Medida_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Medida 2\n",
    "## Piso durante la noche\n",
    "\n",
    " **Ubicacion**: Piso residencial (interior)\n",
    " \n",
    " **Metodologia**:\n",
    " * Medicion de los niveles de presion sonora en el interior del piso, con el fin de cuantificar la intrusion de ruido exterior (trafico, obras urbanas, vecindario etc.).\n",
    " * Toma de mediciones en dos franjas horarias nocturnas: tarde-noche (20:00-22:00) y noche (00:00-02:00).\n",
    " * Toma de mediciones en diferentes dıas de la semana: fin de semana (sabado) y un dıa laboral."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "# initialize directory to store json files with sampled data. \n",
    "path = 'log_files/medida_2'\n",
    "if not os.path.exists(path):\n",
    "    os.makedirs(path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import threading\n",
    "import time\n",
    "import math\n",
    "import serial  \n",
    "import logging\n",
    "import json\n",
    "\n",
    "#Listado de colores para que el texto salga bonito por pantalla.\n",
    "Red = '\\033[91m'\n",
    "Green = '\\033[92m'\n",
    "Blue = '\\033[94m'\n",
    "Cyan = '\\033[96m'\n",
    "White = '\\033[97m'\n",
    "Yellow = '\\033[93m'\n",
    "Magenta = '\\033[95m'\n",
    "Grey = '\\033[90m'\n",
    "Black = '\\033[90m'\n",
    "Default = '\\033[99m'\n",
    "\n",
    "def format_time(unix_epoch, isFileName=False):\n",
    "    \"\"\"format time in unix epoch to human-readable values\"\"\"\n",
    "    if isFileName:\n",
    "        day = time.ctime(unix_epoch)[:3]\n",
    "        formatted_time = day+time.strftime(\" %Y-%m-%d %Hh%Mm%Ss\", time.localtime(unix_epoch))\n",
    "        return formatted_time\n",
    "    formatted_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime(unix_epoch))\n",
    "    return str(formatted_time)\n",
    "\n",
    "#_____________________________________________________________________________________________________________________\n",
    "\n",
    "class AZ8922:\n",
    "    \"\"\"class for sonometer object\"\"\"\n",
    "\n",
    "    def __init__(self, port):\n",
    "        self.port = port\n",
    "        self.ser = None\n",
    "        self.running = True\n",
    "        self.connected = True\n",
    "        self.sound_level = []\n",
    "        print(Green+\"AZ8922: Iniciando la clase.\\n\",flush=True)\n",
    "\n",
    "    def get_sonometry(self):\n",
    "        \"\"\"returns mean and max value of the measurement\"\"\"\n",
    "        if self.sound_level:\n",
    "            sound_level_max = max(self.sound_level)\n",
    "            sound_level_min = min(self.sound_level)\n",
    "            sound_level_linear = [math.pow(10,x/20) for x in self.sound_level] \n",
    "            sound_level_mean = sum(sound_level_linear) / len(sound_level_linear) \n",
    "            sound_level_mean = 20*math.log10(sound_level_mean) \n",
    "        else:\n",
    "            sound_level_max = -1\n",
    "            sound_level_min = -1\n",
    "            sound_level_mean = -1\n",
    "            \n",
    "        # Check if the AZ8922 is off.\n",
    "        # if self.connected and not self.sound_level: logging.warning('AZ8922 is off.')\n",
    "        \n",
    "        # Reset the arrays.\n",
    "        self.sound_level = []\n",
    "        \n",
    "        # Round up.\n",
    "        sound_level_mean = round(sound_level_mean,2)\n",
    "        \n",
    "        # logging.info(f'Sonometry AZ8922:{sound_level_mean}') \n",
    "        # logging.info('Sonometry AZ8922:%f' % sound_level_mean) \n",
    "        # if not self.connected: logging.warning('AZ8922 is disconnected.')\n",
    "         \n",
    "        return sound_level_mean, sound_level_max, sound_level_min\n",
    "\n",
    "    def run_thread(self):\n",
    "        \"\"\"run thread to capture measurements from sonometer\"\"\"\n",
    "        thread = threading.Thread(target=self.sonometro_thread, daemon=True)\n",
    "        thread.start()\n",
    "\n",
    "    def stop_thread(self):\n",
    "        \"\"\"stop sonometer thread\"\"\"\n",
    "        self.running = False\n",
    "        self.connected = False\n",
    "        if self.ser is not None:\n",
    "            self.ser.close()\n",
    "            print(Red+\"RS232 cerrando el puerto serie.\\n\",flush=True)\n",
    "            \n",
    "\n",
    "    def sonometro_thread(self):\n",
    "        \"\"\"reads values from serial port and updates sound level attribute\"\"\"\n",
    "        while self.running:\n",
    "            try:\n",
    "                # If disconnected, check connection every 5 seconds.\n",
    "                if not self.connected: time.sleep(5)\n",
    "                self.ser = serial.Serial(self.port, 2400, serial.EIGHTBITS, serial.PARITY_NONE, serial.STOPBITS_ONE)\n",
    "                self.connected = True\n",
    "                print(Yellow+\"RS232 abriendo el puerto serie.\\n\",flush=True)\n",
    "\n",
    "                #Para sacar valores intermedios\n",
    "                #print(\"Self.conected=\"+str(self.connected)+\"\\n\")\n",
    "                #print(\"self.ser.in_waiting=\"+str(self.ser.in_waiting)+\"\\n\")\n",
    "                #print(\"self.running=\"+str(self.running)+\"\\n\")\n",
    "                \n",
    "                #while(self.connected and self.ser.in_waiting and self.running):\n",
    "                while(self.connected and self.running):\n",
    "                    output = self.ser.readline().decode('utf-8', 'ignore')\n",
    "                    output = output.strip()\n",
    "                    print(Yellow+\"RS232 read data:\"+str(output)+\"\\n\",flush=True)\n",
    "                    if output.startswith('N:') and output.endswith('dB'):\n",
    "                        self.sound_level.append(float(output.split(':')[1].split('dB')[0]))\n",
    "\n",
    "            except (serial.serialutil.SerialException, OSError):\n",
    "                # Thread is not halted upon sensor disconnection.\n",
    "                self.connected = False\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ejemplo toma de 10 medidas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92mAZ8922: Iniciando la clase.\n",
      "\n",
      "\u001b[92mArrancamos el proceso para medir.\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo)=-1,-1 dB\u001b[96m\tTime=2025-03-18 20:10:52\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo)=-1,-1 dB\u001b[96m\tTime=2025-03-18 20:10:53\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo)=-1,-1 dB\u001b[96m\tTime=2025-03-18 20:10:54\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo)=-1,-1 dB\u001b[96m\tTime=2025-03-18 20:10:55\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo)=-1,-1 dB\u001b[96m\tTime=2025-03-18 20:10:56\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo)=-1,-1 dB\u001b[96m\tTime=2025-03-18 20:10:57\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo)=-1,-1 dB\u001b[96m\tTime=2025-03-18 20:10:58\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo)=-1,-1 dB\u001b[96m\tTime=2025-03-18 20:10:59\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo)=-1,-1 dB\u001b[96m\tTime=2025-03-18 20:11:00\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo)=-1,-1 dB\u001b[96m\tTime=2025-03-18 20:11:01\n",
      "\n",
      "\u001b[92mMeasurements saved to log_files/medida_2/type_1 Tue 2025-03-18 20h10m51s.json\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# User Params.\n",
    "MEASURE_PERIOD = 1  # How often (seconds) we poll for measurements.\n",
    "log_file = 'log_files/log.txt'\n",
    "json_file = path+'/type_1 '+format_time(time.time(), True)+'.json'\n",
    "\n",
    "# Initialization.\n",
    "logging.getLogger(\"urllib3\").setLevel(logging.WARNING) # Disable DEBUG from the \"requests\" module.\n",
    "logging.basicConfig(format='%(levelname)s:%(asctime)s %(message)s',\n",
    "                    datefmt='%d/%m/%Y %H:%M:%S', filename=log_file, level=logging.DEBUG)\n",
    "\n",
    "running = True  # Thread running condition.\n",
    "\n",
    "\n",
    "#Llamada a la función y crea objeto de la clase AZ8922\n",
    "port_AZ8922 = 'COM4'\n",
    "sound_level_meter = AZ8922(port_AZ8922)\n",
    "\n",
    "print(Green+\"Arrancamos el proceso para medir.\\n\")\n",
    "sound_level_meter.run_thread()\n",
    "\n",
    "# List to store measurements.\n",
    "measurements = []\n",
    "\n",
    "# Main thread.\n",
    "for x in range(10):\n",
    "\n",
    "        time.sleep(MEASURE_PERIOD)\n",
    "        current_time = format_time(time.time())\n",
    "\n",
    "        # Polling. Esta función devuelve: sound_level_mean, sound_level_max\n",
    "        sound_level_media, sound_level_maximo, sound_level_minimo = sound_level_meter.get_sonometry()\n",
    "        print(Grey+\"Valor medio medido=\"+ str(sound_level_media)+ \" dB\\n\")\n",
    "        print(Grey+\"Valor máximo medido \"+ str(sound_level_maximo)+ \" dB\\n\")\n",
    "        print(Grey+\"Valor minimo medido \"+ str(sound_level_minimo)+ \" dB\\n\")\n",
    "        #print(Magenta +\"Medida(media, máximo)=\"+ str(sound_level)+ \" dB\\n\" )\n",
    "        print(Magenta +\"Medida(media, máximo)=\"+ str(sound_level_media)+\",\"+ str(sound_level_maximo)+  \" dB\" +Cyan+\"\\tTime=\" + current_time+\"\\n\")\n",
    "        logging.debug(\"Medida(media, maximo)=\"+ str(sound_level_media)+\",\"+ str(sound_level_maximo)+  \" dB    Time=\" + current_time+\"\\n\")\n",
    "        \n",
    "        # Store the measurement in a dictionary\n",
    "        measurement = {\n",
    "            \"time\": current_time,\n",
    "            \"mean\": sound_level_media,\n",
    "            \"max\": sound_level_maximo,\n",
    "            \"min\": sound_level_minimo\n",
    "        }\n",
    "        measurements.append(measurement)\n",
    "        \n",
    "# Stop the threads gracefully (close serial ports, clean LCD, etc).\n",
    "logging.debug('Executing finally clause.')\n",
    "running = False\n",
    "sound_level_meter.stop_thread()\n",
    "\n",
    "# Save measurements to a JSON file\n",
    "with open(json_file, 'w') as f:\n",
    "    json.dump(measurements, f, indent=4)\n",
    "print(Green + f\"Measurements saved to {json_file}\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the measurements from the JSON file.\n",
    "with open(json_file, 'r') as f:\n",
    "    measurements = json.load(f)\n",
    "\n",
    "times = [m[\"time\"] for m in measurements]\n",
    "means = [m[\"mean\"] for m in measurements]\n",
    "maxs = [m[\"max\"] for m in measurements]\n",
    "mins = [m[\"min\"] for m in measurements]\n",
    "\n",
    "m_number = list(range(0,len(measurements)))\n",
    "\n",
    "# Plot the measurements\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(m_number, means, label=\"Mean\", color=\"blue\")\n",
    "plt.plot(m_number, maxs, label=\"Max\", color=\"red\", linestyle=\"--\")\n",
    "plt.plot(m_number, mins, label=\"Min\", color=\"green\", linestyle=\"--\")\n",
    "plt.xlim(0, len(measurements)-1)\n",
    "plt.xlabel(\"Samples\")\n",
    "plt.ylabel(\"Sound Level (dB)\")\n",
    "plt.title(\"Sound Level Measurements Over Time\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(maxs,'b',maxs,'ro') #Más opciones de plot aqui: https://matplotlib.org/tutorials/introductory/pyplot.html\n",
    "plt.title('Max Sound Level sampled')\n",
    "plt.ylabel('Sound Level [dB]')\n",
    "plt.xlim(0, len(measurements)-1)\n",
    "plt.xlabel('Samples')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ejemplo toma de medidas para 5 minutos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92mAZ8922: Iniciando la clase.\n",
      "\n",
      "\u001b[92mArrancamos el proceso para medir.\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:01\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:02\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:03\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:04\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:05\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:06\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:07\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:08\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:09\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:10\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:11\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:12\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:13\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:14\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:15\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:16\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:17\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:18\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:19\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:20\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:21\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:22\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:23\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:24\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:25\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:26\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:27\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:28\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:29\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:30\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:31\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:32\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:33\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:34\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:35\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:36\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:37\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:38\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:39\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:40\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:41\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:42\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:43\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:44\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:45\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:46\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:47\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:48\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:49\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:50\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:51\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:52\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:53\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:54\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:55\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:56\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:57\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:58\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:49:59\n",
      "\n",
      "\u001b[90mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[90mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[90mValor minimo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo, minimo)=-1,-1,-1 dB\u001b[96m\tTime=2025-03-18 18:50:00\n",
      "\n",
      "\u001b[92mMeasurements saved to log_files/medida_1/type_2 2025-03-18 18.49.00.json\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# User Params.\n",
    "MEASURE_PERIOD = 1  # How often (seconds) we poll for measurements.\n",
    "log_file = 'log_files/log.txt'\n",
    "json_file = path+'/type_2 '+format_time(time.time(), True)+'.json'\n",
    "MEASUREMENT_DURATION = 5 * 60  # 5 minutes\n",
    "\n",
    "\n",
    "# Get the start and stop time for the measurement.\n",
    "start_time = time.time()\n",
    "end_time = start_time + MEASUREMENT_DURATION\n",
    "\n",
    "# Initialization.\n",
    "logging.getLogger(\"urllib3\").setLevel(logging.WARNING) # Disable DEBUG from the \"requests\" module.\n",
    "logging.basicConfig(format='%(levelname)s:%(asctime)s %(message)s',\n",
    "                    datefmt='%d/%m/%Y %H:%M:%S', filename=log_file, level=logging.DEBUG)\n",
    "\n",
    "running = True  \n",
    "\n",
    "\n",
    "#Llamada a la función y crea objeto de la clase AZ8922\n",
    "port_AZ8922 = 'COM4'\n",
    "sound_level_meter = AZ8922(port_AZ8922)\n",
    "\n",
    "print(Green+\"Arrancamos el proceso para medir.\\n\")\n",
    "sound_level_meter.run_thread()\n",
    "\n",
    "measurements = []\n",
    "\n",
    "# Main thread.\n",
    "while time.time() < end_time:\n",
    "        # Sleep for the measurement period\n",
    "        time.sleep(MEASURE_PERIOD)\n",
    "        current_time = format_time(time.time())\n",
    "\n",
    "        # Store the measurement in a dictionary\n",
    "        measurement = {\n",
    "            \"time\": current_time,\n",
    "            \"mean\": sound_level_media,\n",
    "            \"max\": sound_level_maximo,\n",
    "            \"min\": sound_level_minimo\n",
    "        }\n",
    "        measurements.append(measurement)\n",
    "\n",
    "        # Polling. Esta función devuelve: sound_level_mean, sound_level_max\n",
    "        sound_level_media, sound_level_maximo, sound_level_minimo = sound_level_meter.get_sonometry()\n",
    "        \n",
    "        print(Grey+\"Valor medio medido=\"+ str(sound_level_media)+ \" dB\\n\")\n",
    "        print(Grey+\"Valor máximo medido \"+ str(sound_level_maximo)+ \" dB\\n\")\n",
    "        print(Grey+\"Valor minimo medido \"+ str(sound_level_minimo)+ \" dB\\n\")\n",
    "\n",
    "        print(Magenta +\"Medida(media, máximo, minimo)=\"+ str(sound_level_media)+\",\"+ str(sound_level_maximo)+\",\"+ str(sound_level_minimo)+  \" dB\" +Cyan+\"\\tTime=\" + current_time+\"\\n\")\n",
    "        logging.debug(\"Medida(media, maximo, minimo)=\"+ str(sound_level_media)+\",\"+ str(sound_level_maximo)+\",\"+ str(sound_level_minimo)+  \" dB    Time=\" + current_time+\"\\n\")\n",
    "        \n",
    "        \n",
    "# Stop the threads gracefully (close serial ports, clean LCD, etc).\n",
    "logging.debug('Executing finally clause.')\n",
    "running = False\n",
    "sound_level_meter.stop_thread()\n",
    "\n",
    "# Save measurements to a JSON file\n",
    "with open(json_file, 'w') as f:\n",
    "    json.dump(measurements, f, indent=4)\n",
    "print(Green + f\"Measurements saved to {json_file}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the measurements from the JSON file.\n",
    "with open(json_file, 'r') as f:\n",
    "    measurements = json.load(f)\n",
    "\n",
    "times = [m[\"time\"] for m in measurements]\n",
    "means = [m[\"mean\"] for m in measurements]\n",
    "maxs = [m[\"max\"] for m in measurements]\n",
    "mins = [m[\"min\"] for m in measurements]\n",
    "\n",
    "m_number = list(range(0,len(measurements)))\n",
    "\n",
    "# Plot the measurements\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(m_number, means, label=\"Mean\", color=\"blue\")\n",
    "plt.plot(m_number, maxs, label=\"Max\", color=\"red\", linestyle=\"--\")\n",
    "plt.plot(m_number, mins, label=\"Min\", color=\"green\", linestyle=\"--\")\n",
    "plt.xlim(0, len(measurements)-1)\n",
    "plt.xlabel(\"Sampling Time\")\n",
    "plt.ylabel(\"Sound Level (dB)\")\n",
    "plt.title(\"Sound Level Measurements Over Time\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ejemplo de lectura directa del Serial y almacenamiento en un vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "ename": "SerialException",
     "evalue": "could not open port 'COM4': FileNotFoundError(2, 'Impossibile trovare il file specificato.', None, 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mSerialException\u001b[0m                           Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[63], line 19\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[38;5;66;03m# Sound level meter.\u001b[39;00m\n\u001b[0;32m     18\u001b[0m port_AZ8922 \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOM4\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m---> 19\u001b[0m ser \u001b[38;5;241m=\u001b[39m \u001b[43mserial\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSerial\u001b[49m\u001b[43m(\u001b[49m\u001b[43mport_AZ8922\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m2400\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mserial\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mEIGHTBITS\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mserial\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mPARITY_NONE\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mserial\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSTOPBITS_ONE\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# open serial port\u001b[39;00m\n\u001b[0;32m     22\u001b[0m \u001b[38;5;66;03m# Reset the arrays.\u001b[39;00m\n\u001b[0;32m     23\u001b[0m sound_level \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[1;32mc:\\Users\\Very\\miniconda3\\envs\\ELECTRO\\Lib\\site-packages\\serial\\serialwin32.py:33\u001b[0m, in \u001b[0;36mSerial.__init__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     31\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_overlapped_read \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m     32\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_overlapped_write \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m---> 33\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mSerial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\Very\\miniconda3\\envs\\ELECTRO\\Lib\\site-packages\\serial\\serialutil.py:244\u001b[0m, in \u001b[0;36mSerialBase.__init__\u001b[1;34m(self, port, baudrate, bytesize, parity, stopbits, timeout, xonxoff, rtscts, write_timeout, dsrdtr, inter_byte_timeout, exclusive, **kwargs)\u001b[0m\n\u001b[0;32m    241\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124munexpected keyword arguments: \u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(kwargs))\n\u001b[0;32m    243\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m port \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 244\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\Very\\miniconda3\\envs\\ELECTRO\\Lib\\site-packages\\serial\\serialwin32.py:64\u001b[0m, in \u001b[0;36mSerial.open\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     62\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_port_handle \u001b[38;5;241m==\u001b[39m win32\u001b[38;5;241m.\u001b[39mINVALID_HANDLE_VALUE:\n\u001b[0;32m     63\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_port_handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m    \u001b[38;5;66;03m# 'cause __del__ is called anyway\u001b[39;00m\n\u001b[1;32m---> 64\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m SerialException(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcould not open port \u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mportstr, ctypes\u001b[38;5;241m.\u001b[39mWinError()))\n\u001b[0;32m     66\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     67\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_overlapped_read \u001b[38;5;241m=\u001b[39m win32\u001b[38;5;241m.\u001b[39mOVERLAPPED()\n",
      "\u001b[1;31mSerialException\u001b[0m: could not open port 'COM4': FileNotFoundError(2, 'Impossibile trovare il file specificato.', None, 2)"
     ]
    }
   ],
   "source": [
    "import serial\n",
    "import math\n",
    "\n",
    "#Listado de colores para que el texto salga bonito por pantalla.\n",
    "Red = '\\033[91m'\n",
    "Green = '\\033[92m'\n",
    "Blue = '\\033[94m'\n",
    "Cyan = '\\033[96m'\n",
    "White = '\\033[97m'\n",
    "Yellow = '\\033[93m'\n",
    "Magenta = '\\033[95m'\n",
    "Grey = '\\033[90m'\n",
    "Black = '\\033[90m'\n",
    "Default = '\\033[99m'\n",
    "\n",
    "\n",
    "# Sound level meter.\n",
    "port_AZ8922 = 'COM4'\n",
    "ser = serial.Serial(port_AZ8922,2400, serial.EIGHTBITS, serial.PARITY_NONE, serial.STOPBITS_ONE)  # open serial port\n",
    "\n",
    "\n",
    "# Reset the arrays.\n",
    "sound_level = []\n",
    "\n",
    "#while(ser.connected):\n",
    "for x in range(10):\n",
    "    output = ser.readline().decode('utf-8', 'ignore')\n",
    "    output = output.strip()\n",
    "    #print(\"RS232 read data:\"+str(output)+\"\\n\")\n",
    "    if output.startswith('N:') and output.endswith('dB'):\n",
    "        salida_texto=float(output.split(':')[1].split('dB')[0])\n",
    "        sound_level.append(salida_texto) # Para concatenar los valores y formar un Vector/Array\n",
    "        print(\"RS232 db=\"+str(salida_texto)+\"\\n\")     \n",
    "    \n",
    "ser.close()  #Cerramos el puerto porque no puede quedar cerrado. En el caso de que al ejecutar el código el \n",
    "#Serial se quede abierto y no se pueda volver a ejecutar el código porque el puerto está abierto. Se copia\n",
    "#esta línea en una ventana nueva y se ejecuta. Con eso se cierra el Serial y ya se puede volver a abrir.\n",
    "\n",
    "\n",
    "# Round up.\n",
    "#sound_level_mean = round(sound_level_mean,2)\n",
    "#print(\"Sound Level(Media)=\"+str(sound_level_mean)+\"\\n\") \n",
    "\n",
    "#Calculamos el máximo en dB\n",
    "sound_level_max = max(sound_level) #Esto es el máximo de un vector\n",
    "print(Green + \"Sound Level(Max)=\"+str(sound_level_max)+\" db\\n\") \n",
    "\n",
    "#Calculamos el mínomo en dB\n",
    "sound_level_min = min(sound_level)\n",
    "print(Magenta+\"Sound Level(Min)=\"+str(sound_level_min)+\" db\\n\") \n",
    "\n",
    "#Calculamos la suma de los valores sin decibelios\n",
    "sound_level_linear = [math.pow(10,x/20) for x in sound_level] \n",
    "print(Blue +\"Sound Level(linear)=\"+str(sound_level_linear)+\"\\n\") \n",
    "\n",
    "#Pasamos la suma de lo medido en dB\n",
    "sound_level_mean = sum(sound_level_linear) / len(sound_level_linear) \n",
    "sound_level_mean = 20*math.log10(sound_level_mean)\n",
    "print(Red + \"Sound Level(Mean)=\"+str(sound_level_mean)+\" db\\n\") \n",
    "\n",
    "\n"
   ]
  }
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