Sonometro_AZ.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MEDIDOR DE NIVEL DE SONIDO \n",
    "## Digital Sound Level Meter AZ-8921/8922\n",
    "\n",
    "AZ Instrument Corp. es un líder reconocido a nivel nacional para soluciones innovadoras de medidores de nivel de sonido . En los últimos 20 años, hemos construido nuestra reputación en nuestra capacidad de personalizar nuestras soluciones para satisfacer las necesidades únicas de nuestros clientes. Medidor de nivel de sonido de alta calidad y calidad superior con un diseño ergonómico único que permite una posición de sujeción más cómoda. Nuestra misión es esforzarnos por alcanzar la excelencia en todas las fases de nuestro negocio y garantizar la completa satisfacción del cliente.\n",
    "AZ Instrument Corp. cree en trabajar con nuestros clientes para agregar valor al observar la forma en que se usa el producto, sugiriendo mejoras en el diseño, proceso de fabricación, empaque o métodos de compra para maximizar su eficiencia. Póngase en contacto con nuestra oficina de ventas en Taiwán para hablar sobre la idoneidad del medidor de nivel de sonido para sus aplicaciones o envíenos un correo electrónico para obtener más información.\n",
    "\n",
    "![AZ8922-image](Fotos/front-image.jpg)\n",
    "\n",
    "En la siguiente guía puede consultar las características , la aplicación, la especificación, el manual de funcionamiento y el certificado CE del detector de ruido digital de interfaz USB 8922 AZ.\n",
    "\n",
    "### Caracteristicas profesionales de sonómetro 8922 AZ :\n",
    "\n",
    "Características profesionales del medidor de nivel de sonido 8922 AZ:\n",
    "\n",
    "* Cumple con EN61672-1:2013, estándar de clase tipo 2\n",
    "* Pantalla LCD súper grande de lujo con precio asequible\n",
    "* Absorbente de ruido de fondo único\n",
    "* Cuenta con pantalla digital y gráfico de barras analógico\n",
    "* Rango automático / manual, rango de 6 medidas en 10 pasos\n",
    "* Función de retención máxima y modo de respuesta rápida / lenta\n",
    "* Fácil de calibrar por calibrador estándar\n",
    "* La ponderación del aire acondicionado es conmutable\n",
    "* Salida analógica AC / DC\n",
    "* Interfaz USB\n",
    "\n",
    "\n",
    "### Aplicación del probador de ruido 8922 AZ:\n",
    "\n",
    "El medidor de nivel de sonido profesional 8922 AZ ofrece un rango automático o manual en seis rangos de medición de 30 a 130 dB. La unidad cumple con las normas de clase tipo 2 ANSI S1.4 y IEC60651, estándares de clase tipo 2, y cuenta con una resolución de 0,1 dB.\n",
    "\n",
    "Un absorbedor de ruido de fondo de 8921 AZ SLM permite al usuario medir los niveles de sonido con precisión incluso en presencia de un alto ruido de fondo. Además, tiene una función de retroiluminación, que permite al usuario operar en áreas oscuras.\n",
    "\n",
    "El medidor de nivel de sonido 8922 AZ permite al usuario seleccionar entre tiempos de respuesta rápidos y lentos, así como el pesaje A y C. Las tomas en el medidor proporcionan salida analógica AZ y DC.\n",
    "\n",
    "El medidor de nivel de ruido portátil 8922 AZ puede ser utilizado para mediciones acústicas (sonido que viaja a través del aire), que se ha utilizado ampliamente en aplicaciones relacionadas con HVAC (calefacción, ventilación y aire acondicionado) y industriales. El usuario puede capturar datos de nivel de sonido a través de la interfaz USB.\n",
    "\n",
    "El medidor de prueba de sonido digital Decibel AZ 8921 8922 AZ fue diseñado y fabricado por AZ Instrument, un fabricante con amplia experiencia en instrumentos de medición ambiental.\n",
    "\n",
    "\n",
    "### Especificación de sonómetro 8922 AZ:\n",
    "\n",
    "Especificación del medidor de nivel de sonido AZ-8922:\n",
    "\n",
    "| Modelo  | 8922 |\n",
    "|----------------|----------------|\n",
    "| Rango de medida  | A peso: 30 ~ 130 dB, C peso: 35 ~ 130 dB | \n",
    "| Pantalla digital (6 rangos)  | 30 ~ 80, 40 ~ 90, 50 ~ 100, 60 ~ 110, 70 ~ 120, 80 ~ 130 dB  | \n",
    "| Resolución de pantalla digital  | 0.1 dB  |\n",
    "|Tiempo de actualización digital |\t160 ms |\n",
    "|Barra cuasi-analógica (6 rangos) |\t30 ~ 80, 40 ~ 90, 50 ~ 100, 60 ~ 110, 70 ~ 120, 80 ~ 130 dB |\n",
    "|Resolución cuasi-analógica | 1 dB |\n",
    "|Hora de actualización de la barra | 40 ms |\n",
    "|Precisión a 94 dB, 1 kHz |\t± 1.5 dB |\n",
    "|Salida analógica | AC 0.707 Vrms (a escala completa): DC 10 mV / dB |\n",
    "|Micrófono | Dia 6mm micrófono de condensador eléctrico |\n",
    "|Rango de frecuencia | 31.5 Hz ~ 8 KHz |\n",
    "|Duración de la batería\t| 30 horas tipicas |\n",
    "|Parabrisas\t| SÍ |\n",
    "|Tamaño del LCD\t| 62 (L) * 42 (W) mm |\n",
    "|Temperatura de funcionamiento | 0 ~ 50 ℃ |\n",
    "|RH operativa% | Humedad ＜ 80% |\n",
    "|Temperatura de almacenamiento | -20 ~ 50 ℃ |\n",
    "|Almacenamiento RH%\t| Humedad ＜ 90% |\n",
    "|Tamaño del medidor\t| 254 (L) * 77 (W) * 37 (H) mm |\n",
    "|Peso | ~ 200g |\n",
    "|Batería | Batería de 9 V * 1 PC o adaptador 9V DC |\n",
    "|Paquete estándar | Medidor, batería, manual, estuche |\n",
    "|Accesorio opcional\t| CD de software y cable USB |\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manuales en PDF\n",
    "\n",
    "Bienvenido a descargar y verificar las especificaciones, el manual de operación y el certificado CE del instrumento de medición de sonido 8922 AZ desde el siguiente enlace del sitio web.\n",
    "\n",
    "\n",
    "[8922 Specification](./pdf/8922_Specification.pdf)\n",
    "\n",
    "[8922 Operation manual](./pdf/8922_Operation_manual.pdf)\n",
    "\n",
    "[8922 CE Certificate](./pdf/8922_CE_Certificate.pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import webbrowser\n",
    "\n",
    "path_specification = \"https://www.az-instrument.com.tw/en/product/file/616463-1\"\n",
    "path_op_manual = \"https://www.az-instrument.com.tw/en/product/file/616463-2\"\n",
    "path_ce_certificate = \"https://www.az-instrument.com.tw/en/product/file/616463-4\"\n",
    "\n",
    "# Uncomment the lines below to download the manuals\n",
    "\n",
    "#webbrowser.open(path_specification) \n",
    "#webbrowser.open(path_op_manual) \n",
    "#webbrowser.open(path_ce_certificate) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "%%HTML\n",
    "<div align=\"center\">\n",
    "    <iframe width=\"560\" height=\"315\"\n",
    "    src=\"https://www.youtube.com/embed/LiLmgsPspUw\"\n",
    "    </iframe>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup \n",
    "\n",
    "Para conectar y leer datos del sonómetro 8922 AZ, necesitamos:\n",
    "* `python 3.11.11`\n",
    "* `pyserial 3.5`\n",
    "* un **puerto COM** que corresponda a su dispositivo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: pyserial\n",
      "Version: 3.5\n",
      "Summary: Python Serial Port Extension\n",
      "Home-page: https://github.com/pyserial/pyserial\n",
      "Author: Chris Liechti\n",
      "Author-email: cliechti@gmx.net\n",
      "License: BSD\n",
      "Location: C:\\Users\\Very\\miniconda3\\envs\\ELECTRO\\Lib\\site-packages\n",
      "Requires: \n",
      "Required-by: \n",
      "Python 3.11.11\n"
     ]
    }
   ],
   "source": [
    "# pip install pyserial\n",
    "!pip show pyserial \n",
    "!python --version"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conectar el sonómetro\n",
    "\n",
    "Conecte el sonómetro 8922 AZ a un puerto COM. Para verificar que el controlador (driver) esté funcionando correctamente, navegue en el Administrador de Dispositivos de su computadora. El resultado debería ser similar a la figura que se muestra a continuación.\n",
    "\n",
    "![AZ8922-image](Fotos/device-manager.png)\n",
    "\n",
    "Si encuentra el error: **\"pl2303hxa phased out since 2012. please contact your supplier\"**, siga estos pasos:\n",
    "\n",
    "1. Visite el siguiente enlace para descargar el controlador correcto: [enlace al tutorial](https://rogerbit.com/wprb/2021/04/solucion-a-pl2303hxa-phased-out-since-2012-please-contact-your-supplier-en-windows-10/)\n",
    "2. Siga las instrucciones proporcionadas para instalar el controlador adecuado.\n",
    "\n",
    "### Tomar medidas\n",
    "\n",
    "Se recuerda que las buenas practicas para tomar medidas a través del sonómetro son:\n",
    "* Posicionar el sonómetro lejos de obstáculos que puedan reflejar o absorber el sonido;\n",
    "* Mantener el micrófono del sonómetro a una distancia adecuada de la fuente sonora, generalmente a 1-1,5 metros del suelo y al menos 1 metro de cualquier superficie reflectante;\n",
    "* Evitar obstruir el micrófono con el cuerpo u otros objetos durante la medición.\n",
    "\n",
    "\n",
    "Abra un terminal de comandos y ejecute el siguiente código para conectar el sonómetro y obtener las medidas. Este código requiere que la librería esté instalada en su sistema.\n",
    "\n",
    "### Troubleshooting\n",
    "\n",
    "Para deshabilitar la funcionalidad de AutoPowerOff, siga las instrucciones en la imagen a continuación.\n",
    "\n",
    "![imagen de Auto Powe Off](Fotos/AutoPowerOff.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Código de referencia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Código de la librería para saber qué funciones tenemos disponibles del sonometro. Las siguentes son funciones de la clase **AZ8922**\n",
    "\n",
    "```    \n",
    "    def __init__(self, port):\n",
    "    \"\"\"configuration initial. Assign port COM\"\"\"\n",
    "        self.port = port\n",
    "        self.ser = None\n",
    "        self.running = True\n",
    "        self.connected = True\n",
    "        self.sound_level = []\n",
    "\n",
    "    def get_sonometry(self):\n",
    "        if self.sound_level:\n",
    "            sound_level_max = max(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_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\n",
    "\n",
    "    def run_thread(self):\n",
    "        thread = threading.Thread(target=self.sonometro_thread, daemon=True)\n",
    "        thread.start()\n",
    "\n",
    "    def stop_thread(self):\n",
    "        self.running = False\n",
    "        self.connected = False\n",
    "        if self.ser is not None: self.ser.close()\n",
    "\n",
    "    def sonometro_thread(self):\n",
    "        \"\"\"abre el serial port, lies a 2400 bps.\"\"\"\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",
    "\n",
    "                while(self.connected and self.ser.in_waiting and self.running):\n",
    "                    output = self.ser.readline().decode('utf-8', 'ignore')\n",
    "                    output = output.strip()\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": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Para que las funciones externas se recarguen. Muy útil por si hacemos cambios en el códfigo de éstas.\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable              Type      Data/Info\n",
      "-----------------------------------------\n",
      "path_ce_certificate   str       https://www.az-instrument<...>/en/product/file/616463-4\n",
      "path_op_manual        str       https://www.az-instrument<...>/en/product/file/616463-2\n",
      "path_specification    str       https://www.az-instrument<...>/en/product/file/616463-1\n",
      "plt                   module    <module 'matplotlib.pyplo<...>\\\\matplotlib\\\\pyplot.py'>\n",
      "webbrowser            module    <module 'webbrowser' from<...>TRO\\\\Lib\\\\webbrowser.py'>\n"
     ]
    }
   ],
   "source": [
    "#Para ver qué variables tenemos definidas\n",
    "\n",
    "%whos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[91mAZ8922: Iniciando la clase.\n",
      "\n",
      "\u001b[95mMedida(media, máximo)=(-1, -1) dB    Time=1742984921.4619586\n",
      "\n",
      "\u001b[95mMedida(media, máximo)=(-1, -1) dB    Time=1742984922.464172\n",
      "\n",
      "\u001b[95mMedida(media, máximo)=(-1, -1) dB    Time=1742984923.4655166\n",
      "\n",
      "\u001b[95mMedida(media, máximo)=(-1, -1) dB    Time=1742984924.4673162\n",
      "\n",
      "\u001b[95mMedida(media, máximo)=(-1, -1) dB    Time=1742984925.468691\n",
      "\n",
      "\u001b[95mMedida(media, máximo)=(-1, -1) dB    Time=1742984926.469841\n",
      "\n",
      "\u001b[95mMedida(media, máximo)=(-1, -1) dB    Time=1742984927.4723861\n",
      "\n",
      "\u001b[95mMedida(media, máximo)=(-1, -1) dB    Time=1742984928.4736922\n",
      "\n",
      "\u001b[95mMedida(media, máximo)=(-1, -1) dB    Time=1742984929.4752283\n",
      "\n",
      "\u001b[95mMedida(media, máximo)=(-1, -1) dB    Time=1742984930.476395\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import collections  # Ordered Dictionary.\n",
    "import logging\n",
    "import requests # pylint: disable=E0401\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",
    "#You can chain these to get different colors on the same line.\n",
    "#print('\\033[91m' + 'Red  ' + \"\\033[95m\" + 'Magenta  ' + '\\033[94m' + 'Blue')\n",
    "#print(Red + 'Red  ' + Magenta + 'Magenta  ' + Blue + 'Blue  ')\n",
    "# Handlers.\n",
    "\n",
    "import AZ8922\n",
    "\n",
    "# User Params.\n",
    "SERIALNO = \"HEIMDALSTA2\"\n",
    "LCD_TITLE = 'RUIDO DE FONDO V0.6'  # LCD Title. Max of 20 characters.\n",
    "MEASURE_PERIOD = 1  # How often (seconds) we poll for measurements. (1 medida per second)\n",
    "LCD_PERIOD = 10  # How often (seconds) the LCD switches between screens.\n",
    "log_file = 'log_files/log.txt' #fichero donde cogemos los datos\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",
    "# Sound level meter.\n",
    "#port_AZ8922 = '/dev/serial/by-id/usb-Prolific_Technology_Inc._USB-Serial_Controller_D-if00-port0'\n",
    "#put here COM port\n",
    "port_AZ8922 = 'COM4'\n",
    "\n",
    "#Llamada a la función y crea objeto de la clase AZ8922\n",
    "sound_level_meter = AZ8922.AZ8922(port_AZ8922) \n",
    "sound_level_meter.run_thread()\n",
    "\n",
    "\n",
    "# Main thread.\n",
    "for x in range(10):\n",
    "\n",
    "        # Inintialize the JSON object.\n",
    "        #json_data = collections.OrderedDict()\n",
    "        time.sleep(MEASURE_PERIOD)\n",
    "\n",
    "        # Polling. Esta función devuelve: sound_level_mean, sound_level_max\n",
    "        sound_level = sound_level_meter.get_sonometry()\n",
    "        #print(Magenta +\"Medida(media, máximo)=\"+ str(sound_level)+ \" dB\\n\" )\n",
    "        print(Magenta +\"Medida(media, máximo)=\"+ str(sound_level)+ \" dB    Time=\" + str(time.time())+\"\\n\")\n",
    "        logging.debug(\"Medida(media, maximo)=\"+ str(sound_level)+ \" dB    Time=\" + str(time.time())+\"\\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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[91mAZ8922: Iniciando la clase.\n",
      "\n",
      "\u001b[94mArrancamos el proceso para medir.\n",
      "\n",
      "\u001b[94mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[91mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo)=-1,-1 dB    Time=1742315334.9036264\n",
      "\n",
      "\u001b[94mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[91mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo)=-1,-1 dB    Time=1742315335.9062269\n",
      "\n",
      "\u001b[94mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[91mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo)=-1,-1 dB    Time=1742315336.9073067\n",
      "\n",
      "\u001b[94mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[91mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo)=-1,-1 dB    Time=1742315337.9082851\n",
      "\n",
      "\u001b[94mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[91mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo)=-1,-1 dB    Time=1742315338.9090018\n",
      "\n",
      "\u001b[94mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[91mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo)=-1,-1 dB    Time=1742315339.9101791\n",
      "\n",
      "\u001b[94mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[91mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo)=-1,-1 dB    Time=1742315340.911205\n",
      "\n",
      "\u001b[94mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[91mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo)=-1,-1 dB    Time=1742315341.9130228\n",
      "\n",
      "\u001b[94mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[91mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo)=-1,-1 dB    Time=1742315342.915033\n",
      "\n",
      "\u001b[94mValor medio medido=-1 dB\n",
      "\n",
      "\u001b[91mValor máximo medido -1 dB\n",
      "\n",
      "\u001b[95mMedida(media, máximo)=-1,-1 dB    Time=1742315343.916098\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import threading\n",
    "import time\n",
    "import math\n",
    "import serial  #Hay que tener instalado la librería PySerial\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",
    "#You can chain these to get different colors on the same line.\n",
    "#print('\\033[91m' + 'Red  ' + \"\\033[95m\" + 'Magenta  ' + '\\033[94m' + 'Blue')\n",
    "#print(Red + 'Red  ' + Magenta + 'Magenta  ' + Blue + 'Blue  ')\n",
    "\n",
    "class AZ8922:\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(Red+\"AZ8922: Iniciando la clase.\\n\",flush=True)\n",
    "\n",
    "    def get_sonometry(self):\n",
    "        if self.sound_level:\n",
    "            sound_level_max = max(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_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\n",
    "\n",
    "    def run_thread(self):\n",
    "        thread = threading.Thread(target=self.sonometro_thread, daemon=True)\n",
    "        thread.start()\n",
    "\n",
    "    def stop_thread(self):\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",
    "        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(Red+\"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(Red+\"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",
    "#_____________________________________________________________________________________________________________________\n",
    "\n",
    "# User Params.\n",
    "MEASURE_PERIOD = 1  # How often (seconds) we poll for measurements.\n",
    "log_file = 'log_files/log.txt'\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",
    "# Sound level meter.\n",
    "#port_AZ8922 = '/dev/serial/by-id/usb-Prolific_Technology_Inc._USB-Serial_Controller_D-if00-port0'\n",
    "port_AZ8922 = 'COM4'\n",
    "\n",
    "#Llamada a la función y crea objeto de la clase AZ8922\n",
    "sound_level_meter = AZ8922(port_AZ8922)\n",
    "\n",
    "print(Blue+\"Arrancamos el proceso para medir.\\n\")\n",
    "sound_level_meter.run_thread()\n",
    "\n",
    "\n",
    "# Main thread.\n",
    "for x in range(10):\n",
    "\n",
    "        # Inintialize the JSON object.\n",
    "        #json_data = collections.OrderedDict()\n",
    "        time.sleep(MEASURE_PERIOD)\n",
    "\n",
    "        # Polling. Esta función devuelve: sound_level_mean, sound_level_max\n",
    "        sound_level_media, sound_level_maximo = sound_level_meter.get_sonometry()\n",
    "        print(Blue+\"Valor medio medido=\"+ str(sound_level_media)+ \" dB\\n\")\n",
    "        print(Red+\"Valor máximo medido \"+ str(sound_level_maximo)+ \" 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    Time=\" + str(time.time())+\"\\n\")\n",
    "        logging.debug(\"Medida(media, maximo)=\"+ str(sound_level_media)+\",\"+ str(sound_level_maximo)+  \" dB    Time=\" + str(time.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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ejemplo de lectura directa del Serial y printeo de los datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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[8], line 7\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;66;03m# Sound level meter.\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m#port_AZ8922 = '/dev/serial/by-id/usb-Prolific_Technology_Inc._USB-Serial_Controller_D-if00-port0'\u001b[39;00m\n\u001b[0;32m      5\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----> 7\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      9\u001b[0m \u001b[38;5;66;03m#while(ser.connected):\u001b[39;00m\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m10\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",
    "\n",
    "# Sound level meter.\n",
    "#port_AZ8922 = '/dev/serial/by-id/usb-Prolific_Technology_Inc._USB-Serial_Controller_D-if00-port0'\n",
    "port_AZ8922 = 'COM4'\n",
    "\n",
    "ser = serial.Serial(port_AZ8922,2400, serial.EIGHTBITS, serial.PARITY_NONE, serial.STOPBITS_ONE)  # open serial port\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",
    "        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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ejemplo de lectura directa del Serial y almacenamiento en un vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RS232 db=52.6\n",
      "\n",
      "RS232 db=41.7\n",
      "\n",
      "RS232 db=48.0\n",
      "\n",
      "RS232 db=53.3\n",
      "\n",
      "RS232 db=55.6\n",
      "\n",
      "RS232 db=63.3\n",
      "\n",
      "RS232 db=63.4\n",
      "\n",
      "RS232 db=41.9\n",
      "\n",
      "RS232 db=41.5\n",
      "\n",
      "RS232 db=65.3\n",
      "\n",
      "\u001b[92mSound Level(Max)=65.3 db\n",
      "\n",
      "\u001b[95mSound Level(Min)=41.5 db\n",
      "\n",
      "\u001b[94mSound Level(linear)=[426.57951880159254, 121.6186000646368, 251.18864315095797, 462.38102139926036, 602.5595860743581, 1462.1771744567184, 1479.1083881682073, 124.45146117713844, 118.85022274370189, 1840.7720014689544]\n",
      "\n",
      "\u001b[91mSound Level(Mean)=56.76398936301945 db\n",
      "\n"
     ]
    }
   ],
   "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",
    "#You can chain these to get different colors on the same line.\n",
    "#print('\\033[91m' + 'Red  ' + \"\\033[95m\" + 'Magenta  ' + '\\033[94m' + 'Blue')\n",
    "#print(Red + 'Red  ' + Magenta + 'Magenta  ' + Blue + 'Blue  ')\n",
    "\n",
    "# Sound level meter.\n",
    "#port_AZ8922 = '/dev/serial/by-id/usb-Prolific_Technology_Inc._USB-Serial_Controller_D-if00-port0'\n",
    "port_AZ8922 = 'COM4'\n",
    "\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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CUIDADO\n",
    "\n",
    "Comprueba si la suma de potencias acústicas de SPL se hace como 10·log10(P/Pref)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'sound_level' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[1], line 6\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m#Ploteo de los datos medidos\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m----> 6\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(\u001b[43msound_level\u001b[49m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m'\u001b[39m,sound_level,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mro\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;66;03m#Más opciones de plot aqui: https://matplotlib.org/tutorials/introductory/pyplot.html\u001b[39;00m\n\u001b[0;32m      7\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSound Level samples measured during the test\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      8\u001b[0m plt\u001b[38;5;241m.\u001b[39mylabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSound Level [dB]\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'sound_level' is not defined"
     ]
    }
   ],
   "source": [
    "#Ploteo de los datos medidos\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "plt.plot(sound_level,'b',sound_level,'ro') #Más opciones de plot aqui: https://matplotlib.org/tutorials/introductory/pyplot.html\n",
    "plt.title('Sound Level samples measured during the test')\n",
    "plt.ylabel('Sound Level [dB]')\n",
    "plt.xlabel('Samples')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This text appears above the figures\n",
      "This text appears betweeen the figures\n",
      "This text appears below the figures\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'FigureCanvasAgg' object has no attribute 'set_window_title'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[11], line 10\u001b[0m\n\u001b[0;32m      7\u001b[0m fig2 \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mfigure()\n\u001b[0;32m      8\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThis text appears below the figures\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m---> 10\u001b[0m \u001b[43mfig1\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_window_title\u001b[49m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCanvas active title\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m     11\u001b[0m fig1\u001b[38;5;241m.\u001b[39msuptitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFigure title\u001b[39m\u001b[38;5;124m'\u001b[39m, fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20\u001b[39m)\n\u001b[0;32m     13\u001b[0m \u001b[38;5;66;03m# Create plots inside the figures\u001b[39;00m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'FigureCanvasAgg' object has no attribute 'set_window_title'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "#%matplotlib notebook\n",
    "\n",
    "print('This text appears above the figures')\n",
    "fig1 = plt.figure(num='DORMANT')\n",
    "print('This text appears betweeen the figures')\n",
    "fig2 = plt.figure()\n",
    "print('This text appears below the figures')\n",
    "\n",
    "fig1.canvas.set_window_title('Canvas active title')\n",
    "fig1.suptitle('Figure title', fontsize=20)\n",
    "\n",
    "# Create plots inside the figures\n",
    "ax1 = fig1.add_subplot(111)\n",
    "ax1.set_xlabel('x label')\n",
    "ax2 = fig2.add_subplot(111)\n",
    "\n",
    "# Loop to update figures\n",
    "end = 40\n",
    "for i in range(end):\n",
    "    ax2.cla()  # Clear only 2nd figure's axes, figure 1 is ADDITIVE\n",
    "    ax1.set_title('Axes title')  # Reset as removed by cla()\n",
    "\n",
    "    ax1.plot(range(i,end), (i,)*(end-i))\n",
    "    ax2.plot(range(i,end), range(i,end), 'rx')\n",
    "    fig1.canvas.draw()\n",
    "    fig2.canvas.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(4.444444444444459, 0.5, 'Eje Y')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#from IPython.display import display, clear_output\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython import display as dp\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "m = 10\n",
    "n = 100\n",
    "matrix = np.random.normal(0, 1, size=(m, n))\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "# displaying the title\n",
    "\n",
    "\n",
    "for i in range(m):\n",
    "    ax.clear()\n",
    "    ax.plot(matrix[i, :])\n",
    "    display(fig)\n",
    "    dp.clear_output(wait=True)\n",
    "    plt.pause(0.2)\n",
    "    \n",
    "ax.set_title(\"Linear graph\")  \n",
    "ax.set_xlabel(\"Eje X\")         \n",
    "ax.set_ylabel(\"Eje Y\")         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "import pylab as pl\n",
    "from IPython import display\n",
    "for i in range(10):\n",
    "    pl.clf()\n",
    "    pl.plot(pl.randn(100))\n",
    "    display.display(pl.gcf())\n",
    "    display.clear_output(wait=True)\n",
    "    time.sleep(1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "\n",
    "from IPython import display\n",
    "\n",
    "def pltsin(ax, *,hdisplay, colors=['b']):\n",
    "    x = np.linspace(0,1,100)\n",
    "    if ax.lines:\n",
    "        for line in ax.lines:\n",
    "            line.set_xdata(x)\n",
    "            y = np.random.random(size=(100,1))\n",
    "            line.set_ydata(y)\n",
    "    else:\n",
    "        for color in colors:\n",
    "            y = np.random.random(size=(100,1))\n",
    "            ax.plot(x, y, color)\n",
    "    hdisplay.update(fig)\n",
    "\n",
    "\n",
    "fig,ax = plt.subplots(1,1)\n",
    "hdisplay = display.display(\"\", display_id=True)\n",
    "\n",
    "ax.set_xlabel('X')\n",
    "ax.grid()\n",
    "ax.set_ylabel('Y')\n",
    "ax.set_xlim(0,1)\n",
    "ax.set_ylim(0,1)\n",
    "for f in range(5):\n",
    "    pltsin(ax, colors=['b', 'r'], hdisplay=hdisplay)\n",
    "    time.sleep(1)\n",
    "    \n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ELECTRO",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}