{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(\"ignore\", UserWarning)\n",
    "\n",
    "from __future__ import print_function, absolute_import\n",
    "from tornado.ioloop import IOLoop\n",
    "from client import Client, ConnectionError\n",
    "from boxconfig import parse_config\n",
    "from dejavu.recognize import FilePerSecondRecognizer\n",
    "from dejavu import Dejavu, CouldntDecodeError\n",
    "from endpoint import setup_endpoint\n",
    "from multiprocessing import Process\n",
    "import logging as log\n",
    "import requests\n",
    "import dateutil\n",
    "import math\n",
    "import time\n",
    "import os\n",
    "\n",
    "from queue import Queue, Empty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH = '/tmp'\n",
    "AHEAD_TIME_AUDIO_TOLERANCE = 2  # second\n",
    "MAX_SEGMENT_THREADS = 4\n",
    "THRESHOLD = 10\n",
    "SEGMENTS_TOLERANCE_RATE = 0.6\n",
    "FALL_TOLERANCE_SEGMENTS = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "THRESHOLD_FIXED = 1\n",
    "THRESHOLD_AVERAGE = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "QUEUE_SINGLE = 1\n",
    "QUEUE_THREAD = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "MultiAPI = Process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = parse_config()\n",
    "queue = Queue()\n",
    "\n",
    "cloud_base_url = 'https://storage.googleapis.com/{}' \\\n",
    "    .format(config['bucket'])\n",
    "recognizer = FilePerSecondRecognizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "queue_mode = QUEUE_SINGLE\n",
    "threshold_mode = THRESHOLD_FIXED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def obt_siguiente_trabajo():\n",
    "    url = 'https://api.fourier.audio/na/calendario/pendiente?id=%s' % (config['device_id'],)\n",
    "    response = requests.get(url)\n",
    "    log.info(response.json())\n",
    "    return response.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def descargar_anuncio(ad_path):\n",
    "    anuncio = os.path.basename(ad_path)\n",
    "    path = os.path.join(PATH, 'ads')\n",
    "    os.makedirs(path, exist_ok=True)\n",
    "    ruta_anuncio = os.path.join(path, anuncio)\n",
    "\n",
    "    if os.path.isfile(ruta_anuncio):\n",
    "        return ruta_anuncio\n",
    "\n",
    "    url = '{}/{}'.format(cloud_base_url, ad_path)\n",
    "    response = requests.get(url)\n",
    "\n",
    "    # TODO: Agregar alerta cuando la respuesta no sea 200\n",
    "    if response.status_code == 200:\n",
    "        with open(ruta_anuncio, \"wb\") as fp:\n",
    "            fp.write(response.content)\n",
    "            return ruta_anuncio\n",
    "\n",
    "    else:\n",
    "        print(\"Error al descargar\")\n",
    "        print(response)\n",
    "        return None\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def descargar_media(box, station, media):\n",
    "    ref = '{}/{}/{}'.format(box, station, media)\n",
    "    file = os.path.basename(ref)\n",
    "    path = os.path.join(PATH, 'fourier', box, station)\n",
    "    os.makedirs(path, exist_ok=True)\n",
    "    out_file = os.path.join(path, file)\n",
    "\n",
    "    if os.path.isfile(out_file):\n",
    "        return out_file\n",
    "\n",
    "    filename = ref.replace(\"/\",\"%2F\") \\\n",
    "        .replace(\"+\",\"%2B\")\n",
    "    url = '{}/{}'.format(cloud_base_url, filename)\n",
    "    response = requests.get(url)\n",
    "\n",
    "    if response.status_code == 200:\n",
    "        with open(out_file, \"wb\") as fp:\n",
    "            fp.write(response.content)\n",
    "            return out_file\n",
    "    else:\n",
    "        print(\"Error al descargar\")\n",
    "        print(response)\n",
    "        return None\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def obt_calibracion(calibracion):\n",
    "    default = {\n",
    "        'threshold': 12,\n",
    "        'tolerance': 0.8,\n",
    "        'fallTolerance': 1,\n",
    "        'segmentSize': 5,\n",
    "    }\n",
    "\n",
    "    if 'threshold' in calibracion:\n",
    "        default['threshold'] = calibracion['threshold']\n",
    "    if 'tolerance' in calibracion:\n",
    "        default['tolerance'] = calibracion['tolerance']\n",
    "    if 'segmentSize' in calibracion:\n",
    "        default['segmentSize'] = calibracion['segmentSize']\n",
    "    if 'fallTolerance' in calibracion:\n",
    "        default['fallTolerance'] = calibracion['fallTolerance']\n",
    "\n",
    "    return default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encontrar_resultados(resultados, segments_needed=4, calibration=None):\n",
    "    found_counter = 0\n",
    "    found_down_counter = 0\n",
    "    found_index = None\n",
    "    expect_space = False\n",
    "    expect_recover = False\n",
    "    last_value_in_threshold_index = -1\n",
    "    fall_tolerance = calibration['fallTolerance']\n",
    "    found = []\n",
    "\n",
    "    if threshold_mode == THRESHOLD_FIXED:\n",
    "        threshold = calibration['threshold']\n",
    "    elif threshold_mode == THRESHOLD_AVERAGE:\n",
    "        values = [x['confidence'] for x in resultados]\n",
    "        threshold = math.ceil(float(sum(values)) / float(len(values)))\n",
    "\n",
    "    if segments_needed < 1:\n",
    "        segments_needed = 1\n",
    "\n",
    "    for index, result in enumerate(resultados):\n",
    "        if not expect_space:\n",
    "            if result['confidence'] >= threshold:\n",
    "                found_counter += 1\n",
    "                last_value_in_threshold_index = index\n",
    "                if found_index is None:\n",
    "                    found_index = index\n",
    "                if expect_recover:\n",
    "                    found_counter += found_down_counter\n",
    "                    expect_recover = False\n",
    "\n",
    "            elif fall_tolerance:\n",
    "                if not expect_recover:\n",
    "                    if last_value_in_threshold_index != -1:\n",
    "                        expect_recover = True\n",
    "                        found_down_counter += 1\n",
    "                    else:\n",
    "                        pass\n",
    "                else:\n",
    "                    found_counter = 0\n",
    "                    found_down_counter = 0\n",
    "                    found_index = None\n",
    "                    expect_recover = False\n",
    "\n",
    "            else:\n",
    "                found_counter = 0\n",
    "                found_down_counter = 0\n",
    "                found_index = None\n",
    "                expect_recover = False\n",
    "\n",
    "        else:\n",
    "            if result['confidence'] <= threshold:\n",
    "                expect_space = False\n",
    "\n",
    "        if found_counter >= segments_needed:\n",
    "            found_row = resultados[found_index]\n",
    "            found.append(found_row)\n",
    "            found_counter = 0\n",
    "            expect_space = True\n",
    "\n",
    "    return found"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def enviar_resultados(item):\n",
    "    url = 'https://api.fourier.audio/v1/calendario/resultado'\n",
    "    response = requests.post(url, json=item)\n",
    "    return response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "pendiente = obt_siguiente_trabajo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Procesar elemento pendiente\n",
    "\n",
    "ciudad = pendiente['origen']\n",
    "estacion = pendiente['estacion']\n",
    "calibracion = obt_calibracion(pendiente['calibracion'])\n",
    "tolerance = calibracion['tolerance']\n",
    "tamano_segmento = calibracion['segmentSize']\n",
    "longitud_audio = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    anuncios = []\n",
    "    id_by_ad = {}\n",
    "    item_ids = []\n",
    "    x = 0\n",
    "    for i in pendiente[\"elementos\"]:\n",
    "        x = x + 1\n",
    "        id_by_ad[i['anuncio']] = i['id']\n",
    "        if i['id'] not in item_ids:\n",
    "            item_ids.append(i['id'])\n",
    "\n",
    "        anuncio = descargar_anuncio(i[\"ruta\"])\n",
    "        if anuncio is not None:\n",
    "            anuncios.append(anuncio)\n",
    "        else:\n",
    "            print('[process_segment] ad file missing')\n",
    "\n",
    "except Exception as err:\n",
    "    print('[process_segment] [{}] {}'.format(estacion, err))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    media = []\n",
    "    for i in pendiente[\"media\"]:\n",
    "        archivo = descargar_media(ciudad, estacion, i[\"ruta\"])\n",
    "        if archivo is not None:\n",
    "            media.append((archivo, i[\"fecha\"], i[\"timestamp\"]))\n",
    "\n",
    "except Exception as err:\n",
    "    print(err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dejavu = Dejavu({\"database_type\": \"mem\"})\n",
    "try:\n",
    "    x = 0\n",
    "    for anuncio in anuncios:\n",
    "        dejavu.fingerprint_file(anuncio)\n",
    "except Exception as ex:\n",
    "    print(err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if tamano_segmento == 'integer':\n",
    "    tamano_segmento = int(longitud_audio)\n",
    "elif tamano_segmento == 'ceil':\n",
    "    tamano_segmento = int(math.ceil(longitud_audio / 5)) * 5\n",
    "\n",
    "segmentos_necesarios = int(round(float(longitud_audio) / float(tamano_segmento)))\n",
    "segmentos_necesarios = int(round(segmentos_necesarios * tolerance))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "anuncios_en_paralelo = 5 # Este valor debe venir desde el php\n",
    "\n",
    "dejavu = None\n",
    "cont_media = len(media)\n",
    "cont_anuncios = len(anuncios)\n",
    "resultados = []\n",
    "v = []\n",
    "try:\n",
    "    x = 0\n",
    "    while x < cont_anuncios:\n",
    "        y = 0\n",
    "        dejavu = Dejavu({\"database_type\": \"mem\"})\n",
    "        print(\"Nueva comparación\")\n",
    "        while y < anuncios_en_paralelo and x < cont_anuncios:\n",
    "            anuncio = anuncios[x]\n",
    "            print(\"Agregando anuncio %s %s\" % (x, os.path.split(anuncio)[-1],))\n",
    "            dejavu.fingerprint_file(anuncio)\n",
    "            y += 1\n",
    "            x += 1\n",
    "\n",
    "        for ruta, fecha, ts in media:\n",
    "            values = []\n",
    "            try:\n",
    "                for match in dejavu.recognize(recognizer, ruta, tamano_segmento):\n",
    "                    name = None\n",
    "                    ad = None\n",
    "                    try:\n",
    "                        ad = match['name']\n",
    "                        if match['name'] in id_by_ad.keys():\n",
    "                            name = id_by_ad[match['name']]\n",
    "                        else:\n",
    "                            name = match['name']\n",
    "\n",
    "                    except KeyError:\n",
    "                        pass\n",
    "\n",
    "                    resultados.append({\n",
    "                        'ad': ad,\n",
    "                        'confidence': match['confidence'],\n",
    "                        'timestamp': ts,\n",
    "                        'offset': match['offset'],\n",
    "                        'name': name\n",
    "                    })\n",
    "                    values.append(str(match['confidence']))\n",
    "                    ts = ts + 5\n",
    "\n",
    "                v.append(','.join(values))\n",
    "                print('[process_segment] [{2}] {0} {1}'.format(\n",
    "                    os.path.split(ruta)[-1],\n",
    "                    ','.join(values),\n",
    "                    estacion,\n",
    "                ))\n",
    "\n",
    "            except CouldntDecodeError as ex:\n",
    "                log.error('[process_segment] {}'.format(ex))\n",
    "\n",
    "    try:\n",
    "        encontrados = {}\n",
    "        for i in item_ids:\n",
    "            resultado = [r for r in resultados if r[\"name\"] == i]\n",
    "            encontrados[i] = encontrar_resultados(resultado, \n",
    "                segments_needed=tamano_segmento,\n",
    "                calibration=calibracion,)\n",
    "\n",
    "        for id in encontrados:\n",
    "            for e in encontrados[id]:\n",
    "                for i in pendiente['elementos']:\n",
    "                    if i['id'] == id and i['anuncio'] == e['ad']:\n",
    "                        if 'encontrados' not in i:\n",
    "                            i['encontrados'] = []\n",
    "                        i['encontrados'].append(e)\n",
    "                        break\n",
    "\n",
    "        pendiente[\"archivos_perdidos\"] = 0\n",
    "        pendiente[\"total_archivos\"] = cont_media\n",
    "        # response = enviar_resultados(pendiente)\n",
    "    except ConnectionError as ex:\n",
    "        pass\n",
    "    except UserWarning as warn:\n",
    "        pass\n",
    "\n",
    "except Exception as ex:\n",
    "    print(err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    encontrados = {}\n",
    "    for i in item_ids:\n",
    "        r = [result for result in resultados if result[\"name\"] == i]\n",
    "        encontrados[i] = encontrar_resultados(r, segments_needed=segmentos_necesarios, calibration=calibracion,)\n",
    "\n",
    "    for id in encontrados:\n",
    "        for e in encontrados[id]:\n",
    "            for i in pendiente['elementos']:\n",
    "                if i['id'] == id and i['anuncio'] == e['ad']:\n",
    "                    if 'encontrados' not in i:\n",
    "                        i['encontrados'] = []\n",
    "                    i['encontrados'].append(e)\n",
    "                    break\n",
    "\n",
    "    pendiente[\"archivos_perdidos\"] = 0\n",
    "    pendiente[\"total_archivos\"] = cont_media\n",
    "    response = enviar_resultados(pendiente)\n",
    "except ConnectionError as ex:\n",
    "    pass\n",
    "except UserWarning as warn:\n",
    "    pass\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "dejavu = None\n",
    "resultados = {}\n",
    "try:\n",
    "    dejavu = Dejavu({\"database_type\": \"mem\"})\n",
    "    try:\n",
    "        x = 0\n",
    "        for ruta, fecha, ts in media:\n",
    "            log.info(\"Huellando %s\" % (ruta,))\n",
    "            dejavu.fingerprint_file(ruta, ts)\n",
    "    except Exception as ex:\n",
    "        log.info(ex)\n",
    "\n",
    "    for anuncio in anuncios:\n",
    "        log.info(\"Buscando anuncio %s\" % (anuncio,))\n",
    "        for i in dejavu.recognize(recognizer, anuncio, 5):\n",
    "            if not \"id\" in i:\n",
    "                continue\n",
    "\n",
    "            if i[\"confidence\"] < 50:\n",
    "                continue\n",
    "\n",
    "            obj = i\n",
    "            obj[\"match_time\"] = None\n",
    "            nombre_anuncio = os.path.split(anuncio)[-1]\n",
    "            id = id_by_ad[nombre_anuncio]\n",
    "            dict = {\n",
    "                \"id\": id,\n",
    "                \"anuncio\": anuncio,\n",
    "                \"timestamp\": obj[\"name\"] + int(obj['offset_seconds']),\n",
    "                \"confianza\": obj[\"confidence\"],\n",
    "                \"longitud\": obj[\"length\"],\n",
    "                \"desfase_segundos\": obj[\"offset_seconds\"]\n",
    "            }\n",
    "\n",
    "            if i[\"id\"] in resultados.keys():\n",
    "                resultados[i[\"id\"]][\"longitud\"] = resultados[i[\"id\"]][\"longitud\"] + dict[\"longitud\"]\n",
    "                resultados[i[\"id\"]][\"confianza\"] = resultados[i[\"id\"]][\"confianza\"] + dict[\"confianza\"]\n",
    "                continue\n",
    "\n",
    "            resultados[i[\"id\"]] = dict\n",
    "\n",
    "except Exception as ex:\n",
    "    log.info(ex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'id': 46057082, 'anuncio': '/tmp/ads/-MlReJO2cHvAi7XG40mw', 'timestamp': 1630504399, 'confianza': 980, 'longitud': 30000, 'desfase_segundos': 199.39439}\n"
     ]
    }
   ],
   "source": [
    "for id in resultados:\n",
    "    e = resultados[id]\n",
    "    for i in pendiente['elementos']:\n",
    "        anuncio = e['anuncio'].replace('/tmp/ads/', '')\n",
    "        if i['id'] == e['id'] and i['anuncio'] == anuncio:\n",
    "            if 'encontrados' not in i:\n",
    "                i['encontrados'] = []\n",
    "            i['encontrados'].append(e)\n",
    "            print(e)\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enviar_resultados(pendiente)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"idCampana\": 1768, \"idCampanaPauta\": 1166150, \"ciudad\": \"prueba\", \"idEstacion\": 10, \"origen\": \"g2yJhO8z\", \"fecha\": \"2021-09-01 06:00:00\", \"zonaHoraria\": \"America/Hermosillo\", \"estacion\": \"101_7_SON_OBR\", \"media\": [{\"ruta\": \"2021/09/01/2021-09-01T06-00-00-0700.mp3\", \"fecha\": \"2021-09-01 06:00:00\", \"timestamp\": 1630501200}, {\"ruta\": \"2021/09/01/2021-09-01T06-05-00-0700.mp3\", \"fecha\": \"2021-09-01 06:05:00\", \"timestamp\": 1630501500}, {\"ruta\": \"2021/09/01/2021-09-01T06-10-00-0700.mp3\", \"fecha\": \"2021-09-01 06:10:00\", \"timestamp\": 1630501800}, {\"ruta\": \"2021/09/01/2021-09-01T06-15-00-0700.mp3\", \"fecha\": \"2021-09-01 06:15:00\", \"timestamp\": 1630502100}, {\"ruta\": \"2021/09/01/2021-09-01T06-20-00-0700.mp3\", \"fecha\": \"2021-09-01 06:20:00\", \"timestamp\": 1630502400}, {\"ruta\": \"2021/09/01/2021-09-01T06-25-00-0700.mp3\", \"fecha\": \"2021-09-01 06:25:00\", \"timestamp\": 1630502700}, {\"ruta\": \"2021/09/01/2021-09-01T06-30-00-0700.mp3\", \"fecha\": \"2021-09-01 06:30:00\", \"timestamp\": 1630503000}, {\"ruta\": \"2021/09/01/2021-09-01T06-35-00-0700.mp3\", \"fecha\": \"2021-09-01 06:35:00\", \"timestamp\": 1630503300}, {\"ruta\": \"2021/09/01/2021-09-01T06-40-00-0700.mp3\", \"fecha\": \"2021-09-01 06:40:00\", \"timestamp\": 1630503600}, {\"ruta\": \"2021/09/01/2021-09-01T06-45-00-0700.mp3\", \"fecha\": \"2021-09-01 06:45:00\", \"timestamp\": 1630503900}, {\"ruta\": \"2021/09/01/2021-09-01T06-50-00-0700.mp3\", \"fecha\": \"2021-09-01 06:50:00\", \"timestamp\": 1630504200}, {\"ruta\": \"2021/09/01/2021-09-01T06-54-59-0700.mp3\", \"fecha\": \"2021-09-01 06:54:59\", \"timestamp\": 1630504499}], \"calibracion\": {\"fallTolerance\": 1, \"threshold\": 10, \"tolerance\": 0.7}, \"elementos\": [{\"id\": 46057082, \"ruta\": \"anuncios/-MlReJO2cHvAi7XG40mw\", \"repeticiones\": 1, \"idAudio\": 4565, \"anuncio\": \"-MlReJO2cHvAi7XG40mw\", \"encontrados\": [{\"id\": 46057082, \"anuncio\": \"/tmp/ads/-MlReJO2cHvAi7XG40mw\", \"timestamp\": 1630504399, \"confianza\": 980, \"longitud\": 30000, \"desfase_segundos\": 199.39439}]}, {\"id\": 46057100, \"ruta\": \"anuncios/-MlReBIHRuk0dC4WKiZR\", \"repeticiones\": 1, \"idAudio\": 4561, \"anuncio\": \"-MlReBIHRuk0dC4WKiZR\"}, {\"id\": 46057154, \"ruta\": \"anuncios/-MlRce4eqVUyTkDbIcAN\", \"repeticiones\": 1, \"idAudio\": 4548, \"anuncio\": \"-MlRce4eqVUyTkDbIcAN\"}, {\"id\": 46057172, \"ruta\": \"anuncios/-MlRelzkKzHdUpGZlkmD\", \"repeticiones\": 1, \"idAudio\": 4571, \"anuncio\": \"-MlRelzkKzHdUpGZlkmD\"}, {\"id\": 46057190, \"ruta\": \"anuncios/-MlRcIWglVS9eKoJZLlv\", \"repeticiones\": 1, \"idAudio\": 4542, \"anuncio\": \"-MlRcIWglVS9eKoJZLlv\"}, {\"id\": 46057208, \"ruta\": \"anuncios/-MlWhq2bbe7lED-ApFgp\", \"repeticiones\": 1, \"idAudio\": 4617, \"anuncio\": \"-MlWhq2bbe7lED-ApFgp\"}, {\"id\": 46057226, \"ruta\": \"anuncios/-MlRdwzYUS3k5valF9Ri\", \"repeticiones\": 1, \"idAudio\": 4555, \"anuncio\": \"-MlRdwzYUS3k5valF9Ri\"}, {\"id\": 46057244, \"ruta\": \"anuncios/-MlRcpOv5kyMAJ52pswd\", \"repeticiones\": 1, \"idAudio\": 4549, \"anuncio\": \"-MlRcpOv5kyMAJ52pswd\"}, {\"id\": 46057262, \"ruta\": \"anuncios/-MlReDcD94LuAkmbhq-n\", \"repeticiones\": 1, \"idAudio\": 4562, \"anuncio\": \"-MlReDcD94LuAkmbhq-n\"}, {\"id\": 46057280, \"ruta\": \"anuncios/-MlRe-XN_j1Ns6CDUsP-\", \"repeticiones\": 1, \"idAudio\": 4556, \"anuncio\": \"-MlRe-XN_j1Ns6CDUsP-\"}, {\"id\": 46057298, \"ruta\": \"anuncios/-MlRc_XihusSzapzm6Qa\", \"repeticiones\": 1, \"idAudio\": 4547, \"anuncio\": \"-MlRc_XihusSzapzm6Qa\"}, {\"id\": 46057316, \"ruta\": \"anuncios/-MlRe2hx_xDRiqMsE-pk\", \"repeticiones\": 1, \"idAudio\": 4558, \"anuncio\": \"-MlRe2hx_xDRiqMsE-pk\"}, {\"id\": 46057334, \"ruta\": \"anuncios/-MlRe6KKd5gg8u5lbekH\", \"repeticiones\": 1, \"idAudio\": 4559, \"anuncio\": \"-MlRe6KKd5gg8u5lbekH\"}]}'"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json.dumps(pendiente)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'7798E7A7396865717C5FF58D40533B17D05D90DC': {'id': 46057064,\n",
       "  'anuncio': '/tmp/ads/-MlRe9DwDJVtYlLS-6PS',\n",
       "  'fecha': 1630503000,\n",
       "  'confianza': 78,\n",
       "  'longitud': 5000,\n",
       "  'desfase_segundos': 285.21535},\n",
       " '373A10DF13ADBE563AB4A6F39D9DF0C73AA537FB': {'id': 46057082,\n",
       "  'anuncio': '/tmp/ads/-MlReJO2cHvAi7XG40mw',\n",
       "  'fecha': 1630504200,\n",
       "  'confianza': 980,\n",
       "  'longitud': 30000,\n",
       "  'desfase_segundos': 199.39439}}"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "item[\"elementos\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cloud_download_file(item[\"origen\"], item[\"estacion\"], item['archivos'][0]['filename'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = os.path.join(AUDIOS_PATH, 'fourier', 'ciudad', 'estacion')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs(path, exist_ok=True)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
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  },
  "kernelspec": {
   "display_name": "Python 3.9.7 64-bit ('venv': venv)",
   "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",
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