{"id":3895,"date":"2021-12-03T10:30:13","date_gmt":"2021-12-03T15:30:13","guid":{"rendered":"http:\/\/blog.espol.edu.ec\/girni\/?p=3895"},"modified":"2022-07-25T06:46:18","modified_gmt":"2022-07-25T11:46:18","slug":"lorawan-funciones-girni_lora_lib","status":"publish","type":"post","link":"https:\/\/blog.espol.edu.ec\/girni\/lorawan-funciones-girni_lora_lib\/","title":{"rendered":"4. LoRaWan - Funciones girni_lorawan_lib"},"content":{"rendered":"

El archivo contiene un grupo de\u00a0 funciones usadas para procesar los datos de este prototipo, se separaron del bloque principal de instrucciones con\u00a0 el objetivo de simplificar el desarrollo del las actividades principales.<\/p>\n

Recuerde disponer de este archivo en la misma carpeta que el algoritmo que invoca a la librer\u00eda.<\/p>\n

archivo de libreria: girni_lorawan_libreria<\/p>\n

Instrucciones Python<\/h2>\n
# Girni LoRaWan librerias 2021-10-28<\/span>\r\n# LoRaWan, lecturas de Rssi y SNR<\/span>\r\n# Propuesta: edelros@espol.edu.ec<\/span>\r\n# http:\/\/blog.espol.edu.ec\/girni\/<\/span>\r\nimport<\/span> numpy as<\/span> np\r\nimport<\/span> json\r\nimport<\/span> pandas as<\/span> pd\r\nimport<\/span> datetime as<\/span> dt\r\nimport<\/span> os\r\nimport<\/span> matplotlib.pyplot as<\/span> plt\r\nimport<\/span> matplotlib.dates as<\/span> mdates\r\nimport<\/span> matplotlib.units as<\/span> munits\r\n\r\nimport<\/span> geopandas as<\/span> gpd\r\nimport<\/span> fiona\r\nimport<\/span> utm\r\n\r\ndef<\/span> tablapunto<\/span>(unarchivoDB,unsensor,gatewayDB,fechainicio,fechafin,zonaGMT=0):\r\n    '''Lectura de archivo.json hacia una tabla0\r\n       selecciona registros de \"un sensor\"\r\n       graba unreporte.csv con pandas\r\n    '''<\/span>\r\n    campos = ['domain'<\/span>,'entity_id'<\/span>,'state'<\/span>,'attributes'<\/span>,'created'<\/span>]\r\n\r\n    # Lectura de archivo json, cambia a formato DataFrame<\/span>\r\n    tabla0 = pd.DataFrame()\r\n    archivoexiste = os.path.exists(unarchivoDB)\r\n    if<\/span> archivoexiste:\r\n        tabla0 = pd.read_json(unarchivoDB)\r\n        tabla0 = pd.DataFrame(tabla0,columns=campos)\r\n    else<\/span>:\r\n        print<\/span>(' ERROR: NO se encuentra el archivo...'<\/span>)\r\n        print<\/span>('        revise el nombre de archivo. '<\/span>)\r\n\r\n    # Revisa cada registro <\/span>\r\n    leidos = 0\r\n    tabla  = {}\r\n    if<\/span> archivoexiste:\r\n        # Intervalo de fecha como datetime<\/span>\r\n        hora_desplaza  = dt.timedelta(hours = abs<\/span>(zonaGMT))\r\n        fechaformatoDB = '%Y-%m-%dT%H:%M:%S.%f'<\/span>\r\n        fechaformato   = '%Y-%m-%d %H:%M:%S.%f'<\/span>\r\n        fechatxt     = fechainicio[0:np.min([26,len<\/span>(fechainicio)])]\r\n        fechainicio  = dt.datetime.strptime(fechatxt,fechaformato)\r\n        fechatxt     = fechafin[0:np.min([26,len<\/span>(fechafin)])]\r\n        fechafin     = dt.datetime.strptime(fechatxt,fechaformato)\r\n\r\n        # datos de trama<\/span>\r\n        for<\/span> cadaregistro in<\/span> tabla0.index:\r\n            unatrama   = tabla0['attributes'<\/span>][cadaregistro]\r\n            trama_mqtt = json.loads(unatrama)\r\n            \r\n            # selecciona registro por sensor, en fecha y con datos<\/span>\r\n            cualsensor = (tabla0['entity_id'<\/span>][cadaregistro] == unsensor)\r\n            \r\n            unafecha = tabla0['created'<\/span>][cadaregistro]\r\n            unafecha = unafecha[0:np.min([26,len<\/span>(unafecha)])]\r\n            unafecha = dt.datetime.strptime(unafecha,fechaformato)\r\n            unafecha = unafecha - hora_desplaza\r\n            \r\n            enfecha  = (unafecha>=fechainicio) and<\/span> (unafecha<=fechafin)\r\n            condatos = 'applicationID'<\/span> in<\/span> trama_mqtt.keys()\r\n            \r\n            selecciona = cualsensor and<\/span> condatos and<\/span> enfecha\r\n            if<\/span> (selecciona == True<\/span>):        \r\n                # datos en la mensaje MQTT<\/span>\r\n                publishedAt = trama_mqtt[\"publishedAt\"<\/span>]\r\n                publishedAt = publishedAt[0:np.min([26,len<\/span>(publishedAt)])]\r\n                publishedAt = dt.datetime.strptime(publishedAt,fechaformatoDB)\r\n                publishedAt = publishedAt - hora_desplaza\r\n                \r\n                friendly_name = trama_mqtt[\"friendly_name\"<\/span>]\r\n                deviceName    = trama_mqtt[\"deviceName\"<\/span>]\r\n                dispositivo   = friendly_name.split('_'<\/span>)[2]\r\n                \r\n                dr    = trama_mqtt[\"dr\"<\/span>]\r\n                fCnt  = trama_mqtt[\"fCnt\"<\/span>]\r\n                fPort = trama_mqtt[\"fPort\"<\/span>]\r\n\r\n                objectJSON  = trama_mqtt[\"objectJSON\"<\/span>]\r\n                datosensor  = json.loads(objectJSON)\r\n                \r\n                freq_tx   = trama_mqtt[\"txInfo\"<\/span>][\"frequency\"<\/span>]\r\n                bandwidth   = trama_mqtt[\"txInfo\"<\/span>][\"loRaModulationInfo\"<\/span>][\"bandwidth\"<\/span>]\r\n                spreadingFactor = trama_mqtt[\"txInfo\"<\/span>][\"loRaModulationInfo\"<\/span>][\"spreadingFactor\"<\/span>]\r\n                     \r\n                datostrama = {\"publishedAt\"<\/span>: publishedAt,\r\n                              \"dispositivo\"<\/span>: dispositivo,\r\n                              \"fCnt\"<\/span>: fCnt}\r\n                \r\n                # revisa gateway en la red LoRaWan<\/span>\r\n                tamano = len<\/span>(trama_mqtt[\"rxInfo\"<\/span>])\r\n                i = 0\r\n                while<\/span> i<tamano:\r\n                    gatewayID = trama_mqtt[\"rxInfo\"<\/span>][i][\"gatewayID\"<\/span>]\r\n                    rssi      = trama_mqtt[\"rxInfo\"<\/span>][i][\"rssi\"<\/span>]\r\n                    loRaSNR   = trama_mqtt[\"rxInfo\"<\/span>][i][\"loRaSNR\"<\/span>]\r\n                    channel   = trama_mqtt[\"rxInfo\"<\/span>][i][\"channel\"<\/span>]\r\n                    rfChain   = trama_mqtt[\"rxInfo\"<\/span>][i][\"rfChain\"<\/span>]\r\n                    gtwNum    = gatewayDB[gatewayID]\r\n                    \r\n                    # registra en tabla, incluyendo tramas repetidas<\/span>\r\n                    datostrama['rssi_up'<\/span>]    = rssi\r\n                    datostrama['snr_up'<\/span>]     = loRaSNR\r\n                    datostrama['channel_up'<\/span>] = channel\r\n                    datostrama['rfChain_up'<\/span>] = rfChain\r\n                    datostrama['gtw_rx'<\/span>]     = gtwNum\r\n                    i = i + 1\r\n\r\n                # dato del sensor<\/span>\r\n                equivale = {'Down_datarate'<\/span>: 'dr_down'<\/span>,\r\n                            'Down_rssi'<\/span>    : 'rssi_down'<\/span>,\r\n                            'Down_snr'<\/span>     : 'snr_down'<\/span>,\r\n                            'bateria_V'<\/span>    : 'bateria_V'<\/span>}\r\n                for<\/span> undato in<\/span> datosensor:\r\n                    if<\/span> undato in<\/span> equivale.keys():\r\n                        datostrama[equivale[undato]] = datosensor[undato]\r\n                    else<\/span>:\r\n                        datostrama[undato] = datosensor[undato]\r\n\r\n                # datos restantes<\/span>\r\n                datostrama[\"frequency\"<\/span>] = freq_tx\r\n                datostrama[\"bandwidth\"<\/span>] = bandwidth\r\n                datostrama[\"spreadingFactor\"<\/span>] = spreadingFactor\r\n                datostrama[\"fPort\"<\/span>]     = fPort\r\n                datostrama[\"dr\"<\/span>]        = dr\r\n                datostrama[\"created\"<\/span>]   = unafecha\r\n\r\n                leidos = leidos + 1\r\n                \r\n                # revisa registro repetido<\/span>\r\n                repetido = False<\/span>\r\n                if<\/span> leidos>1:\r\n                    trama_num  = (fCnt == tabla[leidos-1]['fCnt'<\/span>])\r\n                    fecha_pub  = (publishedAt == tabla[leidos-1]['publishedAt'<\/span>])\r\n                    gtwNum_rep = (gtwNum == tabla[leidos-1]['gtw_rx'<\/span>])\r\n                    repetido   = (trama_num and<\/span> fecha_pub and<\/span> gtwNum_rep)\r\n                if<\/span> not<\/span>(repetido):\r\n                    tabla[leidos] = datostrama\r\n                else<\/span>:\r\n                    leidos = leidos - 1\r\n                \r\n        # convierte diccionario en DataFrame<\/span>\r\n        if<\/span> len<\/span>(tabla)>0:\r\n            tabla = pd.DataFrame(tabla)\r\n            tabla = tabla.T\r\n    return<\/span>(tabla)\r\n\r\ndef<\/span> describe_punto<\/span>(tabla,codigopunto,carpeta_rsm,variables):\r\n    ''' Descriptores estadisticos de los datos.csv\r\n        de un dispositivo, revisa media, desviaci\u00f3 est\u00e1ndar, pmf\r\n        graba unreporte.csv con pandas y genera gr\u00e1ficas\r\n    '''<\/span>\r\n    medida = variables['medida'<\/span>]\r\n    medida_unidad = variables['medida_unidad'<\/span>]\r\n    medida_normal = variables['medida_normal'<\/span>]\r\n    movAvg_cual = variables['movAvg_cual'<\/span>]\r\n    medida_grafica = variables['medida_grafica'<\/span>]\r\n    movAvg_color = variables['movAvg_color'<\/span>]\r\n    guarda = variables['guarda'<\/span>]\r\n    precision = variables['precision'<\/span>]\r\n                   \r\n    fechaformato = \"%Y-%m-%d %H:%M:%S.%f\"<\/span>\r\n    # medida intervalo<\/span>\r\n    medida_min = np.min(medida_normal)\r\n    medida_max = np.max(medida_normal)\r\n\r\n    # fechas series a datetime<\/span>\r\n    tabla['publishedAt'<\/span>] = pd.to_datetime(tabla['publishedAt'<\/span>],\r\n                                          format<\/span>=fechaformato)\r\n    tabla['created'<\/span>] = pd.to_datetime(tabla['publishedAt'<\/span>],\r\n                                      format<\/span>=fechaformato)\r\n\r\n    # revisa errores de medida<\/span>\r\n    tabla[\"error_up\"<\/span>]   = 0\r\n    tabla[\"error_down\"<\/span>] = 0\r\n    for<\/span> undato in<\/span> tabla['publishedAt'<\/span>].keys():   \r\n        medida_up = tabla[medida+'_up'<\/span>][undato]\r\n        enrango = (medida_up>=np.min(medida_normal))\r\n        enrango = (enrango and<\/span> medida_up<=np.max(medida_normal))\r\n        if<\/span> not<\/span>(enrango):\r\n            tabla.at[undato,\"error_up\"<\/span>] = 1\r\n        \r\n        medida_down = tabla[medida+'_down'<\/span>][undato]\r\n        enrango = (medida_down>=np.min(medida_normal))\r\n        enrango = (enrango and<\/span> medida_down<=np.max(medida_normal))\r\n        if<\/span> not<\/span>(enrango):\r\n            tabla.at[undato,\"error_down\"<\/span>] = 1      \r\n\r\n    # tasa error trama<\/span>\r\n    leidos = len<\/span>(tabla)\r\n    if<\/span> leidos > 0:\r\n        error_up   = np.sum(tabla['error_up'<\/span>])\r\n        error_up   = error_up\/leidos\r\n        error_down = np.sum(tabla['error_down'<\/span>])\r\n        error_down = error_down\/leidos\r\n\r\n    # descriptor estad\u00edstico, datos sin errores<\/span>\r\n    condicion_up = (tabla['error_up'<\/span>]==0)\r\n    condicion_down = (tabla['error_down'<\/span>]==0)\r\n\r\n    medida_up = tabla[condicion_up][medida+'_up'<\/span>]\r\n    describe_up = medida_up.describe()\r\n    describe_up['error_trama'<\/span>] = error_up\r\n\r\n    medida_down = tabla[condicion_down][medida+'_down'<\/span>]\r\n    describe_down = medida_down.describe()\r\n    describe_down['error_trama'<\/span>] = error_down\r\n\r\n    descriptor = describe_up.copy()\r\n    descriptor = pd.concat([descriptor,describe_down],axis=1)\r\n    descriptor['dispositivo'<\/span>] = tabla['dispositivo'<\/span>][0]\r\n\r\n    pmf_up   = medida_pmf(medida_up,describe_up)\r\n    pmf_down = medida_pmf(medida_down,describe_down)\r\n\r\n    pmf_punto = {'pmf'<\/span>:{'pmf_up'<\/span>   : pmf_up,\r\n                        'pmf_down'<\/span> : pmf_down}}\r\n    pmf_punto = pd.DataFrame(pmf_punto)\r\n    pmf_punto['dispositivo'<\/span>] = tabla['dispositivo'<\/span>][0]\r\n\r\n    # Para gr\u00e1ficas<\/span>\r\n    # medias moviles en movAvg_cual[]<\/span>\r\n    serie_up  = pd.Series(medida_up)\r\n    movAvg_up_mean = []\r\n    movAvg_up_std = []\r\n    m = len<\/span>(movAvg_cual)\r\n    for<\/span> j in<\/span> range<\/span>(0,m,1):\r\n        k = movAvg_cual[j]\r\n        movAvg_up_mean.append(list<\/span>(serie_up.rolling(k).mean()))\r\n        movAvg_up_std.append(list<\/span>(serie_up.rolling(k).std()))\r\n        \r\n    serie_down = pd.Series(medida_down)\r\n    movAvg_down_mean = []\r\n    movAvg_down_std = []\r\n    for<\/span> j in<\/span> range<\/span>(0,m,1):\r\n        k = movAvg_cual[j]\r\n        movAvg_down_mean.append(list<\/span>(serie_down.rolling(k).mean()))\r\n        movAvg_down_std.append(list<\/span>(serie_down.rolling(k).std()))\r\n\r\n    movAvgData ={'movAvg_cual'<\/span>   : movAvg_cual,\r\n                 'movAvg_color'<\/span>  : movAvg_color,\r\n                 'movAvg_up_mean'<\/span>  : movAvg_up_mean,\r\n                 'movAvg_down_mean'<\/span>: movAvg_down_mean,\r\n                 'movAvg_up_std'<\/span>   : movAvg_up_std,\r\n                 'movAvg_down_std'<\/span> : movAvg_down_std\r\n                 }\r\n\r\n    grafData ={'codigopunto'<\/span> : codigopunto,\r\n               'medida'<\/span> : medida,\r\n               'precision'<\/span>: precision,\r\n               'medida_unidad'<\/span> : medida_unidad,\r\n               'medida_grafica'<\/span>: medida_grafica\r\n               }\r\n    resultado = [tabla,descriptor,pmf_punto,movAvgData,grafData]\r\n    return<\/span>(resultado)\r\n\r\n# funci\u00f3n de probabilidad de masa pmf<\/span>\r\ndef<\/span> medida_pmf<\/span>(valores,undescriptor):\r\n    pmin   = np.min(valores)\r\n    pmax   = np.max(valores)\r\n    tramo  = int<\/span>(pmax-pmin)\r\n    conteo = np.zeros(tramo+1,dtype=int<\/span>)\r\n    intervalo = np.arange(pmin,pmax+1,1)\r\n    for<\/span> valor in<\/span> valores:\r\n        donde = np.where(intervalo == valor)\r\n        conteo[donde] = conteo[donde] + 1\r\n    freq_relativa = np.array(conteo)\/np.sum(conteo)\r\n    unpmf = {'intervalo'<\/span> : list<\/span>(intervalo),\r\n             'freq_relativa'<\/span> : list<\/span>(freq_relativa)}\r\n    return<\/span>(unpmf)\r\n\r\n# GRAFICA -----<\/span>\r\ndef<\/span> graf_puntos_serie<\/span>(tabla,descriptor,movAvgData,grafData):\r\n    ''' grafica la serie de tiempo de cada punto\r\n        a\u00f1ade medias m\u00f3viles\r\n    '''<\/span>\r\n    # ajuste de formato de fecha para eje x<\/span>\r\n    converter = mdates.ConciseDateConverter()\r\n    munits.registry[np.datetime64] = converter\r\n    munits.registry[dt.date] = converter\r\n    munits.registry[dt.datetime] = converter\r\n\r\n    # datos para grafica<\/span>\r\n    precision   = grafData['precision'<\/span>]\r\n    medida = grafData['medida'<\/span>]\r\n    medida_unidad  = grafData['medida_unidad'<\/span>]\r\n    medida_grafica = grafData['medida_grafica'<\/span>]\r\n    \r\n    movAvg_cual  = movAvgData['movAvg_cual'<\/span>]\r\n    movAvg_color = movAvgData['movAvg_color'<\/span>]\r\n    \r\n    media_up    = descriptor[medida+'_up'<\/span>]['mean'<\/span>]\r\n    std_up      = descriptor[medida+'_up'<\/span>]['std'<\/span>]\r\n    media_down  = descriptor[medida+'_down'<\/span>]['mean'<\/span>]\r\n    std_down    = descriptor[medida+'_down'<\/span>]['std'<\/span>]\r\n\r\n    # ajuste de intervalo eje y<\/span>\r\n    y_min = np.min([np.min(medida_grafica),\r\n                    media_up - 2*std_up,\r\n                    media_down - 2*std_down])\r\n    y_max = np.max([np.max(medida_grafica),\r\n                    media_up + 2*std_up,\r\n                    media_down + 2*std_down])\r\n    \r\n    # selecciona sin error<\/span>\r\n    condicion_up = (tabla['error_up'<\/span>]==0)\r\n    condicion_down = (tabla['error_down'<\/span>]==0)\r\n\r\n    # grafica<\/span>\r\n    fig_serie,(graf_up,graf_down) = plt.subplots(2,1)\r\n    \r\n    # medida_up -----<\/span>\r\n    graf_up.plot(tabla[condicion_up]['publishedAt'<\/span>],\r\n                 tabla[condicion_up][medida+'_up'<\/span>],\r\n                 color='blue'<\/span>,marker ='.'<\/span>,\r\n                 linestyle=''<\/span>)\r\n    \r\n    # medida_up, medias y std<\/span>\r\n    etiq_up = str<\/span>(np.round(media_up,precision))+' +\/- '<\/span>\r\n    etiq_up = etiq_up + str<\/span>(np.round(std_up,precision))\r\n    graf_up.axhline(media_up,\r\n                    color='blue'<\/span>,label=etiq_up)\r\n    graf_up.axhline(media_up-std_up,\r\n                    color='blue'<\/span>,linestyle='dotted'<\/span>)\r\n    graf_up.axhline(media_up+std_up,\r\n                    color='blue'<\/span>,linestyle='dotted'<\/span>)\r\n    \r\n    # medida_up, medias m\u00f3viles<\/span>\r\n    m = len<\/span>(movAvg_cual)\r\n    for<\/span> j in<\/span> range<\/span>(0,m,1):\r\n        k = str<\/span>(movAvg_cual[j])\r\n        graf_up.plot(tabla[condicion_up]['publishedAt'<\/span>],\r\n                     movAvgData['movAvg_up_mean'<\/span>][j],\r\n                     label='movAvg_'<\/span>+k,\r\n                     color=movAvg_color[j])\r\n    \r\n    graf_up.set_ylim(y_min,y_max)\r\n    graf_up.set_ylabel(medida+'_up ('<\/span>+medida_unidad+')'<\/span>,\r\n                       color='blue'<\/span>)\r\n    graf_up.legend()\r\n    graf_up.grid(True<\/span>,linestyle='dotted'<\/span>,\r\n                 axis='x'<\/span>,which='both'<\/span>)\r\n\r\n    # medida_down -------<\/span>\r\n    graf_down.plot(tabla[condicion_down]['publishedAt'<\/span>],\r\n                   tabla[condicion_down][medida+'_down'<\/span>],\r\n                   color='brown'<\/span>,marker ='.'<\/span>,\r\n                   linestyle=''<\/span>)\r\n\r\n    # medida_down, medias y std<\/span>\r\n    etiq_down = str<\/span>(np.round(media_down,precision))+' +\/- '<\/span>\r\n    etiq_down = etiq_down + str<\/span>(np.round(std_down,precision))\r\n    graf_down.axhline(media_down,\r\n                      color='brown'<\/span>,label=etiq_down)\r\n    graf_down.axhline(media_down+std_down,\r\n                      color='brown'<\/span>,linestyle='dotted'<\/span>)\r\n    graf_down.axhline(media_down-std_down,\r\n                      color='brown'<\/span>,linestyle='dotted'<\/span>)\r\n    \r\n    # medida_down, medias moviles<\/span>\r\n    for<\/span> j in<\/span> range<\/span>(0,m,1):\r\n        k = str<\/span>(movAvg_cual[j])\r\n        graf_down.plot(tabla[condicion_down]['publishedAt'<\/span>],\r\n                       movAvgData['movAvg_down_mean'<\/span>][j],\r\n                       label='movAvg_'<\/span>+k,\r\n                       color=movAvg_color[j])\r\n    \r\n    graf_down.set_ylim(y_min,y_max)\r\n    graf_down.set_xlabel('fecha'<\/span>)\r\n    graf_down.set_ylabel(medida+'_down ('<\/span>+medida_unidad+')'<\/span>,\r\n                         color='brown'<\/span>)\r\n    graf_down.legend()\r\n    graf_down.grid(True<\/span>,linestyle='dotted'<\/span>,\r\n                   axis='x'<\/span>, which='both'<\/span>)\r\n    graf_up.set_title('Serie: '<\/span>+grafData['codigopunto'<\/span>]+' '<\/span>+ medida)\r\n    plt.tight_layout()\r\n    return<\/span>(fig_serie)\r\n\r\ndef<\/span> graf_puntos_pmf<\/span>(pmf_punto,descriptor,grafData):\r\n    ''' grafica funci\u00f3n de probabilida de masa\r\n        para cada punto, media +\/- std\r\n    '''<\/span>\r\n    # datos para grafica<\/span>\r\n    x_pmfup   = pmf_punto['pmf'<\/span>]['pmf_up'<\/span>]['intervalo'<\/span>]\r\n    y_pmfup   = pmf_punto['pmf'<\/span>]['pmf_up'<\/span>]['freq_relativa'<\/span>]\r\n    x_pmfdown = pmf_punto['pmf'<\/span>]['pmf_down'<\/span>]['intervalo'<\/span>]\r\n    y_pmfdown = pmf_punto['pmf'<\/span>]['pmf_down'<\/span>]['freq_relativa'<\/span>]\r\n    \r\n    precision = grafData['precision'<\/span>]\r\n    medida = grafData['medida'<\/span>]\r\n    medida_unidad  = grafData['medida_unidad'<\/span>]\r\n    medida_grafica = grafData['medida_grafica'<\/span>]\r\n    \r\n    media_up   = descriptor[medida+'_up'<\/span>]['mean'<\/span>]\r\n    std_up     = descriptor[medida+'_up'<\/span>]['std'<\/span>]\r\n    media_down = descriptor[medida+'_down'<\/span>]['mean'<\/span>]\r\n    std_down   = descriptor[medida+'_down'<\/span>]['std'<\/span>]\r\n\r\n    prob_max = 0.40\r\n    # ajuste de intervalo eje y<\/span>\r\n    y_min = np.min([np.min(medida_grafica),\r\n                    media_up - 2*std_up,\r\n                    media_down - 2*std_down])\r\n    y_max = np.max([np.max(medida_grafica),\r\n                    media_up + 2*std_up,\r\n                    media_down + 2*std_down])\r\n    # grafica<\/span>\r\n    fig_pmf,graf_pmf = plt.subplots()\r\n    etiq_up = str<\/span>(np.round(media_up,precision)) +' +\/- '<\/span>\r\n    etiq_up = etiq_up + str<\/span>(np.round(std_up,precision))\r\n    graf_pmf.plot(x_pmfup,y_pmfup,\r\n                  label='media_up '<\/span>+etiq_up,\r\n                  color='blue'<\/span>)\r\n    graf_pmf.axvline(media_up,color='blue'<\/span>)\r\n    graf_pmf.axvline(media_up+std_up,\r\n                     linestyle='dotted'<\/span>,color='blue'<\/span>)\r\n    graf_pmf.axvline(media_up-std_up,\r\n                     linestyle='dotted'<\/span>,color='blue'<\/span>)\r\n\r\n    etiq_down = str<\/span>(np.round(media_down,precision))+' +\/- '<\/span>\r\n    etiq_down = etiq_down + str<\/span>(np.round(std_down,precision))\r\n    graf_pmf.plot(x_pmfdown,y_pmfdown,\r\n                  label='media_down '<\/span>+etiq_down,\r\n                  color='brown'<\/span>)\r\n    graf_pmf.axvline(media_down,color='brown'<\/span>)\r\n    graf_pmf.axvline(media_down+std_down,\r\n                     linestyle='dotted'<\/span>,color='brown'<\/span>)\r\n    graf_pmf.axvline(media_down-std_down,\r\n                     linestyle='dotted'<\/span>,color='brown'<\/span>)\r\n\r\n    graf_pmf.set_title('pmf: '<\/span>+grafData['codigopunto'<\/span>]+' '<\/span>+medida)\r\n    graf_pmf.set_xlim(y_min,y_max)\r\n    graf_pmf.set_ylim(0,prob_max)\r\n    graf_pmf.set_xlabel(medida+' ('<\/span>+medida_unidad+')'<\/span>)\r\n    graf_pmf.set_ylabel('frecuencia relativa'<\/span>)\r\n    graf_pmf.legend()\r\n    graf_pmf.grid(True<\/span>,linestyle='dotted'<\/span>,\r\n                  axis='x'<\/span>, which='both'<\/span>)\r\n    return<\/span>(fig_pmf)\r\n\r\ndef<\/span> graf_puntos_std<\/span>(tabla,descriptor,movAvgData,grafData):\r\n    ''' grafica serie de std usando medias moviles\r\n        para cada punto, media_std\r\n    '''<\/span>\r\n    # ajuste de formato de fecha para eje x<\/span>\r\n    converter = mdates.ConciseDateConverter()\r\n    munits.registry[np.datetime64] = converter\r\n    munits.registry[dt.date] = converter\r\n    munits.registry[dt.datetime] = converter\r\n    \r\n    # datos para grafica<\/span>\r\n    precision = grafData['precision'<\/span>]\r\n    medida    = grafData['medida'<\/span>]\r\n    medida_unidad = grafData['medida_unidad'<\/span>]\r\n\r\n    movAvg_cual = movAvgData['movAvg_cual'<\/span>]\r\n    movAvg_color = movAvgData['movAvg_color'<\/span>]\r\n\r\n    # selecciona sin error<\/span>\r\n    condicion_up   = (tabla['error_up'<\/span>]==0)\r\n    condicion_down = (tabla['error_down'<\/span>]==0)\r\n    \r\n    # ajuste de intervalo eje y<\/span>\r\n    y_min = 0\r\n    y_max = np.max([2, 2*descriptor[medida+'_up'<\/span>]['std'<\/span>],\r\n                    2*descriptor[medida+'_down'<\/span>]['std'<\/span>]])\r\n    # grafica<\/span>\r\n    fig_std,(graf_stdUp,graf_stdDown) = plt.subplots(2,1)\r\n    \r\n    # std up<\/span>\r\n    std_up = np.round(descriptor[medida+'_up'<\/span>]['std'<\/span>],precision)\r\n    graf_stdUp.axhline(std_up,label='std '<\/span>+str<\/span>(std_up),\r\n                       color='blue'<\/span>)\r\n    m = len<\/span>(movAvg_cual)\r\n    for<\/span> j in<\/span> range<\/span>(0,m,1):\r\n        k = str<\/span>(movAvg_cual[j])\r\n        graf_stdUp.plot(tabla[condicion_up]['publishedAt'<\/span>],\r\n                        movAvgData['movAvg_up_std'<\/span>][j],\r\n                        label='movAvg_'<\/span>+k,\r\n                        color=movAvg_color[j])\r\n    graf_stdUp.set_ylim(y_min,y_max)\r\n    graf_stdUp.set_ylabel('std_up ('<\/span>+medida_unidad+')'<\/span>,\r\n                          color='blue'<\/span>)\r\n    graf_stdUp.legend()\r\n    graf_stdUp.grid(True<\/span>,linestyle='dotted'<\/span>,\r\n                    axis='x'<\/span>, which='both'<\/span>)\r\n    graf_stdUp.set_title('std: '<\/span>+grafData['codigopunto'<\/span>]+' '<\/span>+ medida)\r\n\r\n    # std down<\/span>\r\n    std_down = np.round(descriptor[medida+'_down'<\/span>]['std'<\/span>],precision)\r\n    graf_stdDown.axhline(std_down,label='std '<\/span>+str<\/span>(std_down),\r\n                         color='brown'<\/span>)\r\n    for<\/span> j in<\/span> range<\/span>(0,m,1):\r\n        k = str<\/span>(movAvg_cual[j])\r\n        graf_stdDown.plot(tabla[condicion_down]['publishedAt'<\/span>],\r\n                          movAvgData['movAvg_down_std'<\/span>][j],\r\n                          label='movAvg_'<\/span>+k,color=movAvg_color[j])\r\n    graf_stdDown.set_ylim(y_min,y_max)\r\n    graf_stdDown.set_xlabel('fecha'<\/span>)\r\n    graf_stdDown.set_ylabel('std_down ('<\/span>+medida_unidad+')'<\/span>,\r\n                            color='brown'<\/span>)\r\n    graf_stdDown.legend()\r\n    graf_stdDown.grid(True<\/span>,linestyle='dotted'<\/span>,\r\n                      axis='x'<\/span>, which='both'<\/span>)\r\n    plt.tight_layout()\r\n    return<\/span>(fig_std)\r\n\r\n\r\ndef<\/span> linealiza_lstsq<\/span>(xi,yi,digitos = 3):\r\n    ''' usa minimos cuadrados para entregar la ecuacion\r\n        digitos: usados en expresion latex\r\n    '''<\/span>\r\n    unaecuacion = {}\r\n    # Eje x en log10()<\/span>\r\n    xilog = np.log10(xi)\r\n    n = len<\/span>(xi)\r\n    \r\n    # m\u00ednimos cuadrados (least square),<\/span>\r\n    # distancia vs medida<\/span>\r\n    A = np.vstack([xilog, np.ones(n)]).T\r\n    [m0, b0] = np.linalg.lstsq(A, yi, rcond=None<\/span>)[0]\r\n    alpha = -m0\/10\r\n    beta  = b0\r\n\r\n    # coeficiente de correlaci\u00f3n<\/span>\r\n    coeficientes = np.corrcoef(xilog,yi)\r\n    r = coeficientes[0,1]\r\n    # coeficiente de determinaci\u00f3n<\/span>\r\n    r2 = r**2\r\n\r\n    # ecuaciones expresion rssi(d)<\/span>\r\n    fdist0 = lambda<\/span> d: -10*alpha*(np.log10(d))+beta\r\n    \r\n    fdtxt0 = r'$ rssi = -10('<\/span> + str<\/span>(np.round(alpha,digitos))\r\n    fdtxt0 = fdtxt0 + ')log_{10}(d)'<\/span> # +('<\/span>\r\n    texto = '+'<\/span>\r\n    if<\/span> beta <0:\r\n        texto = '-'<\/span>\r\n    fdtxt0 = fdtxt0 + texto + str<\/span>(np.round(np.abs(beta),digitos))+' $'<\/span>\r\n\r\n    # Errores respecto a rssi(d) <\/span>\r\n    yi0  = fdist0(xi)\r\n    dyi0 = yi - yi0\r\n    dyi0mean = np.mean(np.abs(dyi0))\r\n    dyi0std  = np.std(dyi0, dtype=np.float64)\r\n\r\n    # ecuaciones expresion d(rssi)<\/span>\r\n    grssi0 = lambda<\/span> rssi: 10**((beta-rssi)\/(10*alpha))\r\n    grtxt0 = r\"$ d = 10^{(\"<\/span> + str<\/span>(np.round(beta,digitos)) + ' - '<\/span>\r\n    grtxt0 = grtxt0 + 'rssi)\/'<\/span> + '(10('<\/span>+str<\/span>(np.round(alpha,digitos))+'))} $'<\/span>\r\n\r\n    # Errores respecto a rssi(d) <\/span>\r\n    xi0  = grssi0(yi)\r\n    dxi0 = xi - xi0\r\n    dxi0mean = np.mean(np.abs(dxi0))\r\n    dxi0std  = np.std(dxi0, dtype=np.float64)\r\n    \r\n    yimin = np.around(np.min(yi),2)\r\n    yimax = np.around(np.max(yi),2)\r\n    \r\n    unaecuacion = {'alpha'<\/span>   : alpha,\r\n                   'beta'<\/span>    : beta,\r\n                   'coef_correlacion'<\/span>   : r,\r\n                   'coef_determinacion'<\/span> : r2,\r\n                   'eq_latex'<\/span>: fdtxt0,\r\n                   'intervalox'<\/span> : [np.min(xi),np.max(xi)],\r\n                   'error_medio'<\/span>: dyi0mean,\r\n                   'error_std'<\/span>  : dyi0std,\r\n                   'eqg_latex'<\/span>  : grtxt0,\r\n                   'intervaloy'<\/span> : [yimin,yimax],\r\n                   'errorx_medio'<\/span>: dxi0mean,\r\n                   'errorx_std'<\/span>  : dxi0std,\r\n                   }\r\n    return<\/span>(unaecuacion)\r\n\r\ndef<\/span> ecuacion_gradiente<\/span>(carpeta_rsm,arch_coord,var_gen):\r\n    ''' desarrolla la ecuaci\u00f3n para un gradiente\r\n    '''<\/span>\r\n    var_gen['movAvg_cual'<\/span>] = [2,4] #cada cuantas muestras<\/span>\r\n    partes = carpeta_rsm.strip('\/'<\/span>).split('_'<\/span>)\r\n    arch_nombre = partes[1]+'_'<\/span>+partes[2]\r\n\r\n    carp_coord = var_gen['carp_coord'<\/span>]\r\n    medida = var_gen['medida'<\/span>]\r\n    precision = var_gen['precision'<\/span>]\r\n    \r\n    # lee coordenadas y su distancia al gateway<\/span>\r\n    dist_Gtw = coordenadas_leer(arch_coord,carp_coord)\r\n\r\n    # crea lista.txt de archivos a usar si no existe<\/span>\r\n    arch_lista = medida+\"_\"<\/span>+arch_nombre+\"_lista.txt\"<\/span>\r\n    archivo_ruta  = carpeta_rsm + '\/'<\/span> + arch_lista\r\n    archivoexiste = os.path.exists(archivo_ruta)\r\n    if<\/span> not<\/span>(archivoexiste):\r\n        puntoUsar = pd.DataFrame(columns=['punto'<\/span>, 'up'<\/span>, 'down'<\/span>])\r\n        for<\/span> unarchivo in<\/span> os.listdir(carpeta_rsm):\r\n            verifica = unarchivo.startswith('describe_'<\/span>)\r\n            verifica = verifica and<\/span> unarchivo.endswith('.csv'<\/span>)\r\n            if<\/span> verifica:\r\n                partes  = unarchivo.strip('.csv'<\/span>).split('_'<\/span>)\r\n                unpunto = partes[2]\r\n                puntoUsar = puntoUsar.append({'punto'<\/span> : unpunto,\r\n                                              'up'<\/span> : 1,\r\n                                              'down'<\/span> : 1},\r\n                                             ignore_index = True<\/span>)\r\n        puntoUsar = puntoUsar.set_index('punto'<\/span>)\r\n        puntoUsar.to_csv(archivo_ruta)\r\n\r\n    # usa lista.txt de archivos seleccionados 1 \u00f3 0 para enlace up,down<\/span>\r\n    if<\/span> (archivoexiste):\r\n        puntoUsar = pd.read_csv(archivo_ruta)\r\n        puntoUsar = puntoUsar.set_index('punto'<\/span>)\r\n      \r\n    # Datos para gr\u00e1fica desde lista.txt<\/span>\r\n    punto_graf  = pd.DataFrame()\r\n    puntoSinDist = []\r\n    for<\/span> unarchivo in<\/span> os.listdir(carpeta_rsm):\r\n        if<\/span> unarchivo.startswith('describe_'<\/span>):\r\n            codigopunto = unarchivo.strip('.csv'<\/span>).split('_'<\/span>)[2]\r\n            \r\n            # lectura del archivo<\/span>\r\n            unresumen  = carpeta_rsm+'\/'<\/span>+unarchivo\r\n            descriptor = pd.read_csv(unresumen,index_col='Unnamed: 0'<\/span>)\r\n\r\n            if<\/span> (codigopunto in<\/span> dist_Gtw.index):\r\n                undato = {'codigopunto'<\/span>: codigopunto,\r\n                          'dist_Gtw'<\/span>   : dist_Gtw[codigopunto],\r\n                          medida+'_up'<\/span> : descriptor['rssi_up'<\/span>]['mean'<\/span>],\r\n                          medida+'_up'<\/span>+'_std'<\/span>: descriptor['rssi_up'<\/span>]['std'<\/span>],\r\n                          'usar_up'<\/span> : puntoUsar['up'<\/span>][codigopunto],\r\n                          medida+'_down'<\/span>: descriptor['rssi_down'<\/span>]['mean'<\/span>],\r\n                          medida+'_down'<\/span>+'_std'<\/span> :descriptor['rssi_down'<\/span>]['std'<\/span>],\r\n                          'usar_down'<\/span> : puntoUsar['down'<\/span>][codigopunto],\r\n                          'dispositivo'<\/span>: descriptor['dispositivo'<\/span>][0]\r\n                          }\r\n                punto_graf = punto_graf.append(undato,ignore_index=True<\/span>)\r\n            else<\/span>:\r\n                puntoSinDist.append(codigopunto)\r\n            \r\n    punto_graf = punto_graf.set_index('codigopunto'<\/span>)\r\n    punto_graf = punto_graf.sort_values('dist_Gtw'<\/span> )\r\n\r\n    # selecciona datos<\/span>\r\n    dist = punto_graf['dist_Gtw'<\/span>]\r\n    usar_up = punto_graf['usar_up'<\/span>]\r\n    usar_down = punto_graf['usar_down'<\/span>]\r\n\r\n    # Eje x en log10()<\/span>\r\n    xi = np.array(dist)\r\n\r\n    # enlace_up<\/span>\r\n    media_up = punto_graf[medida+'_up'<\/span>]\r\n    std_up   = punto_graf[medida+'_up'<\/span>+'_std'<\/span>]\r\n    media_up_techo = media_up + std_up\r\n    media_up_piso  = media_up - std_up\r\n\r\n    # ecuacion enlace up<\/span>\r\n    yi_up = np.array(media_up)\r\n    eq_up = linealiza_lstsq(xi[usar_up==1],\r\n                            yi_up[usar_up==1],\r\n                            digitos = precision)\r\n    alpha_up = eq_up['alpha'<\/span>]\r\n    beta_up  = eq_up['beta'<\/span>]\r\n    fd_up = lambda<\/span> d: -10*alpha_up*(np.log10(d))+beta_up\r\n\r\n    # errores up de puntos vs ecuacion<\/span>\r\n    fi_up = fd_up(xi)\r\n    dyi0std  = eq_up['error_std'<\/span>]\r\n    punto_graf['fi_up'<\/span>] = fi_up\r\n\r\n    # enlace_down<\/span>\r\n    media_down = punto_graf[medida+'_down'<\/span>]\r\n    std_down   = punto_graf[medida+'_down'<\/span>+'_std'<\/span>]\r\n    media_down_techo = media_down + std_down\r\n    media_down_piso  = media_down - std_down\r\n\r\n    # ecuaci\u00f3n Enlace down<\/span>\r\n    yi_down = np.array(media_down)\r\n    eq_down = linealiza_lstsq(xi[usar_down==1],\r\n                              yi_down[usar_down==1],\r\n                              digitos = precision)\r\n    alpha_down = eq_down['alpha'<\/span>]\r\n    beta_down  = eq_down['beta'<\/span>]\r\n    fd_down = lambda<\/span> d: -10*alpha_down*(np.log10(d))+beta_down\r\n\r\n    # errores down de puntos vs ecuacion<\/span>\r\n    fi_down = fd_down(xi)\r\n    dyi1std = eq_down['error_std'<\/span>]\r\n    punto_graf['fi_down'<\/span>] = fi_down\r\n\r\n    resultado = {'tabla'<\/span>         : punto_graf,\r\n                 'ecuacion_up'<\/span>   : eq_up,\r\n                 'ecuacion_down'<\/span> : eq_down,\r\n                 'puntoSinDist'<\/span>  : puntoSinDist}\r\n    \r\n    return<\/span> (resultado)\r\n\r\ndef<\/span> coordenadas_leer<\/span>(arch_coord,carp_coord):\r\n    ''' lista las coordenadas desde un archivo\r\n        ubicado en la carpeta\r\n    '''<\/span>\r\n    if<\/span> len<\/span>(carp_coord)>0:\r\n        carp_coord = carp_coord+'\/'<\/span>\r\n    archivo_ruta = carp_coord+arch_coord\r\n    arch_coord_existe = os.path.exists(archivo_ruta)\r\n\r\n    if<\/span> arch_coord_existe:\r\n        puntos = pd.read_csv(archivo_ruta,index_col='punto'<\/span>)\r\n        # busca gateway y ubicaci\u00f3n de punto observado<\/span>\r\n        Gw_nombre = ''<\/span>\r\n        for<\/span> unpunto in<\/span> puntos.index:\r\n            if<\/span> unpunto.startswith('Gw'<\/span>):\r\n                Gw_nombre = unpunto\r\n        dist_Gtw = puntos['dist_'<\/span>+Gw_nombre]   \r\n    else<\/span>:\r\n        print<\/span>(' ERROR: NO se encuentra el archivo...'<\/span>)\r\n        print<\/span>('        revise el ruta\/nombre de archivo. '<\/span>)\r\n    return<\/span>(dist_Gtw)\r\n\r\ndef<\/span> graf_gradiente<\/span>(arch_nombre,punto_graf,ecuacion,var_gen):\r\n    ''' grafica de un gradiente\r\n    '''<\/span>\r\n    escalabase = 10    # 10<\/span>\r\n    escala = 'log'<\/span>\r\n    medida = var_gen['medida'<\/span>]\r\n    precision = var_gen['precision'<\/span>]\r\n\r\n    # enlace_up<\/span>\r\n    media_up = punto_graf[medida+'_up'<\/span>]\r\n    std_up   = punto_graf[medida+'_up'<\/span>+'_std'<\/span>]\r\n    media_up_techo = media_up + std_up\r\n    media_up_piso  = media_up - std_up\r\n    yi_up = np.array(media_up)\r\n    fi_up = punto_graf['fi_up'<\/span>]\r\n\r\n    # enlace_down<\/span>\r\n    media_down = punto_graf[medida+'_down'<\/span>]\r\n    std_down   = punto_graf[medida+'_down'<\/span>+'_std'<\/span>]\r\n    media_down_techo = media_down + std_down\r\n    media_down_piso  = media_down - std_down\r\n    yi_down = np.array(media_down)\r\n    fi_down = punto_graf['fi_down'<\/span>]\r\n\r\n    dist = punto_graf['dist_Gtw'<\/span>]\r\n    usar_up = punto_graf['usar_up'<\/span>]\r\n    usar_down = punto_graf['usar_down'<\/span>]\r\n\r\n    eq_up = ecuacion['ecuacion_up'<\/span>]\r\n    eq_down = ecuacion['ecuacion_down'<\/span>]\r\n\r\n    dyi0std = eq_up['error_std'<\/span>]\r\n    dyi1std = eq_down['error_std'<\/span>]\r\n\r\n    fig_gradnt,(graf_up,graf_down) = plt.subplots(2,1)\r\n    if<\/span> escala == 'log'<\/span>:\r\n        graf_up.set_xscale(escala,base=escalabase)\r\n        graf_down.set_xscale(escala,base=escalabase)\r\n\r\n    # medida up +\/- std<\/span>\r\n    graf_up.scatter(dist,media_up,color='blue'<\/span>,marker='o'<\/span>)\r\n    graf_up.scatter(dist,media_up_techo,color='blue'<\/span>,marker='+'<\/span>)\r\n    graf_up.scatter(dist,media_up_piso,color='blue'<\/span>,marker='+'<\/span>)\r\n\r\n    # medida down +\/- std<\/span>\r\n    graf_down.scatter(dist,media_down,color='orange'<\/span>,marker='o'<\/span>)\r\n    graf_down.scatter(dist,media_down_techo,color='orange'<\/span>,marker='+'<\/span>)\r\n    graf_down.scatter(dist,media_down_piso,color='orange'<\/span>,marker='+'<\/span>)\r\n\r\n    # linealizado up<\/span>\r\n    graf_up.plot(dist,fi_up,label = '  up:'<\/span>+eq_up['eq_latex'<\/span>],\r\n                 color='blue'<\/span>)\r\n    graf_up.plot(dist,fi_up+dyi0std,\r\n                 color='blue'<\/span>,linestyle='dotted'<\/span>)\r\n    graf_up.plot(dist,fi_up-dyi0std,\r\n                 color='blue'<\/span>,linestyle='dotted'<\/span>)\r\n    # linealizado down<\/span>\r\n    graf_down.plot(dist,fi_down,label = 'down:'<\/span>+eq_down['eq_latex'<\/span>],\r\n                   color='orange'<\/span>)\r\n    graf_down.plot(dist,fi_down+dyi1std,\r\n                   color='orange'<\/span>,linestyle='dotted'<\/span>)\r\n    graf_down.plot(dist,fi_down-dyi1std,\r\n                   color='orange'<\/span>,linestyle='dotted'<\/span>)\r\n\r\n    # ajuste de eje y<\/span>\r\n    y_min = np.min([np.min(media_up_piso),np.min(media_down_piso)])\r\n    y_max = np.max([np.max(yi_up+dyi0std),np.max(yi_down+dyi1std)])\r\n    if<\/span> y_min<-135:\r\n        y_min = np.min([np.min([media_up]),np.min([media_down])])\r\n    graf_up.set_ylim(y_min,y_max)\r\n    graf_down.set_ylim(y_min,y_max)\r\n\r\n    # etiquetas up<\/span>\r\n    for<\/span> unpunto in<\/span> punto_graf.index:\r\n        media_etiq = np.round(punto_graf[medida+'_up'<\/span>][unpunto],precision)\r\n        usarpunto  = punto_graf['usar_up'<\/span>][unpunto]\r\n        \r\n        etiqueta = unpunto #+str(media_etiq)<\/span>\r\n        xi = punto_graf['dist_Gtw'<\/span>][unpunto]\r\n        yi = punto_graf['rssi_up'<\/span>][unpunto]\r\n        if<\/span> usarpunto:\r\n            graf_up.annotate(etiqueta,(xi,yi), rotation=45)\r\n        if<\/span> not<\/span>(usarpunto):\r\n            graf_up.annotate(etiqueta,(xi,yi), rotation=45,color='red'<\/span>)\r\n            graf_up.scatter(dist[unpunto],media_up[unpunto],color='red'<\/span>,marker='o'<\/span>)\r\n            graf_up.scatter(dist[unpunto],media_up_techo[unpunto],\r\n                            color='red'<\/span>,marker='+'<\/span>)\r\n\r\n    # etiquetas down<\/span>\r\n    for<\/span> unpunto in<\/span> punto_graf.index:\r\n        media_etiq = np.round(punto_graf[medida+'_down'<\/span>][unpunto],precision)\r\n        usarpunto = punto_graf['usar_down'<\/span>][unpunto]\r\n        etiqueta = unpunto #+str(media_etiq)<\/span>\r\n        xi = punto_graf['dist_Gtw'<\/span>][unpunto]\r\n        yi = punto_graf['rssi_down'<\/span>][unpunto]\r\n        if<\/span> usarpunto:\r\n            graf_down.annotate(etiqueta,(xi,yi), rotation=45)\r\n        if<\/span> not<\/span>(usarpunto):\r\n            graf_down.annotate(etiqueta,(xi,yi), rotation=45,color='red'<\/span>)\r\n            graf_down.scatter(dist[unpunto],media_down[unpunto],color='red'<\/span>,marker='o'<\/span>)\r\n            graf_down.scatter(dist[unpunto],media_down_techo[unpunto],\r\n                              color='red'<\/span>,marker='+'<\/span>)\r\n\r\n    graf_up.set_ylabel(medida+'_up (dBm)'<\/span>,color='blue'<\/span>)\r\n    graf_up.set_title(arch_nombre+' '<\/span>+medida)\r\n    graf_up.legend()\r\n    graf_up.grid(True<\/span>,linestyle='dotted'<\/span>,\r\n                 axis='x'<\/span>, which='both'<\/span>)\r\n\r\n    graf_down.set_xlabel('distancia'<\/span>)\r\n    graf_down.set_ylabel(medida+'_down (dBm)'<\/span>,color='brown'<\/span>)\r\n    graf_down.legend()\r\n    graf_down.grid(True<\/span>,linestyle='dotted'<\/span>,\r\n                   axis='x'<\/span>, which='both'<\/span>)\r\n    return<\/span>(fig_gradnt)\r\n\r\ndef<\/span> graf_diferencia_updown<\/span>(arch_nombre,punto_graf,var_gen):\r\n    '''Grafica de diferencias up-down\r\n    '''<\/span>\r\n    medida = var_gen['medida'<\/span>]\r\n    medida_unidad = var_gen['medida_unidad'<\/span>]\r\n    movAvg_cual = var_gen['movAvg_cual'<\/span>]\r\n    movAvg_color = var_gen['movAvg_color'<\/span>]\r\n    precision = var_gen['precision'<\/span>]\r\n\r\n    # analiza diferencias de medias y std --------------<\/span>\r\n    punto_graf['usar_dif'<\/span>] = punto_graf['usar_up'<\/span>]*punto_graf['usar_down'<\/span>]\r\n    usar_dif = punto_graf['usar_dif'<\/span>]\r\n    punto_graf['dif_mean'<\/span>] = punto_graf['rssi_up'<\/span>]-punto_graf['rssi_down'<\/span>]\r\n    punto_graf['dif_std'<\/span>]  = punto_graf[medida+'_up_std'<\/span>]-punto_graf[medida+'_down_std'<\/span>]\r\n    punto_graf['dif_top'<\/span>]  = punto_graf['dif_mean'<\/span>]+punto_graf['dif_std'<\/span>]\r\n    punto_graf['dif_bottom'<\/span>] = punto_graf['dif_mean'<\/span>]-punto_graf['dif_std'<\/span>]\r\n\r\n    dist = punto_graf['dist_Gtw'<\/span>]\r\n    usar_dif = punto_graf['usar_dif'<\/span>]\r\n\r\n    # linealiza diferencia<\/span>\r\n    xi_dif = np.array(punto_graf['dist_Gtw'<\/span>][usar_dif==1])\r\n    yi_dif = np.array(punto_graf['dif_mean'<\/span>][usar_dif==1])\r\n    eq_dif = linealiza_lstsq(xi_dif,yi_dif,digitos = precision)\r\n    alpha_dif = eq_dif['alpha'<\/span>]\r\n    beta_dif  = eq_dif['beta'<\/span>]\r\n    fdistdif0 = lambda<\/span> d: -10*alpha_dif*(np.log10(d))+beta_dif\r\n    yi_dif0   = fdistdif0(xi_dif)\r\n\r\n    ecua_dif = pd.DataFrame(eq_dif)\r\n\r\n    # medias moviles en movAvg_cual[]<\/span>\r\n    serie_media  = pd.Series(punto_graf['dif_mean'<\/span>][usar_dif==1])\r\n    movAvg_dmedia = []\r\n    m = len<\/span>(movAvg_cual)\r\n    for<\/span> j in<\/span> range<\/span>(0,m,1):\r\n        k = movAvg_cual[j]\r\n        movAvg_dmedia.append(serie_media.rolling(k).mean())\r\n\r\n    # Grafica diferencia<\/span>\r\n    escalabase = 10    # 10<\/span>\r\n    escala = 'log'<\/span>\r\n    \r\n    fig_dif_updown,graf_dmean = plt.subplots()\r\n    if<\/span> escala == 'log'<\/span>:\r\n        graf_dmean.set_xscale(escala,base=escalabase)\r\n\r\n    # diferencia medida up - down<\/span>\r\n    graf_dmean.scatter(dist,punto_graf['dif_mean'<\/span>],color='purple'<\/span>,marker='o'<\/span>)\r\n    graf_dmean.plot(xi_dif,yi_dif0,label = '  dif:'<\/span>+eq_dif['eq_latex'<\/span>],color='blue'<\/span>)\r\n    graf_dmean.set_ylabel('media Up-Down (dBm)'<\/span>)\r\n\r\n    # medida_up, medias m\u00f3viles<\/span>\r\n    for<\/span> j in<\/span> range<\/span>(0,m,1):\r\n        k = str<\/span>(movAvg_cual[j])\r\n        graf_dmean.plot(dist[usar_dif==1],movAvg_dmedia[j],\r\n                      label='movAvg_dmedia_'<\/span>+k,color=movAvg_color[j])\r\n\r\n    # etiquetas<\/span>\r\n    i=0\r\n    for<\/span> unpunto in<\/span> punto_graf.index:\r\n        media_etiq = np.round(punto_graf[medida+'_up'<\/span>][unpunto],precision)\r\n        etiqueta = unpunto # + ' ' +str(media_etiq)<\/span>\r\n        usar_dif = punto_graf['usar_dif'<\/span>][unpunto]\r\n        xi = punto_graf['dist_Gtw'<\/span>][unpunto]\r\n        yi = punto_graf.iloc[i]['dif_mean'<\/span>]\r\n        i = i+1\r\n        if<\/span> usar_dif:\r\n            graf_dmean.annotate(etiqueta,(xi,yi), rotation=30)\r\n        if<\/span> not<\/span>(usar_dif):\r\n            graf_dmean.annotate(etiqueta,(xi,yi),color='red'<\/span>,rotation=30)\r\n            graf_dmean.scatter(xi,yi,color='red'<\/span>,marker='o'<\/span>)\r\n\r\n    graf_dmean.axhline(0,color='grey'<\/span>)\r\n    graf_dmean.grid(True<\/span>,linestyle='dotted'<\/span>,axis='x'<\/span>, which='both'<\/span>)\r\n\r\n    graf_dmean.legend()\r\n    graf_dmean.set_title(arch_nombre+': '<\/span>+medida+' media Up-Down'<\/span>)\r\n    return<\/span>(fig_dif_updown)\r\n\r\ndef<\/span> graf_gradiente_std<\/span>(arch_nombre,punto_graf,medida,precision):\r\n    ''' Grafica de std_up y std_down ------\r\n    '''<\/span>\r\n    xi_std      = np.array(punto_graf['dist_Gtw'<\/span>])\r\n    yi_std_up   = np.array(punto_graf[medida+'_up_std'<\/span>])\r\n    yi_std_down = np.array(punto_graf[medida+'_down_std'<\/span>])\r\n\r\n    # linealiza std<\/span>\r\n    usar_up   = punto_graf['usar_up'<\/span>]\r\n    usar_down = punto_graf['usar_down'<\/span>]\r\n\r\n    eq_std_up = linealiza_lstsq(xi_std[usar_up==1],\r\n                                      yi_std_up[usar_up==1],\r\n                                      digitos = precision)\r\n    alpha_std_up = eq_std_up['alpha'<\/span>]\r\n    beta_std_up  = eq_std_up['beta'<\/span>]\r\n    fdiststd_up0 = lambda<\/span> d: -10*alpha_std_up*(np.log10(d))+beta_std_up\r\n    yi_std_up0   = fdiststd_up0(xi_std)\r\n\r\n    eq_std_down = linealiza_lstsq(xi_std[usar_down==1],\r\n                                        yi_std_down[usar_down==1],\r\n                                        digitos = precision)\r\n    alpha_std_down = eq_std_down['alpha'<\/span>]\r\n    beta_std_down  = eq_std_down['beta'<\/span>]\r\n    fdiststd_down0 = lambda<\/span> d: -10*alpha_std_down*(np.log10(d))+beta_std_down\r\n    yi_std_down0   = fdiststd_down0(xi_std)\r\n\r\n    # grafica std<\/span>\r\n    escalabase = 10    # 10<\/span>\r\n    escala = 'log'<\/span>\r\n    \r\n    fig_grad_std,(graf_std_up,graf_std_down) = plt.subplots(2,1)\r\n    if<\/span> escala == 'log'<\/span>:\r\n        graf_std_up.set_xscale(escala,base=escalabase)\r\n        graf_std_down.set_xscale(escala,base=escalabase)\r\n\r\n    graf_std_up.plot(xi_std,yi_std_up0,\r\n                     label = '  std_up:'<\/span>+eq_std_up['eq_latex'<\/span>],\r\n                     color='blue'<\/span>)\r\n        \r\n    # medida up +\/- std<\/span>\r\n    graf_std_up.scatter(xi_std,yi_std_up,color='blue'<\/span>,marker='+'<\/span>)\r\n    graf_std_down.scatter(xi_std,yi_std_down,color='orange'<\/span>,marker='+'<\/span>)\r\n\r\n    # etiquetas<\/span>\r\n    i=0\r\n    for<\/span> unpunto in<\/span> punto_graf.index:\r\n        media_etiq = np.round(punto_graf[medida+'_up'<\/span>][unpunto],precision)\r\n        etiqueta   = unpunto # + ' ' +str(media_etiq)<\/span>\r\n        usar_up    = punto_graf['usar_up'<\/span>][unpunto]\r\n        usar_down  = punto_graf['usar_down'<\/span>][unpunto]\r\n        xi  = punto_graf['dist_Gtw'<\/span>][unpunto]\r\n        yiu = punto_graf.iloc[i][medida+'_up_std'<\/span>]\r\n        yid = punto_graf.iloc[i][medida+'_down_std'<\/span>]\r\n        i = i+1\r\n        if<\/span> usar_up:\r\n            graf_std_up.annotate(etiqueta,(xi,yiu), rotation=30)\r\n        if<\/span> not<\/span>(usar_up):\r\n            graf_std_up.annotate(etiqueta,(xi,yiu),color='red'<\/span>,rotation=30)\r\n            graf_std_up.scatter(xi,yiu,color='red'<\/span>,marker='o'<\/span>)\r\n        if<\/span> usar_down:\r\n            graf_std_down.annotate(etiqueta,(xi,yid), rotation=30)\r\n        if<\/span> not<\/span>(usar_down):\r\n            graf_std_down.annotate(etiqueta,(xi,yid),color='red'<\/span>,rotation=30)\r\n            graf_std_down.scatter(xi,yid,color='red'<\/span>,marker='o'<\/span>)\r\n\r\n    graf_std_down.plot(xi_std,yi_std_down0,\r\n                    label = 'std_down:'<\/span>+eq_std_down['eq_latex'<\/span>],\r\n                    color='orange'<\/span>)\r\n    # otras etiquetas<\/span>\r\n    graf_std_up.set_ylabel('std_up (dBm)'<\/span>, color='blue'<\/span>)\r\n    graf_std_up.set_xlabel('distancia'<\/span>)\r\n    graf_std_up.axhline(0,color='grey'<\/span>)\r\n    graf_std_up.grid(True<\/span>,linestyle='dotted'<\/span>,\r\n                    axis='x'<\/span>, which='both'<\/span>)\r\n    graf_std_up.set_title(arch_nombre+': '<\/span>+medida+' std'<\/span>)\r\n    graf_std_up.legend()\r\n\r\n    graf_std_down.set_ylabel('std_down (dBm)'<\/span>, color='brown'<\/span>)\r\n    graf_std_down.set_xlabel('distancia'<\/span>)\r\n    graf_std_down.axhline(0,color='grey'<\/span>)\r\n    graf_std_down.grid(True<\/span>,linestyle='dotted'<\/span>,\r\n                    axis='x'<\/span>, which='both'<\/span>)\r\n    graf_std_down.legend()\r\n\r\n    return<\/span>(fig_grad_std)\r\n\r\ndef<\/span> coordenadas_kml_utm<\/span>(archivo_ruta,zonaNum,zonaLetra,precision):\r\n    # Lectura de archivo<\/span>\r\n    gpd.io.file.fiona.drvsupport.supported_drivers['KML'<\/span>] = 'rw'<\/span>\r\n    puntos  = gpd.read_file(archivo_ruta, driver='KML'<\/span>)\r\n    puntos['dist'<\/span>] = 0\r\n\r\n    tabla = pd.DataFrame()\r\n    for<\/span> i in<\/span> puntos.index:\r\n        # en latitud, longitud y altura<\/span>\r\n        punto_nombre = puntos.loc[i]['Name'<\/span>]\r\n        longitud = puntos.loc[i]['geometry'<\/span>].x\r\n        latitud  = puntos.loc[i]['geometry'<\/span>].y\r\n        altitud  = puntos.loc[i]['geometry'<\/span>].z\r\n        altitud  = np.round(altitud,precision)\r\n        #en UTM<\/span>\r\n        coord_utm  = utm.from_latlon(latitud,longitud,\r\n                                     zonaNum,zonaLetra)\r\n        utm_este  = np.round(coord_utm[0],precision)\r\n        utm_norte = np.round(coord_utm[1],precision)\r\n        \r\n        # distancias a Gateways<\/span>\r\n        dist = 0.0\r\n        if<\/span> punto_nombre.startswith('Gw'<\/span>):\r\n            n_Gtw = punto_nombre\r\n            x1 = utm_este\r\n            y1 = utm_norte\r\n        else<\/span>:\r\n            x2 = utm_este\r\n            y2 = utm_norte\r\n            dist  = np.sqrt((x2-x1)**2 + (y2-y1)**2)\r\n        dist = np.round(dist,precision)\r\n        \r\n        unpunto = {'punto'<\/span>       : punto_nombre,\r\n                   'utm_este'<\/span>    : utm_este,\r\n                   'utm_norte'<\/span>   : utm_norte,\r\n                   'utm_zNum'<\/span>    : int<\/span>(zonaNum),\r\n                   'utm_zLetra'<\/span>  : zonaLetra,\r\n                   'altitud'<\/span>     : altitud,\r\n                   'altitud_gps'<\/span> : altitud,\r\n                   'dist_'<\/span>+n_Gtw : dist,\r\n                   'latitud'<\/span>     : latitud,\r\n                   'longitud'<\/span>    : longitud\r\n                   }\r\n        tabla = tabla.append(unpunto,ignore_index=True<\/span>)\r\n    tabla = tabla.set_index('punto'<\/span>)\r\n    return<\/span>(tabla)\r\n\r\ndef<\/span> revisa_perfil<\/span>(archivo_ruta,alturaGw,alturapunto,\r\n                  plantas,muestras=41,guarda=False<\/span>):\r\n    LineaObs = archivo_ruta.strip('.csv'<\/span>).split('_'<\/span>)[1]\r\n\r\n    planta_desde  = plantas[0]\r\n    planta_hasta  = plantas[1]\r\n    planta_altura = plantas[2]\r\n\r\n    # lectura de datos<\/span>\r\n    puntos = pd.read_csv(archivo_ruta,index_col='punto'<\/span>)\r\n\r\n    # busca gateway<\/span>\r\n    n_Gtw = ''<\/span>\r\n    for<\/span> unpunto in<\/span> puntos.index:\r\n        if<\/span> unpunto.startswith('Gw'<\/span>):\r\n            n_Gtw = unpunto\r\n\r\n    # perfil  <\/span>\r\n    xi = np.array(puntos['dist_'<\/span>+n_Gtw])\r\n    yi = np.array(puntos['altitud'<\/span>])\r\n    etiqueta = puntos.index\r\n\r\n    # plantaciones<\/span>\r\n    conplantas = (xi > planta_desde) & (xi < planta_hasta)\r\n    plantacion = yi[conplantas]+planta_altura\r\n\r\n    # GRAFICA<\/span>\r\n    # plantacion<\/span>\r\n    plt.fill_between(xi[conplantas],\r\n                     yi[conplantas],\r\n                     plantacion,\r\n                     color='lightgreen'<\/span>)\r\n    # perfil<\/span>\r\n    xi = puntos['dist_'<\/span>+n_Gtw]\r\n    yi = puntos['altitud'<\/span>]\r\n    plt.plot(xi,yi,color='brown'<\/span>)\r\n        \r\n    # antenas<\/span>\r\n    for<\/span> unpunto in<\/span> puntos.index:\r\n        xip = xi[unpunto]\r\n        yip = yi[unpunto]\r\n        if<\/span> unpunto.startswith('Gw'<\/span>):\r\n            xigtw = xip\r\n            yigtw = yip+alturaGw\r\n            plt.scatter(xip ,yigtw)\r\n            plt.annotate(unpunto,(xigtw,yigtw))\r\n        else<\/span>:\r\n            xid = xip\r\n            yid = yip+alturapunto\r\n            plt.plot((xigtw,xid),(yigtw,yid),\r\n                     color = 'green'<\/span>,\r\n                     linestyle='dotted'<\/span>)\r\n            plt.scatter(xid ,yid)\r\n            plt.annotate(unpunto,(xid,yid), rotation=45)\r\n    plt.title('Linea :'<\/span>+LineaObs)\r\n    plt.xlabel('distancia'<\/span>)\r\n    plt.ylabel('altura'<\/span>)\r\n    plt.grid()\r\n\r\n    # plt.axis('equal')   <\/span>\r\n    return<\/span>(puntos)\r\n\r\ndef<\/span> revisa_fresnel<\/span>(analiza_punto, archivo_ruta,alturaGw,alturapunto,\r\n                  plantas,guarda=False<\/span>,nFres =1,freq=915,muestras=41):\r\n    LineaObs = archivo_ruta.strip('.csv'<\/span>).split('_'<\/span>)[1]\r\n\r\n    planta_desde  = plantas[0]\r\n    planta_hasta  = plantas[1]\r\n    planta_altura = plantas[2]\r\n\r\n    # lambda Fresnel<\/span>\r\n    lamb = 300\/freq #300e6(m\/s)\/(freq*1e6)<\/span>\r\n    puntos = pd.read_csv(archivo_ruta,index_col='punto'<\/span>)\r\n\r\n    # busca gateway<\/span>\r\n    i_Gw    = 0\r\n    i_punto = 0\r\n    n_Gtw = ''<\/span>\r\n    i = 0\r\n    for<\/span> unpunto in<\/span> puntos.index:\r\n        if<\/span> unpunto.startswith('Gw'<\/span>):\r\n            n_Gtw = unpunto\r\n        if<\/span> unpunto==analiza_punto:\r\n            i_punto = i\r\n        i = i+1\r\n\r\n    # perfil   <\/span>\r\n    xi = np.array(puntos['dist_'<\/span>+n_Gtw])\r\n    yi = np.array(puntos['altitud'<\/span>])\r\n    etiqueta = puntos.index\r\n\r\n    # plantaciones<\/span>\r\n    conplantas = (xi > planta_desde) & (xi < planta_hasta)\r\n    plantacion = yi[conplantas]+planta_altura\r\n\r\n    # a\u00f1ade alturas de antenas<\/span>\r\n    yi_ant = np.copy(yi) + alturapunto\r\n    yi_ant[i_Gw] = yi[i_Gw] + alturaGw\r\n\r\n    # Zona de Fresnel<\/span>\r\n    if<\/span> i_punto>0:\r\n        \r\n        # linea directa<\/span>\r\n        dy = yi_ant[i_punto]-yi_ant[i_Gw]\r\n        dx = xi[i_punto]-xi[i_Gw]\r\n        m = dy\/dx\r\n        alpha = np.arctan(m)\r\n        yi_f = lambda<\/span> x: m*x + yi_ant[i_Gw]\r\n\r\n        xi_D = np.linspace(xi[i_Gw],xi[i_punto],\r\n                           muestras)\r\n        yi_D = yi_f(xi_D)\r\n        dist_D = np.sqrt(dx**2+dy**2)\r\n\r\n        # zona Fresnel 1<\/span>\r\n        F1_up = np.zeros(len<\/span>(xi_D))\r\n        F1_down = np.zeros(len<\/span>(xi_D))\r\n        for<\/span> i in<\/span> range<\/span>(0,len<\/span>(xi_D),1):\r\n            d1 = xi_D[i]\/np.cos(alpha)\r\n            d2 = dist_D-d1\r\n            F1 = np.sqrt(np.abs(nFres*lamb*d1*d2)\/(d1+d2))\r\n\r\n            xiu = xi_D[i]-F1*np.sin(np.abs(alpha))\r\n            d1_u = xiu\r\n            d2_u = dist_D-d1_u\r\n\r\n            xid = xi_D[i]+F1*np.sin(np.abs(alpha))\r\n            d1_d = xid\r\n            d2_d = dist_D-d1_d\r\n            # Fresnel, formula<\/span>\r\n            Fup = np.sqrt(np.abs(nFres*lamb*d1_u*d2_u)\/(d1_u+d2_u))\r\n            Fdown = np.sqrt(np.abs(nFres*lamb*d1_d*d2_d)\/(d1_d+d2_d))\r\n            \r\n            # Linea directa +\/- Fresnel<\/span>\r\n            F1_up[i]   = yi_D[i] + Fup*np.cos(alpha)\r\n            F1_down[i] = yi_D[i] - Fdown*np.cos(alpha)\r\n    else<\/span>:\r\n        print<\/span>('punto no encontrado'<\/span>,analiza_punto)\r\n\r\n    # SALIDA<\/span>\r\n    # print('Radio Fresnel: ',np.max(F1u[i]))<\/span>\r\n\r\n    # GRAFICA<\/span>\r\n    plt.fill_between(xi[conplantas],yi[conplantas],\r\n                     plantacion,color='lightgreen'<\/span>,\r\n                     label = 'plantas'<\/span>)\r\n    plt.plot(xi,yi,label='perfil'<\/span>, color = \"brown\"<\/span>)\r\n    # antenas<\/span>\r\n    plt.scatter(xi,yi_ant,label=\r\n                'Dispositivos'<\/span>,color='blue'<\/span>)\r\n    if<\/span> i_punto>0:\r\n        # Fresnel<\/span>\r\n        plt.plot(xi_D,yi_D,\r\n                 label='Directo'<\/span>,color='orange'<\/span>,\r\n                 linestyle = 'dashed'<\/span>)\r\n        plt.plot(xi_D,F1_down,\r\n                 label='Fresnel1'<\/span>,color='orange'<\/span>)\r\n        plt.plot(xi_D,F1_up,\r\n                 color='orange'<\/span>)\r\n        \r\n    for<\/span> i in<\/span> range<\/span>(0,len<\/span>(xi),1):\r\n        plt.annotate(etiqueta[i],\r\n                     (xi[i],yi_ant[i]),rotation=45)\r\n    plt.xlabel('distancia'<\/span>)\r\n    plt.ylabel('altura'<\/span>)\r\n    plt.title('L\u00ednea :'<\/span>+LineaObs+', Enlace: '<\/span>+n_Gtw+'-'<\/span>+etiqueta[i_punto])\r\n    plt.legend()\r\n    plt.grid()\r\n    # plt.axis('equal')<\/span>\r\n    # plt.show()<\/span>\r\n    return<\/span>(puntos)\r\n\r\n<\/pre>\n","protected":false},"excerpt":{"rendered":"
El archivo contiene un grupo de\u00a0 funciones usadas para procesar los datos de este prototipo, se separaron del bloque principal de instrucciones con\u00a0 el objetivo de simplificar el desarrollo del las actividades principales. Recuerde disponer de este archivo en la misma carpeta que el algoritmo que invoca a la librer\u00eda. archivo de libreria: girni_lorawan_libreria Instrucciones … <\/p>\n
Continuar leyendo \"4. LoRaWan - Funciones girni_lorawan_lib\"<\/span><\/a><\/p>\n","protected":false},"author":8043,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1466681],"tags":[],"class_list":["post-3895","post","type-post","status-publish","format-standard","hentry","category-lorawan-registro-de-estados"],"_links":{"self":[{"href":"https:\/\/blog.espol.edu.ec\/girni\/wp-json\/wp\/v2\/posts\/3895","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blog.espol.edu.ec\/girni\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.espol.edu.ec\/girni\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.espol.edu.ec\/girni\/wp-json\/wp\/v2\/users\/8043"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.espol.edu.ec\/girni\/wp-json\/wp\/v2\/comments?post=3895"}],"version-history":[{"count":8,"href":"https:\/\/blog.espol.edu.ec\/girni\/wp-json\/wp\/v2\/posts\/3895\/revisions"}],"predecessor-version":[{"id":3961,"href":"https:\/\/blog.espol.edu.ec\/girni\/wp-json\/wp\/v2\/posts\/3895\/revisions\/3961"}],"wp:attachment":[{"href":"https:\/\/blog.espol.edu.ec\/girni\/wp-json\/wp\/v2\/media?parent=3895"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.espol.edu.ec\/girni\/wp-json\/wp\/v2\/categories?post=3895"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.espol.edu.ec\/girni\/wp-json\/wp\/v2\/tags?post=3895"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}