Informe_Final_Colab.ipynb

{
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      "source": [
        "<a href=\"https://colab.research.google.com/github/AleC1998/Python_Final_Project/blob/main/Informe_Final_Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZaKwL2GpQd-6"
      },
      "source": [
        "# Proyecto Final de Análisis de Datos y Machine Learning en Python\n",
        "Alejandro Castro Cabrera, Facultad de Física, Universidad de La Habana, 2022"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pbu9vl9MQd-_"
      },
      "source": [
        "# Introducción\n",
        "\n",
        "El uso de los datos de redes de telefonía celular para estudios de movilidad humana ha aumentado considerablemente en las últimas décadas. Las redes de telefonía móvil fueron consideradas fuentes de datos más confiables en comparación con las encuestas de movilidad y otros tipos de sensores. En este sentido, el rápido crecimiento de las redes, el asentamiento y penetración del uso diario de los celulares en el mundo, hicieron de los datos de telefonía un candidato inevitable para el desarrollo de un largo arsenal de herramientas de apoyo para la toma de decisiones y el estudio de la movilidad humana. \n",
        "\n",
        "Los registros celulares permiten evaluar características poblacionales relevantes, en particular las que refieren a la movilidad en escalas intermedias, limitadas por la cantidad de torres distribuidas en el territorio de interés. Las aplicaciones más directas son obviamente las relativas al transporte: obtención de matrices de origen-destino, la identificación de medios de transportes usados por los viajeros, el análisis de patrones horarios, semanales o estacionales de movilidad. Pero esta información tiene valor más allá del análisis del transporte público en campos como la urbanística, la sociología y la epidemiología. Este último campo es donde más se ha aplicado el estudio de los patrones de movilidad.\n",
        "\n",
        "Particularmente en este proyecto utilizaremos matrices de movilidad diarias obtenidas a partir de estos datos celulares y procederemos a continuar su estudio y caracterización, mientras introducimos a la vez herramientas básicas para el análisis de datos y el uso de $Machine$ $Learning$ en $Python$.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y1fUB6OSQd_E"
      },
      "source": [
        "# Datos de telefonía móvil\n",
        "\n",
        "A diferencia de la telefonía satelital, la tecnología imperante para los teléfonos móviles no descansa exclusivamente en el uso de radio frecuencias punto-a-punto. Una red de torres de telefonía se encarga de la comunicación inalámbrica con los móviles, mientras que la red de torres está entrelazada en un soporte de fibra óptica.\n",
        "\n",
        "En una misma torre de telefonía coexisten varias antenas, que gestionan las llamadas, SMS e internet para los\n",
        "teléfonos. Las antenas normalmente son direccionales (cubren un sector angular típicamente de 120 grados con origen en la torre) y pueden diferenciarse también por el tipo de servicio que ofrecen: 2G, 3G, 4G, etc.\n",
        "\n",
        "El área que cada antena cubre define el mapa de cobertura de la red y depende, entre otros, de las características del terreno y de la interferencia generada por las edificaciones.\n",
        "\n",
        "La interacción entre los dispositivos móviles y las antenas generan registros. Estos contienen datos sobre el dispositivo en cuestión, la antena y una marca de tiempo. Al analizarlos es posible obtener información sobre\n",
        "la movilidad poblacional como son: patrones de trayectorias, distancias recorridas, lugares de aglomeración, redes\n",
        "sociales, etc.\n",
        "\n",
        "La suposición fundamental es que a partir de una ubicación aproximada de los usuarios y sus cambios en el espacio-tiempo es posible determinar la ocurrencia de movimiento y las trayectorias recorridas"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L1NeBKgJQd_H"
      },
      "source": [
        "# Registros CDR\n",
        "\n",
        "Los registros que se generan en la telefonía móvil dependen del proveedor de la tecnología de la red, aunque algunas especificaciones se han ido estableciendo de forma general. El más común de estos registros son los llamados $Call$ $Details$ $Records$ (CDR), que se generan al enviar o recibir llamadas, mensajes y al acceder a la red de datos; estos se clasifican como reportes desencadenados por eventos. El propósito fundamental de estos es la facturación a los usuarios por los servicios y por ende las compañías tienden a conservalos por un mayor intervalo de tiempo. Por esta razón, han sido usados en estudios de movilidad. De forma general los CDR contienen: una marca de tiempo, el tipo de operación realizada, el identificador de la antena que gestionó la conexión, los identificadores del dispositivo emisor y del receptor y la duración de la operación.\n",
        "\n",
        "Existen otros registros que son generados por la red telefónica, entre los que se encuentran los $Location$ $Update$ $Records$ (LUR). Los LUR se generan al ocurrir alguna de las siguientes acciones: llamadas, mensajes de texto, cambio entre zonas de cobertura, encendido del teléfono, y cada cierto intervalo de tiempo de no ocurrir ninguna de las acciones anteriores y se clasifican como reportes desencadenados por la red. El propósito de estos registros es que la red pueda mantener un estado sobre la ubicación aproximada de un dispositivo y sea eficiente en el momento\n",
        "de contactarlo para una llamada o mensaje de texto. Los LUR se componen por: un identificador del registro, el identificador del dispositivo, el identificador de la condición que generó el registro, el identificador de la antena a la cual el dispositivo está conectado y una marca de tiempo.\n",
        "\n",
        "Dado que estos registros no se utilizan por las operadores telefónicos para el cobro de los servicios, son desechados luego de un corto período de tiempo. Por este motivo ha sido menos frecuente su uso en estudios. Sin embargo, los LUR poseen una ventaja comparados con los CDR: como son generados cada cierto tiempo aseguran poseer registros independientemente de las acciones del usuario. Esta característica los hacen un buen candidato para estudios de movilidad poblacional.\n",
        "\n",
        "Tanto los CDR como los LUR contienen los identificadores del dispositivo y la antena donde se generó la acción. Obviamente, las compañías telefónicas conocen la ubicación geográfica de sus torres y por ende de las antenas que residen en ella. La ubicación de las antenas en conjunto con alguno de los registros anteriores, permiten estimar la ubicación de los usuarios.\n",
        "\n",
        "Idealmente dado un mapa de cobertura en función de las antenas se puede estimar el área donde se encuentran los usuarios conectados a ellas.\n",
        "\n",
        "La incertidumbre de esta estimación depende de muchos factores, entre ellos la densidad de torres en la superficie, pero suele estar en el orden de los unidades hasta las decenas de kilómetros. La asignación de torres a puntos del espacio no es ni siquiera estática y fluctúa durante el día. Los procedimientos utilizados por la red telefónica\n",
        "para balancear el tráfico en las radio bases involucran múltiples factores: uno de ellos es la cantidad de usuarios que ya son atendidos por las antenas de esa torre. Debido a esto, si una radio base posee un elevado número de\n",
        "usuarios previamente conectados, reduce su potencia y por ende su área de cobertura. De ahí que la conexión de un nuevo usuario será manejada por una antena lejana si la más cercana se encuentra saturada. Este procedimiento es denominado respiración celular. No obstante, la mayoría de los usuarios son atendidos por la torre más cercana. De no ser así se producirián problemas de cobertura y la red no funcionaría correctamente, implicando un reordenamiento por parte la compañía telefónica.\n",
        "\n",
        "Debido a esto se asume que si una operación fue gestionada por una antena entonces esta es la más cercana al usuario. Según esta hipótesis se crean particiones en el espacio, usando el método de Voronoi. Este consiste en asignar a las radio bases el área más cercana a ellas, utilizando la distancia euclidiana. Formalmente este méto-\n",
        "do define una función $V$ que va del conjunto de puntos del espacio $R^2$ al conjunto de torres de telefonía $T$:\n",
        "\n",
        "$V : R^2 → T$, \n",
        "\n",
        "estas regiones permiten delimitar y, por ende estimar la ubicación del usuario al ser atendido por una antena. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OdPagGywQd_J"
      },
      "source": [
        "# Matrices OD\n",
        "\n",
        "Las matrices de Origen-Destino (OD) son los indicadores estándar de los estudios de movilidad. Estas proveen un estimado de la cantidad de personas que viajan entre ubicaciones durante un período de tiempo. Formalmente una matriz OD es una matriz $M$ rectangular de $n$ filas y $m$ columnas que representan las ubicaciones de “origen” y “destino” respectivamente y $M [ p, q ]$ es el número de personas que “viajan” de la zona $p$ a la zona $q$. Es común que el área de estudio sea particionada en el mismo número de orígenes y destinos y por ende $n = m$. En este trabajo se utiliza el enfoque de igual cantidad de orígenes y destinos.\n",
        "\n",
        "Una de las formas de obtener una matriz de este tipo es a través de los datos de telefonía celular. Las matrices que se utilizan en este caso son compuestas por la movilidad diaria entre las áreas de salud de La Habana, para un total de 82 áreas de salud, tenemos entonces que $n=m=82$, generando una serie temporal de 123 matrices de movilidad desde el 4 de marzo hasta el 4 de julio de 2020.\n",
        "\n",
        "En el trabajo previo realizado por nuestro grupo para el estudio de la movilidad con datos de telefonía durante la pandemia, las torres de telefonía aparecen localizadas en la superficie del país, y a cada una se le asigna una superficie de \"cobertura\". Usando el procedimiento de distribuir la población de las zonas de salud en proporción a su intersección con las celdas de cobertura definidas para la telefonía, se pueden generar matrices de origen-destino a nivel de áreas de salud. De esta forma se generaron $t=123$ matrices $M_{AS}(t)$  según la expresión:\n",
        "\n",
        "$M_{AS}(t) [ P p , P q ] = |V_p^q |$\n",
        "\n",
        "donde $V_p^q $ es el conjunto de usuarios que se desplazaron entre las áreas de salud $p$ y $q$ respectivamente."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qO1Aj3PNQd_K"
      },
      "source": [
        "# Análisis y trabajo con los datos"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install scikit-learn-extra"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Jwulhd5SXnT",
        "outputId": "4dc63a6e-f5c7-430d-9b9d-1e8dcc317216"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting scikit-learn-extra\n",
            "  Downloading scikit_learn_extra-0.2.0-cp38-cp38-manylinux2010_x86_64.whl (1.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.9 MB 5.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: scipy>=0.19.1 in /usr/local/lib/python3.8/dist-packages (from scikit-learn-extra) (1.7.3)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.8/dist-packages (from scikit-learn-extra) (1.21.6)\n",
            "Requirement already satisfied: scikit-learn>=0.23.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn-extra) (1.0.2)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.23.0->scikit-learn-extra) (1.2.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.23.0->scikit-learn-extra) (3.1.0)\n",
            "Installing collected packages: scikit-learn-extra\n",
            "Successfully installed scikit-learn-extra-0.2.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "p0LIRNSiQd_L"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "import numpy as np\n",
        "import datetime\n",
        "import seaborn as sns\n",
        "import scipy.stats as stats\n",
        "from scipy.spatial.distance import squareform\n",
        "import scipy.stats\n",
        "import itertools as it\n",
        "import collections\n",
        "import math\n",
        "from sklearn_extra.cluster import KMedoids\n",
        "from seaborn import heatmap\n",
        "import seaborn as sn\n",
        "import networkx as nx\n",
        "from networkx.algorithms.community import greedy_modularity_communities"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D-wM-i7SQd_N"
      },
      "source": [
        "Nuestros datos se encuentran en un archivo tipo $.json$ que podemos ver compuesto por filas de áreas de salud con sus respectivos valores de movilidad para cada día con respecto a otra área."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OhauMuM_Qd_O",
        "outputId": "0ad64536-af4b-4219-bbe5-ca26c5c326bd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Python_Final_Project'...\n",
            "remote: Enumerating objects: 9, done.\u001b[K\n",
            "remote: Counting objects: 100% (9/9), done.\u001b[K\n",
            "remote: Compressing objects: 100% (9/9), done.\u001b[K\n",
            "remote: Total 9 (delta 1), reused 0 (delta 0), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (9/9), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone \"https://github.com/AleC1998/Python_Final_Project\"\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_data=pd.read_json(r\"/content/Python_Final_Project/corrected_mobility_notdiagonal.json\")\n",
        "print(raw_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dIEAiXXGTOeW",
        "outputId": "746d93ad-be11-4a97-a2c2-a623a320a646"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                                            13 DEMARZO  \\\n",
            "13 DEMARZO           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "14 DE JUNIO          [43.13707560203665, 45.453705553159566, 46.780...   \n",
            "15 Y 18 Vedado       [123.28759403815276, 126.25644032813707, 158.5...   \n",
            "19 DE ABRIL          [38.89380047547987, 43.09141345306345, 45.1068...   \n",
            "1RO DE ENERO         [32.88670919745954, 33.97053725975883, 42.7990...   \n",
            "...                                                                ...   \n",
            "WAJAY                [40.55204871892705, 45.36950879417554, 42.4384...   \n",
            "WILFREDO SANTANA     [267.29792012204035, 278.79145038503486, 288.6...   \n",
            "BOYEROS              [14.75412811274402, 10.236970009906837, 15.169...   \n",
            "BETANCOURT NENINGER  [1373.1275674407948, 1446.9634107317283, 1359....   \n",
            "CAMPO FLORIDO        [9.934689531255945, 10.432643505443949, 9.1019...   \n",
            "\n",
            "                                                           14 DE JUNIO  \\\n",
            "13 DEMARZO           [43.13707560203665, 45.453705553159566, 46.780...   \n",
            "14 DE JUNIO          [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "15 Y 18 Vedado       [316.3618456595855, 307.7981682473914, 313.270...   \n",
            "19 DE ABRIL          [213.42395620300337, 208.6758792078228, 190.71...   \n",
            "1RO DE ENERO         [92.4325085737486, 85.72842491431355, 85.28419...   \n",
            "...                                                                ...   \n",
            "WAJAY                [115.93808336356886, 112.08419647635249, 106.9...   \n",
            "WILFREDO SANTANA     [53.461982077949614, 59.75474384575463, 54.106...   \n",
            "BOYEROS              [44.68890483462795, 43.838246200007966, 45.691...   \n",
            "BETANCOURT NENINGER  [31.243005335376488, 35.65772123160251, 40.892...   \n",
            "CAMPO FLORIDO        [1.807182963749202, 2.135187505853401, 2.96374...   \n",
            "\n",
            "                                                        15 Y 18 Vedado  \\\n",
            "13 DEMARZO           [123.28759403815276, 126.25644032813707, 158.5...   \n",
            "14 DE JUNIO          [316.3618456595855, 307.7981682473914, 313.270...   \n",
            "15 Y 18 Vedado       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "19 DE ABRIL          [1735.9973907566664, 1701.9384539157522, 1716....   \n",
            "1RO DE ENERO         [1490.1284109377123, 1478.3679068107542, 1555....   \n",
            "...                                                                ...   \n",
            "WAJAY                [179.4829258193067, 197.4865617756193, 193.782...   \n",
            "WILFREDO SANTANA     [49.39601737583508, 57.02931449408262, 59.9650...   \n",
            "BOYEROS              [57.872987807131715, 55.524785483948364, 58.11...   \n",
            "BETANCOURT NENINGER  [54.93554145385425, 45.66159631960516, 45.5399...   \n",
            "CAMPO FLORIDO        [2.295379731451361, 3.076405794990175, 2.09577...   \n",
            "\n",
            "                                                           19 DE ABRIL  \\\n",
            "13 DEMARZO           [38.89380047547987, 43.09141345306345, 45.1068...   \n",
            "14 DE JUNIO          [213.42395620300337, 208.6758792078228, 190.71...   \n",
            "15 Y 18 Vedado       [1735.9973907566664, 1701.9384539157522, 1716....   \n",
            "19 DE ABRIL          [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "1RO DE ENERO         [331.7560052575982, 328.27553612237966, 317.53...   \n",
            "...                                                                ...   \n",
            "WAJAY                [148.81573205774575, 144.30068889808376, 141.8...   \n",
            "WILFREDO SANTANA     [35.717806151856955, 40.80141438047907, 38.470...   \n",
            "BOYEROS              [52.14311726423461, 48.10754747423465, 48.7213...   \n",
            "BETANCOURT NENINGER  [21.227091152506862, 22.271455655152273, 19.46...   \n",
            "CAMPO FLORIDO        [1.5915634684505782, 1.792893424773431, 1.6618...   \n",
            "\n",
            "                                                          1RO DE ENERO  \\\n",
            "13 DEMARZO           [32.88670919745954, 33.97053725975883, 42.7990...   \n",
            "14 DE JUNIO          [92.4325085737486, 85.72842491431355, 85.28419...   \n",
            "15 Y 18 Vedado       [1490.1284109377123, 1478.3679068107542, 1555....   \n",
            "19 DE ABRIL          [331.7560052575982, 328.27553612237966, 317.53...   \n",
            "1RO DE ENERO         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "...                                                                ...   \n",
            "WAJAY                [85.17111229807145, 102.51350555006232, 88.683...   \n",
            "WILFREDO SANTANA     [19.416155408583148, 26.21236226923745, 20.103...   \n",
            "BOYEROS              [18.567242256328772, 19.009178120807796, 15.51...   \n",
            "BETANCOURT NENINGER  [19.952809328463005, 15.263566029115719, 15.12...   \n",
            "CAMPO FLORIDO        [0.49692826595799305, 2.005837902617381, 0.791...   \n",
            "\n",
            "                                                           26 DE JULIO  \\\n",
            "13 DEMARZO           [46.110974390322944, 46.985718237350206, 44.03...   \n",
            "14 DE JUNIO          [83.76992738049029, 84.4507948083906, 75.18514...   \n",
            "15 Y 18 Vedado       [291.9447294482201, 295.03924673617166, 300.86...   \n",
            "19 DE ABRIL          [166.78320816594814, 184.3824741329044, 165.04...   \n",
            "1RO DE ENERO         [422.3547510532813, 439.68027343627546, 470.22...   \n",
            "...                                                                ...   \n",
            "WAJAY                [184.48050150193245, 202.22935189358992, 181.0...   \n",
            "WILFREDO SANTANA     [29.08387699854922, 28.13045256702294, 43.1423...   \n",
            "BOYEROS              [36.45407833515204, 35.59973342810197, 41.3110...   \n",
            "BETANCOURT NENINGER  [20.40700798697775, 16.619511438009354, 14.195...   \n",
            "CAMPO FLORIDO        [0.682481681829412, 0.8416956440548781, 0.8595...   \n",
            "\n",
            "                                                       27 DE NOVIEMBRE  \\\n",
            "13 DEMARZO           [20.738132601480196, 28.064538931927434, 26.23...   \n",
            "14 DE JUNIO          [83.21178176352726, 80.9993275426992, 74.93304...   \n",
            "15 Y 18 Vedado       [145.88877575553266, 150.05194854356108, 152.6...   \n",
            "19 DE ABRIL          [96.06240682932663, 89.12008318203868, 101.979...   \n",
            "1RO DE ENERO         [102.2319213298736, 98.90115044313613, 85.3176...   \n",
            "...                                                                ...   \n",
            "WAJAY                [1447.9193578768088, 1444.4298598265698, 1411....   \n",
            "WILFREDO SANTANA     [28.3783867582163, 33.81060937490606, 26.66015...   \n",
            "BOYEROS              [149.43238764773713, 143.2241125442266, 127.74...   \n",
            "BETANCOURT NENINGER  [14.768049705558319, 16.274491285531273, 16.03...   \n",
            "CAMPO FLORIDO        [1.3779063537721181, 1.7667961216454162, 3.285...   \n",
            "\n",
            "                                                           28 DE ENERO  \\\n",
            "13 DEMARZO           [25.708775804386583, 30.608338500093126, 34.67...   \n",
            "14 DE JUNIO          [80.26842897893627, 73.77710949262494, 62.4196...   \n",
            "15 Y 18 Vedado       [247.48948832599865, 269.6076410683491, 228.87...   \n",
            "19 DE ABRIL          [149.57273459323287, 149.1015169274972, 122.65...   \n",
            "1RO DE ENERO         [207.47328358765031, 227.66191988587656, 205.9...   \n",
            "...                                                                ...   \n",
            "WAJAY                [248.2787047202024, 215.5976716416375, 233.839...   \n",
            "WILFREDO SANTANA     [23.303448079299066, 28.63435518908237, 20.866...   \n",
            "BOYEROS              [50.87342580345758, 47.70923490303379, 45.7516...   \n",
            "BETANCOURT NENINGER  [23.94897348634346, 20.899130569627403, 21.926...   \n",
            "CAMPO FLORIDO        [2.456290867202751, 2.522248914568325, 1.97612...   \n",
            "\n",
            "                                                       30 DE NOVIEMBRE  \\\n",
            "13 DEMARZO           [3.071169547118322, 1.7530406819243431, 1.2335...   \n",
            "14 DE JUNIO          [85.54255691524706, 82.26104110819186, 77.5585...   \n",
            "15 Y 18 Vedado       [13.352010124820673, 11.29816807745093, 13.954...   \n",
            "19 DE ABRIL          [6.194743474500118, 6.036244742857558, 7.89788...   \n",
            "1RO DE ENERO         [4.700240527995849, 6.53397551517496, 4.642064...   \n",
            "...                                                                ...   \n",
            "WAJAY                [36.988975394815526, 45.52462711587534, 37.609...   \n",
            "WILFREDO SANTANA     [13.967345263233407, 15.67008014949457, 15.312...   \n",
            "BOYEROS              [19.90672699906588, 19.463094988008027, 16.994...   \n",
            "BETANCOURT NENINGER  [7.608684325250341, 7.838102526784434, 8.06571...   \n",
            "CAMPO FLORIDO        [0.63518133727893, 0.8162495406528021, 0.73010...   \n",
            "\n",
            "                                                       5 DE SEPTIEMBRE  ...  \\\n",
            "13 DEMARZO           [0.06805511102551, 0.48708796551826106, 1.2711...  ...   \n",
            "14 DE JUNIO          [1.6149778922734521, 5.174203448885398, 3.8938...  ...   \n",
            "15 Y 18 Vedado       [5.817694334522984, 11.324453319915817, 20.835...  ...   \n",
            "19 DE ABRIL          [2.087708468143297, 5.481735922305525, 5.37765...  ...   \n",
            "1RO DE ENERO         [6.641966769062249, 15.751190011570946, 14.818...  ...   \n",
            "...                                                                ...  ...   \n",
            "WAJAY                [84.01617926778992, 94.05670870718484, 86.3175...  ...   \n",
            "WILFREDO SANTANA     [4.943525376218031, 7.361616059232927, 4.91744...  ...   \n",
            "BOYEROS              [21.172567284621316, 26.928738680206106, 20.38...  ...   \n",
            "BETANCOURT NENINGER  [8.054397422092025, 3.370884589391602, 7.62129...  ...   \n",
            "CAMPO FLORIDO        [0.360304232802628, 0.11533323546203501, 0.439...  ...   \n",
            "\n",
            "                                                 SANTIAGO DE LAS VEGAS  \\\n",
            "13 DEMARZO           [9.578067129188831, 7.08809288719822, 6.770615...   \n",
            "14 DE JUNIO          [28.147451498806902, 24.074798742431472, 30.06...   \n",
            "15 Y 18 Vedado       [31.374789090138478, 24.51797176807214, 27.870...   \n",
            "19 DE ABRIL          [30.642801957317847, 23.07615239069311, 27.745...   \n",
            "1RO DE ENERO         [13.104884602480773, 14.370012881372292, 12.88...   \n",
            "...                                                                ...   \n",
            "WAJAY                [1771.0123686113345, 1688.0625978736932, 1697....   \n",
            "WILFREDO SANTANA     [11.873857355962333, 13.532061056984602, 11.08...   \n",
            "BOYEROS              [1259.9879004690208, 1266.4389671514577, 1184....   \n",
            "BETANCOURT NENINGER  [21.68185253147757, 20.288611040443644, 17.648...   \n",
            "CAMPO FLORIDO        [1.5073306534568132, 0.7170248868081991, 1.928...   \n",
            "\n",
            "                                                           TOMAS ROMAY  \\\n",
            "13 DEMARZO           [95.97575457246822, 93.69386088644433, 91.0617...   \n",
            "14 DE JUNIO          [177.04934187470326, 185.1914821435421, 165.05...   \n",
            "15 Y 18 Vedado       [191.24289628484064, 186.83110349344062, 201.3...   \n",
            "19 DE ABRIL          [134.803465249121, 134.17936339208606, 128.466...   \n",
            "1RO DE ENERO         [60.504754276729045, 62.86519337059004, 63.456...   \n",
            "...                                                                ...   \n",
            "WAJAY                [97.48500926918716, 105.43966403905426, 100.30...   \n",
            "WILFREDO SANTANA     [138.09677622771656, 152.5937077693286, 152.61...   \n",
            "BOYEROS              [36.30579335226212, 35.794460135097935, 34.093...   \n",
            "BETANCOURT NENINGER  [239.37425824257178, 232.61779349278086, 227.3...   \n",
            "CAMPO FLORIDO        [3.09663886562633, 4.104938656659566, 2.935049...   \n",
            "\n",
            "                                                          TURCIOS LIMA  \\\n",
            "13 DEMARZO           [15.76804719656526, 14.369035151430081, 18.554...   \n",
            "14 DE JUNIO          [175.13167713406403, 146.64469588411097, 146.2...   \n",
            "15 Y 18 Vedado       [77.0752787277282, 47.679880636619544, 71.4268...   \n",
            "19 DE ABRIL          [68.87158208227967, 50.90008958981058, 69.7115...   \n",
            "1RO DE ENERO         [31.438613376154205, 20.38779638787041, 23.095...   \n",
            "...                                                                ...   \n",
            "WAJAY                [228.7133829329964, 212.29507744393945, 219.19...   \n",
            "WILFREDO SANTANA     [34.5918771726315, 38.32892927026643, 38.88601...   \n",
            "BOYEROS              [84.79076683815336, 83.22787477253092, 67.3596...   \n",
            "BETANCOURT NENINGER  [30.712253148750154, 23.69733429692217, 29.461...   \n",
            "CAMPO FLORIDO        [0.9892223935670391, 1.173696430371087, 3.0084...   \n",
            "\n",
            "                                                              VAN TROI  \\\n",
            "13 DEMARZO           [79.57025550157807, 80.53979776602853, 85.1524...   \n",
            "14 DE JUNIO          [499.93585497045325, 502.0554553537678, 479.33...   \n",
            "15 Y 18 Vedado       [289.75931195877087, 271.2758503614902, 276.86...   \n",
            "19 DE ABRIL          [285.276355987038, 261.40870557559685, 286.318...   \n",
            "1RO DE ENERO         [72.08511084171614, 74.53892319944255, 74.3784...   \n",
            "...                                                                ...   \n",
            "WAJAY                [168.9547698061593, 159.94310815615611, 149.77...   \n",
            "WILFREDO SANTANA     [71.1956390326742, 73.29979045348547, 68.78770...   \n",
            "BOYEROS              [59.87803129693901, 51.38289293963377, 51.8770...   \n",
            "BETANCOURT NENINGER  [195.1530615091685, 209.6262573690562, 183.564...   \n",
            "CAMPO FLORIDO        [2.939217615993493, 2.587909037089244, 4.23225...   \n",
            "\n",
            "                                                               W PEREZ  \\\n",
            "13 DEMARZO           [24.615880213241688, 26.51287805667624, 24.422...   \n",
            "14 DE JUNIO          [266.0771272054234, 258.3970778252582, 257.325...   \n",
            "15 Y 18 Vedado       [30.019581593802425, 27.318208537808747, 30.75...   \n",
            "19 DE ABRIL          [31.242409893801923, 25.6285449621028, 21.1658...   \n",
            "1RO DE ENERO         [13.205860519565592, 12.145223705086067, 8.653...   \n",
            "...                                                                ...   \n",
            "WAJAY                [77.06191874483851, 78.05275175078171, 76.5583...   \n",
            "WILFREDO SANTANA     [76.0006697538353, 79.04009993074004, 67.09595...   \n",
            "BOYEROS              [37.80520070644588, 31.094354665282225, 27.267...   \n",
            "BETANCOURT NENINGER  [66.8387086436517, 59.9028193331473, 54.184562...   \n",
            "CAMPO FLORIDO        [2.418129799719415, 3.501042341768087, 2.94984...   \n",
            "\n",
            "                                                                 WAJAY  \\\n",
            "13 DEMARZO           [40.55204871892705, 45.36950879417554, 42.4384...   \n",
            "14 DE JUNIO          [115.93808336356886, 112.08419647635249, 106.9...   \n",
            "15 Y 18 Vedado       [179.4829258193067, 197.4865617756193, 193.782...   \n",
            "19 DE ABRIL          [148.81573205774575, 144.30068889808376, 141.8...   \n",
            "1RO DE ENERO         [85.17111229807145, 102.51350555006232, 88.683...   \n",
            "...                                                                ...   \n",
            "WAJAY                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "WILFREDO SANTANA     [25.31776549204608, 30.642958929008827, 29.601...   \n",
            "BOYEROS              [2330.4526233940956, 2356.430118849525, 2280.5...   \n",
            "BETANCOURT NENINGER  [102.1177658031596, 108.2091398251866, 84.1640...   \n",
            "CAMPO FLORIDO        [2.8904204463113183, 1.891987019706899, 2.6866...   \n",
            "\n",
            "                                                      WILFREDO SANTANA  \\\n",
            "13 DEMARZO           [267.29792012204035, 278.79145038503486, 288.6...   \n",
            "14 DE JUNIO          [53.461982077949614, 59.75474384575463, 54.106...   \n",
            "15 Y 18 Vedado       [49.39601737583508, 57.02931449408262, 59.9650...   \n",
            "19 DE ABRIL          [35.717806151856955, 40.80141438047907, 38.470...   \n",
            "1RO DE ENERO         [19.416155408583148, 26.21236226923745, 20.103...   \n",
            "...                                                                ...   \n",
            "WAJAY                [25.31776549204608, 30.642958929008827, 29.601...   \n",
            "WILFREDO SANTANA     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "BOYEROS              [24.67790016872196, 25.689529552628347, 25.457...   \n",
            "BETANCOURT NENINGER  [410.7779494264335, 407.8873698546929, 398.814...   \n",
            "CAMPO FLORIDO        [12.739224420781568, 12.279905614512206, 10.15...   \n",
            "\n",
            "                                                               BOYEROS  \\\n",
            "13 DEMARZO           [14.75412811274402, 10.236970009906837, 15.169...   \n",
            "14 DE JUNIO          [44.68890483462795, 43.838246200007966, 45.691...   \n",
            "15 Y 18 Vedado       [57.872987807131715, 55.524785483948364, 58.11...   \n",
            "19 DE ABRIL          [52.14311726423461, 48.10754747423465, 48.7213...   \n",
            "1RO DE ENERO         [18.567242256328772, 19.009178120807796, 15.51...   \n",
            "...                                                                ...   \n",
            "WAJAY                [2330.4526233940956, 2356.430118849525, 2280.5...   \n",
            "WILFREDO SANTANA     [24.67790016872196, 25.689529552628347, 25.457...   \n",
            "BOYEROS              [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "BETANCOURT NENINGER  [37.60325269031607, 33.20579031338379, 36.3765...   \n",
            "CAMPO FLORIDO        [1.3181724533288501, 1.183910913224248, 1.1291...   \n",
            "\n",
            "                                                   BETANCOURT NENINGER  \\\n",
            "13 DEMARZO           [1373.1275674407948, 1446.9634107317283, 1359....   \n",
            "14 DE JUNIO          [31.243005335376488, 35.65772123160251, 40.892...   \n",
            "15 Y 18 Vedado       [54.93554145385425, 45.66159631960516, 45.5399...   \n",
            "19 DE ABRIL          [21.227091152506862, 22.271455655152273, 19.46...   \n",
            "1RO DE ENERO         [19.952809328463005, 15.263566029115719, 15.12...   \n",
            "...                                                                ...   \n",
            "WAJAY                [102.1177658031596, 108.2091398251866, 84.1640...   \n",
            "WILFREDO SANTANA     [410.7779494264335, 407.8873698546929, 398.814...   \n",
            "BOYEROS              [37.60325269031607, 33.20579031338379, 36.3765...   \n",
            "BETANCOURT NENINGER  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "CAMPO FLORIDO        [45.899863936465486, 50.18776000297037, 45.215...   \n",
            "\n",
            "                                                         CAMPO FLORIDO  \n",
            "13 DEMARZO           [9.934689531255945, 10.432643505443949, 9.1019...  \n",
            "14 DE JUNIO          [1.807182963749202, 2.135187505853401, 2.96374...  \n",
            "15 Y 18 Vedado       [2.295379731451361, 3.076405794990175, 2.09577...  \n",
            "19 DE ABRIL          [1.5915634684505782, 1.792893424773431, 1.6618...  \n",
            "1RO DE ENERO         [0.49692826595799305, 2.005837902617381, 0.791...  \n",
            "...                                                                ...  \n",
            "WAJAY                [2.8904204463113183, 1.891987019706899, 2.6866...  \n",
            "WILFREDO SANTANA     [12.739224420781568, 12.279905614512206, 10.15...  \n",
            "BOYEROS              [1.3181724533288501, 1.183910913224248, 1.1291...  \n",
            "BETANCOURT NENINGER  [45.899863936465486, 50.18776000297037, 45.215...  \n",
            "CAMPO FLORIDO        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
            "\n",
            "[82 rows x 82 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oJQAJ4BYQd_R",
        "outputId": "ade54fa5-3062-4291-ae0d-b6cb1b125492"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(82, 82)\n"
          ]
        }
      ],
      "source": [
        "shape=raw_data.shape\n",
        "print(shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TeS2cAE6Qd_S"
      },
      "source": [
        "O sea, para fijar ideas, tenemos un arreglo tridimensional, y como vemos en el ejemplo, así se obtendría el valor de movilidad entre las áreas de salud 14 de Junio y Wajay para el día 1."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1fC7GNV7Qd_T",
        "outputId": "3dac4da3-d972-4030-86a7-662429018c4f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "115.93808336356886\n"
          ]
        }
      ],
      "source": [
        "print(raw_data[\"14 DE JUNIO\"][\"WAJAY\"][0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FPvkK1QwQd_U",
        "outputId": "102d3561-7898-46db-959b-17eced4a1ec6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "PASTEUR y Los Pinos\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "areas=raw_data.keys()\n",
        "a1=random.choice(areas)\n",
        "a2=random.choice(areas)\n",
        "plt.plot(raw_data[a1][a2])\n",
        "\n",
        "print(a1, \"y\", a2)\n",
        "plt.title(\"Movilidad entre áreas\")\n",
        "plt.xlabel(\"Días\")\n",
        "plt.ylabel(\"No. de personas\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wr_gq26xQd_W"
      },
      "source": [
        "Ahora definiremos algunas funciones que usaremos y explicaremos posteriormente:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f1dBOryvQd_X"
      },
      "outputs": [],
      "source": [
        "def single_matrix(t):\n",
        "        sm=[raw_data[i][j][t] for j in areas for i in areas]\n",
        "        sm=np.array(sm)\n",
        "        sm=sm.reshape((len(areas),len(areas)))\n",
        "        return sm\n",
        "    \n",
        "def single_vector(t):\n",
        "        sv=[raw_data[i][j][t] for j in areas for i in areas]\n",
        "        sv=np.array(sv)\n",
        "        return sv\n",
        "\n",
        "def roc_data(d, v ):\n",
        "    c_bottom_g=0\n",
        "    c_bottom_i=0\n",
        "    c_top_g=0\n",
        "    c_top_i=0\n",
        "    for i in gen_imp:\n",
        "        if i>v:\n",
        "            if d[i]==\"g\":\n",
        "                c_bottom_g+=1\n",
        "            else:\n",
        "                c_bottom_i+=1\n",
        "        else:\n",
        "            if d[i]==\"g\":\n",
        "                c_top_g+=1\n",
        "            else:\n",
        "                c_top_i+=1\n",
        "    return c_bottom_g,c_bottom_i,c_top_g,c_top_i            \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0PNsjOl1Qd_Z"
      },
      "source": [
        "Como pretendemos indagar acerca de la relación que podría tener la movilidad y la naturaleza de los distintos días de la semana, entiéndase por ejemplo, distinguirlos unos de otros, es importante tener a mano un identificador sencillo. De esta manera podemos empezar la metodología de distinción, por ejemplo, podemos tomar todos los índices de los miércoles y domingos."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zq1CnoQdQd_Z",
        "outputId": "af940ab0-710e-4b21-8b3c-4012374b6857"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "18\n",
            "17\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "123"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ini_date=datetime.datetime(2020,3,4)\n",
        "delta=123\n",
        "r=[ini_date + datetime.timedelta(days=idx) for idx in range(delta)]\n",
        "days={}\n",
        "\n",
        "wed=[i for i in range(123) if r[i].weekday()==2]\n",
        "sun=[i for i in range(123) if r[i].weekday()==6]\n",
        "print(len(wed))\n",
        "print(len(sun))\n",
        "len(r)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HIRQepFxQd_Z"
      },
      "source": [
        "# Definición de la función distancia"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3IB7o5Z5Qd_a"
      },
      "source": [
        "Lo primero que necesitamos calcular para hacer un ejercicio de clustering o agrupamiento es la distancia. Antes de agrupar los elementos por su similitud, tenemos que definir la similitud misma. La distancia juega un papel funadmental en el $machine$ $learning$. Esta provee las bases para muchos algoritmos útiles y populares de $machine$ $learning$ como los $k$-$nearest$ $neighbors$ para $supervised$ $learning$ y $k$-$means$ $clustering$ para $unsupervised$ $learning$. \n",
        "\n",
        "Diferentes medidas de distancia deben ser escogidas y usadas dependiendo de los tipos de datos. Una medida de distancia es un marcador objetivo que sintetiza la diferencia relativa entre dos objetos en el dominio del problema.\n",
        "\n",
        "En principio, nosotros propondremos como función de distancia la correlación de Spearman entre las matrices e indagaremos sobre si resulta una buena distancia para el problema."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mUqExtvRQd_b"
      },
      "source": [
        "# Coeficiente de correlación de Spearman"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ht4DsyTtQd_b"
      },
      "source": [
        "En estadística, el coeficiente de correlación de rango de Speraman o el ρ de Spearman, es una medida no paramétrica de correlación de rango (dependencia estadística entre los rangos de dos variables). Refiere a cuán bien puede ser descrita la relación entre dos variables por una función monótona. \n",
        "\n",
        "La correlación de Spearman entre dos variables es igual a la correlación de Pearson entre el rango de valores de esas dos variables; mientras que la correlación de Pearson identifica relaciones lineales, la correlación de Spearman refiere relaciones monótonas, sean lineales o no. Sin valores repetidos, una correlación de Spearman perfecta de +1 o -1 ocurre cuando cada una de las variables es una un función perfectamente monótona de la otra.\n",
        "\n",
        "Intuitivamente, la correlación de Spearman entre dos variables será alta cuando las observaciones tienen rangos (la posición relativa de la s observaciones de la variable) similares o idénticos entre dos variables, y baja cuando no sean similares o directamente opuestos. \n",
        "\n",
        "El coeficiente de correlación de Spearman se define como el coeficiente de correlación de Pearson entre el rango de las variables.\n",
        "\n",
        "Para una muestra de tamaño $n$, los $n$ marcadores puros $X_i$, $Y_i$ se convierten en rangos $R(X_i)$, $R(Y_i)$ y entonces $r_s$ queda como:\n",
        "\n",
        "\n",
        "\\begin{equation}\n",
        "r_s=\\rho_{R(X)R(Y)}=\\frac{cov(R(X),R(Y))}{\\sigma_{R(X)}\\sigma_{R(Y)}}\n",
        "\\end{equation}\n",
        "\n",
        "\n",
        "donde $\\rho$ denota el coeficiente de correlación de Pearson usual, pero aplicado a variables de rango,\n",
        "\n",
        "$\\operatorname {cov} (\\operatorname {R} (X),\\operatorname {R} (Y))$ es la covarianza de las variables de rango,\n",
        "\n",
        "$\\sigma _{\\operatorname {R} (X)}$ y $\\sigma _{\\operatorname {R} (Y)}$ son las desviaciones estándar de las variables de rango.\n",
        "\n",
        "\n",
        "Solo si todos los rangos $n$ son enteros distintos, se puede expresar usando la fórmula:\n",
        "\n",
        "\\begin{equation}\n",
        "r_{s}=1-{\\frac {6\\sum d_{i}^{2}}{n(n^{2}-1)}}\n",
        "\\end{equation}\n",
        "\n",
        "donde,\n",
        "\n",
        "$d_{i}=\\operatorname {R} (X_{i})-\\operatorname {R} (Y_{i})$ es la diferencia entre dos rangos de cada observación,\n",
        "\n",
        "$n$ es el número de observaciones."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Go0z_GZnQd_c"
      },
      "source": [
        "# Función distancia"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "doLsMKwEQd_c"
      },
      "source": [
        "La idea es comprobar preliminarmente si la correlación de Spearman puede ser o no una buena medida de distancia. Aquí usamos la función $single_vector$ que fue definida anteriormente, la cual recibe un día cualquiera y retorna la matriz de movilidad de ese día en forma de vector. Aquí tomamos una parte de la muestra para probar cómo se desenvuelve la función distancia y como vemos al menos es capaz de correlacionar correctamente los días de la misma categoría."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OGmOmykUQd_d",
        "outputId": "4d5721c9-4904-4793-dec4-ee9f7b80e820"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0.975486838613996, 0.9860474723011846, 0.9856524326405578, 0.9888294119073832, 0.9872193407065594]\n",
            "[0.9658955578125302, 0.9795407893551137, 0.974720592078187, 0.990649156168601, 0.9935302396822595]\n"
          ]
        }
      ],
      "source": [
        "wed_gen=[scipy.stats.spearmanr(single_vector(wed[i]),single_vector(wed[i+1]))[0] for i in range(5,10)]\n",
        "sun_gen=[scipy.stats.spearmanr(single_vector(sun[i]),single_vector(sun[i+1]))[0] for i in range(5,10)]\n",
        "\n",
        "\n",
        "print(wed_gen)\n",
        "print(sun_gen)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k354p1xwQd_e"
      },
      "source": [
        "El siguiente paso es usar la función distancia para determinar su efectividad de detección de valores genuinos e impostores, en donde los genuinos serían los días de la misma categoría y los impostores los cruzados."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YCYieKm1Qd_e"
      },
      "outputs": [],
      "source": [
        "ij_imp=list(it.combinations_with_replacement(range(len(sun)),2))\n",
        "\n",
        "impostors=[scipy.stats.spearmanr(single_vector(wed[i[0]]),single_vector(sun[i[1]]))[0] for i in ij_imp]\n",
        "\n",
        "ij_gen_w=list(it.combinations(range(len(wed)),2))\n",
        "ij_gen_s=list(it.combinations(range(len(sun)),2))\n",
        "\n",
        "genuines=[scipy.stats.spearmanr(single_vector(wed[i[0]]),single_vector(wed[i[1]]))[0] for i in ij_gen_w]+[scipy.stats.spearmanr(single_vector(sun[i[0]]),single_vector(sun[i[1]]))[0] for i in ij_gen_s]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UWXoGOHwQd_f"
      },
      "source": [
        "Ahora podemos clasificarlos según su valor real para luego poder comparar."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0TsFapRtQd_f"
      },
      "outputs": [],
      "source": [
        "gen_imp=impostors+genuines\n",
        "\n",
        "d={}\n",
        "for i in range(len(gen_imp)):\n",
        "    if i<len(impostors):\n",
        "        d[gen_imp[i]] = \"i\"\n",
        "    else:\n",
        "        d[gen_imp[i]] = \"g\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AblS2VftQd_g"
      },
      "source": [
        "Si ahora ordenamos los valores, vamos teniendo un diccionario que tiene la siguiente forma:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LwD8rpSUQd_g",
        "outputId": "c18b7d9a-ae98-4e6f-ab9a-5ec331f4cecb"
      },
      "outputs": [
        {
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              "             (0.990380540921245, 'g'),\n",
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              "             (0.9968669531157355, 'g')])"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sd=collections.OrderedDict(sorted(d.items()))\n",
        "sd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "08CZWfNPQd_h"
      },
      "source": [
        "# Curva ROC"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r9hd3b4oQd_i"
      },
      "source": [
        "La curva ROC ($Receiver$ $Operating$ $Characteristic$) es utilizada para visualizar el rendimiento de un clasificador binario. Nos devuelve la relación entre la tasa de positivos verdaderos ($True$ $Positive$ $Rate$, TPR) y la tasa de falsos positivos ($False$ $Positive$ $Rate$, FPR) para diferentes valores umbrales.\n",
        "\n",
        "\\begin{equation}\n",
        "TPR = \\frac{PV}{PV+FN}\n",
        "\\end{equation}\n",
        "\n",
        "\\begin{equation}\n",
        "FPR = \\frac{FP}{NV+FP}\n",
        "\\end{equation}\n",
        "\n",
        "donde\n",
        "PV son los positivos verdaderos,\n",
        "FN son los falsos negativos,\n",
        "FP son los falsos positivos,\n",
        "NV son los negativos verdaderos.\n",
        "\n",
        "Para distintos valores umbrales se obtienen distintos TPR y FPR, de manera que para visualizar qué umbral es mejor para el clasificador se hace la curva ROC, la diagonal con el eje de coordenadas representaría la curva ROC para valores reales por lo que se espera siempre para una buena clasificación, que esté por encima de esta.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B3aaZG5eQd_i",
        "outputId": "bfa9163c-89b7-4018-9fca-c2f821c6e6f7"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "inv_gen_imp=gen_imp\n",
        "inv_gen_imp.sort()\n",
        "inv_gen_imp.reverse()\n",
        "\n",
        "roc_data_curve=[(roc_data(d, i)[0]/(roc_data(d, i)[0] + roc_data(d, i)[2]),roc_data(d, i)[1]/(roc_data(d, i)[3] + roc_data(d, i)[1])) for i in inv_gen_imp]\n",
        "\n",
        "tpry,fprx = zip(*roc_data_curve)\n",
        "x=[i/100 for i in range(101)]\n",
        "y=[i/100 for i in range(101)]\n",
        "\n",
        "fig = plt.figure()\n",
        "ax1 = fig.add_subplot(111)\n",
        "\n",
        "ax1.scatter(fprx,tpry, s=10, c='b', marker=\"s\", label='Datos')\n",
        "ax1.scatter(x,y, s=10, c='r', marker=\"o\", label='Aleatorio')\n",
        "plt.legend(loc='upper left')\n",
        "plt.rcParams['figure.figsize'] = [8, 4]\n",
        "plt.title(\"ROC\")\n",
        "plt.xlabel(\"FPR\")\n",
        "plt.ylabel(\"TPR\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6WWtGgfZQd_j"
      },
      "source": [
        "# G-Mean\n",
        "\n",
        "\n",
        "La clasificación es una de las técnicas de aprendizaje supervisado para realizar análisis predictivos con el resultado categórico, puede ser una clase binaria o multiclase. Hoy en día, hay mucha investigación y casos sobre clasificación utilizando varios algoritmos, desde básicos hasta avanzados como regresión logística, análisis discriminante, $Naive$ $Bayes$, árbol de decisión, bosque aleatorio, máquina de vectores de soporte, red neuronal, etc. Han sido bien desarrollados y aplicados con éxito a muchos dominios. Sin embargo, la distribución de clases desequilibrada de un conjunto de datos tiene un problema porque la mayoría de las técnicas de aprendizaje supervisado desarrolladas son para la distribución de clases equilibrada.\n",
        "\n",
        "\n",
        "La media geométrica o G-mean es la media geométrica de sensitvidad y espeficificidad. Es una de las evaluaciones métricas no sesgadas para la clasificación.\n",
        "\n",
        "Usando G-mean como métrica de evaluación imparcial y el enfoque principal del movimiento del umbral, produce el umbral óptimo para la clasificación binaria. Teóricamente, la observación se categorizará como clase menor cuando su probabilidad sea inferior a este umbral y viceversa."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8Y-03pP6Qd_k"
      },
      "outputs": [],
      "source": [
        "gmean = np.sqrt([list(tpry)[i] * (1 - list(fprx)[i]) for i in range(len(fprx))])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x-CXGSgGQd_k",
        "outputId": "a5539690-fe1a-4e03-fc6e-85623f6dabfc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best Threshold: 0.9434 with G-Mean: 0.7614 at index:  187\n",
            "FPR: 0.0588, TPR: 0.6159\n"
          ]
        }
      ],
      "source": [
        "index = np.argmax(gmean)\n",
        "thresholdOpt = round(inv_gen_imp[index], ndigits = 4)\n",
        "gmeanOpt = round(gmean[index], ndigits = 4)\n",
        "fprOpt = round(fprx[index], ndigits = 4)\n",
        "tprOpt = round(tpry[index], ndigits = 4)\n",
        "print('Best Threshold: {} with G-Mean: {}'.format(thresholdOpt, gmeanOpt),\"at index: \", index)\n",
        "print('FPR: {}, TPR: {}'.format(fprOpt, tprOpt))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "I0R54xzPQd_l",
        "outputId": "58c34bb7-047e-4386-d2f2-6ef21ba3f00e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best Threshold: 0.9463 with Youden J statistic: 0.7565 at index:  176\n",
            "FPR: 0.0327, TPR: 0.5917\n"
          ]
        }
      ],
      "source": [
        "youdenJ = [list(tpry)[i] - list(fprx)[i] for i in range(len(fprx))]\n",
        "\n",
        "index = np.argmax(youdenJ)\n",
        "thresholdOpt = round(inv_gen_imp[index], ndigits = 4)\n",
        "youdenJOpt = round(gmean[index], ndigits = 4)\n",
        "fprOpt = round(fprx[index], ndigits = 4)\n",
        "tprOpt = round(tpry[index], ndigits = 4)\n",
        "print('Best Threshold: {} with Youden J statistic: {}'.format(thresholdOpt, youdenJOpt),\"at index: \", index)\n",
        "print('FPR: {}, TPR: {}'.format(fprOpt, tprOpt))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TFuWtM8bQd_l"
      },
      "source": [
        "# Matriz de distancia\n",
        "\n",
        "El agrupamiento comienza calculando una distancia entre cada par de unidades que desea agrupar. Una matriz de distancia será simétrica (porque la distancia entre x e y es la misma que la distancia entre y y x) y tendrá ceros en la diagonal (porque cada elemento está a una distancia cero de sí mismo).\n",
        "\n",
        "Como habíamos visto usaremos la correlación de Spearman para definir nuestras distancias, pero la correlación funciona de manera inversa a lo que queremos, por lo que en realidad usaremos como distancia $d(i,j)=1-corr(i,j)$. Para ello usaremos los datos no empleados para definir nuestra distancia."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cPplbaGwQd_m",
        "outputId": "fdff3ab2-5ca5-46f6-b0ba-ab5e66326ce6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "111"
            ]
          },
          "execution_count": 128,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "used_data=[wed[i] for i in range(5,11)]+[sun[i] for i in range(5,11)]\n",
        "learning_data=[]\n",
        "for i in range(123):\n",
        "    if i not in used_data:\n",
        "        learning_data.append(i)\n",
        "\n",
        "len(learning_data)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LtUYTJMcQd_m"
      },
      "outputs": [],
      "source": [
        "ij_elements=list(it.product(range(len(learning_data)), range(len(learning_data))))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BZJua6cuQd_n"
      },
      "outputs": [],
      "source": [
        "corr_vector_f=[scipy.stats.spearmanr(single_vector(learning_data[i[0]]),single_vector(learning_data[i[1]]))[0] for i in ij_elements]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n-7IAr_oQd_o",
        "outputId": "12643d19-cf88-4e78-d88a-ed5b568ea06f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "111.0"
            ]
          },
          "execution_count": 132,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "math.sqrt(len(corr_vector_f))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9_Hy143KQd_p",
        "outputId": "3e7782c9-c2c5-4700-a9ea-ad6b79f45ab3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(111, 111)"
            ]
          },
          "execution_count": 133,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cr=np.array(corr_vector_f)\n",
        "corr_matrix_f=cr.reshape((int(math.sqrt(len(corr_vector_f))),int(math.sqrt(len(corr_vector_f)))))\n",
        "#corr_matrix=np.delete(corr_matrix,85,0)\n",
        "#corr_matrix=np.delete(corr_matrix,85,1)\n",
        "#corr_matrix=np.delete(corr_matrix,84,0)\n",
        "#corr_matrix=np.delete(corr_matrix,84,1)\n",
        "np.shape(corr_matrix_f)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qhw8NPVSQd_p"
      },
      "source": [
        "Finalmente observamos la matriz de correlación, en donde salta claramente a la vista uno de los días."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SNcfOqlOQd_p",
        "outputId": "02baf5f0-fe35-4e4c-db8d-c67c018ffb07"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot: >"
            ]
          },
          "execution_count": 134,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "heatmap(corr_matrix_f, cmap='viridis')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6hMLt4-kQd_p"
      },
      "source": [
        "# K-Medoids"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MjPJeSHlQd_p"
      },
      "source": [
        "K-medoids es un algoritmo de agrupamiento ($clustering$) relacionado con los algoritmos $k-means$ y $medoidshift$.\n",
        "\n",
        "Tanto el $k-medoids$ como el $k-means$ son algoritmos que trabajan con particiones (dividiendo el conjunto de datos en grupos) y ambos intentan minimizar la distancia entre puntos que se añadirían a un grupo y otro punto designado como el centro de ese grupo. En contraste con el algoritmo $k-means$, $k-medoids$ escoge datapoints como centros y trabaja con una métrica arbitraria de distancias entre datapoints en vez de usar la norma $l_2$. \n",
        "\n",
        "K-medoid es una técnica clásica de particionado de grupos que divide los datos conformados por n objetos en k grupos (con k conocido de antemano).\n",
        "\n",
        "Es más robusto ante el ruido y a partes aisladas que k-means porque minimiza una suma de disimilaridades (entre pares de puntos) en vez de una suma de distancias euclidianas cuadradas.\n",
        "\n",
        "Un medoid puede ser definido como el objeto de un grupo cuya disimilaridad media a todos los objetos en el grupo es mínima. Es el punto ubicado más hacia el centro en todo el grupo. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S4Fhh15bQd_q"
      },
      "source": [
        "# PAM\n",
        "\n",
        "La aplicación práctica más común de k-medoids es el algoritmo Partición Alrededor de Medoids (PAM). PAM utiliza una búsqueda golosa que puede no encontrar la solución óptima, pero es más rápido que la búsqueda exhaustiva.Trabaja como sigue:\n",
        "\n",
        "    1-Inicialización: seleccionar k de los n puntos como el medoid.\n",
        "    2-Asociar cada punto al medoid más cercano.\n",
        "    3-Mientras el costo de la configuración disminuya:\n",
        "        *Para cada medoid m, para cada no medoid o:\n",
        "            -Intercambiar m y o, recalcular el costo (suma de la distancia de los puntos a sus medoids).\n",
        "            -Si el costo total de la configuración aumentó en el paso anterior, deshacer el intercambio."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zscgQjmDQd_r"
      },
      "outputs": [],
      "source": [
        "kmedoids = KMedoids(n_clusters=7,  metric='precomputed', method='pam', init='heuristic', max_iter=100, random_state=None).fit(1-corr_matrix_f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DTni5MFQQd_r",
        "outputId": "3f339afa-cf2d-4750-ba36-c89583c7a1e6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([5, 5, 5, 3, 3, 5, 5, 5, 5, 5, 3, 3, 5, 5, 5, 5, 5, 3, 3, 5, 5, 5,\n",
              "       5, 5, 3, 3, 5, 5, 5, 5, 5, 3, 3, 5, 5, 5, 5, 3, 1, 1, 5, 5, 0, 0,\n",
              "       0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0,\n",
              "       0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0,\n",
              "       0, 1, 1, 6, 0, 0, 1, 0, 4, 0, 0, 4, 4, 4, 4, 4, 4, 2, 4, 2, 2, 2,\n",
              "       2])"
            ]
          },
          "execution_count": 99,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "lab=kmedoids.labels_\n",
        "lab"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OY27WpSnQd_t"
      },
      "outputs": [],
      "source": [
        "mon=[i for i in learning_data if r[i].weekday()==0]\n",
        "tue=[i for i in learning_data if r[i].weekday()==1]\n",
        "wed=[i for i in learning_data if r[i].weekday()==2]\n",
        "thu=[i for i in learning_data if r[i].weekday()==3]\n",
        "fri=[i for i in learning_data if r[i].weekday()==4]\n",
        "sat=[i for i in learning_data if r[i].weekday()==5]\n",
        "sun=[i for i in learning_data if r[i].weekday()==6]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KXcTBXRwQd_t"
      },
      "outputs": [],
      "source": [
        "m=[lab[learning_data.index(mon[i])] for i in range(len(mon))]\n",
        "t=[lab[learning_data.index(tue[i])] for i in range(len(tue))]\n",
        "w=[lab[learning_data.index(wed[i])] for i in range(len(wed))]\n",
        "th=[lab[learning_data.index(thu[i])] for i in range(len(thu))]\n",
        "f=[lab[learning_data.index(fri[i])] for i in range(len(fri))]\n",
        "s=[lab[learning_data.index(sat[i])] for i in range(len(sat))]\n",
        "su=[lab[learning_data.index(sun[i])] for i in range(len(sun))]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1wZQO2sDQd_t",
        "outputId": "56923db0-de3e-45e7-dd30-e0db22b527a6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(array([0., 6., 0., 5., 0., 0., 0., 0., 0., 0.]),\n",
              " array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.]),\n",
              " <BarContainer object of 10 artists>)"
            ]
          },
          "execution_count": 102,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize =(10, 7))\n",
        "plt.title('Monday')\n",
        "ax.hist(m, bins = [0, 1, 2, 3, 4, 5, 6, 7,8,9,10])\n",
        "fig, ax = plt.subplots(figsize =(10, 7))\n",
        "plt.title('Tuesday')\n",
        "ax.hist(t, bins = [0, 1, 2, 3, 4, 5, 6, 7,8,9,10])\n",
        "fig, ax = plt.subplots(figsize =(10, 7))\n",
        "plt.title('Wednesday')\n",
        "ax.hist(w, bins = [0, 1, 2, 3, 4, 5, 6, 7,8,9,10])\n",
        "fig, ax = plt.subplots(figsize =(10, 7))\n",
        "plt.title('Thursday')\n",
        "ax.hist(th, bins = [0, 1, 2, 3, 4, 5, 6, 7,8,9,10])\n",
        "fig, ax = plt.subplots(figsize =(10, 7))\n",
        "plt.title('Friday')\n",
        "ax.hist(f, bins = [0, 1, 2, 3, 4, 5, 6, 7,8,9,10])\n",
        "fig, ax = plt.subplots(figsize =(10, 7))\n",
        "plt.title('Saturday')\n",
        "ax.hist(s, bins = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n",
        "fig, ax = plt.subplots(figsize =(10, 7))\n",
        "plt.title('Sunday')\n",
        "ax.hist(su, bins = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-cxgwJwDQd_u",
        "outputId": "04f1dbda-6aa6-4a02-f80b-a91537758904"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(12, 8))\n",
        "sn.scatterplot(x=[r[i].weekday() for i in range(len(learning_data))],y=[i for i in range(len(learning_data))], hue=lab, \n",
        "                palette=sn.color_palette('hls', 7))\n",
        "plt.title('KMedoids with 7 Clusters')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CgY5qhhLQd_v"
      },
      "outputs": [],
      "source": [
        "zeros=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==0]\n",
        "one=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==1]\n",
        "two=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==2]\n",
        "three=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==3]\n",
        "four=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==4]\n",
        "five=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==5]\n",
        "six=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==6]\n",
        "\n",
        "fzeros=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==0]\n",
        "fone=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==1]\n",
        "ftwo=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==2]\n",
        "fthree=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==3]\n",
        "ffour=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==4]\n",
        "ffive=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==5]\n",
        "fsix=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==6]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LixR-WRxQd_w",
        "outputId": "59949c2e-63be-4e69-c6bc-71d056b787c4"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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JVlRbDV68nCbo27zwApAjh42KxYYMubOP6dWrcrtSSsW5dg0YNw6oX1++bhceDuzbB6xa5enInEsT9G1y5pQkvXChTdZbjhxJ2+1KKUeaPl2KwEaOTP77zzwD5Msno2jlGZqgkxEaKp8mP/3UdCQZNGtWyvNRxYt7NhallNe6cgUYPx5o1Ah45JHk75M1K9ClC/DVV1JEpqynCToZNWvKV0SEj663xMbKFHaXLkClSrJHIrHAQGDsWDOxKaW8zrRpwOnTKY+e43XvLm8vERGeicvpNEGnICwM2LkT2LjRdCRpdP26zNG/+SbQtSuwbRswYwZQokTCfUaOBNq1MxejUsprXL4MvP020LgxULdu6vctWRJo1kzeUm7e9Ex8TqYJOgXPPeeDxWJnzsgc1aJFwFtvycfcgABJxpGRclZc9uzA1q2mI1VKeYmpU+Wt426j53jh4bJWvXSptXEpTdApyplT8tqiRcC//5qOxgX79gG1a0vy/d//gAEDku6TAICCBYHevaUC7o8/zMSplPIa//0HTJgAPP64vH24omlTGUlrsZj1NEGnIixMZoy9vlhszRqgTh3537Z6NdC2bcr3fe01IHdu4I03PBaeUso7TZkCnDvn+ugZkH5HPXoAP/8M7NplXWxKE3SqqlcHQkK8vLPYvHmyeFSokCyY16mT+v3z5pUk/dVXenSXUg526RLwzjtAixZy5G5adO4MZMmio2iraYK+i7Aw4M8/gQ0bTEdyG2Zg+HCgY0fgoYeAX38FSpVy7Wd79wby5weGDrU2RqWU13r/fVm+S8voOV7+/FKn88knkuiVNTRB38Vzz8l6tFcVi924AXToIP34OnUCli2TkbGrcuYEBg0CVqyQeSqllKNcuABMnAi0bCltPNMjPFwqwL1+CdCHaYK+ixw5gPbtpe7KK4rFzp0DHntMemmPHSvNSDJnTvvjhIcDRYrIKNpr5++VUlaYNEmS9IgR6X+M+++XfhFTp+pbiFUsTdBEFElEO4loOxFtsfJaVgoNlWKxTz4xHMiBA7LG/NtvwIIFwOuv31mp7aps2SQ5r1sHrFzp3jiVUl7r33+B994D2rQBqlVL/+MQySlXu3frRJxVPDGCbsDM1Zg5xAPXskS1asADDxguFlu/XvZBnD8P/PSTHNCaUV26SAMTHUU7lx5F6jgTJ8q68fDhGX+sZ5+V1bWpUzP+WOpOOsXtorAw+aT4yy8GLv7ZZ9KAJF8+qdR+6CH3PG6WLLLdavNm4Jtv3POYynfoUaSOc+6cFIe1bQtUqZLxxwsMBF56SZqWnDiR8cdTSVmdoBnACiLaSkShFl/LUs8+C+TK5eFiMWZgzBhp3Vm7tpSS33efe6/x4otA6dLAsGHSZFc5hx5F6jjvviuFXe4YPcfr3h2IjgY+/th9j6mE1Qn6IWauAeBxAD2J6I5zUogolIi2ENGWM2fOWBxO+mXPLsViixfLLLPlbt6UzYbDhsmFV6yQEbS7Zcok+yz++EP+cso59ChSRzl7Fpg8WQYbFSu673Hvuw9o0kQGL1FR7ntcZXGCZubjcb+eBrAUwAPJ3CeCmUOYOaRAgQJWhpNhoaGyw2nePIsv9O+/8oqfO1fKLOfNk+loqzz7rJx69cYb8lFYOUPBgsnfrkeR2tKECTJBYkUTwZ49ZYr766/d/9hOZlmCJqLsRJQz/vcAGgPw6cZwVasCtWpZXCx28KBUav/yi5SNDx+e/kptV/n5AaNHA/v366ZGp4iNlUr+219bmTPrUaQ2dPo08MEHslpWvrz7H79ZM/lcp53F3MvKEXQhAOuJaAeATQC+Y+ZlFl7PI8LCgL17paja7TZskLXm06dl61P79hZcJAUtW0pf05Ej9Rw5J5g9W04469FDKvmJ5OSzwECgVSvT0Sk3e/tt2SpqVQt+f39Zi161Ctizx5prOJFlCZqZDzJz1bivisxsi4/lzz4rZ024vVjsf/8DGjSQSrQNG4B69dx8gbsgkoK0yEhg5kzPXlt51sWLsoe+bl0ZVkVGyoh69WrpXvHWW6YjVG70zz8ysm3fHihTxrrrdOkiEzDTpll3DafRbVZpFBgoL/QlS2TLQoYxA+PHS+YPCZFtVGXLuuGB06FxY9nCNWYMcO2amRiU9UaPlgOA338/6RR33bqyv37CBEnayhbeeksmxYYNs/Y6BQvK9q25c6VSXGWcJuh0CAtzU7FYVBTQrRsweLA0/f7xR+lCb0r8KPrECWD6dHNxKOvs2yeJ+aWXpE/j7d5+W2oSXnvN87Ept4v/r/zii+7foZmc8HBpgqJb6d2D2Is6SIWEhPCWLb7REfTBB2W71Z496azhunABePpp6Qo2dKis/fp5yeelxo2B7dulYC1HDtPRKHdq1kwKEA8cSLmKe8wYGW6tWiXLLspnvfKKTDnv2+f6YXcZwSyHb8TGyluI1fWtdkBEW1PqtOklGcH3hIXJiz5dPWgjI2U6ce1aOexi9GjvSc5AwhTo5MmmI1Hu9N13wA8/yM6AlJIzAPTrJ20/X3lFt935sGPHgIgIOfDOE8kZkIQcHi5tFYx0XbQZL8oKvqVt23QWi23aJJXax48Dy5dLMxJvU6sW8MQTshZ54YLpaJQ73LwJvPqq1Df06pX6fbNlk5ZTu3Z52TmrKi3GjQNiYjzfGO6FF+S9UbdcZZwm6HQKDJR1nc8/lw49LvniC6B+fXkD3LABaNjQyhAzZvRoSc7vvms6EuUO778v09qTJrl2PGnr1vL6HDbMTdWQypOOHJHWm126yGSIJ2XPLqP2JUuAU6c8e2270QSdAaGhMjCZO/cud2QG3nlH1pyrVpXjIq3oFuBOVasCzzwjb+he3IJVueCff+QDV4sWQNOmrv0MkST1S5es2zyrLPPmm/K28/rrZq7fo4fUwGp/7ozRBJ0BlSpJsVhERCqdxaKj5dXav78k6FWrUl//8yYjR0pvQN0X69sGD5YuFRMnpu3nKlWS1+706bKoqHxCZKSUtnTrZq5ra9mywKOPygqJljGknyboDAoLkw6Za9cm881Ll2Qt96OPgEGDgIULZXrbV5QrB3ToIIe96llyvmnTJmDOHFl/Ll067T8/cqQc+Nu7t54Z7iPGjpUJkMGDzcYRHg4cPSq1iSp9NEFnUNu2QJ48ydTSHD0qTT9WrgRmzJCKDW+q1HZV/AEab75pOhKVVrGxUolduLBs5UuPe+6R6fE1a6TgQnm1gwfl81hYGBAUZDaWJ56QGKZONRuHL/PBjOFdsmVLKBa7tVS7datUQh8+LNtaunY1GmOGlCol8UdEaHcpXzN/vtQ7jB8P5MyZ/scJDQWqVJHmJdphzquNGSMnyA4aZDoSiSMsTMYo+/ebjsY3aYJ2g7AwKYiYOxdy3tojj8jBA7/8Ajz2mOnwMm7IkIQTr5Rv+O8/YOBA4IEHZJkiI/z9pWDs8GEpdlRe6a+/pLth9+7A//2f6WhE166SqLUxYfpognaDChVkNvv6W++DW7WSG377TYps7CAoSIqF5s7Vj8K+4s03gZMnpdmMO5ZW6teXIsdx42T5Rnmd0aNlB93AgaYjSVC4MPDUU3J42tWrpqPxPZqg3SE6GjOyvYyhZ/vgTN1WUjFWuLDpqNxr8GAgSxZgxAjTkai7+esvqdh+8UVZanGXd96RQrEBA9z3mMot4o9yDw/3vreenj2lpcJnn5mOxPdogs6oy5eBVq1QbuUH+CBLP7xSeLF0MbGbggWlknfhQmDnTtPRqNT06ydDqfHj3fu4JUpIcl64EFi3zr2PrTJk1Cgga1bv/Oz00EMymTh1qm4ESCtN0Blx/Djw8MPAsmXAtGn4u8c7+OIrf5w+bTowi7z2mhQbDR9uOhKVkhUrpA5i6FCgSBH3P/7AgUCxYlIdHhPj/sdXabZnj4xOe/XyzhYL8f25f/9dVv6U6zRBp9f27TJ9+NdfwLffAt27IzRUisXmzDEdnEXuuUeS9NKlgI+cOuYoUVFAnz5yrmCfPtZcIzBQerRv3w7MnGnNNVSajBolu0n69zcdScrat5fP9tqfO200QafH99/LvA0RsH79rfaJ5cvLgDoiQrag2lLv3kC+fNaf/q7SbupUGU5NnCj1AlZ55hnZqTBkCPDvv9ZdR93Vn38CixbJhIbJo+TvJmdOKYlYtEg7B6eFJui0+vBD2YFfpozM11StmuTbYWHA338Dq1cbis9quXLJNOeyZfLhRHmHM2ekgK9JE+m5baX4Pt3nz0unMWXMyJFyZHu/fqYjubvwcDm7YNYs05H4jjQlaCLKS0RVrArGq8XEAH37Skli8+ZyEHQymw2fekpmgm19Sl/PnlIqOmSIVn14iyFDgCtX5HATIuuvV62aNHv+4ANg927rr6fusHMnsHixjJ7z5TMdzd1VqCC79aZP1/IFV901QRPRGiLKRUT3ANgGYAYRudx1n4j8ieh3Ivo2I4EadeWKZN733pMp3qVL5WNrMrJmBTp2lLvY9qi1wEBJCD//DPz0k+lo1LZtcmzQyy9L/3RPGTNG5i779NEPagaMGCETWn37mo7EdeHh0pDwhx9MR+IbXBlB52bmSwDaAJjHzLUAPJqGa/QGsCc9wXmFkyeBevWAb76Rpg+TJklnpVSEhkr7atsWiwEJR+XoKNosZvnQmD+/54+FzJ9f5lhXrpTKceUx27fL8fJ9+siMna9o1Uo2F2ixmGtcSdCZiKgIgGcApGkUTERBAJoD8M1TQXfulErtvXuBr76SEYoLypWTnG7rYrEsWSQhbNokVezKjEWLpBZg7Fg5tcXTevSQucu+fYEbNzx/fYcaMQLInVsOKfMlAQEygFm2TGp1VOpcSdCjACwH8BczbyaiUgAOuPj4kwAMAOB7aWr5cqBuXVksWbcuzYU3oaFysoytZ4BffFG29AwbZuNPIl7syhXZW1O9OvDSS2ZiCAiQWaWDB2UJSFlu61YZL/Tta+YzWUZ16ybdZ7U/993dNUEz82JmrsLM4XF/PsjMT93t54ioBYDTzLz1LvcLJaItRLTljLfU30dESCFYqVJSqV29epof4qmnpHDD1sViAQEyxbljB7BkielonOett4Bjx2Tp5S7LLpZ67DGgZUtZk9Zzwy03YkTCEd2+qGhRoHVrqebWw9HugplT/QKQFUBPAB8CmBX/5cLPjQNwDEAkgH8AXAXwaWo/U7NmTTYqJoa5f39mgLlZM+ZLlzL0cP36MWfKxHzypJvi80bR0cwVKzKXLcscFWU6Guc4dIg5a1bm5583HYn46y/mzJmZO3QwHYmtbdokb09jxpiOJGNWrZK/x5w5piMxD8AWTiEnujLF/QmAwgCaAFgLIAjAfy4k/sHMHMTMwQCeA7CKmdun4bODZ129Kg0YJkyQUsOvvsrYGbpIKBabPdtNMXojf39pZbRvH7BggelonOO112Se8O23TUci7r1XNuN+8gmwcaPpaGxr+HApCnvlFdORZEz9+tLYaepU05F4N1cS9H3MPAzAFWaeCyn6cuMROV7g1CmgQQMpi3zvPdnbmSlThh+2TBl5Ic6YYfMl2tatgRo1ZO7t5k3T0djf6tXA55/LCWNBQaajSfD669Ib4JVXbP6CN2PjRtme1L9/hscOxsX35968Wb5U8lxJ0FFxv14gokoAcgNIU0t2Zl7DzBa3N0qn3bulUnvnzoR9C25s9BAWBhw6BPz4o9se0vsQyfrjoUM2ny7wAtHRkgCDg72vfVSOHLIuvnmznB2u3Gr4cNnZ1quX6Ujco0MHIHt23XKVGlcSdAQR5QUwDMDXAHYD8JJ5tQz66SfgwQdle8jPP8smPTdr3Vr+U9m6WAyQfuQPPiinxl+/bjoa+/roI2DXLuDdd+WEBG/Trh1Qp46M7i9dMh2Nbfz6qxxUNmBAij2SfE7u3JKkFy4Ezp0zHY13SjZBE1FtIsoCAMz8MTP/y8xrmbkUMxdkZt8vkJ81S5JKsWIydxQSYsllsmQBOnWSJe2TJy25hHcgkr24x4/r/gmrnDsnW9oaNpRPft6ISKrKT5+WD2vKLYYPl6Mkw8NNR+JePXrI53lbN3XKgJRG0FkAfE1EQUTUN7kvTwbpVrGxslbWpYu80a1fLwfRWyg0VLZT2372t359oFEjYNw44PJl09HYzxtvyKj0/fc90287vUJCgM6dJc79+01H4/PWrZMlsoEDZUrYTqpUkYMBp03TsoXkJJugmXktgE4AKgDImcKX77l2DXj+eUkgoaHSASt3bssvW7q0fBawfbEYIGvRp08DU6aYjsRedu6UmYkePYBKlUxHc3dvvimN6X2t1ZUXGj5czqbp3t10JNbo2VO6iq1YYToSL5TS/isTX5bugz59mrlOHdl8N2ECc2ysdddKxsKFcukffvDoZc1o0YI5Tx7mf/81HYk9xMYyN2jAfM89zOfOmY7GdRMmyIv+u+9MR+KzVq+Wp3DSJNORWOfGDeaCBZmfeMJ0JGYglX3QxCkcdEBEUwCkeAoCM7t9J15ISAhv2bLF3Q8re3SbNZMuR59+Km2+POzmTdkRU7eunHRla9u3S/e1N97Q84Ld4fPPgaeflk2jvrQIefMmULmy/H7nTiBzZrPx+BhmWTX66y/58saaQHcZOlQmXQ4elA0KTkJEW5k52SKo1Kq4twDYCukkVgPSf/sAgGoAvPt/2vz58q/s5ydzQzVqyJromjVGkjMg702dO8uhWLbvhlitGtC2LTBxInD2rOlofNu1a7KdqnJlWZbxJZkzS1+B/ft1ySMdVq2SzSWDB9s7OQOyHZXIAbtd0iqloXX8F4CNADIl+nMAgI13+7n0fLllivvTT5kDA2VeKP6LiPm99zL+2Bl04ICEM3q06Ug8YPduZj8/aZ2q0m/UKHnRrF5tOpL0a9aMOVcu5n/+MR2Jz4iNZa5blzkoiPnaNdPReEarVsz58zNfv246Es9CBlt95gWQK9Gfc8Td5p2GDJG2nYkxy4k7ht13nxQ5z5ghVd22Vr687In94AOb7y+z0NGjUtD49NMy1+mr3ntPZgJef910JD5j5Urgl1/kKcua1XQ0nhEeLhNuixebjsR7uJKgxwP4nYjmENFcANsAvGltWBlw5EjabvewsDAJxREVi8OHA1FRsrik0m7AAPlw+c47piPJmDJl5Oil2bMBK2pMbIZZ/usUL27uFFETGjWSl4p2FkvgynGTsyG9t5cC+AJAHZae3N6pePG03e5hLVtKwwFHrLXce6+8w3z0EXD4sOlofMu6ddJiacAAy/fpe8SwYfLCf+UVyUAqRcuWSe+kIUOk0ZFT+PnJLsING4DffzcdjXdwZQQNZv6Hmb+K+/rH6qAyZOxYIDAw6W2BgXK7F4gvFvv2W2m6ZXtDh0r1h3aVcl1MjCSyYsWkO4Ud5Mol0/UbNkgRp0pW/Og5OFg6EDpNx45SEKejaOFSgvYp7doBEREy6iCSXyMi5HYv0a2bvAfPmmU6Eg8oVkw+Fs+ZAxw4YDoa3zBzpmxVmzDhzg+bvqxjR+D+++VDh3aaS9Z338lZI0OHOnNXWt688lY9fz5w4YLpaMxLcR+0CZbtg/ZCjRsDe/fKAVD+/qajsdipU0CpUtI/+tNPTUfj3f79VxbiKlSQbYHe3NIzPTZuTDhMQ2sTkmCWLqkXLsh7Q0CA6YjM+P132Rn73ntyuKDdpXcfdOIHeIiIOsf9vgARlXRngE4UGipFusuWmY7EAwoVkinbBQvkJCaVslGj5FAMb++3nV61a8sRRu++K/0d1S1ffw1s2ybL9U5NzoD0OKpTR6a5bd8a+S7umqCJaDiAgQAGx90UAECHQRnUsqXkLUcUiwEJp8wPH246Eu+1Z49sS+vWTZq92NX48ZKBXnvNdCReIzZW/mvcdx/Qvr3paMwLD5cVsVWrTEdilisj6NYAngRwBQCY+QR89bAMLxIQIAXO330HHDtmOhoPuOceoG9f4IsvgK1bTUfjfZhlPi97djlwxM7+7/+kRPnLL+WYJoUvvwR27JDuuJkymY7GvKefBvLnl+62TuZKgr4Z1+2EAYCIbHbgmTldu8on55kzTUfiIa++Kol62DDTkXifb76RzfEjRwIFCpiOxnqvvip1Cb17y155B4uNBUaMAMqWlcP2lDRn6dpVpv2PHjUdjTmuJOj/EdFHAPIQUTcAPwKYYW1YzlCqlBSLffwxEB1tOhoPyJVLKnh/+EHaJClx44bMLlSo4FuHYWRE1qzSq333bjkM2ME+/1zOEtHRc1JhYTKxFBFhOhJzXKriJqLHADQGQACWM/NKK4JxUhV3vC++kPM7vvkGaNHCdDQecOWKNDCpUEEXmOKNHy9VzStWAI89Zjoaz2EGmjSRfUX79ztj5uA2MTFAlSryVOzc6YAdHWn0xBPy8jhyxL7bzjJcxc3MK5m5PzO/ZlVydqonnpADtxxTLJY9u6w/rl4N/PST6WjMO3FC1pxbtnRWcgakSn3SJOC//xy77LF4sUwiDB+uyTk5PXvKLs0vvjAdiRmpnQf9H1I/DzpXSt+L+/msAH4GkAVAJgBLmDnVEl4njqAByVfjxwORkdLXw/Zu3ABKlwaKFgV+/dWe24lc9eKLwKJF8i59772mozGjTx9g8mTZY2Tn6vXbxMQAlSpJYv7jD2l1qZKKjU14q/j5Z9PRWCNdI2hmzhmXhN8HMAhAUQBBkC1Xk1y47g0ADZm5KuQM6aZEVDttoTtDt24yxeWYYrEsWWTEtHGjlLE71caNwCefyHnPTk3OgFRI5cvnuD7dCxdKQ5IRIzQ5pyS+P/e6dbIE4DR3XYMmoh1xSTbV2+7yGIEA1gPowcy/pXQ/p46gAeDxx+UFGBnpkEKRqCg5kjJnTtl25bR3qNhYadpx/Diwbx+QI4fpiMyKiJCqoIULgWefNR2N5aKjgYoVpVbu99+d9/JPi3PngKAg6U1ux3rCjK5BXyGidkTkT0R+RNQOcXuiXbiwPxFtB3AawMrkkjMRhRLRFiLacubMGVce1pZCQ+W9+vvvTUfiIQEBMnTYvt2ZC0zz5kn1y1tvaXIGgC5dpIVU//53nuduQwsWSF2cjp7vLl8+4LnnZLLp4kXT0XiWKyPoYMg0d13ImvQvAPowc6TLFyHKAzmu8mVmTrHXo5NH0FFRcq5H9eoOmvV1agnrpUvSb7tUKdlu5uQ1+MTWrQMeeUT2G40caToay0RHA+XKyeTRtm36z++KLVvknJUpU4BevUxH414ZGkEzcyQzt2Tm/MxcgJlbpSU5xz3GBQCrATRNy885SUCADCJ++EG2FDiCv7/0nt6zR4YUTjFmjJSm2rXfdno9/LBMb7/9tq3PD//kE2lDPnKk/vO7KiREEvSHHzqqTMG64ybjDtXIE/f7bAAeA7DXquvZQdeu8uvHH5uNw6Nat5ZpgxEjnNFRav9+2VrUubO846ikJkyQrNW/v+lILBEVJUej16wpWyyV68LD5bP8mjWmI/EcK1c/igBYTUR/ANgMWYP+1sLr+bwSJYCmTaWa2xGdxQBZgBszBjh4EJg923Q01uvbVyqD9KjF5BUrBgwaJBuEbfhOPHeuHDGro+e0e/ZZ6RT84YemI/EcPQ/ay3z1FdCqlTTPb9nSdDQewgzUrStNdw8ckARmRz/8ADRrJqNEPckpZdeuSYV/7txS4W+TbQ03b0rpQaFCssNOE3Ta9e8v50QfOSJnrthBhtagiWhoot9ncWdg6k7Nm8sLzzGdxQB5pxozRo71smvj3Zs3pSFHmTKy31elLFs24J13pHvHDPu0/Z89W5bWdfScft27yw5Fu75N3C7FBE1EA4moDoCnE928wfqQnC1TJikWW7ZM9kQ7RsOG8jV2rPTrtpspU2T9+b337NtU2J2eegqoXx8YOhQ4f950NBl244a8tGvXlvbjKn3uvVeWASMinFGyktoIei+AtgBKEdE6IpoBIB8RlfVMaM7Vtat8wnZMZ7F4o0cDp08DH3xgOhL3OnVKqtWbNZMvdXdEUuV+4YI0qvZxM2fKCs6oUTp6zqjwcODkSVkOtLvUenHXA/AbgF8B3A+gPIDvAKwCUJaZH3R3MLoGnaBFC9kjefiwbMFyjObNgQ0bpJImd27T0bhHly6yt2bXLpniVq7r2VPWe7Zvl8bVPuj6deC++4DgYNnqrQk6Y2JiZCRdsqScuePr0rsG3QSSkO8FMBFALQBXmLmzFclZJRUaKp8Sv3Va3fvo0cC//8pUsB1s2SKLj717a3JOj1Gj5Bzx3r19dgPsjBnSJVBHz+7h7y/9udeskTNm7Cy1wzJeZ+ZGACIBfALAH0ABIlpPRN94KD7HatZMTnBxVLEYANSoIeuPEydKE15fxiwFYQULOvY4xQzLl08+tK1aBSxdajqaNLt2DRg3ThqkNWhgOhr7eOklKeWw+5YrV/ZBL2fmLcwcAeAYMz8EoLPFcTlepkyyFr1ihcz2OsrIkcDly9JRypctWCDT9ePGyShQpU9YmExv9+snGc+HfPSRzITp6Nm9ChSQfdHz5slx4nblSqvPAYn+2CnutrNWBaQSdOki/6kd1VkMkGN+2rWTyud//jEdTfpcvgwMGCA9Cjt2NB2Nb8uUSQrGIiOBd981HY3Lrl6Vc94bNADq1TMdjf2Eh0tynj/fdCTWSVMnMWbeYVUg6k7FislU96xZzthSkMTw4bJ3eNw405Gkz7hxwIkTwOTJelyROzRsCLRpI8/rsWOmo3HJtGlSwG/jcz+MqlVLugRPneqz5Ql3pe8cXi4sTAaR3zht1f+++2Shafp03zs95OBBGem1bw/UqWM6Gvt45x0p4R040HQkd3Xlipwk+uijcgaIcj8iKfLftQtYv950NNbQBO3lmjaVw8odVywGSJMKQLqM+ZJ+/WRa9q23TEdiLyVLSq/HBQvkmE4vNnUqcOaMjp6t9vzzshvTrsVimqC9XOJisYMHTUfjYcWLyxTCrFnAX3+ZjsY1P/4ojdSHDLFPs2BvMmiQfGJ95RUZTXuh//6T+sYmTYAHdUOqpQID5WC4zz/33XKV1GiC9gFdusgypuOKxQDg9ddlP4UvDEWiomS/bqlSwKuvmo7GnrJnl+y3bZvXnn72wQeyQ9AXXrJ20KOH/Nez4/ujJmgfEBQkncUcWSxWuDDw8stSquntXQmmTZMYJ06074lc3uC554CHHpIPbxcumI4miUuXZKm8WTMpYlLWK1MGeOwxWQa02zG9mqB9RGioVIQ6of/sHQYMAHLkAN54w3QkKTtzRirPH3sMePJJ09HYG5FUx589KxuMvcjkyXK2h46ePSs8XIr77VZMqwnaRzRtKkuyjiwWy5cP6NtXFpq2bTMdTfKGDZPFx0mTtCOFJ1SvLsUZU6YAe/aYjgYAcPGiFO8/8YRsf1ee06KFbEu1W7GYJmgf4e8v70c//gj8/bfpaAx49VUgb17vHEVv3y7n3/XqBVSoYDoa5xg7VtakX33VKzbCTpokM+4jRhgOxIEyZZJ60h9/BPbtMx2N+2iC9iEvvSSJ2kZn2Lsud26Z6v7uO2mf6S2YpTAsXz5bHIvoUwoUkGy4fLnxU2Xiz3dp1UraySvP69pVTv6bNs10JO6jCdqHFC0qUzmzZ0uTLcd5+WU5eCJ+f7Q3WLwY+Pln2audN6/paJynZ0+gXDkZRd+4YSyM996TKW4dPZtTqBDw9NPAnDnSKMYONEH7mLAw4PRphxaLZc8ulburVsmXaVevAq+9BlSrJh/flecFBMjc8t9/S79uA86flxCeegqoWtVICCpOeLh8UFqwwHQk7mFZgiaiYkS0moh2E9GfRNTbqms5SePGDi4WA+QTSlCQjKJNrzu+/TZw9KiU7vr7m43FyZo0kcqs0aPl6CgPe/ddqQ/UFQ7z6tYFqlSRYjHTbw/uYOUIOhpAP2auAKA2gJ5EpBU0GeTvD3TrBvz0k+8013KrrFmlYnrDBuCHH8zFcfiwtPJ89llttuwNJk6UdZ/Bgz162bNn5fPZM88AlSt79NIqGUQyit6+Hdi40XQ0GWdZgmbmk8y8Le73/wHYA6CoVddzEkcXiwHS269UKbOj6P795d1gwgQz11dJ3XefrEPPnQv89pvHLvvOO7LeqaNn79GunRy/PnWq6UgyziNr0EQUDKA6gDv+5xBRKBFtIaItZ86c8UQ4Pu///k9m9BxbLBYQINU4v/8OfPGF56+/Zo0Uhw0aJJsvlXcYMgQoUkT6dMfGWn6506elredzz+nuOm+SI4ccwb54sfwb+TLLEzQR5QDwOYA+zHzp9u8zcwQzhzBzSIECBawOxzbCwqR51dKlpiMx5IUXpHr3jTc8e2hCdLRsqypRQkbRynvkzAmMHw9s2gR88onll5swAbh2zTu35jtdjx4yeJk1y3QkGWNpgiaiAEhyns/MBoY69tW4MRAc7OBiMX9/afO4ezfw2Weeu+6MGcAff8jcZrZsnruuck379tIEe9AgqdyyyKlTMoUa/zlReZfy5YEGDeQ4eS899MwlVlZxE4CZAPYw80SrruNUfn5SLLZ6NbB/v+loDHnqKdniNGKEZ04ROX9e1r3r15drK+/j5ydVW//8Y+k54m+/Lduuhw2z7BIqg3r2lFrO7783HUn6WTmCrgugA4CGRLQ97quZhddznM6dpcWdY4vF/Pxka83ff0txkNVGjJBeju+/r/22vdkDDwCdOkn3kAMH3P7wJ0/KNp4OHeQkJeWdnnxS6nV8uVjMyiru9cxMzFyFmavFffnwZxnvU6SIvAjnzDHaRMms5s1lSnPUKGufhF275F25e3fZaKm827hxQJYscsiKm731lkzY6OjZuwUEyCmAy5f77pZU7STm48LCZC+mY4vFiOTQhKNH5cAKKzADffrI3g0vO95QpaBwYcmg334LLFvmtoc9flzWNTt2BO69120PqyzSrZvMMk6fbjqS9CH2onYrISEhvGXLFtNh+JTYWNkCWqKErEc7EjPQsKEcO3jwIBAY6N7HX7oUaNNGjjbs1cu9j62sc+MGUKmSFBTu3ClDqgx6+WV5s9+/HyhZ0g0xKss984yccnXsmPvfGtyBiLYyc7IHlOoI2sfFF4utWWOvY9bShEgKgk6dko2p7nT9OtCvn7zRd+/u3sdW1sqSRdah9+1zy+sifpKmc2dNzr6kZ085bWzRItORpJ0maBtwfLEYIE14H39cFggv3bHdPv3efRc4dEgKwzJlct/jKs9o3hxo2lQK/DLYtWLcOJmsGTLEPaEpz3jkEWkk8+GHpiNJO03QNlC4MNCypRSLXb9uOhqDRo9OOFrIHY4dA958U6a3GzZ0z2MqzyKSUfTVqxnKrIcPAx9/DHTpIstJynfE9+feskV62PgSTdA2ERYGnDtnpvOl16hZU5Lpu+9Kos6ogQOly8E772T8sZQ55cpJ+8+ZM4GtW9P1EG++KW/0r7/u5tiUR3ToIC1AfW0UrQnaJho1kvMjHNtZLN7IkdJBKqOHWPzyixwq27+/LjjawRtvAAUKSJvWNBbGHjokLSO7ddPW674qVy5J0gsXykDGV2iCtgk/P9nz9/PPwN69pqMxqFIl4PnnE7pJpUdMjIy4goKkZaTyfblzyzD4l1/S3Bp27FgpBPfwSZbKzcLDpbDfl/pza4K2kU6dpI7Jqu3APmPECPmfOH58+n5+9mxg2zbp55g9u1tDUwZ17izLIAMGyBmRLvj7b6ntCAsDiuphuT6tUiUpGJs2zSOHnbmFJmgbKVQIaN1aul46ulisdGn5tDJtmuyNSYuLF2Wh8aGH5BxBZR/xfbqPH5eSbBeMGSPbp3UixR7Cw2XJYvly05G4RhO0zYSFSX3U55+bjsSwYcNkrTGtByaMGiWt2bTftj09+CDQrp0U/h08mOpdDxwA5s2TowuLFPFQfMpSrVvLQMZX+nNrgraZBg2kBaHji8VKlJBPK7NmyTylK/bulRFWly5AjRrWxqfMGT9eFpVfey3Vu40eLb1OBg70UFzKcpkzS63O99/LSNrbaYK2mfhisXXr5KhkR3v9dZmfdKV/NjPw6quy5jx2rPWxKXOCguS1sXQp8NNPyd5l3z5g/nzpQlWokIfjU5YKDZX3SV/oz60J2oY6dZK85OjOYoDMS/bqBXz6qfTpTs1338mhCsOHAwULeiY+ZU6/frJ9rk8fIDr6jm+PGgVkzSq77JS9BAXJKYAzZ3p/rY4maBsqWDChWOzaNdPRGDZggHTIHz485fvcuCGj53Ll9DAMp8iaVRra7NoFTJ+OwoWl5CD+a8ECaT6mJ4vaU8+esh968WLTkaROE7RNhYVJg/glS0xHYlj+/JJ8Fy8Gtm9P/j7vvy8Hxk6a5JYTj5SPaNVKOvy88QaiTiXfveLUKc+GpDyjYUOgbFnvLxbTBG1TDRrIMZSOLxYDgL59gbx5pbL7didPSjXQE08ATZp4PjZlDpF8OLt0CaORzGtD2VZ8f+7ffkt391eP0ARtU0RSDPHLL8Cff5qOxrA8eWQx8dtvgY0bk35v8GDg5k1g4kQjoSnDKlbEpQ7hCMNHqIIdpqNRHvTii7L6NW2a6UhSpgnaxjp1km0Fju8sBgAvvyyL80OHJtz222+yUP/qqzLdoByDGVi7FnjqKaDk3JH4F3nxPnoDSFufbuW78uSRLfELFshyoDfSBG1jBQrI4U7z5mmxGHLkkNHyTz8Bq1dLr79XXpFKbz3g1zGuXZPq3WrVgPr1gTVrgNCBeTEUY1Afa/E0nF604Szh4fKamDPHdCTJsyxBE9EsIjpNRLusuoa6u+XLgQsXZConcZVq4cKmIzOge3f52Pz449KoYtMm2W+RM6fpyJTFjh6Vz2fFigFdu8ptH38sR36PGwd8XbAbIlEMn+F5xMAPhxCM5zFf90DbXLVq0lzOW/tzWzmCngOgqYWPr1yQ0tSNI6tTP/9c9s7cuJFw2yefSEcKZTvM0rCnbVvZ8vz220C9ejJq3r5dGsZlyyb3PTFxIYKznEYmxMAPjGAcxoLAUPzzrr427C48XNq6/vij6UjuRJzGs1HT9OBEwQC+ZeZKrtw/JCSEt2zZYlk8TpRaO+kLF+QUPscIDgYOH77z9hIlgMhIT0ejLHL9upwoOXmyJOK8eeUs5/Bw+adOlr42HOvGDZlZefBB4MsvPX99ItrKzCHJfS+Tp4O5HRGFAggFgOLFixuOxlny5JEXZqVKSb/Kl08YWdjKkSNpu135lOPHZaryo4/kvJOKFeX37dvLEk+qUnoNHD4sxYS1ark9XuUdsmSRZY+33pKXgTelIR1B21xqI+jx46WR0q5d0gkzfuaXSIqab0/cpUv7eB8PHSXZDjPw66/AlCnSlCc2VsoKXnlFegG4fCBZSq8NIrnIAw/Ig7ZtK1sjlK0cPgyUKiXHinq6FX9qI2hN0DaX2htU4n/66Gg59Ck+Ycd/7d+fUDwRECDdMG9P3MHB0nze682fL5vDr15NuC0wUPahtWtnLi6VZjduAIsWyTT21q2yVNO1q7RwLFkyHQ+Y0mtj8mSZM588Wf4zFC4sxYZhYQ6ttLSvli2lTcKRIzKq9pTUEjSY2bIvAMEAdrl6/5o1a7Jyr0KFmCUVJ/0qVMi1n792jXn7duZPP2UeNIi5RQvm4OCkjxUYyHz//cydOzO/+y7z8uXMx48zx8Za+3dLl08/ZS5RgplIfv30U9MRqTQ4fpx52DDmggXltVe+PPO0acyXL7vhwVN7bcTEMC9bxvz443LhgADm9u2ZN21yw4WVN1i2TP5p58/37HUBbOEUcqJlI2gi+gxAfQD5AZwCMJyZZ6b2MzqC9h3//SfHWd4+4v7nn4T75M1752i7YkUgXz5zcSvfwyzLwJMnS0v1mBigRQuZcW7UKA3T2O6yfz/wwQfA7NnA5ctA7doSzFNP6fS3D4uNlf7chQoB69d77rrGprjTShO07zt7VlqLxifsnTvl14sXE+5TpMidibtCBeklolS8GzckIU+eDGzeDOTKJVujevYE7r3XdHQALl2SDhdTpshhK0WKSKl4aKgeWeqjJk6Uk0i3bweqVvXMNTVBK6OYpcL29tH27t1JO5yVLHln4i5b1rPrQcq8kyel+nr6dNmvX7asDFBffNFLP8TFxspZ4pMnS2egzJmB55+X9rI1a5qOTqXB+fNA0aLyWvPUQUOaoJVXiokBDh26M3Hv2ydFa4A0/CpT5s7Efe+98j1lH5s2SY773/+AqCigeXNJzI8+6iNFiACwd69Mf8+ZA1y5AtStK3+J1q19fAuEc3TpAixcCJw44Zk+EZqglU+5eVOW+W5P3AcPJlSeZ80q+7VvT9zFiqW8Jlm4cPId1AoVSrp2rjzn5k3ZHjV5sqwz58wJvPSSTGOXLm06ugy4eFHWqKdMkRdu0aIy/d2tmzTJV15r61YgJEReky+/bP31NEErW7hyRfZr3564jx9PuE/OnHcm7UqVZEnQ1S1nynqnTskU4rRp8uGoTBl5M+zY0Wat0WNigB9+kHf7lStlveaFF+QvW7266ehUCmrVkhKD3butL0LUBK1s7d9/kxamxRennT+fcJ8CBYAzZ1J+DC/6b2BrW7ZIrlq0SEbPjz8uM8CNG/vQNHZ67d4t099z58p+64cflr98q1ZAJuNNHVUi8+bJh8WffgIaNrT2WpqgleMwyygtcdKemcomv/79HdDq1JCoKDmnZPJkYMMGKfTq3Bno1UtGzo5z4QIwa5Yk60OHZF0mPFw6reTPbzo6BelNU7SoHEn6+efWXksTtFJIfaoqc2YZ0QEykrv3Xhu2OvWw06elSdu0aVJwc999MrPbqZNsmXK8mBjgu+/kk8tPP0lhRbt28iR5ao+PStGAAbLtKjISCAqy7jqaoJVC6gk6Kkq2st6+vn3ggI1anXrItm2Scz77TD70NGkiM7lNm+rzlKJdu2REPW+e7D2sV0+etCef1OlvQw4elA+Vw4YBI0dadx1N0EohfVXc16/LzpnbE3ficxUCA6VD2u2Ju0gRA12uDImKApYulcT8yy9A9uwyUu7VSz7UKBedP58w/X34sByt1LOnTH/fc4/p6ByneXP5wHn4sHVN4jRBK+Vm8RWetyfuxB8Akmt1WqmSvd5nz5wBZswAPvxQqulLlUqYxs6Tx3R0PiwmBvjmG/nEs3q1FEW0by9PbuXKpqNzjO+/lyS9aBHwzDPWXEMTtFIecubMnRXldmx1un275I4FC6Ql56OPyoxss2baQMbtdu6U/dSffCJTOg0ayJP9xBP6ZFssJkZqT4oXB9asseYamqCVMsgurU6jo4Evv5TEvG6dTO2/+KIM6ipUMB2dA5w7B3z8MTB1KnD0qBQ/9Owpra/y5jUdnW29/TYwcKB8Tqrk0sHJaaMJWikv5CutTs+dS5jGjs8LvXpJxy/NCwZERwNffy2flNaulU9KHTrIqFo/Kbnd2bNSxd2li3w2cjdN0Er5EKtanabVH39IDpg/X2ZWGzaUHNCihc6seo0dOxL+kXStwTIdOwJffCHbBd3d6U4TtFI24Eqr01y5kq8oT+70w5Sq2vPkkW24a9dKbVKHDjKNbcX0nnKTs2cTpjmOHZNqvV69pCOMVutlWL58STsTxnNHH39N0ErZmKutTm9P2nXrpvyYxYvL+3uXLvaqOre9qKiEQoH162W/W8eO8o9Zvrzp6HyWlX38NUEr5TDJtTqN/7py5e4/HxWl/TF83rZtUv29YIGsmzRuLNPfjz+uHWPSSBM0NEErZbXYWODIEUnUTzyR8v286G1BZdTp0wnT3/E9V3v1ks3qnjjw2AZMJWj9GKWUg/j5SRV2ixamI1EeU7AgMGSINJVeuFD+3KePlCa//LJsG1BeSRO0Uko5QUAA8Oyz0ot182agTRs5zaRcOZn2/uGHhMbzyitYmqCJqCkR7SOiv4hokJXXUkqlTaFCabtd2UhIiJxLfeQIMGqUbNdq1kyS9ZQp0st2/vyEk2CCg+XPDlWoEPA85uMQghEDPxxCMJ7HfMv/r1i2Bk1E/gD2A3gMwDEAmwE8z8y7U/oZXYNWSikDbt5MOLR740ZpXxcTk9AxB5CGKBERciSm08yfD4SGAlevJtzmpufDSJEYEdUBMIKZm8T9eTAAMPO4lH5GE7RSShm2aZN0pUmu3D9TJmlt5zT79yf9sBKvRAlZ28+A1BK0lRspigI4mujPxwDUuv1ORBQKIBQAihcvbmE4Siml7uqBB5KOFBOLjnZmO9HdKUz8Hjli6WWN73Rk5ggAEYCMoA2Ho5RSqnjxpIeexytRAli82PPxmBYcnPzzYfGg0soiseMAiiX6c1DcbUoppbzZ2LGyxppYYKDc7kSGng8rE/RmAKWJqCQRZQbwHICvLbyeUkopd2jXTgqgSpSQLh0lSji3QAww9nxY2kmMiJoBmATAH8AsZk7144YWiSmllHISU0ViYObvAXxv5TWUUkopO9JOYkoppZQX0gStlFJKeSFN0EoppZQX0gStlFJKeSGvOg+aiM4ASGY3eLrlB3DWjY/n6/T5SKDPRVL6fCTQ5yIpfT6ScvfzUYKZCyT3Da9K0O5GRFtSKl93In0+EuhzkZQ+Hwn0uUhKn4+kPPl86BS3Ukop5YU0QSullFJeyO4JOsJ0AF5Gn48E+lwkpc9HAn0uktLnIymPPR+2XoNWSimlfJXdR9BKKaWUT9IErZRSSnkhWyZoImpKRPuI6C8iGmQ6HpOIaBYRnSaiXaZj8QZEVIyIVhPRbiL6k4h6m47JFCLKSkSbiGhH3HMx0nRM3oCI/InodyL61nQsphFRJBHtJKLtROToowaJKA8RLSGivUS0h4jqWH5Nu61BE5E/gP0AHgNwDHIu9fPMvNtoYIYQ0SMALgOYx8yVTMdjGhEVAVCEmbcRUU4AWwG0cuLrg4gIQHZmvkxEAQDWA+jNzBsNh2YUEfUFEAIgFzO3MB2PSUQUCSCEmR3fqISI5gJYx8wfE1FmAIHMfMHKa9pxBP0AgL+Y+SAz3wSwEEBLwzEZw8w/AzhvOg5vwcwnmXlb3O//A7AHQFGzUZnB4nLcHwPivuz1iT2NiCgIQHMAH5uORXkPIsoN4BEAMwGAmW9anZwBeyboogCOJvrzMTj0DViljoiCAVQH8JvhUIyJm87dDuA0gJXM7NjnIs4kAAMAxBqOw1swgBVEtJWIQk0HY1BJAGcAzI5b/viYiLJbfVE7Jmil7oqIcgD4HEAfZr5kOh5TmDmGmasBCALwABE5dhmEiFoAOM3MW03H4kUeYuYaAB4H0DNuycyJMgGoAWAaM1cHcAWA5fVNdkzQxwEUS/TnoLjblAIAxK23fg5gPjN/YToebxA3XbcaQFPDoZhUF8CTceuuCwE0JKJPzYZkFjMfj/v1NIClkCVEJzoG4FiiGaYlkIRtKTsm6M0AShNRybiF/OcAfG04JuUl4gqjZgLYw8wTTcdjEhEVIKI8cb/PBims3Gs0KIOYeTAzBzFzMOR9YxUztzccljFElD2ukBJx07mNAThyNwgz/wPgKBGVjbupEQDLC0szWX0BT2PmaCLqBWA5AH8As5j5T8NhGUNEnwGoDyA/ER0DMJyZZ5qNyqi6ADoA2Bm39goArzPz9+ZCMqYIgLlxOx/8APyPmR2/tUjdUgjAUvlMi0wAFjDzMrMhGfUygPlxA7+DADpbfUHbbbNSSiml7MCOU9xKKaWUz9MErZRSSnkhTdBKKaWUF9IErZRSSnkhTdBKKaWUF9IErZSPIqLCRLSQiP6Oa8X4PRGVSe/JZUTUiYj+z91xKqXSRxO0Uj4oruHKUgBrmPleZq4JYDBk72p6dQKQpgRNRLbrpaCUt9D/XEr5pgYAoph5evwNzLwj7gAQADIihhwV2Cvuz98CeAfAOkg3tRDIYQizIAfMhEAaMVwDUAdABQATAeQAcBZAJ2Y+SURrAGwH8BCAz4joCIDhAGIAXGRmp/ZrVsqtNEEr5ZsqQc6yTo9qAIrGnw9ORHmY+UJcB77XmHlLXL/yKQBaMvMZInoWwFgAL8U9RmZmDon7+Z0AmjDz8fjWoUqpjNMErZTzHARQioimAPgOwIpk7lMW8iFgZVyrR38AJxN9f1Gi3/8CYA4R/Q+AHj6ilJtoglbKN/0J4Om73CcaSetMsgIAM/9LRFUBNAHQHcAzSBgZxyMAfzJznRQe+0r8b5i5OxHVAtAcwFYiqsnM51z+myilkqVFYkr5plUAshBRaPwNRFQFSY9ajQRQjYj8iKgY4o4KJKL8APyY+XMAQ5FwbN5/AHLG/X4fgAJEVCfuZwKIqGJygRDRvcz8GzO/ATnUvlhy91NKpY2OoJXyQczMRNQawCQiGgjgOiQh90l0t18AHIIci7cHwLa424sCmE1E8R/QB8f9OgfA9ERFYk8DmExEuSHvFZMgI/fbTSCi0pBR908AdmT8b6iU0tOslFJKKS+kU9xKKaWUF9IErZRSSnkhTdBKKaWUF9IErZRSSnkhTdBKKaWUF9IErZRSSnkhTdBKKaWUF/p/96WRvrTzl1wAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "y=[len(zeros)]+[len(one)]+[len(two)]+[len(three)]+[len(four)]+[len(five)]+[len(six)]\n",
        "x=[i for i in range(7)]\n",
        "\n",
        "fy=[len(fzeros)]+[len(fone)]+[len(ftwo)]+[len(fthree)]+[len(ffour)]+[len(ffive)]+[len(fsix)]\n",
        "\n",
        "y[:]=[x/5 for x in y]\n",
        "fy[:]=[x/2 for x in fy]\n",
        "\n",
        "fig = plt.figure()\n",
        "ax1 = fig.add_subplot(111)\n",
        "\n",
        "ax1.plot(x,y, c='b', marker=\"s\", label='Entre semana')\n",
        "ax1.plot(x,fy, c='r', marker=\"o\", label='Fin de semana')\n",
        "plt.legend(loc='upper left')\n",
        "plt.rcParams['figure.figsize'] = [8, 4]\n",
        "plt.title(\"\")\n",
        "plt.xlabel(\"Clusters\")\n",
        "plt.ylabel(\"# de días\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6J8FbJlqQd_x"
      },
      "outputs": [],
      "source": [
        "kmedoids = KMedoids(n_clusters=5,  metric='precomputed', method='pam', init='heuristic', max_iter=100, random_state=None).fit(1-corr_matrix_f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dbvvbVxsQd_z"
      },
      "outputs": [],
      "source": [
        "lab=kmedoids.labels_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xDJJy9KOQd_0",
        "outputId": "8c9a7281-20de-4e6a-aae8-09cd895ebc50"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(12, 8))\n",
        "sn.scatterplot(x=[r[i].weekday() for i in range(len(learning_data))],y=[i for i in range(len(learning_data))], hue=lab, \n",
        "                palette=sn.color_palette('hls', 5),legend=False)\n",
        "plt.title('KMedoids with 5 Clusters')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kE4N6ZedQd_2",
        "outputId": "d32f301f-d17a-4b78-a96b-3e8cd37420e4"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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x5zxfvnyJCVuyXWQkjB4N110Hw4ZZrm5jTNjz2SruzEhtFbcJMt27w2uvwYoVWlbSC2ltj87wn9/dd8PXX8PatVChQgZPNsaY4BKoVdwm3OzeDWPGwL33eh2cjx71XDsjT55MBOhhw/TfHj0yeKIxxoQWC9DGey+/rDm3+/Xz+pSBAzUb6B9/nFvBZPRofahPPslgGypUgBde0MViv/6awZONMSZ02BC38c62bXDxxZowZPx4r05ZuRLq1IEHHkh5SlwcNGqkmTxXr9ZdVF47fhwuuURPWrhQ56eNMSYE2RC3ybohQ7Tr26ePV4eLwBNPaB6TV15JeX9kpAbtw4eha9cMtqVAAR3q/vtvmDQpgycbY0xosABt0rdpk6bZfOQRzW/uhcmT4ZdfNDiXLJn6MTVqQK9e8PHH8N13GWzT3XdrF/zFFzXKG2NMmLEhbpO+Bx+EKVNg40avMocdOqQj0FFROvfsaZEYaKnuOnV01HrlynSzhp5r8WK44gotSTl8eAZONMaY4GBD3Cbz1q2DDz/U8WovgjNoZs59++Cdd9IOzqBlnsePh3//1fMypF49/fAwejSsX5/Bk40xJrhZgDZp698f8uWDnj29OnzxYg3MTz6pib+80aiRxv833oAFCzLYviFDtH3dumXwRGOMCW4WoI1nK1bAp5/CM8/A+eene3h8vAbaUqW0hHNGvPyydtA7ddLtV14rXVq73t98A7NnZ+xJjTEmiFmANp7166eTws8/79Xh48fDX3/ByJFa3yIjihTRnveKFfDqqxlsZ5cucNFF8NxzEBOTwZONMSY4WYA2qfv7b5g2TYNeiRLpHh4draPgTZpoorHMaNUK7roLBg/WvdFey5tX04+uXq1R3hhjwoAFaJO6vn2heHEN0F544QU4ckQzgaaVezs9o0drBctHHtEhc6/dcgvceKP2+vfty3wDjDEmSFiANinNnw/ffqtD28WKpXv477/DxIm6Tuuyy7L21BdcoJ3h33+H997LwInOweuv66eEDKQiNcaYYGX7oE1KN92kQ9ybN6e7MTk2Vnc7HTyoI8wFC2b96UW0CQsWwKpVkKEKlk8/DW+/Df/8AzVrZr0xxhjjQ7YP2njvl1/g++91QtmLrCFvvQXLlp0dms4OzmnvOTYWHn88gxWvBgzQXn+XLpkolWWMMcHDArQ560yu7dKlNTKmY8cOPfx//4PbbsveplSurFu1vv0WPv88Ayeed56W0Pr5Z/jqq+xtlDHG+JEFaHPWjz9qD7p3by1IkY5u3XRX0xtvZG1hmCdduujw+dNPw4EDGTjx0UehenVt4MmT2d8wY4zxAwvQRolowo/y5XUJdTp++EFLMr/4om5B9oVcubRGx/79GUwUliuXjrlv3gyjRvmmccYY42MWoI2aOVNXZfXpo/uK03DqlKbyvOgi6NHDt82qXVufY9Ik/VDgtWbNdNx98GDYudNHrTPGGN+xAG3Ozj1XrgwdO6Z7+MiRWkPjrbc0Dbav9ekDVapA585a9cprI0boGPyLL/qsbcYY4ysWoA1Mn67bqvr2hdy50zx082ZdvNW2LbRo4Z/m5c8P48bpc2doi/OZ9J8ffKA5SI0xJoTYPuicLi5Ox5FjYzURdq5caR7eujX89BOsWZPB/cnZoHNnnZNesADqp7prMBVHjkDVqlCpkhan9sVqNmOMySTbB208mzoVVq7UspLpBOcZM7RoVP/+/g/OAMOGaaaxTp0yUBOjcGEtlTV/PnzyiU/bZ4wx2cl60DlZbKxuR8qTB5YuhQjPn9eOH9c0noUK6Wh4OiPhPjN9OrRpozHXyxLVmtS7QQNdLLZ2rVcJWIwxxh+sB21SN3myrvYaODDN4AwwdChs3apZNAMVnAFuv10DdP/+sH69lydFROi2q507M1HL0hhjAsN60DlVTAxccolWrFq0KM252bVrNa31PffoeqtA27lTe/N16uh8eDqfLc5q3x6+/FIn0CtV8mELjTHGO9aDNilNnHh2SXYawVlE9zwXKKBzwMHgwgt1B9W8ebpozGuvvgqRkdC9u8/aZowx2cUCdE508qQG5quugptvTvPQqVM1A+jQobpAK1g8/DA0aaKx1us8JOXK6cT1F19odDfGmCBmATonGjcOtm9Pt/f833+6jbhePU1vHUycg7Fj9bPG009n4MTnn4cKFTTRd1ycz9pnjDFZZQE6pzl+XLvDjRtrOsw09O8Pu3frwrDISP80LyOqVNE2TpumX17Jnx+GD9dV6xkaHzfGGP+yAJ3TvP22Rt10es9Ll2qVqkcfhSuv9GP7MqhbN10s9tRTcOiQlye1awfXXqtVu7w+yRhj/MsCdE5y5IgulLrpJg1QHsTHwxNP6ALvIUP82L5MyJ0bxo+HPXsyULjDOd12tX+/bjEzxpggZAE6J3njDdi3T3vPafjgA82KOXw4nHeen9qWBfXqQdeuOrU+d66XJ9WtqynJ3nxTt10ZY0yQ8ek+aOdcMWA8UAMQ4CER+dPT8bYP2ocOHYKoKLjmGs3X6cGBA7o9+pJL4JdfMrDHOMCOH9e92pGROjyfP78XJ+3dqxPZ11wD//d/Pm+jMSY0lS6to3TJXXCBzhhmRSD3QY8GvhORS4HawGofP5/x5PXXNUinM6T74otw8KBOVYdKcAbdpz12rGYX83rU+vzztTzWzJn6ZYwxqUgtOKd1e3bxWQ/aOVcU+AeoLF4+ifWgfWT/fu0933ST7gH2YMECuPpq3Vo1cqQf25eNHnoIPvxQk6PVqePFCadPa9fbOVi2TPOSG2NMEmkVwctqCA1UDzoKiAYmOuf+ds6Nd84VTKVxnZ1zi5xzi6Kjo33YnBxs+HA4ehQGDPB4SFycLgwrU0a3LoWqESOgRAmdXo6N9eKEPHngtdc0n+mYMT5vnzHGeMuXAToXcDnwjojUBY4BKeoPichYEakvIvVLlSrlw+bkUHv26EKoe+7RylUevPsuLFmiI+GFC/uxfdnsvPP0chcv1oXaXvnf/6BFC/0AYx8SjTFJePVB30d8GaC3A9tFZEHCz1+gAdv40yuvaLqtfv08HrJnj24JvuEG3SIc6tq1g9atoU8f2LTJixOc0170sWN6kjHGoFtOH3oocM/vswAtIruBbc65SxJuagas8tXzmVTs2AHvvAMPPABVq3o8rHt3OHFCR3jTmmsJFc7pteTKpYlWvJojqlZNs52MHQv//OPrJhpjgpwIPP44fPSR5xLyvq5P4Ot1uk8Dk51zy4A6wFAfP59JasgQnVxOo1c4b57+AXbvnmYMDznlymlOlh9+yECJzL59dYz82WezvvLDGBOyRHSx7Nix0KuX1iUQSfmV1S1W6bF60OFq61bd4/vww9qLTkVMjK50Pn4cVq7UrUrhJD5eU46vXAmrV3v5affdd/Vj8+efQ9u2Pm+jMSa4iOiU38sva02d11/37cii1YPOiQYN0o3MvXt7PGTUKFi1ShdVhVtwBr38ceN0arlLFy9PeuQRqFVLq16dOOHT9hljgs+QIRqcH33U98E5PRagw9GGDTBpkv6FlSuX6iHbtul2qtatoVUrv7bOry69VEf4P/sszQRqZ0VG6ieXrVtDdzO4MSZTRo7U94v779dkTYFek2ND3OGoQwf48ktdwly6dKqHtG2rybNWrYJKlfzbPH87fVrzdR88qNdbpIgXJ7VtC7Nmwbp1ULasz9tojAmst9+GJ5+EO++EyZN1kak/2BB3TrJqlf51PfWUx+D83Xcav196KfyDM2gukvHjYedOXfDhleHDdYFdzxRb940xYWbiRA3OrVvDxx/7LzinxwJ0uOnfHwoW9Fh78eRJjd2XXKK1lHOKBg3gmWf0U/Lvv3txQlSU/oI+/hj+9FjfxRgT4qZM0bW0N92kU2G5cwe6RWdlKEA754o752r5qjEmi5Yu1dXHzz4LJUumesirr8LGjbpPOG9e/zYv0AYPhooVNQ3oyZNenNCrl+Y+7dJFl4QbY8LK9Ok6I3jddfp9vnyBbtG50g3Qzrm5zrkizrnzgCXAOOfca75vmsmwvn2haFEtjpyKjRt1deLdd0OzZn5uWxAoVEh3Ua1ZA0O92ZFfqJB+olm4UDeLG2PCxqxZcNddcMUVuoA0GHeyeNODLioi/wFtgA9FpAFwg2+bZTJs4UKYMUO3BxUvnuJuER3azpMnZy9ObtEC7rtPP6gsX+7FCe3b6/h4r15w5IjP22eM8b2ffoI2bbSQ3axZwVt/wJsAncs5Vwa4E/jWx+0xmdWnj5Zx8rDhd/p0XRw2cCBceKGf2xZkXn8dihXTLc9xcekcHBGhVTd27dKobowJab//DrfcAhdfDHPm6HtBsPImQA8EZgMbRGShc64ysN63zTIZ8vvvMHu2LgxL5aPg0aM6LV2rlvaic7qSJTXmLlgAb73lxQkNGuhE1ciRXlbfMMYEo4UL4eabNT3E999rnyaY2T7ocNC0qW6v2rhRV3An88ILMGyYxvGGDQPQviAkAi1bwi+/wIoVXmw327FDl77fdBNMm+aPJhpjstHSpXD99dpj/uUXjzmc/C5L+6Cdc/mcc0865952zk0485X9zTSZ8tNP8PPPOkeaSnBeuVIrKT70kAXnpJw7m6L8sce8qI1Rtiy8+KLOFfz0k8/bZ4zJPqtXw4036lvkjz8GT3BOjzdD3B8BpYHmwDygHGCrZYKBiM49ly2raT1TufvJJzVz1quvBqB9Qa5iRV3NPXs2fPKJFyd07apd7WefDWwVd2OM1zZs0F0rkZEanKOiAt0i73kToC8WkT7AMRH5AGgJNPBts4xXvvsO/vhDU4KlsoFv8mQtJ/nyyx63Red4Tz4JV12la+uio9M5OF8+nYdevlyrcBhjgtrWrRqcT5/W0rNZKqk7ebJ+QI+I0H8nT86mVnqW7hy0c+4vEbnSOfcL8ASwG/hLRCpnd2NsDjoDRODKK2HfPli7VvdPJXHokBaKqFhRE2FFWM44j1auhLp1NQfvxx+nc7CIzvkvX655us87zy9tNMZkzM6dmoBk3z6dBaxbNwsPNnkydO6stXnPKFBAC0a3b5+ldmY1F/dY51xxoA8wA1gFDMtSi0zWzZgBixZpcpJkwRl05Ds6WudZLTinrXp1ncKfPFn3RKbJOa12dfAgDBjgj+YZYzJo717tOe/ZowONWQrOoGV7kwZn0J/TKOebHVLtQTvnrgL+FpFTPn32ZKwH7aX4eKhTR/NVrlqVIrP7kiWaHeeJJ7TWs0nfqVP6n/jYMe1RFyqUzgmPP67D3MuWwWWX+aWNxpj0HTigq7XXr9fgfN112fCgERGpryR1LstpgDPTg84LzHDOlXPOdU3tK0stMlnzxRc6xNq/f4rgHB+vsaNUKRg0KDDNC0V582rFq23bdEo/XQMH6p7z557zYgm4McYfDh+G5s01ne/XX2dTcP77b8/DkBUqZMMTeJbqs4rIPKAjcBlQ2MOXCYS4OOjXT3ttd92V4u7x4+Gvv2DEiODOkBOMGjbUUYc33oD589M5uFQp/YA0Zw783//5o3nGmDQcPaq5Df75R/swN96YDQ86cyZce63WOEi+ELdAARgyJBueJA0iEjRf9erVE5OODz8UAZHPP09x1969IsWLizRuLBIf7/+mhYPDh0XKlROpXl3k1Kl0Dj59WuTSS0UuvtiLg40xvnL8uEjTpiIREam+NWbO22/rA9atK7Jjh8jHH4tUrCjinP778cfZ8jTAIvEQEz2u4nbOvQl4HLsTkWey+8OCzUGnIyYGqlXTodXFi1MMuzz8MHz4oWbMsWnRzPv2W83VO3CgLrZL03ffae7A4cO1UIkxxq9OnYLbb9f/ih9+qMVwsiQ+XtMvjhihXfJPP/ViUUrmZXYV9yJgMZAPuBzNv70eqAOkXDZsfO+DDzSd58CBKYLz77/DhAmaS8OCc9a0aqUlOQcP1gxEaWrRQv8TDxyoS0aNMX4TEwP33KO7L957LxuC84kTut9yxAid7/rqK58G5/R4sw96PnCNiMQm/Jwb+FVErsruxlgPOg2nTuku+9KldYLUucS7YmOhXj3d+bNqVUD/nsLG3r06WFGtmubtTXOr2rp1ulfrgQd0EYAxxufi4rSGzZQpWvzmmayO6UZHQ+vWWkVnxAhdAJrkfdZXsroPujhQJMnPhRJuM/70/vvw77+6NDvZH81bb+lun1GjLDhnl/PP1xzmv/8O776bzsFVq2oqsgkTdI+bMcan4uO1XOyUKfDKK9kQnNeu1ZSCZ1aYde3ql+CcHm960A8C/YGfAQdcB/QXTfuZrawH7cGJE3DRRfr1yy/n/OHs3KkZw665RhcTB8HfVNgQ0S0b8+fr3ujy5dM4+PBhqFJFK14le42MMdlHRMvmvv22bmjp3z+LD/jLL3Dbbbpl9ZtvtLysH2WpBy0iE9Hc29OBacDVvgjOJg3vvgu7dumkaLI3/m7dNM/sm29aTMhuzum8VlycTkel+Vm2aFHdcvHbbzB1qt/aaExOIgLdu2tw7t5dA3SWfPKJ7sc6/3z9JO7n4JwuT8u7A/Fl26xSceSISKlSIs2apbjrhx90x1X//gFoVw4ycqT+nj/9NJ0DY2NF6tQRKV9e5Ngxv7TNmJykb1/9v/jUU1ncShofLzJokD5Y48Yi+/dnVxMzjDS2WVmW5mD31lu6eCFZWrBTp7QS00UX6Y4A4zvPPAP168PTT8P+/WkcGBmpq1W2bdNtV8aYbPPKK7pZ4uGH9b9ZpkcMY2KgUyfdQ3nffVpvNkiL3liADmaHD8OwYfC//8HVV59z18iRuq7hrbdSrTRpslGuXLo4++BBL7Y6X3edbtN49VUN1MaYLBs9Wgva3HuvTjtlugDQ4cP6fjphggboDz/UPL9ByqvLdM5dk7BYDOdcKedcCJW8DmFnqiYNHHjOzVu26HT0HXfoNlzje7VrQ48eMGmS1pVN07BhOllmQxvGZNnYsfDss9CmjaaCiIzM5AP9+y80agRz58LEifq+GuQLd9IN0M65fsALQK+Em3ID6VXNNVl14IDu87n9dt3knESXLvoJ8vXXA9S2HKpPH91RlbwsbAoVK2o0nzJFF40ZYzLlo4/gsce00ztlSoraQN5bvFgXgG3frinHOnbMzmb6jDc96NuB1sAxABHZiRXL8L0RI+DIkRQ1h7/5RktB9+uXzrYfk+3y5dNP85s3axnuNPXoAWXL6qepLJajMyYn+vxzjaNNm8KXX6Za9t4733yjU0958mhig2bNsrOZPuVNgD6dsNJMAJxzBX3bJEN0tJZUuvNOqFkz8ebjx3XB0mWX6ZCP8b/GjbUH/frrkOaW/YIFdah7yRIdFzfGeO2bb3S+uWFDLRuZ6XU2Y8boHudq1TRDWPXq2dlMn/MmQE91zr0HFHPOPQL8AIzzbbNyuFdf1eQkyXbgDx2q889vvw25cwekZQZ9eS64QBeCxsSkceA99+g7TK9e8N9/fmufMaFszhxo2xbq1tXkSwUz0yWMi9NsYE89pbny583TNMkhxptEJSOAL4AvgUuAviLypq8blmPt3Kmf+u67T1OEJVi7VnfudOigvTgTOMWK6YekpUt1Nb1Hzuny0717fV831pgwMG/e2Q7vd99BkSLpnpLS8ePQrp0Ocz3zDEyfnskoH3jppvrM8hM4F4lWxtohIq3SOtZSfaKbbd99F9as0U3O6ILgm26ChQs1UF9wQYDbaAD9lP/tt5oHvWrVNA586CH4+GPNF1qlit/aZ0wo+fNPfZ8rX14DdalSmXiQPXu04MXChRqgu3TJ9nZmt0yl+nTOHXHO/efpKwPP3wVIr2ifAd0GMHYsPPhgYnAGzRz5ww/aCbPgHDzefFPnxjp3Tmcd2NChutfS6kUbk6olS7SseunS8OOPmQzOq1drwYvly2HatJAIzunxGKBFpLCIFAFGAz2BskA5dMvVKG8e3DlXDmgJWA0+bwwerP++9FLiTf/9p1XPLr9ctxuY4FGmjC62nzdPi415VLq0vqYzZugEmzEm0YoV2nMuVkyDc5kymXiQuXN1vcfx42fHycOAN9WslopI7fRu83DuF8DL6Las51Mb4nbOdQY6A1SoUKHe1q1bM9D8MLJxo845P/aYds0SdO2q+Urmz4crrwxc80zqRHTXxpIlWov7wgs9HHjqlK4gzZtXJ68zvaHTmPCxbp3ugIqM1KJSSQYOvffxxzqNdPHFMHMmVKqU3c30qazWgz7mnGvvnIt0zkU459qTsCc6nSdtBewVkcVpHSciY0WkvojUL5WpcY0wMXCgvmm/+GLiTcuW6W6rzp0tOAerMxWvTp3SBaMe5c2rK8pWrfKiwLQx4W/zZt3jHB+vPecMB2cRfd/s0EHr7f7+e8gF5/R4E6DvBe4E9iR8tUu4LT2NgNbOuS3Ap0BT55xlIEvNmjX6KfDJJxPHd+Lj4fHHoXhxncI0watKFd0RN326Tn151Lo13HCDZjlJs+qGMeFt+3YNzidO6PqaJBtWvHP6tK7V6dcP7r9fl3wXL+6TtgaUpzJX2fkFNAG+Te+4HFtu8u67RQoWFNm7N/GmCRO0EtqECQFsl/Ha6dNaabJ0aZGDB9M4cPlykYgIkSef9FfTjAkqu3aJVK0qUqSIyMKFmXiAgwdFmjbVN8gBA7JYdzLwsHKTQWz5cvj0U92vlzDEf+CAZops2BAeeCDA7TNeyZ1bF4pFR+tr51GNGjo08s47+tobk4Ps26eDSDt2wKxZWsY1Q7Zs0YIXv/6qlTP69g36ghdZ4ZcALSJzJZ090DlWv366Gz/JFpwXX9QiVu+8k4WyasbvLr9cF/WNG6eLSj0aMACKFtXl+T7OQ2BMsDh0SFdrb9yoqTwbNszgAyxapNuoduzQGs733++LZgYVe/sPpMWLdeKya9fEguF//aVboZ95BmrVCnD7TIb17w+VK8Mjj+j8WqpKlNDFLT/+qFuvjAlzR47oPucVK/Qt7/rrM/gAX3+tKRTz59eMJhl+gNDkTbnJl5J8H7yVrUNR374amBMqX8TF6ehn6dIp0nCbEFGggH7A2rAhRRnvcz32mG676tpVl4AbE6aOH4dWrTS519SpmahhP3q0lt2tXl33m1ar5pN2BqO0Mom94Jy7Gmib5OY/fd+kHOLPP3XPXvfuOtyJbtdZskQz1GUqB60JCs2a6bbM4cPhn388HJQrl25w37RJ/zUmDJ08qTlDfvsNJk/OYP6QuDjNBvbss3DrrTpvlMNSKXpMVOKcuxVoDHQClgJrgJuAm0RkrS8ak6Nycd9wg2503rQJChVizx645BK44gpNNhXG6x5yhAMHtCxouXL6od9jXpJbb4WfftKMDZlKoWRMcDp9WvPVf/MNTJyotZ29duwYtG+vQ9vPPqsp+yIjfdTSwMpsopJDwIvABnSb1OiE23s65/7IzgbmOHPn6vxjr15QqBCgHenjx+Gttyw4h4PzztOEcIsXp9NBHjlSh7iTJKgxJtTFxmpBvm++0cpvGQrOu3dDkyZ68ptv6pBimAbn9KQVoJsD/wdcBLwGNACOiciDIpLR9XfmDBHo00dzQiYk1543Dz76SLfnXHJJgNtnsk3btpqbpG9fXbmaqosv1tXckybpJJ0xIS4+Xqd4Pv8cXntN19V4bdUqXam9ahV89VU66fnCX1rFMl4UkWbAFuAjIBIo5Zz7zTn3jZ/aF36+/14nZHr3hvz5iYnRBGIVK1onKtw4p72H3Lnh0UfT2FHVu7fOrXXpYtuuTEgT0X7HRx9p7Z/nnsvAyT/9pHuvTp3SxNy33OKzdoYKb7ZZzRaRRSIyFtguItcAD/q4XeHpTO+5QgV4+GFAFyiuXKkjOQUKBLh9JtuVLQuvvqozGh984OGgIkXg5Zd14eCUKX5tnzHZRUSni8eN085G794ZOPmDD6B587OLNurV81UzQ4unFGOpfQG1M3J8Rr/CPtXnjBmanm7cOBER+fdfzfB5yy0Bbpfxqbg4kWuuESleXGT37jQOqldPpGxZkaNH/do+Y7IqPl6kZ099e3vuuQxk34yPF+nbV09s1kzk0CGftjMYkV2pPkVkqU8+JeQE8fE6GXnRRYn5O597Tm8ePTqdc01Ii4jQXsWxY5qAxuNBo0drlqRXX/Vr+4zJqsGD4ZVXdHh75EgvF7qeOqXvhQMHauGLmTMTt5waZZnE/GXaNN0U268f5M7Nd9/Bl1/CSy9BVFSgG2d87dJL9fPZ1KlpJA9r1AjuuUc3UOfUuugm5IwYoX/bDzwAY8Z4GZwPHtSMJWcmq99/H/Lk8XlbQ43HfdCBELb7oOPiNG9nfDysWMHJmEhq1NCdA8uWaalgE/5On9biAAcO6CLVVJPRbNumS/lvuQU++8zvbTQmI8aM0YXWd92liUi82g21eTP873+aA2LCBN3vnINldh+0yS6ffabvyAMGQGQkw4bptpsxYyw45yR58sD48bBzJ/Ts6eGg8uX1zqlTdSWrMUFqwgQNzrfeqh1hr4LzX3/pNqo9ezQjUw4PzumxHrSvxcZqSqn8+eHvv9m4OYLq1TXl3aefBrpxJhCee06Tl/z6K1xzTSoHHD+u+YbPO08r+OTQJA0meE2ZorH1pps02ZdXHY3p0/Wk0qW11qQlfQCsBx1YH30E69fDwIGIi+Dpp7Un9dprgW6YCZTBg6FSJa14dfJkKgcUKADDhumahQkT/Nw6Y9I2bRp06KDFpaZN8yI4i2g2sDvu0Km++fMtOHvJArQvnT6tw9r160Pr1nz1lX5wHDhQE4mZnKlgQXj3XVizBoYO9XDQnXdq97p3bzh82K/tM8aTmTPh7rvhyis1E2e6uRvi4nTrQteu0KYN/PwznH++X9oaDixA+9KECboad+BAjh13dOmiHyBzePY6g+Zk6NBB85MsX57KAc7ptqt9+2DQIL+3z5jkfvxRY2ytWtrRSCgj4NnRozqX99Zb8Pzzuq4if35/NDVsWID2lZMndSyzYUNo0YJBg3SB7ttvp1HZyOQor70GxYrpUHdcXCoHXH65ZpwbPRrW+qSAnDFe+e03zStfpQrMnu3FduVdu3QMfOZMXQ07fLju9TcZYr8xX3nvPU06MWgQq1Y7Ro7UvfiNGgW6YSZYlCypsXfBAu1kpGrwYB1H7NbNr20z5oyFC3VXVPny8MMPUKJEOiesWAENGuiHyhkz4Ikn/NLOcGQB2heOH9exy+uvR65vyhNPQOHCliDKpHTPPfrm17s3bNmSygEXXKD52//v/3Rc0Rg/WrpUp2NKldIh7gsuSOeEH37QXkhsrG5TaNnSL+0MVxagfWHMGN3nN2gQn3yi5SRfeUX/yI1Jyjl45x39/rHHPBSzeuYZHVt87jmIifFr+0zOtWoV3HCDLmr88Uct/JKmCRPg5pu1NN+CBVC3rl/aGc4sQGe3I0e0q9y8OYeqN6JbN13x2KlToBtmglWFCjrgMnu2ZmNK4cy+vLVr9cOfMT62YYMG51y5tApkpUppHHymSt/DD0PTpjphXb68v5oa1ixAZ7fRo2H/fhg0iL59ITpaF4bZ+giTliee0ARLzz6rfzMptGypY439+3s4wJjssXUrNGumgzU//qiDNx6dOgX33adrJTp1gm+/9ZDD1mSGhY3sdPCgZo5v3ZolkVcwZgw8/riVNjXpi4zUNKD//eehyL1zmuzh6FGtTGCMD+zYoZ3g//6D77/XJIgeHTgAN94In3yiG/rHjoXcuf3W1pzAAnR2eu01OHyY+P4DeeIJXaU7eHCgG2VCRfXqWuh+8mQP68GqVYMnn9Q3wqVW+dVkr717dVg7OlqnW+rUSePgjRt1C+mCBZr3s1cvL8tYmYywXNzZZd8+rRt5882Mu3EqnTvDhx9qMgpjvHXqlK6tOXZMd6sULpzsgIMHdcyxZk2dHLQ3RZMNDhyA66/XrMSzZ8O116Zx8J9/6qbo+Hj46qt0DjbpsVzc/jBsGBw7xsEu/enZE667TqdmjMmIvHl1qHvbNq0VnkLx4ppZbO5cTYRsTBYdPqzLG85sW04z3n75pY6BFy2qgdqCs09ZgM4Ou3drpon27ek+8TL++08XhlnnxmRGw4Y6kv3mm1pXIIVHHtEe9PPPe6i2YYx3jh7VffhLl2rsveEGDweK6Pqadu10iOfPP6FqVb+2NSeyAJ0dXn4ZTp9myS39eP99XeRTvXqgG2VC2dChuu+0UyetuXKOXLm0XuWWLVYWzWTaiRM6Uj1/vk4je8wpEhurnxi7d4e2bXVptyV18AsL0Fm1fTu8+y7x93fkwSEXU66cLbI1WVe4sCYwWblSk9yk0LSpVi4YOlSX3hqTAadO6Z/P3Lm6VuaOOzwcePQo3Hqr/jG+8IIWsbeCF35jATqrhgwBESaV78OyZdqxSbfKizFeaNVKS/sNHqxZnVIYPlw3q/bq5fe2mdAVE6MpZr/7DsaNg/btPRy4Y4fOMc+erfVRX3nFEjr4mf22s2LzZhg/nmP3dOLZ1yvSooV+KjUmu4werb3pRx7RRbPnqFxZi2h89JGHyWpjzhUXB/ffD9OnwxtvaPKvVC1bpplzNmzQws+PPurXdhplATorBg2CyEh6HO7N6dO6qMcWhpnsdP75mp/kjz+0E5NCr15Qpgx06ZJKBDfmrPh4XdPw6aeajfjppz0cOGcOXHONLgz77TfNr20CwgJ0Zq1bBx98wL8tH+ftr8vSsydcfHGgG2XCUYcOmrCpZ0/dfnWOwoV16PGvvzwk8jZGY+1TT8GkSZottkcPDweOG6fLuqOidFSmdm0/ttIkZwE6swYMQPLl456lPbnoIl0/YYwvOKflxePiNGd3itxC992nFVleeEEX9RiThIjuyHvnHQ3MqS5ijY/XNHadO+unwV9/hXLl/N5Wcy6fBWjnXHnn3M/OuVXOuZXOuS6+ei6/W7kSpkxhfv2n+WPjBbz5pi1sNL4VFaWLxb79FqZOTXZnRIROVu/apVv+jEmiXz/djff00zrYkmIa7uRJuPde/dt59FGdc7aCF0HBZ6k+nXNlgDIissQ5VxhYDNwmIqmtRwVCKNVn27bEz55DhdjNNPhfCb78MtANMjlBXBxcfbVuf169GkqUSHZAhw7w+ee65Lty5UA00QSZl1/WjnGnTjoKk2IR9r59cNtt8PvvOjHdvbstpPGzgKT6FJFdIrIk4fsjwGogvZLfwe+ff+DLL5la9jkORZZg1KhAN8jkFJGROkV48KAOWabwyit6UPfufm+bCT6jRmlwbt9eFximCM4bNmjaukWL4LPPdPzbgnNQ8csctHOuElAXWJDKfZ2dc4ucc4uiQ6HObd++nC5YjMfWPkffvlaX3PhX7dr6PjppkpYDPEfZsvqOPG0a/PxzIJpngsR772lGwzvu0L+VyMhkB/zxh26jOnBAi67ceWcgmmnS4fNqVs65QsA8YIiIpJndP+iHuBcsgKuuYkSxwUy8sDd//w158gS6USanOXlSA3VMDCxfDgULJrnzxAkt4lukCCxerGlBTY7y4YfQsaMuxp42LZX3qKlTdTN0hQowc6ZtPwmwgFWzcs7lBr4EJqcXnENC374cK1CSAYeeYcwYC84mMPLl06HuzZt1AdA58ufXogbLlmlZLJOjTJ0KDz4IzZrBF18ke48S0ap7d90FV1yhBS8sOAc1X67idsD7wGoRCf2M/r/+CnPmMOjUC9x2X2GaNAl0g0xOdt11uuD29dd1CvEcbdpA48Zar/LgwYC0z/jfjBk639yokZZpzpcvyZ2xsfDYY7oV7+67dX4kxSpDE2x82YNuBHQAmjrn/kn4+p8Pn893RJA+fdifpzQfFHyC4cMD3SBjdNHtBRdousaYmCR3OKcrhA4ehAEDAtU840ezZ2slyHr1dCveOdMeR47ALbfA2LGaeW7y5GTR2wQrX67i/k1EnIjUEpE6CV8zffV8PvXTT7h58+h/+kV6DylA6dKBbpAxULSo1h1ftkxHtc9Rp44m8B4zRvdkmbA1d67ulLrsMpg1K9kW5u3bteDF99/rvMjQoVbwIoT4fJFYRgTlIjER4ho0ZM/i7bSpuZ7fF+dLuSLSmABq105zSyxbBlWrJrkjOhqqVNHVurNm2RaaMPTnn5r4q2JFDdTnlGn+5x8t8nzkiE5I33RTgFpp0hKwRWJhYdYsIhfOZ0B8H0a/Z8HZBJ8zmew6d05WL6NUKU28PHu2rtY1YWXJEq1jUaYM/PBDsuA8a5b2nCMitOCFBeeQZAE6LSKceL4Pm4giotODNGgQ6AYZk1Lp0jrEPW8evP9+sjuffBIuuUQ3xZ4+HZD2mey3YoX2nIsV023MZcokufO993TOuUoV3Rpaq1agmmmyyAJ0GuKnfUX+1UsYWbAfg1/JHejmGOPRQw/B9ddrErGdO5PckTu3LvVev1672ibkrV2r26jy5dPgnJgsKT5eV2k/9hg0bw6//AIXXhjQtpqssQDtSXw8h7v0ZS1Vqf96e9uRYIKac7pI99QpLSt4jptv1qwVAwfC3r0BaZ/JHps2aXAG+PHHJCnXT5zQ7VPDhsHjj8PXX0OhQgFrp8keFqA9ODphKsV3rOCTKv154GHLxmSC38UX666q6dM1g9Q5XnsNjh/XvdEmJG3bpsH5xAmdc7700oQ7oqP1js8/17mOMWMsg1yYsFXcqYmNZXepGkQfyk38kqXUrmufY0xoiI3V0tC7dunuqmLFktzZrZsOdy9eDHXrBqqJJhN27dLcM3v26LB2vXoJd6xbp6MjO3bARx9B27YBbafJOFvFnUGbBk2m9KG1LGw1wIKzCSm5cmmGz+joVIpa9emj2aO6dNG0jyYkREfDDTfo2oJZs5IE599+0/qj//2nxVEsOIcdiz7JxJ2MIc8rA1meqy5tP7490M0xJsMuv1w7y+PHJytqVawYDBmiaWs//zxQzTMZcPCg7pDatEkzhDVsmHDHlCk6rF2ypG6GvuqqgLbT+IYF6GR+eXgS5U5v4sBzgyhS1BI7mNDUrx9cdJHujT5xIskdDz+spbC6d092hwkGpUvrgr8zX+edp/lG8udH8/+LwMsvw733QoMGGpwvuijArTa+YgE6iT3/nuLiKYNYXaQB170SmmnDjQEoUEBXdW/YoIu3E0VGwujR8O+/qeQHNYG2Z0/qtx88iCZcf+QRrfl9772avvO88/zaPuNfttQviTntxtFBtrFt1ARchPWeTWhr2lT3Rw8fDnfemWRdWOPGmh/05Ze1cHDiRlrjLwcParnQM1+bNum/nhThMLRsp0H5pZf0U5elbg17too7wW9zjnNR84s4Wb4qUVvn2h+/CQsHD0K1alC2rCaVStx9s2WL3tGmjVY3Mtnq5En9FScPwGe+Dh069/jixSEqStN3JleObfwfLamVa7VmCXvoIX9cgvGTtFZxWw8aHTn68/53uIbdnBz/mQVnEzaKF4e33tIO86hR8PzzCXdUqqQ/DB6sGS/27oUKFXQRWfv2AWxxaIiL051NngLwOdnc0KxflSppEG7YUP9N+nVmO5xzcA+TGUpvKvAvu7mA/JwgAtEl3Dfc4O9LNQFkARp4e9hROu55hb11buT8m64LdHOMyVZ33AG33gp9+8LttydZUxQVpRHhzMTn1q26qgxyfJAWgf37PQfgrVvPrcEdEQHlyumvtHnzc4Nv5cpat9ubKo/3MJlxdKYgxwG4kN3E4+jJywyz4Jzj5Pgh7u3bYXzlofSP6Q3z52MVMUw42rFD6wXXr69ZqJxDu3Rbt6Y8OE8eqFnT3030u7h4OH0KTp3WFKmnT+m/Z34+pzIYOj2QNw/kyQt58+r3efPqz3nyQHYsWzm9eDl5SFnUZFtERcrHbcn6E5igY0Pcaej91GFejxnB8aatKGDB2YSpsmU1TfNjj8GkSfDgg+hK7tScPq37fUJcvMDJE5rh9PhxOJ7w/YmEn08li4OREbr6vUBhKHAB5C+Q8HMBKJDfP9kz87A41dvLi4fXyoS1HB2gZ8+GqK9f5zwOwoiB6Z9gTAh75BFdD9atm9bPKF2hQuo96IoVNStGkBPRqfPUhqA3bdLc1XFxZ4+PjNRp9srVUg5BR0VpPeWALz/xNKpRoYLfm2ICL8cG6JMn4aXH9/NzxGvEtW5DpOUmNmEuIgLGjdPywF26wGdDhuic8/HjZw8qUEAXigWJI0c8B+AtW85tOuhcb+XK5y7EOhOAy5ULgRoSIfCaGP8J9j9Xnxk2DNpsHkFBdxQ3aECgm2OMX1xyiS4We+klaN++Pa3HAr1763B3AFZxnz6tT51aAN68WRdqJVW4sAbcqlXPLsY6E4ArVdJYFtLO/O4D+JqY4JEjF4lt3AjXX7aHdXGVyXfnrfDJJz5/TmOCRUyMFlw4cABWroSiRX33XPHxsHu35wC8Y8e5i7Fy5z67HSl5DzgqShNnBXwY2phsZIvEkhCBZ56BHvIqeeUk9O8f6CYZ41e5c2shjauvhl694O23s/Z4hw55DsBbtuiK6KTKltVg26TJucE3KgouvFDnio0xObAHPX06PNVmB1tzXUyu++6GiRN9+nzGBKuCBVPO4YLO4+7effbn5Fmxks8Je8qKlbz3GxWl68/y5fPlVRkTWqwHneDYMV0cM/K8oUT+F6uTccbkUKkFZ9C8JR06ZD0rljEma3JUgB40CNy2rdyZaxzu4Yf13cQYk8Ivv2Q9K5YxJmtyTIBetQpGjoQfqwwiYqvTVZLGmFSlthXXGONfOeJzsAg8+STUKrCBazdN0nRKVmLPGGNMEMsRAfqTT2DuXJhSbSAuTx5dumqMMcYEsbAP0IcPa2rDdjVWU+Wvj7UrHQZ5ho3JqgsuyNjtxhj/Cvs56D59NF/vu7X647YUhB49At0kY4JC0q1UxpjgE9Y96L//hjFjYHC7pZz3/VTdY1WqVKCbZYwxxqQr7HrQpUufrT9/xmVT+3HYFaVot26BaZQxxhiTQWHXg04enOuxiNv4mhHSTVMcGWOMMSEg7AJ0coPow37OYzRdAt0UY4wxxmthN8SdVEN+52a+owevcoQigW6OMcYY4zWf9qCdcy2cc2udcxuccz19+VxJ3cNkNlOJ37iGOCKIpqS/ntoYY4zJFj7rQTvnIoExwI3AdmChc26GiKzy1XOCBudxdKYgWgkgknje4mlOkRewoufGGGNCgy970FcCG0Rkk4icBj4FbvXh8wHwakTvxOB8RkGO82qE5d42xhgTOnwZoMsC25L8vD3htnM45zo75xY55xZFR0dn+UnLy78Zut0YY4wJRgFfxS0iY0WkvojUL5UdSUQqVMjY7cYYY0wQ8mWA3gEkLRlVLuE23xoyBAoUOPe2AgX0dmOMMSZE+DJALwSqOOeinHN5gLuBGT58PtW+PYwdCxUrgnP679ixersxxhgTIny2iltEYp1zTwGzgUhggois9NXznaN9ewvIxhhjQppPE5WIyExgpi+fwxhjjAlHAV8kZowxxpiULEAbY4wxQcgCtDHGGBOELEAbY4wxQciJSKDbkMg5Fw1szcaHLAnsy8bHC6RwuZZwuQ6wawlW4XIt4XIdYNeSlooikmqWrqAK0NnNObdIROoHuh3ZIVyuJVyuA+xaglW4XEu4XAfYtWSWDXEbY4wxQcgCtDHGGBOEwj1Ajw10A7JRuFxLuFwH2LUEq3C5lnC5DrBryZSwnoM2xhhjQlW496CNMcaYkGQB2hhjjAlCYRGgnXMtnHNrnXMbnHM9U7k/r3Pus4T7FzjnKgWgmeny4jo6OueinXP/JHx1CkQ7veGcm+Cc2+ucW+HhfueceyPhWpc55y73dxu94cV1NHHOHU7ymvT1dxu95Zwr75z72Tm3yjm30jnXJZVjgv518fI6QuJ1cc7lc8795ZxbmnAtA1I5JlTev7y5llB6D4t0zv3tnPs2lfv885qISEh/oaUsNwKVgTzAUuCyZMc8Abyb8P3dwGeBbncmr6Mj8Fag2+rl9VwHXA6s8HD//4BZgAOuAhYEus2ZvI4mwLeBbqeX11IGuDzh+8LAulT+xoL+dfHyOkLidUn4PRdK+D43sAC4KtkxQf/+lYFrCaX3sK7AJ6n9HfnrNQmHHvSVwAYR2SQip4FPgVuTHXMr8EHC918AzZxzzo9t9IY31xEyROQX4EAah9wKfChqPlDMOVfGP63znhfXETJEZJeILEn4/giwGiib7LCgf128vI6QkPB7PprwY+6Er+Qrd0Ph/cvbawkJzrlyQEtgvIdD/PKahEOALgtsS/LzdlL+Z008RkRigcNACb+0znveXAfAHQlDj18458r7p2k+4e31hoKrE4b1Zjnnqge6Md5IGJKri/Zykgqp1yWN64AQeV0ShlL/AfYC34uIx9ckiN+/AK+uBULjPWwU0AOI93C/X16TcAjQOck3QCURqQV8z9lPcCZwlqC5dGsDbwJfBbY56XPOFQK+BJ4Vkf8C3Z7MSuc6QuZ1EZE4EakDlAOudM7VCHCTMs2Lawn69zDnXCtgr4gsDnRbwiFA7wCSfgorl3Bbqsc453IBRYH9fmmd99K9DhHZLyKnEn4cD9TzU9t8wZvXLeiJyH9nhvVEZCaQ2zlXMsDN8sg5lxsNapNFZFoqh4TE65LedYTa6wIgIoeAn4EWye4Khfevc3i6lhB5D2sEtHbObUGnGps65z5OdoxfXpNwCNALgSrOuSjnXB50wn5GsmNmAA8kfN8W+EkSZveDSLrXkWwusDU69xaqZgD3J6wavgo4LCK7At2ojHLOlT4z9+ScuxL9PxWUb54J7XwfWC0ir3k4LOhfF2+uI1ReF+dcKedcsYTv8wM3AmuSHRYK719eXUsovIeJSC8RKScildD34Z9E5L5kh/nlNcmV3Q/obyIS65x7CpiNroSeICIrnXMDgUUiMgP9z/yRc24DuuDn7sC1OHVeXsczzrnWQCx6HR0D1uB0OOemoCtpSzrntgP90EUjiMi7wEx0xfAG4DjwYGBamjYvrqMt8LhzLhY4AdwdjG+eCRoBHYDlCfOEAC8CFSCkXhdvriNUXpcywAfOuUj0Q8RUEfk21N6/EnhzLSHzHpZcIF4TS/VpjDHGBKFwGOI2xhhjwo4FaGOMMSYIWYA2xhhjgpAFaGOMMSYIWYA2xhhjgpAFaGNCVMJe30+dcxudc4udczOdc1Wdh8pbXjxeR+fchdndTmNM5liANiYEJSThmA7MFZGLRKQe0Au4IAsP2xHIUIBOyKJkjPEB+89lTGi6HohJSMoBgIgsTVqX1jnXEagvIk8l/PwtMAL4FU20UB+tNjQBTfxfH5jsnDsBXA1cBrwGFAL2AR1FZJdzbi7wD3ANMMU59y+awCUOzTx2nc+u2pgcxAK0MaGpBpDZZP51gLIiUgPAOVdMRA4lZLJ7XkQWJeS6fhO4VUSinXN3AUOAhxIeI4+I1E84fznQXER2nEn1aIzJOgvQxuQ8m4DKzrk3gf8D5qRyzCXoh4DvE1JaRwJJc3J/luT734FJzrmpQGoFOIwxmWAB2pjQtBLNN52WWM5dZ5IPQEQOOudqA82Bx4A7OdszPsMBK0Xkag+PfezMNyLymHOuAVrgfrFzrp6IBF1hCmNCjS0SMyY0/QTkdc51PnODc64W55aK3ALUcc5FOOfKA1cmHFcSiBCRL4GXgMsTjj8CFE74fi1Qyjl3dcI5uZ1z1VNriHPuIhFZICJ9gehkbTDGZJL1oI0JQSIizrnbgVHOuReAk2hAfjbJYb8Dm4FVaFm/JQm3lwUmOufOfEDvlfDvJODdJIvE2gJvOOeKou8Vo9Cee3LDnXNV0F73j8DSrF+hMcaqWRljjDFByIa4jTHGmCBkAdoYY4wJQhagjTHGmCBkAdoYY4wJQhagjTHGmCBkAdoYY4wJQhagjTHGmCD0/9o6L4CdZCmMAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "zeros=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==0]\n",
        "one=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==1]\n",
        "two=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==2]\n",
        "three=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==3]\n",
        "four=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==4]\n",
        "five=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==5]\n",
        "six=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==6]\n",
        "\n",
        "fzeros=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==0]\n",
        "fone=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==1]\n",
        "ftwo=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==2]\n",
        "fthree=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==3]\n",
        "ffour=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==4]\n",
        "ffive=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==5]\n",
        "fsix=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==6]\n",
        "\n",
        "\n",
        "\n",
        "y=[len(zeros)]+[len(one)]+[len(two)]+[len(three)]+[len(four)]\n",
        "x=[i for i in range(5)]\n",
        "\n",
        "fy=[len(fzeros)]+[len(fone)]+[len(ftwo)]+[len(fthree)]+[len(ffour)]\n",
        "\n",
        "y[:]=[x/5 for x in y]\n",
        "fy[:]=[x/2 for x in fy]\n",
        "\n",
        "\n",
        "fig = plt.figure()\n",
        "ax1 = fig.add_subplot(111)\n",
        "\n",
        "ax1.plot(x,y, c='b', marker=\"s\", label='Entre semana')\n",
        "ax1.plot(x,fy, c='r', marker=\"o\", label='Fin de semana')\n",
        "plt.legend(loc='upper left')\n",
        "plt.rcParams['figure.figsize'] = [8, 4]\n",
        "plt.title(\"\")\n",
        "plt.xlabel(\"Clusters\")\n",
        "plt.ylabel(\"# de días\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SNG8IeLJQd_3"
      },
      "outputs": [],
      "source": [
        "kmedoids = KMedoids(n_clusters=10,  metric='precomputed', method='pam', init='heuristic', max_iter=100, random_state=None).fit(1-corr_matrix_f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Y9f7RljJQd_4"
      },
      "outputs": [],
      "source": [
        "lab=kmedoids.labels_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TOmViDufQd_4",
        "outputId": "9fc90182-0742-4365-fa00-ce5bdf05d780"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(12, 8))\n",
        "sn.scatterplot(x=[r[i].weekday() for i in range(len(learning_data))],y=[i for i in range(len(learning_data))], hue=lab, \n",
        "                palette=sn.color_palette('hls', 10))\n",
        "plt.title('KMedoids with 10 Clusters')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oYqME9V3Qd_4",
        "outputId": "2ae0dfd6-3649-4a14-952c-df00a9207291"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "zeros=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==0]\n",
        "one=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==1]\n",
        "two=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==2]\n",
        "three=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==3]\n",
        "four=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==4]\n",
        "five=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==5]\n",
        "six=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==6]\n",
        "seven=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==7]\n",
        "eight=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==8]\n",
        "nine=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==9]\n",
        "\n",
        "fzeros=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==0]\n",
        "fone=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==1]\n",
        "ftwo=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==2]\n",
        "fthree=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==3]\n",
        "ffour=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==4]\n",
        "ffive=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==5]\n",
        "fsix=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==6]\n",
        "fseven=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==7]\n",
        "feight=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==8]\n",
        "fnine=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==9]\n",
        "\n",
        "\n",
        "\n",
        "y=[len(zeros)]+[len(one)]+[len(two)]+[len(three)]+[len(four)]+[len(five)]+[len(six)]+[len(seven)]+[len(eight)]+[len(nine)]\n",
        "\n",
        "x=[i for i in range(10)]\n",
        "\n",
        "fy=[len(fzeros)]+[len(fone)]+[len(ftwo)]+[len(fthree)]+[len(ffour)]+[len(ffive)]+[len(fsix)]+[len(fseven)]+[len(feight)]+[len(fnine)]\n",
        "\n",
        "y[:]=[x/5 for x in y]\n",
        "fy[:]=[x/2 for x in fy]\n",
        "\n",
        "\n",
        "fig = plt.figure()\n",
        "ax1 = fig.add_subplot(111)\n",
        "\n",
        "ax1.plot(x,y, c='b', marker=\"s\", label='Entre semana')\n",
        "ax1.plot(x,fy, c='r', marker=\"o\", label='Fin de semana')\n",
        "plt.legend(loc='upper left')\n",
        "plt.rcParams['figure.figsize'] = [8, 4]\n",
        "plt.title(\"\")\n",
        "plt.xlabel(\"Clusters\")\n",
        "plt.ylabel(\"# de días\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A-cFSMyQQd_5"
      },
      "source": [
        "Como vimos no hay mucha claridad entre los días de la semana aparte de que el algoritmo sí logra detectar distinta naturaleza entre los fines de semana y el resto de los días. Para verificar que no se trata de una invisibilización por parte de los mismos datos, debido a que las diagonales son muy pesadas, procederemos a quitar las segundas diagonales principales para observar si hay alguna mejoría."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c27IowBgQd_6"
      },
      "outputs": [],
      "source": [
        "for i in range(len(areas)):\n",
        "    for j in range(len(areas)):\n",
        "        for t in range(123):\n",
        "            if i==j and j!=0 and j!=81:\n",
        "                raw_data[areas[i]][areas[i+1]][t]=0\n",
        "                raw_data[areas[i]][areas[i-1]][t]=0\n",
        "\n",
        "            \n",
        "raw_data[areas[0]][areas[1]] = [0 for i in range(123)]\n",
        "raw_data[areas[81]][areas[80]] = [0 for i in range(123)]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "I1bsDNUAQd_6",
        "outputId": "2486a6e2-fb9a-4bf2-c684-be6f9aff728d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot: >"
            ]
          },
          "execution_count": 119,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "used_data=[wed[i] for i in range(5,11)]+[sun[i] for i in range(5,11)]\n",
        "learning_data=[]\n",
        "for i in range(123):\n",
        "    if i not in used_data:\n",
        "        learning_data.append(i)\n",
        "\n",
        "\n",
        "ij_elements=list(it.product(range(len(learning_data)), range(len(learning_data))))\n",
        "corr_vector=[scipy.stats.spearmanr(single_vector(learning_data[i[0]]),single_vector(learning_data[i[1]]))[0] for i in ij_elements]\n",
        "\n",
        "cr=np.array(corr_vector)\n",
        "corr_matrix=cr.reshape((int(math.sqrt(len(corr_vector))),int(math.sqrt(len(corr_vector)))))\n",
        "np.shape(corr_matrix)\n",
        "heatmap(corr_matrix, cmap='viridis')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9x-_fw9OQd_7",
        "outputId": "2de48869-7cc6-47cb-cad5-657608dc1f2f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "kmedoids = KMedoids(n_clusters=7,  metric='precomputed', method='pam', init='heuristic', max_iter=100, random_state=None).fit(1-corr_matrix)\n",
        "lab=kmedoids.labels_\n",
        "plt.figure(figsize=(12, 8))\n",
        "sn.scatterplot(x=[r[i].weekday() for i in range(len(learning_data))],y=[i for i in range(len(learning_data))], hue=lab, \n",
        "                palette=sn.color_palette('hls', 7))\n",
        "plt.title('KMedoids with 7 Clusters')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EElPWmuQQd_7",
        "outputId": "166b788d-7496-4618-d5c9-f9ecf8dd46e0"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "zeros=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==0]\n",
        "one=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==1]\n",
        "two=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==2]\n",
        "three=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==3]\n",
        "four=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==4]\n",
        "five=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==5]\n",
        "six=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==6]\n",
        "\n",
        "fzeros=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==0]\n",
        "fone=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==1]\n",
        "ftwo=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==2]\n",
        "fthree=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==3]\n",
        "ffour=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==4]\n",
        "ffive=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==5]\n",
        "fsix=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==6]\n",
        "\n",
        "\n",
        "\n",
        "y=[len(zeros)]+[len(one)]+[len(two)]+[len(three)]+[len(four)]+[len(five)]+[len(six)]\n",
        "x=[i for i in range(7)]\n",
        "\n",
        "fy=[len(fzeros)]+[len(fone)]+[len(ftwo)]+[len(fthree)]+[len(ffour)]+[len(ffive)]+[len(fsix)]\n",
        "\n",
        "y[:]=[x/5 for x in y]\n",
        "fy[:]=[x/2 for x in fy]\n",
        "\n",
        "\n",
        "fig = plt.figure()\n",
        "ax1 = fig.add_subplot(111)\n",
        "\n",
        "ax1.plot(x,y, c='b', marker=\"s\", label='Entre semana')\n",
        "ax1.plot(x,fy, c='r', marker=\"o\", label='Fin de semana')\n",
        "plt.legend(loc='upper left')\n",
        "plt.rcParams['figure.figsize'] = [8, 4]\n",
        "plt.title(\"\")\n",
        "plt.xlabel(\"Clusters\")\n",
        "plt.ylabel(\"# de días\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "55_8w8LCQd_8",
        "outputId": "8a717ffa-a3f5-431b-9633-ac8e1f3717fa"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "kmedoids = KMedoids(n_clusters=5,  metric='precomputed', method='pam', init='heuristic', max_iter=100, random_state=None).fit(1-corr_matrix)\n",
        "lab=kmedoids.labels_\n",
        "\n",
        "plt.figure(figsize=(12, 8))\n",
        "sn.scatterplot(x=[r[i].weekday() for i in range(len(learning_data))],y=[i for i in range(len(learning_data))], hue=lab, \n",
        "                palette=sn.color_palette('hls', 5),legend=False)\n",
        "plt.title('KMedoids with 5 Clusters')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WIFkjF8LQd_9",
        "outputId": "319b2916-7039-4711-f472-b53f630d0bd4"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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5SWNMIHAYqC4icfRBW7rcpI85dgwOHeJ0nlLsPZ6J4sXhyJGkv19RUVCtml31avuGK2S6rzRkyQLr10OQb12VUUqphHjDcpN1gT0JJWflY6Ki4NgxorNk5cCpTGTObHNocggMtLVKDh+GIaPT24FimzZBHFPQlFLKX6VUgn4emBHXA8aYtsaYtcaYtWFhYSkUjkqy48chMpKTafITGQkFC966IEZS1KwJL70EI0bAngpN4ZFH7BKWJ08m30GUUsqLeTxBG2PSAI2A7+J6XEQmikhVEamaO3fuOF8jJbrh1W2IjITjx4nOko1DpzOSPbu99pzc79OwYZAmDXTsZGyT+vx5m6SVUioVSIkWdANgvYgcT3TPOKRLl45Tp05pkvYmx49DVBTHAvMjAgUK2OR86tQp0qVLl2yHyZ/f5uO5c+HXg2Xh7bdtN/eGDcl2DKWU8lYeHyRmjJkJLBCRKYntG9cgsYiICA4dOhQzR1c5LCoKDh8mOl16Dl7JTebMdloU2C9TBQsWJDg4ONkOd/WqnR9tDGxacZY0ZYvbFTiWL0/ePnWllHJAQoPEPDok1hiTEXgMeO1OXyM4OJiiRYsmX1Aqabp0gZEjaV93M5P/Ks2ePXDXXZ47XNq0tj5Nw4Yw5otsdB4yBF59FWbMgBdf9NyBlVLKYSkyzcpdcbWglRc5dgzuvpuwWs9w169f0K8f9O2bMod+8klYtgx2bosiX5PqcPQo7NgBrkVLlFLKF3nDNCvlD4YMQa5do/2pPtx1F3TsmHKHHjXKdnf36OWag3XkiK1oopRSfkoTtHLPwYMwYQIH67Zixpp76NsXMmdOucMXL26/EEybBqsCHoCXX7bzo3fvTrkglFIqBWmCVu4ZPBgRoU1oL+65x14GTmm9etmR3e3aQfSQ63OwUrAZr5RSKUgTtErc3r0waRLbH2rLop1FGDwYknGgttsyZYIPPoC1a2HKr/mgTx/46Sf45ZeUD0YppTxMB4mpxLVqhcycyX3Z9xBQMD9//+3cDCcRePBB2LULdm6+RraHytsHNm2yLWqllPIhOkhM3bmdO+GLL1h33xusO5qfYcOcnX5sDIwdayt+9h+Sxs7B2rkTPvrIuaCUUsoDNEGrhPXrh6RLx4v/dqd+fahTx+mAoHJlew187FjYWqSBnYM1YICdeqWUUn5CE7SK3+bNMHMmS8u/w+7zdzF0qNMB/WfQIDuK/J13QEaOgmvX7NrRSinlJzRBq/j17Ut0xkw039CZl16CChWcDug/uXPDwIHw++/w4+Z77GjuL76Av/5yOjSllEoWOkhMxW3DBqhcmTkV+/Ls1n7s2AEhIU4HdaPISKhUCS5ehK2rL5K+YknIlw9Wr4YA/e6plPJ+OkhM3b4+fYjMkp2WGzvw1lvel5wBgoJsUbHQUBg+PhMMHw7r1sHkyU6HppRSSaYJWt1q1SqYN4+v83chOnNWevZ0OqD41akDzZrBkCGw/4EXoGZN6NEDzp51OjSllEoSTdDqVn36cC1bbt7c3o7u3SFnTqcDStjw4Xb6VZeurjlYp05Bv35Oh6WUUkmiCVrdaPlyWLSICVm6kTV/Jtq3dzqgxBUpYgdwf/cdLDlbCdq2hXHjYMsWp0NTSqk7poPE1H9EoHZtrmzaRc4zuxnzWQbatHE6KPdcuQJlythyoBsWnSSoTAk7YXrRImcrqyilVAJ0kJhyz++/w/LljAh+jyKlMtCypdMBuS99ehg50k7dHv9drv/mYM2e7XRoSil1RzyaoI0x2Ywxs4wx240x24wxNTx5PJUEItCrFxezF2LQiVcZMsSOkvYlTZrAo4/aNTTCmr4G5cvb+dGXLzsdmlLK102fbqezBATYn9One/yQnm5BfwT8KiKlgArANg8fT92p+fPh77/pF92bqg+kpXFjpwO6fcbYktwXLkCvfq45WPv321FkSil1p6ZPt2Nb9u+3jZn9++3vHk7SHrsGbYzJCvwD3C1uHkSvQTtEBKpU4cz+c9x1ejtLVgTz4INOB3XnOnSwiXrtWqg87DmYOxe2b7ejyZRS6naFhNikfLMiRWwhhiRw6hp0USAMmGKM2WCM+dwYkzGO4NoaY9YaY9aGhYV5MBwVr9mzYcMGul/uyxONfDs5A/TtC7lyQbt2IB+45mB17ux0WEopX3XgwO1tTyaeTNBBQGVgvIhUAi4Bt6xmICITRaSqiFTNnTu3B8NRcYqKgj59OJ69JJOvNmfIEKcDSrps2WDoUFi5Er7+o7AtXDJrFixe7HRoSilfc/UqBAfH/Vjhwh49tCcT9CHgkIj87fp9FjZhK2/y7bewZQudzvejxf8FUqaM0wElj5YtoWpV6NIFLrzeBYoWtUtfRUY6HZpSyleIwOuv29Xy0qS58bEMGWDwYI8e3mMJWkSOAQeNMSVdm+oCWz11PHUHIiOhXz8OZivHD0HP+lXxrYAAW1Ts6FEY/GE6OwdryxYYP97p0JRSvmL0aJg61V43mzzZXnM2xv6cOBGaN/fo4T1aqMQYUxH4HEgD7AVaiciZ+PbXQWIpbNo0aNmSpvxAiW5Pe9V6z8mlZUv4+mvYslko/nY9WLMGdu6061UqpVR8FiyAJ56w8ze/+85jK+QlNEhMK4mlVhERULIku05lp3rAWvbuM2TL5nRQye/oUShZEh5+GOYN3wb33gutWtlvv0opFZedO6FaNdtS/vNPW6LQQ7SSmLrVlCmwbx/tzw+kZy//TM5gl4fu0wd+/hl+3lvaDu3+/HO7LKVSSt3s7Flo1Mhec54716PJOTHagk6NwsOR4sXZdKYgT+VYyY6dhnTpnA7Kc65dsw3n6GjY9Mc50pYvAcWK2W/GWqdbKXVdVBQ8+ST89pud9fHQQwDkzQvHj9+6e548cOxY0g6pLWh1o88+wxw6RIdLgxg4yL+TM9gvwqNHw65dMHpKVjsH66+/UqRUn1LKh3TrBr/+Cp98EpOcIe7knND25KIt6NTm8mWkWDFWny3Jq/csYcM/hsBAp4NKGY0b2/Uzdm6PJn/T++HQIdixAzJndjo0pZTTXINmefttOwUkloQ62pKaQrUFrf7zySeYY8foHD6QocNST3IGO9MqIgK69Yg1B2vQIKfDUko5bdUqW1u7bl0YNcrpaGJogk5NLlwgeugwlgQ/TlDth2jQwOmAUlaxYrbi51dfwZ+R1e235VGj7IhNpVTqdOgQPP00FCoE33xzyzJ+P/3kUFxogk5dxowh4NRJukUMZNiw1Dk+qkcPKFDAFhWLGjQE0qWzq2sopVKfy5ftPOdLl2DOHMiZ84aHf/kFnnnGmdBAE3TqcfYs0cNH8HPgUxR5phrVqjkdkDMyZbKrT65fD5Pn57UVgubPt/OwlFKphwi0bm0/DKZPh7Jlb3h40SLbsC5bFu66K+6XyJPHsyFqgk4tRo0i4NxZ+sgA3n/f6WCc9fzzdoDme+/BmZfaQalS8O67tii+Uip1GDoUZs6E99+Hp5664aElS+xU6BIlbKI+ftzm85tvSZ1ilRhN0KnBqVNEfTiKWeYZqr9WkeLFnQ7IWcbAmDFw+jT0HZzGLh69e7edi6WU8n9z50LPnvDii3ZqVSwrVtip0HffbadD39TrnaI0QacGw4djLl1kaNp+9OnjdDDeoWJFeO01O91xU77H7RysgQPhyBGnQ1NKedLmzXaRiypVbFXBWINx/vrLlt8uVMhOyYyvazulaIL2d8ePE/XRWGbwAk90KUvevE4H5D0GDoQsWaB9e5APR9rVvbp2dTospZSnnDpl+64zZYIff4T06WMeWrMG6te3VcMWL8YrPis1Qfs5GTIUwq8yNntfOnd2OhrvkjOnnQa9ZAl8v+FuOwdr+nRbAlQp5V8iIqBZM9tL9uOPdjqHy4YN8Pjj9jNh8WLIn9+5MGPTBO3PDh8m+pPxTOMVmvcvQZYsTgfkfdq2tXW6O3WCy+17QMGCdkGNqCinQ1NKJacOHey38c8+g+rVYzb/+y88+qjtTVu82HZvewtN0H4setBgoiOimFaoN6+95nQ03ikoyBYVO3AAho3LCCNG2K/TkyY5HZpSKrl8+il8/DF06QIvvxyzeetWm5zTp7fJOSTEuRDj4tFa3MaYUOACEAVExldv9DqtxZ2M9u8nqlhxJka1JvuM8Tz/vNMBebcXXoDZs2HbVqFoq9qwZYtdXSN7dqdDU0olxbJlNgs/9pgtC+aqb7xjB9SqZceILVtmp1Q5wela3HVEpGJiyVklr8h+A4mICmBuuZ48+6zT0Xi/Dz6wf7edu7jmYJ05Y4uYKKV817598L//2Tq/M2bEJOfdu+GRR+wStIsXO5ecE6Nd3P5o924CvpjKBF6n06iCBOi7nKhChWzhkh9+gN/CKsDrr7vmYG1yOjSl1J24eNFOn4yKsi3nrFkBm7MfecTWJVq8GEqXdjjOBHj6o1uAhcaYdcaYtnHtYIxpa4xZa4xZGxYW5uFwUodrPfsTHp2GVbW68+ijTkfjOzp1gqJF7bSriN4D7B/0O+8kfT05pVTKio6GV16xl6q+/Zbr1ZkOHIA6dWzu/u03KFfO4TgT4ekE/aCIVAYaAG8ZYx6+eQcRmSgiVUWkau7cuT0cTiqwdStB305nHG/TbZQXTOTzIenS2cWttm6FT75xzcFauhRmzXI6NKXU7ejXzw4qGTnSXnsGDh+2yfnsWVu+s2JFJwN0j0cTtIgcdv08AcwGUukSDSnncrd+XCIje5p2pVIlp6PxPY0aQb169vLziSZtoUIF1xysy06HppRyx7ff2ipE//d/tgcMu/T7I49AWBgsWGCLiPmC20rQxpjsxph73dw3ozEm8/X7wOPA5tsPUblt40YyzPuOsQHv0n1ELqej8UnG2JLcly7Be70D7Rysgwdh2DCnQ1NKJWbDBrvO+wMP2DEkxnD8ONSta1vQv/xywxRor5dogjbGLDXGZDHG5ADWA58ZY0a68dp5gD+MMRuB1cDPIvJr0sJVCbnQoQ9nyMaFtp0oWtTpaHxXqVL2OvTkybAm3UN2DtawYXZ0iVLKOx0/bgeF5cplR3umTcvJk3aGVWioXVG2Zk2ng7w97rSgs4rIeaAp8IWIVAcSHXokIntFpILrVlZEBic1WJWANWvIvGQu49J0ouOAbE5H4/P69LGF8t95B6KHuuZgderkdFhKqbhcvQpNm8LJkzBnDuTJw+nT9vLz7t12EHetWk4HefvcSdBBxph8wLPAPA/Ho+7Qmbd7c5KcpO3aHh1rl3RZstjlYletgq+WFrRL082ebUeXKKW8hwi8+SasXAnTpkGlSpw9a2trb91qy27Xret0kHfGnQQ9AFgA7BaRNcaYu4Fdng1L3Q7540+yr17AJ5m78Vb3zE6H4zdeecVer+rWDc636WgXiG3f3hbdV0p5hzFj7PWo3r2hWTPOn7erUv37L3z/vR306asSTdAi8p2I3Csib7p+3ysi//N8aMpdJ9/szTHyUGDwW2TM6HQ0/iMgwI4RO3YMBo1IZ0ePbdtma/oqpZy3cCF07AhPPw39+nHxol3Ped06O5j7ySedDjBpEq3FbYxJB7QGygLprm8Xkf9L7mC0Fvfti1q0mMDH6zIo90d0O/wOwcFOR+R/WreGL7+ETf8KJTs8YbvSdu1yfjV3pVKzXbugWjVbBnDlSi4HZOKJJ2DFCpg5064s6QuSWov7SyAvUA9YBhTELoChnCbCyTd6c5CClBvTVpOzh7z/vl3tpv27Bhk12s6Jfu89p8NSKvU6d84WLQgKgrlzuRKYicaNbXL+8kvfSc6JcSdB3yMivYFLIjINaAj40Ewy/xX+46/k2bOSr0N60vi5dIk/Qd2RPHlsYaIFC2DerpLw7ruuOVhrnA5NqdQnKspOfdy9G2bNIjxvCE2bwu+/2z/LF190OsDk406Cvj4i5qwxphyQFdC+PaeJcKZdH/YRwgOf/x/GOB2Qf3v7bVtU/913IbxLb9u93a6drfmrlEo5PXrYiiPjxnGtRi2aNYNff4WJE6FFC6eDS17uJOiJxpjsQG9gLrAV+MCjUalEnf9qLvkOr2VOhT48VDeN0+H4veBg+Ogj2LsXRk3KYguX/P237U9TSqWML7+E4cPhrbeI+L/XeP55mDfPFg1r08bp4JJfnIPEjDH3AxtE5GpKBqODxNwUHc2RPBW5eDKcaxu2Uq5ikNMRpRpNm9qu7h3boin4XE1bXWznTjtxWinlOX//bauNPPAAkT8v4MUWwXz3nf3i7Cq57ZPuZJBYWmCuMaagMaZjXDfPhasSEzZ+FvlPbmLJw/00OaewDz+0l8C6dg+w8y9PnLCF+ZVSnnP4MDRpAvnzEzXzO1q0scl5xAjfTs6JiTNBi8gyoCVQBsgcz005ISqKiJ592WrK8MS055yOJtUpWhS6doUZM2BF+H12xZzRo2H7dqdDU8o/Xblik/PFi0T/OJfWXXPy9dd2doW/V99NdB50StIu7sQdGPwlhXu9wheNZvHKHK0X44TLl+2CGjlywLpfThBYqjjUqGEHruhoPaWSjwi89BLMmEH0Dz/y2s+N+PxzO6uib1+ng0seCXVxx5ugjTFjgXizt4gke8eCJuhERERwJFtpTl7NTMFj68iRy6PLeasEfPstPPccjB8Pr4ePhg4dbJH+Ro2cDk0p/zFsGHTvjgx+n7cO9WD8eFsWf+BA//kufKeFStYC67DVwypj62/vAioCOmzYATvem0b+y3vY9dIATc4Oa9YMate2HxanX3gLypSxSTo83OnQlPIPP/0EPXogzz9Ph+PdGT8eunTxr+ScGHdKfa4CHhSRSNfvwcAKEbk/uYPRFnT8JPwqx7KW4JjkpdTZVaTPkEr+h3qxTZugYkV44w0Y1+Q3u7bd4MFaZUyppNqyBe6/HylZkp4PLmfIRxlo3x5GjfK/5JzUUp/ZgdhzSDK5tqkUtKHdJPJdO8CJdgM1OXuJ8uXtKnfjx8PG3I/aOViDB8OhQ06HppTvOnUKGjVCMmXigxo/MuSjDLz5pn8m58S4k6CHAhuMMVONMdOA9cD77h7AGBNojNlgjNG1pO9QxPkrFJgyiLUZHuLRYY85HY6KpX9/yJ7dTvWQER/aymJduzodllK+KSICnn0WDh1iUsPZdB9XkFdftavKpbbkDO4tNzkFW3t7NvADUMNVk9td7YFtdxaeAlj9fxPIE3WUa70GEhiUCv+XerEcOWyjefly+HZ1SKw5WCucDk0p39OxIyxezE9PTeTVSffTsiVMmGCXfk2NPDrNyhhTEJgGDAY6ikiCq3PqNehbXTx2kfD8dxOa9V6qnP4tVX6L9HZRUVC1Kpw8CdvXXyZjletzsNZBYKDT4SnlGyZOhNdeY83Dnai2fATNm8O0af7/J5TUa9BJMRroCsS7ooAxpq0xZq0xZm1YWJiHw/E9f788jlwSRtphAzU5e6nAQNsFd+gQDB2TwZYb27jRfuAopRK3fDm89Rahpetz//JhPPssTJ3q/8k5MR5rQRtjngSeEJE3jTG1gc7agr49YbvPEVi8KPvy1KDKsZ+dDkcl4qWXYNYs2LpFuPvVujZJ79wJOXM6HZpS3mv/fqhalTOBOSl6fBWPPJ2Nb74h1axvn+QWtDHmQWNMK9f93MaYom48rSbQyBgTCswEHjHGfOVmzApY03w0OThDzo8HOB2KcsOwYXb9+I6djK3gf+4c9OnjdFhKea+LF6FRI65eiqD68bk89GQ2Zs5MPck5MYkmaGNMX6Ab0MO1KRhINNGKSA8RKSgiIcDzwGIReSkJsaYq+9adpubqkWwIeZqQ/1VxOhzlhgIFoFcvW1BswRHXHKwJE2xLWil1o+hoaNGC6E2baXzlG4rVL8GsWZBGy2DFcKcF/TTQCLgEICJH0MUyPG7jyyPIzAUKTurvdCjqNnToAMWKQfv2cK1n7DlY3lPzXimvMGAA/PADnWUEUY/W44cfIG1ap4PyLu4k6GtiL1QLgDEm4+0eRESWJnb9Wf1n429hPLptDJvLPEfuR8o7HY66DWnT2sWtduyAcdOz2yV3li+3xbuVUtasWdC/P1NpyYZa7zJnDqRP73RQ3sedBP2tMeZTIJsx5lXgN+Azz4aVuu1sPYz0XKHotH5Oh6LuQMOG0KCBXXHnWMPWULkydO4Mly45HZpSzvvnHyJfasFf1OCLBybw0zxDhgxOB+Wd3ClUMgKYBXwPlAT6iMhYTweWWi2bcYQnD3zMjqovkblqSafDUXfAGNuKDg+H93oHwpgxdg7WkCFOh6aUs06c4PLjjTl6NQeDKv/AnF/TkimT00F5L7dGcYvIIhHpIiKdRWSRp4NKraKj4eg7QwgikmLTdPSvLytRwl6PnjIFVgfXtHOwhg+HPXucDk0pZ1y7xpk6TSEsjB6l5zD997xk1tFMCYo3QRtjLhhjzsd3S8kgU4u54w7w9MmJhNZpRdoyxZwORyVRr16QNy+8/TZEDxlm54506uR0WEqlPBEON36T7Fv/pH+RKYz5ozLZsjkdlPeLN0GLSGYRyQJ8BHQHCgAFsVOuRqdIdKnI1atwpecgjIFik3s5HY5KBpkzwwcfwJo1MG1Rfujd2zUHa4HToSmVona9O44Cv05iYu6edFn7HDlyOB2Rb3BnPeiNIlIhsW3JITVXEpvaew/NB5XiaKPXKTxHL/H7i+hoePBB27O9c9NVstYsZ+sX/vuvTvhUqcLm0b9RqkN9lmV6kvK7fuCuvKl05Yt4JLWS2CVjTHPXspEBxpjmuOZEq+Rx7hykGz6A6IAgCo1/z+lwVDIKCLB1usPCYMCwWHOwxuqXMOX//vluFwU6PMueNKUpu/5LTc63yZ1/rReBZ4Hjrlsz1zaVTKZ0206zq19x5sW3MPnzOR2OSmZVqkCbNnYw97a7G8ITT9iFpI8dczo0pTxmw9JzpHu+MRIQQNalc8lbXEeE3S53plmFikhjEcklIrlFpImIhKZAbKnC0aOQ//P+RASlJ+/Ibk6Hozxk8GDImBHefRdk1Gg7B6tHj8SeppRP+mddFCceb06x6F1EfD2LvDXcWb5B3Uz7Gxz22TubeDZqJldebQ+5czsdjvKQ3LltZcOFC2HO1uJ2YfqpU+Hvv50OTalktXkzLHuwJ/UifubcwLHkea620yH5LI8tN3knUtsgsR07YGupptRP8zvpj+5Dhzb6t4gIqFTJFhTb+vcF0lcsCQULwqpV9mK1Uj5u2zYYe/9XfHL+Zc69+AZZp3/idEheL8nLTSrP+PyNdTzNbKLad9TknAoEB9tVKEND4cOJsedgTXM6NKWSbOdO6Pjgakaeb8PlarXJOvUjp0Pyee5Ms+olIoNc99OKyFVPBZOaWtCrVsGpGg2pk34VGY7uhaxZnQ5JpZBnnoH582HHdqHQ8zVdc7B26v8B5bP27IFnHzzCzyeqkiNfWtL8swZy5XI6LJ9wRy1oY0w3Y0wN4JlYm/9K7uBSIxGY9vpfNGQ+gd266AdzKjNihP0/0KWrgXHj7Bys/rqsqPJNoaHQoPYVPgtrQu50F0jzy1xNzskkoS7u7dgpVXcbY1YYYz4DchpjdAWHJPr5Z/jfxt5cznwXaTu3czoclcJCQqB7d/jmG1h2oTK8+qqdF711q9OhKXVbDh6EOrWFQcdfpXLUGgK//grK6xK5ySWhBH0WeA/YDdTGlvwE6G6MWenZsPxXVBR8324pj/I7aft0t3NvVKrTtSsUKQLt2kFkv0GQKRO0b2+b1kr5gMOHoU4dePnYcJ6NmA6DBkHjxk6H5VcSStD1gJ+BYsBIoDpwSURaicgDib2wMSadMWa1MWajMWaLMUb78IAvvxD+L7Q3V7LnJ/Ct150ORzkkfXr48EPYtAk+/cE1B+u332ytbqW83LFjULcuVDz8M/2vdYfnnoP3tApicnOrFjfQGqgMDAZ2AGdE5KlEnmeAjCJy0RgTDPwBtBeRVfE9x98HiV25Am0KL2T6yXrIuI8xb73pdEjKQSLw6KOwYQPs3BpJrkcruuZgbbUZXCkvdOKEbTmn3buN1QHVCSpVHFasgAwZnA7NJyV1mtUCEVkrIhOBQyLyINAqsSeJddH1a7Drlqr778aNFd452ZvwPIUxbVo7HY5ymDF22tX589C7f5CtBRoaapvWSnmhU6fsl8qze0/zZ46nCMqcAX78UZOzh7hT6rNrrF9buraddOfFXQts/AOcABaJyC1lk4wxbY0xa40xa8PCwtwK2hedOQPrB8yjOqtJN6g3pE3rdEjKC5QrB2+9BZ9+ChuyP2LnYL3/Phw44HRoSt3gzBl47DHYsyOSf0s/S/qTB2H2bChUyOnQ/NZtFSoRkY23uX+UiFTEriNdzRhTLo59JopIVRGpmtuPS10OfT+arpf6cLVQMWjRwulwlBfp3x9y5oR33gEZPsJu7NLF2aCUiuXcOahXD7ZsgS0NOpFzw+8wYQLUqOF0aH4tRSqJichZYAlQPyWO520OHoQDo3+gEv+QdnBfW1JKKZds2WDIEPjjD5ixsoidg/Xtt7B0qdOhKcWFC9CggR0rsbrt54TMGQMdOkCrRK90qiTyWC1uY0xuIEJEzhpj0gMLgWEiMi++5/jrILHWLaPo9MW9FL87muAdmyEw0OmQlJeJioLq1e3qZjv+uUKm+0pDliywfj0EBTkdnkqlLl2C+vXhr7/g9/5/UKv/I/DIIzBvnv6/TCZO1eLOBywxxvwLrMFeg443OfurzZvh6hffUEa2Evx+f03OKk6BgXaM2JEj8P6o9DBypGsO1qdOh6ZSqcuX4amnYOVK+PGj/dT6qCkULQozZ2pyTiG6mpWHNW4YyYe/liGkdHqC/t2gqxapBL3yiq0wtmWzcM8bj9kW9M6dWjpRpajwcGjUyE7N//qzSzw/tqadYfD331BSi0kmJ13NyiHLl0OO+V9yT/QuggYP0OSsEjVsGKRJAx07xZ6D1dvpsFQqcvUqNG0KixbB5M+jef6XFrY3Z8YMTc4pTDOGh4hAzy7XGBDYn6jKVe3XUaUSkS8f9OkDP/0Evxwoa2uBfvqpHaGjlIdduwbNmsEvv8DEidDy0CD4/nu7NGqDBk6Hl+pogvaQ2bOh7OrJFIraT+DggbYqhVJuaN8eSpSAd9+Faz362u7tdu20TrfyqIgIeOEF++Xw44/h1RzfQ9++dlpox45Oh5cqaYL2gMhI6NstnH5Bg5AaD9gJhEq5KU0aGD3aXnr+aFo2Owfrzz9tF6NSHhAZCS+/DD/8AKNGwZs1N9oBEfffb+c7awPDEZqgPWDyZHhk96fkjTyMGaStZ3X7GjSAJ5+0a2gcrd8Kqla1xUsuXkz8yUrdhqgoO6X5m29sT/a7L56wl+SyZ7cZO106p0NMtTRBJ7NLl2Bon8v0CR6C1Klj5wwqdQdGjbLXBLu/FxBrDtb7Toel/Eh0tF2O/KuvYPBg6NL+mi03e+KErbGdL5/TIaZqmqCT2ejR8L/jH5Mz4jhm4ECnw1E+7J577KW/L76Av6hhuxw//BB273Y6NOUHROCNN2DKFHup+b0eAm+/bVemmjLF9tooR+k86GR08iTcW/QC268VJcsj99mhkEolwcWLdmZLvnywes5RAkqXhFq17Egepe6QiB13+PHH0KOHbT2bj8fZje+9ZzeoFKHzoFPIoEHQ+tJHZLl2yl48VCqJMmWC4cNh3TqY8qtrDta8eTB/vtOhKR8lYntmPv4YOnVyJefFv9tpA089Bdrz5zW0BZ1M9u2D6iXOEBpQlAz1a8GcOU6HpPyECDz0kB3VvXPzNbI9VN5u3LzZDvlWyk0idi2WDz6wq6eNHg1m7x647z7In9/W9cySxekwUxVtQaeA3r2hg4wkw7Vz2npWycoYGDvWXkLp934aW2Fs1y77U6nb0LevTc5vvOFKzhfO2xHbxsDcuZqcvYwm6GSwYQP8Ov0kHQNH2zI8FSo4HZLyM5UqQdu2MG4cbClU33ZFDhhgl79Syg0DB9pbmzb2/5GJjoLmzWHHDpg1C+6+2+kQ1U00QSeD7t2hT7oPSBNxCfr1czoc5acGDbINnPbtQT4c6ZqD1d3psJQPGDrUDl9o0cJWjg0IAHr1suMZxoyBOnWcDlHFQRN0Ev32G2xceIw3osZhmjeHMmWcDkn5qVy5bAvo999h9qZ7oHNnOwdr5UqnQ1NebORIO1L7xRdh0iRXcv76a5u1X3vN9ncrr6SDxJIgOhqqVYM3d7Sn1ZWPMdu328mrSnlIZCRUrmwXudq25iLpK5WCvHntMoC61ri6ydixdjDYM8/YSrFBQcCaNfDww/bDa9EiHWjoMEcGiRljChljlhhjthpjthhj2nvqWE759ls4tu4QLcInYFq21OSsPC4oyPZI7t8PH3wSew7WFKdDU15mwgSbnJs0sQ3moCBsNbomTeyXulmzNDl7OY+1oI0x+YB8IrLeGJMZWAc0EZGt8T3Hl1rQ165B6dIw5OwbNLswCbNrFxQp4nRYKpV47jk76Hb7NqHIyw/D9u12ZHe2bE6HprzA5MnQujU0bGjLaadJA4SH2yI3W7bYyyL33ut0mAqHWtAiclRE1rvuXwC2AQU8dbyUNnEiRO/dxzPnPse0aaPJWaWo4cPtzJjOXVxzsE6f1gGKCoAvv7QjtevVi9VIFrHTAFavtjtocvYJKTJIzBgTAlQC/o7jsbbGmLXGmLVhYWEpEU6SnT9vZ7h8kncgJigQevZ0OiSVyhQubAf+zJoFi09XjDUHa4vToSkHzZgBLVvaQdmzZ8daiOrDD21iHjAAnn7ayRDVbfD4IDFjTCZgGTBYRH5IaF9f6eLu2xdmDNjJjoDSmHfescsOKZXCwsPtpIEMGeCf308RVLq4HUG2aJEucZoKzZoFzz8PNWvaSrAZM7oemD/frl36zDN2TUn9v+FVHKskZowJBr4HpieWnH3FsWP2y+ikwv0x6dLpPFTlmHTp7BSaLVtg/Lc57UTp33+3Fx1VqjJnDrzwAtx/P/z8c6zkvG2bfaBiRTuQUJOzT/HkKG4DTAK2ichITx0npQ0YAMXCt/DgwRl25Zc8eZwOSaVijRvDY4/ZIhRhT7e11xY7doTLl50OTaWQ+fNtAcMqVez9TJlcD5w5Y8t4pktn13aOydrKV3iyBV0TeBl4xBjzj+v2hAeP53E7d9rBYVOL9MVkygRdujgdkkrljLEluS9ehJ59XXOwDhywo8iU31u4EJo2td/Lfv01VintyEg71H//ftujUriwo3GqO+PJUdx/iIgRkXtFpKLr5tNr5PXqBfcF/0Olvd9Dhw6QM6fTISlF6dK2M+fzz2Fdplr2g3noUPvhrPzW4sW2B6VUKZuob5hh16WLHYswYYK9KK18klYSc9Pq1VC9Omwr0YhSJ1bY9SV1zqnyEufOQYkSUKwY/DnzIKZ0KXjiCfjuO6dDUx6wfDk0aGDXt1iyxJaBjXF9EvS77+oAVh+gy00mkQh06wb1sv1NqZ0/2W+nmpyVF8ma1Taa//oLpi8vBO+955qDtdjp0FQyW7nSfvcqXNiuBXBDcv7zT3j9dTswQS9z+DxtQbvhl1/sH8T+UvUofHK9bT3HjMRQyjtER9tRvIcOwY6N4WSu7pqDtWEDBAc7HZ5KBqtXw6OP2kqdy5ZBvnyxHjxwAO67z16IXr0asmd3LE7lPm1BJ0FUlG09P5tvBYW3L7S/aHJWXiggwBYVO3oUBo1IZ7s3t2yB8eOdDk0lg/Xr4fHHIXdu2zFyQ3K+dMlekA4PtzVgNTn7BU3QiZg+HTZtEsZm622/tr75ptMhKRWv6tVtJalRo2BnqUb2E71PH/CRKn0qbhs32l7rbNlsci5YMNaDItCqld1pxgw7alD5BU3QCQgPh9694fXii7lr2zJ7XS9DBqfDUipBQ4bYqa8dOhoYPdq2rrQcrc/avNl2a2fIYJPzLWX/Bw2ygwE/+MBei1N+QxN0Aj75BA4cEIYG9bJfWV991emQlEpU3ry2HO38+fDz3tJ2zcHPP7fLUiqvljevndse+1a+vF0LZckSO2r7BrNn2x6Sl1+GTp0ciVl5jg4Si8fZs3bKyhtF5jNoQ0P49FO7IIFSPuDaNahQwdar2LzyPGnLl7Cf7n/+qeUevVhCb80tH9X//gsPPABly9oRYzErYyhfooPE7sCwYXD6tPBeeG8oWtRe41HKR6RJYyuM7d4NoydniTUHa7rToankEBZmy3hmzWrLeGpy9ktBTgfgjQ4ftpfuRj38IxmWr4epU3WaivI5jz9uB/YOHAgvbXuFAtXGQ9eudmPmzE6Hl2pdvQp79sCuXbZ88K5d/913y7VrdmWq48dtxZIbhnMrf6IJOg59+4JERfPmsT62PFPz5k6HpNQdGTnSLknZrUcAX40da4d5Dxpku4iUx0RGQmho3En4wAE7Z/26XLmgeHE7SnvatEReWMTWdV2+3PaG3HefJ09DOUwT9E22brWrsk2p9y1pftkMX38NQfrPpHzT3XdD584weDC88UY1arZqZedgtW5tv3yqOxYdbYvCxE7C13/u3WuT9HVZstgkXKMGvPKK/acvXtzeYk9ZTjRBjx9vV+zp3h1efNEj56W8hw4Su0mTJrDs90jC8pYjKF2wnVsYoJfqle+6dMkuqJA7N6yZd5zA0iXgwQftwsEqQSK2J/nmVvCuXfb6fnj4f/umT/9f0i1e/L8kXKKE/bd3Z2xe3rz2eDfLkweOfb3YXrdo0MBedw4MTLbzVM5JaJCYNg1j+fNPu/D5nKbTCfphB3z/vSZn5fMyZoQRI+D552HSvDy07dfPrhn988/QsKHT4XmF06dvbQVfv1248N9+wcF2dkfx4jZXxk7C+fMn/ePi2LF4HtizB6o1g5Ilbde2JudUQVvQLiK2UXFwbwSh6UsRkC2rnTeqU1KUHxCB2rVt5c9dWyPIXrsCRETYKhhp0zodXoq4cOHWVvD1n6dP/7dfQACEhNzYCr5+v3BhB654nT9v+8aPHoU1a+w3BOU3tAXthrlz7Soxy16aSsBXe2HePE3Oym8YA2PGQOXK0GdgMGM/+sg2AUeNstcz/cSVK7eOkL7+8+bWacGCNvE2a3ZjMi5a1Iu+s0RHw0svwY4dsGCBJudUxmMtaGPMZOBJ4ISIlHPnOU61oCMj4d57ISDiKpuuFsfkz2/njGqCVn7mrbdgwgT45x8o3+dpW24sd244csQ2DwcP9vpZCxERdkG5uK4LHzx4Y0GPu+66sRv6emv4nnu8uGrv9Om2NOuBA3Y63PnzdhWUt992OjLlAU61oKcC44AvPHiMZDF1KmzbBhtaf4aZdNAueK7JWfmhAQNg5kxb/XNxo4cxP/5oJ/4D7N//X7U8h5N0VJRNtnFdF963zz5+XbZsNvk+9NCNSbh4cVvHw6dMn27fg8uX7e/nz9s+dV2dKlXy6DVoY0wIMM+bW9CXL7v+mAtcZsnBYpgSJWDpUk3Qym+NH28XZbuYK4SMJ/ffukOWLClS1lYELl6EM2fhzBk4c9p1/7QttRsVa65wcDBkz2bzVPYc9n6OHJAtO6RP50d/rhMn2qR8syJF7MRq5Xe8+hq0MaYt0BagcOHCKX78MWNs797yJuMxa47BN9/40V+7Urdq29aWlk+/8UDcO5w/b1eKSQbXv/5HR9ubyI0/A4CcrhvYP72AAAgIBBNk78csHHEJuAQcSpbQvNP1lvPNDsTzXim/lqpb0KdO2TEXj9W4yHdri0KlSrBwYYodXymnrFgBhR4OIYQ4WtB30Fo7dy7uqlm7dtnW8HWBgXYQVlzXhQsV0tlDhITYSw030xa03/LqFrST3n/fTr0YV2IM/HrSFi1WKhV46CFoETSYTyLbkpH/Wm2XyEDnM4MZH8dzLl+2xTniui584sR/+xljk22JEvDCCzcm45AQLWufoMGDb7wGDXY02+DBzsWkHJNqE/T+/TBuHLz+wjnyfDkCnnzS1ilWKpX4IrI5EcD79KQwBzhAYd5jMDPON6fB3FunKl0fS3Zdvnw28T711I1JuFgxXVzpjl0fnHd9FLePjKxXnuHJaVYzgNpALuA40FdEJiX0nJTs4n7lFfjuOzj2ej+yju4P69fbLm6lUgl3hlrkzBl3wY577tEFsZRKDo50cYvIC5567aTauBG++gr6vn2KrJNGQtOmmpyVimXVKpuMc+RwOhKlUq9U2cXdo4edH9k1cISd59G/v9MhKeVV9GqPUs5LdStBLFkCv/wCg9odJ/3EMXYFgXJuDTJXSimlUkyqStAi0K2bHWH62tlhdq24fv2cDkspR+TJc3vblVIpK1V1cc+aZReD+WbkYYLeG//fyulKpULxLm2olPIKqaYFHREB770HZcvCM7vetytk9OnjdFhKKaVUnFJNC/qzz2yRhd8m7Sfg9c+gdWtb0kgppZTyQqmiBX19oPbDD8Mjfw60E0B79nQ6LKWUUipeqaIFPXKkLUX467jdmBem2kVxCxVyOiyllFIqXn7fgj5xAoYPd9Ui+WkApEljJ0IrpZRSXszvE/TAgXDlCoxovc2WD3vrLcib1+mwlFJKqQT5dYLeswcmTIA2baDotH6QMSN07ep0WEoppVSi/O4adN68cPz4jdv++nQj8K0dGJY7tyNxKaWUUrfD71rQNydngP705SxZoVOnlA9IKaWUugN+l6BvVoW1NGEOH9IJsmd3OhyllFLKLX6foAfSm1Pk4CPaOx2KUkop5Ta/uwYd2wP8SQN+pSvDuEAWp8NRSiml3ObRFrQxpr4xZocxZrcxprsnjxXbC0xnHyH8wYNEEUAYuVLq0EoppVSy8FgL2hgTCHwMPAYcAtYYY+aKyFZPHRPg9SzTGXG+LRm5DEAg0YyjHemypAWae/LQSimlVLLxZAu6GrBbRPaKyDVgJtDYg8cDYHz2njHJ+bqMXGZ8dq29rZRSynd4MkEXAA7G+v2Qa9sNjDFtjTFrjTFrw8LCkn7UAwdub7tSSinlhRwfxS0iE0WkqohUzZ0cRUQKF7697UoppZQX8mSCPgzEXjKqoGubZw0eDBky3LgtQwa7XSmllPIRnkzQa4Dixpiixpg0wPPAXA8ez2reHCZOhCJF7LrPRYrY35vrADGllFK+w2OjuEUk0hjzNrAACAQmi8gWTx3vBs2ba0JWSinl0zxaqERE5gPzPXkMpZRSyh85PkhMKaWUUrfSBK2UUkp5IU3QSimllBfSBK2UUkp5ISMiTscQwxgTBuxPxpfMBZxMxtdzkr+ci7+cB+i5eCt/ORd/OQ/Qc0lIERGJs0qXVyXo5GaMWSsiVZ2OIzn4y7n4y3mAnou38pdz8ZfzAD2XO6Vd3EoppZQX0gStlFJKeSF/T9ATnQ4gGfnLufjLeYCei7fyl3Pxl/MAPZc74tfXoJVSSilf5e8taKWUUsonaYJWSimlvJBfJGhjTH1jzA5jzG5jTPc4Hk9rjPnG9fjfxpgQB8JMlBvn0dIYE2aM+cd1a+NEnO4wxkw2xpwwxmyO53FjjBnjOtd/jTGVUzpGd7hxHrWNMedivSd9UjpGdxljChljlhhjthpjthhj2sexj9e/L26eh0+8L8aYdMaY1caYja5z6R/HPr7y+eXOufjSZ1igMWaDMWZeHI+lzHsiIj59wy5luQe4G0gDbATK3LTPm8AE1/3ngW+cjvsOz6MlMM7pWN08n4eBysDmeB5/AvgFMMD9wN9Ox3yH51EbmOd0nG6eSz6gsut+ZmBnHP/HvP59cfM8fOJ9cf07Z3LdDwb+Bu6/aR+v//y6jXPxpc+wjsDXcf0/Sqn3xB9a0NWA3SKyV0SuATOBxjft0xiY5ro/C6hrjDEpGKM73DkPnyEiy4HTCezSGPhCrFVANmNMvpSJzn1unIfPEJGjIrLedf8CsA0ocNNuXv++uHkePsH173zR9Wuw63bzyF1f+Pxy91x8gjGmINAQ+DyeXVLkPfGHBF0AOBjr90Pc+scas4+IRALngJwpEp373DkPgP+5uh5nGWMKpUxoHuHu+fqCGq5uvV+MMWWdDsYdri65SthWTmw+9b4kcB7gI++Lqyv1H+AEsEhE4n1PvPjzC3DrXMA3PsNGA12B6HgeT5H3xB8SdGryExAiIvcCi/jvG5xyznpsLd0KwFjgR2fDSZwxJhPwPfCuiJx3Op47lch5+Mz7IiJRIlIRKAhUM8aUczikO+bGuXj9Z5gx5knghIisczoWf0jQh4HY38IKurbFuY8xJgjICpxKkejcl+h5iMgpEbnq+vVzoEoKxeYJ7rxvXk9Ezl/v1hOR+UCwMSaXw2HFyxgTjE1q00Xkhzh28Yn3JbHz8LX3BUBEzgJLgPo3PeQLn183iO9cfOQzrCbQyBgTir3U+Igx5qub9kmR98QfEvQaoLgxpqgxJg32gv3cm/aZC7Rw3X8GWCyuq/teJNHzuOlaYCPstTdfNRd4xTVq+H7gnIgcdTqo22WMyXv92pMxphr2b8orPzxdcU4CtonIyHh28/r3xZ3z8JX3xRiT2xiTzXU/PfAYsP2m3Xzh88utc/GFzzAR6SEiBUUkBPs5vFhEXrpptxR5T4KS+wVTmohEGmPeBhZgR0JPFpEtxpgBwFoRmYv9Y/7SGLMbO+Dneecijpub5/GOMaYREIk9j5aOBZwIY8wM7EjaXMaYQ0Bf7KARRGQCMB87Yng3cBlo5UykCXPjPJ4B3jDGRAJXgOe98cPTpSbwMrDJdZ0Q4D2gMPjU++LOefjK+5IPmGaMCcR+ifhWROb52ueXizvn4jOfYTdz4j3RUp9KKaWUF/KHLm6llFLK72iCVkoppbyQJmillFLKC2mCVkoppbyQJmillFLKC2mCVspHueb6zjTG7DHGrDPGzDfGlDDxrLzlxuu1NMbkT+44lVJ3RhO0Uj7IVYRjNrBURIqJSBWgB5AnCS/bEritBO2qoqSU8gD941LKN9UBIlxFOQAQkY2x16U1xrQEqorI267f5wEjgBXYQgtVsasNTcYW/q8KTDfGXAFqAGWAkUAm4CTQUkSOGmOWAv8ADwIzjDEHsAVcorCVxx722FkrlYpoglbKN5UD7rSYf0WggIiUAzDGZBORs65Kdp1FZK2r1vVYoLGIhBljngMGA//neo00IlLV9fxNQD0ROXy91KNSKuk0QSuV+uwF7jbGjAV+BhbGsU9J7JeARa6S1oFA7Jrc38S6/ycw1RjzLRDXAhxKqTugCVop37QFW286IZHcOM4kHYCInDHGVADqAa8Dz/Jfy/g6A2wRkRrxvPal63dE5HVjTHXsAvfrjDFVRMTrFqZQytfoIDGlfNNiIK0xpu31DcaYe7lxqchQoKIxJsAYUwio5tovFxAgIt8DvYDKrv0vAJld93cAuY0xNVzPCTbGlI0rEGNMMRH5W0T6AGE3xaCUukPaglbKB4mIGGOeBkYbY7oB4diE/G6s3f4E9gFbscv6rXdtLwBMMcZc/4Lew/VzKjAh1iCxZ4Axxpis2M+K0diW+82GG2OKY1vdvwMbk36GSildzUoppZTyQtrFrZRSSnkhTdBKKaWUF9IErZRSSnkhTdBKKaWUF9IErZRSSnkhTdBKKaWUF9IErZRSSnmh/wez1/efbIXq5gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "zeros=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==0]\n",
        "one=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==1]\n",
        "two=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==2]\n",
        "three=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==3]\n",
        "four=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==4]\n",
        "five=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==5]\n",
        "six=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==6]\n",
        "\n",
        "fzeros=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==0]\n",
        "fone=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==1]\n",
        "ftwo=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==2]\n",
        "fthree=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==3]\n",
        "ffour=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==4]\n",
        "ffive=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==5]\n",
        "fsix=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==6]\n",
        "\n",
        "\n",
        "\n",
        "y=[len(zeros)]+[len(one)]+[len(two)]+[len(three)]+[len(four)]\n",
        "x=[i for i in range(5)]\n",
        "\n",
        "fy=[len(fzeros)]+[len(fone)]+[len(ftwo)]+[len(fthree)]+[len(ffour)]\n",
        "\n",
        "y[:]=[x/5 for x in y]\n",
        "fy[:]=[x/2 for x in fy]\n",
        "\n",
        "\n",
        "fig = plt.figure()\n",
        "ax1 = fig.add_subplot(111)\n",
        "\n",
        "ax1.plot(x,y, c='b', marker=\"s\", label='Entre semana')\n",
        "ax1.plot(x,fy, c='r', marker=\"o\", label='Fin de semana')\n",
        "plt.legend(loc='upper left')\n",
        "plt.rcParams['figure.figsize'] = [8, 4]\n",
        "plt.title(\"\")\n",
        "plt.xlabel(\"Clusters\")\n",
        "plt.ylabel(\"# de días\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hv0tCfUNQd_-",
        "outputId": "47ada87a-f772-4731-8e87-c8eaf4570288"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "kmedoids = KMedoids(n_clusters=10,  metric='precomputed', method='pam', init='heuristic', max_iter=100, random_state=None).fit(1-corr_matrix)\n",
        "lab=kmedoids.labels_\n",
        "\n",
        "plt.figure(figsize=(12, 8))\n",
        "sn.scatterplot(x=[r[i].weekday() for i in range(len(learning_data))],y=[i for i in range(len(learning_data))], hue=lab, \n",
        "                palette=sn.color_palette('hls', 10),legend=False)\n",
        "plt.title('KMedoids with 10 Clusters')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JtgGI24XQd_-",
        "outputId": "cf8e4a7d-2748-4609-b25d-cfecedc6637e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "zeros=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==0]\n",
        "one=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==1]\n",
        "two=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==2]\n",
        "three=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==3]\n",
        "four=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==4]\n",
        "five=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==5]\n",
        "six=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==6]\n",
        "seven=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==7]\n",
        "eight=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==8]\n",
        "nine=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==9]\n",
        "\n",
        "fzeros=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==0]\n",
        "fone=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==1]\n",
        "ftwo=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==2]\n",
        "fthree=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==3]\n",
        "ffour=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==4]\n",
        "ffive=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==5]\n",
        "fsix=[i for i in range(len(learning_data)) if (r[i].weekday()==5 or r[i].weekday()==6) and lab[i]==6]\n",
        "fseven=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==7]\n",
        "feight=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==8]\n",
        "fnine=[i for i in range(len(learning_data)) if r[i].weekday()!=5 and r[i].weekday()!=6 and lab[i]==9]\n",
        "\n",
        "\n",
        "\n",
        "y=[len(zeros)]+[len(one)]+[len(two)]+[len(three)]+[len(four)]+[len(five)]+[len(six)]+[len(seven)]+[len(eight)]+[len(nine)]\n",
        "\n",
        "x=[i for i in range(10)]\n",
        "\n",
        "fy=[len(fzeros)]+[len(fone)]+[len(ftwo)]+[len(fthree)]+[len(ffour)]+[len(ffive)]+[len(fsix)]+[len(fseven)]+[len(feight)]+[len(fnine)]\n",
        "\n",
        "y[:]=[x/5 for x in y]\n",
        "fy[:]=[x/2 for x in fy]\n",
        "\n",
        "\n",
        "fig = plt.figure()\n",
        "ax1 = fig.add_subplot(111)\n",
        "\n",
        "ax1.plot(x,y, c='b', marker=\"s\", label='Entre semana')\n",
        "ax1.plot(x,fy, c='r', marker=\"o\", label='Fin de semana')\n",
        "plt.legend(loc='upper left')\n",
        "plt.rcParams['figure.figsize'] = [8, 4]\n",
        "plt.title(\"\")\n",
        "plt.xlabel(\"Clusters\")\n",
        "plt.ylabel(\"# de días\")\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
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      "file_extension": ".py",
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      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.10"
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    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
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  "nbformat": 4,
  "nbformat_minor": 0
}