{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a42ed887-61b6-43c3-88cc-ca790a45544f",
   "metadata": {},
   "source": [
    "# Introducción a la Ciencia de Datos utilizando Python\n",
    "## Proyecto Imágenes de Satélite\n",
    "\n",
    "### José Luis Villarreal Benítez"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a547e5fa-e8ce-4af6-9175-c0a65338c296",
   "metadata": {},
   "source": [
    "## Introducción"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec4f3a8e-6bdf-4223-9369-677566ca1823",
   "metadata": {},
   "source": [
    "##### La NASA ha liberado grandes cantidades de imágenes de satélite que conforman series de tiempo para cada pixel. Estos datos monitorean la superficie de la Tierra y se pueden contruir índices, como el NDVI o índice de vegetación que registra fenología o variantes de vegetación.\n",
    "##### Con estos datos es posible clasificar dichos pixeles y estudiar sus cambios en el tiempo, sobre todo cambios fenológicos, cambios por la dinámica del hábitat o cambios antropogénicos. Estos últimos son de gran importancia para monitorear reservas ecológicas.\n",
    "##### La NASA genera un producto libre de nubes con el índice NDVI y EVE, cada diesiseis días, a partir de las imágenes MODIS que recogen los satélites Aqua y Terra.\n",
    "##### Este proyecto es un análisis preliminar de clasificación y predicción de cambios en la vegetación a partir de los valores de pixel para el índice NDVI y las bandas rojo, Infrarojo cercano, azul e Infrarojo medio."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5f874d8-347a-43f7-a411-db3590e74e95",
   "metadata": {},
   "source": [
    "### 0. Preparación del entorno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7a48017a-a1ab-4dfb-b22a-db0d1af2b97f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bibliotecas\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "## Clustering\n",
    "from sklearn.cluster import KMeans \n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eac2be4-edc0-4b76-a9cb-59f66c0539a5",
   "metadata": {},
   "source": [
    "#### Carga de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "597084ac-747c-4d13-bb6e-4a2bca79f735",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location_tile</th>\n",
       "      <th>name</th>\n",
       "      <th>group</th>\n",
       "      <th>yearDay</th>\n",
       "      <th>ndvi</th>\n",
       "      <th>evi</th>\n",
       "      <th>QA</th>\n",
       "      <th>reliability</th>\n",
       "      <th>redReflectance</th>\n",
       "      <th>nirReflectance</th>\n",
       "      <th>bluereflectance</th>\n",
       "      <th>mirReflectance</th>\n",
       "      <th>viewZenithAngle</th>\n",
       "      <th>sunZenithAngle</th>\n",
       "      <th>relativeAzimuthAngle</th>\n",
       "      <th>compositeDayOfYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>49</td>\n",
       "      <td>4157</td>\n",
       "      <td>2106</td>\n",
       "      <td>4168</td>\n",
       "      <td>0</td>\n",
       "      <td>567</td>\n",
       "      <td>1702</td>\n",
       "      <td>263</td>\n",
       "      <td>540</td>\n",
       "      <td>4100</td>\n",
       "      <td>3044</td>\n",
       "      <td>13674</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>49</td>\n",
       "      <td>2781</td>\n",
       "      <td>1320</td>\n",
       "      <td>4168</td>\n",
       "      <td>0</td>\n",
       "      <td>587</td>\n",
       "      <td>1285</td>\n",
       "      <td>273</td>\n",
       "      <td>521</td>\n",
       "      <td>4101</td>\n",
       "      <td>3044</td>\n",
       "      <td>13668</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>49</td>\n",
       "      <td>3172</td>\n",
       "      <td>1519</td>\n",
       "      <td>4168</td>\n",
       "      <td>0</td>\n",
       "      <td>582</td>\n",
       "      <td>1380</td>\n",
       "      <td>290</td>\n",
       "      <td>551</td>\n",
       "      <td>4104</td>\n",
       "      <td>3044</td>\n",
       "      <td>13648</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>49</td>\n",
       "      <td>4832</td>\n",
       "      <td>2697</td>\n",
       "      <td>4168</td>\n",
       "      <td>0</td>\n",
       "      <td>568</td>\n",
       "      <td>2038</td>\n",
       "      <td>308</td>\n",
       "      <td>647</td>\n",
       "      <td>4107</td>\n",
       "      <td>3043</td>\n",
       "      <td>13618</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>49</td>\n",
       "      <td>6395</td>\n",
       "      <td>4069</td>\n",
       "      <td>4168</td>\n",
       "      <td>0</td>\n",
       "      <td>558</td>\n",
       "      <td>2890</td>\n",
       "      <td>294</td>\n",
       "      <td>764</td>\n",
       "      <td>4109</td>\n",
       "      <td>3043</td>\n",
       "      <td>13620</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   location_tile  name  group  yearDay  ndvi   evi    QA  reliability  \\\n",
       "0              0     0     20       49  4157  2106  4168            0   \n",
       "1              0     1     20       49  2781  1320  4168            0   \n",
       "2              0     3     20       49  3172  1519  4168            0   \n",
       "3              0     6     20       49  4832  2697  4168            0   \n",
       "4              0    10     20       49  6395  4069  4168            0   \n",
       "\n",
       "   redReflectance  nirReflectance  bluereflectance  mirReflectance  \\\n",
       "0             567            1702              263             540   \n",
       "1             587            1285              273             521   \n",
       "2             582            1380              290             551   \n",
       "3             568            2038              308             647   \n",
       "4             558            2890              294             764   \n",
       "\n",
       "   viewZenithAngle  sunZenithAngle  relativeAzimuthAngle  compositeDayOfYear  \n",
       "0             4100            3044                 13674                  63  \n",
       "1             4101            3044                 13668                  63  \n",
       "2             4104            3044                 13648                  63  \n",
       "3             4107            3043                 13618                  63  \n",
       "4             4109            3043                 13620                  63  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./datosEntrada/ModisLosTuxtlas03.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57bdb166-dbe3-4613-8b88-b356c175ed4f",
   "metadata": {},
   "source": [
    "### 1. Análisis exploratorio de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2b5d416a-f46b-4bd5-8017-0dcdc26f1a9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1097 entries, 0 to 1096\n",
      "Data columns (total 16 columns):\n",
      " #   Column                Non-Null Count  Dtype\n",
      "---  ------                --------------  -----\n",
      " 0   location_tile         1097 non-null   int64\n",
      " 1   name                  1097 non-null   int64\n",
      " 2   group                 1097 non-null   int64\n",
      " 3   yearDay               1097 non-null   int64\n",
      " 4   ndvi                  1097 non-null   int64\n",
      " 5   evi                   1097 non-null   int64\n",
      " 6   QA                    1097 non-null   int64\n",
      " 7   reliability           1097 non-null   int64\n",
      " 8   redReflectance        1097 non-null   int64\n",
      " 9   nirReflectance        1097 non-null   int64\n",
      " 10  bluereflectance       1097 non-null   int64\n",
      " 11  mirReflectance        1097 non-null   int64\n",
      " 12  viewZenithAngle       1097 non-null   int64\n",
      " 13  sunZenithAngle        1097 non-null   int64\n",
      " 14  relativeAzimuthAngle  1097 non-null   int64\n",
      " 15  compositeDayOfYear    1097 non-null   int64\n",
      "dtypes: int64(16)\n",
      "memory usage: 137.2 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dcc73254-8b56-4a9f-b1a9-dea64549d700",
   "metadata": {},
   "outputs": [
    {
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       "      <th>count</th>\n",
       "      <td>1097.0</td>\n",
       "      <td>1097.000000</td>\n",
       "      <td>1097.0</td>\n",
       "      <td>1097.0</td>\n",
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       "      <td>1097.000000</td>\n",
       "      <td>1097.000000</td>\n",
       "      <td>1097.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1793.437557</td>\n",
       "      <td>20.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>6997.863263</td>\n",
       "      <td>4141.395624</td>\n",
       "      <td>3034.330902</td>\n",
       "      <td>0.044667</td>\n",
       "      <td>460.874202</td>\n",
       "      <td>2744.186873</td>\n",
       "      <td>241.233364</td>\n",
       "      <td>798.585232</td>\n",
       "      <td>4143.678213</td>\n",
       "      <td>3099.401094</td>\n",
       "      <td>12888.428441</td>\n",
       "      <td>62.268915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1548.538834</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>856.633914</td>\n",
       "      <td>834.812609</td>\n",
       "      <td>970.749892</td>\n",
       "      <td>0.206667</td>\n",
       "      <td>71.967023</td>\n",
       "      <td>475.505323</td>\n",
       "      <td>31.593683</td>\n",
       "      <td>132.630958</td>\n",
       "      <td>310.803156</td>\n",
       "      <td>202.491222</td>\n",
       "      <td>3232.338291</td>\n",
       "      <td>2.132403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1238.000000</td>\n",
       "      <td>509.000000</td>\n",
       "      <td>2116.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>917.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>328.000000</td>\n",
       "      <td>2239.000000</td>\n",
       "      <td>3029.000000</td>\n",
       "      <td>-3463.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>410.000000</td>\n",
       "      <td>20.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>6653.000000</td>\n",
       "      <td>3682.000000</td>\n",
       "      <td>2120.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>405.000000</td>\n",
       "      <td>2472.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>730.000000</td>\n",
       "      <td>4123.000000</td>\n",
       "      <td>3034.000000</td>\n",
       "      <td>13614.000000</td>\n",
       "      <td>63.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1379.000000</td>\n",
       "      <td>20.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>7162.000000</td>\n",
       "      <td>4222.000000</td>\n",
       "      <td>2317.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>456.000000</td>\n",
       "      <td>2775.000000</td>\n",
       "      <td>238.000000</td>\n",
       "      <td>788.000000</td>\n",
       "      <td>4162.000000</td>\n",
       "      <td>3038.000000</td>\n",
       "      <td>13637.000000</td>\n",
       "      <td>63.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3003.000000</td>\n",
       "      <td>20.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>7597.000000</td>\n",
       "      <td>4754.000000</td>\n",
       "      <td>4168.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>509.000000</td>\n",
       "      <td>3059.000000</td>\n",
       "      <td>264.000000</td>\n",
       "      <td>869.000000</td>\n",
       "      <td>4211.000000</td>\n",
       "      <td>3042.000000</td>\n",
       "      <td>13653.000000</td>\n",
       "      <td>63.000000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>5681.000000</td>\n",
       "      <td>20.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>8466.000000</td>\n",
       "      <td>6249.000000</td>\n",
       "      <td>4169.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>723.000000</td>\n",
       "      <td>4121.000000</td>\n",
       "      <td>374.000000</td>\n",
       "      <td>1320.000000</td>\n",
       "      <td>5034.000000</td>\n",
       "      <td>4073.000000</td>\n",
       "      <td>13693.000000</td>\n",
       "      <td>63.000000</td>\n",
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      "text/plain": [
       "       location_tile         name   group  yearDay         ndvi          evi  \\\n",
       "count         1097.0  1097.000000  1097.0   1097.0  1097.000000  1097.000000   \n",
       "mean             0.0  1793.437557    20.0     49.0  6997.863263  4141.395624   \n",
       "std              0.0  1548.538834     0.0      0.0   856.633914   834.812609   \n",
       "min              0.0     0.000000    20.0     49.0  1238.000000   509.000000   \n",
       "25%              0.0   410.000000    20.0     49.0  6653.000000  3682.000000   \n",
       "50%              0.0  1379.000000    20.0     49.0  7162.000000  4222.000000   \n",
       "75%              0.0  3003.000000    20.0     49.0  7597.000000  4754.000000   \n",
       "max              0.0  5681.000000    20.0     49.0  8466.000000  6249.000000   \n",
       "\n",
       "                QA  reliability  redReflectance  nirReflectance  \\\n",
       "count  1097.000000  1097.000000     1097.000000     1097.000000   \n",
       "mean   3034.330902     0.044667      460.874202     2744.186873   \n",
       "std     970.749892     0.206667       71.967023      475.505323   \n",
       "min    2116.000000     0.000000      255.000000      917.000000   \n",
       "25%    2120.000000     0.000000      405.000000     2472.000000   \n",
       "50%    2317.000000     0.000000      456.000000     2775.000000   \n",
       "75%    4168.000000     0.000000      509.000000     3059.000000   \n",
       "max    4169.000000     1.000000      723.000000     4121.000000   \n",
       "\n",
       "       bluereflectance  mirReflectance  viewZenithAngle  sunZenithAngle  \\\n",
       "count      1097.000000     1097.000000      1097.000000     1097.000000   \n",
       "mean        241.233364      798.585232      4143.678213     3099.401094   \n",
       "std          31.593683      132.630958       310.803156      202.491222   \n",
       "min         167.000000      328.000000      2239.000000     3029.000000   \n",
       "25%         218.000000      730.000000      4123.000000     3034.000000   \n",
       "50%         238.000000      788.000000      4162.000000     3038.000000   \n",
       "75%         264.000000      869.000000      4211.000000     3042.000000   \n",
       "max         374.000000     1320.000000      5034.000000     4073.000000   \n",
       "\n",
       "       relativeAzimuthAngle  compositeDayOfYear  \n",
       "count           1097.000000         1097.000000  \n",
       "mean           12888.428441           62.268915  \n",
       "std             3232.338291            2.132403  \n",
       "min            -3463.000000           50.000000  \n",
       "25%            13614.000000           63.000000  \n",
       "50%            13637.000000           63.000000  \n",
       "75%            13653.000000           63.000000  \n",
       "max            13693.000000           63.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8a19f281-aa05-458a-aff4-d56a1dec190f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualización de la matriz de correlación\n",
    "numericalColumns = ['ndvi', 'evi', 'redReflectance', 'nirReflectance', 'bluereflectance','mirReflectance']\n",
    "\n",
    "corrMatrix = df.loc[:,numericalColumns].corr()\n",
    "\n",
    "sns.set(rc={'figure.figsize':(6,5)})\n",
    "sns.heatmap(corrMatrix, cmap = 'coolwarm', annot = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb909d50-13e7-4b49-ae72-1518734fbcda",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "cb0b88a0-68c3-4dec-8cbd-e3b95cc4b7d2",
   "metadata": {},
   "source": [
    "## 2. Segmentación por el Método de K-Means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b11f541f-2791-429e-8425-40ae592a8b2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ndvi</th>\n",
       "      <th>redReflectance</th>\n",
       "      <th>nirReflectance</th>\n",
       "      <th>bluereflectance</th>\n",
       "      <th>mirReflectance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4157</td>\n",
       "      <td>567</td>\n",
       "      <td>1702</td>\n",
       "      <td>263</td>\n",
       "      <td>540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2781</td>\n",
       "      <td>587</td>\n",
       "      <td>1285</td>\n",
       "      <td>273</td>\n",
       "      <td>521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3172</td>\n",
       "      <td>582</td>\n",
       "      <td>1380</td>\n",
       "      <td>290</td>\n",
       "      <td>551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4832</td>\n",
       "      <td>568</td>\n",
       "      <td>2038</td>\n",
       "      <td>308</td>\n",
       "      <td>647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6395</td>\n",
       "      <td>558</td>\n",
       "      <td>2890</td>\n",
       "      <td>294</td>\n",
       "      <td>764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1092</th>\n",
       "      <td>7790</td>\n",
       "      <td>393</td>\n",
       "      <td>3163</td>\n",
       "      <td>195</td>\n",
       "      <td>788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1093</th>\n",
       "      <td>7788</td>\n",
       "      <td>387</td>\n",
       "      <td>3114</td>\n",
       "      <td>194</td>\n",
       "      <td>771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1094</th>\n",
       "      <td>7820</td>\n",
       "      <td>375</td>\n",
       "      <td>3067</td>\n",
       "      <td>192</td>\n",
       "      <td>715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1095</th>\n",
       "      <td>7853</td>\n",
       "      <td>363</td>\n",
       "      <td>3022</td>\n",
       "      <td>194</td>\n",
       "      <td>650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1096</th>\n",
       "      <td>7845</td>\n",
       "      <td>355</td>\n",
       "      <td>2942</td>\n",
       "      <td>205</td>\n",
       "      <td>611</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1097 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ndvi  redReflectance  nirReflectance  bluereflectance  mirReflectance\n",
       "0     4157             567            1702              263             540\n",
       "1     2781             587            1285              273             521\n",
       "2     3172             582            1380              290             551\n",
       "3     4832             568            2038              308             647\n",
       "4     6395             558            2890              294             764\n",
       "...    ...             ...             ...              ...             ...\n",
       "1092  7790             393            3163              195             788\n",
       "1093  7788             387            3114              194             771\n",
       "1094  7820             375            3067              192             715\n",
       "1095  7853             363            3022              194             650\n",
       "1096  7845             355            2942              205             611\n",
       "\n",
       "[1097 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Se eliminarán las variables categóricas y el índice \"evi\", ya que es redundante, como lo muentra la matriz de correlación (alta correlación entre \"ndvi\" y \"evi\")\n",
    "dfCluster = df.drop(columns=[\"location_tile\",\"name\",\"group\",\"yearDay\",\"evi\",\"QA\",\"reliability\",\"viewZenithAngle\",\"sunZenithAngle\",\"relativeAzimuthAngle\",\"compositeDayOfYear\"])\n",
    "dfCluster"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ecead25-8d85-4a77-82a7-b12b7e5143fc",
   "metadata": {},
   "source": [
    "##### k-means no permite datos nulos, por lo que hay que localizar estos ejemplos y decidir su tratamiento (si son pocos, generalmente se eliminan). En este caso, no hay variables con datos faltantes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "70f6fdff-582a-4896-9019-8d9cf366ba7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datos perdidos en cada variable: \n",
      "ndvi               0\n",
      "redReflectance     0\n",
      "nirReflectance     0\n",
      "bluereflectance    0\n",
      "mirReflectance     0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(f\"Datos perdidos en cada variable: \\n{dfCluster.isnull().sum()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4489e019-cf25-45be-9adf-a90813b75a21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Escalamiento de datos con \"standard scale\"\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(dfCluster)\n",
    "scaled_data = scaler.transform(dfCluster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ee82bb4d-1b11-439f-9724-ce31110def0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-3.31782216,  1.4753174 , -2.19274562,  0.68926956, -1.95054893],\n",
       "       [-4.92484188,  1.75334922, -3.07010739,  1.00593289, -2.09386891],\n",
       "       [-4.46819602,  1.68384126, -2.87022881,  1.54426055, -1.8675742 ],\n",
       "       ...,\n",
       "       [ 0.96016711, -1.19378802,  0.679194  , -1.5590401 , -0.63049651],\n",
       "       [ 0.99870756, -1.3606071 ,  0.58451467, -1.49570743, -1.12080169],\n",
       "       [ 0.98936442, -1.47181983,  0.41619586, -1.14737777, -1.4149848 ]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "152dc489-0c03-4d58-b607-a76715694540",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.plotting.scatter_matrix(dfCluster);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e7ff195f-1c45-408a-a691-8413fef4b0ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "k=4 #Número de clusters a generar (default de 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4c6e2cc7-7140-49cb-8abc-5d78aad75b7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "modelo = KMeans (n_clusters=k)\n",
    "# maximo de iteraciones, valores iniciales, algoritmos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2f94b875-d796-493a-8e59-dc56e2f2a8c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=4)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelo.fit(scaled_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "02185ab7-5801-4c6d-a68c-85bb7b7d5583",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['ndvi', 'redReflectance', 'nirReflectance', 'bluereflectance',\n",
      "       'mirReflectance'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[-2.69867243,  1.44694681, -1.99570098,  1.29028364, -2.20209011],\n",
       "       [ 0.72717386, -0.86892355,  0.53317115, -0.88304153, -0.50137825],\n",
       "       [-1.06499415,  1.16675942, -0.9307667 ,  1.21176406,  1.23589043],\n",
       "       [-0.00571617,  0.28642448,  0.07350861,  0.30088404,  0.26827551]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(dfCluster.columns)\n",
    "centroides = modelo.cluster_centers_\n",
    "centroides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7ca81b61-a137-4937-9c28-32b96ba60cb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ndvi</th>\n",
       "      <th>red</th>\n",
       "      <th>nir</th>\n",
       "      <th>blue</th>\n",
       "      <th>mir</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T1</th>\n",
       "      <td>-2.698672</td>\n",
       "      <td>1.446947</td>\n",
       "      <td>-1.995701</td>\n",
       "      <td>1.290284</td>\n",
       "      <td>-2.202090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T2</th>\n",
       "      <td>0.727174</td>\n",
       "      <td>-0.868924</td>\n",
       "      <td>0.533171</td>\n",
       "      <td>-0.883042</td>\n",
       "      <td>-0.501378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T3</th>\n",
       "      <td>-1.064994</td>\n",
       "      <td>1.166759</td>\n",
       "      <td>-0.930767</td>\n",
       "      <td>1.211764</td>\n",
       "      <td>1.235890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T3</th>\n",
       "      <td>-0.005716</td>\n",
       "      <td>0.286424</td>\n",
       "      <td>0.073509</td>\n",
       "      <td>0.300884</td>\n",
       "      <td>0.268276</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ndvi       red       nir      blue       mir\n",
       "T1 -2.698672  1.446947 -1.995701  1.290284 -2.202090\n",
       "T2  0.727174 -0.868924  0.533171 -0.883042 -0.501378\n",
       "T3 -1.064994  1.166759 -0.930767  1.211764  1.235890\n",
       "T3 -0.005716  0.286424  0.073509  0.300884  0.268276"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coordenadas=(['ndvi','red','nir','blue','mir'])\n",
    "clases=(['T1','T2','T3','T3'])\n",
    "\n",
    "TablaCentroides = pd.DataFrame(centroides, columns=coordenadas, index=clases)\n",
    "TablaCentroides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5e9b41e3-2ef1-4868-a988-e0c4c3455721",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inertia 1962.5417707071922\n"
     ]
    }
   ],
   "source": [
    "print(\"Inertia\",modelo.inertia_) # Suma de cuadrados de las diferencias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "679bc7a5-fd59-44ad-904b-4f651f717f0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 1, 1], dtype=int32)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusterAsignado=modelo.labels_\n",
    "clusterAsignado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dd5ab171-5504-49ba-aa36-8ac3d8555d05",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfCluster[\"grupoAsignado\"] = clusterAsignado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "48557e05-b0ca-4690-9e9b-8140fd013ce0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ndvi</th>\n",
       "      <th>redReflectance</th>\n",
       "      <th>nirReflectance</th>\n",
       "      <th>bluereflectance</th>\n",
       "      <th>mirReflectance</th>\n",
       "      <th>grupoAsignado</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4157</td>\n",
       "      <td>567</td>\n",
       "      <td>1702</td>\n",
       "      <td>263</td>\n",
       "      <td>540</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2781</td>\n",
       "      <td>587</td>\n",
       "      <td>1285</td>\n",
       "      <td>273</td>\n",
       "      <td>521</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3172</td>\n",
       "      <td>582</td>\n",
       "      <td>1380</td>\n",
       "      <td>290</td>\n",
       "      <td>551</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4832</td>\n",
       "      <td>568</td>\n",
       "      <td>2038</td>\n",
       "      <td>308</td>\n",
       "      <td>647</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6395</td>\n",
       "      <td>558</td>\n",
       "      <td>2890</td>\n",
       "      <td>294</td>\n",
       "      <td>764</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7163</td>\n",
       "      <td>554</td>\n",
       "      <td>3443</td>\n",
       "      <td>262</td>\n",
       "      <td>853</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7400</td>\n",
       "      <td>539</td>\n",
       "      <td>3620</td>\n",
       "      <td>255</td>\n",
       "      <td>879</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7399</td>\n",
       "      <td>512</td>\n",
       "      <td>3464</td>\n",
       "      <td>262</td>\n",
       "      <td>860</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7229</td>\n",
       "      <td>492</td>\n",
       "      <td>3107</td>\n",
       "      <td>261</td>\n",
       "      <td>829</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7099</td>\n",
       "      <td>490</td>\n",
       "      <td>2910</td>\n",
       "      <td>258</td>\n",
       "      <td>809</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ndvi  redReflectance  nirReflectance  bluereflectance  mirReflectance  \\\n",
       "0  4157             567            1702              263             540   \n",
       "1  2781             587            1285              273             521   \n",
       "2  3172             582            1380              290             551   \n",
       "3  4832             568            2038              308             647   \n",
       "4  6395             558            2890              294             764   \n",
       "5  7163             554            3443              262             853   \n",
       "6  7400             539            3620              255             879   \n",
       "7  7399             512            3464              262             860   \n",
       "8  7229             492            3107              261             829   \n",
       "9  7099             490            2910              258             809   \n",
       "\n",
       "   grupoAsignado  \n",
       "0              0  \n",
       "1              0  \n",
       "2              0  \n",
       "3              0  \n",
       "4              3  \n",
       "5              3  \n",
       "6              3  \n",
       "7              3  \n",
       "8              3  \n",
       "9              3  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfCA = dfCluster.copy()\n",
    "dfCA.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "342c8c4a-16b1-44fe-ace6-1b91179c9d0e",
   "metadata": {},
   "source": [
    "#### Método *del codo* para aproximar el mejor número de *clusters*\n",
    "Donde se vea el _quiebre_ más pronunciado se determina el valor de $k$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4f17d8bd-111c-4a71-85d0-3158fd58ca59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "maximoClusters=8\n",
    "ks = range(1, maximoClusters) #rango del 1 al máximo\n",
    "\n",
    "kmeans = [KMeans(n_clusters=i, max_iter=500) for i in ks]  #Genera una lista con varios modelos\n",
    "score = [kmeans[i].fit(scaled_data).score(scaled_data) for i in range(len(kmeans))] #Calcula el score para cada modelo\n",
    "plt.figure (num=1, figsize=(8,3))\n",
    "plt.plot(ks,score) #Imprime k,score para cada uno de los modelos generados\n",
    "\n",
    "plt.xlabel('k (valores menores son mejor)')\n",
    "plt.ylabel('Score (cercano a 0 es mejor)')\n",
    "plt.title('Método del codo')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "319973c4-a5aa-4962-8e4a-378a1359f9df",
   "metadata": {},
   "source": [
    "## 3. Clasificación por técnica predictiva: Árbol de decisión"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f0b7edd-cea5-473a-8ba4-e30bfca5a33f",
   "metadata": {},
   "source": [
    "##### La Técnica de Árbol de decisión es una técnica utilizada para predecir valores discretos y en este sentido es una Técnica Predictiva de Clasificación. Para este caso se tomará el dataFrame de Pixeles de imágenes de satélite, agregando la calumna con los valores del grupo asignado (obtenidos mediante Técnica de K-Means)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8b23af1b-c6ee-4189-8f94-463c6efef615",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bibliotecas\n",
    "## Árboles  de decisión\n",
    "from sklearn import tree  # Para un árbol de clasificación\n",
    "from sklearn.tree import export_text # Para ver el árbol en modo texto\n",
    "\n",
    "## Matriz de confusión\n",
    "from sklearn.metrics import confusion_matrix #Para la matriz de confusión\n",
    "\n",
    "## Métricas\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score  # métricas de una regresión lineal\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e02ed01f-6c48-45c7-9f95-99e05854d4d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ndvi</th>\n",
       "      <th>redReflectance</th>\n",
       "      <th>nirReflectance</th>\n",
       "      <th>bluereflectance</th>\n",
       "      <th>mirReflectance</th>\n",
       "      <th>grupoAsignado</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ndvi</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.775732</td>\n",
       "      <td>0.845964</td>\n",
       "      <td>-0.702893</td>\n",
       "      <td>-0.044101</td>\n",
       "      <td>-0.074478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>redReflectance</th>\n",
       "      <td>-0.775732</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.426543</td>\n",
       "      <td>0.865399</td>\n",
       "      <td>0.463029</td>\n",
       "      <td>0.355487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nirReflectance</th>\n",
       "      <td>0.845964</td>\n",
       "      <td>-0.426543</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.425486</td>\n",
       "      <td>0.129581</td>\n",
       "      <td>-0.024750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bluereflectance</th>\n",
       "      <td>-0.702893</td>\n",
       "      <td>0.865399</td>\n",
       "      <td>-0.425486</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.475157</td>\n",
       "      <td>0.381526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mirReflectance</th>\n",
       "      <td>-0.044101</td>\n",
       "      <td>0.463029</td>\n",
       "      <td>0.129581</td>\n",
       "      <td>0.475157</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.524003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grupoAsignado</th>\n",
       "      <td>-0.074478</td>\n",
       "      <td>0.355487</td>\n",
       "      <td>-0.024750</td>\n",
       "      <td>0.381526</td>\n",
       "      <td>0.524003</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     ndvi  redReflectance  nirReflectance  bluereflectance  \\\n",
       "ndvi             1.000000       -0.775732        0.845964        -0.702893   \n",
       "redReflectance  -0.775732        1.000000       -0.426543         0.865399   \n",
       "nirReflectance   0.845964       -0.426543        1.000000        -0.425486   \n",
       "bluereflectance -0.702893        0.865399       -0.425486         1.000000   \n",
       "mirReflectance  -0.044101        0.463029        0.129581         0.475157   \n",
       "grupoAsignado   -0.074478        0.355487       -0.024750         0.381526   \n",
       "\n",
       "                 mirReflectance  grupoAsignado  \n",
       "ndvi                  -0.044101      -0.074478  \n",
       "redReflectance         0.463029       0.355487  \n",
       "nirReflectance         0.129581      -0.024750  \n",
       "bluereflectance        0.475157       0.381526  \n",
       "mirReflectance         1.000000       0.524003  \n",
       "grupoAsignado          0.524003       1.000000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfCA.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a27cdf21-295b-45bf-9f46-0e8de8313ae3",
   "metadata": {},
   "source": [
    "### Separación de variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "96aeb0b9-ac53-4452-92cb-7f7a534d9af2",
   "metadata": {},
   "outputs": [],
   "source": [
    "Xs = dfCA.iloc[ :  , [0,1,2,3,4]]   #Primeras 4 columnas (Xs)\n",
    "ys = dfCA.iloc[ :  , [5]]   #Quinta columna (ys)\n",
    "X_ent, X_pru, y_ent, y_pru = train_test_split (Xs,ys,test_size=0.33)  # Estoy separando el 67% y 33% (2 a 1) de los datos en 2 data sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c7f5d970-d28c-4f91-8eae-d641ccd87aff",
   "metadata": {},
   "outputs": [],
   "source": [
    "arbol = tree.DecisionTreeClassifier (max_depth=4,min_samples_leaf=2)\n",
    "# Entrenamiento\n",
    "arbol = arbol.fit(X_ent, y_ent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "862c0152-b90d-4823-8ae7-61d1c32a1323",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ndvi</th>\n",
       "      <th>redReflectance</th>\n",
       "      <th>nirReflectance</th>\n",
       "      <th>bluereflectance</th>\n",
       "      <th>mirReflectance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4157</td>\n",
       "      <td>567</td>\n",
       "      <td>1702</td>\n",
       "      <td>263</td>\n",
       "      <td>540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2781</td>\n",
       "      <td>587</td>\n",
       "      <td>1285</td>\n",
       "      <td>273</td>\n",
       "      <td>521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3172</td>\n",
       "      <td>582</td>\n",
       "      <td>1380</td>\n",
       "      <td>290</td>\n",
       "      <td>551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4832</td>\n",
       "      <td>568</td>\n",
       "      <td>2038</td>\n",
       "      <td>308</td>\n",
       "      <td>647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6395</td>\n",
       "      <td>558</td>\n",
       "      <td>2890</td>\n",
       "      <td>294</td>\n",
       "      <td>764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1092</th>\n",
       "      <td>7790</td>\n",
       "      <td>393</td>\n",
       "      <td>3163</td>\n",
       "      <td>195</td>\n",
       "      <td>788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1093</th>\n",
       "      <td>7788</td>\n",
       "      <td>387</td>\n",
       "      <td>3114</td>\n",
       "      <td>194</td>\n",
       "      <td>771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1094</th>\n",
       "      <td>7820</td>\n",
       "      <td>375</td>\n",
       "      <td>3067</td>\n",
       "      <td>192</td>\n",
       "      <td>715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1095</th>\n",
       "      <td>7853</td>\n",
       "      <td>363</td>\n",
       "      <td>3022</td>\n",
       "      <td>194</td>\n",
       "      <td>650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1096</th>\n",
       "      <td>7845</td>\n",
       "      <td>355</td>\n",
       "      <td>2942</td>\n",
       "      <td>205</td>\n",
       "      <td>611</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1097 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ndvi  redReflectance  nirReflectance  bluereflectance  mirReflectance\n",
       "0     4157             567            1702              263             540\n",
       "1     2781             587            1285              273             521\n",
       "2     3172             582            1380              290             551\n",
       "3     4832             568            2038              308             647\n",
       "4     6395             558            2890              294             764\n",
       "...    ...             ...             ...              ...             ...\n",
       "1092  7790             393            3163              195             788\n",
       "1093  7788             387            3114              194             771\n",
       "1094  7820             375            3067              192             715\n",
       "1095  7853             363            3022              194             650\n",
       "1096  7845             355            2942              205             611\n",
       "\n",
       "[1097 rows x 5 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af268a8e-7010-4e7b-8793-ab8dcdf2189b",
   "metadata": {},
   "source": [
    "#### Visualización del árbol en modo texto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "56de5244-b91f-4da8-b863-3c20154cc167",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- red <= 434.50\n",
      "|   |--- blue <= 243.50\n",
      "|   |   |--- mir <= 848.50\n",
      "|   |   |   |--- ndvi <= 6202.50\n",
      "|   |   |   |   |--- class: 0\n",
      "|   |   |   |--- ndvi >  6202.50\n",
      "|   |   |   |   |--- class: 1\n",
      "|   |   |--- mir >  848.50\n",
      "|   |   |   |--- blue <= 227.50\n",
      "|   |   |   |   |--- class: 1\n",
      "|   |   |   |--- blue >  227.50\n",
      "|   |   |   |   |--- class: 3\n",
      "|   |--- blue >  243.50\n",
      "|   |   |--- red <= 422.00\n",
      "|   |   |   |--- mir <= 590.50\n",
      "|   |   |   |   |--- class: 0\n",
      "|   |   |   |--- mir >  590.50\n",
      "|   |   |   |   |--- class: 1\n",
      "|   |   |--- red >  422.00\n",
      "|   |   |   |--- class: 3\n",
      "|--- red >  434.50\n",
      "|   |--- ndvi <= 6475.00\n",
      "|   |   |--- mir <= 677.50\n",
      "|   |   |   |--- class: 0\n",
      "|   |   |--- mir >  677.50\n",
      "|   |   |   |--- mir <= 820.50\n",
      "|   |   |   |   |--- class: 2\n",
      "|   |   |   |--- mir >  820.50\n",
      "|   |   |   |   |--- class: 2\n",
      "|   |--- ndvi >  6475.00\n",
      "|   |   |--- blue <= 224.50\n",
      "|   |   |   |--- mir <= 822.00\n",
      "|   |   |   |   |--- class: 1\n",
      "|   |   |   |--- mir >  822.00\n",
      "|   |   |   |   |--- class: 3\n",
      "|   |   |--- blue >  224.50\n",
      "|   |   |   |--- mir <= 1109.50\n",
      "|   |   |   |   |--- class: 3\n",
      "|   |   |   |--- mir >  1109.50\n",
      "|   |   |   |   |--- class: 2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Para visualizar el árbol en forma de texto requiero una lista con los nombres de las variables independientes\n",
    "atributos = [\"ndvi\",\"red\",\"nir\",\"blue\",\"mir\"]\n",
    "\n",
    "arbolTexto = export_text (arbol, feature_names=atributos)\n",
    "print(arbolTexto)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "276e3350-4c3a-42c1-a744-8a050c4304a8",
   "metadata": {},
   "source": [
    "#### Visualización del árbol en modo gráfico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "217aa072-92a9-4bbe-a9df-f1d40e905d6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "tree.plot_tree(arbol, fontsize=11)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83462855-d086-4de3-ac38-e199179e8b00",
   "metadata": {},
   "source": [
    "### Prueba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0930075b-d751-4524-83c7-1f11b22aa79c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Para probar tengo un conjunto de datos con X_pru y y_pru\n",
    "y_pred = arbol.predict(X_pru)\n",
    "y_pru;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c41a329-cb22-444b-aeb9-aa734d5dec45",
   "metadata": {},
   "source": [
    "### Matriz de confusión"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "64818d78-ea7f-4ae6-b11e-0ac571b1126a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3], dtype=int32)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clasesA = arbol.classes_\n",
    "clasesA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dcb97843-3f1d-4ae0-94bd-4582e3cf5ab7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0    1   2    3\n",
       "0  17    0   0    1\n",
       "1   1  144   0    9\n",
       "2   0    0  56   14\n",
       "3   1    9   7  104"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrizConfusion = pd.DataFrame (confusion_matrix(y_pru,y_pred), columns=clasesA, index=clasesA)\n",
    "matrizConfusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "855f1c6d-e72f-435b-a0b8-b6bd1b210b3f",
   "metadata": {},
   "source": [
    "#### La matriz de confusión tiene falsos positivos y falsos negativos; sin embargo, esta situación no es encesariamente una mala clasificación, ya que depende del objetivo del análisis. Por ejemplo, en algunos casos es preferible equivocarnos en los falsos positivos o viceverza."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b781588-95d1-42bf-b882-a919e8f90cdd",
   "metadata": {},
   "source": [
    "## 4. Técnica predictiva de regresión"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d9facf45-3487-46db-a452-a19824b41f87",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Regresión lineal\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "169a0d04-6213-415f-a52d-f4482ae6c558",
   "metadata": {},
   "source": [
    "#### En esta sección se realizará un análisis predictivo con la Técnica de Regresión Múltiple. Se partirrá el dataSet en datos de entrenamiento y datos de prueba. El objetivo es predecir el índice de vegetación (NDVI) a partir de los valores de las bandas de la imagen de satétile."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "833b93af-5109-4479-b47e-35321b0789d4",
   "metadata": {},
   "source": [
    "### Separación en datos de entrenamiento y de prueba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "df5e7868-a952-4928-80fa-ef2666cbbc14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ndvi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>6840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>826</th>\n",
       "      <td>5209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>6754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>7050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>7763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>6197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>7723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1023</th>\n",
       "      <td>5772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>7301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>690</th>\n",
       "      <td>7709</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>767 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ndvi\n",
       "718   6840\n",
       "826   5209\n",
       "46    6754\n",
       "74    7050\n",
       "279   7763\n",
       "...    ...\n",
       "610   6197\n",
       "984   7723\n",
       "1023  5772\n",
       "102   7301\n",
       "690   7709\n",
       "\n",
       "[767 rows x 1 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cuantosRegistros = len(dfCA.index)  # Cuántos registros de procesarán (TODOS en este caso)\n",
    "\n",
    "Xs = dfCA.iloc[ : cuantosRegistros+1 , [1,2,3,4]]   # columnas 1 a 5 (xs)\n",
    "ys = dfCA.iloc[ : cuantosRegistros+1 , [0]]   # primera columna (ys)\n",
    "\n",
    "# Separaciión de datos de entrenamiento y prueba\n",
    "\n",
    "x_ent, x_pru, y_ent, y_pru = train_test_split (Xs, ys, test_size=0.3, random_state=19713)\n",
    "x_ent;\n",
    "y_ent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "13085d24-5556-44b1-b75f-7a890582ffba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg= linear_model.LinearRegression() # Creación del modelo\n",
    "reg.fit(x_ent, y_ent) # Entrenamiento"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "094bbc46-d40b-40e8-a000-5362f8078899",
   "metadata": {},
   "source": [
    "#### Coeficientes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "989c2ecc-853b-4361-a32f-806e04763500",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6921.76771403]\n",
      "[[-6.39365781  0.97561303 -1.86279797  1.00501146]]\n"
     ]
    }
   ],
   "source": [
    "print(reg.intercept_) # beta 0\n",
    "print(reg.coef_) # beta_1 en adelande"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "03bc265a-6993-435c-b566-7cfdd5680f8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NDVIpredicha = -6.39red + 0.98nir -1.86blue + 1.00mir\n"
     ]
    }
   ],
   "source": [
    "print (\"NDVIpredicha = -6.39red + 0.98nir -1.86blue + 1.00mir\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "904c5906-4745-414c-957b-b31b4e44b97b",
   "metadata": {},
   "source": [
    "#### Predicción"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7d9e750f-0204-4b95-8488-6bfa2ebc6588",
   "metadata": {},
   "outputs": [],
   "source": [
    "yPredicha = reg.predict (x_pru) # Datos de prueba (el 30% resultante del split)\n",
    "yPredicha;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "73c2cdea-f3a2-4da5-bcbd-e64c65255d1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9325811590624495"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R2 = r2_score(y_pru, yPredicha)\n",
    "R2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7492cca-436f-4d0c-9aed-aadc5aa1801f",
   "metadata": {},
   "source": [
    "### Inspección gráfica del error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1b4e478b-869d-4cce-9a98-51320ff757fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure (num=1, figsize=(18,10))\n",
    "plt.scatter(yPredicha, (yPredicha - y_pru),  color='black')\n",
    "\n",
    "media = np.full(len(yPredicha),yPredicha.mean()) \n",
    "plt.plot(yPredicha, media-media, color=\"red\", linewidth=3)   \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69b0437d-e024-4e18-9787-773ae51477dd",
   "metadata": {},
   "source": [
    "### 5. Resumen del procedimiento"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0db05f8-6dd8-45b8-803b-f7027c8051f9",
   "metadata": {},
   "source": [
    "##### Para clasificar pixeles extraidos de imágenes de satélite MODIS, con valores de las bandas red, nir, blue, mir y el índice de vegetación ndvi, se realizó un análisis de clustering usando la Técnica de K-Means.\n",
    "##### Antes de proceder, se realizó una inspección de los datos para verificar que cumplieran con el supuesto de distribución normal que esatblece K-Means.\n",
    "##### A través de la inspección de la matriz de correlación, se determinó eliminar una variable altamente correlacionada con el índice \"ndvi\", esta fue el \"eve\".\n",
    "##### Se procedió a realizar la clasificación con la técnica no supervisada y mediante el Método del codo se determinó por inspecciñon que un buen valor de K es 4.\n",
    "##### Con estos valores asigandos por el algoritmo, se construyó un nuevo dataFrame para alimentar la Técnica de Árboles de Decisión, para realizar una predicción de clasificación.\n",
    "##### Este análisis de realizó con separación en datos de entrenamiento y datos de prueba. Con cuyo procedimiento se obtuvo la Matriz de confusión para evaluar el modelo.\n",
    "##### En el resultado del árbol se observó que las bandas \"red\", \"blue\" y \"mir\" son determinantes en la clasificción, pero al final tenemos que \"ndvi\" define la clase, se procedió a realizar un análisis de regresión múltiple para construir un modelo predictivo del índice de vegetación; el cual es un factor ecológico de interés para comprender la dinámica de la vegetación.\n",
    "##### Este modelo se evaluó con datos de prueba y se inspeccionó el comportamiento del error a lo largo de la variable dependiente; con lo cual se determinó, que aunque el ajuste es altamente significativo (R^2 = 0.93), el error no se distribuye aleatóriamente y es recomendable realizar transformaciones a las variables. Es probable que este comportamiento del error se deba a la baja sensibilidad del sensor a valores bajos y a su saturación."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2082e94-4410-4007-aed0-a61384b2c0d4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "pygments_lexer": "ipython3",
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