{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wtWF1n8HOTFV"
   },
   "source": [
    "<table>\n",
    "<table border = 5  align = center bgcolor=\"white\" cellspacing=\"10px\">\n",
    "\n",
    "<tr>\n",
    "<td><a href=\"https://drive.google.com/file/d/15psUSLE0MzkdcMwiJ5xXCl4ycKDDOJrq/view?usp=sharing\"> <img alt=\"Colaboratory logo\" width=\"150px\"  src=\"https://miro.medium.com/max/986/1*S2AyJcdw8EPcn7gwDVSBCA.png\" align=\"left\" hspace=\"10px\" vspace=\"0px\" /> </a> </td>\n",
    "   \n",
    "   \n",
    "<td></td>\n",
    "    \n",
    "<td><a href=\"https://mybinder.org/v2/git/https%3A%2F%2Fgitmilab.redclara.net%2FhalleyUIS%2Fmoncora.git/0936d792b8e87847f8624bb906f74cd24badf030?urlpath=lab%2Ftree%2FBook%2FJupyter_Notebooks%2FapiMakeSens.ipynb\"><img src=\"https://mybinder.org/static/images/logo_social.png\" alt=\"Alternative text\" width=\"150px\"  /></a>\n",
    "</td>\n",
    "\n",
    "</tr>\n",
    "\n",
    "</table>\n",
    "    \n",
    "<br>\n",
    "\n",
    "# Descubriendo los datos de las estaciones \n",
    "## Uso de la API MakeSens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vh3vxnBcOg1s"
   },
   "source": [
    "Instalar Librerias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "2qUuqqUrOist"
   },
   "outputs": [],
   "source": [
    "#!pip install APIMakeSens\n",
    "#!pip install tk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kqjOySK5OzWd"
   },
   "source": [
    "Cargar librería *MakeSens*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "METaVqUqOPPp"
   },
   "outputs": [],
   "source": [
    "# Makesens\n",
    "from MakeSens import MakeSens\n",
    "\n",
    "# Pandas \n",
    "import pandas as pd \n",
    "\n",
    "#  datetime para manejo de fechas\n",
    "import datetime\n",
    "\n",
    "# pytz para manejo de zonas horarias\n",
    "import pytz\n",
    "\n",
    "import pylab as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AHgUeh7F_-py"
   },
   "source": [
    "Definimos el  rango de fechas para descargar los datos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "LulIK6c9_-GT"
   },
   "outputs": [],
   "source": [
    "#Vamos a descargar los datos desde hoy hasta 240 horas atras. Primero definimos la fecha de fin e inicio de la descarga\n",
    "fechaFin = datetime.datetime.now(pytz.timezone('America/Bogota')).strftime('%Y-%m-%d %H:%M:%S')\n",
    "fechaInicio = (datetime.datetime.now(pytz.timezone('America/Bogota'))-datetime.timedelta(hours=240)).strftime('%Y-%m-%d %H:%M:%S')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora usamos la API de Makesens para descargar los datos (preguntale a tu instructor que otras alternativas tienes!)\n",
    ">Para mayor información sobre las funciones de la API de makesens, puedes visitar su [página web](https://docs.makesens.co/help/api-sdk/makesensapi-en-python/makesens.download_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "04N3QvHqRcJ2",
    "outputId": "4d37f2df-91c3-4925-8540-deeaffb1ebca"
   },
   "outputs": [],
   "source": [
    "data = MakeSens.download_data('mE1_00004', fechaInicio, fechaFin, '1H')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exploremos los datos del DataFrame!! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>ts</th>\n",
       "      <th>humedad</th>\n",
       "      <th>humedad2</th>\n",
       "      <th>iluminancia</th>\n",
       "      <th>pm10_1</th>\n",
       "      <th>pm10_1_AE</th>\n",
       "      <th>pm10_2</th>\n",
       "      <th>pm10_2_AE</th>\n",
       "      <th>pm1_1</th>\n",
       "      <th>pm1_1_AE</th>\n",
       "      <th>...</th>\n",
       "      <th>pm_n_10_0_2</th>\n",
       "      <th>pm_n_1_0_1</th>\n",
       "      <th>pm_n_1_0_2</th>\n",
       "      <th>pm_n_2_5_1</th>\n",
       "      <th>pm_n_2_5_2</th>\n",
       "      <th>pm_n_5_0_1</th>\n",
       "      <th>pm_n_5_0_2</th>\n",
       "      <th>presion</th>\n",
       "      <th>temperatura</th>\n",
       "      <th>temperatura2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-08-16 17:00:00</td>\n",
       "      <td>38.897320</td>\n",
       "      <td>41.228504</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>13.700000</td>\n",
       "      <td>13.700000</td>\n",
       "      <td>14.200000</td>\n",
       "      <td>14.200000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>66.700000</td>\n",
       "      <td>71.300000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>969.452265</td>\n",
       "      <td>28.068901</td>\n",
       "      <td>28.687991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-08-16 18:00:00</td>\n",
       "      <td>41.789214</td>\n",
       "      <td>44.269801</td>\n",
       "      <td>39.071429</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.285714</td>\n",
       "      <td>10.285714</td>\n",
       "      <td>7.071429</td>\n",
       "      <td>7.071429</td>\n",
       "      <td>...</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>49.785714</td>\n",
       "      <td>54.071429</td>\n",
       "      <td>3.857143</td>\n",
       "      <td>4.285714</td>\n",
       "      <td>1.428571</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>967.443556</td>\n",
       "      <td>28.146178</td>\n",
       "      <td>28.780804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-08-16 19:00:00</td>\n",
       "      <td>42.114675</td>\n",
       "      <td>44.522121</td>\n",
       "      <td>41.428571</td>\n",
       "      <td>8.428571</td>\n",
       "      <td>8.428571</td>\n",
       "      <td>7.571429</td>\n",
       "      <td>7.571429</td>\n",
       "      <td>5.428571</td>\n",
       "      <td>5.428571</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.071429</td>\n",
       "      <td>39.571429</td>\n",
       "      <td>5.142857</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.285714</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>966.713915</td>\n",
       "      <td>28.195374</td>\n",
       "      <td>28.835164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-08-16 20:00:00</td>\n",
       "      <td>38.560408</td>\n",
       "      <td>40.723931</td>\n",
       "      <td>41.857143</td>\n",
       "      <td>5.857143</td>\n",
       "      <td>5.857143</td>\n",
       "      <td>5.428571</td>\n",
       "      <td>5.428571</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>...</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>26.571429</td>\n",
       "      <td>21.428571</td>\n",
       "      <td>3.428571</td>\n",
       "      <td>2.285714</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>966.744908</td>\n",
       "      <td>28.157669</td>\n",
       "      <td>28.842603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-08-16 21:00:00</td>\n",
       "      <td>38.032839</td>\n",
       "      <td>40.042586</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>6.045455</td>\n",
       "      <td>6.045455</td>\n",
       "      <td>5.363636</td>\n",
       "      <td>5.363636</td>\n",
       "      <td>4.181818</td>\n",
       "      <td>4.181818</td>\n",
       "      <td>...</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>22.818182</td>\n",
       "      <td>3.454545</td>\n",
       "      <td>2.545455</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>982.620261</td>\n",
       "      <td>27.273129</td>\n",
       "      <td>27.939668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   ts    humedad   humedad2  iluminancia     pm10_1  \\\n",
       "0 2023-08-16 17:00:00  38.897320  41.228504     6.000000  13.700000   \n",
       "1 2023-08-16 18:00:00  41.789214  44.269801    39.071429  10.000000   \n",
       "2 2023-08-16 19:00:00  42.114675  44.522121    41.428571   8.428571   \n",
       "3 2023-08-16 20:00:00  38.560408  40.723931    41.857143   5.857143   \n",
       "4 2023-08-16 21:00:00  38.032839  40.042586    42.000000   6.045455   \n",
       "\n",
       "   pm10_1_AE     pm10_2  pm10_2_AE      pm1_1   pm1_1_AE  ...  pm_n_10_0_2  \\\n",
       "0  13.700000  14.200000  14.200000  10.000000  10.000000  ...     0.200000   \n",
       "1  10.000000  10.285714  10.285714   7.071429   7.071429  ...     0.142857   \n",
       "2   8.428571   7.571429   7.571429   5.428571   5.428571  ...     0.000000   \n",
       "3   5.857143   5.428571   5.428571   3.714286   3.714286  ...     0.285714   \n",
       "4   6.045455   5.363636   5.363636   4.181818   4.181818  ...     0.363636   \n",
       "\n",
       "   pm_n_1_0_1  pm_n_1_0_2  pm_n_2_5_1  pm_n_2_5_2  pm_n_5_0_1  pm_n_5_0_2  \\\n",
       "0   66.700000   71.300000    5.000000    7.400000    1.800000    2.000000   \n",
       "1   49.785714   54.071429    3.857143    4.285714    1.428571    1.000000   \n",
       "2   40.071429   39.571429    5.142857    3.000000    1.285714    0.571429   \n",
       "3   26.571429   21.428571    3.428571    2.285714    0.857143    0.857143   \n",
       "4   26.000000   22.818182    3.454545    2.545455    1.000000    0.818182   \n",
       "\n",
       "      presion  temperatura  temperatura2  \n",
       "0  969.452265    28.068901     28.687991  \n",
       "1  967.443556    28.146178     28.780804  \n",
       "2  966.713915    28.195374     28.835164  \n",
       "3  966.744908    28.157669     28.842603  \n",
       "4  982.620261    27.273129     27.939668  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. ¿Cuál es el indice de los datos?\n",
    "2. ¿Cuántas columnas tiene?  ¿Qué tipo de datos tiene cada columna?\n",
    "3. ¿Qué significa cada columna?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['humedad2', 'irradiancia', 'pm_n_2_5_2', 'pm25_1', 'pm25_2',\n",
       "       'pm_temperatura', 'pm_n_2_5_1', 'iluminancia', 'pm1_1', 'pm1_2',\n",
       "       'humedad', 'temperatura', 'presion', 'uv', 'pm_n_10_0_2',\n",
       "       'pm_n_10_0_1', 'pm_n_0_5_2', 'temperatura2', 'pm_n_0_3_2',\n",
       "       'pm_n_0_5_1', 'pm_n_0_3_1', 'pm_n_5_0_1', 'pm_humedad',\n",
       "       'pm_n_5_0_2', 'pm10_2', 'pm10_1', 'pm_n_1_0_1', 'pm_n_1_0_2',\n",
       "       'precipitacion'], dtype=object)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Columnas del paquete de datos que descargamos\n",
    "data.columns.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Mira la página de [makesens](https://docs.makesens.co/productos/minieva/acceso-a-los-datos/campos-de-datos) para entender que representa cada columna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "humedad2          67.164636\n",
       "pm_n_2_5_2        49.521739\n",
       "pm25_1            39.695652\n",
       "pm25_2            41.086957\n",
       "pm1_1_AE          20.962963\n",
       "pm10_2_AE          47.26087\n",
       "pm_n_2_5_1        63.869565\n",
       "iluminancia       54.483871\n",
       "pm1_1             21.962963\n",
       "pm25_1_AE         34.521739\n",
       "pm1_2             22.235294\n",
       "humedad           65.776849\n",
       "temperatura       35.909904\n",
       "presion         1019.413362\n",
       "pm_n_10_0_2        5.952381\n",
       "pm_n_10_0_1       14.727273\n",
       "pm1_2_AE          21.117647\n",
       "pm10_1_AE         50.956522\n",
       "pm_n_0_5_2      1131.764706\n",
       "temperatura2      34.657491\n",
       "pm_n_0_3_2      3592.588235\n",
       "pm_n_0_5_1      1118.037037\n",
       "pm_n_0_3_1      3577.666667\n",
       "pm_n_5_0_1        27.478261\n",
       "pm_n_5_0_2        19.913043\n",
       "pm10_2             53.73913\n",
       "pm10_1            59.478261\n",
       "pm25_2_AE         35.608696\n",
       "pm_n_1_0_1       315.130435\n",
       "pm_n_1_0_2       331.391304\n",
       "longitud               None\n",
       "latitud                None\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Exploremos el valor máximo para cada columna\n",
    "data.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#¿Qué más quieres saber de los datos?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Prueba graficando algunos datos. \n",
    "#Por ejemplo, grafiquemos la temperatura\n",
    "\n",
    "fig=plt.figure()\n",
    "\n",
    "data.plot( y=[\"temperatura\",\"temperatura2\"])\n",
    "\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.ylabel(\"Temperatura [C]\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resumiendo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe width=\"660\" height=\"415\" src=\"https:&#x2F;&#x2F;www.canva.com&#x2F;design&#x2F;DAFwVICi7qs&#x2F;view?embed\" title=\"Pandas Review\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "HTML('<iframe width=\"660\" height=\"415\" src=\"https:&#x2F;&#x2F;www.canva.com&#x2F;design&#x2F;DAFwVICi7qs&#x2F;view?embed\" title=\"Pandas Review\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen></iframe>')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}