validator.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archivo CSV descargado con éxito para el proyecto 13.\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "\n",
    "# Ruta base de la API de Geonity\n",
    "base_url = \"https://geonity.ibercivis.es/api/project/\"\n",
    "\n",
    "# ID del proyecto (según la información que proporcionaste)\n",
    "project_id = 13\n",
    "\n",
    "# URL completa para descargar las observaciones usando el ID del proyecto\n",
    "download_url = f\"{base_url}{project_id}/download_observations/\"\n",
    "\n",
    "# Realizar la solicitud GET a la API\n",
    "response = requests.get(download_url)\n",
    "\n",
    "# Verificar si la solicitud fue exitosa\n",
    "if response.status_code == 200:\n",
    "    # Obtener el contenido del archivo CSV\n",
    "    csv_content = response.content\n",
    "\n",
    "    # Escribir el contenido en un archivo CSV local\n",
    "    with open(f\"observations_{project_id}.csv\", \"wb\") as csv_file:\n",
    "        csv_file.write(csv_content)\n",
    "\n",
    "    print(f\"Archivo CSV descargado con éxito para el proyecto {project_id}.\")\n",
    "else:\n",
    "    print(f\"Error al descargar el archivo CSV. Código de estado: {response.status_code}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    ID                  Timestamp  Latitude  Longitude  \\\n",
      "0  360  2024-04-19 12:24:39+00:00 -0.932642  41.615961   \n",
      "1  374  2024-04-25 17:31:37+00:00 -3.625667  37.161216   \n",
      "2    4  2023-12-19 09:51:48+00:00 -0.885918  41.680452   \n",
      "3   28  2024-02-07 13:48:28+00:00 -3.743405  40.410973   \n",
      "4   16  2024-01-25 19:09:37+00:00 -0.849943  40.815211   \n",
      "\n",
      "  Breve descripción del punto donde se toma la muestra (campo de cultivo, parque, jardín, bosque, etc.)  \\\n",
      "0                                  Zona verde urbana                                                      \n",
      "1     Parcela de huerta en cultivo. Riego habitual.                                                       \n",
      "2                                                NaN                                                      \n",
      "3                                           Frutales                                                      \n",
      "4                                                NaN                                                      \n",
      "\n",
      "                          Ubicación  \\\n",
      "0  Valdespartera. Plantación zona A   \n",
      "1            37,1611840  -3,6255470   \n",
      "2              Fundación Ibercivis    \n",
      "3                 Escuela de la vid   \n",
      "4               40.815119 -0.849962   \n",
      "\n",
      "             Foto del entorno de la toma de muestras  \\\n",
      "0  https://geonity.ibercivis.es/media/images/2024...   \n",
      "1                                                NaN   \n",
      "2                                                NaN   \n",
      "3                                                NaN   \n",
      "4  https://geonity.ibercivis.es/media/images/2024...   \n",
      "\n",
      "  Fecha de la toma de muestras  (dd/mm/aaaa)  \\\n",
      "0                                 02/03/2024   \n",
      "1                                 08/04/2024   \n",
      "2                                 19/12/2023   \n",
      "3                                 07/02/2024   \n",
      "4                                 24/01/2024   \n",
      "\n",
      "   Resultado del indicador 1: Cobertura del suelo  \\\n",
      "0                                             1.0   \n",
      "1                                           100.0   \n",
      "2                                           100.0   \n",
      "3                                             0.0   \n",
      "4                                            10.0   \n",
      "\n",
      "       Fotos de ejemplos de macrofauna y/o lombrices  \\\n",
      "0                                                NaN   \n",
      "1                                                NaN   \n",
      "2                                                NaN   \n",
      "3                                                NaN   \n",
      "4  https://geonity.ibercivis.es/media/images/2024...   \n",
      "\n",
      "   Puntuación promedio del indicador 2: Infiltración  \\\n",
      "0                                               7.30   \n",
      "1                                              10.00   \n",
      "2                                             100.00   \n",
      "3                                               0.00   \n",
      "4                                               3.33   \n",
      "\n",
      "   Puntuación promedio del indicador 3: Compactación  \\\n",
      "0                                                3.0   \n",
      "1                                               10.0   \n",
      "2                                              100.0   \n",
      "3                                                3.0   \n",
      "4                                                3.0   \n",
      "\n",
      "   Puntuación promedio del indicador 4: Diversidad de macrofauna  \\\n",
      "0                                                0.0               \n",
      "1                                                6.0               \n",
      "2                                              100.0               \n",
      "3                                                6.0               \n",
      "4                                                1.3               \n",
      "\n",
      "   Puntuación promedio del indicador 5: N° de lombrices  \\\n",
      "0                                                0.0      \n",
      "1                                                7.0      \n",
      "2                                              100.0      \n",
      "3                                                3.0      \n",
      "4                                                1.0      \n",
      "\n",
      "   Puntuación promedio del indicador 6: pH  \\\n",
      "0                                      5.0   \n",
      "1                                      7.0   \n",
      "2                                    100.0   \n",
      "3                                      6.0   \n",
      "4                                      1.0   \n",
      "\n",
      "   Puntuación promedio del indicador 7: Materia orgánica  \n",
      "0                                                1.0      \n",
      "1                                               10.0      \n",
      "2                                              100.0      \n",
      "3                                                6.0      \n",
      "4                                                8.6      \n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "\n",
    "# Ruta base de la API de Geonity\n",
    "base_url = \"https://geonity.ibercivis.es/api/project/\"\n",
    "project_id = 13\n",
    "\n",
    "# URL completa para descargar las observaciones usando el ID del proyecto\n",
    "download_url = f\"{base_url}{project_id}/download_observations/\"\n",
    "\n",
    "# Autenticación - Reemplaza '<your_access_token>' con tu token de acceso\n",
    "headers = {\n",
    "    'Authorization': 'Bearer <your_access_token>'\n",
    "}\n",
    "\n",
    "# Realizar la solicitud GET a la API\n",
    "response = requests.get(download_url, headers=headers)\n",
    "\n",
    "# Verificar si la solicitud fue exitosa\n",
    "if response.status_code == 200:\n",
    "    # Obtener el contenido del archivo CSV\n",
    "    csv_content = response.content\n",
    "\n",
    "    # Leer el CSV en un DataFrame de pandas\n",
    "    from io import BytesIO\n",
    "    df = pd.read_csv(BytesIO(csv_content))\n",
    "\n",
    "    # Mostrar las primeras filas del DataFrame\n",
    "    print(df.head())\n",
    "else:\n",
    "    print(f\"Error al descargar el archivo CSV. Código de estado: {response.status_code}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>ID</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Breve descripción del punto donde se toma la muestra (campo de cultivo, parque, jardín, bosque, etc.)</th>\n",
       "      <th>Ubicación</th>\n",
       "      <th>Foto del entorno de la toma de muestras</th>\n",
       "      <th>Fecha de la toma de muestras  (dd/mm/aaaa)</th>\n",
       "      <th>Resultado del indicador 1: Cobertura del suelo</th>\n",
       "      <th>Fotos de ejemplos de macrofauna y/o lombrices</th>\n",
       "      <th>Puntuación promedio del indicador 2: Infiltración</th>\n",
       "      <th>Puntuación promedio del indicador 3: Compactación</th>\n",
       "      <th>Puntuación promedio del indicador 4: Diversidad de macrofauna</th>\n",
       "      <th>Puntuación promedio del indicador 5: N° de lombrices</th>\n",
       "      <th>Puntuación promedio del indicador 6: pH</th>\n",
       "      <th>Puntuación promedio del indicador 7: Materia orgánica</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>360</td>\n",
       "      <td>2024-04-19 12:24:39+00:00</td>\n",
       "      <td>-0.932642</td>\n",
       "      <td>41.615961</td>\n",
       "      <td>Zona verde urbana</td>\n",
       "      <td>Valdespartera. Plantación zona A</td>\n",
       "      <td>https://geonity.ibercivis.es/media/images/2024...</td>\n",
       "      <td>02/03/2024</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.30</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>374</td>\n",
       "      <td>2024-04-25 17:31:37+00:00</td>\n",
       "      <td>-3.625667</td>\n",
       "      <td>37.161216</td>\n",
       "      <td>Parcela de huerta en cultivo. Riego habitual.</td>\n",
       "      <td>37,1611840  -3,6255470</td>\n",
       "      <td>NaN</td>\n",
       "      <td>08/04/2024</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2023-12-19 09:51:48+00:00</td>\n",
       "      <td>-0.885918</td>\n",
       "      <td>41.680452</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fundación Ibercivis</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19/12/2023</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>2024-02-07 13:48:28+00:00</td>\n",
       "      <td>-3.743405</td>\n",
       "      <td>40.410973</td>\n",
       "      <td>Frutales</td>\n",
       "      <td>Escuela de la vid</td>\n",
       "      <td>NaN</td>\n",
       "      <td>07/02/2024</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>2024-01-25 19:09:37+00:00</td>\n",
       "      <td>-0.849943</td>\n",
       "      <td>40.815211</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.815119 -0.849962</td>\n",
       "      <td>https://geonity.ibercivis.es/media/images/2024...</td>\n",
       "      <td>24/01/2024</td>\n",
       "      <td>10.0</td>\n",
       "      <td>https://geonity.ibercivis.es/media/images/2024...</td>\n",
       "      <td>3.33</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ID                  Timestamp  Latitude  Longitude  \\\n",
       "0  360  2024-04-19 12:24:39+00:00 -0.932642  41.615961   \n",
       "1  374  2024-04-25 17:31:37+00:00 -3.625667  37.161216   \n",
       "2    4  2023-12-19 09:51:48+00:00 -0.885918  41.680452   \n",
       "3   28  2024-02-07 13:48:28+00:00 -3.743405  40.410973   \n",
       "4   16  2024-01-25 19:09:37+00:00 -0.849943  40.815211   \n",
       "\n",
       "  Breve descripción del punto donde se toma la muestra (campo de cultivo, parque, jardín, bosque, etc.)  \\\n",
       "0                                  Zona verde urbana                                                      \n",
       "1     Parcela de huerta en cultivo. Riego habitual.                                                       \n",
       "2                                                NaN                                                      \n",
       "3                                           Frutales                                                      \n",
       "4                                                NaN                                                      \n",
       "\n",
       "                          Ubicación  \\\n",
       "0  Valdespartera. Plantación zona A   \n",
       "1            37,1611840  -3,6255470   \n",
       "2              Fundación Ibercivis    \n",
       "3                 Escuela de la vid   \n",
       "4               40.815119 -0.849962   \n",
       "\n",
       "             Foto del entorno de la toma de muestras  \\\n",
       "0  https://geonity.ibercivis.es/media/images/2024...   \n",
       "1                                                NaN   \n",
       "2                                                NaN   \n",
       "3                                                NaN   \n",
       "4  https://geonity.ibercivis.es/media/images/2024...   \n",
       "\n",
       "  Fecha de la toma de muestras  (dd/mm/aaaa)  \\\n",
       "0                                 02/03/2024   \n",
       "1                                 08/04/2024   \n",
       "2                                 19/12/2023   \n",
       "3                                 07/02/2024   \n",
       "4                                 24/01/2024   \n",
       "\n",
       "   Resultado del indicador 1: Cobertura del suelo  \\\n",
       "0                                             1.0   \n",
       "1                                           100.0   \n",
       "2                                           100.0   \n",
       "3                                             0.0   \n",
       "4                                            10.0   \n",
       "\n",
       "       Fotos de ejemplos de macrofauna y/o lombrices  \\\n",
       "0                                                NaN   \n",
       "1                                                NaN   \n",
       "2                                                NaN   \n",
       "3                                                NaN   \n",
       "4  https://geonity.ibercivis.es/media/images/2024...   \n",
       "\n",
       "   Puntuación promedio del indicador 2: Infiltración  \\\n",
       "0                                               7.30   \n",
       "1                                              10.00   \n",
       "2                                             100.00   \n",
       "3                                               0.00   \n",
       "4                                               3.33   \n",
       "\n",
       "   Puntuación promedio del indicador 3: Compactación  \\\n",
       "0                                                3.0   \n",
       "1                                               10.0   \n",
       "2                                              100.0   \n",
       "3                                                3.0   \n",
       "4                                                3.0   \n",
       "\n",
       "   Puntuación promedio del indicador 4: Diversidad de macrofauna  \\\n",
       "0                                                0.0               \n",
       "1                                                6.0               \n",
       "2                                              100.0               \n",
       "3                                                6.0               \n",
       "4                                                1.3               \n",
       "\n",
       "   Puntuación promedio del indicador 5: N° de lombrices  \\\n",
       "0                                                0.0      \n",
       "1                                                7.0      \n",
       "2                                              100.0      \n",
       "3                                                3.0      \n",
       "4                                                1.0      \n",
       "\n",
       "   Puntuación promedio del indicador 6: pH  \\\n",
       "0                                      5.0   \n",
       "1                                      7.0   \n",
       "2                                    100.0   \n",
       "3                                      6.0   \n",
       "4                                      1.0   \n",
       "\n",
       "   Puntuación promedio del indicador 7: Materia orgánica  \n",
       "0                                                1.0      \n",
       "1                                               10.0      \n",
       "2                                              100.0      \n",
       "3                                                6.0      \n",
       "4                                                8.6      "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "VALIDATION MODULE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fila 2: pH (4.0) no está entre 5.5 y 7.5.\n",
      "Fila 2: Compactación (1.2) no está entre 0 y 1.\n",
      "Fila 2: Hora (23:00) fuera del rango de 6 am a 10 pm.\n",
      "Fila 2: Todas las variables introducidas están fuera de rango.\n",
      "Fila 3: pH (8.0) no está entre 5.5 y 7.5.\n",
      "Fila 3: Compactación (-0.5) no está entre 0 y 1.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "\n",
    "# Suponiendo que ya existe un DataFrame df con datos anteriores\n",
    "# Definimos el módulo de validación\n",
    "\n",
    "def validar_datos(df, nuevos_datos):\n",
    "    \"\"\"\n",
    "    Función para validar nuevos datos y generar alertas en caso de incumplimiento.\n",
    "    \n",
    "    Parámetros:\n",
    "    df: DataFrame existente con datos previos.\n",
    "    nuevos_datos: DataFrame con nuevas entradas para validar.\n",
    "    \n",
    "    Retorna:\n",
    "    DataFrame consolidado y lista de alertas generadas.\n",
    "    \"\"\"\n",
    "    \n",
    "    alertas = []\n",
    "    muestras_unicas = set(df.apply(tuple, axis=1))  # Crear un conjunto de muestras únicas existentes\n",
    "    \n",
    "    # Recorrer los nuevos datos para validación\n",
    "    for index, row in nuevos_datos.iterrows():\n",
    "        usuario, ph, compactacion, hora = row['usuario'], row['pH'], row['Compactación'], row['hora']\n",
    "        \n",
    "        # Convertir la hora a un objeto de tiempo\n",
    "        try:\n",
    "            hora_objeto = datetime.strptime(hora, \"%H:%M\").time()\n",
    "        except ValueError:\n",
    "            alertas.append(f\"Fila {index + 1}: Formato de hora no válido.\")\n",
    "            continue\n",
    "        \n",
    "        # Inicializar contador de variables erróneas\n",
    "        variables_erroneas = 0\n",
    "\n",
    "        # Validación de pH (ejemplo de rango de pH para suelos saludables)\n",
    "        if not (5.5 <= ph <= 7.5):\n",
    "            variables_erroneas += 1\n",
    "            alertas.append(f\"Fila {index + 1}: pH ({ph}) no está entre 5.5 y 7.5.\")\n",
    "        \n",
    "        # Validación de Compactación (ejemplo de valor aceptable para compactación)\n",
    "        if not (0 <= compactacion <= 1):  # Supongamos que 0-1 es un rango de compactación aceptable\n",
    "            variables_erroneas += 1\n",
    "            alertas.append(f\"Fila {index + 1}: Compactación ({compactacion}) no está entre 0 y 1.\")\n",
    "        \n",
    "        # Validación de hora\n",
    "        hora_inicio = datetime.strptime(\"06:00\", \"%H:%M\").time()\n",
    "        hora_fin = datetime.strptime(\"22:00\", \"%H:%M\").time()\n",
    "        \n",
    "        if not (hora_inicio <= hora_objeto <= hora_fin):\n",
    "            variables_erroneas += 1\n",
    "            alertas.append(f\"Fila {index + 1}: Hora ({hora}) fuera del rango de 6 am a 10 pm.\")\n",
    "        \n",
    "        # Verificación de duplicados\n",
    "        muestra = (usuario, ph, compactacion, hora)\n",
    "        if muestra in muestras_unicas:\n",
    "            alertas.append(f\"Fila {index + 1}: Muestra duplicada.\")\n",
    "        else:\n",
    "            muestras_unicas.add(muestra)\n",
    "\n",
    "        # Alerta si todas las variables (excluyendo el nombre) están fuera de rango\n",
    "        if variables_erroneas == 3:\n",
    "            alertas.append(f\"Fila {index + 1}: Todas las variables introducidas están fuera de rango.\")\n",
    "    \n",
    "    # Concatenar nuevos datos con el DataFrame existente si no hay errores críticos\n",
    "    df = pd.concat([df, nuevos_datos], ignore_index=True)\n",
    "    \n",
    "    return df, alertas\n",
    "\n",
    "# Ejemplo de uso del módulo de validación\n",
    "# Nuevos datos simulados\n",
    "nuevos_datos = pd.DataFrame({\n",
    "    'usuario': ['Usuario1', 'Usuario2', 'Usuario3'],\n",
    "    'pH': [6.0, 4.0, 8.0],\n",
    "    'Compactación': [0.3, 1.2, -0.5],\n",
    "    'hora': ['07:30', '23:00', '12:00']\n",
    "})\n",
    "\n",
    "# Validar los nuevos datos\n",
    "df, alertas = validar_datos(df, nuevos_datos)\n",
    "\n",
    "# Mostrar alertas generadas\n",
    "for alerta in alertas:\n",
    "    print(alerta)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fila 2: pH (8.3) no está entre 5.5 y 7.5.\n",
      "Fila 2: Compactación (-0.1) no está entre 0 y 1.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import requests\n",
    "from io import BytesIO\n",
    "from datetime import datetime\n",
    "\n",
    "# URL del CSV en el sitio web\n",
    "csv_url = \"https://geonity.ibercivis.es/api/project/\"  # Reemplaza con la URL real del CSV\n",
    "\n",
    "# Descargar el archivo CSV de la URL\n",
    "def descargar_csv():\n",
    "    response = requests.get(csv_url)\n",
    "    if response.status_code == 200:\n",
    "        return pd.read_csv(BytesIO(response.content))\n",
    "    else:\n",
    "        print(f\"Error al descargar el CSV. Código de estado: {response.status_code}\")\n",
    "        return None\n",
    "\n",
    "# Función para validar nuevos datos y generar alertas\n",
    "def validar_datos(df, nuevos_datos):\n",
    "    \"\"\"\n",
    "    Función para validar nuevos datos y generar alertas en caso de incumplimiento.\n",
    "    \"\"\"\n",
    "    alertas = []\n",
    "    muestras_unicas = set(df.apply(tuple, axis=1))  # Crear un conjunto de muestras únicas existentes\n",
    "    \n",
    "    for index, row in nuevos_datos.iterrows():\n",
    "        usuario, ph, compactacion, hora = row['usuario'], row['pH'], row['Compactación'], row['hora']\n",
    "        \n",
    "        # Convertir la hora a un objeto de tiempo\n",
    "        try:\n",
    "            hora_objeto = datetime.strptime(hora, \"%H:%M\").time()\n",
    "        except ValueError:\n",
    "            alertas.append(f\"Fila {index + 1}: Formato de hora no válido.\")\n",
    "            continue\n",
    "        \n",
    "        # Inicializar contador de variables erróneas\n",
    "        variables_erroneas = 0\n",
    "\n",
    "        # Validación de pH\n",
    "        if not (5.5 <= ph <= 7.5):\n",
    "            variables_erroneas += 1\n",
    "            alertas.append(f\"Fila {index + 1}: pH ({ph}) no está entre 5.5 y 7.5.\")\n",
    "        \n",
    "        # Validación de Compactación\n",
    "        if not (0 <= compactacion <= 1):  \n",
    "            variables_erroneas += 1\n",
    "            alertas.append(f\"Fila {index + 1}: Compactación ({compactacion}) no está entre 0 y 1.\")\n",
    "        \n",
    "        # Validación de hora\n",
    "        hora_inicio = datetime.strptime(\"06:00\", \"%H:%M\").time()\n",
    "        hora_fin = datetime.strptime(\"22:00\", \"%H:%M\").time()\n",
    "        \n",
    "        if not (hora_inicio <= hora_objeto <= hora_fin):\n",
    "            variables_erroneas += 1\n",
    "            alertas.append(f\"Fila {index + 1}: Hora ({hora}) fuera del rango de 6 am a 10 pm.\")\n",
    "        \n",
    "        # Verificación de duplicados\n",
    "        muestra = (usuario, ph, compactacion, hora)\n",
    "        if muestra in muestras_unicas:\n",
    "            alertas.append(f\"Fila {index + 1}: Muestra duplicada.\")\n",
    "        else:\n",
    "            muestras_unicas.add(muestra)\n",
    "\n",
    "        # Alerta si todas las variables están fuera de rango\n",
    "        if variables_erroneas == 3:\n",
    "            alertas.append(f\"Fila {index + 1}: Todas las variables introducidas están fuera de rango.\")\n",
    "    \n",
    "    # Concatenar nuevos datos con el DataFrame existente si no hay errores críticos\n",
    "    df = pd.concat([df, nuevos_datos], ignore_index=True)\n",
    "    \n",
    "    return df, alertas\n",
    "\n",
    "# Cargar el CSV existente\n",
    "df_actual = descargar_csv()\n",
    "\n",
    "# Nuevos datos que simulan actualizaciones\n",
    "nuevos_datos = pd.DataFrame({\n",
    "    'usuario': ['Usuario4', 'Usuario5'],\n",
    "    'pH': [6.2, 8.3],\n",
    "    'Compactación': [0.5, -0.1],\n",
    "    'hora': ['10:15', '12:45']\n",
    "})\n",
    "\n",
    "# Validar los nuevos datos\n",
    "df_actualizado, alertas = validar_datos(df_actual, nuevos_datos)\n",
    "\n",
    "# Mostrar alertas generadas\n",
    "if alertas:\n",
    "    for alerta in alertas:\n",
    "        print(alerta)\n",
    "else:\n",
    "    print(\"No se han generado alertas.\")\n",
    "\n",
    "# Guardar el nuevo CSV actualizado (si quieres reemplazar el CSV original)\n",
    "df_actualizado.to_csv('datos_actualizados.csv', index=False)\n"
   ]
  }
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