00_AnaDados.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e98fe8c5",
   "metadata": {},
   "source": [
    "# Avaliando os dados da ANA no estado de Pernambuco utilizando a biblioteca hydrobr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3487eb1d",
   "metadata": {},
   "source": [
    "### Bibliotecas utilizadas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9f636fc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install hydrobr\n",
    "import hydrobr\n",
    "import pandas as pd\n",
    "#!pip install geopandas\n",
    "import matplotlib.pyplot as plt\n",
    "import geopandas as gpd\n",
    "import plotly.express as px\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf82f502",
   "metadata": {},
   "source": [
    "#### Explorando o help da biblioteca hydrobr para acesso aos dados das estações\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "8856ff48",
   "metadata": {},
   "outputs": [
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     "text": [
      "Help on class ANA in module hydrobr.get_data:\n",
      "\n",
      "class ANA(builtins.object)\n",
      " |  It provides a connection with the Brazilian National Water Agency (Agência Nacional de Águas - ANA) database\n",
      " |  \n",
      " |  Static methods defined here:\n",
      " |  \n",
      " |  flow(list_station, only_consisted=False, threads=10)\n",
      " |      Get the flow station data series from a list of stations code of the Brazilian National Water Agency\n",
      " |      (ANA) database.\n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      list_station : list of strings\n",
      " |          A list of with the stations code as strings.\n",
      " |      only_consisted : boolean, default False\n",
      " |          If True, returns only the data classified as consistent by the provider.\n",
      " |      threads: int\n",
      " |          Number of parallel requisitions\n",
      " |      Returns\n",
      " |      -------\n",
      " |      data_stations : pandas DataFrame\n",
      " |          The data os each station as a column in a pandas DataFrame\n",
      " |  \n",
      " |  flow_data(list_station, only_consisted=False)\n",
      " |  \n",
      " |  list_flow(state='', city='', source='ANAF')\n",
      " |      Searches for flow/stage stations registered at the Brazilian National Agency of Water inventory.\n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      state : string\n",
      " |          Brazilian state name where the stations are located (e.g., Rio de Janeiro)\n",
      " |      city : string\n",
      " |          Brazilian city name where the stations are located (e.g., Rio de Itaperuna)\n",
      " |      source: string, default 'ANAF'\n",
      " |          The source to look for the data. 'ANA' to get the list of stations from the Brazilian National Water Agency\n",
      " |          (ANA) database, or 'ANAF' to get the filtered list of stations that contain only the stations from ANA\n",
      " |          with registered data.\n",
      " |          More information about ANAF: https://doi.org/10.5281/zenodo.3755065\n",
      " |      Returns\n",
      " |      -------\n",
      " |      list_stations : pandas DataFrame\n",
      " |          The selected list of stations as a pandas DataFrame\n",
      " |  \n",
      " |  list_flow_stations(state='', city='', source='ANAF')\n",
      " |  \n",
      " |  list_prec(state='', city='', source='ANAF')\n",
      " |      Searches for precipitation stations registered at the Brazilian National Agency of Water (ANA)\n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      state : string\n",
      " |          Brazilian state name where the stations are located (e.g., Rio de Janeiro)\n",
      " |      city : string\n",
      " |          Brazilian city name where the stations are located (e.g., Rio de Itaperuna)\n",
      " |      source: string, default 'ANA'\n",
      " |          The source to look for the data. 'ANA' to get the list of stations from the Brazilian National Water Agency\n",
      " |          (ANA) database, or 'ANAF' to get the filtered list of stations that contain only the stations from ANA\n",
      " |          with registered data.\n",
      " |          More information about ANAF: https://doi.org/10.5281/zenodo.3755065\n",
      " |      Returns\n",
      " |      -------\n",
      " |      list_stations : pandas DataFrame\n",
      " |          The selected list of stations as a pandas DataFrame\n",
      " |  \n",
      " |  list_prec_stations(state='', city='', source='ANAF')\n",
      " |  \n",
      " |  list_telemetric()\n",
      " |      Searches for the telemetry stations registered at the Brazilian National Agency of Water inventory.\n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      Returns\n",
      " |      -------\n",
      " |      list_stations : pandas DataFrame\n",
      " |          The list of  all telemtry stations as a pandas DataFrame\n",
      " |  \n",
      " |  prec(list_station, only_consisted=False, threads=10)\n",
      " |      Get the precipitation station data series from a list of stations code.\n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      list_station : list of strings\n",
      " |          A list of with the stations code as strings.\n",
      " |      only_consisted : boolean, default False\n",
      " |          If True, returns only the data classified as consistent by the provider.\n",
      " |      threads: int\n",
      " |          Number of parallel requisitions\n",
      " |      Returns\n",
      " |      -------\n",
      " |      data_stations : pandas DataFrame\n",
      " |          The data of each station as a column in a pandas DataFrame\n",
      " |  \n",
      " |  prec_data(list_station, only_consisted=False)\n",
      " |  \n",
      " |  stage(list_station, only_consisted=False, threads=10)\n",
      " |      Get the stage station data series from a list of stations code of the Brazilian National Water Agency\n",
      " |      (ANA) database.\n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      list_station : list of strings\n",
      " |          A list of with the stations code as strings.\n",
      " |      only_consisted : boolean, default False\n",
      " |          If True, returns only the data classified as consistent by the provider.\n",
      " |      threads: int\n",
      " |          Number of parallel requisitions\n",
      " |      Returns\n",
      " |      -------\n",
      " |      data_stations : pandas DataFrame\n",
      " |          The data of each station as a column in a pandas DataFrame\n",
      " |  \n",
      " |  stage_data(list_station, only_consisted=False)\n",
      " |  \n",
      " |  telemetric(station_code, threads=10)\n",
      " |      Get the Precipitation, Stage and Flow data for the ANA's telemetric stations as a DataFrame.\n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      station_code : str\n",
      " |          The station code a string.\n",
      " |      threads: int\n",
      " |          Number of parallel requisitions\n",
      " |      Returns\n",
      " |      -------\n",
      " |      data_station : pandas DataFrame\n",
      " |          The data os each station as a column in a pandas DataFrame\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      "\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(help(hydrobr.get_data.ANA))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e293212",
   "metadata": {},
   "source": [
    "### Estações meteorológicas em Pernambuco com data final de dados após 1993 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "877ed66b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ----------- Obtendo a lista de todas as estações da ANA\n",
    "estacoesANA = hydrobr.get_data.ANA.list_prec()\n",
    "\n",
    "# ---------- Aplicando o filtro para o estado de Pernambuco e com a data final após 1993\n",
    "estacoesPE = estacoesANA[(estacoesANA['State']=='PERNAMBUCO') &\n",
    "                        (estacoesANA['EndDate']>\"1993/01/01\")]\n",
    "\n",
    "listaEstacoesPE = estacoesPE['Code'].unique()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d74ce056",
   "metadata": {},
   "source": [
    "### Obtendo os dados das estações "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5732249",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # ---------- Criando um df vazio para salvar os dados das estações\n",
    "# df_estacao_PE = pd.DataFrame()\n",
    "\n",
    "# for codigo in listaEstacoesPE:\n",
    "#     # -------- Gravando os dados da estacao em um df\n",
    "#     df_estacao = hydrobr.get_data.ANA.prec(codigo)\n",
    "    \n",
    "#     # -------- Resetando o índice para criar a coluna do periodo\n",
    "#     df_estacao = df_estacao.reset_index()\n",
    "    \n",
    "#     # -------- Salvando o codigo como uma coluna\n",
    "#     df_estacao['CODIGO'] = codigo\n",
    "    \n",
    "#     # ------- Renomeando os campos\n",
    "#     df_estacao.columns = ['PERIODO', 'PRECIPITACAO', 'CODIGO']\n",
    "    \n",
    "#     # ------- Concatenando todos os codigos\n",
    "#     df_estacao_PE = pd.concat([df_estacao_PE,df_estacao])\n",
    "# df_estacao_PE.to_csv(r'Gauges.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ce81f12",
   "metadata": {},
   "source": [
    "### Analisando a completude dos dados "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b76f46d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentual de dados faltantes:  5.32\n",
      "Nº de estações completas:  35\n",
      "Nº de estações incompletas:  110\n"
     ]
    }
   ],
   "source": [
    "df_estacao_PE = pd.read_csv(r\"Gauges.csv\")\n",
    "\n",
    "# --------- Removendo a coluna do eixo\n",
    "df_estacao_PE.drop(['Unnamed: 0'], axis='columns', inplace=True)\n",
    "\n",
    "# --------- Filtrando o período de janeiro de 1992 a dezembro de 2022\n",
    "df_estacao_PE = df_estacao_PE[(df_estacao_PE[\"PERIODO\"]>=\"1992-01-01\") &\n",
    "                              (df_estacao_PE[\"PERIODO\"]<=\"2022-12-31\")].reset_index(drop=True)\n",
    "\n",
    "# --------- Transformando a coluna de codigo para texto\n",
    "df_estacao_PE['CODIGO'] = df_estacao_PE['CODIGO'].astype(str)\n",
    "\n",
    "# ------- Analisando o numero de dados faltantes\n",
    "percentual_de_dados_faltantes = round((df_estacao_PE['PRECIPITACAO'].isnull().sum()/df_estacao_PE.shape[0])*100, 2)\n",
    "print(\"Percentual de dados faltantes: \", percentual_de_dados_faltantes)\n",
    "\n",
    "# ------- Excluir os dados faltantes\n",
    "df_estacao_PE.dropna(inplace = True)\n",
    "\n",
    "#---------- Fazer um merge entre a primeira data e a última data\n",
    "df_estacao_PE_inicio_e_fim = pd.merge(df_estacao_PE[['CODIGO', 'PERIODO']].drop_duplicates(subset = 'CODIGO', keep = 'first'),\n",
    "              df_estacao_PE.drop_duplicates(subset = 'CODIGO', keep = 'last'), how = 'inner', on = 'CODIGO')\n",
    "\n",
    "df_estacao_PE_inicio_e_fim.loc[(df_estacao_PE_inicio_e_fim['PERIODO_x']=='1992-01-01') &\n",
    "                               (df_estacao_PE_inicio_e_fim['PERIODO_y']=='2022-12-31'), \"CLASSIFICAÇÃO\"] = 'COMPLETA'\n",
    "df_estacao_PE_inicio_e_fim.loc[~((df_estacao_PE_inicio_e_fim['PERIODO_x']=='1992-01-01') &\n",
    "                               (df_estacao_PE_inicio_e_fim['PERIODO_y']=='2022-12-31')), \"CLASSIFICAÇÃO\"] = 'INCOMPLETA'\n",
    "\n",
    "df_estacao_PE_inicio_e_fim.columns = ['CODIGO', 'INÍCIO', 'FIM', 'PRECIPITACAO',\n",
    "       'CLASSIFICAÇÃO']\n",
    "\n",
    "n_estacoes_completas = len(df_estacao_PE_inicio_e_fim[df_estacao_PE_inicio_e_fim['CLASSIFICAÇÃO']=='COMPLETA']['CODIGO'].unique())\n",
    "n_estacoes_incompletas = len(df_estacao_PE_inicio_e_fim[df_estacao_PE_inicio_e_fim['CLASSIFICAÇÃO']=='INCOMPLETA']['CODIGO'].unique())\n",
    "\n",
    "print(\"Nº de estações completas: \", n_estacoes_completas)\n",
    "print(\"Nº de estações incompletas: \", n_estacoes_incompletas)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "932eb0e1",
   "metadata": {},
   "source": [
    "#### Utilizando o gráfico de Gantt para analisar a completude dos dados no periodo de janeiro de 1992 até dezembro de 2022 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4a032580",
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "fig = px.timeline(df_estacao_PE_inicio_e_fim, x_start=\"INÍCIO\", x_end=\"FIM\", y=\"CODIGO\", color=\"CLASSIFICAÇÃO\")\n",
    "fig.update_yaxes(autorange=\"reversed\")\n",
    "fig.show()"
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  },
  {
   "cell_type": "markdown",
   "id": "af592d8e",
   "metadata": {},
   "source": [
    "### Analisando espacialmente a completude dos dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "39b39f20",
   "metadata": {},
   "outputs": [
    {
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       "      <td>-35.2000</td>\n",
       "      <td>POINT (-35.20000 -7.58330)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00735041</td>\n",
       "      <td>1992-01-01</td>\n",
       "      <td>1993-05-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>INCOMPLETA</td>\n",
       "      <td>00735041</td>\n",
       "      <td>-7.8000</td>\n",
       "      <td>-35.5833</td>\n",
       "      <td>POINT (-35.58330 -7.80000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00735045</td>\n",
       "      <td>1992-01-01</td>\n",
       "      <td>1993-01-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>INCOMPLETA</td>\n",
       "      <td>00735045</td>\n",
       "      <td>-7.7833</td>\n",
       "      <td>-35.3667</td>\n",
       "      <td>POINT (-35.36670 -7.78330)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00735048</td>\n",
       "      <td>1992-01-01</td>\n",
       "      <td>1993-02-28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>INCOMPLETA</td>\n",
       "      <td>00735048</td>\n",
       "      <td>-7.5833</td>\n",
       "      <td>-35.1000</td>\n",
       "      <td>POINT (-35.10000 -7.58330)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CODIGO      INÍCIO         FIM  PRECIPITACAO CLASSIFICAÇÃO      Code  \\\n",
       "0  00735037  1992-01-01  1994-12-31           0.0    INCOMPLETA  00735037   \n",
       "1  00735038  1992-01-01  1993-02-28           0.0    INCOMPLETA  00735038   \n",
       "2  00735041  1992-01-01  1993-05-31           0.0    INCOMPLETA  00735041   \n",
       "3  00735045  1992-01-01  1993-01-31           0.0    INCOMPLETA  00735045   \n",
       "4  00735048  1992-01-01  1993-02-28           0.0    INCOMPLETA  00735048   \n",
       "\n",
       "   Latitude  Longitude                    geometry  \n",
       "0   -7.9667   -35.8833  POINT (-35.88330 -7.96670)  \n",
       "1   -7.5833   -35.2000  POINT (-35.20000 -7.58330)  \n",
       "2   -7.8000   -35.5833  POINT (-35.58330 -7.80000)  \n",
       "3   -7.7833   -35.3667  POINT (-35.36670 -7.78330)  \n",
       "4   -7.5833   -35.1000  POINT (-35.10000 -7.58330)  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --------- importando o shapefile das mesorregiões de Pernambuco\n",
    "mesorregioes_PE = gpd.read_file(r\"C:\\Users\\flavi\\OneDrive - Universidade Federal de Pernambuco\\Base de dados\\PE 2021\\PE_Mesorregioes_2021.shp\")\n",
    "\n",
    "# -------- Padronizando a coluna codigo\n",
    "df_estacao_PE_inicio_e_fim['CODIGO'] = '00'+df_estacao_PE_inicio_e_fim['CODIGO']\n",
    "\n",
    "# -------- Fazendo o merge com a latitude e longitude\n",
    "df_estacao_PE_inicio_e_fim = df_estacao_PE_inicio_e_fim.merge(estacoesPE[['Code', \"Latitude\", \"Longitude\" ]], how = 'inner', left_on = 'CODIGO', \n",
    "                                right_on = 'Code')\n",
    "\n",
    "\n",
    "# -------- Transformando as colunas latitude e longitude para o formato geometry\n",
    "df_estacao_PE_inicio_e_fim = gpd.GeoDataFrame(df_estacao_PE_inicio_e_fim,geometry=gpd.points_from_xy(\n",
    "    estacoesPE.Longitude,estacoesPE.Latitude))\n",
    "\n",
    "display(df_estacao_PE_inicio_e_fim.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "16cd3012",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --------- Plotando estações meteorologicas\n",
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "base = mesorregioes_PE.plot(ax=ax, color = 'white', edgecolor = 'black')\n",
    "df_estacao_PE_inicio_e_fim.plot(ax=base, column = 'CLASSIFICAÇÃO', legend = True, cmap = 'Pastel2')       \n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}