nivel_para_vazao_manning.ipynb

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      "name": "nivel_para_vazao_manning.ipynb",
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      "include_colab_link": true
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nayanammm/remote_hydrometeorological_monitoring_system/blob/master/nivel_para_vazao_manning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ocR-NYYcKrgO"
      },
      "source": [
        "# Trabalhando com dados Fluviométricos do Córrego da Pedreira \n",
        "\n",
        "Os dados de vazão são essenciais para avaliação do balanço hidrológico em bacias hidrográficas. Entretanto, realizar medições fluviométricas diretas periodicamente é um desafio, devido aos custos operacionais. Sendo assim, uma forma indireta de obter dados de vazão é medindo a altura da lâmina d'água e a área da seção transversal do escoamento. A leitura do nível d'água pode ser obtida de maneira automática com  sensores eletrônicos. \n",
        "\n",
        "A figura abaixo à direita ilustra um sensor ultrassônico instalado em uma seção canalizada do córrego da Pedreira, um afluente do rio Belém. O sistema de medição registra a leitura da distância entre o sensor e a lâmina d'água (linha vermelha na figura da esquerda abaixo). Os dados são obtidos periodicamente e salvos em um cartão de memória.\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=18XqiiHUHq3MoV3koqn0Oq9ggqNTnW-5U\" width=\"300\"/> <img src=\"https://drive.google.com/uc?export=view&id=1NiGpdaX1CgtGZbSxDFo2gf-GNbjd-x8g\" width=\"300\"/> \n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JK0rCn5M7yYE"
      },
      "source": [
        "Com a utilização do sensor ultrassônico, foram realizadas leituras de nível em intervalos de 15 segundos, entre os dias 05/08 e 30/08.\n",
        "Analisando os dados brutos observam-se alguns dados espúrios, necessitando que os mesmos sejam tratados e filtrados antes de serem utilizandos para os cálculos de vazão. \n",
        "\n",
        "**Exercício I:** Organizar os dados de medição de nível, seguindo os seguintes passos:\n",
        "\n",
        "1. Leitura dos dados de distância (linha vermelha da figura acima à esquerda) do arquivo CSV. O arquivo CSV contém uma coluna para a data e hora (no formado ddmmaaaa HH:MM:SS) e outra coluna com os dados de distância em centímetros. Você pode acessá-lo no link:(https://www.dropbox.com/s/htfwy819slzcxkt/dados_ultrassonico.csv?dl=0). **Nessa seção deverá ser realizado os seguintes procedimentos:**\n",
        "\n",
        "**a) Criar um Dataframe e nomear o título das colunas de acordo com a lista: ['data_hora','dist_cm']**\n",
        "\n",
        "**b) Definir a coluna 'data_hora' como o índice do dataframe.**\n",
        "\n",
        "**c) configurar a  coluna 'data_hora'  como o formato de data padrão: aaaa-mm-dd HH:MM:SS**\n",
        "\n",
        "2. Filtragem dos dados espúrios (errôneos). Existem algumas medições onde a distância foi registrada como zero e outras medições em que a distância aparece maior do que 100 cm. O filtro deve ser construído para retirar os valores zero e os valores acima de 100 cm. **Nessa seção pode-se identificar quantas linhas foram retiradas do Dataframe**\n",
        "\n",
        "3. Calcular os valores de nível com base nas medições de distância considerando a distância total entre o sensor e o fundo do canal de 83,80 centímetros (linha verde da figura acima à esquerda). **Nessa seção deve-se criar uma nova coluna no Dataframe intitulada 'dist_cm' com altura da lâmina d'água para cada medição. Além disso, deverá ser calculada a média do nível d'água.**\n",
        "\n",
        "\n",
        "4. Calcular a Média horária dos dados de nível. Não é para calcular o perfil diário (média horária de todos os dias). É para calcular a média de cada intervalo de 60 minutos entre os dias 22-12-2020 e 22-03-2021. **Nessa seção o/deve-se criar um gráfico das médias horárias ao longo do tempo**\n",
        "\n",
        "5. Calcular a Média diária dos dados de nível. **Nessa seção deve-se criar um gráfico das médias diárias ao longo do tempo**\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VikvseXT_DQk"
      },
      "source": [
        "### 1. Leitura dos dados da distância do sensor até a lâmina d'água (medidas em campo com o sensor ultrassônico)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aG4ZWgXy_DQM"
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "import matplotlib.dates as mdates\n",
        "import numpy\n",
        "import pandas\n",
        "%matplotlib inline  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jgngOaEKl8XK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2d964a04-0b4d-47d8-e28f-878a087cd742"
      },
      "source": [
        "from urllib.request import urlretrieve\n",
        "URL = 'https://www.dropbox.com/s/3e4xgkztofh28vg/nivel_05_08_30_09.csv?dl=1'\n",
        "urlretrieve(URL, 'nivel_05_08_30_09.csv')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('nivel_05_08_30_09.csv', <http.client.HTTPMessage at 0x7f27fa7e1d30>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        },
        "id": "H2ckkrGiwNmx",
        "outputId": "119f4eef-9dac-4a84-e284-bc890ad2195f"
      },
      "source": [
        "dateparse = lambda x: pandas.datetime.strptime(x, '%d-%m-%Y')\n",
        "\n",
        "dados=pandas.read_csv(URL,sep=';',index_col=0, parse_dates=['data'], date_parser=dateparse)\n",
        "\n",
        "dados[0:3]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime instead.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    }\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>dist_cm</th>\n",
              "      <th>nivel_cm</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>data</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-08-05</th>\n",
              "      <td>65.10</td>\n",
              "      <td>18.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-05</th>\n",
              "      <td>65.04</td>\n",
              "      <td>18.76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-05</th>\n",
              "      <td>65.20</td>\n",
              "      <td>18.60</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            dist_cm  nivel_cm\n",
              "data                         \n",
              "2020-08-05    65.10     18.70\n",
              "2020-08-05    65.04     18.76\n",
              "2020-08-05    65.20     18.60"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u-_xmwKMpKKn"
      },
      "source": [
        "## CONVERTER INDEX IN DATETIME"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oKVR3Z0at6I5"
      },
      "source": [
        "#dados['data_hora'].pandas.datetime\n",
        "dados.index = pandas.to_datetime(dados.index)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "bb6w6lFRpGmX",
        "outputId": "58759427-792c-4c14-a798-e3272a44f874"
      },
      "source": [
        "dados"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\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>dist_cm</th>\n",
              "      <th>nivel_cm</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>data</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-08-05</th>\n",
              "      <td>65.10</td>\n",
              "      <td>18.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-05</th>\n",
              "      <td>65.04</td>\n",
              "      <td>18.76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-05</th>\n",
              "      <td>65.20</td>\n",
              "      <td>18.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-05</th>\n",
              "      <td>64.95</td>\n",
              "      <td>18.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-05</th>\n",
              "      <td>65.03</td>\n",
              "      <td>18.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-30</th>\n",
              "      <td>69.23</td>\n",
              "      <td>14.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-30</th>\n",
              "      <td>68.86</td>\n",
              "      <td>14.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-30</th>\n",
              "      <td>69.77</td>\n",
              "      <td>14.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-30</th>\n",
              "      <td>69.11</td>\n",
              "      <td>14.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-30</th>\n",
              "      <td>69.48</td>\n",
              "      <td>14.32</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5070 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            dist_cm  nivel_cm\n",
              "data                         \n",
              "2020-08-05    65.10     18.70\n",
              "2020-08-05    65.04     18.76\n",
              "2020-08-05    65.20     18.60\n",
              "2020-08-05    64.95     18.85\n",
              "2020-08-05    65.03     18.77\n",
              "...             ...       ...\n",
              "2020-09-30    69.23     14.57\n",
              "2020-09-30    68.86     14.94\n",
              "2020-09-30    69.77     14.03\n",
              "2020-09-30    69.11     14.69\n",
              "2020-09-30    69.48     14.32\n",
              "\n",
              "[5070 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZgW_U-MUlEsw"
      },
      "source": [
        "###2.Filtragem dos dados espúrios"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OHO-_yxDpLCl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9cec017b-88f2-4ce1-cb3e-cef9790d96b3"
      },
      "source": [
        "type(dados)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iDqsvKxqpST7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "outputId": "3af3cd2e-8bfa-45bf-9699-51b938ba29d1"
      },
      "source": [
        "dados_filtrados = dados.loc[(dados['dist_cm'] > 1.0) & (dados['dist_cm'] < 100.0)]\n",
        "dados_filtrados"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>dist_cm</th>\n",
              "      <th>nivel_cm</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>data</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-08-05</th>\n",
              "      <td>65.10</td>\n",
              "      <td>18.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-05</th>\n",
              "      <td>65.04</td>\n",
              "      <td>18.76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-05</th>\n",
              "      <td>65.20</td>\n",
              "      <td>18.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-05</th>\n",
              "      <td>64.95</td>\n",
              "      <td>18.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-05</th>\n",
              "      <td>65.03</td>\n",
              "      <td>18.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-30</th>\n",
              "      <td>69.23</td>\n",
              "      <td>14.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-30</th>\n",
              "      <td>68.86</td>\n",
              "      <td>14.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-30</th>\n",
              "      <td>69.77</td>\n",
              "      <td>14.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-30</th>\n",
              "      <td>69.11</td>\n",
              "      <td>14.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-30</th>\n",
              "      <td>69.48</td>\n",
              "      <td>14.32</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5010 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            dist_cm  nivel_cm\n",
              "data                         \n",
              "2020-08-05    65.10     18.70\n",
              "2020-08-05    65.04     18.76\n",
              "2020-08-05    65.20     18.60\n",
              "2020-08-05    64.95     18.85\n",
              "2020-08-05    65.03     18.77\n",
              "...             ...       ...\n",
              "2020-09-30    69.23     14.57\n",
              "2020-09-30    68.86     14.94\n",
              "2020-09-30    69.77     14.03\n",
              "2020-09-30    69.11     14.69\n",
              "2020-09-30    69.48     14.32\n",
              "\n",
              "[5010 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4kbDENJfkyld",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c8410a63-55ef-4584-8bdb-f553a051fbaa"
      },
      "source": [
        "len(dados)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5070"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ENUlJOvOOOOE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7cfed48a-c8b5-41f2-ce34-4f080a6b6d37"
      },
      "source": [
        "len(dados_filtrados)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5010"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LisCN1kqM9ul",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2aca52f4-3f9b-463a-e228-c6529fdefaa0"
      },
      "source": [
        "print(len(dados)-len(dados_filtrados))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "60\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gODLe3wTI4eF"
      },
      "source": [
        "3. Cálculo do Nível"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WIkj49qUPWqk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e3bb7ad4-2362-45d5-d3b0-ecfa496a428d"
      },
      "source": [
        "dados_filtrados['nivel_cm'] = 83.80 - dados_filtrados['dist_cm']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hxtS7GjSJMWK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "outputId": "52288aac-6786-4a41-b73b-c9451fcdd9d7"
      },
      "source": [
        "dados_filtrados"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>dist_cm</th>\n",
              "      <th>nivel_cm</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>data</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-08-05</th>\n",
              "      <td>65.10</td>\n",
              "      <td>18.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-05</th>\n",
              "      <td>65.04</td>\n",
              "      <td>18.76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-05</th>\n",
              "      <td>65.20</td>\n",
              "      <td>18.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-05</th>\n",
              "      <td>64.95</td>\n",
              "      <td>18.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-05</th>\n",
              "      <td>65.03</td>\n",
              "      <td>18.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-30</th>\n",
              "      <td>69.23</td>\n",
              "      <td>14.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-30</th>\n",
              "      <td>68.86</td>\n",
              "      <td>14.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-30</th>\n",
              "      <td>69.77</td>\n",
              "      <td>14.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-30</th>\n",
              "      <td>69.11</td>\n",
              "      <td>14.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-30</th>\n",
              "      <td>69.48</td>\n",
              "      <td>14.32</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5010 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            dist_cm  nivel_cm\n",
              "data                         \n",
              "2020-08-05    65.10     18.70\n",
              "2020-08-05    65.04     18.76\n",
              "2020-08-05    65.20     18.60\n",
              "2020-08-05    64.95     18.85\n",
              "2020-08-05    65.03     18.77\n",
              "...             ...       ...\n",
              "2020-09-30    69.23     14.57\n",
              "2020-09-30    68.86     14.94\n",
              "2020-09-30    69.77     14.03\n",
              "2020-09-30    69.11     14.69\n",
              "2020-09-30    69.48     14.32\n",
              "\n",
              "[5010 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yJnfaDvgJVzA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "82e57161-f02a-44ae-8847-a9470631816f"
      },
      "source": [
        "dados_filtrados['nivel_cm'].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15.186139720558916"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dmKNMSw5KFdD"
      },
      "source": [
        "4. Média Horária"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "umbvuthtijCG"
      },
      "source": [
        "medias = dados_filtrados.resample('1d').mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "SsODOkT_ipPy",
        "outputId": "9d3ee4dd-b0e1-443e-ffea-328af7c68dfa"
      },
      "source": [
        "medias"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>dist_cm</th>\n",
              "      <th>nivel_cm</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>data</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-08-05</th>\n",
              "      <td>65.107778</td>\n",
              "      <td>18.692222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-06</th>\n",
              "      <td>64.665532</td>\n",
              "      <td>19.134468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-07</th>\n",
              "      <td>64.131286</td>\n",
              "      <td>19.668714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-08</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-09</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-10</th>\n",
              "      <td>68.856042</td>\n",
              "      <td>14.943958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-11</th>\n",
              "      <td>69.920625</td>\n",
              "      <td>13.879375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-12</th>\n",
              "      <td>69.569474</td>\n",
              "      <td>14.230526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-13</th>\n",
              "      <td>67.331474</td>\n",
              "      <td>16.468526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-14</th>\n",
              "      <td>68.783333</td>\n",
              "      <td>15.016667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-15</th>\n",
              "      <td>63.792500</td>\n",
              "      <td>20.007500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-16</th>\n",
              "      <td>62.945745</td>\n",
              "      <td>20.854255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-17</th>\n",
              "      <td>66.854842</td>\n",
              "      <td>16.945158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-18</th>\n",
              "      <td>63.923474</td>\n",
              "      <td>19.876526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-19</th>\n",
              "      <td>62.907065</td>\n",
              "      <td>20.892935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-20</th>\n",
              "      <td>62.661474</td>\n",
              "      <td>21.138526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-21</th>\n",
              "      <td>65.785275</td>\n",
              "      <td>18.014725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-22</th>\n",
              "      <td>67.277158</td>\n",
              "      <td>16.522842</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-23</th>\n",
              "      <td>67.733438</td>\n",
              "      <td>16.066563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-24</th>\n",
              "      <td>68.381895</td>\n",
              "      <td>15.418105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-25</th>\n",
              "      <td>67.855158</td>\n",
              "      <td>15.944842</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-26</th>\n",
              "      <td>67.973370</td>\n",
              "      <td>15.826630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-27</th>\n",
              "      <td>68.797000</td>\n",
              "      <td>15.003000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-28</th>\n",
              "      <td>68.135745</td>\n",
              "      <td>15.664255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-29</th>\n",
              "      <td>68.092903</td>\n",
              "      <td>15.707097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-30</th>\n",
              "      <td>68.919687</td>\n",
              "      <td>14.880312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-31</th>\n",
              "      <td>69.014271</td>\n",
              "      <td>14.785729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-01</th>\n",
              "      <td>69.222234</td>\n",
              "      <td>14.577766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-02</th>\n",
              "      <td>69.941354</td>\n",
              "      <td>13.858646</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-03</th>\n",
              "      <td>69.036146</td>\n",
              "      <td>14.763854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-04</th>\n",
              "      <td>69.180625</td>\n",
              "      <td>14.619375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-05</th>\n",
              "      <td>70.039167</td>\n",
              "      <td>13.760833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-06</th>\n",
              "      <td>69.369271</td>\n",
              "      <td>14.430729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-07</th>\n",
              "      <td>69.545625</td>\n",
              "      <td>14.254375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-08</th>\n",
              "      <td>70.132553</td>\n",
              "      <td>13.667447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-09</th>\n",
              "      <td>69.276632</td>\n",
              "      <td>14.523368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-10</th>\n",
              "      <td>69.350208</td>\n",
              "      <td>14.449792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-11</th>\n",
              "      <td>70.118404</td>\n",
              "      <td>13.681596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-12</th>\n",
              "      <td>69.294271</td>\n",
              "      <td>14.505729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-13</th>\n",
              "      <td>69.494000</td>\n",
              "      <td>14.306000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-14</th>\n",
              "      <td>70.350526</td>\n",
              "      <td>13.449474</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-15</th>\n",
              "      <td>69.438542</td>\n",
              "      <td>14.361458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-16</th>\n",
              "      <td>69.929688</td>\n",
              "      <td>13.870312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-17</th>\n",
              "      <td>71.443579</td>\n",
              "      <td>12.356421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-18</th>\n",
              "      <td>70.734184</td>\n",
              "      <td>13.065816</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-19</th>\n",
              "      <td>70.735054</td>\n",
              "      <td>13.064946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-20</th>\n",
              "      <td>71.639789</td>\n",
              "      <td>12.160211</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-21</th>\n",
              "      <td>70.977292</td>\n",
              "      <td>12.822708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-22</th>\n",
              "      <td>71.135104</td>\n",
              "      <td>12.664896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-23</th>\n",
              "      <td>71.642105</td>\n",
              "      <td>12.157895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-24</th>\n",
              "      <td>70.552632</td>\n",
              "      <td>13.247368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-25</th>\n",
              "      <td>70.846042</td>\n",
              "      <td>12.953958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-26</th>\n",
              "      <td>71.444787</td>\n",
              "      <td>12.355213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-27</th>\n",
              "      <td>70.375532</td>\n",
              "      <td>13.424468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-28</th>\n",
              "      <td>65.478947</td>\n",
              "      <td>18.321053</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-29</th>\n",
              "      <td>70.298958</td>\n",
              "      <td>13.501042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-30</th>\n",
              "      <td>70.049091</td>\n",
              "      <td>13.750909</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              dist_cm   nivel_cm\n",
              "data                            \n",
              "2020-08-05  65.107778  18.692222\n",
              "2020-08-06  64.665532  19.134468\n",
              "2020-08-07  64.131286  19.668714\n",
              "2020-08-08        NaN        NaN\n",
              "2020-08-09        NaN        NaN\n",
              "2020-08-10  68.856042  14.943958\n",
              "2020-08-11  69.920625  13.879375\n",
              "2020-08-12  69.569474  14.230526\n",
              "2020-08-13  67.331474  16.468526\n",
              "2020-08-14  68.783333  15.016667\n",
              "2020-08-15  63.792500  20.007500\n",
              "2020-08-16  62.945745  20.854255\n",
              "2020-08-17  66.854842  16.945158\n",
              "2020-08-18  63.923474  19.876526\n",
              "2020-08-19  62.907065  20.892935\n",
              "2020-08-20  62.661474  21.138526\n",
              "2020-08-21  65.785275  18.014725\n",
              "2020-08-22  67.277158  16.522842\n",
              "2020-08-23  67.733438  16.066563\n",
              "2020-08-24  68.381895  15.418105\n",
              "2020-08-25  67.855158  15.944842\n",
              "2020-08-26  67.973370  15.826630\n",
              "2020-08-27  68.797000  15.003000\n",
              "2020-08-28  68.135745  15.664255\n",
              "2020-08-29  68.092903  15.707097\n",
              "2020-08-30  68.919687  14.880312\n",
              "2020-08-31  69.014271  14.785729\n",
              "2020-09-01  69.222234  14.577766\n",
              "2020-09-02  69.941354  13.858646\n",
              "2020-09-03  69.036146  14.763854\n",
              "2020-09-04  69.180625  14.619375\n",
              "2020-09-05  70.039167  13.760833\n",
              "2020-09-06  69.369271  14.430729\n",
              "2020-09-07  69.545625  14.254375\n",
              "2020-09-08  70.132553  13.667447\n",
              "2020-09-09  69.276632  14.523368\n",
              "2020-09-10  69.350208  14.449792\n",
              "2020-09-11  70.118404  13.681596\n",
              "2020-09-12  69.294271  14.505729\n",
              "2020-09-13  69.494000  14.306000\n",
              "2020-09-14  70.350526  13.449474\n",
              "2020-09-15  69.438542  14.361458\n",
              "2020-09-16  69.929688  13.870312\n",
              "2020-09-17  71.443579  12.356421\n",
              "2020-09-18  70.734184  13.065816\n",
              "2020-09-19  70.735054  13.064946\n",
              "2020-09-20  71.639789  12.160211\n",
              "2020-09-21  70.977292  12.822708\n",
              "2020-09-22  71.135104  12.664896\n",
              "2020-09-23  71.642105  12.157895\n",
              "2020-09-24  70.552632  13.247368\n",
              "2020-09-25  70.846042  12.953958\n",
              "2020-09-26  71.444787  12.355213\n",
              "2020-09-27  70.375532  13.424468\n",
              "2020-09-28  65.478947  18.321053\n",
              "2020-09-29  70.298958  13.501042\n",
              "2020-09-30  70.049091  13.750909"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eT1-cYc7MQEw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "86897835-0dca-49ea-d5b9-69f51760be88"
      },
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "plt.plot(medias['nivel_cm'],color='blue')\n",
        "#plt.xlabel('nivel (cm)')\n",
        "plt.ylabel('nivel (cm)');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xkvd0lXUd_qb"
      },
      "source": [
        "**Exercício II:** Com os dados já tratados, agora pode-se utilizar de métodos empíricos para o cálculo da vazão. Considere que o córrego está canalizado em uma manilha de 1.50 metros de diâmetro, e a declividade é de 1,31%.\n",
        "\n",
        "Realizar o cálculo da vazão ($Q$) associada ao escoamento no canal circular utilizando os dados horários de nível. Uma aproximação da vazão $Q$ pode ser obtida aplicando a equação de Manning: \n",
        "\n",
        "$\\displaystyle Q = \\ \\frac{A}{n}  Rh^{2/3} S^{1/2}$\n",
        "\n",
        "Sendo que $A$ é a área da seção transversal de fluxo normal à direção do fluxo, $Rh$ é o raio hidráulico, $S$ é a declividade do fundo do canal e $n$ é o coeficiente de rugosidade de Gauckler–Manning. Considere que o valor do coeficiente de Gauckler–Manning é $n = 0.2$ s/m$^{1/3}$.\n",
        "\n",
        "Para obter a vazão o/a discente deverá realizar o produto da velocidade e da área. \n",
        "\n",
        "Observe a figura abaixo e identifique as variáveis: $D$ (diâmetro do tubo), $r$ (raio do tubo), $h$ (altura da lâmina d'água) e $\\theta$ (ângulo em relação ao centro do tubo). \n",
        "\n",
        "<img src=\"https://www.dropbox.com/s/zx1xlfbqd16j5m2/wetted-circle_ok.png?dl=1\" width=\"250\"/> \n",
        "\n",
        "Para proceder com os cálculos sugere-se :\n",
        "\n",
        "1. Calcular $\\theta$ (em radianos) de acordo com as fórmulas abaixo, sendo que $h$ é a altura da lâmina d'água.\n",
        "2. Cálculo da área molhada\n",
        "3. Cálculo do perímetro molhado\n",
        "4. Cálculo do raio hidráulico\n",
        "5. Cálculo da velocidade média\n",
        "6. Cálculo da vazão\n",
        "\n",
        "A seguir são apresentadas algumas equações que podem ser úteis nos cálculos. \n",
        "\n",
        "O raio ($r$) é a metade do diâmetro ($D$):\n",
        "\n",
        "$$r = \\frac{D}{2}$$\n",
        "\n",
        "O ângulo $\\theta$ deverá ser calculado em radianos e pode ser obtido de acordo com a equação:\n",
        "\n",
        "$$\\theta = 2 \\, \\text{arc cos} \\left(\\frac{r-h}{r} \\right)$$\n",
        "\n",
        "A área da seção transversal do escoamento ($A$) é:\n",
        "\n",
        "$$A = \\frac{r^2 (\\theta - \\text{sen}\\theta)}{2}$$\n",
        "\n",
        "O perímetro molhado ($P$) é:\n",
        "\n",
        "$$P = r \\theta$$\n",
        "\n",
        "O raio hidráulico ($R_h$) é:\n",
        "\n",
        "$$R_h = \\frac{A}{P}$$\n",
        "\n",
        "\n",
        "Sugestão de leitura de [material com as equações](https://www.cedengineering.com/userfiles/Partially%20Full%20Pipe%20Flow%20Calculations.pdf).\n",
        "\n",
        "**No final do exercício II o/a discente deverá fazer 3 coisas:**\n",
        "\n",
        "**a) Mostrar um gráfico da vazão horária (m3/s) ao longo do tempo.** (não é o perfil diário de vazão).\n",
        "\n",
        "**b) Mostrar um histograma da Vazão horária (m3/s).**\n",
        "\n",
        "**c) Calcular a mediana da Vazão horária (m3/s)**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_1GjsP7_V0Bz"
      },
      "source": [
        "**Solução:**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "edNXrg9acgqY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2657ad25-8ac7-4e76-fbf4-d5b04f4a7f07"
      },
      "source": [
        "S = numpy.arctan(0.24/18.82)\r\n",
        "S"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.012751699861372612"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x9rA3JXkWA57"
      },
      "source": [
        "import numpy\n",
        "\n",
        "nivel = medias['nivel_cm'].values/100.0\n",
        "\n",
        "theta = 2*numpy.arccos((0.75-nivel)/0.75)\n",
        "\n",
        "area = (0.75**2)*(theta-numpy.sin(theta))/2.0\n",
        "\n",
        "perimetro = 0.75*theta\n",
        "\n",
        "raio_hidraulico = area/perimetro\n",
        "\n",
        "vazao =  (area/0.04) * raio_hidraulico**(2/3) * S**(1/2)\n",
        "\n",
        "vazao_l_s = vazao * 1000\n",
        "\n",
        "medias['theta'] = theta\n",
        "medias['area'] = area\n",
        "medias['perimetro'] = perimetro\n",
        "medias['raio_hidraulico'] = raio_hidraulico\n",
        "medias['vazao(m3/s)'] = vazao\n",
        "medias['vazao(l/s)'] = vazao_l_s"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EDOONOMNXFbM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "cf239c63-13e4-486d-90ed-884f9f9080f7"
      },
      "source": [
        "medias\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>dist_cm</th>\n",
              "      <th>nivel_cm</th>\n",
              "      <th>theta</th>\n",
              "      <th>area</th>\n",
              "      <th>perimetro</th>\n",
              "      <th>raio_hidraulico</th>\n",
              "      <th>vazao(m3/s)</th>\n",
              "      <th>vazao(l/s)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>data</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-08-05</th>\n",
              "      <td>65.107778</td>\n",
              "      <td>18.692222</td>\n",
              "      <td>1.443138</td>\n",
              "      <td>0.126921</td>\n",
              "      <td>1.082353</td>\n",
              "      <td>0.117264</td>\n",
              "      <td>0.085842</td>\n",
              "      <td>85.842210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-06</th>\n",
              "      <td>64.665532</td>\n",
              "      <td>19.134468</td>\n",
              "      <td>1.460901</td>\n",
              "      <td>0.131325</td>\n",
              "      <td>1.095676</td>\n",
              "      <td>0.119858</td>\n",
              "      <td>0.090126</td>\n",
              "      <td>90.125773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-07</th>\n",
              "      <td>64.131286</td>\n",
              "      <td>19.668714</td>\n",
              "      <td>1.482129</td>\n",
              "      <td>0.136704</td>\n",
              "      <td>1.111596</td>\n",
              "      <td>0.122979</td>\n",
              "      <td>0.095439</td>\n",
              "      <td>95.438935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-08</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-09</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-10</th>\n",
              "      <td>68.856042</td>\n",
              "      <td>14.943958</td>\n",
              "      <td>1.284509</td>\n",
              "      <td>0.091465</td>\n",
              "      <td>0.963382</td>\n",
              "      <td>0.094942</td>\n",
              "      <td>0.053739</td>\n",
              "      <td>53.738625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-11</th>\n",
              "      <td>69.920625</td>\n",
              "      <td>13.879375</td>\n",
              "      <td>1.236336</td>\n",
              "      <td>0.082054</td>\n",
              "      <td>0.927252</td>\n",
              "      <td>0.088492</td>\n",
              "      <td>0.046000</td>\n",
              "      <td>46.000226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-12</th>\n",
              "      <td>69.569474</td>\n",
              "      <td>14.230526</td>\n",
              "      <td>1.252403</td>\n",
              "      <td>0.085124</td>\n",
              "      <td>0.939302</td>\n",
              "      <td>0.090625</td>\n",
              "      <td>0.048485</td>\n",
              "      <td>48.484994</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-13</th>\n",
              "      <td>67.331474</td>\n",
              "      <td>16.468526</td>\n",
              "      <td>1.350919</td>\n",
              "      <td>0.105467</td>\n",
              "      <td>1.013189</td>\n",
              "      <td>0.104094</td>\n",
              "      <td>0.065886</td>\n",
              "      <td>65.886069</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-14</th>\n",
              "      <td>68.783333</td>\n",
              "      <td>15.016667</td>\n",
              "      <td>1.287743</td>\n",
              "      <td>0.092119</td>\n",
              "      <td>0.965807</td>\n",
              "      <td>0.095381</td>\n",
              "      <td>0.054289</td>\n",
              "      <td>54.289484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-15</th>\n",
              "      <td>63.792500</td>\n",
              "      <td>20.007500</td>\n",
              "      <td>1.495463</td>\n",
              "      <td>0.140147</td>\n",
              "      <td>1.121597</td>\n",
              "      <td>0.124953</td>\n",
              "      <td>0.098887</td>\n",
              "      <td>98.886531</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-16</th>\n",
              "      <td>62.945745</td>\n",
              "      <td>20.854255</td>\n",
              "      <td>1.528379</td>\n",
              "      <td>0.148860</td>\n",
              "      <td>1.146285</td>\n",
              "      <td>0.129863</td>\n",
              "      <td>0.107768</td>\n",
              "      <td>107.768282</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-17</th>\n",
              "      <td>66.854842</td>\n",
              "      <td>16.945158</td>\n",
              "      <td>1.371120</td>\n",
              "      <td>0.109966</td>\n",
              "      <td>1.028340</td>\n",
              "      <td>0.106935</td>\n",
              "      <td>0.069940</td>\n",
              "      <td>69.940478</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-18</th>\n",
              "      <td>63.923474</td>\n",
              "      <td>19.876526</td>\n",
              "      <td>1.490319</td>\n",
              "      <td>0.138813</td>\n",
              "      <td>1.117739</td>\n",
              "      <td>0.124190</td>\n",
              "      <td>0.097547</td>\n",
              "      <td>97.546503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-19</th>\n",
              "      <td>62.907065</td>\n",
              "      <td>20.892935</td>\n",
              "      <td>1.529870</td>\n",
              "      <td>0.149261</td>\n",
              "      <td>1.147402</td>\n",
              "      <td>0.130086</td>\n",
              "      <td>0.108183</td>\n",
              "      <td>108.183011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-20</th>\n",
              "      <td>62.661474</td>\n",
              "      <td>21.138526</td>\n",
              "      <td>1.539304</td>\n",
              "      <td>0.151819</td>\n",
              "      <td>1.154478</td>\n",
              "      <td>0.131504</td>\n",
              "      <td>0.110835</td>\n",
              "      <td>110.834617</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-21</th>\n",
              "      <td>65.785275</td>\n",
              "      <td>18.014725</td>\n",
              "      <td>1.415571</td>\n",
              "      <td>0.120261</td>\n",
              "      <td>1.061678</td>\n",
              "      <td>0.113274</td>\n",
              "      <td>0.079482</td>\n",
              "      <td>79.482013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-22</th>\n",
              "      <td>67.277158</td>\n",
              "      <td>16.522842</td>\n",
              "      <td>1.353234</td>\n",
              "      <td>0.105977</td>\n",
              "      <td>1.014926</td>\n",
              "      <td>0.104419</td>\n",
              "      <td>0.066342</td>\n",
              "      <td>66.341938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-23</th>\n",
              "      <td>67.733438</td>\n",
              "      <td>16.066563</td>\n",
              "      <td>1.333683</td>\n",
              "      <td>0.101718</td>\n",
              "      <td>1.000262</td>\n",
              "      <td>0.101691</td>\n",
              "      <td>0.062562</td>\n",
              "      <td>62.561742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-24</th>\n",
              "      <td>68.381895</td>\n",
              "      <td>15.418105</td>\n",
              "      <td>1.305471</td>\n",
              "      <td>0.095755</td>\n",
              "      <td>0.979103</td>\n",
              "      <td>0.097799</td>\n",
              "      <td>0.057382</td>\n",
              "      <td>57.382271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-25</th>\n",
              "      <td>67.855158</td>\n",
              "      <td>15.944842</td>\n",
              "      <td>1.328426</td>\n",
              "      <td>0.100590</td>\n",
              "      <td>0.996320</td>\n",
              "      <td>0.100962</td>\n",
              "      <td>0.061572</td>\n",
              "      <td>61.572242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-26</th>\n",
              "      <td>67.973370</td>\n",
              "      <td>15.826630</td>\n",
              "      <td>1.323304</td>\n",
              "      <td>0.099499</td>\n",
              "      <td>0.992478</td>\n",
              "      <td>0.100253</td>\n",
              "      <td>0.060619</td>\n",
              "      <td>60.618904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-27</th>\n",
              "      <td>68.797000</td>\n",
              "      <td>15.003000</td>\n",
              "      <td>1.287136</td>\n",
              "      <td>0.091996</td>\n",
              "      <td>0.965352</td>\n",
              "      <td>0.095298</td>\n",
              "      <td>0.054186</td>\n",
              "      <td>54.185723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-28</th>\n",
              "      <td>68.135745</td>\n",
              "      <td>15.664255</td>\n",
              "      <td>1.316241</td>\n",
              "      <td>0.098006</td>\n",
              "      <td>0.987181</td>\n",
              "      <td>0.099279</td>\n",
              "      <td>0.059322</td>\n",
              "      <td>59.321676</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-29</th>\n",
              "      <td>68.092903</td>\n",
              "      <td>15.707097</td>\n",
              "      <td>1.318107</td>\n",
              "      <td>0.098399</td>\n",
              "      <td>0.988581</td>\n",
              "      <td>0.099536</td>\n",
              "      <td>0.059663</td>\n",
              "      <td>59.662560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-30</th>\n",
              "      <td>68.919687</td>\n",
              "      <td>14.880312</td>\n",
              "      <td>1.281673</td>\n",
              "      <td>0.090894</td>\n",
              "      <td>0.961255</td>\n",
              "      <td>0.094558</td>\n",
              "      <td>0.053259</td>\n",
              "      <td>53.258771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-31</th>\n",
              "      <td>69.014271</td>\n",
              "      <td>14.785729</td>\n",
              "      <td>1.277449</td>\n",
              "      <td>0.090047</td>\n",
              "      <td>0.958086</td>\n",
              "      <td>0.093986</td>\n",
              "      <td>0.052550</td>\n",
              "      <td>52.549706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-01</th>\n",
              "      <td>69.222234</td>\n",
              "      <td>14.577766</td>\n",
              "      <td>1.268117</td>\n",
              "      <td>0.088193</td>\n",
              "      <td>0.951088</td>\n",
              "      <td>0.092729</td>\n",
              "      <td>0.051008</td>\n",
              "      <td>51.007663</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-02</th>\n",
              "      <td>69.941354</td>\n",
              "      <td>13.858646</td>\n",
              "      <td>1.235381</td>\n",
              "      <td>0.081874</td>\n",
              "      <td>0.926536</td>\n",
              "      <td>0.088366</td>\n",
              "      <td>0.045856</td>\n",
              "      <td>45.855630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-03</th>\n",
              "      <td>69.036146</td>\n",
              "      <td>14.763854</td>\n",
              "      <td>1.276470</td>\n",
              "      <td>0.089852</td>\n",
              "      <td>0.957352</td>\n",
              "      <td>0.093854</td>\n",
              "      <td>0.052386</td>\n",
              "      <td>52.386404</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-04</th>\n",
              "      <td>69.180625</td>\n",
              "      <td>14.619375</td>\n",
              "      <td>1.269989</td>\n",
              "      <td>0.088563</td>\n",
              "      <td>0.952492</td>\n",
              "      <td>0.092981</td>\n",
              "      <td>0.051314</td>\n",
              "      <td>51.314323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-05</th>\n",
              "      <td>70.039167</td>\n",
              "      <td>13.760833</td>\n",
              "      <td>1.230871</td>\n",
              "      <td>0.081026</td>\n",
              "      <td>0.923153</td>\n",
              "      <td>0.087771</td>\n",
              "      <td>0.045176</td>\n",
              "      <td>45.176480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-06</th>\n",
              "      <td>69.369271</td>\n",
              "      <td>14.430729</td>\n",
              "      <td>1.261484</td>\n",
              "      <td>0.086890</td>\n",
              "      <td>0.946113</td>\n",
              "      <td>0.091838</td>\n",
              "      <td>0.049931</td>\n",
              "      <td>49.931494</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-07</th>\n",
              "      <td>69.545625</td>\n",
              "      <td>14.254375</td>\n",
              "      <td>1.253487</td>\n",
              "      <td>0.085334</td>\n",
              "      <td>0.940116</td>\n",
              "      <td>0.090769</td>\n",
              "      <td>0.048656</td>\n",
              "      <td>48.656168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-08</th>\n",
              "      <td>70.132553</td>\n",
              "      <td>13.667447</td>\n",
              "      <td>1.226550</td>\n",
              "      <td>0.080218</td>\n",
              "      <td>0.919913</td>\n",
              "      <td>0.087202</td>\n",
              "      <td>0.044533</td>\n",
              "      <td>44.532892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-09</th>\n",
              "      <td>69.276632</td>\n",
              "      <td>14.523368</td>\n",
              "      <td>1.265666</td>\n",
              "      <td>0.087710</td>\n",
              "      <td>0.949250</td>\n",
              "      <td>0.092399</td>\n",
              "      <td>0.050608</td>\n",
              "      <td>50.608163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-10</th>\n",
              "      <td>69.350208</td>\n",
              "      <td>14.449792</td>\n",
              "      <td>1.262345</td>\n",
              "      <td>0.087058</td>\n",
              "      <td>0.946759</td>\n",
              "      <td>0.091954</td>\n",
              "      <td>0.050070</td>\n",
              "      <td>50.070354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-11</th>\n",
              "      <td>70.118404</td>\n",
              "      <td>13.681596</td>\n",
              "      <td>1.227206</td>\n",
              "      <td>0.080340</td>\n",
              "      <td>0.920404</td>\n",
              "      <td>0.087288</td>\n",
              "      <td>0.044630</td>\n",
              "      <td>44.630099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-12</th>\n",
              "      <td>69.294271</td>\n",
              "      <td>14.505729</td>\n",
              "      <td>1.264871</td>\n",
              "      <td>0.087554</td>\n",
              "      <td>0.948653</td>\n",
              "      <td>0.092293</td>\n",
              "      <td>0.050479</td>\n",
              "      <td>50.478962</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-13</th>\n",
              "      <td>69.494000</td>\n",
              "      <td>14.306000</td>\n",
              "      <td>1.255833</td>\n",
              "      <td>0.085788</td>\n",
              "      <td>0.941875</td>\n",
              "      <td>0.091083</td>\n",
              "      <td>0.049028</td>\n",
              "      <td>49.027759</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-14</th>\n",
              "      <td>70.350526</td>\n",
              "      <td>13.449474</td>\n",
              "      <td>1.216415</td>\n",
              "      <td>0.078343</td>\n",
              "      <td>0.912311</td>\n",
              "      <td>0.085873</td>\n",
              "      <td>0.043049</td>\n",
              "      <td>43.049065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-15</th>\n",
              "      <td>69.438542</td>\n",
              "      <td>14.361458</td>\n",
              "      <td>1.258348</td>\n",
              "      <td>0.086277</td>\n",
              "      <td>0.943761</td>\n",
              "      <td>0.091419</td>\n",
              "      <td>0.049429</td>\n",
              "      <td>49.428548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-16</th>\n",
              "      <td>69.929688</td>\n",
              "      <td>13.870312</td>\n",
              "      <td>1.235919</td>\n",
              "      <td>0.081975</td>\n",
              "      <td>0.926939</td>\n",
              "      <td>0.088437</td>\n",
              "      <td>0.045937</td>\n",
              "      <td>45.936982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-17</th>\n",
              "      <td>71.443579</td>\n",
              "      <td>12.356421</td>\n",
              "      <td>1.164426</td>\n",
              "      <td>0.069149</td>\n",
              "      <td>0.873320</td>\n",
              "      <td>0.079180</td>\n",
              "      <td>0.035996</td>\n",
              "      <td>35.996160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-18</th>\n",
              "      <td>70.734184</td>\n",
              "      <td>13.065816</td>\n",
              "      <td>1.198393</td>\n",
              "      <td>0.075076</td>\n",
              "      <td>0.898795</td>\n",
              "      <td>0.083530</td>\n",
              "      <td>0.040500</td>\n",
              "      <td>40.499839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-19</th>\n",
              "      <td>70.735054</td>\n",
              "      <td>13.064946</td>\n",
              "      <td>1.198352</td>\n",
              "      <td>0.075069</td>\n",
              "      <td>0.898764</td>\n",
              "      <td>0.083524</td>\n",
              "      <td>0.040494</td>\n",
              "      <td>40.494148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-20</th>\n",
              "      <td>71.639789</td>\n",
              "      <td>12.160211</td>\n",
              "      <td>1.154876</td>\n",
              "      <td>0.067537</td>\n",
              "      <td>0.866157</td>\n",
              "      <td>0.077973</td>\n",
              "      <td>0.034799</td>\n",
              "      <td>34.798599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-21</th>\n",
              "      <td>70.977292</td>\n",
              "      <td>12.822708</td>\n",
              "      <td>1.186849</td>\n",
              "      <td>0.073028</td>\n",
              "      <td>0.890137</td>\n",
              "      <td>0.082042</td>\n",
              "      <td>0.038926</td>\n",
              "      <td>38.925749</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-22</th>\n",
              "      <td>71.135104</td>\n",
              "      <td>12.664896</td>\n",
              "      <td>1.179302</td>\n",
              "      <td>0.071708</td>\n",
              "      <td>0.884477</td>\n",
              "      <td>0.081074</td>\n",
              "      <td>0.037921</td>\n",
              "      <td>37.921065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-23</th>\n",
              "      <td>71.642105</td>\n",
              "      <td>12.157895</td>\n",
              "      <td>1.154763</td>\n",
              "      <td>0.067518</td>\n",
              "      <td>0.866072</td>\n",
              "      <td>0.077959</td>\n",
              "      <td>0.034785</td>\n",
              "      <td>34.784590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-24</th>\n",
              "      <td>70.552632</td>\n",
              "      <td>13.247368</td>\n",
              "      <td>1.206950</td>\n",
              "      <td>0.076617</td>\n",
              "      <td>0.905213</td>\n",
              "      <td>0.084640</td>\n",
              "      <td>0.041696</td>\n",
              "      <td>41.696236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-25</th>\n",
              "      <td>70.846042</td>\n",
              "      <td>12.953958</td>\n",
              "      <td>1.193093</td>\n",
              "      <td>0.074132</td>\n",
              "      <td>0.894820</td>\n",
              "      <td>0.082845</td>\n",
              "      <td>0.039772</td>\n",
              "      <td>39.771599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-26</th>\n",
              "      <td>71.444787</td>\n",
              "      <td>12.355213</td>\n",
              "      <td>1.164368</td>\n",
              "      <td>0.069139</td>\n",
              "      <td>0.873276</td>\n",
              "      <td>0.079173</td>\n",
              "      <td>0.035989</td>\n",
              "      <td>35.988721</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-27</th>\n",
              "      <td>70.375532</td>\n",
              "      <td>13.424468</td>\n",
              "      <td>1.215247</td>\n",
              "      <td>0.078129</td>\n",
              "      <td>0.911435</td>\n",
              "      <td>0.085721</td>\n",
              "      <td>0.042880</td>\n",
              "      <td>42.880486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-28</th>\n",
              "      <td>65.478947</td>\n",
              "      <td>18.321053</td>\n",
              "      <td>1.428089</td>\n",
              "      <td>0.123259</td>\n",
              "      <td>1.071067</td>\n",
              "      <td>0.115081</td>\n",
              "      <td>0.082327</td>\n",
              "      <td>82.327432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-29</th>\n",
              "      <td>70.298958</td>\n",
              "      <td>13.501042</td>\n",
              "      <td>1.218819</td>\n",
              "      <td>0.078786</td>\n",
              "      <td>0.914114</td>\n",
              "      <td>0.086188</td>\n",
              "      <td>0.043398</td>\n",
              "      <td>43.397785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-30</th>\n",
              "      <td>70.049091</td>\n",
              "      <td>13.750909</td>\n",
              "      <td>1.230412</td>\n",
              "      <td>0.080940</td>\n",
              "      <td>0.922809</td>\n",
              "      <td>0.087710</td>\n",
              "      <td>0.045108</td>\n",
              "      <td>45.107861</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              dist_cm   nivel_cm  ...  vazao(m3/s)  vazao(l/s)\n",
              "data                              ...                         \n",
              "2020-08-05  65.107778  18.692222  ...     0.085842   85.842210\n",
              "2020-08-06  64.665532  19.134468  ...     0.090126   90.125773\n",
              "2020-08-07  64.131286  19.668714  ...     0.095439   95.438935\n",
              "2020-08-08        NaN        NaN  ...          NaN         NaN\n",
              "2020-08-09        NaN        NaN  ...          NaN         NaN\n",
              "2020-08-10  68.856042  14.943958  ...     0.053739   53.738625\n",
              "2020-08-11  69.920625  13.879375  ...     0.046000   46.000226\n",
              "2020-08-12  69.569474  14.230526  ...     0.048485   48.484994\n",
              "2020-08-13  67.331474  16.468526  ...     0.065886   65.886069\n",
              "2020-08-14  68.783333  15.016667  ...     0.054289   54.289484\n",
              "2020-08-15  63.792500  20.007500  ...     0.098887   98.886531\n",
              "2020-08-16  62.945745  20.854255  ...     0.107768  107.768282\n",
              "2020-08-17  66.854842  16.945158  ...     0.069940   69.940478\n",
              "2020-08-18  63.923474  19.876526  ...     0.097547   97.546503\n",
              "2020-08-19  62.907065  20.892935  ...     0.108183  108.183011\n",
              "2020-08-20  62.661474  21.138526  ...     0.110835  110.834617\n",
              "2020-08-21  65.785275  18.014725  ...     0.079482   79.482013\n",
              "2020-08-22  67.277158  16.522842  ...     0.066342   66.341938\n",
              "2020-08-23  67.733438  16.066563  ...     0.062562   62.561742\n",
              "2020-08-24  68.381895  15.418105  ...     0.057382   57.382271\n",
              "2020-08-25  67.855158  15.944842  ...     0.061572   61.572242\n",
              "2020-08-26  67.973370  15.826630  ...     0.060619   60.618904\n",
              "2020-08-27  68.797000  15.003000  ...     0.054186   54.185723\n",
              "2020-08-28  68.135745  15.664255  ...     0.059322   59.321676\n",
              "2020-08-29  68.092903  15.707097  ...     0.059663   59.662560\n",
              "2020-08-30  68.919687  14.880312  ...     0.053259   53.258771\n",
              "2020-08-31  69.014271  14.785729  ...     0.052550   52.549706\n",
              "2020-09-01  69.222234  14.577766  ...     0.051008   51.007663\n",
              "2020-09-02  69.941354  13.858646  ...     0.045856   45.855630\n",
              "2020-09-03  69.036146  14.763854  ...     0.052386   52.386404\n",
              "2020-09-04  69.180625  14.619375  ...     0.051314   51.314323\n",
              "2020-09-05  70.039167  13.760833  ...     0.045176   45.176480\n",
              "2020-09-06  69.369271  14.430729  ...     0.049931   49.931494\n",
              "2020-09-07  69.545625  14.254375  ...     0.048656   48.656168\n",
              "2020-09-08  70.132553  13.667447  ...     0.044533   44.532892\n",
              "2020-09-09  69.276632  14.523368  ...     0.050608   50.608163\n",
              "2020-09-10  69.350208  14.449792  ...     0.050070   50.070354\n",
              "2020-09-11  70.118404  13.681596  ...     0.044630   44.630099\n",
              "2020-09-12  69.294271  14.505729  ...     0.050479   50.478962\n",
              "2020-09-13  69.494000  14.306000  ...     0.049028   49.027759\n",
              "2020-09-14  70.350526  13.449474  ...     0.043049   43.049065\n",
              "2020-09-15  69.438542  14.361458  ...     0.049429   49.428548\n",
              "2020-09-16  69.929688  13.870312  ...     0.045937   45.936982\n",
              "2020-09-17  71.443579  12.356421  ...     0.035996   35.996160\n",
              "2020-09-18  70.734184  13.065816  ...     0.040500   40.499839\n",
              "2020-09-19  70.735054  13.064946  ...     0.040494   40.494148\n",
              "2020-09-20  71.639789  12.160211  ...     0.034799   34.798599\n",
              "2020-09-21  70.977292  12.822708  ...     0.038926   38.925749\n",
              "2020-09-22  71.135104  12.664896  ...     0.037921   37.921065\n",
              "2020-09-23  71.642105  12.157895  ...     0.034785   34.784590\n",
              "2020-09-24  70.552632  13.247368  ...     0.041696   41.696236\n",
              "2020-09-25  70.846042  12.953958  ...     0.039772   39.771599\n",
              "2020-09-26  71.444787  12.355213  ...     0.035989   35.988721\n",
              "2020-09-27  70.375532  13.424468  ...     0.042880   42.880486\n",
              "2020-09-28  65.478947  18.321053  ...     0.082327   82.327432\n",
              "2020-09-29  70.298958  13.501042  ...     0.043398   43.397785\n",
              "2020-09-30  70.049091  13.750909  ...     0.045108   45.107861\n",
              "\n",
              "[57 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "42gsdJnwd-JD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "30f1089d-94d2-434c-80a5-3d993bb7002f"
      },
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "plt.plot(medias['vazao(m3/s)'],color='blue')\n",
        "plt.ylabel('Vazão (m3/s)');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8XSSd6dw-W5p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e63281f-3462-458a-e349-f3a171ff52cd"
      },
      "source": [
        "medias['vazao(m3/s)'].median()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.010101093415513032"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e5PMiZHlaFhH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "388763dd-0b9b-4a3e-e136-c4b81a1c3ec3"
      },
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "plt.hist(medias['vazao(m3/s)'],bins=20,color='blue',histtype='bar',edgecolor='white')\n",
        "plt.xlabel('Vazão (m3/s)')\n",
        "plt.ylabel('Frequência');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/numpy/lib/histograms.py:839: RuntimeWarning: invalid value encountered in greater_equal\n",
            "  keep = (tmp_a >= first_edge)\n",
            "/usr/local/lib/python3.6/dist-packages/numpy/lib/histograms.py:840: RuntimeWarning: invalid value encountered in less_equal\n",
            "  keep &= (tmp_a <= last_edge)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m3QtTuaDtzt1",
        "outputId": "3cf4e548-18cc-4b6f-d02d-895d21b4fa39"
      },
      "source": [
        "type(vazao)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oLJK9ZQluPhK"
      },
      "source": [
        "#medias['vazao(m3/s)'].dropnan\n",
        "\n",
        "medias['vazao(m3/s)'] = medias['vazao(m3/s)'].dropna()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XTfpV40Iu9bA",
        "outputId": "0568be04-4b34-478c-f3a2-1a3d2d1338de"
      },
      "source": [
        "medias['vazao(m3/s)']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "data\n",
              "2020-08-05    0.017134\n",
              "2020-08-06    0.017989\n",
              "2020-08-07    0.019049\n",
              "2020-08-08         NaN\n",
              "2020-08-09         NaN\n",
              "2020-08-10    0.010726\n",
              "2020-08-11    0.009181\n",
              "2020-08-12    0.009677\n",
              "2020-08-13    0.013150\n",
              "2020-08-14    0.010836\n",
              "2020-08-15    0.019737\n",
              "2020-08-16    0.021510\n",
              "2020-08-17    0.013960\n",
              "2020-08-18    0.019470\n",
              "2020-08-19    0.021593\n",
              "2020-08-20    0.022122\n",
              "2020-08-21    0.015864\n",
              "2020-08-22    0.013241\n",
              "2020-08-23    0.012487\n",
              "2020-08-24    0.011453\n",
              "2020-08-25    0.012289\n",
              "2020-08-26    0.012099\n",
              "2020-08-27    0.010815\n",
              "2020-08-28    0.011840\n",
              "2020-08-29    0.011908\n",
              "2020-08-30    0.010630\n",
              "2020-08-31    0.010489\n",
              "2020-09-01    0.010181\n",
              "2020-09-02    0.009153\n",
              "2020-09-03    0.010456\n",
              "2020-09-04    0.010242\n",
              "2020-09-05    0.009017\n",
              "2020-09-06    0.009966\n",
              "2020-09-07    0.009711\n",
              "2020-09-08    0.008889\n",
              "2020-09-09    0.010101\n",
              "2020-09-10    0.009994\n",
              "2020-09-11    0.008908\n",
              "2020-09-12    0.010075\n",
              "2020-09-13    0.009786\n",
              "2020-09-14    0.008592\n",
              "2020-09-15    0.009866\n",
              "2020-09-16    0.009169\n",
              "2020-09-17    0.007185\n",
              "2020-09-18    0.008084\n",
              "2020-09-19    0.008082\n",
              "2020-09-20    0.006946\n",
              "2020-09-21    0.007769\n",
              "2020-09-22    0.007569\n",
              "2020-09-23    0.006943\n",
              "2020-09-24    0.008322\n",
              "2020-09-25    0.007938\n",
              "2020-09-26    0.007183\n",
              "2020-09-27    0.008559\n",
              "2020-09-28    0.016432\n",
              "2020-09-29    0.008662\n",
              "2020-09-30    0.009003\n",
              "Freq: D, Name: vazao(m3/s), dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SYQTLAvtafFk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "18a165b8-e7be-466a-e6b6-9485abec03ac"
      },
      "source": [
        "from google.colab import files\n",
        "medias.to_csv('vazao_medido.csv')\n",
        "files.download('vazao_medido.csv')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_63166d35-d521-4d69-972a-89446a35822e\", \"vazao_medido.csv\", 9068)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_99EFLU4gnoH"
      },
      "source": [
        "###COMPARAÇÃO DAS VAZÕES \n",
        "###ponto exutório na medição de nível\n",
        "###dados metereológicos:\n",
        "* inmet\n",
        "    \n",
        "    vs \n",
        "    \n",
        "* simepar"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B3DEbCiSAmiQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f937ec4a-f216-46d6-f46b-8d19f71a38ba"
      },
      "source": [
        "from urllib.request import urlretrieve\n",
        "URL = 'https://www.dropbox.com/s/e8k9afa5ozl9bx7/watout.pedreira2_2003_2016.csv?dl=1'\n",
        "urlretrieve(URL, 'watout.pedreira2_2003_2016.csv')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('watout.pedreira2_2003_2016.csv', <http.client.HTTPMessage at 0x7f4896731b70>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "dADKKJDNjWVd",
        "outputId": "fb9576fd-40ed-4266-fd76-761a404bfb20"
      },
      "source": [
        "vazao_inmet=pandas.read_csv(URL,sep=';',index_col=0)\n",
        "\n",
        "vazao_inmet[0:3]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-aa5be6d83e9e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvazao_inmet\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mURL\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m';'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mindex_col\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mvazao_inmet\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'pandas' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gLIMfcFXrG-N"
      },
      "source": [
        "yeardatescalendar"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E90aoxiPk4kb"
      },
      "source": [
        "vazao_inmet = vazao_inmet.set_index('Day_Step')\n",
        "vazao_inmet"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qC5QhzWCjiTK"
      },
      "source": [
        "from urllib.request import urlretrieve\n",
        "URL = 'https://www.dropbox.com/s/zj1ffdt4elwc8q6/watout.pedreira3_2003_2016.csv?dl=1'\n",
        "urlretrieve(URL, 'watout.pedreira3_2003_2016.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QwZMEt2vjiKU"
      },
      "source": [
        "vazao_simepar=pandas.read_csv(URL,sep=';',index_col=0)\n",
        "\n",
        "vazao_simepar[0:3]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s7rUq2VNk22A"
      },
      "source": [
        "vazao_simepar = vazao_simepar.set_index('Day')\n",
        "vazao_simepar"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TOFMf5qxj1Jg"
      },
      "source": [
        "plt.figure(figsize=(20,10))\n",
        "#plt.plot(vazao_inmet['FLOWm^3/s_pedreira_inmet'],color='blue')\n",
        "plt.plot(vazao_simepar['FLOWm^3/s_pedreira_simepar'],color='blue')\n",
        "plt.xlabel('Dias');\n",
        "plt.ylabel('Vazão (m3/s)');"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QjRt-TJpj07r"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}