python_Hietograma.ipynb

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      "name": "python_Hietograma.ipynb",
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      "name": "python"
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        "<a href=\"https://colab.research.google.com/github/barbedorafael/Python_workshop_HGE/blob/main/python_Hietograma.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        " HIDROLOGIA\n",
        "\n",
        " Ingrid Petry\n",
        " 12/08/2021\n",
        " Última edição: 09/02/2022\n",
        "\n",
        "##############################################################################################\n",
        "\n",
        " Hietograma de projeto \n",
        "\n",
        " Você está participando do projeto de uma PCH e deve construir uma ensecadeira \n",
        " para desviar o rio durante a obra. Estima-se que a construção irá durar 1 ano \n",
        " e a probabilidade de haver galgamento da ensecadeira (falha) deve ser de apenas\n",
        " 10%. O tempo de concentração da bacia até a PCH é de 8 horas e a área de \n",
        " contribuição é 50 km2. **Estime o hietograma de projeto utilizando o método dos \n",
        " blocos alternados.**\n",
        "\n",
        " Parâmetros da curva IDF: a=1000, b=0.15, c=12, d=0.7"
      ],
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VLf7ZYFpcV8u"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt "
      ],
      "execution_count": 180,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wbJZbx6cbWT_"
      },
      "source": [
        "A = 50              # (km²)\n",
        "f = 0.1             # falha de 10 %\n",
        "d = 1               # duração da obra (anos)     \n",
        "TR = 10             # (anos)\n",
        "dt = 60             # (min)\n",
        "tc = 8              # (horas)\n",
        "\n",
        "# IDF \n",
        "a = 1000\n",
        "b = 0.15\n",
        "c = 12\n",
        "d = 0.7"
      ],
      "execution_count": 181,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cbst_KxTcZvi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c6873ff2-306e-415e-fd72-3063ad5ed04e"
      },
      "source": [
        "# \"DataFrame is a 2-dimensional labeled data structure with columns of potentially different types\"\n",
        "\n",
        "# Nome novo do dataframe: dados\n",
        "\n",
        "dados = pd.DataFrame()\n",
        "\n",
        "print(dados)"
      ],
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Empty DataFrame\n",
            "Columns: []\n",
            "Index: []\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Organiza tabela com tempo em horas e minutos, Intensidade da chuva e volume de chuva por intervalo de tempo\n",
        "\n",
        "intervalo = range(1,tc+1) \n",
        "\n",
        "# Comandos apenas para ver a saída\n",
        "print(intervalo)\n",
        "print(list(intervalo))\n",
        "\n",
        "dados['t(h)'] = pd.Series(intervalo)    # cria coluna os passos de tempo até o tempo de concentração da bacia \n",
        "\n",
        "# O índice indica a distância do elemento inicial. Portanto, o primeiro elemento está a 0 distância do elemento inicial. \n",
        "# Por isso que o index começa em 0. \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bsjPdc-B_H0L",
        "outputId": "1aa39d22-f142-4e37-c727-7d0ffbc70872"
      },
      "execution_count": 183,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "range(1, 9)\n",
            "[1, 2, 3, 4, 5, 6, 7, 8]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(dados)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jp3byVeAn6Ti",
        "outputId": "22fb2ab8-dabb-4803-ae24-1bedc1d0a00b"
      },
      "execution_count": 184,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   t(h)\n",
            "0     1\n",
            "1     2\n",
            "2     3\n",
            "3     4\n",
            "4     5\n",
            "5     6\n",
            "6     7\n",
            "7     8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados['t(min)'] = dados['t(h)']*60\n",
        "\n",
        "dados['I(mm/h)'] = a*(TR**b)/((dados['t(min)']+c)**d)    # Intensidade da chuva mm/h\n",
        "\n",
        "dados['h(mm)'] = dados['I(mm/h)'] * dados['t(h)']        # Volume de chuva mm\n",
        "\n",
        "print(dados)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "54T-kLtzVUp0",
        "outputId": "768fb9e5-dccb-4658-b27d-abc209719929"
      },
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   t(h)  t(min)    I(mm/h)       h(mm)\n",
            "0     1      60  70.772943   70.772943\n",
            "1     2     120  46.301840   92.603680\n",
            "2     3     180  35.619608  106.858825\n",
            "3     4     240  29.445547  117.782187\n",
            "4     5     300  25.356645  126.783223\n",
            "5     6     360  22.419191  134.515148\n",
            "6     7     420  20.191165  141.338155\n",
            "7     8     480  18.434206  147.473645\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hf8saAclkX_q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ae18f465-6302-408b-8922-c8930c382121"
      },
      "source": [
        "# Cálculo do incremento de chuva \n",
        "\n",
        "dados['Incremental'] = dados['h(mm)']\n",
        "\n",
        "for x in dados['t(h)']:\n",
        "\n",
        "  print(x,a)\n",
        "\n",
        "  a = dados['t(h)'][x-1]    \n",
        "  if a>1:\n",
        "    dados.loc[a-1,'Incremental'] = dados['h(mm)'][a-1] - dados['h(mm)'][a-2]  \n",
        "\n",
        "print(dados)"
      ],
      "execution_count": 186,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1 1000\n",
            "2 1\n",
            "3 2\n",
            "4 3\n",
            "5 4\n",
            "6 5\n",
            "7 6\n",
            "8 7\n",
            "   t(h)  t(min)    I(mm/h)       h(mm)  Incremental\n",
            "0     1      60  70.772943   70.772943    70.772943\n",
            "1     2     120  46.301840   92.603680    21.830737\n",
            "2     3     180  35.619608  106.858825    14.255145\n",
            "3     4     240  29.445547  117.782187    10.923362\n",
            "4     5     300  25.356645  126.783223     9.001037\n",
            "5     6     360  22.419191  134.515148     7.731924\n",
            "6     7     420  20.191165  141.338155     6.823007\n",
            "7     8     480  18.434206  147.473645     6.135490\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados['blocos alternados'] = [4, 5, 3, 6, 2, 7, 8, 1]\n",
        "# dados['blocos alternados'] = [5, 6, 4, 3, 7, 2, 8, 1] # outra possibilidade\n",
        "\n",
        "print(dados)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VMEoaHsMo1a3",
        "outputId": "f8cafcf5-7175-4380-ac99-32b31d82885a"
      },
      "execution_count": 187,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   t(h)  t(min)    I(mm/h)       h(mm)  Incremental  blocos alternados\n",
            "0     1      60  70.772943   70.772943    70.772943                  4\n",
            "1     2     120  46.301840   92.603680    21.830737                  5\n",
            "2     3     180  35.619608  106.858825    14.255145                  3\n",
            "3     4     240  29.445547  117.782187    10.923362                  6\n",
            "4     5     300  25.356645  126.783223     9.001037                  2\n",
            "5     6     360  22.419191  134.515148     7.731924                  7\n",
            "6     7     420  20.191165  141.338155     6.823007                  8\n",
            "7     8     480  18.434206  147.473645     6.135490                  1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nNnU_G_pFeYn",
        "outputId": "977bf336-4f94-45f1-dfbb-a579353e7022"
      },
      "source": [
        "table = dados.sort_values('blocos alternados',ascending=True)\n",
        "table = table.reset_index(drop=True) \n",
        "\n",
        "print(table)"
      ],
      "execution_count": 188,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   t(h)  t(min)    I(mm/h)       h(mm)  Incremental  blocos alternados\n",
            "0     8     480  18.434206  147.473645     6.135490                  1\n",
            "1     5     300  25.356645  126.783223     9.001037                  2\n",
            "2     3     180  35.619608  106.858825    14.255145                  3\n",
            "3     1      60  70.772943   70.772943    70.772943                  4\n",
            "4     2     120  46.301840   92.603680    21.830737                  5\n",
            "5     4     240  29.445547  117.782187    10.923362                  6\n",
            "6     6     360  22.419191  134.515148     7.731924                  7\n",
            "7     7     420  20.191165  141.338155     6.823007                  8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "del table['t(h)']\n",
        "del table['t(min)']\n",
        "del table['I(mm/h)']\n",
        "del table['h(mm)']\n",
        "del table['blocos alternados']\n",
        "\n",
        "table['t(h)'] = pd.Series(range(1,tc+1))       # cria coluna os passos de tempo\n",
        "table['t(min)'] = table['t(h)']*60\n",
        "\n",
        "print(table)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ShpeQSGuXSf_",
        "outputId": "ba26084f-a673-4ae6-838d-c61fe8c8dc8f"
      },
      "execution_count": 189,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Incremental  t(h)  t(min)\n",
            "0     6.135490     1      60\n",
            "1     9.001037     2     120\n",
            "2    14.255145     3     180\n",
            "3    70.772943     4     240\n",
            "4    21.830737     5     300\n",
            "5    10.923362     6     360\n",
            "6     7.731924     7     420\n",
            "7     6.823007     8     480\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "-bn6cohx-mte",
        "outputId": "591ffe02-1631-4b96-c7d1-f75a71f92470"
      },
      "source": [
        "# Usando a função plot, temos o gráfico default:\n",
        "\n",
        "# Nome novo do dataframe: table\n",
        "\n",
        "table.plot('t(min)', 'Incremental')"
      ],
      "execution_count": 190,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8766d04a50>"
            ]
          },
          "metadata": {},
          "execution_count": 190
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Adicionando alguns elementos\n",
        "\n",
        "table.plot('t(min)', 'Incremental', kind='bar', color='blue', label='Chuva (mm)')\t\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "3L9ZU9OlFctI",
        "outputId": "dca512c4-46a3-415e-fa46-52844f30428c"
      },
      "execution_count": 191,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8766cbeb90>"
            ]
          },
          "metadata": {},
          "execution_count": 191
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "table.plot('t(min)', 'Incremental', kind='bar', color='blue', label='Chuva (mm)', figsize=(8,5))\t\n",
        "\n",
        "plt.xlabel('t(min)')\n",
        "\n",
        "plt.ylabel('(mm)')\n",
        "\n",
        "plt.title('Hietograma')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "id": "rczu-PpdDnNz",
        "outputId": "54df473f-da96-47bc-9bec-27f35b45c20e"
      },
      "execution_count": 192,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Hietograma')"
            ]
          },
          "metadata": {},
          "execution_count": 192
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}