drain_design_graph.ipynb

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    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyONMFLnxSjQUUgfPnQ+IYDN"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zyCPIZaRVgwF"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df= pd.read_excel('CALCULATION.xlsx',sheet_name=3, skiprows=[0])\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "id": "eq3GN7xGZxfg",
        "outputId": "b3cb4cbf-97ba-4844-be18-862e940eec9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Q (m3/s)  w=6ft: Total Depth, d (ft)  w=6.5 ft: Total Depth, d (ft)  \\\n",
              "0      8.20                    8.966798                       8.154195   \n",
              "1      9.57                   10.155569                       9.215525   \n",
              "2     10.72                   11.155608                      10.099389   \n",
              "\n",
              "   w=7 ft: Total Depth, d (ft)  w=7.5 ft: Total Depth, d (ft)  \\\n",
              "0                     7.501196                       6.961603   \n",
              "1                     8.452298                       7.822767   \n",
              "2                     9.243016                       8.536003   \n",
              "\n",
              "   w=8 ft: Total Depth, d (ft)  \n",
              "0                     6.509783  \n",
              "1                     7.295835  \n",
              "2                     7.947243  "
            ],
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Q (m3/s)</th>\n",
              "      <th>w=6ft: Total Depth, d (ft)</th>\n",
              "      <th>w=6.5 ft: Total Depth, d (ft)</th>\n",
              "      <th>w=7 ft: Total Depth, d (ft)</th>\n",
              "      <th>w=7.5 ft: Total Depth, d (ft)</th>\n",
              "      <th>w=8 ft: Total Depth, d (ft)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8.20</td>\n",
              "      <td>8.966798</td>\n",
              "      <td>8.154195</td>\n",
              "      <td>7.501196</td>\n",
              "      <td>6.961603</td>\n",
              "      <td>6.509783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9.57</td>\n",
              "      <td>10.155569</td>\n",
              "      <td>9.215525</td>\n",
              "      <td>8.452298</td>\n",
              "      <td>7.822767</td>\n",
              "      <td>7.295835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10.72</td>\n",
              "      <td>11.155608</td>\n",
              "      <td>10.099389</td>\n",
              "      <td>9.243016</td>\n",
              "      <td>8.536003</td>\n",
              "      <td>7.947243</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2708266d-3940-4fe2-944a-6cee538e7b45')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "          document.querySelector('#df-2708266d-3940-4fe2-944a-6cee538e7b45 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2708266d-3940-4fe2-944a-6cee538e7b45');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.rcParams[\"figure.figsize\"] = [6,4]\n",
        "plt.rcParams[\"figure.autolayout\"] = True\n",
        "fig, ax = plt.subplots(1,1, squeeze= True)\n",
        "\n",
        "ax.plot(df['Q (m3/s)'],df['w=6ft: Total Depth, d (ft)'],'-D', label='6 ft')\n",
        "ax.plot(df['Q (m3/s)'],df['w=6.5 ft: Total Depth, d (ft)'],'-D', label='6.5 ft')\n",
        "ax.plot(df['Q (m3/s)'],df['w=7 ft: Total Depth, d (ft)'],'-D', label='7 ft')\n",
        "ax.plot(df['Q (m3/s)'],df['w=7.5 ft: Total Depth, d (ft)'],'-D', label='7.5 ft')\n",
        "ax.plot(df['Q (m3/s)'],df['w=8 ft: Total Depth, d (ft)'],'-D', label='8 ft')\n",
        "\n",
        "# plt.yticks(np.arange(50,300,25))\n",
        "plt.title(\"Drain-1 (Rectangular): Subcatchment-1\")\n",
        "plt.xlabel('Flow (cumec)')\n",
        "plt.ylabel('Normal Depth (ft)')\n",
        "plt.grid(visible = True, axis = 'y',  alpha = 0.6)\n",
        "handles, labels = ax.get_legend_handles_labels()\n",
        "fig.legend(handles, labels, loc=(0.1,0.65))\n",
        "#plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.savefig(\"Drain-1 (Rectangular): Subcatchment-1.tiff\", dpi=500)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "WpF9B6CsW9Cp",
        "outputId": "609c263f-43cf-4b14-aad0-52dcaac63d36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kWs5Gus9nffF"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}