{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Overview\n",
        "\n",
        "##  Res1D\n",
        "\n",
        "[Res1D](res1d.qmd) is the main interface for accessing data on a\n",
        "[Network](network.qmd)."
      ],
      "id": "1dce65f6-eb93-4522-ae36-ae1e030e8812"
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/plain": [
              "<mikeio1d.Res1D>"
            ]
          }
        }
      ],
      "source": [
        "import mikeio1d\n",
        "res = mikeio1d.open('../data/network.res1d')\n",
        "res"
      ],
      "id": "4f9e633b"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "##  Locations\n",
        "\n",
        "[Locations](locations.qmd) are where model results exist in the network.\n",
        "The main location types are\n",
        "[nodes](../api/result_network.ResultNodes.html#mikeio1d.result_network.ResultNodes),\n",
        "[reaches](../api/result_network.ResultReaches.html#mikeio1d.result_network.ResultReaches),\n",
        "and\n",
        "[catchments](../api/result_network.ResultCatchments.html#mikeio1d.result_network.ResultCatchments).\n",
        "Each of these location types are accessible via a\n",
        "[`Res1D`](../api/Res1D.html#mikeio1d.Res1D) object."
      ],
      "id": "87a45593-2aa1-4347-82e1-e00e78df1ba7"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "&lt;ResultNodes&gt; (119)\n",
              "    <style>\n",
              "        ul {\n",
              "            margin: 0px;\n",
              "            padding: 0px;\n",
              "            padding-left: 2em;\n",
              "        }\n",
              "    </style>\n",
              "    <details><summary>Quantities (1)</summary><ul><li>Water level (m)</li></ul></details><details><summary>Derived Quantities (3)</summary><ul><li>NodeFlooding</li><li>NodeWaterDepth</li><li>NodeWaterLevelAboveCritical</li></ul></details>"
            ]
          }
        }
      ],
      "source": [
        "res.nodes"
      ],
      "id": "f66df608"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Specific\n",
        "[locations](../api/result_network.ResultLocation.html#mikeio1d.result_network.ResultLocation)\n",
        "are accessible via their unique ID."
      ],
      "id": "d70f6ea2-7d0f-46cd-97ce-0b7c978ad7f1"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "&lt;Manhole: 101&gt;\n",
              "    <style>\n",
              "        ul {\n",
              "            margin: 0px;\n",
              "            padding: 0px;\n",
              "            padding-left: 2em;\n",
              "        }\n",
              "    </style>\n",
              "    <details><summary>Attributes (8)</summary><ul><li>id: 101</li><li>type: Manhole</li><li>xcoord: -687859.5004882812</li><li>ycoord: -1056308.700012207</li><li>ground_level: 196.67999267578125</li><li>bottom_level: 195.92999267578125</li><li>critical_level: inf</li><li>diameter: 1.0</li></ul></details><details><summary>Quantities (1)</summary><ul><li>Water level (m)</li></ul></details><details><summary>Derived Quantities (3)</summary><ul><li>NodeFlooding</li><li>NodeWaterDepth</li><li>NodeWaterLevelAboveCritical</li></ul></details>"
            ]
          }
        }
      ],
      "source": [
        "res.nodes['101']"
      ],
      "id": "9e434994"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "##  Quantities\n",
        "\n",
        "[Quantities](quantities.qmd) are the actual model results, which can be\n",
        "read into a\n",
        "[`DataFrame`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame)\n",
        "or plotted directly."
      ],
      "id": "ad24549e-5635-415f-a853-b069d097c301"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "\n",
              "</div>"
            ]
          }
        }
      ],
      "source": [
        "df = res.nodes['101'].WaterLevel.read()\n",
        "df.head()"
      ],
      "id": "dcdb69eb"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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EslXcAuY/8/VxQW4iAliAEZGMZfnROpBOxQtyswAjUjIWYEQkW5n5jkH4fjUG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\nmkcAAAAASUVORK5CYII=\n"
          }
        }
      ],
      "source": [
        "res.nodes['101'].WaterLevel.plot()"
      ],
      "id": "68a9af68"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "[Quantity\n",
        "collections](../api/result_network.ResultQuantityCollection.html#mikeio1d.result_network.ResultQuantityCollection)\n",
        "enable reading a specific quantity for many locations at once."
      ],
      "id": "99d325d9-32c6-4ce6-a6fb-74e1e244857c"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "\n",
              "</div>"
            ]
          }
        }
      ],
      "source": [
        "df = res.reaches.Discharge.read()\n",
        "df.describe().T.head()"
      ],
      "id": "1f3a91ab"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "##  Xns11\n",
        "\n",
        "[Xns11](xns11.qmd) is the main interface for accessing cross section\n",
        "data."
      ],
      "id": "ad64a9f5-6648-40d8-b346-cef9d51e2463"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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5lNThEBEROTQWWa2IIFxZbuernVk4m18ucURERESOi0VWK9Ml2AtxPYOh1Rvw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\nFhEREZENsMgiIiIisoH/B7VNGcJBCdJiAAAAAElFTkSuQmCC\n"
          }
        }
      ],
      "source": [
        "xns = mikeio1d.open('../data/mikep_cs_demo.xns11')\n",
        "xns['basin_left1','122.042','1'].plot(with_marker_labels=False)"
      ],
      "id": "34e50383"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Access a cross section’s\n",
        "[`raw`](../api/cross_sections.CrossSection.html#mikeio1d.cross_sections.CrossSection.raw)\n",
        "info as a\n",
        "[`DataFrame`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame)."
      ],
      "id": "81642baf-7392-4f36-b8ee-142ddafe1dc2"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "\n",
              "</div>"
            ]
          }
        }
      ],
      "source": [
        "df = xns['basin_left1','122.042','1'].raw\n",
        "df.head()"
      ],
      "id": "3b94828c"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Access a cross section’s\n",
        "[`processed`](../api/cross_sections.CrossSection.html#mikeio1d.cross_sections.CrossSection.processed)\n",
        "info as a\n",
        "[`DataFrame`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame)."
      ],
      "id": "f4e729d1-e0f6-4850-88d6-f71966fb93c0"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "\n",
              "</div>"
            ]
          }
        }
      ],
      "source": [
        "df = xns['basin_left1','122.042','1'].processed\n",
        "df.head()"
      ],
      "id": "539eb0f1"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "##  Additional resources\n",
        "\n",
        "-   [Example\n",
        "    notebooks](https://nbviewer.jupyter.org/github/DHI/mikeio1d/tree/main/notebooks/)\n",
        "-   [Unit tests](https://github.com/DHI/mikeio1d/tree/main/tests)\n",
        "-   [MIKE 1D\n",
        "    API](http://docs.mikepoweredbydhi.com/engine_libraries/mike1d/mike1d_api/)"
      ],
      "id": "2cd209ab-0faa-429e-8f00-e4fc7d150527"
    }
  ],
  "nbformat": 4,
  "nbformat_minor": 5,
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "path": "/opt/hostedtoolcache/Python/3.12.8/x64/share/jupyter/kernels/python3"
    },
    "language_info": {
      "name": "python",
      "codemirror_mode": {
        "name": "ipython",
        "version": "3"
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.12.8"
    }
  }
}