Floorplan_DXF_to_Spatial_.ipynb

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      "name": "python"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Mike-Morrow/CAD-to-Spatial/blob/main/Floorplan_DXF_to_Spatial_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Read DXF -> GeoDataFrames -> Georeferences GeoDataFrame -> Write GeoJson's\n",
        "\n",
        "* Step 1: read in DXF into geopandas geodataframe (gdf)\n",
        "<br><br>\n",
        "* Step 2: split the gdf into multiple gdf's based on geometry type then only keep what we want\n",
        "<br><br>\n",
        "* Step 3: get the control point mappings \n",
        "<br><br>\n",
        "* Step 3: apply a linear transformation to georeference our gdf's\n",
        "<br><br>\n",
        "* Step 4: output gdf's to geojson \n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "9RMBDyc2jxiC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Install geopandas"
      ],
      "metadata": {
        "id": "NAn9s7ogFZYE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install geopandas"
      ],
      "metadata": {
        "id": "NMNtdxWdj5i6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2fa71551-f402-4b11-8bcd-41d56ce6d5c2"
      },
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting geopandas\n",
            "  Downloading geopandas-0.12.2-py3-none-any.whl (1.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m11.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: shapely>=1.7 in /usr/local/lib/python3.9/dist-packages (from geopandas) (2.0.1)\n",
            "Collecting pyproj>=2.6.1.post1\n",
            "  Downloading pyproj-3.4.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.7/7.7 MB\u001b[0m \u001b[31m36.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting fiona>=1.8\n",
            "  Downloading Fiona-1.9.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m16.1/16.1 MB\u001b[0m \u001b[31m26.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pandas>=1.0.0 in /usr/local/lib/python3.9/dist-packages (from geopandas) (1.4.4)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.9/dist-packages (from geopandas) (23.0)\n",
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            "Collecting cligj>=0.5\n",
            "  Downloading cligj-0.7.2-py3-none-any.whl (7.1 kB)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.9/dist-packages (from fiona>=1.8->geopandas) (6.1.0)\n",
            "Collecting munch>=2.3.2\n",
            "  Downloading munch-2.5.0-py2.py3-none-any.whl (10 kB)\n",
            "Collecting click-plugins>=1.0\n",
            "  Downloading click_plugins-1.1.1-py2.py3-none-any.whl (7.5 kB)\n",
            "Requirement already satisfied: attrs>=19.2.0 in /usr/local/lib/python3.9/dist-packages (from fiona>=1.8->geopandas) (22.2.0)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas>=1.0.0->geopandas) (2022.7.1)\n",
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            "Requirement already satisfied: six in /usr/local/lib/python3.9/dist-packages (from munch>=2.3.2->fiona>=1.8->geopandas) (1.16.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.9/dist-packages (from importlib-metadata->fiona>=1.8->geopandas) (3.15.0)\n",
            "Installing collected packages: pyproj, munch, cligj, click-plugins, fiona, geopandas\n",
            "Successfully installed click-plugins-1.1.1 cligj-0.7.2 fiona-1.9.2 geopandas-0.12.2 munch-2.5.0 pyproj-3.4.1\n"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "### Upload CAD File\n",
        "\n",
        "Note we want DXF because DWG is a proprietary format. There might be ways to work with DWG's in Python but I ran into roadblocks. "
      ],
      "metadata": {
        "id": "UxE6ZgMkFgQt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "\n",
        "# prompt the user to upload a file\n",
        "uploaded_file = files.upload()\n",
        "\n",
        "# get the file name and store it as a string\n",
        "file_name = list(uploaded_file.keys())[0]\n",
        "file_path = '/content/' + file_name\n",
        "\n",
        "print('File path:', file_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "RLiDgx0_E2FB",
        "outputId": "81c7bc38-923f-46af-f235-dd3c5d630b85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "<IPython.core.display.HTML object>"
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              "\n",
              "     <input type=\"file\" id=\"files-3a4145ff-4b89-4ad3-83cc-7c65c2902aff\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-3a4145ff-4b89-4ad3-83cc-7c65c2902aff\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving #Sample_Floorplan.dxf to #Sample_Floorplan.dxf\n",
            "File path: /content/#Sample_Floorplan.dxf\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Create GeoDataFrame from DXF file using GeoPandas"
      ],
      "metadata": {
        "id": "NbIWIBdXGbv4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import geopandas as gpd\n",
        "\n",
        "floorplan_gdf = gpd.read_file(file_path)"
      ],
      "metadata": {
        "id": "HZFoZAoPE-YW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Preview the GDF"
      ],
      "metadata": {
        "id": "U3T7q_fMGiyP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "floorplan_gdf.head()"
      ],
      "metadata": {
        "id": "kEfKiL0x6VNJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "outputId": "23fb9935-d5a0-44ac-8911-b7e862c69d90"
      },
      "execution_count": null,
      "outputs": [
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\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",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-78d7659f-7ade-4442-b41c-367fa8fdc6ba 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-78d7659f-7ade-4442-b41c-367fa8fdc6ba');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "floorplan_gdf.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "id": "NxMTvZYW7ut3",
        "outputId": "2f2cea9b-cf32-40c8-d024-7e6ae8ffe66b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "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": "markdown",
      "source": [
        "### Let's see how many unique geometry types we have"
      ],
      "metadata": {
        "id": "jTV5m7fJGl56"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# get the unique geometry types\n",
        "geometry_types = floorplan_gdf.geometry.type.unique()\n",
        "\n",
        "print(geometry_types)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7muuA9roGE4u",
        "outputId": "2c5ce9c8-fe9d-4183-f59b-9e56b50e4158"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['LineString' 'MultiLineString' 'GeometryCollection' 'Point']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Create multiple gdf's for each geometry type"
      ],
      "metadata": {
        "id": "TzbiiIht6aNV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gdf_list = []\n",
        "\n",
        "# create a new GeoDataFrame for each geometry type\n",
        "for geometry_type in geometry_types:\n",
        "    gdf_list.append(floorplan_gdf[floorplan_gdf.geometry.type == geometry_type].copy())"
      ],
      "metadata": {
        "id": "_-CA0aqtGt9j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Preview them"
      ],
      "metadata": {
        "id": "EO7bfW676hna"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gdf_list[0].plot()\n",
        "gdf_list[1].plot()\n",
        "gdf_list[2].plot()\n",
        "gdf_list[3].plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 800
        },
        "id": "YwXeJcXWHepV",
        "outputId": "49ee8338-f81a-4334-c1cb-fc70087025fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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": "markdown",
      "source": [
        "### Let's just keep the linestring and points gdf's"
      ],
      "metadata": {
        "id": "_dOrMKhn6lnS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "linestring_gdf = gdf_list[0]\n",
        "point_gdf = gdf_list[3]"
      ],
      "metadata": {
        "id": "had2gdW5LFnS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "## Let's get our control points (we'll use 5)\n",
        "\n",
        "In order to get control points we need to map out how our local X, Y coordinates relate to actual longitude and latitude on the earth's surface\n",
        "<br><br>\n",
        "*Note latitude and longitude are not linear / planar. \n",
        "<br><br>\n",
        "From wikipedia: \"Although latitude and longitude form a coordinate tuple like a cartesian coordinate system, the geographic coordinate system is not cartesian because the measurements are angles and are not on a planar surface\"\n",
        "<br><br>\n",
        "We're going to cheat and treat lat, lon as linear because our area of interest is so small. As that area becomes larger, we'd expect more distortion. In those cases, we'd want to use the appropriate cartesian coordinate system (i.e. UTM), instead of lat lon. \n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "4z-z59426vdS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "#define the control points\n",
        "data = [\n",
        "    {'X': 2648, 'Y': 2260, 'Lat': 38.90334414, 'Lon': -77.04248049},\n",
        "    {'X': 2645, 'Y': 1075, 'Lat': 38.90333969, 'Lon': -77.04216144},\n",
        "    {'X': 0, 'Y': 1309, 'Lat': 38.90275828, 'Lon': -77.0422264},\n",
        "    {'X': 649, 'Y': 1453, 'Lat': 38.90288602, 'Lon': -77.04226273},\n",
        "    {'X': 3883, 'Y': 127, 'Lat': 38.9036031, 'Lon': -77.04189755},\n",
        "    {'X': 1240, 'Y': 1075, 'Lat': 38.90301213, 'Lon': -77.04215566}\n",
        "]\n",
        "\n",
        "#create a df from the data\n",
        "control_df = pd.DataFrame(data)"
      ],
      "metadata": {
        "id": "A80zW4pY6xed"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## We're going to do 2 linear regressions \n",
        "### First one will map our local x to latitude\n",
        "### Second one will map local y to longitude"
      ],
      "metadata": {
        "id": "YmWg7EA7JyqQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### First we'll \"train\" our 2 models"
      ],
      "metadata": {
        "id": "GjKiI8VKHCvf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "#define our 2 arrays for our input and response variables\n",
        "x_array = np.array(control_df.X)\n",
        "lat_array = np.array(control_df.Lat)\n",
        "\n",
        "#create variables for slope and y intercept \n",
        "m_x, b_x = np.polyfit(x_array, lat_array, 1)\n",
        "\n",
        "print(\"slope: \", m_x)\n",
        "print(\"y-intercept: \", b_x)\n",
        "\n",
        "from sklearn.metrics import r2_score\n",
        "\n",
        "#calculate predicted values of y using the linear regression\n",
        "lat_pred = m_x*x_array + b_x\n",
        "\n",
        "#calculate R-squared \n",
        "r_squared_x = r2_score(lat_array, lat_pred)\n",
        "\n",
        "#print out  R-squared \n",
        "print(\"R-squared: \", r_squared_x)\n",
        "\n",
        "\n",
        "\n",
        "#do the same thing for y\n",
        "\n",
        "y_array = np.array(control_df.Y)\n",
        "lon_array = np.array(control_df.Lon)\n",
        "\n",
        "m_y, b_y = np.polyfit(y_array, lon_array, 1)\n",
        "\n",
        "print(\"slope: \", m_y)\n",
        "print(\"y-intercept: \", b_y)\n",
        "\n",
        "from sklearn.metrics import r2_score\n",
        "\n",
        "\n",
        "lon_pred = m_y*y_array + b_y\n",
        "\n",
        "\n",
        "r_squared_y = r2_score(lon_array, lon_pred)\n",
        "\n",
        "\n",
        "print(\"R-squared: \", r_squared_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IyiDz84lKXfd",
        "outputId": "1acc76c2-467c-408e-891f-415fec3e3936"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "slope:  2.211691186525369e-07\n",
            "y-intercept:  38.90274935395035\n",
            "R-squared:  0.9992396097315736\n",
            "slope:  -2.735711952611875e-07\n",
            "y-intercept:  -77.0418645789743\n",
            "R-squared:  0.9997773262301302\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Now we'll run the model for our x and y points"
      ],
      "metadata": {
        "id": "w_ixrF4qHwmY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from shapely.geometry import Point\n",
        "\n",
        "point_gdf.geometry = point_gdf.geometry.apply(lambda geometry: Point(m_x * geometry.x + b_x, m_y * geometry.y + b_y))"
      ],
      "metadata": {
        "id": "ZEKJvDpjKZe9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Let's preview the results"
      ],
      "metadata": {
        "id": "6TA3z4aRH0Ah"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "point_gdf.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "vuwO6IuiZUWl",
        "outputId": "ae05d7a2-2177-43a3-b8de-e99aa580e120"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "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": "markdown",
      "source": [
        "### The original CAD file is not North-oriented -- or in other words, up does not equal north. So we are going to swap the lat and lon values (we could have done this earlier before finding the control points)"
      ],
      "metadata": {
        "id": "A4nFyLVcBzqH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "point_gdf.geometry = point_gdf.geometry.apply(lambda geometry: Point(geometry.y, geometry.x))"
      ],
      "metadata": {
        "id": "1QuGTiBNaYRI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "point_gdf.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "48PVF96maal8",
        "outputId": "04910a75-e1cc-47b8-b51c-3b9dba0f9c2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "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": "markdown",
      "source": [
        "### Let's output to GeoJSON"
      ],
      "metadata": {
        "id": "tB2jGXX1C2Vg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "point_gdf.to_file(\"points_floorplan.geojson\", driver='GeoJSON')"
      ],
      "metadata": {
        "id": "z9dxrHr-aeob"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Let's georeference our LineString gdf, plot it, and output to GeoJSON"
      ],
      "metadata": {
        "id": "TnLoT-llDDPu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from shapely.geometry import LineString\n",
        "\n",
        "linestring_gdf['geometry'] = linestring_gdf['geometry'].apply(lambda geometry: LineString([(m_x * point[0] + b_x, m_y * point[1] + b_y) for point in geometry.coords]))\n",
        "\n",
        "#swapping x and y\n",
        "linestring_gdf['geometry'] = linestring_gdf['geometry'].apply(lambda geometry: LineString([(point[1], point[0]) for point in geometry.coords]))"
      ],
      "metadata": {
        "id": "y0ZpcSWgLAUF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "linestring_gdf.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "Xhh-hNX7bDL2",
        "outputId": "fde564f2-eca6-4a73-9893-23ce1ec76a1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "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": [
        "linestring_gdf.to_file(\"linestring_floorplan.geojson\", driver='GeoJSON')"
      ],
      "metadata": {
        "id": "xz7PgBmBWuOW"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}