{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0d5468c7-5f9a-4870-856e-420cd4b49cb8",
   "metadata": {},
   "source": [
    "# Test to offset a point perpendicalar to a line\n",
    "O. Kaufmann, 2024."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "314efdfb-0da0-4248-b2ab-ef11fd2f9140",
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "428a2c1f-9bdd-4d1c-9ca2-026dbb7aa383",
   "metadata": {},
   "outputs": [],
   "source": [
    "from shapely.geometry import LineString\n",
    "\n",
    "a = (0, 0)\n",
    "b = (15, 7)\n",
    "cd_length = 6\n",
    "\n",
    "ab = LineString([a, b])\n",
    "left = ab.parallel_offset(cd_length / 2, 'left')\n",
    "right = ab.parallel_offset(cd_length / 2, 'right')\n",
    "c = left.boundary.geoms[1]\n",
    "d = right.boundary.geoms[1]\n",
    "cd = LineString([c, d])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d5a44979-9865-4802-a845-d5184d464e72",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {0:{'geometry': ab}, 1:{'geometry': cd}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9f9fc7f7-8462-4377-a966-8be6ce8468bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf = gpd.GeoDataFrame.from_dict(d, orient='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fce4f40e-04d1-4adc-b190-8bd747cb7108",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gdf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "b719bafe-ec21-459c-ab2c-84aaa9fe7023",
   "metadata": {},
   "outputs": [],
   "source": [
    "l = LineString([[0., 4.], [6.,8.], [9., 11.], [11., 14.], [21.,25.], [26., 33.],])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "0a071c72-fc6a-4b65-96ab-84a8638a04ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-1.16 2.84 28.32 31.319999999999997\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,37.0)\"><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"0.6264\" points=\"0.0,4.0 6.0,8.0 9.0,11.0 11.0,14.0 21.0,25.0 26.0,33.0\" opacity=\"0.8\" /></g></svg>"
      ],
      "text/plain": [
       "<LINESTRING (0 4, 6 8, 9 11, 11 14, 21 25, 26 33)>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a6514bbe-6d13-40f0-ad35-8c07d70d220e",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = 13."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c4a2051f-6ad4-4bb3-a62b-2cc9305429e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterpolate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdistance\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormalized\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Return a point at the specified distance along a linear geometry\n",
       "\n",
       "Negative length values are taken as measured in the reverse\n",
       "direction from the end of the geometry. Out-of-range index\n",
       "values are handled by clamping them to the valid range of values.\n",
       "If the normalized arg is True, the distance will be interpreted as a\n",
       "fraction of the geometry's length.\n",
       "\n",
       "Alias of `line_interpolate_point`.\n",
       "\u001b[0;31mFile:\u001b[0m      ~/.local/share/virtualenvs/geometron-VhFx4lLH/lib/python3.10/site-packages/shapely/geometry/base.py\n",
       "\u001b[0;31mType:\u001b[0m      method"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "l.interpolate?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91669e1f-accd-4125-8aff-6d66dc5a7f65",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}