examples/Notebooks/flopy3_grid_intersection_demo.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id=\"top\"></a>Intersecting model grids with shapes\n",
    "\n",
    "_Note: This feature requires the shapely package (which is an optional FloPy dependency)._\n",
    "\n",
    "This notebook shows the grid intersection functionality in flopy. The\n",
    "intersection methods are available through the `GridIntersect` object. A flopy\n",
    "modelgrid is passed to instantiate the object. Then the modelgrid can be\n",
    "intersected with Points, LineStrings and Polygons and their Multi variants.\n",
    "\n",
    "### Table of Contents\n",
    "- [GridIntersect Class](#gridclass)\n",
    "- [Rectangular regular grid](#rectgrid)\n",
    "    - [Polygon with regular grid](#rectgrid.1)\n",
    "    - [MultiLineString with regular grid](#rectgrid.2)\n",
    "    - [MultiPoint with regular grid](#rectgrid.3)\n",
    "- [Vertex grid](#trigrid)\n",
    "    - [Polygon with triangular grid](#trigrid.1)\n",
    "    - [MultiLineString with triangular grid](#trigrid.2)\n",
    "    - [MultiPoint with triangular grid](#trigrid.3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:33.286297Z",
     "iopub.status.busy": "2022-12-15T13:10:33.285792Z",
     "iopub.status.idle": "2022-12-15T13:10:34.536598Z",
     "shell.execute_reply": "2022-12-15T13:10:34.534288Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.9.7 | packaged by conda-forge | (default, Sep 29 2021, 19:20:46) \n",
      "[GCC 9.4.0]\n",
      "numpy version: 1.21.2\n",
      "matplotlib version: 3.6.1\n",
      "flopy version: 3.3.6\n",
      "shapely version: 2.0.0\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import shapely\n",
    "from shapely.geometry import (\n",
    "    LineString,\n",
    "    MultiLineString,\n",
    "    MultiPoint,\n",
    "    MultiPolygon,\n",
    "    Point,\n",
    "    Polygon,\n",
    ")\n",
    "\n",
    "import flopy\n",
    "import flopy.discretization as fgrid\n",
    "import flopy.plot as fplot\n",
    "from flopy.utils import GridIntersect\n",
    "\n",
    "print(sys.version)\n",
    "print(\"numpy version: {}\".format(np.__version__))\n",
    "print(\"matplotlib version: {}\".format(mpl.__version__))\n",
    "print(\"flopy version: {}\".format(flopy.__version__))\n",
    "print(\"shapely version: {}\".format(shapely.__version__))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id=\"gridclass\"></a>[GridIntersect Class](#top)\n",
    "\n",
    "The GridIntersect class is constructed by passing a flopy modelgrid object to\n",
    "the constructor. There are options users can select to change how the\n",
    "intersection is calculated.\n",
    "\n",
    "- `method`: derived from model grid type or defined by the user: can be either `\"vertex\"` or\n",
    "`\"structured\"`. If `\"structured\"` is passed, the intersections are performed\n",
    "using structured methods. These methods use information about the regular grid\n",
    "to limit the search space for intersection calculations. Note that `method=\"vertex\"` \n",
    "also works for structured grids.\n",
    "- `rtree`: either `True` (default) or `False`, only read when\n",
    "`method=\"vertex\"`. When True, an STR-tree is built, which allows for fast\n",
    "spatial queries. Building the STR-tree does take some time however. Setting the\n",
    "option to False avoids building the STR-tree but requires the intersection\n",
    "calculation to loop through all grid cells.\n",
    "\n",
    "In general the \"vertex\" option is robust and fast and is therefore recommended\n",
    "in most situations. In some rare cases building the STR-tree might not be worth\n",
    "the time, in which case it can be avoided by passing `rtree=False`. If you are\n",
    "working with a structured grid, then the `method=\"structured\"` can speed up\n",
    "intersection operations in some situations (e.g. for (multi)points) with the added\n",
    "advantage of not having to build an STR-tree.\n",
    "\n",
    "The important methods in the GridIntersect object are:\n",
    "\n",
    "- `intersects()`: returns cellids for gridcells that intersect a shape (accepts\n",
    "shapely geometry objects, flopy geometry object, shapefile.Shape objects, and\n",
    "geojson objects)\n",
    "- `intersect()`: for intersecting the modelgrid with point, linestrings, and\n",
    "polygon geometries (accepts shapely geometry objects, flopy geometry object,\n",
    "shapefile.Shape objects, and geojson objects)\n",
    "- `plot_point()`: for plotting point intersection results\n",
    "- `plot_linestring()`: for plotting linestring intersection results\n",
    "- `plot_polygon()`: for plotting polygon intersection results\n",
    "\n",
    "In the following sections examples of intersections are shown for structured\n",
    "and vertex grids for different types of shapes (Polygon, LineString and Point)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id=\"rectgrid\"></a>[Rectangular regular grid](#top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:34.583092Z",
     "iopub.status.busy": "2022-12-15T13:10:34.582140Z",
     "iopub.status.idle": "2022-12-15T13:10:34.588176Z",
     "shell.execute_reply": "2022-12-15T13:10:34.587115Z"
    }
   },
   "outputs": [],
   "source": [
    "delc = 10 * np.ones(10, dtype=float)\n",
    "delr = 10 * np.ones(10, dtype=float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:34.591775Z",
     "iopub.status.busy": "2022-12-15T13:10:34.591497Z",
     "iopub.status.idle": "2022-12-15T13:10:34.596339Z",
     "shell.execute_reply": "2022-12-15T13:10:34.595300Z"
    }
   },
   "outputs": [],
   "source": [
    "xoff = 0.0\n",
    "yoff = 0.0\n",
    "angrot = 0.0\n",
    "sgr = fgrid.StructuredGrid(\n",
    "    delc, delr, top=None, botm=None, xoff=xoff, yoff=yoff, angrot=angrot\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:34.600250Z",
     "iopub.status.busy": "2022-12-15T13:10:34.599464Z",
     "iopub.status.idle": "2022-12-15T13:10:34.781122Z",
     "shell.execute_reply": "2022-12-15T13:10:34.779710Z"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sgr.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id=\"rectgrid.1\"></a>[Polygon with regular grid](#top)\n",
    "Polygon to intersect with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:34.785581Z",
     "iopub.status.busy": "2022-12-15T13:10:34.785206Z",
     "iopub.status.idle": "2022-12-15T13:10:34.791500Z",
     "shell.execute_reply": "2022-12-15T13:10:34.790435Z"
    }
   },
   "outputs": [],
   "source": [
    "p = Polygon(\n",
    "    shell=[\n",
    "        (15, 15),\n",
    "        (20, 50),\n",
    "        (35, 80.0),\n",
    "        (80, 50),\n",
    "        (80, 40),\n",
    "        (40, 5),\n",
    "        (15, 12),\n",
    "    ],\n",
    "    holes=[[(25, 25), (25, 45), (45, 45), (45, 25)]],\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the GridIntersect class for our modelgrid. The `method` kwarg is passed to force GridIntersect to use the `\"vertex\"` intersection methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:34.796072Z",
     "iopub.status.busy": "2022-12-15T13:10:34.795747Z",
     "iopub.status.idle": "2022-12-15T13:10:34.801570Z",
     "shell.execute_reply": "2022-12-15T13:10:34.800604Z"
    }
   },
   "outputs": [],
   "source": [
    "ix = GridIntersect(sgr, method=\"vertex\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do the intersect operation for a polygon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:34.805377Z",
     "iopub.status.busy": "2022-12-15T13:10:34.805080Z",
     "iopub.status.idle": "2022-12-15T13:10:34.826220Z",
     "shell.execute_reply": "2022-12-15T13:10:34.825134Z"
    }
   },
   "outputs": [],
   "source": [
    "result = ix.intersect(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results are returned as a numpy.recarray containing several fields based on the intersection performed. An explanation of the data in each of the possible fields is given below:\n",
    "- **cellids**: contains the cell ids of the intersected grid cells\n",
    "- **vertices**: contains the vertices of the intersected shape\n",
    "- **areas**: contains the area of the polygon in that grid cell (only for polygons)\n",
    "- **lengths**: contains the length of the linestring in that grid cell (only for linestrings)\n",
    "- **ixshapes**: contains the shapely object representing the intersected shape (useful for plotting the result)\n",
    "\n",
    "Looking at the first few entries of the results of the polygon intersection (convert to pandas.DataFrame for prettier formatting)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:34.830862Z",
     "iopub.status.busy": "2022-12-15T13:10:34.830498Z",
     "iopub.status.idle": "2022-12-15T13:10:34.836610Z",
     "shell.execute_reply": "2022-12-15T13:10:34.835750Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rec.array([((2, 3), <POLYGON ((35 80, 40 76.667, 40 70, 30 70, 35 80))>,  66.66666667),\n",
       "           ((2, 4), <POLYGON ((50 70, 40 70, 40 76.667, 50 70))>,  33.33333333),\n",
       "           ((3, 2), <POLYGON ((30 70, 30 60, 25 60, 30 70))>,  25.        ),\n",
       "           ((3, 3), <POLYGON ((40 70, 40 60, 30 60, 30 70, 40 70))>, 100.        ),\n",
       "           ((3, 4), <POLYGON ((50 70, 50 60, 40 60, 40 70, 50 70))>, 100.        )],\n",
       "          dtype=[('cellids', 'O'), ('ixshapes', 'O'), ('areas', '<f8')])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result[:5]\n",
    "# pd.DataFrame(result)  # recommended for prettier formatting and working with result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cellids can be easily obtained "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:34.842129Z",
     "iopub.status.busy": "2022-12-15T13:10:34.841824Z",
     "iopub.status.idle": "2022-12-15T13:10:34.847325Z",
     "shell.execute_reply": "2022-12-15T13:10:34.846432Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(2, 3), (2, 4), (3, 2), (3, 3), (3, 4), (3, 5), (3, 6), (4, 2),\n",
       "       (4, 3), (4, 4), (4, 5), (4, 6), (4, 7), (5, 1), (5, 2), (5, 3),\n",
       "       (5, 4), (5, 5), (5, 6), (5, 7), (6, 1), (6, 2), (6, 4), (6, 5),\n",
       "       (6, 6), (6, 7), (7, 1), (7, 2), (7, 3), (7, 4), (7, 5), (7, 6),\n",
       "       (8, 1), (8, 2), (8, 3), (8, 4), (8, 5), (9, 2), (9, 3), (9, 4)],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.cellids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or the areas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:34.852360Z",
     "iopub.status.busy": "2022-12-15T13:10:34.852087Z",
     "iopub.status.idle": "2022-12-15T13:10:34.858045Z",
     "shell.execute_reply": "2022-12-15T13:10:34.857025Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 66.66666667,  33.33333333,  25.        , 100.        ,\n",
       "       100.        ,  66.66666667,   8.33333333,  75.        ,\n",
       "       100.        , 100.        , 100.        ,  91.66666667,\n",
       "        33.33333333,   7.14285714,  75.        ,  50.        ,\n",
       "        75.        , 100.        , 100.        , 100.        ,\n",
       "        21.42857143,  50.        ,  50.        , 100.        ,\n",
       "        99.10714286,  43.75      ,  35.71428571,  75.        ,\n",
       "        50.        ,  75.        ,  96.42857143,  32.14285714,\n",
       "        41.71428571,  99.35714286, 100.        ,  91.96428571,\n",
       "        22.32142857,   8.64285714,  36.        ,  14.28571429])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.areas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If  a user is only interested in which cells the shape intersects (and not the areas or the actual shape of the intersected object) with there is also the `intersects()` method. This method works for all types of shapely geometries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:34.863240Z",
     "iopub.status.busy": "2022-12-15T13:10:34.862937Z",
     "iopub.status.idle": "2022-12-15T13:10:34.870409Z",
     "shell.execute_reply": "2022-12-15T13:10:34.869308Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rec.array([((8, 1),), ((7, 2),), ((7, 1),), ((6, 1),), ((6, 2),),\n",
       "           ((9, 4),), ((9, 2),), ((9, 3),), ((8, 2),), ((8, 3),),\n",
       "           ((8, 4),), ((7, 3),), ((7, 4),), ((6, 4),), ((5, 1),),\n",
       "           ((4, 1),), ((4, 2),), ((3, 2),), ((5, 2),), ((5, 3),),\n",
       "           ((5, 4),), ((4, 3),), ((4, 4),), ((3, 4),), ((3, 3),),\n",
       "           ((2, 4),), ((2, 3),), ((2, 2),), ((1, 3),), ((8, 5),),\n",
       "           ((7, 5),), ((7, 6),), ((6, 5),), ((6, 6),), ((6, 7),),\n",
       "           ((6, 8),), ((5, 5),), ((5, 7),), ((5, 6),), ((4, 6),),\n",
       "           ((4, 5),), ((3, 6),), ((3, 5),), ((2, 5),), ((5, 8),),\n",
       "           ((4, 8),), ((4, 7),)],\n",
       "          dtype=[('cellids', 'O')])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ix.intersects(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results of an intersection can be visualized with the plotting methods in the `GridIntersect` object:\n",
    "- `plot_polygon`\n",
    "- `plot_linestring`\n",
    "- `plot_point`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:34.874505Z",
     "iopub.status.busy": "2022-12-15T13:10:34.874199Z",
     "iopub.status.idle": "2022-12-15T13:10:35.263918Z",
     "shell.execute_reply": "2022-12-15T13:10:35.262684Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a figure and plot the grid\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "sgr.plot(ax=ax)\n",
    "\n",
    "# the intersection object contains some helpful plotting commands\n",
    "ix.plot_polygon(result, ax=ax)\n",
    "\n",
    "# add black x at cell centers\n",
    "for irow, icol in result.cellids:\n",
    "    (h2,) = ax.plot(\n",
    "        sgr.xcellcenters[0, icol],\n",
    "        sgr.ycellcenters[irow, 0],\n",
    "        \"kx\",\n",
    "        label=\"centroids of intersected gridcells\",\n",
    "    )\n",
    "\n",
    "# add legend\n",
    "ax.legend([h2], [i.get_label() for i in [h2]], loc=\"best\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `intersect()` method contains several keyword arguments that specifically deal with polygons:\n",
    "\n",
    "- `contains_centroid`: only store intersection result if cell centroid is contained within polygon\n",
    "- `min_area_fraction`: minimal intersecting cell area (expressed as a fraction of the total cell area) to include cells in intersection result\n",
    "\n",
    "Two examples showing the usage of these keyword arguments are shown below.\n",
    "\n",
    "Example with `contains_centroid` set to True, only cells in which centroid is within the intersected polygon are stored. Note the difference with the previous result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:35.267911Z",
     "iopub.status.busy": "2022-12-15T13:10:35.267597Z",
     "iopub.status.idle": "2022-12-15T13:10:35.556007Z",
     "shell.execute_reply": "2022-12-15T13:10:35.554907Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CKy9ZO04g5Q2krFJd3vnz52+x1ia0dx9MyAAAOKCn1wEkKTg4WCkpKV7HaJO0tDRJCoi8gZRVCqy8gZRVCqy8ZO04gZQ3kLJKX+f1BRMyAAAOoJABAHAAhQwAgAMoZAAAHEAhAwDgAAoZAAAHUMgAADiAQgYAwAEUMgAADqCQAQBwAIUMAIADKGQAABxAIQMA4AAKGQAAB1DIAAA4gEIGAMABFDIAAA6gkAEAcACFDACAAyhkAAAcQCEDAOAAChkAAAdQyAAAOIBCBgDAARQyAAAOoJABAHAAhQwAgAMoZAAAHEAhAwDgAAoZAAAHUMgAADiAQgYAwAEUMgAADqCQAQBwAIUMAIADKGQAABxAIQMA4AAKGQAAB/hUyMaYfzPGfGKM2WGM+ZsxJsQYE2eM2WiM2WuMedEY08tfYQEA6KraXcjGmKGSHpSUYK0dKylI0h2SnpL0H9ba4ZKOSPqOP4ICANCV+XrIuqekUGNMT0lhkg5IukpSRv3nn5d0m4/3AQBAl2este2/sTEPSfqZpHJJ/yPpIUkf1U/HMsYMk/RG/QR9WjExMXbx4sXtztGZ8vPzJUlRUVEeJ2ldIGWVOibviRMnVFxcooEDB6hHD/+dMsFj23HI2nECKW8gZZXq8s6fP3+LtTahvfvw5ZD1OZJmSIqTFC2pj6Trz+L29xljNhtjNvvyjwLgTD7PztbOnZ9o4/9uUklJiddxAOC0evpw22skZVtrCyTJGPOypCmSIowxPa211ZJiJH3V0o2ttUslLZWkuLg4m5KS4kOUzpOWliZJCoS8gZRV8n/e3Nxc3f/A9zUw5feq/GqX/pq+WP/8j/+oXy35hcLCwnzad3d/bDsSWTtOIOUNpKzS13l94csxvC8lXWaMCTPGGElXS9opKVPSzPrr3C1plW8Rgfb56aKfKezi6QrqE6GwEZN0zuzfKn3dDo24aKzWrVvndTwAaKLdhWyt3ai6k7e2Svp7/b6WSvqRpIeNMXslDZT0rB9yAmclNzdXf/vbCoVeclvjtqDQfgq//mFVXfKPuv7Wf9D9//qQysrKvAsJACfx6SwXa+1Ca+0oa+1Ya+1sa22ltfZza+03rbXDrbWzrLWV/goLtNXJ0/GpmJYBuIh36kKX09J0fCqmZQCuoZDR5ZxpOj4V0zIAV1DI6FLaMh2fimkZgAsoZHQpZzMdn4ppGYCXKGR0Ge2Zjk/FtAzAKxQyugxfpuNTMS0D6GwUMroEf0zHp2JaBtCZKGR0Cf6cjk/FtAygM1DICHgdMR2f6tRp+bPP9qq2trbD7g9A9+PLH5cAnNCR0/GpwkZMUu9hY3Sw6EMF98rt8PsD0H0wISOgdcZ0fKqg0H4yvfuotqam0+4TQNdHISOgdeZ0DAAdiUJGwPJiOgaAjkIhI2AxHQPoSihkBKTWpuOSjRmqyNneZFtFznaVbMzohHQAcPYoZASk1qbj3lEjVLDqycZSrsjZroJVT6p31IhOTAkAbcfLnhBwGqbjgSm/P+11QmLHKXLGoypY9aT6xt+oY1lrFDnjUYXEjuvEpADQdkzICDhtXTsOiR2nvvE3quTDFeobfyNlDMBpFDICytmcWV2Rs13Hstao/+Q7dCxrTbM1ZQBwCYWMgNLW6bhhzThyxqOKuOKuxsPXlDIAV1HICBhnMx1X5u9psmbcsKZcmb+ng1MCQPtwUhcCxtm87rj/pTObbQuJHcc6MgBnMSEjIPCuXAC6OgoZAYF35QLQ1VHIcB7TMYDugEKG85iOAXQHFDKcxnQMoLugkOE0pmMA3QWFDGcxHQPoTihkOIvpGEB3QiHDSUzHALobChlOYjoG0N1QyHAO0zGA7ohChnOYjgF0RxQynFJZWcl0DKBbopDhlC9ycpiOAXRLFDKcUVlZqUOHDjEdA+iWjLXW6wyKiYmxixcv9jpGm+Tn50uSoqKiPE7SukDKKknZ2dmqqq5Vkfp4HaVVA0yZevfsofPPP9/rKG0SSM8FsnacQMobSFmlurzz58/fYq1NaO8+evozEOCLmpoaqbZGVfm7vY7SuqgoVdZIa9eu9TpJmzT8Uhs8eLB69ODAGOAiJwo5ODhYKSkpXsdok7S0NEkKiLyBlFWqy7t27Voti33Z6yitStMsrc2p0eYrN3sdpU0S3ktQRFCQfrZ8udL+9jddccUVXkc6rUB63gZSVimw8gZSVunrvL7gn8pANzG8Vy/9m5Vm3XCjHpo7V2VlZV5HAnASChnoRq7u21f/79xz9cVLL+niESP0wQcfeB0JQD0KGehmIoKC9OSAgUzLgGMoZKCbYloG3EIhA90Y0zLgDgoZANMy4AAKGYAkpmXAaxQygCaYlgFvUMgAmmFaBjofhQzgtJiWgc5DIQM4I6ZloHNQyADahGkZ6FgUMoA2Y1oGOg6FDOCsMS0D/kchA2iXhmn5+0zLgF9QyAB8ck39tJzNtAz4hEIG4LOIoCA9xbQM+IRCRpe0ZH2lMrOrm2zLzK7WkvWVHiU6s4I1BSrdVdpkW+muUhWsKfAo0ek9W1iojWXHm2zbWHZczxYWMi0DPqCQ0SUlRgcpOaO8sZQzs6uVnFGuxOggj5O1LDQuVLnP5DaWcumuUuU+k6vQuFCPkzU3NjRED+flNZbyxrLjejgvT2NDQyQxLQPtRSGjS0qK66n0maFKzijXY5kVSs4oV/rMUCXF9fQ6WovCLwrXsPuHKfeZXB18+aByn8nVsPuHKfyicK+jNXNpWB89HR2th/Py9LvDBXo4L09PR0fr0rA+Ta7HtAycHQoZXVZSXE/NTQjWoverNDch2NkybhB+UbgGJA1QwasFGpA0wMkybnBpWB/dERGh1MJC3RER0ayMGzAtA21HIaPLysyuVurmE1owtZdSN59otqbsmtJdpSrKLFLkrZEqyixqtqbsko1lx7WiuFhzBw7UiuLiZmvKp2JaBlpHIaNLalgzTp8ZqieSQhoPX7tayg1rxsPuH6Yhtw9pPHztYik3rBk/HR2tBwZFNh6+bq2UmZaBM6OQ0SVtyqtpsmbcsKa8Ka/G42QtK88ub7Jm3LCmXJ5d7nGy5naUVzRZM25YU95RXtGm2zMtAy1ze1ENaKd5U3o325YU19PZdeTIGyObbQu/KNzJdeTvDBzYbNulYX1Ou47ckoZp+e1jxzTrhhv17dl36Rf/9/8qLCzMn1GBgMKEDMAzTMvA1yhkAJ46dW1572efqba21utYQKdz8/gdgG7nmr59lRAWpucPHFDffv28jgN0OiZkAM6ICArSwKCeKi9372Q2oKNRyAAAOIBCBgDAARQyAAAOoJABAHAAhQwAgAMoZAAAHEAhAwDgAAoZAAAHUMgAADjAp0I2xkQYYzKMMZ8aY3YZYyYZYwYYY94yxnxW//9z/BUWAICuytcJ+TeS3rTWjpI0XtIuSY9Kesdae6Gkd+ovAwCAM2h3IRtj+kuaKulZSbLWVllriyXNkPR8/dWel3SbbxEBAOj6jLW2fTc0ZoKkpZJ2qm463iLpIUlfWWsj6q9jJB1puHw6MTExdvHixe3K0dny8/MlSVFRUR4naV1HZS0sLNShg4f8uk9JCgkNUVVVlUb2LvD7vv0tX5EqqZSO9T/mdZQ26VvSVz0lVRQVeR2lVcEDBqhHUA+dOFHl1/0aY3TuuUPVv39/v+0zkH4fSIGVN5CySnV558+fv8Vam9Deffjy5xd7Spoo6QFr7UZjzG90yuFpa601xrTY+MaY+yTdJ0nR0dE+xEBnqqys1I4dOzSoX7SCegT5dd9VQXW/gNfm1Ph1vx2h4XfE8d3HvQ3SRn2j+qpa0uEq/5ZcR6h7aGvVu3eJX/dbU2P1ySeFGjw4St/4xgXq0YNzWuEWXwp5v6T91tqN9ZczVFfIB40x51prDxhjzpXU4ihlrV2quglbcXFxNiUlxYconSctLU2SFAh5OyLrd//P/crbXqGExKv9ts8GW0r+KmtrNbbM/Qm5rGaAPi8o0qIh93gdpU1eD96qj3K3KTY21usorSouLlZIyHFNv/Zdv++7pKRGf0g9rhde6K0//3mFLr/8cp/2F0i/D6TAyhtIWaWv8/qi3f9EtNbmS8o1xoys33S16g5fvyrp7vptd0ta5VNCOGP//v164YUXlDR2ptdRgHbp3z9IP3q0n1JSqnX77dfroYfuV1lZmdexAEm+n2X9gKS/GmO2S5og6eeSnpQ03RjzmaRr6i+jC1i86OeaNOIG9Q3llWwIbFMu76M//HGQdu1aqbFjR2jdunVeRwJ8K2Rr7TZrbYK1dpy19jZr7RFrbaG19mpr7YXW2muste6fRYJWMR2jq2Fahms4qwFtwnSMroppGa6gkNEqpmN0dUzLcAGFjFYxHaO7YFqGlyhknBHTMbobpmV4hULGGTEdo7tiWkZno5BxWkzH6O6YltGZKGScFtMxUIdpGZ2BQkaLmI6Bplqalmtra72OhS7El/eyRhfGdAy0bMrlfTT24hD9IXWlqqpqNGrUaK8joYtgQkYzTMfAmTVMy1FRVtu2bfM6DroIChnNMB0DbdOvn3//BCm6NwoZTTAdA4A3KGQ0wXQMAN6gkNGI6RgAvEMho1Fr0/Fb21Zoz1dZTbbt+SpLb21b0Rnxzkrmp/u099DhJtv2HjqszE/3eZTozFI3vqAPc7Y22fZhzlalbnzBo0Snt379emVnZzfZlp2drfXr13uU6MxeXFGsbVnlTbZtyyrXiyuKvQkEnAaFDEltm45jI0fqubcXNZbynq+y9NzbixQbObKzYrbZsAH9tXxDVmMp7z10WMs3ZGnYgP4eJ2vZ+KhRmrtqYWMpf5izVXNXLdT4qFEeJ2suOjpaGRkZjaWcnZ2tjIwMRUdHe5ysZSNH9taiRQcbS3lbVrkWLTqokSN7e5wMaIrXIUNS29aORwyN1z3XLNBzby/S5aNv0bqdq3XPNQs0Ymh8JyZtm+GDB2n2pHgt35ClSRecpw37vtTsSfEaPniQ19FaNDl2olJnPK65qxZqdvxtWp71ilJnPK7JsRO9jtZMXFycZs6cqYyMDCUkJGjz5s2aOXOm4uLivI7WognxoVqwYIgWLTqoW27pp9Wrj2rBgiGaEB/qdTSgCSZknNXa8Yih8bp89C16c+tfdPnoW5ws4wbDBw/SpAvO09s792rSBec5W8YNJsdO1Oz42/SbD5/X7PjbnCzjBnFxcUpISND777+vhIQEZ8u4wYT4UN1ySz/95S/FuuWWfpQxnEQh46zOrN7zVZbW7Vyt6yfepXU7VzdbU3bJ3kOHtWHfl7pm9HBt2PdlszVl13yYs1XLs17RQ5Pv1vKsV5qtKbskOztbmzdv1tSpU7V58+Zma8qu2ZZVrtWrj+quuyK0evXRZmvKgAso5G7ubKbjhjXje65ZoJsT5zQevnaxlBvWjGdPitf1Y0c2Hr52tZQb1oxTZzyuH17xncbD1y6WcsOa8cyZM5WUlNR4+NrVUm5YM16wYIhS5gxoPHxNKcM1FHI3dzbTcU7B7iZrxg1ryjkFuzs65lnLLSppsmbcsKacW1TicbKWfZz/aZM144Y15Y/zP/U4WXN5eXlN1owb1pTz8vI8Ttay3bsrm6wZN6wp795d6XEyoClO6urGGqbjH9/+bJuuP33CHc22jRga7+Q6ctKoC5ptGz54kLPryHMv/cdm2ybHTnRyHXnKlCnNtsXFxTm7jvztOyKabZsQH8o6MpzDhNyN8a5cAOAOCrmb4l25AMAtFHI3xXQMAG6hkLshpmMAcA+F3A0xHQOAeyjkbobpGADcRCF3M0zHAOAmCrkbYToGAHdRyN0I0zEAuItC7iaYjgHAbRRyN8F0DABuo5C7AaZjAHAfhdwNMB0DgPso5C6usrKS6RgAAgCF3MXl5HzJdAwAAcBYa73OoJiYGLt48WKvY7RJfn6+JCkqKsrjJK3Ly8vTsWPH1PNEuIKC3P/T15U9iiVJYTVV3gZpg9qQMFWcqNb5we4/DySpsMcxHas8rt69e3sdpVXV1dUyPWoU3ueI11FaVVk5QOXlViNHjvQ6SpsE0u+vQMoq1eWdP3/+FmttQnv34f5vabRbbW2tJGl/4V6Pk7RNww/e5wVFHidpXVRUL0nSR7nbvA3SRg2PbU5OjsdJWhcVFSXVSJ9+Wul1lFZFRdUNNGvXrvU4SdvUPQ9MwJRcd+NEIQcHByslJcXrGG2SlpYmSQGRNy0tTWvXrtUb//yQ11Ha5NaP1+nEx1uUOcT99e7rgz9VZe4OLYt92esobZKmWVqbU6PNV272Okqrph2YpuO7j2veho+8jtKqzKuu0qbyMiX0nu11lDYpNtu198DH+sUvfu51lFYF0u9a6eu8vmANGQAAB1DIAAA4gEIGAMABFDIAAA6gkAEAcACFDACAAyhkAAAcQCEDAOAAChkAAAdQyAAAOIBCBgDAARQyAAAOoJABAHAAhQwAgAMoZAAAHEAhAwDgAAoZAAAHUMhos+Mr0lSVtanJtqqsTTq+Is2bQGdQsjFDFTnbm2yryNmuko0ZHiU6syXrK5WZXd1kW2Z2tZasr/Qo0ekVrClQ6a7SJttKd5WqYE2BR4nO7NnCQm0sO95k28ay43q2sNCjRKf31rYV2vNVVpNte77K0lvbVniUCJ2JQkabBY8co+In5jWWclXWJhU/MU/BI8d4nKy53lEjVLDqycZSrsjZroJVT6p31AiPk7UsMTpIyRnljaWcmV2t5IxyJUYHeZysudC4UOU+k9tYyqW7SpX7TK5C40I9TtaysaEhejgvr7GUN5Yd18N5eRobGuJxsuZiI0fqubcXNZbynq+y9NzbixQbOdLjZOgMPb0OgMDRKz5REY8tUfET8xR26yyVvbpSEY8tUa/4RK+jNRMSO06RMx5Vwaon1Tf+Rh3LWqPIGY8qJHac19FalBTXU+kzQ5WcUa65CcFK3XxC6TNDlRTn3o9o+EXhGnb/MOU+k6sBSQNUlFmkYfcPU/hF4V5Ha9GlYX30dHS0Hs7L0x0REVpRXKyno6N1aVgfr6M1M2JovO65ZoGee3uRLh99i9btXK17rlmgEUPjvY6GTuDeTzuc1is+UWG3ztLx5f8lSTryg/v8tu8TKSmSpJynbvbbPiWp5MO6w30HV/zYb/usTEmRkWQeP+q3fTZY9H6VFkzt5WQZNwi/KFwDkgao4NUCRd4a6WwZN7g0rI/uiIhQamGh5g4c6GQZNxgxNF6Xj75Fb279i66feBdl3I1wyBpnpSprk8peXSlJGjRokN59911Za/3y37Rp0zRt2jS/7e/dd9/VoEGDtGDBgg7JOtWPWU/OK0mpm080W1N2SemuUhVlFiny1kgVZRY1W1N2zcay41pRXKy5AwdqRXFxszVll+z5Kkvrdq7W9RPv0rqdq5utKaPropDRZg1rxhGPLZEkpaenKzk5WZmZmR4nay4zM1PJyclKT0/XE0884XRWqWleSY2Hr10s5YY142H3D9OQ24c0Hr52tZQb1oyfjo7WA4MiGw9fu1jKDWvG91yzQDcnzmk8fE0pdw8UMtrsxO5PmqwZJyUlKT09XZs2bWrllp1v06ZNSk9PV1JSkiS3s0ot5K1fU96UV+NxsubKs8ubrBk3rCmXZ5d7nKxlO8ormqwZN6wp7yiv8DhZczkFu5usGTesKecU7PY4GTqDu4tUcE6fO1KabUtKSmosEZfMmzev2TZXs0qnyRvX08l15MgbI5ttC78o3Nl15O8MHNhs26VhfZxcR54+4Y5m20YMjWcduZtgQgYAwAEUMgAADqCQAQBwAIUMAIADKGQAABxAIQMA4AAKGQAAB1DIAAA4gEIGAMABFDIAAA6gkAEAcACFDACAAyhkAAAc4HMhG2OCjDFZxpjX6i/HGWM2GmP2GmNeNMb08j0mAABdmz8m5Ick7Trp8lOS/sNaO1zSEUnf8cN9AADQpflUyMaYGEk3SfpT/WUj6SpJGfVXeV7Sbb7cBwAA3YGx1rb/xsZkSPqFpL6SfigpRdJH9dOxjDHDJL1hrR17pv3ExMTYxYsXtztHZ8rPz5ckRUVFeZykdfn5+SopKVHhkKF+3/eJj7do2rRpft1noD22UsdkXbt2rabFBvl1n/mKVEmldKz/Mb/utyP0r+iv2spaVRUWeB2lVWGDIlVtpXAzxOsobVIdXKqKquMaOXKk11FaFUi/D6S6vPPnz99irU1o7z56tveGxpibJR2y1m4xxlzZjtvfJ+k+SYqOjm5vDLTBiY+3eB0BZ2ltTo1f99fwO+347uN+3W9H6BvVV0FBUvi57p9zaoxRdaXV3gMfex2lTQKl3Lqrdk/IxphfSJotqVpSiKR+kv6fpOskRVlrq40xkyT91Fp73Zn2FRcXZ7Ozs9uVo7OlpaVJklJSUjzN0RZpaWlau3atxpb5f9L4Yfrr8uXoSksC7bGVOiarMUa5P3rfr/t8vddWfZS7TbGxsX7db0coLi5WSMhxTb/2Xa+jtGr7x9dq+/YKLVu2zOsobcLPWMdJS0vTnDlzfJqQ2/1PUGvtfGttjLX2fEl3SHrXWvtPkjIlzay/2t2SVrX3PgAA6C464pjQjyQ9bIzZK2mgpGc74D4AAOhS2r2GfDJr7XuS3qv/+HNJ3/THfgEA6C7cP2sCAIBugEIGAMABFDIAAA6gkAEAcACFDACAAyhkAAAcQCEDAOAAChkAAAdQyAAAOIBCBgDAARQyAAAOoJABAHAAhQwAgAMoZAAAHEAho80yP92nvYcON92WmaklS5Z4lKjrWLJkiTIzM5ts+zBnq1I3vuBRotNbv369srOzm2zLzs7W+vXrPUp0Zi+uKNa2rPIm27ZllevFFcXeBAJOg0JGmw0b0F/LN2Q1lnJmZqaSk5OVmJjocbLAl5iYqOTk5MZS/jBnq+auWqjxUaM8TtZcdHS0MjIyGks5OztbGRkZio6O9jhZy0aO7K1Fiw42lvK2rHItWnRQI0f29jgZ0FRPrwMgcAwfPEizJ8Vr+YYsSVJycrLS09OVlJTkcbLAl5SUpPT0dCUnJ0uS5q5aqNQZj2ty7ESPkzUXFxenmTNnKiMjQwkJCdq8ebNmzpypuLg4r6O1aEJ8qBYsGKJFiw7qllv6afXqo1qwYIgmxId6HQ1oggkZZ2X44EGadMF5kqS5c+dSxn6UlJSkuXPnSpJmx9/mZBk3iIuLU0JCgt5//30lJCQ4W8YNJsSH6pZb+ukvfynWLbf0o4zhJAoZZ2XvocPasO9LSVJqamqzdU+0X2ZmplJTUyVJy7Ne0Yc5Wz1OdHrZ2dnavHmzpk6dqs2bNzdbU3bNtqxyrV59VHfdFaHVq482W1MGXEAho832Hjqs5RuyNHtSvCQ1HmKllH3XsB6fnp4uSUqd8bjmrlroZCk3rBnPnDlTSUlJjYevXS3lhjXjBQuGKGXOgMbD15QyXEMho81yi0o0e1K8hg8eJOnrdc9NmzZ5nCzwbdq0qcl6/OTYiUqd8bg+zv/U42TN5eXlNVkzblhTzsvL8zhZy3bvrmyyZtywprx7d6XHyYCmOKkLbZY06oLm25KSWEf2g3nz5jXbNjl2opPryFOmTGm2LS4uztl15G/fEdFs24T4UNaR4RwmZAAAHEAhAwDgAAoZAAAHUMgAADiAQgYAwAEUMgAADqCQAQBwAIUMAIADKGQAABxAIQMA4AAKGQDaqabGeh0BXQiFDADtkJ9/Qvv2VSsmZqjXUdBFUMgAcJb27KnUw/9WpKFDz9cFFwz3Og66CP7aEwCchY0by/SrXx7Tn/70Zx09etTrOOhCmJABoI1ef71Uv/l1hdaseVu3336713HQxRhrvT8pISYmxi5evNjrGG2Sn58vSYqKivI4Sevy8/NVUlKisJoqv+/784IiTZs2za/7DLTHVuqYrGvXrtVlwyb4dZ+HzVGVVpWpRw/3/w1ujJExNerT54jXURpZSUWFNSot7aFx4yYoNLTubykH0nNWCqy8gZRVqss7f/78LdbahPbug0PW3cDnBUVeR8BZMJI+yt3m131GRUVJRjpw6IBf99sRhgwaIkn68kuPg5yiT59+mjhxlIKDg72Ogi7KiQk5Li7OZmdnex2jTdLS0iRJKSkpnuZoi7S0NK1du1Zv/PNDft/3wavi5e/nTqA9tlLHZDXGyC7s59d9zsm5XWEjw/T+ue/7db8dYdKOSYruE62f/vSnXkdpVSA9Z6XAyhtIWaW6vHPmzPFpQnb/+BUAAN0AhQwAgAMoZAAAHEAhAwDgAAoZAAAHUMgAADiAQgYAwAEUMgAADqCQAQBwAIUMAIADKGQAABxAIQMA4AAKGQAAB1DIAAA4gEIGAMABFDIAAA6gkAEAcACFjDY7viJNVVmbmmzLzMzUkiVLPEp0ekuWLFFmZmaTba5mlU6TN7taS9ZXepTo9ArWFKh0V2mTbaW7SlWwpsCjREDXQCGjzYJHjlHxE/MaSzkzM1PJyclKTEz0OFlziYmJSk5Obiw5l7NKLeTNrlZyRrkSo4M8TtZcaFyocp/JbSzl0l2lyn0mV6FxoR4nAwJbT68DIHD0ik9UxGNLVPzEPElScnKy0tPTlZSU5HGy5pKSkpSenq7k5GTNnTtXqampzmaVmuaVpOSMcqXPDFVSnHs/ouEXhWvY/cOU+0yuBiQNUFFmkYbdP0zhF4V7HQ0IaO79tMNpveITFXbrLB1f/l86fPiwrrrqKr/tOyUlRZI0Z84cv+1TkhYtWiRJfs9q5P+sDeYmBDtZxg3CLwrXgKQBKni1QJG3RlLGgB+4+xMPJ1VlbVLZqyvVZ/a/qOzVlYp4bIl6xfvnMHDwx+t04uMtiv3Ra37ZX0XOdhWselJ942/Usaw1ipzxqEJix/ll3717farK3B2yC/v5ZX/S14ep5yYEK3XzCSWd39PZUi7dVaqizCJF3hqposwi9bmoD6UM+Ig1ZLRZVdYmFT8xTxGPLVH4nPsbD1+feqKXCxrKOHLGo4q44i5FznhUBaueVEXOdq+jtaihjNNnhuqJpBClzwxVcka5MrOrvY7WTMOa8bD7h2nI7UMaD1+feqIXgLNDIaPNTuz+pMlE3LCmfGL3Jx4na64yf0+TiTgkdpwiZzyqyvw9Hidr2aa8miZrxklxPZU+M1Sb8mo8TtZceXZ5kzXjhjXl8uxyj5MBgc3N42FwUp87Uppt6xWf6LdD1v7U/9KZzbaFxI7z2yFrf5s3pXezbUlxbh6yjrwxstm28IvCOWQN+IgJGQAAB1DIAAA4gEIGAMABFDIAAA6gkAEAcACFDMAttV4HALxBIQNwRvF7xbLHrAYNGuR1FKDTUcgAPGdrrQpfKpTWShPjJyo8nNc0o/uhkAF4qvZErQqeLdCQ/CHK+t8shYbyZxzRPVHIADxTc7xGB399UAmDEvTh2g85VI1ujUIG4Imqw1XKeypPd15zp1a/vJrJGN2ee2+UC6DLK/+iXAf/86Ae//fH9fD3H/Y6DuAEChlApzr28TEVphXq+T89r9tvv93rOIAz2n3I2hgzzBiTaYzZaYz5xBjzUP32AcaYt4wxn9X//xz/xQUQyIrfK1bJX0r01pq3KGPgFL6sIVdL+oG1drSkyyR9zxgzWtKjkt6x1l4o6Z36ywC6sZNf1rTpw0267LLLvI4EOKfdhWytPWCt3Vr/8TFJuyQNlTRD0vP1V3te0m0+ZgQQwBpe1hR1MEpZ/5ul4cOHex0JcJKx1vq+E2POl/S+pLGSvrTWRtRvN5KONFw+nZiYGLt48WKfc3SG/Px8SVJUVFSH7L+2tlaf792riooKn/cVEhammpoaFZT5vq/OENkvXKqu1pFg91/6MkClsicqVH20wOsobdKzX6TUQyoqLer0+64uq1ZE/wiNGT1GPXq0PgN09M+YPwVSVimw8gZSVqku7/z587dYaxPauw+fT+oyxoRLeknS9621R+s6uI611hpjWmx8Y8x9ku6TpOjoaF9jdBllZWX66sABXdCrl8/7Ku/dWz1MkCKD+vshWSeorpQkVebu8DhI62xUlHr2CFK/sGFeR2mTo6qQaqXhMZ0/nfbu3Vv9+vXr9PsFAo1PhWyMCVZdGf/VWvty/eaDxphzrbUHjDHnSjrU0m2ttUslLZWkuLg4m5KS4kuUTpOWliZJ6qi86enpynvzTc3rH+HzvpZOmSyFDtDw6qt8D9YJthT/VdbWamyZ+1Nn3rFQmR59NTvsOq+jtMmC/Oc0depUzZkzx+soreronzF/CqSsUmDlDaSs0td5feHLWdZG0rOSdllrnz7pU69Kurv+47slrWp/vO7ns88+U0xNjdcxAACdzJcJeYqk2ZL+bozZVr/tx5KelJRujPmOpBxJyT4l7Gb2bN+uC3oEeR0DANDJ2l3I1tp1ksxpPn11e/fb3X22e7eu6hXsdQwAQCfjvawds++LL3ResO8ndAEAAguF7JBjx47pWFmZBvfkHU0BoLuhkB2yd+9ende3r3qY060EAAC6KgrZIZ999pli/fD6YwBA4KGQHcJLngCg+6KQHbJn+3bF8pInAOiWKGSHfLZ7t2J5yRMAdEsUskN4yRMAdF8UsiN4yRMAdG8UsiN4yRMAdG8UsiN4yRMAdG8UsiN4yRMAdG8UsiN4yRMAdG8UsiN4yRMAdG8UsoeWLFmizMxMSV+/5Glj2XE9W1jocbKWvbVthfZ8ldVk256vsvTWthUeJTq9zE/3ae+hw0227T10WJmf7vMo0ZmlbnxBH+ZsbbLtw5ytSt34gkeJAHQ2CtlDiYmJSk5O1uuvv65jZWX6oqpKD+flaWxoiNfRWhQbOVLPvb2osZT3fJWl595epNjIkR4na27YgP5aviGrsZT3Hjqs5RuyNGxAf4+TtWx81CjNXbWwsZQ/zNmquasWanzUKI+TAegsvOjVQ0lJSUpPT9e3vvUthQYF6QcH8vR0dLQuDevjdbQWjRgar3uuWaDn3l6ky0ffonU7V+ueaxZoxNB4r6M1M3zwIM2eFK/lG7I06YLztGHfl5o9KV7DBw/yOlqLJsdOVOqMxzV31ULNjr9Ny7NeUeqMxzU5dqLX0QB0EiZkjyUlJWnmzJkqqqjQHRERzpZxgxFD43X56Fv05ta/6PLRtzhZxg2GDx6kSRecp7d37tWkC85ztowbTI6dqNnxt+k3Hz6v2fG3UcZAN0MheywzM1MrV65UZGioVhQXa2PZca8jndGer7K0budqXT/xLq3bubrZmrJL9h46rA37vtQ1o4drw74vm60pu+bDnK1anvWKHpp8t5ZnvdJsTRlA10YheygzM1PJycl66qmnNLhPHz0dHa2H8/KcLeWGNeN7rlmgmxPnNB6+drGUG9aMZ0+K1/VjRzYevna1lBvWjFNnPK4fXvGdxsPXlDLQfVDIHtq0aZPS09OVmJgoSbo0rK6Ud5RXeJysZTkFu5usGTesKecU7PY4WXO5RSVN1owb1pRzi0o8Ttayj/M/bbJm3LCm/HH+px4nA9BZOKnLQ/PmzZMkZWV9PWFeGtbH2XXk6RPuaLZtxNB4J9eRk0Zd0Gzb8MGDnF1HnnvpPzbbNjl2ol/WkSurq3zeB4COx4QMdGHFFcc0+5V5ihwUKcMfLgGcRiEDXVRuyQF9K/0BXXbTVI0eM9rrOABaQSEDXdD2/N26/cUHdP8jD+rp3/yH13EAtAFryEAX8+6+DfrBW0v0x+eW6vbbb/c6DoA2opCBLuSvH7+q/9j0Z61+8zVddtllXscBcBYoZKALqLW1+uX6Z/Xm/g+17qP1Gj58uNeRAJwlChkIcJXVVfrhW08pP7hEGzZ/pEGD3HxpF4Az46QuB/Tu3VvZR4/qZ0eKtLa0VGW1tV5HQoBoeFlTj9g+eveD9yhjIIBRyA4YPXq0Pvzf/9WYhx7SC1FDNO3LHN17pEjPFRVpT2WFrLVeR4SDTn5Z08pXXlJoaKjXkQD4gEPWjhg/frzGjx+v+T/+sY4eParMzEy98eqrevC111V1+LCmhPXR5KAgTerTRxFBQV7Hhce25+/Wd179dz264Md66N8e8joOAD+gkB3Ur18/zZgxQzNmzJC1Vp999pnefPNNvfHSS3ps40aN7NdPk2U0JTRUY0NCFMQ7MHUrvKwJ6JooZMcZYzRixAiNGDFCDz74oCoqKvTBBx/ojdWv6YnVryo/90tN7ttPk3sYXd4nXJE9+ZZ2ZbysCei6+O0dYEJCQjR9+nRNnz5dT//2N9q/f3/d9Pzyy/rl++8rOiREk3sEaUpIb1lJzM5dAy9rAro+TuoKcDExMbr33nv10po1Kigu1n+9+qqi7v2Ofh8erpLaWhUfP6wPPnlVh4/meR0V7VRZXaWH3vyZNlfs1obNH1HGQBfFhNyF9OzZU5MnT9bkyZO16Oc/19KlS1VUVKS/V3yi//yfH6h3UKhGRl+iEVGX6MLo8eodzFm5riuuOKb7Xlugc0efp3dfXMWZ1EAXZlx4SU1MTIxdvHix1zHaJD8/X5IUFRXlcZLWnZq1tLRURUVFKjxcpNLjxxTaq49Ce4UrtFcfGQcOllT1PCpJCqtx/+/3VgWHyKiHImo77m9Xn6g5oZySPA2MHKgLfJyKA/l567JAyioFVt5AyirV5Z0/f/4Wa21Ce/fBhNyNhIeHKzw8XOedd55qampUXFyswsJCFZUccOK1zucMOEc1NTX6vKDI6yitqvslUavPS3I77k6M0bDY83Ru9Lkddx8AnOFEIQcHByslJcXrGG2SlpYmSQGRN5CySl/n/cUvfuFtkDYI1Mc2EPKSteMEUt5Ayip9ndcX3h+nBAAAFDIAAC6gkAEAcACFDACAAyhkAAAcQCEDAOAAChkAAAdQyAAAOIBCBgDAARQyAAAOoJABAHAAhQwAgAMoZAAAHEAhAwDgAAoZAAAHUMgAADiAQgYAwAEUMgAADqCQAQBwAIUMAIADKGQAABxAIQMA4AAKGQAAB1DIAAA4gEIGAMABFDIAAA6gkAEAcACFDACAAyhkAAAcQCEDAOAAChkAAAdQyAAAOIBCBgDAARQyAAAOoJABAHAAhQwAgAMoZAAAHEAhAwDggA4pZGPM9caY3caYvcaYRzviPgAA6Er8XsjGmCBJv5d0g6TRku40xoz29/0AANCVdMSE/E1Je621n1trqyStkDSjA+4HAIAuw1hr/btDY2ZKut5ae2/95dmSLrXW/uvpbhMTE2MXL17s1xwdJT8/X5IUFRXlcZLWBVJWKbDyBlJWKbDykrXjBFLeQMoq1eWdP3/+FmttQnv30dOfgc6GMeY+SffVX6ycM2fODq+ydHGDJB32OkQXxWPbcXhsOw6PbccZ6cuNO6KQv5I07KTLMfXbmrDWLpW0VJKMMZt9+VcFTo/HtuPw2HYcHtuOw2PbcYwxm325fUesIW+SdKExJs4Y00vSHZJe7YD7AQCgy/D7hGytrTbG/Kuk/5YUJOk5a+0n/r4fAAC6kg5ZQ7bWrpG05ixusrQjckASj21H4rHtODy2HYfHtuP49Nj6/SxrAABw9njrTAAAHOB5IfM2m/5hjBlmjMk0xuw0xnxijHmofvsAY8xbxpjP6v9/jtdZA5UxJsgYk2WMea3+cpwxZmP9c/fF+pMYcZaMMRHGmAxjzKfGmF3GmEk8b/3DGPNv9b8Pdhhj/maMCeF5237GmOeMMYeMMTtO2tbic9XU+W3947zdGDOxtf17Wsi8zaZfVUv6gbV2tKTLJH2v/rF8VNI71toLJb1Tfxnt85CkXSddfkrSf1hrh0s6Iuk7nqQKfL+R9Ka1dpSk8ap7jHne+sgYM1TSg5ISrLVjVXeS7R3ieeuLNEnXn7LtdM/VGyRdWP/ffZJSW9u51xMyb7PpJ9baA9barfUfH1PdL7Whqns8n6+/2vOSbvMkYIAzxsRIuknSn+ovG0lXScqovwqPbTsYY/pLmirpWUmy1lZZa4vF89ZfekoKNcb0lBQm6YB43rabtfZ9SUWnbD7dc3WGpD/bOh9JijDGnHum/XtdyEMl5Z50eX/9NvjAGHO+pHhJGyUNsdYeqP9UvqQhXuUKcL+WNE9Sbf3lgZKKrbXV9Zd57rZPnKQCScvqlwP+ZIzpI563PrPWfiXpV5K+VF0Rl0jaIp63/na65+pZ95vXhQw/M8aES3pJ0vettUdP/pytO6We0+rPkjHmZkmHrLVbvM7SBfWUNFFSqrU2XtJxnXJ4mudt+9SvZc5Q3T96oiX1UfPDrfAjX5+rXhdym95mE21jjAlWXRn/1Vr7cv3mgw2HSer/f8irfAFsiqRbjTFfqG5Z5SrVrXtG1B8KlHjuttd+SfuttRvrL2eorqB53vruGknZ1toCa+0JSS+r7rnM89a/TvdcPet+87qQeZtNP6lf03xW0i5r7dMnfepVSXfXf3y3pFWdnS3QWWvnW2tjrLXnq+45+q619p8kZUqaWX81Htt2sNbmS8o1xjS8Kf/VknaK560/fCnpMmNMWP3vh4bHluetf53uufqqpH+uP9v6MkklJx3abpHnbwxijLlRdetzDW+z+TNPAwUoY8zlkj6Q9Hd9vc75Y9WtI6dLOk9SjqRka+2pJyWgjYwxV0r6obX2ZmPMN1Q3MQ+QlCXpLmttpYfxApIxZoLqTpbrJelzSXNUNyzwvPWRMeZxSd9W3aswsiTdq7p1TJ637WCM+ZukK1X3F7MOSloo6RW18Fyt/0fQf6pumaBM0hxr7Rn/+ITnhQwAALw/ZA0AAEQhAwDgBAoZAAAHUMgAADiAQgYAwAEUMgAADqCQAQBwAIUMAIAD/j8x/iJqxj1LMwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# contains_centroid example\n",
    "\n",
    "result2 = ix.intersect(p, contains_centroid=True)\n",
    "\n",
    "# create a figure and plot the grid\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "sgr.plot(ax=ax)\n",
    "\n",
    "# the intersection object contains some helpful plotting commands\n",
    "ix.plot_polygon(result2, ax=ax)\n",
    "\n",
    "# add black x at cell centers\n",
    "for irow, icol in result2.cellids:\n",
    "    (h2,) = ax.plot(\n",
    "        sgr.xcellcenters[0, icol],\n",
    "        sgr.ycellcenters[irow, 0],\n",
    "        \"kx\",\n",
    "        label=\"centroids of intersected gridcells\",\n",
    "    )\n",
    "\n",
    "# add legend\n",
    "ax.legend([h2], [i.get_label() for i in [h2]], loc=\"best\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example with `min_area_threshold` set to 0.35, the intersection result in a cell should cover 35% or more of the cell area."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:35.560895Z",
     "iopub.status.busy": "2022-12-15T13:10:35.560070Z",
     "iopub.status.idle": "2022-12-15T13:10:35.857248Z",
     "shell.execute_reply": "2022-12-15T13:10:35.855950Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# min_area_threshold example\n",
    "\n",
    "result3 = ix.intersect(p, min_area_fraction=0.35)\n",
    "\n",
    "# create a figure and plot the grid\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "sgr.plot(ax=ax)\n",
    "\n",
    "# the intersection object contains some helpful plotting commands\n",
    "ix.plot_polygon(result3, ax=ax)\n",
    "\n",
    "# add black x at cell centers\n",
    "for irow, icol in result3.cellids:\n",
    "    (h2,) = ax.plot(\n",
    "        sgr.xcellcenters[0, icol],\n",
    "        sgr.ycellcenters[irow, 0],\n",
    "        \"kx\",\n",
    "        label=\"centroids of intersected gridcells\",\n",
    "    )\n",
    "\n",
    "# add legend\n",
    "ax.legend([h2], [i.get_label() for i in [h2]], loc=\"best\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, the intersection can be calculated using special methods optimized for structured grids. Access these methods by instantiating the GridIntersect class with the `method=\"structured\"` keyword argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:35.862255Z",
     "iopub.status.busy": "2022-12-15T13:10:35.861699Z",
     "iopub.status.idle": "2022-12-15T13:10:35.868052Z",
     "shell.execute_reply": "2022-12-15T13:10:35.867120Z"
    }
   },
   "outputs": [],
   "source": [
    "ixs = GridIntersect(sgr, method=\"structured\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is the same as before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:35.873384Z",
     "iopub.status.busy": "2022-12-15T13:10:35.872682Z",
     "iopub.status.idle": "2022-12-15T13:10:35.904749Z",
     "shell.execute_reply": "2022-12-15T13:10:35.903483Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intersection result with method='structured' and method='vertex' are equal: True\n"
     ]
    }
   ],
   "source": [
    "result4 = ixs.intersect(p)\n",
    "\n",
    "check = (result4[[\"cellids\", \"ixshapes\", \"areas\"]] == result).all()[()]\n",
    "\n",
    "print(\n",
    "    \"Intersection result with method='structured' and \"\n",
    "    f\"method='vertex' are equal: {check}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id=\"rectgrid.2\"></a>[Polyline with regular grid](#top)\n",
    "MultiLineString to intersect with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:35.909003Z",
     "iopub.status.busy": "2022-12-15T13:10:35.908597Z",
     "iopub.status.idle": "2022-12-15T13:10:35.916749Z",
     "shell.execute_reply": "2022-12-15T13:10:35.915696Z"
    }
   },
   "outputs": [],
   "source": [
    "ls1 = LineString([(95, 105), (30, 50)])\n",
    "ls2 = LineString([(30, 50), (90, 22)])\n",
    "ls3 = LineString([(90, 22), (0, 0)])\n",
    "mls = MultiLineString(lines=[ls1, ls2, ls3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:35.921053Z",
     "iopub.status.busy": "2022-12-15T13:10:35.920701Z",
     "iopub.status.idle": "2022-12-15T13:10:35.932578Z",
     "shell.execute_reply": "2022-12-15T13:10:35.931313Z"
    }
   },
   "outputs": [],
   "source": [
    "result = ix.intersect(mls)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:35.936910Z",
     "iopub.status.busy": "2022-12-15T13:10:35.936349Z",
     "iopub.status.idle": "2022-12-15T13:10:36.316203Z",
     "shell.execute_reply": "2022-12-15T13:10:36.314765Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "sgr.plot(ax=ax)\n",
    "ix.plot_linestring(result, ax=ax, cmap=\"viridis\")\n",
    "\n",
    "for irow, icol in result.cellids:\n",
    "    (h2,) = ax.plot(\n",
    "        sgr.xcellcenters[0, icol],\n",
    "        sgr.ycellcenters[irow, 0],\n",
    "        \"kx\",\n",
    "        label=\"centroids of intersected gridcells\",\n",
    "    )\n",
    "\n",
    "ax.legend([h2], [i.get_label() for i in [h2]], loc=\"best\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Same as before, the intersect for structured grids can also be performed with a different method optimized for structured grids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:36.320637Z",
     "iopub.status.busy": "2022-12-15T13:10:36.320256Z",
     "iopub.status.idle": "2022-12-15T13:10:36.325908Z",
     "shell.execute_reply": "2022-12-15T13:10:36.324845Z"
    }
   },
   "outputs": [],
   "source": [
    "ixs = GridIntersect(sgr, method=\"structured\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:36.330370Z",
     "iopub.status.busy": "2022-12-15T13:10:36.330069Z",
     "iopub.status.idle": "2022-12-15T13:10:36.357376Z",
     "shell.execute_reply": "2022-12-15T13:10:36.356155Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intersection result with method='structured' and method='vertex' are equal: True\n"
     ]
    }
   ],
   "source": [
    "result2 = ixs.intersect(mls)\n",
    "\n",
    "# ordering is different so compare sets to check equality\n",
    "check = len(set(result2.cellids) - set(result.cellids)) == 0\n",
    "print(\n",
    "    \"Intersection result with method='structured' and \"\n",
    "    f\"method='vertex' are equal: {check}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [MultiPoint with regular grid](#top)<a id=\"rectgrid.3\"></a>\n",
    "\n",
    "MultiPoint to intersect with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:36.361888Z",
     "iopub.status.busy": "2022-12-15T13:10:36.361190Z",
     "iopub.status.idle": "2022-12-15T13:10:36.369833Z",
     "shell.execute_reply": "2022-12-15T13:10:36.368604Z"
    }
   },
   "outputs": [],
   "source": [
    "mp = MultiPoint(\n",
    "    points=[\n",
    "        Point(50.0, 0.0),\n",
    "        Point(45.0, 45.0),\n",
    "        Point(10.0, 10.0),\n",
    "        Point(150.0, 100.0),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For points and linestrings there is a keyword argument `return_all_intersections` which will return multiple intersection results for points or (parts of) linestrings on cell boundaries. As an example, the difference is shown with the MultiPoint intersection. Note the number of red \"+\" symbols indicating the centroids of intersected cells, in the bottom left case, there are 4 results because the point lies exactly on the intersection between 4 grid cells. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:36.375046Z",
     "iopub.status.busy": "2022-12-15T13:10:36.374628Z",
     "iopub.status.idle": "2022-12-15T13:10:36.385247Z",
     "shell.execute_reply": "2022-12-15T13:10:36.384242Z"
    }
   },
   "outputs": [],
   "source": [
    "result = ix.intersect(mp)\n",
    "result_all = ix.intersect(mp, return_all_intersections=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:36.389450Z",
     "iopub.status.busy": "2022-12-15T13:10:36.389160Z",
     "iopub.status.idle": "2022-12-15T13:10:36.673969Z",
     "shell.execute_reply": "2022-12-15T13:10:36.673042Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "sgr.plot(ax=ax)\n",
    "ix.plot_point(result, ax=ax, s=50, color=\"C0\")\n",
    "ix.plot_point(result_all, ax=ax, s=50, marker=\".\", color=\"C3\")\n",
    "\n",
    "for irow, icol in result.cellids:\n",
    "    (h2,) = ax.plot(\n",
    "        sgr.xcellcenters[0, icol],\n",
    "        sgr.ycellcenters[irow, 0],\n",
    "        \"kx\",\n",
    "        ms=15,\n",
    "        label=\"centroids of intersected cells\",\n",
    "    )\n",
    "\n",
    "for irow, icol in result_all.cellids:\n",
    "    (h3,) = ax.plot(\n",
    "        sgr.xcellcenters[0, icol],\n",
    "        sgr.ycellcenters[irow, 0],\n",
    "        \"C3+\",\n",
    "        ms=15,\n",
    "        label=\"centroids with `return_all_intersections=True`\",\n",
    "    )\n",
    "\n",
    "ax.legend([h2, h3], [i.get_label() for i in [h2, h3]], loc=\"best\");"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Same as before, the intersect for structured grids can also be performed with a different method written specifically for structured grids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:36.677565Z",
     "iopub.status.busy": "2022-12-15T13:10:36.677280Z",
     "iopub.status.idle": "2022-12-15T13:10:36.683503Z",
     "shell.execute_reply": "2022-12-15T13:10:36.682378Z"
    }
   },
   "outputs": [],
   "source": [
    "ixs = GridIntersect(sgr, method=\"structured\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:36.687380Z",
     "iopub.status.busy": "2022-12-15T13:10:36.687091Z",
     "iopub.status.idle": "2022-12-15T13:10:36.694548Z",
     "shell.execute_reply": "2022-12-15T13:10:36.693490Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intersection result with method='structured' and method='vertex' are equal: True\n"
     ]
    }
   ],
   "source": [
    "result2 = ixs.intersect(mp, return_all_intersections=False)\n",
    "\n",
    "# ordering is different so compare sets to check equality\n",
    "check = len(set(result2.cellids) - set(result.cellids)) == 0\n",
    "print(\n",
    "    \"Intersection result with method='structured' and \"\n",
    "    f\"method='vertex' are equal: {check}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id=\"trigrid\"></a>[Vertex Grid](#top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:36.698516Z",
     "iopub.status.busy": "2022-12-15T13:10:36.698236Z",
     "iopub.status.idle": "2022-12-15T13:10:36.706429Z",
     "shell.execute_reply": "2022-12-15T13:10:36.705527Z"
    }
   },
   "outputs": [],
   "source": [
    "cell2d = [\n",
    "    [0, 83.33333333333333, 66.66666666666667, 3, 4, 2, 7],\n",
    "    [1, 16.666666666666668, 33.333333333333336, 3, 4, 0, 5],\n",
    "    [2, 33.333333333333336, 83.33333333333333, 3, 1, 8, 4],\n",
    "    [3, 16.666666666666668, 66.66666666666667, 3, 5, 1, 4],\n",
    "    [4, 33.333333333333336, 16.666666666666668, 3, 6, 0, 4],\n",
    "    [5, 66.66666666666667, 16.666666666666668, 3, 4, 3, 6],\n",
    "    [6, 83.33333333333333, 33.333333333333336, 3, 7, 3, 4],\n",
    "    [7, 66.66666666666667, 83.33333333333333, 3, 8, 2, 4],\n",
    "]\n",
    "vertices = [\n",
    "    [0, 0.0, 0.0],\n",
    "    [1, 0.0, 100.0],\n",
    "    [2, 100.0, 100.0],\n",
    "    [3, 100.0, 0.0],\n",
    "    [4, 50.0, 50.0],\n",
    "    [5, 0.0, 50.0],\n",
    "    [6, 50.0, 0.0],\n",
    "    [7, 100.0, 50.0],\n",
    "    [8, 50.0, 100.0],\n",
    "]\n",
    "tgr = fgrid.VertexGrid(vertices, cell2d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:36.709989Z",
     "iopub.status.busy": "2022-12-15T13:10:36.709671Z",
     "iopub.status.idle": "2022-12-15T13:10:36.894087Z",
     "shell.execute_reply": "2022-12-15T13:10:36.892927Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "pmv = fplot.PlotMapView(modelgrid=tgr)\n",
    "pmv.plot_grid(ax=ax);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id=\"trigrid.1\"></a>[Polygon with triangular grid](#top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:36.898883Z",
     "iopub.status.busy": "2022-12-15T13:10:36.898306Z",
     "iopub.status.idle": "2022-12-15T13:10:36.905288Z",
     "shell.execute_reply": "2022-12-15T13:10:36.904267Z"
    }
   },
   "outputs": [],
   "source": [
    "ix2 = GridIntersect(tgr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:36.909443Z",
     "iopub.status.busy": "2022-12-15T13:10:36.908923Z",
     "iopub.status.idle": "2022-12-15T13:10:36.918318Z",
     "shell.execute_reply": "2022-12-15T13:10:36.917226Z"
    }
   },
   "outputs": [],
   "source": [
    "result = ix2.intersect(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:36.921696Z",
     "iopub.status.busy": "2022-12-15T13:10:36.921389Z",
     "iopub.status.idle": "2022-12-15T13:10:37.168558Z",
     "shell.execute_reply": "2022-12-15T13:10:37.167213Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "pmv = fplot.PlotMapView(ax=ax, modelgrid=tgr)\n",
    "pmv.plot_grid()\n",
    "ix.plot_polygon(result, ax=ax)\n",
    "\n",
    "# only cells that intersect with shape\n",
    "for cellid in result.cellids:\n",
    "    (h2,) = ax.plot(\n",
    "        tgr.xcellcenters[cellid],\n",
    "        tgr.ycellcenters[cellid],\n",
    "        \"kx\",\n",
    "        label=\"centroids of intersected gridcells\",\n",
    "    )\n",
    "\n",
    "ax.legend([h2], [i.get_label() for i in [h2]], loc=\"best\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id=\"trigrid.2\"></a>[LineString with triangular grid](#top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:37.173020Z",
     "iopub.status.busy": "2022-12-15T13:10:37.172694Z",
     "iopub.status.idle": "2022-12-15T13:10:37.180697Z",
     "shell.execute_reply": "2022-12-15T13:10:37.179724Z"
    }
   },
   "outputs": [],
   "source": [
    "result = ix2.intersect(mls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:37.184258Z",
     "iopub.status.busy": "2022-12-15T13:10:37.183910Z",
     "iopub.status.idle": "2022-12-15T13:10:37.456925Z",
     "shell.execute_reply": "2022-12-15T13:10:37.455817Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CgoLo3LkzBoOB1atXP/aoF+DPf/4zU6dOpWPHjvTv359mzZoBcObMGX73u9+h0+mws7MjNjb2ieOvWbOGxYsXk5ubS8uWLVm1alWFvh6/FhgYyOLFi3FycuLw4cNs27aN8PBwsrKyMBqN/OY3v6Fjx45P7Pe73/2Oy5cvo6oqr7/+Ol27dqVLly4kJibSo0cPVFXF29ubnTt3MnLkSE6dOkWvXr2wt7dn1KhR/Md//McLH3vVqlXMnTsXRVF44403nvp5TJgwgW+++YYOHTrQtGlTevToYXnb4rM4OjqybNkyRo8ejbOzMwMHDrT8QfHHP/6RkJAQOnXqhF6v589//jPjx49n9erVTJ061fIOin/7t3/jlVdeeertv8j3VQhrZy40kbH1F/LOPHprpN7TEa9ZHbDzrZpnIWua0WgkNjaW9PR0GjduzNy5czU91beilkw0a6lFixbq9evXH9t24cIF2rdv/0L7FxYWWt4/6+rqWm1nvRG1U05ODi4uLqSlpdGnTx8OHTpEw4bWfd7ll/n9eBZZD1xUF2NmAWlrz1F096Flm0MrNzymtUdfzzZPi200GomOjiYzM5PmzZsza9asCpf36tWrmTNnznFVVXtVJpNNPwIvZW9vj5eXF6mpqWRnZwNIiYsXNmbMGDIzMyksLOSDDz6w+vIWwpoV3Mgmbd15zDmPVhKr188P9zEtUfS2+aptYWEh0dHRZGdn07JlS8vbirVWKwocikvc29ublJQUsrOzUVWV+vXrax1L2IAXed1bCFG+h8fvk7Hj8qOVxHQK7mNb4fKq3/N3tGKFhYVERkaSk5NDmzZtmDZtmtaRLGpNgQPY2dnh4+NDSkoKDx48QFXVKl++TQghxONUs0rWF9fJ+f6OZZvO2VC8klhLd+2CVVJ+fj6RkZHk5ubSrl27p76tVEtWXeCqqr70lKLBYLA8Es/JyUFV1XKHkoSwJdYwtyJEKXO+kfRNF8m/lGHZZvB1xmt2Rwwe1X860eqSm5tLVFQUeXl5dOrUiQkTJmgd6QlWW+COjo6kpaXh6elZqRJ/+LB4iEJKXNQGasl64DVxnmUhylOUmkfamnMYU8qsJNbeA48pbdE5WG29lCsnJ4fo6Gjy8/Pp1q0bY8eO1TrSU1ntV7hJkybcvn2blJSUCt+G2Wy2PJVub29vOaOXELbM0dGRJk2aaB1D1HH5VzJI23ARNe/RCavqD2mK6xvNbXIxklLZ2dnExMRQUFBAz549K7zSYE2w2gK3s7OznG60Msr+JdW1a1fGjRtX+XBCCFFHqarKw8NJZH5+FUpOrIZBh8e7bXDu5qNptsrKzMwkNjaWwsJCXn31VUaOHKl1pOeyzZn+l1C6LquzszOnT5+2nG9bCCHEy1GNZjL/7wqZux6Vt66+PT6Luth8eaenpxMTE0NhYSH9+/e3+vKGOlDgULwUaVhYGPXq1ePcuXOPLTYhhBCifKaHRaSsOMPDn+5Zttk1ccE3rBv2TW37LbspKSmW0zkPGjTIsjiStasTBQ7FrxuGh4dTv359Ll269MxzYwshhHhc0b2HJEedpPB6tmWbUzdvfBZ1Qe/q8Jw9rd/9+/dZunQpRqORoUOHVssiWNWlzhQ4FJ/sJTw8HDc3N65cucKaNWu0jiSEEFYt71wayTGnMWWULAOqgOtIfzwmt0Wx02sbrpKSkpJYvnw5JpOJ4cOH29zSvXWqwKH4LWahoaE0aNCAxMREVq5cidlsLn9HIYSoQ1RVJTv+JmnrzqMWmgBQ7PV4zuyA65CmNrmSWFm3b98mLi4Ok8nEm2++Sf/+/bWO9NLqXIHDoxL39PTk1q1brFixQkpcCCFKqEUm0jdfIvurG5Zteg9HfIK74tTBU8NkVSMxMZFVq1ZhNpsZM2aMZSVLW1MnCxxAp9MRHByMt7c3d+/eZfny5VLiQog6z5RVQPLSn8k7/egcHA4t3fAJ6YZdQ9tcBrSsa9eusXbtWsxmM+PGjaNnz55aR6qwOlvgUFziixcvpmHDhty7d48lS5ZIiQsh6qzCWw+4H3WKots5lm31Xm2I17xONrsMaFmXL19m/fr1qKrKhAkT6Nq1q9aRKqVOFzgUl/iCBQto3LgxKSkpxMTEYDQay99RCCFqkdyTySQvPY35QWHxBh24j21Fg3fa2OwyoGVduHCBTZs2oaoqkyZNolOnTlpHqjTb/65UAZ1Ox9y5c2nWrBlpaWlER0dLiQsh6oTSlcTSP70ExuKFchQnA15zO+PSr5HG6arGuXPn2LJlCwDTpk2jffv2GieqGlLgJXQ6HXPmzKFFixZkZmYSGRlJYWGh1rGEEKLamPONpK09z4Pvblu2GXyc8A3thmNrd+2CVaHSM3AqisL06dNp06aN1pGqjBT4r8yaNYvWrVuTnZ1NVFSUlLgQolYypuWRHHua/Ivplm2O7TzwCe6GwdNJw2RV58SJE+zcuROdTsesWbNo1aqV1pGqlBT4U0yfPp127drx4MEDIiIiyM/P1zqSEEJUmfyrmSRHn8J4P9eyzWVwEzxndUDnaLVrXL2UhIQEdu/ejU6nIzAwEH9/f60jVTkp8GeYPHkynTp14uHDh0RERJCbm1v+TkIIYeVyjtwldcVZzLklcz4GhQaTXsH9zRY2vQxoWYcPH2bv3r3odDrmzZtH06ZNtY5ULaTAn2PChAl069aNvLw8IiMjycnJKX8nIYSwQqrJTMbOK2TuvArm4mE1XX07vBd2oV4PX43TVZ3vv/+effv2odfrWbhwIY0a1Y5BvKeRAi/H2LFj6dmzJ/n5+URFRZGdnV3+TkIIYUVMD4tIXXGWh0eSLNvsGrvgE9odh2auGiarWvHx8Rw4cACDwcCiRYvw9a09f5g8jRT4Cyg91V5BQQHR0dFkZWVpHUkIIV5I0f2HJEefouDao/stpy5eeC/qgsHNtlcSK2v//v0cPHgQOzs7goKC8Pb21jpStZMCf0GlJ7svLCwkOjqajIwMrSMJIcRz5V0oWUks/dEgrusbzfGY2g6dvW2vJFbWV199xaFDh7CzsyM4OBgPDw+tI9UIKfCXMHz4cAYNGkRRURExMTGkpqZqHUkIIZ6gqirZ394ibe151ILSlcR0eM5sj+vQZja/klhZe/bs4ciRIzg4OBAaGoq7u7vWkWqMFPhLCggIICAgAKPRyJIlS7h//77WkYQQwkItMpOx5Reyv0yE4lk19O4OeAd1w6mjl6bZqtpnn33GsWPHcHR0JDQ0FFfX2vN6/ouQAq+AQYMGMWzYMEwmE8uXLycpKan8nYQQopqZsgtJXvYzuSeTLdvs/V3xCe2GvZ/tryRW1o4dOzh16hROTk6EhYXh4uKidaQaJwVeQQMGDGDkyJGYTCbi4uK4c+eO1pGEEHVY4e0H3I86SdGtB5Zt9Xo3xHt+Z/Qu9homq3pbt27lzJkzODs7Ex4ejrOzs9aRNCEFXgmvvvoqY8aMwWw2s3LlSm7evKl1JCFEHZR7OpnkJT9jzi6zkthbLXEf3xrFULvu5jdu3Mj58+dxcXHhvffew9HRUetImqld31kN9OzZk7Fjx2I2m1m9ejXXr1/XOpIQoo5QzSpZXyWSvukSGM0AKI4GvOZ0wmVA41o1rAawbt06Ll++jKurK2FhYdjb165nFl6WFHgV6NatG+PHj0dVVcsPmBBCVCdzgZG09Rd4EH/Lss3g7YRPSFcc2zTQMFnVK32AdO3aNdzd3aW8S0iBV5HOnTszceJEVFVl06ZNXLp0SetIQohaypieT0rsafLPp1m2ObzSAJ+Qbth5167Xg81mM6tWreLGjRt4eHgQEhKCwVA7FlypLCnwKtShQwemTJkCwObNmzl//rzGiYQQtU3BtSySo09SdK/MSmIDG+MV2LHWrCRWymw2ExcXx+3bt/Hy8pLy/hUp8CrWtm1bpk+fjqIolklJIYSoCjk/JZESdwbzw5KVxPQKDd59BffRLWvNSmKlzGYzS5cuJSkpCR8fH4KCgtDppLLKkq9GNWjVqhWzZs1CURR27NjBiRMntI4khLBhqkklc9dVMndcebSSmEvJSmK9at+CHWazmZiYGJKTk/Hz82PRokVS3k8hX5Fq4u/vT2BgIDqdjt27d5OQkKB1JCGEDTLnFpG66iw5P961bLPzq4dPaDccmte+M48ZjUaio6NJS0ujSZMmzJ8/X8r7GeSrUo2aNWvGvHnz0Ol07N27lyNHjmgdSQhhQ4qSc4tXEruSadnm1NkL76CuGNxr3/ufjUYjUVFRpKen4+/vz5w5c6S8n0O+MtWsUaNGLFy4EL1eb1kxRwghypN3KZ3k6FMY08qsJDasWa1bSaxUYWEhkZGRZGVl0bJlS2bPni3lXQ756tQAX19fFi1ahMFgYP/+/Xz77bdaRxJCWClVVXlw8DZpq889WknMTofH9Pa4Dmte64bVAPLz84mMjCQ7O5tXXnmFmTNnah3JJkiB1xBvb28WL16MwWDgu+++45tvvtE6khDCyqhGMxlbfyFr7/VHK4m5OeAd1BXnzrVrJbFSpeWdk5NDhw4dmDp1qtaRbIYUeA3y9PQkJCQEOzs7fvjhB/bt26d1JCGElTA9KCRl2c/kniizkljzkpXEGtXOlbZyc3OJiIggNzfXcjIs8eKkwGuYu7s7oaGh2Nvbc/jwYfbu3at1JCGExgrv5JAcdZLCm49WEnPu6Yv3gs7o69fOU4bm5OQQGRlJXl4e3bt3Z/z48VpHsjlS4BooPRG/o6MjCQkJ7Nq1S+tIQgiN5P6cQsqS05iySlYSU8BtdEsavNum1q0kVio7O5vIyEjy8/Pp1asXb7/9ttaRbFLt/OmwAS4uLoSFheHk5MTJkyfZsWOH1pGEEDVINatkfX2D9I0XUYtKVxLT4zWnE/UH1r6VxEplZmYSFRVFYWEhffv2ZfTo0VpHsllS4Boquxj9mTNn2Lp1q9aRhBA1wFxoIn3jBR58c9OyzeDlhE9INxxfqV0riZWVnp5OdHQ0RUVFvPbaa4wYMULrSDZNClxjjo6OhIWF4eLiwvnz59m8ebPWkYQQ1ciYUbySWN7ZMiuJtXHHJ7hrrVtJrKyUlBRiY2MxGo0MHjyY119/XetINk8K3AqUlnj9+vW5dOkS69ev1zqSEKIaFCRmkRx1iqKkh5ZtLgMa4RXYCZ2znYbJqtf9+/dZunQpRqOR119/nSFDhmgdqVaQArcS9vb2hIeH4+bmxtWrV1mzZg1ms1nrWEKIKvIw4R4py89gflhUvEGv0GBCG9zfaoWir52vdwPcvXuXZcuWYTKZeOONN3jttde0jlRrSIFbEYPBQGhoKA0aNCAxMZFVq1ZJiQth41STSubuq2RsvwymkpXE6tnhvaAz9Xo31Dhd9bp16xYrVqzAbDYzatQo+vXrp3WkWkUK3MqUlrinpye3b98mLi5OSlwIG2XOM5K65hw5h8qsJNawZCUxfzcNk1W/sg9C3nrrLXr37q11pFpHCtwK6XQ6goOD8fHxISkpiWXLlkmJv4CPP/6Y+Pj4x7bFx8fz8ccfa5RI2LrK/EwVpZSsJPZLhmWbY0fP4pXEGtS+lcTKunr1KmvXrkVVVcaNG0ePHj20jlQrSYFbKZ1Ox6JFi/Dz8+P+/fvExsZKiZejd+/eTJo0yXKHGx8fz6RJk+Qvf1FhFf2Zyv8lo3glsdQ8y7b6Q5viOb09Oofat5JYWZcuXWLDhg2oqsq7775L165dtY5Ua0mBWzGdTsf8+fNp0qQJqampREdHYzQatY5ltQICAtiyZQuTJk3iT3/6E5MmTWLLli0EBARoHU3YqJf9mVJVlQc/3CF11VnU/DIriU1rh9sb/rVyJbGyLly4YHkr7OTJk+nYsaPGiWo3KXArp9PpmDNnDs2bN7ecBEFK/NkCAgIICgriww8/JCgoSMpbVNqL/kypRjMZ2y+T9fm1MiuJ2eO9qAvOXbxrMLE2zp49y5YtW1AUhalTp9KuXTutI9V6UuA2QKfTERgYSMuWLcnMzCQyMpLCwkKtY1ml+Ph4YmNj+eCDD4iNjX3i9UshXtaL/EyZcgpJWX6G3GP3Ldvsm9XHJ7Q79k3q12RcTZw+fZrt27ejKAozZsygTZs2WkeqE6TAbcjMmTNp06aNZSEAKfHHlb4+uWXLFv76179anvqUEhcV9SI/U4V3c0iOOkXhjWzLNucePngv6FJrVxIr6/jx4+zcufOxBxqiZkiB25hp06bRvn17cnJy+OSTT8jPz9c6ktVISEh47PXJ0tcvExISNE4mbFV5P1N5Z1NJiT2NKbOgeAcF3Ea1oMHEV1Dsav/d69GjR/n8888tL/U1a9ZM60h1iqKqqtYZaNGihXr9+nWtY9iU7du3c/bsWZycnAgNDcXZufaeQ1lUj48++giA999/X+MktkdVVR4cuEX21zcs2xQHPR5T2+HUzkPDZDXn0KFD7N+/H71ez7x58/Dz89M6ks1YvXo1c+bMOa6qaq/K3E7t/xOxlpowYQLdunUjLy+PyMhIcnJytI4kRJ1QvJLYxcfKW+/piE9ItzpT3gcPHrSU94IFC6S8NSIFbsPGjh1Lz549yc/PJyoqiuzs7PJ3EkJUmDGzgJQlp8k7k2rZ5tDaHd+Qbtj51I1nweLj44mPj8dgMLB48WJ8fX21jlRnSYHbuDFjxvDqq69SUFBAdHQ0mZmZWkcSolYquJFNctRJiu4+WkmsXj8/vOZ0rNUriZX19ddfc/DgQezs7AgKCsLLy0vrSHWaFHgtMHLkSAYMGEBhYSExMTGkp6drHUmIWuXh8fukLPsZc07JSmI6Bfd3WtNgbGsUfd24G/3iiy/48ccfsbe3JyQkBA+PuvFygTWrGz95dcCwYcMYNGgQRUVFxMbGkpqaWv5OQojnUs0qmXuvkbH1l0criTkb8J7fCZdX687rvp9//jk//fQTDg4OhISE4OZWuxdisRVS4LVIQEAAQ4cOxWg0smTJEu7fv1/+TkKIpzLnG0lbc46cg3cs2wy+zviEdsehpbt2wWrYZ599xvHjx3F0dCQ0NBRXV1etI4kSlSpwRVH+QVGUc4qinFUUZZOiKI6KorRQFOWooihXFEX5VFGU2n8mAysycOBAhg8fjslkYvny5SQlJWkdSQibU5SaR3L0KfIvlVlJrL0HPsFdMXjU7pXEytq+fTunTp3CycmJsLAwXFxctI4kyqhwgSuK0hgIB3qpqtoJ0ANTgL8B/6OqamsgA5hXFUHFi+vfvz9vvvkmJpOJuLg4bt++rXUkIWxG/pWSlcRSyqwkFtAUz5kd0DkYNExWsz799FPOnj2Ls7Mz4eHhcq4JK1TZp9ANgJOiKAbAGUgChgLbSi5fA4yr5DFEBfTp04e33noLs9nMqlWrSExM1DqSEFZNVVVyfrxL6sqzqHklCwYZdHhMaYvbiNq/klhZGzZs4OLFi7i4uPDee+/h6Fh3nnWwJRUucFVV7wB/B25SXNxZwHEgU1XV0uWybgONKxtSVEyPHj0YN24cZrOZtWvXcu3aNa0jCWGVVKOZzP+7Quauq2Au3qZztcdnURecu/loG66GrV27litXruDq6kpYWBj29vIqqLWqzFPoDYCxQAugEVAPGPkS+y9UFOWYoijHioqKKhpDlKNr165MmDABVVVZv349ly9f1jqSEFbF9LCIlBVnePjTPcs2uyYu+IZ2w75p7V9JrFTps3XXr1/H3d1dytsGVOYp9GHAdVVVU1RVLQJ2AAMA95Kn1AGaAHeetrOqqstUVe2lqmovO7u6cRIErXTq1IlJkyYBsGnTJi5cuKBxIiGsQ9G9hyRHnaTw+qOzGDp188ZnURf0rg4aJqtZZrOZlStXcvPmTTw8PAgJCcFgqDuv99uqyhT4TaCvoijOiqIowOvAeSAeeLfkOrOBzyoXUVSF9u3bM3XqVAC2bNnCuXPnNE4khLbyzqWRHHMaU8ajlcRcR/rjMbktip1e23A1yGw2s3z5cu7cuYOXl5eUtw2pzGvgRykeVjsBnCm5rWXAH4B/VBTlCuAJrKiCnKIKtGnThhkzZqAoCtu2beP06dNaRxKixqmqSnb8TdLWn0ctNAGg2OvxnNkB1yFNKX48UjeYzWaWLFnCvXv38PX1JSgoCJ1OTg9iKyr1Z5aqqn8G/vyrzdeAPpW5XVF9WrZsyaxZs1i3bh07d+7EZDLRo0cPrWMJUSPUIhPp2y6TdzrFsk3v4YjX7A7Y+dbTMFnNM5vNxMTEkJaWRqNGjZg3b56Ut42R71Yd5O/vT2BgIDqdjt27d5OQkKB1JCGqnSmrgOSlPz9W3g4t3fAJ6VbnyttoNBIVFUVaWhpNmzaV8rZR8h2ro5o2bcr8+fPR6XTs3buXw4cPax1JiGpTeOsB96NOUXQ7x7Kt3qsN8ZrXCX29ujVEW1reGRkZ+Pv7M3fuXClvGyXftTrMz8+PhQsXotfr2bdvH99//73WkYSocrknk0leehrzg8LiDTpwH9uKBu+0qTMriZUqLCwkIiKCrKwsWrVqxezZs7WOJCqhbv30iif4+vqyaNEiDAYDBw4cID4+XutIQlQJ1ayS9cV10j+9BMZHK4l5ze2MS79GGqerefn5+URGRvLgwQPatm3LjBkztI4kKkkKXODt7U1QUBB2dnYcPHiQ/fv3ax1JiEox5xtJW3ueB989WgfA4OOMT0g3HFu7axdMI7m5uURGRpKTk0PHjh2ZMmWK1pFEFZACFwB4eHgQHByMvb09hw4d4quvvtI6khAVYkzLIzn2NPkX0y3bHNuVrCTm6aRhMm3k5uYSFRVFbm4uXbp04d133y1/J2ETpMCFhbu7OyEhITg4OHDkyBH27NmjdSQhXkr+1czilcTu51q2uQxuguesDugc697JSXJycoiIiCAvL4/u3bvzzjvvaB1JVCEpcPEYV1dXQkNDcXR05NixY+zatUvrSEK8kJwjd0ldcRZzbulKYgoNJrfF/c0WdWolsVLZ2dlERkZSUFBA7969efvtt7WOJKqYFLh4gouLC2FhYTg5OXHy5El27NihdSQhnkk1mcnYeYXMnVfBXDKsVt8O74VdqNe9bq0kViojI4OoqCgKCwvp168fo0aN0jqSqAZS4OKpnJ2dCQ8Px9nZmTNnzrB161atIwnxBNPDIlJXnOXhkSTLNrvGLviEdsehmauGybSTlpZGTEwMRUVFDBw4kDfeeEPrSKKaSIGLZ3J0dOS9997DxcWF8+fPs3HjRq0jCWFRdP8hydGnKLiWZdnm1NUb70VdMLjVnZXEykpOTiY2Nhaj0ciQIUMYOnSo1pFENZICF89lb29PWFgYrq6uXL58mXXr1mkdSQjyLpSsJJaeb9nm+kZzPKa0RWdfd1YSKyspKYlly5ZhMpkYNmwYgwcP1jqSqGZS4KJcpSXu7u7OtWvXWL16NWazWetYog5SVZXsb2+RtvY8akHpSmI6PGe2x3Voszq1klhZd+7cIS4uDpPJxIgRIxgwYIDWkUQNkAIXL8RgMBASEoKHhwc3btxg1apVUuKiRqlFZjK2/EL2l4lQPKuG3t0B76BuOHX00jSblm7evMnKlSsxm82MGjWKvn37ah1J1BApcPHCSkvcy8uL27dvExcXJyUuaoQpu5DkZT+TezLZss3e3xWf0G7Y+9WtlcTKSkxMtDwj9vbbb9O7d2+tI4kaJAUuXopOpyMoKAhfX1+SkpJYunSplLioVoW3H5AcdZKiWw8s2+r1aYj3/M7oXew1TKatK1eusHbtWlRVZfz48XTv3l3rSKKGSYGLl6bT6Vi4cCF+fn6WqVcpcVEdck8nk7zkZ0zZZVYSe6sl7u+0RjHU3buvS5cusXHjRlRVZeLEiXTu3FnrSEIDdfc3QFSKTqdj/vz5NGnShNTUVKKjozEajVrHErWEalbJ+iqR9E2XwFj8x6HiaMBrTidcBjSus8NqAOfPn2fz5s0ATJkyhQ4dOmicSGhFClxUmE6nY86cOfj7+5Oenk5UVJSUuKg0c4GRtPUXeBB/y7LN4O2ET2g3HNs00DCZ9kpPqqQoCtOmTaNt27ZaRxIakgIXlaLT6Zg9ezatWrUiKyuLyMhICgsLtY4lbJQxPZ+U2NPkn0+zbHN4pQE+Id2w86p7K4mVVXpaY0VRmDlzJq1bt9Y6ktCYFLioEjNmzOCVV16xLKCQn59f/k5ClFFwLYvk6JMU3SuzktjAxngFdqyTK4mVVbqwkE6nIzAwkBYtWmgdSVgBKXBRZaZOnUqHDh3IycmREhcvJeenJFLizmB+WPISjF6hwcRXcB/dsk6uJFbW0aNH2bNnDzqdjrlz59KsWTOtIwkrIQUuqlTpRGxubi4RERHk5uaWv5Oos1STSuauq2TuuPJoJTGXkpXEevpqnE57hw4d4ssvv0Sv1zN//nwaN26sdSRhRaTARZUrfU9qXl4ekZGR5OTkaB1JWCFzbhGpq86S8+Ndyza7RvWKVxJrXjdXEivr4MGD7N+/H71eb3nbphBlSYGLalF6Vqj8/HwiIyPJzs7WOpKwIkXJucUriV3JtGxz6uyF9+KuGNzr5kpiZR04cID4+HgMBgOLFy/Gx6durmsunk8KXFSb0vMyFxYWEhUVRWZmptaRhBXIu5ROcvQpjGllVhIb1gyPae3q7EpiZe3bt4/vv/8eOzs7goOD8fKqu+d5F88nBS6q1YgRI3jttdcoKioiOjqa9PR0rSMJjaiqyoODt0lbfe7RSmJ2Ojymt8d1WPM6fXKWUl988QWHDx/G3t6ekJAQGjSo2+97F88nBS6q3euvv87gwYMxGo3ExsaSkpKidSRRw1SjmYytv5C19/qvVhLrinNneYQJsHv3bn766SccHBwICQnBzc1N60jCykmBixoxZMgQXn/9dYxGI0uXLuX+/ftaRxI1xPSgkJRlP5N7osxKYs1LVhJr5KJhMuuxc+dOTpw4gaOjI6Ghobi6yhCfKJ8UuKgxr732GiNGjMBkMrFs2TLu3r1b/k7CphXeySE56iSFNx+tJObcyxfvBXV7JbGytm3bxunTp3F2diYsLAwXF/mjRrwYKXBRo/r27cuoUaMwm82sWLGCW7dulb+TsEm5P6eQsuQ0pqySU+sq4DamJQ0mtKnTK4mVtXnzZs6dO0e9evUICwvD2dlZ60jChshvkahxvXv35q233sJsNrN69WoSExO1jiSqkGpWyfr6BukbL6IWla4kpsdrTifqv1a3VxIra8OGDVy6dIn69esTHh6Oo6Oj1pGEjZECF5ro0aMH48aNw2w2s3btWq5evap1JFEFzIUm0jde4ME3Ny3bDF5O+IR0w/EVmagutWbNGq5cuYKbmxvh4eHY28vLCeLlSYELzXTt2pV3330XVVUtj0aE7TJmFK8klne2zEpibdzxCe6Knbc8NQxgNptZuXIliYmJuLu7ExoaisFQtxdqERUnBS401bFjRyZPngwUvx544cIFjROJiihIzCI56hRFSQ8t21wGNMIrsBM6ZzsNk1mPsnMfnp6ehISESHmLSpECF5pr164dU6dORVEUtmzZwtmzZ7WOJF7Cw4R7pCw/g/lhUfEGvUKDCW1wf6sVil5e74bi8l6+fDl3797F29ub4OBgKW9RaVLgwiq0adOGGTNmoCgK27dv5/Tp01pHEuVQTSqZn18jY/tlMJWsJFbPDu8FnanXu6HG6ayH2WxmyZIl3Lt3j4YNG7J48WJ0OrnrFZUnP0XCarRs2ZLAwEB0Oh07d+7k+PHjWkcSz2DOM5K65hw5P9yxbLPzq4dPaDcc/OUMYqWMRiMxMTGkpKTQqFEjFixYIOUtqoz8JAmr0qxZM+bMmYNOp+Pzzz/n6NGjWkcSv1KUUrKS2C8Zlm2OHT2LVxJrIG+FKmU0GomOjiYtLY1mzZoxb948KW9RpeSnSVidJk2aMH/+fPR6PV9++SU//vij1pFEifxfMopXEkvNs2yr/3ozPKe3R+cgK4mVKrsCX4sWLSx/lApRleQnSlglPz8/FixYgF6v5+uvv+bgwYNaR6rTVFXlwQ93SF11FjW/zEpi09rhNrw5ik6G1UqVlndWVhatW7dm1qxZWkcStZQUuLBavr6+LF68GIPBQHx8PPHx8VpHqpNUo5mM7ZfJ+vzao5XE3OzxXtwV5y7e2oazMvn5+URERPDgwQPatWvH9OnTtY4kajEpcGHVvLy8CAoKws7OjoMHD/L1119rHalOMeUUkhJ3htxjj1aPs29WH5/Q7tg3lkU3ysrNzSUiIoKHDx8+dn4DIaqLFLiweh4eHoSEhGBvb8+PP/7IF198oXWkOqHwbg7JUacoTMy2bHPu4YP3gi7o68upP8vKyckhMjKSvLw8yxkGhahuUuDCJri5uRESEoKDgwM//fQTn3/+udaRarW8s6mkxJ7GlFlQvEEBt1EtaDDxFRQ7udsoKzs7m6ioKPLz8y3n+BeiJshvorAZrq6uhIaG4ujoyPHjx/nss8+0jlTrqKpK9jc3SVt/4dFKYg56PGd3pP6gJrKS2K9kZWURHR1NQUEBffr04a233tI6kqhDpMCFTXFxcSEsLAwnJydOnTrF9u3btY5UaxSvJHaR7K9vWLbpPR3xCemGUzsPDZNZp4yMDKKjoyksLKR///68+eabWkcSdYwUuLA5zs7OhIeHU69ePc6ePcunn36qdSSbZ8wsIGXJafLOpFq2ObR2xzekG3Y+spLYr6WmphITE0NRUREDBw5k+PDhWkcSdZAUuLBJjo6OhIeH4+LiwsWLF9m4caPWkWxWwY1skqNOUnT30Upi9fr54TWno6wk9hTJycksWbIEo9FIQEAAQ4cO1TqSqKOkwIXNsre3JywsDFdXVy5fvszatWu1jmRzOubXI2XZz5hzSlYS0ym4v9OaBmNbo+jl7uHXkpKSWLZsGSaTiWHDhjFo0CCtI4k6TH5DrdzHH3/8xAlM4uPj+fjjjzVKZF1KS9zd3Z3r16+zatUqzGaz1rGsnmo0MvahQvf8bo9WEnM24D2/Ey6v+mkbzkrduXOHuLg4TCYTI0eOZMCAAVpHEnWcFLiV6927N5MmTbKUeHx8PJMmTaJ3794aJ7MeBoOBkJAQPDw8uHnzJitXrpQSf578LLL+J4b6Rf0tmwze9viEdsehpbt2uaxY2Z+r0aNH8+qrr2odSQgpcGsXEBDAli1bmDRpEn/605+YNGkSW7ZsISAgQOtoVqW0xL28vLhz5w7Lly+XEn+atKsQNwyXBzEoPADA0fUaPgvaYvCQlcSe5vr166xevRqz2czYsWPp1auX1pGEAKTAbUJAQABBQUF8+OGHBAUFSXk/g06nIygoCF9fX+7du8eSJUukxMu6Gg/Lh0LqLxh0SXja/SdZdj/g+fsZ6FzdtU5nlS5fvsy6detQVZXx48fTrVs3rSMJYSEFbgPi4+OJjY3lgw8+IDY2Vhb1eA6dTsfChQtp1KgRKSkpxMTESImrKhxdCusnQH5m8TaDI/sc/dldT0ExyDKgT3Pp0iU2bdqEqqpMnDiRzp07ax1JiMdIgVu50te8t2zZwl//+lfL0+lS4s+m0+mYN28eTZs2JS0tjaioKIxGo9axtGEshN3vwRe/B7V4GVBcGsKcvZzXd9Q2mxU7f/48mzdvBmDKlCl06NBB40RCPEkK3MolJCQ89pp36WviCQkJGiezbjqdjrlz5+Lv709GRkbdLPGHqbBuHJxY82hbox6w8Fto3FOrVFbvzJkzbN26FUVRmDZtGm3bttU6khBPJQVu5X7/+98/8Zp3QEAAv//97zVKZFtmz55Nq1atyMrKIiIigsLCQq0j1Yx7Z2F5ANw49Ghb54kwZy+4ytvEnuXEiRPs2LEDRVGYNWsWrVu31jqSEM8kBS5qvRkzZtC2bVsePHhAZGQk+fn5WkeqXhf3wIo3IPNmyQYFXv8zjF8Odk6aRrNmCQkJ7N69G51OR2BgIP7+/lpHEuK5pMBFnTBlyhQ6duxoWbc5NzdX60hVT1Xh4H/B5mlQVHJaVHsXmLoJBv4jyEpiz3TkyBH27t1rmZ9o1qyZ1pGEKJcUuKgz3n33Xbp06UJubi5RUVG1q8SL8mD7PDjwb4+2uTeHeV9DW1kl63kOHTrEV199hV6vt7yDQQhbIAUu6pR33nmHHj16kJeXR0REBDk5OVpHqrzsu7ByJJwts7Sq/0BYEA++Mj39PN9++y379++3lLevr6/WkYR4YVLgos5566236N27NwUFBURGRpKdna11pIq7fQyWDYGkU4+29ZoLM/8P6nlqlcomfPPNN3z33XcYDAaCgoLw8fHROpIQL0UKXNRJo0aNol+/fhQWFhIVFUVGRobWkV7e6U9h1SjIuV/8saKHUX+HMf8DelkG9Hn27dvHDz/8gJ2dHSEhIXh6yh87wvZIgYs664033mDgwIEUFRURExNDWlqa1pFejNkEX/8J/m8hmAqKtzm6Fz/q7rNA02i2YO/evRw+fBh7e3tCQ0Nxd3fXOpIQFSIFLuq0oUOHMmTIEIxGI7GxsSQnJ2sd6fnys2HTVDj0yaNtXm1hYTy0HKxdLhuxa9cuEhIScHBwsKwlL4StkgIXdd7gwYMZNmwYJpOJZcuWkZSUpHWkp0u/BiuGw+WvHm1rMwLm7wePltrlshE7duzg5MmTODk5ER4ejouLi9aRhKgUKXAhgAEDBjBixAhMJhNxcXHcvXtX60iPu36weCWxlIuPtg14r/g93o7yKLI8W7du5cyZMzg7OxMeHo6zs7PWkYSoNClwIUr07duXUaNGYTabWbFiBTdv3ix/p5qQEAfr3oG8kkE7vT28sxSG/xV0spJYeTZv3sz58+dxcXEhLCwMR0dZ91zUDlLgQpTRu3dv3n77bcxmM6tXryYxMVG7MKYi+PwfYc9vwVyyEIuLLwTuha5TtMtlQ9avX8+lS5eoX7++lLeodaTAhfiV7t27M378eFRVZe3atVy5cqXmQ+SmFz/qPrbi0Ta/bsUnZ2nau+bz2Biz2cyaNWu4evUqbm5uhIeHY29vr3UsIaqUFLgQT9G5c2cmTpyIqqps3LiRS5cu1dzBky8UrySW+P2jbR3Hw5wvwK1xzeWwUWWfPWnQoAGhoaEYDAatYwlR5aTAhXiGDh06MGVK8VPVpa+jVrtLX0LccMhIfLRt6B/h3ZVgL4NX5TGbzcTFxXHr1i08PT2lvEWtJgUuxHO0bduWadOmoSiKZZK5Wqgq/PA/sGkKFD4o3mZXDyavh0G/k5XEXoDZbLa8DdDHx4fg4GB0OrmLE7WX/HQLUY7WrVszc+ZMFEWxvJe4ShXlw46FsP8vgFq8za0ZzNsH7d+q2mPVUmazmdjYWO7fv0/Dhg1ZtGiRlLeo9eQnXIgX0KJFCwIDA9HpdOzatYtjx45VzQ1nJ8HqUXBmy6NtzfoXn1mtYaeqOUYtZzQaiY6OJjU1lcaNG7NgwQIpb1EnyE+5EC+oWbNmzJ07F51Ox549ezh69GjlbvDOieJhtTvHH23rMQtmfQb1vCp323VEaXmnp6fTvHlzy/dHiLpAftKFeAmNGzdm/vz56PV6vvzySw4dOlSxGzqzDVa9CQ9KTtuq6GDk3+CtCDDI251eRGFhIZGRkWRmZtKyZUvLMyRC1BWV+mlXFMVdUZRtiqJcVBTlgqIo/RRF8VAU5WtFUS6X/L9BVYUVwhr4+fmxcOFC9Ho9+/fv5+DBgy++s9kM3/wVts8DY37xNkc3mLEd+i6WYbUXVFre2dnZtGnThpkzZ2odSYgaV9k/Vz8BvlRVtR3QFbgAvA98o6pqG+Cbko+FqFV8fHwICgrCYDAQHx/PgQMHyt+p4AF8OgO+/+9H2zzbwPwD0Gpo9YWtZfLz8/nkk0/IycmhXbt2TJs2TetIQmiiwgWuKIobMAhYAaCqaqGqqpnAWGBNydXWAOMqF1EI6+Tp6UlwcDB2dnZ8//337Nu379lXzkiEFW/ApT2PtrUeVrySmFfras9aW+Tm5hIREUFubi6dOnVi8uTJWkcSQjOVeQTeAkgBVimKclJRlDhFUeoBvqqqlq7HeA/wrWxIIaxVgwYNCAkJwd7ensOHD/PFF188eaXEH4pXEksucyKYfqEwbQs4uddYVluXk5NDZGQkeXl5dOvWjQkTJmgdSQhNVabADUAPIFZV1e7AQ371dLmqqiqWN7Y+TlGUhYqiHFMU5VhRUVElYgihLTc3N0JCQnBwcOCnn35i9+7djy48tgrWjoXctOKP9fYwNgZG/LusJPYSsrOziYqKIj8/n549ezJ27FitIwmhucoU+G3gtqqqpe+l2UZxod9XFMUPoOT/yU/bWVXVZaqq9lJVtZednV0lYgihPVdXV0JDQ3F0dOTEiRPs2LGFnTumon7+m0cridXzhtmfQ/fpmma1NZmZmURHR1NQUMCrr77KmDFjtI4khFWocIGrqnoPuKUoStuSTa8D54FdwOySbbOBzyqVUAgbUbretLNzEduTP+SDB2eJc3MtvrBh5+KVxJq9qm1IG5Oenk5MTAyFhYX079+fkSNHah1JCKtR2bP8hwEbFEWxB64Bcyj+o2CLoijzgBvApEoeQwibca/wHvF+33DLVPxxhIc7ffz60HX8WrCvp204G5OamsrSpUsxGo0MGjSIgIAArSMJYVUqVeCqqp4Cej3lotcrc7tC2KKDtw/yh4N/IMeUY9k2KqM+ZzzH0lXK+6Xcv3+f5cuXYzKZGDp0KAMHDtQ6khBWR05bJEQlqarK6rOrCf0mlJyi4vJ20jvy91bT8GMSV69dZ82aNeXciiiVlJRkKe/hw4dLeQvxDFLgQlRCgamAPx76I/99/L9RS95w0bBeQ9aOWseI1/6Z0NBQ3N3dSUxMZOXKlZjNZo0TW7fbt28TFxeHyWTizTffpH///lpHEsJqSYELUUGpeanM/Wouu67usmzr5t2NTaM30c6jHQAGg4GQkBA8PT25desWK1askBJ/hps3b7Jq1SrMZjNjxoyhT58+WkcSwqpJgQtRAefTzjPl8yn8nPKzZds7rd9hxYgVeDk9vpKYwWAgODgYb29v7t69y/Lly6XEf+XatWusXr0as9nMuHHj6Nmzp9aRhLB6UuBCvKQvE79k9hezuZ97HwCdouP3vX/Pv/b/V+z1T19JTKfTsXjxYho2bMi9e/dYsmSJlHiJy5cvs379elRVZcKECXTt2lXrSELYBClwIV6QWTUTdTKK3333O/JNxSuJ1berT8zrMczsMBOlnJXEdDodCxYsoFGjRqSkpBATE4PRaKyJ6FbrwoULbNq0CVVVmTRpEp06ddI6khA2QwpciBeQW5TLb7/9LUt/XmrZ1ty1ORtGb2BA4wEvfDs6nY558+bRrFkz0tLSiI6OrrMlfu7cObZs2QLAtGnTaN++vcaJhLAtUuBClONuzl1mfjGT/Tf3W7b18+vHhlEbaOHW4qVvT6fTMWfOHFq0aEFmZiZRUVEUFhZWZWSrd/r0abZt24aiKEyfPp02bdpoHUkImyMFLsRznLh/gql7pvJLxi+WbTPazyBmWAxuDm6Vuu1Zs2bRunVrsrKy6lSJnzhxgp07d6LT6Zg1axatWrXSOpIQNkkKXIhn2HF5B/P2zSM9Px0Ag87Av/b/V/7Q5w8YdJU9C3Gx6dOn065dOx48eEBERAT5+flVcrvWKiEhgd27d6PT6QgMDMTf31/rSELYLClwIX7FaDbyt5/+xp9//DPGkpXEPBw9WPHGCsa3GV/lx5s8eTIdO3bk4cOHREREkJubW+XHsAaHDx9m7969ljmApk2bah1JCJsmBS5EGVkFWYR8E8L6C+st215p8AqbRm+ih2+Pajvuu+++S9euXcnLyyMyMpKcnJzyd7Ih33//Pfv27UOv17Nw4UIaNWqkdSQhbJ4UuBAlrmddZ8beGfx490fLttebvc66N9fRyKX6C2fcuHH06NGD/Px8oqKiyM7OrvZj1oRvv/2WAwcOYDAYWLRoEb6+vlpHEqJWkAIXAjh05xDT90wnMTvRsm1Rl0X8vyH/D2c75xrL8dZbb9GnTx8KCgqIjo4mKyurxo5dHfbv3893332HnZ0dQUFBeHt7ax1JiFpDClzUaaqqsu78OoK/CeZB0QMAHPWO/Nfg/yK0eyg6peZ/RUoX8SgsLCQ6OpqMjIwaz1AVvvrqKw4dOoSdnR3BwcF4eHhoHUmIWkUKXNRZhaZC/vzjn/k44WPMavFpTX2dfVn95mpG+o/UNFvpMppFRUXExMSQmpqqaZ6XtWfPHo4cOYKDg4NlRTYhRNWSAhd1UlpeGvP3zef/rvyfZVsX7y5sHrOZjp4dNUz2yNChQwkICMBoNLJkyRKSk5O1jvRCdu3axbFjx3B0dCQ0NBRXV1etIwlRK0mBizrnYvpFpu6Zysnkk5Ztb7d6m5UjVj6xkpjWBg0axLBhwzCZTCxbtoykpCStIz3Xjh07OHnyJE5OToSFheHi4qJ1JCFqLSlwUafsv7GfWV/MIulhcREqKPy252/5twH/hoPeQeN0TzdgwABGjhyJyWQiLi6OO3fuaB3pqbZu3cqZM2dwdnYmPDwcZ+eaG/4Toi6SAhd1gqqqxJ6O5R++/QfyjHkAuNi5EPV6FIGdAstdSUxrr776KqNHj8ZsNrNy5Upu3rypdaTHbNy4kfPnz+Pi4sJ7772Ho6Oj1pGEqPWkwEWtl1uUyz9990/EnIqxbGtavykbRm1gUJNBGiZ7Ob169WLs2LGYzWZWr17N9evXtY4EwLp167h8+TKurq6EhYVhb//0NdGFEFVLClzUavce3iPwy0D23dhn2faq36tsGr2Jlu4tNUxWMd26dWP8+PHFb38rKU6tlP4hce3aNdzd3aW8hahhUuCi1jqVfIopn0/hQvoFy7ap7aYSOyy20iuJaalz585MnDgRVVXZtGkTly5dqvEMZrOZVatWcePGDTw8PAgJCcFgqJoFXoQQL0YKXNRKn135jLlfzSUtPw0Ag2Lgg74f8C+v/gt2OjuN01Vehw4dmDJlCgCbN2/m/PnzNXZss9lMXFwct2/fxsvLS8pbCI1IgYtaxWQ28feEv/PHQ3+kyFwEgLuDO8veWMaktpM0Tle12rZty/Tp01EUxTIBXt3MZjNLly4lKSkJHx8fgoKC0OnkbkQILchvnqg1HhQ+IPRAKGvOr7Fsa+3emk2jN9G7YW8Nk1WfVq1aMWvWLBRFYceOHZw4caLajmU2m4mNjSU5ORk/Pz8WLVok5S2EhuS3T9QKN7JvMH3vdH6484Nl25CmQ1g/aj1N6jfRMFn18/f3JzAwEJ1Ox+7du0lISKjyYxiNRqKjo0lNTaVJkybMnz9fylsIjclvoLB5h+8eZtqeaVzPevS2qvmd5/NJwCfUs6unYbKa06xZM+bNm4dOp2Pv3r0cOXKkym7baDQSFRVFeno6/v7+zJkzR8pbCCsgv4XCZqmqyoYLGwjaH0R2YfHa2Q56Bz4a+BHv9XhPk5XEtNSoUSMWLlyIXq+3rARWWYWFhURGRpKVlUXLli2ZPXu2lLcQVkJ+E4VNKjIV8a+H/5WPfvoIk2oCwNvJm9UjVzO65WiN02nH19fXUuKla3FXVH5+PpGRkWRnZ/PKK68wc+bMKkwqhKgsKXBhc9Lz01nw9QK2X95u2dbJsxObx2ymk1cnDZNZh9LpcIPBwLfffsuBAwde+jZKyzsnJ4cOHTowderUakgqhKgMKXBhUy6lX2Lanmkcv3/csm1Ui1GsGrkKH2cfDZNZF09PT0JCQrCzs+P7779n37595e9UIjc3l4iICHJzcy0njRFCWB8pcGEzDtw8wMwvZnInp3g1LgWF93q8x0cDP8LRIItn/Jq7uzuhoaHY29tz+PBh9u7dW+4+OTk5REZGkpeXR/fu3Rk/fnwNJBVCVIQUuLB6qqqy7OdlvBf/nmUlMWeDMxFDI5jfeb7VrySmpdIFRhwcHEhISGDXrl3PvG52djaRkZHk5+fTq1cv3n777RpMKoR4WVLgwqrlG/P5w8E/EHky0rKtsUtj1o9az5CmQ7QLZkNcXFwIDw/HycmJkydPsmPHjieuk5mZSVRUFIWFhfTt25fRo+vuIKAQtkJOYCys1v2H9wmPD+d82qPzfPdu2Jv/HvzfNHBsoGEy2+Ps7Ex4eDiRkZGcOXMGk8lkuSw9PZ0lS5ZQVFTEa6+9xuuvv65hUiHEi5JH4MLqqKrKf3zzJZN2T36svCe9Momlw5dKeVeQo6MjYWFhuLi4cP78eYqKiiynRy0qKmLw4MFS3kLYEEVVVa0z0LhxYzUsLEzrGMJKHLe7ywWP71F0RgAUVaFXdi9eyX1F42S1g6qqFBYWPrZNr9fLimJC1JDCwkL+/Oc/H1dVtVdlbkd+Y4VVMWPmsssFS3mrJidyb0/naqEPLo4p+OlzkZm1ylEUBYPBgNFotGyT8hbC9ljFb629vT3vv/++1jGElfA/PoaPfw6isMiRvFuzUYs8uQHcyHGla1N3Fg5sychODdHrpMkr4tatW6xevfqxbT4+PpYFUYQQ1evXv38VJb+twupM69mFT8euJDZgNa+37vDYZadvZRKy8QRD/h7Pmh8TyS00PuNWxNMkJiayevVqzGYzBoMBOzs7PD09uXXrFnFxcZjNZq0jCiFekBS4sEptPdoyuE1T4mb3Yv8/DmZqn6bYGx79uN5Kz+PPu87R/6MD/Pe+S6Q8KNAwrW24evUqa9euxWw2M27cOPR6PTqdjuDgYHx8fEhKSmLZsmVS4kLYCClwYfVa+7jwn+O7cOgPQwkb2hp3ZzvLZZm5RUQeuMKAvx3gn3f8zJXkHA2TWq9Lly6xYcMGVFXl3XffpWvXrpbLdDodixYtomHDhty/f5/Y2FgpcSFsgBS4sBne9R347Rtt+fH9ofx1bEeaeThbLis0mtn00y2G/b/vmL8mgZ+up2MN77CwBhcuXGDz5s0ATJ48mY4dOz5xHZ1Ox4IFC2jcuDGpqalER0c/NuQmhLA+UuDC5jjbG5jVz5/4fxpCzPQedG3q/tjl+y8kM2npYcbF/Mien5MwmetukZ89e5YtW7agKApTp06lXbt2z7yuTqdj7ty5NG/enPT0dClxIaycFLiwWXqdwqjOfuwM7s+WRf0Y1t73scvr+sDb6dOn2b59O4qiMGPGDNq0aVPuPjqdjsDAQFq2bElmZiaRkZFPvGdcCGEdpMCFzVMUhT4tPGTgrYzjx4+zc+fOxwr5ZcycOZM2bdpYFjiREhfC+kiBi1pFBt7g6NGjfP755+h0OubMmUOzZs0qdDvTpk2jXbt25OTk8Mknn5Cfn1/FSYUQlSEFLmqlsgNv//p2R5p6OFkuq80Dbz/++CNffvkler2e+fPn06RJk0rd3uTJk+nUqRO5ublERESQm5tbRUmFEJUlBS5qNWd7A7P7+/PtPwWUO/C294xtD7wdPHiQr7/+Gr1ez4IFC/Dz86uS250wYQLdunUjLy+PyMhIcnJq5zMXQtgaKXBRJ7zIwFvwhhME/P1bmxx4i4+PJz4+HoPBwOLFi/H19S1/p5cwduxYevbsSX5+PlFRUWRnZ1fp7QshXp4UuKhTyht4u5mea3MDb19//TUHDx7Ezs6OoKAgvLy8quU4Y8aM4dVXX6WgoIDo6GgyMzOr5ThCiBcjBS7qrNow8PbFF1/w448/Ym9vT0hICB4eHtV6vJEjR9K/f38KCwuJiYkhPT29Wo8nhHg2KXBR59nqwNvnn3/OTz/9hIODAyEhIbi5udXIcYcPH86gQYMoKioiNjaW1NTUGjmuEOJxUuBClLClgbfPPvuM48eP4+joSGhoKK6urjV6/ICAAIYOHYrRaGTJkiXcv3+/Ro8vhJACF+IJ1j7wtn37dk6dOoWTkxNhYWG4uLjU6PFLDRw4kOHDh2MymVi+fDlJSUma5BCirpICF+IZrHHg7dNPP+Xs2bM4OzsTHh6Os7Nz+TtVo/79+/Pmm29iMpmIi4vj9u3bmuYRoi6RAhfiBVjDwNvGjRu5ePEiLi4uvPfeezg6OlbLcV5Wnz59GDNmDGazmVWrVnHz5k2tIwlRJ0iBC/EStBp4W7t2LZcvX8bV1ZWwsDDs7e2r5HarSs+ePRk3bhxms5nVq1dz7do1rSMJUetJgQtRAWUH3qKn9aBrk8cnwKtq4K30Ue3169dxd3e3yvIu1bVrVyZMmICqqqxfv57Lly9rHUmIWk0KXIhK0OsURnfxY2fIAD5d2Jdh7X0eu7wyA29ms5mVK1dy8+ZNPDw8CAkJwWAwVPWnUKU6derEpEmTUFWVTZs2ceHCBa0jCVFrSYELUQUUReHVlp7Eze5dJQNvZrOZ5cuXc+fOHby8vGyivEu1b9+eadOmAbBlyxbOnTuncSIhaicpcCGqWGUH3sxmM0uXLuXevXv4+voSFBSETmdbv6pt2rRh+vTpKIrCtm3bOH36tNaRhKh1bOteQQgb8uIDb8csA29ms5mYmBiSk5Np1KgRCxcutLnyLtWqVStmzZqFTqdj586dnDhxQutIQtQqtvGcnBA2rHTgbUbf5nx59h7LDl7l9O0sy+X7L9zn1IlLfPjzZi62a0+WtxPN/JsTGBhos+Vdyt/fn8DAQFavXs3u3bsxmUz07t1b61hC1Aq2fe8ghA153sDb21d/wD/pCiPjdzNs79eMc3CA/HwN01adpk2bMm/ePHQ6HXv37uXw4cNaRxKiVpACF6KG/XrgbVI3X4bdOma53DMng/sf/htXAoaSEhGBsRYsFlL6coBer2ffvn18//33WkcSwuZJgQuhoSauBnzvfMdXb47gxx5DyHWsZ7nMlJVFakwsV4a+TtIHf6LAxk+O4uvry6JFizAYDBw4cIBvv/1W60hC2DQpcCE0kpubS2RkJDk5OXTu1Y15G2Ppdug7fP/4R+yaNLFcTy0sJHPrVq6NGs2t4BByjx2zmiVNX5a3tzdBQUHY2dnx3XffsX//fq0jCWGzpMCF0EBubi5RUVHk5ubSpUsX3n33XQD09erhMWM6rb76ksb/+z84du782H45Bw5wY8ZMEqdMIfvLr1BNJi3iV4qHhwfBwcHY29tz6NAhvvrqK60jCWGTpMCFqGE5OTlERESQl5dH9+7deeedd564jqLX4zpyJP5bPqX5urW4BAQ8dnn+6Z+585vfcHXkm6Rv2IA5N7em4lcJd3d3QkJCcHBw4MiRI+zZs0frSELYHClwIWpQdnY2kZGRFBQU0Lt3b95+++3nXl9RFJx796ZpbAwt9+7BfeK7KHaPTgxTdOuWzQ68ubq6EhoaiqOjI8eOHWPXrl1aRxLCpkiBC1FDMjIyiIqKorCwkH79+jFq1KiX2t+hZUv8PvyQ1ge+wXPxInRujxZQeWzg7U9/puDa9aqOXy1cXFwICwvDycmJkydPsmPHDq0jCWEzpMCFqAFpaWnExMRQVFTEwIEDeeONNyp8WwZvb3x+8xvaxB94+sDbli1cGzXKZgbenJ2dCQ8Px9nZmTNnzrB161atIwlhE6TAhahmycnJxMbGYjQaGTJkCEOHDq2S29U5OxcPvH35hc0PvDk6OvLee+/h4uLC+fPn2bhxo9aRhLB6UuBCVKOkpCSWLVuGyWRi2LBhDB48uMqPoRgMtWLgzd7enrCwMFxdXbl8+TLr1q3TOpIQVk0KXIhqcvfuXeLi4jCZTIwYMYIBAwZU6/FeduDNITevWvNURGmJu7u7c+3aNVavXo3ZbNY6lhBWSQpciGpw8+ZNVqxYgdlsZtSoUfTt27dGj/8iA29+591pdM6J+wmXajRbeQwGAyEhIXh4eHDjxg1WrVolJS7EU1S6wBVF0SuKclJRlM9LPm6hKMpRRVGuKIryqaIo9pWPKYTtSExMtDxyfPvttzVdfetZA2859RqR5tWVB/U6s23FHT7/3wTuXs60moG30hL38vLi9u3bxMXFSYkL8StV8Qj8PeBCmY//BvyPqqqtgQxgXhUcQwibcOXKFdauXYuqqowfP57u3btrHQl4cuAtveOIxy6/cfEB//ffJ9j+8XGuHE/GbNa+yHU6HUFBQfj6+pKUlMTSpUulxIUoo1IFrihKE2A0EFfysQIMBbaVXGUNMK4yxxDCVly6dImNGzeiqioTJ06k86+mwq1B6cDb8NX/hMH+EC4PLz92+f3r2Xy1/Cwb/nSYM9/epqhA28l1nU7HwoUL8fPzIzk5mZiYGClxIUpU9hH4/wK/B0p/ozyBTFVVjSUf3wYaV/IYQli98+fPs3nzZgCmTJlChw4dNE70fDqdjoxGdiR1zGDqn16l/QA/dAbFcnl2aj4HN//Cmn85xNFd18jNLtQ06/z582nSpAlpaWlER0djNBrL31GIWq7CBa4oyhggWVXV4xXcf6GiKMcURTlWVFRU0RhCaK705COKojBt2jTatm2rdaSX4tGoHkNntmfWv/en58jmODgbLJcVPDRybG8ia//lR+I3XCTj3kNNMup0OubMmYO/vz/p6elERUVJiYs6rzKPwAcAbyuKkghspvip808Ad0VRSu8BmgB3nrazqqrLVFXtpapqL7syb3URwpaUnv5TURRmzpxJ69attY5UYfXcHOg7rhWz/qM/Aye3ob6no+Uyk9HM+e/vsvEvR9kT8zN3r9T8wJtOp2P27Nm0atWKrKwsIiMjKSzU7pkBIbRW4QJXVfWfVVVtoqqqPzAFOKCq6nQgHni35Gqzgc8qnVIIK1S6AIdOpyMwMJAWLVpoHalK2Dsa6BLQlBl/7csb8zvi07z+Y5cn/pzK//29eODt6omaH3ibMWMGr7zyimVhmPz8/Bo9vhDWojreB/4H4B8VRblC8WviK6rhGEJo6ujRo+zZswedTsfcuXNp1qyZ1pGqnE6vo00vX959vxfj/rE7/p09H7v8/vVsvlxWZuCtsOYG3qZOnUqHDh3IycmREhd1VpUUuKqq36qqOqbk39dUVe2jqmprVVUnqqpaUBXHEMJaHDp0iC+//BK9Xs/8+fNp3Lh2z2kqikLjVxowOqTrcwfe1v7zjzU68FY66Z+bm0tERAS5VnqKWCGqi5yJTYiXcPDgQfbv349er7e8vakued7AW/7DohofeCt9r31eXh6RkZHk5ORU+zGFsBZS4EK8oAMHDhAfH4/BYGDx4sX4+PhoHUkzZQfeXpv0jIG3fz3K3tjqH3grPdtdfn4+kZGRZGdnV9uxhLAmhvKvIoTYt28fhw8fxs7OjqCgIBo0aKB1JKtg72ig69CmdB7cmKsnUzj19U2SbzwovlCF66dTuX46Fd8WrnQf3owW3bzR6ZTn32gFjBo1Cr1ez5EjR4iKiiI4OBh3d/cqP44Q1kQegQtRji+++ILDhw9jb29PSEiIlPdTWMPA24gRI3jttdcoKioiOjqa9PT0Kj+GENZEClyI59i9ezc//fQTDg4OhISE4FZmVS/xJK0H3l5//XUGDx6M0WgkNjaWlJSUKr19IayJFLgQz7Bz505OnDiBo6MjoaGhuLq6ah3Jpmg18DZkyBBef/11jEYjS5cu5f79+1V220JYEylwIZ5i27ZtnD59GmdnZ8LCwnBxcdE6ks3SYuDttddeY8SIEZhMJpYtW8bdu3crfZtCWBsZYhPiVzZv3sylS5eoV68eoaGhODo6lr+TKNevB95O7rtJys3qG3jr27cver2evXv3smLFCgIDA2natGkVfTZCaE8KXIgyNmzYwJUrV6hfvz6hoaHY29trHanWKR14a93Th7uXMzn19U0Sz6RZLi8deHP1dqLb601p198PO3t9hY7Vu3dv9Ho9u3fvZtWqVcyaNQt/f/8q+kyE0JY8hS5EiTVr1nDlyhXc3NwIDw+X8q5m5Q68peQ9GnjbXfGBtx49ejBu3DhUVWXt2rVcvXq1qj4FITQlBS7qPLPZzMqVK0lMTMTd3Z3Q0FAMBnlyqiaVO/C259HAW+b9lz9lateuXXn33XdRVZUNGzZw6dKlqowvhCakwEWdZjabWbFiBbdu3cLT05OQkBApbw29yMDbhr8cqdDAW8eOHZk8eTJQPOdw4cKFKs8vRE2SAhd1ltlsZvny5dy9exdvb2+Cg4OlvK1E6cBb6ZKm3s3KLGlaMvBWkSVN27Vrx9SpU1EUhS1btnD27Nlq+gyEqH5ybyXqJLPZzJIlS0hJSaFhw4YsWLAAnU7+nrU2vx54O/n1TW5UcuCtTZs2zJgxg/Xr17N9+3ZMJhNdu3at7k9FiCon91iizjEajcTExJCSkkKjRo2kvG1A6cDbmNKBt/6VG3hr2bIlgYGB6HQ6du7cyfHjx6v7UxCiysm9lqhTjEYj0dHRpKWl0axZM+bNmyflbWM8GtVj6KzKD7w1a9aMOXPmoNPp+Pzzzzl69GhNxBeiysg9l6gzCgsLiYqKIjMzkxYtWljuvIVtepmBt6RnDLw1adKE+fPno9fr+fLLLzl06FBNfgpCVIrce4k6obS8s7KyaN26NbNmzdI6kqgiLzLwtuM5A29+fn4sWLAAvV7P/v37OXjwYA1/BkJUjBS4qPXy8/OJiIjgwYMHtGvXjunTp2sdSVSD0oG3if9cvKRp82ctafrnI08saerr68vixYsxGAzEx8cTHx9f0/GFeGlS4KJWy83NJSIigocPHz72PmBRe1V04M3Ly4ugoCDs7Ow4ePAgX3/9tVafghAvRApc1Fo5OTlERkaSl5dnOROXqFteaODt//uRb0sG3jw8PAgJCcHe3p4ff/yRL774QsP0QjyfFLiV+/jjj594Oi8+Pp6PP/5Yo0S2ITs7m6ioKPLz8y3nwhZ113MH3orMnCsz8JabohISEoKDgwM//fQTu3fv1jC5bZD7KW1IgVu53r17M2nSJMsvR3x8PJMmTaJ3794aJ7NeWVlZREdHU1BQQJ8+fXjrrbe0jiSsxIsOvH295BfeGjwNRwdHTpw4wWeffaZdaBsg91PakDOxWbmAgAC2bNnCpEmTCAoKIjY2li1bthAQEKB1NKuUkZFBbGwsRUVF9O/fn+HDh2sdSVih8s7wdu9aNveuZePn+Rqp/MKpkz9jNBqZMGGChqmtl9xPaUMK3AYEBAQQFBTEhx9+yAcffCC/FM+QmprK0qVLMRqNDBw4kKFDh2odSVi50oG3xq80IP3uQ07tv8mln+5hNha/1exBWgEONMde15hrh++wKXcLU2dO0ji1dZL7qZonT6HbgPj4eGJjY/nggw+IjY2Vt7g8RXJyMkuWLMFoNBIQECDlLV7a8wbeFLMB55zmpB1qQOwf4rhx5oqGSa2T3E/VPHkEbuVKX0sqfToqICDgsY8FJCUlsWLFCkwmE8OGDWPAgAFaRxI2rHTgrcfI5lz4MYnT39ziQVp+8YWqjrxbP7Dt33bi4tWBobOn0aZPN03zWgO5n9KGPAK3cgkJCY/9EpS+1pSQkKBxMutw584d4uLiMJlMjBw5UspbVJmnDbyZjTdRTakA5KSeZ9d//5GNH/wTl4/+iNlsKucWay+5n9KG8rTzA9e0Fi1aqNevX9c6hrAxN2/eZM2aNZjNZkaPHk2vXr20jmRTPvroIwDef/99jZPYBlVVOffdKX7YvJ6HGZeeuNy9oR89R79Dx8FDsXNwfMotCFFs9erVzJkz57iqqpW605Kn0IVNun79OuvWrUNVVcaOHUu3bt20jiRqOUVR6DSkO52GdOfK8Qt8vioCY+ptlJIHQZn3kvhmRQw/bllPtxGj6TZiDM6ubhqnFrWZPIUubM7ly5ct5T1+/Hgpb1HjWvdsT3hENM79XqfQyw8Mjx4L5T3I5vC2TSwPnsPXy6NIv3tHw6SiNpMCFzbl0qVLbNq0CVVVmThxIp07d9Y6kqijdDodi8PC8erWh5zWXdC3bIert4/lcmNRIT/v/5JV/7iYz/7+b9y5dEHDtKI2kgIXNuP8+fNs3rwZgClTptChQweNE4m6TqfTMW/ePJo29yfLsT65bbrwZug/4duy9aMrqSpXEo6w+U+/k4E3UaWkwIVNOHPmDFu3bkVRFKZNm0bbtm21jiQEUFzic+fOxd/fn8zMLPYdP8Xkv/4Xk/70H7Ts8fipRJN+uciu//cfrPqHxZzat5eignyNUovaQApcWL0TJ06wY8cOFEVh1qxZtG7duvydhKhhs2fPplWrVmRlZREZGYlvm3a884c/M/vv0XQKGI6+zOvkpQNvy0Pm8uPWDeRmZ2mYXNgqKXBh1RISEti9ezc6nY7AwED8/f21jiTEM82YMYO2bdvy4MEDIiIiyM/Px6tpc0Ysfo/5USvpM24iDvXqWa5fduBtf1w0GUky8CZenBS4sFpHjhxh7969ltcZmzVrpnUkIco1ZcoUOnbsyMOHD4mMjCQ3NxcAlwYeDJw6m4UxqwmYveCJgbfTX3/Byn9YzGd//3cZeBMvRApcWKVDhw7x1VdfodfrWbhwIY0aNdI6khAv7N1336VLly7k5uYSFRVlKXEAe0cneoway7xPljP6vd8/ZeDt8KOBt59k4E08mxS4sDrffvst+/fvt5S3r6+v1pGEeGnvvPMOPXr0IC8vj4iICHJych67XKfX067/IKb/x/88e+Dtv/+D1f8YxOmv91JUWFCT8YUNkAIXVuWbb77hu+++w2AwEBQUhI+PT/k7CWGl3nrrLXr37k1BQQGRkZFkZT05rKYoCk07dnnmwFtG0l32x8WwPHiODLyJx0iBC6uxb98+fvjhB+zs7AgJCcHT01PrSEJU2qhRo+jXrx+FhYVER0eTkZHxzOvKwJt4GVLgwirs3buXw4cPY29vT2hoKO7u7lpHEqLKvPHGGwwcOJCioiJiYmJIS0t77vVl4E28CClwobldu3aRkJCAg4MDYWFhuLq6ah1JiCo3dOhQAgICMBqNxMbGkpycXO4+MvAmnkcKXGhqx44dnDx5EicnJ8LDw3FxcdE6khDVZtCgQQwbNgyTycSyZctISkp6of1+PfDWovvjq1DKwFvdJAUuNLN161bOnDmDs7Mz4eHhODs7ax1JiGo3YMAARowYgclkIi4ujjt3Xvy17NKBt/Hv/0UG3oQUuNDG5s2bOX/+PC4uLoSFheHo6Kh1JCFqTN++fRk1ahRms5mVK1dy8+bNl74NGXgTUuCixq1fv55Lly5Rv359KW9RZ/Xu3Zu3334bs9nM6tWrSUxMrNDtWAbeolfJwFsdYyj/KkJUDbPZzLp160hMTMTNzY3Q0FAMBvkRFHVX9+7dMRgM7Nixg7Vr1zJt2rQKL9Zj7+RMj1Fj6TZiDL8c+YGE3TtIvn61+MKSgbcrCYdp9Ep7er31Dq16vYpOp6/Cz0bUNLn3FDWi9FHGrVu3aNCgAcHBwVLeQgCdO3dGr9ezdetWNm7cyOTJkyu1XK5Or6fdgMG07T+IW+fOcOzzHVw/ecxy+d1fLrDrvy/QwK8RPUePo8Pg17Gzd6iKT0XUMHkKXVQ7s9lMXFwct27dwtPTUx55C/ErHTp0YMqUKcCj+ZDKUhSFZp0eDbx1HDIMnf5ZA28bZeDNBkmBi2plNpstb5fx8fEhODgYnU5+7IT4tbZt2zJt2jQURbG8Q6OqeDVtzsig37AgagV9xr6Lg/OvB942lgy8xcjAmw2Re1JRbcxmM7Gxsdy/f5+GDRuyaNEiKW8hnqN169bMnDkTRVEs50ioSi4engycFsjCmFUMmbWA+l7elsuKB972svIfFrPrv/+Du7/IwJu1k3tTUS2MRiPR0dGkpqbSuHFjFixYIOUtxAto0aIFgYGB6HQ6du3axbFjx8rf6SXZOznTc/RY5kfEMTr8d/i0aPXoQlXl8k8/sumD37Hpg99xOeGwnOHNSsk9qqhypeWdnp5O8+bNmTt3rpS3EC+hWbNmlt+bPXv2cOTIkWo5TunA24z//F8mfvAftOjW87HL7/5ygV1///eSM7x9IWd4szJyryqqVGFhIZGRkWRmZtKyZUvLIwkhxMtp3Lgx8+fPR6/X89VXX3Ho0KFqO5Zl4O2f/5XZ/xX1jIG3aBl4szJyzyqqTGl5Z2dn06ZNG2bOnKl1JCFsmp+fHwsXLkSv17N//34OHjxY7cf0auYvA282QgpcVIn8/Hw++eQTcnJyaNeuHdOmTdM6khC1go+PD0FBQRgMBuLj4zlw4ECNHFcG3qyfFLiotNzcXCIiIsjNzaVTp05MnjxZ60hC1Cqenp4EBwdjZ2fH999/z759+2rs2DLwZr2kwEWl5OTkEBkZSV5eHt26dWPChAlaRxKiVmrQoAEhISHY29tz+PBh9u7dW6PHl4E36yMFLiosOzubqKgo8vPz6dmzJ2PHjtU6khC1mpubGyEhITg4OJCQkMDu3btrPIMMvFkPKXBRIZmZmURHR1NQUECfPn0YM2aM1pGEqBNcXV0JDQ3F0dGREydOsHPnTs2yvNDAW8jc4oG3e3c1y1lbSYGLl5aenk5MTAyFhYX079+fN998U+tIQtQpLi4uhIWF4ezszOnTp9m2bZu2eZ438FZYUDzw9ptFMvBWxaTAxUtJTU0lNjaWoqIiBg0axPDhw7WOJESd5OzsTFhYGPXq1ePcuXNs3rxZ60iPDbyNCv8dPv7PGHj70++5nHAY1WzWLmwtIAUuXtj9+/dZsmQJRqORoUOHEhAQoHUkIeo0R0dHwsPDqV+/PpcuXWL9+vVaRwKKB97aDxjMjI/+l4kf/PuTA2+XzrPr7//Oqn9cLANvlSAFLl5IUlISy5cvx2QyMXz4cAYOHKh1JCEEYG9vT3h4OG5ubly9epU1a9ZoHcmieOCt66OBt8HPGHgLmcvhbZtk4O0lSYGLct2+fZu4uDhMJhNvvvkm/fv31zqSEKIMg8FAaGgoDRo0IDExkZUrV2K2sqenvZr5MzK4eOCt968H3rKz+HHrhuKBtxWxMvD2gqTAxXMlJiayatUqzGYzY8aMoU+fPlpHEkI8RWmJe3p6cuvWLVasWGF1JQ7FA2+Dnjfwtm9PmYG3ixomtX5S4OKZrl27xtq1azGbzYwbN46ePXuWv5MQQjM6nY7g4GC8vb25e/cuy5cvt8oSh0cDb/M+Wf6cgbd/koG355ACF091+fJl1q9fj6qqTJgwga5du2odSQjxAnQ6HYsXL6Zhw4bcu3ePJUuWWG2JA+gNBhl4qyApcPGECxcusGnTJlRVZdKkSXTq1EnrSEKIl6DT6ViwYAGNGzcmJSWFmJgYjEaj1rGeSwbeXp4UuHjMuXPn2LJlCwDTpk2jffv2GicSQlSETqdj7ty5NGvWjLS0NKKjo62+xEuVDrzNj4qTgbfnkAIXFqVndFIUhenTp9OmTRutIwkhKkGn0zFnzhxatGhBZmYmkZGRFBYWah3rhdX38Coz8Db/6QNv7y1kw7/8I7v/5yOMRUUapq15UuACwHJOZZ1Ox6xZs2jVqlX5OwkhbMKsWbNo3bo12dnZNlfiUDrwNo65/7uMQTPnUd/T+7HL7139hV+O/MDeiP/SKKE2DOVfRdR2CQkJ7N27F51OR2BgIE2bNtU6khCiik2fPp1PP/2UixcvEhERYVkQxZqZzSZSEq9z5+I5bl88x52L58nNynzm9Q0ODjUXzgpIgddxhw8fZt++feh0OubNm0ejRo20jiSEqCaTJ09m+/btnD171lLizs7OWseyMBYWcu/KLyVlfY67v1ygMC/vufvoDXY41HNBVc30ePPtGkpqHaTA67Dvv/+eAwcOoNfrWbBgAb6+vlpHEkJUswkTJmAwGDh16hSRkZGEhITg4uKiSZb8hzncvXTB8uj6/tVfMJUzaOdQrx6N23agcbuONGnfEd+WrdEb7DAZjegNdavS6tZnKyzi4+M5ePAgBoOBhQsX4u3tXf5OQohaYezYsej1eo4fP05UVBTBwcG4urpW+3Fz0tMsj67vXDhHyq0boKrP3cfFw7O4rNt1pHH7jng1aYaie3J8q66VN0iB10n79+/n0KFD2NnZsXjxYjw8PLSOJISoYWPGjEGv1/PTTz8RHR1NcHAwbm5uVXb7qqqSkXS3uKxLXsPOun+v3P0aNGpCk3aPHmG7evuiKEqV5apNKlzgiqI0BdYCvoAKLFNV9RNFUTyATwF/IBGYpKpqRuWjiqrw1VdfceTIEezs7AgODsbd3V3rSEIIjbz55psYDAZ+/PFHoqOjCQoKokGDBhW6rZcdOANQFB0+LVo+eoTdrgPObu4VOn5dVJlH4Ebgt6qqnlAUpT5wXFGUr4FA4BtVVT9SFOV94H3gD5WPKiprz549HDt2DAcHhxp7ykwIYd2GDx+OwWDg4MGDxMTEsGjRIry8vMrdryIDZwY7e/zatKVx+440btuBRq+0w97JeobobE2FC1xV1SQgqeTfDxRFuQA0BsYCQ0qutgb4FilwzX322WecOnUKR0dHTYdWhBDWJyAgAL1eT3x8PEuWLHnqUGv+wxzu/nKBOxfOcbuSA2eialTJa+CKovgD3YGjgG9JuQPco/gpdqGhHTt2cObMGZycnKzubSNCCOswaNAg9Ho9+/fvZ/ny5bwz8m3SfrlFUf4t7lw8R8rNxCobOBNVo9IFriiKC7Ad+I2qqtllhw1UVVUVRXnqd1xRlIXAQoDGjRtXNoZ4hq1bt3L+/HmcnZ0JCwuz+hM3CCFqnqqqZCXn0YAWvOI8hLsnV/HlmX8pdz8ZONNWpQpcURQ7ist7g6qqO0o231cUxU9V1SRFUfyA5Kftq6rqMmAZQIsWLZ7/Z52okI0bN3L58mVcXFwICQmR8hZCAGA2q6TdzuHu5UySrmRy92oWedmPTq+qU+phJvWxfWTgzPpUZgpdAVYAF1RV/X9lLtoFzAY+Kvn/Z5VKKCpk3bp1XLt2DVdXV0JCQrC3t9c6khBCI8YiE8mJ2dy9nEXSlUySrmVRlG965vV1hsaYjbfBzhtTvXoMGj+CHoOGyMCZlanMI/ABwEzgjKIop0q2/QvFxb1FUZR5wA1gUqUSipdiNptZu3YtN27cwN3dnZCQEAx18AQHQtRlBblFJF3NIulKcWHfv5GN2fj8JzodnA00bOVGo9bueDVpj2+L3/LL1V/YsWMH+44ew7NlG1mh0MpUZgr9B+BZL3a8XtHbFRVnNptZtWoVt2/fxsPDg6CgIClvIeqAh5kF3L2SSdKVLO5eySTtTk7x2Tmeo567A41au+HX2p1Gbdzx8KuHonv8Lr1z587o9Xq2bt3Kpk2bmDRpEu3atavGz0S8DLl3ryXMZjNxcXEkJSXh5eVFUFAQOpn+FKLWKR04u3slk6TLmdy9kkl2an65+7n7OhcXdht3GrV2p76n4wsNnHXo0IEpU6bw6aef8umnn/Luu+/SsWPHqvhURCVJgdcCZrOZpUuXkpycjI+PD4sWLZLyFqKWKG/g7GkUBbya1qdRa3f82rjh18odZ9eKz8G0bduW6dOns2HDBrZt24bZbKZz584Vvj1RNaTAbZzZbCYmJoa0tDT8/PyYP3++lLcQNuxlB84A9HY6fP1dadTGHb/WbjRs6Ya9Y9Xevbdq1YpZs2axdu1aduzYQVFRET169KjSY4iXIwVuw4xGI7GxsaSnp9OkSRPmzJkj5S2EjanswJlfa3d8mtVHb1f9v/v+/v4EBgayZs0adu/ejclkonfv3tV+XPF0UuA2ymg0EhUVRVZWFs2bN2fWrFlS3kLYgIdZBSVPh7/EwJmbveW1a7/W7ng2enLgrKY0a9aMefPmsWLFCvbu3YvJZKJv376aZKnrpMBtUGFhIdHR0WRnZ9OyZUtmzpypdSQhxFM8NnB2JZO7V7LITnn+gh9QZuCspLBdvV5s4KymNGrUiIULF7J8+XK++uorjEYjr732mtax6hwpcBuTn59PdHQ0OTk5vPLKK0ydOlXrSFbj448/pnfv3gQEBFi2xcfHk5CQwO9//3sNkwlb9bI/U48NnF0tLuyaHjirKb6+vixatIhly5bxzTffYDQaGTJkiNax6hQpcBuSn59PZGQkubm5dOjQgYkTJ2odyar07t2bSZMmsWXLFgICAoiPj7d8LERFlPczZa0DZzXF29ubxYsXs2TJEr777jtMJhOvvy6nAakptvlTUwfl5uYSFRVFXl4enTt3Zvz48VpHsjoBAQFs2bKFSZMmERQURGxsrOWOV4iKeNrPVMRHy3HMbMaO/zpu1QNnNcXT05OQkBBiYmL44YcfMBqNjBgxQutYdYIUuA3IyckhOjqa/Px8unfvzttvv611JKsVEBBAUFAQH374IR988IGUt6iUh1kFNKnfgVGDJvHhhx8ysucM0hNcSefGM/expoGzmuLu7k5oaCjR0dEcOXIEk8nEqFGjtI5V60mBW7ns7Gyio6MpLCykV69ejB49WutIVi0+Pp7Y2Fg++OADYmNjCQgIkBIXL+RZA2e/3DnJjv0bGNljBj+c280rft14pXF3y37uvs74tX70CNvaBs5qiqurK2FhYURHR5OQkIDRaJQHG9VMCtyKZWZmEhMTQ1FREX379pWnpcpR9vXJ0uIu+7EQZVkGzsoU9q8Hzn65c5KV+z9k7rAPeKVxd9o27sbKb/6ND3/7CWPGjcSvtW0MnNUUFxcXwsLCiIqK4uTJkxiNRnm5rxpJgVup9PR0YmNjLW/PkMGQ8iUkJDxW1qWvXyYkJEiBi5KBsweWwr53NYvCcgbObqX/wu8C/8aIN4cXD5y1GMTYIz1ISEigVQ+fGkpuW5ydnQkPDycyMpIzZ85gMplk4LaaKKpazhkEakCLFi3U69evax3DaqSkpLBs2TKMRiODBw+Wt2aIavHRRx8B8P7772ucpHoU5BmLJ8Nt4AxntZG85fXZVq9ezZw5c46rqtqrMrcjj8CtzP3791m+fDkmk4mhQ4cycOBArSMJYRNs/QxntY2jo6Pl6fRffvmF9evXM2PGDK1j1SpS4Fbk7t27rFixArPZzBtvvEG/fv20jiSEVarMGc5k4Kzm2NvbEx4eTlRUFFevXmXNmjXMnDlTTvtcRaTArcStW7dYvXo1ZrOZUaNGyQIBQpTxIgNnv1Z6hrOyhS0DZzXPYDAQGhpKTEwMiYmJrFq1ShZeqiJS4FYgMTGRtWvXoqoqb731lizRJ+q8igycPXGGsxZu2DvJXZw1KFvit2/fJi4uTpY+rgLy062xq1evsmHDBlRVZdy4cXTt2lXrSELUOBk4q/10Oh3BwcEsXbqUpKQkli1bxsKFC6XEK0EKXEOXLl3i008/RVVV3n33XTp27Kh1JCFqhAyc1U06nY5FixYRFxdHUlISsbGxBAUFSYlXkBS4Ri5cuMCWLVtQFIXJkyfTrl07rSMJUS1k4EyUpdPpmD9/PqtWreL27dtER0cTFBSEwSB19LLkK6aBs2fPsn37dhRFYerUqbRp00brSEJUmV8PnCVdySJXBs5EGTqdjjlz5rB27Vpu3LhBdHQ0ISEhUuIvSb5aNez06dPs3LkTRVGYMWMGLVu21DqSEJVSoYEzgw7fFq6Wwm7YUgbO6hqdTkdgYCDr1q3j2rVrREZGEhISgr29/OH2ouQ3pgYdP36czz//HJ1Ox+zZs2nWrJnWkYR4aQV5Ru5dzbIU9v3E8gfO7J0M+LV2w69k6MynuasMnAkAZs6cycaNG7l8+bKlxB0dHbWOZROkwGvI0aNH+fLLLy1PHTVp0kTrSEK8EMvA2dXiCfHU2zJwJqrWtGnT2LJlCxcuXCAyMpKwsDAp8RcgBV4DDh06xP79+9Hr9cybNw8/Pz+tIwnxVDJwJrQyadIkduzYwZkzZ4iIiCA0NBRnZ2etY1k1KfBqdvDgQeLj49Hr9SxYsABfX1+tIwnxiMnM/UvJ3LtTIANnQnPjx49Hr9dz6tQpy9PpLi4uWseyWlLg1Sg+Pp6DBw9iMBhYtGgRXl5eWkcSdZy5oID8M2e4Hn8ev3Oe5Dk0Ytv/nH3uPjJwJmrS2LFj0ev1HD9+nKioKIKDg3F1ddU6llWS38Jq8vXXX/Pjjz9iZ2fH4sWL8fDw0DqSqINMDx6Qd/IkuceOk3v8OPk//4xaVESaZ2dyOi9+6j72Tgb8WrlZClsGzkRNGzNmDAaDgaNHj1reJ+7u7q51LKsjBV4NvvjiC3766Sfs7e0JDg7Gzc1N60iijihKTibv+HFyj58g9/hxCi5eBPXJiTP3rGuWfzsYH9CkT0satWlAozZueDRyQScDZ0JjI0eOxGAwcOjQIWJiYuSB0FNIgVex3bt3c+LECRwcHOSpH1GtVFWl6MYNco8ftzzCLrp5s9z97P39cevVE+f7B3ngaWDuf/yLnMpSWKVhw4ah1+s5ePAgsbGxLFy4EG9vb61jWQ0p8Cr02WefcerUKRwdHWX4QlQ51WSi4NIlS1nnnjiOKSX1+TvpdDi2a4dTr5449+yFc88eGEpmMe5/9FHJVaS8hfUKCAjAYDBw4MABli5dKsPAZUiBV5Ht27dz9uxZnJyc5O0PokqUDpyVFnbeyZOYc3Keu49ib49Tly6Wwnbq3g29/CEpbNzAgQPR6/V8/fXXLFu2jHnz5tGoUSOtY2lOCrwKfPrpp1y8eBFnZ2c5AYGosCcGzs6cQS18/lu6dPXr49Sje/Gj6149cezUCZ2cilLUQv3798dgMPDFF1+wYsUKAgMDadq0qdaxNCUFXkkbNmzgypUruLi4EBYWJufxFS/MmJLy2OvXBZcugdn83H0M3t6Png7v1ROHNm1Q9PoaSiyEtvr06YPBYGD37t2sXr2amTNn4u/vr3UszUiBV8LatWu5fv06rq6uchJ+8VyqqlJ08+aj16+PH6PoxosNnJV9/dquaVM5w5mo03r06IFer2fnzp2sXbu2Ti8KJQVeAWazmTVr1nDz5k3c3d1lGTzxBNVkouCXXx4r7MoMnAkhHunatSt6vZ7t27ezfv36Orsss7TOSzKbzaxcuZI7d+7g4eEhC9ELoMzA2fET5B4/Rt4JGTgTojp16tQJvV7P1q1b2bhxI5MmTaJ9+/Zax6pR0jwvwWw2s3z5cu7du4eXlxdBQUHyFpw6SgbOhNBe+/btmTp1Kps2bWLLli1MmDCBTp06aR2rxkiBvyCz2cySJUtISUnB19eXhQsXSnnXITJwJoR1atOmDTNmzGD9+vVs374dk8lE165dtY5VI6TAX4DZbCYmJoa0tDQaNWrEvHnzpLxrsQoPnDVv/lhhy8CZEDWjZcuWzJo1i3Xr1rFz505MJhM9evTQOla1kwIvh9FoJCYmhoyMDJo2bUpgYKCUdy1TqYGznj1x7tmzeOBMTvEohGb8/f0JDAxk9erV7N69G6PRSJ8+fbSOVa2kwJ/DaDQSFRVFVlYW/v7+zJ49W+tIogqYCwvLnOFMBs6EqC2aNm3K/PnziYuL44svvsBkMtGvXz+tY1UbKfBnKCwsJCoqigcPHtCqVStmzJihdSRRQaacnDIDZ8fI/1kGzoSorfz8/Fi4cCHLly9n3759GI1GBg4cqHWsaiEF/hT5+flER0eTk5ND27ZtmTJlitaRxEt4bODsxHEKLsrAmRB1ia+vL4sWLWLZsmUcOHAAo9FIQECA1rGqnBT4r+Tm5hIdHU1ubi4dOnRg4sSJWkcSzyEDZ0KIp/H29iYoKIglS5Zw8OBBjEYjw4cP1zpWlZICLyM3N5eoqCjy8vLo3Lkz48eP1zqS+JVfD5zlHT+OMSXl+TvpdDi0a1tydjMZOBOirvDw8CA4OJjY2Fh+/PFHTCYTI0eO1DpWlZECL5GTk0NUVBQFBQV0796dt99+W+tIgqcMnJ08hfnBg+fuo9jb49ils+XRtVP37jJwJkQdVXq665iYGI4ePYrRaGTMmDFax6oSUuBAVlYWMTExFBYW0rt3b0aNGqV1pDqrQgNnLi6PD5x17iwDZ0IIC1dXV0JDQ4mOjub48eOYTCbGjh2rdaxKq/MFnpGRQWxsLEVFRfTr14833nhD60h1ijE19bHXr19k4Ezv7fXo6fBePXF45RUZOBNCPFfpks9RUVGcOnUKo9HIhAkTtI5VKXW6wNPS0liyZInlbQZDhw7VOlKtZhk4K13w49hxCm/cKHc/GTgTQlQFZ2dnwsPDiYyM5OzZs5hMJiZNmqR1rAqrswWenJzMsmXLMJlMDBkyhMGDB2sdqdaRgTMhhLVxdHTkvffeIzIykgsXLrBx40amTZumdawKqZMFnpSUxIoVKzCZTAwbNowBAwZoHalWqJKBs27d0NevX0OJhRB1kb29PWFhYURHR3P58mXWrVvHzJkztY710upcgd+5c4eVK1diNpsZMWIEffv21TqSzaqSgbNOndA5ONRQYiGEKFa2xK9du8bq1auZNWuWTa11UacK/ObNm6xZswaz2cyoUaPo3bu31pFsigycCSFqE4PBQEhICLGxsdy4cYOVK1cyd+5cmynxOlPgiYmJrF27FlVVeeutt+rEUnOVoaoqRbduPXo6/AUHzuyaN3ussO2aNZOBMyGE1Spb4nfu3GH58uUsWLDAJkq8ThT4lStX2LhxI6qqMn78eDp37qx1JKujmkwUXL78WGG/3MBZD5x79pSBMyGEzdHpdAQFBbFs2TLu3bvH0qVLWbRokdWXeK0v8EuXLvHpp5+iqioTJ06kQ4cOWkeyCubCQvLPnn18SU0ZOBNC1FE6nY6FCxcSFxdHUlISMTExBAcHW3WJ1+oCP3/+PFu3bkVRFKZMmULbtm21jqSZ4oGzU5ZH13k//ywDZ0IIUYZOp2P+/PmsWrWK27dvExUVRXBwMAaDdValdaaqAmfOnGHHjh0oisK0adNo3bq11pFqlAycCSHEy9PpdMyZM4d169aRmJhIVFQUoaGhVlni1peoCpw8eZJdu3ahKAozZ86kRYsWWkeqVjJwJoQQVUen0zF79mzWr1/P1atXiYiIIDQ0FHsrW2Oh1hX4sWPH2LNnj+Ub0KxZM60jVbkKDZwpCg7t2lnK2qlHD+x8fGomsBBC2KAZM2awefNmLl26RGRkJCEhITg6Omody6JWFfiRI0f46quv0Ol0zJ07l8aNG2sdqUpUaODMzg7HLl0eFXb37jJwJoQQL2nKlCls3bqV8+fPExkZSVhYmNWUeK0p8EOHDrF//370ej3z5s3Dz89P60gV9sTA2ZkzqAUFz91HBs6EEKJ6TJw4kR07dnDmzBnL0+nOzs5ax6odBX7w4EHi4+PR6/UsWLAAX19frSO9FGNq6mMrdOVfvCgDZ0IIYUXGjx+PwWDg5MmTlqfTXVxcNM1k8wV+4MABvv/+ewwGA4sWLcLLy0vrSM/12MDZiePFA2eJieXuJwNnQgihrbfffhuDwUBCQoKlxF1dXTXLY9MFvm/fPg4fPoydnR1BQUE0aNBA60hPUM3mMktqHiPv+AmMycnP30kGzoQQwiqNGjUKg8HA4cOHLe8Td3d31ySLzRb43r17SUhIwN7enuDgYNzc3LSOBMjAmRBC1HZvvPEGer2eH374gejoaBYvXoynp2eN57DJAt+9ezcnTpzAwcGB4OBgTZ/CMOU8LF5S82UHzrp3txS2Y+fOMnAmhBA25PXXX0ev1/Pdd9+xZMkSFixYgE8NP1NqcwW+c+dOTp8+jaOjoyZDBJUaOOvRo3jgrG1bGTgTQggbN2TIEAwGA9988w3Lli2r8XdA2VSBb9u2jXPnzuHs7ExISEi1j/GrqkrR7duPn+FMBs6EEEKUeO211zAYDHz11VfExcUxb948GjVqVCPHtpkCLz0bTr169QgNDa2WN9KrZnPJGc6OkXf8OLnHjsvAmRBCiOfq27cver2evXv3smLFCgIDA2natGm1H9cmCnzDhg1cuXKF+vXrV+n5aGXgTAghRFXo3bs3er2e3bt3s2rVKmbNmoW/v3+1HtPqC3zNmjUkJibi5uZGcHBwpcpbBs6EEEJUlx49eqDX69m5cydr165l+vTptGrVqtqOZ7UFbjabWb16Nbdu3cLd3Z2QkJCXXs7NmJb22OvXLzRw5uVVXNalZziTgTMhhBAvqGvXrhgMBrZt28aGDRuYPHkybdu2rZZjWWWBm81mVqxYwd27d/H09GTx4sXllneFB86aNbOUtXPPntg1by4DZ0IIISqsY8eO6PV6tmzZwubNm5k4cSIdOnSo8uNYXYGbzWaWL1/OvXv38Pb2ZvHixeh0uieuJwNnQgghrFW7du2YOnUqmzZtYuvWrYwfP57OnTtX6TGsqsDNZjNLliwhJSWFhg0bsmDBAkt5q4WF5J09Z3l0nXvyJObs7OfengycCSGE0EqbNm2YOXMm69atY8eOHZhMJrp161Zlt18tBa4oykjgE0APxKmq+lF5+xiNRpYsWUJaWhqNGjVizpQp5P54+NHA2c8/lz9wVq8eTj16yMCZEEIIq9CiRQsCAwNZs2YNn332GUajscpuu8oLXFEUPRANDAduAwmKouxSVfX8s/ZRVZXl//V3HK9eYWB+Pv4/n+Hy//yvDJwJIYSwec2aNWPOnDmsWrWKPXv2VNnCW9XxCLwPcEVV1WsAiqJsBsYCzyxwp5QUhqxZY/n4WY+zZeBMCCGELWrSpAnz589nxYoVZGRkVMltVkeBNwZulfn4NvDq83bQm558pK0CuT4+PGjShAdNm/CgaVOKSs97/uABfPttVeUVok4qLCwEYPXq1doGEaIO8fHxISkpqUpuS7MhNkVRFgILSz4s6HDp4tknrnTpYo1mquW8gFStQ9QB8nWufvI1rn7yNa5+lX5zeHUU+B2g7Elgm5Rse4yqqsuAZQCKohxTVbVXNWQRJeRrXDPk61z95Gtc/eRrXP0URTlW2dt48g3WlZcAtFEUpYWiKPbAFGBXNRxHCCGEqLOq/BG4qqpGRVFCga8ofhvZSlVVz1X1cYQQQoi6rFpeA1dVdS+w9yV2WVYdOcRj5GtcM+TrXP3ka1z95Gtc/Sr9NVZUVa2KIEIIIYSoQdXxGrgQQgghqpnmBa4oykhFUS4pinJFUZT3tc5TGyiK0lRRlHhFUc4rinJOUZT3SrZ7KIrytaIol0v+XzWnA6rDFEXRK4pyUlGUz0s+bqEoytGSn+dPSwY5RQUpiuKuKMo2RVEuKopyQVGUfvJzXPUURfmHkvuKs4qibFIUxVF+litHUZSViqIkK4pytsy2p/7sKsUiSr7WPyuK0uNFjqFpgZc57eqbQAdgqqIoVb/mWt1jBH6rqmoHoC8QUvJ1fR/4RlXVNsA3JR+LynkPuFDm478B/6OqamsgA5inSara4xPgS1VV2wFdKf5ay89xFVIUpTEQDvRSVbUTxcPH/397d/MSVRSHcfz7AwvSIKmFlBIWtM9WQhFiLXqRbBEVFIkQrVtEUJto0S6iReBG6QUiCJPqDyioTS3KRYt2vYeaEErUwhZPi3NvmTmoM2OXGZ4PDDNz58IcLg/zm3vOveccxVmu1A1gz5xtpbK7F9iSPU4BA4v5gqLPwH9PuyppBsinXbUKSBqT9DJ7/Y30o9dKOrb5nLU3gYOFNLBOREQbsB8YzN4H0A0MZ7v4GFcgItYAO4EhAEkzkqZwjpdDA7AqIhqARmAMZ7kikp4AX+dsLpXdXuCWkmdAc0SsX+g7ii7g80272lpQW+pSRLQDHcBzoEVSPoffONBSVLvqxFXgLJDPBbwOmJKULzfkPFdmEzAJXM+GKQYjognnuKokfQYuAx9IhXsaeIGzvBxKZbesWlh0AbdlFBGrgXvAaUl/LZ6udPuBb0EoU0T0AF8kvSi6LXWsAdgGDEjqAL4zp7vcOa5cNg7bS/rDtAFo4t+uX6uyamS36AK+qGlXbekiYgWpeN+WNJJtnsi7ZbLnL0W1rw5sBw5ExDvS0E83aby2OeuGBOe5Up+AT5KeZ++HSQXdOa6u3cBbSZOSfgIjpHw7y9VXKrtl1cKiC7inXV0G2VjsEPBa0pVZHz0E+rLXfcCD/922eiHpnKQ2Se2k3D6SdAx4DBzKdvMxroCkceBjROSLPuwiLUvsHFfXB6AzIhqz3478ODvL1Vcquw+BE9nV6J3A9Kyu9pIKn8glIvaRxhLzaVcvFdqgOhARO4CnwCv+jM+eJ42D3wU2Au+Bw5LmXmRhSxQRXcAZST0RsZl0Rr4WGAWOSyq1xL0tICK2ki4SXAm8AfpJJx7OcRVFxEXgCOkOllHgJGkM1lkuU0TcAbpIK7tNABeA+8yT3eyP0zXS0MUPoF/SgoudFF7AzczMbOmK7kI3MzOzMriAm5mZ1SAXcDMzsxrkAm5mZlaDXMDNzMxqkAu4mZlZDXIBNzMzq0Eu4GZmZjXoF6uxi3z6lSKEAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "pmv = fplot.PlotMapView(ax=ax, modelgrid=tgr)\n",
    "pmv.plot_grid()\n",
    "ix2.plot_linestring(result, ax=ax, lw=3)\n",
    "\n",
    "for cellid in result.cellids:\n",
    "    (h2,) = ax.plot(\n",
    "        tgr.xcellcenters[cellid],\n",
    "        tgr.ycellcenters[cellid],\n",
    "        \"kx\",\n",
    "        label=\"centroids of intersected gridcells\",\n",
    "    )\n",
    "\n",
    "ax.legend([h2], [i.get_label() for i in [h2]], loc=\"best\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id=\"trigrid.3\"></a>[MultiPoint with triangular grid](#top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:37.461267Z",
     "iopub.status.busy": "2022-12-15T13:10:37.460909Z",
     "iopub.status.idle": "2022-12-15T13:10:37.469808Z",
     "shell.execute_reply": "2022-12-15T13:10:37.468648Z"
    }
   },
   "outputs": [],
   "source": [
    "result = ix2.intersect(mp)\n",
    "result_all = ix2.intersect(mp, return_all_intersections=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:10:37.486223Z",
     "iopub.status.busy": "2022-12-15T13:10:37.485659Z",
     "iopub.status.idle": "2022-12-15T13:10:37.745830Z",
     "shell.execute_reply": "2022-12-15T13:10:37.744798Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "pmv = fplot.PlotMapView(ax=ax, modelgrid=tgr)\n",
    "pmv.plot_grid()\n",
    "ix2.plot_point(result, ax=ax, color=\"k\", zorder=5, s=80)\n",
    "\n",
    "for cellid in result.cellids:\n",
    "    (h2,) = ax.plot(\n",
    "        tgr.xcellcenters[cellid],\n",
    "        tgr.ycellcenters[cellid],\n",
    "        \"kx\",\n",
    "        ms=15,\n",
    "        label=\"centroids of intersected cells\",\n",
    "    )\n",
    "for cellid in result_all.cellids:\n",
    "    (h3,) = ax.plot(\n",
    "        tgr.xcellcenters[cellid],\n",
    "        tgr.ycellcenters[cellid],\n",
    "        \"r+\",\n",
    "        ms=15,\n",
    "        label=\"centroids with return_all_intersections=True\",\n",
    "    )\n",
    "\n",
    "ax.legend([h2, h3], [i.get_label() for i in [h2, h3]], loc=\"best\");"
   ]
  }
 ],
 "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": "3.9.7"
  },
  "vscode": {
   "interpreter": {
    "hash": "dace5e1b41a98a8e52d2a8eebc3b981caf2c12e7a76736ebfb89a489e3b62e79"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}