examples/Notebooks/flopy3_grid_intersection_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id=\"top\"></a>Intersecting model grids with shapes\n",
    "\n",
    "_Note: This feature requires the shapely and descartes packages (which are not a FloPy dependency) so must be installed by the user._\n",
    "\n",
    "This notebook shows the grid intersection functionality in flopy. The intersection methods are available through the GridIntersect object. A flopy modelgrid is passed to instantiate the object. Then the modelgrid can be intersected with Points, LineStrings and Polygons through the different intersection methods. \n",
    "\n",
    "There are three intersection modes: \n",
    "- the first (default mode) builds an STR-tree for fast spatial queries before calculating intersections, thereby reducing the number of grid cells it has to process. This method works for structured and vertex grids.\n",
    "- the second method does not construct the STR-tree, and loops through all gridcells to determine the intersection between the grid and the shape. This method also works for structured and vertex grids.\n",
    "- the third method only works for structured grids and uses information from the structured grid to limit the search space for intersections.\n",
    "\n",
    "This notebook showcases the functionality of the GridIntersect class. \n",
    "\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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import some stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "flopy is installed in /home/david/Github/flopy_db/flopy\n",
      "3.7.6 (default, Jan  8 2020, 19:59:22) \n",
      "[GCC 7.3.0]\n",
      "numpy version: 1.18.1\n",
      "matplotlib version: 3.1.3\n",
      "flopy version: 3.3.1\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import os\n",
    "import platform\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sys.path.insert(1, \"../..\")\n",
    "\n",
    "# run installed version of flopy or add local path\n",
    "try:\n",
    "    import flopy\n",
    "    import flopy.discretization as fgrid\n",
    "    import flopy.plot as fplot\n",
    "    from flopy.utils.gridintersect import GridIntersect\n",
    "except:\n",
    "    fpth = os.path.abspath(os.path.join('..', '..'))\n",
    "    sys.path.append(fpth)\n",
    "    import flopy\n",
    "    import flopy.discretization as fgrid\n",
    "    import flopy.plot as fplot\n",
    "    from flopy.utils.gridintersect import GridIntersect\n",
    "\n",
    "import shapely\n",
    "from shapely.geometry import Polygon, Point, LineString, MultiLineString, MultiPoint, MultiPolygon\n",
    "from shapely.strtree import STRtree    \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__))"
   ]
  },
  {
   "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 the constructor. There are options users can select to change how the intersection is calculated.\n",
    "\n",
    "- `method`: either `\"vertex\"` (default) or `\"structured\"`. If `\"structured\"` is passed, the intersections are performed using structured methods. These methods use information about the regular grid to limit the search space for intersection calculations. \n",
    "- `rtree`: either `True` (default) or `False`, only read when `method=\"vertex\"`. When True, an STR-tree is built, which allows for fast spatial queries. Building the STR-tree does take some time however. Setting the option to False avoids building the STR-tree but requires the intersection calculation to essentially loop through all grid cells.\n",
    "\n",
    "In general the default option is robust (it always works) and fast and is therefore recommended in most situations. If you are working with a structured grid, then the `method=\"structured\"` can speed up intersection operations (especially for points and linestrings) with the added advantage of not having to build an STR-tree. In some cases with vertex grids, it might not be worth your time building the STR-tree, in which case it can be avoided by passing `rtree=False`.\n",
    "\n",
    "The important methods in the GridIntersect object are:\n",
    "- `intersects()`: returns cellids for gridcells that intersect a shape\n",
    "- `intersect_point()`: for intersecting the modelgrid with point geometries\n",
    "- `intersect_linestring()`: for intersecting the modelgrid with linestrings\n",
    "- `intersect_polygon()`: for intersecting the modelgrid with polygons\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 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": {},
   "outputs": [],
   "source": [
    "delc = 10*np.ones(10, dtype=np.float)\n",
    "delr = 10*np.ones(10, dtype=np.float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "xoff = 0.\n",
    "yoff = 0.\n",
    "angrot = 0.\n",
    "sgr = fgrid.StructuredGrid(delc, delr, top=None, botm=None, xoff=xoff, yoff=yoff, angrot=angrot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x7fea028e37d0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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z8nFW2Z0AAAAASUVORK5CYII=\n",
      "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": {},
   "outputs": [],
   "source": [
    "p = Polygon(shell=[(15, 15), (20, 50), (35, 80.), (80, 50), \n",
    "                   (80, 40), (40, 5), (15, 12)], \n",
    "            holes=[[(25, 25), (25, 45), (45, 45), (45, 25)]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the GridIntersect class for our modelgrid. The keyword arguments are shown below, but as these are the default options, they do not need to be passed necesssarily."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ix = GridIntersect(sgr, method=\"vertex\", rtree=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do the intersect operation for a polygon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.21 ms ± 866 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit ix.intersect_polygon(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = ix.intersect_polygon(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",
    "- **lenghts**: 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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rec.array([((2, 3), (((30.0, 70.0), (35.0, 80.0), (40.0, 76.66666666666667), (40.0, 70.0), (30.0, 70.0)),),  66.66666667, <shapely.geometry.polygon.Polygon object at 0x7fea0289fd90>),\n",
       "           ((2, 4), (((40.0, 76.66666666666667), (50.0, 70.0), (40.0, 70.0), (40.0, 76.66666666666667)),),  33.33333333, <shapely.geometry.polygon.Polygon object at 0x7fea0284d5d0>),\n",
       "           ((3, 2), (((25.0, 60.0), (30.0, 70.0), (30.0, 60.0), (25.0, 60.0)),),  25.        , <shapely.geometry.polygon.Polygon object at 0x7fea0289f050>),\n",
       "           ((3, 3), (((30.0, 70.0), (40.0, 70.0), (40.0, 60.0), (30.0, 60.0), (30.0, 70.0)),), 100.        , <shapely.geometry.polygon.Polygon object at 0x7fea0289f890>),\n",
       "           ((3, 4), (((40.0, 70.0), (50.0, 70.0), (50.0, 60.0), (40.0, 60.0), (40.0, 70.0)),), 100.        , <shapely.geometry.polygon.Polygon object at 0x7fea0289f7d0>)],\n",
       "          dtype=[('cellids', 'O'), ('vertices', 'O'), ('areas', '<f8'), ('ixshapes', 'O')])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result[:5]\n",
    "# pd.DataFrame(result)  # for prettier formatting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cellids can be easily obtained "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.cellids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or the areas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "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": 11,
     "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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rec.array([((9, 4),), ((9, 3),), ((9, 2),), ((8, 1),), ((8, 5),),\n",
       "           ((8, 4),), ((8, 3),), ((8, 2),), ((7, 6),), ((7, 4),),\n",
       "           ((7, 2),), ((7, 5),), ((7, 1),), ((7, 3),), ((6, 5),),\n",
       "           ((6, 8),), ((6, 7),), ((6, 6),), ((6, 4),), ((6, 1),),\n",
       "           ((6, 2),), ((5, 1),), ((5, 2),), ((5, 3),), ((5, 4),),\n",
       "           ((5, 5),), ((5, 6),), ((5, 7),), ((5, 8),), ((4, 1),),\n",
       "           ((4, 2),), ((4, 3),), ((4, 5),), ((4, 6),), ((4, 7),),\n",
       "           ((4, 8),), ((4, 4),), ((3, 4),), ((3, 6),), ((3, 5),),\n",
       "           ((3, 3),), ((3, 2),), ((2, 4),), ((2, 2),), ((2, 5),),\n",
       "           ((2, 3),), ((1, 3),)],\n",
       "          dtype=[('cellids', 'O')])"
      ]
     },
     "execution_count": 12,
     "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "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(sgr.xcellcenters[0, icol], sgr.ycellcenters[irow, 0], \n",
    "                  \"kx\", label=\"centroids of intersected gridcells\")\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": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ixs = GridIntersect(sgr, method=\"structured\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The methods are optimized for structured grids, but for certain types of polygons there is no benefit (as can be seen in this example)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.06 ms ± 1.38 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit ixs.intersect_polygon(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is the same as before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rec.array([((2, 3), (((30.0, 70.0), (35.0, 80.0), (40.0, 76.66666666666667), (40.0, 70.0), (30.0, 70.0)),),  66.66666667, <shapely.geometry.polygon.Polygon object at 0x7fea026a8d10>),\n",
       "           ((2, 4), (((40.0, 76.66666666666667), (50.0, 70.0), (40.0, 70.0), (40.0, 76.66666666666667)),),  33.33333333, <shapely.geometry.polygon.Polygon object at 0x7fea026a8b10>),\n",
       "           ((3, 2), (((25.0, 60.0), (30.0, 70.0), (30.0, 60.0), (25.0, 60.0)),),  25.        , <shapely.geometry.polygon.Polygon object at 0x7fea026a86d0>),\n",
       "           ((3, 3), (((30.0, 70.0), (40.0, 70.0), (40.0, 60.0), (30.0, 60.0), (30.0, 70.0)),), 100.        , <shapely.geometry.polygon.Polygon object at 0x7fea026a8450>),\n",
       "           ((3, 4), (((40.0, 70.0), (50.0, 70.0), (50.0, 60.0), (40.0, 60.0), (40.0, 70.0)),), 100.        , <shapely.geometry.polygon.Polygon object at 0x7fea026a8550>)],\n",
       "          dtype=[('cellids', 'O'), ('vertices', 'O'), ('areas', '<f8'), ('ixshapes', 'O')])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result2 = ixs.intersect_polygon(p)\n",
    "result2[:5]"
   ]
  },
  {
   "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": {},
   "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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.22 ms ± 706 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit ix.intersect_linestring(mls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = ix.intersect_linestring(mls)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "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)\n",
    "\n",
    "for irow, icol in result.cellids:\n",
    "    h2, = ax.plot(sgr.xcellcenters[0, icol], sgr.ycellcenters[irow, 0], \"kx\", label=\"centroids of intersected gridcells\")\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": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "ixs = GridIntersect(sgr, method=\"structured\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.42 ms ± 1.17 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit ixs.intersect_linestring(mls)"
   ]
  },
  {
   "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": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "mp = MultiPoint(points=[Point(50.0, 0.0), Point(45., 45.), \n",
    "                        Point(10., 10.), Point(150., 100.)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.27 ms ± 212 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit ix.intersect_point(mp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rec.array([((5, 4), ((45.0, 45.0),), <shapely.geometry.point.Point object at 0x7fea02574650>),\n",
       "           ((8, 0), ((10.0, 10.0),), <shapely.geometry.point.Point object at 0x7fea02574e10>),\n",
       "           ((9, 4), ((50.0, 0.0),), <shapely.geometry.point.Point object at 0x7fea02574810>)],\n",
       "          dtype=[('cellids', 'O'), ('vertices', 'O'), ('ixshapes', 'O')])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = ix.intersect_point(mp)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "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)\n",
    "    \n",
    "for irow, icol in result.cellids:\n",
    "    h2, = ax.plot(sgr.xcellcenters[0, icol], sgr.ycellcenters[irow, 0], \"kx\", label=\"centroids of intersected cells\")\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": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "ixs = GridIntersect(sgr, method=\"structured\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "176 µs ± 3.71 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit ixs.intersect_point(mp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rec.array([((9, 4), <shapely.geometry.point.Point object at 0x7fea0253c6d0>),\n",
       "           ((5, 4), <shapely.geometry.point.Point object at 0x7fea0253c890>),\n",
       "           ((8, 0), <shapely.geometry.point.Point object at 0x7fea0253cbd0>)],\n",
       "          dtype=[('cellids', 'O'), ('ixshapes', 'O')])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ixs.intersect_point(mp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id=\"trigrid\"></a>[Vertex Grid](#top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "cell2d = [[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",
    "vertices = [[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",
    "tgr = fgrid.VertexGrid(vertices, cell2d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x7fea024d8350>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAHWCAYAAACmHPpfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAclElEQVR4nO3db6wld33f8c8X9i6xHUU2tEbEpoIoFgSQEqhFSVJFEQQpJCjmwaI6ShMrovhBaZf8qWKnT6I+aGWkKH9WVEguJHakiKRyUI0ilAo5VGmlxqqBiLA4lRGh4OBgogBB5s8u4tcH91x8d33/njNz5t/rJa3Onbkzc34azb3vnTnnzqnWWgCAYT1r6AEAAIIMAKMgyAAwAoIMACMgyAAwAoIMACNwbJCr6neq6smq+vi+ec+tqg9W1WOrxxtW86uqLlTVJ6vqY1X1qj4HDwBzcZIz5PuS/PhV8+5O8lBr7ZYkD62mk+QNSW5Z/bszybu6GSYAzNuxQW6t/VmSv79q9m1J7l99fX+SN+2b/3tt158nub6qXtDVYAFgrtZ9Dfn5rbUnkmT1eONq/k1JPrtvucdX8wCAI5zpeHt1wLwD781ZVXdm97J2rrnmmn96ww03ZGdnJ1UHbQLYxKVLl5IkZ8+eHXgkMC+ttVy+fDmttTzxxBN/11r7x+tua90gf76qXtBae2J1SfrJ1fzHk7xw33I3J/ncQRtord2b5N4kufnmm9tb3/rWXHfddTl//rxfGtCxe+65J0ly9913H7MkcFKXLl3KO9/5znzlK1/JNddck7vuuuv/bbK9dS9Zvz/JHauv70jy4L75P7d6t/Vrknx579L2UXZ2dvLyl788Tz31VC5cuPDt/80DwBjtj/FLXvKS3HjjjcevdIyT/NnTe5P87yQvqarHq+otSe5J8vqqeizJ61fTSfKBJJ9K8skk/yXJvz7pQM6dOyfKAIze1TG+/fbbO9nusZesW2s/fci3XnfAsi3J29YdzLlz55IkFy9ezIULF1y+BmBU+opxMsI7dTlTBmCM+oxxMsIgJ6IMwLj0HeNkpEFORBmAcdhGjJMRBzkRZQCGta0YJyMPciLKAAxjmzFOJhDkRJQB2K5txziZSJATUQZgO4aIcTKhICeiDEC/hopxMrEgJ6IMQD+GjHEywSAnogxAt4aOcTLRICeiDEA3xhDjZMJBTkQZgM2MJcbJxIOciDIA6xlTjJMZBDkRZQBOZ2wxTmYS5ESUATiZMcY4mVGQE1EG4GhjjXEysyAnogzAwcYc42SGQU5EGYArjT3GyUyDnIgyALumEONkxkFORBlg6aYS42TmQU5EGWCpphTjZAFBTkQZYGmmFuNkIUFORBlgKaYY42RBQU5EGWDuphrjZGFBTkQZYK6mHONkgUFORBlgbqYe42ShQU5EGWAu5hDjZMFBTkQZYOrmEuNk4UFORBlgquYU40SQk4gywNTMLcaJIH+bKANMwxxjnAjyFUQZYNzmGuNEkJ9BlAHGac4xTgT5QKIMMC5zj3EiyIcSZYBxWEKME0E+kigDDGspMU4E+ViiDDCMJcU4EeQTEWWA7VpajBNBPjFRBtiOJcY4EeRTEWWAfi01xokgn5ooA/RjyTFOBHktogzQraXHOBHktYkyQDfEeJcgb0CUATYjxk8T5A2JMsB6xPhKgtwBUQY4HTF+JkHuiCgDnIwYH0yQOyTKAEcT48MJcsdEGeBgYnw0Qe6BKANcSYyPJ8g9EWWAXWJ8MoLcI1EGlk6MT06QeybKwFKJ8ekI8haIMrA0Ynx6grwlogwshRivR5C3SJSBuRPj9QnylokyMFdivBlBHoAoA3MjxpsT5IGIMjAXYtwNQR6QKANTJ8bdEeSBiTIwVWLcLUEeAVEGpkaMuyfIIyHKwFSIcT8EeUREGRg7Me6PII+MKANjJcb9EuQREmVgbMS4f4I8UqIMjIUYb4cgj5goA0MT4+0R5JETZWAoYrxdgjwBogxsmxhvnyBPhCgD2yLGwxDkCRFloG9iPBxBnhhRBvoixsMS5AkSZaBrYjw8QZ4oUQa6IsbjIMgTJsrApsR4PAR54kQZWJcYj4sgz4AoA6clxuOzUZCr6her6mJVfbyq3ltV31FVL66qh6vqsar6w6o629VgOZwoAyclxuO0dpCr6qYk55Pc2lp7RZJnJ7k9yTuS/GZr7ZYkX0zyli4GyvFEGTiOGI/XppeszyS5pqrOJLk2yRNJXpvkgdX370/ypg2fg1MQZeAwYjxuawe5tfY3SX49yWeyG+IvJ/lwki+11r65WuzxJDdtOkhOR5SBq4nx+G1yyfqGJLcleXGS705yXZI3HLBoO2T9O6vqkap65PLly+sOg0OIMrBHjKdhk0vWP5bkr1trX2itXU7yviQ/lOT61SXsJLk5yecOWrm1dm9r7dbW2q07OzsbDIPDiDIgxtOxSZA/k+Q1VXVtVVWS1yX5RJIPJTm3WuaOJA9uNkQ2IcqwXGI8LZu8hvxwdt+89ZEkf7na1r1J7kryS1X1ySTPS/KeDsbJBkQZlkeMp2ejd1m31n6ttfbS1torWms/21r7RmvtU621V7fWvre19ubW2je6GizrE2VYDjGeJnfqWhBRhvkT4+kS5IURZZgvMZ42QV4gUYb5EePpE+SFEmWYDzGeB0FeMFGG6RPj+RDkhRNlmC4xnhdBRpRhgsR4fgSZJKIMUyLG8yTIfJsow/iJ8XwJMlcQZRgvMZ43QeYZRBnGR4znT5A5kCjDeIjxMggyhxJlGJ4YL4cgcyRRhuGI8bIIMscSZdg+MV4eQeZERBm2R4yXSZA5MVGG/onxcgkypyLK0B8xXjZB5tREGbonxggyaxFl6I4YkwgyGxBl2JwYs0eQ2Ygow/rEmP0EmY2JMpyeGHM1QaYTogwnJ8YcRJDpjCjD8cSYwwgynRJlOJwYcxRBpnOiDM8kxhxHkOmFKMPTxJiTEGR6I8ogxpycINMrUWbJxJjTEGR6J8oskRhzWoLMVogySyLGrEOQ2RpRZgnEmHUJMlslysyZGLMJQWbrRJk5EmM2JcgMQpSZEzGmC4LMYESZORBjuiLIDEqUmTIxpkuCzOBEmSkSY7omyIyCKDMlYkwfBJnREGWmQIzpiyAzKqLMmIkxfRJkRkeUGSMxpm+CzCiJMmMixmyDIDNaoswYiDHbIsiMmigzJDFmmwSZ0RNlhiDGbJsgMwmizDaJMUMQZCZDlNkGMWYogsykiDJ9EmOGJMhMjijTBzFmaILMJIkyXRJjxkCQmSxRpgtizFgIMpMmymxCjBkTQWbyRJl1iDFjI8jMgihzGmLMGAkysyHKnIQYM1aCzKyIMkcRY8ZMkJkdUeYgYszYCTKzJMrsJ8ZMgSAzW6JMIsZMhyAza6K8bGLMlAgysyfKyyTGTI0gswiivCxizBQJMoshyssgxkyVILMoojxvYsyUCTKLI8rzJMZMnSCzSKI8L2LMHAgyiyXK8yDGzIUgs2iiPG1izJwIMosnytMkxsyNIENEeWrEmDkSZFgR5WkQY+ZKkGEfUR43MWbOBBmuIsrjJMbMnSDDAUR5XMSYJRBkOIQoj4MYsxSCDEcQ5WGJMUuyUZCr6vqqeqCq/qqqHq2qH6yq51bVB6vqsdXjDV0NFoYgysMQY5Zm0zPk307yJ621lyb5/iSPJrk7yUOttVuSPLSahkkT5e0SY5Zo7SBX1Xcl+ZEk70mS1tql1tqXktyW5P7VYvcnedOmg4QxEOXtEGOWapMz5O9J8oUkv1tVH62qd1fVdUme31p7IklWjzd2ME4YBVHulxizZJsE+UySVyV5V2vtlUmeyikuT1fVnVX1SFU9cvny5Q2GAdslyv0QY5ZukyA/nuTx1trDq+kHshvoz1fVC5Jk9fjkQSu31u5trd3aWrt1Z2dng2HA9olyt8QYNghya+1vk3y2ql6ymvW6JJ9I8v4kd6zm3ZHkwY1GCCMlyt0QY9i16bus/22S36+qjyX5gST/Kck9SV5fVY8lef1qGmZJlDcjxvC0M5us3Fr7iyS3HvCt122yXZiSc+fOJUkuXryYCxcu5Pz58zl79uzAoxo/MYYruVMXdMCZ8umIMTyTIENHRPlkxBgOJsjQIVE+mhjD4QQZOibKBxNjOJogQw9E+UpiDMcTZOiJKO8SYzgZQYYeLT3KYgwnJ8jQs6VGWYzhdAQZtmBpURZjOD1Bhi1ZSpTFGNYjyLBFc4+yGMP6BBm2bK5RFmPYjCDDAOYWZTGGzQkyDGQuURZj6IYgw4CmHmUxhu4IMgxsqlEWY+iWIMMITC3KYgzdE2QYialEWYyhH4IMIzL2KIsx9EeQYWTGGmUxhn4JMozQ2KIsxtA/QYaRGkuUxRi2Q5BhxIaOshjD9ggyjNxQURZj2C5BhgnYdpTFGLZPkGEithVlMYZhCDJMSN9RFmMYjiDDxPQVZTGGYQkyTFDXURZjGJ4gw0R1FWUxhnEQZJiwTaMsxjAeggwTt26UxRjGRZBhBk4bZTGG8RFkmImTRlmMYZwEGWbkuCiLMYyXIMPMHBbl1poYw4idGXoAQPfOnTuXJLl48WIuXLiQ1louXbqUS5cuiTGM1CiCfOnSpdxzzz1DDwNm51nPelaeeuqpK6Y//elP+3mDjnVxxzyXrGHGzpw5c+Q0MB6j+Ok8e/Zs7r777qGHAbOy9wau/f9z39nZyfnz53P27NkBRwbzc9999228DWfIMENXv5v6Oc95zrcvX2/j85SB0xNkmJnD/rRpZ2dnK5+nDKxHkGFGjvs7474/TxlYnyDDTJz0ph+iDOMkyDADp70DlyjD+AgyTNy6t8MUZRgXQYYJ2/Te1KIM4yHIMFFdfVCEKMM4CDJMUNef2iTKMDxBhonp6yMURRmGJcgwIX1/nrEow3AEGSai7xjvEWUYhiDDBGwrxntEGbZPkGHkth3jPaIM2yXIMGJDxXiPKMP2CDKM1NAx3iPKsB2CDCM0lhjvEWXonyDDyIwtxntEGfolyDAiY43xHlGG/ggyjMTYY7xHlKEfggwjMJUY7xFl6J4gw8CmFuM9ogzdEmQY0FRjvEeUoTuCDAOZeoz3iDJ0Q5BhAHOJ8R5Rhs0JMmzZ3GK8R5RhM4IMWzTXGO8RZVifIMOWzD3Ge0QZ1iPIsAVLifEeUYbTE2To2dJivEeU4XQEGXq01BjvEWU4OUGGniw9xntEGU5GkKEHYnwlUYbjCTJ0TIwPJspwNEGGDonx0UQZDifI0BExPhlRhoMJMnRAjE9HlOGZBBk2JMbrEWW40sZBrqpnV9VHq+qPV9MvrqqHq+qxqvrDqjq7+TBhnMR4M6IMT+viDPntSR7dN/2OJL/ZWrslyReTvKWD54DREeNuiDLs2ijIVXVzkp9M8u7VdCV5bZIHVovcn+RNmzwHjJEYd0uUYfMz5N9K8itJvrWafl6SL7XWvrmafjzJTRs+B4yKGPdDlFm6tYNcVW9M8mRr7cP7Zx+waDtk/Tur6pGqeuTy5cvrDgO2Soz7Jcos2SZnyD+c5Keq6tNJ/iC7l6p/K8n1VXVmtczNST530MqttXtba7e21m7d2dnZYBiwHWK8HaLMUq0d5Nbar7bWbm6tvSjJ7Un+tLX2M0k+lOTcarE7kjy48ShhYGK8XaLMEvXxd8h3Jfmlqvpkdl9Tfk8PzwFbI8bDEGWWppMgt9b+R2vtjauvP9Vae3Vr7Xtba29urX2ji+eAIYjxsESZJXGnLjiEGI+DKLMUggwHEONxEWWWQJDhKmI8TqLM3Aky7CPG4ybKzJkgw4oYT4MoM1eCDBHjqRFl5kiQWTwxniZRZm4EmUUT42kTZeZEkFksMZ4HUWYuBJlFEuN5EWXmQJBZHDGeJ1Fm6gSZRRHjeRNlpkyQWQwxXgZRZqoEmUUQ42URZaZIkJk9MV4mUWZqBJlZE+NlE2WmRJCZLTEmEWWmQ5CZJTFmP1FmCgSZ2RFjDiLKjJ0gMytizFFEmTETZGZDjDkJUWasBJlZEGNOQ5QZI0Fm8sSYdYgyYyPITJoYswlRZkwEmckSY7ogyoyFIDNJYkyXRJkxEGQmR4zpgygzNEFmUsSYPokyQxJkJkOM2QZRZiiCzCSIMdskygxBkBk9MWYIosy2CTKjJsYMSZTZJkFmtMSYMRBltkWQGSUxZkxEmW0QZEZHjBkjUaZvgsyoiDFjJsr0SZAZDTFmCkSZvggyoyDGTIko0wdBZnBizBSJMl0TZAYlxkyZKNMlQWYwYswciDJdEWQGIcbMiSjTBUFm68SYORJlNiXIbJUYM2eizCYEma0RY5ZAlFmXILMVYsySiDLrEGR6J8YskShzWoJMr8SYJRNlTkOQ6Y0YgyhzcoJML8QYnibKnIQg0zkxhmcSZY4jyHRKjOFwosxRBJnOiDEcT5Q5jCDTCTGGkxNlDiLIbEyM4fREmasJMhsRY1ifKLOfILM2MYbNiTJ7BJm1iDF0R5RJBJk1iDF0T5QRZE5FjKE/orxsgsyJiTH0T5SXS5A5ETGG7RHlZRJkjiXGsH2ivDyCzJHEGIYjyssiyBxKjGF4orwcgsyBxBjGQ5SXQZB5BjGG8RHl+RNkriDGMF6iPG+CzLeJMYyfKM+XIJNEjGFKRHmeBBkxhgkS5fkR5IUTY5guUZ4XQV4wMYbpE+X5EOSFEmOYD1GeB0FeIDGG+RHl6RPkhRFjmC9RnjZBXhAxhvkT5ekS5IUQY1gOUZ6mtYNcVS+sqg9V1aNVdbGq3r6a/9yq+mBVPbZ6vKG74bIOMYblEeXp2eQM+ZtJfrm19n1JXpPkbVX1siR3J3motXZLkodW0wxEjGG5RHla1g5ya+2J1tpHVl9/JcmjSW5KcluS+1eL3Z/kTZsOkvWIMSDK09HJa8hV9aIkr0zycJLnt9aeSHajneTGLp6D0xFjYI8oT8PGQa6q70zyR0l+obX2D6dY786qeqSqHrl8+fKmw2AfMQauJsrjt1GQq2onuzH+/dba+1azP19VL1h9/wVJnjxo3dbava21W1trt+7s7GwyDPYRY+Awojxum7zLupK8J8mjrbXf2Pet9ye5Y/X1HUkeXH94nIYYA8cR5fHa5Az5h5P8bJLXVtVfrP79RJJ7kry+qh5L8vrVND0TY+CkRHmczqy7YmvtfyWpQ779unW3y+mJMXBa586dS5JcvHgxFy5cyPnz53P27NmBR7Vs7tQ1cWIMrMuZ8rgI8oSJMbApUR4PQZ4oMQa6IsrjIMgTJMZA10R5eII8MWIM9EWUhyXIEyLGQN9EeTiCPBFiDGyLKA9DkCdAjIFtE+XtE+SRE2NgKKK8XYI8YmIMDE2Ut0eQR0qMgbEQ5e0Q5BESY2BsRLl/gjwyYgyMlSj3S5BHRIyBsRPl/gjySIgxMBWi3A9BHgExBqZGlLsnyAMTY2CqRLlbgjwgMQamTpS7I8gDEWNgLkS5G4I8ADEG5kaUNyfIWybGwFyJ8mYEeYvEGJg7UV6fIG+JGANLIcrrEeQtEGNgaUT59AS5Z2IMLJUon44g90iMgaUT5ZMT5J6IMcAuUT4ZQe6BGANcSZSPJ8gdE2OAg4ny0QS5Q2IMcDRRPpwgd0SMAU5GlA8myB0QY4DTEeVnEuQNiTHAekT5SoK8ATEG2IwoP02Q1yTGAN0Q5V2CvAYxBuiWKAvyqYkxQD+WHmVBPgUxBujXkqMsyCckxgDbsdQoC/IJiDHAdi0xyoJ8DDEGGMbSoizIRxBjgGEtKcqCfAgxBhiHpURZkA8gxgDjsoQoC/JVxBhgnOYeZUHeR4wBxm3OURbkFTEGmIa5RlmQI8YAUzPHKC8+yGIMME1zi/KigyzGANM2pygvNshiDDAPc4nyIoMsxgDzMocoLy7IYgwwT1OP8qKCLMYA8zblKC8myGIMsAxTjfIigizGAMsyxSjPPshiDLBMU4vyrIMsxgDLNqUozzbIYgxAMp0ozzLIYgzAflOI8uyCLMYAHGTsUZ5VkMUYgKOMOcqzCbIYA3ASY43yLIIsxgCcxhijPPkgizEA6xhblCcdZDEGYBNjivJkgyzGAHRhLFGeZJDFGIAujSHKkwuyGAPQh6GjPKkgizEAfRoyypMJshgDsA1DRXkSQRZjALZpiCiPPshiDMAQth3lUQdZjAEY0jajPNogizEAY7CtKI8yyGIMwJhsI8qjC7IYAzBGfUd5VEEWYwDGrM8o9xLkqvrxqvq/VfXJqrr7JOuIMQBT0FeUOw9yVT07yX9O8oYkL0vy01X1sqPWaa2JMQCTcXWUv/Wtb228zTMdjOtqr07yydbap5Kkqv4gyW1JPnHYCpcvXxZjACbl3LlzSZKLFy/ma1/72sbb6yPINyX57L7px5P8s6NWaK3lmmuuyde//vXcd999PQwJlm3vkpqfL+jetddem69+9asbb6ePINcB89ozFqq6M8mdq8lv3HXXXR/vYSwk/yjJ3w09iJmyb/tj3/bHvu3PSzZZuY8gP57khfumb07yuasXaq3dm+TeJKmqR1prt/YwlsWzb/tj3/bHvu2Pfdufqnpkk/X7eJf1/0lyS1W9uKrOJrk9yft7eB4AmI3Oz5Bba9+sqn+T5L8neXaS32mtXez6eQBgTvq4ZJ3W2geSfOAUq9zbxzhIYt/2yb7tj33bH/u2Pxvt22rtGe+3AgC2bFS3zgSApRo8yOvcZpNnqqoXVtWHqurRqrpYVW9fzX9uVX2wqh5bPd4w9FinqqqeXVUfrao/Xk2/uKoeXu3bP1y9iZFTqqrrq+qBqvqr1fH7g47bblTVL65+H3y8qt5bVd/huF1fVf1OVT1ZVR/fN+/AY7V2XVi17WNV9arjtj9okNe5zSaH+maSX26tfV+S1yR522pf3p3kodbaLUkeWk2znrcneXTf9DuS/OZq334xyVsGGdX0/XaSP2mtvTTJ92d3HztuN1RVNyU5n+TW1torsvsm29vjuN3EfUl+/Kp5hx2rb0hyy+rfnUneddzGhz5D/vZtNltrl5Ls3WaTU2qtPdFa+8jq669k95faTdndn/evFrs/yZuGGeG0VdXNSX4yybtX05XktUkeWC1i366hqr4ryY8keU+StNYutda+FMdtV84kuaaqziS5NskTcdyurbX2Z0n+/qrZhx2rtyX5vbbrz5NcX1UvOGr7Qwf5oNts3jTQWGajql6U5JVJHk7y/NbaE8lutJPcONzIJu23kvxKkr07yD8vyZdaa99cTTt21/M9Sb6Q5HdXLwe8u6qui+N2Y621v0ny60k+k90QfznJh+O47dphx+qp+zZ0kE90m01Orqq+M8kfJfmF1to/DD2eOaiqNyZ5srX24f2zD1jUsXt6Z5K8Ksm7WmuvTPJUXJ7uxOq1zNuSvDjJdye5LruXUa/muO3HqX9HDB3kE91mk5Opqp3sxvj3W2vvW83+/N5lktXjk0ONb8J+OMlPVdWns/uyymuze8Z8/epSYOLYXdfjSR5vrT28mn4gu4F23G7ux5L8dWvtC621y0nel+SH4rjt2mHH6qn7NnSQ3WazI6vXNN+T5NHW2m/s+9b7k9yx+vqOJA9ue2xT11r71dbaza21F2X3GP3T1trPJPlQknOrxezbNbTW/jbJZ6tq76b8r8vuR7U6bjf3mSSvqaprV78f9vat47Zbhx2r70/yc6t3W78myZf3Lm0fZvAbg1TVT2T3bGPvNpv/cdABTVRV/fMk/zPJX+bp1zn/fXZfR/6vSf5Jdn9A39xau/pNCZxQVf1okn/XWntjVX1Pds+Yn5vko0n+ZWvtG0OOb4qq6gey+2a5s0k+leTns3uy4LjdUFX9hyT/Irt/hfHRJP8qu69jOm7XUFXvTfKj2f3ErM8n+bUk/y0HHKur/wS9M7vvyv5qkp9vrR354RODBxkAGP6SNQAQQQaAURBkABgBQQaAERBkABgBQQaAERBkABgBQQaAEfj/UNfXAE3QzrgAAAAASUVORK5CYII=\n",
      "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": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "ix2 = GridIntersect(tgr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = ix2.intersect_polygon(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "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(tgr.xcellcenters[cellid], tgr.ycellcenters[cellid], \"kx\", label=\"centroids of intersected gridcells\")\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": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = ix2.intersect_linestring(mls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "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(tgr.xcellcenters[cellid], tgr.ycellcenters[cellid], \"kx\", label=\"centroids of intersected gridcells\")\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": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = ix2.intersect_point(mp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rec.array([(1, ((10.0, 10.0), (45.0, 45.0)), <shapely.geometry.multipoint.MultiPoint object at 0x7fea0231ab90>),\n",
       "           (4, ((50.0, 0.0),), <shapely.geometry.point.Point object at 0x7fea02326990>)],\n",
       "          dtype=[('cellids', 'O'), ('vertices', 'O'), ('ixshapes', 'O')])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "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)\n",
    "    \n",
    "for cellid in result.cellids:\n",
    "    h2, = ax.plot(tgr.xcellcenters[cellid], tgr.ycellcenters[cellid], \n",
    "                  \"kx\", label=\"centroids of intersected cells\")\n",
    "    \n",
    "ax.legend([h2], [i.get_label() for i in [h2]], loc=\"best\");"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}