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": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:04:21.241514Z",
     "iopub.status.busy": "2022-03-07T20:04:21.240743Z",
     "iopub.status.idle": "2022-03-07T20:04:22.474913Z",
     "shell.execute_reply": "2022-03-07T20:04:22.475411Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.9.7 (default, Sep 16 2021, 08:50:36) \n",
      "[Clang 10.0.0 ]\n",
      "numpy version: 1.21.2\n",
      "matplotlib version: 3.5.1\n",
      "flopy version: 3.3.5\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 (\n",
    "    Polygon,\n",
    "    Point,\n",
    "    LineString,\n",
    "    MultiLineString,\n",
    "    MultiPoint,\n",
    "    MultiPolygon,\n",
    ")\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()`: for intersecting the modelgrid with point, linestrings, and polygon geometries (intersect can accept shapely geometry objects, flopy geometry object, 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 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": "2022-03-07T20:04:22.481957Z",
     "iopub.status.busy": "2022-03-07T20:04:22.481020Z",
     "iopub.status.idle": "2022-03-07T20:04:22.483709Z",
     "shell.execute_reply": "2022-03-07T20:04:22.484217Z"
    }
   },
   "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": "2022-03-07T20:04:22.490174Z",
     "iopub.status.busy": "2022-03-07T20:04:22.489168Z",
     "iopub.status.idle": "2022-03-07T20:04:22.492262Z",
     "shell.execute_reply": "2022-03-07T20:04:22.492786Z"
    }
   },
   "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": "2022-03-07T20:04:22.505511Z",
     "iopub.status.busy": "2022-03-07T20:04:22.504661Z",
     "iopub.status.idle": "2022-03-07T20:04:22.625266Z",
     "shell.execute_reply": "2022-03-07T20:04:22.626249Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x7f92b83842b0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\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": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:04:22.634101Z",
     "iopub.status.busy": "2022-03-07T20:04:22.633221Z",
     "iopub.status.idle": "2022-03-07T20:04:22.636483Z",
     "shell.execute_reply": "2022-03-07T20:04:22.635587Z"
    }
   },
   "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",
    ")"
   ]
  },
  {
   "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": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:04:22.642289Z",
     "iopub.status.busy": "2022-03-07T20:04:22.641320Z",
     "iopub.status.idle": "2022-03-07T20:04:22.704823Z",
     "shell.execute_reply": "2022-03-07T20:04:22.705444Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jdhughes/Documents/Development/flopy_git/flopy_fork/examples/Notebooks/../../flopy/utils/gridintersect.py:276: ShapelyDeprecationWarning: Setting custom attributes on geometry objects is deprecated, and will raise an AttributeError in Shapely 2.0\n",
      "  p.name = (i, j)\n"
     ]
    }
   ],
   "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": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:04:22.712262Z",
     "iopub.status.busy": "2022-03-07T20:04:22.711375Z",
     "iopub.status.idle": "2022-03-07T20:04:29.687906Z",
     "shell.execute_reply": "2022-03-07T20:04:29.688367Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.48 ms ± 529 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit ix.intersect(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:04:29.692449Z",
     "iopub.status.busy": "2022-03-07T20:04:29.691889Z",
     "iopub.status.idle": "2022-03-07T20:04:29.701584Z",
     "shell.execute_reply": "2022-03-07T20:04:29.702143Z"
    }
   },
   "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",
    "- **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": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:04:29.707658Z",
     "iopub.status.busy": "2022-03-07T20:04:29.707016Z",
     "iopub.status.idle": "2022-03-07T20:04:29.709450Z",
     "shell.execute_reply": "2022-03-07T20:04:29.709907Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rec.array([((2, 3), (((35.0, 80.0), (40.0, 76.66666666666667), (40.0, 70.0), (30.0, 70.0), (35.0, 80.0)),),  66.66666667, <shapely.geometry.polygon.Polygon object at 0x7f92b8384d00>),\n",
       "           ((2, 4), (((50.0, 70.0), (40.0, 70.0), (40.0, 76.66666666666667), (50.0, 70.0)),),  33.33333333, <shapely.geometry.polygon.Polygon object at 0x7f92b83b7670>),\n",
       "           ((3, 2), (((30.0, 70.0), (30.0, 60.0), (25.0, 60.0), (30.0, 70.0)),),  25.        , <shapely.geometry.polygon.Polygon object at 0x7f92b84baa00>),\n",
       "           ((3, 3), (((40.0, 70.0), (40.0, 60.0), (30.0, 60.0), (30.0, 70.0), (40.0, 70.0)),), 100.        , <shapely.geometry.polygon.Polygon object at 0x7f92b83b7910>),\n",
       "           ((3, 4), (((50.0, 70.0), (50.0, 60.0), (40.0, 60.0), (40.0, 70.0), (50.0, 70.0)),), 100.        , <shapely.geometry.polygon.Polygon object at 0x7f92b6713580>)],\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": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:04:29.714539Z",
     "iopub.status.busy": "2022-03-07T20:04:29.713746Z",
     "iopub.status.idle": "2022-03-07T20:04:29.716406Z",
     "shell.execute_reply": "2022-03-07T20:04:29.716793Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:04:29.721181Z",
     "iopub.status.busy": "2022-03-07T20:04:29.720570Z",
     "iopub.status.idle": "2022-03-07T20:04:29.723273Z",
     "shell.execute_reply": "2022-03-07T20:04:29.723662Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:04:29.728126Z",
     "iopub.status.busy": "2022-03-07T20:04:29.727551Z",
     "iopub.status.idle": "2022-03-07T20:04:29.731939Z",
     "shell.execute_reply": "2022-03-07T20:04:29.732299Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rec.array([((8, 1),), ((8, 2),), ((7, 1),), ((7, 2),), ((6, 1),),\n",
       "           ((9, 2),), ((9, 3),), ((9, 4),), ((8, 3),), ((8, 4),),\n",
       "           ((7, 3),), ((7, 4),), ((6, 2),), ((6, 4),), ((5, 1),),\n",
       "           ((4, 1),), ((3, 2),), ((2, 2),), ((5, 2),), ((5, 3),),\n",
       "           ((5, 4),), ((4, 2),), ((4, 3),), ((4, 4),), ((3, 3),),\n",
       "           ((3, 4),), ((2, 3),), ((2, 4),), ((1, 3),), ((6, 8),),\n",
       "           ((8, 5),), ((7, 5),), ((7, 6),), ((6, 5),), ((6, 6),),\n",
       "           ((6, 7),), ((5, 5),), ((5, 8),), ((4, 7),), ((4, 8),),\n",
       "           ((5, 6),), ((5, 7),), ((4, 5),), ((4, 6),), ((3, 5),),\n",
       "           ((3, 6),), ((2, 5),)],\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": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:04:29.746386Z",
     "iopub.status.busy": "2022-03-07T20:04:29.745773Z",
     "iopub.status.idle": "2022-03-07T20:04:30.000499Z",
     "shell.execute_reply": "2022-03-07T20:04:30.000909Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jdhughes/opt/anaconda3/lib/python3.9/site-packages/descartes/patch.py:62: ShapelyDeprecationWarning: The array interface is deprecated and will no longer work in Shapely 2.0. Convert the '.coords' to a numpy array instead.\n",
      "  vertices = concatenate([\n"
     ]
    },
    {
     "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(\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": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:04:30.005069Z",
     "iopub.status.busy": "2022-03-07T20:04:30.004433Z",
     "iopub.status.idle": "2022-03-07T20:04:30.006653Z",
     "shell.execute_reply": "2022-03-07T20:04:30.007234Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:04:30.013496Z",
     "iopub.status.busy": "2022-03-07T20:04:30.012857Z",
     "iopub.status.idle": "2022-03-07T20:04:38.628178Z",
     "shell.execute_reply": "2022-03-07T20:04:38.628618Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.6 ms ± 110 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit ixs.intersect(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is the same as before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:04:38.633459Z",
     "iopub.status.busy": "2022-03-07T20:04:38.632524Z",
     "iopub.status.idle": "2022-03-07T20:04:38.647443Z",
     "shell.execute_reply": "2022-03-07T20:04:38.647934Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rec.array([((2, 3), (((35.0, 80.0), (40.0, 76.66666666666667), (40.0, 70.0), (30.0, 70.0), (35.0, 80.0)),),  66.66666667, <shapely.geometry.polygon.Polygon object at 0x7f92b846e820>),\n",
       "           ((2, 4), (((50.0, 70.0), (40.0, 70.0), (40.0, 76.66666666666667), (50.0, 70.0)),),  33.33333333, <shapely.geometry.polygon.Polygon object at 0x7f92b84dea60>),\n",
       "           ((3, 2), (((30.0, 70.0), (30.0, 60.0), (25.0, 60.0), (30.0, 70.0)),),  25.        , <shapely.geometry.polygon.Polygon object at 0x7f92b8622d90>),\n",
       "           ((3, 3), (((40.0, 70.0), (40.0, 60.0), (30.0, 60.0), (30.0, 70.0), (40.0, 70.0)),), 100.        , <shapely.geometry.polygon.Polygon object at 0x7f92b8622e50>),\n",
       "           ((3, 4), (((50.0, 70.0), (50.0, 60.0), (40.0, 60.0), (40.0, 70.0), (50.0, 70.0)),), 100.        , <shapely.geometry.polygon.Polygon object at 0x7f92b8640700>)],\n",
       "          dtype=[('cellids', 'O'), ('vertices', 'O'), ('areas', '<f8'), ('ixshapes', 'O')])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result2 = ixs.intersect(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": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:04:38.653849Z",
     "iopub.status.busy": "2022-03-07T20:04:38.653259Z",
     "iopub.status.idle": "2022-03-07T20:04:38.655250Z",
     "shell.execute_reply": "2022-03-07T20:04:38.655658Z"
    }
   },
   "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": "2022-03-07T20:04:38.660661Z",
     "iopub.status.busy": "2022-03-07T20:04:38.660068Z",
     "iopub.status.idle": "2022-03-07T20:04:41.854998Z",
     "shell.execute_reply": "2022-03-07T20:04:41.855641Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.95 ms ± 29.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit ix.intersect(mls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:04:41.859905Z",
     "iopub.status.busy": "2022-03-07T20:04:41.859291Z",
     "iopub.status.idle": "2022-03-07T20:04:41.865820Z",
     "shell.execute_reply": "2022-03-07T20:04:41.866262Z"
    }
   },
   "outputs": [],
   "source": [
    "result = ix.intersect(mls)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:04:41.880973Z",
     "iopub.status.busy": "2022-03-07T20:04:41.875272Z",
     "iopub.status.idle": "2022-03-07T20:04:42.062711Z",
     "shell.execute_reply": "2022-03-07T20:04:42.063190Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jdhughes/Documents/Development/flopy_git/flopy_fork/examples/Notebooks/../../flopy/utils/gridintersect.py:1561: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.\n",
      "  for part in ishp:\n"
     ]
    },
    {
     "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, 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": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:04:42.067668Z",
     "iopub.status.busy": "2022-03-07T20:04:42.066825Z",
     "iopub.status.idle": "2022-03-07T20:04:42.068912Z",
     "shell.execute_reply": "2022-03-07T20:04:42.069350Z"
    }
   },
   "outputs": [],
   "source": [
    "ixs = GridIntersect(sgr, method=\"structured\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:04:42.074344Z",
     "iopub.status.busy": "2022-03-07T20:04:42.073533Z",
     "iopub.status.idle": "2022-03-07T20:04:47.590449Z",
     "shell.execute_reply": "2022-03-07T20:04:47.590842Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.79 ms ± 113 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit ixs.intersect(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": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:04:47.595418Z",
     "iopub.status.busy": "2022-03-07T20:04:47.594851Z",
     "iopub.status.idle": "2022-03-07T20:04:47.597409Z",
     "shell.execute_reply": "2022-03-07T20:04:47.597791Z"
    }
   },
   "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": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:04:47.603588Z",
     "iopub.status.busy": "2022-03-07T20:04:47.603041Z",
     "iopub.status.idle": "2022-03-07T20:04:59.123476Z",
     "shell.execute_reply": "2022-03-07T20:04:59.124287Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.48 ms ± 720 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit ix.intersect(mp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:04:59.130769Z",
     "iopub.status.busy": "2022-03-07T20:04:59.129732Z",
     "iopub.status.idle": "2022-03-07T20:04:59.138101Z",
     "shell.execute_reply": "2022-03-07T20:04:59.138885Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rec.array([((5, 4), ((45.0, 45.0),), <shapely.geometry.point.Point object at 0x7f92b86402e0>),\n",
       "           ((8, 0), ((10.0, 10.0),), <shapely.geometry.point.Point object at 0x7f92b8640190>),\n",
       "           ((9, 4), ((50.0, 0.0),), <shapely.geometry.point.Point object at 0x7f92b87c0550>)],\n",
       "          dtype=[('cellids', 'O'), ('vertices', 'O'), ('ixshapes', 'O')])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = ix.intersect(mp)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:04:59.159492Z",
     "iopub.status.busy": "2022-03-07T20:04:59.152007Z",
     "iopub.status.idle": "2022-03-07T20:04:59.277671Z",
     "shell.execute_reply": "2022-03-07T20:04:59.278105Z"
    }
   },
   "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(\n",
    "        sgr.xcellcenters[0, icol],\n",
    "        sgr.ycellcenters[irow, 0],\n",
    "        \"kx\",\n",
    "        label=\"centroids of intersected cells\",\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": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:04:59.282124Z",
     "iopub.status.busy": "2022-03-07T20:04:59.281349Z",
     "iopub.status.idle": "2022-03-07T20:04:59.283486Z",
     "shell.execute_reply": "2022-03-07T20:04:59.283870Z"
    }
   },
   "outputs": [],
   "source": [
    "ixs = GridIntersect(sgr, method=\"structured\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:04:59.289132Z",
     "iopub.status.busy": "2022-03-07T20:04:59.288297Z",
     "iopub.status.idle": "2022-03-07T20:05:06.858340Z",
     "shell.execute_reply": "2022-03-07T20:05:06.858850Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "984 µs ± 567 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit ixs.intersect(mp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:06.863208Z",
     "iopub.status.busy": "2022-03-07T20:05:06.862482Z",
     "iopub.status.idle": "2022-03-07T20:05:06.867096Z",
     "shell.execute_reply": "2022-03-07T20:05:06.867512Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rec.array([((9, 4), <shapely.geometry.point.Point object at 0x7f92b83d3280>),\n",
       "           ((5, 4), <shapely.geometry.point.Point object at 0x7f92b89b6cd0>),\n",
       "           ((8, 0), <shapely.geometry.point.Point object at 0x7f92b89b6d30>)],\n",
       "          dtype=[('cellids', 'O'), ('ixshapes', 'O')])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ixs.intersect(mp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id=\"trigrid\"></a>[Vertex Grid](#top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:06.876167Z",
     "iopub.status.busy": "2022-03-07T20:05:06.875445Z",
     "iopub.status.idle": "2022-03-07T20:05:06.877408Z",
     "shell.execute_reply": "2022-03-07T20:05:06.877809Z"
    }
   },
   "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": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:06.897132Z",
     "iopub.status.busy": "2022-03-07T20:05:06.896248Z",
     "iopub.status.idle": "2022-03-07T20:05:06.987126Z",
     "shell.execute_reply": "2022-03-07T20:05:06.987945Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x7f92b8b3abe0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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(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": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:06.993132Z",
     "iopub.status.busy": "2022-03-07T20:05:06.992418Z",
     "iopub.status.idle": "2022-03-07T20:05:06.995371Z",
     "shell.execute_reply": "2022-03-07T20:05:06.995846Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jdhughes/Documents/Development/flopy_git/flopy_fork/examples/Notebooks/../../flopy/utils/gridintersect.py:337: ShapelyDeprecationWarning: Setting custom attributes on geometry objects is deprecated, and will raise an AttributeError in Shapely 2.0\n",
      "  p.name = icell\n"
     ]
    }
   ],
   "source": [
    "ix2 = GridIntersect(tgr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:07.000987Z",
     "iopub.status.busy": "2022-03-07T20:05:07.000154Z",
     "iopub.status.idle": "2022-03-07T20:05:07.005062Z",
     "shell.execute_reply": "2022-03-07T20:05:07.005472Z"
    }
   },
   "outputs": [],
   "source": [
    "result = ix2.intersect(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:07.027543Z",
     "iopub.status.busy": "2022-03-07T20:05:07.017631Z",
     "iopub.status.idle": "2022-03-07T20:05:07.175551Z",
     "shell.execute_reply": "2022-03-07T20:05:07.176227Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jdhughes/opt/anaconda3/lib/python3.9/site-packages/descartes/patch.py:62: ShapelyDeprecationWarning: The array interface is deprecated and will no longer work in Shapely 2.0. Convert the '.coords' to a numpy array instead.\n",
      "  vertices = concatenate([\n"
     ]
    },
    {
     "data": {
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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(\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": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:07.181729Z",
     "iopub.status.busy": "2022-03-07T20:05:07.180810Z",
     "iopub.status.idle": "2022-03-07T20:05:07.185609Z",
     "shell.execute_reply": "2022-03-07T20:05:07.186122Z"
    }
   },
   "outputs": [],
   "source": [
    "result = ix2.intersect(mls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:07.268936Z",
     "iopub.status.busy": "2022-03-07T20:05:07.268117Z",
     "iopub.status.idle": "2022-03-07T20:05:07.396184Z",
     "shell.execute_reply": "2022-03-07T20:05:07.396800Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jdhughes/Documents/Development/flopy_git/flopy_fork/examples/Notebooks/../../flopy/utils/gridintersect.py:1561: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.\n",
      "  for part in ishp:\n"
     ]
    },
    {
     "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(\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": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:07.402306Z",
     "iopub.status.busy": "2022-03-07T20:05:07.401472Z",
     "iopub.status.idle": "2022-03-07T20:05:07.405573Z",
     "shell.execute_reply": "2022-03-07T20:05:07.406022Z"
    }
   },
   "outputs": [],
   "source": [
    "result = ix2.intersect(mp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:07.410982Z",
     "iopub.status.busy": "2022-03-07T20:05:07.410224Z",
     "iopub.status.idle": "2022-03-07T20:05:07.413211Z",
     "shell.execute_reply": "2022-03-07T20:05:07.413619Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rec.array([(1, ((10.0, 10.0), (45.0, 45.0)), <shapely.geometry.multipoint.MultiPoint object at 0x7f92b8de5a60>),\n",
       "           (4, ((50.0, 0.0),), <shapely.geometry.point.Point object at 0x7f92b87c0b20>)],\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": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:07.433507Z",
     "iopub.status.busy": "2022-03-07T20:05:07.432644Z",
     "iopub.status.idle": "2022-03-07T20:05:07.542937Z",
     "shell.execute_reply": "2022-03-07T20:05:07.543350Z"
    }
   },
   "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(\n",
    "        tgr.xcellcenters[cellid],\n",
    "        tgr.ycellcenters[cellid],\n",
    "        \"kx\",\n",
    "        label=\"centroids of intersected cells\",\n",
    "    )\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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}