examples/Notebooks/flopy3_shapefile_features.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This notebook shows some lower-level functionality in `flopy` for working with shapefiles\n",
    "including:\n",
    "* `recarray2shp` convience function for writing a numpy record array to a shapefile\n",
    "* `shp2recarray` convience function for quickly reading a shapefile into a numpy recarray\n",
    "* `utils.geometry` classes for writing shapefiles of model input/output. For example, quickly writing a shapefile of model cells with errors identified by the checker\n",
    "* demonstration of how the `epsgRef` class works for retrieving projection file information (WKT text) from spatialreference.org, and caching that information locally for when an internet connection isn't available\n",
    "* how to reset `epsgRef` if it becomes corrupted\n",
    "* examples of how the `Point` and `LineString` classes can be used to quickly plot pathlines and endpoints from MODPATH (these are also used by the `PathlineFile` and `EndpointFile` classes to write shapefiles of this output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resetting /Users/jdhughes/Library/Application Support/flopy/epsgref.json\n",
      "3.8.6 | packaged by conda-forge | (default, Oct  7 2020, 18:42:56) \n",
      "[Clang 10.0.1 ]\n",
      "numpy version: 1.18.5\n",
      "matplotlib version: 3.2.2\n",
      "flopy version: 3.3.3\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import shutil\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# run installed version of flopy or add local path\n",
    "try:\n",
    "    import flopy\n",
    "except:\n",
    "    fpth = os.path.abspath(os.path.join('..', '..'))\n",
    "    sys.path.append(fpth)\n",
    "    import flopy\n",
    "    \n",
    "from flopy.utils.geometry import Polygon, LineString, Point\n",
    "from flopy.export.shapefile_utils import recarray2shp, shp2recarray\n",
    "from flopy.utils.modpathfile import PathlineFile, EndpointFile\n",
    "from flopy.utils import geometry\n",
    "from flopy.utils.reference import epsgRef\n",
    "ep = epsgRef()\n",
    "ep.reset()\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": [
    "### write a numpy record array to a shapefile\n",
    "in this case, we want to visualize output from the checker  \n",
    "first make a toy model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = flopy.modflow.Modflow('toy_model', model_ws='data')\n",
    "botm = np.zeros((2, 10, 10))\n",
    "botm[0, :, :] = 1.5\n",
    "botm[1, 5, 5] = 4 # negative layer thickness!\n",
    "botm[1, 6, 6] = 4\n",
    "dis = flopy.modflow.ModflowDis(nrow=10, ncol=10, \n",
    "                               nlay=2, delr=100, delc=100,\n",
    "                               top=3, botm=botm, model=m)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "### set coordinate information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = m.modelgrid\n",
    "grid.set_coord_info(xoff=600000, yoff=5170000, proj4='EPSG:26715', angrot=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "DIS PACKAGE DATA VALIDATION:\n",
      "  2 Errors:\n",
      "    2 instances of zero or negative thickness\n",
      "\n",
      "  Checks that passed:\n",
      "    thin cells (less than checker threshold of 1.0)\n",
      "    nan values in top array\n",
      "    nan values in bottom array\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "rec.array([('Error', 'DIS', 1, 5, 5, -2.5, 'zero or negative thickness'),\n",
       "           ('Error', 'DIS', 1, 6, 6, -2.5, 'zero or negative thickness')],\n",
       "          dtype=[('type', 'O'), ('package', 'O'), ('k', '<i8'), ('i', '<i8'), ('j', '<i8'), ('value', '<f8'), ('desc', 'O')])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chk = dis.check()\n",
    "chk.summary_array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### make geometry objects for the cells with errors  \n",
    "*  geometry objects allow the shapefile writer to be simpler and agnostic about the kind of geometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_vertices = m.modelgrid.get_cell_vertices # function to get the referenced vertices for a model cell\n",
    "geoms = [Polygon(get_vertices(i, j)) for i, j in chk.summary_array[['i', 'j']]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Polygon'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geoms[0].type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((600000.0, 5170707.106781187),\n",
       " (600070.7106781186, 5170777.817459305),\n",
       " (600141.4213562373, 5170707.106781187),\n",
       " (600070.7106781186, 5170636.396103068))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geoms[0].exterior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(600000.0, 5170636.396103068, 600141.4213562373, 5170777.817459305)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geoms[0].bounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12a471c70>"
      ]
     },
     "execution_count": 9,
     "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": [
    "geoms[0].plot() # this feature requires descartes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### write the shapefile  \n",
    "* the projection (.prj) file can be written using an epsg code  \n",
    "* or copied from an existing .prj file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote data/test.shp\n"
     ]
    }
   ],
   "source": [
    "recarray2shp(chk.summary_array, geoms, 'data/test.shp', epsg=26715)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote data/test.shp\n"
     ]
    }
   ],
   "source": [
    "shutil.copy('data/test.prj', 'data/26715.prj')\n",
    "recarray2shp(chk.summary_array, geoms, 'data/test.shp', prj='data/26715.prj')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### read it back in  \n",
    "* flopy geometry objects representing the shapes are stored in the 'geometry' field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rec.array([('Error', 'DIS', 1, 5, 5, -2.5, 'zero or negative thickness', <flopy.utils.geometry.Polygon object at 0x1124420a0>),\n",
       "           ('Error', 'DIS', 1, 6, 6, -2.5, 'zero or negative thickness', <flopy.utils.geometry.Polygon object at 0x12a50e040>)],\n",
       "          dtype=[('type', 'O'), ('package', 'O'), ('k', '<i8'), ('i', '<i8'), ('j', '<i8'), ('value', '<f8'), ('desc', 'O'), ('geometry', 'O')])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ra = shp2recarray('data/test.shp')\n",
    "ra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12a53ad60>"
      ]
     },
     "execution_count": 13,
     "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": [
    "ra.geometry[0].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How the epsg feature works  \n",
    "* requires an internet connection the first time to get the prj text from [spatialreference.org](https://spatialreference.org/) using ```requests```  \n",
    "* if it doesn't exist, ```epsgref.json``` is created in the user's data directory\n",
    "* the prj text is then stashed in this JSON file hashed by the EPSG numeric code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([(26715,\n",
       "              'PROJCS[\"NAD_1927_UTM_Zone_15N\",GEOGCS[\"GCS_North_American_1927\",DATUM[\"D_North_American_1927\",SPHEROID[\"Clarke_1866\",6378206.4,294.9786982138982]],PRIMEM[\"Greenwich\",0],UNIT[\"Degree\",0.017453292519943295]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",-93],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",500000],PARAMETER[\"false_northing\",0],UNIT[\"Meter\",1]]')])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from flopy.utils.reference import epsgRef\n",
    "ep = epsgRef()\n",
    "prj = ep.to_dict()\n",
    "prj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from flopy.utils.reference import getprj, epsgRef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'GEOGCS[\"GCS_WGS_1984\",DATUM[\"D_WGS_1984\",SPHEROID[\"WGS_1984\",6378137,298.257223563]],PRIMEM[\"Greenwich\",0],UNIT[\"Degree\",0.017453292519943295]]'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getprj(4326)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26715:\n",
      "PROJCS[\"NAD_1927_UTM_Zone_15N\",GEOGCS[\"GCS_North_American_1927\",DATUM[\"D_North_American_1927\",SPHEROID[\"Clarke_1866\",6378206.4,294.9786982138982]],PRIMEM[\"Greenwich\",0],UNIT[\"Degree\",0.017453292519943295]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",-93],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",500000],PARAMETER[\"false_northing\",0],UNIT[\"Meter\",1]]\n",
      "\n",
      "4326:\n",
      "GEOGCS[\"GCS_WGS_1984\",DATUM[\"D_WGS_1984\",SPHEROID[\"WGS_1984\",6378137,298.257223563]],PRIMEM[\"Greenwich\",0],UNIT[\"Degree\",0.017453292519943295]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "prj = ep.to_dict()\n",
    "for k, v in prj.items():\n",
    "    print('{}:\\n{}\\n'.format(k, v))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### working with the ```flopy.utils.reference.epsgRef``` handler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "ep = epsgRef()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "ep.add(9999, 'junk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26715:\n",
      "PROJCS[\"NAD_1927_UTM_Zone_15N\",GEOGCS[\"GCS_North_American_1927\",DATUM[\"D_North_American_1927\",SPHEROID[\"Clarke_1866\",6378206.4,294.9786982138982]],PRIMEM[\"Greenwich\",0],UNIT[\"Degree\",0.017453292519943295]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",-93],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",500000],PARAMETER[\"false_northing\",0],UNIT[\"Meter\",1]]\n",
      "\n",
      "4326:\n",
      "GEOGCS[\"GCS_WGS_1984\",DATUM[\"D_WGS_1984\",SPHEROID[\"WGS_1984\",6378137,298.257223563]],PRIMEM[\"Greenwich\",0],UNIT[\"Degree\",0.017453292519943295]]\n",
      "\n",
      "9999:\n",
      "junk\n",
      "\n"
     ]
    }
   ],
   "source": [
    "epsgRef.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### remove an entry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26715:\n",
      "PROJCS[\"NAD_1927_UTM_Zone_15N\",GEOGCS[\"GCS_North_American_1927\",DATUM[\"D_North_American_1927\",SPHEROID[\"Clarke_1866\",6378206.4,294.9786982138982]],PRIMEM[\"Greenwich\",0],UNIT[\"Degree\",0.017453292519943295]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",-93],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",500000],PARAMETER[\"false_northing\",0],UNIT[\"Meter\",1]]\n",
      "\n",
      "4326:\n",
      "GEOGCS[\"GCS_WGS_1984\",DATUM[\"D_WGS_1984\",SPHEROID[\"WGS_1984\",6378137,298.257223563]],PRIMEM[\"Greenwich\",0],UNIT[\"Degree\",0.017453292519943295]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ep.remove(9999)\n",
    "epsgRef.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### start over with a new file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resetting /Users/jdhughes/Library/Application Support/flopy/epsgref.json\n"
     ]
    }
   ],
   "source": [
    "ep.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prj = ep.to_dict()\n",
    "prj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(prj.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other geometry types\n",
    "\n",
    "### Linestring  \n",
    "* create geometry objects for pathlines from a MODPATH simulation\n",
    "* plot the paths using the built in plotting method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "pthfile = PathlineFile('../data/mp6/EXAMPLE-3.pathline')\n",
    "pthdata = pthfile._data.view(np.recarray)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "length_mult = 1. # multiplier to convert coordinates from model to real world\n",
    "rot = 0 # grid rotation\n",
    "\n",
    "particles = np.unique(pthdata.particleid)\n",
    "geoms = []\n",
    "for pid in particles:\n",
    "    ra = pthdata[pthdata.particleid == pid]\n",
    "\n",
    "    x, y = geometry.rotate(ra.x * length_mult,\n",
    "                           ra.y * length_mult,\n",
    "                           grid.xoffset,\n",
    "                           grid.yoffset,\n",
    "                           rot)\n",
    "    z = ra.z\n",
    "    geoms.append(LineString(list(zip(x, y, z))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<flopy.utils.geometry.LineString at 0x12a53a5e0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geoms[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12a53a130>"
      ]
     },
     "execution_count": 28,
     "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": [
    "geoms[0].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig, ax = plt.subplots()\n",
    "for g in geoms:\n",
    "    g.plot(ax=ax)\n",
    "ax.autoscale()\n",
    "ax.set_aspect(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "eptfile = EndpointFile('../data/mp6/EXAMPLE-3.endpoint')\n",
    "eptdata = eptfile.get_alldata()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = geometry.rotate(eptdata['x0'] * length_mult,\n",
    "                       eptdata['y0'] * length_mult,\n",
    "                       grid.xoffset,\n",
    "                       grid.yoffset,\n",
    "                       rot)\n",
    "z = eptdata['z0']\n",
    "\n",
    "geoms = [Point(x[i], y[i], z[i]) for i in range(len(eptdata))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig, ax = plt.subplots()\n",
    "for g in geoms:\n",
    "    g.plot(ax=ax)\n",
    "ax.autoscale()\n",
    "ax.set_aspect(2e-6)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}