kmz_gpd.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\\\\\\\ppeng.com\\\\pzdata\\\\clients\\\\Arvin-Edison WSD-1215\\\\121523003-Frick Unit Pipeline\\\\200 Technical\\\\202 General\\\\KMZs\\\\kmls'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "from pathlib import Path\n",
    "import fiona\n",
    "\n",
    "P = Path(r\"\\\\ppeng.com\\pzdata\\clients\\Arvin-Edison WSD-1215\\121523003-Frick Unit Pipeline\\200 Technical\\202 General\\KMZs\\kmls\")\n",
    "str(P)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Block Reference [9194F].kml\n",
      "Frick Unit Areas.kml\n",
      "Frick Unit.kml\n"
     ]
    }
   ],
   "source": [
    "import zipfile\n",
    "# Open the KMZ file\n",
    "for file in P.glob(\"*.kmz\"):\n",
    "    zipdata = zipfile.ZipFile(file)\n",
    "    zipinfos = zipdata.infolist()\n",
    "\n",
    "    # iterate through each file\n",
    "    for zipinfo in zipinfos:\n",
    "        # zipinfo.filename = str(P.joinpath(f\"{file.stem}.kml\"))\n",
    "        zipinfo.filename = f\"{file.stem}.kml\"\n",
    "        print(zipinfo.filename)\n",
    "        zipdata.extract(zipinfo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Block Reference 9194F'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Polylines'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Frick Unit Areas'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Polygons'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Frick Unit'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Points'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Polylines'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Polygons'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gdbs = {file.stem:{layer:gpd.read_file(file,driver='FileGDB',layer=layer) for layer in fiona.listlayers(file)} for file in P.glob('**/*.gdb')}\n",
    "\n",
    "from IPython.display import display\n",
    "for name, gdb in gdbs.items():\n",
    "\tdisplay(name)\n",
    "\tfor k,v in gdb.items():\n",
    "\t\tdisplay(k)\n",
    "\t\tdisplay(v.plot())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WindowsPath('//ppeng.com/pzdata/clients/Arvin-Edison WSD-1215/121523003-Frick Unit Pipeline/200 Technical/202 General/KMZs/kmls/doc.kml')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gpd.io.file.fiona.drvsupport.supported_drivers['KML'] = 'rw'\n",
    "\n",
    "# READ KML file to a geopandas dataframe \n",
    "file = P.joinpath('doc.kml')\n",
    "file\n",
    "# gdf = gpd.read_file(file,driver='KML')\n",
    "# gdf.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "GEOSException",
     "evalue": "IllegalArgumentException: point array must contain 0 or >1 elements\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mGEOSException\u001b[0m                             Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[20], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m gpd\u001b[39m.\u001b[39mio\u001b[39m.\u001b[39mfile\u001b[39m.\u001b[39mfiona\u001b[39m.\u001b[39mdrvsupport\u001b[39m.\u001b[39msupported_drivers[\u001b[39m'\u001b[39m\u001b[39mKML\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39mrw\u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m----> 3\u001b[0m gdf \u001b[39m=\u001b[39m gpd\u001b[39m.\u001b[39;49mread_file(file, driver\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mKML\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[0;32m      4\u001b[0m gdf\u001b[39m.\u001b[39mhead(\u001b[39m2\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\Denver\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\pandas-analysis-pSGX_PKJ-py3.10\\lib\\site-packages\\geopandas\\io\\file.py:259\u001b[0m, in \u001b[0;36m_read_file\u001b[1;34m(filename, bbox, mask, rows, engine, **kwargs)\u001b[0m\n\u001b[0;32m    256\u001b[0m     path_or_bytes \u001b[39m=\u001b[39m filename\n\u001b[0;32m    258\u001b[0m \u001b[39mif\u001b[39;00m engine \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mfiona\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m--> 259\u001b[0m     \u001b[39mreturn\u001b[39;00m _read_file_fiona(\n\u001b[0;32m    260\u001b[0m         path_or_bytes, from_bytes, bbox\u001b[39m=\u001b[39mbbox, mask\u001b[39m=\u001b[39mmask, rows\u001b[39m=\u001b[39mrows, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs\n\u001b[0;32m    261\u001b[0m     )\n\u001b[0;32m    262\u001b[0m \u001b[39melif\u001b[39;00m engine \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mpyogrio\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[0;32m    263\u001b[0m     \u001b[39mreturn\u001b[39;00m _read_file_pyogrio(\n\u001b[0;32m    264\u001b[0m         path_or_bytes, bbox\u001b[39m=\u001b[39mbbox, mask\u001b[39m=\u001b[39mmask, rows\u001b[39m=\u001b[39mrows, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs\n\u001b[0;32m    265\u001b[0m     )\n",
      "File \u001b[1;32mc:\\Users\\Denver\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\pandas-analysis-pSGX_PKJ-py3.10\\lib\\site-packages\\geopandas\\io\\file.py:360\u001b[0m, in \u001b[0;36m_read_file_fiona\u001b[1;34m(path_or_bytes, from_bytes, bbox, mask, rows, where, **kwargs)\u001b[0m\n\u001b[0;32m    356\u001b[0m     df \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mDataFrame(\n\u001b[0;32m    357\u001b[0m         [record[\u001b[39m\"\u001b[39m\u001b[39mproperties\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39mfor\u001b[39;00m record \u001b[39min\u001b[39;00m f_filt], columns\u001b[39m=\u001b[39mcolumns\n\u001b[0;32m    358\u001b[0m     )\n\u001b[0;32m    359\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 360\u001b[0m     df \u001b[39m=\u001b[39m GeoDataFrame\u001b[39m.\u001b[39;49mfrom_features(\n\u001b[0;32m    361\u001b[0m         f_filt, crs\u001b[39m=\u001b[39;49mcrs, columns\u001b[39m=\u001b[39;49mcolumns \u001b[39m+\u001b[39;49m [\u001b[39m\"\u001b[39;49m\u001b[39mgeometry\u001b[39;49m\u001b[39m\"\u001b[39;49m]\n\u001b[0;32m    362\u001b[0m     )\n\u001b[0;32m    363\u001b[0m \u001b[39mfor\u001b[39;00m k \u001b[39min\u001b[39;00m datetime_fields:\n\u001b[0;32m    364\u001b[0m     as_dt \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mto_datetime(df[k], errors\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mignore\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\Denver\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\pandas-analysis-pSGX_PKJ-py3.10\\lib\\site-packages\\geopandas\\geodataframe.py:635\u001b[0m, in \u001b[0;36mGeoDataFrame.from_features\u001b[1;34m(cls, features, crs, columns)\u001b[0m\n\u001b[0;32m    632\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(feature, \u001b[39m\"\u001b[39m\u001b[39m__geo_interface__\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[0;32m    633\u001b[0m     feature \u001b[39m=\u001b[39m feature\u001b[39m.\u001b[39m__geo_interface__\n\u001b[0;32m    634\u001b[0m row \u001b[39m=\u001b[39m {\n\u001b[1;32m--> 635\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mgeometry\u001b[39m\u001b[39m\"\u001b[39m: shape(feature[\u001b[39m\"\u001b[39;49m\u001b[39mgeometry\u001b[39;49m\u001b[39m\"\u001b[39;49m]) \u001b[39mif\u001b[39;00m feature[\u001b[39m\"\u001b[39m\u001b[39mgeometry\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39melse\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m    636\u001b[0m }\n\u001b[0;32m    637\u001b[0m \u001b[39m# load properties\u001b[39;00m\n\u001b[0;32m    638\u001b[0m properties \u001b[39m=\u001b[39m feature[\u001b[39m\"\u001b[39m\u001b[39mproperties\u001b[39m\u001b[39m\"\u001b[39m]\n",
      "File \u001b[1;32mc:\\Users\\Denver\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\pandas-analysis-pSGX_PKJ-py3.10\\lib\\site-packages\\shapely\\geometry\\geo.py:106\u001b[0m, in \u001b[0;36mshape\u001b[1;34m(context)\u001b[0m\n\u001b[0;32m    104\u001b[0m     \u001b[39mreturn\u001b[39;00m MultiPoint(ob[\u001b[39m\"\u001b[39m\u001b[39mcoordinates\u001b[39m\u001b[39m\"\u001b[39m])\n\u001b[0;32m    105\u001b[0m \u001b[39melif\u001b[39;00m geom_type \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mmultilinestring\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m--> 106\u001b[0m     \u001b[39mreturn\u001b[39;00m MultiLineString(ob[\u001b[39m\"\u001b[39;49m\u001b[39mcoordinates\u001b[39;49m\u001b[39m\"\u001b[39;49m])\n\u001b[0;32m    107\u001b[0m \u001b[39melif\u001b[39;00m geom_type \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mmultipolygon\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[0;32m    108\u001b[0m     \u001b[39mreturn\u001b[39;00m MultiPolygon([[c[\u001b[39m0\u001b[39m], c[\u001b[39m1\u001b[39m:]] \u001b[39mfor\u001b[39;00m c \u001b[39min\u001b[39;00m ob[\u001b[39m\"\u001b[39m\u001b[39mcoordinates\u001b[39m\u001b[39m\"\u001b[39m]])\n",
      "File \u001b[1;32mc:\\Users\\Denver\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\pandas-analysis-pSGX_PKJ-py3.10\\lib\\site-packages\\shapely\\geometry\\multilinestring.py:50\u001b[0m, in \u001b[0;36mMultiLineString.__new__\u001b[1;34m(self, lines)\u001b[0m\n\u001b[0;32m     48\u001b[0m subs \u001b[39m=\u001b[39m []\n\u001b[0;32m     49\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(m):\n\u001b[1;32m---> 50\u001b[0m     line \u001b[39m=\u001b[39m linestring\u001b[39m.\u001b[39;49mLineString(lines[i])\n\u001b[0;32m     51\u001b[0m     \u001b[39mif\u001b[39;00m line\u001b[39m.\u001b[39mis_empty:\n\u001b[0;32m     52\u001b[0m         \u001b[39mraise\u001b[39;00m EmptyPartError(\n\u001b[0;32m     53\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mCan\u001b[39m\u001b[39m'\u001b[39m\u001b[39mt create MultiLineString with empty component\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m     54\u001b[0m         )\n",
      "File \u001b[1;32mc:\\Users\\Denver\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\pandas-analysis-pSGX_PKJ-py3.10\\lib\\site-packages\\shapely\\geometry\\linestring.py:73\u001b[0m, in \u001b[0;36mLineString.__new__\u001b[1;34m(self, coordinates)\u001b[0m\n\u001b[0;32m     68\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(coordinates) \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[0;32m     69\u001b[0m     \u001b[39m# empty geometry\u001b[39;00m\n\u001b[0;32m     70\u001b[0m     \u001b[39m# TODO better constructor + should shapely.linestrings handle this?\u001b[39;00m\n\u001b[0;32m     71\u001b[0m     \u001b[39mreturn\u001b[39;00m shapely\u001b[39m.\u001b[39mfrom_wkt(\u001b[39m\"\u001b[39m\u001b[39mLINESTRING EMPTY\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m---> 73\u001b[0m geom \u001b[39m=\u001b[39m shapely\u001b[39m.\u001b[39;49mlinestrings(coordinates)\n\u001b[0;32m     74\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(geom, LineString):\n\u001b[0;32m     75\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mInvalid values passed to LineString constructor\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\Denver\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\pandas-analysis-pSGX_PKJ-py3.10\\lib\\site-packages\\shapely\\decorators.py:77\u001b[0m, in \u001b[0;36mmultithreading_enabled.<locals>.wrapped\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     75\u001b[0m     \u001b[39mfor\u001b[39;00m arr \u001b[39min\u001b[39;00m array_args:\n\u001b[0;32m     76\u001b[0m         arr\u001b[39m.\u001b[39mflags\u001b[39m.\u001b[39mwriteable \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[1;32m---> 77\u001b[0m     \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m     78\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m     79\u001b[0m     \u001b[39mfor\u001b[39;00m arr, old_flag \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(array_args, old_flags):\n",
      "File \u001b[1;32mc:\\Users\\Denver\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\pandas-analysis-pSGX_PKJ-py3.10\\lib\\site-packages\\shapely\\creation.py:120\u001b[0m, in \u001b[0;36mlinestrings\u001b[1;34m(coords, y, z, indices, out, **kwargs)\u001b[0m\n\u001b[0;32m    118\u001b[0m coords \u001b[39m=\u001b[39m _xyz_to_coords(coords, y, z)\n\u001b[0;32m    119\u001b[0m \u001b[39mif\u001b[39;00m indices \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m--> 120\u001b[0m     \u001b[39mreturn\u001b[39;00m lib\u001b[39m.\u001b[39mlinestrings(coords, out\u001b[39m=\u001b[39mout, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m    121\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    122\u001b[0m     \u001b[39mreturn\u001b[39;00m simple_geometries_1d(coords, indices, GeometryType\u001b[39m.\u001b[39mLINESTRING, out\u001b[39m=\u001b[39mout)\n",
      "\u001b[1;31mGEOSException\u001b[0m: IllegalArgumentException: point array must contain 0 or >1 elements\n"
     ]
    }
   ],
   "source": [
    "gpd.io.file.fiona.drvsupport.supported_drivers['KML'] = 'rw'\n",
    "\n",
    "gdf = gpd.read_file(file, driver=\"KML\")\n",
    "gdf.head(2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pandas-analysis-pSGX_PKJ-py3.10",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}