matplotlib_plot_shape_file.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib plot Shapefile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from osgeo import ogr\n",
    "from matplotlib.collections import PatchCollection\n",
    "\n",
    "SHAPEF_DIR = '/Volumes/kduNTfs128/geo_data/RG2017_rgint_20180911'\n",
    "SHAPEFILE_INPUT = '/Volumes/kduNTfs128/geo_data/RG2017_rgint_20180911'\n",
    "# open ShepFile de Poligonos\n",
    "drvr = ogr.GetDriverByName('ESRI Shapefile')\n",
    "ds_pol = drvr.Open(SHAPEFILE_INPUT, 0)\n",
    "layr_pol = ds_pol.GetLayer()\n",
    "layr_pol_def = layr_pol.GetLayerDefn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.path as mpath\n",
    "import matplotlib.patches as mpatches\n",
    "from matplotlib import pyplot \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature = layr_pol.GetFeature(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "geomref = feature.GetGeometryRef()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = geomref.Centroid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y, z = p.GetPoint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geomref.GetGeometryCount()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "xmin, xmax, ymin, ymax = geomref.GetEnvelope()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "geom  = geomref.GetGeometryRef(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "vertices = [(x, y) for x, y, z in geom.GetPoints()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " (-62.099001714999986, -10.183394458999974),\n",
       " (-62.10014156699997, -10.181943329999989),\n",
       " (-62.10245373499997, -10.17985262900002),\n",
       " (-62.104389981999944, -10.178452730999922),\n",
       " (-62.10572992499997, -10.178410905999925),\n",
       " (-62.10628423099992, -10.180035410999956),\n",
       " (-62.10629072099992, -10.182158786999963),\n",
       " (-62.10722429699998, -10.186276355999947),\n",
       " (-62.110803488, -10.194050199999939),\n",
       " (-62.11266760999997, -10.195589608999946),\n",
       " (-62.11484058999999, -10.196315344999952),\n",
       " (-62.11817246299998, -10.194512906999943),\n",
       " (-62.120084300999984, -10.193947304999952),\n",
       " (-62.121274283999945, -10.192711646999943),\n",
       " (-62.12275354099995, -10.191543204999988),\n",
       " (-62.1246443759999, -10.191228878999915),\n",
       " (-62.126108912999925, -10.191774953999978),\n",
       " (-62.1267279109999, -10.192814712999944),\n",
       " (-62.12700242099996, -10.195666662999926),\n",
       " (-62.12619934899995, -10.197825110999904),\n",
       " (-62.12467399299995, -10.19954819099992),\n",
       " (-62.12289060999992, -10.201928731999978),\n",
       " (-62.12197448599997, -10.20473750499994),\n",
       " (-62.12083230999997, -10.20723839799992),\n",
       " (-62.121391939999995, -10.209705414999938),\n",
       " (-62.12291961599992, -10.211682842999949),\n",
       " (-62.12575778599992, -10.214102580999963),\n",
       " (-62.12809197299996, -10.215412537999992),\n",
       " (-62.132449147999964, -10.219104886999965),\n",
       " (-62.1329045479999, -10.22027489899996),\n",
       " (-62.13463918499998, -10.22472959499995),\n",
       " (-62.13564490299997, -10.228383728999958),\n",
       " (-62.13603900999989, -10.230615589999971),\n",
       " (-62.137361579999904, -10.235157418999975),\n",
       " (-62.137285537000004, -10.235503199999897),\n",
       " (-62.136805871999925, -10.237697575000006),\n",
       " (-62.13717614599992, -10.23928251599996),\n",
       " (-62.13686715199994, -10.242256290999897),\n",
       " (-62.13802975199994, -10.243049617999901),\n",
       " (-62.13817012500001, -10.24567193899998),\n",
       " (-62.13803127399998, -10.247376060999937),\n",
       " (-62.138452096999984, -10.252703199999903),\n",
       " (-62.13898603499993, -10.255607706999971),\n",
       " (-62.14086776799992, -10.258939072999919),\n",
       " (-62.140140882, -10.26057002399995),\n",
       " (-62.141643830999925, -10.261456031999984),\n",
       " (-62.14278223399997, -10.262723633999972),\n",
       " (-62.14469584100003, -10.264379441999928),\n",
       " (-62.145863035999966, -10.263608026999961),\n",
       " (-62.146002642999974, -10.265483422999967),\n",
       " (-62.14539510499998, -10.267775910999934),\n",
       " (-62.14458634499994, -10.269700856999954),\n",
       " (-62.14566731999994, -10.270544458999943),\n",
       " (-62.142089287000005, -10.273473223999886),\n",
       " (-62.138143158999995, -10.27551378999997),\n",
       " (-62.13372225499997, -10.274791643999905),\n",
       " (-62.13370386899995, -10.27617039299994),\n",
       " (-62.135171874, -10.279277386999976),\n",
       " (-62.134637066999986, -10.280884801999925),\n",
       " (-62.13558815799998, -10.282356768999946),\n",
       " (-62.135338221, -10.283607829999898),\n",
       " (-62.139201568, -10.284693897999965),\n",
       " (-62.14092131300001, -10.289729845999908),\n",
       " (-62.139802753999916, -10.290441901999937),\n",
       " (-62.139221436999954, -10.290811015999925),\n",
       " (-62.13953702499995, -10.291716636999983),\n",
       " (-62.13850766999996, -10.292807091999968),\n",
       " (-62.13953889899993, -10.29348801999991),\n",
       " (-62.14025715799994, -10.29485109499998),\n",
       " (-62.13992192099991, -10.295979794999933),\n",
       " (-62.14106278699998, -10.296343635999929),\n",
       " (-62.14172360899995, -10.29781559699995),\n",
       " (-62.14039814999997, -10.29918032299998),\n",
       " (-62.14003395899995, -10.300354526999968),\n",
       " (-62.141393153999964, -10.301674747999957),\n",
       " (-62.141720434999954, -10.30342009999995),\n",
       " (-62.14175862799992, -10.304862793999948),\n",
       " (-62.14122771399995, -10.309408474999916),\n",
       " (-62.140612644999955, -10.310380276999943),\n",
       " (-62.14036592699997, -10.31139931499997),\n",
       " (-62.140646744999934, -10.312370266999928),\n",
       " (-62.138475659999976, -10.315609958999929),\n",
       " (-62.13661413499996, -10.319625573999915),\n",
       " (-62.135364958999986, -10.321787314999899),\n",
       " (-62.13819250899999, -10.324116877999913),\n",
       " (-62.13983439499995, -10.32615449299993),\n",
       " (-62.14019514599996, -10.327353601999935),\n",
       " (-62.139555547999976, -10.331257237999981),\n",
       " (-62.13900778399995, -10.332641144),\n",
       " (-62.13767625699991, -10.334161062999993),\n",
       " (-62.13806024099998, -10.335586172999957),\n",
       " (-62.13749827000002, -10.336879990999893),\n",
       " ...]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vertices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pol = mpatches.Polygon(vertices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "patch = mpatches.PathPatch(pol, facecolor='#6CA043', edgecolor='#1D388C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.31244408947281"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geom.GetArea()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-63.747986524427034"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-9.942811793174531"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.patches import Circle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = Circle((x,y), 0.1, facecolor='#6CA043', edgecolor='#1D388C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.patches.Circle at 0x110f09390>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": [
    "# Prepare figure\n",
    "pyplot.ioff()\n",
    "pyplot.subplot(1,1,1)\n",
    "ax = pyplot.gca()\n",
    "ax.set_xlim(xmin, xmax)\n",
    "ax.set_ylim(ymin, ymax)\n",
    "#ax.add_patch(pol)\n",
    "p = PatchCollection([pol,c], alpha=0.8, match_original=True, )\n",
    "\n",
    "ax.add_collection(p)\n",
    "ax.set_aspect(1.0)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}