Examples/example_05_Extent.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geokit as gk\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.3.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gk.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extent\n",
    "The Extent object represents geographic extents of an area and exposes useful\n",
    "methods which depend on those extents. Just some examples for the extent object.\n",
    "\n",
    "Example: Warp the GHI-rasterfile to the area of the extent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "gk.Extent?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# definition of the extent\n",
    "ext = gk.Extent( 6, 50, 6.75, 51.25, srs=gk.srs.EPSG4326 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\ra.maier\\code\\geokit\\geokit\\core\\raster.py:1560: MatplotlibDeprecationWarning: The 'cmap' parameter to Colorbar has no effect because it is overridden by the mappable; it is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  cbar = plt.colorbar(h, cax=cbax, **tmp)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "AxHands(ax=<matplotlib.axes._axes.Axes object at 0x00000289A8EEFE80>, handles=<matplotlib.image.AxesImage object at 0x00000289A8FA9640>, cbar=<matplotlib.colorbar.Colorbar object at 0x00000289A8FB2190>)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Clip a raster (GHI tif file) to the area of the extent\n",
    "clipped_ghi = ext.warp(\n",
    "    source=gk._test_data_[\"gsa-ghi-like.tif\"],\n",
    "    resampleAlg='bilinear',\n",
    "    pixelWidth=0.01,\n",
    "    pixelHeight=0.01\n",
    ")\n",
    "\n",
    "gk.drawRaster(clipped_ghi, figsize=(6,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get extent as a geometry object / box."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<osgeo.ogr.Geometry; proxy of <Swig Object of type 'OGRGeometryShadow *' at 0x00000289A90615A0> >"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ext.box"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create two shifted extents, combine and get extent of combined box. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AxHands(ax=<matplotlib.axes._axes.Axes object at 0x00000289A90CBBE0>, handles=[<matplotlib.patches.PathPatch object at 0x00000289A90F6340>, <matplotlib.patches.PathPatch object at 0x00000289A90F6970>, <matplotlib.patches.PathPatch object at 0x00000289A90F6B80>], cbar=None)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ext =  gk.Extent( 6, 50, 6.75, 51.25, srs=gk.srs.EPSG4326 )\n",
    "ext2 = gk.Extent( 6.25, 50.25, 7, 51.5, srs=gk.srs.EPSG4326 )\n",
    "\n",
    "ext3 = ext + ext2\n",
    "\n",
    "gk.drawGeoms( [ext3.box, ext.box, ext2.box], figsize=(6,6) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get extent of different types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "xMin: 5.500000\n",
       "xMax: 7.000000\n",
       "yMin: 50.000000\n",
       "yMax: 51.500000\n",
       "srs: GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get Extent of Raster-File\n",
    "ext_from_raster = ext.fromRaster(gk._test_data_[\"gsa-ghi-like.tif\"])\n",
    "ext_from_raster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "xMin: 4035461.383400\n",
       "xMax: 4075451.022500\n",
       "yMin: 3040668.925200\n",
       "yMax: 3101715.202400\n",
       "srs: PROJCS[\"ETRS89-extended / LAEA Europe\",GEOGCS[\"ETRS89\",DATUM[\"European_Terrestrial_Reference_System_1989\",SPHEROID[\"GRS 1980\",6378137,298.257222101,AUTHORITY[\"EPSG\",\"7019\"]],AUTHORITY[\"EPSG\",\"6258\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AUTHORITY[\"EPSG\",\"4258\"]],PROJECTION[\"Lambert_Azimuthal_Equal_Area\"],PARAMETER[\"latitude_of_center\",52],PARAMETER[\"longitude_of_center\",10],PARAMETER[\"false_easting\",4321000],PARAMETER[\"false_northing\",3210000],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Northing\",NORTH],AXIS[\"Easting\",EAST],AUTHORITY[\"EPSG\",\"3035\"]]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get Extent of Vector-File\n",
    "ext_from_vector = ext.fromVector(gk._test_data_[\"Natura2000_aachenClipped.shp\"])\n",
    "ext_from_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get Extent of a geom\n",
    "aachen_pt = gk.geom.point( (6.083, 52.775), srs=gk.srs.EPSG4326 )\n",
    "aachen_buffered_area = aachen_pt.Buffer(1)\n",
    "ext_from_geom = ext.fromGeom(aachen_buffered_area)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cast to different srs\n",
    "Creates a new Extent by transforming an extent from the original Extent's\n",
    "srs to a target SRS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ext_from_geom.castTo?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "xMin: 3980000.000000\n",
       "xMax: 4130500.000000\n",
       "yMin: 3186500.000000\n",
       "yMax: 3421000.000000\n",
       "srs: PROJCS[\"ETRS89-extended / LAEA Europe\",GEOGCS[\"ETRS89\",DATUM[\"European_Terrestrial_Reference_System_1989\",SPHEROID[\"GRS 1980\",6378137,298.257222101,AUTHORITY[\"EPSG\",\"7019\"]],AUTHORITY[\"EPSG\",\"6258\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AUTHORITY[\"EPSG\",\"4258\"]],PROJECTION[\"Lambert_Azimuthal_Equal_Area\"],PARAMETER[\"latitude_of_center\",52],PARAMETER[\"longitude_of_center\",10],PARAMETER[\"false_easting\",4321000],PARAMETER[\"false_northing\",3210000],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Northing\",NORTH],AXIS[\"Easting\",EAST],AUTHORITY[\"EPSG\",\"3035\"]]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pad to 2 percent in all directions and fit resoultion of 500m\n",
    "ext_from_geom.castTo( gk.srs.EPSG3035 ).pad(2, percent=True).fit(500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filter several files for extent\n",
    "Pass a list of sources (Vector-files, raster-files) to '.filterSoruces([ ])'. Filter from that list, the the relevant ones that intersect with the extent. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<filter at 0x289a90cbaf0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter sources \n",
    "ext.filterSources([])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get extent boundary in different forms\n",
    "Get information about the extent geom."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 50, 6.75, 51.25)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ext.xyXY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 6.75, 50, 51.25)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ext.xXyY"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create raster file for extent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'test_raster_with_ext.tif'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raster_matrix = np.array([[1,2,3,1,2,3],\n",
    "                          [2,2,2,2,2,2],\n",
    "                          [3,2,1,3,2,1],\n",
    "                          [3,2,1,3,2,1],\n",
    "                          [3,2,1,3,2,1],\n",
    "                          [3,2,1,3,2,1],\n",
    "                          [3,2,1,3,2,1],])\n",
    "\n",
    "ext = gk.Extent([6, 50, 7, 51.5], srs=gk.srs.EPSG4326)\n",
    "dx,dy = ext.computePixelSize(raster_matrix.shape[1], raster_matrix.shape[0])\n",
    "ext.createRaster( \n",
    "    pixelHeight=dy, \n",
    "    pixelWidth=dx, \n",
    "    data=raster_matrix, \n",
    "    output=\"test_raster_with_ext.tif\" )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Another powerful tool \n",
    "Combine raster files in one single file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[1;31mSignature:\u001b[0m \u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrasterMosaic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msources\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_warpKwargs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_skipFiltering\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;31mDocstring:\u001b[0m\n",
       "Create a raster source surrounding the Extent from a collection of other rasters\n",
       "\n",
       "Parameters:\n",
       "-----------\n",
       "sources : list, or something acceptable to gk.Extent.filterSources\n",
       "    The sources to add together over the invoking Extent\n",
       "\n",
       "Returns:\n",
       "--------\n",
       "* If 'output' is None: gdal.Dataset\n",
       "* If 'output' is a string: None\n",
       "\u001b[1;31mFile:\u001b[0m      c:\\users\\s.ryberg\\fzj\\workdir\\2031_geokit_workshop\\geokit\\geokit\\core\\extent.py\n",
       "\u001b[1;31mType:\u001b[0m      method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ext.rasterMosaic?"
   ]
  }
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