diff --git a/.gitignore b/.gitignore
index 3f9656d..cc241cb 100644
--- a/.gitignore
+++ b/.gitignore
@@ -20,6 +20,16 @@ testing/results/*.cpg
 testing/results/*.csv
 testing/results/eligibility_plots
 
+# Example results
+Examples/*.tif
+Examples/*.shp
+Examples/*.dbf
+Examples/*.shx
+Examples/*.prj
+Examples/*.cpg
+Examples/*.csv
+Examples/*.gpkg
+
 # Extras
 .vscode/settings.json
 .vscode/launch.json
@@ -27,3 +37,5 @@ testing/results/eligibility_plots
 .pytest
 .cache/v/cache/lastfailed
 .pytest_cache/*
+
+
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index c5f6304..9d772b5 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -1,18 +1,25 @@
-image: continuumio/miniconda3:latest
+image: mambaorg/micromamba
 
 stages:
   - test
 
-test:
+.test_template:
   stage: test
-  tags: 
-    - linux
+  before_script:
+    # create environment
+    - micromamba create -y -n geokit --file=requirements-dev.yml
+    - eval "$(micromamba shell hook --shell bash)"
+    - micromamba activate geokit
+    # Install geokit with no --no-deps flag to create a reproducible environment
+    - python -m pip install -e . --no-deps
 
-  script: 
-    - conda install mamba -c conda-forge
-    - mamba env update -n geokit --file=requirements.yml
-    - mamba env update -n geokit --file=requirements-dev.yml
-    - mamba info --envs
-    - source activate geokit
-    - python -m pip install -e .
+test-code:
+  extends: .test_template
+  script:
+    # Test code
     - python -m pytest --cov=geokit geokit/test/
+
+  rules:
+    - if: $CI_COMMIT_TAG
+      when: never
+    - when: always
diff --git a/Examples/README.md b/Examples/README.md
index 24c980d..81f6e02 100644
--- a/Examples/README.md
+++ b/Examples/README.md
@@ -1,5 +1,8 @@
 ## Example Index
 
-* [Create a shape file from a pandas DataFrame](dataframe_to_shapefile.ipynb)
-* [Determine the average of raster values in a region](regional_raster_average.ipynb)
-* [Showcase of RegionMask fuctionalities](RegionMask.ipynb)
+* [Examples for Spatial Reference System](example_01_srs.ipynb)
+* [Examples for Geometry Operations](example_02_geometry.ipynb)
+* [Examples for Vector Datasets and Operations](example_03_vector.ipynb)
+* [Examples for Raster Datasets and Operations](example_04_raster.ipynb)
+* [Examples for Extent](example_05_Extent.ipynb)
+* [Examples for Region Mask](example_06_RegionMask.ipynb)
diff --git a/Examples/RegionMask.ipynb b/Examples/RegionMask.ipynb
deleted file mode 100644
index 6800fa1..0000000
--- a/Examples/RegionMask.ipynb
+++ /dev/null
@@ -1,1052 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import geokit as gk\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/svg+xml": [
-       "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n",
-       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
-       "  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
-       "<!-- Created with matplotlib (http://matplotlib.org/) -->\n",
-       "<svg height=\"288pt\" version=\"1.1\" viewBox=\"0 0 288 288\" width=\"288pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
-       " <defs>\n",
-       "  <style type=\"text/css\">\n",
-       "*{stroke-linecap:butt;stroke-linejoin:round;}\n",
-       "  </style>\n",
-       " </defs>\n",
-       " <g id=\"figure_1\">\n",
-       "  <g id=\"patch_1\">\n",
-       "   <path d=\"M 0 288 \n",
-       "L 288 288 \n",
-       "L 288 0 \n",
-       "L 0 0 \n",
-       "z\n",
-       "\" style=\"fill:#ffffff;\"/>\n",
-       "  </g>\n",
-       "  <g id=\"axes_1\">\n",
-       "   <g id=\"patch_2\">\n",
-       "    <path clip-path=\"url(#p74a9c91dab)\" d=\"M 152.495621 26.576346 \n",
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-       "L 146.276453 27.380113 \n",
-       "L 147.793453 28.260176 \n",
-       "L 148.81571 28.292966 \n",
-       "L 151.329581 25.477273 \n",
-       "L 152.495621 26.576346 \n",
-       "\" style=\"fill:#d9e9ff;stroke:#000000;stroke-linejoin:miter;\"/>\n",
-       "   </g>\n",
-       "  </g>\n",
-       " </g>\n",
-       " <defs>\n",
-       "  <clipPath id=\"p74a9c91dab\">\n",
-       "   <rect height=\"245.9\" width=\"146.520956\" x=\"72.767747\" y=\"14.3\"/>\n",
-       "  </clipPath>\n",
-       " </defs>\n",
-       "</svg>\n"
-      ],
-      "text/plain": [
-       "<geokit.core.regionmask.RegionMask at 0x7f3aac040748>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Create a RegionMask\n",
-    "rm = gk.RegionMask.load(   gk._test_data_[\"aachenShapefile.shp\"]   )\n",
-    "rm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>geom</th>\n",
-       "      <th>name</th>\n",
-       "      <th>osm_id</th>\n",
-       "      <th>type</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>LINESTRING (6.0745324 50.7525618,6.0764633 50....</td>\n",
-       "      <td>Wesertalstrecke</td>\n",
-       "      <td>4342523</td>\n",
-       "      <td>rail</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>LINESTRING (6.0944187 50.7675851,6.0940274 50....</td>\n",
-       "      <td>None</td>\n",
-       "      <td>4964404</td>\n",
-       "      <td>rail</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>LINESTRING (6.0904966 50.7675923,6.0904543 50....</td>\n",
-       "      <td>None</td>\n",
-       "      <td>4980641</td>\n",
-       "      <td>rail</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>LINESTRING (6.0798769 50.7578992,6.0796447 50....</td>\n",
-       "      <td>Wesertalstrecke</td>\n",
-       "      <td>4980642</td>\n",
-       "      <td>rail</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>LINESTRING (6.0798769 50.7578992,6.0848864 50....</td>\n",
-       "      <td>Wesertalstrecke</td>\n",
-       "      <td>5209205</td>\n",
-       "      <td>rail</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                geom             name  \\\n",
-       "0  LINESTRING (6.0745324 50.7525618,6.0764633 50....  Wesertalstrecke   \n",
-       "1  LINESTRING (6.0944187 50.7675851,6.0940274 50....             None   \n",
-       "2  LINESTRING (6.0904966 50.7675923,6.0904543 50....             None   \n",
-       "3  LINESTRING (6.0798769 50.7578992,6.0796447 50....  Wesertalstrecke   \n",
-       "4  LINESTRING (6.0798769 50.7578992,6.0848864 50....  Wesertalstrecke   \n",
-       "\n",
-       "    osm_id  type  \n",
-       "0  4342523  rail  \n",
-       "1  4964404  rail  \n",
-       "2  4980641  rail  \n",
-       "3  4980642  rail  \n",
-       "4  5209205  rail  "
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Find lines which cross into this region\n",
-    "geoms = rm.extractFeatures( gk._test_data_[\"aachen_rails.shp\"] )\n",
-    "geoms.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3aa2379be0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Display these around the RegionMask\n",
-    "axh = rm.drawGeoms(geoms, figsize=(5,6), hideAxis=True, color='b')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3aa35b59e8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Indicate RegionMask pixels within 500 meters of these lines\n",
-    "isNearToRail = rm.indicateFeatures( gk._test_data_[\"aachen_rails.shp\"], buffer=500 )\n",
-    "\n",
-    "# Visualize it!\n",
-    "axh = rm.drawImage(isNearToRail, figsize=(5,6), hideAxis=True, cmap='Reds', )\n",
-    "plt.show()"
-   ]
-  }
- ],
- "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": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Examples/dataframe_to_shapefile.ipynb b/Examples/dataframe_to_shapefile.ipynb
deleted file mode 100644
index 9a81103..0000000
--- a/Examples/dataframe_to_shapefile.ipynb
+++ /dev/null
@@ -1,202 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# DataFrame to Shapefile\n",
-    "\n",
-    "Here a pandas dataframe is converted to a shape file. The dataframe can include any number of columns which contain float, interger, or string-representable values. One column must be called 'geom' which defines the geomentries.\n",
-    "\n",
-    "*last tested: version 1.0.2*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "\n",
-    "import geokit as gk"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Choose an srs for the geometries\n",
-    "srs = gk.srs.EPSG3035\n",
-    "\n",
-    "# Create random point geometries\n",
-    "geoms = []\n",
-    "for i in range(200):\n",
-    "    geoms.append( gk.geom.point(np.random.random(2), srs=srs ))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a DataFrame with collumn called \"geom\" for the geometries\n",
-    "df = pd.DataFrame({\"geom\":geoms})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Add Attributes for each geometry\n",
-    "size = np.random.randint(100, size=len(geoms))\n",
-    "df[\"size\"] = size\n",
-    "\n",
-    "color = [[\"blue\",\"green\",\"red\",\"yellow\"][i] for i in np.random.randint(4, size=len(geoms))]\n",
-    "df[\"color\"] = color\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>geom</th>\n",
-       "      <th>size</th>\n",
-       "      <th>color</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>POINT (0.984231782417238 0.664081842617606 0)</td>\n",
-       "      <td>36</td>\n",
-       "      <td>yellow</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>POINT (0.961004152632402 0.813135688721021 0)</td>\n",
-       "      <td>1</td>\n",
-       "      <td>blue</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>POINT (0.392326232626297 0.389474095301736 0)</td>\n",
-       "      <td>46</td>\n",
-       "      <td>red</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>POINT (0.71471064046312 0.146376162487816 0)</td>\n",
-       "      <td>43</td>\n",
-       "      <td>green</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>POINT (0.716452216684462 0.508822894293676 0)</td>\n",
-       "      <td>53</td>\n",
-       "      <td>green</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                            geom  size   color\n",
-       "0  POINT (0.984231782417238 0.664081842617606 0)    36  yellow\n",
-       "1  POINT (0.961004152632402 0.813135688721021 0)     1    blue\n",
-       "2  POINT (0.392326232626297 0.389474095301736 0)    46     red\n",
-       "3   POINT (0.71471064046312 0.146376162487816 0)    43   green\n",
-       "4  POINT (0.716452216684462 0.508822894293676 0)    53   green"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Show the table for clarification\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'outputShapeFile.shp'"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Save as a shapefile\n",
-    "gk.vector.createVector( df, output=\"outputShapeFile.shp\" )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "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": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Examples/example_01_srs.ipynb b/Examples/example_01_srs.ipynb
new file mode 100644
index 0000000..00fb848
--- /dev/null
+++ b/Examples/example_01_srs.ipynb
@@ -0,0 +1,430 @@
+{
+ "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": [
+    "# Genreal geokit build up\n",
+    "\n",
+    "type gk. and use auto completion (press tab) to see suggestions. Here you can see the modules:\n",
+    "- srs: spatial reference system\n",
+    "- geom: geometries\n",
+    "- raster: raster files\n",
+    "- vector: vector files\n",
+    "- region mask: *ontop of all*\n",
+    "- Extent: *brings *all together*\n",
+    "- ...\n",
+    "\n",
+    "\n",
+    "Each module holdes all necessary function for its operation.\n",
+    "Some more special function are directly available under gk.#function name, but are defined within each module.\n",
+    "The modules are stored under ./geokit/core/#modulename"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For each function, the ducumentation can be assessed by '?'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gk.drawGeoms?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Spatial Reference System (SRS)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "same as:\n",
+    "- CRS - Coordinate reference system\n",
+    "- 'projection'\n",
+    "- proj4 system\n",
+    "\n",
+    "There are different types coordinate systems, for example EPSG4326. This can be accessed by typing 'gk.srs.EPSG4326'. This will create a SRS-Objectk. This object can be later used within other functions, where a SRS is used as an input parameter e.g: gk.raster.extractValues(srs = gk.srs.EPSG4326)\n",
+    "\n",
+    "Commonley used SRS:\n",
+    "- Basic latitude and longitude - EPSG4326\n",
+    "- Lambert azimuthal equal area (LAEA) - EPSG3035\n",
+    "- Mercator - EPSG3857"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'osgeo.osr.SpatialReference'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(type(gk.srs.EPSG4326))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GEOGCS[\"WGS 84\",\n",
+      "    DATUM[\"WGS_1984\",\n",
+      "        SPHEROID[\"WGS 84\",6378137,298.257223563,\n",
+      "            AUTHORITY[\"EPSG\",\"7030\"]],\n",
+      "        AUTHORITY[\"EPSG\",\"6326\"]],\n",
+      "    PRIMEM[\"Greenwich\",0,\n",
+      "        AUTHORITY[\"EPSG\",\"8901\"]],\n",
+      "    UNIT[\"degree\",0.0174532925199433,\n",
+      "        AUTHORITY[\"EPSG\",\"9122\"]],\n",
+      "    AXIS[\"Latitude\",NORTH],\n",
+      "    AXIS[\"Longitude\",EAST],\n",
+      "    AUTHORITY[\"EPSG\",\"4326\"]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(gk.srs.EPSG4326)\n",
+    "#print(gk.srs.EPSG3035)\n",
+    "#print(gk.srs.EPSG3857)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading of other SRS\n",
+    "Other SRS can be loaded from epsg.io or spacialreference.org. For Exampe EPSG:2004 would be loaded by the EPSG-name:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PROJCS[\"Montserrat 1958 / British West Indies Grid\",\n",
+      "    GEOGCS[\"Montserrat 1958\",\n",
+      "        DATUM[\"Montserrat_1958\",\n",
+      "            SPHEROID[\"Clarke 1880 (RGS)\",6378249.145,293.465,\n",
+      "                AUTHORITY[\"EPSG\",\"7012\"]],\n",
+      "            AUTHORITY[\"EPSG\",\"6604\"]],\n",
+      "        PRIMEM[\"Greenwich\",0,\n",
+      "            AUTHORITY[\"EPSG\",\"8901\"]],\n",
+      "        UNIT[\"degree\",0.0174532925199433,\n",
+      "            AUTHORITY[\"EPSG\",\"9122\"]],\n",
+      "        AUTHORITY[\"EPSG\",\"4604\"]],\n",
+      "    PROJECTION[\"Transverse_Mercator\"],\n",
+      "    PARAMETER[\"latitude_of_origin\",0],\n",
+      "    PARAMETER[\"central_meridian\",-62],\n",
+      "    PARAMETER[\"scale_factor\",0.9995],\n",
+      "    PARAMETER[\"false_easting\",400000],\n",
+      "    PARAMETER[\"false_northing\",0],\n",
+      "    UNIT[\"metre\",1,\n",
+      "        AUTHORITY[\"EPSG\",\"9001\"]],\n",
+      "    AXIS[\"Easting\",EAST],\n",
+      "    AXIS[\"Northing\",NORTH],\n",
+      "    AUTHORITY[\"EPSG\",\"2004\"]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(gk.srs.loadSRS(2004))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Otherwise, SRS can be loaded from their Proj4 string:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PROJCS[\"PUWG_42_Strefa_4\",\n",
+      "    GEOGCS[\"Pulkovo 1942\",\n",
+      "        DATUM[\"Pulkovo_1942\",\n",
+      "            SPHEROID[\"Krassowsky 1940\",6378245,298.3,\n",
+      "                AUTHORITY[\"EPSG\",\"7024\"]],\n",
+      "            AUTHORITY[\"EPSG\",\"6284\"]],\n",
+      "        PRIMEM[\"Greenwich\",0],\n",
+      "        UNIT[\"Degree\",0.0174532925199433]],\n",
+      "    PROJECTION[\"Transverse_Mercator\"],\n",
+      "    PARAMETER[\"latitude_of_origin\",0],\n",
+      "    PARAMETER[\"central_meridian\",21],\n",
+      "    PARAMETER[\"scale_factor\",1],\n",
+      "    PARAMETER[\"false_easting\",4500000],\n",
+      "    PARAMETER[\"false_northing\",0],\n",
+      "    UNIT[\"metre\",1,\n",
+      "        AUTHORITY[\"EPSG\",\"9001\"]],\n",
+      "    AXIS[\"Easting\",EAST],\n",
+      "    AXIS[\"Northing\",NORTH]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "poland_special_srs = gk.srs.loadSRS('PROJCS[\"PUWG_42_Strefa_4\",GEOGCS[\"GCS_Pulkovo_1942\",DATUM[\"Pulkovo_1942\",SPHEROID[\"Krasovsky_1940\",6378245,298.3]],PRIMEM[\"Greenwich\",0],UNIT[\"Degree\",0.017453292519943295]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"False_Easting\",4500000],PARAMETER[\"False_Northing\",0],PARAMETER[\"Central_Meridian\",21],PARAMETER[\"Scale_Factor\",1],PARAMETER[\"Latitude_Of_Origin\",0],UNIT[\"Meter\",1]]')\n",
+    "\n",
+    "print(poland_special_srs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Center the SRS to coordinate \n",
+    "SRS can be centerd to a coordinate, for example centered to Aachen:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PROJCS[\"unknown\",\n",
+      "    GEOGCS[\"unknown\",\n",
+      "        DATUM[\"Unknown based on GRS80 ellipsoid\",\n",
+      "            SPHEROID[\"GRS 1980\",6378137,298.257222101,\n",
+      "                AUTHORITY[\"EPSG\",\"7019\"]],\n",
+      "            TOWGS84[0,0,0,0,0,0,0]],\n",
+      "        PRIMEM[\"Greenwich\",0,\n",
+      "            AUTHORITY[\"EPSG\",\"8901\"]],\n",
+      "        UNIT[\"degree\",0.0174532925199433,\n",
+      "            AUTHORITY[\"EPSG\",\"9122\"]]],\n",
+      "    PROJECTION[\"Lambert_Azimuthal_Equal_Area\"],\n",
+      "    PARAMETER[\"latitude_of_center\",50.775],\n",
+      "    PARAMETER[\"longitude_of_center\",6.083],\n",
+      "    PARAMETER[\"false_easting\",0],\n",
+      "    PARAMETER[\"false_northing\",0],\n",
+      "    UNIT[\"metre\",1,\n",
+      "        AUTHORITY[\"EPSG\",\"9001\"]],\n",
+      "    AXIS[\"Easting\",EAST],\n",
+      "    AXIS[\"Northing\",NORTH]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "aachen_centered_srs = gk.srs.centeredLAEA(6.083, 50.775)\n",
+    "print(aachen_centered_srs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Transformation between coordinate systems"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# transforming points between SRS's\n",
+    "new_points = gk.srs.xyTransform(\n",
+    "    [(6.083, 50.775),\n",
+    "     (6.083, 50.875),\n",
+    "     (6.083, 50.975),\n",
+    "     (7.083, 50.175),\n",
+    "     (7.583, 50.775),], \n",
+    "    fromSRS=gk.srs.EPSG4326,\n",
+    "    toSRS=aachen_centered_srs,\n",
+    "    outputFormat='xy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0 0.0\n",
+      "0.0 11124.480318935728\n",
+      "0.0 22249.12252108337\n",
+      "71432.80781956235 -66262.41755972906\n",
+      "105796.52244980003 1069.9939140465938\n"
+     ]
+    }
+   ],
+   "source": [
+    "for x,y in zip( new_points.x, new_points.y ):\n",
+    "    print(x, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Export the SRS\n",
+    "The coordinate systems can be exported. File formats for that are: WKT or Proj4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'PROJCS[\"unknown\",GEOGCS[\"unknown\",DATUM[\"Unknown based on GRS80 ellipsoid\",SPHEROID[\"GRS 1980\",6378137,298.257222101,AUTHORITY[\"EPSG\",\"7019\"]],TOWGS84[0,0,0,0,0,0,0]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]]],PROJECTION[\"Lambert_Azimuthal_Equal_Area\"],PARAMETER[\"latitude_of_center\",50.775],PARAMETER[\"longitude_of_center\",6.083],PARAMETER[\"false_easting\",0],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH]]'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Export as WKT\n",
+    "aachen_centered_srs.ExportToWkt()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'+proj=laea +lat_0=50.775 +lon_0=6.083 +x_0=0 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Export to Proj4\n",
+    "aachen_centered_srs.ExportToProj4()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load SRS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<osgeo.osr.SpatialReference; proxy of <Swig Object of type 'OSRSpatialReferenceShadow *' at 0x00000270AFFE6330> >"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gk.srs.loadSRS(aachen_centered_srs.ExportToWkt())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "geokit",
+   "language": "python",
+   "name": "geokit"
+  },
+  "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": 4
+}
diff --git a/Examples/example_02_geometry.ipynb b/Examples/example_02_geometry.ipynb
new file mode 100644
index 0000000..a056bb9
--- /dev/null
+++ b/Examples/example_02_geometry.ipynb
@@ -0,0 +1,665 @@
+{
+ "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": [
+    "# GEOMETRIES\n",
+    "Within the geometries module, different kind of geometry objects can be defined. Those are: \n",
+    "\n",
+    "1. Point\n",
+    "1. MultiPoint\n",
+    "1. LineString\n",
+    "1. MultiLineString\n",
+    "1. Polygon\n",
+    "1. MultiPolygon\n",
+    "\n",
+    "Each object consists of its data (most likely the location) and its spatial reference system SRS from the lection before"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Point\n",
+    "Ponts can be defined with the location information (lat, lon) and the SRS information. The SRS is an SRS-object as created in the section before"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'osgeo.ogr.Geometry'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "aachen_pt = gk.geom.point( (6.083, 52.775), srs=gk.srs.EPSG4326 )\n",
+    "print( type(aachen_pt) )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Object attributes can be obtaine via *object.Get...*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X value is:  6.083\n",
+      "Y value is:  52.775\n",
+      "srs is:  GEOGCS[\"WGS 84\",\n",
+      "    DATUM[\"WGS_1984\",\n",
+      "        SPHEROID[\"WGS 84\",6378137,298.257223563,\n",
+      "            AUTHORITY[\"EPSG\",\"7030\"]],\n",
+      "        AUTHORITY[\"EPSG\",\"6326\"]],\n",
+      "    PRIMEM[\"Greenwich\",0,\n",
+      "        AUTHORITY[\"EPSG\",\"8901\"]],\n",
+      "    UNIT[\"degree\",0.0174532925199433,\n",
+      "        AUTHORITY[\"EPSG\",\"9122\"]],\n",
+      "    AXIS[\"Latitude\",NORTH],\n",
+      "    AXIS[\"Longitude\",EAST],\n",
+      "    AUTHORITY[\"EPSG\",\"4326\"]]\n",
+      "type is:  POINT\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"X value is: \", aachen_pt.GetX() )\n",
+    "print(\"Y value is: \", aachen_pt.GetY() )\n",
+    "print(\"srs is: \",     aachen_pt.GetSpatialReference() )\n",
+    "print(\"type is: \",     aachen_pt.GetGeometryName() )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'osgeo.ogr.Geometry'>\n",
+      "type is:  POLYGON\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get the buffer around this point\n",
+    "aachen_buffered_area = aachen_pt.Buffer(1)\n",
+    "\n",
+    "print( type(aachen_buffered_area) )\n",
+    "print(\"type is: \",     aachen_buffered_area.GetGeometryName() )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Line\n",
+    "\n",
+    "A line is defined as a polyline and a SRS. A polyline is defined by a number of points with linear connection."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Make a line\n",
+    "aachen_centered_srs = gk.srs.centeredLAEA(6.083, 50.775)\n",
+    "\n",
+    "aachen_line = gk.geom.line(\n",
+    "    [(5.6086412456393614e-11, 1.0605099246605318e-09),\n",
+    "     (5.5966859847770105e-11, 11124.48031893997),\n",
+    "     (5.584717712977507e-11, 22249.12252108337),\n",
+    "     (71432.80781956247, -66262.41755972906),\n",
+    "     (105796.52244980012, 1069.9939140476542),],\n",
+    "    srs=aachen_centered_srs\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Draw functions\n",
+    "Geom objects can be drawn within geokit with gk.drawGeoms()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gk.drawGeoms?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot geoms\n",
+    "Plot the Bufferd area, the defined point and the defined line within the latlon coordinate system. The point is in Aachen, which is at around (6,52)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+    "# Draw some geometries\n",
+    "axh = gk.drawGeoms( aachen_buffered_area, figsize=(6,6))#, srs=aachen_centered_srs )\n",
+    "axh = gk.drawGeoms( aachen_pt, ax=axh)#, srs=aachen_centered_srs )\n",
+    "axh = gk.drawGeoms( aachen_line, ax=axh)# , srs=aachen_centered_srs)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot in different SRS\n",
+    "Same plot as above. This time, the SRS is the Aachen centered with equal area projection. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+    "axh = gk.drawGeoms( aachen_buffered_area, figsize=(6,6), srs=aachen_centered_srs )\n",
+    "axh = gk.drawGeoms( aachen_pt, ax=axh, srs=aachen_centered_srs )\n",
+    "axh = gk.drawGeoms( aachen_line, ax=axh, srs=aachen_centered_srs)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Get Centroid\n",
+    "\n",
+    "Calculates the centroid point of each geom object by obj.Centroid().\n",
+    "The result does NOT contain a srs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "polygon center - X 6.082999999999999\n",
+      "polygon center - Y 52.775000000000034\n",
+      "polygon srs None\n",
+      "line center - X 50860.04492137159\n",
+      "line center - Y -22306.158516041916\n",
+      "line srs None\n"
+     ]
+    }
+   ],
+   "source": [
+    "ctr_polygon = aachen_buffered_area.Centroid()\n",
+    "print(\"polygon center - X\", ctr_polygon.GetX())\n",
+    "print(\"polygon center - Y\", ctr_polygon.GetY())\n",
+    "print(\"polygon srs\", ctr_polygon.GetSpatialReference())\n",
+    "\n",
+    "ctr_line = aachen_line.Centroid()\n",
+    "print(\"line center - X\", ctr_line.GetX())\n",
+    "print(\"line center - Y\", ctr_line.GetY())\n",
+    "print(\"line srs\", ctr_line.GetSpatialReference())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The srs can be assigend by obj.AssignSpatialReference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "line srs PROJCS[\"unknown\",\n",
+      "    GEOGCS[\"unknown\",\n",
+      "        DATUM[\"Unknown based on GRS80 ellipsoid\",\n",
+      "            SPHEROID[\"GRS 1980\",6378137,298.257222101,\n",
+      "                AUTHORITY[\"EPSG\",\"7019\"]],\n",
+      "            TOWGS84[0,0,0,0,0,0,0]],\n",
+      "        PRIMEM[\"Greenwich\",0,\n",
+      "            AUTHORITY[\"EPSG\",\"8901\"]],\n",
+      "        UNIT[\"degree\",0.0174532925199433,\n",
+      "            AUTHORITY[\"EPSG\",\"9122\"]]],\n",
+      "    PROJECTION[\"Lambert_Azimuthal_Equal_Area\"],\n",
+      "    PARAMETER[\"latitude_of_center\",50.775],\n",
+      "    PARAMETER[\"longitude_of_center\",6.083],\n",
+      "    PARAMETER[\"false_easting\",0],\n",
+      "    PARAMETER[\"false_northing\",0],\n",
+      "    UNIT[\"metre\",1,\n",
+      "        AUTHORITY[\"EPSG\",\"9001\"]],\n",
+      "    AXIS[\"Easting\",EAST],\n",
+      "    AXIS[\"Northing\",NORTH]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "## GOTCHYA!\n",
+    "# Apply the old srs\n",
+    "ctr_line.AssignSpatialReference( aachen_line.GetSpatialReference() )\n",
+    "print(\"line srs\", ctr_line.GetSpatialReference())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Boundary\n",
+    "Get the boundary of an object by: obj.Boundary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'osgeo.ogr.Geometry'>\n",
+      "LINESTRING\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get a boundary\n",
+    "boundary_polygon = aachen_buffered_area.Boundary()\n",
+    "print(type(boundary_polygon))\n",
+    "print(boundary_polygon.GetGeometryName())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+    "#draw the boundary with a buffer\n",
+    "axh = gk.drawGeoms( boundary_polygon.Buffer(0.2), figsize=(6,6), srs=aachen_centered_srs )\n",
+    "gk.drawGeoms( boundary_polygon, figsize=(6,6), srs=aachen_centered_srs, ax=axh )\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Spatial Relation\n",
+    "\n",
+    "1. Contains\n",
+    "1. Crosses\n",
+    "1. Touches\n",
+    "1. Overlaps\n",
+    "\n",
+    "[More information here](https://en.wikipedia.org/wiki/Spatial_relation#/media/File:TopologicSpatialRelarions2.png)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n",
+      "False\n",
+      "False\n",
+      "False\n",
+      "False\n",
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "# test for containment\n",
+    "print(aachen_buffered_area.Contains( aachen_pt ))\n",
+    "print(aachen_buffered_area.Contains( aachen_line ))\n",
+    "print(boundary_polygon.Buffer(0.2).Contains( aachen_buffered_area ))\n",
+    "print(boundary_polygon.Buffer(0.2).Crosses( aachen_buffered_area ))\n",
+    "print(boundary_polygon.Buffer(0.2).Touches( aachen_buffered_area ))\n",
+    "print(boundary_polygon.Buffer(0.2).Overlaps( aachen_buffered_area ))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Transform SRS and get points\n",
+    "\n",
+    "Get points within a geometry within a choosen SRS:\n",
+    "\n",
+    "1. Transform object to another SRS by: gk.geom.transform( [obj1, obj2], toSRS)\n",
+    "1. Extract gk.geom.extractVerticies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 788475.95328876, 6941486.97274921],\n",
+       "       [ 788323.39379549, 6931862.13450442],\n",
+       "       [ 787866.13347064, 6922275.17314893],\n",
+       "       [ 787105.42563299, 6912752.18910776],\n",
+       "       [ 786043.35532983, 6903319.01626946]])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get Points\n",
+    "boundary_polygon_3857 = gk.geom.transform(boundary_polygon, toSRS=gk.srs.EPSG3857)\n",
+    "gk.geom.extractVerticies(boundary_polygon_3857)[:5,:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<osgeo.ogr.Geometry; proxy of <Swig Object of type 'OGRGeometryShadow *' at 0x0000018CBD9E5B70> >"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "boundary_polygon_3857.SimplifyPreserveTopology(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Intersect and Union\n",
+    "\n",
+    "Example for Intersection and Union of two geometries area1 and area2. \n",
+    "* Intersection of geoms: 'area1.Intersection(area2)'\n",
+    "* Union of geoms: 'area1.Union(area2)'\n",
+    "\n",
+    "Important: All geometry operations need to happen in the same SRS!\n",
+    "If they are not in the same srs, change to one srs: area2 = gk.geom.transform(area2, toSRS=area1.GetSpatialReference())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AxHands(ax=<matplotlib.axes._axes.Axes object at 0x0000018CBDB3D3A0>, handles=[<matplotlib.patches.PathPatch object at 0x0000018CBDB685B0>], cbar=None)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Geom 1:\n",
+    "axh = gk.drawGeoms(aachen_buffered_area, figsize=(5,5), srs=4326)\n",
+    "# Geom 2:\n",
+    "box = gk.geom.box(6,52,7,53, srs=gk.srs.EPSG4326)\n",
+    "gk.drawGeoms(box, figsize=(5,5), srs=4326, ax=axh)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Intersection of geoms\n",
+    "intersect_geom = aachen_buffered_area.Intersection(box)\n",
+    "axh = gk.drawGeoms(intersect_geom , figsize=(5,5), srs=4326)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Union of geoms\n",
+    "unioned_geom = aachen_buffered_area.Union(box)\n",
+    "axh = gk.drawGeoms(unioned_geom, figsize=(5,5), srs=4326)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Getting areas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Box area in degrees^2: 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "box = gk.geom.box(6,52,7,53, srs=gk.srs.EPSG4326)\n",
+    "print(\"Box area in degrees^2:\", box.Area())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Box area in meters^2: 7556368901.8809\n"
+     ]
+    }
+   ],
+   "source": [
+    "ctr = box.Centroid()\n",
+    "box_centered_laea_srs = gk.srs.centeredLAEA( ctr.GetX(), ctr.GetY() )\n",
+    "box.TransformTo(box_centered_laea_srs)\n",
+    "print(\"Box area in meters^2:\", box.Area())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "geokit",
+   "language": "python",
+   "name": "geokit"
+  },
+  "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": 4
+}
diff --git a/Examples/example_03_vector.ipynb b/Examples/example_03_vector.ipynb
new file mode 100644
index 0000000..e1330b3
--- /dev/null
+++ b/Examples/example_03_vector.ipynb
@@ -0,0 +1,2118 @@
+{
+ "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": [
+    "# VECTOR\n",
+    "\n",
+    "Vector files contains geometries of one type (points, lines, polygons) and additional data. They are used, to store data with geospacial information.\n",
+    "\n",
+    "These files are stored in shape-files (*.shp*). \n",
+    "A shape file comes with more additional files, which have the same name and different file type which are needed (*.cpg* / *.dbf* / *.prj* / *.shx*). Always store them together (!).\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Example Data**\n",
+    "See geokit/geokit/test/data for the 'gadm36_DEU_1'. The shape file is 'gadm36_DEU_1.shp'(Downloaded from gadm.org) and it covers the federal state of Germany. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Read a vector file.\n",
+    "Vector files can be loaded with 'gk.vector.extractFeatures(PATH)'. It is stored similar to a pandas dataframe. It has one special column 'geom', where the geometric information are stored."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>geom</th>\n",
+       "      <th>GID_0</th>\n",
+       "      <th>NAME_0</th>\n",
+       "      <th>GID_1</th>\n",
+       "      <th>NAME_1</th>\n",
+       "      <th>VARNAME_1</th>\n",
+       "      <th>NL_NAME_1</th>\n",
+       "      <th>TYPE_1</th>\n",
+       "      <th>ENGTYPE_1</th>\n",
+       "      <th>CC_1</th>\n",
+       "      <th>HASC_1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>MULTIPOLYGON (((8.70802116 47.6895752,8.707655...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.1_1</td>\n",
+       "      <td>Baden-Württemberg</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Land</td>\n",
+       "      <td>State</td>\n",
+       "      <td>08</td>\n",
+       "      <td>DE.BW</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>POLYGON ((9.74066353 47.55353546,9.74057579 47...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.2_1</td>\n",
+       "      <td>Bayern</td>\n",
+       "      <td>Bavaria</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Freistaat</td>\n",
+       "      <td>None</td>\n",
+       "      <td>09</td>\n",
+       "      <td>DE.BY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>POLYGON ((13.17135715 52.3977623,13.17135334 5...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.3_1</td>\n",
+       "      <td>Berlin</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Land</td>\n",
+       "      <td>State</td>\n",
+       "      <td>11</td>\n",
+       "      <td>DE.BE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>MULTIPOLYGON (((12.26715565 52.2313118,12.2704...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.4_1</td>\n",
+       "      <td>Brandenburg</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Land</td>\n",
+       "      <td>State</td>\n",
+       "      <td>12</td>\n",
+       "      <td>DE.BR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>MULTIPOLYGON (((8.71142387 53.04463196,8.71033...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.5_1</td>\n",
+       "      <td>Bremen</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Freie Hansestadt</td>\n",
+       "      <td>State</td>\n",
+       "      <td>04</td>\n",
+       "      <td>DE.HB</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>MULTIPOLYGON (((10.21060085 53.52004623,10.211...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.6_1</td>\n",
+       "      <td>Hamburg</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Freie und Hansestadt</td>\n",
+       "      <td>State</td>\n",
+       "      <td>02</td>\n",
+       "      <td>DE.HH</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>MULTIPOLYGON (((8.68058777 49.62247086,8.68134...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.7_1</td>\n",
+       "      <td>Hessen</td>\n",
+       "      <td>Hesse</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Land</td>\n",
+       "      <td>State</td>\n",
+       "      <td>06</td>\n",
+       "      <td>DE.HE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>MULTIPOLYGON (((11.4958334 54.03180695,11.4958...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.8_1</td>\n",
+       "      <td>Mecklenburg-Vorpommern</td>\n",
+       "      <td>Mecklenburg-West Pomerania</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Land</td>\n",
+       "      <td>State</td>\n",
+       "      <td>13</td>\n",
+       "      <td>DE.MV</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>MULTIPOLYGON (((6.76194477 53.61875153,6.76194...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.9_1</td>\n",
+       "      <td>Niedersachsen</td>\n",
+       "      <td>Lower Saxony</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Land</td>\n",
+       "      <td>State</td>\n",
+       "      <td>03</td>\n",
+       "      <td>DE.NI</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>POLYGON ((8.05965614 50.69504166,8.0413866 50....</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.10_1</td>\n",
+       "      <td>Nordrhein-Westfalen</td>\n",
+       "      <td>North Rhine-Westphalia</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Land</td>\n",
+       "      <td>State</td>\n",
+       "      <td>05</td>\n",
+       "      <td>DE.NW</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>POLYGON ((6.6504283 49.54951096,6.65105438 49....</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.11_1</td>\n",
+       "      <td>Rheinland-Pfalz</td>\n",
+       "      <td>Rhineland-Palatinate</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Land</td>\n",
+       "      <td>State</td>\n",
+       "      <td>07</td>\n",
+       "      <td>DE.RP</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>POLYGON ((6.84392643 49.15551376,6.84085464 49...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.12_1</td>\n",
+       "      <td>Saarland</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Land</td>\n",
+       "      <td>State</td>\n",
+       "      <td>10</td>\n",
+       "      <td>DE.SL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>POLYGON ((14.79049587 50.82408524,14.79012299 ...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.14_1</td>\n",
+       "      <td>Sachsen</td>\n",
+       "      <td>Saxony</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Freistaat</td>\n",
+       "      <td>State</td>\n",
+       "      <td>14</td>\n",
+       "      <td>DE.SN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>MULTIPOLYGON (((11.44027328 51.19976044,11.441...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.13_1</td>\n",
+       "      <td>Sachsen-Anhalt</td>\n",
+       "      <td>Saxony-Anhalt</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Land</td>\n",
+       "      <td>State</td>\n",
+       "      <td>15</td>\n",
+       "      <td>DE.ST</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>MULTIPOLYGON (((9.53010368 53.67519379,9.53016...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.15_1</td>\n",
+       "      <td>Schleswig-Holstein</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Land</td>\n",
+       "      <td>State</td>\n",
+       "      <td>01</td>\n",
+       "      <td>DE.SH</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>POLYGON ((11.70222759 50.40244293,11.70159817 ...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.16_1</td>\n",
+       "      <td>Thüringen</td>\n",
+       "      <td>Thuringia</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Freistaat</td>\n",
+       "      <td>State</td>\n",
+       "      <td>16</td>\n",
+       "      <td>DE.TH</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 geom GID_0   NAME_0  \\\n",
+       "0   MULTIPOLYGON (((8.70802116 47.6895752,8.707655...   DEU  Germany   \n",
+       "1   POLYGON ((9.74066353 47.55353546,9.74057579 47...   DEU  Germany   \n",
+       "2   POLYGON ((13.17135715 52.3977623,13.17135334 5...   DEU  Germany   \n",
+       "3   MULTIPOLYGON (((12.26715565 52.2313118,12.2704...   DEU  Germany   \n",
+       "4   MULTIPOLYGON (((8.71142387 53.04463196,8.71033...   DEU  Germany   \n",
+       "5   MULTIPOLYGON (((10.21060085 53.52004623,10.211...   DEU  Germany   \n",
+       "6   MULTIPOLYGON (((8.68058777 49.62247086,8.68134...   DEU  Germany   \n",
+       "7   MULTIPOLYGON (((11.4958334 54.03180695,11.4958...   DEU  Germany   \n",
+       "8   MULTIPOLYGON (((6.76194477 53.61875153,6.76194...   DEU  Germany   \n",
+       "9   POLYGON ((8.05965614 50.69504166,8.0413866 50....   DEU  Germany   \n",
+       "10  POLYGON ((6.6504283 49.54951096,6.65105438 49....   DEU  Germany   \n",
+       "11  POLYGON ((6.84392643 49.15551376,6.84085464 49...   DEU  Germany   \n",
+       "12  POLYGON ((14.79049587 50.82408524,14.79012299 ...   DEU  Germany   \n",
+       "13  MULTIPOLYGON (((11.44027328 51.19976044,11.441...   DEU  Germany   \n",
+       "14  MULTIPOLYGON (((9.53010368 53.67519379,9.53016...   DEU  Germany   \n",
+       "15  POLYGON ((11.70222759 50.40244293,11.70159817 ...   DEU  Germany   \n",
+       "\n",
+       "       GID_1                  NAME_1                   VARNAME_1 NL_NAME_1  \\\n",
+       "0    DEU.1_1       Baden-Württemberg                        None      None   \n",
+       "1    DEU.2_1                  Bayern                     Bavaria      None   \n",
+       "2    DEU.3_1                  Berlin                        None      None   \n",
+       "3    DEU.4_1             Brandenburg                        None      None   \n",
+       "4    DEU.5_1                  Bremen                        None      None   \n",
+       "5    DEU.6_1                 Hamburg                        None      None   \n",
+       "6    DEU.7_1                  Hessen                       Hesse      None   \n",
+       "7    DEU.8_1  Mecklenburg-Vorpommern  Mecklenburg-West Pomerania      None   \n",
+       "8    DEU.9_1           Niedersachsen                Lower Saxony      None   \n",
+       "9   DEU.10_1     Nordrhein-Westfalen      North Rhine-Westphalia      None   \n",
+       "10  DEU.11_1         Rheinland-Pfalz        Rhineland-Palatinate      None   \n",
+       "11  DEU.12_1                Saarland                        None      None   \n",
+       "12  DEU.14_1                 Sachsen                      Saxony      None   \n",
+       "13  DEU.13_1          Sachsen-Anhalt               Saxony-Anhalt      None   \n",
+       "14  DEU.15_1      Schleswig-Holstein                        None      None   \n",
+       "15  DEU.16_1               Thüringen                   Thuringia      None   \n",
+       "\n",
+       "                  TYPE_1 ENGTYPE_1 CC_1 HASC_1  \n",
+       "0                   Land     State   08  DE.BW  \n",
+       "1              Freistaat      None   09  DE.BY  \n",
+       "2                   Land     State   11  DE.BE  \n",
+       "3                   Land     State   12  DE.BR  \n",
+       "4       Freie Hansestadt     State   04  DE.HB  \n",
+       "5   Freie und Hansestadt     State   02  DE.HH  \n",
+       "6                   Land     State   06  DE.HE  \n",
+       "7                   Land     State   13  DE.MV  \n",
+       "8                   Land     State   03  DE.NI  \n",
+       "9                   Land     State   05  DE.NW  \n",
+       "10                  Land     State   07  DE.RP  \n",
+       "11                  Land     State   10  DE.SL  \n",
+       "12             Freistaat     State   14  DE.SN  \n",
+       "13                  Land     State   15  DE.ST  \n",
+       "14                  Land     State   01  DE.SH  \n",
+       "15             Freistaat     State   16  DE.TH  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "federal_states_DE = gk.vector.extractFeatures(gk._test_data_[\"gadm36_DEU_1.shp\"])\n",
+    "federal_states_DE "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Draw the geoms of the vector-file\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AxHands(ax=<Axes:>, handles=0     [PathPatch19((8.70802, 47.6896) ...), PathPatc...\n",
+       "1                 PathPatch2405((9.74066, 47.5535) ...)\n",
+       "2                  PathPatch201((13.1714, 52.3978) ...)\n",
+       "3     [PathPatch5((12.2672, 52.2313) ...), PathPatch...\n",
+       "4     [PathPatch128((8.71142, 53.0446) ...), PathPat...\n",
+       "5     [PathPatch219((10.2106, 53.52) ...), PathPatch...\n",
+       "6     [PathPatch10((8.68059, 49.6225) ...), PathPatc...\n",
+       "7     [PathPatch6((11.4958, 54.0318) ...), PathPatch...\n",
+       "8     [PathPatch41((6.76194, 53.6188) ...), PathPatc...\n",
+       "9                  PathPatch1426((8.05966, 50.695) ...)\n",
+       "10                PathPatch1093((6.65043, 49.5495) ...)\n",
+       "11                 PathPatch363((6.84393, 49.1555) ...)\n",
+       "12                PathPatch1294((14.7905, 50.8241) ...)\n",
+       "13    [PathPatch5((11.4403, 51.1998) ...), PathPatch...\n",
+       "14    [PathPatch66((11.2781, 54.4493) ...), PathPatc...\n",
+       "15                PathPatch1356((11.7022, 50.4024) ...)\n",
+       "dtype: object, cbar=None)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# draw all geoms \n",
+    "gk.geom.drawGeoms(federal_states_DE, figsize=(6,6))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# highlight one geom\n",
+    "g2 = federal_states_DE.geom.iloc[3]\n",
+    "axh = gk.geom.drawGeoms(federal_states_DE, figsize=(6,6))\n",
+    "gk.geom.drawGeoms(g2, ax=axh, figsize=(6,6), fc='red')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# use a buffer around the highlighted area with '.Buffer()'\n",
+    "g2 = federal_states_DE.geom.iloc[3].Buffer(1)\n",
+    "axh = gk.geom.drawGeoms(federal_states_DE, figsize=(6,6))\n",
+    "gk.geom.drawGeoms(g2, ax=axh, figsize=(6,6), fc='red')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Export vector file "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/storage/internal/home/c-winkler/Python/packages/geokit/geokit_dev/Examples/example_file_df.shp'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Simple vector creation\n",
+    "gk.vector.createVector(g2,output='example_file_df.shp' )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Filtering spatially\n",
+    "Example: For a given point filter for the geom in which the point is located. Filter for the federal state which contains the city of Aachen."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>geom</th>\n",
+       "      <th>GID_0</th>\n",
+       "      <th>NAME_0</th>\n",
+       "      <th>GID_1</th>\n",
+       "      <th>NAME_1</th>\n",
+       "      <th>VARNAME_1</th>\n",
+       "      <th>NL_NAME_1</th>\n",
+       "      <th>TYPE_1</th>\n",
+       "      <th>ENGTYPE_1</th>\n",
+       "      <th>CC_1</th>\n",
+       "      <th>HASC_1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>POLYGON ((8.05965614 50.69504166,8.0413866 50....</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.10_1</td>\n",
+       "      <td>Nordrhein-Westfalen</td>\n",
+       "      <td>North Rhine-Westphalia</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Land</td>\n",
+       "      <td>State</td>\n",
+       "      <td>05</td>\n",
+       "      <td>DE.NW</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                geom GID_0   NAME_0     GID_1  \\\n",
+       "0  POLYGON ((8.05965614 50.69504166,8.0413866 50....   DEU  Germany  DEU.10_1   \n",
+       "\n",
+       "                NAME_1               VARNAME_1 NL_NAME_1 TYPE_1 ENGTYPE_1  \\\n",
+       "0  Nordrhein-Westfalen  North Rhine-Westphalia      None   Land     State   \n",
+       "\n",
+       "  CC_1 HASC_1  \n",
+       "0   05  DE.NW  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Point of Aachen\n",
+    "aachen_pt_real = gk.geom.point( (6.083, 50.775), srs=gk.srs.EPSG4326 )\n",
+    "\n",
+    "# Filter for the federal state containing Aachen\n",
+    "federal_states_DE_df_geom_filtered = gk.vector.extractFeatures(gk._test_data_[\"gadm36_DEU_1.shp\"], geom=aachen_pt_real)\n",
+    "federal_states_DE_df_geom_filtered"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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kChUq8OGHH7J7926Cg4M5ceIEZrMZb29vatWqRf/+/alSpcpD+8TFxSV/HhoaSmho6DMHd3R05IsvvmDXrl2cOHGC8+fPExMTg6enJy1atOCVV16hU6dOWsVeREREnpuQkBCaNG3KnTt3mbtkM2Uq/Pkz0OAPxnHv7h02b1jFrq0bmf7zL0yYMAEHBwdq1qpF47/KpZdeekk/4IukM7PZzOqAAHq8NiD536yAnb09xUq+xP79B4yOkmUkJibSs2dPHJ1c6N1/+HM/n8lkYt2qxfzw9TjgzwW+ixUrpv/PyHNhYTabzUaHyEhScys8EREReXFdu3aNqlWrYmFlw88L1lHAt8i/bmsymTh76ji7tgWya+tG9u/ZRmxMDK6urmTPkYN7d+9y9uxZPD090/EKRF48ZrOZX375hX79+jF9XgB1GjY3OlKGMuLt1wg5d4yDB1QopYXExETKli1L0VIV+fy72c/1XPt2beXjEQM4d/okjZs0YdzYsVStWvW5nlOyntT0IWl+NzcRERGRrC4iIoJmzZuTaIKFq7aRM/eT12e0tLSkuF8ZivuV4bW3hhAfF8fhA7s5enAPd+/cYvZP3+Dl5UW+/PnxLViQ999/n/r16xMbG0tsbCwAuXPnTo9LE3kq0dHRLFiwgNmzZ/Paa68REBDASy+9xIgRIzLMWi3Xr1+ndu3aBAcHY2dvT+XqdY2OlKGcOnGE3dsD8Sv5Yt/h7lmsWLGC27dvY2Njg42NDf379yc8PJx6zTo+l/OZTCa2b1rH0oWzWbdyEW5ubuzatYtq1ao9l/OJ/C+NTPoHjUwSERGRJ0lKSqJ5ixbs2b2HeSt3ULREqWc+ZvCZIA7t20nopQtMn/L5Y7d55ZVX+Prrr/Hy8nrm84mktfr167N582Z8ixTnwrnTeHh6EREeRrFixRgyZAidOnXCxsaGLVu2EBwcTN68efHx8cHX1xc3N7fnmi02NpZvvvmG8ePHExUVRddX3+TDTyZjq/XLkt28fpVGVQvjV7IkS5YsoUCBAkZHylRCQ0P5+utJfPvt5Ieet7a2pnXHlxn4/kd4++RP8/Mu9Z/NiLd7U6p0aV7r3Zvu3buTM2fOND+PvDhS04eoTPoHlUkiIiLyJBMnTmTEiBHMXLiB6nUapvnxI8LD2BCwFDs7e+zs7bG1tePK5YtM+fIjChcuxLatW3FwcEjz84o8i6FDP+Crr75kzpJNXA0NoWGztly/epkvP/6AnVs34ujoiNlsJjo6GktLS0wmE/DnqL169evz5htv0LHjf4/eMJvNnD17ll27dnH48GHKly9P27ZtcXNzw2QysWvXLhYvXkxQUBCzZ89m7969DBkyhNDQUHq8NpDuvfvj4uKGh1f25/1Hkqns2bGZVzvU5/Tp0xQrVszoOBleVFQUH3zwAWfPniX0yhXOnjkDwKChY+n/3mgSExNJTEjA0tIS++f0/ToiPIym1YvSuFFD5s2bp7WCJU2oTHoGKpNERETk3xw+fJgqVarQ+80hDBk1IV3PffLYIXq0qUXLFi3w9/fXgqqSoSQkJNCsWTMOHDzEyE+/pXK1OuTJmw+Aq6GXWLZwNjY2ttRt1JIixf24e+cW16+GEnT8EP5zpnHqxBE2btxIw4b/X9D+7x2owsPD6d6jB3t27+bevXtYWFjg7ZOfK5dDKFiwIB07duTs2bOsWLGCHDlzYzKbiI6MICYmhtoNmjF83CQKFdH0rX+zeP5MPny3D7GxsbrjZAocPHiQihUrAlC1Zn0O7d/Jmm1B+BTwTbcMn374Nsv8Z3LmzBny5MmTbueVrE1l0jNQmSQiIiL/plnz5py/eJmlGw8Zsg7MxjXLGNi7PWPGfMS4cWPT/fwiTxIWFoa7uzsAr/V/n2EffZmi/WIePKBSMXcSExJo3bo1Xl5e7Nm7l4sXLlCiZEmio6M5e+YMJpOJ9l170aJdN8qUr4KziytrVy7Cf/ZUgs+e5M7tWwDsOXWHmAfRfDx8AN16vaVFtlPg24ljWOY/g2tXrxodJdNYv349r7/+Ordu3yZw7/n/XDsvLZ0+eYx2Dcvx+eefM3To0HQ7r2R9WoBbREREJI0FBQWxbu1aJn4/17AFhRs1b8egoWP57LNP6NixA6VLlzYkh8jjuLm58c033/Duu+/S/dW3UryfQ7ZsHLkYzYLZU1m/ejHnLlym5EtVaNCiM1cvh+Dk7EKD5p1wcnahR+8B2NnbJ+/brHUnmrXuRHx8PAHLFhAZEY6rmzvuHp5M+23V87jMLMlsNhMRHs6NGzfIlSuX0XEyBT8/P8xmM/WbtE7XIgngyqULmEwmlixdStGiRWnVqpVGq0q608ikf9DIJBEREXmcvn37sXLVav44EGLo3ani4+NpU+8l8ubJyZYtW7ROhmQoPXr0YP78+XR5uR8Nm7ejdv2mRkeSFAgPu0+TakXIkzsXX375JU2bNtX3lic4efIkpUr9efOFwL3n03V6G/w5BXRr4Bp++X4iB/buoFjx4nw4ciQvv/xyuuaQrCc1fYjqSxEREZEnWLp0KX369OHXX+fSs88gw29zbmtry6jxU9i2bRsBAQGGZhH5p7NnzwKw8NfpfP3JMBITEw1OJCnh6ubO9PlrcXD2oHnz5jRs2JCoqCijY2VY+fPnx8PDA4Dd2/9I9/NbWlpSr3FL5q3czoLVO7l79z6vvPIK06ZNS/cs8uLSyKR/0MgkERER+V8ODg7ExsYC0Kx1Z9p0epmqNevjkC2bobmaVCtKsyYN+fHHHw3NIfK/Ll++zN69e4mOjqZ3794A/LH/InnzFTA2mKSI2Wxm84bVvPdmV7p17caMGb8YHSlDCQsLw8LCgn379hEYGMgXX3yBu4cne07dMTTX1dBL1K9YgIoVK7J//35Ds0jmpjWTRERERNJIWFgYW7duJSAggFWrV7N25e/Y2dtTtWZ96jRsQd2GLfD2yZ/uuarUrM8fmzal+3lFniRfvnzky5cPs9lM0aJFqVGjBg0qFeT7WUtp1Lyd0fHkP1wNvYSFhQXFSrzEiZMnjI6TIURGRjJ37lzmzZ/P7l27Hnqtc8++vPdh+t7Z85+2b17P613/nE7aoEHD/9haJO2oTBIRERF5Ajs7Oxo3bkzjxo2ZPHkyZ86cISAggICAAMaPepuPhw+gSHE/6jRsQb1GLalYtVa65KpWqwEL5/7E1atX8fZO38VfRf6LhYUF1atXZ/fu3dSsWZMrly8aHUn+w8F9O+nTpTExDx5gY2PDhx9+aHSkDOHq1asMHDgQS0tL2nftRamylXBwyIZv4eKUrVjV6Hg4ZHNM/tze3o7ff/+de/fuYWNjQ58+fQxMJlmdprn9g6a5iYiISEqFh4ezceNGAgICWLJkCTnz+BCw7WS6nPvendtU88vB3LlzteiqZFizZ8+md+/ezF78B9Vq1Tc6jvyLyyHnaVnbjypVqvDbb7/h7e39Qt4dLDExkfPnz3Pnzh1q1KhBSEgIBQsWBOCbn/xp3raLwQkfL2CZP++92e2h57Jly0ZERARWVlYGpZLMSAtwi4iIiKQDV1dXOnbsyLRp08idJw8VKtdMt3N7eGWnhF8Z1q1bl27nFEmNHTt20Lt3b5q17kzVmvWMjiNPcDboOHFxcVSuXBkfH58XskgC+P77HyhevDg1a9akfIUKyUXS28M+ybBFEkCLdl3ZdeImgfsucOBcGLN+38iDBw8IDg42OppkYS/mdwkRERGRNBAVFcWkSZMoVKgQ586epUbdxul6/hbtu7N48WJu3LiRrucVSYndu3cDYGNrS8iFcwankSfJV7AwABERkZhMJoPTGKdMmZeSPy9UvBzder3Fj3NW0P+9UQamShnP7DnwyV8QZxdXChf3A+DkyfQZKSsvJpVJIiIiIk9h9erV+Pr6MmzYMKrUakTAtpM0adkhXTN07tkXaxsbpk6dmq7nFfkvYWFhrFmzBoCVi3/j91+nG5xInqRwsZK8O+Izfv55Oq1atyazr4RiNpu5evUqQUFBKd4nIiIiuXx5691RfPbNL4yd+CMNmrZ+XjGfm9BLFwCSR1aJPA9agFtEREQkFWJiYhgy5H2mTv2Reo1aMubzH8iTN58hWVzd3GnfpTc//vgjw4cPx8HBwZAcIv8UHR3Nli1bAOjeuz/dew8wNpA8kaWlJW+8PYIVi+ZibW2NhYWF0ZGeycSJExkxYgQ2NjacOHGCgYMG4ezkRPbs2cmePTteXl4ULFiQJk2akJSUxLFjx6hWrRoATVp1pMdrmfvva+CaZTg4OFCqVCmjo0gWpgW4/0ELcIuIiMiT/P2zQqGiJQjYdtLwX7ouXQymSbWiVKlShcKFC5MnTx5atGhBzZo1X9h1T8R4JpOJ+fPnM2LECG7dukWrDj0pUtyPvPkK4pPfl7z5CuLkrJ+1M5IdWzbQp0sTNm3aRL169YiLi+Ojj8Zy8+YNxo4dS/78+Z/quDExMdy7d4/cuXOny/cks9mMo6MjMTExWFtbk5iYCEDl6nWIjork/t3b3L17m7jY2Ef2/W35VipVq/3cMz5PWwPX0K9HCz777DNGjhxpdBzJZFLTh2hkkoiIiEgq7Nu3D4A+/YcaXiQB5C9YmPdGjufE0QOcOX+ZjYGb+OKLL/D29qZz58507dqVSpUqZYis8uKwtLSkZ8+etG/fnq+++gr/hQtZs3wBMTExydu4uXtQtVYDur36FlVq1NXfUYNFRUYAcPv2bY4fP06Pnj05feoUzi6uzJ8/nzfeeJMxY0bj5eX1xOMkJSVx+PBhAgMDCQwMZMeOHcTFxWFvb09BX1+KFC5MoUKFKPw/H/Pnz4+1dcp/NU1MTOTYsWPs27eP3r17Y2lpyb59+7h9+zYHDx4kJiaGWvWb0qpDD6KjIilZqhxlK1Z96BjHjxwg+MxJrK1tsLaxwcnZJVMXSZvWr+KbCSO5fuUyLVq2ZPjw4UZHkixOI5P+QSOTRERE5En++OMPGjZsSK833mXEx5OMjvMIk8nEkYN7WLPMn7WrfufOrZsUKFiQbl270rVrV0qXLq1f2sUQZrOZW7ducfHiRS5evMjZs2dZ4O/PmdOn8S1cjC6vvEnbzq/g5u5hdNQX0rnTJ2lZ5/+nRRUp7seXP/xGvoKF+fXn75jxwxeUfqk0W7dseeh282azmQsXLhAYGMjGjRvZtGkT9+/fJ5ujI5Wr1aV6nUbkzVeQK5cvcvliMJcvnefyxWCuXL6YPGrIysqK/AUKUKhQoUfKJl9f3+QpvGazmXfffY9vv538n9ezeP1+SpetmLZ/SBnQg+hofv3lOyaN/3MUUvfu3fn+++9xd3c3OJlkRqnpQ1Qm/YPKJBEREfk3169fp0qVKji7efHbim04OjoZHemJkpKS2LdrK2uW+7MhYAlh9+9RvEQJunXtSpcuXShWrJjREeUFZzab2bZtG9OmTWPJkiVYWllRu34zivuVoWiJ0hQtURqf/L4PlRfyfJjNZjatX8XKJb+xb9cWVm0+jleOnMmvH9i7g5fb1qF69er069cPOzs7NmzYSOAfgVwKCcHKyoqyFapSrXZDqtVuyEvlKmNra/uv50tMTOT61VAuhwRzOeTPgulyyPnkr2P/ZxRbHm9vihQujLu7O8uXL6dF265UqVGPMUPfoLhfGWrXb0bFqrUoXbYS2RydsLO3fyFK868+Gc6vM74jKTGRfv3eYNSoD8mVK5fRsSQTU5n0DFQmiYiIyONER0dTp04drl67we9r95Izt7fRkVIlPj6e3dsCCVjuzx9rlxMVFUnZcuXo2qULXbp0oUCBAkZHlBfcrVu3mDlzJhs3buTYsWPcuXMHAIds2ShSzI/ylWvSvE0XTCYTZStWfSHKAqMkJCRgY2PzyPOBa5Yz95dv2btzCwBFipWkaq2GVK/dkMrV66TZOlhms5lbN68TGnKeSxf/v2wKvXQevzIVGffF1OTtXsS/B8cO76f/K625fesGdnZ2BAUF4evra3QsyQJUJj0DlUkiIiLyTyaTifYdOrBu3Tr8V++iZOlyRkd6JrExMWzbtJaA5f5s2bia2JgYqlStSreuXenUqRN58uRJ90w3btzAxcWFbNmypfu5JeMxm83cvHmT48ePc/z4cY4ePcr69eu5efMmAPUbt+Kzb2bg4ZXd4KQvptBLF7G1syNnrvT/XiGwbtVi3n69EwDXrl0jd+7cBieSrEJl0jNQmSQiIiL/FBgYSKNGjQBw9/Ckyytv8u6ITw1OlTaioiLZvH4VAcv92bF5HYmJidSpU4euXbvSoUOH/1xsN7WCg4M5fvw4ERERREREEBkZSUREBAsWLCAuLo4PPviAN998U6WSPCIxMZHAwEAOHz7MpEmTKFS0FNPnr8H+r/V0RF4U8fHxlPaxA2DdunU0adLE4ESSVahMegYqk0RERORv9+7dY8iQIcyePfuR1w5diMzwayalVnjYfQLXLmfNcn92b/8DgIaNGtGta1fatm2Lq6trqo9pMpnYv38/K1asYPmKFZwKCkp+zc7ODidnF5ydXbG0tsbDMzuH9+/C09MzuVRydHRMs+uTrGPbtm3Ur18fR0cnWnXsSeeefSnuV8boWCLpZu7P3zFx7BDOnDmjKW6SZlQmPQOVSSIiIi+G8PBwTp8+TVhYGJGRkTRp0gRLS0u2bdtG1apVsbGxwdnZGYD3R0+kT//3+XbiGK5duUSNuo1p2+llg6/g+bp7+xbrA5awZrk/+3dvw9bWliZNmlCoUCHc3d1xd3fHw8Mj+fP/fZhMJjZt2sTy5StYvXoV169fx93Dk7qNWtKgaRvKV6qBs4srtnZ2j5w3NOQC074dz/Lf5+Du7s4HH3zAW2+9pVJJHhEcHMyMGTOYNWsWN2/e5KVylejUoy/N23XFycnZ6Hgiz01SUhKj3nudVUvmERsbi6WlpdGRJItQmfQMVCaJiIhkbVevXmXSpG+YPv0noqKikp/PnTs3ERERREdHY2NjQ2JiIn//mLRmxykKFSluVGTD3bh2hXUrF7Fpw0ru3LpBePh9IsLuEx8f/9jtLS0tMZlM5C9QiPpN29CgaRvKVaqOtbV1is8Zeuki07+bwFL/Wbi7uzN06FD69++vUkkekZCQwOrVq/n5l19Yt3YtDtmy0aJtN1q07Ypn9pwULVHqvw8ikkns2bGZCaPf4XTQMYYNG87nn08wOpJkISqTnoHKJBERkazp1KlTfPHFl8yb9xsODtno1qs/zdp0xtXNgwfRUUz5aizFS5ahVv2mHNizHWcXV0qWLkehoiWfeHvrF5XZbCY2JoaI8PuEh91/6GNMzAMqVa1N4WIln/lOS1cuhzD9uwksWTATKysrChcpQuFChYiIiODmzZscPHgQe3v7NLoqyexCQ0OZOXMmM2bMIDQ0FBsbG774/le8fQpgZ2+vqXCSqR3av4tuLWtQtVo1vvv2WypVqmR0JMliVCY9A5VJIiIiWUNUVBTfffcdm7ds4fbt2xw9coScufLw6hvv0uXlfml2C2tJH1dDL/HHuhVcPH+GyyHnsbayZktgAK+91odPP/1EdzOShyQlJREUFMSgQYPYunUrABYWFmw9ckV3IJNMKSkpiY5NKpHNzoo9e/ZgZWVldCTJglLTh6R8rLGIiIhIJhAbG8u0adMYP3484eHh1KrfjOKlK9Ol12Bate/+2HV6JOPz9snPK30HJ3+dlJTEt5+P5qfvJnDpUgiBgYEGppOMxsrKitKlS7Nq1SpCQ0PZtWsXffv2Zf+urbRs383oeCKpdu70CYKOH2by5MkqkiRDUJkkIiIiWUJCQgJz5sxh3LhxXLt2jXZdejFgyBi8ffIbHU2eAysrK977cDw/fTeBQ4cOGR1HMihnZ2dKlizJ5MmTsbS0pHKNukZHEkmVM0HHGfRaey5dDAagTBlN1ZSMQcu+i4iISKZmMplYsGABJUuWpG/fvrxUoQYB24MYP3mGiqQs7nLIeQDu379P27ZtGTJkCPfv3zc4lWREH3/8MSaTienfarFiyfjMZjNRkRH8NuN7ureuiYOdNf7+/ly9epW6desaHU8E0MgkERERyYTMZjOHDx/G39+fhQsXcvnyZeo3bsXX0xdrgd0XiEO2/7+z24oVKwBwdXVjzJjRRkWSDCpXrlxUr16dNSsXMuKTbzRNSNJdUlISs6ZNImcu779u7lDikW0O7d/FyLd7c/PGVR5ERwNgbf1nkaQRSZLRqEwSERGRTCMoKAh/f38W+PsTfO4cHp5eNGnVia+79OKl8pWNjifpLHuOXEz7dRUFChVl2uTPWP77XNq0aW10LMmgvvnmG6pUqcKqJfNo2/kVo+PIC2bV0vl8+fEHyV+XrVCVNp1foaBvUbzzFSBXHh9m/vgVF8+f5auvvsLb2xtvb2+KFClCrly5DEwu8ni6m9s/6G5uIiIiGUtwcDALFy5kgb8/J0+cwMXFlUYtOtC8bReq1qyPtbXeG3vRXbkcwoBebTl76ji3bt3C09PT6EiSQbVv356Dh4+xYc85LCwsjI4jL5A3e7Zi88bVHDhwgMuXL/Pj1Kn8ERjI37+OW1hYYDabyZYtG9euXcPV1dXgxPIi0t3cREREJFMLDQ3l999/x9/fnwMHDpDN0ZH6Tdow8IPx1KzbWHdkk4eEh93j9MmjTJs2TUWSPNGgQYOoX78+xw7to0yFKkbHkRfEnVs3OXfmBDY2NlSoUIEKFSrQrl074uPjCQ0NJSQkhJCQEC5dukSJEiVUJEmmoJFJ/6CRSSIiIsbasWMH9erVw8rKijoNW9C8bVfqNmyBQ7ZsRkeTDCoxMZG2Dcpy5dIFqlSpwsSJE6lcWdMe5VFJSUnkzZuXJq27MvKTb4yOI1nYz99/wcXgM9y5fYNjh/ZiZ2vDhg0bKF26tNHRRP5VavoQlUn/oDJJRETEONevX6d8+fL4FCzKtF9X4eSs/xdLykRFRjB/9lTWLPfnzs2rhISEkE0FpDzGkCHv88OPPzBt7iqq12lodBzJQiIjwkmIj+f6tVDaN6rAS2XK4ObqyvXr11m3bh2+vr5GRxR5IpVJz0BlkoiIiDESEhKo36ABZ88GszTwENlzaMFRSb3z507TvGYJrK2t6dWrN5Mnf4Ojo+N/7ygvjLi4ONq1b8+mTZuYt2I7pctWNDqSZAH37t6hVR0/7ty+BYCbmxs3b97E1tYWk8mEpaWlwQlF/pvWTBIREZFMZ+zYcezZvZu5y7aoSJKnVqhIcRYG7Gbfri1898UYChYswMiRI42OJRmInZ0dS5csoWDBgqxftVhlkqSJa1cucef2Lb7++muyZ8+Ou7s7tra2ACqSJEtSmSQiIiKGu3//PpMmfU3fQcOpULmG0XEkkytbsSplK1blQvBpPvzwQ/bs3cuvc+dqUVtJZm9vT82aNVmxaC7unl4ULVGaMuWr4OLqZnQ0yYS2bVrHqiXzgD/vGFigQAFjA4mkA5VJIiIikm5Onz7N2rVr8fLywsvLC2dnZ4KDg9m7dy+xsbEULaGFSSXtjJ88k9r1m/HRB29So2ZNfl+4kJIlSxodSzKIjz/+mF69e/PFuKEANGjamh/nrDA4lWQ2t25ep2+3ZgBMmDCBvHnzGpxIJH2oTBIREZHn4ubNmyxevJi6detSsmRJAgICaNWq1WO3zV+wMI1btCdXHv0QLmnH0tKS5m27ULREad7o2QI/Pz+qV69OsWLF6NmzJ/Xr1zc6ohioRIkS7N2zh08//ZTRo0fj7VPQ6EiSCR3Ysx2Ac+fOUbhwYYPTiKQflUkiIiKSpuLj4/nuu+/4+OOPiYyMBCB/gQLw1z0/Nu4NJkfOPITdv0tEeBje+Qrg6OhkYGLJzMLD7nPqxBFOnzhCbGwMzi6uOLu4/fnR1Q1nZ1dcXN1YtG4/2zevY9P6lfz6669cDAlRmfSCCwkJwcbGhh9++IEuL/fjw08nGx1JMqH9u7cCYGNjY3ASkfSlMklERETShNlsZvXq1bw3ZAgXL1yg26tv0e/tEZw9dZzNG1YRcv4s0/03kq9AIQByOeTVSCR5KhvXLGPbH2uJj49jzXJ/4uPjsbe3x9nZmbCwMBISEh67n6WlJU7OLiQmJjJ40KB0Ti0Zye3bt6lSpQq3bv15561X33jX4ESSWXl4ZgfAy8vL4CQi6UtlkoiIiKSJAQMGMnXqj9So04jJvyyjSHE/AHLmykOtek0MTidZycDe7QEoU7YsH374IZ06daJIkSJYW1tjNpuJjY0lPDycsLCw5I//+7mFhQVt27Y19iLEULt27eLWrVvkL1iY1/q/T6EixY2OJJmQ2WzmwJ7tNGzUCEdHR6PjiKQrlUkiIiLyzGbNmsXUqT8y5vMf6N7rLSwsLIyOJFlYuYrVOHH0ADt37HjkFzgLCwscHBxwcHAgV65cBiWUjK5169aUKFECG3snurzcz+g4kkn98PXH7NmxiXnz5hkdRSTdqUwSERGRZ3LkyBH69+9Ppx6v06N3f6PjSBYXcuEchw/sxs3NTSMB5KmFhIRw6tQpvv1lkcpveWpHDu6hYsWKdO/e3egoIunO0ugAIiIikjk9ePCA33//nfbt21OoaElGj59idCR5AeQvWBjP7DkoVaoUSUlJRseRTOrvItLaWosmy9Nr0qIDBw8e5NSpU0ZHEUl3KpNEREQkRcxmM+Hh4axfv55XX32VnDlz0qVLF5zds/PtL4uxs7c3OqK8ACwsLKhQuSY7duygbLlybN261ehIkgm5uLgAEB0difmvO02KpJTZbGbdqsX8/P1EAHbu3GlwIpH0Z2HWd8+HRERE4OrqSnh4ePL/ZERERF4U9+7dY+bMmdy6dYtbt25x8+ZNbv71+e1bt4iPjwfAt3AxWrTvTqv23clfsLDBqeVFc/vWDQ7v38WMH77kyME9dO3alS+//JK8eXV3QEkZs9mMnZ0dFpaWDPvoK3r2GWh0JMnA4uPjOXZoL4cP7Obw/l0cPbibO7dv0bRZM76YOJHSpUsbHVEkTaSmD1GZ9A8qk0RE5EV1+vRpWrVqRWhoKLny+ODplQMPrxwPffT0ykHBwsUo7ldG64yI4UwmEysW/cqXn3xA7INoRo0axbvvvoudnZ3R0SQTCAgIYODAgZQqX52vp2oBZfl3m9av4q1XWmNra0vNmjUpUqQI3bp1o06dOkZHE0lTKpOegcokERF5Ea1fv54uXbqQPZc30+auwqeAr9GRRFIsMiKc778ax6+/fEfBggXx9/enQoUKRseSTOCtt94iYO16AvddMDqKZGChIRcYOqAnhw/spnz58hw6dIh69eqxadMmo6OJpKnU9CFaM0lEROQFZjab+e6772jevDllK9VgYcBuFUmS6Ti7uDLi40n0GzyC4OBg1qxZY3QkySTOnz9P6KWLWjdJnsingC/zVm7nw0+/JTomAUADD+SFZ210ABERkawkPDycU6dOERQUxMWLF7l+/ToAdnZ22NvbJz/+9+unec3S8tnfD0pISGDAgIH8/PN0er/5HkPHfIGVldUzH1fkeUpKSmLHlg3EPIjG0tISCwsLEhISWPTbz+zaFkiPHj0YNmyY0TElk+jSpQsbN27kwJ7tVKpW2+g4koFZWVnx8uuDuBB8muCzQQwYMMDoSCKGUpkkIiLyFGJiYjh48CBBQUEEBQVx8q+P165eBf6841TOXHnInjM3lpaWxMfFERcXS1xcLPHxccTF/vV5XNxTvSNuY2Pz5+Kx/7Fu0ZNeT0xMJCEhgU8n/UKnHn1SnUEkvYVcOMc7r3fi1Mmjj7xWsmRJli1bRps2bbSel6RIdHQ030yeTMFCRSlRqqzRcSQTuHv7FgtmT8XT05Po6GiCg4MpWLCg3oiRF5LKJBERkacwatRoJk36GisrK/IXLEyhoiVp3akXhYqUoHCxkhQsVAyHbNn+8zhms5mEhATi4+OIj31M2fQfzz/2mDxaTv1bYVWuYnXKVaqWuosXMcj8WT9yIfg0O3bsoGTJkpjN5uSHu7u7fqGTVOnbty8XL15k0dp9ODlrypL8Nw+v7PR+8z2WLZxNu3btAHBwcKBEyZKULlWKUn89Spcujbe3t8FpRZ4vlUkiIiIpZDab+eWXX5j2008cPXKEPgOG8s6wT7B9hjtHWVhYYGtri62tLTg5p2FakawnNjYGDw8PatSoYXQUyQKCgk4RGxPD2pW/M2joWKPjSCZgaWnJ8HFfM2zsV9y5fZNzp05w9tRxzp4+weHjJ1i0eDEPoqMBWLt2LU2bNjU4scjzo7u5/YPu5iYiIv9m3LhxjB07lsYt2lO1Zn3adHpZ72aLpJO42Fiq++Vg8OBBfPbZZ0bHkSzg5s2b5MqViwKFirJk/X59P5dnZjKZOHXiCO0bVSBXrlzk8fbG0dERn7x5KVu2bPIje/bsRkcVeazU9CEamSQiIpJCW7ZsoVHzdkyZucToKCIvHAsLC6KiIvH11d0GJW14eHhQqlQpTpw4wfzZU+k3SAu3y7OxtLTE76XyjB4/hdBLF4h5EM2DB9GcPneR5StWJI9aKlO2LD9Nm0aVKlUMTizy9FQmiYiI/Iv4+HgiIiI4fvw4f/zxBzt27ODNdz40OpbIC8nWzo6iJUrx3nvvUbx4cU11k2cWFRVF69atOXfuHMsWzqbvwA+0eLukiZ59Bj7ynMlk4nLIeYKOHWLGj19StWpV6tWrR+/evenQoQPZUrDOokhGomlu/6BpbiIiL44JEyawe/duoqKiiIyMIio6iqioKKKj/vyYkJCQvK2jkxOdevTl/dETsbGxMTC1yIsrKjKCbq1qULRwQVatXGl0HMnkSpUqxYWLF3nl9bfpM2Aorm7uRkeSF0RiYiKrly1g6YKZ7N25BWdnZzp37sKnn35Crly5jI4nL7DU9CEqk/5BZZKIyIth4cKFdO3alWq1GuDplYNsjk6PPBz/+liwUDF8ixTXnaJEDGY2m6lbzoeqVSuzbOlSo+NIJnbhwgUKFSpEi7ZdmfTTAqPjyAssNOQCyxfNZcHsH7G0gM6dO1OxYkUqVKhAiRIlsLbWZCJJP1ozSURE5DESExPZvn07ixcvYc6c2bRs142vps7TtAaRTOL+3TvcuH6Vnj16GB1FMjkHBwcAPLxyGJxEXnQ+BXwZNHQs3Xv1Z9L4kazb8Ac//vgjZrMZBwcHypYrR7euXRk0aJDRUUUeojJJREQynbi4OK5fv46npyfOzs5P3DY+Pp5NmzaxePESVqxYzp07d8jt7UOH7q/z9vBPVCSJZCL2DtlwdnbhyJEjdOjQweg4konlypWLAgULEvMg2ugoIgB4Zs/BZ9/8Avw5pTfo+GGOHNjN15+N4O6dOyqTJMNRmSQiIhlaUlISf/zxB/PmzePU6dOEXr7MjRs3kl/38PCgWPHidOvalZIlS3Lx4kUuXLjw5+PiRU6fOkVkZCT5CxamXdc+NG7ZgdJlK6pEEsmEYh5EY++QjZs3bxkdRTKxkJAQPv74E65dvcqOLesxmUxYWloaHUteIAkJCZw8epC9OzfTuGUHChYq+tDrYffvsXPLBgLXLcfW1pbRo0cblFTk32nNpH/QmkkiIhlLYGAgjRo1AqBDt9fI7e1Dbu98ZM+Zm/D797h25RLHDu9ja2AAiYmJWFhYkNvbB598vuTN70sB3yLUbtCcYiVLq0ASyYROnTjCkvkzye9bhGnffoaF2cSOHTsoWrTof+8s8hidOnXij02bea3/ULq83E8Lb0u669ayBof27wIgX4FCLAzYjau7B8cO7WNLYADzZkzBxsaaJk2aMGDAAN29UtKNFuB+BiqTREQyjqCgIMqXL4+7Z3YmT/+dcpWq/eu29+7cJjIynNze+bC1tU3HlCLyvISH3adlbT9u3bwOQOMmTZgze7budiRP7fr16+TLl49hY7/mlb6DjY4jL6Do6CgqFXFj7NixdOnShRo1anD//n0cHLIRGRmBu7s7HTt24ssvv8DV1dXouPKC0QLcIiKSJSxbtgxrGxvW7DiFo6PTE7f18MqOh1f2dEomIs/b7m1/MHRAT27fukGXLl347rvv8PLy0nQkeSqXLl3iww8/ZMWKFVhZW9Om08tGR5IX1JEDu0lKSmLlqlX4+fmxc+dOAgMDuXv3LvXr16dKlSq6e6xkCiqTREQkw1q+YgU16zX9zyJJRLKeT0cN5vatG1SrVo3JkyeTI4fuuiVPb86cOcybN49yFasx4bvZmtomhqlcvS7f/rKI+bN+oH379vTr9wbfffctdnZ2RkcTSRW9tSMiIhlSQEAAB/bvp3mbLkZHEZF0dOTAHsYNH8Dd2zdp3bo1u3bt0rQ2eWZdu3alWvXqHD6wm+nffW50HHmB2djY0LRVR+Yu3cwnX01n+vSf+PXXX42OJZJqKpNERCTDOXv2LG+++Sa16jWhSUvd/lvkRbJrWyDzZ/3I/Xt36dlTU5EkbRQtWpSdO3bw008/sdR/FmtXLjI6kgi58+YD/iyYRDIblUkiIpKhnD9/nooVK2Jt68C4L3/SHdhEXjA9+wzEt3AxANasWWNwGslKLCws6NevH46OjrzTtzNXLocYHUlecKuWzCN79uy8/LKKc8l8VCaJiEiGcevWLbp264a7Z3YWr9+Pt09+oyOJSDpzcXVj8LBPAAgOPmdwGsmKNmzYgIWFBT98PY7wsPtGx5EsIDIinPNnTz3yfHR01BP3a9m+O7dv39Y0N8mUVCaJiEiGcODAAUqVKsXFiyF8PXUBzi66Ha7Ii6rUSxXIkTM3e/bsYe3atUbHkSymevXqjB8/gYBlC6hczIMWtUoy4u3e+M/9idMnj5KQkGB0RMkkYmNiKJbTgopF3GheqyTzZv6Q/Nr9e3dpXbc0/Xq04PTJo4/dv1a9JpSrWI3XX3+d06dPp1dskTShu7mJiEiGsHTpUsLCwthyKBSvHDmNjiMiBvIp4Mv8VTtpWNmXmJgYo+NIFjR8+DBefrkngYGB7N27l9179rBi0a8kJSVhaWlJrtze5PEpQJ68+fH2KYC3TwHy+hQgj09+8uTNrzVuBID4+LiHvv54xEB2b/+DUmUqsmn9SmKiI9kauIatgWsI3HcBn/wFH9p+2uTxHD6wm5EjP6RYsWLpGV3kmVmYzWaz0SEykoiICFxdXQkPD8fFxcXoOCIiL4RvvvmG9957j2693mLsxB+NjiMiGcD5s6fo0KQiMQ8e0KpVK3777Tf9bCbP1YMHDzh48CCnT5/m0qVLhISEcDEkhJCQEK5fu8bfvza5uLjSuGVHWnfsSZUadY0NLRlCjza1ObBne/LXzs7O5MuXj6lTp9K7d2/Onz/PrhM38cyeA5PJRFRkBPYO2fhgwMucCzrEuXOa0isZQ2r6EJVJ/6AySUQk/ZjNZkaPHsNnn33KG4NH8O7Iz7TgtogkCw25QMMqhQDw8vJi1KhRtG/fHi8vLxwcHAxOJy+S+Ph4QkNDuXjxIjNnzmTBggUAnLgSr1FKQtj9e/Tu1JDgMyeJj4/n999/p0WLFmTLlo0hQ97nm28mka+AL+Fh94kID8NkMgFga2dHfFwc169fJ1euXAZfhYjKpGeiMklE5NmZzWZiY2OJiIggPDw8+ePdu3cJDg7m7NmznDl7lnNnz3L37l2GjvmC1wcMNTq2iGRAxw7vx9HRiVnTJrFkwUxMJhNeXl6sWLGC6tWrP7RtQkICe/fuxcfHh/z5tYC/pK2IiAj69evHokWLcPf0ole/d+kzYChWVlZGR5MMICoyggvBZxg++NXkxbiPHj2Kn58fU6ZM4dq1a3h4eODh4YGbmxvR0dFcuXIFT09P+vfvb3B6kT+pTHoGKpNERJ7enj17+OqrrwgICCA2Nvax27i4ulGwcDEKFipGAd+ilClfhep1GqZzUhHJjC6HnOdi8Bk+fLcPt2/doHCRIri6uuLm6orJZOLcuXNcuXIFgCFDhtCgQQNq1aqFk5MT8GfRfenSJW7evJn8uHXrFjdv3iQyMpJmzZrRqlUrjXqSR4SGhjJ48GDWrFnDiE8m075LL+z190T+xdyfv+OzUW/j5eXFhQsXcHZ2NjqSSIqoTHoGKpNERJ5OUlISnp6eOLu607lnP3J5++Dk5IKziyvOLq44Obng4uaOi6ubprKJyDO5d/cOKxbN5dbN60RFhBMZEY6lpSVOLq4c3reTfAULc+LIfm5cv4qjoyODBg0ie/bsBAYGPnR3OAsLCzw8vfDMnhMLCwvOBB3H2dmZXr1689133xp4hZKR7Nq1i4YNG5LNyZnhYyfRumMPoyNJJjB9ykS+/nQ4jRs3plixYgwdOhQfHx+jY4k8kcqkZ6AySUQk9eLj4/npp58YPHgwn076hU49+hgdSURecGazmQvBZ1jmP5t5s77HZDJRtERpWrTpStVaDfDKnhM3D0+srf//5sYhF87x2ai32fbHWk6cOIGfn5+BVyAZwcaNG+nZsyce2XMzb+V2nJw0wkRSJjo6itHv9SVguT8AI0eO5LPPPjM4lciTpaYPsUynTCIikgWZzWaWLFmCn58fb7/9Nu279qZuoxZGxxIRwcLCgkJFivP+6M/ZcfwGh85HsmjtXnq9+S7F/V7CK0fOh4okgAK+RXjtzSEAlCpVipEjRxITE2NEfDGQ2Wzm1VdfxdPTk8aNG2Nr78gv/utUJEmqODo64eLmTrZs2ShRsiSdOnUyOpJImrL+701EREQetWvXLt4fOpTdu3ZRq35TJv28lGIlSxsdS0TkEY6OTv/6WmJiIiePHsTNwxOTycTShbN5qVxlYmMeMGHCBBISEvjyyy/TMa0YberUqcydO5e+A4dRtERpSpYuR/YcutOWpM7tWzdYPO8XPv74Y4YPH250HJE0pzJJRERS5cSJE4waNYoVK1ZQolRZZv2+UQtoi0imFbDcnw8GvPzY17y8vPjggw/SOZGkN7PZzJUrV1i1ahUL/P3ZsX073Xv35/3RnxsdTTIxR0fn5LvbxsXFYWdnZ3QkkTSlMklERFKlQYMG3Lp1i4lT5tC6Y08sLTVjWkQypxvXriQXSW+//TY1atTAz88PKysrLl26RMWKFfHw8DA4paSlo0ePMmz4cPbt3Uu+/PnBbOb48eOYTCasrKyoUbcxE6fMoVnrzkZHlUwum6MjZStWY9y4cZjNZsaNG2d0JJE0pTJJRERSLCoqilu3bvH6wA9o2/kVo+OIiDyTub98l/z52LFjcXNzS/66WLFiBiSS5yUkJITRo0czb948CvgWoV6Ttiz1nwWAm7sH4yfPolyl6nh4ehmcVLKSuUs306hKIeLi4lK1X0JCAhYWFlhZWekOuJJh6e1kERFJsUWLFmFhYUH3Xv2NjiIi8swGD/3/kQILFiwwMIk8D1FRUbRt144cOXJQqFAh1m8IZNwX01i19QQTvp3JmZtmjlyMZu/puzRo2lpFkqQ5CwsLzGbzf5ZJYWFhlClTBgsLC3Lnzo2trS02NjZYWlpy4MCBdEorkjoqk0REJMV+mTGD6rUb4u2T3+goIiLPzN7Bgf1n7wOwbPlyY8NImjl58iQDBgzE29ub9evX0/mVt/jk65/ZsCeYLq/0w8bGJnlbh2zZDEwqWd307z7n+tVQmjdv/tjXExMT+eabb3B3d+fYsWN4++SnQfMOdOj2GmM+/wGAn3/+GQCTyYTZbE637CL/RdPcREQkRc6cOcOunTv55id/o6OIiKQZF1c3AOJTOQ1FMpa4uDiWLl3Kj1OnsmP7drxy5KT7a4Po3LOv3gARQxzav4tvJ45mxIiRNGrUKPn5s2fP8uOPU4mJiSEgYDVXr16lW6+36PbqWxQtUSp5WttHH7wFwPTp0/H39yciIoIVK1bQunVr4M9y6Z/rVt64cYOjR49iNpsxm80sW7aMFi1a0KZNm3S6anmRqEwSEZEUmTlzJq5u7jRoqh9IRCTrCLt/D4CtW7canCTzCQ4OJjQ0lHr16hma49KlSzRq3JhzZ89StWY9vpm+kIbN2mJra2toLnmxbVi9BHd3dz78cCSJiYmsWrWKH378kT8CA3H38MTW1o6bN67R47UBjJnw/SP7d3m5H4f27aRB0zY4u7iyduXvtGvXjtq1a9OiRQv8/f0pV648P/00DUtLS86cOUPTpk0JCQl56Dg///wzM2bMwMbGBhsbG5o3b46Li0s6/SlIVqYySURE/lNCQgJz5syhVfse2NnbGx1HRCTNZHN0olOP11k8fwbx8fEqIB7DbDYzd+5cLC0tKVy4MPv372fTpk2sWLECR0dHIiMjDVsk+NSpUzRu3BhLa1tWbj5GsZKlDckh8k+ubh7cu3eP4sWLYzabuXLlCuUqVuOLH36lacuOWFlbYzKZ/vV7TsnS5Vi15Vjy1+279mZjwFI2BCxh1KhRxMXFcfDgQc6fD8bZ2ZmAgADy5ivIik1HcHP35PatG3RsUgmAPn36JB+ncJEiLF60iDJlyjzfPwDJ8izMmnj5kIiICFxdXQkPD1djKyICHDhwgPHjx7Ns2TLGTPieHq8NMDqSiEia2r19E706NuDs2bMUKVLE6DgZzsGDB6lYsWLy17a2trxUrjIH9u6gU6dO/P7774bkioiIwNfXF88cuZmxcAM5cuY2JIfIvzl/9hRTJ3+Gvb0D3Xq9hd9L5dPkuHGxsVwIPs2c6ZO5d/c2kRHhNG3ViU49Xiebo+Mj25tMJhITEwm9dIEhb3bj/NkgWrduzYwZM/Q7rzwkNX2IRiaJiMhjRUVF8eGHo5gy5Tvy5ivAm+98SO36zYyOJSKS5nzy+wJw4cIFlUmPMW/ePACW/3GYhIQEipUojaWVFaV97Dh//jyvv/467733HiVLlnxk30OHDjF79hx27dpJ0aJFqV69OrVq1cLOzo6wsDCqVKny1KOaHB0duXv3Lm++O0ZFkmRIhYqW4Ksff0vz49rZ21OiVFk+/252ira3tLTE1taWQkWKszBgN5M/H8Wc6ZOJfvCAVStXYmVlleYZJevTyKR/0MgkEXkRxcfHc//+/eRHSEgII0eO5MbNm7wz/FNe6fu2ftAQkSwrMTGRl/LZM2XKFN566y2j4xjGbDYzadIk7t69i5ubG25ubri6ujJhwgRss7ny67ItDxU/P076lP27t3Ly2EHCw/68K97Ro0fx8/PDysqKwYPfZsqU7wBwc/dIXp/qfzVt1ozvp0zBx8cn1VMMw8PDcXNzA+BgcDhOzvrZXSSlXu3QgD07NhEaGkrevHmNjiMZhEYmiYhkQeHh4dSoUQMLS0ss//GwsrJ6+Ou/nrP46/P/3S4pKYmw8PDk4ijs/n1iYmIeOV/VGvX4eWEg+QoUMuBqRUTSRmJiIhFh94mPjyMhPv7Pjwnx2Nja4eziirOzK/YODnj75OfixYtGxzXMiRMn+PTTT1m4cCHePvmJiowgMiI8eU2Xn34LeGQEUf/3RgGjuH/vLoNea8/+3dsoU6YMDg4OFCtenCOHD2NrZ8fhC1FYW1uTkJBAZEQ4Z04e5e6dW2xcu4wj+3dRuHBhAOrVq0euXLkAmD179n+WS66urnz//fcMHz6cDwa+wvezlj5ydysRebx3hn9Kjza16NWrF4GBgUbHkUxII5P+QSOTRCSj2rt3L1WrVqV1x544u7iSlJSE2WTC9NfDbP7z40PPm03Jn//9PBYWuLi64eLqnvzR1c39ka9z5vY2bEFVEZFnZTabmTZ5PIvnz+DK5SeXRNbW1iQlJdGzZ0/mzp2bTgkzjrt371KuXDkSTdBv8Ai69/pzdJbJZOJBdBTx8fF4eHr953Hu3bnNmaBjnA46ypmgY/gWLk6PPgNxdHT6130iI8LZvnk9YffvsnLRr5jMJo4e3Iuvry/ffvstLVu2fOI5Q0NDad68OUFBQRy+EIW9g0PqLl7kBXLv7h327tzMjWuhnDx2iFVL5hEYGEiDBg2MjiYZhEYmiYhkQadPnwZg3BfTHru4ooiI/MlsNvP5R0OY/dM3VKlShc/Hf4Knpye2trbY2dlhY2NDXFwc4eHhDz1eeeUVo6Onm6SkJOLi4ti8eTPvDRlCVPQDlgUeJre3T/I2lpaWqZo65uGVnWq1G1Ctdsp/MXV2caV5m84AySXWhoClDHqtA8OGDcNkMpE/f37y5cuHm5sbiYmJnDx5kv3793PgwAGmT5+OhYUFgz/4WEWSyH9YOPcnJn8+CoCixYrxwQfDVCTJU1OZJCKSSZw+fZo8efOpSBIReQKTycTHIwayYPZUvv/+ewYM0B0o/+nEiRO0bNmSS5cuAX9Oa/5q2qKHiiQjlS5bic49+7Jp/QratGmT/LyzszMJCQnExsZiaWlJkeJ+NGjahi9//O2Jo59EXmQJCQns2hbIpvUr8Z8zDYAHDx7goPJVnpGmuf2DprmJSEbVrl077oQ9YMbC9UZHERHJMMxmM3GxsRzct4PDB3az4ve5hF66wPTp03n99deNjpfhhIeHkzNnTuLi4ihYuBhjJ06lSo26GXJas8lk4s7tm1wLvcS1q5e5duUS1lbWlCpXiZKlyunNFZEUCLlwjibVipI/f37atWtHhw4dqFmzptGxJIPSNDcRkSzo1OnTVK7Z2OgYIiKGCg+7z7kzJzl3+gRbNwZwKSSY6MgIbt64hqOjIx07dqRv3znUqFHD6KgZislkYunSpQwZMoS4uDhm/r6BGnUaGR3riSwtLcmRMzc5cuambMWqRscRyZSsrP78lX/s2LFcv36dX3/9lYSEBEqXLo2np2eGLJIlc1CZJCKSgd2/f59Zs2aRmJjImdOn6Tt4tNGRRETS1c0b1/h8zHtEhN/n3OkT3LxxDQArKyvy58+Pt7c35Zo1pkmTJjRq1AgbGxuDE2c84eHh1Kpdm+PHjlGzbmO+nLpQ5YzIC6Jri2oA9O7dGzt7e3LmysP06dMB+OKLLxg6dKiR8SQTU5kkIpJBzZw5k/79+2MymUhISKC4Xxmat+1idCwRkecqKiqSNcv82bd7K8cO7eHSxfMA2NraMnToUPz8/ChVqhRFixbFzs7O4LSZw5IlSzhx/Dhzlmyias16RscRkXRUolRZtm9eT5OWHfjgo6/w9snP4QO7ebtPR3bs2EH79u3x9fXVCCVJNZVJIiIZVHx8PHFxcbz5zoe82vdtkkxJWFlZGR1LROS5uHQxmN9mfM8y/1k8eBBNhYoVadu6JVWrVqVKlSoUKFBAv+ykUlxcHBMmTGDixIlUrVlfRZLIC2j8t7M4f/YU1WrVT36ufKXqlHypPCtXrmTlypXkz5+f9u3b06FDB6pVq4alpaWBiSWz0ALc/6AFuEUkowgLC+Ptt99m7ty59H9vNG8P+9joSCIiae74kQOMH/0Oh/btxNPTk379+vHWW2/h45Mx7iyWme3cuZOaNWvS6413GfTBOJycnI2OJCIZhMlk4sa1K5w7fYLNG1cTuGYZt2/dIFeuXMydO5dGjTL2mmryfKSmD1HlKCKSQWXLlg1fX18AjUgSkSxrx+b1HNq3E4CFCxcyfvx4FUlpICEhgfeHDsXVzV1Fkog8wtLSkjx581GnYXPGTvyRbUevMn/VDgoW8aNDhw4cO3bM6IiSwalMEhHJgDZs2ECp0qX55JNP6P/eaAYMGWN0JBGR56LvoGF8P2spFavWomnTpvz8889GR8qQbt26RVRUFGFhYSnafsmSJezZvZsf565UkSQi/8nS0pIKlWvww5zl+BQoTIsWLbh69arRsSQD0zS3f9A0NxFJD4mJiYSEhHDt2jVu37790OP06dNs3LiRStVqM3r89xQrWdrouCIiz11CQgLjR7/D/Fk/MmnSJN59912jIxkmMTGRCxcucPr0aU6dOkVQUBBz585Nft3b25u+ffty+/ZtWrVqRWRkJM2bNydbtmwkJiYSFBREmTJlyJkrD9uO6pdBEUmdmzeu0aV5VbJ7ebBj+3acnVVIvyhS04doAW4RkXQQEBDA3r17//yl4NQpgs+dIz4+Pvl1Kysr3D08cffMjodHdr76cR4t23fTYrMi8sKwsbFhzITvcXJy4b333iMuLo7hw4cbHQsAs9mcbt+P16xZQ4sWLZK/dnJyxrdoCRo0bYOrmzuREeEAfPzxx5hMJn744Ye/tnPCZDLx4MGD5H1f669bfotI6uXMlYeffgugR+ualCtXjvHjx9O5c2ejY0kGo5FJ/6CRSSKS1qKiopLf0alaox6+RUtQqEgJfAsXJ5e3Dx6e2XFxddOdM0RE+LO4+f6rcXz/1TjGjh3HmDGjDS3Wd+/ezcCBA8nm6Mi4sWOpX///74h06dIlBg0aRFh4OOHh4Tg7O/PlF19w//59fHx8KFCgwH++o5+QkMBvv/3GtWvX2L5jB+vXrcPGxobJPy+idLlK5MiZ+5HrN5vNPHgQzb07tzm0fyc5c3lzcN8OnJxccHL+81Hcrwz5CxZ+Ln8mIpK1LZw7nV9/+ZZzZ4KSn7t+/Tq5cuUyMJWkh9T0ISqT/kFlkoiktQcPHpAnTx5ad3yFUeO/MzqOiEimMG3yeL6Z8CHDh4/gs88+TdfCPSIigmXLlrF8+XKWL19OsRKlOXPqOACjR48mLi6OW7ducfLkSQ4dOkTztl1xcnZhy8bVXL8a+tCx3N3d8StVipo1avDZZ59haWlJYmIie/fuZd26dUycOJGEhAQAKlatRcv23enYvQ82Njbpdr0iIn8zm820qV8GJwdbunbtio+PDz4+PlSsWBFbW1uj48lzpjLpGahMEpG0ZDab6d69OytWrmTR2n0UKe5ndCQRkUxj5tSvmTj2fUqWLMnIkSPp0qUL1tbPf5WGTp06sXjxYl4qX5mO3frQsUcfFs37hXUrF3HuzAkcHZ3w8MyBh1cOSpQqy6ChYwG4ce0KZ4KOUahoSW7dvMa10EtcuXyRoOOHWL96CVOnTuXu3bt8/fXX3L9/Hzd3D8pXrknLdt0oX7kGub11FzsRMc7qpQv4Ytz73LxxjWXLltG2bVujI0k6U5n0DFQmiUhaOXfuHBMnTmTGjBlM/vl3mrXuZHQkEZFM5/D+3Uyd/ClbA9fwww8/0L9//zQ/x4MHDzhx4gSHDx9m9+7dzJkzh7z5CvLH/gtpdo43erRkS2AAAC+/PphW7btTqmxFrKys0uwcIiLPYuiAl1m5+DcCAwOpW7euvj+9gLQAt4hIBvD7778zY8YMer/5nookEZGnVK5SNVq068bWwDUULVo0TY99584dvvzyK77/fgoPHjzAysqKQkVLMHjYx3Tq8Xqanuu7GYvZvmkdPgUK6S6dIpIh5StQCIDatWurSJL/pDJJROQ5iY6OBqB5264GJxERydxCQ84D0KtXL/LkyUOePHlo0aIFffv2fepjLliwgH79+mEym3n59bdp1KI9RYr5Ye/gkFaxH2Jnb0/D5m2fy7FFRNLCzi0baNa8udZskxTRNLd/0DQ3EUkrBw4coFKlSiz/4zAlSpU1Oo6ISKYVFxvL/j3b2LF5PTu3buDsqRMAVKhQgZYtW1K5cmUqVapE9uzZU3S8wMBAunbtyksVqjN+8kw8PL2eZ3wRkQwv9NJFGlb2JWfOnFy/ft3Qu2iKcTTNTUQkA/j77jxxsbFERUY8dptsjk7peociEZHMyM7enpp1G1OzbmMATCYTP3z9MUcP7eW7Kd9z/95dAAoULEiVypWp/NejfPnyZMuW7aFjnT59mkaNGlGhcg3GfTlNRZKICJA3XwE69+zL77/9zMKFC+naVSPr5clUJomIPCdOTk5YWFjQpUW1f90me45c1GnYgrqNWlC9TiMcHZ3SMaGISOZkaWmZfAc1s9nMlUsXOXZ4H8cO7+P44X2sWLmS2JgYrKys8CtVipo1atC+fXsqVKjAmDFjsLa25mf/dfqeKyLyFwsLC65dvQxAkyZNDE4jmYGmuf2DprmJSFravn07N27ceOxrSUlJHDx4kFWrV3Pm9GlsbW2pXL0udRu1pG7DFvgU8E3ntCIiWUNCQgLBZ05y7NA+jh/Zz65tG7kaeglLS0vsHRwYM+EH2nV51eiYIiIZyqxpk/j8oyFMnjyZt99+2+g4YoDU9CEqk/5BZZKIGCE4OJiAgABWBwSwdcsWEhISKFy0BHUataReo5aUq1Qda2sNJhUReRpms5njRw6wecMq2nZ+hfwFCxsdSUQkw7l39w6NKvsSFRXJkiVLaN++vdGRJJ2pTHoGKpNExGiRkZFs3LiRVatWs3btGm7evImHpxdvvjOKbr3ewtbW1uiIIiIiIpIFRYSH8W6/LuzYsoGmzZoxYvhwateubXQsSSep6UOeatXXXr16YWFh8cRHbGzsQ/scPnyYMWPGUKdOHby8vLCxsSFHjhw0a9aMZcuWPU2MZKdOnaJHjx7kzp0be3t7ChUqxPvvv09YWNgzHVdExAjOzs60b9+eWbNmcu3aNfbt20e7tm35/KP3aFGrJGtXLkLvA4iIiIhIWnNxdWPab6v54odfOXc+hObNm5OUlGR0LMmAnmpkUq9evZgzZw5FihQhR44cj91m06ZNye+enz9/nsKF/384ccGCBfHw8ODChQvcv38fgFdffZWZM2em+q5GmzdvpkWLFsTExJA9e3Z8fHw4ffo0Dx48wNfXl127dpEzZ84UH08jk0Qkozpx4gRDP/iAdWvXMnr8FNp37U02R0ejY4mIiIhIFrRz60Ze69yYo0eP8tJLLxkdR9LBcx+Z9LeRI0eyY8eOxz7+dxqG2Wwmd+7cTJw4kWvXrnHhwgUOHDjAnTt3mDJlChYWFsyZM4cff/wxVeePjIykS5cuxMTEMHjwYK5evcrBgwe5fPkyNWrU4MKFC/Tp0+dZLlFEJMMoVaoUa9esoUuXLnwychDlfJ14vVszjh3aZ3Q0EREREcliylSoirdPfjp27MjevXuNjiMZzDOVSSmVN29egoOD+eCDD8idO/f/n9zSkoEDB/LGG28A8PPPP6fquNOmTeP27duUKFGCSZMmYWNjA4Cnpyfz58/H2tqagIAADh06lHYXIyJisJkzZ7Jnzx6mTZvGneuX6NSsCv16tODY4f1GRxMRERGRLMLJyZlvf1nMuXPneLVXL0wmk9GRJANJlzLJ3t6ebNmy/evrjRs3BuDs2bOpOu7SpUuBP6fdWVlZPfRavnz5aNiwIQCLFy9O1XFFRDKybNmyUaVKFd544w2OHz/O/PnzuXHlAp2aVubNnq04cfSg0RFFREREJJM7eewQ334+CoCrV64QHR1tcCLJSJ6pTFq8eDFt27alfv36dO3alSlTphAeHp7q4/y9WLeDg0OK90lMTOTgwT9/YapRo8Zjt/n7eQ3JE5GsysrKim7dunHyxAl+++03rlw6S4fGFXnz5dacPKZRmSIiIiKSOklJSaxZvpD2jSpw/sxxJk+ezL59+3B2djY6mmQg1s+yc0BAwENfL1y4kI8++oj58+fTtGnTFB/n999/B/69FHqckJAQEhISAPD19X3sNn8/f+7cuRQfV0QkM7KysqJHjx506dKFBQsW8PHHH9O+UQUaNG3DoKFjKVGqrNERRURERCQTGPRae/5Yt5LixYtz5MgR7OzsjI4kGdBTjUwqVKgQ48eP5+jRo0RERBAZGcmGDRuoUqUK9+/fp23bthw4cCBFx9qwYQPLly8HYOjQoSnO8Pdd4ADc3d0fu83fz//vtv8UFxdHRETEQw8RkczK2tqal19+mVOnTjFnzhxCzp2gbYNyDH69k9HRRERERCQTyOOdH4ALFy6QJ08e6tWrx/79WptTHmZhNpvNaXWw+Ph4atWqxb59+6hfvz5//PHHE7e/fPkyFSpU4M6dO/Tv358ffvghxefavn07tWvXBv4chmdp+WgvtmnTJho0aICVlRWJiYmPPc7YsWMZN27cI8+n5FZ4IiIZXWJiIu7u7kRFRfFa//fJlTsv7h5euHt6UbpsJdzcPYyOKCIiIiIZzKkTR1i5ZB6b1q8k5PxZatepw9YtW4yOJc9ZREQErq6uKepD0nQBbltbWz755BMAtmzZ8sQRQffu3aNZs2bcuXOHunXrMmnSpFSdy97ePvnz+Pj4x24TFxcHPHktphEjRhAeHp78CA0NTVUOEZGMzNramjFjPgJg4+rfmTxhJEMH9OT1rk35YlzKR4OKiIiIyItja+Aa5s2YQsj5sxQvUYKRI0YYHUkymDS/m1u1atUAMJlMXLhw4bHbREVF0bx5c4KCgqhQoQIrV65M9TzM/53a9m+l1d/P/9s0OAA7OztcXFweeoiIZCVDh76P2Wzm8qVLREdHEx0dzRdffMGyhbM5f+600fFEREREJANJSkri+6/G0rlzZ+7fv8+poCCaNGlidCzJYNK8TLKxsUn+/HFTy+Li4mjTpg179+6lZMmSrFu37qlWhS9QoEDyuf6ttPr7+SJFiqT6+CIiWVW2bNkYPHgwrq6urPh9rtFxRERERCQDMJvNzP35Oz4Y+AoJCQl06dIFNzc3o2NJBpXmZdLJkyeTP8+bN+9DryUmJtK5c2c2bdqEr68vGzduxMvL66nOY21tTfny5QHYuXPnY7f5+/kqVao81TlERLIqW1tb4uPjcfd8uu/BIiIiIpK1HD+8n89Gvc3qpfMZOXIkjRs3NjqSZGDWaX3Ar7/+GoDixYvj7e2d/LzZbKZXr16sXLmSPHnyEBgYSJ48eZ7pXO3bt2fv3r3Mnj2bIUOGYGVllfza5cuXCQwMBKBDhw7PdB4RkawmISGB+Ph44mJjjY4iIiIiIhlAidLlqFWvCds3r2fSpEnky5ePN954I8X7P3jwgB07drBp0ybCwsLo168f58+f5/Tp05w+fZqYmBjq1atHo0aNKFasGBYWFs/xauR5S/Xd3DZu3MimTZvo168fBQsWTH4+PDyc0aNHM2XKFADmz59Pt27dkl8fPHgwU6ZMwcvLi23btlGiRIkUnW/x4sW8//775M2blx07djz0WkREBIUKFeLOnTsMHjyYr776ChsbG+7evUubNm3YuXMnzZo1Y82aNSm+vtSsXi4ikpm9+uqrBG7awsa957G2TvP3FkREREQkE7p+NZSBr7Xn8oWzrF69mlq1aj12u6FDPyAo6CQ5c+YkJCSEnTt3Eh8fj5OTM1FRkcnbuXt4UqhICbCw4OjBPSQkJADQrl07unTpQpcuXdLluuS/paYPSXWZtHz5ctq1aweAt7c3efLkISEhgaCgIOLj47GwsGDMmDGMHTs2eZ/du3dTvXp1AHx8fMiXL9+/Hv+fhdHs2bPp3bs3+fPnJyQk5JHt//jjD1q2bElsbCzZs2cnX758nDp1igcPHlCgQAF2795Nrly5Unx9KpNE5EUxZ84cevXqxbLAQ5QsXc7oOCIiIiKSQURGhNOuUXkaN6jHL7/88sjr/v4L6datKwAvlauEZ/ZcVK/dkOq1G1KoaAlOnzxKdHQUvoWL4/E/yypER0dR3vfhNZMjIyNxcnJ6vhckKZKaPiTVb0VXqFCBDz/8kN27dxMcHMyJEycwm814e3tTq1Yt+vfv/8gaRXFxccmfh4aGEhoamtrT/qsGDRpw4MABPv30UzZt2sTx48fx9vamXbt2jBo16ol3chMRedEkJiYSExPDgwcP+Pzzz6lasz4lSpU1OpaIiIiIZCCxsTGEhlzg9OncJCQkYDKZmDJlCi+//DJTpnzPZ599CsDe03dxc/d4ZP9/+/nS0dGJH2Yt48jBPfz8/UQAbt26pTIpE0r1yKSsTiOTRCQrSUhI4MSJE0yfPp0ZM2YkDysGsLCwYFngIZVJIiIiIvKQB9HR1C3vQ3jYfbJly0bevHk5e/bsQ9vsDrr90Kij1DCbzWzesJq3XmnNyJEjGTp0qO4clwE812luWZ3KJBHJCvz9/fluyhQOHzpEbGwsVlZWNGzejjoNmmNv74CdvQMeXtkpX6m60VFFREREJAOKj4vjxLGDHN6/i8P7d5GYmEhCfBw7tmwAYPfJW3h4ZX/q45tMJppUK8rlkPMALFy4kM6dO6dJdnk6z3Wam4iIZHwLFizgytUbvDtiPKXLV6ZkqXI4ZMtmdCwRERERySRs7ewoX6n6I28+LvWfTcnS5Z6pSJo38we2/rGG8LB7yc9FRkY99fEk/alMEhHJgk4GBdGgaRt6vfmu0VFEREREJAtp37XXMx9jxo9fcjX0Em+99RbZsmXD29ubVq1aPns4STcqk0REspiYmBguXrhA77dKGh1FRERERCTZFx9/wJXLF7kaeolixYrxww8/YGFhYXQseQqWRgcQEZG0dfbsWUwmEwUKFTU6ioiIiIhIsoN7t7N+1WIAIiMjGTJkCGFhYcaGkqeiMklEJIu5ffs2AFdDQ4wNIiIiIiLyP6bOWckPs5fz5jsfcvv2bb755htmz55tdCx5CiqTRESymF27dmHv4EDdRpp3LiIiIiIZh4dXdho2a8Pbwz4mISEBgPLlyxucSp6G1kwSEckirly5wpdffsX06T/RpvOruLl7GB1JREREROQRS/1nA9C6dWtq1aplbBh5KiqTRESyiPz582MymRgwZAyv9nvH6DgiIiIiIo91++Z1AGrUqMG9e/fw9PQ0OJGklqa5iYhkcmazmSlTpmAymShToQqDPxiHq5u70bFERERERB6r3+Dh9Bs0nGHDhuHl5UXXrl2JjY01OpakgoXZbDYbHSIjiYiIwNXVlfDwcFxcXIyOIyLyRDdu3KD3a6+xbu1aXu4ziPdHT8TewcHoWCIiIiIi/2nl4nmMfKd38vpJ77zzDp9//jnnz59n7dq1nD59mqioKKKioqhduw5Dh75vcOKsLTV9iMqkf1CZJCLPymw2c+nSJY4fP06TJk2wtbV9LudZvXo1r732GmYLS8ZPnkWdBs2ey3lERERERJ4Xk8nEl58MY+aPX2FjY4OtrS3R0dHY2dtTtHgpHJ1cSExM4MCe7Xz55Ze0aNECk8lEbGwspUqVws7OzuhLyDJUJj0DlUki8jTCw8NZsmQJ8+cvYP/+fURERABQrVo1SpYsyalTp5g6dSovvfTSUx0/KSmJM2fOsH//fvbv38/effs4sH8/9Ru34tNJv+CZPUdaXo6IiIiISLoxm82EXrpAeNh9Vi2ZR426jalSvW7yiHuz2czId15j2cI5/G+FMXz4CCZMGG9U7CxHZdIzUJkkIikVFxfHhg0b+O2331i5ciVxcXFUrVmf6rUbUriYH1v/WEPI+bNERoQRcv4sAwb05/PPP8fCwiLV52rcpAkbN2wAwLdwMUqVrUStek1p1aH7Ux1PRERERCSzuX/vLudOn8DGxpaNa5cxe9okli1bRqtWrYyOliWoTHoGKpNE5N+YzWZOnTrFhg0bWL9hA9u2buXBgwcUL/kSrTu9TMt23ciZ2/ux+44Z+iYL5/4EwJo1a2jW7L+npJlMJs6fP8+uXbsYMGAA5avU4puf/HF2cU3T6xIRERERyWwSEhIY1Ls9mzeuZtSo0XzyycdGR8r0VCY9A5VJWdfff9UfN4rDbDZz8eJF9uzZw549e9i9Zw8vlX6J77+fgoMWM37hRUVFMWTI+wQErObq1avY2dlRoUotatRtTO36zShaotR/HiMyIpzlv8/l0w8HY29vz/z582nbtu1j/z76+/vz7XffcfzYMaKjowGoWbcx385YjJOTc5pfn4iIiIhIZmQ2mxn1Xl+2Ba7i5s2bRsfJ9FQmPQOVSVnT6dOnmTdvPus3rGfIe+/RuHFjDh8+nFwe7dmzh9u3bwNQwLcIxUuVZfOGVZQsUYJXX32V5cuXM3z4cBo3bqwpRVlUXFwcx48f5+DBgxw8eJADBw8SGRFB6dKlOX36NKGhV+j8cj9q1G1MxSq1nvqOaWeCjvPlJx+wfdM6ataqxddffUXlypWTXz906BDVq1enbMXq1GnYnOJ+ZShesozWRBIREREReYxlC+cwfHAvIiIicHbWG6/PQmXSM1CZlPVMmTKFwYMHA1CoaAnOnz2V/Jqzswsvla9CmQpVKVuhKqXLVcbD0wuA0yePMui1DlwOOU/+goW5dDGYunXrMmHCBKpWrWrItUjaiIuL49ixYw8VRydPnCAhIQErKyuKFPejZOkKJCYmcP/eHWxt7eg3aDhlK6bdf/cdWzbwxdj3OXPqOF27dsXHx4ftO3Zw6OBBipYozYJVO7HVnSlERERERJ4o5MI52tQvQ9cuXZk5c4be/H8GKpOegcqkrOPevXt07NiRzZs3A1C4WElm+K/n3t3bBB0/TNkKVfEtUhxLS8t/PUZEeBhngo5RsWottmwM4JvxIzlz6jit27Sh7+uvU716dTw8PNLrkuQZXL58mVmzZrF8xYrHFkelylSgVJmKFCv50lOPOkqtpKQkli2cw7efj8LKyopylWpQvnINmrTqSI6cudMlg4iIiIhIZjfi7dcIWDafu3fv4ujoaHScTEtl0jNQmZR1BAYG0qhRo0eef6lcJZq06kSTlh3xyV8wVcdMSkoiYJk/U778iMsh56nfoAF/BAamVWRJYwkJCaxatYrpP//MhvXrcciWjYbN2lG2QtV0L46eJDExEWtra6NjiIiIiIhkOmazmZqlc3Pn9k3i4+OxsbExOlKmpTLpGahMyjqSkpLYsWMHOXPmJG/evNy7d48dO3awePFi1q5dS2xsLKXKVKBJq068PmDoE0co/a/9u7cxf9aPrFmxEPj/hb3FGFevXiU6OpoFCxbQtGlTqlSpwtmzZ/nll1+YM2cOt27dokyFKnTq/jrN2nbRAtYiIiIiIllIdHQUFQq54ObmRmhoqEYmPYPU9CF6K1yyLCsrK+rUqZP8tZOTE927d6d79+5ERUUREBDA3Llz+frT4eTIlYe2nV5O0XH/LpIaNWrE0KFDn1d8SYGtW7fSvn177t27B8DYsWPx8vLizp07OLu40q5LLzp270OxkqUNTioiIiIiIs9DfFwcVlZWfPDBByqS0pHKJHkhOTk50aVLFzp37kzXrl0ZMbgXPvl9qVC5xn/uO3jYx2zftJazZ89qVJIB7t27x4IFC5g9Zw4H9u+nSvW61G7YnOw5c7NqyTz27dyMV/ac/LH/YoaYwiYiIiIiIs/PwT3byeboxIEDB4yO8kLRNLd/0DS3F09SUhL58+enYYtOjPzkmxTtE3rpImPe78eubYG88sorjBw5kmLFiqVJnuvXr3Py5EkALCwsKFu2LJ6enmly7NQIDg7Gw8PD8AXGHzx4wN27d4mLiyM4OJiWLVuSlJREvcataNflVeo3aZ08L9psNuvuDSIiIiIiL5AuzasR9yCcuXPnUrFiRaPjZGpaM+kZqEx6MfXt248/Nm9l3a4zKd7HbDaz1H82E8cOITzsPuXKl6d7t2506dIFHx+fR7a9ffs2EREReHh44OrqipWV1SPHPHPmDA0bNuTKlSvJz1lZWVG3bl3atWtH27Zt8fb2fvoLTaGTJ09SqlQpnJycWL16NZUqVWL37t24ubkRHBxMx44dH5s/Ld2+fZtvv/2W77//nvDw8OTni5YoxWeTZvBS+crP9fwiIiIiIpLx1SiVk8GDBjJ69Gijo2R6KpOegcqkF9Py5ctp164dPV4bwBuDR5AjV54Uj3CJjYlh6x9rCFi2gC0bVxMXF0fNWrUoWaIEIZcucenSJS5fukRMTEzyPhYWFri5ueHu4YGHuweenh44Ojqybt068vgUYNI0fxydnElIiGfPjk0ErlnGnh2bSExMpHKVKrzcsycDBw58Xn8cvPZaH2bNmkn+goW5dDEYCwuLh6b0lSpdmlEffki5cuUoWLBgmt4x4dKlS4wYMYLly5djYWlJ11fepHqdRtja2uHi6kZxvzIafSQiIiIiIkRHR1He15nffvuNHj16GB0n01OZ9AxUJmVsP/00nbXr1vLuO+9Qu3btNCsVEhMTmTJlCmPGjCEqKgpXN3eatupEzz6DKFqiVIqPExUZQeDa5axetoA7t27g7VOAPHnz4+1TAG+f/Dg5uxIZEUb4/Xvcv3+X8LB7hN+/R3jYPcLu36V85Zq89tYQXN3cHzl2eNh9Nm9czbCBrwBw//593Nzc0uT6/9ehQ4eoWLEio8dPoVPPvhw7vI8Th/dTvkpN7t+9TUzMA3795TsO7NkOgLW1Nf7+/nTo0CF5BNbo0WM4cPAABQoUYOaMGbi6uqb4/O3bt2fZsmUMfP8jevYZhLtH+k/xExERERGRjO9M0HFa13uJXbt2Ua1aNQBiYmLYvn07GzZsIDg4mAIFCuDr60vhwoVp0KABdnZ2BqfOuFQmPQOVSRnXwoUL6dq1K+4enty/d5fqNWow6sMPadq0aZqVSjdv3mTXrl0cOHCAWbNmcf36dUqVqcCSDRlnMbe5P3/HZ6Pext/fny5duqT58ffu3UvVqlUpUaosrw/4gBbtuj7y52s2m7l5/SoXg8/wyciB5M2Tk+rVq/PTTz9x79497OztadmuOxsClpDXOw8rV66kcOHC/3rO+/fv88cff7B27ToWLvSnfJVa/LJgbZpfm4iIiIiIZB2Ba5YzoHc7bty4wZEjR/h60iS2b9tGbGwsOXPloXAxP25cC+XK5YvExcWRv0ABqlapgqurK66urnh5edGuXTuKFCmS4nMmJSWxc+dOAI4dO0ZQUBCLFi2ie/cefPvt5Od0pelDZdIzUJmU8Zw5c4ZZs2axatUqgoKC2H3yFscO72PqN59y5OAeypcvz6hRo2jTpg2WlpZpdt74+Pjk1vrMzYz1z6R1vZeoVrkCs2bNei7H/+mnn1i1ahUBAQHUqNOI4eMmUaS432NLuzUrfmf0e68D0KF7HypVq02xki+Rr0AhLgSfof8rrQm7d5tZs2bRpEkT7O3tOXz4MCtWrCAsLIx9+/ezd88eTCYTRYqVpFb9ZgwcOhZHR6fncm0iIiIiIpJ5mUym5GU4hg16lZWLf+Pdd99l8uTJlK1QlSatOlKzbhMKFyuZ/PuLyWTi7KnjzP7pG25cu0JkRBhREeHcunWdB9HR1K1bl759++Lj48Ply5e5c+cOVlZWWFtbY2Njg7W1NdbW1iQkJPDN5MkcO3o0OY9v4WJcCP5z7d179+7h7v7oLJPMQmXSM1CZlPEMGjSY77+fQqkyFejy8ht0frkv8OfomD07NjNt8qfs2bEZPz8/Ro4cSefOnbG2tk6Tc+fx9qZtl9d4Z/gnaXK8tDJx3FDWLP2N0NDQ5Gv9+59yWq4nFBAQwICBA7kUEoKrmzuNmrfn00k/P3KOu7dvYWtnh7PLo9PZwsPu894bXdmxZQPW1tZky5aNiIgI3Nw9yJ4jF75FSlCzbhNq1mtCnrz50iy7iIiIiIhkPcVyPvr7jouLK6/1H8obb49I1QCDuNhY1q9ewqJ5P7Nv19bk5+0dHDCbTCQmJpKUlPTQPuUqVuPdkePJkSsPAF7ZczLyndfYuWU9N27cwMkp874prjLpGahMyjgiIyNZsGAB3333HSdPnmTRun28VK7SY7c9uG8n0yZ/xrY/1pIvXz4aNmxEhQrlyZcvH/nz5yd//vxP9d+zVKlSODi5M2/l9me9nDQVdPwwHRpXpFOnTtjb23MyKIhTQUFER0djZ2eHnZ0d9vb22NnbJ3+eI3t2OnXqROfOnfHw8EjxuR48eMCGDRto164dAMcuxWBnb5+qvElJSZw6cYRjh/dx/PA+vLLn4u3hn6RZ6SciIiIiIi+GVzs0YM+OTQDY2Nry4aff0rpjz2ee2XA55DwJ8fHk8vZ56Fgmk4mkpCSSEhNJTEzA0ckZgONHDrBmuT+rls4jLuYBP/74Iz179nymDEZTmfQMVCYZIz4+ngMHDlC1alWio6P5+ONPmDr1R2JiYqhdvxmdevalfpNW/9kynzh6kKX+s9i/eysXg8+QkJCQ/JqdnR22trbY2tpiY2ODzV8fPT09qVypEpUrV6Zy5coULVo0+Tw//fQTb731FieuxGe44mPy56OZO30yhYqVpHDRkhQqWhIXFzfi4+OIi4slLjY2+fP42FguXTzHrm2BWFlZ0aJFC4YOHZq8SN1/mTBhAiNHjmTM5z/Qo3f/53xlIiIiIiIiGdOZoONMHDuEnVs3kiNHDjp27MSwYR+QL1/mn2WhMukZqExKPwkJCWzYsIFFixYlr58D4OnpSfSDB7z21vt0ebkfufLkfarjJyUlcefWDa5eucT1K5e5d/c2iYkJJMTHk/DXx8SEBG7euMqJI/uT57m6urpSsVIlqlSuTGxsLJMmTWLn8Rt45ciZVpeeJv6eK5yaaW13bt1k9bIFLJr3M7euX+HgwYNPXBgb/iz6XF1dKVzMD/+A3djY2DxrdBERERERkUzlzq2bfPfFGBbN+wXfQoX4YuJEWrdujZWVldHR0ozKpGegMin1kpKSnuof0Ndff83777+Pb+FiNGnViXwFCrHUfxZu7p6M+PgbvH3yP4e0/y4iPIwTRw5w9NBejh3ex7HDe7lz6ybZHB1Zt+ssOf+aE5sVREVG0KhKIV7v8xoTJ0584rYmk4kGDRpw5Ogxfl+7l/wFn1w+iYiIiIiIZCUJCQlUK5kdaytLPvroI9566y1sbW2NjpXmVCY9A5VJKRcXF8ewYcPx91/AW2+9xZtvvknOnE8evXPnzh02bdrEokWLWLx4Ma069ODLH35N00Wj04rZbObGtSskJMSTr0Aho+OkufaNKnDy2CF27dqFjY0N586dIzw8nKJFi1K8eHFy586d/N/l/feH8vXXX1G/cSum/rrS4OQiIiIiIiLpq1GVwri7OhEQEIC3t7fRcZ6L1PQhGWsRGMk0zp07R9euXTlx4gT1m7Zh4hdf8Mknn1CocGFKlyqFn58fpUqVws3NjYiICPbu3cvGwECOHD4MQJHifnz2zQzadn4lQxZJ8Odd0XJ7+xgd47l5pd87fPnxUKpXr578nJWVVfLdCnx9fRk+fDivvvoqERERAPQZMNSQrCIiIiIiIkb6bsZi2jYoR968eTlx4gR+fn5GRzKURib9g0Ym/bd58+bx5ptv4pk9F99MX4jfS+UJD7vPhtVLOHPqOOdOnyD4zAnu3L6VvE/2HLmoXqcR1Ws3pFrthllqylhmlpSUxKF9O7F3yEYB3yLYO2TjyuWLnD97ilVL57F+1WLy5s1L586d+frrr3m13zuM/OQbo2OLiIiIiIikK7PZTLWS2bl/7y7t2rVj6dKlRkdKc5rm9gxUJv27xMRE+vbty+zZs2nVoQdjv5iK01+3RXyce3duExPzAAeHbLh7emXYEUjy74LPBDF18mesWe6Pm7sHw8Z+TdvOrxgdS0REREREJF2F3b9H0+pFuX/vLhcvXqRAgQJGR0pzmuYmz82cOXNwcXWjQdM2ZMvm+MRtPbyyp1MqeV4KFyvJ11PnMWHyTGzt7IyOIyIiIiIiYojI8DA8PLOD2YSTk5PRcQxnaXQAyTysra3x9/cnIjyMd/p2pkRuK27euGZ0LEkHKpJERERERORFdXj/brq0qIqFOZE9e/bg5eVldCTDqUySFLt06RK9evV66LnoqEhjwoiIiIiIiIg8Z3t2bObVDvUoUaI4u3fvpmjRokZHyhBUJkmK+fj4EBMTk/x1k5YdMP115y8RERERERGRrOaPdSuIi4vjozFjNCLpf6hMkhSztLQkNjaWYcOG0bFjR47s30GL2n70f6UN165cNjqeiIiIiIiISJrq1ustAKZOnYruX/b/VCZJqtjZ2fH555+zaNEiLl++zK+//srpEwdpUbsks3+aTJJGKomIiIiIiEgW4Vu4GE1admDp0qW4uLhQqXJloqOjjY5lOJVJ8tRsbW3p2bMnQUFB9O7Vm88/eo8uzasSdPyw0dFERERERERE0sQHH33FD7OWUaioHwcPHODixYtGRzKchVnjtB4SERGBq6sr4eHhuLi4GB0nU9mzZw/VqlUD4MxN/bUSERERERGRrMFsNlPzpdzEPoimQ4cOVKpUiQEDBhgdK02lpg/RyCRJM6VLlwbAyVklnIiIiIiIiGQdFhYWjJs4DUdnV+bMmcPAgQMfukHVi0ZlkqSZ/v37A/Drsi3GBhERERERERFJQ1dDL/HRB29y8/pVmjRtyrZt23BwcDA6lmGsjQ4gWUeFChWYO3cuXVpUo27DFri6eVDcrwxtu7yKk5Oz0fFEREREREREUiUqMoIvPv6AhXN/AmD//v1UrFjR4FTGU5kkaeb1118nJOQSDx5Es2jRIu7duwdArtx5adi8rbHhRERERERERFLhTNBxBvfpwL07Nxk1ahTdu3enRIkSRsfKEFQmSZrJli0bkyZ9DUCxYsV57713KVq8FJVr1DU2mIiIiIiIiEgqLF/0Kx8NfYMiRYqwYd0BihQpYnSkDEVrJslz8e23k8meIxcrtxzDxdXN6DgiIiIiIiIi/ykuNpYxQ99k2MBX6NK5M3t271aR9Bgqk+S5KFGiJLdv3aB3p0bcuHbF6DgiIiIiIiIiT7R4/kxqvpSb5QtnM336dGbNmkW2bNmMjpUhWZjNZrPRITKSiIgIXF1dCQ8Px8VFt7h/WklJSSxcuJAePXrg7uHJnlN3jI4kIiIiIiIi8q86N6+Ko50Vv/zyywu5NlJq+hCtmSRpzmw2s3btWiZ8/jkAr/Z71+BEIiIiIiIiIv9u9/ZNHDu0j1mzZr2QRVJqqUySNLNv3z5+/fVXdu3axaFDh6hSoy4LA3ZTtmJVo6OJiIiIiIiIcO/Obe7eucXfk7QSEuJZPH8G82f9SLXq1WnVqpXBCTMHlUnyTP4ehfTFF1+wdetWvH3yU6pMRWYMm0CNOo2wsLAwOqKIiIiIiIgIALOmTWL6lM8fes7a2pomTZuyJiAAS0stLZ0SKpPkqSQkJODv78/EL77g5IkTlKlQhe9nLaV+k9ZYWVkZHU9ERERERETkEffu3sbPz49ffvkFAEtLS0qWLImTk5PByTIXlUmSapcvXyZ//vwA1G3Ygt8+/YGKVWtpFJKIiIiIiIhkWIf272Ln1g2UK1OaqlW1HMuz0PgtSbWLFy8CMGzsV/w0bzWVqtVWkSQiIiIiIiIZ1pzp39K9VU188ubhyy+/NDpOpqeRSZJqCQkJAFSr1eCh581mMwkJCdjY2KhcEhERERERkQwhKSmJbyaMpHXr1ixZskRLs6QBlUmSauXLl8fLy4t3+nbGydmFyIgwwsPuExEehslkwsLCAnsHBxwcsmFv74CLmzutOvSkU4/XcXVzNzq+iIiIiIiIvECsrKyoXL0uW7ZsYeLEibRv357ixYsbHStTszD/fT88ASAiIgJXV1fCw8NxcXExOk6GtWTJEvz9/XF3d3/oYW9vT2xsLDExMcmP4ODgP9tfa2vadHqFVh16UK5iNbXBIiIiIiIiki6iIiMYN3wAgWuX8SA6muIlSvDuO+/Qp08f/W76l9T0ISqT/kFl0vNx8+ZNpk2bxk8//cT169fxypGT+o1b07B5O6rVrI+tnZ3REUVERERERCSLi42JYefWjaxc8hvrVi6if//+/PDDD0bHyhBS04doAW5JFzlz5uSjjz4iNDSUnTt30uuVlzmwexP9ujenul8O3nujG2tXLiIxMdHoqCIiIiIiIpJF2Ts40KBpa/q89T6WlpZYWqoWeRoamfQPGpmUfsxmMydOnGDZsmUsXbaMo0eO4JPfl6lzV1KkuJ/R8URERERERCQLOrR/F327NuWlMi+xds0a/e7/F01zewYqk4yzdetW6tatC0C/QcPp1PN18hUo9H/s3XV4lfXjxvH3WW+MjdiI0bERkxzdIqGE0khKSHcIAgIiigjSIC1dgqR0dyPdOXpswLp3fn/ol58gKoNtz+J+Xde52DnnifvZpbDd5/P5PMaGEhERERERkWSlX+fmXL1wkpMnT+Lo6Gh0nERD09wkSapcuTItWrQAYNaU0dQo486+XVsMTiUiIiIiIiLJSZq06Xn06BHPnz83OkqSpTJJEoWgoCAGDBjA9Rs3AHBycsJsNmtVfREREREREYlTPQd+g62dA23atCEkJMToOEmSprm9QtPcjFG/QQO2bt1K9VoNcEzthJ2dPfnfK8onjVoaHU1ERERERESSmUN7d9C1zScULFCA9evX4+bmZnQkw2nNpHegMskYJUqWJFvugvwwZYHRUURERERERCQFuHjud7q2/hgTMRw8eJCcOXMaHclQWjNJkpz3PD25ef2y0TFEREREREQkhShYqBi/bDlGYGAQ8+fPNzpOkqIySRIFLy8vzv1+nCXzfjI6ioiIiIiIiKQQgQH+RESEY2GheiQ29N2SRKFbt27UrVuXX5fONTqKiIiIiIiIJHMR4eFMn/AdDaoXx83Njb59+xodKUlRmSSJgoWFBba2tty6cYWYmBij44iIiIiIiEgydXDvdj5+vzBTf/yabl27cebMGRwdHY2OlaRYGR1ABODOnTusXLkSz8LFNbxQRERERERE4s34UYMJDQ7g9OnTeHp6Gh0nSdJv7ZIoZMuWDSsrK65cPKuRSSIiIiIiIhJvPm7YEh8fH/bu3Wt0lCRLI5MkUbCwsMDd3R2TlZ1GJomIiIiIiEi8adGuG/fv3qZbt248evSYb74ZYXSkJEe/tUui4efnh8lk4qnvE6OjiIiIiIiISDJlZWXF4JETqN/0M1b9usroOEmSyiRJNObPn8+j+3eo+34hJo4eyuH9uwgLDTU6loiIiIiIiCQzd25d56nfE27euKGlVt6CprlJovHRRx9x/vx5vvjiC35ZOJ3pE77FxsaGoiXKUqpcFUqXf5+iXmWwsbU1OqqIiIiIiIgkMVFRUUyf8C0bVi3mzu0bWFtbM2zYcC218hZMZrPZbHSIxCQgIABnZ2f8/f1xcnIyOk6KFRMTw/nz59mzZw+7d+9m7969PHv2DFs7O4p5laVUhfcp4FkU71vXqfrhx2TPmcfoyCIiIiIiIpKIdW/bgF1b19OuXXvq1KlN1apVcXR0NDpWohGbPkRl0itUJiVOMTExnD17lt27d7N792727duHv7//i/ddM2TCLVsOps5bg52dPamdnPF74kNYWChZs+c0LriIiIiIiIgYLiIigmK5UjF69Gj69etndJxEKTZ9iKa5SZJgYWFB0aJFKVq0KH369CE6OpobN27g7OxMz169sLSwYN26dVQs7AZAjlx5eer3hMAAf3LnzUeF9z+kYtUPKVW2Mnb29gZfjYiIiIiIiCSk2zeuEhUVRalSpYyOkiyoTJIkydLSEg8PDwBWLF8OwOnTp7l9+zahoaFs27YdS0tLataswbZt29m8+VcWzp7E590H8MXQH4yMLiIiIiIiIgnsl0WzcHR0pHDhwkZHSRZUJkmy8b+RSwDNmjV78Xrjxo05evQoZcqUwf/ZU4PSiYiIiIiIiBH8nz9j0dwp1K9fH9tY3NDp5s2bbN26FW9vb7y9vYmKisLT05Nhw4bFY9qkQWWSJHu//PILzZs3B6Bhs3YGpxEREREREZGEljZdetasWUPVDz6gT+/eBAUFcffu3T+Kort3efjwIXa2djinccbZyYnHPj4cPHAAKysrMrllxS1rDo4d2gv8MWChQIECBl+RsbQA9yu0AHfyYjabX9zmcd+Z+2TM5GZwIhEREREREUloQUGBnD5xmEE92+Dz+CEALhkyktktG5mzZMc1Y2YiwsMJDPQnKDAAa2sban3SlOq16mPv4ADA0YN7aN3gfQAOHTpE2bJlDbue+KAFuEX+NHfuXACGjZ6mIklERERERCSFcnRMTYUqNdh35j7+z5/h4JAKm1hMeQOwsbHFZDJhNpuJjIyMp6RJg4XRAUTiS0xMDOPGj6d6rfq0aNvV6DgiIiIiIiJiMJPJRJq06WJdJB09uId2TatTslQpjhw5QqVKleIpYdKgkUmSbG3dupXLly4x9IdZRkcRERERERGRJMj/+TPmThvL/JnjqVipEuvWrsXhz2lvKZnKJEm2xo0fT+FiJfEqVd7oKCIiIiIiIpKIBQUGcOPqJa5fvcj1qxe58efjnvdt7O3t6devH0OHDsXOzs7oqImCyiRJls6ePcvOHTsYP2MZJpPJ6DgiIiIiIiJikFFD+/D7iUPcvn6FqOgo4I/pbrny5MP/+TOCgwJ46uf74vUcOXPiWbAgzZo2pmDBgtSoUQM3N63B+1e6m9srdDe3pC0mJoapU6eyaNEiHjzyYduR61hbWxsdS0RERERERBKI3xMfQkNDSJveBRsbWwpls6VKlSrUrFkTOzs7zGYz0dHRnD59mk2bNlGwYEE6d+5MwYIFyZcvX4qdxqa7uUmKdf78eXr16gXAL5uOqEgSERERERFJ5oKDgzhxeB+H9u3g8P4dXLl47sV7NjY2mM1mBgwYwIcffvi3fc1ms2azvAWVSZKs/K9BnvLzrxTxKm1wGhEREREREYkvOzavY96McZw+cZioqCiyZM1K9WrVGTp4IBkyZMDX1xc/Pz/s7OyoUaPGa4+hIuntqEySZOXgwYOYTCaKlihrdBQRERERERGJR/t2bebEkf107dqVXr164e7urnIogVgYHUAkLq1evZpiJcqSIWNmo6OIiIiIiIhIPPq4YQty5fFg1qxZzJ49h+DgYKMjpRgqkyTZCA8PZ8eOHdy5fZ15M8YTHBxkdCQRERERERGJJyXKVGT97rN07/81U6ZOoUCBAly+fNnoWCmCyiRJNmxtbTlx4gR1a9fix5EDeb94diaPGf7iFo8iIiIiIiKSvNjY2tKlzxA27r3A8+f+VKxYkRkzZhgdK9lTmSTJSoECBZg3bx43btzgs9atmTf9R973ys63Q3rx4J630fFEREREREQkHmTLmZvVO07hWbQUXbp0oWvXbuzfvx8/Pz+joyVLJrPZbDY6RGISEBCAs7Mz/v7+ODk5GR1H3pGvry9TpkxhypQpBAYGUrr8+zRq8Tm1PmlidDQRERERERGJYwH+z5kxaRQbVi3G5/HDF6///vvvFC1a1LhgSUBs+hCNTJJkzcXFhREjRuDt7c348eMxRYfyRdcWnDiy3+hoIiIiIiIiEsecnNMwYNgYdp/yZu3O31+8vmTJUgNTJT8qkyRFcHR0pEePHuzatYvy5cvT8/NGPHpwz+hYIiIiIiIiEg9CQ4I5sGcbAF9++SVffjnQ4ETJi6a5vULT3JI/Hx8fvLy8SJfBjYW/7sbewcHoSCIiIiIiIvKOzGYza1YsYN3KhZw4sp+oqCg6dOjIrFkzjY6WJGiam8i/yJAhA2vWrOHqxbN8/H5hjhzYbXQkEREREREReUfzZ05gUK+2OKWyYdKkSdy6dUtFUjxRmSQpUokSJThz5gw5s2fhs4ZV+apvBwL8nxsdS0RERERERN7C98P6Mnp4P7p06crWLVvo2rUrOXPmNDpWsqUySVIsDw8Pdu/ezfTp09myfgW1Kxbkl0WzCQkONjqaiIiIiIiIvKHo6GhOHNlH/vz5mTZtqtFxUgSVSZKiWVhY0LlzZy5evEiFCuUY9kUnKhbOzNcDu3L3zi2j44mIiIiIiMi/+P34YRrVLMn5Myf58ccfMZlMRkdKEVQmiQBZs2bl11WruHnzJr1792Lnpl/p1KIWoSEhRkcTERERERGR11i3ajGf1imHnbWJI0eOULt2baMjpRi6m9srdDc3Abh06RJeXl7Ua9qGr3/4yeg4IiIiIiIi8hcRERHUKJOXcmVKsWLFCiwtLY2OlOTpbm4i76hAgQKMHz+eZfOns3PLeqPjiIiIiIiIyJ/OnjpG01pl8PV5RJcuXVQkGcDK6AAiiVWnTp3YtHkzX/VtT6FiZ8mQMbPRkURERERERFKkA3u2cXDPNq5ePs+B3VspVLgwR48epVixYkZHS5E0ze0VmuYmf/XkyRMKFy5M3vyFmb1sMxYWGswnIiIiIiKSkMxmM5WKZCE0JIgyZcvSuFEj2rVrpxFJcUzT3ETiiKurKwsXLuTAnm0snDXJ6DgiIiIiIiIpjslkwufxQywtLdm6ZQsdOnRQkWQwlUki/6F69er06dOXcd99yaXzp42OIyIiIiIikqKsW7UYAHt7e0wmk8FpBDTN7W80zU1eJzw8nFKlSxMUHMaP05fiWbi40ZFERERERESSvQD/55T0SAvAjRs3yJ07t8GJki9NcxOJY7a2tixftgyTOYoG1b34vNlHnDh6wOhYIiIiIiIiydr3w/oC0KJFCxVJiYjKJJE3VKBAAS5fvsySJUt4+vguLT6uSMt6lTmwZxsa4CciIiIiIhL3vG9fJ126dCxcuNDoKPIXKpNEYsHKyormzZtz9uxZ1qxZgzkyhPZNa9Lko9LcvX3T6HgiIiIiIiLJSlhoCE+fPmXv3r1GR5G/eKsyqU2bNphMpn99hIWFvbTPo0ePWLhwId27d6dUqVLY2tpiMpn4/PPP3zr8/Pnz/zPHli1b3vr4Iv/EwsKCevXqcezYMbZu3UqQvx8dmn/Es6d+RkcTERERERFJNmYt2QTAkCFDDE4if2X1Lju7u7uTIUOG175nYfFyT7V8+XL69OnzLqf7RxkyZMDd3f2176VNmzZezikCf9yiskaNGmzdupWyZcvS9bNPmL9yB7Z2dkZHExERERGRRCAoMAB7h1S6lf1bun/vDgBWVtYGJ5G/eqcyafDgwbRp0+aNtnVycqJ69eqUKlWKUqVKsWPHDqZMmfIup3/ho48+Yv78+XFyLJG3kTdvXjZs2MD777/PgB6tmTBz+d8KVRERERERSTmuXDzHd1/15OjBPZQoU5GFq3erUPoHIcHBeN++Tn7PIgDExMRgMpm4e+cmrRtUoWSpUixevMjglPJX71QmxUa7du1o167di+enTp1KqFOLJIgyZcqwdOlSGjZsyMScX9F3yCijI4mIiIiISAKIiori4T1vvO/c4MKZk2xau5xLF86QM2dOOnbsyKxZs/C+fYNceTyMjppomM1mrlw8y8Tvv2Lfrs1ER0djZ29PZEQE0dHRpHfNgN8THwDWrV1L5syZDU4sf5VgZZJISlC/fn169erFosVz6DP4O0wmk9GRREREREQkjkSEh3Px3O+cPnmE2zevcvf2De7eucH9u3eIiooCwN7enrp16/L9dyP48MMPCQgIYNasWUz+YRgjx8/G0TG1wVeROHw9sCvLF8zA0dGRPn36YG9vT/r06bG1tSUiIoIvv/wSgBYtW5IxY0aD08qr3qlMWrVqFWvXriUgIIAMGTJQvnx5WrdujbOzc1zleyNnzpyhefPmPHr0CCcnJ4oVK0bLli3JkydPguYQAahWrRoTJ07k3p1bZMuZ2+g4IiIiIiLylp74POL0icOcOn6I08cPceHsScLDw7GzsyOvuzt58+ShtFc98uTJ8+KRPXt2rK3/f30fV1dXFi5cSNeuXWlQrTg/Tl9K4WIlDbwqY+3btYV+nZsR4P+c8ePH07VrV2xtbf+2XatWrXB0dHzpeymJxzuVSRs3bnzp+YoVKxg+fDhLly7lww8/fKdgsXH69GlOnz794vm6desYOXIkI0aM0IrvkuDKlCkDwJlTR1UmiYiIiIgkIbduXOXQvh2cPnGY0ycO4X37JgBZs2WjXNmytG7RhHLlylGkSBFsbGze+LitWrWibNmyNGvenKa1ylCleh3yexYhPDyM8NBQwsPDCAsLpdpH9fiwbiMeP7zPb2uWsWf7bwQHBTJg+I+UqfB+fF12ggkODqJHuwZUqFCBbl27Uq9evX/cVjfTStzeqkzKkycPo0aNonbt2uTKlQuTycThw4cZOnQoR48epV69ehw4cIASJUrEdd6XpEmThh49evDpp5+SN29enJ2duXTpEuPHj2fRokV89dVXODs707179388Rnh4OOHh4S+eBwQExGtmSf6srP7430pT3EREREREEr973rdZs2I+W39bxbXLF7CysqJY8eI0qPcxZcuWpWzZsmTLlu2dz5M3b14OHjjAvHnzmD1nDhtWncbOzg47Ozvs7e05dfIk+3ZuYuOaZZw8doDgwAA+qFYNU0w4nzWsSvVa9Rk4/Mck8YH1lYvneHjfm8cP7/P40X0eP7yPtY0Ny+ZPB+CH0aMpXry4wSnlXZjMZrM5rg4WERFBxYoVOXbsGFWrVmXnzp3/uO3XX3/NiBEjaN++PXPmzImrCC/06dOHiRMn4uzszN27d0md+vXzUv+X41X+/v44OTnFeS5J/i5evIinpydL1u2jRJmKRscREREREZFXmM1mHj24x8I5k1k8Z/KLdY4aNWpE9erVcXBwSPBMd+7coWevXoSEhJAta1ZGjRpFpkyZMJvNLFu2jIEDB+Lj48NnHfvQte9QHFKlSvCMb2LFwlkM+6IT8McH7BkzZSJLliyEh4dz/tw5hg//mq+/Hm5wSnmdgIAAnJ2d36gPidMyCWDbtm3UrFkTCwsLfH19/3FoWnyXSf9bxyk8PJy1a9fyySefvHa7141MypYtm8okeWvbt2+nRo0a7Dh6I0l8aiAiIiIiktzdv3uHTetW8PvxQ9y9cwPv2zcICw3FwcGBgQMH0q9fP1Il0nLmf4KDgxk6dCgTJkzgl01HKOJV2uhIL4QEB7Np3Qqe+j1h3Ldf4ujoyKVLl8iUKdOLmRvwR4mnGRyJV2zKpDi/m1vZsmUBiImJ4ebNm3h5ecX1Kd6Ik5MTnp6enDp1iuvXr//jdra2tq9d7EvkbTk6OmIymVi6YDoDh481Oo6IiIiISIrk8/ghW9avZNPa5fx+4jD29vZUqFiR6lUrkydPO/LkyUPp0qWTzJ3CUqVKRdOmTZkwYQK2dvZGx3lh8pjh/DR+JGaz+UVxNGrU92TNmvVv26pISj7ivEz660rr/7s1olH+l8XoHJKylC1blokTJ9KrVy+y58xDs886Gx1JRERERCTFiIqK4rc1y/iqT3sAan74If2XLKFu3br/uPxJUrFixS/Y2tnhmiGT0VEAOHH0ANPGfUPZsmWZPn06RYoUMTqSJJA4L5MuXLjw4uvXNZEJJTo6mitXrhieQ1Kmnj17cu7cecaNHEjdBs1xTK0pkyIiIiIi8c1sNlO9dB4e3POmadOmTJ8+PdncFezQoUNMnDiBL4aNIb1rBqPjMKhXW1Yvn4+Xlxfz5s0jX758RkeSBGQR1wccN24cAPnz5ydLlixxffg3NnfuXJ4/f46lpSVVqlQxLIekXF9/PZywsFBWLJpldBQRERERkRTBZDJha2tH69atWbZsWbIpkkJDQ2nTti1FipemTac+RscB4InPI4oXL86xY8dUJKVAsS6Ttm/fzqBBg7h169ZLr/v7+9OzZ0+WLVsGwLBhw+Ik4KpVq8iZMycVKlR46fWAgACaNWvGsWPHXno9Ojqa2bNn06tXLwDat29vaKklKVeWLFlo1qwZyxfMMDqKiIiIiEiKUa5ydX777TeCgoKMjhJnhg0bjvedO4ya+DOWlpZGxwGgQuUaXLhw4aUbWknKEesyKTg4mNGjR5M7d26yZs1KqVKlKFasGBkyZGDKlCmYTCaGDx9Os2bNXtrv7t27uLi4vHiMGTMGgMWLF7/0+sGDB1/aLygoiDt37nDv3r2XXo+JiWH58uWULl2atGnTUrx4cUqVKoWLiwsdO3YkLCyMjz76iEmTJsX2EkXiTM6cOYmM0F+uIiIiIiIJpUP3gQQGBlKrVq2//X6ZFB05coTx48fR/YsR5PEoYHScF1wyZCI8PJywsDCjo4gBYr1mkpeXF0OGDOHw4cNcv36d8+fPYzabyZIlCxUrVqRr166ULv33WxRGR0fj5+f3t9fDw8NfajIjIyPfKEeqVKkYM2YMhw4d4vz589y4cYPQ0FDSp09P7dq1ad26NY0bN9Zq8WKoR48e4ZQmeQytFRERERFJCjJnycbUeWuYOPorKleuzIABAyhbtix169Y1OlqshYWF0aZtW94r4kW7Lv3i/Xxms5mrl86zffMafB49oETpipQoUxG3rNn/tu2Na5fIlClTsplKKLFjMpvNZqNDJCYBAQE4Ozvj7++Pk5MWTZZ3816hQhQsUoZvx882OoqIiIiISIoSFRXFN4O6s3ntcgIC/OnXrz9jx45JUgMObt++Ta5cuShWoiwt2nXn/Rp14u3mPls2rGLct1/iffsGqVOnJrObG1f/vKmVW9bseJWuSMFCxUjtlIb0Lhn4ZfFsTFHB7Ny5M17ySMKLTR8S5wtwi8gfnj9/zsULFyheqrzRUUREREREUhwrKyu+GTuD49eeM+ibCYwb9yMrVqwwOlas5MyZkzVr1hAVEUz/ri34efq4eDnPnVvXGfvNF3jfvsHmzZt58uQJVy5fxsfHh9WrV9O0cUPu377M1LHD+arv53Rp/TG7t22gYMGC8ZJHEr9YT3MTkTdz+PBhzGazyiQREREREYO16dSbU8cP0qtXL2rWrJmkpmbVq1eP7Nmz4+XlRX7PInF+/MsXztKqfmVc0qdj7969VKpU6cV7rq6u1K9fn/r16794LTo6mhs3/iidWrRoEed5JGnQyCSRePLw4UMAsmbPZXASERERERGpWqMuPj4+nD9/3ugosWZvbw9Aaqc0sd73wtlT/NPqNt63b/D5pzXJnSsnp06deqlI+ieWlpZ4eHjQq1cvXFxcYp1HkgeVSSLxwNfXF19fXwCCAgMMTiMiIiIiIkW8ypA2XXoqVarETz/9ZHScWPHw8MDFxYVp40YQFhr6RvuEhYYybfxIGlT34vNPP+TWjat/22Zov444OzmydetWnJ2d4zq2JGNagPsVWoBb3lVYWBh58+bl/v37ZMzkxqaDl3F0TG10LBERERGRFG/T2hX06fQpAIGBgTg6Ohqc6M0dOnSIDz74gFSOqSlT4QM8i3iRPWcenvo+4fGj+zx+eJ/Hj+7j8+fXz589BaB9+8/ZtWsn9+/fp12X/nTsNYjgoEDu3rlJ55Z1+KJ/P7766iuDr04Sg9j0ISqTXqEySd5VZGQkNjY29Bgwgtaf98TJOY3RkURERERE5E91Kxfi6uXzHD9+nBIlShgdJ1auXLnCnDlzOHT4MGdOnyY4OBgLCwsyZspEFrcsZM2ahSxZ/ni4ubmRJ08eKlSoQGhoKKNHj+aHH34gPDz8xfHSpk3L6tWrqVKlinEXJYmGyqR3oDJJ4kKaNGno0HMwHboPMDqKiIiIiIj86fmzp5TOnx6A0NBQ7OzsDE709qKjo/H19SV9+vRYWb3ZvbX+t3B29uzZyZ49O56enlhbW8dzUkkqYtOH6G5uIvEgbbp0PHvqa3QMERERERH5iysXzwIwa9asJF0kwR8LYWfMmDFW++TJk4fu3bvHUyJJSbQAt0gcCwgI4N7du2TJmsPoKCIiIiIi8hfpXTIA0LFjRwp6euKWJQsODg7kdXendevW/PTTT5w9e9bglCKJn0YmicSx3bt3ExUVRYX3axodRURERERE/sItaw6q1vwYJ+c0ODmnxTlNWhwdnbjnfYszp46wbNkyoqKiOHbsGCVLljQ6rkiipTJJJI5t3bqV7DlzkyNXXqOjiIiIiIjIXzikSsX0hev+8f1De3fQtkl1bGxsEjCVSNKjaW4icWzrtm2Ur6JRSSIiIiIiSc3vJw/j6OjIe++9Z3QUkURNZZJIHLpx4wY3b9yggsokEREREZEkJ3VqZ4KCgvj22++MjiKSqKlMEolDW7duxcrKijIV3jc6ioiIiIiIxMLBvds5feIwAF9/PRxvb2+DE4kkXlozSSQObd26lWIly+GY2snoKCIiIiIi8oZCgoPp3LIOERER1KlTh+zZs5MpUyajY4kkWiqTROJIREQEu3btokOPQUZHERERERGRWHBIlYrcefOTJ1c2NmzYYHQckURPZZJIHDl8+DBBQUFcPHeKMd8MIF161xePMhWqYmdvb3REERERERF5jccP73P54lk6tP/M6CgiSYLKJJE4YjKZsLKy4syJg1y7dBo/X1/8/f0BGD9jGbXrf2pwQhEREREReZ2MmbNQ1KsMGzZsoG/fvkbHEUn0VCaJxJFKlSrx+PFj0qZNi8lkYtWqVbRu3Rr3AoUoWa6y0fFERERERJK9iIgIDu7ZRmhoCJjNmP/yiDHHAODimpHsOfPiljU7lpaWABw/vA/v29d55piK4OBgUqVKZeRliCR6KpNE4lC6dOkAmDp1Kj169KB2vU8ZNfFnTXETERERkRQtKDCAM6eOcurYwT/umGYykTOXOzly//Hwe/KYwAB/WnfoiclkIjQkBHsHhzc+fnhYGKuWzmX2lNE8fHDvjfaxtrYma/Zc5Mjlzp4dGwG4euWyiiSRN6AySSQe9OjRA4BxM5ZiMpkMTiMiIiIikjCCggI5sm8noaEhBAcHcvnCGX4/fpArF89hNptJly4dZcuWxcrKipNHdrFyyWzCw8MxmUyYzWZuXLvEg7u3ObRvBx17DqL3lyP/9XyhISGsWDiTuT+NxffJY5o1a8bAgQPJkSMHJpPpxcPCwuLFz+UPHjzg+vXrXL9+nWvXrnH9+nVcXFyoWfPDFx8Oi8i/M5nNZrPRIRKTgIAAnJ2d8ff3x8lJt3eXt9OuXXtW/forRy75YmWlzlZEREREkrfo6GhWL5/PpNFf8cTn0YvX8+XPT/ly5SlfvhzlypXDw8MDCwuLl/a7d+8ezs7OjBr1PWPHjqFipUpYmEycv3iJb8bOonK1WlhbW790vqCgQJbO+4n5M8bh//wZLVu2YvDgQbi7uyfYNYskN7HpQ1QmvUJlksSF06dPU6xYMab8/Cs1ajcwOo6IiIiIyGtFhIcTFhaKk3OaWO8bGRnJPe9bXL5whhkTv+PyhTM0b96c4cOHkzVrVuzs7F4qjv5LTEwMDx8+JEuWLJw6dYpOnTpx4sQJ0rtm4JPGrfmkUUu8b99g62+/smfbBsLDw2jbth2DBn1Jzpw5Y51fRF6mMukdqEySuFK+QgViTLYs+HWn0VFERERERAB4/uwpe3duYtfW9Rw9sItnT/2ws7enaatOFC5WijTp0vPMz5fsOfNQoFAxbGxsgD+KnqMHdrNv9xZuXb/C7RtXuHvnJlFRUQCUKVuWCePHU6ZMmTjNe+bMGX7+eR5LlizGz88PgPcKFaJhgwa0b9+ebNmyxen5RFIylUnvQGWSxJVly5bRvHlzNu2/SB6PAkbHEREREZEUbOtvv7J47hROHj1AdHQ0JUuV4qMPPyRnzpzs3r2bvfv24X3nzkv72Nra4lnYC+c06bh+9QJ379wia7ZsvPfee+TPlw8PDw/y/flnlixZ4nWt0PDwcLZt2/bifCIS91QmvQOVSRJXIiIiyJ49O9VrN2bo91OMjiMiIiIiKVRkZCQVi7jxzM+XmTNnUrduXTJnzvy37Xx9fQkKCiJdunRcvnyZQ4cOcfToUYKDg3Fzc6NVq1aUK1dON5gRSaZi04doZWCReGJjY0OHDh2YOHESfYaMwtExtdGRRERERCQFsra2Zuh3U+jbuRkBAQGvLZIAXFxccHFxAaBUqVKUKlUqIWOKSBLy5quhiUisderUidDQENatXGR0FBERERFJocxmM05p0lKzTkMGDhzI7t27jY4kIkmcRiaJxJObN2/Sp29foqOjOX/6uNFxRERERCSFiYiI4PqVC6xYNIvlC2YAUKBAAQoVKmRwMhFJ6lQmicSxoKAgRo36nnHjfiS9SwbGz1hGrXpNjY4lIiIiIinIU98n9O3cjMP7d2JpacnUqVPJmjUrXl5eL6ayiYi8LZVJInHEbDazdOlSBgwYgJ+fH593H0iH7gNxSJXK6GgiIiIikkJs37SG6OhohvbrQID/c6pUqcKvv/5KunTpjI4mIsmIyiSROHDq1Cl69OzJoYMHqVG7AQOG/0i2HLmMjiUiIiIiKUhYaCgDurci5M+7r23ftpUiRYpga2trdDQRSWa0ALfIO3jy5AkdOnSkRIkSPPF7zvyVO5jy868qkkREREQkXvn6PGbYF50Z1Ksd58+cxGw2ExjoT0hwMCNHjmTz5s2UKlVKRZKIxAuNTBJ5S9u3b6dx48aYMTHk20k0a9MFKyv9LyUiIiIi8cNsNnP+zEm2bFjJsvk/YWNtjaOjI6uXz8PO3p6MGd0AyJYtG4ULFzY4rYgkZ/rNVyQWoqOjef78OX5+fixatAh/f39Wbj5K4eKljI4mIiIiIsnYxXO/0/WzT3h4/y7p06enY4eOfPXVEJycnNi1axcXL17k1q1bPHv2jMaNGxsdV0SSOZVJIv/h8OHDNGnShODgYJ4/f47ZbH7pfRtbO4OSiYiIiEhKMW/GeOxsrNi1axcVK1Z8aUR8jRo1qFGjhoHpRCSlUZkk8h/mz5/PvXv36Dt4FGnTuZAmXXqc06QjTdr0pE3vQoaMmY2OKCIiIiLJ1FM/X86dPo73revkyJGD999/3+hIIiIqk0T+zaxZs5g1axatO/SiY88vMZlMRkcSERERkRTg7u2b/DxjHKuXzyMsNBQLCwuWL19udCwREUBlksg/mjt3Lp06daJV+x4MHjlBRZKIiIiIJIjIyEjaf1qT4MDnDPryS5o1a4atrS3Zs2c3OpqICKAySeS1bty4QYcOHWjauhNDvpukIklEREREEszFs6e4c+s6+/bto2LFikbHERH5GwujA4gkRrt378ZsNvNR3cYqkkREREQkQV25eBYAV1dXg5OIiLyeyiSRVyxcuJAOHTpQr0lrvMrokyARERERSTgzJ33P0P4dqV2nDtmyZTM6jojIa2mam8grNm3ahHt+T0ZPnq9RSSIiIiKSYK5dvsDMSaMoW7YsG9av18+iIpJoaWSSyF/8+OOPrFixgjr1m+sfbxERERFJEI8e3GPMNwNo3aAKufPkZu3atfpZVEQSNZVJIn+aMGECX3zxBZ17D6FTr0FGxxERERGRFCAmJoZJPwxl+fyfqFy5Ent27yZDhgxGxxIR+VcqkyTFi4yMpF+//vTt25eOPb6k95cj9UmQiIiIiMS7X5fNo2IRN1Yvn8/IkSNZ/euvpEuXzuhYIiL/SWsmSYp29+5dmjRtyonjxxk0YjyfdeqtIklERERE4lVQYABjvhnAioUzadmyJR07dqRcuXJGxxIReWMqkyTF2rx5M61atcLGzoEl6/ZTtEQZoyOJiIiISDIW4P+cOVPHsGz+T4SHhzF79mw+//xzo2OJiMSayiRJkTZs2MDHH39MlWq1GT1lAWnTpTc6koiIiIgkY4EB/rRvWoMbVy/SuXMX+vTpTZYsWYyOJSLyVlQmSYoTERFB3379KF+5OtMXrcfCQkuHiYiIiEj8CQoKpEOzj/C+dY39+/dTrFgxoyOJiLwTlUmS4kyfPp2bN24wYfZqFUkiIiIiEu+6tKrLjasX2LFjR6Ipkm7fvg1Azpw5Dc0hIkmTyiRJUUJCQpg3bx5VqtfBo8B7RscRERERkRTg8cN7WFpacuHCBUqUKGHoDV/279/P2rVrWb58Ob6+vtStW5fg4GD8AwKIiYnB1cUFV1dXXFxcyJcvH+3btzcsq4gkXhqWISnGsWPHKFqsGJevXKFuwxZGxxERERGRFGL5b4epVK0Obdu25YMPPmDt2rU8f/48wXOEhoYyePBgxo8fT868BalRpxEPfJ4TbWFP5uweZMlZgOAIE7+fvcTiJcvo0KEDDx48SPCcIpL4mcxms9noEIlJQEAAzs7O+Pv74+TkZHQciQORkZF8881Ivv9+FJ6FizN6ykLyuOc3OpaIiIiIpDAH927n+2F9uHb5AhYWFpQsVYoVy5eTI0eOOD2Pr68vW7duZdOmTRw/cYKIiAiioqII8PcnJCSEzzr2ZuDXP/7rMU4cPUCLjyvStWtXJk2ahJWVJrWIJHex6UNUJr1CZVLycvHiRVq1asWZM2fo2m8YnXoOwtra2uhYIiIiIpKC3fO+zaG925k0ZijVP6jK0qVLY30MX19fjh49ypEjR3j06BHR0dFER0dz+coVjh87htlsxrNwcUqUqYS9QyqsrKywtrbho0+akCNX3v88vt8THz6qkB//58+ws7OjWPHifD18ODVq1HibSxaRJEBl0jtQmZQ8xMTEMGnSJAYNGkTW7Ln4YeoiChUtYXQsEREREZEXVi39mSF92nPo0CHKli37RvscOnSIVq1acfPmTQBcXDOQJVtOLCwssbCwwDWTG5U/qEXFqh/imiHTO+WLjIzk5NEDXD5/mu+H98XBwYHnz5/rw1mRZCo2fYjGKkqyc+fOHdq0acOePXv4rGNv+g4ehZ29vdGxREREREReUr/pZ/w8/Ufq1KnD4cOH8fDw+M99Ll68yM2bNxkwfCw1ajcka/ac8bagt7W1NWUqvM/enZsAGDx4iIokEQG0ALckI2azmQULFlC4cGGuXLvB/FU7GTxygookEREREUmULC0t6djjS54+fcqmTZv+dVs/Pz/Gjx/PgAED+ODDT2jXpR/ZcuSK9zvDPfXzZcemNTRs2JAhQwbH67lEJOnQyCRJFh4/fkznLl1Yu2YN9Zq05qvvJpPaydnoWCIiIiIi/+rjRi05cWQ//fr14+nTp3zwwQeEh4cTERFB3rx5ef78OdOnT2fFihXExMRQs04jhnw3Od5LJICoqChaN6hCUMAzBg9WkSQi/09lkiRZMTEx7Nixg7lz57J27VocUzsx5edfqVG7gdHRRERERETeiIWFBSPGzsA1Y2Z++OEHRo4c+bdtsuXIRfcvRtDw07akd82QYNmsrKyoUKUmy+b/pPVkReQlWoD7FVqAO+moUaMm27dvI2++gjRq3p56TT4jbbr0RscSEREREXkrT3weERwUiI2NLZZWVly/coGYmBjKV66OhYUxK5SEBAdTsXBmunfvxvfff29IBhFJGFqAW1KEgMAAMmfJxuptJ7G1szM6joiIiIjIO3HNkOmlO7BlzORmYJo/2NrZEREZwY0bNzCbzQkyvU5EEj8twC1J1tw5c/D1ecS0cd8YHUVEREREJFmytLTku/FzWblyJf369UcTW0QEVCZJEubp6cmwYcOYM20M58+cNDqOiIiIiEiy9HGjFgwdNYUJE8azfPlyo+OISCKgMkmStIEDB/JeoUIM6d2OiIgIo+OIiIiIiCRLRbzKAODmZvzUOxExnsokSZJiYmI4cuQIW7Zs4eO6dbl88SyzJmtBQBERERGR+JA2nQtWVlYcPXrU6CgikghoAW5JcrZt20aHDh3w9vZ+6fWoyEiDEomIiIiIJG9Zs+ekYfP2jB07lq5du+Lo6Gh0JBExkEYmSZITFBSEt7c3Py1Yx6Hzjzl25SknbwTQY8AIo6OJiIiIiCRbnXsNxt/fn2LFi7N06VKio6ONjiQiBlGZJEmOu7s7AGnSpSe9awac06TF0TE1lpaWBicTEREREUm+3LJmZ+WWY2TPlZ8WLVqQI0cOOnToyNq1awkMDDQ6nogkIJVJkuTkyZMHAO9b1w1OIiIiIiKSshR4ryjTF61n5ZZjVK/ThF179lG/fn3Sp09PtWrVuHDhgtERRSQBqEySJMfBwQG3LFm4ffOa0VFERERERFKkwsVKMuib8Ww+eJntR6/zaevO7Ny5k/XrNxgdTUQSgBbgliTJw91dI5NERERERAzk98SHH78dyM4t6/B//ozMmTPTpUtno2OJSAJQmSRJUt68eTl87JTRMUREREREUqR1qxbz3ZCeWFqY6N2rJ0WLFqVEiRKkSZPG6GgikgBUJkmS5O7uzooVv2A2mzGZTPFyDp/HD9mz7TciIsJp3OJzbO3s4uU8IiIiIiJJSXR0NN8O7kGF8uWYP38+rq6uRkcSkQSmMkmSJHd3dwIDA3jq+4T0rhni5Jhms5kbVy+xc8s6dm5dx5mTR7Gw+GNZsVmTv6dTr8E0bvE5Nra2cXI+EREREZGkKCQ4iAD/5zRv3lxFkkgKpQW4JUnKmzcvALdvvdsi3FFRURw/vI/Rw/tRo4w7tSt5MnPSd+TJkYUFCxbg4+PD5cuXqVH9A74d0pMaZfKy4delcXEJIiIiIiJJUirH1KRK5ciGDVpsWySl0sgkSZLy5MkDgPet63iVKh+rfYODgzi4Zxs7t6xj746NPHvqR6ZMmahb92Pq1ZtM1apVsftzSpvZbMbHx4cCBQqQPXt2bt++zdYNK6nbsHmcX5OIiIiISFJgYWGBV5mKhIeHGx1FRAyiMkmSJAcHBwoWLMiiOZP56OMm2Nnb/+c+N65eYs60MWxcs4zw8HAKenrSpXMnPv74Y0qWLPliSltMTAyHDx9m7dq1rFm7lmtXr+KQKhUVq35Ety++pXK1WvF9eSIiIiIiiZrJZOLq1avcvXuXbNmyGR1HRBKYyWw2m40OkZgEBATg7OyMv78/Tk5ORseRf3H69GnKlSvHhx834ftJ8/51Ie5lC2YwclB3MmXKRPfu3WncuPGL0U0A4eHh7Nq1izVr1rJ+/ToeP36Mi2sG3q/xMdU+qkfZih9oAW4RERERkT+dOLKfFp9UAmDSpMn07NnD4EQi8q5i04doZJIkWUWLFmXWrFm0atWKHLncqVG7AS4ZMuHknAaTycTFc7+zb+dmDu7dxrFDe+nevQfjxv2IjY3NS8fZunUbjRo1JCgoiOw581C7YSuqfVSPol5lsLS0NOjqREREREQSp+joaHZuXf/ieZo0aYwLIyKGUJkkSVrLli05duw4E0d/xcTRXwFgY2ODc9p0PHn8CEdHRypVrszixYtp0aLFa49haWmJtbU1AFmz56KoVxkKFyulIklERERE5DUWzp7Ezz/9SP/+/Rk4cCAuLi5GRxKRBKZpbq/QNLekJyoqitu3b/Po0aOXHunSpaNHjx4viqJ/ExoayqpVq5g+YwaHDx3CNUMmGrX4nCYtO+CWNXsCXIWIiIiISOJlNps5enAPyxfMYPP6X7CzsyM4OPjFuqMikvTFpg9RmfQKlUly9uxZZsyYyeLFiwgODqbyB7Vo2roTlT74SKOVRERERCRF2r5pDd3bNiBf/vx06dyZ1q1bkzZtWqNjiUgcUpn0DlQmyf8EBQWxbNkyZsyYwalTp3DLmp3ZSzeTN19Bo6OJiIiIiCSIsNBQli+cyeypozGZY3j8+PG/3vhGRJIuLcAtEgccHR2pUaMGAQEB3L9/n8cP7/P8mZ/RsURERERE4l14WBgrFs1i9pTR+Pn60Lr1Zwwd+pWKJBEBVCaJ/KPDhw9Trlw5bG1tqVj1I3p88TX5PYsYHUtEREREJF5ERUXxzaDuXL5wmof3vPF98phWrVoxdOhQ8uTJY3Q8EUlEVCZJihYVFcXNmzc5c+YMW7ZsZevWLWTPkYOwsDAePnwIwPRFGyhfubrBSUVERERE4k9ERARfdGvJ9o2radKkCWVKFKVv3754eHgYHU1EEiGVSZKi+Pr68tVXQ1m0aCFhYWHExMS8eC+zW1aq125EgP8zbO3sKVPZlWw5clOuUjUDE4uIiIiIxK+nvk8YPqAz2zauZuzYsfTv39/oSCKSyKlMkmTPbDZz9+5dTp48Sf/+/fH29qZjz0FkyOSGjY0tmdyykr9gEdK7ZjA6qoiIiIhIgjqwZxtfdG1BRHgYQ4YMoUOHDkZHEpEkQGWSJCuPHz8mLCyMM2fOsG7dOs6dP8+lixcJCgoCIF/BQoyevIC6DZsbnFRERERExFjTxo9kypjhVKtencWLFpEhgz5cFZE3ozJJko3IyEgqVKzI9WvXAHDP78l7RUpQuWZD3PN5kjefJ1mz5zQ2pIiIiIhIIrB5/Uom/zCM9u3bM3PmTCwtLY2OJCJJiMokSTY6duzE9WvXGDNtEQU8i+JR4D2jI4mIiIiIJEprVswHYNCgQSqSRCTWLIwOIPIuzGYzM2fOon79+syfP49CRUvySaOWKpJERERERP5FJrdsAHz33SiDk4hIUmQym81mo0MkJgEBATg7O+Pv74+Tk5PRceRfhIaGsnDhQjp37vziteNXn+HknMa4UCIiIiIiSYDZbCZ/pj/GFri5uXHy5EkyZcpkcCoRMVJs+hCNTJIkJSoqirNnzzJnzhxq1qxJ586dcUztxJhpi9hzyltFkoiIiIjIGzCZTGw/ep2ho6bg7x/AoEGDePbsGRs2bCAwMNDoeCKSyGlk0is0MinxGjXqe7777ltCQkKwsLDAPb8nnoW9+HLEeJzTpDU6noiIiIhIklS9dF68b9948TxVqlR4eHiwbt06smXLZmAyEUlIselDtAC3JAk//fQTQ4YMpmW77nz0SRMKFCpGqlSORscSEREREUmSYmJisLD4Y6LKjMW/ce70cUwmE9ZW1owc3IPff/+d48ePq0wSkdfSyKRXaGRS4nPv3j2yZctGztzubDl0BZPJZHQkEREREZEk6eypYyyZN43N61ZgY2NLOhdX0qZzJZ1LBkKCg7h47hQB/s8B8PX1JX369MYGFpEEo5FJkizExMQwY8YMvvzySzJmcmPCrBUqkkREREQkyXji84hzvx/H1+cRDqkcKV+lBs5p0hIY4E/A82cEBQVgMllgbWODjbUN1jY2WP/5p2Nqpxcjh95VeFgYm9f/wpKfp3L29+PkzJWLr776Cnt7e3x8fHjy5Ak+Pj44uzpT54v+eHl54eXlpSJJRP6RyiRJlKZPn87ESZO4euUKTVp2oP/QH7QukoiIiIgkGXduXeezhlV5eP8uJpMJs9n84oPRN5kc4uiYmveKlCCdSwbsHVJhb+9A2vQuFCpWiqJeZd7oZ+P7d++wfMEMVi2dw1M/X2p++CHfbdjARx99hKWl5Ttfo4ikXCqTxFDh4eHY2toCf0xn27t3L+vWrWPlypV89HETho+dQ4nSFQxOKSIiIiLyelFRUfzwdX/Cw8NIm86FtGnT4+//jJWLZ5M2rTN7rlwhd+7c+Pj4sH37diIiIkibNi1p06bFyckJs9lMREQEERERREZGEhERQXh4ONevX+fEiRM8f+6L3+M7hISE8OD+ffz8/ADI456fIl5lyJvPk3TpXf84dzoX0qZ34e6dmyz5eRq7t20gderUtGnTlm7duuLu7m7wd0tEkgutmfQKrZmUMPz9/Vm4cCFDhw6lZs2aHD9xgls3bwLgnq8grT7vRdPWHQ1OKSIiIiLyeuFhYdjY2jLiy26sXDyb9woVws/Pj6d+ftjZ2VG1alWmTJlCxowZ4+ycZrOZGzducOTIEQ4fPsyhw4e5eeMGAQEBf9v2vUKF6NG9Oy1atCBVqlRxlkFEkq/Y9CEqk16hMil+hYSE0L17D+bN+xmAUuWqEOj/jBJlKlGyXGVKlK5IetcMBqcUEREREflnPo8f0vjDUjx/5kdYaCizZ8/m888/f/H+X6e0JYSIiAj8/Pzw9fXF19cXe3t7SpcurfVGRSRWtAC3JDrPnj1j5syZTJkyhafPnjFoxHhKla9CgfeK6h85EREREUkSbly7zPIFM9i5ZS0WxDDym29InTr1S0USkOA/39rY2JA5c2YyZ86coOcVkZRLZZLEu3379tGkSROeP39O3YYt+bz7AHLl8TA6loiIiIjIG/P1eUzvDo157udDrVq16Nu3L4UKFTI6loiIIVQmSbzatm0btWrVwqt0BVZOX0rGTG5GRxIRERERiZVL50/TtnE1rCwt2L59O0WKFDE6koiIoVQmSbwIDQ1lz549tG3blrIVP2Dmko1YWek/NxERERFJepzTpOPZUz9mzZqlIklEBLAwOoAkPyNHfouDgwO1atXCPpUTP0xZqCJJRERERJIst6zZKVPhfbp06UK58uWpU6cOffv25dSpU+h+RiKSEulubq9IqXdzi4mJYffu3VSoUAFbW9u3Pk5ERMSL/Tfuu0AejwJaYFtEREREkryIiAgWz53ClYtn8X/+jHOnj+Hr85jq1WuwYcP6d/oZWkQkMYhNH6KRScL+/fspWbIk1apVw8PDg+XLl7/VcaKiovj2228B6N5/OHnzFVSRJCIiIiLJgo2NDe269OOHKQuYsWg9e3+/x6Q5K9m3by/t2rUjJibG6IgiIglGZVIKdvv2bZo0aUKlSpWIjLFg8txV5PJ4j7Zt2/L48eNYHWvbtm0UKFCAkSNH0mPACLr3Hx5PqUVEREREjGdlZcWHdRvRrd9wli5dyqFDh4yOJCKSYFQmpUBms5nRo0eTP39+9u47wA9TFvDL5qPUrNOQsdMWY2llxaRJk2J1zM2bN3P9+nUmzv6F7v2GaUSSiIiIiCR70dHRbFq3grLlylG+fHmj44iIJBitipxMhYWF8eDBA54+ffq3x9mzZ1m5ciXtu31Bt37DSJXK8cV+zmnS8ulnXZg2bRoDBw7E2dn5jc5369YtAIoULx0v1yMiIiIikpiEhYbSvV0DLl84w+HDh/VhqoikKCqTkqkPPqjGoUMHX3rNysqKtOnS45wmHcNGT6NF266v3fezjr1ZNHsSM2bMYODAgW90vosXL1K4eCkyuWV95+wiIiIiIomV2Wxm+oTvmPTDUAAaNGhAmTJlDE4lIpKwVCYlUzdv3uCDDz+hxxdf45wmHc5p0+HgkOqNPjHJmMmN+k3b8N1331GsWDFq1Kjxn/tYWFpSwLMoFhaaOSkiIiIiyVN0dDTfDunJ0nk/0a5dO3Lnzs2gQYOMjiUikuD0m38y1aRJU3ZtXc+Fs6dwy5qdVKkcYzX09othYyheuiIfffQRkydPxmw2/20bPz8/xo0bR9Fixbhy+TIFCxePy0sQEREREUk0IiMj6dOxKcsXzGDAgAE8fPiIffv24+fnZ3Q0EZEEZzK/riVIwQICAnB2dsbf3x8nJyej47w1s9lM167dmDlzBl37DqVNpz44OaeJ1TGio6P5ceRAfp4+jjVr1lCvXj2CgoKwsLDA3t6ePn36MmnSRGrWbcTHDVvywYcfa664iIiIiCRLfk98KPdeRipWrMj+/ftfeu/WrVvkzJnTmGAiInEkNn2IyqRXJJcyCf4olIYM+YrRo7+nXdf+DBg25q2O0aHZR1y9eJp06dNz+dIlzGYzNjY2f3yfAgI4ec0fG1vbeLgCEREREZHEY+XiOYz4siuOqZ1o1/UL7t25yYpFs1i4cCGtWrUyOp6IyDtRmfQOklOZBPD48WMyZcrED1MWUK9J67c6xo2rl+jXpTmFi5emSPHSWFvb4PvkETs3r6Vek89o3PLzOE4tIiIiIpI4+T3xIW16FywsLDhz8ihNapUhS5Ys3L17V6P0RSRJU5n0DpJbmVS9enWOnzjJ+t1ndac1EREREZE4NnxAF5YvmIGVlRUbNmzgww8/NDqSiMhbiU0f8lYLcLdp0waTyfSvj7CwsJf2efToEQsXLqR79+6UKlUKW1tbTCYTn3/+7qNaLl26RIsWLcicOTN2dnbkyZOH/v378/z583c+dlJXsWJFwsJCsbC0NDqKiIiIiEiy88XQH5gwawWFi5emW7duhIaGGh1JRCTeWb3Lzu7u7mTIkOG17716i/jly5fTp0+fdznda+3evZvatWsTGhqKq6srnp6eXL58mXHj/lg0+tChQ2TMmDHOz5tU9OzZkwkTJjBn6hgGj5xgdBwRERERkWTFMbUTtT5pQr6ChalVoQAODg5ERERgbW1tdDQRkXjzViOT/mfw4MEcOHDgtQ8bG5uXtnVycqJ69eoMGTKEdevW0aNHj3cKDhAYGEjTpk0JDQ2lZ8+e3L9/n5MnT+Lt7U358uW5efMm7du3f+fzJGVp0qShUaNG7N62gTB9SiIiIiIiEueOHtxDszrlXjyPjo42MI2ISPx7p5FJsdGuXTvatWv34vmpU6fe+ZgzZszgyZMnFChQgPHjx2P551Su9OnTs3TpUvLkycPGjRs5deoUxYsXf+fzJVWNGjViyZIlNK1dlveKlKCoVxmq125AmrTpjI4mIiIiIpLk3fO+hf/zZ8yYMYMqVapgZ2dndCQRkXj1TiOTjLZ69WrgjzWcLF9ZEyh79uxUq1YNgFWrViV4tsSkZs2aHDx4ECdHO65dPMmwLzpR/r2MrFmxwOhoIiIiIiJJjvftG/w0/lvGfDOAnu0bMbh3O0qULEnTpk3Jly+f0fFEROLdO41MWrVqFWvXriUgIIAMGTJQvnx5WrdujbOzc1zl+0dRUVGcPHkSgPLly792m/Lly7NlyxaOHj0a73kSu2LFinH0yBHgj8XQ8+TJw7OnvganEhERERFJemZNHs3KJXPIkTMnLi4uTJ06lc6dO//tA24RkeTqncqkjRs3vvR8xYoVDB8+nKVLl8b7LTFv375NZGQkALlz537tNv97/dq1a/GaJanJlCkTADMmfsexg3soVrIczdp0wck5jbHBREREREQSsejoaKaN+4Z1KxeSLXt2bt28iclkMjqWiEiCe6tpbnny5GHUqFGcOXOGgIAAAgMD2bZtG6VLl+bZs2fUq1ePEydOxHXWlzx79uzF12nTpn3tNv97/a/bvio8PJyAgICXHinB2rVr6d2rJwFPHzJ+1GBCQ4KNjiQiIiIikig99fNl9PB+VCmWjWnjvqFBgwacO3tWRZKIpFhvNTJp6NChf3utevXqVK5cmYoVK3Ls2DEGDhzIzp073zngPwkLC3vx9at3jvsfW1tbAEL/5S5m33//PSNGjIjbcElA9erVqV69OhkzZuTMmTO4ZMhkdCQRERERkUQnMjKSPh2acPHcST77rA2tWrWkRIkSKpJEJEWL0wW4bWxsGDlyJAB79uz51xFB7+qvd0iIiIh47Tbh4eEA2Nvb/+NxBg0ahL+//4vH3bt34zZoIuft7U0mt6ya3y0iIiIi8hpTxgznyMHdrFmzhsmTJ1GyZEkVSSKS4r3TmkmvU7ZsWQBiYmK4efMmXl5ecX0K4OWpbc+ePSNz5sx/2+Z/ZdY/TYODP0Yv/W8EU0p0584dMrll+8/tIiMjued9izs3r3H7xlVu37pGmrTp6f3lyARIKSIiIiKSsKKjozl98gh7dmwka9aslCtXzuhIIiKJRpyXSdbW1i++joqKiuvDv5AzZ06sra2JjIzk5s2bry2Tbt68CYC7u3u85UiqYmJiGDVqFCtWrKBF226v3eap7xN+nj6ObRt/5Z73LaKjo4E/RnplyZKF69ev45wmLc3bdMX2LyPFgoIC8Xn0gFx5PDCbzYQEBxEcFEhQUCDBgQF/fv3HnxHh4Tg6OePknIZ8BQvjqul2IiIiImKwTet+4dshPfB74oOrqyuzZs16aWaEiEhKF+dl0oULF158nTVr1rg+/AtWVlYUL16co0ePcvDgQcqXL/+3bQ4ePAhA6dKl4y1HUnT27FnatGnD6dOn6dp3KN36DXvp/f+VSEvmTcXCZKJ169YULlwYd3d3PDw8cHNzA6Bjx06MGfEF82eM5/PuA4mMjGDvjk2cPLqfyMhIbO3sCP/L2lb/pXWHXgz5dmJcXqqIiIiIyBsLDwvj++F9WTZ/Oo0bN6ZPnz6UKlVKS0KIiLwizsukcePGAZA/f36yZMkS14d/SYMGDTh69Cjz58+nX79+L/0l7+3tzY4dOwBo2LBhvOZIatauXcfvv/9Oxx5f0nPACGJiYrhx9RKnjh3k5LEDbP1tFRYmE7179aJv376kT5/+tceZM2c2X3zRn5EjR/LdV72wtbWlyvvvM378eHLnzs2VK1dInTr1i4eTk9NLf6ZOnRpra2u2bt1KrVq1yONRIIG/EyIiIiIif7hz6zq9Pm/MzWuXmDlzJh06dNDaSCIi/8BkNpvNsdlh+/bt7Nq1i44dO5IrV64Xr/v7+zN06FCmTJkCwNKlS2nWrNk/Hufrr79mxIgRtG/fnjlz5vzjdqtWraJ///5kzZqVAwcOvPReQEAAefLkwdfXl549e/Ljjz9ibW2Nn58fn3zyCQcPHuSjjz5i06ZNb3x9AQEBODs74+/vj5OT0xvvl5QEBQVRtWpVjh8/TpZsOQgKDMD/+TMsLCwoVLgwdevUoXfv3v9YIr2Ot7c3rq6u/7rY+etER0dTokQJosyW/LL5qD71EREREZEE9+ypH9VK5sLW1oYdO3ZQtGhRoyOJiCS42PQhsR6ZFBwczOjRoxk9ejRZsmTBzc2NyMhILl68SEREBCaTiWHDhv2tSLp79y7FihV78TwkJASAxYsXs3bt2hevr1u37qUpa0FBQdy5c+e1WZycnFi+fDl16tRh8uTJLFu2jOzZs3Pp0iVCQkLImTMnP//8c2wvMdlzdHRkypQp7Nixg+DgYOzs7ChbtiylS5d+6wIte/bsb7XfjBkzOHPmDL9sOqIiSUREREQSlNls5vbNa5w6dpCgoECCguJ33VcRkeQi1mWSl5cXQ4YM4fDhw1y/fp3z589jNpvJkiULFStWpGvXrq9doyg6Oho/P7+/vR4eHk54ePiL55GRkbHK88EHH3DixAm+/fZbdu3axblz58iSJQv169fnq6+++tc7uaVkpUuXNnwtqSdPnvDVV1/RqHl7ChcvZWgWEREREUk5zGYzE0YNYd3KhTx6eP+l92L7+4iISEoU62luyV1KmOaWGISEhNCmTRu2bd/BlkNXSZfexehIIiIiIpJCXL5wlk+qFgFg48aNlCtXDisrK8xmM6lTpzY4nYiIMeJ1mpvIuzCbzSxbtoyBAwfi4+PDtxPmqkgSERERkQSV37Mw2XLkJq2zIx999JEW2hYRiSWVSZKg1qxZS4sWLXBOk5bFa/dRxMvYqXYiIiIikjK5ZsyM76O7hIWFxfomMiIiKZ2F0QEkZfHyKk6DBg0ICQ6ixScVmTdjvNGRRERERCQF8nvymDRp0mBnZ2d0FBGRJEdlkiSoHDly8Ouvv/LgwQMyZ87MsYN7jI4kIiIiIilMdHQ0d25d5+zZs5w5c8boOCIiSY7KJDFE+vTpef78OZ5FvIyOIiIiIiIpyK0bV2lVrzIAPXr0xN3d3eBEIiJJj9ZMEkOYTCZKlS7Nud+PGx1FREREXnHm5FFWr5jP7q3rSe3kTLacecntnp/qH9WnaIkyREZEYGNra3RMkVjbtXUDXVp/TJ48edi7dy+VKlUyOpKISJKkkUlimPLlynH65GFiYmKMjiIiIiJ/2rtjE83qlufQ7k00b9aUj2pWw94qmk2rF/NpnXLkz2RBoex27Nyy3uioIrF2+sRhAPbt26ciSUTkHWhkkhimfPnyjBgxgoE9PsOzsBf5ChTCo0Ah0rtmMDqaiIhIinT29+P06tCY2nXqsPrXX7G0tHzxXnR0NHv37uXUqVMMHTqU/l2bM2PRb5QuX8W4wCKxlN+zCAAODg4GJxERSdpMZrPZbHSIxCQgIABnZ2f8/f1xcnIyOk6yFhgYSPfu3Tl77hyXLl4kPDwcABfXDLjnL0SF92tSuvz77Nn+G7XrNyN33nwGJxYREUm+bl6/QstPKuLunpddO3f+663Sg4OD+aBaNY4eOYJXqfK0/Lwn6V0yEBMdjWvGzNy+cZUl86bxzdiZZMuZOwGvQuTfrVr6M0P6tKdkqVJsWL+ejBkzGh1JRCTRiE0fojLpFSqTjBEVFcWNGzc4d+4c586d48yZM2zcuJGoqCgsLCywsLCgQ48v6f3lSKOjioiIJCs3rl1mztQxbPh1Mbly5+bQwYOkT5/+P/eLiYnht99+44cxYzh08ODf3k+dOjWOqZ1Zsv4AWbLliI/oIm9l/HeDmTn5e9q1a8fcuXONjiMikmioTHoHKpMSj8uXL3P9+nWKFStG1qxZ8SxcnI49B+GWNQfhYaGsXj6PXHnykdu9APkKFiZbjlzAHz/cWlhoOTAREZHXeer7hPDwMJ48fsisKaPZsXktmTNnpl+/fnTo0IHUqVPH+pi3bt0iKiqK48ePs3rNGjp36sSFCxfo3bs3U37+lRq1G8TDlYi8nae+T6hcLCu1a9dm9erVRscREUk0VCa9A5VJiU9gYCAuLi5ERES89HqOHDl4/vw5/v7+AJQsW4lL50+TKpUjO47fwsbGxoi4IiIiicajB/fYt3Mzh/btwNrGhsiICHZuWUt0dDTR0dG4e3jw5cCBtGjRAts4vDvbwoULadu2LfWatObb8XNeWntJJKGYzWaOHtzDe0VL4OiY+qXXF86ezKihvTlz5gyFCxc2MKWISOIRmz5EC3BLopc6dWoCAgJ48uQJDg4OXLp0iZs3b9K8eXMsLCx4+PAh+/fv59NPPwUgKDAAa2trg1OLiIgYr2GNEvg+eUz69OnJX6AAwcHBfPfdd38USe7ufPLJJ3Fe9KxYsYI2bdrQqHl7vvlxpkYLiyEiIyP5ekAXVi2di62tLcVKlMPO3oHnz/y4eO4UNja25MmTh4IFCxodVUQkSdLIpFdoZFLSdPjwYapWrUrlarUZ8u0kMmbOYnQkERERw307uCeL5k4BwN3Dg25du9KrV694O19AQAB58+bFq2wVJsxcriJJDHHh7Cm+H9aH0ycOM2HCBMLDwzl48CDR0dHExMRQsmRJQkJCqFixIrVq1TI6rohIoqFpbu9AZVLS8/33o/nqqyEUKlaSeSt3kCqVo9GRREREEo2Q4GAO799J7w6N6d69O+PGjYuX8yxfvpyhQ4dy7/59th66Sia3rPFyHpF/cv7MSab9OIJd2zaQ192dObNnU7lyZaNjiYgkGbHpQ/RxkSRpZrOZceN+5ONGLVm6/oCKJBERkVc4pEpFgfeKEhERwapVq2jQoAGrV68mPDw8Ts8zYsQIrl+/zqI1e1UkSYI6d/oEnVvWpWGNEty9fYVFixZx6eJFFUkiIvFIZZIkab///jt+fn7UadAcKystASYiIvI6mbNko0P3gZQo9wHXbnrTsGFD3N3d8fPzi7NzNG7cGAC3LNnj7Jgi/2XU0D40qlmS+97XWLx4MZcuXqRly5b6uVBEJJ7pb1lJ0iZOnIhb1uyUqVDV6CgiIiKJlslkov/Q0S+eL184k+FfdGbr1q00b978nY9vNps5dOgQhYqWwCVDxnc+nsh/iYyMZO60sSyYNZG2bdsye/Zs3TVQRCQBqUySJOvu3bssW7aML4aN1d3bRERE3tCjB/dY+vM0SpQoQdOmTd/5eFFRUXz8ySfs3LmTCbNWxEFCkX/mffsG+3dv5ZdFs7h2+TwDBgzk66+Hq0gSEUlgKpMkyTp48CBRUVF8WLeR0VFERESSjE/rlCMqIowlizbHyS/gI0aMYPOmTUxfuJ6qNevGQUKR15sxcRQTvh+ClZUVlSpVYvGCo3h5eRkdS0QkRdKaSZJkFS9eHEdHRwb1aktMTIzRcURERBK9iIgIHj24R8+ePePsl3AfHx8ASpatFCfHE/knp08cJlOmTPj5+bFz504VSSIiBlKZJEmWh4cHP/74I4f27WDD6qVGxxEREUn0bGxsaPBpW8aMGYO3t3ecHLNOnToAHD2wm9CQkDg5piROT32fcOHsKQ7t3cH2TWtY+8tCli2YwfHD+4iIiIj38zunTUdqJ6f/vF21iIjEP5PZbDYbHSIxCQgIwNnZGX9/f/1DlQR4e3tT88MPuXzpEh993IQfpy/R3TtERET+RWCAP9VL58HLqzjbtm7FZDK90/HCw8PJli0bT548wdLSknwFC1O4eGmKepWhcPHSZHbLhr2DA1FRUVw8e4pTxw9S65OmZMycJY6uSOLbtcsX+GFEf/bv2vKP2yxas4dS5SrHa47qpfPyfuUKzJ8/P17PIyKSUsWmD9Fv3ZKkZc+enYsXLtCoUSNWr/6FPoO/I0euvEbHEhERSZTMZjOrls7l2VM/goOD4+SYtra2PHjwgAsXLnDkyBGOHDnKkaN7Wb5gxottbGxsMFlYEB4WBsDC2ZNo0a47Lq4ZccmQiXKVqmFhoQHzidHBvdvp2a4hbm6Z+fnnn3nvvfdwcXEhderUpE6dmtOnT1OmTBmCAgPi/Nx379zi3p2bPHp4jwf3vXl435tcuXLF+XlERCT2NDLpFRqZlDRNmjSJ3r17M3fFVipUqWF0HBERkUTp2uUL1Kn8Hm3atOWHH0aTIUOGeDvX8+fPOXXqFI8ePeLp06dERUVRpkwZMmbMSPMWLbh44QIBAX8UEO269GPg1z/GWxZ5O+FhYTSpVYbHD7y5c+cOqVOn/ts2YWFhVKtenYsXL7Hst0PkyuMRJ+f+bfUy+ndtwf9+VXF1dcUjXz62btlCqlSp4uQcIiLyMo1MkhQn5M81GiLCww1OIiIikjhFR0cz5psvcHZ2Ztq0qTg4OMTr+dKkSUPVqlVf+97hQ4cACA0NZcyYMXz99dd06fMVTs5p4jWTvJmI8HBmTf2BpT9Pxc/3CevWrXttkQRgZ2fH+nXrKFe+PB2bf8Ty3w6T3vXdSso92zcysEdrWrZsybBhw8iaNSt2dnbvdEwREYlbGpn0Co1MSnpWrFjB4MGDsbSxZ/W2k9jY2hodSUREJNHxf/6MMgVccHV1ZezYsbRq1croSABcv34dd3d33q9eh7z5PPn0s85kzZ7T6Fgpktls5taNqwzs0ZrL50/Tvv3n9O7dCw+P/x5tdPv2bcqWLYujczr6DBpFtY8+efFeTEwMN65e4tL501y6cJorF85gZW1Ntuy5cUqTlkcP7mIyWTBi7Aysra35afy3TPphKEeOHKF06dLxeckiIvIXGpkkKcbWrVv59NNPAVi5+aiKJBERkX/gnCYtP/+ynVmTv6d169bMnjOHCePHG3579Tx58tC8eXOuXLnC7Km/UaZiVZVJBogID6d7uwbs3bGJbNmycfDgQUqUKPHG++fMmZMtW7bQsmVLurWpR9vOfYmKjCQiMoITR/Zx4+olAHLkzEnRIkWIiYnh5JFdPHv2jGzZsvH7qVM89XtCJres7N+1GYB9+/apTBIRSaQ0MukVGpmU+ISHhzNv3jzc3d354IMPXrxuNpvJmjUrzukyMHz0TxQrWdbAlCIiIklDaEgIv61eytL5P3Hr+mWWLVvGJ5988t87xrMLFy7w3nvvUaxkOeav3IGdvb3RkVKMS+dPM2JgVy6d/51Zs2bx8ccf4+zs/FbHmj17NqO+/x5bW1usra2xsbEhe/bsdOncmVKlSpEmTZrX7rdixQoGDhyItY0N1T74gHr16lGzZs13uCoREYmt2PQhKpNeoTIp8QgNDWXt2rWMHDmSS5cukTp1ap4+fYqV1R8D6m7dukXu3LmZvnA9VWvWNTitiIhI0mA2mzl1/BA///QjOzavpWvXbkybNtXoWMTExPD999/z7bff0qj55wz9forRkVKETet+oV/nZuTPn59Zs2ZRvnx5w7KYzWZMJpNh5xcRSek0zU2SpICAAObMmcP58+fxvnuX48eOERAQQOnyVWjYrCy/LvuZzz77jEqVKlG/fn32798PQLGS5QxOLiIikrjs2b6R82dP0qx1578thjxiYFeWLZhBmjRpmDhxIm3atDEm5CssLCwYMmQIadKkoXv37vj5+jB68vx/HaH0uvIhMjKSQP/npHNxje/ISd7jRw+YOfE7KleuzNatW7G2tjY0j4okEZGkQ2WSGOqXX37h2LFjXL16lf379xMaGkp+zyJkcsvGZ536UrdhC3Lkyouvz2NCQ4K5dPUWy5cvp2vXrqRLlw73/J6kTZfe6MsQERFJNLZsWEW/zs2Ijo5m5sTvKFepOk5p0mJv70Bu9wKcO30cgOfPn5M6deq3ns4UX7p27Yq9vT2dO3fm4/dPUbBQcQoXL0Xdhi2wt3cgODiIQ3u3s2/nZg7t205MdDSuGTNTuVpt8nkWYfaU0dzzvkXL9j34vNsA/ZzwGv7PnzF7yg8smjsZezs7fpo60fAiSUREkhZNc3uFprklnHv37pEjRw4yZs6Ce/73KOBZlBbtupExc5Z/3e+p7xN2blnH7u2/Ub/pZ1SvVT+BEouIiCReT/18WbV0LhNGDaZJkyZMmjSJ+fPns2vXLkJCQwkKCuLihQuEh4e/2KdPn76MHz/OwNT/bP369WzdupXjJ05w/Nixl94zmUx4lSjBhzVr4uTkxM2bN1m6dCkBAQGULVeOihUqMG3aNCwsLJk0dxXlK1c36CoSl5DgYBbNmcycqT8QFRVJnz596N+//z+uYyQiIimL1kx6ByqTEs63337L0KFDOXndH8fU+l6LiIi8rbEjBzJ/xngAOnbsxOTJk7C0tPzbdkFBQezcuZP16zcQHR3F4MGD3+i270Y7cOAAz58/JyQkBAsLCypXroyr68vT2EJCQoiIiMDZ2RmTycSTJ0+oXr06aVyzMGvJRoOSJx4P79+l/ac18b51nU6dOvPVV0PImDGj0bFERCQRUZn0DlQmJRxPT08uXrzIrCUbqVytltFxREREkqz61YqTI2smFixY8LeSJSXr0qUL6zZs5Le957G2tsHWzs7oSAkqODiIdSsXcfzQXo4d3kMqezs2b95MgQIFjI4mIiKJUGz6EIsEyiTyN9Wr/zHkvGiJsgYnERERSZr6dWlOvowmLp0/TenSpVUkvSJHjhz4+jyipEdaCuewZ//urUZHShBms5k508ZSuUgWRg7qTuDTB3xcpzaHDh1SkSQiInFCZZIY5n9l0rhvv+TksYMGpxEREUlajhzYzW+rl5EhQwZ++OEHateubXSkROfLL7/k+vXrxMTEAOCcJp3BieJXVFQUMyaOonrpPIz9ZgBt2nzGrVu32LdvH3PnzsXNzc3oiCIikkxomtsrNM0t4ZjNZrp27caMGdMBuPJY/ymKiIj8k/CwMK5ducCl86c59/sxVi2dS5X332f5smW4uLgYHS/RCgkJwd3dHae0rvy67QRWVsnzZsY+jx/S6/PGnD5xmPbt29O4ceMXH9yJiIi8idj0IcnzX1NJEkwmE9On/4StrS2TJk3k2VM/3b5XRERStCMHduPz6AEVqtTA1t6eOzevsWrpXI4d2sPNa5eJjo7GwsICj3z5GDRoMMOHD0u25UhccXBwoHz58qxatQo/Xx8yZkqeo3M+a/A+ocEB7Nu3j/LlyxsdR0REkjn99CGG8/BwB2Dp/J/o1neowWlERESM8eypH581rAqArZ0d4WFhAGTOnJlPPvmE/n16UrRoUd577z0cHByMjJqkmM1m/AMCyJYjd7Iski5fOMPIwT24ef0KP/74o4okERFJECqTxHDdunUDoE2nPgYnERERMY69/R8FkYWFBblz5aJVq1bkypWLRo0aafTRO1i3bh3btm5l1MSfjY4SL8aPGsyJI/tZtGgRjRs3NjqOiIikEFqAW+LF77//ztChQzl+/Pg/bnP+/Hlq16kDgJ29PalSOSZUPBERkUTHzt6eFRsP41m4OJcuXWLw4ME0a9aMn376yehoSZqHhwcAIcFBBieJew/ueXN43w5GjhxJy5YtsbW1NTqSiIikECqTJM6tXLmS4sWLM2rUKEqVKkWVKlU4duzYS9scPnyYChUqcOnyNX6YupAT1/wNSisiIpJ4FC1RhpVbjrFozR469xoMwMqVqwxOlbQVLFgQtyxZ8PP1MTpKnIqIiGDEwK44OzvTu3dvo+OIiEgKozJJ4tSKFSto0qQJBd4rypnbIUybt4aHPn6ULl2aZs2acfz4cUaOHEn16tVxL1CYVVuPU69xK6ytrY2OLiIikiiYTCZKlavM3Ts3ARg7dozBiZK20NBQHj18SCa3rEZHiRNms5kzJ4/SpVVdDu3bzrx583B01OhuERFJWCqTJE598cUXVKlWmznLtmBja0u1WvVYu/M0346fw67deylVqhSjvv+eOg1bMmfZFhxT//vtBkVERFIq14yZAfjtt43ExMQYnCZpunnzJu9XrfpnQVfF6DjvxOfxQ+ZMG0vtSp40qVWG29cvsmHDBmrXrm10NBERSYG0mqPEmYcPH3L37l0yZ8vNtSsXsLCwIJ2LK5aWljRu0Z7a9T5l19b1VKz6Ic5p0hodV0REJFEb+PWPOKVJy3ffDSN79mx07NjR6EhJTq1atQgODWfxun3kzpvP6DixFhERwe5tG1i9bB77d2/BysqK+vXr89OUiXzwwQdYWloaHVFERFIojUySOJMxY0YWL17Mk4fetGn0AWU9M1CpSBYWz51KREQEDqlSUadBMxVJIiIib8DCwoLWn/cknYsrixcvNjpOkvTw4UOat+1G8ZLljI4Sa5GRkfRs15Ce7RsRHPCEqVOn8ujRI5YtW0aNGjVUJImIiKE0MknijIWFBS1atKBJkyZcvXqVy5cv89tvG/l2SE8Wzp7E5oOX9YOPiIhILOzftYWnvk+YM+eA0VGSHLPZjMlkIjAg6dzk45dFs1m9fB4OqRy5fuUCT/2e8Ntvv2kqm4iIJDoms9lsNjpEYhIQEICzszP+/v44OWk9n7iwYMEC2rRpQ5VqtRkwfCx5PAoYHUlERCRRMZvNLJw9mW0bfyUkKBCnNGkZM3URS36exm+/LuTevXtGR0xyIiIiKFmyJIHBYWw+eBmTyWR0pP+UL+MfGevVq0f+/Pn58MMPqVy5ssGpREQkpYhNH6JpbhLvWrZsyYIFC7hx5Sy1KhbkswZV2bR2BREREUZHExERSRTCw8IYNbQ39tZQpVI57t+5TrO65dm4djlFihQxOl6StHDhIs6ePYu9Q6oks4D5/JU7AHj//ff5/vvvVSSJiEiipTJJ4p2lpSWtW7fm6tWrLF68GCuLKPp0+pQqxbIyduRAvG/fMDqiiIiIoaKio7CwsKBBgwZMmzaNgwcPYm9rzT3vW5ri9JaKFy8GQLnK1ZPMNPuylT4gY+Ys9OrVi4CAAKPjiIiI/COVSZJg7OzsaNGiBfv37ePChQu0aN6cVYtnUb10Xto2rs6Zk0eNjigiImIIR8fUfPRxEyZPnkxUVBTZsmXj6NGjHDp0iC5duhgdL0kqXrw4bdq0Zf+uzUZHiRWPAoUAKFCgAPv37zc4jYiIyOupTBJDFCxYkEmTJvLgwQMmTpzIoX07WDJvmtGxRERE4szzZ09Zs2IB+3dv5cbVSwQG+OP//Bl+T3y4f/cOU8Z+zeypY7hx9RIXz/2OhaUlt27d4uDBgwCkS5eOsmXLJom1fhKrWrU+4srFc4z5ZgBRUVFGx3kjs5ZsZMn6/Tx99oxKlSpx6tQpoyOJiIj8je7mJob7ZeVK0qZLT+feQ4yOIiIi8tYO79tJZFQki+dO4c7Na/g9eUxg4H9PVfpx5MAXX48fP55KlSrFZ8wUpXHjxkycOJHevXuzZf0vTJz1C4WLlzI61r+ysLAgV24PoiIjAXRDGBERSZRUJolhoqOjada8OadOnWLBql3kzpvP6EgiIiJvZfnCmQz/ojMAefLkoWH9ejg4ONCpUyeioqLw9vbm4cOHWFpaYmNjg62tLRkzZiRXrlzs37+fJUuW0KdPH0qWLGnwlSQ/vXr1onLlyrRv356enzdi8MiJVK9VP1GP+LKytsbeIRU21lb8/PPPZMuWnU6dOmJhoUkFIiKSOJjMZrPZ6BCJSWxuhSfv5unTp2TIkIEufb6ixxdfGx1HRETkrZw+cYSmtcuSNWtWNm3ahIeHB7a2tkbHklfcunWLDh06sHPnTuo0aE7nXoNxz+9pdKx/5OvzmB+/HciaFQsAKFKkCNu3b8fV1dXgZCIiklzFpg/RxxtimHTp0lGhQgXOnT5udBQREZG3snvbbzStXRZ3Dw9GjBhBoUKFVCQlUrly5WLHjh106tSZnZvXUKfyezSo7sX8mRN5/uyp0fH+xiVDRkZPns/xq89o27kvZ86c4cSJE0bHEhERAVQmicE+/vhjjuzfybaNq4n8c20AERGRpGLfn3cK+3XVKtq1a2dwGnkTM2ZM59mzZ6xevRqPPDn4ceQAGn9Ykru3bxod7bWcnNPgVboCABMmTDA4jYiIyB80ze0VmuaWsLy9vWnStClHjxzBJUNGGn7ajsYtPidbztxGRxMREflPIcHBVC2Rg4IF8nPgwAGj48hbuHXrFjVq1CAgMJi5K7bhUeA9oyO9Vr6Mf6zxNH78eNq1a4ezs7PBiUREJLnRNDdJMrJnz86Rw4c5c+YMTRs3ZvmCn6hWOg9tG1fn4rnfjY4nIiIpSER4ODu3rKdf5+Z0bvUxly+cAeDfPnfzefyAZ0/9NCopCcuVKxcHDhwgU6YMtKxXidMnjhgd6bXW7DiFrZ0d/fv3x9XVld27dxsdSUREUjCVSZIoFC5cmKlTp/DgwQNmzZrFoX07uHLxrNGxREQkmYuJieHQ3h0M6fM55Qtloutnn3D7+nnu3rrMJ1WLUji7HaXzp+eLbq3YvH4lQYEBL/aNiIh48Txt2rRGXYLEgYwZM7J3zx48PQvStkk1FsyalOim3xcsVIzjV57xzdiZREZGUrVqVZ48eWJ0LBERSaGsjA4g8lcODg58+OGHAKRzyWBwGhERSW7MZjO3b14jZ253dm5ex5Sxw7l88Sy5cuemR/duNGvWDE9PTyIjI1mxYgXPnj3j8ePHrN+wgd6rFmNtbU3p8u+TN58nG35djJ/vE+zs7EiXLp3RlybvKE2aNGzfto0+ffry/bA+rP1lAXOWbSG9a+L5ecTWzo406dK/eB4WFmZgGhERScm0ZtIrtGaSse7cuUPjJk04/fvvrNt9ljzu+Y2OJCIiyci3g3uyaO6UF8+rVq3K8OHDqVixIiaT6V/3vX37Nhs2bGDd+vVcvXqVah9Uo3HjRhQqVIisWbPGd3RJQCdPnqRu3brY2jvSa+BIqtdugLW1tdGxXvD1eUyNsnnp2aMHo0aNMjqOiIgkE7HpQzQySRKVKlWqcPv2bVZuPqoiSURE3klIcDDHDu3hwX1v7O0dcMuag43rlgPg4uLCypUrqVKlyhsfL2fOnPTo0YMePXrEU2JJLLy8vNi3bx/tP/+cPp0+xTVDJlp37E37rv2xtLQ0Oh4H9m4jOjo6URVcIiKSsmhk0is0MslY6dOn5+nTp1x5bGb6hO/wvn2DZm264Fm4eKL44U1ERBK/u7dvcvPGFaaPH8nvJw7/7f38+fNz6dIlA5JJUnT+/HkmT57C3LlzqFj1Q8ZNX0pqJ+PupHbh7CnaNq5G2TKlmTt3Lm5uboZlERGR5CU2fYjKpFeoTDKO2WwmW7Zs3L9/n6/HTOfrAV1wdHQkKCgIh1SpKFS0JIWLlaaoVxkKe5UmQ8bML+0fExPD0YN7yJ03Hwf3bifg+TMiIiOIjIwgQ0Y36tRvhr2Dg0FXJyIi8e3yhbOcPXWUof07An98QLFixQpSp05NhgwZiIiI4Pbt23h4eJAzZ05jw0qSs2XLFj799FPKVKrO5DkrDcvxYfn83Lp+hdu3b5MjRw7DcoiISPKjMukdqEwy1r59+6hSpQpmsxkrKysePHjApUuXOHr0KEeOHOHIkSM8ePAAALcs2UjlmJqwsFDCw8IICQ4iKCgQG1tbIsLDSZ06NTY2NlhbW+Pj44NzmrQ0a9OVbv2GYWWlGZ4iIvEhODgIGxvbf51+ExMTQ0xMTKz/Lo6OjsbX5xGWlla4ZMj44lhhoaHMmjKa6RO+fbHtoUOH8PLywsbG5u0uROQ1vvzySxYtXsruU96GnH/LhlX0+rwxY8aM4YsvvjAkg4iIJF9aM0mSrIIFC5I7d25u3LhB5SpVcHV1xdXVlUqVKr3Y5t69exw9epTjx48TERGBnZ0d9vb22NnZUahQIUaPHk2PHj1o2LDhi31u3rzJhAkTmTp+JHk9ClK7/qdGXJ6ISLL21M+X+tWKYW1tzceNWnHP+xb3vW/xSePWVKz6ISEhwYwbOZDDB3ZiaWFJtVr1+WLomH+8W1Z4WBizp43h3KljFCxcnJWLZ/PE5xEAjo6piYgIJyIiAgCTycSoUaPo3r07qVOnTrBrlpSlePHi/PDDDzz1fUI6F9cEPfel86f5sudnfPrpp/Tv3z9Bzy0iIvIqjUx6hUYmGS8kJIRDhw5RsGDBt1oHwGw2/+MdeWrUrMm167dYsekIadLqNs4iInElOjqa0vnTExjgT4kSJbh48SKe773H8WPHXtouR86cdO3ShdDQUKZOnYrZZMHXP0yn2kf1sLCweLFdRHg4e3duonvbBi9eK1GiBMOHDycoKIh79+5hb2//4pE7d25Kly6dYNcrKdO1a9fw8PBg7oqtVKhSI8HOGxQYQN0qhciUwYX9+/fjoGn7IiISDzQySZI0BwcHqlWr9tb7/9utnadOmULx4sX57qtejJ226K3PISIiLwsOCiQwwB+ArVu3kjZtWkwmE1FRUdy6dYurV6/i6+tL06ZNsbOzA6BTp060bdeOHu0akjlLNtyyZMdkMhEQ8Jyb1y4TFRVF/vz5OXToEGnTpv3XDwtEEsL/PuS6530rQc97++Y1HtzzZtUvy1UkiYhIoqAySVIUBwcHQkJCsHdIZXQUEZFk4/jhfUweMxw7Oztu3rxJunT/P/LTysoKd3d33N3d/7ZfpkyZ2LRxI0ePHmXp0qUEBgYSExODg4MDfXp2o2jRohQuXPjFL88qksRoe/fuBaBE6Yr/uE1MTAyb1/3Cnh0beb9GXWp90uSl99+mFPV//hT44/8ZERGRxEBlkqQomTJlokePnkyePAlLS0uGfDtJi3GLiLyjMd98QcBTHxYuXEjmzJn/e4e/MJlMlClThjJlysRTOpG4s3HjRrJmz0kejwKvfT80JIQ+nT5l97YNZMmShU1rlzPx+8GEhoQQFhpCaGgIqZ2c6dhzEM0+64Kdvf1/njMqKorpE74lU6ZMZMyYMa4vSURE5K3ot2hJUaysrJg0aSKengXp1q0b9+/eZtaSjUbHEhFJksxmMxUKZcb3yWNq1KhBqVKljI4kEm/MZjO//fYbrhmy8PjhfVwzZsbS0hKA61cu0q9zMy5fPAvA0qVLadSoESNHjiQiIgIHB4cXj1OnfmfsNwOYP2M8vQd9R/2mn710nqCgQEKCg8iQ8Y9idsL3Qzh17CC7d+/WFDcREUk0tAD3K7QAd8oxefJkevXqRbuu/WnTsTcZM2cxOpKISJKTL+P/T9cpXboMR44cNjCNSPyJjo4md+7ceHt7A398QJUxcxZcMmTizMmjeL73Hh07dCBnzpzUrVv3X6eyXbt2jWHDhrF8+XLade3PF0N/wMLCgkcP7tG5ZR0uXThDtuy5yP9eUbZvWsPYsWN1BzcREYl3WoBb5A00b96cu3fvMWvWTI4f2sOSdfux/XNRWBEReTNXHpt5eP8uVYpnJyoq0ug4IvHG0tKSc+fOcefOHe7evYu3t/eLR+vmTWnT5rOX1gv7N+7u7ixbtoyyZcvSu3dvnjx+yKiJP9OuSXUiwoKZOXMmX3/9NdcunWbw4MH069cvnq9OREQkdjQy6RUamZTy7Ny5k2rVqlGvSWt+mLLA6DgiIklOTEwMBTJb4urqio+Pj9FxRJKUX375hVatWlG2UjVuXb9MvY/rMGnSJIKCgkiVKpUWnhcRkQQTmz7EIoEyiSRaH3zwAblz5+bUsYNGRxERSZIsLCzInTcfT548Yc+ePUbHEUlSmjRpwsiRI9m7YxPet29SunRpABwdHVUkiYhIoqUySQRo2rQp3rdvMG/GeKKjo42OIyKS5PT76gcA3n///RdryojIm6lXrx7z5s3jt99+o169ekbHERER+U9aM0kEaNGiBZcvX2b08H78fvwQbTv3o2iJMvpEUETkDd3zvvXiazc3NwOTiCQ9Hh4eeHh4GB1DRETkjWlkkgjg6enJqlWrGDZsGJfOHufTOuXo26kZjx7cMzqaiEii9/D+XUJDgkmVypFvv/0WKyt9ViUiIiKSnKlMEvmThYUFI0aM4NatW/Tq1ZtN61bQusH7RscSEUnULl84Q9NaZZg56TssLS3InTu30ZFEREREJJ7po0ORV1hYWDBx4gRcXV346quvuHn9Crnz5jM6lohIonNo7w56tGuAh4c7p38/SaZMmYyOJCIiIiIJwGQ2m81Gh0hMYnMrPEnewsLCcHd3J2sOd6YtXIejY2qjI4mIJCqfNfyA8OBn7Nu3D0dHR6PjiIiIiMg7iE0fomluIv/Azs6OefPmcfHcSZrVKcfdO7f+eycRkRQkOjoKT09PFUkiIiIiKYzKJJF/Ua1aNY4cOcLjB3dpVb8yERERRkcSEUk0oqOiWL58OUFBQUZHEREREZEEpDJJ5D/Y2tri7+/Pe0VKGB1FRFKI4OAgAvyfExoSQlBggNFx/pFnYS+ioqI4dOiQ0VFEREREJAGpTBL5Dzly5KBNmzbs3LKOWhXyc+PqJaMjiUgyZTab6d2hKcVzp6akR1qK5krF1wO7Gh3rbyLCw9mxeR1+vj4A7Nu3z+BEIiIiIpKQVCaJ/Idt27aRJk0aihQpwt07t7h47nejI4lIMjVr8mg2r/+FvHnzMnjwYLJkycL1y+eNjgVAVFQUB/duZ1CvdpR/LyPd2tTj7q1LfP/993To0MHoeCIiIiKSgKyMDiCS2I0ZO5Y9u3eTJm066jZsQZXqtY2OJCLJ0D3v24wfNZjevfswfvw4TCYTERER/LpmfYLmiIiIYNXSucyb/iM5cntQpVptiniV4ZtB3Th76hh58+alV6+efPrppxQsWDBBs4mIiIhI4qAySeQ/LFq4kPaff862rVtxcEiFhaWl0ZFEJJkJCgxg8phhWFtbM2LE15hMJgDs7e0JDwuN13OHBAfzw4j+nDyyj+fPnhIeHkZggD+NGjXi6dOnjB7el8jISNzc3NizZw+VKlV6kU9EREREUiaVSSL/IWvWrGzZvJmZM2fSv39/Du3bzvgZy/EoUIjxowZz/+5tmrftSlBgAKkcU5Mhoxtp0qXn+uULFC9VHjt7e6MvQUQSud/WLGPDr0v44YcfcHJyevG6g4MDoaEh8XLOmJgYzp8+wZe92vDw3h3atGkDwJMnTxg+fDienp4ABAQEsH37dsqUKUOWLFniJYuIiIiIJC0qk0TegMlkonPnznh6elKlShXmz5zAqeMH8fV5RNasWWnXpMZr93NIlYqqNT7mo0+aUqnqh9jY2r52u6DAAM6fOUmuvPnImMktPi9FRBKhs6eOUahwYfr37//S6/b29oSGhnD/7h0sLS1J7ZwGB4dUhIWGEuD/jAD/59ja2ZE9Z56/HTM6OhrLP0dSRkdHc/vGVc6fPcnFs6e4cPYkl879TlBQIIUKF+bkyZPkz5//tdmcnJxo2LBh3F+0iIiIiCRZKpNE3tCiRYto3bo1AFcvnePh/bt89913fPnllxw8eJDcuXMTGRnJgwcPePDgAba2tpw+fZoVv/xCtzbLSJ3aiUof1MLaxobAAH+CAv0JCvAnIOA5D+55Ex0dDUDBQsWYPGcV2XLmNvJyRSQB3b55ldDQUMxm80tTyBwcHAgLDaVqiZwvXjOZTJjN5pf2z5XHg9DQECIjwunYczAODqn4acJITCYTrhkzc+3yeUKCg//YNnduSnh50fCTIRQvXpyKFStiZ2eXINcpIiIiIsmDyfzqT6QpXEBAAM7Ozvj7+7801UDkr2WSlZUV7dq1Z9q0qVhZ/Xcne+HCBX755Rc2bd6MtbU1adOkwdnZmTR//pkzZ07KlCnDtWvX6NKlC5Wrf8yoiXPj+5JExECXL5xlxaKZACyd9xM2NjYEBwe/9HfKkiVLaNmyJQBTpkzBxcWFgIAAUqVKRdq0aTlz5gzBwcGEhYURHBzMjBkzgD8KpypVquDl5cXDhw8pUqQIXl5eFCtWjLRp0yb8xYqIiIhIohebPkRl0itUJsm/CQwMZP369ZQpU4Y8ef4+reRdRUdHk9fdncrVP2HwyAlxfnwRSRy+HdyTJfOm4ebmRnoXF1KnTs1XQ4ZQs2bNl7a7cOECS5cuxcvLiwYNGrzRsW/fvk2GDBlwcHCIj+giIiIikkypTHoHKpPEKDExMbRv356FCxcyfeF6qlSvbXQkEXlHT32fsGH1UgoWKkahoiWxs7cnLDSUorlS0bNnT8aMGYONjY3RMUVEREREYtWHaM0kkUTAbDbTuXMXFixYwNhpi1UkiSRit29eY9Loody/d5umrTrR4NM2L61z9D+RkZHULOdBgP9zAKytrcmaPReRkRGYzWaaNGmiIklEREREkiSVSSIGMpvNbN68meFff82J48cZNfFn6jZsbnQskWQtKioKvyePyZg59re5f+LziIbVvXB1dcHDw4PBvdtxYPdWJsxa/rdt2zepQYD/c5YuXUqBAgU4ePAgN2/exMrKCmtrazw9PePickREREREEpzKJBGDbNmyhaHDhnHi+HG8SpVnwa+7KFPhfaNjiSRbp08cYfyowRw9uJvUTs5MmrOS8pWrx+oYNja2BAUFMnToVwwYMICpU6fSo0cPnj31pVHz9pSrXJ106V0AuHr5HN27d6dZs2YAFC1aNK4vSURERETEECqTRAwyZcoULl++wvyVOyhTseprp8mISNzYsWktQ/t34KmfL15eXoSFhdGuSQ1atOtGo+btKVio2BsdZ+qPIwBeLIbdtWtXMmbMyDcjR9Kvyx+jCtOmS88njVvz7KkfFStWjJ8LEhERERExkIXRAURSqvfff5+gwABy5c2nIkkknjzxeUTPzxvTrW19ypYtg7e3NydOnGDEiBHkzJWLJT9P44fh/d7oWPNnTmTh7EkUKlSI3LlzA2BhYUHjxo05d/Ys9+7dY+jQobi6pGfv9nVkypSJ/Pnzx+fliYiIiIgYQndze4Xu5ibx7cCBAyxZsoTly5eT/73izF62WYvwisSD82dO0q5JdWysrZg8eTJNmzb9W3E7fvx4+vXrR7uu/WnUrB15PAq89lhBQYGUyZ+eNm3aMn36T1haWibEJYiIiIiIJJjY9CEqk16hMkniUlhYGE2bNiVVqlQUKlQIH58nTJo0kYyZ3MiQyY25K7bh5JzG6Jgiyc7jh/dpWa8SrunTsmXLFlxcXF67XWhoKPny5ePRo0dERkZSwLMIteo34+OGLcjklhWz2czxw/uYOfl7DuzeyvXr18mTJ08CX42IiIiISPxTmfQOVCZJXOrduw8zZs7As1Bxrl0+T1hYKD0HjqR91/4a2SAST04dP0TPdg2xtbFi37595MqV61+3j4mJISIigi1btrBs2TJ++eUXAFq068aSn6cB4OnpSb9+/Wjbtm285xcRERERMYLKpHegMkniyqVLlyhbtiwfftyUb36cidlsJigwgNROzkZHE0m2Vi6ew4gvu1KqdGl+XbWKjBkzxvoYp0+fpkWLFlhaWWFnZ0enjp1o166t1jYTERERkWRNZdI7UJkk7yIqKoqNGzcyc9YstmzeTO68+Vi0Zi/pXTMYHU0kWYuMjGTU0N4snfcTnTt3YdKkiVqLTEREREQkFmLTh1glUCaRZO/MmTNUr16dJ0+eULhYSUb+OIta9T8lVSpHo6OJJGs3rl7iy15tuHTud2bOnEnHjh2NjiQiIiIikqypTBKJA5GRkXTv3p0nT54wdNQUWrbvbnQkkWQvOjqaeTPGM/mHoeTImZP9+/dTunRpo2OJiIiIiCR7KpNE3pLZbCYmJoYDBw6wfPkKDhw4QLESZVUkiSSAG9cuM7hXW86cOkrfvv0YOfIb7O3tjY4lIiIiIpIiqEwSiSWz2cxvv/1G3759uX79OgBuWbPTrmt/OvcabHA6kcTPbDZz68ZVrl0+D0DGTFkoWqLM37YLCw1lw+qlrF+1iDs3r+FRsDA5cubF5/ED9u7YSLbs2Tlw4ADlypVL6EsQEREREUnRtAD3K7QAt/ybo0eP0v+LLziwfz8ZMmYmS7acDPx6HEW8SmNhYWF0PJFEKzwsjF8Wz8bf/xlTxgx/6T2TycRHHzeh7+BRZMuZ+8XrOzatpVvb+nzwwQd4eXlx8eJFbty8iZubG2XLlGHQoEE4ODgk9KWIiIiIiCRLWoBbJI6ZzWZatGjBsmXLyFegEHOWb6FClRq6VbjIG7h0/jQDurfi5rXLuLi6ki9/fkqWKMHYsWPZs2cPJ06cYNy4cWTI5Eb2nHm4e+cmrhn/r707D6uqXP8//tkggqICiogiDjibs6mJUw6ZGE5lDmmYQ2qjptkxLYcsM61TZudYDuWQU5lDJsexNAVzFiTngcRQSQUhBRn2+v3hD76pKBvZuGHzfl0XV7jXs559L7m5jZtnPaus9u76Vd7e3tq8eTPfawAAAEAewsqkO7AyCZm5cuWKKleurISEBH25eJ3adgy0dUhAvrB6xUK9O/pF1apdW98uXqy6deveNcYwDDVo0EARERFydHRUhYoVdT4qSs7Ozpo0aZLeeOMNG0QOAAAAFCysTAKsrFSpUjpx4oSqV6+uIxEHaSYBFnJwdFRKSopWLF+umjVrZjrGZDIpLCxMiYmJKly4sBwdHXXt2jWZTCaa+gAAAEAexCYvgIW8vb3l4OAgFxeeGAVY6omAHnItVkzLly/PcmyRIkXk6OgoSXJzc6ORBAAAAORRNJOAbEhKSlLUH2e0d9evupmUZOtwgDyvqKurOnXppVmzZmnJkiW2DgcAAACAFbBn0h3YMwn3U69+fR0OD5ckFSlaVL36D9XQ18bK06uMjSMD8q7z5yL1zqghOnHkkC5cuCAnJydbhwQAAADgDtnph7AyCciGXaGhOnfunA4dOqQxb76p1cu/Vodmfvr3B+N0/frftg4PyJPKV6iktybO0JUrV/Tzzz/bOhwAAAAAOUQzCcgGV1dX+fr6qn79+po0aZLmzJmjcmXLavH8z8UiPxREycnJ+jPqDx3a95sux1y66/iFP6PUuWUt9ejQSCaTie8TAAAAwA7wNDfgAZjNZo0aNVozZ34mSeodNEzFihW3bVDAQ3L9+t/6dOp4rV+9VFevXL7t2NJ1O9Xw0eYK2b5ZyxbM1tYNayVJo0aNUqtWrdSpUydbhAwAAADAitgz6Q7smQRL/Prrr2rTpo2GvPqWOnV5Vo/UayQHBxb6oWDYviVYQ/s9pTZt2mjAgAEqV66c/vWvfyksLEyS5FGylGKvXlGdunX18ksvqX///ipenGYrAAAAkJdlpx/yQCuTXnjhBS1cuPC+YxITE+Xi4nLX67t27dK0adMUGhqqv//+W5UrV1bfvn01ZsyYTMffz4IFCzRw4MD7jvnf//7Hb8JhdXXq1FHt2rX10w9LFB8XK5ODgxwcHOTp5a0Xho5UseI0ImG/Hm3eWsWLl1D79u0zanDr1q119epV/fDDD4qOjlaXLl3k7+8vk8lk42gBAAAAWFuObnOrVq2avLy8Mj2W2SqNJUuWaMCAAUpLS5OPj498fX0VERGhCRMmaN26ddq2bZuKFi2a7Ti8vLxUrVq1TI95eHhkez4gKyVLltTmzZsVFBSkM8cOyWyYZTabdejgQUVFnta/Jn2ikqU8bR0mkCtcXYuphLuHpkyZom8WLFDxYsXUrVt3vffeZL3++uu2Dg8AAABALstRM2ncuHF64YUXLBobGRmpwYMHKy0tTdOnT9ebb74pk8mkP/74Q08++aT27t2rt956S1988UW24wgICNCCBQuyfR6QE+XKldOWLVtue23s2Lf10UfTdGBPiDb9dpJVGbBbP2zar+C1K3Qk/IBWLp2v8PBwvfvuO3JycrJ1aAAAAABy2UPb5GXGjBm6efOmOnbsqDFjxmT8kF2xYkV9/fXXkqQ5c+bo0qW7nwYE5BcvvTRcknQu8rR+3rjOxtEAucejZCk922+IzkWekoeHh5YuXUojCQAAACggHkozyTAMrV69WpI0ePDgu477+/urZs2aSklJ0dq1ax9GSECuqFixosxms9p36KDpk9/U7+EHsj1HamqqkpOTcyE6wLoWzZ2pPaHbVbVqVf3+++9KSUmxdUgAAAAAHoIcNZNWrlyp7t27q127durTp49mzZqla9eu3TXu3LlzunDhgiSpRYsWmc6V/vru3buzHUdYWJiee+45tWvXTt27d9fkyZN1+vTpbM8DWIPJZNInH3+s1ORE9Q301/lzkZmOO3v6hN57+1VNHvuKbly/LunWiqb+3VrriaZ++uT9t/Xe269q0r9eVmpqaqZzxFy6kHEu8LC1fLyj2nTorFSjkD766CM1bdpUgwcPVlRUlK1DAwAAAJCLTIZhGNk96X5Pc0u/3eGfT1DbsmWLnnjiCTk7OysxMTHTfWSmTp2q8ePHq1WrVvr1118tiuN+T3NzdHTU5MmTNX78+PvOcfPmTd28eTPjz/Hx8fL19bXoUXjA/cTExKhMmTLq1PVZffrVcp0/d1Z/nD2lc2dPafvWYG3fEqzSpUvrr7/+kiQNHD5KKxZ/Je8yZdSyZUutW7dO5Xx8dPzYMXXo3EOPd3hKZbx95FHKU3tCt2vTTyu1f0+I3D1KKujFkXp51Ds2vmIUZBt/+kFTxr2mvy5d0IIFCzVgQJCtQwIAAACQDfHx8XJzc7OoH/JAK5OqVKmiqVOnKiwsTPHx8UpISNCmTZvUrFkzxcbGqnv37tq3b1/G+NjYWEmSu7v7PTckTn/qWvpYS7i7u+u1115TSEiILl26pKSkJB08eFDPP/+80tLS9M4772S5ofeHH34oNze3jA9fX1+L3x+4n/QmZej2zXqxb4CeaFZVQ/p00rSJo3Qj/rIWLFigqKgo9evXT3Xr1dPGdSvUt09fhYWFaeHChbp69aoiDh/WF198oX2hv2js6y9oYK8n1L19Q3085S2VKe2uefPmqX69upr50bt6Y2gf7Q7Zds9VTEBuKlzYWX9duqARI0aqf/9+tg4HAAAAQC56oJVJ95KcnKxWrVppz549ateunbZu3SpJWrx4sYKCguTr66tz585leu7XX3+twYMHq0qVKjp16lSOY3njjTf02Wefyc3NTVFRUSpevHim41iZhNz0/fffa+DAgXJ2dtbnn3+uFi1ayNfXV46OjtmeKykpSdHR0YqOjlbdunXl5uYmSUpJSdG8efP06WefMsXrdQAAJtxJREFU6eSJE3J2dpZftVpq3LSl3p7yqQoVytFDG5EPGYahqD/OKPzAHjVt8bi8ypTN9fer6e2Q8TkAAACA/Cc7K5Os2kySpE2bNunJJ5+Ug4ODLl++LA8PD33//ffq1auXypQpo4sXL2Z63uzZs/Xyyy+rTp06Onz4cI7jiI+Pl5eXl27evKk1a9aoW7duFp9n6V8eYIkbN27o+vXrKl26dK6+j2EYCg0N1b59+xQeHq6FCxeqctUaKlKkqFJTUvTH2VPq3nuAJk77T67GgYcvKTFREeH7dXBvqA7uDVXY/l26/FeMJMm1WDG98fZUPT/ktVyNoUaZW6tOjx49qpo1a+bqewEAAACwvly/ze1+mjdvLkkym806c+aMpP+7hS0uLu6ev7VOv70tfWxOlShRQo888ogkWWWlE/CgihYtmuuNJOnWxt8tWrTQiBEjNH/+fM2fP1+t/JuplHsxHf09TDduXNeyBbNzPQ48HEu+/o9Ctm/Wsd/DFNjmEfXr2kqz//2ejOQEDR82VOvXr9eZM2f0RIcO+uLjSbkez8ZdJ1S+QiW1b99eR44cyfX3AwAAAGA7Vr//xcnJKePz9L1bqlWrJunWLWXR0dHy8fG567z0xlP6WGvGwh4yKIgGDBigAQMGqE6dOirt5a1hI8bp2X5DbB0WrODXnzfovbdfzfhz9Ro1tHv3bjVq1Oiu2xr79OmjNWvWKC72qtw9SuZaTJ5e3vLy9tGBPSFKS0vLtfcBAAAAYHtWX5n0+++/Z3xevnx5SVKFChXk7e0tSQoJCcn0vPTXmzVrZpU40tLSdPz48dviAAqaxMRERUdH67mBr+j5Ia/JpUgRW4eEHApe+51eDuqqTgEBOnDggL788kuF7Nyppk2bZro/VnqD/uDe0FyN6/OPJujg3lDNnDlTdevWzdX3AgAAAGBbVm8mffLJJ5KkmjVrZqxAMplM6tGjhyRp/vz5d50TGhqqY8eOycnJSV27drVKHPPnz1dcXJwcHR31+OOPW2VOID9JS0tT69atFRsbq8bNWto6HDyAhPhrWrbwS/Xu3Fyt6/sosE0djRrWR7169dKPa9eqYcOGGjZsmDw9Pe85R4MGDdSmTRu9P/41/f13Qq7FWrFyVRmGIRcXl1x7DwAAAAB5Q7abSZs3b9bbb7+ts2fP3vb6tWvX9Prrr2vZsmWSpAkTJtx2fMyYMSpcuLA2bdqkGTNmZOyd9Mcff2jQoEGSpCFDhmSsYEq3cuVKVapUSS1b3v7DcHx8vPr27as9e/bc9npaWprmzp2rESNGSJIGDx6c6W11gL1zdHRUyv+/xXPblvU2jgYPYua0d/Xe2Fd0aP9vunQxWieP/S4fHx8tWrTotluK78fBwUFff/21rl6OUafm1fXR5DE6cTTC6rH+ef4PSVL16tWtPjcAAACAvCXbT3Nbs2ZNxiojHx8flStXTikpKTpy5IiSk5NlMpk0YcIETZo06a5zFy1apIEDB8psNsvHx0deXl6KiIhQSkqKGjdurO3bt8vV1fW2cxYsWKCBAweqYsWKioyMzHg9Li4uY7Nud3d3Va5cWYUKFdLJkycVFxcnSQoICNCqVauy9ZtynuYGexIZGanJkydrwYIFCt5xRFWq17J1SMiGTv415KA0GYahM2fOyLN0GTVt0ljr12e/ORgWFqZ58+Zr2bKlunLlimrVaaDmrdqrfuPH1PDR5ipT1rKme/y1OB3cG6oDe0K0O3SbrifEy6Okp3aHbpN06+ELJpMp2/EBAAAAsK1cfZpb48aNNX78eLVr106Ojo6KiIjQsWPH5OPjo6CgIO3atSvTRpIkBQUFaceOHQoMDFRiYqKOHDkiPz8/TZo0STt37ryrkXQ/rq6umj59urp37y5PT0+dPn1ahw4dkouLi5566imtWLFC69ev55YLFGjlypXTjh071KLNE/KrxuPa8xOz2azeQcPk4uqmmylpCujaS5f/uqTg4GAtWrRIKSkp2Zqvfv36mjXrc0VHR2vNmjVqULeWtgSv1Ighz6p1g/J6vKGvdvyy8b5zbA5erVb1y2lov6e0atk8XY35UyWKF1G5Mh6aMWOGTpw4QSMJAAAAKACyvTLJ3rEyCfYkNTVVZcuWVWqaWa+/9Z56Bw3LdJNm5H03rl9X9/YN9MfZU5Kk0aPf1Mcfz8jxvBcuXNBvv/2mL774Qrv37FHnbn00fOR4lSxVWkX/f4PfMAx98+W/NX3yGPXs2VNTp05VlSpVZBiGHBysvvUeAAAAABvITj+EZtIdaCbB3ly4cEHjxo3XwoULVMqztDy9vFW9Vj31Dhomp0JOOvr7IaWlpanfwJdtHSqykJqaquNHwvX0E43l4uKiq1evqoiVntCXkJCgd999V999950uXLigeo2aatb8H/TWq88r5mK0zp4+obFj39YHH7xPAwkAAACwQzSTcoBmEuzV/v37tXr1asXFxWnDhg06ffq0pFsbNJvNZn25eJ3adgy0cZTISsylC2pVr5ymTp2qt99+2/rzx8Ro2rRp+vTTTyVJ3t7e6tSpk1q1apXxsAQAAAAA9odmUg7QTEJBYDabtX37dpUoUUK1a9dW127d9OeFv7Rm60Fbh4Z7MAxD+3fv1Feff6hft/5P8+fPz9Xmzp49e7R161YFBQXxREwAAACgAMhOP4TNU4ACyMHBQW3bts34c98+fTRkyBBdi4uVm7uHDSPDncxms7ZvCdaczz/Ugb2heqROHX377bfq3bt3rr5v06ZN1bRp01x9DwAAAAD5ExtfAFCbNm0yVr4g7wg/sEedW9bS8Oe7yNlJWrduncLDwtSvXz82UgcAAABgMzSTAMjPz0+SNHksm3DnJRt+Wqmzp09o+fLlCg0JUWBg4G2bX6elpdkwOgAAAAAFFb/aBqArV65Ikt585yMbR4J/qtfw1m1mU6dOVWhoqMxms3r16qWLFy+qV69ecnd3V0xMjJycnGwcKQAAAICChGYSAG3cuFGSVLdhExtHgn96vMNTmvLxHK1cNl8bNv+s+LhYffHFFxnH4+LiuN0NAAAAwEPH09zuwNPcUNBERUWpQYMGavBoC/130VqZTCZbh4R7+DshXl9+NlWBT/dVyPbNmj55DLUKAAAAgFVkpx/CnklAAZaSkqI+ffvKuYirPvx8AY2kPK5Y8RJ6891pqvlIfbV8/ElJkpubm2rUrKmZM2cqOTnZxhECAAAAKAi4PwIowCZMmKjdv/2mb9f+KnePkrYOB9lQo3ZdffPdZp06cUQRYfs0atQobdq0WevX/2Tr0AAAAADYOZpJQAG2Zs1qte3YRY2a+Ns6FDwA/zYd5N+mg44fOawt/1utYsWL2TokAAAAAAUAt7kBBVi3bt10cG8Ij5jP514d2ENVqlTR/HnzbB0KAAAAgAKAZhJQgHXr1k1XLv+lg/t22ToU5EDMpWgNHjRIxYqxMgkAAABA7qOZBBRg+/fvlyQVcuSO1/zqZlKSDMMQD+YEAAAA8LDQTAIKqBMnTmjcuHHqHTRMDR59zNbh4AEc+z1cPTo0lDktTVWqVLF1OAAAAAAKCJpJQAG0fPlyPfrooypV2lujx39o63DwAM6fi9Sg3k/ItUhhHTx4UIGBgbYOCQAAAEABQTMJKGBWrFihvn37qk2HQP2waZ/c3D1sHRIewI8rv1Vq8k1t2bJFjzzyiK3DAQAAAFCA0EwCCpDr169r+fLlqlDJTx/PXqJixUtk6/yrVy5r146flZqamksR2kZ+fJpd+ME9kqTDhw/bOBIAAAAABQ3NJKAAMJvNWrJkiWrUqKHg4GD1feFlmUyme443DENRkWe0af0qzfxogob376LHG/qqee3SeqFne3364fiHGH3uOBd5WvP/+7H6POWv4c93sXU42TZ5+mzVqttYnTp10s6dO20dDgAAAIAChEc4AQXAq6++ptmz/6uOTz2ttybMkG8lv3uO3b71fxo9rI8SEuIlSV5eXmrQsKEGBPVXgwYNdPToUU2ePFleZcrpmecGqVix4g/rMnLsYvR5rVw6X1uCV+vo72FycXFRo0aN9OvW/2n/nhA1btrC1iFarExZH81bvkGDe3XU008/rb1796pixYq2DgsAAABAAUAzCbBzP/zwg2bP/q+GvjZWo9/JerPtlORkJSbekCSFhYWpXr16tx03DEOhoaGa+u5ImUwmBb34eq7EbW2pqal6dWAPRZ4+ri5duui9Se+oU6dOOnTokNq3b68fln6dr5pJkuTk5KTP5n2vXgFN1aVrV4WGhKhYsWK2DgsAAACAnaOZBNi58uXLy8PDQ3NmTVNqWqrefGeaHB0d7zm+Q0A3deryrH5avUyFCt1dIq5cuaKLly7J0dFR/q075GboOZKWlqZxIwfp7OnjuhZ7VbFXLysh/ppCQ0PVrFmzjHFBAwYoOTlZMZeiNfqlfvrjzAm1ahegwKefU1pqqqrXqmPDq8hayVKe+u/CH9XzyUfVp08f/fjjj3Jw4A5mAAAAALmHnzgAO9esWTNduHBBM2bM0MKvPtWTzatpxpR/6dTxI/c8JzHxhlq3bq0aNWrcdSw0NFSHw8NVytNLf5w9lfF6WlqaDu7dpe1bgrVz2yZFnjmZK9eTzjAMXb1yWRf+jLrr2LW4WE2fPEZrv1+s+o/UUM+nu2nsv97SmjVrbmskSdKQwYMlSccO71fcX1GqVd1PX/93hjq3rKUuj9e16DpuJiXpx5VLdPrkMetcXDZVr1VH73wwS+vXr1eFChW0evVqu9skHQAAAEDeYTIMw7B1EHlJfHy83NzcdO3aNZUokb0nXQF53e7duzVv3nytXr1KyckpWrouJNOVN0P7PaXtW4LVpk0bzZgxQ02aNMk4lpSUpFWrVikoKEilSpdR8I4jcnZ2Ud8uLRQRtj9jXLFixfXLgXMq4eZutfhTU1M1d9ZH+nnjWkWeOan4a3GSpE9mL1UpTy9FhO/X4UN7tX3LeqWlpmrcuPGaNGnifec0DEOxsbEqWbJkxmt//vmnpkyZoq+++kqbfjupipWrZnru6RNH9d23c7Xmu4WKi70qlyJFNO69z9Tr+Rfvu8F5bjCbzVq3aqmWfv0fHdr/m7y8vORXpYpef+119e3b56HGAgAAACD/yU4/hGbSHWgmoSBISEhQy1atdOVqnL4L3i1PrzK3HU9LS9PqFQs1/o3BcnJyUnR0tBwdHeXh4ZExpkOHDtqxY4cqVK6qvy5d0LW4WI0YMVJvvTVGGzdu1KBBg/Tzvkj5+FpnU+jTJ45q/BuDFXZgt/r06aNHHnlE58+f1+zZszPGuLq6qmGjRgro1EmDBw9WmTJl7jPj/W3evFkdO3bU/0KOya/q3Su0UlJSVL9iEbm7u2vgwIHq37+//vOf/2ru3Dl6onMPTZ7+pUp6ls71plLs1SvaHfKLnujcI+P2xd/DD2jjupVaNG+mWrVspY0bN+RqDAAAAADyv+z0Q9gzCSiAihcvrvU//aTGjRtrWP+n9O2aX1WkaNGM446OjvIsfasRk5KSotKlS0u6tYon3ZYtW3Ts2DEFBgbqWlysSpUqpU8//bdMJpMCAgLk7OysgBY1VKlKdTVq2lLvTp11372a7nT1ymWVLOWpyzGX1KKutySpUqVK2rFjh/z9/SXdWiVVpEgRNWzYUI0bN1b16tWz9R73Yjab9dlnn0mSyvtWynSMk5OTfHwrqlfPpzVjxgxJ0pw5XykgoJOGDBki/zpl9Pzg1zTu/c9yZQ+jyDMnFbJ9syIO7dOq5d/Iu1x5NXi0+a2vkWHofFSkEm/cUO/evaz+3gAAAAAKNppJQAFVvnx5tWzZUqtWrVL39g300axFavDoYxnH/Vt3kFeZsoq5dEGS1LNnTxmGcdtKm5o1a2r37t3asmWLAgMDM455e3tr27Zt2rNnjyIiIjR37myVLeerYSPetii2hXNm6qNJo9Xg0ebav3unJGnVqlXq2LGjXF1dM8a5uLjok08+yfHfxZ327t2r4OBguRQpIqfChe85rkLlajp58vY9lXr06KEmTZpo4MCBWjx/lpKSEvX+v+daJa6UlBT9vPFHLVswW7t2bL3tmP9jTXUtPlYmk0nbt21TSkqKypUrp+rVq1vlvQEAAAAgHc0koAB77rnnFB4erhs3rqv3U83VIaC7mjRvowFDR6iws7O+WrJePTo0Uv/+/bVo0aJMb9kqVaqUevfufdfrjz32mB577DFt2rRJc+fOlV+1mhbFFH8tTtMnv6n27dvrwsWLGXP16NEjZxebDYsXL5YkrVj/2z1vU0u8cUOxV/6SKdX1rmPly5fX5s2bNXPmTI0cOVLDR45X+QqVLHrv5Js3FXnmpM6ePq6zp47f+u/p47r4Z5TMZrP+irmo5v7+Wrx4sfz9/fX557PUrFmz2/ZFMgxD+/btU4MGDeTk5JT9vwAAAAAAuA/2TLoDeyahIIqJidGUKVM0e/ZspaWlSZI+mrVQXZ7ppxWL52jyv17WiBEj9dlnn2Z77kebNJEcXbRk7a9Z7h/0V8xFrVg8R7OmT9TUqVM1duxYBQcHq2XLlnJzc3uga3sQs2fP1ssvvyxHR0f1DhqmN9/9SK6uxW4b0/Gxavrj7CmFhoaqefPmmc7z999/y8/PTyU9vTX/u00q7eV9z/e8mZSkb7/+Ql/NnKprcbGSJA8PD1WvUUNV/PwUGhqqTp0C9NJLw1WvXj3rXSwAAAAAiA24c4RmEgqy2NhYrV+/XmPHjtWff/6pmrXrafS7H+nFvgGqWq2aTp44ke05S5UqpQHDRmv4yHH3HBMVeUaffPC2NgevkpOTk/r2fU7vvz9FZcuWzcnlPDDDMPThhx9qyZIlOnLkiIoWddWM/y7Rr1uD9ef5P3TpwnmdPPa7BgwYoAULFtx3rqNHj6pDhw5yci6qb77fctuG5EmJidod8ot+2fyTtv5vja5cjtGQIS+qf/9+qlGjhjw9PTMacHfeYggAAAAA1kQzKQdoJgG37Nq1S2PeekshO3eqUKFCqlKliiIiIlSokOV3x5rNZtWtW1fFPbz08eylOnH0sGrUrnfbCp2Q7Zv1xtDecitRXKNGjVJQUNBtT42ztXnz5mn8+PGKiYlR2bJl9dhjj8nb21tt27ZVz549LWrwnD17Vg0bNtSzzw/TqHFTtWrZN9q68Uft2rFFSYmJqlS5sroEBuqVV15RjRp3PzkOAAAAAHIbzaQcoJkE/B/DMNSyZUsdP3FKcbFXNH78eE2ePNmicyMiIjRs+HCFhoRIuvWEuLS0NBUqVEgfz16qSn7VdDMpSb2funWL2NWrV/NUE+mf4uLi9Msvv6hdu3YPfLtd1WrV5P94Z8Ve+UvBa1eoRcuW6hIYqMDAQNWsWZNVRwAAAABsKjv9EDbgBnBPJpNJDRs21O7du5WWlqb33ntPbm5uGjVqVJbnjhg5MqOR5Ofnp1dffVVPPvm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+      "text/plain": [
+       "<Figure size 1200x1200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Draw the federal state\n",
+    "gk.drawGeoms(federal_states_DE_df_geom_filtered)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Filtering and clipping spatially\n",
+    "Example: Filter for the federal states which overlap with a random box polygon and clip the federal state gemetries to the shape of the input polygon."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>geom</th>\n",
+       "      <th>GID_0</th>\n",
+       "      <th>NAME_0</th>\n",
+       "      <th>GID_1</th>\n",
+       "      <th>NAME_1</th>\n",
+       "      <th>VARNAME_1</th>\n",
+       "      <th>NL_NAME_1</th>\n",
+       "      <th>TYPE_1</th>\n",
+       "      <th>ENGTYPE_1</th>\n",
+       "      <th>CC_1</th>\n",
+       "      <th>HASC_1</th>\n",
+       "      <th>areaShare</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>POLYGON ((9.04122543 50.00000763 0,9.03525352 ...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.2_1</td>\n",
+       "      <td>Bayern</td>\n",
+       "      <td>Bavaria</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Freistaat</td>\n",
+       "      <td>None</td>\n",
+       "      <td>09</td>\n",
+       "      <td>DE.BY</td>\n",
+       "      <td>0.025930</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>POLYGON ((9.93442822 50.41932678 0,9.93427849 ...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.7_1</td>\n",
+       "      <td>Hessen</td>\n",
+       "      <td>Hesse</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Land</td>\n",
+       "      <td>State</td>\n",
+       "      <td>06</td>\n",
+       "      <td>DE.HE</td>\n",
+       "      <td>0.842050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>POLYGON ((9.99879646 51.40369415 0,9.99525547 ...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.9_1</td>\n",
+       "      <td>Niedersachsen</td>\n",
+       "      <td>Lower Saxony</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Land</td>\n",
+       "      <td>State</td>\n",
+       "      <td>03</td>\n",
+       "      <td>DE.NI</td>\n",
+       "      <td>0.052680</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>POLYGON ((8.0413866 50.69665527 0,8.03969002 5...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.10_1</td>\n",
+       "      <td>Nordrhein-Westfalen</td>\n",
+       "      <td>North Rhine-Westphalia</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Land</td>\n",
+       "      <td>State</td>\n",
+       "      <td>05</td>\n",
+       "      <td>DE.NW</td>\n",
+       "      <td>0.254207</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>MULTIPOLYGON (((8.00022984 50.75508118 0,8.001...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.11_1</td>\n",
+       "      <td>Rheinland-Pfalz</td>\n",
+       "      <td>Rhineland-Palatinate</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Land</td>\n",
+       "      <td>State</td>\n",
+       "      <td>07</td>\n",
+       "      <td>DE.RP</td>\n",
+       "      <td>0.016208</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>MULTIPOLYGON (((9.99682236 50.67557526 0,9.996...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.16_1</td>\n",
+       "      <td>Thüringen</td>\n",
+       "      <td>Thuringia</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Freistaat</td>\n",
+       "      <td>State</td>\n",
+       "      <td>16</td>\n",
+       "      <td>DE.TH</td>\n",
+       "      <td>0.010095</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                geom GID_0   NAME_0     GID_1  \\\n",
+       "0  POLYGON ((9.04122543 50.00000763 0,9.03525352 ...   DEU  Germany   DEU.2_1   \n",
+       "1  POLYGON ((9.93442822 50.41932678 0,9.93427849 ...   DEU  Germany   DEU.7_1   \n",
+       "2  POLYGON ((9.99879646 51.40369415 0,9.99525547 ...   DEU  Germany   DEU.9_1   \n",
+       "3  POLYGON ((8.0413866 50.69665527 0,8.03969002 5...   DEU  Germany  DEU.10_1   \n",
+       "4  MULTIPOLYGON (((8.00022984 50.75508118 0,8.001...   DEU  Germany  DEU.11_1   \n",
+       "5  MULTIPOLYGON (((9.99682236 50.67557526 0,9.996...   DEU  Germany  DEU.16_1   \n",
+       "\n",
+       "                NAME_1               VARNAME_1 NL_NAME_1     TYPE_1 ENGTYPE_1  \\\n",
+       "0               Bayern                 Bavaria      None  Freistaat      None   \n",
+       "1               Hessen                   Hesse      None       Land     State   \n",
+       "2        Niedersachsen            Lower Saxony      None       Land     State   \n",
+       "3  Nordrhein-Westfalen  North Rhine-Westphalia      None       Land     State   \n",
+       "4      Rheinland-Pfalz    Rhineland-Palatinate      None       Land     State   \n",
+       "5            Thüringen               Thuringia      None  Freistaat     State   \n",
+       "\n",
+       "  CC_1 HASC_1  areaShare  \n",
+       "0   09  DE.BY   0.025930  \n",
+       "1   06  DE.HE   0.842050  \n",
+       "2   03  DE.NI   0.052680  \n",
+       "3   05  DE.NW   0.254207  \n",
+       "4   07  DE.RP   0.016208  \n",
+       "5   16  DE.TH   0.010095  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# instead of the point of Aachen, let us define a random square box between 8-10° lon and 50-52° lat in the center of Germany\n",
+    "center_box = gk.geom.box((8, 50, 10, 52), srs=4326)\n",
+    "\n",
+    "# now extract all \"Bundeslaender\" that overlap with this box AND clip their geometries to the box extent\n",
+    "# the parameter 'scaleAttrs' would allow to scale numeric attributes of the states linearly with area share (but we have none in the sample data)\n",
+    "federal_states_DE_df_geom_clipped = gk.vector.extractAndClipFeatures(gk._test_data_[\"gadm36_DEU_1.shp\"], geom=center_box, scaleAttrs=None)\n",
+    "\n",
+    "# we get the expected federal states in the center of Germany\n",
+    "# also note the area share of each state area that is contained in the box, Hesse is nearly completely contained\n",
+    "federal_states_DE_df_geom_clipped"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plotting the results shows how the province geometries where clipped to our random box polygon shape\n",
+    "gk.drawGeoms(federal_states_DE_df_geom_clipped)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot feature in region\n",
+    "Example: Get region from shape file and show feature. Get Aachen region and show wind turbine placements in this region."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "aachen_rm=gk.vector.extractFeatures(gk._test_data_[\"aachenShapefile.shp\"])\n",
+    "gk.drawGeoms(aachen_rm, figsize=(5,5), srs=4326)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "turbine_placements = gk.vector.extractFeatures(gk._test_data_[\"turbinePlacements.shp\"])\n",
+    "axh = gk.drawGeoms(aachen_rm, figsize=(5,5), srs=4326)\n",
+    "gk.drawGeoms(turbine_placements, ax = axh)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Get information about the Vector file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Show information about a vector file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COUNT 16\n",
+      "SRS GEOGCS[\"WGS 84\",\n",
+      "    DATUM[\"WGS_1984\",\n",
+      "        SPHEROID[\"WGS 84\",6378137,298.257223563,\n",
+      "            AUTHORITY[\"EPSG\",\"7030\"]],\n",
+      "        AUTHORITY[\"EPSG\",\"6326\"]],\n",
+      "    PRIMEM[\"Greenwich\",0,\n",
+      "        AUTHORITY[\"EPSG\",\"8901\"]],\n",
+      "    UNIT[\"degree\",0.0174532925199433,\n",
+      "        AUTHORITY[\"EPSG\",\"9122\"]],\n",
+      "    AXIS[\"Latitude\",NORTH],\n",
+      "    AXIS[\"Longitude\",EAST],\n",
+      "    AUTHORITY[\"EPSG\",\"4326\"]]\n",
+      "ATTRIBUTES ['GID_0', 'NAME_0', 'GID_1', 'NAME_1', 'VARNAME_1', 'NL_NAME_1', 'TYPE_1', 'ENGTYPE_1', 'CC_1', 'HASC_1']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Vector info\n",
+    "vinfo = gk.vector.vectorInfo(gk._test_data_[\"gadm36_DEU_1.shp\"])\n",
+    "print(\"COUNT\", vinfo.count)\n",
+    "print(\"SRS\", vinfo.srs)\n",
+    "print(\"ATTRIBUTES\", vinfo.attributes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Show information about points in a shape file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " , Lon      , Lat\n",
+      "0, 5.98520  , 50.79725 \n",
+      "1, 5.99469  , 50.79421 \n",
+      "2, 5.99411  , 50.80094 \n",
+      "3, 6.00475  , 50.78443 \n",
+      "4, 6.00417  , 50.79116 \n",
+      "...\n",
+      "554, 6.32384  , 50.83216 \n",
+      "555, 6.32331  , 50.83889 \n",
+      "556, 6.32278  , 50.84563 \n",
+      "557, 6.33332  , 50.82909 \n",
+      "558, 6.33279  , 50.83582 \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# PD point series is okay\n",
+    "turbine_placements.geom\n",
+    "\n",
+    "# numpy array\n",
+    "np.array([ (pt.GetX(), pt.GetY()) for pt in turbine_placements.geom])\n",
+    "\n",
+    "# LocationSet\n",
+    "location_set = gk.LocationSet(turbine_placements.geom)\n",
+    "print(location_set)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Extract Feature and show information about the filtered data with '.attr'.\n",
+    "\n",
+    "Example: Filter for the federal state which contains the city of Aachen and show the information."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'GID_0': 'DEU',\n",
+       " 'NAME_0': 'Germany',\n",
+       " 'GID_1': 'DEU.10_1',\n",
+       " 'NAME_1': 'Nordrhein-Westfalen',\n",
+       " 'VARNAME_1': 'North Rhine-Westphalia',\n",
+       " 'NL_NAME_1': None,\n",
+       " 'TYPE_1': 'Land',\n",
+       " 'ENGTYPE_1': 'State',\n",
+       " 'CC_1': '05',\n",
+       " 'HASC_1': 'DE.NW'}"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "aachen_pt_real = gk.geom.point( (6.083, 50.775), srs=gk.srs.EPSG4326 )\n",
+    "federal_states_DE_df_geom_filtered = gk.vector.extractFeature(gk._test_data_[\"gadm36_DEU_1.shp\"], geom=aachen_pt_real)\n",
+    "federal_states_DE_df_geom_filtered.attr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Making subregions\n",
+    "Making subregions with tileize. Deconstruct a given geometry into a set of tiled geometries.\n",
+    "Example for federal state Bavaria."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tiled_geoms = list(gk.geom.tileize(federal_states_DE.geom.iloc[0], zoom=8))\n",
+    "print(len(tiled_geoms))\n",
+    "\n",
+    "gk.geom.drawGeoms(tiled_geoms, figsize=(6,6))\n",
+    "plt.show()\n",
+    "\n",
+    "# Or use https://mapshaper.org/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Voronoi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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q+/bt7nnV1dVauHCh++e2Hn/8cV9XM6BshfvqJfg6DKn6yAnZCvd5LwMAAFpUW//+9vmauDNnzmjp0qVaunSp4uLiFBsbq5qaGu3fv1/V1dWyWCxKT0/Xww8/XKfe1Vdfrddee01PPPGE5syZo5dfflnR0dEqKipSTU2NUlJSlJWVVW95P/zwgw4dOuSxLytXrtRPf/pTHT58WLfddpvi4uIUFRWlb7/91n269T/+4z903333+bqaAVVz/PuAlgMAAM2vrX9/+3wkLiUlRfPnz9fw4cMVFBSkoqIiFRcXKy4uTpMmTVJBQYEWLVrkse6kSZP08ccfa+zYsaqqqtL+/fuVmJioRYsW6ZNPPtEVV1zhU1969+6tvXv3KiMjQwMHDpTNZlNRUZFCQ0M1ZswY/e1vf9Orr77q6yoGXHB094CWAwAAza+tf39bDG/P/Gin7Ha7IiIiZLPZAvbMOMPpVOGtU1R97KTn8+oWyRoTpUHbX5clKCggywQAAP5pje9vX3JIwG9sQH2WoCAlps/414eLZ57/k7jw5wQ4AADakLb+/U2IayGRo1OV/McFsl4VWWe6NSZKyX9cwHPiAABog9ry9zenUy/SHKdTL2Q4nbIV7lPN8e8VHN1dEYP6cwQOAIA2rqW+v33JIQH/xQY0zBIUpK43D2jtbgAAAB+0xe9vTqcCAACYECEOAADAhAhxAAAAJkSIAwAAMCFCHAAAgAkR4gAAAEyIEAcAAGBChDgAAAATIsQBAACYECEOAADAhAhxAAAAJkSIAwAAMCFCHAAAgAkR4gAAAEyIEAcAAGBChDgAAAATIsQBAACYECEOAADAhAhxAAAAJkSIAwAAMCFCHAAAgAkR4gAAAEyIEAcAAGBChDgAAAATIsQBAACYECEOAADAhAhxAAAAJkSIAwAAMCFCHAAAgAkR4gAAAEyIEAcAAGBChDgAAAATIsQBAACYECEOAADAhAhxAAAAJkSIAwAAMCFCHAAAgAkR4gAAAEyIEAcAAGBChDgAAAATIsQBAACYECEOAADAhAhxAAAAJkSIAwAAMKEmhbgpU6bIYrE0+Dp79qzHugUFBRo3bpyioqIUFhamfv36KTMz02v5xtq6davuv/9+xcbGKiQkRDExMfrZz36mrKwsv9oFAABoizr6U7lPnz6Kjo72OK9Dh/r5cM2aNZo8ebKcTqfi4uIUHx+voqIipaena/PmzcrLy1OnTp186oNhGHr66ae1cuVKSVLPnj11/fXX68SJE8rPz1dRUZFmz57t+8oBAAC0YX6FuHnz5mnKlCmNKltaWqpp06bJ6XRq2bJlmjVrliwWiw4dOqQ777xThYWFmjNnjl555RWf+jB//nytXLlSP/nJT7R69WoNGjTIPc9ut+ujjz7yqT0AAAAzaLFr4rKysuRwODRq1CjNnj1bFotFktS7d2+tXr1akvTqq6/q2LFjjW6zqKhIy5YtU1RUlN5///06AU6SwsPDdc899wRuJQAAANqIFglxhmFow4YNkqRp06bVmz906FAlJyerpqZGmzZtanS7r7zyipxOp5577jmvp3UBAAAuR36dTs3OztbGjRtlt9sVHR2t1NRUTZo0SREREXXKHT58WEeOHJEkpaamemwrNTVVxcXF2rlzp5588slGLX/z5s2SpLFjx2r37t1atWqVvv76a3Xq1ElDhgzR9OnTCXcAAOCy5FeI27JlS53P69evV0ZGhtauXavRo0e7px84cECSFBISotjYWI9tJSYm1il7KUePHlV5ebksFos+/PBDzZo1S06n0z3/vffe04svvqi//e1vGjFihNd2HA6HHA6H+7Pdbm/U8gEAAFpTk06nJiUlafHixdq7d6/sdrsqKyuVm5urIUOGqKKiQuPHj9euXbvc5SsqKiRJXbt2dV8Ld7Fu3brVKXspriN7FotFv/71rzV48GDt3r1bDodD+/bt08iRI2W323X//ferrKzMaztLlixRRESE+xUfH9+o5QMAALSmJoW4hQsXKi0tTQMGDFCXLl3UuXNnjRw5Utu3b9fgwYPlcDg0d+5cd3nXM+CsVqvXNkNCQiRJVVVVjerDmTNnJEm1tbXq3LmztmzZooEDB8pqtapfv37atGmTYmNjZbfb9Yc//MFrO2lpabLZbO5XQ4EPAACgrQjojQ1Wq1WZmZmSpLy8PPdRtdDQUElSdXW117quU5phYWGNWparTUmaNGmS+0ieS1hYmGbMmCFJysnJ8dpOSEiIwsPD67wAAADauoDfnXrLLbdIOn+ErKSkRNK/T5WePn1ahmF4rOcKfBeHMW8uLJecnOyxzHXXXSfp/DPqAAAALicBD3HBwcHu9+fOnZN0/pcdpPNH28rLyz3WcwU+V9lLSUhIcJ+Cdf29mGv6hTc8AAAAXA4CHuL27dvnft+zZ09JUq9evRQTEyNJys/P91jPNX3IkCGNWk5QUJD74b6uAHgx1/S4uLhGtQkAAGAWAQ9xy5cvl3T+FKcrPFksFk2YMEGStGrVqnp1duzYoeLiYgUHB+vee+9t9LIefPBBSdK6detUU1NTb/6bb74pSRo+fLhvKwEAANDG+Rzitm7dqrS0NB08eLDOdJvNppkzZ2rdunWSpPT09DrzZ8+eLavVqtzcXGVlZbmvjTt06JCmTp0qSZo+fbr7iJ1Ldna2EhISNGzYsHp9mT59uuLj41VaWqrnnnvOfeOE0+nU/PnztWfPHlmtVj3//PO+riYAAECbZjG83WngxcaNG91H1eLi4hQbG6uamhrt379f1dXVslgsSk9P16JFi+rVfeutt/TEE0+otrZWcXFxio6OVlFRkWpqapSSkqKPPvpIV1xxRZ06b7zxhp544gn17t3b4w0Ku3bt0h133CG73a5u3brpmmuuUWlpqU6cOKGgoCCtWrVKkydPbvT62e12RUREyGazcacqAABoUb7kEJ+PxKWkpGj+/PkaPny4goKCVFRUpOLiYsXFxWnSpEkqKCjwGOCk848C+fjjjzV27FhVVVVp//79SkxM1KJFi/TJJ5/UC3CNcdNNN+mLL77Q9OnTdcUVV+jzzz+XJN13333asWOHTwEOAADALHw+Ene540gcAABoLc16JA4AAACtjxAHAABgQoQ4AAAAEyLEAQAAmBAhDgAAwIQIcQAAACZEiAMAADAhQhwAAIAJEeIAAABMiBAHAABgQoQ4AAAAEyLEAQAAmBAhDgAAwIQIcQAAACZEiAMAADAhQhwAAIAJEeIAAABMiBAHAABgQoQ4AAAAEyLEAQAAmBAhDgAAwIQIcQAAACZEiAMAADAhQhwAAIAJEeIAAABMiBAHAABgQoQ4AAAAEyLEAQAAmBAhDgAAwIQIcQAAACZEiAMAADAhQhwAAIAJEeIAAABMiBAHAABgQoQ4AAAAEyLEAQAAmBAhDgAAwIQIcQAAACZEiAMAADAhQhwAAIAJEeIAAABMiBAHAABgQoQ4AAAAEyLEAQAAmBAhDgAAwIQIcQAAACZEiAMAADAhQhwAAIAJEeIAAABMqEkhbsqUKbJYLA2+zp4967FuQUGBxo0bp6ioKIWFhalfv37KzMz0Wt5X27Ztc/dhxIgRAWkTAACgrenoT+U+ffooOjra47wOHernwzVr1mjy5MlyOp2Ki4tTfHy8ioqKlJ6ers2bNysvL0+dOnVqcn/Onj2rp556qsn1AQAAzMKvEDdv3jxNmTKlUWVLS0s1bdo0OZ1OLVu2TLNmzZLFYtGhQ4d05513qrCwUHPmzNErr7zS5P688MIL+uabb3Tvvffqvffea3I7AAAAbV2LXROXlZUlh8OhUaNGafbs2bJYLJKk3r17a/Xq1ZKkV199VceOHWtS+19++aWysrI0ZswYTZgwIWD9BgAAaItaJMQZhqENGzZIkqZNm1Zv/tChQ5WcnKyamhpt2rSpSe3//Oc/V4cOHfw6kgcAAGAWfoW47OxsjR8/XsOHD9fEiRO1YsUK2Wy2euUOHz6sI0eOSJJSU1M9tuWavnPnTp/7sWrVKn388cdKS0tTYmKiz/UBAADMxq9r4rZs2VLn8/r165WRkaG1a9dq9OjR7ukHDhyQJIWEhCg2NtZjW67w5SrbWCdOnNDcuXN1zTXXaO7cuT7VlSSHwyGHw+H+bLfbfW4DAACgpTXpSFxSUpIWL16svXv3ym63q7KyUrm5uRoyZIgqKio0fvx47dq1y12+oqJCktS1a1f3tXAX69atW52yjfX888/r+++/1yuvvKKQkBCf12XJkiWKiIhwv+Lj431uAwAAoKU1KcQtXLhQaWlpGjBggLp06aLOnTtr5MiR2r59uwYPHiyHw1HnqJjrGXBWq9Vrm64AVlVV1eh+vP/++1qzZo0eeOAB3XnnnU1ZFaWlpclms7lfZWVlTWoHAACgJQX0xgar1arMzExJUl5envuoWmhoqCSpurraa13XKc2wsLBGLevs2bOaMWOGOnfurN///vdN7nNISIjCw8PrvAAAANq6gN+desstt0iSamtrVVJSIunfp0pPnz4twzA81nMFPlfZS3nxxRf1zTffKCMjQz179vS32wAAAKbi140NngQHB7vfnzt3TtL5X3aQzh9tKy8vV1xcXL16rsDnKnspe/bskSQtW7ZMv/vd7+rMc52S/fjjjxUTEyNJKiws5Ho3AABw2Qh4iNu3b5/7vesIWa9evRQTE6OjR48qPz9fDz74YL16+fn5kqQhQ4b4tLwTJ054nVddXe1+eLDT6fSpXQAAgLYs4KdTly9fLklKTk52H3GzWCzuX1FYtWpVvTo7duxQcXGxgoODde+99zZqORs3bpRhGB5fr7/+uiTpjjvucE9LSEgIwNoBAAC0DT6HuK1btyotLU0HDx6sM91ms2nmzJlat26dJCk9Pb3O/NmzZ8tqtSo3N1dZWVnua+MOHTqkqVOnSpKmT5/uPv3pkp2drYSEBA0bNszXrgIAAFy2fA5xZ86c0dKlS5WYmKiePXtq8ODBGjhwoKKjo7VixQpZLBZlZGTo4YcfrlPv6quv1muvvaYOHTpozpw5io+P14033qg+ffroq6++UkpKirKysuot74cfftChQ4f03XffNX0tAQAALjM+h7iUlBTNnz9fw4cPV1BQkIqKilRcXKy4uDhNmjRJBQUFWrRokce6kyZN0scff6yxY8eqqqpK+/fvV2JiohYtWqRPPvlEV1xxhb/rAwAA0C5YDG/P/Gin7Ha7IiIiZLPZeGYcAABoUb7kkIDf2AAAAIDmR4gDAAAwIUIcAACACRHiAAAATIgQBwAAYEKEOAAAABMixAEAAJgQIQ4AAMCECHEAAAAmRIgDAAAwIUIcAACACRHiAAAATIgQBwAAYEKEOAAAABMixAEAAJgQIQ4AAMCECHEAAAAmRIgDAAAwIUIcAACACRHiAAAATIgQBwAAYEKEOAAAABMixAEAAJgQIQ4AAMCECHEAAAAmRIgDAAAwIUIcAACACRHiAAAATIgQBwAAYEKEOAAAABMixAEAAJgQIQ4AAMCECHEAAAAmRIgDAAAwIUIcAACACRHiAAAATIgQBwAAYEKEOAAAABMixAEAAJgQIQ4AAMCECHEAAAAmRIgDAAAwIUIcAACACRHiAAAATIgQBwAAYEKEOAAAABMixAEAAJgQIQ4AAMCEmhTipkyZIovF0uDr7NmzHusWFBRo3LhxioqKUlhYmPr166fMzEyv5Rvy9ddfa8mSJRo1apRiYmIUHBys7t276/bbb9frr7+u2trapqweAABAm9fRn8p9+vRRdHS0x3kdOtTPh2vWrNHkyZPldDoVFxen+Ph4FRUVKT09XZs3b1ZeXp46derUqGU7nU717dvX/blnz5664YYbdPjwYeXl5SkvL0/vvPOONm3apNDQ0KatIAAAQBvl1+nUefPm6ZNPPvH4slqtdcqWlpZq2rRpcjqdWrZsmcrKyrR7924dOHBAffv2VWFhoebMmdPoZRuGoa5du2rBggX69ttvVVZWpsLCQh07dkzr169XWFiYcnNztWDBAn9WEQAAoE1qsWvisrKy5HA4NGrUKM2ePVsWi0WS1Lt3b61evVqS9Oqrr+rYsWONai8oKEglJSXKzMxUYmJinXkPPvigMjIyJEmrV6/mtCoAALjstEiIMwxDGzZskCRNmzat3vyhQ4cqOTlZNTU12rRpU6PatFgs6tatm9f5o0aNkiRVVFToxIkTTeg1AABA2+VXiMvOztb48eM1fPhwTZw4UStWrJDNZqtX7vDhwzpy5IgkKTU11WNbruk7d+70p0tuF94oERYWFpA2AQAA2gq/bmzYsmVLnc/r169XRkaG1q5dq9GjR7unHzhwQJIUEhKi2NhYj225Tom6yvrrr3/9qyTpJz/5icLDw72Wczgccjgc7s92uz0gywcAAGhOTToSl5SUpMWLF2vv3r2y2+2qrKxUbm6uhgwZooqKCo0fP167du1yl6+oqJAkde3a1X0t3MVcp0ZdZf1RVFSkP/3pT5J0yZsllixZooiICPcrPj7e7+UDAAA0tyaFuIULFyotLU0DBgxQly5d1LlzZ40cOVLbt2/X4MGD5XA4NHfuXHd516nNi+9YvVBISIgkqaqqqildcjt9+rTuv/9+VVdX66677tLjjz/eYPm0tDTZbDb3q6yszK/lAwAAtISA3thgtVqVmZkpScrLy3MfVXM9p626utprXdcpTX+uX3M4HBo/fry+/vpr9e/fX2+//fYl64SEhCg8PLzOCwAAoK0L+N2pt9xyiySptrZWJSUlkv59qvT06dMyDMNjPVfga+iO04acO3dODz30kD766CMlJCQoNze3yW0BAFqX4XTq9Kdf6MR7eTr96RcynM7W7hLQ5vh1Y4MnwcHB7vfnzp2TdP6XHaTzR8rKy8sVFxdXr54r8LnK+sIwDD3xxBPatGmTevTooW3btnm9gQIA0LadzMlXyW9WqvroSfc0a0ykEtNnKHK05yccAO1RwI/E7du3z/2+Z8+ekqRevXopJiZGkpSfn++xnmv6kCFDfF7mL37xC7399tu68sortXXrViUlJfncBgCg9Z3MyVfxMy/UCXCSVH3spIqfeUEnczx/hwDtUcBD3PLlyyVJycnJ7iNuFotFEyZMkCStWrWqXp0dO3aouLhYwcHBuvfee31a3vz58/WnP/1JXbp0UU5Ojvr37+/nGgAAWoPhdKrkNyslT1fd/GtaSeZfOLUK/IvPIW7r1q1KS0vTwYMH60y32WyaOXOm1q1bJ0lKT0+vM3/27NmyWq3Kzc1VVlaW+9q4Q4cOaerUqZKk6dOnu4/YuWRnZyshIUHDhg2r15eXXnpJixcvVlhYmP7+97/rpptu8nV1AABthK1wX70jcHUYUvWRE7IV7vNeBmhHfL4m7syZM1q6dKmWLl2quLg4xcbGqqamRvv371d1dbUsFovS09P18MMP16l39dVX67XXXtMTTzyhOXPm6OWXX1Z0dLSKiopUU1OjlJQUZWVl1VveDz/8oEOHDtWbXl5erlmzZkmSunTponnz5nntc3Z2dr1wCABoW2qOfx/QcsDlzucQl5KSovnz56ugoEDffPONioqKZBiG4uLidOutt+rpp5/2el3bpEmTdM0112jJkiXasWOH9u/fr8TERD388MOaO3eu+1EkjVFdXe0+mnf8+HEdP37ca9kLf4ILANA2BUd3D2g54HJnMbw986OdstvtioiIkM1m45lxANCCDKdThbdOUfWxk56vi7NI1pgoDdr+uixBQS3eP6Al+JJDAn5jAwAATWEJClJi+ox/fbh45vk/iQt/ToAD/oUQBwBoMyJHpyr5jwtkvSqyznRrTJSS/7iA58QBFwj4w34BAPBH5OhUXTnyZtkK96nm+PcKju6uiEH9OQIHXIQQBwBocyxBQep684DW7gbQpnE6FQAAwIQIcQAAACZEiAMAADAhQhwAAIAJEeIAAABMiLtTL+L6AQu73d7KPQEAAO2NK3805ge1CHEXqayslCTFx8e3ck8AAEB7VVlZqYiIiAbL8NupF6mtrVV5ebm6dOkii+Xi333BpdjtdsXHx6usrIzfnvUTYxk4jGVgMI6Bw1gGzuU2loZhqLKyUrGxserQoeGr3jgSd5EOHTqoZ8+erd0N0wsPD78s/jG1BYxl4DCWgcE4Bg5jGTiX01he6gicCzc2AAAAmBAhDgAAwIQIcQiokJAQZWRkKCQkpLW7YnqMZeAwloHBOAYOYxk47XksubEBAADAhDgSBwAAYEKEOAAAABMixAEAAJgQIQ4AAMCECHEAAAAmRIiDnE6nXnvtNd12222KjIxUaGioevfurfHjx2vTpk2NbmfPnj1KT093txMcHKzo6GiNGTNGGzZs8FrvjTfekMViafCVk5MTiFVtdoEay0WLFl1yTIqLi73W//LLL/Xoo4+qR48eCg0NVVJSkmbNmqXTp08HYC2bX6DG8VJj6Hq9+eabdepdDttkaWlpo9f/o48+8qntgoICjRs3TlFRUQoLC1O/fv2UmZmps2fPNljPjNtlc4xje91XNsdYtvd9JT+71c5VVFTorrvu0qeffiqLxaJrr71WCQkJKi8v16ZNm9SxY0eNGzfuku18++23uvHGG92fr776aiUkJKikpEQ5OTnKycnR5MmTtXr1aq+/BRcdHa0+ffp4nNetW7emrWALCtRYXig+Pl69evXyOK9Tp04ep3/44Ye6++67VVVVpaioKPXv31/FxcVavny5NmzYoB07duiqq67yef1aSiDHMTU1tcHl7N+/X5J08803eyxj5m0yNDS0wfU/cuSISkpKFBoaqhtuuKHR7a5Zs0aTJ0+W0+lUXFyc4uPjVVRUpPT0dG3evFl5eXket02zbpeBHsf2vK9srm1Sap/7SkmSgXbL6XQaw4YNMyQZ9913n1FWVlZnfllZmfHRRx81qq0DBw4YPXr0MF588UWjvLy8zjJWrFhhWCwWQ5KxYsWKenVff/11Q5IxefJkv9anNQVyLA3DMDIyMgxJRkZGhk/9sNvtRlRUlCHJmDlzplFdXW0YhmGcPHnSSE1NNSQZd999t09ttqRAj2ND5s+fb0gyBg8eXG/e5bBNXsqjjz5qSDIefPDBRtc5ePCgERISYkgyli1bZtTW1hqGYRilpaVG3759DUnGM888U6+e2bfLhvg6ju19X9mQpmyT7XVf6UKIa8f+/Oc/G5KM22+/3XA6nX61VVVVZZw5c8br/BkzZhiSjAEDBtSbdznsmAI5lobR9B3TsmXLDEnGddddZ5w7d67OvEOHDhkdO3Y0JBmfffaZ331sDoEeR29qa2uNhISEdvtlWVlZaVxxxRWGJGPz5s2Nrvf0008bkoxRo0bVm5efn29IMoKDg42jR4/WmWf27dKbpoxje99XetPUbbK97itduCauHXv55ZclSZmZmV4P2zdWaGio10PWkjRq1ChJ0tdff+3XctqqQI6lP959911J0pQpUxQUFFRnXq9evTRixAhJUnZ2dov3rTFaahw//vhjlZaWKjg4WBMnTmy25bRV7777rs6cOaOoqCiNHj26UXUMw3BfrzVt2rR684cOHark5GTV1NTUu27R7NulN00Zx/a+r/SmKWPp7/Ik82+TXBPXTh04cEDFxcXq3r27hg4dqk2bNul//ud/dOTIEUVFRWnEiBF6/PHHA/ZbdK4LnsPCwryW2bt3rx555BEdPXpU4eHhGjhwoB577DElJSUFpA/NpTnH8sMPP9S+fft06tQpde/eXYMHD9akSZMUExNTr+y5c+f02WefSfJ+LVhqaqpycnK0c+dOn/vS3Fpym3z77bclSaNHj1ZkZKTXcmbdJi/Ftf4TJ05Ux46N+xo4fPiwjhw5Iqnh7au4uFg7d+7Uk08+Kcn822VDmjKOl3I57ysb4u9Ytqd9ZR2tfSgQrWPdunWGJGPo0KHu6xAufiUnJxulpaUBWd748eMNScbYsWPrzXOdIvD0CgoKMl544YWA9KG5NMdYuk4ReHqFhYUZr7/+er06Bw4ccJe58FqbC61Zs8aQZMTHxzd1dZtNS22TZ8+eNbp27WpIMv761796LGP2bbIh5eXlRocOHQxJxv/93/81ut7WrVsNSUZISIj7WriL/fa3vzUkGbfeeqt7mtm3S2+aOo6XcjnvK73xZyzb477yQpxObadc/0ddWFioNWvWaPr06SotLdXZs2e1bds2JSYmqri4WPfff79qa2v9WlZubq42btwoSZo9e3a9+V27dtWzzz6r/Px8HTt2TGfPntWePXv0+OOPy+l0asGCBXrllVf86kNzao6x7NGjh+bNm6fCwkKdOnVKP/74o/Lz8zVmzBhVVVVp6tSp2rx5c506FRUV7vfe7lBzTb+wbFvRUtvk5s2bdfr0aUVEROiee+7xWMbs22RD1qxZo9raWvXt21eDBg1qdD3XNtO1a1dZLBaPZTxtX2bfLr1p6jg25HLfV3rjz1i2x31lHa2dItE6MjMz3f8ncuH/Nbt8/vnn7ruk3nvvvSYv59ChQ0ZkZKQhyXj66ad9rv/LX/7SkGREREQYdru9yf1oTi01loZx/oL8CRMmGJKMpKSkOkdEtm/f7u6Ht5sC3n//fff/tbc1LTWO48aNMyQZ06dPb1J9M2yTDbn++usNST4ftXnrrbcueWRi1apV7m3TxezbpTdNHUdv2sO+0ptAj6VhXN77ygtxJK6dCg0Ndb9/7rnn6s2//vrrdfvtt0tSkx8e+f3332vMmDE6efKkfvazn+mll17yuY3//M//VEhIiGw2mz744IMm9aO5tcRYulgsFi1dulTS+edNffHFFx77UV1d7bG+w+GQ1PD1Nq2lJcbx1KlT+t///V9J0qRJk5rUhhm2SW/+8Y9/aO/evbJYLHrsscd8quv67+Nt25I8b19m3y498WccPWkv+0pPAj2WLpfzvvJChLh26sJDyMnJyR7LXHfddZLOP2XbVz/88IPuuusu7d+/XykpKXrvvfeadEF6eHi4+vfvL0n65ptvfK7fEpp7LC927bXXqnv37pLqjsmF/fB2CsA1vS0+ELQlxnH9+vWqqalRQkKChg0b1qQ2zLBNevPf//3fkqSf/vSn6t27t091Xf99Tp8+LcMwPJbxtH2Zfbv0xJ9xvFh72ld6EsixvNjluq+8ECGunerbt6/7vbcdhmu60+n0qW2Hw6Fx48Zp586d6tevn3JyctSlS5cm9zU4OFjS+TuK2qLmHEtvPI1JQkKCe3pJSYnHeq7p3p723ppaYhxdd8A99thjXq/raoy2vk16Ultbq3Xr1kmSHn/8cZ/ru7YZh8Oh8vJyj2U8bV9m3y4v5u84Xqi97SsvFsix9OZy3FdeiBDXTg0cONB9SPlSG3FcXFyj2z137pwefPBBffDBB0pMTNTWrVsbfITDpTidTn311VeSpJ49eza5nebUXGPpzcmTJ3X8+HFJdcekY8eO7p/zyc/P91jXNX3IkCF+9yPQmnscv/32WxUUFEiSX6dtzLBNevLhhx/qu+++U2hoqB544AGf6/fq1cv9uAZfti+zb5cX83ccXdrjvvJigRpLby7XfWUdrX1RHlrPfffdZ0gyHnnkkXrzjhw5YoSFhRmSjLfffrtR7dXW1rofDREbG2uUlJT43ce//OUv7otLv/vuO7/bay6BHsuGpKWluS9gdjgcdea9+OKLjXoK+a5du/zuR3NoznF0PYrA089s+cIs2+TFJk+ebEi+/aTRxZ566ilDjfjFhiNHjtSZZ/bt8kKBGMf2vK+8UCDGsiGX877ShRDXjn3++edGUFCQ0aFDB+ONN95wT6+oqDDuvPNOQ5KRmJhYZ+P//e9/b/Tu3dt46KGH6rX37LPPGpKMyMhIY//+/Y3qg81mMyZOnGjs3LmzzvRz584Zr776qhEaGmpIMp588skmrmXLCORYFhUVGU899ZRRVFRUZ3pVVZXx29/+1v08pcWLF9frh81mc9/h5u33AMeMGRPIVQ+oQG+TF7rmmmsMyfPPbF3octkmL/Tjjz8aXbp0MaRL/6RRQ+NZUlJiWK1WQ/L+26lPPfVUvXpm3y5dAjWO7Xlf6RKIsWzP+0oXQlw79+c//9n92IZevXoZN910k9GpUyf3DmbPnj11yruOZtx22211pu/YscN9y3Z8fLyRmprq9XWhiooKd72uXbsaAwcONAYNGuR+GKvrH1JVVVUzj4T/AjWWe/bsca97VFSUkZKSYqSkpLjbkmRMmzbN6wNXt23b5t6hu+q76iYkJNQ7StLWBGocL+TaPoODg40TJ040uPzLaZt0Wbt2rXt7qKmpabDspcbzzTffdH85xsXFGQMHDjSCg4MNSUZKSorxww8/eKxn9u3SMAIzjuwrzwvEWLb3faVhEOJgnH9mzj333GNERkYaVqvVSEhIMJ555hmPh+S9/WP68MMP3f9oLvW6UHV1tbFs2TJj/PjxxjXXXGOEh4cbwcHBRkxMjHH33Xcb69ev9/oPsC0KxFhWVFQYmZmZxpgxY4yrr77a6Ny5s2G1Wo2ePXsaDzzwgJGTk3PJfhQVFRkTJ040oqOjDavValx99dXGr371K+P7778P1Ko2q0CM44VcpwHvueeeSy77ctsmDcMwxowZY0gynn322UuWbcx45ufnG2PHjjW6d+9uhISEGH379jUWLVp0yQBh9u0yEOPIvvK8QIwl+0rDsBiGl3vFAQAA0GZxdyoAAIAJEeIAAABMiBAHAABgQoQ4AAAAEyLEAQAAmBAhDgAAwIQIcQAAACZEiAMAADAhQhwAAIAJEeIAAABMiBAHAABgQoQ4AAAAEyLEAQAAmND/BxwI0dPLRELXAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Voronoi\n",
+    "aachen_centered_srs = gk.srs.centeredLAEA(6.083, 50.775)\n",
+    "aachen_points = [\n",
+    "     gk.geom.point(5.6086412456393614e-11, 1.0605099246605318e-09, srs=aachen_centered_srs),\n",
+    "     gk.geom.point(5.5966859847770105e-11, 11124.48031893997, srs=aachen_centered_srs),\n",
+    "     gk.geom.point(5.584717712977507e-11, 22249.12252108337, srs=aachen_centered_srs),\n",
+    "     gk.geom.point(71432.80781956247, -66262.41755972906, srs=aachen_centered_srs),\n",
+    "     gk.geom.point(105796.52244980012, 1069.9939140476542, srs=aachen_centered_srs)]\n",
+    "\n",
+    "\n",
+    "gk.geom.drawGeoms(aachen_points, figsize=(6,6))\n",
+    "plt.show()\n",
+    "\n",
+    "# Use function scipy.spatial.Voronoi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>geom</th>\n",
+       "      <th>GID_0</th>\n",
+       "      <th>NAME_0</th>\n",
+       "      <th>GID_1</th>\n",
+       "      <th>NAME_1</th>\n",
+       "      <th>VARNAME_1</th>\n",
+       "      <th>NL_NAME_1</th>\n",
+       "      <th>TYPE_1</th>\n",
+       "      <th>ENGTYPE_1</th>\n",
+       "      <th>CC_1</th>\n",
+       "      <th>HASC_1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>POLYGON ((4398502.17663101 2825444.29747721,43...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.1_1</td>\n",
+       "      <td>Baden-Württemberg</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Land</td>\n",
+       "      <td>State</td>\n",
+       "      <td>08</td>\n",
+       "      <td>DE.BW</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>POLYGON ((4571474.71024872 2876300.01040813,45...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.2_1</td>\n",
+       "      <td>Bayern</td>\n",
+       "      <td>Bavaria</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Freistaat</td>\n",
+       "      <td>None</td>\n",
+       "      <td>09</td>\n",
+       "      <td>DE.BY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>POLYGON ((4687445.32670111 3279417.05473998,46...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.3_1</td>\n",
+       "      <td>Berlin</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Land</td>\n",
+       "      <td>State</td>\n",
+       "      <td>11</td>\n",
+       "      <td>DE.BE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>POLYGON ((4687406.62967122 3276216.21474307,46...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.4_1</td>\n",
+       "      <td>Brandenburg</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Land</td>\n",
+       "      <td>State</td>\n",
+       "      <td>12</td>\n",
+       "      <td>DE.BR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>POLYGON ((4370767.36023941 3343531.62977978,43...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.5_1</td>\n",
+       "      <td>Bremen</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Freie Hansestadt</td>\n",
+       "      <td>State</td>\n",
+       "      <td>04</td>\n",
+       "      <td>DE.HB</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>POLYGON ((4454947.63911716 3383942.86010936,44...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.6_1</td>\n",
+       "      <td>Hamburg</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Freie und Hansestadt</td>\n",
+       "      <td>State</td>\n",
+       "      <td>02</td>\n",
+       "      <td>DE.HH</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>POLYGON ((4393993.60615276 3055118.46532246,43...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.7_1</td>\n",
+       "      <td>Hessen</td>\n",
+       "      <td>Hesse</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Land</td>\n",
+       "      <td>State</td>\n",
+       "      <td>06</td>\n",
+       "      <td>DE.HE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>POLYGON ((4621202.81347589 3414622.42247221,46...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.8_1</td>\n",
+       "      <td>Mecklenburg-Vorpommern</td>\n",
+       "      <td>Mecklenburg-West Pomerania</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Land</td>\n",
+       "      <td>State</td>\n",
+       "      <td>13</td>\n",
+       "      <td>DE.MV</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>POLYGON ((4399247.10847504 3295845.90234923,43...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.9_1</td>\n",
+       "      <td>Niedersachsen</td>\n",
+       "      <td>Lower Saxony</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Land</td>\n",
+       "      <td>State</td>\n",
+       "      <td>03</td>\n",
+       "      <td>DE.NI</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>POLYGON ((4290577.32178673 3152201.93412347,42...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.10_1</td>\n",
+       "      <td>Nordrhein-Westfalen</td>\n",
+       "      <td>North Rhine-Westphalia</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Land</td>\n",
+       "      <td>State</td>\n",
+       "      <td>05</td>\n",
+       "      <td>DE.NW</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>POLYGON ((4281428.62823477 2978059.94127456,42...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.11_1</td>\n",
+       "      <td>Rheinland-Pfalz</td>\n",
+       "      <td>Rhineland-Palatinate</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Land</td>\n",
+       "      <td>State</td>\n",
+       "      <td>07</td>\n",
+       "      <td>DE.RP</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>POLYGON ((4244977.13709376 2919589.99870464,42...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.12_1</td>\n",
+       "      <td>Saarland</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Land</td>\n",
+       "      <td>State</td>\n",
+       "      <td>10</td>\n",
+       "      <td>DE.SL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>POLYGON ((4695655.04309472 3118274.75326386,46...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.14_1</td>\n",
+       "      <td>Sachsen</td>\n",
+       "      <td>Saxony</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Freistaat</td>\n",
+       "      <td>State</td>\n",
+       "      <td>14</td>\n",
+       "      <td>DE.SN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>POLYGON ((4574954.70366724 3217915.49222278,45...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.13_1</td>\n",
+       "      <td>Sachsen-Anhalt</td>\n",
+       "      <td>Saxony-Anhalt</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Land</td>\n",
+       "      <td>State</td>\n",
+       "      <td>15</td>\n",
+       "      <td>DE.ST</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>POLYGON ((4439188.68199957 3454387.04273875,44...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.15_1</td>\n",
+       "      <td>Schleswig-Holstein</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Land</td>\n",
+       "      <td>State</td>\n",
+       "      <td>01</td>\n",
+       "      <td>DE.SH</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>POLYGON ((4533713.06687209 3092446.94063045,45...</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>DEU.16_1</td>\n",
+       "      <td>Thüringen</td>\n",
+       "      <td>Thuringia</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Freistaat</td>\n",
+       "      <td>State</td>\n",
+       "      <td>16</td>\n",
+       "      <td>DE.TH</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 geom GID_0   NAME_0  \\\n",
+       "0   POLYGON ((4398502.17663101 2825444.29747721,43...   DEU  Germany   \n",
+       "1   POLYGON ((4571474.71024872 2876300.01040813,45...   DEU  Germany   \n",
+       "2   POLYGON ((4687445.32670111 3279417.05473998,46...   DEU  Germany   \n",
+       "3   POLYGON ((4687406.62967122 3276216.21474307,46...   DEU  Germany   \n",
+       "4   POLYGON ((4370767.36023941 3343531.62977978,43...   DEU  Germany   \n",
+       "5   POLYGON ((4454947.63911716 3383942.86010936,44...   DEU  Germany   \n",
+       "6   POLYGON ((4393993.60615276 3055118.46532246,43...   DEU  Germany   \n",
+       "7   POLYGON ((4621202.81347589 3414622.42247221,46...   DEU  Germany   \n",
+       "8   POLYGON ((4399247.10847504 3295845.90234923,43...   DEU  Germany   \n",
+       "9   POLYGON ((4290577.32178673 3152201.93412347,42...   DEU  Germany   \n",
+       "10  POLYGON ((4281428.62823477 2978059.94127456,42...   DEU  Germany   \n",
+       "11  POLYGON ((4244977.13709376 2919589.99870464,42...   DEU  Germany   \n",
+       "12  POLYGON ((4695655.04309472 3118274.75326386,46...   DEU  Germany   \n",
+       "13  POLYGON ((4574954.70366724 3217915.49222278,45...   DEU  Germany   \n",
+       "14  POLYGON ((4439188.68199957 3454387.04273875,44...   DEU  Germany   \n",
+       "15  POLYGON ((4533713.06687209 3092446.94063045,45...   DEU  Germany   \n",
+       "\n",
+       "       GID_1                  NAME_1                   VARNAME_1 NL_NAME_1  \\\n",
+       "0    DEU.1_1       Baden-Württemberg                        None      None   \n",
+       "1    DEU.2_1                  Bayern                     Bavaria      None   \n",
+       "2    DEU.3_1                  Berlin                        None      None   \n",
+       "3    DEU.4_1             Brandenburg                        None      None   \n",
+       "4    DEU.5_1                  Bremen                        None      None   \n",
+       "5    DEU.6_1                 Hamburg                        None      None   \n",
+       "6    DEU.7_1                  Hessen                       Hesse      None   \n",
+       "7    DEU.8_1  Mecklenburg-Vorpommern  Mecklenburg-West Pomerania      None   \n",
+       "8    DEU.9_1           Niedersachsen                Lower Saxony      None   \n",
+       "9   DEU.10_1     Nordrhein-Westfalen      North Rhine-Westphalia      None   \n",
+       "10  DEU.11_1         Rheinland-Pfalz        Rhineland-Palatinate      None   \n",
+       "11  DEU.12_1                Saarland                        None      None   \n",
+       "12  DEU.14_1                 Sachsen                      Saxony      None   \n",
+       "13  DEU.13_1          Sachsen-Anhalt               Saxony-Anhalt      None   \n",
+       "14  DEU.15_1      Schleswig-Holstein                        None      None   \n",
+       "15  DEU.16_1               Thüringen                   Thuringia      None   \n",
+       "\n",
+       "                  TYPE_1 ENGTYPE_1 CC_1 HASC_1  \n",
+       "0                   Land     State   08  DE.BW  \n",
+       "1              Freistaat      None   09  DE.BY  \n",
+       "2                   Land     State   11  DE.BE  \n",
+       "3                   Land     State   12  DE.BR  \n",
+       "4       Freie Hansestadt     State   04  DE.HB  \n",
+       "5   Freie und Hansestadt     State   02  DE.HH  \n",
+       "6                   Land     State   06  DE.HE  \n",
+       "7                   Land     State   13  DE.MV  \n",
+       "8                   Land     State   03  DE.NI  \n",
+       "9                   Land     State   05  DE.NW  \n",
+       "10                  Land     State   07  DE.RP  \n",
+       "11                  Land     State   10  DE.SL  \n",
+       "12             Freistaat     State   14  DE.SN  \n",
+       "13                  Land     State   15  DE.ST  \n",
+       "14                  Land     State   01  DE.SH  \n",
+       "15             Freistaat     State   16  DE.TH  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Operating on geom column - Make new file with centroids\n",
+    "federal_states_DE_center_df = federal_states_DE.copy(deep=True)\n",
+    "\n",
+    "def find_centroid(g):\n",
+    "    g2 = g.Centroid().Buffer(2)\n",
+    "    g2.AssignSpatialReference(g.GetSpatialReference())\n",
+    "    g2 = gk.geom.transform(g2, toSRS=gk.srs.EPSG3035)\n",
+    "    return g2\n",
+    "\n",
+    "federal_states_DE_center_df.geom = federal_states_DE.geom.apply( find_centroid )\n",
+    "federal_states_DE_center_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/storage/internal/home/c-winkler/Python/packages/geokit/geokit_dev/Examples/example_centroid_df.shp'"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# save this a new shapefile\n",
+    "gk.vector.createVector(federal_states_DE_center_df, output='example_centroid_df.shp')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### DataFrame to Shapefile\n",
+    "\n",
+    "Here a pandas dataframe is converted to a shape file. The dataframe can include any number of columns which contain float, interger, or string-representable values. One column must be called 'geom' which defines the geometries.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Choose an srs for the geometries\n",
+    "srs = gk.srs.EPSG3035\n",
+    "\n",
+    "# Create random point geometries\n",
+    "geoms = []\n",
+    "for i in range(200):\n",
+    "    geoms.append( gk.geom.point(np.random.random(2), srs=srs ))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a DataFrame with collumn called \"geom\" for the geometries\n",
+    "df = pd.DataFrame({\"geom\":geoms})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add Attributes for each geometry\n",
+    "size = np.random.randint(100, size=len(geoms))\n",
+    "df[\"size\"] = size\n",
+    "\n",
+    "color = [[\"blue\",\"green\",\"red\",\"yellow\"][i] for i in np.random.randint(4, size=len(geoms))]\n",
+    "df[\"color\"] = color"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>geom</th>\n",
+       "      <th>size</th>\n",
+       "      <th>color</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>POINT (0.8057656850059 0.011353649021707 0)</td>\n",
+       "      <td>50</td>\n",
+       "      <td>blue</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>POINT (0.490977180171505 0.434269014972137 0)</td>\n",
+       "      <td>54</td>\n",
+       "      <td>yellow</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>POINT (0.521765899401118 0.237738175183063 0)</td>\n",
+       "      <td>70</td>\n",
+       "      <td>green</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>POINT (0.260401208439103 0.494542155718579 0)</td>\n",
+       "      <td>25</td>\n",
+       "      <td>yellow</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>POINT (0.667251186319504 0.989181100065715 0)</td>\n",
+       "      <td>68</td>\n",
+       "      <td>red</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            geom  size   color\n",
+       "0    POINT (0.8057656850059 0.011353649021707 0)    50    blue\n",
+       "1  POINT (0.490977180171505 0.434269014972137 0)    54  yellow\n",
+       "2  POINT (0.521765899401118 0.237738175183063 0)    70   green\n",
+       "3  POINT (0.260401208439103 0.494542155718579 0)    25  yellow\n",
+       "4  POINT (0.667251186319504 0.989181100065715 0)    68     red"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Show the table for clarification\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/storage/internal/home/c-winkler/Python/packages/geokit/geokit_dev/Examples/outputShapeFile.shp'"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Save as a shapefile\n",
+    "gk.vector.createVector( df, output=\"outputShapeFile.shp\" )\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dataframe to Geopackage\n",
+    "\n",
+    "Similarly, pandas DataFrames containing geometry objects can also be saved to geopackages containing multiple layers of geometries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Choose an srs for the geometries\n",
+    "srs = gk.srs.EPSG3035\n",
+    "\n",
+    "# Create random point geometries\n",
+    "geoms = []\n",
+    "for i in range(200):\n",
+    "    geoms.append( gk.geom.point(np.random.random(2), srs=srs ))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create two DataFrames with collumn called \"geom\" for the geometries\n",
+    "df_1 = pd.DataFrame({\"geom\":geoms[:100]})\n",
+    "df_2 = pd.DataFrame({\"geom\":geoms[100:]})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add Attributes for each geometry\n",
+    "size = np.random.randint(50, size=int(len(geoms)/2))\n",
+    "df_1[\"size\"] = size\n",
+    "df_2[\"size\"] = size\n",
+    "\n",
+    "color = [[\"blue\",\"green\",\"red\",\"yellow\"][i] for i in np.random.randint(4, size=int(len(geoms)/2))]\n",
+    "df_1[\"color\"] = color\n",
+    "df_2[\"color\"] = color"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>geom</th>\n",
+       "      <th>size</th>\n",
+       "      <th>color</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>POINT (0.157951965463824 0.892680845572919 0)</td>\n",
+       "      <td>39</td>\n",
+       "      <td>green</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>POINT (0.164058968466453 0.50463264791745 0)</td>\n",
+       "      <td>6</td>\n",
+       "      <td>yellow</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>POINT (0.023772071378047 0.267115483619039 0)</td>\n",
+       "      <td>10</td>\n",
+       "      <td>yellow</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>POINT (0.506936698670426 0.040470046024073 0)</td>\n",
+       "      <td>8</td>\n",
+       "      <td>yellow</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>POINT (0.380670447091158 0.930788833400302 0)</td>\n",
+       "      <td>46</td>\n",
+       "      <td>yellow</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            geom  size   color\n",
+       "0  POINT (0.157951965463824 0.892680845572919 0)    39   green\n",
+       "1   POINT (0.164058968466453 0.50463264791745 0)     6  yellow\n",
+       "2  POINT (0.023772071378047 0.267115483619039 0)    10  yellow\n",
+       "3  POINT (0.506936698670426 0.040470046024073 0)     8  yellow\n",
+       "4  POINT (0.380670447091158 0.930788833400302 0)    46  yellow"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_1.head()"
+   ]
+  },
+  {
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+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
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+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            geom  size   color\n",
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+       "4  POINT (0.229364121948998 0.122049215419284 0)    46  yellow"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_2.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/storage/internal/home/c-winkler/Python/packages/geokit/geokit_dev/geokit/core/vector.py:883: UserWarning: Overwriting existing file\n",
+      "  warnings.warn(\"Overwriting existing file\")\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'outputGeoPackage.gpkg'"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Save the first DataFrame to a geopackage\n",
+    "gk.vector.createVector( df_1, output=\"outputGeoPackage.gpkg\", driverName=\"GPKG\", layerName=\"layer1\")\n",
+    "\n",
+    "# Save the second DataFrame to the same geopackage without overwriting the first layer\n",
+    "gk.vector.createVector( df_1, output=\"outputGeoPackage.gpkg\", driverName=\"GPKG\", layerName=\"layer2\", overwrite=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['layer1', 'layer2']"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Read from the saved geopackage\n",
+    "\n",
+    "# Check the layer names\n",
+    "gk.vector.listLayers(\"outputGeoPackage.gpkg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Extract the features from the geopackage\n",
+    "\n",
+    "df_1 = gk.vector.extractFeatures(\"outputGeoPackage.gpkg\", layerName=\"layer1\")\n",
+    "df_2 = gk.vector.extractFeatures(\"outputGeoPackage.gpkg\", layerName=\"layer2\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "      <td>yellow</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
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+       "                                            geom  size   color\n",
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+   ]
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+   "metadata": {},
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+       "                                            geom  size   color\n",
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+    "df_2.head()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
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+   "name": "python3"
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+}
diff --git a/Examples/example_04_raster.ipynb b/Examples/example_04_raster.ipynb
new file mode 100644
index 0000000..c830ccf
--- /dev/null
+++ b/Examples/example_04_raster.ipynb
@@ -0,0 +1,314 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.2.8'"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gk.__version__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Raster\n",
+    "\n",
+    "Information included in raster-files:\n",
+    "1. width: number of pixel x-direction\n",
+    "1. height: number of pixel in y-direction\n",
+    "1. resolution\n",
+    "1. extent: relevant geospatial extent\n",
+    "1. datatype\n",
+    "1. srs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a raster from a matrix\n",
+    "raster_matrix = np.array([[1,2,3],\n",
+    "                          [2,2,2],\n",
+    "                          [3,2,1],])\n",
+    "\n",
+    "ras = gk.raster.createRaster( \n",
+    "        bounds = [5,48,8,51], \n",
+    "        pixelWidth=1, \n",
+    "        pixelHeight=1, \n",
+    "        data=raster_matrix,\n",
+    "        srs=gk.srs.EPSG4326)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "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": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "axh = gk.drawRaster(ras, srs=gk.srs.EPSG4326, figsize=(6,6))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3],\n",
+       "       [2, 2, 2],\n",
+       "       [3, 2, 1]])"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# extract matrix\n",
+    "gk.raster.extractMatrix( ras )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extract value from a raster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2.927 2.936 2.948]\n",
+      "[2.926      2.93450004 2.94799995]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# extract value at a given location by interpolating \n",
+    "value_near = gk.raster.interpolateValues( \n",
+    "                source=gk._test_data_['gsa-ghi-like.tif'],\n",
+    "                points=[(5.6,51.1),(5.7,51.1),(5.8,51.1)])\n",
+    "print(value_near)\n",
+    "\n",
+    "\n",
+    "value_interp = gk.raster.interpolateValues( \n",
+    "                source=gk._test_data_['gsa-ghi-like.tif'],\n",
+    "                points=[(5.6,51.1),(5.7,51.1),(5.8,51.1)],\n",
+    "                mode=\"linear-spline\")\n",
+    "print(value_interp)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Polygonize a raster \n",
+    "Poligonize a raster xyz with 'gk.raster.polygonizeRaster(xyz)'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# polygonize a raster\n",
+    "ras_boolean = gk.raster.createRasterLike( \n",
+    "                source=ras,\n",
+    "                data=raster_matrix==2)\n",
+    "\n",
+    "polygonized_geoms = gk.raster.polygonizeRaster(ras_boolean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcIAAAGtCAYAAACFunTKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAfRklEQVR4nO3de7SddX3n8ffHBAJyKUkBFV0BhDproOEiYawUp0CxFIZLrbdGqDMVS1tdKE4RFuIoI1iQS1lWtJopa8pMQFs6MIUWwapEOy1BA6IltrBQwkWLXEK4lgjxO3/sHd1szmXvnXNyOPm9X2s9a7N/z/N7nu+PX+CT53ZOqgpJklr1kpkuQJKkmWQQSpKaZhBKkppmEEqSmmYQSpKaZhBKkpo2d6YLmCo77rhj7bbbbjNdhiTpReiWW255uKp2GmvdZhOEu+22GytXrpzpMiRJL0JJ7hlvnZdGJUlNMwglSU0zCCVJTTMIJUlNMwglSU0zCCVJTTMIJUlNMwglSU0zCCVJTTMIJUlNmzQIkxySpMZY1vZss12SC5MsT/J4d/0hgxaR5CVJzkiyOskzSb6d5M0jjUiSpCEMc0b4PuD1PcvhPet+HngX8BzwdyPUcTZwFnAJcCSwArgyyVEj7EuSpIEN80O3/7mqVoyz7p6qWgCQ5HDgNwfdaZKdgVOB86rqwm7zjUn2BM4DrhuiRkmShjIl9wirqjai+xHAlsCyvvZlwKIku2/EviVJmtAwQXh5kvVJHklyRZKFU1TD3sA64K6+9lXdz72m6DiSJL3AIJdGHwMuAr4GPA7sD3wIuCnJ/lX14EbWsABYO8ZZ5Zqe9dJGeftvLeEv/+ILbLPttjNdiqbYnDlz+OpXvsIBBxww06Volpo0CKvqW8C3epq+luTrwDfoPEDz4Y2sIcBYl1YzacfkJOAkgIULp+oEVZujVatW8b+vXs5e+7x2pkvRFDvtPcezevVqg1AjG+k31FfVrUnuBA6cghrWAPOTpO+scH7P+vHqWAosBVi8ePHG3KfUZm7u3Llss+12bLvtdjNdiqbYFltsMdMlaJbbmIdlxjuTG9YqYB6wR1/7hnuD352CY0iSNKaRgjDJYuA1wM1TUMP1wI+B4/vaTwBur6q7p+AYkiSNadJLo0kuB+4GbgXW0nlY5gzgB8CnerY7EtgGWNRt+pUkOwJPVdUXe7Z7Drisqk4EqKoHk1wMnJHkie5x3g4cBhy3sQOUJGkig9wjvB1YApwMvBR4ALgK+GhVPdyz3Z8Cu/Z8P6v7eQ+wW0/7nO7S60zgSeD9wMuBO4C3VdW1gwxCkqRRDfLU6LnAuQNst9sgB6yqFzwNWlXrgXO6iyRJm4y/fUKS1DSDUJLUNINQktQ0g1CS1DSDUJLUNINQktQ0g1CS1DSDUJLUNINQktQ0g1CS1DSDUJLUNINQktQ0g1CS1DSDUJLUNINQktQ0g1CS1DSDUJLUNINQktQ0g1CS1DSDUJLUNINQktQ0g1CS1DSDUJLUNINQktQ0g1CS1DSDUJLUNINQktQ0g1CS1DSDUJLUNINQktQ0g1CS1DSDUJLUNINQktQ0g1CS1DSDUJLUNINQktQ0g1CS1DSDUJLUNINQktQ0g1CS1DSDUJLUNINQktQ0g1CS1DSDUJLUNINQktQ0g1CS1DSDUJLUtEmDMMkhSWqMZW3fdvOT/FmSh5M8leTLSRYNUkSS1eMc4zdGG5YkSYOZO8S27wO+2fP9uQ3/kCTANcDuwMnAo8AZwI1J9quq+wfY/w3AWX1tdwxRnyRJQxsmCP+5qlaMs+5Y4GDgsKq6ESDJTcDdwGl0QnQyD0+wf0mSpsVU3SM8FvjhhhAEqKrHgGuB46boGJIkTblhgvDyJOuTPJLkiiQLe9btDdw+Rp9VwMIk2w6w/2OSPJ1kXZIV3h+UJG0KgwThY8BFwLuBw4CzgcOBm5Ls3N1mAZ37gv3WdD/nT3KMa+ncWzwCOB54Brg6yQkTdUpyUpKVSVY+9NBDAwxFkqTnm/QeYVV9C/hWT9PXknwd+Aade38fBgLUGN0zSBFVdfLzOiVXAyuAc4FlE/RbCiwFWLx48VjHlyRpQiPdI6yqW4E7gQO7TWvonBX223AmONbZ4kT7Xw9cCbwqyStGqVGSpEFszMMyvWeBq+jcJ+y3F3BvVT054v5h7DNNSZKmxEhBmGQx8Brg5m7TNcArk/xKzzbbA8d01w27/7nAW+mE6AOj1ChJ0iAmvUeY5HI67wPeCqwF9qfzsvwPgE91N7sGuAlYluSD/OyF+gDn9+3vOeCyqjqx+30JnVcsrgPuA14GvBc4AFiyUaOTJGkSg7xQfzudQDoZeCnwAHAV8NGqehigqn6S5GjgQuAzwFZ0gvHQqrqvb39zussGdwM7AxfQuc/4NJ2fYPPrVXXDiOOSJGkggzw1ei6dpzcn224N8K7uMtF26fu+gs5rGZIkbXL+9glJUtMMQklS0wxCSVLTDEJJUtMMQklS0wxCSVLTDEJJUtMMQklS0wxCSVLTDEJJUtMMQklS0wxCSVLTDEJJUtMMQklS0wxCSVLTDEJJUtMMQklS0wxCSVLTDEJJUtMMQklS0wxCSVLTDEJJUtMMQklS0wxCSVLTDEJJUtMMQklS0wxCSVLTDEJJUtMMQklS0wxCSVLTDEJJUtMMQklS0wxCSVLTDEJJUtMMQklS0wxCSVLTDEJJUtMMQklS0wxCSVLTDEJJUtMMQklS0wxCSVLTDEJJUtMMQklS0wxCSVLTDEJJUtMmDcIkhySpMZa1fdvNT/JnSR5O8lSSLydZNEgRSV6S5Iwkq5M8k+TbSd484pgkSRrY3CG2fR/wzZ7vz234hyQBrgF2B04GHgXOAG5Msl9V3T/Jvs8GTgXOBG4Bfgu4MsnRVXXdEDVKkjSUYYLwn6tqxTjrjgUOBg6rqhsBktwE3A2cRidEx5RkZzoheF5VXdhtvjHJnsB5gEEoSZo2U3WP8FjghxtCEKCqHgOuBY6bpO8RwJbAsr72ZcCiJLtPUY2SJL3AMEF4eZL1SR5JckWShT3r9gZuH6PPKmBhkm0n2O/ewDrgrjH6Auw1RI2SJA1lkEujjwEXAV8DHgf2Bz4E3JRk/6p6EFgArB6j75ru53zgyXH2vwBYW1U1Tt8FA9Q4JZYsOZ7v/NN3NtXhtAnd/f3vsfVLt5npMjQNXrrNdpxyygf4yEfPmulSNA0+fcmnOOSQQ6b1GJMGYVV9C/hWT9PXknwd+Aade38fBgL0Bxnd9smM3DfJScBJAAsXLpxk68ldf8MX+fRl17Dddj+30fvSi8tWW2/NrrvvOdNlaBp8+NxLuP+eu2e6DE2DSz9zAbfccsvMB+FYqurWJHcCB3ab1jD2mdv87uejE+xuDTA/SfrOCuf3rB+vjqXAUoDFixePFaZD2/M1e7HD/E12EippI22zzbb8u70GelNLs8yCn99pkxxnYx6W6T2TW0XnXl+/vYB7q2q8y6Ib+s4D9hijL8B3N6JGSZImNFIQJlkMvAa4udt0DfDKJL/Ss832wDHddRO5HvgxcHxf+wnA7VXlNQ9J0rSZ9NJoksvpvA94K7CWzsMyZwA/AD7V3ewa4CZgWZIP8rMX6gOc37e/54DLqupEgKp6MMnFwBlJnuge5+3AYUz+6oUkSRtlkHuEtwNL6PzEmJcCDwBXAR+tqocBquonSY4GLgQ+A2xFJxgPrar7+vY3p7v0OpPOU6XvB14O3AG8raquHWVQkiQNapCnRs8Fzh1guzXAu7rLRNu94GnQqloPnNNdJEnaZPztE5KkphmEkqSmGYSSpKYZhJKkphmEkqSmGYSSpKYZhJKkphmEkqSmGYSSpKYZhJKkphmEkqSmGYSSpKYZhJKkphmEkqSmGYSSpKYZhJKkphmEkqSmGYSSpKYZhJKkphmEkqSmGYSSpKYZhJKkphmEkqSmGYSSpKYZhJKkphmEkqSmGYSSpKYZhJKkphmEkqSmGYSSpKYZhJKkphmEkqSmGYSSpKYZhJKkphmEkqSmGYSSpKYZhJKkphmEkqSmGYSSpKYZhJKkphmEkqSmGYSSpKYZhJKkphmEkqSmGYSSpKYZhJKkphmEkqSmjRSESa5PUknO6Wvfr7vuySSPJ7kmyZ4D7nN1d5/9y2+MUqMkSYOYO2yHJEuAfcdo/wXg74HbgeO7+/4o8PUk+1XVgwPs/gbgrL62O4atUZKkQQ0VhEl2AC4GPgBc0bf6dGA9cGRVre1ufzNwF3AqcNoAh3i4qlYMU5MkSRtj2Euj5wOrqurzY6z7JeCmDSEIUFX30zlDfNPIFUqSNI0GDsIkBwPvBN4zzibrgR+P0b4O2CPJVgMc5pgkTydZl2SF9wclSdNtoCBMsgXwOeDCqhrvnt0dwAHdbTf02w7YGwgwf5LDXAucDBxB5x7jM8DVSU6YoK6TkqxMsvKhhx4aZCiSJD3PoGeEpwNbAx+fYJtPAq8EPpvklUl2Bf4nsG13/U8mOkBVnVxV/6uq/r6q/gr4VWAlcO4EfZZW1eKqWrzTTjsNOBRJkn5m0iBMshA4E/hvwLwkO3QfmqHn+5yq+gfgvcBbgPuB1cAOwGV0LpmuGaawqloPXAm8KskrhukrSdKgBjkjfDWwFbAMeLRngc7ToI8CiwCq6jPAzsAvAgur6nBgF+Dmqnp2hPrS/awR+kqSNKlBXp+4DTh0jPYb6YTjpXRekQCgqtYBqwCSLAIOp/OQzVCSzAXeCtxbVQ8M21+SpEFMGoTd1yGW97cnAbinqpZ3v78K+APgH+k8KXoA8CHgqv7XLZI8B1xWVSd2vy8BjgOuA+4DXkbnMusBwJJRBiZJ0iCG/skyE3gWeB3we8B2wPeAj9F5iKbfnO6ywd10LqleACwAnga+Cfx6Vd0whTVKkvQ8IwdhVaXv+4/oXAYdpe8K4LBRa5EkaVT+9glJUtMMQklS0wxCSVLTDEJJUtMMQklS0wxCSVLTDEJJUtMMQklS0wxCSVLTDEJJUtMMQklS0wxCSVLTDEJJUtMMQklS0wxCSVLTDEJJUtMMQklS0wxCSVLTDEJJUtMMQklS0wxCSVLTDEJJUtMMQklS0wxCSVLTDEJJUtMMQklS0wxCSVLTDEJJUtMMQklS0wxCSVLTDEJJUtMMQklS0wxCSVLTDEJJUtMMQklS0wxCSVLTDEJJUtMMQklS0wxCSVLTDEJJUtMMQklS0wxCSVLTDEJJUtMMQklS0wxCSVLTDEJJUtNGCsIk1yepJOf0te/XXfdkkseTXJNkzwH3+ZIkZyRZneSZJN9O8uZR6pMkaVBDB2GSJcC+Y7T/AvD3wM8BxwO/A+wGfD3JzgPs+mzgLOAS4EhgBXBlkqOGrVGSpEHNHWbjJDsAFwMfAK7oW306sB44sqrWdre/GbgLOBU4bYL97tzd5ryqurDbfGP3bPI84Lph6pQkaVDDnhGeD6yqqs+Pse6XgJs2hCBAVd0P3A68aZL9HgFsCSzra18GLEqy+5B1SpI0kIHPCJMcDLyTMS6Ldq0HfjxG+zpgjyRbVdUz4/Tdu7vdXX3tq7qfewF3D1qrJEmDGigIk2wBfA64sKruGGezO4CDkmxRVc92+21HJ+QCzAf+dZy+C4C1VVV97Wt61k+7uXPn8sH3vIMtt9xyUxxOm9BWW2/DWed/lu22/7mZLkVT7G+v+jzX/fVYF6k02935L6vY95T3TftxBj0jPB3YGvj4BNt8Engr8NkkH+nu+yJg2+76n0zQN0B/CG5oH79TchJwEsDChQsn2nQgX/nyl1m9evVG70cvPu9//yncu/p77L3Pa2e6FE2xL/3tX3HAvntx0EEHzXQpmgZvfOMbp/0YkwZhkoXAmcC7gXlJ5vWsntd9gOaJqvqHJO8FzgXe1V3/FeAy4AR+dnY3ljXA/CTpOyuc37P+BapqKbAUYPHixWMF6VD22Wcf9tlnn43djV6EPvLRs2a6BE2jAw88kGOPPXamy9AsNcjDMq8GtqLz4MqjPQt0nvR8FFgEUFWfAXYGfhFYWFWHA7sAN2+4XDqOVcA8YI++9r26n98doE5JkoY2yKXR24BDx2i/kU44XkrPQy5VtY7uQy5JFgGH03nIZiLX03nQ5njgv/e0nwDcXlU+KCNJmhaTBmH3dYjl/e1JAO6pquXd768C/gD4RzpPgB4AfAi4qv91iyTPAZdV1YndYzyY5GLgjCRPALcCbwcOA44bbWiSJE1uqBfqJ/Es8Drg94DtgO8BH6PzEE2/Od2l15nAk8D7gZfTeQr1bVV17RTWKEnS84wchFWVvu8/onMZdOi+3bb1wDndRZKkTcLfPiFJappBKElqmkEoSWqaQShJappBKElqmkEoSWqaQShJappBKElqmkEoSWqaQShJappBKElqmkEoSWqaQShJappBKElqmkEoSWqaQShJappBKElqmkEoSWqaQShJappBKElqmkEoSWqaQShJappBKElqmkEoSWqaQShJappBKElqmkEoSWqaQShJappBKElqmkEoSWqaQShJappBKElqmkEoSWqaQShJappBKElqmkEoSWqaQShJappBKElqmkEoSWqaQShJappBKElqmkEoSWqaQShJappBKElqmkEoSWqaQShJappBKElq2khBmOT6JJXknL72vZNcleSHSZ5KsirJHyaZO8A+V3f32b/8xig1SpI0iEkDql+SJcC+Y7TvAiwHfgCcAjwM/CpwAbAzcPoAu78BOKuv7Y5ha5QkaVBDBWGSHYCLgQ8AV/StPhrYEfjlqrqz2/bVJHsA72SwIHy4qlYMU5MkSRtj2Euj5wOrqurzY6zbsvv5eF/72hGOI0nSJjFwQCU5mM6Z3XvG2eRKOpdDL0mye5Ltk7wJ+G3gogEPc0ySp5OsS7LC+4OSpOk2UBAm2QL4HHBhVY15z66qfgS8Hvj3wPeBx4D/A3yiqs4f4DDXAicDRwDHA88AVyc5YYK6TkqyMsnKhx56aJChSJL0PIPeIzwd2Br4+HgbJNkJuAp4CngL8AhwGPDhJOuq6hMTHaCqTu7b39XACuBcYNk4fZYCSwEWL15cA45FkqSfGuS1hoXAmcC7gXlJ5vWsntd9gOYJ4DRgN2DXqnq0u355kjnA2UkuraqHBy2sqtYnuRL4RJJXVNW/DtpXkqRBDXJp9NXAVnTOyh7tWQBO7f7zou5yV08IbvANYAtgzxHqS/fTsz1J0rQY5NLobcChY7TfSCccLwXuAh4ADkoyvy8MX9f9/MEwhXVfwn8rcG9VPTBMX0mSBjVpEFbVWjovyj9PEoB7qmp59/tn6Tzk8qUkF9C5R3gInbPGq6vqvp6+zwGXVdWJ3e9LgOOA64D7gJcB7wUOAJaMODZJkiY19E+WGU9VrUjyBuAjwCeB7YHVwMd44esTc7rLBnfT+ekzFwALgKeBbwK/XlU3TFWNkiT1GzkIqypjtK0Ajhq2b7ffYaPWIknSqPyJL5KkphmEkqSmGYSSpKYZhJKkphmEkqSmGYSSpKYZhJKkphmEkqSmGYSSpKYZhJKkphmEkqSmGYSSpKYZhJKkphmEkqSmGYSSpKYZhJKkphmEkqSmGYSSpKYZhJKkphmEkqSmGYSSpKYZhJKkphmEkqSmGYSSpKYZhJKkphmEkqSmGYSSpKYZhJKkphmEkqSmGYSSpKYZhJKkphmEkqSmGYSSpKYZhJKkphmEkqSmGYSSpKYZhJKkphmEkqSmGYSSpKYZhJKkphmEkqSmGYSSpKYZhJKkphmEkqSmGYSSpKYZhJKkpo0UhEmuT1JJzulr3zvJVUl+mOSpJKuS/GGSuQPs8yVJzkiyOskzSb6d5M2j1CdJ0qCGDsIkS4B9x2jfBVgOvBo4BTgG+L/ABcDHB9j12cBZwCXAkcAK4MokRw1boyRJg5r0TK1Xkh2Ai4EPAFf0rT4a2BH45aq6s9v21SR7AO8ETp9gvzsDpwLnVdWF3eYbk+wJnAdcN0ydkiQNatgzwvOBVVX1+THWbdn9fLyvfe0Axzmi239ZX/syYFGS3YesU5KkgQwchEkOpnNm955xNrkSeBi4JMnuSbZP8ibgt4GLJtn93sA64K6+9lXdz70GrVOSpGEMdGk0yRbA54ALq+qOsbapqh8leT3w18D3NzQDZ1XV+ZMcYgGwtqqqr31Nz3ppo1z66fNZsOPOM12Gpth3b78NeMdMl6FZbNB7hKcDWzPBQy9JdgKuAp4C3gI8AhwGfDjJuqr6xAT7D53QHKt9/E7JScBJAAsXLpxoUzXuM5++hJUrV850GZoGr9vvv3LUUT5Tp9EN8lrDQuBM4N3AvCTzelbP6z5A8wRwGrAbsGtVPdpdvzzJHODsJJdW1cPjHGYNMD9J+s4K5/esf4GqWgosBVi8ePFYQSoBcNBBB3HQQQfNdBmSXoQGuUf4amArOg+uPNqzQOdJz0eBRd3lrp4Q3OAbwBbAnhMcYxUwD9ijr33DvcHvDlCnJElDGyQIbwMOHWOBTjgeSuchlweAPZPM7+v/uu7nDyY4xvXAj4Hj+9pPAG6vqrsHqFOSpKFNemm0qtbSeVH+eZIA3FNVy7vfP0snyL6U5AI69wgPoXPWeHVV3dfT9zngsqo6sXuMB5NcDJyR5AngVuDtdO4xHjfy6CRJmsRQL9RPpKpWJHkD8BHgk8D2wGrgY7zw9Yk53aXXmcCTwPuBlwN3AG+rqmunqkZJkvqNHIRV9YInOqtqBTDp41vj9F0PnNNdJEnaJPztE5KkphmEkqSmGYSSpKYZhJKkphmEkqSmGYSSpKYZhJKkphmEkqSmGYSSpKYZhJKkphmEkqSm5fm/B3f2SvIQcM8U7GpHYLxfILy5aWms0NZ4WxortDXelsYKUzfeXatqp7FWbDZBOFWSrKyqxTNdx6bQ0lihrfG2NFZoa7wtjRU2zXi9NCpJappBKElqmkH4QktnuoBNqKWxQlvjbWms0NZ4WxorbILxeo9QktQ0zwglSU3b7IMwySFJaoxl7QB9t0pyQZJ/TfJvSW5K8h83Qdkj28jxjtWvkuw3/ZWPLslRSb6e5MkkjydZmeSwSfrMurndYMTxzqq5TbJ8gpqvn6TvrJvbjRzvrJpbgCS/nORLSR7s/hm+Ncm7Bug3LXM7d2N3MIu8D/hmz/fnBuhzKfCfgA8C3wfeC9yQ5PVVdduUVzi1RhkvwJ8Dn+tru3MqCpoOSX4PuKS7nE3nL3f7AS+dpOusnNuNGC/Mrrl9D7B9X9vrgT8Grpmk72yc240ZL8yiuU2yD/BlYAXwu8DTwFuAS5PMq6o/naD79MxtVW3WC3AIUMDhQ/bbt9vvd3ra5gJ3ANfM9LimerzdvgWcM9NjGKLe3YB/A05pZG5HGu9snNtxxnApsA5YsLnN7ajjnY1zC/wR8GNg2772FcBNMzG3m/2l0Y1wLPAs8BcbGqrqOeALwBFJ5s1UYfqpdwE/AT47ZL/ZOrejjnfWS7I18Fbg2qpaM8Gms3Vun2eI8c5GW9KZo3/ra1/LxLfrpm1uWwrCy5OsT/JIkiuSLJxk+72Bu6vq6b72VXQmcs9pqXLqDDveDf4gybokTyf5apI3TGuVG+dg4F+A30ryvSTPJbkryXsn6Tdb53bU8W4wm+a2328C2wGXTbLdbJ3bfoOOd4PZNLd/3v38kyS7JNkhye8CvwpcPEG/aZvbFu4RPgZcBHwNeBzYH/gQcFOS/avqwXH6LQAeHaN9Tc/6F6NRxwuwDPgb4IfArnSuw381yRuravm0Vj2aXbrLBXTG+D06f4u+JMncqvrkOP1m69yOOl6YfXPb753Ag8AXJ9luts5tv0HHC7Nsbqvq9iSHAFfTuTcKnTO936+qL0zQdfrmdqavF8/QNerX0nl4ZNzr6sDfMcb1auCNdK5Tv2GmxzGV4x2n33Z0fpD5/5vpMYxT353dufjNvvYvAg/QfU92c5nbUcc7G+e2r9ZdgPXAHw+w7ayc21HHOxvnFvgF4F7gBuBoOmeCf0InDI+fiblt6dLoT1XVrXT+p3LgBJutYey/YczvWT8rDDjesfo9AfztsP02oUe6n3/X1/4l4GXAK8bpN1vndtTxvsAsmNteJ9C5jTPIZcLZOre9hhnvC8yCuf0jOqF3dFX9TVV9pareB/wl8Mkk4+XStM1tk0HYFTp/ixjPKmD3JP2Ppe9F54mnu6arsGky2Xinut+msGqc9nQ/fzJBv9k4t6OOdzwv5rnt9U7g21X17QG2na1z22uY8Y7nxTy3i+iM79m+9m8APw/sPE6/aZvbJoMwyWLgNcDNE2x2DbAFnXswG/rNBd4OfKmq1k1rkVNowPGO1W97Ou/sDNVvE7q6+3lEX/sRwP1V9cA4/Wbr3I463heYBXML/PTP7t4MfnY0W+cWGGm8Y+3jxT63DwD7Jdmyr/11wDOMf2Y3fXM709eLN8H16MuBc+g8hXUY8Id0fsnjvcCO3W12pXMP7SN9fb9A5+bsu+lcx/6r7kS9dqbHNdXjBU4F/gfwDjrvIv5n4J/o/E3rRXlfhc7fer9K55Lh7wO/RucH9BbwXzbDuR1pvLNxbntq33Dv6GVjrNts5nbU8c7GuaXz8nzRuUd4XPfP8SXdtj+eibmd8X8pm+Bf+hnAd+g8TfkscF/3fx6v6Nlmt+4knNXXd2s6P9nhge6/7JuBQ2Z6TNMxXuAY4B/ohOaz3f/ZXgP8h5ke0yTj3R74NPCj7n/83wHesTnO7ajjncVzuwXwEJ136cZav7nN7dDjncVzeySwvDveJ4Db6DxBOmcm5tbfPiFJalqT9wglSdrAIJQkNc0glCQ1zSCUJDXNIJQkNc0glCQ1zSCUJDXNIJQkNc0glCQ17f8DMSNeQTIBnicAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# draw polygonized geom\n",
+    "gk.drawGeoms(polygonized_geoms.geom[polygonized_geoms.value==1], figsize=(6,6))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+    "# draw polygonized geom with buffer\n",
+    "gk.drawGeoms(polygonized_geoms.geom[polygonized_geoms.value==1].iloc[0].Buffer(0.5), figsize=(6,6))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warp raster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "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 0x000001A557C7D430>, handles=<matplotlib.image.AxesImage object at 0x000001A557CDAD30>, cbar=<matplotlib.colorbar.Colorbar object at 0x000001A557CE70A0>)"
+      ]
+     },
+     "execution_count": 21,
+     "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"
+    },
+    {
+     "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": [
+    "# Warp\n",
+    "gk.drawRaster(gk._test_data_['clc-aachen_clipped.tif'], figsize=(6,6))\n",
+    "warped_raster = gk.raster.warp(gk._test_data_['clc-aachen_clipped.tif'], srs=gk.srs.EPSG4326, resampleAlg='near')\n",
+    "gk.drawRaster(warped_raster, figsize=(6,6))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "geokit",
+   "language": "python",
+   "name": "geokit"
+  },
+  "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": 4
+}
diff --git a/Examples/example_05_Extent.ipynb b/Examples/example_05_Extent.ipynb
new file mode 100644
index 0000000..5a8b1bb
--- /dev/null
+++ b/Examples/example_05_Extent.ipynb
@@ -0,0 +1,482 @@
+{
+ "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?"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "geokit",
+   "language": "python",
+   "name": "geokit"
+  },
+  "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": 4
+}
diff --git a/Examples/example_06_RegionMask.ipynb b/Examples/example_06_RegionMask.ipynb
new file mode 100644
index 0000000..0afeccf
--- /dev/null
+++ b/Examples/example_06_RegionMask.ipynb
@@ -0,0 +1,1402 @@
+{
+ "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",
+    "from os.path import join\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": [
+    "# RegionMask\n",
+    "The RegionMask object is the geospatial representation of the given region, allows to combine raster and vector operations and many functions. \n",
+    "The following shows only a few examples of these. \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Example for the Aachen-Region"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define the srs \n",
+    "aachen_centered_srs = gk.srs.centeredLAEA(6.083, 50.775)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n",
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+       "<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n",
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+       " <metadata>\r\n",
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+       "   <cc:Work>\r\n",
+       "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\r\n",
+       "    <dc:date>2021-10-08T11:15:34.110260</dc:date>\r\n",
+       "    <dc:format>image/svg+xml</dc:format>\r\n",
+       "    <dc:creator>\r\n",
+       "     <cc:Agent>\r\n",
+       "      <dc:title>Matplotlib v3.3.4, https://matplotlib.org/</dc:title>\r\n",
+       "     </cc:Agent>\r\n",
+       "    </dc:creator>\r\n",
+       "   </cc:Work>\r\n",
+       "  </rdf:RDF>\r\n",
+       " </metadata>\r\n",
+       " <defs>\r\n",
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+       "L 142.770168 22.909091 \r\n",
+       "L 144.0975 24.03219 \r\n",
+       "\" style=\"fill:#d9e9ff;stroke:#000000;stroke-linejoin:miter;\"/>\r\n",
+       "   </g>\r\n",
+       "  </g>\r\n",
+       " </g>\r\n",
+       " <defs>\r\n",
+       "  <clipPath id=\"pce302824ca\">\r\n",
+       "   <rect height=\"266.4\" width=\"165.173064\" x=\"61.413468\" y=\"10.8\"/>\r\n",
+       "  </clipPath>\r\n",
+       " </defs>\r\n",
+       "</svg>\r\n"
+      ],
+      "text/plain": [
+       "<geokit.core.regionmask.RegionMask at 0x1f737c54250>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#load the region mask from the passed shape file\n",
+    "aachen_rm = gk.RegionMask.load(gk._test_data_[\"aachenShapefile.shp\"], pixelRes=20, srs=aachen_centered_srs)\n",
+    "aachen_rm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "aachen_rm.extent (-7640.00000,-31220.00000,23820.00000,19500.00000)\n",
+      "aachen_rm.pixelWidth 20\n",
+      "aachen_rm.pixelHeight 20\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print information about the region mask\n",
+    "print(\"aachen_rm.extent\", aachen_rm.extent)\n",
+    "#print(\"aachen_rm.srs\", aachen_rm.srs)\n",
+    "print(\"aachen_rm.pixelWidth\", aachen_rm.pixelWidth)\n",
+    "print(\"aachen_rm.pixelHeight\", aachen_rm.pixelHeight)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Region Mask and Raster-File\n",
+    "\n",
+    "A RegionMask object allows raster data to be warped onto the RegionMask's characteristics (as in the RegionMask's resolution, extent, and spatial reference system). This create a numpy matrix, from which only the pixels which lie in the region can be easily extracted and operated on.\n",
+    "\n",
+    "\n",
+    "Example: Extract the GHI-data for Aachen from a raster-file for the GHI in Germany.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ras = aachen_rm.warp(gk._test_data_['gsa-ghi-like.tif'], returnMatrix=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Draw Raster file in RegionMask."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "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 0x000001F73CB4EB50>, handles=<matplotlib.image.AxesImage object at 0x000001F73EAC8EE0>, cbar=<matplotlib.colorbar.Colorbar object at 0x000001F73EAD7B50>)"
+      ]
+     },
+     "execution_count": 7,
+     "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": [
+    "gk.drawRaster(ras, figsize=(6,6))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Raster values: Average values and histogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.9310975"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get the average of raster values which are within the region mask \n",
+    "aachen_rm.warp(gk._test_data_['gsa-ghi-like.tif'])[aachen_rm.mask].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([1.93000e+02, 1.28100e+03, 4.91500e+03, 6.80170e+04, 3.40446e+05,\n",
+       "        1.96429e+05, 2.64498e+05, 2.99985e+05, 4.26737e+05, 1.71008e+05]),\n",
+       " array([2.789051 , 2.8100007, 2.8309503, 2.8519   , 2.8728497, 2.8937993,\n",
+       "        2.914749 , 2.9356985, 2.9566483, 2.977598 , 2.9985476],\n",
+       "       dtype=float32),\n",
+       " <BarContainer object of 10 artists>)"
+      ]
+     },
+     "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": [
+    "# plot histogramm of the values\n",
+    "plt.hist(aachen_rm.warp(gk._test_data_['gsa-ghi-like.tif'])[aachen_rm.mask])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Region Mask and shp-file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Extract feature in a df."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>geom</th>\n",
+       "      <th>FID</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>POINT (5.98519518411592 50.7972538800796)</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>POINT (5.9946850348875 50.7942084158756)</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>POINT (5.99410693905033 50.800938904832)</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>POINT (6.0047500934257 50.7844315976052)</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>POINT (6.00417362866228 50.7911621621515)</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                        geom  FID\n",
+       "0  POINT (5.98519518411592 50.7972538800796)    0\n",
+       "1   POINT (5.9946850348875 50.7942084158756)    1\n",
+       "2   POINT (5.99410693905033 50.800938904832)    2\n",
+       "3   POINT (6.0047500934257 50.7844315976052)    3\n",
+       "4  POINT (6.00417362866228 50.7911621621515)    4"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "aachen_points = aachen_rm.extractFeatures(gk._test_data_[\"turbinePlacements.shp\"])\n",
+    "aachen_points.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. Example: Show features in the region mask."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "axh = aachen_rm.drawGeoms(aachen_points, figsize=(5,6), hideAxis=True, color='b')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. Example: Indicate features directly in region mask. Include a buffer around the features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Indicate RegionMask pixels within 500 meters of these points\n",
+    "isNearToTurbine = aachen_rm.indicateFeatures(gk._test_data_[\"turbinePlacements.shp\"], buffer=500 )\n",
+    "# Visualize it!\n",
+    "axh = aachen_rm.drawImage(isNearToTurbine, figsize=(5,6), hideAxis=True, cmap='Reds', )\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Export a raster-tif-file for the region "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'rm_raster.tif'"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a raster\n",
+    "example_data = np.ones_like(aachen_rm.mask)\n",
+    "aachen_rm.createRaster(data=example_data, output=\"rm_raster.tif\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Indicate areas with values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AxHands(ax=<matplotlib.axes._axes.Axes object at 0x000001F73FE0D310>, handles=<matplotlib.image.AxesImage object at 0x000001F73FE73C40>, cbar=<matplotlib.colorbar.Colorbar object at 0x000001F73FE88490>)"
+      ]
+     },
+     "execution_count": 15,
+     "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": [
+    "# Show areas with GHI values > 2.9\n",
+    "tmp = aachen_rm.indicateValues(\n",
+    "    source=gk._test_data_['gsa-ghi-like.tif'],\n",
+    "    value=(2.9,None),\n",
+    ")\n",
+    "\n",
+    "aachen_rm.drawImage(tmp, figsize=(6,6))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AxHands(ax=<matplotlib.axes._axes.Axes object at 0x000001F73FEA1A90>, handles=<matplotlib.image.AxesImage object at 0x000001F73FAC4670>, cbar=<matplotlib.colorbar.Colorbar object at 0x000001F73FAC45E0>)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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POoh1qSkunrx7JOJ2kc92yrQo2iEZY9qwxGe6lYgznLgnP0g43su295MlCxP43jed/XsbWUFAG6nT6ihHZUzPRFT6fIlllvhMt1pWdAZDzvFCpmdu+lo2xx7vJ0Ajr/Ef3uFFqrUi2mEZc0Q2x2cM4G6z+Fetw9djbrfw+B25+FOdkywiRFjFaxzUgihHZszwZonPdCtF0knxZVNdozz3an20wxlSxo3x8pdfj2DpsX5mzvYQIsh6lrNJVxLWULTDM6YTQlhdfb7EstiOzsSM3NBoAD56+QFWru28YLTp3EUfTuGNf49l/QsT+M1PRuDyuSlkN2t5K9qhGXMY5zw+V58vsSy2ozMxY3RkPH63s8jlO7eURTmaoUdEEBG+dkUmN1+bDkCIIIW6h3ItjnJ0xgwvlvhMj/gkgVnhBQBs3BqIbjBD3HvrnANqq6lgEyvYxaYoR9R7B3QvDVoX7TBMP4v3xS2xU+vJxLw6nENoRaCpSfH7Y/vFHaumTfRz2cdd1NVHeOqZBhoZWvOmYQ3RQB1hQmxiJUmkcJyejke80Q7N9ANVifk5ur6yxGd6LIcxbGMtRcVhvvp/Jdz3q5xohzQk3fYDp/ZoU5OS9tIeGhvradR6EiT2q+IENcBa3qSSQ8PddVSzjuUs0JNwSXy/YZr4YK9S02PS5uXy4MPV3PWnqihGM/T5/cJZpzmfPUsojHI03WvSBlayrF3SWzjPh9sjlHNwSA/ZmvYiSJ8vscwSn+kxH35y3ONbv//BL8p4YdnQGqaLNZd+KAOAYvZHN5Bu1GstK2QZtVQxZYqbj1+QSM5IF3Nn+AmHFI/4yGN89w9kYp6zgd1qdRoDOCsT54aP4zTOJ8OXR1lFhHMuLeTeh6zn11sfPisJt89FBSXUa020w+lUjVayQl6lQWs5doGPt54az8O/H80NX87kL/9wYp6g00i2I5jMEGGJzxwVEcEjXhYGjmeSazYAX/pWCWs3NkU5sqEpI93NJR/zA7CPnVGO5nAVWsJKeY2ANnL6KQm89M+xZI9w43IJX/xMOh861zmfcRebKNDYi9/0hm1gN6ZTbvEwRWcy1j0NgN/dXxndgIaw6z/vLHYpZC8hDUY5mkNKtJDVvEFIA1z4kWSeeWgMqSmH3jKSklw8ed8Yvvm1NBRlC6vZqmtQq2s3pNkGdmOOIKIRGsLOUNeytxrsDa+XFs71s+h4H2GCbGF1tMNptZvNRHAO0v37Xbmdbl9xuYSf/18OD/x2JAjsY4edQWhiniU+02vbWEsZRSQlC0/9JQ+R2F7JFcseui0Xd4KHIvIp1D2DXsdTVVufM6Jhtus6qnFOklh6rJ+GxiN/qEn0u0DBLV7GMGnA4zUDK6zS50sss8Rnei2X8SAu6uuUxm7eGM2RzZjm466fZQCwiRW8wpODdn5fSIOsYzmbWUWtVvEur7CXbbjcwg++mcWyJ8eSktz1W0VRcYgvfccpuzZN55AkKQQ1QKkeGJT4Tf+yE9iNOYIMGcFY1xQA/v28la3qqysuSeejH0to/X4dywlruNO2jVrfL/OBNVrJO7xECfspIp93eIlaKhk/3s1rT47m+9/IwuM58qf3G35YSlWl4sFLDmMB2MlG1vIWRbqPRq1nn+7oc6zG9BdLfKZPssNO9ZY/PVJDfb2dUtsXIsL1nx3R+n2IIE2dlDOr1Sre42W2s77Pz1lCIQ3Utn6vRLj8khTWvTSBExYn9ugxbrw2i8w8PyGCrGQZJVpIAbtQlA28wxv8l62s4aDu63O8ZnBE1NXnSyyL7ehMzPORAAj7CkPc8Ufbz9cXTz5byzmfcCq4pMsIlnAGSZLark25FrNCXqWJRvazizI92Ovni2iEJhraXfeP+3J58De57VZvdmfGNB9r/pPHhGl+6qhuPm5JmTOzfe3ODbxHpZb2Ol5j+oslPtNrEY2wmjcAJT1N+NyltoG5t359dwUXXlFEfb2Sy3gW6an4pX2P66AWsJo3CWmQSRPdAGxmZa+HPNfxNvvZjccrPPCbHEKFU7jwwym9eqyxoz2seGo08471AZCT4+L1p8Zx6w8yndvHuFEirOWtmN2obxxWuaUPRGSsiPxeRN4WkXoRURGZ2Em7TBG5X0RKRaRORF4UkbmdtEsQkdtE5ICINDQ/7qmdtHOJyHdFZI+INIrIWhG5sIsYrxSRLSLSJCJbReSL/fLDDxMucTHS48zpfP6ydLJHuKMc0dAVaR4l9uJnFotxS/vfZb5uZz3LUcJ8+XPpbHx1IjPmeGmknh29HPLMYwIALlHmzvD1eVVuVqabN/8xlg+dlcSvfjCStFQXN3xxBG89M5adyydy6hl+ggRYzRsE1AoexCql7ys6h/OqzqnAxUAF8HpnDcT5S3saOAf4GnAh4AVeEZGxHZo/AFwJfB/4MHAAeE5EFnRo92PgZuAPwAeB5cA/ReTcDs99JXAP8Hjz8/8TuFNEvnT0P+rw1RhxFrVMnmhH0vTFNV/IIG+SnyBN7GN76/WqynZdxzbWAvCT72bxu59k4/cLf/9tLuJxUcCuXh1mO0rGMs4znUAAPvHFg1TX9H2ONinJxRMP5vGJjx3qOR6/KAGPR/j3vWOYNttHA3Ws5c0uF+4YM9AGMvG9pqqjVPVcnKTSmfOAk4FPq+rfVfV/zde5gG+1NBKR+cBlwHWqep+qvoSTVPOBH7VplwN8E7hVVX+pqq+o6tXAK8Ctbdp5gJ8AD6nqjc3tbgL+BPxYxA4W64k6raE8UkRCAlx6fu+GyIzD5xPuvyULgF1spkkbiGiEjbzXurXgwd/m8J1rslp7ZvNm+bnxWmcOcBMrCHWx969ci1mlr3fay5oamonXlcjOPUG++t2SfvlZPB7ptPeYkuzizpudKjVVlFMQgyXajMMqt/SSqvbk4+N5QKGqvtLmflXAv4HzO7QLAo+2aRcCHgHOFhF/89VnAz7grx2e56/AXBFp2Vl7AjCyk3YPASNwkrHpxn52AfCJj6WSmWHDnH11zhnJnPl+P2FC7GADlZRQRD4At3w7i8svPnwO9aZrspkyy9/lkGeB7mI1b1DOQVbwCvV6aAVnRCNsZiXBSAM+v7TrpQ2EPfuCfOZrRQCMIJdxTB3Q5zO9o4rV6hxgs6HT+kYbgfEiktKm3W5V7bi2eyNOopvapl0T0HHT0Mbmr7PatKOT5+7YznQhpEEKXc6b8pc/mx7laOLHXT/OxeVzc4C9uPAwwT0TgN/eV8n+A4f36Lxe4Z+/y0HcLgrYSYU6vbaIRtiqa9jCKpQII0e5qaeWFbxCjVYS0TDrWU4R+0hMdvG/h0fzwTOTB+Rnqq6JcM9fqjjtggIOFEXIkJHMY6kdWmuiJtqvvCxorovUXnnz18wetstq87VSDy8a2Vk7OnnMju1MJ97Vl3iVpwlFmliyyM+ieQnd38n0yJSJXr7xRScBbWU1gbDzWe9gSZhPXF1EJHJ4hZz5s/18+xrnM+IWVhHWEOt5h33swN28YnPb6xNYeqKPAE2s5g1W8yYlFJKWJrz0j9G878Se7dnrjdvuqODL3y5hX0GYVMligZ6IWzwD9nymr/p+CK0dRHtkgrN6trPrB7odXbTtkohcJSIrRGRFkOG7Kq3e1QAok6e6+c7XMrttb47O976eTWaujxoqOcBexAWXXZTCvb/KweXq/A3le9dkkzfRRx01vMOLlLCftHQnqX32kjTSUl28/PC45uTXSAXFjMh2sexfYzl+0cB+cLnu6gx8KU6im6qz8dgUekxTbKhzoJXTee+q5d20ooftytt8zZTDZ9Y7a0cnj5nV4fZ2VPVeVV2sqou9+DtrEveqtYJI876xd54ez/nn2KKW/pac5OL3P8jA7YbPXJLCljfG89Dvc5kxzdflfRISXPzp587CkXpq8ScK//3raE5Zeqgn5/cL//3jGKbN8JKX5+L1f41l3qyBfx1nZbq54WqnF7ubzXaKh4m6aCe+jRyab2trFpCv2joTvxGYJCJJnbQLcGhObyPgB6Z00g5gU5t2dPLcHduZNvboVt7lZSIaZtIET6fH1Jj+cen5qex8ZwJ//E0uUyd1nfDaev+pSVxwfiIeDzx+X26nJcfS09y89PAY3nhqHNOn9uxx+8M3rsoiMc1LJaWUc/RbL8zgsg3sA+tpYIyIvK/lChFJAz7SfFvbdl7g423aeYBLgOdVW9dp/w8nEX6yw/N8Ctigqrubv38bKO2iXTnwZh9+prjUoHXNqwaVa69OZ93L40lOivbLJ36JCOPGHP2Q4O9+OIqH7hh1xIUqY/I8TBw3uMON6WlubvyKs/ViJxut1xfDFCGifb/EsgGdYRaRi5r/eWzz1w+KSAlQoqrLcBLa28BfReQGnKHN7+LMwf2i5XFUdY2IPAr8pnmP3W7gS8Ak2iQvVS0WkduB74pIDbAKJzmeQZvtEaoaFJHv4WxY3w+82NzmCuBrqhro51/FkFePU2bqlOMT+NXNI6McjelK3igPF5+X2n3DKLjk/FS+/4tyqsPllHKAkYyOdkhmmBropVUdN67f2fx1GXCaqkZE5MPAL5tvS8BJhKerHlbK/XM4m85vATKAtcA5qrqqQ7sbgVrg60AusBW4WFX/3baRqt4tIgp8A7gBZzP8V1X1TuJcRCM0Uk8iyT0uU1WPU6Fl2pTBGx4z8WHvviC/vKuSBx+uItJcrKWcYkt8MSzWhyr7akATn2r3/V1VLcfpaV3RTbsG4Prmy5HahXGS4y09eO57cMqWDRvO/q13KWE/Hrwcq+8jVTK6vV9Dc+KbPN6WoZujc99fq7mz+eSOEa48Jkdmki62YyhWKcT8sUJ9Fd8/nWmnSRtZw5uUsB9wzntrqQzSnUaXc3yN1eQ0R+sH38xifvOpDRJR0rAtMCa6LPENE/Vaw5v8j3KKSc+Q1v135fSsPmO929lIPWWCJT5zdLxe4Xffc+aFyygmRN9PjjcDSQj3wyWW2bjVMFCoe9nBOiKEGJ3n4pG78zhY4ky2+HuwH1FVaQhXA05lEWOO1mP/cRZH5TIOr9g8cSyzoU4zpB3QfFbr62ziPQI0MWKkiz//LpeTliRSW+fUEHfTfSIL0EQ4EiQzw2XFqM1Ra2iI8MdHnMQXIULEjiMyUWaJL46ENESj1lOvtWzTtWzkXco4iMcr/PqH2RSsmMQZJzs1AErLnTefEN3v3GjAqSMwebz19szRS0x08ZtbsnH53BxkHytZRqM2RDsscwQ21GliWpM2UEQ+9dRSzH6CbRKZ2yNcf3U6X7o8nQkdNiyHmz90l3GQt/Q53HgYSR4TmXFY1fzWFZ02zGl66XOXpDNvhp9zriihvLCcd3mReXoCGZId7dBMB6piQ50mdtVrLct5ke2sZz+7CRLA43ORnuXig2cl8J+H8rj1puzDkh7A5Aleps3wIG6hnhpqqGAXm1jO8xTqHiJtjlNsSXy2sMX0xbHzE9j03GiWnOgnQBMrWcY+3WFVXIYxERknIo+JSJWIVIvIEyIyvof3HS8ifxaRfBGpF5FtInKLiHR7vpb1+IaYsIYJ0Eg1FWyWVYQ0wLELvXzqY+ksmufnpCVOpf3uNqZf+OEULvxwCo2NETZsCbBxW4Dv/KqK4vxaNrGC3Wxmok4nj4k0uOohYj0+03cjs928/uhYrr/lIHfcU8tW1lBNBTN0EW6x+eNYMRinKzTXXn4Z5wzVy3HW1dwCvCIi81S17gj3TcapuOUFvodTgOQ44IfANJyKXV2yxDdERDRCATvZzeZDw5kKH/pAIg/9Ppf0tN69aSQkuFi8IIHFCxK47GOp/PXxam78bQ0H99SxmVXsYjNNEWc+ZspEe7mYvvN4hN/dnMvS+dV89royDjTtpZYq5ukJJHb/Yd0MMIXBOk/vSmAyMF1VdwCIyDpgO3A18Osj3PcknAR3tqo+33zdKyKSBXxTRJI6Obi8lQ11xrCwhliv77BG3+QdXmQba1uTns8v/PaWbJ760+heJ72OvF7hc5ems++NMTx0xygmTPPRxKFFCNN6eEqAMT1x2cfSWPHf0eSM81NDJe/yEvt1d7thdhPXzgOWtyQ9gOaDBN6kTW3lLrS8GVV3uL4SJ68dMXNb4othlZRxkH2UcoA6qhmV6+KCDyXzt7tGUbxhEl/9fEaPa20eDbdbuOyCVHa8Oo5H78tl9iwPF30khTF51uMz/WveLD8bnxvNye/zEyTAZlbyNs8dNs9sBpMM1kG0s4ENnVy/kUNHxHXlRZye4c9FZJaIpIjIGTg1mu8+0jAp2FBnTKtorqpy8fkpfOGTaSyc4ycrc/DmQVwu4aIPp3Dhh5KprbMFCGZgZGW6eflvY/n7kzV8+5fVFO2pa51nnqQzyWPCgHzAM51zNrD3y+87W0RWtPn+XlW9t833WRw6bLytcjhyXTtVbRSRk4HHOXS+KsD9wFe7C8x6fDGs0u28Jj55YSpnnpI0qEmvLREhNcVeKmbguN3Cpy5MY+/rY/jpjU4B6wbqKOVAlCMzfVCqqovbXO7tpE1nn6i7zboikgA8CuQAnwbeh3PKziXAHd3d33p8MSqsIaoipbhccHLzSk1j4l1Do/LIk06Vl3TJZrYusd5eFAzSsUQVOL2+jjLpvCfY1ueB04Cpqrqz+brXRKQKuFdE7lbVtV3d2RJfjKqiDNUwC+f4yUi3Zd4m/gWDysVXHmDdxiBJksp8PcG2OERBywnsg2AjzjxfR7OATd3cdy5Q0SbptXi3+etMnDNbO2XjVzGqglIA3ndiYpQjMWbgqSpf+nYxz7/agE8SWKAn4ZPuC6ibIe1pYKmITG65QkQm4mxVeLqb+xYBmSIytcP1xzd/3X+kO1vii1GVnnLAEp8ZHn5yewV//HsNLvEwX08gSVKiHdKwFsHV50sP3AfsAZ4SkfNF5DzgKWAfbQ4IF5EJIhISke+3ue+fgBrgvyJyuYicLiI3AL8EVuJsieiSJb4YpKpUh53E98KyeiIRW1Fp4lcgoLz8prPXWDVCI13uOzaDQBXCKn2+dP88WgecAWwDHgL+BuwGzlDV2jZNBXDTJl+p6h5gKbAGp9rLf3E2xN8LnKV65L0wNscXg0SE2RzHetc73PFgFXk5br779c7mgI0Z+nw+4blHxnDVdw/wl7/Vs5/djGJctMMyg0BV84ELu2mzh05WeqrqJuDi3jyv9fhiVA6jSRNnK8vUyVYxxcQ3r1f40XXOKe3VVFKuxVRrBQ1aR0iDVsh6kEVU+nyJZdbji1ENWkdVuJTEROHcM5OiHY4xA27saA+pmR5qKgKs4rV2t01kOlOZG6XIhhdnVWd894ks8cWo4uZFSR8+K5nkpPh+ERoDzhD/d76Yxlur6ymsUKqqlMJ9YRrrwniwUQ/TfyzxxagmaQKFMbm2j8kMH9+5Jou2e5ov/3oRf/1HLV5LfIMq1k9Q7yvrSsSokZoLwL+fr7f5DTPsRCLKvQ9V8dT/nFrDXmxPn+k/1uOLURlk4/Uks3NPHRu2BJg70/7wzfCwYUsTX7yhmLdXNAEwQvLIUFvVPFj6sUh1zLIeX4wSEXJkNAB3/amK2jo7osXEv607Ahx71j7eXtGE35XEXI5ngZ6IT6xe7eBxFrf09RLLYju6YW5kMAeAe/5Szag5u7no8wd49KkaAgEb+jTx6X8v1xMKOf+eHpnPKBlnRapNv7PEF8OyGMUxspCMxLE0Nir/+m8dl33xII8/U9v9nY0ZgvYVhlr/fYC9UYxkeIsgfb7EMkt8McwlLsYzhUUNS0ghHQC32ykpZEw8Out9h2rTTmVOFCMZvgarZFk0WeIbAgTB43HmOK75QgaXXZAa5YiMGRinHJ+I2+u8ab7Ly2zVNdRpTZSjMvHGEt8QICJMDDmnb/zv5booR2PMwElKcvHo3aOYf5yfMCH2sYO3eY5tXZ8pagaALW4xMSGTHEDYvjtIMGhjnSZ+fezcFFY9PY6nH8prvS6bvCPcw/SnloNo47lWpyW+IcItbhJ96YRCsHNPMNrhGDPgHn3SWcSVwxiyJCfK0Zh4YolvCElWZ27vhWV2XpmJb2+vaOBvj9fgEjfTmBftcIYdW9VpYkZOaBQAN/yolN351usz8SkSUb7+vRIAxupkEiU5yhENLy2VW2yo08SEdB2BIASDUFdvlVxMfKpvUCI489hF5FOtFVGOyMQbS3xDRJHms5znUZS5s3zMnm7V6k18Skl28epj4zjpFB8BmljJMiq0JNphDSu2qtNEXVADbHOtR1E+dXEK/3lotJVxMnEtJdnFCw+NY+48D2FCrOZ1arQy2mEND/0wzGlDnabPyjhIINIAQGVlhKTE2H5RGdMf/vBgJevXOSXMxnNMa/UiY/rKjiUaAnJlHLVaxR628J/n69mdHyQr0w6oNfFJVfnOLWX88s5KAI5hPuNlWnSDGkYUYn5VZl9Zj2+IqKS09d+fv66Y8opwFKMxZmCEQsrnryvml3dWIriYwxJLelEQ70Od1uMbAmq1ikacvXsZGcLtP862Hp+JO/X1ES79YhHPvFCPW7zM1ePJltxoh2XikCW+GHdQC9jECsKEmDfHy5MPjmbCOG+0wzKmX1VUhjn/8gO8+W4jXvGzQE8kXUZEO6xhaTicwG6Jrx8d1AJ2s5kRjGIys3FL73tlTdrIbjZRwC4APnFhMvfdNorERBudNvGlsCjEBy/bz4bNQRJcySyMnESypEU7rGHNEp/pVkQjbGMtBewEoJYqSjjAbF3cq0+tAW1ilbxOnVbhcsNt38vm61el2xYGE3e27wrwgUv3k78vTLKkszByEgmSFO2wTJyz7kM/2MIqCtiJxyt8+uOp5ExIoJ4a3uNVdupG9ChPjt0sq6jTKqZN9fDOf8dy7dUZlvRM3Fm/uYmTzysgf1+YdBnBsXqqJb0YMBxOZ7AeXx81aj0HyMfthpcfG81JSxJpbIzw3V8U87u7a9mtm2mgjlm6GJd0/zmjXmspYT9+P7z46FjGjrb/RSY+jRrpRpoPnU3VdHzij3JEpoVtZzBHVMx+lAjnn5PMSUsSAUhIcHH793P5z99ycSd6KCKftbxFSI9cWFpV2SEbALjsglRLeiau5WR7+PE3sgAoYJedtG4GjSW+Ptilm9mGczL0hLGHr7T84OkpvPF4LgkZfsoo4h1eOmLB3QPspVgLSE0Vvndd1oDFbUwsKCkN8/1flgEwgWNIltQoR2QA0Pjfx2eJr5eUCHtlKwBuD5xzRudzE0sWJrDmmVFMnuGngVre4xX26Y7D2lVrBVvFSaK//8lI27Jg4t5nrimi+GCEDLKZwpxoh2Oa2bFEpksBGgmrU0fw4btyef+pXU/KT5vsY91/x/DZzyShRNjKGnbqRsAZ3izUPazgVcIa5KKPpPCpi+yTr4lvlVVhnn+lAUGYw/E9mv82pr/YJFIvBQgA8NYzYzl+UUK37RMTXTzw89GcuriKK64tZXdkM0ENUEtVazmyL3wyjd/9ZKSt4DRx73+vOJWI0hlBgiRGORrTUaz32PrKEl+vKdOnenuU9Nq6/OPpeD3Cp79aQkHE2feXluni19/P5nOX2qZdMzw8/oyzkCWHMVGOxHTUsp0hnlni64Npk3s3D3fZx9JwueCqb5Twlc+l8+2vZpKRbrU3TfwKBJR/PF3Lxq1NbNwa4PllzjFblvhMNFji64MpE3q/AOXS89M476wUkpJsbsPEv9/eV8l3bilrd10aWbZhPUap9fhMV1yuvr04LOmZeBMIKKrg9x/62ygoDPHDX5cDzoGy6WSRQhqJpEQrTNMN28BuunT7PZWc9fH97Np75I3pxgwHgYByyVVFLDlnHyvXNrZe/42bS2ioV0YyhmNkHqNkLMmSZis5TdRYj6+X/CTRRD0vv9HAeZ8u5I5bc5g4zmP778yw1NSkfPzKAzzzgrNa84QPFfDdazJZemwCj/27Dpd4mK7zoxyl6QlVW9VpuuDDz3xOYINvNZu3l3PGhfsBmDzBw69/NJIZU71Mm+yLcpTGDLymJuWiLxzgvy/W4xU/Oa6x7A/v4pbbK2jZmTNJp9t83hAS73N8NtbQB2mSyezAArL8Y0nzOydF79ob4qOXH2Dmyfl879YyIpGjO5nBmKGksTHChZ8vbE56CSzSU5gZWcixnEqiLxNVSJJUJnBMtEM1ppX1+PooXbJYFFgKQBONvOt+FRUlEKrjp7+tYOfeIA/enkNCgn3GMPHFSXoH+N/LDc1J72RSJQOATBnJ0sD72M56sjUPVx8OZTaDzfbxmaPglwRODp+NiFAqB1nvfpdHn6ylYH+IJ/6YR/YI++M38eOmW8v538vOfrzZemxr0mvhFg8zWBiFyExf2VCnOSot5cayGcXi0Cn4/em8+V4jJ32kgO27AlGOzpj+c+O1mcyZ7yzm2sJqGrQuKnGo6lEf9myGN0t8AyhVMljSdDKp/lHs2B3k0185GO2QjOk3mRlulj06lrkLfTRSz2re6PbMyf62X3eznBc4wN5Bfd54ZqczmD7zSyIzm5wjVxqb7FOpiS8Z6W6WPTKGidP91FPDXrYN6vNvYy11VLOPw4/6MqYrlvgGgav517w7P8jj/6mlti4S5YiM6T/paW7++LNsAPaxgyotG5Shx0ZtIIxzNFgNldRr7YA/57Cgzl6+vl5imSW+QZBEKsm+LGrrlIuvLOLyrx20bQ4mrpyyNIETTvETIsh7vMIynmaVvk6h7hmQ4c+gBtjAO+2us+HO/hNB+nyJZZb4BoFLXCwIHE+WZzQATz5bx09+UxHlqIzpPyLCC38ew2c/nUTqCB8hgpRzkE2sYDOr+uU5yrSIA5rPPt3Jcl6kklJGjnTxt7tGAU7is0Uupics8Q2SRElmUfhEFsjJgHDzbeVc//0SNm5tinZoxvSLxEQXD/xiNBXrx7F35UR+8n9ZABxkH9Xa9w96G3iPjbzLVlbTRD2LF3l569/juPi8FJIzPDRSTx01fX6e4U5xtjP09RLLLPENsmxymeZ2ahb+9r4qFn9gH8+9Ep1l4MYMBBFh1Eg3lVWH5rKVvvXEwhoiiPMh8cKPJXDHrSN548nxTJ7gpaY2Qn1VCMFFEsl9eh4DLRvY43lVp21gj4Lx4Smkk0GBdy9Fgd1ceEURK18Yx/SpVtvTxIfHn6nltjsqAWE800iXrD49XkvaTEoU/nHn2Ha3vbemCVVII8MqxJgesR5fFIgIGZLN7OAicr2TaGhUTj6vgMf/Y6vSzNBXURnmimudPatTmc0xMq/PjxnCKf4gnXQk3lnpHIGUTt+Sqzkk3ld1Dusen4iMA24HzgIEeBG4VlXzB+n5OSY4h5AvRGnFPi6+soi5M32ce2YS11yZQW7OsP7fY4ao4tIwTc1T12OY3KvHCGgju9lCFeXkMo4qnNPbzz69/QkPjY0RHnm6GoB0RvQ+aNNOrM/R9dWw7fGJSBLwMjADuBz4NDANeEVEBm2iwCd+5geWMF0W4Xb7Wb85wM//UMm0pXv5zT2VhMMx/tHJmA7czaONLty9mttr0kZWyDL2sYNqytnGWg5SAMAXL09vbffg36uZcOweNm0JkSgpZJPbL/Gb+DdsEx9wJTAZ+KiqPqmqTwHnAROAqwczEBFhHJM5NXwuiziVkd4J1Dco37i5lBM/VMCmrVbj0wwd40Z7GTPRS4Qwm3jvqO9fyG7qtYbp0zzc/K1Mxk9085lLk3nxsdGccXJia7v0VBel5c4Cmrl6PB6xQ6D7gzNUGd+rOofzWNp5wHJVba11pKq7ReRN4Hzg14MdkFvcZJFDViiHEsawxbueFWtrOOX8Ah6+axSnnZiE3x/bLyhj/H7h7SfGMHHpPkoDRdRrLUmS0q6NqtJALdVUUE0FCSQxktEkSjJ1UgsKX/9CJld/Jp3vXdf5EObJxye0/juF9E7bmN6J9VWZfTWcE99s4KlOrt8IfHyQYznMSBlNZnAkm3zrKK7azbmXHcDjceY4vnd9FsctSOj+QYyJkjF5Hi48z8c/H2tgP7uYRvsFLq/zDAEa2123jbWkaDr1OIu8GhuPPEyakXZoBWctlaTZ4hbTQ8N5qDML6GxXbTmQOcixdMojXuYEFjLBM5MkXyahkPDMC/Wc9rECXn6jPtrhGXNE11/h9NQK2UNYw+1vdDtvPae/z89N12fwwQ8l4EvyUEsVEcJcfmkKX/hU2hEf/0+PVrf+O4hNB/QnW9UZ3zr739NlH19ErgKuAkggqatm/colLqaFZzMtPJsAjexwb6KwcRdXfL2Y9cvGk5oynD+7mFi2ZGECM+b52bKuiYPsYzQTW29zu3wQrufOn4zimCnO/tXGxggvvdFAIKB87NyULh71kM9flsYf/1XFe8sD7GMHI2xxS7+J9Tm6vhrO75oV0OnYSCad9wRR1XtVdbGqLvbiH9DgOuOTBNLDGQDU1keoq7dTHkxs++bnnLm3fLZzQPdS2HzRsHOqQnXNoddwQoKLD70/uUdJD8DjEe76kVOnswGrfmR6bjj3+DbizPN1NAvYNMix9Eij1rNdNoDCb3480vb5mZh36fkpXHNzObVVVWxsu8KzOd9V1/btw5u3dSFnfPdQBpMS+6sy+2o4v3M+DfxSRCar6i4AEZkInAR8J5qBdWUrawlpgA9/IIlPXtizT8XGRFNiootf3ZjJK8vrWlOTiHPxutxkpvetxNjKdc5OeT+22Ks/xfgUXZ8N58R3H/BV4CkRuQnn//WPgX3APdEMrDNlepAS9pOUJNz18xyks9pNxsSgqz6dzlWfHpjtBv/8j3MaQwbZRDRCJaVkMtL+PswRDds5PlWtA84AtgEPAX8DdgNnqMbeUc47XBsBuOnaLEbnDufPK8Y4IhFly3ZnNecuNvMa/2EVr7FR3rNz+fpiEDewi8g4EXlMRKpEpFpEnhCR8T0NVURmisg/RaRURBpEZKuIfL27+w3bxAegqvmqeqGqpqlqqqp+VFX3RDuujuq0hppIOenpwtevtI26xgC4XMKaFyfwlatTwHWokHWJFlqPr6+0Hy7d6GvZSBFZDLwD+IEvAOcCvwK6HT+3rsMQUMhuAM77QAoJCcP6s4ox7aQku/jdzbl84eImPnDxfkrKIkxkerTDMj3TUjZyeksFLRFZB2zHKRvZZfUsEXEBfwZeUtWPtbnplZ48sb2LxrgKLSFfdgJw9WeOvKHXmOFq5jQfjc4OCXIYE91g4sAgDXV2WjYSaCkbeSSn4azA71VpSUt8MSygTayVt1AN89XPp3PC4sTu72TMMPTqWw3UVEVIJo1ksQ+IfTVIlVtmAxs6uX4jTlI7kpObvyaIyHIRCYpIsYj8TkS6faO0xBfDDrCXkAY5ZWkCv/5hdrTDMSZmPf6Ms7rTentDSl/KRo5u/voo8DzOmaq/wJnre7i7J7Y5vhgWkAAonLwkEbfbJuuN6Uwkojz+rFO5JZ0sQhrEjccWuPSS0m8ly7JFZEWb7+9V1Xs7ebqOevLkLZ22v6rq95v//aqIuIFbRWSWqnZZiMQSXwxLVWcF57rNTVGOxJjY5XIJZ5+ewN//Wc863iZCBC9+ZugCRsm4aIc39CjQP4mvVFUXH+H2oy4b2UZZ89cXOlz/PHArsIAjVOCyoc4YlkoGAGs2WOIz5kjuuTWX8VO8RJproQVpYj3vUKZFtqcvdvWlbOTG5q8d/+e2ZOwj1sKzxBfDkkjB7fax/0CY4tJQtMMxJmYlJ7l4+eHRvPfcWBrzp3D9l50FLqt5o3U7kOm5QVrc8jSwVEQmt1zRpmzk093c91mgCTinw/VnN39dwRFY4othIkKqx1nUsnq99fqMAVi+spErrj3ICefu4/PXHeTv/6qhuDTEpPFeFs1LwOsVPnvxoUIPFZRGMdohahA2sOOUjdyDUzbyfBE5D+dw8HZlI0VkgoiERKRlLg9VLQN+BnxRRH4qIu8Xke8A3wf+3HaLRGdsji/GpYVSqQTWbAhw9undFjMwJm4VFIb47k9KefiJQxUF313dxJ8ecVZ0zp/t47ILUvnURalcfNUBAFIli6k6NyrxmiNT1ToROQO4HadspAAvAdd2KBspONVYOnbUfgTUAF8GvgkcAG7Dqbl8RJb4YlxKOBWAVdbjM8PYT24v52e/r6ChQXGJh3E6hSxyqJEqKjzlVISLWLsxwNqNZXz7x866Bz9JLNZTcYu9zR2dwTuWSFXzgQu7abOHTlZ6qjN5+2t6sYndXhExzha4GAN3PlRJQ4MyUsZwjM4jsbmU4whGMTEEYQ1TzkEKffspCe7DI16mReZY0uutOF8PZK+KGJdMGiJuduwOUl0TIS3VpmXN8DNmopeiA02M1UmtSa8tt7gZyWhGBkfTpHNxqwePJT3TBXsXjXEucZHqHQHAuZcVsmGL9fzM8DN3spPEKijptq1fEizp9cUgHksULZb4hoDJgWn4vKm8vaKRY8/ax40/LaOh4YjbVIyJK5/7eAYA+ewgrLa1Z8ANzqrOqLHENwRkSx4nBs9gnGc6oZBw6+8rmHd6Pi8sq492aMYMinmz/ABECBPCEp/pG0t8Q4RHvEwPz2Uxp5Hiy2bX3hDnXFrIp79SZJvbTVyLRJQrv3EQgDQy8eKLckTDgfTDJXZZ4htiMmQESwKnMtU1D3F5ePiJWmafms+Df6+20kwm7oTDyjdvLuWxf9fhFh+zOQ6X2NvWgLOhThNrXOJioh7DCZH3M8I3lvKKCFdeX8yZF+5n645AtMMzpl+EQso5lxby2/uqEIS5usTO2jP9whLfEJYkKSwIHM9sWYLXm8yytxtZcGY+P/pVOU1NMf6Ry5huHCwJ8/IbDQAs5GSyJTfKEQ0j1uMzsUxEyGM8JwbPZIxnKoEA/PCX5a0lm4wZqn5zbyUA6YwgS0ZFNxgTV2yzS5zwio+Z4QV4cbOHrYweZf9rzdC1dmMTv7mvEhCmMz/a4Qwv/XceX8yyd8c40+hqhAgsmuePdijG9NqPfl1GJAxjmUyadHZWqRlI8b5OzoY640yNqxqAOTNsybcZelSVH/yijCf/W4/gYhIzoh2SiUOW+OJMy6q3l19viHIkxhydcFj5yndKuOX2CkCYySL8khjtsIYnW9xihpLRwbEA/P1fNVGOxJiea2pSLvvSQe75SzUucTOfExgtE6Md1vCl0vdLDLM5vjgTbi7nNGGcN8qRGNMz4bDy0c8W8vyrDXhcfuZHlpIpI6MdloljlvjiTBUVABy/KCHKkRjTM3f/uYrnX23A50pkYeQkUiUj2iENexLjQ5V9ZYkvjtRoJftlFygsPdYSnxkaHvhHFQDTI/Ms6cWCITBH11c2xxcnmrSBNfIWYQ3xyYtSOOt9tijAxL7GxggbNjnD8yOwyixmcFiPLw6ENcRaeZsmreeE4/zc98tRiMT25LIxAGs2BggHlWTS8IjNS8eG2F+c0leW+IY4VWUjK6jWcsaPd/OvB0fj98f3i9bEj/dWNwKQhm1Sjyk21GliWYgg5a4SAK64JJ2R2e4oR2RMzx0ocYY5E0mOciRmOLEe3xAXJACRCAAjMu1zjBlagkGna+GK8YNLh5047/FZ4hvitrKGEEFcLjj7dPvUbIaWXflBAHzYKuSYEueJz7oIQ1yyKwOAtFTXYT2+3flBIpE4fwWbIUtVWbbcmeNLZ0SUozHDiSW+IW5sZAIA9Q0RUlPa/++84usHmX7iXn51VwUNDZFohGdMl3bsDlJRFsFHAkmkRDsc06LlWKI4LllmiW+IK8dZ2OL1CK42/zcjEWX5miZ27Q3xrR+VccnVRVRUhqMUpTGHe/2dQ709234TW0T7folllviGuFqcqhfHzvO3e/PYvitIoNF59bndPp55oZ6TP1LAHx+pprzCEqCJvqeecwqpZ9gwpxlklviGuCxyAHhteSO3311BIOAku5XrmgDIJo8l4dNJ8mawZUeQL1xXTN683XzwE4U88HA1pWWWBM3gW7m2kf8814ALN7mMi3Y4piM7lsjEsgSS8HtSAfjmD8u46oaDAKxc17IxOJNkSeW44KnMkEWM8I8jFHbx/Kv1XPWNYkbP380HLtnPvQ9VUVwa6nM82uHo5vr6CDt2BygoDFFfH2l3+40/LWPztkCfn9PEto2BBp6v97Ze3myMcNMvSgEYxxQ7c88MOtvOMMSlSSZLQ6ezjKcBKK9wFrEsX1vv3E4mAF7xMZbJjA1MJqBNlEghJf5SSpv28dJrDbz0WgNf/W4J7z81iU9emMpHP5hMclLPPxepKk8/V8fNt5Xz6L25HDPFxzU3lnDHg1Xt2vl8kJXhJiPNxZYdQW79fQUf/WAy37kmk+MW2JL2/vB4bRp7A9lcn7Ur2qHwZF0Kd3zhc/i2FrZeV+Eq5539f8ctXibo9ChGZ4YrS3xxwCs+xrinsj+0g/POTiYSUdasdxJganPia8snfsYwiTFNkwjqfEoopNhXTFmwgOdeqee5V+pJThI++sEUPnlhCmeekoTLBRWVEUrLw62X+gan96YKDz9Rw7MvOcn2os8X8bUvpDcnPSHBm0ZEIgRDDQQCIYqKwxQVHxpiffLZOp58to5vfCmDX3w/e+B/YXGsNFzHbT/6IgllYT529zomeaO3WrI+EuCHt3+GnGVv0XYsYZsuA2C8TsEn/ugEZ44o1hen9JUlvjgRUmcjcEqyi+27gjTWhfGTiF+O3Ivyio/RTGR0cCIBnU+x7KfIe4DK+gP87fEa/vZ4DUmJQmOTthSI6ZJHfHg8iWzcWsUXb3BWm85gIWNDk1vbhAkTJECIAEECNNJAnbuWPbqFX91VydyZPj798bS+/TKGsat2f5TMx9agoRAXrPkCq497JGqx/LxsIXkPb6TtLHK5FlNBCR68jOeYqMVmuhHj2xH6yhJfnGg5eb2yKtK6sCWVjKN6DJ/4neHQ4GTqqaVICijyFlLfUA6Ax5OA152EDz++iBe3Ng+FKnjDHibqDELBAO+6XyUcDpDJSMYwqd1zuMWNm0SgzbxOBDwuLztYy2evKeaVNxt44PYcW+LeC+vfnsrkxrcBcP03k/DiCG6JzlT+3144hSmVy1u/V1V2shGACRyDV3xRicsYS3xxIiWcSinwjZtLmTnNOd4lrZNhzp5KkhQmM4NJgekECeDBiyvsgq4WgTbnKD8JzAovYiPvMoNFPU5eE8JT8YiHba51/PnRGj7xsVTOel9Sr+MfrlxtxhQ9DdGLY01TE1MfrWu3uK+MIqoow4uPcUyNWmymG0NgVWZf2arOODGF2Yz1TaexUVm93lkpebQ9vs6ICD7x4zqKXsMoGctiTidZUo/qecYyiUmRGQD89r7Kow3VxJALXvsSumJD6/dte3sTmWFn78W6ON/OYD2+OCEiTA/MIUF8uNVDKulRPeMsTXrX2xyjE9kpG3jxtXqqayKkpdpns6Hm7soxzLi1hnCbrSslFFJDJT4SGMvkI9zbmIFniS+OiAgTmc5QPuHFJ34yfXlUNBXyv1fquPi8nvcazdHbGGigKNz5ys+genitZjr1kZ7PxW2rziF48yhcm1e3uz6fbQBMYgZusbedWGerOo0ZZJmBTCoo5LW3Gy3xDaA7Ksfx1OfPwLvnYOcNVIlUVqHhoyhwrsW4QoXtrmrQOiopw4WbPCb0IWIzaCzxGTO4vOrM/yx7q4FwWHG7h3AXNkYdCNXy8A/OJeXtd+h7vZ4jK2IfACMZbXN7JibYBIqJOaOZSIIvnU3bAtz31+pohzOkRNyd/7ujK3d9nNSn1wx4PABF5ANYTc6hJM4Xt1jiMzHHLR6mBWYC8L2fl9lpEkdh8nH7EK8PRCg9MdTlHr5NG8ajTU0DHk+NVlJHNV58jCB3wJ/P9F1/HEkU63OElvhMTMphDBm+PMorIvzr2bpohzNk/GnaI9R8dCGyeA5/PfOerhsO0htTS28vh7FHtSXGmIFkc3wmJokIiZFEKgGPvUp7LM+TwuU/+jcb6sZwUkJ0E42qts7v5TE+qrGYo2Qly4wZfKpKjVYCkJFmPYWjcVV6IaQXdt9wgFVSShMNJJBEuh02O7TE+FBlX9k7iolJZRRRGy4nL9fNOacnRzsc0wttF7VY3VUTS6zHZ2JSNZUAeFxCfUMEv/8ISxRNzIlohIMUAJBrw5xDTqwvTukr6/GZmDSR6aT7RrGvMMQXri8+7GR3E9vKKCJEkBTSSZH0aIdjjpZtZzBm8LnExezAQtxuP08+W8edf6zq/k4mZtjePRPLLPGZmJUkKcwKLwTgmz8sZfX6gd93NhyENULy3oEbOg5pkBKcxTWCy3rrQ43t4zMmukbJWMa6pxEIwKVXF1FTexR1I02nXm30Mu7JAwP2+OUUE8H5/7SddWzgHUIaHLDnM+ZoWeIzMW9aeA6p3mx27A7yxW/ZfF9fhDXC1U9eSXjnngF7jhwZw0l8kGnMw42bgxTwDi+2bk8xQ4DN8RkTXW5xMye4CJfbxyP/quXuv1j9zt76xO6zOObWnTDAHx4SJZkJcgxLeD9JpNJAHaUMXC/T9LM4T3y2ncEMCcmSxszIAjbyLl/7bgnzZvo4aUlitMOKSWGN8LXCE3lj/6R219eUpDDrp8WES/YOWizJkkq6ZlFPDT4SBu15jTkSS3xmyMhjPAfd+ykN72fj1oAlvi5csusDNHzcTV7R5nbX58GAH0HUmQCNAPgt8Q0Zsb44pa9sqNMMKckR52DasnI7saEzu4O1VNw8gVBRF4fLRkETzmpc6/GZWGGJzwwpvuZDav/6WA2lZZb8Orps0+V4l62NdhjttPT4fPijHIkxDkt8ZkgZzSRSfNls2RFkzvv28vv7K2lqivNxmR4Kapimp3PQUDQGNDunqm0Sn/X4how4X9xiic8MKV7xsSBwPBneXErKIlz7vVJmnryXklLr/d1bNZG8J3dHO4x2As3DnF58dh7fUGEb2I2JPQmSyLHBk5jPiXg9SewtCLG/KHZ6OdEQ1DB3PvQRQgeKoh1KO9bbM7HIEp8ZkkSEkTKaNJdzzturbzZEOaLo+vCW85lw9+buGw4yW9E5RMX5UOeAbWcQkeuB04HFQC7wQ1W9uYu2VwLfACYBe4DbVfXuTtp9FPgBMBM4CNwH/ExVwx3anQz8AlgIVAEPAzeqakOHdrOB24ETgSbgaeAbqlrem5/ZDL6xgXGUsY9v/rCUcET5xpcyox1SvwhqmE/sOpud5dndtq08kMasnxQSqqgYhMi6V6+1lHOQMg5STjFgPb4hJ8YTV18N5D6+K4Fq4Engi101ak569wA/A14EzgTuFBFR1bvatDsbeBx4ALgeJ6n9FEgFvt2m3TzgBeA54MM4yfQ2YAxwSZt2o4FXgS3ARUBGc7v/iMjJqmpFIYeAbPIY7Z1KYXAH3/pRGRPHebnwwynRDqvPztp4IUmXVJFTsaXbtjlEZ39eiyZtoIpyyimmjCIaqGt3exIpjGBUlKIz5nADmfhmq2pERDx0kfiab/sJ8JCq3th89SvNSenHInK/amt121uBN1T1qjbtUoCbROR2VW2Z3PghUAB8vOW+IhIA/iwiP1fVVc3tbgC8wEdUnSKCIlIILAM+CjzRD78DM8BEhFmhBXjFw17dwvd/XjbkE9/KpgAJ/5dCuGJPtEM5TEQj1FBBFeVUUUYV5TRS366NBy9Z5DCCUWQxikRJjlK0pjeE2F+c0lcDlvh62GM6ARgJ/LXD9Q8BnwNOxklw44AFwFWdtPsh8EHgjyLiBc4Bft0mYQL8A2dY9HygJfGdBzzTkvSaY35NRPKb21niG0KqKAPgC59Mi3IkfffplVcwbuXGaIcBOEcMlXGwOcmVUUNl68kLLdx4SCeLDLIZwSjSyEJEohSx6ReW+AbU7OavGzpc3/JXPwt4pat2qrpbROqb2wFMARI6adcoIjtb2olIIs4Q6P2dxLSxzeOZIUBVqXfXQRhOPzkp2uH0SX0kQOpTqQNeRLqnCtnDNtpviE8mlTRGkEEW6YwgmTRLdKZXmjs1twNn4XQ2XwSuVdX8o3yc7+JMfb2pqid31z7aiS+r+WvHWfnyDrd31a7lup60K29zeybOL7mrdtO7DtnEGhFhhORygF28/EY9C+YM3QohPyg+nhH/3kSs7EpsWZySxwRGMY50svCKL8pRmQE1SPvwRCQJeBlnYeHlzjNzC84o3zxVrTvS/ds8zmTgRmh+sfZAj7YziMj7RUR7cHm1p0/c8tDNX7v7NR+pnQxgu/Y3iFwlIitEZEUQOw08lowIjQTg2Zfqu2kZu2ojjbx071LClVXRDgVw5vMqKAFgCnPIllxLesPF4GxnuBKYDHxUVZ9U1adwpqAmAFcfRbR3AX8Deryfp6c9vrdwthB052jfddr27Noe1pXV4faOPcC2MnrYLpNDQ6gVOP9rumrX6XYGVb0XuBcgTbJiYyzKALSuGnz9nQYaGiIkJg6tLapBDbPwtS8y9S9riZXlxFWUESZEMqkkiJ2EYfrdecByVd3RckXz9NWbOOssft3dA4jIZcAi4BMcxbqMHiU+Va3HWfbf31oS0WzaJ76WObZNnbR7u6WRiEwEktq024nTbW6ZE2xpl4DzyeKf4Pw8IrKnY7s2z73sqH8SE1UtCy6CQcjfH2L61Oj3TJo0yAkrP0XN5s4+X7WXWCxMu28D4frY6bFWNi8YyiQnypGYQTc4H+tnA091cv1G4OPd3VlEMnHmB7+lquVHM88c7Tm+t4FS4JM4k5otPoXT63oTQFXzRWRtc7v7O7QLAs82twuIyP+Ai0XkZlVt2d50EeDH2aDe4mngchFJV9UqaN34PqFDOzME1FDZ+u9zLi1k/bLxpCRHt9c3/80rmPS5XYys29qj9rEyr9ci0hyRl+h/iDCDa5C2M2TR9TqLnlSiuA3YBvzpaJ94ICu3LAYmcmgecZaIXNT87/+qar2qBkXkezgb1vfjJL8zgCuAr6lqoM1D/h/O5vJ7gL/jbGC/Cfhtmz18ADfjJNR/iMgdzTHcBjymqivbtLsNJ3E+LSI/A9Jxqr28C/yrH34FZhBlkcNCTmG7bzP5+0tZcvY+HrpjFMfOj07FkP/V+5n8g0bCdT2an49J0vynq/G+tt0MlGwRWdHm+3ubp4vaOqp1Fq0NRE4BPgMsUj36JdAD2eP7Ks5KnRYf51D3taU0Gap6t4goTsmyG4B84KuqemfbB1PV/zYnzh8An8UpWfZTnA3wbdutaa7y8nPgGZySZX/BSZxt2+0XkdNxxpEfBwI43e5vWNWWocclLkYwCn8ggXW+lWzdWc4DD1dHLfF9admnOWbziu4bxjBpfv+xxDcM9c//8lJVXXyE29uuyG8rk857gm3dg1PFq0BEMpqv8wDu5u8bVLXLFYgDuYH9szgJqidt78H5Qbpr9wQ9mMBU1ddwNsd31249zv4REydSJJ0ZgTms4jWeebGe5SsbWXrs4Ca/0nAdE/8xqE85IFoSXyTmBmHNgBq8ItMb6XqdxaZOrm9rZvOls6pgFcB1wG+6uvPQWvpmTA+kkYnfk0pBYYiTPlzAFdce5GDJ4FWz/OKe80l4vbu/29iXiFNqrJj9RNSSn+l3TwNLm/fhAa0LFk+i+3UWp3dyWYtTvOR04LEj3dkSn4k7HvFyQuhMJrpmIS43f360hhkn7eV391USDA7sR9kDoVoO/H4qkRhandlbOYwhmTSaaGA/sXXArRlYg3QQ7X04U15Picj5InIeznTTPtqMAIrIBBEJicj3W65T1Vc7XoBKoKr5+4IjPbElPhOXPOJhqs7ihMj7yfaMpbpGue77pXzt/0oG7DmrIg2c8ugNpD4xtOf2WogIU5pHonazhbD1+oaPQdjA3lyZ5QyclZkP4WxC3w2coaq1bZoK4KYf81W0tzMYM6CSJJUF4aVsZhX72cXWHc5C4QOhWk565Rpcxf1X3ix9B0z90yo0FD+nwY9kNKlkUEMlu9nMFJ1tdTlNv2muyXlhN2320IOVnqp6Wk+f1xKfGRYmM5MD7n28tryRkz6yj+1jzmXev1fh7ueRz3hb/ygiTNHZrOFN9rCFCoqZrgtIk+435ZuhK96PJbKhTjMs+CWRmZFFuNxelq9oouypf/FO5DkbvuuBEeQyi8X48FNFOe/yMpt0BQFtjHZoZqAMTq3OqLHEZ4aNPMZxavhcZuFsLaqnhnIORjmq2CcijJaJnMg5TOAYBKGQPbzFc+w7VGZxyKnUUvKHcPym9yzxmWHFI17qqGn9Pq3T/bOmMx7xMk3msZQPMIJRhAiyjbWE9NCcpqoS0iARjdCLghqDQlXZo1tYyTIqGbjFTkNWf/T2YvN/fSub4zPDzghGsRenfqaPoXt2X7QkSyoL9GTe42WqqaCKUjI1h33sYD+7qOfQgrwUzWAex5MkqQCUaRGpZOKT6PzeA9rERt6jDKfKYT1Dt6TcQBF6sJJkiLPEZ4adDLLx4idIE7VUkUpGtEMackSETB1JNRXsZzd72d46bOzChTb/V0sl7/ASx+h8SiiklAP4SWS2HkeWDO6pD5VaynrepYl6UtNc1FRHaKAWVbWVqsOMDXWaYcclLnIYDThVSUzv5DIeF26K2U85B/HiZx4ncBof5Uy5kNM4nxzGECbEZlZS2nzyWBMNrOI1dugGIoNUFrdQ97KSZTRRz/GLfax/eRwj8ryECdlroDNxPtRpic8MSzmMAaCYIxZ4MEeQKhnM5XgEIYkUjuN0cmQMLnHeVjziZS5LmcY8BGEUYzmZc5nUfKb1Hraws/WozYGTr9vZxHsoyrVXp7PsiXGMG+Pl+19NA2gd9jaHDFLllqixoU4zLGWSgwdnoUutVpMiadEOaUgaKaOZqyeQwYhO5+1EhAkcQ66Oxy9OsfApzCZDR7CaN8hnO2N0YuscYH9SVXaxid1sBuCXPxjBdV88dMxbSrIbgCDBfn9uE9usx2eGJZe4GNnc69uHLWnvixwZ3e1ilZak12KE5DKKsSgRSpqHQPtDhZZQrsUArUlPXHD/7Tntkl5ZeZhv31IKwHim9dvzxw0b6jQmPo1nKoKwn12tb5Zm8CSRAkCYvpd4U1V26SZW8hqbWEG1VrCHrYjAP+/L5XOXtu/RX/f9UkpLI2TISMYyuYtHHcYs8RkTn1Ilo3W+aRMrCKkNeQ0md/NMS18TX5M2sIrX2cUmQGmknhW8ihLhc59I42PnprRrX18f4eEnnL2cM3SBregchizxmWFtIjNIJYNG6tnG2pjddB2PpPntJ0LvV3aWahHLeYkKisnKdvGHn41EPC4ihElKEn70rcMLFCQluZh0jBeAkM3vHa4fFrbE+uIWS3xmWHOJi9kchwsXhexhj63w6z8uN7UfP56dt52Ae/b0w25uSToevO2uD2uYBj3yxvKIRtiu61jDGwRp5PRTElj/0ni+9Nl0vne9s1DmO1/NJG9U5+v33n+CMydZZiXrhiVLfGbYS5F0ZrMEgJ1soFD3RDegOFH2uSU8+qtfseOTd7Hob5twT2s/l1ZLFQAJJNGkjRToTtbomyzjaVay7Iinvq/nHfayDZdbuOU7WTz/yBhyc5wk939fzeYjZydz3dUZXd7//LOc5NifC2viSpzP8dl2BmOAUTKWJl3ANtawmZUkaxrpdvROr3kmjOOaG/7JWI8zv3ZLznpm/nQBEz7haT2vsIoyAEo5wFbWEOFQomuknkL2MJYp7R43oE2UUEgJ+0lMEp77+2hOWpLYro3XKzzxYC4uV9dzd6edmIgv0U1tQyWNWk+CJPXLzx0vYn2osq+sx2dMswxGAE7JrUSSoxzN0LbjC2P5ZGr7lbL/XXIXwVPnt37vw9niUEIhEcKMIJeZHMsMFgLOqe8tc64hDbFKX+M1/s1mVgJwzRXphyW9FkdKegAJCS7OfJ/zub/Uen3DjvX4jGnWMr83hslRK6IcD9xpaVzx0RdxS/vP1ZO8Key6TDjmZef76SxgDW+SQjrTmEeGOB88WoaaE3F6YWVaxE42Uk0FHr+L44718P6lyVzzhYw+xXnh2ek8+79iDrAXnyYgCMmkDshm+iEnznt8lviMAeq0hmIKEITxHBPtcIa2xARmJnRe/zJ3TEXrvzNlJCfqOfjwt9tScIC9AHjx8w4vUUslADk5Ll7711imTfb1S5gfen8SCFRpOet4G3B6oUv1rGH/wceGOo0ZBlrqNaaQYUcV9VG4pIybN3+kR239ktAu6TVoHRXNZ+SVUEgtlbgzUrj1phFseX1CvyU9gJxsD9/8cjof+nACHzjbz5iJXgI0spH3bFtLnLMenzE4tTsPkE8NFazhDebqUrzSf2+yw0okTPqdaRTcW9u6uKUdlxu6OJXhAHvbDbNlTj2W954tYFLKwAw//vymka3/3rc/yIwzCimrLmIfO4ZvKbMhsCqzr6zHZwyQJ+M5lvfhw085xbzHK9Rrbfd3NJ3yv7iaU168lmCHLQk/O+Zf8EIevDim08uI389lxJIJAPiykvjX40UDlvQ6GjfGyx9+6NTz3M56GrSOfHawTYdhYQPbzmDM8JAhIzhOz2ANb1JHNe/xMnN16aAfmBoPNBRi5jd2MjPh8zy49E8kS4AFPg+nJcJpM//T9R1nAhd6Wbl2LDV1EU7J6XzV5kB5/lVn4/xIRlNPLdt0DQCpZJLH+EGNxQwc6/EZ00aiJHMcp5NNLkECrOZ1qrQs2mENSeGKCqZevomfn/ohvvupq9gWbOzxfY+dn8BpJw7u3rr31jTyj6fqcOFhGnNppL71th2sHza1XAUrWWbMsOMRL/M5iSxyUJQ6bMiztzQYIFSwH8/BKoIx/nZz253OitNxTCZRktslvpZT44MaiFZ4g8uGOo0ZfhRt3cRuhYzjX2VVmCeeqUNwMa55UUujNIDCd6/J5L4n6iktqGAly8jVcdRSTRMN+EnATxKjmUCyHWY8ZFjiMwZnL1cxBTTRQAJJFLOf+uaeXtgSX5/VzRzJBE/sdgNCIWehqQc3/uaKMo2uRgjDqSck8sXL01lyYREH91Sxo7nGaFv5bCNVM/DiYz4n4ZLY7t12R+J8MY8lPjNsqSolFJLPNirpeh7Penx9I34/9VdXku4a3IUqRyMpUUhMdtFQF3SqyWg6NeoMfU4Y62HsaA+rnsrlKzceZGyul7kz/Iwb7aGoJMwDj1Xy5usBqnHa11JJGkO4zusQGKrsK0t8ZtgSEWq0sjXpZZNLgCYaqcdHAgEayWU8GWRHOdIY1YMDXMXtpuC6Y3lzwa+AGE58SS7++vscLryiiDKcCxHnRxw/xnmbzM3x8Ph9Yw677ycvSOV7Py/jF3+oBKCKiqGd+IYBS3xmWJvMLBqp5wB7qaKChZxMEil4xIuq2uncnRC/n/1fP5acDxTg6mb5XrInwAuTfkG6q5ON7DHmox9MYee7E/jP83UUHAxy/IJE3ndCIomJRx62vPIbB/nLPw4tgKqmHDqcKjHUxPqqzL6yxGeGNRFhph5LE42Uc5D1vMNxnN56m+lAhH3fOJY3vvRLMt093W4Q+0mvxcRxXr76+Ywetz9YEmpNetNZgBdf68nyQ1qcJ744+D9kTN+4xMU8lpJCBg3UspY3CWso2mHFJDl2Ng984fdHkfTi28gRbrJGOSfI+0ggV8YzSsZGOSrTHUt8xuDs3VvISSSQRBXlbODd4Vemqge2fimBpQnuaIcRM1wu4QdfSwdgP7uiHE3/sQ3sxgwTfklkISfjwUsJhWxljSW/NtzZI/jBSU9HO4yY86mLUvEkuCmnOH7qu8b5BnZLfMa0kSxpzOdEBBcF7Gw9rsiApKWyKCE/2mHEnIx0Nxd92DnJo5DdUY7G9IQlPmM6yJSRzGEJADvYwAG1N3tzZF/6lLN9oZA9RLo4cmnI6IdhThvqNGYIGiVjOYb5AGziPcq1uMu2tVrNVl1DuMMRPCY+rN3YxKe/UsTc9+VzynkF3PXnKsLh9u/sJy1JIDvPS4AmGuKhtmucD3XadgZjujBeptGo9eSznbW8xWw9jhw5tIFZVdnOOvaxA0WpopxcHcdoJuHGTZgQHvF2+fgRjSCIbZuIYRWVYc64qIDKykPv5G+918ia9U3c88tDx1WJCNOnCqUHoIE6krG6nbHMEp8xRzCNeTTRyEH2sY63GauTmSGLACjlAPlsb21bTTnVlLONta3XpWkmoxhHJaWMYhwZjGAv28gih91swYWLqTqHDLHqMLFod36wNekdx+nUUcsW9xru/1s1r808m4zFk1vbbos8CWwlxNDeCtNyLFE8s8RnzBGICHN0CQ3UUU05EZz5G1VlBxsAOIb55DGBYgrYzZZ2x9lUU9Faw7GEwtbr97Gj9d8reJVszWMqc0iR9MH4sUwPTZ/qI2u0n/LCJsopZpLMJBhuZDvr2XfTfxnDB3CLG1UlRBFAa5HrIS3OVzPbHJ8x3ailimrKEVzkMp639Xne4L/UUU0CSYxlMl7xMUYmM4tjSSKFZNI4gQ8wl6WMZHS7x8tkJB68pJDORKbjwk0pB1jOC2zU92jQuij9pKaj5CQXN/08E4BdbKJWqxjHNJJJo4E69rAFgAqKqaUKD15SyYxmyKYHrMdnzBE4Pbv1AIxlMpmMRFGaaACcWp8uObShO0tGcSLntH6fTBqjGEtQAygR6qklQ7JbT/P2iJdxOo3dbGY/uzjAXorIZ6xOYSIz8Evfew81WkkpBxjNRPwSu4WiY9Xik5MYmzKHgtoNbGQFx3E6M1nECl5lD1tJ0CTy2QbABI7BI0P/bdWGOo0ZhoIaYD+7KWAnjdTjxsMkZiIi5Ol4drKRTEaSx4QePZ5XnH1evuZhsLaLXvySwAwWMl6nsYtNFJHPPnZQyG7G6zHNb6ZdL5LpSFUJEqCCEvaxg0pKAdjNZrI0hxHkkk0eiZLc48cc7mZknEJJ7U5qqKCCYkZILmN0EvvZzWZWAs7/23FMjXKk/WAIrMrsK0t8xnQQ1hBv8AxhnO0JiSQzhTn4xE+tVrUmkirKnZWb9DwpHUmSpDCHJUzQY9jJRko5wG42U8BOJuoMMhlJEw0ECRCgiSBNBAm0+T7Qel1bbjz4SaSeGkopopQidrKRJXomSTJ0CkhHk8flJ5fx7GUrFZQyglymsxAfCZRygHRGMJmZR/UBxUSPJT5jOnCLhyRNpYZKRjGW2SzBJS5qtJKVLCNEEBcuxjBpQJ4/VTJYwElUaik7WE8lZWxn3VE9hgcvCSQzhonkMQGPeGnSBso4yH52UUU561jOcXo6brHam92JaJhGnLlXwdl+4hIXU5jNFGZHM7QBIUN8D353LPEZ04lscqmhkoMU0EAdE3UGW1hNiCDZ5DGTY/tl/u1IMiSbY/U0yihiBxtopJ50RuDDjxcfXvz4mr+2/d6DD5ccvm7NL4mMZiI5OoZ3eIlaKtnNZqYyZ0B/jqGipjbCT39bztqNAdxuyEx3k5XhYmtZmFf3P0CAegQhm9xohzrwbKjTmOFnMrNJIpXtrKeaCtbxNgAZZDOXpYPWSxIRssljhOYSIYy7HxZOOAtqprCNtYQI9kOUQ8ebjRG+ve2idu/rV058g1Mr9nP2pQXs2tN19Z1k0pjGXNJlxMAHagaUJT5jOiEi5DGBkTqaPWxhL9tJJpUFnBSVoUERwd2Pf67a/NbvGkY7mgpCtXz9pzcw4oHl7fap/X3J2Tw88Xfs2hMmWdKZorMQhCBBgjThwUMaWaSQPmyq7NiqTmOGMY94mcpcRqtThixeFi9EmhfuuBg+83tnvv1lJj747uGbs99dz7sHRgJlTNHZ5MhoVJ0SdI248OAhkZRhk/ScVZ3xnfks8RnTA/G2+rGlAs3R9PjU68HL0Fz1UB8JkPvXBIh0PpSZcKCOGqCBWvJ1O/vYQQOHCgm48bBQT7bScnFi+IxzGGNa9abHV3TGSKZ6/QMV0oDyi4fyGV1/zs8MOscKbWcd21hLA3XkjXFzzrl+xsxIJkyIdSwf+kcO9ZAdS2SMiSuqSjklAHh6OOjjycvlnKvexDtEtz64xcXnLv8fnkmdFxwYyxRSlszE7XVx7GIv/7w/l73vTOSZB8bx0L9zSPJkEKCRsuZ6nHEvzo8lssRnzDBTgFOBxE8iuT2oPOMekcXeO0bw45w1Ax/cALo+axclf/DhGTP6sNtc4mLq9z9G455JvPvvCVzwoRTcbmdOz+MRxqY4e/WK2xQaN0OXJT5jhpEmbWg9VWI6C7qtK+keOZKDf8pm7fEP4e5kb+BQ8/aCR0l8JIBn4vhOb3e5Ol/AkuJxhkKDNA5YbLGi5VgiG+o0xsSFbawjTIhs8g47NaIj8XjY8stxvLvokbhIeuAMeT425UWKfp+AK6HnBQjcLmc1b0sZOzO0xcer2RjTrTIt4iD7cOFmOgu6XZ7feNZCXjv9d3GT9Np6ccGfqP7I/B63rw60zIn6Biqk2KHaP5cYFn+vaGPMYcIaZgurAecopW5PZnC5Kb2ynrGe+NrG0SLTnUTC1QcQT/eLe159tpbtVW8BMIaJAxxZbLChTmPMkLeHLTRQRzJpjGdat+3dUydy/4K/DEJk0fPl8a/iyuj6xPvKqjA3/rSMH3ylmIiGGcMkRgyHOp3DgG1gNybO1WkNe9gKwEwWdVrAuqP9HxrFcf74rlTilRB08bu484+V3HhrGdXVTtdlKnOYwPThVb0ljlniMyaOhTXMZlaiRBjNxB5VHvFMmsCnPv9cXM7t9cSzL9Xxtf9zzlzMcuUyOTKjVxVbAtrEbja31kUdSmJ9qLKvLPEZE6eatIG1vEU1FXjxMZW53d7HM3YM4QdC3JC1cxAijE2btzsH+eYwhnl6AvSyk7eBdynnYD9GZvqLJT5j4lC1VrCWt2iigQSSWMBJ+OTI5cY8Y8cQ/gv8b8YzgxRldCVIEPEdXnTc07xx3cuh31dAmyhiH+Hm8xhTJeOIjx3WEBXN1XGGHAUi8d3ls8RnTBxpGV4rYBdKhHRGMJ8T8HVzaK74/RTekcrqGY8MUqTRN8tXQTgnE/a3r8Yyd5azZaGUQoI6h2rK2cRKmmgAYCcbydIcshiFjwR8+Egjq90Hi0rK0CFa0BuwOT5jTOxTVfawlT1sIUwIgDFMZjrzcfWgvmb9OfN5btHtQDfbHOKIGzpd137q0kRmzPWxZX0jb/Js62G9ixd5mTHdyyP/aqK8sZhyilvvI7iYqNOZzCxEhIo2t5nYMzxnr42JM1tYzU42ECbECHI5nvczUxb1KOnhctN4VQU57uGT9I7E7Rb++0AeKZleQgRxeYSffDeLt54ez59/PZoDq8Zz4Y/nMt4/m1zGk8lIlEhrTxvAOeSo/XDpUBLv+/isx2fMEFel5exnFy5czGUpI+XIpcg6cqel8OUpywYouqEjEjnUD5gwzkv+8rG8vaKR2dN9jBtzaC4wK9PNfVeUc957Xyfh3+8CUKT5bOBddrCekZrXOiyaRubQPNFhkCqviMg44HbgLJxlRC8C16pqfjf3WwxcBZwKjAdKgdeBm1R1d3fPaz0+Y4a4lpWDuUw46qQHgLjwitWgbHovi3Cb8/bS09ycc0Zyu6TXepsrkfIralsrv+TKeEYymjAh8tlOgCaA1mFnczgRSQJeBmYAlwOfBqYBr4h0V1qIS4HZwO+ADwLfARYBK5qT6RFZ4jNmiPM2148MNr/Zmt6Z9NA+Hqkd2eP2zxx7L5WXLD50f2YAsI8dNFIPQCWl/RvkIBmkoc4rgcnAR1X1SVV9CjgPmABc3c19f66qJ6nqnaq6TFUfBs4BMpsf94gs8RkzxGWTBzg9v4haz623Qnv3ccvfLqE+EuhR+/GeFG74wcMUfutEir98Io1f+TDJoycftmE9hzEDEe7A6Y9DaHuW+M4DlqvqjtandoYp3wTOP2KIqoftFVHVvUAJdP8LH5DEJyLHiMhvRWSdiNSKyAEReVpEOi2HLiJXisgWEWkSka0i8sUu2n1URFaLSKOI7BWRm0QOn70XkZNF5C0RaRCRIhH5tYgkdtJutog83xxjmYj8UUSy+v4bMGbwJEgSKaQTJkzFEO1hxIqJv1rL8e99lmAPP0BcmFLN+mvvZPVNd/Lut+9gWlZBu9sTXakcQ89PgRhmZkPz4ZDtbQRmHe2DichMIAfY3F3bgVrc8gHgdODPwCogA/gW8I6InKSqK1saisiVwD3Az3AmNs8E7hQRUdW72rQ7G3gceAC4HlgI/BRIBb7dpt084AXgOeDDwCTgNpxPAZe0aTcaeBXYAlzUHONtwH9E5GRVHcKbcMxwk00utVRRygFGMCra4QxZkbo6xl1xgGN+8iU2nvcHklztjyEqKg7xl3/UsHJdE2mpLjLSXKSnukhPc7F9d5A1Gw71FjNkJPMiS539fTG+yrEt5yDafgk4W0RWtPn+XlW9t833WUBFJ/crxxmy7DER8QB34/T4Huiu/UAlvkeAO1QP/fZE5GVgD/B14DPN13mAnwAPqeqNzU1faU5KPxaR+1U12Hz9rcAbqnpVm3YpwE0icruqtiyd+iFQAHy85b4iEgD+LCI/V9VVze1uALzAR1S1srldIbAM+CjwRL/9NowZYC3Daw3URTmSoS9cUcGM69dxfN5nWbfkb1RWRXj9nUYe/Hs1/32xjnA3ncHJzCKFdLI1t2fbSWJR/3zsL1XVxd206SzD9qZI3B+AE4EPqWpnybSdAUl8qnrYeIuqVonINtqPv54AjAT+2qH5Q8DngJNxEtw4YAHO8tWO7X6Is6rnjyLixZng/HWbhAnwD+A+nHHjlsR3HvBMS9JrjvE1EclvbmeJzwwJjVrPPpxpkknMjHI08SHS2EjeT4XUDbtoqD/03iziYqRrNCMjuUSIEJIgIQ0SdoUJuSO4g8IkZg6fUxz6pgKn19dRJp33BDslIj/DyQ2Xq+rzPbnPoO3ja547mwP8sc3Vs5u/dhzn3dj8dRbwSlftVHW3iNRzaDx4CpDQSbtGEdnZ0q55vm8ScH8nofZqfNmYaNnBBiJEGMU40nsxRa0T8ljkfwZI6v/ghrDAitU0NNesTPGMIDc0mjydgJ+E9n0Swem3hOh1QetY009Dnd3ZyKH39rZmAZt68gAiciPOVoZrVPWhnj7xYG5g/z3Oy+I3ba5r+SvtmN3LO9zeVbuW63rSrrzN7ZnNsXTVbnon1yMiV9Hc60ywNwkTRbVaBUCECEXkI7iYypxePdbOi9OZ6bPXc0eNkVoAUsng+PDpcZPUutXzVZl99TTwSxGZrKq7AERkInASTjI7IhG5BrgFuFFVf380T9yjxCci78dZMNKdZap6Wif3/y5wGfD5tktXOfRS6u7XfKR2HT979We7dponZu8FSJOsITRdbeJJtVawkmXtNkePYwqJ3e757cTSedx7yT39GF38aPn9uq3A1UC5D/gq8JSI3ITzfvxjYB/OgkcARGQCsBP4kar+qPm6S3E6Uf8DXhaRpW0et1pVj9hj7On/0begR5MH9R2vaN6a8FOcUjIPdri5bc/uQJvrszrc3rEH2FZGD9tlcmgItQLnl9xVu/JOrjcm6uq0htW8QZgQbjyECeHB27u5vaXzmP6HzZyWaAuYO9NyuoKLIbpApdd0UEqWqWqdiJyBU7LsIZxOx0s4Jctq2zQVnJribbffndN8/TnNl7aWAacd6bl7lPhUtR5n2f9REZFPA3cCv1LVn3TSpCURzaZ94muZY9vUSbu32zz+RJyJiZZ2O4EmOowbi0gCToWAf7b8PCKyp2O7Ns9thQtNVJRrMSGC+PDjxU8SKa0LJRq1gdW8TpAmshjFAk4iQCNBAnjF180jtxc5eQGfvv8/fCZtaO77q48EiHSz9DBRfH06RT7cmviGyxjnIYNVZLq5JueF3bTZQ4eROFX9LPDZ3j7vgPXhReRjOAtZ7lfVb3bR7G2c4qKfxNnD1+JTOL2uN8H55YjI2uZ293doFwSebW4XEJH/AReLyM2q2jIWdBHgxxlTbvE0cLmIpKs6EyYicjJOuZy27YwZFEENsJa32g1hJpPKOJ1KNnms5g0aqSeNLOZxAi5xkUDSUc83u9PSGPmLPUM26V26+wwO/Gwq7sYj7CtwCbsvcLHxw4fvxespX3MpuGoqCGkQjxxes9MMTQOS+ETkVODvwDrgTx3GX5tUdTWAqgZF5Hs4G9b34yS/M4ArgK+patvaQf+Hs7n8nubHXgjcBPy2zR4+gJtxEuo/ROQOYCLOxvTH2m6cb77uU8DTzcth04FfAO8C/+r7b8GYo5PPdsKESCAJPwnUU0cdNWxhNbAagGTSWMBJeKT3f7rFF8/msfG/BXqXEKLp6bokqr6UQ8K6d7ttO+O1BI4f9VnWH/9wr54rnRH4x46jqWAf21nHTI7t1eMMSYN0OkO0DFStzjNwelgLcXptb7e5tEsqqno38CXgYpxqK58Avqqqd3Ro91+cntvS5nbX4cwdfqdDuzXA2UAe8Exzm7/gVP9u224/TnWZAE5FmLtxtk6ca1VbzGALabB1L95sjuM4OYNT+BBzOZ50RgDOSuKFnNLupO+jJV4fYz+9q9e9oGj75uOXE1nXs1mXSGMjo3/mZmOgoVfPJSJkXvFxXF4X+9lNmR7s1eMMOQoS6fsllg3UBvabcXpePW1/D21W8Ryh3RP0YGO5qr6Gszm+u3brcc6BMiaqCthFiCAZZJMpzgkBLnExinGMYhzVWoEHLwmHl5w9Ku7Ro7hh3FMMxfr0GwMNTHm44qiKisiqzVy782JemPnvw25rVJBQ5IhLyn1jR/H9b6Rx862VbGYlS/UsG/KMA0Pv1W9MHKqlEoA8JnR6e5pkkiQpfX8il4tkCXbfLgZds+MSdPOuo7qPhkKUPTaWJj38Z7695HTYVdDJvdr77ldGMHOej0bq2cPWo3r+IUu175cYZonPmBjQRCMACfStRxevSsN1BO7MQ4M9OzKordxHtnDTwSXtrquPBHjj/sVEamq6vb/HI9z+f9lOHO0Wn8exwTmWKGos8RkTZbVa1XpgaSL90KuLQ+eu+yzJz6zp9Db37OnsvvUEGj66BDqpkRmuqGDZ75ZSHD5UwPtTuz5E7iM9qooFwMq1zgeT4benLz5Z4jMmilSVraxBUcYwuX+GM+NIWCNcXXAC2dcr2nT4CfOeSRNY9LdNbPvMXfzhN7+j4jNLO3kUyPrre7zvvht4tcHFDUULqb8uh3BlVY9i+OMj1dx0q1PTYvIwKQIuqn2+xDJLfMZEUQE7qaAEL75e19qMZyev+zgFF2QR3rrj8Btdbrb8KItbctYDMM+XwLdv/BvumdMOa6qhEON//Da3nXYuG8/MQFduPKxNZ+reWc2V1xejClOZQ7bk9ennGTJsjs8Y099CGmSDvstW1gAwlblHXX0l3j1QlUvmNUqoYH+nt+vSOTx76h/aXXdhSjVbvtzFCRWqhPYVEK7o2Yk3tVpF+Z8fa056c5koM44qfhO7LPEZM8iqtJx3eJEi8nHhZiaLGM3EaIcVU8Ia4dd/uYDw9q5Xce68KJFjvIcX5r717EfwjBndp+dXVbawGg2FGM1EJkqnB7bEJ8U5iLavlxhmic+YQaKq7NYtrOAVGqgjhQyO50zGyGQ7uLSD/FA945+tPHKjnMPn/ADOTy6l5P2dbwvpqSYaqKQUNx6OYX6fHmuoEfo+vxfrc3x23oYxg6BR69nIe1RQAsB4pjGVObjEVgl2JgJIINRtu874xcvkq7ZS9Vgykbq67u/QiVqqAecsPtuwHn+sx2fMACrVIl7Tf/MG/6WCEnz4WcjJHCPzLekNoAcn/I+9184H19H/jsMaYo/fGWJNI7O/QxsabHGLMaa36qkhgDMkN4JclnIWIyQ3avFodQ1rmsZF7fkHS5LLx/NX/4LdtyzBlZDQ4/tFNMy6hNVUNhWSIMmM55gBjNJEiyU+YwbQfna3/nsWi/FJz9+EB4I2NHIwmH7U9wtqmI2BBtYFGlkXaGRjoIGgHuFYoBgw1pPCms/8ll03Lex0Y3tHEY2wwb+Gssa9+CSRhXpyn2ujDllx3uOzOT5jBkiD1tGAM8c0lbn4o5z0eqtJg8x49kvM/EUFRJqX67ndbP5WBlvOuQt/DM+BJbl8PPmpX3HNc1/B9frqLtupKptYQXFTPh7xsVBPIllSBzHSGNKyqjOOWeIzZoBs5D0ihMkmjwlDeMjswu3nMfP6bYSrq9tdP/O6NC6cch7/OebZKEXWMzN9SeR/NczEN90Q6byXupdtFJGPGw8L9SRSJWNwgzSDyoY6jRkgtTglsVJIH7LbFSrC9VT/ZtxhSQ8gXF1N7S/GtquBGasePO5PuKdN6vS2ci1mB071l9ksIV1GDGZoMSnetzNY4jNmAKgqIZyjcIShmfQA7qpYSMqrXR/Fk/jqRn5a/L5+f959oTSkvrHfHm+Or4lAXtph1zdqPet5B4CJzCBH+rbxPW7E+RyfJT5j+lmVlrOK1wDw4B3Sw5x//N8ZRyzmHKmv59lnjyOs/Tsp9LV1lxLK77xUWX9p0DpWsowgTWSRwxRmD+jzmdhhic+YflKnNazT5bzHy1RQggcvM1g4ZDdAv9YIU//afV3LqQ8U8nhd/+13OxCqJevBlC7n48Trw5WQgMt1dL2KsN/lbG0QoUJLeI+XaaCONDKZy9IhOxzd//qht2c9PmPiT7kWU64HUVWatIHNuorlPE8xBbhwMYFjOIkPkivjox1qr9RHAlz9py8TWd/9ieOh3Xv58X2fpCrS0OfnDWqYE5+/lsRnV3V6e/i0RaS9kspJ71byzxPu6fHjpoifT/3mP5ywvIKVFzexitcINPf0FnGqFQhvS4n7xGerOo3phVIOkM92fPgJESKC0zsZzSQmM5MESYpyhL1XFWlgwfNfY8av1hLp4RvYmN+s4Lis6/nLJX8g1XX0p6QDBNXFBa99yVlBGjq8XJlrwSw+cfd/+GxacfM1fsIa4ZHakZSHuj7H8JSkbSzw+1lSuJerbyimYqVTUMApGzcXl9jn/+HGEp8xR0FVOcg+ynHefFuqsoxkNFOZQ7IcvoBiKKmNNLL4b9cz/aaVRII9T2AaDDDpxnf58b0XoJ7elWKTcIRj9m0g3MnzisfDvu9Lm6TnKIs08KcvnIdnTSfn9TX79a8/wKOjf8+ZF+2nvl7xu5KYHplPjozpVZzDgu3jM8a02M0WdnHoENNJzCCbvLhZAn/uxk8w9YdrjyrptYqECe3e2/9BAcH3zeffx/4WaN+zC6vibggSqanp+r77ivngN52kN0rGMTOyaMjOuw6WWN+O0FeW+IzpoQLd2Zr0JjGTTEaSzgjccVJsujhch+u32UTqd3ffeJDt/owyydv1cGZXGrSO4t/cT7hKySaP2XqcDW0aS3zG9ESR5rMFp+TVTBYxRiZHOaJeikQoDXaeQH5a/D4SX90Yc6Ncnrxcbj7+6V7dt4BdhKuqSJds5upSS3o9Fec9PnsVGHMEqsou3cxG3gNgKnOGbtIDIo2N/OuV4w+7PqwRnvv3EiL19VGI6sj2fHYyn0g92Kv7anMaz9HRcdMzH3AKRLTvlxhmic+YLjgbnF9lFxtRlEnMZKLMiHZYfTbtL1W81NA+CTxUk8vkB/K7vI94o7Pcv/6C47nryjvx9jJpRZoT31CunmP6nyU+YzpRpPks5wUqKcNHAgs5hSkSH5U9Ims387UHr6Y+4ixgqYo0cPs9FxHaV3B4YxFKvnQCE950U3Tdib062LW36i84nh/fdh+n9uFQC8Xpebjsre4oxP8GdpvjM6aDfN3ONtYCzjaFmRyLT/xRjqp/Tbh9DfPTv85lZ7/GI/8+lUl3rqSzt6ryzy7l8e/cxiRvCgeue4FzA98i5463Bjy+0JnHcvNt93NaYt9mHBtxhm492CrOoxLjiauv7GOQMR1UUdb678nMirukB06Nzcnfept3T0hn4k1vo01Nh7XxjBnNZ2/4T+tqyjxPCt++5u94Jk0Y0NhcSUmk/yCfMxP7dtBtRCNUUgpABtn9EZqJE5b4jOnAi691aGwD7xDWw6uIxIsjLWbJv2wiX0xvvy/v4yllbL9qYE8wqD1nLn+c3LtVnG3VUEGYEEmkDOlKOlER50OdlviM6WCGLOJUPkIyqdRRwzbWRTukQSceD9PP24a7w/J/t7g49wPvIf6B6wUXnBsm3ZXY58cppwSATEb2+bGGFVvVaczw5BEvMzkWgDKKohxNFIiLsUmVnd6U56tC3AO3yEWaev7YJREPrrrDh2kBKprLymWS0y9xmfhhic+YLnhxejW2FH5wTfh3hAOh2h61/fyGzxDZfnilGWd+z5mrtR7f0VLQSN8vMcwSnzFd0NY9YPZnMpj8L63lgo2Xd9tud7CWlN+noZ2c5ACHtjK4sY3rR83m+IwZnmwPWHRoMED69xJ5oCq3yza1kUbO+uc38b2wutPbXeIimVQA6qgekDjN0GX7+IzpglX9iB5dsYFHv3AOd3y7gZyU9sOeqkL+6+OZdusqIl2c0g6QQjq1VFFLNenEx+kZg6JlcUscs8RnTBdahjqtx9eeSwZn/kbeXMPI86XTjfXjtaDbYtrJOGcj1lLV77HFvRgfquwr+4s2pgstQ502x9feosQ9uLIyB+fJ+jB/lNKc+Gyo03RkPT5jumBDnZ0b4aoHb+y/dRzq8VniO2rW4zNmeBrOQ50aDrOiZHynt/23Zh5aUTm4AfVCIsm4cBOgkaD24kT5YSv+i1QPv79oY3poWA91RsI0/nMUTRo87KY/PXMG4crYnzcTkdbhTpvnM20Nw79oY3qms6FOjfFPsv0p5/FNXJl/ZrvrHqjKZdp9B6IU0dFLtnm+o6dAJNL3SwyzxGdMF1p6fNVUsEVX846+xKs8ySZdSZ3G/xtpuLKK/B9NZ3mjs2UgP1TLPT//GKFde6Ib2FHw4hygGyJ+C40PiDgf6oz9GWpjosQ5pcFNEw0UsLP1+kJ2U8huRulY5nA8IvG7+MX/7Ht881tfJnRFGaHHRzLiL+9GO6Qe26c7yGc7AD7i72gp03uW+IzpwggZxan6EYopoJEGMhiBFx8F7OQAezlIAZnkMJbJ0Q51QCU/9g48LqA7oh1Kj4U1zA42tH5vie8oxXiPra8s8RlzBB7xMJqJ7a6bybFk6Eg28i5bWEWj1jOZWbgkjmcOhtgbYSmFhJuHN+dwPGlkRTmioST2jxXqqzj+SzVm4OQyjsnMAmAPW1jJMhq160NdzeA6gHOA7jEsIFfG4ZP2Pb4GraNUD1CgO6nTmmiEaKLIenzG9IKIMJlZZOpINvAOVZSxnBeYqnMYw+S4nveLdRVaQhkHEYRcxrW7LaRBdrKRAna2KULuZo4eT44M7MnyQ4aCxvixQn1lic+YPsiUkRyvZ7GJ9yiliC2spoBdzNcTSZTkaIc37FRoCat5A0UZyxR84nd6dxygnGIqKCVEAHHBiSf4aGxQVq0KspP1jNQ8+8DSIs6HOi3xGdNHPvEzX08in+1sZx0N1NliiigpZj8RwuQwhuksoFyLWc9yghyq3HLcYi93/yyXBXP8BINK3nH5VBysoYwissmLYvRmsNgcnzH9QERoogGAXMbjFvtMGQ0txQZSSGcdy1nFawQJkC7ZZF1xAZveHsvbT49nwRzng4nXK1x0dgIAddhcX6s438dnic+YflJDJQAjsbmiaGlJfCUUUsJ+XLiZwmyO1VNY/Fg652z8GnXa1NpeVdm2y+kN+kiISsxm8NnHUmN6SVXZxlqK2d/a2wNIIDGKUQ13TuJr+RAyhdlMkGMAiNTUMONr65l//5fYecYfAfj13ZUse6MJt3hI00E6ainWqcZ8ybG+ssRnTC8VsIt9tN/U7cZDAklRish0PEKqY+870tjI9JsrefL4FHzLD/LtH5cBMEsXkyypgxZnzIvxocq+ssRnTC+UazHbWAM4G9oL2Y0LN3NZike80Q1uGBOEBJLIYQw5jCFJUg5rE96xm2tfuoTgD25EFSYzi1EyNgrRmmixxGfMUWrQOtazHEWZwDGMkUkkaxppZMZ39ZYhYDzTmMysbrclpD+4kw1FERJJYRIzBym6oUNtqNMY09ZW1hAkQBY5TGUuwP+3d+7BcdXXHf989bYtW5Ys29jyS7J5ydgkYIKdODbgdKAlMZmBJG3DJE0a8mjaSYY0k7RD0yQlpS0Z0jbTUGgKk0CmQCEJbgmPEF4p2MQG/MRvW/Lbsixbtt4Pn/5x765W65W0sla7a+/5zPxmdX/3/Pb+9ox2z/397nkwUZMyPCsHoFBFSckdX/cSEGTg8di9eLLfK3Ok+O2p4wyT2JiwSPYP5/yh13po6A1Smk0jcZV558LGDZ/jDJOLWUA+BTTRwC42ZXo6zjBp4BC99FJGBWPdoeVsjCBzy0hbFuOGz3GGyURVspAlABxhf05VZb8QOEJktTc7wzPJYuzMyFsW44bPcc6BCqZQTAlddNDM8UxPx0mSTmuPJrCegnty5iru3OI454AkLrJZ1LODd1nHeCunmy666aKHLkopo5Jp9NBNPgVUUe1OFFnAEfYDUMm0s0oVOQEGWJq2KiXNBH4A/B5B9oEXga+a2b4kxpYAfwfcDkwE1gPfMLPXhhrrhs9xzpFqajnKAdpooY2WfufaaeUYh6LHjRxmoS3xcIcMc9i3OYfGLC1blZLGAi8BncCnCWzu3cDLkhaaWesQb/GfwM3A14E9wJeB5yUtMbP1gw10w+c450iBClhg19JME4UUU0ghhRSTRx6HqaeLTgoo5DB1NHKYdloYx4RMTztnabFmWmimgEIquSjT03HgDqAGuNTMdgFI2gjsBL4A3DfQQElXAn8MfNbMHg77XgW2AN8FVg52YTd8jjMCyjSJMs6O4RvPxOjfbXaaJhpo5oQbvgzRY91s4x0ApjKTPOVneEbZTZq2OlcCayJGD8DM9kp6HbiFQQxfOLYbeDxmbI+kx4BvSio2i8lGHofvuzjOKBNxoqhjK83WlOHZ5B7d1sXb/JaTNFLMGGZzSaanlP2kx6tzPrA5Qf8WoDaJsXvNrC3B2CJg3mCDfcXnOKPMRQROMG20sJaXKLfJXMn7PadnGuiyTt7ht5zmJCWM5SqWJczf6fRxmhPPv2hPVqbgrUokrYs5ftDMHow5rgBOJBjXBAxVKmOwsZHzA+KGz3FGmQIVMM+uYBNrADjBMdbyEu+xpYzRuAzP7sKl0zp4m9do5RRjKOVqllEir5wxFGZ2Uzovl6AvGfdnjWCsGz7HSQeNHI7+XcEUqqih2Ov2jRod1s7bvEYbpxnHeK5iGcVyfWcZJ0i8Misn8WouliZImG+uPOb8gPgzPsdJA7HPlZpoYA/vemjDKNFurbzFK7RxmlLKuJrlbvSyky0Ez+riqQXeTWJsdRgSET+2C+IKZcbh3zzHSQOlKmMy06OFUts47anORoE2a2Edr9BOKxMo52qWU6SSTE/LScwqYLGkmkiHpDnAB8JzQ40tBD4WM7YA+ATwwmAeneCGz3HSxpV6Pyt0K2MYh2H9AtydkdNsTazjFTppp4xJXMWypMsUORnhP4A64GlJt0haCTwN7AceiAhJmi2pR9K3In1hgPrjwD9L+pykFcBjQDXwt0NdeFQMn6Txkp6QtEtSq6STkt6UdPsA8ndI2iapU9J2SV8cQO6jkt6R1CGpXtJd0tkBOZKWSnpDUrukI5Luk87e65A0X9ILklokHZf0sKRBvYEcZ6TMDD2t69mR4ZlcGLTYKTbYatbyEl10UM5k3ssH3Ws2ywkzs9wA7AAeAX4G7AVuMLPYVEgC8jnbXn0GeJgg28szwEzgJjN7e6hrj5ZzSxHQA9xDYNGLCZagj0iabGY/iAhKuoPAut9DkKdtBfAjSTKz+2PkbgSeIkhTcyfwXuDvgfHAN2LkFgK/Bp4HPkxwB3AvUBXOISI3HXgF2AbcRpDr7V7gfyUtNcvy9OLOect0qtnNFpo5Tos1U6qyTE/pvKTNWtjL1mgasjzymMk8aqglX+63dz4Q5uS8dQiZOhJ4a5pZO4EtuHO411U6nzNIWg2UmtmC8LgAOAQ8a2afjpF7iCAyf5qZdYd97wCnzGx5jNy3gLuAWWZ2JOz7BXAFUBsz9lPAT4CrI3cDkn5AcMcwx8xOhn3LgFeBW83s54N9lgmqsGu1YoQacXKVrfYWB9nLbC7hYi3M9HTOOw7aXrbxNoYhRBU1VHNZ1jqxvGhPvmVmizI9Dycg3bdFxwlWfxGWAJOBR+PkHiEwSksJEpbOBN4DfD6B3HeA3wcellQI3ATcFzF6IU8Q7CffAkSWwSuBZyJGD8DMXpO0L5Qb1PA5zkiYxmwOspfD1FNl1V4QNaTHujnOUQoooJcztIcJwCOvhpFHHu0E+YsvYhY11HpQujMsRtXwKajDkg+UESxnbwT+NEYk4soan7ZmS/haC7w8kFyY162NvvQ2c4GSBHIdknZH5MLnfdXAjxNMO5l0OY4zIsqYxFjG08ZpVvNrqu1yanR5Wudwxs7QQ1fWeD3uti3UsR1j6KcM+RRQQy2z5enHnOEz2iu+LwM/DP/uBr5iZj+NOR9xJIkPVoxPOzOQXKQvGbmmmPPlBHvGA8ldmqAfSZ8nXHWW4BkgnHNHEovsOnaykcPUs4ctzLZLyE9B8mQzG7L2n5nxLms5zlFqbRGTNX3E1x0JrXaKOrZhGBMoR4gCihhLKWMYF76WAkYvPZQy0eMgnXMmKcMn6UMEDiND8aqZXRdz/DiwBqgk2Fr8oaReM4u4qka+nUM9aBxMTqMo148wz9yDEDzjG0jOcZKhSMXM5xqarYk2TnOSRiYx9Zzey8zYx06OcoAOWplnC5nGrIQGsMd62Mo6jnIAgA28wQyrYSbzOMI+mmmKPjtbyJJR9Y7stV4OsDtq9Kqo5nJdPWrXcxxIfsX3BpDMPky/TNlmdgw4Fh4+F0bZf1/SQ+EzuNiV3eGYoZGVWVPca6JQg4lJypXTt4V6gsDoDSTnKfSdtDGVGexlK9tZz2L70LBL5vRaDzvZxAF2R/veZS1GL1XU9JM1M7bwO45xiDzyqaKaA+yJtnj2sZOac9z577JO8sinYAAPy05r5y1ejRbxnUglNQkTeThOaknK8IWlH7al4HrrCCrtTgUO0GeI5tPf8EW+aZG0NbFyqyNCYZT/2Bi53QTVfPt9e8IS9TXAf0PweSTVxcvFXPvVYX0qxxkBc7gsrOR+mn3sZA6XJT22wQ6ygw10hPec05hDAQXsZxd1bGe6Vfdb9dWzg2McooBCFnEdpSpjus1hM2/SRitTqGIqM+mmk628RT07mGFzKVLxQFM4i27rYh872McuihnDAruW8ZrYT8bM2MSbtNHCWMZzCQuZxEVDbtE6TipI9yb5cqAFaAiPVwONwCfj5G4nWHW9DtFYjw0DyHUDz4ZyXcBzwMfDUIkItxF4k8amwVkF3Cz1BVFJWgrMZuh0OY6TMvKVzzRmA9DK6aTGmBk7bSMbWU0HbYxnItdwPfO1iFlcDEA3XUiKpkZrsgZ2sQmA+VwTjR8cr4m8jxV8kJtZoGuZoulUqZpJTKWXHuqSvOftsR722lZe51n2so1eemjjNGt5if22q1+Ktnq2c5JGiihmEcup1DQ3ek7aGBXnFklfABYTBKQfACYBHycwQN8MDRRm1i3pbwgC1g+G8jcAnwX+IiIX8tcEweUPAP9FEMB+F/AvkRi+kG8TGNQnJP0bMIcgMP1JM3srRu5eAsO5StI9BJ6n/wT8DvhFilThOEPSZA3Usx2A0gEqtJsZp2iiNXTtb+Y4TTQgxMUsZCbzooYjP/xa99DNy/ZLeumhzCqiRnUOl53lzJKvgui4CHO5guMc5QC76bVexlIadTIZw7ioI06v9XKQPdSxjS6CFInlTKaayznKAQ6yh+2s5yTHWcC1HLH97A43cWpZlDVepU7uMFpenZsIYuG+T/AcrRHYCnzYzJ6JFTSzf5dkwNeArwP7gD83sx/Fyf1K0m0Eedj+BDhKkLnle3Fy68MsL/9IkMamGfgpgeGMlTso6XqC8vZPEWT0fhr4mmdtcdJFp3Wwnv/jDGcoYxJVzI2eO2NnaOIoDRzkGIfopqvf2DzyWcgSKnVRv/5CiphIJSdppJceAJrDx9YVTGVuks/RJqicKTaDhtB4xVNiYymkiA7aonObQAXzmE85U5BEBVMot8ls5k2Osh+ZOMI+AKq5nEpNS1JTjpM60pq55ULCM7c4qaLOtrGLzRRQyPtYEQ3G3mkb++XzHEMpEyiPuveXMYlxAwS+mxmdtEdXcZGE2FOZMax0Xt3WRTPHaYsLJO+gDYtxii6ljLnMp5KztyzNjJf5BWfC+Lw88pjLFczi4pzZ3vTMLdmFJ7RznAwzm0tppoljHGIjq5lv19DIYerZgRBzuIypzGAcE5I2FJL6xZpOZ845za1QRVRy9qrsjJ2hg1YaOUIRJUxlxoBzk8RVtoz1vM44JlDLogENtuOkA1/xnSO+4nNSSY918ya/oZ2Wfv2XcCWzdHGGZpVaOq2dIkpyZpUXi6/4sgtPfeA4WUCBClnIYvLoi+GbSGW0hNGFQLHG5KTRc7IPX/GdI5KOQVgPJXepJHBcclKH63R0yLReZ5vZ5Axe34nBDZ9zzkha59s3qcV1Ojq4Xp1YfKvTcRzHySnc8DmO4zg5hRs+ZyQ8mOkJXIC4TkcH16sTxZ/xOY7jODmFr/gcx3GcnMINnzMsJM2U9KSkZkmnJP1c0qxMzyudSJoh6YeSVktqk2Rhiax4uXJJP5bUKKlV0ouSFiSQK5F0r6TDktrD912WQC5P0l9JqpPUIWmDpFsHmOMdkrZJ6pS0XdIXU/LhRwFJt0l6SlJ9+Pm3S7pH6p/exfXppAwz8+YtqUZQ+3AnsBn4KEEi8k0EdRDHZXp+adTDdQRJ0n8FPE9Q1HhOnIyA3xJUJ/kj4CaCOo+NwIw42Z8BJ4E7gBXAz4F24D1xct8jqDf5l8D1wAPAGeAP4uTuCPu/F8rdHR5/KdO6G0Cfa4AnCMqOLQe+GupjDZDn+vSW8v+5TE/A2/nTgK8AvcC8mL5qoAe4M9PzS6Me8mL+/twAhu+WsP/6mL4ygjqT/xrTd2Uo95mYvgJgO7Aqpm9K+CP9nbjr/AbYGDe2AfhJnNxDoZEozLT+EuhzcoK+T4V6ucH16S3Vzbc6neGwElhjZrsiHWa2l6Bg8C0Zm1WaseTKVq0EDpnZyzHjmoH/ob+uVhIUU348Rq4HeAy4UYqWPr8RKAIejbvOo8ACSdXh8RJgcgK5RwjqYi5NYu5pxcyOJeheG75Wha+uTydluOFzhsN8gm3OeLYAtWmeS7YzmK5mSWHtoUBur5m1JZArgmiyzvkEK5RdCeSgT/+RYnvx146Xy3aWh69bw1fXp5My3PA5w6ECOJGgvwkoT/Ncsp3BdAV9+hpKriLm9aSF+2xDyJHgPePlshZJVcB3gRfNbF3Y7fp0UoYbPme4JAr89JT7ZyOS09VoyDGAbNYTrtyeJnhu/JnYU7g+nRThhs8ZDidIfIdbTuK77FymiYF1BX36GkquKea1XGfX9UkkR4L3rIg7n3VIKgFWATXAjWZ2IOa069NJGW74nOGwhb5nHrHUAu+meS7ZzmC62mdmLTFy1ZLGJpDrou8Z1BagGJibQA769B959hR/7Xi5rEJSIfAU8D6CcIJNcSKuTydluOFzhsMqYLGkmkhHGLj9gfCc08cqoEpSxEkDSROAj9BfV6uAQuBjMXIFwCeAF8ysM+x+juCH+5Nx17kd2Bx61wKsJnCzTyTXROCBm1VIyiOIvVsB3GJmaxKIuT6d1JHpeApv508DxhHcMW8icCFfCWwA9gClmZ5fmnVxW9juJ3j+86XweHl4Pg94A9gP/CGB+/wrBD+WM+Pe6zGCrbrPEfz4Pwl0AFfFyf1D2H8nQRD9/QSB1B+Jk/ti2H93KPfd8PjLmdbbALqM6PBuYHFcm+H69Jby/7lMT8Db+dWAWQRbUqeA08AviQvezoUW/lAnaq/EyFQQBDo3AW0EwdFXJnivMcB9wJHwh/hN4LoEcvnAXUA9gSv+RuC2Aeb3BWBHKLcT+LNM62wQXdYNos9vuz69pbp5dQbHcRwnp/BnfI7jOE5O4YbPcRzHySnc8DmO4zg5hRs+x3EcJ6dww+c4juPkFG74HMdxnJzCDZ/jOI6TU7jhcxzHcXIKN3yO4zhOTvH/2D8sJ3Tour8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Show areas with GHI values > 2.9 with buffer\n",
+    "tmp = aachen_rm.indicateValues(\n",
+    "    source=gk._test_data_['gsa-ghi-like.tif'],\n",
+    "    value=(2.9,None),\n",
+    "    buffer=100\n",
+    ")\n",
+    "\n",
+    "aachen_rm.drawImage(tmp, figsize=(6,6))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "geokit",
+   "language": "python",
+   "name": "geokit"
+  },
+  "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": 4
+}
diff --git a/Examples/regional_raster_average.ipynb b/Examples/regional_raster_average.ipynb
deleted file mode 100644
index 1fb14fa..0000000
--- a/Examples/regional_raster_average.ipynb
+++ /dev/null
@@ -1,109 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Regional average of raster values\n",
-    "\n",
-    "Here an average of raster values is desired over the context of some region. A RegionMask object is created, which then allows raster data to be warped onto the RegionMask's characteristics (as in the RegionMask's resolution, extent, and spatial reference system). This create a numpy matrix, from which only the pixels which lie in the region can be easily extracted and operated on.\n",
-    "\n",
-    "*last tested: version 1.0.2*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "from os.path import join\n",
-    "\n",
-    "import geokit as gk"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# (Here a shape file of Aachen, Germany will be used)\n",
-    "regionPath = join(gk._test_data_, \"aachenShapefile.shp\")\n",
-    "\n",
-    "# Create a region mask from a region definition\n",
-    "rm = gk.RegionMask.fromVector(regionPath)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Warp a raster file onto the RegionMask's context (Here, a simple elevation raster file is used)\n",
-    "rasterFile = join(gk._test_data_, \"elevation.tif\")\n",
-    "\n",
-    "warpedMatrix = rm.warp(rasterFile)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean raster value: 307.869\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Compute the average of pixels the region mask\n",
-    "meanValue = warpedMatrix[ rm.mask ].mean()\n",
-    "\n",
-    "print(\"Mean raster value:\", meanValue)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [default]",
-   "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": "3.4.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/README.md b/README.md
index 3a45eb9..34501e4 100644
--- a/README.md
+++ b/README.md
@@ -4,7 +4,7 @@
 
 ---
 
-<a href="https://www.fz-juelich.de/iek/iek-3/EN/Home/home_node.html"><img src="http://www.fz-juelich.de/SharedDocs/Bilder/IBG/IBG-3/DE/Plant-soil-atmosphere%20exchange%20processes/INPLAMINT%20(BONARES)/Bild3.jpg?__blob=poster" alt="Forschungszentrum Juelich Logo" width="230px"></a>
+<a href="https://www.fz-juelich.de/en/iek/iek-3"><img src="https://github.com/FZJ-IEK3-VSA/README_assets/blob/main/FJZ_IEK-3_logo.svg?raw=True" alt="Forschungszentrum Juelich Logo" width="300px"></a> 
 
 # GeoKit - **Geo**spatial tool**kit** for Python
 
@@ -28,6 +28,20 @@ Therefore, it is our pleasure to offer it to anyone who is interested in its use
 
 ## Installation
 
+### Installation via conda-forge
+The easiest way to install GeoKit into a new environment is from `conda-forge` with:
+
+```bash
+conda create -n geokit -c conda-forge geokit
+```
+
+or into an existing environment with:
+```bash
+conda install -c conda-forge geokit
+```
+
+### Installation from a local folder
+
 1. First clone a local copy of the repository to your computer, and move into the created directory
 
 ```
@@ -41,22 +55,28 @@ cd geokit
 git checkout dev
 ```
 
-2. When using Anaconda (recommended), GeoKit should be installable to a new environment with:
+2. When using [Anaconda](https://www.anaconda.com/) / [(Micro-)Mamba](https://mamba.readthedocs.io/en/latest/) (recommended), GeoKit should be installable to a new environment with:
 
 ```
 conda env create --file requirements.yml
+conda activate geokit
+pip install . --no-deps
 ```
 
 2. (Alternative) Or into an existing environment with:
 
 ```
 conda env update --file requirements.yml -n <ENVIRONMENT-NAME>
+conda activate geokit
+pip install . --no-deps
 ```
 
 2. (Alternative) If you want to install GeoKit in editable mode, and also with jupyter notebook and with testing functionalities use:
 
 ```
 conda env create --file requirements-dev.yml
+conda activate geokit
+pip install . --no-deps -e
 ```
 
 ## Examples
@@ -105,19 +125,21 @@ docker run -it \
 
 MIT License
 
-Copyright (c) 2017 David Severin Ryberg (FZJ IEK-3), Jochen Linßen (FZJ IEK-3), Martin Robinius (FZJ IEK-3), Detlef Stolten (FZJ IEK-3)
+Active Developers: Julian Schönau, Rachel Maier, Christoph Winkler, Shitab Ishmam, David Franzmann, Julian Belina, Noah Pflugradt, Heidi Heinrichs, Jochen Linßen, Detlef Stolten 
+
+Alumni: David Severin Ryberg, Martin Robinius, Stanley Risch
 
 You should have received a copy of the MIT License along with this program.  
 If not, see <https://opensource.org/licenses/MIT>
 
 ## About Us
 
-<a href="https://www.fz-juelich.de/iek/iek-3/EN/Home/home_node.html"><img src="http://fz-juelich.de/SharedDocs/Bilder/IEK/IEK-3/Abteilungen2015/VSA_DepartmentPicture_2017.jpg?__blob=normal" alt="Abteilung VSA"></a>
+<a href="https://www.fz-juelich.de/en/iek/iek-3"><img src="https://github.com/FZJ-IEK3-VSA/README_assets/blob/main/iek3-square.png?raw=True" alt="Institute image IEK-3" width="280" align="right" style="margin:0px 10px"/></a>
 
-We are the [Process and Systems Analysis](http://www.fz-juelich.de/iek/iek-3/EN/Forschung/_Process-and-System-Analysis/_node.html) department at the [Institute of Energy and Climate Research: Electrochemical Process Engineering (IEK-3)](http://www.fz-juelich.de/iek/iek-3/EN/Home/home_node.html) belonging to the Forschungszentrum Jülich. Our interdisciplinary department's research is focusing on energy-related process and systems analyses. Data searches and system simulations are used to determine energy and mass balances, as well as to evaluate performance, emissions and costs of energy systems. The results are used for performing comparative assessment studies between the various systems. Our current priorities include the development of energy strategies, in accordance with the German Federal Government’s greenhouse gas reduction targets, by designing new infrastructures for sustainable and secure energy supply chains and by conducting cost analysis studies for integrating new technologies into future energy market frameworks.
+We are the <a href="https://www.fz-juelich.de/en/iek/iek-3">Institute of Energy and Climate Research - Techno-economic Systems Analysis (IEK-3)</a> belonging to the <a href="https://www.fz-juelich.de/en">Forschungszentrum Jülich</a>. Our interdisciplinary department's research is focusing on energy-related process and systems analyses. Data searches and system simulations are used to determine energy and mass balances, as well as to evaluate performance, emissions and costs of energy systems. The results are used for performing comparative assessment studies between the various systems. Our current priorities include the development of energy strategies, in accordance with the German Federal Government’s greenhouse gas reduction targets, by designing new infrastructures for sustainable and secure energy supply chains and by conducting cost analysis studies for integrating new technologies into future energy market frameworks.
 
 ## Acknowledgment
 
-This work was supported by the Helmholtz Association under the Joint Initiative ["Energy System 2050 – A Contribution of the Research Field Energy"](https://www.helmholtz.de/en/research/energy/energy_system_2050/).
+This work was supported by the Helmholtz Association under the Joint Initiative ["Energy System 2050   A Contribution of the Research Field Energy"](https://www.helmholtz.de/en/research/energy/energy_system_2050/).
 
 <a href="https://www.helmholtz.de/en/"><img src="https://www.helmholtz.de/fileadmin/user_upload/05_aktuelles/Marke_Design/logos/HG_LOGO_S_ENG_RGB.jpg" alt="Helmholtz Logo" width="200px" style="float:right"></a>
diff --git a/geokit/__init__.py b/geokit/__init__.py
index 678c45c..2f10fb5 100644
--- a/geokit/__init__.py
+++ b/geokit/__init__.py
@@ -1,6 +1,6 @@
 """The GeoKit library is a collection of general geospatial operations"""
 
-__version__ = "1.3.0"
+__version__ = "1.4.0"
 
 # maybe set GDAL_DATA variable
 from os import environ as _environ
diff --git a/geokit/_algorithms/combineSimilarRasters.py b/geokit/_algorithms/combineSimilarRasters.py
index fb71dd5..851156f 100644
--- a/geokit/_algorithms/combineSimilarRasters.py
+++ b/geokit/_algorithms/combineSimilarRasters.py
@@ -3,33 +3,35 @@
 from geokit.raster import rasterInfo, createRaster, extractMatrix
 from os.path import basename
 import os
+import sys
 from json import dumps
 from glob import glob
 from osgeo import gdal
+from warnings import warn
 
 
-
-def combineSimilarRasters(master, datasets, combiningFunc=None, verbose=True, updateMeta=False, **kwargs):
-    '''Combines several similar raster files into one output file 'master'
+def combineSimilarRasters(datasets, output=None, combiningFunc=None, verbose=True, updateMeta=False, **kwargs):
+    """
+    Combines several similar raster files into one single raster file.
 
     Parameters
     ----------
-    master : [string]
-        [folder path to output. If it is a file, datasets will be added to master, but dont use that, its buggy. Just write to an new file.]
-    datasets : [string]
-        [glob string for datasets to combine. Or lsit of gdal.Datasets or iterable object with paths]
+    datasets : string or list
+        glob string path describing datasets to combine, alternatively list of gdal.Datasets or iterable object with paths.
+    output : string, optional
+        Filepath to output raster file. If it is an existing file, datasets will be added to output. Recommended to create a new file everytime though. If None, no output dataset will be loaded or created on disk and output dataset kept in memory only, by default None
     combiningFunc : [type], optional
-        [description], by default None
+        Allows customized functions to combine matrices, by default None
     verbose : bool, optional
-        [description], by default True
+        If True, additional status print stamenets will be issued, by default True
     updateMeta : bool, optional
-        [description], by default False
+        If True, metadata of output dataset will be a combination of all input rasters, by default False
 
-    Raises
-    ------
-    GeoKitError
-        [Oops, something went wrong]
-    '''    
+    Returns:
+    ----------
+    output dataset: osgeo.gdal.Dataset
+        Raster file containing the combined matrices of all input datasets.
+    """ 
 
 
     # Ensure we have a list of raster datasets
@@ -61,15 +63,32 @@ def combineSimilarRasters(master, datasets, combiningFunc=None, verbose=True, up
     dataYMin = min([i.yMin for i in infoSet])
     dataYMax = max([i.yMax for i in infoSet])
     
-    # Maybe create a new master dataset
-    if not (os.path.isfile(master)): # we will need to create a master source
-        #check if geokit has to create a folder:
-        if not os.path.isdir(os.path.dirname(master)):
-            os.mkdir(os.path.dirname(master))
-        
+    # Maybe create a new output dataset
+    if isinstance(output, str):
+        if not os.path.isfile(output): # we will need to create a output source
+            
+            # Determine no data value
+            noDataValue = kwargs.pop("noData", None)
+    
+            if noDataValue is None:
+                noDataSet = set([i.noData for i in infoSet])
+                if len(noDataSet)==1: noDataValue = noDataSet.pop()
+                
+            # Create Raster
+            dx = infoSet[0].dx
+            dy = infoSet[0].dy
+            dtype = infoSet[0].dtype
+            srs = infoSet[0].srs
+            
+            createRaster(bounds=(dataXMin, dataYMin, dataXMax, dataYMax), output=output, 
+                         dtype=dtype, pixelWidth=dx, pixelHeight=dy, noData=noDataValue, 
+                         srs=srs, fill=noDataValue, **kwargs)
+        else:
+            warn('WARNING: Overwriting existing output file. Sometimes writing to an non empty output fails. Recommended to write to a non existing location instead and include maser into datasets.')
+    elif output is None:
         # Determine no data value
         noDataValue = kwargs.pop("noData", None)
-
+  
         if noDataValue is None:
             noDataSet = set([i.noData for i in infoSet])
             if len(noDataSet)==1: noDataValue = noDataSet.pop()
@@ -80,34 +99,35 @@ def combineSimilarRasters(master, datasets, combiningFunc=None, verbose=True, up
         dtype = infoSet[0].dtype
         srs = infoSet[0].srs
         
-        createRaster(bounds=(dataXMin, dataYMin, dataXMax, dataYMax), output=master, 
+        outputDS = createRaster(bounds=(dataXMin, dataYMin, dataXMax, dataYMax), 
                      dtype=dtype, pixelWidth=dx, pixelHeight=dy, noData=noDataValue, 
                      srs=srs, fill=noDataValue, **kwargs)
     else:
-        print('Warning, sometimes writing to an non empty master fails. Write to a non existing location instead and include maser into datasets.')
+        sys.exist("output must be None or a str formatted file path to an existing output file or a file to be created.")
 
-    # Open master dataset and check parameters
-    masterDS = gdal.Open(master, gdal.GA_Update)
-    mInfo = rasterInfo(masterDS)
+    # Open output dataset if required and check parameters
+    if not output is None:
+        outputDS = gdal.Open(output, gdal.GA_Update)
+    mInfo = rasterInfo(outputDS)
     mExtent = Extent(mInfo.bounds, srs=mInfo.srs)
 
     if not mInfo.srs.IsSame(infoSet[0].srs):
-        raise GeoKitError("SRS's do not match master dataset")
+        raise GeoKitError("SRS's do not match output dataset")
     if not (mInfo.dx == infoSet[0].dx and mInfo.dy == infoSet[0].dy) :
-        raise GeoKitError("Resolution's do not match master dataset")
+        raise GeoKitError("Resolution's do not match output dataset")
     if not (mInfo.dtype == infoSet[0].dtype) :
-        raise GeoKitError("Datatype's do not match master dataset")
+        raise GeoKitError("Datatype's do not match output dataset")
     
-    masterBand = masterDS.GetRasterBand(1)
+    outputBand = outputDS.GetRasterBand(1)
     
     # Make a meta container
-    if updateMeta: meta = masterDS.GetMetadata_Dict()
+    if updateMeta: meta = outputDS.GetMetadata_Dict()
 
-    # Add each dataset to master
+    # Add each dataset to output
     for i in range(len(datasets)):
         if verbose: 
-            if isinstance(datasets[i], str): print(i, basename(datasets[i]))
-            else: print(i)
+            if isinstance(datasets[i], str): print(f"{i+1}/{len(datasets)} ({basename(datasets[i])})")
+            else: print(f"{i+1}/{len(datasets)}")
         # create dataset extent
         dExtent = Extent(infoSet[i].bounds, srs=infoSet[i].srs)
 
@@ -119,8 +139,8 @@ def combineSimilarRasters(master, datasets, combiningFunc=None, verbose=True, up
         # Calculate starting indicies
         idx = mExtent.findWithin(dExtent, (mInfo.dx, mInfo.dy), yAtTop=mInfo.yAtTop)
 
-        # Get master data
-        mMatrix = masterBand.ReadAsArray(xoff=idx.xStart, yoff=idx.yStart, win_xsize=idx.xWin, win_ysize=idx.yWin)
+        # Get output data
+        mMatrix = outputBand.ReadAsArray(xoff=idx.xStart, yoff=idx.yStart, win_xsize=idx.xWin, win_ysize=idx.yWin)
         if mMatrix is None: raise GeoKitError("mMatrix is None")
 
         # create selector
@@ -133,18 +153,18 @@ def combineSimilarRasters(master, datasets, combiningFunc=None, verbose=True, up
         else: 
             writeMatrix = dMatrix
 
-        # Add to master
-        masterBand.WriteArray(writeMatrix, idx.xStart, idx.yStart)
-        masterBand.FlushCache()
+        # Add to output
+        outputBand.WriteArray(writeMatrix, idx.xStart, idx.yStart)
+        outputBand.FlushCache()
 
         # update metaData, maybe
         if updateMeta: meta.update(infoSet[i].meta)
     
-    if updateMeta: masterDS.SetMetadata( meta  )
+    if updateMeta: outputDS.SetMetadata( meta  )
 
     # Write final raster
-    masterDS.FlushCache()
-    masterBand.ComputeRasterMinMax(0)
-    masterBand.ComputeBandStats(0)
+    outputDS.FlushCache()
+    outputBand.ComputeRasterMinMax(0)
+    outputBand.ComputeBandStats(0)
 
-    return
\ No newline at end of file
+    return outputDS
\ No newline at end of file
diff --git a/geokit/core/extent.py b/geokit/core/extent.py
old mode 100755
new mode 100644
diff --git a/geokit/core/geom.py b/geokit/core/geom.py
index 389fee8..1644bbc 100644
--- a/geokit/core/geom.py
+++ b/geokit/core/geom.py
@@ -328,8 +328,8 @@ def line(points, srs=4326):
 
     Parameters:
     -----------
-    Points : [(x,y), ] or Nx2 numpy.ndarray
-        The point defining the line
+    Points : [(x,y), ], Nx2 numpy.ndarray or list of osgeo.ogr.Geometry points.
+        The points defining the line
 
     srs : Anything acceptable to geokit.srs.loadSRS(); optional
         The srs of the line to create
@@ -342,9 +342,12 @@ def line(points, srs=4326):
     # Make the complete geometry
     g = ogr.Geometry(ogr.wkbLineString)
     if not srs is None:
-        g.AssignSpatialReference(srs)
+        g.AssignSpatialReference(SRS.loadSRS(srs))
 
     # Make the line
+    if all([isinstance(p, ogr.Geometry) for p in points]):
+        # convert points into a list of coordinate tuples in correct srs
+        points=[(transform(p, toSRS=srs).GetX(), transform(p, toSRS=srs).GetY()) for p in points]
     [g.AddPoint(x, y) for x, y in points]
     # g.AddGeometry(otr)
 
@@ -532,7 +535,7 @@ def polygonizeMatrix(matrix, bounds=None, srs=None, flat=False, shrink=True, _ra
     # Make sure we have a boolean numpy matrix
     if not isinstance(matrix, np.ndarray):
         matrix = np.array(matrix)
-    if matrix.dtype == np.bool or matrix.dtype == np.uint8:
+    if matrix.dtype == np.bool_ or matrix.dtype == np.uint8:
         dtype = "GDT_Byte"
     elif np.issubdtype(matrix.dtype, np.integer):
         dtype = "GDT_Int32"
@@ -708,7 +711,7 @@ def polygonizeMask(mask, bounds=None, srs=None, flat=True, shrink=True):
     if not isinstance(mask, np.ndarray):
         mask = np.array(mask)
 
-    if not (mask.dtype == np.bool or mask.dtype == np.uint8):
+    if not (mask.dtype == np.bool_ or mask.dtype == np.uint8):
         raise GeoKitGeomError("Mask must be a 2D boolean numpy ndarray")
 
     # Do vectorization
@@ -1175,12 +1178,16 @@ def drawGeoms(geoms, srs=4326, ax=None, simplificationFactor=5000, colorBy=None,
     # Check Geometry SRS
     if not srs is None:
         srs = SRS.loadSRS(srs)
+        transformed_geoms = []
         for gi, g in enumerate(geoms):
             gsrs = g.GetSpatialReference()
             if gsrs is None:
                 continue  # Skip it if we don't know it...
             if not gsrs.IsSame(srs):
-                geoms[gi] = transform(geoms[gi], srs)
+                transformed_geoms.append(transform(geoms[gi], srs))
+            else:
+                transformed_geoms.append(geoms[gi])
+        geoms = np.asarray(transformed_geoms)
 
     # Apply simplifications if required
     if not simplificationFactor is None:
@@ -1457,3 +1464,65 @@ def partition(geom, targetArea, growStep=None, _startPoint=0):
 
     # Done!
     return output
+
+
+def shift(geom, lonShift=0, latShift=0):
+    """Shift a polygon in longitudinal and/or latitudinal direction.
+
+    Inputs:
+        geom : The geometry to be shifted
+            - a single ogr Geometry object of POINT, LINESTRING, POLYGON or MULTIPOLYGON type
+            - NOTE: Accepts only 2D geometries, z value must be zero.
+
+        lonShift - (int, float) : The shift in longitudinal direction in units of the geom srs, may be positive or negative
+
+        latShift - (int, float) : The shift in latitudinal direction in units of the geom srs, may be positive or negative
+
+    Returns :
+    --------
+    osgeo.ogr.Geometry object of the input type with shifted coordinates
+    """
+    if not isinstance(geom, ogr.Geometry):
+        raise TypeError(f"geom must be of type osgeo.ogr.Geometry")
+    # first get srs of input geom
+    _srs=geom.GetSpatialReference()
+
+    # define sub method to shift collection of single points
+    def _movePoints(pointCollection, lonShift, latShift):
+        """Auxiliary function shifting individual points"""
+        points=list()
+        for i in range(len(str(pointCollection).split(","))):
+            points.append(pointCollection.GetPoint(i))
+        # shift the points individually
+        points_shifted=list()
+        for p in points:
+            assert p[2]==0, f"All z-values must be zero!"
+            points_shifted.append((p[0]+lonShift, p[1]+latShift))
+        return points_shifted
+
+    # first check if geom is a point and shift
+    if "POINT" in geom.GetGeometryName():
+        p=geom.GetPoint()
+        return point((p[0]+lonShift, p[1]+latShift), srs=_srs)
+    # else check if line and adapt
+    elif "LINESTRING" in geom.GetGeometryName() and not "MULTILINE" in geom.GetGeometryName():
+        return line(_movePoints(pointCollection=geom, lonShift=lonShift, latShift=latShift), srs=_srs)
+    # else check if we have a (multi)polygon
+    elif "POLYGON" in geom.GetGeometryName():
+        if not "MULTIPOLYGON" in geom.GetGeometryName():
+            # only a simple polygon, generate single entry list to allow iteration
+            geom=[geom]
+        # iterate over individual polygons
+        for ip, poly in enumerate(geom):
+            assert "POLYGON" in poly.GetGeometryName(), f"MULTIPOLYGON is not composed of only POLYGONS"
+            # iterate over sub linear rings
+            for ir, ring in enumerate(poly):
+                assert "LINEARRING" in ring.GetGeometryName(), f"POLYGON (or sub polygon of MULTIPOLYGON) is not composed of only LINEARRINGS"
+                poly_shifted=polygon(_movePoints(pointCollection=ring, lonShift=lonShift, latShift=latShift), srs=_srs)
+                if ip==0 and ir==0:
+                    multi_shifted=poly_shifted
+                else:
+                    multi_shifted=multi_shifted.Union(poly_shifted)
+        return multi_shifted
+    else:
+        raise TypeError(f"geom must be a 'POINT', 'LINESTRING', 'POLYGON' or 'MULTIPOLYGON' osgeo.ogr.Geometry")
\ No newline at end of file
diff --git a/geokit/core/raster.py b/geokit/core/raster.py
index 761ec0c..4b30c81 100644
--- a/geokit/core/raster.py
+++ b/geokit/core/raster.py
@@ -202,6 +202,10 @@ def createRaster(bounds, output=None, pixelWidth=100, pixelHeight=100, dtype=Non
             else:
                 raise GeoKitRasterError(
                     "Output file already exists: %s" % output)
+    
+        #check if writeable:
+        if not os.access(os.path.dirname(output), os.W_OK):
+            raise PermissionError(f"Writing permission error for path: {os.path.dirname(output)}")
 
     # Ensure bounds is okay
     # bounds = UTIL.fitBoundsTo(bounds, pixelWidth, pixelHeight)
@@ -239,7 +243,7 @@ def createRaster(bounds, output=None, pixelWidth=100, pixelHeight=100, dtype=Non
                                getattr(gdal, dtype), opts)
 
     if(raster is None):
-        raise GeoKitRasterError("Failed to create raster")
+        raise GeoKitRasterError(f"Failed to create raster")
 
     # Do the rest in a "try" statement so that a failure wont bind the source
     try:
@@ -305,7 +309,7 @@ def createRaster(bounds, output=None, pixelWidth=100, pixelHeight=100, dtype=Non
         raise e
 
 
-def createRasterLike(source, copyMetadata=True, **kwargs):
+def createRasterLike(source, copyMetadata=True, metadata=None, **kwargs):
     """Create a raster described by the given raster info (as returned from a
     call to rasterInfo() ).
 
@@ -321,6 +325,9 @@ def createRasterLike(source, copyMetadata=True, **kwargs):
 
     if not isinstance(source, RasterInfo):
         raise GeoKitRasterError("Could not understand source")
+        
+    if copyMetadata and not metadata is None:
+        raise GeoKitRasterError("If metadata is given, copyMetadata cannot be True!")
 
     bounds = kwargs.pop("bounds", source.bounds)
     pixelWidth = kwargs.pop("pixelWidth", source.pixelWidth)
@@ -332,11 +339,36 @@ def createRasterLike(source, copyMetadata=True, **kwargs):
     if copyMetadata:
         meta = kwargs.pop("meta", source.meta)
     else:
-        meta = None
+        meta = metadata
 
     return createRaster(bounds=bounds, pixelWidth=pixelWidth, pixelHeight=pixelHeight, dtype=dtype, srs=srs,
                         noData=noData, meta=meta, **kwargs)
 
+def saveRasterAsTif(source, output, **kwargs):
+    
+    '''Write a osgeo.gdal.Dataset in memory to a GeoTiff file to disk.
+
+    Parameters
+    ----------
+    source : osgeo.gdal.Dataset 
+
+    output : str
+        A path to an output file
+
+    Returns
+    -------
+    str
+        Path to the saved file on disk.
+    '''
+    # assert os.path.isdir(os.path.dirname(output)), 'Output folder does not exist!'
+    assert output.split('.')[-1] in['tif', 'tiff'], 'Wrong type specified, use *.tif or *.tiff'
+
+    sourceInfo = rasterInfo(source)
+    data = extractMatrix(source)
+
+    return createRaster(bounds=sourceInfo.bounds, pixelWidth=sourceInfo.dx, pixelHeight=sourceInfo.dy,
+                          noData=sourceInfo.noData, dtype=sourceInfo.dtype, srs=sourceInfo.srs, 
+                          data=data, output=output, **kwargs)
 
 ####################################################################
 # extract the raster as a matrix
@@ -464,7 +496,7 @@ def extractMatrix(source, bounds=None, boundsSRS='latlon', maskBand=False, autoc
     # Correct 'nodata' values
     if autocorrect:
         noData = sourceBand.GetNoDataValue()
-        data = data.astype(np.float)
+        data = data.astype(np.float64)
         data[data == noData] = np.nan
 
     # make sure we are returing data in the 'flipped-y' orientation
@@ -679,7 +711,12 @@ def rasterInfo(sourceDS):
     sourceDS = loadRaster(sourceDS)
 
     # get srs
-    srs = SRS.loadSRS(sourceDS.GetProjectionRef())
+    if sourceDS.GetProjectionRef() == '':
+        # return None directly if raster has no srs
+        srs=None
+    else:
+        # generate an srs object if we have srs information
+        srs = SRS.loadSRS(sourceDS.GetProjectionRef())
     output['srs'] = srs
 
     # get extent and resolution
@@ -862,13 +899,13 @@ def loadPoint(pt, s):
     yStarts = yIndexes - winRange
     window = 2 * winRange + 1
 
-    inBounds = xStarts > 0
-    inBounds = inBounds & (yStarts > 0)
-    inBounds = inBounds & (xStarts + window < info.xWinSize)
-    inBounds = inBounds & (yStarts + window < info.yWinSize)
+    inBounds = xStarts >= 0
+    inBounds = inBounds & (yStarts >= 0)
+    inBounds = inBounds & (xStarts + window <= info.xWinSize)
+    inBounds = inBounds & (yStarts + window <= info.yWinSize)
 
     if (~inBounds).any():
-        msg = "WARNING: One of the given points (or extraction windows) exceeds the source's limits"
+        msg = "WARNING: One of the given points (or extraction windows) exceeds the source's limits. Valies are replaced with nan."
         warnings.warn(msg, UserWarning)
 
     # Read values
@@ -877,7 +914,7 @@ def loadPoint(pt, s):
 
     for xi, yi, ib in zip(xStarts, yStarts, inBounds):
         if not ib:
-            data = np.empty((window, window))
+            data = np.ones((window, window)) * np.nan
         else:
             # Open and read from raster
             data = band.ReadAsArray(
@@ -910,6 +947,11 @@ def loadPoint(pt, s):
         # Append to values
         values.append(data)
 
+        #check if not inbounds, then replace values with nan
+        for i in range(len(values)):
+            if not inBounds[i]:
+                values[i] = np.nan * np.ones_like(values[i])
+
     # Done!
     if asSingle:  # A single point was given, so return a single result
         if _onlyValues:
@@ -2005,3 +2047,125 @@ def warp(source, resampleAlg='bilinear', cutline=None, output=None, pixelHeight=
     if not cutline is None:
         del tempdir
     return destRas
+
+#--------------------------------------------------------------------------------------------
+# Sieve raster datasets
+#--------------------------------------------------------------------------------------------
+
+def sieve(source, threshold=100, connectedness=8, mask='none', quiet_flag=False, output=None, **kwargs):
+    
+    """
+    Removes raster polygons smaller than a provided threshold size (in pixels) and 
+    replaces them with the pixel value of the largest neighbour polygon. 
+    It is useful if you have a large amount of small areas on your raster map.
+
+    Parameters:
+    -----------
+    source : Anything acceptable by loadRaster()
+    
+    threshold (int): minimum polygon size (number of pixels) to retain.
+
+    connectedness (int): either 4 indicating that diagonal pixels are not considered directly 
+                         adjacent for polygon membership purposes or 8 indicating they are.
+
+    mask (str): 'none' or 'default'. An optional mask band. All pixels in the mask band with a 
+                value other than zero will be considered suitable for inclusion in polygons. 
+
+    quiet_flag (bool): 0 or 1. Callback for reporting algorithm progress
+
+    output : str; optional
+        The path on disk where the new raster should be created
+
+    **kwargs:
+        * All kwargs are passed on to SieveFilter()
+        * See gdal.SieveFilter for more details
+
+    Returns
+    -------
+    * If 'output' is None: gdal.Dataset
+    * If 'output' is a string: The path to the output is returned (for easy opening)
+
+    """
+    
+    gdal.AllRegister() 
+
+    try: 
+        gdal.SieveFilter 
+    except: 
+        print('') 
+        print('gdal.SieveFilter() not available.  You are likely using "old gen"') 
+        print('bindings or an older version of the next gen bindings.') 
+        print('') 
+    
+    ### Open source file
+    # if output is None: 
+    #     src_ds = gdal.Open( source, gdal.GA_Update ) 
+    # else: 
+    #     src_ds = gdal.Open( source, gdal.GA_ReadOnly )
+
+    src_ds = loadRaster(source)
+
+    if src_ds is None: 
+        print('Unable to open %s ' % source) 
+     
+
+    srcband = src_ds.GetRasterBand(1) 
+
+    if mask == 'default': 
+        maskband = srcband.GetMaskBand() 
+    elif mask == 'none': 
+        maskband = None 
+    else: 
+        mask_ds = loadRaster( mask )
+        maskband = mask_ds.GetRasterBand(1).GetMaskBand() 
+
+    ### Create output file if one is specified.
+
+    if output is not None: 
+
+        format = 'GTiff' 
+        driver = gdal.GetDriverByName(format) 
+        dst_ds = driver.Create( output, src_ds.RasterXSize, src_ds.RasterYSize, 1, 
+                             srcband.DataType, COMPRESSION_OPTION) # ["COMPRESS=LZW"]
+
+    else:
+
+        format='Mem' 
+        driver = gdal.GetDriverByName(format)  # create a raster in memory
+        dst_ds = driver.Create( '', src_ds.RasterXSize, src_ds.RasterYSize, 1, 
+                             srcband.DataType, COMPRESSION_OPTION) # ["COMPRESS=LZW"]
+
+    wkt = src_ds.GetProjection() 
+    if wkt != '': 
+        dst_ds.SetProjection( wkt ) 
+    dst_ds.SetGeoTransform( src_ds.GetGeoTransform() ) 
+    dstband = dst_ds.GetRasterBand(1)
+
+    if quiet_flag: 
+        prog_func = None 
+    else: 
+        prog_func = gdal.TermProgress_nocb 
+
+    result = gdal.SieveFilter(
+                              srcband, 
+                              maskband, 
+                              dstband, 
+                              threshold, 
+                              connectedness, 
+                              callback = prog_func,
+                              **kwargs 
+                             ) 
+
+    dst_ds.FlushCache()
+
+    # Return raster if in memory
+    if output is None:
+        return dst_ds
+
+    # Return output path to raster if on disk
+    else:
+        src_ds = None 
+        dst_ds = None 
+        mask_ds = None
+        return output
+
diff --git a/geokit/core/regionmask.py b/geokit/core/regionmask.py
index 68fab5f..f6d0079 100644
--- a/geokit/core/regionmask.py
+++ b/geokit/core/regionmask.py
@@ -179,7 +179,7 @@ def __init__(self, extent, pixelRes, mask=None, geom=None, attributes=None, **kw
                 raise GeoKitRegionMaskError("geom does not have an srs")
 
             if not gSRS.IsSame(self.srs):
-                GEOM.transform(self._geometry, toSRS=self.srs, fromSRS=gSRS)
+                self._geometry = GEOM.transform(self._geometry, toSRS=self.srs, fromSRS=gSRS)
         else:
             self._geometry = None
 
@@ -270,13 +270,15 @@ def fromGeom(geom, pixelRes=DEFAULT_RES, srs=DEFAULT_SRS, start_raster=None, ext
         # make sure we have a geometry with an srs
         if (isinstance(geom, str)):
             geom = GEOM.convertWKT(geom, srs)
-
-        geom = geom.Clone()  # clone to make sure we're free of outside dependencies
+        
+        # clone to make sure we're free of outside dependencies
+        # convert to regionmask srs to ensure that rm.geometry.GetSpatialReference() is equal to rm.srs
+        geom = GEOM.transform(geom.Clone(), toSRS=srs)  
 
         # set extent (if not given)
         if extent is None:
             extent = Extent.fromGeom(geom).castTo(
-                srs).pad(padExtent).fit(pixelRes, start_raster)
+                srs).pad(padExtent).fit(unit=pixelRes, start_raster=start_raster)
         else:
             if not extent.srs.IsSame(srs):
                 raise GeoKitRegionMaskError(
@@ -454,7 +456,7 @@ def buildMask(self, **kwargs):
 
         self._mask = None
         self._mask = self.rasterize(self.vectorPath, applyMask=False,
-                                    **kwargs).astype(np.bool)
+                                    **kwargs).astype(np.bool_)
         self.height, self.width = self._mask.shape
 
     @property
@@ -592,6 +594,11 @@ def applyMask(self, mat, noData=0):
         """
         if (noData is None):
             noData = 0
+
+        # set matrix datatype to float if float noData value (like e.g. nan) is passed
+        if isinstance(noData, float):
+            mat=mat.astype(np.float64)
+
         # Get size
         Y, X = mat.shape
 
@@ -690,7 +697,7 @@ def processor(data):
 
     def indicateValues(self, source, value, buffer=None, resolutionDiv=1, forceMaskShape=False, applyMask=True,
                        noData=None, resampleAlg='bilinear', bufferMethod='area', preBufferSimplification=None,
-                       warpDType=None, **kwargs):
+                       warpDType=None, prunePatchSize=0, threshold=0.5, **kwargs):
         """
         Indicates those pixels in the RegionMask which correspond to a particular 
         value, or range of values, from a given raster datasource
@@ -817,11 +824,23 @@ def indicateValues(self, source, value, buffer=None, resolutionDiv=1, forceMaskS
               - This method can be made more accurate by increasing the 
                 'resolutionDiv' input
 
-        preBufferSimplification: numeric
+        preBufferSimplification: numeric; optional
             If given, then geometries will be simplified (using ogr.Geometry.Simplify)
             using the specified value before being buffered
             - Using this can drastically decrease the time it takes to perform the 
               bufferring procedure, but can decrease accuracy if it is too high
+        
+        prunePatchSize: numeric; optional
+            If given, then isolated non-indicated patches below the given size
+            will be removed. The given value corresponds to the minimum area in
+            the unit of the regionmask SRS that will not be removed. Defaults 
+            to 0, i.e. no patches will be removed.
+            Note: This is applied to the geoms after buffer application and can
+            deviate from the patch size after final rasterization.
+
+        threshold: float; optional
+            The cell value ABOVE which cells count as positively indicated, 
+            relevant for partial overlaps with buffer method 'area'. Defaults to 0.5.
 
         kwargs -- Passed on to RegionMask.warp()
             * Most notably: 'resampleAlg'
@@ -832,6 +851,7 @@ def indicateValues(self, source, value, buffer=None, resolutionDiv=1, forceMaskS
         numpy.ndarray
         """
         assert bufferMethod in ['area', 'contour']
+        assert isinstance(prunePatchSize, int)
 
         # format value input
         if isinstance(value, str):
@@ -923,13 +943,58 @@ def processor(data):
             if bufferMethod == 'contour':
                 geoms = self.contoursFromMask(final)
             elif bufferMethod == 'area':
-                geoms = self.polygonizeMask(final > 0.5, flat=False)
+                # Check for results
+                if not (final > threshold).any(): 
+                    # no cells > threshold exist that would be buffered, so return empty
+                    return self._returnBlank(resolutionDiv=resolutionDiv, forceMaskShape=forceMaskShape,
+                                            applyMask=applyMask, noData=noData)
+                geoms = self.polygonizeMask(final > threshold, flat=False)
 
             if preBufferSimplification is not None:
                 geoms = [g.Simplify(preBufferSimplification) for g in geoms]
 
             if len(geoms) > 0:
                 geoms = [g.Buffer(buffer) for g in geoms]
+                if not prunePatchSize ==0:
+                    # create Union of all geoms, this will merge overlapping
+                    # TODO speed up via cascaded union or better sieve raster or even inverted raster (approximate exclusions based on geoms directly)!
+                    union=geoms[0]
+                    if len(geoms)>1:
+                        for g in geoms:
+                            union=union.Union(g)
+                    
+                    # create a new empty list of geoms that will be filled with manipulated shapes partly without holes
+                    geoms=[]
+                    # if regionmask extent contains only a single polygon, make it a single-entry list to make it iterable
+                    if union.GetGeometryName()=='POLYGON':
+                        union=[union]
+                    # iterate over union first to separate independent polygons
+                    for polygon in union:
+                        # check if the independent iteration polygon has more than one geometry, if so has holes/inner rings
+                        NoIndivPol=polygon.GetGeometryCount()
+                        if NoIndivPol>1:
+                            print('inner polygons found')
+                            # create a new base polygon containing the outer ring
+                            newpolygon = ogr.Geometry(ogr.wkbPolygon)
+                            newpolygon.AddGeometry(polygon.GetGeometryRef(0))
+                            # then iterate through the other geometries = inner rings
+                            for i in range(1,NoIndivPol):
+                                # only if the area of the inner ring exceeds the prunePatchSize criterion, add back as inner ring
+                                # else do nothing i.e. drop the inner ring
+                                if polygon.GetGeometryRef(i).Area()>=prunePatchSize:
+                                    # TODO this part might be faster with polygon.RemoveGeometry() without creating a 'newpolygon' above
+                                    newpolygon.AddGeometry(polygon.GetGeometryRef(i))
+                                else:
+                                    print('a innerring was dropped!')
+                            # add the new polygon with possibly less holes back to geoms
+                            geoms.append(newpolygon)
+
+                        # if the polygon has only one geometry, it has no inner ring and can be added to geom list as such
+                        else:
+                            geoms.append(polygon)
+
+
+
                 areaDS = VECTOR.createVector(geoms)
                 final = self.rasterize(areaDS, dtype="float32", bands=[1], burnValues=[1], resolutionDiv=resolutionDiv,
                                        applyMask=False, noData=noData)
@@ -1052,7 +1117,7 @@ def doBuffer(ftr):
                 return {'geom': geom.Buffer(buffer)}
 
             source = self.mutateVector(source, where=where, processor=doBuffer,
-                                       matchContext=True, keepAttributes=False, _slim=True)
+                                       matchContext=True, keepAttributes=False, _slim=True, **kwargs)
 
             where = None  # Set where to None since the filtering has already been done
 
@@ -1466,7 +1531,7 @@ def extractFeatures(self, source, **kwargs):
         """
         return VECTOR.extractFeatures(source=source, geom=self.geometry, **kwargs)
 
-    def mutateVector(self, source, matchContext=False, **kwargs):
+    def mutateVector(self, source, matchContext=False, regionPad=0, **kwargs):
         """Convenience wrapper for geokit.vector.mutateVector which automatically
         sets 'srs' and 'geom' inputs to the RegionMask's srs and geometry
 
@@ -1489,6 +1554,11 @@ def mutateVector(self, source, matchContext=False, **kwargs):
               mutating
             * If False, only selects the geometries which touch the RegionMask
 
+        regionPad: int, optional
+            Will buffer the regionmask geometry by a value (in the unit of the 
+            regionmask srs) before mutating the source vector onto it. Defaults
+            to 0.
+
         **kwargs:
             All other keyword arguments are passed to geokit.vector.mutateVector
 
@@ -1506,7 +1576,9 @@ def mutateVector(self, source, matchContext=False, **kwargs):
             ext = self.extent
 
         # mutate the source
-        return VECTOR.mutateVector(source, srs=ext.srs, geom=self.geometry, **kwargs)
+        if regionPad==None: #cannot calculate with none, so use 0
+            regionPad=0    
+        return VECTOR.mutateVector(source, srs=ext.srs, geom=self.geometry.Buffer(regionPad), **kwargs)
 
     def mutateRaster(self, source, matchContext=True, warpArgs=None, applyMask=True, processor=None,
                      resampleAlg="bilinear", **mutateArgs):
diff --git a/geokit/core/srs.py b/geokit/core/srs.py
index 9dad4e6..fa44b9d 100644
--- a/geokit/core/srs.py
+++ b/geokit/core/srs.py
@@ -1,68 +1,21 @@
 import os
 import numpy as np
-from osgeo import osr, gdal
+from osgeo import osr, gdal, ogr
 import warnings
 from collections import namedtuple
 import smopy
 from typing import Iterable
 
 from . import util as UTIL
-
+from . import geom as GEOM
 
 class GeoKitSRSError(UTIL.GeoKitError):
     pass
 
+warnings.filterwarnings("always", category=DeprecationWarning)
 
-######################################################################################
-# Common SRS library
-
-# Some other srs shortcuts
-
-
-class _SRSCOMMON:
-    """The SRSCOMMON library contains shortcuts and contextual information for various commonly used projection systems
-
-    * You can access an srs in two ways (where <srs> is replaced with the SRS's name):
-        1: SRSCOMMON.<srs>
-        2: SRSCOMMON["<srs>"]
-    """
-    # basic latitude and longitude
-    _latlon = osr.SpatialReference()
-    _latlon.ImportFromEPSG(4326)
-
-    @property
-    def latlon(self):
-        """Basic SRS for unprojected latitude and longitude coordinates
-
-        Units: Degrees"""
-        return self._latlon
-    # basic latitude and longitude
-    _europe_m = osr.SpatialReference()
-    _europe_m.ImportFromEPSG(3035)
-
-    @property
-    def europe_m(self):
-        """Equal-Area projection centered around Europe.
-
-        * Good for relational operations within Europe
-
-        Units: Meters"""
-        return self._europe_m
-
-    # basic getter
-    def __getitem__(self, name):
-        if not hasattr(self, name):
-            raise ValueError("SRS \"%s\" not found" % name)
-        return getattr(self, "_"+name)
-
-
-# Initialize
-SRSCOMMON = _SRSCOMMON()
-
-################################################
 # Basic loader
 
-
 def loadSRS(source):
     """
     Load a spatial reference system (SRS) from various sources.
@@ -75,6 +28,7 @@ def loadSRS(source):
         Example of acceptable objects are...
           * osr.SpatialReference object
           * An EPSG integer ID
+          * A standardized srs str definition such as 'EPSG:4326' or 'ESRI:53003'
           * a string corresponding to one of the systems found in geokit.srs.SRSCOMMON
           * a WKT string
 
@@ -98,7 +52,13 @@ def loadSRS(source):
             # assume a name for one of the common SRS's was given
             srs = SRSCOMMON[source]
         else:
-            srs.ImportFromWkt(source)  # assume a Wkt string was input
+            try:
+                # try handling as a standardized epsg or esri etc. code
+                srs = osr.SpatialReference()
+                _val = srs.SetFromUserInput(source)
+                assert _val==0
+            except:
+                srs.ImportFromWkt(source)  # assume a Wkt string was input
     elif(isinstance(source, int)):
         srs.ImportFromEPSG(source)
     else:
@@ -107,6 +67,9 @@ def loadSRS(source):
     if gdal.__version__ >= '3.0.0':
         srs.SetAxisMappingStrategy(osr.OAMS_TRADITIONAL_GIS_ORDER)
 
+    # assert that the srs is valid (may be invalid if e.g. wrong integer codes were passed)
+    assert srs.Validate()==0, f"Created srs is invalid."
+
     return srs
 
 
@@ -116,29 +79,52 @@ def loadSRS(source):
 EPSG3857 = loadSRS(3857)
 
 
-def centeredLAEA(lon, lat):
+def centeredLAEA(lon=None, lat=None, name="unnamed_m", geom=None):
     """
-    Load a Lambert-Azimuthal-Equal_Area spatial reference system (SRS) centered on a given set of latitude and longitude coordinates.
+    Load a Lambert-Azimuthal-Equal_Area spatial reference system (SRS) centered
+    on a given set of latitude and longitude coordinates. Alternatively, a geom
+    can be passed to center the LAEA on.
 
     Parameters:
     -----------
     lon : float
-        The longitude of the projection's center
+        The longitude of the projection's center. Required if no geom is given.
 
     lat : float
-        The latitude of the projection's center
+        The latitude of the projection's center. Required if no geom is given.
+
+    geom: osgeo.ogr.Geometry
+        The region shape to center the LAEA in. If given, lat and lon must not 
+        be given, instead they will be defined automatically as the coordinates
+        of the region centroid.
 
     Returns:
     --------
     osr.SpatialReference
 
     """
+    if geom is None:
+        assert isinstance(lat, float) and isinstance(lon, float), "If geom is not passed, lat and lon must be given as float values."
+    else:
+        assert isinstance(geom, ogr.Geometry), "geom must be given as osgeo.ogr.Geometry class object if not None."
+        assert lat is None and lon is None, "If geom is given, lat and lon must not be given."
+    
+    # check if lat/lon can be used or if it must be extracted from geom first
+    if not geom is None:
+        # transform to EPSG:4326 in case it not already is lat/lon projection
+        geom = GEOM.transform(geom, toSRS=4326)
+        # extract lat/lon centroid coordinates to center LAEA upon
+        lon = geom.Centroid().GetX()
+        lat = geom.Centroid().GetY()
+
     srs = osr.SpatialReference()
-    srs.ImportFromProj4(
-        '+proj=laea +lat_0={} +lon_0={} +x_0=0 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs'.format(lat, lon))
+    srs.ImportFromWkt('PROJCS["{}",GEOGCS["GRS 1980(IUGG, 1980)",DATUM["unknown",SPHEROID["GRS80",6378137,298.257222101],TOWGS84[0,0,0,0,0,0,0]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433]],PROJECTION["Lambert_Azimuthal_Equal_Area"],PARAMETER["latitude_of_center",{}],PARAMETER["longitude_of_center",{}],PARAMETER["false_easting",0],PARAMETER["false_northing",0],UNIT["Meter",1]]'.format(name, lat, lon))
     
     if gdal.__version__ >= '3.0.0':
         srs.SetAxisMappingStrategy(osr.OAMS_TRADITIONAL_GIS_ORDER)
+
+    # assert that the srs is valid (may be invalid if e.g. wrong integer codes were passed)
+    assert srs.Validate()==0, f"Created srs is invalid."
         
     return srs
 
@@ -245,3 +231,88 @@ def tileIndexAt(x, y, zoom, srs):
 
     return Tile(xi, yi, zoom)
 
+######################################################################################
+# Common SRS library
+
+# Some other srs shortcuts
+
+
+class _SRSCOMMON:
+    """The SRSCOMMON library contains shortcuts and contextual information for various commonly used projection systems
+
+    * You can access an srs in two ways (where <srs> is replaced with the SRS's name):
+        1: SRSCOMMON.<srs>
+        2: SRSCOMMON["<srs>"]
+    """
+    # basic latitude and longitude
+    _latlon = osr.SpatialReference()
+    _latlon.ImportFromEPSG(4326)
+
+    @property
+    def latlon(self):
+        """Basic SRS for unprojected latitude and longitude coordinates
+
+        Units: Degrees"""
+        return self._latlon
+    # basic latitude and longitude
+    _europe_laea = osr.SpatialReference()
+    _europe_laea.ImportFromEPSG(3035)
+    _europe_m = _europe_laea.Clone()
+
+    @property
+    def europe_m(self):
+        
+        warnings.warn(
+            "SRSCOMMON.europe_m is deprecated and will be removed in a future release. \
+            use SRSCOMMON.europe_laea instead.",
+            DeprecationWarning,
+        )
+         
+        return self._europe_m
+
+    @property
+    def europe_laea(self):
+        """Equal-Area projection centered around Europe.
+
+        * Good for relational operations within Europe
+
+        Units: Meters"""
+        return self._europe_laea
+    
+    # define a centered LAEA on the centroid lat/lon of ECOWAS region
+    _ecowas_laea = centeredLAEA(lon=0.782051665138668, lat=13.564515698612, name="LAEA ECOWAS")
+    
+    @property
+    def ecowas_laea(self):
+        """Equal-Area projection centered around ECOWAS (Western Africa).
+
+        * Good for relational operations within Western Africa
+
+        Units: Meters"""
+        return self._ecowas_laea
+    
+    # define a centered LAEA on the centroid lat/lon of SADC region
+    _sadc_laea = centeredLAEA(lon=26.6605715570689, lat=-14.5952938182064, name="LAEA SADC")
+    
+    @property
+    def sadc_laea(self):
+        """Equal-Area projection centered around ECOWAS (Western Africa).
+
+        * Good for relational operations within Western Africa
+
+        Units: Meters"""
+        return self._sadc_laea
+
+    # basic getter
+    def __getitem__(self, name):
+        
+        if not hasattr(self, name):
+            
+            raise ValueError("SRS \"%s\" not found" % name)
+        
+        return getattr(self, f"_{name}")
+
+# Initialize
+SRSCOMMON = _SRSCOMMON()
+
+################################################
\ No newline at end of file
diff --git a/geokit/core/util.py b/geokit/core/util.py
index 39149f0..63c687c 100644
--- a/geokit/core/util.py
+++ b/geokit/core/util.py
@@ -617,3 +617,18 @@ def drawImage(matrix, ax=None, xlim=None, ylim=None, yAtTop=True, scaling=1, fon
 
     # Done!
     return AxHands(ax, h, cbar)
+
+def compare_geoms(geoms_1, geoms_2):
+    
+    """Compare two lists of geometries and return a list of booleans indicating whether each pair of geometries are equal.  
+    The order of the lists is important, as the first geometry in the first list will be compared to the first geometry in the second list, and so on.
+
+    Returns
+    -------
+    list    
+        A list of booleans indicating whether each pair of geometries are equal.
+    """
+    
+    equal = map(lambda g1, g2: g1.Equals(g2), geoms_1, geoms_2)
+    
+    return list(equal)
diff --git a/geokit/core/vector.py b/geokit/core/vector.py
index a599b21..fffc9ff 100644
--- a/geokit/core/vector.py
+++ b/geokit/core/vector.py
@@ -6,6 +6,8 @@
 from collections import namedtuple, defaultdict, OrderedDict
 from collections.abc import Iterable
 import pandas as pd
+from binascii import hexlify
+import numbers
 
 from . import util as UTIL
 from . import srs as SRS
@@ -238,15 +240,43 @@ def vectorInfo(source):
 
     return vecInfo(**info)
 
+####################################################################
+# List layers within a multi-layer vector dataset e.g. a geopackage
+def listLayers(source,):
+    """Returns the layer names for each layer that is stored in a geopackage.
+
+    Parameters
+    ----------
+    source :  Anything acceptable by loadVector()
+        The vector datasource to read from
+
+    Returns
+    -------
+    list
+        A list of layer names for the source geopackage.
+    """
+    layer_names = []
+    ds = loadVector(source)
+    
+    # Loop over the layers to get their names.
+    for i in range(ds.GetLayerCount()):
+        name = ds.GetLayer(i).GetName()
+        layer_names.append(name)
+    return layer_names
+
 ####################################################################
 # Iterable to loop over vector items
 
 
-def _extractFeatures(source, geom, where, srs, onlyGeom, onlyAttr, skipMissingGeoms, ):
+def _extractFeatures(source, geom, where, srs, onlyGeom, onlyAttr, skipMissingGeoms, layerName=None):
     # Do filtering
     source = loadVector(source)
-    layer = source.GetLayer()
-    filterLayer(layer, geom, where)
+    if not layerName is None:
+        layer = source.GetLayerByName(layerName)
+        filterLayer(layer, geom, where)
+    else:
+        layer = source.GetLayer()
+        filterLayer(layer, geom, where)
 
     # Make a transformer
     trx = None
@@ -284,7 +314,7 @@ def _extractFeatures(source, geom, where, srs, onlyGeom, onlyAttr, skipMissingGe
             yield UTIL.Feature(oGeom, oItems)
 
 
-def extractFeatures(source, where=None, geom=None, srs=None, onlyGeom=False, onlyAttr=False, asPandas=True, indexCol=None, skipMissingGeoms=True, **kwargs):
+def extractFeatures(source, where=None, geom=None, srs=None, onlyGeom=False, onlyAttr=False, asPandas=True, indexCol=None, skipMissingGeoms=True, layerName=None, **kwargs):
     """Creates a generator which extract the features contained within the source
 
     * Iteratively returns (feature-geometry, feature-fields)    
@@ -296,7 +326,7 @@ def extractFeatures(source, where=None, geom=None, srs=None, onlyGeom=False, onl
     * Sometimes they may only overlap
     * Sometimes they are only in the geometry's envelope
     * To be sure an extracted geometry fits the selection criteria, you may 
-      still need to do further processing
+      still need to do further processing or use extractAndClipFeatures()
 
     Parameters:
     -----------
@@ -338,6 +368,9 @@ def extractFeatures(source, where=None, geom=None, srs=None, onlyGeom=False, onl
 
     skipMissingGeoms : bool; optional
         If True, then the parser will not read a feature which are missing a geometry
+        
+    layerName : str; optional
+        The name of the layer to extract from the source vector dataset (only applicable in case of a geopackage).
 
     Returns:
     --------
@@ -354,7 +387,8 @@ def extractFeatures(source, where=None, geom=None, srs=None, onlyGeom=False, onl
             srs=srs,
             onlyGeom=onlyGeom,
             onlyAttr=onlyAttr,
-            skipMissingGeoms=skipMissingGeoms)
+            skipMissingGeoms=skipMissingGeoms,
+            layerName=layerName)
     else:
         fields = defaultdict(list)
         fields["geom"] = []
@@ -366,7 +400,9 @@ def extractFeatures(source, where=None, geom=None, srs=None, onlyGeom=False, onl
                 srs=srs,
                 onlyGeom=False,
                 onlyAttr=False,
-                skipMissingGeoms=skipMissingGeoms):
+                skipMissingGeoms=skipMissingGeoms,
+                layerName=layerName
+                ):
             fields["geom"].append(g.Clone())
             for k, v in a.items():
                 fields[k].append(v)
@@ -514,9 +550,157 @@ def extractAsDataFrame(source, indexCol=None, geom=None, where=None, srs=None, *
     return extractFeatures(source=source, indexCol=indexCol, geom=geom, where=where, srs=srs, **kwargs)
 
 
+def extractAndClipFeatures(source, geom, where=None, srs=None, onlyGeom=False, indexCol=None, skipMissingGeoms=True, layerName=None, scaleAttrs=None, **kwargs):
+    """
+    Extracts features from a source and clips them to the boundaries of a given geom.
+    Optionally scales numeric attribute values linearly to the overlapping area share.
+
+    Parameters:
+    -----------
+    source : Anything acceptable by loadVector()
+        The vector data source to read from
+
+    geom : ogr.Geometry
+        The geometry to search with
+        * All features touching this geometry are extracted and clipped to the geometry boundaries.
+
+    where : str; optional
+        An SQL-like where statement to apply to the source
+        * Feature attribute name do not need quotes
+        * String values should be wrapped in 'single quotes'
+        Example: If the source vector has a string attribute called "ISO" and 
+                 a integer attribute called "POP", you could use....
+
+            where = "ISO='DEU' AND POP>1000"
+
+    srs : Anything acceptable to geokit.srs.loadSRS(); optional
+        The srs of the geometries to extract
+          * If not given, the geom's inherent srs is used
+          * If srs does not match the inherent srs, all geometries will be 
+            transformed
+
+    onlyGeom : bool; optional
+        If True, only feature geometries will be returned
+
+    indexCol : str; optional
+        The feature identifier to use as the DataFrams's index
+        * Only useful when as DataFrame is True
+
+    skipMissingGeoms : bool; optional
+        If True, then the parser will not read a feature which are missing a geometry
+        
+    layerName : str; optional
+        The name of the layer to extract from the source vector dataset (only applicable in case of a geopackage).
+
+    scaleAttrs : str or list, optional
+        attribute names of the source with numeric values. The values will be scaled linearly with the 
+        area share of the feature overlapping the geom.
+
+    Returns:
+    --------
+    * pandas.DataFrame or pandas.Series
+    """
+    # assert and preprocess input source
+    if isinstance(source, pd.DataFrame):
+        # check validity of input dataframe
+        if not 'geom' in source.columns:
+            raise AttributeError(f"source is given as a pd.DataFrame but has not 'geom' column.")
+        if not isinstance(source.geom.iloc[0], ogr.Geometry):
+            raise TypeError(f"source is given as a pd.DataFrame but value in 'geom' column is not of type osgeo.ogr.Geometry.")
+        # return empty dataframe with empty expected "areaShare" column if no geometries contained since vector cannot be created without geometries
+        if len(source)==0:
+            source['areaShare']=None
+            return source
+        # generate a vector from source dataframe
+        source = createVector(source)
+    elif isinstance(source, str):
+        if not os.path.isfile(source):
+            raise FileNotFoundError(f"source is given as a string but is not an existing filepath: {source}")
+        # load as vector file
+        source=loadVector(source)
+    elif not isinstance(source, gdal.Dataset):
+        raise TypeError(f"source must either be a pd.DataFrame, a gdal.Dataset vector instance or a str formatted shapefile path.")
+    
+    # extract only the overlapping geoms, first define srs
+    if srs is None:
+        srs=geom.GetSpatialReference()
+    else:
+        geom=GEOM.transform(geom, toSRS=srs)
+    df = extractFeatures(source=source, geom=geom, where=where, srs=srs, onlyGeom=onlyGeom, indexCol=indexCol, skipMissingGeoms=skipMissingGeoms, layerName=layerName, **kwargs)
+    if scaleAttrs is None:
+        scaleAttrs=[]
+    elif isinstance(scaleAttrs, str):
+        scaleAttrs=[scaleAttrs]
+    else:
+        assert isinstance(scaleAttrs, list), f"scaleAttrs must be a str or a list thereof if not None."
+    for _attr in scaleAttrs:
+        if not _attr in list(df.columns):
+            raise AttributeError(f"'{_attr}' was given as scaleAttrs but is not an attribute of the source dataframe.")
+        if not all([isinstance(_val, numbers.Number) for _val in df[_attr]]):
+            raise TypeError(f"All values in column '{_attr}' in scaleAttrs must be numeric.")
+
+    
+    # check if we have any features to clip at all
+    if len(df)==0:
+        # if not, add the mandatory areaShare column in case that it is not there already and return empty dataframe
+        df['areaShare']=None
+        return df
+    # else add the expected areaShare column
+    assert not 'areaShare' in list(df.columns), f"source data must not contain a 'areaShare' attribute."
+    df['areaShare']=1.0
+    # check if we need to clip the geometries at all
+    if df.geom.iloc[0].GetGeometryName()[:5] == 'POINT':
+        # we have only points and no further clipping is needed
+        return df
+
+    # else we need to add an ID column and generate a new vector
+    assert not 'clippingID' in list(df.columns), f"source data must not contain a 'clippingID' attribute."
+    df['clippingID']=range(len(df))
+    _vec = createVector(df)
+    # extract only these features intersected by the outer geom boundary
+    outer_df = extractFeatures(source=_vec, geom=geom.Boundary(), where=where, srs=srs, indexCol=indexCol, skipMissingGeoms=skipMissingGeoms, layerName=layerName, **kwargs)
+    del _vec
+    if len(outer_df)==0:
+        # we have no features intersecting with the geom boundary, return all included features
+
+        return df.drop(columns='clippingID')
+    
+    # else clip these features that are intersected by the geom
+    _clippedIDs = list()
+    _clippedGeoms = list()
+    _areaShares = list()
+    for i, feat in zip(outer_df.clippingID, outer_df.geom):
+        _clipped = feat.Intersection(geom)
+        _areaShare = _clipped.Area()/feat.Area()
+        if _areaShare==1.0:
+            # the feature is only touched by the boundary but not reduced
+            continue
+        elif _areaShare==0.0:
+            # the feature is fully outside the geom and only touches the geom boundary
+            # set clipped feature geometry to np.nan to filter out later
+            _clipped=np.nan
+        _clippedGeoms.append(_clipped)
+        _areaShares.append(_areaShare)
+        _clippedIDs.append(i)
+
+    if len(_clippedIDs)==0:
+        # we have not clipped any feature at all, return df
+        return df.drop(columns='clippingID')
+    
+    # else replace the original feature geometries with the clipped ones where needed and add area shares
+    df.loc[df.clippingID.isin(_clippedIDs), 'geom'] = _clippedGeoms
+    df.loc[df.clippingID.isin(_clippedIDs), 'areaShare'] = _areaShares
+    for _attr in scaleAttrs:
+        df[_attr]=df.apply(lambda x : x[_attr]*x.areaShare, axis=1)
+
+    # return the adapted dataframe
+    return df.drop(columns='clippingID')
+    
+
+
 ####################################################################
 # Create a vector
-def createVector(geoms, output=None, srs=None, fieldVals=None, fieldDef=None, overwrite=True):
+def createVector(geoms, output=None, srs=None, driverName="ESRI Shapefile", layerName="default", fieldVals=None, fieldDef=None, checkAllGeoms=False, overwrite=True):
     """
     Create a vector on disk from geometries or a DataFrame with 'geom' column
 
@@ -541,6 +725,18 @@ def createVector(geoms, output=None, srs=None, fieldVals=None, fieldDef=None, ov
           * If not given, the geometries' inherent srs is used
           * If srs does not match the inherent srs, all geometries will be 
             transformed
+            
+    driverName : str; optional
+        The name of the driver to use when creating the vector. 
+        Currently supported options are:
+            - ESRI Shapefile
+            - GPKG
+            
+        For a list of all supported vector drivers by OGR, see: https://gdal.org/drivers/vector/index.html
+
+    layerName : str; optional
+        The name of the layer to create within the vector. (Only applicable for GeoPackages)
+        * If the layer already exists, it will be overwritten
 
     fieldVals : dict of lists or pandas.DataFrame; optional
         Explicit attribute values to assign to each geometry
@@ -562,6 +758,11 @@ def createVector(geoms, output=None, srs=None, fieldVals=None, fieldDef=None, ov
         * The ogrType() function can be used to map typical python and numpy types
           to appropriate ogr types
 
+    checkAllGeoms : bool, optional
+        If True, all geoms will be asserted in object type and exact srs. Else, only
+        the first geom in the geom column/iterable will be checked fpr performance reasons.
+        By default False.
+
     overwrite : bool; optional
         Determines whether the prexisting files should be overwritten
         * Only used when output is not None
@@ -575,15 +776,6 @@ def createVector(geoms, output=None, srs=None, fieldVals=None, fieldDef=None, ov
     if(srs):
         srs = SRS.loadSRS(srs)
 
-    # Search for file
-    if(output):
-        exists = os.path.isfile(output)
-        if (exists and overwrite):
-            os.remove(output)
-        elif(exists and not overwrite):
-            raise GeoKitVectorError(
-                "%s exists but 'overwrite' is not True" % output)
-
     # make geom or wkt list into a list of ogr.Geometry objects
     finalGeoms = []
     geomIndex = None
@@ -605,10 +797,12 @@ def createVector(geoms, output=None, srs=None, fieldVals=None, fieldDef=None, ov
         raise GeoKitVectorError("Empty geometry list given")
 
     # Test if the first geometry is an ogr-Geometry type
-    if(isinstance(geoms[0], ogr.Geometry)):
+    if isinstance(geoms[0], ogr.Geometry):
         #  (Assume all geometries in the array have the same type)
         geomSRS = geoms[0].GetSpatialReference()
-
+        if checkAllGeoms:
+            # check if all other geoms have the same SRS
+            assert all([geomSRS.IsSame(g.GetSpatialReference()) for g in geoms]), f"Not all geoms have the same SRS, srs of first geom: {geomSRS}"
         # Set up some variables for the upcoming loop
         doTransform = False
         setSRS = False
@@ -665,12 +859,35 @@ def createVector(geoms, output=None, srs=None, fieldVals=None, fieldDef=None, ov
         raise RuntimeError("Could not determine output shape's geometry type")
 
     # Create a driver and datasource
-    #driver = ogr.GetDriverByName("ESRI Shapefile")
-    #dataSource = driver.CreateDataSource( output )
-    if output:
-        driver = gdal.GetDriverByName("ESRI Shapefile")
-        dataSource = driver.Create(output, 0, 0)
+    # driver = gdal.GetDriverByName("ESRI Shapefile")
+    # dataSource = driver.Create(output, 0, 0)
+
+    if (output is not None and overwrite):
+        
+        # Search for directory
+        if os.path.dirname(output) == '': # If no directory is given, assume current directory
+            output = os.path.join(os.getcwd(), output)
+            
+        elif not os.path.isdir(os.path.dirname(output)): # If directory does not exist, raise error
+            raise FileNotFoundError(f"Directory {os.path.dirname(output)} does not exist")
+        
+        # Remove file if it exists
+        if os.path.isfile(output):
+            os.remove(output)
+        
+        driver = ogr.GetDriverByName(driverName)
+        dataSource = driver.CreateDataSource( output )
+        
+    elif output is not None and overwrite == False:
+        
+        warnings.warn("Overwriting existing file")
+        dataSource = ogr.Open(output, 1)
+        assert dataSource is not None, f"Could not open {output}"
+        
     else:
+        # driver = ogr.GetDriverByName("Memory")
+        # dataSource = driver.CreateDataSource("")
+        
         driver = gdal.GetDriverByName("Memory")
 
         # Using 'Create' from a Memory driver leads to an error. But creating
@@ -687,12 +904,16 @@ def createVector(geoms, output=None, srs=None, fieldVals=None, fieldDef=None, ov
     # Wrap the whole writing function in a 'try' statement in case it fails
     try:
         # Create the layer
-        if(output):
-            layerName = os.path.splitext(os.path.basename(output))[0]
+        if output is not None and overwrite == False:
+            
+            layerName = layerName
+            assert layerName not in listLayers(output), f"Layer name '{layerName}' already exists in {output}. Please Specify a new layer name or set overwrite=True."
+            
         else:
-            layerName = "Layer"
-
+            layerName = layerName
+                       
         layer = dataSource.CreateLayer(layerName, srs, geomType)
+        assert layer is not None, "Could not create layer!"
 
         # Setup fieldVals and fieldDef dicts
         if(not fieldVals is None):
@@ -776,7 +997,7 @@ def createVector(geoms, output=None, srs=None, fieldVals=None, fieldDef=None, ov
 
     # Delete the datasource in case it failed
     except Exception as e:
-        dataSource is None
+        dataSource=None
         raise e
 
 
@@ -882,9 +1103,6 @@ def createGeoJson(geoms, output=None, srs=4326, topo=False, fill=''):
             pass  # We already wrote to the file!
         return None
 
-####################################################################
-# mutuate a vector
-
 
 def mutateVector(source, processor=None, srs=None, geom=None, where=None, fieldDef=None, output=None, keepAttributes=True, _slim=False, **kwargs):
     """Process a vector dataset according to an arbitrary function
diff --git a/geokit/geom.py b/geokit/geom.py
index e572f05..c5850ed 100644
--- a/geokit/geom.py
+++ b/geokit/geom.py
@@ -22,4 +22,5 @@
                               drawGeoms,
                               partition,
                               extractVerticies,
+                              shift,
                               )
diff --git a/geokit/raster.py b/geokit/raster.py
index 150b01e..01225dd 100644
--- a/geokit/raster.py
+++ b/geokit/raster.py
@@ -17,4 +17,6 @@
                                 drawSmopyMap,
                                 drawRaster,
                                 warp,
+                                saveRasterAsTif,
+                                sieve,
                                 )
diff --git a/geokit/test/data/gadm36_DEU_1.cpg b/geokit/test/data/gadm36_DEU_1.cpg
new file mode 100644
index 0000000..3ad133c
--- /dev/null
+++ b/geokit/test/data/gadm36_DEU_1.cpg
@@ -0,0 +1 @@
+UTF-8
\ No newline at end of file
diff --git a/geokit/test/data/gadm36_DEU_1.dbf b/geokit/test/data/gadm36_DEU_1.dbf
new file mode 100644
index 0000000..1401c00
Binary files /dev/null and b/geokit/test/data/gadm36_DEU_1.dbf differ
diff --git a/geokit/test/data/gadm36_DEU_1.prj b/geokit/test/data/gadm36_DEU_1.prj
new file mode 100644
index 0000000..a30c00a
--- /dev/null
+++ b/geokit/test/data/gadm36_DEU_1.prj
@@ -0,0 +1 @@
+GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]
\ No newline at end of file
diff --git a/geokit/test/data/gadm36_DEU_1.shp b/geokit/test/data/gadm36_DEU_1.shp
new file mode 100644
index 0000000..6c4913f
Binary files /dev/null and b/geokit/test/data/gadm36_DEU_1.shp differ
diff --git a/geokit/test/data/gadm36_DEU_1.shx b/geokit/test/data/gadm36_DEU_1.shx
new file mode 100644
index 0000000..74b3f16
Binary files /dev/null and b/geokit/test/data/gadm36_DEU_1.shx differ
diff --git a/geokit/test/data/gsa-ghi-like.tif b/geokit/test/data/gsa-ghi-like.tif
new file mode 100644
index 0000000..0007f0b
Binary files /dev/null and b/geokit/test/data/gsa-ghi-like.tif differ
diff --git a/geokit/test/data/raster_gdal_244.tif b/geokit/test/data/raster_gdal_244.tif
new file mode 100644
index 0000000..a0c771c
Binary files /dev/null and b/geokit/test/data/raster_gdal_244.tif differ
diff --git a/geokit/test/data/turbinePlacements.dbf b/geokit/test/data/turbinePlacements.dbf
new file mode 100644
index 0000000..f0ecb5a
Binary files /dev/null and b/geokit/test/data/turbinePlacements.dbf differ
diff --git a/geokit/test/data/turbinePlacements.prj b/geokit/test/data/turbinePlacements.prj
new file mode 100644
index 0000000..a30c00a
--- /dev/null
+++ b/geokit/test/data/turbinePlacements.prj
@@ -0,0 +1 @@
+GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]
\ No newline at end of file
diff --git a/geokit/test/data/turbinePlacements.shp b/geokit/test/data/turbinePlacements.shp
new file mode 100644
index 0000000..184e6a4
Binary files /dev/null and b/geokit/test/data/turbinePlacements.shp differ
diff --git a/geokit/test/data/turbinePlacements.shx b/geokit/test/data/turbinePlacements.shx
new file mode 100644
index 0000000..a36d39a
Binary files /dev/null and b/geokit/test/data/turbinePlacements.shx differ
diff --git a/geokit/test/helpers.py b/geokit/test/helpers.py
index 874f7cf..79294d3 100644
--- a/geokit/test/helpers.py
+++ b/geokit/test/helpers.py
@@ -27,7 +27,8 @@ def result(s): return join(dirname(__file__), RESULT, s)
 pointsInAachen4326 = [(6.06590,50.51939), (6.02141,50.61491), (6.371634,50.846025)]
 pointsInAachen3035 = [(4042131.15813425, 3052769.53854268),(4039553.19006358, 3063551.94787756),(4065568.41552701, 3087947.74365965),]
 pointInAachen3035 = (4061794.7,3094718.4)
-
+pointInAccraEcowasM = (535733.2772457143, -1508102.6643880515)
+pointInWindhoekSadcM = (-5255934.573657837, 3239007.1380184838)
 
 POLY = "POLYGON ((10.1 32, 10.9 35.1, 12 36, 14.6 38.1, 13.5 35, 12.9 35.1, 11.1 33, 10.6 32.2, 10.5 30.5, 10.1 32))"
 SUB_POLY1 = "POLYGON ((7 49.7, 7 49.9, 7.4 49.75, 7 49.7))"
@@ -88,6 +89,7 @@ def result(s): return join(dirname(__file__), RESULT, s)
 
 CLC_RASTER_PATH = source("clc-aachen_clipped.tif")
 CLC_FLIPCHECK_PATH = source("clc-aachen_clipped-unflipped.tif")
+RASTER_GDAL_244 = source("raster_gdal_244.tif")
 
 SINGLE_HILL_PATH = source("elevation_singleHill.tif")
 ELEVATION_PATH = source("elevation.tif")
diff --git a/geokit/test/test_02_srs.py b/geokit/test/test_02_srs.py
index 308144d..4955f66 100644
--- a/geokit/test/test_02_srs.py
+++ b/geokit/test/test_02_srs.py
@@ -1,45 +1,82 @@
-from .helpers import MASK_DATA, np, pointInAachen3035, pointsInAachen4326, osr
-from geokit import srs
+from .helpers import MASK_DATA, np, pointInAachen3035, pointsInAachen4326, pointInAccraEcowasM, pointInWindhoekSadcM, AACHEN_SHAPE_PATH, osr
+from geokit import srs, vector
+import pytest
 
+@pytest.mark.parametrize("points, fromSRS, toSRS, expected", 
+                         [
+                            (
+                                pointsInAachen4326, 
+                                "latlon", 
+                                "europe_laea", 
+                                [(4042131.1581, 3052769.5385, 0.0), 
+                                (4039553.1900, 3063551.9478, 0.0), 
+                                (4065568.415, 3087947.743, 0.0)]
+                            ),
+                             
+                            (   
+                                pointInAccraEcowasM, 
+                                "ecowas_laea", 
+                                "latlon", 
+                                (5.562, -0.1389, 0.0)
+                            ), 
+                            
+                            (
+                                pointInWindhoekSadcM, 
+                                "sadc_laea", 
+                                "latlon", 
+                                (-22.389, 17.398, 0.0)
+                            )
+                          ]
+                        )
 
-def test_xyTransform():
-
-    # test single point
-    p1 = srs.xyTransform(
-        pointInAachen3035, fromSRS='europe_m', toSRS='latlon')[0]
-    real = (6.313298792067333, 50.905105969570265)
-    assert np.isclose(p1[0], real[0], 1e-6)
-    assert np.isclose(p1[1], real[1], 1e-6)
-
-    # test multiple points
-    p2 = srs.xyTransform(pointsInAachen4326,
-                         fromSRS='latlon', toSRS='europe_m')
-    real = [(4042131.1581, 3052769.5385), (4039553.1900,
-                                           3063551.9478), (4065568.415, 3087947.743)]
-
-    assert np.isclose(p2[0][0], real[0][0], 1e-6)
-    assert np.isclose(p2[0][1], real[0][1], 1e-6)
-    assert np.isclose(p2[1][0], real[1][0], 1e-6)
-    assert np.isclose(p2[1][1], real[1][1], 1e-6)
-    assert np.isclose(p2[2][0], real[2][0], 1e-6)
-    assert np.isclose(p2[2][1], real[2][1], 1e-6)
+def test_xyTransform(points, fromSRS, toSRS, expected):
 
+    if isinstance(points, tuple):
+        # test single point
+        p = srs.xyTransform(
+            points, fromSRS=fromSRS, toSRS=toSRS, outputFormat="raw")[0]
+        assert np.isclose(p[0], expected[0], 1e-3)
+        assert np.isclose(p[1], expected[1], 1e-3)
+    
+    else:
+        
+        # test multiple points
+        p = srs.xyTransform(
+            points, fromSRS=fromSRS, toSRS=toSRS, outputFormat="raw")
+        
+        assert np.isclose(p[0][0], expected[0][0], 1e-6)
+        assert np.isclose(p[0][1], expected[0][1], 1e-6)
+        assert np.isclose(p[1][0], expected[1][0], 1e-6)
+        assert np.isclose(p[1][1], expected[1][1], 1e-6)
+        assert np.isclose(p[2][0], expected[2][0], 1e-6)
+        assert np.isclose(p[2][1], expected[2][1], 1e-6)
 
 def test_loadSRS():
     # Test creating an osr SRS object
     s1 = srs.loadSRS(srs.EPSG4326)
-
     # Test an EPSG identifier
     s2 = srs.loadSRS(4326)
-
+    # Test an EPSG code
+    s3 = srs.loadSRS("epsg:4326")
     # Are they the same?
     assert s1.IsSame(s2)
+    assert s1.IsSame(s3)
+
+    # test an invalid srs, must raise error
+    with pytest.raises(AssertionError):
+        s3 = srs.loadSRS(1000)
 
 
 def test_centeredLAEA():
+    # first test procedure using lat and lon coordinates
     s1 = srs.centeredLAEA(6.8, 50.0775)
     assert isinstance(s1, osr.SpatialReference)
 
+    # then test procedure using a geom
+    Aachen_geom = vector.extractFeatures(AACHEN_SHAPE_PATH).geom[0]
+    s2 = srs.centeredLAEA(geom=Aachen_geom)
+    assert isinstance(s2, osr.SpatialReference)
+
 def test_tileIndexAt():
     tile = srs.tileIndexAt(6.083, 50.775, zoom=8, srs=srs.EPSG4326)
     assert tile.xi == 132
diff --git a/geokit/test/test_03_geom.py b/geokit/test/test_03_geom.py
index 1429f22..41d40e6 100644
--- a/geokit/test/test_03_geom.py
+++ b/geokit/test/test_03_geom.py
@@ -5,8 +5,6 @@
 import pandas as pd
 
 # box
-
-
 def test_box():
     # fun func
     b1 = geom.box(0, 0, 5, 10, srs=EPSG3035)
@@ -99,11 +97,44 @@ def test_point():
     assert np.isclose(p2.GetY(), y)
     assert p2.GetSpatialReference().IsSame(EPSG3035)
 
+@pytest.mark.parametrize(
+    "points_input, srs, output_length, output_bounds",
+    [
+        (
+            # test input as list of tuples
+            pointsInAachen4326,
+            4326,
+            0.52498095,
+            (6.02141, 6.371634, 50.51939, 50.846025)
+        ),
+        (   
+            # test input as nx2 np.array
+            np.array([[tup[0], tup[1]] for tup in pointsInAachen4326]),
+            4326,
+            0.52498095,
+            (6.02141, 6.371634, 50.51939, 50.846025)
+        ),
+        (   
+            # test input as list of osgeo.ogr.Geometry point objects
+            [geom.point(tup, srs=EPSG4326) for tup in pointsInAachen4326],
+            4326,
+            0.52498095,
+            (6.02141, 6.371634, 50.51939, 50.846025)
+        ),
+    ]
+)
+def test_line(points_input, srs, output_length, output_bounds):
+
+    # test input as list of tuples
+    l = geom.line(points_input, srs=srs)
+    
+    assert l.GetSpatialReference().IsSame(EPSG4326)
+    assert np.isclose(l.Length(), output_length)
+    assert np.isclose(l.GetEnvelope(), output_bounds).all()
 
 @pytest.mark.skip("No test implemented for: geom.empty")
 def test_empty(): assert False
 
-
 def test_convertWKT():
     g1 = geom.convertWKT(POLY, srs=EPSG4326)
     assert np.isclose(g1.Area(), 7.8149999999999995)
@@ -116,7 +147,7 @@ def test_polygonizeMatrix():
                           [0, 1, 1, 1, 0],
                           [0, 1, 0, 1, 0],
                           [0, 1, 1, 1, 0],
-                          [0, 0, 0, 0, 0]], dtype=np.int)
+                          [0, 0, 0, 0, 0]], dtype=int)
 
     g1 = geom.polygonizeMatrix(boxmatrix, shrink=None)
     assert np.isclose(g1.geom[0].Area(), 8.0)  # polygonizeMatrix: simple area
@@ -134,7 +165,7 @@ def test_polygonizeMatrix():
                               [2, 2, 0, 1, 0],
                               [0, 0, 0, 1, 1],
                               [1, 1, 0, 1, 0],
-                              [3, 1, 0, 0, 0]], dtype=np.int)
+                              [3, 1, 0, 0, 0]], dtype=int)
 
     g2 = geom.polygonizeMatrix(complexmatrix, shrink=None)
     assert np.isclose(g2.shape[0], 4)  # polygonizeMatrix: geometry count
@@ -163,7 +194,7 @@ def test_polygonizeMask():
                         [0, 1, 1, 1, 0],
                         [0, 1, 0, 1, 0],
                         [0, 1, 1, 1, 0],
-                        [0, 0, 0, 0, 0]], dtype=np.bool)
+                        [0, 0, 0, 0, 0]], dtype=bool)
 
     g1 = geom.polygonizeMask(boxmask, shrink=None)
     assert np.isclose(g1.Area(), 8.0)  # polygonizeMask: simple area
@@ -178,7 +209,7 @@ def test_polygonizeMask():
                             [1, 1, 0, 1, 0],
                             [0, 0, 0, 1, 1],
                             [1, 1, 0, 1, 0],
-                            [0, 1, 0, 0, 0]], dtype=np.bool)
+                            [0, 1, 0, 0, 0]], dtype=bool)
 
     g2 = geom.polygonizeMask(complexmask, shrink=None, flat=False)
     assert np.isclose(len(g2), 3)  # polygonizeMask: geometry count
@@ -229,7 +260,7 @@ def test_transform():
                             [1, 1, 0, 1, 0],
                             [0, 0, 0, 1, 1],
                             [1, 1, 0, 1, 0],
-                            [0, 1, 0, 0, 0]], dtype=np.bool)
+                            [0, 1, 0, 0, 0]], dtype=bool)
 
     polygons = geom.polygonizeMask(complexmask, bounds=(
         6, 45, 11, 50), flat=False, srs=EPSG4326, shrink=None)
@@ -270,6 +301,7 @@ def test_drawGeoms():
     # Draw single polygon
     r = geom.drawGeoms(SUB_GEOM)
     plt.savefig(result("drawGeoms-1.png"), dpi=100)
+    assert SUB_GEOM.GetSpatialReference().IsSame(EPSG4326)
 
     # Draw single linestring
     r = geom.drawGeoms(SUB_GEOM.Boundary())
@@ -286,12 +318,16 @@ def test_drawGeoms():
     # Change projection systems
     r = geom.drawGeoms(SUB_GEOMS, fc='r', srs=3035)
     plt.savefig(result("drawGeoms-5.png"), dpi=100)
-
-    # Draw from a dataframe
+    assert SUB_GEOMS[0].GetSpatialReference().IsSame(EPSG4326)
+    
+    # Draw from a dataframe, once without and once with SRS adaptation
     df = pd.DataFrame(dict(geom=SUB_GEOMS, hats=[1, 2, 3]))
 
-    r = geom.drawGeoms(df, srs=3035)
+    r = geom.drawGeoms(df)
     plt.savefig(result("drawGeoms-6.png"), dpi=100)
+    r = geom.drawGeoms(df, srs=3035)
+    plt.savefig(result("drawGeoms-6b.png"), dpi=100)
+    assert df.geom[0].GetSpatialReference().IsSame(EPSG4326)
 
     # Set individual mpl args
     df["MPL:hatch"] = ["//", "+", None]
@@ -303,3 +339,26 @@ def test_drawGeoms():
     plt.savefig(result("drawGeoms-8.png"), dpi=100)
 
     assert True
+
+def test_shift():
+    # test point, no srs
+    assert geom.shift(geom=geom.point((0,1)), lonShift=5).Equals(geom.point((5,1)))
+
+    # test line, epsg 3035
+    l1=geom.line([(0,0), (1,1)], srs=3035)
+    l1_check=geom.line([(0,-10), (1,-9)], srs=3035)
+    assert geom.shift(l1, latShift=-10).Equals(l1_check)
+
+    # test polygon, srs 4326
+    b1 = geom.box(-170, 60, -160, 70, srs=4326)
+    b1_check=geom.box(10,-30,20,-20, srs=4326)
+    assert geom.shift(geom=b1, lonShift=180, latShift=-90).Equals(b1_check)
+
+    # test multipolygon
+    b2 = geom.box(-120, 10, -100, 30, srs=4326)
+    b2_check=geom.box(60,-80,80,-60, srs=4326)
+    b_multi=b1.Union(b2)
+    b_multi_check=b1_check.Union(b2_check)
+    assert geom.shift(geom=b_multi, lonShift=180, latShift=-90).Equals(b_multi_check)
+
+
diff --git a/geokit/test/test_04_raster.py b/geokit/test/test_04_raster.py
index 035b7c1..82ccc35 100644
--- a/geokit/test/test_04_raster.py
+++ b/geokit/test/test_04_raster.py
@@ -1,11 +1,11 @@
 from .helpers import *  # NUMPY_FLOAT_ARRAY, CLC_RASTER_PATH, result
 from geokit import raster, geom, util
 from osgeo import gdal
+import os
 import pytest
 
 # gdalType
 
-
 def test_gdalType():
     assert raster.gdalType(bool) == "GDT_Byte"
     assert raster.gdalType("InT64") == "GDT_Int32"
@@ -30,7 +30,6 @@ def test_rasterInfo():
 
 # createRaster
 
-
 def test_createRaster():
     ######################
     # run and check funcs
@@ -82,7 +81,6 @@ def test_createRaster():
 
 # Get values directly from a raster
 
-
 def test_extractValues():
     points = [(6.06590, 50.51939), (6.02141, 50.61491), (6.371634, 50.846025)]
     realValue = [24, 3, 23]
@@ -292,7 +290,6 @@ def test_loadRaster():
     s3 = util.isRaster(raster.loadRaster(CLC_RASTER_PATH))
     assert s3 == True
 
-
 def test_createRasterLike():
     source = gdal.Open(CLC_RASTER_PATH)
     sourceInfo = raster.rasterInfo(source)
@@ -313,14 +310,24 @@ def test_createRasterLike():
     # From rasterInfo, no output
     newRaster = raster.createRasterLike(sourceInfo, data=data*4)
     newdata = raster.extractMatrix(newRaster)
-    assert np.isclose(data, newdata/4).all()
+    assert np.isclose(data, newdata/4).all()   
 
+def test_saveRasterAsTif():
+    
+    source = gdal.Open(CLC_RASTER_PATH)
+    data = raster.extractMatrix(source)
+    
+    # Saving from osgeo.gdal.Dataset, with output
+    raster.saveRasterAsTif(source,
+                           output=result("saveRasterAsTif.tif"))
+    
+    newdata = raster.extractMatrix(result("saveRasterAsTif.tif"))
+    assert np.isclose(data, newdata).all()
 
 def test_rasterStats():
     result = raster.rasterStats(CLC_RASTER_PATH, AACHEN_SHAPE_PATH)
     assert np.isclose(result.mean, 15.711518944519621)
 
-
 def test_indexToCoord():
     rasterSource = gdal.Open(CLC_RASTER_PATH)
 
@@ -383,6 +390,16 @@ def test_polygonizeRaster():
     assert np.isclose(geoms.geom[is3].apply(lambda x: x.Area()).sum(),
                       120529999.18190208)  # geom area
 
+    geoms = raster.polygonizeRaster(RASTER_GDAL_244, flat=True)
+    assert np.equal(geoms.shape[0], 2) # geom count
+
+    # geom areas
+    assert np.isclose(geoms.loc[0, "geom"].Area(),  949049962.3788521)
+    assert np.isclose(geoms.loc[1, "geom"].Area(),  5584949959.933687)
+    assert np.isclose(geoms.geom.apply(lambda x: x.Area()).sum(), 6533999922.312539)
+
+    # geom validity
+    assert geoms.geom.map(lambda g: g.IsValid()).all()
 
 def test_contours():
     geoms = raster.contours(AACHEN_ELIGIBILITY_RASTER, contourEdges=[0.5])
@@ -442,3 +459,99 @@ def test_warp():
                     output=result("warp6.tif"))
     v6 = raster.extractMatrix(d)
     assert np.isclose(v1, v6).all()
+
+@pytest.fixture()
+def sieve_ds():
+    
+    data_arr = np.array([[0,0,1,1,1,0,0],
+                         [1,0,0,0,0,0,1],
+                         [1,0,0,1,1,1,0],
+                         [0,0,0,1,0,1,0],
+                         [1,0,0,1,1,1,1],])
+    
+    
+    data_raster = raster.createRaster(
+                                    bounds=(0,0,7,5),
+                                    pixelHeight=1,
+                                    pixelWidth=1,
+                                    srs=3035,
+                                    data=data_arr,
+                                )
+
+    return data_raster
+
+@pytest.fixture()
+def sieve_mask():
+    mask_arr = np.array([[1, 1, 1, 1, 1, 1, 1],
+                            [1, 1, 1, 1, 1, 1, 1],
+                            [1, 1, 1, 1, 1, 1, 1],
+                            [1, 1, 1, 1, 0, 1, 1],
+                            [1, 1, 1, 1, 1, 1, 1]])
+        
+    mask_raster = raster.createRaster(
+                                        bounds=(0,0,7,5),
+                                        pixelHeight=1,
+                                        pixelWidth=1,
+                                        srs=3035,
+                                        data=mask_arr,
+                                        noData=0,
+                                    )
+    return mask_raster
+    
+@pytest.mark.parametrize("source, threshold, connectedness, mask, expected_output",
+                         
+                         [
+                            (   
+                                "sieve_ds",
+                                2,
+                                4,
+                                "none",
+                                np.array([[0, 0, 1, 1, 1, 0, 0],
+                                         [1, 0, 0, 0, 0, 0, 0],
+                                         [1, 0, 0, 1, 1, 1, 0],
+                                         [0, 0, 0, 1, 1, 1, 0],
+                                         [0, 0, 0, 1, 1, 1, 1]],)
+                                
+                            ),
+                            
+                            (   
+                                "sieve_ds",
+                                2,
+                                8,
+                                "none",
+                                np.array([[0, 0, 1, 1, 1, 0, 0],
+                                        [1, 0, 0, 0, 0, 0, 1],
+                                        [1, 0, 0, 1, 1, 1, 0],
+                                        [0, 0, 0, 1, 1, 1, 0],
+                                        [0, 0, 0, 1, 1, 1, 1]],)
+                            ),
+                            
+                            (   
+                                "sieve_ds",
+                                2,
+                                8,
+                                "sieve_mask",
+                                np.array([[0, 0, 1, 1, 1, 0, 0],
+                                          [1, 0, 0, 0, 0, 0, 1],
+                                          [1, 0, 0, 1, 1, 1, 0],
+                                          [0, 0, 0, 1, 0, 1, 0],
+                                          [0, 0, 0, 1, 1, 1, 1]],)
+                            )
+                            
+                            
+                        ]
+)
+def test_sieve(source, threshold, connectedness, mask, expected_output, request):
+    
+    if mask == "none":
+        arr_out = raster.extractMatrix(raster.sieve(source=request.getfixturevalue(source), 
+                                                    threshold=threshold, 
+                                                    connectedness=connectedness, 
+                                                    mask=mask))
+    else:
+        arr_out = raster.extractMatrix(raster.sieve(source=request.getfixturevalue(source), 
+                                                    threshold=threshold, 
+                                                    connectedness=connectedness, 
+                                                    mask=request.getfixturevalue(mask)))
+
+    assert (arr_out == expected_output).all()
diff --git a/geokit/test/test_05_vector.py b/geokit/test/test_05_vector.py
index d1342d5..e54f59a 100644
--- a/geokit/test/test_05_vector.py
+++ b/geokit/test/test_05_vector.py
@@ -1,6 +1,8 @@
 from .helpers import *
 from geokit import vector, raster, geom, util
-
+from os.path import join, dirname
+import pytest
+from functools import reduce
 # ogrType
 
 
@@ -99,10 +101,21 @@ def test_extractFeature():
     else:
         assert False
 
+def test_extractAndClipFeatures():
+    multiFeatures = vector.extractFeatures(MULTI_FTR_SHAPE_PATH)
+    multiFeatures['testAttr']=100
+    box = geom.box((6.6, 49.0, 7.6, 50.0), srs=4326)
+
+    clipped = vector.extractAndClipFeatures(source=multiFeatures, geom=box, where="id < 4", scaleAttrs='testAttr')
+
+    assert len(clipped)==2
+    assert all(np.isclose(clipped.areaShare.values, np.array([0.827164, 1.0])))
+    assert all(np.isclose(clipped.testAttr.values, np.array([82.716413, 100.0])))
+
 # Create shape file
 
 
-def test_createVector():
+def test_createVector(tmpdir):
     # Setup
     out1 = result("util_shape1.shp")
     out2 = result("util_shape2.shp")
@@ -111,8 +124,16 @@ def test_createVector():
     ######################
     # run and check
 
+    # assure that check for directory works as expected
+    with pytest.raises(FileNotFoundError):
+        vector.createVector(
+            geoms=POLY, 
+            output=join(dirname(__file__), "nonexisting_folder", "util_shape1.shp"), 
+            srs=EPSG4326, 
+            overwrite=True)
+
     # Single WKT feature, no attributes
-    vector.createVector(POLY, out1, srs=EPSG4326, overwrite=True)
+    vector.createVector(geoms=POLY, output=out1, srs=EPSG4326, overwrite=True)
 
     ds = ogr.Open(out1)
     ly = ds.GetLayer()
@@ -159,7 +180,48 @@ def test_createVector():
     for i in range(len(POINT_SET)):
         ftr = ly.GetFeature(i)
         assert ftr.GetGeometryRef() != ogr.CreateGeometryFromWkt(POINT_SET[i])
-
+        
+    # Multiple points, save to shapefile 
+    output_shp = tmpdir.mkdir("temp_1").join("mpoints_temp.shp").__str__()
+    
+    # create a vector in memory
+    vec_shp = vector.extractFeatures(vector.createVector(POINT_SET, srs=EPSG4326))
+    
+    # create a vector on disk
+    vector.createVector(POINT_SET, output=output_shp, srs=EPSG4326)
+    
+    vec_shp_disk = vector.extractFeatures(output_shp)
+    
+    assert len(vec_shp) == len(vec_shp_disk)
+    test_equal_shp = util.compare_geoms(vec_shp["geom"].to_list(), vec_shp_disk["geom"].to_list())
+    
+    assert all(test_equal_shp) 
+    assert all([g.IsValid() for g in vec_shp_disk["geom"]]) 
+    
+    # Multiple point layers, save to geopackage 
+    output_gpkg = tmpdir.mkdir("temp_2").join("mpoints_temp.gpkg").__str__()
+
+    # create new vector layer in memory
+    vec_gpkg_lyr_1 = vector.extractFeatures(vector.createVector(POINT_SET, layerName="layer_1", srs=EPSG4326))
+    
+    # create new geopackage on disk
+    vector.createVector(POINT_SET, output=output_gpkg, layerName="layer_1", srs=EPSG4326)
+    
+    # append new layer to existing geopackage
+    vector.createVector(POINT_SET, output=output_gpkg, layerName="layer_2", srs=EPSG4326, overwrite=False)
+    
+    layers = vector.listLayers(output_gpkg)
+    assert len(layers) == 2
+    assert "layer_1" in layers
+    assert "layer_2" in layers
+    
+    vec_gpkg_lyr_1_disk = vector.extractFeatures(output_gpkg, layerName="layer_1")
+    
+    assert len(vec_gpkg_lyr_1) == len(vec_gpkg_lyr_1_disk)
+    test_equal_gpkg = util.compare_geoms(vec_shp["geom"].to_list(), vec_shp_disk["geom"].to_list())
+    assert all(test_equal_gpkg) 
+
+    assert all([g.IsValid() for g in vec_gpkg_lyr_1_disk["geom"]]) 
 
 def test_mutateVector():
     # Setup
diff --git a/geokit/test/test_07_locations.py b/geokit/test/test_07_locations.py
index 30bb0f6..01f7c15 100644
--- a/geokit/test/test_07_locations.py
+++ b/geokit/test/test_07_locations.py
@@ -270,18 +270,18 @@ def test_LocationSet_splitKMeans():
 
     sublocs = list(sublocsGen)
 
-    assert sublocs[0].count == 3
-    assert sublocs[0][0] == (2, -1)
-    assert sublocs[0][1] == (2, -1.5)
-    assert sublocs[0][2] == (2, -1.25)
-
-    assert sublocs[1].count == 2
-    assert sublocs[1][0] == (-1, -1)
-    assert sublocs[1][1] == (-1, -1.5)
+    assert sublocs[0].count == 2
+    assert sublocs[0][0] == (2, 1)
+    assert sublocs[0][1] == (2, 1.5)
+    
+    assert sublocs[1].count == 3
+    assert sublocs[1][0] == (2, -1)
+    assert sublocs[1][1] == (2, -1.5)
+    assert sublocs[1][2] == (2, -1.25)
 
     assert sublocs[2].count == 2
-    assert sublocs[2][0] == (2, 1)
-    assert sublocs[2][1] == (2, 1.5)
+    assert sublocs[2][0] == (-1, -1)
+    assert sublocs[2][1] == (-1, -1.5)
 
 
 def test_LocationSet_bisect():
diff --git a/geokit/test/test_08_regionmask.py b/geokit/test/test_08_regionmask.py
index c5cbd19..012ca8c 100644
--- a/geokit/test/test_08_regionmask.py
+++ b/geokit/test/test_08_regionmask.py
@@ -73,7 +73,7 @@ def test_RegionMask_fromGeom():
     # fromGeom with wkt
     rm1 = RegionMask.fromGeom(geom.convertWKT(
         POLY, srs='latlon'), pixelRes=1000)
-    assert (rm1.extent.xXyY == (4329000.0, 4771000.0, 835000.0, 1682000.0))
+    assert (rm1.extent.xXyY == (4330000.0, 4728000.0, 835000.0, 1682000.0))
     assert (rm1.extent.srs.IsSame(EPSG3035))
     assert rm1.mask.sum() == 79274
 
@@ -262,9 +262,9 @@ def test_RegionMask_applyMask():
                       * 3).reshape((rm.mask.shape[0] * 3, rm.mask.shape[1] * 3))
 
     # test applying
-    data1 = rm.applyMask(data1)
-    assert data1.sum() == 39296070
-    assert np.isclose(data1.std(), 3020.0893432)
+    data1 = rm.applyMask(data1, noData=np.nan)
+    assert np.nansum(data1) == 39296070
+    assert np.isclose(np.nanstd(data1), 2501.063544487093)
 
     data2 = rm.applyMask(data2.astype('int64'))
     assert data2.sum() == 3183264630
@@ -441,7 +441,7 @@ def test_RegionMask_warp():
     assert warped_1.dtype == np.uint8
     assert warped_1.shape == rm_3035.mask.shape
     assert np.isclose(warped_1.sum(), 88128)
-    assert np.isclose(warped_1.std(), 9.52214123991)
+    assert np.isclose(warped_1.std(), 9.400516136589552)
     #rm_3035.createRaster(data=warped_1, output=result("regionMask_warp_1.tif"), overwrite=True)
 
     # basic warp Raster (FLIP CHECK!)
@@ -450,7 +450,7 @@ def test_RegionMask_warp():
     assert warped_1f.dtype == np.uint8
     assert warped_1f.shape == rm_3035.mask.shape
     assert np.isclose(warped_1f.sum(), 88128)
-    assert np.isclose(warped_1f.std(), 9.52214123991)
+    assert np.isclose(warped_1f.std(), 9.400516136589552)
     #rm_3035.createRaster(data=warped_1f, output=result("regionMask_warp_1f.tif"), overwrite=True)
 
     assert (warped_1 == warped_1f).all()
diff --git a/geokit/util.py b/geokit/util.py
index d65202b..d6ca948 100644
--- a/geokit/util.py
+++ b/geokit/util.py
@@ -4,4 +4,4 @@
 # 							   isVector, isRaster, scaleMatrix, KernelProcessor, drawImage)
 
 from geokit.core.util import (
-    GeoKitError, isVector, isRaster, scaleMatrix, KernelProcessor, drawImage)
+    GeoKitError, isVector, isRaster, scaleMatrix, KernelProcessor, drawImage, compare_geoms)
diff --git a/geokit/vector.py b/geokit/vector.py
index 95be413..6fd343b 100644
--- a/geokit/vector.py
+++ b/geokit/vector.py
@@ -5,7 +5,9 @@
                                     extractFeatures, 
                                     extractFeature, 
                                     extractAsDataFrame,
+                                    extractAndClipFeatures, 
                                     createVector, 
                                     createGeoJson,
                                     mutateVector, 
-                                    rasterize )
\ No newline at end of file
+                                    rasterize,
+                                    listLayers)
\ No newline at end of file
diff --git a/requirements-dev.yml b/requirements-dev.yml
index e33d7fe..95fb88d 100644
--- a/requirements-dev.yml
+++ b/requirements-dev.yml
@@ -17,7 +17,6 @@ dependencies:
   - numpy
   - scipy
   - scikit-learn
-  - gdal>2.2.0,<=3.2.1
-  - pip:
-      - smopy
-      - -e .
+  - gdal>=2.4.0, 3.4.*
+  - smopy
+
diff --git a/requirements.yml b/requirements.yml
index 28a75f2..cc0b44d 100644
--- a/requirements.yml
+++ b/requirements.yml
@@ -17,7 +17,6 @@ dependencies:
   - numpy
   - scipy
   - scikit-learn
-  - gdal>2.2.0,<=3.2.1
-  - pip:
-      - smopy
-      - .
+  - gdal>=2.4.0, 3.4.*
+  - smopy
+
diff --git a/setup.py b/setup.py
index b5977e0..948ac2d 100644
--- a/setup.py
+++ b/setup.py
@@ -2,13 +2,13 @@
 
 setup(
     name='geokit',
-    version='1.3.0',
+    version='1.4.0',
     author='GeoKit Developer Team',
     url='https://github.com/FZJ-IEK3-VSA/geokit',
     packages=find_packages(),
     include_package_data=True,
     install_requires=[
-        "gdal>2.2.0,<=3.2.1",
+        "gdal>=2.4.0, ==3.4.*",
         "numpy",
         "descartes",
         "pandas",