diff --git a/lecture_material/15-regex_2/regex_2.ipynb b/lecture_material/15-regex_2/regex_2.ipynb
index e490b747b54eb619e2d78ac8d9cc1b98cf91e87b..623d72461d7b75a77c129d08ff070d364c0d74bb 100644
--- a/lecture_material/15-regex_2/regex_2.ipynb
+++ b/lecture_material/15-regex_2/regex_2.ipynb
@@ -1630,7 +1630,7 @@
    "id": "42758038",
    "metadata": {},
    "source": [
-    "#### Find all commit autho names."
+    "#### Find all commit authors names."
    ]
   },
   {
diff --git a/lecture_material/15-regex_2/regex_2_lec_001.ipynb b/lecture_material/15-regex_2/regex_2_lec_001.ipynb
index 8595cb67a49eb290c5b6d11619bd814ab775ee5e..db06eb7fa32b923d38c746a1408a37396c9ee014 100644
--- a/lecture_material/15-regex_2/regex_2_lec_001.ipynb
+++ b/lecture_material/15-regex_2/regex_2_lec_001.ipynb
@@ -660,7 +660,7 @@
    "id": "ecfc71e6",
    "metadata": {},
    "source": [
-    "#### Find all commit autho names."
+    "#### Find all commit authors names."
    ]
   },
   {
diff --git a/lecture_material/15-regex_2/regex_2_lec_002.ipynb b/lecture_material/15-regex_2/regex_2_lec_002.ipynb
index 8595cb67a49eb290c5b6d11619bd814ab775ee5e..db06eb7fa32b923d38c746a1408a37396c9ee014 100644
--- a/lecture_material/15-regex_2/regex_2_lec_002.ipynb
+++ b/lecture_material/15-regex_2/regex_2_lec_002.ipynb
@@ -660,7 +660,7 @@
    "id": "ecfc71e6",
    "metadata": {},
    "source": [
-    "#### Find all commit autho names."
+    "#### Find all commit authors names."
    ]
   },
   {
diff --git a/lecture_material/16-viz-1/Target_plot.png b/lecture_material/16-viz-1/Target_plot.png
new file mode 100644
index 0000000000000000000000000000000000000000..d6e38cd4c818af2eaa0afb84aa9b58c27b4e803c
Binary files /dev/null and b/lecture_material/16-viz-1/Target_plot.png differ
diff --git a/lecture_material/16-viz-1/vis_1.ipynb b/lecture_material/16-viz-1/vis_1.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..8d286004567d1246fc2598e5812ee27a3dfd1489
--- /dev/null
+++ b/lecture_material/16-viz-1/vis_1.ipynb
@@ -0,0 +1,883 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "471a762b",
+   "metadata": {},
+   "source": [
+    "# Visualization 1\n",
+    "\n",
+    "- Advanced visualization, example: https://trailsofwind.figures.cc/\n",
+    "- Custom visualization steps:\n",
+    "    - draw \"patches\" (shapes) on the screen (what):\n",
+    "        - lines\n",
+    "        - polygons\n",
+    "        - circle\n",
+    "        - text\n",
+    "    - location of the \"patches\" on the screen (where):\n",
+    "        - X & Y co-ordinate\n",
+    "        - \"Coordinate Reference System (CRS)\":\n",
+    "            - takes some X & Y and maps it on to actual space on screen\n",
+    "            - several CRS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5df39a4b-d55b-4ba0-ab78-bd06fac8047e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import statements\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import pandas as pd\n",
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fcd95b4",
+   "metadata": {},
+   "source": [
+    "### Review: drawing a figure\n",
+    "\n",
+    "- `fig, ax = plt.subplots(figsize=(<width>, <height>))`\n",
+    "\n",
+    "### Drawing a circle\n",
+    "\n",
+    "- Type `plt.` and then tab to see a list of `patches`.\n",
+    "- `plt.Circle((<X>, <Y>), <RADIUS>)`\n",
+    "- To see the cicle, we need to invoke either:\n",
+    "    - `ax.add_patch(<circle object>)`\n",
+    "    - `ax.add_artist(<circle object>)`\n",
+    "    - this invocation needs to be in the same cell as the one that draws the figure\n",
+    "    - Is there a difference between `ax.add_patch` and `ax.add_artist`?\n",
+    "        - `ax.autoscale_view()`: automatically chose limits for the axes; typically works better with `ax.add_patch(...)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "152cd4b0-7334-491f-841d-c0bfe3fce3c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.patches.Circle at 0x7f28b7358640>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6, 4))\n",
+    "# Let's draw a circle at (0.5, 0.5) of radius 0.3\n",
+    "c = plt.Circle((0.5, 0.5), 0.3)\n",
+    "# Add the circle to the AxesSubplot\n",
+    "ax.add_artist(c)\n",
+    "# ax.autoscale_view()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e6b1fa0f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6, 4))\n",
+    "# Let's draw a circle at (0.5, 0.5) of radius 0.3\n",
+    "c = plt.Circle((0.5, 0.5), 0.3)\n",
+    "# Add the circle to the AxesSubplot\n",
+    "ax.add_patch(c)\n",
+    "ax.autoscale_view()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9918baa3",
+   "metadata": {},
+   "source": [
+    "Type and MRO of circle object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8a714acd-e33d-4fa0-9fdd-de8438e94418",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "matplotlib.patches.Circle"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "392e6b98",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(matplotlib.patches.Circle,\n",
+       " matplotlib.patches.Ellipse,\n",
+       " matplotlib.patches.Patch,\n",
+       " matplotlib.artist.Artist,\n",
+       " object)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(c).__mro__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "418cedd8",
+   "metadata": {},
+   "source": [
+    "### Making the circle circular\n",
+    "\n",
+    "1. Have same figure width and height\n",
+    "2. Aspect ratio\n",
+    "3. Transformers: let's us pick a Coordinate Reference System (CRS)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c6446c47",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Option 1: Have same figure width and height\n",
+    "fig, ax = plt.subplots(figsize=(4, 4))\n",
+    "c = plt.Circle((0.5, 0.5), 0.3)\n",
+    "ax.add_patch(c)\n",
+    "ax.autoscale_view()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4bd1648d-55ce-4156-b94e-6b9a10db4da7",
+   "metadata": {},
+   "source": [
+    "### Aspect Ratio\n",
+    "\n",
+    "- `ax.set_aspect(<Y DIM>)`: how much space y axes takes with respect to x axes space"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "06b32774-26a2-4627-b363-f79eb2838d07",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6, 4))\n",
+    "c = plt.Circle((0.5, 0.5), 0.3)\n",
+    "ax.add_artist(c)\n",
+    "# Set aspect for y-axis to 1\n",
+    "ax.set_aspect(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1699cae2",
+   "metadata": {},
+   "source": [
+    "What if we want x and y axes to have the same aspect ratio? That is we care more about the figure being square than about the circle being circular."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7fa11875-34ba-4550-8e8b-9a9f92e58a9a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6,4))\n",
+    "# Set x axis limit to (0, 3)\n",
+    "ax.set_xlim(0, 3)\n",
+    "c = plt.Circle((0.5, 0.5), 0.3)\n",
+    "ax.add_artist(c)\n",
+    "# Set aspect for y-axis to 3\n",
+    "ax.set_aspect(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2429f83-1603-4aaf-b767-a60969fc20d7",
+   "metadata": {},
+   "source": [
+    "### Transformers: let us pick a Coordinate Reference System (CRS)\n",
+    "\n",
+    "- Documentation: https://matplotlib.org/stable/tutorials/advanced/transforms_tutorial.html\n",
+    "- `ax.transData`: default\n",
+    "- `ax.transAxes` and `fig.transFigure`:\n",
+    "    - (0, 0) is bottom left\n",
+    "    - (1, 1) is top right\n",
+    "        - these are true immaterial of the axes limits\n",
+    "- `None` or `IdentityTransform()`: disabling CRS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aae7da52",
+   "metadata": {},
+   "source": [
+    "### Review:\n",
+    "- `fig, ax = plt.subplots(figsize=(<width>, <height>), ncols=<N>, nrows=<N>)`:\n",
+    "    - ncols: split into vertical sub plots\n",
+    "    - nrows: split into horizontal sub plots\n",
+    "- `ax.set_xlim(<lower limit>, <upper limit>)`: set x-axis limits\n",
+    "- `ax.set_ylim(<lower limit>, <upper limit>)`: set y-axis limits\n",
+    "\n",
+    "### `ax.transData`\n",
+    "- `transform` parameter in \"patch\" creation function let's us specify the CRS\n",
+    "- `color` parameter controls the color of the \"patch\"\n",
+    "- `edgecolor` parameter controls outer border color of the \"patch\"\n",
+    "- `linewidth` parameter controls the size of the border of the \"patch\"\n",
+    "- `facecolor` parameter controls the filled in color of the \"patch\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "5adb1223-0fc4-422c-95ef-1446ad811940",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.patches.Circle at 0x7f28b4d88d90>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a plot with two vertical subplots\n",
+    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(6, 4))\n",
+    "# Set right subplot x-axis limit from 0 to 3\n",
+    "ax2.set_xlim(0, 3)\n",
+    "\n",
+    "# Left subplot: plot Circle at (0.5, 0.5) with radius 0.2\n",
+    "# Specify CRS as ax1.transData (tranform parameter)\n",
+    "c = plt.Circle((0.5, 0.5), 0.2, transform=ax1.transData)\n",
+    "ax1.add_artist(c)\n",
+    "\n",
+    "# Right subplot: plot Circle at (0.5, 0.5) with radius 0.2\n",
+    "# default: transform=ax2.transData\n",
+    "c = plt.Circle((0.5, 0.5), 0.2) \n",
+    "ax2.add_artist(c)\n",
+    "# Observe that we get a different circle\n",
+    "\n",
+    "# Transform based on ax1, but crop based on ax2\n",
+    "# Left subplot: plot Circle at (1, 1) with radius 0.3 and crop using ax2\n",
+    "c = plt.Circle((1, 1), 0.3, transform=ax1.transData, color=\"lightblue\") # where to position the shape \n",
+    "ax2.add_artist(c)  # how to crop the shape\n",
+    "\n",
+    "# Right subplot: plot Circle at (1, 1) with radius 0.3 and crop using ax1\n",
+    "c = plt.Circle((1, 1), 0.3, transform=ax1.transData) # where to position the shape\n",
+    "ax1.add_artist(c)  # how to crop the shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ffc89cd",
+   "metadata": {},
+   "source": [
+    "### `ax.transAxes` and `fig.transFigure`\n",
+    "\n",
+    "- (0, 0) is bottom left\n",
+    "- (1, 1) is top right\n",
+    "    - these are true immaterial of the axes limits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "38aa99c6-039a-468e-9cb1-b1a3d9b36a80",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.patches.Circle at 0x7f28b4ccfeb0>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(6, 4))\n",
+    "ax2.set_xlim(0, 3)\n",
+    "\n",
+    "# Left subplot\n",
+    "c = plt.Circle((0.5, 0.5), 0.2, transform=ax1.transAxes)\n",
+    "ax1.add_artist(c)\n",
+    "\n",
+    "# Right subplot\n",
+    "c = plt.Circle((0.5, 0.5), 0.2, transform=ax2.transAxes)\n",
+    "ax2.add_artist(c)\n",
+    "\n",
+    "# whole figure\n",
+    "# edgecolor=\"red\", facecolor=\"none\", linewidth=3\n",
+    "c = plt.Circle((0.5, 0.5), 0.2, transform=fig.transFigure, edgecolor=\"red\", facecolor=\"none\", linewidth=3)\n",
+    "fig.add_artist(c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bfa71840",
+   "metadata": {},
+   "source": [
+    "### No CRS (raw pixel coordinates)\n",
+    "\n",
+    "- `fig.dpi`: dots (aka pixesl) per inch\n",
+    "- increasing dpi makes the figure have higher resolution (helpful when you want to print a large size)\n",
+    "- Review: \n",
+    "    - `plt.tight_layout()`: avoid unncessary cropping of the figure --- always needed for No CRS cases\n",
+    "    - `fig.savefig(<relative path.png>)`: to save a local copy of the image\n",
+    "    \n",
+    "- Jupyter command to avoid cropping:\n",
+    "    - `%config InlineBackend.print_figure_kwargs={'bbox_inches': None}`\n",
+    "        - bbox_inches stands for bounding box"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "4c571b0f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Jupyter commands begin with %\n",
+    "%config InlineBackend.print_figure_kwargs={'bbox_inches': None}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e09ef243-ba52-4b70-a980-6ff4735f0fc2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100.0\n",
+      "300.0 200.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(6, 4))\n",
+    "ax2.set_xlim(0, 3)\n",
+    "\n",
+    "# What is the dpi?\n",
+    "print(fig.dpi)   # dots (aka pixel) per inch\n",
+    "\n",
+    "# Calculate total width and height of the figure using dpi and dimensions\n",
+    "width = 6 * fig.dpi\n",
+    "height = 4 * fig.dpi\n",
+    "\n",
+    "# Calculate (x, y) in the middle of the plot\n",
+    "x = width / 2\n",
+    "y = height / 2\n",
+    "print(x, y)\n",
+    "\n",
+    "# Make sure to invoke plt.tight_layout()\n",
+    "# matplotlib does the cropping better than Jupyter\n",
+    "#plt.tight_layout() \n",
+    "\n",
+    "# Draw a circle at (x, y) with radius 30\n",
+    "# Make sure to set transform=None\n",
+    "c = plt.Circle((x, y), 30, transform=None)\n",
+    "fig.add_artist(c)\n",
+    "# Save the figure to temp.png\n",
+    "fig.savefig(\"temp.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78040b6e-0446-4aab-8e0b-feb4f2646142",
+   "metadata": {},
+   "source": [
+    "### Mix and match\n",
+    "\n",
+    "- `ax.transData.transform((x, y))`: converts axes / data coords into raw coordinates\n",
+    "- How to draw an arrow:\n",
+    "    `matplotlib.patches.FancyArrowPatch((<x1>, <y1>), (<x2>, (<y2>)), transform=None, arrowstyle=<STYLE>)`\n",
+    "    - arrowstyle=\"simple,head_width=10,head_length=10\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9ef4efdf-ce83-4096-a411-fa4ae968514e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "382.2164351851851 209.11111111111111\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.patches.FancyArrowPatch at 0x7f28b4b592e0>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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wAMBGG/Ye175jeXbHqFKv1+ACCZSFggUANlqz/Ue7I1S5T/ccVX5hkd0xAK+iYAGAjbYfPml3hCpX7LK084ccu2MAXkXBAgCbFBa59E3mKbtjeMV2bgKNWoaCBQA22Z2Zo8LimnXtq7Js/56ChdqFggUANqlNR3Vq09cKSBQsALDNjlpUOr47eooXupchPz9fCxYsUGpqqnJzc+2OA0Pq2B0AAGqrrJwCuyN4jcuSjuUWqlVTfu383q5du3T//fdLkvz8/NShQwf16dNHkZGRioyMVPv27eVwOGxOifLiJx0AbHK6hl69vSyni2rX13uuOnXqpICAABUWFsrlcmnXrl369ttvtWDBAklSw4YN1atXL/Xq1UuRkZHq0aOHgoODbU6NP0LBAgCbFNSwmzv/kYIztevrPVdOp1ORkZH67LPP3LcVKir67XRqbm6uPvzwQ3388ccqLi6Ww+HQxRdf7D7K1adPH7Vv396u+CgDBQsAbFLbTvpwlqtsUVFRWr9+vYqLPR/lsyzL/ZhlWfrmm2+0b98+LVq0SH5+ftq/f79atWrlzcj4A7zIHQBsEljX3+4IXhVYl185ZfnTn/5UZrkqy69HucaOHavw8PCqiIVK4AgWANikthUOZ53aVSjL46qrrpLT6VRBwbm98cHhcMjhcGju3LnuF8jDt9SuvRsAfEirJufZHcFrnHX81Dw40O4YPic/P18pKSnq37//OZcrf39/1atXT8nJyZQrH8YRLACwyeUX1J53gnVsEaS6/vxN//PPP+vf//63Zs2apY8++qjc6/v7+yskJEQffPCBOnfuXAUJYQo/7QBgk04ta0/B6twyyO4Itjh9+rQ+/vhj3XDDDXI4HKpfv76io6NLlKvhw4friy++0Lp16866LT8/P3Xq1EmbN2+mXFUDHMECAJu0bXaezgvwV15hzb8+VOdaUiZPnz6tDRs26LnnnlNycrLHMbfffrtGjx6t7t27q06d334NFxYWKjAwUKdPny61jsPh0IABA/TWW2/pvPNqz6nl6owjWABgEz8/hy4Lqx3Fo6YerSsoKNAnn3yim266SQ6HQ/Xq1VNUVFSJcvXXv/5VGzZsUGFhoSzL0uuvv66ePXuWKFeSFBAQoF69enm8avuYMWO0cuVKylU1whEsALBR11aNtPHAT3bHqFL1A/zVPrSh3TGMKCgoUFpammbPnq1//vOfHscMHjxYDz/8sCIiIlS3bt1ybb9fv35at26d+4KikvTSSy/poYceqnR2eBcFCwBs9L9XXqAFn+2zO0aVurlrWLV9gXthYaG++OILzZ49WytXrvQ45n//93/1yCOPKCIiQgEBAZV6vl+vh+Xn5yen06l3331XN954Y6W2CXtQsADARh2aN1SPNk1q9FGsYT3b2B3hnBUWFmrjxo164YUXtHz5co9jBg0apDFjxigyMrLSher3rrrqKtWvX19BQUFKSUlRly5djG4f3kPBAgCb3RHZusYWrCtbNdKlYb77DsIzZ85o48aNmjNnjpYtW+ZxzE033aQxY8aoV69ecjqdVZqnbt262rBhg8LCwhQSElKlz4WqRcECAJvd2Km5mjVw6tipc7vQZHUyLLK13RFKOHPmjDZt2qQ5c+borbfe8jhmwIABiomJUa9evRQY6P2Lo3bt2tXrzwnzKFgAYLO6/n4actUFSvr0O7ujGNXkvAD179zC1gxFRUXatGmTkpKS9Prrr3scc+ONN+rRRx9V7969bSlUqJkoWADgA+65uq2W/ef7GnUUa2zUxV6//2BRUZG2bNmipKQk/f3vf/c45vrrr9djjz2mq6++WvXq1fNqPtQeFCwA8AGNzwvQk4M66YHXN9sdxYjItk11R8+qPz1YXFysLVu2aN68eXr11Vc9jomKitJjjz2mPn36qH79+lWeCZAoWADgM27o1FwDu4TpX9t+sDtKpZwX4K+Zt17u8YKZlVVcXKz09HTNmzdPixYt8jjmmmuu0eOPP66+fftSqGAbChYA+JCpN12mtO+OV+tThRP6X6LwJmaKTXFxsbZt26aXX35ZCxYs8Dimb9++GjdunPr27cuVzuEzKFgA4EOq+6nCXu2a6o6IVhVe3+Vy6auvvtLLL7+s+fPnexxz9dVXa9y4cbr22mspVPBZFCwA8DE3dGquR/pdrBdSv7U7Srm0O/88zRl6RblODbpcLm3fvl0LFy5UUlKSxzG9evXSuHHj1K9fPzVo0MBUXKBKUbAAwAeNva69ck8XafGG/XZHOSctG9XT6/dEqGmDs1+I0+VyaceOHVq0aJFefPFFj2MiIiI0YcIE9evXTw0b1ox7GKL2oWABgI964n8ukcuytOTzA3ZHOavwJvX0xt091SK49CUPXC6Xvv76ay1evFizZ8/2uH737t0VGxurqKgoBQX57lXfgfKgYAGAj3I4HJpy02UKrlfXZ08Xtg9toH/cHaHQoF8u0GlZlnbu3KlXX31Vzz77rMd1rrzySk2YMEHXX3+9goODvRkX8BoKFgD4uLHXtVe7kAaK/+cOncg/Y3cct1uuaKn4gZfqx4Pf6dknX9UzzzzjcVyXLl00ceJEXX/99WrUqJF3QwI2oWABQDVwU5cwRbZtqkkrt+vDnVm25bAsS0EFR9QpL12z/zpHsz2M6dy5syZOnKgbbriBQoVai4IFANXE+Q2dWjC8u/6Zfljxq7/WSS8czbIsS0U/Hdaprz9RTtrb7uU7/mvMpZdeqkmTJunGG29U48aNqzwTUB1QsACgmrm5a0v1atdML33yrVZsOazcgiJj27YsS0UnflDe158q+/OlHsd07NhRkyZNUv/+/dWkSRNjzw3UJBQsAKiGzm/oVMLNnTTuho5alX5Y/0g7qN2ZueXejmVZKjr54y+FasNbHse0adtOCfFxGjBggJo2bVrZ6ECtQMECgGrsPGcd3R7RWrdHtNamAz/p7U2HtPngCe0/lieXVXr8L4UqU3k71/5SqCxXqTENzm+pofeP0aQH7lDrls298FUANY+fqQ0lJSWpTZs2CgwMVEREhDZu3HhO6y1dulQOh0ODBg0yFQUASqgt81P3Nk0089YuSn30T9o+JVpv3x+pyQMu0bVhlup9/U9lPHOzMmYO1A8L7lX2+jfc5apxaEs9MmWWtuzeL8uylHvkey2Y9hjlCqgEI0ewli1bppiYGM2fP18RERGaPXu2oqOjtWfPHoWEhJS53oEDB/TYY4+pT58+JmIAQCm1cX46cOCAli5dqvj4eBUWFpZ6PDw8XE888YRuvvnms34PAFSckSNYzz33nO69916NHDlSl156qebPn6/69etr8eLFZa5TXFys22+/XQkJCWrbtq2JGABQSm2Ynw4ePKiZM2eqfv36cjgcuvDCCxUbG+suV2FhYZo/f74yMzNlWZYyMjJ07733Uq6AKlTpglVYWKjNmzcrKirqt436+SkqKkppaWllrjd16lSFhITo7rvvrmwEAPCops5PGRkZmjVrlho2bCiHw6E2bdpo/Pjx+vnnnyVJzZs319y5c/XDDz/IsiwdPnxY999/v0JDQ21ODtQelT5FeOzYMRUXF5facUNDQ7V7926P66xfv16LFi1Senr6OT9PQUGBCgoK3J/n5ORUKC+A2qOmzE+HDh3SO++8o4SEBI/bDgkJUVxcnG655RaFhYUZfW4AFeP1dxHm5uZq2LBhWrhwoZo1a3bO6yUmJiohIaEKkwGo7Xxlfjp8+LCWL1+uhIQEnThxotTjTZs2VXx8vP785z+rZcuWxp4XgDmVLljNmjWTv7+/srJK3rohKytLzZuXfgfKd999pwMHDmjgwIHuZS7XL+9kqVOnjvbs2aN27dqVWi82NlYxMTHuz3NychQeHl7Z+ABqsOoyP/3www969913lZCQoOPHj5d6vFGjRoqPj9ett96qCy644Jy3C8A+lS5YAQEB6tatm1JTU91vZXa5XEpNTdXo0aNLje/YsaO2b99eYtnkyZOVm5urF154ocxJyel0yul0VjYugFrEV+enH3/8UStWrNDUqVN15MiRUo8HBQUpLi5OgwcP5g9JoJoycoowJiZGI0aMUPfu3dWjRw/Nnj1beXl5GjlypCRp+PDhatmypRITExUYGKhOnTqVWP/Xm4H+fjkAVJYvzE+ZmZlatWqVEhISlJmZWerxBg0aKD4+Xn/5y1/UunXrCj8PAN9hpGANGTJER48eVVxcnDIzM9W1a1elpKS4X1iakZEhPz9j1zQFgHNmx/z061GpSy65RD/88EOpx+vVq6cpU6Zo8ODBatOmjdHnBuAbHJZlebiZgu/LyclRcHCwsrOzFRQUZHccAOegpu63R44c0erVqzVt2jRlZGSUejwgIEBTpkzR0KFDKVSAjzI9P3EvQgAop6NHj+pf//qXpk2bpgMHDpR6vE6dOioqKlJ6erouv/xyORwO74cEYCvO2wHAHzh27JiWLFmidu3ayeFwuC9C+mu5cjgcSkhI0N69e+VyudzvBLzwwgspV0AtxREsAPid48ePa82aNXryySf17bffehwTHx+vO+64w126AOC/UbAA1Ho//fSTkpOT9dRTT5V5hffJkydr2LBhuvjiiylUAP4QBQtArXPixAm9//77euqpp7Rz506PY2JjYzVixAi1b9+eQgWg3ChYAGq8kydPKiUlRdOnTy91IdFfjR8/Xnfeeac6dOhAoQJQaRQsADVOdna2PvjgA02fPl3btm3zOObxxx/XyJEj1bFjRwoVAOMoWACqvZycHH344YdKTEzUli1bPI6JiYnRXXfdpUsvvZRCBaDKUbAAVDu5ubn66KOPlJiYqE2bNnkcM2bMGN1111267LLLuJMEAK+jYAHwebm5uUpNTdWMGTP05Zdfehzz8MMP65577lGnTp0oVABsR8EC4HNOnTqlTz75RE8//bQ+//xzj2NGjRqle++9V507d6ZQAfA5FCwAtsvLy9Onn36qp59+WuvXr/c45oEHHtB9992nLl26UKgA+DwKFgCvy8/P1/r16zVz5kytW7fO45j77rtP999/v7p06SJ/f38vJwSAyqFgAahy+fn5WrdunRITEyVJLVq0KDXmrrvu0kMPPaSuXbtSqABUexQsAMb9/PPP+uyzzzRr1ix9/PHHHseMHDlSDz74oK688koKFYAah4IFoNJOnz6tf//735o1a5Y+/PBDj2OGDx+uESNGqF+/fsrOzlZQUJCXUwKA91CwAJTb6dOntWHDBj377LN6//33PY654447NGrUKHXv3l116vwy1eTk5HgzJgDYhoIF4A8VFBRow4YNeu6557RmzRqPY4YOHaqHH35Y3bt3V926db2cEAB8CwULQCkFBQVKS0vT888/r9WrV3scM2TIED388MPq0aMHhQoAfoeCBUCFhYX64osv9Pzzz2vVqlUex9x6663629/+poiICAUEBHg3IABUMxQsoBYqLCzUl19+qRdeeEHvvvuuxzG33HKLxowZo549e1KoAKCcKFhALXDmzBlt3LhRL774ot5++22PY26++WaNHTtWPXv2lNPp9HJCAKhZKFhADXTmzBn95z//0Zw5c7R06VKPYwYOHKixY8eqV69eFCoAMIyCBdQARUVF2rRpk+bMmaM333zT45j+/fsrJiZGvXv3VmBgoJcTAkDtQsECqqGioiJt3rxZSUlJ+sc//uFxTHR0tB599FFdffXVqlevnpcTAkDtRsECqoGioiJt3bpVc+fO1ZIlSzyOue666/TYY4+pT58+FCoAsBkFC/BBxcXF2rp1q+bNm6fFixd7HNOvXz899thj+n//7/+pfv36Xk4IADgbChbgA4qLi7Vt2zbNmzdPr7zyiscxf/rTn/T444+rb9++Ou+887ycEABQHhQswAYul0vbtm3Tyy+/rJdfftnjmD59+mjcuHG65pprKFQAUM1QsAAvcLlc+uqrr7Rw4ULNnTvX45jevXtr3Lhxuvbaa9WgQQMvJwQAmETBAqqAy+XS9u3b9corr+ill17yOCYyMlLjx4/Xtddeq4YNG3o5IQCgKlGwAANcLpe+/vprvfLKK3rxxRc9junRo4cmTJigfv36KSgoyMsJAQDeRMECKsCyLH399ddavHixnn/+eY9junXrptjYWF133XUUKgCoZShYwDmwLEs7d+7UkiVLNGvWLI9jrrjiCsXGxur6669XcHCwlxMCAHwJBQvwwLIs7d69W0uWLNHMmTM9junSpYtiY2MVHR2tRo0aeTcgAMCnUbAA/VKo9uzZo9dee00zZszwOKZTp06aOHGibrjhBjVu3NjLCQEA1QkFC7WSZVn69ttv9fe//11PPfWUxzGXXHKJJk2apBtvvFFNmjTxckIAQHVGwUKtYFmW9u7dq9dff11Tp071OKZDhw6aNGmS+vfvr6ZNm3o5IQCgJqFgoUayLEvfffed3njjDU2ZMsXjmIsuukiTJ0/WgAED1KxZM+8GBADUaBQs1AiWZWnfvn168803NWXKFLlcrlJj2rZtq8mTJ+t//ud/dP7559uQEgBQW1CwUC1ZlqUDBw7orbfeUnx8vIqKikqNad26tZ544gkNHDhQISEhNqQEANRWFCxUGwcOHNDSpUs1ZcoUFRQUlHo8PDxcTzzxhG666SaFhobakBAAgF9QsOCzDh48qGXLlikhIUH5+fmlHg8LC1NcXJxuvvlmNW/e3IaEAAB4RsGCz8jIyNDbb7+thIQEnTp1qtTjzZs3V1xcnAYNGqQWLVrYkBAAgHNDwYJtvv/+e73zzjuaMmWKcnJySj0eEhKiuLg43XLLLQoLC7MhIQAAFUPBgtccPnxYy5cvV0JCgk6cOFHq8aZNmyo+Pl5//vOf1bJlSxsSAgBgBgULVeaHH37Qu+++q6lTp+rYsWOlHm/UqJHi4+N166236oILLrAhIQAAVYOCBWN+/PFHrVixQlOnTtWRI0dKPR4UFKS4uDgNHjxY4eHhNiQEAMA7KFiosMzMTK1atUoJCQnKzMws9XiDBg3chap169Y2JAQAwB4ULJyzrKwsrVq1StOmTdPhw4dLPV6vXj1NmTJFgwcPVps2bbwfEAAAH0HBQpmOHDmi1atXa+rUqTp06FCpxwMCAjRlyhQNHTqUQgUAwH+hYMHt6NGj+te//qVp06bpwIEDpR6vU6eO4uPjddttt+nCCy+Uw+HwfkgAAKoBClYtduzYMb333nuaNm2a9u3bV+pxPz8/xcfH6/bbb1fbtm0pVAAAnCMKVi1y/PhxrVmzRk8++aS+/fZbj2Pi4+N1xx13qF27dhQqAAAqiIJVg/30009KTk7Wk08+qT179ngcM3nyZA0fPlwXXXQRhQoAAEMoWDXIiRMn9P777+upp57Szp07PY6JjY3ViBEj1L59ewoVAABVhIJVjZ08eVIpKSmaPn26tm/f7nHM+PHjdeedd6pDhw4UKgAAvISCVY1kZ2frgw8+0PTp07Vt2zaPY8aNG6c777xTHTt2pFABAGATP1MbSkpKUps2bRQYGKiIiAht3LixzLELFy5Unz591LhxYzVu3FhRUVFnHV9bZWdna/ny5bryyivlcDjUqFEjDRkypES5evTRR7Vjxw65XC5ZlqWnn35al1xyCeUK+C/MTwC8zUjBWrZsmWJiYhQfH68tW7aoS5cuio6O9ng/Oklau3athg4dqk8//VRpaWkKDw/X9ddf7/Hq4LVJTk6OVqxYoauuuspdqP7yl79o69at7jFjxozR9u3bVVxcLMuyNGvWLF122WUUKqAMzE8AbGEZ0KNHD2vUqFHuz4uLi62wsDArMTHxnNYvKiqyGjZsaL322mvn/JzZ2dmWJCs7O7vceX3Jzp07rYiICEuSx4+//e1v1rZt26zi4mK7owKVZsd+y/wE4FyY3m8r/RqswsJCbd68WbGxse5lfn5+ioqKUlpa2jltIz8/X2fOnFGTJk3KHFNQUKCCggL35zk5ORUP7UNmzZqlL7/80v356NGjdc8996hz587y8zN2BheolZifANil0r/Bjx07puLiYoWGhpZYHhoaqszMzHPaxvjx4xUWFqaoqKgyxyQmJio4ONj9ER4eXqncvuKZZ57RV1995T7lN2fOHHXp0oVyBRjA/ATALrb/Fp8xY4aWLl2qlStXKjAwsMxxsbGxys7Odn94uvlwddSkSROOVgE+qrbPTwAqrtKnCJs1ayZ/f39lZWWVWJ6VlaXmzZufdd1Zs2ZpxowZ+vjjj3X55ZefdazT6ZTT6axsXAC1CPMTALtU+rBJQECAunXrptTUVPcyl8ul1NRURUZGlrnezJkzNW3aNKWkpKh79+6VjQEApTA/AbCLkQuNxsTEaMSIEerevbt69Oih2bNnKy8vTyNHjpQkDR8+XC1btlRiYqIk6emnn1ZcXJzefPNNtWnTxv1aiAYNGqhBgwYmIgGAJOYnAPYwUrCGDBmio0ePKi4uTpmZmeratatSUlLcLyzNyMgo8RqjefPmqbCwULfeemuJ7cTHx2vKlCkmIgGAJOYnAPZwWJZl2R2iInJychQcHKzs7GwFBQXZHQfAOagt+21t+TqBmsT0fstb1wAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMM1awkpKS1KZNGwUGBioiIkIbN2486/h33nlHHTt2VGBgoDp37qzk5GRTUQCgBOYnAN5mpGAtW7ZMMTExio+P15YtW9SlSxdFR0fryJEjHsd//vnnGjp0qO6++25t3bpVgwYN0qBBg7Rjxw4TcQDAjfkJgB0clmVZld1IRESErrrqKr300kuSJJfLpfDwcD388MOaMGFCqfFDhgxRXl6e3nvvPfeynj17qmvXrpo/f/45PWdOTo6Cg4OVnZ2toKCgyn4JALzAjv2W+QnAuTC939ap7AYKCwu1efNmxcbGupf5+fkpKipKaWlpHtdJS0tTTExMiWXR0dFatWpVmc9TUFCggoIC9+fZ2dmSfvmGAKgeft1fDfxdd06YnwCcK9PzU6UL1rFjx1RcXKzQ0NASy0NDQ7V7926P62RmZnocn5mZWebzJCYmKiEhodTy8PDwCqQGYKfjx48rODi4yp+H+QlAeZmanypdsLwlNja2xF+VJ0+eVOvWrZWRkeGVidqknJwchYeH69ChQ9Xy9AH57VOds0u/HNlp1aqVmjRpYncUo2rS/GS36v4zbie+d5Vjen6qdMFq1qyZ/P39lZWVVWJ5VlaWmjdv7nGd5s2bl2u8JDmdTjmdzlLLg4ODq+0PUlBQULXNLpHfTtU5u/TLaTpvYH6qvqr7z7id+N5Vjqn5qdJbCQgIULdu3ZSamupe5nK5lJqaqsjISI/rREZGlhgvSR999FGZ4wGgIpifANjFyCnCmJgYjRgxQt27d1ePHj00e/Zs5eXlaeTIkZKk4cOHq2XLlkpMTJQkPfLII+rbt6+effZZDRgwQEuXLtWmTZu0YMECE3EAwI35CYAdjBSsIUOG6OjRo4qLi1NmZqa6du2qlJQU9wtFMzIyShxy69Wrl958801NnjxZEydO1MUXX6xVq1apU6dO5/ycTqdT8fHxHg/L+7rqnF0iv52qc3bJnvzMT9UL37uK43tXOaa/f0augwUAAIDfcC9CAAAAwyhYAAAAhlGwAAAADKNgAQAAGObTBSspKUlt2rRRYGCgIiIitHHjxrOOf+edd9SxY0cFBgaqc+fOSk5O9lLS0sqTfeHCherTp48aN26sxo0bKyoq6g+/1qpW3u/9r5YuXSqHw6FBgwZVbcCzKG/2kydPatSoUWrRooWcTqfat29fbX52JGn27Nnq0KGD6tWrp/DwcI0dO1anT5/2UtrffPbZZxo4cKDCwsLkcDjOeu++X61du1ZXXnmlnE6nLrroIi1ZsqTKc1a1iu47tV1Ffn7wi8TERF111VVq2LChQkJCNGjQIO3Zs8fuWNXCvHnzdPnll7svzhoZGan333/fzMYtH7V06VIrICDAWrx4sfX1119b9957r9WoUSMrKyvL4/gNGzZY/v7+1syZM62dO3dakydPturWrWtt377dy8nLn/22226zkpKSrK1bt1q7du2y7rzzTis4ONj6/vvvvZz8F+XN/6v9+/dbLVu2tPr06WPdfPPN3gn7O+XNXlBQYHXv3t3q37+/tX79emv//v3W2rVrrfT0dC8n/0V587/xxhuW0+m03njjDWv//v3WBx98YLVo0cIaO3asl5NbVnJysjVp0iRrxYoVliRr5cqVZx2/b98+q379+lZMTIy1c+dOa86cOZa/v7+VkpLincBVoKL7Dsr/84PfREdHW6+++qq1Y8cOKz093erfv7/VqlUr69SpU3ZH83mrV6+21qxZY33zzTfWnj17rIkTJ1p169a1duzYUelt+2zB6tGjhzVq1Cj358XFxVZYWJiVmJjocfzgwYOtAQMGlFgWERFh3X///VWa05PyZv+9oqIiq2HDhtZrr71WVRHPqiL5i4qKrF69elmvvPKKNWLECNsKVnmzz5s3z2rbtq1VWFjorYhnVd78o0aNsq699toSy2JiYqzevXtXac4/ci6/IMeNG2dddtllJZYNGTLEio6OrsJkVauy+z5+QcGqnCNHjliSrHXr1tkdpVpq3Lix9corr1R6Oz55irCwsFCbN29WVFSUe5mfn5+ioqKUlpbmcZ20tLQS4yUpOjq6zPFVpSLZfy8/P19nzpyx5Ya4Fc0/depUhYSE6O677/ZGTI8qkn316tWKjIzUqFGjFBoaqk6dOmn69OkqLi72Vmy3iuTv1auXNm/e7D4NtW/fPiUnJ6t///5eyVwZvrLPmmJi3wdMyM7OlqQad1P1qlZcXKylS5cqLy/PyK2xjFzJ3bRjx46puLjYfaXlX4WGhmr37t0e18nMzPQ4PjMzs8pyelKR7L83fvx4hYWFlfrl4w0Vyb9+/XotWrRI6enpXkhYtopk37dvnz755BPdfvvtSk5O1t69e/XQQw/pzJkzio+P90Zst4rkv+2223Ts2DFdffXVsixLRUVFeuCBBzRx4kRvRK6UsvbZnJwc/fzzz6pXr55NySrGxL4PVJbL5dKYMWPUu3fvct19oDbbvn27IiMjdfr0aTVo0EArV67UpZdeWunt+uQRrNpsxowZWrp0qVauXKnAwEC74/yh3NxcDRs2TAsXLlSzZs3sjlNuLpdLISEhWrBggbp166YhQ4Zo0qRJmj9/vt3RzsnatWs1ffp0zZ07V1u2bNGKFSu0Zs0aTZs2ze5oAGwwatQo7dixQ0uXLrU7SrXRoUMHpaen68svv9SDDz6oESNGaOfOnZXerk8ewWrWrJn8/f2VlZVVYnlWVpaaN2/ucZ3mzZuXa3xVqUj2X82aNUszZszQxx9/rMsvv7wqY5apvPm/++47HThwQAMHDnQvc7lckqQ6depoz549ateuXdWG/j8V+d63aNFCdevWlb+/v3vZJZdcoszMTBUWFiogIKBKM/+3iuR/4oknNGzYMN1zzz2SpM6dOysvL0/33XefJk2aVOIee76mrH02KCio2h29kiq37wMmjB49Wu+9954+++wzXXDBBXbHqTYCAgJ00UUXSZK6deum//znP3rhhRf08ssvV2q7Pjn7BgQEqFu3bkpNTXUvc7lcSk1NLfO8aGRkZInxkvTRRx8ZOY9aHhXJLkkzZ87UtGnTlJKSou7du3sjqkflzd+xY0dt375d6enp7o+bbrpJ11xzjdLT0xUeHu6z2SWpd+/e2rt3r7sUStI333yjFi1aeLVcSRXLn5+fX6pE/VoWLR+/zaiv7LOmVHTfByrLsiyNHj1aK1eu1CeffKILL7zQ7kjVmsvlUkFBQeU3VOmXyVeRpUuXWk6n01qyZIm1c+dO67777rMaNWpkZWZmWpZlWcOGDbMmTJjgHr9hwwarTp061qxZs6xdu3ZZ8fHxtl6moTzZZ8yYYQUEBFjLly+3fvzxR/dHbm6u17NXJP/v2fkuwvJmz8jIsBo2bGiNHj3a2rNnj/Xee+9ZISEh1pNPPlkt8sfHx1sNGza03nrrLWvfvn3Whx9+aLVr184aPHiw17Pn5uZaW7dutbZu3WpJsp577jlr69at1sGDBy3LsqwJEyZYw4YNc4//9TINjz/+uLVr1y4rKSmpRlym4Wz/fyjbH/38oGwPPvigFRwcbK1du7bE75D8/Hy7o/m8CRMmWOvWrbP2799vffXVV9aECRMsh8Nhffjhh5Xets8WLMuyrDlz5litWrWyAgICrB49elhffPGF+7G+fftaI0aMKDH+7bffttq3b28FBARYl112mbVmzRovJ/5NebK3bt3aklTqIz4+3vvB/095v/f/zc6CZVnlz/75559bERERltPptNq2bWs99dRTVlFRkZdT/6Y8+c+cOWNNmTLFateunRUYGGiFh4dbDz30kHXixAmv5/700089/hz/mnfEiBFW3759S63TtWtXKyAgwGrbtq316quvej23aWf7/0PZ/ujnB2Xz9H2TVCP2p6p21113Wa1bt7YCAgKs888/3+rXr5+RcmVZluWwLB8/jwAAAFDN+ORrsAAAAKozChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwLD/D98AR9/gnqI6AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 600x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# GOAL: draw a visual circle at axes / data coords 0.5, 0.5 \n",
+    "# with raw co-ordinate radius 30 on right subplot\n",
+    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(6, 4))\n",
+    "ax2.set_xlim(0, 3)\n",
+    "\n",
+    "# crop now (after .transform, we don't want to move anything!)\n",
+    "plt.tight_layout() \n",
+    "\n",
+    "x, y = ax2.transData.transform((0.5, 0.5))\n",
+    "print(x, y)\n",
+    "# Draw a circle at (x, y) with radius 30 and set transform to None\n",
+    "c = plt.Circle((x, y), 30, transform=None)\n",
+    "ax2.add_artist(c)\n",
+    "\n",
+    "# GOAL: arrow from 0.2, 0.2 (left) to 2, 0.5 (right)\n",
+    "# Use axes / data coords from one subplot to another subplot\n",
+    "x1, y1 = ax1.transData.transform((0.2, 0.2))\n",
+    "x2, y2 = ax2.transData.transform((2, 0.5))\n",
+    "# arrowstyle=\"simple,head_width=10,head_length=10\"\n",
+    "arrow = matplotlib.patches.FancyArrowPatch((x1, y1), (x2, y2), transform=None, color=\"k\",\n",
+    "                                           arrowstyle=\"simple,head_width=10,head_length=10\")\n",
+    "fig.add_artist(arrow)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8878fb2b-b39c-4c32-b54d-0e9fc909c400",
+   "metadata": {},
+   "source": [
+    "### Custom Scatter Plots with Angles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "263cc5c9-2751-4dbb-be8a-9dc5fc0b5217",
+   "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>x</th>\n",
+       "      <th>y</th>\n",
+       "      <th>a</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6</td>\n",
+       "      <td>6</td>\n",
+       "      <td>45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>180</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   x  y    a\n",
+       "0  2  5   90\n",
+       "1  3  1    0\n",
+       "2  6  6   45\n",
+       "3  8  1  180"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame([\n",
+    "    {\"x\":2, \"y\":5, \"a\": 90},\n",
+    "    {\"x\":3, \"y\":1, \"a\": 0},\n",
+    "    {\"x\":6, \"y\":6, \"a\": 45},\n",
+    "    {\"x\":8, \"y\":1, \"a\": 180}\n",
+    "])\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "24c2de6e-86e7-48fc-9f89-48d8cff68736",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2 5 90\n",
+      "3 1 0\n",
+      "6 6 45\n",
+      "8 1 180\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 300x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(3,2))\n",
+    "ax.set_xlim(0, 10)\n",
+    "ax.set_ylim(-1, 10)\n",
+    "\n",
+    "for row in df.itertuples():\n",
+    "    print(row.x, row.y, row.a)\n",
+    "    # v1: draw a circle of radius 10 for each scatter point\n",
+    "    \n",
+    "    # x, y = ax.transData.transform((row.x, row.y))\n",
+    "    # c = plt.Circle((x,y), radius=10, transform=None)\n",
+    "    # ax.add_artist(c)\n",
+    "    \n",
+    "    # v2: draw an arrow for each scatter point (correct angle)\n",
+    "    x, y = ax.transData.transform((row.x, row.y))\n",
+    "    # Calculate angle: math.radians(row.a)\n",
+    "    a = math.radians(row.a)\n",
+    "    # Calculate end axes / data coords using math.cos and math.sin\n",
+    "    x_diff = math.cos(a) * 25\n",
+    "    y_diff = math.sin(a) * 25\n",
+    "    c = matplotlib.patches.FancyArrowPatch((x,y), (x+x_diff, y+y_diff),transform=None, color=\"k\",\n",
+    "                                           arrowstyle=\"simple,head_width=10,head_length=10\")\n",
+    "    ax.add_artist(c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9730e3e9-ed3c-49d8-aaf0-e0c29290fb90",
+   "metadata": {},
+   "source": [
+    "### Plot annotations\n",
+    "\n",
+    "- Target plot:\n",
+    "\n",
+    "<img src = \"Target_plot.png\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19eb08f2-3949-465c-9629-fe9c33d9aa8d",
+   "metadata": {},
+   "source": [
+    "- `ax.text(<x>, <y>, <text>, ha=<someval>, va=<someval>)`\n",
+    "    - `ha`: horizontalalignment\n",
+    "    - `va`: verticalalignment\n",
+    "        - enables us to modify \"anchor\" of the text\n",
+    "    \n",
+    "### More patches\n",
+    "- `plt.Line2D((<x1>, <x2>), (<y1>, <y2>)))`\n",
+    "- `matplotlib.patches.FancyArrowPatch((<x1>, <y1>), (<x2>, (<y2>))`\n",
+    "- `plt.Rectangle((<x>,<y>), <width>, <height>)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "3fe00736-a62f-41ce-ae99-a6e95a36f9f8",
+   "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>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>2</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>8</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>9</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    A   B\n",
+       "10  1   5\n",
+       "20  2   7\n",
+       "30  8  12\n",
+       "40  9  15"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.rcParams[\"font.size\"] = 16\n",
+    "df = pd.DataFrame({\"A\": [1,2,8,9], \"B\": [5,7,12,15]}, index=[10,20,30,40])\n",
+    "ax = df.plot.line(figsize=(4,3), legend=False)\n",
+    "ax.set_xlabel(\"Day\")\n",
+    "ax.set_ylabel(\"Amount\")\n",
+    "plt.tight_layout()\n",
+    "# Enables us to control borders (aka spines)\n",
+    "ax.spines[\"top\"].set_visible(False)\n",
+    "ax.spines[\"right\"].set_visible(False)\n",
+    "\n",
+    "# 1. Replace legened with line labels\n",
+    "for col in df.columns:\n",
+    "    ax.text(df.index[-1], df[col].iat[-1], col, ha=\"left\", va=\"center\")\n",
+    "\n",
+    "# 2. Draw a vertical line at x=20\n",
+    "p = plt.Line2D((20, 20), ax.get_ylim(), color=\"r\", linestyle=\"--\")\n",
+    "ax.add_artist(p)\n",
+    "\n",
+    "# 3. Highlight a region from x=25 to x=35\n",
+    "p = plt.Rectangle((25, 0), 10, ax.get_ylim()[1], color=\"k\", zorder=50, alpha=0.2, linewidth=0)\n",
+    "ax.add_artist(p)\n",
+    "\n",
+    "df"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/lecture_material/16-viz-1/vis_1_lec_001.ipynb b/lecture_material/16-viz-1/vis_1_lec_001.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..767640ca28ba6815ec2637c0716da8adbc21ab3c
--- /dev/null
+++ b/lecture_material/16-viz-1/vis_1_lec_001.ipynb
@@ -0,0 +1,526 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "084b5333",
+   "metadata": {},
+   "source": [
+    "# Visualization 1\n",
+    "\n",
+    "- Advanced visualization, example: https://trailsofwind.figures.cc/\n",
+    "- Custom visualization steps:\n",
+    "    - draw \"patches\" (shapes) on the screen (what):\n",
+    "        - lines\n",
+    "        - polygons\n",
+    "        - circle\n",
+    "        - text\n",
+    "    - location of the \"patches\" on the screen (where):\n",
+    "        - X & Y co-ordinate\n",
+    "        - \"Coordinate Reference System (CRS)\":\n",
+    "            - takes some X & Y and maps it on to actual space on screen\n",
+    "            - several CRS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5df39a4b-d55b-4ba0-ab78-bd06fac8047e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import statements\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import pandas as pd\n",
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af357b87",
+   "metadata": {},
+   "source": [
+    "### Review: drawing a figure\n",
+    "\n",
+    "- `fig, ax = plt.subplots(figsize=(<width>, <height>))`\n",
+    "\n",
+    "### Drawing a circle\n",
+    "\n",
+    "- Type `plt.` and then tab to see a list of `patches`.\n",
+    "- `plt.Circle((<X>, <Y>), <RADIUS>)`\n",
+    "- To see the cicle, we need to invoke either:\n",
+    "    - `ax.add_patch(<circle object>)`\n",
+    "    - `ax.add_artist(<circle object>)`\n",
+    "    - this invocation needs to be in the same cell as the one that draws the figure\n",
+    "    - Is there a difference between `ax.add_patch` and `ax.add_artist`?\n",
+    "        - `ax.autoscale_view()`: automatically chose limits for the axes; typically works better with `ax.add_patch(...)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "152cd4b0-7334-491f-841d-c0bfe3fce3c9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6, 4))\n",
+    "# Let's draw a circle at (0.5, 0.5) of radius 0.3\n",
+    "\n",
+    "# Add the circle to the AxesSubplot\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5064c802",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b760ce1",
+   "metadata": {},
+   "source": [
+    "Type and MRO of circle object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8a714acd-e33d-4fa0-9fdd-de8438e94418",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2617ab22",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "085918f5",
+   "metadata": {},
+   "source": [
+    "### Making the circle circular\n",
+    "\n",
+    "1. Have same figure width and height\n",
+    "2. Aspect ratio\n",
+    "3. Transformers: let's us pick a Coordinate Reference System (CRS)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1bf67506",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Option 1: Have same figure width and height\n",
+    "fig, ax = plt.subplots(figsize=(6, 4))\n",
+    "c = plt.Circle((0.5, 0.5), 0.3)\n",
+    "ax.add_patch(c)\n",
+    "ax.autoscale_view()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4bd1648d-55ce-4156-b94e-6b9a10db4da7",
+   "metadata": {},
+   "source": [
+    "### Aspect Ratio\n",
+    "\n",
+    "- `ax.set_aspect(<Y DIM>)`: how much space y axes takes with respect to x axes space"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "06b32774-26a2-4627-b363-f79eb2838d07",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6, 4))\n",
+    "c = plt.Circle((0.5, 0.5), 0.3)\n",
+    "ax.add_artist(c)\n",
+    "# Set aspect for y-axis to 1\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65f0928e",
+   "metadata": {},
+   "source": [
+    "What if we want x and y axes to have the same aspect ratio? That is we care more about the figure being square than about the circle being circular."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7fa11875-34ba-4550-8e8b-9a9f92e58a9a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6,4))\n",
+    "# Set x axis limit to (0, 3)\n",
+    "\n",
+    "c = plt.Circle((0.5, 0.5), 0.3)\n",
+    "ax.add_artist(c)\n",
+    "# Set aspect for y-axis to 3\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2429f83-1603-4aaf-b767-a60969fc20d7",
+   "metadata": {},
+   "source": [
+    "### Transformers: let us pick a Coordinate Reference System (CRS)\n",
+    "\n",
+    "- Documentation: https://matplotlib.org/stable/tutorials/advanced/transforms_tutorial.html\n",
+    "- `ax.transData`: default\n",
+    "- `ax.transAxes` and `fig.transFigure`:\n",
+    "    - (0, 0) is bottom left\n",
+    "    - (1, 1) is top right\n",
+    "        - these are true immaterial of the axes limits\n",
+    "- `None` or `IdentityTransform()`: disabling CRS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84c0e7b7",
+   "metadata": {},
+   "source": [
+    "### Review:\n",
+    "- `fig, ax = plt.subplots(figsize=(<width>, <height>), ncols=<N>, nrows=<N>)`:\n",
+    "    - ncols: split into vertical sub plots\n",
+    "    - nrows: split into horizontal sub plots\n",
+    "- `ax.set_xlim(<lower limit>, <upper limit>)`: set x-axis limits\n",
+    "- `ax.set_ylim(<lower limit>, <upper limit>)`: set y-axis limits\n",
+    "\n",
+    "### `ax.transData`\n",
+    "- `transform` parameter in \"patch\" creation function let's us specify the CRS\n",
+    "- `color` parameter controls the color of the \"patch\"\n",
+    "- `edgecolor` parameter controls outer border color of the \"patch\"\n",
+    "- `linewidth` parameter controls the size of the border of the \"patch\"\n",
+    "- `facecolor` parameter controls the filled in color of the \"patch\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5adb1223-0fc4-422c-95ef-1446ad811940",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a plot with two vertical subplots\n",
+    "\n",
+    "# Set right subplot x-axis limit from 0 to 3\n",
+    "\n",
+    "\n",
+    "# Left subplot: plot Circle at (0.5, 0.5) with radius 0.2\n",
+    "# Specify CRS as ax1.transData (tranform parameter)\n",
+    "\n",
+    "\n",
+    "# Right subplot: plot Circle at (0.5, 0.5) with radius 0.2\n",
+    "# default: transform=ax2.transData\n",
+    "\n",
+    "\n",
+    "# Observe that we get a different circle\n",
+    "\n",
+    "# Transform based on ax1, but crop based on ax2\n",
+    "# Left subplot: plot Circle at (1, 1) with radius 0.3 and crop using ax2\n",
+    " # where to position the shape \n",
+    " # how to crop the shape\n",
+    "\n",
+    "# Right subplot: plot Circle at (1, 1) with radius 0.3 and crop using ax1\n",
+    " # where to position the shape\n",
+    " # how to crop the shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0167a871",
+   "metadata": {},
+   "source": [
+    "### `ax.transAxes` and `fig.transFigure`\n",
+    "\n",
+    "- (0, 0) is bottom left\n",
+    "- (1, 1) is top right\n",
+    "    - these are true immaterial of the axes limits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "38aa99c6-039a-468e-9cb1-b1a3d9b36a80",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(6, 4))\n",
+    "ax2.set_xlim(0, 3)\n",
+    "\n",
+    "# Left subplot\n",
+    "c = plt.Circle((0.5, 0.5), 0.2, transform=???)\n",
+    "???.add_artist(c)\n",
+    "\n",
+    "# Right subplot\n",
+    "c = plt.Circle((0.5, 0.5), 0.2, transform=???)\n",
+    "???.add_artist(c)\n",
+    "\n",
+    "# whole figure\n",
+    "# edgecolor=\"red\", facecolor=\"none\", linewidth=3\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc52379c",
+   "metadata": {},
+   "source": [
+    "### No CRS (raw pixel coordinates)\n",
+    "\n",
+    "- `fig.dpi`: dots (aka pixesl) per inch\n",
+    "- increasing dpi makes the figure have higher resolution (helpful when you want to print a large size)\n",
+    "- Review: \n",
+    "    - `plt.tight_layout()`: avoid unncessary cropping of the figure --- always needed for No CRS cases\n",
+    "    - `fig.savefig(<relative path.png>)`: to save a local copy of the image\n",
+    "    \n",
+    "- Jupyter command to avoid cropping:\n",
+    "    - `%config InlineBackend.print_figure_kwargs={'bbox_inches': None}`\n",
+    "        - bbox_inches stands for bounding box"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "222eb737",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Jupyter commands begin with %\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e09ef243-ba52-4b70-a980-6ff4735f0fc2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(6, 4))\n",
+    "ax2.set_xlim(0, 3)\n",
+    "\n",
+    "# What is the dpi?\n",
+    "   # dots (aka pixel) per inch\n",
+    "\n",
+    "# Calculate total width and height of the figure using dpi and dimensions\n",
+    "width = ???\n",
+    "height = ???\n",
+    "\n",
+    "# Calculate (x, y) in the middle of the plot\n",
+    "x = ???\n",
+    "y = >>>\n",
+    "print(x, y)\n",
+    "\n",
+    "# Make sure to invoke plt.tight_layout()\n",
+    "# matplotlib does the cropping better than Jupyter\n",
+    "\n",
+    "# Draw a circle at (x, y) with radius 30\n",
+    "# Make sure to set transform=None\n",
+    "\n",
+    "\n",
+    "# Save the figure to temp.png\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "744d76e5",
+   "metadata": {},
+   "source": [
+    "### Mix and match\n",
+    "\n",
+    "- `ax.transData.transform((x, y))`: converts axes / data coords into raw coordinates\n",
+    "- How to draw an arrow:\n",
+    "    `matplotlib.patches.FancyArrowPatch((<x1>, <y1>), (<x2>, (<y2>)), transform=None, arrowstyle=<STYLE>)`\n",
+    "    - arrowstyle=\"simple,head_width=10,head_length=10\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db8a296e-6eae-4e57-a94e-b7901c476ae2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# GOAL: draw a visual circle at axes / data coords 0.5, 0.5 \n",
+    "# with raw co-ordinate radius 30 on right subplot\n",
+    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(6, 4))\n",
+    "ax2.set_xlim(0, 3)\n",
+    "\n",
+    "# crop now (after .transform, we don't want to move anything!)\n",
+    "# plt.tight_layout() \n",
+    "\n",
+    "# Transform (0.5, 0.5) to transData CRS\n",
+    "\n",
+    "print(x, y)\n",
+    "# Draw a circle at (x, y) with radius 30 and set transform to None\n",
+    "\n",
+    "\n",
+    "# GOAL: arrow from 0.2, 0.2 (left) to 2, 0.5 (right)\n",
+    "# Use axes / data coords from one subplot to another subplot\n",
+    "\n",
+    "# arrowstyle=\"simple,head_width=10,head_length=10\"\n",
+    "arrow = matplotlib.patches.FancyArrowPatch((x1, y1), (x2, y2), transform=None)\n",
+    "fig.add_artist(arrow)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2272a0b-f8f1-4bdd-87d5-971aee1ce160",
+   "metadata": {},
+   "source": [
+    "### Custom Scatter Plots with Angles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "468492f8-0e1e-4a37-b16c-d08e818a1b63",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame([\n",
+    "    {\"x\":2, \"y\":5, \"a\": 90},\n",
+    "    {\"x\":3, \"y\":1, \"a\": 0},\n",
+    "    {\"x\":6, \"y\":6, \"a\": 45},\n",
+    "    {\"x\":8, \"y\":1, \"a\": 180}\n",
+    "])\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4963466b-acab-468e-bdde-97f9908933a8",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(3, 2))\n",
+    "ax.set_xlim(0, 10)\n",
+    "ax.set_ylim(0, 10)\n",
+    "\n",
+    "for row in df.itertuples():\n",
+    "    print(row.x, row.y, row.a)\n",
+    "    # v1: draw a circle for each scatter point\n",
+    "    \n",
+    "    # x, y = ax.transData.transform((row.x, row.y))\n",
+    "    # c = plt.Circle((x,y), radius=10, transform=None)\n",
+    "    # ax.add_artist(c)\n",
+    "    \n",
+    "    # v2: draw an arrow for each scatter point (correct angle)\n",
+    "    #x, y = ax.transData.transform((row.x, row.y))\n",
+    "    # Calculate angle: math.radians(row.a)\n",
+    "    #a = ???\n",
+    "    # Calculate end axes / data coords:\n",
+    "    # using math.cos(a) * 25 and math.sin(a) * 25\n",
+    "    #x_diff = ???\n",
+    "    #y_diff = ???\n",
+    "    c = matplotlib.patches.FancyArrowPatch((x,y), (x+x_diff, y+y_diff),transform=None, color=\"k\",\n",
+    "                                           arrowstyle=\"simple,head_width=10,head_length=10\")\n",
+    "    ax.add_artist(c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ddfa239-804f-4c86-b8e9-6f94e7709ab6",
+   "metadata": {},
+   "source": [
+    "### Plot annotations\n",
+    "\n",
+    "- Target plot:\n",
+    "\n",
+    "<img src = \"Target_plot.png\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dff3b3a7-171f-4dab-9bb4-9c5fc76ca0db",
+   "metadata": {},
+   "source": [
+    "- `ax.text(<x>, <y>, <text>, ha=<someval>, va=<someval>)`\n",
+    "    - `ha`: horizontalalignment\n",
+    "    - `va`: verticalalignment\n",
+    "        - enables us to modify \"anchor\" of the text\n",
+    "    \n",
+    "### More patches\n",
+    "- `plt.Line2D((<x1>, <x2>), (<y1>, <y2>)))`\n",
+    "- `matplotlib.patches.FancyArrowPatch((<x1>, <y1>), (<x2>, (<y2>))`\n",
+    "- `plt.Rectangle((<x>,<y>), <width>, <height>)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "648f8e12-d357-43ec-b7de-1f8a14b7264b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.rcParams[\"font.size\"] = 16\n",
+    "df = pd.DataFrame({\"A\": [1,2,8,9], \"B\": [5,7,12,15]}, index=[10,20,30,40])\n",
+    "ax = df.plot.line(figsize=(4,3))\n",
+    "ax.set_xlabel(\"Day\")\n",
+    "ax.set_ylabel(\"Amount\")\n",
+    "plt.tight_layout()\n",
+    "# Enables us to control borders (aka spines)\n",
+    "ax.spines[\"top\"].set_visible(False)\n",
+    "ax.spines[\"right\"].set_visible(False)\n",
+    "\n",
+    "# 1. Replace legened with line labels\n",
+    "\n",
+    "\n",
+    "# 2. Draw a vertical line at x=20\n",
+    "# color=\"r\", linestyle=\"--\"\n",
+    "\n",
+    "\n",
+    "# 3. Highlight a region from x=25 to x=35\n",
+    "# color=\"k\", zorder=50, alpha=0.2, linewidth=0\n",
+    "\n",
+    "\n",
+    "df"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/lecture_material/16-viz-1/vis_1_lec_002.ipynb b/lecture_material/16-viz-1/vis_1_lec_002.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..767640ca28ba6815ec2637c0716da8adbc21ab3c
--- /dev/null
+++ b/lecture_material/16-viz-1/vis_1_lec_002.ipynb
@@ -0,0 +1,526 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "084b5333",
+   "metadata": {},
+   "source": [
+    "# Visualization 1\n",
+    "\n",
+    "- Advanced visualization, example: https://trailsofwind.figures.cc/\n",
+    "- Custom visualization steps:\n",
+    "    - draw \"patches\" (shapes) on the screen (what):\n",
+    "        - lines\n",
+    "        - polygons\n",
+    "        - circle\n",
+    "        - text\n",
+    "    - location of the \"patches\" on the screen (where):\n",
+    "        - X & Y co-ordinate\n",
+    "        - \"Coordinate Reference System (CRS)\":\n",
+    "            - takes some X & Y and maps it on to actual space on screen\n",
+    "            - several CRS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5df39a4b-d55b-4ba0-ab78-bd06fac8047e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import statements\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import pandas as pd\n",
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af357b87",
+   "metadata": {},
+   "source": [
+    "### Review: drawing a figure\n",
+    "\n",
+    "- `fig, ax = plt.subplots(figsize=(<width>, <height>))`\n",
+    "\n",
+    "### Drawing a circle\n",
+    "\n",
+    "- Type `plt.` and then tab to see a list of `patches`.\n",
+    "- `plt.Circle((<X>, <Y>), <RADIUS>)`\n",
+    "- To see the cicle, we need to invoke either:\n",
+    "    - `ax.add_patch(<circle object>)`\n",
+    "    - `ax.add_artist(<circle object>)`\n",
+    "    - this invocation needs to be in the same cell as the one that draws the figure\n",
+    "    - Is there a difference between `ax.add_patch` and `ax.add_artist`?\n",
+    "        - `ax.autoscale_view()`: automatically chose limits for the axes; typically works better with `ax.add_patch(...)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "152cd4b0-7334-491f-841d-c0bfe3fce3c9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6, 4))\n",
+    "# Let's draw a circle at (0.5, 0.5) of radius 0.3\n",
+    "\n",
+    "# Add the circle to the AxesSubplot\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5064c802",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b760ce1",
+   "metadata": {},
+   "source": [
+    "Type and MRO of circle object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8a714acd-e33d-4fa0-9fdd-de8438e94418",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2617ab22",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "085918f5",
+   "metadata": {},
+   "source": [
+    "### Making the circle circular\n",
+    "\n",
+    "1. Have same figure width and height\n",
+    "2. Aspect ratio\n",
+    "3. Transformers: let's us pick a Coordinate Reference System (CRS)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1bf67506",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Option 1: Have same figure width and height\n",
+    "fig, ax = plt.subplots(figsize=(6, 4))\n",
+    "c = plt.Circle((0.5, 0.5), 0.3)\n",
+    "ax.add_patch(c)\n",
+    "ax.autoscale_view()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4bd1648d-55ce-4156-b94e-6b9a10db4da7",
+   "metadata": {},
+   "source": [
+    "### Aspect Ratio\n",
+    "\n",
+    "- `ax.set_aspect(<Y DIM>)`: how much space y axes takes with respect to x axes space"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "06b32774-26a2-4627-b363-f79eb2838d07",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6, 4))\n",
+    "c = plt.Circle((0.5, 0.5), 0.3)\n",
+    "ax.add_artist(c)\n",
+    "# Set aspect for y-axis to 1\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65f0928e",
+   "metadata": {},
+   "source": [
+    "What if we want x and y axes to have the same aspect ratio? That is we care more about the figure being square than about the circle being circular."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7fa11875-34ba-4550-8e8b-9a9f92e58a9a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6,4))\n",
+    "# Set x axis limit to (0, 3)\n",
+    "\n",
+    "c = plt.Circle((0.5, 0.5), 0.3)\n",
+    "ax.add_artist(c)\n",
+    "# Set aspect for y-axis to 3\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2429f83-1603-4aaf-b767-a60969fc20d7",
+   "metadata": {},
+   "source": [
+    "### Transformers: let us pick a Coordinate Reference System (CRS)\n",
+    "\n",
+    "- Documentation: https://matplotlib.org/stable/tutorials/advanced/transforms_tutorial.html\n",
+    "- `ax.transData`: default\n",
+    "- `ax.transAxes` and `fig.transFigure`:\n",
+    "    - (0, 0) is bottom left\n",
+    "    - (1, 1) is top right\n",
+    "        - these are true immaterial of the axes limits\n",
+    "- `None` or `IdentityTransform()`: disabling CRS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84c0e7b7",
+   "metadata": {},
+   "source": [
+    "### Review:\n",
+    "- `fig, ax = plt.subplots(figsize=(<width>, <height>), ncols=<N>, nrows=<N>)`:\n",
+    "    - ncols: split into vertical sub plots\n",
+    "    - nrows: split into horizontal sub plots\n",
+    "- `ax.set_xlim(<lower limit>, <upper limit>)`: set x-axis limits\n",
+    "- `ax.set_ylim(<lower limit>, <upper limit>)`: set y-axis limits\n",
+    "\n",
+    "### `ax.transData`\n",
+    "- `transform` parameter in \"patch\" creation function let's us specify the CRS\n",
+    "- `color` parameter controls the color of the \"patch\"\n",
+    "- `edgecolor` parameter controls outer border color of the \"patch\"\n",
+    "- `linewidth` parameter controls the size of the border of the \"patch\"\n",
+    "- `facecolor` parameter controls the filled in color of the \"patch\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5adb1223-0fc4-422c-95ef-1446ad811940",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a plot with two vertical subplots\n",
+    "\n",
+    "# Set right subplot x-axis limit from 0 to 3\n",
+    "\n",
+    "\n",
+    "# Left subplot: plot Circle at (0.5, 0.5) with radius 0.2\n",
+    "# Specify CRS as ax1.transData (tranform parameter)\n",
+    "\n",
+    "\n",
+    "# Right subplot: plot Circle at (0.5, 0.5) with radius 0.2\n",
+    "# default: transform=ax2.transData\n",
+    "\n",
+    "\n",
+    "# Observe that we get a different circle\n",
+    "\n",
+    "# Transform based on ax1, but crop based on ax2\n",
+    "# Left subplot: plot Circle at (1, 1) with radius 0.3 and crop using ax2\n",
+    " # where to position the shape \n",
+    " # how to crop the shape\n",
+    "\n",
+    "# Right subplot: plot Circle at (1, 1) with radius 0.3 and crop using ax1\n",
+    " # where to position the shape\n",
+    " # how to crop the shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0167a871",
+   "metadata": {},
+   "source": [
+    "### `ax.transAxes` and `fig.transFigure`\n",
+    "\n",
+    "- (0, 0) is bottom left\n",
+    "- (1, 1) is top right\n",
+    "    - these are true immaterial of the axes limits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "38aa99c6-039a-468e-9cb1-b1a3d9b36a80",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(6, 4))\n",
+    "ax2.set_xlim(0, 3)\n",
+    "\n",
+    "# Left subplot\n",
+    "c = plt.Circle((0.5, 0.5), 0.2, transform=???)\n",
+    "???.add_artist(c)\n",
+    "\n",
+    "# Right subplot\n",
+    "c = plt.Circle((0.5, 0.5), 0.2, transform=???)\n",
+    "???.add_artist(c)\n",
+    "\n",
+    "# whole figure\n",
+    "# edgecolor=\"red\", facecolor=\"none\", linewidth=3\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc52379c",
+   "metadata": {},
+   "source": [
+    "### No CRS (raw pixel coordinates)\n",
+    "\n",
+    "- `fig.dpi`: dots (aka pixesl) per inch\n",
+    "- increasing dpi makes the figure have higher resolution (helpful when you want to print a large size)\n",
+    "- Review: \n",
+    "    - `plt.tight_layout()`: avoid unncessary cropping of the figure --- always needed for No CRS cases\n",
+    "    - `fig.savefig(<relative path.png>)`: to save a local copy of the image\n",
+    "    \n",
+    "- Jupyter command to avoid cropping:\n",
+    "    - `%config InlineBackend.print_figure_kwargs={'bbox_inches': None}`\n",
+    "        - bbox_inches stands for bounding box"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "222eb737",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Jupyter commands begin with %\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e09ef243-ba52-4b70-a980-6ff4735f0fc2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(6, 4))\n",
+    "ax2.set_xlim(0, 3)\n",
+    "\n",
+    "# What is the dpi?\n",
+    "   # dots (aka pixel) per inch\n",
+    "\n",
+    "# Calculate total width and height of the figure using dpi and dimensions\n",
+    "width = ???\n",
+    "height = ???\n",
+    "\n",
+    "# Calculate (x, y) in the middle of the plot\n",
+    "x = ???\n",
+    "y = >>>\n",
+    "print(x, y)\n",
+    "\n",
+    "# Make sure to invoke plt.tight_layout()\n",
+    "# matplotlib does the cropping better than Jupyter\n",
+    "\n",
+    "# Draw a circle at (x, y) with radius 30\n",
+    "# Make sure to set transform=None\n",
+    "\n",
+    "\n",
+    "# Save the figure to temp.png\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "744d76e5",
+   "metadata": {},
+   "source": [
+    "### Mix and match\n",
+    "\n",
+    "- `ax.transData.transform((x, y))`: converts axes / data coords into raw coordinates\n",
+    "- How to draw an arrow:\n",
+    "    `matplotlib.patches.FancyArrowPatch((<x1>, <y1>), (<x2>, (<y2>)), transform=None, arrowstyle=<STYLE>)`\n",
+    "    - arrowstyle=\"simple,head_width=10,head_length=10\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db8a296e-6eae-4e57-a94e-b7901c476ae2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# GOAL: draw a visual circle at axes / data coords 0.5, 0.5 \n",
+    "# with raw co-ordinate radius 30 on right subplot\n",
+    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(6, 4))\n",
+    "ax2.set_xlim(0, 3)\n",
+    "\n",
+    "# crop now (after .transform, we don't want to move anything!)\n",
+    "# plt.tight_layout() \n",
+    "\n",
+    "# Transform (0.5, 0.5) to transData CRS\n",
+    "\n",
+    "print(x, y)\n",
+    "# Draw a circle at (x, y) with radius 30 and set transform to None\n",
+    "\n",
+    "\n",
+    "# GOAL: arrow from 0.2, 0.2 (left) to 2, 0.5 (right)\n",
+    "# Use axes / data coords from one subplot to another subplot\n",
+    "\n",
+    "# arrowstyle=\"simple,head_width=10,head_length=10\"\n",
+    "arrow = matplotlib.patches.FancyArrowPatch((x1, y1), (x2, y2), transform=None)\n",
+    "fig.add_artist(arrow)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2272a0b-f8f1-4bdd-87d5-971aee1ce160",
+   "metadata": {},
+   "source": [
+    "### Custom Scatter Plots with Angles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "468492f8-0e1e-4a37-b16c-d08e818a1b63",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame([\n",
+    "    {\"x\":2, \"y\":5, \"a\": 90},\n",
+    "    {\"x\":3, \"y\":1, \"a\": 0},\n",
+    "    {\"x\":6, \"y\":6, \"a\": 45},\n",
+    "    {\"x\":8, \"y\":1, \"a\": 180}\n",
+    "])\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4963466b-acab-468e-bdde-97f9908933a8",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(3, 2))\n",
+    "ax.set_xlim(0, 10)\n",
+    "ax.set_ylim(0, 10)\n",
+    "\n",
+    "for row in df.itertuples():\n",
+    "    print(row.x, row.y, row.a)\n",
+    "    # v1: draw a circle for each scatter point\n",
+    "    \n",
+    "    # x, y = ax.transData.transform((row.x, row.y))\n",
+    "    # c = plt.Circle((x,y), radius=10, transform=None)\n",
+    "    # ax.add_artist(c)\n",
+    "    \n",
+    "    # v2: draw an arrow for each scatter point (correct angle)\n",
+    "    #x, y = ax.transData.transform((row.x, row.y))\n",
+    "    # Calculate angle: math.radians(row.a)\n",
+    "    #a = ???\n",
+    "    # Calculate end axes / data coords:\n",
+    "    # using math.cos(a) * 25 and math.sin(a) * 25\n",
+    "    #x_diff = ???\n",
+    "    #y_diff = ???\n",
+    "    c = matplotlib.patches.FancyArrowPatch((x,y), (x+x_diff, y+y_diff),transform=None, color=\"k\",\n",
+    "                                           arrowstyle=\"simple,head_width=10,head_length=10\")\n",
+    "    ax.add_artist(c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ddfa239-804f-4c86-b8e9-6f94e7709ab6",
+   "metadata": {},
+   "source": [
+    "### Plot annotations\n",
+    "\n",
+    "- Target plot:\n",
+    "\n",
+    "<img src = \"Target_plot.png\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dff3b3a7-171f-4dab-9bb4-9c5fc76ca0db",
+   "metadata": {},
+   "source": [
+    "- `ax.text(<x>, <y>, <text>, ha=<someval>, va=<someval>)`\n",
+    "    - `ha`: horizontalalignment\n",
+    "    - `va`: verticalalignment\n",
+    "        - enables us to modify \"anchor\" of the text\n",
+    "    \n",
+    "### More patches\n",
+    "- `plt.Line2D((<x1>, <x2>), (<y1>, <y2>)))`\n",
+    "- `matplotlib.patches.FancyArrowPatch((<x1>, <y1>), (<x2>, (<y2>))`\n",
+    "- `plt.Rectangle((<x>,<y>), <width>, <height>)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "648f8e12-d357-43ec-b7de-1f8a14b7264b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.rcParams[\"font.size\"] = 16\n",
+    "df = pd.DataFrame({\"A\": [1,2,8,9], \"B\": [5,7,12,15]}, index=[10,20,30,40])\n",
+    "ax = df.plot.line(figsize=(4,3))\n",
+    "ax.set_xlabel(\"Day\")\n",
+    "ax.set_ylabel(\"Amount\")\n",
+    "plt.tight_layout()\n",
+    "# Enables us to control borders (aka spines)\n",
+    "ax.spines[\"top\"].set_visible(False)\n",
+    "ax.spines[\"right\"].set_visible(False)\n",
+    "\n",
+    "# 1. Replace legened with line labels\n",
+    "\n",
+    "\n",
+    "# 2. Draw a vertical line at x=20\n",
+    "# color=\"r\", linestyle=\"--\"\n",
+    "\n",
+    "\n",
+    "# 3. Highlight a region from x=25 to x=35\n",
+    "# color=\"k\", zorder=50, alpha=0.2, linewidth=0\n",
+    "\n",
+    "\n",
+    "df"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/lecture_material/17-viz-2/vis_2.ipynb b/lecture_material/17-viz-2/vis_2.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..761bceffe77c42f530b550a86557057a4ac4a716
--- /dev/null
+++ b/lecture_material/17-viz-2/vis_2.ipynb
@@ -0,0 +1,2825 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "471a762b",
+   "metadata": {},
+   "source": [
+    "# Visualization 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2478daaa-cb6c-4d73-92af-01ae91e773fe",
+   "metadata": {},
+   "source": [
+    "### Geographic Data / Maps\n",
+    "\n",
+    "#### Installation\n",
+    "```python\n",
+    "pip3 install --upgrade pip\n",
+    "pip3 install geopandas shapely descartes geopy netaddr\n",
+    "sudo apt install -y python3-rtree\n",
+    "```\n",
+    "\n",
+    "- `import geopandas as gpd`\n",
+    "- `.shp` => Shapefile\n",
+    "- `gpd.datasets.get_path(<shp file path>)`:\n",
+    "    - example: `gpd.datasets.get_path(\"naturalearth_lowres\")`\n",
+    "- `gpd.read_file(<path>)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e6f50cc3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import math\n",
+    "import requests\n",
+    "import re\n",
+    "import os\n",
+    "\n",
+    "# new import statements\n",
+    "import geopandas as gpd\n",
+    "from shapely.geometry import Point, Polygon, box"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "257671bc-1e79-47c2-aa7a-1725562d23ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "naturalearth_lowres.cpg  naturalearth_lowres.prj  naturalearth_lowres.shx\n",
+      "naturalearth_lowres.dbf  naturalearth_lowres.shp\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls /home/gurmail.singh/.local/lib/python3.8/site-packages/geopandas/datasets/naturalearth_lowres"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e7269706-188a-48e3-882a-ac068170b1af",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "__init__.py  naturalearth_cities       naturalearth_lowres\n",
+      "__pycache__  naturalearth_creation.py  nybb_16a.zip\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls /home/gurmail.singh/.local/lib/python3.8/site-packages/geopandas/datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "273ed288",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_13458/2175405820.py:2: FutureWarning: The geopandas.dataset module is deprecated and will be removed in GeoPandas 1.0. You can get the original 'naturalearth_lowres' data from https://www.naturalearthdata.com/downloads/110m-cultural-vectors/.\n",
+      "  path = gpd.datasets.get_path(\"naturalearth_lowres\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Find the path for \"naturalearth_lowres\"\n",
+    "path = gpd.datasets.get_path(\"naturalearth_lowres\")\n",
+    "# Read the shapefile for \"naturalearth_lowres\" and\n",
+    "# set index using \"name\" column\n",
+    "gdf = gpd.read_file(path).set_index(\"name\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "cf4d871c-d6d6-4d99-be96-75faf804d4a7",
+   "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>pop_est</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>iso_a3</th>\n",
+       "      <th>gdp_md_est</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Fiji</th>\n",
+       "      <td>889953.0</td>\n",
+       "      <td>Oceania</td>\n",
+       "      <td>FJI</td>\n",
+       "      <td>5496</td>\n",
+       "      <td>MULTIPOLYGON (((180.00000 -16.06713, 180.00000...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tanzania</th>\n",
+       "      <td>58005463.0</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>TZA</td>\n",
+       "      <td>63177</td>\n",
+       "      <td>POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>W. Sahara</th>\n",
+       "      <td>603253.0</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>ESH</td>\n",
+       "      <td>907</td>\n",
+       "      <td>POLYGON ((-8.66559 27.65643, -8.66512 27.58948...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Canada</th>\n",
+       "      <td>37589262.0</td>\n",
+       "      <td>North America</td>\n",
+       "      <td>CAN</td>\n",
+       "      <td>1736425</td>\n",
+       "      <td>MULTIPOLYGON (((-122.84000 49.00000, -122.9742...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States of America</th>\n",
+       "      <td>328239523.0</td>\n",
+       "      <td>North America</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>21433226</td>\n",
+       "      <td>MULTIPOLYGON (((-122.84000 49.00000, -120.0000...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              pop_est      continent iso_a3  gdp_md_est  \\\n",
+       "name                                                                      \n",
+       "Fiji                         889953.0        Oceania    FJI        5496   \n",
+       "Tanzania                   58005463.0         Africa    TZA       63177   \n",
+       "W. Sahara                    603253.0         Africa    ESH         907   \n",
+       "Canada                     37589262.0  North America    CAN     1736425   \n",
+       "United States of America  328239523.0  North America    USA    21433226   \n",
+       "\n",
+       "                                                                   geometry  \n",
+       "name                                                                         \n",
+       "Fiji                      MULTIPOLYGON (((180.00000 -16.06713, 180.00000...  \n",
+       "Tanzania                  POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...  \n",
+       "W. Sahara                 POLYGON ((-8.66559 27.65643, -8.66512 27.58948...  \n",
+       "Canada                    MULTIPOLYGON (((-122.84000 49.00000, -122.9742...  \n",
+       "United States of America  MULTIPOLYGON (((-122.84000 49.00000, -120.0000...  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5c983208-7851-4d25-92e7-3f110039411a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(geopandas.geodataframe.GeoDataFrame,\n",
+       " geopandas.base.GeoPandasBase,\n",
+       " pandas.core.frame.DataFrame,\n",
+       " pandas.core.generic.NDFrame,\n",
+       " pandas.core.base.PandasObject,\n",
+       " pandas.core.accessor.DirNamesMixin,\n",
+       " pandas.core.indexing.IndexingMixin,\n",
+       " pandas.core.arraylike.OpsMixin,\n",
+       " object)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(gdf).__mro__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "653edec9-8b82-4684-ae82-a9fa399459ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "name\n",
+       "Fiji                        MULTIPOLYGON (((180.00000 -16.06713, 180.00000...\n",
+       "Tanzania                    POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...\n",
+       "W. Sahara                   POLYGON ((-8.66559 27.65643, -8.66512 27.58948...\n",
+       "Canada                      MULTIPOLYGON (((-122.84000 49.00000, -122.9742...\n",
+       "United States of America    MULTIPOLYGON (((-122.84000 49.00000, -120.0000...\n",
+       "                                                  ...                        \n",
+       "Serbia                      POLYGON ((18.82982 45.90887, 18.82984 45.90888...\n",
+       "Montenegro                  POLYGON ((20.07070 42.58863, 19.80161 42.50009...\n",
+       "Kosovo                      POLYGON ((20.59025 41.85541, 20.52295 42.21787...\n",
+       "Trinidad and Tobago         POLYGON ((-61.68000 10.76000, -61.10500 10.890...\n",
+       "S. Sudan                    POLYGON ((30.83385 3.50917, 29.95350 4.17370, ...\n",
+       "Name: geometry, Length: 177, dtype: geometry"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# All shapefiles have a column called \"geometry\"\n",
+    "gdf[\"geometry\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e2eb071a-b29a-4edd-8747-50b02fd43108",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(geopandas.geoseries.GeoSeries,\n",
+       " geopandas.base.GeoPandasBase,\n",
+       " pandas.core.series.Series,\n",
+       " pandas.core.base.IndexOpsMixin,\n",
+       " pandas.core.arraylike.OpsMixin,\n",
+       " pandas.core.generic.NDFrame,\n",
+       " pandas.core.base.PandasObject,\n",
+       " pandas.core.accessor.DirNamesMixin,\n",
+       " pandas.core.indexing.IndexingMixin,\n",
+       " object)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(gdf[\"geometry\"]).__mro__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "da5b5783-d78e-460f-bd22-b41e7f24f871",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fiji\n"
+     ]
+    },
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"300\" height=\"100.0\" viewBox=\"-194.4 -32.68799 388.8 31.067107743258774\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,-34.308872256741225)\"><g><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"2.592\" opacity=\"0.6\" d=\"M 180.0,-16.067132663642447 L 180.0,-16.555216566639196 L 179.36414266196414,-16.801354076946883 L 178.72505936299711,-17.01204167436804 L 178.59683859511713,-16.639150000000004 L 179.0966093629971,-16.433984277547403 L 179.4135093629971,-16.379054277547404 L 180.0,-16.067132663642447 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"2.592\" opacity=\"0.6\" d=\"M 178.12557,-17.50481 L 178.3736,-17.33992 L 178.71806,-17.62846 L 178.55271,-18.15059 L 177.93266000000003,-18.28799 L 177.38146,-18.16432 L 177.28504,-17.72465 L 177.67087,-17.381140000000002 L 178.12557,-17.50481 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"2.592\" opacity=\"0.6\" d=\"M -179.79332010904864,-16.020882256741224 L -179.9173693847653,-16.501783135649397 L -180.0,-16.555216566639196 L -180.0,-16.067132663642447 L -179.79332010904864,-16.020882256741224 z\" /></g></g></svg>"
+      ],
+      "text/plain": [
+       "<MULTIPOLYGON (((180 -16.067, 180 -16.555, 179.364 -16.801, 178.725 -17.012,...>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# First country's geometry\n",
+    "print(gdf.index[0])\n",
+    "gdf[\"geometry\"].iat[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "8f4da997-567d-47f6-8594-f0a37b92b9e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tanzania\n"
+     ]
+    },
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"28.90093389661636 -12.160001698450722 11.854719799667649 11.64906539473471\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,-12.670938002166736)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.23709439599335297\" opacity=\"0.6\" d=\"M 33.90371119710453,-0.9500000000000001 L 34.07261999999997,-1.0598199999999451 L 37.69868999999994,-3.0969899999999484 L 37.7669,-3.6771200000000004 L 39.20222,-4.67677 L 38.74053999999995,-5.9089499999999475 L 38.79977000000008,-6.475660000000005 L 39.44,-6.839999999999861 L 39.47000000000014,-7.099999999999966 L 39.19468999999998,-7.703899999999976 L 39.25203000000005,-8.00780999999995 L 39.18652000000009,-8.48550999999992 L 39.53574000000009,-9.112369999999885 L 39.94960000000003,-10.098400000000026 L 40.316586229110854,-10.317097752817492 L 40.31659000000002,-10.317099999999868 L 39.52099999999996,-10.89688000000001 L 38.42755659358775,-11.285202325081656 L 37.827639999999974,-11.26878999999991 L 37.471289999999954,-11.568759999999997 L 36.775150994622805,-11.594537448780805 L 36.51408165868426,-11.720938002166735 L 35.31239790216904,-11.439146416879147 L 34.55998904799935,-11.520020033415925 L 34.27999999999997,-10.160000000000025 L 33.940837724096525,-9.693673841980285 L 33.73972000000009,-9.417149999999992 L 32.75937544122132,-9.23059905358906 L 32.19186486179194,-8.930358981973257 L 31.556348097466497,-8.762048841998642 L 31.15775133695005,-8.594578747317366 L 30.740009731422095,-8.34000593035372 L 30.74001549655179,-8.340007419470915 L 30.199996779101696,-7.079980970898163 L 29.620032179490014,-6.520015150583426 L 29.419992710088167,-5.939998874539434 L 29.519986606572928,-5.419978936386315 L 29.339997592900346,-4.4999834122940925 L 29.753512404099865,-4.452389418153302 L 30.11632000000003,-4.090120000000013 L 30.505539999999996,-3.5685799999999404 L 30.752240000000086,-3.3593099999999936 L 30.743010000000027,-3.034309999999948 L 30.527660000000026,-2.807619999999986 L 30.469673645761223,-2.41385475710134 L 30.469670000000008,-2.4138299999999617 L 30.75830895358311,-2.2872502579883687 L 30.816134881317712,-1.6989140763453887 L 30.419104852019245,-1.1346591121504161 L 30.769860000000108,-1.0145499999999856 L 31.866170000000068,-1.0273599999999306 L 33.90371119710453,-0.9500000000000001 z\" /></g></svg>"
+      ],
+      "text/plain": [
+       "<POLYGON ((33.904 -0.95, 34.073 -1.06, 37.699 -3.097, 37.767 -3.677, 39.202 ...>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Second country's geometry\n",
+    "print(gdf.index[1])\n",
+    "gdf[\"geometry\"].iat[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "d380b32f-c401-4601-b6d3-02ac344b3f08",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"113.21256665112256\" height=\"100.0\" viewBox=\"-175.98416862700688 14.72313197588435 113.21256665112256 60.82768962517304\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,90.27395357694175)\"><g><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"2.0\" opacity=\"0.6\" d=\"M -122.84000000000003,49.000000000000114 L -120.0,49.000000000000114 L -117.03121,49.0 L -116.04818,49.0 L -113.0,49.0 L -110.05000000000001,49.0 L -107.05000000000001,49.0 L -104.04826000000003,48.99986000000007 L -100.65000000000003,49.000000000000114 L -97.2287200000048,49.0007 L -95.15906950917206,49.0 L -95.15609,49.38425000000001 L -94.81758000000002,49.38905 L -94.64,48.84 L -94.32914000000001,48.67074 L -93.63087000000002,48.609260000000006 L -92.61000000000001,48.44999999999993 L -91.64,48.14 L -90.83,48.27 L -89.60000000000002,48.010000000000105 L 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+      ],
+      "text/plain": [
+       "<MULTIPOLYGON (((-122.84 49, -120 49, -117.031 49, -116.048 49, -113 49, -11...>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Geometry for \"United States of America\"\n",
+    "gdf.at[\"United States of America\", \"geometry\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3528f252-36b7-4ca5-9108-0951367837cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tanzania <class 'shapely.geometry.polygon.Polygon'>\n",
+      "United States of America <class 'shapely.geometry.multipolygon.MultiPolygon'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Type of Tanzania's geometry\n",
+    "print(gdf.index[1], type(gdf[\"geometry\"].iat[1]))\n",
+    "\n",
+    "# Type of United States of America's geometry\n",
+    "print(\"United States of America\", type(gdf.at[\"United States of America\", \"geometry\"]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47711b72-a24c-4da8-a804-3faf88a5fe8b",
+   "metadata": {},
+   "source": [
+    "- `gdf.plot(figsize=(<width>, <height>), column=<column name>)`\n",
+    "- `ax.set_axis_off()`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9874c7de-226e-4296-af60-45e091836a19",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = gdf.plot(figsize=(8,4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "f5547cc5-3404-421e-aeed-4b4cf8ee8382",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Set facecolor=\"0.7\", edgecolor=\"black\"\n",
+    "ax = gdf.plot(figsize=(8,4), facecolor=\"0.7\", edgecolor=\"black\")\n",
+    "# Turn off the axes\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "58da716e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Color the map based on population column and set cmap=\"cool\"\n",
+    "ax = gdf.plot(figsize=(8,4), column=\"pop_est\", legend=True, cmap=\"cool\")\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7a85431-bdab-46ab-8e29-5f91d8a2e0bc",
+   "metadata": {},
+   "source": [
+    "#### Create a map where countries with >100M people are red, others are gray."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "962a552b-94a6-4690-8018-f82173dd6096",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a map where countries with >100M people are red, others are gray\n",
+    "\n",
+    "# Add a new column called color to gdf and set default value to \"lightgray\"\n",
+    "\n",
+    "# Boolean indexing to set color to red for countries with \"pop_est\" > 1e8\n",
+    "\n",
+    "# Create the plot\n",
+    "# ax = gdf.plot(figsize=(8,4), color=gdf[\"color\"])\n",
+    "# ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "11214c90",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a map where countries with >100M people are red, others are gray\n",
+    "\n",
+    "# Add a new column called color to gdf and set default value to \"lightgray\"\n",
+    "gdf[\"color\"] = \"lightgray\"\n",
+    "# Boolean indexing to set color to red for countries with \"pop_est\" > 1e8\n",
+    "gdf.loc[gdf[\"pop_est\"] > 1e8, \"color\"] = \"red\"\n",
+    "# Create the plot\n",
+    "ax = gdf.plot(figsize=(8,4), color=gdf[\"color\"])\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a32c52c-509f-4f7f-8dd6-bc7fe53c64fd",
+   "metadata": {},
+   "source": [
+    "### All shapefile geometries are shapely shapes. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "33e92def-a7db-4d90-adc8-a3d01d472ac1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "shapely.geometry.polygon.Polygon"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(gdf[\"geometry\"].iat[2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39c95c23",
+   "metadata": {},
+   "source": [
+    "### Shapely shapes\n",
+    "\n",
+    "- `from shapely.geometry import Point, Polygon, box`\n",
+    "- `Polygon([(<x1>, <y1>), (<x2>, <y2>), (<x3>, <y3>), ...])`\n",
+    "- `box(minx, miny, maxx, maxy)`\n",
+    "- `Point(<x>, <y>)`\n",
+    "- `<shapely object>.buffer(<size>)`\n",
+    "    - example: `Point(5, 5).buffer(3)` creates a circle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "61716db9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-0.08 -0.08 2.16 1.1600000000000001\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,1.0)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.0432\" opacity=\"0.6\" d=\"M 0.0,0.0 L 1.2,1.0 L 2.0,0.0 L 0.0,0.0 z\" /></g></svg>"
+      ],
+      "text/plain": [
+       "<POLYGON ((0 0, 1.2 1, 2 0, 0 0))>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "triangle = Polygon([(0, 0), (1.2, 1), (2, 0)])   # triangle\n",
+    "triangle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "6a36021a-8653-4698-a0ba-818c14091d79",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "shapely.geometry.polygon.Polygon"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(triangle)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "bddd958d-6fde-42df-8ae3-459e25fa3f04",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-0.04 -0.04 1.08 1.08\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,1.0)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.0216\" opacity=\"0.6\" d=\"M 1.0,0.0 L 1.0,1.0 L 0.0,1.0 L 0.0,0.0 L 1.0,0.0 z\" /></g></svg>"
+      ],
+      "text/plain": [
+       "<POLYGON ((1 0, 1 1, 0 1, 0 0, 1 0))>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "box1 = box(0, 0, 1, 1) # not a type; just a function that creates box\n",
+    "box1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "eb7ade8d-96d2-4770-bce9-b3e35996b0b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "shapely.geometry.polygon.Polygon"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(box1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "e5308c61-b1fa-433b-bb05-485ca7bd23da",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"4.0 4.0 2.0 2.0\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,10.0)\"><circle cx=\"5.0\" cy=\"5.0\" r=\"0.06\" stroke=\"#555555\" stroke-width=\"0.02\" fill=\"#66cc99\" opacity=\"0.6\" /></g></svg>"
+      ],
+      "text/plain": [
+       "<POINT (5 5)>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "point = Point(5, 5)\n",
+    "point"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "5c669619-af78-477d-b807-3e6d99278f2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "shapely.geometry.point.Point"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(point)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "f015d27e-8fd8-446f-992f-4f7a88cc582d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"3.92 3.92 2.16 2.16\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,10.0)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.0432\" opacity=\"0.6\" d=\"M 6.0,5.0 L 5.995184726672197,4.901982859670439 L 5.98078528040323,4.804909677983872 L 5.956940335732209,4.709715322745538 L 5.923879532511287,4.61731656763491 L 5.881921264348355,4.528603263174002 L 5.831469612302545,4.444429766980398 L 5.773010453362737,4.365606715836354 L 5.707106781186548,4.292893218813452 L 5.634393284163646,4.226989546637263 L 5.555570233019602,4.168530387697455 L 5.471396736825998,4.118078735651645 L 5.38268343236509,4.076120467488713 L 5.290284677254462,4.043059664267791 L 5.195090322016128,4.01921471959677 L 5.098017140329561,4.004815273327803 L 5.0,4.0 L 4.901982859670439,4.004815273327803 L 4.804909677983872,4.01921471959677 L 4.709715322745538,4.043059664267791 L 4.61731656763491,4.076120467488713 L 4.528603263174002,4.118078735651645 L 4.444429766980398,4.168530387697454 L 4.365606715836354,4.226989546637263 L 4.292893218813452,4.292893218813452 L 4.226989546637263,4.365606715836354 L 4.168530387697455,4.444429766980398 L 4.118078735651645,4.528603263174002 L 4.076120467488713,4.61731656763491 L 4.043059664267791,4.709715322745538 L 4.01921471959677,4.804909677983871 L 4.004815273327803,4.901982859670439 L 4.0,5.0 L 4.004815273327803,5.098017140329561 L 4.01921471959677,5.195090322016128 L 4.043059664267791,5.290284677254462 L 4.076120467488713,5.38268343236509 L 4.118078735651645,5.471396736825998 L 4.168530387697454,5.555570233019602 L 4.226989546637263,5.634393284163645 L 4.292893218813452,5.707106781186548 L 4.365606715836354,5.773010453362737 L 4.444429766980398,5.831469612302545 L 4.528603263174002,5.881921264348355 L 4.6173165676349095,5.923879532511286 L 4.709715322745538,5.956940335732209 L 4.804909677983871,5.98078528040323 L 4.901982859670439,5.995184726672197 L 5.0,6.0 L 5.09801714032956,5.995184726672197 L 5.195090322016128,5.98078528040323 L 5.290284677254462,5.956940335732209 L 5.38268343236509,5.923879532511287 L 5.471396736825998,5.881921264348355 L 5.555570233019602,5.831469612302546 L 5.634393284163646,5.773010453362737 L 5.707106781186547,5.707106781186548 L 5.773010453362737,5.634393284163646 L 5.831469612302545,5.555570233019602 L 5.881921264348355,5.471396736825998 L 5.923879532511286,5.3826834323650905 L 5.956940335732209,5.290284677254462 L 5.98078528040323,5.195090322016129 L 5.995184726672197,5.098017140329561 L 6.0,5.0 z\" /></g></svg>"
+      ],
+      "text/plain": [
+       "<POLYGON ((6 5, 5.995 4.902, 5.981 4.805, 5.957 4.71, 5.924 4.617, 5.882 4.5...>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "circle = point.buffer(1)\n",
+    "circle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "39fe5752-8038-46cc-b4a6-320e6a78bbd1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "shapely.geometry.polygon.Polygon"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(circle)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "800cc48d-c241-4439-9161-ff309e50373f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-3.3199593907741214 -3.319994088007126 8.639840376192394 7.639985892064196\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,0.9999977160499447)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.17279680752384788\" opacity=\"0.6\" d=\"M 0.0,-3.0 L -0.29315396407645233,-2.9856424356152 L -0.583501943543646,-2.9427071688975084 L -0.8682648118621171,-2.8716051637511106 L -1.1447169015543412,-2.7730169879205264 L -1.4102120935022777,-2.6478862987937988 L -1.6622091448310923,-2.49741081098803 L -1.8982960129479625,-2.32303083217289 L -2.1162129429132928,-2.126415476863884 L -2.31387409715874,-1.9094466901429394 L -2.4893875205186675,-1.674201234226615 L -2.6410732494755527,-1.4229308103012759 L -2.7674793922828913,-1.1580405058932604 L -2.867396026051358,-0.8820657740695307 L -2.939866777779323,-0.5976481648172466 L -2.9841979784774866,-0.3075100408944149 L -2.9999653027669955,-0.014428520164173887 L -2.9870178303987425,0.27879110617088887 L -2.9454794908181587,0.5693422250014475 L -2.875747876948555,0.8544437653970423 L -2.778490439547124,1.1313668182093857 L -2.654638098560126,1.3974607563982173 L -2.505376332627515,1.6501786060635695 L -2.332133832025667,1.8871014253402698 L -2.1365688236589673,2.1059614578069885 L -1.9205531989934397,2.3046638387921274 L -0.7205531989934397,3.3046638387921274 L -0.48230548273878826,3.483917925928112 L -0.2274414479240734,3.6386380791515203 L 0.04152158500240777,3.767296110282502 L 0.32192703821822866,3.8686212496228545 L 0.6110053158417735,3.9416126974898225 L 0.9059011595391244,3.985549509225993 L 1.2037018502285248,3.999997716049945 L 1.5014659773310561,3.9848146114142216 L 1.796252491410417,3.94015016053379 L 2.0851497532456618,3.8664455191629132 L 2.365304292415968,3.76442867625065 L 2.633948991345682,3.635107263512949 L 2.8884304164327097,3.4797586029420016 L 3.126235026307631,3.2999170905547093 L 3.3450139983616642,3.097359040992387 L 3.5426064283290906,2.874085142663273 L 4.342606428329091,1.874085142663273 L 4.51413065303249,1.6368100254710147 L 4.6617091685800425,1.3839451946868921 L 4.783936370928579,1.1178990484927562 L 4.879648113324297,0.8412055298366848 L 4.947932794159122,0.5564999920226339 L 4.988140039473731,0.2664940984223267 L 4.999886897411147,-0.026050004624205612 L 4.9830614856221676,-0.3183459957305381 L 4.93782405688309,-0.6076099166397639 L 4.864605472776324,-0.8910866878928989 L 4.764103099971219,-1.1660763494426483 L 4.6372741681908085,-1.42995977628515 L 4.4853266531261315,-1.6802236241824668 L 4.309707771133386,-1.9144842678815743 L 4.112090195295574,-2.1305105038314895 L 3.8943561241330404,-2.326244801167677 L 3.6585793546996865,-2.49982289855981 L 3.4070055308087106,-2.6495915602737146 L 3.142030754512665,-2.7741243223307843 L 2.866178764551686,-2.872235078791378 L 2.582076899132601,-2.9429893787603407 L 2.292431071980201,-2.985713326516883 L 2.0,-3.0 L 0.0,-3.0 z\" /></g></svg>"
+      ],
+      "text/plain": [
+       "<POLYGON ((0 -3, -0.293 -2.986, -0.584 -2.943, -0.868 -2.872, -1.145 -2.773,...>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "triangle_buffer = triangle.buffer(3)\n",
+    "triangle_buffer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "6b9478d3-7cc9-4e80-ae84-e56d7bfd0b71",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "shapely.geometry.polygon.Polygon"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(triangle_buffer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a340443-d750-43d5-99cd-28e7034ce898",
+   "metadata": {},
+   "source": [
+    "#### Shapely methods\n",
+    "\n",
+    "- Shapely methods:\n",
+    "    - `union`:  any point that is in either shape (OR)\n",
+    "    - `intersection`: any point that is in both shapes (AND)\n",
+    "    - `difference`: subtraction\n",
+    "    - `intersects`: do they overlap? returns True / False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "d1c5f9f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-0.08 -0.08 2.16 1.1600000000000001\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,1.0)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.0432\" opacity=\"0.6\" d=\"M 1.2,1.0 L 2.0,0.0 L 1.0,0.0 L 0.0,0.0 L 0.0,1.0 L 1.0,1.0 L 1.0,0.8333333333333334 L 1.2,1.0 z\" /></g></svg>"
+      ],
+      "text/plain": [
+       "<POLYGON ((1.2 1, 2 0, 1 0, 0 0, 0 1, 1 1, 1 0.833, 1.2 1))>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "triangle.union(box1)   # any point that is in either shape (OR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "8a2d3357",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-0.04 -0.04 1.08 0.9133333333333334\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,0.8333333333333334)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.0216\" opacity=\"0.6\" d=\"M 1.0,0.8333333333333334 L 1.0,0.0 L 0.0,0.0 L 1.0,0.8333333333333334 z\" /></g></svg>"
+      ],
+      "text/plain": [
+       "<POLYGON ((1 0.833, 1 0, 0 0, 1 0.833))>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "triangle.intersection(box1)   # any point that is in both shapes (AND)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "153a6b72",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"0.96 -0.04 1.08 1.08\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,1.0)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.0216\" opacity=\"0.6\" d=\"M 1.2,1.0 L 2.0,0.0 L 1.0,0.0 L 1.0,0.8333333333333334 L 1.2,1.0 z\" /></g></svg>"
+      ],
+      "text/plain": [
+       "<POLYGON ((1.2 1, 2 0, 1 0, 1 0.833, 1.2 1))>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "triangle.difference(box1)   # subtraction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "9082b54a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-0.04 -0.04 1.08 1.08\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,1.0)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.0216\" opacity=\"0.6\" d=\"M 0.0,1.0 L 1.0,1.0 L 1.0,0.8333333333333334 L 0.0,0.0 L 0.0,1.0 z\" /></g></svg>"
+      ],
+      "text/plain": [
+       "<POLYGON ((0 1, 1 1, 1 0.833, 0 0, 0 1))>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "box1.difference(triangle)   # subtraction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "59493a5b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "triangle.intersects(box1) # do they overlap?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51a82cf3-fe28-46d2-a019-d87f6a0f41b6",
+   "metadata": {},
+   "source": [
+    "Is the point \"near\" (<6 units) the triangle?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "7a87b70f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-1.48 -1.48 12.96 12.96\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,10.0)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.25920000000000004\" opacity=\"0.6\" d=\"M 0.8299166312941222,0.6915971927451019 L 0.7573593128807152,0.7573593128807143 L 0.3619372798235778,1.193640295018127 L 0.01118232618472792,1.666578601882387 L -0.29152758609012963,2.171619579044013 L -0.54327719506772,2.7038994058094605 L -0.7416420143932534,3.258291936473226 L -0.8847116824193826,3.8294580679032286 L -0.9711083600331811,4.411897158022635 L -1.0,4.999999999999999 L -0.9711083600331811,5.5881028419773635 L -0.8847116824193826,6.17054193209677 L -0.7416420143932534,6.741708063526772 L -0.5432771950677209,7.296100594190538 L -0.2915275860901305,7.828380420955986 L 0.011182326184727032,8.333421398117611 L 0.36193727982357693,8.806359704981872 L 0.7573593128807143,9.242640687119284 L 1.1936402950181244,9.63806272017642 L 1.666578601882387,9.988817673815271 L 2.171619579044013,10.29152758609013 L 2.703899405809458,10.54327719506772 L 3.2582919364732255,10.741642014393253 L 3.8294580679032277,10.884711682419383 L 4.411897158022637,10.971108360033181 L 4.999999999999999,11.0 L 5.588102841977361,10.971108360033181 L 6.17054193209677,10.884711682419383 L 6.741708063526772,10.741642014393253 L 7.2961005941905395,10.54327719506772 L 7.828380420955986,10.29152758609013 L 8.333421398117611,9.988817673815273 L 8.806359704981872,9.638062720176421 L 9.242640687119284,9.242640687119286 L 9.63806272017642,8.806359704981876 L 9.988817673815271,8.333421398117613 L 10.29152758609013,7.8283804209559875 L 10.54327719506772,7.296100594190542 L 10.741642014393253,6.741708063526775 L 10.884711682419383,6.170541932096772 L 10.971108360033181,5.5881028419773635 L 11.0,5.0 L 10.971108360033181,4.4118971580226365 L 10.884711682419383,3.8294580679032304 L 10.741642014393253,3.258291936473226 L 10.54327719506772,2.7038994058094614 L 10.29152758609013,2.1716195790440143 L 9.988817673815271,1.666578601882387 L 9.638062720176421,1.193640295018127 L 9.242640687119286,0.7573593128807152 L 8.806359704981872,0.3619372798235778 L 8.333421398117615,0.011182326184728808 L 7.828380420955987,-0.29152758609012963 L 7.2961005941905395,-0.54327719506772 L 6.741708063526774,-0.7416420143932534 L 6.17054193209677,-0.8847116824193826 L 5.588102841977364,-0.9711083600331811 L 5.0,-1.0 L 4.4118971580226365,-0.9711083600331811 L 3.829458067903231,-0.8847116824193826 L 3.258291936473227,-0.7416420143932534 L 2.7038994058094614,-0.54327719506772 L 2.171619579044014,-0.2915275860901305 L 1.6852351860055625,0.0 L 0.0,0.0 L 0.8299166312941222,0.6915971927451019 z\" /></g></svg>"
+      ],
+      "text/plain": [
+       "<POLYGON ((0.83 0.692, 0.757 0.757, 0.362 1.194, 0.011 1.667, -0.292 2.172, ...>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "triangle.union(point.buffer(6))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "e04daa60",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "triangle.intersects(point.buffer(6))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf500303",
+   "metadata": {},
+   "source": [
+    "#### Extacting \"Europe\" data from \"naturalearth_lowres\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "410b08cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Europe bounding box\n",
+    "eur_window = box(-10.67, 34.5, 31.55, 71.05)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d45fdfcb-b244-4eff-a9d6-5cc4fefdfbd8",
+   "metadata": {},
+   "source": [
+    "Can we use `intersects` method?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "24f4a32b-9ed4-468b-a194-ebe0395fcdb6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "name\n",
+       "Fiji                        False\n",
+       "Tanzania                    False\n",
+       "W. Sahara                   False\n",
+       "Canada                      False\n",
+       "United States of America    False\n",
+       "                            ...  \n",
+       "Serbia                       True\n",
+       "Montenegro                   True\n",
+       "Kosovo                       True\n",
+       "Trinidad and Tobago         False\n",
+       "S. Sudan                    False\n",
+       "Length: 177, dtype: bool"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf.intersects(eur_window)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "ff455178-84da-45bf-b5b9-a71a7f9160f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Incorrect v1\n",
+    "gdf[gdf.intersects(eur_window)].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "03aa8da8-72f4-45fd-8931-e410d1204226",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Incorrect v2\n",
+    "gdf[~gdf.intersects(eur_window)].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4173f3c5-017f-44ff-b713-158219fcf3e5",
+   "metadata": {},
+   "source": [
+    "Can we use `intersection` method?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "d7226fd9-3bb2-48c3-a5e9-5ce1ca0dd88f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "name\n",
+       "Fiji                                                            POLYGON EMPTY\n",
+       "Tanzania                                                        POLYGON EMPTY\n",
+       "W. Sahara                                                       POLYGON EMPTY\n",
+       "Canada                                                          POLYGON EMPTY\n",
+       "United States of America                                        POLYGON EMPTY\n",
+       "                                                  ...                        \n",
+       "Serbia                      POLYGON ((18.82984 45.90888, 19.59604 46.17173...\n",
+       "Montenegro                  POLYGON ((19.80161 42.50009, 19.73805 42.68825...\n",
+       "Kosovo                      POLYGON ((20.52295 42.21787, 20.28375 42.32026...\n",
+       "Trinidad and Tobago                                             POLYGON EMPTY\n",
+       "S. Sudan                                                        POLYGON EMPTY\n",
+       "Length: 177, dtype: geometry"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf.intersection(eur_window)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "108b8b8a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gdf.intersection(eur_window).plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f13e4e16-286a-4681-8bc5-9bd3fcf599a7",
+   "metadata": {},
+   "source": [
+    "How can we get rid of empty polygons (and remove the warning)?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "8c0dd051-a808-4fd4-b5ff-fee6ed580753",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "name\n",
+       "Fiji                         True\n",
+       "Tanzania                     True\n",
+       "W. Sahara                    True\n",
+       "Canada                       True\n",
+       "United States of America     True\n",
+       "                            ...  \n",
+       "Serbia                      False\n",
+       "Montenegro                  False\n",
+       "Kosovo                      False\n",
+       "Trinidad and Tobago          True\n",
+       "S. Sudan                     True\n",
+       "Length: 177, dtype: bool"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eur = gdf.intersection(eur_window)\n",
+    "eur.is_empty"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acb1b886-f7c8-41d4-979a-2309c2b973bd",
+   "metadata": {},
+   "source": [
+    "Remove all the empty polygons using `is_empty`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "55a76a00",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "name\n",
+       "Russia              MULTIPOLYGON (((31.54002 52.74205, 31.30520 53...\n",
+       "Norway              POLYGON ((29.39955 69.15692, 28.59193 69.06478...\n",
+       "France              MULTIPOLYGON (((6.65823 49.20196, 8.09928 49.0...\n",
+       "Tunisia             POLYGON ((8.14098 34.65515, 8.37637 35.47988, ...\n",
+       "Algeria             POLYGON ((-1.79299 34.52792, -2.16991 35.16840...\n",
+       "Sweden              POLYGON ((11.46827 59.43239, 12.30037 60.11793...\n",
+       "Belarus             POLYGON ((29.22951 55.91834, 29.37157 55.67009...\n",
+       "Ukraine             POLYGON ((30.74875 46.58310, 30.37761 46.03241...\n",
+       "Poland              POLYGON ((23.52754 53.47012, 23.80493 53.08973...\n",
+       "Austria             POLYGON ((16.90375 47.71487, 16.34058 47.71290...\n",
+       "Hungary             POLYGON ((22.64082 48.15024, 22.71053 47.88219...\n",
+       "Moldova             POLYGON ((26.85782 48.36821, 27.52254 48.46712...\n",
+       "Romania             POLYGON ((28.67978 45.30403, 29.14972 45.46493...\n",
+       "Lithuania           POLYGON ((26.58828 55.16718, 25.76843 54.84696...\n",
+       "Latvia              POLYGON ((27.77002 57.24426, 27.85528 56.75933...\n",
+       "Estonia             POLYGON ((27.98112 59.47537, 28.13170 59.30083...\n",
+       "Germany             POLYGON ((14.35332 53.24817, 14.07452 52.98126...\n",
+       "Bulgaria            POLYGON ((22.94483 43.82379, 23.33230 43.89701...\n",
+       "Greece              MULTIPOLYGON (((26.16500 35.00500, 24.72498 34...\n",
+       "Turkey              MULTIPOLYGON (((30.62162 36.67786, 30.39110 36...\n",
+       "Albania             POLYGON ((20.99999 40.58000, 20.67500 40.43500...\n",
+       "Croatia             POLYGON ((16.88252 46.38063, 17.63007 45.95177...\n",
+       "Switzerland         POLYGON ((9.63293 47.34760, 9.47997 47.10281, ...\n",
+       "Luxembourg          POLYGON ((6.24275 49.90223, 6.18632 49.46380, ...\n",
+       "Belgium             POLYGON ((6.04307 50.12805, 5.78242 50.09033, ...\n",
+       "Netherlands         POLYGON ((7.09205 53.14404, 6.84287 52.22844, ...\n",
+       "Portugal            POLYGON ((-8.67195 42.13469, -8.26386 42.28047...\n",
+       "Spain               POLYGON ((-7.53711 37.42890, -7.16651 37.80389...\n",
+       "Ireland             POLYGON ((-6.03299 53.15316, -6.78886 52.26012...\n",
+       "Italy               MULTIPOLYGON (((11.04856 46.75136, 11.16483 46...\n",
+       "Denmark             MULTIPOLYGON (((9.28205 54.83087, 8.52623 54.9...\n",
+       "United Kingdom      MULTIPOLYGON (((-6.95373 54.07370, -7.57217 54...\n",
+       "Slovenia            POLYGON ((14.63247 46.43182, 15.13709 46.65870...\n",
+       "Finland             POLYGON ((28.44594 68.36461, 29.97743 67.69830...\n",
+       "Slovakia            POLYGON ((22.28084 48.82539, 22.08561 48.42226...\n",
+       "Czechia             POLYGON ((15.49097 50.78473, 16.23863 50.69773...\n",
+       "Morocco             POLYGON ((-1.79299 34.52792, -1.79025 34.50000...\n",
+       "Bosnia and Herz.    POLYGON ((17.67492 43.02856, 17.29737 43.44634...\n",
+       "North Macedonia     POLYGON ((22.88137 41.99930, 22.95238 41.33799...\n",
+       "Serbia              POLYGON ((18.82984 45.90888, 19.59604 46.17173...\n",
+       "Montenegro          POLYGON ((19.80161 42.50009, 19.73805 42.68825...\n",
+       "Kosovo              POLYGON ((20.52295 42.21787, 20.28375 42.32026...\n",
+       "dtype: geometry"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eur = eur[~eur.is_empty]\n",
+    "eur"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "08c59df7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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IV6GHQhUCnVCS9v7+fsyfPx/nnHMOvvzySyQkJODUqVNQq0fmbFxwwQVYv36953uhMPoJcSyxyY6TPfjn1ycovy4/QYr9MbAs/PevZ2BGhorWc+bES7GvMXxlSgQBdETRqoaSaP39739HRkbGCEHKyckZc5xQKERycnLoo2P5WdPcZ8Yd7wZu6DcEn0PA5nIzflm4eskkXDw9lfbz5sSHt8xmZoYK1c26sL6HPygtD//3v/9h9uzZuPzyy5GYmIiSkhK88sorY477/vvvkZiYiMmTJ2PlypXo6/NdG2az2WAwGEZ8sbBY7C7c+nYV9BYH5deWZKnRomWOaZ03LpqegjsX59N6TpebxCc1bdi4r5nW846mn2JSL91QEq3Tp09j3bp1KCgowNdff42VK1fizjvvxJtvvuk55oILLsB///tfbN26FX//+9+xfft2LFu2DC6X9/KJxx9/HEql0vOVkZER2hWxxDwkSeL+zYdwPAhX0fxEWUwsC2emq2izTyZJEl8d6cSyZ3bgro01aOoLXxwvO06CxjCePxAIkgy8eksgEGD27NnYtWuX57E777wT+/btw+7du72+5vTp08jLy8OWLVuwePHiMc/bbDbYbD8F9AwGAzIyMqDX66FQRN8GgyXyvPZjAx757Bjl1/E5BJJVIsbPsgDgN3My8MSK6SGfx2B14J73D+LbY+N30KaLWZkqHAhgeZifKMOWNWcHdE6DwQClUhnQfU9pppWSkoKioqIRj02ZMgXNzb6no7m5uYiPj0ddnfcsZqFQCIVCMeKL5efLrvpe/O2L40G9ls8lolbWQpXTNLQsq+004pL/7IyoYAHA8U4j0lSiiL7ncCiJ1vz581FbWzvisZMnTyIrK8vna1pbW9HX14eUlJTgRsjys6FNZ8EdG6oD6gjtDbPDjRRF9G4mKtT3UMs5G82nB9tx6fM70dAb+T6EFrsLQj4XvChleVJ629WrV2PPnj3429/+hrq6OmzYsAEvv/wyVq1aBQAYGBjAH/7wB+zZsweNjY3YunUrLrnkEuTn52Pp0qVhuQCWiYHF7sLKt6soOzeM5lCbHiWZKnoGFUb6THbozdQ3GRwuNx757BjueLcaFkf0bHZO95gwK0sTlfemJFpz5szBRx99hHfffRfFxcV45JFH8PTTT+Pqq68GAHC5XBw6dAi/+MUvMGnSJNx4440oLS3FDz/8wOZqsfikd8CGK1/Zg0OtelrOx2VC//hxUIh4MDuolcB0G624+pW9eO3HhjCNihqVDVpMS4t8OIdSID4SUAnIscQ+dd0D+N0blbQFz/kcAhqZAF2G6GRrB0JOvBSvXjcbeQmB51NVNWmx8u0DUbGC8YdSzAePQ3idITMiEM/CQie76/tw2Qs7ad3tK8lSMVqw5uXF4aPfzwtYsEiSxJu7GnHFS3sYJ1gAoLc4IlI2NBzWmoYlKmw+0Ir7PjwEhyu4if6kJBmEPA5c7sGkSqfbDZIETnaFFuAOJ1eVZ+KhX0wN2MXBYnfhTx8dxkdR8ssKFJM9srE1VrRYIgpJknhm6yk8vYVaQ4rhSAVctPRbYInwzRIsHAL4y8VFuG5edsAJpY29Jtz2dhVOdEbWYI8qiXIhjrZHtoqFFS2WiGF3unH/5kPYfCC0mUNRqiKsBcF0Ihfy8J+rZ+HsSQkBv+a7E124a2MNjNbIe1VRJSdeGvFlKytaLBFBb3bg1rf3Y8/p0EtsOvVWGkYUfjI1Erx+/WzkJ8oDfs2WY1245a39jC/2HqJZG/k8MVa0WMJOi9aM69dXop6GLPCiFDmORdiTPBjKczR48ZpSqCkY8h1q1QXlahEtCpPlUVm+sqLFElaqm/tx83/3o3eAHmcAfrTSsAOEIIBVi/Jx95IC8CjYJrdozbjhjX1RTRilipjPBQEg0hrLihZLWLDYXXhzdyOe+vYkbE566gET5UIcpikBNRzEy4R4+oqZOKsgntLrdGY7rl9fSZuwR4rqFh0Kk+XoNtigNUdu7KxosdCK3enGe/tb8NzWU7QHaKMR9A2UBQXxePLXM5FAsYGpzenCLW9V0bJ0jgYnOo3QSAURXSqyosVCCy43if8dbMNT354Kiy87jwOc7GJeLIvHIXDv0sm4ZUEuOBzq5UP3fXAo4q2/6EZrskNntqM8RxORVm2saLGEBEmS+PZYF/79zUnUhlFU8hOjE/T1R5pKjOeuKsGsTPX4B3uhskGLj2vaaR5VdHCTwN4GLaalKdHYOwCjLXyxOVa0WIJmV10v/vF1LWpadBF4N2ZtqS0rTsYTK6ZDKeYHfY5ntp6kcUTM4HCbHskKERLDaBHEihYLZWpadPjX17X4sa43Yu/pDLLch24EPA7+cnERri7PDMkueX+jFjvrfPdOiGU6DVbwuQQWT0kKy/lZ0WIJmJNdRvzr61p8E2GnTABwMiB5KUMjxsvXzsaUlNDdR57ZGnwZUyzgcJEgw/Q7Y0WLZVxatGY89e1JfFTThmgZGTFBtG5dmEeLYB1o7scPpyI3S40GmRpJyO6svmBFi8Uvh1v1+PVLu6Oe9Oh0Rd/7/bwiepY79d3MdaKgiySFEP1BOLMGArPTi1miSmu/GTe8yYwsbXuUY1ozM1RIoim4fF5REnhBpEfECioJP6ybM6xosXjFYHXghjf2oYchyZyOKM+0zp9KX1BZJRGgIi+OtvMxjfxEWdA+aYHAihbLGBwuN37/9gFGGeo5aCoFCpbzi5JpPd+y4onZnaosR4P9YbYNYkWLZQQkSeL/Pjoc0XSGQLBHcaaVmyBFfmLgfu6BcP7UJEy0FWJZjiYi2f2saLGM4PltdXh/f2u0hzEGN4mo3eR0z7KAweLqspzotOAKB+UREiyAFS2WUXx2qCPaQ/CJIEq2NHTGs4YzUZaIZdmRqTkcghUtlhHEyQI3rYs0CbLI985MkAsxM10VlnNfUEz/DC7SzMlWo7IxsgXfrGixjEAjZW5TXQGPAz43smvE84qSgnJvCIQkhQjZcZKwnDsSlGVrouLVz4oWywjiKNgDR5r6HhNmZqgi+p7n05RQ6g2SJBndo9EfZdmaiM+whmBFi2UETBYtANjX2I9ZmaqIvJdMyAtrPlWP0caIxF2qlOVET7AAVrRYRqFhcExriOMdBmRoxGF/n7m5cRDyuGE7f1MYzBLDTSR3CX3BihbLCJg+0wIAi8ONFEX4RWt3fS869Jawnb+pL7ZEK1LOpOPBihbLCJgciB+OOwJ2Eya7C49+djxs52/uix1feKYIFsCKFssomJzyMByjNTwOAqP5/HAHtp/sCcu5Y2V5yCTBAljRYhlFLCwPAaBNF7ku03/55AisYQiYx8LysIxhggWwosUyCoWIDy7Di+IS5UIM2JwRe7+mPjPWfV9P+3nD0bWITspy1FEPunuDFS2WEXA4BNQSZs+2EhWRj7ut216Phl76YlBuN4nJSXLazkc3ZdlqVDZEPnE0EFjRYhkD05eIEkH40hB8YXe6cdfGauhpcuPkcAhsuLkcr/x2NnLjpbScky4GS3OYKVgAa7fM4oU4mQCIfO+KgImWS82hVj2ufGUP3rqxDHE01EESBIHzipKwaHICNuxtxtNbTobNong4ChEPKokAagkfyjP/qiUCKMV8CHkcPPkts1ubURattrY23Hffffjyyy9hNpuRn5+P9evXY/bs2QAGSxMefPBBvPLKK9DpdJg/fz7WrVuHgoIC2gfPEh40DJ9p6cz2qL33sQ4Drnh5D965qZw2+2U+l4Pr5mXj0pI03LvpIL6lsdtRUYoCy2ek4tzCRCTIhVCIeOBx/S+w5ubF4Y4N1WjThS9HLRQoLQ/7+/sxf/588Pl8fPnllzh27Bj+/e9/Q63+qcPuP/7xDzz77LN48cUXsXfvXkilUixduhRWa+R2e1hCg+nLw2jfTHXdA/j1S7vR2k9vIF0p5uMfK6aH/PPPjZfirsUF2LJmIb64awFWLsrD5GQ5NFLBuIIFALMy1fj8zrOwJEx9C0OFIMnAs/Tuv/9+7Ny5Ez/88IPX50mSRGpqKu655x7ce++9AAC9Xo+kpCS88cYb+M1vfjPuexgMBiiVSuj1eigUobdrYqHOM1tO4aktzFwipCiF6NAzo8g4VSnChpvnIpvmmNQnNW24a2MNpdekqcRYPiMVy2ekoChFEVIj2SFIksRrPzbgiS9PBNXCLT9Rhi1rzg7oWCr3PaWZ1v/+9z/Mnj0bl19+ORITE1FSUoJXXnnF83xDQwM6OzuxZMkSz2NKpRLl5eXYvXu313PabDYYDIYRXyzRhckJpulq5li5tOutePRz+jPmfzEjFedMTvB7jJDHQWmWGjcvyMGHK+fhx/vOwf3LCjE1VUmLYAGDMbebFuTi/dsqoBTzaTknHVCKaZ0+fRrr1q3DmjVr8Kc//Qn79u3DnXfeCYFAgOuuuw6dnZ0AgKSkkdPKpKQkz3Ojefzxx/HQQw8FOXyWcMDU5WFJhioq/k3+uG5eFu3nJAgCj/5yGs5/cjtM9sGk1rwEKWZmqDEzQ4mZGWoUpsjBD2CpFypOlxtfHOqA3hKZCoRAoCRabrcbs2fPxt/+9jcAQElJCY4cOYIXX3wR1113XVADWLt2LdasWeP53mAwICMjI6hzsdADEwPxyQohTjGsyWlJpgpn5ceH5dxpKjFe+e1suEgS09NVUZnpaE123PHuAeys64v4e/uDkmilpKSgqKhoxGNTpkzBhx9+CABITh60j+3q6kJKyk/+111dXZg5c6bXcwqFQgiFsVGk+3OBactDLgHIRXx0Msww787FBbQtxbwxL0yCGAjH2g245a39aO1n3g4ipfnl/PnzUVtbO+KxkydPIitrcIqck5OD5ORkbN261fO8wWDA3r17UVFRQcNwWSJBqkqMMN6LlJmdrWHcLGt6uhKLJvmPO0WTAZsT+xq12HaiGwaKxeWfHmzHZet2MlKwAIozrdWrV2PevHn429/+hl//+teorKzEyy+/jJdffhnA4Fr87rvvxqOPPoqCggLk5OTggQceQGpqKi699NJwjJ8lDEgEPOQlyFDHAKGYmqpgXMEuANx5bnhnWVToMdpwtF2Po+0GHGs34Gi7Ho3DirE5BFCcpsTc3DhU5MZhdrYactFPy023m4TZ4YLJ6sDrOxvx0o7T0biMgKEkWnPmzMFHH32EtWvX4uGHH0ZOTg6efvppXH311Z5j/vjHP8JkMuGWW26BTqfDWWedha+++goiET2JeCyRYXqaMiKiRRDAY5dOw6763jHty/hcAp165uX3FaUosHhKYlTH0G+y47ND7dhc3YbqZp3fY93kYDb/oVY9Xt5xGhxicDZtdbhgsrlGWD5PT1eGeeShQylPKxKweVrM4PUfG/DwZ8fC+h4cAvjnr2ZgRWk6+gZsOP+pHegz/ZTtPidbzbjdQgB48ZrSqLT/sjld2HaiG5sPtGFbbTccLvpv3ZIMFapbdLScK1x5WmztIYtXwv2Jy+UQePqKmVg+IxUAECcT4uFLirFqwwEAAAGgjYExlcJkeVg79PjiyW9P4s1djWFNPZALeWG1l6YL1uWBxStFqYqwtaHncwk8f9Usj2ANcdH0FCw7M4OZmaFCOwOXhnecWxC2Poj+2N+oDXuuVG6ClHE7tN5gRYvFKxIBD/mJMtrPK+Bx8PK1s30urx6+pBgqMY/yjlckWDgpwSOqkSY+zN2152SrcbBVH9b3oAt2ecjik+I0JU520ReMF/E5ePW3c3BWge/8owS5EA9dUky59i5cpKnEuGxWGi6blY6cKPpeJcjDJ1p5CVJUNzMvdugLVrRYfDI9TYnNB9poOZdUwMXr189Bee74zU/PK0qCiM+B1REd4yyCAH5ZkoZfzUrH3Ny4qCwHRxOumZaQx4HZ7oIzSh5lwcCKFkWOtg9OofMSZBDxxzpoWh0udOit6NBZoBDzUZzG/C1kX0yjKRgvF/Hw5g1lmJWpHv9gDC5Nz5mciC+PeK9XDTepSjGe/PXMqLy3L1KU4UkZcrlJ9BiZFzv0BytaFNBbHLj2tUpoTXYQBJCpkaAgUQYOQaBDb0W7zuLZsudxCHx194Iojzg0Zmao8cilxfjHVydgtAbXSEIp5uPtG8spC+AFxclRE62sOOY4SQyxfEYqKhu12LC3mdbzOt0kcuOlOE2j/324YUWLAs9sOQXtGVEiycEuLb7aQN14Vg7yE5nbuCAQuBwC187NwgVTk/H4l8cpLxXjpAK8fVM5pqRQz7c7tzARAi4H9ih4KzNRtLgcAo9dWgyNRID/bKuj9dwaqSCmRIvdPQyQum4j/ru7MaBjkxRC3LF44thLJ8iFePLXM/HeLXMxKWn8HUUhj4NFkxOw8Za5QQkWMFggvcBPwD6cZGqY1WhiCIIgcO/SyfjzRVOQoRHjP1eVIDch9LEypBopYNiZVgCQJImHPzsesHvj/11UBJlw4v1oy3Pj8PmdC7B+ZwOe3nIKZvtP5R/pajHOLUzEOZMTUZEX5zXeR5Vl01Kw9UR3yOehChNnWsO5aUEufjc/BxwC+MOmQyGfj0leWYEw8e6sMPDdiW7sCLA1ekVuHJZPTxn/wBiFz+XgloV5uHh6Kv6zrQ45cVKcU5iAvAQZ7QXE501JAo9DBGX1GwpMFy1gcLmoNdlH1A0GS4uW+Vnww2FFaxzsTjceCbAGj8ch8NAlUxlT/R9OUlVi/O2X08L6HkoJHxV5cfjhVG9Y32c0WXHMXB6Opn1Ugw8Rn4MEmRAtFMufLA4X0tVixlrRjIaNaY3DG7saRth8+ON387MxicFdg2ORC6dFdtYaJxXEzNK+td8CPpfAjAwVSjJVAAm09FtQlKLA1FRqscRwJq/SDSta47B+Z2NAxyXKhbhryaTwDmaC4/ayDDy/KClsNZDeyGTA0rDbEFjeVM+AFQIuBwdbdKhu1sF6JkP0WIcBR9sNKM1SQRxoN25mmb34JTY+UqJEp96KjgCLdv/voimQCXnQme2obtbhQHM/jncYQZIkCIIAhwA4BAEOB/j17AwsmhxdPyYm0dpvxuNfnMDKRXljknHjZEKU5Wiw53RkjACzNNETrb4BG/7xVS0+qm7Dp3echcnJ/mftxzuMnsYX3qhq0iFFKUK6SuzX+ZWpFkC+YEXLDzUB+grJhDzsONmLZ7aewume8fNdrimnv4NLLGKxu7Buez1e2l4Pm9ONNed7n6kuK06JnGhFIZ7lcpPYsLcJ//rmpGcn774PD+HDlfPA9TPNbAjgb61DbwWHAMpzNNjXqMXoyWxRihxVTbEjWAArWn452KoL6LgBmxMfHmgN+LxFFOMNE5F9jVrc+W71iJmsxMdS5oLiZDz82TG4IrCLGOmdw4MtOvzfx4dxpG1kv8+aFh3e3NWIG87K8fna072BFbO7SWBvgxYZavEZm2XyTG4WAYWIN0bImA4rWn6oGcfGNhjS1WKoJMzqdhNpOvVW3PZW1QiXUgAQ+8jtSlKIsKw4eYwdcziIpGjZnW7c+lYVOn3EsP71TS3OK0pChpcl64DNiS6K3leDu4pjdwhnZapwtF0PmzM21IsNxPvA5SZxuI1+f6Hi1NgtoKYDh8uNVRsOjBEsPpfwGzS+aUFuuIcGILLZ8B9Vt/oULAAw2134v4+PwJsjeiONZTcHmnXIS4idXW92puWD0z0DGLAFVyTsD6pb0bFM74ANHx1oQ5vOgg69Be06K1r7zeg3j83AvmZuFoQ836I1M0OF2Vlq7A9j/EUi4CI+Qj0fXW4SL24fv+vNjpM9+Ki6DZfNSh/xeH0PvU1HWvsDS+thAqxo+YAuc//RxLJVDVX+/uUJbKoaP9YnF/Fw57nj12retCAX+5uq6BiaV4rTlBFLDP7qSCcaApwtPfzZMWhNdlgdg51zLHY3qlvoFW+D1QkBl4A9DM0y6IYVLR8cDJNoTU37ecy0DrboAhIsALiuIhtq6fgznPOKkpCpkaBZG55ZwWUlaWE572hIksQL3wfu1KAzO/Do58fDOKJB4uVCtOuY763FxrR8EEjqAlVWzEpHovzn0f/xH1+fCPjYPlNgAWUuh8AN87ODHJF/BDwOlkUo+35vgxZH2w3jHxhhFMMauDIZVrR8cNMC31vNwaAQ8bD2wkJaz8lUWrRm7KzrC/j4XfWBH3v57AwoRPQvEM4rSoJSHJmblqltuiJ1/aHCipYPFk9JwhIauwj/YenksHdUYQpUctaAQTPFBz85gpYAln1SIQ9XhSE5d8WsyCwNgcFUBybibyeTSbCi5Ye/XDwVAl7oP6Jpacqw3GhMJRirkzd3N2HRv77H3RurvdYgDue6eVng0ViQGC8TYEFBAm3nGw8bQ0Wrqc+MdLU42sMYF1a0/JAZJ8HKs/NCOgdBAI9eWuy3HGOiwecGd60uN4l+s2Pc7jcpSjEuptGz7Bcz0sDnRu5W8DXTSlaIMDlZjqw4CeJlAp/JtkMoRDwUJssxJ1uNOdmBNQ0ZjzQV80WL3T0ch9vOzsMzW08F/foryzIxI0NF34BigFAE+sqyjICOWzYtBR/XtAf9PsO5LIJLQ2DsTKswWQ4hj4ODrfoxSzQuAcjFfEgFXIgFXIh4XLhIEl16G7RmOwydRgAAhxi01RmdtEuVbiPzO0yzojUOYgE36AYLGqkAf1w6OQyjYjZUZy0z0pW4bl42XvmhAecWJvk91u50w+UmMZ1Cd5+FkxLwn6tK0KI1n/myoFlrRrPWDIKIfMKv3ekGl0NgRroSeosDJ84Ijzdc5GDKg85LQu5w3CSQIBcgL1EGk82Jhl7TCDvsQImTCpAgE6BDb6VsJhgpWNEKACE/ONG6f1nhz7LOkEq8iSCARy4txvR0FS6dmeZ1aUiSJI60GfDhgVZ8UtOGVefk48azchAvE6J3YPyZwe/mZ0Mh4mNqqhJTGVBGJRVyoZEIcIDm2tYTnT9lySfIhEhWiALuspOXIAVBECMqDiYnySET8XCkTceoukRWtAJAzOcG1ffvqW9PYsPeZiQphEhSiJCkECE7ToqLJrCHPADwKMy0ri7PxPR0FQCMEaxuoxWfVLfjg6pW1Hb9NBt5f38LbjwrB9PTlfhunMYXufFSnB3BIHsgKMV89AQgtqHQM2CDwWrHzAwlalp819AqxXxMSpJ59dMa+pnLhDxMT5czxnOLFa0AGN5Zhs8l4Aiw1KHDh4lgXuICFCZP3Mz4QGdacVIB/nD+2Ny1r450YNP+Vnx/sserHc3JrgEcbNVjWtr4onXdvGxGtLUfTl7C+G3Y6MDmJFHTokd5jgZ7G0b6kXE5BGZlqnCi0ziuGA3YnNjX2I/CZLlnKZusEKKTossEXbC7hwEwtIszJUWBt28sD3kn8JUdDXQMi7HwAtw9vH9ZIZSSsQmNf/+qFltPdPv1z3p/f8u4cS2ZkIcVpel+j4kGuRESrSH2NmgxK1MFAY8DiYCLsmwN4qQC7Gvsp7SCGHptbrwUnQYbkhRClOdooAmgBItOWNEKABGfAwGXg6eumIHy3DisXhJaI9b/HWxDZ4A2zrFIIIH42VlqrJjlXVCmBVBUvvd037jHXT47nZFNKjRSAVRexDqcHGjWoThVAQ6AykZtULuEB5p1qGzUeuJkXQYb9jZoobc4MDNDieIIbWiwohUAQj4X95w/ybOk+938nJCSTh0uEut3TtzZljfTutE8cHGRz2VbIDuDvyrNQKJChGSF91pOghgsxGYqufGRt3Xu1FsxEMSO4ni43IPL0CPtBqSqwl9bS+nO++tf/wqCIEZ8FRb+FJNYtGjRmOdvu+022gcdac6elDDChO69fS0hl2Js2NsMozW2OvsGygVTk5E4TksqfyUjZTkavx14JAIurirLBABcVZ6JFbPSccP8HKxeMgl/XV6Ep66YgXduLEd2FIQhUCIV1xpOJHKw1BGYQVKeO0+dOhVbtmz56QS8kae4+eab8fDDD3u+l0ii35IpVG47O88Tx7I6XHhxe33I5zTanHi3shm3LAwt456JCHgcXDcvG//8utbnMZ8f6sDSqclen5uersK6a0px57vVXktefj07wxMLu3NxaEv1aBHpuBYAON0kEuXCsIqXRBD+5Tjld+DxeEhO9v7HBgyKlL/nmcLxDgM69BZoTQ5oTTZoTQ5cVZbpte/d8MD7+/tbaPulv/5jI66fF9pSk6lcVZaJ5747BavD+4x0y/EuWOwunxbLS6cm452bynHjm/s9HWqAwczvG+bT68ARSVxuEj+c6sHB1n6U52igNdtR1z0QkbaDcVIBwr2PyomAiSJl0Tp16hRSU1MhEolQUVGBxx9/HJmZmZ7n33nnHbz99ttITk7G8uXL8cADD/idbdlsNthsP4mAwRB+n6G39jTh05o2VI7a6v2ouhXv3DQX+Ym+PwU/O0hfc4VOgxWfHmxn5A5XqKilAqyYlY539jZ7fd5sd+H72m6/HlazszX4cGUFrnt9H9rOtIBfOjWZEQ1VqdKiNWNTVSs+2N+C9lGbMHIhF/mJMnA5HDT0mkIuxfHG7Cw1TnQa0GejP6YVaSh9xJeXl+ONN97AV199hXXr1qGhoQELFiyA0TiYu3HVVVfh7bffxrZt27B27Vq89dZbuOaaa/ye8/HHH4dSqfR8ZWQEVnsWLBsrm/HAx0fg8LKd3mWw4YqXduNou+9kvOvmZdM6nld+OO21ccFEYEVpOsqyNWOCs1IBF3Oy1X7LV4bIT5Rj8+/nofBM49JINbigA5vThU8PtuPa1/Zi4T+34dmtp8YIFgAYbS5Ut+ixv6kfBosD5TkaCGmafacqRShKUWB/Uz8GwixYYj4XxzvDP+kgyBDuGJ1Oh6ysLDz55JO48cYbxzz/3XffYfHixairq0NenvfYjbeZVkZGBvR6PRQKerdQP6hqxR8+OAiSHLQj6R3w/ommEPHw5g1lKMkcWznvdpP45Qs7cbCVvk49b95QhrMnMStrmw7qewaw+N/bAQBpKhHSVBK4SBJH2/SwOt24qjwTf/vltIDOZbA68MK2ety/LPJGilVN/fj2WBdmZ6lRkRcHaQBpFF0GKy54eofXJh6BkJ8oRV13aO65RSly1HUPRMz3fXSn6vxEGbasOTug1xoMBiiVyoDu+5CiZiqVCpMmTUJdnXe/6/LycgDwK1pCoRBCYfjN8T6ubvMIFgD0DtiRrBB53cUyWJ245tW9ePW6OajIixvxHIdD4MOV83C8w4jKRi0qG/qwr7Ef2hCm9C/vqJ+QojU8R6pNZ0XbKP9xKjuwChE/ooLldLnx1dFOvPpDw4hO43wugdlZGpw9OQFnT0rAlBTvN9g3x7qCFiwAkNOQXyYV8iLaqKLzTDfrcDd/DeknMzAwgPr6elx77bVen6+pqQEApKREt9Zu84FW3Lvp4JhgZ4rKu2gBgMnuwvXrK/HStaVYNHmkgymPy8G0dCWmpStx41k5IEkS9T0mVDZo8coPpwPusjLEzro+vP5jA341Oz1mfLoDwVfH6CG6jTbYnC6/rcMijcHqwPv7WrB+Z6MnjjYch4vE7tN92H26D3//6gSO/HWp15nXN0c7QxoHnxv6zyTSTqQt/RaU5ahR2RDeGkVKonXvvfdi+fLlyMrKQnt7Ox588EFwuVxceeWVqK+vx4YNG3DhhRciLi4Ohw4dwurVq7Fw4UJMnz49XOMfl7f3NOHPHx/x+tx41h02pxtv7W4aI1qjIQgC+Yky5CfKMDVVgV++sJPyp83Dnx3DP74+gQunpeCK2Rkoz40b/0UMZ7zt7x0ne1D+t624dGYaLp+dHrADw1B5D53Gip16K1754TTe29cScL9LkgROdBpRmjUyjKC3OLCbgu+9V0K8NJWEH5SDbKgcbzdCKeZBb6G/Z+gQlESrtbUVV155Jfr6+pCQkICzzjoLe/bsQUJCAqxWK7Zs2YKnn34aJpMJGRkZWLFiBf785z+Ha+zj8vKOevztC+9dYfgcAqcDaHh5K0Xn0hkZKty8MBcvBdCIczRWhxubD7Rh84E2fLt6IQqSYqfrrze4HAJKMX9EysJodGYH3tjViDd2NWJqqgK/np2BFaW+y2/0FgdWv1eDTr0VDy4vClnc23QWrPu+Du/vaw3KfujOd6sRJxOAxyHA53I86SslmSoQGPxg7DHa0EUxTcYRxFiGkxMnRbVZF9I5gsFoc46JbdFNSIH4cEAlIOcLkiTx9JZTfh1HJyfJR9ideGN2lhofrJxH+f2tDhcufOaHgL2MvLHmvEkxmzg5nD9+cBDv76fW6OKdm8oxPz9+zON13Ubc8t+qET/Xi6anYO2yQqSrqaVBNPeZ8cL3dfjwQGvArh2hIORxkKIUQSnhgwABEoN/pyQJuM/cgi43OeJxtUTgyeOiAo9DYFqaAtV+LGnCCUEAWRoJeFwO8wLxTIQkSTz2+XG8+qP/2r5AClZ/f05w2eoiPhf/+NV0XP7S7qCTBr843DEhROuyWemURWu4tQ1JkjjZNYBvjnbipR2nxyzdPj/UgS3HunDr2Xm47ezccZekp3sG8Py2enxc0+bXRYJubE43GvvMQBCrxuGWMOMxPU2JPpM9aoIFDC6bRXxu2FJ5JpRoud0k/vzJEWzwkdA44liSxOwsNQgCIECgsc80ItO9MFmOc8aJZfljdrYG11Vk441djUG9/kSnEQ29JuQwuH4uEMqyNUhXi9FKwbqXBLCrrhffHu/CluNd48ZmbE43nt16Cpv2t+Dfv56BeXljZ2kNvSY8veUkPj3YHvbdLbo50WnE1FSF3wavmRoJFCIeDrVFT6yGc6LTiIowxWUnjGg5XW784YND+Ki6LaDjR6+5OQRQkqGCwepAfY8JKxflgQixJOHOxQVBixYAfHmkA79flB/SGKJNY58JBj8xLW/89rXKoOJLHXorbnpzPzbeMtfjhkqSJDZUNuORz475LCmKDbwrrULEw+RkOaqa+hknxuFyZ50wRW+bq9sCFixvuEmgukWH+h4TpqcrcRENLdK7Qtxy/vJwaNvm0UZvduCmN/fDQNGqOhjBGsJsd+F36/ehsdeEvgEbbv5vFf7voyMxLljA0XYj+BxAwCUg5nMgFXAhF3Jhcbiwr5F5ghVOJsxMqzxHQ9u5DrXqYbK7oBSHpumne0LLaD7cpkeL1hyQP1W0MNud6NRb0WmwolNvhVoqwDmTE+F0ubFqw4GQNiOCpc9kx/XrK2G2O9FtpL+OL1oI+dzBUpwIJowykQkjWllxUkoBy/Fo7jNjGoU2Vd4IJKViPL460ombFzKz3u7dymas3Xx4xGNyIQ8/3HcOnvr2JH6s643SyIDGPvNgPHACiZZUwAt7/WAsMGGWhwBwXpH/nnlUaNL6nyE4XW70Ddj8tnCnY5bxxRH6XCWCxWx3YlddL2padKjvGUC3wYoeow3//ubkmGONNid++3ol3tzdFIWRjmQ8I8JYw5eNz8+NCTPTAoDzi5Lx3Hfe6yCp0tRn9vv80XYDLnl+J3gcAolyIRIVIk+rsKHvD7XqQh5HdbMOHXoLUpSRb1dusbvwzt4mvLi9fkxxuVzEg9lH5vghGovJQ2GiOcMmyIWDaRM/cyaUaBWnKZCiFHlt20WVxnFmSbVnlqFON4l2vdWr5QhdfHawI6JLRKvDhQ17m7Fuez16fGRyG61OTEtT4HBb+K1IguVU9wBEPA6sIVpjM4VI1xIylQm1PCQIgrYl4ngzLbpiZ4Hw3z2NEUmEtDpceHNXIxb+Yxse/uyYT8EaQsCgQmdvKER8v17zsUaL1oLiADoVMQW6PMFGM6FEC6AvrlU0Tjuk2q7IzTBatBZsOd4V1vf4vrYbi/75PR7839GA7aTN9vAVxdJBZpwE5hhPdRhNgC0lo05hsjxs1ssTTrTKc+IgF4W26hXyOPj9Iv8lPLURnGkBwOvjlCWFylPfnqS8/KDD8ylc5MZLUd2si/YwaMdbow8mYne5w/ahNuFES8DjhJyz9duKLCT66KcHAFqT3afrabjY26DFBU/vwCOfHcPW4120BpnrewZodWJlAqd7TSjw4/Ufq0SicQQdnO4xITsuPCVozP2oDBKD1YG9p7VBv14i4OK2cexoVGI+Zmepsb8pvGZnoznRacSJTiNe+7EBXA6B6elKzM+Lx5KiJMzMUAV93k+CrCQwBug7FS2cbjcI+CqAiU1iRLMAhG6v44sJN9N6e09TSDfT9fOyESfzn9/D4RB49JfFI9wIIo3LTaK6WYf/bKsLaelIkiQ+qglOtLrCuGNKBw29ZszJpq9SItrEywTgc2Pnlg3Xjnrs/AQCwOpw4fUfG4N+vVzIwy0BphYUJitw41nM6L+XEkIr8qqm/qAcLpViPrQheKBHinZ95N076UbEHwx5DFidI/zqf65MKNH6oKoVvSFUlt9wVg5UEkHAx9+5uACpyuAFgy7SVMEnngZbZJ7CgOsOhNZ+C5JiPDN+epoSexu0EybfLFQmjGg5XW68vIO6xfEQSjEfNy6gNnOSCnm4LwotrUYTbLa83enGZ4eCKxMKdYc2ksRic9fh1HYNgD+REs5CZMKI1hdHOtGsDa7EoTBZjnXXzKLUCUdvceDZrafw4P+OBvWedBLsrOf72m6//u3+iKXgNpVWZUxEb3GgOMTi/YlE7Hxc+oEkSaz7vp7y65IUQtxz/mSsmJUecGcXndmO139swPpdjTBS9IkKB0IeBxkU/dGH+DjIALyIz8GJDuaW74zmFEWPdSZidcSWuwOfS0AdgKV5MEwI0fr+ZA+OU7iJpAIuVi7Kw41n5VKqnH9+Wx1e2FYH0zitxyKFVMDFa9fPgTKIPw69xYEtx7uDet/iVGXE0z1+7hzvMEIjYfbmh4TPQU6CDG06C3RmR0jNav0xIUQr0FkWl0PgqrJM3LWkAPHjpDV4o3fAxhjBUoh4ePOGMpRkqsc/2AtfHu4IeNnEIeBpjQUAOktseVQlKUSUG+gyEZGAC4wSgkS5EFlxEhAgQBA/LdtJkoTF7oLO4kCXwRr2jkMEgLxEWUQK6GNetJwuN64sy0BJpgq1nUac6DB6LUc5vygJ9y0rRF5C8FnSQ40qmNB07U8XTglasABqu4ZTUkY2Vajrji0BUIonRtfu0SGMyUkydBttAfUYjJMKkB0vhdXhwrF2A+0xyZmZqoiVTcW8aPG4HPyyJH3EY3qzAyc6DajtMqK+ewAXTU9FGQ12zNnxUpwzORHfnQhuWUUnH9e04TdlmUG9tk1nwd6GwKsGmBC7C4V+c2zNDH3B4/w0252dpcbBVl3AM6g+kx19psGfg0YqQH6CFH0mO+pDtAQfgs+J3J5ezIuWN5QSPspz48LSWv7/LpqCmRlnZnWdBjT0mqLSVGDPaS2qm/spz7YMVgf+sOkgpdfoYvymb+ozIz9RRrnpKdPI1EggF/HQb7aHFFPUmuyoPCNg6WoxkhQicAgCBEGCAAGny42qM7MmMZ+D6emqcT/kIlleNCFFK5zkJchGNFG1Olyo6x7AiU4jajsNONFpxL5GbUS6v7y4vR4vXTs74OM79Bb8bv0+r15gYgEXRSkKdButYzLkDVYn+BwCjhhu+aKaAEtEs91Juytsa7/Fa0/KgkQZxAIu+s127G3Qoixbg8pG38LlimDMhBWtEBHxuShOU3piZQeadRFrV/XNsS7UdQ8gfxw3A5Ik8WNdL17ecXpMvK8oRQExn4Oj7QZUNfUjSS6ERiqA1jRydqWRCdBlCE8fu0hwrMMQ8y6m9T0DESsAH50mUtOqQ2mWGjanCxa7C3qLAzqzA4lyITI0kjGdv8MJK1ohUtdtxFu7m7C5ui3isZ8EmdCvM2e/yY4PD7Riw97mEU02NBI+UlQi9BjtODYqVaTLaENuvBQWuwuWYblBchE/pkXLbHehNEuNqhhO1dCaHON2mg4Xdqfb688u3Fbj3mBFKwSOtRvwqxd3wRyFNIg0lRjv3FSO7PiRnkUkSWJvgxZbj3fhzd1NXtMatGaH33yf070mTEsbvDmGVoTSGOwEI+Zz4CZJ2JyDF2F3MiNdJRTE/Nj7PdANK1pB0m2w4qY390VFsDI1Emy4uRzpwzLh23UWfFjVik1VrWjWmjE7Sx1S+crhNgNmZapwrMMAq8M9Ik8rVpiULId2wI6EMwXTR9oNKMvRoJLCzinTONFpjPn4YqiwohUEVocLN/93f8SnxQCQlyDFOzfNRfKwesPnt9XhX9/Ujsgfa+0PvdXUgWYd0lViCPmcmHHMBAaXv3mJMk/+UsuwQHNlgzYqBo50MWBzYka6csI5zVKBFS2KuN0k7nn/YFj+aIQ8DhIVQrjdgyZ/LpKE+8y/LjeJ7Dgp1v9uzphs/m+PdY1JeO002FCapYbF7sKAzRl0MXmrzgIugRGzOqaSpZEgQS7EwVad34TLA839mJWpwvEOAywx2PiC8zN3fGBFiyJPbzmJzw8HZucytCzx1YqLIAbr+M4qiMdZ+fEozVJDRDFmYXMOZjh7Y3jgdHKyPOhmHC6S+QW7Q0H2pgDE2U0OziIJAkhTi5EkF8JFkjjYEhuzl1NdsZ1vFiqsaFHg4+o2PBtgB2sxn4vXr5uDNLUYf9p8GF8d7QQwGI+anx+PBQXxqMiNg1oauOmgN460GWAPwItbZ7JDKuAGXTtJMqF2yQdzstUBlbKMhiSBtn4L2vottFRMRIoUpWhCOFcECytaAbK/UYs/fnAooGMJAnjmNzMx7YwH0rprZmFnXR8yNRLaDemqmwO7WbuMNpRmqVDVpAvqfQIRxkgj5nMwLV2JyobQ41OjRXlykgyNfSbPziOT0IT4QRfrUNoS+utf/wqCIEZ8FRb+5NxptVqxatUqxMXFQSaTYcWKFejqCm+T0UjQ3GfGLW9VjblxfRXi/t+FU3D+1GTP9wRB4KyC+LA4aFIpUq1q0mFWpiqo9zHZmLM8lPA5KMvRQMDj0iJYwGBOW/aZ30+iXIjargG4SWBKipwxs7DsOAnmZKtxrD02lrHhgvJMa+rUqdiyZctPJ+D9dIrVq1fj888/x6ZNm6BUKnH77bfjsssuw86dO+kZbRQwWB248c19IzLEOQTw6KXTcGVZBlr7LTjcpsehVj0Ot+kwKUke0YYXBwKcaQ1xuFWPKSlyHO+gFt8K1uGUbqalKdDUZ6Y9baGuxwQuh/D0zOw22uBwkZ6fU7pKjFZd9JpkDJXRNPaFvisc61AWLR6Ph+Tk5DGP6/V6vPbaa9iwYQPOPfdcAMD69esxZcoU7NmzB3Pnzg19tFHgvcqWEfEDIY+D564s8cykMjQSZGgkuHBaSsTH1txnRgfFtAuHm0RLnxnZcZKAbwClmA9tCA1D6MTqcMEQpsoDl5v0WRicIBdGTbQKk+V+6/5+blDOGDx16hRSU1ORm5uLq6++Gs3NzQCAqqoqOBwOLFmyxHNsYWEhMjMzsXv3bp/ns9lsMBgMI76YxM0Lc/H69bORphJDKebjnZvKRyz9ooHN6cIrO07j4ud+COr1A3YXjFYnEgPoUlOWo4He4sD0EJrB0smpblNI3YeCxekmUZKpQl6CFDJh5LLShTwODDR2E58IUJpplZeX44033sDkyZPR0dGBhx56CAsWLMCRI0fQ2dkJgUAAlUo14jVJSUno7Oz0ec7HH38cDz30UFCDjxTnFiZh7po49BrtUe3sQpIkPj3UgX98dcJrZT4V+kx2ZKjFKEpRQC7iwWh1QibijVl2uc5kXnforeAQiIoNz2jSVGK0RXjWc7htZBwpQyNGp84a9sz0GekqdpY1CkqitWzZMs//p0+fjvLycmRlZeH999+HWBzcp9/atWuxZs0az/cGgwEZGRlBnSucSAQ8ZMZFb7O1skGLx744joM0NusczBQfefPPylThcJveYy5nOZMi0aG3QsznMCIZ082A9IsWrSXsJUGTkmSsYHkhpLtQpVJh0qRJqKurw3nnnQe73Q6dTjdittXV1eU1BjaEUCiEUBjbzTSpsr9Ri36zA+cVJY177OmeATzx5Ql8cywyu7AHmnUoTJajXWeBweqE/owfvIDHDMECgAEbM5ZLR9v0UEn40IWhgYOAx4lKXWssEFIV7MDAAOrr65GSkoLS0lLw+Xxs3brV83xtbS2am5tRUVER8kAnEkkKEW7+7348+tkxOHzkP/UN2PDgJ0dw/lM7IiZYQ5zoNEIu4iNNJUb3mWx+JvmsM8Uix2R3oWAcL7NgKclQhRwCmKhQmmnde++9WL58ObKystDe3o4HH3wQXC4XV155JZRKJW688UasWbMGGo0GCoUCd9xxByoqKmJ25zBcpKnEEPO5ePXHBlQ19+O5K0s8tX1Whwuv72zAum31MEbQWG00bToLFCIupqYqUdOig0rM91mOFGn6zQ5GuDXkJkhBkoM1j3EyATr0ViTJRahp1QV8Dm/GhDnx0qhfG5OhJFqtra248sor0dfXh4SEBJx11lnYs2cPEhISAABPPfUUOBwOVqxYAZvNhqVLl+KFF14Iy8BjGQ6HQEGSDIda9ahu1uGiZ3/EP381HSa7E//8qjYq7hHeMFhdqGnRQSbg4lT3QNDlMuGgskGLGelK1HUPRKWtG49DwGxz4nSPCUIex1PzqDXZR/jRD2btq0CSJPrNDtR1DyBdJUaqWozeARua+8woy9GgqlGLoR4Vdpc7pjp4RxqCZFhRmcFggFKphF6vh0KhiPZwwsa9mw7ig6rWaA+DMuU5GkqdfMJNukoMo80Z0eTXwmQ5BFwODrV5z0xXS/gQ8jhIUYnRojWjd+CnxGSlmO91rDnxUpAk6cmdy42XjnCbjUXyE2XYsubsgI6lct/HnrPbBGFSUnhiIeFmb4MWxakKlOdoMDlZDmGUzQFbdRakqcR+bafphsclfAoWMLh81VkcqG7WjRAswHdlQUOvCa39FpTnaMAhgPgAcuh+rrAF01FiUpI82kMImiPDrHA4xOAsIV4mAEkOduFu1pojms91rMMwbreYIbgcAsIQdubEAi7adeMv34NpbuI8k5E/N1fjyY9jGQs704oSsSxaw3GTg7OEfY392N/Uj8Y+M3JG+dZHgspGLUr8FINzOQTKcjSQi3gQ8jh+j/WFVMAFBxjTqYhOBDwOWrRmxsQOmQg706IJl5vE4TY9dpzswY6TPbA6XZibE4eKvDjMydFAIRqZMpCiFEEu5EV1hzAcFCbLvfZVjAS9RhtmZqhQ4yUBd1amasSOXH+zDtPTlWjrt3g6L49GLuSBwyFgtDowJUUBp8uN2jAb8M3MULE7h+PAilYIdBms2H5GpH6s6x2TZHikzYBXf2wAhwCmpSkxNzcOc/PiMCdbA5mQh4IkGQ5QsJaJBaIZ40pWirCvsR+zs9Q42m7wtEDztet5qFUPmZCL8hwNSHIwaVUi5OFYuwFmuwtpahFOdA6KVCTadhEEcLIrOoIfS7C7h0HSprNg/hPfBfVaLofA9HQl+gbsQXu3M5E0lQjteusYv/pIoBDxYHe6PTlPaWcacoh4XJzoNFCKsSXJhciOl9K+S5oTL4XT5R7RaGM4BYmyCeVIGq7dQ3amFSRqSfAZ4i43Scm8L1YYLGSOTo7ZlBTFCJFpO9OQQ8TnUt4U6DLaxvSTDAWxgIs4qQANZ1IYZqQrYba7xgjUz92RNFBY0QoSiYAHEZ8T1C7RREQu4vlNAwgnmRoJar0sq1wkgk48tYXQM3I009OUIwR1qJPTlBQ5uBwCR9oMZ8qkIjdFzUuQIk4qhNXpgtnuAkmSqO+JjbwwdvcwBOKkbC7NEJOT5FER8LJsDVr7zbQXLYcSmxtqESkVcFGWrcE+H6kYxzuMONJmQE68FCIeB/sa+z3OqeGCzyUwNVWB+h4TKhu1ONSqR133AJrOZObHQnMydqYVAnEyQcR9nZgKN0q9+Fr6TWHJCbMFIcB8LoGSDDVO9w4gUyPBiQ5DQLljDcMy3/c2aDErU4UjbXrYXfRcmEYqQFacBAQGl8t1XuJmTjeJygYtJifJYbA6KDviRhJWtEIgjo1BeHBGKRlSLRGgQ09/IbfF4QooMK6RCpCfKANJkoPe9WdEanQmPBUONOtQmqlGVXM/kuRCZGgkON1jgtYc3Dkz1OIRMVQBj4OpqXIcbR+7pB5aZqslfCQrRTBYHFGLU/qCFa0QyNQwv+typLBEyftJKgzPn7BEwMWRdj1mZar8pqXkJ4THkYHHJVCYLEenwYr9Z5ru5idIoZEJRyzh2nQWrxY2Ej4H2fEyr260dqcbWpMDAi7hczbXb3ag3+xAvEwQ9aYeo2FFKwT++oupWDYtBW/tacLXRzqjNttgAsYo+ZhziPAsS/lcDhwuEgeadSjLUeNIm2FE6Y9awkd2nNRnYmoocInBpWZd98CIv6m6HhMwKlguF/I8Cb18DoGSLDU69Va09JtxrMN3blmH3hqQvU/vgB0aqQDJCiE6GeJjxopWCBAEMZgwmhuHLoMVGytbsKGyiTEmdZGk18vNW5atgcnuBEnC7w0UCr5MFOmksqEfciEXZTkakCSJDp0VrToL+s062t9rKH/vx7q+gI432pyo7xlAWbYGPQM2SrO+6uZ+aKSCccuStCY7CpNlrGhNNJIUIty1pAC/PycPW4514b+7m7D7dGB/eLGOTMCBTMSDXMiDy02CJEmoJAIcaO73zBQKEmWQCLie7X4qcIjBm9nhcsNodUEu4kEq5GFfo5bWJrLDZx6jA+hGmyvs5TUlGSpUB9EDwOEig/KSd7hI5CfKArquvgFmWFwDrGjRDp/LwbJpKVg2LQUnu4y46c39EyrrHRjcyk9SiHC61wQuAeQmyMfkaGlHpSAMBbQzNGKkKMUgMNigoqZFB4eLhFTAxZQUhWcX0ukiYXe5IRFwcbrHhJqWsWI3J1sNgsZN+oMtgc08wkFOvDQowQqV4+2GcRuWcDkEehnS9xJgRSusTEqS443fzcGKdbvQH4bmB9FCyOPgdK8JqUoRZBSTSlu0FnBAeJw+C5PlaO23YHKS3BNwDpR9jf0Q0VjraHOSSFbyoiJacTLBiNSHSGG0Oce19XG5SUxPU6JRa4LBEv0Cfza5NMzkJsjw6nVzom6WRye6M0Z27XorTgbhemAfFoc60WlEvEwAsyO4m2G0v3oopKpEaIpC23m5iIfDQSyb6eJkt3HcBrSH2vQQ8bgRNVv0xcS5kxhMaZYaz/ymBGHa6Io4s7PVIb1+dKpIY58Zxzui726QropOCktRioLWsiGq6MwOFKUoxz2u22hDVlzkvdJGw4pWhLigOBl/XT412sMImcJkOaqadCGdYyAIDzGlmIdZmSrww/hRH632800MiHlWNfcjUzN+w+V4WfQTqlnRiiDXzcvGkinjN2hlKnFSAbqN1pCtgOu6B1AWQI1dbrwU5TkaTEqSwWB14kCzDtPSx58RBINKwvdadB1upqcr0cmAkhmXm4RMOL5zCRMyEVnRijB/ubgIghiMb/E4g8FirSn02YjN6UZlgxY58RKUZqlRnqNBeY4GCvFP+0IlGSqc7jVhb4MWJ7sGPB5d4VpG5cdLo+IDZohgF6HxONZhwMwMld9jTvcMRL18LfbunhgnM06ClWfnRXsYlCnJVAcVdPdHQ68ZVU392Nughd3lhsXuQkmGCjMylDjS7j0wfbTdgKIUes0hxXwO2vRWzAkxVkeV0ky1p2UYUxhv6a41OaCWCiDgRi9Ay6Y8RIGVi/Lw4YHWmGl7LuFzwt5owWRzwuEiA8pVOtZhgEYqQG6CFA6nG7WdxoB3EXPjpVBK+OASBDgcgAABk92JI22D9swcAhHpJMTnEGjVMUuwgMBqSAeX92pUNnj/m1CK+SjJVOHsSQl0Dw8AK1pRQcTn4oGLi3DrW1XRHkpA8HgcIMxeWf0Ul51ak92TT8XnEihOU0DM56Kpz4xu49hESAJnMt4btSB7vZ9TZ3ZgaqoiIn7wJVlqRjawsDoCrDDwIux3nJuPS0vSkBsvBRHGrXJWtKLE+UVJOHtSAraf7In2UMaFR/wURQjFrTVZIUSfyQ7HKGeB0kw1DrQEP5NzuEgcaftJaCYlySDgcSAR8ECSJMx2F1xnegr6Y2aGEi4XiclJsrB23ZEJuKiNUsei8RgtWnwugWlpStic7hFi3mmwQSXhe8wX42VC3HFuQUTitaxoRQmCIPDg8iIsfXrHmJuYafC4BJIUQqSpxOgx2tCqswQVtBYLeOCYHZiWpoCIz4HR6gSXQ6Cqmb6lp4BLUI69TUqSgQAxolSoPEdDe2OLIYrSlIycZQFjJ1AOF4m6ngFI+FyUZKjA5xIgCAIGq2NEedoVc9IjtsHEilYUyU2Q4aYFuVj3fX20h+IXrckGAoTHvcJXb0F/pCiFnjKVw2H0kk9WiinVevoqUt7boA24a3UgqCV8FCTJ0KGzMlawACD5TE3pcJRiPlq0Fp8uDwQB/GZOZiSGB4DdPYw6d5ybjxSlKNrD8IvTDTiGRacD8c4qyx6Zh5WhiUwmtYpClyS5iOc38F/ZqMXsLGo7iiUZKhQmyzEjXYmyM6kcszKUMFqdqGzo99k+jCkoR/38pqcp0aL1P+ZFkxKQEUFDTHamFWUkAh5WL5mEP354KNpDCZj6HhOmpSlwuM17wHp2lhqVjYMzFTdJgsshIuZ0IeAG/jmcpBDBaPW/lNzf1I+SDBUOtergbxWfnygFl+B4FcE52eqYMYgc/fMLpErghrNywjUcr7CixQAU4uB7KEYLiYDnVbhmZ6k9bg10La2oQKXRiEIU2J9/dYsOeQlSqCUCHG3Xj7BxUUv4mJwkxx4/S759jf0oy1ajMsxpI3QzLU057lL+4ukpWFAQntQGX7CixQCiZVUcCma7C0faDSjL0cBqd0Es4MJkc1K2l6GbDr0VeQnScXv45SfKYKRQAzl4PhPEAi5mZ6lhtDohFXJR3xNYN6D9Tf0RS6cIheG7hyK+/1mrQsTDX5YXhXtIY2BFiwEEU0AcfUiQJKIeVM5PlOH+CwpxtN2ALce7cLhNjziZ0K9ojdeswh8Wu2uMMHcbx68ddJPwmj/GNIZn6Nd3+xf+/7toChLlkY/HsqLFAIzWWBSt6HNVeSYeuKgIYgEXS4qScNeSAnTqrXhjV8MIMZ2TrUaXweaJq9HdDKOxz4zceOmYXbfR9BhtSJQLGS1eeosD6WoxkhQiVPmZNZfnaPDr2RkRHNlPsKLFAGJxeXi4zYCceAkaeiNfiqIU8/H3FdNwQXHKmOd+ONWDA026QRcJkgRBENjboIWIx0FZtgZWhwucMNjbyMWB3UqpKjGjRQsY9DvbVd836jExms/sIgq4HPztsmlhzXr3R0gpD0888QQIgsDdd9/teWzRokUgCGLE12233RbqOCc0sTrTUkkiX+3P5xL48q4FXgULAGpadKhs1KKyQYvKxn5PgqjV6R5sA9+mD8uS9mDLYI/E8egbsKEgSUb7+9PJrvo+TE6SI0UpQn6iFJOT5WjWWjAjXYkEmRC3n5uPvIToXUPQM619+/bhpZdewvTp08c8d/PNN+Phhx/2fC+RsE1N/RGrosXnRD7Nz+EikSgX+nzeMiyQLBVwYQqgAJjHGcxFAwZrFFUSPmRCHnoHbH4bPozmVPcA5CKe399nS78FBDGYx3akTQdzmGs6g6W2ywgBj4MO/U/jO9iqh0TAxQ3zI5viMJqg/uoGBgZw9dVX45VXXoFaPTb5TiKRIDk52fOlUNBrJTLRoLKLxRSmpiqiktIAAAY/ouAetpX3119MxbZ7F+GWhblQ+0g61Uj4mJQkh4BLQC7kQi3lo9/sQEu/BS4SmJGuRGmWeoTXly/SVKKAPoBIcjAdJFHB7KRiuxfnDLPdFZFek/4ISrRWrVqFiy66CEuWLPH6/DvvvIP4+HgUFxdj7dq1MJt9xz1sNhsMBsOIr58bsRbT4hLR3QnTmX13y/lN2WA5iUYqwPIZqciJl+JPF07B7rWLMd2L62luggzHOoyYnKzApGTFCJNDu9ONg616VDX1w2RzoTBZjunpSkxNVWByshzT0pSeWR+XAMx2ajcz07PjvSERcOGOhlviMCgvDzdu3IgDBw5g3759Xp+/6qqrkJWVhdTUVBw6dAj33XcfamtrsXnzZq/HP/7443jooYeoDmNCEWvLQxfp/VM4Uuj9uH3OzY3DoskJKE5VQsT/qcOMiM+Fxovj5tCsYbwkSpebxAkvzgwqCR+FyXIoxHzKsTKXm0S8TIDegci3LAsUqYALq9MNl5vE1FQFnruyBHEy38vzSEBJtFpaWnDXXXfh22+/hUjkfWp7yy23eP4/bdo0pKSkYPHixaivr0de3ljHzrVr12LNmjWe7w0GAzIyorOVGi1ibaYFABlqsV/xCCcJfmJaAPDHpYVeBUrEG9smK1TnUJ3ZgQGrE7kJUhSlyHG800jJAUMtYbZoLSlKwt1LJmHr8S5cW5EFoZefYaShtDysqqpCd3c3Zs2aBR6PBx6Ph+3bt+PZZ58Fj8eDyzU26FleXg4AqKur83pOoVAIhUIx4uvnRqzNtABAKoxOtkxWnATpav8bO0WpCiR7KUL3tmtXmCwPeUxON4mTXQMQ8bmULXv4XA7ko3oOehPcaJEVJ0VOvBQ3LchlhGABFEVr8eLFOHz4MGpqajxfs2fPxtVXX42amhpwuWMvqqamBgCQkuJ9i/rnjss9aFIXawTscEkz4zVe8MfvF+UjK26k4O1r1CI7jp7dbWcQvmjHOgxQiAVIVw+275qTrYbV4cLUVGZ8eNP1s6ETSh+XcrkcxcXFIx6TSqWIi4tDcXEx6uvrsWHDBlx44YWIi4vDoUOHsHr1aixcuNBragQLMBCDsyxgsMYvGuw53Qenyw0eBTeHIcQCLp64bDqufGWP5zE3CfC4HBAEQurGIxdycawjuE2kNp0FUgEXZTkaVDX1Q8LnQm9mRsiACc1ZR0Nroo1AIMCWLVtw/vnno7CwEPfccw9WrFiBTz/9lM63mVBEq0FoqHQbbQF7V/lKNwiGLoMtJIvqirw4XDN3pGFdXfcAZd+s0UgEPPCDENIhTHYXKhu0cLlJTElVoJWCW0U4ifmZlje+//57z/8zMjKwffv2UE/5syIW41lDZMdJUWPW+Xw+TSXGbYvycHlpOqqbdfjjhwfHNZQLhI37WrA4yKa3FrvLa+1hffcA+FwiaOvrLqMtpELs4Rxu0yNBJkBPlAP0ciGPUfG1IVjn0igTizuHQ9S06FCYLEdpphrCUf7guQlSfLN6Ia6dmwURn4uKvDh8dddC/LYiK+T3/e5Ed0DOCt7gcgjs9+JrpTU7vOZxUeFAs46W3ok8gsCkpNA3CPwh4XNQkqnyO1vOjJNErb7QH6xoRZlYnmkBwIlOI6qa+5Gf+NPOnIjPwQtXzxqzwygV8vDwJcXYcFN5SMsOl5vEE1+cgDOIzGwBj4OnrpjptQkDHSkcNS06ZIZoPTwpWY4DzTrIhPTu1smFXJRmqTA9XQmHm0R1sw5CHsfn7yKbgfEsgBWtqPP10c5oD4EWegd+ypB/+BfFKEz2vfs1Lz8e392zCO/cVI5LZqaCG4TrwubqNtz6VhXMduqiPzlZjj8unTzm8bpu06A7RAg4XCQkgtDE5mSXERaHC0Upoc38hpiVqUJxmgIWhxtVTTocatV7lsFdBhu6jDbM8DLLHL3TyhRY0Yoi9T0D+PBAa7SHETIiHsfTqeeyWWm4fHb6uK/hcAjMz4/HM78pQXFacDfn1hPduPKVvegboF5SdMP8HMzNHStQVY3aEbPGYGgJ0Q8/+UxNYmu/GXOy1ZiWpkRBogxl2Rq/xeKj4XGA0iw1DjTrcKTN4NOn3mJ34WCrHuU5Ggzvds/UmdbPyk+rttOI3fW9KMuJQ2GyPCy+SlR48tuTEWnBHm7SNYOmcedMTsRV5ZmU4yBTUxU4SLEl2RAHW3RYsW4X3ryhjNL2PIdD4F+Xz8Cyp38YUbDuIoEBqwMyIS8oR9kUpRAd+tDqMhWiwThTu96Kdi+pJZOSZFBLBHCTJJxuEk6XG3YnCavDCYvDDZPNCR6XQIpS7NfIbzR7G7SQCXkoSJSBwwFy4lnRiirbartxx4Zqzx+iQsQ70+IpDmU5GkxNVQSV+xMsR9v1+PxQR8TeL1yI+Bw8duk0lOfGBX2OUBMpG/vMuOyFXXj9+jmYQSH5NF0twYO/mIp7Nx0c8XinwYapqXLU95god9NOUohCFq3xND/QZrR6C/Uu1gM2J6pbdDivKAmzadhUCAcTXrRIksQbuxrxyGfHRsxqDFYnthzvxpbj3QAGq9cVIj7cJAk3Ofi6of+7SRIKER8VeXE4Kz8e8/LjQvbG/vc3J0N6PRMQ8Dh49bdzQhIsAJiaGnrsps9kx/2bD+PLuxZQet2KWWnYe7oPm6pGLtOPthtRmCxHs9YcUMWCiMdBWY7Ga1E1Vaqa+1GYLKflXMGQrhbjX7+awcidQwAgSDLKPhOjMBgMUCqV0Ov1IdchOlxuPPTpUby9p5mm0f3E5CQ55ufHIydegh6jDZ0GKzoNNnQbrHC5SYj4XAh5HIj4XIj4HAh5XAj5Z77ncfDGrkbGLA1Ls9TQme3jdrAZDp9L4OVrZ+OcwsSQ39/qcGHqg1/DRcMP5Is7F6AoiJnb3tN9eOKrE6gelWflr7/jEDIBFwkKEToNVqSpxKjrDmwm5I8UpQg6s52SCSEdCLgcfLCyAtPTVRF9Xyr3/YSdaektDty+4QB+ONUblvPXdhlR2xX8JyGdLdeDISdeihWz0nBpSZqnALnfZEd1Sz+qmga/alp0XpdHPA6B56+aRYtgAYO2MXkJ0oCXPf74qLoVRanU21qV58Zh88p5+PpoF/759QmPgB9uM6AoReGzRIdDAFnxUk9rsL4BGxRiHgwW7/Gw4S6p/ujQWzEnW419Ee6V+IelkyMuWFSZkDOtpj4TbnhjH6WZQ6QR87mQi3gRNdNTivlYPiMFl81KR0mGatzpv8PlxokOI6qatKhq1qGqUYtOgxXPXTkLF02ntwB+9Xs1+Ki6LeTzxMuE2LP23JDik06XG2/sasSjnx8HAL+ddspzNB4f+iGmpiqgNdnGxLbyEqSIlwnHHO8LEZ8DHkFgIEIF9TwOgaoHzoMyCs2Df9YzrcoGLW59az/6GVJw6guLw4WCJFlERCs/UYZ7zpuEc6ckUrIX4XM5mJauxLR0Ja6fP/iY3uIIyx/11FQFLaLVO2DDD3W9OGdy8LNAHpdzxoqFgwc+OYrTvSaUZqpR1Tx21uOtdvRouwF8LoGyHI3HGDBdLUZ9jwmNfeYRj/ujOFUZ0ea35bmaqAgWVSZcntam/S2MF6whDrXqUZAoQ2mWCnxu+IKe5xclYdm0FFr8kEL5o3a63D6TaYOJQ/li84HQxQ8Arq3IxgMXDy41a7sMKPHSbceX24XDRaKyQYvZWWpwiMEYFTCYzV/ZoB23c09Jpiri3brPC7KeM9JMONG6d+lkiPnMMCsLhFPdA6hq0kHM56I8R4M0lZj290gNwzmp4nC5cefGaqzf2eD1+bJsDUpDdFoY4pujnbS5Z9x4Vg7+dGEhTHYXqpt1KM1UQ3om410h5kE3zgfk/qZ+VOTFoc80svj5dK/J5/UKeBycCiFeGixLiljRigpJChFWLhpr68x0DFYn9jZo0aazYGqqAiWZKoyefKUohZiTrYZcRG1Vn6qKbtcXu9ONVe8cwBeHO9Hmw3KFx+Xg6StmQk6DI6rN6cb7+1pCPs8QtyzMw4cr56EgUYaq5n7IhDxMTpIj1Ys76mhy4qXYWdeH06PiqzqzAzUtOq8JnFOS5RiwRdZksShFMa4jLFOYcKIFADcvyA3oD4qpHG03oLpZB6VEgPKcwdKNVJUITjewr7EffC6HkiNBNGdabjeJlW9X4ZtjXQCADp3VZ2pDhkaCRy4t9vocVf7+1Qnso3F3dlamGp/deRbuXFyAPpMdJ7uMnnKb0ShEPGSccSL19wEz6FrrxNTUkY4OkUxyHuK8GJllARNUtMQCLhYUJER7GCGjNdmxt0GL3gEbDBYnes4E7bUmOw61DnY0DmTWlaKMnmgdaO7H1hPdnu+dbtJzHd64tCQNl8xMDfl9HS4St75VFXId4HCEPC7WnDcJn95xFqalK72W2ADAlBQFUlVilGaqYBzHOaLLYEOvceTS0R2FBL6UGPqQn5CiBQDmKHmYhwM3Ca91cAeadeBziREV+hwCyNIM3jBlORoUpyogC9F1IBS+ODw28N6m8y8kj1xa7PFMDwWtyY6b3txPu2fZlBQFPrytAkunJo8puUmUC3GguR97GwbTRFTjmOjlxkvRNUrE+V5sc8LNl0dix21kwoqWJQjLklhEa3LgYKseRSkK5CVIweUQaNJaUNWsQ2WDFkfaDVETcLebxJdHxtZXto7TpFQh4uM/V83y6nlFldouI+7aWENLtv1w+Dwu7jl/Mt74XdkIO+lMjWSE+2l1sw6TvHQBGkIu4kEm5A3GMTNUKMvW4Hi7/x6M4eDHul5oTcxtZTacCStaQ/ViQh4H5TkalGWH5pPEdI51GFDfY/JqFxwto0Gr04VOw9gllK9g/HBmZqjw78tn0DKO705044kvj9NyrtGcPSkBj/1yGoDBJZY3V4X67oEx6RKJciHKczSwOVww252DccwWHSobtchNkCHSBiQuN4mvYmS2NaFFK0khhFrCx94GLep7Qi8RiVWCsVihA4mAh/yEsbMMQYCB5uUzUnHv+ZNoGcsrPzTgvX3016ACwAVTkzE1VQE+lwNv8zkXCY8v/eQkOUoyVegx2rC3QYsTXQNjalAPtupRkhE+hwUCQGmWCjMylCjP0SAvYXAH89OD7WF7TzqZsKJFEIP5Lp1nzOn6TPaQzd1ilWhaOnurY6vIC9wVYtU5+bhsVhotY/nzx0fw46le6Mx29A0MFrd36C0ItZKNwyFw7/mT/W4wWB1OFCTKUNtlRHWzzqu4DaequR/lIbqoemNWpgppajGqmnQ42KI/84FuglzEg8XhgjYIQ8VIM+HKeIYQ8rhjOr/ESQUY3uc6O04ClVgAAY8AQRBo7bcEtHSJJQhisIYtWszMUI5wZ1VL+Jjix4p5NARB4PHLpqFVawm5wNzhInHNa3vHPP7UFTPwy5Lx3Vb9cU5hIl64ZhbWfnjI80E5HL3FQXkXl86GuDMzVNCa7D67BRmtTtS06HCsw4izCgJ3R40GE3amNWAbu2PU0GtCWbYGZWdiXBa7CzWtOlQ2Du72RDsJMxz8+aIiWvyqgmX0TKsiL46yY6yQx8VL15aGrQffX/93LOjuPsM5Z3Ii1l44xetzCpGAsmODLRA7iACYk61Gn8mG5gDSP451RH4TgCoTVrTUkrFbzd1GGyobtahs0KKyUTtmq/lU90DEA6Dh5LcVWbhhfnZUx1CYIh9RV1mRFx/UedRSAd74XVlY8olyE6Qei+NQWegjP5BqFQNA70xLZ3b43cUcYshih8lMWNFKkFGf4urMDkxJoa9wN5qcMzkBf7m4KOruk0IeF7+tyPZ8P49CPGs02fFSvH9rRcgtuoYjF/Lw8rWzIaKpXnX36T6vjwfTNcgyjmipJXyU5Wg8tZD+MFqdaOw1jfuz+/xQBx7/4jhMUdq8CYQJK1pnFQT3iR5q+ycmUJSiwH+umhWVchBvPHBxEZ67sgT5iTLkhtgsIUMjwabbKmjbVDHanLjz3WraMud/rPNuOhlMrN/XBgqfQ6A8RwO7043KBi00MoHf0MbQx5bTTaJ/nFwsp5vESztO47wnt+OrI50hb1KEA2b8VYeBJUVJAW+tDyfaM5NQSVaI8Pr1c8Y0So02y2ek4n+3z6fl55ukEOG9W+aiiKZZ8e7TfVj69A68tacp5BKaOh/uq8H8PjI0EpRkKDEtTYGCJBky1GLMzFAhXj5oJGg6k4vYorXAaB1bwwgASQqhZ6dyUpJ8ROchf7Trrbjt7Src+Ob+oAU9XOVIzPrLphGFiI+zJyfg2zOFuoHgzYUyVuAQg0Hvx35ZjGSG1pFxaPxAiJMJ8e7Nc7GhshlNfSY09JrQ2Gfy9F+kitnuwgMfH8GXhzvw9xXTkRHkErTf7H0mQzU+VZql9pqo2uKjmsBodeJ4hxFlORq43SQMVgfa+i2DzVjP/EyCidt9d6Ibl8xMRbJShANN/UhViZGsFIE/zoRAZ7bjH1/X4m9nEm/pZMKKFjDohhmoaJXFoGClqcRYOCkeCwoSMC8vDiovmw9M4USnAbdvqMYnq+bTNgtUSvhjbIie/PYknt16Kuhz7qrvwwVP78D/XVSEK8syKM8MfYmWL7PAIfISpFBJ+OASHBAEgmqO4Sbh1xG1XU89nSdFKcKF01Kws64X16/fB2AwjSZBJkSqSoxUlQgpSvHg/5UipKjEEPI4uOPdasrvFSgTWrSoOHUyOfA4HLmQh3uXTsaCgnjkxEtjYjl7qsuIq1/Ziz6THW/ubsTvF+WH7b1WnZOHr450hNQkw2R34U8fHcbXRzvx9xXTA565kiTp0xSw22jz2cykMFmOFq05rD0N0lRinzWfQh4HZ09KgFoigFoqgEbKh0oigEYiQE6CFHwuZ0SPTpIcvJ5uow01fmzLwpXMPcFFK/CYFgPjjV4pTlPiunnZ0R6GX9p0Fvz5o8PIiZchUyPG89/Xe5w7X95xGtfOzYKcphSD0Qh5XPx9xXSsWLcr5BZt20/24PyntuORS4txyczxs/KNNqfP1vMAUNmo9XTYiZcJkK6WQMDl4GBrP2zO4AZLEMDcnDhcWpIKAY+DLce7saO2Z0Tsis8hkKgQ+kycfvLXM/02KrE73R4/NCYwsUWLQia4O0ZUqyCAXJtok6IQoaZFh221PWOe05kdeP3HRty1pCBs71+SqcZdiyfhma0nQxYug9WJuzbWoNtgw80Lc/0eqzONb4Gzr7EfMzOUqGnRo3cgeFeFqakKXDozDctnpI6YCf6yJB0Olxudeit0Zge0ZjvW72zA915+FwCwesmkcTsr7azvhX4cX7BIMmF3DwGga5w4QiwSC3lkHA6BOX5cNV798TT0YW4+cteSAmy7dxGumZtJi8XNY18cx6s/nPZ7jMMdWAZ7KFbKF05LxpY1C/H5nQtw88Jcr0tXPpeDDI0E09KVOHtSgs/Qxy9mpOLOxeMv1b84NNZeKJpMaNGqaQ28JCEWZlqFyXKsmBVajVykKPNT7Gu0OvHKOAJAB1lxUjx66TTsvO9cFCaPTQegyqOf+xcuX/bLw5EJeTCEMGs51KpHgpza7nC8l0TrcyYn4F+Xzxg3Jmp3+u6gFC0mrGiRJImDLbrAjw/fUGiBxyHwr8tn0DJriAT+RAsANlXR13hiPBLkQtwwP4fSa3wVmT/6+XG8/qP3jkJSIQ8KP+U6eQlSyITcgHtdeutz0NpvwR8/OEgp6fM3ZZkjvi/L0eCFq0s9f0suN4kBmxPdRiua+kw43mFAVVM/fjzVi9d+bIAhii4h3gjpDnjiiSdAEATuvvtuz2NWqxWrVq1CXFwcZDIZVqxYga6uyAfxmvrMlNbh0fDlpsKqc/JRnBa9wmeqFKUowPVTyBnpNm/LZ6T6FZThaKQCHHjgPGy4qRzXz8seIx4Pf3YMGyu9e3P5c3KQCLheHSC8UZatweu/mwOel5/h10e78OauxoDOAwAL8uM95TvT0pR47brZEAu4qGzQovCBL5H3py9Q/ODXKHtsK87+5/dY9swPWLFuF655bS/+/tWJgN8nUgQtWvv27cNLL72E6dOnj3h89erV+PTTT7Fp0yZs374d7e3tuOyyy0IeKFUOtuoCPrYgSeaz7TkTKEpRYNU54UsTCAc8Lsfv7q0iwp2MxQIuLp+dEdCxK2alQSLgYV5+PP76i6nYef+5+OyOszxmeQCwbnu919nOZD/LUIMl8BnLjQtyUJiswI0LvM8Q//bFCRwOMPzB4RC4qjwT+YkyvHlDGeQiPgxWB1a/VwOrgx4niUgSlGgNDAzg6quvxiuvvAK1+ieHRb1ej9deew1PPvkkzj33XJSWlmL9+vXYtWsX9uzZQ9ugA4FKtbqYz2VsygOHQEwtC4fjbZYwRDTar9+8IBdLp47fKuuKOSOXUwRBoDhNiWevLPGUhvmayf/tsmk+m7D2mQLP1u86Y1N91+ICrw187S43Vr9fA6crMNH59ewMvHVjGTRnGm389X9HY9Y7Lqg7YdWqVbjooouwZMmSEY9XVVXB4XCMeLywsBCZmZnYvXu313PZbDYYDIYRX3RA5RdCl29ROJiVqaa1ZXwk8VfqQZcVDBWSlSK8dO1sfHHnAiTKvbuAlGVrfCZFTk1V4r5lhQCA6elKrxUIMiEPb95QNka4ZmYoKe0aDpXwSAQ8PHLp1BHPcQhgxax0rL9+TsBF8RqpwLN0/fxQBzYfaAt4LEyDcp7Wxo0bceDAAezbt2/Mc52dnRAIBFCpVCMeT0pKQmen9x2Ixx9/HA899BDVYYxLJ4V0B7uTue3GzilMjPYQgoY3ukX2MBTi6KUIFqUqcNmsdLy4vX7Mc1fM8b+E/N28bOw42eN3o0Em5OGN383Br1/ag+MdhkHbb4rpN8PrDs8tTMLGW+bC7nSDIIB0tcRrZ+pA6NRb8aePDgf1WqZAaabV0tKCu+66C++88w5EInqKcteuXQu9Xu/5ammhZ1eJyh8Jk9f1Z0+K3aazPA5zYlqj8eY7L+RxsGxast/Xcc7s4l48TkKmXMTHnOzB2VZJhirgAPwQrf0WzxIRAObmxmHhpAQsKEgIWrDcbhL3bjrIqETRYKAkWlVVVeju7sasWbPA4/HA4/Gwfft2PPvss+DxeEhKSoLdbodOpxvxuq6uLiQne/9jEAqFUCgUI75C5Wi7Hh0BFIcSAKakyEPKmwknCXIhpsbo0hDACMfS0URjeTicSUlyTBu1G2tzuvHHDw6N68iQIBciK2584UhWihAvE1BKvRnOAS8uD6Hwxq5Gn35fsQQl0Vq8eDEOHz6Mmpoaz9fs2bNx9dVXe/7P5/OxdetWz2tqa2vR3NyMiooK2gfvDYfLjXs3HRq3fCNNJYJCzMfxDqPHl4hpLJqUEBMF0b7wF2+JRiB+NL8sGTvb+uxQB658ZY/fzjqBsvLsPFw8PRXWIGOm/93dRJsJX22nEU8wMH0hGCiJllwuR3Fx8YgvqVSKuLg4FBcXQ6lU4sYbb8SaNWuwbds2VFVV4Xe/+x0qKiowd+7ccF3DCF7YVo/jHeMH89t01qB8uyPJosmxG88C/O8eRnt5CACXlqR5NYqsbtbh0ud3orbTGNL5CYIIqVZ09+k+vL8/9HCJy03i7vdqYGfwhhMVaN9Hf+qpp3DxxRdjxYoVWLhwIZKTk7F582a638YrHXoLnvsucC+lVIotnSIJl0MEbRnNFPylaTBhpqWRCnzGsNp0Fqx+rybk98gJYBnpj0c/P45uL126qcDlEKjIDd6bn2mELFrff/89nn76ac/3IpEIzz//PLRaLUwmEzZv3uwznkU3rf0Wv9YgozneaWBs/pNMyIt41jjdjJ5pDY9xBZqdHm6umZvl8zk6WsrNylJ7zbMKFKPVib98cjTkcfzpwsIJI1zMvGODZDzT/tEYrU5Mi2JPQH/oLQ7GFapSJWXYzcrnEvhw5Tzs/dNiPH/VrIAC2ZFgdpbaZ2utTE3oYxTxubjn/EkhnWNfoxY6H46ogcLjcvCfq0pCElCmMKFEy5drpD9MdmbuHALA23uaoj2EkHjs0mJP9537LijE9HQVkhQiXDQ9xZOZHW0IgsDV5d5nW1k0NYe9dGZa0JZCSQoh3ru1ghYr7TiZEC9dWxrVjuN0ENujH4Uvf25/0NlsgW72Nmhxsiu0YHA0UUkEWP+7Obi8NB03nkXNZSGSlOd6TxTNpEm0OBwC95/JpKfKny6cQqttcXGaEmflx27uHzDhRIv6rClctr90Eeuzraw4Kf4ZgG9TNPnX1yc9/4+XCfDYL4vxzeqFONtHt+hgWFgQj/n51GNKT3x5gnLYwx96swM7Tnl3MY0VJpRoBVo8OpxQYwXh5odTsZ8MyGQqG7TYcvwn66RflWbg6vIsTEqSg+MnZYMqBEHg/gumUH5dh96KP354iLZ8rf8dbIv51IcJJVoXFFPbpRTzOagLYweUUOFyCDxxGf1941h+YueoDPFflYbPGXZauhLnF43vMjGab4914S2aZtybqlppOU80mVCiVZqlptR2PS9RBheDzf/+dOEUlE+QbWqmMrwwuSRTFba2V0OsPi+4ncRHPz8edDnQELWdRhyiYEHOVCaUaBEEEbDRGzBo+8FUls9IxQ3zs6M9jAmN0+VGdfNPonV5aeB/O8EyJUUxbrG1N+xON655be+I8VJlEw3Z9UxgQokWANy8IAdv3ViGK2ZnjLu1qzXZ/FoCR4t0tRhPXDaN0cHricCJzp/qToU8Di6eQV1MguHWhXnjH+QFo9WJa17d67eLtC8cLjc+roldD63hMHeqESQ8LgcLCgYtPNr1Fr+B7LpuE8pzNNgbxB9BOFlQEE9b63iWQSx2F453GnC03YBj7XoUJMpH1D9eUJwcMeeJyclycDlEUKEJk92F69dX4tjDF1B6XbfRxrgGFcEyoe+MBC+tk0ZT2aBFmlqMNh8tw6NBLDWwiBZ3bayGweJAhkaCDLUEGRox0tWD/+8z2VDbacSJTiNqO42o7TKisc80wlJ73dWzRvhKhTMAPxoBj4OsOAlOB7kJZLa74HaTlHY301RiPHppMf74waGg3pNJTGjRivdhqTscHpeANoROv+Fgepoq2kNgNCRJ4rsT3TAGOXPgcgjMy4/HF4c7kKER4/yiZMzLi2xxel6CLGjRAgAXSYIDauGDX8/OwOFWPW07kdFiwsW0hhPITKswWQ7LOKZvkUTA5WBScnh3sGKdbqMtaMECgJkZKijFfPyyJA07/nAOHri4KOKxTSq7lI9cWowZGaoRjzldwe16P3BxEWb7aLwRK0xs0QpgpsW0hIfJyXIIebHt7hBuTnUNhPT6hWcy3UV8btQ2O/ITAhctu9ONj38/DxtuKsdZ+YMzQqc7uARRAY+DF66ZhSTF+PcGU5nQojWeZ9P0dCWOtNHT/YcOBFwOfjEjNdrDYDwNvaGJ1oJJ0fcpm5Tkuz/iaP53sB0EMbikffumcnyyar7fTkfjkSgX4ekrSoJ+fbSZ0DEtf5a5Ai6BborNBsJFvEyIa+Zm4uryrIBmhz93rpiTCYvDhae3nIKZolW2UszHjHRVeAZGgaJUBdQSfkD1sgdbdGjsNSH7TOL06KViMMzNHWyVVtcd2gdANJjQMy1/vQ9nZqjRGaIjJB3cd0Ehdt5/Du5eMokVrAAR8Di4ZWEett5zNi6imKg5KUnGiNw8LofAggALsvlcAgdCSCr1BkEQXj3yY4EJLVrtfkTLzYCW0iWZKtx2du7PKoa1q64Xa96vwab9Leg2hvahkaIU4/mrZuHtG8uRmxBY+dahVj0sDGlkMl57uKvLM7H++jk4+OD5uGwW/SkZsRqKmNDLw3Y/bcQGbNFPtPvzRVN+Nlnv3UYrHvv8OD6paQcAT4fjohQF3vjdHCQqgrc2PqsgHl/dtRCv/diAZ7ee8rsbbHO6sau+F4unUC9cppuF44jW8hmpmBvG2tMMjQRlOZqgMuyjycQWLZ3vT3Kni0RZjgYHmvop+crTxUXTUlCa5btLMdPo0Fvwzp5m2F1uuNwkXG4SbnLw36VTk33egC43iXf2NuGfX9d6TVM41mGAfZSlUJfBiiSKIibgcbByUR5+MTMVz209hc8Pd/hMi2joZYazR4JciOI0hc/NoF31fWEVLWCwjRorWgyBJElPTGtqqgINvSZP0FYt4ePFa2chP1GOAZsTe+r78MOpHvxwqhenI/AHzecSuO+C4Jwso8EnNW144OMjPstA3t/fgmd+U4ILp42MLx1s6cefPz6Kw23+nQVEZxp4uNwknt9Wh3crm7Hr/nODmoWmqcR4YsV0/PUXU/F9bQ+e3nISJ0a1AitlUJ7SokmJvkWrrhdrgnSFCJQLi1Pw4CdHx3xwMJkJK1pmuwu3n5OPS2amIitOCr3ZgY37mrH5QBv+dfkM5CcObjnLhDwsKUrCkjM+R1e+vAe7T/eFdWzXVWTTZuUbCiRJYsDmRL/JgT6TDYkK0YjGBzqzHX/++Ag+O9Th9zwOF4nbNxzA9fNyYLQ60NhnwuleE9xuMqDdMRGfO9iya2MNKhu1kAq4sDrcEAuCj/WJ+FxcUJyMhZPi8YdNh/D54cFrkAq4jCqTWjQ5Af/ZVuf1uZoWHUw2Z1jrUJUSPhZPScSXR2KnicqEFS2pkIc7Fxd4vldK+Lj17Dzcerb/CvvFUxLDKlpKMR93nFsw/oEhQpIkegYGa/BqO41o7bdAa7KP+Rr+CcvjELhsVhp+vygfTVoz/vjBQXQFmBbiJoHXdzZ4vk9Xi9EaYD3n1uNd+PPHRzzLuTXnTw5JsIYjEfDwn6tKMGWbHP/65iRKszUh5TjRTWmWGmuXFeLxL8d2f3a6SVQ2anFOmJv2XlqSxopWLLN4ShIe/fx4QMemKkW4em4WbE43bE4XbI7Bf+1OEjwOAR6XAJ/LOfN/DvhcArMy1VBK6HUTMFodONk1cEagDKjtGhQqqp75TjeJ9/e34oOqVoQa5ktRigIWrbs21nj+X5SiwHUVvnsRBgNBELj93AJMTlagw8/mTDQgCAK3np2HFJUY975/cMwybXd9X9hFa9HkBFwxOwNcLgEBlwMhjwMBjwMBl4Mf6noDinmVZKqwsCABJpsTJrsTAzYXlOLwyAsrWqPIiZciN146bmwrN16Kt24qj1ofObebxOeHO/Cf7+pQS3PHHjr2JYKpMiEI4LFfFoMXppnQeUFYHUeKX8xIRZJciJv/u39E7HC0HXQ4EPK4+Puvpnt97uaFuVj9Xs24M7FbF+biguLI+JExZ57MIBZP8f/JNjVVgfdvq4iKYJEkia3Hu3DRcz/ijneraRcsuugwUJ/RXFWWiZJM5gTJI015bhw2/34eJifJkaoUISdeCpebhNkevfQcEZ+L/1w1C1eXZ/o8RiLgYlGYZ4PDYWdaXrhsVjpe39no1aStJFOFN28oi5hh3HB21/fhn1+fwIFmXcTfmyrB9JNcUBD9msBok58ox9erF0Z7GCPgcgg8emkx4mVCPLP11Jjnl0xJ8uwARwJ2puWFKSkKrPQSsBfwOHjq1zOjIlgfVrXiylf2xIRgAQjqZ/RzSbSNRQiCwOrzJuGRS6Zi6Nc0NVWB3y/KC3taxmjYmZYP7licj2+OdeLkMBuUlWfneYpWI8m3x7rwxw9jy3EymNbrTO72zTLItRXZmJauQqpSFFIVQyiwMy0fCHmDa/lflaYjQS5EVpwEKxcF15AgFPac7sOqDQcY3erMG8GkFTAoE4HFDzMzVFETLICdafllUpIc/7p8BkiShMHqjOi63eZ0YVddH+54tzomOwIHI7Hf1/bg3ELm7vCxMANWtPygNztgc7rA4RDgEASsDlfYhMvtJnGi04iddb348UxuDJNsoKniCKIs5L+7m1CQJMe1c+nN02KZWLCi5Yffvr4XB4d15E1VivDxqvm0To1JksRT357EO3ub0WdiVoONUAjW/uWv/zuKswsSGFHmxESqmrQxVWgfDtgogg869dYRggUA7XorbnxzP215My43ibWbD+PZ7+omlGABgMFCLRt/CJebBJfLBuS90a6z4MqX92JbbXe0hxJVWNHywbfHu7w+frhNj7s21oQcGLc73bhrYzU27psYrcpHYwrBaC9OKqBxJBOHnXW9sLvcuPWtKuw42TPmeXeMbdYEC6Xl4bp167Bu3To0NjYCAKZOnYq//OUvWLZsGQBg0aJF2L59+4jX3HrrrXjxxRfpGW0E+faYd9Eaeu6Rz45hWXEy2vUWtPVb0KazoE1nhVzIw/IZqTinMMGnI6nF7sLv36nCttqxf3gThUSFcEQz1EBRiHgR3fCIJXbVDxby251u3Pzf/biqPBPdRpvn7693wIZUpRi5CVLkJchw9qQEnFMYuUz1SEFJtNLT0/HEE0+goKAAJEnizTffxCWXXILq6mpMnToVAHDzzTfj4Ycf9rxGIom92ITR6sDuev81X2/sasQbuxq9Pvf54Q7IRTxcWJyCS2amojw3zuNLbrQ6cOOb+2POeI0qyiATcANpsPtzZdHkBNidbuys74XO7MD6nY1jjhn88LTgh1O9sNhdyE+U4ePqNmTGSXDJzNj0hB8NJdFavnz5iO8fe+wxrFu3Dnv27PGIlkQiQXJyMn0jjALfneiGI8hmmEMYrU68t78F7+1vQaJciLwEGcQCLrgcYsILFgAEmycaSIPdnyuXzEzDJTPT4HaTONZh8Ow072vUwuoYu1v7xZEOvLd/MPzw4cqKSA83bAS9e+hyubBp0yaYTCZUVPz0A3nnnXfw9ttvIzk5GcuXL8cDDzzgd7Zls9lgs/3k2WQwRL8P4QdVrbSer9toQ/eZdmZTUxW0npupmGzBxbTYmdb4cDgEitOUKE5T4taz89Cpt2Lu41vHHGe0OiHgcpCmFmM6A9qm0QVl0Tp8+DAqKipgtVohk8nw0UcfoaioCABw1VVXISsrC6mpqTh06BDuu+8+1NbWYvPmzT7P9/jjj+Ohhx4K/gpopkVrxo9htAM53mGARsKHlqLXVazRGaRvFTvToo6Iz8GNZ+UgUS5EglyIRLkIiQohEuVCKMX8CVfTSZAktV5adrsdzc3N0Ov1+OCDD/Dqq69i+/btHuEaznfffYfFixejrq4OeXneS2C8zbQyMjKg1+uhUER+VvLktyfxrJdKdjqZk63GvkZ6+9gxCZWED12Qovz0FTNxaYz242MJHoPBAKVSGdB9T3mmJRAIkJ+fDwAoLS3Fvn378Mwzz+Cll14ac2x5eTkA+BUtoVAIoZAZn64uN4lN+8OfgkDVUTTWSFWKghKtX5Wm45KZsdmLjyVyhJyn5Xa7R8yUhlNTUwMASEmJjKNhqOw41YMOffi7Ttd1DyBNFb2C03AjDCJlYVqaEo9eWjzhljIs9ENpprV27VosW7YMmZmZMBqN2LBhA77//nt8/fXXqK+vx4YNG3DhhRciLi4Ohw4dwurVq7Fw4UJMn+7dypVpcAkCf1g6OSLvJRVwQ0rAZCp8LgG7040lFJuhXlqSxuZnsQQEJdHq7u7Gb3/7W3R0dECpVGL69On4+uuvcd5556GlpQVbtmzB008/DZPJhIyMDKxYsQJ//vOfwzV22lk4KWHcrr8sLCzRhXIgPtxQCcixsLBMDKjc92ztIQsLS0zBihYLC0tMwYoWCwtLTMGKFgsLS0zBihYLC0tMwYoWCwtLTMGKFgsLS0zBihYLC0tMwYoWCwtLTMGKFgsLS0zBihYLC0tMwYoWCwtLTMGKFgsLS0wRdGOLcDFkOsGEBhcsLCyRYeh+D8R0hnGiZTQaAQAZGRlRHgkLC0ukMRqNUCqVfo9hnJ+W2+1Ge3s75HJ5QNa7Q40wWlpaJpz/1kS+NoC9vliG7msjSRJGoxGpqangcPxHrRg30+JwOEhPT6f8OoVCMeH+MIaYyNcGsNcXy9B5bePNsIZgA/EsLCwxBStaLCwsMUXMi5ZQKMSDDz7ImN6JdDKRrw1gry+Wiea1MS4Qz8LCwuKPmJ9psbCw/LxgRYuFhSWmYEWLhYUlpmBFi4WFJaaIadF67LHHMG/ePEgkEqhUKq/HNDc346KLLoJEIkFiYiL+8Ic/wOl0RnagQfL8888jOzsbIpEI5eXlqKysjPaQgmLHjh1Yvnw5UlNTQRAEPv744xHPkySJv/zlL0hJSYFYLMaSJUtw6tSp6AyWIo8//jjmzJkDuVyOxMREXHrppaitrR1xjNVqxapVqxAXFweZTIYVK1agq6srSiOmxrp16zB9+nRPEmlFRQW+/PJLz/PRuLaYFi273Y7LL78cK1eu9Pq8y+XCRRddBLvdjl27duHNN9/EG2+8gb/85S8RHil13nvvPaxZswYPPvggDhw4gBkzZmDp0qXo7u6O9tAoYzKZMGPGDDz//PNen//HP/6BZ599Fi+++CL27t0LqVSKpUuXwmq1Rnik1Nm+fTtWrVqFPXv24Ntvv4XD4cD5558Pk8nkOWb16tX49NNPsWnTJmzfvh3t7e247LLLojjqwElPT8cTTzyBqqoq7N+/H+eeey4uueQSHD16FECUro2cAKxfv55UKpVjHv/iiy9IDodDdnZ2eh5bt24dqVAoSJvNFsERUqesrIxctWqV53uXy0WmpqaSjz/+eBRHFToAyI8++sjzvdvtJpOTk8l//vOfnsd0Oh0pFArJd999NwojDI3u7m4SALl9+3aSJAevhc/nk5s2bfIcc/z4cRIAuXv37mgNMyTUajX56quvRu3aYnqmNR67d+/GtGnTkJSU5Hls6dKlMBgMnk8KJmK321FVVYUlS5Z4HuNwOFiyZAl2794dxZHRT0NDAzo7O0dcq1KpRHl5eUxeq16vBwBoNBoAQFVVFRwOx4jrKywsRGZmZsxdn8vlwsaNG2EymVBRURG1a2NcwTSddHZ2jhAsAJ7vOzs7ozGkgOjt7YXL5fI69hMnTkRpVOFh6Pfg7VqZ/Dvyhtvtxt1334358+ejuLgYwOD1CQSCMTHXWLq+w4cPo6KiAlarFTKZDB999BGKiopQU1MTlWtj3Ezr/vvvB0EQfr8m2o3LMjFYtWoVjhw5go0bN0Z7KLQyefJk1NTUYO/evVi5ciWuu+46HDt2LGrjYdxM65577sH111/v95jc3NyAzpWcnDxmx21oZyM5OTmo8UWC+Ph4cLncMbswXV1djB53MAxdT1dXF1JSUjyPd3V1YebMmVEaFXVuv/12fPbZZ9ixY8cIa6Xk5GTY7XbodLoRM5JY+l0KBALk5+cDAEpLS7Fv3z4888wzuOKKK6JybYybaSUkJKCwsNDvl0AgCOhcFRUVOHz48Igdt2+//RYKhQJFRUXhuoSQEQgEKC0txdatWz2Pud1ubN26FRUVFVEcGf3k5OQgOTl5xLUaDAbs3bs3Jq6VJEncfvvt+Oijj/Ddd98hJydnxPOlpaXg8/kjrq+2thbNzc0xcX3ecLvdsNls0bu2sIX4I0BTUxNZXV1NPvTQQ6RMJiOrq6vJ6upq0mg0kiRJkk6nkywuLibPP/98sqamhvzqq6/IhIQEcu3atVEe+fhs3LiRFAqF5BtvvEEeO3aMvOWWW0iVSjViJzRWMBqNnt8NAPLJJ58kq6uryaamJpIkSfKJJ54gVSoV+cknn5CHDh0iL7nkEjInJ4e0WCxRHvn4rFy5klQqleT3339PdnR0eL7MZrPnmNtuu43MzMwkv/vuO3L//v1kRUUFWVFREcVRB879999Pbt++nWxoaCAPHTpE3n///SRBEOQ333xDkmR0ri2mReu6664jAYz52rZtm+eYxsZGctmyZaRYLCbj4+PJe+65h3Q4HNEbNAWee+45MjMzkxQIBGRZWRm5Z8+eaA8pKLZt2+b193TdddeRJDmY9vDAAw+QSUlJpFAoJBcvXkzW1tZGd9AB4u26AJDr16/3HGOxWMjf//73pFqtJiUSCfnLX/6S7OjoiN6gKXDDDTeQWVlZpEAgIBMSEsjFixd7BIsko3NtrDUNCwtLTMG4mBYLCwuLP1jRYmFhiSlY0WJhYYkpWNFiYWGJKVjRYmFhiSlY0WJhYYkpWNFiYWGJKVjRYmFhiSlY0WJhYYkpWNFiYWGJKVjRYmFhiSlY0WJhYYkp/h9CrNCWpIkEZgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "eur.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97a12d23",
+   "metadata": {},
+   "source": [
+    "#### Centroids of European countries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "eb5c83b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_13458/1089923979.py:3: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
+      "\n",
+      "  eur.centroid.plot(ax=ax)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the centroids\n",
+    "ax = eur.plot(facecolor=\"lightgray\", edgecolor=\"k\")\n",
+    "eur.centroid.plot(ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dbfab5e7-5941-438a-baf2-e05369106004",
+   "metadata": {},
+   "source": [
+    "### Lat / long CRS\n",
+    "\n",
+    "- Long is x-coord\n",
+    "- Lat is y-coord\n",
+    "    - tells you where the point on Earth is\n",
+    "- **IMPORTANT**: degrees are not a unit of distance. 1 degree of longitute near the equator is a lot farther than moving 1 degree of longitute near the north pole\n",
+    "\n",
+    "Using `.crs` to access CRS of a gdf.\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "89251896",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Geographic 2D CRS: EPSG:4326>\n",
+       "Name: WGS 84\n",
+       "Axis Info [ellipsoidal]:\n",
+       "- Lat[north]: Geodetic latitude (degree)\n",
+       "- Lon[east]: Geodetic longitude (degree)\n",
+       "Area of Use:\n",
+       "- name: World.\n",
+       "- bounds: (-180.0, -90.0, 180.0, 90.0)\n",
+       "Datum: World Geodetic System 1984 ensemble\n",
+       "- Ellipsoid: WGS 84\n",
+       "- Prime Meridian: Greenwich"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eur.crs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a6ba447-6f1a-4d31-8c33-ec1eeb9e0deb",
+   "metadata": {},
+   "source": [
+    "#### Single CRS doesn't work for the whole earth\n",
+    "\n",
+    "- Setting a different CRS for Europe that is based on meters.\n",
+    "- https://spatialreference.org/ref/?search=europe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "5451e78f-ebaa-4bb3-af34-24ffe92d0582",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setting CRS to \"EPSG:3035\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "586038b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Projected CRS: EPSG:3035>\n",
+       "Name: ETRS89-extended / LAEA Europe\n",
+       "Axis Info [cartesian]:\n",
+       "- Y[north]: Northing (metre)\n",
+       "- X[east]: Easting (metre)\n",
+       "Area of Use:\n",
+       "- name: Europe - European Union (EU) countries and candidates. Europe - onshore and offshore: Albania; Andorra; Austria; Belgium; Bosnia and Herzegovina; Bulgaria; Croatia; Cyprus; Czechia; Denmark; Estonia; Faroe Islands; Finland; France; Germany; Gibraltar; Greece; Hungary; Iceland; Ireland; Italy; Kosovo; Latvia; Liechtenstein; Lithuania; Luxembourg; Malta; Monaco; Montenegro; Netherlands; North Macedonia; Norway including Svalbard and Jan Mayen; Poland; Portugal including Madeira and Azores; Romania; San Marino; Serbia; Slovakia; Slovenia; Spain including Canary Islands; Sweden; Switzerland; Türkiye (Turkey); United Kingdom (UK) including Channel Islands and Isle of Man; Vatican City State.\n",
+       "- bounds: (-35.58, 24.6, 44.83, 84.73)\n",
+       "Coordinate Operation:\n",
+       "- name: Europe Equal Area 2001\n",
+       "- method: Lambert Azimuthal Equal Area\n",
+       "Datum: European Terrestrial Reference System 1989 ensemble\n",
+       "- Ellipsoid: GRS 1980\n",
+       "- Prime Meridian: Greenwich"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Setting CRS to \"EPSG:3035\"\n",
+    "eur2 = eur.to_crs(\"EPSG:3035\")\n",
+    "eur2.crs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "d46c124c-9aff-4c2f-81fe-08885b606800",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = eur2.plot(facecolor=\"lightgray\", edgecolor=\"k\")\n",
+    "eur2.centroid.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "045b9c33",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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iIr169aJw4cJMmTIl1XFFiTNA4cKFsbe35/Pnz3Kld8tLcjGktFwspUqVokaNGkLURj4ny+Ls7+9PiRIlKFeuHL169eLdu3fpjo2LiyMiIiLFj4BAfmf+/PncunWLBQsWyAR4zpw5mJubU7duXVkSira2tkLWa9u2LQAPHjxQyHzwP3GuW7dumsdtbW05ceIE3759U9iaAoolS+Jsbm7Otm3bOHXqFOvWreP169c0bdo03V/wwoUL0dPTk/3Im5IqIJBXXL9+nblz5zJ48OAU7aZOnjxJqVKlGDx4MLNnz2bHjh0Ky7KrVq0a6urqChXnd+/eoa6unioRJRkbGxtiY2M5fvy4wtYUUCxZEufWrVvTpUsXatWqJfvmDQsLS/f2aMqUKYSHh8t+Mqs7KyCQl3z79o2ePXtSvXr1FFmAQUFBREVF0bVrV5ydnenUqVOO+wT+jL6+Pnfv3lXonImJienWcC5RogS1atUSOqTkY3IU51yoUCEqVarEixcv0jyuoaGBrq5uih8BgfzKv//+y4cPH1i4cCGqqv8LZDp8+DBSqZT69esrbe3q1avz9OlT4uLiFDKfk5MT5cqVY/Lkybx69SrNMba2tpw6dUpwN+ZTciTOkZGRvHz5EiMjI0XZIyCQJyQkJLBmzRratGlD6dKlUxy7fPkyhQsXpmzZskpbv2XLliQmJvL06VOFzKelpcXatWspUKAAvXv3TrPYv42NDfHx8Rw9elQhawooliyJ8/jx47l06RJv3rzBx8cHR0dHVFRU6NGjh7LsExDIFY4cOcKHDx/o1atXqmOvXr3C3Nw8zUp0isLKygqxWMz9+/cVNqeRkRFr1qwhJiaGbt26pUoXL168OKampoJrI5+SJXF+//49PXr0oHLlynTt2hV9fX2uX7+ukHbwAgJ5yapVqzAzM6NKlSopXg8ODiYqKkqpLg34vtPV0dFRqDjD91ZWc+bM4d27d4wYMSLVcTs7O86cOZNmPRCBvCVL4rxv3z4+fvxIXFwc79+/Z9++fZQvX15ZtgkI5Ap3797l6tWr9OzZM9UxDw8PpFIpZmZmSrejbNmy3L17N9PGrlnF3t4eJycnLl++zOrVq1Mca9WqFYmJiRw5ckShawrkHKHwkcAfj6urKyVKlKBFixapjl24cIEiRYpgYmKidDsaNmzI169f+fDhg8LnHjFiBC1atGDz5s0pIjiKFStG3bp1BddGPkQQZ4E/mqCgIPbu3Uu3bt1SRGgk8+rVK5o0aaJUf3MyyckoinZtwPdCSIsWLaJ8+fJMnjw5RYSVra0tZ8+eldUMEcgfCOIs8EezceNGxGIxnTp1SnXs9evXREdH07Bhw1yxxcTEBE1NTaWIM3z3a//7779oaWkxYMAA2evW1tYkJSUJro18hiDOAn8s8fHx/Pvvv7Rr1w49Pb1Ux5OTq5LrOOcGhoaGXL16NcNmrTnByMiI/v37p4htNjAwwMzMjH379illTYHsIYizwB+Lu7s7nz59SvNBIMCVK1coU6YMRYsWzTWbhgwZwvv379m7d6/S1hCLxancNHZ2dpw/f57g4GClrSuQNQRxFvhjWbVqFebm5lSsWDHN44GBgTRp0iRXbXJwcKBixYq4urry6dMnpayRlJSU6jUrKyukUimHDx9WypoCWUcQZ4E/kps3b3Ljxo10d803b94kLi4u1/zNP7J8+XJZ95Xnz58rfP60QvX09fVp0KCB4NrIRwjiLPBH4urqSsmSJWnWrFmax93d3RGLxbkS3/wjt27dwsXFBZFIxIcPH+jcuTMTJkxItz5GdpBIJGlGn9jY2HDx4kWF1pUWyD6COAv8cQQGBnLgwAG6d++OiopKmmPu3LlDjRo1FFazOTMiIiKYPXs2AwYMoGTJkjx69IgPHz6wceNGfH196dChA5MnT+bNmzc5Xiu9rt+tWrVCJBLh4eGR4zUEco4gzgJ/HBs2bEBNTQ1HR8c0j0dHRxMSEoKFhUWu2HP27FkcHR05c+YMa9eu5fLly1SpUgU1NTWcnJzw9/fn33//5f79+zg4OODq6pqj9aRSaZo758KFC2Nubi64NvIJgjgL/FHExcWxfv167O3t0y1hu3btWhITE7Gzs1O6PWfOnGHMmDGYm5vz5MkThg4dilic8mOpoaHB0KFDefHiBaNGjWLr1q2EhIRke02JRIJUKk0zXM/W1pbLly8r7WGkgPwI4izwR3HgwAE+f/6c7oNAAE9PT6pXr55mc1RF4+7uTqNGjTh69CglS5bMcKympibTpk1DJBJx6tSpbK9Zp04dEhMTcXR0TCXQVlZWqKio4O7unu35BRSDIM4CfwxSqZSVK1fSqFEjypUrl+YYX19fQkJC6Ny5s9LtCQoK4vr16/Tr10/u9HB9fX1at26Nl5dXttdt1KgR8+fP5+3bt3Ts2DGFQOvp6WFhYSG4NvIBgjgL/DF4enpy9+5d+vbtm+6YlStXoq6unisujRMnTqCmpkaXLl2ydF6NGjV49OgRsbGx2V7b3t6e+fPn8+bNm1QCbWNjw9WrV5VSgElAfgRxFvgjSEpKYsqUKTRo0CDdB32JiYncu3cPOzu7XInS8PT0pH379uk2YU2LkydPsnjxYnr27ImmpmaO1re3t2fevHmpBLply5aoqqoKro08RhBngT+CvXv34uvry8iRI9N1Iezdu5e4uDg6duyodHuePXvGs2fP+Ouvv+Q+5+nTp3Tt2pWmTZsyceJE2etJEikPP8Vy6XUUDz/FkiSRvx50+/btmTt3Lm/evKFTp04kJiaiq6tLo0aNBNdGHpO6RqKAwG9GfHw806dPp2XLltSuXTvdcbt376ZkyZLUrVtX6TYdP34cAwMDud0nUqkUZ2dn9PX1+eeff2Tx2T7votl4O5Tg6P+lZBtoqTDYrAiNSmvJNbeDgwOJiYnMnj2bffv20bt3b2xtbZk6dSoBAQGUKlUq6xcokGOEnbPAb8/GjRsJCAhIs01TMh8+fCAwMJBOnTrlSu3mhw8f0qRJE9TU1OQav2/fPi5fvszUqVPR0vouuj7vollw+UsKYQYIjk5iweUv+LyLltueVq1aAchC6Fq0aIG6ujqHDh2Sew4BxSKIs8BvTWRkJHPnzsXe3j7D0LhVq1YB33eRuYGpqSk+Pj7pZuv9yLdv3xg7diytWrWiUaNGwHdXxsbbGRfH33g7VG4XR4ECBYDvCTgA2traNG7cWOiQkocI4izwW7Ny5UrCwsIYNmxYhuMuX75MkyZNcq1ZsaWlJUFBQdy+fTvTsbNnzyYsLCyFn9k3KC7VjvlngqOT8A2Kk8seNTU1xGIxUVFRstdsbW25ceMGb9++lWsOAcUiiLPAb0twcDBLliyha9eulChRIt1xp0+fJioqKlceBCZjamqKrq5upvHKvr6+uLq6MnjwYIyMjGSvf43JWJizOk4kEqGurp5CnJs3b46GhgYHDx6Uaw4BxSKIs8Bvy8KFC0lKSmLQoEEZjtu0aROFChXC0tIylywDVVVVGjVqhKenZ7pjpFIpw4cPx9jYOFVsduECaRds+hl5x8H3DMSYmBjZ/wsWLEjTpk0F10YeIYizwG/J+/fv+ffff+nbty9FihTJcOzr169p0aKF3A/nFIWpqSl3794lMjIyzeP79+/n0qVLTJ48GXV19RTHqhfVwEArY+E10FKhelENue0xMDDg3r17LFq0SBbzbGtry+3btxVaslRAPgRxFvgtmT17NlpaWvTp0yfTscbGxnh7e/Pt27dcsOw7cXFx7N27l8aNG1OwYMFUx799+8a4ceOwsrJKsxuLiljEYLOMv3QGmxVBRSx/5MmyZcswNzdn9+7dNG7cmC1btmBpaYmmpqbg2sgDBHEW+O3w8/PDzc2NQYMGpRC+9JI1Fi1aRHR0NG5ubrlm46ZNm/j48SMbN25MM3Tv77//JjQ0NMVDwJ9pVFqLqZaGqXbQBloqTLU0lDvOOZly5cqxbt063NzcKFeuHCtXrsTW1hZdXV3BtZEHiKRp9axREhEREejp6REeHp5uuUYBgZyQkJBA69atefr0KceOHUND4/ttfWbJGl26dOHt27fcvHlT6Ta+evWKzp07M2nSJObOnZvq+JMnT6hduzZDhw5l8ODBmc6XJJHiGxTH15gkChf47srIyo45LaRSKefPn2fFihW8ffsWkUjE5s2bGTBgQI7mFZBfBwVxFvhtkEqlDBkyBDc3N9atWyfr/5ecrJEeUy0NmTmgPfr6+krfIUokEgYMGEBYWBiPHz9OVR9DKpViZWXFy5cvOXz4cCpfc26TmJjIsWPHcHV1JTQ0lGvXrmFubp6nNv3qyKuDgltD4Ldh6dKlbNq0iZkzZ8qEWZ5kjfU3g/kSHIKNjY3SbTxy5Ah37txhw4YNaRYu+u+//7hw4QITJkzIc2GG71ElHTt2ZOPGjUilUh48eJDXJv0xCOIs8Ftw6NAhJk6cyKBBg1K0n5InWSM0VopaiaqyFGZlERISwvLly+nTpw9WVlZpjlm7di1ly5bN1bA+eUj2iwcEBOSxJX8OgjgL/PJcv36dv/76i9atW+Pi4pLimLxJGJVqm1GmTBllmCdjyZIlqKqqsnTp0jSPBwYG4u7uTrdu3VK1qsprkr2f586dy2NL/hyEqnQCvzSvXr2iffv2VK1alblz56YSNXmTMCaPyji9O6dcunQJLy8v3Nzc0k0R37RpE2pqarRv316ptmSHZHG+du1aHlvy55C/vp4FBLLA169fadOmDQUKFGDVqlWyyIwfUUayRlYJCAhg6tSptG3bNt0uLAkJCaxfv562bdvmy4flP8aAP3nyJA8t+XMQxFnglyQ+Ph5HR0c+f/7M2rVrKVy4cJrjlJGskRViYmIYM2YMhoaG7Nq1K91ypMeOHSMwMJDu3bsrxY6ccu7cOVRVVSlQoAAHDhzIa3P+CARxFvjlkEqlDBo0iGvXrrFq1apMfcWKTtbIip1z5swhICCAw4cPZ9iOateuXdSuXZvKlSsrxZackJSUxIkTJyhfvjzW1tbs37+fXIzA/WMRxFnglyIqKorhw4ezY8cO5s6dK3fXkkaltVjXxpAv+6dRK+oOC1oVY0sHY6UJM3xve+Xp6cnmzZupWbNmhmNv376dKx1YssPt27cJDQ2lR48e2Nra4ufnx+PHj/ParN8eQZwFfhlOnz5N9erV2bp1K9OnT6dNmzZZOv/UyRNEv3lAlwYm1CquqTRXBsC9e/dYsmQJI0eOpEePHhmODQkJ4f3791StWlVp9uSEkydPoqGhgaOjIxYWFujq6gqujVxAEGeBfM+XL1/o3bs3dnZ2GBkZ4eHhQbdu3bI8j5eXFwUKFKBGjRpKsPJ/BAcHM378eBo2bJhu2NyP3L9/HyBfujQSEhI4deoUVatWRSwWo6amRsuWLQXXRi4giLNAvkUqlbJ9+3aqVKmCl5cX8+bNY+PGjZQuXTpb8/n5+WFubq7U0qAJCQmMHz8eFRUVDh48KNda9+7do0CBAkqPs84Oly9fJioqKkWUia2tLf7+/jx8+DAPLfv9EcRZIF/y8uVLrK2t6devHxYWFhw5cgQHB4ccNV+VSCTpRnXIS3qV7ZJZvnw5Dx484NChQxQvXlyuOR88eEDFihVlHbXzE3v37kVbWztF9qS5uTmFChUSKtUpGSEJRSBfkZCQwIoVK5g9ezZFihRh3bp1adYzzg4qKiop2jBllcwq2717945du3axYsUKWSNWeTAyMuLYsWNER0fLOmvnB16/fs2NGzfo0qVLitfV1NSwsrJi//79zJ8/P1e6lf+JCDtngXzD7du3MTMzY8qUKXTp0gUPDw+FCTN8F5XsinNyZbuf63QERyex4PIXfN5F8+bNGwA6d+6cpbldXFyIioriyJEj2bJNWezfvx9VVVXGjh2b6piNjQ2vXr3i3r17eWDZn4EgzgL5gpkzZ2Jubk5CQgJ79uxhwoQJCt9FampqEhERkeXz5Klst/F2KAHvP6CmppaiEas8lC5dms6dO7N7926SkuSrBaJsoqOj8fDwoHr16mhra6c63qBBAwoXLiy4NpSIIM4Cec62bduYO3cuzs7O7Nmzh+rVqytlHU1NzWztnOWpbBccncTT4ARKly6dLd/x2LFjeffuHZcuXcryucrA09OTmJgYJkyYkOZxVVVVWrVqJURtKBFBnAXylPv37zN06FA6duzI0KFDUVVV3mMQLS2tbImzvJXtPoVHU7Zs2SzPD993oo0aNWLnzp3ZOl+RSKVSdu/ejaGhIbVr1053nK2tLW/fvuX27du5aN2fgyDOAnnG169f6dixI+XKlWPq1KlKX69gwYJER0dn+Tx5K9uFfXpHuXLlsjx/MuPGjeP27dv4+vpmew5FcOfOHV69ekWvXr0yHFevXr1c6R7zpyKIs0CeIJFI6NOnDyEhISxbtizNinKKRltbm5iYmCyfJ29lu8+PrmBiYpJd83BwcKBs2bLs2LEj23Mogr1796KpqUn//v0zHCe4NpSLIM4CecKiRYvw9PRk4cKFlCxZMlfW1NXVJTExkYSEhCydJ09luz41ChD29Wu23RrwPdRv1KhRnD59OkWJztwkKCiIs2fP0qRJE6SIMozphu+ujffv33Pjxo08sPb3RohzFsh1QkNDmTFjBk5OTrnajim5TnJUVFSGFeLSIrmyXXpxzvox39s35WTnDFCnTh2SkpIIDQ1FR0cnR3Nlh0OHDgHQqt94Bh75kG5MdzJ169bF0NCQ/fv3y/o2CigGQZwFcp1v374hkUioX79+rq6bLM6RkZFZFmf4LtDmJQvgGxTH15gkChf4XqRfRSzi/PkPADnaOQMyu/Ji55yQkMD+/fsp09iBdY8SUx1Pjun+scyqiooKrVq14sCBAyxbtizftdf6lRHeSYFcRyKRAOT6B9nAwAD43qsvu6iIRdQqrkkzk4IpKtt9/PiRAgUKUKxYsRzZmJxenp147Jxy7tw5Qr+GodUk7W4tyWy8HZrCxWFnZ8fHjx+FFlYKRhBngVwn+eFRbqf9WltbU6BAARYuXJhlv3NmfPjwgTJlyuT4mpJ3zrktzuHh4WzcuBHd8nWIlGR8Qx0cnYRvUJzs/6amphQtWpR9+/Yp28w/CkGcBXKdvBJnLS0tpk+fjr+/P9u2bVPo3B8+fMixSwO+R5SIxeJccWtERUXh6enJ8OHDad68Of7+/iSqFZTr3B9jv8ViMTY2Nhw6dCjfZDj+DgjiLJDrJLs18qJgTvv27alZsyZr167l1atXCps3JiaGkJAQ2bVlF7FYjJ6entLEOS4ujrNnzzJu3DiaN2/OlClTZMWmnj9/Tp8uDnLN83Pst62tLZ8+feLq1avKMPuPRBBngVwnr3bOyaxduxYVFRWmT5+usPjcQYMGcevWLTZv3pzjuZQhznFxcSxfvpzmzZszZswYgoKC+Pvvv3nz5g0+Pj64uLhQsWJF1swaQzEddSD99yWtbuW1atXCyMhISEhRIII4C+Q6yYKYV0/2CxUqhL29PY8ePSI8PFwhczZo0ABHR0cmTJjAx48fc2yfIn3O/v7+9OzZkz179jBq1Cj8/Py4d+8eEydOTFXgX0Us4m+HGohI/4szrW7lYrEYa2trwbWhQARxFsh18tKtkUxUVBRisVgWXqcIxo0bh7q6Oi4uLjmap2TJkpw5c4YNGzbk6MsjuUZG9+7dUVNT49atW8ybNy/Tdlh2NYxY17su+gVSykNm3crt7OwICgri8uXL2bZZ4H8I4iyQ6+S1WwPg06dPFClSRKG7dz09PSZPnszhw4fx8PDI9jzr16+nR48ebN68GRsbG/75558sh/8FBwczbNgwFi1axJAhQ7h161amHcB/xKZaMcRes1G7spZxFkXk6lZeo0YNjI2NBdeGghDEWSDXyQ8759DQ0BzHJKeFjY0NzZs3Z/jw4YSFhWVrDmNjY9auXcvbt28ZN24cXl5etG7dmsOHD8t1/sWLF+nYsSP+/v6cOHECV1dXChQokCUbduzYwe1bN5k+qCstyuvI1a1cJBJhY2ODh4cHiYmpk1gEsoYgzgK5Tn7YOUdERMjd4y8riEQipk2bRmRkJG3atCE0NOMi/RlRtGhR5syZw7t37+jTpw9z586VdepOi5cvXzJp0iRGjBhBo0aNePjwIa1bt87yuuHh4UyaNInWrVtTr169LJ1ra2vLly9fuHjxYpbXFUiJIM4CuU5ePxCE79ELyRmDiqZ48eJs2rQJPz8/mjZtyvv373M0n7a2NuvXr6d+/fqMHTuWL1++pDj+5MkTxowZg6OjIw8ePGDTpk0cP36cokWLZnmtuLg4nJyciIiIYNy4cVk+v1q1apQqVUpwbSgAQZwFcp384NaIj4/PlnjJS40aNdi2bRthYWE0atSIZ8+e5Wg+dXV13N3dUVNTY+zYsSQkJHD37l2GDh1Kt27dePXqFRs3buTVq1c4OTml+d6GhoZy//59Xr9+TWhoqMz1EBERwb59++jevTuGhoacPXuWuLg49PT0smznj64NRWdh/mkI4iyQ6+S1WyMyMpLExEQMDQ2Vuk65cuXYvn076urqNGnShFu3buVovuLFi3Po0CF8fX1p06YNffv25evXr+zZs4dnz57h5OSEurp6qvPevn3LiBEjKFmyJHXq1KFcuXLo6+ujpqaGtrY2BgYG9OjRg0ePHtGnTx+cnZ2RSqXMnTs3W3ba2dkRGhrK+fPnc3S9fzpCVTqBXCevd87+/v4ASt05J1O8eHG2bduGi4sLLVq04Pnz55QoUSLb81lYWLBlyxbc3NxYv3499vb26bqHfH19Wbx4MXv27EFbW5v+/fvTpEkToqKiiIyM5Nu3b3z79g0NDQ2aNm0qsyu5i3h23T6VK1emTJky7N+/H1tb22zNISCIs0AekNc75+S0bWXvnJMpVKgQAwYMYNSoUdnqYfgzf/31F3/99Ve6xz9+/IiLiwuHDx+mePHijB07lk6dOsndzfzcuXOIxWL69euXLfuSXRsHDx5k/fr1ae7mBTJHcGsI5Dp5Lc5PnjwBck+cAa5du0bZsmWpUKGCUtc5cOAANWrU4MqVK8yZM4cTJ07w119/yS3MAGfPnsXAwEBWvjQ72NraEhYWxtmzZ7M9x5+OIM4CuU5eujViY2M5fvw4pqam2Sq4n12uXbuGra2t0q45LCyM3r17061bN+rXr4+HhweOjo6oqamlGJckkWbYeiooKIjHjx/TrFmzHNlTqVIlypUrx4EDB3I0z5+M4NYQyHXyMpRu+vTpxMbGMnny5Fz7cggICODt27fY2dkpZf5z587Rt29fIiIiWLhwIW3btk3z2nzeRafbZis58+/ChQuIRKJMm7tmRrJrY+/evcTFxeVKA9/fDWHnLJDr5NXOOSAggHPnztGhQweqV6+ea+v6+PigqqpKy5YtFT7369evsba2xsjICHd3d9q1a5euMC+4/CWFMMP/Wk/5vIsG4M6dO2hqalKqVKkc22Zra0t4eDhnzpzJ8Vx/IoI4C+Q6qqrfb9hyO8V39OjRqKqqMnLkyFxd9+rVqzRs2FChRZaSMTIyokCBAlhaWmJkZJTmmCSJlI23M85UTG49ZWFhQUxMDP/991+ObatQoQIVKlQQXBvZRBBngVwnOW06ODg419Y8f/48/v7+DB8+XGmZgWmRkJDAzZs3lebS0NTUpHnz5hkWufcNiku1Y/6Z5NZTbdu2pVChQixdulQh9tnY2HD06FFiY2MVMt+fRI7EedGiRYhEIkaPHq0gcwQyIz4+nhEjRrB8+XK+fv2a1+Zki2RxDgoKUvjc79+/x9nZmefPn6d4fc6cORgbG9OrVy+Fr5kR9+/fJyoqSqnxvq1bt+bu3btER0enefzHllIZ8TUmCXV1dXr37s2bN28U0inG1taWb9++cerUqRzP9aeRbXG+desWGzZsoFatWoq0RyADpFIpw4cPZ/369UyePBljY2MGDBjA+fPn8fX15f3793z79k1h3T2Uhbq6OoaGhgoX55CQEIYMGcLVq1eZP3++7H04e/YsISEhDB06NFX0grLx8fHBwMCAunXrKm0NOzs72Q49LX5uKZUeyeO6deuGqqoq8+bNy7Ft5cqVo3LlyoJrIxtkS5wjIyPp1asXmzZtylEspEDWWLNmDZs3b2bmzJmcOXMGJycnTp48iZWVFTVq1KBUqVLo6uqiqqpK4cKFWbt2bV6bnC4lSpRQqDhHR0czfPhw4uLi2LRpE3fv3uX06dMAbN68GR0dnWxVaMspPj4+2NjYKDUyJdm3u3btWgICAlIdr15UAwOtjAX6x9ZThQoVwtHRkXv37imkI4uNjQ3Hjh0jJiYmx3P9SWTrL2b48OG0bduWVq1aZTguLi6OiIiIFD8C2ePs2bOMGTOGv/76C0dHRwwMDBg8eDAnT57kyJEj7Ny5k7Vr17J48WJq1KhBWFhYqhZE+YmSJUsqTJwTEhIYM2YM796949SpUzg5OdG+fXuWL19OeHg4z58/x97ePld3zQEBAYwdO5YnT57Qvn17pa+3d+9eYmNj6dKlC8eOHSM6Opphw4Z975coFjHYrEiG5//ceqp3794kJSXxzz//5Ng2W1tboqKiOHHiRI7n+pPIsjjv27ePu3fvsnDhwkzHLly4ED09PdmPIsJz/kT8/f3p0qULDRs2ZOzYsSmOqaqqUr58eUxNTWnatCkFCxbk0aNHzJw5k7Zt2+aRxZlTokSJVKUvs4NEImH69Oncvn2bo0ePYmpqCsCyZcsICQmhf//+JCQk4OAgX1fpnBIREcHSpUtxcHDgyZMn7Ny5k65duyp9XTMzMx48eEDnzp2ZNm0abdu25b///uPOnTsANCqtxVRLw1Q76PRaT5UtWxZLS0vOnDmT447iZcqUoWrVqoJrI4tkSZwDAgIYNWoUu3fvRlNTM9PxU6ZMITw8XPaT1i2XQMaEh4djb29P4cKFWbx4sSwMLS3evHnDpEmTaNeuHbNmzcpFK7OOsbGxQsR52bJlnDx5kl27dtGiRQvZ6xUqVGDUqFG8ePGCcuXKUbVq1RyvlRk+Pj60a9eOQ4cOMWvWLJ4/f07v3r1zLZ5bR0eHbdu2sXfvXkqXLk2LFi1S3J00Kq3FZocSLGhVjAmNDTJtPWVtbU1sbCxPnz7NsW02NjZ4enoqpLbIn0KWxPnOnTsEBQVRt25dVFVVUVVV5dKlS7i6uqKqqpqq666Ghga6uropfgTkJykpie7duxMYGIirq2uG719kZCSjR4/G2NiYXbt25Wkhe3kwNjYmODg4R7HO27ZtY8eOHbi6utKlS5dUx6dPn466ujr169fPFYE8dOgQRkZG+Pv7M23atCzVs1Ak3bt3586dO7Rt25agoCCkUimBgYE4Oztj0dCcDXPH8fH6MUwKJmTYeurcuXNoaWkp5IvN1taW6OhovLy8cjzXn0KWPsFWVlY8evSI+/fvy37MzMzo1asX9+/fR0VFvqfCAvJx8OBBTp06xeLFiylbtmy64yQSCdOmTePLly8cPXr0l/gSLFGiBBKJhE+fPmXr/OPHj7Ns2TKmTp2abrdrXV1devbsmeM6yvKSkJBAhQoV0k0GyW2MjY2Jjo5mx44dtG/fnps3b1K1alUePnzIwoULsba25t9//02zw3dwcDCXLl2iSZMmCvmiL1WqFDVq1BBcG1kgS7U1dHR0qFGjRorXChYsiL6+fqrXBXKGVCpl6dKlNGzYkMaNG2c4dsOGDVy4cIFjx45l2vY+v1CnTh309PRwdnZmxYoVVKxYUe5zr1y5wsyZMxkwYECm4V4ODg64ubnx5s2bDL/gFEF8fHy+Ko+ZHE++dOlSTExM2LBhg+yL48mTJ8yePZuNGzeybds2evToQdu2bXn+/Dl37tzBx8cHqVRKXFwc0dHRCrkLsLGxYe3atURGRqKtrZ3j+X538ve97x/M1atXuXPnDn369Mlw3IULF1i7di1z5syhXbt2uWRdzjEyMuL27dvo6OjQq1cvuW93Hz58yNixY7Gzs2PDhg2Zuiusra0pUKAAFy5cUITZGZKQkJCvxLlIke8RGr179+bYsWMpdvTVqlXjwIEDuLu7U6lSJbZv307nzp2ZOnUqJ06cQEtLi/r163P58mWaNm3K8ePHc2yPjY2NrCqgQOaIpLmYsRAREYGenh7h4eG/xK13XtKxY0cePHjA4cOH072tfPXqFb169UJPT48pU6YQGRlJREQEGhoa1K5dm9q1a1OmTJk87dWXGdHR0Tg7O7Nz50569OjBhAkT0gx5i46OxsfHhzlz5lClShXOnj0r926uffv2fPjwge3btyva/BT06dOH2rVr4+bmptR1kjl+/Dj+/v6MGTMmzd/x169fKVKkCMuWLcPGxibDuQICAjhz5gw2NjYpoqqeP38uy6q8ceNGjl0cvXr1wsTEhCNHjuRonl8ZeXVQKBmaD3n58iVHjhxhxowZGX4Yjhw5QmRkJJGRkYwaNQodHR20tbWJioqSpXbr6elRu3ZtbG1tmTp1am5dgtxoaWmxfft2LCwsGDVqFE+fPmXJkiUUL16c9+/fc/nyZS5fvsytW7eIj4/H3NwcT0/PLN1mOzg4MHjwYEJDQ2W7SWWQmzvn7du3M2DAACQSCSKRiDFjxqQao6enh1gsTtOn/DOlSpVi4MCBqV6vVKkSa9euZcCAARw8eJBu3brlyG4bGxtWr15NRESEsEHLBGHnnA8ZOXIku3bt4syZMxmGLCYlJfH161cKFiyIpqambPcklUr58uULfn5+PHv2DHd3dxITE7P98C23uHHjBp06dSImJoYiRYrw4sUL1NTUsLS0xN7enrZt22ark8jnz58xMjLi77//xtHRUQmWf6dz585YW1uzevVqpa0BsH79eoYOHUqnTp3Q0dFh+/btHDlyJM1kFwMDA3r37o2TkxNJEim+QXF8jUmicIHvGYEZRWskI5FIsLa2pmDBghw7dixHtgcGBmJjY8POnTvp3bt3jub6VRF2zr8oYWFhuLm50bNnz0xjyVVUVNKssCYSiShatChFixalcePG7Nu3L1cSIXKKubk59+7dY+zYsaioqLBo0SKsra1z/EVerFgxGjZsyIULF5Qqzrmxc16xYgVjx46lV69eTJo0CalUyvv37+nRowf//fdfqhoehQsXJjw8XK5C++khFotxcHBg69atOd7xGhkZYWpqyv79+/9YcZYX4YFgPmPz5s3ExcXRvXt3hcx38+ZNgoKCMmwImp8wNDRk586dbNu2jU6dOinsDsvBwYFr164ptXRlQkKCUjt+7Nq1i7FjxzJgwAAmTZqESCRCLBazYMECTExMsLe358OHDynO0dfX501iIbkK7WeEg4MDEomEKVOm5Pg6bG1tOXPmDGFhYTme63dGEOd8hEQiwdXVldatWyus+ainpycVKlSgfv36CpnvVyEqKipFUpSDgwOxsbFcv35daWsqe+e8Z88eGjRowOjRo1M8ACxQoACrV69GKpXSrl07IiMjCQsLw8vLi9CvYbwzMM9w3uRC+xlRpkwZevbsyZUrV3j48GGOrsPa2pr4+HiOHj2ao3l+dwRxzkckJSXx5csXhXVojo6O5ty5c/Tp0ydfR2wokoSEBObNm0eRIkXYt2+f7PUqVapQsWJFpYbUKTPOOSkpiatXr2Jubp7m79LQ0JDVq1fj7+9P+fLlKVKkCO3ateObZlGSNHQynDu50H5muLi4UKhQoVT1XbJKsWLFqFu3Lvv378/RPL87gjjnI9TU1GjWrBnXrl1TyHwXL14kKioq1wvM5xX37t2jfv36zJ49m/j4+FS3zQ4ODly6dCnHhXzSQ5k754cPHxIREUG9evXSHVO5cmXWrFlDixYt+Pvvvzlx4gTT5i2Wa355CvJra2szefJkPn/+zPr16+W2PS1sbW3x9vb+ZRtG5AaCOOcz7OzsuHPnjkJq3x47dgwLCwvKlSunAMvyNwsXLqR+/frExsaye/dugFT+XwcHB0JCQnJ8W54eyhTnS5cuoa6uTs2aNTMcZ2ZmxvTp03F0dKRUqVIUKSDfM395C/Lb2dnRoEEDNmzYwKBBg1iyZAmnT58mJCRErvOTsba2Jikp6Y+Od84MIVojn2Fra8uYMWO4ffs2TZs2zfY87u7uXL16VSZUvzNeXl5MnTqVAQMG4OLiItsZ/yzOFhYWlCpVioMHD8pKiyoSZYrz5cuXqV27dpbnTy60n1EPwR8L7WdGeHi4rNLdgwcPUvjw1dXVqVWrFlu2bCEyMpKJEyeSkJBAmTJlqFChAjVq1KBKlSqyTjhmZmbs27eP/v37Z+ma/hQEcc5nVKlShZIlS+Lj45Ntcb579y7z58/H2dmZnj17KtjC/ENyGNnAgQOxtLSUPSiLjIwEUouziooKI0eOZOrUqYwePVphD13hu084KSlJaeJ85cqVbIUBJhfaX3A5/fKsPxfaT4+oqCicnZ15//49rq6uNGnShLCwMN6+fStrdPDff/8xZMgQHj16RGxsLNra2inaZ4lEIsqVK8eRI0ewtbVl4cKFhISEoK+vn+Vr+90R3Br5DJFIRKlSpQgNzbiVfXoEBgYyZswYLCwscHV1VbB1ec/SpUuxsbGhatWq6OjoULp0aeLj4/n7779lD8pu3LgBkKY7x8nJCXV19RQPCxXBjxmZykAsFmc7dbpBCXXaFQpEJS5lJyL9AuI0C+2nRVxcHCNGjMDPz4+5c+fStGlTRCIRhQsXxtTUlPbt27N27Vrs7e25fv06Ojo67N69mytXrnDjxg2OHDnCv//+S+/evXn58iXnz5/HysoKqVTK4cOHs3VdvztChmA+QyKRoKenx6BBgxgwYECWzo2OjqZv377ExMRw69Ythe4M8wPnzp2jVatWNG7cGBMTE4oXL07x4sWpXbu2rAKbVCqlW7duFCtWLN3IDHkzMLPCmTNnGDduHB8+fKBEiRIKmfNHGjduTJEiRdLtQCSRSAgJCeHTp0+pfm7fvk1oaCgFtbUxb9eLyrUbsG39KsQhr1BTVUVFRYV27doxePDgND+XiYmJjB07lkuXLjFhwoQMk0ciIiI4cOAAnTt3plChQqmOJyQkyBKLjh07hpOTE9ra2pw9ezbb782vhpAh+Ivy4sULIiMjqVKlSpbOk0qlTJ8+nYCAAHx8fH47YZZIJIwfP57atWuzbt26dEMDr1y5wtOnT/n333/TnWvUqFGsWbMGT09POnfurBD77t69i4mJiVKEGaB8+fI8evQoxWvBwcEsXryYu3fvEhwcnCKuWywWo66ujqamJsWKFWPSpEm0adNGdrxIpD3e3t7Ad7HYuXMn7u7uuLi40LVrV1nxqdjYWKZNm8bFixcZPHhwpll9urq6ODk5pXtcTU2NXr16sWbNGl6/fo2trS3z5s3jy5cvv93fbE4RxDmfce/ePYAsd5/YsGED3t7eeHh4UKtWLWWYlqccOnSI+/fvs3Pnzgxjtt+9e4eqqiqNGjVKd0z58uVp3749u3btolOnTgqJAb9z5w6WlpY5nic9ypUrx8mTJ2X/v3jxItOnTycqKooqVarIKhBWqFCBqlWrUqZMmQzdIN27d0+Rhern58fEiRNZtGgRu3btYvz48dSqVYsRI0bw9OlT+vTpk25Tg6zSuXNn1q5di6urKzNmzGD+/Pl4eHgwZMgQhcz/uyCIcz7j/v37FC9enMKFC3PhwgW0tLQwN884w+vcuXP8+++/zJ49W6m1I/KSzZs3U69evUyjLExNTUlMTOTOnTsZCvTYsWNp1qwZPj4+mTYzyIxv377x7Nkzxo8fn6N5MqJ8+fIEBwcTHBzMmjVrcHd3x8DAgP3791OpUqUcz1+lShWOHTvG6dOnmT9/PqNHj5btnufOnavQDuK6urpIpVLEYjFFihShQYMG7Nu3TxDnnxAeCOYz7t69S5UqVTh48CAjR45kxowZqXoz/kyyb/Xff/+lb9++HDhw4LeqW/DhwwfOnTsnl0BUrlwZHR0dhg0bhpeXF+k9UmnatCl16tRh586dObbv/v37SKVSpe+c4Xud78OHD9O2bVvOnTunEGH+EVtbWy5fvszQoUNJSEjA1tZWocIM8OXLF5KSkihfvjzwPXb68uXL+b5qYm4jiHM+QiqVcv/+fd68eSPrbBIYGMjVq1czPO/vv/9mx44dODg4cP36dbp164ahoeFvE63x4MEDJBJJujvhJImUh59iufQ6iifBiaxe8y9isZh27dphbm6eZj1jkUjE2LFjuXr1Ki9evMiRfXfv3qVo0aIKS7tPi+QWW3Fxcaxdu5ZFixbJHb3x4/vz8FNspnU0AIYNG0bNmjUVlq36I8lNfU+fPk1iYiItW7ZELBbj4eGh8LV+ZQRxVjKxsbF069aNOnXqMGnSJLZu3YqPj0+aghEYGEhQUBBv3rzBxcWFY8eOUa1atUzb+qioqFCnTh1GjRqFu7s7Z86coVGjRmzYsEFZl5WrJH+YVVVTe+F83kUz8MgHpp79zJKrwUw9+5nVbwwZMncd/fv35+7du+l2+O7atSvFixdn165dctkRGhpKZGRkqt343bt3sbS0VGr9EmNjYypWrEiLFi2y5IZJ6/0ZeOSDXJXoOnToQGhoKM+fP8+J6akoWbIks2fP5vXr13Ts2BFtbW0aNmyo8PDGXx1BnJXIf//9R61atThw4AD+/v7s2bMHJycnGjduTJkyZXj58mWK8c+ePQNgxIgRuLq6cuXKFfz8/LK8IzMyMsLOzo4nT54QHByssOvJK5Iz/n7u7u7zLjrdUpgL/wvmVmACrVq1SjfBQV1dnQEDBnD+/Pl03R/J3Lp1i2bNmmFhYUGdOnVkDQB69erFw4cPc5TNKS/dunXjypUrJCQkyDU+o/dHnlKhlpaWSotD7tSpE/PmzePNmze0b98eKysrrly5wsePHxW+1q+KIM5KICIiAmdnZywtLSlYsCA6OjrUqVOHkydPcvPmTQ4ePEjBggUZPnx4ClFo2LAhJ0+eZNWqVQQHB9O9e3fq1q2bZvugzDAzMwO+f0H86iT73H+8jU+SSNl4O/1EHalUwlcTK7r3SDtD8tatW7L39evXr7x+/TpDGzw9PSlbtiz79u1jzZo1jB8/nk6dOmFmZkaXLl0UFpKXEQ4ODoSHh8siejIis/cHMi4VmpCQwKxZsxCLxTRp0iRb9mZG+/bt+eeff/jw4QNr165FKpXi7u6ulLV+RYRoDQWS/Mc1evRowsLCmDJlCt27d6d58+ayVGJNTU2qVKnC5MmTGTlyJAcPHpR1KSlQoAB2dnZIJBJ69+5NbGws//zzT5q385lhZGREyZIluXjx4i8fwZGWOPsGxWVYL0IkEqOqa0ipuv8rFBQfH8+hQ4dwdXXlxo0bqKmpUbx4cUQiEffv30+3QFRCQgLnz59n6NChOe6hlxPq1atHiRIlOH/+PA0aNMhwbGbvD/yvVGit4ikTcSQSCVOnTuXatWuMGjUqx9EsGdG6dWtUVVUZP348IpGIWbNmMWLECKWt9ysh7JwVRHKca5cuXShfvjweHh707NkTsViMRCJJVXOhRYsWtGzZklGjRqXyPz948IAzZ84wefJkihYtmm2b6tWrx8WLF7N9fn4hLbeGPCUuASKTVPj8+TNz5syhTJky9OrVC5FIhKurK1u3biUgIICyZctmuBu9desWYWFhdOnSJWcXkkNEIhEODg5cvHgxUzeMvO/Pz+OkUikLFy7k1KlT/PXXX9m6a8sq1tbWuLq6oqurS1hYGO/evVP6mr8CgjjnkI8fP9K/f3/q169PYGAg69ev599//02RKZZeQZwpU6YQERHBtGnTUrxesWJFVFVV5eqanBH169fn0aNH2a7TkV9Ia+csb4lLt7WrKFWqFIsWLcLS0pLDhw+zadMmWrRoQe3atalcubJs55weZ86coVy5ckqpZJdVOnTowIcPHzJ9SCfv+/PzuGvXrrFv3z7atGnDhAkTsm1nVmnWrBleXl6IxWK8vLxybd38jCDO2SQ6Opq5c+dSqVIljh49yrRp0zh06FCqW8CzZ88SGRlJnTp1Us1RvHhxhg0bxtq1a7l9+7bsdW1tbczNzTlz5gzfvn3Lto1mZmZIpdJf3u+cljgnl8JMDxFQTEedo5uX0a5dO7y9vZkxY0aKh6sikYhOnTrx5s0b3rx5k+aXWLJLo2vXrvmim0y5cuXQ0NBg6tSpzJ49m40bN3L8+HHu3r3Lly//qzyX2fsDaZcKTX4PJk+erHjjM0FPTw8zMzOhxvP/I4hzFpFKpezZs4fKlSszd+5cOnfujKenJ926dUvTN/zPP/9QrFixdAP5e/XqRaVKlRg8eHCKZJPhw4fz4MEDbGxscHV1zXIxc/gefmVsbPzLuzbSEufkUphpkSyhfzvUoFRJY/T09NKtFmdubi5zm6S1e7516xZfv37NlQd+GREXF8eCBQtkNZ2/fv3KqVOnWLduHVOnTqVv3760bNmSPn364OXlRVJiQrrvTzJplQpNfi+SswNzmxYtWnDhwgUiIiIyH/ybIzwQzAKRkZFYW1tz/fp1WrZsyYYNGyhdunS648+ePcunT5+YNWtWun/sqqqq1K1blwMHDhAUFISRkREAPXr0oHnz5ixbtoz169ezc+dOOnXqRL9+/WQV2OThd/A7FyxYEICgoKAU7qJGpbWYamnIqv8+ECX9n9uouJ4ms+yrYVfDiAYNGuDr65vu3OfPn0dTU5PChQtz6dIlNDU1CQwM5OPHjwQGBvLgwQPKli1L3bp1lXeBmXDixAlGjhzJ27dv6dWrF87Ozmhra8uOBwcH8/DhQ65evcrp06eZPHky165dY968eUy1NGTj7dAUDwcNtFQYbFYkzVKhyV+EymxUmxEtWrRg0aJFnDp1Svag/E9FEOcscOvWLa5fv87y5cuxtrbOdPw///xD4cKFadeuXbpjLl++zN69e/nnn39kwpyMkZERS5cuZerUqaxevZpVq1Zx4MABRo8eTZ8+feSy2czMjOPHjxMWFpZmCcdfgTZt2qCtrc3Ro0cZOnRoimONSmsRoHafZTuOsGnXAcoZGdDA5H87QjMzM06ePMn79+8pWbJkinOlUilHjhyhc+fOSKVSdu/ejYeHByKRCCMjI8qUKUOjRo1kDxFzm1evXjFq1Cg8PT0xNzdn2bJlspTnHzEwMKBly5a0bNmSGTNm0KRJE6Kjv8cwNyqthXnJAvgGxfE1JonCBb67MtIrrp+8c85OhJAiKFGiBJUrV+bo0aN/vDgLbo0skLybSOsDkhYhISF8/fqVxo0b069fP65cuZLi+MePH5k6dSpt27bNsGhOkSJFmDVrFm/fvqVz586sW7dO9uHLjN/B76ytrU23bt04cuRImnVGrFq2IPbdIxJeXMOivH4K4enTpw/FixenT58++Pn5pTjv7t27vH37loEDB7Jy5UouXrzIq1eviIuL48OHD/j4+LB3794Mv1yVxeXLl6lWrRp37txh2bJlbNq0Se6/O6lUSoECBWT/VxGLqFVck2YmBalVXDPDrifJSSBZKeyfnfTwjGjRogVeXl5yJ9v8rgjinAWSY5Xj4+PlGp8cDteoUSP8/PwYOnQoc+bMIT4+noSEBCZMmEChQoXYsWOHXB8GHR0dFixYQFRUFKdPn5bLhpIlS2JkZMSBAwfSTWP+FRg0aBAfP35M0bMuGQMDA2rXrp1mbYYSJUpw7do1SpUqRb9+/WRdUgAOHz6MiYkJlpaWGBgY0KxZM0xMTJTib5VKpdy8eZNXr17J1f3b09OTwoULc+TIEWxsbLK0c9fQ0EiVfSoPO3bsYOPGjVSsWFHuc3KSHp4eLVu2JDw8nDNnzmR7jt8BQZyzQPLOWd5vdAMDA3r16sXq1au5fv067dq149ChQ/Tp04c5c+bw9OlTDhw4QJEiGT+4+ZGyZctiY2MjdyaVSCSiR48e7N69m3r16v2yO+gGDRpQrVo19u/fn+buuUWLFpw+fVrWP/BHihYtysWLF2nUqBHDhg3j1KlTREZGcubMGQYOHJjt9k/y8uDBAywtLTE3N6d8+fLo6upibm6Ok5MTq1atShFlkUxy2r6WVuYtpH4kPj6emjVr4uvry/nz5+WqTiiVSnF1dWXJkiXUrl2bQ4cOybVWTtPD0yM5Qmnx4sXZOv93QfA5Z4H0ds5JEqlcPr2FCxfSoEED5s6di6+vL6tWrco00ystBg8eTKfOXThx25+C+iUy9SMmx2EvWLAAS0tLBg8e/MsVRRKJRDRs2JCtW7fSvHlzrK2tadWqFdra2nh4eODp6UlCQgLe3t5pZkTq6Ojg6enJgAEDmDhxIo0aNSIuLo6+ffsqzeaQkBCmT5/Oxo0bMTExYfXq1aiqqvLixQv8/f25du0aO3fu5Ny5cxw7dizFuU+fPsXCwiLD+d++fculS5e4e/cuL168ICQkhOjoaCQSCSoqKowaNQr4vklwdXWlZs2aqeZISkpi3rx5HDp0iGbNmrFmzRq5rk3e9HDzkgXkah77IytXrkQkEvHixQukUmm+CGHMCwRxzgJp7Zx93kVn6Wm4o6MjoaGhuLq6Mnz48OzZUa4BpYdvY62fOhCc6ZoANWrUYNeuXaxfv55169Yxb968X64tUKNGjdi6dSslSpTg2LFjHDx4EPge9lW5cmUeP36Mjo5Ouuerq6uzY8cOihcvzrJly7Czs0v1kFARJCUlsXHjRqZPn058fDzjxo2jR48eMnfJj7Uqtm/fzurVq3F3d+fTp098+PCB9+/f8/LlS16+fImnpycikUjW4FUsFhMfH09kZKRsk6Cqqkq5cuWwsrKiUqVKVKpUidKlS+Pn58ft27dxd3dnwoQJeHh4yHbiiYmJvHnzhn///Zdz587h6OjInDlz5L7GnKSHZ0bXrl159OgRHz9+5P79+2nmCPwJCOKcBX4W5+Tbup9Jvq1Lr7NxXFwchoaGqaqsycOpx4GM2PcAUcHCWVoTvj/ksbOzY926dfj6+tK8efMsr5+XJDdj3b59O+rq6nh7exMVFUWbNm2IiIjAysoq0+cBYrGYpUuX0qhRozR3kjnFx8eHYcOG8eDBAxwcHBg9ejQGBgbpjm/ZsiUrVqygc+fOqKqqUrRoUYoWLYqVlZUs9T8xMZGkpCSSkpJITEykQIECMhFOFuK0oitKlChBy5YtadKkCUOHDmXcuHEYGhri6+vLq1evSExMRCQSMXDgQNkuW16ymx4uDx06dODly5ds27aN/v37Z5i9+TsjiHMW+NGtkZPbutDQUIoVK5bl9ZMkUv4+/oTvz8LTvtXL7FayVKlSqKmp/ZLi/OP7r6mpia2trexYbGws8L2dlaGhIWZmZhneDnfs2FGhtgUHBzNx4kTc3NyoXr06u3btonbt2pmeV6pUKU6dOoWamhqFCxdWiv+7UaNGtG7dmhMnTqCpqYmhoSHNmjXDzMwMKyurVCGc8pDd9HB5GT16NL6+vjluhPArIzwQzAI/7pyzclv3MyEhIdkS55uvQwkMj83WmsmoqalRtmzZDBMz8isZRcvo6ekxcuRIrl27RoMGDahVq1ammZWJiYkMHTqUihUrsmvXLrmiKH5GIpGwceNGKleujLu7O9OnT2f37t1yCXMyxYsXR19fX6kPJsePH49UKsXR0REvLy9WrlxJ7969syXMkP30cHlRUVGhS5cufP36lYCAgGzN8asjiHMWeP/+PfD96XZObutCQkIyzPLbsmULbdu2Ze3atSn+MIO+ZSzMGa35I+XLl+fx48dyzZWfSM4UTCsCQSQSMWjQIFlKs5GREePGjaNEiRKsW7cu1fioqCgcHBzYvHkz+vr6/PXXX9SpU4fTp09nWvEtmbt372JhYcGQIUNo0qQJx44do1u3btlyVykbAwMDypQpk2YoYnbIKH0+mbTSw7NC48aNUVVVzbQT0O+KIM5yEhwcjL29PeXLl6dJkyY5uq37+PFjuqVAExMTmTFjBnfu3GHUqFGULl0aU1NTZs6cydePb7O95o+UL1+eJ0+eyC1C+YWGDRuipaWVYTq6iooKTZo0Yfny5Zw9e5YyZcqkSP5J7tPYvHlzLl68yJo1a9i8eTM7d+5EVVUVOzs7rKysUhSi+pmwsDBcXFyoX78+oaGhuLm5MX/+/HQ7ruQXGjZsSEBAQLbuENIiOX3+5x20gZZKhs8+5EVXVxczMzOOHj2ao3l+VQRxloPY2FhZF4o1a9ago6OT7du6U6dO8fnzZ+zt7dM858SJEwQGBrJ69WouXbrE4sWLMTY2ZtWqVfRv1xRpVChSafofLnluJStUqEBISAifP3/OcFx+Q0tLC3t7e7kTcPT19WUp648ePWLGjBlUrlyZOnXqEBAQgJubm6yKoKmpKdu2bWP16tW8e/eO+vXr89dff8l82cmcP3+eKlWq4ObmxtixYzlw4ICs60x+x8HBgYSEBObNm6ewORuV1mJLB2MWtCrGhMYGLGhVjC0djHMszMk0b978jy2EJIhzJkgkEvr378+dO3dwdXWVhV5l57bu0aNHTJ8+nV69emFpaZnmORs3bqR69epUrVoVXV1dWrduzT///MPFixfZumUz1eOfIkIEpL3rledWMjkN+Ff0O3fo0AE/Pz+ZiykzpFIpBw4ckPmgq1evzrp16zh58iTVqlVLMVYkEtG8eXPc3d35+++/OXToEB07diQu7rsPf926ddjY2GBiYsLRo0fp27dvnlVvyw41a9akTZs2eHh48OjRI4XNm5X08KwQFhbGrVu3SExMlDv++ndCiNbIhJkzZ7Jv3z6WLVtGrVq1UhxLvq2TJ87506dPjBo1ijp16rB58+Y0Iwnev3/PyZMnUxXfh+8P8urXr0/9+vWzHFv9M6VKlUJdXR1fX1+srKzkeh/yAzExMaxcuRJDQ0N0dXXlOqdVq1YYGxtjY2ODhYWFXGKqoqJCx44dMTIywsXFhS5dumBkZMTGjRvp1asX48ePz7PCQDll6tSp3Lhxg+HDh3Px4kWlZ0dmh/j4eIYMGcL9+/dJSkpCQ0ODu3fv5rVZuc6v+ReWS2zbto358+czZswYbGxs0hwjT9Wv6OhoRowYgaamJkeOHJHF6/6Mm5sbGhoatGnTJkO7slpp7GdUVVUxMTHJlzvnb9++ERQUREhICCEhIYSGhtKkSRNKlSpF//79efDgAW5ubnKLc69evbJti4WFBStXrmTkyJEkJSUxc+bMPG9VlVP09PSYPn06Y8aM4fz587Rq1SqvTUrFkiVLuH37Nr169aJnz554eHhw7NgxkpKS8uXDVmUhiHM6XLhw4XuadKdO9O/fP8Oxybd1aSGRSJgyZQrv37/Hx8cnwxC6c+fOUa9evRS1erOzpjyUL1+eBw8eZPt8ZeDl5YWjo2Oq2iWNGzemWbNmHDhwgOXLl1OjRo1cs6lp06b069cPNzc32rZtm2vrKpPkkgHXrl3Ld+IskUg4duwYDRs2lHVjadasGVu2bOHWrVs0bNgwjy3MPQRxTgM/Pz86duyImZkZ06ZNy1Fu/6pVq7hw4QLHjh3LNCOtRYsWLFmyhJiYmBQlH5VBvXr1WLBgAeHh4el2CVEW/v7+bNy4kcjISIyNjSlZsiT6+vo4OztTr149Bg4cSKFChShUqBD3799nwoQJXL16lbFjx+aJmNjZ2bF582a2bdvGsGHDcn19RaOrq0uxYsXy5Z3TxYsXiY6OpmfPnrLXatWqRaFChfD09PyjxDn/OZzyAWPGjCEsLIxmzZple46AgADWr1/P1q1bWbp0qVw1gf/66y+ioqI4f/58tteVl8aNG5OUlMS5c+eUvhZ8fzB35swZ2rRpQ6VKldiyZQsXL15k+fLl9O/fn/bt2xMYGMiIESNo2LAhVapUoXjx4tjY2FCnTh169uxJv379csXWn0lOkfb09MyT9RVNYmIiRYoUITAwMK9NSYWJiQnw/flCMsnhkX9avLMgzmmwcOFC2rRpw6JFi2jXrh27d+/OtLh9cHAwXl5ezJw5Ezs7O9q0acO6desYOXIkY8aMkWvdcuXK0bhx41z5IzQ2NqZcuXJK73QcFRXF+vXrqVatGra2trx69Yo5c+bg7e3NwYMHuXz5Mnfu3GHbtm1IpdJUDQnEYjHbt29nypQpeVadTCQS0bp1az5+/JhmSdJfiYiICJydnfHz88POzi6vzUmFiYkJGhoaqXb1lpaWPHz4UO4ond8BQZzTwNTUFC8vLx48eCBzNSQXDAoPDwe+/5GfO3eOhQsX0qFDB1q0aMHkyZN59uwZnTp14ujRo4SEhLBq1aosiUrfvn25du0aQUFByro8GVZWVmzdupXGjRuzY8cOuburyMPXr1+ZOHEiJUuWZPjw4ZQsWRI3NzcOHjyIo6OjLBUbvqfF161bl2LFinH27NlUc+WHkpFxcXFIJJJfOt7206dP9OjRg9u3bzN27FimTJmS1yalSaFChVKF+jVq1AgVFRWlbybyE4I4Z0CtWrXYtWsX/v7+9OjRg61bt2JjY0O3bt1o2rQpo0ePxsfHh2bNmrFnzx4+ffokq9Pcvn37LPXsi46O5uzZszx69AiJRIK3t7fyLuz/GTp0KEuWLEEikdC3b19KlCiBi4sLDx8+zPHcY8aMYe3atXTo0IETJ06wYsWKDIsRiUQiTExM0iw8n9d8+/YNd3d3SpcunaLB7K/GtWvXePfuHX369MkzF5GHhwddu3YlODhY9lpsbCxr1qzB2tqaRo0aERISwpMnT1I0VdDT06Nu3bp/lGtDJM3FHN6IiAj09PQIDw+XOxQqPxEUFMTq1at58+YNzZo1w8rKSuYjyw63bt3C09OT8+fPc+PGDRISEihSpAhmZmY4OztnqV1QTgkICMDDw4MjR44QHByMhYUFp06dytbv6cOHD5iYmDBq1KgsFbN3cnLi5cuXXLhwIctrKpPdu3ezaNEiuSvN5VcSEhLo0qULnz594uLFi+mGdGaERCLh8+fPBAUFYWJikuHfR0REBN7e3nz+/Jlu3brx6dMnevfuLSt76uLigpeXF/7+/iQkJFChQgWqVKlCcHAwGhoauLq6pojD3rZtG2vXriUkJETpD8yVibw6KIhzHpGQkICGhgY6Ojqy5JIGDRpQoUKFPL2NT0hIYPXq1bi5ufHp06dsVc+bMGEC69evx9vbW66wQPj+obewsKBmzZps3rw5y2sqk4cPH9K7d28aNGiQa7bJ210nqzx69EiWoZpR1l1AQACHDh3Cx8eH4OBgYmNjSUhIICEhIUVtDlVVVdTV1WV/y0WKFCEiIoLPnz8THR0tq9+S3CigUKFCLFq0iGnTpvH582d0dXVp3749Dg4OVKlSJUPbX716hYODA15eXpnmAuRn5NVBIZQuj1BTU6N06dI0a9aMCRMm5LU5MtTU1IiJiaFChQrZEuaIiAg2btxIly5dZMIsj9C8e/eO6OjofBkqVatWLQYNGsSmTZs4evQoDg4OSl0vpxmgGVGzZk169erFnj17uHPnDvXq1ZMdi4yMZNiwYTx//pyoqCjgeznTKlWqYGBgIPvR19dHW1ub0NBQgoKCZD+fPn0iICAAHR0dWrVqRc2aNalRowYFCxbk0qVLsszE6tWrc+jQIfz9/TE1NZU7Bd7ExIRSpUpx/PjxX1qc5UUQ5zykQYMG+bJ05/3791O0UsoKmzZtIiYmht69ewPyC03y+/BjAf38hLOzM//99x/z5s2jdevWstreiia73XWywogRI9i/fz87duyQibNEIqFr1658/PiR1q1b06BBAxo0aICxsXGO1kqmbNmyKVxchQoVon79+lmaQyQSYWlpiaenJ2vXrs0XD4qVifBAMA9p0KABT58+latDcm7x7ds3nj17lm5hpoxISEhgxYoVtGnThqJFi2apO/Pjx4/R1NSkVKlSOb4GZaCmpsbUqVOJjY1lz549SllD3u46SZKceSK1tLSoWrUqT58+lb02bNgwAgICmDdvHgsXLsTR0VFhwqxILC0tef/+vUILN+VXBHHOQ+zs7JBIJFhZWTF+/HiuXr2a4gl1XvDgwQOkUmm2ds779u3jw4cP9OvXL8tC8/DhQwoXLpzh+Lymdu3aGBkZcejQIaXMn5PuOlmldu3ahIaG8ubNG0aMGMHVq1cZPHiwXMlSeYmZmRlaWlq/TUJQRgjinIfUqFGDd+/esXDhQgICAnB2dsbOzo7Vq1fnSWueoKAgNm7cSPHixalQoUKWzpVKpSxevJimTZtSoUKFLAlNQkICfn5+VK1aNSfmKx2RSIS9vT3v379XSjKKMpum/kytWrWIi4vD3t6eS5cu0bFjx2x3g89N1NXVsbCwEMRZQPkULVqUsWPH8vjxY27cuEH79u3Zt28fbdq0oU+fPmzbti1VISBlcPnyZTp37kxgYCD79+/Psj/vzJkzPH78WBY/mxWh8fPzIyEhgUaNGmXV7Fynbdu2JCUlsWnTJoXPreymqT9ibm6OpqYm+vr6nD9/nr///jtflg9Ni2bNmnH9+vUUsdK/I7/Gb+MPQCqVEhUVRaVKlWTlSe/du8eyZcs4c+aMUtfev38/w4cPx8LCgocPH2bL37xkyRKqV68ue8iTFaHZvXs36urq6XaHyU+UK1cOExMTpdQkUXbT1B8pXLgwLi4uhISEcOfOnRzPl5s0bdoUqVTKyZMn89oUpSKIcz4gJCQEe3t7WrZsyfTp02Up4FOmTGHDhg20bt1a6etramri7u6OoaFhls+/d+8e586do1+/frIdt7xCUzgxmJMnT2JpaYmWlmJaGykbVVVVpRTbz42mqT/Ss2dPSpcuzZw5cxTWVzA3MDAwoEaNGr+9a0MQ5zzGx8eH2rVrc+3aNVxdXbl+/Tp79uxh9uzZ9OzZk0aNGin9dtPW1pbY2Nhs79CXLl2KsbFxinKeGQqNVApSKQWfnWTK5MmIxWJmzpyZrbXzgrCwMHR0dJQyt7Kbpv6Impoa06ZNIyIigkWLFils3tzA0tKS06dP54rLL68QxDmPkEgkLFmyBEtLS4oWLcqBAwdo0aJFnnR6KF++PBUrVmT//v1ZPvf58+fs37+fv/76K9VuMj2hSfwWTJjXUu57buPFixe0bt0630dq/EhkZKTS4pxB+U1TU6zVqBFWVlYcOnTol/LhWlpaEh4eztWrV/PaFKUhJKHkASEhIfTp04cTJ04wcOBAhg8fnueNQu3s7Ni6dSvR0dFyuxf279/PkCFDMDIywtHRMc0xP7fUUk2MwrnTQJwGDmDUvl+zaWejRo24cuUKsbGx2apPIQ857XSTFSZMmMDly5cZNWoUu3fvzpU1c0rVqlUpWrQonp6eNG/ePK/NUQrCzjmX+dGN8e+//zJ69Og8F2aAihUrEhUVlWbJzp95/vw59evXp3v37lhYWLBv374Ugv7hwwcOHjzItGnT8Pf3T9GduV4pXZD+2qU3Bw0aRFxcHMuXL89rUxSCsbExgwYN4tGjR/j4+OS1OXIhFot/+wL8gjjnEmm5MeSNioiKiuLTp09Ks2v79u2MGzcOU1PTFLUWkklISODSpUtMnjyZWrVqUblyZW7fvg18zyg8cuQIZ8+eZcGCBdjZ2WFnZ8fcuXPx8vJi+PDhshrYABoaGohEIqXECSdJpDz8FMul11E8/BSb40y69KhevTqNGjXiyJEjJCYmKmWN3KZ///6IRCKlJdgoA0tLS54/f86LFy/y2hSlILg1coGcuDGePn3K2LFjZYX7LSwsFGZXcHAw06ZNw8fHh7Fjx7JgwQI0NDRITEzk7t27XLp0iTNnznDx4kVZa6PGjRvTq1cvYmJi8Pb2xtfXl2vXriGRSNDQ0MDY2JgBAwbQtWtXPn36xMCBAxk/fjzr169HRUUFkUiEpqamrLCOolBmsaC0GDJkCH379uXff/9l1KhRCp8/t1FTU0MikVCwYMG8NkVuLCwsUFdXx8vL67f4HfyMIM5K5u3btzRu3Jjo6GjWrl1L06ZNAWQtmfbu3Uu1atXo1KkTRkZGKc49fPgw8+fPp1q1alStWhUXFxcWLVpElSpVCAkJSfcnNDSUiIgIjIyMKFOmDGXLlqVs2bKyfxcsWJDLly8zY8YM1NTUOH36NDY2NgQFBTFw4EAuXrxIZGQkBQoUoEaNGiQmJjJixAicnJxSRI507twZ+P6A7NWrV1StVp2nwQl8jUkiREUF0zolGDFiBCtXrmT16tWMHj0agAIFCii060puFAuC73cZjx8/platWtStW5eaNWuyc+dOXFxc8uRBriK5dOkSgFKiUJRV/lRLS4v69etz/PhxQZwFsk5cXBwRERGULl1a1n37yZMnLF++nBs3blCnTh12797Npk2baNq0KV26dKF+/fosXrwYd3d3nJycWL16NWKxmB49ejB27NgU84vFYgwNDSlWrBjFihWjRo0aFCtWDD09Pd6+fYufnx/u7u4p2l4ZGhry5csXWrduzbZt2yhatCgA69at48KFCzg5OWFmZkb16tVRU1OjXr16xMTEpBvSp62tTWShCgw6Fph652rdg6Z37rBlyxbKly+Pvb29QsVZ3hoe5iUL5FgQBg0axM2bNylRogRaWlq8evUKiUSCg4MDHh4eSo3gUBafPn1i/vz5XLx4kUKFCim8Q4qy72iaNm3KsmXL+Pbtm9LCG/MKodh+LnD37l3s7OzQ1dWlSpUqeHl5UblyZRYvXoy9vT1RUVHs3buXdevWce/ePdTV1RGLxaxdu5b+/fvL5klMTOT06dNoaGhQrFgxihcvjr6+vlxx0GFhYfj7+/Ps2TOePXtGuXLlUiSNSKVSKlWqRPXq1Zk3b16Kc5s2bYquri729vZUqFCB5s2bpwibS2/nmszkpvosGNqVgIAA+vfvz6VLlxCJRBw5ciSL72RqHn6KZerZz5mOW9CqWI6iH9auXcu6deto1aoVt27dAqBXr14UKVKEBQsWYGhoyJEjR+RuLpAfkEqldOvWjRcvXtCjRw/GjRun0Jj6zP4uFHFHExAQQJs2bXB3d6djx445miu3EDqh5DOeP3+OjY0NsbGx/P333wwcODDNLLPbt29z9OhROnXqhKmpaa7Zd/PmTczNzdm0aVOqgvdLly7Fw8ODmJgYEhMT6dOnj6xBQJJEysAjHzIscmSgpcKm9kaMHTOaS5cuIZVKKV68uEL6JF56HcWSq5nH505obEAzk6z7U3ft2sWGDRsICwvD3NycDRs2yLLpkp8bnD9/nnHjxiGRSKhSpQqGhoaUKFGC8uXL06lTJ6VkEyqCa9euMXjwYEaNGoWTk5NC55b372JLB+Mc39F06NCBpk2b4ubmlqN5cguhE0o+o1KlSvj6+iIWizPsf2ZmZoaZmVkuWvadnTt3UrRo0TQLoI8fP57x48cD3x+E7dq1CwcHh+/XJGf1uafB39tfHThwgIULF/Lt2zeF2K2sYkHHjh3jn3/+ISIigooVKzJz5kysrKwQi8Wp/MstW7Zk69atuLu7c+fOHV6+fElc3PeynmFhYQwZMiRLa+cWW7ZsQUtLiwEDBih87qxUJcxpPLelpSVeXl5IJJJfpniTPPw+V/ILULBgwXzZmDIhIYH9+/fTunXrTB9srVixAnV1debNm4dUKs1ymcuuXbuiq6tLuXLlcmw3KKdY0Pnz55k5cybFihVj9erVuLu7Y21tneEHv06dOsybN4+TJ09y+/Ztmcsm+f0MCgpi0KBBzJgxg/379/P69Wu57VEGvr6+3Lhxg44dOypF0HKz/KmlpSVBQUGy8M7fBUGcBfD29ubLly9yFVrX0tJi+PDh3Lt3j2PHjmV55xoZGUloaGimzTzlRdHFgvz8/JgwYQImJibs2LGD5s2bZ6sdUnL8s6+vL4mJiaxcuZLr169z9OhR5s2bR/v27albt26exehu3boVDQ0NxowZo5T5c7P8qampKbq6ur9dIaQsifO6deuoVasWurq66OrqYmFh8duX7cuvKKqK2KdPn3B1daVChQpUrlxZrnP69euHsbEx8+bNQyvqY5Z2rsk7xjp16uTM8B9QRLGgiIgIRo0aRY8ePdDR0WHdunU5erhnbGxMvXr1OHv2LI0aNcLb25saNWpw8+ZNDh06xOLFi0lMTGTLli3ZXiO7vH37Fm9vb6ysrJQWYZKb5U9VVVVp3Ljxny3OJUuWZNGiRdy5c4fbt2/TsmVLHBwc8PX1VZZ9AmkQERGBgYEBq1atytb5kZGR7Ny5ExsbG4yNjblw4QIDBgzI0g5xx44dAIxwGU7vahn7DH/cub569QpA4YX1s1ssKDo6mokTJ9K8eXMuXrxIu3bt2Lt3L8WLF5d77bQyE7W1tdm2bRvbt2+nTp06xMbGYmlpiaamJpUrV6Z169aYmppy5coVrl69ys2bN3n48CFv377N6VuRKW5ubqioqDBt2jSlrZHb5U+bNWvGvXv3+PDhg0Lmyw/kOFqjSJEiLFmyhIEDB2Y69k+O1lAknz9/lonHggULmDJlSqbnJCQk4O3tza5duzh69CjR0dGYmZnRtm1brK2t0dPTy7Id169fx9nZmYYNG9J78lI23w1LN571eyJCLOu37+Wez0WuHN6usA9mTrC2tubz58+0bt2aYcOGUaZMmSydL28cb0BAAEWLFkVD4387xZ07d7J48eJUc7Zq1YoVK1Zk42oy58uXL9jY2NCgQQM2bNiglDV+JLcyN8PDw7G0tGT9+vUMGjRIYfMqA6WH0iUlJXHw4EH69u3LvXv3qFatWqoxcXFxsqfWyUaVKlVKEOcc8vXrV4oUKYKpqSn3799n+vTpzJkzJ9XOVyqVcuvWLXbu3Mn+/fv58uUL5cuXp127drRp04YSJUrk2JaNGzeyevVqBg0axKDBQ7jo+563QV+pU6U8ZmUKoSIW4fMumnU3vvD1h76k+loqDFFSanVW6N27N8+fP+fixYtZLvaf0zjehIQEnj59Snx8PElJSSQlJeHt7Y27uztTp06le/fuWbJHHrZu3cqKFSs4ceJErnU6V1aG4M/07dsXY2Njjh07pvC5FYnSQukePXqEhYUFsbGxaGtrc/jw4TSFGWDhwoX8/fffWV1CIBOS/YQ9evSgRYsWzJs3j5iYGJYsWYJIJOLFixfs3r2bnTt38vLlS4oWLUqbNm1o164dlStXztYDrvQYPHgw9+7dY9OmTWzZskXmCy9fvjwbN27kRaz2/wuYFPjfuiFRiQpNrc4uEyZMoHfv3nh5edGlSxe5z1NEZqKamhq1atVK8VqDBg14+fIlixcvpm7dulSqVAkgS2FiISEhzJkzhytXrqCqqoqVlRXjxo1DX19flqXq5uaWaw0Ocqv8qaWlJRs3blRqKdfcJMs75/j4eN69e0d4eDiHDh1i8+bNXLp0Sdg55yIJCQmycDYHBwd2797NokWL6NSpE+/fv+fGjRsULFgQKysr2rVrR4MGDZRa+0EikTB79mzi4uKoVasWmpqazJ8/H30DA4o5bSIsPv1zFZWIkBOaNWtGhQoVsvRwTpmZicHBwXTq1ImkpCSOHTvG6NGjefjwIZ06dWLGjBmycRKJhFmzZvHixQuqVq1KnTp1OHbsGLdv30YikdCmTRsiIiL477//EIvFVKhQgZEjR3L48GFZUSsDA4Ms2ZafefHiBY6Ojpw8eRI7O7u8NiddlLZzVldXp0KFCgDUq1ePW7dusWrVqjT9VxoaGil8bAKKITnjLLlFT69evdDU1GTlypXUrl2bJUuW0KxZs1yLqRaLxcyZMyfFaxUqVGDorOUZCjMoLhEhJ1SsWJHHjx8jlUrlvqtQZhzvrVu3kEgkhIeH06xZM8RiMXXq1OHAgQM8f/4cNzc3vnz5wl9//cXnz5/R19fn8ePHHDx4EFVVVTp16kT//v0xNjYG4P379xw6dIiDBw8yfPhwChQoQEJCAn379sXLyyvL9mWViIgI5syZw4ULF4Dv73fPnj1p166dQmOsy5cvj7GxMZ6envlanOUlxxmCEokkxe5YIHNev37N58+fCQ8PJywsjPDw8BT/FovF9O7dmwYNGqR5vkgkQl1dPUX/tE6dOtGpU6fcuoRMqV27NmOmzmb788zHKiIRISfUrVuXGzdu8PHjR5mgZYay4ngTEhJYuHAhiYmJVK9eHZFIxMyZM6lSpQrbtm1jxYoVtGrVim/fvqGqqsrKlSuxsrLi27dvvHz5kpIlS6baDZcsWZLRo0czbNgwjhw5wty5cwF49+5dlmzLKvHx8UyaNIlLly6RmJiInZ0dOjo6nDp1imnTpjFnzhwqV66MgYEBgYGBJCYmMnr06Gx1f4fvn4umTZty/PhxVq9erVD3XV6QJXGeMmUKrVu3pnTp0nz79o09e/Zw8eJFTp8+rSz7fjtu3LiRqnaFWCxGW1sbHR0ddHR0CA8PZ/Xq1dSrV48RI0bQrVu3VD60n8U5P1K5tBE8z/zWXxGJCDmhVatWrFu3TpYxJw/JcbyZ1Y7IShzvs2fPmDNnDl+/fpWJcDJJEin1WvfAuWh1dm1aS7miMaxYvoySJUsC30t9ZlaLRV1dnejoaEQiEatXr05VolbRdO/eHX9/f9q3b8+gQYMoW7Ys8F1Hbt26xenTpzl9+jR+fn4ULVqU6Ohohg8fTqlSpZg9e3a6m5OMaNasGfv27ePJkydUr15dwVeUu2RJnIOCgujTpw+BgYHo6elRq1YtTp8+jbW1tbLs++3w9/cHvvffK1KkCNra2mhpaaW4vUtKSuLKlSvs27ePfv36MW7cOJycnHB2dpb9gf8K4qwMAVMGlSpVokiRIsyePZuXL18yYsSITB8oJcfxZhStkdU43k2bNvHkyROGDh2aQphThqMVo0jnv9EqIOadpAgl5Z79e/TO/v37KVGiBM2aNcvCmfITEhKCl5cXR48exd/fH1NTU5ycnGR/t/DdLWdhYYGFhQUzZ85EJBIhEomIj4/H3d2ddevW4eTkhKura5b7A9avX58CBQrg6en5y4tzlhw+W7Zs4c2bN8TFxREUFMTZs2cFYc4iHz9+RFdXl2rVqlG8eHG0tbVT+d1UVFRo1qwZ69atw9PTk9atW7N27VrKlStH+/btOXPmDKqqqvlenDNKRJBKJSCV4lSvUL6Id/b29qZJkybs2rWLjh078vTp00zPUURmYjJSqZTAwEAMDQ0ZNmyY7PXkcL2fv+BCYiQsuPwFn3fy18UOCgri/fv3tG3bVu5zskJgYCDW1tYsWbKEgIAADA0NuX//Po6Ojhw8eDDNc8Riscz9oK6uTo8ePThx4gRSqVTmo84KGhoamJub/xa9BYXaGrnMx48fZcXt5aFMmTJMmjSJs2fPMmPGDJ49e4atrS1BQUH5XpwhfQFTT4gi6MgC9vwzkcOHD/PmzRtysXptKtTV1Vm7di2rV68mJCSEAQMGyFX3IruZiT8zZswYHj58mGK3J2+4nry9EpOzD+vWrZsl2+Rl0aJFJCUlsXv3bq5du8b58+fx9vamVq1azJs3j7Vr18o1T7KL7+PHj9myo1mzZly7do2QkJBsnZ9fEEqG5jIfP37MVviSlpYWXbp0oXPnzty9e5ejR48qtJ+gMmlUWgvzkgV+SkQozbroWuzevZv//vsPAF1dXerVq0fdunUxNTWVdWLJTSwtLTl06BCdOnVi4MCB7Nq1K8NkjaSkJK5du8ahQ4dITEzEyckJleKmcq+XlJTEoUOHuHjxIvXr10+RGZiVspsmBRPw8PDg0aNH1K5dmwYNGlCxYsUUd2Vv3rxBJBIptK4JfA8KcHd358qVK9ja2qaI3S5evDgbN25k4sSJrF+/HolEgouLS6o5fk5UKVbciLt37zJ58mSGDBmCiYmJ3PY0bdoUiUTCqVOn6NWrl0KuMS8Qiu3nMo0bN8bAwID58+fntSn5hsDAQI4dO8bVq1d59eoV3759QyKRoKamRs2aNVmwYIHcURSK4vnz5/Ts2ZPChQuzc+fONGttJCYm4uLiwtWrV2VhizExMZibmzNs2DDZDlUqlRIUFIShoSFisZjLly+zevVqoqKiiIiIIDw8nDJlyrB169YUd1XyNhLoaRLHyjG9iY2NRUtLi+joaKRSKTo6OjRs2JAGDRpQp04d5s+fz6NHj6hRowYLFizIcYZgZGQky5Yt4+TJk0RFRWFsbCxzv/1MUlISY8aM4fLly/zzzz/Y2trKjqWV4q2jkojKg8M8PLEDkUjEmjVrZP035aFbt27Url2bPXv25OgalYHQCSWfYmJiQsOGDZVadOZXJzo6Gm9vb1asWEFISAgeHh5UrFgx1+148OABAwYMwNDQkHnz5qVqgrB8+XK2bduGk5MTLi4uSCQSVq5cyYEDB4iJiaF+/foYGBhw7do1wsLCqFChAi4uLuzcuZOHDx9SoUIFNDU1cXR0xNHRMdX68ia6FPPdywPvQ+zatYsqVaoQERHBkSNHOHv2LC9evCAyMhKpVIpYLKZp06Zcu3aNpKQkTE1NWbVqFXp6ety+fZsnT57ImgKXKFGCBw8ecPXqVR49ekRQUBD29vYMGzYMsVgsa5qQmJiIhYUFvXv3pkmTJhnGLUdHR9O7d2/evn3L3r17qVSpUqYp8C6mGiwZ1QuxWJylzjlr1qyRlSzIb51oBHHOp7Rr1w4vLy/atWvHuHHjfqsMLUUSGRmJpaUlLVq0YNmyZXlmR9++fXnw4AFJSUnY2Ngwfvx4jIyMOHnyJBMnTqRFixa4urqmOCcxMRFXV1fZQzATExOqV6/OqVOnCAsLA6Bt27YsWrQow7XlafWko5LI44Ud6dypI7NmzUpzTHBwMB4eHlStWpWmTZsSHByMm5sbO3bswNTUlPbt2zNv3rx0y9AWKVKEQoUK8erVKwoWLEiTJk04e/YstWvXZvbs2VlyOXz69ImuXbuSkJCAx+HDTPFJzDSax55bzJ41k4ULF8pVczw+Pp41a9bg5ubG9evXMTc3l9u+3EAQ53yKRCLBzc2NSZMmERcXh4uLC926dct33+55zejRozl//jxHjhxRWNeUrJKcDtyxY0dEIhHHjh1DJBLRrVs39u/fj6GhISdOnMhSllufPn24d+8ex48fTxFelh7p7yylIIWgIwtQ++SLt7d3lgs3LVu2jJ07dwJQq1YtZs+ezbdv3/j69SthYWEUK1aMypUrU6RIEaRSKTdu3MDV1ZVHjx5Rp04d1q9fn+U1AR4/fszAgQNRM65GoY6zMx0/t4UB0wd35evXrxw+fJhixYqlOS4xMZFjx46xYcMGPn36RJ8+fVi9enW+a7orrw4K0Rq5jFgsZuDAgTx79oyePXvyzz//0KNHDx48eJDXpuUbgoODuXTpEh06dMgzYYbvNavV1NSYNGkSs2fP5tSpU1SuXJldu3ahqqrK3r17s5x+XLBgQdTV1eX2oacX7ZIYEUzo8cXYm5bi0qVL2RLJpKTvO9ZKlSrJfMW1a9emefPmdOjQAQsLC4oU+R4KKRKJaNiwIbt372bv3r1s2LAhW2sC1KhRg+3bt6OmK99dY0Q8TJs2jbi4OFq3bs2SJUtS7fIjIyNxdHRk1qxZNG7cGF9fX9zc3PKdMGcFYeecx9y8eZOhQ4dy9+5dOnbsyOjRoylcuHBem5WnODk5cefOHU6cOKH0LLb08Pf3p2vXrjRs2JB169alOPbkyRP09fXT3cFlxIkTJ5g0aRKbN2+W63Y7ICCAixcvcvvOXa6//IK6rgGd29lSu0RBateqma2/ldjYWAYNGsT9+/cxNzdn6dKlFCpUKMvz5JQz91/j+jjzL7fk4lGBgYHMnz+fS5cupap5PW3aNI4dO8bdu3cVHo2iaISd8y9CgwYNuHnzJmvXruX8+fPY29vLmoP+iQQEBHD79m26d++eJ8IcGxvLkiVL6Ny5MyoqKkyfPj3VmGrVqmVLmAHs7OxQU1PD29tbrrhuZ2dnlixZwu1bN6ljrI3XujmM6tGa5s0ssyXMr1+/xsrKiocPHzJq1Cg2btyYJ8IMYFWrLIU1yPB9+DGD1MjIiDVr1lCyZEmePHlCfPz/qmo9ffoUZ2fnfC/MWUEQ53yAiooKQ4cO5fnz59jb2zNjxgyuXr2a12blCUePHiUpKYmePXvmyfoXL15kx44d1K1bl/Pnzys8hE8sFqOmpsb+/fsZNmwYAQEB6Y5Nzuhr06YNV69eZf369Tm+45wzZw7x8fG4ubnh5OSklM7b8qIiFjHU3PB7hmA6Aj2gji4hwSl97lWrVuXjx4+0adMG+F4s6s2bN798uvbPCE+h8hGGhoZs376doKAgZs6cibu7e57tavIKPz+/LPlkFU3p0qWB71E1Pwuhojp6qKuro62tzc2bN3FwcKBfv3506NCB0qVLExISwrlz5zhx4gR3794FoHXr1jm/sP/n6dOnNG/eXGlZglkl2ae+4vJ7YvhfjRUDLRX61izItrmjuX//PrNmzSI8PJynT5/y4MEDxGIxNjY2wPe7rYSEBEGcBZSLWCzGzc2NmjVr8vfff7N8+fJfvvRhVnj79i1lypTJsx1d+fLlEYvF3Lx5M0UJVkX2wpNKpdSrV49JkyYxYsQItmzZwqZNmzAyMuLTp08AFCpUCAcHBwYPHixLFsnpl4Ofnx9RUVHZLsmpLBqV1sLYUpNOzpNp2bYDvTu1p3pRDfbu2c2tW7dQU1Nj+vTpiMViChYsSLFixZgyZYqsONTLly8B0u3I9KsiiHM+pESJEmzatIlOnTpx9OhROnTokNcm5RqhoaE0btw4z9bX0NCgbNmyPHv2TPZaeuFswdFJcrXaio2N5e+//+bVq1eEhYURERGBhoYG+vr67Nmzh5CQELZv387ly5dp06ZNmunKivhy2L17NyKRiCZNmsg1PjcpU7o0xUThPDm5g1rDuwLfm++uWLGCatWq4ezsTL169dKsFvjo0SMMDQ2zVLPmV0DwOedTOnbsSL9+/Vi0aFGGfsnfjZiYmFTxvxKJhNatW9OoUSMWLVpEdLT8ldiyQ7Vq1QgKCgIUU3zoy5cveHl54e/vT7FixbC2tsbBwUF2XF9fn7Fjx3LkyBEWLVqUpjCnVZku+ctB3sp0Pj4+1KyZvQiP3KB+/foEB/8vXb1o0aLo6OgQFxdH48aN0xTmpKQkvLy8ZLHovxOCOOdjXF1dMTQ0ZOHChXltSq7w/v17EhISCAsLw8fHB19fXz58+MCsWbN4//49pUqVYvfu3TRp0gRnZ+ccfWnFxsZy/Phx+vXrh7W1NZ06dWLAgAF4eHhQpUoVoqKiiI+Pz1LxofQoVaoUTZo0ISEhgREjRrBs2bJUqeDpoajKdJGRkYSEhGS5PnJuUqhQIRITE2X/9/PzIyQkRPbgLy2uXr1KUFAQTk5OuWFiriK4NfIxOjo6zJs3j969e+Pn50eVKlXy2iSFsmrVKnx8fOjcuTNt27Zl8ODBAOzdu5e9e/emGJuc7vzs2TN2797N8ePHadeuHWXLlsXJyYmYmBgiIiLo3bs3mpqafPnyBU9PTwoUKEDBggXR0dGR1c729vbm8OHDREVFoaenR6lSpYiOjsbf359Zs2ZRpUoVpFIpsbGxfI2Rr0tLZq22FixYQNOmTfHz86N+/fpyv0dZ+XLIqA/j7t27SUpKynf+5h/R1dVNIc6XLl1CRUWFrl27pnvO4cOHqVmzJvXq1csNE3MVQZzzOd26dWP69Ols2bKFJUuW5LU5CsXT05NPnz4xZ84c5s2bB3xPJqhRowYxMTHExsYSExODRCKRde6oXLkyc+bMYdSoURw8eJA9e/Ywbdo0WXPWU6dOsXr1atzc3Ni3bx+QOo5WVVWVKlWq4OLiksq/7eLiwqVLlzAwMEBXV5fC0bFyXUtmrbaSi7/Lu2NORlGNZPfv30+5cuWoVKlSltbPTXR1dUlKSiI+Ph51dXUePXqErq5uupmIISEhXLx4kWXLlv12Lg0QxDnfo6qqysSJE3FxcWHEiBGyUK/fAXV1dapVq8b48eP577//sLS0lFu89PX1cXZ2pkOHDlhbW9O3b1+qV6/O9OnT6dy5MxKJhGrVqrFnzx4iIiIICgriy5cvhIeH07x583Q/8GvWrGH37t2yuxRFtdo6e/YsOjo6VK5cWa7rS0YRjWTv3LnDly9fGDp0qMJFTFHhhfA9fhm+F+2fOXMmnz59ytA/7unpiVgs/qVrNmeE4HP+Bejfvz+GhoZs3bo1r01RGP/99x+BgYEYGBhQv359xo4dm0KYkyRSHn6K5dLrKB5+ik3Xp5rc7cLY2JjWrVtz6NAhRCIRkZGR9O/fH7FYTKFChahUqRKNGzemTZs2mdaE6NWrl+w2OaNWW8nI0yvQ39+fhg0bZjlEMPnLISMy+3JYunQpBQsWVHh7Kp930Qw88oGpZz+z5GowU89+ZuCRD1lqnfUj1atXx8rKiqNHj8q+UPX19WXHExMTZVX9pFIphw8fxtHRMcWY3wlh5/wLoKmpyZgxY5g5cybDhg1TWsiQIndBGXH9+nVGjRpFyZIlmTt3bqrjWQkb27VrF+rq6rKYZBMTE86ePcuNGzcybGIqkUi4evUq7u7uqKurs3jx4nTHJidKZDeULSgoiMjIyFRd1+Uhp41kw8LC8PPzo2fPntkuVJQWOQ0vTI/Ro0dz4cIF+vbtS1hYmCxy5+HDhzg5OREbG0vBggUpXrw4L1++TFX35HdCKHz0ixAREUHp0qXp0KED48ePV/j8ikyyyIibN2/i7OyMkZER27dvT1XPOrPi6z9/6Nu2bcuHDx+wt7dn1qxZGZZeDQoKYu/evVy4cIGAgADi4+MRiURIpVJWrFiRouN1WmT3y2vevHns37+fihUrcuDAgWyVh83u72fGjBkcPXoULy+vHHc+SUaeOtMGWips6WCcrS/3efPmyWphDx48GAMDA/755x+KFClCv379ePToEdevXyc8PJzw8HAKFiyY7WvJC4R6zr8h06ZNY+XKlZw5cwY9PT2FzZtVQcwud+7cYfDgwRQtWpQdO3ZgaGiY4nh2PvSfP39m+fLlnDhxAi0tLYyMjNDV1UUkEtGhQwccHBwQi8VMnToVT09PpFIpxsbGWFpa0qRJE0xNTXF0dERNTY1Tp07l+Bp/Jjo6mmbNmlGgQAG+fv1K3759s/3lmtUvh/j4eJo2bUq9evXkbq4qD/J2aEmuJpdVIiIiWLNmDefOnZPFm1evXp01a9bIvsxfvXqFg4MDR44cSREz/isgiPNvSFBQEGXKlGHAgAEMHTpUIXMqexeUzKtXr+jSpQv6+vrs2LEjzZ58OfnQ3759m61btxIYGEhISAhfv34Fvmf8VapUiUePHtG8eXMmTpyYage5Z88eFi5cSMeOHWnbti1mZmbZSh93c3Pj6tWrfP78mfDwcGJjY4mL+x7/vH//fpYuXcrDhw/x8fFRanOF+Ph4li5dyuHDh4mNjWXLli00aNBAYfPL29twQmMDmplkf1crlUp5+fIlSUlJlClTJkUSyufPn2nbti3jx4+XRfr8Ksirg4LP+ReiaNGiDBw4kD179tC3b1+F+BAVFUebGXv37iU+Pp6tW7emKcyQs7AxMzMz2QNFb29vxo4dy5gxY3j79i3Hjx9HJBIxdOjQNG/tO3bsyLFjx/Dw8MDDwwNVVVUKFSpExYoVsbCwoE2bNhmWCA0LD6fv+Hl8Co9GQyLFQF2DOnXqYGRkhJGREaamplSpUoXu3btz48YNunXrhqurq8KLOyUmJrJ8+XIOHjxIbGwsTZo0wcXFReEFgRQRQSIPIpGIChUqpHr969evDBkyBENDQ1ls/O+IIM6/GBMmTGDDhg0cO3aM7t2753g+RcXRZkZwcDBqamqULFky3TGK+tA/ffoUAHt7ewwNDZk+fTqJiYmyDtk/o6mpyb59+/j69SuPHj3i4cOH3L9/n/v373Pt2jWWL1+OpqYmBQoUoEyZMhQrVkxWyyFAqs+1OGNUmrmQ7KTR1VKh2w++4Li4ODZt2sT69etRVVXlxYsXtG3bFnNzc1asWJGtL9lXr14xatQovnz5gkQiQSKRkJSURGJiImZmZowZM4ZatWpleV55UFR4YVZ49eoVixcvpnjx4vj6+vLt2zf++++/3yq09GcEcf7FKFGiBGpqarLb5ZySW7ugr1+/oqury9u3bzl48CDa2to4OzunGJOVD/3p06eJiorCzs4uhbhFR0dz7tw51NTUZP5JNTU11NTUMrWxcOHCWFpayrLoJBIJb9684cGDB/z33394e3sTHx/Po0ePSEpKokAlCww7TEXlp6mTIxamNDUg4fUtFi5cSGBgIFWrVmXlypWoqqqyYcMGDh48SMuWLZk3b16mDyOTkUgkLF68mP3796OpqUnbtm3R1NREQ0ODuLg49uzZg5+fHyoqOft9ZUROI0iyg7+/P1evXqVq1apIpVJOnz6drxNqFIHgc/7FuHfvHnXr1mX79u0KqcmbWz7njh078vLlSyQSiSxC4vDhw6luW+V5OFlNN57mzZuTlJSEiooKenp6VK1alaJFi3LixAni4+MZNGgQI0aMyLa9P+Pl5cXkyZPZt28f1apV41tkJMNOhhIWLwXSfl80JTE8W9qNwoX0mDdvXqrUaV9fXyZPnszbt2+xsLBg1apVaRb3ScbPz49hw4bx5csXrK2tmTp1aqpol0ePHjFy5EjCwsIYN24cvXv3zvG1p0duRfgA3LhxAycnJ168eEH58uUVOnduI/icf1Nu3LiBioqKLJsqp+TWLqhixYoEBQVhbW2Ns7Mzbdq0YePGjanii+WJKZ4/fwVJSUksX76cd+/ecePGDW7fvi2rXjZu3DgqVqyYI3t/5vXr18D3hggikYg3UWqExUN6wgwQKy5A0eoWeO9em+YDxurVq3Po0CH+/fdftm3bRosWLejXrx9t2rRJ5RufOXMmR48eBWD58uVYW1unuWbNmjU5dOgQ48ePZ/HixcTFxTFw4MDsXXQmNCqthXnJArkSG58cnRQaGvrLi7O8COL8i3Hjxg0qVaqUrv80O+Q0yUIe/vnnnxT/t7Ky4tSpUwwdOjRViczkD/3jzzGs3rSDJ3evMX3KcBqW/u6vPnXqFHXq1JEJ1MCBA0lISCA4OFhpfQffv3+PSCSSZaPJ64PXLVYqw8gPDQ0Nxo4dS8uWLZk6dSpr1qxhzZo1aGpqYmhoSI0aNfDz85N9OVhZWaUrzMno6+uzadMmevXqxfbt25UmzvD9yz0nD4rlJbkjUHJG6J+AIM6/GNevX1fKg57c3AXB953gmTNnGDFiBAcOHEj1UCwmOorpg3vw5s0bChYsyAiX4ZQoUYJv374RFhaWoksJfPcrK7Mh7NevX9HQ0EAikaCioiK3D15PXb75TU1N8fLy4u3btzx8+FD2UPL06dOoqKgwa9Ysrl27hre3N76+vplGYKiqqtK9e3dmzpzJ7du3s1xwKb+RvHP+k8RZqK3xCxEeHs6zZ8+oWbOmUuZP3gU1MylIreKaShNmAG1tbQoWLMjbt29l3aCTef78OTY2Nrx//5558+axZcsWSpYsiaqqKhUqVKBTp07Y2toqzba06Ny5M7GxsZw5cwaQo+aFVEpixBdqGcl/1yESiShbtizt27dn+vTpHDp0iOvXr3Px4kU6d+7M/Pnz0dTUZO7cuSQkJGQ6n52dHTo6OgwaNIjOnTtz/PhxJBKJ3PbkJzQ1NVFXVyc0NOPa1r8TwgPBX4izZ89ibW3N0aNHKVeuXF6bk2PMzc2pWbMmr1+/JjQ0FC0tLeLi4oiPj6dQoUKsXr2a2rVr54ltUqmUa9euIZFIqFGjBoUKFcLS0hJDQ0NZcaX0Hl5KpRJARLXQq/wzoodC+yHu37+f+fPn06JFC5YuXZppFMqbN284dOgQx44d4+vXr2hqajJ8+HD69eunMJtyi1atWuHs7Mzs2bPz2pQcITwQ/A25ceMGOjo6qdo4/arEx8dTuXJlli5dyqpVq4iJiaFYsWIUK1aMVq1a5VlPuODgYObOncv58+dlr5UuXRotLS2eP3/OzZs3MTc3x6JUAcq8P8M7/QZICxSSjU36FkKtxGcsHpW6lGVOi0t169aNL1++sHHjRsaPH5+pQJctW5bx48czevRorl+/zooVK9i4ceMvKc56enp/1M5ZEOdfiBs3blC9evU860ytaCpWrMihQ4cYMmQIs2bNymtzkEqlnDx5koULF6Kuro67uzu1atXi5s2b+Pj4sGXLFuB7jRMnJyc+ffrE5d1bUNfQRFS0ItXNLNBWSeKyuxs1bKyBlOKsqNAzFxcXXr16hbe3N7dv38bCwiLTc1RVVWnSpAlfvnxh5syZvH79OtWD2Lzi9OnTLFu2DDU1Nf766y+6du2a5t+4np6e4HMWyH9IpVJu3LihNH9zXjBv3jxiY2PZuXNnXptCcHAwY8aMYdKkSdja2uLr60vHjh2pUKECPXv2pEaNGsTGfu+KEhQUxIIFC9iyZQvlypXjms9VrGqW4tWFAzzwPkQhPd1UMc3pNmmNSmTBpSAW7TrF27dvU9klkUjw8/Pj9OnTMn+xRCLh1q1blC5dOkstrwBZ55c9e/akOnbw4EF8fX2zNF9OuHnzJra2towfPx4VFRUSEhKYP38+5ubmjBgxQhahkoyenl6KBrC/O3/szlkqlRIYGIiRkdEv0eLm7du3BAUFKS0lNy+oVKkSlSpVYvv27fTv31+h9YazSnJyx4EDB+jSpUuq42KxGLFYzIMHD4D/NUwtXbo0IpEoVajgj7Ru0waRw3zEBdMo2i8SIZVKuBhhwG779qip/l97Zx0WVfr+4XtoKUHRFRVbsRUDVAQLA127O9fAXHPFtdu1G1YxEXbtTmxUTBREag0sUlqpmfn94Y/5itQMDKXnvq65gDPnvOeZYeZz3vO8T6iira2NhoYG8fHxJCQkyERZR0dHVtM4KiqKZcuWKVxAqXTp0piamnLs2DHEYjGzZ89GS0uLgwcPsnr1alk9i4MHD+bZ/8Pf358//viDwMBADA0NWbBgAT169EBVVZX79+9z5MgR3NzcuHHjBnp6erJZ9JcvXwrM1VUQ/HTiHBISwoEDB9i9eze+vr40adKEOXPm0LNnzzxNec0tHh4eAD/UzPnNmze8evVKlthRkHz+/Jnhw4dnKMwZMW/ePK5evUrZsmVp3rw5LVu2pFmzZujq6qbZLyQkhHCRAb9kJMz/j0ikgpp+KaYu20LiWy8CAwOJj4+nQoUKmJiYUKFCBVRUVHBwcGDTpk2y41atWkVkZCQ9evTIdOyMfNxr1qxh3bp1HD58mJMnT9KmTRuuXr1Ko0aNiI+PJyAggMjISKWLc0hICLNnz8bT05NixYoxdepUBg0alCZmv1mzZjRr1ozIyEhOnz4ti+L5/Pkzt27d+mHWW+ThpxDnlJQUzp8/z65duzh37hwqKiq0a9eOQYMGcfbsWfr27UvVqlWZNWsWw4YNU2qCh7Lw8PCgXLlyP0xLnqioKAYOHIiWlhaOjo4F/p6XLl2a9+/fZ/q8trY2EomE//77D3V1da5du8b06dMRi8WcP3+eo0ePUrp0aa5cuZLmQuPq6oqKTuZ98L6ler3GtOqWeXdsa2trrl+/zoIFC/jy5Qvv3r0jNjY20/0z93GXYdu2bfj6+uLo6MilS5fQ0dGhT58+2NvbY2Njo/SKeT4+PgwdOhSpVMrw4cMZPXp0ljXJDQ0NGTZsmOzvLVu28ODBA5ydnZVqV2Hmhw6l8/X1Zc+ePezfv5/g4GBq165Njx496Ny5c5oPhre3N3v27OHKlSsYGRkxZcoU7Ozssmwumd+0aNGC4sWL/zAduCdPnszNmzc5cOBAoXDVzJ07l8jISG7fvp3h81++fKFRo0aIRCLq1KnD9evXefv2rawWhpOTE6NHj+bOnTvo6enJjuvZsydf9Mqj1iH7AvuKFKd3dHRky5YtnDhxIsN0ZkUaKLx69QqpVIpUKmXw4MGIxWIOHTqktBT4t2/f0rt3b3R0dNi/f7/CHVlCQ0Pp0qULU6dOZdWqVUqxqSCRVwd/uAXBz58/4+TkhKWlJbVq1cLBwYG2bdty+PBh/vnnHwYOHJjuil23bl3WrVvH6dOnad26NUuWLMHExIQhQ4awZcsWPDw8lFYFLickJyfz5MmTH8qloaenh1QqzbBeb0FQpkwZ3r17l+nzxYoVw9nZmYCAAI4cOcKECRPSFClKrZAWFpZWEENDQ9H9EkLJYll/1b4tsSlPc1s3NzeMjIwyjHcXS6Q4Psw65Mzx4SfZuJUrV6ZKlSpUrVqVOXPmkJCQwIgRI5SSsPL27Vv69++Pmpoau3fvzlGrrO3bt6Ojo8PcuXNzbU9R4odza8yaNYvt27fTokUL/vrrL9q0aYOmpnx1ZStUqMD8+fOZMGECrq6u3Llzh8OHD5OUlIS6ujoNGjTAwsICCwsLzM3NqV69er6EtT179oyEhIRCMcNUFi1atOD06dP4+/vTsGHDgjaH2NhYNDSyzrVu1KgRK1asYMWKFdjZ2aV5LlV07ty5k0Ywf/vtN9avX491wEUiymdeEyO1uJS84XavX7+mWbNmGfrqc9pAQSKR4OLigqamJjExMcyYMYMNGzYgkUh4+PAhBgYGVKpUKd37lJCQwOPHj/H09OTFixcEBQURERHBly9fSEpKQlNTk7179+YocerLly+cOHGClStXKrU1W1HghxNnc3Nztm/fzoIFC3LsNzMyMmLSpElMmjSJpKQk/P39efbsGd7e3pw7d45t27YB//OLTZ06NU9jRj08PFBTU6NmzZp5do78pm3btohEInx8fAqFON+5c4du3bplu9+sWbOYPHlyutKeFStWZMKECaxdu5YKFSrIQulGjBhBUFAQh5030XNyCYKMzDMVXkU6WpuYmHD16lV27NjB+PHj04h0Thso+Pr68uLFC3777Te+fPmCs7Mzv/32G15eXsTHx8v2S62Pra6uTlJSEgkJCaR6RzU0NKhQoQLNmjWjYsWKVKpUCTMzsyybLGTF+/fvEYvFcsVy/2j8cOLcp08fJk2axKlTp5TSZ09DQ4O6detSt25d2baYmBi8vb25d+8e+/btY8uWLXTv3p3p06djaWmp1MiD+Ph4HB0dqVOnTpa1fosa2traaGtry7qWFCTh4eG8ffuWZs2aybV/Zv+HLVu28OHDB2bOnMnu3btlbqgFCxbw/v17jm9ZyKLFS6huY5suQ1BeV4RF+WKoqoj4999/mTBhAtu3b+ft27csXrxYlimY0wYKqbPi58+fs2XLFjw9Pbl37x7m5uYMHPg1DT0iIkL2iIyMpFSpUjIRrlSpEqVLl1bq3eTbt28BfohyBYryQy4Ijhw5kitXrnD27Nk8dzt8+fKF06dP4+zszMuXL2ncuDHTp0+nb9++cnXfyAqxWEyvXr24cuUK+/bt+6FmzvC1jZSqqionTpwoUDtS32dTU1POnTuXq7G+fPlCu3bt8PX1xcXFRXb3JpFI6NmzJ69fv+bYsWPpFvFy2tx2xYoVuLq6Ym1tzdatW7++nlw0UJg9ezbXr1/n/v37SKVSoqKiCnRh/MCBA2zevJnPnz8XeLilsvhpFwQBRo0axbt373j8+HGen6tYsWL069eP48ePs337djQ0NBg8eDDa2tqUL18ec3NzevTogZ2dHcuXL+fMmTN8/PhRrrFnzZrFmTNn+Ouvv344YQaoXbs2r169kmuxVSwWc/PmTebPn8/Ro0cRi3PX0/BbVFVVGTduHOfPn+f+/fu5GkssFhMfH09UVFSajD8VFRWcnZ1RV1dnyZIlfD8nyqkrwt7eHjMzMx48eCDbltpAISsya6Bga2vLly9fWLp0KSKRKMfCLM+ipjy8ffuWypUr/zDCrAg/nFsDvnargK+RG/mFiooKVlZWWFlZERAQwKNHjwgLC5M9/Pz8+PjxI9HR0cDX6ICmTZvSuHFjGjduTJMmTdJ0pd6xYwcbNmxg7ty56VKBfxSsrKw4d+4cv/32Gz179qR9+/bpkjgkEglOTk64uroSEhJClSpVOHHiBK6ursyePVvh9OXM6NixI46OjixcuJDz58/neBxVVVXi4uL45Zdf0s2OdXV1mTBhAhs3buT48eP06tVL9lxuejnGx8eny5zLaQMFa2trfv31V/7991/ev3/P9u0Zd3HJCmW2r3r37l2hiejJb35It8a6deuYN28et27dKvDkhm9JTRn38fHBx8eH58+f8+LFCyIjIwEwNjamSZMmGBgY4OzszMCBA/njjz8K2Oq8ZdWqVZw9e5aoqCg0NDSYMWMGgwYNAr6+X2vWrOHgwYOMHTuWsWPH0qhRI+7fv8/UqVPx8PDAxsaG6dOn5yhE63vOnz/P7NmzuXbtGq1bt87xOG/evMHKygo1NTWcnJzS9fnr2rUrQUFBLFu2jK5duwK5c0W0a9eOKlWq8Pfff6c7JidV8KRSKdu2bcPBwYGqVavi6uoq93qHIvHV8tCtWze6devGxo0b5T6msCOvDv6Q4ty6dWskEgnbt2/Ps3Moi1TBfv78uUy0nz59SlJSErdv3y7QehP5iZeXF0OHDqV3797Mnz8fgK1bt+Lg4MD27dvTLe5KpVIOHTrEnDlzCAsLo0ePHnTo0IHGjRsrXG8iFbFYzPDhw/Hx8WHx4sXMmjUrx2MFBgZiZWWFvr4+Tk5OacLAEhIS6N27N0FBQcybN48BAwYAORO2z58/06ZNG2xsbFi+fHmObM2Mo0ePsmTJEn755RdZk4GsyItmwT179qRMmTJcv369UJdXUISf1uccFRWFu7s7rVq1Ijo6mlu3brFjxw5mzJiBn59fQZuXDpFIRNmyZWnfvj1Tp07FwcGB3bt3yyp0/SwYGBggFotlYXV79uzBwcGB1atXZxh1IxKJGDx4MH5+fsybN4+7d+8yZswY2rZtm+OLsqqqKk5OTgwbNow///wTS0vLHH9mqlWrhpubG58+fWLcuHFp0qy1tLQ4efIkNWvWZPny5ezcuROJRCJzRXzfYcVIWzVDYV6+fDnNmzfn8+fPmJmZ5cjOrOjatSvGxsZyuwcVia+WF3t7e9zd3bMsLPWj8sOJ86VLl0hJScHb25u2bdtiZ2eHo6Mjly9fTlM8vTBTp04dOnXqxPnz52Uujx+ds2fPAl/rDm/evJn169czb948Zs+eneVxOjo6LFiwgDdv3nD//n1+/fVXduzYkeP3TUNDg2nTprFv3z5CQkJo2LAhmzZtylG2XO3atbl8+TLv379n4sSJaUROTU2Nf/75h6ZNm7Jt2zaGDRvG69evaVFBm909yrHC5hdmWRqxwuYXdvcol06YlyxZgqurK7q6uohEojQlRb/H3d0da2trWrVqpVA95F27dvHhwweWLFki1/45XdTMiqZNmzJmzBgWLlyY68XaosYPJ847d+4E4NSpU1SvXp2dO3fy6NEjdHV1CQoKKmDr5GfKlCmIxWLs7OxkdYR/VJKSkrhw4QLwNZRr7969zJ49m6VLl8o9hkgkomnTprJjnjx5kiubGjZsyOHDh+nTpw/Tpk2jTZs26eoLyzvOhQsXCAgIYNKkSXh7e8siNVRUVHBycmLOnDn4+vrSs2dPTp8+nW0vx6NHj3LkyBF69OjBZTc3eo2fw7NoDXqNn4N/QGA6G2bPno2GhgafP3+mU6dO2NnZZTsbfvnyJY6OjjRs2JC2bdvK9Vpzs6iZFRMmTKBWrVoMHDgwy0JPyiAfvbzZ8sP5nA0NDVFRUWH9+vU0btxYtt3W1hYDAwNcXFzy5Lx5wfbt29mxYweampp06dKFuXPnFppEFIlEwuvXr0lISKBGjRo59s0GBwczffp0/Pz8GDJkCOPGjaN+/fo5fp1SqRQTExPat2/PjBkzcjTG9zx48ID58+ejpaXFixcvchS/fvPmTfr3709wcDAVKlSgY8eOdO7cWRaJEBkZyeDBg3n//j1OTk5pPrvf4u/vz4ABA6hduzZjl27H6UlMGldCSkwYX9z306VBeWbO/FpsqVGjRkycNInSdVpw6vINvO7fppxGAs4HD2S6prFu3TqcnZ25fv263N9VZfick5KSsLe358GDByQnJyORSJBIJIjFYiQSCc2aNePWrVty2aMovr6+2NrasmfPnlwtCGfHT7kgmJKSQrFixfjjjz/o379/mudGjx6Nr68v7u7uSj9vXuLr68uOHTu4evUqmpqadO7cGXt7+3wX6cjISLy8vHj69CleXl54e3vLZjG6urqYmZnRpEkTmjZtSq1ateQSaw8PD2bPno2Ojg7Hjh2jSZMmSrF10KBBPH/+XKnlJf38/OjTpw+7d+9m1KhRORojJSWF69ev4+LiwrFjx4iKiqJ69ep06tSJ/v37o66uTocOHYCvXUmMjY3TjTFv3jxOnTrFXy6X2eqZke9WClIIPbEC1Q9e9OnTh3/u+FGlzxzixP/7n6TEhBF1dRcdapfO0G0xZMgQgoODuXLlikKvMTfRGg8fPmTKlCnExcVRrVo16tati4aGBurq6mhoaBAUFMT169d5+fKlUqJzviU6OpqmTZsSEBDA+PHj2bFjh1LH/5afUpxfvnxJ1apVcXBwoEWLFmmeW7VqFc7OztStW5emTZvSqFEjIiIi+Pvvv6lSpQo2Nja0bt2aEiWyDt4vKL4VaQ0NDTp37szcuXPzJJpDKpXy/Plznj59yrNnz/Dy8pKl0ZYqVQpzc3OaN2+OhYUFWlpa3Lx5k2vXrnHnzh0+f/6Mjo4OjRo1onHjxjKx/na2KZVK2bt3Lxs3bqRNmza4urqmCzfLDdu3b2fq1KncuXNHqaGU06dPx9/fn4CAgFxnfyYmJnLp0iVcXV05ceIEenp6LFmyhDJlytC3b18qVqzInj170hX7GTduHF7e3tSY8U+WM1R9NTFeK3qiU6M5Jbv/kUESx/9E3CD2Na1atWLevHnA1yqIFhYWWFpasmXLFoVfm6JxzhKJhMWLF3Py5ElKlSrF6tWradSoUbr94uPj6dSpk6xapDJITEzE19cXe3t7bt26Rf369QkJCSEgIEAp42fETynOz549o0GDBuzfvz/d6nVMTAzLly/nyZMnREREkJSUBHwtsp6QkEBMTAwikYhWrVqxfv36XH/58go/Pz927NiBm5tbpiLt7++Ph4cHQ4cOzdE5Tp8+jb29PRoaGpiZmcm6U1hYWFCpUqVMs7WSkpJ49OgR169fl4l1fHw8Ojo6NGzYkCZNmmBmZoazszOXL19m9uzZLF++PMcukcxI/Rw4OTkpLUkFICAggN69e+Pg4MBvv/2mtHHfvn3LyJEjcXNzY9CgQdSuXZvFixejqqrKsGHDGDhwoOzi1bdvXz7rlkPU7vdsxw0/PB+jLtNB2wDI+H+mKf6M/7oBFNfXk9WyTkxMpFWrVqipqckmA4oib3z1mzdvGD16NCEhIXTr1o25c+emS0T6lh07duDk5MT06dNln0l5WldJpVI+fPjAs2fPZI+nT5/i5+dHSkoKGhoabNiwgYSEBGbMmEFQUJDSZ+ep/JTi/OXLF3R0dFi8eDE9e/bMdD+JRMLjx4+JiorCxsYG+BqXumbNGu7evcvhw4cLfbq0n58fO3fu5MqVKzKRnjNnDosWLeLKlSuIxWLKli3Lrl27FP6QzZ8/X5blKG+51YxITk5OI9bu7u7Ex8ejq6vLvn370mTIKROJREKJEiUYMmQI48ePz3Z/RRI1Zs2axfPnzwkMDMyRaGVl85YtW/jjjz8oW7YsHTp04OrVqwQGBqKiokKXLl0YOnQokydPpoRZB+LqZd9OK8rdBQPLgdnu9/nMCsqpf07TaPfPP//k9OnTXLp0iV9++SVXry0ztm7dyu7du9HS0mLJkiW0b595SdVUYmNjWbp0KQ8ePJA1e61UqZJMqAcNGoSuri7e3t5pRNjb25tPn74WltLV1aV69eqyHpY1atSgWrVq6OrqEhUVhbW1NU5OTowYMSJPXvdPKc4AVatWxcrKSrYYogjh4eG0adOGJUuWZCnuhYlvRVpFRQWJRELv3r1p0KABq1atIjk5mSlTpij0QevatSudO3eWlUZVFqlNA8qWLZvjEpLf8vr160x7ynXu3Jno6GgcHByyHEPRW/CXL1/So0cPtm/fLpfwK4qPjw+jR4/m3r17lC1bFmtra/z9/fHy8iI5ORlVVVWadx/G2+q9sx0r4dFxtBpn/zkOO7WGrvWNMTEx4enTpzx58oTw8HC0tLRwdnaWNRJQNi1atCA2NpYyZcowcuRIevToIbebLjV5K1WAvby88PT0ZM6cORw8eJD379+joqJCxYoV0wlx2bJls6zV0a9fPxo3bpxnXeF/2iQUU1NTXr9+naNjjYyMZCvyRQVTU1M2bNjA0aNHGT58OAcOHGDRokX07NmTEydOUL9+fdatW0f//v2JiYnJdrxPnz7x+vVrLC0tlW6ruro65ubmuRbmxMREJkyYQOXKlenTp0+G+1hbW/P06VNZLZOMSF28+t53m1o/+U5Q+nCzKlWqYGtry7Jly/IkxLF27drcvXuXBw8e0KlTJ06dOsXTp09p1qyZLEHHtIRqukSVb5FKJUjiIogJfCTXOcVxkfzzzz+sX78eDw8PypQpQ7du3VBXV6dfv35Mnz5d5gZUJrVq1ZLV7Vi1ahVt27bFx8dHrmNTk7c6deokc4/B1+Sf9+/fM3/+fO7du8epU6dYt24d48aNo02bNpQrVy7bIkoWFhZcuXKlwMPqfjhxfvfuXa4WlwwNDXn+/LkSLcofatSowfTp09MUrjc2NsbJyYmZM2fi7++PjY0Nly9fznIcT09PgDwRZ2Xw9u1brKyscHJyAkhT+e1bBg4cSLFixZg0aVKGIqpoK6dvGT9+PB8/fmTmzJlKaeWUEU2aNGH37t18+PCBzZs3ExkZiaenJ2KxGKlEwogGmfllpYhEKvzZuSadGlUlJSYMMhEZqVRKSkwYkhB/bG1tuXTpEu7u7ri4uLB8+XLOnz9Pr169uHz5Mra2tkoX6AYNGiCRSDhw4ADz588nPj5e5nqQl6ioKE6ePMnChQtRV1eXVdFr2bJljheDLSwsCA4OxtfXN0fHK4sfSpwTExN58eJFjv3Ft27dIjY2Nk9XavMbFRUVhg8fjqurK8bGxsyYMYN+/frJ6lh06dIFGxsbrK2tsbS0ZM6cOejp6VGhQoWCNj0dbm5uNGrUiHfv3rFv3z4mT57My5cvM9y3YsWKnD17Fn9/f2bPnk1KSkqa53OTaly5cmXmzp3Ljh076NOnT55WPyxevDh2dnY8e/aM8+fPY25uzt69e1k0ugfNUrwomS7VW00WrrZk8SJqfXmOlK+z6W9J/btjqVg8PDwYMn0Jvp9105T3LF68OAsWLGDmzJmEhobi5uYml83fv9eZYWVlBXxdwE4VZXk6Cn348IGDBw8yatQoWrduzZ9//omamhr79u3j9evXaGtrp6nwqCiNGjVCXV1d7tebV/xQ4vz8+XNSUlIUFucrV67Qvn177Ozs0NTUzJG/urBjamrKv//+y/Dhw/n48SMfPnwgOTkZfX19KleuTKNGjWjVqhVlypTJ1idXEPj5+dGxY0eqV6/OP//8Q926dalQoQKfPn3KNFXb3Nycw4cPc/PmTZYvX57mNjW3qcYDBgxg06ZNXLhwgdatWxMSkn2h/NwgEono1KkTHh4e+Pn50atXL/5db0/TjyfpZ/SBsFNr6K73Kl2q94YZw7FroI44Nm3ato4omXmtSmNhYcFvpz5ifyWEv9zDsb8SwugT79O4dAYMGICamhoXL17M0saUlBSmTJlCo0aNaNasGfPmzcswXTwpKYldu3Zhb28PfL378fHxQV1dPcPYbvg68XJ0dKRfv3507NiRDRs2ULJkSbZv386HDx+4e/cuAwcOxMfHhypVquSqyYa2tjb169cvcHH+oeo5nzt3Dg0NDYUWMBISEpg5cyalS5dmwYIFdO/eXamr8IUJTU1NZsyYkWXmnL29vcK3lvnB1q1bMTAwYOvWrbL/T2oUyn///ZdpAkvnzp3ZtWsXI0eOpHTp0rIiSspINW7dujV79uxh8uTJWFhYcP78eWrVqqXIy8oR1apVw9HRkUqVKjFv3jx+++03Pr+4iWa0RYZRJl3ql2P5hH4kFa9IMcPSbF27grq/FMPj3Re5ehZqampSr149vLy8MrXJy8sLOzs7oqKi6NOnD8HBwZw+fZqzZ89StWpVJk+ezOfPn9m3bx8BAQEkJydTvnx5Jk6cSLdu3Thy5AgVK1bMVFQ3b96Mi4sLvXv3ZuHChdja2ma4mObt7a2UllYWFhYcOnQIsVhcYNXwfhhxjoiI4K+//qJv374KJWakhp2tXr06Typ7FTVSUlIKXYx3bGws+/btY+DAgWkunKmul8DAwCyzC0eMGMGHDx+YN28ehoaGDBgwgDqlNTHSVs021bhO6axDCevUqYOzszMTJ06kRYsWHDt2jDZt2ij4CnPG3LlzefbsmSyq4MKFCwwbNizDfUuWKMHr115oROnRwFhb4Z6FTZs2xcvLC4lEkk5AV69ejYuLCwYGBjg6Osqasb57946jR49y+PBhJk+eDHx1lfTr149ff/2VOnXqyO7QIiIiMm3i6unpyYEDB1i9ejWzZs3K1F6pVMqLFy/SJaDlBAsLC7Zv387jx4+VGiuvCD+MOK9atQqxWKxwcsD169dRUVHJlxlPUUAsFis9KSS3uLq68vnzZ/r2TRvbq6enR4kSJQgMTF/s53vmzp1LaGgoy5cvJzo6+mvx/iYlskw1zqyV0/cYGxuzb98+ZsyYQYcOHdi1axfDhw/P/oXlEpFIhJOTExMnTsTZ2RlfX198fHyoXbt2un1VVFRQVVVl7969gGI+9/pltGjcuDGOjo7s3bsXDQ0NAgMDCQoKwsfHh/j4eDp06MCCBQvSZDSWL1+eqVOnYmdnx40bN9DU1KRZs2bpLv7+/v7ExcVlGBaZkJDA/PnzMTc3Z/r06ZnaGhgYyMmTJ4mNjU3XgSYn1KtXD21tbdzc3ARxzg3v3r1j69atjBw5kpIlSyp0rLe3N6ampoWmoFBBIpVKiY2NLdCGnhmR6ivO6Da2fPnycomzSCRiw4YNGBkZMX/+fMLDw5k7d26OWjllhJ6eHtu2bWP58uWMGDECPz8/2rZtS1hYGKGhoYSFhREeHk6vXr1k9TOUgba2Nnv27GH58uV07doVOzs73Nzc0t2Kf/r0iTZt2shcfor43JOTk7lx4wYAGzZskD1nYGBAkyZN6NKlC506dcp0nUJFRYV27drJnpdKpYSEhBAUFMSLFy/YunUrxYsXp1u3bumOXbduHR8/fuT8+fNpXlNcXBzXrl3jwoULXLhwgZcvX6Kurk7z5s2Vcgesrq5O48aNuXLlSoF1I/ohxHnRokUUK1Ys01u6rIiIiKBVq1Z5YFXR4vPnzyxdupS7d++ydu3agjYnDdbW1ojFYjw9PdPcsiYnJxMUFMSvv/4q1zgikYg///yTX375hfHjxxMZGcmKFSuw6FFO4VZOGaGurs7ChQsxMTFh5cqVrFy5Evjq6y9ZsqSsmP+xY8fktlleypYtS9euXdm8eTMqKirpsh4TEpPSZPop4nP/999/OXToEHXr1mXkyJFUq1YNY2PjbEPVgoOD2b17N8eOHSMpKQkNDQ20tLRITk7my5cvALLEqd27d6fLZD179iyurq5s374dU1NTvLy8uHDhAufPn+f27dskJydjYmKCpaUlv//+O+bm5kqtNWNhYcHWrVtJSEgokMlbkRfnjx8/snfvXn7//fcsc/Iz4uXLlyQkJNCgQYM8sq5o8PLlS2bMmMHHjx9xdnaW9fArLJiamlKqVCkePXqURpwfPHggW4BShN9++w0jIyMGDhzIxIkT2bhxI/XL6CjFVpFIxOjRo7G1tZWlkRcrVgyRSERycjIzZ86kd+/ePHjwgPr16yvlnKncv3+fOnXqcPftl3R3A6VH7+Tmo8PMkUoRiURy+NylaIq/cGjTEm7euE758uXlLrcbHBzMrl27OH78OLq6utjb21OuXDm+fPlCQkICampqsqy91O/f93Wa//vvPxYvXsyQIUOoVasW5cuX58OHD2hpaWFubs7MmTNp2bJlnoZ8NmvWjLVr13L37t18W0f4liIvzgcOHEBVVZUePXoofGxq942fWZwvXLjAokWLMDEx4f79+xn6KwsakUiElZUVjx8/TrP90qVLVKlSJU3ijbz07NmTixcv0q1bN0aNGsX27dsVdollRdmyZdNtS0lJ4dOnT+jp6WFgYKC0c6Xy9OlTmvQYk6EfXaRtCFZjOXDNi2Ft66OqIsrU5/41BlpE8LmtBL/zxMDAgL/++ivb838vyosWLWLSpEno6ellekxKSgq1a9dm5cqVHDp0CH19feLj45k+fTqVK1dm8+bNmJubY2RkxKJFi2jUqFGu6r0oQvXq1TE0NMTNza1AxLlIxzlLpVKcnJxo165dmoUIsUTKs+AEbryKTxNU/y2HDh3CyckJExOTDL9IOUWecxcGkpKSWLFiBbNmzaJbt248ePCgUApzKtbW1jx79ozExK9JIcnJyVy9epW+ffvmOCa7VatW3Lx5k0+fPjFkyBCePXumTJPT8ddff+Hp6cn8+fPzpOKZWCLBT6dexk+KRICUY69VZJ/JzHoWSuI+MbGBBu7/bMPd3Z1z585Rp06dTM8bHBzMsmXL6Ny5M1euXGHx4sW8fv2auXPnZinM8LVd18mTJ4mOjuaPP/5ALBazcOFCwsLCOHbsGHv37uXly5csXLiQ5s2b55sww1eXi7m5ucI1rZVFkZ05S6VStm7dip+fX5pVXHkK2UgkEtatW0ft2rXZtGmT0hIuFC2ik98kJCQQFhZGcHAwGzZswM/PT1bAp7AlnXyPtbU1SUlJnDx5kr59+/LgwQMiIyPTRXAoSoMGDbh79y79+/dn2LBhTJgwgTFjxuRJbKuVlRX3799n2rRp7N+/n2nTptGvXz+lCY562dokqGTuBxaJVEjR0GPCgrXMHzcAExMTmhqrk1TyJcdfPOORTyCihBhctyyjnAITlj///BMPDw/Gjh3L2rVrsxXk76lWrRouLi506dKFYcOG8ezZMw4fPkyJEiVYvHgxffv2zbPiS9lhYWHB8uXLiYmJybNibZlRJKvSRUREMGbMGE6cOEH//v2xt7dHRUVF7i4M169fZ/LkyezYsYOWLVvm2I5vyU0HCGUSFhbGpUuXCAsLk0UKhIeHExYWlqYIUIUKFThy5EiBhQkpikQiYcCAARw+fBhLS0vU1dV5/fo1L1++VMqFJTk5mSVLlrBixQoaNmzIihUrKFeunBIsT4tEIuH27dscOnQId3d3Spcuzfjx4xk/fnym2XHyUrlVX6TNR2S7X+TZdcS/uImVlRUPHjwgPj4eLS0t6tSpw/LlyxV+3S9evGDmzJl8+vQJBweHHK9ZrFmzhjlz5jBt2jQ2bNjA+PHjcXFx4cyZMwUWQfT27Vs6d+7MqVOn6Nq1q1LGzJOSoStXruTYsWP4+vpSrFgxWrRowerVqzE1NVWqUVlx584d+vXrR3x8PIsWLaJdu3aAYv3Lfp82lVu3bimtU4YyeqcpC3t7e86fP0+5cuUoW7as7Gfqw9jYmLJly1K1atV8vUVUFqdPn2by5Mm8efOGWbNmsWbNGqWOf+vWLQYPHkxkZCR//vkn7du3z7OM0ZcvX+Li4sKpU6dITk6mT58+TJ48mWbNmuXoglPdsjPJVhOz3c++mQ5b5k/Gx8eHmjVrMmbMGNq1a5dtynNqDZGMIiLi4+NZunQpZ8+eZeTIkWzZsgUdHcUWWaVSKXfv3sXc3BwfHx/MzMyYOXNmjptGKFKnOyubbG1t6dOnDxs3bsyRHd+TJ+LcqVMnBgwYQNOmTUlJScHe3h5vb298fHzk+kfkVpylUim1atWSdS34NjToWXAC9leyr2/QXtWHf7YspXbt2uzatUthGzJC3nOvsPmF+mXyLiQnOTmZVq1aMW3aNLnb2RdF4uLicHJyYsCAAXJ1wVCUqKgo7OzsZNEJ2traFC9enOLFi2NgYMC4ceOU1u8Qvn4vTp48iaurK0FBQZiZmTFlyhQGDBigUAhXnbr1SO40nyS1zL+LqZOEx48eMmrUqAxbumXG6NGjefPmDTt37pQ1pv0WqVTKyZMnWbFiBRUrVuTff/+lXr1MfOBZIJVKadeuHa9eveLYsWM5ylhVpotxwYIF+Pn5Ka1aZZ7Uc75w4QIjRoygTp06NGjQgL179xIUFMSjRxnXjU1MTCQmJibNIzd4eXnh5+fH+PHj03VnkDeo/tDxM8TGxsrd7l0ecltER1ncvXuX2NhYevfOvhB7USUyMpJ+/foxdepUWdlQZWNgYICzszM3b95k3759LFu2jNGjR9O2bVuSkpKYPHmyUmt+6+vrM3ToUE6fPs22bdvQ1dVl5MiRVKhQgb1798pdV1hLUwOTMI8s9xnbpAQScQr//PMPgNx3T15eXty/fx+RSMTw4cN58uRJun1EIhE9evTAwcGBgIAA2rZti1is+Gf+5MmTXLt2jVmzZuVYmBWt050VFhYW+Pj4EBwcrLAtuSFX0RqpPszMmqKuXLlSNuMoXrx4rleo//nnH4oXL55hDr68QfVL5s7g3LlzDBgwIFe25OTc8u6XE168eMH8+fNp1KiR0uNnC4qwsLA0X24vLy+aNGnCnTt3qFGjBosWLcqz8q6p4XvDhg3j999/Z+nSpWzbto0bN25Qs2ZN7OzsZE1vlYWKigrW1tZs376dM2fOYG5uzsiRI2nfvj3//fdftsfXq1ePD/fPZRiBYaStir11KZqUUWPMmDFcunQJGxsbGjduLJdtZ8+epVSpUjx//pwGDRrw22+/cf369TT7SKVSLly4wLRp09DX12fTpk0KL6wmJiYyY8YMLC0tZSVFFSE3dbozw8LCAoCrV68qbE9uyLE4SyQSpk2bhqWlJXXr1s1wn7lz5xIdHS175ObDLJVKcXV1pW3bthleTVOD6rMYAHFsOG3qVsDExCRXJQUVPjfyFdHJKQ8ePGDUqFFUqVKFixcvFvrIC3lwc3OjdOnSFCtWjBo1amBrayury+Di4sLBgwcpXbo0Y8aMybOC9xmhq6vLuXPnMDQ0ZNy4cUoX6FQqVqzIqlWr2LlzJy9evKBevXqsWbMmy1rJbdu2xdfXl9r6SezuUY4VNr8wy9KIFTa/yEqJvnjxgsePHzNy5Mg0qdjZ0bBhQ8LCwnj16hWXLl3C1taWadOm4ejoyI4dO5g2bRqdO3dm1qxZtG7dGh8fnxwtDG7atEm2npCTz3Fu6nRnhpGREdWqVcv3kLocK9TEiRPx9vbG1dU10300NTXR19dP88gpjx8/5uXLl3Tq1CnD51OD6jNFBBFXHAjw98uxDZmR7bmRv4iOoly9epXx48djYWHB1atXc9UFpjCRWvbRxsaGZs2a8eXLF7p3786BAwcwMTGhWLFizJ8/n5s3b+aZeyMzSpUqxaVLlxCLxXTu3Jn+/fuzY8cOXrx4ofTWRpaWlhw/fpw+ffowd+5cmjZtmi4ZJ5XURIkHDx6gqiKifhktWlXWoX4ZLdlnL/VORN4Zcyo2NjaYmJiwevVqtLS0OHLkCKNHj2bLli38+++/wNfee+fOnePo0aM5KnYfFRUli5fOafGivHIxWlhY4Obmlq+tq3IkzpMmTeLMmTNcu3ZNKY065eH48eMYGhpibm6e6T6ZBdUbaasyw6I4Sf/dx93dPU/sy+rceRVGd/z4cX7//Xe6d+/O2bNnFY4vLcxUrlyZGjVqoK2tzezZs9m6dSv29vZpomuaN29O9+7dmTVrFh8/fsxX+ypVqoSPjw+HDh2ifv36HDx4kH79+tGzZ88s+xZmRnh4OOvXr8fZ2Zlbt24RFBQkmyWnvgfOzs58+fKFpk2bcvDgwXRjmJiYUK1aNY4ePZouHTqVVHFWtPKgmpoaI0aM4OjRowQGBqKqqoqDgwPR0dGEhoZy5coV1q1bh62trYKv/H+oqKhQpUoVzp49y+rVq4mPj1d4jLxyMVpYWBAUFJRp5528QCFxlkqlTJo0iePHj3P16lW5WsooCy0tLcRicbbuiBYVtDO8pWtT3RBDQ0Nu3bqVZzZmdu68EOY7d+6wYMECxowZg4uLS5EMi8sOW1tb3N3ds5ytzJw5ExUVFVm94PykePHiDBw4EBcXlzTx5Zs2bVJ4rH379nHw4EE2bNiAnZ0dXbp0oWnTpnTt2pXp06fz4cMH6tati4uLCzY2NkyfPj3DBfYlS5bg6elJt27dOH36dLr37sKFC6iqquYoqaNbt24YGhqmSeXW19dXmhtNX1+fhw8fsnr1ao4dO0aPHj0U9vPmlYuxSZMmqKio5Gt3FIXEeeLEiRw8eJBDhw6hp6dHcHAwwcHBsgpTeYmlpSUxMTFyLYxkdkunra2dZz7C7M6tTKRSKZs2baJFixbs3LmzwDo15DW2trYEBwdn+T83MDBgzpw5HD16lLt37+ajdWnR0NCgffv2LFu2jCNHjmTZNeR7EhMTOXnyJJMnTyY+Pp7Xr19z+fJlNm3aRI8ePfD392fIkCH4+vqirq7O+PHjCQsL49q1a+nGGjhwIL6+vrRt2xZ7e3tGjhwpe/8CAwM5fPgwVlZWOXJ/aWlpMXjwYPbu3cuFCxcUPl4e1NTUmDlzJs+fP8fMzIypU6cydepUue9G8srFqKenR926dQuvOO/YsYPo6Ghat26NsbGx7JEalpOXmJubo6qqmmEIjzxcvnyZoKAgRo4cqWTL8p+rV6/i4+PD8uXLf4jFv8xo1aoVWlpa3Lt3L8v9UqNT8rLRqrxMmDCB+vXrs2zZMrkbnV6+fJnIyEjGjh2LqqoqFStWxMbGBjs7OzZs2ICHhwcVKlRgxIgR3LlzB39/f4BMszvLly/PP//8w5UrV4iOjub3338HYO3atWhoaMhKmSqKWCLFrGN/6v06kt4T/mCng2OOxpGHSpUqcfbsWbZt28bVq1d5+vSp3MfmlYsxdV0nvxagFXI85acz/Ht0dHRo0qQJu3btQl9fn44dO8otTBKJhMWLF2NiYlLoymEqilgsZuvWrbRr147WrVsXtDl5ipaWFgYGBpn6T1NJ7XmYFwkpiqKmpsbOnTtp0aIF7dq1w9ramvbt22NlZZXp5/Xw4cO0adMm00zbX375hevXr9OvXz8mTpyIiYkJDRs2zLZgV7t27Vi4cKEshtrd3Z0RI0YoXFoXvkvqqN6VUtW7suxpGDdnr+HgqplKjX5KRSQSoaWlhYqKisKVI1tU0MaifDGl1OlOxcLCgr///hsvL698qWRZpKrSHTx4kMaNGzNr1iwGDx4st3N+/fr1REdHM3fu3ELXH09RLl68SGBgIEuXLi1oU/IFkUiU7aQgtcNzYRBn+FoH+NGjR4wdO5YXL14wceJEjh07luG+AQEBPH78WNZ4NjN0dXU5deoUw4YN49WrV3Tp0kUuW1q2bImWlhb29vbo6enJZtGKkFlSh5qeEe6i2nQYNTvPZpNXrlyhTp06aapOyouyXYwNGzZEU1Mz31wbRUqcq1WrxunTp7l27RqBgYGcP38+22OioqJwcXHBysoqR0HthYmUlBS2b99O586dM22G+aOhoqKSrTh/+vQJkUik1HrMucXMzIyVK1fi6+vLwIED2bZtW4ZulyNHjmTaoul71NTU2LVrFydOnMiy0em3VKpUCV9fX6pUqYKpqanCM9wskzpEIkSAn049wsIjFBpXHiQSCVeuXJElgRQ0mpqamJmZ5Vu8c5ES51Ssra1JTk6Wa1Fj/fr1JCUlMWPGjHywLG85c+YMb968+WlmzfC/NkZZ8enTJ0qWLFnoGtOmsnLlSmJiYtizZ0+65zQ1NYmOjqZly5ZyRSaIRCK6d++u0EyyYsWKTJkyhWfPnhEXF6eQ7dkmdYhEqOoZ8TJW+VLi7e1NWFhYoRFn+OrauHXrFsnJyXl+riIpzuHh4aSkpMglzpaWlgB5Ft+cn5w9e5aWLVvSqFGjgjYl35B35lyqVKl8skhxKlasyNSpU9m7dy8hIWkLZE2fPh0nJyeSkpJo164dHTt2xNPTU+k29OrVC5FIxKJFixSqdyFvskZobEJOTcsUNzc32Wy1sGBhYUFcXBz379/P83MVSXFOTTiQx8fYsWNHqlWrxtatWwkPD89r0/KURo0a8eTJk2wXyH4kRCJRtjPniIiIQuNvzgx7e3t0dXUzjIFOTSrZsGED/v7+sqp0ysTExAQXFxcuX77M8uXL5V7clzdZw0hH+Ws5ly9fxszMrFDF8NeuXRs9Pb188TsXSXH+8OEDgNyzpY0bN5KUlJTvab7KpmfPnnz+/DlfQhcLC/LMnCMjI9NVKSxsFC9enJUrV3L69OkMZ10ikQgbGxuOHz9Op06dOHHihNJt6NmzJ3///TeHDx9m69atch0jT82alJgwFkwYnO6uIDdIpVJu3ryJrq5uvrgQ5EVVVZWmTZvmi9+5SIrz69evFVoAevToEWKxuNB/gbOjTJkyWFpaKq0OdVFAXrdGYZ85A4waNQpLS0uWLl0q64X4PWpqahQrVixHtSnktWHNmjU4Ojpy4MCBbPfPvmaNiAGmajx76knDhg05dOhQjsqEph9WxKRJk7h27Rp9+/bl4cOHuR5TWVhYWHDv3r0cpZcrQpEU50OHDmFubi5XWNybN29YuXIltWrVYvDgwflgXd7Sq1cvPDw8lFb4u7Aj74JgURBnFRUVHB0def/+fZYX2PDw8Fy3rMqKWbNmybrInD59Otv9s0vqGNGuIYcPH5Z9x2rXro2zs3OuRXrVqlU8evQIIyMjRo4cyZ9//imLaS9ImjVrRnJyMrdv387T8xQ5cfb19eX27dtyFZS/evUqvXr1kmVFFdbVfEVIjVvdu3dvQZuSL2Q0c/7vv/+YN28emzdv5sKFC0VGnOGrz3L27Nns3r070zj9T58+5dnMOZXVq1czYsQIFixYwJ07d7LdP7u6MUZGRmzevBkXFxeMjY0ZMmQItWvX5v3797mys0GDBty5cwcHBwdu3rxJ9+7dOXr0aL6Wif2eypUrU7p06Tz3Oxc5cd61axeGhoay3oGZsWXLFn7//XdMTEz4999/c1yCsDARHBzMuHHjSE5OzlH7n6KIvr4+9+7dk82YXr58yejRo3n06BGnT59m1qxZSCSSXDdyyE/+/PNPjIyM2LJli2xbdHQ0p0+fZu7cuQQEBOTpzBm+ug0cHR1p164dM2bMwNfXN9tj5EnqqFu3Llu3bsXV1ZWXL19y6tSpXNuqoqLC2LFj8fX1pVu3bixatIh169bletycEhISQrly5fLc71ykxDkxMZF9+/bx66+/Ztl0083NDUdHR6ytrTl06FCedFHOb27evEm/fv0IDQ3l5s2bDBs2rKBNyhccHByIioqS1ZUYM2YMZcqU4cmTJ3z8+JHQ0FA8PDzo2LFjQZsqN7dv3+bjx4+yPoRSqZSRI0dib2/P27dvmTp1KuPGjctzO9TV1Tly5Ag1atTAzs4u17Pcb6lTpw5Vq1ZValhg6dKl2bdvHxs2bGD//v1cunRJaWNnhlQq5e3btxw/fpx58+bRqVMn2rdvz5MnT/L+AqpIg9fcktsGr//++y/9+/fn5MmTsmLsGbFgwQKOHz/Oo0eP8qxzcn4hFovZvHkzTk5OdO7cmf379xeqTLj8IDAwEBsbG968eUOtWrW4fv16kXFjfI9YLKZy5cqUK1cOBwcHVFRU8PDwYMyYMVy+fBkbG5t8tykkJIRmzZpRunRpdu/erbRx582bx8ePH5UeEyyVShkwYADnzp3DxcWFSpUqKXX8VMRiMUuWLOHYsWOIRCIaNGhAq1ataNWqFS1btsxxbL28OliknLCOjo6ULFmShw8f4uHhgZqaGp06dUpXZD71epMXxVjym3PnzuHk5MTq1atltYt/NqpVq8adO3fYuHEjM2bMKLLCDPD8+XPevn3L4sWLZf/Lw4cPU7NmzWxddXnFL7/8wuLFixk+fDjBwcEK+bvFYjH9+/dHQ0ODqlWrUq1aNdnPGjVqcOnSJVJSUpS63iMSidi1axdNmjRhxowZHDx4ME0TBmXx9u1bjh07xrRp01i4cCEGBgZKP0dWFClxDg4OJiIiguXLl6OqqopYLGbHjh3MmjWLTp06IRKJeP/+vcx/dujQIXr37o2OTuat4gs7hw8fpm3btsyePbugTSlQypYty5o1awrajFxz584d1NTUZH03w8PDcXNzY+3atQVa/rVHjx5oaWlx7tw5Ro0aJfdxqqqqGBkZ4e7uTmJiIpcuXZLVEFFTUyMlJYXAwEBq1qypVHv19PQ4evQoFhYWLFu2jGXLlin9/UvVEXt7+3wXZihiPmcvLy8kEglisZikpCTevHmDlZUVs2fPZty4ccyfP59ff/2V0NBQzM3N2bBhA+3bt2f9+vVKDZDPL/z9/Xny5Em2FcsEig7u7u7UqlVLNtM7ceIEampqBb6GoK+vT7du3Th37pzCxy5atAhdXV2aNWtGbGysbCFw6dKlskX5vKBu3bo4ODhw6tQpjh49mul+EokEX19fLly4oFALMX9/f4yNjQusNECRmjl/f2UsX748R48e5ezZs0ycOJFXr16xZs0axo4di46ODu/evWPTpk2ygHtbW1uGDx+ead3cwsbhw4cpU6YM3bt3L2hTBHKJVCpFKpVy+/ZtWrZsCXwVjaNHj9K/f38MDQ0L2EIYNGgQ//77L4GBgVSrVk3u48qUKcOcOXOYP38+vXv3plu3blSuXJmuXbvmobVfGTJkCLdv32blypXUrl2b2rVrA/Du3Tvu3bvHvXv3uH//PpGRkcDXjjXW1tZ07doVKyurTHMlnj59ypUrV/KlbnNmFKkFwaxI/fBn5JONiYlh9+7dbNiwgbdv37J79+4sG8UWBj5//ky7du2YOnUqy5YtK2hzBBQkOjqaBw8ecP/+fTw8PDh16hQdO3bk4sWLrFmzRtYfcfz48dy5c6dQlIBNSkqiTJky9OnTR+HaHqGhoQwePBipVMq7d+/yNacgMTERS0tLgoODadasGR4eHrx79w4VFRWaNm2KjY0NNjY2VKlShWPHjrFv3z48PT0xNDSkY8eOdOvWjbp16yISifDy8mL79u3cvn2b2rVrs2fPHqVrhbw6+MOIszykpKRgYmJC165dlV5YJrdIpVKCg4MJCAjA39+fhw8fcufOHV69ekXFihUL2ryfDolEIvfia2hoKE+ePOHJkyd4enry+PFjAgICgK9F8uvWrZum1daGDRtkF96wsDCePXtWaNqNTZgwgX/++Ydz585l2zElJSWF27dvc+zYMW7evImGhgZDhw5l27Zt+Z7w9fr1a5o1a4ahoSHt27enXbt2tGrVKlNfsbe3NwcOHODAgQN8/PiRSpUqYWxszN27d6lZsyaLFi2ib9++ebIAL4hzJnTu3Jm4uDi2b99eIOf/ng8fPrBo0SK8vb1l1eb09fWpU6cOAwcOLJCu0j87V69epUOHDmhoaKCvry97FC9enOLFi6Ovr4+Ojg6vXr3iyZMnBAcHA1+FuEaNGtSsWZNatWpRv359WZhXu3btGDZsGOvXr6dx48aEhITw7t07HBwcGDt2bAG+2rS8e/eO6tWrM2zYsGw/exs3bmT37t00bNiQsWPHMnDgwAJZOEtFKpUqfJETi8Vcu3aN/fv34+/vz5QpU+jfv3+eNk3+IUPplEGjRo1wdMy7xpSK8OHDB0aNGoW6ujr29vbUq1ePevXqYWJiUmhmUj8jV69eRV9fn7FjxxIXFyd7xMfHExoayuvXr4mLi6NMmTJ07doVU1NTatWqRfny5TOcaXl7exMeHk737t05fPgwHz58oFu3bvTq1Yv27dsXwCvMnPLlyzN16lQ2b95M//79swxbbNy4Mbt372b58uV07tw5H63MmJx8Z1RVVWVuj8LGTzdzPnr0KH369OHatWs5ag+vLFKFWUNDgxs3bhSp9OMfnS5duhATE8OOHTuUMt62bdtwdXUlLCwMiUSCmppaoY5Xj4qKomrVqrRp04ZFixZlup9UKmXUqFEkJibi6elZqF9TYUJeHfzp3s3Urgo+Pj4KHRcYGMjWrVs5cuQIfn5+cre9zwhBmAs3np6eSo3LvXHjBra2tqipqaGhoVHoRczAwID58+dz/PjxLJsoi0Qipk6dipeXF66urvlo4c/BTzdzlkqlVKtWjTJlyrBz584sb4WkUil3795l//79uLu7Y2BgQExMDBKJBC0tLWrWrEndunWpW7cuderUoXz58tkuhAjCXLgJCwujdOnSrFixQimhYCEhIdjY2HDo0CEGDhyoBAvzh8TERExNTalSpQqbN2/Oct8pU6bw+vVrfH19i3y5hPxA8DlngkgkYuPGjXTr1o2rV69mmDIrkUg4deoU+/btIzAwkIYNG3Lw4EH69u1LcnIyT5484f79+zx48AB3d3cOHjwIfC0kU6FCBSpVqpTmUblyZYoXLy4IcxHgy5cvFCtWjEWLFnHr1i369u1LkyZNcrwGcPfuXQA6dOigTDPzHE1NTVauXMmgQYPYs2cP/fv3R1tbO8N9J0+eTO/evfn777+ZOHFiPlv64/LTzZxT6dKlC56enpw4cSJNXv7nz5/5888/uXz5Mr/++iszZ87E2to6yy9nREQET548wc/PD19fX3x9ffHz8+Pt27eyfUqUKIFEIsHQ0FAQ5kJOeHg4e/fuxcHBgcDAQKpUqULv3r0V7noNX5MZhgwZwvHjx+nRo0feGJxHSCQSxo0bx549e9DR0aFPnz4MGjQow45C8+bNw8PDg8DAwGxD8H52hFC6bAgMDKROnTpUr16doUOH0qFDByIiIpgyZQpBQUE4OzvnOjMvPj6egIAA/Pz88PPzIzw8nFmzZgnCrAQiIyPZtWsXFy9epFatWlhYWGBhYUG1atWUFukilUq5fv06Dg4OsspkHTp0oG/fvpiZmcl9nhEjRqCmpsbdu3eLZBROUFAQmzZt4u+//yYhIQFbW1uGDRuGqakpERER+Pr6cuPGDVxcXFi7di0zZswoaJMLNYI4y8G1a9dYvnw5bm5ulCpVColEgra2NmfOnKF+/foFbV6RRyqV8ujRI8LDwylWrBhaWlrpfurq6ipUUczf35+NGzeyb98+UlJSMDc35927d7x+/RoAQ0NDrKyscHFxyfQ2PCeEhobKZtMvX76katWq9OnTB2tr62xDH0+fPo29vT1BQUFF+sIcHR3Nrl272LhxI+/evaNkyZJEREQAXwsRNWzYkD///LPIuXDyG0Gcs2HJkiUEBgZibm5OqVKluHLlCrGxsWzevLlIl6QsDCQnJ3P06FHWrl3Lo0ePstxXVVWVuXPnsmjRokwD/6VSKVevXmXDhg2cPXuWkiVL0r9/f/r27SsLh4yOjsbb25vLly9z9OhR/P39qV69umyMgIAA3NzcGDt2bK6iJSQSCVevXsXBwYETJ06QkpKCvr4+tWvXpkGDBvTt2zfdbf+2bdv4559/CAsLy9Pkhvwi9f/r7e1NgwYNMDMzo0qVKoU+CqWwIIhzFpw9e5Zff/2VqlWrEhQURHJyMhoaGjRs2JAePXrwxx9/FMnbz4iICK5fv87t27eJiooiMTGRhIQEEhMT0/3+7bakpCQkEgnq6uqoqamhpqaGurq67O/vf9fQ0MDExIQJEyakqTsQHR3N33//zaZNm3j37h3NmjVj6NCh1KhRI915k5KSSEhI4Pnz5+zatYuWLVvK+s/FxcURGhpKaGgoFy5c4NixY3h7e2NqasrgwYPp3LkzmpqaGb4HN27cYNKkSXz48AFjY2NCQkJYvHgxf//9NykpKbi5udG2bVulvN+fPn3i/v37PHz4kAcPHnDz5k2+fPnCwIEDGTVqlKyY0dChQ6latSpHjhxRynkFijZCtEYmREZGMmbMGKysrNi2bRtJSUn4+fnh5eWFp6cn9vb2qKqqFon6yTExMdy8eZOrV6/i5ubGs2fPADAxMcHIyEgmqhoaGmhoaFCiRAk0NDRk2zQ1NWW/q6ioIBaLSU5ORiwWk5KSQkpKimxb6u+pP93c3Ni7dy8WFhbY2dnx+PFjdu/eTWJiIra2tmzcuFGu6n9t27alefPmzJkzh6pVqyKVSklMTEyzT8uWLdm1axfm5ubZXjRTawlLpVIWLVrE2rVrUVVVZdKkSezdu5dz584pTZxLlChBp06d6NSpE/D1/7F+/XrWrVvHkSNHGDZsGL169cLLyytf2k4J/Fj8dDPn4cOHc/z4cY4fP57hqvPmzZvZtWsXZ86cKRQpqd8jlUo5duwYa9as4dGjR4jFYoyNjWnatCkWFhaYm5vneedm+FqT4MaNGzg7O3P//n2KFy9Ov379GDBgQI7cQp8+feL48eNoaWlRokQJDA0N2bZtGwEBAWmKBmXHsWPHWLhwIUZGRsTExDB48GBGjx7NiRMnWLt2LUeOHJGrc3tuCAsLY9WqVWzbtg2pVEpSUhKBgYE/RJNhRUlJSUFFRUUpLo+EhARCQkKKfCEwwa2RAadPn6Zbt24sXbo007AmiUTC1KlTefLkCR4eHoWq9vN///3HxIkTuXjxIi1atKB9+/ZYWFhQvnz5AnXDfPjwAQMDA6UuwMXFxWFlZUWLFi3Ytm2b3MedOXMGe3t7unXrxsSJEzE2Nsbd3R07OztmzpzJ6tWrlWZjdrx7944lS5YQFhYmi/b42WjVqhXv379nwYIFDBo0KMMkrdDQUC5duoS/v7/MnRUcHExoaCjwtbnxsWPHWLlyJbGxsURHRxfp91IQ5+8ICQmhYcOGVK9enW3btmX5z42Li2PIkCGIVFTZfvgSn6VqlNbTwrxyiQzbwec1CQkJrFmzhhUrVlCyZEn++OMP2rRpk+925CenTp1i3rx5ODs7KxQ5k5KSQlxcnKw62uvXrxk0aBBWVlacOnXqh1iQKyokJiaiq6tL2bJlCQoKomrVqixYsIABAwbw+PFjzp8/z9mzZ3n8+DFSqZRffvmFkiVLYmhoKGtifOrUKfT09IiPj8fAwABdXV3evHlTwK8sdwji/A2JiYm0bt2a//77D1dXV7luu08+eoPDgwhUdP/X6Vo9OY76Kf40Kq0icyXUq1cvV7Z9/vwZT09PoqOjiYmJITY2lpiYGNkjNjaWa9eu8ebNG4YPH87YsWOVOkMtrNjZ2fH06VPc3d1zPEZcXByDBg1CQ0MDDw8PhRNIBHLH48ePady4MQcOHEBTU5OdO3dy9epV1NXVSU5Opnjx4jRv3pyWLVtiaWmZrhBZdHQ0PXv2pFmzZrI7odatW3Px4sUCekXKQVgQ/H+kUinjx4/n8ePH7NmzRy5hvhP0mb9fgIpuiTTbk9V0eKRmxq2jmwl7chmANm3aMGPGDGxtbRXyq8XGxrJjxw7WrVsnu31LRVdXFx0dHdnPqlWrsm7dunzxWYolUp6HJhL5RYxhMVXqlNYskLuF6OjodF3VFeX27du8evWK58+fC8JcADx58gT4unCqra3NggUL6N+/P/fu3aNJkybUr18fNTU12cL0tyQkJODl5UXHjh25e/cup0+fBmDlypX5/joKih9enDdt2sTevXtZsWKFXLfHYokUx4ef/v+v70Tp/10hFXpM56zjSm5cv8a+ffv49ddfqVGjBtOnT2fo0KFZzmyjoqLYsmULGzduJDY2lm7dutGvXz9KliwpS8goqHjRO0GfcXz4ifDPYtk2I21VxjYpQYsK+TtbL1euXJa3r/JcRFIrB1auXDlPbRXImNTEoC5duqR7bs+ePbLfS5QowZUrV3jz5g3u7u7cuXOHhw8fkpSURLly5ejYsSMrV66kXbt2lChRIt1YPyo/tDhfvHiRGTNmMHLkSLkrjD0PTUwjThkR/llMQJSUTp060bFjRzw9Pdm/fz8TJkxg3rx52NnZMWHCBIyNjWXHREREsHHjRjZv3kxCQgK9e/dm1KhR+RJZIQ93gj6z4mZYuu3hn8WsuBmGvXWpfBVoY2NjEhISMnxO3otIqseuKC8eFWVmzZqFpaUlEokEiUSCVCpN81MikeDl5cWSJUvo0KED4eHhaGlpYWVlxcqVK+nUqRO1atX6af9/P6w4f/78mf79+6OiooKOjg43b96kZs2alCpVKst/duSXrIX5+/1EIhFmZmaYmZnx9u1bnJ2dWbduHUuXLqVy5co0bNiQmJgY7t27h0QioV+/fgwfPrzA2q1nRNq7hYxxfPgJi/LF8s3FYWxsTFJSEklJSWnKUCpyERHEuWDR19eXxYBnRtu2bbl79y5169alY8eOWFtbK5TO/yPzw4qzuro6kyZN4tatWxw4cIDo6Gjg6y1Uap83U1NTTE1NqVy5sizEx7CYfKv5Ge1nYmLCH3/8gZ2dHTdu3MDX1xcfHx8ePnxI/fr12bJlS6G8LZP3buF5aCL1y2jli01ly5ZFKpUSEBBAnTp1AMUvIqkRG8uWLWPJkiWCSBdCSpQowaVLlwrajELJDy3Oy5YtA77OoN68eYOnpydPnz7F09OTa9eusXfvXgA0NDSoXr06pqamVKteA3215sSkZC7SRtpffZyZoa+vT9euXWWulN69exMaGloohRkUv1vID1LdPT4+PjJxVvQiYmVlxdSpU1m2bBmBgYHs2bMHLa38ubgICOSWH1acv0UkEskK33+bfBIVFcWzZ8/w9PSUPc6cOYNqpcaU6mEvO/Z7xjZRLN7Z1taWLVu2EBkZKau3UJjIzd1CXpHqrw8MDJRty4nLacyYMVSoUIG5c+diamqaZU88AYHCxE9dRsrAwABra2umTJmCk5MTjx8/Ji4uDo8jDgysEIc2aWs8pMSEEXdhA7sWTWL16tWcOHECX19fkpOTszxPhw4dkEgk7N69Oy9fTo6pU1oTI+2shTe7uwVlo6enR7FixQgKCpJty+lFpEOHDjIXh4BAUeGnmDkrgrq6OvXq1WNVvXosl0i5/+oToTEJiBJjEYXDs3Jt8PT05O7du2naU1WpUkXmw059pApChQoVqF69OpcuXWLmzJkF+OoyRlVFxNgmJTJcaEtF0bsFZaCjoyNbK4D/XUSycm1kdBFJSkoiNDSUChUq5JmtAgLKRhDnLFBVEdG8aslvttSk66//i9mMjY2VVbN7+vQpT5484dKlS7IQMGNjY2rUqIGpqSlqamp8/PiRkJCQDAsuFTQtKmhjb12q0MQ5BwcHEx4eniZGNqcXkZCQEABBnAWKFII45wI9PT1atGhBixYtZNtSUlIICAiQLTw+efKEEydOyLIAbWxs0NHRoWTJkpQsWZISJUrIfs/obx0dnXyLMmhRQRuL8sUKRYZgaiW6/v37p7NR0YtIcHAwIIizQNFCEGclo6amRq1atahVqxYDBgyQbf/48SMXLlxATU2N0NBQQkJCCAkJITg4GH9/f0JCQggLC5NltaWiqamJkZFRGtH+XsBTH/r6+rkWclUVUb6Fy2XF/fv30dHRybCtk6IXkY8fPwIU6RZRAj8fgjjnE8bGxowcOTLLfSQSCZGRkTLhzujx5s0b7t+/T2hoaLqi9Orq6jLhzm5GbmBgUGgrtEmlUtzd3alUqVKm+yhyEfn48SNGRkZCcoNAkUIQ50KEioqKTDxr166d5b5SqZSYmJhMRTy1Ju6zZ88IDQ0lPj4+3bkyE/LvtxkaGqYrTJOXvHr1ik+fPjFo0CCljPfx40fBpSFQ5BDEuYgiEokoXrw4xYsXp0aNGtnuHx8fn6mIp7pX/Pz8CA0NTRMhkUpqjd3shLxkyZJp0q1zgoeHByKRiD59+uRqHIA7d+5w/vx5pQm9gEB+IYjzT4KOjg5VqlShSpUq2e6bkJCQxi/+rYinCvmjR48IDQ0lIiIi3fF6enoZCndGwv59Bb+kpCTOnDmDgYGBrOB6Trlw4QL29vbY2NiwadOmXI0lIJDf/BTF9gXyjuTkZMLCwjIU8VQhT/09PDwciUSS5nhtbe004h0WFsbz58+pVasWjRs3RlVVVdaDTkVFRa6/RSIRwcHB7Nq1i8GDB+Pk5JSvbhkBgawQiu0L5Avq6uqULVuWsmXLZruvWCwmIiIiQxFPFfIPHz5gYmKCRCKRVfITi8WIxeJ0v2f2nFgsRiQS8fvvv/PXX38VWH1sAYHcIIizQL6hqqpK6dKlc9SdW1GkUqlQhU6gSCNMKQR+SARhFijqCOIsICAgUAgRxFlAQECgECKIs4CAgEAhRBBnAQEBgUKIIM4CAgIChRBBnAUEBAQKIYI4CwgICBRCBHEWEBAQKIQI4iwgICBQCBHEWUBAQKAQIoizgICAQCFEEGcBAQGBQoggzgICAgKFkHwtGZpa1z8mJiY/TysgICBQaEjVv+z6nOSrOMfGxgJCi3oBAQGB2NhYihcvnunz+dqmSiKR8OHDB/T09IpMvd2YmBhMTEx4+/at0ForhwjvYe4R3sPcU1jeQ6lUSmxsLGXLls2yS0++zpxVVFQoX758fp5Saejr6wtfilwivIe5R3gPc09heA+zmjGnIiwICggICBRCBHEWEBAQKIQI4pwNmpqaLFy4EE1NzYI2pcgivIe5R3gPc09Rew/zdUFQQEBAQEA+hJmzgICAQCFEEGcBAQGBQoggzgICAgKFEEGcBQQEBAohgjhnwo4dO6hfv74sYL158+acP3++oM0qsqxatQqRSMS0adMK2pQixaJFixCJRGkeNWvWLGizihzv379nyJAhlCxZkmLFilGvXj0ePnxY0GZlSb5mCBYlypcvz6pVq6hevTpSqZR9+/bRvXt3njx5Qp06dQravCLFgwcPcHBwoH79+gVtSpGkTp06XLlyRfa3mprwtVWEyMhILC0tadOmDefPn6dUqVIEBARgaGhY0KZlifBfzoSuXbum+Xv58uXs2LGDe/fuCeKsAHFxcQwePJi///6bZcuWFbQ5RRI1NTXKlClT0GYUWVavXo2JiQl79uyRbatcuXIBWiQfgltDDsRiMa6ursTHx9O8efOCNqdIMXHiRLp06YKNjU1Bm1JkCQgIoGzZslSpUoXBgwcTFBRU0CYVKU6dOkWTJk3o27cvpUuXxszMjL///rugzcoWYeacBV5eXjRv3pyEhAR0dXU5fvw4tWvXLmizigyurq48fvyYBw8eFLQpRRYLCwv27t2LqakpHz9+ZPHixVhZWeHt7Y2enl5Bm1ckePnyJTt27GD69OnY29vz4MEDpkyZgoaGBsOHDy9o8zJFyBDMgqSkJIKCgoiOjubIkSPs2rWLGzduCAItB2/fvqVJkyZcvnxZ5mtu3bo1DRs2ZOPGjQVrXBEmKiqKihUrsn79ekaPHl3Q5hQJNDQ0aNKkCXfu3JFtmzJlCg8ePODu3bsFaFnWCG6NLNDQ0KBatWo0btyYlStX0qBBAzZt2lTQZhUJHj16RGhoKI0aNUJNTQ01NTVu3LjB5s2bUVNTQywWF7SJRRIDAwNq1KhBYGBgQZtSZDA2Nk43oapVq1ahdw8Jbg0FkEgkJCYmFrQZRYJ27drh5eWVZtvIkSOpWbMmc+bMQVVVtYAsK9rExcXx33//MXTo0II2pchgaWmJn59fmm3+/v5UrFixgCySD0GcM2Hu3LnY2tpSoUIFYmNjOXToENevX+fixYsFbVqRQE9Pj7p166bZpqOjQ8mSJdNtF8icmTNn0rVrVypWrMiHDx9YuHAhqqqqDBw4sKBNKzL8/vvvtGjRghUrVtCvXz/u37+Po6Mjjo6OBW1algjinAmhoaEMGzaMjx8/Urx4cerXr8/Fixdp3759QZsm8BPx7t07Bg4cSEREBKVKlaJly5bcu3ePUqVKFbRpRYamTZty/Phx5s6dy5IlS6hcuTIbN25k8ODBBW1alggLggICAgKFEGFBUEBAQKAQIoizgICAQCFEEGcBAQGBQoggzgICAgKFEEGcBQQEBAohgjgLCAgIFEIEcRYQEBAohAjiLCAgIFAIEcRZQEBA4Btu3rxJ165dKVu2LCKRiBMnTig8hlQqZe3atdSoUQNNTU3KlSvH8uXLFRpDSN8WEBAQ+Ib4+HgaNGjAqFGj6NWrV47GmDp1KpcuXWLt2rXUq1ePT58+8enTJ4XGENK3BQQEBDJBJBJx/PhxevToIduWmJjIvHnzcHFxISoqirp167J69Wpat24NwIsXL6hfvz7e3t6Ymprm+NyCW0NAQEBAASZNmsTdu3dxdXXl2bNn9O3bl06dOhEQEADA6dOnqVKlCmfOnKFy5cpUqlSJMWPGKDxzFsRZQEBAQE6CgoLYs2cPhw8fxsrKiqpVqzJz5kxatmwpayD78uVL3rx5w+HDh9m/fz979+7l0aNH9OnTR6FzCT5nAQEBATnx8vJCLBZTo0aNNNsTExMpWbIk8L+mHPv375ftt3v3bho3boyfn5/crg5BnAUEBATkJC4uDlVVVR49epSum4+uri7wtS2WmppaGgGvVasW8HXmLYizgICAgJIxMzNDLBYTGhqKlZVVhvtYWlqSkpLCf//9R9WqVYGvbbEAhVpjCdEaAgICAt8QFxcna6BrZmbG+vXradOmDSVKlKBChQoMGTIEd3d31q1bh5mZGWFhYbi5uVG/fn26dOmCRCKhadOm6OrqsnHjRiQSCRMnTkRfX59Lly7Jb4hUQEBAQEDGtWvXpEC6x/Dhw6VSqVSalJQkXbBggbRSpUpSdXV1qbGxsbRnz57SZ8+eycZ4//69tFevXlJdXV3pL7/8Ih0xYoQ0IiJCITuEmbOAgIBAIUQIpRMQEBAohAjiLCAgIFAIEcRZQEBAoBAiiLOAgIBAIUQQZwEBAYFCiCDOAgICAoUQQZwFBAQECiGCOAsICAgUQgRxFhAQECiECOIsICAgUAgRxFlAQECgEPJ/I07glgtx89AAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = eur2.plot(facecolor=\"lightgray\", edgecolor=\"k\")\n",
+    "eur2.centroid.plot(ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0634941f",
+   "metadata": {},
+   "source": [
+    "#### How much error does lat/long computation introduce?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "c0b72aff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_13458/2170279601.py:3: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
+      "\n",
+      "  eur.centroid.to_crs(\"EPSG:3035\").plot(ax=ax, color=\"r\")  # red => miscalculated\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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4+Hh69epF4cKFmTp1arLjqhJngMKFC2Nra8unT58USu9WlMRiSCm5WIyNjalataoQtZHLUVqc/f39MTQ0pEyZMvTq1Yu3b9+mOjYmJobw8PAkPwICuZ2FCxfi4+PD33//LRfgefPmUbduXWrVqiVPQilQoIBK1mvbti0A9+7dU8l88H9xrlWrVorHbWxsOH78ON++fVPZmgKqRSlxrlu3Ltu3b+fkyZOsX7+eV69eYWlpmeo/8KJFiyhYsKD8R9GUVAGBnOL69evMnz+fwYMHJ2k3deLECYyNjRk8eDBz5sxh586dKsuyq1y5MpqamioV57dv36KpqZksESURa2troqOjOXbsmMrWFFAtSolz69at6dq1K9WrV5d/84aGhqZ6ezR16lTCwsLkP+nVnRUQyEm+fftGz549qVKlSpIswKCgICIjI/nrr78YOnQonTt3znSfwJ8pWrQovr6+Kp0zPj4+1RrOhoaGVK9eXeiQkovJVJxzoUKFqFChAs+fP0/xeJ48edDR0UnyIyCQW/nnn394//49ixYtQl39/4FMhw4dQiaTYWFhkWVrV6lShSdPnhATE6OS+RwcHChTpgxTpkzh5cuXKY6xsbHh5MmTgrsxl5IpcY6IiODFixcYGBioyh4BgRwhLi6OtWvX0qZNG0qWLJnk2KVLlyhcuDClS5fOsvWbN29OfHw8T548Ucl8+fLlY926deTNm5fevXunWOzf2tqa2NhYjhw5opI1BVSLUuI8YcIELl68yOvXr7l69SodO3ZETU2NHj16ZJV9AgLZwuHDh3n//j29evVKduzly5fUrVs3xUp0qqJFixaIxWLu3r2rsjkNDAxYu3Yt379/p1u3bsnSxfX19TEzMxNcG7kUpcT53bt39OjRg4oVK/LXX39RtGhRrl+/rpJ28AICOcnq1asxNzfH1NQ0yevBwcFERkZmqUsDEna62traKhVnSGhlNW/ePN6+fcvIkSOTHW/VqhWnT59OsR6IQM6ilDi7ubnx4cMHYmJiePfuHW5ubpQtWzarbBMQyBZ8fX25cuUKPXv2THbMw8MDmUyGubl5lttRunRpfH19023sqiy2trY4ODhw6dIl1qxZk+RYy5YtiY+P5/DhwypdUyDzCIWPBP54nJ2dMTQ0pFmzZsmOnT9/niJFimBiYpLldtSrV4+vX7/y/v17lc89cuRImjVrxpYtW5JEcOjp6VGrVi3BtZELEcRZ4I8mKCiIvXv30q1btyQRGom8fPmSRo0aZam/OZHEZBRVuzYgoRCSk5MTZcuWZcqUKUkirGxsbPD29pbXDBHIHQjiLPBHs2nTJsRiMZ07d0527NWrV0RFRVGvXr1sscXExAQtLa0sEWdI8Gv/888/5MuXjwEDBshft7KyQiKRCK6NXIYgzgJ/LLGxsfzzzz+0a9eOggULJjuemFyVWMc5O9DV1eXKlStpNmvNDAYGBvTv3z9JbHOxYsUwNzfHzc0tS9YUyBiCOAv8sbi7u/Px48cUHwQCXL58mVKlSlG8ePFss2nIkCG8e/eOvXv3ZtkaYrE4mZumVatWnDt3juDg4CxbV0A5BHEW+GNZvXo1devWpXz58ikeDwwMpFGjRtlqk52dHeXLl8fZ2ZmPHz9myRoSiSTZay1atEAmk3Ho0KEsWVNAeQRxFvgjuXnzJjdu3Eh113zz5k1iYmKyzd/8IytWrJB3X3n27JnK508pVK9o0aLUqVNHcG3kIgRxFvgjcXZ2pkSJEjRp0iTF4+7u7ojF4myJb/4RHx8fHB0dEYlEvH//ni5dujBx4sRU62NkBKlUmmL0ibW1NRcuXFBpXWmBjCOIs8AfR2BgIPv376d79+6oqamlOOb27dtUrVpVZTWb0yM8PJw5c+YwYMAASpQowYMHD3j//j2bNm3i0aNHdOjQgSlTpvD69etMr5Va1++WLVsiEonw8PDI9BoCmUcQZ4E/jo0bN6KhoUHHjh1TPB4VFUVISAj169fPFnu8vb3p2LEjp0+fZt26dVy6dAlTU1M0NDRwcHDA39+ff/75h7t372JnZ4ezs3Om1pPJZCnunAsXLkzdunUF10YuQRBngT+KmJgYNmzYgK2tbaolbNetW0d8fDytWrXKcntOnz7N2LFjqVu3Lo8fP2bYsGGIxUn/LPPkycOwYcN4/vw5o0ePZtu2bYSEhGR4TalUikwmSzFcz8bGhkuXLmXZw0gBxRHEWeCPYv/+/Xz69CnVB4EAnp6eVKlSJcXmqKrG3d2dBg0acOTIEUqUKJHmWC0tLaZPn45IJOLkyZMZXrNmzZrEx8fTsWPHZALdokUL1NTUcHd3z/D8AqpBEGeBPwaZTMaqVato0KABZcqUSXHMo0ePCAkJoUuXLlluT1BQENevX6dfv34Kp4cXLVqU1q1b4+XlleF1GzRowMKFC3nz5g2dOnVKItAFCxakfv36gmsjFyCIs8Afg6enJ76+vtjb26c6ZtWqVWhqamaLS+P48eNoaGjQtWtXpc6rWrUqDx48IDo6OuEFiYT8Pj4UPH6c/D4+kEIc88/Y2tqycOFCXr9+nUygra2tuXLlSpYUYBJQHEGcBf4IJBIJU6dOpU6dOqk+6IuPj+fOnTu0atUqW6I0PD09ad++fapNWFPixIkTLFmyhJ49e6KlpYWOtzcVbGwwGTAA48mTMRkwgAo2Nuh4e6c7l62tLQsWLEgm0M2bN0ddXV1wbeQwgjgL/BHs3buXR48eMWrUqFRdCHv37iUmJoZOnTpluT1Pnz7l6dOn9OnTR+Fznjx5wl9//YWlpSWTJk1Cx9sb43Hj0Pj0Kck4jaAgjMeNU0ig27dvz/z583n9+jWdO3cmPj4eHR0dGjRoILg2chhBnAV+e2JjY5kxYwbNmzenRo0aqY7bvXs3JUqUoFatWllu07FjxyhWrJjC7hOZTMbQoUMpWrQoixcvRg3Qd3ICmYyfv2pEMhnIZOgvXqyQi8POzo7Zs2fz8uVLuSDb2Nhw7do1AgIClLwyAVUhiLPAb8+mTZsICAhIsU1TIu/fvycwMJDOnTtnS+3m+/fv06hRIzQ0NBQa7+bmxqVLl5g2bRr58uUjv68vmp8+JRPmRESA5seP5Pf1VWj+li1bAshD6Jo1a4ampiYHDx5U6HwB1SOIs8BvTUREBPPnz8fW1jbN0LjVq1cDCbvI7MDMzIyrV6+mmq33I9++fWPcuHG0bNmSBg0aACD+yZWRGoqOy5s3L5CQgANQoEABGjZsKHRIyUEEcRb4rVm1ahWhoaEMHz48zXGXLl2iUaNG2dasuHHjxgQFBXHr1q10x86ZM4fQ0FAmTZokf+3x168KraPoOA0NDcRiMZGRkfLXbGxsuHHjBm/evFFoDgHVIoizwG9LcHAwS5cu5a+//sLQ0DDVcadOneJ7ZCQjq1VTKhwtM5iZmaGjo5NuvPKjR49wdnZm8ODBGBgYyF9/WLgwAUBq+24p8Pa/cYogEonQ1NRMIs5NmzYlT548HDhwQKE5BFSLIM4Cvy2LFi1CIpEwaNCgNMe9XrGCtyIRXf75R+lwtIyirq5OgwYN8PT0THWMTCZjxIgRGBkZJYvNLqanx+j//v9ngU78fcx/4xRFS0uL79+/y3/Pnz8/lpaWgmsjhxDEWeC35N27d/zzzz/Y29tTpEiRVMfpeHuz+sMHDH+qcaxMOFpGMTMzw9fXl4iIiBSP79u3j4sXLzJlyhQ0NTWTHKtVqxZX9fToCvycKvIO6Apc09dXKvKkWLFi3LlzBycnJ3nMs42NDbdu3VJpyVIBxRDEWeC3ZM6cOeTLl4++ffumPkgiSQhHg5TD0UDhcDRliYmJYe/evTRs2JD8+fMnO/7t2zfGjx9PixYtUuzGoqamxpQpUzgkEmECNAV6/PffMsAhkYjJkyenWhI1JZYvX07dunXZvXs3DRs2ZOvWrTRu3BgtLS3BtZEDCOIs8Nvh5+eHi4sLgwYNSiJ8EokEHx8fjh8/jo+PD1o+Pmh++pTqH4FIJlMqHE0ZNm/ezIcPH9i0aVOKoXtz587ly5cvSR4C/kzLli1ZsWIFxfT0uAi4ARcBXX19VqxYIQ+PU5QyZcqwfv16XFxcKFOmDKtWrcLGxgYdHR3BtZEDqOe0AQICqiQuLg5HR0f09fX566+/5K97e3vj5OTEpx9Cy4bo6LBBgTnVP39WqY0vX75k27ZtTJ48mcqVKyc7/vjxY1avXs2wYcPSfJAJCQLdrFkzfH19+fz5M7q6utSqVUupHfPPmJubs2fPHs6dO8fKlSt58+YNnz9/Ztu2bQwYMCDD8wooh0iWUkOxLCI8PJyCBQsSFhaWai1dAYGMIpPJGDJkCC4uLqxfv17e/8/b25tx48Yl653XFDivwLyvtm0j0sJCJTZKpVIGDBhAaGgoDx8+REtLK9k1tGjRghcvXnDo0KFkvubsJj4+nqNHj+Ls7MyXL1+4du0adevWzVGbfnUU1UHBrSHw27Bs2TI2b97MrFmz5MIskUhwcnJKsanpJUgzHE0mEhGrr0+kCtO5Dx8+zO3bt9m4cWMyYQb4999/OX/+PBMnTsxxYYaEqJJOnTqxadMmZDIZ9+7dy2mT/hgEcRb4LTh48CCTJk1i0KBBSdpP+fr6JnFl/IgUUg1Hk/3nB/44eTJkwkXwIyEhIaxYsYK+ffvSokWLFMesW7eO0qVL07hxY5WsqSoS/eJCrY3sQxBngV+e69ev06dPH1q3bo2jo2OSY5/T8RcfAroAn34S4Dg9PQJWrCBcyYdqabF06VLU1dVZtmxZiscDAwNxd3enW7duyVpV5TSJdx5nz57NYUv+HIQHggK/NC9fvqR9+/ZUqlSJ+fPnJxM1RdKxDwHtN26kiViM+ufPxOvqJrgyVLRjBrh48SJeXl64uLikatPmzZvR0NCgffv2KltXVSSK87Vr13LYkj8HQZwFflm+fv1KmzZtyJs3L6tXryZPnjzJxtSqVQs9PT2CgoJS9DuLRCL09PSoaW5OpArF+EcCAgKYNm0abdu2TbULS1xcHBs2bKBt27a58mH5t2/f5P//+PHjFKNMBFRL7rp3EhBQkNjYWDp27MinT59Yt24dhVOpIZGYrAEkiydO/F3ZZA1l+P79O2PHjkVXV5ddu3alWo706NGjBAYG0r179yyxI7OcPXsWdXV18ubNy/79+3PanD8CQZwFfjlkMhmDBg3i2rVrrF69mlKlSqU5PjFZo3jx4kle19PTy1CyhjJ2zps3j4CAAA4dOpRmO6pdu3ZRo0YNKlasmCW2ZAaJRMLx48cpW7YsVlZW7Nu3L8W7EAHVIrg1BH4pIiMjmThxIjt37mTx4sUK145o2bIlDRs2pF69ejRr1oxevXplPFlDIiG/r2+6/um9e/fi6enJnj17qFatWppT3rp1CysrK+VtyQZu3brFly9fGDVqFLq6uhw9epSHDx+me00CmUMQZ4FfhlOnTjFkyBA+fvzIjBkzaNOmjVLnnzx5EqlUir29PTVr1syQDTre3ug7OaH5Q3herJ4eH6dMSRLZcefOHZYuXcqoUaPo0aNHmnOGhITw7t07KlWqlCGbspoTJ06QJ08eOnbsiEQiQUdHh/379wvinMUIbg2BXM/nz5/p3bs3rVq1wsDAAA8PD7p166b0PF5eXuTNm5eqVatmyA5FG6oGBwczYcIE6tWrl2rY3I/cvXsXIFe6NOLi4jh58iSVKlVCLBajoaFB8+bNBddGNiCIs0CuRSaTsWPHDkxNTfHy8mLBggVs2rSJkiVLZmg+Pz8/6tatq3DfviQkVrBLraEqCRXs4qKjmTBhAmpqahw4cEChte7cuUPevHnT9Z3nBJcuXSIyMjJJlImNjQ3+/v7cv38/By37/RHEWSBX8uLFC6ysrOjXrx/169fn8OHD2NnZZar5qlQqTTWqIz3Sbaj6XwW741Oncu/ePQ4ePIi+vr5Cc9+7d4/y5ctnWcRIZti7dy8FChRI8tC0bt26FCpUSKhUl8UI4iyQq4iLi2PJkiVUq1YNPz8/1q9fj5OTE0WLFs303GpqaknaMCmDopXpHnp7s3TpUnkjVkUwMDDg9evX8uaquYVXr15x48YNWrduneR1DQ0NWrRoIbg2shhBnAVyDbdu3cLc3JypU6fStWtXPDw8Uiw0n1E0NDQyLM433r5VaFwg0KVLF6XmdnR0JDIyksOHDytvWBayf+9emovFLKxaNVlfRWtra16+fMmdO3dy0MLfG0GcBXIFs2bNom7dusTFxbFnzx4mTpxIvnz5VLqGlpYW4eHhSp8nkUgYdeBAug1VQ7W1ua6unqQRqyKULFmSLl26sHv3biRZ3FhWUfJ4ebFo717OSqWUnz07WV/FOnXqULhwYcG1kYUI4iyQ42zfvp358+czdOhQ9uzZQ5UqVbJkHS0trQztnH19fQkMCkq3oeqasmUpUapUhnzH48aN4+3bt1y8eFHpc1WNjrc35aZM4ecy/z9Gpairq9OyZUvBtZGFCOIskKPcvXuXYcOG0alTJ4YNG4a6etaF3ufLly9D4pxY2S6xgl1KDVW7APvj4yldunSGbKtTpw4NGjTA1dU1Q+erjP+iUmQkF4ef+yra2Njw5s0bbt26le1m/gkI4iyQY3z9+pVOnTpRpkwZpk2bluXr5c+fP0MP3X6sIncIKE3Shqom/70eERFBmTJlMmzf+PHjuXXrFo8ePcrwHJklMSpFkb6KtWvXpmjRooJrI4sQxFkgR5BKpfTt25eQkBCWL1+eYkU5VVOgQAG+f/+u9HmJle0Sw/ikkKShqkwkQl9fn69fv2JiYpJh++zs7ChdujQ7d+7M8ByZRdGoFPXPnwXXRhYjiLNAjuDk5ISnpyeLFi2iRIkS2bKmjo4O8fHxxMXFKXWeIpXtRo8eTVhYWIbdGonrjB49mlOnTiUp0ZmdfFbQrRT/392EjY0N796948aNG1lp1h+JIM4C2c6XL1+YOXMmDg4O2dqOKbFOckb8zulVtitfvjxApnbOADVr1kQikfDly5dMzZNRtj57RgCQ2j5YBkn6KtaqVQtdXV3BtZEFCIWPBLKdb9++IZVKsVBRR2tFSRTniIiINMt3pkbLli1p1qwZvr6+fP78GV1dXXllu3PnzgFkaucMyO3KiZ1zXFwcbgcO8L1gQbaEhSV7KJgYlXKqdWtM/otIUVNTo2XLluzfv5/ly5fnuvZavzLCOymQ7UilCX/m2f2HXKxYMSChV19GUVNTw8LCgjZt2mBhYSEPm/vw4QN58+ZFT08vUzYmppdnJB47s5w9e5YvX75wSCRKNSqlKzD4xIkk8ditWrXiw4cPQgsrFSOIs0C2k/jwKDN1MjKClZUVefPmZdGiRUr7ndPj/fv3lCpVKtPXlLhzzm5xDgsLY9OmTWhqahIaGppqVIoH8PHjR3x9feXnmpmZUbx4cdzc3LLV5t8dQZwFsp2cEud8+fIxY8YM/P392b59u0rnfv/+faZdGpAQUSIWi7PFrREZGYmnpycjRoygadOm+Pv7ExsbKz/+c1TKj8k3P3Y1F4vFWFtbc/DgwVyT4fg7IIizQLaT6NbIbnEGaN++PdWqVWPdunW8fPlSZfN+//6dkJAQ+bVlFLFYTMGCBbNMnGNiYvD29mb8+PE0bdqUqVOnEhcXx8qVK3n27Bn9+vVTaJ6fO4jb2Njw8eNHrly5kgVW/5kI4iyQ7eTUzjmRdevWoaamxowZM1QWnzto0CB8fHzYsmVLpufKCnGOiYlhxYoVNG3alLFjxxIUFMTcuXN5/fo1V69exdHRkfLly7NlyxYMDX9O3P4/ov9iun9uD1a9enUMDAyEqA0VIoizQLaTKIg59WS/UKFC2Nra8uDBA8LCwlQyZ506dejYsSMTJ07kw4cPmbZPlT5nf39/evbsyZ49exg9ejR+fn7cuXOHSZMmJSvwr6amxpo1axCJREp1KxeLxVhZWQmuDRUiiLNAtpOTbo1EIiMjEYvF8vA6VTB+/Hg0NTVxdHTM1DwlSpTg9OnTbNy4MVNfHjKZjN27d9O9e3c0NDTw8fFhwYIF6bbD6tSpEwcPHkzmukivW3mrVq0ICgri0qVLGbZZ4P8I4iyQ7eS0WwMSIg6KFCmi0t17wYIFmTJlCocOHcLDwyPD82zYsIEePXqwZcsWrK2tWbx4sdLhf8HBwQwfPhwnJyeGDBmCj4+PUg1ZO3ToQMmSJTE2NmbRokVs27aNkydPpirMAFWrVsXIyEhwbagIQZwFsp3csHP+8uVLpmOSU8La2pqmTZsyYsQIQkNDMzSHkZER69at482bN4wfPx4vLy9at27NoUOHFDr/woULdOrUCX9/f44fP46zszN58+ZVyoadO3dy69Yt5s+fT7t27ZLEdKeGSCTC2toaDw8P4uPjlVpPIDmCOAtkO7lh5xweHq5wjz9lEIlETJ8+nYiICNq0aZOpNOzixYszb9483r59S9++fZk/f768U3dKvHjxgsmTJzNy5EgaNGjA/fv3k7WYUoSwsDAmT55M69atqV27tlLn2tjY8PnzZy5cuKD0ugJJEcRZINvJ6QeCkBC9kJgxqGr09fXZvHkzfn5+WFpa8u7du0zNV6BAATZs2ICFhQXjxo1LEmMM8PjxY8aOHUvHjh25d+8emzdv5tixY8nqgChCTEwMDg4OhIeHM378eKXPr1y5MsbGxoJrQwUI4iyQ7eQGt0ZsbGyGxEtRqlatyvbt2wkNDaVBgwY8ffo0U/Npamri7u6OhoYG48aNIy4uDl9fX4YNG0a3bt14+fIlmzZt4uXLlzg4OKT43n758oW7d+/y6tUrvnz5Inc9hIeH4+bmRvfu3dHV1cXb25uYmBgKFiyotJ0/ujZUnYX5pyGIs0C2k9NujYiICOLj45NFI6iaMmXKsGPHDjQ1NWnUqBE+Pj6Zmk9fX5+DBw/y6NEj2rRpg729PV+/fmXPnj08ffoUBwcHNDU1k5335s0bRo4cSYkSJahZsyZlypShaNGiaGhoUKBAAYoVK0aPHj148OABffv2ZejQochkMubPn58hO1u1asWXL1/kxaAEMoZQlU4g28npnbO/vz9Alu6cE9HX12f79u04OjrSrFkznj17lmaSR3rUr1+frVu34uLiwoYNG7C1tU3VPfTo0SOWLFnCnj17KFCgAP3796dRo0ZERkYSERHBt2/f+PbtG3ny5MHS0lJu1+vXrwEy7PapWLEipUqVYt++fdjY2GRoDgFBnAVygJzeOSembWf1zjmRQoUKMWDAAEaPHp2hWtI/06dPH/r06ZPq8Q8fPuDo6MihQ4fQ19dn3LhxdO7cWeFu5mfPnkUsFiucyv0zia6NAwcOsGHDhhR38wLpI7g1BLKdnBbnx48fA9knzgDXrl2jdOnSlCtXLkvX2b9/P1WrVuXy5cvMmzeP48eP06dPH4WFGcDb25tixYrJy5dmBBsbG0JDQ/H29s7wHH86gjgLZDs56daIjo7m2LFjmJmZZajgfka5du0aNjY2WXbNoaGh9O7dm27dumFhYYGHhwcdO3ZEQ0MjyThJbCwBrq68+vtvAlxdkfxQhQ4gKCiIhw8f0qRJk0zZU6FCBcqUKcP+/fszNc+fjODWEMh2cjKUbsaMGURHRzNlypRs+3IICAjgzZs3tGrVKkvmP3v2LPb29oSHh7No0SLatm2b4rW9WrGC2jt2UOOHynkfli3jtr09JuPGAXD+/HlEIhH9+/fPlE2Jro29e/cSExOTLQ18fzeEnbNAtpNTO+eAgADOnj1Lhw4dqFKlSrate/XqVdTV1WnevLnK53716hVWVlYYGBjg7u5Ou3btUhXmti4u6P9U0lRfKqWtiwuvVqwA4Pbt22hpaWFsbJxp22xsbAgLC+P06dOZnutPRBBngWxH/b8Oz9md4jtmzBjU1dUZNWpUtq575coV6tWrp9IiS4kYGBiQN29eGjdujIGBQYpjJLGx1N6xA0j+B5/4e60dO5DExlK/fn2+f//Ov//+m2nbypUrR7ly5QTXRgYRxFkg20lMmw4ODs62Nc+dO4e/vz8jRozIsszAlIiLi+PmzZtZ5tLQ0tKiadOmaRa5/7BvH4ZSaap/7GLASCrlw759tG3blkKFCrFs2TKV2Gdtbc2RI0eIjo5WyXx/EpkSZycnJ0QiEWPGjFGROQLpERsby8iRI1mxYgVfv37NaXMyRKI4BwUFqXzud+/eMXToUJ49e5bk9Xnz5mFkZESvXr1UvmZa3L17l8jIyCyN923dujW+vr5ERUWleDw+IECheeIDAtDU1KR37968fv1aJZ1ibGxs+PbtGydPnsz0XH8aGRZnHx8fNm7cSPXq1VVpj0AayGQyRowYwYYNG5gyZQpGRkYMGDCAc+fO8ejRI969e8e3b99U1t0jq9DU1ERXV1fl4hwSEsKQIUO4cuUKCxculL8P3t7ehISEMGzYsGTRC1nN1atXKVasWLLOIaqkVatW8h16Sqgr6D9OHNetWzfU1dVZsGBBpm0rU6YMFStWFFwbGSBD4hwREUGvXr3YvHlzpmIhBZRj7dq1bNmyhVmzZnH69GkcHBw4ceIELVq0oGrVqhgbG6Ojo4O6ujqFCxdm3bp1OW1yqhgaGqpUnKOiohgxYgQxMTFs3rwZX19fTp06BcCWLVvQ1tbOUIW2zHL16lWsra2zNDIl0be7bt06AlLYJRt268YHsZjUuhtKgfdiMYbdugEJSTMdO3bkzp07KunIYm1tzdGjR/n+/Xum5/qTyNAnZsSIEbRt2zbNwtuQUOEqPDw8yY9AxvD29mbs2LH06dOHjh07UqxYMQYPHsyJEyc4fPgwrq6urFu3jiVLllC1alVCQ0OTtSDKTZQoUUJl4hwXF8fYsWN5+/YtJ0+exMHBgfbt27NixQrCwsJ49uwZtra22bprDggIYNy4cTx+/Jj27dtn+Xp79+4lOjqarl27cvToUaKiohg+fHhCv0RNTW7b2wMkE+jE333t7VH7IZOvd+/eSCQSFi9enGnbbGxsiIyM5Pjx45me609CaXF2c3PD19eXRYsWpTt20aJFFCxYUP6jivCcPxF/f3+6du1KvXr1GPdfPGoi6urqlC1bFjMzMywtLcmfPz8PHjxg1qxZtG3bNocsTh9DQ8NkpS8zglQqZcaMGdy6dYsjR45gZmYGwPLlywkJCaF///7ExcVhZ2eX6bUUITw8nGXLlmFnZ8fjx49xdXXlr7/+yvJ1zc3NuXfvHl26dGH69Om0bduWf//9l9u3bwNgMm4cXv378/GnHXygWIxX//7yOOdESpcuTePGjTl9+nSmO4qXKlWKSpUqCa4NJVFKnAMCAhg9ejS7d+9GS0sr3fFTp04lLCxM/pPSLZdA2oSFhWFra0vhwoVZsmSJPAwtJV6/fs3kyZNp164ds2fPzkYrlcfIyEgl4rx8+XJOnDjBrl27aNasmfz1cuXKMXr0aJ4/f06ZMmWoVKlSptdKj6tXr9KuXTsOHjzI7NmzefbsGb179862eG5tbW22b9/O3r17KVmyJM2aNUtyd2IybhxBN29yYtIkjvXowYlJkwj28UkmzIlYWVkRHR3NkydPMm2btbU1np6eKqkt8qeglDjfvn2boKAgatWqhbq6Ourq6ly8eBFnZ2fU1dWTdd3NkycPOjo6SX4EFEcikdC9e3cCAwNxdnZO8/2LiIhgzJgxGBkZsWvXrhwtZK8IRkZGBAcHZyrWefv27ezcuRNnZ2e6du2a7PiMGTPQ1NTEwsIiWwTy4MGDGBgY4O/vz/Tp05WqZ6FKunfvzu3bt2nbti1BQUHIZDICAwMZOnQo9Rs1wun6da6bmFCoQ4ckroyfOXv2LPny5VPJF5uNjQ1RUVF4eXlleq4/BaX+glu0aMGDBw+4e/eu/Mfc3JxevXpx9+7ddHuMCSjHgQMHOHnyJEuWLKF06dKpjpNKpUyfPp3Pnz9z5MiRX+JL0NDQEKlUysePHzN0/rFjx1i+fDnTpk1Ltdu1jo4OPXv2zHQdZUWJi4ujXLlyqSaDZDdGRkZERUWxc+dO2rdvz82bN6lUqRL3799n0aJFWFlZ8c8//6TY4Ts4OJiLFy/SqFEjlXzRGxsbU7VqVcG1oQRK1dbQ1tamatWqSV7Lnz8/RYsWTfa6QOaQyWQsW7aMevXq0bBhwzTHbty4kfPnz3P06NF0297nFmrWrEnBggUZOnQoK1eupHz58gqfe/nyZWbNmsWAAQPSDfeys7PDxcWF169fp/kFpwpiY2NzVXnMxHjyZcuWYWJiwsaNG+VfHI8fP2bOnDls2rSJ7du306NHD9q2bcuzZ8+4ffs2V69eRSaTERMTQ1RUlEruAqytrVm3bh0REREUKFAg0/P97uTue98/mCtXrnD79m369u2b5rjz58+zbt065s2bR7t27bLJusxjYGDArVu30NbWplevXgrf7t6/f59x48bRqlUrNm7cmK67wsrKirx583L+/HlVmJ0mcXFxuUqcixQpAiREXhw9ejTJjr5y5crs378fd3d3KlSowI4dO+jSpQvTpk3j+PHj5MuXDwsLCy5duoSlpSXHjh3LtD3W1tbyqoAC6SOSZWPGQnh4OAULFiQsLOyXuPXOSTp16sS9e/c4dOhQqreVL1++pFevXhQsWJCpU6cSERFBeHg4efLkoUaNGtSoUYNSpUrlaK++9IiKimLo0KG4urrSo0cPJk6cmGLIW1RUFFevXmXevHmYmpri7e2t8G6uffv2vH//nh3/1ZfIKvr27UuNGjVwcXHJ0nUSOXbsGP7+/owdOzbFf+OvX79SpEgRli9fjrW1dZpzBQQEcPr0aaytrZNEVT179kyeVXnjxo1Muzh69eqFiYkJhw8fztQ8vzKK6qBQMjQX8uLFCw4fPszMmTPT/GM4fPgwERERREREMHr0aLS1tSlQoACRkZHy1O6CBQtSo0YNbGxsmDZtWnZdgsLky5ePHTt2UL9+fUaPHs2TJ09YunQp+vr6vHv3jkuXLnHp0iV8fHyIjY2lbt26eHp6KnWbbWdnx+DBg/ny5Yt8N5kVZOfOeceOHQwYMACpVIpIJGLs2LHJxhQsWBCxWJyiT/lnjI2NGThwYLLXK1SowLp16xgwYAAHDhyg23+JKhnF2tqaNWvWEB4eLmzQ0kHYOedCRo0axa5duzh9+nSaIYsSiYSvX7+SP39+tLS05LsnmUzG58+f8fPz4+nTp7i7uxMfH5/hh2/ZxY0bN+jcuTPfv3+nSJEiPH/+HA0NDRo3boytrS1t27bNUCeRT58+YWBgwNy5c+nYsWMWWJ5Aly5dsLKyYs2aNVm2BsCGDRsYNmwYnTt3Rltbmx07dnD48OEUk12KFStG7969cXBwQBIby4d9+4gPCEDd2BjDbt3SjNZIRCqVYmVlRf78+Tl69GimbA8MDMTa2hpXV1d69+6dqbl+VYSd8y9KaGgoLi4u9OzZM91YcjU1tRQrrIlEIooXL07x4sVp2LAhbm5u2ZIIkVnq1q3LnTt3GDduHGpqajg5OWFlZZXpL3I9PT3q1avH+fPns1Scs2PnvHLlSsaNG0evXr2YPHkyMpmMd+/e0aNHD/79999kNTwKFy5MWFiYQoX2U0MsFmNnZ8e2bdsyveM1MDDAzMyMffv2/bHirCjCA8FcxpYtW4iJiaF79+4qme/mzZsEBQWl2RA0N6Grq4urqyvbt2+nc+fOKrvDsrOz49q1a1laujIuLi5LO37s2rWLcePGMWDAACZPnoxIJEIsFvP3339jYmKCra0t79+/T3JO0aJFKX75skKF9tPCzs4OqVTK1KlTM30dNjY2nD59mtDQ0EzP9TsjiHMuQiqV4uzsTOvWrVXWfNTT05Ny5cphYWGhkvl+FSIjI5MkRdnZ2REdHc3169ezbM2s3jnv2bOHOnXqMGbMmCQPAPPmzcuaNWuQyWS0a9eOiIgIQkND8fLyIjQkhJHPnwPpF9pPi1KlStGzZ08uX77M/fv3M3UdVlZWxMbGcuTIkUzN87sjiHMuQiKR8PnzZ5V1aI6KiuLs2bP07ds3V0dsqJK4uDgWLFhAkSJFcHNzk79uampK+fLlszSkLivjnCUSCVeuXKFu3bop/lvq6uqyZs0a/P39KVu2LEWKFKFdu3aUCwykBKn/of9YaD89HB0dKVSoULL6Lsqip6dHrVq12KfAmn8ygjjnIjQ0NGjSpAnXrl1TyXwXLlwgMjIy2wvM5xR37tzBwsKCOXPmEBsbm+y22c7OjosXL2a6kE9qZOXO+f79+4SHh1O7du1Ux1SsWJG1a9fSrFkz5s6dy/Hjxxlsa6vQ/IoU5C9QoABTpkzh06dPbNiwQWHbU8LGxoYzZ878sg0jsgNBnHMZrVq14vbt2yqpfXv06FHq169PmTJlVGBZ7mbRokVYWFgQHR3N7t27AZL5f+3s7AgJCcn0bXlqZKU4X7x4EU1NTapVq5bmOHNzc2bMmEHHjh0xNjZGo2RJheZXtCB/q1atqFOnDhs3bmTQoEEsXbqUU6dOERISotD5iVhZWSGRSP7oeOf0EKI1chk2NjaMHTuWW7duYWlpmeF53N3duXLlilyofme8vLyYNm0aAwYMwNHRUb4z/lmc69evj7GxMQcOHJCXFlUlWSnOly5dokaNGkrPb9itGx+WLUM/lR6CUhLKhhoqGL8cFhYmr3R37969JD58TU1NqlevztatW4mIiGDSpEnExcVRqlQpypUrR9WqVTE1NZV3wjE3N8fNzY3+/fsrdU1/CoI45zJMTU0pUaIEV69ezbA4+/r6snDhQoYOHUrPnj1VbGHuITGMbODAgTRu3Fj+oCwiIgJILs5qamqMGjWKadOmMWbMGJU9dIUEn7BEIskycb58+XKGwgATC+23dXFBStJb5R8L7ZsoYHdkZCRDhw7l3bt3ODs706hRI0JDQ3nz5o280cG///7LkCFDePDgAdHR0RQoUCBJ+yyRSESZMmU4fPgwNjY2LFq0iJCQEIoWLar0tf3uCG6NXIZIJMLY2JgvX75k6PzAwEDGjh1L/fr1cXZ2VrF1Oc+yZcuwtramUqVKaGtrU7JkSWJjY5k7d678QdmNGzcAUnTnODg4oKmpmeRhoSr4MSMzKxCLxRlOnTYeNYp1zZrx4afXUyu0nxIxMTGMHDkSPz8/5s+fj6WlJSKRiMKFC2NmZkb79u1Zt24dtra2XL9+HW1tbXbv3s3ly5e5ceMGhw8f5p9//qF37968ePGCc+fO0aJFC2QyGYcOHcrQdf3uCBmCuQypVErBggUZNGgQAwYMUOrcqKgo7O3t+f79Oz4+PirdGeYGzp49S8uWLWnYsCEmJibo6+ujr69PjRo15BXYZDIZ3bp1Q09PL9XIDEUzMJXh9OnTjB8/nvfv32NoaKiSOX+kYcOGFClSJNUORFKplJCQED5+/Jjs59atW3z58oUCefNiX6YMVYoUYf+//3Irb17U8+RBTU2Ndu3aMXjw4BT/LuPj4xk3bhwXL15k4sSJaSaPhIeHs3//frp06UKhQoWSHY+Li5MnFh09ehQHBwcKFCiAt7d3ht+bXw0hQ/AX5fnz50RERGBqaqrUeTKZjBkzZhAQEMDVq1d/O2GWSqVMmDCBGjVqsH79+lRDAy9fvsyTJ0/4559/Up1r9OjRrF27Fk9PT7p06aIS+3x9fTExMckSYQYoW7YsDx48SPJacHAwS5YswdfXl+Dg4CRx3WKxGE1NTbS0tNDT02Py5Mm0adNGfvy9mxtRZ84ACWLh6uqKu7s7jo6O/PXXX/LiU9HR0UyfPp0LFy4wePDgdLP6dHR0cHBwSPW4hoYGvXr1Yu3atbx69QobGxsWLFjA58+ff7vPbGYRxDmXcefOHQClu09s3LiRM2fO4OHhQfXq1bPCtBzl4MGD3L17F1dX1zRjtt++fYu6ujoNGjRIdUzZsmVp3749u3btonPnziqJAb99+zaNGzfO9DypUaZMGU6cOCH//cKFC8yYMYPIyEhMTU3lFQjLlStHpUqVKFWqVJpukO7duyfJQvXz82PSpEk4OTmxa9cuJkyYQPXq1Rk5ciRPnjyhb9++qTY1UJYuXbqwbt06nJ2dmTlzJgsXLsTDw4MhQ4aoZP7fBUGccxl3795FX1+fwoULc/78efLly0fdunXTPOfs2bP8888/zJkzJ0trR+QkW7ZsoXbt2ulGWZiZmREfH8/t27fTFOhx48bRpEkTrl69mm4zg/T49u0bT58+ZcKECZmaJy3Kli1LcHAwwcHBrF27Fnd3d4oVK8a+ffuoUKFCpuc3NTXl6NGjnDp1ioULFzJmzBj57nn+/Pkq7SCuo6ODTCZDLBZTpEgR6tSpg5ubmyDOPyE8EMxl+Pr6YmpqyoEDBxg1ahQzZ85M1pvxZxJ9q//88w/29vbs37//t6pb8P79e86ePauQQFSsWBFtbW2GDx+Ol5cXqT1SsbS0pGbNmri6umbavrt37yKTybJ85wwJdb4PHTpE27ZtOXv2rEqE+UdsbGy4dOkSw4YNIy4uDhsbG5UKM8Dnz5+RSCSULVsWSIidvnTpUq6vmpjdCOKci5DJZNy9e5fXr1/LO5sEBgZy5cqVNM+bO3cuO3fuxM7OjuvXr9OtWzd0dXV/m2iNe/fuIZVKU90JS2JjCXB15dXffxO4dy9rV69GLBbTrl076tatm2I9Y5FIxLhx47hy5QrP/6s9kVF8fX0pXry4ytLuUyKxxVZMTAzr1q3DyclJ4eiNH9+fAFfXdOtoAAwfPpxq1aqpLFv1RxKb+p46dYr4+HiaN2+OWCzGw8ND5Wv9ygjRGllMdHQ09vb2PHv2DGtraypWrIipqSlVqlRJFnb14cMHjIyMgIQ6Bs7OzlStWpWSJUuydOlShdcMDAxkwYIFBAcH8+jRI5VeT05w9OhR7OzsOH/+fLISqYmlMA1/LIUpFnO7b18OiUTs3LmTT58+pRhHGxsbS6lSpWjYsCFz5sxJ144vX76gqalJ/vz5k/ip7e3tKVOmDAcOHMj4RSpAhQoVqFChAk5OTgqfk+r7o0Cp0P3797NgwQIOHjyo8h26u7s7c+fOpXTp0nh4eDBy5EhEIhGXLl1S6Tq5EUV1UNg5ZyH//vsv1atXZ//+/fj7+7Nnzx4cHBxo2LAhpUqV4sWLF0nGP336FICRI0fi7OzM5cuX8fPzU3pHZmBgQKtWrXj8+DHBwcEqu56cIjHj7+fu7q9WrEi9FOb27eQ5fpyWLVummuCgqanJgAEDOHfuXKruj0R8fHxo0qQJ9evXp2bNmvIGAL169eL+/fuZyuZUlG7dunH58mXi4uIUGp/m+6NAqdDGjRtnWRxy586dWbBgAa9fv6Z9+/a0aNGCy5cv8+HDz9HYfy6COGcB4eHhDB06lMaNG5M/f360tbWpWbMmJ06c4ObNmxw4cID8+fMzYsSIJKJQr149Tpw4werVqwkODqZ79+7UqlUrxfZB6WFubg4kfEH86iT63H+8jZfExlL7v56AqZXCnPrpEz1SaTLg4+Mjf1+/fv3Kq1ev0rTB09OT0qVL4+bmxtq1a5kwYQKdO3fG3Nycrl27qiwkLy3s7OwICwuTR/SkhSLvT1qlQuPi4pg9ezZisZhGjRplwurUad++PYsXL+b9+/esW7cOmUyGu7t7lqz1KyJEa6iQxA/XmDFjCA0NZerUqXTv3p2mTZvKU4m1tLQwNTVlypQpjBo1igMHDsi7lOTNm5dWrVohlUrp3bs30dHRLF68GHV15f+ZDAwMKFGiBBcuXPjlIzhSEucP+/Yl6erxM2KgJFDh0yf5a7GxsRw8eBBnZ2du3LiBhoYG+vr6iEQi7t69m2qBqLi4OM6dO8ewYcMy3UMvM9SuXRtDQ0POnTtHnTp10hyryPtjJJVyf98+jH9qxCCVSpk2bRrXrl1j9OjRmY5mSYvWrVujrq7OhAkTEIlEzJ49m5EjR2bZer8Sws5ZRSTGuXbt2pWyZcvi4eFBz549EYvFSKXSZDUXmjVrRvPmzRk9enSyB1b37t3j9OnTTJkyheLFi2fYptq1a3PhwoUMn59bSMmtoUiJSwDZhw98+vSJefPmUapUKXr16oVIJMLZ2Zlt27YREBBA6dKl09yN+vj4EBoaSteuXTN3IZlEJBJhZ2fHhQsX0nXDKPr+/DxOJpOxaNEiTp48SZ8+fTJ016YsVlZWODs7o6OjQ2hoKG/fvs3yNX8FBHHOJB8+fKB///5YWFgQGBjIhg0b+Oeff5JkiqVWEGfq1KmEh4czffr0JK+XL18edXV1hbomp4WFhQUPHjzIcJ2O3EJKO2dFS1wevnEDY2NjnJycaNy4MYcOHWLz5s00a9aMGjVqULFiRfnOOTVOnz5NmTJlsqSSnbJ06NCB9+/f8+zZszTHKfr+/Dzu2rVruLm50aZNGyZOnJhhO5WlSZMmeHl5IRaL8fLyyrZ1czOCOGeQqKgo5s+fT4UKFThy5AjTp0/n4MGDyW4Bvb29iYiIoGbNmsnm0NfXZ/jw4axbt45bt27JXy9QoAB169bl9OnTfPv2LcM2mpubI5PJfnm/c0ribNitGx/EYlK7cZcC79XUWO7jQ7t27Thz5gwzZ85M8nBVJBLRuXNnXr9+zevXr1P8Ekt0afz111+5optMmTJlyJMnD9OmTWPOnDls2rSJY8eO4evry+fPn+XjFHp/UigVmvgeTJkyJYuuIHUKFiyIubm5UOP5PwRxVhKZTMaePXuoWLEi8+fPp0uXLnh6etKtW7cUfcOLFy9GT08v1UD+Xr16UaFCBQYPHpwk2WTEiBHcu3cPa2trnJ2dlS5mDmBkZISRkdEv79pISZwTS2ECyQQo8feAceMwMjamYMGCqVaLq1u3rtxtktLu2cfHh69fv2bLA7+0iImJ4e+//5bXdP769SsnT55k/fr1TJs2DXt7e5o3b07fvn3x8vJCAum+P7729qj9dEeX+F4kZgdmN82aNeP8+fOEh4fnyPq5CeGBoBJERERgZWXF9evXad68ORs3bqRkGp0mvL29+fjxI7Nnz071w66urk6tWrXYv38/QUFBGBgYANCjRw+aNm3K8uXL2bBhA66urnTu3Jl+/frJK7Apwu/gd86fPz8AQUFBSdxFJuPG4QXU2r4dox98sIFqagSMG0e9JUuo8/JlmrHe586dQ0tLi8KFC3Px4kW0tLQIDAzkw4cPBAYGcu/ePUqXLk2tWrWy7PrS4/jx44waNYo3b97Qq1cvhg4dSoECBeTHg4ODuX//PleuXOHUqVNMmTKFa9eusWDBArwgWZxzoFicUMM5hTjnxC/CrGxUmxbNmjXDycmJkydPyh+U/6kISShKcP78eZo3b86KFSuwsrJKd7yVlRUxMTFplqa8dOkSI0aMYPHixUyaNCnFMV++fGHNmjWsXr2aiIgIxowZQ9++fRWy+dChQ8yePZsvX76kWMLxVyAiIgIDAwP69u3LsGHDkh3fv3cv//79NzMGDqRo1apUGz5cviN0cnJi/vz5uLu7U6JEiSTnyWQybG1tsbS0RCaTybvGiEQiDAwMKFWqlPwhYrt27bL+Qn/i5cuXjB49Gk9PT+rWrcvUqVPlKc9p0ahRI+rUqcOK/+KYJbGxfNi3j/iAANSNjTHs1i3ZjjkRd3d35syZw7179zJcPzqzdOnShdq1a/+2XXyEJJQsIHE3ocgfCEBISAhfv36lYcOG9OvXj8uXLyc5/uHDB6ZNm0bbtm3TLJpTpEgRZs+ezZs3b+jSpQvr168nKipKIRt+B79zgQIF6NatG4cPH06xzkhzKysuikQENGqE2ZgxSYSnb9++6Ovr07dvX/z8/JKc5+vry5s3bxg4cCCrVq3iwoULvHz5kpiYGN6/f8/Vq1fZu3dvjgjzpUuXqFy5Mrdv32b58uVs3rxZ4c+dTCYjb9688t/VNDUx7tMHk2nTMO7TJ1VhBuRJIMoIc0bSw9OiWbNmeHl5KZxs87siiLMSJMYqxyr44UsMh2vQoAF+fn4MGzaMefPmERsbS1xcHBMnTqRQoULs3LlToT8GbW1t/v77byIjIzl16pRCNpQoUQIDAwP2798vr2nwKzJo0CA+fPiQpGddIsWKFaNGjRop1mYwNDTk2rVrGBsb069fP3mXFEi4qzAxMaFx48YUK1aMJk2aYGJikiX+VplMxs2bN3n58qVC3b89PT0pXLgwhw8fxtraWqmHkXny5EmWfaoIO3fuZNOmTZQvX17hc16tWIGuhQWtlyzBdu9eWi9Zgq6FRbrZh2nRvHlzwsLCOH36dIbn+B0QxFkJEnfOin6jFytWjF69erFmzRquX79Ou3btOHjwIH379mXevHk8efKE/fv3U6RIEYVtKF26NNbW1hw6eJD8Pj4UPH6c/D4+kErlOpFIRI8ePdi9eze1a9f+ZXfQderUoXLlyuzbty/F3XOzZs04deqUvH/gjxQvXpwLFy7QoEEDhg8fzsmTJ4mIiOD06dMMHDgwy2/f7927R+PGjalbty5ly5ZFR0eHunXr4uDgwOrVq5NEWSSSmLafL18+pdaKjY2lWrVqPHr0iHPnzilUnVAmk+Hs7MzSpUupUaMGBw8eVGitzKaHp0ZihNKSJUsydP7vguBzVoKnT59iamrK9u3bqV27tvx1ZXx6hw4dYv78+cTFxbF69WpGjRqltB3XJ03CaOlSfoxQjdXT4+OUKYS3bJniOQ8fPuTvv//mwYMHDB48mI0bNyq9bk4zcOBAtm3bRqFChbCysqJly5YUKFAADw8PPD09iY2Nxd3dPdWMyNjYWAYMGMCePXto0KAB165d482bN8l80aoiJCSEGTNmsGnTJkxMTBgzZgzq6uo8f/4cf39/nj9/zvPnz7GxseHo0aNJzi1fvjz169dP9TkEwJs3b7h48SK+vr48f/6ckJAQoqKikEqlqKmpyb/EihUrhrOzM9WqVUs2h0QikRc3atKkCWvXrlXo2iSxsehaWKTb1TvYxydNN0pK9OzZk4cPH2JgYMC7d+9yRQijKlFUBwVxVoJXr15RpkwZNm/eTL169RJey0DVr61bt+Ls7ExsbGyyYj7p4uGBrHNnZCTvpCwCAlauTFWgpVIpGzZsYP369QQFBf1ybYG2bt2Kg4MDlStX5sWLF8TExAAJYV8VK1bk4cOHnDlzhpapXD8kvAeTJk1i+fLltGrVKkl3EVUhkUjYtGkTM2bMIDY2lmHDhtGjR48U3SU7duxgzZo17N69m48fP/L+/XvevXvHrl27AChUqBAikUje4FUsFhMbG0tERITcvaaurk6ZMmWoVKmSvHJdyZIl8fPz49atW7i7u1O4cGE8PDzkO/H4+Hhev37NP//8w9mzZ+nQoQPz5s1T+BoDXF1prcDO9sSkScnSw9Pj8OHDzJw5E0h4LpBSjsCvjNBDMAv42a2ReFv3M4m3dV6QokDHxMSgq6urvDBLJEQNHowWKRezkQKF584lvFkzSGFusVhMq1atWL9+PY8ePaJp06bKrZ/DJEa87NixA01NTc6cOUNkZCRt2rQhPDycFi1apPs8QCwWs2zZMho0aJDiTjKzXL16leHDh3Pv3j3s7OwYM2ZMsjKnP9K8eXNWrlxJly5dUFdXp3jx4hQvXpwWLVrIU//j4+ORSCRIJBLi4+PJmzevXIQThTilGHtDQ0OaN29Oo0aNGDZsGOPHj0dXV5dHjx7x8uVL4uPjEYlEDBw4kNGjRyt1nRlND1eEDh068OLFC7Zv307//v3TzN78nRHEWQl+fCCYXtUvKQlVv4IdHZPd1n358gU9PT2l15dcuEC+NJJRxIB2aChaPj5E/7ez/xljY2M0NDR+SXH+8f3X0tLCxsZGfiw6OhpIaGelq6uLubl5mrfDnTp1UqltwcHBTJo0CRcXF6pUqcKuXbuoUaNGuucZGxtz8uRJNDQ0KFy4cJb4vxs0aEDr1q05fvw4Wlpa6Orq0qRJE8zNzWnRooU8tl4ZMpoerihjxozh0aNHmW6E8CsjPBBUgh93zh/27cMwFX8b/L/q14d9+5IdCwkJyZA4P1UwmeSdj0+qxzQ0NChduvQvWYQ/rWiZggULMmrUKK5du0adOnWoXr16upmV8fHxDBs2jPLly7Nr1y6Foih+RiqVsmnTJipWrIi7uzszZsxg9+7dCglzIvr6+hQtWjRLH0xOmDABmUxGx44d8fLyYtWqVfTu3TtDwgwZTw9XFDU1Nbp27crXr18JyMDu+3dAEGclePfuHZDwdDszt3UhISFpZvlt3bqVtm3bsm7duiQfzEAF7UxvXNmyZXn48KGCs+UeEjMFU4pAEIlEDBo0SJ7SbGBgwPjx4zE0NGT9+vXJxkdGRmJnZ8eWLVsoWrQoffr0oWbNmpw6dSrdim+J+Pr6Ur9+fYYMGUKjRo04evQo3bp1U95dlQ0UK1aMUqVKpRiKmBEUSZ9PKT1cGRo2bIi6ujrHjh3L8By/MoI4K0hwcDC2traULVuWRo0aZeq27sOHD6mWAo2Pj2fmzJncvn2b0aNHU7JkSczMzJg1axYvDA0JIPkfQyJS4C0Qk06t37Jly/L48WOFRSi3UK9ePfLly5dmOrqamhqNGjVixYoVeHt7U6pUqSTJP4l9Gps2bcqFCxdYu3YtW7ZswdXVFXV1dVq1akWLFi2SFKL6mdDQUBwdHbGwsODLly+4uLiwcOHCVDuu5Bbq1atHQEBAhu4QUsJk3Di8+vfn4087/kCxGK/+/dNtg5UeOjo6mJubc+TIkUzN86siiLMCREdHy7tQrF27Fm1t7Qzf1p08eZJPnz5ha2ub4nnHjx8nMDCQNWvWcPHiRZYsWYKRkRGrV69myPDhjP3Pj5rabmVuoULU/K8LSmqUK1eOkJAQPv1QiP5XIF++fNja2iqcgFO0aFF5yvqDBw+YOXMmFStWpGbNmgQEBODi4iKvImhmZsb27dtZs2YNb9++xcLCgj59+sh92YmcO3cOU1NTXFxcGDduHPv375d3ncnt2NnZERcXx4IFC1Q2p8m4cXz28eHEpEkc69GDE5MmEezjk2lhTqRp06Z/bCEkQZzTQSqV0r9/f27fvo2zs7M8JjYjt3UPHjxgxowZ9OrVi8aNG6e43qZNm6hSpQqVKlVCR0eH1q1bs3jxYi5cuMC2bdsIbtyYLsD7n857B3QFqs2ene5tdWIa8K/od+7QoQN+fn5yF1N6yGQy9u/fL/dBV6lShfXr13PixAkqV66cZKxIJKJp06by5qMHDx6kU6dO8pC99evXY21tjYmJCUeOHMHe3j7HqrdlhGrVqtGmTRs8PDx48OCByuZVJj1cGUJDQ/Hx8SE+Pl7h+OvfCSFaIx1mzZqFm5sby5cvp3r16kmOJVZFU6Tq18ePHxk9ejQ1a9Zky5YtKUYSvHv3jhMnTiQrvg8JD/IsLCywsLDA29ubuosWUSEoCAMSfMz+enpMnDIlzRjfRIyNjdHU1OTRo0e0aNFC4fcip/n+/TurVq1CV1dX4Tj5li1bYmRkhLW1NfXr11dITNXU1OjUqRMGBgY4OjrStWtXDAwM2LRpE7169WLChAkZah2WG5g2bRo3btxgxIgRXLhwIceKG6VFbGwsQ4YM4e7du0gkEvLkyYOvr29Om5XtCEkoaZAYZzl27FgGDBiQ6rj0MgSjoqKwt7cnMjISHx+fVCM15s+fz6JFizh37lySkpAprimRyAus6+rqUqtWLaUeRHXp0oUmTZrkukzBb9++ERQUREhICCEhIXz58oVGjRphbGxMz549OXLkCC4uLlStWjVb7Pn3338ZNWoUEomEmTNn5nirKlXg7e3N2LFjWblypUJf5tnNwoULcXNzo1evXvTs2RMPDw+OHj1KYGBgrnzYqixCEkomOX/+PIMHD6Zz5870798/zbGJt3UpIZVKmTp1Ku/evePq1atphtCdPXuW2rVrpyvMkLC7s7CwSHdcapQtW5Z79+5l+PyswMvLi44dOyarXdKwYUOaNGnC/v37WbFiRbYJM4ClpSX9+vXDxcWFtm3bZtu6WUlic9hr167lOnGWSqUcPXqUevXqybuxNGnShK1bt+Lj4yPPzP0TEMQ5Bfz8/OjUqRPm5uZMnz49U7n9q1ev5vz58xw9ejTdjLRmzZqxdOlSvn//nqTkY1ZQu3Zt/v77b8LCwlLtEpJV+Pv7s2nTJiIiIjAyMqJEiRIULVqUoUOHUrt2bQYOHEihQoUoVKgQd+/eZeLEiVy5coVx48bliJi0atWKLVu2sH37doYPH57t66saHR0d9PT0cuUzhwsXLhAVFUXPnj3lr1WvXp1ChQrh6en5R4lz7nM45QLGjh1LaGgoTZo0yfAcAQEBbNiwgW3btrFs2TKFagL36dOHyMhIzp07l+F1FaVhw4ZIJBLOnj2b5WtBwoO506dP06ZNGypUqMDWrVu5cOECK1asoH///rRv357AwEBGjhxJvXr1MDU1RV9fH2tra2rWrEnPnj3p169fttj6M4kp0p6enjmyvqqJj4+nSJEiBAYqGjmffZiYmAAJzxcSSQyP/NPinQVxToFFixbRpk0bnJycaNeuHbt37063uH1wcDBeXl7MmjWLVq1a0aZNG9avX8+oUaMYO3asQuuWKVOGhg0bZsuH0MjIiDJlymR5p+PIyEg2bNhA5cqVsbGx4eXLl8ybN48zZ85w4MABLl26xO3bt9m+fTsymSxZQwKxWMyOHTuYOnVqjlUnE4lEtG7dmg8fPqRYkvRXIjw8nKFDh+Ln50erVq1y2pxkmJiYkCdPnmS7+saNG3P//n2Fo3R+BwRxTgEzMzO8vLy4d++e3NWQWDAoLCwMSPiQnz17lkWLFtGhQweaNWvGlClTePr0KZ07d+bIkSOEhISwevVqpUTF3t6ea9euERQUlFWXJ6dFixZs27aNhg0bsnPnToW7qyjC169fmTRpEiVKlGDEiBGUKFECFxcXDhw4QMeOHeWp2JCQFl+rVi309PTw9vZONlduKBkZExODVCr9peNtP378SI8ePbh16xbjxo1j6tSpOW1SihQqVChZqF+DBg1QU1PL8s1EbkIQ5zSoXr06u3btwt/fnx49erBt2zasra3p1q0blpaWjBkzhqtXr9KkSRP27NnDx48fefToEatXr6Z9+/ZK9eyLiorC29ubBw8eIJVKOXPmTNZd2H8MGzaMpUuXIpVKsbe3x9DQEEdHR+7fv5/puceOHcu6devo0KEDx48fZ+XKlWkWIxKJRJiYmKRYeD6n+fbtG+7u7pQsWTJJg9lfjWvXrvH27Vv69u2bYy4iDw8P/vrrL4KDg+WvRUdHs3btWqysrGjQoAEhISE8fvw4SVOFggULUqtWrT/KtSGE0ilBUFAQa9as4fXr1zRp0oQWLVrIfWQZwcfHB09PT86dO8eNGzeIi4ujSJEimJubM3ToUKXaBWWWgIAAPDw8OHz4MMHBwdSvX5+TJ09m6N/p/fv3mJiYMHr0aOz/S9RRBAcHB168eMH58+eVXjMr2b17N05OTgpXmsutxMXF0bVrVz5+/MiFCxdSbTqcFlKplE+fPhEUFISJiUman4/w8HDOnDnDp0+f6NatGx8/fqR3797ysqeOjo54eXnh7+9PXFwc5cqVw9TUlODgYPLkyYOzs3OSOOzt27ezbt06QkJCsvyBeVYiFNvP5cTFxZEnTx60tbXlySV16tShXLlyOXobHxcXx5o1a3BxceHjx48Zqp43ceJENmzYwJkzZxQKC4SEP/r69etTrVo1tmzZovSaWcn9+/fp3bs3derUyR7bJBK0fHx45+NDIAm1Umqam6skxvfBgwfyDNW0su4CAgI4ePAgV69eJTg4mOjoaOLi4oiLi0tSm0NdXR1NTU35Z7lIkSKEh4fz6dMnoqKi5PVbEhsFFCpUCCcnJ6ZPn86nT5/Q0dGhffv22NnZYWpqmqbtL1++xM7ODi8vL9q0aZPp9yKnEOKcczkaGhqULFmSJk2aMHHixJw2R46Ghgbfv3+nXLlyGRLm8PBwNm3aRNeuXeXCrEgbr7dv3xIVFZUrQ6WqV6/OoEGD2Lx5M0eOHMHOzi7L1tLx9qbwnDloh4VR7r/XAjZtYnbBglSfMyfToYTVqlWjV69e7Nmzh9u3bydptxYREcHw4cN59uwZkZGRQEI5U1NTU4oVKyb/KVq0KAUKFODLly8EBQXJfz5+/EhAQADa2tq0bNmSatWqUbVqVfLnz8/FixflmYlVqlTh4MGD+Pv7Y2ZmpnAKvImJCcbGxhw7duyXFmdFEXbOOchff/3Fixcv2PFf0f7cQteuXalXrx4uKXR5SY/ly5czdepUTp48SfHixRVu4+Xp6cnUqVM5fvw4xhks0J6VxMXF0atXL169esWVK1fktb1ViY63N8Zjx6bYggwSaqdYqiCrLyoqikaNGmFpacnq1asT1pBKadeuHR8+fKB169bUqVOHOnXqYGRklKm1VI2TkxMXL17k7du3ueJBcUZQVAeFB4I5SJ06dXjy5IlCHZKzi2/fvvH06dNUCzOlRVxcHCtXrqRNmzZyYVa0O/PDhw/R0tLKlcIMCXcU06ZNIzo6mj179qh+AYkEfSenZMLMD7+vBJY6OaXYfVwZ8uXLR6VKlXjy5In8teHDhxMQEMCCBQtYtGgRHTt2zHXCDAkhde/evVNp4abciiDOOUirVq2QSqW0aNGCCRMmcOXKlUz/4WWWe/fuIZPJaNSokdLnurm58f79e/r165duGy9IaOMl+a+ryf379ylcuHAmLM96atSogYGBAQcPHlT53Pl9fdH89CnNzjolgfKfPqmkCFCNGjX48uULr1+/ZuTIkVy5coXBgwcrlCyVk5ibm5MvX77fJiEoLQRxzkGqVq3K27dvWbRoEQEBAQwdOpRWrVqxZs2aHGnNExQUxKZNm9DX16dcuXLpn/ADMpmMJUuWYGlpSbly5ZRq4xUXF4efnx+VKlXK9DVkJSKRCFtbW969e6fyZBR1BUMIDUAl4YbVq1cnJiYGW1tbLl68SKdOnRgxYkSm581qNDU1qV+/viDOAllP8eLFGTduHA8fPuTGjRu0b98eNzc32rRpQ9++fdm+fXuyQkBZwaVLl+jSpQuBgYHs27dPaX/e6dOnefjwoTx+Vpk2Xn5+fsTFxdGgQQNlzc522rZti0QiYfPmzSqdN15XV6FxgYCugmPTom7dumhpaVG0aFHOnTvH3Llzc2X50JRo0qQJ169fTxIr/Tvya/xr/AHIZDIiIyOpUKEC1tbWANy5c4fly5dz+vTpLF173759jBgxgvr163P//v0M+ZuXLl1KlSpV5JXylGnjtXv3bjQ1NVPtDpObKFOmDCYmJiqvSRJZqxaxenrptiDz19OjVq1amV6vcOHCODo6EhISwu3btzM9X3ZiaWmJTCbjxIkTOW1KliKIcy4gJCQEW1tbmjdvzowZM+Qp4FOnTmXjxo20bt06y9fX0tLC3d09Q7uyO3fucPbsWfr16yffcSvaxkvWqBEnTpygcePG5MuXL+MXkY2oq6urvti+mhofp0xBROqddcYCE6dMUVlN4549e1KyZEnmzZunsr6C2UGxYsWoWrXqb+/aEMQ5h7l69So1atTg2rVrODs7c/36dfbs2cOcOXPo2bMnDRo0yPLbTRsbG6KjozO8Q1+2bBlGRkZJQrwUaeO12sSEaTNnIhaLmTVrVobWzglCQ0PR1tZW+bzhLVsSsHIlkT+VcH0HDCpUSCVhdD+ioaHB9OnTCQ8Px8nJSWXzZgeNGzfm1KlT2eLyyykEcc4hpFIpS5cupXHjxhQvXpz9+/fTrFmzHOn0ULZsWcqXL8++ffuUPvfZs2fs27ePPn36JNtNptad+R3QXU2NjZ8/8/z5c1q3bp3rIzV+JCIiIkvinCFBoN9cvMjzzZu5MHgwewcP5tyWLYy6cCFLalk3aNCAFi1acPDgwV/Kh9u4cWPCwsK4cuVKTpuSZQhJKDlASEgIffv25fjx4wwcOJARI0bkeKPQTZs2sW3bNoKCghR2L+zbt48hQ4agra2Nu7t7quf9mCEYU6QI3f75hwEODowePVqVl5BtjBkzhsuXL3P58uUM1afIbbx//x5bW1sqVarE7t27c9ochZBKpVhZWdGnTx+WLVuW0+YohZCEkkv50Y3xzz//MGbMmBwXZoDy5csTGRmZYsnOn3n27BkWFhZ0796d+vXr4+bmlkSY379/z4EDB5g+fTr+/v5JujOX7tcPKWRd6U2JhPw+PhQ8fpz8Pj6QBXHjgwYNIiYmhhU/JNH8yhgZGTFo0CAePHjA1atXc9ochRCLxb99AX6htkY2IZVK5anN1atXZ8mSJejr6yt0bmRkJN++fVN4vLJ2ubq6snr1aszMzJLUWkgkLi6Oq1evcuLECY4fP54kO+vbt28cPnwYIyMjbt68yaVLl3j//j0ikQixWIyPjw8HDhyQt8LKkycPIpEoS4rWFzh9Gt0FC8j/9av8tVg9PT5OmUK4Cl0CVapUoUGDBhw+fJhJkyb9sp24f6R///5s2LCBgwcP/hIhjZDg2vDw8OD58+dKx+X/Cgg752wgMRpj0qRJ9OvXj61btyostE+ePKFLly60b9+ea9euqdSu4OBghg0bxrJlyxg5ciTXr1/HyMiI+Ph4bt68ydKlS7GysiJfvnw0bdqUzZs3U7p0aZycnJg9ezYNGjTg0aNHrFixgrFjx+Lh4UGePHkYMGAAJ06cYOvWrQQFBTFhwgR55qNIJEJLS0teWEdVvFqxgpLjx5P3B2EG0Pj0CeNx49BR4I5AGYYMGcL379/5559/VDpvTqGhoYFUKiV//vw5bYrC1K9fH01Nzd+2AP+v/5Wfy3nz5g0NGzYkKiqKdevWYWlpCSBvybR3714qV65M586dMTAwSHLuoUOHWLhwIZUrV6ZSpUo4Ojri5OSEqakpISEhqf58+fKF8PBwDAwMKFWqFKVLl6Z06dLy/8+fPz+XLl1i5syZaGhocOrUKaytrQkKCmLgwIFcuHCBiIgI8ubNS9WqVYmPj2fkyJE4ODgkiRzp0qULkPCA7OXLl1SqUIGPBw4QHxCA9Nw5zLp1Y+TIkaxatYo1a9YwZswYAPLmzavSritnT5+m039Fmn7ebYj+e6/1Fy8mvFkzyMQDV6lUysOHD6levTq1atWiWrVquLq64ujomCMPclXJxYsXAbIkCkWRqoQZIV++fFhYWHDs2LFf9vlFWgjinMXExMQQHh5OyZIl5d23Hz9+zIoVK7hx4wY1a9Zk9+7dbN68GUtLS7p27YqFhQVLlizB3d0dBwcH1qxZg1gspkePHoz7oZIbJPjedHV10dPTQ09Pj6pVq6Knp0fBggV58+YNfn5+uLu7J2l7paury+fPn2ndujXbt2+nePHiAKxfv57z58/j4OCAubk5VapUQUNDg9q1a/P9+/dUQ/oKFCiAtrc3en36UPPH6nPLltHc3p7blpZs3bqVsmXLYmtrq1JxlkgkXFywgLT+NEWA5seP5Pf1JfK/JJmMMGjQIG7evImhoSH58uXj5cuXSKVS7Ozs8PDwyLIIjqzk48ePLFy4kAsXLlCoUCGVd0hJrEpY46fPxc9VCTOKpaUly5cv59u3b1nyxZKTCOKcxVSoUIELFy7QqlUr+vXrh6mpKV5eXlSsWJEjR45ga2tLZGQke/fuZf369Tg6OqKpqYlYLGbbtm30799fPte+ffs4deoUefLkQU9PD319fYoWLapQHHRoaCj+/v48ffqUp0+fUqZMmSRJIzKZjF27dtGyZUscHBySnJsvXz5Onz5Nnjx5KFeuHE2bNk3iZ02sPvczidXnZPb2vH79mmnTpuHv70+ePHmIjo7O6FuaBF9fX/L85MpIDUXrV6TEunXruHnzJi1btsTHx4fIyEiGDh1KkSJF+Pvvv2nTpg2HDx9WuLlAbkAmkzFq1CieP39O3759GT9+vEpj6tP7XHhBpgW6cePGODk5cebMGTp16pSpuXIbQihdNvHs2TOsra2Jjo5m7ty5DBw4MMUHSbdu3eLIkSN07twZMzOzbLPv5s2b1K1bl82bNycreL9s2TI8PDz4/v078fHx9O3bV94gQBIbi66FBfqpFDmSAoFiMUE3bjBu4kQuXryY4GbQ11dJn8Tjx49zfPJkLigw9tW2bUrvnHft2sXGjRsJDQ2lbt26bNy4UZ5Nlxhlc+7cOcaPH49UKsXU1BRdXV0MDQ0pW7YsnTt3zrUPDK9du8bgwYMZPXp0si/kzKLo5yLYxyfTLo4OHTpgaWmZofrjOYHQCSWXUaFCBR49eoRYLE6z/5m5uTnm5ubZaFkCrq6uFC9eXF4b40cmTJjAhAkTgIQHYbt27cLOzo4KFSrwYd++JLesP5NYfe7+gQOsWbOG/fv3s2jRIr59+6YSu3V1dfkXCACMSPkJtxSIKlKESCVqUhw9epTFixcTHh5O+fLlmTVrFi1atEAsFifzLzdv3pxt27bh7u7O7du3efHiBTExMUDCHcuQIUMyenlZytatW8mXLx8DBgxQ+dwKfy727cO4T59MrdW4cWO8vLyQSqW/TPEmRfh9ruQXIH/+/LmyMWVcXBz79u2jdevW6T7YWrlyJZqamixYsACZTKZU9TlI6P6io6NDmTJlMm03QK1atdDV02PMf7+nlioePH26wg8Dz507x6xZs9DT02PNmjW4u7tjZWWV5h9+zZo1WbBgASdOnODWrVscPnwYQP5+BgUFMWjQIGbOnMm+fft49eqVwteYFTx69IgbN27QqVOnLBE0ZT8XmaFx48YEBQVx69atTM+VmxDEWYAzZ87w+fNnhQqt58uXjxEjRnDnzh2OHj2qVPU5SIjs+PLlS7rNPBVFTU2NKVOmcEgkoivw/qfj7wCv/v2J+K/SX3r4+fkxceJETExM2LlzJ02bNs1QO6T4+HggQQTj4+NZtWoV169f58iRIyxYsID27dtTq1Ytnj9/rvTcqmDbtm3kyZOHsWPHZsn8yn4uMoOZmRk6Ojq/XSEkpcR5/fr1VK9eHR0dHXR0dKhfv/5vX7Yvt6KqKmIfP37E2dmZcuXKUbFiRYXO6devH0ZGRixYsIBoCwuFqs8ZdusGIN8x1qxZUwXWJ9CyZUtWrFjBFT09SgNNgR5Ap8KFObR8uUIPncLDwxk9ejQ9evRAW1ub9evXZ+rhnpGREbVr18bb25sGDRpw5swZqlatys2bNzl48CBLliwhPj6erVu3ZniNjPLmzRvOnDlDixYtsizCRNGqhImfi8ygrq5Ow4YN/2xxLlGiBE5OTty+fZtbt27RvHlz7OzsePToUVbZJ5AC4eHhFCtWTN6cU1kiIiJwdXXF2toaIyMjzp8/z4ABA5TaIe7cuROAUWPHcq17dyB1l4Kvvb38oc/Lly8BVJ6F1rJlS06dOsWWbdtos3gx1tu2Mfv8eVqks2OOiopi0qRJNG3alAsXLtCuXTv27t2rVDamJDaWAFdXXv39NwGurkhiYylQoADbt29nx44d1KxZk+joaBo3boyWlhYVK1akdevWmJmZcfnyZa5cucLNmze5f/8+b968yexbkS4uLi6oqakxffr0LFtDkaqEP34uMkuTJk24c+cO79//fO/065LpaI0iRYqwdOlSBg4cmO7YPzlaQ5V8+vRJLh5///03U6dOTfecuLg4zpw5w65duzhy5AhRUVGYm5vTtm1brKys5OnVynD9+nWGDh1KvXr1mFSuHOaurkm6bL8Xi/H9L55VEhvLezc3Lh84wI23b1mhgqf0qsDKyopPnz7RunVrhg8fTqlSpZQ6X9Hu4gEBARQvXpw8efLIX3N1dWXJkiXJ5mzZsiUrV67MwNWkz+fPn7G2tqZOnTps3LgxS9b4kZTenx8/F6oiLCyMxo0bs2HDBgYNGqSyebOCLI/WkEgkHDhwgMjISOrXr5/imJiYGPlT60SjBDJP4q2omZkZ06ZNIyoqinnz5iXb+cpkMnx8fHB1dWXfvn18/vyZsmXLMmjQINq0aYOhoWGm7KhXrx7Dhw9nzZo1eFaujMGVK5zdvJmoFy/QMzPDpG9fTDQ1ebViBbX+S0Ro89+5HywsVJaIkBn09PQICwtj9uzZShf7VyaON6Wu4t27d6dGjRrExsYikUiQSCScOXMGd3d33Nzc6P7fHYkqOXbsGPHx8cyYMUPlc6eEybhxfHZ05N5PGYImKv5iLliwIGZmZhw7dizXi7OiKL1zfvDgAfXr1yc6OpoCBQqwZ88e2rRpk+LYOXPmMHfu3GSvCzvnzBEZGUmBAgVYvHgxHz9+ZOXKlYwfP56lS5ciEol4/vw5u3fvxtXVlRcvXlC8eHFat25Nu3btqFixYoYecKXFsGHDuHz5MmKxWO4LL1u2LJs2bSJy1y65gP3oQ0vcR3n175+jAn3v3j169+7NrFmz6Nq1q8LnZVUcb3x8PAMGDODhw4e4ublRoUKFhPmUCBMLCQlh3rx5XL58GXV1dVq0aMH48eMpWrQoPj4+DBgwgK5du/5SDQ4UYevWrWzatEne2Se3oujOWWlxjo2N5e3bt4SFhXHw4EG2bNnCxYsXqVy5crKxKe2cjY2NBXHOJHFxcfJwNjs7O3bv3o2TkxOdO3fm3bt33Lhxg/z589OiRQvatWtHnTp1srT2g1QqZc6cOcTExFC9enW0tLRYuHAhxQoXxic4GINsSETIDE2aNKFcuXJKPZwLcHWldQouiZ85MWmS0nG8wcHBdO7cGYlEwtGjRxkzZgz379+nc+fOzJw5Uz5OKpUye/Zsnj9/TqVKlahZsyZHjx7l1q1bSKVS2rRpQ3h4OP/++y9isZhy5coxatQoDh06xMWLFzl9+jTFihVTyrbczPPnz+nYsSMnTpygVatWOW1OqmSZW0NTU1Nenq927dr4+PiwevXqFP1XefLkSeJjE1ANiRlniS16evXqhZaWFqtWraJGjRosXbqUJk2aZFtMtVgsZt68eUleK1euHC729hhlUyJCZihfvjwPHz5EJpMpfFeRlXG8Pj4+SKVSwsLCaNKkCWKxmJo1a7J//36ePXuGi4sLnz9/pk+fPnz69ImiRYvy8OFDDhw4gLq6Op07d6Z///4YGRkB8O7dOw4ePMiBAwcYMWIEefPmJS4uDnt7+2yp6BYeHs68efM4f/48kPB+9+zZk3bt2qk0xrps2bIYGRnh6emZq8VZUTKdISiVSpPsjgXS59WrV3z69ImwsDBCQ0MJCwtL8v9isZjevXtTp06dFM8XiURoamom6Z/WuXNnOnfunF2XkC41atSgn40NHD+e7lhVJCJkhlq1anHjxg0+fPggF7T0yKo43ri4OBYtWkR8fDxVqlRBJBIxa9YsTE1N2b59Oyv/6yP47ds31NXVWbVqFS1atODbt2+8ePGCEiVKJNsNlyhRgjFjxjB8+HAOHz7M/PnzAXj79q1StilLbGwskydP5uLFi8THx9OqVSu0tbU5efIk06dPZ968eVSsWJFixYoRGBhIfHw8Y8aMyVD3d0j4u7C0tOTYsWOsWbNG5e677EYpcZ46dSqtW7emZMmSfPv2jT179nDhwgVOnTqVVfb9dty4cSNZ7QqxWJxQ2U1bG21tbcLCwlizZg21a9dm5MiRdOvWLZkP7Wdxzo0UrVpVIXFWRSJCZmjZsiXr16+XZ8wpgmG3bnxYtixdn7MycbxPnz5l3rx5fP36VS7CiUgkEqpVrszf1tY88vZGZGhIz/XrKfFfdIm2tna6tVg0NTWJiopCJBKxZs2aZCVqVU337t3x9/enffv2DBo0iNKlSwMJOuLj48OpU6c4deoUfn5+FC9enKioKEaMGIGxsTFz5sxJdXOSFk2aNMHNzY3Hjx9TpUoVFV9R9qKUOAcFBdG3b18CAwMpWLAg1atX59SpU1hZWWWVfb8d/v7+QEKFuSJFilCgQAHy5cuX5PZOIpFw+fJl3Nzc6NevH+PHj8fBwYGhQ4fKP+C/gjhnhYBlBRUqVKBIkSLMmTOHFy9eMHLkyHQfKCXG8bZ1cUFKyg87fe3tlYpK2Lx5M48fP2bYsGFJhNnb25v7c+YwNywM+ddYQADfevXi65w5Cnd5kclk7Nu3D0NDQ5o0aaKwXcoQEhKCl5cXR44cwd/fHzMzMxwcHOSfW0hwy9WvX5/69esza9YsRCIRIpGI2NhY3N3dWb9+PQ4ODjg7O9O0aVOl1rewsCBv3rx4enr+8uKslMNn69atvH79mpiYGIKCgvD29haEWUk+fPiAjo4OlStXRl9fnwIFCiTzu6mpqdGkSRPWr1+Pp6cnrVu3Zt26dZQpU4b27dtz+vRp1NXVc704K5KIcLtPn1wR73zmzBkaNWrErl276NSpE0+ePEn3nNS6iweKxUpHochkMgIDA9HV1WX48OHy1729vfl37Fi2hIXxs8Mlf1gYxmPHKtzlJSgoiHfv3tG2bVuF7VKGwMBArKysWLp0KQEBAejq6nL37l06duzIgQMHUjxHLBbL3Q+ampr06NGD48ePI5PJ5D5qZciTJw9169b9LXoLCrU1spkPHz7Ii9srQqlSpZg8eTLe3t7MnDmTp0+fYmNjQ1BQUK4XZ0hdwN4DXYBlL19y6NAhXr9+TTZWr02GpqYm69atY82aNYSEhDBgwACF6l6YjBvHZx8fTkyaxLEePTgxaRLBPj5KhweOHTuW+/fvJ9ntSSQSlixaxKr/fv/5j1UMyAB9JyeFGtkmZh/WUqI6nzI4OTkhkUjYvXs3165d49y5c5w5c4bq1auzYMEC1q1bl/rJPzTm1XvyhIIFCvDhw4cM2dGkSROuXbtGSEhIBq8kdyCUDM1mPnz4kKHwpXz58tG1a1e6dOmCr68vR44cSTX5J7eRWiJC8U2bOLN7N//++y8AOjo61K5dm1q1amFmZibvxJKdNG7cmIMHD9K5c2cGDhzIrl27UkwgSUQikXDt5k0O3r5NfHw8Dm3aYKzEnYBEIuHgwYNcuHABCwuLJJmBvr6+VAgKIi2PvBjQ/PQJ0eXLbH/9mgcPHlCjRg3q1KlD+fLlk9yVvX79GpFIpNK6JpAQFODu7s7ly5exsbGhevXq8mP6+vps2rSJSZMmsWHDBqRSKY6OjknO1/H2Rt/JCc1Pn+SvPVFTY/StW0yZMoUhQ4ZgYmKisD2WlpZIpVJOnjxJr169Mn+BOYRQbD+badiwIcWKFWPhwoU5bUquITAwkKNHj3LlyhVevnzJt2/fkEqlaGhoUK1aNf7++2+FoyhUxbNnz+jZsyeFCxfG1dU1xVob8fHxODo6cuXKFXnY4vfv36lbty7Dhw+X71BlMhlBQUHo6uoiFou5dOkSa9asITIykvDwcMLCwihVqhTbtm1Lcld1/Phx7kyezF4F7LXX0MA1Pp58+fIRFRWFTCZDW1ubevXqUadOHWrWrMnChQt58OABVatW5e+//07zS0cRIiIiWL58OSdOnCAyMhIjIyO5++1nJBIJY8eO5dKlSyxevBgbGxsgQZiNx45FRsp++67AYbGYtWvXyvtvKkK3bt2oUaMGe/bsyfD1ZRVZloSSHUb9zpiYmFCvXr0sLTrzqxMVFcWZM2dYuXIlISEheHh4UL58+Wy34969ewwYMABdXV0WLFiQrAnCihUr2L59Ow4ODjg6OiKVSlm1ahX79+/n+/fvWFhYUKxYMa5du0ZoaCjlypXD0dERV1dX7t+/T7ly5dDS0qJjx4507Ngx2fo+Pj7sGDBAoS4v1hoaDN6zB1NTU8LDwzl8+DDe3t48f/6ciIgIZDIZYrEYS0tLrl27hkQiwczMjNWrV1OwYEFu3brF48eP5U2BDQ0NuXfvHleuXOHBgwcEBQVha2vL8OHDEYvF8qYJ8fHx1K9fn969e9OoUaM045ajoqLo3bs3b968Ye/evVQoW5ZSTZqQPyws1QfG33R0MM2TB9TUlOqcs3btWnnJgtzWiUYQ51xKu3bt8PLyol27dowfP/63ytBSJRERETRu3JhmzZqxfPnyHLPD3t6ee/fuIZFIsLa2ZsKECRgYGHDixAkmTZpEs2bNcHZ2TnJOfHw8zs7O8odgJiYmVKlShZMnTxIaGgpA27ZtcXJySnNtiURCa2trbgQFpdnl5R0wsXNnZs6Zk+I8wcHBeHh4UKlSJSwtLQkODsbFxYWdO3diZmZG+/btWbBgQaplaIsUKUKhQoV4+fIl+fPnp1GjRnh7e1OjRg3mzJmjlMvh48eP/PXXX8TFxXFu1iwsJk1K95zt9vb037GDRYsWKVRzPDY2lrVr1+Li4sL169epW7euwvZlB4I451KkUikuLi5MnjyZmJgYHB0d6datW677ds9pxowZw7lz5zh8+LDKuqYoS2I6cKdOnRCJRBw9ehSRSES3bt3Yt28furq6HD9+XKkst759+3Lnzh2OHTuWJLwsNRKjNRJjHVK69e+bNy+jLlxQunDT8uXLcXV1BaB69erMmTOHb9++8fXrV0JDQ9HT06NixYoUKVIEmUzGjRs3cHZ25sGDB9SsWZMNGzYovSbAw4cPGThwIJ1jY9n5X1OCtDjn4ED/06f5+vUrhw4dQk9PL8Vx8fHxHD16lI0bN/Lx40f69u3LmjVrcl3TXUV1UIjWyGbEYjEDBw7k6dOn9OzZk8WLF9OjRw/u3buX06blGoKDg7l48SIdOnTIMWGGhJrVGhoaTJ48mTlz5nDy5EkqVqzIrl27UFdXZ+/evUqnH+fPnx9NTU2FfegtW7bEcuVKHAoWTLHLy8oGDZhw9WqGRFLyX4RHhQoV5L7iGjVq0LRpUzp06ED9+vUpUqQIkJB9V69ePXbv3s3evXvZuHFjhtYEqFq1Kjt27OCTgu/dJ7GY6dOnExMTQ+vWrVm6dGmyXX5ERAQdO3Zk9uzZNGzYkEePHuHi4pLrhFkZBHHOIYoWLcrGjRu5fv06+fLlo3fv3syePZuvX7/mtGk5zpQpU4CEanc5hb+/P8eOHaNu3bpyESpevDh79uzBzc2No0ePUrhwYaXntbW1JTY2Fl9fX4XGBwQEEBgYyJvatSkjEtFSTY1/Gjbk0KhRvDl/HpuNG5W+64qOjqZPnz64urpibm7Opk2bFBYxkUhE1apVM123xdTUlKYzZxJA8hj4RKTAWyCmTh0aNGiAp6cnDRo0YOfOnYwfPz7J2EWLFvH69Wt8fX3Zv3+/ytqg5SSCWyMXIJFI2LRpE9OmTUMmkzFhwgQ6dOiQ02blCAEBAdja2tKjRw8mT56c7etHR0ezZs0adu3ahYaGBkeOHFFppIhUKsXc3JxOnToxffr0dOs/tG3bloCAALS1talWrRpLlizJ1N/Oq1ev6N27NxEREYwcOZIBAwbkWMdqiUTCSktLtv3XiT0ll82gQoUYdeFCkqqKrVu3RiqVcuzYMXlt806dOtGiRQvWr1+fTdZnHMGt8QuhpqbGsGHDePbsGba2tsycOZMrV67ktFk5wpEjR5BIJPTs2TNH1r9w4QI7d+6kVq1anDt3TuUhfGKxGA0NDfbt28fw4cMJSKPoU2JGX5s2bbhy5QobNmzI9KZm3rx5xMbG4uLigoODQ44JMyR87s3mzaMLKTfm7QpUmj49WTJJpUqV+PDhg7yOfFxcHK9fv/7l07V/RngKlYvQ1dVlx44dBAUFMWvWLNzd3SlUqFBOm5Wt+Pn5KeWTVTUlS5YEEqJqfhZCSWwsH35KpMlI6rmmpiYFChTg5s2b2NnZ0a9fPzp06EDJkiUJCQnh7NmzHD9+XO76aN26deYv7D+ePHlC06ZNsyxLUFlatmwJK1dSbeZMzCIiMAACAX89PUaNHcv+/fuZNm0as2fPJiwsjCdPnnDv3j3EYjHW//WHDAgIIC4uThBngaxFLBbj4uJCtWrVmDt3LitWrPjlSx8qw5s3byhVqlSO7ejKli2LWCzm5s2bSUqwJvbCq/Fjr8BlyzLUaksmk1G7dm0mT57MyJEj2bp1K5s3b8bAwICPHz8CUKhQIezs7Bg8eLA8WSSzXw5+fn5ERkZmuCRnVtGyZUvKlSuHra0tTZo0wd7enlq1arF37158fHzQ0NBgxowZiMVi8ufPj56eHlOnTpUXh3rx4gVAig0/fmUEcc6FGBoasnnzZjp37syRI0f+KP/zly9faNiwYY6tnydPHkqXLs3Tp0/lrynTKzAloqOjmTt3Li9fviQ0NJTw8HDy5MlD0aJF2bNnDyEhIezYsYNLly7Rpk2bFNOVVfHlsHv3bkQiEY0aNVJofHZSunRpSpYsyevXr7GwsAASmu+uXLmSypUrM3ToUGrXrp1itcAHDx6gq6urVM2aXwHB55xL6dSpE/369cPJySlNv+Tvxvfv35PF/0qlUlq3bk2DBg1wcnIiKioqS22oXLkyQUFBQMJutfaOHUDKhYcAau3YgSQ2NtX5Pn/+jJeXF/7+/ujp6WFlZYWdnZ38eNGiRRk3bhyHDx/GyckpRWFu6+KC/k/hY4lfDq9WrFDouq5evUq1atUyFGWSHVhYWBAcHCz/vXjx4mhraxMTE0PDhg1TFGaJRIKXl5c8Fv13QhDnXIyzszO6urosWrQop03JFt69e0dcXByhoaFcvXqVR48e8f79e2bPns27d+8wNjZm9+7dNGrUiKFDh2bqSys6Oppjx47Rr18/rKys6Ny5MwMGDMDDwwNTU1MiIyOJ/c+NYJhKPWr4f6utD/v2pbqWsbExjRo1Ii4ujpEjR7J8+fJkqeCpoYovB0iIAw4JCVG6PnJ2UqhQIeJ/SErx8/MjJCQk1QbSAFeuXCEoKAgHB4fsMDFbEdwauRhtbW0WLFhA79698fPz+y1iN39k9erVXL16lS5dutC2bVsGDx4MwN69e9m7N2m5n8R056dPn7J7926OHTtGu3btKF26NA4ODnz//p3w8HB69+6NlpYWnz9/xtPTk7x585I/f360tbXltbPPnDnDoUOHiIyMpGDBghgbGxMVFYW/vz+zZ8/G1NQUmUxGdHS0ynoF/v3331haWuLn5ye/bVeED/v2JXFl/IyifRh3796NRCLJdf7mH9HR0UkizhcvXkRNTY2//vor1XMOHTpEtWrVqF27dnaYmK0I4pzL6datGzNmzGDr1q0sXbo0p81RKZ6ennz8+JF58+axYMECAKZPn07VqlX5/v070dHRfP/+HalUKu/cUbFiRebNm8fo0aM5cOAAe/bsYfr06fLmrCdPnmTNmjW4uLjg5uYGkKxOtLq6Oqampjg6Oibzbzs6OnLx4kWKFSuGjo4OYSrqFZhY/F3RHXMiqvpy2LdvH2XKlKFChQpKrZ+d6OjoIJFIiI2NRVNTkwcPHqCjo5NqJmJISAgXLlxg+fLlv51LAwRxzvWoq6szadIkHB0dGTlypDzU63dAU1OTypUrM2HCBP79918aN26ssHgVLVqUoUOH0qFDB6ysrLC3t6dKlSrMmDGDLl26IJVKqVy5Mnv27CE8PJygoCA+f/5MWFgYTZs2TfUPfu3atezevVt+l6KqVlve3t5oa2tTsWJFha4vEVU0kr19+zafP39m2LBhqhUxiQQtHx/e+fgQSEImX01z8yQJI8pQqVIlIKFo/6xZs/j48WOa/nFPT0/EYvEvXbM5LQSf8y9A//790dXVZdu2bVm70A/dKPL7+CjUXSOj/PvvvwQGBlKsWDEsLCwYN25cEmGWxMYS4OrKq7//JsDVNVWfamKCgpGREa1bt+bgwYOIRCIiIiLo378/YrGYQoUKUaFCBRo2bEibNm3SrQnRq1cv+W2yIq22fO3t0w1p8/f3p169ekqHCBp268YHsTjNFOf36Xw5LFu2jPz586u0PZWOtzelmjSh3KBBNN20iR6bNtHCwYHVTZrgrWDbrJ+pUqUKLVq04MiRI/Iv1KJFi8qPx8fHy6v6yWQyDh06RMeOHZOM+Z0Qds6/AFpaWowdO5ZZs2YxfPjwLAkZSqkbRayeHh+nTFG4gaiiXL9+ndGjR1OiRAnmz5+f7LgyYWO7du1CU1NTHpNsYmKCt7c3N27cSLOJqVQq5cqVK7i7u6OpqcmSJUtSHWsybhxeQO0dOzD8waZAsTihiWs6oWxBQUFEREQk67quCJltJBsaGoqfnx89e/bMcKGin/mxQP6PGAFbwsLoOnYs/NQ9XFHGjBnD+fPnsbe3JzQ0VB65c//+fRwcHIiOjiZ//vzo6+vz4sWLXyJdO6MItTV+EcLDwylZsiQdOnRgwoQJKp07rW4UIiBg5UqVCfTNmzcZOnQoBgYG7NixI1k96x9jilMSop8bp7Zt25b3799ja2vL7Nmz0ywCFBQUxN69ezl//jwBAQHExsYiEomQyWSsVEBMMpoEsmDBAvbt20f58uXZv39/hsrDJn5h/fjl8F6BL4eZM2dy5MgRvLy8Mt35BACJhAo2Nqh/+pRmfen6enocP3UqQy6OBQsWyGthDx48mGLFirF48WKKFClCv379ePDgAdevXycsLIywsDDy58+fqUvKboR6zr8h06dPZ9WqVZw+fZqCBQuqZlKJJN1uFJGFCvHmwgXIoC8xkdu3bzN48GCKFy/Ozp070dXVTWpKbCy6Fhbp+neDfXzkovjp0ydWrFjB8ePHyZcvHwYGBujo6CASiejQoQN2dnaIxWKmTZuGp6cnMpkMIyMjGjduTKNGjTAzM6Njx45oaGhw8uTJTF1fSkRFRdGkSRPy5s3L169fsbe3z/CXq7JfDrGxsVhaWlK7du20m6sqQX4fH0wGDEh3XFPAfts2pSJTEgkPD2ft2rWcPXtWHm9epUoV1q5dK/8yf/nyJXZ2dhw+fDhJzPivgCDOvyFBQUGUKlWKAQMGqKycptb165QbNCjdcc83byY6A7flibx8+ZKuXbtStGhRdu7cmWJPvgBXV1qn4V5I5MSkScnCxm7dusW2bdsIDAwkJCREXno1T548VKhQgQcPHtC0aVMmTZqUbAe5Z88eFi1aRKdOnWjbti3m5uYZSh93cXHhypUrfPr0ibCwMKKjo4mJiQESoiWWLVvG/fv3uXr1apY2V4iNjWXZsmUcOnSI6Ohotm7dSp06dVQyd8HjxzFWoFpgD6Dm4sVpxiinh0wm48WLF0gkEkqVKpUkCeXTp0+0bduWCRMmyCN9fhUU1UHB5/wLUbx4cQYOHMiePXuwt7dXiQ/xnY8P5RQcVywT4rx3715iY2PZtm1bisIMmQsbMzc3lz9QPHPmDOPGjWPs2LG8efOGY8eOIRKJGDZsWIq39p06deLo0aN4eHjg4eGBuro6hQoVonz58tSvX582bdqk2n0D4GtwMKu7dEEjJISCmpqElixJzZo1MTAwwMDAADMzM0xNTenevTs3btygW7duODs7q7y4U3x8PCtWrODAgQNER0fTqFEjHB0dVVoQKP6nu53UCASsFRybGiKRiHLlkn86v379ypAhQ9DV1ZXHxv+OCOL8izFx4kQ2btzI0aNH6d69e6bnC1RiXGa6HQYHB6OhoUGJEiVSHaOKsDFIqLwGCYXtdXV1mTFjBvHx8akWiNfS0sLNzY2vX7/y4MED7t+/z927d7l79y7Xrl1jxYoVaGlpkTdvXkqVKoWenp68lkORCxfo6+vLwcTJYmP58PIlty0t5b7gmJgYNm/ezIYNG1BXV+f58+e0bduWunXrsnLlygx9yb58+ZLRo0fz+fNnpFIpUqkUiURCfHw85ubmjB07lurVqys9b3pE1qpFrJ5euj5nfz09lVa+e/nyJUuWLEFfX59Hjx7x7ds3/v33398qtPRnBHH+xTA0NERDQ0N+u5xZYurUIWDTpnQbiMZk8rb469ev6Ojo8ObNGw4cOECBAgUYOnRokjHKxBSfOnWKyMhIWrVqlUTcoqKiOHv2LBoaGnL/pIaGBhoaGunaWLhwYRo3bizPopNKpbx+/Zp79+7x77//cubMGWJjY3nw4AESiYSOQEppQfKCSDIZb2rXZtGiRQQGBlKpUiVWrVqFuro6Gzdu5MCBAzRv3pwFCxYoHNkglUpZsmQJ+/btQ0tLi7Zt26KlpUWePHmIiYlhz549+Pn5ZTjWOF3U1Pg4ZQrGY8emGj0yFpg4ZYpKbfD39+fKlStUqlQJmUzGqVOncnVCjSoQfM6/GHfu3KFWrVrs2LFDJTsTiUTC6iZN2BIWBijejUJZOnXqxIsXL5BKpfIIiUOHDiW7bVUkWqPwgAE0bdoUiUSCmpoaBQsWpFKlShQvXpzjx48TGxvLoEGDGDlyZIbt/RkvLy+mTJmCm5sblStXJvzrV0o3a4ZBGl8k70UiSstkFCxcmAULFiRLnX706BFTpkzhzZs31K9fn9WrV6dY3CcRPz8/hg8fzufPn7GysmLatGnJol0ePHjAqFGjCA0NZfz48fTu3VsFV58cHW9vCs+Zg/Z/nxtIaCk1t1Ahqs2enaEwurS4ceMGDg4OPH/+nLJly6p07uxG8Dn/pty4cQM1NTV5NlVmUVNTo/qcOXQdO5ZVwI8Og3ck7IIsZ8/O9C6ofPnyBAUFYWVlxdChQ2nTpg2bNm1KFl+sSEzxwoULkUgkrFixgv+1d95xNfb/H3+eNpWyFSErIyuUlZl9cxuRTdy3vWWFzKyvPe/cpJCZvckWkhGF1m0klUIl0Trn/P7o7vykdarTcl/Px+M8qutc1+d6n+t0XudzvT/vERQUhIeHBw8fPpRVL5s5cyY1a9bMlb0/8/r1ayC5IYJIJOLLuXNUzKLmhYFUStfixVl540a6C4z16tXD1dWVbdu24eTkRPv27Rk5ciTdu3dP4xu3s7Pj1KlTAKxfv55OnTqle9769evj6uqKjY0Na9asIT4+ntGjR+fwVWfMFwsLvrRvnyZDcEouMgQzIyU66fPnz0VenOVFmDkXMaytrfHw8ODIkSMKHdfNzY01K1dSKzw8VTeKWXPnKnwWBDB79mwuXrzIqVOn0pTIhOSwseCDB7l56BCPQ0MZtH07Zi1bAmBubo6hoSF79+6V7Z+YmMjHjx/R09NTuK2Q3HT2/PnzPHnyBGVlZV6vWEHPn4ozpccEXV0m3L6d5X5eXl7Y2trKKu1paGhQtmxZjI2N8fX1lX05dOzYkY0bN2Y5XlJSEkOGDCE0NJRbt25luX9hJywsjE6dOnHhwgW6du1a0ObkCmHm/Ity//79PFnosbCwoH379jx+/JiIiAg6ly2LiYlJnvku7ezsuHz5MpMnT+bIkSNpFsW+JyQwydWVN8HBaGpq4j51Kvr6+sTExBAVFZWqSwkk+5XzSpgh2Weurq6ORCJBWVlZ7sXL73K2GWvUqBHnzp3j7du3PHv2TLYoeenfRI5FixZx7949rly5wvPnz7OMwFBRUWHgwIHY2dnx8OHDbBdcKmykzJx/7if4KyPU1ihCREdH4+fnR/369fNkfGVlZZo1a0b37t1p1qxZ3i0qAVpaWmhqavL27Vs6duzIs2fPZM/5+/vTuXNngoODWb58Obt376ZSpUqoqKhQo0YN+vXrR5cuXfLMtvSwtLQkLi6Oy5cvA/LVvAgCRNko0SkSiahatSq9evViwYIFuLq6cv/+fW7cuIGlpSX29vZoaGiwbNkyEhMTsxyva9euaGtr8+eff2JpacmZM2eQZOKKKcxoaGigpqbG58+fC9qUfENwaxQh3Nzc6NSpE6dOnaJatWoFbU6uMTMzo379+rx+/ZrPnz9TvHhx4uPjSUhIQFdXly1bttCwYcMCsU0qlXLv3j0kEgnGxsbo6urSpk0bypYtKyuulNXipV3dugw4eFCh/RAPHz6Mvb097du3Z+3atVlGobx58wZXV1dOnz5NZGQkGhoaTJw4kZEjRyrMpvzCwsKCcePGsXjx4oI2JVcIbo1fEA8PD7S1tdO0cSqqJCQkYGRkxNq1a9m0aRPfv3+nfPnylC9fHgsLiwLrCffx40eWLVvGtWvXZNsqV65M8eLF8ff358GDB5iZmVF1+nTm3rrF5H/+SbOQ+nfdugxMpztKbpu0WllZERERwc6dO7GxsclSoKtWrYqNjQ3Tpk3j/v37bNiwgZ07dxZJcdbR0flPzZwFcS5CeHh4UK9evQLrTK1oatasiaurK2PHjmXRokUFbQ5SqZQLFy6wcuVK1NTUOHbsGA0aNODBgwfcvXuX3bt3A8k1Tv744w/CwsLY/c8/bFZRoXlSEk309Pimo8NOX186GRjwcxFPRXXwnjRpEq9eveLKlSs8fPiQFi1aZHmMiooKrVu3JiIiAjs7O16/fp3uQmxBcOnSJdatW4eqqirDhg1jwIAB6f6P6+joCD5ngcKHVCrFw8Mjz/zNBcHy5cuJi4tj3759BW0KHz9+ZPr06cyZM4cuXbrw/Plz+vbtS40aNRg8eDDGxsbExcUByTVOVqxYwe7du6lWrRp3PTzQ7N4dx+/fORgaSol/XSA/klWT1nOjR/P27ds0dkkkEnx9fbl06ZLMXyyRSPD09KRy5crZLiyU0vnlwIEDaZ47evQoz58/z9Z4ueHBgwd06dIFGxsblJWVSUxMxN7eHjMzMyZPniyLUElBR0cnVQPYX53/7MxZKpUSGhqKnp5ekWhx8/btW8LDw/MkUqOgqFWrFrVq1cLZ2Rlra2uF1RvOCSnJHUeOHKF///5pnldSUkJJSYmnT58C/98wtXLlyohEIlavXp3h2N27dME9JCR5nJ/HJdlHPejBAwx/+w1lVVWKFy+OmpoasbGxxMXFyURZU1NTVtM4KiqK5cuXZ7uAUrly5TAyMuL48eOIxWJmz56NhoYG+/fvZ/Xq1bJ6Fvv378+z98Pf35+5c+cSGBhIyZIlsbOzo3fv3igrK/PgwQNcXV25evUqN2/eRFtbWzaL/v79e4G5ugqC/5w4f/jwgX379rF79258fX1p2rQpc+bMoU+fPnkanZBbPDw8AH6pmfPbt295/fq1LLGjIPn27RsjRoxIV5jTY/78+Vy7dg19fX1atGhB69atad68OVpaWqn2+/DhA9VCQsgs8E4JqAz8r2dPAipWJDAwkNjYWCpXroyBgQGVK1dGSUkJBwcHNm3aJDtu1apVREZG0rt37wzHTs/HvWbNGtatW8fRo0c5deoU7du359q1a5iYmBAbG0tAQACRkZEKF+cPHz4we/ZsvLy8KFasGFOnTmXw4MGpap40b96c5s2bExkZyZkzZ2RRPN++feP27du/zHqLPPwnxDkpKYkLFy6wa9cuzp8/j5KSEh07dmTw4MGcO3eO/v37U716dWbNmsXw4cMzLJBTkHh4eFCxYsVfpiVPVFQUgwYNQkNDg507dxb4NS9Xrhzv37/P8PnixYsjkUj4559/UFVV5fr168yYMQOxWMyFCxc4duwY5cqVw83NLdUXzaFDh5A3+rqmlhadJ07M8Pk2bdpw48YN7Ozs+P79O8HBwcTExGS4f0Y+7qARI9i2bRu+vr7s3LmTy5cvo6mpiaWlJba2tlhYWCi8Yt6LFy8YNmwYUqmUESNGMHr06ExrkpcsWZLhw4fL/t6yZQuenp64uLgo1K7CzC8dSufr68uePXvYu3cvYWFh1K1bl969e9O9e/dU/xg+Pj7s2bMHNzc3ypQpw5QpU5gwYUKmzSXzm5YtW6Kjo/PLdOCePHkyt27dYt++fYXCVTNv3jwiIyO5c+dOus9///4dExMTRCIR9erV48aNG7x7905WC8PR0ZHRo0dz9+5dtLW1Zcf16dOHuuHhnPryJUsb0qtTnRE7d+5ky5YtnDx5Mt105ux0lHn9+jVSqRSpVMqQIUMQi8UcOHBAYSnw7969o1+/fmhqarJ3795sd2QJDw+nR48eTJ06lVWrVinEpoJEXh385RYEv337hqOjI61ataJOnTo4ODjQoUMHjh49yuHDhxk0aFCab2xjY2PWrVvHmTNnaNeuHUuXLsXAwIChQ4eyZcsWPDw8FFYFLickJiby5MmTX8qloa2tjVQqTbdeb0FQoUIFgoODM3y+WLFiuLi4EBAQgKurK+PHj09VpCilQlpERESq48LDwwmoUIH32WjSKk9z26tXr1KmTJl0493FCQk0cXYG0vdxA5g4O8vGNTQ0pFq1alSvXp05c+YQFxfHyJEjFZKw8u7dO6ysrFBRUWH37t05apW1fft2NDU1mTdvXq7tKUr8cm6NWbNmsX37dlq2bMn//vc/2rdvj7q6ulzHVq5cmYULFzJ+/HgOHTrE3bt3OXr0KAkJCaiqqtKwYUPMzMwwMzPD1NSUmjVr5ktY27Nnz4iLiysUM0xF0bJlS86cOYO/vz+NGjUqaHOIiYlBLYt4YxMTE1asWMGKFSuYMGFCqudSROfu3bupBPPPP/9k/fr1bKpWjVX//JNlk1Z5w+3evHlD8+bN0/XVhxw+nOr4n1ECKkokPDt8ONVMXSKRcPDgQdTV1fny5QszZ85kw4YNSCQSHj58iK6uLlWrVk1zneLi4nj8+DFeXl68fPmSoKAgPn36xPfv30lISEBdXR0nJ6ccJU59//6dkydPsnLlSsW1Zisi/HLibGpqyvbt27Gzs8ux36xMmTJMmjSJSZMmkZCQgL+/P8+ePcPHx4fz58+zbds24P/9YlOnTs3TmFEPDw9UVFSoXbt2np0jv+nQoQMikYgXL14UCnG+e/cuvXr1ynK/WbNmMXny5DSlPatUqcL48eNZu3YtlStXloXSjRw5kqCgIP539CjajRox+tmzDKvt/eiK+BFZuB3IBNrAwIBr166xY8cOxo0bl0qkc9pRxtfXl5cvX/Lnn3/y/ft3XFxc+PPPP/H29iY2Nla2X0p9bFVVVRISEoiLiyPFO6qmpkblypVp3rw5VapUoWrVqjRu3DjTJguZ8f79e8RisVyx3L8av5w4W1paMmnSJE6fPq2QPntqamoYGxtjbGws2/blyxd8fHy4f/8+zs7ObNmyhd9//50ZM2bQqlUrhUYexMbGsnPnTurVq5dprd+iRvHixSlevLisa0lB8vHjR969e0dzOdtwZfQ+bNmyhZCQEGxsbNi9e7fMDWVnZ8f79++xu3sXqZ0dzeLiUkVPGKqpZemKkJDsivg4aRLKamocOXKE8ePHs337dt69e8eSJUtkmYI57SiTMit+/vw5W7ZswcvLi/v372NqasqgQYNQUlLi06dPskdkZCRly5aViXDVqlUpV66cQu8mU6r0/QrlCrLLL7kgaG1tjZubG+fOnctzt8P37985c+YMLi4uvHr1iiZNmjBjxgz69+8vV/eNzBCLxfTt2xc3NzecnZ1/qZkzJLeRUlZW5uTJkwVqR8p1NjIy4vz587ka6/v373Ts2BFfX18OHjwou3uTSCT06dOHN2/ecPz48TSLeDltbrtixQoOHTpEmzZt2Lp1a/LryUEX8xRmz57NjRs3ePDgAVKplKioqAJdGN+3bx+bN2/m27dvBR5uqSj+swuCAKNGjSI4OJjHjx/n+bmKFSvGgAEDOHHiBNu3b0dNTY0hQ4ZQvHhxKlWqhKmpKb1792bChAnY29tz9uxZQkPl69w3a9Yszp49y//+979fTpgB6taty+vXr+VabBWLxdy6dYuFCxdy7NgxxGKxwuxQVlZm7NixXLhwgQcPHuRqLLFYTGxsLFFRUaky/pSUlHBxcUFVVZWlS5fy85wop64IW1tbGjdujKenp2ybspoaj0aMAEizCPmjjzu9mh7dunXj+/fvLFu2DJFIlGNhlmdRUx7evXuHoaHhLyPM2eGXc2tAcrcKSI7cyC+UlJQwNzfH3NycgIAAHj16REREhOzh5+dHaGgo0f+29alQoQLNmjWjSZMmNGnShKZNm6bqSr1jxw42bNjAvHnz0qQC/yqYm5tz/vx5/vzzT/r06UOnTp3SJHFIJBIcHR05dOhQckJHtWqcPHmSQ4cOMXv27GynL2dEly5d2LlzJ4sWLeLChQs5HkdZWZmvX79Svnz5NLNjLS0txo8fz8aNGzlx4gR9+/aVPZeb5raxsbFpMufk6SiTHm3atOG3337jyJEjvH//nu3bt2f77lNRNUQAgoODC01ET37zS7o11q1bx/z587l9+3aBJzf8SErK+IsXL3jx4gXPnz/n5cuXREZGAqCnp0fTpk3R1dXFxcWFQYMGMXfu3AK2Om9ZtWoV586dIyoqCjU1NWbOnMngwYOB5Ou1Zs0a9u/fz5gxYxgzZgwmJiY8ePCAqVOn4uHhgYWFBTNmzMhRiNbPXLhwgdmzZ3P9+nXatWuX43Hevn2Lubk5KioqODo6punz17NnT4KCgli+fDk9e/YEcueK6NixI9WqVePvv/9Oc1xOquBJpVK2bduGg4MD1atX59ChQ3Kvd2QnvloeevXqRa9eveTq/lJUkFcHf0lxbteuHRKJhO3bt+fZORRFimA/f/5cJtpPnz4lISGBO3fuFGi9ifzE29ubYcOG0a9fPxYuXAjA1q1bcXBwYPv27WkWd6VSKQcOHGDOnDlERETQu3dvOnfuTJMmTbJdbyIFsVjMiBEjePHiBUuWLGHWrFk5HiswMBBzc3NKlCiBo6NjqjCwuLg4+vXrR1BQEPPnz2fgwIFAzoTt27dvtG/fHgsLC+zt7XNka0YcO3aMpUuXUr58eVmTgczIzRdMRvTp04cKFSpwI5cNhgsT/1mfc1RUFO7u7rRt25bo6Ghu377Njh07mDlzJn5+fgVtXhpEIhH6+vp06tSJqVOn4uDgwO7du2UVuv4r6OrqIhaLZWF1e/bswcHBgdWrV6cbdSMSiRgyZAh+fn7Mnz+fe/fu8ccff9ChQ4ccfykrKyvj6OjI8OHDWbBgAa1atcrx/0yNGjW4evUqnz9/ZuzYsanSrDU0NDh16hS1a9fG3t6ev/76C4lEkuyKsLYm7Cc3QqiSUrrCbG9vT4sWLfj27RuNGzfOkZ2Z0bNnT/T09OR2D4YcPox+BsIM/x9fHZJOneuMsLW1xd3dPdPCUr8qv5w4X758maSkJHx8fOjQoQMTJkxg586dXLlyJVXx9MJMvXr16Nq1KxcuXJC5PH51zp07ByTXHd68eTPr169n/vz5zJ49O9PjNDU1sbOz4+3btzx48IDffvuNHTt25Pi6qampMW3aNJydnfnw4QONGjVi06ZNOcqWq1u3LleuXOH9+/dMnDgxlcipqKhw+PBhmjVrxrZt2xg+fDhv3rzBcMYMIjw9uTB7NmcGDeLC7Nl89PRMI8xLly7l0KFDaGlpIRKJUpUU/Rl3d3fatGlD27Zts1UPedeuXYSEhLB06VK59s/pomZmNGvWjD/++INFixblerG2qPHLifNff/0FwOnTp6lZsyZ//fUXjx49QktLi6CgoAK2Tn6mTJmCWCxmwoQJsjrCvyoJCQlcvHgRSA7lcnJyYvbs2SxbtkzuMUQiEc2aNZMd8+TJk1zZ1KhRI44ePYqlpSXTpk2jffv2aeoLyzvOxYsXCQgIYNKkSfj4+MgiNZSUlHB0dGTOnDn4+vrSp08fzpw5g7KaGgbDhmFoa4vBsGFpXADHjh3D1dWV3r17c+XiReaYmVH1/n2WdeyI34sXaWyYPXs2ampqfPv2ja5duzJhwoQsZ8OvXr1i586dNGrUiA4dOsj1WnOzqJkZ48ePp06dOgwaNCjTQk+KIB+9vFnyy/mcS5YsiZKSEuvXr6dJkyay7d26dUNXV5eDcrSzLyxs376dHTt2oK6uTo8ePZg3b16hSUSRSCS8efOGuLg4atWqlWPfbFhYGDNmzMDPz4+hQ4cyduxYGjRokOPXKZVKMTAwoFOnTsycOTNHY/yMp6cnCxcuRENDg5cvX+Yofv3WrVtYWVkRFhZG5cqV6dKlC927d5dFIkRGRjJkyBDev3+Po6Njqv/dH/H392fgwIHUrVuXJQ0b0mz//lTRGO+AWaqqqA0ciI2NDZCcdj5x3DgaREfje+MGD4ODCalenX0HDmS4prFu3TpcXFy4ceOG3J9VRficExISsLW1xdPTk8TERCQSCRKJBLFYjEQioXnz5ty+fVsue7KLr68v3bp1Y8+ePblaEM6K/+SCYFJSEsWKFWPu3LlYWaVuEjR69Gh8fX1xd3dX+HnzEl9fX3bs2MG1a9dQV1ene/fu2Nra5rtIR0ZG4u3tzdOnT/H29sbHx0c2i9HS0qJx48Y0bdqUZs2aUadOHbnE2sPDg9mzZ6Opqcnx48dp2rSpQmwdPHgwz58/V2h5ST8/PywtLdm9ezejRo3K0RhJSUncuHGDgwcPcvz4caKioqhZsyZdu3bFysoKVVVVOnfuDCR3JdHTS1tsdP78+Zw+fZrDVlZY/uu7TW/x0BK4oqWFpaUl0U5ObBGJqPjDR/0dMA2Q9umTrtti6NChhIWF4ebmlq3XmJtojYcPHzJlyhS+fv1KjRo1MDY2Rk1NDVVVVdTU1AgKCuLGjRu8evVKIdE5PxIdHU2zZs0ICAhg3Lhx7NixQ6Hj/8h/UpxfvXpF9erVcXBwoGXLlqmeW7VqFS4uLhgbG9OsWTNMTEz49OkTf//9N9WqVcPCwoJ27dpRqlQphdulCH4UaTU1Nbp37868efPyJJpDKpXy/Plznj59yrNnz/D29pal0ZYtWxZTU1NatGiBmZkZGhoa3Lp1i+vXr3P37l2+ffuGpqYmJiYmNGnSRCbWP842pVIpTk5ObNy4kfbt23Po0KE04Wa5Yfv27UydOpW7d+8qNJRyxowZ+Pv7ExAQkOvsz/j4eC5fvsyhQ4c4efIk2traLF26lAoVKtC/f3+qVKnCnj170hT7GTt2LD5Pn/Ly+/dMZ6ghIhFVpFJ6A0f/3Z6RiN/X06Nt27bMnz8fSK6CaGZmRqtWrdiyZUu2X1tKnPOPM/r3mcRXSyQSlixZwqlTpyhbtiyrV6/GxMQkzX6xsbF07dpVVi1SEcTHx+Pr64utrS23b9+mQYMGfPjwgYCAAIWMnx7/SXF+9uwZDRs2ZO/evWlWr798+YK9vT1Pnjzh06dPJPybsVSuXDni4uL48uULIpGItm3bsn79+lx/+PIKPz8/duzYwdWrVzMUaX9/fzw8PBgmZ23gnzlz5gy2traoqanRuHFjWXcKMzMzqlatmmG2VkJCAo8ePeLGjRsysY6NjUVTU5NGjRrRtGlTGjdujIuLC1euXGH27NnY29vn2CWSESn/B46OjlknqYjFaD5+jEpEBEllyxJrYgIZhGwFBATQr18/HBwc+PPPPxVm77t377C2tubq1asMHjw42WWxZAnKysoMHz6cQYMGyb68+vfvT+2wME5ERWU5rgWwB6hI+otLEuC9SERVqRRtHR1ZLev4+Hjatm2LioqKbDKQXeSNr3779i2jR4/mw4cP9OrVi3nz5qVJRPqRHTt24OjoyIwZM2T/k/K0rpJKpYSEhPDs2TPZ4+nTp/j5+ZGUlISamhobNmwgLi6OmTNnEhQUpPDZeQr/SXH+/v07mpqaLFmyhD59+mS4n0Qi4fHjx0RFRWFhYQEkx6WuWbOGe/fucfTo0UKfLu3n58dff/2Fm5ubTKTnzJnD4sWLcXNzQywWo6+vz65du7L9T7Zw4UJZlqO85VbTIzExMZVYu7u7Exsbi5aWFs7Ozqky5BSJRCKhVKlSDB06lHHjxmW4Xwk3NyqsWoXahw+ybbElSxKxYAFf/3Uv/MysWbN4/vw5gYGBORKtzGzesmULc+fORV9fn86dO3Pt2jUCAwNRUlKiR48eDBs2jMmTJ2OZmMhmORqdLgXs5Dh392LFCDUyStVod8GCBZw5c4bLly9Tvnz5nL+wTNi6dSu7d+9GQ0ODpUuX0qlTpyyPiYmJYdmyZXh6esqavVatWlUm1IMHD0ZLSwsfH59UIuzj48Pnz5+BZDdczZo1ZT0sa9WqRY0aNdDS0iIqKoo2bdrg6OjIyJEj8+R1/yfFGaB69eqYm5vLFkOyw8ePH2nfvj1Lly7NVNwLEz+KtJKSEhKJhH79+tGwYUNWrVpFYmIiU6ZMydY/Ws+ePenevbusNKqiSGkaoK+vn+MSkj/y5s2bDHvKde/enejoaBwcHNJ9voSbGwYzZoBUyo/3AVn5Rl+9ekXv3r3Zvn17psKfU168eMHo0aO5f/8++vr6tGnTBn9/f7y9vUlMTERZWZnR1avj4O+f5VgrlJWxlaMGySBAamWFgYEBT58+5cmTJ3z8+BENDQ1cXFxkjQQUTcuWLYmJiaFChQpYW1vTu3dvud10KclbKQLs7e2Nl5cXc+bMYf/+/bx//x4lJSWqVKmSRoj19fUzrdUxYMAAmjRpkmdd4f+zSShGRka8efMmR8eWKVNGtiJfVDAyMmLDhg0cO3aMESNGsG/fPhYvXkyfPn04efIkDRo0YN26dVhZWfFFjlZJnz9/5s2bN7Rq1UrhtqqqqmJqapprYY6Pj2f8+PEYGhpiaWmZ7j5t2rTh6dOnslomqRCLqbBqVRphhv//QDTcs4er6WTFVatWjW7durF8+fI8CXGsW7cu9+7dw9PTk65du3L69GmePn1K8+bNZQk64pYtCcmis0oQ4CZncahQ4PDhw6xfvx4PDw8qVKhAr169UFVVZcCAAcyYMUPmBlQkderUkdXtWLVqFR06dOBFOqGA6ZGSvNW1a1eZewySk3/ev3/PwoULuX//PqdPn2bdunWMHTuW9u3bU7FixSyLKJmZmeHm5lbgYXW/nDgHBwfnanGpZMmSPH/+XIEW5Q+1atVixowZqQrX6+np4ejoiI2NDf7+/lhYWHDlypVMx/Hy8gLIE3FWBO/evcPc3BxHR0eAVJXffmTQoEEUK1aMSZMmpRFRzcePUfvwIY0wp5DSDfvm8uXpVr8bN24coaGh2NjYKKSVU3o0bdqU3bt3ExISwubNm4mMjMTLywuxWIxUSQmPIUOAjKvOPbW2plTv3rxLZ58f9w0C7ikr061bNy5fvoy7uzsHDx7E3t6eCxcu0LdvX65cuUK3bt0ULtANGzZEIpGwb98+Fi5cSGxsrMz1IC9RUVGcOnWKRYsWoaqqKqui17p16xwvBpuZmREWFoavr2+OjlcUv5Q4x8fH8/Llyxz7i2/fvk1MTEyertTmN0pKSowYMSK5C7SeHjNnzmTAgAGyOhY9evTAwsKCNm3a0KpVK+bMmYO2tjaVK1cuaNPTcPXqVUxMTAgODsbZ2ZnJkyfz6tWrdPetUqUK586dw9/fn9mzZ5OUlCR7TuWnPn8ZoR4ZmW7ZWUNDQ+bNm8eOHTuwtLTM0+qHOjo6TJgwgWfPnnHhwgVMTU1xcnLiz/PnWd20aaap3ouXLWOHkRGQsYi7tmrF/fv3mVy/Pt92705V3lNHRwc7OztsbGwIDw/n6tWrctn847XODHNzcyB5ATtFlOXpKBQSEsL+/fsZNWoU7dq1Y8GCBaioqODs7MybN28oXrx4qgqP2cXExARVVVW5X29e8UuJ8/Pnz0lKSsq2OLu5udGpUycmTJiAurp6jvzVhR0jIyOOHDnCiBEjCA0NJSQkhMTEREqUKIGhoSEmJia0bduWChUqZOmTKwj8/Pzo0qULNWvW5PDhwxgbG1O5cmU+f/6cYaq2qakpR48e5datW9jb28tuU5P+LSmbFaGkbdiawsCBA9m0aRMXL16kXbt2fPhhUTEvEIlEdO3aFQ8PD/z8/Ojbty/zHz1i6ahRbO7Th0HAut9+S5PqPdjVlSP9+/P+p/FCRCLOWVtTs1YtypuZ0W3NGnoePEi3NWso26wZr9evT/VaVVRUuHTpUqY2JiUlMWXKFExMTGjevDnz589PN108ISGBXbt2YWtrCyTf/bx48QJVVdV0Y7sheeK1c+dOBgwYQJcuXdiwYQOlS5dm+/bthISEcO/ePQYNGsSLFy+oVq1arppsFC9enAYNGhS4OP9S9ZzPnz+PmppathYw4uLisLGxoVy5ctjZ2fH7778rdBW+MKGurs7MmTMzzZyztbXN9q1lfrB161Z0dXXZunWr7P1JiUL5559/Mkxg6d69O7t27cLa2ppy5coxfvx4Yk1MiC1ZkmKRkRmGmAUDt4ERmQh5u3bt2LNnD5MnT8bMzIwLFy5Qp06d3L1QOahRowY7d+6katWqzJ8/nz///JNDgEGdOumGqxnb2WF8/jyNY2MxUFZm4PTpVBo0CLZulatnobq6OvXr18fb2ztDm7y9vZkwYQJRUVFYWloSFhbGmTNnOHfuHNWrV2fy5Ml8+/YNZ2dnAgICSExMpFKlSkycOJFevXrh6upKlSpVMhTVzZs3c/DgQfr168eiRYvo1q1buotpPj4+CmlpZWZmxoEDBxCLxQVWDe+XmTl/+vSJ//3vf/Tv3z9biRkpYWerV6+mf//+v6wwy0tSUlKhi/GOiYnB2dmZfv36pXp/UlwvgYGBmR4/cuRI7O3t2b59O4cOHQJlZSIWLAAyvt2fDpSrUCHdZIgfqVevHi4uLqipqdGyZUuuX7+enZeWK+bNm4eVlZUsqiClPkl6lCpblpvAqeLFqfJvl5TMehZCcs/CFBdHs2bN+Pz5c7o+9tWrVzNs2DCUlZVlDQt27NjB+fPnsba25sOHD0yePJk5c+bw/v17BgwYwMGDBzl//jzjxo1DR0eHT58+ZVhU38vLi3379mFvb8/BgwexsrJKV5ilUikvX75UmDhHRUXlSzeljPhlZs6rVq1CLBZnOzngxo0bKCkp5cuMpyggFosVnhSSWw4dOsS3b9/o379/qu3a2tqUKlUqS3GGZCELDw/H3t6e6OhoxowZwzlraxrt2cOPUeDBJAvzCZGI9XPmyDVr0tPTw9nZmZkzZ9K5c2d27drFiH8FMC8RiUQ4OjoyceJEXFxc8PX15cWLF9StWzfNvkpKSigrK+Pk5AQkl/dsmMliZkp5z2eHD2MwbBhNmjRh586dODk5oaamRmBgIEFBQbx48YLY2Fg6d+6MnZ1dqozGSpUqMXXqVCZMmMDNmzdRV1enefPmab78/f39+fr1a7phkXFxcSxcuBBTU1NmZFKkPzAwkFOnThETE5OmA01OqF+/PsWLF+fq1asK67aTXQrXpzCHBAcHs3XrVqytrSldunS2jvXx8cHIyKjQFBQqSKRSKTExMQXa0DM9UnzF6c2WKlWqJJc4i0QiNmzYQJkyZVi4cCEfP35k3rx5HDc25uby5ahHRhJKsiujXIUKrJ8zR5agJA/a2tps27YNe3t7Ro4ciZ+fHx06dCAiIoLw8HAiIiL4+PEjffv2ldXPUATFixdnz5492Nvb07NnTyZMmMDVq1dRhlSZj1GfPtG+fXuZyy875T0TExO5efMmABs2bJA9p6urS9OmTenRowddu3bNcJ1CSUmJjh07yp6XSqV8+PCBoKAgXr58ydatW9HR0aFXr15pjl23bh2hoaFcuHAh1Rfl169fuX79OhcvXuTixYu8evUKVVVVWrRooZDa1qqqqjRp0gQ3N7cC60b0S4jz4sWLKVasGMOHD8/2sZ8+faJt27Z5YFXR4tu3byxbtox79+6xdu3agjYnFW3atEEsFuPl5ZWqZkpiYiJBQUH89ttvco0jEolYsGAB5cuXZ9y4cURGRrJixQradezI48ePiYiIYETZspiYmOTIz6iqqsqiRYswMDBg5cqVrFy5Ekj29ZcuXVpWzP/48eNy2ywv+vr69OzZk82bN6N77Rp6q1enynx8Crj+0Eg3O+U9jxw5woEDBzA2Nsba2poaNWqgp6eXZahaWFgYu3fv5vjx4yQkJKCmpoaGhgaJiYl8//4dQJY4tXv37jSZrOfOnePQoUNs374dIyMjvL29uXjxIhcuXODOnTskJiZiYGBAq1atmD59OqampgqtNWNmZsbWrVuJi4srkMlbkRfn0NBQnJycmD59eqY5+enx6tUr4uLiaNiwYR5ZVzR49eoVM2fOJDQ0FBcXF1kPv8KCkZERZcuW5dGjR6nE2dPTU7YAlR3+/PNPypQpw6BBg5g4cSIbN25U2K2rSCRi9OjRdOvWTZZGXqxYMUQiEYmJidjY2NCvXz88PT1p0KCBQs6ZwoMHDxhfvjyVZ8zg5/QJfWDK7du8u3KFmE6d0LeyImTt2kyLJ70Xidjh48ON27epVKmS3OV2w8LC2LVrFydOnEBLSwtbW1sqVqzI9+/fiYuLQ0VFRZa1l/L5+7lO8z///MOSJUsYOnQoderUoVKlSoSEhKChoYGpqSk2Nja0bt06T0M+mzdvztq1a7l37x7t27fPs/NkRJEX53379qGsrEzv3r2zfWxK943/sjhfvHiRxYsXY2BgwIMHD9L1VxY0IpEIc3PzNIszly9fplq1aqkSb+SlT58+XLp0iV69ejFq1Ci2b9+ebZdYZujr66fZlpSUxOfPn9HW1kZXV1dh50rB28sL58+fkZL+Ip8E0LazI6ZDB5TV1Hg0YgQ99uxBQvoV62aIRNy8cwddXV3+97//ZXn+n0V58eLFTJo0CW1t7QyPSUpKom7duqxcuZIDBw5QokQJYmNjmTFjBoaGhmzevBlTU1PKlCnD4sWLMTExyVW9l+xQs2ZNSpYsydWrVwtEnIt0tIZUKsXR0ZGOHTumWogQJyTwbt8+Xq9YkSqo/kcOHDiAo6MjBgYG6X6Qcoo85y4MJCQksGLFCmbNmkWvXr3w9PQslMKcQps2bXj27Bnx/96aJyYmcu3aNfr375/jmOy2bdty69YtPn/+zNChQ3n27JkiTU7D//73P7y8vFi4cGGeVDxr+v07ZePjM+3hV+rrVzQ8PQEy7FkYDBwdMIBFT5/i7u7O+fPnqVevXobnDQsLY/ny5XTv3h03NzeWLFnCmzdvmDdvXqbCDMntuk6dOkV0dDRz585FLBazaNEiIiIiOH78OE5OTrx69YpFixbRokWLfBNmSHa5mJqaZrumtcLOXyBnVQBSqZStW7fi5+eXatb8ev16yjZrlmlQvUQiYd26ddStW5e9e/cqLOFCnnMXJHFxcbx79w5PT09GjhzJsWPH2L59Oy4uLtl2CeU3bdq0ISEhgVOnTiGVSvH09CQyMjJNBEd2adiwIffu3UNfX5/hw4fj4OCQbsq2IjA3N6dKlSpMmzaNpk2bsm/fPtmXjSIoK6fdu5cvl9XnrjR5Mic2bGBSvXoMVVKis6oqL86epd6/HdDlYcGCBRw+fBhra2tev37N3LlzsxTlH6lRowYHDx7E3d2d4cOHc+nSJRwdHSlVqhRLliyhf//+eVZ8KSvMzMx4+PChXHVpFE2RrEr36dMn/vjjD06ePImVlRW2trYoKSnJ3YXhxo0bTJ48mR07dtC6deucv6AfyE0HCEUSERHB5cuXiYiIkEUKfPz4kYiIiFRFgCpXroyrq2uBhQllF4lEwsCBAzl69CitWrVCVVWVN2/e8OrVK4V8uSYmJrJ06VJWrFhBo0aNWLFiBRUrVlSA5amRSCTcuXOHAwcO4O7uTrly5Rg3bhzjxo3LMDtOXnrr6nIyvUJPP9FRSYmb/7qKPD09iY2NRUNDg3r16mFvb5/t1/3y5UtsbGz4/PkzDg4OOV6zWLNmDXPmzGHatGls2LCBcePGcfDgQc6ePVtgEUTv3r2je/funD59mp49eypkzDwpGbpy5UqOHz+Or68vxYoVo2XLlqxevRqjf/P3FWVUZty9e5cBAwYQGxvL4sWL6dixI5C9/mXTbWy4ffu2wjplKKJ3mqKwtbXlwoULVKxYEX19fdnPlIeenh76+vpUr149X28RFcWZM2eYPHkyb9++ZdasWaxZs0ah49++fZshQ4YQGRnJggUL6NSpU54lJr169YqDBw9y+vRpEhMTsbS0ZPLkyTRv3jxHXzhVKlXizvv3mRbXDwZOb9yIw65dvHjxgtq1a/PHH3/QsWPHLFOeU2qIpBcRERsby7Jlyzh37hzW1tZs2bIFTU3NbNkvlUq5d+8epqamvHjxgsaNG2NjY5PjphHyFvzPyqZu3bphaWnJxo0bc2THz+SJOHft2pWBAwfSrFkzkpKSsLW1xcfHhxcvXsj1RuRWnKVSKXXq1JF1LfixCPi7ffvoJscH1d7CgtUeHtStW5ddu3Zl24b0kPfcF2bPxiCH/2jykJiYSNu2bZk2bZrc7eyLIl+/fsXR0ZGBAwfK1QUju0RFRTFhwgRZdELx4sXR0dFBR0cHXV1dxo4dq7B+h5D8uTh16hSHDh0iKCiIxo0bM2XKFAYOHJitEK569erRIDAQl3/XOdK7g/tTV5cpN27w+PFjRo0alW5Lt4wYPXo0b9++5a+//ko3m08qlXLq1ClWrFhBlSpVOHLkCPXr15fb/h/H6dixI69fv+b48eM5ylhNr1VWiJISjzJolZUZdnZ2+Pn5KaxaZZ7Uc7548SIjR46kXr16NGzYECcnJ4KCgnj06FG6+8fHx/Ply5dUj9zg7e2Nn58f48aNS9OdQd6geh83N2JiYuRu9y4P2Qnoz0vu3btHTEwM/fr1y9PzFCSRkZEMGDCAqVOnysqGKhpdXV1cXFy4desWzs7OLF++nNGjR9OhQwcSEhKYPHmyQmt+lyhRgmHDhnHmzBm2bduGlpYW1tbWVK5cGScnJ7nrCmtoaPDGxIT+kKbQUTDQH6i/aBESiYTD/zaHlffuydvbmwcPHiASiRgxYgRPnjxJs49IJKJ37944ODgQEBBAhw4dcuS/P3XqFNevX2fWrFk5FuYee/ZQ4acMyJSaIdldAzIzM+PFixeEhYVl25bckKsFwRQfZkZNUVeuXCmbcejo6OR6hfrw4cPo6OjQokWLNM/JG1TffdQozp8/z8CBA3NlS07OLe9+OeHly5csXLgQExMThcfPFhQRERGpPtze3t40bdqUu3fvUqtWLRYvXpxn5V1TwveGDx/O9OnTWbZsGdu2bePmzZvUrl2bCRMmyBbVFIWSkhJt2rRh+/btnD17FlNTU6ytrenUqRP//PNPlsfXr1+fqKgozDdswKxcOdqR3OWkHdCifHnMN2ygTZs2/PHHH1y+fBkLCwuaNGkil23nzp2jbNmyPH/+nIYNG/Lnn39y48aNVPtIpVIuXrzItGnTKFGiBJs2bcp2Mk98fDwzZ86kVatWspKi2UGckJCtmiHyYGZmBsC1a9eybU9uyPGCoEQioVevXkRFRckaQ/5MfHx8qtXoL1++YGBgkCO3hlQqpUaNGjRs2DDdW3Z5/L7BwMcHD1BTYEdmec+dlz5nT09PpkyZQu3atblw4YJCO1kXFFevXsXCwgJVVVWqVq1K9erVuXXrFgYGBrI07H79+mFoaMj169dzVSIyu0RERNCqVSvi4uJwcHDIs0agAO7u7ixdupTIyEgWL17MjBkzMqx9snfvXkaMGMHt27fR1taWZT2W/SHr8enTpwwdOpRRo0Yxffp0ue24ePEis2bN4uHDh9SrV49BgwZx5swZJkyYgFgsxs/PDz8/P4KDg+nbty/btm3LUU3lNWvWYGtry7Fjx3JUIyOvXIx9+vRJ1eQhN+R5m6qJEyfi4+OTXOUrA9TV1SlRokSqR055/Pgxr169omvXruk+nxJUDxlXGpsGBGRQnD03yHPuxyNG5IkwX7t2jXHjxmFmZsa1a9d+CWEGZJXFLCwsaN68Od+/f+f3339n3759GBgYUKxYMRYuXMitW7fyzL2REWXLluXy5cuIxWK6d++OlZUVO3bs4OXLlwpvbdSqVStOnDiBpaUl8+bNo1mzZhlWSktJlPD09ERZWZlmzZrRvXt3mjVrJpvBptyJyDtjTsHCwgIDAwNWr16NhoYGrq6ujB49mi1btnDkyBEguffe+fPnOXbsWI6EOSoqShYvndPiRXnlYjQzM+Pq1av52roqR+I8adIkzp49y/Xr1xXSqFMeTpw4QcmSJTE1Nc1wn4yC6kOVlDg1dCinlZVxd3fPE/syO3dehdGdOHGC6dOn8/vvv3Pu3LlsxZYWdgwNDalVqxbFixdn9uzZbN26FVtb21TRNS1atOD3339n1qxZhIaG5qt9VatW5cWLFxw4cIAGDRqwf/9+BgwYQJ8+fdLvW5gFHz9+ZP369bi4uHD79m2CgoJkHUVSroGLiwvfv3+nWbNm7N+/P80YBgYG1KhRg2PHjqVJh04hRZyzW3lQRUVFFhsfGBiIsrIyDg4OREdHEx4ejpubG+vWraNbt27ZfOX/j5KSEtWqVePcuXOsXr2a2NjYbI+RVy5GMzMzgoKCMuy8kxdky60hlUqZPHkyJ06c4MaNG9SsWTNbJ8tNtMby5cv53//+x+3bt7O8hc0ohKZ9+/ZUqlQpz7rqZnZuRXP37l3Gjh3LmDFj2L59e4EVBM9Lpk2bxuHDh7l8+XKGoWVRUVH8/vvvtG/fHldX13y28P9JSEjg5s2bWFpa0qVLF+zs7LJ1/Lp163BxcUFJSUnmClRRUaFSpUrUrFkTGxsb9PX1SUxMZO7cuTx58oTAwMA0n6ODBw/y559/UqxYMWbMmMFvv/2W6totW7aMY8eO4ebmlu27rLi4OLp27Uq/fv0y7GqeW5KSkti4cSOLFi2iRIkSzJs3L1uL93nlYoyJiaF169bs2LGDMWPGyH1ceuSJW2PixIns37+fAwcOoK2tTVhYGGFhYbIKU3lJq1at+PLli1wLI8pqahgMG4ahrS0Gw4bJ3oTixYsrfBFH3nMrEqlUyqZNm2jZsiV//fXXLynMAN26dSMsLCzT91xXV5c5c+Zw7Ngx7t27l4/WpUZNTY1OnTqxfPlyXF1dM+0a8jPx8fGcOnWKyZMnExsby5s3b7hy5QqbNm2id+/e+Pv7M3ToUHx9fVFVVWXcuHFERESkW9h/0KBB+Pr60qFDB2xtbbG2tpZdv8DAQI4ePYq5uXmO3F8aGhoMGTIEJyenTAv75wYVFRVsbGx4/vw5jRs3ZurUqUydOlXuu5G8cjFqa2tjbGycr62rsiXOO3bsIDo6mnbt2qGnpyd7pITl5CWmpqYoKyunG8IjD1euXCEoKAhra2sFW5b/XLt2jRcvXmBvb1/oev0pkrZt26KhocH9+/cz3S8lOiUvG63Ky/jx42nQoAHLly+Xu9HplStXiIyMZMyYMSgrK1OlShUsLCyYMGECGzZswMPDg8qVKzNy5Eju3r2Lv78/QIbZnZUqVeLw4cO4ubkRHR0tW/hbu3YtampqslKm2UWckEBHZWUmlynDmu7dcdi+PUfjyEPVqlU5d+4c27Zt49q1azx9+lTuY/PKxZiyrpNXHdd/JluOp/x0hv+MpqYmTZs2ZdeuXZQoUYIuXbrILUwSiYQlS5ZgYGBQ6MphZhexWMzWrVvp2LEj7dq1K2hz8hQNDQ10dXUz9J+mkNLzMC8SUrKLiooKf/31Fy1btqRjx460adOGTp06YW5unuH/69GjR2nfvn2Gmbbly5fnxo0bDBgwgIkTJ2JgYECjRo2yLNjVsWNHFi1aJIuhdnd3Z+TIkTmqo5KS1NFQIiGlJP67iRNZ5eTE7Pv38yRaRiQSoaGhgZKSUrYrRxrOmEHEpEk8/cnFaJiLO1kzMzP+/vtvvL2986WSZZEqfLR//36aNGnCrFmzGDJkiNzO+fXr1xMdHc28efMKXX+87HLp0iUCAwNZtmxZQZuSL4hEoiwnBSkdnguDOENyHeBHjx4xZswYXr58ycSJEzl+/Hi6+wYEBPD48WPGjx+f6ZhaWlqcPn2a4cOH8/r1a3r06CGXLa1bt0ZDQwNbW1u0tbWzFT6XQkZJHRWB2Z6ezK9TJ89mk25ubtSrVy9V1Ul5UbSLsVGjRqirq+eba6NIiXONGjU4c+YM169fJzAwkAsXLmR5TFRUFAcPHsTc3DxHQe2FiaSkJLZv30737t3TTcT5FVFSUspSnD9//oxIJFJoPebc0rhxY1auXImvry+DBg1i27Zt6bpdXF1dM2zR9DMqKirs2rWLkydPMmvWLLnsqFq1Kr6+vlSrVg0jI6Nsz3DlSeoY7+9PRB5kz0kkEtzc3GRJIAWNuro6jRs3zrcSokVKnFNo06YNiYmJci1qrF+/noSEBGbOnJkPluUtZ8+e5e3bt/+ZWTP8fxujzPj8+TOlS5cudI1pU1i5ciVfvnxhz79VC39EXV2d6OhoWrduLVcGmkgk4vfff8/WTLJKlSpMmTKFZ8+e8fXr12zZHnL4MPoZRD5AsoBUBkL/jXVWJD4+PkRERBQacYZk18bt27dJTEzM83MVSXH++PEjSUlJcolzq1atAPIsvjk/OXfuHK1bt8bExKSgTck35J05ly1bNp8syj5VqlRh6tSpODk58eGHvn4AM2bMwNHRkYSEBDp27EiXLl3w8vJSuA19+/ZFJBKxePHibNW7kDdZ45scUVTZ5erVq7LZamHBzMyMr1+/8uDBgzw/V5EU55SEA3l8jF26dKFGjRps3bqVjx8/5rVpeYqJiQlPnjzJcoHsV0IkEmU5c/706VOh8TdnhK2tLVpaWmzatCnNcylJJRs2bMDf319WlU6RGBgYcPDgQa5cuYK9vb3ci/vyJmuoV62aC+vS58qVKzRu3LhQlbatW7cu2tra+eJ3LpLiHBISAiD3bGnjxo0kJCTke5qvounTpw/fvn3Ll9DFwoI8M+fIyMg0VQoLGzo6OqxcuZIzZ86kO+sSiURYWFhw4sQJunbtysmTJxVuQ58+ffj77785evQoW7dulesYfSsrQpSU0sQMpyABgoC5586luSvIDVKplFu3bqGlpZUvLgR5SUmLzw+/c5EU5zdv3mRrAejRo0eIxeJC/wHOigoVKtCqVSuF1aEuCsjr1ijsM2eAUaNG0apVK5YtW5ZheyoVFRWKFSuWo9oU8tqwZs0adu7cKVemrDxJHRe7dMHL25tGjRpx4MABhbT5EolETJo0ievXr9O/f38ePnyY6zEVhZmZGffv389Renl2KJLifODAAUxNTeUKi3v79i0rV66kTp06DBkyJB+sy1v69u2Lh4eHwgp/F3bkXRAsCuKspKTEzp07ef/+faZfsB8/fsx1y6rMmDVrlqyLzJkzZ7LcP6ukjpZr13L06FHZZ6xu3bq4uLjkWqRXrVrFo0ePKFOmDNbW1ixYsEAW016QNG/enMTExAyrcSqKIifOvr6+3LlzR66C8teuXaNv376yrKjCupqfHVLiVp2cnAralHwhvZnzP//8w/z589m8eTMXL14sMuIMyT7L2bNns3v37gzj9D9//pxnM+cUVq9ezciRI7Gzs+Pu3btZ7m84YwYRnp5cmD2bM4MGcWH2bD56esqy7cqUKcPmzZs5ePAgenp6DB06lLp16/L+/c9l/7NHw4YNuXv3Lg4ODty6dYvff/+dY8eO5VuWXnoYGhpSrly5PPc7Fzlx3rVrFyVLlpT1DsyILVu2MH36dAwMDDhy5EiOSxAWJsLCwhg7diyJiYk5av9TFClRogT379+XzZhevXrF6NGjefToEWfOnGHWrFlIJJI8ramsaBYsWECZMmXYsmWLbFt0dDRnzpxh3rx5BAQE5OnMGZLdBjt37qRjx47MnDkTX1/fLI+RJ6nD2NiYrVu3cujQIV69esXp06dzbauSkhJjxozB19eXXr16sXjxYtatW5frcXPKhw8fqFixYp77nYuUOMfHx+Ps7Mxvv/2WadPNq1evsnPnTtq0acOBAwfypItyfnPr1i0GDBhAeHg4t27dYvjw4QVtUr7g4OBAVFSUrK7EH3/8QYUKFXjy5AmhoaGEh4fj4eFBly5dCtpUublz5w6hoaGyPoRSqRRra2tsbW159+4dU6dOZezYsXluh6qqKq6urtSqVYsJEybkepb7I/Xq1aN69eoKDQssV64czs7ObNiwgb1793L58mWFjZ0RUqmUd+/eceLECebPn0/Xrl3p1KkTT548yfsv0Jx2QskJuW3weuTIEaysrDh16pSsGHt62NnZceLECR49epRnnZPzC7FYzObNm3F0dKR79+7s3bu3UGXC5QeBgYFYWFjw9u1b6tSpw40bN4qMG+NnxGIxhoaGVKxYEQcHB5SUlPDw8OCPP/7gypUrWFhY5LtNHz58oHnz5pQrV47du3crbNz58+cTGhqq8JhgqVTKwIEDOX/+PAcPHqRqHoTxQfJ7tXTpUo4fP45IJKJhw4a0bduWtm3b0rp16xzH1surg0XKCbtz505Kly7Nw4cP8fDwQEVFha5du6YpMp/yfZOfrYvyivPnz+Po6Mjq1auxsbH5JV5TdqlRowZ3795l48aNzJw5s8gKM8Dz58959+4dS5Yskb2XR48epXbt2lm66vKK8uXLs2TJEkaMGEFYWFi2/N1isRgrKyvU1NSoXr06NWrUkP2sVasWly9fJikpSaHrPSKRiF27dtG0aVNmzpzJ/v37UzVhUBTv3r3j+PHjTJs2jUWLFqGrq6vwc2RGkRLnsLAwPn36hL29PcrKyojFYnbs2MGsWbPo2rUrIpGI9+/fy/xnBw4coF+/fmhqahaw5Tnn6NGjdOjQgdmzZxe0KQWKvr4+a+ToDVfYuXv3LioqKhgbGwPJkRlXr15l7dq1BVr+tXfv3mhoaHD+/HlGjRol93HKysqUKVMGd3d34uPjuXz5sqyGiIqKCklJSQQGBlK7dm2F2qutrc2xY8cwMzNj+fLlLF++XOHXL0VHbG1t812YoYj5nL29vZFIJIjFYhISEnj79i3m5ubMnj2bsWPHsnDhQn777TfCw8MxNTVlw4YNdOrUifXr1ys0QD6/8Pf358mTJ1lWLBMoOri7u1OnTh3ZTO/kyZOoqKgU+BpCiRIl6NWrF+fPn8/2sYsXL0ZLS4vmzZsTExMjWwhctmyZbFE+LzA2NsbBwYHTp09z7NixDPeTSCT4+vpy8eLFbLUQ8/f3R09Pr8BKAxSpmfPP34yVKlXi2LFjnDt3jokTJ/L69WvWrFnDmDFj0NTUJDg4mE2bNskC7rt168aIESMyrJtb2Dh69CgVKlTg999/L2hTBHKJVCpFKpVy584dWrduDSSLxrFjx7CysqJkyZIFbCEMHjyYI0eOEBgYSI0aNeQ+rkKFCsyZM4eFCxfSr18/evXqhaGhIT179sxDa5MZOnQod+7cYeXKldStW5e6desCEBwczP3797l//z4PHjwgMjISSO5Y06ZNG3r27Im5uXmGuRJPnz7Fzc0tX+o2Z0SRWhDMjJR//vR8sl++fGH37t1s2LCBd+/esXv37kwbxRYGvn37RseOHZk6dSrLly8vaHMEskl0dDSenp48ePAADw8PTp8+TZcuXbh06RJr1qyhW7duuLu7M27cOO7evVsoSsAmJCRQoUIFLC0ts13bIzw8nCFDhiCVSgkODs7XnIL4+HhatWpFWFgYzZs3x8PDg+DgYJSUlGjWrBkWFhZYWFhQrVo1jh8/jrOzM15eXpQsWZIuXbrQq1cvjI2NEYlEeHt7s337du7cuUPdunXZs2ePwrVCXh38ZcRZHpKSkjAwMKBnz54KLyyTW6RSKWFhYQQEBODv78/Dhw+5e/cur1+/pkqVKgVt3n8OiUQi9+JreHg4T5484cmTJ3h5efH48WMCAgKA5CL5xsbGqVptbdiwQfbFGxERwbNnzwpNu7Hx48dz+PBhzp8/n2XHlKSkJO7cucPx48e5desWampqDBs2jG3btuV7wtebN29o3rw5JUuWpFOnTnTs2JG2bdtm6Cv28fFh37597Nu3j9DQUKpWrYqenh737t2jdu3aLF68mP79++fJArwgzhnQvXt3vn79yvY87H+WHUJCQli8eDE+Pj6yanMlSpSgXr16DBo0iMmTJxewhf89rl27RufOnVFTU6NEiRKyh46ODjo6OpQoUQJNTU1ev37NkydPCPu30LyWlha1atWidu3a1KlThwYNGsjCvDp27Mjw4cNZv349TZo04cOHDwQHB+Pg4JDrbs6KJDg4mJo1azJ8+PAs//c2btzI7t27adSoEWPGjGHQoEEFsnCWglQqzfaXnFgs5vr16+zduxd/f3+mTJmClZVVnjZN/iVD6RSBiYkJO3fuLGgzgGRhHjVqFKqqqtja2lK/fn3q16+PgYFBoZlJ/Re5du0aJUqUYMyYMXz9+lX2iI2NJTw8nDdv3vD161cqVKhAz549MTIyok6dOlSqVCndmZaPjw8fP37k999/5+jRo4SEhNCrVy/69u1Lp06dCuAVZkylSpWYOnUqmzdvxsrKKtOwxSZNmrB7927s7e3p3r17PlqZPjn5zCgrK8vcHoWN/9zM+dixY1haWnL9+vUctYdXFCnCrKamxs2bN4tU+vGvTo8ePfjy5Qs7duxQyHjbtm3j0KFDREREIJFIUFFRKdTx6lFRUVSvXp327duzePHiDPeTSqWMGjWK+Ph4vLy8CvVrKkzIq4P/uauZ0lXhxYsX2TouMDCQrVu34urqip+fn9xt79NDEObCjZeXl0Ljcm/evEm3bt1QUVFBTU2t0IuYrq4uCxcu5MSJE5k2URaJREydOhVvb28OHTqUjxb+N/jPzZylUik1atSgQoUK/PXXX5neCkmlUu7du8fevXtxd3dHV1eXL1++IJFI0NDQoHbt2hgbG2NsbEy9evWoVKlSlgshgjAXbiIiIihXrhwrVqxQSCjYhw8fsLCw4MCBAwwaNEgBFuYP8fHxGBkZUa1aNTZv3pzpvlOmTOHNmzf4+voW+XIJ+YHgc84AkUjExo0b6dWrF9euXUs3ZVYikXD69GmcnZ0JDAykUaNG7N+/n/79+5OYmMiTJ0948OABnp6euLu7s3//fiC5kEzlypWpWrVqqoehoSE6OjqCMBcBvn//TrFixVi8eDG3b9+mf//+NG3aNMdrAPfu3QOgc+fOijQzz1FXV2flypUMHjyYPXv2YGVlRfHixdPdd/LkyfTr14+///6biRMn5rOlvy7/uZlzCj169MDLy4uTJ0+mysv/9u0bCxYs4MqVK/z222/Y2NjQpk2bTD+cnz594smTJ/j5+eHr64uvry9+fn68+6E5ZqlSpZBIJJQsWVIQ5kLOx48fcXJywsHBgcDAQKpVq0a/fv2y3fUakpMZhg4dyokTJ+jdu3feGJxHSCQSxo4dy549e9DU1MTS0pLBgwen21Fo/vz5eHh4EBgYmGUI3n8dIZQuCwIDA6lXrx41a9Zk2LBhdO7cmU+fPjFlyhSCgoJwcXHJdWZebGwsAQEB+Pn54efnx8ePH5k1a5YgzAogMjKSXbt2cenSJerUqYOZmRlmZmbUqFFDYZEuUqmUGzdu4ODgIKtM1rlzZ/r370/jxo3lPs/IkSNRUVHh3r17RTIKJygoiE2bNvH3338TFxdHt27dGD58OEZGRnz69AlfX19u3rzJwYMHWbt2LTNnzixokws1gjjLwfXr17G3t+fq1auULVsWiURC8eLFOXv2LA0aNCho84o8UqmUR48e8fHjR4oVK4aGhkaan1paWtmqKObv78/GjRtxdnYmKSkJU1NTgoODefPmDQAlS5bE3NycgwcPZngbnhPCw8Nls+lXr15RvXp1LC0tadOmTZahj2fOnMHW1pagoKAi/cUcHR3Nrl272LhxI8HBwZQuXZpPnz4ByYWIGjVqxIIFC4qcCye/EcQ5C5YuXUpgYCCmpqaULVsWNzc3YmJi2Lx5c5EuSVkYSExM5NixY6xdu5ZHjx5luq+ysjLz5s1j8eLFGQb+S6VSrl27xoYNGzh37hylS5fGysqK/v37y8Iho6Oj8fHx4cqVKxw7dgx/f39q1qwpGyMgIICrV68yZsyYXEVLSCQSrl27hoODAydPniQpKYkSJUpQt25dGjZsSP/+/dPc9m/bto3Dhw8TERGRp8kN+UXK++vj40PDhg1p3Lgx1apVK/RRKIUFQZwz4dy5c/z2229Ur16doKAgEhMTUVNTo1GjRvTu3Zu5c+cWydvPT58+cePGDe7cuUNUVBTx8fHExcURHx+f5vcftyUkJCCRSFBVVUVFRQUVFRVUVVVlf//8u5qaGgYGBowfPz5V3YHo6Gj+/vtvNm3aRHBwMM2bN2fYsGHUqlUrzXkTEhKIi4vj+fPn7Nq1i9atW8v6z339+pXw8HDCw8O5ePEix48fx8fHByMjI4YMGUL37t1RV1dP9xrcvHmTSZMmERISgp6eHh8+fGDJkiX8/fffJCUlcfXqVTp06KCQ6/3582cePHjAw4cP8fT05NatW3z//p1BgwYxatQoWTGjYcOGUb16dVxdXRVyXoGijRCtkQGRkZH88ccfmJubs23bNhISEvDz88Pb2xsvLy9sbW1RVlYuEvWTv3z5wq1bt7h27RpXr17l2bNnABgYGFCmTBmZqKqpqaGmpkapUqVQU1OTbVNXV5f9rqSkhFgsJjExEbFYTFJSEklJSbJtKb+n/Lx69SpOTk6YmZkxYcIEHj9+zO7du4mPj6dbt25s3LhRrup/HTp0oEWLFsyZM4fq1asjlUqJj49PtU/r1q3ZtWsXpqamWX5pptQSlkqlLF68mLVr16KsrMykSZNwcnLi/PnzChPnUqVK0bVrV7p27Qokvx/r169n3bp1uLq6Mnz4cPr27Yu3t3e+tJ0S+LX4z82cR4wYwYkTJzhx4kS6q86bN29m165dnD17tlCkpP6MVCrl+PHjrFmzhkePHiEWi9HT06NZs2aYmZlhamqa552bIbkmwc2bN3FxceHBgwfo6OgwYMAABg4cmCO30OfPnzlx4gQaGhqUKlWKkiVLsm3bNgICAlIVDcqK48ePs2jRIsqUKcOXL18YMmQIo0eP5uTJk6xduxZXV1e5OrfnhoiICFatWsW2bduQSqUkJCQQGBj4SzQZzi5JSUkoKSkpxOURFxfHhw8finwhMMGtkQ5nzpyhV69eLFu2LMOwJolEwtSpU3ny5AkeHh6FqvbzP//8w8SJE7l06RItW7akU6dOmJmZUalSpQJ1w4SEhKCrq6vQBbivX79ibm5Oy5Yt2bZtm9zHnT17FltbW3r16sXEiRPR09PD3d2dCRMmYGNjw+rVqxVmY1YEBwezdOlSIiIiZNEe/zXatm3L+/fvsbOzY/DgwekmaYWHh3P58mX8/f1l7qywsDDCw8OB5ObGx48fZ+XKlcTExBAdHV2kr6Ugzj/x4cMHGjVqRM2aNdm2bVumb+7Xr18ZOnQoyoDT6NFIQ0IoXr069SdMSLcdfF4TFxfHmjVrWLFiBaVLl2bu3Lm0b98+3+3IT06fPs38+fNxcXHJVuRMUlISX79+lVVHe/PmDYMHD8bc3JzTp0//EgtyRYX4+Hi0tLTQ19cnKCiI6tWrY2dnx8CBA3n8+DEXLlzg3LlzPH78GKlUSvny5SldujQlS5aUNTE+ffo02traxMbGoquri5aWFm/fvi3gV5Y7BHH+gfj4eNq1a8c///zDoUOH5LrtfrpoEW2OH+fHwKdgYE+jRihZWspcCfXr18+Vbd++fcPLy4vo6Gi+fPlCTEwMX758kT1iYmK4fv06b9++ZcSIEYwZM0ahM9TCyoQJE3j69Cnu7u45HuPr168MHjwYNTU1PDw8sp1AIpA7Hj9+TJMmTdi3bx/q6ur89ddfXLt2DVVVVRITE9HR0aFFixa0bt2aVq1apSlEFh0dTZ8+fWjevLnsTqhdu3ZcunSpgF6RYhAWBP9FKpUybtw4Hj9+zJ49e+QS5tfr1zPo+PE02/WB+V5eDH35kgX/Llq1b9+emTNn0q1bt2z51WJiYtixYwfr1q2T3b6loKWlhaampuxn9erVWbduXb74LMViMY8fPyYiIoKyZctiYmJSILPN6OjoNF3Vs8udO3d4/fo1z58/F4S5AHjy5AmQvHBavHhx7OzssLKy4v79+zRt2pQGDRqgoqIiW5j+kbi4OLy9venSpQv37t3jzJkzAKxcuTLfX0dB8cuL86ZNm3BycmLFihVy3R6LExJo4uwMpC3ZpwRIgP8lJjL93j1u3rmDs7Mzv/32G7Vq1WLGjBkMGzYs05ltVFQUW7ZsYePGjcTExNCrVy8GDBhA6dKlZQkZBRUv6ubmxpqVK6kVHo4eEArML1eO2fPm5Xu924oVK2Z6+ypOSCDk8GGS3r1DxcAAfSurNC6nlMqBhoaGeWqrQPqkJAb16NEjzXN79uyR/V6qVCnc3Nx4+/Yt7u7u3L17l4cPH5KQkEDFihXp0qULK1eupGPHjpQqVSq/zC9wfmlxvnTpEjNnzsTa2lruCmMhhw/TUCLJ8HkloKJEwrMTJ+g6bBhdunTBy8uLvXv3Mn78eObPn8+ECRMYP348enp6suM+ffrExo0b2bx5M3FxcfTr149Ro0blS2SFPLi5uXF7+nQ8IJUr5114ONOmT4cNG/JVoPX09IiLi0v3udfr19PE2TnV+xSydi2PRozAcMYM2bYUj11RXjwqysyaNYtWrVohkUiQSCRIpdJUPyUSCd7e3ixdupTOnTvz8eNHNDQ0MDc3Z+XKlXTt2pU6der8Z9+/X1acv337hpWVFUpKSmhqanLr1i1q165N2bJlM32zk34oVpQZKfuJRCIaN25M48aNeffuHS4uLqxbt45ly5ZhaGhIo0aN+PLlC/fv30cikTBgwABGjBhRYO3W00MsFvNs8WKOpvNcReAo8OeSJbRv3z7fXBx6enokJCSQkJCQqgzl6/Xr6fHDrCuFChIJPfbs4RzIBFoQ54KlRIkSshjwjOjQoQP37t3D2NiYLl260KZNm2yl8//K/LLirKqqyqRJk7h9+zb79u0jOjoaSL6FSunzZmRkhJGREYaGhrIQHxU5ax+kt5+BgQFz585lwoQJ3Lx5E19fX168eMHDhw9p0KABW7ZsKZS3ZU8ePmTJv9cnI1fOoqgorj18SFMzs3yxSV9fH6lUSkBAAPXq1QPkczmZODvzcdIklNXUZBEby5cvZ+nSpYJIF0JKlSrF5cuXC9qMQskvLc7Lly8HkmdQb9++xcvLi6dPn+Ll5cX169dxcnICQE1NjZo1a2JkZETNatVoIBKhJ5Wm2yZGAoQqKaFvZZXhuUuUKEHPnj1lrpR+/foRHh5eKIUZQP3BAzL7SlICKv+7H/kkzinunhcvXsjEWW6X0+HDGAwbhrm5OVOnTmX58uUEBgayZ88eNDQ08sN8AYFc88uK84+IRCJZ4fsfk0+ioqJ49uwZXl5essfZs2fxl0pxJVmIfxToFFl4PGIEhtmId+7WrRtbtmwhMjJSVm+hMKGX9S7Z2k8RpPjrAwMDZdty4nL6448/qFy5MvPmzcPIyCjTnngCAoWJ/3QZKV1dXdq0acOUKVNwdHTk8ePHfP36lSXPnuHYrRshP90GBwND1NRY5uPD6tWrOXnyJL6+viQmJmZ6ns6dOyORSNi9e3cevpqcU6lZM4Xupwi0tbUpVqwYQUFBsm05dTl17txZ5uIQECgq/CdmztlBVVWV+vXrU//8ecQJCXht305sYCCJZcrwpWFDjF+8IMnLi3v37qVqT1WtWjWZDzvlkSIIlStXpmbNmly+fBkbG5sCfHXpE9esGTE6OmhGR2foyonV1SUuH8UZQFNTU7ZWAKBvZUXI2rVUkEiy5XJKSEggPDycypUr563BAgIKRBDnTFBWU6PRtGmptvXq00f2e0xMjKya3dOnT3ny5AmXL1+WhYDp6elRq1YtjIyMUFFRITQ0lA8fPqRbcKlAUVYmcvFitKZPT9eVIwIiFy2CfExGCQsL4+PHj6liZJXV1Hg0YgQ99uzJlsvpw4cPAII4CxQpBHHOBdra2rRs2ZKWLVvKtiUlJREQECBbeHzy5AknT56UZQFaWFigqalJ6dKlKV26NKVKlZL9nt7fmpqa+RJl8MXCgncbNlBh1SrU/hUzgKTy5QmbO5cv+ZyEklKJzuqnWbDhjBmcA5o4O6P/w+JgqJJSsjD/EOecQlhYGCCIs0DR4j9RW6MwEBoaysWLF1FRUSE8PJwPHz7w4cMHWfWtDx8+EBERIctqS0FdXZ0yZcqkEu2fBTzlUaJEidwLuViM5uPHqEREkFS2LLEmJvk6Y07B1taWa9euZVguVJ4MwRRSiih9+/ZNiKEVKHCE2hqFDD09PaytrTPdRyKREBkZKRPu9B5v377lwYMHhIeHpylKr6qqKhPurGbkurq66SeUKCsTm8++5Z+RSqW4u7tTtWrVDPdRVlPDYNgwucYLDQ2lTJkygjALFCkEcS5EKCkpycSzbt26me4rlUr58uVLhiKeUhP32bNnhIeHExsbm+ZcGQn5z9tKliyZpjBNXvL69Ws+f/7M4MGDFTJeaGio4NIQKHII4lxEEYlE6OjooKOjQ61atbLcPzY2NkMRT3Gv+Pn5ER4enipCIoWUGrtZCXnp0qVTpVvnBA8PD0QiEZaWlrkaB+Du3btcuHBBYUIvIJBfCOL8H0FTU5Nq1apRrVq1LPeNi4tL5Rf/UcRThPzRo0eEh4fz6dOnNMdra2unK9zpCfvPFfwSEhI4e/Ysurq6soLrOeXixYvY2tpiYWHBpk2bcjWWgEB+IywICuSKxMREIiIi0hXxFCFP+f3jx49Ifkq/Ll68eCrxjoiI4Pnz59SpU4cmTZqgrKws60GnpKQk198ikYiwsDB27drFkCFDcHR0zFe3jIBAZggLggL5gqqqKvr6+ujr62e5r1gs5tOnT+mKeIqQh4SEYGBggEQikVXyE4vFiMXiNL9n9JxYLEYkEjF9+nT+97//FVh9bAGB3CCIs0C+oaysTLly5XLUnTu7SKVSoQqdQJFGmFII/JIIwixQ1BHEWUBAQKAQIoizgICAQCFEEGcBAQGBQoggzgICAgKFEEGcBQQEBAohgjgLCAgIFEIEcRYQEBAohAjiLCAgIFAIEcRZQEBAoBAiiLOAgIBAIUQQZwEBAYFCiCDOAgICAoUQQZwFBAQECiH5WjI0pa7/ly9f8vO0AgICAoWGFP3Lqs9JvopzTEwMAAYGBvl5WgEBAYFCR0xMDDo6Ohk+n69tqiQSCSEhIWhraxeZertfvnzBwMCAd+/eCa21cohwDXOPcA1zT2G5hlKplJiYGPT19TPt0pOvM2clJSUqVaqUn6dUGCVKlBA+FLlEuIa5R7iGuacwXMPMZswpCAuCAgICAoUQQZwFBAQECiGCOGeBuro6ixYtQl1dvaBNKbII1zD3CNcw9xS1a5ivC4ICAgICAvIhzJwFBAQECiGCOAsICAgUQgRxFhAQECiECOIsICAgUAgRxDkDduzYQYMGDWQB6y1atODChQsFbVaRZdWqVYhEIqZNm1bQphQpFi9ejEgkSvWoXbt2QZtV5Hj//j1Dhw6ldOnSFCtWjPr16/Pw4cOCNitT8jVDsChRqVIlVq1aRc2aNZFKpTg7O/P777/z5MkT6tWrV9DmFSk8PT1xcHCgQYMGBW1KkaRevXq4ubnJ/lZRET622SEyMpJWrVrRvn17Lly4QNmyZQkICKBkyZIFbVqmCO9yBvTs2TPV3/b29uzYsYP79+8L4pwNvn79ypAhQ/j7779Zvnx5QZtTJFFRUaFChQoFbUaRZfXq1RgYGLBnzx7ZNkNDwwK0SD4Et4YciMViDh06RGxsLC1atChoc4oUEydOpEePHlhYWBS0KUWWgIAA9PX1qVatGkOGDCEoKKigTSpSnD59mqZNm9K/f3/KlStH48aN+fvvvwvarCwRZs6Z4O3tTYsWLYiLi0NLS4sTJ05Qt27dgjaryHDo0CEeP36Mp6dnQZtSZDEzM8PJyQkjIyNCQ0NZsmQJ5ubm+Pj4oK2tXdDmFQlevXrFjh07mDFjBra2tnh6ejJlyhTU1NQYMWJEQZuXIUKGYCYkJCQQFBREdHQ0rq6u7Nq1i5s3bwoCLQfv3r2jadOmXLlyReZrbteuHY0aNWLjxo0Fa1wRJioqiipVqrB+/XpGjx5d0OYUCdTU1GjatCl3796VbZsyZQqenp7cu3evAC3LHMGtkQlqamrUqFGDJk2asHLlSho2bMimTZsK2qwiwaNHjwgPD8fExAQVFRVUVFS4efMmmzdvRkVFBbFYXNAmFkl0dXWpVasWgYGBBW1KkUFPTy/NhKpOnTqF3j0kuDWygUQiIT4+vqDNKBJ07NgRb2/vVNusra2pXbs2c+bMQVlZuYAsK9p8/fqVf/75h2HDhhW0KUWGVq1a4efnl2qbv78/VapUKSCL5EMQ5wyYN28e3bp1o3LlysTExHDgwAFu3LjBpUuXCtq0IoG2tjbGxsaptmlqalK6dOk02wUyxsbGhp49e1KlShVCQkJYtGgRysrKDBo0qKBNKzJMnz6dli1bsmLFCgYMGMCDBw/YuXMnO3fuLGjTMkUQ5wwIDw9n+PDhhIaGoqOjQ4MGDbh06RKdOnUqaNME/kMEBwczaNAgPn36RNmyZWndujX379+nbNmyBW1akaFZs2acOHGCefPmsXTpUgwNDdm4cSNDhgwpaNMyRVgQFBAQECiECAuCAgICAoUQQZwFBAQECiGCOAsICAgUQgRxFhAQECiECOIsICAgUAgRxFlAQECgECKIs4CAgEAhRBBnAQEBgUKIIM4CAgICP3Dr1i169uyJvr4+IpGIkydPZnsMqVTK2rVrqVWrFurq6lSsWBF7e/tsjSGkbwsICAj8QGxsLA0bNmTUqFH07ds3R2NMnTqVy5cvs3btWurXr8/nz5/5/PlztsYQ0rcFBAQEMkAkEnHixAl69+4t2xYfH8/8+fM5ePAgUVFRGBsbs3r1atq1awfAy5cvadCgAT4+PhgZGeX43IJbQ0BAQCAbTJo0iXv37nHo0CGePXtG//796dq1KwEBAQCcOXOGatWqcfbsWQwNDalatSp//PFHtmfOgjgLCAgIyElQUBB79uzh6NGjmJubU716dWxsbGjdurWsgeyrV694+/YtR48eZe/evTg5OfHo0SMsLS2zdS7B5ywgICAgJ97e3ojFYmrVqpVqe3x8PKVLlwb+vynH3r17Zfvt3r2bJk2a4OfnJ7erQxBnAQEBATn5+vUrysrKPHr0KE03Hy0tLSC5LZaKikoqAa9Tpw6QPPMWxFlAQEBAwTRu3BixWEx4eDjm5ubp7tOqVSuSkpL4559/qF69OpDcFgvIVmssIVpDQEBA4Ae+fv0qa6DbuHFj1q9fT/v27SlVqhSVK1dm6NChuLu7s27dOho3bkxERARXr16lQYMG9OjRA4lEQrNmzdDS0mLjxo1IJBImTpxIiRIluHz5svyGSAUEBAQEZFy/fl0KpHmMGDFCKpVKpQkJCVI7Oztp1apVpaqqqlI9PT1pnz59pM+ePZON8f79e2nfvn2lWlpa0vLly0tHjhwp/fTpU7bsEGbOAgICAoUQIZROQEBAoBAiiLOAgIBAIUQQZwEBAYFCiCDOAgICAoUQQZwFBAQECiGCOAsICAgUQgRxFhAQECiECOIsICAgUAgRxFlAQECgECKIs4CAgEAhRBBnAQEBgULI/wHxTLBEGOMD4QAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = eur2.plot(facecolor=\"lightgray\", edgecolor=\"k\")\n",
+    "eur2.centroid.plot(ax=ax, color=\"k\") # black => correct\n",
+    "eur.centroid.to_crs(\"EPSG:3035\").plot(ax=ax, color=\"r\")  # red => miscalculated"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "ca9e306e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "shapely.geometry.multipolygon.MultiPolygon"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(eur2.iloc[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "f489c88d-5964-4358-b8c4-fc80d28b5491",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(geopandas.geoseries.GeoSeries,\n",
+       " geopandas.base.GeoPandasBase,\n",
+       " pandas.core.series.Series,\n",
+       " pandas.core.base.IndexOpsMixin,\n",
+       " pandas.core.arraylike.OpsMixin,\n",
+       " pandas.core.generic.NDFrame,\n",
+       " pandas.core.base.PandasObject,\n",
+       " pandas.core.accessor.DirNamesMixin,\n",
+       " pandas.core.indexing.IndexingMixin,\n",
+       " object)"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(eur2).__mro__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85880c73",
+   "metadata": {},
+   "source": [
+    "#### Area of European countries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "3e4874d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "name\n",
+       "Russia              2.024604e+11\n",
+       "Norway              3.286343e+11\n",
+       "France              5.595585e+11\n",
+       "Tunisia             6.547702e+10\n",
+       "Algeria             2.043067e+11\n",
+       "Sweden              4.505641e+11\n",
+       "Belarus             2.039346e+11\n",
+       "Ukraine             2.857955e+11\n",
+       "Poland              3.103969e+11\n",
+       "Austria             8.506301e+10\n",
+       "Hungary             9.247413e+10\n",
+       "Moldova             3.232099e+10\n",
+       "Romania             2.383473e+11\n",
+       "Lithuania           6.382974e+10\n",
+       "Latvia              6.392371e+10\n",
+       "Estonia             4.467593e+10\n",
+       "Germany             3.574253e+11\n",
+       "Bulgaria            1.102150e+11\n",
+       "Greece              1.319524e+11\n",
+       "Turkey              2.217875e+11\n",
+       "Albania             2.969486e+10\n",
+       "Croatia             5.752945e+10\n",
+       "Switzerland         4.618538e+10\n",
+       "Luxembourg          2.416819e+09\n",
+       "Belgium             3.012566e+10\n",
+       "Netherlands         4.002133e+10\n",
+       "Portugal            9.340841e+10\n",
+       "Spain               5.023083e+11\n",
+       "Ireland             5.845217e+10\n",
+       "Italy               3.150991e+11\n",
+       "Denmark             4.275938e+10\n",
+       "United Kingdom      2.499857e+11\n",
+       "Slovenia            1.911811e+10\n",
+       "Finland             3.412330e+11\n",
+       "Slovakia            4.706754e+10\n",
+       "Czechia             8.120719e+10\n",
+       "Morocco             4.002044e+10\n",
+       "Bosnia and Herz.    5.060437e+10\n",
+       "North Macedonia     2.506166e+10\n",
+       "Serbia              7.638898e+10\n",
+       "Montenegro          1.344348e+10\n",
+       "Kosovo              1.123012e+10\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eur2.area # area in sq meters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "95f55824",
+   "metadata": {},
+   "source": [
+    "What is the area in **sq miles**?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "85ee20c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "name\n",
+       "France              216045.735320\n",
+       "Spain               193941.416516\n",
+       "Sweden              173962.955903\n",
+       "Germany             138002.030052\n",
+       "Finland             131750.182704\n",
+       "Norway              126885.828356\n",
+       "Italy               121659.867544\n",
+       "Poland              119844.344075\n",
+       "Ukraine             110345.741832\n",
+       "United Kingdom       96519.589998\n",
+       "Romania              92025.972522\n",
+       "Turkey               85632.236650\n",
+       "Algeria              78882.882393\n",
+       "Belarus              78739.245551\n",
+       "Russia               78170.023756\n",
+       "Greece               50946.870966\n",
+       "Bulgaria             42554.046136\n",
+       "Portugal             36065.021674\n",
+       "Hungary              35704.295877\n",
+       "Austria              32842.862855\n",
+       "Czechia              31354.126319\n",
+       "Serbia               29493.813018\n",
+       "Tunisia              25280.702563\n",
+       "Latvia               24680.969176\n",
+       "Lithuania            24644.688797\n",
+       "Ireland              22568.405096\n",
+       "Croatia              22212.141886\n",
+       "Bosnia and Herz.     19538.365073\n",
+       "Slovakia             18172.796262\n",
+       "Switzerland          17832.192898\n",
+       "Estonia              17249.391945\n",
+       "Denmark              16509.413633\n",
+       "Netherlands          15452.249586\n",
+       "Morocco              15451.907072\n",
+       "Moldova              12479.148232\n",
+       "Belgium              11631.530122\n",
+       "Albania              11465.196302\n",
+       "North Macedonia       9676.315936\n",
+       "Slovenia              7381.508356\n",
+       "Montenegro            5190.531452\n",
+       "Kosovo                4335.952028\n",
+       "Luxembourg             933.134867\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Conversion: / 1000 / 1000 / 2.59\n",
+    "(eur2.area / 1000 / 1000 / 2.59).sort_values(ascending=False)\n",
+    "# careful!  some countries (e.g., Russia) were cropped when we did intersection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "cd600837",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_13458/3689342177.py:2: UserWarning: Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
+      "\n",
+      "  eur.area\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "name\n",
+       "Russia              35.748174\n",
+       "Norway              61.455274\n",
+       "France              65.673811\n",
+       "Tunisia              6.540885\n",
+       "Algeria             21.165124\n",
+       "Sweden              79.446214\n",
+       "Belarus             27.622448\n",
+       "Ukraine             35.483088\n",
+       "Poland              40.759231\n",
+       "Austria             10.179604\n",
+       "Hungary             10.980058\n",
+       "Moldova              3.837658\n",
+       "Romania             27.621146\n",
+       "Lithuania            9.022101\n",
+       "Latvia               9.398691\n",
+       "Estonia              6.905922\n",
+       "Germany             45.923594\n",
+       "Bulgaria            12.119548\n",
+       "Greece              13.743985\n",
+       "Turkey              23.109344\n",
+       "Albania              3.185163\n",
+       "Croatia              6.570063\n",
+       "Switzerland          5.440201\n",
+       "Luxembourg           0.301516\n",
+       "Belgium              3.829997\n",
+       "Netherlands          5.264180\n",
+       "Portugal             9.802468\n",
+       "Spain               53.268425\n",
+       "Ireland              7.860299\n",
+       "Italy               34.685652\n",
+       "Denmark              6.168457\n",
+       "United Kingdom      34.202954\n",
+       "Slovenia             2.225310\n",
+       "Finland             63.782393\n",
+       "Slovakia             5.753425\n",
+       "Czechia             10.136967\n",
+       "Morocco              4.053127\n",
+       "Bosnia and Herz.     5.696666\n",
+       "North Macedonia      2.706996\n",
+       "Serbia               8.604719\n",
+       "Montenegro           1.479321\n",
+       "Kosovo               1.231641\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# area on screen, not real area\n",
+    "eur.area"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "daf1245f-9939-468d-9a5f-34225c2dbc51",
+   "metadata": {},
+   "source": [
+    "### CRS\n",
+    "\n",
+    "- `<GeoDataFrame object>.crs`: gives you information about current CRS.\n",
+    "- `<GeoDataFrame object>.to_crs(<TARGET CRS>)`: changes CRS to `<TARGET CRS>`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9da3ee4c",
+   "metadata": {},
+   "source": [
+    "### Madison area emergency services\n",
+    "\n",
+    "- Data source: https://data-cityofmadison.opendata.arcgis.com/\n",
+    "    - Search for:\n",
+    "        - \"City limit\"\n",
+    "        - \"Lakes and rivers\"\n",
+    "        - \"Fire stations\"\n",
+    "        - \"Police stations\"\n",
+    "\n",
+    "- CRS for Madison area: https://en.wikipedia.org/wiki/Universal_Transverse_Mercator_coordinate_system#/media/File:Universal_Transverse_Mercator_zones.svg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "a6f80847",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "city = gpd.read_file(\"City_Limit.zip\").to_crs(\"epsg:32616\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "7f8595be",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Projected CRS: EPSG:32616>\n",
+       "Name: WGS 84 / UTM zone 16N\n",
+       "Axis Info [cartesian]:\n",
+       "- E[east]: Easting (metre)\n",
+       "- N[north]: Northing (metre)\n",
+       "Area of Use:\n",
+       "- name: Between 90°W and 84°W, northern hemisphere between equator and 84°N, onshore and offshore. Belize. Canada - Manitoba; Nunavut; Ontario. Costa Rica. Cuba. Ecuador - Galapagos. El Salvador. Guatemala. Honduras. Mexico. Nicaragua. United States (USA).\n",
+       "- bounds: (-90.0, 0.0, -84.0, 84.0)\n",
+       "Coordinate Operation:\n",
+       "- name: UTM zone 16N\n",
+       "- method: Transverse Mercator\n",
+       "Datum: World Geodetic System 1984 ensemble\n",
+       "- Ellipsoid: WGS 84\n",
+       "- Prime Meridian: Greenwich"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "city.crs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "9ebd361f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "water = gpd.read_file(\"Lakes_and_Rivers.zip\").to_crs(city.crs)\n",
+    "fire = gpd.read_file(\"Fire_Stations.zip\").to_crs(city.crs)\n",
+    "police = gpd.read_file(\"Police_Stations.zip\").to_crs(city.crs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "723e2bff-545f-4a8c-9f72-8a9906a99b1b",
+   "metadata": {},
+   "source": [
+    "#### Run this on your virtual machine\n",
+    "\n",
+    "`sudo sh -c \"echo 'Options = UnsafeLegacyRenegotiation' >> /etc/ssl/openssl.cnf\"`\n",
+    "\n",
+    "then restart notebook!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6069860-7dd9-43f2-970e-fd15980135ef",
+   "metadata": {},
+   "source": [
+    "#### GeoJSON\n",
+    "\n",
+    "How to find the below URL?\n",
+    "\n",
+    "- Go to info page of a dataset, for example: https://data-cityofmadison.opendata.arcgis.com/datasets/police-stations/explore?location=43.081769%2C-89.391550%2C12.81\n",
+    "- Then click on \"I want to use this\" > \"View API Resources\" > \"GeoJSON\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "3a095d5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "url = \"https://maps.cityofmadison.com/arcgis/rest/services/Public/OPEN_DATA/MapServer/2/query?outFields=*&where=1%3D1&f=geojson\"\n",
+    "police2 = gpd.read_file(url).to_crs(city.crs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "248be81e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = city.plot(color=\"lightgray\")\n",
+    "water.plot(color=\"lightblue\", ax=ax)\n",
+    "fire.plot(color=\"red\", ax=ax, marker=\"+\", label=\"Fire\")\n",
+    "police2.plot(color=\"blue\", ax=ax, label=\"Police\")\n",
+    "ax.legend(loc=\"upper left\", frameon=False)\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "3a609d81",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fire.to_file(\"fire.geojson\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77600884",
+   "metadata": {},
+   "source": [
+    "### Geocoding: street address => lat / lon\n",
+    "\n",
+    "\n",
+    "- `gpd.tools.geocode(<street address>, provider=<geocoding service name>, user_agent=<user agent name>)`: converts street address into lat/long\n",
+    "\n",
+    "\n",
+    "#### Daily incident reports: https://www.cityofmadison.com/fire/daily-reports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "6b0b2aa0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Response [200]>"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "url = \"https://www.cityofmadison.com/fire/daily-reports\"\n",
+    "r = requests.get(url)\n",
+    "r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "bee28b41",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r.raise_for_status() # give me an exception if not 200 (e.g., 404)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "0bae00e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# doesn't work\n",
+    "# pd.read_html(url)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "47173ec2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# print(r.text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39f166c5",
+   "metadata": {},
+   "source": [
+    "Find all **span** tags with **streetAddress** using regex."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "ac7b9482-5512-49e4-b0b8-7f9ed34b5844",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# <p>1700 block Thierer Road<br>\n",
+    "# addrs = re.findall(r'<p>1700 block Thierer Road<br>', r.text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "8e9b49d2-3e0d-4a39-9dcf-13b288a165e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0                      1700 block Thierer Road\n",
+       "1                       6400 block Bridge Road\n",
+       "2                  800 block W. Johnson Street\n",
+       "3                      700 block Vernon Avenue\n",
+       "4    U.S. Highway 12 WB &amp; John Nolen Drive\n",
+       "5                   900 block Delaplaine Court\n",
+       "6                        6900 block Odana Road\n",
+       "7                             East Campus Mall\n",
+       "8         N. Stoughton Road &amp; Hoepker Road\n",
+       "9                      0 block West Towne Mall\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "addrs = re.findall(r' <p>(.*?)<br>', r.text)\n",
+    "addrs = pd.Series(addrs)\n",
+    "addrs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4fc8ba0c-9d45-4251-8055-1310cabf15a9",
+   "metadata": {},
+   "source": [
+    "#### Without city name and state name, geocoding would return match with the most famous location with such a street name."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "095b9ebe-b583-4098-a3a2-47dd46610d59",
+   "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>geometry</th>\n",
+       "      <th>address</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>POINT (-117.39118 47.64686)</td>\n",
+       "      <td>1300, East 9th Avenue, 99202, East 9th Avenue,...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      geometry  \\\n",
+       "0  POINT (-117.39118 47.64686)   \n",
+       "\n",
+       "                                             address  \n",
+       "0  1300, East 9th Avenue, 99202, East 9th Avenue,...  "
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "geo_info = gpd.tools.geocode(\"1300 East Washington Ave\")\n",
+    "geo_info"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "e52ee0fc-d73c-4942-9bec-d1586a702f68",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1300, East 9th Avenue, 99202, East 9th Avenue, Spokane, WA, United States'"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "geo_info[\"address\"].loc[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e80b714c-a962-4a47-b7cc-3a19d0da8ac0",
+   "metadata": {},
+   "source": [
+    "#### To get the correct address we want, we should concatenate \"Madison, Wisconsin\" to the end of the address."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "cf3f590b-8d00-46c4-ac57-6f2f61509f68",
+   "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>geometry</th>\n",
+       "      <th>address</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>POINT (-89.29503 43.13823)</td>\n",
+       "      <td>East Washington Avenue, 53701, Madison, Wiscon...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     geometry  \\\n",
+       "0  POINT (-89.29503 43.13823)   \n",
+       "\n",
+       "                                             address  \n",
+       "0  East Washington Avenue, 53701, Madison, Wiscon...  "
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "geo_info = gpd.tools.geocode(\"1300 East Washington Ave, Madison, Wisconsin\")\n",
+    "geo_info"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3caf8c12-67a3-4e19-a758-d556d010eead",
+   "metadata": {},
+   "source": [
+    "#### Addresses with \"block\" often won't work or won't give you the correct lat/long. We need to remove the word \"block\" before geocoding."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "54103e4a-5ac2-4f19-811b-385c02623ed2",
+   "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>geometry</th>\n",
+       "      <th>address</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>POINT (-91.22788 43.88605)</td>\n",
+       "      <td>800, Madison Street, 54650, Madison Street, On...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     geometry  \\\n",
+       "0  POINT (-91.22788 43.88605)   \n",
+       "\n",
+       "                                             address  \n",
+       "0  800, Madison Street, 54650, Madison Street, On...  "
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gpd.tools.geocode(\"800 block W. Johnson Street, Madison, Wisconsin\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "66072f4b-2286-4d66-ba2c-18ccd2e37ed6",
+   "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>geometry</th>\n",
+       "      <th>address</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>POINT (-88.74367 42.84436)</td>\n",
+       "      <td>University of Wisconsin-Whitewater, 800, West ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     geometry  \\\n",
+       "0  POINT (-88.74367 42.84436)   \n",
+       "\n",
+       "                                             address  \n",
+       "0  University of Wisconsin-Whitewater, 800, West ...  "
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gpd.tools.geocode(\"800 W. Johnson Street, Madison, Wisconsin\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "cf982302",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0                       1700 Thierer Road, Madison, WI\n",
+       "1                        6400 Bridge Road, Madison, WI\n",
+       "2                   800 W. Johnson Street, Madison, WI\n",
+       "3                       700 Vernon Avenue, Madison, WI\n",
+       "4    U.S. Highway 12 WB &amp; John Nolen Drive, Mad...\n",
+       "5                    900 Delaplaine Court, Madison, WI\n",
+       "6                         6900 Odana Road, Madison, WI\n",
+       "7                        East Campus Mall, Madison, WI\n",
+       "8    N. Stoughton Road &amp; Hoepker Road, Madison, WI\n",
+       "9                       0 West Towne Mall, Madison, WI\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fixed_addrs = addrs.str.replace(\" block \", \" \") + \", Madison, WI\"\n",
+    "fixed_addrs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6a267ed-6876-4866-a66f-74390a3d4ee1",
+   "metadata": {},
+   "source": [
+    "#### Using a different provider than the default one\n",
+    "\n",
+    "- `gpd.tools.geocode(<street address>, provider=<geocoding service name>, user_agent=<user agent name>)`: converts street address into lat/long\n",
+    "    - We will be using \"OpenStreetMap\", for which the argument is \"nominatim\"\n",
+    "    - We also need to specify argument to `user_agent` parameter, indicating where the request is coming from; for example: \"cs320_bot\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "ab0e699f",
+   "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>geometry</th>\n",
+       "      <th>address</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>POINT (-89.31206 43.12188)</td>\n",
+       "      <td>1700, Thierer Road, Mayfair Park, Madison, Dan...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>POINT (-89.33876 43.04856)</td>\n",
+       "      <td>6400, Bridge Road, Bridge-Lakepoint, Monona, D...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>POINT (-89.39985 43.07217)</td>\n",
+       "      <td>800, West Johnson Street, State-Langdon, Bowen...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>POINT (-89.30584 43.08825)</td>\n",
+       "      <td>700, Vernon Avenue, Rolling Meadows, Madison, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>POINT (-89.40073 43.05908)</td>\n",
+       "      <td>900, Delaplaine Court, Greenbush, Madison, Dan...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>POINT (-89.50167 43.05667)</td>\n",
+       "      <td>6900, Odana Road, Madison, Dane County, Wiscon...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>POINT (-89.39917 43.06979)</td>\n",
+       "      <td>East Campus Mall, State-Langdon, Bowens Additi...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>POINT (-89.50622 43.05752)</td>\n",
+       "      <td>West Towne Mall, Madison, Dane County, Wiscons...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     geometry  \\\n",
+       "0  POINT (-89.31206 43.12188)   \n",
+       "1  POINT (-89.33876 43.04856)   \n",
+       "2  POINT (-89.39985 43.07217)   \n",
+       "3  POINT (-89.30584 43.08825)   \n",
+       "5  POINT (-89.40073 43.05908)   \n",
+       "6  POINT (-89.50167 43.05667)   \n",
+       "7  POINT (-89.39917 43.06979)   \n",
+       "9  POINT (-89.50622 43.05752)   \n",
+       "\n",
+       "                                             address  \n",
+       "0  1700, Thierer Road, Mayfair Park, Madison, Dan...  \n",
+       "1  6400, Bridge Road, Bridge-Lakepoint, Monona, D...  \n",
+       "2  800, West Johnson Street, State-Langdon, Bowen...  \n",
+       "3  700, Vernon Avenue, Rolling Meadows, Madison, ...  \n",
+       "5  900, Delaplaine Court, Greenbush, Madison, Dan...  \n",
+       "6  6900, Odana Road, Madison, Dane County, Wiscon...  \n",
+       "7  East Campus Mall, State-Langdon, Bowens Additi...  \n",
+       "9  West Towne Mall, Madison, Dane County, Wiscons...  "
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "incidents = gpd.tools.geocode(fixed_addrs, provider=\"nominatim\", user_agent=\"cs320bot\").dropna()\n",
+    "incidents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ad73b2c-5171-45c2-a3f0-eef30f93c492",
+   "metadata": {},
+   "source": [
+    "It is often a good idea to drop na values. Although in this version of the example, there are no failed geocodings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "41a7d12e-73be-4442-b43c-285229e6cfdb",
+   "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>geometry</th>\n",
+       "      <th>address</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>POINT (-89.31206 43.12188)</td>\n",
+       "      <td>1700, Thierer Road, Mayfair Park, Madison, Dan...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>POINT (-89.33876 43.04856)</td>\n",
+       "      <td>6400, Bridge Road, Bridge-Lakepoint, Monona, D...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>POINT (-89.39985 43.07217)</td>\n",
+       "      <td>800, West Johnson Street, State-Langdon, Bowen...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>POINT (-89.30584 43.08825)</td>\n",
+       "      <td>700, Vernon Avenue, Rolling Meadows, Madison, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>POINT (-89.40073 43.05908)</td>\n",
+       "      <td>900, Delaplaine Court, Greenbush, Madison, Dan...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>POINT (-89.50167 43.05667)</td>\n",
+       "      <td>6900, Odana Road, Madison, Dane County, Wiscon...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>POINT (-89.39917 43.06979)</td>\n",
+       "      <td>East Campus Mall, State-Langdon, Bowens Additi...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>POINT (-89.50622 43.05752)</td>\n",
+       "      <td>West Towne Mall, Madison, Dane County, Wiscons...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     geometry  \\\n",
+       "0  POINT (-89.31206 43.12188)   \n",
+       "1  POINT (-89.33876 43.04856)   \n",
+       "2  POINT (-89.39985 43.07217)   \n",
+       "3  POINT (-89.30584 43.08825)   \n",
+       "5  POINT (-89.40073 43.05908)   \n",
+       "6  POINT (-89.50167 43.05667)   \n",
+       "7  POINT (-89.39917 43.06979)   \n",
+       "9  POINT (-89.50622 43.05752)   \n",
+       "\n",
+       "                                             address  \n",
+       "0  1700, Thierer Road, Mayfair Park, Madison, Dan...  \n",
+       "1  6400, Bridge Road, Bridge-Lakepoint, Monona, D...  \n",
+       "2  800, West Johnson Street, State-Langdon, Bowen...  \n",
+       "3  700, Vernon Avenue, Rolling Meadows, Madison, ...  \n",
+       "5  900, Delaplaine Court, Greenbush, Madison, Dan...  \n",
+       "6  6900, Odana Road, Madison, Dane County, Wiscon...  \n",
+       "7  East Campus Mall, State-Langdon, Bowens Additi...  \n",
+       "9  West Towne Mall, Madison, Dane County, Wiscons...  "
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "incidents = incidents.dropna()\n",
+    "incidents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b30733e6-9491-4be0-bf20-bba64903334d",
+   "metadata": {},
+   "source": [
+    "#### Self-practice\n",
+    "\n",
+    "If you want practice with regex, try to write regular expression and use the match result to make sure that \"Madison\" and \"Wisconsin\" is part of each address."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "843bbba2-3de5-4cc0-b76b-92e7bebdd379",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1700, Thierer Road, Mayfair Park, Madison, Dane County, Wisconsin, 53704, United States\n",
+      "6400, Bridge Road, Bridge-Lakepoint, Monona, Dane County, Wisconsin, 53713, United States\n",
+      "800, West Johnson Street, State-Langdon, Bowens Addition, Madison, Dane County, Wisconsin, 53706, United States\n",
+      "700, Vernon Avenue, Rolling Meadows, Madison, Dane County, Wisconsin, 53714, United States\n",
+      "900, Delaplaine Court, Greenbush, Madison, Dane County, Wisconsin, 53715, United States\n",
+      "6900, Odana Road, Madison, Dane County, Wisconsin, 53719, United States\n",
+      "East Campus Mall, State-Langdon, Bowens Addition, Madison, Dane County, Wisconsin, 53715, United States\n",
+      "West Towne Mall, Madison, Dane County, Wisconsin, United States\n"
+     ]
+    }
+   ],
+   "source": [
+    "# self-practice\n",
+    "for addr in incidents[\"address\"]:\n",
+    "    print(addr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "1a04c2b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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DHgnD1tYWmqahaVo1urfyb+VnnIW2XYsUHlilR6mxXRHKVq+r9kj7jxJShULREcvLyziO0/LvRfQGl66QkgU/Q3HLpZ1CZefPnx/blI1OhLRYLDI52X3E8uLiYrWSmhLS4aGEVKFQtM329jY7OzsHHlPQzAeJ4HvM+Dmisv29wHG2SDt10+7u7pJKpQ79XCtEo9GqOzgajWJZ1oGvq4S0/yghVSgUbVEqlVjeV/jaR7ClhSkJnekgj4/GuvagAIMtPSb9AjHZ2oJtxjhP/p1YpAC5XI7FxcVDhXRqaor5+fmWiwxlkQ4P9QkrFD1kt+RyP1Ok6Pm4ftD3HMFBEQQBd+/epRTAllbO+yyi85YxwbYeJq9ZLOlxSkKvs0Yn/AJx6dCpfTnOk3831nQmkyEejx94TC6XY2lpqeU1ZRhluygcDjMzM1N9fJw/y3FBfcI94nd+53cO7dzymc98hve85z0DGY9isNxNF/hPt9bZLDikbANT0zD18Q6aqWV1dZV8sciyHiMelK3LQAhkzfvzhM6WFiYSONV6n7taCL9jGR1f1+6tW7fq0lk0TWNmZubQjlDpdLrle67U19V1vSE/tZZz584RCoWYnJwkHo9jmiaGYSCEIAiC6o+UEiklhUKBbDZLJpMhm81Wf/L5PIVCgWKxSKlUwnEcXNfF8zx83687j6LMiXbtfvzjH2dnZ4c/+IM/OPK5fuRHfoQf+IEfOPqgOuR3fud3+NSnPtX2/oqit+wUXb5ybwtTE7xnLslchw2qx4FsNsvm5iYSwaKfQd+rVqRJiSYDgpro3EBoFDDRkAQICprJ2yKFKX1m/RwhmhcfEEKg63q1Hu24WlGVvsa1eJ7Xlru3tgh9LXNzc20FXq2srFAsFtE0jTt37lTTcN566602Rt4909PTzM/P9/U1Rp2REVLf93nxxRdZXl5mYWGBZ555Bn2MagQalk1i8vgllysOJpASxw9471zqWIqo7/vcv38foCqgAJtamE0t3BBUBCCFQNZYob7Q0JBYLUQUyoEy8Xi8ugc4rhbp/hzSIAjY3t4+0jkdxyGfz1c7wNh243Umpaym0Gia1vdqSbWM63fVS0Zi2feFL3yB8+fPc+XKFX7sx36MK1eucP78eb7whS8MbAwf/OAH+cQnPsHP/dzPMTk5yfz8PJ/5zGfqjtnZ2eHv/t2/y9zcHKFQiHe+85380R/9EQC//q/+f8xOTdW5O65du8bc3BzxeJyf+ImfoFgsNrzu//6//+88/vjjhEIhHnvsMX7jN36j+re3334bIQRf+MIXuHLlCpFIhHe/+9189atfBeDLX/4yf/tv/+1qUrcQojrm3/iN3+DSpUuEQiHm5uaqnWcUvcP1A4pewDNnJlmMH68ydxWWl5cbKu8U0VuKaCtm/BwaEIvF6lqI1bK9vc3NmzcpFotjOTlXXJ69ZmlpibfeeoubN2+2bA5Q+7qDqN9by7h6D3rJ0D+BL3zhC3zsYx9rSPC+f/8+H/vYxwYqpr/7u79LNBrlpZde4p//83/OL/3SL/Gf/tN/AsoX6oc//GH++I//mN/7vd/jlVde4dq1a1WreSJkookHq7P/6//6v/jMZz7Dr/zKr/D1r3+dhYWFOpEE+PznP88v/MIv8M/+2T/j1Vdf5Vd+5Vf4x//4H/O7v/u7dcf9/M//PJ/+9Kf5xje+wSOPPMKP/uiP4nke73//+/nc5z5HIpFgeXmZ5eVlPv3pT/P1r3+dT3ziE/zSL/0S3/nOd/gP/+E/8IEPfGAAn+DJwfUDvCDgVDzETOT4WaIAhUKhYcvAQWNDj7QWUSkJBS4T/oNuJpoMiEoXIQSzs7MsLCy0nHwdx+Htt9/u0TsYLP0Q0f20+txqX/soxR26QQnpkF27vu/zyU9+sunqqdIx4VOf+hQf+chHBuLmffLJJ/nFX/xFoNzc+9d//df50pe+xPd///dz/fp1/vRP/5RXX32VRx55BIALFy60PNfnPvc5fuInfoKf+ImfAOCXf/mXuX79ep1V+ou/+Iv8r//r/8pH93rFPfTQQ7zyyiv81m/9Fn/rb/2t6nGf/vSn+Rt/428A8E/+yT/hiSee4M033+Sxxx4jmUwihKjbo7hz5w7RaJQf/MEfJB6Pc+7cOd773vf26FM62WRKHiXfJxUyCZsjszPSF0KhEPPz86ysrABQQue2kTzUEvWFRkY82OZIBiUEVN2SmqYRjUab1oWF8Z2YB2EFtpoHa125u7u7fR9HLeP6ffWSoX4CL7744oGlxqSU3L17lxdffHEg43nyySfrfl9YWGBtbQ2Ab3zjG5w+fboqoofx6quv8vTTT9c99t3f/d3V/+dyOW7evMlP/MRPEIvFqj+//Mu/zM2bN1uOa2FhAaA6rmZ8//d/P+fOnePChQv8D//D/8DnP//5thsHK1qTdTyils50xD4RRb+FEExPT3Pu3Dk0TSOt2Ye7c4XAFTqe0BFSMuXnmQ7K157v+9Ui94lEAh+Bh2C/HTeuE3Mv3dGVBt7hcJhQKIRt20xMTJBMJpseXwnQGkZ5xXH9vnrJUJfU+5O7j3rcUTHN+mLOlbBx4NDw9U6pdLf/V//qXzUI7v5VZ+24KjfJQW6keDzOjRs3+PKXv8z/8//8P/zCL/wCn/nMZ/izP/uzQ1N0FM3Juz6+lGhjuHfXKb7vk8/nq3mNkUgEIznFdqaDABYpOevtYu8LMMpkMiQSCcLxBIsXIoR1QeB5ZNK7ZNJpXNcdqyDDWnrp2k2lUszMzJDL5arxD0KIaq7ofsLhMO94xzuAckOBfkfq1qKEdMhCWrGuenVcP3nyySe5d+8er7/+eltW6eOPP85LL73Ej//4j1cf+9rXvlb9/9zcHKdOneKtt97iv//v//uux2VZVtMIPcMwePbZZ3n22Wf5xV/8RVKpFP/5P//nqhtZ0SknQ0SDIODOnTvkcjnm5+dJJpN89a37bEsTRPsTZkS6DSIKZSGVUpJzXKbCNkU/wDYNwuEQC/PzFAqFA3MlR5leCqmu6wghWFlZqfs82kk1GWTELighhSEL6TPPPMPp06e5f/9+0/0FIQSnT5/mmWeeGcLo6vme7/kePvCBD/BDP/RDvPDCCzz88MO89tprCCH40Ic+1HD8Jz/5ST7+8Y/zvve9j7/yV/4Kn//853n55Zfr9lX/yT/5J3ziE58gmUzyoQ99iFKpxNe//nW2t7e5evVqW+M6f/482WyWL33pS7z73e8mEonwn//zf+att97iAx/4ABMTE3zxi18kCAIeffTRnn0ex5Vg7zrcL5rpksfsMUxvqcVxHJaXl6vu1+WVFb65kWVX2HRaU6Hizt2P53m89tpr6LoBU5MEQYAVT+LL8mc/HYkQiUSaPnfU6eUeacVFOzExUd2jhrJL/DAGEfRUy7h6EHrJUIVU13V+7dd+jY997GMNPfkqLszPfe5zI/NF/d//9//Npz/9aX70R3+UXC7Hww8/zLVr15oe+yM/8iPcvHmTn/u5n6NYLPJDP/RD/P2///f5j//xP1aP+cmf/EkikQj/4l/8C372Z3+WaDTKu971Lj71qU+1Pab3v//9/L2/9/f4kR/5ETY3N/nFX/xFnn32Wb7whS/wmc98hmKxyKVLl/g//o//gyeeeOKoH8GxJ13ysHRBZF8gkW3oI2uRZjIZbNuuFi+vxXEcstkshUKhek/V/mvbNvF4nPX1dba2turvQfbEocO3bUifkGxtFfm+j+/7LC8vI4XgdDROImSxXXQpej72mFaE6qUlWLHyUqlUnZCurq4Si9X3c93//0HHQyiLFIQcgTpPX/jCF/jkJz9ZF3h05swZPve5zylXpGJgFD0fP5DommCn5JKwTEq+T8wycP2gQVxHhfv377Ozs8Pc3BwTExPk8/lq6beD2p1Bef+9UvZtPz6Cu0YCR5Tft5CSRFCiqBmUROvPIhI4nPabR+RalkXJcUjNLmCbBpZtYxoGm0WX+ahN3vUxNUHUGs3P+iDS6TR37tzpybnOnj1btT5v3bpV9RKMIo899ljLvduTwkgIKYx/ZSPF+LNVcJgMl606xw/IuT6WJjB0DVsf3VX3xsZGndXSK7a1EOt6tPr7nJclKUv4CEpCx0VnVY82RPJO+nmmg0LdY+FwmPn5ecLhMNu5PJOxciswxwvYKbnMRCw2Cy5TYZNAgq6Nn0W6vb1drQLVDaFQqOqZO3XqVNXF3a/vt1e84x3vOPFW6cgsI3Rd54Mf/OCwh6E4oVQs0QqWrmGNsHjW0qxkXC/IifoodmMvUUVHEpEe4BEEgg0tUi1eX7Faa5mdnSU+MYkbSFZyDhOhcDkiXkoc30cXgrRTdqkLIdDHT0OBxvKAnXL27Nmm7vlRb3A+jm74XjMeM4VC0WcCJMYQJwTH7z5ApJWQSqAgDJwOb3MHjRU9Sl4rT+q29Jjy80SaNOaeCIpc8LaZ9bLMeVnOertY+zJDZ2ZmyLo+Cdtkq+jgS0m65PJnyzvkvADb0IiaOm/tjHeu81H3SFtZdbZtV9PvUqlUy1zSYTCMvNVRRAmpQgEUXH+o+3IbBYec051FY5pm08msIAzu63HeNlKsa5GGwge1OGjkhUFBGNwxkqS1ULm0X+BgSw8PDbfFdKEjSckSSVlqSHnRNA03kHiBpOj5LMZCFDyfkKFzeS5JzCxv3+hCEBvDfdFajrpL1kpIK4UxoLzHPEr7kSfdpVthdL4RheIEcyoW6vq5QggikUhDQEpOWNUWZ9t6mLRmE5IeIekxERSqslhC574RxxMPYhIm/TxR6VJCJ7W3L5oVFrtCJxE0CmYrdF3H0jWStomxF0QkgKIXELN0vEAQ3hPThyei5PN5DMNo6uIcdY6SdlLp7NKKSqRuLpcbqcCjw5qR95pK6dhRQy0nFCeegucj5YMc0nGkNr8woNzmbFurF2dfaOQ0i009wi1jglUtypYW4o6RrBNRU/pMBQXC0kPba52mI0nKUjlqVxisa5GWFmotuq4jpcTQBAKBF0g8KQkbGgXPp+gHbBfLLmMpJTs7O2PbMPooe6SHWXa6rnPq1Kmuz98PotHowMYkpeTb62n+5P7RWtL1C2WRKk48lqbhiGBk80QrOH7AqxsZCl6ArWtoGpxNRJgImaRSKdbW1vB9n3t6gqJmHnguX2js6o1W8JSfJxkUq6mjtvQJeLDitvDLj0nICov0noVqtnAch8NhdkseUVMn7bhkHJ/T8RBFL8DQBCHjgYBvbW2xtbXF7OxsF59O50gpKRQKmKbZUB602/N1SiVKtx0X6cTExMA7u7QiFApx9uzZgVmHXiC5tZPn4kQUNwgwR8ylrIRUceLZLDhEzdFPtbqxssNStj4i9uZ2nu9aSHE6EWZubo57S0uHimgrQoHL1L60FQufWnmoTJsakJAORanjCg2HB59fgMAgwJIeVihEICWmruHlXRajITQhiJg6XlBfdnFzcxPof6UcKSW7u7tsbm5SKBR4+OGHhyKkkUiECxcutP08KeXAy/+14tSpU1VvQz/FNJCStVyJ+ViIv3pmEj+QIyeioIRUoWA2apPpMtBnkLjBgwl3JmLxrpkE97NFcq7HV+5u8t/MJ4lOlDiLjRQCJ5BETJ23d/JIyj1zd0suQYt5eyJobDwvOLiwkYZsqGJUQq/uoRaKDqmooOB6rC4tseq7xGIJpidTRKNRSqUSlmVVe5UuLS21NTl7ntd10M3W1lZdI4xeBcx0ukdaWTC0I0RHzVHtNffv3+fcuXPs7Oz0zYOwVXB4aWmbghdwNhFGSknYLHdfGjWUkCoUlMVit+QSM42RLQYwG7FZz5crFV2ajJIKmaRCJlJKSn6Gb61n+K7FxgYP75iOU/J84pbBcrbEny5vl4seCIEZeFjSJRGU9nJDO0OjUZWNGjdvNr1DfGKSiKGDW6o+lk3voOs6QRBw7tw5YrEYqVQKTdN45ZVXOH/+fNP8Sd/32djYIJfLHdgP+CDS6XT9e+iRkHZqkXbyutFo9PCDBkipVOKNN97oaypOKmTyXacm+PryDnfSBSxd8OTM4bWGh4ESUoUCqqkXW4VynuN02Bq56MBUqOx+XIyHmI8+2N8UQvDkbKLlRG7XVGY6FQ/xtJggZOikbINXX331SNGmehMhrRVXGQQkbINCodBwXMVNubm5SS6XQ9O0aklDx3FwXRfDMBBC4Ps+W1tbbGxs4Pt+14Xta3uiVsfbIyHt1O3ayetaloVlWYeWfBwkUsq+uuE1IZgKW3zP2Sle28ySdTzOJiOUPB9dEyPVE1gJqUJRw2TYwgsC7meKnE70tgftUans406Gmu/ntSv802ELU9eQUh65U0izV6x9TErJ2tpaUyGtkMlkyGTqa/MuLS0BZfenpmkN9YC7devufx2AO3fuoOt69bUq/69YyO0QBEHHUbudCngsFhuZYKMK/cojDaREynKAnaVrZByPub3uS2/t5EnYBovx0bk/lZAqFPswNA1DE6zlSsxGbVw/wByBcoEVg/ON7RwRUz9wIqkIT21T6IrQVt7LoNptVYKIuqHSKWY/tRN4JwEvzYQ0m802PTaVSrU3yD10Xe9ITDsVoWg0OnJCCtRdZ73irZ08tq6xkXfYLDgsxEK8tpnlVCyEbWgjF3CkhFShaMJc1Cbr+qzkiiBh/ggFE3pF1NKJGDp5z2cj7xwopEEQ8OabbzYVoV5PesPAMAwcx2FtbQ0hBIuLi4c+R0rZVEib0elnpGlax8LYqVu0F5HFvWZ9fZ319XWAugVbIpFgcnKy+nullGA7n5MfBGQdl4WJGJqAc8kw63mHv/7QDKaucSE1WvvFoIRUoWiKEIK4ZRA2NHJO/1IOpJRI6huJSynZLrpICcmQUd0LEoClC/Ie7JYOtnxM0+TMmTO8/fbbzV9zTIseVNjd3WVjYwMoW47tWKW5XK5tK7ybGrKdWvidCm84HMY0TVy3sebxKFB7XW1vb7O93Vg8YWZmhrm5uQPPcz9TxNA0vra0zZVz02hCkLCNkdoT3Y8SUoXiAPxAspIrkWyxL3lUvEASAPZey5NyBG7AX66l2Sq6GEJwPhVBSokbSHb2BNRvQwhjsRgzMzNVi+E44boutm2zuLjYduBRu9YodLf3128hFUKQSqXG+vts5z2fTUb485Ud0iWP/3pnkzOJMBcnRs8KrUUJqUJxAEII3D7uJfp75fOKns93Nst7dZoQbO2VzfOk5M3txtqqEbO9ZPjZ2Vmy2eyBwT7jhBCCZDKJZVlMT093JEadCGmnbtduAre6iXidnJw89kJaKbpgaOX7IGzqSkgVinHG1AQ5x6fg+YSN3oX6S1muOatrgv96d4udYqO7Lmrq5NzmbuVLE9G2XI9CCCzLOjZCCuVC6Z3mL5ZKpY5TRxzHqUbyHvZZdxO41Y3Va5omyWSS3d3djp87CrSzeNC1cjrXhVSE1VyJhRGITzgMJaQKxQEIIYhaOt/ZzHJpItqTVmt+IPnKvU02CwfvdTUTUV3Au2eTTIYbu6P4vt8QoQtHb+81SDRNO1CUui2T14k1ClAsFnn99derv+9Pjdn/003wVrepI9PT02MrpJ00oY9Zxti01huPUSoUQ2Qj7zATtdE1QcbxyJQ8NA1sXcfUBFGzvYm04Pq8sZ3jbrpAqcNG3lFTZyZi8/hUrNp2bD9vvvlmXSBKRVDHSUjj8TjpdPrAMXfjEt1fzahTKmk4vQz0eeuttxqiWoUQnD59utrIuxnhcJh4PN7x4mDYTE9PH/i+xhklpArFISRsk4Rl4PgBCdskvrdK3im6uEHjPmUgZUMnmZzr8aW3N/BaFbo9gFTI5INnpw7tTrPfkhvH6Nx2LC3HcTrKHfU8j3w+f9Sh9Zza76fWyt6fA1xLPp9naWlpoMXrRT7PE08/DcDLL72E7KKqVCgUGlhXn2GghFShOISHUhG+sbrLbsnl8lySU/EQhqaRtA02C437bn4g0faicAMpybk+Rc/vSkTjlsF3L0601eJtUAUWhs3q6irT09NtHz9ullttylKtxapp5WpUo5r+0oqKld2vKkijgBJSheIQJkImp2Ihso7H11d2sdbTPJSMlIOPBOiaxlquxGbB4aFUhNmITd71CRkaf3xvq1povhME8N75JOeT7a3+x9H67JZO9yTHTUhrqXyvw1gkiT0rXqsJVNMKhWpLgnYt09nZWUKh0Q8YOgpKSBWKFvhBwJ10kd2Siy4E33d+hje3c2Qcj6VsqaH1mgBWciVsXePpUxPcSee7ElEACby+mSVmGkxHGgOL9nNSrFHoLEgnCIKWJQAVB1Nx59by+Ac/WP3/t7/1rUPPEYlEOvIejCtKSBWKGqSU5F0fJ5C4fsCZRIjzIly1gJ6cfdDGyQsky9kiaccjaRnMx0JsFx2+dn+bP1ve7jigaD9Z1+e/3t3kXTNxHkqUV/TNaufCyRLSToKNOqlmpOgtmqZx+vTpsS9H2Q5CnhR/kKIj8q7PVsFhOmIR6mH+5CgTSEnB8zGEwD7Ce35lI8OjkzFe38ry6mZvrKELlo+R22l4vFZQT4pgRKNRHnroobaOXVpaGslC7+NArWu3Yom++uUvE+xF3spIBF3Xq+5n0zSr/5dSMj8/z8TExLCGP1CURapoQErJ15a22Sm6TIVNvufs8XfNQLmiUNQ8+i3xjuk4d9KFnokowEpgskhj27KTtDdaoV3XbidF6hWNVPZAa5dnWjzOY+99b/WaO84BRJ2gPgVFU8JG+dLIu/6Jm6iPyk7R5S9WepswX/QDMA/fKz0JtOvaLRaLYxfhOorU9n61bbvtLi4nCWWRKhoQQvD0qQnSJa/tYgOKMoGU/MXqbltF5TtBAFLoTRtpnxRM02RiYqIvReoVrZk4fbraDPd8h8/1AslWwWEiZI5ET99+cXzfmeJIaEKQOuYXfz/4zmaW7SZ1c49K3NQRXqnn5x0nXNdlbW2t7ShcJaS9oZtKUhXWciU00V63onFGWaQKRY/Iuz43m3Rq6QVxLYATEkx0GO1M7K7rHqtC/cPkKC7c+ZiNF0isY74gP97vTqEYEFJKbqzs4HRRvagdAsSJduvW0o6QKmu0N1QK9beD3NvWqI2p0IQ49iIKyiJVKI6MlJK3dvKsdVl8oR3SvmC0OzIOjnYsJCWkveHRRx9tW0iFELynJs/6JKGEVKE4An4geWlpm5Vcf/cvM67PnBVGc5S78rCJXVUz6h2dunVPamCiElKFoku8IOBP7m2z0aRwfT/I2THiSkgpFosNvUFrJ/xsNqtSttqk0vgd6nOSK/+eVGHsFCWkCkUX+IHkT5d2BiaiADsexAf2aqPL6upqw2NCCHRdxzCMgbYYG3csy+LSpUvDHsbYc/x3gRWKPvDaZqbv7tz9jLuRJfJ53vmud/HOd72rWn6uV0gp8TxPFWHoEFVUoTcoi1Sh6IKtPuSKHsaCGUBx4C+rOMZIKUmn00BjQ4TKz3FvgdYLlJAqFB2yWXC6bo/WLedjJsb2ykBfs1f0qq+lovcUi0Xu3LnT8u9CCJ544omGx3d3d4nFYkcq1nCcUEKqUHSAlJKX7m8P/HXTbsC4tg7oRV9LxXBo5votFovcu3ePxx57bAgjGk2Ug1yh6AAvkBj64CMZk6h9P8Xg2S+kvu9z584dpJRqf7WGE2WRBlKiqXBuxRG4mymQdQYbFTph6ZjZzYG+5lGRgI/AQPLySy8BrftatoPvw40bUdbXDWZmPC5fzqG8iv2nViyllNy/fx/HKW9rqNSYB5wYIXX9gOtvr/P0qQkmw6odlaI78u7gUyvyfoDUdEQwXmkdu1qIojCYi5QFtbZScBAOt703ev16gmvX5lldfXDfzs05PP/8Cs8+m+7xqBW11Irl5uZmNTBJWaP1nJhPw9Q1HpuKUxjCRKg4Hkgp2RhwkBFAyZfctSaR5uhHT2aERRGddS1CVDq4QsPfm2a6Kbl//XqCq1fPsLpq1j2+tmZy9eoZrl8ffkk6TdO4cOEC586d48yZM223eRsHKoKZy+VYWVlpeFxR5sRYpAAPpY7PBa4YPJsFdyhpL1Bu7H1Xj3Na09BKvc3BbEUlbbWVA0/u/S0A0ppNCYOsZmFJHwkkgxKalGQ0C096hCLRjgKLfB+uXZvfy5+tH4WUAiEkn/3sPFeupIfq5hVC1IlnJBLhzTffPBaFITRNw/M87t69W/d4t27dXC5Hfi+Ku1I9KZVKVasrjSsnSkgViqPwna3h1m8t+gE3CbMYjxDObffE1SsBaUfwDRtX03HRKPhQCgJKfnmiMzVBxNAIa5J4KYvYK1O4q9nsaCGEhIh0mQ1yzAY5BJAXBkVh4AqdLc3klJdGo7OKEjduROvcuQ1jl4KVFYsbN6I89VR/2td1g2mahEIhcrnRGVO3CCG4e/cunufVPd6NRep5Hru7u2xtbdU9Ho+Pf70uJaQKRRsUPZ/VAVcyaoYE7hUlujHBtC1IFHcRbvvuZmmY+FaEkm6RDwRp18cPAKdy9kZxdgPJruOzC6wQYTEeI5JZJxmUyAmLeT+Lvk8kI9IDPLLSIicsXKGTAwwCQrK9BcD6envTU7vHKTqnVd3ibizSN998s0GQ4Xi4idUVqFAcgh9IvjaE3NGD8KVktShZF3HCYQ1NgCEE06VthFfvfg5CMfJmmEIg2HZ8cAG3+ybh94sB8/EZ4pl1pv08K3qMZFAkKt06B6yPoCAMEkGRiaDzkkwzM42T7lGOU3ROq+L/3YjfY489xt27d9nd3T3yuUYNJaQKxSF8az09tL3Rwwgk5LwHohgJp4hm1pGGCbqJY4a46wiCYvfC2YyVYoCIzxDLrBMLHDb0CGsIJAKdAF1KTvkZTBkQDroTusuXc8zNOaytmUjZaAEJIZmbc7l8ebgu1GbW2XHvPtOp+AVBwNbWFqVSo1fnOAjp+L8DhaKP7BRd3toZTHBPL9hywQ8nuKkluKdF2Qp0gj7N6cvFgEJ8mqQskfKLey7egABBgGBTC6MTdLw3WkHX4fnny5GiQtSfo/L7c8+tDD2f1Pd97t69y/3791laWmJlZeXYF87v1LUrhCAWi1EsNnomjoOQCnncl04KRZdIKfnynU22R9QabUbK0glrkuUeW6AH8bBehGKOYK8AQwUfwY4WIh6UsLpKfinTLI90ft7huedUHumwME2TeDzeUODesixSqVTD8b7vs7u7y9LSUt3jQgjOnDnT9DUq5x8HlJAqFC341lqaN7ZHO/JSF7BgC5ZLMGFpTOQ3KUQnuFcc3G0dMzUW8uv4CAQ0BB71AlXZaDyIxWKcP3++4fFCocDNmzc7OtcTTzwxNkKq9kgViibcyxRGXkQBfAmalFz0d5BZHwHYXgldWPgD0tKiLwkiCYx8Gr9l1unR0HVGKsXlpBOPx9F1nZ2dnbrHW7lpO7XXKhbuuDD+zmmFosdkHI8bK7uHHzgibPga0n8gYVppsIKjAUvSRtIfa1QxepRKpQYRhdZ7p0HQmWt/3PZNlUWqUNQQyHKqi9evCJ0+kPcCpBlCuOVADi+cxB9gJUMnkBBIphfPEje1anHzZjmDiuNNKwFUQqoYWW7t5HH9gILnE8hyCcRUyDz8iceIvOuzXXRYjLffSeQg3trJk3HGTwBWzDgzVhhf01muFlcYLJ5uEouVv4fFxUVu37498DEohkuvXLtKSBUDYyVXZDn7IC/LMrQTJaRSSl5eTyOE6ImQBlLy5hjsizYj6wZk0RiGgFZI2g+mk3g8zvT0NBsbG0Mbzzjg+z43btxgfX2dmZkZLl++jD7GUVS9cu1COUCpndfRNG3otXqVkI4pS5kimzWdSExNcLZHVtm4UPACAglPn0r15HxvbOWG0iZt3ImaOpcmoyTs+kXc7Ows2Wy2ae6gAq5fv861a9dYXV2tPjY3N8fzzz/Ps88+O8SRHczk5CSe51X7ktbSK9duqVRqO8rXMAweffTRoQYnjZf9rADg9m6ery1tl/emgMmQyYcuzBK3T9a6KGLqvG8h1ZNzOX4w9KL040jU1Pn+h2a4kIo2/E3TNE6fPj2EUY0+169f5+rVq3UiCrC2tsbVq1e5fv36kEZ2OJOTk9i23fRvvXLtdoLneUNfrJ2smfeYoGvlfD0JPD4V49JkFGOAewqBlGgjEpqua70Zx53dwlgFGI0Kl+eTB14LoVCIqakpNjc3Bziq0UXk8zz29NN8mOZOeCklQgg++9nPcuXKlZFz84ZCIaSULYWxl67dTshms4TDw/PIKYt0DDkdD/NdpyZ4cjbBY1OxgYooMFaVftrB8QNe28wMexhjx2IsxEyk0TJx/IBvr6fJueWgrZmZmbELHuknLwL3Dvi7lJKVlRVu3LgxqCG1xcLCAmfPnsU0TVKpFIbRaIcNwyIFhh4hrizSMWUxHhro6zl+QM71iRga6ZLHVHi8G/FWCKTkG6u7VTe5oj0ihs675xJN/5Z3/boFnmEYTE9Ps7a2NsghjhRir5m1Viiw3OZz1tfX+zegDrFtG8dxSKfLwX25XK6peA3LIh02SkgVbWHpGqZWrjYSs3SyjkfMGu/LJ+t4/Mm9LbIqwKhjHp2KETKaux2bRY5PTk6yvr5+7LuitOKJp5+u/n/1gONqmZmZOdJrmqbZVNgmJiaIRCLV36WUFIvFOpdt5f+bm5sYhoFlWWxubqJpGolEgrm5OaLRaLVfaeXHNJtnDfRSSPe/p0gkwtzcXM/O3w3jPRMqBkrlAo6aOs6g6s/1iZVskRsruxT9471S7geWrjEV7izNyjAMEolEQy/Kk8gzwGngPs33SYUQzM3Ncfny5a7Or+s60WiUYrHYNLLW8zyi0frgsFgs1vRcW1tbuK5b7WZjWRaLi4sATE1NMTU11daYerWAmp2dZXZ2tifn6iVq40LRMV4g6VGMz8AJpOTryzv8yf1tJaJd8MhklL/20ExDqks7TE5O9mFE48HLL73Eyy+9xKtf/jI68Gt7j++/jSqL1eeee67rQKNwOMzZs2dbRtbmcu3nSu8XwGKxyMsvv9xxm7heWaSjutc+mqNSjAyBlOwUXfyaPcSCFxA1x8+ZsZIt8uXbG9xJt070VrTmXDLMO2cSWHp300YkEiEUGuze/qggIxFkJEKwF1n6UeCFa9eY3eeSnJub44UXXjhSHqlt2y33MKEshr5/+HbGQdG5rQQtnU6zs7PD7u4u6XSaTCZDLpfrWX/WURXS8ZsNFQNF29sT9aVE31s/T4ct1gsl5qPjMSlKKfnmWnqsGnSPGjFL5/Gp+JHOIYRgcnKyoSflSeX7r1zhyoc+1PPKRolEglu3bh14TD6fJx4/+Ps8yB3bStBWVlaaupN7xah2hFFCqjiQiiUqeJDjdjdT4GxiPKoo+YHkz5a3WaoppajonHdOJ4iYR89pTCaTrKysHPsozlbISIRvf+tb1d914KmnnurZ+YUQbe1HtiOkQggeeeSRumCiys+wonOVRaoYS3RNEEgQAkp+gBtI4pbRs4IMXrAX7delu/AgAil5aWmblZwS0aMQNXUWYs332zpF13VSqRRbW1s9OZ+iHk3T2qry084+qRCi4xq2SkgVihbUiuZytshi7Ogu3W+vpwkkuEHAZMjioVTk8Cd1yDdX00pEe4Claz11qU1MTCgh7RO+77OysnLocfl8HsdxDhTKIAjwPK8jMe1VdO758+fr9tMr5x21Sk8VlJAqmtKsDKDjB7i+JLovf1RKiYSOrNTHp+LV8n6BlD0vO3gvU+DWrtoT7QU7RZeC5xNukTfaKeFwmHA4fGB3D0X/yWazB0ZSF4tF3nrrLR566KGGdJlmHBSc1CmO41TPFYlEmlZRGiVG005WDBU/kE3rzmYcj1KLaD+nw1SS2hq5uyWPQg+LIjh+wF+sqHzFXiGBv1jZxe2h2+4kp8KMCoe5dysVqRzHoVQqVX9aBRP10q27tLTE7du3uX379tDL/7XDaMu8Yii4QdBQtSaQElvXm6Y+CCFaVrlph4ke91B9dTODq0r+9ZSVXIn/emeTp09N9KSiVSXoqJ00DEV/yOcP9thYlsX8/Hzb5+vX/ujq6mp1b3R+fn4krdPRG5Fi6DTbD8u7Pp4c/fzRkhdwS6W59IXdksf1t9dJ2iYzEYupsIUXSDKOx3q+BAimIxYXkhHCh0T4appGKpVSXWGGiOu6h+6TdkK/hDSdTlf/34mwD5LRnhUVAyeQsqHaCpQjN1dypZFPe3ljO4syRvtHIMvdf8odgBpdg5sFh7d38jw2FePixMH7apOTk0pIh0yhUOhYSKWUBEGAEKK66BZCDCSlSUXtKtrGD2TP+mx2ihfIlu7bmbCFMYRxSSlZzzv4UmJoZTdy1NQbgpN2ii43t5U1OmxKfsA318pWxEFiats20Wi0o5J1it5SKBRIJpMdPSedTnP37t2GxwdRLEEVZFC0jS8lnicxdTHwBtoHRd2FTX1PaHszJikluyWvoVuI4wdsF102Cw45x2O94FD06le7uii7ERdiNhMhi9c2MyyrogsjxTfX0sQtg9lo6xzUyclJJaRD5KDI6c3NTWKxGJZl1QlYK8uz3519ai3gUUMJ6QhSaVc2aAIpD2wSbmiCvOtjaJ0LvJSSrT1xdPyAs4kwBS9gJvLArZR3fb65ttuWIPpSsporsaryREea17eyBwppIpHAMIyxiMw8jpRKre8f3/d54403mJubY2pqqipkw6pKJaXk5ZdfBuCxxx4bqZxSJaQjyCBFVEpJ0Q8I6RqO3xitW4smBBFTp+j5RNoMOir5ASvZIm/t5Pf21cq8vVvg8akYc3uT7Ebe4av3t1S07TFjLe+wW3JJtugWI4RgYmJipJpYnyQ8zyMIgqZ7jxXBzGazZDIZ8vn80C3CitV77949zp07N9Sx1KKE9IQjhCBslMXRbqNMnybEgVZrhUBK3trO88pmpmlOquMHfHs9gyYEWcfj5k5OBQkdU9bzTkshhQdNvxXDwXGcpl15KqJV63ofhcbsQoiO93X7jRJSBVDec+zVajPv+nz1/ha7pYPddb6U/MWqKpxw3NkqOHBA0JFpmiQSibo0B0X/0DStzj3byq0+qo0FpJSsra2RSqWGPZQqoxlLrGgLKSWOH9T1Cu0GL5AdRePmHI/dUvP+giU/4MW7m4eKqOLksFk4vK2WqnQ0GIQQzMzMtHVsKyGdm5vj7NmzvRxWx4xaGsxojUbREY4vMTVx5NJt3l5OWLukQiZZx8P1A6SUpEsugZT4geRba2lyPSz3pxh/Cl5A/pBrIhqNjmTFmuOEEIK5ubkGgWwlSq2E1DCMoe+V2nZvuhH1CnXljihSSnzJgZZiuVR8ec+yUuvW0ASCzgKWOk0NFUKwnC1xcztPsBeNa+sagZQqWEjRlJ2ie2A/UyEE0WiU3V3l6u8XFy5cIBwOs729Xfd4KyGdmJggGo2Sz+fr3O6+7/fV7ZtMJllcXOT27dtNU6N0XR+5CkdKSEeUoh8ggEr6pLl3sZf8gJChoYlyCooQAkMAiKqbNwDsJrmebhAgJXUFFxw/QBedOSa2iw530vX5Z6UOi9YrThZbRYdT8ebt95aWligUCrhu8+0CRW+oWHG1aSOaprUU0kQiAZT3UCORSLU9m+u6WJbVtw4+lTFVxDqZTCKEQNM0kskkkUhk6BbxfpSQDplKC7LaVmTaXiRtMyKaTt71MDWtwZKsCGTR83H8oKFCkR9IbF2j5Plomqj69TutonQ/c3jjYIWilp1ia5EsFosDa6nmITBo7TWJRqNEo1GCIKhaXrU/+x8bJyriU+nekkgkSCaTh5YINAyDeDyO67oEQcDExAShUIh8Pt83Ia287tmzZ4nFYiO3J7ofJaRDRoiyK5YOVlghQz/QfRsydPKuj6WX01AqVYEsvWzB2jUi3U1K80b+8OARhaKWgtdadAYpSOVK0gcL6ezs7KHnSafT3LlzZ++c5Z9Rnuo1Tata/NFolHg8TjQaRdM0PM+rFltoVT3Itm0WFhbqHutl555EIoHruoRCoaolPEp5ooehhLSGgutT9IOet/XqNe1UFbJ0QdHzCSSEDa2nrpDDAkcUiv1kHI/VXIk3tnK8byFZV/hjlCy7dqrl5PN5VlZWCICA8kJYO0CcR4EgCHj99dfrHstkMi2P3y+sFddqrdAeVBXpMOLxePVcpmmiaVo1lzWfz5PP59vOWbVte+h5pUpIayj6Absld+SFtB0MTcPowxK5UglJoeiUP763ha1rDQvBQQqpOETw2hHStbU18o4Lh7iJxxkpZV+LLxwk4p2STCaVkI4SEyHzWIhoP1FBuYpumQ5bhA2tYe9+kEJ6mOV4mJB6vk/O8dCQI+3KPUmMQuCREtI+Msx2aArFqPG+mQhClPfkKpGZlVqvg8SndWzAYUEtS2vrSKfIrhZCk5K0ZmPiM+kXsFCemmEwCoFISkj7jJRyJFZMCsUwiRgab928WfeYEIJ4PD7QcWiUI3fLYtponTazSB3HQdd1hBAUikUCYF2LgBDEgyJpLYQtfaxARbMPg1GYX4cv5ccYTYzGl9wtzl7lolrG+O0ohsiU2Wg5hEKhodTXNZAELXZLa4VUSsn29jZvvvkmhUKB19+8SSGXQwOi0sWQPlN+gUUvzYQSUQBEPs873/Uu3vmudyHy+cG85ghMSsoi7SOj8AV3Q8Hzeen+NgXP56+emSJuPbhMNCHKuagq4EjRARdnJkguTpNOp0kkEgRBwNLS0tDGYxDgoWHuc8dWhNTzPO7fv18Ninn77bdx0LD25HfWz+EjsAiwZPN7QdO0Oo/UKEUnHydGYZ5VQqpoIGzoRE2dRyZjdSJaIWQoIVW0z9lEmMlwOel/YmICKKc49DJys1MqU6+s+T+UJ+Xd3V2Wlpbq8iRdNIwa0TUJOCgsMZVKkUwmuX37NlJKDMOoCmkl7aPfkbGDpGJ9ajUFGrRCofqJyUikf6+thFQxqjw5m6gr3FBL2NBVdxdF27xjun4fVEpZLTc3TPZbpZqmce/evYZ6v90UXLBtu26C1zSNJ554ou6xzc1NlpeXj/AORocnnn664bHHP/jB6v+//a1v9e21R0FI1R5pj5BSUtqr3nLUtmajQCsRBZpaqQpFM6bCVkOx+t3d3SMl8/eK8vT74F4NgqBp0Xy3iQv4MIrFItlstv71hpg/e5wZBSFVM2KP+NZ6hrvpAqYmeHIuwXy0eYHu40DCVpeNol3qF5VBELC6ujqksTTj4JKBknJ0b6dT9e7ubl0N22Yu3OMkpC+/9BJQdudWLNFXv/xlgnC476+thPQYcSoW4nwyjG3o2PrxNvSVkCraReyToO3t7ZHq8qIT4COapsIAOOjYdF4Sc35+Hl3XuX//PtC8Lu1xEtLKHmjtOwrC4b7ujVZQQnqMmI4c3EGhgh9Iso5Hwh5+c9xuSVgmmlBVjhSHE7OMauSq7/usra0Ne0h1aIDbQkj3Bxh1gmEYDek+pVKpaplKKUdqQTHOjMI8KuRxCRsbYRw/oOD6fH1lBy+Q5Fyfh5IR3js/3PqQnbC9vU2pVELTNHRd55WcZL2gJgLF4UQMnffGJKVSaSh5o4fh7O2B7p+Oi+iEurBGFYPlzJkzqtbuScDSNbaLbl2k691MYayEtFAosLW1hbTDlKwoyOPtvlYcDUMIvL01etH3WV/fHPKIWmPuRe9qBAR71XiDBqe0YlQZBYtUzYYDImXXZ52djvd/E76XVNxUUmhoMiBV3FYTjaIlEfPB1DJljvaVIiiLqb73r0WAjU8wAhP0OGLbNrFYjHe+85088sgjfX89JaQnCNvQmN5LShfAY1Oxnpy35AVsFvrfaLsipFoxh5XdQnguU3Y3bcEVJwEBJC2dpKmRyI2uNdqKg2N5Fa2YnZ3l0qVLTE5OkslkBlJwYhSEVLl2B8hfOT1JxvEQgobcum750+Vt1vMO75qJc3Ei2lbT725o1mEh5RfYoL0gK8XJYtcp7y2e9XbJCZO4dMbOg2HKoKsc0pNMJTo5kUiwurraVn/Xo6KE9ISha4KkbeD3aJW2lC2yni9bo99az/DKRgZT10jaJk8tpBr6PraL7/vk8/m6zhyxWIxwOEyhpgSYKGSYic2yXjoZARmagJSpUQokOU/ZK4cxYwKuRAhJVliEpTtWjbBNAgrCwGxRS1fRSG2az9TU1EAqWI2CkCrX7oARQmD0qH/eX66lqW136ksoegGruRLfXOs+OnJtbY3bt2+zs7NTfSwUCnH69Om64wSQKm6jj8CF3G8MTXDelkzn1jlV2GDOUJNrK6YtwUPkSeQ3cYWOJQNcoVEUBqWWnUBHDx+BrkS0I2qLUBiGUa2t3E9GQUiVRdpnpJRkXb8vZfWeOT1J3vP5k3vbDVbubrHz1JSdnR2y2WxVQO/duweUC3BDOYhA07S6RHLhuZwPO2T0EGvH0DKdsHQmgyJ6MQOl8mcsgHhhm2Jkhl1XTbS1TFo6E7k1BLAjbMLSw8bHlRqGDJBATphE5einTrlCIySP3zXdTypzRYWTskeqLNI+I4ToW23aqGUwE7H5rlOphr9dnIh2fD7TNOusUKChiHezPQ+9kCaZW2cxPD7WxmGcDutc0gvM5NbQC2nYNyFowHRpB1Mb/k08SkwEhepeaES6bOgRPAQh6bGpR7DxKaKzo9kj7eT19vqVjvIYR41wOFxnkYISUsUIIaXEDcpNttfzJTby9QW/F2IhLs8nORMP8a6ZOP/NfJKHUp2X5opGo5w9e7YhsOju3btsbGwgpWwZPCCkJJpe43xIYIzAhd0tYUPjEdMlkl5DFA9uTKz7LuePb0nljombGlrhQWs0i4AZP0das9GQTPt5NrQIQkA8cNjWQjgjOgWVhEFI+vgIFWrUJoVCgc3NTXZ2dsjn85RKpYFUbxoFIVWVjcaAN7ayvLWTR0DVTfzs+em+XUDFYpHbt2833ASTk5MUi0Xy+YMFBivEkhEnO2Zuz/mwQaqwQ+C235lEAhuxWba7dGsfp1KLl/QiophreDwvDDa18sIuJD2mgnxVPrPCREMSkaPTls9FI0BUa+wGlAs0dFO8/qRy/vx5YrEYu7u73L17t6+v9eijj2KaB3WH7T9qj7QHBEHAzs4OQgg0Tas27q387H9M07TqTztMhi2+tf5gpZ9xPNbyDnNRuy/vJxQKcfHiRe7evUsu92Bi3Nraau8ETpEFp0gmPsNKcbTFVBcwF9KJuTlI5zq2PgQw66RJi1jH0dimJjgv8twXUfLeaH9OhxEzNUS+UUQBItJD83N4QiMq3ToxikmXnDBJC4uE7H8+dDv4QtTtjWqAhiQAJaRtUomjGERh/lGwSJWQ9gDf91laWuroOadPn27YmG95/iYmy2qu1DchhXLE3blz53jllVe6er4A4pl14obJuj3BjjM6QRu6EMyEDWJ+Ca2Ug/TRJnDpFDkfMnjLb3/fL2ZqLLi7uOEk+cJ4iyjApHbwewjhQ4vAnah0yQuDbS1EKigOTKykEKDpoJvgFhFSIg0Ty7ChmG04fpBOaN/3uXHjBuvr68zMzHD58uWB5GT2iuXlZVZXV5t2vek1SkjHlGKxyP379zl9+jS2bXd1sXRyU8xELN45E+fbNVbpcrbIu2Ye5HnWeugrlu9ROepqUgB4LjNih7SID92FGdI1TofAyu3i7fbW+tGLWR6K6txyDs+UNDTBgruLYYW4VToefl2r1Cg8nRCRHrqUZPcieo8qWlIIpB0l0HSEAKRE8z2k0PAMmzQ62yW//F1JMKwoSVMjkDDt7Bzx1Y/G9evXuXbtWl3f1rm5OZ5//nmeffbZIY6sfQbZ2WYUhFTtkbZJpQWU7/uk02mCIMCyLIIgqP50wsWLFwkf0vQ2CAKWlpaqkbQegrtGElfomNLjrJdu2v5pbm6OmZmZtsfiOA6ZTKbqdo7FYhiGgeM4vP766x29r1asRGZID3HP9JGJCMb2Kp7bX/dhEI6zrYfZqkzSNRiaIGVqxHXJjq+xXRqdfcGjMGHpzOR60x7NQ5DWbGKBg9Who11aIUpWlCw6u46P3+XMljB15pxthDf4FJ3r169z9erVhmjXili88MILYyOmg+KJJ54Yupgqi7RNXNdlc7O+ZqjjdD8p7w8T34+Uknv37tW1nTKQLHpp7hkJIoFbDYDYT7t7rxUKhQLLy8vV323b5vz58z11ywxrtRYxdC7PJ5mwNL6zNoCaxIUMU2SYFAJMGyk0hJQQeGgYrMgEt/I+HKNY0MmgcPhBbWIgmQiKpIWNS9BWvmkQirKpR9l2fCgBR2x9lnZ9CnqSc2apLgq53/i+z7Vr15qmjFR6un72s5/lypUrY+Xm7TfDFlFQQto2vfb1NxM73/dZXV2tNgBuFh1rEbDgZfBF6xqgnd5k+99bqVTirbfe6ukFGtIEg5uSyjyUjPDkbAJdE5RK7Ufi9gIhJTg1+32ROHdkiIJzPKzQCqdDAj3T229WAElZwkVjV9jEZamlqzcIx7npWcge359uILklLR6yI2ilQ6LUe8SNGzfq3Ln7kVKysrLCjRs3eOqppwYyplFnFEQUlJC2Ta+jz27fvo2u69VG2Zqmsb293dbeQviAwA3o3CJt9t5qxyGBLS3M1BEsj1Rply0R69rd1gmagMvzKc4mHrjOBxE9COXPShrmA7dgJMGOHma96HGcrFBdCM6ZHkZmp2+vYRIQlyV2tBDJoNTgfZGmze3ARvbJ3+FLuCMiLEYtjNxO34Og1tfXe3rcSUAJaYf4vl+1tIIgwPf9hjSTfn6ovbZIs9mjBWccRC+EtO7vCJJB8ShDQrglzsci3Cz1N/YxaRtcnk8xEarPKxtIGH4owpoeY8fxSUR0XCkpuAG4x8sKNTXBeZlD5Hrn0m2FBkwERXb2R/TqBktGArfPaUNOILnl6CSjs8w6O4g+7rG3G9fQSfzDQfg+3LgRZX3dYGbG4/LlHOPmMVZC2iG13Ujy+Txvv/12wzHNhLVVbmenfzu0CMEIcVTXbsP5erTi17PbPJSY4Vaf0j0WYyHet5BCb1K2r99CGpgh3g4ieHufZdodnXSfXnNWdxD5o4uoyOd54umnAXj5pZeQkebVuASQDIrsajbJoASGyYo1QW6An/Gu4+MZSRblDpOJOLZt18UV9ILLly8zNzfH2upq0ztOCMHc3ByXL18+8mtdv57g2rV5VlcfxGrMzTk8//wKzz7bfcOLQaOEtE2klDiOg+d5eJ6HrustJ0Up5UBqO4469+7dq7qLawtAtPp/sXg0a7MTzPQ6ZxMz3OmhmArgHdNxHpmMtryxeiWkaWGR1Sym/EK18o2wQuTtON6IF5/oBXMhHT0z+EbdGuWyghkrzo4WojiEhUrOC0jHpnnnqalyuc71dTyvd94GXdd5/vnnufo//U8NjcUr1/Vzzz135ECj69cTXL16Zn/5aNbWTK5ePcMLL9wdGzFVQtomvu/zxhtvVH+/cOFCTy/e40glsEYC9/U4c35mpJoT2+l1zoXi3AusI++Zzkdt3jWTIG4ffCn3QkgzwmLFKHtFisJgwc8SMXQ8Cc7ofLx9w9YFiXyb1a0OQOx5d7Sa3rZaoVC9QltZppqus62FKPnD+7BXcg4b+RLTEZtYLNbQ5OEoiHye73//+/ncL/0S/+IXfoF7NX+bm53luR7kkfo+XLs2vyei9SIkpUAIyWc/O8+VK+mxcPOOipCOfB5pL3MZFaNBKT5FXuokgwIIneXAJNfBXpcuBLNRiwupaNvVnTzPw3GcqtdCSlnN/93//0Kh0LCH7SF420gRCA2kZDrIY8iAmCEQnsNWZIpNZ6RvpSNz0XDQe5AO8s53vevAv3/7W99qeEwCu7HZkWjVNxky+Z6zU6TT6Z7Wka39XHzgRWAZWACeAV5t8rl0yp/9WZS/83ceOvS4f/2vb/HUU81LPo4Stm1z6dKlYQ9j9C3SfgblKIaDIwwmCluwF9m6qGnkolNsuJJSCxPV1jXilsFs1OZ8MkzI6Gy5bBgGhtHe5b65udlw3QUIJAJNBiz6GcJ7RdalW34/oWNeiXUxpPU8zaVdJJCNz7BWHL6IAmwVXVbzJaajnbcqbBcd+GAfzru+3t490O5xw2ZULNKR/7QyQ7p5Ff1BAvHCTlVEAQgCopl1IgCmTaCbsHeDzCeiTE9NNQ0g6hfNgq8sAiLSxZR+VUShLJ229DDcAtB567pxYC6kE8n2LuXi5ZdeAsru3Mc/+EEAXv3ylwmaVPoKgJ3YLBsjIqIVbu3kmV+cJBwOUyj0Jnq5k8+lW2Zm2tsWa/c4RZmRFtIgCEbCIjUMQ+3L9ggB0KJNmQD0wGNhbgbf99E0jXg8PlARhdZRzImgRFE0v2V0p8CZeJS7xePl3p2xdRKZtZ7a2pU90FpnfhAON+yNyr2GB7sj4M7dz3K2RN71icViPRPSdj+Xo3D5co65OYe1NRMpG79VISRzcy6XL3fm1p2cnCSTyQy0xi6MjkU6ml1198hmsyMRhdtulxbF0dE0jVQqxdTUFBMTE227Y3tJq6jImHSIH5BPG8psMGeP9C3VNmFd47wtSWV7K6LtIAEnOsHbepLdEeoatJ/lbJFYLDbsYXSErsPzz68AZdGspfL7c8+tdBxoNDU1VTdXd9Im8iiMipCOtEU6Km5dZY2eLKIt9r4EIIUGsnlglAAS+U0y9tRY9RcVQNLSiWgSHYnlFtCKub4LqIxE6gKLpBAUYlNsuIKiEzC8Cs3tsVFweCiZRAjR0wX//s+l1zz7bJoXXrjbJI/U5bnnussjFUKwuLhYDdxLJBJIKXn11Vd7OfSmrzsKjLSQ7i+CIIQgFArh+/6RCsZ3Si9D3BUHMwrFuMPhMLFYrBrFW5uf7DsHu65EELAY5HhLROoafcdNjRk9oIjG0ojkm2oCTtmCcGEXkRt8p5NapGmxaqZIF31GXUArbBUcNE0jEomQy41+hGstzz6b5sqVdM8qGwkhqgVzKpwkA2SkhbSyV7WwsFBtORaJRPB9n0wmQyQS4datW7iuy8WLF3Ech3v37o2EO1jRHYd1xRkEmqZx/vz5pn978803KRaLpIWFK/Sm9YdFKc+5iMEt16xKQkgTGJlNYsDDtd1KKq8pym3W3Jqo5X5exQlTZ85NIzKDK8bRCi+S4I5n4Y1ZNaiCF+AHEsuyxk5Ioezm7VWKSzPLcBDzsLJIKa9YgiBoKM3neV7VRQBgmiaWZSGEIAiC6j4alOtOLi0tYVkWoVCob1+epmmqctIA6OTGCIIAz/MGKr6VazIhHUqy9fLdyKd5SOgUrSimLrByDwoZaMUcM+SYNm2kpiM8B3yvvpqNYbIVmmCzx4E2hhCctgLMIex97keaFsVQYi9Aazzvq5IfDGQvcNRpdt8GQUAymazzMhWLxZ6WW1VCCmxtbbG2dnhD4Dt37jQ8VlsLV9M0dnd3KZVKTE5OVgVXStkzt+zp06cRQrC6ulpXUu/cuXPcvXt3YN1FjjutbgwpJblcjlwuR6FQQNd14vF4dZE1KGq/Z/uQvpeG9DFKOUz8pqIl3FLD49XfPZep7BqTpk3JjpFDJ+dJil1W9dEEnLYFdm4LMeQyTBJwY1PccSAY4yhnAZgaY2mN9ppWFmk8Hq8L1mxWI73XrzsMhiqkR+moIqWsPt80TSYnJ5se0yshrRXzaDTKwsICvu9z69atnpxfUabVjeE4Tt1NODMzM5Ro6k49ElIItCNohXBLhNwSIWCqck7dwLejeHrZdewh2PUg7wUNNVpNTTBtCWLFNCIz2J6s+5GaTjE6wYYnKJTGf+G5EAtRKhQGWqt6VGklpLXWqOM4PU9nVEJK7wqJtwpQ6ZeVePr0aUzTHHiz6JNAoVBgbW2tobD+/kWX67pks1k0TSMcDg/shur0mtJaRPgeBeF7GPndups3TjnvMjBsHDRsIRFeCVFyYEiXqdR0/FAUV7coSI1tJ8AfYzduBVvXeNdMnMV4mDu33x72cEaCZvef53mEQqHq71tbR6/TPKqMhUW6sLBAIpFgZ2enaQf5VkLa6x6iFSp7Isqd23uKxWJbK/ydnZ06b0Ot6Lb7U3lOKBSqu+EP4vHHH6/W5U2n06ysrBx4vBzgTqTwXHTPpXd1cLpDAk5sijulcglFXAmHuMHHhbmozdOnUhiaRj6fV25dWluFrutWI3lLpRKbm73vGqQsUtoXItM0MU2z5YfWarO/X0JaCXjq1/kVnVNx9Xf7nYTDYSYnJ0kmkwcGj9Qmmh+WqjOqdle7fUC7IbAjbJuxngdJjQICeHI2gbH3/a+v965s4jjTbF7O5XJE9q4rKSX379/vS6CmElJaC13thyOEYHd3l0Kh0DLaa9AW6Xe+8x2gtYArxo9CocD9+/dZWVkhlUoxOTmJbR/cWeaw66soDGzZ/1w63/e5ceMG6+vrzMzMcPny5YHn4wbhOJtauJzScwxFFGAxHiJuladMx3FGpmDMKJBOp5FSks/nyefzRKNR5ufngXITiF5G6taihJTWFmntykVKye7u7oHnGbSQVlCu3eOH7/tsbm6yublJNBplcnKSYrFYrdYSiUSqN+9B15ePwJBB32twXr9+nWvXrtVteczNzfF8k96VnfQB9X3aStaXmk46MslqKeC4uG9bcTH1oOKVCjB6QBAEdcGYQgjOnDkDlD+nZttxvUIJKb0TolaW4UmqrKHoPZV0mwqbm5sIIbBtG8MwDgw20ymX2+sn169f5+rVqw0us7W1Na5evcoLL7xQJ6YVd24tlS4j8KAP6PXriSbl4xyef76+fJzUDZasCXLHIAL3MOKWwWTYrP6uFtGtmZycxLIspJQnpkDOUH2T/Y7aVXuYil4jpaRYLJLNZgfe6aIW3/e5du1a00mq8thnP/vZju+B69cTXL16htVVs+7xtTWTq1fPcP16ovrYdniyo4bs48ylyWid9aOEtDmapjEzMwM8uFf6ibJI6b9FqoRUcVy5cePGgS4zKSUrKyvcuHGDp556Cji836Xvw7Vr85R1WOw7n0AIyWc/O8+VK2m81NTI9QjtF/NRm3OJ+ljok2BldUMkEsFxHFZXV6uC2k9OvJD2styeskgVJ412I0Zrjzus3+WNP4vWuXP3I6VgZcXia68uMPPewTWNGCaTIZOnTqUaJmxlkTYnm81Wiy4MYmvtxAspwJkzZ1hbWztyYYNOhTSZTDI9PY2Ukkwmo8LYjwH9TOkYRdpd7Tcrr9mK9fX2poPX7mvMvLft044luhBcnIjw+FTzxvKGYRCNRhuMAdd1O3L5G4bB4uIiOzs7hwZVjhuDiGo+8UIqhCCZTBKLxdjY2DiSmGUyGRzHaSh+v7/VmqZpGIZBJBKpdpOpVN+obZfl+75acSoGTieLgcuXLzM7O3torerf//3f56d+6qfqFput+l3OzLRnQUzMjPe9ETI0ZsIWBS9go+BwKhbibCLMTMTCCyR3MwXOJyNYeusQkomJCSYmJhoeX19f7yhKtVIz2vO8Yyekg+DEC2mFXuS7tVsxIwgCHMepiihAIpEgkUjUHdfpzaAYHp2kdBwndF3nh3/4h/mX//JfHnjc6upq3T7pQVy+nGNuzmFtzUTKJhOUkEzNBTz+vvF1655NhHnvXLJqZXpBUC2wAGDq5T3Rg0T0IDrdrqoIwSj04VV0z9CFFAa/37C+vo5hGA0WbOVHlf0aH9pN6Rhlul0MnD17tq3zt+vt0XV4/vkVrl49gxCyXkxFWSD+zv+c7rr587CZiVhcnk+i1VgxRpNAxYRtNjy2n1KpVDdvVQSxm0hux3GUB6xLlEVaw6CDglRFEsUo0e1ioN190k6iJ599Ns0LL9xtyCOdmgv4O/9zmv/2r41nIQJNwHvn6kX0KKyurpJOpw8/8BCKxSKvv/56D0Z0MlFCWoNajSm65bCUjuPM5cuXmZubY21tralLUQjB3Nwcly9f7ui8zz6b5oNX0rz46iJv3S/viT7+PmdsLVGAC6koMat3052as0YDJaQ1qItS0S2HpXSMA90uBnRd5/nnn+fq1asIIerEtDLBPPfcc13tvxVTMyy+t8jiMYjO1YXgkcno4Qd2gJqzFLUMrbLR+vo6r7zyCq+++mrf9iRt28Y0TQzDQNf1ausshWKUkJEIMhKpE87KYuCwBcGzzz7LCy+8wOzsbN3jc3NzDSUC28UPx1kqHh+heMd0jJDRW3NaFWQYDUZlPh+aRdrvFBPTNLl06VLLv9emu0gpcV2Xmzdv9m08iv7SKqXjJPDss89y5cqVnnSAkUKwQoh6G398eXgiysMTvbVGQVmko8KJF9J+X4iHTSKVCN0KqgqSYtgcZTGg63pbKS7NCIBlPY4rNMKGfizq5xqa4LsWUszH2mvY3ilKSEcDJaR9vhA77RWqboz+UELHwmc0LndFM9a1KDnN4rS3y4aRYnRbkh/ORMjkPXNJkrbRswjdZijXrqKWobp2+0mnbi11Y/SHHS3ETJAbCSFtt8fmSaKEzq5WbmBeNEIU/fG9D+aiNt+9ONEgoJV7u5fWi1p4jwYn3iI9e/Ysr7zySt8ErFMhVTdGf5gK8sPt1bdHuz02jxMeggCBQesG42nNBiEIBS66ZcKYtvCdj9o8tZBqaoVKYKfoMhluXZC/U8Zp4T0zM8P29vax7M984oUUYGpqCiklQRBUg35yuVzdFy6EIBQK1R3jed6hF3IQBGSzWYQQddG6mqah63rdHmmlvu5xZxiF3Y0RcBNWemzuv2QqPTZfeOHusRJTCazqUdJaeX9QSMlkUGAyKFQ9AwGQFRY7e8fY0kc3bBjD/dHpsMV3L060nFQ1Ici5PpM9Si3uZeeqQTA5OcnMzAxbW1tsbGz0XVA9BBKBOYCAtRMvpEII5ufnGx6/fft2XeUh27a5ePFi3THLy8sH1tfVdZ1sNtuTyiOK8aaTHpvj7uYNgIIwcYVWFVEoR+Ju6hGymsWkXy5DuKmHcdAJSY+iMLGlR9YfjUmpUxbjoZYTqpQSX0pO9TDoaNy8VxVjYnp6msnJSXZ2dkin09V2Z71mVwthSR9Tjm9N5k4ZiYIMtey/SIvFIq+99lqdZXlYPUtN08buYu8E3/c7SnU4qYXdobwn2k6PzRs3ojz11HjXWF7VY2T29jubURIGy0a87jEfDU0G2PhseOPnlRHAwgEi+ZdraSKmzqXJWM9ec5ysUSgLaTabxXVdkskkk5OTTE5O8u1vf7svrzcVFA4/qEeceIu0Fc0EsFNXRDeFo/uNaZoEQXBkF/L169e5du1aXXeaubk5nn/++ZbJ98ehsHu3tNtjs93jRon9C6qJ7/rejkusuEInFLhY0icYL30AytZoxGy+iMw4HguxEEaTfqJHYdwW6UIINjc3yWQyxGIxNE0bu8VAK5SQtuAoX/AoN3c2DINEInGk9mzXr1/n6tWrDZ/R2toaV69e5Vd/9VeZmJg4clL+caLdHpvtHjcqNFtQTc0v8Hf+53/Kf/vXfqCjc8k9l3fE0MiP2R7powdYmq4fMBttbaF3y7gJqeu6RKNRTNOspgUqIe0tIyek43aRtoumaVWR6+Y9+r7PtWvXmt4Alcd+9md/tu7cFUtVnOjC7gf32BRCMjfncvny+Lh1Wy2oNldX+Bef/Cl+9tf+VUdiqhOwqkeZMQJuj9F64lQsRDLUvOVZ3vXJOF5PI3UrjJsI2baNbdcvKMbtPYw6o5CZUEc3X7DI5xH5fMMeYOXxUUDTNAzDYG5urqvn37hx41Brdr9AVyzV//Qnf9J1Lddxp9JjE8qiWUvl9+eeWxl4oJEEXDTSwmJHs8kIiy3t8ICYgxZUlbDkf/0rv9DRFoIrdLKajZvLEDVGbkpoyYVU62vX8QPOJftzbdu2zaVLl7h06RKJRKIvr9FLlpaWqj/Ly8ssLy8fyTM2SiiLtAXdWGvjsAcYBAH5fJ5wOIxpmh3v47bbnLkWKSVCCD772c9y5cqV0fuyB0SrHptzcy7PPTfYPNJdYSOQbOoRXKFjS49o4OAJ2NbCJIISAtBbpA0duqCSks2VJV79+ku88+n3tzUmU/q4QmdVizIZOOTG4EqxdY2ZSHNrU0rJdtEl1cJaPSqaplUtvEgkMvLZAVtbW8MeQt/otIJdvxi5O+a4unZzuRxvvfVW18/vpDlzLVJKVlZWuHHjBk899dRILCqGwbPPprlyJT3UykYFYbBqxFj00pzxdquC6SNY1mOEpYsjdAwZtBTSdhdU2+vtWxyeKE9GvtAI3CLzUZuV4mhH8M5F7ZbWiOMHnE70p8bufkZlIj+pKIu0Cd0mOp+E5s6HNXE+jG4s2uOGrjPUFJdgL6hnSY+TCErYeISC8qZkMighAVMGGATVnNCiMLCkT0w6CNpfUE3MtLeFYEofIamm2G5pYc7ntgjbExRGOPDooCCiO+lCT9NdDmJUJvKTyqgsZEZOSLt63jFo7nwYBzVxboduLVpFb3DQcIWGkBIpBLv6nsW0zyLWZEBEuuSFSbBnKSIlM0EeW3qHL6iEYGpugcff17jd0Qxbesz5OTz/wYRk4HPOz+BGYiAlupS87Wg4I5QfE9JbT6BJuz8u3Vo8z8N13WNZdm+cGJWFzGjI+R7H1a3bK1o1cT5oVVapIHX58uV+D0/RgoywuG2kWNNjyENu/EBoZDWbQGhEA4eH3G0uetvEAod7eoJNM85zzz8PNE4iQggE8DP/6P+LoWmY0icaOFjSqwYi1SEliaCEjsTGr/4IIHCK6Lsb6OlNyGxxTmYxe5yPeRTcA+YK8wCRLXlBTyJW79+/z82bN49N0M44sr8V5jARcoTioF3X5Tvf+c6whzHy7E/E397e5tOf/jRQb9VXLrIXXnihZbEGRf9w0NjVQmzr3W0xzPg5JoJi9fdNrXweS/p87T99kc/uyyOdn5/nueeea/pdS8BDq+68ekLDlEFn9VATU7w+uKI1B3IxFeHdc8mmf9suuky0CDTaKjg9SYnJ5XJIKdna2hr5YKPjiqZpvOMd7xj2MIARE9JSqcQbb7wx7GGMJc0S9A+aWBXdU7OlWP3dQ8MROgKJh0Zas8lrR5uwz7k72PgNr+cj2NVsEm6Ov+igVGQvKCVnuZ0ffiCSLuD7zs8Qsxp3p3aaROwWPJ+X1zPczxR5bCrGo1O92UNdWVlhY2OjJ+dSdIZhGDz22GPDHgYwYkJaKBS4efPmsIcxtnRag1fROS4a94wEC14GV+ikNZuiMPBFb3dJooHDol9u3nDLSHHe26kTU2dPuGNysOUwJZCPz3C/OPxtmKip8/SpCcKmTrrk4vgBIV0n43pEjHLcc8nz2Si43M8UcGv2eN9/eoL56NEjezc3N+saaDjOySnUPmxM0+TRRx8d9jCAERPSXC7HrVu36h47d+4coVCoGtH79ttvj2Qt3VEhFAoRiUSwbRvDMBBCsLS0hOd5zM3NYZpmtW3cysrKsIc7VuwKm13NxpY+Oc3EE/1bpCx4GeLSQQI3jQlm/Rxh6aHX9BZd0yLMBPmBNk0voaMhMeJJ7nnGSET2PpSM8J65RMN+2Uq2yJ/c3276nFOxEP/t4kTPx/L6668rMR0QlcIYo8BIRe02CzYyTRPTfOCmGSHdH0nOnj2LZdW7FJeXlwFIJBLVRPIgCJSQdoCHwBUap/wMd4xkX0UUHqTKeGgkghJpzWZFi2NIn9NeGouAkPTIC5PogKzSIjo6sryvmtnmDBCYIVbNOBl3eIL69m6ehG0QswxMTVDyA/xA8p2t1m3CtopOtWBJLxmV4JeTwCh91iMVtdtMJPdHpKrI3oNpFsG7sLBAKpWq2z89CY3Me4kjdJJBiRU91ncRhQdFEvLCZCooIBGEAxcPjbfNCXaETUw6FER/18Jy7wcghN8QnKS5ReaLmySt4W0hSODb62lMTTAZtliIhUjYBulS69SUqbCF34c1+ShN7sedUckhhRG3SIUQdXt8UkolpIfQbOGh6zqGYbC9vc1bb72l9k3bYE2LMB3kqytNW/pkNOvIAUTtEiBw0TDxSWs2U0GerLCwpE8qKLKjhXClV7Vc+4GLhreX+1pJi2mGCAJmi1v49gTZIVmmvoQ/vrfFQiyEFwRsFd0WtaHA0gSn471vrwblfO3KIjWfz7Ozs3Pg8ZOTk4TDYRzHUUVTOmSUFi0jLaShUKhBSEeVw1ZHg1oAbG1tVfOrhBD4vk+hUMB1XXzfJz8iRfxHGR+BL7S99mLla04gWdeiA3l9TQakgiKu0DBkQIBPRHpE5AMLy8Inr5kEfZhMJGU3rklAWLZXcED4HqecHdZDE2yXhuPtcAPJnfTh+TluIFnLOyzGe1/5LJl8kJIjpTxQSFOpFAsLCwghcF1XCWmHKIu0BfvFplgs8uabb6Jp2kitPvZj2zYPP/zwgWN87bXXBlIFRe17Hh0dyYJfv7+WFdahxRR6xbSfxyRgR4RIyWLTyNxw4FLQTGJBbwNbAiAnLCLSbVnvtyWey3R2jdlInJwZISQCXDRWigElf3Q8SRJYzZX6/jrNFs9CCGZmZtA0jampqeqcYRjdTcWj3IO534ySJoy0kEopKRaLLY4eHebm5g79UpVLerwJVaoD9fnmFVKiISkIAxMfvUXBBBufHBY6koDeBDs4aJSEUa3r2w0CkPkMETJ744SzkThv+INxiR9G3NSwNEFpALdjs3t+ZmamoTIZlEWhm65QJxllkbagV2ITDoeZnJyspsxU9lZLpRK7u7tNn1MRwk7dx7FYjHg8fuhxSkjHg+JeeodZs/soAYOAmHTIitbF0iOBQyxwsPHxEWQ0u9GSlZK4dIgHJSzpUxAmu5qNJzR8NGaCHDHpIBHoMmgpkJUzmtLHQ8PqpEJRE/LCQABx2dzCrdwVnQpsEIqyQhgYfnDbeVtiZtcJECybSfKOR6RJQYdesf+etyyL6enplsd3IqSVPsv7ezBXXvEkWKbKIm1Br8TGsiwmJhpzxHZ3d5sKaSqV4vTp00C5Gfba2lpbryOEqO5xHISUksXFRXZ3d8lmW4fkK4aPsRdU41G+OSpRqwKISJcsjUIqpGTWz5GU9e7CmO9Wqx65e1G4IenViaMlSyT8cg/SimXpIart1PZXNaoggWhQemC9yu7CjiTlWsAh6R0oxhtaBEfozPlZjDZdvtKOcCsIDzVCXBeCGVsQkj5WdqtatGRtfZ2NR97Bj/5/Ptz34DshBA899BDhcLhuwb5/3qhN8zuMcejB3G+URXoA3XQ22U+rD7iVUNcePzU1VRXSWCxGOBxuGQQwMzNTzcs8iCAIuH///qHHKYaLg4ZHWcxsAnxEnWgICfNehrRm4widAEFYesz6uZY1awVgEmDK1iJVmU41ysIWIOoCi5qRF2WbOSQdYtIlKywEEonAxMeS/qHu3gDI7KXRtNoPlcCOFmImyBMAa3qUOT93qGhLYM2I4Tu9EVEBhA2tnCO6N1RLE8ybEquYRmgGnhnC0QyKUpD3JAkD4l4eLVNundesjOY/On2aX/u1X+OjH/1oT8ZZy/z8PBMTE3ieR6TGQgykbPr57c//VhzMKFmkI1XZqEJlSLWu2WY/y8vL1SjU6elpNE1DSomu6xSLRYQQdW7XbDbbtFt8MpkkGi1HZNYWKgiHwxiGQSaTaXiOaZpcvHixLhCq8m8+n8e27epKd1yL8RuGgaZpJ6ZSS0BZAJrZJw7lKF4bf684g952RGu7SMqVg0JtuEE3tDCTQaFBLCXltJXSXn6phsSUPgECKQQ+om4Sj0j3QFH0EOxoIaaDsgtxSY8x5RewDxmjblq8yuFbHu1wxoZQYRfhe2U3uWEhNR1Rar+q0/Xr17l69WrDIr1yz/7+7/9+X8S0GX4QoDdZ7G9ublaLpxxGrWu3WQ/mbl27U1NTWJbV9jiGyezsbNP95mEwchYp0CBM7TA1NVXnGrl58ya6rpNIJKqPlUrNI/USiQQrKysN+xOFQutQetd1ee2115qO+7jQbyGtuD2NPWuu8ukVhEGAGFjFngq1U1tOmHUiU+v2NJAI6e/lefZu79tDO1SggKort5nFKfbGajXb6+xiyawjKQqDrDAxpU8scLDaGGPJikIPLpuzNoSyD2rZCinBLXXkxvZ9n2vXrjX1dFVcrJ/61Kf4yEc+MqAc6+aj7yRyt189mCvlRXvhGew3ozTfjo6TuQtqXbXNChEUCgXefvvtqhC0cu1ub28fOTVF13Usy8KyrGpZQ8MwMAyjWhBB13V0XUfTtJFP6YFy+lEv804r+40uGmlhccdIcs9IsKzHuK/HcfeKEOxoIdLa4S7zflEJODro26mWyushog2lCyi7dROy/+kbFTfzKT+DsbcHm2gzordotOem1ET5pxm6ENg1ItotN27cOLBvqJSSu3fv8uKLLx75tY7CKOz5aZpGKBTi4sWLwx7KoYzC51VhJC3SdqkNYnj99dcBmJiYYH5+Ht/38X2fbDbLG2+8ceAKqxcBQMlkklOnTnX0nN3dXe7evXvk1x4XdoXNjh4iGrgPenRKSVQGNcXXJZN+gbRmtwy06Tch/K6st6Oi77VgaybQEigKg7wwSQXFgXwuYm9MQFvu5urzdJ314uHHnwppRPPboGns2im23ABvr0OLrQtO6R6iB1Ztu4UOXn/7Lk8WHExNI273Z2o8yMrrxgKUkUhPA4s0TSMIgrFoDTdKhshYC2mthVkR1cqHWyuylT3VXqHrOtFotG6/NhTqvCXTSat3GwiBIwwc/cFlt+hnGly4Nj4zwcmrwNRqWggoF4QwCZgMCkNZXLSi2WJHWGFc7+D77VRII5bZEzgfUu4aSSEIQnEQEi2f6dn7nJmZaeu4R86fYTJssZ4vEe/T1CgBvYUJPgopcp7n8dZbb3WUvy8pbzeEpTfQa1NZpD2g0gpsP5WAo37698PhMGfPnj3yeYQQzM7O4vs+W1tbI78ncVQmgyKGDMhpFqmgiCn9tlMpjiPNCikUhEFWaMQCB4HEQKJRdqmOGpJyWsz+RU9QzJGMRNhtEbEb1jWiucagPyEleiF94GtalkU8HicUCmEYxqEBiVJK/vpf/+ssLCywsrLS9B4TQrBwapFHLn8Xy9kik6H+Rc8GUqLVWFJSSpZ2MuhIbCSpVIpMJjPwRXZlQXTv3r2O5iEJLOsxsprNtJ9jMhhcAR1lkTbB931ef/31ujqxzX5s2+bUqVMEQcDMzEy1v2blJxaLIaVkenqaIAjY2dnp+UqvVyshXder+a47OzsnwkJNSIeEP3qiMGh8BGt6lAm/gBTl3VF/r0i8h8amHkYimPOzIxvIIKC550BKZgsbGJFpNpvU3T1FHhF0fq1PTU0xPz/f1QT667/+63zsYx9r2OKpnOvXfu1znEpE2Cm6vLqZQROCs4kwqVD7uZ3tUKtRUkoCCdtrK5RcH4Og87KMXVKOUH/wOYpKZekOF/N5YZLdi2fIaDbJoDSw96As0iYEQdCWkOTz+eqe5oULF1pGus3PzwO0rGR0FHr1Bfq+Ty6XQwjRF7dOt9VoFP2nrp5v7bxzTAx0EQRM5daJxaa4XwJPSlKWziQltFyus3MJwalTp0ilUl1bIR/96Ef5/d//fT75yU9y79696uOnT5/mV/7FrzLz1Pfw9eUd7qYL1a/g5naO98wlSVjlXqd5zyfreJyKhVq6Zw+jcm4pJXnXx9Q1dBm0Fa3dS7S60XRPSHrYgYcnyn1zA4QS0mHSrjUmpcRxHDRNOzRUvZX796j0yqVQKBT6WqhhQ4sQIJgLOpu4FIqeICV2ZoOHhAChQa51K7aDT7PXgeeI991HP/pRPvKRj/Diiy+yvLzMwsICzzzzDAiN/3p3s6FzjAT+YnWXpG3w8ESUiZBJyNAIpETvcnnqBZKs4+AFkpmIVX5PY7yloyM54+9Wy1RWgi5v377d905TyrXbhE4Fz7Kstkrz9YNeWY/9duWa+GhjfJMqjgdCSpBHu9Y7KZ93ELqu88GaUnoV/pv5JCFDZzVXYjlb5H6mWLWrMo6H4wck7KOPIWxo2LqGJrqv7z1qaDzIs/Y8byAiCsoibUqnotJO8nK/hKpXSdv9jtJLBf3PNVQoekkqlaqmYNQ2nOhF+bx0yW0phpXHzyTCnEmEybseJa9c6ziQEkvvzaS9XXRZzzs8OhWrPvboo4/WHfPmm2+ObReYXIdu+6OgLNImdCoq7YhZv4SqVyuhUQh3VyhGidnZ2b7VnO3Eoiz5Af/vnfpiEB+6MEvE1BsEeSVbJBUyCRltzEkSImb9cfvnslGytPZT6acai8XqispkMpmB90Iepc9pZIQ0Ho9z8eLFqmvgMEqlEqurq00jeytfcL9WdUpIFaOK78ONG1HW1w1mZjwuX84xkKp3PaJX95YXSO6mC6RsAyEg4/icjofatmJCho4mysIH5epLWccjXXKJ7mu9lnY8km1G9wZSYvfIuh0G4XC4Wte8lkG4cvejLNIm6LpOOBxuOxG4WCwOren3xsYG29vbVeEG2vp/5ffKv61q/yoU3XD9eoJr1+ZZXX1g0c3NOTz//ArPPntwfuao0CshNTTBQ6m9erRSkrTNjibesKHz/sVJXlraxgski7EQs9HmZSu9oJwbKqXE8QMsvbyQz7s+y9kiedfH1jUWYiF8KdFHSAA6JZ/Pc+vWLXRdx/f9aunTYczFo2SRjlz3l1wux61bt4Y9jLGgiM6WHuaUr3qcnnSuX09w9eqZvQDQ2oVb+fZ+4YW7YyGmTzzxxEhZGl4g+dOlbdKOx+l4iIzjEUi4NBGtCuvL6xkkZQu44AWEDY24ZbCedxoSQSxd43Q8xJOzCQLfr+4Fw4Ogo9u3b4/0HqmmaZw5c4allVXc0nCMGRita2WkhFRKycsvvzzsYYwFErinJ5gICsQG3CVFMVr4Pvz1v/4Iq6smzbKGhZDMzbn8h//w+si7eaenp0kkEnX9O4fNqxsZlrNFIjrYvstGYJBxfeajNlFTZ7PgsFPqrOnFuUSY+SDH9vZ2n0bdX4Rp8bqMcdHbGkrBECEETzzxxBBeuTkj49rd2dk5MX0v28U/ILlZAGf80bcwFP3nxo1onTt3P1IKVlYsbtyI8tRTo51TvLGxgWVZIyOk99c3ye1kiFkhIrvlQu7zpkVgJFnJdb81cztdAAuG1+PoaASuwxl2h1Z1a5TcujBCQrq9vT3Q0Olek0ql2NnZ6dn5SujcNlOEApeYdKqltxKJBLquH7qSHfegE0X7rK+3dxu3e1wt8XicRCKBpmkUi8W2O6kchVGYJAMpWc2VWF9fJxR4UHqwfSJch1PuOkEohmfY7EqdnRZ1hQ/ijiN4CDGW9aYFnXUE6vnrj4hLt8LICOns7Cye51Vzx4rFIpubR+9FOAgMwyCZTPZMSCWwoZdX5BqSib22WZWO8Lu7uwcK6XEIOlG0z8xMe27Fdo4zDAPbtolGoySTSWz7gc00KIEbtpDullwcP2AuYrEZNP/MBKAXs+hkmdYNMnoSv0M9lAiM2dM8MZN4cN59RRra+ffOnTsdBftMTEwwMTHBxsYG6fTh84Gu65imObTgzmYM+xrZz8gIaTQarfs9k8mMvJCmUilSqRTRaLSji8w0TTRNIxaLoes6W1tb1cbi09PT+IFkqwA5DwLdZCKeQkpJOFzu4XlQIEJ90MkD1tZMrl49MzZBJ4r2uXw5x2nucp/FvV4x9QgC5ubLXolmGIZBIpEgmUwSiURarvYzmUxPx92KYU2SXiDZLDjELZ2kbVfvycMQlC3Ybqpar+Yd3tnk866N7u8HnXgADcPgwoUL3Lt3ry3hHQRKSNtklDuh2LbN6dOnq8IGneWEzs7OYts2pmlimma1EL+madVi+3NBwI2VXc4kwizEQgRBQKFQrgVa+Xc/vg/Xrs03RG5CeZ9MCMlnPzvPlStp5eY9Rug6/MNrRa4+LxDIhq4eIHjuuZW671zXdVKpVDWwp51ym8dZSCtW6GzkQenRducg4XtM24L1LmL+0qWyF+4ogtlpvGgul+soHqWSm3/mzBlWVlZGwsBRrt02GVUhNU2TixcvNtzsnQiprusEQUAmk2FycrLp6tPQNL7r1ETd+St/b/VaxynoRNEZz/6NEi/Yd7n2v8yxuvbAHTs37/Lccw9c+pFIpFqZppPJqFQqDSwl4yhCej9TQBeirqSfJgRZ1yOka0yF62t0u35AxvEImzrJfZWPOpmDuh2xBHwJxhF0oVMhjUQimKbZtkVa+T6EECwsLGCa5sCrGLUa06ighLRDDMNo+iV2Mt67d+9WzxUEQdUtfNDFUbtqbSWk/Qw6UYw+zz6b5nufXmHr/T/HMgsUf+MXuPx+v2qJaprGuXPnuqoVPShrFJrfB/sbYrei6AVcnIg2PB7fa4O2VXQJpKxufWT28kOFEDh+gKk9KKDSrmtXAkG5m2dbx9eii/LPUehUSMPhcEdCun/BValstLS01NHr9hIlpG0yquXzWn2BnYy3cuG7rlu3sjvIQgiCoPrasVgMwzAaeq32MuhEMZ5o8QjT3/p1pgH2RVWaptl1w4XhCykUPR+Q+BL8QOIFZSd2yNSIHFLnVtcEcatxurMNDbvFc9tdHJuRGHnZnZDGLePIbspOhTSbzXbk2m32fUxOTqJpWl1v10GihLRNTNMkFotVo3j3/wwr57TVFxiPx7Es60jjOujiCIKg2vFmZmYGz/MahPSoQSeK4VKpNZ3JZNja2ur5+c+fP9/V8zzPG2gt1Wb3gaEJDK1R8PxAUvR8dkouri8Jt1E4vpaDJKxdIS3pFrlidwv/uVioq+fV0qmQlkolHMfh1KlTBy6s1tbWKJVKFAoF7ty5U/c6MzMz1U49d+/eHXgrOLVH2iZTU1NMTU01/VsQBLzyyisDHlEZ3/erkWuZTAbLstjd3UXTNDzPq0bjVmgWri6EwDTNaq3dyuJA07SmUXG+75PP50kmk5RKJaSUTd0y3QSdKEYDIUQ1eC0Wi+F5Xs8jJNPpdMt76iCy2cGVoKwEtrSLrgmilkGjM/fotOvaddCBzoVUE3AhdfTCE52KWDQarWYcHPRZV4KKml2LlesokUhw9uzZBqHtN8oi7QHDdPvWWoKWZZFIJFhdXa3+PRwOc/bs2Z6+5v3799ne3m6rnFi7QSfDpLI3PKru+2Gwv7HBqVOnyOfzbU/m7bCyslLNee6EYbt1+8lBlk07Fqk0bTwJti4odZhI+vBEtGMLuhdUhPQwDhLG2u8pHo9z7tw5bt++PTAxVULaA4Y5ATuOU3XfmqZZXa3Pz89jWVbPmn7X0un7PSzoZJgIIbh06RKlUolsNlu1viKRCOvr601D65PJJNFotNrkufLj+35Pq0kNk/0Tem2vx14hpeTu3bvk83kWFhbafs5xFtKDOExIpRAsGQlypYBZW8c1YLvUnjv4VCzEO6bjvRhmx+LV7nV10Hn3nyMWi3Hq1Cnu37/f0Vi6ZZSuE1BCWn9eOgtjd123mhIQi8UIhY6+37GfUqnUVUWRg4JOhomUktdee43HH38cy7Kq+75QtuYrbu/aveZoNMrk5GTTc6XT6UOvh4sXL2LbdrXTRkWQa/+tFWnf9wce3r9/Ytrc3OxbukknpTillCwuLrK5uTmQfdKBW6QH/O0wIS3Fpqp7o2sln6ihMRc2WC088CLUhiDpAlIhi3PJMOcS4Z4slCrXbif0Q0ihXDFJSjmQaF61R9oD+iGkLhoFYZCQ3QUL9XoCCIKAjY0N1tfXB76R32+klNy7d6/BBZ5KpYjH4ywtLdUJaSsr33GcQ2+oaDRa3Xts9zsqlUp9E9K5ublqHnHlZ39CfhAEfa07bZrtNaGG8meWTCYHVtHGcRzeeOMN4EEv34p1rmla9UcIgY9ACgHiwd+LAZi6hkBQCCSBBMvQiJg6YcNA1wS6riMQZByX1AENuQ9yq4vEFHcL9fdlzgvQheAhOyCNiS4gWcowt7hI2DSImnpbKTz9phdC2upempycJAiCvi9ElUXaA8LhMI8//ji+79f9pNPphkjWdjEJMLsUUXjwxW5sbLC1tcUjjzzS9bmy2WyDmBw3SqUSmUyGeLzevVVpGFzLQSlHh1kN09PTHY+tnznMyWQSy2pdNAMe5HveunWLUqlU17OyU0zTJBqN1gl3bUWudhnUYk5K2XXD+yI6Nn6dlRkARQQZJD4aGhIJSKFhCsmSZoAQhDUaRLvl/RdL8XqhebJL2vVxDYPTQRZRKlvwMxG7bxZUN9/Lzs5OU+9CIpGouzampqbwfZ9sNttw/Pr6OpqmVV/ftu1qANL+cq/9QAlpDxCivKrcb6l0ewP2gsqN4roujuPw+uuvI4TgzJkzLV2+hUKBUChUlwC+srJybPb9DiIIAvL5fIOQQqOQtbJIK8XVDcMgk8k0eCps266LoG6XfgppuxOAEIILFy5Uf5dS8p3vfKfj4KNTp041/Yw7ZRy8IlpdnHrlMXggecGDrRvpgwQtcHDRKLUZdRtEEtwstWpuWKbgBdwUYc7GQpjZrZFzQ7YyNizLahBSeHCv1rI/8DEej1ePH8S1Mmqf6VgKaSuGGYS0vr5ed8FVVrMHfeGu61IoFJiYmGB7e5vV1dWRrejUa6LRKHNzc03/tv97bCWkmqbx0EMPAeXJwXGc6vcAZWu0mxuun99BtxOAEKLjcdm23RMRhdEXUheB0UUKSifIUJS3PWvPpj2YQMLbJcF0bBYvCDBGzIJqRq/EybZtLly4gBCCjY2Nrr2EB6GEtI8MU0g3NjaaPn7QFx6Px7l3715LV8txppO0g3YioSspHVLKajGDTtM8KvQy5WQ/uVyuGtik6zqRSKQu4KoV3bh3ayOjj8qoCmkAeGiYe0X6WiFhz17t7n0EoShvB2G8Dj+HjZLP/3t7k/ctpJg4YD+231Q6TlWCkzoJZGvnu6+9nyvXNfTPBatcu31kFPMSD9rfW19fJ51Oj+wk1Q22bTM9PV3dv/R9H8/zGvazC4UCu7u7TcWu3T3SZszOzjIzM4Prul3fbP20SO/cuVP3+/nz59sSum7H9Pbbb6PrejVIx7Iszp071/F5RvEa9REECKw2LNEAgdZERMtlSg5+b9IKcVt2LqIVMo7Hf72zwXedmmChB5WMuuHcuXPVLSYpJS+//PJAXrdfc7KySPvIuAhpLpdjaWlpqHu6nXDp0qWqa/EgcfR9n3A4zMTExKHnbDUx77e8Oq10U3nOYQE9BzFI93q7ecdHGVPluwHa+m6a0er7qjT/rk0dCoIAz/OqP/0S4QCB2YE7t9XUe+COp6axpMdxvaPNLb6El5a2eebMFFPh7q/NXtDq+ziKOLV6rqZpddd4r+4tJaR9ZBBCeubMGba2ttpOT3j77bc5c+YMpmni+z6rq6t9qaPaTwzD6HmhiVY3Qjdu3V7TT9fufgYhpBUikUjbJQKllHieh2GUi6ofJKSJROLAc+3Pz+1FgYeAwy3JWsLhMBfPn2vIG3Zdt8FLUEs+Nk2u0JvJP5Dw1ftbvH9xkskhi2knHGUhtLi4yOLiItDblD4lpD1gaWmJTCZTXe1UXFfdFC7oBCEEyWSyo4mgsveZTqdZWloa6CTdKwa5HzEKQtqNaMViseo+VKufynVa2zi6nf3RbsdUi6ZpnD59uu0JSAjBnTt3KBaLmKbZdE/Nsqy2LP/K+2/3vbaDh9aWS7eCsTdP7L+emn0eeVEeZ0jXuN8jEa3g+JIX727xvoUUi/HhuHmHhaZpzM7OYtt2tZXkUc41SoylkHqeN7Amw7VUvrxEIoFpmtWVtuu6LQt767peLcs2jlSS4gdFt/uj+4sa9HIMhxGLxTh37lxfP6duhHRychIpJbquE4vFOnZ3z87Oksvlqq58eFDgIhQKdbXI6YVl7XURodsqarbZeHLCYiIoUDAiyD54+X0peWlpm/fMJbiQ6n/OZbu0un47DTY6jGQyycbGBoVCoe3nHOX1BsFYCumwUkRqhbTWnVXZE/J9Hyklvu/jOA6bm5s4jjO2IgqDv2APs0h3ii6bBYe865NzffKeT8H1CRs633u+8+ILzejUazA3N9f3z2n/5xIKhUilUuw6PlGt7KYsFAp1k9PCwsKRxhWPx3uWPlPhqB6ZgHL0bbPAoYMwWhSHb7UdZCDxfRfoX6TtN1bTOH7AY1Pdf8bdfL+jEDj20EMPsbq62rS2djsoIe0BwwoqamUdVaIhoZwburu7y9bW1khcsEdl0C6Ug4TUDyRfW9om79YfMxux+O7Fxlq83TIxMcHGxkZb318qlepLjeX9VASoss+ZSCTKhUmKDhOh8rW3vr5eFVJd10dusoGjL4L9vVSXTml1HTcbz0xQXvjaboHTsQj3+hgT+NpmltPxMLEmTcd7yVGuhV5bpFD+PhYWFrAsi+Xl5Y7HpFy7PWBYQnrQxSKlZH19/djVxh30ZHxQMYa/WN1tEFEoR0TqWu/GOTc3x8TEBKurqy2TyXVdrx63/zNazZWYCJlYeu9u9lQqxczMTIOFXhFRqBeFXu5H9pKzZ8/i+gGeHxDsBfxAOfjHDwKCQCJlQCD3Os/s7sDe/RTUuHQty0IIURc8JKvna+SgNLSDmKXEqYVZ/nS590UFoByA9OLdTZ45M9V3Me0F8Xgc0zQbAia7nSempqYQQnRc6H7UFomj/801YdQsUikly8vLYxeN2w6DXvlpmlZN5pZSYtsPeqr6QfMFyqWJ3u8zWZbFmTNnmJmZYWtri0KhgJQSy7KIxWIkk8kGUcu5Hi8t7SCl5LGpGIvxzmvatqIdq7fWbTqMIK126LQO68u729VKQrVpKmfPnm35mezv8FPZJ241ntOnTzd9jpSS6elpdF1nJedwJ939nt5BFLyAP7m/xfedm+lqQdhs0bRfaPY3KqgsRGqPb/UZWZZVvSdnZ2fxPK+hcttRvDKVvfza63f/2PY/NmoWqZBjaD4Vi8W6PMbKHmWr6kK9IhaLcf78+YbHd3d3jxyFNqqEw2EuXrw47GEA8OcrO9zPFDmXCOMGkpIfcDoe4lwyMuyhAZAuuQghiA/JsigWi7iuW62a1IuKRsPkoMIBjzzyyJFyhTvlbrrAny3v9PU1LqYivHuuu2pciuEylhZps9XPIIS0lTthGBHEg2KUVn5nE2EWYyHmh1Qd5jAS9vBKwEH5vhjEfu2gOMjzNOjr0uzh1kErbu7keXgiSnQMXLyKeo7NNzYId+9Brt3jyigJ6UzEPvwgxbFhlITUH9AtvlV0lZCOIaMzSx6RSrGEWCxGOBzGsqye7xN1G7AwzoySkCpOFgfdV4MPNhmMkm7kj28P4uPMsVn6mKbJmTNnGh7vZYFmZZEqFIMlHA43BAINukgIQMwczFSZccav8pniGAlpK3ppLba6eZVFqlD0Htu2RybQLWEbzEdtdE1wP9O/UqQbBYfNgjP0wvaKzjj2s2QvRU5ZpArFyUQIwftPT/K++RT9jjv6xurusZ5TjiPH3iLVdZ0LFy60bAFWqZfb7Gf/xaz2SBVHpZc1gRWDQUpJ0Qt4eSPDbsmlRTpzz9gteWRdv29pVK5bTtMa1aId48ix/yRrE/w7pbIvU/lpFbw0NTVFPB5vKsae55FOp4/yFoaKEtLe4QUBf76yy9OnuusJqhgcrh/w+laW2+kCxSP2Iu0Go49mrxLS3qM+yQOoVPs4LPo3Eok0FesgCHAcRwmpAoB0ySOngklGnvV8ia8v71LwhtMcA/orpN0aForWKCHtI0fpbjAqKCHtHUnb5MlZVblmlFnOFvnq/e1hD6Pv7mNFb1GzZB85DnunSkh7h64JpiMqGnOUubUzGi0Ptwoqn3ScULNkH1FCqlCMF6NiCN7rY4qNoveoWbKPHIcQdiWkipNE0h6N3a7VbPFYzB8nBTVL9hFlkSoU40XIGI32c04gcdVG6digZsk+ooRUoRgvMqXRiarWVb7x2KBmyT6ihFShGB8CKVnKjsbeZMzUu2ryrRgOapbsI8dhj0MJqeKkcD9TpOSPxuL34cnosIeg6AA1S/aRcbdIh9FlY9QoeD7Xb60PexiKPiOl5LXN7LCHAcBEyOShpCqaME6MRojaMWXchVRZo2DrGu+dV0UUjjt3M8WhtzATwGI8xONT8RO/gB03lJD2Edu2G4rjjxPqZgZNCNXS6hjj+AGGJnh9yNaorWu8Zy7JYjzEaq5IwfPRhCrgMS4oIe0jFy5cqPu9tkFxEASk02lWVlaGNLrDURap4rhjaoL1vEN6iNaorWt8/0MzWHr5fsu5PhdSao90nFBCOkAqe44VgTqsGP6gsG2bZDJZFfrKj+oOoTjuCCGqAjYsZiJWdQxSSiZC5lDHo+gcNVMOkVFx9YZCIWZnZ4c9DIViKCRsA1MTQyuAUNt3tOAFCNSWyrihfHdDZFTSY5QLV3GS0YRgMR4a2uvX9jsNRmROUHSGmkGHiO8Pr99hLUpIFSed0/Hw0F77fqZQFdCoqbNTcoc2FkV3qBl0iIyKa1cJqeKkMx2xhlaSzwkky9kSUN6zHffSgFsFh7w7GkbCoFAz6BAZFdeuSnNRnHQ0IQgbw5sONwsOgZRsFRzGvTJg1DTYLp6sfqpKSIfIMF27pmkyOzvLzMwM0agKtVecbKSUOEP0EKVLLv/lziZfvrNJSNf506XtsW3ubRsai0N0lQ8DFbU7RIbp2q0IqUKhgPW8g+MPz0M0HbG4uZ0nZupMhAwWYiH+fGWXUzGbx6fjaMprNNIoi3SIjMoeqUJxkil4Pn++sjO014+ZOmFDJ2YZvGcuiaZpnEmEuXJumoIXcDddGNrYuuW1zQzf2czWRSE7fkD6mAZSKSEdIq2EVNf1vhZriEQiyhpVKAAvkHz13hYFb/CLWl3AI5NRvvf8DI4f8ORsgtmoXf27oZXTctbz4+fifXgixkxNecO3dnL8+5urQxxRf1Gu3SHSSkjPnj1LJBLhlVde6VlAkm3bJBIJUqkUtm0f/gSF4hiTLrlsFlxu7+bZGUIz73PJMO+YjhM2ygvmS5Oxpse9uZ3jXTOJQQ6tJxiaYLKmRvVb23mmI3Zd8YnjxPF8V2PCxMQEruvW1d8NggDTNJFSYllW3ePdiuq5c+eIxWIto3M38w7bJZfT8RAhYzTKFioU/UBKyVfvb7OSKw1tDI9OxnhiJn7oca4fsJF3SNrjP01/9+IEvpTHNkNg/L+hMWZ6evrAv1+6dKnu9/2CGwQBQgiklPi+z927d5tGAruu2/ICllLy9ZUdcq6PBlyYUBG8iuNLzvWHKqKmJnhsqrn1uZ+dkksyZB4L8YkeU0u0wvF+d8cMIUTL/dOKgM7OzrK2tlb3N8dpvsdS8HxeXs+Qc30EcE41E1Ycc0r+cAP8UiETvc1E0ZCusxBV2zDjgBLSY8La2hrnzp0jFAo1CGmrPdF76QJ39iICJ8NW2ze4QjGuWLqGAAaV6LL/tTrtNHNQ2ouUklu7eTbzDqau8dhUTG3NDAklpMeEmZkZDMPA8x4ETpimyeLiIrFYc1fS+VTZAnUDyXlljSpOAHHL4J0zcb61nun7a81FbZ4+leLmdp63dnJETJ1LHWydvLmdYyHe2iL1Asmd3QK7JQ9fSnZLHt+9OPH/b+/Oftu4rjCAf/fOykXctFvxUttFGgRwgrZu6xZFi/Yh6B/c1wIFmgRGCySN6zaLnVq1ZVuiRFFchsO5M3P7QFKWA8miNJzhou/35gdJhGDx47lz7jmprYUb9WgswlHzpDFIF8Rod+ioE7hWq2F9ff2d12gsKc/sFiRaVFkMN6i6Fh5sVRFEMQqWgT/dWb/w97AMgfAdQyK2Wz34UYxPbq/i76+aqHsB/vxkF7Yh4ZoSZcfC7Ur+re7Zy1JxjIc7h7i/WYVjMkh/SOhZGfhKExGGIZRSyOUGI7o8FSKINCpcFkyE1x0fD182EaX8tndvrYS71QJ0gk7Vbw862PX6+PVW7a3HLlprfLXXQt0L8GCrCj+M8dlO48x9qu8tufh4vZy4Uo21nukJS2GsYU7p8RQHMiwY0zSPQ9QfNhM9qrem/KqIpu+gF+DzncPUQxR4s6w7yTHo3VoBP64W8NlOA4/rLdS9PsI4xvdND+0gxB9uDSYf/e3F2SEKAC/aPv76v334YbLZ3rMcou2+wneNDtSU5pfzaHeBfdfoItQaH66cf2eNaJE1fYWHO4eZNRlVJ3ACJIXARtHFWsHB604fTw891L0+1goO7l+rQgqB563eWB8M2kGEL3aP8Ktr1YV8xhnrwYcXc0orIXm0u6CavsKnLxooOSZ+e3152i+HaGr2un18+qKRWYhuFh082Kql8r1PHhXHWqMThLANiUNf4fOdw3O//qO1Eu7wrvjE8Wh3QeVMA6YUWMvzHhpdbd80OpmG6M82Kql9/5PVpBQCJceCaxrYLLpwx3gG+rjevnJLt7PAo90F5ZgSn9zmYHqitOVMiY/Xy6jlbDgpXT05j6ci+GMMmwi1xpPDLu6tzd/83lnGICWihTZOpXZZAsCDrdrUu+Iv0q3amsKQ/kXHo905pLVe2L1+ROfpqQjhKV2qsdbwwwjtIETTV1DDCi3Nfd0frZdODdF+FKPRy2792UU6chX3IE8cK9I5o7XGfi9AzU1+yZpoHtmGxLMj77hpRmuNXa+Pf7w6QnDieNM1Jd6vFVH3kg+pL1iDxdtRrBHGMUwpcbOcO3U+tYpiPK63Mp1d3fTH/2A9y9dY5hW7dueMp0K4pgEBjuqiqyWMNdRwnaCnYnRUiJWcjS93j7CX4vLrraKL+9cqlwogrTW+b3pYydsoO5M//vVUhH4U4Vmzh/8eeWN9zYcrS3h/zA00NB5WpFPkhxFiDRhSwBCDu1CWFBBCwFMRXFO+9cerohiWlPxESVeSKQVMORh5qXWIpq/xl+39U495J2k1b1/6by7WGnUvQCtQuLdanvhiCNsQyFs2DuzxK1LX5BO9SeNvdAqCKEYvjGBKgbxlwDEkTCkHmymGf7A5U8IPB8dUWmu0g8FgamtKXYFEs6Rgm7hbK6CWQZPPZTeqPDvy8KrTxy+vVbDi2qnsQR0NILhIs1MWk52uGr4rZ0jFMTp9BSnE8J7n2b9+IQSkGFShQaxRtAyuSCI6YfQBM23V3MXDuh2E8FSErSUXYjihyFNhaq83vEAD0aO9Fp6NeQxM4+HRbobiWANCIIxjqAgQYnD0Y0l5aqXJ4CQ6ndYaXx900AvT7UAVuPj1ma4K8WivhV9cqxyfMFmGxErORhoPZRq9ABXXQs210Bij6WhrKYd/1dvoBCG2lnITGWd41bHZKCMqimFIARVpOCeeUfSjeGqXuInmSS+M8LrTh2tKNH2F7VYv9Sk9ZcfEH2+tXuhrumpQdRasN3VKZ1iJFu3J1y6jsYGvOj4e19tonVP1ruRsHPrq+Ij3TjWPe6slNi8mwHfwDGitEcbDFUQ/+L/KECU6n9Yae90+bpRygz2b1QJ+f2MZRTvdU5sbpdyFv+Zl23/r393hMW8aIQq86d7fLLr43c1lfLBcfGflu98LBv0WUqDqWsPF47Nz1BvPYW3HijQDQRQfd+O2+gq2Mei8TWuTPdGi6YcxpBwsox9p+gov2j0YQuA/B52J/8zVvI3fvFe7VMfuq46PtbwDQwp0gjC1ED1LOwjxzUEHLzv+cVezFMCPynk4poGyY2I1b8OUEq+7Pv6524IUAptFB9dLORQtExp6KttUukGIQsa/r6QYpEQ0l7pBiFYQYrPo4slhF1/tJd+7W3UtmFJgo+Bgo+Ai0nrq4/8uS2sNDaCrInSCECXbPDOggijGTtvH9pGHhq9QsAzcrRawUXAyD7VDX83dc1sGKRHNpeetHjaKznGV+u/9Nr4+pzI1xOAI1JISXhjBFAJF28Rm0UHJeXufpaci9FSE5Xx2U8RG905rrjWVq25aa+x2+9jvBdg+6mG94ODnm5XMfn6sNZq+Qi03X5Pb5qt+JiLC4A03GA4oGflguYicaeDbRgfdE01ItiGRMyW2llxsFt2xJgzte334UZz5GsK6F2Cn3cNqhuF90uiqTsW1UPcC3Cpf/BlxEirWEx9akQVWpEQ0N+Jh4972kYfrpdypV8S01nje6uFp08OSbeKnG+W5mAamtcbTpoeaa029Its+8tALY/wk41GCngoBDAbVzBNWpEQ0N9pBiJ6KcLtSOLNyEULgRjmPG5cYGq+iGAd+gI2Cm/SlXthRP0TRMqYeosCgMp5GVdyPNEopd2KngUFKRHOj7FipDH8fsQyJ9YyPc0fKjjkTjU2j56Q3Mz7WBd7ct583vH9BRHTCtAYTzMpABA3gViU/lVWNaS8gSAufkRIR0UwIongu79czSImIiBKYv+gnIiKaIQxSIiKiBBikRERECTBIiYiIEmCQEhERJcAgJSIiSoBBSkRElACDlIiIKAEGKRERUQIMUiIiogQYpERERAkwSImIiBJgkBIRESXAICUiIkqAQUpERJQAg5SIiCgBBikREVECDFIiIqIEGKREREQJMEiJiIgSYJASERElwCAlIiJKgEFKRESUAIOUiIgoAQYpERFRAv8Hw9wGKdG1gyYAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = city.plot(color=\"lightgray\")\n",
+    "water.plot(color=\"lightblue\", ax=ax)\n",
+    "fire.plot(color=\"red\", ax=ax, marker=\"+\", label=\"Fire\")\n",
+    "police2.plot(color=\"blue\", ax=ax, label=\"Police\")\n",
+    "incidents.to_crs(city.crs).plot(ax=ax, color=\"k\", label=\"Incidents\")\n",
+    "ax.legend(loc=\"upper left\", frameon=False)\n",
+    "ax.set_axis_off()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/lecture_material/17-viz-2/vis_2_lec_001.ipynb b/lecture_material/17-viz-2/vis_2_lec_001.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..a530b4c2668fbbc8d7e0d3a09f652eed5488f1bc
--- /dev/null
+++ b/lecture_material/17-viz-2/vis_2_lec_001.ipynb
@@ -0,0 +1,1224 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "471a762b",
+   "metadata": {},
+   "source": [
+    "# Visualization 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2478daaa-cb6c-4d73-92af-01ae91e773fe",
+   "metadata": {},
+   "source": [
+    "### Geographic Data / Maps\n",
+    "\n",
+    "#### Installation\n",
+    "```python\n",
+    "pip3 install --upgrade pip\n",
+    "pip3 install geopandas shapely descartes geopy netaddr\n",
+    "sudo apt install -y python3-rtree\n",
+    "```\n",
+    "\n",
+    "- `import geopandas as gpd`\n",
+    "- `.shp` => Shapefile\n",
+    "- `gpd.datasets.get_path(<shp file path>)`:\n",
+    "    - example: `gpd.datasets.get_path(\"naturalearth_lowres\")`\n",
+    "- `gpd.read_file(<path>)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e6f50cc3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import math\n",
+    "import requests\n",
+    "import re\n",
+    "import os\n",
+    "\n",
+    "# new import statements\n",
+    "import geopandas as gpd\n",
+    "from shapely.geometry import Point, Polygon, box"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "257671bc-1e79-47c2-aa7a-1725562d23ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!ls /home/gurmail.singh/.local/lib/python3.8/site-packages/geopandas/datasets/naturalearth_lowres"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7269706-188a-48e3-882a-ac068170b1af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!ls /home/gurmail.singh/.local/lib/python3.8/site-packages/geopandas/datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "273ed288",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Find the path for \"naturalearth_lowres\"\n",
+    "path = \n",
+    "# Read the shapefile for \"naturalearth_lowres\" and\n",
+    "# set index using \"name\" column\n",
+    "gdf = "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf4d871c-d6d6-4d99-be96-75faf804d4a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gdf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5c983208-7851-4d25-92e7-3f110039411a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(gdf).__mro__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "653edec9-8b82-4684-ae82-a9fa399459ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# All shapefiles have a column called \"geometry\"\n",
+    "gdf[\"geometry\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e2eb071a-b29a-4edd-8747-50b02fd43108",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(gdf[\"geometry\"]).__mro__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "da5b5783-d78e-460f-bd22-b41e7f24f871",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First country's geometry\n",
+    "print(gdf.index[0])\n",
+    "gdf[\"geometry\"].iat[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f4da997-567d-47f6-8594-f0a37b92b9e6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Second country's geometry\n",
+    "print(gdf.index[1])\n",
+    "gdf[\"geometry\"].iat[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d380b32f-c401-4601-b6d3-02ac344b3f08",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Geometry for \"United States of America\"\n",
+    "gdf.at[<row_index>, <column_name>]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3528f252-36b7-4ca5-9108-0951367837cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Type of Tanzania's geometry\n",
+    "print(gdf.index[1], type(gdf[\"geometry\"].iat[1]))\n",
+    "\n",
+    "# Type of United States of America's geometry\n",
+    "print(\"United States of America\", type(gdf.at[\"United States of America\", \"geometry\"]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47711b72-a24c-4da8-a804-3faf88a5fe8b",
+   "metadata": {},
+   "source": [
+    "- `gdf.plot(figsize=(<width>, <height>), column=<column name>)`\n",
+    "- `ax.set_axis_off()`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9874c7de-226e-4296-af60-45e091836a19",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = gdf.plot(figsize=(8,4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f5547cc5-3404-421e-aeed-4b4cf8ee8382",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set facecolor=\"0.7\", edgecolor=\"black\"\n",
+    "ax = gdf.plot(figsize=(8,4))\n",
+    "# Turn off the axes\n",
+    "# ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58da716e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Color the map based on population column, column=\"pop_est\" and set cmap=\"cool\" and legend=True\n",
+    "ax = gdf.plot(figsize=(8,4))\n",
+    "# Turn off the axes\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7a85431-bdab-46ab-8e29-5f91d8a2e0bc",
+   "metadata": {},
+   "source": [
+    "#### Create a map where countries with >100M people are red, others are gray."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "962a552b-94a6-4690-8018-f82173dd6096",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a map where countries with >100M people are red, others are gray\n",
+    "\n",
+    "# Add a new column called color to gdf and set default value to \"lightgray\"\n",
+    "\n",
+    "# Boolean indexing to set color to red for countries with \"pop_est\" > 1e8\n",
+    "\n",
+    "# Create the plot\n",
+    "# ax = gdf.plot(figsize=(8,4), color=gdf[\"color\"])\n",
+    "# ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11214c90",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a map where countries with >100M people are red, others are gray\n",
+    "\n",
+    "# Add a new column called color to gdf and set default value to \"lightgray\"\n",
+    "gdf[\"color\"] = \"lightgray\"\n",
+    "# Boolean indexing to set color to red for countries with \"pop_est\" > 1e8\n",
+    "gdf.loc[gdf[\"pop_est\"] > 1e8, \"color\"] = \"red\"\n",
+    "# Create the plot\n",
+    "ax = gdf.plot(figsize=(8,4), color=gdf[\"color\"])\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a32c52c-509f-4f7f-8dd6-bc7fe53c64fd",
+   "metadata": {},
+   "source": [
+    "### All shapefile geometries are shapely shapes. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33e92def-a7db-4d90-adc8-a3d01d472ac1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(gdf[\"geometry\"].iat[2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39c95c23",
+   "metadata": {},
+   "source": [
+    "### Shapely shapes\n",
+    "\n",
+    "- `from shapely.geometry import Point, Polygon, box`\n",
+    "- `Polygon([(<x1>, <y1>), (<x2>, <y2>), (<x3>, <y3>), ...])`\n",
+    "- `box(minx, miny, maxx, maxy)`\n",
+    "- `Point(<x>, <y>)`\n",
+    "- `<shapely object>.buffer(<size>)`\n",
+    "    - example: `Point(5, 5).buffer(3)` creates a circle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61716db9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "triangle = Polygon([(0, 0), (1.2, 1), (2, 0)])   # triangle\n",
+    "triangle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6a36021a-8653-4698-a0ba-818c14091d79",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(triangle)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bddd958d-6fde-42df-8ae3-459e25fa3f04",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "box1 = box(0, 0, 1, 1) # not a type; just a function that creates box\n",
+    "box1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb7ade8d-96d2-4770-bce9-b3e35996b0b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(box1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e5308c61-b1fa-433b-bb05-485ca7bd23da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "point = Point(5, 5)\n",
+    "point"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5c669619-af78-477d-b807-3e6d99278f2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(point)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f015d27e-8fd8-446f-992f-4f7a88cc582d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "circle = point.buffer(1)\n",
+    "circle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "39fe5752-8038-46cc-b4a6-320e6a78bbd1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(circle)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "800cc48d-c241-4439-9161-ff309e50373f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "triangle_buffer = triangle.buffer(3)\n",
+    "triangle_buffer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b9478d3-7cc9-4e80-ae84-e56d7bfd0b71",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(triangle_buffer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a340443-d750-43d5-99cd-28e7034ce898",
+   "metadata": {},
+   "source": [
+    "#### Shapely methods\n",
+    "\n",
+    "- Shapely methods:\n",
+    "    - `union`:  any point that is in either shape (OR)\n",
+    "    - `intersection`: any point that is in both shapes (AND)\n",
+    "    - `difference`: subtraction\n",
+    "    - `intersects`: do they overlap? returns True / False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d1c5f9f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# union triangle and box1\n",
+    "# it will give any point that is in either shape (OR)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8a2d3357",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# intersection triangle and box1\n",
+    "# any point that is in both shapes (AND)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f327bb99-be34-4a00-a216-a6f40df48268",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# difference of triangle and box1\n",
+    "# subtraction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9082b54a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# difference of box1 and triangle\n",
+    "box1.difference(triangle)   # subtraction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0def4359-78f6-4f14-80ae-05c25ff64bc5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check whether triangle intersects box1\n",
+    "# the is, check do they overlap?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51a82cf3-fe28-46d2-a019-d87f6a0f41b6",
+   "metadata": {},
+   "source": [
+    "Is the point \"near\" (<6 units) the triangle?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7a87b70f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "triangle.union(point.buffer(6))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e04daa60",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "triangle.intersects(point.buffer(6))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf500303",
+   "metadata": {},
+   "source": [
+    "#### Extacting \"Europe\" data from \"naturalearth_lowres\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "410b08cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Europe bounding box\n",
+    "eur_window = box(-10.67, 34.5, 31.55, 71.05)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d45fdfcb-b244-4eff-a9d6-5cc4fefdfbd8",
+   "metadata": {},
+   "source": [
+    "Can we use `intersects` method?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "24f4a32b-9ed4-468b-a194-ebe0395fcdb6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gdf.intersects(eur_window)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ff455178-84da-45bf-b5b9-a71a7f9160f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Incorrect v1\n",
+    "gdf[gdf.intersects(eur_window)].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03aa8da8-72f4-45fd-8931-e410d1204226",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Incorrect v2\n",
+    "gdf[~gdf.intersects(eur_window)].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4173f3c5-017f-44ff-b713-158219fcf3e5",
+   "metadata": {},
+   "source": [
+    "Can we use `intersection` method?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d7226fd9-3bb2-48c3-a5e9-5ce1ca0dd88f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gdf.intersection(eur_window)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "108b8b8a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gdf.intersection(eur_window).plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f13e4e16-286a-4681-8bc5-9bd3fcf599a7",
+   "metadata": {},
+   "source": [
+    "How can we get rid of empty polygons (and remove the warning)?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8c0dd051-a808-4fd4-b5ff-fee6ed580753",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eur = gdf.intersection(eur_window)\n",
+    "eur.is_empty"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acb1b886-f7c8-41d4-979a-2309c2b973bd",
+   "metadata": {},
+   "source": [
+    "Remove all the empty polygons using `is_empty`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "55a76a00",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eur = eur[~eur.is_empty]\n",
+    "eur"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08c59df7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eur.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97a12d23",
+   "metadata": {},
+   "source": [
+    "#### Centroids of European countries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb5c83b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot the centroids\n",
+    "ax = eur.plot(facecolor=\"lightgray\", edgecolor=\"k\")\n",
+    "eur.centroid.plot(ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dbfab5e7-5941-438a-baf2-e05369106004",
+   "metadata": {},
+   "source": [
+    "### Lat / long CRS\n",
+    "\n",
+    "- Long is x-coord\n",
+    "- Lat is y-coord\n",
+    "    - tells you where the point on Earth is\n",
+    "- **IMPORTANT**: degrees are not a unit of distance. 1 degree of longitute near the equator is a lot farther than moving 1 degree of longitute near the north pole\n",
+    "\n",
+    "Using `.crs` to access CRS of a gdf.\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89251896",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eur.crs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a6ba447-6f1a-4d31-8c33-ec1eeb9e0deb",
+   "metadata": {},
+   "source": [
+    "#### Single CRS doesn't work for the whole earth\n",
+    "\n",
+    "- Setting a different CRS for Europe that is based on meters.\n",
+    "- https://spatialreference.org/ref/?search=europe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5451e78f-ebaa-4bb3-af34-24ffe92d0582",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setting CRS to \"EPSG:3035\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "586038b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setting CRS to \"EPSG:3035\"\n",
+    "eur2 = eur.to_crs(\"EPSG:3035\")\n",
+    "eur2.crs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d46c124c-9aff-4c2f-81fe-08885b606800",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = eur2.plot(facecolor=\"lightgray\", edgecolor=\"k\")\n",
+    "eur2.centroid.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "045b9c33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = eur2.plot(facecolor=\"lightgray\", edgecolor=\"k\")\n",
+    "eur2.centroid.plot(ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0634941f",
+   "metadata": {},
+   "source": [
+    "#### How much error does lat/long computation introduce?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0b72aff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = eur2.plot(facecolor=\"lightgray\", edgecolor=\"k\")\n",
+    "eur2.centroid.plot(ax=ax, color=\"k\") # black => correct\n",
+    "eur.centroid.to_crs(\"EPSG:3035\").plot(ax=ax, color=\"r\")  # red => miscalculated"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ca9e306e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(eur2.iloc[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f489c88d-5964-4358-b8c4-fc80d28b5491",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(eur2).__mro__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85880c73",
+   "metadata": {},
+   "source": [
+    "#### Area of European countries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3e4874d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eur2.area # area in sq meters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "95f55824",
+   "metadata": {},
+   "source": [
+    "What is the area in **sq miles**?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "85ee20c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Conversion: / 1000 / 1000 / 2.59\n",
+    "(eur2.area / 1000 / 1000 / 2.59).sort_values(ascending=False)\n",
+    "# careful!  some countries (e.g., Russia) were cropped when we did intersection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cd600837",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# area on screen, not real area\n",
+    "eur.area"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "daf1245f-9939-468d-9a5f-34225c2dbc51",
+   "metadata": {},
+   "source": [
+    "### CRS\n",
+    "\n",
+    "- `<GeoDataFrame object>.crs`: gives you information about current CRS.\n",
+    "- `<GeoDataFrame object>.to_crs(<TARGET CRS>)`: changes CRS to `<TARGET CRS>`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9da3ee4c",
+   "metadata": {},
+   "source": [
+    "### Madison area emergency services\n",
+    "\n",
+    "- Data source: https://data-cityofmadison.opendata.arcgis.com/\n",
+    "    - Search for:\n",
+    "        - \"City limit\"\n",
+    "        - \"Lakes and rivers\"\n",
+    "        - \"Fire stations\"\n",
+    "        - \"Police stations\"\n",
+    "\n",
+    "- CRS for Madison area: https://en.wikipedia.org/wiki/Universal_Transverse_Mercator_coordinate_system#/media/File:Universal_Transverse_Mercator_zones.svg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a6f80847",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "city = gpd.read_file(\"City_Limit.zip\").to_crs(\"epsg:32616\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f8595be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "city.crs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ebd361f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "water = gpd.read_file(\"Lakes_and_Rivers.zip\").to_crs(city.crs)\n",
+    "fire = gpd.read_file(\"Fire_Stations.zip\").to_crs(city.crs)\n",
+    "police = gpd.read_file(\"Police_Stations.zip\").to_crs(city.crs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "723e2bff-545f-4a8c-9f72-8a9906a99b1b",
+   "metadata": {},
+   "source": [
+    "#### Run this on your virtual machine\n",
+    "\n",
+    "`sudo sh -c \"echo 'Options = UnsafeLegacyRenegotiation' >> /etc/ssl/openssl.cnf\"`\n",
+    "\n",
+    "then restart notebook!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6069860-7dd9-43f2-970e-fd15980135ef",
+   "metadata": {},
+   "source": [
+    "#### GeoJSON\n",
+    "\n",
+    "How to find the below URL?\n",
+    "\n",
+    "- Go to info page of a dataset, for example: https://data-cityofmadison.opendata.arcgis.com/datasets/police-stations/explore?location=43.081769%2C-89.391550%2C12.81\n",
+    "- Then click on \"I want to use this\" > \"View API Resources\" > \"GeoJSON\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a095d5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "url = \"https://maps.cityofmadison.com/arcgis/rest/services/Public/OPEN_DATA/MapServer/2/query?outFields=*&where=1%3D1&f=geojson\"\n",
+    "police2 = gpd.read_file(url).to_crs(city.crs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "248be81e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = city.plot(color=\"lightgray\")\n",
+    "water.plot(color=\"lightblue\", ax=ax)\n",
+    "fire.plot(color=\"red\", ax=ax, marker=\"+\", label=\"Fire\")\n",
+    "police2.plot(color=\"blue\", ax=ax, label=\"Police\")\n",
+    "ax.legend(loc=\"upper left\", frameon=False)\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a609d81",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fire.to_file(\"fire.geojson\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77600884",
+   "metadata": {},
+   "source": [
+    "### Geocoding: street address => lat / lon\n",
+    "\n",
+    "\n",
+    "- `gpd.tools.geocode(<street address>, provider=<geocoding service name>, user_agent=<user agent name>)`: converts street address into lat/long\n",
+    "\n",
+    "\n",
+    "#### Daily incident reports: https://www.cityofmadison.com/fire/daily-reports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b0b2aa0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "url = \"https://www.cityofmadison.com/fire/daily-reports\"\n",
+    "r = requests.get(url)\n",
+    "r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bee28b41",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r.raise_for_status() # give me an exception if not 200 (e.g., 404)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0bae00e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# doesn't work\n",
+    "# pd.read_html(url)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47173ec2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# print(r.text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39f166c5",
+   "metadata": {},
+   "source": [
+    "Find all **span** tags with **streetAddress** using regex."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ac7b9482-5512-49e4-b0b8-7f9ed34b5844",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# <p>1700 block Thierer Road<br>\n",
+    "# addrs = re.findall(r'<p>1700 block Thierer Road<br>', r.text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8e9b49d2-3e0d-4a39-9dcf-13b288a165e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "addrs = re.findall(r' <p>(.*?)<br>', r.text)\n",
+    "addrs = pd.Series(addrs)\n",
+    "addrs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4fc8ba0c-9d45-4251-8055-1310cabf15a9",
+   "metadata": {},
+   "source": [
+    "#### Without city name and state name, geocoding would return match with the most famous location with such a street name."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "095b9ebe-b583-4098-a3a2-47dd46610d59",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geo_info = gpd.tools.geocode(\"1300 East Washington Ave\")\n",
+    "geo_info"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e52ee0fc-d73c-4942-9bec-d1586a702f68",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geo_info[\"address\"].loc[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e80b714c-a962-4a47-b7cc-3a19d0da8ac0",
+   "metadata": {},
+   "source": [
+    "#### To get the correct address we want, we should concatenate \"Madison, Wisconsin\" to the end of the address."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf3f590b-8d00-46c4-ac57-6f2f61509f68",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geo_info = gpd.tools.geocode(\"1300 East Washington Ave, Madison, Wisconsin\")\n",
+    "geo_info"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3caf8c12-67a3-4e19-a758-d556d010eead",
+   "metadata": {},
+   "source": [
+    "#### Addresses with \"block\" often won't work or won't give you the correct lat/long. We need to remove the word \"block\" before geocoding."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "54103e4a-5ac2-4f19-811b-385c02623ed2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gpd.tools.geocode(\"800 block W. Johnson Street, Madison, Wisconsin\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "66072f4b-2286-4d66-ba2c-18ccd2e37ed6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gpd.tools.geocode(\"800 W. Johnson Street, Madison, Wisconsin\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf982302",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fixed_addrs = addrs.str.replace(\" block \", \" \") + \", Madison, WI\"\n",
+    "fixed_addrs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6a267ed-6876-4866-a66f-74390a3d4ee1",
+   "metadata": {},
+   "source": [
+    "#### Using a different provider than the default one\n",
+    "\n",
+    "- `gpd.tools.geocode(<street address>, provider=<geocoding service name>, user_agent=<user agent name>)`: converts street address into lat/long\n",
+    "    - We will be using \"OpenStreetMap\", for which the argument is \"nominatim\"\n",
+    "    - We also need to specify argument to `user_agent` parameter, indicating where the request is coming from; for example: \"cs320_bot\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab0e699f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "incidents = gpd.tools.geocode(fixed_addrs, provider=\"nominatim\", user_agent=\"cs320bot\").dropna()\n",
+    "incidents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ad73b2c-5171-45c2-a3f0-eef30f93c492",
+   "metadata": {},
+   "source": [
+    "It is often a good idea to drop na values. Although in this version of the example, there are no failed geocodings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "41a7d12e-73be-4442-b43c-285229e6cfdb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "incidents = incidents.dropna()\n",
+    "incidents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b30733e6-9491-4be0-bf20-bba64903334d",
+   "metadata": {},
+   "source": [
+    "#### Self-practice\n",
+    "\n",
+    "If you want practice with regex, try to write regular expression and use the match result to make sure that \"Madison\" and \"Wisconsin\" is part of each address."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "843bbba2-3de5-4cc0-b76b-92e7bebdd379",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# self-practice\n",
+    "for addr in incidents[\"address\"]:\n",
+    "    print(addr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1a04c2b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = city.plot(color=\"lightgray\")\n",
+    "water.plot(color=\"lightblue\", ax=ax)\n",
+    "fire.plot(color=\"red\", ax=ax, marker=\"+\", label=\"Fire\")\n",
+    "police2.plot(color=\"blue\", ax=ax, label=\"Police\")\n",
+    "incidents.to_crs(city.crs).plot(ax=ax, color=\"k\", label=\"Incidents\")\n",
+    "ax.legend(loc=\"upper left\", frameon=False)\n",
+    "ax.set_axis_off()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/lecture_material/17-viz-2/vis_2_lec_002.ipynb b/lecture_material/17-viz-2/vis_2_lec_002.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..a530b4c2668fbbc8d7e0d3a09f652eed5488f1bc
--- /dev/null
+++ b/lecture_material/17-viz-2/vis_2_lec_002.ipynb
@@ -0,0 +1,1224 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "471a762b",
+   "metadata": {},
+   "source": [
+    "# Visualization 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2478daaa-cb6c-4d73-92af-01ae91e773fe",
+   "metadata": {},
+   "source": [
+    "### Geographic Data / Maps\n",
+    "\n",
+    "#### Installation\n",
+    "```python\n",
+    "pip3 install --upgrade pip\n",
+    "pip3 install geopandas shapely descartes geopy netaddr\n",
+    "sudo apt install -y python3-rtree\n",
+    "```\n",
+    "\n",
+    "- `import geopandas as gpd`\n",
+    "- `.shp` => Shapefile\n",
+    "- `gpd.datasets.get_path(<shp file path>)`:\n",
+    "    - example: `gpd.datasets.get_path(\"naturalearth_lowres\")`\n",
+    "- `gpd.read_file(<path>)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e6f50cc3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import math\n",
+    "import requests\n",
+    "import re\n",
+    "import os\n",
+    "\n",
+    "# new import statements\n",
+    "import geopandas as gpd\n",
+    "from shapely.geometry import Point, Polygon, box"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "257671bc-1e79-47c2-aa7a-1725562d23ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!ls /home/gurmail.singh/.local/lib/python3.8/site-packages/geopandas/datasets/naturalearth_lowres"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7269706-188a-48e3-882a-ac068170b1af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!ls /home/gurmail.singh/.local/lib/python3.8/site-packages/geopandas/datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "273ed288",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Find the path for \"naturalearth_lowres\"\n",
+    "path = \n",
+    "# Read the shapefile for \"naturalearth_lowres\" and\n",
+    "# set index using \"name\" column\n",
+    "gdf = "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf4d871c-d6d6-4d99-be96-75faf804d4a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gdf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5c983208-7851-4d25-92e7-3f110039411a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(gdf).__mro__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "653edec9-8b82-4684-ae82-a9fa399459ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# All shapefiles have a column called \"geometry\"\n",
+    "gdf[\"geometry\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e2eb071a-b29a-4edd-8747-50b02fd43108",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(gdf[\"geometry\"]).__mro__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "da5b5783-d78e-460f-bd22-b41e7f24f871",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First country's geometry\n",
+    "print(gdf.index[0])\n",
+    "gdf[\"geometry\"].iat[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f4da997-567d-47f6-8594-f0a37b92b9e6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Second country's geometry\n",
+    "print(gdf.index[1])\n",
+    "gdf[\"geometry\"].iat[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d380b32f-c401-4601-b6d3-02ac344b3f08",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Geometry for \"United States of America\"\n",
+    "gdf.at[<row_index>, <column_name>]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3528f252-36b7-4ca5-9108-0951367837cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Type of Tanzania's geometry\n",
+    "print(gdf.index[1], type(gdf[\"geometry\"].iat[1]))\n",
+    "\n",
+    "# Type of United States of America's geometry\n",
+    "print(\"United States of America\", type(gdf.at[\"United States of America\", \"geometry\"]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47711b72-a24c-4da8-a804-3faf88a5fe8b",
+   "metadata": {},
+   "source": [
+    "- `gdf.plot(figsize=(<width>, <height>), column=<column name>)`\n",
+    "- `ax.set_axis_off()`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9874c7de-226e-4296-af60-45e091836a19",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = gdf.plot(figsize=(8,4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f5547cc5-3404-421e-aeed-4b4cf8ee8382",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set facecolor=\"0.7\", edgecolor=\"black\"\n",
+    "ax = gdf.plot(figsize=(8,4))\n",
+    "# Turn off the axes\n",
+    "# ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58da716e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Color the map based on population column, column=\"pop_est\" and set cmap=\"cool\" and legend=True\n",
+    "ax = gdf.plot(figsize=(8,4))\n",
+    "# Turn off the axes\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7a85431-bdab-46ab-8e29-5f91d8a2e0bc",
+   "metadata": {},
+   "source": [
+    "#### Create a map where countries with >100M people are red, others are gray."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "962a552b-94a6-4690-8018-f82173dd6096",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a map where countries with >100M people are red, others are gray\n",
+    "\n",
+    "# Add a new column called color to gdf and set default value to \"lightgray\"\n",
+    "\n",
+    "# Boolean indexing to set color to red for countries with \"pop_est\" > 1e8\n",
+    "\n",
+    "# Create the plot\n",
+    "# ax = gdf.plot(figsize=(8,4), color=gdf[\"color\"])\n",
+    "# ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11214c90",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a map where countries with >100M people are red, others are gray\n",
+    "\n",
+    "# Add a new column called color to gdf and set default value to \"lightgray\"\n",
+    "gdf[\"color\"] = \"lightgray\"\n",
+    "# Boolean indexing to set color to red for countries with \"pop_est\" > 1e8\n",
+    "gdf.loc[gdf[\"pop_est\"] > 1e8, \"color\"] = \"red\"\n",
+    "# Create the plot\n",
+    "ax = gdf.plot(figsize=(8,4), color=gdf[\"color\"])\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a32c52c-509f-4f7f-8dd6-bc7fe53c64fd",
+   "metadata": {},
+   "source": [
+    "### All shapefile geometries are shapely shapes. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33e92def-a7db-4d90-adc8-a3d01d472ac1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(gdf[\"geometry\"].iat[2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39c95c23",
+   "metadata": {},
+   "source": [
+    "### Shapely shapes\n",
+    "\n",
+    "- `from shapely.geometry import Point, Polygon, box`\n",
+    "- `Polygon([(<x1>, <y1>), (<x2>, <y2>), (<x3>, <y3>), ...])`\n",
+    "- `box(minx, miny, maxx, maxy)`\n",
+    "- `Point(<x>, <y>)`\n",
+    "- `<shapely object>.buffer(<size>)`\n",
+    "    - example: `Point(5, 5).buffer(3)` creates a circle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61716db9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "triangle = Polygon([(0, 0), (1.2, 1), (2, 0)])   # triangle\n",
+    "triangle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6a36021a-8653-4698-a0ba-818c14091d79",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(triangle)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bddd958d-6fde-42df-8ae3-459e25fa3f04",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "box1 = box(0, 0, 1, 1) # not a type; just a function that creates box\n",
+    "box1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb7ade8d-96d2-4770-bce9-b3e35996b0b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(box1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e5308c61-b1fa-433b-bb05-485ca7bd23da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "point = Point(5, 5)\n",
+    "point"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5c669619-af78-477d-b807-3e6d99278f2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(point)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f015d27e-8fd8-446f-992f-4f7a88cc582d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "circle = point.buffer(1)\n",
+    "circle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "39fe5752-8038-46cc-b4a6-320e6a78bbd1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(circle)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "800cc48d-c241-4439-9161-ff309e50373f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "triangle_buffer = triangle.buffer(3)\n",
+    "triangle_buffer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b9478d3-7cc9-4e80-ae84-e56d7bfd0b71",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(triangle_buffer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a340443-d750-43d5-99cd-28e7034ce898",
+   "metadata": {},
+   "source": [
+    "#### Shapely methods\n",
+    "\n",
+    "- Shapely methods:\n",
+    "    - `union`:  any point that is in either shape (OR)\n",
+    "    - `intersection`: any point that is in both shapes (AND)\n",
+    "    - `difference`: subtraction\n",
+    "    - `intersects`: do they overlap? returns True / False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d1c5f9f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# union triangle and box1\n",
+    "# it will give any point that is in either shape (OR)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8a2d3357",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# intersection triangle and box1\n",
+    "# any point that is in both shapes (AND)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f327bb99-be34-4a00-a216-a6f40df48268",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# difference of triangle and box1\n",
+    "# subtraction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9082b54a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# difference of box1 and triangle\n",
+    "box1.difference(triangle)   # subtraction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0def4359-78f6-4f14-80ae-05c25ff64bc5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check whether triangle intersects box1\n",
+    "# the is, check do they overlap?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51a82cf3-fe28-46d2-a019-d87f6a0f41b6",
+   "metadata": {},
+   "source": [
+    "Is the point \"near\" (<6 units) the triangle?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7a87b70f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "triangle.union(point.buffer(6))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e04daa60",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "triangle.intersects(point.buffer(6))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf500303",
+   "metadata": {},
+   "source": [
+    "#### Extacting \"Europe\" data from \"naturalearth_lowres\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "410b08cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Europe bounding box\n",
+    "eur_window = box(-10.67, 34.5, 31.55, 71.05)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d45fdfcb-b244-4eff-a9d6-5cc4fefdfbd8",
+   "metadata": {},
+   "source": [
+    "Can we use `intersects` method?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "24f4a32b-9ed4-468b-a194-ebe0395fcdb6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gdf.intersects(eur_window)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ff455178-84da-45bf-b5b9-a71a7f9160f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Incorrect v1\n",
+    "gdf[gdf.intersects(eur_window)].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03aa8da8-72f4-45fd-8931-e410d1204226",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Incorrect v2\n",
+    "gdf[~gdf.intersects(eur_window)].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4173f3c5-017f-44ff-b713-158219fcf3e5",
+   "metadata": {},
+   "source": [
+    "Can we use `intersection` method?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d7226fd9-3bb2-48c3-a5e9-5ce1ca0dd88f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gdf.intersection(eur_window)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "108b8b8a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gdf.intersection(eur_window).plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f13e4e16-286a-4681-8bc5-9bd3fcf599a7",
+   "metadata": {},
+   "source": [
+    "How can we get rid of empty polygons (and remove the warning)?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8c0dd051-a808-4fd4-b5ff-fee6ed580753",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eur = gdf.intersection(eur_window)\n",
+    "eur.is_empty"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acb1b886-f7c8-41d4-979a-2309c2b973bd",
+   "metadata": {},
+   "source": [
+    "Remove all the empty polygons using `is_empty`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "55a76a00",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eur = eur[~eur.is_empty]\n",
+    "eur"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08c59df7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eur.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97a12d23",
+   "metadata": {},
+   "source": [
+    "#### Centroids of European countries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb5c83b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot the centroids\n",
+    "ax = eur.plot(facecolor=\"lightgray\", edgecolor=\"k\")\n",
+    "eur.centroid.plot(ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dbfab5e7-5941-438a-baf2-e05369106004",
+   "metadata": {},
+   "source": [
+    "### Lat / long CRS\n",
+    "\n",
+    "- Long is x-coord\n",
+    "- Lat is y-coord\n",
+    "    - tells you where the point on Earth is\n",
+    "- **IMPORTANT**: degrees are not a unit of distance. 1 degree of longitute near the equator is a lot farther than moving 1 degree of longitute near the north pole\n",
+    "\n",
+    "Using `.crs` to access CRS of a gdf.\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89251896",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eur.crs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a6ba447-6f1a-4d31-8c33-ec1eeb9e0deb",
+   "metadata": {},
+   "source": [
+    "#### Single CRS doesn't work for the whole earth\n",
+    "\n",
+    "- Setting a different CRS for Europe that is based on meters.\n",
+    "- https://spatialreference.org/ref/?search=europe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5451e78f-ebaa-4bb3-af34-24ffe92d0582",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setting CRS to \"EPSG:3035\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "586038b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setting CRS to \"EPSG:3035\"\n",
+    "eur2 = eur.to_crs(\"EPSG:3035\")\n",
+    "eur2.crs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d46c124c-9aff-4c2f-81fe-08885b606800",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = eur2.plot(facecolor=\"lightgray\", edgecolor=\"k\")\n",
+    "eur2.centroid.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "045b9c33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = eur2.plot(facecolor=\"lightgray\", edgecolor=\"k\")\n",
+    "eur2.centroid.plot(ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0634941f",
+   "metadata": {},
+   "source": [
+    "#### How much error does lat/long computation introduce?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0b72aff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = eur2.plot(facecolor=\"lightgray\", edgecolor=\"k\")\n",
+    "eur2.centroid.plot(ax=ax, color=\"k\") # black => correct\n",
+    "eur.centroid.to_crs(\"EPSG:3035\").plot(ax=ax, color=\"r\")  # red => miscalculated"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ca9e306e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(eur2.iloc[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f489c88d-5964-4358-b8c4-fc80d28b5491",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(eur2).__mro__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85880c73",
+   "metadata": {},
+   "source": [
+    "#### Area of European countries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3e4874d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eur2.area # area in sq meters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "95f55824",
+   "metadata": {},
+   "source": [
+    "What is the area in **sq miles**?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "85ee20c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Conversion: / 1000 / 1000 / 2.59\n",
+    "(eur2.area / 1000 / 1000 / 2.59).sort_values(ascending=False)\n",
+    "# careful!  some countries (e.g., Russia) were cropped when we did intersection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cd600837",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# area on screen, not real area\n",
+    "eur.area"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "daf1245f-9939-468d-9a5f-34225c2dbc51",
+   "metadata": {},
+   "source": [
+    "### CRS\n",
+    "\n",
+    "- `<GeoDataFrame object>.crs`: gives you information about current CRS.\n",
+    "- `<GeoDataFrame object>.to_crs(<TARGET CRS>)`: changes CRS to `<TARGET CRS>`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9da3ee4c",
+   "metadata": {},
+   "source": [
+    "### Madison area emergency services\n",
+    "\n",
+    "- Data source: https://data-cityofmadison.opendata.arcgis.com/\n",
+    "    - Search for:\n",
+    "        - \"City limit\"\n",
+    "        - \"Lakes and rivers\"\n",
+    "        - \"Fire stations\"\n",
+    "        - \"Police stations\"\n",
+    "\n",
+    "- CRS for Madison area: https://en.wikipedia.org/wiki/Universal_Transverse_Mercator_coordinate_system#/media/File:Universal_Transverse_Mercator_zones.svg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a6f80847",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "city = gpd.read_file(\"City_Limit.zip\").to_crs(\"epsg:32616\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f8595be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "city.crs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ebd361f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "water = gpd.read_file(\"Lakes_and_Rivers.zip\").to_crs(city.crs)\n",
+    "fire = gpd.read_file(\"Fire_Stations.zip\").to_crs(city.crs)\n",
+    "police = gpd.read_file(\"Police_Stations.zip\").to_crs(city.crs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "723e2bff-545f-4a8c-9f72-8a9906a99b1b",
+   "metadata": {},
+   "source": [
+    "#### Run this on your virtual machine\n",
+    "\n",
+    "`sudo sh -c \"echo 'Options = UnsafeLegacyRenegotiation' >> /etc/ssl/openssl.cnf\"`\n",
+    "\n",
+    "then restart notebook!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6069860-7dd9-43f2-970e-fd15980135ef",
+   "metadata": {},
+   "source": [
+    "#### GeoJSON\n",
+    "\n",
+    "How to find the below URL?\n",
+    "\n",
+    "- Go to info page of a dataset, for example: https://data-cityofmadison.opendata.arcgis.com/datasets/police-stations/explore?location=43.081769%2C-89.391550%2C12.81\n",
+    "- Then click on \"I want to use this\" > \"View API Resources\" > \"GeoJSON\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a095d5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "url = \"https://maps.cityofmadison.com/arcgis/rest/services/Public/OPEN_DATA/MapServer/2/query?outFields=*&where=1%3D1&f=geojson\"\n",
+    "police2 = gpd.read_file(url).to_crs(city.crs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "248be81e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = city.plot(color=\"lightgray\")\n",
+    "water.plot(color=\"lightblue\", ax=ax)\n",
+    "fire.plot(color=\"red\", ax=ax, marker=\"+\", label=\"Fire\")\n",
+    "police2.plot(color=\"blue\", ax=ax, label=\"Police\")\n",
+    "ax.legend(loc=\"upper left\", frameon=False)\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a609d81",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fire.to_file(\"fire.geojson\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77600884",
+   "metadata": {},
+   "source": [
+    "### Geocoding: street address => lat / lon\n",
+    "\n",
+    "\n",
+    "- `gpd.tools.geocode(<street address>, provider=<geocoding service name>, user_agent=<user agent name>)`: converts street address into lat/long\n",
+    "\n",
+    "\n",
+    "#### Daily incident reports: https://www.cityofmadison.com/fire/daily-reports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b0b2aa0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "url = \"https://www.cityofmadison.com/fire/daily-reports\"\n",
+    "r = requests.get(url)\n",
+    "r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bee28b41",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r.raise_for_status() # give me an exception if not 200 (e.g., 404)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0bae00e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# doesn't work\n",
+    "# pd.read_html(url)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47173ec2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# print(r.text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39f166c5",
+   "metadata": {},
+   "source": [
+    "Find all **span** tags with **streetAddress** using regex."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ac7b9482-5512-49e4-b0b8-7f9ed34b5844",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# <p>1700 block Thierer Road<br>\n",
+    "# addrs = re.findall(r'<p>1700 block Thierer Road<br>', r.text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8e9b49d2-3e0d-4a39-9dcf-13b288a165e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "addrs = re.findall(r' <p>(.*?)<br>', r.text)\n",
+    "addrs = pd.Series(addrs)\n",
+    "addrs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4fc8ba0c-9d45-4251-8055-1310cabf15a9",
+   "metadata": {},
+   "source": [
+    "#### Without city name and state name, geocoding would return match with the most famous location with such a street name."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "095b9ebe-b583-4098-a3a2-47dd46610d59",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geo_info = gpd.tools.geocode(\"1300 East Washington Ave\")\n",
+    "geo_info"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e52ee0fc-d73c-4942-9bec-d1586a702f68",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geo_info[\"address\"].loc[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e80b714c-a962-4a47-b7cc-3a19d0da8ac0",
+   "metadata": {},
+   "source": [
+    "#### To get the correct address we want, we should concatenate \"Madison, Wisconsin\" to the end of the address."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf3f590b-8d00-46c4-ac57-6f2f61509f68",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geo_info = gpd.tools.geocode(\"1300 East Washington Ave, Madison, Wisconsin\")\n",
+    "geo_info"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3caf8c12-67a3-4e19-a758-d556d010eead",
+   "metadata": {},
+   "source": [
+    "#### Addresses with \"block\" often won't work or won't give you the correct lat/long. We need to remove the word \"block\" before geocoding."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "54103e4a-5ac2-4f19-811b-385c02623ed2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gpd.tools.geocode(\"800 block W. Johnson Street, Madison, Wisconsin\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "66072f4b-2286-4d66-ba2c-18ccd2e37ed6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gpd.tools.geocode(\"800 W. Johnson Street, Madison, Wisconsin\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf982302",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fixed_addrs = addrs.str.replace(\" block \", \" \") + \", Madison, WI\"\n",
+    "fixed_addrs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6a267ed-6876-4866-a66f-74390a3d4ee1",
+   "metadata": {},
+   "source": [
+    "#### Using a different provider than the default one\n",
+    "\n",
+    "- `gpd.tools.geocode(<street address>, provider=<geocoding service name>, user_agent=<user agent name>)`: converts street address into lat/long\n",
+    "    - We will be using \"OpenStreetMap\", for which the argument is \"nominatim\"\n",
+    "    - We also need to specify argument to `user_agent` parameter, indicating where the request is coming from; for example: \"cs320_bot\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab0e699f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "incidents = gpd.tools.geocode(fixed_addrs, provider=\"nominatim\", user_agent=\"cs320bot\").dropna()\n",
+    "incidents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ad73b2c-5171-45c2-a3f0-eef30f93c492",
+   "metadata": {},
+   "source": [
+    "It is often a good idea to drop na values. Although in this version of the example, there are no failed geocodings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "41a7d12e-73be-4442-b43c-285229e6cfdb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "incidents = incidents.dropna()\n",
+    "incidents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b30733e6-9491-4be0-bf20-bba64903334d",
+   "metadata": {},
+   "source": [
+    "#### Self-practice\n",
+    "\n",
+    "If you want practice with regex, try to write regular expression and use the match result to make sure that \"Madison\" and \"Wisconsin\" is part of each address."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "843bbba2-3de5-4cc0-b76b-92e7bebdd379",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# self-practice\n",
+    "for addr in incidents[\"address\"]:\n",
+    "    print(addr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1a04c2b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = city.plot(color=\"lightgray\")\n",
+    "water.plot(color=\"lightblue\", ax=ax)\n",
+    "fire.plot(color=\"red\", ax=ax, marker=\"+\", label=\"Fire\")\n",
+    "police2.plot(color=\"blue\", ax=ax, label=\"Police\")\n",
+    "incidents.to_crs(city.crs).plot(ax=ax, color=\"k\", label=\"Incidents\")\n",
+    "ax.legend(loc=\"upper left\", frameon=False)\n",
+    "ax.set_axis_off()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}