diff --git a/topic_10/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/topic_10/.ipynb_checkpoints/Untitled-checkpoint.ipynb
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index 0000000000000000000000000000000000000000..26b9d2ed1c2d4e47555e7bd14c130ae309a447ed
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@@ -0,0 +1,254 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# The Lifecycle of a Plot\n",
+    "\n",
+    "\n",
+    "This notebook is a summary that aims to show the beginning, middle, and end of a single\n",
+    "visualization using Matplotlib. We'll begin with some raw data and\n",
+    "end by saving a figure of a customized visualization. Along the way we'll try\n",
+    "to highlight some neat features and best-practices using Matplotlib.\n",
+    "\n",
+    "Our data\n",
+    "========\n",
+    "\n",
+    "We'll use the data from the post from which this tutorial was derived.\n",
+    "It contains sales information for a number of companies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sphinx_gallery_thumbnail_number = 10\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.ticker import FuncFormatter\n",
+    "\n",
+    "data = {'Barton LLC': 109438.50,\n",
+    "        'Frami, Hills and Schmidt': 103569.59,\n",
+    "        'Fritsch, Russel and Anderson': 112214.71,\n",
+    "        'Jerde-Hilpert': 112591.43,\n",
+    "        'Keeling LLC': 100934.30,\n",
+    "        'Koepp Ltd': 103660.54,\n",
+    "        'Kulas Inc': 137351.96,\n",
+    "        'Trantow-Barrows': 123381.38,\n",
+    "        'White-Trantow': 135841.99,\n",
+    "        'Will LLC': 104437.60}\n",
+    "group_data = list(data.values())\n",
+    "group_names = list(data.keys())\n",
+    "group_mean = np.mean(group_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Getting Started\n",
+    "=============\n",
+    "\n",
+    "First lets generate our figure containing out axes. Recall: the Figure is like a canvas, and the Axes is a part of that canvas on which we will make a particular visualization.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once we have our figure and axes, we can generate our plot within them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<BarContainer object of 10 artists>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.barh(group_names, group_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Things to notice:\n",
+    "* There is no heading on the plot.\n",
+    "* X-axis is missing a label.\n",
+    "* It is difficult to compare the bars because there is no backing grid to line up the bar ends.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Applying Style to a Plot\n",
+    "===================\n",
+    "\n",
+    "Matplotlib has a wide range of built-in style sheets that can be used to modify the appearance of plots. Styles are applied to the pyplot object and we must re-make plots between style changes to see the results.\n",
+    "\n",
+    "[Review the stylesheet reference](https://matplotlib.org/3.1.0/gallery/style_sheets/style_sheets_reference.html)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark-palette', 'seaborn-dark', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'seaborn', 'Solarize_Light2', 'tableau-colorblind10', '_classic_test']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(plt.style.available)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can activate a desired style using the `plyplot.style.use( ... )` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.style.use('seaborn-bright')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can re-make the plot using the new style."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<BarContainer object of 10 artists>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x396 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.barh(group_names, group_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The style controls many things, such as color, linewidths, backgrounds, etc. Not all styles apply to all types of plots. You should experiment to see how the appearance of different plot-types are affected."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Customizing a Plot\n",
+    "=================\n",
+    "\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/topic_10/.ipynb_checkpoints/lifecycle-checkpoint.ipynb b/topic_10/.ipynb_checkpoints/lifecycle-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..10d6bac39b1493a64522d43023cd92cb7b62538c
--- /dev/null
+++ b/topic_10/.ipynb_checkpoints/lifecycle-checkpoint.ipynb
@@ -0,0 +1,717 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# The Lifecycle of a Plot\n",
+    "\n",
+    "\n",
+    "This tutorial aims to show the beginning, middle, and end of a single\n",
+    "visualization using Matplotlib. We'll begin with some raw data and\n",
+    "end by saving a figure of a customized visualization. Along the way we'll try\n",
+    "to highlight some neat features and best-practices using Matplotlib.\n",
+    "\n",
+    ".. currentmodule:: matplotlib\n",
+    "\n",
+    "<div class=\"alert alert-info\"><h4>Note</h4><p>This tutorial is based off of\n",
+    "    `this excellent blog post <http://pbpython.com/effective-matplotlib.html>`_\n",
+    "    by Chris Moffitt. It was transformed into this tutorial by Chris Holdgraf.</p></div>\n",
+    "\n",
+    "A note on the Object-Oriented API vs. Pyplot\n",
+    "============================================\n",
+    "\n",
+    "Matplotlib has two interfaces. The first is an object-oriented (OO)\n",
+    "interface. In this case, we utilize an instance of :class:`axes.Axes`\n",
+    "in order to render visualizations on an instance of :class:`figure.Figure`.\n",
+    "\n",
+    "The second is based on MATLAB and uses a state-based interface. This is\n",
+    "encapsulated in the :mod:`.pyplot` module. See the :doc:`pyplot tutorials\n",
+    "</tutorials/introductory/pyplot>` for a more in-depth look at the pyplot\n",
+    "interface.\n",
+    "\n",
+    "Most of the terms are straightforward but the main thing to remember\n",
+    "is that:\n",
+    "\n",
+    "* The Figure is the final image that may contain 1 or more Axes.\n",
+    "* The Axes represent an individual plot (don't confuse this with the word\n",
+    "  \"axis\", which refers to the x/y axis of a plot).\n",
+    "\n",
+    "We call methods that do the plotting directly from the Axes, which gives\n",
+    "us much more flexibility and power in customizing our plot.\n",
+    "\n",
+    "<div class=\"alert alert-info\"><h4>Note</h4><p>In general, try to use the object-oriented interface over the pyplot\n",
+    "   interface.</p></div>\n",
+    "\n",
+    "Our data\n",
+    "========\n",
+    "\n",
+    "We'll use the data from the post from which this tutorial was derived.\n",
+    "It contains sales information for a number of companies.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sphinx_gallery_thumbnail_number = 10\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.ticker import FuncFormatter\n",
+    "\n",
+    "data = {'Barton LLC': 109438.50,\n",
+    "        'Frami, Hills and Schmidt': 103569.59,\n",
+    "        'Fritsch, Russel and Anderson': 112214.71,\n",
+    "        'Jerde-Hilpert': 112591.43,\n",
+    "        'Keeling LLC': 100934.30,\n",
+    "        'Koepp Ltd': 103660.54,\n",
+    "        'Kulas Inc': 137351.96,\n",
+    "        'Trantow-Barrows': 123381.38,\n",
+    "        'White-Trantow': 135841.99,\n",
+    "        'Will LLC': 104437.60}\n",
+    "group_data = list(data.values())\n",
+    "group_names = list(data.keys())\n",
+    "group_mean = np.mean(group_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Getting started\n",
+    "===============\n",
+    "\n",
+    "This data is naturally visualized as a barplot, with one bar per\n",
+    "group. To do this with the object-oriented approach, we'll first generate\n",
+    "an instance of :class:`figure.Figure` and\n",
+    ":class:`axes.Axes`. The Figure is like a canvas, and the Axes\n",
+    "is a part of that canvas on which we will make a particular visualization.\n",
+    "\n",
+    "<div class=\"alert alert-info\"><h4>Note</h4><p>Figures can have multiple axes on them. For information on how to do this,\n",
+    "   see the :doc:`Tight Layout tutorial\n",
+    "   </tutorials/intermediate/tight_layout_guide>`.</p></div>\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have an Axes instance, we can plot on top of it.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<BarContainer object of 10 artists>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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Wk7QeMAqYnsp3kvSZNLd/VK6tjrwNrJV7PznFgqT9gXU62Y6ZmVWJE38fERFvAvsB35d0CHAGcKKkucC/A99MVc8AGtJNf08Ap+WamQbcDUwFfhQRL6fyKcAFwDzgBeC2NsK4W9KC9LqJbLpgq9LNfcAPgD0kzQT2BXrLtw/MzCzxQ3p6uYgYkFv+X+AzudV7tVL/DbJee2uejogxrZQviYi2tim1O6qNVTu2eL9vbvlb7bVpZmbV5x6/mZlZgbjHXxARcV4b5Q8AD1QzFjMzqx0nfqupYUMG0ehnepuZVY2H+s3MzArEid/MzKxAnPjNzMwKxHP8VlNNC5upH3t3rcMws3a86PtwPlHc4zczMysQJ34zM7MCceI3MzMrECd+MzOzAnHiryFJi3LLB0h6RtLGZbQzStJdaflgSWO7Kb5rJB3Roqxe0rw26p8tab6keZLmSDquO+IwM7Pu47v6ewFJXwB+BewbERU90S4i7gDu6JbAukDSacA+wE4R8ZakQcCh1Y7DzMza5x5/jUnaHZgAHBgRz6Wy9SXdIml6eu2ayteUdHUqm5Uez9uyvRMkXZqWr5F0iaRHJT1f6r1LWknSZZIel3SXpP9u2bMvwznA1yLiLYCIaI6I31XYppmZdTP3+GurP3A7MCoi5ufKLwYuioiH09D/RGBL4HvAXyLiJElrA9Mk/bmDfdQBuwFbkI0E3Ax8CagHhgEbAE8CV5d7EJLWAtYqXbh0ov4YYAxAv4Hrl7tbMzMrgxN/bb0PPAqcDHwzV743sJWk0vuBKbnuCxws6exUvhrQ0T0B/xURy4EnJG2YynYDbkrlf5N0f4XHISA6WzkixgPjAfrXDe30dmZmVjkn/tpaDnwZ+LOkcyLiJ6l8JWCXiFiar6zsSuDwiHiqRfmGtO3dfNUWP7tFmtNfLGnTiHi+O9s2M7Pu5Tn+GouIJcBBwGhJJ6fiScDppTqShqfFicA30gUAkrYvc7cPA4enuf4NgVFltpN3PvBrSQNTbAPTkL6ZmfUiTvy9QES8CewHfD/dsHcG0CBprqQngNNS1R8BqwBz01fqflTmLm8BFgDzgCuAx4DmNupeIWlBek1JZZvnyhZIOhL4DXA/MD3F9iCwpMz4zMyshyjCU6xFJGlARCyStB4wDdg1Iv5W7Tj61w2NuuPHVXu3ZtYFfkhP7yJpRkQ0lLu95/iL6670zYBVgR/VIumbmVn1OfEXVESMqnUMZmZWfU78VlPDhgyi0cOIZmZV45v7zMzMCsSJ38zMrECc+M3MzArEc/xWU00Lm6kfe3etwzCzXsJfHex57vGbmZkViBO/mZlZgTjxm5mZFYgTv5mZWYE48VeRpEUVbHuCpEu7uM0Dkhpy7+vTA3SQ1CDpknLbbmefa0v6Wne0ZWZm3c+JvxdSpkc/m4hojIgzurNNSf2AtQEnfjOzXsqJvwYkfVvS9PTY3R+ksnpJT0q6DJgJfFrSiZKelvQgsGtu+/Ul3ZLamC5p1zZ21V4MoyTd1Ur5NZIul/RQ2vdBqbyfpAtzcX8l1879kv4ANAEXAJtJmi3pwnLOj5mZ9Rx/j7/KJO0LDAV2AgTcIWkP4K/A5sCJEfE1SXXAD4ARQDPZs+5npWYuBi6KiIclbQxMBLZsY5fXS1qallcFlncizHrg88BmwP2SPgscBzRHxI6S+gOPSJqU6u8EbBMRL0iqT8vD2zkHY4AxAP0Grt+JcMzMrLs48VffvulVSuIDyC4E/gq8FBFTU/nOwAMR8TqApBuBz6V1ewNbSSq1OVDSWhHxdiv7Gx0RjamNeuBjvfxW/CkilgPPSHoe2CLFvK2kI1KdQSnu94BpEfFCJ9oFICLGA+MB+tcNjc5uZ2ZmlXPirz4B50fEFR8pzJLy4hZ120qKKwG7RMTSfKGkicCGQGNEnFJBjC33G2RxfyMiJrbY5yg+HreZmfVSnuOvvonASZIGAEgaImmDVuo9BoyStJ6kVYAjc+smAaeX3kgaDhARX4yI4RUmfYAjJa0kaTNgU+CpFPdXUyxI+pykNVvZ9m1grQr3b2ZmPcQ9/iqRtDLwbkRMkrQlMCUN1S8CjgWW5etHxCuSzgOmAK+Q3fDXL60+A/i1pLlkn+Fk4LRuDPcp4EGy0YPTIuIdSVeSzf3PVBb468ChLTeMiL9LeiR9bfCeiPh2N8ZlZmYVUoSnWKtB0nbAhIjYqdaxtEfSNcBdEXFzNfbXv25o1B0/rhq7MrM+wA/p6ZikGRHR0HHN1nmovwoknQbcAHy/1rGYmVmxeai/CiLicuDyWsfRGRFxQq1jMDOznuPEbzU1bMggGj20Z2ZWNR7qNzMzKxAnfjMzswJx4jczMysQz/FbTTUtbKZ+7N21DsPMegF/la863OM3MzMrECd+MzOzAnHiNzMzKxAnfjMzswLpMPFLWiZpdu5V30qdT0m6OS0Pl3RAOcFIekBSl/7+cC6+eZLulLR2Ofvu4j7r00NoqtqupG9JekfSoDLaPkHSpZVFaGZmfV1nevxL06NeS68X8yslrRwRL0fEEaloOFBW4i9TKb5tgDeBr1dx39V2DDAdOKynd5SeJmhmZp8wZQ31p97jTZLuBCaVeqqSVgV+CByVeuFHSfp8brRglqS1UhvfkdQkaY6kC3LNHylpmqSnJe3exdCmAENS+6Mk3ZWL+VJJJ6TlCyQ9IWmupJ+nsiPTMcyRNDmV9ZN0oaTpqe5XOjgvAyTdJ2lmOrZDUnm9pCclTZD0uKRJklZP60akfU6hnYsWSZsBA8ge9HNMrvwESbdK+h9Jz0j6WW7diek8PgjsmitfX9It6bimS9o1lZ8nabykScC1krZOn8XsdPxDU72z0rmaJ+nMjo7RzMx6j8706laXNDstvxARpd7mLsC2EfFmafg/It6TdC7QEBGnA6SLg69HxCOSBgDvSNqf7FnuO0fEEknr5mOKiJ3SdMF/AHt35kAk9QO+AFzVQb11yXrMW0RE5KYGzgW+GBELc2UnA80RsaOk/sAjKSm29Szjd4DDIuItSYOBqZLuSOuGAsdExKmS/gQcDvwe+C3wjYh4UNKF7YR+DNkT/h4CNpe0QUS8ltYNB7YH3gWekvQr4APgB8AIoBm4H5iV6l8MXBQRD0vaGJgIbJnWjQB2i4ilqZ2LI+L6dFHXT9II4ERgZ0DAY+nC4h/tHKOZmfUSXR3qzw8x3xsRb3Zi+0eAX0o6A1g7Ij4gS+a/jYglAC3auTX9nAHUd6L90oXJ34F1gXs7qP8WWYK+UtKXgCW5OK+RdCrQL5XtCxyX2n8MWI8subVFwE8kzQX+TDb6sGFa90JElC6gZgD1aa5+7Yh4MJVf107bRwN/jIjlZOfoyNy6+yKiOSLeAZ4ANiFLzA9ExOsR8R5wY67+3sCl6bjuAAaWRmKAOyJiaVqeApwj6f8Cm6Ty3YDbImJxRCxKsZRGZj52jK2eJGmMpEZJjcuWNLdzyGZm1t0quat/cWcqRcQFwCnA6mQ94C3IEmRbveZ3089ldG5EYmlEDCdLdquyYrj8Az56fKuleD4AdgJuIRt1+J9UfhrZMPqngdmS1ktxfiN34fOZiJjUTiyjgfWBESmmV0v7zR1X/tjaOw8fkrQt2QXHvZJeJLsIOCZXpbW2aaftlYBdcsc1JCLeTus+/Fwj4g/AwcBSYKKkvVLMbWkrjo+IiPER0RARDf3W6PJ9imZmVoGe+Drf20Cp94ikzSKiKSJ+CjQCWwCTgJMkrZHqrNtqSyvaGCLpvvbqREQzcAZwtqRVgJeArST1Tz3rL6S2BgCDIuK/gTPJhslLcT4WEecCb5BdAEwEvpraQ9LnJK3ZThiDgNci4n1Je5JdjLQX8z+BZkm7paLRbVQ9BjgvIurT61PAEEnttf8YMErSein+/AjBJOD00htJw1trQNKmwPMRcQnZyMC2wGTgUElrpHNxGNn0g5mZ9QE9cef2/cDYNIx8PrBbSoLLyIah74mId1OyaZT0HvDfwDnttFlH1oNvV0TMkjQHODoirkvzzHOBZ1gxv70WcLuk1ch6r99K5Remm9cE3AfMSdvWAzMlCXidbJSgLdcDd0pqBGYD8zuKmWy+/GpJS8guNFpzNLB/i7LbUvmrrW0QEa9IOo9suP4VYCYrpjDOAH6dpiRWJkvmp7XSzFHAsZLeB/4G/DDd03ENMC3VuTKd9/r2D9PMzHoDRXQ40lxzkk4H/hoRd3RY2fqU/nVDo+74cbUOw8x6AT+kp3MkzYiILv3Nm7w+8V3tiPAfnjEzM+sG/pO9ZmZmBeLEb2ZmViB9YqjfPrmGDRlEo+f1zMyqxj1+MzOzAnHiNzMzKxAnfjMzswLxHL/VVNPCZurH3l3rMMysDP7efd/kHr+ZmVmBOPGbmZkViBO/mZlZgTjxm5mZFUhNE7+kZZJm5171PbCPgyWN7aDOKEl3tSi7RtIRaflKSVul5RclDU7Li7o73nZifEDSxx7KIOkgSbMkzZH0hKSvtNNGvaR5FcbxaBvl+fN1ZumRy2Zm1rvU+q7+pRHR6rPgASStHBEdPo63PemJfhU91S8iTqlk+54iaRVgPLBTRCyQ1J/sMcI9JiJGdqLamcDvgSU9GYuZmXVdrxvql3SCpJsk3QlMkjRA0n2SZkpqknRIqlcvaX7qjc+TdL2kvSU9IukZSTvl2qvo6X5t9bZz6+skTU6jFvMk7d5KnXMlTU/rx0tSru2fSpom6enStpJWl/RHSXMl3Qis3squ1yK7ePs7QES8GxFPpe03lHRbGgmYI6mUsPtJmiDpcUmTJK2ei+OidBxPStpR0q3pXP5n7jgWpZ+SdGkaZbgb2CCVnwF8Crhf0v1dO9NmZtbTap34V88N89+WK98FOD4i9gLeAQ6LiB2APYFflJIm8FngYmBbYAvg34DdgLOBc7oYy+75aQfg4C5s+2/AxDR6sR0wu5U6l0bEjhGxDVkSPyi3buWI2Imsp/wfqeyrwJKI2Bb4MTCiZYMR8SbZaMZLkm6QNFpS6TO9BHgwIrYDdgAeT+VDgV9HxNbAP4HDc02+FxF7AJcDtwNfB7YBTpC0XovdHwZsDgwDTgVGppguAV4G9oyIPVs7WZLGSGqU1LhsSXNrVczMrIf01qH+e1NSAxDwE0l7AMuBIcCGad0LEdEEIOlx4L6ICElNdH3I+6GI+DAZS7qmC9tOB65OQ+//FRGtJf49JX0HWANYlywR35nW3Zp+zsjFvQdZ8iYi5kqa29qOI+IUScOAvckuePYBTgD2Ao5LdZYBzZLWITtnpfjy+4MVUyJNwOMR8QqApOeBT5NGFnLx3ZDaflnSX1o9M63HPJ5sioL+dUOjs9uZmVnlat3jb8vi3PJoYH1gRLpIeBVYLa17N1dvee79cqp4URMRk8kS4ULgOknH5ddLWg24DDgiIoYBE1hxDLAi7mV8NO5OJcWIaIqIi8iS/uEdVM+fs5b7y5+/lue2tfPppG1m1sf01sSfNwh4LSLel7QnsEm5DUk6TNL53Rfah+1uQhbjBOAqsqH1vFKSf0PSAOCITjQ7meyiB0nbkE1ntNzvAEmjckXDgZfS8n1k0wVI6idpYOeOptMmA0entuvIpmFK3ia7/8DMzHqZWg/1d8b1wJ2SGsnmzudX0NZmwFvdEtVHjQK+Lel9YBFpiL0kIv4paQLZEPqLZFMDHfkN8Ns0xD8bmNZKHQHfkXQFsJRspOSEtO6bwHhJJ5P17L8KvNKlo2rfbWTTCU3A08CDuXXjgXskvdLWPL+ZmdWGIoozWivp98C3IuL1Wsdimf51Q6Pu+HG1DsPMyuCH9NSGpBkR0eY3zTrSF3r83SYijq11DGZmZrXUF+b4zczMrJsUqsdvvc+wIYNo9HChmVnVuMdvZmZWIE78ZmZmBeLEb2ZmViCe47eaalrYTP3Yu2sdhplVkb8GWFvu8ZuZmRWIE7+ZmVmBOPGbmZkViBO/mZlZgTjx9yKSlkmaLWmOpJmSRpbRxjndGM95ks5upXxRG/WPkzRP0uOSnmhtWzMzqy0n/t5laUQMj4jtgO8CnX6EsDIrAd2W+LtC0v7AmcC+EbE12aOJm2sRi5mZtc2Jv/caCPwDQNIASfelUYAmSYek8npJT0q6DJgJXAWsnoWjVDkAAAfMSURBVEYNrk91zkq98HmSzmyx3YTUO58kafUK4/0ucHZEvAwQEe9ExIQK2zQzs27m7/H3LqtLmg2sBtSRPe8e4B3gsIh4S9JgYKqkO9K6zYETI+JrAJKOjIjhaXkEcCKwMyDgMUkPkl1QDAWOiYhTJf0JOBz4fQWxbwPM6ExFSWOAMQD9Bq5fwS7NzKyr3OPvXUpD/VsA+wHXShJZ0v6JpLnAn4EhwIZpm5ciYmob7e0G3BYRiyNiEXArsHta90JEzE7LM4D67j+c1kXE+IhoiIiGfmsMqtZuzcwMJ/5eKyKmAIOB9YHR6eeI1Jt/lWxUAGBxO82onXXv5paXUfnoz+PAiArbMDOzHubE30tJ2gLoB/wdGAS8FhHvS9oT2KSdTd+XtEpangwcKmkNSWsChwEP9VDI5wM/k/QvKf7+ks7ooX2ZmVmZPMffu5Tm+CHrrR8fEcvSjXp3SmoEZgPz22ljPDBX0syIGC3pGmBaWndlRMySVN+FmL5fuikQICI2AtaQtCBX55cR8UtJGwJ/TtMTAVzdhf2YmVkVKCJqHYMVWP+6oVF3/Lhah2FmVeSH9FRG0oyIaCh3ew/1m5mZFYgTv5mZWYF4jt9qatiQQTR62M/MrGrc4zczMysQJ34zM7MCceI3MzMrECd+MzOzAnHiNzMzKxAnfjMzswJx4jczMysQJ34zM7MCceI3MzMrED+kx2pK0tvAU7WOowyDgTdqHUQZ+mrc0Hdj76txQ9+Nva/GDZ2LfZOIWL/cHfhP9lqtPVXJU6ZqRVKj466uvhp7X40b+m7sfTVuqE7sHuo3MzMrECd+MzOzAnHit1obX+sAyuS4q6+vxt5X44a+G3tfjRuqELtv7jMzMysQ9/jNzMwKxInfzMysQJz4rSYk7SfpKUnPShpboxg+Lel+SU9KelzSN1P5upLulfRM+rlOKpekS1LMcyXtkGvr+FT/GUnH58pHSGpK21wiSd0Yfz9JsyTdld5/RtJjKYYbJa2ayvun98+m9fW5Nr6byp+S9MVceY99PpLWlnSzpPnp3O/SF865pG+lfyfzJN0gabXees4lXS3pNUnzcmU9fo7b2keFcV+Y/q3MlXSbpLVz67p0Lsv5vCqJPbfubEkhaXB6X9tzHhF++VXVF9APeA7YFFgVmANsVYM46oAd0vJawNPAVsDPgLGpfCzw07R8AHAPIOBfgcdS+brA8+nnOml5nbRuGrBL2uYeYP9ujP8s4A/AXen9n4Cj0/LlwFfT8teAy9Py0cCNaXmrdO77A59Jn0m/nv58gN8Bp6TlVYG1e/s5B4YALwCr5871Cb31nAN7ADsA83JlPX6O29pHhXHvC6ycln+ai7vL57Krn1elsafyTwMTgZeAwb3hnFf1F61ffkUE6R/vxNz77wLf7QVx3Q7sQ/aXBOtSWR3ZHxkCuAI4Jlf/qbT+GOCKXPkVqawOmJ8r/0i9CmPdCLgP2Au4K/0yeCP3C/LDc5x+6eySlldO9dTyvJfq9eTnAwwkS6BqUd6rzzlZ4v/f9At55XTOv9ibzzlQz0cTaI+f47b2UUncLdYdBlzf2jnq6FyW83+kO2IHbga2A15kReKv6Tn3UL/VQumXaMmCVFYzaWhve+AxYMOIeAUg/dwgVWsr7vbKF7RS3h3GAd8Blqf36wH/jIgPWtnXh/Gl9c2pflePpztsCrwO/FbZNMWVktakl5/ziFgI/Bz4K/AK2TmcQd845yXVOMdt7aO7nETW2y0n7nL+j1RE0sHAwoiY02JVTc+5E7/VQmtzrjX7XqmkAcAtwJkR8VZ7VVspizLKKyLpIOC1iJjRidjaW1fVuJOVyYZDfxMR2wOLyYYn29IrYk/zpoeQDSl/ClgT2L+dffWKuDupT8Qq6XvAB8D1paI24ign7m4/JklrAN8Dzm1tdRv7q8o5d+K3WlhANu9VshHwci0CkbQKWdK/PiJuTcWvSqpL6+uA11J5W3G3V75RK+WV2hU4WNKLwB/JhvvHAWtLKj1/I7+vD+NL6wcBb5ZxPN1hAbAgIh5L728muxDo7ed8b+CFiHg9It4HbgVG0jfOeUk1znFb+6hIusntIGB0pDHtMuJ+g65/XpXYjOxCcU76v7oRMFPSv5QRe/ee80rmkPzyq5wXWa/v+fSfonTzzdY1iEPAtcC4FuUX8tGbZX6Wlg/kozfkTEvl65LNW6+TXi8A66Z101Pd0g05B3TzMYxixc19N/HRG5e+lpa/zkdvXPpTWt6aj94c9TzZjVE9+vkADwGbp+Xz0vnu1ecc2Bl4HFgjtfs74Bu9+Zzz8Tn+Hj/Hbe2jwrj3A54A1m9Rr8vnsqufV6Wxt1j3Iivm+Gt6znv8l6tffrX2Irur9Wmyu2+/V6MYdiMbLpsLzE6vA8jm9u4Dnkk/S//xBPw6xdwENOTaOgl4Nr1OzJU3APPSNpdSxg1DHRzDKFYk/k3J7vx9Nv2C65/KV0vvn03rN81t/70U21Pk7n7vyc8HGA40pvP+X+kXXK8/58APgPmp7evIEk6vPOfADWT3IrxP1ls8uRrnuK19VBj3s2Tz3qX/o5eXey7L+bwqib3F+hdZkfhres79J3vNzMwKxHP8ZmZmBeLEb2ZmViBO/GZmZgXixG9mZlYgTvxmZmYF4sRvZmZWIE78ZmZmBfL/AfD7Qx/nxgRaAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.barh(group_names, group_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Controlling the style\n",
+    "=====================\n",
+    "\n",
+    "There are many styles available in Matplotlib in order to let you tailor\n",
+    "your visualization to your needs. To see a list of styles, we can use\n",
+    ":mod:`.style`.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark-palette', 'seaborn-dark', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'seaborn', 'Solarize_Light2', 'tableau-colorblind10', '_classic_test']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(plt.style.available)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can activate a style with the following:\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.style.use('fivethirtyeight')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's remake the above plot to see how it looks:\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<BarContainer object of 10 artists>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.barh(group_names, group_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The style controls many things, such as color, linewidths, backgrounds,\n",
+    "etc.\n",
+    "\n",
+    "Customizing the plot\n",
+    "====================\n",
+    "\n",
+    "Now we've got a plot with the general look that we want, so let's fine-tune\n",
+    "it so that it's ready for print. First let's rotate the labels on the x-axis\n",
+    "so that they show up more clearly. We can gain access to these labels\n",
+    "with the :meth:`axes.Axes.get_xticklabels` method:\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.barh(group_names, group_data)\n",
+    "labels = ax.get_xticklabels()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we'd like to set the property of many items at once, it's useful to use\n",
+    "the :func:`pyplot.setp` function. This will take a list (or many lists) of\n",
+    "Matplotlib objects, and attempt to set some style element of each one.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.barh(group_names, group_data)\n",
+    "labels = ax.get_xticklabels()\n",
+    "plt.setp(labels, rotation=45, horizontalalignment='right')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It looks like this cut off some of the labels on the bottom. We can\n",
+    "tell Matplotlib to automatically make room for elements in the figures\n",
+    "that we create. To do this we'll set the ``autolayout`` value of our\n",
+    "rcParams. For more information on controlling the style, layout, and\n",
+    "other features of plots with rcParams, see\n",
+    ":doc:`/tutorials/introductory/customizing`.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.rcParams.update({'figure.autolayout': True})\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.barh(group_names, group_data)\n",
+    "labels = ax.get_xticklabels()\n",
+    "plt.setp(labels, rotation=45, horizontalalignment='right')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we'll add labels to the plot. To do this with the OO interface,\n",
+    "we can use the :meth:`axes.Axes.set` method to set properties of this\n",
+    "Axes object.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0, 0.5, 'Company'),\n",
+       " (-10000, 140000),\n",
+       " Text(0.5, 0, 'Total Revenue'),\n",
+       " Text(0.5, 1.0, 'Company Revenue')]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.barh(group_names, group_data)\n",
+    "labels = ax.get_xticklabels()\n",
+    "plt.setp(labels, rotation=45, horizontalalignment='right')\n",
+    "ax.set(xlim=[-10000, 140000], xlabel='Total Revenue', ylabel='Company',\n",
+    "       title='Company Revenue')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also adjust the size of this plot using the :func:`pyplot.subplots`\n",
+    "function. We can do this with the ``figsize`` kwarg.\n",
+    "\n",
+    "<div class=\"alert alert-info\"><h4>Note</h4><p>While indexing in NumPy follows the form (row, column), the figsize\n",
+    "   kwarg follows the form (width, height). This follows conventions in\n",
+    "   visualization, which unfortunately are different from those of linear\n",
+    "   algebra.</p></div>\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0, 0.5, 'Company'),\n",
+       " (-10000, 140000),\n",
+       " Text(0.5, 0, 'Total Revenue'),\n",
+       " Text(0.5, 1.0, 'Company Revenue')]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8, 4))\n",
+    "ax.barh(group_names, group_data)\n",
+    "labels = ax.get_xticklabels()\n",
+    "plt.setp(labels, rotation=45, horizontalalignment='right')\n",
+    "ax.set(xlim=[-10000, 140000], xlabel='Total Revenue', ylabel='Company',\n",
+    "       title='Company Revenue')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For labels, we can specify custom formatting guidelines in the form of\n",
+    "functions by using the :class:`ticker.FuncFormatter` class. Below we'll\n",
+    "define a function that takes an integer as input, and returns a string\n",
+    "as an output.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def currency(x, pos):\n",
+    "    \"\"\"The two args are the value and tick position\"\"\"\n",
+    "    if x >= 1e6:\n",
+    "        s = '${:1.1f}M'.format(x*1e-6)\n",
+    "    else:\n",
+    "        s = '${:1.0f}K'.format(x*1e-3)\n",
+    "    return s\n",
+    "\n",
+    "formatter = FuncFormatter(currency)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then apply this formatter to the labels on our plot. To do this,\n",
+    "we'll use the ``xaxis`` attribute of our axis. This lets you perform\n",
+    "actions on a specific axis on our plot.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6, 8))\n",
+    "ax.barh(group_names, group_data)\n",
+    "labels = ax.get_xticklabels()\n",
+    "plt.setp(labels, rotation=45, horizontalalignment='right')\n",
+    "\n",
+    "ax.set(xlim=[-10000, 140000], xlabel='Total Revenue', ylabel='Company',\n",
+    "       title='Company Revenue')\n",
+    "ax.xaxis.set_major_formatter(formatter)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Combining multiple visualizations\n",
+    "=================================\n",
+    "\n",
+    "It is possible to draw multiple plot elements on the same instance of\n",
+    ":class:`axes.Axes`. To do this we simply need to call another one of\n",
+    "the plot methods on that axes object.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8, 8))\n",
+    "ax.barh(group_names, group_data)\n",
+    "labels = ax.get_xticklabels()\n",
+    "plt.setp(labels, rotation=45, horizontalalignment='right')\n",
+    "\n",
+    "# Add a vertical line, here we set the style in the function call\n",
+    "ax.axvline(group_mean, ls='--', color='r')\n",
+    "\n",
+    "# Annotate new companies\n",
+    "for group in [3, 5, 8]:\n",
+    "    ax.text(145000, group, \"New Company\", fontsize=10,\n",
+    "            verticalalignment=\"center\")\n",
+    "\n",
+    "# Now we'll move our title up since it's getting a little cramped\n",
+    "ax.title.set(y=1.05)\n",
+    "\n",
+    "ax.set(xlim=[-10000, 140000], xlabel='Total Revenue', ylabel='Company',\n",
+    "       title='Company Revenue')\n",
+    "ax.xaxis.set_major_formatter(formatter)\n",
+    "ax.set_xticks([0, 25e3, 50e3, 75e3, 100e3, 125e3])\n",
+    "fig.subplots_adjust(right=.1)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Saving our plot\n",
+    "===============\n",
+    "\n",
+    "Now that we're happy with the outcome of our plot, we want to save it to\n",
+    "disk. There are many file formats we can save to in Matplotlib. To see\n",
+    "a list of available options, use:\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'ps': 'Postscript', 'eps': 'Encapsulated Postscript', 'pdf': 'Portable Document Format', 'pgf': 'PGF code for LaTeX', 'png': 'Portable Network Graphics', 'raw': 'Raw RGBA bitmap', 'rgba': 'Raw RGBA bitmap', 'svg': 'Scalable Vector Graphics', 'svgz': 'Scalable Vector Graphics', 'jpg': 'Joint Photographic Experts Group', 'jpeg': 'Joint Photographic Experts Group', 'tif': 'Tagged Image File Format', 'tiff': 'Tagged Image File Format'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(fig.canvas.get_supported_filetypes())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then use the :meth:`figure.Figure.savefig` in order to save the figure\n",
+    "to disk. Note that there are several useful flags we'll show below:\n",
+    "\n",
+    "* ``transparent=True`` makes the background of the saved figure transparent\n",
+    "  if the format supports it.\n",
+    "* ``dpi=80`` controls the resolution (dots per square inch) of the output.\n",
+    "* ``bbox_inches=\"tight\"`` fits the bounds of the figure to our plot.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Uncomment this line to save the figure.\n",
+    "# fig.savefig('sales.png', transparent=False, dpi=80, bbox_inches=\"tight\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/topic_10/.ipynb_checkpoints/lifecycle_of_plot-checkpoint.ipynb b/topic_10/.ipynb_checkpoints/lifecycle_of_plot-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..26b9d2ed1c2d4e47555e7bd14c130ae309a447ed
--- /dev/null
+++ b/topic_10/.ipynb_checkpoints/lifecycle_of_plot-checkpoint.ipynb
@@ -0,0 +1,254 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# The Lifecycle of a Plot\n",
+    "\n",
+    "\n",
+    "This notebook is a summary that aims to show the beginning, middle, and end of a single\n",
+    "visualization using Matplotlib. We'll begin with some raw data and\n",
+    "end by saving a figure of a customized visualization. Along the way we'll try\n",
+    "to highlight some neat features and best-practices using Matplotlib.\n",
+    "\n",
+    "Our data\n",
+    "========\n",
+    "\n",
+    "We'll use the data from the post from which this tutorial was derived.\n",
+    "It contains sales information for a number of companies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sphinx_gallery_thumbnail_number = 10\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.ticker import FuncFormatter\n",
+    "\n",
+    "data = {'Barton LLC': 109438.50,\n",
+    "        'Frami, Hills and Schmidt': 103569.59,\n",
+    "        'Fritsch, Russel and Anderson': 112214.71,\n",
+    "        'Jerde-Hilpert': 112591.43,\n",
+    "        'Keeling LLC': 100934.30,\n",
+    "        'Koepp Ltd': 103660.54,\n",
+    "        'Kulas Inc': 137351.96,\n",
+    "        'Trantow-Barrows': 123381.38,\n",
+    "        'White-Trantow': 135841.99,\n",
+    "        'Will LLC': 104437.60}\n",
+    "group_data = list(data.values())\n",
+    "group_names = list(data.keys())\n",
+    "group_mean = np.mean(group_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Getting Started\n",
+    "=============\n",
+    "\n",
+    "First lets generate our figure containing out axes. Recall: the Figure is like a canvas, and the Axes is a part of that canvas on which we will make a particular visualization.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once we have our figure and axes, we can generate our plot within them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<BarContainer object of 10 artists>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.barh(group_names, group_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Things to notice:\n",
+    "* There is no heading on the plot.\n",
+    "* X-axis is missing a label.\n",
+    "* It is difficult to compare the bars because there is no backing grid to line up the bar ends.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Applying Style to a Plot\n",
+    "===================\n",
+    "\n",
+    "Matplotlib has a wide range of built-in style sheets that can be used to modify the appearance of plots. Styles are applied to the pyplot object and we must re-make plots between style changes to see the results.\n",
+    "\n",
+    "[Review the stylesheet reference](https://matplotlib.org/3.1.0/gallery/style_sheets/style_sheets_reference.html)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark-palette', 'seaborn-dark', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'seaborn', 'Solarize_Light2', 'tableau-colorblind10', '_classic_test']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(plt.style.available)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can activate a desired style using the `plyplot.style.use( ... )` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.style.use('seaborn-bright')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can re-make the plot using the new style."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<BarContainer object of 10 artists>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x396 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.barh(group_names, group_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The style controls many things, such as color, linewidths, backgrounds, etc. Not all styles apply to all types of plots. You should experiment to see how the appearance of different plot-types are affected."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Customizing a Plot\n",
+    "=================\n",
+    "\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/topic_10/examples/acceleration_line_plot.py b/topic_10/examples/acceleration_line_plot.py
new file mode 100644
index 0000000000000000000000000000000000000000..4fe459659b807f35caff6f79a5679700602c875d
--- /dev/null
+++ b/topic_10/examples/acceleration_line_plot.py
@@ -0,0 +1,54 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Jun 30 10:45:44 2020
+
+@author: mwelch8
+"""
+
+#Import the data from the dataset.csv
+
+import csv
+import matplotlib.pyplot as plt
+import numpy as np
+
+# The segment that we want to analyse 
+# Try changing this to 1000, 1850 to view a single 'cycle'
+segment_to_view_start = 1000
+segment_to_view_end = 1850
+
+# Create empty 3D np.arrays to store the accelerometer and gyroscope data 
+ax_set = np.empty((0,3), float)
+gx_set = np.empty((0,3), float)
+
+
+# Read the data line-by-line into the 3D arrays
+with open('imu_dataset.csv', newline='') as f:
+    reader = csv.reader(f)
+    for row in reader:
+        #We are only interested in the accelerometer data
+        temp_Arr = np.array([float(row[1]), float(row[2]),float(row[3]),
+                             float(row[4]), float(row[5]),float(row[6])])
+        ax_set = np.append(ax_set,[temp_Arr[0:3]], axis = 0);
+        gx_set = np.append(gx_set, [temp_Arr[3:6]], axis = 0)
+
+
+# Plot the acceleration data
+fig, ax = plt.subplots(nrows=2,figsize=(30,20))
+ax[0].set_title('Accelerometer Data')      
+ax[0].plot(ax_set[segment_to_view_start:segment_to_view_end,0], 'r-')
+ax[0].plot(ax_set[segment_to_view_start:segment_to_view_end,1], 'g-')
+ax[0].plot(ax_set[segment_to_view_start:segment_to_view_end,2], 'b-')
+ax[0].set_xlabel('Time')
+ax[0].set_ylabel('Acceleration')
+
+# Plot the gyroscope data
+ax[1].set_title('Gyroscope Data')
+ax[1].plot(gx_set[segment_to_view_start:segment_to_view_end,0], 'r-')
+ax[1].plot(gx_set[segment_to_view_start:segment_to_view_end,1], 'g-')
+ax[1].plot(gx_set[segment_to_view_start:segment_to_view_end,2], 'b-')
+ax[1].set_xlabel('Time')
+ax[1].set_ylabel('Angle')
+
+
+
+
diff --git a/topic_10/examples/imu_dataset.csv b/topic_10/examples/imu_dataset.csv
new file mode 100644
index 0000000000000000000000000000000000000000..16eae5fa48c95e7f1aa214bbf5d05fa510baaa15
--- /dev/null
+++ b/topic_10/examples/imu_dataset.csv
@@ -0,0 +1,5001 @@
+1,-14624,-4184,-4528,4,26,-39
+2,-15016,-4016,-5256,-20,-45,-90
+3,-14328,-4680,-4808,22,13,-33
+4,-14400,-4320,-4728,28,5,-16
+5,-15152,-4840,-4960,-4,6,4
+6,-14744,-4264,-4712,0,7,-19
+7,-14736,-4504,-4536,-9,25,-9
+8,-14640,-4552,-4936,-1,19,9
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diff --git a/topic_10/examples/lifecycle_of_plot.ipynb b/topic_10/examples/lifecycle_of_plot.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..f775cce0e5e3f6021dfbcca26cce1b8bb186fd03
--- /dev/null
+++ b/topic_10/examples/lifecycle_of_plot.ipynb
@@ -0,0 +1,672 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# The Lifecycle of a Plot\n",
+    "\n",
+    "\n",
+    "This notebook is a summary that aims to show the beginning, middle, and end of a single\n",
+    "visualization using Matplotlib. We'll begin with some raw data and\n",
+    "end by saving a figure of a customized visualization. Along the way we'll try\n",
+    "to highlight some neat features and best-practices using Matplotlib.\n",
+    "\n",
+    "Our data\n",
+    "========\n",
+    "\n",
+    "We'll use the data from the post from which this tutorial was derived.\n",
+    "It contains sales information for a number of companies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sphinx_gallery_thumbnail_number = 10\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.ticker import FuncFormatter\n",
+    "\n",
+    "data = {'Barton LLC': 109438.50,\n",
+    "        'Frami, Hills and Schmidt': 103569.59,\n",
+    "        'Fritsch, Russel and Anderson': 112214.71,\n",
+    "        'Jerde-Hilpert': 112591.43,\n",
+    "        'Keeling LLC': 100934.30,\n",
+    "        'Koepp Ltd': 103660.54,\n",
+    "        'Kulas Inc': 137351.96,\n",
+    "        'Trantow-Barrows': 123381.38,\n",
+    "        'White-Trantow': 135841.99,\n",
+    "        'Will LLC': 104437.60}\n",
+    "group_data = list(data.values())\n",
+    "group_names = list(data.keys())\n",
+    "group_mean = np.mean(group_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Getting Started\n",
+    "=============\n",
+    "\n",
+    "First lets generate our figure containing out axes. Recall: the Figure is like a canvas, and the Axes is a part of that canvas on which we will make a particular visualization.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once we have our figure and axes, we can generate our plot within them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<BarContainer object of 10 artists>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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Wk7QeMAqYnsp3kvSZNLd/VK6tjrwNrJV7PznFgqT9gXU62Y6ZmVWJE38fERFvAvsB35d0CHAGcKKkucC/A99MVc8AGtJNf08Ap+WamQbcDUwFfhQRL6fyKcAFwDzgBeC2NsK4W9KC9LqJbLpgq9LNfcAPgD0kzQT2BXrLtw/MzCzxQ3p6uYgYkFv+X+AzudV7tVL/DbJee2uejogxrZQviYi2tim1O6qNVTu2eL9vbvlb7bVpZmbV5x6/mZlZgbjHXxARcV4b5Q8AD1QzFjMzqx0nfqupYUMG0ehnepuZVY2H+s3MzArEid/MzKxAnPjNzMwKxHP8VlNNC5upH3t3rcMws3a86PtwPlHc4zczMysQJ34zM7MCceI3MzMrECd+MzOzAnHiryFJi3LLB0h6RtLGZbQzStJdaflgSWO7Kb5rJB3Roqxe0rw26p8tab6keZLmSDquO+IwM7Pu47v6ewFJXwB+BewbERU90S4i7gDu6JbAukDSacA+wE4R8ZakQcCh1Y7DzMza5x5/jUnaHZgAHBgRz6Wy9SXdIml6eu2ayteUdHUqm5Uez9uyvRMkXZqWr5F0iaRHJT1f6r1LWknSZZIel3SXpP9u2bMvwznA1yLiLYCIaI6I31XYppmZdTP3+GurP3A7MCoi5ufKLwYuioiH09D/RGBL4HvAXyLiJElrA9Mk/bmDfdQBuwFbkI0E3Ax8CagHhgEbAE8CV5d7EJLWAtYqXbh0ov4YYAxAv4Hrl7tbMzMrgxN/bb0PPAqcDHwzV743sJWk0vuBKbnuCxws6exUvhrQ0T0B/xURy4EnJG2YynYDbkrlf5N0f4XHISA6WzkixgPjAfrXDe30dmZmVjkn/tpaDnwZ+LOkcyLiJ6l8JWCXiFiar6zsSuDwiHiqRfmGtO3dfNUWP7tFmtNfLGnTiHi+O9s2M7Pu5Tn+GouIJcBBwGhJJ6fiScDppTqShqfFicA30gUAkrYvc7cPA4enuf4NgVFltpN3PvBrSQNTbAPTkL6ZmfUiTvy9QES8CewHfD/dsHcG0CBprqQngNNS1R8BqwBz01fqflTmLm8BFgDzgCuAx4DmNupeIWlBek1JZZvnyhZIOhL4DXA/MD3F9iCwpMz4zMyshyjCU6xFJGlARCyStB4wDdg1Iv5W7Tj61w2NuuPHVXu3ZtYFfkhP7yJpRkQ0lLu95/iL6670zYBVgR/VIumbmVn1OfEXVESMqnUMZmZWfU78VlPDhgyi0cOIZmZV45v7zMzMCsSJ38zMrECc+M3MzArEc/xWU00Lm6kfe3etwzCzXsJfHex57vGbmZkViBO/mZlZgTjxm5mZFYgTv5mZWYE48VeRpEUVbHuCpEu7uM0Dkhpy7+vTA3SQ1CDpknLbbmefa0v6Wne0ZWZm3c+JvxdSpkc/m4hojIgzurNNSf2AtQEnfjOzXsqJvwYkfVvS9PTY3R+ksnpJT0q6DJgJfFrSiZKelvQgsGtu+/Ul3ZLamC5p1zZ21V4MoyTd1Ur5NZIul/RQ2vdBqbyfpAtzcX8l1879kv4ANAEXAJtJmi3pwnLOj5mZ9Rx/j7/KJO0LDAV2AgTcIWkP4K/A5sCJEfE1SXXAD4ARQDPZs+5npWYuBi6KiIclbQxMBLZsY5fXS1qallcFlncizHrg88BmwP2SPgscBzRHxI6S+gOPSJqU6u8EbBMRL0iqT8vD2zkHY4AxAP0Grt+JcMzMrLs48VffvulVSuIDyC4E/gq8FBFTU/nOwAMR8TqApBuBz6V1ewNbSSq1OVDSWhHxdiv7Gx0RjamNeuBjvfxW/CkilgPPSHoe2CLFvK2kI1KdQSnu94BpEfFCJ9oFICLGA+MB+tcNjc5uZ2ZmlXPirz4B50fEFR8pzJLy4hZ120qKKwG7RMTSfKGkicCGQGNEnFJBjC33G2RxfyMiJrbY5yg+HreZmfVSnuOvvonASZIGAEgaImmDVuo9BoyStJ6kVYAjc+smAaeX3kgaDhARX4yI4RUmfYAjJa0kaTNgU+CpFPdXUyxI+pykNVvZ9m1grQr3b2ZmPcQ9/iqRtDLwbkRMkrQlMCUN1S8CjgWW5etHxCuSzgOmAK+Q3fDXL60+A/i1pLlkn+Fk4LRuDPcp4EGy0YPTIuIdSVeSzf3PVBb468ChLTeMiL9LeiR9bfCeiPh2N8ZlZmYVUoSnWKtB0nbAhIjYqdaxtEfSNcBdEXFzNfbXv25o1B0/rhq7MrM+wA/p6ZikGRHR0HHN1nmovwoknQbcAHy/1rGYmVmxeai/CiLicuDyWsfRGRFxQq1jMDOznuPEbzU1bMggGj20Z2ZWNR7qNzMzKxAnfjMzswJx4jczMysQz/FbTTUtbKZ+7N21DsPMegF/la863OM3MzMrECd+MzOzAnHiNzMzKxAnfjMzswLpMPFLWiZpdu5V30qdT0m6OS0Pl3RAOcFIekBSl/7+cC6+eZLulLR2Ofvu4j7r00NoqtqupG9JekfSoDLaPkHSpZVFaGZmfV1nevxL06NeS68X8yslrRwRL0fEEaloOFBW4i9TKb5tgDeBr1dx39V2DDAdOKynd5SeJmhmZp8wZQ31p97jTZLuBCaVeqqSVgV+CByVeuFHSfp8brRglqS1UhvfkdQkaY6kC3LNHylpmqSnJe3exdCmAENS+6Mk3ZWL+VJJJ6TlCyQ9IWmupJ+nsiPTMcyRNDmV9ZN0oaTpqe5XOjgvAyTdJ2lmOrZDUnm9pCclTZD0uKRJklZP60akfU6hnYsWSZsBA8ge9HNMrvwESbdK+h9Jz0j6WW7diek8PgjsmitfX9It6bimS9o1lZ8nabykScC1krZOn8XsdPxDU72z0rmaJ+nMjo7RzMx6j8706laXNDstvxARpd7mLsC2EfFmafg/It6TdC7QEBGnA6SLg69HxCOSBgDvSNqf7FnuO0fEEknr5mOKiJ3SdMF/AHt35kAk9QO+AFzVQb11yXrMW0RE5KYGzgW+GBELc2UnA80RsaOk/sAjKSm29Szjd4DDIuItSYOBqZLuSOuGAsdExKmS/gQcDvwe+C3wjYh4UNKF7YR+DNkT/h4CNpe0QUS8ltYNB7YH3gWekvQr4APgB8AIoBm4H5iV6l8MXBQRD0vaGJgIbJnWjQB2i4ilqZ2LI+L6dFHXT9II4ERgZ0DAY+nC4h/tHKOZmfUSXR3qzw8x3xsRb3Zi+0eAX0o6A1g7Ij4gS+a/jYglAC3auTX9nAHUd6L90oXJ34F1gXs7qP8WWYK+UtKXgCW5OK+RdCrQL5XtCxyX2n8MWI8subVFwE8kzQX+TDb6sGFa90JElC6gZgD1aa5+7Yh4MJVf107bRwN/jIjlZOfoyNy6+yKiOSLeAZ4ANiFLzA9ExOsR8R5wY67+3sCl6bjuAAaWRmKAOyJiaVqeApwj6f8Cm6Ty3YDbImJxRCxKsZRGZj52jK2eJGmMpEZJjcuWNLdzyGZm1t0quat/cWcqRcQFwCnA6mQ94C3IEmRbveZ3089ldG5EYmlEDCdLdquyYrj8Az56fKuleD4AdgJuIRt1+J9UfhrZMPqngdmS1ktxfiN34fOZiJjUTiyjgfWBESmmV0v7zR1X/tjaOw8fkrQt2QXHvZJeJLsIOCZXpbW2aaftlYBdcsc1JCLeTus+/Fwj4g/AwcBSYKKkvVLMbWkrjo+IiPER0RARDf3W6PJ9imZmVoGe+Drf20Cp94ikzSKiKSJ+CjQCWwCTgJMkrZHqrNtqSyvaGCLpvvbqREQzcAZwtqRVgJeArST1Tz3rL6S2BgCDIuK/gTPJhslLcT4WEecCb5BdAEwEvpraQ9LnJK3ZThiDgNci4n1Je5JdjLQX8z+BZkm7paLRbVQ9BjgvIurT61PAEEnttf8YMErSein+/AjBJOD00htJw1trQNKmwPMRcQnZyMC2wGTgUElrpHNxGNn0g5mZ9QE9cef2/cDYNIx8PrBbSoLLyIah74mId1OyaZT0HvDfwDnttFlH1oNvV0TMkjQHODoirkvzzHOBZ1gxv70WcLuk1ch6r99K5Remm9cE3AfMSdvWAzMlCXidbJSgLdcDd0pqBGYD8zuKmWy+/GpJS8guNFpzNLB/i7LbUvmrrW0QEa9IOo9suP4VYCYrpjDOAH6dpiRWJkvmp7XSzFHAsZLeB/4G/DDd03ENMC3VuTKd9/r2D9PMzHoDRXQ40lxzkk4H/hoRd3RY2fqU/nVDo+74cbUOw8x6AT+kp3MkzYiILv3Nm7w+8V3tiPAfnjEzM+sG/pO9ZmZmBeLEb2ZmViB9YqjfPrmGDRlEo+f1zMyqxj1+MzOzAnHiNzMzKxAnfjMzswLxHL/VVNPCZurH3l3rMMysDP7efd/kHr+ZmVmBOPGbmZkViBO/mZlZgTjxm5mZFUhNE7+kZZJm5171PbCPgyWN7aDOKEl3tSi7RtIRaflKSVul5RclDU7Li7o73nZifEDSxx7KIOkgSbMkzZH0hKSvtNNGvaR5FcbxaBvl+fN1ZumRy2Zm1rvU+q7+pRHR6rPgASStHBEdPo63PemJfhU91S8iTqlk+54iaRVgPLBTRCyQ1J/sMcI9JiJGdqLamcDvgSU9GYuZmXVdrxvql3SCpJsk3QlMkjRA0n2SZkpqknRIqlcvaX7qjc+TdL2kvSU9IukZSTvl2qvo6X5t9bZz6+skTU6jFvMk7d5KnXMlTU/rx0tSru2fSpom6enStpJWl/RHSXMl3Qis3squ1yK7ePs7QES8GxFPpe03lHRbGgmYI6mUsPtJmiDpcUmTJK2ei+OidBxPStpR0q3pXP5n7jgWpZ+SdGkaZbgb2CCVnwF8Crhf0v1dO9NmZtbTap34V88N89+WK98FOD4i9gLeAQ6LiB2APYFflJIm8FngYmBbYAvg34DdgLOBc7oYy+75aQfg4C5s+2/AxDR6sR0wu5U6l0bEjhGxDVkSPyi3buWI2Imsp/wfqeyrwJKI2Bb4MTCiZYMR8SbZaMZLkm6QNFpS6TO9BHgwIrYDdgAeT+VDgV9HxNbAP4HDc02+FxF7AJcDtwNfB7YBTpC0XovdHwZsDgwDTgVGppguAV4G9oyIPVs7WZLGSGqU1LhsSXNrVczMrIf01qH+e1NSAxDwE0l7AMuBIcCGad0LEdEEIOlx4L6ICElNdH3I+6GI+DAZS7qmC9tOB65OQ+//FRGtJf49JX0HWANYlywR35nW3Zp+zsjFvQdZ8iYi5kqa29qOI+IUScOAvckuePYBTgD2Ao5LdZYBzZLWITtnpfjy+4MVUyJNwOMR8QqApOeBT5NGFnLx3ZDaflnSX1o9M63HPJ5sioL+dUOjs9uZmVnlat3jb8vi3PJoYH1gRLpIeBVYLa17N1dvee79cqp4URMRk8kS4ULgOknH5ddLWg24DDgiIoYBE1hxDLAi7mV8NO5OJcWIaIqIi8iS/uEdVM+fs5b7y5+/lue2tfPppG1m1sf01sSfNwh4LSLel7QnsEm5DUk6TNL53Rfah+1uQhbjBOAqsqH1vFKSf0PSAOCITjQ7meyiB0nbkE1ntNzvAEmjckXDgZfS8n1k0wVI6idpYOeOptMmA0entuvIpmFK3ia7/8DMzHqZWg/1d8b1wJ2SGsnmzudX0NZmwFvdEtVHjQK+Lel9YBFpiL0kIv4paQLZEPqLZFMDHfkN8Ns0xD8bmNZKHQHfkXQFsJRspOSEtO6bwHhJJ5P17L8KvNKlo2rfbWTTCU3A08CDuXXjgXskvdLWPL+ZmdWGIoozWivp98C3IuL1Wsdimf51Q6Pu+HG1DsPMyuCH9NSGpBkR0eY3zTrSF3r83SYijq11DGZmZrXUF+b4zczMrJsUqsdvvc+wIYNo9HChmVnVuMdvZmZWIE78ZmZmBeLEb2ZmViCe47eaalrYTP3Yu2sdhplVkb8GWFvu8ZuZmRWIE7+ZmVmBOPGbmZkViBO/mZlZgTjx9yKSlkmaLWmOpJmSRpbRxjndGM95ks5upXxRG/WPkzRP0uOSnmhtWzMzqy0n/t5laUQMj4jtgO8CnX6EsDIrAd2W+LtC0v7AmcC+EbE12aOJm2sRi5mZtc2Jv/caCPwDQNIASfelUYAmSYek8npJT0q6DJgJXAWsnoWjVDkAAAfMSURBVEYNrk91zkq98HmSzmyx3YTUO58kafUK4/0ucHZEvAwQEe9ExIQK2zQzs27m7/H3LqtLmg2sBtSRPe8e4B3gsIh4S9JgYKqkO9K6zYETI+JrAJKOjIjhaXkEcCKwMyDgMUkPkl1QDAWOiYhTJf0JOBz4fQWxbwPM6ExFSWOAMQD9Bq5fwS7NzKyr3OPvXUpD/VsA+wHXShJZ0v6JpLnAn4EhwIZpm5ciYmob7e0G3BYRiyNiEXArsHta90JEzE7LM4D67j+c1kXE+IhoiIiGfmsMqtZuzcwMJ/5eKyKmAIOB9YHR6eeI1Jt/lWxUAGBxO82onXXv5paXUfnoz+PAiArbMDOzHubE30tJ2gLoB/wdGAS8FhHvS9oT2KSdTd+XtEpangwcKmkNSWsChwEP9VDI5wM/k/QvKf7+ks7ooX2ZmVmZPMffu5Tm+CHrrR8fEcvSjXp3SmoEZgPz22ljPDBX0syIGC3pGmBaWndlRMySVN+FmL5fuikQICI2AtaQtCBX55cR8UtJGwJ/TtMTAVzdhf2YmVkVKCJqHYMVWP+6oVF3/Lhah2FmVeSH9FRG0oyIaCh3ew/1m5mZFYgTv5mZWYF4jt9qatiQQTR62M/MrGrc4zczMysQJ34zM7MCceI3MzMrECd+MzOzAnHiNzMzKxAnfjMzswJx4jczMysQJ34zM7MCceI3MzMrED+kx2pK0tvAU7WOowyDgTdqHUQZ+mrc0Hdj76txQ9+Nva/GDZ2LfZOIWL/cHfhP9lqtPVXJU6ZqRVKj466uvhp7X40b+m7sfTVuqE7sHuo3MzMrECd+MzOzAnHit1obX+sAyuS4q6+vxt5X44a+G3tfjRuqELtv7jMzMysQ9/jNzMwKxInfzMysQJz4rSYk7SfpKUnPShpboxg+Lel+SU9KelzSN1P5upLulfRM+rlOKpekS1LMcyXtkGvr+FT/GUnH58pHSGpK21wiSd0Yfz9JsyTdld5/RtJjKYYbJa2ayvun98+m9fW5Nr6byp+S9MVceY99PpLWlnSzpPnp3O/SF865pG+lfyfzJN0gabXees4lXS3pNUnzcmU9fo7b2keFcV+Y/q3MlXSbpLVz67p0Lsv5vCqJPbfubEkhaXB6X9tzHhF++VXVF9APeA7YFFgVmANsVYM46oAd0vJawNPAVsDPgLGpfCzw07R8AHAPIOBfgcdS+brA8+nnOml5nbRuGrBL2uYeYP9ujP8s4A/AXen9n4Cj0/LlwFfT8teAy9Py0cCNaXmrdO77A59Jn0m/nv58gN8Bp6TlVYG1e/s5B4YALwCr5871Cb31nAN7ADsA83JlPX6O29pHhXHvC6ycln+ai7vL57Krn1elsafyTwMTgZeAwb3hnFf1F61ffkUE6R/vxNz77wLf7QVx3Q7sQ/aXBOtSWR3ZHxkCuAI4Jlf/qbT+GOCKXPkVqawOmJ8r/0i9CmPdCLgP2Au4K/0yeCP3C/LDc5x+6eySlldO9dTyvJfq9eTnAwwkS6BqUd6rzzlZ4v/f9At55XTOv9ibzzlQz0cTaI+f47b2UUncLdYdBlzf2jnq6FyW83+kO2IHbga2A15kReKv6Tn3UL/VQumXaMmCVFYzaWhve+AxYMOIeAUg/dwgVWsr7vbKF7RS3h3GAd8Blqf36wH/jIgPWtnXh/Gl9c2pflePpztsCrwO/FbZNMWVktakl5/ziFgI/Bz4K/AK2TmcQd845yXVOMdt7aO7nETW2y0n7nL+j1RE0sHAwoiY02JVTc+5E7/VQmtzrjX7XqmkAcAtwJkR8VZ7VVspizLKKyLpIOC1iJjRidjaW1fVuJOVyYZDfxMR2wOLyYYn29IrYk/zpoeQDSl/ClgT2L+dffWKuDupT8Qq6XvAB8D1paI24ign7m4/JklrAN8Dzm1tdRv7q8o5d+K3WlhANu9VshHwci0CkbQKWdK/PiJuTcWvSqpL6+uA11J5W3G3V75RK+WV2hU4WNKLwB/JhvvHAWtLKj1/I7+vD+NL6wcBb5ZxPN1hAbAgIh5L728muxDo7ed8b+CFiHg9It4HbgVG0jfOeUk1znFb+6hIusntIGB0pDHtMuJ+g65/XpXYjOxCcU76v7oRMFPSv5QRe/ee80rmkPzyq5wXWa/v+fSfonTzzdY1iEPAtcC4FuUX8tGbZX6Wlg/kozfkTEvl65LNW6+TXi8A66Z101Pd0g05B3TzMYxixc19N/HRG5e+lpa/zkdvXPpTWt6aj94c9TzZjVE9+vkADwGbp+Xz0vnu1ecc2Bl4HFgjtfs74Bu9+Zzz8Tn+Hj/Hbe2jwrj3A54A1m9Rr8vnsqufV6Wxt1j3Iivm+Gt6znv8l6tffrX2Irur9Wmyu2+/V6MYdiMbLpsLzE6vA8jm9u4Dnkk/S//xBPw6xdwENOTaOgl4Nr1OzJU3APPSNpdSxg1DHRzDKFYk/k3J7vx9Nv2C65/KV0vvn03rN81t/70U21Pk7n7vyc8HGA40pvP+X+kXXK8/58APgPmp7evIEk6vPOfADWT3IrxP1ls8uRrnuK19VBj3s2Tz3qX/o5eXey7L+bwqib3F+hdZkfhres79J3vNzMwKxHP8ZmZmBeLEb2ZmViBO/GZmZgXixG9mZlYgTvxmZmYF4sRvZmZWIE78ZmZmBfL/AfD7Qx/nxgRaAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.barh(group_names, group_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Things to notice:\n",
+    "* There is no heading on the plot.\n",
+    "* X-axis is missing a label.\n",
+    "* It is difficult to compare the bars because there is no backing grid to line up the bar ends.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Applying Style to a Plot\n",
+    "===================\n",
+    "\n",
+    "Matplotlib has a wide range of built-in style sheets that can be used to modify the appearance of plots. Styles are applied to the pyplot object and we must re-make plots between style changes to see the results.\n",
+    "\n",
+    "[Review the stylesheet reference](https://matplotlib.org/3.1.0/gallery/style_sheets/style_sheets_reference.html)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark-palette', 'seaborn-dark', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'seaborn', 'Solarize_Light2', 'tableau-colorblind10', '_classic_test']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(plt.style.available)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can activate a desired style using the `plyplot.style.use( ... )` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.style.use('fivethirtyeight')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can re-make the plot using the new style."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<BarContainer object of 10 artists>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.barh(group_names, group_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The style controls many things, such as color, linewidths, backgrounds, etc. Not all styles apply to all types of plots. You should experiment to see how the appearance of different plot-types are affected."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Customizing a Plot\n",
+    "=================\n",
+    "Now we've got a plot with the general look that we want, so let's fine-tune\n",
+    "it so that it's ready for print. First let's rotate the labels on the x-axis\n",
+    "so that they show up more clearly. We can gain access to these labels\n",
+    "with the :meth:`axes.Axes.get_xticklabels` method:\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.barh(group_names, group_data)\n",
+    "labels = ax.get_xticklabels()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Matplotlib supports the use of setp() (\"set property\") and getp() to set and get object properties, as well as to do introspection on the object. If we'd like to set the property of many items at once, it's useful to use\n",
+    "the :func:`pyplot.setp` function. This will take a list (or many lists) of\n",
+    "Matplotlib objects, and attempt to set some style element of each one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.barh(group_names, group_data)\n",
+    "labels = ax.get_xticklabels()\n",
+    "plt.setp(labels, rotation=45, horizontalalignment='right')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some times the plotting functions can cut off some of the labels on the bottom. We can tell Matplotlib to automatically make room for elements in the figures that we create. To do this we'll set the autolayout value of our rcParams. For more information on controlling the style, layout, and other features of plots with rcParams, see :doc:/tutorials/introductory/customizing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.rcParams.update({'figure.autolayout': True})\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.barh(group_names, group_data)\n",
+    "labels = ax.get_xticklabels()\n",
+    "plt.setp(labels, rotation=45, horizontalalignment='right')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we'll add labels to the plot. To do this with the OO interface,\n",
+    "we can use the :meth:`axes.Axes.set` method to set properties of this\n",
+    "Axes object.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0, 0.5, 'Company'),\n",
+       " (-10000, 140000),\n",
+       " Text(0.5, 0, 'Total Revenue'),\n",
+       " Text(0.5, 1.0, 'Company Revenue')]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.barh(group_names, group_data)\n",
+    "labels = ax.get_xticklabels()\n",
+    "plt.setp(labels, rotation=45, horizontalalignment='right')\n",
+    "ax.set(xlim=[-10000, 140000], xlabel='Total Revenue', ylabel='Company',\n",
+    "       title='Company Revenue')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also adjust the size of this plot using the :func:pyplot.subplots function. We can do this with the figsize kwarg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0, 0.5, 'Company'),\n",
+       " (-10000, 140000),\n",
+       " Text(0.5, 0, 'Total Revenue'),\n",
+       " Text(0.5, 1.0, 'Company Revenue')]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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VbOYohBDvgZ2dHdevX3/n6be5JT09nXr16nHjxg3OnTsn/+sXHzxpgRFCCMHWrVu5ePEi7du3l+RF5AsyBkYIIT5iM2fO5J9//mHNmjUUK1aMYcOG5XVIQmSLJDBCCPERmzx5Mnp6elSrVo2JEye+875HQrwvMgZGCCGEEPmOjIERQgghRL4jCYwQQggh8h1JYIQQQgiR70gCI4TgwoULeR1CjitodSpo9QGpU37wIddHEhghhBBC5DuSwAghhBAi35EERgghhBD5jiQwQgghhMh3ZCE7IQSqlX/ndQhCfDQSfMzyOoRsu3Dhwge7I7a0wIiPnqenp9r+L2/6LIQQIu9JAiMKhBUrVmBqasqLFy+UshcvXmBiYoKzs7PauZcuXUKlUnHgwAEA1qxZw7hx49762VFRUahUKh48eJDl8alTp+Lk5PTae8THxzNgwABq1qyJoaEhdnZ2dO3alSNHjrx1XEIIUZBJAiMKBDc3N549e8bx48eVst9//53SpUtz8eJF7t+/r5RHR0dTpEgRHB0dAShTpgylSpV67zFnOnnyJO7u7vz111/MmjWLI0eOsG7dOurUqcPw4cPzLC4hhPiQyW7UokCoUqUKJiYmREVFKa0dUVFRuLu7c/XqVaKjo2nTpo1SXq9ePYoWLQpkdBHVqFGDmTNnvve409PT6devH5UqVWL37t0UKlRIOVarVi169Ojx3mMSQoj8QFpgRIHh6upKVFSU8jkqKgoXFxdcXFzUyqOjo3F1dc2LEDWcOXOG8+fPM2jQILXkJZNKpcqDqIQQ4sMnLTCiwHBxcWH48OE8f/6c9PR0fv/9d+bPn4+5uTkjR44EIC4ujtu3b+Pm5pbH0Wa4fPkyADY2Nlpfm7NLfBfPwXsJIV7nQ16ePyt5Fe+bZj9JAiMKDDc3N5KSkjh69Cjp6emUK1cOKysrDA0NiY+P586dO0RFRVG8eHHq1q2b1+ECGV1IbytHpzZGyzRqId6XD3VaclY+5GnUksCIAsPS0pKKFSsSHR1Neno6DRs2BKBEiRLUqVOH6OhooqOjadCgAYULF87jaDNUrlwZyGgZsre3z+NohBAi/5AxMKJAyRwHkzn+JZOLiwuRkZFER0d/MN1HALVr16Z69eoEBgaSmpqqcTwhISEPohJCiA+ftMCIAsXV1ZWNGzcCsGjRIqW8YcOG+Pj48OTJk1wbwHvu3DkMDAzUymrVqgVAUlISZ86cUTtWvHhxqlSpwsKFC2nTpg2ff/453333HdWqVePZs2dERETw66+/sn///lyJVwgh8jNJYESB4urqyosXLzAzM8PKykopb9CgAYmJiZQuXZo6derkyrO9vLw0ym7cuAFkLFT3cstPnTp12L9/Pw4ODuzfv58ffviBoUOHcu/ePYyMjPj000/zZGq3EELkB7IXkhBC9kIS4j2SvZByhrTACCE45vLsg/0h9bY+5B+8b6Og1QekTuLdyCBeIYQQQuQ7ksAIIYQQIt+RMTDindnZ2eHr68vAgQPf6RyRd2QMjBDifXvXsUDSAiMUK1aswNTUlBcvXihlL168wMTEBGdnZ7VzL126hEql4sCBA9m69759++jZs6fyWaVSERoa+k7x7t+/H5VK9dqv4ODgd3rGm5w7dw6VSkVcXFyuPkcIIYQ6GcQrFG5ubjx79ozjx48rOzr//vvvlC5dmosXL3L//n3Kly8PZGyIWKRIERwdHbN178zrcpKzszOxsbHK5/Hjx3P16lV+/vlnpezldVkAUlNT0dHRQVdX8nchhMiv5Ce4UFSpUgUTExONHZ3d3d355JNPiI6OViuvV68eRYsWBTIWahsyZAgVK1akRo0aBAYGqt3bzs6O+fPnK98DdOvWDZVKpXwG2LlzJ+7u7hgZGVG7dm0mTZqk1iL0X/r6+hgZGSlfxYoV0ygrWrQoS5cuxcbGhq1bt1K/fn0MDQ25fv06MTExeHl5YWVlhYWFBZ6enpw6dUq5/9OnT1GpVKxdu5ZOnTphYmLCp59+qrQcPX36VGmZql+/PiqVio4dOwIZSVJAQAC2trYYGhri6upKRESEcu8vv/ySsWPHKp9HjBiBSqXi/PnzAKSlpWFpacn27duz9WcnhBAfG0lghJrMpfgzZS7J7+LiolYeHR2ttqLtokWLqFGjBgcOHGDw4MGMGzeOo0ePZvmMffv2ARAYGEhsbKzyec+ePfj6+tK7d28OHz7MggULCA0NZeLEie9cr0ePHrF48WIWLlxITEwMFSpU4N9//6Vbt26Eh4cTHh6OtbU1HTp04PHjx2rXTp06lS+//JKDBw/StGlT+vbty927dylRogRhYWFARuIVGxvLkiVLAPjhhx9YtmwZU6dO5eDBg7i7u9OpUycuXrwIkOX7LFeunFJ29uxZHj9+rOznJIQQQp0kMEKNi4sLx44d4/nz5yQlJfH777/j6upKw4YNlV+ucXFx3L59W21l2f/973/4+vpibW1Nnz59sLa2fuX4mMzuJAMDA4yMjJTPs2bNYuDAgXh7e2NlZYWbmxsTJkxg5cqV77RrM8Dz588JDAykXr162NjYULx4cZo0acIXX3xB1apVqV69OvPmzSMpKYnIyEi1a7t160bbtm2xtrZmwoQJPH/+nKNHj6Kjo0PZsmUBKFeuHEZGRqhUKtLT01mwYAHfffcdbdq0oWrVqgQEBFCzZk0WLlyovOczZ87w6NEj/vnnH+Li4vDz81PecXR0NDVr1kSlUr1TvYUQoqCSMTBCjZubG0lJSRw9epT09HTKlSuHlZUVhoaGxMfHc+fOHaKioihevDh169ZVrqtZs6bafYyNjbl3755Wzz59+jQnTpxg3rx5SllaWhqJiYncuXOHoKAgZs+erRw7fPgwFStWzNa9S5YsqbG41M2bN5k8eTIxMTHcv3+f1NRUnj17piz/n1XdihUrhoGBAffv33/ls27fvs3jx49p0KCBWnmDBg04ffo0APb29pQsWZJDhw6RkpKCnZ0dzZs3Z9GiRaSnpxMdHa22GWVWLly4kK26Z0/xHLyXEEK82Zt+hr1pQUBJYIQaS0tLKlasSHR0NOnp6UoXRokSJahTpw7R0dFER0fToEEDChcurFz33+8BdHR0tG41SUtLY8SIEbRp00bjWPny5enRowdt27ZVykxMTLJ97+LFNX9B9+zZk5SUFGbMmIGZmRlFihShadOmJCcnq52XVd3S0tJe+azMeuvo6GgcyywrVKgQDRo0ICoqitTUVFxdXalRowY6OjqcPXuWmJgYvL29X1unHF3tM1qmUQsh3q93/RkmCYzQkDkOJj09nU6dOinlLi4uREZGEh0dTf/+/d/pGYULFyY1NVWtzN7enri4OKytrbO8pkyZMpQpU+adnpspNTWVo0ePsnz5cpo0aQLAtWvXePDggVb30dfXV+6XycTEhNKlSxMTE4ODg4NSfvjwYT799FPls4uLCxs2bCAtLY0JEyago6ODs7MzixcvlvEvQgjxBjIGRmhwdXXl999/5/jx42oDdRs2bMjmzZu5d++eWvnbsLCw4MCBA9y5c4eEhAQAhg8fzsaNG5k8eTLnzp0jLi6O0NBQxo0b907PykqhQoWwsrJi3bp1XLhwgaNHj+Lr60uxYsW0uo+JiQmFCxcmIiKCe/fu8fjxY3R0dBg4cCCzZs0iNDSUCxcuMHbsWP7880/69eunXOvi4sKff/7JhQsXlO4mFxcXQkJCZPyLEEK8gSQwQoOrqysvXrygQoUKWFlZKeUNGjQgMTGR0qVLU6dOnXd6RkBAAFFRUdSsWVNJhho3bkxISAjR0dE0btyYxo0bM2fOHMzNzd/pWa+yZMkSbt++jYuLC35+fvj5+Wm9Xk2JEiUICAhg8eLFVKtWjd69ewMwdOhQevbsyYgRI3B2dmb//v2sW7eOKlWqKNdmjoOpXbs2pUqVAjLefWpq6hvHvwghxMdOthIQQshWAkKI9+5dtxKQMTBCCI65PMvZQcEfgAsXLhSoOhW0+oDUKT/4kOsjXUhCCCGEyHckgRFCCCFEviNjYIQQMgZGCKH479gU6UISQgghhMhBksAUECqV6rVffn5+uR6DjY0NS5cuzfXnvGzFihVqda1YsSJNmzZl79697z0WIYQQ74fMQiogYmNjle93797NoEGD1MqKFi2a5XXJyckaS+XnR2XKlOHw4cMAPH78mB9//JHOnTtz+vRpjIyM3uqe6enppKSkaLyfgvLOhBAiP5MWmALCyMhI+TIwMMiyLC4uDpVKxZYtW2jRogVGRkasW7eOu3fv4uPjg62tLSYmJjg5ORESEqJ2/yZNmjBq1CjGjh2LpaUlNjY2TJw4Udn3p0mTJty9e5fhw4ejUqnUkobNmzfj6OhIhQoVqFWrltpmjQsXLlRb1Xfnzp2oVCoWL16slH399dcMGzbstfXX0dFR6lq1alVGjx5NUlKSWhK3Zs0a3N3dMTc3x8bGhh49enDnzh3leEREBCqVij179uDu7o6hoSEHDx5kwoQJeHh48PPPP1O7dm0MDQ1JSUkhMTGRYcOGUblyZYyMjPj88885duyYcr+GDRtq1KNs2bL8888/ADx69Ihy5cpx6tQpAA4cOMD//vc/TE1NsbCwoEmTJjm8YaMQQhQcksB8hCZMmEC/fv04cuQITZs2JTExkbp16xIcHExMTAw9evTAz8+PmJgYteuCgoIoXbo0e/bsISAggLlz5xIWFgbAhg0bKF++POPGjSM2NpazZ88CcOTIEXr27EmHDh2IiYlh1KhRTJs2jV9++QXIWDr/jz/+UH6pR0dHU65cOaKiooCMVpBDhw5ptTJtcnIya9eupUSJEtSoUUMpT0lJYdy4cURHRxMUFMTNmzfp06dPlu/H39+fo0ePUrt2bSBjINuOHTtYvXo1UVFRFCpUiFGjRrF9+3aWLFnCgQMHsLa2pn379sp+Si4uLhr1KFOmDAcPHgTg0KFDlChRAjs7O54/f06XLl3w8PDg0KFD/Pbbb/Tq1QtdXfknKoQQWdGqC+mvv/6ievXquRWLeE/69+9Py5YtNcoy9e7dm3379rF582acnJyU8tq1aystIZUrV2blypVERkbi5eVFmTJl0NXVpWTJkmqtLwsWLKBJkyZ89913AFSpUoULFy4wd+5cunXrhp2dHQYGBkRHR+Pl5UV0dDQDBgxg3rx5pKWlcf78eR4+fPjGBObhw4eYmWWMnE9MTKRYsWIsXrxYbWuA7t27K99bWloyY8YM3NzcePDgAeXKlVOOjRkzBg8PD7X7p6SksGTJEmUzyYSEBFavXs2yZcuUzSADAwOJjIxkxYoVDBs2DBcXFwYOHKjUIy0tja+//pqoqChatmxJdHQ0Tk5OFCpUiLt37/L06VNatGiBpaUlANWqVXttnXO2dUZzt24hxMfp5Z8tedUS/KbZT1olME5OTtjb29OxY0fat2+PoaHhOwUn8sYnn3yi9jklJYVZs2axdetWbt68SXJyMs+fP1d+MWeqWbOm2mdjY2Pu3bv32mfFxcXRoUMHtbIGDRowb948kpKSKFq0KE5OTkRHR+Pm5sa5c+cIDQ1l8eLFnD17lsOHD2Nra0u5cuV4/vy52k7VX3/9NdOmTQMyBjFnDtr9999/2bNnD76+vpQuXVpJRo4fP86MGTP4888/SUhIIC0tDYDr16+rJTAvvx+ASpUqqe2EfenSJVJTU3F0dFTKChcujIODg9Jt5eLiwqNHj5R6NGzYEFdXVyZMmABktDZ98cUXQMamkO3bt6dly5a4u7vj7u5OmzZtMDU1feW7zdGpjdEyjVoIkeG/P1sKzDTqGTNmoK+vz+jRo6lZsyYdOnRg06ZNJCUl5VZ8IhcUL67+v+1Zs2axbNkyvvnmG7Zt20ZUVBRNmzYlOTlZ7byXB67q6OgoScCrpKeno6Ojo1Guo6OjlGd2tRw6dIgaNWqgUqlo2LAhUVFRREdH07BhQwD09fWJiopSvjJbdQB0dXWxtrbG2toaOzs7hgwZgoODA3PnzgUyWkzatWuHSqVi6dKl7N27l3Xr1gFo1PPl95NVWebYn1fVDTIGFteoUUOph6urK05OTsTFxREfH88ff/yhNv5n+fLl7Nq1C0dHR7Zt24aDgxxjojIAACAASURBVAORkZGvfb9CCPGx0iqB6d27N+Hh4Zw8eZKhQ4dy+fJlevXqhY2NDf3795cftvnU4cOHadmyJR06dMDOzg5LS0suXbqk9X309fVJTU1VK6tWrZoyO+i/z7O0tKRIkSJARgJz/vx5QkNDla6i/yY1mWU6OjpKkmJtbf3GnaMLFSpEYmIikNH9+ejRI/z9/XF2dsbGxoa7d+9qXcdMVapUoVChQmp1S05O5vjx42pdPy/XI3P36RkzZijjX/6rTp06DB06lJ07d+Lg4EBwcPBbxyiEEAXZW40QtLS0ZNSoURw/fpzw8HA6duzI7t27adOmDbVq1WLixInExcXldKwil1SpUoU9e/Zw9OhRYmNjGTJkCLdv39b6PhUrVuTgwYPcvHmThw8fAjBw4EAiIiL44YcfuHTpEmvXrmXJkiUMHjxYuS5zHMyGDRuUFgkXFxciIiKyNf4FMlpE7ty5w507d7hy5Qo//fQT0dHRtGjRAsjoAipcuDBLlizhypUr7NixgxkzZmhdx0wqlYquXbsyZswY9uzZw19//cWgQYN48uQJPj4+ynmZ9QCUAcUuLi6EhIQo418go5l20qRJHD16lGvXrrF//35iY2NlzJkQQrzCO09xqFWrFvXr16dmzZqkp6dz7949Fi1aRIMGDejYsSM3btzIiThFLho1ahQ1a9akbdu2tGzZkgoVKtCqVSut7zN27FguXbpEnTp1sLW1BaB+/fosX76ckJAQGjRowJQpUxg5ciTdunVTrtPV1cXZ2RlAGTRcrVo1ypcvr4x/eZN//vmHatWqUa1aNRo0aMBPP/3E+PHjGThwIJAxxmTBggVs2rQJR0dH5syZQ0BAgNZ1/K8pU6bQvHlzfH19cXd359KlS2zatEktXhcXF9LS0mjYsKHSteTq6kpqaqpaYlaiRAnOnz9P165dqVu3LgMHDqRr167069fvnWIUQoiC6q32QkpPT2f//v0EBwezfft2/v33X+rUqUOnTp344osvKFSoEOvXr2f69OnY2toqU22FEB+mD3mg3tsqaHUqaPUBqVN+8CHXR6tZSGfPniU4OJhNmzZx584djI2N6dmzJ506ddKY8unr64uOjg5jxozJ0YCFEEIIIbRKYNzc3ChWrBienp506tQJDw+P1y60Va1aNerVq/fOQQohhBBC/JdWXUirVq2ibdu2lCpVKjdjEkK8Z6qVsg6MECLvJPiYaX2NVoN4u3btKsmLyPemTp2qtsKwEEKI/Efr3ahTU1PZu3cvV65c4Z9//lEW9Mqko6PD8OHDcyxAITL5+fnx8OFDtbVRdu3ahY+PD/369WPs2LF5GJ06lUrFL7/8QuvWrfM6FCGEKJC0SmDOnDmDt7c3N27c0EhcMkkCI96X9evXM2jQIPz9/fHz88vrcIQQQrxHWnUhfffddzx9+pTVq1cTHx/PP//8o/GVuYCZELlp8eLFDBo0iMDAQLXkxc/Pj44dO6qd+6YuoxMnTtC2bVusra2pWLEizZo14+jRo2rnrFy5EgcHB4yMjKhcuTLt2rUjJSUl2/GqVCp+/vlnunXrhqmpKfb29hqr7N66dYvevXtjZWWFiYkJLi4usrq1EEK8glYJzJkzZxg8eDCenp6oVKrcikmI1woICGDixImsXr2ar7766p3v9+TJEzp27MjOnTvZs2cPdnZ2dOjQgQcPHgBw8uRJvvvuO0aMGMGxY8fYsmULjRs31vo5M2bMoEWLFkRHR9OuXTsGDBjAtWvXgIwNKD09Pbl27Rpr1qzh0KFD0pIphBCvoVUXkqGhIXp6Wg+bESLH7Nu3j927dxMcHMznn3+eI/d0d3dX+zxjxgy2bt1KREQEHTt25Pr165QoUYLmzZsrg9hf3sMoOzp27Ki0Dn3//ff8+OOPxMTEYGFhwcaNG7l79y6//fabspKvlZXVa++Xs1vca25gKYQQ70tWP8/etICeVtmIr68v69evx9fXV2NnYiHeB1tbWx4/fsz06dNxdHTMkZbAe/fuMXnyZKKiorh37x6pqakkJiYq22A0atQIc3Nz7O3tady4MY0aNcLLy0vrGXk1a9ZUvtfT06NcuXLcu3cPyGjdrFmzZra2TciUo6tjRss0aiFE3nmbn2daJTCmpqbo6enh5OSEt7c35ubmymZ0/9W2bVutAxEiO4yNjVm3bh2tWrWiTZs2bNmyRS2J0dXV1Rhg/qaxKn5+fty9e5cpU6ZgYWFBkSJFaNWqFS9evACgVKlSREZGcvDgQfbv38+cOXOYNGkSe/fuxcTEJNuxv5z06+joKLG+alC8EEKIrGmVwPTs2VP53t/fP8tzdHR0JIERucrU1JSwsDBatWpFq1at2LJlC2XLlgWgfPnynD17Vu38lz+/7PDhw0ybNk3pkrp79y537txRO0dPTw93d3fc3d0ZNWoUVapUYffu3XTv3j1H6mRvb09ISAgPHjzQqhVGCCE+VlolMNu2bcutOITQirGxMWFhYbRu3RovLy+2bt1KuXLlcHNzY968eaxevZqGDRuybds2Dh8+jJnZq1d5rFy5MiEhIdStW5dnz54xbtw49PX1leO7du0iPj4eZ2dnypQpQ1RUFE+fPsXGxibH6vPFF18wZ84cunTpwrhx4zA1NeXcuXOULFkSNze3HHuOEEIUFFolMC4uLrkVhxBaMzQ0ZNu2bUoSExoaSuPGjRkxYgQBAQEkJibSoUMHevXqxc6dO195nwULFjBkyBA8PDwwNjZm5MiRygwkAAMDA7Zv386MGTNITEzEysqKwMBAnJ2dc6wuJUqUYPv27YwZM4avvvqK5ORkqlSpwpQpU3LsGUIIUZBotReSEKJgunDhQs4OCv4AFLQ6FbT6gNQpP/iQ66P1nOh79+6xevVqTp06xaNHj0hLS1M7rqOjw9atW3MsQCGEEEKIl2mVwPz11194enry77//UrlyZc6fP0/16tVJSEjg1q1bWFlZvXasgRBCCCFETtCqC+mrr77i1KlT7Nq1i1KlSlGlShW2bNmCu7s7GzduZPjw4WzevJk6derkZsxCiBymWinrwAiR2xJ88t9/8D/kLiStthKIiYnBx8cHS0tLdHUzLs1cv+KLL76gXbt2H9SOwELkZ2ZmZgQFBeV1GEII8UHSKoFJTk5WFu4qWrQoAI8ePVKO29nZcfLkyRwMT3xsstqMcdeuXZiYmDBp0qQ8iir7rl69ikqleuW/gzdtLCmEECJ7tEpgzM3Nlc3nihUrhrGxsdquvefOnaNEiRI5G6H4qK1fv56uXbsybtw4ad0TQgih0CqBcXV1ZceOHcrnDh06sGTJEgYOHEj//v1Zvnw5LVq0yPEgxcdp8eLFDBo0iMDAQPz8/JTytLQ0ZsyYQc2aNTE0NMTZ2Znt27erXXvz5k169OhBpUqVqFSpEl9++SWXLl1Sjme2hKxatYpatWphbGxM586d1dZ/yWwNmjlzJlWrVsXMzIx+/fqRmJj4VvUJCgpi+vTpnD9/HpVKhUqlUrqILl++jKenJ0ZGRtStW5ddu3a91TOEEOJjodUspCFDhuDm5kZSUhJFixbl+++/5/Hjx/z6668UKlSIjh075otmfvHhCwgIYOHChaxevVpj1+nFixczf/58Zs+ezSeffEJwcDBff/01+/fvp3bt2jx79gwvLy/q16/P9u3b0dfXZ/78+bRu3ZqjR49SvHjGzsvXrl0jODiYoKAgEhMTGTJkCP3792f9+vXKsw4ePEjRokUJDQ3l1q1bDBgwgPHjxzNjxgyt69SuXTvOnz/P7t27CQsLA6B06dKkpaXh7e2NgYEB4eHhJCYmMnLkSJ4/f/4Ob1AIIQo2rRKYihUrUrFiReVzkSJFmDt3LnPnzs3xwMTHa9++fezevZvg4GCN5AUyVs4dMGAAHTp0AOD777/n0KFDLFiwgKVLl7Jp0ybS09NZtGgROjo6AMydO1fZvyhzr67ExER+/PFH5e/0nDlzaN68OZcuXaJy5cpAxuaQCxcupGTJktSoUYMJEyYwcOBAxo8fr3V3abFixShRogR6enoYGRkp5Xv37uWvv/7i9OnTSixTp06lefPmr71fVtvPv73iOXgvIURWcvbf7PuTV3G/afaT1gvZ/deTJ0+AjN16hcgptra2PH78mOnTp+Po6Ki22/Tjx4+5desWDRo0ULvGycmJ8PBwAE6fPs3Vq1cxNzdXO+fZs2fEx8crn01NTdUS8rp166Krq0tsbKySwNSsWZOSJUsq59SvX58XL14QHx9PrVq1cqS+sbGxr4zldXJ0amO0TKMWIrd9qNORX+dDnkatdQJz+fJlpk2bxm+//abMQDIwMKBp06YMHz6cKlWq5HiQ4uNibGzMunXraNWqFW3atGHLli1qScyrZLa2pKWlYWdnx4oVKzTOKVOmTI7H+64ylyIQQgiRfVoN4j1+/DgeHh78+uuv1K9fHz8/P/r27Uv9+vX59ddfadSoEb///ntuxSo+IqampoSFhfHs2TNatWrFw4cPgYwxIyYmJhw+fFjt/JiYGKpVqwaAvb09ly9fpmzZslhbW6t9/TeBuXnzJjdu3FA+Hz9+nLS0NOU+kDGz7t9//1U+Hzt2DH19faysrN6qXvr6+qSmpqqVVa9e/ZWxCCGEyJpWLTAjR46kZMmS7Nu3T2liz3Tx4kW8vLwYPXq00pQvxLswNjYmLCxM2W1669atlCtXjoEDBzJ16lQqV65MnTp1CA4OJiYmhv379wMZs+Pmz59P586dGT16NObm5vz999/s2LGDHj16KH93ixUrhp+fH5MnTyYpKYmhQ4fy+eefq/3dTk1NZcCAAQwfPpzbt2/j7+9Pt27d3jj+5eLFixQqVEitzMbGBgsLC65fv86pU6eoWLEiJUuWxMPDAxsbG/r27cuUKVNISkpi9OjR6Om9Uw+vEEIUaFr9hPzjjz8YNmyYRvICUKVKFXr16sWsWbNyLDghDA0N2bZtm5LEhIaG0rdvX54+fcr48eO5e/cuVatWZdWqVdSuXRuA4sWLs2PHDiZMmED37t15/PgxxsbGuLq6qnVFWVhY0L59ezp16sSDBw9o1KgR8+fPV3t+w4YNqV69Ol5eXiQmJuLl5YW/v/8b4+7du7dG2aFDh2jVqpVSn0ePHrFw4UK6dOnCmjVrGDRoEE2aNMHc3JyAgIAs7yGEECKDVnsh2dvb4+Pjw5AhQ7I8PnfuXH7++WdOnTqVYwEKkRumTp3K1q1biYmJeeU5fn5+PHz4kODg4PcYWd74kAfqva2CVqeCVh+QOuUHH3J9tBoD079/f5YtW6bWV5/p2rVrLFu2jP79++dYcEIIIYQQWdGqCykxMREDAwPq1q1LixYtsLa2BuDSpUvs2rWLypUr8+zZM+bNm6dco6Ojw6BBg3I2aiGEEEJ81LTqQnqbKag6OjrKDBIhxIfpQ24mflsFrU4FrT4gdcoPPuT6aJXAZG7kqC0LC4u3uk6I7IqKisLLy4tLly5Rrlw5jc/i9VQrZSE7IXJSgo9ZXoeQIz7kBEarMTAWFhZv9SU+XpkbIv7Xrl27MDExydV9sxwdHYmNjaVs2bK59oxMKpWK0NDQLI9FRUWhUqnUNol82ZMnTwgICMDR0RFjY2OqVq2Kp6cnGzdulLVghBDiFWShCfFerV+/nkGDBuHv76+2w3RO09fXV9tv6EOVkJBA8+bNSUhI4Pvvv8fBwQF9fX1iYmKYOXMm9erVo1KlSnkdphBCfHC0aoEB2LFjB506dcLJyQlbW1uqV6+u9mVra5sbcYoCYPHixQwaNIjAwECN5GXNmjU4OjpiZGSEg4MDCxcuVGt9ePToEYMHD6ZKlSqYm5vTokULTp48+cpnvdzyERQUhJmZGQcOHMDJyQlTU1NatmzJlStX1K6bPXs2VatWxczMjD59+jBt2jTs7Oxy7iW8ZNKkSVy9epWIiAi8vb2xtbWlcuXKeHt7c+DAgXyRhAkhRF7QqgVm+vTpTJ8+HQMDA2rVqqXMQhLiTQICAli4cCGrV6/W2GH6l19+YcqUKcyYMQN7e3vOnz/P4MGDKVy4ML6+vqSnp9OxY0dKly5NcHAwZcqUYe3atbRq1Ypjx45hbGycrRieP3/O7NmzWbBgAUWKFMHPz4+hQ4eyefNmADZt2sT06dOZOXMmzs7ObN26lblz52JgYJDj7wMy9mzatGkTHTp0wMxMs7+8aNGiufJcIYQoCLRKYH766Sfc3d1Zv349RYoUya2YRAGzb98+du/eTXBwsEbyAjBz5kz8/f1p3bo1AJaWlsTHx7N8+XJ8fX2JjIzk7NmzXLx4kWLFigEwZswYdu3aRXBwMIMHD85WHCkpKcyaNUsZkDZw4ED69+9PWloaurq6/Pjjj3Tu3JmuXbsCMHToUKKiorh48WJOvAYNDx48ICEhARsbm1y5vxBCFGRaJTDJycm0atVKkhehFVtbWx4/fsz06dNxdHRUW87//v373Lhxg2+++YZvv/1WKU9JSVF2aT59+jTPnj3T2Ok8KSmJ+Pj4bMdRpEgRtdH0xsbGJCcn8+jRI8qUKUNcXJySvGRycHDItQTmXXehvnDhQg5FAlA8B+8lhMjZf595K6/q8qbZT1olMP/73/84efIkPj4+7xSU+LgYGxuzbt06WrVqRZs2bdiyZYuSxGSOc5k9ezaOjo5ZXp+WloahoSE7d+7UOFaqVKlsx/Hy5og6OjpqMfy37H0oX748KpWKuLi4t7o+R6c2Rss0aiFy0oc69VhbBWYa9cyZMzl58iTTpk3j+vXr7/w/SPHxMDU1JSwsjGfPntGqVStlcUNDQ0NMTU2Jj4/H2tpa4wsy9uC6e/cuurq6GscrVKiQYzHa2Nhw4sQJtbKXP+ckXV1d2rVrx4YNG/j7b80EIikpiaSkpFx7vhBC5GdatcCUL1+e9u3bM3HiRGbMmJHlOTo6Oq9d80J8vIyNjQkLC1N2lt66dSvlypVj5MiRDB8+HAMDAz777DOSk5M5ffo0t27dYujQoXh4eNCgQQM6d+6Mv78/VatW5e7du0RERODh4YGzs3OOxNe3b1/69+/PJ598grOzM2FhYfz+++9qXV6vcu3aNc6cOaNWZmlpqXx/7tw5jcHAtWrVYty4cURHR9OkSRNlGnWRIkU4evQoc+fOJTg4WKZRCyFEFrRKYCZMmEBgYCCVKlXCwcGB0qVL51ZcooAyNDRk27ZtShITGhpK165dKV68OIGBgUycOJGiRYtia2tL7969gYykOCQkhICAAAYPHsy9e/cwNDTE0dGRTp065Vhs7du358qVK/j7+5OYmEjLli3p0aMHO3bseOO1Y8eO1Shbv349JUqUAMDLy0vj+I0bN1CpVPz2228EBgYyb948rl27RqlSpahWrRrDhg2jYsWK714xIYQogLTaSsDa2hpnZ2fWrFmTmzEJ8cHo0qULKSkpBAcH53UouUq2EhAiZ8lWArlPqxaYtLQ0GjdunFuxCJGnnj17xvLly2nSpAl6enps3bqVHTt2sGrVqrwOLdcdc3n2wf6Qelsf8g/et1HQ6gNSJ/FutBrE27x5c6Kjo3MrFiHylI6ODhEREbRo0QI3Nzc2b97MkiVLsuz+EUIIkbe0aoH59ttv6dGjB4MHD+brr7/G3NycQoUKaZyXkzNDhHhfihUr9spNGYUQQnxYtBoDU6ZMmf+/8DXrZWROkRVC5A8yBkaI/OV9jbH5kLvEtGqBGT58+Htd6EuIkydP0qhRI06fPv1ephNHRUXh5eXFpUuXKFeunMZnIYQQHwatEphRo0blVhwin/Pz8+Phw4cf7GwdT09PatSowcyZM9XKQ0ND6datGwkJCQA4OjoSGxtL2bJl33uMV69exd7enn379vHJJ5+89+cLIUR+otUg3pc9efKEJ0+e5FQs4iP24sWLvA4BAH19fYyMjN57S+OHUn8hhMgvtE5grl27Rp8+fbC2tqZSpUpUqlQJa2tr+vbty7Vr13IjRpEPrVmzBkdHR4yMjHBwcGDhwoVqew6pVCp++uknvL29MTU1ZeLEiQBERERQr149jIyMaN68eZYbKR45coQWLVpgYmKCra0tQ4cO5fHjxzkSd1RUFCqV6pWrSQcFBWFmZsbOnTtxcHDAyMiIli1bcuXKFbXzdu7cibu7O0ZGRtSuXZtJkyapJSl2dnZMnTqV/v37Y2FhQe/evbG3twegUaNGqFQqPD09c6ROQghREGmVwFy4cAEPDw82btzIJ598Qt++fenTpw+ffvopGzZsoFGjRrm2c6/IP3755RcmTZrE6NGjOXLkCAEBAcybN49ly5apnTd9+nQ+++wzDh06RK9evbhx4wZdunTBw8ODqKgofH19GT9+vNo1f/75J+3atVOm9K9evZqzZ88yYMCA91a/58+fM336dBYuXEh4eDipqal06dJF2Rtsz549+Pr60rt3bw4fPsyCBQsIDQ1VkrRMixYtwsbGhv379zNu3Dj27t0LwKZNm4iNjZUFI4UQ4jW0GgPj7+9Peno6+/bto3bt2mrHzp49S+vWrfH392f16tU5GqTIX2bOnIm/vz+tW7cGMvYEio+PZ/ny5fj6+irntW3blq5duyqfJ06ciLm5OTNmzEBHRwcbGxsuXrzI5MmTlXMCAwNp27YtAwcOVMp++OEH3NzcuHfv3mun8P/888+sXbtWrSw1NVXr+qWkpDBt2jQaNGgAwJIlS6hTpw4HDhzAw8ODWbNmMXDgQLy9vQGwsrJiwoQJ9OnTh0mTJindU87OzgwePFi579WrVwEoW7YsRkZGb4wjZ7e4L56D9xJC5Lac/ff/4Tzrv940+0mrBCY6Opq+fftqJC+Q0STeu3dvlixZol2EokC5f/8+N27c4JtvvuHbb79VylNSUjR2L395oGpsbCx169ZVG39Sv359tXNOnz7N5cuX+fXXX5WyzPvGx8ezb98+vvnmG+XYhg0blM0e27Zty8iRI9XuFxERwfDhw7Wqo66uLg4ODspnCwsLTExM+Ouvv/Dw8OD06dOcOHGCefPmKeekpaWRmJjInTt3MDY2zrL+2srRqY3RMo1aiPzkfU1tLjDTqF+8ePHaDRwNDAxkMOJHLnOcy+zZs3F0dHztuZkbHWZ6OcF51f27du1Kv379NI5ljompW7euWlkmAwMDrK2t1a7JTkuHttLS0hgxYgRt2rTROFa+fHnl+5frL4QQIvu0SmBq1KhBcHAwPj4+FCtWTO3Y8+fPCQ4OpkaNGjkaoMhfDA0NMTU1JT4+XuudoqtXr87WrVtJT09XWmGOHTumdo69vT3nz5/XSET+q1SpUtoHroW0tDROnDihJGjXr1/n1q1bVKtWTYkxLi7utTFmRV9fH3i7bi0hhPjYaJXADB06FG9vbxo1akTPnj2VZqW4uDhWrFjBhQsXZOChYOTIkQwfPhwDAwM+++wzkpOTOX36NLdu3WLo0KGvvM7Hx4cFCxYwcuRIevXqxblz51i5cqXaOYMHD6Zp06Z88803dO/enVKlShEXF8euXbuYO3dublcNAD09PUaNGsW0adMoWrQoo0ePpnr16nh4eAAZCz527NiRihUr0rZtW/T09Dh//jzHjx/XGMj7XxUqVKBYsWLs2bMHCwsLihQpgoGBwXupkxBC5DdazUJq0aIFS5cuJSEhgeHDh9OuXTvatWvHiBEjSEhIYOnSpTRv3jy3YhUfsLS0NGVfrK5du7JgwQKCg4NxcXGhefPm/PLLL29cSbdixYqsXr2aPXv24OLiwqJFizRmIdWqVYsdO3Zw7do1WrZsiYuLCxMnTnyv+28VKVKEb7/9lr59+9KkSRPS0tJYs2aN0mrUuHFjQkJCiI6OpnHjxjRu3Jg5c+Zgbm7+2vvq6ekxffp0Vq9eTfXq1encufP7qI4QQuRLWu2FlCklJYVTp04p675YWFhQp04d9PS0atARBUjbtm2xsrJi9uzZeR1KrgoKCmL48OH8/XfBGvT6IQ/Ue1sFrU4FrT4gdcoPPuT6vFXGoaenR926ddUGS4qP04MHDzh8+DAHDx7Ex8cnr8MRQgjxkXhjF9KdO3eoV68ekyZNeu15kyZNon79+ty/fz/HghMfvu7duzN8+HAGDRqEl5dXXocjhBDiI/HGLiR/f39WrVrFqVOnXju74/Hjx3zyySf06NGD77//PscDFULkHtXKgtUlJkRBluBj9t6e9SF3Ib2xBSY8PJx27dq9cWpq6dKlad++PTt37syx4LJSo0YNFi1alGv3DwgIwMXFJdfu/6FZtWoVFhYWefLsOXPm5Omuy998842yWrAQQoj85Y0JTHx8PLVq1crWzWrWrMnly5ezda6fnx8qlUrj68yZM6+9LjIyku7duwMZg4lVKhVhYWHZeub7FBAQoNSpbNmy2Nra4uvry82bN/M6tHzp22+/pWzZsjJNXwghBJCNBEZHR0dtF+HXSUtLU1sG/k08PDyIjY1V+3rVQniZK/yWL1+e4sXzx74ttra2xMbG8ueff7Js2TLOnj1Lz5498zqsfCcxMZGNGzcyZMiQD26freTk5LwOQQghPkpvTGAsLCw4fvx4tm524sQJrbojihQpgpGRkdpX5lTsZs2aMWzYMEaPHk3lypXx9PQE1LuQMvdk8vb2RqVSKd0R165d46uvvsLS0hITExPq16/Pli1blOf+/fff9OzZEysrK0xMTHBzcyM6OlottpCQEOzt7alYsSLe3t78888/2a5XpkKFCmFkZISJiQkNGzbE29ubw4cP8++//yrnZNUl1qxZM7U9e0JDQ3F2dsbY2BhLS0s8PT2VwdJvquuNGzfo3r07lSpVwtLSko4dOxIfH69VPQIDA3F2dsbU1JQaNWowZMgQHj16pBzP7Ibat28fjo6OmJqa4uXlpUyzzzR79myqVq2Kubk5fn5+PHv2LFvPDw0NpUqVKgwdOpQ//viDuLg4teOZ3X6vWxIeVwAAIABJREFU+zNLSUlh1KhRWFhYYGlpyffff6+RmKelpTF79mzs7e0xNjbG2dmZjRs3KscvX76MSqVi8+bNtGzZEiMjI1avXk1CQgK9e/emcuXKGBkZUadOHZYuXapcd+3aNTp16oSZmRkVK1bk66+/5tatW1rFL4QQQt0bE5jPP/+cTZs2afzSeFlcXBwbN26kWbNmORbcunXr0NPTY9euXSxYsEDj+N69ewFYuHAhsbGxREREABkrBicnJxMWFkZMTAyTJ09W9nB68uQJLVq04ObNm6xdu5ZDhw6pbToIcOXKFbZt28batWvZsGEDJ0+eVNsR+W3cvn2bsLAwChUqpCz4lh03b96kZ8+eeHt7c+TIEXbs2EGHDh2U46+r69OnT2nZsiUlS5Zk+/bthIeHU65cOVq3bk1iYmK2Y9DV1WXatGkcOnSIJUuWcOTIEUaNGqV2TmJiIvPmzWPx4sXs3r2bhw8f8t133ynHN2zYwLRp0xgzZgz79+/H0tKSH3/8MVvPX7VqFV9++SUlS5bE09OTVatWaZzzpj+zefPmERQUxPz58wkPDycpKYnNmzer3cPf35/169cze/ZsDh8+zODBgxk4cKDy9yrThAkT8PX15ciRIzRr1oyJEycSFxfHhg0bOHbsGIGBgcoeS2lpaXz11VckJCSwfft2tmzZwt9//83XX3+ttvdTbvydE0KIguyN68AMGDCANWvW4OXlxeTJk2nTpo3agnUpKSls2bKFMWPGUKpUKQYMGJDth0dERGBm9v+jqZ2cnNT+x2tlZfXapdczN8YzMDBQ25Tv+vXrdOjQQRm7Y2lpqRwLCQnh4cOH7N+/nzJlyijP+a+0tDQWLVqkDFz++uuv2bRpU7brlencuXOYmZkpOxED9O/fn6JFi2b7Hrdu3SIlJYU2bdpgamoKoNbN9rq6bty4kcKFCzN//nylay8wMBBra2t+++03WrVqla0Y/vtnamlpyfjx4/Hx8WHhwoXKfZOTk5kzZ47yLvv376+2bcDixYvx9vamW7duAIwYMYLIyMg3jgn6v/buPK6m/H/g+KvbRmmxVEoqksqaNSHUKNkpxDD2GGOnbA0ZpmGUZcQo+1qYb7IMyjITYWxjJxGiLJE1tN76/eHXGXeylMl0az7Px6PHwz3n3HPfn+7Ved/P+bw/nxs3bnDq1CkpaenduzfDhg3Dz88PdXV16biPvWfLli1jwoQJ0qDdgIAADh48KO1PTU0lODiYXbt2SStgW1hYcPr0aVauXEnbtm2lY0eMGKHwu0tMTKR+/fo0bNgQQKEX8uDBg8TFxXH+/HlpJt4VK1bQuHFjjhw5gqOjY4Hif5eiXeK+ZNyWFQShqP/vK9/r5flY9dNHE5hKlSrxyy+/0LdvX4YNG8aYMWOoUaMG5cqV4+XLl8THx5Oeno6xsTGbN2+mYsWKBQ6uefPm/PTTT9Ljv1/YP7VC5euvv8bHx4d9+/bRqlUrOnfuTP369QG4cOECdevWlZKXdzEzM1OoujI2NubRo0eFjsPKyoqwsDAyMjLYtWsXkZGRfPvtt4U6R/369WnZsiX29vY4OTnh5OREly5dpN/zh9p67tw5bt68mW8K+9evX5OQkFDgGKKjo1mwYAHXr18nNTUVuVxOWloaKSkp0hT+WlpaComgsbEx6enpvHjxAl1dXa5du4aXl5fCef9+u+tdNmzYgLOzs5Sstm7dGnV1dfbs2aNQQfSh9+zJkyekpKTQpEkTab9MJqNRo0bSrbjY2FgyMjLo3r27wutnZWXlW5Tx75/LIUOGMGjQIM6cOYOTkxNubm60aNECgLi4OKpUqaLwHlhaWmJgYEBcXJyUwHzKZ65ISxuPiDJqQSgp/s2yZmUuoy7QTLwNGjTgjz/+YM2aNURGRhIXF0dqaio6OjrUq1eP9u3bM3DgwEIvPKelpfXBFXs/dbDuoEGDcHFxYf/+/URHR+Pi4oKPjw8+Pj4K3fbv8/clEQozkPlt6urqUvtsbW2Jj49n0qRJBAUFScfIZLJ8Mb09MFRNTY1du3Zx8uRJfvvtN9asWcPMmTPZu3cvtWrV+mBbc3JysLOzY8WKFfliq1ChQoHakJCQgKenJ4MHD2b69OmUL1+eM2fOMGzYMGlgdV6cb8vrmfmU31ue7OxswsLCePjwoUJinJOTw4YNGxQSmA+9ZwV5z/OO3bJli9TTleftnh7I/7l0c3Pj4sWL0nvQs2dPevToweLFixVW1v67t7cX1WdOEAThv6LAiznq6ekxbtw4IiMjuXXrFikpKdy6dYvIyEjGjh1bLKvm5o0nkcvl+faZmpoyaNAg1q1bx6RJk1i3bh3wpkfj0qVLxTJAcvLkyYSGhnLx4kVpW8WKFUlOTpYep6WlER8fr/A8FRUV7O3tmTp1KocOHcLAwICIiAhp/4faeuPGDSpVqkT16tUVfvT19QsU85kzZ5DL5fj7+9OkSRNq1KihMAC1oGrWrMnp06cVtp06deqDz4mKiiI1NZVDhw4RExMj/YSGhvLbb7+RlJRUoNeuWLEilSpVUnj9nJwczpw5Iz22tbVFQ0ODpKSkfL+rqlWrfvQ1KlWqRJ8+fQgJCWHRokVs3LiRrKwsbGxsSEpKUoj1xo0bPHr0CGtr6wLFLwiCIORXqNWolY2KigqmpqYcPnyY5ORknj17BoCPjw8HDx4kISGB8+fPc/DgQeli0atXL/T19fnyyy/5448/SEhI4Ndff81XhfQ5WFpa4urqqjA4s1WrVmzZsoWjR48SGxvLyJEjFb55nzhxgvnz53P27FkSExP59ddfuXfvHjY2Nh9tq6enJ+XLl+fLL7/k2LFjJCQkcOTIEaZOnVrgW0jVq1cnOzub4OBgEhIS2Lp1q0KFTUF9/fXXbNy4kfXr1xMfH09AQADnz5//4HPWr1+Pq6sr9erVo1atWtJP+/btsbCwYNOmTYV6/YULF7Jz506uXbvG5MmTFZa90NPT45tvvmHatGls2rSJW7ducf78eVauXPnOQcNv+/7779m9ezc3btzg6tWr/Prrr1haWqKurs4XX3yBtbU1Xl5enDt3jjNnzuDl5UWjRo3+UxMmCoIgFLUSncAA+Pv78/vvv1O7dm2cnJyAN9+uvb29sbe3x8PDAxMTE5YuXQqAjo4Ou3fvxtDQEE9PT5o3b05AQECh5q+Jjo5GX1+fP/74o9Dxjho1isjISKk0feLEiTRv3pw+ffrg4eGBo6MjtWvXlo7X1dXl2LFj9OzZk8aNG+Pn58fUqVPx8PD4aFvLlSvH3r17MTU1pX///jRt2pSRI0fy8uXLAveY2dnZ4e/vT1BQEM2aNSM0NJTvvvuu0O3u1asX3t7ezJo1i9atW3P9+nWGDx/+3uPv37/PgQMH3jtTbrdu3di0aVOBbg8BjBs3Dk9PT0aNGkXbtm1RVVXNN97Fz88Pb29vfvrpJ5o2bYq7uzu7d+/G3Nz8g+dWV1dn1qxZtGzZEjc3NzIyMggNDQXe3CLcvHkz+vr6dOzYkS5dulClShWlm89GEAShpPnoWkhCfuvWreOHH37g1KlTUsmyIJRkyjxQ71OVtjaVtvaAaFNJoMztKfE9MMVh3759zJo1SyQvgiAIglBMClSFJCgqzNgLQRAEQRCKnriFJAgC+mvEPDCC8Lk9G1Tl4wcpGXELSSiQYcOG8eWXX36Wc+et45O32vfHHiuTJk2aEBAQ8NnO/zlXNc9bJ+pDFi5c+MmTNgqCIPxX/acTmBEjRqCvr5/vp7gu4gEBAfkWdvyY9yU9p06dQl9fn7t333yzNjc3/+Bq3yWZXC5nwYIFNG3aFGNjYywsLHBycvqkcu+i1rNnzwIvhprncyaygiAIpcV/fgxMmzZtCAkJUdj2vuUQMjMz0dDQ+GyxfM7JAPNWxi6N/P39Wb9+PQEBATRo0ICXL19y/vz5T5pwr6iVLVuWsmXLFncYgiAIpc5/ugcGQFNTEyMjI4WfvGnd3dzc8PHxYdq0aVhaWtKxY0fgzYKIzZs3x8TEhFq1ajFu3DieP38unTPvtkFkZCSNGzfG2NiYL7/8khcvXhAREUGDBg0wMzNjxIgRpKenS8/7N28hfUxmZiY+Pj5YW1tjaGhI7dq1mT179nuPT0lJYfDgwdSqVQtjY2McHBwICwtTOMbNzY1Jkybh5+dHtWrVsLKyws/PT2HivocPH9K7d28qV65M3bp1pflUPmTv3r0MGTKE7t27Y2FhQZ06dejbt6/CatgAGzduxMHBASMjI2rWrJlv4dEnT57w1VdfYWJigp2dncLConm/v4iICNq3b0/lypVp1aoVsbGxXLp0ibZt22JiYkL79u25c+eO9Lx33UJasGABVlZWmJqaMmLECF6/fi3t+/7779m6dSt79uyRegQ/Zb4hQRCE0u4/n8B8TFhYGGpqakRGRrJkyRLgzeRkc+fO5dixY4SEhHDixAmmTp2q8Ly0tDRCQkJYuXIlERERnD59mgEDBrB161Y2btzIhg0b2L17N2vWrCmOZn3Uzz//zN69e1mzZg2nT59m1apVH1y3Ki0tjQYNGrB582b++OMPvLy8GD16NDExMQrHbd68mbJly7J//37mzJlDUFAQO3fulPYPHz6cO3fusHPnTjZs2MCGDRuk22DvY2hoSExMzAcXP1yxYgXe3t589dVXHD16lC1btuSbyv/HH3+kS5cuHDlyhM6dO/PNN9/ke+05c+YwceJEDh8+jLa2NkOGDGHKlCn4+flx4MABXr58me+z8LZffvmFuXPn8u233xIdHY2FhQXBwcHS/nHjxtGlSxfatm1LXFwccXFxNG7c+IPtFwRB+C/6z99COnDgAFWq/DUy3MHBQeGbd7Vq1Zg1a5bCc97+5m5hYYGfnx+DBg1i6dKl0oy+WVlZLFiwQFqh2d3dnZUrV3L9+nVpJWw3NzdiYmIYMWLEP2pDVFSUQhvgny2iCJCYmIiVlRUODg6oqKhgZmZGs2bN3nt81apVGT16tPR48ODBREdHs23bNmnFZYDatWszZcoUAGrUqMHatWs5dOgQ3bp14+rVq/z+++8cOHBAumgvW7bsowNcf/jhBwYOHIi1tTXW1tY0adIEV1dXOnbsiIqKCrm5uQQGBjJq1Ci++eYb6Xl/P2+fPn3o2bMnANOnTyckJITjx49Lsx4D0ky+ACNHjqRfv36EhoZKbfTy8vrgiuPLli2jX79+DBgwAHizPtbhw4e5d+8e8Gb25DJlypCdnf3RW35Fu8T9py2cKghCwRXt/9l/T3HF/bHqp/98AtO8eXN++ukn6XGZMmUU9r/r4hkdHc2CBQu4fv06qampyOVy0tLSSElJwcDAAHizYnFe8gJvegkqV64sJS8ABgYGBV6T6EMcHR1ZsGCBwraLFy9KF8lP0bdvX9zd3WncuDHOzs64urryxRdfIJO9u9MuOzubBQsWsH37du7fv09mZiYZGRnS8g553l4mAcDY2FjqOYmLi0NNTU3hd25hYYGhoeEHY61duzYnTpzg7Nmz/PHHHxw7doz+/fvj4uLC5s2befDgAcnJybRq1eqD56lTp470bw0NDSpWrJivV+ftY/LientgtIGBAS9evHjveKlr167h5eWlsK1p06Zs3779g7G9S5GWNh4RZdSC8LkpaznyhyhzGfV/PoHR0tL64K0RLS3Fb6YJCQl4enoyePBgpk+fTvny5Tlz5gzDhg0jMzNTOi5vHE0eFRUV1NXV8237pz0l72vD48eP/9E5GzZsyIULFzh48CCHDx9m2LBhNGjQgPDw8HeuG7Vo0SKCg4OZM2cOtra2lCtXDj8/P168eKFw3Lt+L3//HRRmXao8MpmMRo0a0ahRI0aNGsWmTZsYOXIkx48f/+haRoWJ7e1j8uJ8+33N21YU76sgCILwfmIMTCGdOXMGuVyOv78/TZo0oUaNGkpR7fI56Orq0r17dxYuXEhYWBi//fYbt2/ffuexx48fp0OHDnh6elKvXj2qVatGfHx8oV7P2tqa7Oxszp49K21LSEjg4cOHhY49b7XuV69eYWxsjKGhIYcPHy70eYpazZo1OX36tMK2U6dOKTxWV1dHLpf/m2EJgiCUOP/5HpjCql69OtnZ2QQHB9OhQwdOnjypFPONFLWgoCBMTEyoW7cuqqqqhIeHo6urS+XKld95vKWlJbt37+bEiRPo6+sTHBzM3bt3P3r75202Nja0adOGMWPGsGjRIjQ0NJg2bdpHy5D79u1LixYtsLe3x8DAgFu3bjFr1iyMjIxo3LgxKioqTJw4ET8/PypWrIirqysvX74kJiYmXyXS5/b1118zevRo6tevT/PmzYmIiOD8+fNUqlRJOsbMzIxDhw4RHx9P+fLl0dPTy9c7JAiC8F8nemAKyc7ODn9/f4KCgmjWrBmhoaF89913n+W11q9frzAZ3b9JW1ubRYsW4eTkhJOTE1euXCE8PDzfGKE8U6ZMoX79+nh4eNCxY0f09PTo3r17oV83ODgYU1NTOnXqRN++fenTp0++Acp/17ZtWyIjI+nduzeNGzfmm2++wcLCgp07d6Kvrw+8qW6aO3cua9asoVmzZvTs2ZNr164VOr5/qlevXnh7ezNr1ixat27N9evXGT58uMIxgwYNwtLSkjZt2mBpaZmvh0YQBEEQayEptdmzZ7N3715iYmJQVVUt7nCEUkyZB+p9qtLWptLWHhBtKgmUuT2iB0aJ7d+/n8DAQJG8CIIgCMLfiBvrSkwZBp0KgiAIgjISt5AEQUB/jZgHRhCU0bNBHx4D+LmJW0iCIAiCIAhFSCQwQrEYMWKEtFihvr4+1atXx9PTs8gqgzp27IiPj0+RnOtjYmJi0NfXf+/kgXPmzMHBweGD57h16xajRo2idu3aGBoaUrduXfr378+JEyc+R8iCIAglnkhghGLTpk0bacHCbdu2kZaWRr9+/f7RObOzs8nNLVl3Rc+ePUvr1q25evUqgYGBnDhxgrCwMOzs7Jg0aVJxhycIgqCURAIjFBtNTU2MjIwwMjLCzs6Ob775hmvXrpGWliYdM3PmTBo3bkzlypWpW7cuM2bMID09Xdqf17uxadMm7OzsMDQ0ZMCAARw9epQVK1ZIPTx5MwgfPXqUL774AiMjI6ysrJg6darCEhAdO3Zk4sSJzJo1i+rVq1OjRg2+/fbbz7Y0QG5uLt988w3m5uZERUXRvn17qlWrRp06dZgwYQI7duz4LK8rCIJQ0okqJEEppKamsm3bNmrVqqUw866WlhZLlizB2NiYuLg4JkyYgIaGhsKKz7dv3+Z///sfa9euRUNDgypVqvDgwQOsrKyYMWMGAJUqVeLevXv07NkTT09Pfv75Z27dusWYMWOQyWT4+/tL5/vll18YPnw4+/bt4+LFiwwdOhQ7Ozt69OhR5O2+cOECsbGxrFix4p3l8nkT8QmCIAiKRAIjFJsDBw5Is+y+evUKU1NTtm7dqnDM27dQzM3NmTBhAkFBQQoJTGZmJiEhIQrLFqirq6OlpYWRkZG0bdWqVRgZGTF//nxkMhnW1tb4+fkxfvx4fH19pYU7ra2t8fX1BaBGjRqsW7eOQ4cOfZYE5ubNm8CbNZIKq2iXuNf6+CGCIPzrivb/ecmK4WPVTyKBEYpN8+bN+emnnwB4+vQpK1euxN3dnQMHDmBqagrAjh07WLZsGTdv3uTVq1fI5fJ8Cx2amJgUaM2luLg4mjRpgkz2151TBwcHMjMzuXnzJnXq1AGgdu3aCs+rXLkyjx49+kdtfZ9/Ml6nSEsbj4gyakFQRsVdwizKqAXhHbS0tKhevTrVq1enUaNGLFmyhNTUVNauXQu8WaV58ODBODs7s3nzZg4fPoyvry9ZWVkK59HW1i7Q6+Xm5qKiovLOfW9vV1dXz7fvcw0MtrS0BCiWdZkEQRBKMpHACEpDRUUFmUwmDeI9fvw4xsbGTJo0iYYNG2JpaUliYmKBzqWhoZGvp8bGxoZTp04pDMj9448/0NDQoFq1akXXkEKoV68eNjY2LF68OF+8AM+ePSuGqARBEJSfuIUkFJuMjAySk5OBNxfqFStW8PLlS9zc3IA340/u37/P1q1badq0KQcPHiQ8PLxA5zYzM+PPP//k9u3blCtXjvLlyzNkyBCWLVvGxIkT+frrr0lISOC7777Dy8tLGv/yT1y5cgU9PT2FbXm3pdLT07lw4YLCPi0tLWrUqMHSpUvp1q0b7dq1w9vbG2tra16/fs2BAweIiIggOjr6H8cmCIJQ2ogERig20dHRWFtbA6Cjo4OVlRVr167F0dERgPbt2zNmzBimTp1Keno6Tk5OTJs2jYkTJ3703KNHj2bEiBE0a9aMtLQ0zp8/j7m5Ob/88gszZszA0dERPT09evToIVUq/VOdO3fOty0pKQl4M1Fdq1atFPbZ2dkRHR1No0aNiI6OZv78+UyYMIFHjx5hZGREw4YNCQgIKJLYBEEQShuxFpIgCGItJEFQUmItpPcTPTCCIHCq5Wul/SP1qZT5D++nKG3tAdEm4Z8Rg3gFQRAEQShxRAIjCIIgCEKJIxIYQRAEQRBKHJHACIIgCIJQ4ogERhAEQRCEEkeUUQuCIAiCUOKIHhhBEARBEEockcAIgiAIglDiiARGEARBEIQSRyQwgiAIgiCUOCKBEQRBEAShxBEJjCAIgiAIJY5IYAShFMrOziYrK6u4w/hscnPF7A+C8F8nVqMWhFLm6tWr/Pjjjzx48IBq1arh7OxMjx49ijusfyQ1NZXU1FS0tbXR0dFBJpORk5ODTCa+gykz8R4pv9zcXFRUVIo7jE8iJrIThFIkPj4eZ2dn3NzcqFatGtHR0bx69Yr69euzdOnS4g7vk1y+fJmxY8eSkpKCjo4Otra2BAYGoqurW9yhfbJHjx6RlJQEgKmpKQYGBkDJvpgkJiZy5MgRVFVVMTExoWXLlsUd0j92+/Ztzp49y4MHD2jXrh0VK1ZEV1e3xL5P8fHx7Ny5k9u3b9O6dWvq1q2LlZVViW2PSGAEoZTIzc3F39+fa9eusX79egBev37NunXrWL9+Pba2tqxevbqYoyycO3fu4OzsTK9evXBycuLixYts376dJ0+esG3bNmrWrFni/vhevnwZT09PdHV1uXbtGs2aNaNr1654eXkBJTOJuXz5Ml27dsXc3Jx79+6RkZFB165dmTVrFjo6OsUd3ie5fPky3bp1o0qVKty7dw+5XI67uzuDBw/G1ta2xL1PsbGxuLm50axZM5KTk8nMzCQnJ4e5c+fSpk2bEtlbpjplypSZxR2EIAj/nIqKCps3byYxMZGvvvoKAHV1dWrXrk2ZMmU4cOAASUlJtG7dupgjLbjo6Gji4uL4+eefsbW1xcHBgZYtW3L69GmWLVtG9+7d0dXVJScnp0RcTFJSUujatSudOnUiMDAQFxcXnj59yrJly3j16hWtWrVCRUWlRF0cX7x4Qb9+/XB1dWXt2rV07dqVevXqsWjRIs6ePYujoyPlypUr7jAL5fnz5wwZMgQ3NzeWLFmCt7c3MpmMY8eOcfjwYWrVqoWRkVFxh1lgmZmZTJ48mTp16rB69WoGDRqEra0tycnJzJo1iwYNGmBpaVmiPncgBvEKQqmQN6i1fv36ZGdnc+XKFWmflpYW7u7utG7dmujoaB49elRcYRbaw4cPiYuLU7gAWllZERQURNWqVenZsydpaWkl5ptjUlISZcuWZfjw4VSpUoXWrVvj4+PDpEmTCAoKIiAgAKBEXUTkcjlZWVm4uLigqqqKmZkZ3bp1Y/v27Zw+fZrJkydLx5aUwdcZGRk8ePCAxo0bS7cqR48ejZeXF5mZmcybN4+EhITiDbIQ5HI5t2/fxszMTPpsNW/enClTptCzZ0+8vLw4ceJEifrcgUhgBKFUyPvD4+rqSkJCAosXL+bZs2fSfj09PUaPHs3Zs2c5efJkcYVZYDk5OQC0bt0aIyMjgoKCkMvl0v6qVasyffp0cnNz2bdvX3GFWWhqamrcunWLa9euSdsMDQ3p27cvkydPZsOGDURGRhZjhIWnoqJCUlISsbGx0racnBzs7OzYsGED+/fvZ9GiRdKxJYWOjg73798HkD573bt3p2/fvsTHx/Pbb78Bf31WlVnZsmWpWbMm58+f58WLF9J2CwsLRo8ejb29PSEhIbx+/boYoyw8kcAIQilSrVo11qxZQ0REBLNmzeLhw4fSPnV1derWravUg1/T09OBN9+AASpXrkyTJk3YvXs3e/fulY5TUVGhcePGpKenc/HixWKJ9VMYGBhgb2/P7t27uXfvnrRdX18fDw8PTE1NOXv2bDFGWHj6+voMGjSILVu2cOjQIQBkMhlyuRwHBwe8vLyIjo7m5cuXJaYHxtDQkNq1axMcHExSUhKqqqpSEtOnTx8aNWrEihUrAEpM71+jRo2IjY1l79690v8vABsbG1xcXDh69CgvX74sxggLr2T85gVBKLBWrVqxdu1aNm3axNixY9m8eTOXL19m8eLFUmm1MoqNjWXIkCF06NCBr776it27d6Orq8vs2bPJzc0lKCiI8PBw6XhNTU2srKwoX758MUb9Yc+fP+fevXskJycDYGRkRI8ePdi2bRthYWE8efJEOtbc3Jxq1apx/Phxhd4mZfPw4UPOnTvH0aNHpd6H7t27o6enx/Llyzlx4gQAqqqqwJukLTk5GVVVVaXtgXn06BFnz56VKo4AFi9eTNmyZfnqq694+vSp1B4AR0dHNDU1SUtLK66QPygxMZGwsDC2bt3K4cOHARg+fDgNGjTA19eXffv2KSQrebfKXr16VVwhfxIxiFcQSiErKytcXFyIjIxk586dbN++nZs3b7Jx40Zq1qxZ3OHlc+PGDdq2bUvTpk2lBGvWrFk8f/4cNzc3OnToQFRUFL///jvHjh3j9evXrFq1in379jF79mwqVKhQzC3I79KlSwwaNIjly5cTERHWj19HAAAfqElEQVTBoUOHcHZ2xt7eHplMhr+/P5qamhgbG0vx79ixAyMjI9q1a6eUF/tLly7h7u7O9u3b2bx5M2FhYZiZmdGyZUtMTEzYvXs358+fR1tbGysrK7Kzs9m5cyfp6el0794dDQ2N4m5CPpcvX6ZTp04cPHiQRYsWcebMGV68eEGzZs1o2bIloaGh/O9//6NBgwZoa2ujqanJmjVreP78OZ6enqirqxd3ExRcvnwZNzc3bt68ya5duwgPDyc+Pp727dvTrVs3jh07RmhoKADGxsaoqamxfPlybt26hZeXF2XKlCnmFhScKKMWhFLsxYsXPH36lFevXmFkZETFihWLO6R38vf35+TJk+zYsUPatnXrVkaMGEH//v1ZuHAhT548YdOmTezevZuXL19Svnx5fvjhB+rWrVuMkb/bnTt3aNu2Lb1796ZFixYkJSWxYsUKMjIyWLNmDXZ2dixdupTg4GBMTEwwNTVFRUWFqKgooqKiqFWrVnE3IZ+HDx/Srl073N3d6dWrFwAzZ87k7NmzjBo1ilGjRhETE0NISAjR0dHUqFEDLS0tLl++zK5du6hXr14xtyC/lJQUvvjiCzp06MCoUaOIj4/n119/ZcuWLQwfPhxfX18SExMZPHgwd+/eRVdXFxMTE06fPs3u3buV7rP34sULOnfuTLNmzfjhhx+4e/cuZ86cYdy4cTRq1Ij169ejra2Nt7c3J0+eJDY2lrp163L79m22bdtG/fr1i7sJhSJm4hWEUkxXV1epx7zkefz4sfRNNicnh9zcXHr16oWmpiaDBw/GwsKCsWPHMnLkSEaPHk1qaipqamqULVu2mCN/twsXLmBkZMSECRPQ19cHoEuXLgwYMIB+/fqxY8cORo4ciZWVFRcuXOCPP/6gevXqSpu8ANy/f5/c3Fx69OiBtbU1AGFhYfj4+BASEoKWlhaDBw+mWrVqxMXFcfDgQUxMTFi8eDE1atQo5ujf7e9VYVWqVMHW1hZzc3NmzZqFhoYGPj4+7N+/n40bN3L//n00NDQICAjA0tKyuMPP510VYWZmZlhYWNCjRw+GDRvGpk2bCAwM5OrVq1y9epWyZctSq1YtqlatWtzhF5pIYARBKHa1atUiLCyM69evS7cecnNzpcnQvv/+e9q1a4eNjQ2A0k+OlpycTGJiopS8ZGdnY2BgwObNm3F3d2fgwIHExMTg6uqKq6srubm55ObmKvWA0BcvXvDs2TMpxtevX6OlpUVAQADp6enMmTMHZ2dnLCwsMDU15YsvvijmiD/u7aowCwsL4M0A3n79+pGZmcmaNWuwtbWlU6dO9OvXr3iDLYC3K8Latm0LKFaE9ejRg8DAQLy9vbGxsZH+P5VUYgyMIAjFrkqVKpw5c4b9+/fj4OBAhQoVyM7ORlVVlcqVKxMREUHDhg2VcvzO2/ImAjMwMCA0NJT09HRatGghVeWULVuWhg0bEhoaipaWFnZ2dtIMqMo45uVtZmZmbNmyhXPnztGjRw/U1dXJyMhATU2NDh068Msvv3Dz5k06dOhQ3KEWWG5uLmfOnOHRo0fUr19fSozLlCmDiYkJMTExlClTBkdHR+m9VebJ3sqUKcOzZ8/YtWsXlpaWWFhYoKKiglwux8zMjNTUVE6ePEnHjh1RV1dX2nYUlPKm+4IglErx8fH4+voycuRI5s2bx507dzAyMsLLy4uXL18ybdo0bt26Jd1SKl++POXKlVPq+Tbyyr/zVgDX19fH3d2d/fv3s3nzZuCvqpxq1aqho6PD3bt3AeUtw83rccmbT0hFRYXvvvuO8+fP4+3tDbypBMvMzATAzs5O6atY/klVWN7FXpku+p9aEaampqa0n7vCED0wgiD8a65evUrbtm3R0tJCJpPxyy+/cOTIEdTV1fHw8EBLS4ujR4+yceNGqlWrxoMHDwgNDeXEiRNMnjwZPT294m5CPrGxsYwbN461a9cSGhpKxYoVqVWrFra2thw+fJiTJ08il8uxs7MDQENDg6ioKMzMzGjevLlSfqO/fPky/fr1Y8OGDfz44488e/YMfX19aYHGrVu3cvnyZTp06CBdHCMiItDQ0KB9+/aoqKgoXZtKW1VYaawIKyxRhSQIwr8iMzOTESNGoKWlRVBQEPCmCmTcuHEkJSUxYMAABg0axIkTJ1i1ahXbt2/H3NwcmUzG8uXLlbJC4ubNm7Rp0wYPDw8MDQ1JSEggPDycoUOH4uPjw+vXr5kxYwZXrlyhdu3aODk58eeffxIeHs7vv/+ulINbExMTcXJyokePHri4uHD16lXCwsLQ09Nj4sSJODs7s3r1aubOnYuBgQENGjQgIyODPXv2cODAAWxtbYu7CfmUtqqw0lgR9inEIF5BEP4VGhoaPHv2TOpFkcvlVKpUiaCgIGkafXNzc+lb8aRJk9DS0kJTU1Npy783b95Mw4YNWbhwobStefPmzJs3j8zMTGbOnMm8efPYu3cvq1at4tatW+jo6LBnzx6lTF4Ajhw5gpmZGf7+/qiqqvLFF19Qr149QkJC8Pf3R01NjcGDB+Ps7ExAQACpqaloa2srbfICpa8qrDRWhH0KkcAIgvDZ5Y0N0dLSksZ+qKqqkpWVRfny5Zk7dy69evXi559/xtnZGaBE/KF9/fq1VDEll8tRU1NjwIABqKmp4evri6mpKRMmTKB///7079+fjIwMcnNzlXqyMJlMxr1790hKSsLc3Bz4a+bZ+fPns3z5cqpXr46FhQVLly4F3iSjb89Uq2xKW1VYaawI+xTK+e4IglAq5K3FpK6ujrq6OmPHjmX//v3SLSR1dXUyMzOpUKECgYGBHD58mHPnzhVnyIVStWpVzp07x927d1FTU5MGtPbt25fx48czb948bt68KR2vqamp1MkLgImJCRkZGfz555/AX4sVNm3alMGDBxMdHU1cXJzCc5T1Qp+39pKrqyuamprSat9qamrI5XJ0dXVZtGgRz58/Z926dcCb9qqoqChdm94exJ43zsXPzw9488Ugb32joKAgKleuTGBgYLHE+W9SrndIEIRS4+LFizg6OkoL/OXm5tK4cWNmzJiBn58fISEhAAqDCc3MzJRyoO77DB48mLp169KvXz8eP36MhoaGVJE0cOBADAwMOHXqVDFH+WGXL1/mwIED0mNHR0c8PDwYP348Z8+elUrAAdq1a4e1tTUHDx5UOIeyDXAtbVVhsbGxzJgxg/T0dCnBmjFjBpcuXSqxFWFFQfneKUEQSryLFy/i4uJC7969ad26NfDXRa5Pnz5MmDCBadOm4efnx7Vr13j48CG//vorOTk5lCtXrjhDf6/r168zbdo0Bg8ezMKFCzl37hzq6upMnToVgP79+/P06VOFHpZy5cop7WzB8FeS+ffelhkzZtC8eXM8PDw4evSodFGXy+XSHCnKKjY2lgEDBtCxY0e6dOnC3r17KVeuHKNGjaJ8+fKsW7dO6m0B0NbWpkqVKlIirWwrZue9R/r6+pQpU0Z6L5o1a8bw4cM5ePAgo0aNAv76MpCenk6ZMmWQy+VK156iJKqQBEEoUlevXqVNmzaMGzeOKVOmkJubS1JSEg8fPqR+/fqoqamRkZFBeHg4U6dORUtLCy0tLdLS0ggNDZXKjZXJ1atXcXV1xcHBAV1dXX7//XcsLS3p0aMHXl5eREVFMWfOHFJSUggICEBTU5Njx46xbt06Dh48iJmZWXE3IZ8LFy7g5ubG0KFDmTVrVr79KSkpTJw4kX379uHl5YWBgQH3799n06ZNHDx4UCnHKJW2qrBLly7h6uqa7z3KG3P09OlTtm3bxrx586hUqVKJqAgrSiKBEQShyDx//pxevXqRmJjIlStXABg0aBBxcXHcuHEDExMTJkyYQNeuXdHV1eXevXtcuXIFmUyGtbU1VapUKeYW5JeVlcXo0aNRV1eXxu7cvn2b+fPn8+eff9K7d29Gjx7NtWvXCAgI4LfffkNPT48yZcrw888/K2VCduvWLZo2bcq4cePw9fUlMzOT8PBwEhISqFixIi1atKB27doALFy4kN9//51Hjx5hbGzMzJkzlbYM94cffuDkyZNs375d2rZu3TrmzZtHu3btmDlzJhkZGVJVmEwmQ0dHB39/f6VbmDEhIYE2bdrQtm1bVq5cSU5ODgsWLCA+Pp6UlBSGDh2Kq6srMpmMhIQEAgICePnyJdra2owePbrUJy8gqpAEQShCenp6dOrUiaioKLy8vLh+/TpGRkZMnToVGxsbAgMDCQgIQFdXl65du2JiYqLUtyPgzUDj5ORkKc7c3FzMzc2ZOnUqCxYsYNu2bVSpUgV3d3dWrFjBtWvX0NHRQVNTU5oQTdn8+uuvaGpqSgt99unTh4cPH6KiokJiYiI2Njb079+fPn36MH78eIYOHYq6ujpyuRxtbe1ijv79SlNV2NWrV9HW1qZ8+fKcPXuWGTNmAG9mpi5Tpgx9+vTB19eXsWPHlqiKsKIkemAEQSgSb88oGxwczJo1azA1NWXJkiUYGxtLx3Xv3p309HT27t1bXKEWSN6FICMjgwkTJvDkyRNWrlyJlpaWVGJ7584dxo4di4aGBlu2bAFQypl18yQkJPD48WOsra1Zvnw5mzZt4smTJzRr1oy5c+dibm7O7du3mThxIunp6WzcuBF9fX2lbtPbQkJCmD17NsePH8fU1JTMzExpXMhPP/3EnDlzOHbsGNWrVy/mSAsmPDycxYsXc//+fezs7FiyZAkVKlRATU2NoKAgZs+ezf79+xUmeSwp71VREEsJCILwj7x8+ZLXr1+Tnp4uDVht3Lgx5cqVo3nz5tSvXx8VFRWys7ORyWTExsZy9+5dpV7d99y5cwwZMoTu3btTtmxZ9PT08Pf3R19fH3t7e1RUVMjJyUFfX58aNWowc+ZMOnTogJGRkdJePGJjY+nUqRO3bt3iyy+/xNzcnMzMTMqUKcOUKVOwtrYmNzcXfX19atasyezZs2nXrh1Vq1ZV2jb9Xf369fntt9/YunUrnTt3RkdHh/T0dNTU1LC1tWXLli3UqFGDOnXqFHeoBVKrVi10dHR49uwZ3t7e1KxZUxrE27BhQ1avXo2JiQlNmzaVnlNS3quiIG4hCYLwya5evcrkyZN5/Pgx9+/fZ9q0afTu3RttbW369OlDVlaW9AdXTe3Nn5sHDx5gY2MjlYMq2x/cixcv0rFjRwYOHIi2tja5ubm0bNkSPz8/ZsyYQdmyZRkyZIjULm1tbaytrdHS0irmyN/v4sWLuLq6Ur16dY4ePcrNmzepXr06Q4cO5ebNmwrjJXJzc8nIyMDKykqh50zZXL9+nTVr1vDgwQPq1q2Lk5MTdnZ2TJ06lRkzZtC/f382btxI+fLlpecoc1VYQkICO3bs4M6dOzRo0ABPT0/U1dXp0aMHdevWlSYVzHPv3j2MjIykmXj/i0QCIwjCJ4mLi6Njx4707t0be3t7Lly4wKRJk6hVqxYODg4A0orS8Ka0MzAwkIMHD7J3716lnG/j0qVLuLm5MWTIEKnqQ0VFhfT0dMaMGUNOTg4+Pj7cvn2brl27Ym5uztatW0lPT5fGkyibixcv0q5dO0aOHImvry+Ojo4sXbqUOXPmYGhoiKGhoXRsXjIZFRWFvr6+0rbp71VhS5cuJTIyUqoKmzp1KnPmzMHR0VGhKiwlJUUpB1VfvnyZnj17YmNjQ2ZmJmvXruX27dv4+voCvDNJ2bBhA3K5XBps/V8kxsAIglBoT58+ZciQIdSoUYN58+ZJ2z08PKhcuTJLly5VuBe/b98+li5dyvXr1wkLC1PKhRmTk5Np1aoVtWvXZtu2bcjlcqZOncqNGze4ceMGffv2xcXFhbt37zJx4kRycnLQ09Pj1atXStumS5cu8cUXXzBq1CimT59Obm4u48eP5/jx4+zfvx8dHR1ycnKkZPL8+fNERESwatUq9u7dq5S3WkpbVVhiYiLdunWjU6dO+Pn5IZPJiIiIYPz48URGRmJjY6NwfN4XgF9++aVULcz4KUQPjCAIhZaVlcWzZ8/o2rUr8NeA1+rVq5OcnAwo3otv2bIl58+fJzAwECsrq2KJuSCaNGnC7du32bVrF+vWrSM7O5tGjRphY2PD//73Py5dusSSJUvYv38/d+7cITMzE2tra6WtpMrMzGTMmDH4+vpKCaW3tzctW7Zk+fLlTJw4UUpeHjx4wI8//khCQgJ79uxRyuQFSldVWE5ODuHh4VSrVo3x48cjk8nIzc2lXr16lCtXjuzsbIXjX758ybVr1zh//jx79uz5T/e+gOiBEQThE8XHx0sTf2VlZaGurs78+fO5dOkSa9askY579OgRBgYGxRVmoTx48AA/Pz+2b99O8+bNWbVqlXTR27lzJ2PHjiUwMBAPD49ijvTT5OTkSFVVd+/eZf369dLSDSoqKiQkJKCpqam0Y1/kcjk5OTmMGzeuVFSFARw9epQDBw5I6xrlsbOzY/78+fkWYpTL5aSmpkoLU/6XKd9NaEEQSoS85CUnJ0ca65KZmUlKSop0TGBgIPPnz5fWplF2lStXZubMmYwcOZJx48ZRoUIFaXr9Ll26YGhoyPHjx4s5yk8nk8koW7Ys7u7uxMTE8Oeff0oX99zcXCwsLJQyecnriVBVVUVdXZ0+ffqwb98+1qxZIy28mJOTg5mZGb6+vuzbt48LFy4AylmV83bPSosWLaTk5e1p/1VUVKS1nAB27drFpUuXUFVVFcnL/xMJjCAI/8jfB+PmTaLl7++Pv78/X375pVJOFPY+xsbGjBs3ThqInNet/+zZMypUqKCUY10Ky8XFBRcXF4KDg3nx4oVSVoPliY+PZ968edy4cUPa1rJlS2bOnMmMGTNYvXo1QImpCntXe/ISl7zpBtLS0pDJZFLv2OzZsxk4cKDSDqouLmIMjCAI/1jeQFA1NTVMTU1ZvHgxixcv5vfffy+Rgwz/fqFQUVFh6dKlJCcn4+joWExRFa02bdowe/ZskpOTlfbCePPmTdq1a8eTJ094/vw533zzjVROPGTIEF6/fs3EiRO5c+cOnTt3VvqqsPe15+3kUSaTST1K6urqzJs3j+DgYA4cOKCUa2oVJ5HACILwj+V9+5XJZGzatAldXV0iIyOVruLjU4SHh3PkyBEiIiLYsWNHvvk4Spq8MSF9+/Zl27ZtCqXuyuTVq1csXLgQFxcX7O3t8fX1RS6XM3r0aMzNzdHS0sLHxwdzc3P8/PwIDQ1VqAp7uzxcGXysPXlkMhmampro6Ojg7e3NlStXiIqKokGDBsUYvXISCYwgCEXG2dmZ77//nqioqHzlnyWVtbU1W7duZe/evaVigby8b/t6enrs3LlTaW+1yGQy6tWrR/ny5enRowcGBgYMGzYMgFGjRmFhYYFMJqN37944ODiQlJREeno6tra2SlkV9qH2vJ3EyOVyXrx4QUJCAq9eveLw4cP/+Wqj9xFVSIIgFKlXr14p9YJ/n+LtNXWEf8/fP0s7d+7k66+/pk+fPowZMwZzc3Oys7O5f/8+VatWLcZIC+ZD7Rk7dixmZmbI5XKePXvGuXPnMDExKRVJ8+ciemAEQShSpS15AUTyUkzyPktyuRyZTEaXLl3Izc1lxIgRqKioMGLECFavXs2dO3cIDg5GS0tLaQcjQ8Has2rVKu7cucPy5cuVtndMWYgeGEEQBEHp5ebmSvO97Ny5k1GjRlGhQgWSkpL47bffStxg8dLWnuIgyqgFQRAEpZc330tubi5dunShUaNGPH/+nMOHD5fIi31pa09xELeQBEEQhBIjJyeH6dOnEx0dTUxMDLVq1SrukP6R0taef5PogREEQRBKFBsbGw4dOqS06zUVVmlrz79FjIERBEEQShRlX9+osEpbe/4togdGEARBKFFK28W+tLXn3yISGEEQBEEQShyRwAiCIAiCUOKIBEYQBEEQhBJHJDCCIAiCIJQ4IoERBEEoRWrWrMn48eOLOwxB+OxEAiMIglBI+vr6BfrZtGlToc77888/s2XLls8UtaK2bdsqxFq5cmVatmzJ8uXLyc0Vs2sIyk/MxCsIglBIISEhCo/Xrl3L6dOnWbJkicJ2e3v7Qp132bJl1KxZE09Pz38cY0GYmpoyffp0AFJSUtiyZQuTJk3i6dOnTJ48+V+JQRA+lUhgBEEQCunvCUZ0dDRnzpz51xKPoqKvr68Q88CBA2nUqBHBwcFMmjRJzE8iKDVxC0kQBOEze/XqFd9++y116tTB0NAQOzs75s6dS1ZWlnRMzZo1SUxM5ODBg9JtnSZNmgCQlpbG999/T6tWrTAzM8PY2BgXFxf2799fpHFqa2tTv359nj59yvPnzxX2paenM2fOHBo2bIihoSE2Njb4+Pjw4sUL6ZiuXbvSqFGjd567a9euNGzYUGHb/v37ad++PVWqVKFKlSp07dqVP//8U+GYmTNnoq+vz507dxg1ahTm5uaYmpoyZMgQnj17phCfvr4+CxcuzPfabdu2xcPDo9DtEZSb6IERBEH4jHJycujduzcxMTH07duXBg0acOTIEebOncv169dZtWoVAAEBAUyYMIHKlSszZswYAHR1dQF48uQJGzZsoFu3bvTv35+0tDTCwsLw9PRkx44dODo6Flm8iYmJqKmpUa5cOWmbXC7H09OTU6dO0b9/f2xsbKTYL168yO7du1FVVcXDw4MxY8Zw7tw57OzspOc/fPiQI0eOKAwu3rhxI6NHj8bZ2Znp06eTlZXFhg0b6NixI1FRUdSvX18hrv79+1OlShWmT59OXFwcq1atokyZMixdurTQbSxoewTlJhIYQRCEz2jXrl3ExMTw7bff4u3tDcDQoUOZMGECq1evZujQoTg4ONC1a1e+/fZbKleunO9WlJGRERcvXkRDQ0PaNnToUFq0aMGSJUs+OYHJycnh8ePHADx+/JjVq1dz5coV3N3dUVP76/IQGhpKTEwMe/bsoVmzZtJ2e3t7+vfvz65du+jWrRtdunTB29ub8PBwhQRm+/btyOVyqRfk+fPnTJ06lYEDByr0mAwcOBB7e3v8/f3ZunWrQqx169YlKChIepydnc3GjRsJDAykbNmyhWp3QdsjKDdxC0kQBOEzioyMRF1dna+//lphe14vy759+z56DjU1NSl5ycjI4MmTJ7x+/RoHBwfOnz//ybFduXIFS0tLLC0tadq0KcHBwXh4eLBo0SKF47Zv346trS1WVlY8fvxY+nFwcEBDQ4PDhw8Db8bUODk5ERERoVDJFBERQa1atbC1tQXgwIEDpKam0rNnT4XzZWZm0rJlS2JiYvJVQg0ZMkThcYsWLcjKyuLevXuFbndB2yMoN9EDIwiC8BnduXMHExMThVsyABYWFpQtW5Y7d+4U6DyrVq0iJCSE69evK1zcC9v78LZq1aqxcOFCcnJyiI+PZ/78+dy/fx91dXWF4+Lj47l9+zaWlpbvPM+jR4+kf3t4eBAVFcXJkyext7fn7t27HD9+HF9fX4XzAXTo0OG9sb18+RIdHR3psZmZmcJ+fX19AJ4+fVrA1n5aewTlJRIYQRAEJbdx40YmTpxI586dmTBhAgYGBqiqqrJmzRoiIyM/+bza2tq0adMGAGdnZ5o2bYqzszNz587lu+++k47Lycmhbt26zJ49+53nqVSpkvTvDh06oKWlRXh4OPb29mzbto3c3FyFQbQ5OTkArFy5UuG5b/t7YiaTvfuGQV4y96GKKblcrvC4MO0RlJdIYARBED4jMzMzTp48ycuXLxV6YW7fvk1aWppCz8L7LsIRERFYW1uzYcMGhe15A4CLip2dHT169CAkJISvv/4aY2Nj4E1Pzc2bN2nduvVHS6vLlSuHq6srO3bsYO7cuWzbto0GDRpQrVo16Zi8fxsYGNC6desiiV1TUxMtLa181VPwphesQoUKCq9f0PYIykuMgREEQfiM3NzcyMrKYvny5Qrb8wakurq6Stu0tbUVSoPz5FXEvH3r6Pr160RFRRV5vGPGjCE9PV2husfDw4OkpCTWrVuX7/isrKx8SYOHhwfJycls3LiRs2fP4u7urrDfzc2NcuXKMW/ePIVS8jwpKSmfFLuFhQVHjhxR2LZt2zZpoPKntkdQTqIHRhAE4TPq3Lkzjo6OzJ49m4SEBOrVq8fRo0eJiIjA3d0dBwcH6Vg7Ozu2bt1KQEAA1atXR0dHB1dXV9q3b8/48ePp168fLi4uJCUlsWrVKmrWrMn169eLNN46derg5OTE2rVrmThxIuXLl6dfv378+uuvjBs3jkOHDtGsWTNyc3O5efMmO3bsYP78+XTq1Ek6h6urK7q6uvj6+qKiokL37t0VXkNfX5/58+czYsQIHB0d8fDwwNDQkKSkJA4fPkylSpUKvQwDwKBBg/Dx8aF///44OTkRGxvLjh078o2fKWx7BOUkEhhBEITPSCaTsXnzZn744Qe2b99OWFgYJiYmTJ48WSqrzjN9+nQeP37M4sWLSU1NxcrKCldXVwYOHMiTJ09Yu3YtBw4cwNLSkvnz53PhwoUiT2AARo8ejbu7OyEhIUyZMgVVVVXCwsJYtmwZYWFh7NmzB01NTczMzOjbt6804V4eTU1NOnbsSFhYGA4ODpiamuZ7DU9PT0xNTVm4cCFLliwhIyMDIyMjmjRpwoABAz4p7sGDB5OUlMSmTZvYt28fjRs3Jjw8nHHjxikcV9j2CMpJ5dmzZ2LVLkEQBEEQShQxBkYQBEEQhBJHJDCCIAiCIJQ4IoERBEEQBKHEEQmMIAiCIAgljkhgBEEQBEEocUQCIwiCIAhCiSMSGEEQBEEQShyRwAiCIAiCUOKIBEYQBEEQhBJHJDCCIAiCIJQ4/wfPawyu+Fe0uAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8, 4))\n",
+    "ax.barh(group_names, group_data)\n",
+    "labels = ax.get_xticklabels()\n",
+    "plt.setp(labels, rotation=45, horizontalalignment='right')\n",
+    "ax.set(xlim=[-10000, 140000], xlabel='Total Revenue', ylabel='Company',\n",
+    "       title='Company Revenue')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For labels, we can specify custom formatting guidelines in the form of\n",
+    "functions by using the :class:`ticker.FuncFormatter` class. Below we'll\n",
+    "define a function that takes an integer as input, and returns a string\n",
+    "as an output.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def currency(x, pos):\n",
+    "    \"\"\"The two args are the value and tick position\"\"\"\n",
+    "    if x >= 1e6:\n",
+    "        s = '${:1.1f}M'.format(x*1e-6)\n",
+    "    else:\n",
+    "        s = '${:1.0f}K'.format(x*1e-3)\n",
+    "    return s\n",
+    "\n",
+    "formatter = FuncFormatter(currency)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then apply this formatter to the labels on our plot. To do this,\n",
+    "we'll use the ``xaxis`` attribute of our axis. This lets you perform\n",
+    "actions on a specific axis on our plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6, 8))\n",
+    "ax.barh(group_names, group_data)\n",
+    "labels = ax.get_xticklabels()\n",
+    "plt.setp(labels, rotation=45, horizontalalignment='right')\n",
+    "\n",
+    "ax.set(xlim=[-10000, 140000], xlabel='Total Revenue', ylabel='Company',\n",
+    "       title='Company Revenue')\n",
+    "ax.xaxis.set_major_formatter(formatter)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Combining multiple visualizations\n",
+    "=================================\n",
+    "\n",
+    "It is possible to draw multiple plot elements on the same instance of\n",
+    ":class:`axes.Axes`. To do this we simply need to call another one of\n",
+    "the plot methods on that axes object.\n",
+    "\n",
+    "`axvline` - Add a vertical line across the axes.\n",
+    "`text` - Add arbitrary text to the plot, specifying the position and style attributes.\n",
+    "`set_xticks` - Specify the xticks that are to be displayed.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8, 8))\n",
+    "ax.barh(group_names, group_data)\n",
+    "labels = ax.get_xticklabels()\n",
+    "plt.setp(labels, rotation=45, horizontalalignment='right')\n",
+    "\n",
+    "# Add a vertical line, here we set the style in the function call\n",
+    "ax.axvline(group_mean, ls='--', color='r')\n",
+    "\n",
+    "# Annotate new companies\n",
+    "for group in [3, 5, 8]:\n",
+    "    ax.text(145000, group, \"New Company\", fontsize=10,\n",
+    "            verticalalignment=\"center\")\n",
+    "\n",
+    "# Now we'll move our title up since it's getting a little cramped\n",
+    "ax.title.set(y=1.05)\n",
+    "\n",
+    "ax.set(xlim=[-10000, 140000], xlabel='Total Revenue', ylabel='Company',\n",
+    "       title='Company Revenue')\n",
+    "ax.xaxis.set_major_formatter(formatter)\n",
+    "ax.set_xticks([0, 25e3, 50e3, 75e3, 100e3, 125e3])\n",
+    "fig.subplots_adjust(right=0.5)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Saving our plot\n",
+    "===============\n",
+    "\n",
+    "Now that we're happy with the outcome of our plot, we want to save it to\n",
+    "disk. There are many file formats we can save to in Matplotlib. To see\n",
+    "a list of available options, use:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'ps': 'Postscript', 'eps': 'Encapsulated Postscript', 'pdf': 'Portable Document Format', 'pgf': 'PGF code for LaTeX', 'png': 'Portable Network Graphics', 'raw': 'Raw RGBA bitmap', 'rgba': 'Raw RGBA bitmap', 'svg': 'Scalable Vector Graphics', 'svgz': 'Scalable Vector Graphics', 'jpg': 'Joint Photographic Experts Group', 'jpeg': 'Joint Photographic Experts Group', 'tif': 'Tagged Image File Format', 'tiff': 'Tagged Image File Format'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(fig.canvas.get_supported_filetypes())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then use the :meth:`figure.Figure.savefig` in order to save the figure\n",
+    "to disk. Note that there are several useful flags we'll show below:\n",
+    "\n",
+    "* ``transparent=True`` makes the background of the saved figure transparent\n",
+    "  if the format supports it.\n",
+    "* ``dpi=80`` controls the resolution (dots per square inch) of the output.\n",
+    "* ``bbox_inches=\"tight\"`` fits the bounds of the figure to our plot.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "fig.savefig('sales.png', transparent=False, dpi=80, bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/topic_10/examples/sales.png b/topic_10/examples/sales.png
new file mode 100644
index 0000000000000000000000000000000000000000..21d10e15cca89105c32d125384783442f6caa52d
Binary files /dev/null and b/topic_10/examples/sales.png differ
diff --git a/topic_10/topic_10.md b/topic_10/topic_10.md
new file mode 100644
index 0000000000000000000000000000000000000000..36d7c7690627897df37a6b69fcc76dbac2cc879a
--- /dev/null
+++ b/topic_10/topic_10.md
@@ -0,0 +1,98 @@
+
+class: bottom, left
+background-image: url(assets/g.png)
+
+<h2 class="title_headings_sml">COSC102 - Data Science Studio 1</h2>
+
+<h1 class="title_headings_sml"> Topic 10 - The Lifecycle of a Plot </h1>
+
+<h3 class="title_headings_sml"> Dr. Mitchell Welch </h3>
+
+---
+
+## Reading
+* [Open the notebook for this topic]()
+* Review the [Matplotlib Lifcycle Tutorial](https://matplotlib.org/tutorials/introductory/lifecycle.html#sphx-glr-tutorials-introductory-lifecycle-py)
+* Review the [Effectively Using Matplotlib Blog](https://pbpython.com/effective-matplotlib.html)
+
+---
+
+## Summary
+
+* Plot Basics
+* Applying Style to a Plot
+* Customizing a Plot
+* Combining Multiple Elements
+
+---
+## Plot Basics
+
+.center[![Matplotlib](../assets/topic_08/anatomy.png "Plot Anatomy")]
+
+---
+
+## Plot Basics
+
+* Recall: We create our figure and (1 or more) axes:
+
+```python
+# One Figure, one axes
+fig, ax = plt.subplots()
+
+# One Figure, 2 axes
+fig, axs = plt.subplots(1,2)
+```
+* [Open the notebook for this topic and create the figure]()
+---
+
+## Applying Style to a Plot
+
+* You can customize almost everything:
+
+.center[![Matplotlib](../assets/topic_10/matplotlib_example.png "Plot Elements")]
+
+.center[(Source: https://pbpython.com/effective-matplotlib.html)]
+
+---
+
+## Applying Style to a Plot
+
+* Matplotlib has builtin-styles that control colour and appearance of the different plot-types.
+* The `style` package adds support for easy-to-switch plotting "styles"
+* [Review the style reference.](https://matplotlib.org/3.1.0/gallery/style_sheets/style_sheets_reference.html)
+* [You can even define your own styles!](https://matplotlib.org/3.2.2/tutorials/introductory/customizing.html) - I'll leave this one to you!
+* [Open the notebook for this topic and apply some styles]()
+---
+
+## Customizing a Plot
+
+* Things we cna change:
+  * Add labels and apply rotation.
+  * Add titles.
+  * 'Ticks' on the axis.
+  * Adjust the limits on each axis within the plot
+
+---
+
+## Combining Multiple Elements
+
+* Adding multiple plots to a single axes. (E.g. denoting a mean/standard deviation).
+* Adding text and additional markers.
+* Formatting labels using formatting functions.
+
+---
+
+## Summary
+
+* Plot Basics
+* Applying Style to a Plot
+* Customizing a Plot
+* Combining Multiple Elements
+
+---
+## Reading
+
+* Review the [Matplotlib Sample Plots Tutorial](https://matplotlib.org/tutorials/introductory/sample_plots.html)
+* Review the [Matplotlib Gallery (hundreds of examples!)](https://matplotlib.org/gallery/index.html)
+
+---
diff --git a/topic_11/example/3d_scatter.py b/topic_11/example/3d_scatter.py
new file mode 100644
index 0000000000000000000000000000000000000000..87540bed000249d6e656dbf0d68395265423f825
--- /dev/null
+++ b/topic_11/example/3d_scatter.py
@@ -0,0 +1,50 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Jul 12 15:13:07 2020
+
+@author: mwelch8
+"""
+
+# This import registers the 3D projection, but is otherwise unused.
+from mpl_toolkits.mplot3d import Axes3D  # noqa: F401 unused import
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+fig = plt.figure(figsize=[15,10])
+ax = fig.gca(projection='3d')
+
+# Plot a sin curve using the x and y axes.
+x = np.linspace(0, 1, 100)
+y = np.sin(x * 2 * np.pi) / 2 + 0.5
+ax.plot(x, y, zs=0, zdir='z', label='curve in (x,y)')
+
+# Plot scatterplot data (20 2D points per colour) on the x and z axes.
+colors = ('r', 'g', 'b', 'k')
+
+# Fixing random state for reproducibility
+np.random.seed(19680801)
+
+x = np.random.sample(20 * len(colors))
+y = np.random.sample(20 * len(colors))
+c_list = []
+for c in colors:
+    c_list.extend([c] * 20)
+# By using zdir='y', the y value of these points is fixed to the zs value 0
+# and the (x,y) points are plotted on the x and z axes.
+ax.scatter(x, y, zs=0, zdir='y', c=c_list, label='points in (x,z)')
+
+# Make legend, set axes limits and labels
+ax.legend()
+ax.set_xlim(0, 1)
+ax.set_ylim(0, 1)
+ax.set_zlim(0, 1)
+ax.set_xlabel('X')
+ax.set_ylabel('Y')
+ax.set_zlabel('Z')
+
+# Customize the view angle so it's easier to see that the scatter points lie
+# on the plane y=0
+ax.view_init(elev=40., azim=-70)
+
+plt.show()
\ No newline at end of file
diff --git a/topic_11/example/iris_3d_scatter.py b/topic_11/example/iris_3d_scatter.py
new file mode 100644
index 0000000000000000000000000000000000000000..c0cf8f56f56ba58f71bdf415087121c9b2d647bd
--- /dev/null
+++ b/topic_11/example/iris_3d_scatter.py
@@ -0,0 +1,32 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Jul 12 15:17:47 2020
+
+@author: mwelch8
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.colors import ListedColormap
+from sklearn import datasets
+# This import registers the 3D projection, but is otherwise unused.
+from mpl_toolkits.mplot3d import Axes3D  # noqa: F401 unused import
+
+# import some data to play with - the iris dataset
+iris = datasets.load_iris()
+
+X = iris.data
+tgt = iris.target
+
+cmap_bold = ListedColormap(['red', 'green', 'blue'])
+fig = plt.figure(figsize=(20,15))
+ax = fig.add_subplot(111, projection='3d')
+
+
+ax.scatter(X[:,0], X[:,1], X[:,2],c=tgt, cmap=cmap_bold,
+                              edgecolor='k', s=30)
+ax.view_init(elev=30., azim=-35)
+
+ax.set_xlabel(iris.feature_names[0])
+ax.set_ylabel(iris.feature_names[1])
+ax.set_zlabel(iris.feature_names[2])
\ No newline at end of file
diff --git a/topic_11/example/plot_kde_1d.py b/topic_11/example/plot_kde_1d.py
new file mode 100644
index 0000000000000000000000000000000000000000..39b1c38c1f59d5e5f9d88fcd39f1794db6f94990
--- /dev/null
+++ b/topic_11/example/plot_kde_1d.py
@@ -0,0 +1,154 @@
+"""
+===================================
+Simple 1D Kernel Density Estimation
+===================================
+This example uses the :class:`sklearn.neighbors.KernelDensity` class to
+demonstrate the principles of Kernel Density Estimation in one dimension.
+
+The first plot shows one of the problems with using histograms to visualize
+the density of points in 1D. Intuitively, a histogram can be thought of as a
+scheme in which a unit "block" is stacked above each point on a regular grid.
+As the top two panels show, however, the choice of gridding for these blocks
+can lead to wildly divergent ideas about the underlying shape of the density
+distribution.  If we instead center each block on the point it represents, we
+get the estimate shown in the bottom left panel.  This is a kernel density
+estimation with a "top hat" kernel.  This idea can be generalized to other
+kernel shapes: the bottom-right panel of the first figure shows a Gaussian
+kernel density estimate over the same distribution.
+
+Scikit-learn implements efficient kernel density estimation using either
+a Ball Tree or KD Tree structure, through the
+:class:`sklearn.neighbors.KernelDensity` estimator.  The available kernels
+are shown in the second figure of this example.
+
+The third figure compares kernel density estimates for a distribution of 100
+samples in 1 dimension.  Though this example uses 1D distributions, kernel
+density estimation is easily and efficiently extensible to higher dimensions
+as well.
+"""
+# Author: Jake Vanderplas <jakevdp@cs.washington.edu>
+#
+import numpy as np
+import matplotlib
+import matplotlib.pyplot as plt
+from distutils.version import LooseVersion
+from scipy.stats import norm
+from sklearn.neighbors import KernelDensity
+
+# `normed` is being deprecated in favor of `density` in histograms
+if LooseVersion(matplotlib.__version__) >= '2.1':
+    density_param = {'density': True}
+else:
+    density_param = {'normed': True}
+
+# ----------------------------------------------------------------------
+# Plot the progression of histograms to kernels
+np.random.seed(1)
+N = 20
+X = np.concatenate((np.random.normal(0, 1, int(0.3 * N)),
+                    np.random.normal(5, 1, int(0.7 * N))))[:, np.newaxis]
+X_plot = np.linspace(-5, 10, 1000)[:, np.newaxis]
+bins = np.linspace(-5, 10, 10)
+
+fig, ax = plt.subplots(2, 2, sharex=True, sharey=True)
+fig.subplots_adjust(hspace=0.05, wspace=0.05)
+
+# histogram 1
+ax[0, 0].hist(X[:, 0], bins=bins, fc='#AAAAFF', **density_param)
+ax[0, 0].text(-3.5, 0.31, "Histogram")
+
+# histogram 2
+ax[0, 1].hist(X[:, 0], bins=bins + 0.75, fc='#AAAAFF', **density_param)
+ax[0, 1].text(-3.5, 0.31, "Histogram, bins shifted")
+
+# tophat KDE
+kde = KernelDensity(kernel='tophat', bandwidth=0.75).fit(X)
+log_dens = kde.score_samples(X_plot)
+ax[1, 0].fill(X_plot[:, 0], np.exp(log_dens), fc='#AAAAFF')
+ax[1, 0].text(-3.5, 0.31, "Tophat Kernel Density")
+
+# Gaussian KDE
+kde = KernelDensity(kernel='gaussian', bandwidth=0.75).fit(X)
+log_dens = kde.score_samples(X_plot)
+ax[1, 1].fill(X_plot[:, 0], np.exp(log_dens), fc='#AAAAFF')
+ax[1, 1].text(-3.5, 0.31, "Gaussian Kernel Density")
+
+for axi in ax.ravel():
+    axi.plot(X[:, 0], np.full(X.shape[0], -0.01), '+k')
+    axi.set_xlim(-4, 9)
+    axi.set_ylim(-0.02, 0.34)
+
+for axi in ax[:, 0]:
+    axi.set_ylabel('Normalized Density')
+
+for axi in ax[1, :]:
+    axi.set_xlabel('x')
+
+# ----------------------------------------------------------------------
+# Plot all available kernels
+X_plot = np.linspace(-6, 6, 1000)[:, None]
+X_src = np.zeros((1, 1))
+
+fig, ax = plt.subplots(2, 3, sharex=True, sharey=True)
+fig.subplots_adjust(left=0.05, right=0.95, hspace=0.05, wspace=0.05)
+
+
+def format_func(x, loc):
+    if x == 0:
+        return '0'
+    elif x == 1:
+        return 'h'
+    elif x == -1:
+        return '-h'
+    else:
+        return '%ih' % x
+
+for i, kernel in enumerate(['gaussian', 'tophat', 'epanechnikov',
+                            'exponential', 'linear', 'cosine']):
+    axi = ax.ravel()[i]
+    log_dens = KernelDensity(kernel=kernel).fit(X_src).score_samples(X_plot)
+    axi.fill(X_plot[:, 0], np.exp(log_dens), '-k', fc='#AAAAFF')
+    axi.text(-2.6, 0.95, kernel)
+
+    axi.xaxis.set_major_formatter(plt.FuncFormatter(format_func))
+    axi.xaxis.set_major_locator(plt.MultipleLocator(1))
+    axi.yaxis.set_major_locator(plt.NullLocator())
+
+    axi.set_ylim(0, 1.05)
+    axi.set_xlim(-2.9, 2.9)
+
+ax[0, 1].set_title('Available Kernels')
+
+# ----------------------------------------------------------------------
+# Plot a 1D density example
+N = 100
+np.random.seed(1)
+X = np.concatenate((np.random.normal(0, 1, int(0.3 * N)),
+                    np.random.normal(5, 1, int(0.7 * N))))[:, np.newaxis]
+
+X_plot = np.linspace(-5, 10, 1000)[:, np.newaxis]
+
+true_dens = (0.3 * norm(0, 1).pdf(X_plot[:, 0])
+             + 0.7 * norm(5, 1).pdf(X_plot[:, 0]))
+
+fig, ax = plt.subplots()
+ax.fill(X_plot[:, 0], true_dens, fc='black', alpha=0.2,
+        label='input distribution')
+colors = ['navy', 'cornflowerblue', 'darkorange']
+kernels = ['gaussian', 'tophat', 'epanechnikov']
+lw = 2
+
+for color, kernel in zip(colors, kernels):
+    kde = KernelDensity(kernel=kernel, bandwidth=0.5).fit(X)
+    log_dens = kde.score_samples(X_plot)
+    ax.plot(X_plot[:, 0], np.exp(log_dens), color=color, lw=lw,
+            linestyle='-', label="kernel = '{0}'".format(kernel))
+
+ax.text(6, 0.38, "N={0} points".format(N))
+
+ax.legend(loc='upper left')
+ax.plot(X[:, 0], -0.005 - 0.01 * np.random.random(X.shape[0]), '+k')
+
+ax.set_xlim(-4, 9)
+ax.set_ylim(-0.02, 0.4)
+plt.show()
diff --git a/topic_11/example/rand_3d.py b/topic_11/example/rand_3d.py
new file mode 100644
index 0000000000000000000000000000000000000000..224b2c2d033f1cdd898916bf3c7696223ffa6bb6
--- /dev/null
+++ b/topic_11/example/rand_3d.py
@@ -0,0 +1,42 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Jul 12 15:15:05 2020
+
+@author: mwelch8
+"""
+
+# This import registers the 3D projection, but is otherwise unused.
+from mpl_toolkits.mplot3d import Axes3D  # noqa: F401 unused import
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Fixing random state for reproducibility
+np.random.seed(19680801)
+
+
+def randrange(n, vmin, vmax):
+    '''
+    Helper function to make an array of random numbers having shape (n, )
+    with each number distributed Uniform(vmin, vmax).
+    '''
+    return (vmax - vmin)*np.random.rand(n) + vmin
+
+fig = plt.figure(figsize=[15,10])
+ax = fig.add_subplot(111, projection='3d')
+
+n = 100
+
+# For each set of style and range settings, plot n random points in the box
+# defined by x in [23, 32], y in [0, 100], z in [zlow, zhigh].
+for m, zlow, zhigh in [('o', -50, -25), ('^', -30, -5)]:
+    xs = randrange(n, 23, 32)
+    ys = randrange(n, 0, 100)
+    zs = randrange(n, zlow, zhigh)
+    ax.scatter(xs, ys, zs, marker=m)
+
+ax.set_xlabel('X Label')
+ax.set_ylabel('Y Label')
+ax.set_zlabel('Z Label')
+
+plt.show()
\ No newline at end of file
diff --git a/topic_11/topic_11.md b/topic_11/topic_11.md
new file mode 100644
index 0000000000000000000000000000000000000000..43385564f99aa975301487d913d897c070ad594c
--- /dev/null
+++ b/topic_11/topic_11.md
@@ -0,0 +1,75 @@
+class: bottom, left
+background-image: url(assets/g.png)
+
+<h2 class="title_headings_sml">COSC102 - Data Science Studio 1</h2>
+
+<h1 class="title_headings_sml"> Topic 11 - Advanced Plots </h1>
+
+<h3 class="title_headings_sml"> Dr. Mitchell Welch </h3>
+
+---
+
+## Reading
+* [Open the notebook for this topic]()
+* Take a look at the [documentation for the kernel density estimation function in Scikit-learn](https://scikit-learn.org/stable/modules/density.html#kernel-density)
+* Review the [3D Plotting Tutorial](https://matplotlib.org/3.1.1/tutorials/toolkits/mplot3d.html#sphx-glr-tutorials-toolkits-mplot3d-py)
+
+
+---
+
+## Summary
+
+* Kernel Density Estimators
+* 3D Plotting
+
+---
+
+## Kernel Density Estimators
+
+* Need to be careful when working with histograms.
+* The size of bins and placement of the bin *edge* can dramatically impact the visualization generated.
+  * This might lead to incorrect conclusions from the data.
+* **Kernel Density Estimation (KDE)** can help us understand the *shape* of our data.  
+
+---
+
+## Kernel Density Estimators
+
+* A formal definition of KDE is beyond the scope of this unit.
+* Put simply, KDE estimates a distribution by placing a *kernel* function over each data point and summing them all together. 
+
+.center[![Matplotlib](../assets/topic_11/kde_hist.png)]
+
+---
+
+## Kernel Density Estimators
+
+* KDE isn't a silver bullet - there are still parameters to tune:
+  * Kernel type/shape (this is usually a normal distribution)
+  * There is a *smoothing* parameter called the *bandwidth*
+* By manipulating these, you can reveal trends in the distribution of the underlying data.
+* KDE is used to create impressive visualizations of distributions.
+* Lets play with an [example.](plot_kde_1d.py)
+
+---
+
+## 3D Plotting
+
+* So far we have only looked a 2D plots.
+* Matplotlib has the capability to produce 3D plots using the `mpl_toolkits.mplot3d` module.
+* You then need to create a 3D-enabled axes:
+
+```python
+fig = plt.figure(figsize=[15,10])
+ax = fig.add_subplot(111, projection='3d')
+```
+* We will explore some 3D scatter plots: 
+
+---
+
+## Summary
+
+* Kernel Density Estimators
+* 3D Plotting
+
+---