Data Visualization with Matplotlib Project

Data Visualization with Matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualization with Matplotlib\n",
    "\n",
    "\n",
    "This project is all about Matplotlib, the basic data visualization tool of Python programming language. I have discussed Matplotlib object hierarchy, various plot types with Matplotlib and customization techniques associated with Matplotlib. \n",
    "\n",
    "\n",
    "This project is divided into various sections based on contents which are listed below:- \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "\n",
    "\n",
    "1.\tIntroduction\n",
    "\n",
    "2.\tOverview of Python Data Visualization Tools\n",
    "\n",
    "3.\tIntroduction to Matplotlib\n",
    "\n",
    "4.\tImport Matplotlib\n",
    "\n",
    "5.\tDisplaying Plots in Matplotlib\n",
    "\n",
    "6.\tMatplotlib Object Hierarchy\n",
    "\n",
    "7.\tMatplotlib interfaces\n",
    "\n",
    "8.\tPyplot API\n",
    "\n",
    "9.\tObject-Oriented API\n",
    "\n",
    "10.\tFigure and Subplots\n",
    "\n",
    "11.\tFirst plot with Matplotlib\n",
    "\n",
    "12.\tMultiline Plots\n",
    "\n",
    "13.\tParts of a Plot\n",
    "\n",
    "14.\tSaving the Plot\n",
    "\n",
    "15.\tLine Plot\n",
    "\n",
    "16.\tScatter Plot\n",
    "\n",
    "17.\tHistogram\n",
    "\n",
    "18.\tBar Chart\n",
    "\n",
    "19.\tHorizontal Bar Chart\n",
    "\n",
    "20.\tError Bar Chart\n",
    "\n",
    "21.\tStacked Bar Chart\n",
    "\n",
    "22.\tPie Chart\n",
    "\n",
    "23.\tBox Plot\n",
    "\n",
    "24.\tArea Chart\n",
    "\n",
    "25.\tContour Plot\n",
    "\n",
    "26.\tStyles with Matplotlib Plots\n",
    "\n",
    "27.\tAdding a grid\n",
    "\n",
    "28.\tHandling axes\n",
    "\n",
    "29.\tHandling X and Y ticks\n",
    "\n",
    "30.\tAdding labels\n",
    "\n",
    "31.\tAdding a title\n",
    "\n",
    "32.\tAdding a legend\n",
    "\n",
    "33.\tControl colours\n",
    "\n",
    "34.\tControl line styles\n",
    " \n",
    "35.\tSummary\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Introduction\n",
    "\n",
    "\n",
    "When we want to convey some information to others, there are several ways to do so. The process of conveying the information with the help of plots and graphics is called **Data Visualization**. The plots and graphics take numerical data as input and display output in the form of charts, figures and tables. It helps to analyze and visualize the data clearly and make concrete decisions. It makes complex data more accessible and understandable. The goal of data visualization is to communicate information in a clear and efficient manner.\n",
    "\n",
    "\n",
    "In this project, I shed some light on **Matplotlib**, which is the basic data visualization tool of Python programming language. Python has different data visualization tools available which are suitable for different purposes. First of all, I will list these data visualization tools and then I will discuss Matplotlib.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Overview of Python Visualization Tools\n",
    "\n",
    "\n",
    "\n",
    "Python is the preferred language of choice for data scientists. Python have multiple options for data visualization. It has several tools which can help us to visualize the data more effectively. These Python data visualization tools are as follows:-\n",
    "\n",
    "\n",
    "\n",
    "•\tMatplotlib\n",
    "\n",
    "•\tSeaborn\n",
    "\n",
    "•\tpandas\n",
    "\n",
    "•\tBokeh\n",
    "\n",
    "•\tPlotly\n",
    "\n",
    "•\tggplot\n",
    "\n",
    "•\tpygal\n",
    "\n",
    "\n",
    "\n",
    "In the following sections, I discuss Matplotlib as the data visualization tool. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Introduction to Matplotlib\n",
    "\n",
    "\n",
    "**Matplotlib** is the basic plotting library of Python programming language. It is the most prominent tool among Python visualization packages. Matplotlib is highly efficient in performing wide range of tasks. It can produce publication quality figures in a variety of formats.  It can export visualizations to all of the common formats like PDF, SVG, JPG, PNG, BMP and GIF. It can create popular visualization types – line plot, scatter plot, histogram, bar chart, error charts, pie chart, box plot, and many more types of plot. Matplotlib also supports 3D plotting. Many Python libraries are built on top of Matplotlib. For example, pandas and Seaborn are built on Matplotlib. They allow to access Matplotlib’s methods with less code. \n",
    "\n",
    "\n",
    "The project **Matplotlib** was started by John Hunter in 2002. Matplotlib was originally started to visualize Electrocorticography (ECoG) data of epilepsy patients during post-doctoral research in Neurobiology. The open-source tool Matplotlib emerged as the most widely used plotting library for the Python programming language. It was used for data visualization during landing of the Phoenix spacecraft in 2008.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## 4. Import Matplotlib\n",
    "\n",
    "Before, we need to actually start using Matplotlib, we need to import it. We can import Matplotlib as follows:-\n",
    "\n",
    "`import matplotlib`\n",
    "\n",
    "\n",
    "Most of the time, we have to work with **pyplot** interface of Matplotlib. So, I will import **pyplot** interface of Matplotlib as follows:-\n",
    "\n",
    "\n",
    "`import matplotlib.pyplot`\n",
    "\n",
    "\n",
    "To make things even simpler, we will use standard shorthand for Matplotlib imports as follows:-\n",
    "\n",
    "\n",
    "`import matplotlib.pyplot as plt`\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dependencies\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Matplotlib\n",
    "\n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Displaying Plots in Matplotlib\n",
    "\n",
    "\n",
    "Viewing the Matplotlib plot is context based. The best usage of Matplotlib differs depending on how we are using it. \n",
    "There are three applicable contexts for viewing the plots. The three applicable contexts are using plotting from a script, plotting from an IPython shell or plotting from a Jupyter notebook.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting from a script\n",
    "\n",
    "\n",
    "\n",
    "If we are using Matplotlib from within a script, then the **plt.show()** command is of great use. It starts an event loop, \n",
    "looks for all currently active figure objects, and opens one or more interactive windows that display the figure or figures.\n",
    "\n",
    "\n",
    "The **plt.show()** command should be used only once per Python session. It should be used only at the end of the script. Multiple **plt.show()** commands can lead to unpredictable results and should mostly be avoided.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting from an IPython shell\n",
    "\n",
    "\n",
    "We can use Matplotlib interactively within an IPython shell. IPython works well with Matplotlib if we specify Matplotlib mode. To enable this mode, we can use the **%matplotlib** magic command after starting ipython. Any plt plot command will cause a figure window to open and further commands can be run to update the plot.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting from a Jupyter notebook\n",
    "\n",
    "\n",
    "The Jupyter Notebook (formerly known as the IPython Notebook) is a data analysis and visualization tool that provides multiple tools under one roof.  It provides code execution, graphical plots, rich text and media display, mathematics formula and much more facilities into a single executable document.\n",
    "\n",
    "\n",
    "Interactive plotting within a Jupyter Notebook can be done with the **%matplotlib** command. There are two possible options to work with graphics in Jupyter Notebook. These are as follows:-\n",
    "\n",
    "\n",
    "•\t**%matplotlib notebook** – This command will produce interactive plots embedded within the notebook.\n",
    "\n",
    "•\t**%matplotlib inline** – It will output static images of the plot embedded in the notebook.\n",
    "\n",
    "\n",
    "After this command (it needs to be done only once per kernel per session), any cell within the notebook that creates a plot will embed a PNG image of the graphic.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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Z9W1w91Hiu028sPcsIT7u3JZpWZa0I/nu/ATqWjp5+8gltaXoABRshH+tgTPb1FYyNO4+0FQG+/+kGDTgjYMXaevq4eF5zhGFNxj3zRmPn6crf/pEHzVciy0MQ3+PNQOl1d4BvCel7Pt4GmvqQXoX8AchRL/jUyHEwyYDkl1T40SNZ8YEQMa9Slx3Yxm5ZY18eqaGr30lDk83J0hIGoIZcUFcNz6QFz89R2e3UW05oxsp4bM/QNAEmLhIbTVDIwTM/T7UFUPhFtq7enht/wXmJ4aSGOHgek4W4OfpxgNz49iZX8WZKj2voS+2MAylQN9H42hgoPrOd3CNG0lKWW56PwfsATL621FK+aKUMlNKmRka6oCSw+Yw61sgjXD477yw5yy+Hq7cM2uc2qqGzXfmJ1De2K5nQ6tNyWElb2H2d5wjy3k4JK+GgHHw+R9572gpdS2dfNPBRSKt4f7Z4/BwNfDq5xfUluJU2MIwHAEmCiHihBDuKDf/L0UXCSESgUDgQJ9lgUIID9PnEGAuoL3Mk8DxMPu71I2JZ0tuBXfPGoefp5MkJA2DGyeFkhThy6ufX9Cb+ajJwb+Cpz9MvVNtJcPHxVUJ3S49zIE9m5kaE8DMOPXqgZlLsI8HazOi+OBYKZf1kvRfYLVhkFJ2A48A24HTwDtSyjwhxC+FEH2jjO4E3pJX33mSgWwhRA6wG3haSqk9wwCw6EleaJiBixA8OHe82mrMQgjBA3PGc7qiicPn69WWMzrpaoOyYzD9AXC3fxMbm5JxN4UpP2B/QyDfmhePcPZJ82t4cG4cHd1G1unzbF9gk3RcKeUWYMs1y/73mu+/6Ge//cBkW2hQm9bObjZmF/GLmFOE+yxWW47ZrE6P4tdbC3ht/wVmxgerLWf04TYGvn8MutvVVmI+7t48cXkpPkFtLEqNUFuN2SRG+DI3IZh/7r/IN66Px82JIwkdhf4vYCPWHy8nvfM491Q9CwWb1JZjNmPcXbhjRgw78qsoa7C+t7WOGXR3QHenUg/J0U14bEBBpTLSfCK+GJfjrw+9gxPytblxVDa1szW3Um0pToFuGGyAlJLX91+gLOxGpH80HHlZbUkWce+scUgp9YQ3R3P0dfjjFLhSrbYSi3jj4EU8XA3c2LlXSfbs0t6DxfzEMMYHe/GK3sgH0A2DTTh0vp7CqmbunRuPmP4AnN+rNPTRGNGBXmSlhLPu8CW9JLejkFLppew3VumvrDGa27v48FgZK6eOxX3Ww0oDq7z1assyG4NB8ODcOE6UNHCixLL2pSMJ3TDYgNf3XyDAy43V6VFKToPBFY6+qrYsi3hgThwNrV1sODFQxLGOTbl0AGrPKD0+NMiHx8to6ezh3lnjlLajwROVXiUa5OZpUYxxc2HdIX0SWjcMVlLR2MaO/Cq+mhmjJLT5RkDScqjVZjblrPggJoX78O9DujvJIWS/Ch7+kHqz2krMRkrJvw5cZGq0P1NjApSEt+segrJsKD+utjyz8fV0Y9XUsWzIKad5lBeW1A2DlbybXUqPUXL3zD4JbWtfhLvfUU+UFQghuOO6WHJKG8kvb1JbzsimtR7yP4Ipt4O789YVGoiD5+opqr5ydTJn+p0QOdWsnujOxJ0zY2nr6uGjUT5i1g2DFRiNkneyS5ibEExscJ8ftpun8t6uzeJcazOicHcx8E52ydAb61iOZwDc9ZZSb0uDvHn4Ev5j3FjZt4Kwpz9881OYsEA9YVYwNdqf5Eg/3jx0aVQne+qGwQr2n62j9HIbt/dXLK9oJzw7EaryHC/MSgK93VmSFsEHx0r1SWh7YjAoN9ABylc7Mw2tnWzPq2RtRlT/NcE6W6HurOOFWYkQgrtmxJBf0cSpMm0+2NkC3TBYwdvZJfiPcWNxf0k9UdOV96PajOu+47oYmtq72abHdduHkiOw/WeKO0mDbMgpp7Pb2P9DEcAbN8P7DzlWlI1YnRGFp5uBdYdH7yS0bhgs5HJLJ9tzB3li8gpSJqFPvaskL2mMWfHBjAv24i29TIB9OPISHPsXuHqqrcQi3skuIS3Kj5Sxfv1vkLxKKQiowRGzn6cbK6eM5aMT5Vzp6FZbjirohsFC1p8oo7PHyFevG6TnQvrd0Favjdr612AwCG7PjOHguXrO17aoLWdk0d4E+Rtg8q2anHTOK28kt6xp4NECKBPqBjc4/m/HCbMhd8yIpbWzh80nR+cktG4YLEBKydtHSr6YqBqQCfPBNxJOvOk4cTbktunRuBiEPmqwNfnrobsN0u9SW4lFvJtdirurYfC2td4hkLgUTr6lyRHztNgA4kO8ef/Y6CxFrxsGCzhV1khBZTO3DzZaAKWm/qq/wKJfOUaYjQnz82R+Yijrj5fRYxy9ERo258SbSiJY7zyUhujo7mH9iTIWp0YQ4OU++MYZ90JrHZz/1DHibIgQglumR3P4fD0l9a1qy3E4umGwgA+OleHuamDFlEGemHqZuBBCnLcp+lCszYimqqmDA2fr1JYyMujphsA4JdNZY+WpAXbmV9HQ2sXtmdFDbzxhATy8BxJusrcsu7AmIwoh4P1jpWpLcTi6YTCTrh4jG3PKyUoOx3/MMJvxXDoE2x5X6uJojJuSw/D1dOWDUfjjsAsurrD2BaVLmwZ572gpY/09mTMhZOiNXVxhbIYmDSBAVMAYZscH88GxslGX06AbBjP59EwNdS2drM2IGv5O1XlKdy4NlgnwdHNhxZRItuVV0jJKIzRshtGoySidXmqvdLCvqJY1GVG4GIZ5s+/uhPXfVUp/aJBbpkVzqb6V7IvazOS2FJsYBiHEEiFEoRCiWAjxWD/rHxBC1AghTpheD/VZd78Qosj0ut8WeuzJB8fLCPJ254ZEM/pOp94MLh6Qs27obZ2QtRnRtHb2sD1Pz2mwiksH4IU5cFp7/ToANuWU02OUrDHnocjVHapy4ehrdtNlT5akReDl7sL7R0fXiNlqwyCEcAGeB5YCKcCdQoiUfjZ9W0qZbnr9w7RvEPBzYCYwA/i5ECLQWk32oqm9i535VaycEmlel6cxAZC4BHI/UHzMGiNzXCDRgWP48PjojNCwGTnrwN1HiVbTIB+eKCcl0o9J4WY2E5pyO1ScgJoz9hFmR7w9XFmaFsnmkxWjqgqALUYMM4BiKeU5KWUn8Bawepj7LgZ2SinrpZSXgZ3AEhtosgtbT1XQ2W1k7bRhTLxdy+TbobUWzu2xuS57YzAIbs6I4vPiWiobNdh60hnoalcK5iWv0l5PZ+B8bQs5JQ3muVB7SbsFhAFOabOw5C3Tomju6GZnfpXaUhyGLQxDFNC32lqpadm13CKEOCmEeE8I0RvnOdx9nYIPjpURH+LN1Gh/83eemAURk6Gz2fbCHMDaadEYJXx0Qh81WETRDuhogim3qa3EItYfL0MIri6YN1x8IyDuBqUKgAYncWfGBxPu58GGnNGT7GYLw9DfLNS1//sbgfFSyinAx0BvAaHh7KtsKMTDQohsIUR2TU2NxWItpfRyK4fO15tC2CyIsnD1gG99BqlrbS/OAcSFeJMeE6C7kywl70PwDoXx89RWYjZSStafKGPOhGAi/C0s4ZH5NZi0RJNtP10MgpVTxrKnsJrG1tHRp8EWhqEU6JvpFQ1cZVqllHVSyg7T15eA6cPdt88xXpRSZkopM0NDzZj4tREbcyoAWJNu5YCmp1uzvX1Xp4+loLKZ4mptjnpUZdWf4a53lBBOjXGipIGLda3WXfspq2DpbzRZAgRgVfpYunok2/Iq1JbiEGxhGI4AE4UQcUIId+AOYEPfDYQQkX2+rgJOmz5vBxYJIQJNk86LTMucjo055aTHBFzdd8ESXpoPG39gG1EOZvnkSIT4j5HUMQMPH4iaprYKi1h/vAwPVwNL0vqpImwORiNc+FyTJTImR/kzPthr1LiTrDYMUspu4BGUG/pp4B0pZZ4Q4pdCiFWmzb4vhMgTQuQA3wceMO1bD/wKxbgcAX5pWuZUnK25Qn5Fk2X+1WuJm6f0atBgueUwP09mxQWzMad81CX8WMXWx+D4G2qrsIjuHiObT1WwMDkcX89hJnQOxNld8NoyOLfbNuIciBCCVVPHsv9sHdVNIz8AwyZ5DFLKLVLKSVLKCVLKp0zL/ldKucH0+XEpZaqUcqqUcr6UsqDPvq9IKRNML6fMgtmUU4EQyhOz1Uy+DYxdSoSKBlk5dSznalvI09t+Do8rNXD4RagrVluJRRw6X0/tlU5WTLHBtR93g9LhLe9D64+lAqvSxyIlbDo58kfMeubzEEgp2ZBTxozxQZZPvPUlcqpSQC33feuPpQJL0yJwNQg2jtJyxGaTvx5kj/JAoEE2nSzH292F+Ulh1h/M1R2SVkLBZiV8V2MkhPmSHOk3KtxJumEYgoLKZs7WtLDCFm4kUOrGpN0MFz+HZu3FRQd6u3P9xBA25VTo7qThcOo9CE2G8FS1lZhNV4+RrbmVLEwJ778ZlSWkrVXCds/uss3xHMzq9LGcKGngUt3IrriqG4Yh2HSyHBeDYKm1E299ybgX7tug1KzXICunjqWsoY1jlxrUluLcNJZCyUElwUuDfF5cS0Nr1/CqCA+XuBtgTBAUbrHdMR1I7zzjSB8x64ZhEKSUbMypYM6EYEJ8PGx34IAYiLte6degQbJSwvFwNbBxFAypraK9SSk9rdHclU0nK/D1cGXeJBs+wLi4wYNbYMUfbHdMBxIVMIb0mAA2j/B5Bt0wDMLJ0kYu1bfaJhrpWhouwbafQrP2CtP5eroxPzGMzacqMOoNfAYmPAXu/RBCEtRWYjad3Ua251WSlRqOh6uNH2DCkhUDoVGWT44kv6KJCyO45a1uGAZh86kK3FwEi1Ns6EbqpbMVDj6v9P7VIMumRFLT3DHqyhEPm7YGTc4h9bKvqIbm9m5W2tKN1JfP/6g8GGmQpZOV+8GW3JE7atANwwBIKdlyqoK5CSH4e9nh6SYsSZmU1Gjo3oKkMDxcDWw5NXJ/HFaRsw5+n6TMM2iQTScr8B/jxtwEO82DNVyC7Feg44p9jm9HogO9SI8JGNHXvm4YBiC3rInSy20sS7NB/PZApK5VavQ3ae8C8/Fw5YZJoWzN1d1J/ZK3HsJSwN+CSrwq09Hdw8f5VSxODcfd1U63iNS10N0GRU5Z6GBIlk+OJLesacRGJ+mGYQC25FbgYhBkpYTb7ySpawAJpzXqTpocSVVTB8dLdHfSVTSWKdFIKWvUVmIRnxfX0tzRzVJbJHQOROxs8A7TrCu11520eYSOGnTD0A9SSraeUqKRAr3d7Xei0ESInQMd2ixKd1NyGO4uBjaf1N4Eul3pNfSp2jQMW05V4uvpytzh9HW2FIMLJK9QysN0au+pOzrQi6kj2J2kG4Z+OF3RzIW6VpbZ84mplwe3wLxH7X8eO+Dr6ca8SSG6O+la8j+C8DQImai2ErPp6jGyM7+KrGQ7upF6SbsFJi2G9kb7nsdOLJ8cwamyxhHpTtINQz9sza3AIGCRPd1IvQihNC/R6Khh2eRIKhrbySnVk92+4JZ/wMo/qq3CIg6craOxrcv6SqrDYfxX4LZXwc8BD2B2YKlp/nEkRifphuEapJRsPlXBrPhggm2Z1DYYb34V3r7HMeeyMTclh+PmIkbskNoi/KMhOlNtFRaxNbcSb3cX5k1yYM+T2mLo7hh6OycjJsiLKdH+bMsdea5U3TBcw5mqK5yrabHvxNu1hKfA+X2aLMXtP8aN6yeGsuVUpV47CeCTp6BAm+UeeoySHXmVzE8Ks11tpKE4vw/+Ml2TvdABFqdGcKKkgYpG7XWmGwzdMFzD1lylxPbiVAe4kXpJXqVU4NRo/ZglqRGUNbTppbhb62Hf76AsW20lFnH4fD11LZ2OmVvrJWYGePhrNjqp1+W2I0+7yYz9oRuGa9iWW0nmuEDCfG1QYnu4jM0A/xjN/jgWpoRjELA9b+QNqc2icIti4JNXqq3EIrbmVuDpZuDGRAe6kVw9IHEJFG6GHu31U54Q6sPEMB+2jrB5BpsYBiHEEiFEoRCiWAjxWD/r/1sIkS+EOCmE2CWEGNdnXY8Q4oTppeqd8WJdCwWVzSxOdcDEW1+EUG4m53Yrhdc0RpC3OzPjgkekr9UsTm8E/1hDqHBRAAAgAElEQVSITFdbidkYjZLteZXcMCkUL3cH96VOXgVtl+HCPsee10YsSYtQRltXtDdPMhBWGwYhhAvwPLAUSAHuFEKkXLPZcSBTSjkFeA94ps+6Nilluum1ChXpfeJ1uGEAmHYfrHkBXOyYN2FHFqeGU1R9hbM12itxYBM6muHsJ4qBF0JtNWZzorSBqqYOx0QjXUvCTeDmBac3Of7cNmBJWgRGCR+fHjnuJFuMGGYAxVLKc1LKTuAtYHXfDaSUu6WUvcG+BwGnrBOwPa+KlEg/YoK8HH/ysGSYfCu4OdCFZUMWmYzpqHUnNZZBSKJm3Ujb8ypxNQgWJDpwbq0XtzFw1ztw0xOOP7cNUO4ZY0bUiNkWhiEKKOnzvdS0bCC+Dmzt891TCJEthDgohBgwVVQI8bBpu+yamhrrFPdDdVM7xy5dVueJqZemcvj8T9ClvQiHsQFjmBrtz/YR9OMwi7Ak+PZnMG622krMRkrJjrwqZk8Itk/ByOEQdz2MCVTn3FYihGBJagSfF9fR1K69eZL+sIVh6G/c3G/cohDiHiATeLbP4lgpZSZwF/AHIcSE/vaVUr4opcyUUmaGhtp+cmxHfhVSquRG6qU6H3Y+od3QvbQIckobKW/QnmGzip4uTRrzXoqqr3C+tuWLUZ9qHHoRjr6mrgYLWZIWQWePkd0F1WpLsQm2MAylQEyf79HAl1p7CSEWAj8DVkkpv5ilkVKWm97PAXuADBtoMpvteZXEhXgzKdxHjdMrjJ+nhO5p1dea2hu6N8pGDcW74JkJUHFSbSUW0TvKc0im/2AUblFGzBrMh8mICSTM12PEuJNsYRiOABOFEHFCCHfgDuCq6CIhRAbwdxSjUN1neaAQwsP0OQSYC+TbQJNZNLZ2ceBsHYtSwxFqThy6usOkRcoPpKdbPR0WEm8K3ds+wmK6h6Rgk1IULjRJbSUWsT2/kozYAML9VJ7fSl4B9WehpkBdHRZgMFVi3numhvauHrXlWI3VhkFK2Q08AmwHTgPvSCnzhBC/FEL0Rhk9C/gA714TlpoMZAshcoDdwNNSSocbhk8Kq+g2yi+eeFUlaTm01StlmzXIkrQIDp2vo76lU20pjsHYA4VbYeIixbBrjJL6VnLLmtR1ofaSuFx51+iIeVFqBK2dPew/W6u2FKuxSR6DlHKLlHKSlHKClPIp07L/lVJuMH1eKKUMvzYsVUq5X0o5WUo51fT+si30mMuOvCrC/TyYGh2gxumvJiEL3H2g+rTaSixiUYoSuvfJCPG1DknJIWitVZ52NciOfGV05xSGwS8Soq+Dgo1qK7GI2fHB+Hq4jogs6FGf+dze1cPeMzVkpYRjMDhB/LmHDzxaBDO+obYSi0iL8iPS33P0zDMUbFZyTxIWqq3EIrbnVTIp3Ie4EG+1pSikrAYPP+hqV1uJ2bi7GrgxKYyPT1fRo/Ey9KPeMHxeXEtrZw+LUpzgiakXd1MehQYn4YQQLEoJ59OiGto6te9rHZKpd8CKP4CHr9pKzKa+pZPsC/XOMVroZfYj8MAm7ebzpIRTe6WTY5e03dVw1BuGHXlV+Hq4Mis+WG0p/6GnG15fCXufGXpbJ2RRagTtXUb2Fdk+38TpiJgMGXerrcIidp2uwihxroei3uCPzhZ1dVjIjYmhuLsYND9iHtWGocco2VVQxY1JYfbvVmUOLq6Kccj/SG0lFjEjLgg/T9cv/NcjljPblVBVjbIjv4pIf0/SovzUlnI1R1+HZ+I1WYbe19ONOQnBprwo7Y34e3Giu6HjOX7pMrVXOtWP3+6PpOVQnQeXL6itxGzcXAzclBzOrtNVdPcY1ZZjPz55UrOjurbOHvYVKXNrqoZo90d4GnS3Q9EOtZVYxKKUCC7WtXKmSrt1w0a1YdiRX4Wbi3BsmeHhkrRMeddo05eslHAut3aRfVHbvtYBabgElScVA65B9hXV0N5ldC43Ui9jM8A3UpnY1yALU8IQGi9DP2oNg1IfppI5E0Lw9VSpPsxgBMVDWIpmm/fMmxSKu6thRITu9UuvwdaoYdiZX4Wvpysz44PUlvJlDAZIXKq46TQYnRTm60lGTAA7NexKHbWGobj6ChfqWlnkyE5t5jL7u5qt1unj4cpXEkLYkT9CW34WblYynYP7Le3l1Chza9UsSArDzcVJbwFJy6GrBc7vVVuJRWSlRHCqrFGzLT+d9KqwP70TowuTndgwZNwDM7+ptgqLWZQSTunlNk5XNKstxbZ0d0LdWUhcprYSizh68TL1LZ3O6UbqZfw8WPSkMt+gQbJM85Yfa3TUMHoNQ14lU2OcoD7MULTWw4XP1FZhETclhyMEmh5S94urO/y/XJj3qNpKLGJHXiXuLgZucMa5tV5c3WHO98B/sAr+zktCmA/xId6ajcwblYahqqmdnNJG54xGupZPnoR/36bJss6hvh6Kr/W0difhBsRgAHcnyRY2AyklO/KrmJMQjI+Hg1t4mktXG5x6D2oK1VZiEVkp4Rw8p80eDaPSMPQ+wWrCMCQtg65WOKddX2tuWdPI6dHQ3QHPz1RuWBrkTNUVLtW3fuHqcGp6OuHDb8HxN9RWYhFZKeF09Uj2FGov0XPUGoZxwV4khKnYe2G4jL8e3H2VyU4N8oWvdaT0wz3/qVIW2tNfbSUWsTNfGb059dxaL57+MP4rmo3My4gNJNjbXZOu1FFnGK50dHPgbB1ZyU6Y2NMfrh4wMUsp7WzUXu2hXl+rFn8c/VKwWal+GzdPbSUWsTO/Shtza70kLYe6YqgtUluJ2bgYBDclh7GnoJrObm0leo46w7C3sIbOHqM2htK9JC2HlhqoyFFbiUVo2dd6FUajYqATblIMtsbQ1NxaL5OWKO8aTXbLSomguaObQ+fr1JZiFjYxDEKIJUKIQiFEsRDisX7Wewgh3jatPySEGN9n3eOm5YVCiMW20DMYO/MrCfRyY/o4DTUen7QEvncMoqaprcQitOxrvYry43Cl8j8NZTRGrztPUw9FATEQMUXJMtcgX0kIwdPNoLkRs9WGQQjhAjwPLAVSgDuFECnXbPZ14LKUMgF4DviNad8UlFagqcAS4K+m49mFrh4jnxRUsyApHFdnTezpDw8fTSZS9aJlX+tVuHsruSUTs9RWYhG9c2sTtTC31pf7N8Ktr6itwiLGuLtw/cRQPtZYUT1b3B1nAMVSynNSyk7gLWD1NdusBl43fX4PuEkoDv7VwFtSyg4p5Xmg2HQ8u3DkfD1N7d3aemLqpbYY3n1QSazSGFr2tV5FWBKsfh68nLCMxBBc6ehmf3EdC7Uyt9aXMU7QWdEKslLCKW9sJ6+8SW0pw8YWhiEKKOnzvdS0rN9tTD2iG4HgYe5rM3bkV+HhamDepBB7ncJ+uHpA3gdK43kNolVf6xdcqYGKk5psngTw6RkNzq31ZccTSuiqBrkpKQyDQFPJbrYwDP09flz76xlom+HsqxxAiIeFENlCiOyaGst81V09RhYmh+Pl7uSJPf0REKM0hdFotVWt+lq/4OTb8PfrlaqqGmRnfhUBXm5kamlurS/dHZD3oSYb+AT7eDB9XKCmrn1bGIZSIKbP92igfKBthBCugD9QP8x9AZBSviilzJRSZoaGWpbK/9TayfzlrgyL9nUKEpcrzeevaG8SV6u+1i8o3AJhqRA4Tm0lZtP9xdxamLbm1vqStEzp0XB2t9pKLCIrJZzTFU2U1LeqLWVY2OIqOQJMFELECSHcUSaTN1yzzQbgftPnW4FPpHJ32ADcYYpaigMmAodtoGlANOdf7UvSMkDCmW1qK7GIrGTt+VoBaKmDSwf+0yNDYxy+UE9jW5e2wlSvZdxcJeFNo8luWaaChbs0kuhptWEwzRk8AmwHTgPvSCnzhBC/FEKsMm32MhAshCgG/ht4zLRvHvAOkA9sA74rpdReFpejiJgCcTeAQYOuMGBBcpg2i+oVbQdp1Gw11Z35Vbi7Grh+ohMXzRsKFzeYuEh5KNJgomdciDcJYT7s1IhhsMkdRkq5BdhyzbL/7fO5HbhtgH2fAp6yhY4RjxBw/7WDMe0Q4uPB9FjF1/pfWZPUljN8zmwD37FKZzGNIaVkZ34VX0kIwdvZi+YNxdQ7wCdcmWfwdLI+1cMgKyWclz49R2NbF/5jnLA5WB806nAc5fR0abJROig/jvyKJkova8PXCsCaF+DudxTDrDEKKpspvdym3WikviQshMVPadIogHLtdxslewqr1ZYyJLph0BpSwp+nwc4n1FZiEZpsYOLurUSEaZCd+VUIATclh6ktxTb0dMOlg5oMG06PDiDU10MTYau6YdAaQkDMTCjUpq81PtSHCaHemvG18tlzcOCvaquwmJ35VaTHBBDmq5GieUNx4g14ZbEmezQYDIKFyWHsLayho9u5f7u6YdAiicugtRZK7BrAZTeyUiI4dK6exlYnL6pn7IEDz0PpEbWVWERFYxunyhpHhhupl4mmcmoaLUO/KCWCKx3dHDzn3K5g3TBokYSFYHDTcBa0ydd6xsl9raXZSlXbJI0WzdNSQ6rh4hcJY6dpNtFz9oRgvNxd2JHn3F0NdcOgRTz9lH4ABZs16WvNiAkgxMeDHXlO7k4q3KyEBicsVFuJRezIryIuxJsJoRormjcUScugLBuanfvm2h+ebi7cmBjKx6erMBqd97erGwatcsNPYI02fd8GgyArJZw9hdXO7Wst2Kx00NNgEbem9i4OnqsjK0WDRfOGorfsuVYTPVPCqWrq4GRZo9pSBkQ3DFoldiaMm6PJEEpQ3BstnT3sP+ukRfU6rkBgHKSuUVuJRewprKGrR46s+YVewpLhwa2QfrfaSixifmIYLgbxRZtVZ0Q3DFqm/Djs/7PaKixi9oRgvN1dnNed5OED97wH0x9QW4lF7MirJMTHnWmxGi2aNxhCKA9FLs6dJDYQAV7uzIwLct5rH90waJvij2HH/0BThdpKzMbTzYUbnNnXqtEEQoCO7h72FNawMDkcF4M2R5RD0tGsXPtFH6utxCKyUsIpqr7C+VrnrBarGwYtk7RCeddoYbFFKRHUNHdworRBbSlX01wFzybA0deH3tYJOXC2jisd3SxKHYFupF7cvODEm5CzTm0lFtHr4nNWd5JuGLRMaBIExWs2bHV+YhiuBuF8Q+rCLSB7IGq62kosYkd+FV7uLsyZoMGGVMPF4AKJS6FoB3R3qq3GbKIDvUiJ9HO+a9+Ebhi0jBDKqOH8p9DmZE/dw8Dfy42Z8UHscLanpoLNEDgewlPVVmI2RqNSNO/GxFA83ezWPt05SFoBHU1wYZ/aSixiUWo4Ry9dpqa5Q20pX0I3DFonaQW4+0DtGbWVWMSilAjO1bRQXH1FbSkK7U1wfq/y76rBiK8TpQ3UNHewyFT/f0QTfyO4eSuGXIMsTo1ASvjYCcvD6IZB60RfBz86CzEz1FZiEb2+1u3OkglatAN6OiF5pdpKLGJHXhWuBsH8xBFSNG8w3MZA2lqlH7oGSYrwJSZojPNc+33QDYPWMRjAxVXJgDYa1VZjNmMDxjAl2t95SgTEzYMVf1AMrgbZkV/JrPhg/L20GcppNqufhyW/VluFRQghWJwSwf7iOprbnatumFWGQQgRJITYKYQoMr1/KWhaCJEuhDgghMgTQpwUQny1z7rXhBDnhRAnTK90a/SMWurOwp8yoHin2kosYnFqBDmljVQ0tqktBXzCIPNBZXJTYxRXN3OupmVkRyMNhAbn2AAWp0XQ2WNkd6Fz9XG3dsTwGLBLSjkR2GX6fi2twH1SylRgCfAHIUTfGgM/klKmm14nrNQzOvGPgdY6zUYnLU5V/OGqR2iUHoVj/4SudnV1WMh207/fiMx2Hoz134V/aLOe1bTYQEJ83J1nxGzCWsOwGugN9n4d+FL9ACnlGSllkelzOVANaLj5rBPi6g4Ts5SKkxrs0ZAQpvRoUN3Xeuw12PZTTU46A2zLrSQ9JoBI/zFqS3EsY9OhrkiTPRpcvqgb5lw9Gqw1DOFSygoA0/ugM15CiBmAO3C2z+KnTC6m54QQ2pxFcgaSVig9Gi4dVFuJRSxJi+DQ+Xout6gUk27sUQzrpEWanMwsvdzKqbJGlqSNgmika+kti35am/3QF6UqPRr2FztP3bAhDYMQ4mMhRG4/r9XmnEgIEQn8C3hQStk7S/o4kARcBwQBPxlk/4eFENlCiOyaGufyxzkFE7PAxUPT7qQeo1QvdK/kkGJYNdp7odeN1OuWG1X4jVWCBU5vVFuJRcyZEIyPh6v6I+Y+DGkYpJQLpZRp/bw+AqpMN/zeG3+/nVeEEH7AZuB/pJQH+xy7Qip0AK8CA8ZcSilflFJmSikzQ0N1T9SX8PCFBf8DE25SW4lFTI7yZ6y/5xc3OIdzehO4uENCljrnt5LteZUkRfgSF+KtthR1SF4JFTlw+aLaSszGw1Xp0bAzv4oeJ6kbZq0raQNwv+nz/cBH124ghHAHPgT+KaV895p1vUZFoMxP5FqpZ3Qz9/swUZuTcEIIFqVGsK+ohtbObscLuHxBMaqefo4/t5XUNHdw5EL96Bwt9JJ2C9z8D/AKVluJRSxJi6CupZMjF5yjeKO1huFpIEsIUQRkmb4jhMgUQvzDtM3twDzggX7CUv8thDgFnAJCgCet1KNTWwzntVkiYHFqBB3dRvaoEbp355twuzaL5n18ugopGZ3zC734R8OU25Ry6RpkfmIYHq4GtuU6hzvJKsMgpayTUt4kpZxoeq83Lc+WUj5k+vyGlNKtT0jqF2GpUsoFUsrJJtfUPVJKJ6mLoGG2/BA2/kCTLT+vGx9IsLc7Wx394+hNDNTgpDMo0Ujjgr1IivBVW4q6XKlR+pNccfJe4v3g7eHKDZNC2ZZb6RRl6PXM55FG8iqoPwvVp9VWYjauLgYWpUbwyekq2rscFLonJfx9Hux91jHnszGNbV3sP1vLktSIkdfC01xaapQeDRoNwFg6OYLKpnaOl6ifrKcbhpFG0gpAaDZCY2laBC2dPewrqnXMCavyoOoUeGvTN/1JQRVdPZJFo3l+oZewZAiaAPnaDFtdkBSOm4tgW676jbd0wzDS8A2H2FmajemePSEY/zFubD3loB/H6Q2A+E/TI42x5VQlEX6eZMQEDL3xSEcISFmllKHXYAc+/zFuzE0IYWtuJVJlV7BuGEYiySuVLNBm55jIMgc3FwOLUsLZebrKMZmg+RuU/sE+2qtG2tzexd4zNSydHIFhpLbwNJeUNUqTJY26k5alRVJ6uY288iZVdeiGYSSScQ/8qAh8teleWDo5guZ2B2SC1hZBzWllXkaDfFJQTWe3keWTI9WW4jxEToXgBKg/r7YSi8hKUfp0b1XZnaQbhpGIpz+M+VKhW80wNyEEXw9XttjbneTuDfN+pNneC1tOVRDu58G0WO3+X9scIeDbB2Dhz9VWYhGB3u7Mig9S3Z2kG4aRSkUOvLxYKcmtMTxcXViYEs6O/Cq6euzYY8JvrJIt7h9lv3PYiZaObvYU1rA0LVJ3I12Lq7vyrsGCkgBL0yI5V9NCYVWzahp0wzBSGRMEJQchf73aSixiSVoEjW1dHDhrJ3dSQwmc2a7JRvIAuwqq6eg2skx3I/XP+u/AW3errcIilqRFYBCwKUc9d5JuGEYqATEQPQPyPlRbiUXcMCkUb3cXNp+0048jZx28ebtSOE+DbDlZQZivB5njdDdSv3gFQ/HH0HZZbSVmE+LjwZwJIWw6Wa6aO0k3DCOZ1DVQeUqT7iRPNxeyUsLZlldJZ7cd3El5H0LsbMWdpDFaOrrZXVjN0jQ9GmlAUteAsUsppa5Blk+J5EJdq2rRSbphGMmkmCqja3TUsGLKWBrbuvis2Ma1k6oLoDofUm+27XEdxO5CxY20VHcjDczYaRAQq11XamoELgbBJnuNmIdANwwjGf9omPEwBE9QW4lFXD8pBD9PV9v7WvM+BEzJUBpkY045Yb4eXDc+SG0pzosQSk7D2d2aTHYL9HZnbkIIm0+p407SDcNIZ9mzkLpWbRUW4eHqwuLUCHbk27h20qUDMG6uJvM8mtq72F1Yw4opY3HR3UiDk34XLPwFGFzUVmIRK6ZEUlLfxsnSRoefWzcMo4HWeig/obYKi1g5dSxXTKGZNuPe9Zotsb09V5lzWTlVdyMNSVgyzHlEyevRIItTInBzEWw6We7wc+uGYTTw7gPw/kOaLMU9Z0IwQd7utv1xGAzgHWK74zmQjScriA3yIl2vjTQ8Olsg521oVqkzoBX4e7lx/cRQNp+scHgpbt0wjAbSboa6IiXpTWO4uhhYkhbBrtPV1nd2kxJeXgRHXraNOAdTd6WDz4trWTk1Ui+xPVway+DDhyH3fbWVWMSKKZGUN7Zz7JJjw26tMgxCiCAhxE4hRJHpvd+gaiFET5/ubRv6LI8TQhwy7f+2qQ2ojq1JXgUGN8h9T20lFrFyyljaunrYddrKBiyl2VByCFw9bSPMwWw5VUGPUbJyqvZCbFUjdBJETNbstb8oNQJPNwPrT5Q59LzWjhgeA3ZJKScCu0zf+6OtT/e2vqEgvwGeM+1/Gfi6lXp0+sMrCBIWwqn3/9OtTEPMiAsi3M+Dj05Y6U469Y5iFDRaG2ljTgWTwn1IitBeX2pVSbsVyo5qsrCej4crWSkRbD5ZYd/yMNdgrWFYDfTO4r0OrBnujkIZCy8Aek25WfvrmMnkW6G5HCq0NwntYhCsTo9iT2E19S0WlrDo6YLcD2DSEvDU3o21vKGNwxfqWaWPFswn7RblXaPupDXpY7nc2sWnZxzXC91awxAupawAML0PVNTeUwiRLYQ4KITovfkHAw1Syl7HcSmgvWpmWiFxGXz/OERNU1uJRaxJj6LbKNls6ST0uT1K+Yspt9tUl6PYmKP83Sum6IbBbAJiIGYWVJ5UW4lFzJsUSqCXG+utHTGbgetQGwghPgb6C/j+mRnniZVSlgsh4oFPhBCngP5yvQecehdCPAw8DBAbG2vGqXUAcPeCoHi1VVhMcqQvieG+fHi8jHtnjzf/AH5jIfPrkJBlc232RkrJB8fKyIgNYHyIt9pytMnd72pypAhK86rlUyJ572gpVzq68fEY8rZtNUOOGKSUC6WUaf28PgKqhBCRAKb3fmcHpZTlpvdzwB4gA6gFAoQQvX9lNDCgSZRSviilzJRSZoaGhprxJ+p8wZVqePteKNqpthKzEUKwdloUxy41cLGuxfwDhKfCit//pySzhsivaKKwqpmbp0WrLUW79BoFjZbiXpMeRXuXkR15junKaK0raQNwv+nz/cBH124ghAgUQniYPocAc4F8qeR57wZuHWx/HRsyJhAu7odj/1RbiUWsmjoWIWD9cTOH1OXHlZcG8zgAPjhWhpuLYOUUPanNKg69CH+eDj1Whj2rwPRxgUQHjnGYO8law/A0kCWEKAKyTN8RQmQKIf5h2iYZyBZC5KAYgqellPmmdT8B/lsIUYwy56DNAHOt4OIGU74KhVs1WT9mbMAYZsUFs/5EmXn1Yz55Et66R5OGobvHyEcnyrgpKZwAL+2NdpwK/yi4fB7OfqK2ErMRQrA6fSyfFdVQ09xh9/NZZRiklHVSypuklBNN7/Wm5dlSyodMn/dLKSdLKaea3l/us/85KeUMKWWClPI2KaX9/+LRTvqdSjniU9qM6147LYrztS3kDLd+TFO5ciOYeoeS8awx9hXVUnulk5un6XEZVpOQpTSwylmnthKLWJsRxS3Tounotr87THu/FB3riJisvHLeVFuJRSxNi8DD1cD7R0uHt0POOpBGpaCaBnn/WCmBXm7cmDhQwJ/OsHF1V8K2CzZDW4PaaswmIcyXZ2+bSnSgl93PpRuG0cjsR2DiIk36Wn093ViSFsFHJ8qGrrgqJZx4E2LnaLL0eGNbFzvyq1g1dSzurvpP1SZMvRN6OjTbp8FR6FfbaGTqHTD/p+Bi/7A3e/DVzBia2rvZPlSERsNFpXhahjZ7/245VUFnt1GPRrIlYzNgwRPKw4LOgOiGYbTS06VMQmtw1DArPpiYoDG8faRk8A0Dx8OjhUpJBA3y9pESEsJ8mBKtzbLRTokQMO9RpYaSzoDohmG0UrQD1t0BZ3eprcRsDAbB7dNj2H+2jkt1rf1vZDQqriR3b3DTXtG8gsomTpQ0cOeMWL2Sqj248BmcfFdtFU6LbhhGKwlZ4B0KR7XZsObWzGgMAt49OsCo4dQ78NfZ0OyYhCBb89bhEtxdDNycoUcj2YWDL8C2x6DbwtpbIxzdMIxWXN0h/W44s00J6dQYkf5jmDcplPeOltLTXxOTY/+C7nbwCXe8OCtp7+rhg2OlLEmLINBbz12wC9MfVGpnFW5WW4lTohuG0cz0+0H2KDdRDXJ7ZgwVje3sK7qm6mR1AVz8TPn7NOiG2XKqgqb2bu6YEaO2lJHLhPngHwNHX1NbiVOiG4bRTFA8xM+HC/vUVmIRC5PDCfJ2Z93hS1evyH4FXNwh4151hFnJW4dLGB/sxez4YLWljFwMLjDtPqXqbv05tdU4HbphGO3c+grct2Ho7ZwQd1cDt2fGsDO/ivKGNmVhZ4uS1JayWpN9nYurr3D4Qj136JPO9if9bvCN1A1DP+iGYbTjFaSUitBg2CrA3TOVEuxvHjKNGgxusOI5mPN9FVVZzluHL+FqENyi5y7YH/8o+K98pbuhzlXohkFHKcP9u0RoGCIvwAmJCfJiQVI46w5fUmrI9JY9iJyitjSzaeno5u3sEpakRRDq66G2nNGBwaCU4m6qUFuJU6EbBh0ITYTWOjj6qtpKLOK+2eOoa+lk/75PYN/voL2/HlDOzwfHy2hu7+bBuXFqSxldvHEzvHOf2iqcCt0w6EBALCQth+xXoXOAhDEn5isJIcSHeCMP/Q32/V6TkUhGo+S1z88zJdqfabEBassZXUxaCqWHofSo2kqcBt0w6CjM+g601cPJt+IVFSMAABCbSURBVNRWYjYGg+AbGd7MbdtD3YS14OGrtiSz2Vdcy9maFh6cO16fdHY06XeBuy8cekFtJU6Dbhh0FMbNgch0OPBXpZyExljbvQU3enipa4naUizitc/PE+rrwfLJY9WWMvrw9INp90Leh/pcgwmrDIMQIkgIsVMIUWR6D+xnm/lCiBN9Xu1CiDWmda8JIc73WZdujR4dKxAClvwa1rygvYY2nS14Hn+FgoDrefm0gcrGdrUVmcW5mivsLqzh7pmxenlttZjxsDIJffJttZU4BdZehY8Bu6SUE4Fdpu9XIaXcLaVMl1KmAwuAVmBHn01+1LteSnnCSj061jBuDsRcp7YK82mth+hMgrN+iFHCy59pKy79tf0XcHcxcPfMcWpLGb0ExcFDH8Oc76mtxCmw1jCsBnqrsL0OrBli+1uBrVJK7c1wjhaaq2DD96AyV20lwycgBu55n/C0G1kxJZI3D12isbVLbVXDoqa5g7ePlLA6faweoqo20ZlKRrQGe4PbGmsNQ7iUsgLA9D5U/8E7gGsbrj4lhDgphHhOCDHgL0MI8bAQIlsIkV1TUzPQZjrW4uKm9IPe/ye1lQyPihxo+E9JjG/Om0BLZw//OnhBPU1m8PJn5+nqMfLtG7XXYW5EcmId/P36UV91dUjDIIT4WAiR289rtTknEkJEApOB7X0WPw4kAdcBQcBPBtpfSvmilDJTSpkZGhpqzql1zMErCDK/BqfehdpitdUMjpSw6b/gjVu+eMpLGevHjYmhvPr5haFbf6pMQ2sn/zpwgRVTxhIf6qO2HB1QyqhUnhr1cw1DGgYp5UIpZVo/r4+AKtMNv/fGXz3IoW4HPpRSfjHGl1JWSIUO4FVghnV/jo5NmPsDcPGAT59VW8ngFO2EsqNKqG2fEM9v3TCBupZO3j1aqqK4oXn18wu0dPbw3fkJakvR6SVhoRKd99nvNVsmxhZY60raANxv+nw/8NEg297JNW6kPkZFoMxPaMixPYLxCYPrvq40u6k7q7aa/pESdj8FAeMg456rVs2MCyIjNoC/7TmrlMlwQprbu3ht/wUWpYSTGKG9vIsRixAw70dKYb28D9RWoxrWGoangSwhRBGQZfqOECJTCPGP3o2EEOOBGGDvNfv/WwhxCjgFhABPWqlHx1bM+b7iUnLzUltJ/xRshooTcMNPlHmRPggh+GFWImUNbfz74KUBDqAubxy8RGNbF48s0EcLTkfiMghLgU9/q8mcHlvgas3OUso64KZ+lmcDD/X5fgH4Uo9CKeUCa86vY0d8w2H579RWMTC1hRCaDFO+2u/qr0wMYc6EYJ7fXczt18Xg42HVpW5TGtu6+PunZ7lhUihTovXyF06HwQDLfqs8cGgtp8dGjM6/Wmf4lByGz50wQun6H8I3PwWXgW/4P1qcSF1LJ698dt6Bwobmr7uLaWzr4idLktSWojMQ4+dCzOid8tQNg87gFG6BnU9AabbaShR6uqDkiPLZdfB+yBmxgSxODeelT89R3+Ic4Ycl9a28+vkFbpkWTcpYP7Xl6AxGTxds+D7s/7PaShyObhh0Buf6H4JPOGx7zDkSfw6/BC8vhPLjw9r80UWJtHR288Ie5wi9/e2OQgwG+OGiSWpL0RkKFze4UgV7n4GWWrXVKP1SOq445FS6YdAZHA9fuOl/ofSIktugJs1VsOfXkJClhBQOg4nhvtwyLZrX9l+guLrZzgIHJ6ekgY9OlPPQV+KJ9B+jqhadYZL1S6Vd7N7fqKvDaIT3vw6vLXPIA5puGHSGZupdyo1458+VH4lafPwL6G6Hpb8xq+fCT5Ym4eXuys8+zEWqNOoxGiVPbs4nxMedb+lZztohNBGm3w9HXoaaQvV0HP8XlByCGd90SL8R3TDoDI3BAEufUXIbhIs6Gi4dgpw3YfYjEGzejTXEx4PHliZx6Hw97x8rs5PAwfn34UscuXCZHy9OcqoIKZ1hcOPj4OED23+qzvmlhJPvwLi5Su8IB6BfoTrDI3am8lKLplIlPHXeoxbt/tXMGN47Wsr/bTnNTUlhBHoPPnFtS0ovt/L0ltNcPzGE2zKjHXZeHRvhEwa3vQ7BKuWcCAH3rYe2yw7rTqiPGHTMo3gXvL4Kuhzc8yDtFvj/27v34KjqK4Dj37MJb6QBeUkeAhLEgAXkUShWREBxqkYRK1ioVUSGYn12FPuYMqVWbZkCM2WsVGhFmVAePrCiIgJGqzKSIAoBSUSRQDRBHpGHhJDTP36bacIAIdm99y675zOTSfbu7v2dO9m75/5+9/eY8h40btGgt4dCwmM39aL86HH+tHJrlIM7PVXl0Rc+AeDx0Zfa6mznqouGuVl8VeG7g/6VW7rNlZfUyCUon1hiMPWjCp+/DWtm+FPezvcg/zlXboSDjXp0bMWkK7qyNK+YVz/2Z6WupXnFvFO4l2nX9iCtdYyOIjdnb+nPIec2f0ZEH94Lz90EOeN87xFoicHUT+YIN1XG+3Phi3e9Levoflg+yU1oVhmdGsoDI7rTNyOFR5Z/zOd7vb2RXlR6iBmvFDCwSxtbhCdedBsOO9+F9z0e21BVBS9OhiPfuJUVfa5pWmIw9TdyBrTu7L60y/d4U4YqvHI/HPoKbn4GGkWne2fj5BB/u+0ykpOEqYvyPZua++DR40xauIEmjULMurUPoZA1IcWFvhMgK9v1kCt807ty/jsbila7pHBBb+/KOQ1LDKb+mrSEW5+DY+WumccL6x6Hgpfgqt9Car+o7jo1pRmzftKHgpJypq/YEvUurCeqlHtzNlK8/whPje9HaoqNWYgbIm5d9A49YdmdULY9+mV8+QGs+SP0HO1q5wGwxGAapuOlbq6ioQ9Hf99fbXYDivqOhyH3R3//wLAe7Zk67CIWf7iLmas+jWpyePL1bby9vYw/ZPdiQOc2UduviRGNW8DYHGh9IVR4MBK5bXfoNRqun+N7E1I1665qGq56PME3n8GmxXDlNLdmbqQ69oKfrXD9tj08MR4aeTH7Dlcwd+1nJIdCPDAysmkqVJUnXtvGvNwdTBh0IeMGZkQpUhNzUtJh8jvu81ndU6lZhDPllm13yaZ5G9d8GiCrMZjIFbwEuX92vSeONXDaCVU3unR7eOXXrkPPOHNqNIRCwmM3Xsot/dKY81Yhc1YXNrjmUHmiikeWf8zTuTsYPyiD6Tf0jHK0JuZUX7Tk/gXmDXWL+zTUno0wfySsbNg4nWiLKDGIyC0iskVEqkSk/xleN0pEPhWRIhGZVmN7FxFZLyKFIvJvEfFv1JGJnh895NZuKFoN86+BA/VcHKfiCLz0C3j1QTfC00ehkPDEzd9n9GWpzFq9nUkL89h76Fi99rHvcAVTFuWzZEMx9w7PZEZ2L5LsZnPi6DrM1RjmX+NG6NdHVZXr4Tf/amjc0p1LMSDSGsNmYDSQe7oXiEgSMBe4FsgCxolIVvjpJ4FZqpoJ7AcmRhiPCcqAu+CnS+HgLvj75Wc3+6kq7FjnTopNOTB0Goz+h+ehniwpJMwc05vfXZdFbmEZo2bn8mbB13XWHk5UKc9/sJNhM9exdlsp06/P4sGR3W0QW6JJHwB3roJGTWHB1e4i51Bp3e87sg8W3eym2ug2Aia/7Xr7xYBIV3DbCtR1IgwEilR1R/i1i4FsEdkKXAVUT/7xLDAdeCqSmEyAug2HSWvg3dluaUSA3fmgVa4XR6NmboH140egaSvYnQcLs9203rctge5XBxZ6KCRMvLwLl3dry32LNzJp4Qa6tW/JmH5p3NgnlQ6tmiAiqCoFJeXkbt/LK5v2UFBSzuCu5/OH7J5kdrC1mxNWu+4w5X3XrLT+aRg81Y1UPrALmrV2PflU3Wf/yDeQkuHux5VsgutmQb87ArvRfCoSjd4YIrIO+FV4Sc+TnxsDjFLVu8KPJwA/wCWBD1S1W3h7OvCaqvaqq7z+/fvrhg0xsnCMOb3KYzCnD3y7x02+d15HN799VjaMWeBOlK0rIPMad7UVI45VnuDF/N0szSsmb+d+wJ2zLRonIwLfflcJQI+O5zHlyou4oXcnqyWY/zuyz91ABnffrXAVNE1xzU1Vx924hMnhRpbvDkLT7/kWmojkqeppm/2r1VljEJHVQMdTPPUbVX35bGI5xTY9w/bTxXE3cDdARob19jgnJDeBiaug5CN3ZXRgF7TqBGkD3PMiLknEmCbJSYwdmMHYgRnsKDvEmm2llB89zqFjJ6g4cYI+6a25IrMt7VvFTjIzMaR5jS7Kg+9xU3cfPeB6LTVNgfaXuIsiEV+TQn3UmRhUdUSEZRQD6TUepwF7gL1Aiogkq2plje2ni2MeMA9cjSHCmIxfUtLdzyXXBx1Jg3Rt15Ku7VoGHYY5V3Ue4n7OMX50V/0QyAz3QGoMjAVWqGvDWguMCb/uduBsaiDGGGM8FGl31ZtEpBgYDLwqIm+Et3cSkZUA4drAPcAbwFZgiapuCe/iEeBBESkCzgfmRxKPMcaYyEXl5rPf7OazMcbU39nefLaRz8YYY2qxxGCMMaYWSwzGGGNqscRgjDGmFksMxhhjajkneyWJSBmws4Fvb4sbXJdI7JgTgx1z/Iv0eC9U1XZ1veicTAyREJENZ9NdK57YMScGO+b459fxWlOSMcaYWiwxGGOMqSURE8O8oAMIgB1zYrBjjn++HG/C3WMwxhhzZolYYzDGGHMGCZUYRGSUiHwqIkUiMi3oeLwkIukislZEtorIFhG5L+iY/CIiSSKyUUT+E3QsfhCRFBFZJiLbwv/vwUHH5DUReSD8ud4sIjkiEnerJonIAhEpFZHNNba1EZE3RaQw/Lu1F2UnTGIQkSRgLnAtkAWME5GsYKPyVCXwkKpeAgwCpsb58dZ0H26K90QxB3hdVXsAvYnzYxeRVOBeoH94KeAk3Dov8eZfwKiTtk0D3lLVTOCt8OOoS5jEAAwEilR1h6pWAIuB2FtXMkpUtURV88N/f4v7skgNNirviUga8GPgmaBj8YOItAKuILyWiapWqOqBYKPyRTLQTESSgeacYfXHc5Wq5gL7TtqcDTwb/vtZ4EYvyk6kxJAK7KrxuJgE+KIEEJHOQF9gfbCR+GI28DBQFXQgPukKlAH/DDefPSMiLYIOykuquhuYCXwJlAAHVXVVsFH5poOqloC7+APae1FIIiUGOcW2uO+SJSItgeXA/apaHnQ8XhKR64BSVc0LOhYfJQOXAU+pal/gMB41L8SKcLt6NtAF6AS0EJHxwUYVXxIpMRQD6TUepxGH1c+aRKQRLiksUtUXgo7HB0OAG0TkC1xT4VUi8nywIXmuGChW1era4DJcoohnI4DPVbVMVY8DLwA/DDgmv3wtIhcAhH+XelFIIiWGD4FMEekiIo1xN6tWBByTZ0REcO3OW1X1r0HH4wdVfVRV01S1M+7/u0ZV4/pKUlW/AnaJyMXhTcOBggBD8sOXwCARaR7+nA8nzm+417ACuD389+3Ay14UkuzFTmORqlaKyD3AG7heDAtUdUvAYXlpCDAB+EREPgpv+7WqrgwwJuONXwKLwhc8O4A7Ao7HU6q6XkSWAfm43ncbicMR0CKSA1wJtBWRYuD3wBPAEhGZiEuQt3hSto18NsYYU1MiNSUZY4w5C5YYjDHG1GKJwRhjTC2WGIwxxtRiicEYY0wtlhiMMcbUYonBGGNMLZYYjDHG1PI/zBC4Ap2aEbwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "\n",
    "x1 = np.linspace(0, 10, 100)\n",
    "\n",
    "\n",
    "# create a plot figure\n",
    "fig = plt.figure()\n",
    "\n",
    "plt.plot(x1, np.sin(x1), '-')\n",
    "plt.plot(x1, np.cos(x1), '--');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Matplotlib Object Hierarchy\n",
    "\n",
    "\n",
    "There is an Object Hierarchy within Matplotlib. In Matplotlib, a plot is a hierarchy of nested Python objects. \n",
    "A**hierarch** means that there is a tree-like structure of Matplotlib objects underlying each plot.\n",
    "\n",
    "\n",
    "A **Figure** object is the outermost container for a Matplotlib plot. The **Figure** object contain multiple **Axes** objects. So, the **Figure** is the final graphic that may contain one or more **Axes**. The **Axes** represent an individual plot.\n",
    "\n",
    "\n",
    "So, we can think of the **Figure** object as a box-like container containing one or more **Axes**. The **Axes** object contain smaller objects such as tick marks, lines, legends, title and text-boxes.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.\tMatplotlib API Overview\n",
    "\n",
    "\n",
    "\n",
    "Matplotlib has two APIs to work with. A MATLAB-style state-based interface and a more powerful object-oriented (OO) interface. \n",
    "The former MATLAB-style state-based interface is called **pyplot interface** and the latter is called **Object-Oriented** interface.\n",
    "\n",
    "\n",
    "There is a third interface also called **pylab** interface. It merges pyplot (for plotting) and NumPy (for mathematical functions) together in an environment closer to MATLAB. This is considered bad practice nowadays. So, the use of **pylab** is strongly discouraged and hence, I will not discuss it any further.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Pyplot API \n",
    "\n",
    "\n",
    "**Matplotlib.pyplot** provides a MATLAB-style, procedural, state-machine interface to the underlying object-oriented library in Matplotlib. **Pyplot** is a collection of command style functions that make Matplotlib work like MATLAB. Each pyplot function makes some change to a figure - e.g., creates a figure, creates a plotting area in a figure etc. \n",
    "\n",
    "\n",
    "**Matplotlib.pyplot** is stateful because the underlying engine keeps track of the current figure and plotting area information and plotting functions change that information. To make it clearer, we did not use any object references during our plotting we just issued a pyplot command, and the changes appeared in the figure.\n",
    "\n",
    "\n",
    "We can get a reference to the current figure and axes using the following commands-\n",
    "\n",
    "\n",
    "`plt.gcf ( )`   # get current figure\n",
    "\n",
    "`plt.gca ( )`   # get current axes \n",
    "\n",
    " \n",
    "**Matplotlib.pyplot** is a collection of commands and functions that make Matplotlib behave like MATLAB (for plotting). \n",
    "The MATLAB-style tools are contained in the pyplot (plt) interface. \n",
    "\n",
    "This is really helpful for interactive plotting, because we can issue a command and see the result immediately. But, it is not suitable for more complicated cases. For these cases, we have another interface called **Object-Oriented** interface, described later.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code produces sine and cosine curves using Pyplot API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3rZBVIG3ZnG/3sr+wkhcnxhPg7W50HIdwzwXd6dHOj/tXpNv1tSa7LvZ3fj7AtuxSnro0lvb+nkbHcRgRQd48OroXGzOP8anclWqTNmcd4/1fD3LFwAjO6x5idByH4elm4pUp8ZRW1/Pomgyj45w1uy323fnlzP1uHxf3ac/YeBlbNLdpA8IZ1iOE577eTVZRpdFxxEkq6xq5d8V2IgK9efjinkbHcTi9O/pz18jufJmezxfbjxgd56zYZbHXNzZz99I0/L3cefrSPjIEYwFKKV64LA4PVxP3Lt9OkwzJ2IxnvtxN7vEa5kyKl7tLLeTGc6OIDw/gsTUZFJbXGh3njJml2JVSo5RSe5VSmUqpB83xnP/k9R/2s6eggucm9CHQR8YWLaVdG0+eHNebbdmlDr9+tb34aW8hi7dkM3NoFEmRgUbHcViuJhfmTIqnpr6Jh+xwobBWF7tSygS8AVwE9AKmKaV6tfZ5/05aTilv/nSAyxLDuKBXO0sdRpwwNr4jF8W255Vv97G3oMLoOE6trLqBB1fuoHs7X+6+QG7Cs7ToUF8eGBXD+j2FLE/ONTrOGTHHGfsAIFNrnaW1rgeWAOPM8Lz/o7ahiVnL0gj18+DxMRb7t0OcRCnF05fG4ufpyj3LHGs9DXvzny92UlRZx5xJCbJxhpXMGBzJwC6BPLl2F7nHq42Oc9rMUeydgJPXvcw98TOze/mbvRwoquKFy+Lw95IFvqwlyNeDZ8b3YeeRct740XHW07An3+4sYFVqHrcNj6ZPmL/RcZzGHyugaq15YGW63Uz/NUex/9WVy/959UqpmUqpZKVUclHR2S00dX7Pdtw9sjvnyvQuqxsV257xfTsx74dMduSWGR3HqZRUtSzz0LtjG24bEW10HKcTHujNw6N7sjHzGAs328f0X3MUey5w8m4WYcD/zBHSWr+rtU7SWieFhJxdMZ/TNYg7R3Y7u5Si1Z4Y05sgX3dmLU+TtdutRGvNo6t3UF7TyJzJ8biZ7HIim927fEAEQ7sF8+xXezhkB2u3m+NdshXoppTqopRyB6YCn5vheYWN8fd24/nL4th3tJK53+8zOo5T+CI9n692FHDXBd2Iad/G6DhOSynFixPjcDUp7lth+9N/W13sWutG4DbgG2A3sExrvbO1zyts0/AeoUwbEM57P2c5zBKntqqwvJbHVmfQNyKAmUOjjI7j9Dr4e/HEmN5sPXScD3617em/Zvlcp7X+SmvdXWvdVWv9jDmeU9iuR0b3omOAF/cs2051vf0vcWqLtNY8uGoHdY1NzJkUj6sMwdiECYmduKBXO17+Zh/7jtru9F95t4gz5uvhyksT4zl8rJrnvpLt9Cxh6dYcfthTyP3/jiEqxNfoOOIEpRTPTeiDr41P/5ViF2flnK5BXDukC5/+fphf9st2euaUfayap9bu4pyoIGYMjjQ6jviTYF8Pnh0fS0ZeOfNsdDs9KXZx1u4f1YOuIT7ctzydspoGo+M4hKZmzb3Lt+OiFC9PjsdFtrmzSaNiO7RM//0xk+05pUbH+R9S7OKsebqZeGVyAkWVdTzxuVwvN4cPfs1iy6ESZo/tLdvc2bgnxvYm1M+Du5elUVNvW9N/pdhFq8SHB3D7iGg+S81jbbp9LnFqK/YUlPPyN/u4sFc7Lku0yM3bwoz8vVq208sqquL5r21rOz0pdtFqtw2PJiE8gEc+y6CgzP6WOLUFtQ1N3LUkjTZerjw7QZaitheDo4O5dkgXPv6tZd9lWyHFLlrN1eTC3CkJ1Dc2c+9y2U7vbLz8zV72FFTw0sR4gn09jI4jzsD9o3rQLdSX+5Zvt5nt9KTYhVl0CfbhsUt68WtmMfM3HTI6jl3ZmFnM+78eZPqgzgyPCTU6jjhDnm4m5k5J4Hh1PQ+uSreJtdul2IXZTBsQzsie7Xjh6z3sOlJudBy7UFpdz6xl24kK8ZFt7uxYbCd/7vt3D77ZeZQlW3NO/QcsTIpdmM0f62kEeLtxx5JUm5spYGu01ty/Ip1jVXW8OqUvXu6yxro9u/5fUfwrOpgnv9jFAYP3CZZiF2YV6OPO3CkJHCiq5Kkvdxkdx6Yt2JzNt7uO8sCoGFlj3QG4uCjmTI7H082FO5ekUtdo3ImNFLswuyHRwcw8N4pFm7NZl5FvdBybtKegnKfW7uK87iFcO6SL0XGEmbRr48kLl8WRkVfOC1/vNSyHFLuwiFkX9CA+zJ/7V6STU2I/W4pZQ019E7cvSqWNpxsvT5K7Sx3Nhb3bM2NwJB9uPMh3u44akkGKXViEu6sL8y5PRAO3LdpGfaNtLpZkhMfXZJBZVMncKfGE+MnURkf00MUxxHZqw73LtxuyV6oUu7CY8EBvXpoYx/bcMp6zsTvzjLIsOYflKbncNjyaod1ki0dH5eFqYt60RJqaNXcsTrX6KpBS7MKiRsV2YMbgSOZvPOT04+2788t5bHUGg7sGcdfI7kbHERYWGezDcxP6sC271OrLW0uxC4t76OIY4sP8uXd5OpmFxk4DM0pFbQO3LNyGv5cbr07ti0nG1Z3CmPiO/zfeviYtz2rHlWIXFufhauKtK/vh4erCjZ8mU1HrXEv8Njdr7lm2neySal6f1lfG1Z3Mwxf3JKlzWx5cuYO9BdbZdUmKXVhFxwAvXr+8L4eOVTvdejKvrt/Pd7uO8ujongyMCjI6jrAyd1cX3rwiEV9PV25akEK5FU5spNiF1QzuGsxDF8Xwzc6jvPmTbe48Y27rMgp4df1+LksMk92QnFhoG0/euDyR/LIath60/CbwrhY/ghAnue5fXcjIK+Plb/cRFeLLxX06GB3JYvYWVDBrWRrxYf48Mz5WluJ1cgO6BPLrAyOssnqnnLELq1JK8fxlcfTr3Ja7l6aRZoPbiplDYXkt1360FW8PV96e3g9PN1kHRmC1JZml2IXVebqZeGd6P0L8PLj+42TySmuMjmRWVXWNXPvxVo5X1zN/Rn86+MsWd8K6pNiFIYJ9PZg/oz91DU1cM38LpdW2sUFBazU2NXP74lR2HSnnjcsTie0ki3sJ65NiF4bp1s6Pd67qx6Hiaq79aCvV9Y1GR2oVrTWPrs7ghz2FPDkuVjbNEIaRYheGGtw1mNem9SUtp5SbFtjvmjJaa55cu4slW3O4fUQ0Vw7qbHQk4cSk2IXhRsW25/kJcfy8r4i7l6ZZfV0Nc3jlu33M33iIa4d04Z4LZLkAYSyZ7ihswuT+4ZTXNvD0l7tpata8Nq0v7q62f96htWbeD5m8/kMmU/uH89glPWVaozCc7f/mCKdx/dAoZo/pxbqdBdyyMMXQHWhOh9aa59ftYc53+5jQtxPPjO8jpS5sghS7sCnXDOnCU5fG8v3uQq7/OJnKOtu8oNrUrHlkdQbvbMhi+qDOvDwpXhb2EjZDil3YnOmDOvPixDg2HTjGpLd/I7/Mtua5V9c3cvvibSzanM0tw7ry5LjesguSsClS7MImTU4KZ/6M/uSUVHPpGxvJyCszOhIAeaU1THr7N9ZlFPDo6J7cPypGhl+EzZFiFzbr3O4hrLx5MK4uLkx8exNLt2ajtXGrQm45WMK4eb+SfayaD2b05/qhUYZlEeKfSLELm9ajvR+f3TqYfp3b8sDKHdyxJM0qy56erL6xmZe+2cPUd3/D18OVz24dzPAecvORsF0y3VHYvFA/Tz65diBvbzjAK9/tIzX7OE+O682ImHYWP/a+oxXcsyyNjLxyJieF8dglvfDzdLP4cYVoDSl2YRdMLopbh0czKCqQ+1ekc+1HyYzs2Y7ZY3oRHuht9uMdq6zj1fX7Wbg5G38vN96Z3o9/925v9uMIYQmqNWOWSqlJwBNAT2CA1jr5dP5cUlKSTk4+rYcK8T/qG5uZv/Egr67fT2Oz5rLEMG4Y2oWoEN9WP/exyjoWb8nmnQ1ZVDc0cfmACO4a2Y0gKy23KsQ/UUqlaK2TTvW41p6xZwATgHda+TxCnDZ3VxduPK8rYxM68voPmaxIyWXJ1mwu6NmO8X07cV6PELzdT/+t3djUzLbsUpZsyWZtej71Tc2cHxPKQxfHEB3qZ8FXIoRltKrYtda7AZnuJQzRwd+LZ8f34e6R3flo00EWbc7m211H8XB1YWi3YOLCAujezo9u7Xzx83DFzeSCi4uiqKKWnOM1HC6u4vesEjYeKKaithEfdxNTB4QzfVBnurWTQhf2S8bYhd0L8fPgvn/HcPfI7mw5VMK3O4/y495Cvt9deMo/28Hfk4tjOzC0ezDDeoTi6yG/EsL+nfJdrJT6Hvirq0aPaK3XnO6BlFIzgZkAERERpx1QiNPlanJhcNdgBncN5gl6U1XXSGZhJVnFlVTXN9HQ2ExjsybY14PwQC/C2noT6uchnziFwzllsWutR5rjQFrrd4F3oeXiqTmeU4h/4uPhSnx4APHhAUZHEcKq5AYlIYRwMK0qdqXUeKVULnAO8KVS6hvzxBJCCHG2WjWP/awPqlQRcPgs/3gwUGzGOPZAXrNzkNfsHFrzmjtrrUNO9SBDir01lFLJpzNB35HIa3YO8pqdgzVes4yxCyGEg5FiF0IIB2OPxf6u0QEMIK/ZOchrdg4Wf812N8YuhBDin9njGbsQQoh/IMUuhBAOxq6KXSk1Sim1VymVqZR60Og8lqaUCldK/aiU2q2U2qmUutPoTNaglDIppVKVUmuNzmINSqkApdQKpdSeE3/X5xidydKUUnefeE9nKKUWK6U8jc5kbkqpD5VShUqpjJN+FqiU+k4ptf/Ef9ta4th2U+xKKRPwBnAR0AuYppTqZWwqi2sEZmmtewKDgFud4DUD3AnsNjqEFb0KrNNaxwDxOPhrV0p1Au4AkrTWsYAJmGpsKov4CBj1p589CKzXWncD1p/43uzsptiBAUCm1jpLa10PLAHGGZzJorTW+VrrbSe+rqDlF76TsaksSykVBowG3jc6izUopdoA5wIfAGit67XWpcamsgpXwEsp5Qp4A0cMzmN2WuufgZI//Xgc8PGJrz8GLrXEse2p2DsBOSd9n4uDl9zJlFKRQF9gs7FJLO6/wP1As9FBrCQKKALmnxh+el8p5WN0KEvSWucBLwPZQD5QprX+1thUVtNOa50PLSduQKglDmJPxf5Xi2Y7xVxNpZQvsBK4S2tdbnQeS1FKXQIUaq1TjM5iRa5AIvCW1rovUIWFPp7bihPjyuOALkBHwEcpdaWxqRyLPRV7LhB+0vdhOODHtz9TSrnRUuoLtdarjM5jYUOAsUqpQ7QMtY1QSi0wNpLF5QK5Wus/PomtoKXoHdlI4KDWukhr3QCsAgYbnMlajiqlOgCc+O+pt/k6C/ZU7FuBbkqpLkopd1outnxucCaLUi1b+3wA7NZav2J0HkvTWj+ktQ7TWkfS8vf7g9baoc/ktNYFQI5SqseJH50P7DIwkjVkA4OUUt4n3uPn4+AXjE/yOXD1ia+vBk57F7ozYTcbPGqtG5VStwHf0HIV/UOt9U6DY1naEGA6sEMplXbiZw9rrb8yMJMwv9uBhSdOWLKAawzOY1Fa681KqRXANlpmfqXigEsLKKUWA8OA4BP7VswGngeWKaWuo+UfuEkWObYsKSCEEI7FnoZihBBCnAYpdiGEcDBS7EII4WAMuXgaHBysIyMjjTi0EELYrZSUlOLT2fPULMWulPoQ+OPmkthTPT4yMpLk5GRzHFoIIZyGUurw6TzOXEMxH/G/i90IIYQwgFnO2LXWP59Yy8SidueXU1RRR1tvdwK83Qjx88DTzWTpwwphmIraBo6W11JYXgeAl7sJHw9XOgZ44ethN7ehCCuz2jtDKTUTmAkQERFxVs+x4PfDLNyc/X/fm1wUPdr5kdg5gAFdghjZMxRvd3mzC/u172gFGzOL+e3AMbYeKuF4dcNfPk4p6BLkQ6+ObRgUFcRFse0J8vWwclphq8x2g9KJM/a1pzPGnpSUpM9mjD2/rIbc4zUcr6qntLqB7JJqUnOOk5ZdSlV9E97uJi6K7cDEfmEMigqk5W5lIWxbZV0jn6cdYfGWbHbklQEQEejNwC6BRIf60t7fkxA/DxSKmoZGquqaOFhcRUZeGRl5ZRwpq8XkohjcNYiJ/cIY3acDriaZ8OaIlFIpWuukUz7Onoo5t+RcAAAbQ0lEQVT97zQ1a1IOH2fVtly+TM+noq6RxIgA7rmgB0Oig6TghU2qrGvk/V+yeP+Xg1TWNRLT3o+p/cMZ2asdYW29T+s5tNbszq/gyx1HWJuez+Fj1UQEenPzsK5MSOyEh6sMVToSpyr2k9U2NLFyWy7zfsgkv6yWgV0C+c+43sS0b2OR4wlxphqbmlm0JZvX1u+nuLKei2Lbc8O5UfQND2jVSUhzs+b73UeZ92Mm6bllhAd68eS4WIb3sMiS38IAVi32kxe7AY4Cs7XWH/zd4y1Z7H+oa2xiyZYcXlu/n7KaBm4ZHs2tw7vKGYwwVGZhBbOWp7M9p5SBXQJ58KIY+kaYd9tLrTUb9hXx5NpdZBVVMTquA49f0ot2bRxuW1GnY/Uz9jNhjWL/Q0lVPU+t3cVnqXl0C/XltWl96dlBzt6FdTU1az789SAvfbsXb3cTT46LZUxcB4sOE9Y1NvHuhixe/zETLzcTL0+K54Je7Sx2PGF5Uux/8uPeQh5YkU55bQPPju/DhMQwqx5fOK+ymgbuWpLKj3uLuKBXO54ZH0uon/XOng8WV3H74m1k5JVzw9Au3D8qBje5uGqXpNj/QlFFHbct2sbmgyVcOSiCxy/pjburvMGF5ew/WsHMT1PIKalm9tjeXDkwwpCL+bUNTTzz5W4+/f0w/Tq35Z3p/QiW6ZF253SL3alaLcTPg4XXD+TGc6NY8Hs213y0hYrav54nLERr/bi3kEvf2EhFbSOLZw5i+qDOhs3Q8nQz8dSlsbw+rS8ZeWVMeHMTWUWVhmQRludUxQ7ganLhoYt7MmdSPL9nlTD13d8pqqgzOpZwMGvS8rjh42Qig3344vYh9I8MNDoSAGPiO7J45iAq6xq57K1NpBwuMTqSsACnK/Y/XNYvjPevTiKrqIrL3trE4WNVRkcSDuLT3w5x19I0+nVuy5KZg+jg72V0pP9PYkRbVt08GH8vNy5/bzO/7C8yOpIwM6ctdoDhPUJZPHMQFbUNTHv3d7KPVRsdSdi5tzcc4LE1Ozk/ph0fXzsAP083oyP9pchgH1bePJguwT5c93EyG/ZJuTsSpy52gITwABZeP4jqhiamvfc7OSVS7uLsfPjrQZ7/eg9j4jvy9pWJNr9AXZCvB4tuGETXEF9u+CSZn/YWGh1JmInTFztAr45tWHDdwJYz9/d+J6+0xuhIws4s3HyYJ9fuYlTv9rwyOd5u1moJ9HFn0fUDiQ7xZeYnKWw6UGx0JGEG9vHus4LYTv4suH4gZTUNXPXBZo5X1RsdSdiJz1JzeeSzDEbEhPLatL52N0e8rY87C68fSOcgb2Z+kkLGiYXIhP2yr3eghcWFBfDeVUnklNRw/SfJ1DY0GR1J2Lhf9xdz3/J0zokK4s0rEu32voi2Pu58ct0A2ni6MmP+Fg4Vy2QCe2af70ILGhQVxH+nJrAt+zi3L06lsanZ6EjCRu3OL+emBSlEh/ryzlX9bH5M/VQ6+HvxyXUDaWrWTP9ws0wDtmNS7H/h4j4deGJMb77bdZQn1+4yOo6wQfllNVwzfyu+Hq7Mv6Y/bWx09suZig71Zf41AyiqqOPGT+VTq72SYv8bVw+OZOa5UXzy22E+/f209o8VTqK6vpHrPkqmqq6R+df0t7l56q2VEB7AK5MT2JZdygMr0zFi2RHROlLs/+CBUTGMiAnlic93silTZguIliVx71uezu6Ccl673HFXCr24TwfuvbA7a9KO8MaPmUbHEWdIiv0fmFwUr05NICrYh5sXbpMLSoJ5P2Ty5Y58HhwV4/AbWNw6PJrxfTvx8rf7+GZngdFxxBmQYj8FP083Pri6Py4KZn6aTHV9o9GRhEG+23WUOd/tY3zfTsw8N8roOBanlOK5CX2ID/Nn1rLtHJBFw+yGFPtpiAjy5rVpfdlfWMlDq3bImKMTOlhcxT1L04gL8+e5CX2cZh9dTzcTb13ZD3dXF276NIXKOjmxsQdS7KdpaLcQZl3QMub4yW9yMdWZ1DY0cfOCFEwmxZtX2P5SAebWMcCLedP6cqCokvtXbJcTGzsgxX4GbhkWzfkxoTy1dpcsd+pEHludwd6jFcydkkBYW2+j4xhicHQwD4yK4asdBXy48ZDRccQpSLGfARcXxSuTE+gY4MVti1Jl2QEnsGxrDstTcrl9eLTDXyw9lZnnRnFhr3Y8//VutueUGh1H/AMp9jPk7+3GvMv7UlxZx30rZI6vI9t3tILH1mQwJDqIO0d2NzqO4ZRSvDQxnlA/T25dtI2yGtl9zFZJsZ+FuLAAHryoJ9/vPsp8+VjqkGrqm7ht0Tb8PF2ZOyUBk4tzXCw9FX9vN16/vC8FZbU8ICc2NkuK/SxdOySSkT3b8dzXu9mRK6vhOZqnvtzFvqOVvDI5gVA/T6Pj2JTEiLbcP6oH63YWsEDuyrZJUuxnqeVjaRzBvh7cvngbVTINzGF8mZ7Pos3Z3HheFOd2DzE6jk26/l9RDOsRwtNf7mbf0Qqj44g/kWJvhbY+7vx3SgKHS6p58gtZLMwRHCmt4aFV6cSHB3DvhT2MjmOzXFxaxtv9PF25Y3GqLBZmY6TYW2lgVBC3DOvK0uQcvt6Rb3Qc0QpNzZp7lqXR1Kx5bWqC3W2YYW0hfh68NDGePQUVvLBuj9FxxEnknWsGd43sTlyYPw+u2kF+mWyrZ6/e+yWL37NKmD22N52DfIyOYxeGx4QyY3Ak8zcekj1TbYgUuxm4mVx4dWpf6hubmbVsO83NMlPA3mTklTHn271cFNueSf3CjI5jVx68KIYe7fy4b0W63NthI6TYzaRLsA+zx/Ri04FjzN90yOg44gzUNjRx19I0An3ceXa886wDYy6ebibmTkmgtLqehz+TtZRsgRS7GU3pH87InqG8sG6PzBSwIy+s20NmYSUvT4qnrY+70XHsUq+ObZh1YQ++zijgs9Q8o+M4PSl2M2pZ5jQOXw9X7l6aRn2j7Jdq6zZlFjN/4yFmDI5kaDeZ2tgaNwyNYkBkILPX7CT3eLXRcZyaFLuZhfh58NyEPuw8Us5r6/cbHUf8g7KaBu5dvp2oEB8eGBVjdBy7Z3JRzJkcjwa51mQwKXYL+Hfv9kzsF8abP2WyLfu40XHE3/jP5zs5WlHH3MkJeLk711K8lhIe6M3jl/Ri88ESudZkICl2C5k9phcd/L24d9l2aurl5g1bsy6jgFWpedw6PJr48ACj4ziUSUlhnB8Tyosnrl0I65NitxA/TzdemhhHVnGV3LxhY4or63jksx3EdmrD7SOijY7jcJRSPHdZH7zdTcxalkZjk1xrsjYpdgsaHB3MjMGRfLTpEJsOFBsdRwBaax79LIOK2kZemSx3l1pKqJ8nT1/ah+25Zbz50wGj4zgds7yrlVKjlFJ7lVKZSqkHzfGcjuKBUTF0CfbhvuXpVNTK+tVGW5N2hHU7C5h1YXe6t/MzOo5DGx3XgbHxHXlt/X52HpEVUK2p1cWulDIBbwAXAb2AaUqpXq19Xkfh5W5izuR48stqeObL3UbHcWoFZbU8viaDfp3bcv3QKKPjOIUnx/WmrY87s5Ztp65RrjVZiznO2AcAmVrrLK11PbAEGGeG53UYiRFtmXluV5ZszeHHPbKehhG01jywMp2GJs3Lk+Jl4wwrCfB254XL+rCnoIJXv5fpv9ZijmLvBOSc9H3uiZ/9f5RSM5VSyUqp5KKiIjMc1r7cfUE3urfz5YGV6ZRWy3oa1rZ0aw4b9hXx4EUtQ2PCekbEtGNSvzDe3nCAVJn+axXmKPa/OvX5nzsTtNbvaq2TtNZJISHOd4efh6uJVyYnUFJVzxOf7zQ6jlPJKanmqbW7OCcqiOmDOhsdxyk9NqYX7dt4Mkum/1qFOYo9Fwg/6fsw4IgZntfhxHby57YR0axOOyJrt1tJc7Pm/hXpKKV4cWIcLjIEY4g2nm68ODGerOIqXvpmr9FxHJ45in0r0E0p1UUp5Q5MBT43w/M6pFuHRxPbqQ2PrM6guLLO6DgO7+PfDvFb1jEeHd2T8EBvo+M4tX91C+aqczrz4caD/HbgmNFxHFqri11r3QjcBnwD7AaWaa1lrOFvuJlceGVyApW1jTy8SpY4taQDRZU8//UehvUIYUr/8FP/AWFxD14UQ+cgb+5bsZ1K2SfYYswyj11r/ZXWurvWuqvW+hlzPKcj697Oj1kXdufbXUdZnSZLnFpCY1PLpieebiZeuCxO1li3Ed7ursyZFE9eaQ3PfCn7BFuK3HZnkOuHRpHUuS2Pr9kp2+lZwDs/Z5GWU8pTl8bSro2n0XHESZIiA5k5NIrFW2T6r6VIsRvE5KJ4eVI8Tc2a+5anyxKnZrTrSDn//X7f/935KGzP3Rd0p0c7P+5fKdvpWYIUu4Eig314ZHRPfs0s5pPfDhkdxyHUNjRx99I0ArzdeXpcrNFxxN/wdDPxypR4SqvreXR1hlxrMjMpdoNdPiCC4T1CeO5rWeLUHOZ8u5e9Ryt4cWKcbHNn43p39Oeukd35ckc+n2+XGdLmJMVuMKUUL1wWh7e7iXuWpdEgS5yetU0Hinn/14NcOSiC4T1CjY4jTsON50aRGBHAY6szOFIq15rMRYrdBoS28eTZ8X1Izy3jddlO76yU1zZw3/J0IoN8ePjinkbHEafJ1eTC3CkJNDZr2U7PjKTYbcRFfTowsV8Y837MZOuhEqPj2J3HVmdQUF7LnMnxeLu7Gh1HnIHOQT48MaY3v2Ud4/1fs4yO4xCk2G3IE2N7E9bWm7uXplEua7efttWpeaxJO8IdI7qRGNHW6DjiLExKCmNU7/a89M1edh0pNzqO3ZNityG+Hq7MnZJAflkts9fIzbunI6ekmkdXZ5DUuS23Du9qdBxxlpRSPDuhD2293blzSaosFNZKUuw2pl/nttw2PJrPUvNYnSp3pf6TxqZm7lqahgLmTknAVba5s2uBPu7MmRzP/sJKnpK7UltFfhNs0O0joknq3JZHPtvBoeIqo+PYrNd+yCTl8HGeHh8rC3w5iKHdQrjxvCgWbc6WFVBbQYrdBrmaXHh1Wl9cTS7ctnibbCn2FzZlFvP6D/u5LDGMcQn/s6+LsGP3XtiD+PAAHliZTu7xaqPj2CUpdhvVKcCLFyfGkZFXzovrZP3qkxVV1HHn0jSign146tLeRscRZuZmcuH1qX3RGu5cIvd2nA0pdhv2797tmTE4kg9+Pcj3u44aHccmNDdr7lmWRnlNA29ckShTGx1URJA3z07oQ8rh47wsG3OcMSl2G/fgRTHEdmrDPcvSyD4mH0vn/ZjJL/uLeWJsb2LatzE6jrCgMfEdmT6oM+/8nMU3OwuMjmNXpNhtnKebibeu6IdSipsXplDb4Lzj7Rv2FTH3+32MS+jIVNk4wyk8eklP4sL8uXf5djmxOQNS7HYgPNCbuVPi2Xmk3Gnnt+eUVHPnklR6tPPjuQl9ZOMMJ+HhauKNyxNxkRObMyLFbidGxLTj9hHRLE3OYfGWbKPjWFVtQxM3L0yhqVnz9pX9ZFzdyfxxYrMrv5yHZDvJ0yLFbkfuGtmdc7uH8PiaDKdZT0ZrzcOf7SAjr5xXJicQGexjdCRhgBEx7bhnZHc+S83j/V8OGh3H5kmx2xGTi+L1aX0Jb+vNTZ+mkOcEy5y+83MWq7blcdfIblzQq53RcYSBbhsRzcV92vPc17v5eV+R0XFsmhS7nfH3cuO9q5Oob2rm+o+Tqa533J3ev9t1lBfW7WF0XAfuPL+b0XGEwZRSvDQxnu7t/Lht0TYOFMnGNH9Hit0OdQ3x5fVpfdlbUM6dS9JocsA1rPcUlHPXklT6dPLn5YnxcrFUAODj4cp7VyXh7urCjPlbKK6sMzqSTZJit1PDeoQye0xvvtt1lNmfO9aekXmlNcz4cCu+nq68Oz0JL3eT0ZGEDQkP9Ob9q/tTVFHHdR8ny0qQf0GK3Y5dPTiSG8+LYsHv2bz50wGj45jF8ap6rv5wC1X1jXx0zQDa+3saHUnYoITwAF6b2pf03FLuWJLqkJ9aW0OK3c498O8YLk3oyEvf7GVZco7RcVqlpr6Jaz/eSnZJNe9dlUTPDnJnqfh7F/ZuzxMnPrU+tCpdttU7iUwItnMuLooXJ8ZTXFnPgyvT8XQzMTa+o9GxzlhtQxM3LUhhe04pb16RyKCoIKMjCTtw9eBIjlXV89r6/Xi5mXhibG+5HoOcsTsEd1cX3r2qH0mRgdy9NM3u1rH+o9Q37CviuQl9GBXbwehIwo7cPbIbNwztwse/HeaFdXsd6nrT2ZJidxDe7q58OKM/CeEB3L441W4WTaprbOLmBSn8tLeI5yf0YUr/CKMjCTujlOLhi3tyxcAI3t5wQModKXaH4uvhykfX9Ce2kz+3LNzGipRcoyP9o6q6RmZ+ksKPe1vO1KcOkFIXZ0cpxVPjYrn8RLk/ujrDqcfcpdgdjJ+nGwuuH8g5UUHcu3w772ywzdkyRRV1TH33d37NLObFy+KYJqUuWsnFRfHMpbHcPKwrCzdnc9dS592kQy6eOiBfD1c+mJHErGXbee7rPRwtr+Phi2NsZrPng8VVXPXhZoor6nnvqn6MiJGlAoR5KKV4YFQMfp6uvLhuL4UVtbx5RT8CfdyNjmZVtvGbLszOw9XEa1P7cs2QSD7ceJCrPtzCMRu4S2/97qNc+sZGquqaWDxzkJS6sIhbhkUzd0o827JLGTvvV3bnlxsdiayiSq58fzNHrLDGkxS7A3NxUcwe05uXJ8WTfPg4Y+dtJD231JAsjU3NvLBuD9d9nExYWy9W3zKEhPAAQ7II5zC+bxjLbjyHhqZmJry5iVXbcg27qLo6NY8xr/9KxpEyskssv2GIMuKFJiUl6eTkZKsf15ntyC3jpgUpHC2v5dbh0dw6PBp3V+v8u55VVMkDK9PZeug40waEM3tMbzzdZJkAYR1Hy2u5deE2kg8f56LY9jwzvo/VhmYq6xp58oudLEvOpX9kW16d2peOAV5n/XxKqRStddIpHyfF7jxKq+v5zxe7+Cw1j5j2frw0MZ4+Yf4WO159YzPv/nyA137IxMPVhf+M7c2ExDCLHU+Iv9PUrHn35yxe+W4v/l7uzB7Ti0viOljsZiatNV+k5/PMl7sorKjj1mHR3DWyW6uvc0mxi7/1/a6jPPzZDooq6xgb35G7R3Y36wYWTc2ar3bk8+r6/WQWVjI6rgOzL+lFaBtZ90UYa3d+ObOWbWdXfjkJ4QE8Mron/SMDzXqM9NxSXli3h42Zx4jt1IanL+1jtmFHqxS7UmoS8ATQExigtT6ttpZiN15ZTQPvbDjA/I2HaGhq5rLEMK4YFEGfTv5nfRZT29DEuowC5v2YSWZhJdGhvjx0UQzn95QLpMJ2NDVrVm7LZc63ezlaXsfQbsFMH9SZETGhZ31GrbVmY+Yx3tqQycbMY7TxdOXef/fgioGdMbmY71OBtYq9J9AMvAPcK8Vufworapn3QyZLt+ZQ19hMTHs/JiR2YnDXYHp2aHPKN2V5bQPbDh9nbXo+6zIKqKxrpFuoL3ec342L+3Qw65taCHOqrm9k/sZDfPrbYQrKa+no78nYhE4M7RZMv85tT3kdqLGpme25pXy9o4B1OwvIPV5DqJ8H1/2rC5cPjMDP083sma06FKOU+gkpdrtWVtPA2vQjLEvOZXtOy8wZXw9X4sP9ad/Gi2BfdwK83ampb6S8tpGSqnoyjpSRVVQFgJ+HK6Ni2zMuoRODuwbhIoUu7ERjUzPr9xSy4PfD/HbgGI3NGg9XF3p2aEOntl6EBXjRxsuNusZm6hqbKKqoY29BBfsLK6lvbMbNpPhXdDCj4zoyJr4DHq6Wmxhgc8WulJoJzASIiIjod/jw4VYfV1jGkdIath4qYcvBEjLyyiiqqKO4sp76pmaUail8fy83Ytr7ERcWQHx4AAO7BMpMF2H3quoa2XzwGL/uP8a+oxXkldaQV1pDfWPLHazuri4EeLnRo70fPTu0IbaTP8N6hNDGAmfnf8Vsxa6U+h5o/xf/6xGt9ZoTj/kJOWN3aFprahqa8HQ1ydm4cCrNzZqG5mbcTS6GLwl8usV+yiUFtNYjzRNJ2DOlFN7usgKFcD4uLgoPF/v6NCp3ngohhINpVbErpcYrpXKBc4AvlVLfmCeWEEKIs2XIDUpKqSLgbK+eBgPFZoxjD+Q1Owd5zc6hNa+5s9Y65FQPMqTYW0MplXw6Fw8cibxm5yCv2TlY4zXLGLsQQjgYKXYhhHAw9ljs7xodwADymp2DvGbnYPHXbHdj7EIIIf6ZPZ6xCyGE+Ad2VexKqVFKqb1KqUyl1ING57E0pVS4UupHpdRupdROpdSdRmeyBqWUSSmVqpRaa3QWa1BKBSilViil9pz4uz7H6EyWppS6+8R7OkMptVgp5XCL9SulPlRKFSqlMk76WaBS6jul1P4T/21riWPbTbErpUzAG8BFQC9gmlKql7GpLK4RmKW17gkMAm51gtcMcCew2+gQVvQqsE5rHQPE4+CvXSnVCbgDSNJaxwImYKqxqSziI2DUn372ILBea90NWH/ie7Ozm2IHBgCZWussrXU9sAQYZ3Ami9Ja52utt534uoKWX/hOxqayLKVUGDAaeN/oLNaglGoDnAt8AKC1rtdaG7PjuHW5Al5KKVfAGzhicB6z01r/DJT86cfjgI9PfP0xcKkljm1Pxd4JyDnp+1wcvOROppSKBPoCm41NYnH/Be6nZQMXZxAFFAHzTww/va+UMt8+hTZIa50HvAxkA/lAmdb6W2NTWU07rXU+tJy4AaGWOIg9FftfrZfpFFN6lFK+wErgLq11udF5LEUpdQlQqLVOMTqLFbkCicBbWuu+QBUW+nhuK06MK48DugAdAR+l1JXGpnIs9lTsuUD4Sd+H4YAf3/5MKeVGS6kv1FqvMjqPhQ0BxiqlDtEy1DZCKbXA2EgWlwvkaq3/+CS2gpaid2QjgYNa6yKtdQOwChhscCZrOaqU6gBw4r+FljiIPRX7VqCbUqqLUsqdlostnxucyaJUy6r+HwC7tdavGJ3H0rTWD2mtw7TWkbT8/f6gtXboMzmtdQGQo5TqceJH5wO7DIxkDdnAIKWU94n3+Pk4+AXjk3wOXH3i66uBNZY4iN3snKC1blRK3QZ8Q8tV9A+11jsNjmVpQ4DpwA6lVNqJnz2stf7KwEzC/G4HFp44YckCrjE4j0VprTcrpVYA22iZ+ZWKA96BqpRaDAwDgk8sbz4beB5YppS6jpZ/4CZZ5Nhy56kQQjgWexqKEUIIcRqk2IUQwsFIsQshhIORYhdCCAcjxS6EEA5Gil0IIRyMFLsQQjgYKXYhhHAw/w83FDtRmKHe8gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a plot figure\n",
    "plt.figure()\n",
    "\n",
    "\n",
    "# create the first of two panels and set current axis\n",
    "plt.subplot(2, 1, 1)   # (rows, columns, panel number)\n",
    "plt.plot(x1, np.sin(x1))\n",
    "\n",
    "\n",
    "# create the second of two panels and set current axis\n",
    "plt.subplot(2, 1, 2)   # (rows, columns, panel number)\n",
    "plt.plot(x1, np.cos(x1));\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure(432x288)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get current figure information\n",
    "\n",
    "print(plt.gcf())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "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": [
    "# get current axis information\n",
    "\n",
    "print(plt.gca())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization with Pyplot\n",
    "\n",
    "\n",
    "Generating visualization with Pyplot is very easy. The x-axis values ranges from 0-3 and the y-axis from 1-4. If we provide a single list or array to the plot() command, matplotlib assumes it is a sequence of y values, and automatically generates the x values. Since python ranges start with 0, the default x vector has the same length as y but starts with 0. Hence the x data are [0,1,2,3] and y data are [1,2,3,4]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OLWd2o2YN/RpKxYo2FL5DybUQfh1phd0GuD24skTkK5tz87hrWiZvLd7Mce0b8ejIwfRqrQZ2EoxyQ8HMqgGfuXu/rx5z943AxiALE6nq3J2XP1/Lr2YvpqComLsu6M11p3amulpUSIDKDQV3LzazhWaW7O5fVkZRIlXdmu17uWNqBh+v2M6QLk155NL+dGpeL+yypAqIdvdRGyDLzD4D9n71oLtfHEhVIlVUUbHz4ker+PWbS0mqVo2HRqRwxcAOamAnlSbaULg/0CpEhKWbShrYfbF2F2f3asmDI/rRppEa2EnliioU3P09M+sIdHf3t8ysLqDr9olUgPzCYp55N5un38mmQe0kfv+9E7iofxs1sJNQRPuJ5huBUUBToCvQDpgAnB1caSKJb+HaXYyeks7SzbsZfnxbxl3Ul6b1aoZdllRh0e4+ugUYBMwFcPflZtYysKpEEtz+/CJ+86+lPP/hKlo2qM3zV6dydm81sJPwRRsKB9w9/6vNWTOrAXhgVYkksI9XbOOOqRms2b6P7w9OZuz5vWhYWw3sJDZEGwrvmdmdQB0z+zbwY+C14MoSSTy5eQU8PHsJL332JR2b1eWlG4dwUtdmYZcl8j+iDYWxwPVABnATMBv4U1BFiSSatxZt5q7pGWzdfYBRp3Xh5+f0oE5NnashsSfas4+KzWwSJccUHFjq7tp9JFKO7XsOcP9ri5ixcAO9Wjdg4lWpHNehcdhliRxWtGcfXUjJ2UYrAAM6m9lN7j7nCM/pAEwGWgPFwER3f/KQMWcArwKrIg9NdfcHjnYSIrHG3ZmxcAP3zchiz4FCfvHtHtx8elc1sJOYF+3uoyeAM909G8DMugKzgMOGAlAI/NLdF5hZA2C+mf3L3RcdMu4Ddx92tIWLxKqNOfu5e1om/16yheM7NOaxkf3p0apB2GWJRCXaUNjyVSBErAS2HOkJpZvmRa7YtpiSzzccGgoiCaG42Hnp8y95ePYSioqde4b14ZqTO6mBncSVI4aCmV0auZllZrOBVyg5pnA58Hm0L2JmnYATiHzO4RAnmdlCYANwm7tnRft9RWLFqm17GZuWztxVOzilWzMeHtGf5GZ1wy5L5KiVt6VwUanbm4HTI7e3AlFd7snM6gNpwM/cPfeQxQuAju6+x8wuAKYD3cv4HqMo+UQ1ycnJ0bysSKUoLCrmhY9W8cSby6hZoxqPXpbCd1I7qEWFxC0L8iQiM0sCZgJvuPtvohi/Gkh1922HG5Oamurz5s2ruCJFjtHijbmMSUsnfV0O3+7Tigcv6UerhrXDLkukTGY2391TyxsX7dlHnYH/AzqVfs6RWmdbyZ9KzwOLDxcIZtYa2OzubmaDKLnE5/ZoahIJy4HCIp5+ZwXPvJNNozpJPPX9E7gwRQ3sJDFEe6B5OiVv8K9RcnppNE4BrgIyzOyLyGN3AskA7j4BGAn8yMwKgf3AFfr8g8SyBV/uZMyUdJZv2cOlJ7TjnmF9aKIGdpJAog2FPHf//dF8Y3f/kJLPNBxpzFPAU0fzfUXCsC+/kF+/sYwXP15Fm4a1efHagZzZUz0hJfFEGwpPmtk44E3gwFcPuvuCQKoSiSEfZW9j7NR01u7Yz1VDOjJ6aE8aqIGdJKhoQyGFkl1BZ/Hf3UceuS+SkHL2F/DQrMW8PG8tnZvX4+VRQxjcRQ3sJLFFGwojgC7unh9kMSKx4s2sTdw9PZPte/O5+fSu/Oyc7tROUgM7SXzRhsJCoDHlfIpZJN5t3X2A+17LYlb6Rnq3acjzVw8kpX2jsMsSqTTRhkIrYImZfc7/HlM47CmpIvHE3Zn2n/U8MHMR+w4Ucdu5Pbjp9K4kVVcDO6laog2FcYFWIRKi9bv2c9e0DN5dupUBySUN7Lq1VAM7qZqivZ7Ce0EXIlLZioudv81dwyNzluDAfRf14aqT1MBOqrZoP9G8m/9ek7kmkATsdfeGQRUmEqSVW/cwNi2Dz1bv4Fvdm/PQiBQ6NFUDO5FotxT+Z1vazC4BBgVSkUiACouKee6DVfz2rWXUrlGNx0f2Z+SJ7dWiQiQi2mMK/8Pdp5vZ2IouRiRIWRtyGJOWTub6XM7r24rxw/vRUg3sRP5HtLuPLi11txqQyn93J4nEtLyCIv7w9nImvLeSJnVr8uyVAzg/pU3YZYnEpGi3FEpfV6EQWA0Mr/BqRCrY/DU7GD0lnRVb93LZgPbcM6w3jeuqgZ3I4UR7TOHaoAsRqUh7DxTy+BtLmfTJato2qsOk6wZxeo8WYZclEvPKuxznvUdY7O4+voLrEfnG3l+2lTumZrAhZz8/HNKR24f2on6tYzp8JlLllPebsreMx+oB1wPNAIWCxIycfQWMn7WIKfPX0aVFPV656SQGdmoadlkiceWIoeDuT3x128waAD8FrgX+ATxxuOeJVLbXMzdyz6tZ7Nibz4/P6MqtZ6uBncixKHeb2syaAr8ArgQmAQPcfWfQhYlEY8vuPMa9msWczE30adOQF68ZSL92amAncqzKO6bwOHApMBFIcfc9lVKVSDncnSnz1/HgrMXsLyji9vN6Muq0LmpgJ/INlbel8EtKuqLeDdxV6lOfRsmBZrW5kEq3dsc+7pyWwQfLt5HasQmPXNafbi3rh12WSEIo75iC/uySmFFc7Ez+ZDWPvbEUAx4Y3pcfDO5INTWwE6kwOk9P4kL2lj2MTUtn3pqdnNajBQ+N6Ef7JmpgJ1LRFAoS0wqKipn4/kqefGs5dWpW54nLj+PSAe3UwE4kIAoFiVmZ63MYPSWdRRtzuSClNfdf3I8WDWqFXZZIQlMoSMzJKyjiyX8vZ+L7K2laryYTfjCAof3UwE6kMgQWCmbWAZgMtAaKgYnu/uQhYwx4ErgA2Adc4+4LgqpJYt/nq3cwZko6K7ft5fIT23P3hX1oVDcp7LJEqowgtxQKgV+6+4LIp6Hnm9m/3H1RqTHnA90jX4OBZyP/ShWz50Ahj72+hMmfrKF9kzr85fpBfKu7GtiJVLbAQsHdNwIbI7d3m9lioB1QOhSGA5Pd3YFPzayxmbWJPFeqiHeWbuGuqRlszM3j2lM6cdu5PamnBnYioaiU3zwz6wScAMw9ZFE7YG2p++sijykUqoCde/MZP3MRU/+znm4t6zPl5pM5sWOTsMsSqdICDwUzqw+kAT9z99xDF5fxlK9d0c3MRgGjAJKTkyu8Rqlc7s7sjE2Mm5HJrn0F/OTMbvzf2d2oVUMN7ETCFmgomFkSJYHwN3efWsaQdUCHUvfbAxsOHeTuEynpv0RqaqouAxrHtuTmcff0TN5ctJmUdo2YfN1g+rRVtxSRWBHk2UcGPA8sdvffHGbYDOAnZvYPSg4w5+h4QmJyd/45bx3jZy0iv7CYsef34oZTO1NDDexEYkqQWwqnAFcBGWb2ReSxO4FkAHefAMym5HTUbEpOSdVlPxPQl9tLGth9mL2NQZ2b8silKXRpoQZ2IrEoyLOPPqTsYwalxzhwS1A1SLiKip0/f7yaX7+xlOrVjAcv6cf3ByWrgZ1IDNN5fxKI5Zt3Mzotnf98uYszerbgoREptG1cJ+yyRKQcCgWpUPmFxUx4bwVPvZ1NvVrV+d13j2f48W3VwE4kTigUpMKkr9vF6CnpLNm0m2H923DfxX1pXl8N7ETiiUJBvrG8giJ++69lPPfBSprXr8XEq07k3L6twy5LRI6BQkG+kU9XbmdsWjqrt+/je4M6MPb83jSqowZ2IvFKoSDHZHdeAY/MWcLf5n5JctO6/P2GwZzcrXnYZYnIN6RQkKP29pLN3DUtk825edxwamd+cW4P6tbUj5JIItBvskRtx958Hngti+lfbKB7y/o886OTOSFZDexEEolCQcrl7ryWvpH7ZmSRu7+An57dnR+f2VUN7EQSkEJBjmhTTkkDu7cWb+a49o149MbB9GqtBnYiiUqhIGVyd/7x+VoemrWYguJi7rqgN9ed2pnqalEhktAUCvI1a7bvZWxaBp+s3M6QLk155NL+dGpeL+yyRKQSKBTkoKJi58WPVvHrN5eSVK0aD41I4YqBHdTATqQKUSgIAEs3lTSwW7h2F2f3asmDI/rRppEa2IlUNQqFKi6/sJhn3s3m6XeyaVA7iSevOJ6Lj1MDO5GqSqFQhX2xdhdjpqSzdPNuhh/flnuH9aGZGtiJVGkKhSpof34RT7y5lBc+WkXLBrV5/upUzu7dKuyyRCQGKBSqmI9XbGNsWgZf7tjH9wcnM/b8XjSsrQZ2IlJCoVBF5OYV8PDsxbz02Vo6NqvLSzcO4aSuzcIuS0RijEKhCnhr0Wbump7B1t0HGHVaF35+Tg/q1FSLChH5OoVCAtu+5wD3vbaI1xZuoFfrBky8KpXjOjQOuywRiWEKhQTk7rz6xQbufy2LPQcK+cW3e3Dz6V2pWaNa2KWJSIxTKCSYDbv2c/f0TN5esoXjOzTmsZH96dGqQdhliUicUCgkiOJi5++ffckjc5ZQVOzcM6wP15zcSQ3sROSoBBYKZvYCMAzY4u79ylh+BvAqsCry0FR3fyCoehLZqm17GZuWztxVOzilWzMeHtGf5GZ1wy5LROJQkFsKfwaeAiYfYcwH7j4swBoSWmFRMc9/uIrf/GsZNWtU49HLUvhOage1qBCRYxZYKLj7+2bWKajvX9Ut2pDLmLR0Mtbn8O0+rXjwkn60alg77LJEJM6FfUzhJDNbCGwAbnP3rJDriXkHCot46u1snn13BY3rJvH09wdwQUprbR2ISIUIMxQWAB3dfY+ZXQBMB7qXNdDMRgGjAJKTkyuvwhgzf81OxqSlk71lD5ee0I57hvWhSb2aYZclIgkktFBw99xSt2eb2TNm1tzdt5UxdiIwESA1NdUrscyYsC+/kMffWMqfP15Nm4a1efHagZzZs2XYZYlIAgotFMysNbDZ3d3MBgHVgO1h1ROrPly+jbFT01m3cz9XDenI6KE9aaAGdiISkCBPSX0JOANobmbrgHFAEoC7TwBGAj8ys0JgP3CFu1e5rYDDydlfwK9mLeKVeevo3LweL48awuAuamAnIsEK8uyj75Wz/ClKTlmVQ7yRtYl7pmeyfW8+PzqjKz89uzu1k9TATkSCF/bZR1LK1t0HuG9GFrMyNtK7TUOev3ogKe0bhV2WiFQhCoUY4O5MXbCeB2YuYn9+Ebef15NRp3Uhqboa2IlI5VIohGz9rv3cOTWD95ZtZUBySQO7bi3VwE5EwqFQCElxsfPXuWt4dM4SHLjvoj5cdZIa2IlIuBQKIVixdQ9j09L5fPVOvtW9OQ+NSKFDUzWwE5HwKRQqUUFRMc99sJLfvbWc2jWq8fjI/ow8sb1aVIhIzFAoVJLM9TmMSUsna0MuQ/u25oFL+tKygRrYiUhsUSgELK+giD+8vZwJ762kSd2aPHvlAM5PaRN2WSIiZVIoBGje6h2MTktn5da9XDagPfcM603jumpgJyKxS6EQgL0HShrYTfpkNW0b1WHSdYM4vUeLsMsSESmXQqGCvbdsK3dOzWBDzn6uPqkTt5/Xk3q19N8sIvFB71YVZNe+fMbPXEzagnV0aVGPf950EqmdmoZdlojIUVEoVIA5GRu559Usdu7L55Yzu/J/Z6mBnYjEJ4XCN7AlN497X83i9axN9G3bkEnXDaRvWzWwE5H4pVA4Bu7OlPnrGD9zEXmFxYwe2pMbv6UGdiIS/xQKR2ntjn3cOS2DD5ZvY2CnJjxyWX+6tqgfdlkiIhVCoRClomLnL5+s5rE3lmLA+OF9uXJwR6qpgZ2IJBCFQhSyt+xmTFoG89fs5PQeLfjViH60b6IGdiKSeBQKR1BQVMwf31vB7/+dTd1a1fnNd45jxAnt1MBORBKWQuEwMtfncPuUdBZvzOXClDbcd3FfWjSoFXZZIiKBUigcIq+giN+9tZznPlhJ03o1mfCDExnar3XYZYmIVAqFQimfrdrB2LR0Vm7by3dTO3DnBb1pVDcp7LJERCqNQgHYnVfAY68v5S+frqF9kzr89frBnNq9edhliYhUuiofCu8s3cJdUzPYmJvHdad05rbzelC3ZpX/bxGRKiqwdz8zewEYBmxx935lLDfgSeACYB9wjbsvCKqeQ+3cm8/4mYuY+p/1dGtZnyk3n8yJHZu3Y6qIAAAFWklEQVRU1suLiMSkIP8k/jPwFDD5MMvPB7pHvgYDz0b+DZS7MytjI+NezSJnfwG3ntWNW87qRq0aamAnIhJYKLj7+2bW6QhDhgOT3d2BT82ssZm1cfeNQdW0OTePe6Zn8uaizaS0a8RfbxhM7zYNg3o5EZG4E+bO83bA2lL310UeCyQU3lmyhVv/8R/yC4u54/xeXH9qZ2qogZ2IyP8IMxTK+liwlznQbBQwCiA5OfmYXqxz83oMSG7CfRf3pXPzesf0PUREEl2YfyqvAzqUut8e2FDWQHef6O6p7p7aosWxXeu4U/N6TLpukAJBROQIwgyFGcAPrcQQICfI4wkiIlK+IE9JfQk4A2huZuuAcUASgLtPAGZTcjpqNiWnpF4bVC0iIhKdIM8++l45yx24JajXFxGRo6fTb0RE5CCFgoiIHKRQEBGRgxQKIiJykEJBREQOspKTgOKHmW0F1hzj05sD2yqwnDBpLrEpUeaSKPMAzeUrHd293E//xl0ofBNmNs/dU8OuoyJoLrEpUeaSKPMAzeVoafeRiIgcpFAQEZGDqlooTAy7gAqkucSmRJlLoswDNJejUqWOKYiIyJFVtS0FERE5goQMBTMbamZLzSzbzMaWsbyWmb0cWT63nMuGhiqKuVxjZlvN7IvI1w1h1FkeM3vBzLaYWeZhlpuZ/T4yz3QzG1DZNUYrirmcYWY5pdbJvZVdYzTMrIOZvWNmi80sy8x+WsaYuFgvUc4lXtZLbTP7zMwWRuZyfxljgnsPc/eE+gKqAyuALkBNYCHQ55AxPwYmRG5fAbwcdt3fYC7XAE+FXWsUczkNGABkHmb5BcAcSq7INwSYG3bN32AuZwAzw64zinm0AQZEbjcAlpXx8xUX6yXKucTLejGgfuR2EjAXGHLImMDewxJxS2EQkO3uK909H/gHMPyQMcOBSZHbU4Czzaysy4OGLZq5xAV3fx/YcYQhw4HJXuJToLGZtamc6o5OFHOJC+6+0d0XRG7vBhZTcp300uJivUQ5l7gQ+b/eE7mbFPk69OBvYO9hiRgK7YC1pe6v4+s/HAfHuHshkAM0q5Tqjk40cwG4LLJpP8XMOpSxPB5EO9d4cVJk83+OmfUNu5jyRHY/nEDJX6Wlxd16OcJcIE7Wi5lVN7MvgC3Av9z9sOulot/DEjEUykrLQ1M2mjGxIJo6XwM6uXt/4C3++9dDvImXdRKNBZS0FDgO+AMwPeR6jsjM6gNpwM/cPffQxWU8JWbXSzlziZv14u5F7n48JdeuH2Rm/Q4ZEth6ScRQWAeU/mu5PbDhcGPMrAbQiNjcHVDuXNx9u7sfiNx9DjixkmqraNGst7jg7rlfbf67+2wgycyah1xWmcwsiZI30b+5+9QyhsTNeilvLvG0Xr7i7ruAd4GhhywK7D0sEUPhc6C7mXU2s5qUHISZcciYGcDVkdsjgbc9csQmxpQ7l0P2715Myb7UeDQD+GHkbJchQI67bwy7qGNhZq2/2r9rZoMo+T3bHm5VXxep8Xlgsbv/5jDD4mK9RDOXOFovLcysceR2HeAcYMkhwwJ7DwvsGs1hcfdCM/sJ8AYlZ++84O5ZZvYAMM/dZ1Dyw/MXM8umJF2vCK/iw4tyLrea2cVAISVzuSa0go/AzF6i5OyP5ma2DhhHyQE03H0CMJuSM12ygX3AteFUWr4o5jIS+JGZFQL7gSti9I+OU4CrgIzI/muAO4FkiLv1Es1c4mW9tAEmmVl1SoLrFXefWVnvYfpEs4iIHJSIu49EROQYKRREROQghYKIiBykUBARkYMUCiIicpBCQUREDlIoiIjIQQoFERE56P8BSKuWplX8BPcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 2, 3, 4])\n",
    "plt.ylabel('Numbers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plot() - A versatile command\n",
    "\n",
    "\n",
    "**plot()** is a versatile command. It will take an arbitrary number of arguments. For example, to plot x versus y, \n",
    "we can issue the following command:-"
   ]
  },
  {
   "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 2, 3, 4], [1, 4, 9, 16])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### State-machine interface\n",
    "\n",
    "Pyplot provides the state-machine interface to the underlying object-oriented plotting library. The state-machine implicitly and automatically creates figures and axes to achieve the desired plot. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": [
    "x = np.linspace(0, 2, 100)\n",
    "\n",
    "plt.plot(x, x, label='linear')\n",
    "plt.plot(x, x**2, label='quadratic')\n",
    "plt.plot(x, x**3, label='cubic')\n",
    "\n",
    "plt.xlabel('x label')\n",
    "plt.ylabel('y label')\n",
    "\n",
    "plt.title(\"Simple Plot\")\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Formatting the style of plot\n",
    "\n",
    "\n",
    "For every x, y pair of arguments, there is an optional third argument which is the format string that indicates the color and line type of the plot. The letters and symbols of the format string are from MATLAB. We can concatenate a color string with a line style string. The default format string is 'b-', which is a solid blue line. For example, to plot the above line with red circles, we would issue the following command:-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "plt.plot([1, 2, 3, 4], [1, 4, 9, 16], 'ro')\n",
    "plt.axis([0, 6, 0, 20])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **axis()** command in the example above takes a list of [xmin, xmax, ymin, ymax] and specifies the viewport of the axes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Working with NumPy arrays\n",
    "\n",
    "\n",
    "Generally, we have to work with NumPy arrays. All sequences are converted to numpy arrays internally. The below example illustrates plotting several lines with different format styles in one command using arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": [
    "# evenly sampled time at 200ms intervals\n",
    "t = np.arange(0., 5., 0.2)\n",
    "\n",
    "# red dashes, blue squares and green triangles\n",
    "plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.\tObject-Oriented API\n",
    "\n",
    "\n",
    "The **Object-Oriented API** is available for more complex plotting situations. It allows us to exercise more control over the figure. In Pyplot API, we depend on some notion of an \"active\" figure or axes. But, in the **Object-Oriented API** the plotting functions are methods of explicit Figure and Axes objects.\n",
    "\n",
    "\n",
    "**Figure** is the top level container for all the plot elements. We can think of the **Figure** object as a box-like container containing one or more **Axes**. \n",
    "\n",
    "\n",
    "The **Axes** represent an individual plot. The **Axes** object contain smaller objects such as axis, tick marks, lines, legends, title and text-boxes.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code produces sine and cosine curves using Object-Oriented API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First create a grid of plots\n",
    "# ax will be an array of two Axes objects\n",
    "fig, ax = plt.subplots(2)\n",
    "\n",
    "\n",
    "# Call plot() method on the appropriate object\n",
    "ax[0].plot(x1, np.sin(x1), 'b-')\n",
    "ax[1].plot(x1, np.cos(x1), 'b-');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Objects and Reference\n",
    "\n",
    "\n",
    "The main idea with the **Object Oriented API** is to have objects that one can apply functions and actions on. The real advantage of this approach becomes apparent when more than one figure is created or when a figure contains more than one \n",
    "subplot.\n",
    "\n",
    "\n",
    "We create a reference to the figure instance in the **fig** variable. Then, we ceate a new axis instance **axes** using the \n",
    "**add_axes** method in the Figure class instance fig as follows:-\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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 = plt.figure()\n",
    "\n",
    "x2 = np.linspace(0, 5, 10)\n",
    "y2 = x2 ** 2\n",
    "\n",
    "axes = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "\n",
    "axes.plot(x2, y2, 'r')\n",
    "\n",
    "axes.set_xlabel('x2')\n",
    "axes.set_ylabel('y2')\n",
    "axes.set_title('title');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure and Axes\n",
    "\n",
    "\n",
    "I start by creating a figure and an axes. A figure and axes can be created as follows:\n",
    "\n",
    "\n",
    "`fig = plt.figure()`\n",
    "\n",
    "`ax = plt.axes()`\n",
    "\n",
    "\n",
    "\n",
    "In Matplotlib, the **figure** (an instance of the class plt.Figure) is a single container that contains all the objects representing axes, graphics, text and labels. The **axes** (an instance of the class plt.Axes) is a bounding box with \n",
    "ticks and labels. It will contain the plot elements that make up the visualization. I have used the variable name fig \n",
    "to refer to a figure instance, and ax to refer to an axes instance or group of axes instances.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "ax = plt.axes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10. Figure and Subplots\n",
    "\n",
    "\n",
    "\n",
    "Plots in Matplotlib reside within a Figure object. As described earlier, we can create a new figure with plt.figure() \n",
    "as follows:-\n",
    "\n",
    "\n",
    "`fig = plt.figure()`\n",
    "\n",
    "\n",
    "Now, I create one or more subplots using fig.add_subplot() as follows:-\n",
    "\n",
    "\n",
    "`ax1 = fig.add_subplot(2, 2, 1)`\n",
    "\n",
    "\n",
    "The above command means that there are four plots in total (2 * 2 = 4). I select the first of four subplots (numbered from 1).\n",
    "\n",
    "\n",
    "I create the next three subplots using the fig.add_subplot() commands as follows:-\n",
    "\n",
    "\n",
    "`ax2 = fig.add_subplot(2, 2, 2)`\n",
    "\n",
    "`ax3 = fig.add_subplot(2, 2, 3)`\n",
    "\n",
    "`ax4 = fig.add_subplot(2, 2, 4)`"
   ]
  },
  {
   "attachments": {
    "Subplots.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above command result in creation of subplots. The diagrammatic representation of subplots are as follows:-\n",
    "\n",
    "\n",
    "![Subplots.png](attachment:Subplots.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 11. First plot with Matplotlib\n",
    "\n",
    "\n",
    "Now, I will start producing plots. Here is the first example:-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": [
    "plt.plot([1, 3, 2, 4], 'b-')\n",
    "\n",
    "plt.show( )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`plt.plot([1, 3, 2, 4], 'b-')`\n",
    "\n",
    "This code line is the actual plotting command. Only a list of values has been plotted that represent the vertical coordinates of the points to be plotted. Matplotlib will use an implicit horizontal values list, from 0 (the first value) to N-1 (where N is the number of items in the list)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Specify both Lists\n",
    "\n",
    "\n",
    "Also, we can explicitly specify both the lists as follows:- \n",
    "\n",
    "\n",
    "`x3 = range(6)`\n",
    "\n",
    "\n",
    "`plt.plot(x3, [xi**2 for xi in x3])` \n",
    "\n",
    "\n",
    "`plt.show()`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAHNlJREFUeJzt3Xu8VXP+x/HXxxERJqYz/XIrcmemcDSGGIWEoVyGXPolKYZmNGI0DWOQycyQS/pFN0pIKLoZRanJKHNKN3KbMqTo5FYqXT+/P767EU6d3b6ctdfa7+fjsR5nn332aX22nHfrfNb3Yu6OiIjE33ZRFyAiIrmhQBcRSQgFuohIQijQRUQSQoEuIpIQCnQRkYRQoIuIJIQCXUQkIRToIiIJsX11nqxOnTreoEGD6jyliEjszZgxY5m7l1b1umoN9AYNGlBeXl6dpxQRiT0z+086r1PLRUQkIRToIiIJoUAXEUkIBbqISEIo0EVEEkKBLiKSEAp0EZGEUKCLiOTR6tXwm9/AsmX5P5cCXUQkT9zh6quhd2+YMSP/56sy0M2sppm9ZmazzewNM7s19fwjZrbQzGaljsb5L1dEJD4GDIBHHoGbb4bTTsv/+dKZ+r8GaO7uX5lZDWCqmT2f+toN7v50/soTEYmn8nLo3BlatIBbbqmec1YZ6O7uwFepT2ukDs9nUSIicfbpp3D++fA//wOPPw4lJdVz3rR66GZWYmazgKXABHefnvrSHWY2x8zuMbMd81aliEhMbNgAl1wCS5bA00/DD39YfedOK9DdfYO7Nwb2BpqY2RHA74FDgGOAPYAbK/teM+tkZuVmVl5RUZGjskVECtPtt8MLL8D998Mxx1TvubdplIu7fwG8DLR09yUerAEeBpps4Xv6uXuZu5eVlla5nK+ISGyNGQO33grt2kGnTtV//nRGuZSaWe3U452AU4C3zKxe6jkDWgPz8lmoiEghe+ed0Go56ijo2xfMqr+GdEa51AMGm1kJ4R+A4e4+xswmmlkpYMAs4Ko81ikiUrBWrIBzzoEddoARI2CnnaKpI51RLnOAIyt5vnleKhIRiRF3uPxyeOstmDAB6tePrpZq3YJORCRp/vrXMJrlb3+D5hFf5mrqv4hIhiZMgO7d4cILoWvXqKtRoIuIZGThQmjTBg4/HAYOjOYm6Hcp0EVEttGqVXDuubBxI4wcCbVqRV1RoB66iMg2cIcrr4TZs2HsWGjYMOqKvqFAFxHZBr17w9ChYUbo6adHXc23qeUiIpKmyZPhuuugVatwM7TQKNBFRNLwwQdwwQVwwAEwZAhsV4DpWYAliYgUlpUrw1X511/Ds8/CbrtFXVHl1EMXEdkKd2jf/puboIccEnVFW6ZAFxHZijvugKeeCjNBC+0m6Hep5SIisgXPPRf2A7300sKYCVoVBbqISCXmzg1B3qQJ9O9fGDNBq6JAFxH5jmXLwk3QXXcNM0Fr1oy6ovSohy4ispl16+CXv4TFi2HKFNhzz6grSp8CXURkM7/9Lbz8chhr3qTSjTULl1ouIiIpDz0EffrADTdA27ZRV7PtFOgiIoT2SufOYWhiz55RV5MZBbqIFL2FC+G888LKiY8/DiUlUVeUmSoD3cxqmtlrZjbbzN4ws1tTz+9nZtPN7F0ze9LMdsh/uSIiufXll/CLX8D69TBqFNSuHXVFmUvnCn0N0NzdGwGNgZZmdizwF+Aedz8Q+BzokL8yRURyb/36sODWO+/AiBFw0EFRV5SdKgPdg69Sn9ZIHQ40B55OPT8YaJ2XCkVE8qRLFxg/Hvr2hWbNoq4me2n10M2sxMxmAUuBCcC/gS/cfX3qJYuAvfJToohI7vXuHUa0XH89XHFF1NXkRlqB7u4b3L0xsDfQBDi0spdV9r1m1snMys2svKKiIvNKRURyZNy4cHXeqhXceWfU1eTONo1ycfcvgJeBY4HaZrZpYtLewOItfE8/dy9z97LS0tJsahURydrcudCmDTRqBI89Ft8RLZVJZ5RLqZnVTj3eCTgFmA9MAs5Pvawd8Fy+ihQRyYVPPoGzzgprtIwaBbVqRV1RbqUz9b8eMNjMSgj/AAx39zFm9iYwzMx6AK8DA/NYp4hIVlavhtatoaIiTCLae++oK8q9KgPd3ecAR1by/AJCP11EpKC5w+WXw7RpYXji0UdHXVF+aKaoiCTerbfCsGHhBug550RdTf4o0EUk0YYODYHevj387ndRV5NfCnQRSayJE0OrpVkzePDBeOw6lA0Fuogk0rx5cO65YTr/iBGwQxGsNqVAF5HEWbwYzjgDdt45TCKK84Jb20I7FolIoqxYAWeeCZ9/HoYn7rtv1BVVHwW6iCTGpv1A586FMWPgyO8NuE42BbqIJII7XH01vPAC9O8PLVtGXVH1Uw9dRBKhZ08YMAD+8IfkrJ64rRToIhJ7Q4eGIL/0Urj99qiriY4CXURibdKkb8aaDxyY/LHmW6NAF5HYmjMnTOU/8MDiGWu+NQp0EYml998PNz532QWef754xppvjUa5iEjsLFsGp50WlsSdOrW4xppvjQJdRGJl5cowceiDD2DCBDj88KgrKhwKdBGJjU0Th8rLQ8+8adOoKyosCnQRiQX3ML78+eehX7+wwbN8m26KikgsdOsGQ4bAbbdBx45RV1OYFOgiUvDuuQf++tcwtf+mm6KupnBVGehmto+ZTTKz+Wb2hpldm3r+T2b2kZnNSh1n5L9cESk2jz8O110H550H999f3BOHqpJOD3090NXdZ5rZrsAMM5uQ+to97n5X/soTkWI2YQJcdhn8/Odhen9JSdQVFbYqA93dlwBLUo9XmNl8YK98FyYixe3VV6F1azj0UHj2WahZM+qKCt829dDNrAFwJDA99VRnM5tjZoPMbPctfE8nMys3s/KKioqsihWR4jBnTthxaM89Yfx4zQJNV9qBbma7AM8AXdx9OdAXaAg0JlzB313Z97l7P3cvc/ey0tLSHJQsIkn27rvQogXUqgUvvgh160ZdUXykFehmVoMQ5o+5+wgAd//E3Te4+0agP9Akf2WKSDFYtAhOPRU2bAj98/r1o64oXtIZ5WLAQGC+u/fa7Pl6m73sHGBe7ssTkWKxbFm4Mv/sM/j730PvXLZNOqNcjgfaAnPNbFbque7ARWbWGHDgfeDKvFQoIom3fHlYOXHhwrCF3NFHR11RPKUzymUqUNnIz3G5L0dEis3q1XDWWTB7dhjNcuKJUVcUX1rLRUQis2mxrX/8I0wgOvPMqCuKNwW6iERiw4YwaWjsWHjwQWjTJuqK4k9ruYhItdu4Ea66KlyV9+wJV+oOXE4o0EWkWrnDb34DAwbAzTeHVRQlNxToIlJt3OGGG6BPH7j+erj11qgrShYFuohUmz/+Ee6+Gzp3DsvhauXE3FKgi0i16NEjHB07wn33KczzQYEuInl3112hX962bRjRsp2SJy/0n1VE8uqBB0Lf/IILYNAghXk+6T+tiORN//7w61+HDZ2HDoXtNfMlrxToIpIXjz4axpe3bAlPPgk1akRdUfIp0EUk5x59FNq1g2bNYMQI2HHHqCsqDgp0EcmpIUO+CfPRo2GnnaKuqHgo0EUkZwYPDuuzNG8ewnznnaOuqLgo0EUkJwYPhvbtQ5iPGqUwj4ICXUSy9sgjIcxPPllhHiUFuohk5eGH4fLLFeaFQIEuIhkbNAg6dIBTTglhrhug0Upnk+h9zGySmc03szfM7NrU83uY2QQzezf1cff8lysihWLQILjiCjj1VHjuOYV5IUjnCn090NXdDwWOBa4xs8OAbsBL7n4g8FLqcxEpAv37hzBv0SLsA6owLwxVBrq7L3H3manHK4D5wF5AK2Bw6mWDgdb5KlJECsd990GnTmEGqMK8sGxTD93MGgBHAtOBuu6+BELoAz/KdXEiUlh69oQuXeCcc2DkSKhZM+qKZHNpB7qZ7QI8A3Rx9+Xb8H2dzKzczMorKioyqVFEIuYelr/t3h0uvhiGD9d0/kKUVqCbWQ1CmD/m7iNST39iZvVSX68HLK3se929n7uXuXtZaWlpLmoWkWrkHraL69Ej9M2HDNGqiYUqnVEuBgwE5rt7r82+NApol3rcDngu9+WJSJQ2boSrr4ZevcIyuA89BCUlUVclW5LOv7PHA22BuWY2K/Vcd+BOYLiZdQA+AH6ZnxJFJAobNoQx5oMHw403hv65to0rbFUGurtPBbb013hybssRkUKwbl3YLu7JJ+G22+CmmxTmcaBOmIh8y9dfw4UXhpmfd90FXbtGXZGkS4EuIv+1fHnYLu7ll6FPn9A/l/hQoIsIAEuXwumnw5w58NhjYXiixIsCXUT4z3/CNP4PPwytltNPj7oiyYQCXaTIvflmCPOVK2HCBDj++Kgrkkxp+VyRIjZ9OpxwQhiiOHmywjzuFOgiRWrChLApRe3a8Mor8JOfRF2RZEuBLlKEnnoKzjwTGjaEqVNh//2jrkhyQYEuUmQefDCMM2/SJLRZ6tWLuiLJFQW6SJHYtGLir34VRrGMHx/aLZIcGuUiUgTWrYOOHcO6LB06hKt0rZiYPLpCF0m4FSvgrLNCmP/pT2H7OIV5MumvVSTBPv443PycPRsGDAhX55JcCnSRhHr77bDv59KlYfbnGWdEXZHkmwJdJIH++c/QZikpCQttHXNM1BVJdVAPXSRhnn02TBjaYw949VWFeTFRoIskhDvcfTecey40ahSu0hs2jLoqqU4KdJEEWLcujC+//no47zyYOBG0J3vxUaCLxNwXX4SRLA89BN26hW3jdt456qokClUGupkNMrOlZjZvs+f+ZGYfmdms1KH75yIRWLgwrJA4aRIMGhQ2ct5Ol2lFK52/+keAlpU8f4+7N04d43JblohU5dVX4ac/hcWLwzT+9u2jrkiiVmWgu/sU4LNqqEVE0vTkk9CsGey2G0ybFh6LZPPLWWczm5Nqyey+pReZWSczKzez8oqKiixOJyLu0KMHtGkThiNOmwYHHxx1VVIoMg30vkBDoDGwBLh7Sy90937uXubuZaW67S6SsVWr4KKLwoqJl14KL74IdepEXZUUkowC3d0/cfcN7r4R6A80yW1ZIrK5Dz6Apk1h+HD4y19gyBDYcceoq5JCk9HUfzOr5+5LUp+eA8zb2utFJHNTp4ax5V9/DaNHhyGKIpWpMtDN7AngJKCOmS0CbgFOMrPGgAPvA1fmsUaRojVgAFx9NdSvH9ZkOfTQqCuSQlZloLv7RZU8PTAPtYhIyrp10LUr9O4NLVrAsGGw+xaHHogEmoIgUmA+/TQse9u7N1x3HYwdqzCX9Gj5XJECMnt2WFxr0aKww9D//m/UFUmc6ApdpEAMHQo/+1m4+Tl5ssJctp0CXSRia9dC587Qti00aQIzZ8Kxx0ZdlcSRAl0kQh99BCedBH36hJugL74IdetGXZXElXroIhGZPBkuuABWrgxrs1xwQdQVSdzpCl2kmrlDr15hm7jateG11xTmkhsKdJFqtGJFWFira1c4+2z417/gsMOirkqSQoEuUk1mz4ajj4ann4Y774RnngnL34rkigJdJM/cw/ZwP/1p6JdPnAg33ghmUVcmSaNAF8mj5cvDkrdXXRVGs8yaBT//edRVSVIp0EXy5PXXQ4vlqafgz3+GceNAWwJIPinQRXLMHfr2DZODVq8OqyT+/vfavFnyT/+LieTQl1/ChReGJW9PPjm0WE44IeqqpFgo0EVyZOpUaNQIRowIo1jGjNEWcVK9FOgiWVq/Hv74x3Czs6QkBPuNN6rFItVPU/9FsrBgAVxyCUybBu3awf33a2y5REeBLpIB97Dc7TXXhCvxYcNC71wkSlX+Umhmg8xsqZnN2+y5Pcxsgpm9m/qo/VSkaHzxBVx8cVivvHHjMANUYS6FIJ0u3yNAy+881w14yd0PBF5KfS6SeFOmhBufTz0FPXrApElhA2eRQlBloLv7FOCz7zzdChicejwYaJ3jukQKyurV8NvfhtmeNWrAK6/AH/4QboKKFIpM78PXdfclAKmPP8pdSSKFZfp0OPJIuPfeML589uywLotIocn7wCoz62Rm5WZWXlFRke/TieTMmjXQvTscdxysWgUTJsADD0CtWlFXJlK5TAP9EzOrB5D6uHRLL3T3fu5e5u5lpVrIQmJi1iw45hjo2TMMR5w7F045JeqqRLYu00AfBbRLPW4HPJebckSitW4d3H57CPOKChg9GgYNgh/8IOrKRKpW5Th0M3sCOAmoY2aLgFuAO4HhZtYB+AD4ZT6LFKkOs2ZBhw4wc2bYVeiBB+CHP4y6KpH0VRno7n7RFr50co5rEYnE6tVw223wt7+FAH/qKTj//KirEtl2mikqRW3KFOjYEd55By67DO6+G/bYI+qqRDKj5YOkKC1fDr/6VVhQa+1aGD8eHn5YYS7xpkCXojNmDBx+OPTrFyYLzZsHp54adVUi2VOgS9FYtCj0xs86C2rXhldfhV69NK5ckkOBLom3bl3ojR9yCIwdC3fcATNmQJMmUVcmklu6KSqJ9soroVc+dy6ceSb07g377Rd1VSL5oSt0SaRly+CKK6BpU/j8cxg5MkwSUphLkinQJVE2boQBA0J7ZfBguOEGmD8fWrcGs6irE8kvtVwkMV55Ba69NvTHmzaFvn3hiCOirkqk+ugKXWJv0aKwr2fTpvDxx/DYY2HCkMJcio2u0CW2Vq8Oo1d69oQNG+Cmm6BbNw1DlOKlQJfYcYcRI+D66+H99+G888I6LLrhKcVOLReJlenTw3T988+HXXeFl16Cp59WmIuAAl1i4r334IIL4Nhj4e23ww3PmTOhefOoKxMpHGq5SEGrqAgbTvTtCzvsALfcAl27hqtzEfk2BboUpFWr4L774M47YeXKMEnollugXr2oKxMpXAp0KShr14Yt33r0gI8+glatwiiWQw+NujKRwqceuhSE9evhkUfg4IPD2iv168PkyfDsswpzkXQp0CVSGzfCsGFhffL27cMWcM8/D1OnwoknRl2dSLxk1XIxs/eBFcAGYL27l+WiKEk+d3juObj55rDBxBFHhAW0WrXSmisimcpFD72Zuy/LwZ8jRWDjxhDcd9wBr78OBx0ETzwRhiRup98XRbKiHyGpFuvXw9Ch8OMfh0lBK1aEPTzfeAPatFGYi+RCtj9GDow3sxlm1qmyF5hZJzMrN7PyioqKLE8ncbNmDfTvH252tm0bgvuJJ+Ctt+Cyy2B7jbMSyZlsA/14dz8KOB24xsy+dxvL3fu5e5m7l5WWlmZ5OomLlSvD7kAHHACdOsEee4QRK7NnhyvykpKoKxRJnqyuj9x9cerjUjMbCTQBpuSiMImnjz+GBx4IMzs/+wxOOAEGDoRTT9XNTpF8yzjQzawWsJ27r0g9bgHclrPKJFbmzYNevcJa5OvWhdEqXbuGNcpFpHpkc4VeFxhp4bJre+Bxd/97TqqSWHAPqx3edRe88ALstFOYot+lCxx4YNTViRSfjAPd3RcAjXJYi8TEypXhSrxPH5gzB+rWDVP1r7oqTAwSkWhojIGk7e234f/+L0zRX74cGjUK/fFLLoEdd4y6OhFRoMtWrV8PY8aEq/EXX4QaNcI48muugeOO041OkUKiQJdKffABDB4cxpB/+CHsvXdYl7xjx9BiEZHCo0CX/1qzJqyvMmgQjB8fbnqefDLcey+cfbYmAYkUOv2ICnPmhF740KFh7Pg++8BNN4XVD7VXp0h8KNCL1JIlMHw4PPoozJgRtndr3RouvxxOOUUzOUXiSIFeRL78EkaMgMcfh4kTw8qHjRuHlsqll2rIoUjcKdAT7uuvYdy4EOJjxoQ++f77Q/fucPHF2g1IJEkU6An01Vdh158RI2Ds2LBU7Y9+BFdeGUK8SRMNNxRJIgV6Qnz6KYweHTaPeOGFcCVeWhpWNjz/fGjeXKNURJJOP+Ix9u67oZ0yejS8/DJs2AD77hum4J97Lhx/vG5uihQTBXqMrF4NkyeHdsq4cfDee+H5Qw6BG28MIX7UUWqniBQrBXoBcw9btE2cGCb6TJwYQr1mzdBC6dIFTj893OQUEVGgFxB3WLAgLEk7cSJMmgRLl4avNWwIHTrAGWfASSeFpWpFRDanQI/Qhg3hCvyf/wzH5MlhDRWAevWgRYtwJd6sGTRoEGmpIhIDCvRq9OWX8Npr3wT4tGlhGVoIC141bQrduoUQP+gg9cJFZNso0PPk889h5sxwzJgRjk03Mc3gxz8O64gfd1w49ttPAS4i2VGgZ2nNGnjnHXjzzdA+eeMNeP11WLjwm9fUrw9HHw2XXQbHHAPHHgu77RZZySKSUFkFupm1BO4DSoAB7n5nTqoqMO5hMat///ubY/78EN7vvht64QDbbQcHHABlZWFW5lFHhUNrpIhIdcg40M2sBOgDnAosAv5lZqPc/c1cFVdd1qyBxYvho49g0aJvPi5YEMJ7wYIwXHCTTcF9+OFhFuZhh4XHBx+srdhEJDrZXKE3Ad5LbRaNmQ0DWgGRBPrGjbBqVVjHZOXK8PGrr8I6Jp9+Go5ly779eNmyEN7Lln3/z9t55zC++4AD4LTTwrDBTUf9+mErNhGRQpJNoO8FfLjZ54uAn2ZXTuVuvz2sFrh+feXH2rUhzKtiBrvvDnXqhDbIvvuGfvZee4Ut1vba65vjBz/QTUoRiZdsAr2yuPPvvcisE9AJYN99983oRHvuCT/5SVhc6rtHSUm4Wt5ll3DUqvX9x5sCfPfdtbaJiCRXNoG+CNhns8/3BhZ/90Xu3g/oB1BWVva9wE9Hhw7hEBGRLdsui+/9F3Cgme1nZjsAbYBRuSlLRES2VcZX6O6+3sw6Ay8Qhi0Ocvc3claZiIhsk6zGobv7OGBcjmoREZEsZNNyERGRAqJAFxFJCAW6iEhCKNBFRBJCgS4ikhDmntFcn8xOZlYB/CfDb68DVLLqSizpvRSmpLyXpLwP0HvZpL67l1b1omoN9GyYWbm7l0VdRy7ovRSmpLyXpLwP0HvZVmq5iIgkhAJdRCQh4hTo/aIuIIf0XgpTUt5LUt4H6L1sk9j00EVEZOvidIUuIiJbEYtAN7OWZva2mb1nZt2iridTZjbIzJaa2byoa8mGme1jZpPMbL6ZvWFm10ZdU6bMrKaZvWZms1Pv5daoa8qWmZWY2etmNibqWrJhZu+b2Vwzm2Vm5VHXkw0zq21mT5vZW6mfm5/l5TyF3nJJbUb9DpttRg1cFMfNqM3sROArYIi7HxF1PZkys3pAPXefaWa7AjOA1jH9OzGglrt/ZWY1gKnAte4+LeLSMmZm1wFlwG7u/ouo68mUmb0PlLl77Mehm9lg4B/uPiC1f8TO7v5Frs8Thyv0/25G7e5rgU2bUceOu08BPou6jmy5+xJ3n5l6vAKYT9hjNnY8+Cr1aY3UUdhXOVthZnsDZwIDoq5FAjPbDTgRGAjg7mvzEeYQj0CvbDPqWIZHEplZA+BIYHq0lWQu1aKYBSwFJrh7bN8LcC/wO2Bj1IXkgAPjzWxGam/iuNofqAAeTrXCBphZrXycKA6BntZm1FL9zGwX4Bmgi7svj7qeTLn7BndvTNgXt4mZxbIdZma/AJa6+4yoa8mR4939KOB04JpUyzKOtgeOAvq6+5HASiAv9wLjEOhpbUYt1SvVb34GeMzdR0RdTy6kfg1+GWgZcSmZOh44O9V7HgY0N7Oh0ZaUOXdfnPq4FBhJaL/G0SJg0Wa/+T1NCPici0OgazPqApO6kTgQmO/uvaKuJxtmVmpmtVOPdwJOAd6KtqrMuPvv3X1vd29A+DmZ6O6XRlxWRsysVuqGO6n2RAsglqPD3P1j4EMzOzj11MlAXgYQZLWnaHVI0mbUZvYEcBJQx8wWAbe4+8Boq8rI8UBbYG6q9wzQPbXHbNzUAwanRlNtBwx391gP90uIusDIcO3A9sDj7v73aEvKyq+Bx1IXpQuA9vk4ScEPWxQRkfTEoeUiIiJpUKCLiCSEAl1EJCEU6CIiCaFAFxFJCAW6iEhCKNBFRBJCgS4ikhD/D3SgdFa3d4p0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x3 = np.arange(0.0, 6.0, 0.01) \n",
    "\n",
    "plt.plot(x3, [xi**2 for xi in x3], 'b-') \n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 12.\tMultiline Plots\n",
    "\n",
    "Multiline Plots mean plotting more than one plot on the same figure. We can plot more than one plot on the same figure.  \n",
    "It can be achieved by plotting all the lines before calling show(). It can be done as follows:-\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": [
    "x4 = range(1, 5)\n",
    "\n",
    "plt.plot(x4, [xi*1.5 for xi in x4])\n",
    "\n",
    "plt.plot(x4, [xi*3 for xi in x4])\n",
    "\n",
    "plt.plot(x4, [xi/3.0 for xi in x4])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {
    "Parts%20of%20a%20plot.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 13.\tParts of a Plot\n",
    "\n",
    "\n",
    "There are different parts of a plot. These are title, legend, grid, axis and labels etc. These are denoted in the following figure:-\n",
    "\n",
    "\n",
    "![Parts%20of%20a%20plot.png](attachment:Parts%20of%20a%20plot.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.\tSaving the Plot\n",
    "\n",
    "\n",
    "We can save the figures in a wide variety of formats. We can save them using the **savefig()** command as follows:-\n",
    "\n",
    "\n",
    "`fig.savefig(‘fig1.png’)`\n",
    "\n",
    "\n",
    "\n",
    "We can explore the contents of the file using the IPython **Image** object.\n",
    "\n",
    "\n",
    "\n",
    "`from IPython.display import Image`\n",
    "\n",
    "\n",
    "`Image(‘fig1.png’)`\n",
    "\n",
    "\n",
    "In **savefig()** command, the file format is inferred from the extension of the given filename. Depending on the backend, \n",
    "many different file formats are available. The list of supported file types can be found by using the get_supported_filetypes() method of the figure canvas object as follows:-\n",
    "\n",
    "\n",
    "`fig.canvas.get_supported_filetypes()` \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the figure\n",
    "\n",
    "fig.savefig('plot1.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore the contents of figure\n",
    "\n",
    "from IPython.display import Image\n",
    "\n",
    "Image('plot1.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ps': 'Postscript',\n",
       " 'eps': 'Encapsulated Postscript',\n",
       " 'pdf': 'Portable Document Format',\n",
       " 'pgf': 'PGF code for LaTeX',\n",
       " 'png': 'Portable Network Graphics',\n",
       " 'raw': 'Raw RGBA bitmap',\n",
       " 'rgba': 'Raw RGBA bitmap',\n",
       " 'svg': 'Scalable Vector Graphics',\n",
       " 'svgz': 'Scalable Vector Graphics',\n",
       " 'jpg': 'Joint Photographic Experts Group',\n",
       " 'jpeg': 'Joint Photographic Experts Group',\n",
       " 'tif': 'Tagged Image File Format',\n",
       " 'tiff': 'Tagged Image File Format'}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore supported file formats\n",
    "\n",
    "\n",
    "fig.canvas.get_supported_filetypes() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15.\tLine Plot\n",
    "\n",
    "\n",
    "We can use the following commands to draw the simple sinusoid line plot:-\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create figure and axes first\n",
    "fig = plt.figure()\n",
    "\n",
    "ax = plt.axes()\n",
    "\n",
    "# Declare a variable x5\n",
    "x5 = np.linspace(0, 10, 1000)\n",
    "\n",
    "\n",
    "# Plot the sinusoid function\n",
    "ax.plot(x5, np.sin(x5), 'b-'); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 16.\tScatter Plot\n",
    "\n",
    "Another commonly used plot type is the scatter plot. Here the points are represented individually with a dot or a circle.\n",
    "\n",
    "\n",
    "### Scatter Plot with plt.plot()\n",
    "\n",
    "We have used plt.plot/ax.plot to produce line plots. We can use the same functions to produce the scatter plots as follows:-\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": [
    "x7 = np.linspace(0, 10, 30)\n",
    "\n",
    "y7 = np.sin(x7)\n",
    "\n",
    "plt.plot(x7, y7, 'o', color = 'black');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 17.\tHistogram\n",
    "\n",
    "\n",
    "Histogram charts are a graphical display of frequencies. They are represented as bars. They show what portion of the \n",
    "dataset falls into each category, usually specified as non-overlapping intervals. These categories are called bins.\n",
    "\n",
    "The **plt.hist()** function can be used to plot a simple histogram as follows:-\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": [
    "data1 = np.random.randn(1000)\n",
    "\n",
    "plt.hist(data1); \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 18.\tBar Chart\n",
    "\n",
    "\n",
    "Bar charts display rectangular bars either in vertical or horizontal form. Their length is proportional to the values they represent. They are used to compare two or more values.\n",
    "\n",
    "\n",
    "We can plot a bar chart using plt.bar() function. We can plot a bar chart as follows:-\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": [
    "data2 = [5. , 25. , 50. , 20.]\n",
    "\n",
    "plt.bar(range(len(data2)), data2)\n",
    "\n",
    "plt.show() \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 19.\tHorizontal Bar Chart\n",
    "\n",
    "\n",
    "We can produce Horizontal Bar Chart using the plt.barh() function. It is the strict equivalent of plt.bar() function.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": [
    "data2 = [5. , 25. , 50. , 20.]\n",
    "\n",
    "plt.barh(range(len(data2)), data2)\n",
    "\n",
    "plt.show() \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 20.\tError Bar Chart\n",
    "\n",
    "\n",
    "\n",
    "In experimental design, the measurements lack perfect precision. So, we have to repeat the measurements. It results in \n",
    "obtaining a set of values. The representation of the distribution of data values is done by plotting a single data point \n",
    "(known as mean value of dataset) and an error bar to represent the overall distribution of data.\n",
    "\n",
    "\n",
    "We can use Matplotlib's **errorbar()** function to represent the distribution of data values. It can be done as follows:-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": [
    "x9 = np.arange(0, 4, 0.2)\n",
    "\n",
    "y9 = np.exp(-x9)\n",
    "\n",
    "e1 = 0.1 * np.abs(np.random.randn(len(y9)))\n",
    "\n",
    "plt.errorbar(x9, y9, yerr = e1, fmt = '.-')\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 21. Stacked Bar Chart\n",
    "\n",
    "\n",
    "We can draw stacked bar chart by using a special parameter called **bottom** from the plt.bar() function. It can be done as follows:- "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": [
    "A = [15., 30., 45., 22.]\n",
    "\n",
    "B = [15., 25., 50., 20.]\n",
    "\n",
    "z2 = range(4)\n",
    "\n",
    "plt.bar(z2, A, color = 'b')\n",
    "plt.bar(z2, B, color = 'r', bottom = A)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The optional **bottom** parameter of the plt.bar() function allows us to specify a starting position for a bar. Instead of running from zero to a value, it will go from the bottom to value. The first call to plt.bar() plots the blue bars. The second call to plt.bar() plots the red bars, with the bottom of the red bars being at the top of the blue bars."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 22. Pie Chart\n",
    "\n",
    "\n",
    "Pie charts are circular representations, divided into sectors. The sectors are also called **wedges**. The arc length of each sector is proportional to the quantity we are describing. It is an effective way to represent information when we are interested mainly in comparing the wedge against the whole pie, instead of wedges against each other.\n",
    "\n",
    "Matplotlib provides the **pie()** function to plot pie charts from an array X. Wedges are created proportionally, so that each value x of array X generates a wedge proportional to x/sum(X)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "\n",
    "x10 = [35, 25, 20, 20]\n",
    "\n",
    "labels = ['Computer', 'Electronics', 'Mechanical', 'Chemical']\n",
    "\n",
    "plt.pie(x10, labels=labels);\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 23. Boxplot\n",
    "\n",
    "\n",
    "Boxplot allows us to compare distributions of values by showing the median, quartiles, maximum and minimum of a set of values.\n",
    "\n",
    "We can plot a boxplot with the **boxplot()** function as follows:-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": [
    "data3 = np.random.randn(100)\n",
    "\n",
    "plt.boxplot(data3)\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **boxplot()** function takes a set of values and computes the mean, median and other statistical quantities. The following points describe the preceeding boxplot:\n",
    "\n",
    "\n",
    "•\tThe red bar is the median of the distribution. \n",
    "\n",
    "•\tThe blue box includes 50 percent of the data from the lower quartile to the upper quartile. Thus, the box is centered on the median of the data. \n",
    "\n",
    "•\tThe lower whisker extends to the lowest value within 1.5 IQR from the lower quartile. \n",
    "\n",
    "•\tThe upper whisker extends to the highest value within 1.5 IQR from the upper quartile. \n",
    "\n",
    "•\tValues further from the whiskers are shown with a cross marker.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 24. Area Chart\n",
    "\n",
    "\n",
    "An **Area Chart** is very similar to a **Line Chart**. The area between the x-axis and the line is filled in with color or shading. It represents the evolution of a numerical variable following another numerical variable.\n",
    "\n",
    "We can create an Area Chart as follows:-\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": [
    "# Create some data\n",
    "x12 = range(1, 6)\n",
    "y12 = [1, 4, 6, 8, 4]\n",
    "\n",
    "# Area plot\n",
    "plt.fill_between(x12, y12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have created a basic Area chart. I could also use the stackplot function to create the Area chart as follows:-\n",
    "\n",
    "`plt.stackplot(x12, y12)`\n",
    "\n",
    "The fill_between() function is more convenient for future customization.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 25. Contour Plot\n",
    "\n",
    "\n",
    "**Contour plots** are useful to display three-dimensional data in two dimensions using contours or color-coded regions.\n",
    "**Contour lines** are also known as **level lines** or **isolines**. **Contour lines** for a function of two variables are \n",
    "curves where the function has constant values. They have specific names beginning with iso- according to the nature of the variables being mapped.\n",
    "\n",
    "\n",
    "There are lot of applications of **Contour lines** in several fields such as meteorology(for temperature, pressure, rain, wind\n",
    "speed), geography, magnetism, engineering, social sciences and so on.\n",
    "\n",
    "\n",
    "The density of the lines indicates the **slope** of the function. The **gradient** of the function is always perpendicular to the contour lines. When the lines are close together, the length of the gradient is large and the variation is steep.\n",
    "\n",
    "\n",
    "A **Contour plot** can be created with the **plt.contour()** function as follows:-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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72lsN4lTiVp7mhnIz7RR1DXyw1q3Dtsgd2OoZ0cXek8XHLyOTShjStEzo3g99DoCPT7ngrtpUEng2BK8WHhiaGlR6P3buCqC4uJTx7yvfxv13wngYn8yikb3R09asJTobmmGuo09QWhwj3RpXei5NPM1OY3nYZY7FPsBQS4eZPh2YVK9FtbXT6tDctQ47po5i+pZDfLhhP3MGdGZYc1+0tWV881VfnJ0tWb/hEvEJmfzy4xAsLAzxaevN4vM/AJCSmsPs7/diaKjDrz8NRU9PmxMhEaw4fZ1+fl583KWlxnNXCG5tzY6t1wl5lghAwzq2Kvvq2VnhaWvJ0aCHjGr1ckbV3aEevmZ2LA+7TD/nBuhIZZhqW9LCoic3Uo/TzmogVrplArhUUcLt9NN4GjXBXNsGgK2P7+BoYEIHW7eKYwZGx2Gkq4OHjUXFNtkLhUH+hhq3OqQyKfVbelK/pSfj5g4jLzuf4AthBJ4JJvBMCLeO3wPA0sEc/26N8O/eiMZdfDC1KnMiX9p9nWsHbjFp4VicvBwqO5UKdnamfDKtG78tOsYfy04ycUIHzMw0vzeCIDDIvwFtPZz56dB5Fp+4wqn7j/llaDfcbSzf/CZUglQiYWmLAfQ5tY6ZNw6yq8t7Fc8VlJlin+QGcyjuH5wM6mGmba3UPzD9PDoSfeqbtEAURfZFh9DYwgE3Y9Xx9vT15FJ4FPefJ9LIyY727eqxu1I7hjLvJHMyNTWHR48S8HC3YdLEDvy+cAQH9s1gx7ap/DhvMGNGt6ZZM9dKhTbAwcAw4jKy+aRbq0ojNibUa4G5jj6LQy4C0NKyN0Yyc3bGLCazJIWWFj2JyknnSuJTRro1prhUzsHAB3T38cDSqOzhCQuLw9zcADvbsoe0XHBLZepvUU5GLo9uR9Kka+X27ZTUHA4evku3rj44Ob18MbMLCllx+hpNXOzp1dCz0mMIgoCfhT1Bb6BxP8/L5OtbR+hx8h/OxT1mindrLvaZxvQGbd+q0C7H2dKUf6eOpIVbHX44cJaFRy9SKlcgCAKjR7bix3mDiYlJ5ePpm3kcmVjRr6CgmO++30teXhHzfx6KpaUR92Limb3nFP4uDvw0pFulz4B2heCuvsYd8iwRa2MDbE3Uazt9/bwIik2oMP1A2W/xRcOOxOdns/PJvYrtHawHoyXR5mzSy7fvQfZNckszaWFRZlKIyEzmZkoso938kb4ydb4bE09jZ3ulbW9L464MA2N9Wg9oxid/TmJTxAq2Pv2Lmf98hHcrT64fusX80WVmlalNv2LdN9v485P11GvmxtBZfd/ofN27+TB0SDNOnr7PmPdWs2bdRbKyCyrtY2VsyIqx/Vg8qjfPM7IYsnI7q8/fpET+bu6LvYEJ85v1Jjg9nuWhl5X2SQUpw5zK8g93x/6BXHw5hkJ5PmFZN2ho2gYtiQ73MxJ4lJXCYBf18qHcXFI+o5s4vkONxvlOBLebmzXbt37MD3MHMXpkK5r618XEuPqaEJTZH/+5cBM/JzvaerpU2tZIS4ep3m24lhTFtcQotCU6dLYZTp48GyOZOd4mzdkReReZIGGEqx/Hg8PJKSyqcEpCmWPSp4FjhXCoysYddD4UhULEv3vl9u1t268jiiLvjW2jtH31+Ztk5Bfwbd+O1Qoj9LNw4HF2KtnFhVW2BUgqyGFe4Em6Hl/F4ZgwPvBoxsW+0/iiYSdMdWr2W9QUI10d/n5/IGNbN2brtXtM23KInMIiANq28WTlsrFIBIEZM7dz+UoEcrmCX+Yf5unTZL7/bgBubjY8S8/kk62HsTMxYsXYfmjLKp8car2R4E5QayYpp4+fF4IAx4IeKm1vY1OXltbO/PXgGnklxQAYykxpY9mP0KzrxOU/ASAg9QTm2rZ4GPkBsD0yEG2JlGGuL5+ZjLwCnian08TFXukc70LjrgpbF2t6T+7K3N2fsyd5PSsD5vPBzyPRNdRl79Kj5OcU8sWGaZWaDytDEASmTunChnWTaNPag127Axg9dhUbNl0mJ0fzcy0IAr0a1uPwZ+/RrYEHK89cZ+RfO3iU+PZ8Pq/Sq443w+s2YvXD6wQkxyjtM9e2YYDDR8TmR3DxFdNYaNZ1SsRimph1BmB/VAg6Uhl9neqrPcfr5pKahHzCOxLc0krMC9Vl7+1QErNymV6Ftl3OaPcm2OsbsyjkAqIo4m/eBVdDXzpaD6FYLrInKpgejl5Y6Rqy40YwnraWNHEue1lSUnNITMyqMJNA1TbuwNPB6Bvr4dXcXe1+gISETI6fCKZ3r0bY2b2MAY1KSWf79SAGN/WhvoNNte5HuZ07JD2+0nZphXnMDzpLp2N/s/PJPYbWbcT5PlP5rnE3LHUrN+m8TWRSCd/268i8gV0IiIxl9KqdxKa9cFq62fD3n+/hWteKH346wGeztnMjIJLpU7vSsoUbWQWFfLzpIAqFyKoPBmJaSVxvOdo1NJWk5+bzLD0LXzVmknJsTYxo7lqHI0HhKpEGn/t2JK0oj82Pb1dsb2vVHz2pIWeS/iWxIJqY/Ie0sOiBRJCQU1LEwZhQ+jo1UIrHvxtdNotq4qJseigX3P9V405MzGTxkuPcuv20Rv2kUilezT0Y890Qll78if1pG9n0aAUuDepU3bkKnOpY8N23/Vm/dhLNm7mybft1Ro9bxZatV8nN0yzAzQ31WTyqNyvH9SclJ48Rf/3Ltmv3NNrk/wtzGnfHxciczwMOkVmkPCtoZNaOxmYduZC8l+i8so/63fQLWOo4UEffkyJ5KYdjw+juUA/jShSJHr6eJGblcP95osY2mvj/ZZGpwpJS1ly4hb+LAy3dnKrVR0cqY4ZPe+5nJHDqeQRSQcpE1x9padmLo7FhZJcUMtbdn+DYBMITUhjVslHFByEs7IV9u8GrgluzjVsURQLPBOPXyQeZlmZNcMvWq0ilEsaObq20/fdjl9HVkvFpt9YaeqrS0NweAc0OyuziQpbev0inY3+z8dEtetfx5kzvKfzStHe1kmneFcNbNGTNhMGk5uQx6u8d3H5adq/NzQ1Zung0Xbs0IOxBHIMHNWXgAH+KS+V8tu0oz9KzWPFa2F9l1FTjLn9ZGlWicUOZkzI2LZP7z5RfriaWjnSx92BN+A2yistebF2pAe2tBvE45x77n/+NlqBdoYEdiA4hr7SYcR7+SscJjI5DWybF11H5Ay6RCEgkwhtHlYiiyJmzoUz+aCPHT4bwzezdLF12kvz8ojc6nr6RHtZ13q5t2cXZknnfD2TtPxPw83Ni05arjBm3mu3/Xq90nJ3ru3FgxjhauTux4OhFpmw6SEpO3lsdm4GWNn+0HEhaUR6z7xxT+Tj0s5+MmbYVe2L/4Hl+JDH5D/E364QgCJyLf0xWcaGKU/J1Onm7IpNKOHX/UaXt1FEt52RNeRifTLN5fyIRBCSCAAIV/xYq/kbp/5IXQlQiCBTL5aTk5LFoZO9qadvlDHL2ZV14AEvuX6SrgycyiQRRFNkaGYiniRXNrOrwze6TGOpo08fPq6JfaOhzdHW1cHd76Wx4aSpRFdzxTxJJjE5h2BcDNI4lJiaVM+fCGDKoKZaWL22ol8OjuBwRxZe921fY16uDkZYOHiZWKnbuvJJiNj++zdrwALJLCuldx5sZPu1xV+MQKScxK4egmHh6+HrW6P6+KS3c6rBz6iimbTnEpPX7mDuwC0Oa+aCtLePbr/syYlhz6ta1RhRFfjp4jltPn7FweE+aqgn700RNBXfIs0QkgkB9B+tK23XzcefnQ+c4fO8hDZ2Uhfws3w70PbWONeEBfNmwLJKgpWVvrqceJa4gEn+zLujLjBBFke2Rd2lkbk9Dc2WTyN3oeHwdbdWagmQyaY3juKEs3PaP5ae4cPEhPg0c+WJWL06eCmHXnpsEBkbx1Zd9aNSwegrR/wVurtb8/MMQHj1KZNPWK6zfeJm9+28zYnhLBvRrjJ6etkofC0N9/npvALtuhvD7sUsMWr6Vn4d0o5O3m5ozvBm+5nZ87tuJhcHn2PU0SCkwQEeqx3CnWayJnM3Gpz8gIMHvRez23qhgbPWMaG3tUunxjfV0aePuzOnQx3zZu32NxvZOBLeZgT7DmvuieJHloxCp+HfZ/0XE8m2IKBTK7UBksH8DmrlW/8WFMq/w5w07MuXqXvZHhzDc1Y+gtHjCMhL52b8n6XkFnLr/mOHNfSvSpQHuhz3Hy8uuYnoKrzonVU0lgadDAPDvrvmLunnrVXR0tBg1slXFtuJSOb8du4SLpRmjW/nV6NqgzFxy+nkEoihSrJDzb+RdVj28TlpRHp3t3Znp04H6Zpqn/gDP0jMZv3YvCZk5FJfK6d9EvQ2uOuSXFiMVJOhIq36MnC3N+PfjkXz+7zHm7j/Dk+Q0Pu/VDqlEgptbmba55sItDgSGMbVLS/ppCPvTxJsIbncbC6XnQB2Gujp0ru/GiZAIvu7bAa1XQuC8TG3o59SATY9u8YFHM6z0DNGW6NDFZiSH4v6hpWVvAG4kxxCZncqi5v2Ujp1fXMLD+GTGt1fWwiuu6Q0Ed3BwLAt+O0pqWg7jP2jH6JGtkEolfDi5E61bebBw0VFmffEvQwY3Y+L49ui8o5T4N8HT05b5Pw/j4cN4Nm25wpq1F9iz5yYjR7akf9/GKmMVBIGRLRvRzNWRr3aeYPqWw4xo0ZAve7evNEqrJkys14KriU/5+d5pmlrVUVKI6uh70NV2JKcTt+Np1ARjLXOSCnK4kviUj7xaKTmbNdHd14NLEVEqM7qqeCeC29bEkK/61MxL+rboau9JYwsHlodepr9TA7ZFBmIo06a/sw87rgVTIpcrOSULCop58iSZ0aNaKR2nMht34JlgbF2ssHdTLyQjI5O4eCmcsWNaY2r60p75740golMzWPX+QLTfwMHT2MKB3U+DWB52mT1Pg0ksyKG1jQuzfIbR2LLq8KyY1EwmrNtDQXEpXnZWzD9ykeZudTRGVVSGKIqMOLeFuPwsRrk1YZy7f5Wx4MZ6uqz6YBC/HbvE5qt3iUrJYNHIXhjq6nAiJILlp6/R18+LqZWE/WmiXKCaaFVt41YoREKfJdLdt3pFLvv6eXMi5BHXHsXQ8bWw1Bk+7Tn+7CF/PbjGD/5lsdpNzbviZdy0on7Jtsg7mOvo0+c1R1VIbAKlCgX+Lup/O6lMQmlJ9UwlJSVyNm6+wq7dAdjbmbFy+Ti8vZS1ex8fR9aunsCatRfZu+82t2495Zuv++JVr3Jz0f813t72/LZgBKGhz9m4+QqrVp9n9+5bjB7Vij69G6GtrSy23Kwt2DF1JMtPX2fTlUBuRz3n9xG98LavfDZVHSSCwKIW/ehzah2f3TjAvq7jlRSVdlYDkYtyGpi0AOBg9H0UosiQulUn5UGZ2UcmlXAqtNJkdNVx1aj1/wCCIPBlw04kFuSwPOwKx589YJBLQ/SkWuy6GUILtzq4Wr9MvX/4MB6FQlSyb4NmG3dpSSlBF0Lx79ZIo5lhw+bLGBrqMHxo84ptabn5rDoXQDtPF9p71X2jayt3UK4Mu4q9vgnbOo5ha8cx1RLa0SkZfLB2N4UlpWyYPJSlo/tSKpczd9+ZN3LuhGYk8iAzCXt9Y9aE36DD0b+YFXCI++kJlfaTSSV8178T3w/ozLXH0YxZvYvjwRHM3nOKJi72/FxF2J8mKpyTOlVr3DFpGWQXFlUaUfIqbTydMdXX5ehr0SUALkbmDHNtxM6nd3mWW+Z8FQShQmjH52dzNu4Rw+o2UpmZBEbHIQjg52yvclx4oXFXI+wtNjaN6TO2sHNXAL16NmLN6vEqQrscPT1tZnzand8XjqCgsJjpn25hw6bLNc7c+7/Ax8eRJYtGsXTxKOwdTFn51xnGffAPR47eUxmvtkzGl73bs27iYHIKihj59w42XQlUSZl/E6z1jPiteV8eZiazOOSC0j6JIKWzzXBsdJ3LYrejQvC3dKzIiq2KcnNJTe3c70RwJxfmcjXxabVD1942LaydaW/ryprwGxQr5Ixxb8Kl8CgSMnMY1VL5S3g/7DmCAPXrKz/ommzc4bciyc8u0BgG+OBBHAEBTxgxrAWGr1RN+/PMdQpLSv/TTMTD2JKvGnZiXbsR7O7yHq1sXKrV72lyOu9X1r+AAAAgAElEQVSv3U2pXMHGycPwsrPC2dKUmT3bce1xDHtvh1Z9kNc4EhOGlkTCto5jOd97KuM8mnIu7hEDz2xg1PmtnH4eUWmm28iWjfhn/CCSs3L5cudxbIwNWTG2f5Vhf5qoENzV0LgrS7xRh5ZUSq+G9Tj/4ElFWOOrTK/fFgGBFWGqGXU7ntxFIYqMdm+isi8wOo56tlYY6eqo7INyjVuzQBVFkUNH7vLR1I0kJWXz47xBfDGrl5JNWBRFgmMTKHgtrb2pf13Wr5lIty4N2Lb9OtM+2czTqGSN5/p/iV8jZ5YtGcOi30ZiZWnEH8tP8f6ENTx4oOqob+XuzMEZ4+jo5cqi45eZvGEfSVm5/3kMne09GOfelA2PbnEp4YnaNkFp8TzJSWNoNbXtcrr7epCQmVOjPu9EcCcV5PD+pR00PrCEbsdX88XNw2x9fIeQ9HiK31Hg/Ot8Ue4ssnbGw8SKnQHB2BgbqjgvQkOfU7euFYYGypqaJhv33TMhSCQCfhrS3DdsuoyZqT6DB71MaY5JzWTfnVCGt2iopO3XFEEQ+Mi7NZ3s3autlUYmpfLB2j2IImycPAxP25c2ulEtG9HCrQ6/H7tEXIbmGhKvI1coOBIbRgc7d0x19KhjaMqcxt242u8TZvt1JS4vi4+v7aXridVsfnS7Itb5dVq5O7Nj6igG+tfnn/GDKi3nWRXlNm7TamjcwbEJGOho1+i36NfYm6JSOWdDI1X22eobM86jKQdj7vM4K6Vie5G8lF1Pguhs74GjgXJJ0BK5nJDYBI1mEijXuNV//DIy8vhu7l6WrziNr08d1v0zgXZt6ym1kSsULDhykdGrdiqFY5ZjaKjL11/15ecfB5OWlsuUqZv4d+eNKlPS/18gCAL+TVxYuXwcC+cPRwA+/2oH166raqqmBnosG9OXHwd3JTg2gUHLt3I2TPV3qynfNOqMp4kVX948Qmqh6sdgX3QwulIZverUzD9Tbi6pCe9EcNc3tWVzh1HM8u1AXSNzLic85Ye7pxh0ZiON9i9i6NlN/Hz3NIdjQonJzXgncZgNzGxZ3moQP/r3JCY1g2uPYxjW3FfpBsnlCh6Gx6uYSeCljft1U0ngmWA8m7ljZGao0udeUAx378UwalQrJa3n73M30JJK+bBTc5U+75JHiamMX7sXiSCwafIw3F9JqYaykLNfhnRHEAS+23u62tPKgJQYkgtz6e/UQGm7kbYuE+u14HyfqaxsPRgLHX1+uneaNkdWsDD4HPH5qlXpXKzM+HVoj2qH/WmiXHAbV6NWyf1nifg42lTLeVROwzq2OFmYckSNuQRgindr9F+pmQNw6nk4aUV5SnVJynkYn0JBSalK4s2ryGQStSaMgJtPmPThegIDo5n2cRcWzh+uFLkEUFBcwmfbj7L9RhB9/bxIzMphxJ//cilcNZ67TWtP1q+dROtWHqxbf4kZs7bz/Hm6xnH9v0QQBJo3c+XPFe/h4mLF3B/2c/BQoNp2Q5v5sveTsTiaGzNj2xHm7j9DXpF6JaI66Mq0WNZyIDklhXx166iS3CosLeFo7AN6Onor1WuvDsZ6urR2d65Rn3eTgCMItLV1ZVr9tqxpN5ybA2Zwue90VrQaxDiPpkgFCTuf3mNmwCE6H/ubZgf/YMLlnawIvczFhEgyivLfyjj6OtXH3diSXTdDkEkkDG2uXE41KiqF/PxiDYJbVePOzcwj/OZjtWVcRVFkw8bLWFoa0b/vy7ChyKRUjgWHM7qVH1Y1CP/7r4QnpDB+7R5kUgmbJg9T0i537r7Jh1M2kJ6Rh72ZMV/1ac/tp8/ZERBUrWMfjgnFUKZNF3v1zj2ZRELvOt7s7foBe7t8QDtbVzZE3KTj0T+ZceMAwW+hWNbrVAjuKqJKCktKeZSYWm0zSTmCINDPz4tbT5+RmKU6rTXX0WdivRaceh5BSFpZktTWx4G4GJrTxkbVp1GeeFOZxi2TSZW038LCEpavOM3sOXswMzNg1V/vM2RwM5Xia2m5+Yxfu5cLD58wu19HfhvRiz3Tx2BvZszUzYf46+wNlY+0qak+874fyHff9iM2NpXJUzZw4ODbsRG/Snlk2X/FzMyApYtG0bKFOyv+PMM/ay+oHauLlRnbpoxkcsdm7L8TyrCV22scwfEq9Uytme3XlUsJT9j8+OV6MqfjHpFTUlRl7LYmKisyp453ElWiEJW1BEEQcDAwwcHApMKzXqpQ8CgrmaC0eELS4wlKi+dywhPKb72fuT3LWw9SmWLWlILiEg7cCaObj7uK4Hy9sJTSNaixcQddeJHmrqaM681bTwl7EMfMGT2UvN4rz9xAX1ubiTX8Yf4LD+KSmLR+P3raMjZMGoaz5ct7uHXbNTZuLrPF/rH8JD/NG8yQpj6cDY1k6cmrtPV0qVT7LZKXcvJ5BN0dvapV66SxpQMrLQcTl5fF5se32fU0iKOxD/C3dGSCZwu6OXjWSPPVhLZEioFMW6kokDoexCVRqlBU2zH5Kn38vPnrXADHgsKZ2KGZyv7x9Vqw5fEdloZe4quGnbib9pw5ft0qchReJTA6jjrmJlgZq87cypFpSSs07seRicxfcISY2DSGDWnGxAkdVKIroCwrd8rGA6Tm5rNibH861y8zDTqam7D945H8dPAcf58LIPR5EgtH9MRE7+WHThAEunRuQKOGTixeeoKVf53h6rVHfPlFb2yruXJVZeRl5/Ntz194HhGPi68TdX2cqOvrTF1fJ1x86mBgXLMVnvT0tPnph8Gs/OsMu3bfJDk5m6+/7KNyX7RlUj7r0ZY2Hi58s/sEY1fvYnq3Vkxo3/SNnr2x7v5cTnzCb8HnaGnthJepDfujQ3DQN6Gldc0053JqGvr8TgR3YmE0m6N+wc+0Pd4mzdGWqGpBMomE+ma21DezZTRljpvckiLupydwLy2OteEBDDqzkVVthtLU6s3TbE+ERJBdWKTilISy+iSWlkbYWKuGsanTuANPB6NnqIt3S2VNU6EQ2bDpMnZ2pvTq+fKLGxaXxNmwSKZ2aVmttO23QejzRCav34+BrjYbJw+ljnmZ0BZFkc1brrJl2zV6dPPBycmStesvcvZcGN26+vDTkG4M+GMLs/ecYstHwzU+0BfiI8ktKWKAcwO1+zXhYGDCbL+ufNqgHXuigtn86DbTru+jjoEp73k0pYuDJ04Gpm+cEDS4bkPqmVZdSL/cMVlZqrsmnC1NaVTHjqMaBLeRlg4f12/D/KCzZBTloyfVUquBlS+c8Hpo4evIpBKKS0rZuSuADZsuY2Kiz6LfRuLfxEVt+8Co50zfehgtqZSNk4epzCp0tWT8OrQ7DevYsuDoRUb8+S/LxvbDy075vllaGrHg12EcOxHMqtXnmfTheqZ93JWePXzf+PdRKBQsHLeCiNtP6DK2HXGPEji79TL5OS/TyW1drFQEuqOnXaXZyVKphBmfdMfWxoQ16y6SlpbLzz8OwchIVeY0c3Vk/4xx/HjgLMtOXePqo2gWDO+psQ6+JgRBYGGzvvQ5tZbPbhxkVduhXE18yvQGbdV+pN8F70RwG8rMSC6MZfezZWjH6dLApCV+pu1xNfRFImjWiAy1dGhl40IrGxd6Onox6couxl3czoJmfRjo4quxnyZEUWTHjWA8bCxUakFAWUVAnwYOah9GdTbuu2dD8OusmuZ+9VoEkZFJfPNVH6UknpWnr2Oip8v7bVUjCt4FIbEJfLjxAMZ6OmycPBQHszItqdyMs33HDXr1bMjnM3shiiI3AiJZ8ecZ/PycsbY04rv+nfh690k2X72rcep2KCYUS10DWlaRFaYJQy0dxns25z33ppyNf8SGiFv8GnSWX4POYqNnSDMrJ5pbOdHMygkP4+qt4wngb+mIfzVW7rn/LBE7U6M3Nlv1a+zNL4fPE5GQQj071Q/FGLcmbIi4SWhGIiNc/dSabp6mpJOZX0gT58rDOLW0pAQFxRIUFEu7tp7MmtlLY7G2EyERfLv7FI7mxqz6YGDFB/t1ypNWvOysmPnvUcas2smPg7qq1DkXBIG+vf3wb+zC74uPsWjJcW4ERDJ3zgClZ7y6bJm3m4AjgUxbMYGB08uqJYqiSFJMClH3Y1/8iSHqfiy3jt+rmPFqacuo4+XwQit3wrVhmUC3dDCveDYEQWDkiJZYWRnz++JjfPrZVhbMH652lmCip8uSUX1oX+8Bvx6+wJhVOznw6bgaK1YWugYsbtGf9y/tYMyFbYigsRLgu6BaglsQhJnAJMrWZb0PjBdFUWOsn7GWOZ97rSYm7yFBmZcIzbzOvYyLGMnMaGjaFj+zjtjpulT6UroaW7Cv6wdMu76fz28eJjI7lVm+HWv0RQt5lsiD+GTmDuyscq6k5CySU7IZ7qPeYfh6dcD4J4nEP0li0Iw+Su3kcgUbN13B2cmCLp1faqF3o+O48iiaWT3bYqgh3Ottci8mno82HsDcQI8Nk4dWaBGiKLJ23UV27r5Jn96NmDmj5wubqMDXX/Zh8pQNLFl6ggW/DqOPnxdnwiJZcfo67eu5qNQ9ziou4GJCJKPdmtR4pZzXkUok9HD0ooejF0+yU7mZHMOtlGfcSonhaOwDoMxu3NSyzgtBXgdv05o5FNUR/CyhyvokldGzoScLj17kSNBDtYJbV6bFTJ8OzL5zjPfUOCWhzEwCldu3AQwMddDV1eKTaV3p2aOh2vdFFEXWX77DHyev4u/iwIpx/THVf/mxuJv6nG9vH2Ogsw8febeueH/8nO3ZM30Mn+84xte7TxLyLJEverdXSQyzszNlyaLR7N5zkzXrLrJ2/SU+/qhz5TfpNa7sC2D7r/voOb4TA6b1rNguCAK2LtbYuljTqt/Le1VcVMKz8LiXAj00lpBLDzi3/WW4pamVMTPXTKH1gJczny6d62NpYcj3P+xj+qdbmP/LMDw9VGdWgiAw0L8BnraWjFq1kx8OnuWP0X1rPJtoa+vKpHotWRcRQAsrJ5wM/5uDvSZUKbgFQXAAPgXqi6JYIAjCbmAksKmyfhJBQl3DBtQ1bEBf+0lE5AQSlHGJgLQTXEs9grVOHfzMOtDItB2m2uqnuGY6+mxqP4p5d0+y6uF1onLSWdSin8pKzJrYcSMIAx1t+vqphueEhpa9POock6BajzvwTFmae9PX4rfPnX9ATGwa874fWFEVURRFVpy+joWhPqPeILW9pgRGxzFl4wEsjQzYOHloRSakKIqs/uc8e/bdpn+/xnw6vbuSI8vBwYzJEzuy8q8zHDsRTN/efswd2IUBy8pMJts/HqmU4n3qeQTFCjkDnCtf8aemuBlb4mZsyWh3f0RR5FleJrdSYrmdEsut5FhOx0UAZdq6v6VjhUbua2aHdjVXYQFIyckjITOHsa3fbDEKADMDPdrWc+FYUDgze7RV+yEZ6tqIzg4eSlUAX+VuVBwWhvpKvgd1fDGrN/JSOebm6u3gpXIF849cYNfNEHo1rMevQ7uj88ps8OSzcGbdPISWIGHx/YsEpcezuHk/jF7MAiyNDFg3cQhLT1xly7W7PIhP5o/RfVTs7hJJmUablJzNnr23aOjrSJvWldeQLyfqfgy/f/An3i09+OTvydUSjto6Wrg1csGtkYvS9pyMXKJDnxF1P5aTG8/zw+BFTFsxQelj0KiRE8v/GMu33+3hs1nb+WHuIJo3U2+Squ9gw6fdWrP05FUO3X3AQP+amf8APvftSEFpMQPewCLwX6iu+iID9ARBkAH6QOW1RUufIub/i6goiw7RkmjjY9KKsS7f8I33evo7fISuVJ/TidtYFP4R657M5U76WUoUqqE62lIp85v2ZrZfV049D2fk+a0kqgkrexW5QsHSk1c4EhTO4KYN1NajCA0rKyzl5qo+LfZ1G/fdsyHYOFvh4PFSWystlbN561Xc3W2UYmgDnsRyO+o5H3Vqjv5bqpmgiTtRz/lo4wGsjQ3Z/OEwJaH916pz7Nl3m0ED/ZnxyUuhnZmSxc1jZSFUA/o3wc/PiVWrz5OYmImFoT5zB3YhLC6ZdRdvK53rUEwoLobm+Jq/uxRpQRBwMjRjaN1G/Na8Hxf6TuNqv0/4o+VA+js1ID4vi0UhFxh+bjONDyxm7IXtrAi9zJ2UZ1Ue+34NE2800c/Pi+TsvIpKh+rQJLShbOGEJi72VQoxE2M9jUI7r6iYT7YeZtfNECZ2aMrvI3opCe0NETeZfn0f9U1tON9nKnP8unEh/jGDzm7k0Sux5lpSKV/37cCikb0Jj09m6J/bK2YEr/PxR53x9LBl4e/HiE/IVNvmVbLTcpg78Hf0jfWZu/cLtP9jXRQjM0N823nTf2oPll76iZb9/Pnzk/Ws/WorileSveq6WPHn8nE4Opgxe84ejp8I1njMD9r506yuI78evqC0YEZ10ZZK+alpr2qZ6d4mVQpuURTjgMVALJAAZImieLryTvmI2T8g5q1V2aUvM6KFRQ8+cl/Ax+6/Ya5tS1ReKAee/82ttFNqDycIAhM8mzPA2YewjERWP7xe6emDYhNYf+kOlkb6TOmsvu5FVFQKLi6WGmuHv27jfnTnCfWaKye+PAyPJyEhk6GDmyppskeDwtGRSRnW/N1/hX8/dpmC4hImtG+K9SuaUkxsGvsP3MHMzICRw1sojfvI36fZsfAAaQkZSCQCfXo1oqCgmGMnymYVbT1dsDE2ZPPVuxWhW6Iocjf1OfXNbP5PKgq+ip2+Mf2dG/Bz016c7PURtwZ8xt9thjDKrQnZJYWsfHCVEee3EJFZeebf8eAItKTSatdA10S5v+ROlGbBrYni0lLiMrIxN6hZBMXrLH/hXJs7sDOzerZTev4S8rP5Negsnezd2dZxDBa6Boyv15xtHceSVpjPt7eOqhyvd6N67Jg6CgNtbT7csF+tENPWljHv+4EIAvyx7GSVYzy25iyJUcl8uXEalvZvnnimDl19Hebt+4LGXXzZvfgw4beUE2wsLY1YtnQMVlZGLPnjBHl56svESiUS5g/vgVQiof+yzczYdoTzD55Q/A5XHnobVCm4BUEwAwYAdQF7wEAQhLFq2n0oCMIdQRDulMplCEZfIxhMUDmeXJQTnn2HHTGLWPPkO9KLE7HTrUsf+4k0s+imdgzx+dl8cGkHB2NCaWtTlyneldex9nOy48NOzUnPLWDpCfULezo6mhMXpzn5R15SCpQ5RwDq+joRdT9WqU3dulZoaUl59DhJaXtjZ3uKSuWEx6fwrpk3qAvuNhbM3X+Gb3adIPPFquTOThZM+7gL+flFTJi0nmPHgyuudcD0niw4OQcLOzPu3o1m8dITODtZMGhAE/KKipn6or7xd/07KTmAhrv6ceLZwyoXc3jXWOga0MPRizmNu3G4+0TGuvujI5VVamO88PAJJ0Ii+LBjM3QriVKoDtuu30MQygrh1xRtmYz+jb3ZdyeU6JQ3W708r6iYg3cf0NfPixEtVKOlTLR1kQgCPmZ2SiGbza2d0JdpUUfDffKwtWTTh8PQkkqZu199QpadnSnjxrYh8G40QcExao7ykpZ9y5zyEbf/e9aiOsKuRRBy6QFNezSiXlPVcq7nLz4kKSmbIYObYWCg2c9kb2rMzqmjGNPKj3sx8Xyy9TCdF65lwZELPIhLeicJgv+V6phKugJRoiimiKJYAuwHVCSnKIprRFFsKopiU5muF4LBRATJy2yupMJYTsRv5veHk9kaPZ+nuWG0sOjJdI8lTPdcQmvLPiphg6IosvdpML1OruFu2nN+9u/Fpg6jqqxCJ5VImNG9DUOb+XA06CGZ+ap+VG8ve3JyComLU//ylBSXCW6pVpmppFGHBjwLjyM98WV7QwNdWjR349KlcKVEiR6+nujIpBy8+6DScb4NGjjYsGf6aD7u3IITIY/ov2wLp0MfIwgCQwY3Y92aiXh42LDkjxN89c0uEhMzMbYwQs9Al9t3opj9/V7s7cxYung0OgbaTNl0gLsxcSwc3lOltOoXvh2x1DVgzp0TlL7jFbdrwqWEJ7S0dkZPQ1x5dkEhPx08h6etJZM6/rfs1ZTsXLZfD6JvIy88bN9sYYFZvdqhK5Px65ELbyQUjtx7SF5RsVKVy1fRl2njZmRBWIZyoklCfjaJBTkVxcrUYW1syNd9OnAnKo5dN0PUtunftzEWFoZs3HSl0vHX9XWmWa/GHFx5gqKCN1vAQROJ0cn8NHQx9m42fLdjpkppiuCQWJavOE2zpnX5aHIntcc4GhTO6vM3gbJEna/6dOD8N5P5+/0BNHd1ZNfN+wz789+KVeZTsv97zZO3RXUEdyzQUhAEfaFM/eoCqM/7fY380hwCUk/w9+OvWPHoM66nHqWOvidjnL/ha++19LGfgJ2e+kp5yQU5fHh1N1/fPkoDUxuO9ZjMaPcmNZqmj2jZiKJSOYfUCFAvrzI77cNw9dqj/EXiQ7nG3bBjmeMi5JLysTp38iYtPZf7oS9trEa6OnRp4M6JkAiKS0urPd43RVsmY3q31uyaPhprY0Nmbj/KzO1HSc3Jw8HejMW/j2LmjB48DI9nwuT1HDgYSMDNJ8yZuxdHRzOWLBqFTE/Ghxv2ExybwKKRvZUWmqi4Lm1d5jbpQVhGIltfyRr7r+SVVu6zqIyonDRicjPoZKd5CbnFx6+QmpPPz0O6vVE53Vf558ItSuUKpnZtpbHNlcSn9Dq5hqicNLX7rYwMmN6tFdcfx9S4hoYoiuwICKa+vXWltvoGZraEZihXaixfhKOxheY0e4CB/vVp7eHMkpNX1Naw0dHRYuzo1twPfc6dO1GVHmvElwPITM7izJbLlbarLqIoEnE7krkDfkNequCnQ19jaKoc2pmYmMkPPx3A3t6U778boNYcevHhU77dfZJV5wKU0uBlUgkdvFxZOrovl777kLkDO6Ovrc3iE1fovHAdUzYe4ERIBEUl7/69rozq2LhvAnuBu5SFAkqANZX1KZTnsyNmMQsfTuRI/FrkYil97CfwTf11jHX5hvomzZFJVLUjURR5kJHIyrAr9Dy5hmtJ0cxp3I1tnca+UaiNl50Vfs527AoIVpn2uThboqurRXiE+jKkLzXuMsHt7ueCvrEewRfDlNq1aO6Grq4WFy4of8sGNKlPdkERFx7WbK2//4KXnRU7po7ksx5tuPDwKQOWbeFoUDiCAP36Nmb92on4+jiy8q8zzJ6zB2cnC5YsGo1ER8Lk9fsJfZ7EklF9KjUB9HL0oqOdG3+EXlJbe6SmJBbEsODBBJ7kqNfuquJifFmlto4aBPf1xzHsuxPK+Pb++Dj+N6fk8/Qs9ty+z5BmPjhZqI8IKSgt4bvbx3mUlcLs28dfLAyiyqiWfnjaWvLbsUvkv1a5rzICo+OITEpjVCvNZYUBfMzsSCrIJaXgpZZ4Ly0ObYkUb9PK74MgCPw4qCsC8MP+s2q16l49G2JjY8yGzZVr3Q071KdeMzf2LDmM/D8UmEuOTWHHggNMbDCT6S2+JT4ykTm7ZuLoqfwRys8vYs7cfWULUP80VKlCZzmhzxP5YscxzAz0KFUoNPoqTPR0GdGiEf9OHcnRWe8zsUNTHiel8sWO43SYv4Z5+89wNzrurZhSNj26XXWjV6hWVIkoivNEUfQSRdFHFMVxoihWOu9JK07gaW4oLSx6MM1jCZ94LqW1ZV8MZKoB8YWlJVyIf8z3d07Q9shK+p1ez/LQy9Q3teVo90mM92z+n7KRRrZoRExaJjefKtunpVIJnh62VWrcshemEqlMim87b4Jf07j19LRp1dKdy1cjlFYraeXuhLWxAYfvVWty8tbQkkqZ3LE5+z4dg7OFGV/vOsH0rYdJysrFxtqEhfOH8+3XfZn/yzAW/z4KUSYwaf0+HiYk88eYvnTzqXxxAUEQ+NG/J3JRwU931TuTa8KT3BBEFDzMvvVG/S8kROJubEkdQ1VBmldUzLz9Z6lrZcbULpo15Ory19kbSAWBjzq10NzmwVXi8rMY4erHrZRYdj65p7adTCphTv9OJGTmsPZC9a99x41gjPV06NWwXqXtGrxYCSn0FXNJUFocDcxsqxVCaW9mzOe92nE9MpYDgWEq+7W1Zbw3tg0REQlcv6F51iAIAsO/HEB8ZCLXD9ZMOOXnFHBq0wW+7PIDY+tOY8N3/1bEb++MW6NSekKhEFnw21GiY1KZO2cgdRxVHaLP07OYuvkQZgb67Jg6Em2ZlIDIqiOS6lqZ81mPtpz+aiLrJg6mk7crx4LCGffPbnov2URQzH/z+xyKuV+j9u+kyJS5tu0LU8hE7NWYQpIKctjx5C4fXtmN/8GlTLqym0MxoTSysOe35n0JGDCDbZ3G4GpsoeboNaO7jwem+rrsDFDV6Ly97XnyJJniYtVpT0lxCRKpBMkrcbrq7NxQZi7Jyirg3r2XzhqpREJfP2+uRkSTlvt2imbVBDdrC7ZOGc7XfToQEBnLgGVb2H+nrO52t64+tGzhhkIKE9ft5VFiGsvH9Kuoa1EVjgamfNqgPWfiHnHmRYz1mxKbX9Y/MldzyJYmckuKuJUSo1HbXnbqGglZ2fw0uNt/dkhGJqVyJOgho1v7YWOiPkQvMjuVdREBDHLx5demvWlt48JvwedI0DAz8a/rSD8/LzZeCSQmtWpHZUp2LmfDIhnk36DKpbnqm5VFzpTbuYvlckIzEmlciX37dYY3b0izuo78fuyy2prW3bv54mBvxsbNlystRtVmUHPs3W3Z9fvBKrVTuVzO7VNBLBy3guG2k1g84W+SY1MZN28YWyL/ZOmln+g9qYuKeQRg46bLXLv+mKlTutDUX1XuZBUU8vGmg5SUylk9fiAOZiY0drbn5tOqBXc5UomEVu7OLBjek0vffcQvQ7tTWFLCohNvbgpKyM8mpIoFSF7nnQhuPamBkilEIYqEpMeXLSd2ej2tD69gzp0TRGQlM9zVj00dRnF74Ez+bjOUoXUbYamrufBOTdgfFUJUXhqDm7V9zuMAACAASURBVPpw4eETlYfPq54dJSVynjxVDSOTl8gr7NvlaLJzN2vqioGBDucvvm4u8aZUoeBYUPjbuByAGk3LpBIJ77Vtwv5Px+FlZ8X3+87w4Yb9xGVkkZ6bz/h1e3maks7K/4+5846K6vy+/mdm6L03KVIUsIAdpCg2sMUaTWyJJtFoiprENGOiKaaaojHNNHvvPSqCCqKoNJWidAWk9zrlvn8MIDhDM/H3ffdaWVnOnTuFmTn3PPvZZ+95kzr0zXgUL7gPoaexJZ/HhT62x7ogCGRVJyMRaVBYn0N5Q1GXzo/Mz0CqUDDCTrVw38i4z86oOOYM7afW7qCraDILe2mYqkcJKN/L6hun0ZVo8Z73KEQiEWsHjUfeeHtbn9tb4wLR0pCw9mjHG5X7rt1CplDwjE/Ho9UGmto4G5o189zJ5fnUy2Xtbkw+CrFYxMfTxiCVy/n4sCplIpGIef65ANLTC7l4qe3vuEQiYcZbk0i5lkbCRfUb9hk3s9j09lZmOy5h5bi1XD0Rw5jnhvNDxGdsvvMj8z6aga1L2zLO0POJ7NgVxYTx3kydoprh2SCTsXTbUe6VlLNh3iRcrZRNoa+rIyl5hZQ8RnOlr63F1IG9WRA4iLisPG7dfzzXwdCcrqe8P7HoshpZA2dzUnj/2gn8j25g6tm/2ZgYgY5Eg3e8RnBq7CLCJ7zK6gEhBNq4dCpwtitIKSvg7ehjrIn5h5k+yuDi/ddaL0ea4p2Sk1WvdtIGWbOipAlt8dxaWhoE+PckIuJOq+7dzdqC3t2s1G6OdgW5ZRXsjIpj0V8HGbR6Y5djjpwsTPjrpaf5cPJI4rLzCP76LwLX/kZWUSk/PTeZQPfuXX5NmmIJ73iNJLuqlL3p6umAjlAuLaJSVkJ/U+Wuf1e77vDcVAwbpylbok4q48MDZ7E3NWJZSMBjvbaWuHnvAedupzI/cGCbnhbHsm9zpSCLt72UyhsARwNT3ug7nNDcu5y4p54yszQy4LXRQ4m8m0VoovpkFVAGL+yLTsC/h1Onvcv7mNo2d9wPNya7dhFzsjBhabA/F5Iz1DYgI4I8cXKyYPOWiHYDGMY8NwwTK2P2fnOk+baSB6Xs/+4Yiwe8zSLvFRxcfxL3wa58tO8t9uRuYtkvi+jt596hICE5JY9vvj2JV18Hlr4WrHJ/hUJg5b4zXM/I4bOng1s58fm4Kg3sorvQdT+KqYN6o6elyY7LnbNFfhRncu7gYtg1duGJFO7MqhIGHvqOxRH7OXkviUGWDnzrM4mrk5ezd9TzvOzpR09jyyc6yNE0pHOt8B414gYCenRnX/RNpC26Q0tLQ8zM9ElOUeWnZGo67rZ4boCRQZ5U19Rz7XrrzcjJA3qRnFdISl7nNd0KhUDCvQdsOHOZqeu3MearP1l7NIz7JeVoaUgeq4MXi5XmQkeWP0dgz+74ujrw8/wp+PV4PBtKgCBbV4ZYOvJjYgQ1sq4b1DfRJEPMgjHQMCG1qvMblIIgEJ6XRqCNi4qV68Zzl8kqLuPjaWP+k8nVDWciMdXXbdMsrLKhjrVx5/Ays+UZl9bj9PN7DMbLzJaPY/5p02d+9tB+9LA258vj4SoRY00IS0qnoKK6TQmgOvQ2tSG3poKS+hriinOx0jHAtgMprTrM9euHt4Mtnx8LV5HWSiRiFjwfQPa9YkLPt92gaOtqM+X1cUSfjOWvD3aycsLnzHJYzG8rtqKhKeHVDS+wO+c3Pjn8LoHTfdHS6ZytRVFRJR+uPoCZmT5rPpqKpqYqf7/+TCSnElJYHuLPxEfUUr27WWOgrcWVtMcv3IY62kwZ2JuTCSkUVlZ36dzyhlquFmQRbN/+nsWjeCKFu0EuZ67bQLYFzeH6lDf40W8aU7r3bXcMuAkKhUBmYalKzFJXkFVVyvF7iUzv7oWWWMLOtBie8fWisLK6lcpDJBLh6WFHkpqOW9Yga1aUtERbPHf//k4YG+ty/hF1yXhvDzQk4g43KWsbpJxPTOOjg2cZ8eUmZv28i9/DozHS1WbFuECOv/k8J1csYGzfnkSlZj/2ZJedqRG/LpjKny89ja+r42M9RhOagpmL6qr5+07XNxezq5PREutgo9sdVwMv0irjUQid04ffLn1AQV2VCr99894DtlyK4enBffB1+3fvD+Bq2j0up2azMGiIWusEgO9uXaC4rppPBo5T8S6RiMV8OXgiFQ11fBZ7Tu35GhIxqyaPVG5UhqvfwNsVFY+tiSHDuxA03adxg/J26QNii3PoZ67eCbMjSMRiVk8dRUVdHZvCrqocD/B3x83Nmi3bIlpt0D+Kp5YEI5aI2fXFITJvZjPz7cn8cft7Nl79kimvjcPEsmue3/X1Uj5cc5CamgY++2Q6Jiaq9WXv1QT+uHCNmUP68pIaK14NiZhBLvZc/ReFG2DO0H7I5Ar2taF9bwthuanIBAVjunVtmOuJ2Lr2NLbkg/7qpyBbokEm425+MUm5BSTnFpKUV0BKXhG1DVJ0tTT55+0XMDfo+mjwpqQoNERiVngFIRcEDmfe5M2Jw7E1MWT3lXiCWygnPNxtibx8l8rKulYevjKZTKXjhtY8d9Az/s23a2hIGBbgztnQ29TWNjRHl5nq6zLc3ZljsUm8ERLQKjqtoKKK8OR0whPTuZKWTb1Mjr62FoE9uxPk6UJgz+4qS/NAd2f2Rt8kNiu3eZn3v8QAC3tGd+vJpuQrzHYdgGknLs5NyKpJwV6vBxKRBDcDL+LLLpJfl9Wmtr8lwvJSEQHDbR9uqDbIZKw6cAZLQ31WjB/2OG+nFZRmYZHYGBvwbBu88q2SPLan3mCu28A2PVzcTaxY7OnHxsQIJjn1bvWamzDI2Z6J/Tz46+J1Jg/wbEWHpBUUE51+j+Uh/l1yR2xSllzKSye7qpRnXR7fXMvd1pIpA3qzMyqe2UP7YW/2sMiKxSJemB/IylX7OX3mJhPHqzdWMzIz5B/pHm5fTsHTt0erjf+uQhAE1n13ijt38vhkzTRcnFU9hy4mZ/DpkfMMc3fmg0mqDqFN8HFxIDwpndyyii57czehu6Upw9yd2X01gZeCBnc69Ppszh2sdAzwMmtfW/8onhjH/SgqauuITr/H1ogY3t97mqnrtzF49U/M3LiT1QfPcTQ2CYlIzLSBvXl3wnDqpTI2X1LNkusID2oqOJiZwAxnb6x0DZnjNoAqWQMn7iUyc4gXV9PukVH4ME+vmed+RM8tU8NxQ9s8N8CIEZ7U1Um5Gt2aq5w8oBfFVTVE3s0kKbeAn0OvMHPjDkZ88TsfHwoltaCYGUP68seL04hYtZhvZ0/gqf6eavlUH1cHNCRiLt1pf/ChI7SlL34crOgbRI2sgV868JBpiQZFHQ9qM3DUUy4RXQ2VFEBnee7w3FS8zOya+WSATWHRpOYXs3rqqDaT07uCC8kZxGXnsXikbysDpybIFQo+unEaUy093uwb1O5jvdLLHzcjC1ZdP9lmePKKxo3KL46Ft9oI3H0lAU2JhOmDuubKaKSlg6OBKdvTlL+jrvLbj+L1MX5IxCJ++CdS5ZjPEFc8PezYtj1SrUqrJXr7uf+rog2wa88VQs8n8sL8YWqdCm/n5PPmrhN42Fqybtb4dsN4m1ZmVzshC1SHu6kPqKuTMsevH8VVNfxz826nzquTSbn4II0x3Xp2WfL8RDpuqVxBWFIaybmFJOcVkpRbQE7pQ0mUpaE+HraWBHm44GFniYetFQ5mxq2Mcm7dz2dXVBwLAgdi1oWu+8+Uq8gFBQs9lLrd/ubd6GVizY7UGDb7zeKn0Cj2XE3gvYlBAPR0t0EkguSUXAYPetjpqeO4oX2eu28fB8zNDDgflkTQ8Ifj4oHuzpjo6bB02zFkCgUiEXjZ27I8xJ8gDxfcrM07vYTV19ZiYPduRN7JYsW4Tv9ZWmH9rYvsTIthe9Acehh3nBrTFu4+KEJfW4seppZM696XbXevM7/HYOz0O17y3q9JRYECRz0l52isaY6ltj2plQkEWk5p99ziumriS3JZ1udhV52SV8jv4dd4qp8Hwz26ppBRB4VCYP2ZSJzMTZgysJfa++xJjyO+JJdvfSa1Ckyol9dytzKWXsY+zcEh2hINPh88gWdCt/DtzXA+GhCs8niWRga8OnooX5+4QFhSOiN7uVJd38CRmETG9u3Rpd9BE/qY2pBdVYpEJKKP2b8bQLI2NuD5gIH8FnaV5wIGtJrcFIlEvLBgGG+/u5vjJ+OYNuXJRPUJgsCBQ9f5868LjBzRi9mzVPX5uaUVvLLlMCZ6Ovz8/OQ2Ka4m9LA2x9xAj6tp2Uwd1DVr11u377Psje2EBPfl7bfG42JpxrbIWCb28+jwNx2Zn0GNTMqYLvLb8IQ67jsPCnlt61F+Co0iNb+YvvY2LA/x59f5U7mwchHhKxfx64KpLAvxJ6RvT5wsTFQCTxeP9KFOJmNzROe77pL6GnalxfKUYx+66RuxNeNzrpecY7bbAJLLC8huKGNM7x4cuZHYvAlkoK+Do4O5yiBOWx03tM1zSyRihg/34Gp0GlXVDzdxtDQkLAvxZ2QvVz6bHsyFlS+z85VnWRg0hB42nU95aUJAz+7ceVCkNrS2I+zPiGfD7UuU1NfwcsQ+yhtqOz5JDarq6nl+0z6WbDmMXKFgWW9lEV1/u3N61uwa5Qaro97DbsnNwJvM6kS19r4tcfFBOgKtpyU3hUWjr63VfEH+tziVkMKdB0W8NsavlSd5E4rqqll3MwwfKycVf/ITuX+xK3sdUUUnW90+0MKeeT0GsfXuNWKK1E/rzR7qjZu1OV8cU25UHo9r35ekIzTRJbZ6Rp32sW8PLw4fhLmBHutOXlSRBw7o74S3lwM7d0VRV9f5adDOoqFBxrrvTvHzL6H4+/Xk7bfGqfx2ymvreHnzIeqlcn6dP7XdTM8miEQihrg4cCXtXpfktg0NMr797hSCoJQjlpXVMHtoP27n5BOX3bEu+2zOHQw1tfGxdCK2NKzTzwtPqHDbGhuy7eWZXF39Kifems+3syewMGgIge7dsehkZJSLlRnjvNzZGRVPaXXnisuWO9eolUtZ7DmUxPIrpFReJ6xgHxMdPDHQ0GJH6g2e9fWioq6eUwkPB0c83G1JSc5r9aG11XFD23puUKpLpFI5ly+3Xi7NHOLF93MmMnVQ78fi7VsisGd3ACLuZHbpvCsFWay6fhJ/a2d2BM0lt6acpZcPPZZh1M6oeMpr60jNL+ZYbBJ2+sbMcxvEwcyb3C3vWEGTXZ2ClbYDuhoPf1huht7IhAayq9tXzYTlpmKpo99clBpkMi6mZBDct8d/ku0plcvZeC6KnjYWjG1j/P/r+PNUSxv4ZODYVsUjqzqZG6WhaIt1OftgJyUNrZ0jV/Qdga2eEe9fO0G9XJVS0JRIWDVpJLllFfxx4Rq7ryTgaWeFt+PjeaB7mii1z1ri/2Zxra+txSujfLmRmaNi5yASiVgwfxglJdUcORbznzxfE0pLq1nxzm5OnU7gubn+rPloKtqP+Hs3yGQs236M7OIy1s99CjfrzkvsfFwdKKysJr0FjdoRdu6KIiu7mCWLRyKVyjl+Mo5JAzwx1NFmx+X2JbIyhYLQ3LuMsHVDSyLheklop58XntTkpIEeA7p363CJ0hEWj/ShTirtFNddKa1n693rhNi742ZkQXjBATRFWpRLi7hXe5Op3b04dS+J7jYmuFmbs/vKQy7Vw9OO0rIa8vMfGuq0pSqB9nluT087rK2NVNQl/yXcrM2xMTbgUkpmp89Jryjmlcj9OBmYsdFvGkOsHPlk4Dgi8jP4OuF8l56/qq6ezZduMMzdmd7drNh4Lop6qYzFvfzQ09Di25vh7Z6vEBRk16TgqN96ieis3xsxknZ5bplCwaUH6Qy3dW3mBa+k3qOmQcqoXm0bTXUFh28kkl1cxrJgf5WVIMC1wmwOZCbwkocvbkYPHQLlgpyjOb9hpGnGYrevEIvEHL7/S6uGQF9Ti88GjSe1oohfklS5YlCG2k7w9mBTWDR3HhTxrK/62LLOoL95NxZ6+PLF4Akd37mTmD64D86Wpnx3+lIreS2AV18HBg9yZtfuK9TU/DeOgKmp+Sx5bQt3Ux/w0arJzH8+UOVzEQSBVfvPci39PmunB3d5497XTXn/zoy/g9LPf+fuKEaN7MWM6UMYPMiZo0dj0RJLmD64D2du3W13RRxTdJ+S+hrG2LtTJSsjq4Nm5VH8n21OgjKfsbS0mozMQuLjs7lwMZkjx2LYui2CDRvP8OnaI7z19i5eWvQnKz/cR3dzU8b2dWdnVFyHXffO1BtUSOtY7OnH3apY8uoyGG/3AoYaZkQXn2a22wAaFHIOZCbwjI8Xt3MKmhNRPN1VNyhlUvWqEmif5xaJRIwY7smNmEzKKx6PhugIIpGIgJ7duZKarfLDUYeS+hpevLQHiUjMH4HPUCbN4njOn0zt7slzPQbxZ8pVDmZ0XsbU1G2/MsqXN8YGkFdWyZ6rCZhp67HQ3ZezOXfapAIAiutzqZVXNW9MNkFbooujvnu7hTum6D4V0jpG2D5UBoUmpqKvrYWP679PIamTyvgl9Ar9nGzVSu+kCjkf3TiNnZ4Rr/byb3XsStFJHtRlMcHuRax07Am2mUtaVYLKMni4rStTnPrwS+LlNsMfVowPREdTA0MdbSZ4qzo1dhZGWjq85z2KQZb/nQJJUyLhzbGBZBSWcuDaLZXj858PpKKilgOH/r2D5MVLySx9YzuCILD++7mt9o5aYsOZy5yIT2ZZsL9K8HFn4GBmQjdTo04N4sjlCtZ9dwo9PW1eXTIagGlTB1FcUsXFSynM8vVGEGCPGpuNJpzNSUFLLGGYjQvJFdcR6Nqq94kU7qLiKr7/4TSrPz7Isje3M/+F35kyfT3B475m+swfeXHhn7yxYicff3qY9RvOsHlrBOdCb3P37gMaGmQYGely5UoaFy+lsHikD7VSKVva4brrZFL+TIkm0MYFLzM7wvMPYKxpwQDTEQw2H8PdqjjMdeT4WDqyKy2WCf3c0dXSbPYbdnFRBiIkJT3kudvjuOEhz12cp+oxMWKEJ3K5gksR/87Hoz0EujtTVd9AXFb7XFq9XMaSiP08qKngt4AZ6GrWsDVzLVHFJzh8/1fe9x6Fr5UTH1w/SXyx+siqlmjqtoe7O9PXwYahbk4MdXNkU3g0VXX1LOg5BAsdfb5JaHuEO6tx8KZpY7IlXA28yKvNaNPqNSwvFU2xGH8bZVGVKxSEJaUzzL17pyVY7WH3lXjyK6pYHhygtsvdfOcad8oL+WhAcCvOuFxazLn8XfQ0HEBvI2Xq0hDzEJz0PDmZt5lKaevvyQf9x2CkpcP7104gV0NVWRkZ8P2ciXw5c2yHviT/C4zwdGGQczd+Dr1CVV3rztrTww6/oW7s3RdNZWWbmeLtQhAEtm6LYM0nh3FxtuSXjc+rDf4F2Bd9k03h0Uwf3IeFQeotCToDH1cHotPuqf08WuLI0RiSknN5dcmoZu344EEu2NubcfDQdezNjBnh6cLe6ATq1Ni/CoLAmZwU/K2dMdDUJrE8GhPNrokEnkjhLimu4mJECtnZxYgQ0b27BcOHeTB3th+vvTqaVSsnse6rZ/n9txfYt/s1zpx6m6OH3mDr5pf5cf081n09CycnC7Zuj8TF0oyxfXuy43Jcc7rLo9iXEU9xfTVLPP3IrE4kqyaJQMvJaIg1GWw2BjFioov/YbbbQO5VlxFTmsNT/Tw4GZ9MWU0dGhoSevSweaTjbpvjhvZ5bjdXa+ztzVSsXv9L+Lo6oCEWt8tzC4LAymsnuF50j298JuFuYsTWjLWIEeNrPo64sgtEl5zgR79pWOkasCRyPwW17W94NnXbS0Y9jIRbHuJPaXUtmy/dQF9Ti9d6BRBdmM2FB+pHuLOrk9GVGGChrapddTPwRkAgrY0pyrDcVAZZOGKoqZT7xWfnUVxVw6je/54mySoq47ewq/j1cGo1Ft2E3JoKNty+yEg7N0bbtea+T+X+jUKQM9HupeaCLxaJmWr/ClJFPcdz/2h1fzNtPT4aEEx8SS5b7qofuglo1PP//wiRSMSKccMorqrhz4uqnfWC5wOprq5n34GuD2bV1jbwyWdH2Lw1guAxffhu3ew2szcvpWTw6ZFQAnp258PJbWu1OwMfV0cq6upJym07Au9Bfjl//HWBIYNdGD1KWQMEQUAsFjF18kCSknNJSspljl9/ymrq1E45J5cVcL+6nGB7d+rltaRVxdPLuG3HSXV4IoW7Z08bDu1fxt9/LuSH7+aw5qOpvLEshAXzhzFtyiBGjujFgAHdcXWxwtzcAI1HzO3FYhHz5viRlVXExUvJLbpu1Q0PqULOpuSo5vTv8IID6EuMGGg2GkGej6GGHp7GQ7hRGsoI2+5Y6OizM+2GSsiCp4ctd+4+aJ78kjXImi1d1aE9nlskEjEyyJO4+CyKi59MaoaBjjb9ney41E7h3pgYweGsW7zZdzhj7XuwM+tryqVFzOn+HhPtXqKPsR+n87ZR3JDCpoCZVErreSXygNpNM1DttgsKKigvr6GPvQ0hfXuwOSKGospqnnHpj6O+CesSwtTqxbNrUnDUU8qlBEGOULOXJqfgbnpu6Ij11BbunOpy7lYUtjKVCk1MQ1Miad6wfVyUVNWwePMhxCIRqyaNVHuftbFnUQgCH/UPaVUg7lbGcbP8MsOtpmOu3bortNTpxgjrGdwqjyKxvPXU4USHXoy0c+O7mxfIrnq8GLP/Jfo62DDOy52tETEqBm6urtYEDffgwMHrlJV13sApv6CcZW/u4FJECosXjeDdtyeg1UYDlZiTzxs7T9DD2oLvZk9Qq/5pD5H5GRzKfOhf1MSLtzVFKQhCc9bm8mXK70BZYTkv9lrOiU1nCQnug76eNgcPX2eIiz09bSzYfjlWZeV5JicFETDSrgd3K2ORCVJ6Gf1/ULj/Cwwf5oGTo3lj121OcJ8e7IiKU/FKOJJ1i9yaCpZ4+pNbm87dylj8LZ9CkzqEogkIFZ/gYz6OWnkVd6quMtOlH2G5qRgYabYKWfBwt6O+XkZmptKhTiaVo9FOx93EcydcUC3cAEFBnggCXGjHNa2zEASBpKRcvv3+FNNmbCAiUmkyFeDenZS8QgrURCodzbrFD7cuMq27F0s8/Dh0/2cyqxOZZv8ajroOII1nusNrWOs4sDvrW8x1ZXwzZBKxxTl8dOOUWpqjZbddXy/l1de38t4H+xAEgaVj/GmQydgUFo2WRMIbfYNIKivgeHbrv0+NrJLC+vs4NW1MNkQgVKyCWqX5kEQkwdmgD6mV8SqvISxXqdRpSrsRBIHQ26n4ujpg8C8GbuqkMl7bepT88kp+em4yThaq3t7heamcvp/Mq70CWnl/SxUNHMv5HXMt2zb154GWU7DR6c7RnE3Uyh96WYhEIuWYvEjEquvq/+b/PyCzsoQ/U67yw60LKq9xeYg/coXAj2dVh6+enxdAfb2U3XuvdOp5bt2+zyuvbiEvt4y1nz7NzBk+bXbQuWUVLNlyGGNdHX6ZP6XLQogLeWm8eHE3K6+doFamlC5aGurjamXWpm/JudDbXLuewUsvDMfGWjmr8OubW7iXksv2T/ejpSlh7Ni+hF9Ipri4irl+/bnzoIhrjwQ1nM1JYaCFAxY6+iRWRKMnMcRRv2v7GJ0JC3YXiURxLf6rEIlEy7v0LI8BiUTM3Dn+ZGYWcfFSCktG+lJd38DWFl23XKHgt6QoPE2sCLJ15ULhAbTFeviYj4Wa7SBUQO0xnHXtsdDuxtXif5jl0h+RSMTutNhWIQuensple5OeW9lxt8+Zeg/vzb2UXLU8d3cnC1ycLQkPf/zCXVZWw7790by46E9eXbqV0POJ1NVJOReqLIZtyQKvF97j3ejj+Fg6snbQeMIL9xNXdoFR1s/iZeKDULoQoWQmmvX/MLf7+4hFGmzP/IIgO0de7x3A/owEtjwSTfZot33sRBzFJVWkpORxKSKF7pamTBvUhz3RCdwrKWOiYy96mVjz/c0LrWxfsx/ht4WG2Mb/P/zhuxl4UyYtpLihNX8fnpeGo4EpzoZKg/zU/GLulZQzsnfnfMTVQa5Q8O6eUyTcz+OrZ8bRz0mVvqmTSfn4xj+4GJrzonvrzuhS4WGKG/J4qttCNMXqi4dEpMFU+1eokpVzOm9Lq2O2eka84z2SyPwMDnRhg/hJQq5QEFN0n6/jzxNy6jdGnfyFz+PO8ePtCBWXQ3szY+b49eNwzG0VIzUnJwtGjezF4SMxHa48T/+TwFtv70JPT5uNG+bhM6Ttz7SkqoYlmw9T1yDj1/lTsOqEVrslrhfe45XI/Rhq6tCgkLfaSPd1dSQmM0clcrCsrIaffgnF08OOyZOUZmPRp2IJ3XEJr+G9KMopIfJQNFMmD0ShUHDseCwT+nlgoqfD9siH0sB7VWUklRUQbO+OTCElpeI6HkaDkYi6tlroTHRZiiAI/QRB6AcMBGqAQ+2dkxafyTPdFjHP5RVe8FzGy/1W8JrPe7wx7EPeHv0xKyd8zpppX7N21vd8PX8j3y/6lY2v/8mvb23hz5U72fbxPi4dvErQcA8cHczZtj0SVytl1739cmxz130mJ4X0ymKWePpTWJ9DYvlVfC3GoS0CoXoLaLgD9YjqDuFjHsK9mjsgLmakrRt70+MJ8nTGVF+X3VcSsLUxxthYt5nnlkk7Ltzt8dygtLy8dfs++QWquX1tQS5XcDU6jTWfHGLmrI388tt5dHW0ePONsezb/RojR/Tixo1MZDI5PW0ssDZqLQvMqiplSeR+7PSM+cl/OkkVlwnN300/k+EEWT6NUP4BSK+DxBmh/ANMRPnMdnqb4voH7M3+gdd6BTCmW08+jztLZP7DsfpHu+3de67g7eWAk5MFf/59EblcwSujfJGIRGw8G4VYJGKF1wiyq8vYnf7wYptdk4IYSFfOFQAAIABJREFUMd30GukOaeOx+iiERoMpt6bx98qH6pI6mZSogkxG2Lo1d2HnbqciEsEIz8cv3F+fuMi526m8O2F4m+k/vyVHkV1dxpqBIa3sh4vrH3Ch4AB9jf3oYajen6MJ9npuBFhO4nrJOdKrWtsLz3IdwGBLB9bGnWsVNfZ/iVqZlLM5KbwXfZyhRzcwI3QLf6ZcxUrXgA/7B3N+wit4mljxTcJ5FSpt0YghGOpos+7UJZXHfW5eADKZnJ27o9Q+r1yu4JdfQ/l6ndKW9acfn8PJqe0Q5uS8Qp75aRf3istYP++pLgc23y59wIuX9mCrZ8SB0fPREIm5XJDZfNzXzZE6qYz47Nbe2j/9co6amnpWvDUOiURMTWUt65dswtGzG5+fXImdqzUHN5ykm50pvj5uHDsRh1iAGUP6EpaUzv0SZQ1oCh4Z060nmdW3qVPU0Muo6wHWXaVKRgFpgiBktXcnQ1MDfMYPoE+gJy7eTtg4W2FkYYSGlgbSeinlhRXkpuWTFpfJzYuJXDkRw/ldEZzYdJZ9646y9eO9fPL0OlJjM5g314+MzEL+OZPAklHKrvuXUOWX4HDWLQw1tQm2dyeh7BICAgNNR0FDBAhliAxeVxaoujP0MVYG098uv8J4h14U11dzs/wBIz1dCUtKQ6ZQ4OFuR3xCNoIgoJAraKhvf4JPWq9cYiVfVe9NYNy44xwd3fncyb83X+L9D/Zx8VIKzs6W/P7rAn768Tkmju+Hvr42/fs5Ul1TT3p6ISKRiMEu9tzIfKgGeefqMRSCwB/DnkFXAw7e/4nu+r2Yav8KooYLUHcEkcFSROa7QGyJUL4KZ4PeTLB7gZTK61wuOsI6n0m4GFqw7PIhiuqUS/tDN27j5WBDXwcboq+lU1JSzdQpg3h62mDu3SshPj4bKyMDJvbz5ER8MtX1DQyzcaGHkSW7WsR35dVmYK5th5a4kdqQNnZwQinIMwEw17JFV2KgvNA24lbpA+rkMrxaGDldy7iPg5kJFl0caKppkHLo+m3m/LKb7Zdjmeffn3n+6i1b6+Uyfk26TH/zbvhbt5YHRhQeQSZICbJ6ulPPO9xyGhoiTc4+2NnqdrFIxLLew6iQ1vFHiqr73v8F5oXvYHHEfvZlxGOvb8z3vlOInrKcbUFzmN9zME4GpiztPYz71eWcz239fTfW1eH5gIFcvptFekHrAZZudqaMHNGL4yfi1Pp1Hz5yg30HrjF1ykC+/HwmRkZtD1DVNkhZ8Ps+ZAo5W1+e+Vjulm9dOUqVtJ6P+gfjaGCKm5EFsS3UVH3slQNL8fcervaysooIPZ/IqJG9ce6uVH+c+iOUguwinls9E21dbUbMCiDxcgqF94sZM7o3ZWU1xMVnM7GfJwpB4OytuygaDe/cja1wNDAltvQCmiJt3Ay9Eeqe7OTks8AudQdEItEikUh0XSQSXRe0FLz5+2Le3fI6q3a/yceH3uHzEyv55txqfrj0GT9Ff8mm+G/5K2k929J/Zk/OJg4W/c2xyu2cbtjNgaK/MLcz5Zv5G/HzccXby4GNP4eiL5cwy9eb7ZfjiLyTxQhbt+bBmx6G/REhJqxgL2j2A5ExQtlrIM9ApO3PleJTALjoe7Et9Tpm2npYaRjyz807DHFxQFMiYUSQB7m5ZVy/kcnAYG+ijlynslR9B3Tyj1DeGfUxNs5WjF80utUxhUJg85ZLfPf9adzdbfH3az/HsSVCQvoyNqQv+nra3L2bzzvv72XjT2dJSspFEATS0guRSMTY2ip51jsPinCxVFIHpfU13Ci6x/yeg3E2NKNGXolMaGCg6cjGRKJGflIrEJHYDDR7NN/mYz6W3ka+hObvpUFRykb/aVTJ6vkiTjnR5ePqwO2cfJJyC/Bwt0VXV4tjx2M5ejwGExM9XF2tKKio4uytu/RztENXU5OogizuVhQS0KLg2em6UFSfS7m0MQFds9EbQmwJEqWSI6smSanzbsH7eZpYY6NryO/JV5onPYe5O5NdXNZuAEFLJObk88nhUEZ8volVB85QXlvHexOH83Y7ToLaEg2mdfcitjhHJdC1n+kwNERaHLr/C/Xy9jX7MoWUfffWIxOkDDFv7VNSUFvJquunMNDU5ilH9b4oTxrL+gwj0MYFsUhEfEkuW+9e41jW7VYe4jdL8xABHiaqTny37j/ARE8HGxNDlWP37pdg381M7TBTTa2yOXp54Qi1aewtkVlUSkVtPe9NDHrs4OfXewdgoqXLooi9LIs6RHJ5QasgjtDbyu9S/xaUmZ2dKV59HTgflkhcvLJn9X1qIEbmhvy1ahcF94oI3XEJp172mFgZceJkPPr62vRws+bAtVuIRDDE1YEDGfHcLstnoYcv92ruEFd2AV+LcWiKtRHqTnTpfXS6cItEIi1gErBP3XFBEDYJgjBIEIRBDeWyf7XRYmRmyFt/LCEr8T7bP97H++8+hUQi4vMvj7E82B9XKzNW7j/NGKueBHdzZ11CGNUNJgRZPU1saTg3K+8iMv6s+fEKJeOJKDxCf9MRXCuQElucw3veo/jt7FUa5HI+nKxUEQQN98TUVJ8DB68xdel46mrqOfVH61FUaYOU9Us28f2iX/EK6s1P177EyfPhB19VXceHqw+wdXskIcF9Wf/dnDalTOrgYG/GOysmcGDf66z5aCp9+9hz7EQcry7dytznf2P3niv083bE0FCH7OIy7jwoas6KvFKQhQDNnaFCUHLLTUZHaDR2KPLG4GT5/eZiKRKJmNDtRSQiCUdzNuFqaM4ij6EczrpJZH4Gb4wNwERPlzWHzmFmbsAL8wO5EZPJ3bv5LF8ajJGRLmsOnqNBLmft08HUyqW8f+043Q3MWjnnDTAdiYCCuNJw5fPqTFIe0JmA8isGEYVH0ZMY0t/04Xn6mlp8NCCY5PICNjd6f8/164+7rSWfHw2juo3VUVVdPXuuxjPjxx3M2LiTwzduM7KXK1sXzeDYG88zz39Ah1apHw8cS3A3dz6NPcPRrIcDJ076njzr+Ca5tWnszPoGmUK9P4dckLEn+ztSKm8wudvLzYk/oPQ8mRe+k4K6Sv4a9ix92rCGfdIItHFh8/BZRDz1Ou95j6JGJmV1zD8MPbqeJRH7OZR5k79Togm2d8f5kbSW9IISwpLSmT20n0pwRVpaPikpeYwfp37607zxt1FS2nEAQUahci+pqVF5HExw7MWZcS8z0bE3x7OVFOc8N2XUmUIhsC0yhj721gxoUbg1NSV8+vF07GxN+HD1QTIyC+nmZsunR9+l6H4xc5yW8CCjgFc3vEDU1XRuxGSyYH4gxQ217IiKZcbgvthbGvF1QhiDLByY5NiLYzl/YKhhygirGconkXbNIqArHfc4IEYQhPyO7lhbVceZLeFdeiGPYvDY/kxYOJp93x6jMDWPN5ePIyk5l717rvLNs+Mpr6nno0PnWDtoHKbauiyPOsxQ8yk46rlz5P6vlIn7I7K6AVaxHMn9Gy2xDkNMn+brhPP4WDpiIzXiZHwKC4cPbvY+1tLSYPJT/Ym+lo6WmRHeQb058tNp5I0SweK8UlaM/Jjjv51l5tuTWXvifYzMHnYY2dnFvPr6VqKvpbP0tTG8s2J8m1KmjqClpcGwQHfWfDSVA3tf5+23xmNrY4ypqX6zI9r5xk6zqXBH5mdgoKHV7O0rF5SFRCJqfA2NRRq5kg5CngOShxN1xprmjLGZzd2qOG6WR/KKpz9OBqZ8eP0UOloavDthOLfu57P7SgJTJg9k/nMBLF40gmGBHhyNTeJCSgbLgv1xsjDlq/hQcqrL+WrIRHQ1Hv6YzbVt6K7fixsl55WvQXcqYps7iAzfB6CoPpfkimsMMQ95SKc0IribO6PserD+9kVyq8vRkIhZM2UUBZVVrVQNgiAQl5XLqv1nCPp8E58cPo9MoWDlUyMIW7mIL2aOZaCzfac1vxpiMT8MnYKPpSNvXz3GhbyHHb6n8RCm2C8htSqOA/d+VAmCUAhy9mdvILHiKhPsXmCIeUjzsbL6Wp4P38n96jL+CHxGJYLtfwFrXUMWevhycuxCjge/xHM9BhNbfJ8VV49SK5eyyEPVjW/zpRvoaGowS40R1snTCWhqShg9Wr0lrZmZ0ruopBOy2YzCEkQicDRXVf10BeY6+qzzmcTx4Je4O3MlVrrK3/CFlHSyist4PmCgynfD0FCHLz6fiY62Ju+v3EdhUSW9hrqzcqdSpxH0jB+efh78/GsoLs6WTJrYn7VHwzHQ1mZZsD8/3LpIWUMtqwcEE1sWTk5tKmNtn0Nboosgf6BsorqArhTuWbRBkzwKXX1tfn9nGxXFXXeva4lF657D2smCr+dvxGdQd8aG9GXnrijqi+p4c2wA4UnpnIlL5RufSaRVFvNlfBgzHJcjILAvez0KkR5xZdFkVicSYjOPH29fp0bWwCrvMXx2NAxHcxNefCQV46mJ/dHUlHDo8A2mLh1PQXYRkYejSbxyh1cHv0t6XCYf7FrOwq/mImmhG428fIdXXt9CVWUd675+limTVT/8x4WBgQ7jxnqx7utZ7Nv9Gv37KePGwpLScLe1bDa1j8zPxNfKCY3GDlLe2HE3FW6RSAfE1giybFAUg1CLSNK6WPiYj6Wbrisncv9CoJ5PB44jq6qUnxMjGe/tjl8PJ9afiaSoqobn5gUwc4YPBRVVfHksnP5Odszx68fl/Ex2pMWwoOcQtaPWA0xHUNyQR3ZNcvPfqOn/l4uOIxZJ8DVX9awViUSsHhCCIMDHsWcA8HK0ZeYQL3ZcjuNKajbbImOYsn4bc37dw+mbdxjfz4Ndr8zi4NK5zPHrh7GujsrjNkEQBD7Y/4/a7EBtiQa/Bcygp7Elr0YeILboIS860GwUITbzSCiP4ETun82rTYWg4OD9n0kojyDEZh5+FhObz6lsqOP5CztJryzmt4AZ+FipRshV1zdQWfff+H08DjxNrVnZbzQRTy3lr2HPEjdthUrYcEFFFUdjk5gysLeK7WxDg4xzobcJ8O+JcRvcddNqtLikc4Xb3tRYrTf648DT1LqVD/aWiBhsjA0Z00f9MJeNtTFfrJ1BZVUd73+wl+rqevynDOGMfC8f7HqDXbujKCio4PXXxnAuMZXo9HssC/HjgbSC7ak3mOM6AGcjQ87kbcNRzx1vk0aKrqHrhlydKtwikUgPGAMc7Mz9rZwsqSyt5o/3dnT5BbWEnqEuK/56ldzUB/z53g5ee2U0NjbGfP7lMaZ4e+LXw4mvT1zAGiNecvdlZ1oMNwrLmNRtEVk1SfyTt5VTeVtw1HNHJnPjYGYCL7n7Eh6bQXZxGR9OHqnyJTA11WfUyF78c/YmvYN6Y+NsxW8rtrIiaDWa2pqsv7y2VfJNE5/94eqDODiY88tP8/H2+veRWW2hiScsqaohJjOXUY3d9r2qMrKrSvFrwSfLH6VKACSOSqpE3qhVfaRwi0USptgvoVpWwT8PtuNv48wUpz78lnyZtMpiPpw8EplczhfHlZspgiCoUCTvRatSJC3Rx9gPLbEOMY94eNTIKokpOU8/k+EYaqoPxO2mb8zSPoGcy7nTvEO/LMQfU31dXvzzAF8ev4COpgZrpo7mwspFfDJtDF4ONp26iGYVlXH4RiKfHwvjx7OXVeg+Qy0d/h7+LJa6Brx0aU8rF8RAyykEWEziSvEpwgqU2vajOZuILQ1jlPWzDLOa2nzfKmk9Cy7uJqW8gJ/8pxNgozodmVlYypQftjH2m78IS+och/+koCEWM9zWtXlatSW2X45FrlCozeO8FHGHyso6xo9r25K2mSop6RxV4vwvaJL2kJiTz7X0+8z169fuII+bmzVrPppKVlYxaz45hFQqRyQSkZNbyu69Vxk1shdu7jZ8c/IinnZWTB/UhzUx/2CipcvyPsMJy99Ljbyy1YStIL0Boq65WnaqcAuCUCMIgrkgCJ3StWnrajF9+QRO/RnKrch/N4DiHdSbqUvHc+Sn06RcucMH70+iuLiK9RvOsHZ6MHraWryz5xSv9wqgt4k170efwE5nAN4mgUQWHaNGXsF424WsiTmDvb4xT1n3ZlP4tebuUR2mTx1EXZ2U02duMfV1ZdfdxGe7eD08Rx2fbWX1eNFHXcWF5AwUgtBMk1xulO75WXdvvo9CUMq2mqkSUPLc8nsPl2Yaqh2xna4LfhYTuFZyhqzqZN7vNxo9DS1WXT+JvZkxS0b5cvZWKuFJ6RyJSXyEIjlPbo0qRdIS2hJd+hgP5WZZJA2KhwNVV4v/QSo04G/5VLvvfUHPIbgbW/FxzBmqpQ0Y6+rw9bPjWBA4kANL57Ln1dnMGNK3y0MZl1OVG09Bni78ev4qX51QHTix0DFgy/BZaIrFzL+wi9xq5U9CJBIRYvsc/U1HEJq/m9W3nuFayRmGW057yGOilN0turSXhJJc1g+dykg71Y3rm/ceMPe3PdRJpVgbG/La1qOsPRpGvRrfi/8lqurq2Xv1JsF9eqilL06eisfGxrh5hagOJiZ6iMWiDjtuhUIgs6iU7p1MuO8qtkbEoKelyfTBHacMDR7kzIo3x3EjJpN1351EEAR+/iUUDQ0JLy8cwe/h0Twor+KDSSM4eT+Ja4X3WNE3iAahiKiikwwyG003vRYS1oYY0Oya3/oTm5yct3oGlg7mbHjld2T/8gv3wuezse9py7oXfsbRzoT5zwUQFp5E7NUMPpseTEpeIT+fvcL3Q6dQK5fy9tVjTLRbSKDlFKbbv87xrDxSK4pYMyCEb45fQltDwjsThrf5fK6u1vTzduTw4RtMfCWYr85+9ET57MdBaGIqdiZGeNgq5UmR+ZlY6Rg8YjPaVLgfdhAiiQMoChBkjZIuifo4q1HWz2KsacGRnF8x1dbmPe9RXCu8x4GMeJ4PGIibtTnLth9j9cFzDOhux1y//kTmZ7AzLYYXevp06EY3wHQk9Ypabpcrp+pkCilXik/Sw7A/1jrtr1g0xRI+HTSOvJoKNtxWaod9XR1ZMX5Y89/jcXAlNRt7UyN+nDuJuX792BYZy+pD51RMhxwNTNk8fBbVsgbmX9hFSaPyQulNsoQehv2RCzL8LZ5ijM2c5s6qXi5jccQ+oguzWeczmRB71Wm5y3ezWPDHfvS0NNm++Bl2v/Is8/z7szMqjmd/3kVqfvFjv7//Gvuv3aKyrp4FwwaqHMvJLSU2LotxY73UqkmaIJGIMTHR65DjflBeSZ1UhrPVf1+488urOJVwh+mD+2DUDpXWEiHBfVkwP5Cz527z8pK/ibqSyry5/lQJMv6+dINJ/T3pYWfOF3GheJnZ8rSzN8dz/0RLosMYm9nNjyMoqkCWBJqqf8P28EQKd21tA7oGury6/gUybmZzaP3Jjk9qBzp62ry9+TWK7hfz65tbePYZX7y9HNiw8Sw9jE151tebLRExPHhQzar+Y4jIz2BX2m3G2j6HpVZ/fkyMIMTenZoCOVGp2SwL8ceyg0CH6dMGUVBYQdSVNAaM6vt/xmd3BjUNUi7fzWJkL1dEIhEKQSCqIBM/a+dWr+NRjlv5j8ai2HAFxJaI2liiaUt0earbQvLrsokoPMrTzt4MtnTgy/jzVMrqWD1lFDKFAplCwWfTQ6iRN/B+9AmcDc14s2/bF8UmdNfvhZmWNTElSi/w+LKLVMnKCLBov9tuwkALe55x6cffd66SVNrhfnmHkMkVRKffx9fNEbFYxHsTg1g0YggHrt3i3T2nVexzPUys+T1wJvdrynnp4p7mHEmJSIN53VfySd99jLOd3/x5NMjlvBp5gIj8DL4cMpFJTqoRWSfjU1iy5TAOZsbsWPwMThamaGlo8N7EIH55forSB+anneyNTvifj8c3yORsjYjBx9VBrTTv9OkExGIRY4P7dvhYZmYGFHdAlTQpSpwt/nuqZGdUHApBYK5f18KU5872Y/w4b1LTCtDT02LalIF8cTwcbQ0N3hwXyMbECArqqlg9IITkymjSqhIYbT0LfY0W0X7SeECBSEv9HEFbeCKF+0F+OfX1UvwmD8Z34kC2fryXguyOU1HaQy/fnsx8ezKn/zrP9X/iHkoEvzjGGy0kgsFW7ozp1pN1CWEklj5gTcxpNERi3vAcztcnL9C7mzXPtJHY3RK+Pm7Y2ppw4ODD0e/H5bNlcgXH45L5++J1UvIK//WP7vLdLOpl8maaJKWsgJL6Gvxb0CTwsOMWtyrcjZ2wNE6F334UnkaD6W3ky/n8vZQ15PPpwHFUy+r5Ij6UAd27EfreS5x950WcLEz4Mj60kSJ5Cp02KJKWEIlE9DcdQXr1LUoa8oksOoaNjhOuBp1fMr7jNRJjLV0+vHHqX4cfJ+bkU1lXz9DG4FiRSMSyYH/eGBvAqYQUlu84rkJVDLZ0ZMPQqdwqzeOVyP3No/0SkQSJSNJctKUKOcuiDhGWl8qnA8fxtLPqe9xxOZZ39pykn6MtWxbNUIncGubhzMGlc+nvZMfHh0JZvuO4im/P/yVOxieTX1HFgkDVbEm5XMHpMzcZMtgFS8uOqUNzM4MOqZKmgG9ny/+2466ub2Dv1QRG93ZrlVzfGYhEIt5YFsKWvxex9e9FRKRmEXEnk1dHD6VCqOXvlGhmOHvT29SCk7l/Y63j2EpVBDTKAMWg2bWLxhMp3NIGObt2X0EkEvHqhhcQFAI/L//7Xz/uvDUz6d7Hge8W/oqOhog3l49tlgh+3SgRXH3oHGsHjcdES5enzvxJeF4ay/sMY8+lm5RU1bJ66qgOdbugXMJNnTKQW7fvk5yS91h8tkIhcCIumSnrt/LunlOsO3WJaRu2M+rLP/jowFnO3rqr4mXcGYTeTsVIV5uB3ZU0R6Qafhse6rhb+SBotLjQdFC4gVbabjcjCxZ6DOVQ5k0u52diY2yInakRkQ8y2JUWywvuPl2StA0wHYEIEfuz15Nfl42/xaQurVxMtHVZ2W80scU57E5rPyqqI0SlZSMSKa09W+Kl4YNZNWkk4UnpvLLlsIpefHS3nnw+aAIR+RmsuHpU5QIiVyhYcfUoZ3JSWNV/DLPdWndWgiCw4cxlPj8WzkhPV35bMK3N5bqlkQGbFkzjrXGBhCelM33Ddm5kdE1G9l9AEAT+vnSDnjYWBPRU5a+jr6VTXFzV7qZkS5ib6VPSQeFOLyzBSEf7X8f+PYojMYlU1NWr3VztDCQSMQ72ZugZ6vDV8XBcrcyY5evFxzFn0NXQZIXXCC4VHqFMWshEu5dUPEmEhhjQ6IlI3DW/lSdSuA2NdNm15wr37pdg092KuR/NIPLwNaKO/btEDC1tTd7d8jrlhRX8tPQvgoZ7EhKslAg2FNXxxtgAwpLSORefyjof5WCHgaY2A3Qd2H01nlm+3vTuZt3p5xsX4oWenhZ//nWhS3y2QiFw5tZdpm7Yxjt7TqEhFrN+7lOcf28hn04fg7ejLWdu3WX5juP4f/or8zft448L17jzoKjDblwmVxCenE6QhwsajZNmkfkZuBlZYKPX+kIiV1e4RSYgavzyd6JwG2uaM7qFtvtVT38cG7Xd9XIZldJ63rt2HBdDc97s0zFF0hImWpa4GPQlqyYZAw0TvEwCunQ+wBSnPvhaOfFNQhhFdY/v8xF1NxtPWytM1WRWzhrqzeczQohOv8+ivw5SUdu6033axZt3vEZy4l4in8ScaSEHFHjv2gmOZyfyjtdIFvRs7UkhVyj4+HAov4VdZfqgPnw3eyI6HUjdxGIRLwwbxPbFz6ApETP/9/38dC4KmZpx8ieFSymZpOYX88KwQWovtCdPxWNqqo+vT+c8ZMzMDSgrq1E7Et+EzKJSulua/qeUpFyhYFtkLN4OtmrNxbqCvy5e535pBSsnjSDsQapyaK3PcCTiai4UHKSvsR8uBq03PgVBplz5dpEmgSdUuC0tDdHS0mD9hn8QBIHpb0zAqZc9Py39i9rqf7e8c+vvzJxV0zm/M4JLB67w+qsPJYJTmyWCF7ERG5H09Htcm7yctUfCsDDQ5/Vgvy49l76+NmNDvLgRk9kpPlsQBM4npjFj4w7e2HEchUJg3azxHFw6j9G93bA2NmDaoD58P2cil1a9zJZFM5gfOJCKunq+Px3B1PXbGP3VH3x08Cznbqeq7cZvZOZQUVvfHBxQL5dxrfCeSrcNLQdwHlIXyteu/PejGu624Gs+FrsmbbeogU8HjiWzqoRfkiL5Kj6UvJoKvhoysVMUyaPwNgkEwMskoHEsv2sQiUR8OnAcdXIpa2PPdfl8UO4ZxGbn4uvWNu01eUAvvps9gVs5+Sz4fT8lVa09pl/2HMpL7r5sS73OxsQIBEHgoxunOJiZwPI+w3jZs/XgSr1Uxps7T7Av+iaLgobw8bTRzRfizqCvgw0Hls5lYj8Pfg69woI/9pFbpj416L/GXxevY2NsyFgv1SDl4uIqoq6kEjKmj4rPflswNzNAoRAoL2/btzujsOQ/lwJeSFbKgp8PfLxuuwk5peX8ER7N2L498Xay5bPYs7gbWzHHbSCn87YgAsbaPq96ouwOCNWINFXppo7wRAq3hkTMSy8MJyY2i/NhiWhqabL054XkZxWy87MD//rxZ70/lR4DXVi/ZBP1VXUqEkFdLU3e3n0KBIF9V2+SmFvAuxOHY/gYns2zZw1lxvTB7fLZgiBwKSWTZ3/exevbjlJd38AXM0I4vHwe47zc1e6qa0okDHK2542xARxcOpfz7y3kk2lj6Otgwz8Jd1i2/ZiyG/99H39dvM7dxm78fGIq2hqSZiljXHEOtXKpigESqBl5b0bj65G0r/xoglgkYUq3xVTLKjiTt50AGxcmN2YmNlEkAx5z6m+A6UjWeh1knO38xzofwMXInMWefhzNvk1YbmqXz7+RkYNMrmjmt9vCmD49+Om5yWQWlfL8pn0q4QHveo9kWncvfrh1kV77v2JXWixLPP14rVfrlURlXT0v/32Ic7dTeX9iEMtC/B+rk9Q3REFyAAAgAElEQVTX1uKLmWP5cuZYknMLmb5+O2duqTc8+6+QcO8B1zLu81zAALV65zNnb6FQCIzrJE0CD6cn2+K5q+sbKKio/k/57bKaOr48Ho69qdG/CpkWBIEvj19QumGOH8avSZfJralgzYAQsmpuc6s8iuFW0zHRUqN2amhkIP5/6bgBJk7oh7u7LT//ep6qqjq8hvUieH4Q+749RubtziUptwUNTQ3e2fwaNRW1rF/8Gx7uts0SwbjohxLBNYdCWX/mMn49nBjbV7U76AzMTPVZsnhUm3z2lbRs5v22l8WbD1FSVcsn08Zw7M3nmTSgV6e49CZYGxswfXAffpjzFBEfLmbzwhk8HziA8po6vj11iSnrtzH6qz85GpOEXw+nZk+IyPwMJCIRPv+PurMMjOL+vv5nduPuCTFCiCIhuLu7a6E4RdoChQqUtpS2VKC0WClO0eIuIbgESSAQCIEIcSfusjvPiyUJIZuNwa//57wh7M7OzMrcud97zz3HvGLQKS2V8NYF9loTBGn1l4c2Og3pYDaQ+6leROW8YKlnL3TVNRQskhqWSMqdyhs2X3XBbPcOOBuY8c2D82QV1axvcDcsCg01KS0clFMj30QnFwc2Tx1OYmY2H24+SHRqeulzEkFgZesBdLFypFAuY5pLGxY17VYuKCdn5TB5y2H8I+P4dWx/JnasWVNKGQY3d+fopxOxNzNi4b4zLD9+ibxC5bopdcXOG34YaGkySgnfWRRFzp1/jEdTO+xsq58dl05PpihnlpQ1Jt9Nxi2Ty/ni33MkZeawatyAGq103kSxTM63x7y58iyMub3aU6RWzJbndxhi35iW5tacid2GsYYlncyHKn29WPQQJFYINbgOS/BeAnd6bj5SqYSF8/uSkZHL9h03AJj560R0DLRZN3drnZkVDo3tmPLDOG6f8OXyvpuMG9sOj6Z2rF1fRhE8+fAZRTIZy4bUzYtOGR5ExDJl62GmbztKXFom3w7rwdlFUxjZukmNLZTehrpUSmtHWz7r15nj8yeVZuNNbC1RV5MyolXZReOTGIGHiTX6GhUbWjJlAziAYHYRweJhtWrcb6KE230idhPGmpqc7D2dQz0n16pE8q6hKVXj59aDSMjNZHVAzSQy74RG0by+dZX15RK0amDL9ukjycov5MPNhwhLKuNWq0ukbO08lhejl7DUs1e5311USjqT/j5IVEo6f00exiDP2ru3vw17UyP2fDSW6V1bcfj+E8Zs3F/B2KCuiHyVjndgCOPaNVM63BQQEE1sXFq1m5IlMC3RK6kk4y6lAr6jjHvjpbvcDolk6ZBueNjXTtSrsLiYRQfOcswvkLk92zG1c0t+8vdGXSLlK8+e3EvxIqkgmgH1pig12BBFEQofgEbN+NsleD90wPQsXmXl4OJsxbAhLTh15iHPX8RjZG7IjF8m8uRmUJ1FqABGfjaIRh1c2fjpDtIS0ljy1aByFMFOLg58MaCrUiuq2iIgKp6ZO47x4eZDvExKZcmgbpxfPJWxbZuhUc2aXk1Rko2vnTiYG19/VEoDzCrMJyA1Tml9G0COEh43IEh0ECR6CDXMcjWl2gy2nkFifhS3k09hp2eEiea77fLXBc3NbJji0oa9oQ+4nxRVrde8ysrhRXxylWWSt9HUzop/Zo1GJheZvOVwOYNZNYkENYmkXNAOikti4t8HycovYMeMUXRUwsaoKzTUpHzWrzPbpo8gMy+fcX8dYJ/Po3fG+f7n1gPUpVImdFBuGnH2vELOtEtn1xrttyq9kvDkNKQSATuTul/HV56FlTaDR7eummOuDDkFhczZdYJLgaF8Nagr83q151p8GJfiQvi4cSd01WVcTjiAk54n7pWZJMjjQJ6IoF67+vp7GfWTiyKrz93gl7H9mTqlC9dvvOCPtRf4a/1k+k3rjteuq2z9Yg/tB7fCwLSifm91IZVK+XznPGZ7LmbNzL/56exSPlvQjxU/nuTQwXtsnjq86p1UE89iE9ngfYfrL8Ix1tVmcf/OjGvXDG2N/y7bvJcchUwUlda3AWTyEh73u7uhuBu2oZFBW64kHqKJYQdMNGuni/y+8FnTrlyKDWaJ71nO9p1R5WqgxBi2NqL8zlZm7PloDNO3H2Xq1iNsmjKsnI7zm8f4ZM8pDLQ12TptdLWW/HK5yIlTD5DLRUYOV87eqAztnepz/NNJfH3kIitPX+V2SATd3B1Rk0iQCAJqEglSqQTp6xuMVCIgLffv249LKCgq5viDQIY0d1c6vJadnc+Nmy/o17cpWlqVf+bhT6PQN9HDzLrsM9DQUENfX4tUFaUSW2PDOidG4cmpfHXoAo1tLPl6SPdarcLTc/KY888JAmMTWTm6L0NbNKJAVswP/hdx1DdlinMbzsZvplBewCDraZUfo0RYqpYZ93sJ3Gb6Opx+9JzhrZrQtqEd8+b2ZMWPJzl5+iEjhrVi/l8zmdPyC7Z9tY/Pts6u07Fsnesx49eJbPx0B+e3XWbAzF7cu/+S/Qfu0KplAzyaVq8BVxleZeXww8krXHrNnZ7fpyMfdPCssQ7G+8DtxHC0peoVFNtKIEN5qaSuGGQ9nT+DP+VU3FYmOyz7n06NVgUdNQ1+aj2AD6/tZ23gTb5sptyxvQR3w6Iw0NakkU1Fc4DqoL6ZMbs/GsOMbUeZuf0o6z8cQnunsmza60kwXx68QH0zI7ZMHYGlYdV83aysfH757Qx37ioarRHhySxc0K9Ko4E3YaKnw1+Th7LXx581F25x/Xl41S+qAoIAUzorDzSXrzyjsLBYZZmkqLCIxd2X49mjCd8c/Kz8+aoYwglPTqWBRd3q2zkFhczfexoNNSl/ThxUK4XBpMxsZu44RlRKOn9+MLh05bvjxT0is9PY1XU8SQXhPEi9TEezwZhrVV6KVAhL6YJa7Xpv7yVwm+vrYm5iyA8nL3Ps04l07eJGq5YB7Nh5gy6dXHH0qM/IBQM5/Ptp+k7tTuMONVtavY0hc/ty+8R9/l70Dy16e/DJvF48eRrNyl9Os23zNPT0qqc/8DZSsnOZtu0IsWmZzOvZjkmdWtSKmfK+4JMYQWtzu3I+iG9CqTrgO4Chhhm9rSZwNm4HD9Ou0NKk5zvdf22QVZRGTnEmVtr16WjZgDGOnmx7cZf+dm6l+uRvQxRFfEKiaNvQrkaN5LdhbWTAPx+NYdaOY8zZdZI1EwbSo1FDDt57zA8nr9DMvh4bPxyGkU7Vv8Pg4ASW/3CcV6+y+HhuLzIz89i99zYpqTl8u2wo2trVTxgEQWBSxxaMaNWEnIJCiuVyZHI5crn4+m8RmVxOsUzxuEwuRyaWPCaWPlayraWBbqWrhbPnH+PkZImLc+UrsIfeAWSmZBHsW5H5U9kQjkwuJzIlnU6vjbFrA1EUWXbkIuHJaWydNgJro5oLwUWlpDNj+1HScvL4e8pw2jZUJIRX40JYF3iTPjaudLR0YEvYUnTVDOluOUb1DgsfgLonQi2TqvdS4xYEga+H9CA8OY2dNx8oxoc/6UNRkYxNmxXaFCUiVGvnbKmzCJVEImHx9rkIgsDqaX+hpaVeShFc/MW/xMSkVr2Tt5Cancv010F705RhzO3V/v9U0E7IzSQ081WlZRJQ0AElSOrM2FCGdqb9sddx5VjMRo5Erye3uG7a63XB80w/1gbPZ1PoFyTnKyYJlzTriZmmLkvuny3nMv8molLSScjIqnF9WxnM9XXZOXM0bvXMWbDvNIsPnGPFiSt0cW3Atmkjqwzaoihy+ow/nyzYg1wm8ufvHzBieCumTO7MZwv64ev3ks8+P0BaNZxi3oaupgYWBnpYGxlgZ2JEfTNjGlqY4mJlhru1BU3trPCsb03LBra0cbSjvVN9Ors60M3dkZ6Nnejb1IUBzVxp2UB5BhkckkBoaCIDq2hKXj14G4CEiOQKloCVZdxx6ZkUFsvqxCjZefMBF5+GsLBfJ5Vc/crwIj6ZSZsPklNQyI4Zo0qD9qnIp8y+dQQXQ3N+aj2Ax+nXic4Npq/VRLSklfd+RHkWFAcj1LJMAu+RDtjZ1YE+TZzZfOUe0anp2NgY88H49ly9FoSvX3h5Eap15+t8PMv65sxeM5nH1wI5ueEC7m7WLP92GPEJ6cyas5MLXtUX5knPyWP69qNEpaSz8cOhtHGsW7nlfcAnMQJAZeCWicXldUreISSClGmO39PNYhSP026wNng+TzOUO3m/L8jEYs7H/cOeiJUYqZujLtHkeMxfyEU5BhparGjVj+cZSWx5rvy8fEIUDcx2DctKG/n5RcjltWvmGelosX3GSFrUt+F8wAuGtmjE2omDq+yD5OUV8stvZ/hjrReezezZvGkKjRqVlb8GDfTk++9GEB6ezKcL9hIbl1ar83tfOHf+MRoaavTsUblfZkFeAT4nfKnnqJhcfvm4vN+4qakeqak5Fa7R8CTFe3WoJaPkblgUf1y4RZ8mzkytpMyjCv6RcUzZchipIGH3R2NoaqdYUewJ8eOzuydpaW7H3u4T0VGDC/F7sNNxxvMNyz2lKPIHRKhlYxKqb6RgJAjCEUEQnguCECQIQkX/ojeQkKfIvr4a1A2pRMKPJ68iiiLjxrbD1taEteu9yotQLT9YZxEqgH7TetBmQHO2L9lHTHAcHTu4sG3zNNxc6/Hb6nP8uPIU2dmqJzfTc/OZvv0oEa/SWD9paK3u0MogimKdhZDexO3ECEw0dXBVYtxaAplYXEEb4V1CXaJBb6sJzHH+DQN1Ew5ErmJ/xG9kFlV/hXMnMYJOp9dzOiqwRsdOK0xia9gybr06SVvTfnzk9DMD6k0lMvc591IuANDbxpWBdo3Y+OxWOdODEtwNi8LayAB7U4W4UHGxjElTNjN73i7CXiZV2L460NXU4O+pw9k+YyQ/jepTJTU0KjqFeZ/u5tLlQKZO7szPP43B0LBittaxgzO/rxpPVlYen8zfw4sX8Ur29r9Hfn4Rl688o2sXV5UlyXtnH5KXnc+UH8YBEOpfvuZuYqJLUZGMrKzy12f4q9r7TMalZ7L4wDkamBvz46g+Ne7F3AqOYOb2oxjparN39lgaWpgiiiIbAm+x/KEXPa2d2dllHPrqmlxNPExOcQaDrGdUucIVCx8C0hprcL+J6mbca4ELoii6Ac2AIFUbv8rP4UV6EpaGenzSuwO3giO4+DQEDQ01FnzSh7i49PciQiUIAgu3zEZDS53fpmxAJpNhbm7Aql/HMWNaV67feM6Mj3bw5KnyAaCMvHxm7ThKWFIq6yYOqTFlSy6KJORmcjcpkoNh/vz6+Apzbx9hwIWtNDn6G57HVjPh6l5+fXyFC9HPicvNrBVVSxRFfBLD6WDpUM566W3IRNk7r28rg7V2A2Y7/Upfq4m8yHrA2uD5PEi9XOV7C85IZs7tIyTlZbHo7km8YqpnuhGU6cvGkMUk5Uczzn4RQ2xmoS7RoLlxN5z1m3MxYS9phYrA+22LPuiqabDE92w5XW2ZXM69sGjaO9mXXtDPn8eTkpJNZOQr5szbxZ69tykuVl5mUQUtdTXaNbSvMlBcux7EnHn/kJaWw68/j2XSxI4qtasbN7Jh3dpJaGqqsXDxfu7d/2+dcQBu3HxBTk4BA/qpDkLXDt7G2NKQrmPaY2ptTOij8oG7MtPg8ORUjHS0ymnI5BYXsirgqspBq4KiYhbsPUNRsYy1EwcrJROcO/+YTX8r/51eCHjBvN0nqW9mzJ6PxmBtbIBcFPnp0SX+eHqd4Q5N2dhxFJpSNZLzY/B5dYYWxj2w1alojFEBRQ9BzR1BolpaWhWqDNyCIBgAXYDtAKIoFoqimK7qNVJB4NcARS17QntP3OqZ8/Ppa8SmZdCihQM9ezTiwMG7PA2MKSdC5bWrZoMTb6Igr4BAnxdcP+iDqbUJQXdDuLxXIbIvlUqYML496/+cpBgMWrSfR28t1QCWHLrA8/hk1k4cRGdXhxod3yvmOR5HV9Hx9Ho+uLqXpX7n2PL8Dl4xLwjJTMZBzwQTTR3uJUWy5fkd5vkcpfPp9fQ9v5kCWc1q/JHZaSTlZyMXRZU/Xrkoo0heSFCm73vXb5YKUrpYjOATlz+w0qrPsZiNnI3brvI1n/gcQ0uqxpm+M/EwsWb+nePE5Kj8afEs4x57I35GV2rAPOfVNDUqs5ETBIFhNrORizK84ncDYKaly1eePfFPieXEGw7tvi9jyMovKLeiuuUTjCDAjq0z6NLZjZ3/3GTmRzs4c/YReXnKXeRrg7y8Qv5Y68WKH0/i2MCcLZum0qpl5SWvN2FvZ8qGtR9ia2PM198c+c8z73PnH2NjbYyHR+XlRJlMxr2zD2nVzxOpVIpDE3teBpS//kpWGW83KEMTUyqUSU5EPOXvIB82Pbtd6TF33PAjMDaRn8f0U1ofj45JZfWa8xw+6ktgYGy55/wj41j87zma2lqxc+YozF7TH7c8v8PO4PtMdm7Nb20Gl3q7Xkr8F6kgpU+9Dyo9nxKIoghFAaBeUY+9JqhOxu0IJAM7BUHwFwRhmyAIFW4VgiDMEgTBTxAEP6lcJDVfIRijJpWwcnRfCoqLmbLlCLFpGcyd3RNLS0O+XHKIp09jGLlwIM17NuX36Zvw3n29yhOSy+VEPY/l4j/XWDd3K3NbfcFQw8ks6LSMvxf9Q05mLp1HtcOtbfm7n6trPWxtjFFTk5aS/t9EQwtTZHKRzLyaS6066psy0N6drvUa0tjYCittfdRff7FyUeR5RhLRr4OSukRCA30TutZryAB7d9Rq2Dy01Nanv60b56KD6H5mI1ue3yGvuOKIc3Pjbhiqm7I34mc2h31FWPaTGr+vmsJM05rpjitoatgB/7RrpcwWZciXFdHMxBoXQ3MG2jei6DWrQfX+bTBUNyO1MJGA9NuleiwliM0Lo1gswlyrLJA8eBWDgOI7AoWo1PcnLlPPSJ+uboqA+TI8iWPH/ejerRE2NsYsWzqEFctHoKYmZc2fFxg7YSObNl8hLl71jaUqBD6LZebsHZw568/YMW354/cJ1dKsfhOmpnp06+aOXC6irfPf0VKjolMIeBLNgP7NVK4upFIpbm2duXn4LkH3Qnh+LwR7t/IU1jt3Q1FTk+BQv8zBKTkzm8dR8RV6TGmFitiSVIUapCBAE1vlaqDlqJVvnbqZvg7qUimWBnrlJHbraSu+JycD03IrXXNNW4rEQuLzqqZcCoIAao2g4JLC/aaWqE7nSg1oAXwiiuI9QRDWAl8B37y5kSiKW4AtAEYuDqKxZtnSxrWeOdunj2T69qNM3nKYXTNHs2bVeD77/ABfLj3ELytHs+Lkl3w37FdWTd2IKIr0mdyt9PVpiek8vx/K83shPL8fwgvfMHJeK4np6Gvj0rohoxcPwb2tM65tnDCtp7yRceTofXz9wlnwaV/s7UwrPP9pnw48iU7gu2PeOFma4m5dfW6vs6E5v7Yp794iiiIZhfm8KsghOS8bHTV1zLX0sNTWrxP9TFtNnQ0dR/IkNZ41T67x6+Mr7Hxxn3mNOjLGsTkar+uq9XXdmO+6loepV7madIgdL7/DUa8pvS0nYK9bNwqmKkgECY0N2/Mkw4fY3NBKj9XZypFTkYEUyWVcig3G2cCc+nqqm1AWWrZ84ryGU7FbuJS4n5Csh4yyn4+JhiVZRWmciPkba+2GdLMYCcDl2GAOvXzER27taW6mCBa/n79JdGo6O2aMQldTA5lMzm+rzqGnp8XHc3uVHqtTRxc6dnDmydMYjp94wNFjvhw5ep92bZ0YPqwlLVs4VLtuWlQkY/eeWxw4eBdzc33WrJpAs2a165/I5SJnzz2mmYed0t/x/wrnLwQglUro26dqn8al+xcwt+UXfNp+KQDDPhlQ+lxOTgEXvALo1tW9XEJ19vEL5KLIkObu5fYVna24ecblVq6G2KepMxsu3cH7aQgfKHG2sa5XNoXp6lJ+7N3OxIiPurdlvbcPQ583osvrm/uQ+o05EvGY3wKu0svGBQttxfBgN4uRPM24zYmYTXzqshZNqWrjX8FgKWLKSMScvxD0v1C5baX7qGoJLQiCFXBXFEWH1//vDHwliuLAyl6j72wvztqzgd/bDSn3eFBcEtO3HUVbQ51ds0ahI6rx2ef7SU7O4peVY3B1suDbYb/hf+kJwz7pT2pCGs/vhZIYqWgsSaQSHD3q49raCbe2zri3dcLOzQZJNYJgcHACH8/fTdu2DVnx3YhKL7iU7FzGbNiHVCLh0LwJGCnRZ/6/Bt/kKFYHXMPvVTS2uoZ82rgLw+o3KXdzKJIX4pt6kWuJR8iRZdLYsB1j7T9758M5JcgpzmTlsyn0shxPd8vRSrfxinnO3NtH+bvTKObdPsost/Ys9uherf2Losjj9Bucit0KiAy2mcnTdB9Csx8zz/l3LLRsScnPof+FrZhr6XKs91Q0pWrcCo7go53HmdypRanv6IF/77J1+zW+XTaUbl3dKz1m8qssTp/x58zZR6Sn52JnZ8KwIS3p26cJOjqVU0XDw5P5+dfThIYl0b+fB3Nn90RXt/bUUl+/cL5ccpCvlwxRyeR4nygqkjF2wkaaNLZlxfIR1XrNk5tBfNb1W4wtDTkYt7X0Gjx23I8Nf13irw2TcXMtC6Ij1u1FXSrh4LwJ5fYz4coe7iVHYaGlx52h8ys93vC1e9DT0mDPR2OVPi+TyZFIBKWxoLBYxqj1e8krLObEgkmlNfLwrFQGXNhCLxsX1ncoe9+ROUFsDVtGO9P+DLKZUeVnIc9YCnknEczOIqg5ACAIwgNRFKul8VplxBNFMQGIFgShJG3qCTxT9ZpiuRxTJRoW7tYWbJ8xkrzCIqZsOUwOxfyxegIW5gZ8tfQQz4MTWXHiC1r1bcbxded4fi8Ut7ZOzFr1IX/cWMHJjN1sevAbC/6eRb+p3anfyK5aQTs3t4AffjqJsbEun382QGWWZKqnwx8fDCYpM4fPD56vYBT7fxGtze35t8ckdnQZh6GGNl/cP80Ar62cjw4qZbKoSzToYDaIRW6b6G4xmsCMu9xOPvXezklXzYB6Wg0Iyw6odJsOFg5IBYEf/b2RiSK9bKo/RSYIAp7GXfnEZQ1WWg4ciV7H8yw/+lhNxELLFlEUXysF5vN7uyFoStVIz83nm6MXaWhhwvw+itp4VFQKu3bfpHMnF7p2US36ZG6mz7QpXfh331y++mIQOjqarN/ozZjxG1m/0Zvot+YFZDI5hw7fY/a8XbxKyeaH70fw+aIBdQraAGfO+mNgoE3nTrWbunsXuHM3hPT0XAYOqD4zomlnd87m7uNQ/LbSa1AuFzl+4gGN3K3LBe3ghFe8iE+ukG0DRL0uOSblZ6vs8fRp4szDiLgK8rslkEollcYCDTUp3w3vRVx6JhsvldFJG+ib8HHjTpyLDuJKXJmEbn1dd9qZ9uduynkic1RyNwAQ9BaCoIGY9UuV2ypDddfrnwD7BEEIADyBlao2liNioqWcgO5ubcGOmaPILypmypbDZMmLWfP7BCwtDFiy7DBBwQn8dHYpJ9J2sTf8L5b9+xmjFw2mSSd3tFRkNaqwfoM38QnpLP1yMAYGVWfQHnZWLBvaHZ+QSNZ7+9TqmP9rCIJA13oNOdl7Ghs7KMoEH/scY5j3Dq7Fh5Y2JzWl2vSyGl+qN5JaWHej3crgpN+MqNwXFMjylD6vr6FFc1NbYnIyMNfSrXTCURWMNSyY0XAFQ20+YmbDH2lvplgInoh8ilfMCxY06YqbkaLO+dOpK6Rm5/HLmH5oqqspSiSrz6Ktpc78T6pPF9PQUKNP7yZs2jCZjes+pEN7Z06f8Wfy1C18ueQgd++FEhefzqLPD/D3lqu0aePI9i3T6dih7oE2JSUbnzuh9O3TVKULUwmSs3IYsHonN97ByPubOHvuMebm+tVuqpZAQ6t8Tf6+bxixcWmMGF4+0Tzl/ww1iYT+HuXLbAWyYhJyM2n0+jsNz6zc9b7vayln71pqlLd0sGFMm6bsue3Ps9iy62Sma3ucDcz57sGFUpNogN5WH2CobsrxmL8okqtuZgtScwTduVBwBbHgVo3PrVqBWxTFR6IothJF0UMUxWGiKFY5AaBKNc6tnjk7ZoyiUCZjytZDZMkK+X31BCwtDVm67AiPHkeha1h7qsybuHwlEC/vp3wwvn2NaoqjWjdldJumbL3mW+sv/r+AIAj0s3PjXN+ZrGozmMzCfKbfOMi4K3vKKeYNsp6OIEg4FbvlvTFOnPQ8kInFRKjIQNpZKCiXnqY2KqmNqiARpLQx7YuDbiMkgoS4nAyWP/SilZkdM1zbAnA+4IXCRb1nOxq9tq87dsKPZ0FxzJvbS2mzujpwd7dm6VeD+XffXKZ82ImXL5NZuuwIEz/8m9CwRL5YPIAV343A2Pjd/J4veAUgk8kZNFC5Qt/b2HL1PpEp6ey4UTfbwDeRkJiB34Nw+vf1qJF+ijIcO/4AU1O9coqCMrmcs4+e08nVAZO3PCZjczIQga71FDohYVmVB25HCxNcrMy48CS41ue3sF8nTPS0WX78Uqk9nIZUyo+t+hOXm8mfT8vIFJpSbYbZziG5IJZrSUeq3rnuZJDaI2atRBRrpp/+3iYnq5L7dH0dvItkcqZsPUxmcSFrVo3HysqQpcsOc+16EKGhiUREviImJpWExAxSUrLJyMwjJ6eAwsLiKifc4uLT+WOtF40b2fDhpJr7GS4d3A0POyu+PHieSZsPsuTQBTZ4+3DiQSB+4THEp2f9ny2lSCUSRjTw4GL/2axo2Y+o7DTGX93DlOsHCEiNK9UbCcny52lG7VYV8qw1iLn7Kn2+vq47aoI6YdmPK92mv50bzU1tmORcc/smpeckinxx/wxyUc6qtoORSiQkZWaz4sRlPOysmNG1NQCxsWns2HmDdu0a0qtn3ahZoBjZ/nBSJ/bvncM3Xw9l9MjWbNs8jX59Pd6ZCJdcLnL2/GM8Pe2rZVQQl2p1/1YAACAASURBVJbJofsBmOhq4xseU043vC64cEFR/urXz6NO+4mMfIXfg3CGDm5Rzubs/stokjJzKimTKHLGTlaOSAWBlyoCNyiybv/IysslVcFAW4slg7oRGJvE/juPSh9vZW7H+IbN2RXiy5PUMkqms35zmht350bSceKqYJkIggaC/ldQHAq5B2p0Xu+nM0XVgRvAxcqMnTNGMW3bEaZsPczOmaNYs2oCiz4/wIofT1brOBKJgJqaFHU1KWrqEtTUpKipSVBXk5KVna/QTVk6pFaZgYaaGmsnDmbjpTtEvErj/ssYEjOzeDNBVZNKsDYywNbYABsTQ2xNDBV/Gyv+NtLR+k/V8zSkUj5waslIBw/2hD5gc5APw713Msi+Eb+1GYh/2jXOxG3HWd8TLWkNs8K8k4iCFoKOcv6qukST+rruhKqoc7sZWXKk15SaHVcFdof4cScpgp9aDcBez7hUYKiwWMbPo/uhJpUgl4us+v0campSFs7v906/H3V1Kd27udO9W+VNztrC70E4CQkZzJzerVrbb7pyFwGBzVOHM37Tvxy694Qlg6v32sogk8k57xVAq5YNsLI0rNO+jp14gLq6tMLq4dTDIPS1NOnm5ljhNVHZisDdUN8UO11jlaUSgL5NnVnv7cPFp8FM6li7EfO+TV04+TCIdd4+9GriVCpS9YVHDy7FBvO13zmO9ZpayuseUG8KwVkPOR6zkdlOv6qeXtbsCRodELPX1+ic/tPADQpN450zRzFt21FF8J4xmvVrJ/LkaQxFRTKKi2Wv/5Ur/i6WUVwko1gmp7hIRtHrx4uLZRQXySkqliErVjwuk8kZMrh5nX5gFgZ6fD+id+n/C4uLiU/PIiY1k9i0DGLSMohNzSQ6NYNnT0NIzy0/squjoY6tiSE2xgZ0dXNkdJvaibfXFVpq6sx0a8e4hs3ZHOTDpiAfmhjXY5j9HDaFfsnFhH0MsZlV7f2JogjyVKAAUZaMIFXiqQc01PPgYsJesorS0Fd/d56ByhCa+YrfAq7QvZ4TYx0VweDQ/QBuh0Ty9ZDupYMcp04/JOBJNJ8v6o+5We314P/XOH3WHyMjHTp1rLpWHpGcxsmHzxjfzpNGNpb0buzMqYfPWNC3Y5005H39wklOzmLenLopQmZl5eN96Sk9ezTCyKgsVuQWFuEdGMqAZq5KpVejs9PRlqpjpqWLo4GJylIJKOzOSsoltQ3cgiCwbGh3hv6xm59OXWXDpCEIgoCBhhbfNO/Dp3eOsyv4PjPc2gGgo6bPEOuZHIhajU/yaTpbDFO5b/SXIqYMqXQbZXhvgVsZq6QyOFmaseOtzLttm4bv69TqBA01NeqbGVPfTHkQyikoJCY1g9i0zNf/ZhCdmkFYUipXg14Sm5bB/D61M4d9F9BX12RR026EZCSz9ukNBth99Lobfo7mxt2w06lmA03MAV539Avvg7ZydqiTXjMuspew7AA8jWvvTVkViuQyFt89hY5UnZ9bD0QQBCJfpbHq7A06ONkzvp2C/RAfn86Wbddo3aoB/frWban/v8SrV1ncuRPKmFFtUFevWsZg4+U7aEilzOymKA2NbevB+YAXXHgSzPCWtS8NnTv/GCMjHdq3q8Zot6r9XHhMfn4RI99qSl4JDCWvsEhpmQQUGbednhGCIOCob8athHBkcrnKuYh+Hi6su+hDQkYWVoa1u1HbGBvyce8OrDp3A+/AUPo0Ubz/AXbunIh8wh9Pr9PLxgUHfUUJq7FhexoZtOVS4r+4G7bBTLPyxrug7oKoMwlYVu3zeT+yrgjoqdeMAeJkacrOmaMAmLr1CKGJ76Ye97+GrqYGrvXM6dGoIR92asGSwd35a/Iwznw2mVGtm7D1mi+/nLlWawW6dwFBEPiuRV8EAb57cIGeluPQVzfmRMymUp/KKiEvo76Jhfcr3ayetgPaUj2VtMC6QhRFfn50mSdp8fzYagDm2noUy+QsPeyFulTKj6P6IggCoijy+x/nEQSBzxa+2xLJ+8Z5rwDkcpGBA6puSgYnvOJ8wAsmdmxeOq7dqoENjuYmHLpX++8hNTWbO3cVjJbq3Dwqg0wm58TJBzTzsKNhw/KTjaf8g7A2MqBFfeXmIFE56djrKoZnHPVNKJTLiM3NUHm8vk0UycjFJ3UjGUzs0Bx3awtWnrpKVr4iaREEgR9a9kdNImWp37lS+q0gCAy2mYmaoMaJmE3IRdW9MEF/aY3O5b0EbjWhcn6kKjS0MGXXzNEIAoxav4+pW4+w9dp9AmMT/9NA9y4glUhYPrwXkzo2Z6/PI7477v2fNjatdQ1Z0KQrV+NDuR4fzSDrGSTkR+Lz6kz1dlASuAVtKLxX6WYSQUpDPQ9Csx+/F/aKXBRZ/tCLf0J8mezcmn52Ci72zpt+PIqKZ9nQHqWuM2fPP+ahfyQfzeyOpUXd6rP/S8hkcs6ee0yL5vWxsam63LTe2wc9TU2mdinLZgVBYEzbpgREJ5Tzx6wJvC4+RSaT07+OTUmfOyEkJmYyfFj5bDs5M5s7oVEMau6mVGxLFEWis9Oxfz1d29BAMR5fVYPSwdwY13rmeD2tPbsEFP2s5cN7kpKdyx8Xyih8VjoGLGnWk3tJkfwb5l/6uIG6Cf2tpxKeE4hfqrfKfdc0Xr6fwF2HcW5HCxP2zR7HBx08Sc/N40+v24zZsJ8uKzez+MBZjvk9JT79vxPtrwsEQeDLgV2Z3aMtx/wC+fLgBYoqEfl/n7gdHMmfXrf4wLEljYwsWeHvhZ22J276rbiccLBUWU8lSgK3ZjeQvUSUvap004Z6HmQWpfKqILbSbWoDmVzOUt+z7A19wHTXtnzTXNGLCIpLYsOlO/R7bQAAkJSUyd+br+DpaV9tKt3/Ffj6hZOUlMmggRVHt99GQHQCV56FMaVzSwy1y8usDmnRCE01KYfv11yzRhRFzp1/jEfTuo/ZHzvuh6WlAR07lC+3VDbiXoJX+TnkyYqw01Nk3A1elyVeVtGgBOjX1IVHkfF1jh1NbK2Y0N6TQ/cDeBQZV/r4WEdP2ls48Ovjy+VG8Vsa96ChngcX4neTUVj5NVJTvJfALa2j44qtiSGfD+jC8fmTuLZ0Fr+M6UdnFwd8w2P45qg3vX7dxuA1//Dz6atcC3pJTsG7U25ThtpIe1YGQRD4pHcHPuvXifMBL1i47wwFdXQAqi5yCgr5/vglZu08xtZrvhy8F8CPrfqTlJfNH0+vM9hmJoIgcDp2W9XZsVxxsQha/RX/V1EucdJX1JdDVdACa4oiuYxF905xOPwxHzfqxJJmPREEgYKiYpYcuoCxjjbfDO1RWiJZ8+cF5HKRxZ8NUCmd+n8RZ876Y2ykUyHQKcP6i7cx1tVmUseKQd5QW4v+zVw57R9U42vmcUAUsXFpKj0lq4PQ0EQeB0QzdEjLCkyv0/5BNLG1rNTtpmRisiTjNtHUwUhDu8qMGxTsEoCL72Am49M+HbA00GP58UuliZcgCKxsPQCZKPKN37nS6+dNxcqTsZvf2arz/1zG/TbM9XUZ3Nydn8f049qSWRyfP4nPB3ShnpE+h+8/Yd7uk3T4YRNTthxm89V7PIlOeKcliMtXntF3wCrGjN/Il0sOsmnzFS54BRAcnEB+fs1I829ietfWfD2kO1eDXvLx7pPkFtZ+X9XBg4hYRq7by2HfJ0zt3JLOLg6s9/bBQmrARKdW7AnxIyqriJ6WY3mR5cezzMrLH0BZxq3RCQRdlXVuEw1LTDQsVdICa4JCmYxPfY5zOiqQxU27sbBp19Kl5npvH0ISU/hhZO9SnZmL3k+57/uSGdO6lhMX+v8BycmZ3L0XRr9+HlXWlX1fxuATGsWMrq0rNbMe28aD3MIizj6qnvZ5Ca5ee46Wljpdu9RenEwURQ4duY+WlnoFm7OQhFc8r2TEvQQlVEB7XUXgFgSBBvom1cq465sZ41bPnIt1GMYpga6mBl8P6UFIYgq7bj4ofdxez5hFTbtxLT6MU5FlxiAmmlb0tvqAF1kPCEiv+ZSkMrwXVklNZUrfxPP0RDY+u01MTjofOrdmsH3j0huBIAi4WJnhYmXGlM4tKSgqxj8yjtshkdwJjWLdRR/WXfTBUFuLdk4K77wBzVxr7cgeE5PKmj8v4ORkSQMHc8Ijkjl56iGFhcWvzwes6xnj0MCMBg7mODYwx8HBvFQ6tipMaO+JtoY63x715qOdx/hr8rB37mtZUFTMem8fdt16gK2xIf/MHE3LBrbEpmUw5I/drDx9lZVj++AV85xlfuc43Gsy/mnXOR27jYZ6HpV654nyFBB0ECR6iOotoKjywA2KcklA+q3Xrjy1/9kVyIqZe/sI1+LDWNa8N1Nd2pQ+5xcew65bDxjTpimdXRWj2K9eZbFx0yWaNrFl2NDae/z9Vzh3oXpNSVEUWXvxNhYGuoxrV3lW3NTOCrd65vx7L4DRbZpWq7YqiiK+fi9p0bw+mpq1oxKWaJBfuhzI6FFt0NcvX8Y55R+kdMT9TURnpyEANrpl/YmGBqZcj6+eoUTfpi6svXib+PQs6hnVjQbao1FDejV2YtPlu/Rt6oK9qSIh+NC5FWejn/GD/0U6WjXATEvRHG5vNoCA9FuciduOk34zdNVqblj8Jt5L4K5NqeRJajwbnt3iUmwwemoaWOkYsPjeKdYF3mSee0eGOjRBXVI+GGqqq9HOyb5UDD8lO5e7oVH4hEbiExKJ15MQzge8YNu0kTVeHhcWFvPDypOoqUn4YflILCwUH7RMJicuPp3w8GQiIpJ5GZ5MRMQr7twJLW2gqqlJsLczxaE0mCsCu6WlYYXzGN6yMdrqanx58ALTtx1ly9Th70yR8FlsIksOexGamMKYNk1ZPKBL6U3MxtiQeb3a8/v5m9wPieHbFn342OcY+0P9GWY3h82hX3Ep4QCDbKYr37k8FSSKJa2g0RYxezWiLAVBqrz+6aTXDN9Ub2JyQ6mvq1rMqTLkFhfy0a3D3EmM4MdW/RnfsIyXm51fwNLDXtgaG7J4QBdAEXD+XOdFYaGMzxe93xKJTCYjKeoVMS/iiH4RR3FhMUM/7oemdu1vxDKZnHPnH9OqZYMqVwq3giPwj4zj22E90FLCfy6BIAiMbevB9ycu8yQ6AQ/7epVuW4Lo6FQSEjIYN6Zdjd8DKEwLln9/nIjIZKZO6cwH4zuUe75kxL2jS/0KI+5vIjI7HSsdAzSlZe/PUd+UI+EBZBXmo6+h2pC5b1Nn1l68zcWnIUzuVHu/xxIsHdydwaFRrDhxma3TFIqjUomEX9oMYrDXNr5/6FWqICgRpIywm8vGkM85G7eDMfYL6nTs95Nx16BU4v8qlg3PbnItPgwDdS3mN+7MZJfW6KtrcTkumPWBt/jS9wzrn91ktnsHRjo0K9WbfhumejoM9HRjoKcboiiy/85jVp6+yqH7ASqzEGXYuv0aISGJ/PD9iNKgDQpFMTtbE+xsTcrpKxQWFhMVlUJ4RDLhEa+IiEjmaWAMV66WCSk6O1vy809jMHlLt6Kfhyta6uos3H+GKVsPs3X6SMz1a69tUSSTsfWaL5uv3MNET5u/pwwv5+jz8GEEjwOimDChPaf9g1h5+ionF3xI93pOrHl6jb52s2lj2o+7KefwNO6i3I7pjcCNRtvXB74P0v5Kz8lRrykCAmHZAbUK3FlFBcy4cZCHKTH81mYwIxqUMRsKi4v56tAF4tOz2P3RmNKb05WrQfjcCWX2rO7YVmNEvDrIycgh+nVwVgTpWKJfxBEbkkBRQflyV9C9EJYdXIi0Ct/JynDf9+XrYZdeKrcTRZF1F32wNTZgeMuqtbEHerqx6twNDt4PqFbgvueryGjbtK6ZoBTAzVsv+G3VOaRqEn5ZOZbWrSruw/dlDImZ2XwxsIvKfUXnpJVSAUvgaKBIFF5mpdDMVDmFsAT1zYxxt7bAKyD4nQRuS0M9FvbtxI+nrnDKP4ihLRQSu04GZnzcuBNrnlxncMwL+tgq4oSlVn26WozkSuJBPIw64WZQe5mH9xK4ddWqLk3cT4piw7Nb3E4Mx1hDm8VNuzHRuRX6b/C/e9u40svahWvxoawPvMUyv/NsfHab2W7tGe3oWe7O+zYEQWBC+2ZcCwpj9fmbdHSpj51J9eqbd+6GcvSYH8OHtay2opuGhhpOTpY4OZXnpWbn5BMZmcLz53Fs33mDhYv2sfrXcRVcT7q5O7Jp8jA+3n2SyZsPsX3GqFot58KSUlhyyIvA2EQGNnPj66HdS9kFeXmFbNl2jZOnHgIKu6jvhvVk4uaDbLh0h+Xd+9L3/Ga+f+jF2vYTeJZxlxMxfzPH+beKY7vyVJC+fq/qjV7Xue+VNSvfgo6aPtbajoRmPaaH5Zgavaf0gjym3jjAs7RE1rYfzgC7sjpoflEx8/ee5lZwBMuG9KB5fcWgQ2paDus3euPuZs3IEa1rdDxZsYz48KTS7DnmRSzRwYpAnZZYxhmWSCXUc7TEzs2a1n09sXW1wc7VGltXa64euMWmhbv4a/5OPl4/vVb02PMXAjAx0aVDeyeV23kHhvIsLomVo/uiUY0Sna6mBoObu3PiQSBfDOxagX3yNnx9w7G3M8XKqvr9AZlMzvYd1/n30D1cXaz47tvhlU4wn/IPQk9Tg27uqofuorLT6Fav/GdR4mr0Miu1ysANiqz7T6/bxKVnlo6u1wVj23pw5lEQv565Tkfn+qW8+Vlu7bkQ/ZxvH5ynrYU9hhqKVXRX8xEEZtzhVOxmHHQbVVqKrArvpTlZWUAVRZHbieFMuLKH8Vf38CIjia+a9eT6oI+Z06gj+uqa5BZn4Z2wn8NRa4nODUYQBLpbO3O01xR2dR1PPW0DvnvoRfezG9kV7Eu+EsuuEgiCwIqRfZAKAt8c8a4WFzz5VRa/rjpLQ0cLPppZPVF/VdDT1aJxIxtGjmjNrz+PISUlm/mf7VNqgdXOyZ4t00aQkp3Lh5sPEZVSfZssuVzkn1sPGb1+H7FpGayZMJDfxvUvvSgDn8Uya/ZOTp56yMgRrWjVsgHbd97ARkePMW082OfziMz0AuY36cKl2GBuxMcw0Ho68fnh3H11TskBU8pKJYI6qLeAQl+V59hQrxnRucGVyrwqQ0p+DhOv7eV5ehIbO44sF7RzCgqZs+sEt0MiWDGiN+Pbl62q1q2/SF5eIZ8vHlBtnZqiwiKWDlzJIN0PmOr6Kd8M+YUtn+/m9on7yGVy2g5owYxfJvL98S/Y/uxPzuTsZdeLdfxw8itmrfqQATN60rSzO8YWhoyYP5DRiwZz6i8vDv5WPd2dN1FYWIzfg3A6d3RV2S+RyeWs9/bB0dyEQZ7VX8mMaetBQbGMUw9VSuuTn1/E44Ao2rSpqBtSGVLTcvj8q3/599A9Bg/0ZO0fEysN2nmFRXg/DaFvUxeVJZ684iKS83NKqYAlsNczRk2QEJZZPapdn3c0jFMCiURgxcje5BYWsfJ0mWeuukTKL20GkVqQy8pHl0ofV5OoM9x2HplFaXgl7Kn1cd/byPubEEWRGwkvWR94E/+UWCy19fimeR/GOXqipaZodmQXp3Mr+RT3Ui5QKM9HU6LNo/TrOOt50t1yNPV13els5UgnywbcSYpkQ+BNfvC/yKag28x0bccEpxboKMn06xnp8+Wgrnxz1JsDdx/zQYfKmzwymZyVP5+isLCYb5YNrZbecU3QtIkdv/82ni+XHGT+wr2s/nUc9d/w2ANo4WDDzpmjmLnjGB9uPsS26SNxslTNm41Ny+DrwxfxDY+hm7sjy4f3Ki21FBYWs3vPbf499Noya/V4PJvVJz4+nWkzt7F+4yUWfzmAS4GhLD9+md2zR3Mi4infP/TiQr9ZuOi34FLiARobtsdIQ3GupTolkrLyg6DRBjH7d9V1bv1m3Eg+RnhOYLWWiUl5WUy6tp+YnHS2dB5DZ6uy4JGVX8DsXcd5Ep3AL2P6lwta128858bNF8yY1rWch2FVOLz6NL7n/Rk6rx9OLRyxc7XGztUaA9PaNbJm/DqRV3GpbF+yD1NrY3pPqv7If8CTaPLzi2jbVnXAPPf4BS+TUlkzYWCN7PDc6pnTzK4eh+49YWKH5pWuCB49jqKoSEbrVtUL3E8DY/j+hxNkZeXz5ecD6dtHtTbP5Wdh5BYWMVgFmwTeYJS8ZW2nLpFip2dEeFaqspdVQH0zI0W55EkwUzq/m2Z1QwtT5vRsy7qLPlwKDKVXY8WqoLGxFbPc2rMpyIdB9o1Lf792Os50MBvI7Ven8TDsRAO9mksQVOubFgQhQhCEJ4IgPBIEodrCvqIocik2mOHeO5l2418S87JY0bIfVwfOY4pLa7TU1MksSuVs3A5WB83mVvIp3Axa86nLn3zpvo2+VpOIy3vJlrCv2f7yO8KzAxEEgQ6WDuzvMYn93SfiYmjOz48v0/XMRjYH+ZCtxBFjeMvGdHZx4I8LN4l8VXkWu2+/D48Dopn/SZ/SIQNRFAl7HEFuVvWzRFVwda3Hmt8nIJeLLFy8n9DQikYGjWws+WfWaEREpmw9XOmkmyiKHPF9wrA/9/AsLokfR/Zhw6QhpUE77GUScz/5h/3/3qFfn6Zs2zwdz2YK/et69YyYNLEjt24H88Q/miWDuxEYm8jR+0/5sVV/EvOyWBd4kyE2sxBFOWfitr1x4BygCOGNwF1W564867bXcUVN0FAp81qCuJwMxl3ZQ1xuBju6jCsXtNNz8pi+7QhPYxJZPX5guaCdkZHLuvUXcXa2ZMzoNsp2rRTx4Yns+/EInUe25eP10+k3tTuNO7jWOmgDSCQSFu+Yh2ePJvw+fRMPvKvPY793Pwx1dWnp96UMRTIZGy/dwa2eOb0bK+d4F8ll/Pn0OrE5FcfCR7dtysvkVPzCKx+Muu8bhpaWOs1UuLiD4rd47IQfCxftR0NDjQ1rJ1UZtEHB3a5npE9LB9VljhKj7bdr3KAol4RVgxJYgr5NnQmITiAurXLPyppiWpdWuNUz54eTl8nIKxOa+6RxZxz1Tfna91w504VeVuMx0bB8bbpQc3PympRKuoui6FldT7Tz0UEMvriNj24dJqMwj59bD+TygLl84NRSYSNV+IpTsVv5/fkc7r46RxOjjixwXcdY+4VYatmjKdWmi8VwFrv/Tf96U0jKj2bby2/YGraMsKwARFGkrUV99nT7gEM9J9PE2IrfAq7S9cxGNj67RVZh2YcnCALfj+iNmlTKsqNeSksmjwOi2L33Nr16NqZP77IGz/6fjjG7+ecMM5rM9MYL+G3KBk6sP8+zu8EU1MINHsCxgQV/rvkADXWF5+azZxUvHCdLM3bPGouWuhpTtx4pN6UFivHgef+c5Ltjl2hia8WJ+ZMY3qoxgiAgk8nZf+AOc+btIi0tl59+GMXiNyyzAm48Y+8PRxg5vCUNHMxZv8Gbrk4OdHSuz9qLPlirGzK+YQt2hfgSmy2jh+VYgjLv8yzjNbf79fANkjcya/XGIOggqhh/V5do4KDrTmiWaj53ZHYa467sIbUgl3+6TqCtRVnwSsnOZeq2I4QkprBu4uBSsZ8SbPjrElnZ+XyxeGC1KJkl+GvBTiRSCbPXTKn2a6oDDU11lh9dTP1Gtnw/cjWh/tVzornv+xLPZvZoaVVOvzv+IJDo1Aw+7dOhUsbMsfAA1gfeYkPgzQrP9WvqgoGWJgdV6JeUnIeq1WdeXiE//XyaDRsv0bp1A/7eOKVCr0cZkrNy8AmJZLCne5WMn8oyblA0KCOzU6s9v1HijPMuhnFKoNDE6UNaTh6/nX3TXEGNn1sPJC43g1UBZaUUDYkWw2zmkFIYz5XEQzU+3nupcYdkvuJjn2Pky4pZ1WYw3gPmMMbRs5QNcjv5NGtezMUv1RtP424sdN3AKLtPlCpoaUi06GQ+hMVumxhkPZ3UwgR2hC9n+8vvKJYr6tstzWzZ2XU8x3pNoYWZDWueXKfLmY3l+J2WhnosGdyNhxFxrPf2KRe8CwuLWfnLaczM9FnwaZmFVeijcHYvP0jrfp6MXjSY4sJivHdfZ+P8Hczv8DVDDSczp+UXhD2OqPFnZGdrwp9/fICBgTaLv/yXyMiKNbr6Zkbs/mgMxrrazNhxjNBExTYv4pMZ+uce7oZFsWRQN7ZPH4m1cRld8bPP97Ntx3U6dnBhx9bptG9X1tA5tvYsi7p9xz/fHeTynpssXNCXpORM9h+4yzfDelAsk7H63E0+9+iOiaYO3z24QEfzwVhq1edM3DaFJVPJ8M2bpRJBHdSbQ6HqBVlDPQ+SCqLJKlJuopScl834K7vJLS5kb7cPaGFmW/pcbmERU7YeJjolnU2Th9H1Lb3me/fDuHzlGR9M6EBDRwuV5/Em/C4+5u7pB4xcMAgLu+qXVqoLXUNdlu6fT3FhMV8PXEleduWrt9zcAo6d8CM6OpU2rVWXJzZfuY+nfT26uFbO9tj47DYAp6ICyS0uPy2praHOkBaN8A6sKEcMEBuXRlxcusoySXGxjE8X7OXK1WdMm9KFH78fVYGjXRm8AoKRiyI9GlWtBBqVnY6GRKq0f6anpkmhXEZMFWJTJbA3NaKRtQVHfJ8Qk1q911QH7tYWTOvSihMPnnE7OLL08VbmdkxybsWeUD/8kqNLH2+o70FL457cSj5JbG71uOglqG7gFoGLgiA8EARBqWizIAizBEHwEwTBr6C4iD42rnj1+4gRDTwq0AOTCqKRicXMc17NcNs5mGhaVXkC6hJN2psNZJHrJux0nInPe1lhm2amNmztPJYD3SeRWZRfzpkCYEhzd4a1bMSWa/f5dO+pcksaA31tkpMzOXzUF9lriyI1dTV0DHTwvfCIQ6tPERdWVtYQBAEjCwM0tDVqrTJnaKCNvp6WyqapkY42htpayOXyUgOHuPRMMvLyWTm6LxM7Nq+QrTx/Hk+vno35dtlQDA3Ld60DfV5gZG6AIAhEPI2iSWNbnJ0tCQlNwM7EOTj3JwAAIABJREFUiN5NnHkcFY+BhhYfOrfiUWoc+cVyeliMJqMohYT8CBBeX5jysqWmKM+G4ufls3AliMp9gYZEC3WJcubRvy/9SczL5p9uE2hiUp6qdulpCC+TUlk9fmApd/9NHDnqi4WFARPGtVd5Dm9Dx0AbHQNtjv5xhsO/nyYzJYuidzDJKooiQfdCWD3tL+a1/oqiwmKsGlgo/b6DgxP4/Y/zjBq7gQ0bL+HibEWP7qod3DXUpGTmFajUuymZfRBRqHa+jcy8fNQkEpQlvOLr81Q1sSmKoKmpCKYlY/HVhbWxAWoSCV8dulClGmg7i/oUi3JGXfqHyNfZd15xEcsfePHH0+s01DfFWKP68w+ze7QlIT2LyVsOvVO9oDk92uFgZsyPp66Uk7JY3LQ71joGfPPgPEXysuP1t56CjpoBJ2M31+g41e2+dRRFMU4QBAvAWxCE56Io3nhzA1EUtwBbACzcGor3k6MoksuUNkxamfTGL/USETnPsNSqvg8kQL48h7i8cNqY9EVNonwZWfJjdTUqL+4vCAI/juxDI2tLfjt3nTHr97Hmg0E0trFk/dpJ/LnOi3923yIwMJavlwzGobEdf/n9StC9EJKiXmFhZ4q5nRkW9maYWhujpqILXhWKimR8u/wYIaGJrFg+okKTEhQ1zIX7zhAYm8jaiYNxtlJs09JB4c8YkphCv7deI5VKcHS04FVKltIbip2LNTcOK1yr9Y0VqnmiXCwtK+hraZbqWNi9Hi2Oz8vERsfp9d/h2Gp3B9QRi4MQGKTYR84WkKcg6C+q9D2HZwcSlHmf3lYTlLrtyEWRI+GPaW/hQGPjijfzU/5B2Bob0NWtYoaZkJjBQ/8IPpzUqcaSo43aubDt6R/8OXszWz7fzZbPdwPw/5h774Ao7u/7+zW79A7Si9JFKYIC9t6wl9hLbLEksUZN7/kkpscY04wtJvbeUcTeRRAQKQqiKL33tju/P5a2sEsxyfd5zj/o7uzszu7Mnfs+995zNDSl6OjroGugg46+Njo1fxX/10FXv+ZxfW10DXQbPKdNYW4xwVvPk3gvGR19bQbP7MuoxUNx71afXZaVVXL+QiwnTkYQn5COtrYGAwd0YswoPzw8bLh8JZ6Skgq1+iDvjhnIku2H2Xr5LksGdVe5zUddhzHv8h7muweiq6F8vaTk5nMyMo4ZPX0xUtESaGdnipmZPpFRT9UKc2lqSvnxh1kcPxHBlq2XeWXRFqZO6c7M6T1bnLIc1NmFra+8xKpdJ5n+y26+mDycoV6qufogBw+2ak5jxY3DjD+7lXnuARx7GsPjolzmugWw1mdgXaNDazDY05Vvpo9k6Y5jXIp7XFdQ/KfQ1tTgg3GDWLDlIJsv3eH1IYokQl9Tiw+7DmPJ1QNsT7jDwhrTBV2pPiNt5rIvZX2b3qdVkUcUxdSav5mCIBwGAoHL6rZvp6NPfmUZx57GMMW56Q9ur+uKra4zt3KCCTQb3qaMNTz3PDKxmsB2w9VuE1+gKOZ5GDfl2QRBYGYvX7zsrXhj10lm/rqXd8cMYHKgN2+tHYWXlz0/bQxh0avb+PD98Xh2tsPGuWW+ri2Qy0W++uYkd8OTWbt6JL16Nj1Z5XKRDw6c5WpCMp9MHKK0nDTS1cHTzoqbiU9ZNrRXk9e6u1lz/sIDRFFs8t06eNQXgfRrnEeqZXI0NBQ3WH1trTrdFFs9Bf3yvKQAF0NndCR6pJUlIwhaiBquUKXQuxBlaVCyDXTGImiqLkjJRTmn07ZjrNmO3uZjVG5zIyOZZyUFrPZu2oaZUVDMzcSnLB7YXeX5EhJyH6BVBTFVsLBvx/+Ov0NS1BMiL8RQVlxOeUl5zd8Kykrq/5+XUUB5cQblJRV1j1VXNc3anLt0YPkvCxk0ow/6RvUrn8SkTE6cuEdI6H1KSytxdDRn6etDGDbECwOD+gC6/c8rpDzLxc3NCjfXpjeyvh0dCfJ25/cLtxjh467S3KOfjQuJU99TecybL95BIkiY30912UoQBLr4tOde5FOV51ItpFIJ48d1o1/fjvy26QJ/77xOaGgMy5YOpUf35gNiNyd79i+dycqdx1m58wQLBwSwbGgvlQlfX2tnjgydz6tXD/BjzBVs9Yz4e8BMelo5Nvse6tDX3QlrYwMO3I7+R4FbJpMrtZz2cG3PyC4d2XzpDqN9Pep+l6F2HRls68aGmMuMat+57vryMelLWO454HCr37PFwC0Igj4gEUWxqObfw4BPm3uNvoYWjsYW7Hh4h8lOXZr84IIgEGg2nCPPf+VJaRyO+q3z55OLcm7nhuCk74Wljr3a7eILMtHX0FLSNGiMLu1tOLBsJm/tPc0nR0KJeJLKB+MHM3qkLx3drfn40yOsfGMnSxYNZOIE/39NdF8URX75LZTzFx6wcMEAldrGoijy7enLHL8Xx4phvZkU0DQY9XRtz5bLdygur8Cgkb6Jm5sVx09EkJqWj52t8sXs4FFfR6jNuKur5XUZt562JlUyGZXVMmxrvr/U0gIEQcBG14m08primmYnqFDcu8Wi7wERwXCV2uOOzr/K87JEJjksR1Oiegx83+N7GGvpMNy+qV7Fycg4RBG1IkS3bifSycP2H9nUCYKASxdHXLo4tvm1VZVVNYG8grLiciQSATs3m7rzpry8iouXYjlx8h4PYlPR1JTSv58HY0f74elp1+T8Ki4p52lKDqII3/8QzMYNL6vsR397dH+uJiTz2dHzdWPXrUFafhFHwh/wkr8XlkbqHe67+LTnwsVYnj/Pa3H61MzMgHffHsPIET78uOEs775/gN693Xj91SHN/i5Wxgb8uWgynx+7wB8X7xCbmqU0g9AQ7Q1M2T9kDkVVFehJNVscc28OGlIJE7p58tuFW6TmFdbVidqCqioZM2b/yphRvkqG5G+O7MfluMf879gFNs2bUPe7fNh1GMNP/87/Is7yS2+FcYwgCEzrsIZX+KzV79sajtsKuCoIQiRwGzgpimJwSy962c2f2PxMwrJTVD7fxbQvOhI9bue0uKs6PCy6R15lBoHthjW7XXxBFu7GFkhaOIlN9RXj4EuH9OT4vVim/7KbpMxc3Fyt+f2XufTo7sLPv4Yyf+EWjh2P4OatRyQmZVJUVP7C8oy7997k0OEwXproz7Spqpe3267c5c+r4czq5VtnPdUY3V0dkMlFla1c7m6K7Ozhw/Qmzzl0rA/cBjWj97JqWX3GraXgnksrq7DSMUAqCHX6wja6jqSXPUEuyhA0OoE8G7H8ApQfBf25CFLVLV1V8grOpv+Nra4zXUxUjzXnVZRy9lk84zp4qSxAHY+IxcfBWmVWWVxSTlx8Gt26Oqrc9/8FNLU0MTQ1wMK+He097LB3t1XUEZ5ks/HnEKZM38jX356iqLicV5cMYt+epbz79hi8vOxVBtv4+HREEUYE+RCfkF437doYFkYGrBzehxuPnnIyMr7Vn3fr5TAQqXO9VwffLgoqMzJK9XWs+jUd2PTbfBYuGMDdu8nMW/AHu/bcoErFqqQWWhoafDJxKB9NGMzNxKdM3biL+LQsldvqaWhhpWv4j4J2LSYGKDrIDobdf6HXP4h9Tk5OMX/vuk7Ks/pecgsjA5YP68X1h0+Uulfs9U1Y2rkPZ57FcyH1Ud3jbRWdajHjFkUxCWizCO+4Dl58HXmBHQ/DCLBoymNrSXTwMx3A7dyzjKqej75Gy5nS7dwzGGiY0NlIdcCr+bzE52eqzNpUQSIReHVwD7q0t+HNvaeZ+vMuPn1pKCN8OvLpxxPZu/82wcFRrN9wRul1OjqaWFoYYWlphKWFIRa1f+seM2rSynU6OJLNWy4xeFBnXl08WOUFe+RuDN+dvsIIn468NWqA2gzKr70t2hpSbiY+ZUAn5aq/k6MFmppSEhLSGdBfOUPVNagv4BjWBO6qRhk3QGlFJSZ6OljrGpFaUhO4dZyoEivIrcygnaZiv2L+YhBMEfQXq/2Ob2SfJL8qm5ccliNRI0B29EkMlXJZnclvQ8SlZZGQns17Y1VPskZGPkUuF+n6/2HgboiionJu3nrEiZP3iL7/DA0NCX37dGTMaF+6+LRvVVYcG6to/1yyaBA5OcVs2XaZPr3dlXRzajGluzdHwx/w9clL9O3o2OIIe1ZhMQfuRDOua+cWs0wHBzNMTRU896iRrQ8DmppSpk/rwaBBnfj5l1A2b7nE2bP3WbF8GH6+6nvTpwT64G5lzsqdJ5j56x4+mzSsWcXAfwpbEyP6ujtxKOw+rw7qgUYrp2xrERHxBIlEQFtLk582hvDVuil1v+/U7l04fPcBX564SG+3DnUr4wUde3A4OZpPw8/Q07JDm7j5Wvxnk5N6GlpMcfZlW8It0koLsdFreoIEtgviRs4p7uaG0s9yYrP7y6/MIr7wLv0sJ6gtSgJklheTX1mGh0nr28EAerl14MCymazedZI1u08RkZzKmpH9mDalO5NfCiA3r4TMzEKysgrJzCwkM6tI8e+sIhKTMsnLK2myTyNDHSwsjLCwNMTEWI+zIfcJ8HfizTWjVPatXopL4sNDIfR0bc8Xk4c329uqramBXwdbbiU2zYQ0NaU4OVqQoCLjbgiDGqpEVi2rO2H1ahzASysVBUpbfSPSatqsrHUVRcG0sse0M6qnbwSDZQgS1YMqJdUFXMw8hIdRAM4GqgWQRFFkX9I9vE1t8DBpWk843oLk593wZHR0NOncqWWtiv8ChYVlREWnEBn1lMjIpyQmZSKKYGtrwqKFAwka5q3kZN4axMal4uBghqGhDiuWDVNMuf4Swmcfv9RkW6lEwkcTBjP15138EHyVjyc0L0q1/Wo41TI5C1rItqGW53ZokedWBytLYz79eCI3bz3ip40hrF67m5FBPix9fajaHnXfDrbsXzqDVTXXYsyzDFYO79PmoNpaTA70Ztlfx7gc/7hVrYkNER7xBDc3a4YM6szPv4Zy5WpCnfichlTCh+MGMeO3Pfx87iZvjVZMzmpJpXzSLYhZF3fya+x1Vnm33UT7Px15n+XajS3xN9mVGM5q7wFNnrfUscdJ34vbuSH0sRiHpLGQUQPcyQ0BRALMhjb7nvH5isJkR+O2BW4Aa2NDti+azPenr7LjWjjRz9L5bsYobE2MsDA3xMLcEFAdHCorq8nOKSYrs5DMrEKysorIaBDoY2Ke4+fbgY8/nKCy6yHiSSpv7DqJh40lP84a0yqxoB6u7Vl/5hrZRSV14ja1cHOz4vKVeJUXm4amlOoqWR1VUi2TI21QnAQoqagtUBpzt4bustS2RypokFb2GG+T3ohoAxWgN1XtZwzN2EeVvJwg69lqt4nOTSO+IJPPujUVqKqV/Ozb0RFTNXK34eFP8PF2+EcGtm1BQWEZUTVBOjIqhaTHikCtpaWBZ2c75szug59fBzw727+QlKwoisTFpRJQ08dtY2PCnNl92LT5IlevJdCnd1Phs062lszu3ZXtV+4yrmvnOrGtxsgtLmXvzUhGdelIB/PWiUZ16dKei5fiVNZMWose3V3x8+3Ajr+vsWfvTR7EpfLR++NVdlOBgmrY+sokvjp5iW1X7hKbmsm300epPQdeBF9Hnici5zk7+s3AwlCfA7ej2xS4y8oqiY1LZcrkQMaP68bp4Ch++TWUAH8ndHUV15FPexsmB3iz80YE47p1xsNG0enW08qRcR282BR3g/GOXjgZts0O7j8N3A4GJgy2dWNPYgRLO/dRyV12bzecPU+/42HRPToaqdYOkInVhOWew92wK6ZazQfkuIIXD9ygmIB6a3R//Bxtef/AWSZt2MlXU0coyaKqgpaWBrY2Ji/ksPIoI5vX/jyClZEBv84d32rjhx4u7YFr3EpMYVQjgSF3N2tOnookPb0Am0af6auQD4m//Qhjc0WWXFUlQ7OWKmmccesZcbK0CJlcjoZEEwttO9LKkwGQWEc3m4VllT/jTs4ZAtoNw6KZYvLepHvoSDUY075p3/LNRylkFZWo1bLIyirkaUpOm5bxbUVBQSmRUfUZddJjBfeqra0I1HPn9KWLT3s8Otr8K/o2GRkF5OWX0qlTffCd9FIA587HsGFjCF39OqCn17TA+9rgHgRHJfDJ4XPsXzYTTRVysjuuhVNeXc3Cga2XA+jiU8NzRz594cANoK2tycIFA/Dt0oEvvjzGq0v/ZNWK4QwdonolpqUh5YNxg/Cys+LTo6FM2biLH2eNprPdP+/ySinOZ2vCLarkciLzUpng78nmi3faZLIQGZWCTCanq58jUqmE5cuGsfKNnezafYMF8+uz6JVBfTgX84jPjoTy1+KpdTfzd7oM5nzqQz68G8yO/jPa9Pn/m7VHA7zsFkBuRSknnqpWIetkFIiBhgm3cs6ofB4gtuA2xdX5zbYA1iI+PwsrXQNMtP/ZnXmYlxv7Xp+BlbEBS7YfZv7mA4TGPPrXndnT8otYvO0wWhpSNs2fSLtmhOQbo7OdJUY62irpktoCpSq6xKdfZyavGYtmTZCWyeRI6zjuxhm3EdWinKzyYgBFZ0lZ/dh2c0vn4PS/0JRoM9hKfUZeWl3JiacxjHTorLLYdDziAYY62gzwUD29Fx6hmFD7N/nt/PxSLl2OY8PGsyxYuIUJkzbw8aeHOR0champPvPn9mPDD7M4dngV3349ndkze+Pj7VAXtGVy+T8ypY2NUwyOdWrQAaShIeWNFUHk5BSxdZvqTlyFpdZAHmbk8OfVpsXMgrJydt2IZJiXGy6Wrc/wOrRvh4mJHvcin7bxSFQjwN+JTb/Nx93NmnVfneDb705RUaF+4GmCvyc7Fk1BLsqZ9dveFhUNW4OND64gIGCgocXuxAhe8vdCRORwG4qUERHJaGpK8fJUrMJ9vB0YOsSTvftvKRUqjXV1WD2iL/eepnHobv3+LXQNWO09gOsZyZxIadsx/eeBu5eVI65G5ux4eEdlJ4aGRBN/syEkFN1V6y5+K/cMJpoWuBu27HKdUJD5wtl2YzhamLLr1WmsHN6bJ9n5LP/7OCO+3cbWy2EqR4TbivySMhZtPURJRRWb5k3E3qxtrWxSiYRAFwduPHra5Lt1crJAKpW0yHODYmy5tqtEr7arpKKW465tCawvUBZV51Fc1bzkbFLxfeIK79Df8qVmC887HoZRXF3JdJemv21JRSXnYh4x3NsNbTXDTnfDkzE10cPJ0ULl821BQWEZK9/YycTJG/jksyMEn4mmXTsDFszrx4b1szh6aCXffDWNWTN74eVl34SaEUWRK/HJTPppJ0O+2sz5B20bY65FbFwqWloaODspH1PnznaMHd2Vw0fvEhefpvK1DS21Go9z77wWQUlFJYsHqi/uq0JtP3dkVNPz7EVhYW7Id99MZ+aMXpwKjuL1ZTt4+lT99KS3gzX7ls7Ex8GGd/afYd3xCy+cRD0uyuFQcjSzXLsx3tGbUykPMNDXopdrBw6G3W/1fsMjnuDpaac0aLR44cC6QmXD72pc1874O9nx/emr5JXUSx7McOmKt6kNn0eEtOkY/vPALQgCL7v5cz8vnYgc1SpkCt5aqOGxlZFV/pyk4mgC2g1tlgMHhRLao8JsOraxMNkcdLU0WTggkDNr5/PDzNHYmhjx3ekrDP7yDz46FEJCeut0gBujtLKKV/88wrO8Aja+PJaONi8WeLq7OJCaX0hKo4tUS0sDJ0dzHj5sqj7YEDKZYpS+tvCjX3MSNuS4AaWWQKCOLlGF2mEbE01zepmPUrtdXkUpv8VeZ5Ctq5ImSS1CYx5RVlWttndbFEXCI5Lx83P8x7ZkJSUVvP3uPmLjUpk3ty8//TibY4dX8vWXU5k5oxdenk0DdUPEPM9gwZaDLNl+mNKKSpwtzXj/wFnSC9qeecfGpuLuZq1SJGvBgn6YmRrw/Q+n66QZGuOdMQOQCAKfH7tQFzyKyyv463oEAzs5v9C55uvbnqysIp49b/1Ie0uQSiUsmNePr76YQk5uMUte307o+Ri127cz0OOPBROZ2dOXv6/f40QbDY9r8eP9K2hLNFjcqRfTnP2olMs48uQ+kwO9SS8o5mpCcov7yM8vJTEpk66+jkqPm5kZMHdOH8LuPubK1XpjYkEQ+GDcIEoqKvk+uF7wSyqR8Kl/ENnlTZsbmsN/HrgBxnfwxlBTmx0PVQsQmWhZ0NGwK2G55+qEo2pxJ/csEqR0Mx3c4vskF+VSKZf9axl3Q2hIJQzzcmP7oskcWj6LUb4eHI+IZcKPfzH3j/2E3H9ItZoLqTGqZDLe2HlCIUs6bST+Tur535ag4LnhZmLTZay7mzUJD9ObzZKqqxW9tRqaylRJLcdd2w2UWttZouMIoESXNEZU/hVSy5IYaj1T7bANwC8PrlFSXclan0Eqn68dcffroLognPwkm9zcErp1Vd9e1hpUVFTx3gcHePgwnY8+GM/smb3x7GzXKnXBpzn5rNl9iikbd5GQns07owdw/I25/DRrLJUyGW/vDW5TZlhdLePhoww6eai2FDPQ12Hp60N4lJjJocOqrydrY0OWDe3F5fjHdT3Eu29GUlhW0eZsuxa1QlN37jTVCPqnCAhwZtOv83BzteLzdcf57ofTaqkTTamUd8YMwNnCjJ3XI9q8AojPz+TE0xjmugdgrqNPJ1MrfMxs2JMYQX8PJ9oZ6LH/dnSL+4m4V0PR+TU998aP64azkwW//BZKWVm9sJerlTkv9+nKobAYwpPrk1gfM1vmuLXNpen/JHDra2oxyakLp1NiyShTnYEEtguipLqAB4X1sqBV8grC8y7gadwdQ82WiyLxBYqikce/GLivpT9m5Y0jSoJVHW0s+HTiUM6/vZA3gvrwLLeAlTtPEPTtVjZfukN+iXr1N7lc5MODIVxJSObD8YMZ/A81EpwsTLE00ufWo6Y8t5u7NYWFZWRkqtcdrq6uEdSqybh1NTUQhPqM21BTGyNNHVJr9Jz1NAwx0TRXm3Erhm12Yqfrgo9JX7Xvm1Kcz1+Pwpjk6IO7cdMMsHbEfbSfesnP8HDFZ/gn/HZVlYyPPz1C9P0U3nlrjEr5AVXILS7li2MXGPPDn1yITWTRwECC18xjVm8/NKUSTI11eH/sQO48fsamC7db/XmSkrKorKxWKkw2Rr++HenRw4Vtf14hPUO1ut2Mnr50trVk3fGLZBUW8+fVcHq7dcDboWVBN1WwtTHB3s6U2/9B4AawsDDi+29nMGNaT06eiuT15TuUeOKGUNgS+hLzPJPIp6opI3X44f4l9DW1eaVjvfHxNGc/HhZmEZ2fzoRunlyKe0xGQXGz+wmPSEZfT5uOHW0oKSxF3uDmXFuozMwsZNfuG0qve3VwD6yNDfn0SKiSuNX7fs13yzXG/0ngBkVroEyUsztR9QSYm6EvplpW3G5QpIzOv06ZrJjAdo2llFQjLj8DqSDUGYj+ExRVlvPOnZO8fGkXJ57G8NK5bXwVeV7JKs1EX5cF/QMIXjOfH2eNwcHMhB+CrzLoyz/48GAIcSomv74PvsKxiFiWDe3F5MAX09VoCEEQ6OHSnltJKU1U59xr9C0SEtTz3LWrhNrsUhAE9LS06jJuUBQoa6kSUPRzp5clq9zf9ewTFFRlM8JmrtphG4Dvoy8iFSSs8FI9SdnSiDvA3Yhk7O1MsbJ8sTF3mUzOF18e59btRFatCGLwoObV+EBBcf12/hZB325jz61IJnTz5PSaeawY1hsDHW3SSwt55cpeAo78gJmtDqN9Pfgl9KZShtUcYuMUgzceHuoDtyAILF+qmB7e8NNZ1bUjqaK3O6e4lAHr/iCvpEytEFVrERDgTGTUUyorq1ve+AUglUp4ZUF/1n0+mezsYpa8tp3Q86qLdmO7dsJAW4udN+61ev9RuamEPE/glY7dMdHWJTz3PCdTtzK6vSf6GlrsSYzgpQAv5KLIoRaKlOERT+jSxQFZVTWznV9n01plG7LaQuW+A7eVbkB6Wpq8O2YADzNy2Hm9/rO3tT/+/yxwOxqa0d/Ghd2JEVSqkFGUCBICzYbxuCSGjHLFsv92bjAW2nY46bfO2iehIAtnw3bNmgi3BhfTHhEUvIkDjyNZ6NGDG2NXMMmpC5vibjDqzGZuZT5R2l5DKmGIpyvbFk7i8IrZjO3amZORcby04W/mbNrHmegEqmVytl0OY9uVu8zo6cviNrRj1aKkqpJfHlwjrVQ5g+7h2p68kjISMpT5dmdnCyQSQeXoey1kNVRJbR83KHju0gZLVVt9ozqqBBQFyqyK502cO4qr87mUeYhORoHN2jFF56Zx7GkM89wDsVYxmAXNj7iDglKIjEx54WxbLhf5fn0wly7HsWTRQLXqd3XvJ5Oz71YUI77dyk8h1+nh4sCRFS/z8YQhWBoZIIoih5OjGRH8Bzczn2Cla8ja28d5bXh37EyNeHPvaSUZYXWIjUvF1FQfK0sj/njrb76YuV5lYLa2MmbenL7cvJXI5SuqR9297K2Z3kPRJulsaUbXFlxmWkKAvxMVFdVERbd+/L0xZDJZi/RG90AX/vhtHi7Olny+7hg/rA9uQp3oa2sxvpsnZ6MfklXYfHZcix+iL2Gipctc90DKqos5mbqV69knKJFlMraDJ6dSHmBiqENP1/bNFinT0/NJS8unq58j96/FU5RbzOEfTzbR5V+8cCBamhps/Fm5UDmoswv9PZzYeO7GC3cf/SeBu1YWtDHmuAWQXV7C6ZRYlc93MxuEVNDgds4ZUsuSSCl9SGC71qsHxhdk4v4PaJKCyjLevHWcBZf3Yqipzf7Bc3i7y2AsdA34ImAUfw2YiVyUM+PC33wQdpoiFTZp7tbmfDxhCKFvL2TNiL6k5Rfxxq6TDPlqM9+evkKQtztvj+7f5jtsclEuL53bznfRF9kSr+wyU8dzP1LmubW1NXF0NOehCnu0WlTVBG7NBnxu44zbTs9YKeO20XVERF53g61FaMZequSVBNm8rPb9RFHk66h8ygoDAAAgAElEQVTzmGrpsthDtW527Yh7cz6EcXFplJVV0tXPUe02zX2GX34L5XRwFLNn9mLK5OYlFM7FPGL8jzv45EgoDmYm/L1kKhtmj8XZUiG6lFVWzJKr+1lz6xhuxuacGL6QzX2nUlRVwUeRZ/hq6giyCkv46FBIi0ErNi6VTh4KcarQnZe5sPsaF/ZcU7ntxAn+uLpasfHncxSXqL4pLB/Wi31LZ/Db3PGt/HbUo4tPezQ1pS/McxfmFrHQezXLe71HamLz3U4K6mQ606f14PjJeyxd/hfPGlEnM3r6IhPl7GsFJx2WlcLl9CQWefTEUFObq9nHKJeXIkFKWG4I05z9KJdVc7SmSJmWX8T1h09U7qu2BdXPrwMRodFIpBIMTA3YuGyL0u9bW6i8E/aYq9eUC5XvjhmIKIp8dfJii59dFf6TwJ1ZqLpC2sfaGSdDM3Y8VO1LqK9hjJdxLyLyLnE16yiaghZ+JgNa9Z5FVRU8Kylo86h7LUKfJxB0ehNHnkTzaqdeHB22AN92dlTJK0kqjkYuyuhl5cjJoIUscO/OnqQIgk7/zoVU1fZHJno6zOvnz+k18/hp9ljcrNox1MuVdVOGt8nUFeBSWiITQraRVV6Mi2E7rqQrXzhWxgY4WZg2CdzQcoGyjuNulHGXNMhwbPSMKagsr/PztKkbfU+u2yaz/BlhOSF0bzdcpZNRLa6kJ3E9I5nXPfuoFQlqacQdFDSJINCs7oU6/LnjKocOhzFxgj9z56jn4cOTnzPr972s+Ps4AgI/zR7LX4un1E0liqLIsScxBAVv4krGY971HcLugbNxMjTD2ciE932HcCU9iTulT1g5vDch9x81W/gqKionJSWXTh62ZD3LISc1D6mGlF9Xbacwt2lmJpVKWL0yiLz8ErZsUd3bbaCjjaedFXamL66aWAtdXS18vB24c7d19msNIauW8fm0H0hPyuBZfCpL/NYS8telZl+joSFl4YIBfPG/yWRlFbL4te1cuFif9HUwN6GvuxP7bkdRWa1ewEoURb6Pvoi5jj6z3bpRUl3A9ewTeBv3prNxd8LzLtDRpB1eptbsToxggIczZvq6HLijmi4Jj3iCmZk+jh3MuXc+Go/ubrzy5UzuX43j3N/Kv0NtofLnX0MpL6+/puzNjFk8sDsh9x9xJb7t3+d/ErhLKyuJed40y5MIArNdFa4qUTmpKl6pmKSskJcSmX8Fb5M+6Gqol5xsiISawmRHFYWu5pBXUcobN4+y6Op+TLX1ODhkHmt8BqIt1eBpSTw/P1zDlqSP2JT4LhnlT9HT0OJdvyHsHzwHQ00dXrmyj1U3jpCjpp1HKpEwqLMLfyx4ifUzx6Cl0XoaRxRFfou9zoLLe7DVM+LI0PlMcfblUWG2UgYMiqw7LPl5kxPYzdWa/PxSsrJUL8lkNYFbKlWfcdvqK+iMWorGVNMSbYlevcQrEJy2Ay2JDgOtpqg9HplczleR53HQN2GGS1e127Q04g6KwqS7u02rbbJqsW//LXb8fY0RQT68tkS10FdiZg5Ldxxl9u/7eJ5byMcThnB4xWwGdXap2z67vITXrx9k1c0jOBqYcnzYAhZ07E65vJg9T77js5hZ9LDSJsjeg2+jL9C1sy293Trw5YmLdRZ0jVHbm93Jw5a4W4qEYOlPCyjMKWLL2ztVvqZjRxvGj+3KsRPhKr1L/20E+DuRnJxNZjMFb1XY/PZOws9Fs/yXhfx+7xtc/Zz4es5Gvpy9gZLC0mZf26O7C5t+m4+zkwWffX6UH348U8ezz+jpS3ZRabP+kdczk7mV9ZRXO/VGT0OLy5mHqZJXMthqKgFmQymTFfOg4CZTnf2IL8gktjCD8d08uRCb2ISGEUWRiHtP8PPtQElBKQlhifgN8mL4vIF4dHfjjzf/oqSgPhY0LFTubFSonNu3K04Wpnx+7ALlVW2rG/wngVsiCErEe0NMdPJBX0OLP9W0BrbXqx/d7t6KScla1GmUtCHjPvMsjqDgTZx8+oDlnn05MnQ+3mY2VMkrOJ26nU2J71EpL2eI1QxyKjL4+eEazmfso1pehW87O44NW8AKz76cfhZLUPAmjj2J+dcGFEqrK1l+4zDfRF1gpENn9g+Zg4OBSZ3b+dVGWXcP1/aUVVYR/Ux5Cerurn6CEqC6pt7QMOPWa5Rx1/ZyP6/huRXa3I51LYGJxdHEF4UxwHJSs/KUR5/cJ64gk9U+A9TWIVoacQeFL+OD2FS6tZEmOXHqHr9tukD/fh68sTKoSbdKtUzO58cuMH79X9xOesbyYb04tWYekwO9lQSOTqXEMiJ4ExdSH/GmzyD2DZ6Di5E5Dwpu8WPCCh4U3kJXasD+Z+v5oGtfzHUMWHnrCO+OH4CBjjard59SeaHGxaUiCIpgHHfrIZpaGgybO4CXVo7i1OZQoq+ophjnz+uHubkh368Prmvv/K9Q2xYY1oasO3TnFQ58f5xxrwcRNH8Qlu0t+Ob8R8z5ZCoX9lxjid9aHtxMaHYflpZG/PDdDKZN6c7xExEsXfEXBYVl9HbrQId2JuxSE29EUeSH6EtY6xoy3cWPwqpcbuYE42vaHwsde5wNvDHTsuJObghjOniip6HJnsQIJgV4IZOLHL6rXBxNTs4mL6+Ern4diLr0ALlcxG+wNxKJhGUbF5CfWcifHymb//p4OzBksCf79t9Sonu0NDR4f9wgUnIL+ONi6zuP4D8K3CZ6upyKjCenuOmd1FBTm4mOPpxMiSG7vGlRQRAE3uz0B2s9fsdOt/WtcgkFmRhoaGGn1/KSMKe8hOXXD/PatYNY6RhweOg8Vnj1Q0sq5UlJLBsTVnM1+xj+ZkNY7r6egVaTWNnxRzyNexKasYdfHq3lWelDtKRSlnv14+jQBTjom7Dq5hEWXd3XpHjYVjwtzmPSuT8JfhbHmz6D+LHnePQ0FP3V7sYWWOsaNqFLApztkQhCE7rExdmy2QJldZVyVwnUZNwVyl0lQJ28KygKlOnlT5CJMk6nbsdE04Ie5iPVHlOFrJrv71/Cy9SaUQ7quzdaGnEHiIqu0YhoQ//2+QsP+GF9MIEBzrz79pgmpgRyuciHh0LYdeMeU7v7cGbtfBYP7F6n3QKK82bZ9UMsu34Ie31jjg1bwOJOPamQl7Dv6Xp2PvkKIw0zXnP9mpcd36OkupBzGVv5ofs4npXk81PCFdZNDuJRRo6SE3gtYuNSad/eHH19bWJvP8TF1xEtbU1mfzwFqw4WrF/yu0ovTD09bZYvHUrS4ywOHFRNQ/5bcHQ0x9zcsNVtgQl3E/l+4a90GeDJku/n1D0ulUqZ9cEkvr/0KaJcZFXfD9j1xSFkzfg/amhIWbRwIP/79CUePcrgyNG7SCQKR6vIlDSiU5qe4xfTHhGR85ylngqtpIuZB5GLMgZZTgYUTRH+ZkN5XBJDuSyb0e09OfH0AWZGugQ6O3DgTrRSt1Z4RDIAfn6ORIRGo62rRaceCtEv924ujFo0hKMbT5MUpcyPL1mkKFT+1KhQ2cOlPaN9PdhyqXmT7cZodeAWBEEqCEKEIAgnWtrWzECXKpmMA3dU83kvu/lTJZezOzFC5fPGmu0w0bJoUwEvrqYw2dxrRFHk5NMHBAVv4uzzOFZ59efg0Hl0NrWmUl7OydQt/JH4PjKxmvlOHzPefgk6UoV2iL6GMVPbr2KW4zuUyYr57dE7nE7dTqW8go4mluwfPIf3fIdwPSOZoOBN7E4MR/4C2ffV9CTGh2wlrbSQLX2nsrhTT6VjEgSBPtZOXMt4rFT1NtbVoZOtZZNBHB0dTTq0NydBzQRl3QBOY467AVViqWOAhiBR6iyx1nWkUl7O+Yy9pJU/ZrjNbLUGwAB/PrxDWmkhb3UZrNbgojUj7qAYc9fS0sDLs3WDS9dvPGTdVyfw8Xbgk4+aqjOKosi6Exc5Gv6A14f05P1xg5rQNLWrs5Dn8azxHsD+wXNxM7YgtvAOPyasIDr/GoOtpvGq21dY6zpiq+vEcOvZxBbeRiaJUazontwnQ7OAef26sfdWFCENlveiKNYVJmXVMh6GJeERqOgp19XXYdnPr/A09jn7vj6m8hh793Knd283/vzrKmlpzcsR/BMIgkCgvxN3w5PVTm7WIi8jn48nfIOplQnv712l0qPVs1dHfov4hn6TerDt/d28Pewzsp83bxzcq6cbgQHOHD8RQVWVjHFdO6OnpcmuRq2B8ppsu72+CZOcupBXmUlYbgjdzAZjpm2NWHkHsfQgXU0H1hUppzv7USar4tjTGCYHevM8r5AbDZKh8Ign2NqaYG1lTMT5aLz6dkKrwcj7vM+nY2BqwE9LNzcpVM5RUagEWDOyHzpt9K9tS8a9AlC9VmsEbQ0Nerm2Z8/NSJUOys5G7ehr7czuxHAlx+MXhcI8IauJOXBDZJcX8/r1gyy/cRg7PWOODl3AUs8+aEqkPC6O4aeEVVzPPkn3dkEsc/8BF0MfRLEasfh35FlDEEv3IooinYwCWOH+I/5mQ7iafYyfElaRVByNVCJhfsfunA5ahLepDe+HnWbWxZ0kF6keIlB1DH/E3WTe5T1Y6hhyeOg8+tmolpjsa+1CQWU5UY1c7Hu6tifqaXqTrh43Nyu1BcrGfdxQm3HXZ3ZSiQRrPUOllYRNzQTlxcwD2Ou64W3cW+2x5VeU8euD6/S3caFXM/6ALY241yI8/AneXvatUuILj0jmk8+O4Opqxf8+naTSwHbD2evsunGPuX278WqjXue8ilJW3jjCa9cOYqNnxNGhC3i1c2+qxDIOpPzE38nr0Ncw5lW3rxhkNQWJWI684D3EzG70NLbH3bArp9O2M9HJju4W7fkoPJgxPTrhZW/FhwdDSM1XfKdp6QUUFJTRycOW5JgUyksr8OhePwzUfWRX+k3uyc7PD/Lsoeqhk+WvD0UikbB+w5l/jbJTBX9/Z0pKKup6zlWhqrKKTyd/R2FOER8fXouJhfqVsIGJPu/uWsnqLa8Rd/sRi7qs4frR5lcO48d1Ize3hMtX4jHQ0WZ8N09ORyUorfLPPosnJj+DZZ590ZRIuZCxHwGBgZaTEMVKxPy1iIXvYyCpopNxAOF5F+hkYk5nEyt2J4YzuLMzJno67L8TBSj6/iOjntLVz5GctDyePHiG3yDlWQwjM0MWrFMUKkN3XlF6boKaQqWFoT4fjFM9PawOrQrcgiDYA6OAza3d8cxefmQWlhAa80jl83Pc/MkoK+ZIcsutPM1BFEWuZjymsKpc7aj7tfTHDD9dy0kO5MCQuXVceHDaDjYnfQAIvOL8GWPsFqIt1UWU5SDmTEEs/g5EGWLhB4h5cxDlxehI9Rlvv4QFzgrrzS1JH3HiuaIVqL2BKX8NmMG6gFE8yEtn5Jk/OJAU2eIxvHX7BF9GhtLJ2JK9g2fjaKje36+3lSMCcCVdWcSoh6sD1XJ5k2EPN1cr8vJKyM5pSk3VZdzSRn3clVVKS0RbPeM6jhvAUseh7t8jbOc2u9L5MeYyRVXlvOmj2sGmFscjYrEyMlA74g6Qm1vM4+QsvLxazrYj7j3h/Q8PYm9nxpdfTEFfX3n8Xi4X+eXcDTZdvM2kAC/WjOirdBxRuakEBW8i+FmsYnVWc96klT3mx/gVROZdYqDlZF5z/RpbXWfE6keI2aOg7CCgCQWrecl+ATpSffanrOfr7qPQkmiw9s4xvpwahEwUeXtvsCLbrnG8aViY9GhktPva+nlo6Wjy01LVl6GFhREL5vXjTthjLl1uvY1ZW9Gtq0Ibprm2wM1v7eT+1TjWbH0NV1+nFvcpCAJB8wby692vsXa04KMJX/P7mh1qtw8McMbO1pQjR+8CMKNnlyar/PX3L+Fi2I5xHbzIrUgnIu8Cge2GYaxlrviN5KmADMoOEmA2jFJZEbFFt5nm4kdsfibxhVmKIuWDJLKLSoiPT6O0tJKufh24d17RceI3uKkkbdD8gXgEurJp7Q5Ki+qnqJUmKvcoFyobyzK3hNZm3OuBNwG1ayNBEBYJghAmCEJYVlZWnbhOhZpiSX8bV7xNbXg37BSb4m60OUMoqqrg74dhjDqzmbmXdmOspaM2Q72cnkR+ZRnb+k9ncadeaDRox4vOv4ahhinL3L9XHhqRpUD1fdCdhmARCtqDofImyOszaGcDL5a5/4CJpjnRBfW9toIgMMXZl/2D51AhqyZUTctgLUQgNl9BZcTkZ9Dz2Aamhu7gq8jznH0W36QW8NfDMESo470BKqtl/F1ToGnIy+bmFnP8xD10dDRVZqhmZoqunYbZk2PN0Mul+PoL09PUmns5z3leM/quKdFinN1iBlhOUmv2XFsY2vEwjBkuXVW62zSEIAhkFBazevdJsopUd+nI5CL6+trs3nOTPftuqVyuV1fL2LL1Emve3I2FhSFffzkVYyNl6iOrsJjF2w/zc+hNxvp14sPxTTtMEvKzyC4vwUbXiH42zmhKFKuSzPJnFFXnYqRphrth13pHpupEkKeBxBzEcpBloieImGlZkV+ZhYGGBDs9I54W52Gsr4OlkT7J2XlUy+V1GZiWlgblJYq2Sy0dZepJEEBHX5v0pAy110utgH9q2r8nBtUYWlpStLQ0KC5pOsdQi8hLMXh0d2PAVPUrMVWwd7dl/bXPsWxvztVDN9VuJ5EItG/frq5bytbECF0tTdIbDLTkVJRiqq2HRBAolRUhR16vVCmvp2NEeR66UsV1UFpdhLGW4lzJqyzDRE+Harmc4vLKuuM1MNChOF9xftYabit/Ngl2bjYU5hRTnKd87fp4O9CrpyvBZ6L/0aqoxcAtCMJoIFMUxbvNbSeK4iZRFP1FUfQ3MDZh3YmL9HV3ZIyv6otaIgjsHDiLIHsPvoo8z/IbhympUj240xCxeRm8H3aK3sc28FH4GTQEgc/9R3Jl9DI6GKiespvm4ocAXM9oWgnvazGeouo8npQ0YoE0u4CmP1ScB1kqVN4AnZEIGsr+mSkl8eRXZdPfsqnD9oHHUQjAMs8+NAeJIHB8+CtcHbOMn3pNZKZLV2SinG0Jt3j12gG6H/2RASd+ZtWNIwSd3sSPMVeY5OTD/I6KZX1ltYw3dp3gYmwS748dRLca0arcvBJWr91NZlYhX34xpUnwAnDsYI6fbwcOHblbZ+Y63McdBzNjNobcqMu6F3TsjoDAxpj65V9gu+EMtVYtAC8XRT6NOMvGB1eZ7NSFj7q23CH085xxrBjWm4txSYz94U8OhzXt0rEwN2Tr5lcUms5/XOC1ZX/yqMGA0fPneSxf9Tc7d98gaLgPv/08l3btlC+u8w8SmbDhb8KTn/PRhMF8MVl1b/0k5y5s7juVSrmMSee2803UBSpk1XQx7cs8pw8B2JT4LqdTt1Mlr0DQGY5gugPFrbgcwfBtLmRf5mlpPOPsF/FLbBgx+Rl8FTiaDcHXeZyVxxeTh6MpldaJFYXdfYzfYMXy++7Z+pVaZUUVn0z6jpL8Uj7Yv1rlCic2NpX1G87g59uBqc0MFf1ThN1Npry8ih7d1TcP+A7wJPFeMuWl6oO7OsTdekjm02wmrhytdpuCwjLuhCXRv5+i1/9iXBJllVUM9653B1rtPYCw7BSOPLmPvZ4bnYwCuZhxUCEfrVc/JCbozeBK1hG0JXp0MenLprgbOBma4d/Ogb+uRdDLrQOOFqZ4e9mjoSHhbnhy/W8UEtXks90NiSR05xWmvjkOy/ZN6duAAGeys4tI/Qe1iNZk3L2BsYIgJAN7gEGCIPzd3AtScguwMjLgy6kjmpXb1NfUYkPPCbzVZRDBz+J46dw2Hhc1LUxUyKo59DiKSee2M/rsZg4lRxNk78HBIXM5OmwB01z80NdUXxhzMjRjsJ07ux6FU1atXJX3NxuCiaYFIem7lYKEIAgIhqtAnomYPQjECgSDFUqvFUWRs+k7MdY0J9BMOTClFOez4+EdJjr60Nm0dcI+NnpGjHToxHt+QzkwZC6RE9eyb/Ac3ukyGE9Ta25mPiG3opSZLl1ZFzAaiSBQWS1j9e6TXIhN4r2xA5neUzHinJtXwuo1u8jILGTd55Px8XZQ+75TJgeSnV3EhYuK1idNqZTXBvcgLi2Lcw8e1X226S5dOZgc1SJvXy2X8/btE+x4GMZ890DWBYxq1dCRplTKooGBHFo+Czcrc94/eJZXthwkJVf5BLcwN+TTjyfy8YfjFZoWr29n0+aLHD0ezqJXt/HsWS4ffTCetatH1mWgAGWVVXx6JJRlfx3D2tiQ/UtnMiXQp1maZ6CtK6eDFjHR0YffYq8z9uwWIrKf42roy3L3HwkwG1ZT63iD5JJYBO0eCOZnEaxieCzvwoXMA/iZDiC/zJ4t8beY7doNea7A/tvRLOjvTx93R8X3a2OCvb0Zd8Ie4+TdHjMbU8LOKlZQoiiycekWHlyPZ82211VSD9nZRXz48SHM2xnw4Qfjm3TN/Ju4ejUeAwPtOgd4VfAP8qOqooqoS203Pdj5+UFMLI0Z8Yp6RdBLl+KorpYzZIhilXw8IhZLI30CnOsptCnOvvia2bLuXigFlWWMsp0PwMnUrQgSIwTLWwiWEeTItIgpuEH3dkHczc4kJi+dRR49ORERS05xKQsHKJT7dHW18PKy586dJBw62mLZ3pywM8oNFmUl5axfsgl7dxtmvt/UHxSouxajol5cOqDFX1cUxXdEUbQXRdERmAacF0VxVnOvqZbLWT9zNCZ6LQ9HCILAIo+ebO8/nezyEsaHbOPcc0XV9XFRLl/cO0evYxtYe/s4+ZVlis6NMcv5uvsYfNvZtbrz5JWO3cmrLONQsvIdUkOiyUCrKTwve0RcoXJBRNAKAI0aDktnLIKG8gUTW3iHZ2UPGWQ1tYmB8ddR55EKEt54ASPQWmhLNehmbs8rHj34ufdLXB+7nEujX+dT/xF1QXvN7pOcf5DIe2MHMqOnQm+jNtOuDdq11lPqEBjgjJOjBfv23667eY3y9cDZwoyNIdfrulde7dQLTYmUn2KuqN1XhayaFTcOczA5ihWefXnXd0ibx/udLMzYvnAyH44fRPSzDMav/4vtV+4qyeYKgkC/vh5s37KQ4cO82bP3Jj9uOIu7mxWbf19A/37KnGFcWhZTft7F3ltRzOvbjV2vTq0bWW8JRlo6fBk4mm39plFSXcmU83+y7l4ooqjBOPvFzHf+BLkoY3Pi+5xM3UIVUkplpexLWU87bRsCTKex9tZxOptYMcvBn48OncO3vQ3LhvZSep8AfyfuRT6hqkqG//AuhIdEIZPJOP7rWU5vCWXGuxPpP7mpTEBlZTUffXKYktIKPvv0JZUrq38L1dUyrt98RM8ers3qk/v064S2rhZ3TqvuHFOH2FsPCQ+JYvLqMeiosGerRci5+zg6muPqYkVeSRlX4pMZ5euhlCBIBIFP/UeQV1nK99GXMNWyZJDVZGILbxNXGIYgMUWQ6HMl6whSQYPe5qP5NfY6VroGjLbvzNZLYfg4WBPQQHY50N+ZpMdZ5OQUEzDcl4jQ+1Q36Mnf8dE+0h9nsmrTkiZUVy0cO5hjbKxLVNSLOwr9J7dlOxOjNvvC9bZy4uiwBTgamLL46n5c9n7OkFO/8mfCHXpZOfLXgJmEjFjC/Bplr7bC39wBHzMbtsbfbtKm52c6gHZaNpzL2I1cVOZMBZNvEUz/UGTfDSAXZZzL2IW5li1+pgOUngvPfsaplFhe8eiuVkTpRSAIAroaihtElUzG2j2nCH2QyLtj6oN2Xm3Qzihg3f9aDtq1+508KYCkx1mE3U0GFJ0krw3pQWJmLqejFDdSC10DZrv5c/TJfR4WNFU+LK2uZPHV/QQ/i+N936Es9+rX5qBdC4lEYGr3Lhxb9TI9XNvzzanLzPh1TxPFRUNDHdauHslvv8xl6+ZX+Pbr6Vha1n/ncrnIn1fDmfbzborKKvhj/kTWjOzXpgnWWvSzceF00CKmOPmyOf4mo89uJiwrBRcDb5a5/0D3dkFczz7JhoRVfBO7hDJZMZMdVvH27TNUyKv5NnAc7+47i1QQ+GbayCaekLUiTtH3n+E/zJeivBL2f3OMX1Zuo8eYbsz5tKkFnCiK/LA+mNi4VN5+czTOTv++Fn1DREalUFRUTt8+6uUIQMHPdxnoye3gtgXuXZ8fxNDMgNFLhqnd5mlKDjEPnjNksCeCIHA6Kp5quVwlLetpas1sV392PrpLVG4qvczHYKntwInnm6mUV1BQlUNE3kW6mg3iUWEJNzOfMN+9OxceJPEsr5BX+gconcO1Bs53wh7jH+RLaVEZD24oro/4sEQOrT/BqEVD8emnflZBEAS8vRz+kVhXmwK3KIoXRVFUTzzVwLgVmbYq2Okbs2/wHF7p2IORDp1Y5dWfK2OW8lOvifSycnzhIACKL2tBxx4kF+cSmqrcRykVpAyymkJ6+RNiCpQLIoKGM4J2fwSpMt0RlX+VjPKnDLGejrSBM48oinxx7xwWOvos7FifHR2PiGXN7lN8d/oKe25GcjnuMY8ystUKcjWHKpmMNbtPcS7mEe+OGcDMXg2C9puKoP3F/ybRpZmlbGMMGtiZdmYG7NtfL2A13Msdd2tzfjl3oy7bXeTREz0NLTY0yroLK8uZe2k31zIe82XAKOZ1bLv6oSpYGxuycfZYvps+kvSCIqZs3Mn6M1epaDR56O5mjWMHcyWKoLYA+fXJS/Tt6MjhFbPp5fbPTBcMNbX5PGAkOwbMoFImY9r5HfwvIgS5qMEYu4W84vwZ1fIqqsQKRtrO40BSCreznvJZtxEcufaAmOcZfDZpGLamTW/otSJOt+8k0W2oDwBb3t2FnZs1b/+1HIkKuunQ4TDOhNzn5dm96de3+WD6b+DK1Xh0dDTx79Zyp0hAkB+pj9J5/qh1mtmPIh5z88RdJq4YhZ6h6uRMFEU2/nwOPT0tgoYpeOYTEXG4W5urdfZZ5dUPcx19Pgg7jYCUsXaLyKvK5JpYa2cAACAASURBVFLmQa5nHUcU5fS1GM9vsdcx1tJhqrMvmy/dwdnSjIGdlBsenJ0saGdmwJ2wJPwGeSGRSrgTfI/qqmq+X/grJlYmLPxqZovH2sXHgbT0gjZLB9TiP3V5fxFoSzV4x7dlt5sXQZC9B3Z6xmyJv8VQO+WT3MekD5cyDxGasRtP4+7N2qTJxGpCM/Zgo+OEp7Hy0vVUSiwROc9ZFzCqjndPzS/k48Pn0JJKKa2qauKUY6yrg52pEbamRtiYGGJrYtTg/0YY62rX3bQaBu23R/dnZi+FV2N+filr3txDWlo+6z6fjG+XtgUoLS0NJkzoxuYtl0hMzMDFxQqJRGDpkJ4s//u4wu3H3xMzbT3mugfw84NrvJaXQSdTK7LLS5h3aTcPC7P4secERjo034fdVgiCQJBPR3q4tOeb05f54+IdQu4/4pOJQ9S6B12ITeSDgyGUVVbx0fjBTA70/kc3/sbobeXEqaCFfBN1gW0Jtzmf+pAvA0YTaOnJGx4bkQqa3Mx4ysaYXbzk6INxmR47rp1jRk9fhqgxz9DV1cLby56wsMe8ungQnXq4EXvzIZ8ceQt9o6Ym0nfDk/n19/P06e3Oy7OaL4D/G5DLRa5eSyAwwBkdnab98I0REKRIKO4E38NuqWpHn4bYte4Qeka6jF82Qu02ly7HEXb3MUtfG4KZmQFPsvOITElj9Qj1YmGGWjq85zeUlTeOsDsxnFlu/via9Odi5gEAupj0JbdcSsjzBJZ27kNEUhoJ6dl8PmlYkxqdIAj4+ztx/cZDdAx08ezVkbtn76FvpEtS5BM+PrQWfWP9Fo/Vx6eG545OYcjg1slWN8T/7wL3fwkNiYS57gF8fu8cUTmp+LSrV7GTCFIGW01l99Nvicy/0oT+aIi7uaHkVmbwsuN7SmYBFbJqvom6gIexJS85+tQ9/v3pK4iiyMHls7A2NiSrqITU/EJS8wob/C0iKSuXawnJlDXKJvW0NBWB3MSIoooKwpNTeWtUf2b3Vgg15eeXsnrtblLT8vjif80H7crySv78cC8xN+L58swHSjzimFF+/L3zOvsO3Oadt8YACu1gTztLfgm9yShfD7Q0pCzo2J0dD8NYf/8SH3ULYs7FXaSWFvB7nyn0V9OS+W/ARF+XzycNZ1QXDz45fI45m/YzJdCbN0b0xVBHcRxllVV8c+oye29F4WFjwdfTRrTJ0bwtMNDU5pNuQYxw6MQ7t08w/cJfvOzmz1qfgRRWl7P61lGcDduxxKU3s37Zi4eNBWuaCTCgGG7Z9McFsrKL2HD9C+RyucpM+3lqHp/+7wgd2pvz9puj/rHnZmvwIPY5ubkl9O3j3vLGgJ2rDbau1twJjmD8UvXBGODJgxSuHrzF9HcmYGCiOvCVlFTw8y+huLlZMW6s4tw/cS8OQYBRXZrvgx7t0Jl9Sff4NvoiQQ4ejLCZw718hexAP4v/x955BUR1f137mRl67006KgIWlGbvNYkFe41GY4mJJaaaRE3UFE0sicaosffee0MUC6KoWBBUkCa9I33mfBdDcWSAATHv/+JbN+icwmHmzD77t/faaw1mWdgNtCRqjG/qw6wtx7Ey1K+WW+3r48zZcw+IiEjEu48nm3/YzdPQaDoN8aPDINVWms5OFujqanI/LPb/B25VMMzZkz8fXWVDRDB/tfdX2OZu2BZrLScuJe+jpVFHJKKqb0+JrIhLyftx0GlGU31FhbttT28T9yqLrV1GVTRJbkfHczoskuk92lYsjy0N9bA01KuQB30dgiCQ+apAHtCzcnmZmUNiVg4JZcE981U+cz/oytgOr2fau0l4mckvi4fWKHMaeec5S8evJuZxPAA3jt2m28hKnq2+vhbv9WvF0WOhTPqoCxYWBohEImb0as+0LUc4fOchI/xaYaihzceubVnxMJALL5+ip67Jli6j8DFXvTTzNmjfxIHDsz9k9fnrbL92l8tPopg3sAc2xgZ8tecUUSkZTOjkxaze7etVy64r2lo4cLLvZP4Iu8y2pyFcevmUlII8RCIRGzqOZMGB85RIpSwb9X6No/wAvj5ymuPt21H069tKadDOzy9i3oKDiIBFC4egU0MTryFxNSgSNTUxfn6qP5x9+7bm9MaLFBcWV9usA9j962E0dTQYPLt6c+nNW66QkZnHooVDkEjECILA8bvh+DnbYWlYs4qoSCTipzZ9ee/sen69d4llbQfwhesa1MQa5BVLOBbzkDGNvYhOzCT0xUvmftC1Sg+iHG3aOCISQcidaNr1acXmH3YD8Olfk1R4R+SQSMS0aG5b7zr3f+aA878CfXVNRjh7ciY+vGKYpBxikZieVqPIKE4iNCNA6fHB6WfILc2gl9UYhaV3RlE+fz8Ooou1Cx3LFPykMhm/HL+MlaE+Ezt7q3R9IpEIEz0dmtta0bt5EyZ08mJu/26s/nAgh2aOJWDulIqgnZ0tD9rxCZn8smhotaYCpSWlbPtxHzPafser7Hx+Pvkd5ramXNxZVcN56GBvBEHg0JFK0ZuOTR3xdLBm3aVbFbXlCU190FeXB4ydXcf8Z0G7HDoa6nz9fhd2fTISIx1tZmw/xvDVOysakF/VswFZ7+tR02B+m97s7j6OUpmMYpmUHzx7celOFKEvXrJgUA8czWv3TXVyNMfUVI+Q28rV92QygV+XniA2Np35PwzCxtqoof8UpRAEgaCgCNq0dkRPV/UelndfT4oKigm7Ur1aRvzTRAJ2B9F/Wm8MzZQ38yMjkzhyLJQBH7Shmau87HIvNpG4jOwalSRfh7OBKZObteNIzAOCU2Iw0bTCQN2EDWXGJB+7+rHxcgjGutoM8ak6EVkOQwNtmrnaEBISRePWTqy4uojlgQsxta79830dLVvYEReXQUZm3Rze4X80cBeVlBKfodwEtSEwocxReWtkVT0EV30v7HSaEJCyv4rjfKE0n8CUQzTR86xizbXq0VVelRbzbavK+vzBkIdEJKby1Xud0NaovSZYF2Rn5/NFWdD+edHQai28oh/GMqPtd2xfuJ/uozvy74Pl+PZrTffRHQk5c4/MFMX32crKiC6dm3Hi5L0KV5XyrDs5J499t+R0Sj11Te4N/pInw76luUnt9cuaUBcX9DfRws6KfZ+NZnafDgxo7d4gDci3gY+5PRfe+4SIYXNxEkxZfzkYfy8PPlAxuIhEIry9qhdx2rYjiGvXnvLJ1O5vZZJcVzx/nkJiUnatbJI30aqrB+qa6tyugV2y97fDqGmoMfSL/kq3S6UyVvx5BkNDHSZNrPQoPX43HC11NXo1V83gGWC6WwdsdQ1ZcOcMxVIpGUX57I26ywCH5uTmFBMYEc2Ydp61fl99vJ14EpFIXl4RzTs0o0Wnuvd1yhlf9eFz/88FbkEQmLP7JH1+30T/5Vv58+w1HsZX7+BSH9joGvKenTt7o+6SW6xo+SQSiehpOYrskjRuZ1xQ2HYt7Tj50lx6WSl2jaNz09n1LJQRzp4VjuXZBYX8ee4aPk62CtNcDYHsnAK+/HoP8fGZLF44BC8lX2CpVMrepUf51Psb0uLTWXDwS77ZOqOifthzXBdkUhmBe69XOXb4UF/y84s5dapycq+tiz0+zrb8ezmE/NekRcvHwOuLhxmJND+4lN6n1rLgzhnOxD0hs6hmYf03oS6RMLmrL4uH9q7RfOG/graaOlmvCvhm3xmczE34bkDNGi1vwtfHmdzcQiIiFNkYV65GsG37Nfr0bsFgf9VWcA2B5JRslq04jUQipn071aWWAbR0NGnZxZ1bZ5TrZSfHpHJ++xXe+7gnJlbKM9YTJ+8REZnE9Gnd0dOTZ/vFpVLOhEXS3d0FXc3qSzBvQltNnQVt+vA0J43NkcFsjQyhUFrKlGZt2Rh4Gx0NdUa1q9l/FOS0QJlMqJB5rQ+aNLFES0udsAd153P/zwXu/bcecDk8igGt3TA30GXjlRBG/L2bXks38uvxAEKi4t8qQyvHJFc/8kqL2RtV9YZy0WuFo647ASn7KS4zxH1VmsO11GN4GLalkY5ije+3+5fQlKgx+zXH8jUXbpBTUMS3/bs2KJshO6eAL7/aTWxcOot+GqKUlpXwLJE5XRaw4dsd+H3gxb8Pl9PRX3EE2tHDDhdPR6XlEldXazxb2XPw8G0FYf4ZvdqTnpfP7jo4a9eG1Y+D0JKoY6tnxKEXYXx6/SDeR1bQ/+wGfrl7gYCXT5V6e/4vQyYTmLv/LHmFRSwf9b6CdowqeL2GWo6o6BR+W3oCt2Y2fD5LdR/Wt0XI7WimfrKFuLJpVGPj2hkTb8K3b2viniSQ9CKlyra9S44gEsGwrwYoPTYjI48NGwNp09qB7t0qudFBkS/ILiisVlKjJnS3aUKvRk1ZGhbA6sdB9GrUFC2pOqfDIhju11KlwcFmrnL3JVV1yZVBTU2Ch3ujemXc/1PNyejUDJacDKR9Y3t+HtoHsVhE1qsCAp5EcfHRM/bdesCO6/cw1tWmu5sLPTxcaNfYvl61zBYm1viZ27PlaQjjm/ooZI7lWfeGqHncSj9DR/OBXEk5RLGsiJ6WoxTOE5wSw4WESL5o0RUzLXmD5FlyGrtv3meYbwualXFLMzLyuH0nmpISGaWlUkpLpZSUyigtkVIqlVJaIqOkVIq07HX5TymlpbKyfeX7xCdkkJ6ex+KFQ/HxVgzaMpmM4/+cY8M3O1DTUOPb7TPpPrpjtV/yHmM6s/6rbcRFJGDnqqjIN3yYL9/9cIDLgU8qut5ejo3o0MSBTVduM7JtqzplOsoQkZXC+YRIZnl0YmbzzpTIpIRlJHIj+QU3Ul6w/dltNkYGIxGJaGliQ1sLB9pZOuJlaouWWsOWnhoKMWlZLDxygZvP41jg34MmVmZ1PoehgTaurtaEhEQxflxHsnMKmDf/ILo6mvy0wF8lOdu3hUwmsHPXdbZsu4qjgzk/LvDHzla1SdM34d3XE+bIaYH9XxusSXuZwZlNl+g9visWdsrfpzVrL1FcUsqsmYoPq2N3H2Oiq13vsti81r05XzahPc2tPZuvyE0ZPuyg3FLvTUgkYtq0diTkdjSCINT7QdqypR2bt1wlJ6eg9p1fwzu5A16V1m+o5Ju9Z9BUU+PnYX0q6E1Gutr4e3ng7+XBq6JigiJfcOHRM84+iOTg7YfoamrQ2dWRnh6N6eTqVKdgMsnVjylB+zkd94QBDoo1ayc9DxrrtSIw5TCu+t7cTD+Np3EXBTlTmSDw870LWOsYMLGpnAYkCAK/nQhEV0OjYqS5pETK7Dk7iU+oXrFNIhGjpiZGTU2CuroEiUSMuroENTVJ5etqEqysDPni835VyiMpsan8Mekf7l58gE9fT+b8Ow2zRjXT4LqN6sCGb7ZzcedVJiwcqbDN18cFB3tT9u4Lpkd394obc0av9oxcs5vt1+4yrfvbCRmtCb+GrpoGH5b1HNTFErzMbPEys+Uzj44UlpYQmp7AjZQX3Eh+wfonN/gn/DoaYgmtzWxpZ+FAOwtHWpnavHXJ5m1RIpWy5eod/rl4E3WJhPmDujPMp0XtB1YDX29nduy6TlZWPot+Pkpaeh4rl43BzEy/Aa9aOXJzC/l1yXFuBj+nR3d35szuq6D5UlfYudpg5WhOyJm7CoH7wB/HkEpljPzWX+lxd0JfcCngMR+O7aDw0MguKORyeDQj/BQt5eqCRrqGHO01EXWxBFOJLofvPGJgG/da2Smvw9fHicArT4h+kVrvidXyOveDh3XLut9J4E4uUG5MWxP+uRjMo4RkVoz5AAsD5W+erqYGfVo0pU+LphSXlhL8PI4Lj54REB7F6bBINNQktGtsT0+PxnRzc6m13tnNpglO+iZsjLhJf3v3Kk/NnlajWPvsW1ZGzkAiUqP7G0a4R2Me8igziWV+AyoywEuPn3PjWSzf9e9a8fsPHr5NfEIm388dIFcYU5eg9lpglkjE9ebhCoLA2S2X+efzzXILqHVT6fexchPcN2FmY4JnjxZc3HmV8T+NUDhGLBYxfJgvvy87TejdmIoHRQs7K7q5ObPl6h1GtWuFoXb9pmSjctI5GfuYKc3aVSthoKWmTntLR7n5QgvIKykiJDWOGykvuJn8gj8fXmElV9BRU8fbzI5+dm4Mc2r1n5URynE/NpEfD18gMimNXs0b813/btXew6rC29uJbTuuMXnaJtLT8/jmq/dxc6tKH21oPH2WxI8/HSY1LZeZn/Vi4IA2b/1+ikQivPt4cmHHFUqKS1DXUCczJZsT687TY0wnrJ2rymMUF5fy519nsbExYvQoxSG3tReDKZVJ8feunvmhCsqb6ivPBlEilTKxU936Bt5l/pu3QqLrHbibuVqjri6pc7nkndS4X5UW8zBDtTFXgNAXCfx7+Rb+Xh70VrFDrKGmRidXJ34a3IuAuZPZNmUYI/1a8TQpnXkHz9P553VM+Hc/j5W4zZdDLBIxsakvDzOTuJVatUFgp9MUhzLzYi/jHphoVN5gBaUl/BEWQAtjawY4yG+gopJSlp4MpLGlKSP8ylT6MvLYsfMabf1c6NHdHQsLA0yMdTEw0EZbWwN1dUm9g3ZGUibzBy1h2aQ1NG7txLr7f/De5LqJOvUc05mk6BQeXa8qvN+juwfGxroKY/AAn/VqT25hEVuvhtbrugHWhl9HU6JWIU2rCvTUNelm05jvPHtyrM/HhAz6nDUdhjDEsSXxr7KZG3KST68f/M9q4nmFRSw+eokxa/eQU1DIqnEDWDmmP+b6uuTmFtZ+ghrg1kwepNPT8xgy2Js+veufvauK02fCmDFrByWlMlYsG8OggV4N9hD06deawldFPAx6AsChFScoLiypNtvevfcm8QmZzJrRR6E0FP4yhZ037jLMp7IM+TbILSxi94379G7eRCW65uswN9PHydGc27frX+fW0FDD3c2G+/8LgVuMqFoX9zeRV1jE3H1naGRswNz+XQE4eiyU2XN28vhxQs0Hl0EiFuPlZMs3H3Th3NcTOTBjDJO7+vIsOZ1fjl+u8Vh/x5YYa2izMSJY6fZh9rP5pPFSelgpCvxsigwmqSCX7zx7Vngobg0KJT4zh7kfdK1Ywm3YFEhxcSnTpzXsGH/gvut83HwOoefD+GT5BH6/uABrp7oJewF08PdFU1uDizuqNik1NNQYPMiLkNvRPI+qbCw1szanT4smbL8WSuarutXmAOJfZXEk5gEjnVtjplX3Zlc5jDV16GPbjB+9+nKu31S+8+zJhYRIBp3bRERW1UZYQ+Lio2cMWLGNPcH3GdPOk2Ofj6e7uwtx8Rl88fVuBg5eyffzDhAdXVWMSxVIJGKW/zGK8R92ZNqUutla1RXFxaUsX3mG35edwsO9EevWTMDDvXoXovrAs1tz1NQlhJy+S05GLkf/PkOX4e2wb1b198THZ7Br9w26dXVT6OPIZAILj1zEUFuL2X0bZsR/b3AYeUXFTOriU6/jfXycePAwnoKCupeHy9GyhR3PnlefYCrDOwncxpo6nIh9RFph7cTyX09c5mVWLr8O74uupgaRT5NYveYCDx/FM2P2dv7861wFn1gViEQi3GwsmNm7PZM6e3M35iXPktOq3V9bTZ0xjb24+PIpUTlVtcCNNSyw1WmMnlrloENqQR7rwm/Qu5ErvhbyGlVydh7rL9+ip0dj2jaWv/YkIpEzZx8wZLAPtvVs7LyJnPRcfh69ksUjV9CosRX/hP7O4NnvK52wUwU6+tp08PclcN91iouqOoj3/6A1WlrqHDhwS+H1T3u2o7CklH8uVu9SUh3Wh99AIhLzcbO29bpmZZCLiPmxo+tYXpUWMeTCFo7FPGyw85cjOTuPWTuOM3PHcYx0tdn1ySjm9u+GhljCjp3X+XjKRp4+TWZA/9aEhcXx8dSNLFl6gqTkus8leLZyYPy4ju9UWzspOZtZn+/gxMl7jBrZlqW/jagXc6Q26Ohr07yTGyFn73Hkr9MU5BUyau7gKvsJgsCfq86hoa7GJ1MVH1gHQh4QFpfEV+91rneJ7nUUlpSyLSiU9o3t8aijmmk5fL2dKSmRcjnwSb0py61a2ivYBKqCd3JHmGrqUCyTsvt5zUvpsw8iOXLnMVO6+dLawYbCwhJ++fU4RkY67Ng2Df9B3hw/eZePJm6o1xszsI07ahIx+2/V/AUe18QLDbGEzZG3atyvHCsfXqFIWsrXrSpvrBVnriKVyfjqPTklUCYTWP33eYyNdRk7un11p6oWgiCQmZzFvYCHHFl9mr+m/8ucrvMZ5/IpQQdvMmHRSFYGLVaasdQVPcZ0JjfzlVLtZAMDbfr1bcnFgMekplX2LlwsTBnh15KdN+5xoRpfUWVILshlf/R9Bju2xLoBJW/L4Wthz7Hek/AwtuLzm0f58c5ZipUYVtcVUpmMXTfu0X/FVoIiXzCnb0f2fjqKlnZWPHwUz9Tpm9m05Qrt2zVhy8bJzJ7Zhx3bpzF0iC+XLocz/qP1rFl7kezsunHU3yVCQqKY9slm4uMzWfjjYCZP6vpOHxK+fVvz4mEc2xfup/1AH5xbVmWEBFwO507oCyZ+1FmhEZuW+4oVZ4LwcbZVeVKyJpRKZawLCCY9L5+Pu9ZfybJ5c1vMzfX5fdkpxny4lnX/BvAkIrFOscrNzabO7/s7aU5qStTwsnJm17NQpjZrj4aSmf/k7Dx+PHyB5raWFeyEdesDiI1L5/clI7GyNOSz6T3p1cOD5X+eYeHiI/j5ujBrRi+srFQb8zXR06GXR2OO3X3MnL4dq9WJMNPSY5BDcw6+COPzFl0w0ayqxFaOiKwU9kXf48PG3jiVGfrei3nJ8XtPmNLNF1sTuafdhYuPeBz+kq+/fK+KSe3rKA/QMY/jiXkUT8zjOPm/H8eTk14ZKHUNdXDwsKPr8Pb0n95HJQNWVeHVqyVGFoZc3HlFqUjO0ME+HD0WyuHDt5kyuXKY5Ov3O/MgPonv9p/FxcIEJ/PaVxUbntxEKsiY6lbVEKChYKGtz45uY1h6/xKbIm/xMDORVe0H1/tBEZmUxo+HLnA/LpH2je2Z798DOxMj8l4V8vfGCxw/cRdzMwN+XjSUdm0bIwgCOem5GJrq88nU7gz292LrtiAOHb7N6dNhDB/uy9DBPm/F1HgbvE71c3I058f5/g22IqwJ3n09Wf/1dgBGf1/VHSbvVSFr1l6kaRMrBvRvrbBt2emr5JeUMH+gao336iAIAgHhUaw4G0RUSgbd3V3wda7deLo6aGiosWHdJIKuRRJ45QkHDoawd18wVlaGdO7kSpfOzWjmal3jNWtra+Da1IoLZ1X/vbUGbpFIpAVcATTL9j8gCMKC2o6b0NSHSVf2ciY+vKJ5Vw6ZTOD7A2cpKZWyZHg/1CUSbgY/5+jxUIYN8VGgurm6WrNm1XgOH73Dps1XmDh5I+M/7MgQf2/U1GqngA31bcHpsEjOP3xa49jxRFc/9kXfZ+ezO8zwqF7B7bf7F9FT06zYRyYT+OX4ZSwMdPm4rE6Wn1/Evxsu06yZNb17yZtKgiCQkVQeoMuDs/xnbkaloaiekS4OHrZ09PfFwcMOB3dbHDzsMLU2fmdsCYmahG4jO3Bi7TlyM/OqGKBaWxvRuZMrx0/cY8zo9hUPIg01NVaO6c+w1TuZteM4u6ePqpGOmV74il3PQxng0Bz7avxB64NiWSGphQloSrQx05Q39dTFEr5v3QtP00Z8G3KCAec28mc7fzlDRUUUlpSy9tJNNl+5g762JkuG961QjLty9QmrVl8gM+sVQwb78NH4TmhraxAXkcDKaesJC3yM3/ttmPTLaJxaOPD1l+8zbKgvmzZfYfOWqxw9Fsq4sR14v18rle7jhkJOTgG/LjlB8K3n9Ozhweez+vxnDxBHDzsat3ZCx0AbV++qQlWbNl8hM/MVPy8cqpCBBj+P49jdcKZ281PZtUgZwmIT+eP0Ve68SMDRzJg/x/anh7vLW3+v9PW16Ne3Jf36tiQ3t5Br1yMJvBLBocO32bf/FpaWBnTu1IyunZvRrJnyIN5SBcOT1yGqLaUXyX+LriAIeSKRSB0IAmYJglBtcdPb21u4FRJC79NrMVTX4mCvjxS2bwsKZcnJQBb492C4b0syM1/x8ZSNGBvrsmb1+GoHDFJScvhr9Tmu33iGi7MFc2b3rZUiJZMJvL98CxYGumydMrzGfSde2cPDjCSu9v8MTUnVa7iaFMWEwN1859mTSWVsiEO3HzLv4HmWDO9b8WD4d+Nldu+5yd9/fVhxfcsmreHM5krhKn1j3bLAXBmcHdxtMbEy+s/pbCB37/jM91s+XzeV9yb3rLL9SUQi0z/byifTujNsiGJWfvNZLJM3HaJPiyb8PvK9aq//j7AA1oZf52y/qbgY1H0wpVD6ipTCeFKK4kktjCelKI7UwngyS+SNSBFieluNoZP5IIVreJaTxvSgA0TnZfBli65Madau1vf45rNYfjx8gbiMbAZ5ufNVv84Y6Wor3IONXSz44vN+uLpaU1Jcwt4lR9n180E0dTTpMaYTF3de5VV2Pj3GdmL8TyOwcpRTxh4+iuffjZd58CCeRjbGTPyoM106N3vn0qxPnyWx4KfDpKXl8uknPRnQv/X/yb2mDBERiUyfsZWBA9ow87NKrndxaSmD/9pBqVTGkdkfolWLuqIyxKRlsvLsNc49fIqpng6f9mzLYO/m1ar/NRRycwu5fuNphYZ4aakMCwsDupRl4m5uNhXvf25uIQYG2ncEQVCJk1hr4FbYWSTSQR64PxEEQTkNA3ngvn37NtuehvBT6DkO9pyAp6m8Fvs0KY3hf++ifRMHVo8bgEgkYt6Cg9wKieKfv8fj7GRBVFgMD66GM2C68tHeoGuR/LX6POnpuQzo34ZPP+lRY9ayMTCE5WeCODr7QxpbVj+Uci0pmg8Dd7HYux+jXBQnqKQyGf3PbaSgtJgz/aaiKVEjt7CI95dtwc7UkB1T5TzoGxOzLgAAIABJREFUhJeZTPx4A926uvHt13KzoHsBD/mqx0/0mdCN7mM64ehhi7Hl/02Arg6CIDDJfTZGloYsv7xQ6T6z5+wkKTmbHVunVnm/NwSGsOJMEN9+UKkT/jqyiwvofHw1na1dWNW+alNKGSJyQnmWd7csWMeRU1JpUqwmUsdMsxEWWnaYazbCQtOWB9nXeJh9A3cDP0bYz1HwAc0rKWJuyElOxYXTq1FT/mznr/ThXFBcwqKjlzga+hh7UyMW+PegrYs8Gzp+4i5r1wcgCAITPuzIkME+SCRiosJi+GX0SmIex9NtVAc+WT4BY0sjcjPz2PPbEY6sOoUgE+j/SR+m/D4OiZoEQRAIvvWcDRsDiYpOpUkTS+bM6our69sJdlWHCxcf8fuyUxgZ6bDgh0G4NzBr5G0glcr4dMY20tJz2bJpsoL64LqAYP46d521E/zp5OpYp/MWl0pZejKQ/bceoK4mYWInL8Z38nrrid/6IC9PHsQvX3nCnTsvKCmRYmFuQOfOrvTt0wJnJwtEIpHKgVulx5dIJJIAd4DGwN/KgrZIJJoCTAGwt5ff6IMdW/LrvYucjY+oCNznHz6luFTKj4Pktar8/CKuXX/K8GG+FST2xSNXEPckgc5D22JsWbWe3bFDU9q0duD3Zac5eiyUvr1b1HjDD/LyYO2lYKZsOsRf4/rT3Fa563p7S0damlizMPQcaiIxw5wrxWb2Rt0jIjuF1e0HV3zh114KJuNVPv9MqMzwHj1KoKREiqNDZUZ5L0DeHHVqYU/r7s3/pwJ2OeIiXpKfW8CrakZvc3IKKCwsIS0tl6ys/CoTfJM6e3PqfgTH74YrDdw3kmPIKy1mfBPVaVdnEreSUlTJbxUhwlmvBV7G3XE39ENdrNg7cDP0I/XpFzzOCSalKA4bbeeKbXrqmqxsO4jnOemcT4jkaXaqUlXD29EJHA19zFCf5szt360iwxMEgb9Wn8fF2YIF8wZh/Zqc6o7FB0hLyODnk9/h26+yNqtvrMfkJWMZNKMfm3/YzaE/T9K8kxudBvshEolo69cYH29nLl56zLp/A1i56hz/rB6v8vtTF+zYeR1dXU3WrfkII6Pqezj/F4iOTiXyaRJzZvetIhl74t4T/Fzs6hy0ASKTUtl98z52JoZsnzYCc/2GZ8uoCj09LXr3akHvXi3Ie1XI9evPCLzyhKPHQjlwMESpUFxNUKmVKQiCVBAET8AW8BWJRFVGlgRBWC8IgrcgCN6mZvI6lJ66Jg56JkTlVNLxLMpGSovKxIu0tTXQ0lKntLRSOOrPa4vZ8HC50qBdDh0dTYyNddDSUsfZueapJVM9HbZNHY5YLGLcun0cC32sdD+RSMSmziPxNrfj25CT/HjnLCUyKVlFBSx/cBk/Cwf62sprnFEpGey4dpch3s0VqEQ9urvTrm1jNmwKJKRMgGbEN4Po4O/L2i+28lGzWez65RApcdVTFP9rPA2NYk7neUhLZfxy6rsq29PT8/j8i11Ev0jlx/n+SseuZYJAYmYOzWyUfxaZxXI2RSNdQ5Wv65MmS/jYeRF9rMbibuCHnpoRz/PC2Be3ksWPPmTts285+XITYVlBZBQlcTh+DcmFsfSxGqsQtMuvb27ISSKyU/iqZbdqpWhb2FkhEoGlob7CslwkEmFpaYCNjZFC0C7fZmpjrBC0X4e5rSnTV8rLhUnRivxyiURM717N6dbVjZiYtDrTwlRF3z4tyMrKJy2t7lPN7xpZZUwbe3slq2EBjHXqp/jY3NaKDzu0IS4jm3MPIms/4D+Cnq4WvXs15+dFQ9m35zM+ntiFFzF1iwd1NQvOAi4DfWvaL19aeXM4G5gQlVu5xHUwlTelYtOzAPlNb21lRFJSVsU++sZ6OLhXaoJUhzuhL2jZwg519dprVW42Fuz7dDSt7K2Zu/8sS04EVvF+BDkHfXPnUUxq6sf2Z7cZf3kX8++cIbukkAWteyMSiRAEgV9PXEZbQ51ZvTsoHC+RiPnhuwE4OZqzcPFRXsSkoa2rxXc7Z/HlpumY2hiz+YfdjHWczjd9FnFpdxBFBf936ncProbzZfcf0dTRZMXVRbi0clTYnpiYxaw5O0hMyuLXxcPo2EG5RG1kUho5hUX4VOMBmVMmn2uooTr/VkOshZOeB50tBjPG8Ru+cdvAV83WM9L+S9qa9kOMmFvp59gbu5xlEdO5mxlAD8uRdLZQLMUIgsD8O6c5+CKMWR6dmOZWPT3TSEcLN2sLgp8rmaS1NSU+PqPK65raGhTl1zyAoWuog46+NikxygdyHOxNKSwsISW1fuaxteGD9z3R1tZg3wHVKK//JcoFlgwMqgZosViEVKi/GuiX73Wiu7sLv50IJPBJ/Scc3xUMDbQZPaodu7Z/Uqfjag3cIpHIXCQSGZX9WxvoCTyp6ZhSWeVN7KxvSmxeJiUyeYZtbyrPVsoDN4CVlSFJSXUbUEhJySEuLgMvL0eVjzHR0+HfiYMZ086TbddCmbr5EFlKJv/UxGK+a92TP/wGEJway8m4x4xx8cLVSJ5NBoRHcf1pDJ/1bIeJXtVlp7a2Bj8vGoqmlhrf/7CfrKx8NLQ06DOhG8sCfmLr01WM+WEICZGJ/DrmT4ZbT2bl1HU8vhHRoLrjteHW6bt822cRpjYmrAxajG0TxSz0RUwaMz/fQW5uIcuWjqpRuD8kSm6HVp15b05JEepiMdqS+qv6iUQijDTMaGHUnn42E5jS+BfmN9/B9Ma/099mMl+4rqGbxTCFYwRBYOHdc+x+fpdP3NrXyBgqh19jO+7FJlJQrDiQZGtrTHxCZpXPSB64a374ikQiLBzMql1plWebsbFVh8AaAnp6WrzXryUBl8NJfUcPh/qiXB7AQL/qQ10sEr3VKkQiFrNkRD/cbMz5Yvcpwl82zERtcamUoMgXLDxykX5/bGb+ofNkF9Rf5kCV5PN1qJJxWwMBIpEoDAgBzguCcKKmA0qEyhvexcCMUkFGbJ5cGc9cXxctdTWFwG1tZUhiUnadgla5gHlda0PqEgnfDejG4iG9ufPiJSP+3kVkkvIvk79jCw73+ohTfSYzp0UXQK5HsuTEZbkeSduWSo8DsLAwYNFPQ0jPeMX8Hw9RXFxpAGzjYsX4n0aw7flqfr+4gPYDfbiw4wqzOvzAJPfZ7PntMGkJ7+YLXI7AfdeZP3AJDu62LA/8CXNbxWVqREQis+fsRBAEVi4bUyt753Z0PHYmhlgbKVevyy4uQF9dq8Hr+xKRGo10XGhr1g8TTSuF8wuCwG/3L7Ht6W0mNfXjixaqaaP7OdtTKpUR+uKlwuu2tibyOn96nsLrmjqaFKkw8mxhb0ZytRm3vCcSE/vuSmhD/Mtt6e68s99RH5Rn3Pr6VTNuiViM9C2TGR0NdVZ/OBBDbS2mbz1CcnZe7QcpQW5hEafuR/Dl7pN0WryWqZsPc+xuOI2MDThy5xH9l2/l7IPI/yT5qrU5KQhCGKC8eFcNSmXFFRq1zmVDKlG56bgYmCEWi7AzMSTm9Yzb2oiCgmJycgowNFStcXIn9AXGxro4OdZPaMbf2wNnCxNm7TjO6H/28MuwPkoFrlqaKAasLUF3iM/MYeOkIbXSidya2fDt1++zcPFR/lh+mrnffPCGAp8Yz27N8ezWnM9WTeLqgZuc23qZjd/tYvMPu2nTqyV9JnSj/UCfGo1Wq0PBq0JSYlJJjkkjOSZV/u9Y+f/Db0TSvGMzFh37Bl1DxabNvfsx/DD/IIYG2ixdMpJGNjVzrmUygdvRCfTwqN5ENqe4qE5lkobA8oeBbIi4ybjGXsz1VH1ww8upEWoSMTefx9KhaeV0n10j+b0cH5+B+Wt1/vKMuzZdZkt7c8JvPlW6zchIBwMD7QbPuAVB4NLj57RtbF9pS3fiHmNf4+P/XyM7pwAdHQ2lWefbZtzlsDDQY834gYxbt4/pW4+wbepwldglKTl5BIQ/5+Kj5wRHxVEqlWGqp0Oflk3p4e5CWxd7NNXVCH+ZwvyD55mz6yRd3ZyZN7A7VobvToL3nUxOypCRU5KOoYYZzvryTC4qJx3KGEgOZsZEpVbWCq2t5A2rxKRslQK3IAiEhr7Ay8vprTK4VvbW7P9sNLN2nODznSeY0s2XGT3bV8unTczK5d+AW/RqXqlHUhu6dnEjLj6DzVuuoqYmZs7svkqpi7oGOvSd2J2+E7uT8CyR89sCOb8tkJ9HrUTPSJeuI9rTe0I3mvk2rqiz52bmkVIWlCsDcxrJL1JIjklTmLwEUFOXYG5nhqWDGYNnv8+ERSPResMh/MbNZ/y48DA2NsYs/W2EQoCqDk+T08guKKy2vg2QU1KIgfp/F7hXPwpizeNrDHf2ZH6bujnG6Gio08rOukqd285OHrjj4jJo7VkZ0DV1NJHJBEqKS9HQrL4UZGFvRm5GHgV5BWjrVc0uHexNGzxwbw0K5fdTVxjh15L5g3owfKgvAZfDOXX6PsOG1n/UuyGRk1OgtL4NIBGLGsTxCsDV2pxlo97n021H+GrPKVaNG4BEicZPVEoGFx8/49Lj54TFJQFgZ2LIuPat6e7uQit76yrHudlYsHv6KHZcv8uq89cZsGIbn/fpyAi/lu+En//OrDRSiuIx1DBDX0MLcy1donIrb0g7U0MCn0QjlcmQiMUVI+xJSVkVDs41ISo6lcysfNq0fntTWHMDPbZMGcriowGsD7hFRGIqS0b0Q1+rajay7PRVZIJQoUeiKsaObk9xUSk7d98gMjKJ2TP70Lx59UGuUWNrJiwcyYc/DudewCPObQ3g/LZATqw7T6Mm1qhrqJEck0pBnmJNTUtHEwsHMywdzGnq3RhLB3MsHc3lPx3MMLYyQlLDKuHipUf8tvQkjV0s+O2X4Sqvfirq2zWMDucUF2LwH2Xc65/cYMXDQPwdW/Cz93sV6o11QVsXO9Zcukl2QWGFoJGpqT5aWurEJyg2KMsffkX5RTUHbgf56jAlNk1p893e3pSrQQ3Hfrgfm8iKM0Foa6hz8PZDJnf1rbSlO3Qb/0Fe/+nUZnXIzS1EX0l9G8qakw0UuAE6uTryXf9uLDp6iaUnA5nbvxsymcCD+KSKYB2dKi/rejSyZGbv9vRwd8HFwrTWh7+aRMyETl708HBh4eGLLD52iRP3w/nJv1eN8yP1wbsL3IVxNNGX86CdDcx4/prynr2pESVSKcnZedgYG1Rm3ImqNShDQ18Ada9vVwcNNTV+GtwTNxsLfjtxmVFrdrNq3AAF7Y3b0fGcDotgeo+2NDJWndIG8sbUxI864+pqzeq/LzDz8x2817clkyd3w7CaTAPkpZQ2PVrQpkcLXq3O58r+G1w9eBN1TXU8uzcvC8jmWDiYY+VojoGpfr1XIEePh/LXqnO0amnPop+G1GkZHRIdj62xATZG1WuBZBcXYqurmsbM22Bz5C2W3L/EB/buLPH5oF5BG8DPxY6/L94kJCqenh5yg1yxWEQjG+MqzBJNHfmSu6igGP0aqkoWZXXs5JjqA3dOTgFZWflvzbXOyi/ki90nsTTU469xAxjx9y42BIYwb2D3Clu6wCtP6NHdo/aTAaUlpeSk51Zr6Ps2yMkpwEBJfRtAInr7GvebGNm2FbHpWWwNCiUuI5tHCcmk5eajJhbj42zL6HaedHNzqbZfUxvsTIxYP3Ewx+6Gs+REIENW7WBKV18md/Wpl82iMryTwC1GojA44axvwqm48Ioa4OvMEhtjA3R0NDEw0FagBNaEO6EvsLczxdy84dTlRCIRo9q1orGlKXN2nWDk37tZOrIfXZo5UyqV8fOxAKyN9JnYuX7u2iKRiI4dmuLVxpFtO65x4GAIQdefMnVyV/r0rn05pWugQ79JPeg3qWF1vQF27bnBho2BtGvbmPk/DESzhqzxTcjr2/F0dXOucb+cksJ3XuPe9ewOi++ep4+tK3/4KV8Gq4oWdtZoq6tx81lsReAGeYPy+RvayRplWh+1MUssX8u4leH1BqWRUd20K16HIAj8cOAsqbmv2D51BM2szfH38uBgyEMmd/WptKXbf4vu3ao6PynDkVWn2fT9blbd/KUKZfRtkZNTgJWV8mRILG6YGveb+KJfJ15m5RAUGUOnpo70cHehUzOnesnFljfeX08SRCIRA9u407GpI0tOBLLm4k3OPIhk4eBetHZ4eyejd6LhqC7WIKWwMnC76JuRXVxIRpGcaP8mlxsqmSW1obi4lLAHcbRp8/ZlEmXwcbZl72ejsTM14tNtR1kfcIv9tx4QmZTGV+91RruOjt1vQltbg6mTu7H+n49wsDfl92WnmT1nJ1HR71b4XxkEQWD9vwFs2BhIj+7u/LTAv05BG+BZSjpZ+TXXtwVBeOelkgNR95l35wzdbRqzsq3/W3tQaqhJ8HKyJfi5ojOJnZ0JLxOzKCmplIqtLJXUzCwxsTZCoiapllnSUJTAbdfuEhAexRd9O9HSTj4lPLmrD4IgsOFyCGKxiGFDfXn2LJm792JUOmfkneeUFJXw29i/KC6sv2mAMuTk1lDjFr0dj7s6SMRiVoz+gOAF01kx5gM+aO1W56BdKpOx5vE12h79k4lX9vCqpOr7Yqqnw9KR/Vg7YRAFxSWMW7eXRUcvkVf4drMb7yRwq4nVSS6Mq6DFOBuUNSjL6tyWBnpoqEne4HIbqZRxh4e/pLCwBK82DSdr+iZsjAzYPnU4/Vq68ue5a/xyPABfZzuVbdVUgZOTOSuWjeGrL94jNi6dKdM2s3b9pbdy0qgLpFIZK/48y559wQzs34a53/SvV72zvL7tU0N9u0BaQqkge2fNyWMxD/k25ASdrJxZ3X6IUhlhVXEnLZ70MgMQPxc7olIzSMmppI/ZNjJBJhNIfO1e1SwL3IW1ZNwSiQRzWxNSq+FyW5gboKWlTsxbBO6wuCSWn7lKd3cXxnWQk8ESXmVjY2TAQC93DoQ8JDk7j549ym3pVBvIiQ1PwKyRCS8exbFx7q56X9+bkEpl5OUVKuVwg7xc+K6mSUUiUb1XZRFZKQy5sJllDy7T2syW68nRjL28o+LeeROdXJ04OvtDxrZvzd7g+wxYsY1Lj5/X+9rfTcYt0qBIlk9OqbwWqMAsAaWUQGsrQ5JTcmr9kG6HvkAsFtGqVe2TlW8DbQ11lo7ox5f9OmFpqMd3/VXjACuDIAisOn+dufvOkPMaSV8sFtGvb0u2bp5C3z4t2bf/Fh9N2sDVoHc7iFNQUMwvvx3nxMl7jBnVjpkzetW78x0SHYeNkUGNdf/ssqlJA42Gp5+diXvCl8HH8LVw4J8OQ5UKR6mKqJx0Rl7axoqHgYC8QQkoZN3lbuOv17nLa9zFKjx0zWvgcovFIuzsTIip4/hzObILCvli10ks9PVYNEQ+5XshIZLOJ1bzT/h1pnT1RRAENgaGoKGhhv9AL26FRNVqryaVSol7kkCX4e0Z9Fk/Dv15kjvn79frGt9E3qsiBEH51CSUZdzvKHDXB+VZ9qDzm3iZn8Oq9oPZ1W0s/3QYSkR2KsMvbiP+lfIEVFdTg28/6MquaSMx1NFixvZjzNl1gtTc2p3C3sQ7yrjlN3J5ucRGxwBNiRrPX2OWOJgZV8m4S0qkpKfXrKUQGvqCZs1sqojRvAlBEBi+ehdbg+pvaCsSifioszcXvvmYJlZ1lyEtx+ard1h7KZhjd8MZvnpXlektQwNtvpzTj79WjkVPX5MFPx3m+3kHSExUreZfGwoKirl9J5qNmwKZMWs7A/xXEnA5nCmTuzFpYpe3eiDdjk7A26lmpbnKcff6aU5Uh0svnzLrxmFamTTi347D0VZ7uzLWyodXkAkCV5OiEASBZtYWGGprcfM1WmCjRvIyn0LgVrHGDfIGZXU1bpDXuWPj6p5xC4LAvAPnSMnJY9mo9zDS0SLhVTZf3zoOwIaImxjqaTKwjTv7Qx6QkpNH//5yW7r9B2vOulNi0iguLMHB3ZaPl4zB3q0Rv3/0NzkZb697UtO4O5TVuP/DaeKa8HqW3bNRU870ncJ7dnIp5x6NmrKty2gyi/MZdmErT7Kq95BsaW/Nvs9GM6t3BwLCoxiwfCsHQ+pms/fOMm6oDNwSsRhHPROicxWZJXHpWRUZ9utc7uqQm1tIRGQi3iqwScq7xQFvsRxpCJy6H8Gy01fp17Ip26cOp7hUyuh/9ij9oJp72LJuzUd8MrU79+7HMnHyBnbuuq5QT1UFhYUl3Al9wcbNV5g5ewcDB6/k62/3snvvTfkDbZgvf64Yw8jhqjusK8PzlHQyXxXg61zz6ienpCzjVm+4jDsoKYpPrx3EzciSTZ1HoKv+dlKd4ZnJnIx7jL2uEfGvsonJy0QsFuHrYsvNZ5VlPwMDbQwNtYlPyKw4trJUUnvGbWlvTlpCBtJS5Z+pg70pqam55KvwEHgdO67f5eLj53zetyMt7a0pkUmZdeMwUpmMv9r5k11cyNant5nczQepTMbGwBAMDbTp26cFFy4+Ij29+mnC2HB5OczezRZNbU2+3T6T7NQcVk5b/9Yrw9oCt0QsblA6YH3wZpa9uv1gVrUfjOkbRtfe5nbs6f4hYpGIkZe2E5xSff9AXSJhSjdfDs8ch6u1OfMPna/TNb0bl3eRBB2JgQKzxMXA9A1KoCFFpVKSy+qHVtaVXO7qcO9+DDKZUKNmRjlCX8gd4h8mJP+fffAhUfF8t/8s3k6N+HloH9o4NuLAjDF4OTZi/qHz/HDgHIUlpQrHSCRihg31Zeumyfj5urBx8xUmT93EvfvV3wSFhSWEhr5g0xZ5oB7gv4KvvtnD7j03kEplDBvqy5JfhnPs8GxW//Uhkyd1pUXzty81hUTJ32NvZ9Uy7oZqTganxDA1aD/O+qZs6TIK/QY474qHgeira/Jne39A/mAA+fh7UnYusemVCYWdrSlxr2XF5c1JVUolFvZmyKQy0l9WFauC1xqUccq3K8ODuCT+OH2Vrm7OjO8ol9T9I+wyd9MT+MXnfd63d6e7TWM2RQZjpK/NgNbu7L/1gNScPIYM9kEmEzhcwxh8zOPywC3/nJu0cWb8wpFcPXCTC9uvqHydypCbW1vg/r/NuJVl2f3sqnfSampozv4e47HU1mdC4G7Oxtco64SjuTGbPh7Kj/5VzUtqwjtzBrXUslNgljjpmxD/KosiqTxQvSk2ZWlhgEgEL2soD4SGxqCtrYF7LboZAHdj5DoTBcUlPEt+t7ofyvAsOZ2Z249hZ2LIn2MHVPhdmujpsO4jf6Z19+PwnUeM/mcPMWlV/2ZzcwN+nO/PL4uHUVJSypwvd/PrkuNkZr6iqKgyUM/6XB6ov/xmD7t2ywP10CG+/FYWqP9eJQ/UPj7O6Og0TMYrlcl4kpjK2YeRWBnqY1sLr72ixt0AzcnQtHgmX92Hra4RW7uOxkjz7csv99ITuPjyKZObtaWFsTV2ukYEJUcD0LZxeZ27slxia2tMfHxlxl1OB6ytOQmKQzjKUE4JjFVRsySnQM7XNtfX5eeh8gnRSy+fsiHiJmNc2vC+vTsAMzw6kV1cyPant5nSzZdSmYyNV27TyMaYjh2acuxEaLWN8djwBEysjBQs7YZ92Z8WndxYPWMjSS/qz4iqyLhr4HG/q+ZkTVA1ywaIznvEq9JK4S4bXUP2dB+Hh7EVn10/xK5nNZdrxWIRw3xb1On63tkAjrmWLWGZVyu42y76ZkgFgdi8TJoYmitQAv1c7NDQUMPUVL9GlcA7odG0ammnEvshNOYlzuYmRKVmcD8uEVfr+mma1AepOXlM23IYdTUJaz8ahJGOFjJBQBAEJGIxErGYGb3a42lvzTd7zzB89U5+GdaHHq/xhcvR1s+F1p4fs3P3DfbsvUnQtaeUlkopKZEiFoto2tSKoYN9aNXKnuYetu9EfyK7oJCw2ETuxiRyP/YlYXFJ5Jcp541t71lrjTynRB7Q3pbH/SAjkYlX9mCmpcv2rqMxU/Ilqg+WPwjERFOHCU18EYlEdLB04kTcY0plMhzNjLE00OPm81iG+8lFxWxtTThz9gH5+UXo6GiipVO3GjfIh3Cad6y63cbGCIlErBKzRBAE5h08T3J2HlunDMNIR4uX+Tl8FXwcdyNLvm/dCwCZIKOliQ3drBuzMSKYD5t407+1G/uCw5jUxYcRw/y4cjWC02fCGOxfdU4hJjy+Itsuh0Qi4eutnzHV80uWfLiKPwJ+rHEqtzpkq1Dj/q9XzBFZKXx96zgPM5N4z86NH9v0URqwC6X5HEtYz/2sK5hoWDLBaT6mmvLJb2NNHbZ1Hc3M64eZd+c06UWv+My9Y4OJrL27jFvTnsLXmSVvUgIN9VCXSJRwuZVn3EnJ2cQnZKpUJsl8VUBUSgb9W7thpKPFgzK9gf8Cr4qK+WTrUbLyC/lnwiAaGRtSWFrC2IAd9Dj1D2EZlYpznVydODBjDI7mxszccZw/Tl1RqhGuqanOxAmd2bB+Ep07uTLY35tffx7G0UOzWbNqPFMmd8PP16VBgrZMJvAsOZ0DIQ+Yd/Ac/Zdvpf3Cf5i25Qj/Xr5FVn4hA9q489vwvpz56iO+/aBrrefMKS5Tf3uLjPtJVjITAndjoK7Fjq5jsNBWnGp7npJeL3rVzZQYriVH84lb+4o6eScrZ/JKirif8RKRSISfix3Bz+MqMj/bcrGpsjq3poo8bqgM3NVl3GpqEmwbGavE5d514x4XHj1jdp8OeDrYyOva1w9TIpPyV5lTU2JBNEvCPyYsK4iZHp3IKi6QZ91d/SiVydgUeBs3NxtaNLflwKEQpG/cf4IgEBsej12zqnRPK0cLZqz+mIdBT9i39Fit16sMOTmFiMWiau9dOavkvwncpTIZfz8OYuD5jbVm2XH5T/n76ReEZQXRzvR9CqX5rHv+HQn5lfegjpoG/3QcymDHlqx8eIUFd8402N/yzjJuCy35EjO1MA5DdVOcylUCy+rcErEYOxPDKrrc9+5XFbBOozMxAAAgAElEQVSHuo25l5dJvBxtCH2RUCEUUx8IgkBaep5KYkslUilzdp0kMimV1eMG4tHIEpkg8GXwMYJTYzHT0mXExW3Ma92bUS5yo1YbYzlnfMnJK2y+eoewuCSWjXoPcwO9Kue3tzPlm6/er/ffogx5hUWExSVxLzaRezEveRCXRE7ZcIChthae9tZ80LoZnvY2NLe1rJdfX05JEXpqGqjVkzP7PCeNcZd3oSVRY0e3Mdi84aLzJDGVj/7dT05BEUtG9OODMif22iAIAsvCLmOprcfo1zxG21k6IBaJCEqKwsvMFj8XO47dDScyOY1m1uYKYlNNm1ihpq6GRE2ikiGGtq4WBqb61VICQV7njn5Rc6nkYXwSS09doUszJ8Z39ALkK4fQ9HhWthuEk74JRdIC9sQsI680i3OJO/i82Wq6WLuUZd0+vN+qGftuhTGpizfDh/kyb8EhrgZF0LVLZQ03PTGT/JwCHNyV8/R7jOnEzRO32bpgL959WtGkTc0TtG8iN7cAfX2taumo4gaQdVUFqmbZMkFGUOoRziftxkDdmI9dFuGo64afaV+2RP/Ehqh5jHX4Fhd9+epMXSxhqe8HmGvpsu7JDdKL8lneduBb0VbhHWbcFlryDzq5rM6tp66Jlba+AiXQztTwjYzbiLS0XAXt6nLcCX2BiYmugpdjdbgb8xJ1iYTmtla0tLPieUp6vSaV8vOL+HHhYUaM+pubwc9q3FcQBBYdvURQ5AvmDexB52byAaHf7l3kdPwT5rbqwZm+U2hr4cC8O6f5Mvg4+aXyDE1DTY15A7uzZEQ/HickM2TVTm5FxdX06+qNopJSLj1+zk+HL+D/53baLlzD5E2HWHPxBik5efRu0YTFQ3tzYs54rs2bxpoJg5jazQ8/F7t6m6xmFxfWu4EYk5fJuMs7EYtEbO86Bns9Ra2MZ8npTN54EB0NDTwdrJl38Bz3Yl5WczZFXE58Tmh6PJ+5d0TrNSqhoYY2LY2tCUqS17n9ysyCbz6TJxWNbIwRiVAQm1LFBaccljUYKgDY25vx8mVmtWyinDK+tpmeLr8M7YNYLCLg5TPWP7nBKJfW9Lf3QBAEjiasJb04iU7mg8gsSeFuZiAzPTqRWVzAjme3mdbdj+JSKZuv3qFd2ybYNjJm7/5bCkyR2Dcak29CJBIxc81kjC0N+XXsXyrV+RX+lpwCpTrc5ZC8o5H3ctQly84pyWBL9ELOJu3A3dCXz5osx1FX/pAz12rElMa/YqxhwdYXi3mQda3iOJFIxNetuvO9Z0/OxD/hoyt7yC2uv+kCqOaAYycSiQJEIlG4SCR6JBKJZqlyYj01ozJmSXzFa076pkS/xixxMJVzuctvFCsrQwRB7m7zOmQygbv3YmjT2lGlGlFoTAIejSzQVFejpb01giBnl9QF8fEZfDpzO9euP8XERJe/Vp+nsLCk2v3XBdziYMhDpnTzrWg0bIkMYWOkvKY4ydUPY00dNnYeyezmnTka84AhF7YoUCQ/8GzGnk9HYaCtyaQNB9kQGNIggzjFpaUEhD/n272n6fTzOmZsP8ap+xGY6esyvXtb1n80mBvzp3Nk9of8NLgX/l4eOJmbNFg9LqekEMN6lEkSXmUzNmAHxVIp27qMrii3lSMmLZNJGw8gEYvZ9PEQVo8biKWBHjN3HOdlZs0uLzJBYMWDy9jpGjHUybPK9g5WTtzPSCCnuBBrI30cTI0qBnE0NNSwtDSsMoSjSo0byrjcNWTcDvamyGRCFRVCKLNhO3SexOxc/hj1Hka62iTm5/BV8DHcjCz4wVNe176dcYH7WVfpYTmCPlbjsNF2ITDlAC1MLOls5cyGiGDMDHXl99zN+2TmFzBsqC8REYmEPahMGmLD5cwhe7fqJ2MNTPT5astnxD1JYMM3O1R6D8pRk6QryPW438XIuyAI3E6NY8iFzSx/EEivRq41Mkae5NxmVeQcYl89YVCjTxhp/yXaaoqrYkN1Uya7/IydTlP2xi7nRtophe0TXf1Y7jeQO6lxjArYTkpB/XnwqmTcpcAXgiC4AW2BT0UikbsqJ7fQsiWlsLL04WJgyvPc9IpgZG9qRGFJKSk58smhchPWN7ncUVEpZGXl462CTVlRSSmP4lNo7ShnnrQo02qoS537ZvAzPvlsK5mZr1j62wjmfTeQpKRsdu25oXT/o6GP5Rq8rd2Y2UvuZ3g2/gmL756jdyNXfvDsVREExSIRMzw6sbnLKFIL8hh0bhOn48IrztXY0oy9n46md/MmrDgTxIztxxSmLVVFcamUwCdRzN13hk6L1/HZtmMERkTTp0UT1k7wJ2jeNP6dOJjpPdvRoamDUhnbhkJ9dEqS8nMYe3kneSXFbO06usI2rhwJmdlM3HAQqUxgw6QhOJgZY6yrzZrxgygulTJ965EaV1ln/x95/x0Vxf2+/+OP2aX3XkWqAiIoogIqYO8tGruxRU2i0ZhEU0wxPdFYYmyJvcbYu6LYAAELiAUBBZVepPe2u/P9YxUkgIIx78/vdX7XOZ7j2d0ZZnZ27rmf933d15UWz73CbOa7+TU6Iu9n4YBcFIl4kgSAj1NrIh+nUSNXZsGtrI3+EbjVqWymd6hZa1OepOQ2+VB+RglMTm5Y59579TZBMYl80L87nrZWyBQKPog4QrVCzppuo9BQUSWrIpmTGVtw1PEgwGwUgiDQ23ws+dXZ3C4IYb6bP/lV5exOjOKdXk+z7pBI+vdrj76+Zr0x+OTYVHQMtDGyeLGyY6c+7oxeMIRj6wK5fia6Wd8DPAvcTf82pK955D2/qpyt968xKHAj4y7ufGmWXaOo5mT6ZnYl/YSeqhFz2iyni3G/JpMaTak20+y/wkWvCyczNhOU9Ve96zzCrj2b/ceRXFrA2As7eVzSfNrn83hp4BZFMVMUxZtP/18CxFFrifBimGnY8KQqrfbA7XWNKampIq9KGaj/SQm0tGicyx311KbM09PupX8zJj2bGrmcTrbKQ9TX1MDOxLBZdW5RFNm1J4wvvjqIpaUBf6yfRidPOzp0aE2/vm7s23+twVRbRGIyXx8KwsfRhm9HKS/ozdw0Prx6jI7G1qzyGYFUIqGoJo/cqucakxYOHB8wkzb6prwffpgfo4NqfTm11dVYPmEwnw/tSej9pEanLRuDXKEg9P5jFh84i/+PfzJnxzEuxT2ib3sn/pg2kuDF7/D96P74Odu91L3ndaKlgTu3spS3Lv9FfmUZ2wLG42ZoUe/9rKISZmw6SHl1NZvfHl1P69jBzIhVk4bwKCefRX+fabQZJFco+C0mBCc9E0bYtm/0GDoaW6OtokZYbbnEhvLqGmLSlCs3Gxsj0tLq/Cc1tNSbxeMGZcZdUVpJSUHjQy/Pxur/SQmMTc9m2akQ/J3tme6nZH+sigkmKjeNHzoPxl7XmCp5BXtTlqMh1WKszQIkgvI6u+h2xkrTgUtPDuJhbIGfhQOb71/FzFCbwR2c+fvqbUprahg5wouIq4m1zdGU+HRau1o3a/U146eJ2LW3YcXb6ynKbZ6vZXFJJfovKJVIXtPIe2ROKvPCD9P9+O/8eOs82ipq/NxlCJeGzGkyy86pTOePxE+JyDtNN5MhvOv0S20J+BlEsRqxdB1izb3a11Ql6kywXURno35cfnKQI2nrUYh1ZS8/Cwf29JxMmayasRd2cDc/s8Xn06IatyAIdihtzK418t5sQRAiBUGIzMnJQSEqKK7Oo1pegeypB2X60xn+wqf1nWcBu+RpZmRsrIOamgqPntNOKCgo49jxmzjYmzarQZicq+z0P6+la66vQ2rey8fHU1OVTjWuLlb8vmoyFuZ1TbC3pwdQUyPn/IW6CyRXKPjiwDnsTA35bfIw1J7SFL+IPI25hg5/9hhTWzs9kLKaVfff50llXenISkuPv3q9xdQ2Xdj64Drf3zz3/PfJ5O6e7Jg9hhq5nBmbDpKa3/g55JeWs+nydQb+uo13tx/lYuxDerdzZMPUkYR88Q4/vjkAP2f72uP7v4YCkZDMhyyJCmxSx+EZ7uZnMipoO5nlxWz2H0dH44Y5wr5rd0grKGZU5/a4NELz7OpgQ2f7VoTcf0xsesMH3oOiHBKLcwmwdGxSZEhVIsVO14i4QuX2Dk+12ROylcG0tY0xZeVVtfRVLX0t8popUVCQrdymoqTxlVRJSSWqqtJaqhxATkkZ83efwEhbk5/GKOvap1Pj+CMunHEOHRluq9TVji+OJLcqnR6mw9FRrcuSBUGgg4E/+dVZPKlMYYRte/KryonOTWdoR1cqamRcTUxhxLBOqKmpcPhoJAD5mQVoN9NQo/BJEUYWBuRnFXLlcIMQ0QAnTkaTm1uCmVnT8sz6WhoUV1ZyIjquyc+8DDUKOZMu7eZSRiITHTtxesAsDvWbzliHjui8YJr3QvZesiqTcdHrwgCLKahKGunxVIUhlq5GLP6+3stSQcpgy2kYqVkQVXCBtPL6PTIPYyv295mCplSV+eGHW1wSbXbgFgRBBzgELBBFscHjVBTFjaIodhZFsbOpqSnns/4iviSS/paTUZWoceDRLbY+uM54B08cdY25mpjCLycv062NLX7OdsqTlUrw6mRHREQioihSVVXDl18foqCgjI8/GtSs4/Rra4eKVMLRKGWAfVJcSuTjtNpm4YtgY2OEj48jCYnZDWiJiU81mN3a1QWSm0kZZBeX8m5v79pSQ1Z5MQ+Kcpjg1Kl26SWKIo/LlCPufyUvo0ped0OqSaV83ak/s1x82PPwJocf36n3dzvaWrFj9lgQ4IPdJ+s5j99JzeLz/YH0WbqZ386GYW2kx8qJQwj5YjY/jRmAv8v/u2D9PP7sMYaRdu7sexRN71Pr+fjqcRKKGtZ4Dzy6xdgLO1Ag8levyXQxbVyTemr3TnRrY8v20Ci+PhxE1XPTp1U1MhbuPc21h6lM9/PCzdq8wfYuBmb0s27LzoQb9eiZzyOltIB7BVn4WypZEudiEhAE6N5GKSf8jN107bqS/tUhoB3x1xKazKKfIe5aAgdXnqDPZL9afe7noVCILFt+CqlUwqiRyqy6skbGvJ3HKSyrYM2U4Rhqa3IrL52F147jZdKKJZ0G1G7fTr8rlhr2BD85TH51XV9HIcqJzA/CTN0GM3Ub9j68ibmmDp1NbThw/Q466mp0b2uHgYEW/n7OXLgQS0VFNd6DO3Hz/F0Kspt+KFVVVLHruwPMcF1AzJV4piwZS78pAU1+Xi5XsG7DeVatPkvXLg6Me4H0wjQ/L7rY2/DZ/kC2vGLPR1UiZZitG3JRwbS2XRuU3ZrCQMtpuOp1Jb74BusSFvK49F7DD6n7IZicRTBYVe/lJ5WpbEj8lILqJ/Q1n0ArrYbKova6xsx38yOlrJCYgpYx35oVuAVBUEUZtPeIonj4ZZ8vlxUTnHOYLkb96GEygrDsx3wZeYYe5vZ84zWAh0/yWbDnJHamhqycOKTest2vR1ue5BQTfz+Tn5eeJP5+Bl98PhxXl+aJj5vq6TDQvS1HomIprazicOQ95AqRN7u8fDJJEAQWfTQYHW11fvz5eD12y6XLcejpatSjI565cx9NVRUCXOooUGHZSQD0MK97UBTVKLM0d/1u5FZlcDhtXYMf4EL3Xnib2fJl1Bli/3ERWxnps2zcIB5k5fDd0QscjbrH2LV/MWH9Xs7fS2RU5/YcW/AW22eNYYB729fmsvG60FrH8OmydC5T23ThbFo8AwM38u6VA9zOS6dKLuOLG6f57MYpvExsONZvBh7GTV9vA21N/pg2ktk9u3LoRgxTNu4ns7CEksoq3tl+hHMxCSwa7M/Cwf6N0swEQeCXLkMx1dDhg/AjjXb4Dz6+jQCMtu+AXKHgSNQ9fBxb16ogtmplRKtWRkRcVWZSPkO9UMgV3Ai81eRxV5RVsnTKGoytDHn/97cb/cyRo5HcjE5mznt9sLY2RKEQWXwgkJj0LJaOG4SbtTlpZYXMDj2AuYZOA0VEVYk6E20XISKyN/lXahTK8s3twlByqtLpYz6OsOxkonLTmNuuB4lZeVyIfchUPy8MtJTlrCGDO1BWXkVwSDyDZvVFLpNzbkdwg2MVRZGQgxG83e5Ddn6zH++hXmyNW81bS8Y0aW5dXl7FV98c4tDhSEa90Znvvx39woleXQ11/pw+kkEebVkZeIVfTl5+pZr3QveeSAUJS29faPY2BmomTLb7jLfsFlOjqGLzo684kLKakpq6qVlBUEFQsUeQ1lku3ioIZn3CJ5TLS5jusIRe5mOQCI2H2j7WbZAKwktH4/+J5rBKBGALECeK4srm7LSwJhcnnY4Ms55FYnEuc8MOYa9rzNpuoygqq2TOjqOoqUjZMHVkg6aYr28bJBKBufN2EhJ6n3ff6U2P7m1bdFKTu3lSVlXNoch7HLoRg69T69p6+stgaKjNwo8H8+hRDlu3K3UYKitrCAtPwM/PuXZqs0Yu51xMAj1dHdF6zlwhLPsxRupauBjUZXpZlUqdEV+ToQywmExMUThhuSfq/V0ViYTffUdioKbB3LBDFFVX1Hvfz9meOb19OB4dxxcHz1FRU8MXw3tx6fNZfDWiN07mzVcvlCsUZBe9ODP8L2CppccXnv0IGfY+89x6cD0nhVHnt9Pu4FL+fhTNbBdftgdMaLRJ9E9IJRI+GNCd3ycP43FOAX2Xbsbn2/VEJ2WwdOxApvl5vXB7A3VNfvN9g/TyIhZHnq73IJUrFBx6fAc/CwestPS4mphKZmEJo7vUr4f7+jhx63YKFRXVOHd1wsBUj2unmtb82LRoFxmJWXy6Yx46Bg3PMSk5l42bL+Pj48iQQR0AWBMUztm7CXw80I8+bk6UVFcyM2Q/1QoZm/3HNfpdGalb8KbNfDIqHnEyYzNyUcbF7P1YaNjhqufNyrvBWGvpM8a+I2vOhaOvqcGUp9rdAB7uNtjYGHHq9G1sXVvh1t2ZM1su1PuOHt9N5pO+3/L92JVo6Wmy/OI3fLXvo0ZXEc9w63Yyc97fyfXrj/hgfn/en9MXqfTluaOaigrLxg1mSvdO7A6/xcK/T9VbZTUHFlp6vOPqy5m0eK4/aXxWpCm46HVmvvNqepq9yd2iMH67P4+I3NP16tagbGQeTdvAgdTVWGs58n6bFTjqvDhhNFTXwtfMjtOpLSsFNSfj7g68BfQWBOHW03+DX7SBiqDKBNuPKaiqZGboPtSlKmzxH4eqoMK8XcfJKy1n3ZQRWBk2rG3p62li8rSWPXJEJ94c1aVFJwRKJknH1pYsOxVMRmFxi3UAfH2cGD7MkwMHr3MzOomr1x5SWVlDr551TYxrD1MpKKtgcAfn2tdEUSQs+zHdzO3q2RhlVSYBYK7Rmh6mI3DT8+Fs5s4GSy8TDR3WdhtNZkUxC68dbyCu825vHxYP68mWmaM5vmAKE307ovMKbJBNl28w4NetpOU3z+PzdcNIXYsF7QMIGfo+n3Xow/vterCu22g+7dC7xUM6fdyc2Dd3Am0tTLA00GX91JEM9WxaBOh5eJm04iP3npxOjePvR3VMiNDsR2RVlDDWQUkTPBwZg76mBn3aOdbb3tfHkZoaOVE3k5BKpXQZ7MmNM9GNKv9dPxPNiT/OMfrDoXTo2dDnsaZGzk+/nEBLS42FHw1GEASO3Yxl4+XrvNmlPdP8vJApFMyLOMLjkjzWdR+No17TD2tXvS4EmI0mMv88mx9+RX51Fn0tJnAxM5G7BZnMc+vBvbRsQh8kMSOgc73fkSAIDB7UgXux6TxOymHwzL6kJ2RyJySW4vwS1s7bwruei3h4K4n317zNhqhljZ7TM+TllfLTLyf4aOFeqmtkLP15HCOGdWry841BIhH4dGgAiwb7c/ZuAu9sO0JZVctMR2Y6+2CppccP0edaPMGoJlGnn8VE5rf9DWstJ05mbGZD4qeklivNnfOqsvgz8XNu5AfhbzqKGQ7foqdq9JK9KjHQxoXk0oKXf/A5NIdVckUURUEURQ9RFDs+/Xf6RdsYq1uCqMY7oQfIrSxjU4+xWGrqsfhAIHfTlEu+ZzS9xrB54ww2bpjO3Pf6vjKXePJzGUQvV8cXfLJxvDu7N61aGbH011OcOBmNoaE2HTzqaq5n7txHV0OdHm3rLNQeFOWQW1lWr0wCkF2RgqGqGRpSLQRBYJTN+xipWfB3ygqKa+rTgTqZtOKLjv24mJHI+tiweu9JJAKTunni49j6lb+XapmMvyJuUSOXs/NfaJW/DuioqjPLxYcP3QMYaNO8acfGYG9qxOH5kwn65G26P3c9moPZLr74WTjwfXQQ9582Ig88uo2RuhZ9rNpSWFbBhdiHDPN0aVCCeqYN86xc4ju0MyUFZdwLv1/vc0W5xax4ez127W2Y/sP4Ro9j564rJCZm8/GHgzAy1Obhkzy+O3qBrg42fDmiNwDf3TxLaNYjvvUaSHfzl/ds+pqPx1HHg5Ty+5ioW9NWx4vf7oZgq2PIG3YerAkKx1hHi4m+DXnsA/q5o6Ii4fSZ2/iP8UVLV5OFvb5huvMHnNhwliHv9Gf7gzWMmDsQaRN9FLlcwcHDN5g6YyPBIfG8NakbWzfN/Fcm39P8vFg6bhCRSWksPdmwfPMiaKqo8olHL+4VZnMk6e4r/X0TdSum2y9hfOuPKakp5M/Ez1kWN4vfHyygsCaHt+wWM8ByMlKh+b2lftbOLTa1/k8mJ6WCCouun+B2fgYrfUbgYWzF6nNhtUu+vo2IKT0PHW0NnJzMm7WMagp93ZyY7u/FigmDX6lBp6GhyuJPh5GTU0L0rWQC/J1rj6daJuN8TCJ93Bzr3czPFOW6W9Qf+82qTMZcsy6gaEi1mGj3CdWKSvYm/4pMUX+wZ7KTFyNs2/NbTDAhma9XTzzwzgPySstxNDPicGQMhWUVL9/ofwCCILzSw0wiCCz3HoaeqjrzI46QVlbIhYwHjLRtj5pUyolbcdTI5Yzq3JA2qKIipWsXB65ee6iUG+7ngYqqlGsn68oloijy27sbKc4r4bNd8xut/cbEpLF331UGDfSgR/e2VMtkfPL3macuTANRlUrZnnCDPQ9vMsvFh/GOng320fi5SRnbegHT7L9msu1nBKbFE1/0hA/c/Il6nM61h6nMDOhSr9T3DAYGWnTv1pagoBgkKlJGfzgUALv2NqyPWsb8dTPRM26a5XXnbirvvLeN9Rsu0N6tFVs3zWT6NH80NP6d2QUoB9VmBnThUGQM5++9eKL5nxjW2g1PY2uW371Eac2r+T4KgoC7QXc+dF5DN5Oh1ChqsNZ0YG6b5bjotdxM3ERDm65NNOKbwn8SuLMqSjidGsenHfowoJULh27EsDn4BmO7ur+09vi6oCqVsnCQPwM9nF/+4Sbg7GzJxPG+aGioMqBfXbnlyoNkSquqGehef99Xsh7hoGuMlVZdCahGUU1uVToWGvUzQXON1rzRag4p5fcJzNxZ7z1BEPih8yDa6pvx4dVjL6XQNReiKLI7PBoHMyOWTxhCRY2MvVdfjwXV/zJMNHRY4TOCh8W5BJxcR41CwRiHjoiiyOHIe7hZmzepLunr40RBQRn3H2SiraeFR0A7Ip4L3Od3hXDl8DWmfT+hUXf08vIqfl52EnMzPea+1weA38+FE5+Zw3ej+mGqp8OF9Af8GB1EP+u2fOLRu0XnpqNiQBvdjhipWbI6JoQ2eiYMsXFlTVA45no6jHuqeNgYhgzuQHFJJaFXHjDlm7Ecyt3K8ovfvNDlPb+gjF+WnWTBR3soLaviu29G8fOPY2qdg14X5vTxpZ2VGUsOB5FT3Px+jSAIfNGxHzmVZfwRF/6vjkFdqslgq+l83m4rsxx/xFCteWyVxjClTcsC/n8SuHMqS5ng6MlMZ2+uJqbw3dELdGtjy+LhvV7bGPX/FWa+HcDpEx/j7FzXNT5z5z4GWhq1Ws0AVXIZN3JS6W5uV2/7nKo0FCiw0Kj/OoCHQQ+6mQwlIu8UtwtC672npaLG+u6jkYsK5oYdqtUx/ze4lZLJvfQnTPLtSFsLEwKc7dkTcauBmcP/P6K7uT1z2nUHlLrhbfVNiUnL5kFWLqO7NF2/7dLFAYlE4Gotu6QzqfHppCdmkpX0hLXztuDu58qbHw9tdPv1f1wkK6uQzz4dipaWOhGJyWwLjWKctwe92zkSW5DFgqtHcTO0YKXPiBYvqZ/heMo9HpbksaB9ABGJqUQnZzC7V9danfjG0MnTDksLfU6dUTJl9Ix0m7x/5XIFR45GMXX6Ri5eimXSBF+2bZ5Jj+5t/5N7Xk1FytJxg6iskfHFwXMtogl6mlgzwrY9W+5fey1JkUSQ/OtzHNCqZaXC/yRw66qos6RTHe3PvhHa3/8qyqtruBT7kP7t29Q7n+i8dCrkNXS3qF97zKpQMkosNBpfCg20nIKtlitH0taTXVnf5cZO14jl3sOJKcjim5tn//Wx7wmPRldDnWFPm3czAjpTUFZRy3n/fwGFQiTycdprkbv8t7ou8938OdZvBucGvQMom5IaqioM7tD0TaWvp4mbm3U9WiBAxPFIlk1bC8AnO95vVKs6LDyB02duM36cD+7tbSgoq+Dz/WdxMDNi0WB/sitKmBW6Hz1VDTb6jUVL5dVEvmoUcn6/F0o7A3P6WbdlTVA4VgZ6jZZ/nofSzLoDt26lkJ7edPMs5l4a783dzpp1Qbg4W7J549u8PSMATc1/Zyf3Mjz7nsISkvkromkaZmNY6NELiSDwfthhVt0NZt/DaEKzHpFYnEtZTcuanv8v8J8Qfm11DOvR/tY3Qvv7X0VI/CMqamQM6tCwTCIVBHxM65dEsiuTURHUMFK3pDFIBRXG237MuoSF7Elaxpw2y9CQ1lG8+lq35T3XbmyIC8fT2LqW6dBSZBWVcC4mgbe6d6pV+fOys8bDxoLtoVGM6ere5BRhU8itLEVdovKvrMN2XIli+ZlQFg7yY7p/y+uDz7DqbjDn0lF+gacAACAASURBVO+zr/eUV7ZIU5FIaG+kvE4V1TWcvn2f/u3bvPS36+vtxMbNl8nJKcbSwRzbdq34c6Gy/LVw6xws7BouoQsKylix6gyODmZMm+JXKx71TMddRUXC3JBDlNRUsa/3FMw1Xz413BQOP75DSmkBm/zGEhz/mHvp2fwwun+zej8DB7izfWcop87cZvbMng3OYdOWywSevYupqS5LvhqJv59zk9lnSl4hKwND6epg02hD9FUwztuD4PjHrDgTirejTbNpsVZaenzl2Z8190JZHxfWgMFloKaJpZYuVlr6WGnpYamlh+XT/1tp62OmofPKMsWvA//ZpMYz2t/2WWMapf39r+LMnQeY6mrjZVd/FDs8O4kORtYNglhWZTJmGjYv7DLrqRoxwXYhWx5+zcHUNUyy/bTej//D9gHcyc9kSVQgrgbmuBs1/hB4EfZdvYMowgSfDrWvCYLADP/OLNhzkvP3Ehng3ny+vCiKTLi4G0c9Y/7oMabFxwNK84Pfg8KRCAJbQiIZ6+3xStKxGWVFbIyPoFoh54vI0/zu+8a/Xrqei0mgtKr6pVkpKOvcGzdf5uq1hwwb6olHgBvJsWl0GeRJ/6k9G3xeFEVWrDpDWVkVK5ZNQFVVyv7rd7gY+5BFg/1xtTJj5V2lZ+Tvvm/gathw+rO5qJLLWBt7hY5GVgSYO/LmgT3YGhvUrrpeBhMTXXy8HTl77i4zpvmhoiJFLldw8vQttmwNpqKihvFjvXlrcvcmM2yZXMGusJusPR9BZY2MkPjH9G7niIX+qz+MnjlrCYLA96P78cbqXXy6L5C9c8Y3ewBtvKMn4x09qVHIeVJRSmZ5MRnlRWSUF9f+P72siMjc1Fr7vWeQCALmmrpYaelhraXP1LZdGpVn+K/wnzwy0gqKm0X7+19DSWUVIfcfM8C9bb3stKi6grsFmXS3sGuwTVZlcoPGZGOw027HQMupxBVfJzTnSL33pBIJv/mOxERDm7lhhyioKm/RcVfWyNh//Q69XB1oZVTfhKB3O0dsjQ1aPE6cUJzLo5I8QrMeUSlrWu62KcjkChYfOIumqiqrJw+joKyCPeEtW+4+w/q4MERE3nLy4nRqHAcf//uG6+HIGFobG9DZ/uU3Y+vWxlhaGtSWS6Z9P45Nd1fyyfa5jT5ATp+5Q3hEIjPfDsDe3pRHT/JZejKYbk6tmdK9EzdyUtgQF85oO49az8hXxbrYK2SUF7PQoxdB9xJ4kJXLnL6+qLSAsTVkcEcKCsoIj0gkLj6DufN2svr3czg5mbP5zxnMntWryaAdl/GEiRv2svxMKL5Ordn1zlgUIqwNalxpszl4kJDF5Cl/cPOp+JyJrjbfje5HfGYOa15hv6oSKdba+nQ2tWG4bXvede3Gt14D2eQ3jlMDZ3HzjY+5PWohgQNns9V/PD92Hsx7rt3wMbNFVSLlSvZjJl/aUytI9n+B/yRwF1dUsnCQ/0tpf/9ruBj7kGqZvN7QDUBEdjIKUaSHeX0aYKmskFJZYbMCN0A3k6G463fnXNZfPCypr1lipK7Fum6jyaooZkNsy7rhp2/HU1heyaRuShrZ46QcNm8JRi5XIJVImObvxb30J1x/lPaSPdXhYkYCAJVyGddyWjaJBrAtNJKYtGy+Gtmb3u0cCXCxZ1toZK3gWHORWlrIgUe3GefgyVee/fExs+Xbm+dqnZZeBcm5BUQ+TmdUZ7dmZe6CIODr7cjN6GQqK2vQM9LFzs0GA9OGJsrpGQWs23Cejh1bM/qNLkrq374zaKiq8OOYAZTKqvjo6jFaaRvwdaf+r3wOAAlFOWyMj2CkrTtdTVuz7vxVnMyNGeTRsknkrl0cMDHRZfWac7w/fyd5eaV8uXg4K5ZNwLYJY5PKGhmrAq8wbt1fZBWVsnLiENa8NZxOdtZM7taRozfvcT+zaU3yF2HT5stkZhXxy7JTtWbDvVwdGdvVnW2hkf+JCYmOqjpt9E0JsHRkvKMnH7n3ZLn3cPb0msyZgbOw1TFkZug+zqc/eO1/uzH8J4HbSFuLqT1aNhn1v4Azt+9jZaCHxz9WEVeyH6GjokaHf+hrZFcoA1pzA7cgCLzRag6m6tbsS1lJYXV9WU8PYysGtnJl/+NbzW6gKCmAt2hjbkxXB6Uk5eYtwfz1dwThEcrgO8KzHcY6WmwNiWzWPgEupCfQRs8UdakKlzNbxqV9kJXL2vMRDHBvw6CndM15/bpRXFHV4qGgtbGhSASB91y7IZVIWOE9HA2pCgsijrwyE+dw5D0kgsCITs3Pdn1921BdLavNAhuDXK7g56UnkUolfLpoCBKJwO/nwonLeML3o/tjqqvNV1FnyK4oYZXPiBcq170MClHki8jT6Kios7hjH07diudRTj5z+/q2uJchlUoYMcyToqJy3hzdlR3bZtG7V7smH2o3HqUxavUuNgffYLhnO45/OJUB7nXsklm9uqKroc7KwCstPq9bt5OJupnEgP7uFBSUsfK3wNqV4qIhAbQ2MuDz/YEUvYKG/avCREOHPb0m42pgzpywg5xI+e+b/f9J4LY0aJo29L+KwrIKIhJTGOjRkN4Ulp2E99Nl0/N4plFiodn8aT51qSYTbT9BJtbwd0rD4ZypbbtQUlPFseTmTX5FJaVzPzOHyd2VHpfpGQW1Nmx/77uGKIqoq6owuZsnVx4kNSsLyqssIzovjUE2Lvia2XI5I7HZZZYauZzFB86ip6HOl8OVnOTqahmuVmb0dXNi55WbFJY376Z7XJLPkaS7THLywuIpd95CS4+fuw7hXmE2K+5cbtZ+nodMruDYzVj8Xewxa8T3syl4uNugpaXG1WtND0z9ve8qsbHpfDCvP+Zm+rXUv7Fd3endzpEjSXc5mRLLB+396Whszd3CMG7mX2rxOQDsexRNVG4an3Xsg56qBusvXFV+x+1ebRU8YbwvRw5+wHvv9G5SFKq4opJvjpxn2qYDKESRzW+P4oc3+9eKVz2DvqYG7/Ty5sqDJCISkxvdV2MQRZEtW0MwNtZhwfz+TJ/mR0jofYLOK5U3tdRUWTpuELkl5fxw7OIrneerwkBdk509J+JlYsOHEUfZ/+jVyn7Nxf+7tuj/GILuJSJTKBqUSVJLC0kpLaBbIyPIWZVJ6KgYoK3ScMn8IphqWDPaZh6p5Qmcytxa771Oxta0N7RgR0Jks4LlrrBo9DU1GPKU0nb0WBQSiYRJE7sRF5/B3RhleWScjweaaqpsa0bWfSkzERHoY9WGnpZOpJQVklTaPCePTZeuE5fxhCVv9MVIR4vrNx4xYtRvhEck8H5fX8qqq9ke2rzMf+29UFQlUt519SWu6DqnMraiEOX0s3ZmspMXWx5cI7iFk6ehDx6TU1LG6GY0JZ+HqqqUzl72RFxt/CH2ICGL7Tuv0DPAhT6921FYVsHiA2dxMDXikyEBJJcW8M3Ns3Q1bc27Lt1ILU9gf8oqDqetJakstkXH8qSihKW3L+JjZstoOw+O3YwlNb+I9/v6NmnK+zJIJAK6uk2zdS7cS2T4qp0cuhHDdD8vjnzwFr5OTScsE307YG2ox/LToc1W+7t2/RH3YtN5a1J31NVVGTfGG3f3Vvy+NojMp1ro7jYWzOnrw+nb9zl5q2WKe/8WOqrqbPUfj5+FA5/fOMW2B9dfvtEr4v8nA/fF2Id8vj/wlSy7/iucuXMfOxPDBsL9V7IfAdDDorHAndzo4E1z4Kbvg5/pSK7nnSW6oC7rEgSBqW26kFicS/hTCdmmkFFQzMXYh4zp6o6mmirl5VUEBt6lZ4ALkyb4oq+vyb79SsF7fU0NxnRx5/Sd+2QUvti95GJGAhaaurgZWhBgqdSBudyMABmX8YQ/L11nSAcX+ro5UVJSya8rTlNVJWPz1mAcTI0Y6O7M7vBb5Je+uAGbWJzLseQY3mrTGT01FY6kbyA89yShOccA+LxDH9rqm7Lo2glyK5s/WXc48h7GOlq1GvEtga+PE3l5pSQk1vc3raqq4edfTmCgr8WC+Urt7K8PB5FfVsGy8YNQUZHwUcRRJILACu/hyMQq9qesQk/VCEM1Mw6m/E6lvPkN6e+jg6iSy/ih8yCqZXI2XLiGh40FAc3QpG8pckrKWLDnhNLkQUeLvXPGs3CwP5qNjNEXVlXwQ3QQp1PjUFNRYX7/7sRn5nDy1suV8RQKka3bQ7C00GfQQOW0p1Qq4fNPhyEg8PPSk8jlylmAmQFd8LS14vujF17qPfpvEZGYzJg1e9h4SWmyrKmiyh89xtDf2pkfooMa6A29LvwngfvfeMRVVNfw7dHzHI+OY+rGAzxpwTjrf4Wc4lKuP0plUCNlkvDsJCw0dXHUrW9kKxflPKlMbXLwpjnoZzEJB+32HE37k4yKuo71kNbtMFbXZkfCjRdu/9fVWwiCMpsGOBcUQ1l5FaNGdkZDQ7XWpiopWVlLf6uHJwICu6407RlYJZdxJesxvaycEASB1jqGOOoavzSzrZbJWHzgLAbaGiwe3guAdRvOU1BQxvhxPiQl5XI5OJ65fX2oqpGx5SWZ/+qYELRU1Jjt4svV3DOUyYqw1nTifNZeUssT0FBR5TefkZTKqlh07UQDnm5jSM0vJDj+ESO92r3SsFjXLg4IArVTlM+waUswySl5fLJwMHp6mhy4cZcLsQ/5cEAPXK3MWHvvCrfyM/ih82CstPU5mbGZguonjGm9gDdtPqCwJpfTGduadQyXMhI5nRrH3HY9sNc15o9L18gqKmHBgB6vtXwpiiKHbsQwfOUOguMfs2BAd/bNnUD7Vo2zyM6mxTMg8E+2PbjO4hunKKgqZ7CHM27WZqw+F/5SmdbQK/dJTMxm6pQeqKrWXRsLc30+mN+fmHtp7P37KqBkYf0ydiAi8PmBwNcy3PVP5JWW89m+M8zccpjU/CJWnwtjxZlQZelRqsKabqMYadueFXcv8+udS6/F9Pt5/CeBOzkll/j7LfdRA6UZam5JOfP6dSMtv4iJG/7m0ZNXM9R8XTgXk4Ao0kD3RK5QEJ6dRHdz+wY3RX5VJjKxpkX17X9CKkgZ1/ojtFR02Ju8jAqZ8iGmLlVhgqMnFzMSmpSDLK+u4dD1GPq0c8LKQA+FQuTw0ShcXCxxdVU2UUcO74S6ugr7DyizbisDPQZ3cObAjbtNNneuPUmmTFZNH6s6R4+elk5ce5JMuazphumGi9d4kJXLt2/0w0BLg7DwBM4FxTBpgi8zZwTgYG/Kjl1XaG1kwNCOLuyNuNWkBkV8YTanU+OY2rYL2ioCoTlHaaPryXSHJeipGrI/ZSWV8nKcDcxY3KEPIVmP2N6MZeuW4EikEgmTuzVPxOmfMDTUxtW1booSIOpmEoePRDJyRCe6dHGopf75Pkf9Wx8Xxig7D4a2bqesaxdcIsBsFHba7bDVdsHfdCRRBReILXrxOZTLqlkSFUgbPRNmu/iSmJ3L1uBIhnu64u1o88JtW4Lk3EJmbD7E14eDaGtpwpH5bzGrZ9dGH3a5lWXMCz/MnLBDmGnosMpnJGWyatbHhiGRCHw8yJ+sohL2vGDyUS5XsG17KLatjenT243qqhqCdgVTWa5kIPXp3Y5ePV3ZsetKbdxpZaTP4mE9iXyczrbQpjXSWwqlfk0Mw1bu4MzdB7zb25vLi2czwacD20Kj+P7YRRQKERWJhF+9hzPB0ZM/4sL59ubZZiUPzcV/ErhFUWTeB7v46++I2uVLc1BaWcXmyzfo3saWd3t7s332GKplct76cx+3U17tQfA6cPrOfdpamNQzpQW4V5hFYXVFoxKbzxqT5q9YKnkGHVUDJrReRFFNHgdSV6MQld/nRKdOSAUJuxMaz0xP3oqjuLKqVt42MuoxaWn5jH6jbjpRX1+LQQM9OH/hHjm5JQBM9+9MRXUN+67eaXS/FzMS0JCq4GtWd14BVo5UK+RENFG6uZuaxZbgG4z0akdPVweKiitY+Vsgjg5mTJ7UHYlEYOqUHqSl5XP+wj3e6+ODTKFg4+XGVxSrY0LRUVVnprM34bmnKJeX0Md8PJpSbca2/pCC6hxOpG8CYJKTF32t27LszkXuvcAeKquohKNRsbzh5daipuQ/4evtyP0HWeTllVJSUsnSX0/R2saY2TN7US2T11L/fvoH9W9Jp/4UVudwNP0PbLTa0Nt8LKKiHFFRSm/zcVhq2HM0bT2lNU1ra/wWE0J6eRE/dB6MiiBhyZHz6GiosWiw/yufz/OQyRVsDYnkjdU7iU3PZskbfdg2cwx2pg0FpERR5HhyDAPP/Mn59Ad85B7A4X7TGW7rxpv2HdiVGElKaQHejjYEONuz8dL1JpUqz1+4R0pqHtOn+SGVSji86iTLpq5l/QfKVYggCCyYPwBjIx1++uUEFU9Nm0d0akf/9m1YExRObHp2o/tuCR7n5DN900G+OhSEo7kRh+ZNZl6/bmioqvDF8F68HdCZfdfu8MXBs8jkCiSCwPdeg3jb2ZtdiVF8dv0ksteU/TfHAWerIAhPBEGIae5O7WxN6dG9LZu3BLPo07/JyWlenWnnlZsUVVQyv383ANyszdn97jh0NdSZsfkgwfGPmnsIrw1JOQXcSs6spa09j2c15qYGbyRIMFNv1eC9lqK1tjODLadzvySK4CeHADDX1GWQjSv7H99uQA0URZE94bdwtTKjk60yuz58JBJjIx38/errbowZ3VWZjR9RPgDaWpjg19aO3eHRDZavoihyISOBHhYOtSbIAJ1NbNBWUWu0zl1VI2PxwbOY6Gjz6RClD+GatUEUF1fw2SdDape9Pbq3pU0bc3buDsNKX5eRXm4cuH63Qb09Jj+Tc+n3ebttV9SkCq7kHsNFtzM2Tz39bLVd6WU+hluFwdwqCEYQBH7uMkRp3hBxtMlVwbaQKERR5O2AVx+7B/D1VbI2rl57yOo15ygoKOPzT4eioaHK70Fh9ah/X0cF1lL/tFRUnj6Y5Yyx+RAJNYj5byLmjUBKFWNaf0CVooIjaesbXXbH5Gey7cF1Jjh60tnUhoM37nIrOZNFg/0x0mme2e+LEJuezYT1e1lxJpTube04/uFUxnb1aLTZmVVezOwr+/nw6jHsdI043v9t5rbrUcu6WtDeHxVByoq7lwH4cGAPyqqq+fNSwxVFTY2cHbuu0KaNOX49nCktLGP/r8fQ0tPkzJYLhB1VbqOrq8GnnwwhPT2fP/5UMkoEQVA2wbU1+XRfYD2/1pagWiZj/fkI3li9m/uZOXzzRl92zBpbL5ETBIEPB/Rgfv9uHI+OY+Hfp6iWyREEgc879OEDNz8OJd3hw6tHqZY3NNpoKZqTcW8HBrZopxKBr78cwaKPBxN/P5OZ72wlJPTFHd7Csgq2X7lJXzenenWy1sYG7H53HA6mRszbdZwjkf93gkgXYx8yccPfaKmpMrRjQ6GhK1mPcdE3w0SjYYaWVZGEibo1KpJ/rz8M4G08kI4GAVzI/rvWOWdqmy6U1lRxJKl+dnztYSqJ2XlM7tYRQRBISc3j+o1HDB/miaqqVGnEXKFcZlpaGhDg78KJk9GUlinLIzMCOpNXWs6x6PpshvjCJ2SUF9crk4CydNPN3I7LmQ0ZFWvOh/PoST7fje6HnqYGIaHxXLwUy5TJ3XF0NCftQQaL+n5L0r1Upk/1IzOzkMBzd3m3lzciIhv/cTP/FhOCvpoG09t2JSznBJXyMvpYTECUJSNWKm/YnmZvYqvlyrH0P8mrysJIXYsV3iN4XJLH9zfPNfhu80rLOXjjLkM9XWo9JV8V9nammJnpsWLVGeV5vtUdZ2dLriamsC2kjvp3NDmGEyn3mO+mpP6F5BwhqSyWYVazMFa3QCxeCrJEkKcjlvyEuUZr+ltMJr4kkqiC+r6JMoWCLyJPY6yuxScevckpLmVl4BW8HW1axEVvDGVV1fxy8jLj1u0lu7iUVZOG8vvkYZjrN/zNi6LIvofRDAjcSHh2El907Mu+3lNoo1+/oW+uqcvbzt6cTInlTl4GbSxMeMPLjb+u3iI1v/6K4nTgbbKyipgxzR9BEDi48gQlBWUsPfcVbTrZs3LWH+RlKsuFnh1tGTvGmxOnbtXOKBhoafDTmAE8yslnxZn6CpzNQeTjNEb/vod1F67S182J4x9NZUxX93oPrOwnRdTUKIP0O728+WxoAEExiczfdZzKGhmCIDC/vT+fd+jD6dQ45oQdfKVp4+fRHAecEKBFReaaauXBDhrowcYN07GyMuSb746yfMXp2mXMP7ElJJLy6mrm9fNt8J6JrjbbZ49RuoEcOsemy9dfe7H/eVTVyPjx+CXm7TqOtaEeB+dNaqC3UiGrISo3tUknkuzKlGYP3jQHgiAwotW76Koacvlp1t3R2AoPI0t2JkTWq5/tCo/GSFuzdpVw5GgUqqpShgx5asX12ymGak+u/cGPG+tNeXk1J08q64xd7FvRvpU520Oi6jV2Ljydluxl2ZAL3NPSiYzyYhKK64aGopMz2B4axZtd2tOjrR0FBWWsWn2Wtm0smDDeB7lczrJpa7l1MYbNn+3Gu6sjri5W7N4djom2Fm92cedI5L3amzk6N51LmYnMcvZBKqkhPPcEbvo+WGrYIhbORyx8F7EqBOlTAwGJIGV/yirkogxfczvede3G/se3G/j77bxykyqZjFkBXf/dRUJ5nTp2UDaknZzMmTjel8KyCj4/EIi9qSGLhgSQUlrAN1GBdDG14T1XJfXvQtY+3PW742nYE7HyAlT8BVpvg/YsqDiIWHkeX5MhOGi351TGVvKr6so+uxIiiSnI4ivP/uipafDLyWCqZDKWjOxDTY2c39ee41xQTIvKlvCM4reD3eHRjOnqzsmPptK/fZtGm5yppYVMDd7L4sjTuBlacGrALGY4ezc57DPLxQdjdW1+ua30sny/ny8qEgmrz9ZNBVdV1bB7Tzjt3VrRtYsDhTlFHP7tFP5jfHHp2obPdn9AVXkVy2fUmW9Pn+qHo4MZy1eeIb+gDABfJ1umdO/E3qu3CYlv3lh6YXklXx8KYurGA1TJZPwxbSTLJwzGVLdOAK6srIo164KYOHkDCz/dS1mZMhl6q3snvhvVjysJSbz7nMXaTBcfvvcayOXMRN4O3fevVAj/kxp3RmLdj6pVKyPW/DaZvn3cOB14h01bLjf4fFBMArvDoxnSwaVJdS9tdTXWTx3J4A7O/HY2jMv/Ydlkw8Wr/BVxi4m+Hdnz3jhsTRrW8GILs6lWyBERGzxEFKKcYlk+ZfLiBgM0/wZqEnU8DXuRWHqLclkJgiAwxr4jD0vyiC9U1vCqZXKC4x8R4OJQq7V8JewB7u1bYWSo/NH98fEOAK6eUJZH2jiZ06qVEZeDlasiQRAY5OFMcl5hvYGc6zkptNEzxVSzYbblbaYMVpHPjb//fOISlvp6fPK0RLJnbwTl5dV8umgIKipSrhy+TtzVBDwC2nH9dDTXz0QzfZofT3KK2bf/GrN7dkUqEVh2KgRRFNn78CYCMMahA7cLQ6lSVNDZqC/U3AaZMhiLJcsAMFAzxdd4MGkVCcQUKoPBe67d0VVV5+db5+sd+5Goe/i3tW+0VvsqmPJWDz5aMJAlX45EIhFYcuQ8+WUV/Dpe6cb00dVjCILASu8RSCUSgrL2ACKDraYjCILyHAQ9BN0PEHTeB0EfsWQFEkHCYKvpVCsqax/esQVZrLh7mZ6Wjgy2cSUpp4DAuw+Y4d8ZWxNDLl6K5eixm/yy7CT7DzSfV7zx0nXm7z6BnqYGe94dz9cj+6Cn2TiPOzo3nWHnNnM7L53vvQayu+ck7HRf7Leoo6rOB+39uJaTwunUOMz0dJjaw4szd+5z46n0QmRUEnl5pYwY3glBELhy+DoVpZUMn6OkVLZ2sWbI7H5Enr3NrUvKSq6amgqzZ/aksLCcAwfrzvf9fr5oqamy6mzzpjWXHA7iUGQMA9zbcHTBFPycGyZo6zac58jRKMzM9Lh7N40du+r2PbpLez4f2pMbj9PYcOFq7esTnbz41Xs413NSmBm675Uz79cWuAVBmC0IQqQgCJEq2vV3Gx2dTEREIsZGOgweWKdOJ4oiW4JvsGDPSVwtzWproE1BTUXKj28OQEUqITo543UdegP4OCmDkLGOVpNKYx5Glgy2cWXrg+ssiQqs13SQCFL6mU/kYekdtj3+lnJZyWs7tpzKNHRVjGqlXx8W56ImkdJaRxl01FSkBLg4cPp2PEk5yoy6R/e2RN9K5s5dpYbDpzvnMWRWX7z6K69F4Nm7pKXl4++vzNALyirYHhqFo5kRDmZ1dTwXfTOSSvMorm7IOLmbr3xYt9VXSpimFxQpTRu6daxV/IuPz8CtnTX29sqlc06qMjv/8u8PcfCw5ddpa7E116V3r3bs2HWF7JQC5vfvzsXYh/wVcZuRdkoXomW3L+Gq1wVNqQ5BWXuRS9uAVHljCdozAbhXdJWQnCNYatjTRteTclk1c8IOUlJTxbuu3eodu7OFCYlP8v4VjfV5WFkaMHRIR6ytDTkeHcf5e4nM69cNVyszNsZHEJ2XzndeA7HSVpZluhr1R0TkZMZmFKIcQWsSiMWIJcsRS34DsQhBazwyRQ2nMrYhFVToZNSbrPJiZoXuR19Ng5+6DEEQBIx1teqZLTwz3oa6+vvLcOpWPKvPhTGkgwv7359Ih9ZNq1FG5aYxLfgvDNW1ODlgFhOdvJpt9jDOwZMORlYsiQokt7KUmT270NrYgC8PnqWsqhq3dtYYGGix/+A1ZDI5nn3ao6Glzr6lRxFFkTshsZz88xzOXRxx8VaW7woLy1m74Tza2ur066s0wFAoRL4/eoHy6ppmy8lO6e6JsY4Wl+Mecfp2fKMr/BHDOmFqqkt2trIP07d3neFGaWUVhyPvoaWmypB/lFnfsHNnhfcIbuSkMC/iMDWKlte8X1vgFkVxoyiKnUVR7GxtW6fZcezETT7/8gAWFvqsWzMFJyelRGW1ueD+nAAAIABJREFUTM7Xh4NYGXiFQR5t2TrrzWY1UdRUpDiYGvEgM/eln31V+Di2pn/7Nmy6fJ3MwsaDrqpEymrfN3jHxZc9D2/yzpX99ZY+fmYjGWuzgNTyB/z5cHG9pW1jkIsyimrySC9/yP3iKCLzL3D5yUFOpm9mb/JyNj38kpXxc4krvkF7fV8kggSFKHImLQ5/S8d6uhbfjOyDuqoKXxw6i1yhYPbMnliY67Ns+SkqKqrpO9mfBX++g4WdGWlp+axZF0THDq0ZN8YbURT56tA5Cssr+XX8YDSec0gZZONKjUJRWzJ5HkHp9zHV0KaTibIZeylWuSLq5aoU3lIoRB49zqkN2gAVJcoHgJ6JLl/u+5Cqimp+nvw78+f2xdLCgB9+Os4IdxcCnO359XQIBjJN5rbrwaGkO1xMz2RUq/fJqHjI2ex9CCaBCOYxCJojuVUQzN/Jy7HWdORtx++QK1SZHvw34U+SWNp1KJOc6tvnjejUjvSCYm4mp7/wGrUUGYXF/HT8Ep3srJju58W9gixWx4QwxKYdw1rX3eTtDboxyHIa94quciZzB4L2FNCaBuU7oXwLaE1E1HyLY+l/8LgshlGt5mKq5sjs0P2U1FSx2W9crV63roY6btbmXHuofEh7uNuwY9ts1v0+BbsmBKGeR9TjNL44eI7O9tb88Ga/F/LZI3NSmR68FxMNHf7qNRkbHYMWfT8qEgnLvIdRJqvmi8gzaKqq8OOb/UkvLGbFmVAMDJQDSwkJ2fy1NwJrJ0ve/mUSNwJvsW7+Vr4a/gvmtqb8eGoxmtoalJdX8fkX+8nOLubH79/Ewd4MURRZdjqYE7fimd+/G2O6ur/8wAAv+1Ycmj+ZjrZWLDl8nk/3BTZwlXd2tmTjHzP4del4Nm6YTtu2yt5ctUzOgj0nSczOY9WkobhaNdRjH27rxrdeA7mYkchn10+2mCr4n+lxy+UKNvx5kcNHIvHxduTLxcNrNQ6KKipZsPsk1x+l8k4v7wajuDKFgjJZFfpqmo3u28XSlKsPW65I1xIsGuxPyP3HLD8dwoqJQxr9jEQQ+KRDbyWd62YgEy7uZJN/3U3UwdAfPVVj9iQv5Y/Ez+hoGICuqiGlNYWUyoookRVQKiukpKaQcnnjzBsNqTa6KgboqBhiremIi15nupkMA+BmbhrZFaV8ZlNfW9lUT4fFw3rx2f5AdoVFM83Pi0ULB/PRwr1s2RrM+3P7ASCTyfnxlxOoqkr57JOhSKUS9l27zaW4R3w6JKCBz2JHYysstfQ4kxrHG3Z1N0CVXEZw5kOG27rVZluX4h7iYGZUW2bKzCqksrIGB/u6H3F5SQUa2upIpVJsnK35YMNslk5Zw5GVJ/j6qxG8P38Xvy4/zQ+fDWHM2j18vPc0f88dz42cFJbcDORovxl0MxlCeO4p7LXb007fm+t5ZzmevhF7nfZMtv2McpnItOA9xBc+YbXvGwy2aahD3dvNCS01VY7djKWz/b9nAYHyQbX4gJK7+/OYgchEBR9fPYaxhhbfeQ1sUCfubjqMwponhOeexEDVlG4mn4G0NSCC1gSCcw5zs+ASvc3G4q7vx5ywg8QVPWFjj7EN9Lp9nGzYFhJFWVU12upq2LR6cdniGZJyCpi36wStjPRYPXn4C3Wtrz1JZmboPiw09djda9IrGz046ZnwsXtPfr59gaPJMbxh587UHl5sD42ir5sT/n7O9Ondjl17wvH1dWL4nAGEHbnGsXWBGJrr88vZL9E30aO6WsbX3xwmITGb774ZhYe7kre+OfgGu8KimdzNk9k9W9bDMNXVZtOMUWy8dJ31F64Sk5bFiolD6gVifT3Neq71oijy9aFzRCSm8MOb/enR1q7hjp9ikpMXBVUVrIoJbjLWNYWXBm5BEPYCPQETQRDSgCWiKG550TYKhchX3xzi6tWHjB7VmXdn9651SE/OLWTOjqOkFxTz85gBDP9H1zultIA5YQfJrigleOjcRu2anC1NOR4dR0FZBYbaLTvh5sLKUI+3A7qw7nwE4x550NWh6QGGiU6dsNTSY37EYUaf384Wv3E4Gygvrr2OG+84/sSRtA2E5Z4AQEVQUwZjVQOM1Cyw1XJFR9WgNkDrqhqgo6L8pypp2ljgTGocahIpvf/B8gClE/bZuw/4/VwYAS72dOxgy8gRnTh8NAo/P2c6eLRmx84r3L+fyZKvRmJmpsfDJ3ksOxVCtza2jQ6hCILAoFYu7E6MoqS6stY0Iiz7MWWyavpbK0stxRWVRD5Or2cM/ejREwAcHOoeBuXF5Wjp1l2/vpP9uXUxhj0/HMLdvx3vvdOb39cGcf7MXZaNH8z0TQf4/uglVgwfwfCgLcwLP8KBPpNJLovnUNpaHpXeJSLvNM66Xkyw/f/aO++oKI8uDj8vvXcUEJBu772h2DXWWGPXGGONUaNJLKnqF9PsPfYee0VBxa6gEjsiTZRepHfY9/tjF6Ts0gJqztnnnD0su7O7wzB735k79/7ufBKyshh35QChqW/Y1HEobnLGCaTiRL0auXDhcQAL+7vJTdeuKLtv+nI3OIyfh/TA2sSQZf94EpAcxw7XkRhpyp+zfSwnkJQdj3vkTgzVTWloNAaAR4k38IzaTxMjV7rWHMHyBxe5GBHA98174mZV0v3R1tGWrVfucj8kHNdyprknpGUwbdcJVFQENowfVEIYqjC3ol/y2fVDWOsasbfLaLlnHhVhoktrPML9+dH3Au1q1GZWj/Zcex7CkqMenPhyHLNm9ODBg1es+PUsG9aNZ8GuWcSFv0HHQJsatuYFqou+/4Ty9fyPaN9O+n8+cvcxqy7cpF/Tunz9UedKZY6qqqgwrVtbWtpbs+DQOUZtPMiCvp0Z2bax3PdbdeFmwep+cAvFtUrzmVG/A4nZGRXWNSlPVMknoihaiqKoLoqidVlGG+D163h8fIKZ/UVPZkzrXmC074eE8cmGAySmZ7Dt049LGO0rkYEM8txOaGoCb7LSORkqP3S8jqV0y1dZPd/yMsm1JbWMDVh+yqvENqk4blZOHOw6jjyJhOGXd3Mz+u3ptbmWNZ85LmVenQ0sabCHHxoe4Kt6m5jq9Atj7L5hoPXndKs5gtamvahv2BobHReMNWqUarQVuUnyEQSB72Uuk8VHPMiTSPjs0y5YWhrx6+/nuOMdxP6Dt+nTuzGdXetKdaEPuqOtoc7yoT0VihH1tqlHtiSviLvEI8wffXVN2sqScq77vyRXIilwkwCEhMQiCBTZrqenZqKtX9SIzVg7CZu6VvwyZjWubRzo1NGFrduuop0OM7u3x/2RPzeehvJbm/74J8Xwv4dejLCdR2ZeGrfjz9HIsD2jai8gJiODkZf3EJ6eyDbXkQqNdj4DmtcjLSubS88qJkoF0lVWTHIqdwJfse/WA34+eZnVHjdxq+fA4BYNuBX9ku0vfBjr1AJXmbaLPFQEVYbZzsZGx4XDr1fzMs2PV2n+HH29lto69fjYegb7g3zZ/sKH8c6tGOfcSu77NK1thYaaKt7l1KXOysll5p6TRCWlsG7sAGxNFbs8bkQFM/n6IWz1jNnnNuZfG22QGsdfW/cnVyLh27tn0VRTZdmwXsQkp7HizFUMDLSZN7c3wSGx7Nl7E3NrU+q1caZ2PWtEUWTNOk+uXnvO51Pc6NVTuhP0fBLAj8cv0cnFjqWlzOfy0srBmqOzxtDawYalpy4zd//ZEtrx+28/4K+rdxneulG5V/eCILCwaXeG2zcpu3EhqiWqJCcnj/8tHcbA/m81uU/5PmPStqOY6Gmzf9ontCi0JZWIImufXmfytUNY6Rhwttdn1DOqwd6A+3IPBepYSFdtz8sw3Hl5kn8VNqilrsa3/bsQEB3PgJW7uPD4Ranv18DYgqM9JmKlY8CkqweLVGIRBAETTQu0VHWrRDMi303ykZytfz7mBnos6u/Gg1eR7L7hi7a2Bgu+6ktkZCILFx+mlpUxM6d3B2C1xy2eR8aydEhPzEvJHGxmWgsLbX3cw6QRKLkyn7ebpRMaMn+ol18Qpno6NLZ5e6gVHBJLLSvjIpVSMlIy0CmmOKetq8XiQ3NJS0pnxbi1zJvTG3NzfX5efpIRLRrR3smW/532wkowYkrddhwI+ofb0W+YV2cD051+Y7jtHF6npTDi8m7eZKWzq/Mo2te0K3M8W9pZY2VkwElfxUp8uXkSXsYmcPlZEFuv+LDw8AVGrj9A2x834Pa/rXy67SjLT3tx5h8/WtjV4sePe5CSk8UCn9PY65vwdZNuZfZDXUWTsXYLMVI3Z2vQIjYHfYuBuimj7b7mZvQrfvS9gJulE4uadlf4HlrqajS1teJOYNnuRIlEZNGRCzwIjeSX4b1pWttKYdurkUF8dv1v7PVM2NtlNGZaugrbykMURZ4m3SEms+QFxU7fhK+bdOVaVDCHgh/Q2MaCyV1acfz+U674BdO2jRO9ezXiwKE7+Pm9DUzYtfsGp8/8w8jhbRgxrA0gzWOYf9CdxjYW/Dm6X5UVKTfR02Hj+EHM7d2RS88CGbpmL49fS8+uLj4NZPlpL7rUc2DRgK4V+o6rCAL/a92vQn2pFh+3ja0prVq9PZRae/EWW7x8aONow8rR/TAsFFaUnJ3JPO+TXI4IZFDtRixt2QdtNXXGOLVk0b1z3I8Lo6V5UTeFiZ4O5vq6Za64P/1sGy1a2DFL5tOtDG71HNk3dQQ/n5ReZds712bxADe5IYIgLUL6d9dxzLh1jK99zhCWlsjsBq5Vrk9empukMB81rYvHkwDWeN6ic10HmjS2ZeSItnhefMLCbwegra3BrYBQdl6/z8i2TehSaJUsDxVBoLd1XfYH+ZKSk8XThCjeZKXT01rqJsnOzeO6/0t6NXIpssoJDo7BwaHoIU16SkaJFTeAfUNbZqz5lJVTNnFm3XmWLBzIF3P28sef7iyf24sha/cxb/9Z9k4fwd3YVyy6e5ZTvSZTW88C/8QYxl3dj0QU2ec2hgbG5Sudp6Ii0L9ZPbZe8SE0LoG0rGyCYxMIjnlDcGw8wTFvCI1PJLdQLHQNA10czE0Y0Lw+9ubGONQwwdHcBDP9txfneXdOEZORwuFu49FWK58LRkdNn/H2i/nTfyYiEsbbLyYsNZMvbh3DxdCcVe0GlVkMoY2jDWs9b5GYloFRKe7ENZ43cX/0grm9O5Zac/RGVDBTbxzGycCM3V1GYaxZsWzMN1lRHA/fSHDqY+x06/OZ49ISbUY7teBCmD/LH1yko4U907q25YpfMN8f9+Sk7TimT+uGr28ov/x2hi0bJ3LW/SG7996kd69GfCYrZPwsPJpZe05ha2rI+vGD0KkCt1dhVFQEPu3ciuZ2tfjqwDnGbD5E13qOXHkeTCNrC34f2bdCZeEq3Y/qeFNNDen1IDMnl/kHz7HFy4chLRuyeeLgIkbbPzGGgZ7buRYZzA/Ne/F7m/4Fk3tA7Qboq2uyW4EWh4uFGf5RiiNLUlMzefU6nuMn7vPkSflLcsmjaW0rDs0Yxbf9uvDoVSQDV+1hrectMhUomulraLHNdQRD7Ruz9ukNvvI+XSVprvmU5SYpjCAILBnUDW119QKXyZTJXTh8cCZ161iSINOFdqxhUm5Niz4yd4lXRAAeYf5oqKjiaiF1AdwLCSM1Kxu3+m8vABkZ2YRHJBSJKAFpVImOgQJ/76ddcfukA7u+O0huXBJTJnfhxs0X3Ljsz68j+hAS94Zfz1xldbvBqKqo8MWtY9yPC2OU115UBYH9FTDa+QxoVg+JKNL3j50MW7efrw+5s/WKDy8i47A1NWJ8x+YsG9qTA9M/4c730/H6dgrbJg9l0QA3RrVrSltHW8wN9AqMtvtrP06EPmZG/Y40qWAhWRNNC35q9DdLGx1FItHn0+uH0FXXYGunEeWqjNNWJipVmrvkyN3HbL1yl2GtGzHJVXGqf3ByPDNvHcNe35Q9XUZXyGhLxDxuxp5mzYs5hKcHYq/bkNA0P1JzS2quqAgCK1r3QwC+9jmDmqoKy4f1IjEtk2WnvdDT1WL+V315/foNY8ZvZt36i7Rv58S8OX0QBIHQuAQ+33EcA21Ntkz6uFQ//b+lWW0rjn4xhk4udng8CcBEV5sN4wdVyflIeai2qJK4lDRm7TnF47Ao5vXpxMROLYqsOk+FPmXh3bPoq2uyv+tYWpgVPc3XUdOQitEE3CMmI4UaxU6t61ia433Tl5y8PLlboYgI6cRQURH4c/V5Nm+YWEQOsqKoqaowpkMzejVy5nf362y67M2Zf/z4tr+b3FWquooqv7Tqh42uMSufXCUqI5kNHYZU+PRYHoqiSRRhrq/LogFuLDjkzq4bvgVf0sKhf5smDi4S+lcazc2sqamtx9nXfjxLiKKThQO66lIXiJdfMFrqarR1fCtn+zI0DlEEh2KGW3o4Kd+gCYLAl5s+x/9uEMtHrWKj76/88zCUTVsus3b1WKa6tWHjZW9aO9iwonU/pt44wvBLu7DWNWRPl9EFce0Vwc7cmIX93UjOyJSunmuYYGtqVO6q4YWJyUhh8T13GhlbMr1+hwq/HkBFUCE9N5sp1w+TmJXBwa5jsdQxKPuFQAPrmuhoqOMd9FruSvrmi1B+OnGJji52LC5la5+UncGUG3+jrqLK1k7DFR6syiMmM4zjYet5le6Pi35zBtaaSkZeCusC5uGXdJdWpiV3wrV0DVnYtDsL751jb+A9xjm3YmrXNqy7eJvuDZzo1dyFAf2bcer0P9SvX4sliwaiqqpCTHIqn20/hghsnfTxv6ogX16MdLRYO3YA8anp6GpqvDOjDdW04s7KyeWTDQd5ERXHqtH9meTasmBi5Ejy+NnXgzl3TtDQxIJTPT8tYbTzGe3UglxRwiE5ZYDqWJqTmychJFa+rGl4hPTxSRNcefkyjsNHq6YahbmBHitG9GH75KFoqKsxY/dJZu05RXhCUom2giAws0FH/mgzgIfxETQ//idTrv/NNn9vnryJrLROcHndJIXp26QO3Rs4sdbzFkEx0kK6h30e4+UXzJzeHUsUiCgNFUGgl3VdLoa/ICI9ucBNIooil58F0c7JtsgkDgmRurRKukoy0dZTbAh09LVZcmguSbHJ/D5pA/Pn9cXISIeflp5gXNtmtLK35ucTl3BUN+d/rT7i0zptONh1XKWMdj6j2zdlWre29GrkglNNs0oZbVEU+ebuWTLycvij7YASJe3Ki0QUmXfnFE8SIlnVbhANTRQnwhRHXVWVVvbWBfHchXkRFcec/WdwrGHKH58o3trnSiTMvn2CsLRENnYYSi3d8um45Im5eEUfYV3AXGKzwhlmM5txdosw0jDDQssOE42aPEv2Vvj64Q5N6WzpyIqHlwlJecPkLq1oUKsmP5+8THxqOtOnduPsqbn8snwYmprqJGVkMmX7MRLSMtg0YRD25uULf6wKBEHATF/3nRptqCbDHRz7hpy8PHZ/PrxIpffYjFTGXtnHzoC7THBuxR4FoUSiKJIrycFe34ROFg4cCPItkV1Ut4zIkvBwqeEePKgFrp3qsHvPTSIiFUtiVpQ2jjYcnTWGub07cjsglAErd7PFy4fs3JLuk0F2jfDoO5WRDs0ISo5n+YOLDPTcTssTK/n8+t/s8PfhWUJUuYLwK+ImKYwgCCwZ2BUdDXUWHfYgICqOFWev0t65NmMroT9dOB46X3TqeWQsUUkpdK1fNHIiODgGLS11LC2KRivIO5wsjlMzez7/YzzeZ325uO0yixcOICoqidVrPPhleG+0NNSZd+AsA2wasrBp93KvSKuLZwlRTLx2kKuRQXzTpCuOBmUnvSji90deeIT7s6hpD7rXqlhldoDWjja8jEsgKultEllMcirTdp5AV1OdjRMGoaeleA798vAS16OC+alFnxLnTIoITw9iQ8ACLkbvp75Ba76ss4amxm9D8QRBoL5BG4JSHyms6pOv6KihosYC79OoCALLh/UiLSubn05cQl1dFW1tDfR0tcjIzmHGrpO8jEtk7dgBCgs5fOgERsdXqH21GG4NNTUOzviEBrXeJgbcjwtjgMc2nryJYmXbQSxp3lPuSiQq4yUbAxew0n8GOZJsxjq1IDojtUTZezszEzTUVBUb7ogETE310NbWYMb07qipqbB6zYUqFafSUFPl086tOD13Ap3q2LHa4yaDV++VWwDVSseAZa36cumjadzsP4s/2wykt01dApLjWPrAk/4e22h54k+m3jjMzhd3eZ4YLdeQlyeaRBFm+rosGtCVx2FRDFq9p8zQv9JoYSb9ItfWMy7weXo9C0IQwLWYrkNwSCwO9uZFPicvN4+sjGy5h5PFGTC9Fx0/bsO2b/ehlpbJxPGd8Lrix71bQfxvWC9eRMWx4syVCv8NVcmr1ATm3D5Bf49tPHoTwcKm3RnjVHmJWM9wfzY/v81Ih2ZMcJEf9lcW+dIN+avutKxsZuw6SVJGJhvGDyrVnXA4+AE7XvgwwbkVwx3KThPPkWRzIXIvmwK/JjU3kdG1v2Zk7a/QUysZWljfsC15Yi4vUnwVvl9NbX2+b94T3/gwtr/wwammKbN6tOfi00DOympJ5uTlMW//WR68imDFiN4Ff+9/jezcPObsP1Oh11SLj9ve3LhgUoiiyJ7Aeyz75yJWuobs7PxJQXJKYXIlOVyJOcrVmKOoq2iQJcngSdJNulh2ppaOIXsC79OnkLFSU1XBqYapQsMdEZFALSvpltncTJ9JE11Zt/4iXlf86Or276Qui2NppM+q0f257v+SZacuM3nbMfo0dmF+385y5S8tdAwYaNeQgXbSgrQR6cn4xIRyJyYU75hQPGUXKWMNbVrXsKWNeW3a1qiNs6F5pdwkhenT2IXj959yKyC0zNC/0lARBIJGLCrymJdfME1sLTErpKAmiiLBwTF06lRUzzw9RSqar1MOwy0IAvP+msa05vNZNnIl6++t4MHDV6xbf5EN68YxybUl26/do7WjjVzd9OokLjONdU9vcDDYF1VBhWn12jOlbjsMNCp/MPYqNYH53qdpaGzBd817VjoiyaWmGca62twJfEW/pnVZcMid55GxrB83UG4adj73Yl+z5L47HWva820pYYf5hKb5cSxsA3FZ4TQ37kpfywloqymeVzY6LuipGfE06Q6NjToqbDewdkPOhz3nT5mI1viOzbn0LJBlp7xo6WDN6gs3ueofwveDupUaEfOhs+P6vQpX+aoWw52f9pyRm8Pie+6cCH1MVysn/mgzUO6EDksP4FjYeqIzX9HEyJWPrCaxNWgRd+LO08zYjdFOzfn1kRcvkmJxKaTtW8fSnGv+8mUawyMSaNP67ZZ9YP/meHo+Yf3GS7Ru5YCeXtWfOHeqY8fJL8ex7do9tl7x4erzEGZ0b8fo9k1LjSW10jFgkF2jAhGl8LQkvPMNeWwoF8L8ATDR1CErL7fCbpLCCILAlomDC+5XFZGJKTyLiGFO76JfxLj4VJJTMoukuoPUTQIojCopjp6RLgsPzGFOpyWsnLKJb7ZO4/NpO/jp55OsXTMG35fhLD7igZa6Gm71FCe5VBWpOVls8/dmm783mXk5DLNvyhcNO1U69TufrLxcZt46hoDAuvZD0FSt/FdURUWgtYPUz73izFWu+AWzeEDXUrMpw9OSmHbzCLV0jFjTfjBqpYQdZuVl4Bm1jzvx7hiqmzHB/juc9ctenasIKtQzaM3DxGvkSLIVJpoJgsDSln3oc34r871Pc6T7BJYP7cXHa/YyfN1+4lOlJQ6Ht2lc9mBUI34J0djpm5Q73LMwoXGJbL7sTa9GzijOIChJtQUcvkpNYNilnZwMfcyXDV3Z3HF4CaOdI8nCPWIXmwK/JSMvlbF2Cxlu+yW6aga0Me1DWEYAYemBDHNoioaKKvsCi9aOq2NpRnxqOrEpaUUez8jI5s2btIIVN0grQs+d05ukpHS2brtaXX82mupqTO/WlpNfjqOlvTW/nbvG8HX7uf+y/AJGtXQN+di+Mb+26c/VfjO52m8GK1r3o7OlI1Y6BowtJpJUUQRBqPK48it+UlGprvVK+rehaKo7SA8moXwr7nzqtXFm8i+juXnch+v7r7Pwm/68Dotnw8bLrB07AMcapszee5qjd8tdrKnCZOXlsvPFXdzObmDN0+u4WjhwvvfnLGvV918bbYBl/3jyNCGK39r0RyVHKDhIrixtHW2JTk5l3+0HTOjUgk/aKc7QS8vJ5vMbf5MjyWNLp2GlRkAFpjxgzYsvuRPvTlvTPnzhsqpcRjufBoZtyZZkEpj6sNR2Zlp6/NSiN48TItnkd4vaZsbM7d2R+NR0Rrdryudu/15DvbJIRJHfH3nRz+Mv5nufqvDrRVFk6clLqKuq8k2/LhV6bbWsuFNyshjkuR1RhL9cR9BFjvD+y7RnHHu9nvjsSFqa9KCP5bgCqVKAZsZd8Ijai3e8O0NsZvGRbX2Ov3zMV43d0JetNvNFkF5ExhUROI+QRZRYWRWNLnB2suDjwS05cvQuPXs0pEH9isXWVgRbUyM2jB/I5WdB/O/MFcZt/pt+Tesys0c7bEwqpqJmrWvEUHsjhlYwLfZd4uUXRG1TI+yLaVoXRJTYl0y+Acrl4y7MkDn9eHjlKVu+2s3qW8sYO7oDu/fepFlTW3Z8NpQ5+87w3TFP4lLTmNKldZVdoCSiyOnQp6x8cpXXaYm0rVGbBY3d5MZnJ+XEE5jygGbGXVARyh9Rcir0CfuCfJlcpy1NDWoxZO1e3qRmMKpdE2b1aF/qQaIi8v2+3Rs4Ma93p1L/vvk+p/BPiuWvTiMUHqpm5KZyLnInvgmXMdOwYrLjUux0K37eYq/bAC0VHZ4leVPPoHQffh+bevSzrc+6Z9fpauXEqHZNaeVgg1MN0ypfgJSXlOxM5soSB50NzHEPe87NqBA6WJRPGwbg3EN/bgW+YmF/twrXOa2eKu+pb7DSMeBkz0kljHZWXgZA0rpyAAAVhklEQVSnwreyNWgxEiRMcviRwdbTihhtAC1VHZoad+ZR4g3Sc1MY69SCtNxsTrx8XNDGRZb67h9V1M+dHwpYq1ZJAzlxfCdqmBvw58rz5OZWXVKMPARBoFsDJ07NGc+ULq3xfBJAvz928dOJS8QoqGD+XyQ1Mwvv4Nd0re9Y4osUHBJLDXMD9ItFj6Qn5/u4K+ayEgSB+TtmYFTDkKUjV/LxgGY0aWzDqjUexEensG7cQPo3rcsaj1ssO+VV6ZDLfERRlCofevzFXO+T6KlrsMN1JHu7jC5htCWiBJ94D1b7z+ZY2Hq84y+U+3MCk+NYdO8cLc1smNPQlQWH3EnNzKZ/s3rsu/2A/uWQXJCHrakRR2aN5reRfUs9hF7z9BoXwvz5pkk3OivQU3mW5M2qF1/wIOEKruYfM9Plj0oZbQA1FXXqGrTiefJd8sSyv4c/NO+FkYY2871PkyOR4GJh9q/1RypLSEo8Qy7u5FpkMD8278XJnpOw1TPmB98L5U60S8rIZMXZqzS0rsnIthV39VSL4TbS0OZwtwkl4mkDUh6w5sVsfOLP097sI75wWYmjnmJ93LamfcgVc7j35iJNTGvRyNiSvYFv9UuMdLSwMNQrcUAZHi4N+7OyLBnPq62twayZPQh5GcvhI/KriFc1OhrqzO7VgfPzJzG0dSOO3n1C79+28/u5ayQoqGz9X+JmQCi5eUVFpfKRprqXjBHPqOSKG8DAVJ+F+2cTFRLD2hl/sfDb/mhqqvHdD8dITkxn+bDeTOzUggN3HvLVgXMlih6Xlwfx4Yy+so9J1w6SmpPNyrYDOdVzMq6WJS9Q8VlRbA/+gZPhm7DSdsBOtz4Xo/bLzRAsTnpuNjNvHkVLVZ3V7Qax5fJdvINes3hgV5YP68X+aZ9goqvD3P1nmb7rJGFvSuYMlEY9qxpoqCle+Z977cfapzcYYteYSS7yXQ+Xog+xL3QFempGTHVaQS/LMairVO6cJZ/6hm1Iz0shNK1s766xpg7LWvbleVIMa59VvHZkVXElMpDBnjtIyM5gV5dRjHFuiaaqGt8160FwSjw7A8qXL7Lq/A0S0jL4YXB3VFVU2LTlcoX6US2G21rXqIijPiM3laOv17Ez5CfUVDT5zHEZH1l9ioaK4tWWKIrU1LLFXrcBPvEXkIh5jHFuQWByHHdi3obb1bEwL2m4IxIwNtJBV1f+xOrQ3pmOHVzYvfcGkVUY210WNQz0WDKwK2fnTaB34zrsuuFLr9+2s87zVgmlseoiPC2JObdP0P3cRg4HP6iwgLs8Lj8LwkhHq4RAUU5OHqGv4kukukOhqJJyHk4Wp2HHeoz/cQRXDt7k7nEfflgymNi4FGZ9uYew8Dd81deV+X1d8XgSwOc7jldofIOT45l+8whDLu4kICmW75v3xKPPVAbUbliiuotEzONG7CnWvviSiIxgBtWaxqcOPzKo1lRyxGwuRO4p9bNEUWTJvfMEJsexqu0gAl6/YbOXNx+3bFAgC9rYxoJDM0bx9UeduRcSxsBVu9l6xYfsKtgxPk2IYr73KZqbWvNzyz5yXQ+PE29yOfoQTY06M935V2rpVM3hr7N+U9QEDZ4m3Sm7MdCtlgtD7Bqz2e8Wj+KrrwKWPERRZLPfbSZfO4S1riEnekyibY23NWXdrJzpZuXM2qfXiUqXr62fz4PQCP72eczYDs2oZ1UDf//IImXWykO1q6EU3l51Nv+Ymc5/UFu3ZMX0fMTcV0jeTESM64soZtPGtA8JOTG8SPmHfjb1MdLQZm+hQ8o6luaExCYUSXyJiEgo4d8uzqwZ3VFRUWH1Wo9qLTwsD2sTQ5YP68WJL8fSwbk2Gy970+vX7Wy7epeM7KqrUVmYlOxMfn14me7nNnIh3B8tVTW+uXuWEZd245cQXen3zcnL49rzEDrXdSghfPTqdTx5eZIS/m14W/2mIoeTxRn5zSCa92jM+tnbMVARWfn7KLKzcvniy708eybVA18xog//vIpg3Oa/y+We+uXhJXqf38yNqBBmN+iE10fTGefcqkD5sDDRmaFsDlyIe+ROHPQaM9tlFa1MeyAIAuZa1nQw649vghev0vwVft7fwQ84EfqYLxp0wkHLjG/+dsfFwoyF/d34/U935n99kJCXsaipqjCuY3NOzx1PRxc7Vl24ydC1e7kfUnkdnrjMVD6/cRhjTR02dpQfwRKeHsTR12ux1anDYOvpqApVdyymoaKFs35T/JLLX/x7SbMemGvp8ZX3KaIzqq4kYGlk5OYw585Jfn10mT429fi723i5WaSLm/UgVyLhfw8vKXyvnLw8fjh+EQtDfWZ0b0denoQ/V5/H2LhiSovVZrhTcxM5GPp7ke1VT8sxCkN/RDEXMXUrYtxHkH0X8oIg05P6hq3RVzPhTrw7WmrqDHNogme4P5Gyq1odSzNyJRKCCsVBhkckUKtW6Ybb3NyASRM64XM3mKvXnlfdH14BHGuYsnJ0Pw7PHCWVoDx/g96/b2f/7QdVspoCadry3oB7dD23kc3Pb9PXpj4X+07jVM/JrGjdj5epbxjguY2ffT1IkVNLsix8X0aQnJlVIlsSCqe6l1xxpyVLs+Yq4yrJR0VFhW92z0LPSJefR6zEppYRa1aPRU9Pk3kLDnDHO5B+Teuycfwgwt4kMXrjIUJiFcfLPk+MZuvzO3xkU5/LH03ni4aucsMucyU5XIo+xPqA+bzJjma4zRzG2n2LoUbRA70uNYZiqG7KqfAtSOT4cZ8lRPGD7wU61rRnSt12fHXgLDl5ElaO6se5Mw845/6Qx0/CmDJ1B1u3XSEzMwcLQ31Wj+nP+nEDycjOZdyWwyw56lFhl1tWXi5TbxwhMSuDzR2HYaZV8nAsJSeBfaG/oKNmwOjaX6OmUvVp3Q0M20hL9mUElqu9voZWwbx1Pb2OuXdO8uhN9a2+I9KSGHFpF2dePeWrRl1Y025wkeIuuZIcbsaeJjYzDFs9Y6bWa8+ZV8+4Hf1S7vvtvuFLQHQ8iwa4oaupwfGT9wkIiGbm9IopmJbLcAuC0FsQBH9BEAIFQfimrPYZeams9p/Ns2Qfutf8pMztlZjzBDF+CGLqb6DpimB+EVRtENP3oSqo0cq0BwEp/xCfFcloxxZIZBW/4W1kSb42d1ZWDrGxKUVCARUxaGALXJwtWLfhIqmpFTda5SUqOokLHo+5dv05eXklD8vq16rJpomD2f35cGqbGrPslBf9/tzJ8ftPi8iIVgRRFLkcEUDf81v43vcCzgZmnOgxiT/aDsBKxwAVQWCofRMu9p3KJw7N2BVwlx7umzgV+qRCOxAvvyA01FRpJydrLTg4BnV1VbmlszJSMlBTV0VD898ZA+OaRnyz9wvC/CNYPXULlhaGrFk1FltbUxZ/dxT3849o71ybnVOGkZmTw5hNh3j0KlLue/0d/BANFVW+a95TodZ0WHoAGwLnczn6EA0N2/FlndU0Me5U4GIQxTzEtF1I4keiQRJ9LCcQmRmCT7xHkfdJyc5kxq1jmGjq8Gfbgaw+f4sHryL5eUgP0mPT2bTFi/btnDi4bzo9ujfgwME7TPrsL+54Sw1cl3oOnJwzjk87t+SUr1/BfCnP/07qnnHnn/hwfmvTX66KYo4km32hK0jPTWWM3bfoqVcsEqq81NFviQqqPE1SrF1SnI4WDnj2ncYYpxZcCn/BYM8dDL+0C/fXfkWKdv9b7sa+YqDndkLTEtnaaTjT6nco4kpKzU1kR8iPnIvcwcFXfyIR8/i8bjtsdI340fdCCZmOsDdJbLh0h271Hela35HY2GR27LxO61YOdHatWOJYmYZbEARVYD3QB6gPfCIIQqmph2+yozHVtGCm8x+41RymcHslStKRJP+CGD8UJPEIRmtRMV6PoGqBoDMKcu4h5vjTyqQHKqjiHX8eGz0julg6cSj4Adl5ediaGqGlrlZQPDhfj6QsVwnIYru/7E1iYjrbtl8rs315iYlJxsPzMb/+fpZRYzcyasxGVvx2lh9+OsGUqTu4cVN+dEALu1rsmjKMzRMHY6SjxeIjHgxevYcLj19UqAL5s4Qoxl7Zz2fX/0aCyKaOQ9nnNoZGckSKDDW0+allH471mIiljgFz7pxkzJV9BCaXXYxZFEW8ngXRztG2oJJ7YYJDYqlta4qanIOx9JSS1W8qS7OujRj/0wgu7bvO8lGr0NPRYOXvo2jWtDa//XGO/QduU9+qBnunjkRfS5NJfx3h2vOiiVtZebmcCH1MT+s6cmVLs4vlHIyx+5bhtnPQVXu7ZRZzAxHfjEJMWQY5vogpv9DQsD0Oeo3wjN5PWq70UFEURb6+e4bwtERWtxuMb2AEu2/6MrpdU9rWtuGnpScxN9fn6/n9MDLSYcFXH7Hyj1FoaqixcPERvv/xGLGxyehoqDO3dyeOzBqNvbkJi494MGHr4TJ1L7a/8OHoy0d80aBTkWzkgr9DFDkZvonX6S8YavsFVtrlD3GrKDpq+tjrNeBZ0p0KLRhq6xmzpHlPbgz4gsXNehCbkcrMW8dwO7uerc/vkFyJ3WNh9gfeZ4zXPgw1tDjWfUKJCkoRGcFsCFhAWHogrUx6EJX5Ep94D7TU1FncrAcByXFFJKlFUWTZKS9UBIFv+7sBsHb9RSQSCbNnVTw7tjwr7tZAoCiKwaIoZgMHgYGlvcBQ3ZQpjsupoVWGME3WeUjfDtrDEMzOIWj1evuc9hBQqQF5IRiom9DQqD2psok/1rklOmoahKUloqqiQjsn24JT86ysXBwdamBjUz6FMBcXCwYPasHzF5FkZ1cu+qAwUdFJjBy9gV9+PcutWwE4O9Vk5ozu/LVlEt8tHkhuXh7f/3iM16/lb9kFQaCjix2HZoxi1eh+CALM3X+WF9Hlq2ovEUVm3z7B88RovmvWE/feU+hRq06ZE6OxiRVHuk3g5xZ98EuMZsk99zI/KykjC0MdbdzkuEkAdHU1C4q2FsfEwog6raouw3H0oiFM+W0cN4758OJuIDo6mixfOoyubvU5cuwuiYnp1DYzYu/UEdibm7DZy7vIxfB1WiKGGtoMt5efROKX5M2NuJO0NOnGbJfVcmOPxeTlkBuCYPg7gt4syA0CMZ3+Vp8hIBCRIU1Sis5I4WF8BAsad6WluQ2XnwXRyNqCr/q68uxZOKlpmXy/eFCREMomjW3ZsmkSkyd1xtsnmJ27bxQ852xhxu4pw/np4x4ERMWz9KTiCIVciYQzr57S27ousxrIj+tOzU0kKPURXWuOoKFhu9IHvgpoYNgOTVVtMiXyRadKQ19dk4kurbnYdxqbOg7FRs+YXx5e4n8PLla6PwlZ6ax8co2OFvYc6z5Rbky7V/RhAKY4LmNgrak46jUmTObu6WblTBdLRx4WOkBNSMsgNC6BmT3aY2mkT0pKJq9exTN2TAcsLSu+mxHKusoJgjAU6C2K4mTZ72OBNqIozizWbgowRfZrQ6D60teqHjOgfJbxw0DZ3+pF2d/qRdlf+dQWRbFc+srlOSKWt1QrYe1FUdwCbAEQBOGeKIqVl0Z7xyj7W70o+1u9KPtbvXyI/S2PqyQMKLzftQbebRClEiVKlCgpoDyG+y7gLAiCvSAIGsBIoOKKKkqUKFGipEoo01UiimKuIAgzgQuAKrBdFMWnZbxsS1V07h2i7G/1ouxv9aLsb/XywfW3zMNJJUqUKFHyYVHtKe9KlChRoqRqURpuJUqUKPmPUWnDXVYavCAImoIgHJI97y0Igt2/6ei/QRAEG0EQvARB8BME4akgCLPltOkiCEKSIAgPZLfv3kdfi/XppSAIj2X9uSfneUEQhDWyMX4kCELz99FPWV/qFBq7B4IgJAuC8GWxNu91jAVB2C4IQowgCE8KPWYiCIKnIAgBsp9yU24FQRgvaxMgCML499jf3wRBeC77fx8XBEFu9kZZc+cd9vcHQRDCC/3P+yp4bYVkNaqxv4cK9fWlIAgPFLz2nY9vEURRrPAN6SFlEOAAaAAPgfrF2kwHNsnujwQOVeazquIGWALNZff1gRdy+tsFOPO++qig3y8Bs1Ke7wu4I421bwt4v+8+F5ofUUgTCj6YMQZcgebAk0KP/Qp8I7v/DbBCzutMgGDZT2PZfeP31N+egJrs/gp5/S3P3HmH/f0B+Koc86VUe/Ku+lvs+T+A7z6U8S18q+yKuzxp8AOBXbL7R4BuwnuqMySKYqQoir6y+ymAH1B9dcveHQOB3aKUO4CRIAglBUnePd2AIFEUQ8ts+Q4RRfEaUFxroPA83QUMkvPSXoCnKIpvRFFMADyB3tXWURny+iuKoocoivnaDHeQ5lV8ECgY3/JQYVmNqqC0/sps1XDgQHX3ozJU1nDXAl4X+j2MkoawoI1soiUBppX8vCpD5rJpBsiTI2snCMJDQRDcBUFo8E47Jh8R8BAE4b5MUqA45fk/vA9GonjCf2hjXFMUxUiQXuCBkuLhH+44T0K645JHWXPnXTJT5trZrsAV9SGObycgWhTFAAXPv9fxrazhLk8afLlS5d8lgiDoAUeBL0VRLF6mwhfp1r4JsBY48a77J4cOoig2R6rMOEMQBNdiz3+IY6wBDAAOy3n6Qxzj8vAhjvMiIBfYp6BJWXPnXbERcASaApFI3Q/F+eDGF/iE0lfb73V8K2u4y5MGX9BGEAQ1wJDKbaOqBEEQ1JEa7X2iKB4r/rwoismiKKbK7p8D1AVBkF/q+h0himKE7GcMcBzplrIwH6IcQR/AVxTFEmV1PsQxBqLz3UuynzFy2nxQ4yw7HO0HjBZlDtfilGPuvBNEUYwWRTFPFEUJsFVBPz608VUDPgYOKWrzvse3soa7PGnwp4D80/ehwGVFk6y6kfmrtgF+oij+qaCNRb4PXhCE1kjHpnRh42pEEARdQRD08+8jPZQqrrh4Chgniy5pCyTlb/vfIwpXKh/aGMsoPE/HAyfltLkA9BQEwVi21e8pe+ydIwhCb+BrYIAoinJ1UMs5d94Jxc5cBivox4cmq9EdeC6Koty6cB/E+P6LE9m+SKMzgoBFssd+QjqhALSQbpcDAR/A4X2dwAIdkW69HgEPZLe+wFRgqqzNTOAp0hPtO0D799VfWX8cZH15KOtX/hgX7rOAtMhFEPAYaPme+6yD1BAbFnrsgxljpBeUSCAH6SrvU6TnLpeAANlPE1nblsBfhV47STaXA4GJ77G/gUj9wfnzOD9yywo4V9rceU/93SObm4+QGmPL4v2V/V7CnryP/soe35k/Zwu1fe/jW/imTHlXokSJkv8YysxJJUqUKPmPoTTcSpQoUfIfQ2m4lShRouQ/htJwK1GiRMl/DKXhVqJEiZL/GErDrUSJEiX/MZSGW4kSJUr+Y/wfFOOTdl0LVFsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a matrix\n",
    "matrix1 = np.random.rand(10, 20)\n",
    "\n",
    "cp = plt.contour(matrix1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **contour()** function draws contour lines. It takes a 2D array as input.Here, it is a matrix of 10 x 20 random elements.\n",
    "\n",
    "The number of level lines to draw is chosen automatically, but we can also specify it as an additional parameter, N.\n",
    "\n",
    "`plt.contour(matrix, N)`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 26. Styles with Matplotlib Plots\n",
    "\n",
    "\n",
    "The Matplotlib version 1.4 which was released in August 2014 added a very convenient `style` module. It includes a number of \n",
    "new default stylesheets, as well as the ability to create and package own styles.\n",
    "\n",
    "We can view the list of all available styles by the following command.\n",
    "\n",
    "\n",
    "`print(plt.style.availabe)`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# View list of all available styles\n",
    "\n",
    "print(plt.style.available)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can set the **Styles** for Matplotlib plots as follows:-\n",
    "\n",
    "\n",
    "`plt.style.use('seaborn-bright')`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": [
    "# Set styles for plots\n",
    "\n",
    "plt.style.use('seaborn-bright')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have set the **seaborn-bright** style for plots. So, the plot uses the **seaborn-bright** Matplotlib style for plots.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 27. Adding a grid\n",
    "\n",
    "\n",
    "In some cases, the background of a plot was completely blank. We can get more information, if there is a reference system in the plot. The reference system would improve the comprehension of the plot. An example of the reference system is adding a **grid**.\n",
    "We can add a grid to the plot by calling the **grid()** function. It takes one parameter, a Boolean value, to enable(if True)\n",
    "or disable(if False) the grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": [
    "x15 = np.arange(1, 5)\n",
    "\n",
    "plt.plot(x15, x15*1.5, x15, x15*3.0, x15, x15/3.0)\n",
    "\n",
    "plt.grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 28. Handling axes\n",
    "\n",
    "\n",
    "Matplotlib automatically sets the limits of the plot to precisely contain the plotted datasets. Sometimes, we want to set the\n",
    "axes limits ourself. We can set the axes limits with the **axis()** function as follows:-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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": [
    "x15 = np.arange(1, 5)\n",
    "\n",
    "plt.plot(x15, x15*1.5, x15, x15*3.0, x15, x15/3.0)\n",
    "\n",
    "plt.axis()   # shows the current axis limits values\n",
    "\n",
    "plt.axis([0, 5, -1, 13])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that we now have more space around the lines."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we execute **axis()** without parameters, it returns the actual axis limits.\n",
    "\n",
    "We can set parameters to **axis()** by a list of four values. \n",
    "\n",
    "The list of four values are the keyword arguments [xmin, xmax, ymin, ymax] allows the minimum and maximum limits for X and Y \n",
    "axis respectively.\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can control the limits for each axis separately using the `xlim()` and `ylim()` functions. This can be done as follows:-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 12.0)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8zt3+6XwV/o4jsg7imYI/cr5q8URiooCXpPPZAE/UqMU7OqsvDxRcz9nZx6tkQyQOCnhJmp3Wx9/9L3K/f6Zq8USagAJeml3dWrxT3MXM8P6eE7OPTPbQRDKKAl6azdZwGQ/5/8FDgX+wzZZzZvZxTPSM5bjsw5M9NJGMpICXJrcp/D0P+J/mb/7nq2vxJnrHcJT7kGQPTSSjKeClyawLb+Y+/0ym+F/ER4CLc05hgmcM/dy9kz00kRZBAS8J921oA/f4p/N44BWChPhRzmncqlo8kWangJeE+TK0lrv805gaeB2A0blncotnpGrxRJJEAS9xWxH6mjt9U3mq4g3cZPGz3HO5SbV4IkmngJeYLQl+wR3+Uv5R8V+85HKN52Ju8FymWjyRFBFXwBtj2gCPAYcDFvixtfaDRAxMUteC4Aom+Up4KVKLd3OkFq+DavFEUkq8M/gHgX9Zay8yxuQAalvIYB8GlzLJV8LrlR/QxhTyG8+PucZzsWrxRFJUzAFvjGkFDAXGAlhrK4CKxAxLUsnsyk+Y5Cvhv6rFE2kW1sLna2DOIpizOPafE88M/gBgE/CkMWYAMB+4zlq7s+aTjDHjgHEARUVFcbycNCdrLW8EP2Kyr7S6Fu9u7y8Z5zlPtXgiCRYOw6ero4E+ZxGs3+oc69Am9p9rrLWxfaMxxcCHwGBr7VxjzINAmbX2N7v7nuLiYjtv3rzYRirNoqFavJu9l6sWTySBgkFY9IUT5rMXwjtLYGuZc6xrezjxCDhxAAztD32KwOUy8621xfv6OvHM4NcAa6y1cyO/fxYYH8fPkyQK2zAvVs5msq+UhaHP6enqzN/ybmF07pmqxROJU0UlzFsRnaG/uwTKdznHeneB8wY7YX7iEdCzEyTqZqoxB7y1dr0x5ltjTB9r7QpgGLAsMcOS5lK3Fu9gV3eeyL+NETk/UC2eSIx8AZi7DGZHAv2DT53HAA7tAZefCkMHwJB+0G3/phtHvH+DrwFmRHbQfAlcEf+QpDlU2iBPVfybO33T+Dz8LYdl9WJ6/u+4OOcU1eKJ7KPyXfD+0uiSy0efQWXQmYkP6A0/O8dZcjmhH+zfjLuJ4wp4a+1CYJ/XhSR5AraC0sBr3O2fzurwOo7MOph/FEzivOyhqsUTaaTvy51lltkLnVBfsBJCYchywVF94PqLnCWXwf1gv8LkjVP/Bm8hfDbA44GXudf/FGvCGzkmqy8PFtzAWdnHqT1JZC82bIV3Fkdm6ItgyZfOVsacbBh0KIz/kbN+flxfKEihTWYK+Ay3w+6qrsXbYLdygnsAj+ZP4FT30Qp2kd1YszG6fj57Iaz41nk8zwPHHwa/v8KZoQ86FDwpvLlMAZ+htod38NeAU4u3xW5nmLuYmd7/Y2j2EckemkhKsRa+/C4a5nMWw1frnGOt8p118x+f5QT6wIOdWXu6UMBnmK3hMv7sf4a/BJ5lmy3nrOzjmeAZo1o8kQhrYfnXtS8qWrvZOdaulbO75doLnCWX/gdAVhrvOVDAZ4iN1bV4z7EDH+dnD2WidywD3X2SPTSRpAqFnDXz2TUCffN251jndtH950P7O1sYXRm010ABn+bWhTdzr+8ppgRexE8Fl+QMY7xntGrxpMWqDDq7WqrC/J3FsD1yA5WeneCsYyOhPgB6d03cRUWpSAGfpr4Jrece/wyeiNTiXZ7zA271jqKPavGkhfEH4OMV0fXz95fCTr9zrE93uORkJ9CHDoCijskda3NTwKeZL0JruNs/vboWb0zuWdziGckBWV2TPDKR5rHT51wZWrVlce4yCFQ6x/odAGPPcJZchvSDTu2SO9ZkU8Cnic8itXgzI7V443LP40bPj1SLJxlv+w54b2lkDX2Rc0+XYMhZKx94EFw93JmhD+kPbVVNUIsCPsUtCX7BZH8Jz1b8r7oW70bPCDq72id7aCJNYvM25+6KcxY5ob7oC+d2utluOPoQuPkyJ9CPP9zZxii7p4BPUfODnzHZV8pLlXMoJI9bPCO53nOpavEk43y3ufaWxU9XO497cuC4w+A3o5wll0GHOhcaSeMp4FPMB5VLmeR/kn9VfqhaPMlIq9dF18/nLIJVa53HC7zORUWXn+bM0Iv7QK7uVB0XBXwKsNYyO+jU4v0vOJ/2pg1/9P6cX3gupJXRv0ElfVkLK7+Nzs7nLIZvNjjH9it01s2vOs8J9CMOBLcSKaH0nzOJqmrxJvlKeC+4uLoW7+ee88k33mQPT2SfVVXPVW1ZnLMINnzvHNt/P2fv+c2XOksuh/XMrIuKUpECPgmstbxS+R6TfaV8HFpGN9f+PJj3K36c+0PV4klaCQZh4arokss7i51b6QJ06wCnHhW9SvTg7pl9UVEqUsA3o7AN80LlbCb7SlgUWkUvVxceybuV0blnkmPS6A5G0mJVVc9VrZ+/tzRaPXdgVxg+JHqVaI8EVs9JbBTwzSBog9W1eMtCq1WLJ2ljlx/mLo9uWfxwWbR6rm9PGHla9CrRLtq5m3KULk2o0gaZUfFv7vRNZVV4DYdl9WJG/u+5KOdk1eJJSirfBe8tia6f16yeO+JAGHeOs+RyQj/o0CbZo5W9UcA3gYZq8Z4tmMy52UNUiycpZWuZUz1XNUNf8LnzQak7y9mm+KuLI9Vzh0ObJFbPSWwU8AnkswEeC7zMvb4ZrLWbVIsnKWfD1tpbFquq53KzYVBfuG2kE+jHplj1nMRGAZ8AO+wuHvG/wAP+p9lgtzLEfQSPe29jmLtYwS5J9e3G6Ox8zqJo9Vy+x7nU/+IrnCWXo/ukdvWcxEYBH4ft4R08HHiOByO1eKe6j1YtniSNtfDF2ugMffYiWL3eOdY6Uj33k7Oj1XPZ+tuf8fQWx2BruIwH/bP4S+BZttsdnJ09mAne0RzrVi2eNJ+q6rmq2fmcxc59XQDat3Z2tlx/kbNlsV+aV89JbBTw+2Bj+Hvu98/kEf/z7MDH8OwTmegdy5Hug5M9NGkBQiFY/GV0dv7O4mj1XJf2TpBXbVk8tIf2oIsCvlG+C2/iXt9TPBp4iQCV1bV4h7sPSPbQJINVVc9VzdDfXRKtnuvVGc4+NnqV6AFdFOhSnwJ+D74Orece/3SeCLxCiDCX5/yA8d7RHJxVlOyhSQbyB5x951Uz9Pc/dS40AjikCC49JTpD775/cscq6UEB34BVoTXc7Z/G1MDrGAxjc8/mZs/lqsWThNrpc0K8av28qnrOGGfN/CdnRZuKOrZN9mglHcUd8MaYLGAesNZae078Q0qe5aHV1bV42bj5ee753OS5nO5ZLaypV5rEtvLa1XPzVzrVc1kuZ1fLL4c7Sy6DD1f1nCRGImbw1wHLgbT9I7k4uIrJ/lKei9TiXee5lBs8l6kWT+KyaZvzQWjN6jlrne2JxxwCt4yIVs8V6qIiaQJxBbwxphtwNjAJuCEhI2pG84LLmewr5eXKd1SLJ3Grqp6bHVlyWbbaedyb61TP3T7G2ekyqK/zmEhTi3cG/yfgFmC3d6kwxowDxgEUFaXGh5PvVy5hkr+Ef0dq8X7r/QnX5F7EfqrFk0ay1rmIqOZVol985xwrzHMuKhp1mvOBaHEfyNHdoCUJYg54Y8w5wEZr7XxjzEm7e561dgowBaC4uNjG+nrxstbydnABk3wlvB1cQHvThkneK7nKc4Fq8WSvqqrnal5U9O1G51jbVjCkH1w93FlyGdBb1XOSGuL5YzgYONcYcxbgAVoZY6Zba0cmZmiJYa3lP5UfMclfwvvBxXQy7bjHew3jPOepFk92KxyGpV/VXnLZGKme67if82HorSOcJZe+PVU9J6kp5oC31k4AJgBEZvA3pVK4O7V47zLJV8q80HK6uzry57wb+HHuOXhUiyd1BIPwyaro7Lxm9VxRRzj96Oge9IO66aIiSQ8Z9w/JsA3zfOXb3OErZVFoFQe4uvD3vFsZpVo8qSFQ4VTPzVnsFES/txR2+JxjB3WDC4ZErxLt0Sm5YxWJVUIC3lr7NvB2In5WrII2yDMVb3GHbyrLw6vp4yriyfxfMyLnNNyqxWvxdvmdurmqGfoHn4K/wjl2WE8YfXr0oiJVz0mmSPvkq7RBplf8i7t801gVXsPhWQfwVP7vuVC1eC1a2U5nVl4V6B9HqudcLqd67spznfXzE/pBe1XPSYZK24AP2ApKAq9yt386X4fXMzCrj2rxWrAt2yPVc5Ell09WRavnjj4EbrjYWT8ffDi0Lkj2aEWaR9oF/C7rr67F+85uZlDWYTxUcCNnqhavRVm/pX71HDjVc8dGqudOHOB8na/NUtJCpU3Al9udPOJ/kQf8M9lov2eo+0ie9P6GU9xHKdhbgG821N6yuLJG9dzgfnDpyU6gH30I5OYkd6wiqSLlA357eAd/CTzLg/5ZbLVlnOY+honeMQxRLV7Gqqqeqwrz2Qvh6w3OsTYFzgehPzvbWXI58iBVz4nsTsr+1dgS3s6D/lk8HHiuuhZvoncMg9yHJXtokmDhsFM9VxXmcxbDui3OsQ5tnN0tN1zizNAP76XqOZHGSrmA3xDeygP+p6tr8S7IPokJ3jGqxcsgoZBzZ8Wq9fM5i2BLmXOsa3s4+cjoRUWHFOmiIpFYpUzArw1v4j7fDB4NvFxdizfBM5rDVIuX9iqDMH9FdMnl3SXONkZwquZ+eHykT3SAU0WnQBdJjKQH/Neh9dztn8aTgVcJEWZkzunc6h2lWrw05g/A3OXRJZcPltWunhtxihPmQ/tDN1XPiTSZpAX8qtAa7vJNY1pFtBbvFs9IemV1SdaQJEY7dtWpnlsOFZHquf6R6rkTBzgfju6vW+2LNJtmD/jlodXc4Svl6Yo3ycHNlbnDucl7Od1cmsqli23lzjJL1ZLL/BUQCjvVc0f1gWsvcGboJ/SD/XbbFCAiTc1Y23y3aG87sIstf/MA8vDwc8/53OAZQSdXu2Z7fYnNpm21PxCtqp7LyXaq56rWz48/DApUPSeScMaY+dba4n39vmadwZeFdzDeM4rrPZfS3qUbgKSqtZtqb1lc/rXzuDfXCfHfjXVC/ZhDVT0nksqaNeD7uXvzx7yfN+dLyl5YC1+ti87QZy+CLyPVc63ynWWWMac7M/SjDlb1nEg6adaAz0JXqCSbtbDim+j6+ZxFsGaTc6xtK2dnyzXDnUAf0FsXFYmks6Rvk5SmFQ47N+KqueSyaZtzrFPb6Pr50P6qnhPJNAr4DBMMwoLPa1fPbdvhHOvREc44JhrqB3bVRUUimUwBn+YCFU6ZRdX6+fs1qucO7g4XnRidoat6TqRlUcCnmV1+p26uav38w2XR6rnDe0U/EB3SHzprB6pIi6aAT3FV1XNV6+cffwbBkLNWfuSBcNV50eq5dq2TPVoRSSUK+BSzZbuzbl615LIwUj2X7XbKLG66NFo91yo/2aMVkVSmgE+ydVucQJ+9yFlyWfqV87gnx6mb+/WoaPVcnie5YxWR9KKAb2Zfr4+un89eBJ+vcR4v8Dqz8hHDnA9EVT0nIvFSwDcha2HV2uj6+ZxF9avnfv7DSPXcgeDWuyEiCaRISaBwGJatjq6fz1kE67c6x/bfz5mZ33hptHpOFxWJSFNSwMchFHI+BK2anb+zOFo9160DDBsY3YPeR9VzItLMFPD7oKIS5q+MLrm8tzRaPde7C5w7OHqVaM9OCnQRSa6YA94Y0x2YCnQCwsAUa+2DiRpYKvAFYO6y6Az9/U+dxwAO7QE/GhadoXftkNyxiojUFc8MPgjcaK1dYIwpBOYbY96w1i5L0NiaXfku5yrRqvXzjz6LVs8N6A0/Oyd6UZGq50Qk1cUc8NbadcC6yNflxpjlQFcgbQL++0j1XNWWxQUro9VzxX3gugud2fkJ/aCNqudEJM0kZA3eGNMTOBKYm4if11Q2fl/7oqLFX0ar5wYdChMud5Zcjuur6jkRSX9xB7wxpgB4DrjeWlvWwPFxwDiAoqKieF9un6zZWPuios++cR7P8zjVc7+/wpmhDzoUPKqeE5EME1fAG2OyccJ9hrX2+YaeY62dAkwBKC4ubrKG76rquarZ+ZzF9avnrjjTCfSBqp4TkRYgnl00BngcWG6tvT9xQ2oca50ZedXsfM4iWLvZOdaulbPUcs1wOPEI6H8SXIHtAAAJqklEQVSAqudEpOWJZwY/GBgFLDHGLIw8NtFa+1r8w6ovFIpWz1XN0Kuq5zq3q109d2gPXSUqIhLPLpp3gSa7lKcyCJ98Hp2dv7skWj3XsxOcOSgS6v2ht6rnRETqSZkrWQMVzr7zqiWX95fCTr9zrE93uPgkJ8yHDoCijkkdqohIWkhawO/0OXVzVTP0D5dBoNI51u8AGHuGs34+pB90UvWciMg+a9aA374Txv/dCfV5K6LVcwMPgquHOzP0If2hbavmHJWISGYy1jbZzsX6L1ZYbLMHzePoQ6Lr58erek5EZI+MMfOttcX7+n3NOoM/uDt88oqq50REmkOzBnxhnsJdRKQxrLXYnTuxZeUx/4yU2UUjIpIJbGUltrwcW1ZOuKwcW1aGjfxvuKwcW76j+rHax8sJR77PlpVjy8udmrg4KOBFpMWz1sKuXbUCN1wVvLsJ5HAkhKsDORLg+P2Nek1TWIhp5fxytWqFKSzA3aUTplWryGOFzteFBXDVz2I6LwW8iKSterPl8tpha2sGco2grhXg+zJbzs7GtG6Fq2Y4d+6Eq89Bkd87gVwdzpH/dUWeWx3qBQWYfbncXgEvIumg1mw5lkCOPB4uKwefr1GvaQoKqgO5KnDdnTtWB26DgVxYUH82nZtet51VwItIo9hgsMZsuaz20kT5jtrhXHNtuUYgVz2nUbNlt9uZLVcHbyGuTh0xBx9YK5BrLmXUeyyW2XIGUcCLZDBrLfh89T/s20sg1wvwWGbLkcB1tSrE1bED7prhW1hYf/mi6njk+8jNxegmU3FRwIukoJqzZVteXmv2W2spIxLUDQVy1cyZUGjvL+h215n9FuLquD/moN6NDuTq2bLuzZ0yFPAiCWKtBb9/j2Hb0Na4hgKcXbsa9ZomP7/e2rJr//a1Z8tVyxetW9f7ALDqa82WM5MCXlo8GwrtdrZcf/mi5vE6M+rycggG9/6CWVlO2NZYrnB1aI/p3av+h32FBbVny3XXljVblj1QwEtastZCIFBvyaJm2DZ2d4bdubNRr2ny8+t9kOfq3at6tmwKC+svX9T6ANDZ74zHo9myNAsFvDQrGwphd0Q/4Nvzh327m01HdmI0drZcJ2xd7dthDuhZay9zvbXlunuZCwowbv11kfSiP7HSKLaB2XKtQG5gK1yDV/vt2NG4F8zLqx2whQVk9eqJu6FAbnAvs2bLIgr4DGbD4dqz5b0sX9T9MLDm7gwqK/f+gjVny1Vb5Nq1xfTqUWsvc0OBXGspo7BQs2WRBNDfohRUa7a8290Xjbi4pLyRd6HLy6u91a1VIVk9e9Tey1xYgGndqnYg19k6h9er2bJIClHAJ4gNh6tv7blv68n19zJTUbH3F3S56q8tt90P06N7w3uVawVyYXRGrdmySMZq8X+zbUVFgxePNLiXuc56cr2bFTWG11tvZ0VWUff6H/bt7rLryGyavDzNlkVkj9Iy4OvNlst3NHzf5RqBvLv7LhMI7P0Fq2bLNba6mTatcRd123sg19w6V1iIyc5u+v9AIiI0d8BbS3jLlnqBu7f7Ljd4I/zGdMl6PPV2VmQVda8O6oaWMpztcbXvKqfZsoiko2YN+MoFC9nUvseen2RM7a1uhYWY1q1wd++6973KdbfOabYsIi1YswZ8VpfOFI6/rd5e5ZrLGyY/X7NlEZEEaNaAd3XuRN41VzXnS4qItFgt8y74IiItQFwBb4w5wxizwhizyhgzPlGDEhGR+MUc8MaYLOBh4EygLzDCGNM3UQMTEZH4xDODPwZYZa390lpbATwNnJeYYYmISLzi+ZC1K/Btjd+vAQbVfZIxZhwwLvLbgDFmaRyvmeraA5uTPYgmlMnnl8nnBjq/dNcnlm+KJ+Ab2stY7+oja+0UYAqAMWaetbY4jtdMaTq/9JXJ5wY6v3RnjJkXy/fFs0SzBuhe4/fdgO/i+HkiIpJA8QT8x8BBxphexpgc4DLg5cQMS0RE4hXzEo21NmiM+SXwbyALeMJa++levm1KrK+XJnR+6SuTzw10fukupvMztjE37RIRkbSjK1lFRDKUAl5EJEMlPOCNMU8YYzbubr+7cfw5cnuDxcaYgYkeQ1NqxPmdZIzZboxZGPn12+YeY6yMMd2NMf8zxiw3xnxqjLmugeek7fvXyPNL5/fPY4z5yBizKHJ+v2/gObnGmFmR92+uMaZn8480No08v7HGmE013r+fJmOssTLGZBljPjHGvNLAsX1/76y1Cf0FDAUGAkt3c/ws4HWcffTHAnMTPYam/NWI8zsJeCXZ44zx3DoDAyNfFwIrgb6Z8v418vzS+f0zQEHk62xgLnBsnef8Angk8vVlwKxkjzvB5zcW+EuyxxrHOd4APNXQn8FY3ruEz+CttXOArXt4ynnAVOv4EGhjjOmc6HE0lUacX9qy1q6z1i6IfF0OLMe5YrmmtH3/Gnl+aSvynuyI/DY78qvuLorzgNLI188Cw0yaFDA08vzSljGmG3A28NhunrLP710y1uAbusVBxvwlizgu8s/I140xhyV7MLGI/PPvSJxZUk0Z8f7t4fwgjd+/yD/xFwIbgTestbt9/6y1QWA70K55Rxm7RpwfwIWR5cNnjTHdGzieqv4E3AKEd3N8n9+7ZAR8o25xkMYWAD2stQOAh4AXkzyefWaMKQCeA6631pbVPdzAt6TV+7eX80vr989aG7LWHoFzZfkxxpjD6zwlrd+/RpzfP4Ge1tr+wJtEZ7wpzRhzDrDRWjt/T09r4LE9vnfJCPiMvsWBtbas6p+R1trXgGxjTPskD6vRjDHZOOE3w1r7fANPSev3b2/nl+7vXxVr7TbgbeCMOoeq3z9jjBtoTRouOe7u/Ky1W6y1gchvHwWOauahxWowcK4xZjXOnXlPMcZMr/OcfX7vkhHwLwOjI7sxjgW2W2vXJWEcTcIY06lqXcwYcwzOf+MtyR1V40TG/Tiw3Fp7/26elrbvX2POL83fvw7GmDaRr73AqcBndZ72MjAm8vVFwH9t5FO7VNeY86vzedC5OJ+zpDxr7QRrbTdrbU+cD1D/a60dWedp+/zeJbyT1RgzE2cnQntjzBrgdpwPQ7DWPgK8hrMTYxWwC7gi0WNoSo04v4uAq4wxQcAHXJYuf4FwZhGjgCWRdU6AiUARZMT715jzS+f3rzNQapwyHhfwjLX2FWPMH4B51tqXcf4PbpoxZhXO7O+y5A13nzXm/K41xpwLBHHOb2zSRpsA8b53ulWBiEiG0pWsIiIZSgEvIpKhFPAiIhlKAS8ikqEU8CIiGUoBLyKSoRTwIiIZ6v8BXsNLECrzlooAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x15 = np.arange(1, 5)\n",
    "\n",
    "plt.plot(x15, x15*1.5, x15, x15*3.0, x15, x15/3.0)\n",
    "\n",
    "plt.xlim([1.0, 4.0])\n",
    "\n",
    "plt.ylim([0.0, 12.0])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 29. Handling X and Y ticks\n",
    "\n",
    "\n",
    "Vertical and horizontal ticks are those little segments on the axes, coupled with axes labels, used to give a reference system\n",
    "on the graph.So, they form the origin and the grid lines.\n",
    "\n",
    "Matplotlib provides two basic functions to manage them - **xticks()** and **yticks()**.\n",
    "\n",
    "Executing with no arguments, the tick function returns the current ticks' locations and the labels corresponding to each of them.\n",
    "\n",
    "We can pass arguments(in the form of lists) to the ticks functions. The arguments are:-\n",
    "\n",
    "1. Locations of the ticks\n",
    "\n",
    "2. Labels to draw at these locations.\n",
    "\n",
    "We can demonstrate the usage of the ticks functions in the code snippet below:-\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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": [
    "u = [5, 4, 9, 7, 8, 9, 6, 5, 7, 8]\n",
    "\n",
    "plt.plot(u)\n",
    "\n",
    "plt.xticks([2, 4, 6, 8, 10])\n",
    "plt.yticks([2, 4, 6, 8, 10])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 30. Adding labels\n",
    "\n",
    "\n",
    "Another important piece of information to add to a plot is the axes labels, since they specify the type of data we are plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": [
    "plt.plot([1, 3, 2, 4])\n",
    "\n",
    "plt.xlabel('This is the X axis')\n",
    "\n",
    "plt.ylabel('This is the Y axis')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 31. Adding a title\n",
    "\n",
    "\n",
    "The title of a plot describes about the plot. Matplotlib provides a simple function **title()** to add a title to an image.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": [
    "plt.plot([1, 3, 2, 4])\n",
    "\n",
    "plt.title('First Plot')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot displays the output of the previous code. The title `First Plot` is displayed on top of the plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 32. Adding a legend\n",
    "\n",
    "\n",
    "Legends are used to describe what each line or curve means in the plot. \n",
    "\n",
    "Legends for curves in a figure can be added in two ways. One method is to use the **legend** method of the axis object and \n",
    "pass a list/tuple of legend texts as follows:-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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": [
    "x15 = np.arange(1, 5)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(x15, x15*1.5)\n",
    "ax.plot(x15, x15*3.0)\n",
    "ax.plot(x15, x15/3.0)\n",
    "\n",
    "ax.legend(['Normal','Fast','Slow']);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above method follows the MATLAB API. It is prone to errors and unflexible if curves are added to or removed from the plot.\n",
    "It resulted in a wrongly labelled curve."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A better method is to use the **label** keyword argument when plots are added to the figure. Then we use the **legend** method without arguments to add the legend to the figure. \n",
    "\n",
    "The advantage of this method is that if curves are added or removed from the figure, the legend is automatically updated\n",
    "accordingly. It can be achieved by executing the code below:-\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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": [
    "x15 = np.arange(1, 5)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(x15, x15*1.5, label='Normal')\n",
    "ax.plot(x15, x15*3.0, label='Fast')\n",
    "ax.plot(x15, x15/3.0, label='Slow')\n",
    "\n",
    "ax.legend();\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **legend** function takes an optional keyword argument **loc**. It specifies the location of the legend to be drawn. \n",
    "The **loc** takes numerical codes for the various places the legend can be drawn. The most common **loc** values are as follows:-"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ax.legend(loc=0)  # let Matplotlib decide the optimal location\n",
    "\n",
    "ax.legend(loc=1)  # upper right corner\n",
    "\n",
    "ax.legend(loc=2)  # upper left corner\n",
    "\n",
    "ax.legend(loc=3)  # lower left corner\n",
    "\n",
    "ax.legend(loc=4)  # lower right corner\n",
    "\n",
    "ax.legend(loc=5)  # right\n",
    "\n",
    "ax.legend(loc=6)  # center left\n",
    "\n",
    "ax.legend(loc=7)  # center right\n",
    "\n",
    "ax.legend(loc=8)  # lower center\n",
    "\n",
    "ax.legend(loc=9)  # upper center\n",
    "\n",
    "ax.legend(loc=10) # center"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 33. Control colours\n",
    "\n",
    "\n",
    "We can draw different lines or curves in a plot with different colours. In the code below, we specify colour as the last argument to draw red, blue and green lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": [
    "x16 = np.arange(1, 5)\n",
    "\n",
    "plt.plot(x16, 'r')\n",
    "plt.plot(x16+1, 'g')\n",
    "plt.plot(x16+2, 'b')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The colour names and colour abbreviations is given in the following table:-\n",
    "\n",
    "\n",
    "**Colour abbreviation**      **Colour name**\n",
    "\n",
    "b                               blue\n",
    "\n",
    "c                               cyan\n",
    "\n",
    "g                               green\n",
    "\n",
    "k                               black\n",
    "\n",
    "m                               magenta\n",
    "\n",
    "r                               red\n",
    "\n",
    "w                               white\n",
    "\n",
    "y                               yellow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several ways to specify colours, other than by colour abbreviations:\n",
    "    \n",
    "•\tThe full colour name, such as yellow\n",
    "\n",
    "•\tHexadecimal string such as ##FF00FF\n",
    "\n",
    "•\tRGB tuples, for example (1, 0, 1)\n",
    "\n",
    "•\tGrayscale intensity, in string format such as ‘0.7’.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 34. Control line styles\n",
    "\n",
    "\n",
    "Matplotlib provides us different line style options to draw curves or plots. In the code below, I use different line styles \n",
    "to draw different plots.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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": [
    "x16 = np.arange(1, 5)\n",
    "\n",
    "plt.plot(x16, '--', x16+1, '-.', x16+2, ':')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above code snippet generates a blue dashed line, a green dash-dotted line and a red dotted line."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the available line styles are available in the following table:\n",
    "\n",
    "\n",
    "**Style abbreviation**  **Style**\n",
    "\n",
    "-                    solid line\n",
    "   \n",
    "--                   dashed line\n",
    "   \n",
    "-.                   dash-dot line\n",
    "   \n",
    ":                    dotted line\n",
    "   \n",
    "\n",
    "\n",
    "Now, we can see the default format string for a single line plot is 'b-'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 35. Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, I discuss Matplotlib (the basic plotting library in Python) and throw some light on various charts and customization techniques associated with it.\n",
    "\n",
    "\n",
    "In particular, I discuss Matplotlib object hierarchy, Matplotlib architecture, Pyplot and Object-Oriented architecture. I also discuss subplots which is very important tool to create graphics in Matplotlib.\n",
    "\n",
    "\n",
    "Then, I discuss various types of plots like line plot, scatter plot, histogram, bar chart, pie chart, box plot, area chart and contour plot. \n",
    "\n",
    "\n",
    "Finally, I discuss various customization techniques. I discuss how to customize the graphics with styles. I discuss how to add a grid and how to handle axes and ticks. I discuss how to add labels, title and legend. I discuss how to customize the charts with colours and line styles.\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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}