diff --git a/14_stats_and_montecarlo/.ipynb_checkpoints/16_stats_and_montecarlo-checkpoint.ipynb b/14_stats_and_montecarlo/.ipynb_checkpoints/16_stats_and_montecarlo-checkpoint.ipynb
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--- a/14_stats_and_montecarlo/.ipynb_checkpoints/16_stats_and_montecarlo-checkpoint.ipynb
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@@ -1,20612 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": true,
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "setdefaults"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": true,
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%plot --format svg"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "# Statistics and Monte-Carlo Models\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "## Examples of Monte Carlo models:\n",
-    "- [Eigenvlaues in supercritical systems](https://link.springer.com/chapter/10.1007%2FBFb0049064)\n",
-    "- [average time between failures for reliability](http://www.egr.msu.edu/~mitraj/research/pubs/proc/singh-mitra_em_stdby_ias95.pdf)\n",
-    "- disordered materials (physics)\n",
-    "- [Calculation of the energy output of a wind farm](http://www.mdpi.com/1996-1073/9/4/286/pdf)\n",
-    "- [US Coast Guard rescue missions](https://en.wikipedia.org/wiki/Search_and_Rescue_Optimal_Planning_System)\n",
-    "- [Radiation shielding](http://www.sciencedirect.com/science/article/pii/S0920379612000580)\n",
-    "- [Predict number of asteroids that hit body of water](https://cneos.jpl.nasa.gov/sentry/intro.html)\n",
-    "- [Financial modeling](https://en.wikipedia.org/wiki/Monte_Carlo_methods_in_finance)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "- What is the standard deviation of the vector created with 'normrnd(100,10,[10000,1])'? (rounded to nearest 10)\n",
-    "- The bar is, on average,1mm x 2 mm and fails at 940 MPa. What is the average failure load?\n",
-    "- In statistics, we can describe precision and accuracy with two descriptions of our data. How do we do this?\n",
-    "- The ratio of point inside the circle to total is pi/4. What is the ratio of points outside the circle to total?\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "## Statistics\n",
-    "\n",
-    "How do we describe *precision* and *accuracy*?\n",
-    "\n",
-    "- mean\n",
-    "\n",
-    "- standard deviation\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "## Monte Carlo Simulations\n",
-    "\n",
-    "Monte Carlo models use random numbers to either understand statistics or generate a solution. \n",
-    "\n",
-    "### Example 1:\n",
-    "#### Calculate $\\pi$ with random numbers. \n",
-    "\n",
-    "Assuming we can actually generate random numbers (a topic of philosophical and heated debates) we can populate a unit square with random points and determine the ratio of points inside and outside of a circle.\n",
-    "\n",
-    "![Unit circle and unit square](MonteCarloPi.gif)\n",
-    "\n",
-    "![1/4 Unit circle and 1/4 unit square](MonteCarloPi_rand.gif)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "The ratio of the area of the circle to the square is:\n",
-    "\n",
-    "$\\frac{\\pi r^{2}}{4r^{2}}=\\frac{\\pi}{4}$\n",
-    "\n",
-    "So if we know the fraction of random points that are within the unit circle, then we can calculate $\\pi$\n",
-    "\n",
-    "(number of points in circle)/(total number of points)=$\\pi/4$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false,
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "function our_pi=montecarlopi(N)\n",
-    "    % Create random x-y-coordinates\n",
-    "\n",
-    "    x=rand(N,1);\n",
-    "    y=rand(N,1);\n",
-    "    R=sqrt(x.^2+y.^2); % compute radius\n",
-    "    num_in_circle=sum(R<1);\n",
-    "    total_num_pts =length(R);\n",
-    "    our_pi = 4*num_in_circle/total_num_pts;\n",
-    "end\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false,
-    "slideshow": {
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-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean value for pi = 3.141720\n",
-      "standard deviation is 0.000502\n",
-      "actual pi is 3.141593\n"
-     ]
-    }
-   ],
-   "source": [
-    "test_pi=zeros(10,1);\n",
-    "for i=1:10\n",
-    "    test_pi(i)=montecarlopi(10000000);\n",
-    "end\n",
-    "fprintf('mean value for pi = %f\\n',mean(test_pi))\n",
-    "fprintf('standard deviation is %f',std(test_pi))\n",
-    "fprintf('actual pi is %f',pi)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Example 2\n",
-    "#### Determine uncertainty in failure stress based on geometry\n",
-    "\n",
-    "In this example, we know that a steel bar will break under 940 MPa tensile stress. The bar is 1 mm by 2 mm with a tolerance of 1 %. What is the range of tensile loads that can be safely applied to the beam?\n",
-    "\n",
-    "$\\sigma_{UTS}=\\frac{F_{fail}}{wh}$\n",
-    "\n",
-    "$F_{fail}=\\sigma_{UTS}wh$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": false,
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-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ans =  1.8803\n",
-      "ans =  0.021802\n"
-     ]
-    },
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-    "N=1000;\n",
-    "r=rand(N,1);\n",
-    "wmean=1; % in mm\n",
-    "wmin=wmean-wmean*0.01;\n",
-    "wmax=wmean+wmean*0.01;\n",
-    "hmean=2; % in mm\n",
-    "hmin=hmean-hmean*0.01;\n",
-    "hmax=hmean+hmean*0.01;\n",
-    "\n",
-    "wrand=wmin+(wmax-wmin)*r;\n",
-    "hrand=hmin+(hmax-hmin)*r;\n",
-    "\n",
-    "uts=940; % in N/mm^2=MPa\n",
-    "\n",
-    "Ffail=uts.*wrand.*hrand*1e-3; % force in kN\n",
-    "hist(Ffail,20,1)\n",
-    "xlabel('failure load (kN)')\n",
-    "ylabel('relative counts')\n",
-    "mean(Ffail)\n",
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-    "Normally, the tolerance is not a maximum/minimum specification, but instead a normal distribution that describes the standard deviation, or the 68 % confidence interval.\n",
-    "\n",
-    "So instead, we should generate normally distributed dimensions."
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-      "ans =  1.8800\n",
-      "ans =  0.026571\n"
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-   "source": [
-    "N=100000;\n",
-    "wmean=1; % in mm\n",
-    "wstd=wmean*0.01; % standard deviation in mm\n",
-    "hmean=2; % in mm\n",
-    "hstd=hmean*0.01; % standard deviation in mm\n",
-    "\n",
-    "\n",
-    "wrand=normrnd(wmean,wstd,[N,1]);\n",
-    "hrand=normrnd(hmean,hstd,[N,1]);\n",
-    "uts=940; % in N/mm^2=MPa\n",
-    "\n",
-    "Ffail=uts.*wrand.*hrand*1e-3; % force in kN\n",
-    "hist(Ffail,20,1)\n",
-    "xlabel('failure load (kN)')\n",
-    "ylabel('relative counts')\n",
-    "mean(Ffail)\n",
-    "std(Ffail)"
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-   "cell_type": "markdown",
-   "metadata": {
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-   },
-   "source": [
-    "# Thanks"
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-  },
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-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "# Using data from Dart board experiment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 173,
-   "metadata": {
-    "collapsed": false
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-       "\t<path d=\"M333.0,362.4 L333.0,349.9 M333.0,16.7 L333.0,29.2  \" stroke=\"black\"/>\t<g fill=\"rgb(0,0,0)\" font-family=\"{}\" font-size=\"16.00\" stroke=\"none\" text-anchor=\"middle\" transform=\"translate(333.0,386.4)\">\n",
-       "\t\t<text><tspan font-family=\"{}\">0</tspan></text>\n",
-       "\t</g>\n",
-       "</g>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<path d=\"M400.3,362.4 L400.3,349.9 M400.3,16.7 L400.3,29.2  \" stroke=\"black\"/>\t<g fill=\"rgb(0,0,0)\" font-family=\"{}\" font-size=\"16.00\" stroke=\"none\" text-anchor=\"middle\" transform=\"translate(400.3,386.4)\">\n",
-       "\t\t<text><tspan font-family=\"{}\">5</tspan></text>\n",
-       "\t</g>\n",
-       "</g>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<path d=\"M467.7,362.4 L467.7,349.9 M467.7,16.7 L467.7,29.2  \" stroke=\"black\"/>\t<g fill=\"rgb(0,0,0)\" font-family=\"{}\" font-size=\"16.00\" stroke=\"none\" text-anchor=\"middle\" transform=\"translate(467.7,386.4)\">\n",
-       "\t\t<text><tspan font-family=\"{}\">10</tspan></text>\n",
-       "\t</g>\n",
-       "</g>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<path d=\"M535.0,362.4 L535.0,349.9 M535.0,16.7 L535.0,29.2  \" stroke=\"black\"/>\t<g fill=\"rgb(0,0,0)\" font-family=\"{}\" font-size=\"16.00\" stroke=\"none\" text-anchor=\"middle\" transform=\"translate(535.0,386.4)\">\n",
-       "\t\t<text><tspan font-family=\"{}\">15</tspan></text>\n",
-       "\t</g>\n",
-       "</g>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "</g>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<path d=\"M63.6,16.7 L63.6,362.4 L535.0,362.4 L535.0,16.7 L63.6,16.7 Z  \" stroke=\"black\"/></g>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<g fill=\"rgb(0,0,0)\" font-family=\"{}\" font-size=\"16.00\" stroke=\"none\" text-anchor=\"middle\" transform=\"translate(19.1,189.6) rotate(-90)\">\n",
-       "\t\t<text><tspan font-family=\"{}\">y position (cm)</tspan></text>\n",
-       "\t</g>\n",
-       "</g>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<g fill=\"rgb(0,0,0)\" font-family=\"{}\" font-size=\"16.00\" stroke=\"none\" text-anchor=\"middle\" transform=\"translate(299.3,413.4)\">\n",
-       "\t\t<text><tspan font-family=\"{}\">x position (cm)</tspan></text>\n",
-       "\t</g>\n",
-       "</g>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "</g>\n",
-       "\t<g id=\"gnuplot_plot_1a\"><title>gnuplot_plot_1a</title>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<g onmousemove=\"gnuplot_svg.showHypertext(evt,'')\" onmouseout=\"gnuplot_svg.hideHypertext()\"><title> </title>\n",
-       "\t<use color=\"rgb( 51,  51,   0)\" transform=\"translate(382.3,96.8) scale(3.00)\" xlink:href=\"#gpPt5\"/></g>\n",
-       "\t<use color=\"rgb( 51,  51,   0)\" transform=\"translate(413.3,58.6) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb( 51,  51,   0)\" transform=\"translate(390.3,135.4) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb( 51,  51,   0)\" transform=\"translate(453.1,79.8) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb( 51,  51,   0)\" transform=\"translate(361.5,211.8) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb( 51,  51,   0)\" transform=\"translate(382.3,96.8) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "</g>\n",
-       "\t</g>\n",
-       "\t<g id=\"gnuplot_plot_2a\"><title>gnuplot_plot_2a</title>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<g onmousemove=\"gnuplot_svg.showHypertext(evt,'')\" onmouseout=\"gnuplot_svg.hideHypertext()\"><title> </title>\n",
-       "\t<use color=\"rgb( 31, 225, 254)\" opacity=\"0.00\" transform=\"translate(346.0,133.7) scale(3.00)\" xlink:href=\"#gpPt5\"/></g>\n",
-       "\t<use color=\"rgb( 31, 225, 254)\" opacity=\"0.00\" transform=\"translate(349.3,157.9) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb( 31, 225, 254)\" opacity=\"0.00\" transform=\"translate(478.9,146.0) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb( 31, 225, 254)\" opacity=\"0.00\" transform=\"translate(346.0,133.7) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "</g>\n",
-       "\t</g>\n",
-       "\t<g id=\"gnuplot_plot_3a\"><title>gnuplot_plot_3a</title>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<g onmousemove=\"gnuplot_svg.showHypertext(evt,'')\" onmouseout=\"gnuplot_svg.hideHypertext()\"><title> </title>\n",
-       "\t<use color=\"rgb(153, 102,   0)\" transform=\"translate(398.9,125.7) scale(3.00)\" xlink:href=\"#gpPt5\"/></g>\n",
-       "\t<use color=\"rgb(153, 102,   0)\" transform=\"translate(379.4,105.8) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(153, 102,   0)\" transform=\"translate(318.2,103.6) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(153, 102,   0)\" transform=\"translate(295.3,236.8) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(153, 102,   0)\" transform=\"translate(342.2,196.7) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(153, 102,   0)\" transform=\"translate(398.9,125.7) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "</g>\n",
-       "\t</g>\n",
-       "\t<g id=\"gnuplot_plot_4a\"><title>gnuplot_plot_4a</title>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<g onmousemove=\"gnuplot_svg.showHypertext(evt,'')\" onmouseout=\"gnuplot_svg.hideHypertext()\"><title> </title>\n",
-       "\t<use color=\"rgb(204, 153,   0)\" transform=\"translate(323.1,96.1) scale(3.00)\" xlink:href=\"#gpPt5\"/></g>\n",
-       "\t<use color=\"rgb(204, 153,   0)\" transform=\"translate(280.7,168.9) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(204, 153,   0)\" transform=\"translate(294.4,157.8) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(204, 153,   0)\" transform=\"translate(316.9,249.0) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(204, 153,   0)\" transform=\"translate(366.5,167.0) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(204, 153,   0)\" transform=\"translate(323.1,96.1) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "</g>\n",
-       "\t</g>\n",
-       "\t<g id=\"gnuplot_plot_5a\"><title>gnuplot_plot_5a</title>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<g onmousemove=\"gnuplot_svg.showHypertext(evt,'')\" onmouseout=\"gnuplot_svg.hideHypertext()\"><title> </title>\n",
-       "\t<use color=\"rgb(255, 204,   0)\" transform=\"translate(88.8,81.4) scale(3.00)\" xlink:href=\"#gpPt5\"/></g>\n",
-       "\t<use color=\"rgb(255, 204,   0)\" transform=\"translate(156.8,294.1) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(255, 204,   0)\" transform=\"translate(466.3,67.1) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(255, 204,   0)\" transform=\"translate(440.6,193.6) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(255, 204,   0)\" transform=\"translate(326.8,113.2) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(255, 204,   0)\" transform=\"translate(459.5,131.1) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(255, 204,   0)\" transform=\"translate(374.1,108.2) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(255, 204,   0)\" transform=\"translate(428.3,114.1) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(255, 204,   0)\" transform=\"translate(293.2,170.0) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(255, 204,   0)\" transform=\"translate(343.5,208.6) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(255, 204,   0)\" transform=\"translate(88.8,81.4) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "</g>\n",
-       "\t</g>\n",
-       "\t<g id=\"gnuplot_plot_6a\"><title>gnuplot_plot_6a</title>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<g onmousemove=\"gnuplot_svg.showHypertext(evt,'')\" onmouseout=\"gnuplot_svg.hideHypertext()\"><title> </title>\n",
-       "\t<use color=\"rgb(  0, 255,  51)\" transform=\"translate(347.2,145.2) scale(3.00)\" xlink:href=\"#gpPt5\"/></g>\n",
-       "\t<use color=\"rgb(  0, 255,  51)\" transform=\"translate(368.1,193.5) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(  0, 255,  51)\" transform=\"translate(434.3,110.9) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(  0, 255,  51)\" transform=\"translate(310.0,156.6) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(  0, 255,  51)\" transform=\"translate(291.4,99.5) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(  0, 255,  51)\" transform=\"translate(363.0,206.3) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(  0, 255,  51)\" transform=\"translate(347.2,145.2) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "</g>\n",
-       "\t</g>\n",
-       "\t<g id=\"gnuplot_plot_7a\"><title>gnuplot_plot_7a</title>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<g onmousemove=\"gnuplot_svg.showHypertext(evt,'')\" onmouseout=\"gnuplot_svg.hideHypertext()\"><title> </title>\n",
-       "\t<use color=\"rgb(102,   0,  51)\" transform=\"translate(369.4,201.6) scale(3.00)\" xlink:href=\"#gpPt5\"/></g>\n",
-       "\t<use color=\"rgb(102,   0,  51)\" transform=\"translate(339.4,187.1) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(102,   0,  51)\" transform=\"translate(231.9,98.1) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(102,   0,  51)\" transform=\"translate(288.5,164.9) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(102,   0,  51)\" transform=\"translate(279.2,239.0) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(102,   0,  51)\" transform=\"translate(369.4,201.6) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "</g>\n",
-       "\t</g>\n",
-       "\t<g id=\"gnuplot_plot_8a\"><title>gnuplot_plot_8a</title>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<g onmousemove=\"gnuplot_svg.showHypertext(evt,'')\" onmouseout=\"gnuplot_svg.hideHypertext()\"><title> </title>\n",
-       "\t<use color=\"rgb(153,   0,  51)\" transform=\"translate(399.3,156.3) scale(3.00)\" xlink:href=\"#gpPt5\"/></g>\n",
-       "\t<use color=\"rgb(153,   0,  51)\" transform=\"translate(366.1,169.1) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(153,   0,  51)\" transform=\"translate(461.3,110.5) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(153,   0,  51)\" transform=\"translate(334.9,204.3) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(153,   0,  51)\" transform=\"translate(513.4,61.5) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(153,   0,  51)\" transform=\"translate(399.3,156.3) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "</g>\n",
-       "\t</g>\n",
-       "\t<g id=\"gnuplot_plot_9a\"><title>gnuplot_plot_9a</title>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<g onmousemove=\"gnuplot_svg.showHypertext(evt,'')\" onmouseout=\"gnuplot_svg.hideHypertext()\"><title> </title>\n",
-       "\t<use color=\"rgb(204,   0,  51)\" transform=\"translate(337.4,128.0) scale(3.00)\" xlink:href=\"#gpPt5\"/></g>\n",
-       "\t<use color=\"rgb(204,   0,  51)\" transform=\"translate(320.4,121.4) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(204,   0,  51)\" transform=\"translate(324.4,153.0) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(204,   0,  51)\" transform=\"translate(323.3,161.8) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(204,   0,  51)\" transform=\"translate(289.3,175.3) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(204,   0,  51)\" transform=\"translate(337.4,128.0) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "</g>\n",
-       "\t</g>\n",
-       "\t<g id=\"gnuplot_plot_10a\"><title>gnuplot_plot_10a</title>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<g onmousemove=\"gnuplot_svg.showHypertext(evt,'')\" onmouseout=\"gnuplot_svg.hideHypertext()\"><title> </title>\n",
-       "\t<use color=\"rgb(255,   0,  51)\" transform=\"translate(369.0,151.4) scale(3.00)\" xlink:href=\"#gpPt5\"/></g>\n",
-       "\t<use color=\"rgb(255,   0,  51)\" transform=\"translate(333.4,155.0) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(255,   0,  51)\" transform=\"translate(358.3,158.1) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(255,   0,  51)\" transform=\"translate(302.5,154.4) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(255,   0,  51)\" transform=\"translate(344.5,208.5) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(255,   0,  51)\" transform=\"translate(338.2,150.5) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(255,   0,  51)\" transform=\"translate(362.1,106.2) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(255,   0,  51)\" transform=\"translate(305.8,145.0) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(255,   0,  51)\" transform=\"translate(315.9,199.3) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(255,   0,  51)\" transform=\"translate(369.7,118.2) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(255,   0,  51)\" transform=\"translate(325.8,104.8) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(255,   0,  51)\" transform=\"translate(369.0,151.4) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "</g>\n",
-       "\t</g>\n",
-       "\t<g id=\"gnuplot_plot_11a\"><title>gnuplot_plot_11a</title>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<g onmousemove=\"gnuplot_svg.showHypertext(evt,'')\" onmouseout=\"gnuplot_svg.hideHypertext()\"><title> </title>\n",
-       "\t<use color=\"rgb(  0,  51, 102)\" transform=\"translate(330.4,110.6) scale(3.00)\" xlink:href=\"#gpPt5\"/></g>\n",
-       "\t<use color=\"rgb(  0,  51, 102)\" transform=\"translate(318.9,312.7) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(  0,  51, 102)\" transform=\"translate(496.3,135.0) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(  0,  51, 102)\" transform=\"translate(285.8,105.0) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(  0,  51, 102)\" transform=\"translate(296.9,135.5) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(  0,  51, 102)\" transform=\"translate(330.4,110.6) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "</g>\n",
-       "\t</g>\n",
-       "\t<g id=\"gnuplot_plot_12a\"><title>gnuplot_plot_12a</title>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<g onmousemove=\"gnuplot_svg.showHypertext(evt,'')\" onmouseout=\"gnuplot_svg.hideHypertext()\"><title> </title>\n",
-       "\t<use color=\"rgb( 51,  51, 102)\" transform=\"translate(437.9,120.4) scale(3.00)\" xlink:href=\"#gpPt5\"/></g>\n",
-       "\t<use color=\"rgb( 51,  51, 102)\" transform=\"translate(333.0,115.5) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb( 51,  51, 102)\" transform=\"translate(412.6,175.1) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb( 51,  51, 102)\" transform=\"translate(426.3,204.4) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb( 51,  51, 102)\" transform=\"translate(366.6,122.1) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb( 51,  51, 102)\" transform=\"translate(293.7,140.7) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb( 51,  51, 102)\" transform=\"translate(295.5,108.5) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb( 51,  51, 102)\" transform=\"translate(333.3,158.6) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb( 51,  51, 102)\" transform=\"translate(433.4,149.2) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb( 51,  51, 102)\" transform=\"translate(315.9,164.9) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb( 51,  51, 102)\" transform=\"translate(437.9,120.4) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "</g>\n",
-       "\t</g>\n",
-       "\t<g id=\"gnuplot_plot_13a\"><title>gnuplot_plot_13a</title>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<g onmousemove=\"gnuplot_svg.showHypertext(evt,'')\" onmouseout=\"gnuplot_svg.hideHypertext()\"><title> </title>\n",
-       "\t<use color=\"rgb(102,  51, 102)\" transform=\"translate(354.2,203.3) scale(3.00)\" xlink:href=\"#gpPt5\"/></g>\n",
-       "\t<use color=\"rgb(102,  51, 102)\" transform=\"translate(322.6,162.8) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(102,  51, 102)\" transform=\"translate(366.6,164.9) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(102,  51, 102)\" transform=\"translate(392.1,124.4) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
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-       "\t</g>\n",
-       "\t<g id=\"gnuplot_plot_14a\"><title>gnuplot_plot_14a</title>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<g onmousemove=\"gnuplot_svg.showHypertext(evt,'')\" onmouseout=\"gnuplot_svg.hideHypertext()\"><title> </title>\n",
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-       "\t<use color=\"rgb(153, 102, 102)\" transform=\"translate(353.5,125.6) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
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-       "\t</g>\n",
-       "\t<g id=\"gnuplot_plot_15a\"><title>gnuplot_plot_15a</title>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<g onmousemove=\"gnuplot_svg.showHypertext(evt,'')\" onmouseout=\"gnuplot_svg.hideHypertext()\"><title> </title>\n",
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-       "\t<use color=\"rgb(204, 102, 102)\" transform=\"translate(412.0,106.9) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(204, 102, 102)\" transform=\"translate(451.9,114.5) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
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-       "\t<g id=\"gnuplot_plot_16a\"><title>gnuplot_plot_16a</title>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<g onmousemove=\"gnuplot_svg.showHypertext(evt,'')\" onmouseout=\"gnuplot_svg.hideHypertext()\"><title> </title>\n",
-       "\t<use color=\"rgb(255, 102, 102)\" transform=\"translate(307.3,132.3) scale(3.00)\" xlink:href=\"#gpPt5\"/></g>\n",
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-       "\t<use color=\"rgb(255, 102, 102)\" transform=\"translate(314.8,173.2) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(255, 102, 102)\" transform=\"translate(328.6,201.6) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(255, 102, 102)\" transform=\"translate(320.5,211.8) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
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-       "\t<use color=\"rgb(255, 102, 102)\" transform=\"translate(304.8,167.0) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(255, 102, 102)\" transform=\"translate(389.6,146.4) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
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-       "\t<use color=\"rgb(255, 102, 102)\" transform=\"translate(313.3,173.5) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(255, 102, 102)\" transform=\"translate(307.3,132.3) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
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-       "\t</g>\n",
-       "\t<g id=\"gnuplot_plot_17a\"><title>gnuplot_plot_17a</title>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<g onmousemove=\"gnuplot_svg.showHypertext(evt,'')\" onmouseout=\"gnuplot_svg.hideHypertext()\"><title> </title>\n",
-       "\t<use color=\"rgb(  0, 153, 153)\" transform=\"translate(473.0,98.2) scale(3.00)\" xlink:href=\"#gpPt5\"/></g>\n",
-       "\t<use color=\"rgb(  0, 153, 153)\" transform=\"translate(290.9,31.1) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
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-       "\t<use color=\"rgb(  0, 153, 153)\" transform=\"translate(463.2,224.5) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
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-       "\t<g id=\"gnuplot_plot_18a\"><title>gnuplot_plot_18a</title>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<g onmousemove=\"gnuplot_svg.showHypertext(evt,'')\" onmouseout=\"gnuplot_svg.hideHypertext()\"><title> </title>\n",
-       "\t<use color=\"rgb( 51, 153, 153)\" transform=\"translate(312.8,190.5) scale(3.00)\" xlink:href=\"#gpPt5\"/></g>\n",
-       "\t<use color=\"rgb( 51, 153, 153)\" transform=\"translate(379.6,184.6) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
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-       "\t<use color=\"rgb( 51, 153, 153)\" transform=\"translate(346.0,162.3) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
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-       "\t</g>\n",
-       "\t<g id=\"gnuplot_plot_19a\"><title>gnuplot_plot_19a</title>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<g onmousemove=\"gnuplot_svg.showHypertext(evt,'')\" onmouseout=\"gnuplot_svg.hideHypertext()\"><title> </title>\n",
-       "\t<use color=\"rgb(102, 153, 153)\" transform=\"translate(324.3,119.9) scale(3.00)\" xlink:href=\"#gpPt5\"/></g>\n",
-       "\t<use color=\"rgb(102, 153, 153)\" transform=\"translate(375.4,185.8) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(102, 153, 153)\" transform=\"translate(287.3,167.2) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(102, 153, 153)\" transform=\"translate(369.0,135.5) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(102, 153, 153)\" transform=\"translate(337.2,139.4) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(102, 153, 153)\" transform=\"translate(324.3,119.9) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
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-       "\t<g id=\"gnuplot_plot_20a\"><title>gnuplot_plot_20a</title>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<g onmousemove=\"gnuplot_svg.showHypertext(evt,'')\" onmouseout=\"gnuplot_svg.hideHypertext()\"><title> </title>\n",
-       "\t<use color=\"rgb(153, 204, 153)\" transform=\"translate(334.3,137.7) scale(3.00)\" xlink:href=\"#gpPt5\"/></g>\n",
-       "\t<use color=\"rgb(153, 204, 153)\" transform=\"translate(332.0,151.7) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(153, 204, 153)\" transform=\"translate(352.7,161.8) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(153, 204, 153)\" transform=\"translate(340.0,194.0) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(153, 204, 153)\" transform=\"translate(339.1,162.8) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(153, 204, 153)\" transform=\"translate(334.3,137.7) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
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-       "\t<g id=\"gnuplot_plot_21a\"><title>gnuplot_plot_21a</title>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<g onmousemove=\"gnuplot_svg.showHypertext(evt,'')\" onmouseout=\"gnuplot_svg.hideHypertext()\"><title> </title>\n",
-       "\t<use color=\"rgb(204, 204, 153)\" transform=\"translate(322.6,155.8) scale(3.00)\" xlink:href=\"#gpPt5\"/></g>\n",
-       "\t<use color=\"rgb(204, 204, 153)\" transform=\"translate(314.4,143.1) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(204, 204, 153)\" transform=\"translate(337.7,172.0) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(204, 204, 153)\" transform=\"translate(329.7,151.2) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(204, 204, 153)\" transform=\"translate(338.5,176.0) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
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-       "\t</g>\n",
-       "\t<g id=\"gnuplot_plot_22a\"><title>gnuplot_plot_22a</title>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<g onmousemove=\"gnuplot_svg.showHypertext(evt,'')\" onmouseout=\"gnuplot_svg.hideHypertext()\"><title> </title>\n",
-       "\t<use color=\"rgb(255, 204, 153)\" transform=\"translate(333.0,135.2) scale(3.00)\" xlink:href=\"#gpPt5\"/></g>\n",
-       "\t<use color=\"rgb(255, 204, 153)\" transform=\"translate(356.3,155.0) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(255, 204, 153)\" transform=\"translate(333.0,66.1) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(255, 204, 153)\" transform=\"translate(372.0,157.2) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(255, 204, 153)\" transform=\"translate(333.0,95.7) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(255, 204, 153)\" transform=\"translate(333.0,135.2) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
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-       "\t</g>\n",
-       "\t<g id=\"gnuplot_plot_23a\"><title>gnuplot_plot_23a</title>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<g onmousemove=\"gnuplot_svg.showHypertext(evt,'')\" onmouseout=\"gnuplot_svg.hideHypertext()\"><title> </title>\n",
-       "\t<use color=\"rgb(  0, 255, 204)\" transform=\"translate(289.2,159.2) scale(3.00)\" xlink:href=\"#gpPt5\"/></g>\n",
-       "\t<use color=\"rgb(  0, 255, 204)\" transform=\"translate(322.6,78.3) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(  0, 255, 204)\" transform=\"translate(368.5,89.2) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
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-       "\t<use color=\"rgb(  0, 255, 204)\" transform=\"translate(399.6,246.1) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(  0, 255, 204)\" transform=\"translate(289.2,159.2) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
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-       "\t</g>\n",
-       "\t<g id=\"gnuplot_plot_24a\"><title>gnuplot_plot_24a</title>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<g onmousemove=\"gnuplot_svg.showHypertext(evt,'')\" onmouseout=\"gnuplot_svg.hideHypertext()\"><title> </title>\n",
-       "\t<use color=\"rgb( 51, 255, 204)\" transform=\"translate(346.8,137.0) scale(3.00)\" xlink:href=\"#gpPt5\"/></g>\n",
-       "\t<use color=\"rgb( 51, 255, 204)\" transform=\"translate(348.9,162.8) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb( 51, 255, 204)\" transform=\"translate(360.3,159.5) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
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-       "\t<g id=\"gnuplot_plot_25a\"><title>gnuplot_plot_25a</title>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<g onmousemove=\"gnuplot_svg.showHypertext(evt,'')\" onmouseout=\"gnuplot_svg.hideHypertext()\"><title> </title>\n",
-       "\t<use color=\"rgb(102, 255, 204)\" transform=\"translate(498.8,143.4) scale(3.00)\" xlink:href=\"#gpPt5\"/></g>\n",
-       "\t<use color=\"rgb(102, 255, 204)\" transform=\"translate(454.3,102.5) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(102, 255, 204)\" transform=\"translate(405.2,209.3) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
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-       "\t<use color=\"rgb(102, 255, 204)\" transform=\"translate(300.2,140.0) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(102, 255, 204)\" transform=\"translate(498.8,143.4) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
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-       "\t<g id=\"gnuplot_plot_26a\"><title>gnuplot_plot_26a</title>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<g onmousemove=\"gnuplot_svg.showHypertext(evt,'')\" onmouseout=\"gnuplot_svg.hideHypertext()\"><title> </title>\n",
-       "\t<use color=\"rgb(153, 255, 204)\" transform=\"translate(346.4,164.2) scale(3.00)\" xlink:href=\"#gpPt5\"/></g>\n",
-       "\t<use color=\"rgb(153, 255, 204)\" transform=\"translate(359.8,163.5) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(153, 255, 204)\" transform=\"translate(373.2,162.1) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
-       "\t<use color=\"rgb(153, 255, 204)\" transform=\"translate(386.8,162.7) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
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-       "\t<use color=\"rgb(153, 255, 204)\" transform=\"translate(346.4,164.2) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
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-       "\t</g>\n",
-       "\t<g id=\"gnuplot_plot_27a\"><title>gnuplot_plot_27a</title>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<g onmousemove=\"gnuplot_svg.showHypertext(evt,'')\" onmouseout=\"gnuplot_svg.hideHypertext()\"><title> </title>\n",
-       "\t<use color=\"rgb(255,   0, 204)\" transform=\"translate(387.0,144.7) scale(3.00)\" xlink:href=\"#gpPt5\"/></g>\n",
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-       "\t<g id=\"gnuplot_plot_28a\"><title>gnuplot_plot_28a</title>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<g onmousemove=\"gnuplot_svg.showHypertext(evt,'')\" onmouseout=\"gnuplot_svg.hideHypertext()\"><title> </title>\n",
-       "\t<use color=\"rgb(  0,  51, 255)\" transform=\"translate(388.9,153.9) scale(3.00)\" xlink:href=\"#gpPt5\"/></g>\n",
-       "\t<use color=\"rgb(  0,  51, 255)\" transform=\"translate(255.7,125.2) scale(3.00)\" xlink:href=\"#gpPt5\"/>\n",
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-       "\t<g id=\"gnuplot_plot_29a\"><title>gnuplot_plot_29a</title>\n",
-       "<g color=\"black\" fill=\"none\" stroke=\"currentColor\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\" stroke-width=\"0.50\">\n",
-       "\t<g onmousemove=\"gnuplot_svg.showHypertext(evt,'')\" onmouseout=\"gnuplot_svg.hideHypertext()\"><title> </title>\n",
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-       "\t</g>\n",
-       "\t<g id=\"gnuplot_plot_30a\"><title>gnuplot_plot_30a</title>\n",
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-      "text/plain": [
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-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "darts=dlmread('compiled_data.csv',',');\n",
-    "x_darts=darts(:,1).*cosd(darts(:,2));\n",
-    "y_darts=darts(:,1).*sind(darts(:,2));\n",
-    "\n",
-    "colormap(colorcube(length(darts(:,3))))\n",
-    "\n",
-    "scatter(x_darts, y_darts, [], darts(:,3))\n",
-    "xlabel('x position (cm)')\n",
-    "ylabel('y position (cm)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 174,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mu_x =  0.90447\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "mu_x=mean(x_darts)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "s_x =  4.2747\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "s_x=std(x_darts)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 175,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mu_y =  0.88450\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "mu_y=mean(y_darts)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 177,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "s_y =  4.6834\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "s_y=std(y_darts)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 180,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
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-   "source": [
-    "x_vals=linspace(-15,20,30);\n",
-    "hist(x_darts,x_vals,1);\n",
-    "[histFreq, histXout] = hist(x_darts, 30);\n",
-    "binWidth = histXout(2)-histXout(1);\n",
-    "bar(histXout, histFreq/binWidth/sum(histFreq));\n",
-    "pdfnorm = @(x) 1/sqrt(2*s_x^2*pi).*exp(-(x-mu_x).^2/2/s_x^2);\n",
-    "%cdfnorm = @(x) 1/2*(1+erf((x-mu_x)./sqrt(2*s_x^2)));\n",
-    "%hist(x_darts,x_vals,trapz(x,f))%,cdfnorm(max(x_darts))/2)\n",
-    "hold on;\n",
-    "plot(x_vals,pdfnorm(x_vals))\n",
-    "n=2; % n=1, 68% confidence, n=2, 95% confidence, n=3, 99% conf\n",
-    "    plot([mu_x+n*s_x,mu_x+n*s_x],[0,0.1],'r-')\n",
-    "    plot([mu_x-n*s_x,mu_x-n*s_x],[0,0.1],'r-')\n",
-    "\n",
-    "xlabel('x position (cm)')\n",
-    "ylabel('relative counts')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 183,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
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-   "source": [
-    "y_vals=linspace(-15,20,30);\n",
-    "hist(y_darts,y_vals,1);\n",
-    "[histFreq, histXout] = hist(y_darts, 30);\n",
-    "binWidth = histXout(2)-histXout(1);\n",
-    "bar(histXout, histFreq/binWidth/sum(histFreq));\n",
-    "pdfnorm = @(x) 1/sqrt(2*s_y^2*pi).*exp(-(x-mu_y).^2/2/s_y^2);\n",
-    "%cdfnorm = @(x) 1/2*(1+erf((x-mu_x)./sqrt(2*s_x^2)));\n",
-    "%hist(x_darts,x_vals,trapz(x,f))%,cdfnorm(max(x_darts))/2)\n",
-    "hold on;\n",
-    "plot(y_vals,pdfnorm(y_vals))\n",
-    "n=2; % n=1, 68% confidence, n=2, 95% confidence, n=3, 99% conf\n",
-    "    plot([mu_y+n*s_y,mu_y+n*s_y],[0,0.1],'r-')\n",
-    "    plot([mu_y-n*s_y,mu_y-n*s_y],[0,0.1],'r-')\n",
-    "\n",
-    "xlabel('x position (cm)')\n",
-    "ylabel('relative counts')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {
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-   },
-   "outputs": [
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-   "source": [
-    "x_exp=empirical_cdf(x_vals,x_darts);\n",
-    "plot(x_vals,x_exp)\n",
-    "hold on;\n",
-    "plot(x_vals,normcdf(x_vals,mu_x,s_x),'k-')\n",
-    "legend('experimental CDF','Normal CDF','Location','SouthEast')"
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-   "source": [
-    "% plot the distribution in x- and y-directions\n",
-    "gauss2d = @(x,y) exp(-((x-mu_x).^2/2/s_x^2+(y-mu_y).^2/2/s_y^2));\n",
-    "\n",
-    "x=linspace(-20,20,31);\n",
-    "y=linspace(-20,20,31);\n",
-    "scatter3(x_darts, y_darts,ones(length(x_darts),1))\n",
-    "xlabel('x position (cm)')\n",
-    "ylabel('y position (cm)')\n",
-    "hold on\n",
-    "[X,Y]=meshgrid(x,y);\n",
-    "view([1,1,1])\n",
-    "\n",
-    "surf(X,Y,gauss2d(X,Y))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 187,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
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-   "source": [
-    "gauss2d = @(x,y) exp(-((x-0).^2/2/1^2+(y-0).^2/2/5^2));\n",
-    "\n",
-    "x=linspace(-20,20,71);\n",
-    "y=linspace(-20,20,31);\n",
-    "scatter3(x_darts, y_darts,ones(length(x_darts),1))\n",
-    "xlabel('x position (cm)')\n",
-    "ylabel('y position (cm)')\n",
-    "hold on\n",
-    "[X,Y]=meshgrid(x,y);\n",
-    "view([1,1,1])\n",
-    "\n",
-    "surf(X,Y,gauss2d(X,Y))"
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-   "execution_count": 190,
-   "metadata": {
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-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "hist(darts(:,2))"
-   ]
-  }
- ],
- "metadata": {
-  "celltoolbar": "Slideshow",
-  "kernelspec": {
-   "display_name": "Octave",
-   "language": "octave",
-   "name": "octave"
-  },
-  "language_info": {
-   "file_extension": ".m",
-   "help_links": [
-    {
-     "text": "MetaKernel Magics",
-     "url": "https://github.com/calysto/metakernel/blob/master/metakernel/magics/README.md"
-    }
-   ],
-   "mimetype": "text/x-octave",
-   "name": "octave",
-   "version": "0.19.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/14_stats_and_montecarlo/.ipynb_checkpoints/lecture_16-checkpoint.ipynb b/14_stats_and_montecarlo/.ipynb_checkpoints/lecture_16-checkpoint.ipynb
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-{
- "cells": [],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 2
-}
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-x1=linspace(0,1,1000)';
-y1=0*x1+0.1*rand(length(x1),1);
-
-x2=linspace(0,1,10)';
-y2=1*x2;
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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "###### Content under Creative Commons Attribution license CC-BY 4.0, code under BSD 3-Clause License © 2017 L.A. Barba, N.C. Clementi"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Seeing stats in a new light\n",
-    "\n",
-    "Welcome to the second lesson in \"Take off with stats,\" Module 2 of our series in _Engineering Computations_. In the previous lesson, [Cheers! Stats with Beers](http://go.gwu.edu/engcomp2lesson1), we did some exploratory data analysis with a data set of canned craft beers in the US [1]. We'll continue using that same data set here, but with a new focus on _visualizing statistics_.\n",
-    "\n",
-    "In her lecture [\"Looking at Data\"](https://youtu.be/QYDuAo9r1xE), Prof. Kristin Sainani says that you should always plot your data. Immediately, several things can come to light: are there outliers in your data? (Outliers are data points that look abnormally far from other values in the sample.) Are there data points that don't make sense? (Errors in data entry can be spotted this way.) And especially, you want to get a _visual_ representation of how data are distributed in your sample.\n",
-    "\n",
-    "In this lesson, we'll play around with different ways of visualizing data. We have so many ways to play! Have a look at the gallery of [The Data Viz Project](http://datavizproject.com) by _ferdio_ (a data viz agency in Copenhagen). Aren't those gorgeous? Wouldn't you like to be able to make some pretty pics like that? Python can help!\n",
-    "\n",
-    "Let's begin. We'll import our favorite Python libraries, and set some font parameters for our plots to look nicer. Then we'll load our data set for craft beers and begin!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy\n",
-    "import pandas\n",
-    "from matplotlib import pyplot\n",
-    "%matplotlib inline\n",
-    "\n",
-    "#Import rcParams to set font styles\n",
-    "from matplotlib import rcParams\n",
-    "\n",
-    "#Set font style and size \n",
-    "rcParams['font.family'] = 'serif'\n",
-    "rcParams['font.size'] = 16"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Read the data\n",
-    "\n",
-    "Like in the previous lesson, we will load the data from a `.csv` file. You may have the file in your working directory if you downloaded it when working through the previous lesson. In that case, you could load it like this:\n",
-    "\n",
-    "```Python\n",
-    "beers = pandas.read_csv(\"beers.csv\")\n",
-    "```\n",
-    "\n",
-    "If you downloaded the full set of lesson files from our public repository, you can find the file in the `/data` folder, and you can load it with the full path:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Load the beers data set using pandas, and assign it to a dataframe\n",
-    "beers = pandas.read_csv(\"../data/beers.csv\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Note:\n",
-    "\n",
-    "If you don't have the data file locally, download it by adding a code cell, and executing the following code in it:\n",
-    "\n",
-    "```Python\n",
-    "from urllib.request import urlretrieve\n",
-    "URL = 'http://go.gwu.edu/engcomp2data1?accessType=DOWNLOAD'\n",
-    "urlretrieve(URL, 'beers.csv')\n",
-    "```\n",
-    "The data file will be downloaded to your working directory, and you will load it like described above."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "OK. Let's have a look at the first few rows of the `pandas` dataframe we just created from the file, and confirm that it's a dataframe using the `type()` function. We only display the first 10 rows to save some space."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "pandas.core.frame.DataFrame"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(beers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Unnamed: 0</th>\n",
-       "      <th>abv</th>\n",
-       "      <th>ibu</th>\n",
-       "      <th>id</th>\n",
-       "      <th>name</th>\n",
-       "      <th>style</th>\n",
-       "      <th>brewery_id</th>\n",
-       "      <th>ounces</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0.050</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1436</td>\n",
-       "      <td>Pub Beer</td>\n",
-       "      <td>American Pale Lager</td>\n",
-       "      <td>408</td>\n",
-       "      <td>12.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.066</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2265</td>\n",
-       "      <td>Devil's Cup</td>\n",
-       "      <td>American Pale Ale (APA)</td>\n",
-       "      <td>177</td>\n",
-       "      <td>12.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>0.071</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2264</td>\n",
-       "      <td>Rise of the Phoenix</td>\n",
-       "      <td>American IPA</td>\n",
-       "      <td>177</td>\n",
-       "      <td>12.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>0.090</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2263</td>\n",
-       "      <td>Sinister</td>\n",
-       "      <td>American Double / Imperial IPA</td>\n",
-       "      <td>177</td>\n",
-       "      <td>12.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>0.075</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2262</td>\n",
-       "      <td>Sex and Candy</td>\n",
-       "      <td>American IPA</td>\n",
-       "      <td>177</td>\n",
-       "      <td>12.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>5</td>\n",
-       "      <td>0.077</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2261</td>\n",
-       "      <td>Black Exodus</td>\n",
-       "      <td>Oatmeal Stout</td>\n",
-       "      <td>177</td>\n",
-       "      <td>12.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>6</td>\n",
-       "      <td>0.045</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2260</td>\n",
-       "      <td>Lake Street Express</td>\n",
-       "      <td>American Pale Ale (APA)</td>\n",
-       "      <td>177</td>\n",
-       "      <td>12.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>7</td>\n",
-       "      <td>0.065</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2259</td>\n",
-       "      <td>Foreman</td>\n",
-       "      <td>American Porter</td>\n",
-       "      <td>177</td>\n",
-       "      <td>12.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>8</td>\n",
-       "      <td>0.055</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2258</td>\n",
-       "      <td>Jade</td>\n",
-       "      <td>American Pale Ale (APA)</td>\n",
-       "      <td>177</td>\n",
-       "      <td>12.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>9</td>\n",
-       "      <td>0.086</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2131</td>\n",
-       "      <td>Cone Crusher</td>\n",
-       "      <td>American Double / Imperial IPA</td>\n",
-       "      <td>177</td>\n",
-       "      <td>12.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Unnamed: 0    abv  ibu    id                 name  \\\n",
-       "0           0  0.050  NaN  1436             Pub Beer   \n",
-       "1           1  0.066  NaN  2265          Devil's Cup   \n",
-       "2           2  0.071  NaN  2264  Rise of the Phoenix   \n",
-       "3           3  0.090  NaN  2263             Sinister   \n",
-       "4           4  0.075  NaN  2262        Sex and Candy   \n",
-       "5           5  0.077  NaN  2261         Black Exodus   \n",
-       "6           6  0.045  NaN  2260  Lake Street Express   \n",
-       "7           7  0.065  NaN  2259              Foreman   \n",
-       "8           8  0.055  NaN  2258                 Jade   \n",
-       "9           9  0.086  NaN  2131         Cone Crusher   \n",
-       "\n",
-       "                            style  brewery_id  ounces  \n",
-       "0             American Pale Lager         408    12.0  \n",
-       "1         American Pale Ale (APA)         177    12.0  \n",
-       "2                    American IPA         177    12.0  \n",
-       "3  American Double / Imperial IPA         177    12.0  \n",
-       "4                    American IPA         177    12.0  \n",
-       "5                   Oatmeal Stout         177    12.0  \n",
-       "6         American Pale Ale (APA)         177    12.0  \n",
-       "7                 American Porter         177    12.0  \n",
-       "8         American Pale Ale (APA)         177    12.0  \n",
-       "9  American Double / Imperial IPA         177    12.0  "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "beers[0:10]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Quantitative vs. categorical data\n",
-    "\n",
-    "As you can see in the nice table that `pandas` printed for the dataframe, we have several features for each beer: the label `abv` corresponds to the acohol-by-volume fraction, label `ibu` refers to the international bitterness unit (IBU), then we have the `name` of the beer and the `style`, the brewery ID number, and the liquid volume of the beer can, in ounces.\n",
-    "\n",
-    "Alcohol-by-volume is a numeric feature: a volume fraction, with possible values from 0 to 1 (sometimes also given as a percentage). In the first 10 rows of our dataframe, the `ibu` value is missing (all those `NaN`s), but we saw in the previous lesson that `ibu` is also a numeric feature, with values that go from a minimum of 4 to a maximum of 138 (in our data set). IBU is pretty mysterious: how do you measure the bitterness of beer? It turns out that bitterness is measured as parts per million of _isohumulone_, the acid found in hops [2]. Who knew?\n",
-    "\n",
-    "For these numeric features, we learned that we can get an idea of the _central tendency_ in the data using the **mean value**, and we get ideas of _spread_ of the data with the **standard deviation** (and also with the range, but standard deviation is the most common).\n",
-    "\n",
-    "Notice that the beer data also has a feature named `style`: it can be \"American IPA\" or \"American Porter\" or a bunch of other styles of beer. If we want to study the beers according to style, we'll have to come up with some new ideas, because you can't take the mean or standard deviation of this feature!\n",
-    "\n",
-    "**Quantitative data** have meaning through a numeric feature, either on a continuous scale (like a fraction from 0 to 1), or a discrete count. \n",
-    "**Categorical data**, in contrast, have meaning through a qualitative feature (like the style of beer). Data in this form can be collected in groups (categories), and then we can count the number of data items in that group. For example, we could ask how many beers (in our set) are of the style \"American IPA,\" or ask how many beers we have in each style.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Visualizing quantitative data\n",
-    "\n",
-    "In the previous lesson, we played around a bit with the `abv` and `ibu` columns of the dataframe `beers`. For each of these columns, we extracted it from the dataframe and saved it into a `pandas` series, then we used the `dropna()` method to get rid of null values. This \"clean\" data was our starting point for some exploratory data analysis, and for plotting the data distributions using **histograms**. Here, we will add a few more ingredients to our recipes for data exploration, and we'll learn about a new type of visualization: the **box plot**.\n",
-    "\n",
-    "Let's repeat here the process for extracting and cleaning the two series, and getting the values into NumPy arrays:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "#Repeat cleaning values abv\n",
-    "abv_series = beers['abv']\n",
-    "abv_clean = abv_series.dropna()\n",
-    "abv = abv_clean.values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "#Repeat cleaning values ibu\n",
-    "ibu_series = beers['ibu']\n",
-    "ibu_clean = ibu_series.dropna()\n",
-    "ibu = ibu_clean.values"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's also repeat a histogram plot for the `abv` variable, but this time choose to plot just 10 bins (you'll see why in a moment)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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gIv4FfBHYCNgdQNIuwG7ApyJifp7nFGAUcEKjEkkbA5OB0xsXAo2I84A7gdOG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\nB/sIHixzk7epAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1081850b8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pyplot.figure(figsize=(6,4))\n",
-    "pyplot.hist(abv, bins=10, color='#3498db', histtype='bar', edgecolor='white') \n",
-    "pyplot.title('Alcohol by Volume (abv) \\n')\n",
-    "pyplot.xlabel('abv')\n",
-    "pyplot.ylabel('Frequency');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can tell that the most frequent values of `abv` fall in the bin just above 0.05 (5% alcohol), and the bin below. The mean value of our data is 0.06, which happens to be within the top-frequency bin, but data is not always so neat (sometimes, extreme values weigh heavily on the mean). Note also that we have a _right skewed_ distribution, with higher-frequency bins occuring in the lower end of the range than in the higher end.\n",
-    "\n",
-    "If you played around with the bin sizes in the previous lesson, you might have noticed that with a lot of bins, it becomes harder to visually pick out the patterns in the data. But if you use too few bins, the plot is also unhelpful. What number of bins is just right? Well, it depends on your data, so you'll just have to experiment and use your best judgement."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's learn a new trick. It turns out that `pandas` has built-in methods to make histograms directly from columns of a dataframe! (It uses Matplotlib internally for that.) The syntax is short and sweet:\n",
-    "\n",
-    "```\n",
-    "dataframe.hist(column='label')\n",
-    "```\n",
-    "\n",
-    "And `pandas` plots a pretty nice histogram without help. You can add optional parameters to set these to your liking; see the [documentation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.hist.html). Check it out, and compare with our previous plot."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Uxgoe78bO4+2+Xu1KWgFsU1G+DbCqvLVgZmbd06vEsBAYqCjfHbi1u10xM7O8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\nwczMCpwYzMys4P8DL87SZtIJwm8AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x108162cf8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "beers.hist(column='abv', edgecolor='white')\n",
-    "pyplot.title('Alcohol by Volume (abv) \\n');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Which one do you like better? Well, the `pandas` histogram took fewer lines of code to create.  And it doesn't look bad at all. But we do have more fine-grained control with Matplotlib. Which method you choose in a real situation will just depend on the situation and your preference."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exploring quantitative data (continued)\n",
-    "\n",
-    "In the [previous lesson](http://go.gwu.edu/engcomp2lesson1), you learned how to compute the mean of the data using `numpy.mean()`. How easy is that? But then we wrote our own custom functions to compute variance or standard deviation. It shouldn't surprise you by now that there are also NumPy functions for that!\n",
-    "\n",
-    "\n",
-    "##### Exercise:\n",
-    "\n",
-    "* Go to the documentation of [`numpy.var()`](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.var.html) and analyze if this function is computing the _sample variance_. \n",
-    "**Hint**: Check what it says about the \"data degrees of freedom.\"\n",
-    "\n",
-    "If you did the reading, you might have noticed that, by default, the argument `ddof` in `numpy.var()` is set to zero. If we use the default option, then we are not really calculating the sample variance. Recall from the previous lesson that the **sample variance** is:\n",
-    "\n",
-    "$$\n",
-    "\\begin{equation*}     \n",
-    "     \\text{var}_{sample} = \\frac{1}{N-1}\\sum_{i} (x_i - \\bar{x})^2\n",
-    "\\end{equation*}\n",
-    "$$\n",
-    "\n",
-    "Therefore, we need to be explicit about the division by $N-1$ when calling `numpy.var()`. How do we do that? We explicitly set `ddof` to `1`.  \n",
-    "\n",
-    "For example, to compute the sample variance for our `abv` variable, we do:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.000183378552053\n"
-     ]
-    }
-   ],
-   "source": [
-    "var_abv = numpy.var(abv, ddof=1)\n",
-    "print(var_abv)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can compute the standard deviation by taking the square root of `var_abv`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.0135417337167\n"
-     ]
-    }
-   ],
-   "source": [
-    "std_abv = numpy.sqrt(var_abv)\n",
-    "print(std_abv)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You might be wondering if there is a built-in function for the standard deviation in NumPy. Go on and search online and try to find something.\n",
-    "\n",
-    "**Spoiler alert!**\n",
-    "You will. \n",
-    "\n",
-    "##### Exercise:\n",
-    "\n",
-    "1. Read the documentation about the NumPy standard deviation function, compute the standard deviation for `abv` using this function, and check that you obtained the same value than if you take the square root of the variance computed with NumPy.\n",
-    "\n",
-    "2. Compute the variance and standard deviation for the variable `ibu`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "### Median value\n",
-    "\n",
-    "So far, we've learned to characterize quantitative data using the mean, variance and standard deviation.\n",
-    "\n",
-    "If you watched Prof. Sainani's lecture [Describing Quantitative Data: Where is the center?](https://youtu.be/tQ5slNYRcC4) (recommended in our previous lesson), you'll recall that she also introduced the **median**: the middle value in the data, the value that separates your data set in half. (If there's an even number of data values, you take the average between the two middle values.)\n",
-    "\n",
-    "As you may anticipate, NumPy has a built-in function that computes the median, helpfully named [`numpy.median()`](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.median.html). \n",
-    "\n",
-    "##### Exercise:\n",
-    "\n",
-    "Using NumPy, compute the median for our variables `abv` and `ibu`. Compare the median with the mean, and look at the histogram to locate where the values fall on the x-axis."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Box plots\n",
-    "\n",
-    "Another handy way to visualize the distribution of quantitative data is using **box plots**. By \"distribution\" of the data, we mean some idea of the dataset's \"shape\": where is the center, what is the range, what is the variation in the data. \n",
-    "Histograms are the most popular type of plots in exploratory data analysis. But check out box plots: they are easy to make with `pyplot`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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x+uPlzHppG4cCz0bEGkkPAueSnIL7a6An/eXfq4h4TdL3gDmSvgBMAcZHxG/T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\n/wOpkdOWZBfOAAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x108162c88>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pyplot.boxplot(abv, labels=['Alcohol by volume']);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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S1GIwSJJaDAZJUovBIElq2WHUBUizWZIZGaeqpjyONCwjC4YkDwX+Chhrmq4CTqiqH46q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\nAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1109544e0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pyplot.boxplot(ibu, labels=['International bitterness unit']);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "What is going on here? Obviously, there is a _box_: it represents 50% of the data in the middle of the data range, with the line across it (here, in orange) indicating the _median_. \n",
-    "\n",
-    "The bottom of the box is at the 25th _percentile_, while the top of the box is at the 75th percentile. In other words, the bottom 25% of the data falls below the box, and the top 25% of the data falls above the box. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "_Confused by percentiles?_\n",
-    "The Nth percentile is the value below which N% of the observations fall. \n",
-    "\n",
-    "Recall the bell curve from our previous lesson: we said that 95% of the data falls at a distance $\\pm 2 \\sigma$ from the mean. This implies that 5% of the data (the rest) falls in the (symmetrical) tails, which in turn implies that the 2.5 percentile is at $-2\\sigma$ from the mean, and the 97.5 percentile is at $+2\\sigma$ from the mean.\n",
-    "\n",
-    "The percentiles 25, 50, and 75 are also named _quartiles_, since they divide the data into quarters. They are named first (Q1), second (Q2 or median) and third quartile (Q3), respectively. \n",
-    "\n",
-    "Fortunately, NumPy has a function to compute percentiles and we can do it in just one line. Let's use [`numpy.percentile()`](https://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.percentile.html) to compute the `abv` and `ibu`  quartiles. \n",
-    "\n",
-    "** abv quartiles **"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The first quartile for abv is 0.05\n",
-      "The second quartile for abv is 0.056\n",
-      "The third quartile for abv is 0.067\n"
-     ]
-    }
-   ],
-   "source": [
-    "Q1_abv = numpy.percentile(abv, q=25)\n",
-    "Q2_abv = numpy.percentile(abv, q=50)\n",
-    "Q3_abv = numpy.percentile(abv, q=75)\n",
-    "\n",
-    "print('The first quartile for abv is {}'.format(Q1_abv))\n",
-    "print('The second quartile for abv is {}'.format(Q2_abv))\n",
-    "print('The third quartile for abv is {}'.format(Q3_abv))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "** ibu quartiles **\n",
-    "\n",
-    "You can also pass a list of percentiles to `numpy.percentile()` and calculate several of them in one go. For example, to compute the quartiles of `ibu`, we do:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The first quartile for ibu is 21.0\n",
-      "The second quartile for ibu is 35.0\n",
-      "The third quartile for ibu is 64.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "quartiles_ibu = numpy.percentile(ibu, q=[25, 50, 75])\n",
-    "\n",
-    "print('The first quartile for ibu is {}'.format(quartiles_ibu[0]))\n",
-    "print('The second quartile for ibu is {}'.format(quartiles_ibu[1]))\n",
-    "print('The third quartile for ibu is {}'.format(quartiles_ibu[2]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "OK, back to box plots. The height of the box—between the 25th and 75th percentile—is called the _interquartile range_ (IQR). Outside the box, you have two vertical lines—the so-called \"whiskers\" of the box plot—which used to be called \"box and whiskers plot\" [3]. \n",
-    "\n",
-    "The whiskers extend to the upper and lower extremes (short horizontal lines). The extremes follow the following rules: \n",
-    "\n",
-    "* Top whisker: lower value between the **maximum** and `Q3 + 1.5 x IQR`. \n",
-    "* Bottom whisker: higher value between the **minimum** and `Q1 - 1.5 x IQR`\n",
-    "\n",
-    "Any data values beyond the upper and lower extremes are shown with a marker (here, small circles) and are an indication of outliers in the data.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Exercise:\n",
-    "\n",
-    "Calculate the end-points of the top and bottom whiskers for both the `abv` and `ibu` variables, and compare the results with the whisker end-points you see in the plot. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### A bit of history:\n",
-    "\n",
-    "\"Box-and-whiskers\" plots were invented by John Tukey over 45 years ago. Tukey was a famous mathematician/statistician who is credited with coining the words _software_ and _bit_ [4]. He was active in the efforts to break the _Enigma_ code durig WWII, and worked at Bell Labs in the first surface-to-air guided missile (\"Nike\"). A classic 1947 work on early design of the electonic computer acknowledged Tukey: he designed the electronic circuit for computing addition. Tukey was also a long-time advisor for the US Census Bureau, and a consultant for the Educational Testing Service (ETS), among many other contributions [5].\n",
-    "\n",
-    "##### Note:\n",
-    "\n",
-    "Box plots are also drawn horizontally. Often, several box plots are drawn side-by-side with the purpose of comparing distributions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Visualizing categorical data\n",
-    "\n",
-    "The typical method of visualizing categorical data is using **bar plots**. These show visually the frequency of appearance of items in each category, or the proportion of data in each category. Suppose we wanted to know how many beers of the same style are in our data set. Remember: the _style_ of the beer is an example of _categorical data_. Let's extract the column with the style information from the `beers` dataframe, assign it to a variable named `style_series`, check the type of this variable, and view the first 10 elements."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "style_series = beers['style']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "pandas.core.series.Series"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(style_series)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0               American Pale Lager\n",
-       "1           American Pale Ale (APA)\n",
-       "2                      American IPA\n",
-       "3    American Double / Imperial IPA\n",
-       "4                      American IPA\n",
-       "5                     Oatmeal Stout\n",
-       "6           American Pale Ale (APA)\n",
-       "7                   American Porter\n",
-       "8           American Pale Ale (APA)\n",
-       "9    American Double / Imperial IPA\n",
-       "Name: style, dtype: object"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "style_series[0:10]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Already in the first 10 elements we see that we have two beers of the style \"American IPA,\" two beers of the style \"American Pale Ale (APA),\" but only one beer of the style \"Oatmeal Stout.\" The question is: how many beers of each style are contained in the whole series? "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Luckily, `pandas` has a built-in function to answer that question: [`series.value_counts()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.value_counts.html) (where `series` is the variable name of the `pandas` series you want the counts for). Let's try it on our `style_series`, and save the result in a new variable named `style_counts`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "American IPA                      424\n",
-       "American Pale Ale (APA)           245\n",
-       "American Amber / Red Ale          133\n",
-       "American Blonde Ale               108\n",
-       "American Double / Imperial IPA    105\n",
-       "Name: style, dtype: int64"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "style_counts = style_series.value_counts()\n",
-    "style_counts[0:5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "pandas.core.series.Series"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(style_counts)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "99"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(style_counts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `len()` function tells us that `style_counts` has 99 elements. That is, there are a total of 99 styles of beer in our data set. Wow, that's a lot!\n",
-    "\n",
-    "Notice that `value_counts()` returned the counts sorted in decreasing order: the most popular beer in our data set is \"American IPA\" with 424 entries in our data. The next-most popular beer is \"American Pale Ale (APA)\" with a lot fewer entries (245), and the counts decrease sharply after that. Naturally, we'd like to know how much more popular are the top-2 beers from the rest. Bar plot to the rescue! "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Below, we'll draw a horizontal bar plot directly with `pandas` (which uses Matplotlib internally) using the [`plot.barh()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.plot.barh.html) method for series. We'll only show the first 20 beers, because otherwise we'll get a huge plot. This plot gives us a clear visualization of the popularity ranking of beer styles in the US!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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4AmgmGkvqHZQEaCCOoZy8d4mk/VufpSRpVeBrlOVnO7UdRtGTycD7ge9IGme79VlE91A+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dM6yrqGtO92jYhhwL8AP5Z0VJ11OQj4VUQsAi4E1sppnZJ0OKl37PQ8rHBHYDgwRZI6WX0PUqBS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\nzMzMzMysxIGSmZmZmZlZyf8HM9xsOyl1ooIAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x110975828>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "style_counts[0:20].plot.barh(figsize=(10,8), color='#008367', edgecolor='gray');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Visualizing multiple data\n",
-    "\n",
-    "These visualizations are really addictive! We're now getting ambitious: what if we wanted to show more than one feature, together on the same plot? What if we wanted to get insights about the relationship between two features through a multi-variable plot? \n",
-    "\n",
-    "For example, don't you want to know if the bitterness of beers is associated with the alcohol-by-volume fraction? We do!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Scatter plots\n",
-    "\n",
-    "Maybe we can do this: imagine a plot that has the alcohol-by-volume on the absissa, and the IBU value on the ordinate. For each beer, we can place a dot on this plot with its `abv` and `ibu` values as $(x, y)$ coordinates. This is called a **scatter plot**.\n",
-    "\n",
-    "We run into a bit of a problem, though. The way we handled the beer data above, we extracted the column for `abv` into a series, dropped the null entries, and saved the values into a NumPy array. We then repeated this process for the `ibu` column. Because a lot more `ibu` values are missing, we ended up with two arrays of different length: 2348 entries for the `abv` series, and 1405 entries for the `ibu` series. If we want to make a scatter plot with these two features, we'll need series (or arrays) of the same length.\n",
-    "\n",
-    "Let's instead clean the whole `beers` dataframe (which will completely remove any row that has a null entry), and _then_ extract the values of the two series into NumPy arrays."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "beers_clean = beers.dropna()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1403"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ibu = beers_clean['ibu'].values\n",
-    "len(ibu)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1403"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "abv = beers_clean['abv'].values\n",
-    "len(abv)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Notice that both arrays now have 1403 entries—not 1405 (the length of the clean `ibu` data), because two rows that had a non-null `ibu` value _did_ have a null `abv` value and were dropped."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the two arrays of the same length, we can now call the [`pyplot.scatter()`](https://matplotlib.org/devdocs/api/_as_gen/matplotlib.pyplot.scatter.html) function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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gI4CFoccm+28GwZrm9fDX2kfq9aC/76fhzZh+a+QYX4A3oU3hxV3og8V66sg+dvr/XgSv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\nU3d9+B52dsjYBMAkLvczraxN933S0IgpJ0jaPokoXekbCgD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tRzwB7GJmC0puj4iISOMN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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x110bb6a58>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pyplot.figure(figsize=(8,8))\n",
-    "pyplot.scatter(abv, ibu, color='#3498db') \n",
-    "pyplot.title('Scatter plot of alcohol-by-volume vs. IBU \\n')\n",
-    "pyplot.xlabel('abv')\n",
-    "pyplot.ylabel('IBU');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Hmm. That's a bit of a mess. Too many dots! But we do make out that the beers with low alcohol-by-volume tend to have low bitterness. For higher alcohol fraction, the beers can be anywhere on the bitterness scale: there's a lot of vertical spread on those dots to the right of the plot. \n",
-    "\n",
-    "An idea! What if the bitterness has something to do with _style_? Neither of us knows much about beer, so we have no clue. Could we explore this question with visualization? We found a way!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "### Bubble chart\n",
-    "\n",
-    "What we imagined is that we could group together the beers by style, and then make a new scatter plot where each marker corresponds to a style. The beers within a style, though, have many values of alcohol fraction and bitterness: we have to come up with a \"summary value\" for each style. Well, why not the _mean_… we can calculate the average `abv` and the average `ibu` for all the beers in each style, use that pair as $(x,y)$ coordinate, and put a dot there representing the style. \n",
-    "\n",
-    "Better yet! We'll make the size of the \"dot\" proportional to the popularity of the style in our data set! This is called a **bubble chart**.\n",
-    "\n",
-    "How to achieve this idea? We searched online for \"mean of a column with pandas\" and we landed in [`dataframe.mean()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.mean.html). This could be helpful… But we don't want the mean of a _whole_ column—we want the mean of the column values grouped by _style_. Searching online again, we landed in [`dataframe.groupby()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html). This is amazing: `pandas` can group a series for you! \n",
-    "\n",
-    "Here's what we want to do: group beers by style, then compute the mean of `abv` and `ibu` in the groups. We experimented with `beers_clean.groupby('style').mean()` and were amazed… However, one thing was bothersome: `pandas` computed the mean (by style) of every column, including the `id` and `brewery_id`, which have no business being averaged. So we decided to first drop the columns we don't need, leaving only `abv`, `ibu` and `style`. We can use the [`dataframe.drop()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.drop.html) method for that. Check it out!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "beers_styles = beers_clean.drop(['Unnamed: 0','name','brewery_id','ounces','id'], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>abv</th>\n",
-       "      <th>ibu</th>\n",
-       "      <th>style</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>0.061</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>American Pale Ale (APA)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>0.099</td>\n",
-       "      <td>92.0</td>\n",
-       "      <td>American Barleywine</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>0.079</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>Winter Warmer</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>0.044</td>\n",
-       "      <td>42.0</td>\n",
-       "      <td>American Pale Ale (APA)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>0.049</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>Fruit / Vegetable Beer</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>0.049</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>Fruit / Vegetable Beer</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>0.049</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>Fruit / Vegetable Beer</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>0.070</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>American IPA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>0.070</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>American IPA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>0.070</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>American IPA</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      abv   ibu                    style\n",
-       "14  0.061  60.0  American Pale Ale (APA)\n",
-       "21  0.099  92.0      American Barleywine\n",
-       "22  0.079  45.0            Winter Warmer\n",
-       "24  0.044  42.0  American Pale Ale (APA)\n",
-       "25  0.049  17.0   Fruit / Vegetable Beer\n",
-       "26  0.049  17.0   Fruit / Vegetable Beer\n",
-       "27  0.049  17.0   Fruit / Vegetable Beer\n",
-       "28  0.070  70.0             American IPA\n",
-       "29  0.070  70.0             American IPA\n",
-       "30  0.070  70.0             American IPA"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "beers_styles[0:10]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We now have a dataframe with only the numeric features `abv` and `ibu`, and the categorical feature `style`. Let's find out how many beers we have of each style—we'd like to use this information to set the size of the style bubbles."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "style_counts = beers_styles['style'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "American IPA                      301\n",
-       "American Pale Ale (APA)           153\n",
-       "American Amber / Red Ale           77\n",
-       "American Double / Imperial IPA     75\n",
-       "American Blonde Ale                61\n",
-       "American Pale Wheat Ale            61\n",
-       "American Porter                    39\n",
-       "American Brown Ale                 38\n",
-       "Fruit / Vegetable Beer             30\n",
-       "Hefeweizen                         27\n",
-       "Name: style, dtype: int64"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "style_counts[0:10]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "pandas.core.series.Series"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(style_counts)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "90"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(style_counts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The number of beers in each style appears on each row of `style_counts`, sorted in decreasing order of count. We have 90 different styles, and the most popular style is the \"American IPA,\" with 301 beers…\n",
-    "\n",
-    "##### Discuss with your neighbor:\n",
-    "\n",
-    "* What happened? We used to have 99 styles and 424 counts in the \"American IPA\" style. Why is it different now?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "OK. We want to characterize each style of beer with the _mean values_ of the numeric features, `abv` and `ibu`, within that style. Let's get those means."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "style_means = beers_styles.groupby('style').mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>abv</th>\n",
-       "      <th>ibu</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>style</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Abbey Single Ale</th>\n",
-       "      <td>0.049000</td>\n",
-       "      <td>22.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Altbier</th>\n",
-       "      <td>0.054625</td>\n",
-       "      <td>34.125000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>American Adjunct Lager</th>\n",
-       "      <td>0.046545</td>\n",
-       "      <td>11.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>American Amber / Red Ale</th>\n",
-       "      <td>0.057195</td>\n",
-       "      <td>36.298701</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>American Amber / Red Lager</th>\n",
-       "      <td>0.048063</td>\n",
-       "      <td>23.250000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>American Barleywine</th>\n",
-       "      <td>0.099000</td>\n",
-       "      <td>96.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>American Black Ale</th>\n",
-       "      <td>0.073150</td>\n",
-       "      <td>68.900000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>American Blonde Ale</th>\n",
-       "      <td>0.050148</td>\n",
-       "      <td>20.983607</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>American Brown Ale</th>\n",
-       "      <td>0.057842</td>\n",
-       "      <td>29.894737</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>American Dark Wheat Ale</th>\n",
-       "      <td>0.052200</td>\n",
-       "      <td>27.600000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                 abv        ibu\n",
-       "style                                          \n",
-       "Abbey Single Ale            0.049000  22.000000\n",
-       "Altbier                     0.054625  34.125000\n",
-       "American Adjunct Lager      0.046545  11.000000\n",
-       "American Amber / Red Ale    0.057195  36.298701\n",
-       "American Amber / Red Lager  0.048063  23.250000\n",
-       "American Barleywine         0.099000  96.000000\n",
-       "American Black Ale          0.073150  68.900000\n",
-       "American Blonde Ale         0.050148  20.983607\n",
-       "American Brown Ale          0.057842  29.894737\n",
-       "American Dark Wheat Ale     0.052200  27.600000"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "style_means[0:10]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Looking good! We have the information we need: the average `abv` and `ibu` by style, and the counts by style. The only problem is that `style_counts` is sorted by decreasing count value, while `style_means` is sorted alphabetically by style. Ugh."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Notice that `style_means` is a dataframe that is now using the style string as a _label_ for each row. Meanwhile, `style_counts` is a `pandas` series, and it also uses the style as label or index to each element.\n",
-    "\n",
-    "More online searching and we find the [`series.sort_index()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.sort_index.html) method. It will sort our style counts in alphabetical order of style, which is what we want."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "style_counts = style_counts.sort_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Abbey Single Ale               2\n",
-       "Altbier                        8\n",
-       "American Adjunct Lager        11\n",
-       "American Amber / Red Ale      77\n",
-       "American Amber / Red Lager    16\n",
-       "American Barleywine            2\n",
-       "American Black Ale            20\n",
-       "American Blonde Ale           61\n",
-       "American Brown Ale            38\n",
-       "American Dark Wheat Ale        5\n",
-       "Name: style, dtype: int64"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "style_counts[0:10]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Above, we used Matplotlib to create a scatter plot using two NumPy arrays as the `x` and `y` parameters. Like we saw previously with histograms, `pandas` also has available some plotting methods (calling Matplotlib internally). Scatter plots made easy!\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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UfboUhx1Kg5QoiOSJQ4b34smvHc74Qd0oKYyQ7mxHM29MwuAepdx9YTnnHjSk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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x110f963c8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "style_means.plot.scatter(figsize=(8,8), \n",
-    "                         x='abv', y='ibu', s=style_counts, \n",
-    "                         title='Beer ABV vs. IBU mean values by style');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "That's rad! Perhaps the bubbles are too small. We could multiply the `style_counts` by a factor of 5, or maybe 10? You should experiment. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But we are feeling gung-ho about this now, and decided to find a way to make the _color_ of the bubbles also vary with the style counts. Below, we import the [`colormap`](https://matplotlib.org/api/cm_api.html) module of Matplotlib, and we set our colors using the [_viridis_ colormap](https://matplotlib.org/examples/color/colormaps_reference.html) on the values of `style_counts`, then we repeat the plot with these colors on the bubbles and some transparency. _What do you think?_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from matplotlib import cm\n",
-    "colors = cm.viridis(style_counts.values)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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8LJrH8J3A31P0nSjyF0T9S1/CMmoHi/xXi+ZqxaK5Qef6Zl49VvzP4yXg9vh3cIDoWlqa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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x110a76b00>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "style_means.plot.scatter(figsize=(10,10), \n",
-    "                         x='abv', y='ibu', s=style_counts*20, color=colors,\n",
-    "                         title='Beer ABV vs. IBU mean values by style\\n',\n",
-    "                         alpha=0.3); #alpha sets the transparency"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It looks like the most popular beers do follow a linear relationship between alcohol fraction and IBU. We learned a lot about beer without having a sip!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "_Wait... one more thing!_ What if we add a text label next to the bigger bubbles, to identify the style? \n",
-    "\n",
-    "OK, here we go a bit overboard, but we couldn't help it. We played around a lot to get this version of the plot. It uses `enumerate` to get pairs of indices and values from a list of style names; an `if` statement to select only the large-count styles; and the [`iloc[]`](http://pandas.pydata.org/pandas-docs/version/0.17.0/generated/pandas.DataFrame.iloc.html) slicing method of `pandas` to get a slice based on index position, and extract `abv` and `ibu` values to an $(x,y)$ coordinate for placing the annotation text. _Are we overkeen or what!_ "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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N7Cgu54MPPmD16tVceumlgDdYZODAgXvUxowcOZLDDjsMoMmm2b/97W+cfvrp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Tk3STqfQtM08kNZrn56i/Hjwy/sVBVFUR4vFRAqivJE9XsOy7fWKQ7nd30+XUjSqrbRdY3ACzCs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\nrj7OPjtMx4YW4hPTHHjkBA0t0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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x102c29dd8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = style_means.plot.scatter(figsize=(10,10), \n",
-    "                               x='abv', y='ibu', s=style_counts*20, color=colors,\n",
-    "                               title='Beer ABV vs. IBU mean values by style\\n',\n",
-    "                               alpha=0.3);\n",
-    "\n",
-    "for i, txt in enumerate(list(style_counts.index.values)):\n",
-    "    if style_counts.values[i] > 65:\n",
-    "        ax.annotate(txt, (style_means.abv.iloc[i],style_means.ibu.iloc[i]), fontsize=12)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## What we've learned\n",
-    "\n",
-    "* You should always plot your data.\n",
-    "* The concepts of quantitative and categorical data.\n",
-    "* Plotting histograms directly on columns of dataframes, using `pandas`.\n",
-    "* Computing variance and standard deviation using NumPy built-in functions.\n",
-    "* The concept of median, and how to compute it with NumPy.\n",
-    "* Making box plots using `pyplot`.\n",
-    "* Five statistics of a box plot: the quartiles Q1, Q2 (median) and Q3 (and  interquartile range Q3$-$Q1), upper and lower extremes.\n",
-    "* Visualizing categorical data with bar plots.\n",
-    "* Visualizing multiple data with scatter plots and bubble charts.\n",
-    "* `pandas` is awesome!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## References\n",
-    "\n",
-    "1. [Craft beer datatset](https://github.com/nickhould/craft-beers-dataset) by Jean-Nicholas Hould.\n",
-    "2. [What's The Meaning Of IBU?](https://beerconnoisseur.com/articles/whats-meaning-ibu) by Jim Dykstra for The Beer Connoisseur (2015).\n",
-    "3. 40 years of boxplots (2011). Hadley Wickham and Lisa Stryjewski, _Am. Statistician_. [PDF available](http://vita.had.co.nz/papers/boxplots.pdf)\n",
-    "4. [John Wilder Tukey](https://www.britannica.com/biography/John-Wilder-Tukey), Encyclopædia Britannica.\n",
-    "5. John W. Tukey: His life and professional contributions (2002). David R. Brillinger, _Ann. Statistics_. [PDF available](https://www.stat.berkeley.edu/~brill/Papers/life.pdf)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Recommended viewing\n",
-    "\n",
-    "From [\"Statistics in Medicine,\"](https://lagunita.stanford.edu/courses/Medicine/MedStats-SP/SelfPaced/about), a free course in Stanford Online by Prof. Kristin Sainani, we highly recommend that you watch this lecture:\n",
-    "* [Looking at data](https://youtu.be/QYDuAo9r1xE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
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-       "}\n",
-       "\n",
-       ".text_cell_render h5 {  /*Use this for small titles*/\n",
-       "    font-family: 'Source Sans Pro', sans-serif;\n",
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-       "    font-size: 130%;\n",
-       "    color: #e31937;\n",
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-       "    margin-top: 1em;\n",
-       "    display: block;\n",
-       "}\n",
-       "\n",
-       ".text_cell_render h6 { /*use this for copyright note*/\n",
-       "    font-family: 'Source Code Pro', sans-serif;\n",
-       "    font-weight: 300;\n",
-       "    font-size: 9pt;\n",
-       "    line-height: 100%;\n",
-       "    color: grey;\n",
-       "    margin-bottom: 1px;\n",
-       "    margin-top: 1px;\n",
-       "}\n",
-       "\n",
-       "    .CodeMirror{\n",
-       "            font-family: \"Source Code Pro\";\n",
-       "\t\t\tfont-size: 90%;\n",
-       "    }\n",
-       "/*    .prompt{\n",
-       "        display: None;\n",
-       "    }*/\n",
-       "\t\n",
-       "    \n",
-       "    .warning{\n",
-       "        color: rgb( 240, 20, 20 )\n",
-       "        }  \n",
-       "</style>\n",
-       "<script>\n",
-       "    MathJax.Hub.Config({\n",
-       "                        TeX: {\n",
-       "                           extensions: [\"AMSmath.js\"], \n",
-       "                           equationNumbers: { autoNumber: \"AMS\", useLabelIds: true}\n",
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-       "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
-       "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
-       "                },\n",
-       "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
-       "                \"HTML-CSS\": {\n",
-       "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
-       "                }\n",
-       "        });\n",
-       "</script>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Execute this cell to load the notebook's style sheet, then ignore it\n",
-    "from IPython.core.display import HTML\n",
-    "css_file = '../style/custom.css'\n",
-    "HTML(open(css_file, \"r\").read())"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.7"
-  },
-  "widgets": {
-   "state": {},
-   "version": "1.1.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks_en/.ipynb_checkpoints/4_Life_Expectancy-checkpoint.ipynb b/notebooks_en/.ipynb_checkpoints/4_Life_Expectancy-checkpoint.ipynb
deleted file mode 100644
index 19921b8..0000000
--- a/notebooks_en/.ipynb_checkpoints/4_Life_Expectancy-checkpoint.ipynb
+++ /dev/null
@@ -1,2115 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "###### Content under Creative Commons Attribution license CC-BY 4.0, code under BSD 3-Clause License © 2017 L.A. Barba, N.C. Clementi"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Life expectancy and wealth\n",
-    "\n",
-    "Welcome to **Lesson 4** of the second module in _Engineering Computations_. This module gives you hands-on data analysis experience with Python, using real-life applications. The first three lessons provide a foundation in data analysis using a computational approach. They are:\n",
-    "\n",
-    "1. [Lesson 1](http://go.gwu.edu/engcomp2lesson1): Cheers! Stats with beers.\n",
-    "2. [Lesson 2](http://go.gwu.edu/engcomp2lesson2): Seeing stats in a new light.\n",
-    "3. [Lesson 3](http://go.gwu.edu/engcomp2lesson3): Lead in lipstick.\n",
-    "\n",
-    "You learned to do exploratory data analysis with data in the form of arrays: NumPy has built-in functions for many descriptive statistics, making it easy! And you also learned to make data visualizations that are both good-looking and effective in communicating and getting insights from data.\n",
-    "\n",
-    "But NumPy can't do everything. So we introduced you to `pandas`, a Python library written _especially_ for data analysis. It offers a very powerful new data type: the _DataFrame_—you can think of it as a spreadsheet, conveniently stored in one Python variable. \n",
-    "\n",
-    "In this lesson, you'll dive deeper into `pandas`, using data for life expectancy and per-capita income over time, across the world."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## The best stats you've ever seen\n",
-    "\n",
-    "[Hans Rosling](https://en.wikipedia.org/wiki/Hans_Rosling) was a professor of international health in Sweeden, until his death in Februrary of this year. He came to fame with the thrilling TED Talk he gave in 2006: [\"The best stats you've ever seen\"](https://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen) (also on [YouTube](https://youtu.be/RUwS1uAdUcI), with ads). We highly recommend that you watch it! \n",
-    "\n",
-    "In that first TED Talk, and in many other talks and even a BBC documentary (see the [trailer](https://youtu.be/jbkSRLYSojo) on YouTube), Rosling uses data visualizations to tell stories about the world's health, wealth, inequality and development. Using software, he and his team created amazing animated graphics with data from the United Nations and World Bank.\n",
-    "\n",
-    "According to a [blog post](https://www.gatesnotes.com/About-Bill-Gates/Remembering-Hans-Rosling) by Bill and Melinda Gates after Prof. Rosling's death, his message was simple: _\"that the world is making progress, and that policy decisions should be grounded in data.\"_"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this lesson, we'll use data about life expectancy and per-capita income (in terms of the gross domestic product, GDP) around the world. Visualizing and analyzing the data will be our gateway to learning more about the world we live in.\n",
-    "\n",
-    "Let's begin! As always, we start by importing the Python libraries for data analysis (and setting some plot parameters)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy\n",
-    "import pandas\n",
-    "from matplotlib import pyplot\n",
-    "%matplotlib inline\n",
-    "\n",
-    "#Import rcParams to set font styles\n",
-    "from matplotlib import rcParams\n",
-    "\n",
-    "#Set font style and size \n",
-    "rcParams['font.family'] = 'serif'\n",
-    "rcParams['font.size'] = 16"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load and inspect the data\n",
-    "\n",
-    "We found a website called [The Python Graph Gallery](https://python-graph-gallery.com), which has a lot of data visualization examples. \n",
-    "Among them is a [Gapminder Animation](https://python-graph-gallery.com/341-python-gapminder-animation/), an animated GIF of bubble charts in the style of Hans Rosling. \n",
-    "We're not going to repeat the same example, but we do get some ideas from it and re-use their data set. \n",
-    "The data file is hosted on their website, and we can read it directly from there into a `pandas` dataframe, using the URL."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Read a dataset for life expectancy from a CSV file hosted online\n",
-    "url = 'https://python-graph-gallery.com/wp-content/uploads/gapminderData.csv'\n",
-    "life_expect = pandas.read_csv(url)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The first thing to do always is to take a peek at the data. \n",
-    "Using the `shape` attribute of the dataframe, we find out how many rows and columns it has. In this case, it's kind of big to print it all out, so to save space we'll print a small portion of `life_expect`.\n",
-    "You can use a slice to do this, or you can use the [`DataFrame.head()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.head.html) method, which returns by default the first 5 rows."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1704, 6)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "life_expect.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>country</th>\n",
-       "      <th>year</th>\n",
-       "      <th>pop</th>\n",
-       "      <th>continent</th>\n",
-       "      <th>lifeExp</th>\n",
-       "      <th>gdpPercap</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Afghanistan</td>\n",
-       "      <td>1952</td>\n",
-       "      <td>8425333.0</td>\n",
-       "      <td>Asia</td>\n",
-       "      <td>28.801</td>\n",
-       "      <td>779.445314</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Afghanistan</td>\n",
-       "      <td>1957</td>\n",
-       "      <td>9240934.0</td>\n",
-       "      <td>Asia</td>\n",
-       "      <td>30.332</td>\n",
-       "      <td>820.853030</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Afghanistan</td>\n",
-       "      <td>1962</td>\n",
-       "      <td>10267083.0</td>\n",
-       "      <td>Asia</td>\n",
-       "      <td>31.997</td>\n",
-       "      <td>853.100710</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Afghanistan</td>\n",
-       "      <td>1967</td>\n",
-       "      <td>11537966.0</td>\n",
-       "      <td>Asia</td>\n",
-       "      <td>34.020</td>\n",
-       "      <td>836.197138</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Afghanistan</td>\n",
-       "      <td>1972</td>\n",
-       "      <td>13079460.0</td>\n",
-       "      <td>Asia</td>\n",
-       "      <td>36.088</td>\n",
-       "      <td>739.981106</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       country  year         pop continent  lifeExp   gdpPercap\n",
-       "0  Afghanistan  1952   8425333.0      Asia   28.801  779.445314\n",
-       "1  Afghanistan  1957   9240934.0      Asia   30.332  820.853030\n",
-       "2  Afghanistan  1962  10267083.0      Asia   31.997  853.100710\n",
-       "3  Afghanistan  1967  11537966.0      Asia   34.020  836.197138\n",
-       "4  Afghanistan  1972  13079460.0      Asia   36.088  739.981106"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "life_expect.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can see that the columns hold six types of data: the country, the year, the population, the continent, the life expectancy, and the per-capita gross domestic product (GDP). \n",
-    "Rows are indexed from 0, and the columns each have a **label** (also called an index). Using labels to access data is one of the most powerful features of `pandas`.\n",
-    "\n",
-    "In the first five rows, we see that the country repeats (Afghanistan), while the year jumps by five. We guess that the data is arranged in blocks of rows for each country."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can get a useful summary of the dataframe with the [`DataFrame.info()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.info.html) method: it tells us the number of rows and the number of columns (matching the output of the `shape` attribute) and then for each column, it tells us the number of rows that are populated (have non-null entries) and the type of the entries; finally it gives a breakdown of the types of data and an estimate of the memory used by the dataframe."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 1704 entries, 0 to 1703\n",
-      "Data columns (total 6 columns):\n",
-      "country      1704 non-null object\n",
-      "year         1704 non-null int64\n",
-      "pop          1704 non-null float64\n",
-      "continent    1704 non-null object\n",
-      "lifeExp      1704 non-null float64\n",
-      "gdpPercap    1704 non-null float64\n",
-      "dtypes: float64(3), int64(1), object(2)\n",
-      "memory usage: 80.0+ KB\n"
-     ]
-    }
-   ],
-   "source": [
-    "life_expect.info()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The dataframe has 1704 rows, and every column has 1704 non-null entries, so there is no missing data. Let's find out how many entries of the same year appear in the data. \n",
-    "In [Lesson 1](http://go.gwu.edu/engcomp2lesson1) of this module, you already learned to extract a column from a data frame, and use the [`series.value_counts()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.value_counts.html) method to answer our question."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2007    142\n",
-       "2002    142\n",
-       "1997    142\n",
-       "1992    142\n",
-       "1987    142\n",
-       "1982    142\n",
-       "1977    142\n",
-       "1972    142\n",
-       "1967    142\n",
-       "1962    142\n",
-       "1957    142\n",
-       "1952    142\n",
-       "Name: year, dtype: int64"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "life_expect['year'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We have an even 142 occurrences of each year in the dataframe. The distinct entries must correspond to each country. It also is clear that we have data every five years, starting 1952 and ending 2007. We think we have a pretty clear picture of what is contained in this data set. What next?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Grouping data for analysis\n",
-    "\n",
-    "We have a dataframe with a `country` column, where countries repeat in blocks of rows, and a `year` column, where sets of 12 years (increasing by 5) repeat for every country. Tabled data commonly has this interleaved structure. And data analysis often involves grouping the data in various ways, to transform it, compute statistics, and visualize it.\n",
-    "\n",
-    "With the life expectancy data, it's natural to want to analyze it by year (and look at geographical differences), and by country (and look at historical differences). \n",
-    "\n",
-    "In [Lesson 2](http://go.gwu.edu/engcomp2lesson2) of this module, we already learned how useful it was to group the beer data by style, and calculate means within each style. Let's get better acquainted with the powerful `groupby()` method for dataframes. First, grouping by the values in the `year` column:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "by_year = life_expect.groupby('year')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "pandas.core.groupby.DataFrameGroupBy"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(by_year)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Notice that the type of the new variable `by_year` is different: it's a _GroupBy_ object, which—without making a copy of the data—is able to apply operations on each of the groups."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The [`GroupBy.first()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.core.groupby.GroupBy.first.html) method, for example, returns the first row in each group—applied to our grouping `by_year`, it shows the list of years (as a label), with the first country that appears in each year-group."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>country</th>\n",
-       "      <th>pop</th>\n",
-       "      <th>continent</th>\n",
-       "      <th>lifeExp</th>\n",
-       "      <th>gdpPercap</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>year</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1952</th>\n",
-       "      <td>Afghanistan</td>\n",
-       "      <td>8425333.0</td>\n",
-       "      <td>Asia</td>\n",
-       "      <td>28.801</td>\n",
-       "      <td>779.445314</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1957</th>\n",
-       "      <td>Afghanistan</td>\n",
-       "      <td>9240934.0</td>\n",
-       "      <td>Asia</td>\n",
-       "      <td>30.332</td>\n",
-       "      <td>820.853030</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1962</th>\n",
-       "      <td>Afghanistan</td>\n",
-       "      <td>10267083.0</td>\n",
-       "      <td>Asia</td>\n",
-       "      <td>31.997</td>\n",
-       "      <td>853.100710</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1967</th>\n",
-       "      <td>Afghanistan</td>\n",
-       "      <td>11537966.0</td>\n",
-       "      <td>Asia</td>\n",
-       "      <td>34.020</td>\n",
-       "      <td>836.197138</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1972</th>\n",
-       "      <td>Afghanistan</td>\n",
-       "      <td>13079460.0</td>\n",
-       "      <td>Asia</td>\n",
-       "      <td>36.088</td>\n",
-       "      <td>739.981106</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1977</th>\n",
-       "      <td>Afghanistan</td>\n",
-       "      <td>14880372.0</td>\n",
-       "      <td>Asia</td>\n",
-       "      <td>38.438</td>\n",
-       "      <td>786.113360</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982</th>\n",
-       "      <td>Afghanistan</td>\n",
-       "      <td>12881816.0</td>\n",
-       "      <td>Asia</td>\n",
-       "      <td>39.854</td>\n",
-       "      <td>978.011439</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1987</th>\n",
-       "      <td>Afghanistan</td>\n",
-       "      <td>13867957.0</td>\n",
-       "      <td>Asia</td>\n",
-       "      <td>40.822</td>\n",
-       "      <td>852.395945</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1992</th>\n",
-       "      <td>Afghanistan</td>\n",
-       "      <td>16317921.0</td>\n",
-       "      <td>Asia</td>\n",
-       "      <td>41.674</td>\n",
-       "      <td>649.341395</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1997</th>\n",
-       "      <td>Afghanistan</td>\n",
-       "      <td>22227415.0</td>\n",
-       "      <td>Asia</td>\n",
-       "      <td>41.763</td>\n",
-       "      <td>635.341351</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2002</th>\n",
-       "      <td>Afghanistan</td>\n",
-       "      <td>25268405.0</td>\n",
-       "      <td>Asia</td>\n",
-       "      <td>42.129</td>\n",
-       "      <td>726.734055</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2007</th>\n",
-       "      <td>Afghanistan</td>\n",
-       "      <td>31889923.0</td>\n",
-       "      <td>Asia</td>\n",
-       "      <td>43.828</td>\n",
-       "      <td>974.580338</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          country         pop continent  lifeExp   gdpPercap\n",
-       "year                                                        \n",
-       "1952  Afghanistan   8425333.0      Asia   28.801  779.445314\n",
-       "1957  Afghanistan   9240934.0      Asia   30.332  820.853030\n",
-       "1962  Afghanistan  10267083.0      Asia   31.997  853.100710\n",
-       "1967  Afghanistan  11537966.0      Asia   34.020  836.197138\n",
-       "1972  Afghanistan  13079460.0      Asia   36.088  739.981106\n",
-       "1977  Afghanistan  14880372.0      Asia   38.438  786.113360\n",
-       "1982  Afghanistan  12881816.0      Asia   39.854  978.011439\n",
-       "1987  Afghanistan  13867957.0      Asia   40.822  852.395945\n",
-       "1992  Afghanistan  16317921.0      Asia   41.674  649.341395\n",
-       "1997  Afghanistan  22227415.0      Asia   41.763  635.341351\n",
-       "2002  Afghanistan  25268405.0      Asia   42.129  726.734055\n",
-       "2007  Afghanistan  31889923.0      Asia   43.828  974.580338"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "by_year.first()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "All the year-groups have the same first country, Afghanistan, so what we see is the population, life expectancy and per-capita income in Afghanistan for all the available years.\n",
-    "Let's save that into a new dataframe, and make a line plot of the population and life expectancy over the years."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "Afghanistan = by_year.first()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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64EbgLHcfD/wQeNzMTqt/tUVERFJH5FKp0w7rkZB9xhvAtwA3u/saAHevBP47LH9OdYXM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\nrkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x112abdf60>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "Afghanistan['pop'].plot(figsize=(8,4),\n",
-    "                       title='Population of Afghanistan');"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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9FQFe/nQNf/ggn+17yhk1pCM/O70PHbMzaz0tJWsREZE65Jzj3fkFPD5xCWu27Ob4nq25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\nJwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x112ad9c50>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "Afghanistan['lifeExp'].plot(figsize=(8,4),\n",
-    "                       title='Life expectancy of Afghanistan');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Do you notice something interesting? It's curious to see that the population of Afghanistan took a fall after 1977. We have data every 5 years, so we don't know exactly when this fall began, but it's not hard to find the answer online. The USSR invaded Afghanistan in 1979, starting a conflict that lasted 9 years and resulted in an estimated death toll of one million civilians and 100,000 fighters [1]. Millions fled the war to neighboring countries, which may explain why we se a dip in population, but not a dip in life expectancy."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also get some descriptive statistics in one go with the [`DataFrame.describe()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.describe.html) method of `pandas`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>pop</th>\n",
-       "      <th>lifeExp</th>\n",
-       "      <th>gdpPercap</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>1.200000e+01</td>\n",
-       "      <td>12.000000</td>\n",
-       "      <td>12.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>1.582372e+07</td>\n",
-       "      <td>37.478833</td>\n",
-       "      <td>802.674598</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>7.114583e+06</td>\n",
-       "      <td>5.098646</td>\n",
-       "      <td>108.202929</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>8.425333e+06</td>\n",
-       "      <td>28.801000</td>\n",
-       "      <td>635.341351</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>1.122025e+07</td>\n",
-       "      <td>33.514250</td>\n",
-       "      <td>736.669343</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>1.347371e+07</td>\n",
-       "      <td>39.146000</td>\n",
-       "      <td>803.483195</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>1.779529e+07</td>\n",
-       "      <td>41.696250</td>\n",
-       "      <td>852.572136</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>3.188992e+07</td>\n",
-       "      <td>43.828000</td>\n",
-       "      <td>978.011439</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                pop    lifeExp   gdpPercap\n",
-       "count  1.200000e+01  12.000000   12.000000\n",
-       "mean   1.582372e+07  37.478833  802.674598\n",
-       "std    7.114583e+06   5.098646  108.202929\n",
-       "min    8.425333e+06  28.801000  635.341351\n",
-       "25%    1.122025e+07  33.514250  736.669343\n",
-       "50%    1.347371e+07  39.146000  803.483195\n",
-       "75%    1.779529e+07  41.696250  852.572136\n",
-       "max    3.188992e+07  43.828000  978.011439"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Afghanistan.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's now group our data by country, and use the `GroupBy.first()` method again to get the first row of each group-by-country. We know that the first year for which we have data is 1952, so let's immediately save that into a new variable named `year1952`, and keep playing with it. Below, we double-check the type of `year1952`, print the first five rows using the `head()` method, and get the minimum value of the population column."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "by_country = life_expect.groupby('country')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The first year for all groups-by-country is 1952. Let's save that first group into a new dataframe, and keep playing with it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "year1952 = by_country.first()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "pandas.core.frame.DataFrame"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(year1952)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>year</th>\n",
-       "      <th>pop</th>\n",
-       "      <th>continent</th>\n",
-       "      <th>lifeExp</th>\n",
-       "      <th>gdpPercap</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>country</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Afghanistan</th>\n",
-       "      <td>1952</td>\n",
-       "      <td>8425333.0</td>\n",
-       "      <td>Asia</td>\n",
-       "      <td>28.801</td>\n",
-       "      <td>779.445314</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Albania</th>\n",
-       "      <td>1952</td>\n",
-       "      <td>1282697.0</td>\n",
-       "      <td>Europe</td>\n",
-       "      <td>55.230</td>\n",
-       "      <td>1601.056136</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Algeria</th>\n",
-       "      <td>1952</td>\n",
-       "      <td>9279525.0</td>\n",
-       "      <td>Africa</td>\n",
-       "      <td>43.077</td>\n",
-       "      <td>2449.008185</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Angola</th>\n",
-       "      <td>1952</td>\n",
-       "      <td>4232095.0</td>\n",
-       "      <td>Africa</td>\n",
-       "      <td>30.015</td>\n",
-       "      <td>3520.610273</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Argentina</th>\n",
-       "      <td>1952</td>\n",
-       "      <td>17876956.0</td>\n",
-       "      <td>Americas</td>\n",
-       "      <td>62.485</td>\n",
-       "      <td>5911.315053</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             year         pop continent  lifeExp    gdpPercap\n",
-       "country                                                      \n",
-       "Afghanistan  1952   8425333.0      Asia   28.801   779.445314\n",
-       "Albania      1952   1282697.0    Europe   55.230  1601.056136\n",
-       "Algeria      1952   9279525.0    Africa   43.077  2449.008185\n",
-       "Angola       1952   4232095.0    Africa   30.015  3520.610273\n",
-       "Argentina    1952  17876956.0  Americas   62.485  5911.315053"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "year1952.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "60011.0"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "year1952['pop'].min()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Visualizing the data\n",
-    "\n",
-    "In [Lesson 2](http://go.gwu.edu/engcomp2lesson2) of this module, you learned to make bubble charts, allowing you to show at least three features of the data in one plot. We'd like to make a bubble chart of life expectancy vs. per-capita GDP, with the size of the bubble proportional to the population. To do that, we'll need to extract the population values into a NumPy array."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "populations = year1952['pop'].values"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you use the `populations` array unmodified as the size of the bubbles, they come out _huge_ and you get one solid color covering the figure (we tried it!). To make the bubble sizes reasonable, we divide by 60,000—an approximation to the minimum population—so the smallest bubble size is about 1 pt. Finally, we choose a logarithmic scale in the absissa (the GDP). Check it out!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Esdzx5xDtfiIikhIKvkVEOpeLgL+aqK8S7WP8zzTXqSO5F6hPR3gX8IxVKjwRSSEF3yKS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\nza5SonbNFWjmbNjf4HmJ+GitseOsBBSxHMpDUcpCUYJtvIOsEEJ0BJJ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cO6kXKb6qlViX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\nIiIiEidKvkVERERE4uT/A2fstcoMx1QIAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x112bc0240>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "year1952.plot.scatter(figsize=(12,8), \n",
-    "                       x='gdpPercap', y='lifeExp', s=populations/60000, \n",
-    "                       title='Life expectancy in the year 1952',\n",
-    "                       edgecolors=\"white\")\n",
-    "pyplot.xscale('log');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "That's neat! But the Rosling bubble charts include one more feature in the data: the continent of each country, using a color scheme. Can we do that?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Matplotlib [colormaps](https://matplotlib.org/examples/color/colormaps_reference.html) offer several options for _qualitative_ data, using discrete colors mapped to a sequence of numbers. We'd like to use the `Accent` colormap to code countries by continent. But we need a numeric code to assign to each continent, so it can be mapped to a color.\n",
-    "\n",
-    "The [Gapminder Animation](https://python-graph-gallery.com/341-python-gapminder-animation/) example at The Python Graph Gallery has a good tip: using the `pandas` _Categorical_ data type, which associates a numerical value for each category in a column containing qualitative (categorical) data. \n",
-    "\n",
-    "Let's see what we get if we apply  `pandas.Categorical()` to the `continent` column:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Asia, Europe, Africa, Africa, Americas, ..., Asia, Asia, Asia, Africa, Africa]\n",
-       "Length: 142\n",
-       "Categories (5, object): [Africa, Americas, Asia, Europe, Oceania]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pandas.Categorical(year1952['continent'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Right. We see that the `continent` column has repeated entries of 5 distinct categories, one for each continent. In order, they are: Africa, Americas, Asia, Europe, Oceania.\n",
-    "\n",
-    "Applying [`pandas.Categorical()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Categorical.html) to the `continent` column will create an integer value—the _code_ of the category—associated to each entry. We can then use these integer values to map to the colors in a colormap. The trick will be to extract the `codes` attribute of the _Categorical_ data and save that into a new variable named `colors` (a NumPy array)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "colors = pandas.Categorical(year1952['continent']).codes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "numpy.ndarray"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(colors)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "142"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(colors)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[2 3 0 0 1 4 3 2 2 3 0 1 3 0 1 3 0 0 2 0 1 0 0 1 2 1 0 0 0 1 0 3 1 3 3 0 1\n",
-      " 1 0 1 0 0 0 3 3 0 0 3 0 3 1 0 0 1 1 2 3 3 2 2 2 2 3 2 3 1 2 2 0 2 2 2 2 0\n",
-      " 0 0 0 0 2 0 0 0 1 2 3 0 0 2 0 2 3 4 1 0 0 3 2 2 1 1 1 2 3 3 1 0 3 0 0 2 0\n",
-      " 3 0 2 3 3 0 0 3 2 0 0 3 3 2 2 0 2 0 1 0 3 0 3 1 1 1 2 2 2 0 0]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(colors)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You see that `colors` is a NumPy array of 142 integers that can take the values: ` 0, 1, 2, 3, 4`. They are the codes to `continent` categories: `Africa, Americas, Asia, Europe, Oceania`. For example, the first entry is `2`, corresponding to Asia, the continent of Afghanistan."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we're ready to re-do our bubble chart, using the array `colors` to set the color of the bubble (according to the continent for the given country)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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52XnW/eKYxwlDPr0U+Cmho9YI8N/A4+aZ/2Vx3heukuPqU3G7VW/fbYQfC72E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\nOlHNBkRWiRULXM3sUuA5QO3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G7r+RwnA/hYkhyvnQjCA/1Iud8iQSTQ+QSGXIdPaQzLaQ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\nTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x112bd0e10>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "year1952.plot.scatter(figsize=(12,8), \n",
-    "                         x='gdpPercap', y='lifeExp', s=populations/60000, \n",
-    "                         c=colors, cmap='Accent',\n",
-    "                         title='Life expectancy vs. per-capita GDP in the year 1952,\\n color-coded by continent',\n",
-    "                         logx = 'True',\n",
-    "                         ylim = (25,85),\n",
-    "                         xlim = (1e2, 1e5),\n",
-    "                         edgecolors=\"white\",\n",
-    "                         alpha=0.6);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Note:\n",
-    "\n",
-    "We encountered a bug in `pandas` scatter plots! The labels of the $x$-axis disappeared when we added the colors to the bubbles. We tried several things to fix it, like adding the line `pyplot.xlabel(\"GDP per Capita\")` at the end of the cell, but nothing worked. Searching online, we found an open [issue report](https://github.com/pandas-dev/pandas/issues/10611) for this problem.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Discuss with your neighbor:\n",
-    "\n",
-    "What do you see in the colored bubble chart, in regards to 1952 conditions in different countries and different continents?\n",
-    "Can you guess some countries? Can you figure out which color corresponds to which continent?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Spaghetti plot of life expectancy\n",
-    "\n",
-    "The bubble plot shows us that 1952 life expectancies varied quite a lot from country to country: from a minimum of under 30 years, to a maximum under 75 years. The first part of Prof. Rosling's dying message is _\"that the world is making progress_.\" Is it the case that countries around the world _all_ make progress in life expectancy over the years?\n",
-    "\n",
-    "We have an idea: what if we plot a line of life expectancy over time, for every country in the data set? It could be a bit messy, but it may give an _overall view_ of the world-wide progress in life expectancy.\n",
-    "\n",
-    "Below, we'll make such a plot, with 142 lines: one for each country. This type of graphic is called a **spaghetti plot** …for obvious reasons!\n",
-    "\n",
-    "To add a line for each country on the same plot, we'll use a `for`-statement and the `by_country` groups. For each country-group, the line plot takes the series `year` and `lifeExp` as $(x,y)$ coordinates. Since the spaghetti plot is quite busy, we also took off the box around the plot. Study this code carefully."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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GDfP0/hHb23a7J+tjfkPpJ3e0zPvH5eKN2sjsqES2vrC/CGu5+3sAc6Wv3zOIyBu1zslS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D5ltdYawwbGOrJsyBhHzJ6YDTF7GDiQKYjgwKRErAQoYZNRBUCx0b11BG2Z0AGPWosnE4H6N954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Gr//OiB/AE0qjAGCvNXfYtxZfia40u/F5dRCv61kysGXyoBtuvW3OXdp16zs2EA1YYV8evqUv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pNqL52fuw8RWMv0/biNrvpE9fLjpH710eM0UOcRDZIaIge6hm6VbRxd6Ko7xJW4k7i5WcIm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MgDOb3RZYyPd3FhvJtqoVkgwqaDUqz1So8saCGzs1c+TQhjlpfHgkVZXF0eVk8sF8NLG7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j0OoSvpFveOawmeW97PK2cY3Ims192vlwpSdxgpXnPS9IrTF6qxQApgISK0FF5ZV/nIZU2M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8N6J8PX3uORNxo98Y4qbHry+ef9p7GIC81+jpjKcQb26nsHNzbWE7KnqtDzZ7PQy7WeK9\nGuwaiAFvA/dxytn7pDJTAM72Hucvd8dOP/BPYwb8NGL2/QaAi2/HPaenEc8CKEpgxR+lLx9/nN58\nJA072UTKushP3LzIT17HTlD80Iz1HjrnUZmsFnT6yphCF/IUHRsQCwLmiz4//krlD/+YY7sJexTA\n/kDCvnoaIy0TQ40UIY0UhBHgz/6H37/679/q6/1+jfc9UD7xhRNyUoi5g13+Y6aLD+W6Yma0g/S+\nFpeHmbBbRrp92TzZnTcPJj1TmyCynwERJBRSNxJ4w6b4Koh4/vXa8as7OjsHR+wj+qBS2TqASiP4\nlY7Dn8pipyF3n1n2m8Sy5qVNeYU2EosmxhqGMWRrRrqtm622Li+H4kZ1qHGnRCL/CGHGiGCJAg2G\nQf2RSPKHOj6kVgisNSE2mxCro7TtHZKqqRFqZXQSccTA5DqAm71EIRPbgHhH+SmSiNTMKMlmVtG7\nSSnha5IUyoRHEmkLQgLPSTK2kizypUSG1fVMoGpmmJZMlhUiMmQrlJS1INLf0FpzqzPheHqM5/JZ\nYTIJ1Ebc8Xzz8bOmoA3/BJg4AYKDAhglMk0JSggk0gYla0ImL8GQvwNgqfLUKT6QtLUbGJuIQUOI\nGCwwADYHR0fpaA29gZbaoh21ozjUEw5Lk8gfTaQaI7mUlRpXI0lWZBIkM+76+Nh6czq7GkynV9qG\n7LXRY4wRyynugqwvnLuQ2wA/bkMc8YEDNsQIBwgDIgKsScCtCLh6A+zq185/6s2kFEkAJSeSp9qh\nfqQj5Nk1opYaMk1WqBKuq9ReMUhj1SC1RoLVOQVHDIgakTzqeuYP6aF+PMWU0bSgBh/xa/Kj3Wvu\nJ+rfCj9V+wbNRx0dABgBqw6ZmWayOBNigmpbZ9eTtQ9uIV7AbgO4PXH+dOfZZy7kEAO9KcRSgTHE\n7KTRu7dt3GuhN/i1ix4w/uznz7wl3WSvS32QYeuXREMMMBwAtvYCOHvETV8H8GrlqVNv2Q+1xx6O\nIGawxhF/Zsl9fp31nm8UQMmHUFsQ7ip4fRE8akMkbQrNNmjCMaREYEiUyoSZIGGWk+5kIDanHdGZ\ntrmTjkARb0BUbANg4WRuTfiplaFU9bGvyEG2hj3s9ib0T7PeOY+xgEyFtjQVOlIyciU96Mi+35GD\noCuHoSPZEHf9rB3EVbe+BvNtB3v8TRnlcnkcwNMALpTL5QfanT1orJ67WEIsqSggnuu/NXH+9N3q\n0okvnOinvvabs7MAIHGZHd/44eR080QhEaZqepj8yi8/+/S1t/+K/npHz1v3BIAnCOD9IrT5vw9t\nAm8BIO81yuWyhhhsqwBWi5UfbiKeE3azxIPuNg7ubbDbM33zLY1yJgfgk4grN19HuX3zIc5fQaxH\nngTwarlcfqjgjd71TOP+tmwBgJqMqPrP5N83/650capAuhrizdbLKLdX39qLGzzpTN9m9TgTqVkm\nikmfH7Ys56M8cAtZEdgpEVhm11+nFboWrcubfFWvBWu5KKxmiegk0O0kYBudvKk35kbMwH/+Dz/3\nu8+97fN5j8f7Hij/0//tyG+pNj6Q6yIz1eBK0k/6VW3Uv2oe9pe1WZ2bSjZhdBK67CKld5oS9V+j\nWvd3Hx997cJP/fhrey/wsYfo/Uq/WwA2Qr6vVg//GY/EVL+RaRjbDWMcQC0C27ohrbPr0lqiSe2h\nrmZMtg1zpJlIJ9qJlBXJUqBaK7Jhr5U4s8Z9rhQY1yQSDEU8KHR4UNiMIFW6EIvtOGN+s0is5lPq\n7WSChPsRg9+Igi2ewtWNv4WvMuxMYdpRfgoU0l4v6dpWXs3apjTOZGoyRs0gSFDXSfmOm2HRVlIT\nVTUltWCQACDpyKBFP6Q5r00y4SqR+eVUZ/byqYWfNRAHfeQRL/KLlzN06R8fkEw/EichcERwMUok\npECJKmTqQSI1odBlaNJVULIAYPGZr/1JgO3S0BhiGU22dy1p77FdxKDMDWjgbOlboqbXaFNrql2l\nq/kkjHhYNLk7oTF3CtwblTKOlCiFnck8bSaLet0dH9vY2D9z505Kdxxspz6tnj0zfxcsPHvu+eEG\nxHEX4ogPMecCQwIgAggpsCwBt0LgyjL4jS+e/7H7s5YDriYNIk3chvHIFtSZGuSRJpHTVUlmDUr9\npkwrVY2sdwy2Gai8ghjINAE0OoX/nvrmh4YATFPBiiNB3Zx21+UPtV8PPt58STzeecNTBBO9a7PB\nhbrZjn6R2uwTQ4C0z02sZesjL85YSot03eRydf70YuQU+3ZBg82NAjEAawxclwp2Js9ZbyW2957L\nEUfF9p93FNsLno+dDi+NB7Ew94ubrjx16qGjU3sM2GBiZwnxwiQQf7b7UhsbMbPEAETPIZgYAv2Y\nChzrQpjXwJxvItrqErC0qRTSpqInNck0ZcnIcrAhRsJRj7tFX3RKnmjlA9Gfa/rphvccXnLFXvpw\n+e+Aso2zZ+b/cvsCZmh3VZ9iITkoGJkTnEwynyaZTxORR4PIo83Ik9qhI7XBSR8M7zjeS+/iH9TR\nK/V/BoBTLpf/9J08Vu++OQLgg11qpy6ZN2qfH/mDWkNpD2pfBzd6GwC2Lv/cZf7sMxeyAH4Y8b22\njjjZ74Fa3x/UsXruIvkzBI9dBfvpCBieAb3+41D+nyzo7bdTWehJVQa9qvuguA8W+yFDu1ni966p\nMV7/fwTx/PA6gO/s1gjf/dVYBvip3jl/o1wu33cjNH3uuSS2AXHfN77fuxRhp8/5FoDOLls2injN\nfQwxkN9ADJh3pEQ+xGs7DOAoC0ghtCVqreu12m3TWdel0maW5Bo5LWvlsrlO1jSDhKFSJUGZLPsa\nSVgaVyqca0s3vVH3Da8ktZmpI8Y73108//QDQ5H+Osf7Hij/9j/84Je6UW5/m46G88lZ0VRLlOuG\nlDZb2kiywvKJzUbB3Hplaujm78ty+I2zZ+bvXTDKGQM7S7/53k8YgCoXasVmP+52o79HONIFxDf5\noAdsG/HNXd0kreaX1e+ZAYkmAUx6spppJVLFdjKfdrWs6kKYsreWEk6l4LNOKhJUiZjOeZhviqCw\n4of5xS7IjSherKsAqtO0UXtSnR8FcFhBeCAJO5VDOziAO61TuOoZ8Ad1oXfDSADUW2m5e/loOu8r\n9GQUqScZU0phqCY9L+U7djaw7WxHWIqr1ISiNwLZtByuKXYkTVkenbMCZaK7STW+SUPz+vS3/udQ\n8QuziHfOxKeofakodZ4tItuSyEEIMQ0uEpCoJigCqNSCQjeFJq1BpncKVrty+tb37FKnYSIGKn0t\nWl93OahnbQCoNNWmu55Yp1vGltpSWwlGWQoAeFDQI2eGMGcf4d6YzMO8n5IiOkOWRvZFy+PDpJoc\n1rdaxYnmfHrSukroXTlF9eyZeQ4A/+u5r45ZECd84IgPMev3Sv4C8BVgSQJuMOCNG2A3/vL8px7k\ngU0A5ENg9JqmHrpD1WMVaHMNIhe7Qk66XPJtIbsdSjc7lN50NT7fTIULTBL9MqG9NfXbFPHEvE/l\nwcyIXy+NBPXMIWeRf7B9JXq8c6Wz313ppwZWmJArl52faNeCfzCsUhyjwIFIIOVxIbWYCLoMCCCo\npLcnJc2aJBCEBYlK5GVvQUh9Bn25d7QGPYvfyRhwHNnL4aVvJdY/Wg/jYVx64ZKEGKQ8ipjxXgLw\nUuWpU40H/mFv9GKix7ANjvsbyCa2gfHGxPnTwQDLMzQMMvpDkE8dhHSqADJMAdqGaHdU2qaaJCsq\nNSKFKhIlIQVEMkLH7GmNxx2+roid6YOIo4rvu8G68MW5Q7SLH0teoN8uXmOGrLM5ImEfiBiHIBoA\n8JBYLKQ1HpDlwJIXeUhr2GaKO/fYOr3PR7lcfgSxfOIPyuXyfd1bHjROfOHEXY/7VGROPtX54LE5\nb3KUg3u3jOXvfjn7re8ywtYBNPYK1gHuusMcQqyDlRA3ml56p1Zy368xILF4DEDehej8Olz+TURZ\nEa81FxbPP/0gdxwZ98Z757HThi3AvSxx851s0t/2KGeoLfTTAjjegbn+K+Ez3/o2PyYQVzUVAPI4\nbWVPyJUzBCJxmw29cosVG4gBvrLH176EDYiBZQPbgHgLQOuhXUXinqjDiOfDBOK5/GWU2/d11bH/\ncXEMwMcIwfEgItktX5Fv+bp/XVKjRpImqhmgm4DTNUinZaLTMbEmCKkDqI0E+davrf530h2/dPA2\n2KMNiMMCUFIgzizowgcgXxoFfWPi/Om3z3C/x+N9D5R/+pf/zfMREvuEmmIFsxNMpJfZRH6+PpxZ\nuaYmmn9FKL949sz8Tq1RLEkYBMZ90BsBqPj8SMeOPhW5/KOSgN5PPeprkPs6s/6x9e/15zXEZuv7\nAJQo1dKuni3VzaF8Q8sU2tTLhX4lGfkVQ0QWDQTnghlNFuZW3GDomhcWXkMMHKqIvYs9lDPkCg4W\nrmH/hzxoj1Dw4QRcbQyb7RmsbA6jYSNmE+8pP/3+kUdziuI/SQl/AkQc5FzOhaFGfD9hu256w7Ez\nG3DkSmbJ9oqVWirTaqspt+OIo07ofJjL4YzgQoMPYL5w+yc3hxb+dgHAHAe0DZ3QL+UI+fMsSa9p\nZBwRjzVihERQqCU0ahGVVhMIgiGr3ZmubThzW2u6wtkY4onRRAyMOWLWroOYZbkTkrC+mFoUy8ll\npaW2kiAYQQyMwIM8jew5xpxZyrwJRYQ5plFGSuamOpte5PvFwkihVS9pQaASSdSNIe/F/MH2d6gs\n1vupZr9+7iv7fOBECBz0IebCbSbIVYEFGbgJ4Eod4vZ/PP/J+09a8c6+0APGB5dl+fAqtKkqUUou\nlxMOU3gYyU3mqyuOkK9xKm62kuG15ZJbH0zG6sUOT6Uia/9w0DhSDJqFobCZPGrNR0fs+eYjrfm2\n7JndWjhtVcLD9qp/MujyYcWkGDMomdUISoAwQgHV4/A9ASsQdzXbHmKAtirprcrwI38wlCy9UZA0\nuwHgG2fPzL8rk1pPKtPXr/UbWXcnQm0g9jV9aOa3d30oYg3jB7AdCvFS5alTD9QO9rSlfVA8jm0G\n28I2MF7/EgJlHWIihJggwBiJA1+GE4A5BlocAsknQMAl4jY12mjqtFs3JdFRSGTJxGcEVUlgOR2J\n+UcbbOHRFms/TMPXtcNHZMRzTv/IyYloPDrEf1SiPGPWeJVQyFQSAaFiSwBLPKQLflu+5TXU/4+9\nN4+S67rPA7973/5qr+qq3jc0CKBBgIJEiqQlQRIBy7JDO7ZlOd5NeybHozPKOKORJwfjJJMaZzyB\nT5KZOR5rovFknEMnsa2J5Xgk8tiSBWgBSVESCYLE0th636qX2uvt7907f7z3uqsbjYUkKJGWfufc\nU3vVq7fc+93v/n7ft3Y/LZ//tkdUGPrLAK6Uy+UX7uUzR58+Gq+AxC0GczaiNLtfX/9J6+PVjxym\noP0IWcAXhk4fv6ve8Gc+cVZHqKoxgXCC841PfvbEPefCfrcjAshjCK/BPMIJ2XkA00Onj/OxU8+O\nIDTvkAC8+N83tCmE53Q3IM5hp+JEgC6N6vj2jRi2RDm9ewHT+P7ux/dyG9/Hj9Fv9Z+gr0y0kLD+\nXfDRy0u8ZANAnhipA8LGgwBwPShervFEE+GKgn+b29g4bAOhDOubB/9hrdRhhLrXKkIC4aWpPxto\nAigQgfXSB+yHgx7/UV/lQx2RyLNUti9ISm1FFWptjbTaGmr1JBZ8kWxg29SscfGpi0G0f+OV44MI\nj6snAjMfg7z+m1AFbK8SvjJ0+vgPivnervG7/8u/+L/yic3xgdKVupysViStdoVQ/h0AV7fku0Kn\nnO6OLxV93A14vmoFjzkW+wBz2IMSwqrSeOYXL4dsgeKh08fbETjoBTAqcWFC4/KQDDndULPFVT3X\nO68l81XSTHvBhs78Cmdux+Xcb3qBtuIGqdeY3fcd5vbORt/ZmFN/UcbOpaf8LIYmruDAYQ+inEa7\n3ofNG/sxe1WGH5/Q1TI+ZQHIqFqrP5NZfTChtx4WJWdSELxeAIQxwXVdfcm29cummXst2NDmHnzp\nqj60tFQSg6APAPH6mWWcZMR6F9N4AhxAQ2mNzAy+8t9CcvL7mhJ6Z1RSfDFJlOfSJHVT4ioAiQfM\nASVtWea2IjGoxGcpx5L7WlXS16wJeaPFKHgSIXtnYhsULyEs0litaBXjQuGCZEhGAduSYxQAmJtz\nfONAEBj7aWANK9zPEEoYKWmb0kR2lh3K3xAeyEwTpeqkWovJAacpu8wnCwT4amc18Wpn/B/CCgej\nIy74QQfYx6IBjwIdGZgWQa4BuPSf4M7dNj89PH8ogKIH9F9R5ANLonhgUZB61yH3G0xULV+ivie1\nuamuwpGnMh3p1WJTuf7pzz1zC+PZ99ULuRFr5UCvW32XxPyJnGP25G1TG+o0rOFm2yg0UDW8PqcZ\nDLgtv9f2ofgAIBNoSYK8JpASBZIMEGzGDYujbjGs8hAEVrFzErcjZ+/M2YkBhHqvWYTGCi/slri7\nW0TnfgkhAI0LWfdUeCmXy2+ocjxyoRtHWPWdQTi4fLvyxLE9dW2XTp1Tom2J84xjDXUOoMHAW0tg\nfBksawN9AVBiQI8PvsVmCYCvg3R6QGiCUq2tUbKUFMxX8mLtUoY211VqN2RSZ4TEbPxK5Yljd9x3\nU4cmtWhbctgJjJOCzFRRD7KiGqSlZKCSNJOcIT6s+OxS0giel5P+dTkVzL8ei90fxN5RLpdPIFy9\n+g+75cSOPn30tg5mCPutWJFi9eJTF2/J6V46dW4CIUucQJgK9K3dslt7xWc+cXYE4bWYBHAFwLfv\n16rOnSJaMdmr0e7HGRDyO9DGRkHfLQJ5F2jeQHCxDGvOAojEQY64QroU0Bzl6J2X2Ptd8OEcI85B\nT5hROGEc4B5B2yG8ZRDebFDerIisNScyMwirAOiubYiLZV8PmO0uAryXuBOYjW/j+z4A79eEv879\nPeFrj0vwgzPsPV99njyOAdp8PwE6myzxzFe8A/XXozF9P2Pq0CRV824xO2G+z0mzD7QJ7av4Iq1J\nVJI11gOJCx2BtK/J8vzLmjK1nKLTlkLWEGGIi09dvIW8iCYfQwhZ67jQsoKwSHFm7vSTtxRHLp06\nR/eSW3y7xPc9UP7SXz309wXZVAnhCwir3mdPfmNzNzBOAADnkuOwSdNmj3gOOwaPj6kA7Z7p1tEF\nigHU4oNfLpdllctjSa4cpaCHJC4UfC7n16RU9oaeys0ntGxLqqs+WxfhV1z4nVrArFXO5Ctgyku+\nMfHacbe+8rT8e7G+Yzcw3ipSsCH7z+OR4iIGcgxkdQxLXz6Bb94o41NC9N68JFn9imqMK7K5P5Go\njyhqpySJjkYF3yEgqwETLrqO9q3N6ui3P/pvzlkIT/b90S3lAm8bH2Sm8USgBj3IAGDEl+d7r/5y\nm6x/sLSmkCMLEoavaiR9QYd4TeE+C3xXd21HFX1HUggRBZZWAy+tuw7JGy230GkGacfsYLsafx3b\noLhqCmbty0NfFgMa9CIExb3YnrAw5mWafvtB7ncO0sAaUcA0DQCySkPYn531D+Wv08n8dbEvseFx\nBtaYSQu1a5msXVd8zxQXHZJ8ZXPk1yVKhMMe+EEPGOPRgCcCDQVkWgKuUeDi757+yJ3Z1HBpq+gB\nAxFj/MCSKBbWidzbYaJi+jJhtuwwU63IprKYMcTL2Y58DcDypz/3TLftMtlIC3p9ePmhQHHea4nC\nYQo+KAUsmbKDoNS2rZ62X0l2pDWTZVudoKfBIMXKEW2NQMkJZFyhGCNAyQdUl6Fpcb7RCTAXhIN4\nfL5u7qXZujsi45h3IVy2YwitUy/vdhHcsTtCV8NhbBf9xYzxJrqK72J78DcTfV+9sCWLhMjatfLE\nsbnu90SSRwPYlpmKwY3kgdstcNIEF2vgggGesMATDBAcwDXA7SBksNYArHCBrNMeVZEVOmEJ9EBV\nIekljVpzSbq8pNN1bOdtL1eeOHYLUIqsYlPoYoe77m/VCFCJUSXjCWrWl5Ssp0nJAJLuW1IiqFIB\ny9cmEkObBTlpq8K/3TM97AfxhqNcLvcD+AkAX/v8+OdvYmdqUD9uswKyF4jYK6Lz8RjC64ojZFwv\nDp0+fkfWMDLAeARh3r3lgz//v2XtCrZl/3TslAHUEYLD24HdPUHvrnbHIAAegVB4FOJoBiTRBjdf\nY35lhnEzxUgiwYiucyRUTnTaBVIdwFoVA3WD8rxHuMWAyybFakDwRgBKgNcJZu/lvbd1yr2XCMm2\nH11B6dBzeC+u4MBFhBrJ3xUDmDimDk2mWxp6Z/vIRD2BcVPFUCNBMm0NiYLs6o+oZmmUunmVcKsl\n0EvfTshf/MNs5iWH0urFpy7eUQ1o7NSzaYTg+ADCc81CuMp6be70k+/owt/ve6D81b/ZNzCyZOkT\n82Z3AZ7GOYHPB+GwBx2HHfVdPkkCXlQB2q23282+bXbryC6dOkdeFeZGOsR+yCPBpAc2ZnGhWIWQ\nXKUJdUZPJlZSkmLLNZWTdYZgtU2C5jJljRsEwSv7Wf3qZ1vna4N+EC9DxUtQ8e93O0NVAdT+GB9T\nZjD6GMKOfBFhh50VBK9PVox+WbIzqtrp0RP1gqa1JEWxqCB4VUqDaVF0XhQE9tLJE9ObU4cm43zX\n/QiZOQmA6Y6w1dbPBKL7AI9lv9rK2g+tN5Z/udD0k4/UBIyuScjOiC5ZFhwr8AwjwR1HkpgcqFKK\niYJKOVd0x3Yzdmczb7QqScfeRAgkFqL9WANQ/fz452PWvYRttnjL9pZ56ZrfOsa99oMiswZ1QMwA\ngCZa2JeZcx8sXCUPFq7KA8kKp2HhfoMzLC2/0CtWr2YHXaZmneTBhJF60LXkYs4nZATRgKcAmwrI\ntAxcTYBcPHX6h++8tBkuYZUixvjgoijuX5LEfIWK+Q6TRNOXwWzZR0erqoaymelIN9OmdB3AopL5\nDYPQZAohWEoRBCk/VR9rZdzJlk72tzXaywlXKOcsZ9mdguksyHkoyAAAIABJREFU9Lbcq5Kp36z5\nI0sB5Dgn2wWQVAmG0gI5KhJMCgQFAAg4GgHH1TbjF00W7udPfvbEm2Iaz5ydSCNktIYQAt5zJ09M\nbwBbVdwD0WvD2F427WB78rNSLpfvmxlC31cv9CHUn+0D0B4y2av/8QVjLREg3rdDCFObhgD0cXDF\nBQQT3G+Bu1XwYAXMrYITA9xpgdsd8I4NrLbAlytgK1UB6+5jRfCUVFAC3jvZCo7mHX5YD5AKCPym\nRObmEvTCik7nEQLkzdjNL0qXyOBWdjiD7bQsIBxcGoIaGMkBW070OppW8JJSItBICFOc6LtDAF5u\nNiJb8l8CcO3kienn7tc+/UEAR58+SgEUP7D6gV81RTN1vnh+FtuSe03sBMZvaAUkjsgl7XGEqQot\nhOkYCwAwdupZGdvAd8ftgE/7J13h3TJHoS7w2kXZv2lQdE+WLITkQ6wPfzsN6ZjRY3d4T/d7tx6L\nAP8lJo8+zMWHNU5KNuds0WOblYA7lIf9NgE4AyyX8LpN0DAob9Qor81IQasm8FiiM+5XsgjVaF5C\nCFTvtl0sav73ip29W/zr8m/90Htw6ecHUMEYlv5CgXfubs55byamDk1qs70YXOoh+zsqxm0ZI22N\n5AwVekDBbRkdT+bNI9S230MtcZB6fiZg9SxjF3XO41U5ASETfH4vbejIuGQfwtSKfoTHYhHhsVt4\nux6L1xvf90AZ5czfBdAX8IzksQnBYZOuyw8yj40LDLl4Rh87hsVM8fru5bGlU+eSLvzeJVo92CTm\n4Q5x91cQ9FdBMzUuiStEJSt6klRTXAz0mkiEiifw1abENmd6/fmZh73Kwo+abeNDpgU5HEzVrq83\nsK3vWI1uG2V8SsK2RuEHEDILRBC8eUXpUFm2U6ralvREQ9L1hqxpHUWSbFsQgipCLcsLAG7Gyg1T\nhyZ7EYLjiej3XS7yudbHAsv4IMuDYhgAr1oPGpWVv1fwm0PHwNmEy/z0umAHNd6xXb/ZoRI8T5UF\nU9MSnigCII7m2Ztpy5wrdhqvJh1rruu/ND8//nkgnAjsyRYD2GR+qubVH+Ne85jEvZ544gCB+MFo\nesk5UpjiDxUvyaPpJSkCxjYiZQrfFpaf/9MPjEDp/wlfzh9w5HzakvKEU9UihHAVWFdBbirAVBrk\n4qdO//Cdi7xCYNznR6kUC6L4wJIkZiuCmGlxEUYggVmKR1p6O9XOtPJGTysR9K9TcahJpXGDUF2N\n/l8KYALTWvl21h409KDP0HnOkblIiO+J8Ftpz7xRsDuvHaxWv+U098399P/xm0GkWbxlOkOBvgTF\nuE5JSSbIypRYEsEqgIsuw8szLlt407JHt4kzZyf2cU7eZ5qZ0ubGaG15ebIZBFJsQe0jBHRLAJbu\nasN6jxHlPWoAkn85KA0/VxQe64hkNOtx8v4Nf+VHV/2WxLcc5rIMPGsDmgmOBri9CtaZRmDfAGuu\ngZlVcKcOvuF3VcUDqNofHSTYrjAvAehJ+Fzf3w76h01e7LOYnfb5qk/w7UsZ4Tv/9H/6VSfaLh0h\nAO4GxKmuvxA7CjYQFdIpWc8YeLShqnmviHCSUYje6yEEYjE4ru6uoD9zdiIuOPvz3aY2P4jXF5Ed\ndLeKTgmAONQZGhg0B/tnUjP/34a2cQ0hMH7TaS1jp54VsAv4fhTS6PsgvlcG8gtgnS/AXVoG30vL\n10WUliZwmI/bYv+4L+wTOZwW5S/+le6+YlGY9xusREZCOQA5AuT7JTJZEMm7RJCcB26te3x+3edL\nuNXNsP7Jz5646+Q42ifvRWja0gBwZu70k9X7+R++mxGlm30QwAEKdvW38QeuiOAhhNfzV1BuvmHd\n9jimDk3KLx4kI9UUHnBkjLsiGTYV9FgyVADckWB6IjYCikVTIbODaWvtH7SaPfs9b5iGK1c1hCvq\nN7acBcsZHeFKx+HoZ64AuIBy0xw79WwRIXs8gZBg2pKWfT26zO+U+L4HytXf/hcfD3hxxOODHkfS\nQTiIxRWlMTCud0vXRDmNRQBFE87gOm0dXCLG6BxxxmvguQaHvMEVXoNirOtJu5NzCU2uiwpdRhIr\nZslf2pi0F9feazbaj9qOs9/zYtDdLXi+1cr4lIudupBdKRc8Tal/VBD8lKa11vOFpVVdbwWa1gxU\ntaNQymPpqgZCcDwNYPbkiekmAEwdmsxjGxynEC5bzTsH2XLtE36SKzhgsETPqj3eU107PsgbE6NJ\nV+ojzE8YxPHq3Gh3eKvmKILTTiS8ViIpeILoeYJYE1kwlXSsqyPVyuUeo1Upl8s2EArq4w5sMYA1\n5ic23c0T8JoPq2Bq/B4iURdDqRXnYO6mf7TnCtmfnVFFyihCQF1BBMp+78y/cvZ7yhHFq38I3Hss\nIGIviOhxQd9UqHpTJeRGAmRqAPTiU6dP3M3cQwbQaxMycE2WDi6K4r5FScytCWK6xXXfdvKiYOa5\n1O5hKTPDknbGl3nOoDRrESHdBEm1SUQHEgQOTayrrUKnZKTskqHxoi0TmVDf4zSoq9y+KXP/Kge5\n+O8Gf3qh8sQx/plPnE1jp+lMDwAhQZFLCSSdEYiSoMTQKNZSlFxQKLk2dPr43i6K9ykik48hAEOU\n+qOp1OZBRTUGRcGrua76tc3NsW8hNPh4I0YNFNtFm92Oclv3F3SS/EqvNLqQoEWZcf9YPVg6seZX\nA8ZTJnjBANfb4OI6mH8DrHMdweZVsEYL3EQXGI7u1+yPDorYBsVFACXCmJoyDTHTaQuHNhr0kQ0r\nv79uZ/ra7SBdXV1lm9dXWWPOQgiOY/3o7vCxh9QagObkz68QbOsvD2Bbfzk2SVlGOJBu3Il1OnN2\nggD4eQCdkyemv/h69/X3exx9+qiK8DjEmutbdQ7YTg1aOdg4WDtSP/KzAK6Vy+W7svZRLm88aepO\nf9h9q+7x8UAErB+HVHgM4rAO4i+DXf4TuOeXwJqI2OG9cj0ji/jjCK/NdYTFfm9o8hQpTcTpQN3F\ndUkA6BFJoVckQzolBMB6M+Dfvumw1xhQe7MrVgAwdurZIYSFfiqAbwO4eMdakLdhRKtrH0F4PF4q\nl8vnwxcyBxCC5w6AL6HcvGdFld//+cNiW8e4J5AJX8A+T8CwK6HPE8Ix1BNhcaACggVbxuxCkdys\npsn6xacuWihnhhA6540ixAWzAC7fUSIu1PB/T40nD59jDxWfCR73n2dHmiZUG2GtyrW500/eu8Tc\nOzC+74Hy0qlzP4Sw0+pOofC7Xo9ze7cG0Dox+y6Q5ugMsffViNPT4FANENrkSrPKlXpLV9fVwqai\npFbzGl1Sk7xOS86684C9sfqwubnyLsepDvrBVppB1227jE/J2AmGCwg7KAEACGFcVdsskWiIHHyC\nMWFUkS2jt+/GVDpdizsnFeFFEOf6ziAExy0AmDo0mUIIjGMtZQ5gKRBx8/o/LGpugTxsUfU9LT8z\nZDYn+khzPK/aOV1iRAiY5zaJudlAZ2YzKTVq6ay3mcoEtqQYniDWGCFTbVW/6InScuWJY360dHlH\nthjAOvP1DXv14yzoHM4iBA8lADQlt5XB5Kq/PzvjT+ZvkInMLJEEP15uqyHK//y/n/snVtocnBSA\nScb5AwKz90uBVRB5IGrMX5Wo/E1dLnx9gkiXP3b6iTt34pHrnU3IwEWh58Elkjm4SlPFOhJZi6eY\n56VBnAxkK8MUWxY0V7ZEJjmgap0QfZXQzCKhahVAWyFtUy9eSNWKxlg7wUfasjpiCHoSAHwitCTm\nz4g8mOqI2sW/LP3w0j/9XE1FCIS7mcyt4tA0RVCSqFoQSSojEEejpBUd35sAKm+Vy1nkhNWPCBxj\nWwLRQjQ5OTT5dadYXHgk2v4lhOoYt0xCIkY4g53gd7fF8u58SAtAezpBgz8dlYank3RAdVjygU3P\neHfFYUKAnAWeMAFWAWssgjVvIlhYAV9G1zX2V3/5W52vv/tR/fK+A8OWogwGVBggQL8QBDndsSTV\ncaRcu+X2NKpOT6Pp9nqClifJLBWVJBjzuVWvsPbKMnc79Wib4mbuetwEYExenQqPR1jQWcK2kkYv\nQkAWa6vHwHgN5eY9Ty7OnJ0YAfCjAL5y8sT0XV3fvt8jkmrrBsaxYlF8HNYQKVPstoMul8sfBjD+\nojfy/14NemXsBL2784J13HoOx7rlcXGy0XV/63bu9JNbDOPSqXMawpSig9F7voXQ9OeO1/lnPnF2\nP4D3IWT7XgNw/nY1CNEKVRo7U/zy0XPxf2AAGgSojctUHZbJsE6JLBGsE0JiFYv7vsw+dupZFSGg\nHEN4jXxt7vSTO1Zzozx/IWrd+3x3XvVej9+y25X+fv3VY+96IhCEXM/m5rcef/FbM92vFw61i5lx\n64MAxPay+vzGa+mV7m00FJArIyS5WESqniB5EIwEFKO+gF4e4QFO4FCGZQIsBBSzm2ly48ooWd3h\n2Bi66cbW0lmEq61TAK6g3Lxj4Wg04RsEcCiH1qEjdG7sMJnXH6IzSw/T62f7SP08ys2/9TUR3/dA\nuTu6BvAYoBQBFCrw9ZdJa/QmMYbqxC6aCNIuOLEgGBanLYH7i2mlXpN71/N+ojXgUrOHc5dkvGZr\nn1ObPWasvPqIbV8b9v1VREtQZXwqiH5rS6kCt7oH2YLgNdPpdZ7NVqRMZk1JJOu6aWYya2sTB1xX\nJanUxlx//815QQhiRosj7OjnAMydPDFtAFsV9PsQguNeTxSlTipprPb11+eODgZsxH5UUq1HPSL0\n+34qRVrjsmL0C7KvuGLA2haz5mqCdf7VvuTiar6gNbSkYsmK5YlSnMYxB2CtuPArCu7CFgNYY76+\nYc791+BeTy8i4waB+HJObST7E2vBvswcO5Cbxlh6wVVFN7bdXfMZXbuwcbR19urHMeQURiWQgz74\nhAhSJJwTNbC0lFeH5taaqle7Irvrz9jO6vVPf+6ZPU/0z3zirDYgXeopSTfHKbXGa5J3sEHJaJMq\nuTaUlAPZdyHZ8BSbOqojO5ypNjPEwO+A8zVCxFnQ5E1B2jdHaKL1yb6fJt/MPDRyObn/aENMH2yK\nybGWmEwAgEOlhsSCGYBfrUuZi5MvTG5kTRa7KcVC8smuzWsAWC+JxN2n0FRRJEVKiI6QqZxDCI6X\n3qpq4cjoIwbG/QiPYzdzv4jQ5KNbGYMgXKp7FOH5+Epm8cOv9U39WrcyQB+2C6CA7UndLbrBANr/\nJTq8kpMG8wXtx6gsPJLyeGFfw2PjNa8mBPCqPKgabmelbW4uCLXZzcnanPXujRtuxjVkTxD0SqHY\nU8vk+gxVK7qS1GPLyhYQl3zPSVhWO2UazaRprPfWN9c1onli35FemhkZIHISoEKVO+3XvIUXrrD6\nbBuAswWAb7vzMrExSQyMY2MSIJwcbpuzlJv3ZpG7R5w5OxGbFfxJrO/9gwgjmqR3u3RuyUUizPWO\n7c0rADbbU6c59ga/CQB6kXQGDorrP7QYZGfmWX63lJuDW0HvbiBsvdE0iKVT50oIgW8p2u4Xhk4f\n37jTZyLL5scQguwWgOcQTuB2axF32zhzbOvQd6dNtH4yK40glHkrRN9zR4AcAdjdNu67rdzvqX1p\n5L1jL/ceelBgAT60fOHa45Ur9V3f97aKdjKpXT9w4CgTqDg6Nz9V2tjYkzH20oGemDTeFWg8O+0o\n65d8rdXRiGoqUG0ZCu8C9iKDLfpYFhgWKMesK+H6l99DV2+nvY1yJosQHB9AmFe/jjC9YuZuk/Gx\nU8+mEJ43BxCOSQ5Cx9yrc+ovMoRFpPsQjsuvArj0Zvqxt3u8IaBMCDmHMCd2nHM+d7836rsZS6fO\nDWPbVrpog6szYKkLsAs3SKe3Sax+TuwcQyBQBABYK8GNyjiWFg+r18z5kthzScuNVQXkXRBP5PZG\nzjcuHDDmv/F+s/7ayX9UaUZanLsB8RZLjO3CvKqmtcze3mmx0DOv6Ho7h+1cRR4EQm1x4WjOdhIP\naGqbF0tzFV1vxQUaSwjB03ys+zt1aFJyJWnC0rRjjNL9niQljESCr/eW/LmhYdEpYVhJtg5KqlUM\nIErEGiBqa8hJGNlOwhOW5cB/rZLAq59/IL05k89nGKW5aFtqAGZJ0JzvWf4HwF3YYoTAeM248T8q\n2AYO/bpoJrNKM92bWOfj6QU+npnno+nFVkKyfMPT2rPNUfNafb9zaXPSabaH1D4IYwmQoRRITgdJ\nAiAq4CQ4FpJOpaW1L4swbhg+d9cR2qHOq7n/Ls4HjlnLlEqahbSwPizRzqAntQZM0Sp0BKZ1BKbY\nhFumEDRcjg1m06pu+F665TLFtTucd9rg7jxCgLj46c89Y6KcUS8kD46+mjp4pCplD9al9GhLTOoc\nhFtUaYg8uMkJrtbc0Wvv+2bBk9gOUNytmNLCtnj8xuMJweuVaKw2ko325xJCcDx3L3q7rzciK9hB\nbCtUxJO2RvTbSwiL8O7420unzolG7sqIlZ/6EcKkByWrKKrNiRnZKjWxLQG3hsi1D4A5dPo4iwpD\ncgAKicDrecR1DvYSdsjOq4faaXmQcl8YrjZbB+YXZlP1yprSXt1INxYbKafNaTSgtPSEUs3k0s1k\nKtlMphL1VEYxVS2wZcV1JckUWLAme15Ft63lfSuLKw9fvVQDYE1enXIiq+EjCNkrilBX9NLQ6eN7\nysrt3HkZATtBWbcaQh3bwHgV5eZ9KWKMCip/HsDLJ09Mv3w/vvOdHEefPirj1jSKeHLSYn6ixuyh\nttd8l+G33sUBIV7BiJu2x9cG6AK9J6Qbjwpg9t94B7+AbQBsvilFhHuMiMh5ACH41RDmhH5n6PTx\nOyonfOYTZwcQsrJhf8MZITygAvNMwbdbktdpa3a1lW7NG6X1lw3NqXUDXEHad2JELB0+AlHJcde0\ngvXL097CC+tg/t0A7+uVXovD36vNpvvkz+//8JGGkkwNdDbnfuna37yacQ2n6z1xsR+wXeSHrsff\nldvLhw8X58dGPwiAj8zNnO0E1+2b/SS5mUGqpSNhySTpSEi5EpIBhSxzTn/S7kwMB15+mYorX5RS\n56lPWoqHtu7wVk8L7bE13viRV/jaPU7QRxAC5KFon0wjTK+4raEIsJUbPoYw93gwenoJ4Xk2f4t2\nczlTQAiYRxGy1BcQstRv+bXw3Y7XDZQJIT8D4M+jhzuAMiEkCeA0wpycAOFO/hTn/PJ92dq3IL5+\n6msfX0Zw8DyYNg2rYNHOgEKMggonKRMPClw7j+bmGJaXPkAu3Ewq695fFA4NfivR21+R1IJNBNcj\nqDiEflPwNp97cuZAXQu0HZrG2MkSxyYfNQDVgcEpf2zsgioIfty5x0DTxzbjUavV+nsNI/+kJDrD\nimpsptPrU4IQzCDMMVo6eWLaL5fLCRoEhZH5hYPZRuOo4jgHAlHIO4pCNgsFZ6FvQNzIZ1Ni2uhN\narW0IHiy6iZctTHS7Gn0r/VY+lyRCVPLef36HxxU2LW0UOrangr11laTjT+1FOtlHXdgixGlsLSn\nTicRAuMBCjaYVZs9abmVLmpVOpJe4kPJZScpG47laWTV6LWXOgPOUnvQXzV6iRCoWi9oJgeSyYCk\nC6A0BWJlQDoysKSBzCaYN72x8oWUzZxHCFF6iJC1qLRvQZDGTWyDY1EmhpwUNjMqbWQDeSNvy7VU\nUzakumSIbdFtNiS32hLdWdMKVvs2KB9a13TdEWOQU0MIjBcArH168px2KbF/9LXUgQc3pNzBmpQd\nbkgpnYEwU1DrAmczPlOuKssTqwdnZSIFiA1nup0YO+gCxQil2ZzI6GIiasXovasIwfHsnVzZ3kh0\naRrHrHEpeslFCOoWERbh3bGaf+nUuRigxIzxVp6nnVzgrd5v9rvaPPfdykX9S61va6/S4NWeicxy\nsthvCXKfJ4glTmhPkoj9BSCX4UgrhCQX+wrKXG+P4ojE7q2uLzx2+eWLA0s3lsADA4DVTCS96yPj\n6nz/kLZU7NPXCj2peipDWomk10qmLEPT19BVhFt54tgttr9RatUEQoDcE/33qwCuDJ0+fvvc9TA9\nZzcoiye+sQlO2N4iLeMzZycej7b7T16vnvXfhjj69NEUomPAmTjAmdIPpiicKTL3MlbglkxmD7qB\nOeZyPyPjVuZxLztwo6uZc6ef3DGpKZfLhxGSRP+5XC7fkdF9qyK63t6D8Nj73LPOG1/73VlurMe5\n8t1NB6AFVE5s9hwdJ8wXVLtm6daGKQb2HVlFoXioIJaOjBA5meS+ZQUbV6f9tUsV8KBbWu31tOBu\n75m8OnU3ppMiZLXfjfA6Ozt3+sk7AsC3OqIC0DSA9Gh79FDGzXzIFmzMJ+evOaKze8LAEJ5nrV2t\nfW5+aSLL2DGE/dWXX1e/EfZHhxCu5qUQnr9XAFxFuXnHidTYqWcLCNnjBxAW9nUQKl1cmzv95N2V\nXMqZEkLAPIQQB7wS/e533xHxLYrXBZQJITKASwgp+L+DW4HyXyE8YT7COTcJIf8cwCcAHOOc352V\n+R7ER/+HP/o/JeIeTRE7lSSOkIDtZ0l7cwSV+WPk+o1j9Pr1V1TV+bc9Dw29mijtrwt6n09FQiC1\ndE++MVEXbzy4LhkUNF7OuoUljlpN1+u1hx95JontwbXbntdGl6YsQkA9yhgZb7d7HrWt1BjntKMo\nxlcSyfp3zr/847bnaTHjXNANY7xnc/NAutUaEoIg5YkiWSv0WNMDo+ZM75DCNS+TU9YSBWldSbnM\nLLRK9YHq2PxQu2++CGnjtZzY/NNR2TtXEtMAVHDGBX+lKZvnTa3zVV8INnO4DVsMYO3iUxc70XLN\nAIBBTbRGM0qzLy130mm5peWUhqSJti9Rz7MCzV8zi17VKvgNJ91uOmkjDUEdBFVGQIURULUPFHkO\nqwBqpxhZN33eXnZqZM2uawxuL8BL4KzEwTVCpA4ReuapkK8BcDTa8IrijJKV5jRbW+htqLXsutJR\nVxVTrkusVae02RLofNtRl8eWUphYSagJWyxExy5m50PWePIcvZLYN3Ipuf/BDSl/cEPOD9SldCIg\nNDAEvU4CMqu20suJ1YGN4QoVBL4FiuMlMwMRGI5uNz752RPdOYgqQhmeiWjfIXrvTYTLmvfVRe02\nmsYc27rVSwA2fu7PPhc7VknRe7bu01R/Qug9MkATpT6ipHqJqObBmQgWEGY3DGast1lruRPUZs3A\nt4WGnsy0huT9dlocNbkmG51k07FUUyaClBBkLUVlISWqTBEkWxAk4/JQ0fv2aC9dTyht5pmXHr70\n0vNHp6+t/ctf/o3E7OBIHtv5291KEjVsTzzWAdRiaba9IpqUHEZoba0iZH0vAbixJ1sf6qB2A+N4\ndSW+FuIl/LW7DUz3I86cnRAROsYtnTwx/ZW3+ve+lzF26llClVVNTF0cplJrDNQeIdQbBJDlTFLA\nFJF5WYt5uRZzi01m97fBZYZw0N4NhLdady7wvUa5XJYR7vfpcrn89fv2J3fF1KFJCXsA3u5Gk/09\n4vBjR6iaLXLPML2V89OsNt2tLONiZw69HT13R7Cqvve/6hfy+44QUcmB0AYYe4nI+vW3kyHE2Kln\n+wE8gZCEehnAK29lod/Rp4/qCPub9K6WQmRElXWy/Rk3sz8gQX1NW3veE7wqIhCMbUBs3DZVAgDK\nmfHofzkIi/zuXJQdsroPIgS5AkL8cBnA3J2KgCO5wf0IwXUPwn5sFiFJsPKG9mU5049QraQP4TV2\nHsD1t1IC77sVrxcofwrhjrgO4J+hCygTQj4C4MsATnLOz0bPyQgHjz/lnH/y/m76/YnyP/2tP1YQ\nDPSgcXOYrF18F73xWh7rS7/Tkxf+c/7B8UDIPkqhH6RcSkk86WRcbXWsIa2M1r1m1zRxB0sctcbx\nD/57ATsH124Xsha2B9fVkyemm2fOTmQRLn2MASi5rqpXq8PjrWaJGmZmxujkrgA0hxDgJGkQpHK1\neilfr+Uk20k4okQq+aJ1aeQB68rgPt+jRCqJK6kxYYYXec0ZM9XWWG18daC5r6r6uebFjOD91YDo\nfalfEh1iq9Tf1ER31pTtV13Zeo1Qbsdg7xa2+OJTF/2xU8/qAAYI2EBWaUyqojOuCk6PKjppXbSI\nQH2fgAcek1uGp7fabrLVcDI1y9c3xkDZUS7Ih7iQGec0XwRVZA7J5SBt33fqvik0PEsxAkPg3E2A\nezoHF4DAB3ctcFYD+BqhqanxlL06qZ9J9MiXM/OaNTEnSYNLophZEQW9LgidukCbTUrnKkSanlhK\nBYfnUlLalPqwnfrQQMgYL/76vpfsRrYw9Erq0OF1uXBwXc731qRM0ieC36Z6TTPUVaWZXU+v51ul\nOkSBI4dtUGzhVqb4FlZg6dQ5CeFy1X6EgJVG2zCNsFDnFubzbtGVD7gD2Bq6rq8MDAyaCX3Ik6RB\nTkiGBkyQPdfTDbOearcbuXq9Jfk+wU5gvJUbR9SsQjNDaZrszVCtkCGSrgMA50HA3U6Dm9VN1q5s\ntjqV5qqaktb1rFZVM3pNTettWdddKnFHkHxNZdLBQnu4XyHpAs9Y+fb+q1kpv6kKStOhWPk3+xXp\nC4PSQEsmsdzQAsLOvwTs2M8ddEk1ItQrvqf8uK70ivHo++YQplesbL0pshjHzms3duFzsTO3deN7\nsdR45uzEIYTL6V88eWL6HV1xHqXc7EyDEIyMoC4NUrk6SMR2LxE6WUJYWMDEZIf76RrzUxXmlJaZ\nPbgKCLcww/fF5nePKJfLH0R47f6Hcrl8z4VMU4cmZdwB+O56TrzN1zjYVTwqjX84Kw09eojISZkz\n76a/dvk598pfbNyNnd0dS6fOjSJka3sQXn/nEfZHb0ugE4G94whJhgqAFwA03kgaTJTPvhsIdz/e\nfTxiVrhNOGk9VHtovMfuGVMC5ZoWaF+6W3raHSMEvx9FOIH/GsrNmV2vU4T914MI+yYfIXl5GeXm\nbRVOosK8foTs8T6EfWsVIXt8Y/fqyZvY/iGEDHMJ4T6jebEPAAAgAElEQVR6CcD0blnLd1LcM1Am\nhOQRMi7vA/BruBUofxbAfwEgyTl3uz73RQDv5Zz33dctv0/xpfJP9MvwGr85vuxmnew4aOZhW0q/\nLxCSYyBqRuJpL+UlKgNt+eZww1yVWNANiGsAqrG7zpmzEzp2ssUFbFvhVrE9uFbipdIzZyeKAMY4\nJ/tcVx3wPDVh20lvc3Mk12z0jbmuThEOzD6iYqpEs4Vkq51TTCvnUhHrmTy91j9mX+qbMJuKHoxg\nnk/Qm+I+edoed6k50h62++uTrcAa7txIqOLXSsT9m5Kl1cSmRoOqIrkzruRcM0VnpkngB9iDLQa2\nfNv3K9Se1CXziET9UUnwcyLxU5LgBargmBL1mj4Xq14grRpecsnpFDd6XNV9lAupB7lQ6CO0JBPS\nz4Ek41xxuc87vu11fDNo+SazghYHN8G5a4PZDofbAvcq4M4yC5rL4O21Xxi9EAzo7V4HGJxS5IPz\nktQfA+OaILRqAm02qDBfEYUbpZrafPxyXsx25FgXVYz25QoFW/xw74xR7Ge58+nJBytyz4FVpVis\nSZmkC8l1/YypdrRNtZneTNdSZr7NRcq3wJqDnaB445OfPXFb9jeSPBtGOMCORtthIALH7b/8jRbC\njjFuStT2ZHVvcz8sq9e0pKnrWVtV854kpUFACOeB7LpNxXZqumluaLbdwLbrlNt13xPyE5JQnMzS\n9ECWavkcJE0lVAgAaoGzlQB8+XnCmv9SVdymIHars3SnGJkK0PgQJP4+iMpRCHoPSEIA4XZqLtcp\nnS/5SmNOXv7In/zI44cHAXwIYV5czNDGS37xft6Sa6w8cex1MbaR81mcXlHAdnrF5aHTx9uR/F+s\nnbt7QttB13ULoP526PDPnJ34GQDk5InpP7/rm7+H0SWTtjsfuLupRGzJVKmkqbyZplIjQ8SWBOI5\nhHoO5+I6mLzEA22BOX1zXuOxtfs2qL+BKJfLPQA+BuD5n/uzz03jzoC3u92u6My6TdutpGJNXp3a\nE7RGKUQPIUxJIAjzRV+9l1qGdxpA3h1jp559AGE6TGwEYyFkcXc0IXnFUXu/SKlcj+tVukFxctfX\n+tjJBG+lSABoX3zqYgBspa99GGG/fgXA893FzW84yhkNwI8gnKyfR8iaa9hOr9Cj7bkC4Nqd6h7G\nTj2bQFiUdzD6ry7CFcurc6effOtkRMuZ3S6pLyFkur/n/efrjdcDlP93AC7n/B8RQsq4FSi/AGCA\ncz6263O/D+C/AdDLOf+e5hLtFb/4v/7ijxmS956WKh11pETRFxMqRc5UWHqxYOkvj9Wbr6Vts4KI\nJS6Xy1udx5mzExlsF+50M5Q+wkE9HlzXY1vZM2cnaLNZGmNMOEoIO8QZLfm+nDTMrGB08nar1UN8\nXx1BCDo2AVx3BKHToXKQrTcL2UajP2AoWIJCp3uG7Gs9o8ZqIr82jhkcVS/aR/SbfMRLCb1mH082\nDgS2PezMyCn5hbypfzNvSgtqU6RBTRS9pbrozGyK3vwaAd+dWywhVOQoAhjXRfOgLLiHKGGDMnU1\nSfAkmXpWQrTaSepW9EBckpxkVWwX62mjYPQHRO4RaV+Sol+jJKMSkgYCMeC+4gWOYwQdo+3VzJa7\n4dhBw+DctMFMizNjDfA20TUJiQrmtnKdAQzMSGLpBU0bmJKlbFUQOjVBaNYEOrsmijdFn1Q+8p0S\neutqCWFRQ5wb3KKELR7NVMyJIUO+knvgwKpcPLCilnqqYjbp+wnmu1lX66h1tZmqJ1uanTaZKXBw\nhB3LVuoEQlDc3ut8iphdGYACQVal0Q+M0GTvASKq+3jg6dwzOTfWNoL6fI21li2Ax6D4ToUvDDvB\n7I77hq7T9VIp38xk8qauF3xJFH1RDMCxIbBgUXGcuX3TM8uljQ1n8urUjoEzKhDKYaciRcye2jb4\n2nn41l/D878OnwbboDgGkrFGd1UEqj8NmX0csjYI2hN9nxBt/5pPsPzX/WLjXx1S+WHxxSNJtH7q\nBg72LGKkBUJNhJOGa+hiiytPHLsnG+C9YunUuSTCQeUQwslHDcDlHum3l1XhtSK2gXEet05oQ9b4\nLvJJ34s4c3aiF8BPIpTfu/JW/97UoUkB25MyGV3n6my6X7icH9OXUkW9KScThqQmLFFNuFRMeIKo\n+1RIMBAKAJyQaLBhHlc3wfVViWkbClcaOpdMSojrUOoYEneWEw6r5Nu88sRrfOP45S3iZXfh1F7P\n3ct7AIDHhVFHnz6aRAgSC5TxRKkB88g87/yd7zBrqAoJewDfV9597Dg41HdfuPDKHrssloG7LeDt\nes6+a4HW64gopegxhMCtA+DFodPH95QNfKcD5O4IwaDfT5WNXkLtEqhXAlgPCPKEeDqIp4Kw0CGQ\nug6hjg3itQn1NkD8TSLYa0Qw1qm6VKGi2bwXM5koDedHEI5L3y6Xyxfu658KC4Q/gBDgVrHtzLuI\nML1i8XagM8rlHo0+O4ywf1tBSBDMfTeKTwHExYXjCBnmLML/8R2Umwvfld+/T3FPQJkQsh/ANwBM\ncs6btwHK1wE4nPOjuz77PwP4xwAOc86n9vju3wDwG9HDP+Sc/+Eb/zuvP97/9G9+1hGFiUDoqXKh\nNCuh9zmZ0Zef/7kf3yH/c+bsRLwc280Yx1XScX7xltTQyRPTLJptZmXZLObyy4c0rX1IFN0DnJMc\nY4JkWWnPNLKGYWSbQSC3EILjvC2IRiVTuDqb768PVFYHBlZW9+UajX6fCOqGmrFm0wPLN1IDi6Pq\nbPXxwkv8aGI1M+wUEonOUFJs7WOrLE9nRSVxKe1oV5OWclNv+wyttuCtbEju3DXRW5hlXnrT2fiI\n5TffG4PiLIAcJcGYJlqjMnX7RMIKCvUTCgmQoq6Zo24zx7BWdJRKws62xE7B5kwOJAIxI5BMkpJU\nknJNJn4C3KWM29T223bb27TabqXdcBY3XGZ4uDVVpQag8enPPRMuFYaOQP3YNmNIOwB5XtdSZ3VN\nuy7LdF0UqlVBuAxg9tHLudbh+XQMjAcRDupMJEFlPFkzRvsMuto7MrKslCZW5FK+QYq5wE3Dd9Ke\naqgttZNoq6ZcS5usJfAtF8YNwvzNQu1Kc/LqH7uSb+1me9XbNIVouTRNFItEzRZBBRmcB9xpVblZ\nXWPGRgWcWdE5Ezdn1+O4uQDc3SxSpGnch+1c41s0jQEsl8vlvdI+uiWzYmCsAEATzJ8GM1+B774A\nP7gGpkbnRhwutlOLqgCqf4SEewBCrGQyGO0DmALaU2mh9Y2SaD8zIAZNmcZSiEp0HfVneL1nBHOJ\nAjbPvYDjf+QS9Y55xfcaO9MrGGRyo5YU/3JTF87F+y1mj7oLZsPJ4jtA4ujM2YkTCM/1/3jyxPRt\nt3cPgCtj5yrEPT3vUlFuKEm9Lem6Iam6JSqaK0iKR0U1oHTnkjQHl5jvysy35cBzlMB1BO547bQl\n1jO23Eh5WjPJEn5kjqB4cAtt3iw20R7a5M2hTRgiw1vCOPkUpJKDVsmR5GYayUaSJFo6krFRAwAu\n+/BdMWQmCcATNqxch7cLLbRKTb4xtIENzUPn+gMPFJeGhybHZ2a/PD43t4SdYPju8oFvcSydOtcP\n4P0Ir7kVhHJytei1dzxAjoxi4kl7T3Sbwa7COc5Jm7sFN3BLjNl9jDl9nLklwtwcBVdiCcBubeVu\nqcq9mjl3+klWLpcTAH4M4bj59XK5fOMt+7PlzEMIVwtmEKZX3DY1b+zUs1mExMABhH2xgTBV9trc\n6SffMOnwpiMEzPsRnndphGTId1Buvi1r13bHvQLlvwDwNc7570ePy7h3oPy7AH4btwHK3+sY+Jsv\nH2dCASDCjcoTx7bAcVQsU8I2mOjFNovWRld+MYDmuW/8ioIukxBRdPqTydoRXW8OyYpZAqBwRgPb\nSdRMM1MxOvkVxsRljwrrK5mCKjL27oDS0kYqazYDyZyYnx8aqlT6RdsVfS50FrWehWv64IXcUHX9\nieHnlHfLxmDGHOgjnb6SaRXlZZ6TZmVJvpry1RnddVZU22SsVaG2tSBU3TleL7V9cz8HU5IIL+6k\nwCEmqduvCc6wTP0eiQQZlfiKwjkyNHAKnNd7fCznfKXC7WwzsNMdgHR0Cq8oEiVFvLRKnKIAN8uY\nJXrMlA2/4Rt+s9Vyq82mu970uLuDIQZQ+/Tnntm5dF7OqNF+jgFXzAK705LY+PfptHZOV7MbghBw\nQlpCQKY+8p1Ss6+2xRrnAUAkgTWot4yBnMlbQ6VCRe8dX6bDpRYvFjw3zampIdEWLcUQ6gnDb6cN\ny9ScZlu1No2kuWqmW3N2srPsk5DljQHxbtOAOBi6QC1N9opC4YEemh4sESWlEEFxOA/mud267i2+\ncJPV54zJq1NvWJj9jWgaA1tLst3ncV8ALtXB9QoYvwHmXEYQXICPyk58EmupxrbOm3Onn+xEFfcD\n0XYMMiDTlIi2phJ6I0XN83nRfbEgsA2VKl3f5UX7MYFwydNB2FG+/Ef8FzQF7sMAXjt5YvrFN7p/\novSK/QTWUYnMj0l0TlPoxZZCX7EF0ooBgImdE9ra27HQ5E4A1xtkycYvBD8lLZKF7OfEa7vet/v+\nvejLbq1WeETwN/SsuKbn1Q0to9XVtNqQk3pH1hRHkHyXSoErSC7lrKkEnqkGrqF5tplxDTNvt8zh\n9rq5v7lsLfY68ksP0NJKHj3NBClZynaRs+qilTH4Rm8dmw/N8c13z3ADtzdu2Ou5e3pPPQHx0hjJ\nLhVIpppGtq0jZ8okzUm4HZQjSNi8lTHQ7Gnx5sgGGpMLvJ2y4c/2Ai89QNTrAyRRyZNEI4G0E+bN\nAyGQqotMrL1n8z2Py4E89Vz/c1+Il+LfThFNig8hXP6WEa7WxCo87xiAHKmbdAPiAnamShjYHmO6\n0ySMi09dvCPAiZjXuF/aqyV2fYTpcDEuVA+qxA/WWfJriyw3jZ1A+rs6SRo79ayEMLXsEMK+niGU\ntrwGYPFt5WYY5lcfQKjakkSIn76DcnO3LvnbKu4KlAkhxwH8PwAe5Dz0m//blHoRx5mzEyp2ssU9\n2J6dbuUXm2Z67eWXflLCTk3kQQBFUXQyiWStX9daeUUxNEKZxzlpO05i0bZSl5vN3uubaq716tB+\ncaY4oHqi1LNvY/mhwer6JHX9lFI3zIH5FTnTagmC6xl1ITUzl+477z/kzf7sA19K9tn5Y8wsTRKr\n0NfwcsoKTytTksZuKpQsSoHT4rRJLF5BR1xHU9sUPU1WOTSZE0XmUFVwISk4WZm6RYH6eZAgI9GA\nimB+kvpWimM9BT5b8MQbxOiZC5xMDUB7WHSFHNkckOEOEkLHOWclj7uJgHnUCoyW4Teaba+22nDX\n5wPub2AbFDc//blnbu2Ew5zQGBgPYFsr2gew6gErv9OTJ19IJvoYISMAuGYLlSMz6dbkfEqmnAwK\nAdMSgauWBIPnFVvws8lcQ+sdqpL+XhPZLPd0Cl+D7MCSbdZRbc9Kmo2qZjdaql1r69ZaW7M2LMoZ\nRyhbdCdm95bXJq9OedHSfmz/XcC22Uss5/aGgHG5XI5BZSzdNox71DSOCgW3pNpM8IE6eKoJnlwF\nY9MIvGsI2A2wTg3cjf57dxFqFUBt7vSTbvR9WxJyHRFjTYmMNSSSbEpEnUlS92pa8K5khPZCgprR\n/29ie9CKXS1j1tpByIpMA1itPHGMA8CZsxPvR1iY8uLJE9OvvZ591fjHv9XDIX6AIHgPJbUekVSI\nSFZWJbKwToi/oy4A5eae6TL3O6YOTSYRTrTuhcHd67XbpuGYjwXDzgE+lvpr4dviBmliOxXHvcf7\n7oaa8Z8+/GPqq8X9yU0tm8G2+UQGO53YmthpOlEH0No98B59+mgWO/vOOAUtQLg6s6UIcvGpi/c9\ntzhSJNgNpLpXQmyE5+Rm123rjuoDe/9GsbsVreIRzdd6lxPL3wxosI6d6Vm11/P9b2VEyjqPIFR4\naeNtCpCjgrosdh7HHmzrkcf9S/dxrF586uJ9lc3sjghIb2nwD9DmWC9tP+FwUZoPctcNKLv3Ybf8\nW2yetNXmTj9532Qcx04924cwtWICIXHSQJhacWPu9JNvufLOm4owreQQwpx6HcDLKDfftlrw9wKU\nfwfALyE80HHEDOsUws73twH8XbwDi/nOnJ14FKHKRMxixp37arudr12/9n7fNLPxjLaEEBTHyf+a\nJFlyMlnTEsm6rCiGKIpukxC+6nnKddtKXf5P2s9UXhs8GEvC9QLIwmey3jIKh5bm9g1V1wZy1Tov\nza/WNdvuBITONpTUy/x9zUv5fZsPSp2Bj8ApHPKdXO9mkJY3eBrzghKsUcFpEMEkPm0qllhTTbGu\negg0RniCAwlGiE58SJKZgGhnfNHNuQJLBzQAqO/JglfTqTudpN5UL3Ff0Tf23/z7v/er5h/8ws8m\nhhIHJ3Qx/YBElX0ikUcAngFAAu57VtCpO4Gx0Pbqs1VndTbgXswS376zKmfE6P/HqRQ9CAfkAOGS\n9zKAlY8N9rVvyPLBtMEfyhooFBtCYmJZ8scrsqA7NCtzX9eJl5AlSJ6eStmJQrKllFKGmNF9nuKM\nKwEJSIsGwqbqWCvpTnNBt+prurW+lmlObwjM253yEIPee87Xiixl9yHsnOJzeh0hOJ4ZOn38njrC\nyNwjrqxO7bqfwjZYuqOmcTQI9gXgfZvgEwb4SBs82QZPLIOxBTBnPrRzbnXCzru6q7V2u4W98s+e\nyy/q9FBAcJAA+ywB6Y5IEmsqdRd1Ur+ZFBrX0nTdp2QHuEbYUecRTh72IewAd7gI7pVaEbn5nYw+\n89WTJ6Zvv4wZuk31OezQ4YAXH+aQhwGAorUhkI3XZDo3hW2Ztre04Gvq0CRFZJCCvQf2vaI73/x1\nAVw/x/2Nf+L9FBdQPfmj01+82/ZFhXRJbAPh+DaLnWzzbie2OkL1gFuO1dGnjwoIgWKsCNKLWyUu\n43SWzfvJtB59+mjsnLp7yb3bKKT7HI+B1FuSZ/4r//pX/n/23jw6rus+E/zufXvtVdhXggR3CxJl\nyZbtiFFIJrZjyVkmiZ2TuI97uqcT92Q6k2nPTHNO5ky/6UwSnp72iafT7s45nZkMZ+KeyEnnOInk\nLQZom14lWYJEkeAGAiS2Agq1b+/VW+78cd9DPRRAEiRBghLrO+edAglUva2q7ne/+/2+31DUin5q\nPjw/MxObKYFfF38lxfWOIRgNmd9J8jx/8qwCwHoYCPLY6TFfbAreyxSa33s2mitaa98x61ozP2Do\nur4HwHHw99hXdV0vtyS2xFoeo9jYxMbB5paOCvh38S1Jv1dQ7xfmJcC/I6bBC/MeWiHypuDc4DCA\nBejF7E4fzs1wt535dGxUlD8M4OsAjjHGvuX9nwwurf/FwxoPNz4xepQxEi4WeqorK7vNTGaEuK7o\nR7D1oNnKVIbXpUnTSiwWWxFisQxUtQJRMquUOjcgOxf+Wvzl0lfJzykIDiKOK5Kao9JSg5FCQ3xy\n4fLgwfL83rBRi8mFWho1+9pcIjE3tVszEyl2uAvimGqHBqgT1kwWIjWmOXmiGFUi1mwmFEMNcTVu\nkmLCYNmwjUqIETPiwowoJacWycYKarWzLFqdVcriDcIYo7YFwV6SpdolRWhcTCjFc11fWykASIpE\n6uzWdu0Ji/FRhYaGVSHUQYkgAoALt2i55nXLMWbKdu7KXPXSdXCV+HbdgXzbim+l6AJAmQtmVYWi\nUZAKlSWlWL6hGa5NtSv9GFroIAfqEtkj2yQZNgTEqqITN4jNZCFkRlKaGeuQ65GUVNE6ZFOMiyYN\nmYagVRhTMjaTrwiOcClZpedEpi0ByP7WnxzftgHasxuMgBPAAXCSn0cz63iD98vzEbcS4ODPrWTK\nQPNL06+uzgHIBAtI50+eDa/AHVyGu98G9jpAXxUsUgHTVsGqS3CL1+EWrsG5UW0O0jkA2c1Uht4z\nk+oHVu2Bw0XnUKrB9kYtNsIIkgygZZHUlzSamw/R62/H6XReocvwBqz0sSO1wGv45HjUOzcHnNhf\nBXAjfezIbQe38YlRAdzz1wvg6yeOT8+1drtjTOyz2PCwzfr6XRaTXSSyAHtDJHPfDwnfu3E/bRRe\ntFdHy7bZwO4P6jVsJLzWnUzKWjE+MToCXjz0jRPHp2eDv/OiGlsJcRLNJACAL1G3EuL8rQp7PCU1\n2HlzrZkMuLq3RozPffpcYdMXuQt4TRz8SUhQYfTtb653/EGlOHvu0+fu2t50N9B1/ecBKLquf8k7\n7iiaqrPfhdP/rDtokmf/s1l4WJTn+4WA4t/qJ/ZhYD0hXgVQvJ1t4kFC1/UxAB8Ef79/Xdf1LU3C\nPSJ9M1tHFM2JlQ8/baN1A3hW8jD45y8Nbq24Nnvq+Ye+ruKdjm0jyt7/fw2cVH7EazjyvwL4r/EQ\nNxzRdf1nwGc0cTTbmFLwD28VQAVgS8nkotnRMadFY5m4otSYJDWMCsLZN/B09Wt4vjFLRmPwBxGH\nCaRsCXTVUGjBDJGyrWm1ujBSTvf1ktIIpYg2wMwqMF+MKXYkJPR0EtrTweSIxhRRgOpaTDXKVClU\nqLzqQsrETbacMHEjVXHmRRclKlVrWs95JRvPdGdFe7BCWH/RjqQsVxQdl1ouE5ZFal9lIBdD1yuz\n71m4KALoEIncmVJ694TFxFBYjMdUMRwVIECkUg1A2mXuNRfutEDEix/84/9q0/70Gy9inDbKQp9d\nF0Zdh4wwFwNOg2quRWWrKhiNilBtlEWjUREtuMQFgIYIYa6T9Cwl6UgpROOWIIghS7MUYYg6ke5Q\nI5JSTC0pW1JMcETJMolWZ66YsyBN1xG5JDj0al/eno7VWea3/uT4tqsMnrfXbyE9DK7AleHFuf2p\nOp7Hem9bKyEOtbykrySsRQwFf75ZHuvnT473ysBBCRhVQHYzoNsE02zAWQUrpuFm03BnpuDM1Png\nmwUnP+smCr1nJv1lzY5kw+1+Iu/s7a+z3d2G2xu3WBgAXIJqWSTXswq9diFGL/64Q5wDUNhMBe49\nMxlDkxwnwZdFF+C12E4fO3LHhOX6n/ZG8nHp11TTGRpaNC6Ha44MQHBZWDHdw7GGezhssaG6zfqu\n22zXK+DLx9t+76cOHgpjPTnrwPp245sO7Pe7gGt8YvR5w1Y6/sXZf/n3FSsSJMNJNFVd//haCfGa\nneZm8Ja+U9i8Jb2/0uan+Syf+/S5bVnevUlhVrBpT7CI1L/uO6rO+tB1fT94PNhLuq4vtv7eU8GD\n5Nkn0P4ExgY/p6Bt46EiiVuFd64xrL+PndghxX874NngPgBgDLwhx5l7ykhugZcFvWbt2GQLCip1\nNAvztm1S2sbtcacNRz4G4A/QYr1gjB3xfh/FxhbWv/Mwt7DWdf2/Az+XCviAsgAgI4pmfvfu15Wu\n7tmkINiDANQi4vIM9hiTeK/xw8YHpIoZTZKGo8B0FFqxRFKxFVKzw8RwwtSBqDAmdBllscssRKOo\ndQoKi1MBYAIpxlXV7hVpuBuiplAFBKqdk9X8khiZn1HU+YpIbsg2riUrztXHrzfmRj6sEyGW7rqS\nH92/Uus8UGpEh/JGImEzUWw4ctVxhTSAK4YpXxy+OpffX70aB9AnU7U/IXd3RqWOeFiMxUNiTJCo\nUpeoUqZEmJeIdDUsJa4JREwPnjq66cA3dfCQgqayHiaURZSE1S+FnCEqsn5CWQ9jRAEA1yIVx6QF\n2xCKVk3IMIcU0eyQVXtlrxiZ3CM/nomFD1N5YEQhfeG43amESFIA1SQqUBNEMF3QnEPJNRPaVBnJ\nc/Eqru7K2Ku/9SfH79vg6Plx+8EJ4IgJK1QmdWRoaeU6Xc3OC1kbTSIcwUYvqe9J20CGN0ugCGLk\n5MuCBiTfB3H3EOjeOMieMMiQ5HmTG4BVAlsqgM0uwJ2ehH2t6hXYtb5W75nJEIKFpS7rGK24g/vL\nbsdgzU10GW4k7KCuOaxsE8zYhFyVXTb17KqzMHjq6E2/EHrPTG7WYjsNz5N9p/nGXrc739faAyBp\nC0RaTUmPmzI14it7v+9UPyab7uNRFwkLzeYg29Jgw7NOxLGRFAdJp++5XhvcD12cuu/toj0lao0M\nD0Xnhz46Mv7R89mDi99ffGbO+zML6wlxDnyStKX74BHUbjRJcTA/2i989KMjs9tho7iDwqygSrxz\n1fq3ga7rIrg1cV7X9fGtPCdgIfEVZ588+9feQpM4+4+lh4k8exacJNYT4odO8b8XeKuCx8AtYW8D\n+MG2ZCTfATwiHQOfWC1vZodq4/7jrhTldxN0XR8GVy6yR3/y/20AGLZdYXfNDu3LOJ09q05n/Jq5\nh1wx94o3jGFmNUSZWK4gGg6TTVeWTFeRGkyTGASVERZibj3ploQeIxdOmoWQzAyVhZhGwxIJq6rR\no8jluKCoDUkhZUk1Z7Rw5mIkcvV7nanLNYnOArjxPvaDxd/Bv4majtw7Wxzal6l37MsZqZ68EY9Z\nrkRqtlayXXHJcYXLxXL00tPzr5sj9RtxAH0CEXuTcm8yLnclE3KXGJGSTKFqRRa0skK1WZHK8/AG\nwPKXf8MCn+2HW7YIuCLqPTJR1NyIqDlxUXUTVHJjgsRcIromIci6Npl3TDpXz8mzVkUsgA921UMX\np+w/+tQ/p+WwcnCh233eUsgxQsMjEouGFaaREJNqMoQakdwcpe4MI7hoEuXNFWfkjT/9F8/ed7+S\nruuixITIXqd3NMLUQwLoXgtOwiCWUCK1apHUcmVSLzKyFgexmT3C/7kStEjcCl4AfEoBOt8DYWQA\ndE8KZLALNC4DEgUcAuTrwPUa2PQK2OUfwp757qmfXTfI9J6Z9AerYGFpCoA6UHO1gyUnsa/sansq\nrpSwmBm3WFWz2Y3OBrssu5gHsHw7Rbb3zKTfYnsveIEg0GyxfS197MgGor4peOSfTwz8x2C3uzSA\ntO12r15Xf7u/Fs98SrCiofDq2I8EO/wmgAuDp9kbi6cAACAASURBVI5ubV+bwGsJnMJ6gpZC06u7\nWVFj9tDFqfu6rDly8mUBnDSlsN42EQ38mfP87q/37EtOJ1+89It/uVTtWwInxFu+Hh45S2C9WuzX\nZbTmR681GbpbeOp00L/tX/dgYVYBG9XF+1aYdb+g6/oHwYtRv+g3n7pTBO5PUHnuQPP92Zrlvvqg\nJhBjp8f8SMfgZyfYKdNCCyEGV/wfuiSQrcKrIfkw+Hfej3Rdf3OHD6mNHcQjT5SP/cG/P3yo49Jj\nqVBhnyg7+w2mJipOJLRkDrBMrb+Rr3VZok0dpcFszWLQTEYVh6kyA0QGWyGGqYqVmkIKTtxY0KLV\n1aRmVkXGahaLd9W0zsF4SokpYU1wGppmLakyuaqFMxfC0WtvhDsu2FS8PsYmM7+NfwPRsXqul4b2\nZeqdu1frHcm8EY/Vbc2q2Vqp4ciLdVu9WCmEZ5/LnHWHjfkEgD4C2hWXO6NJuSeRVHpoTOokmhAp\nqUK4IBrljJOfKdnLb5ftpTcM2GYI6wlxCBvjz1wAVSlsQ01amhK3I3LEjgqqywTFNQXZzYiaM0sF\nzANYgl5cGxi+8JkJ0RKQWolUD1i08hRo9QMNyX7MlJEEYZIAYinEySdhXVFF+y2JmG+5kvHKP//t\n35vDfUAgPWKDPSLMlP4o04ZDTOkWGFUYYa4JK1MjjZk8qcy4hBWxRXvEzeCRIJ/4dChA114Iu4ZB\nu/tA450gsRCIHQIqAsiKC3ZNAbm8D8LVQ6d+ct0g2HtmMoL1ZNj3+REAiDdcvC/n0CN5R9xfdkM9\nhstSJqtq7vqUjMFTR29LRHrPTEpY78le12I7fezIrVtsc1IcJMSdWG9FKYAP9n7RV37eeCnYHERp\naCtmZv+L/dWOt68x0fzyiePTWy7Mmzp46HYpCCbWD+pZAIWbdT3bDnjV8zE0rRI+Mb5t0sTvfej3\na/2R5U8BmD1xfPrMVvbnFUsF1eIeNEmqgYCFAkDmXoqkxk6Pydh8EvLQFmZtJ3RdTwD4BLa56UQg\nBaKVPPvX1X8frzVC2oYJzlrzFTQ/Q0HFv4aNtqPyw6R23yt0XY+A10vEAXxL1/WrO3xIbewwHnmi\n/Nn/+79/USD2voYTZmWju1ap9dWcSkdBs0hNtlxLNpkL11VsQmAIhmNLBdNWM2VTXTIs9YbVU1hV\nhpcL8YFVIxwzpVpcGylHB99jWh2Dh6uCMGrIgriYFPKzEW3uXCRy9ZLaNXkI5wsfx5cb+5xL0blK\n/950tac/U+uM58xEtGqFapVGuGQ6ynyxEbsklxrp57LfRZ+ZTsLrIhYVU6Gk0hNNST00QSJqyHJc\nuWHaqGbrbnmh6hbnG2457YI5rSTYgqf2tm6J0SpN7q3GlbjdQSj60fSVlcEjz/jmdSr7wmcmVAAd\ny3FhsKpgL2GVxyTUDjvU6reomWpIhlqX6iBCsRwi5Ws9tPqtJ5H+xjOVySudv3t92zxpLekRrT7h\ndfYIhUlqhxuJJFkkFmaKqECqE0ZmHeKcX6bFqatCunS3S2u+SowAkdWAjn0QYsOg8QHQyCCoEAGp\nR0EqKrCQAr0aA5kDsOSnZXgqcbAttP9z0KtWDtus8NyyjecytvpYwQl3mSzknWgD/F7NA5jfrNBw\nM/SemRTRbLHte7IraJLjzRV+Pe77eX1CHCTFvmoYXEbOBpt6zJ882w/eHGSX918z4PaK9PjEaD+A\nj3nPffnE8el1xMrrguinIAQH96AnsowWUnzo4tQ9kYnbwXsv+PfunpMmxidGD4N36PryiePTm1a2\nj50ei2E9Kfa7DcJ7fd9CsXzu0+duPdG5BbzCrFYidVv/9ruJSG0GXdc/Dj4pf/F+Ls8HfOTBiWhw\nUmJgfbFgZjMPcICEtyr+weIyX/EPWice7tixe4Su6ynw7xwRwDc285238eihTZQ//8Xfk+qhTrOW\nWrZMp15r2E6BuFqZWmJNXRUNNW0b4ZkqC825VF41iWCYvTkmP3OJaYdvsHBPPYouZZ8bGjzoFHf3\n9KbDdLTgsKEVySELcWHmzZ7oK1SuTD+JH9eedn9IC+XY8FK1N7VS60ys1jvUshWpVBrhUtUKzeWM\n5OXe2nLhQ/kfkl5zJQmgjzAWTzhyrJMkOlKIKBGoIdVyqGCZDGaZsUa5yIxiwa1lC3AaJvgAXERL\n8Dq4FaKpiPK20H5cWz+aOb01eHFt4MS4/IXPTMQAdJQ00lcMC6OWgGFG7AFCy7soMzqBWsgVaqIp\n5pghrTQsOZ1h4uKFLpb5Zpdtfvfkb1/fWlHgTeCR4XhgiwV+bk2P8K9BGUAp6YatQ85gR7+b7Iox\nLUZBGZq+2mtbUViDaFWJEbA7RABxP4TYXgjyKCgdBBVjILUESCUOMkdBlrx9pwdPHTU8W8Otiph8\nD2pOdFn2U7MN/Op1S+tsMN/Xu9YeGvyezQNY3Wr8k1fgNwDuOd4N7oOro5l1vOxnHQPwSXGQEHeh\nSUp9UuwP0HzTixtUQy+mag/4cnUKfHC/iE3sFeMTo7sB/AwamO/5n6UfCxUSVC5TWO+J3Mw6cd89\nkd57wu+UOAT+/vBxx0kTrRifGP0VAM6J49N/DawlQnRiPTH274OFACkGsHK3vtAAKQ76aIMrAyW0\nkOKttP59N0LX9VHwiMOv6Lo+/yD37fmFffLsb0FrRB3NQl8NN7cdBfOJ3zWK/1ah63o/uN3CAo9/\ny+3wIbXxkOCRJ8pP/A9f+YkKdYZdJdsjyCtRqmRUqizZVM7YRCqWqFitAyjGqiz3wiuu+v7LSPY7\n/V1SYnfS7R+VVvd0da4mpdSKyhIZZmhFp46yXF+Qugo/2q+dK0jlemyp0htfqXfGlqvdtNyIVEuN\naKlsRebKjei1kdps9bmV74nDlZUByXFGJMdNqQ4JR2kkEWWqrBFFk4nMBEZMMMditrEKy5hza6uz\nrJ5bxnpiXLtp5T1fDvfj2vrR9EAa8Ehx1hpa+ovsvxUAdNRk0lMK0T0NkQw1RJJsSG5CJOVuinIY\npBxWnRwN2dkqw0KtpKULq8pqIatZ8xmZnqtS+haAG3dSla7rut9KezMyrLb8eRkbJwNr9giPiPm+\n2n7vOVk049y2pChuphIjQGRTIPRxCOwxCMI+CHI/qJgCqSogFvjS9hKApWthuvyJZ8P+ABUkxcGu\nT34R0xrp+OZE2U1YvAMeAu2hwQe1BW9bGjx1dMs+2t4zkwSc1I2Ck1UVXIWeASfHi+ljR1xvIhUk\nxMHqdQZO+ILV+tnNSLGP+ZNnE+Cq8bC3f+Kd59vg92TtuVMHD627VrVnnMfNA+xJaYEsR8aFK2j6\nNYPXK38/rROtGDn5chRNYjyAZqfEJfBovBVsIWnidhifGO0rOuSXvl8Rp75ekmrgpDjYDKmE9UV3\n+btRbwOkOEiMN7PLrEUOvpMKs+43vMKvXweQ1nX9Gzt9PN5kqpU8J7D+s+M/Fh+GBJGdhK7re8HT\nS4rgJPm+rjq18c7CI0+UD/zRb/w3RCqOULFUIVKxQIRygRCsAFiRLLb8r/7ckUdX1d1CYteTND7Y\nZfbsimd2dUQyHUpsMWqHV8M1J08rllYsCJFG1ZDE4nxDauSXa13aUrUHpUa0WjRjJaOmLfYXs4X3\nZd4SnshfikTsaj8DGRBdNy64kGUxJISECFOEMJGlqCuK4RKRtAoImQdzrzGzfNVeeO36wTd/tLWl\nL94Wuj+w+YU7JoClktO18lrlE42p+k8LDQHdpRAdMSUyYMgkbkg04oh2mIgVKtB8WLMzatRYEmPG\ncj1hZKuxxurMdKJeeKXbta9FULUoqYFnOl64VYGJVyEeJMDBn1vj1KpokuECAhMCXdc3FIl4LYx3\ngZPjIXAiUQInx1cHTx29aZzOrVTiwJ9VdoHWfgIiPQJB2gdB6wCRKefMNjyleFUmy//omZC1GKLB\n1+pAMw4qWMS0RvbSx44YHpncrMNZDVwtXgCwsNWmJkH0npnsQjOxIuwd83UA01//8T/JPVG57BN4\nnxT75x4kxUH7xC3VJi9erw+cGA8HziUL4AaA6+Uv/0YGzTip4AQi+F6oAFgt/pLdZb7HHQbwg+d+\nbfpbd3r+9wrvPdKHJjn2P09lcGJ8A8DSvWaaekviHfCU4ic0+4RI0DdZE37kNCdgQbX4jpfCx06P\nadjoIQ9O2oIrAxnwJfd2VuttoOv6MwAeBy/qe+iU9bHTY8I7ucDufkHX9ScAPAM+yf3GVjOS23h0\n8MgT5bHTY0+BK2Ur3lb80h/aURLueVxIDD9Dw11Dpe7B7sxAXM10ydGlpKHlwyVWDWUrUSyvsgVF\nMPKheN6ONLJWaNGsyiWnIlrxUr2yq7RU31+6Lu8qL0QV1kjaAo0zEAEARDFkqmpHLaT1mFqo35K1\njipR4zWiJm5QJXodnBQtb7mLUrMttK8Yp7zfWAW7P3/dfLJ+zfigtWAfloqaMGzIpK+mkKghkUhN\noZKlWA0ql0nYTUtdxoycKs+r0XJBSNVyNalhLQvUvXQxac99aa/jVmQ2BL5stwLgAoBr/jKdrut+\n0dJmZDhYFALwJcHNyHBxK1mVXpzbILw4N3BVrwbPVzt46mim9TleY4ZW/2/Q7uAXHuWegdD4GGTp\n/RC1KEgXmmTCBJBeVEn2Pw9Jxou7ZMEQSPC1fLXPrwZfp3ymjx2xvWNfa6bhba0dzhbBifFdWVd6\nz0wmvGuzF/w+uAPG8urzq98u/LMbXzS7rIIf79RKioMkKXc7UuzD61roE+NB8MmBA/5evt6Y/e6K\nOfn/RNGMg0th/QTCJ+RB68TaoDU+MfoseMHfD04cnz53F5fkjjBy8uUYmsS4H/z95aCpGs/da56p\nF9HW2tBDBIAQZdaxqPUel+H1r5bkvwcnrHek/HmkuDVtpJUUBwvC2qT4LqHregzArwJ4Tdf113f6\neNq4Nbxi7w+C10hcA89Ibk8k2tiAR54o+5g6eEgS+596kqiJD7mh5IF8Z3dvpk+NrXQJ4ZVUjRYi\nReLKy3m5UC2QFbdYSidpqZoaQYNIqCMbK5cWByqZSl8t4/YaGSIxK2xIomIJ1GyIQl2QosVYZLga\nj+8zE/H9rqIk/QvvZzfPg3tXt7acqcclcMLhK8adFpOFgj2oZqzd9bR1wFiyDtoL4mCsoogddYVE\nDIlESyEqlDVasxXDUqWsO2BfNUcKl2Od+ZVoKF9UFNNwTFcsMkZmHEbfviRK57/0odUEozgMPtDa\nhJEro6XRG0/knnCxkQxHsT5Jw4RHftEkxT4ZvuOl2/mTZ33rgN8mWfH2MQOuHi8NnjrKtqoSw6vA\nl4Dsb0HFL0AKiSB94JMO/29rCxopvZoSjb/vFe1XOwTJJaQ1/7W1GjwLoOR7fOdPnvVTCHq91w5m\n1vrL575/+a7JV++ZySi4arw3YZX6uhvZ6IHabP2ncq9WP7b6HTtpl1tzToNK8ZZJsY/5k2c70STH\n3d5/VwFct9PncsYbpxkzS13g5+2rsC7We5l968QtBymv1fVPg9tqJk4cn97WanQvuzioGvtJGSV4\nxBjA4p34i4PwIsCSaJLi4KqBC34t1tTizw/VDgJ4L4C/OHF8+rZFmXdIilfBPcVtUryN0HX9Y+Dv\n8//vQWfutrF1eKubx8C/S84B+GH7frVxMzzyRHn6v/jdI4SQn6iGtffnk5GBXIecyCQctRLK0zpb\ntUg1b7l5Uq7n1GKhmnAtS7YBIRpnNS1hVsrdpZWpnkp6xRVdsaZIiiGJpiEJNaiJTH/kgNUf2ks7\n1QFNoopfTVxFkxgvbnkZnbfz7QHQ7zLaX3E6h4tOb7TsdGl5e9Badne788KwnJFTrCYLIUOmWiFE\nUdGIUQoJFcjlRi+ZqTxWn8LBzJVUx+pyhJaMcMVWVMMRi6YrrDRc4XzODJ13Qef//KMLWsgOPSW5\n0pjqqPGQFbK6jK7VLqOrLDIxjPXNNizcnAxvSy7q/MmzHWiqo0HrwJVfRyV7He5NvcTe3/rh91kA\nuSch5P4Y4QQ4Merzrq1sE5BllbArUaH+elKwvt8puLMRIYSb57/61ol1S+CeuhpUizu94wlm1vrE\n+J6WaXvPTIaOlKbGJGY/ITB3JG6Xo7vrC+6R8sX8hwpvrHZbeQPrlWLfPnHH6olncRkAJ8a74Nkk\nGHNXnOyVojU93rCXJv1z35CT7G2Z25Him6Gl1fXXThyfvqfCKU81HgYnxn1oqsaLaKrGd5US4cWm\ntUa0+Qq6v2rgE+PVYPHU+MQoBfBrALInjk9/dZPXVrHRPhGcuBWx0T7R9hTfZ+i6zotPga/pun5j\np4+njY3wisM/Av4d8gNd1+/76lQb72yIt/+TdzeuDYr/2la0AyW5RmtYdsq1omsW3XrFDBXyRqxQ\nN3bnTcgVybWXE6xS6dGKgyHRzBK7nmFWPl1MMLuYSLgSVbKDoQPWaHg/7VaHQiKVfaWogWbh1cLg\nqaNbG3T1OAXQVbK7R4pO737DfXak6iaiNTcZXsagkBG67YzUYeeUOAohiRTD1K2otFoI02oxhHSK\nLpWeLp+nHy2+LT8+fyGi5YuxQl0byTW0cNWWa3OOWjSc6Bt5mpi1El1FK9nlOITFs2r2J2pi7dDj\n2Y5+CkoUR1mNWtGlsB3OoUmEpxGwS9wvP978ybMxeOoogGQDjF2EU/w27Gsvo2FUOBn+KWyuEs96\njzkAxe8i5k80egE8BaC7TqFmFRJe1Kh7OUob5xICO5cQ2IpK/UYskvcaiwgon+ljRzZLcgh2metD\nU410wC0qk+BL9itbXjW4GfR4bCq8u3889YHH55Xew78sasMCc6WkVaoeqk4vv6/09g/21Bfmsd4+\ncddLivMnz0bQVI0HAAjMtR23cKNiL02uWHM/AjPySTQV5Qr4NfOJcX672jufOD7tjE+MfgPAxwF8\neHxi9KWbRaZtBk817kdTNfY/p0Xw5I05cK/xllRjz1Mcadn8lsW+/YmBvw+voBnRdjuFeDf4ROM7\nHiluTZ9oJcVptEnxw4Dr4KtLh8F96208RNB1PQo+0Y4B+Kau69d2+JDaeAfgkVeUP/+7//g75YbY\nXRbkasGJrJSM2MyqnVy2IcyJsJa67KXqoeplWYqH3+8ooYPEdRpiKX9BrtcvD4b3myORx2i3OqQJ\nVPIr0R3wQcsnx6u3agvs42v/7e/Rbml6D4ADLoS9DhN31Vk0VhAS0ZwYd7Jiyl5V4o3FcKyaD8v1\nskZrhTCt5qLCEqP26ocKk+5Hst+Tfir/qjpan49nTS2xVI91rhhhLWNFaRkRs0yjlboczzhauOZo\n4QYEMQRAatCGUpJKvTWx1mMKpuMQJy+78vkuo+vtqBX1kzWqD2JpylNiR5fhHl6CuzsNNzwD1zwP\np3YRjmHw6wusV4n9WLDc7KnnTe91ZDRJa19RwkBOprGiRELzIcKuRAX7cpRal6NCsSgTG3xwW9cl\nDAHrRMsxEnDVOqgY++qpifXq6ergqaN373vjbZ67AHRWBK3ne4knD54P7+2/ofalXEJB4Wb6zMzF\nJ8qX3vpo9nvXcI+kGFg7v240UypSzDZFt7IsONkrdTt9znVWLwmBnO5c4HzT9zunGADGJ0ZDAH4e\nfDLztyeOT9+qWDMB7pkeBn8/CODvn6BqvClx9bqSbUaE/Z9bi1AB7r0P2igyWyWuPin+xUTjlyoO\n6f5mWTzPQFpJcat9ok2KHyLouv4+AEfA7Rft9ISHBLqud4CTZBHA13VdX9rhQ2rjHYJHnih//A/+\n988U3FjnSr3zYrRWyR+sXHIPVK6IIbfeBSDiKFrESnTucSXFjjD16vucfVPD6qhMidCL5jJqBk1i\nfNu2wF/4zERIQCPVI10ZCQn5fRT2bosKu6qSEq6IilKSImxFjVbS4Wg1HYplMhG1mI8IpXKIpgFk\nE1ap8I8W/pr8g6W/U7saue4qQoMlFo4smYnOdCMeXrVioQo0UofmWKJadSWlzCQ5x0SpAkIYgDID\nKy5ry0paS3cXlEK8LtRNQzSuuMQ9D2DuQTUIGDn5snAYQvcRCGMqyGM22O4iWLgI1siArVyHk6ms\nJ8NrKvHsqefXjtG3OjQI+ldUsrcmkKGSTCIlkYTmQsSdD9HabJgWL0eFYlUirV3CNlgngvDsBl3g\nJMsvRPPvfQVNkrgEoLCVidEG8GLMYMe2FIAOk0jKj+JjqTeihzouRPaqOSlWq1M14xLyVlrunHz9\nZz+yfMf72gTexGIQnBwPMbOScI18zC3OW072imOvXrZYdaUO7qX1u7qlASwHC+4eJMYnRmMAfgGc\n9H75xPHpGrBONfYtFX4UYgFNr/FS9NBJhmar9s1IcATN++zDAb/nm21lANWtJgt4JLzVPhGNUhY6\npDpP5Rxy7qop/BhtUvyOgtfZ7dcAvK7r+ms7fTxtALquD4JbYhrgWdf3lO3fxqOFR54o/86nf/O9\nXY3VbtU1u9Fcwq8zQtK090C3GO48FKURetgZWhlwU77FoIgmMV4cPHV0U6Lwhc9MiOD2gBSAjihd\nGdSEwqgjm0O2ZHfXJSlclQWa15TGaihcymjhlZl4Mr0Qjyy5lKwCyCarpfKzC2+4v5A+kwqz+i4B\nzhCAHhOyVneVUN4OyRVLoXVHFE1XtFzQBgRhlQniDVdWrzNRyiJgkxjvHzcLSmE/gEPgy09+s4ep\nc58+V74vF9lDMHFCBjp3g+5Pgu5NgHQQgDpAtQY2vQT3wjTc62hRiYOYP3k2sqSS4WWV7rcpRm2C\nvopIwlWRqCsKqSyGaPF6iOauROlsXSQrCJDszawTLa+tYL1a3IWmJ3uderrVTOY1cEuN/54Ibmuq\nYUZK4LXYY/hO8in59ejh6LLSYWbkVNYhwjS47SW9mdJ9p5g/eTYOYBdznV3MLI0yo5hwjYLm5mdt\np3C95uSmc7CNOrbJX3w/MD4x2gng4zkj4fzhK7/zds5I+QWuFNRkVM7khNBMQYpNFgVtodUmEcbG\nFu4Gbk6CKwCMu8wpVrCx0C4a+JMSPEL8X3aYu/erTrdGcfpOWne38fBA1/WPgt/n/6Tr+iOdUbzT\n0HV9H4DnwMfAr+q6vm2dYdt4NPDIe5SHjAW/i9GNqJTKPpk6QUg4NTAtLH/UhtOdYKGVA9bA2yok\nP7JtYTNy9IXPTEQRUAIBpASh0get1OfKZo8rNTqWVVcqhSjqslLLheKZxUhkKiNH5x1bKHRUS0ZX\nedU+unzR7jZyoSSKoyrMZ1WYKRmNsAWJ5CGzuiPVqg2hUTOpVW+QPLNZnbhWEcSapoReZkroyu9+\n/t9tKKAbOz3WA+BJ8KQIAZz0vAZgZruzNW+WOEEAtQ8k3g/aPQgaTYGaCZCcAPzAAiY/D+NqUCUO\n4s/+6AfdpoBDAsM+ycUet1fsNCk0ixI7o5DSskqW50P0tfNxYcYUyFqQ/lYIpefB9W0avWh2VvPT\nGd6Cp57ebFK0AXqcgJOxVkIcR5N0u3Uql16NjRnfTb7X/HH0sHwpvFtdlZO+ilkFcB48zWMhfezI\nPQ24XiRdL7OMPW499xis2iBrVGNuLSO45aWiU7iRcws3LgMsSIy3zV+8ndjzv/xfEhEre0D/6f7B\ncHr4PR2Xnz449OWfmyx1X3alXEmQVwwi5+qEuP77D+D3s4qmh3oDId6ObmR3QIoveI9rSvH4xKgM\n7qG/1CbJ72hMgReM7QJP5GljB6Dr+hEA7wf/vH/jbpKW2mjjkSfKLwz+5vmwlOgHMMDA9l+l6cFF\nYWHIgrPa7ca/+Liz65XBU0fXWll+4TMTMj4z0YsWAlSXSNRSGwkiV3pctRpraEasrjqaJdlOXYZZ\nlsSs60pZ2xazouFWUis59+nZG7bIXCgwu+Mox8OoxWMoqxHURBWmoaBRB2PninWpuFjWnGxJFJld\n8z2ROYkXjlwHkPnsiy9tIDPegD0KXliSAk+n8NXjbWnPuZVc4kFQ+ykIoTEIiWEIziDo5RjIVXgE\n0M+K/h3vCY999XXtaMYe7TbYwajNRjUHw1TgRVdVgVhFjSwVJPLKfIheeaVDmDYFkksfO7KlgkLP\nf5vEesXYV3Mb4J7Sq/DU09vZaAD4zV1aCXES65ftKwByb4f3rvxN93HhW8mnxQvh0bBDxQ40iXMV\nza5uKwAy6WNH7tVvrLrl9F7XKDwB5h5kttEBqx5y67miW1rMOcUbr7BqZhYP0F+8FXidxdZ5gx2z\nu5c1UiPMVYflDqEbgADiOitCrQC7eHVfNNv1TOyC83pNmHRBfAU4uNW201LkpVok0ZwU+j+3tnn2\nSbFvn7gVAd4P/r18fruOs40dwRz45/kQ2kT5gcPLSP4J8LHvKoBvtZX9Nu4Wj7z1Yv7k2RcA9OZJ\npfKqOD2QoxVSg/kWsUPfTWWfktGiErsEsapCQjWFJUzVittaNdHQjIQr1cNMMmRLdCC5VoPabkmw\n3FVikbRYd1cjDcMEL/SqxlFyB5BWepEJdyIXjqMshVEzo6hWBbhLdVtcOZsZwVSxO2ozYQg8gYGB\ne2BnAdz47Isv3az4KALefGMEXCH1WwVfAHD1bnNT7yCXOAcg+34IjU9BiT8BoVcASYCreXPgX1rX\nfQLae2YyFLFY91N5e3SgxvbFLTaSaLAe2WUiADCCQk0gs2WRTC+r5OKXh+SZ9LEjW1bavA5xfo6v\nv/lRbzU0vcVpALlb+ov1uIiNPuIUmq2dAb50nwOQy0jJ0r8f+lXyVz0fVjJyKgVeIOeTKBvNgq8V\nACvpY0e2ZUnw2if+1S4I0hFCpcOgwgjsRog5jYZbzayy2uoVJ3v1PGuUF7CD/mIv8/dW3mCVuYLg\nWqkEs2NJZkeSzAkR5ioGGM0Sal0nUnFaDF+aJYJZBlD5/FBtH/jgePHE8envbOOxymjaZZKBLZhR\n7BeX+ttWSPE6eDnRnwBgnjg+/eXtOfo2dgq6rr8XwNMA/kLX9dvmYLexPfAyko+Dj4FvAnilnZHc\nxr3gkVeUTVjf+SvhjYMWI0eFhqoqxuhMzfS5XwAAIABJREFUykzEXer8Y0M2YjUFCUNGvKJBqmiu\n1lDMGJFqKhXrkkjrQsSqN2JWvS43rEW5al8XTfdKzQ7NMdAKgIoAu/IsXhWewtvxGCp+QZhPlgx4\nrW8vFjsLp5fGIjYThsErpgVwhXMOXDWe++yLL2066I6dHkuhSY47vf8ugH9JzJz79LkNHepuhS12\nr8uDk/a1xAkvgs2Pc+vy/nYR3Low8/RHohKAzrDN3vuev3xttL/ujv6mybo7TRYTXSaoDmoEWLEJ\nvu0SXAnbbOrXP/uhdcr3n9zm2L2iND8Grs87DiFwTa6h6S/efPDiPuIYNhLiWOCvbDSLw3J1Kuf+\nx32fdf6y96NRb/99AN4TuGa+r90nxrl7tVIAwNTBQ5RG+7uErkNjRI0/RuTQPiIoMQBgZinPzPI5\nt7z0tp2Zugin8UD8xV5kWhibE2D/30LL02wAZdfshFMfpo7Zp7qNzjCzIxazo9eYE5kBE68DmJ89\n9fzNVO/z4xOjGoD3jk+M1k8cn371Do9bwnoi7G/B1An/vi+CfwZy3mNlG9TqAXBrzpl7fJ02Hg5c\nArfRHATwyg4fyyMBXddVAB8FFyW+r+v62zt8SG28C/DIK8r/+rN/9n8QJhy0BNEyRbVal6lc0UBL\nmoCaygDJIFSsiTGnTHoaWStlFStdjXw+2She6zLyUxakmSnsnf2M/h94agInWSk0G1kEWxPXwNXL\nJQBL//bSh4jlCrvAfWzBDNpZcHK89NkXX9pAprwOXz1okmOfwK14z5099+lzt+3udqcqMTZJnPCI\n6W5wctwPTgxX30zQxT86oJbfTgjhkM26D5Sc0eGa291fZ/FOk0XCNjMiNqtQhgXZxXRf3b3UY7K5\nO22+MX/ybAjN69yLph+VgS95BwvvNjY/0eNhbCTECTSJHAMnuT4hygHIPf3Mi8a82tuNZkOJbjSV\n6gaa9glfLd6WxitTBw+JALppbHBESO4eI1piP1ETHYQIAiitMdu8yqzaBbe8NGnP/XDxfviLPVtE\nkAC3/hzCxiK5OpoFcWsFck5t2DTSvxh3zb4e8IQKX6XNgU8i5wGkZ089v+VJxfjE6E+Ck5Pvnzg+\nvWGg9I6/lQyvK6gET7coYD0ZzgMo369EmPGJ0Q+Dv4e/eOL49ENTMNnG3UPXdf+e/nl76f/+wmsh\n/rPgn+MJXdfblpc2tgWPvKJ8YTASrcsySiEpV1FRUoQaehur9m5j3tlr3HBHKvPFw/XpbJddWABX\nkRYBLK9l1epx4Tm80gX9Px1Ak7D53tQSPMUYwNLnpo5WvN+PAHgCzQKfFQCvArj+2Rdf2tQ7PHZ6\nTABXnEbAibUGbmfwFdvZc58+d1OSebcq8U0SJ4T5k2eHAex1gOEljUSuRAX2akoofrdLrJckkthf\ndvYfKDvxY8t2tL/u0qjNqjGLlUM2Xusy3athZ61l9x0RSC+qbbOGETa4WvtjNBt7NP3FelyGjlZv\neRK8BbaPmnfuC95jDkCh97lvu97f+9nC70OzHTPzrts0+H1cxhaLCLeCqYOHVAC9ILRXSO4+IO16\ndi+N9HQSORqBKFcJFdOMuT8igjIp9rxn+p4ymz14Wb5BArzBFtHyFL9Irgx+7VqTIip+wejIyZcp\n+HtvAMDj4JMMimZjHj/X+F5sKN8FoDZcPPv7f7tf/Yu8UsR6H3GwsM4FJ8RprLdOlB5URCIAjE+M\nRsDfW2+2SfK7ChfAv7N3g39HtHEfoOt6JzhJpgBe1nU9vcOH1Ma7CI88Ua4Ml764y1wcfF/xbfXx\n1cvqaG3OUJjlgnsMfXKchl7kpIv7VHuhx4OZur76mAfvvsV9r3qx+rlPviCD57k+7T3K4GrVAoA3\nwP3GmxJczxs5DP5FOwROwC1wMjEDnne8ror3DlXiWdwkl7gV8yfPEoNiYDpCj2S7hMPLKk0ualS5\nHKW1rEzKHQ1GR6pu3z+caQiDNRcxi1USFltOmexHMsMiOHldvpuudF7XuyHvWvRjfcOI8+DXOzt4\n6qjrtfpOANgNfR0hDqqFDe+cr6FJiHPQiybAfdPgpHjUe+xC87NSByfEl9EsuLsr3/dmmDp4KArf\nNiKqA0LXoREaH0zRcHeSKDGDyOEiEaRXiZp4i2rJ6cFTR+8oD9SzRfjZwTdThVu/F2w0Se8KNsam\n3bRIzifGIydf7gO/d0GPeBZ8kjcHYPlOVOOWc/LvuacOh5IiWOchzXlMJfhQh+C+nXVoDpwQr4Av\niQcJ8cOg9B32Hi/s6FG0sd1YAP+MHEKbKN8X6LougyeMOAD+Ttf1266mttHGneCRt15Aj78APoD7\nrYo5qdOLDe/36zq8gXuAKbiSmEXTSpGGXjQA4HOffCEKrg6NeM+l4H5kP6Vi/rMvvrRpmsLY6bEQ\nmpaKfu+5dXiWCgCLAXVuqyrxpt3rboXeM5MUQOJE2trfZbLHRRcHyxJSFiUwKAohB/X+mlveV3GF\n/rpLkg1W6TBZUeCKqt++eGVLqREt8Arw+tAkx3476CI4qboRFr6WTkr/LoTN49f88/fVwhzWE+I1\nj2vvmUkB/J4GLRSRwPNX0bRQLKePHdnWrOmpg4f8BA5OjGODfTTaGyfh7rAQ7WNEiZaJEs0SNX6e\nCPIsgPlbqfCbpEW0qsI3yw4O2iLW/XwnBWkBxXgzYux7e5fA20Tfkc3GI8RxbEyaiGH9PS8ByEUo\nK38i2XiqR3KJRtmXfvFnpu/Iq/+gMD4xKgD4dQDpE8env7HTx9PG9iIQUfalNonbfui6fhTcavU3\nuq5vuZ19G21sFY+8ogy+TGv4JNeL+hoIKMZ+cVwwU3cJ3H7RAIDPffIFAqALn3xhDJwgp7zn5L2/\nvw5gZbMINwAYOz2WQJMc+17lEoBzAGZr1//JqlMb9YnB+0dOvrwtKrEPjxQnvXPt7K27I++tuwc6\nTNaruNDAGEk1WPZQiV0bqLvV3jpzkg1WF9a3a14Cj1O7K3XOyzL2O6kNABAB1xGQzcv0wnRY+HpJ\nFd4SwYnST4ATPxp4iRL49Z5BkxQXoRfXHU/vmckYzkzuBb/O3WhOfAB+DZfBr/syeMe+bVsGnzp4\nyCeRvQD6iBIdEroOddNIb5xGulUa7naJEi0SLblClOgcIXQR/L2z7F/XsdNjCk6jEzf3B7faIhia\n2cFL2EiE7yk7eOTky347735sToyn4ZHjrRJjT/X2CXEwaSI4CQp6x6fRVIgLQYV4fGL0HHj3vp8d\nnxj9mxPHp4t3e673EXvA71tbTX534hL4iuIhAD/Y4WN5V0HX9QHw6/pWmyS3cb/QVpR5MVdfYPO9\npza4iugrxitr9gsAn/vkCyI4ofOL8YIRbtfB/cY3i3DjxLpJjv19ZlyzM93IPle2iu8Tsc0qMbCm\noCbRbIbQCSAVsZi2q+p2DdSc1MGSq+6quegxWKnbYIWExVYpX9YywEmxrxhn76pdM5rNLwBniKK8\nnxCjl6KiCSTPJDJTVejbdZmedyhZd1o2Al0GwYkSJ0h6cYP9offMpOSdp68U96BJJG3wiU8wnu2O\nFM7bYergIcHbLyfGoc5dNNbfQSM9cRrukWm4wyJKrEjUeJ4qsVkbTvr18FTx/+z569oNJR2MTwuS\n4daWyjZuogSjmR28bdaCLRDjJWyRGHuE2E8XCRbWBZuyMDQnQf6WA1DcaqOc8YnROICfB7ct/Y3f\n6vphwfjE6C+Ae+W/dOL49CP+hfzuhK7rPw0+XnxR1/V7bmrTBqDrugTgV8DHpv/cvq5t3C+0FWXg\np8EJVAOc/F2Gp462qpGf++QLGprE2FM91zzDs7h1hBsFJxYjAEYYo2FmR0Ku2V11aqMVqzTWYFZn\nFM1YNYATnSzuQiUGgN4zkz7Z7gxsKXgkRLWZ+2Teln5yxel6rOh09tddMWrDopzoZNBcKk8DWLpT\nP+w66PFQ3Xm613THDrgssVel4V0EdoTAUASSqUh0PieRyzmJLlXQJMH+xv+tF29a4NV7ZpKATyiC\nFopU4E8K4IWVfsFdfjvi2YKYOnioGU1HSB+N9u+msf4kDXfFaKRXcLWYWYhQIxtly3PhQm5afbt2\nUZsxrqnzjkPcCHgsIG15Wb+lsh8v5xPgMrgavC1pGjfDbYhxEVtQjL3YtTj4/WndNlsVuI5m0kTx\nXrvlnTg+XRyfGP0qgI+DK8t/d+L49EPRoctrwd0NntDRJsnvXlwAXznYDV7H0sa94xlw8eBv2yS5\njfuJtqKsx3vBVbks9OKGi/G5T76QRNNvHIxw8/3Gi5tFuAFrBGHItWIHmaschKMlmKOpjtlnOvVd\nhlMbycPVbG//vl1gTSmePfX8lgdzTz3twHpSnERTiTYBZJKmW/hnl82eD646I10NtgucrPhxZtPg\n1oUlcGJ8ZyH5elwDJ0QxAHHGpESD7R+23KFdNuvvchGLAACBaQgkNy+SxRmFvjUtkGIeTVJc3ew+\ntJwrBVdZE+ATC99GEYxn85XiZfCCu21vrLGWSAH0gUoDdrJvd7mzs6sWj3ZU41GlFCZ2UWNWJmxU\n00q+uiLlGqtSoVgUyjXWdAnX0EJ+sT4tYtsKBbeCFmLsr7IEibHvMV5sJcZjp8fC2EiE41hfSMnA\nz8/3jgctE/d1sBufGB0Ez1hdBvCVhyFdYnxi9DnwotE/f1jIexv3B7qufxJAXdf1v93pY3mnQ9f1\nfgAvADin63rbztLGfUWbKLfgc598wbMErCnHfvxYBk1LRXaz546cfFkQo2/3Cdr1x4hYOQja2ANX\nijBXgdvoyLrGQNapD82DyRmsJ8VbVokBoPfMpIz1hLgTTfsGwIv/VgGsdhtu/l+eM8gzOWcfeCTd\nPvDl+wY4MX4T3EO3NHjq6O0jubiHOwZOgIJbDIDssLjUcA8kLbYrabtDmouIxaDUAXuOwrxCSfFi\nUvrC3BbIMAEnWK37iYOT5KBXNYeAhQLbGM8WxH/4lcMp6mKPS7EbgjxCtNRuKxxJNDQtboRUqaYw\n25aEWkV2ciWpVigI5XxOLC2VxMoqbk6EdzRx4U6JcaCgLoGNKnHQFmKBk+HWrbRVy8T9wPjE6F7w\nrl0zAL65kyru+MSoCl7Ed/nE8emzO3UcbTwY6Lr+OIAPAPgrXdc3jQFt4/bwOu/9svfPv2qryW3c\nb7StFwC8CLchcGLcGuH2JniE2zoSOXLyZb9RRQdVloYE7cZ+uSu/m4rlOAAwR6kys2vGNXsu2dW9\nV8GULO5QJQaA3jOTKppKse8rDnaIq4KT4mkAqx+ftwr/8rwRByf7h7ytB5zEFMDzml8H8OZN0xN4\n0sdmRDiO9bnDjDFaabDDMJwnaYPt1xzWpbksWnYRuQEI18FtKfPrY+H+IHh+oU325e8v2L3NBidu\n/rn6CnRuu+LZvMSRNU/wYIb1dZbYYMjEgOLKQ8qejg5ZCGuiEJYha4wJUlUQtLwtCm9YgjNXpfW5\naXV+dk5J53Cb2LSdwhaI8TUAi1Lyezm19+/81s2dAPaOnT6ZwPpJCsAJfwF8srVGiG+V6b2TOHF8\n+qpHUD8E4FkAO0lQ94O/x8/v4DG08eBwGTyD/RCA7+3wsbyT8X7w8eHv2iS5jQeBR15R/twnXzgG\nvvTpR7jdAPcEL3z2xZesQC5xMIItRZV0p6De6KBKpoOIJZFQswJgkbnKReaE3jKXf/76najEANB7\nZlLDRqU42ByhDE8p9rfXvl5mWN8FcAjN7F8LfFn7AjhBvrbWkEKP+77RViIcx8bkBN8jWwRQarj7\njKL9D8Km+1gKkAe8v2fgau6cdw3XCv08sh8DJ13BxzjWq5Au1nuT17Z7LbQL5AdHsUl+MGEskqgg\nnqogFquxeMLUuqJuJBR3E0KUJEhYSFZVEitoQiItSfHLKSc+029138BdRuA9KASIsR/XFiDGdomq\n6aIYuVgVY2+agpJR0VSHgxMiB/w+bFCI77dd4n5hfGL0/eCe8B+fOD794x3YPwHwSQC1E8en20vx\njwh0XT8GLsj8eZvk3Tl0Xe8F8HMAzuu63p5stPFA0FaUOQE4B+D6fxz+h2VD0HxCPPrHJ1/22xkT\nwCFUmwuLoWsS1ebCVCw5RKjliFB7jQjmVfDOeFvO2O09MxnGRlIcDvxJEZx4XoBHitPHjphejJrf\n3e+D3vHJ4GRIRrNT3A8EsjiTkj6/qtALvmr7LPQ1UhxqOaSqt89ZrCep5XnjJcd7/WHwYpRufk1g\nwOuk9uUBKf2/Paaq3n6GAYzhzKRPvNep0OCEvwheJBjcV+VuLROeJWCz3OBN84Opw0hXEVJ/jkn9\neaoNVRKpTqcjlEK3HBd7ERO6VzQxVSBqfJmqcd+3nYaD7ODv310E3oPApsSYGhqVciEqrzpUXawJ\n6nyDqosOFWsRrF+dqIMT4GtYT4grD5syfq84cXz6lfGJUQ3AU+MTo/UTx6cfdDSb31Xy1Qe83zZ2\nFlPg9re9AC7u8LG8o+BZLn4KfPx4ZWePpo1HCY88Uf7j3f/UBVdfD6A1l5gaeSn2hiFGp6JUXYwR\noVIhBA6ASXCP442tpA70npmMYH0cWyd4nJwPP13CV4qz6WNHGgAwf/JsHJz07Jv/+tk+rCnMDqMo\nqZSU4hTFsEBWHJGsVGV6NSfTqbJAyp3gRCmIOppNO5ppEjxRYp26MX/yrAxgELzN8BCAUIOCzIZp\n9c2EkP52t1h+pUNwXULi4L67VuLtp1dMY32kW+lu0ia8LoU3a6scxfrrCbTkB0fqrPbUVaYevs7C\ne1ekeL8z0K+E+pI01BGj4W6BqLEq0WILRI0tUCU6Cy8fevDU0Ye6QYBHjFOA00/k3B4hVN5DxHKc\nCOUQlYqMyqsNIuUMKhWLRDAa4Kp9DRvV4eKdNBZ5l+As+Gf+2fGJUePE8elrD3Dfh8Hvw8wD3Gcb\nOwxd19O6rufB7RdtonxneBp8cvmSrusPtMi5jUcbj7z1YuTky8fBP3w5AFkhNF1R+/8yQqXCIDhR\nFMATI26AD2rzN1tu9grQoljvJ+5EU1H11d5V8OJAnxTbAG8TDSAJ2H0UpRFCGrsIzASBoVBSISLS\nBiU5RSClBGAmCAglpFEXyOqKSBYyAinlsb69sE8UOSn2uw3eBPMnz6bqFLvSGt2XlcnuVYWEVhUi\nXovQ6nREMGYi1KyJJPgF5RPv1q3kn9NWMXZ6TMPmLZX9R7nlKQ42ZgavPf7hn9nOaJpHtRE5MkRj\nA3topCdBQp0xGu4CUSIlosRKVI3PEil0A01ifPuCxh3E4T/5iGhXD4zAlfcB2APiDhNqxEANjVCz\nQcRakQjlApVKGSLUV7B5Md1Dq4g/aIxPjIoAPga+SvKVE8enFx/APmMAfhXA6yeOT792v/fXxsMF\nXdcfA/fI/7Wu66s7fTzvBOi63gOehT6l63q78LWNB4pHXlGePfX8xNjpsSi4lWEPuK2BgBOuKXAr\nQrqVXHikOI6N9gmf0Lng5HsGTWKcSx874kCPKwAiLgtHDPfpZ4rfHNzlIjGo0XA/wCIEtkxIvSGQ\nTFHEUpGSgs2ghR3W1+Ei6jisu0RI/Q2JzE1pwg/mwAlxtVUVvhm8Yw8PVt2Op/L2vqiFvRJju42D\nSrIqELUqoppVSG5ZpflllWRdQgrY3De8pcJEzx8cxkYlOPhvoeVpDTTJ7xJauskBqAftAFMHD/mN\nY0ZJqGM3jfYN0z3dcRrqjJJwh0PkSJEqsRxRE68QUVkAJ8bLg6eOPpQqqldYmGCMJlyjb5jZsd2M\nicNg3b1ULPHPLbHqoPYSoY3LVMrPUDm7hKZ3uL6Tx/9OwYnj0/b4xOjXwX2PH/Eylu83eTkMPmme\nus/7aePhxGXwgrRD2Nli0ncEPMvFc+Df+z/a4cNp4xHEI68oj50e+wh4cQXQbAE9e+7T59YGSy+3\n16/+D27+RMMBj3pbTVmF/AuZb9f+p5n/aCXtchhNghhhTI5abKTPYkMdttsTd5GKMYgCGGGEWAWK\n4pJAVudlemXOZfFGzT3a1XD39jqsizKEDXDSfRXA4lZaRXtFdH6MVxxAvKfu9nWbbHeywTqjFosT\ngBCgYRPMV0QyOxOmV+fDdBlNMnxba8nY6TERN1eCN/iDPdSxeSc5v5HGLUn41MFD3JJCaC+N9O6l\nkZ5+GumOk1BHmKpJkyjRIlGiWaImrhJB8pumZB62wrux02MR8HuUBJBkjCSYHRtidrSbOeE4c7Q4\ncyUCV6szV1oFMAvCZqhYuiIlXlt6pxbTPWwYnxgNgytWAnj3vjvLEN/6fkTwSLiFE8env3k/9tHG\nww9d19fys3Vdb+dn3wK6rj8DHm36FV3X53f6eNp49PDIK8oA5uG1nT736XNFjxSnes9MHkSTEHcA\nEChzSMyu0l4zU91dXyg/XrlsPlN8q/FU6QJTmBUGV6TXfM4uUwWL7Yla7ohisSHVYT0qg2q5TDUB\ndpGA3WAQZxzWOdN/6uO1+ZNnY+BFHnvByZMLTtyvALhxM5LnNRvx2/+mAo8h0WW0v84SPYYbH665\noU6DsYTF6iGbTYsMV1SHXXwu48zciniPnR5TsLkv2H9sTcnw/cGbqcFlANU7IXhTBw95lhT0QVQH\nhNjAfnHoA9001BEjWodG1XjNI8ZXiBqfJlTkhXf30GJ7O+G1LI+Cn8MaKQaQYIzIzI5FmB2Lu1Zc\nY1ZKcu1Yg9nRGrPDV5gdnXatjmkwcWn21POVnTyPdzNOHJ+ujk+MfgWcLH9sfGL0b04cn74fqvwo\nuBWrHQn3aGMKvC5mL3jB9v/f3p1Ht3mddx7/XhDcKYoStVErLWq1LMV27CxulTiknaVK0zhpM2mb\njs+kmUzb007bzLRl09OZ22TmVNPldJn2TJouE7dJk8aTLmmUpE5Ex5ZjO3ESy5ZEypJoURKtnfsO\nArjzx31f8SUESZQEEgD5+5yD8wrABXCFC4APLp77XMnCWrsCv07mqIJkyZcFP6O86smD4Rayy2qT\nQw3VqfGG6uRoZXV6rLw2ORJbM35h4o6x7sTm0VOpbaMnkxvGzo6XMO05C1MEhlOufmIs9abKifSO\nqkm3flHaLa1JU5WAeBqffhEGcOfDGsbdrQcq8H88N+HrHRO0OQ6cjNY6XvXkwXCzh6VMD4ijJeSS\nSybSo/f0pcru7k9Vbx1MVzeMpxPLx91IqaOboHzb2r27h+FKEFfJ9RfKRUu4ga9pHE2DyMwRHr2d\nPNiObdtjBJUbYotWN8VqVmw2lUuXxSqXLDIVdaWmrHrAlNcOmPJFZ2KVS15laovtWZkFnKkgxaSW\nSCAcOZYAOBczbnJJPDWxMpYebyhLT6yoSE8uSaQn60dIVYevkXPAeQXGc29/W9MK/I5f/cBXcr1b\n3v62pvcBJS3NnY/n8n6l+Fhr3x8cv5TvvhQia20J8D58OuPjmnmXfFnwgfIvfOH3fz1pSjbVJodj\nKxK9o6sSl4fXjZ8fbho9M7xm4sJICS5cEHfVIrnexMfSo+nmsBRXAz5oBT8TfJGpoOfC2r27ryyC\n6249EMfnRG/CV5Qw+LSPE8CJ+96xaISpgCsaFC/G13sOH2MguF3vO89O8oHTiUU7BtLLSvwsOMDg\npEmeebHq6OU/bfjcSE/pwLQNNSLH8D5DE1wdBEfTInI609axbXsJsNyU166LLV631VQs3hirXLLU\nVNTVmtKqpCmrHjBl1T2mvPZErHJJF8EOfNfcMGWWRXanywyIo+MDMOzSpQOpsQ0mNbKxPDXWWJka\nX11F+soEfC/TA+OC3KRjodnf1rQOeAf+C9jXcrXVdRCEvxd4Jg/l6KTAWGu3A7uBf7bWXsx3fwqN\ntfZ+4B7ga9baM/nujyxcCz714oPnv/qDhonL7ZvHTofbDE8Fw3ZgWkDY3XpgET4gviM4Lg6uSuK3\nUP4ePui5Khe2u/VADF9FYxM+SI4Dw68sih37u8aynq+vLg03Nnl7cIwubhtkKn+6F+j73YNjQw9f\nSDaMxMY2jsbG75+IJZaOxSbKn1/UM3Ks4tTA92raR16t6C7BLxzKzA8eDf6Pl/B5z9MWyt0oP/h2\ndWzbXgqsLFm6qclUL9tW2vRQY6yirs6UVVcTLx81ZTUDJl5+wlTUHY9VLjmNf2775jqNYudjO0uZ\nPiscBsaZW2iH5e9OpSdrhxI9b4knB++udqmaFfjXSRg8h7sKhoFxXgJ9ub6W5s4z+9uangLeBrxt\nf1vT/hxtdb0DvwnQ8RzclxS/E/jSmnfiv/xLwFq7DL8h0DEFyZJvC35G+Xq6Ww/UMX3Xu5rgqgSR\nmUDg8rVyfLtbD6wENvWVmm1nqszSs1Wx0sOLSwa+t7RkpLMmhjMmWvZslGCGODj11V78g/Hy8Zcq\ngZr1Ew319w3f2bRysv6OxamatSnSVROxhLlQ2tvXVf5aT3vlq71D8ZErqSBkT40YOfTooZzMkM1U\nx7bt5aZ6xZqSug3bTfmiLaa0aoOpWFxrSkrjxCuGTbyyx5SUdZqqpcdilUvO4GeL56waRVCjOXN2\neAlT4w1TM/h9+KC4D+gb6/6p0eTQrjAgbsCXBTT4APoS02eM9dNhEdnf1rQLH8i0tzR3PnOb91WJ\nX8TX0dLcqR3FBABr7W78BiSfs9YWZAWeuWatjeFTLirwKRd6XiSvFvyMciioYbyUqYCngalFamP4\nYOel4HjN2c1VTx4sfeRMYsPqMXd3iXN3jm4qW95bZirPV8QGX6syF89UxXrSxowDvbjJ/vjEqfGy\n8UOJ8pHnkvHkuQp8cFaPn3WuXjexctHWsTfWr0usql+SrF0Uc7Fk2qQHzpZe6uwqP3vy+zXtXSMl\nY4NMBcJj+d5FrWPb9sp4wz1bTOWSbSZesal8x4+vNWXV1RgD8cpBE4u/RknZM7FFq47GKhafZY5m\ni3c+trOC7AFxdLOUFD4QPs/0oHjw0KOH0o2t+yrwX5oa8CuxwzSXMN3mIEG6TdfePSqKX8Ramjtf\nDgLc1+1vaxptae78wW3c3Vb8LwtZ4tp6AAAgAElEQVRaxCdRHfgycZuBw3nuS6G4F/+3+N8UJEsh\nWPAzyt2tB+7Eb7m8iqkayMNMzQSeW7t390Dm7YKFdWGwtXTZeLph/Wh6W13CNValqAHcYDzZ3xsf\nPn264tJrk6nTY/FEV7I0cSxdkrwU7jKXuZsdpen42K7RLWU7RzfXNI2vra1N1cQq0+UTcVfSXZEu\nP7YkVdu5du/unll5Mm7Rsbd+oC5Ws+ouU1qxlZLSjSZe2WBipaUYk6KkrBdjTpqS8mOx2tWvxCqX\nnJvt2eKdj+2sJkuFCaZX55gkY3Y4+PdQ9ItGY+u+SqZ/eQrz0FP4lJDwdXKxa+8elWqbZ/a3NRl8\nDdctwIGW5s6brn28v60pht9gZLClufMrOe6iFDlr7XuBMmvtF/Pdl3yz1tYDjwCvWmvb8t0fEdCM\nMvi84VrgVaYC4yvVBlY9eTDGkwevBMRXjs4tLk+NV60ZGWuon5hYVTc+Wl2WGkomXO/QudiZkxdi\nHZfGU2cnzISjfIRF5T6vNc1UOsSZ8N93jW5KPHrxPXVbxjcsL3Ola/D5yUP4LU5P4atUFMxCr873\nfXw9cKeJxbcQi28sXX3vUjCGWCyJ34r7GUrKj5YsveNorKJuVmaLg2odNWQPiKPpLBP4IPgkkaD4\n0KOHsu7A19i6r7qxdV80MK4LrkriZ5nDHONLXXv3zGkKi8y9luZOt7+t6Wmmb3V9s9tOr8e/Vp/L\neQdlPugA3mqtXWWtPZ/vzuRLkHLxIP4z+9n89kZkigJl+GaYX7zqyYM1wFKePLgJl6iPpUYaStzE\nStxElXET5SY9URFPDaSXjw1XLx8braufmKyomhxNmtTgQG9pb3dX2aXX+uNDFwkCYHOd3eSC/OcN\n+J9kV+LzWkfxCzxO4TcVyfsM5elf/FJZqvfEZpea3I4xm40paYyVLw5mwt2Yc+kul04+a2Lx9pIl\nG4+t/7P35ywPNwiGq5naMKU249/RChOj+ED4OJGZ4htV6Ghs3Rcu0FyN/1WhNrgqgQ+MX8EHxpe7\n9u7R1s8LUEtzZ3p/W9M38WXjmve3NX21pbnz3E3cxQ58pZxTs9JBKXadwJvxi/oWbKCMX7xXDzxh\nrdVCZykYCz71Yt1XH3snxDZhzErjUtW4RIVxk+UmPeJi6aGRWGpgtCTZN7JibLxk80hF6fqJ+kWL\nUtWpynT5wEQscfxM2YX2b9Q9d5obVIsIql6swgfHG5gKyC4Dp/EVLfK6QUZ36wGTHuurS/Wd3EYq\nsRVf33kt6XQcwKUSvaQnX8WljzmXbi/f8q7TuehvsF3z4oxTGBRHq3+kmL6Ndlhtou/Qo4dmlM7R\n2LqvDj8OYWAcLtibIJJuA/R07d2zsN8cMs3+tqYK/FbXVcC/tjR33jAFan9bUx3wAeCFlubOF2e5\ni1KkrLUP4APlzy7EINFauxS/gO+ktXZ/vvsjErXgZ5RLE6d2QXqdSQ8Px1JD3bFU/4X4ZPe5WHqw\n/01Du0ofHLhv+dbxB1ctm1xSEqckwU1sI93deqAcXyd5Q3Aswwd7Z4GXiWz8kQ/drQfK0hODq9L9\np7a5ZGILcAepRD3OlTiXTDI5dt6lJr/tkuNH3Vjvkc37/67vVh9r52M7K7l6Vjg8RV+HaXwAPIDf\nNTEaFI/czELFxtZ94Y5+0cA4zAu/aoGmAmO5npbmzvHI7n3vCnbvG7rBze7Ev6aPznoHpZh1AHfh\nc+FfznNf5lQk5SIBqCKMFJwFP6O887Gdi4Bk+BP9NbaRPsMNtpEOBbcPZ40b8CkVY/hZ41PAa9HN\nR+ZKUNVjcXq8f0166Pw2UonNLp1aS3KsFodxkyOjbnK02yXHT7ix/vZUz/Hj2176zk3lRQdbXWcG\nwWFwHM0bTuPTUsIAODpLPHyrVTuCwDi6AcwqphbwjRCZMe7au6f/Vh5DZH9b0xL8zPIEcM2trve3\nNZXiS8Kdbmnu1MIkuS5r7Xvwu6R+0Vq7YP4wW2vvBt4AfNNa+2q++yOSacHPKB969NBQd+uBiu7W\nAzu4ehvpA2RsI50pCEBXMhUch4u/evGlwk7hNyCZ0w++YPe/Fenx/vVu5PI2l0o0ueR4PcnxGpdO\nptz4wKBLjJx0iZFj6YHTHemhc2e2H+24YfpCUHM4W87wYqA80tTh87IH8NUhMoPh2873bWzdF8OX\nZ4sGxmFAPoT/cnIWX8M4r9tby/zR0tzZt7+t6evAHvzM8r+2NHdm+/K7Cf96VEk4mYkO/CY3q4HX\n8tyXOWGtXQLch0+5UJAsBWnBB8rdrQcexNewDLeR/i5w4nopEd2tB8rw1TLClIoK/CzpWaAdOLV2\n7+4b/SSbU1cC45FLd6TH+u4knWxyk6N1JBPVbnJ4JD02MOASw4fc6OWjqb6u46QnL2w/2pF1ZjvY\nkS4zCK7l6hJr4IPhQfyClOjs8FAuNzYJZosX4zf0WI4PkJcx9RoewFcuCQPjvKW0yPzX0tx5IVjg\n9w7g7fvbmr7W0tyZ+eVvB9DT0tx5Ye57KEXoVeABfF3leR8oW2vD0osJ4LY29BGZTQs+9aK79cC9\n+GDrxNq9u3uv024RPjBej//GH8P/9BqmVHSv3bt7znZeCwPj1ED3ZpcY2k461eQmxxaTSlSkJ4aG\n3VhfrxvvO57q6+pw4/1ngIvbj3ZcCVx3PrYzjg9+wwA4Ghhn1nceZfqMcJgyMXjo0UM5r8wRCYqX\nMRUY1wOlQZMkfhHkZfzM//muvXsKpnyeLBz725q24PMrO4G2cKvr/W1NDcCPAk+3NHcqP1lmxFr7\nJnyu8t9ba+f1Z5q1Ntz5ss1aeyLf/RG5lgU/o7x27+6su20FKRXLmUqpCDea6AcO4YPjizda0Jcr\n3a0H4i6VWJEe6N7mJke349Ib3eRYHelkaXpiaNiN919y4/0HU31dHW6s9wzQ84HfjAPUQnwxsIPH\ndkZnh2syHmKMqxfQDeCD4VnLqQ6C4lqmB8XLmAqKU/iA+Bh+S+jLQL9KtUkhaGnuPBbs3vdG/Hso\nrP+6Az9TpgBAbsZRYBd+Ud/BPPdl1lhr64D7gS4FyVLoFvyMclQwSxumVKzHL6xw+AVgp/EpFVft\n0jdbfXGJ0ZWpgTN3khrfhmOjS47VkU6VpCeGht3EwAU3PvBKqu9kxxNbei9/5qGYmyw1dUzNCtfh\nNzkxkbud4OpZ4QFg4Hql7XKpsXVftqA4zCtOAT34YPhScFJQLAVvf1vTm4GdwAv42ts/BRxuae58\nPq8dk6JjrX03/rP7C/NxUV+QcvEe/N+ox+f7zLkUvwU/o9zdeqCaqcA43BUvga90cQo4M9tbLgf9\niKdHexrSQ+d2uNTkNmAjyfFaXDqWnhgaHk33nT9dfvHQ9+tPnXtu49hI93ITLqp7PZRExzGJD34v\n42ezrgTGM601nCvBZh5hMBwewwV/aXxQfIKpwLhPQbEUqefxX6zvx3+WxPDrFURuVjvwEH7S5kye\n+zIb7sIvgH9SQbIUgwUfKAMPAyvws6vt+OD4/GynVHS3Hoin+k+vTY/27MCltoG5I5Uaqx0sHS+9\nXHI5eaG8r6er+vLx9iWXel9dPpkYrTAl+PFaB8YxVV7tbHDsD46jt1pe7XY0tu6rYfos8XKmB8W9\n+MUqYfpEr4JimS+Cra6/hV/suhY409LcqUorciu68Gk825lngbK1thb/ZfK0tfZ4vvsjMhMLPvWi\nu/XASmBi7d7ds1pXt7v1QDx56eh6NzG0s79s9K6h0sSWodjIksGy8XhfyUDqQsXAwGtVA31nqwbP\nn69N9k6WmhRTecNhEBz+ezAX5dVuVRAUZ6ZPhNUw0vgtpMPUiTAozlkFDJFCFdROvh842tLcec3F\nwSLXY619A/A6/KK+kXz3JxeClIt34xdmPz5f/l8y/y34GeW1e3fPSummX/vjn6vceja2qzTp7nOw\nPVnChtFl47XDpRPxCcYnhuNjl/tKR7rOVQ2duVA72TUZN31cCYjNANA/V3nD19PYuq+aq4PiyuBq\nhw+KTzEVFPcoKJaFKqin/OwNG4pc31HgbmAb8P089yVX7sTXu39KQbIUkwU/o3w7dj62M0ZQXq06\nWb70nksrd9RNVNxV6ko2xl18VSxNqcO5JBMjKZfsHmf82LnK3kNHV46/mig1ffi84YL5wGhs3VfF\n1UFxWCouDIqjC+16u/buyXl5OBGRhc5a+yPAEuDz1tqiTlMLUi5+HDhvrf1qvvsjcjNuOKNsjGkC\nfh6/YxD41bgXgL3OuX2RdqXAfwN+Ar+gbBD4dedc0RcS3/nYzmqmKkksBhaXpUrqNw8u2bxyrGb1\n4kTFyqpUeX1FKk7NZGmiPOEux5LJZ1xq4tBg+tILR5Zc7PzLvUcK6oOusXVfJdPziZczvX5yH75U\nXBgY9ygoFhGZM+3A2/GbWp3Kc19uWZBy8Rb8ZMvTee6OyE2bSerFu4APAg86504YY2LAXuDLxphm\n59xTQbv/DTQDP+Scu2SM+QjwDWPMm51zBV8PMmNr5rqMY7wsGYvfMbi4oWF00crVo7V1KyZqaxdP\nlKcWTZYlasbMa7VjfKNqLHkoNXT28JZv/UNPHv8rV2ls3VfB1QvtqiNN+vE7QUWD4lmrnSwiIjd0\nGr/Z050UcaCMX5S4GnjaWqsdU6Xo3DD1whjzCFDvnPuryGV1+BnHP3LOfcwYsxW/T/1HnHN/E2l3\nBOhyzu2Zld7nwM7Hdr4FXx6uMnKxq06UjN9zYXnd1v7ly9eN1q1YkahbUjdRkapJliVdavI8qcQJ\nlxxvT4/1Htr8xF/PSW3lmQjqFC/FB8ThMbq5yABT+cSXgMsKikVECo+19j7gXnz6xVC++3OzrLU1\n+F+ZL1pr992ovUghuuGMsnPun7JcXBscLwXHR/AbWzyZ0a4N+DljTI1zrlC/SY4Apzb0lE/+yMnG\nVXf1r17dMFG3Nl5SuQZiBkPapSbPkk5+16XG29OTvYeb/vWP8/5/aWzdF8fnr9VHTkuZ2rzD4YPi\nC8Bhgi2fu/buyfsCQRERmZGjwD34RX0v5Lkvt+It+NhAKRdStG56MZ8xZg3wKfxPKW9zzg0aY/4e\n+HdAmXMuFWn7MeAPgTc6576bu27nTud7f+OnTLzidaa0osEHxibl0slu0snjuPQRSsqObPzCx8fy\n2cdgkV3mLPFipnbdm8Rv3tFLUI4NLbQTESl61tp34Gv9f66YFvVZa7fhA+VnrLXafEeK1ozLwwWL\n+v4NaAK+BrzXORcW1F8GjEaD5EB4ff117vejwEeDs592zn16pn3KBVOxeIUxsQnn0k9A6kisemX7\nHX/1s3O6g12osXVfDB8A12ecomkhw/ig+NXg2AMMde3do/IlIiLzTwc+PbAR/7lf8IKUizfhN8Tq\nyHN3RG7LjANl51wnsMkYUwt8AnjJGPOeG1S1MNe5LrzfTwNzGhxHlTXu/pO1e3fnYye7Mq6eJV6C\n30IbpnazO83UbHFP1949eQniRUQkL87gJ0i2UySBMrAbv43709ZaTeJIUbulOsrGGIMvXTPpnNtV\nzKkXc6Gxdd8irp4lXhRpMs7U7HB46tcWzyIiYq29F7gP+AdrbcEsHs/GWrsFeBB41lp7OM/dEblt\nM6mjXAmMu0hE7ZxzxphDwI8bY8qBl4GfxNd77Irc/A58TeUF8dNLY+u+EvyscHSWOLrADnwptov4\n56QXv8BudI67KiIixeMo8Hr8rPLzee7LNVlrq4AHgPPAkTx3RyQnZlIe7lvAbzrnnsu4/LvAFnxg\nuAUf+H3YOfeZSJvDwKlCLg93q4INOzJnieuYvsCul+mzxFpgJyIiN81a+zB+C+jPWWsz1wMVBGvt\nO4E1wP8r9JlvkZmaaY7y7xhjftI51xOkXfwicD/wyWCm+RVjzKeB3zTGfMU5d9kY82H8wr8PzU7X\n50Zj6z6DX2CXOUsc3cUuXGB3Ei2wExGR3OvA/0p7B3Aiz325irV2M7AeeF5BsswnMwmUfwv4CPCU\nMSYJVOADwQ8Bfx9p90vAfwe+bYyZBIaAtxfDrnyhyAK7zNrE0QV24dbOV2aKtcBORERm2Wv4SlLb\nKbBAOZJycQE4lOfuiOTULS3mm08aW/ftwNeErmdqIxXQAjsRESkg1trXAW8EHrfW9uW7PyFr7dvx\na5S+ZK3tz3d/RHJpxuXh5rE1+Dzry8ArTM0Sj+S1VyIiItO9gk973A48m+e+AGCtbcLXeP6OgmSZ\njzSj3LrPKJdYRESKgbW2BT97+1lrbV4Xh1trK4GfwKeEfLmYdg4UmalYvjuQbwqSRUSkiLTjS45u\nzHdHgB/C9+UpBckyXy34QFlERKRYWGvP4evx35nnfmzEB+vfL6R8aZFcU6AsIiJSXDqAFdba+nw8\nuLW2Avhh/Nqel/LRB5G5okBZRESkuBwDUvhFffnwAD7l4ltKuZD5ToGyiIhIEbHWTgCdwGZrbekc\nP3YjsAn4gbW2dy4fWyQfFCiLiIgUnw6gFL8D7pyw1pYDu/FlVItmMzGR26FAWUREpMhYay8Avczt\nor4HgHKUciELiAJlERGR4tQOLLPWLp/tB7LWrgc2AwettT2z/XgihUKBsoiISHE6ASSZ5UV91toy\n4C34GewfzOZjiRQaBcoiIiJFyFqbwAfLm4Jgdra8GahEKReyAClQFhERKV4dQByfFpFz1tp1wFbg\nJWvt5dl4DJFCpkBZRESkSFlrL+E3/sh5+kUk5aIf+H6u71+kGChQFhERKW7twFJr7coc3++bgCp8\nykUqx/ctUhQUKIuIiBS3TmCSHM4qW2vXANuAl621F3N1vyLFRoGyiIhIEbPWTgLHgaZgU5Dbvb9S\n4K3AAEq5kAVOgbKIiEjx6wBKgC05uK83AjX4lItkDu5PpGgpUBYRESlywSYgF7nN9Atr7Wr8bn+H\ngt3/RBY0BcoiIiLzQztQZ61tuJUbW2vj+JSLQeCFXHZMpFgpUBYREZkfXgUS+BnhW/EGYBFKuRC5\nQoGyiIjIPBAEt8eAO6y1lTd521XAXcBha+352eifSDFSoCwiIjJ/tOP/ts94UV+QcvEgMAR8d3a6\nJVKcFCiLiIjME9bafuAcsN1aa2Z4s/uBWuAppVyITKdAWUREZH7pwAe+a27UMNjNbyfQbq09O9sd\nEyk2CpRFRETml5PAODcoFRepcjEMfGcO+iVSdBQoi4iIzCPW2hTwCtBora26TtPXA3X4lIvJOemc\nSJFRoCwiIjL/dAAG2JbtSmvtCmAXcNRa+9pcdkykmChQFhERmWestYPAa8C2zEV91toSfMrFKPB8\nHronUjQUKIuIiMxPHUANsC7j8tcDS4CnrbWJOe+VSBFRoCwiIjI/deFnja8s6rPWLgNeB7xirT2T\np36JFA3jnMt3H0RERGQWWGvvB+4GPo8Pmt8HVABf1GyyyI1pRllERGT+ii7quxdYilIuRGZMgbKI\niMg8Za0dBk4DO/Azy8ettafz2yuR4qFAWUREZH7rAMrxm5A8m+e+iBSVeL47ICIiIrPqDHACv4Bv\nIt+dESkmWswnIiIiIpKFUi9ERERERLJQoCwiIiIikoUCZRERERGRLBQoi4iIiIhkoUBZRERERCQL\nBcoiIiIiIlkoUBYRERERyUKBsoiIiIhIFgqURURERESyUKAsIiIiIpKFAmURERERkSwUKIuIiIiI\nZKFAWUREREQkCwXKIiIiIiJZKFAWEREREclCgbKIiIiISBYKlEVEREREslCgLCIiIiKSRVEFysaY\nj+a7D3JrNHbFSeNWvDR2xUtjV5w0bvNTUQXKgF6ExUtjV5w0bsVLY1e8NHbFSeM2DxVboCwiIiIi\nMicUKIuIiIiIZFFsgfKn890BuWUau+KkcSteGrvipbErThq3ecg45/LdBxERERGRglNsM8oiIiIi\nInNiTgJlY0yDMebrxhhNXxcZjV3x0tgVJ41b8dLYicw/sx4oG2MeAZ4Dmm7QboMx5vPGmJPGmOPG\nmO8ZY34sS7vPGGNOGGMOZpxas7R9vTHmKWPMYWPMK8aYPzDGVOTufze/5XrsgrbLjTH/xxjzojHm\nkDHmlDHmi8aYuox2GrvbkMuxM8Y0GmOGs7znDhpjJowxn8xor7G7RbPweVlmjPltY0y7MeZlY0yH\nMebTxpiVWdpq3G7DLIxdpTHm94K/d0eNMceMMa3GmKv+bmvsbp0x5m5jzF8aY75vjHkpeK/8qTFm\neUa7GmPMnwXPb7sx5gljzI4s91dqjPlkMGaHjTHPGmN++BqP/SuR9+YPjDHvna3/p9wG59ysnoDv\nAJuBz/iHy9pmBfAa8E9AWXDZB4EU8O6Mtp8BHpzB424GBoFfDs7XAYeAz8/2/3m+nGZh7JYBx4Ff\nYCo//l5gHGjU2BXm2AGNwLey3H4NkAS2a+wKb9yCy/8EGAXuCc7XA4eB7wIxjVtBj90TweflmuD8\nDuAy8HsZ7TR2tzduR4EvAdXB+TXBZceAyki7rwHfBqqC858ELoXjE2n3qeC2y4PzHwHGgLsz2rUG\n49kUnH8YmATele/nRKeM18isPwDEg+P1Pjw+AThga8bl3waOZlz2GWYWKH8O6CIIyILLfiJ4nPvz\n/cQXw2kWxu4vgH1Z7uOh8MNHY1d4YwdUAw9nuf1vAwcyLtPYFci4BZddBP4l47Jfzby9xq2wxg5o\nDtr9p4x2/xP/5XRN5DKN3e2N21FgU8ZlPxs8f+8Pzj8cnG+OtCkDeoE/j1y2FUgDH864vyPRv334\nLzMjwCcy2u0DjuT7OdFp+mnWUy+cc8kZNLsP/03qWMblLwNbjTFbbuYxjTFx4MeAp1zw6gu0Bcf3\n38z9LVS5HDtjTCXw08BXsjzON51zo0E7jV0O5HLsnHMjzrlvRBsYYwzwYSLlkDR2t28WPi+TQDyj\nXXi+BDRuuZLjsbsvOB7J0q4E2AMauxzZ5Zw7kXHZ2eC4JDi+Hz9uz4QNnHMJ/Bec6HP8CGCAJzPu\nrw14uzGmJjj/TqDqGu3uNMZsu4X/h8ySQql6MYJ/cZmMy9PBMfNF8zNB3k+HMeZ5Y8zHgg+M0Eb8\nLNjJ6I2ccz3AELArd11f8GY6drvwYzJijPlUkJN1zBjzf40x6yO309jNnZt930U9DCwGHo9cprGb\nGzczbp8AWowxLeDzzYGfA/Y759qDNhq3uTPTsRsJjpl/ozPbaexuUxDwZtqCn0F+Oji/Czibpe1J\nYKUxZkWkXRo4naVdHLgz0i68PLNd9HopAIUSKL+IfxHtzLj87uBYG7lsCLgA/Aj+Rfdx4LeAL0Ta\nLIu0zTSIz9OT3Jjp2K0Ljn8OPBtc/wA+//W5yMIJjd3cuZn3XaaPAH/rnBuPXKaxmxszHjfn3KeA\nXwS+ZIw5B5zA577uidxO4zZ3Zjp2LwbHe27QTmOXY8aYEvyvZX/tnAtn/pdx7ecYpp7nZcCocy41\ng3Zkuc/MdlIACiVQ/jP8Tx1/ZIypN8bEjDH/kalvVWNhQ+fcLznnPu6c63deG7AXeL8x5odm8FiZ\n3+Tl9sx07MIV2N9xzv2tcy7tnLsM/GdgNX6B341o7HJrxu+7KGPMMvzPvTezC5XGLndmPG7GmN8H\nfhd4p3OuAb9QaTvwuDGmbAaPpXHLrRmNnXPuWXya2q8ZY3YCGGPeDPxMtN0NaOxuzW/jU5Z+dQZt\nZ/oc57qdzKGCCJSdc4PAD+NXAz+P/za9E/hY0OTMDe7iO8HxTcHxcnBclKXtIqDnljsr09zE2IXf\nnA9m3MVhfO7X/cF5jd0cuY333b8HXoj8dB/S2M2BmY5bULrqvwJ/6px7PrjtBeCXgR8Ffj5or3Gb\nIzf5nvsg8FngC8aYI/ix/FBGO41dDhlj/gPwAXzlieHIVZe59nMMU8/zZaAqmJW+Ubvo5ddqJwUg\nc5FH3jjnTjL1bRkAY8yvA8P4BQzhTyKLnXO9GTcPf+YIX5yv4nO8GjPurx7/Qnw5l31f6GYydviV\nxZDx5cw554wx6cjlGrs5NMOxy/Sz+F9xMmns5sgMx+2u4Hg84+bhz8lvCI4atzk00/ecc24EX0Ks\nNdIuHLNng6PGLkeMMT8D/Bd8ZYuLGVe/DNxnjCnLyFO+A7gQaf8y8JP4VMOujHZJoCPSDvy4ZbaL\nXi8FoCBmlI0xVcaYh7Jc9W7gs5E8yHX4gu6ZXh8cfwBXVh9/GXhrsDo/9Lbg+I+332uBmY+dc+4V\n/B/oXRm33wyUAy8E7TR2c+Qm3nfR2zwANDB9ER+gsZsrNzFu4R/v9RntNgTHHtC4zaWbec8ZY95l\njCnN0q4DX21BY5cjxpgPAb8BPOScOx9c9m5jzEeDJv8IlOLX1YS3KQvOfylyV/+EXwT4YMZDvA14\nwjkX/rL6dXx982zt2p1zR5HCMVd16Lh+bclG/KYTYVH8GP5nphPAsox2Dvho5LJdwDngKabXkQyL\nsP9ScH4x/luairDnYeyC634MP/v/nuB8OfDP+J8hl2vsCnfsIrf5G+BPrvN4GrsCGTf8L2wv4n+m\n3xRcVgX8S3D7XRq3why74Lou4Ocj55vxG1y8OaOdxu72xuun8TnfYWpLePoLwEbafR04wNSGI7/D\ntTcceSUcT/zCwGttOHIJ2BicfwhtOFKQp9l/APh9fF5qLz7IPRicyiJt6vAzVKfwOwq9FLzYVmTc\nVxnwUWB/8EHwSvAB878IdtXJaH8fPoA+gp/N/EOgIt9PerGccjl2kfaP4Gf+jwV/CD4PbNDYFcXY\nLcL/PLzjBo+tsSuQcQOWBvd5JPKZ+c8EgZrGraDH7veC8TqK/8LzZTKCLY1dTsYtHK9sJxtptwhf\ntekYflb/G9k+C/Ezz/8jGLvD+F/Bd1/jsX8FaA/emy8C783386HT1adwG2EREREREYkoiBxlERER\nEZFCo0BZRERERCQLBcoiIiIiIlkoUBYRERERyUKBsoiIiIhIFgqURURERESyUKAsIiIiIpKFAmUR\nERERkSwUKIuIiIiIZKFAWen5ebMAAAAMSURBVEREREQki/8PO5qT0n/BMyIAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x118a4e5c0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pyplot.figure(figsize=(12,8))\n",
-    "\n",
-    "for key,group in by_country:\n",
-    "    pyplot.plot(group['year'], group['lifeExp'], alpha=0.4)\n",
-    "    \n",
-    "pyplot.title('Life expectancy in the years 1952–2007, across 142 countries')\n",
-    "pyplot.box(on=None);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Dig deeper and get insights from the data\n",
-    "\n",
-    "The spaghetti plot shows a general upwards tendency, but clearly not all countries have a monotonically increasing life expectancy. Some show a one-year sharp drop (but remember, this data jumps every 5 years), while others drop over several years.\n",
-    "And something catastrophic happened to one country in 1977, and to another country in 1992.\n",
-    "Let's investigate this!\n",
-    "\n",
-    "We'd like to explore the data for a particular year: first 1977, then 1992. For those years, we can get the minimum life expectancy, and then find out which country experienced it. \n",
-    "\n",
-    "To access a particular group in _GroupBy_ data, `pandas` has a `get_group(key)` method, where `key` is the label of the group.\n",
-    "For example, we can access yearly data from the `by_year` groups using the year as key. The return type will be a dataframe, containing the same columns as the original data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "pandas.core.frame.DataFrame"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(by_year.get_group(1977))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "pandas.core.series.Series"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(by_year['lifeExp'].get_group(1977))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can find the minimum value of life expectancy at the specific years of interest, using the `Series.min()` method. Let' do this for 1977 and 1992, and save the values in new Python variables, to reuse later."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "31.219999999999999"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "min_lifeExp1977 = by_year['lifeExp'].get_group(1977).min()\n",
-    "min_lifeExp1977"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "23.599"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "min_lifeExp1992 = by_year['lifeExp'].get_group(1992).min()\n",
-    "min_lifeExp1992"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Those values of life expectancy are just terrible! Are you curious to know what countries experienced the dramatic drops in life expectancy?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can find the row _index_ of the minimum value, thanks to the [`pandas.Series.idxmin()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.idxmin.html) method. The row indices are preserved from the original dataframe `life_expect` to its groupings, so the index will help us identify the country. Check it out."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "221"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "by_year['lifeExp'].get_group(1977).idxmin()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Cambodia'"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "life_expect['country'][221]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>continent</th>\n",
-       "      <th>gdpPercap</th>\n",
-       "      <th>lifeExp</th>\n",
-       "      <th>pop</th>\n",
-       "      <th>year</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>216</th>\n",
-       "      <td>Asia</td>\n",
-       "      <td>368.469286</td>\n",
-       "      <td>39.417</td>\n",
-       "      <td>4693836.0</td>\n",
-       "      <td>1952</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>217</th>\n",
-       "      <td>Asia</td>\n",
-       "      <td>434.038336</td>\n",
-       "      <td>41.366</td>\n",
-       "      <td>5322536.0</td>\n",
-       "      <td>1957</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>218</th>\n",
-       "      <td>Asia</td>\n",
-       "      <td>496.913648</td>\n",
-       "      <td>43.415</td>\n",
-       "      <td>6083619.0</td>\n",
-       "      <td>1962</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>219</th>\n",
-       "      <td>Asia</td>\n",
-       "      <td>523.432314</td>\n",
-       "      <td>45.415</td>\n",
-       "      <td>6960067.0</td>\n",
-       "      <td>1967</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>220</th>\n",
-       "      <td>Asia</td>\n",
-       "      <td>421.624026</td>\n",
-       "      <td>40.317</td>\n",
-       "      <td>7450606.0</td>\n",
-       "      <td>1972</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>221</th>\n",
-       "      <td>Asia</td>\n",
-       "      <td>524.972183</td>\n",
-       "      <td>31.220</td>\n",
-       "      <td>6978607.0</td>\n",
-       "      <td>1977</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>222</th>\n",
-       "      <td>Asia</td>\n",
-       "      <td>624.475478</td>\n",
-       "      <td>50.957</td>\n",
-       "      <td>7272485.0</td>\n",
-       "      <td>1982</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>223</th>\n",
-       "      <td>Asia</td>\n",
-       "      <td>683.895573</td>\n",
-       "      <td>53.914</td>\n",
-       "      <td>8371791.0</td>\n",
-       "      <td>1987</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>224</th>\n",
-       "      <td>Asia</td>\n",
-       "      <td>682.303175</td>\n",
-       "      <td>55.803</td>\n",
-       "      <td>10150094.0</td>\n",
-       "      <td>1992</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>225</th>\n",
-       "      <td>Asia</td>\n",
-       "      <td>734.285170</td>\n",
-       "      <td>56.534</td>\n",
-       "      <td>11782962.0</td>\n",
-       "      <td>1997</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>226</th>\n",
-       "      <td>Asia</td>\n",
-       "      <td>896.226015</td>\n",
-       "      <td>56.752</td>\n",
-       "      <td>12926707.0</td>\n",
-       "      <td>2002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>227</th>\n",
-       "      <td>Asia</td>\n",
-       "      <td>1713.778686</td>\n",
-       "      <td>59.723</td>\n",
-       "      <td>14131858.0</td>\n",
-       "      <td>2007</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    continent    gdpPercap  lifeExp         pop  year\n",
-       "216      Asia   368.469286   39.417   4693836.0  1952\n",
-       "217      Asia   434.038336   41.366   5322536.0  1957\n",
-       "218      Asia   496.913648   43.415   6083619.0  1962\n",
-       "219      Asia   523.432314   45.415   6960067.0  1967\n",
-       "220      Asia   421.624026   40.317   7450606.0  1972\n",
-       "221      Asia   524.972183   31.220   6978607.0  1977\n",
-       "222      Asia   624.475478   50.957   7272485.0  1982\n",
-       "223      Asia   683.895573   53.914   8371791.0  1987\n",
-       "224      Asia   682.303175   55.803  10150094.0  1992\n",
-       "225      Asia   734.285170   56.534  11782962.0  1997\n",
-       "226      Asia   896.226015   56.752  12926707.0  2002\n",
-       "227      Asia  1713.778686   59.723  14131858.0  2007"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "by_country.get_group('Cambodia')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We searched online to learn what was happening in Cambodia to cause such a drop in life expectancy in the 1970s. Indeed, Cambodia experienced a _mortality crisis_ due to several factors that combined into a perfect storm: war, ethnic cleansing and migration, collapse of the health system, and cruel famine [2].\n",
-    "It's hard for a country to keep vital statistics under such circumstances, and certainly the data for Cambodia in the 1970s is uncertain.\n",
-    "However, various sources report a life expectancy there in 1977 that was _under 20 years_.\n",
-    "See, for example, the World Bank's interactive web page on [Cambodia](https://data.worldbank.org/country/cambodia)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Something is strange about the data from the The Python Graph Gallery. Is it wrong?\n",
-    "Maybe they are giving us _average_ life expectancy in a five-year period.\n",
-    "Let's look at the other dip in life expectancy, in 1992."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1292"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "by_year['lifeExp'].get_group(1992).idxmin()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Rwanda'"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "life_expect['country'][1292]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>continent</th>\n",
-       "      <th>gdpPercap</th>\n",
-       "      <th>lifeExp</th>\n",
-       "      <th>pop</th>\n",
-       "      <th>year</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1284</th>\n",
-       "      <td>Africa</td>\n",
-       "      <td>493.323875</td>\n",
-       "      <td>40.000</td>\n",
-       "      <td>2534927.0</td>\n",
-       "      <td>1952</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1285</th>\n",
-       "      <td>Africa</td>\n",
-       "      <td>540.289398</td>\n",
-       "      <td>41.500</td>\n",
-       "      <td>2822082.0</td>\n",
-       "      <td>1957</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1286</th>\n",
-       "      <td>Africa</td>\n",
-       "      <td>597.473073</td>\n",
-       "      <td>43.000</td>\n",
-       "      <td>3051242.0</td>\n",
-       "      <td>1962</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1287</th>\n",
-       "      <td>Africa</td>\n",
-       "      <td>510.963714</td>\n",
-       "      <td>44.100</td>\n",
-       "      <td>3451079.0</td>\n",
-       "      <td>1967</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1288</th>\n",
-       "      <td>Africa</td>\n",
-       "      <td>590.580664</td>\n",
-       "      <td>44.600</td>\n",
-       "      <td>3992121.0</td>\n",
-       "      <td>1972</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1289</th>\n",
-       "      <td>Africa</td>\n",
-       "      <td>670.080601</td>\n",
-       "      <td>45.000</td>\n",
-       "      <td>4657072.0</td>\n",
-       "      <td>1977</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1290</th>\n",
-       "      <td>Africa</td>\n",
-       "      <td>881.570647</td>\n",
-       "      <td>46.218</td>\n",
-       "      <td>5507565.0</td>\n",
-       "      <td>1982</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1291</th>\n",
-       "      <td>Africa</td>\n",
-       "      <td>847.991217</td>\n",
-       "      <td>44.020</td>\n",
-       "      <td>6349365.0</td>\n",
-       "      <td>1987</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1292</th>\n",
-       "      <td>Africa</td>\n",
-       "      <td>737.068595</td>\n",
-       "      <td>23.599</td>\n",
-       "      <td>7290203.0</td>\n",
-       "      <td>1992</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1293</th>\n",
-       "      <td>Africa</td>\n",
-       "      <td>589.944505</td>\n",
-       "      <td>36.087</td>\n",
-       "      <td>7212583.0</td>\n",
-       "      <td>1997</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1294</th>\n",
-       "      <td>Africa</td>\n",
-       "      <td>785.653765</td>\n",
-       "      <td>43.413</td>\n",
-       "      <td>7852401.0</td>\n",
-       "      <td>2002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1295</th>\n",
-       "      <td>Africa</td>\n",
-       "      <td>863.088464</td>\n",
-       "      <td>46.242</td>\n",
-       "      <td>8860588.0</td>\n",
-       "      <td>2007</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     continent   gdpPercap  lifeExp        pop  year\n",
-       "1284    Africa  493.323875   40.000  2534927.0  1952\n",
-       "1285    Africa  540.289398   41.500  2822082.0  1957\n",
-       "1286    Africa  597.473073   43.000  3051242.0  1962\n",
-       "1287    Africa  510.963714   44.100  3451079.0  1967\n",
-       "1288    Africa  590.580664   44.600  3992121.0  1972\n",
-       "1289    Africa  670.080601   45.000  4657072.0  1977\n",
-       "1290    Africa  881.570647   46.218  5507565.0  1982\n",
-       "1291    Africa  847.991217   44.020  6349365.0  1987\n",
-       "1292    Africa  737.068595   23.599  7290203.0  1992\n",
-       "1293    Africa  589.944505   36.087  7212583.0  1997\n",
-       "1294    Africa  785.653765   43.413  7852401.0  2002\n",
-       "1295    Africa  863.088464   46.242  8860588.0  2007"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "by_country.get_group('Rwanda')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The World Bank's interactive web page on [Rwanda](https://data.worldbank.org/country/rwanda) gives a life expectancy of 28.1 in 1992, and even lower in 1993, at 27.6 years. \n",
-    "This doesn't match the value from the data set we sourced from The Python Graph Gallery, which gives 23.6—and since this value is _lower_ than the minimum value given by the World Bank, we conclude that the discepancy is not caused by 5-year averaging."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Checking data quality\n",
-    "\n",
-    "All our work here started with loading a data set we found online. What if this data set has _quality_ problems? \n",
-    "\n",
-    "Well, nothing better than asking the author of the web source for the data. We used Twitter to communicate with the author of The Python Graph Gallery, and he replied with a link to _his source_: a data package used for teaching a course in Exploratory Data Analysis at the University of British Columbia. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<blockquote class=\"twitter-tweet\" data-lang=\"en\"><p lang=\"en\" dir=\"ltr\">Hi. Didn&#39;t receive your email... Gapminder comes from this R library: <a href=\"https://t.co/BU1IFIGSxm\">https://t.co/BU1IFIGSxm</a>. I will add citation asap.</p>&mdash; R+Py Graph Galleries (@R_Graph_Gallery) <a href=\"https://twitter.com/R_Graph_Gallery/status/920074231269941248?ref_src=twsrc%5Etfw\">October 16, 2017</a></blockquote> <script async src=\"//platform.twitter.com/widgets.js\" charset=\"utf-8\"></script>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%%html\n",
-    "<blockquote class=\"twitter-tweet\" data-lang=\"en\"><p lang=\"en\" dir=\"ltr\">Hi. Didn&#39;t receive your email... Gapminder comes from this R library: <a href=\"https://t.co/BU1IFIGSxm\">https://t.co/BU1IFIGSxm</a>. I will add citation asap.</p>&mdash; R+Py Graph Galleries (@R_Graph_Gallery) <a href=\"https://twitter.com/R_Graph_Gallery/status/920074231269941248?ref_src=twsrc%5Etfw\">October 16, 2017</a></blockquote> <script async src=\"//platform.twitter.com/widgets.js\" charset=\"utf-8\"></script>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note one immediate outcome of our reaching out to the author of The Python Graph Gallery: he realized he was not citing the source of his data [3], and promised to add proper credit. _It's always good form to credit your sources!_\n",
-    "\n",
-    "We visited the online repository of the data source, and posted an [issue report](https://github.com/jennybc/gapminder/issues/18) there, with our questions about data quality. The author promptly responded, saying that _her_ source was the [Gapminder.org website](http://www.gapminder.org/data/)—**Gapminder** is the non-profit founded by  Hans Rosling to host public data and visualizations. She also said: _\" I don't doubt there could be data quality problems! It should definitely NOT be used as an authoritative source for life expectancy\"_\n",
-    "\n",
-    "So it turns out that the data we're using comes from a set of tools meant for teaching, and is not up-to-date with the latest vital statistics. The author ended up [adding a warning](https://github.com/jennybc/gapminder/commit/7b3ac7f477c78f21865fa7defea20e72cb9e2b8a) to make this clear to visitors of the repository on GitHub. \n",
-    "\n",
-    "#### This is a wonderful example of how people collaborate  online via the open-source model.\n",
-    "\n",
-    "##### Note:\n",
-    "\n",
-    "For the most accurate data, you can visit the website of the [World Bank](https://data.worldbank.org)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Using widgets to visualize interactively\n",
-    "\n",
-    "One more thing! This whole exploration began with our viewing the 2006 TED Talk by Hans Rosling: [\"The best stats you've ever seen\"](https://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen). One of the most effective parts of the presentation is seeing the _animated_ bubble chart, illustrating how countries became healthier and richer over time. Do you want to make something like that?\n",
-    "\n",
-    "You can! Introducing [Jupyter Widgets](https://ipywidgets.readthedocs.io/en/latest/user_guide.html). The magic of interactive widgets is that they tie together the running Python code in a Jupyter notebook with Javascript and HTML running in the browser. You can use widgets to build interactive controls on data visualizations, with buttons, sliders, and more.\n",
-    "\n",
-    "To use widgets, the first step is to import the `widgets` module."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from ipywidgets import widgets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After importing `widgets`, you have available several UI (User Interaction) elements. One of our favorites is a _Slider_: an interactive sliding button. Here is a default slider that takes integer values, from 0 to 100 (but does nothing):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4a5474f75f08400090ca199b832ec70b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "A Jupyter Widget"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "widgets.IntSlider()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "What we'd like to do is make an interactive visualization of bubble charts, with the year in a slider, so that we can run forwards and backwards in time by sliding the button, watching our plot update the bubbles in real time. Sound like magic? It almost is.\n",
-    "\n",
-    "The magic happens when you program what should happen when the value in the slider changes. A typical scenario is having a function that is executed with the value in the slider, interactively. To create that, we need two things:\n",
-    "\n",
-    "1. A function that will be called with the slider values, and\n",
-    "2. A call to an _interaction_ function from the `ipywidgets` package.\n",
-    "\n",
-    "Several interaction functions are available, for different actions you expect from the user: a click, a text entered in a box, or sliding the button on a slider.\n",
-    "You will need to explore the Jupyter Widgets documentation [4] to learn more.\n",
-    "\n",
-    "For this example, we'll be using a slider, a plotting function that makes our bubble chart, and the [`.interact()`](http://ipywidgets.readthedocs.io/en/stable/examples/Using%20Interact.html#) function to call our plotting function with each value of the slider.\n",
-    "\n",
-    "We do everything in one cell below. The first line creates an integer-value slider with our known years—from a minimum 1952, to a maximum 2007, stepping by 5—and assigns it to the variable name `slider`.\n",
-    "\n",
-    "Next, we define the function `roslingplot()`, which re-calculates the array of population values, gets the year-group we need from the `by_year` _GroupBy_ object, and makes a scater plot of life expectancy vs. per-capita income, like we did above. The `populations` array (divided by 60,000) sets the size of the bubble, and the previously defined `colors` array sets the color coding by continent.\n",
-    "\n",
-    "We also removed the colorbar (which added little information), and added the option `sharex=False` following the workaround suggested by someone on the open [issue report](https://github.com/pandas-dev/pandas/issues/10611) for the plotting bug we mentioned above.\n",
-    "\n",
-    "The last line in the cell below is a call to `.interact()`, passing our plotting function and the slider value assigned to its argument, `year`. Watch the magic happen!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bbc97145cc924ae3a765f37a10594f60",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "A Jupyter Widget"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "slider = widgets.IntSlider(min=1952, max=2007, step=5)\n",
-    "\n",
-    "def roslingplot(year):\n",
-    "    populations = by_year.get_group(year)['pop'].values\n",
-    "    \n",
-    "    by_year.get_group(year).plot.scatter(figsize=(12,8), \n",
-    "                         x='gdpPercap', y='lifeExp', s=populations/60000, \n",
-    "                         c=colors, cmap='Accent',\n",
-    "                         title='Life expectancy vs per-capita GDP in the year '+ str(year)+'\\n',\n",
-    "                         logx = 'True',\n",
-    "                         ylim = (25,85),\n",
-    "                         xlim = (1e2, 1e5),\n",
-    "                         edgecolors=\"white\",\n",
-    "                         alpha=0.6,\n",
-    "                         colorbar=False,\n",
-    "                         sharex=False)\n",
-    "    pyplot.show();\n",
-    "    \n",
-    "widgets.interact(roslingplot, year=slider);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## References\n",
-    "\n",
-    "1. [The Soviet War in Afghanistan, 1979-1989](https://www.theatlantic.com/photo/2014/08/the-soviet-war-in-afghanistan-1979-1989/100786/), The Atlantic (2014), by Alan Taylor.\n",
-    "\n",
-    "2. US National Research Council Roundtable on the Demography of Forced Migration; H.E. Reed, C.B. Keely, editors.  Forced Migration & Mortality (2001), National Academies Press, Washington DC; Chapter 5: The Demographic Analysis of Mortality Crises: The Case of Cambodia, 1970-1979, Patrick Heuveline. Available at: https://www.ncbi.nlm.nih.gov/books/NBK223346/\n",
-    "\n",
-    "3. gapminder: Data from Gapminder (R data package), by Jennifer (Jenny) Bryan, repository at  https://github.com/jennybc/gapminder, v0.3.0 (Version v0.3.0) on Zenodo: https://doi.org/10.5281/zenodo.594018, licensed CC-BY 3.0\n",
-    "\n",
-    "4. [Jupyter Widgets User Guide](https://ipywidgets.readthedocs.io/en/latest/user_guide.html)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
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-       "<link href='http://fonts.googleapis.com/css?family=Source+Code+Pro:300,400' rel='stylesheet' >\n",
-       "<style>\n",
-       "\n",
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-       "    src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n",
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-       "\n",
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-       "div #notebook { /* centre the content */\n",
-       "    background: #fff; /* white background for content */\n",
-       "    width: 1000px;\n",
-       "    margin: auto;\n",
-       "    padding-left: 0em;\n",
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-       "#notebook li { /* More space between bullet points */\n",
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-       "div.cell.border-box-sizing.code_cell.running { \n",
-       "    border: 1px solid #111;\n",
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-       "div.cell.code_cell {\n",
-       "    background-color: rgb(256,256,256); \n",
-       "    border-radius: 0px; \n",
-       "    padding: 0.5em;\n",
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-       "    margin-left:auto;\n",
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-       "\n",
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-       "\n",
-       ".text_cell_render h4 {    /*Use this for captions*/\n",
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-       "    line-height: 120%;\n",
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-       "\n",
-       ".text_cell_render h5 {  /*Use this for small titles*/\n",
-       "    font-family: 'Source Sans Pro', sans-serif;\n",
-       "    font-weight: regular;\n",
-       "    font-size: 130%;\n",
-       "    color: #e31937;\n",
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-       "    margin-bottom: .5em;\n",
-       "    margin-top: 1em;\n",
-       "    display: block;\n",
-       "}\n",
-       "\n",
-       ".text_cell_render h6 { /*use this for copyright note*/\n",
-       "    font-family: 'Source Code Pro', sans-serif;\n",
-       "    font-weight: 300;\n",
-       "    font-size: 9pt;\n",
-       "    line-height: 100%;\n",
-       "    color: grey;\n",
-       "    margin-bottom: 1px;\n",
-       "    margin-top: 1px;\n",
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-       "\n",
-       "    .CodeMirror{\n",
-       "            font-family: \"Source Code Pro\";\n",
-       "\t\t\tfont-size: 90%;\n",
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-       "/*    .prompt{\n",
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-       "\t\n",
-       "    \n",
-       "    .warning{\n",
-       "        color: rgb( 240, 20, 20 )\n",
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-       "</style>\n",
-       "<script>\n",
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-       "                           equationNumbers: { autoNumber: \"AMS\", useLabelIds: true}\n",
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-       "                tex2jax: {\n",
-       "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
-       "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
-       "                },\n",
-       "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
-       "                \"HTML-CSS\": {\n",
-       "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
-       "                }\n",
-       "        });\n",
-       "</script>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Execute this cell to load the notebook's style sheet, then ignore it\n",
-    "from IPython.core.display import HTML\n",
-    "css_file = '../style/custom.css'\n",
-    "HTML(open(css_file, \"r\").read())"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.7"
-  },
-  "widgets": {
-   "state": {},
-   "version": "1.1.2"
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- },
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- "nbformat_minor": 1
-}