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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Lineshape Comparison and Analysis\n",
      "\n",
      "By James Keaveney (<james.keaveney@durham.ac.uk>)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We're going to start with a complex-looking plot, and break it down step-by-step into its component parts. The code used here should then be portable into pretty much any other Python script you'll write."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The end result of this tutorial is this plot (click on it for pdf) - a comparison of a lineshape that is fitted to various functions. The physical significance of this is the inhomogeneous broadening caused by the Doppler effect. As is well-known, the bare atomic response has a Lorentzian lineshape. Doppler broadening is governed by the Maxwell-Boltzmann distribution, which is Gaussian. Combining these requires a convolution of both lineshapes, resulting in a Voigt profile. For different temperature ranges, either the Lorentzian or Gaussian may be a good approximation. If this is the case, the residuals should be flat, and normally distributed around 0. In this plot we show the residuals for all three (Lorentzian, Gaussian, Voigt) functions. \n",
      "\n",
      "<a href=\"images/lineshape_analysis.pdf\"><img src=\"images/lineshape_analysis.png\" alt=\"The final product\" width=\"400\"></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So, what are the main features of the plot:\n",
      "\n",
      "1. Multiple panels, which aren't equally sized\n",
      "2. Maths (LaTeX) in the axes labels\n",
      "3. Analysis of (randomly generated) data with least-squares fitting\n",
      "4. Histogrammed residuals\n",
      "\n",
      "And some less obvious features:\n",
      "\n",
      "* Y-axis labels are aligned\n",
      "* Axes are constrained to share (some of) the same limits\n",
      "* Legend is placed so that it doesn't go over the plot elements\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "The Data Part"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Everything in this example is going to be imported from the script used to make this plot, which you can download <a href=\"files/code/lineshape_analysis.py\">here</a>. So let's find out what the current working directory is:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#cfg = get_ipython().config\n",
      "#cfg['InlineBackend']['close_figures'] = False\n",
      "#print cfg\n",
      "from os import getcwd\n",
      "getcwd()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "'D:\\\\Dropbox\\\\Dropbox\\\\Examples\\\\python_notebooks'"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Ok, great - now we can tell the system to look in the subdirectory called 'code', then import from our script file:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys\n",
      "sys.path.append(getcwd()+'/code/')\n",
      "from lineshape_analysis import gaussian, lorentzian, voigt, generate_lineshapes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The functions we have imported are the standard Gaussian (Normal) and Lorentzian curves, and the Voigt profile, which is a convolution of the Gaussian and Lorentzian. Look up the functions in <a href=\"./files/code/lineshape_analysis.py\">lineshape_analysis.py</a> if you're interested in the details."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now let's generate some noisy data, using numpy's random module:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "from numpy import random, arange\n",
      "from matplotlib.pyplot import figure, subplot2grid, setp, clf\n",
      "from IPython.display import display\n",
      "\n",
      "x = arange(-30,30,0.2)\n",
      "wL, wG = 1, 5\n",
      "#generatae the Lorentzian, Gaussian, and Voigt lines:\n",
      "yL,yG,yV = generate_lineshapes(x,0,wL,0,wG)\n",
      "#add noise to the Voigt\n",
      "y_noise = random.randn(len(x))*0.03\n",
      "y_data = yV + y_noise"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now let's take a quick look at it:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = figure(1)\n",
      "clf()\n",
      "ax = fig.add_subplot(111)\n",
      "ax.plot(x,y_data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "[<matplotlib.lines.Line2D at 0x8cd9c50>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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CS0Seesr/3syZwMMPl16uuNiqkT77zF4/+aT1Gp8+3f32RZoRI4Dly2vmuyIm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\nYdAnIooj/x/PQb57XH+k/gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8cb0c18>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Great - there's some noise, but the overall lineshape looks ok."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For fitting the data, we use a least-squares minimisation routine, which is part of <a href=\"http://docs.scipy.org/doc/scipy/reference/\">scipy</a>. Specifically, we are using <a href=\"http://docs.scipy.org/doc/scipy-0.13.0/reference/generated/scipy.optimize.curve_fit.html\">curve_fit</a>, which is a handy wrapper for scipy's least-squares routine.\n",
      "\n",
      "Before using it, we must import it:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.optimize import curve_fit"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Then we can run the fit three times, one for each function (L, G, V). Personally, I like the curve_fit wrapper as it makes everything more neat and tidy. The only mandatory curve_fit inputs are the function, x_data and y_data. Optionally you can include y-axis error bars (with the option sigma=*array*), or initial parameters with p0=*list* - as we do here. curve_fit returns (by default) a list of the optimum parameters (popt), and the covariance matrix (perr). The errors on each of the fit parameters can be found from the square root of the diagonal elements of perr."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# FITTING:\n",
      "\n",
      "# 1. Lorentzian\n",
      "pin = [0,1]\n",
      "popt, perr = curve_fit(lorentzian,x,y_data,p0=pin)\n",
      "y_L = lorentzian(x,*popt)\n",
      "y_LRes = y_data-y_L\n",
      "\n",
      "# 2. Gaussian\n",
      "pin = [0,1]\n",
      "popt, perr = curve_fit(gaussian,x,y_data,p0=pin)\n",
      "y_G = gaussian(x,*popt)\n",
      "y_GRes = y_data-y_G\n",
      "\n",
      "# 3. Voigt\n",
      "pin = [0,1,0,1]\n",
      "popt, perr = curve_fit(voigt,x,y_data,p0=pin)\n",
      "y_V = voigt(x,*popt)\n",
      "y_VRes = y_data-y_V"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So that's all the analysis done, we just need to plot it all."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "The Plot Part"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now let's create the final figure with all the panels. The <a href=\"http://matplotlib.org/users/gridspec.html\">subplot2grid</a> module is very useful here for making more complex panel arrangements than is possible just using <a href=\"http://matplotlib.org/api/pyplot_api.html?highlight=subplots#matplotlib.pyplot.subplots\">subplots</a>. Both of these modules are part of matplotlib.\n",
      "\n",
      "subplot2grid creates a grid of subplots, in this case an 8 x 8 grid, then we just make panels span multiple elements in the grid using the *rowspan* and *colspan* options. The first argument *(yy,xx)* is the total number of panels, the second argument (*(yy-3,0)* for ax_LRes) is the top-left corner of that particular panel. Just remember that most indexing in python starts from 0; in this case panel 0,0 is the top left, and x-1,y-1 is the bottom right\n",
      "\n",
      "We can share axes limits between panels with the *sharex* and *sharey* options. Turn off the axes tick labels that are not required, using *setp*.\n",
      "\n",
      "Finally, we label the axes. Notice that we can use LaTeX commands for any maths in the labels (or anywhere else that text goes), just insert it using a <a href=\"http://www.tutorialspoint.com/python/python_strings.htm\">raw string</a>:\n",
      "\n",
      "       # Normal string\n",
      "       print ' Hello \\n world' # prints 'Hello' and 'world' on two lines\n",
      "       # Raw string\n",
      "       print r' Hello \\n world' # prints 'Hello \\n world' in one line with no breaks\n",
      "       \n",
      "For the LaTeX stuff, this just avoids any confusion with all the back-slashes."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "### Create figure panels using subplot2grid\n",
      "fig = figure(1,figsize=(6*0.75,7*0.75))\n",
      "clf()\n",
      "fig.subplots_adjust(left=0.15,bottom=0.08,top=0.97)\n",
      "\n",
      "#rows\n",
      "yy = 8\n",
      "#cols\n",
      "xx = 8\n",
      "\n",
      "#main panel\n",
      "ax = subplot2grid((yy,xx),(0,0),rowspan=yy-3,colspan=xx-1)\n",
      "\n",
      "#3 residual panels\n",
      "ax_LRes = subplot2grid((yy,xx),(yy-3,0),rowspan=1,colspan=xx-1,sharex=ax)\t\n",
      "ax_GRes = subplot2grid((yy,xx),(yy-2,0),rowspan=1,colspan=xx-1,sharex=ax,sharey=ax_LRes)\t\n",
      "ax_VRes = subplot2grid((yy,xx),(yy-1,0),rowspan=1,colspan=xx-1,sharex=ax,sharey=ax_LRes)\n",
      "\n",
      "#residual histogram panels\n",
      "ax_LHist = subplot2grid((yy,xx),(yy-3,xx-1),rowspan=1,colspan=1,sharey=ax_LRes)\n",
      "ax_GHist = subplot2grid((yy,xx),(yy-2,xx-1),rowspan=1,colspan=1,sharey=ax_GRes)\n",
      "ax_VHist = subplot2grid((yy,xx),(yy-1,xx-1),rowspan=1,colspan=1,sharey=ax_VRes)\n",
      "\n",
      "#turn off unwanted axes tick labels\n",
      "setp(ax_LRes.get_xticklabels(),visible=False)\n",
      "setp(ax_GRes.get_xticklabels(),visible=False)\n",
      "setp(ax.get_xticklabels(),visible=False)\n",
      "setp(ax_LHist.get_yticklabels(),visible=False)\n",
      "setp(ax_GHist.get_yticklabels(),visible=False)\n",
      "setp(ax_VHist.get_yticklabels(),visible=False)\n",
      "setp(ax_LHist.get_xticklabels(),visible=False)\n",
      "setp(ax_GHist.get_xticklabels(),visible=False)\n",
      "setp(ax_VHist.get_xticklabels(),visible=False)\n",
      "\n",
      "#set axis labels\n",
      "ax.set_ylabel(r'Intensity ($I/I_0$)')\n",
      "ax_VRes.set_xlabel(r'Detuning ($\\Delta/\\Gamma$)')\n",
      "ax_LRes.set_ylabel(r'$L$')\n",
      "ax_GRes.set_ylabel(r'$G$')\n",
      "ax_VRes.set_ylabel(r'$L \\otimes G$')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<matplotlib.text.Text at 0x898dcc0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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Wz63bnAh8iGow/h7gLbZv7jpOklTEgJSapHZo+uB6vOg44DsTMyM09DWqe/72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\nFO3/A7CfvFKtEd4rAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8b3ff60>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The y-axis tick labels for the bottom 3 panels look quite bad at the moment, but we can sort that out later when we know the range of the residuals.\n",
      "\n",
      "Now let's populate the plot - we start with the data we generated previously:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# plot initial data\n",
      "ax.plot(x,y_data,'k.',alpha=0.6)\n",
      "ax.set_xlim(-30,30)\n",
      "\n",
      "fig.canvas.draw()\n",
      "display(fig)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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6dCh79uxBRBg1ahQ//elPCQQCXHjhhZSXlzNz5kwrl+hF4b364A4WI0aMoKSk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\nxpgk0Fs9sM+p6m+BE0Tky8Aod5VwXCd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       "text": [
        "<matplotlib.figure.Figure at 0x8b3ff60>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Then add the fits..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# add fits to main panel\n",
      "ax.plot(x,y_L,'b',lw=2,label = 'Lorentzian')\n",
      "ax.plot(x,y_G,'r',lw=2,label = 'Gaussian')\n",
      "ax.plot(x,y_V,'g--',lw=4,label = 'Voigt')\n",
      "\n",
      "#and optimise the axes limits\n",
      "ax.set_ylim(-0.1,1.05)\n",
      "\n",
      "fig.canvas.draw()\n",
      "display(fig)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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jIyOJNCfQKOpk3dFviQsrZk0YwAuQMhhn5y0YDEeZN2+eja1rHFOmAM9ZTzAX\nepcS5BXkcILb0sTExBATE2NrM+pFSNlwJ0UIcUhK2f+qLijEKGCRlHJK5fHzgElK+Xq1PolUCVMg\nUAQ8KqX8rsa5ZGPsV2iZ5t7RXlRgsrS5vrWPJx76nMWLHW9JiZRajamTU3pBYNXM3saZG7mhZ23r\n4RV1IYRASml347+NHZPaKYQYWH+3y7IH6CGECBNCuAD3AFbiI6UMl1J2k1J2QxuXeqymQCmujH0Z\n+6wEigvXMKJ/hUMKFGj1pW65BThyNxFJQ3lvLfzvxy4M72hn+3EprpjGitS1wN7KbdbNlRAaVQVB\nSmkEngA2AEeAL6WUR4UQc4QQcxppj6KR/Jb2m3VD6kheemmYQwqUmVtuAba8iu6793hsD9ywP4tP\nP1pua7MUTURjw72wypeSauNEUsqkpjSqoahwr/HM/HomKw+vtBy7bF5K3k/zcXW1oVFXSVmZVlI4\nP1+S6+KJb1kxxUeO4N6nj61NcyhaS7h3Bs2b+kOlMJmA9k1tlKL5MAxayM8fQXSMBxy+mzEh1zm0\nQIG2hu/GGwEEpwK00Qj3g6rMWWuhsSL1HjAauLfyuKCyTeEgeO4/zLVnYFL8dfC/r/j9pCG2NqlJ\nuOUW7fmw51TtxW+/1d1Z4VA0VqRGSinnAsUAUsoLXMGyGIUN2b0bgA0XhuPkBLfdZmN7moibbwa9\nHlae1lLuZOwuCsoKbGyVoilorEiVVS5XAUAIEQTVp4oUdk+lSMXK4Vx/PQQE2NieJiIwEMbdmMXW\nEXu5+24IGbWT279oJQrcxmnswPn9aEtjIoDlwF3AX6SUXzWPefXaowbOG4PJBH5+kJ9PBzJ49cMO\ntKak7P/79wn+cran5dhNuvB0xZ/xdPdU6/gagL0OnDdKpACEEH3QyrQAbK65MLglUSLVcJZ9uIzs\n/b/y4nvLSSGEMF0yp0+X0KVL6/nDzc6WBP0tGDyrlnvemXUnngWeREREOFw2fUtjryLV2AoGr0sp\nj0op36l8HDXvu6ewbz776TP+0n45PZ+EP0zT4zP6TdasaV0LcAMDBe1Lx1i1pYpUtYOMg9PYManJ\ntbSpfffsHIPBwOH8wwCcaAdbhyRT1uNjcnJyWl1N8PHh1iJV0dlxs+kVGmrfvTZAcnIydLFu6+Pp\nT2JiYqsrZ/LgpCqR0pmgc2iwEigHp6Ge1ArgVrQ1drdUvr4ViJBS3tdMtimaCDc3N0qDiq3a8g9n\n4e/v3+qLX3uyAAAgAElEQVTCoMieEXQ69hTvftKbi6/BHT+1a3XeYltD7bvXBpg2axrFzqWWY32F\nCz5FPoSEhLQ6L8Pd2Z3rGExZ0o14lUHxpk2tzltsazSqnlRlueA7gbBqn5VSysVNbJeiCblYcZHw\nsgASXS4A4H7elbGjxzJ37lwbW9Y89OuXxk5dP6JMMPCijh733lv/hxR2S2MHzr8FfgeUoy2JKQAK\nm9ooRdMytstYfvhoAGf/DjdtvptOpzu2Si/KzNNPz+d053IABhad54vPPrOxRYqrobGVOTtLKW9s\nFksUzcaFs+WEZv6GBxDgGcytfW9ttV4UgIeHB9feJTj+Zg96yhPM6DWs/g8p7BZbFL1TtDA/RO/H\ng2JSPHoyfGqPNjEl/+qrf+CAz1By3eD/3v4bjy58lOjoaDWI7oA01pO6FnhQCHEaMI/ESimlEi47\npaICTn+2HQA5emybybrekrqFl+fFMkMHJt1a+hT0pexMGQaDoc18B62FxorUlGaxQtFsrFsHg3K2\nAhBy73U2tqbl0AkdR52SLMcnjRlMboUpF22BBomUEKKAund5kYBPk1mkaDKklPxp9RKWdt1CaSq4\nTpxga5NajHFdxqEXeiqktul2uV8OPu4+rT7MbY00eoGxPaEWGF+er7Ye5Z6f+wLgbhRMHXA7q6av\nsrFVLcdIw0irmu43FT3OUBd/3N3dVVWEWmgVC4wVjsWrn2+2vC52khSVt61B4wlh1p7jluTTZGVl\nqd2NHQwlUq2U+HiIL9hs1Tax28Q6erdObrxGy5bpmA8z41wp2fcY6elBqiqCg6HCvVbK9Jll/K9b\nILjmW9q2P7Cdsd3G2tCqlqWsoox9Z/bRd+gUfHIvMpg4znYoID6+N4GBgbY2z+5Q4Z6ixThwAP4X\nu91KoNyMbuz+brcNrWp5XPQujOo2Cp/b7wDgBrGRzMwx/OMfG2xsmaIxKJFqhTz3HHB2AHMPT+OW\nBHA1Cq6R1zD70dm2Ns02TNTC3Fs9/gfoiIubYVt7FI1ChXutjE2b4IYbwMcHzvWPxGXnNtY8eD+D\nXl9Mt6ButjbPNmRmQseOlDm70Mktj/P5rmzcqH1PiipUuKdodkwmeOYZ7fXLURdx+W0H6HTc9sbb\nbVegADp0gP79cSkv462ZsQD8+c9aNr7C/lEi1Yr44AOIi4POneHxXpvAaIQxY8Df39am2RQpJQmT\nh/LWSPg0+EE8Z87m4EH4z39sbZmiIdhMpIQQU4QQx4QQJ4QQz9by/n1CiANCiINCiB1qYfPlOXu2\nciwKWLoUXLes1w6mTrWdUXbCzpSd9Pb5lKipsFGfSFn3z0FfxrPPapGgwr6xiUhVbjD6DtpawL7A\nzMqtsqqTCFxXuXj5r8AHLWulY/GnP8HFizD5pmKm3loIP/6ovaFEilEhowjyCLIcl+uLCL1uBbm5\n8NRTNjRM0SBs5UmNAE5WliAuB1YC06p3kFL+KqW8WHkYC4S0sI0Oww8/wOefg5sbjJ/3XwKX+DNz\nVBrL++oY9cQfmTx5MtnZ2bY202bodXpu7327VduIWTG4u8PKldr3p7BfbCVSnYGUaseplW118TCg\nfkq1kJ0NDz2kvV68GLac/ZISUc7KATBruolDvoc5deoUc+bMsa2hNubOvndaHcdkruXlV4wAPPyw\n9j0q7JPGlmppKhqcNyCEmAA8BNSaKr1o0SLL68jISCIjI6/SNMdBSpgzRxuPuu46uG/2WZ57a6tV\nH790PwICAli2bJmNrLQPJoRNwN/Vj5zSXABcdM787oFE1n3fk19+gUcfhdWrQdjdBHzzERMTQ0xM\njK3NqBeb5EkJIUYBi6SUUyqPnwdMUsrXa/QbCKwGpkgpT9ZynjadJ/XBB5pIeXvDwYPw4pr7WHFx\nheX9YGMgo+LHYjAY1DIQ4IXNL1Dy2cfctSWTUW+vRnfb7SQnw8CBkJcH//639n22VVSelDV7gB5C\niDAhhAtwD9qefhaEEF3QBOr+2gSqrbN7Nzz5pPb6/fchLAy252636tMpL5Trr79eCVQl0ROj+WeP\nJxiTArrv1wLQtav2/QHMmwe//XaZEyhsgk1ESkppBJ4ANgBHgC+llEeFEHOEEOZ72UuAP/C+ECJO\nCKF+PpVkZ8Odd0JZGTz+ONx3H5ikiQ76DniUVd0ITQdNrXIr9avidm0AvWjFCqbfdhvPPfccSUnR\nPPpoOWVl2vealWVjGxVWqGUxDkZxMUyaBDt3wqhRsG0buLho7xWdPk1F73C+HKTn1aHtmVg+FaPR\nSEREhKrrXY3M4GA6ZGVxn48P2/38GDduHBERo1m16gl27oSxY+Gnn8Dd3daWtiwq3FNcNSYT/P73\nmkCFhsKqVVUCBeCxYQPeZfBI8FRmd3kCo9GoaifVwqbK8PfWoiIKCgrYsWMH+fnn+fTTIkJCYMcO\nuP9+tWzGXlAi5SBIqY1BrVoFvr6wfj106qS9ZzAYWLhwIUl//7vWcOedREVFERER0Sa2r2oMBoOB\nNa6uAAzwNlI09CLGQUbWrFnDN9+8x/r12ve7erX2fbcxR90usVUKgqIRSAlPPw3vvQeurvDNN9Cv\nX9X7ycnJGE+doktiIkYnJ5ymTcPDw0OFeLWQnJxMgm85w2cJ9oRJoIKzhWfxOaXtJdK/P6xZAzfe\nqA2o63Twr3+1rdQEe0OJlJ1jMmkC9dZb4Oys3eEnTNA8guTkZJzcnNA76Rly5Ag6wHTbbW1+QfHl\ncHd3p1dIL74LOWxpM3oa0fXXkZ2dTXR0NFFRUXzzjQd33AHvvqv9H7zzjiZYipZHfe12TFmZNgZl\nFqj//Q9uukl7Lzk5maysLL7M/hKD+JALHqco14HT7DZa2K6BREVFcd3w63igj3Xhuwu9LnAh54Jl\nk4abbtI8KldXzaN6+GHt/0PR8qjZPTslP1+bDv/pJ/Dy0jyo6kXaoqOjiT8Rz6quqygX5QB0KdDz\ncNiLlGea1LZN9XAs+xh93rVe0z4lbQr9fPpZjeNt3Ai33abNql5/PXz9det1VNXsnqLBHDsGI0dq\nAtW+PcTEXFpFMioqioLeBRaBAij1dKM4s0xt29QAegf25ma/EZZjIQXJFcmEhISwZMkSoqOjKSoq\nYvJkLc0jOBi2bNHKc51UqcUtihIpO2P1ahgxAo4ehcDALO6//3369Lk0GdOoN7K11Hqd3pND5uDt\n7k1JSYlKPWgAT93yKl7lgqhfYeH+axluHM769esvEfnhwyE2VpusOHYMhg6Fr76ysfFtCCVSdkJh\nIcydq4V4+fnQt288t976KtnZu6w8InO6wR1/u4NCWWhp9yyFrF+NlJWVMWDAAJV60ACuD59EmudL\nvLkBpu1NJCgoiDFjxtQq8l27avlT5v+fe+7R/r+Ki234D2gjKJGyA379FQYP1gZonZ3hjTfg3nu/\np6Li4iV/LOYBc6dUJ3wLvS3tXfe5ERsTy+nTp3FxcVEC1QCEEPg89BjSxYUhaWn89fe/Z8GCBXXm\nl/n6apMX77yjJdG+/z4MGgS//GKjf0AbQYmUDblwAR57TFuGcfIkDBigLRx++ml46qnakzHd3d0p\nKSmhv3d/krr8Hx98B93Ow9l4D8rLy1WY11iCgxGzZiGkxP2ttyz5ZR4eHhav1Tw+BVq+1OOPazeW\nfv3gxAmtTM7jj2v/n4qmR83u2QCjET7+GJ5/Hs6fBycnbfeSRYu0Ke/LUVRUhMFg4JEHH8Rj2DA4\nfpxFYV05d9PNdOvWjblz5yovqrGcOgU9e2qJUCdPUh7SiYNnD7Lm/TVkZWVRUlJCREQEHh4eJCcn\nW2ZO9XoPoqMhOlr7Pw0I0P4P//hHzSN2NOx1dk+JVAtiMmlT2AsXwvHjWltkpBY+VM8gbxArV8LM\nmZi6duW9+fN5aM4cJU5XwclRo+geG8vfBwXx6o2llLiXMHLPSEJ9Q+nYsSOLFy9myZIlVqJlzug/\neBCiomBr5TxG796acE2b5lgJoPYqUg70FTouFRWaOEVEaAOux4/DNdfAF19o09oNFSiLIJeXa7ds\nQPfiizzx1FNKoK6S9YMGsSgSnpt2jjyPPMpEGQe7HmTP3j14eXkBVaF2zZB64EDYvFlL/uzeXZsB\nvOMObbzqyy/VQuWrxeGXxSxcuNBuExcLCrSw7s034fRpra1zZ3jpJXjwwfpDAvPSF3d3d4bfNZy/\n7vgr/73jv3T5fC0kJHDe358PMzKYV1TEihUrrEIRe/su7J38rl05f8wdk65quu5i+4v0nNKT06dP\nYzAYiIqK0kLtRx655PsVQvOcpk7VKqa+/jocOgQzZmiR5JNPwgMPaDtLKxqHw4d7s2fPvsT9tiVS\nwp492saTX3yhlaUFCA/Xtk96+OGG1ylauHAh27Zt40TGCbJuy8LkZcK53In/fq9j+sEy/n3DDfza\nsSMRERGcO3eu1lBE0TCKior47NW/8tnZ19jRpapdSEHEoQgmdp+Ij49Pg28ApaWwfDksWQJJSVqb\nl5cmVLNna16WvWGv4Z7Di9QDDzxAUFCQzfOC0tK0BL+PP4b4+Kr2MWO0PfGmTQO9vu7PV/ea/Pz8\nyMjIIDY2ltSzqZwYdwJje6Olr5Dw5YcuRAf1YXxkJNHR0SxdupSEhAS7+C4cmR9+dy33991OTrUb\nybjycfQ915eSkhLKysro3r17gz1WoxG+/VYbd6y+58GAAVrNqnvvhRA72azNXkXK4cO9iIiIWt3v\nliA5Wavv9PXX2pS0mcBAbWHwQw9ppT8adi4t/yk+Pp7MzEwCAwMJDQ8luXuylUAB/GkH7B48Cedz\n5wgJCcHDw+OyoYii4eztPZr3ftjNzDtLca/Qs/yeLzjx7QkSSrQbgJOTk8VjNRgMVh5r9RuNWcCc\nnLQE0JwcAz17FnPgwGhOnIggPl7w7LPartPXXqvdxKZN08YqFdY4vCdltr+2H0hTU1wMP/+sLTrd\nsAEOV1X7wM1NG4+47z649Vbripm1YTAYWLNmDYWFhZZtuBITE0lISMBoNJKTk4P3YG/iB8VjkibL\n524+DrdsDuKjLmFcd911ymtqYqKjo3HbvBm3/C1EpjnT98e9FF1zjeUGcDmPdeHChRYBq+lxmWcG\nN23ahMnkhK/vPXTp8iIbN7pSWlp1/b59td/PpEla/lxzlTCu7e9FeVLNjNkTqe0Od6UUFmq7h+zc\nqYnTzz9DSUnV+56ecPPNcNddmkBVTgI12N709HQKCgpYt24d06dPJyIigvDwcI4fP87evXuZGDaR\nXrpefG36GpM0MSQDXtvsz0d33s10lRPVLERFRWHw8mL23hCcdn8KM2bw5dy5nDt3jqVLlzJ79mxW\nrFhRq8daffavpsdlfq+kpARvb2/y8z/H2fkwZ8+uYv16+O47bSflI0e0x+uvaze6MWNg4kQtVSUi\nom7RauxNujn+XpqLViNSdU0PN5SKCi3rOy5OC9127tRe15w+HjoUJk/WKjeOHl1/8qWZmj8i98pf\nm7OzM2PGjLEIjjlZMzQ0lJycHNyy3XhBN5gfnfexfpU7P/zxMV598UUlTs2EpaJpYaG2qvjIEYa/\n+y6x48ZRXFrKihUrOBp+lKV7l/LM2Gdw0ml/QgaDgby8POLi4rjhhhvYt28fQUFBdOzY0fJ7NBgM\npKamkpSUZNmw1ddXmwGcMUOrV/Xzz5qXvnkz7N+vjWOZx7KcnLQB91GjtMfIkVrKgxCNF52r/Xtp\nSVpNuGfJxK5nTMZgMHD8eCb5+SH06zeTI0dciYvTEvJq7vwkhIlBg2DcOB1jx2r1hNq3v/R8td3B\narYvWbKEbdu2kZubS5cuXfjkk0/45JNPAJg7dy7ny8/z7ZffcjblLO7u7pSXl5OYmMj1OTk8sHYt\nFULi9NXX2gCHomU4ckS7E+Xlsa5fP7beeCMj/zCK6d9MB2Bk55G8NeUtRoaMtIR6v/76K97e3oSF\nhVFSUsJnn31m9XvMzs5mzpw5LFu2rN79EM+fh7/8ZRO7d3uTmtqNrKwgakZjPj7aIHx5+V6EOES3\nbvm8+eZDdOhw+ZtYbX8v9hrutRqRqkl5OaSkQEKC9jh2THvesyePgoLak1VCQmDIELh4cTMXLnyP\nyfQr3boF8dVXX9UpfOY0AbP4mPtWH5+IiIigoKCAlStXIoSgf//+jBw5knnz5pGWl8bfd/6d9/e8\nz+DywQzOHExJSQkDBgyg5/Hj3PrJJ4jycvjrX+Evf2my707RMNZHRTH5X/9CbzKRPO8RRoR+R1ah\n9cZ8d/a6k5BTIeQczyExMZEuXbpYstTrumk11BOu/jvq128Uw4c/xq5dmpMXGwuZmbV/rksX6NFD\ne/TsWfW6W7e6x0vtVaQcPtzbuFGbZTM/kpK057Q0bRnKpfig05UREJDNpEntGTbMicGDNTfafGOL\njo5l5cotpKenkJWVyNChQ9m+fXutdz53d3dyc3MRQuDr62txtWtzp3fu3Imvry8dO3ak84TO3PnV\nnXx77FsqpBZT/iZ+4/Se04Tpw7g9L49b165FGI1aDsOLLzbPF6i4LDu9vUmeOJFHNm0i7RsDpgfd\nLlmnsSphFavvXE3KlhTeeOMNqzErszht376d8PBwjEZjo8aAqv+OnnjiD3h4aDXuzZw9q6W8HDxY\n9Xz4MJw5oz02b7Y+n16vlZ3p2lXb9br6a3vF4T0pqN1+IbQtn3r1sn506VLMxo0GZs9+2CpL25yb\n5O7uzuzZs5k1axaxsbGYTCZ8fX0ZMmQIq1atsrqGwWDg5MmTrFmzhkGDBpGTk4Ner6ekpIQxY8bg\n7+9vNbhtdrGdnJyIzoomTaRdYrfzBT2ffhXAjMxzWsOzz2oZgWq7EpsQHR1NQkICkbm5zFq/niyX\nch6c4cX60AJLn97tenP48cN89J+PrLylTz7/hA+/+RCnbCfKSstwdXVt9IxsbWFZfV7ZsmX/4eDB\nPOLiCvHwGERBQUc8PYeQmKgnOfly23TZpyfl8CIVGSlrvSuEhFS5tXX9p1Z3pffs2YNr5Sj4vffe\ny9y5cxk6dChlZWW0a9eO++67j/Pnz1udw/z5goICSkpK6N6jO+u3ridXl4u+o55uI7rx+xt/z4ND\nHrSye+HChfxU+BOxvrFW7b6lbjyz1cSzsWVInR7TP/+Jix3PurQFrERi3z64+27IzOTbXs7MuVFw\nNqCMmypuYqzbWPLy8sjJybGE+Luyd/GF/gsoA322nk504qn7nmJKnyn0CerT4PHM6iJkMBhYvnw5\nqampeHp6EhYWdslwRM3xse7du1tWIZSWVkUbNZ937LBPkXL4cM+88vxy1Jz5MJfciI2NtczABAYG\nkpGRgXPlgjoPDw+2b9/O7DmzGT5uOEuXL6XAVICfvx8uLi74+fmxfft2cnNzGT9+PDf+8UZu+/I2\nym6q2lIkqTQJz2Oel4iUu7s7ndI7ga923M/nGh474s0jH+/HtQIutG+P+5o1uI8e3WTfk+LKsNq/\ncNw42LePpHHjmJaQyK3H4b0BbhwrS+DwEF8u5uXRrl07S4j/vzf+BybABSo6VZBCCk9vfZp129bx\n3Z++Izk52TKeuXPnTkInhnIi5wRpx9Po0q4L+nI9hcsKeeHxF/B08QS033JpaSm5ubnk5OQAMH36\ndG666SZLJODk5ERJSQnFxcWYTCYSExN54403AG02ulcv+OUXA999V5Wn9+GHC/D0tMU3XD82ESkh\nxBRgKaAHDFLK12vp8zYwFSgCZkkp42o719P/9zQz75+JzllHaYWWFTcmdIxVH3d3d/Yf2c9J35P8\nvOdnTDoTvgG+lPYtJbk8mfnh89m9ezdZWVl0796dwsJCjqQfYcQnIygcWMg3ed/Abdq5ci/kEhsb\nS2ZmJkVFReh0OkJCQujs35kyLt3z6HDW4UvaoqKi+OzttxnkYmLCL2e4dk0cQjMUFiwg4JlnqP6L\naYlEVUUD6diR9gcPsuGPf2TM6tU8cbAIOEViSibBCxbwpRCUBAayZMkSTulPQS1+SWlmqSV3qvp4\n5rcnvyWjfQb0gWMcA2B93nr6J/Rn5oCZgPZb7tGjB9nZ2eRH5HPK5xQpIoWfd/1M5/ad0Rfomdpx\nKhEREQQGBrJr1y66dOnCihUruPbua1mxegXnM88THxdPfm4+pYWlfL/+e/zteAucFhcpIYQeeAeY\nBKQBu4UQ30kpj1brcxPQXUrZQwgxEngfGFXb+d40vsmbn7xpOQ70COTcgnNWfaKioti4cyMXhl/g\nAtblEzt4diAjI4Pu3btz4sQJnJycSExMZPVXqyksL6QmOi8daWlpnDlzBhcXF8LCwpg7dy6lovSS\nvgCnc09TWJLP90vfxRgXR3hmJiONRubs3q1NQYKWBTprFjzzDISGXnIOR0q8awt4eHqSMn48M5KS\nmHjyJH+4cIHwwkJYtIiHgKzAQA4GB/H9+DLyA90o0JVYfb6LZ5daJ1PcvWvP1PRz87PcqPR6PUOG\nDOGtt96iT3QfSnxLKEE7v1nYxjuPZ+PGjRw5cgQfHx+LZ3f76tvZmL1R+6sfXnX+QfmDeOSRR5g/\nf36Tf1dNgS08qRHASSllEoAQYiUwDTharc/vgOUAUspYIYSfECJYSnm2vpMXFubDmTOs/OIL0lJT\ncXN1xdfHh6Dcglr7XyzMIXnDBgry85kQHEzndu1oLwR/GDGOhT9d2r/C2Uj4xVyCTSY8S0t595FH\n8Fi3DvfCQjyFKxWygrAKH/oWe9I3x4l+aeXogoKYUVBDxHQ6LfHq7rth5kytgHYdOFLiXWuhvvGi\n7du30yU8nJWlpYj583mqVy/4/HNYv5722dlMys5m/2FtWic1wIm4gUHs6eLMXtd8/unpjcc//wk+\nPqy69VY2xcZiKinhC+fz4HapLX7n8jm6fz8V589TVFpK1wEDCCwooOc1XcnOzr6kf0VKJsakJDqZ\nTOQkJZGxZw+GxYsp6ppT67/1j3dMx8NcrsMOafGBcyHEXcCNUspHK4/vB0ZKKZ+s1ud7YImUcmfl\n8SbgWSnl3hrnkiyyPr+rEUpevfS6xU7gUUuakZBQ8cqlXrlJgP5l7bVXKbQrhnZF2vN3X4Cb8ZJT\nkeMGfiW1evhkCkGyhwf5ffow7vnncZswAcOqVQ0K4xqaqKpoOmrmuZnHMc2pBHFxcTg5OeHn52eZ\n0Y2MjGTB/Pksf+IJnH7+maEmE91LSvC9cOFyU2oW/jUCTgVAhhdccIdcN+2xbgX0PH9p/6FzIK7j\npe27P4Bh6Ze2j3wEfqul4sKvBhiVqv1u1cC5RkNVseaXVevnfNeDkwl0JvDvpKNTR2dkaBB5efmU\nG43o9HqETkepsZw5+8rxdXbHzaTD1STwqtDDxWJSfQWuOicC27dHp9OBEOiE4OIaE55GHXqhtWWc\nPUt5eTlJLiU4ebui8/HB5OpKMVAgJcVC4NepE0OnTIHgYD7dsIHPNm5kf34+eULg4ezMi/fcQ9KF\nCyT/858Nzp2xGrxVtAg1vVfzAuGLFy8SHx/PtddeS2hoKNnZ2WzcuNGyBtPf35+Cvn1J0OtJqFyE\njJRa0lJamvbIyIC8PA7v2kVZdjYFGRm4lJYy/GA5o6XEz8uLiuJi9IAsK8NFCC74akumdEBFRYWW\nxrJZx0VvPRdlGaWugnxdBcUu4G90I89HbxYdSoqLkSYTvTNN6Cqg1El75J6FgnT4ME/PGj12W0LU\nFiKVBlQfeAkFUuvpE1LZdgnP/O7/al2V7lxUxPLKnKQzZ85w6NAhJk2YxPnz53Gt5rlU91J09UwB\nf1yZM2NeQGqebj5//rxlVmfx4sVQ2T81PZ3D27aRV1CAl5cXM2bMwMPDg+XLl1NaWkppaSlJSUkE\nBwcTHh5OUVGR8pTshJqlb8yiZRYnc/5bdHQ0ULUGs/o6PSvPt08f7VGNlZXe2u7duy2D55988glr\n1qxh06ZN7Nq1iyIhCA4Opl27duTk5CCEIDc3l9DQUCZMmMDixYstlRkSEhLod00/Fnc1WlIOioqK\neGj6dJKSkjj32znKy8vR6/V4eHjw1JNPUlZWRkLPBIxBQdpeanaILcI9JyABmAikA78BM2sZOH9C\nSnmTEGIUsFRKecnAuRBCPvvssxw6dIhPPvmk1ozw6m57ZmYmYWFhxMfH4+3tzfjx42sNs2q6+mYv\npqioiIcffpiuXbtaLSB97rnnePLJJ+natatV9caioiKWLl3Kzp07GTt2LPPnz2fJkiVs2LCB8+fP\nExISgrOzM127dsVoNKqKmnZMXSF3UVER7733HkCjq1KYE0VTU1MRQjBu3Djat29PRkYG27Zt4/Tp\n0xQUFODq6sqMGTPw9PTkp59+Yvjw4VY7A5lty8nRluXUvGEXFRUxffp0UlJSyMjIoF27dtx4440W\ngTX/uzw9Pe0y3LNJMqcQYipVKQj/kVIuEULMAZBSLqvs8w4wBSgEHpRS7qvlPHL27NlWolM9czwq\nKsqq/o+3t7elZlO/fv3qFIboah5TXTWDzAmc5gWkl6slVP3z0dHRHD58mIyMDL766is++OAD1q5d\ni8lkYurUqSxYsEB5U20Es7ikp6dbvHLzjdTsXXl5edGuXTt69uyJm5sbXl5edYph9ZtozVLHZjEt\nKyvDxcWl1nOotXvVkFKuB9bXaFtW4/iJhpyrpKQEk8lESEgIa9euJTMzEyklnp6e7Ny5k08++cSy\nlgq0u0Z4eDgbNmzAZDLVGmZdrsql2e2vuYDU3d2d+Ph4TCYTrq6upKSkkJeXx86dO60ygmueOyoq\nim3bthESEkJiYqJKMWgjVB9ScHd3JyMjg6CgIMLDw0lMTLQKK2tupVXXTczDw4Pu3buTlZVFRkaG\n5bdUX56d+X17xeEzzs2F4hITEzGZTAQEBHDq1ClLNu6sWbOsRMIcp+/cubNWYaj+H1obdQlYdbGJ\ni4sjMzOToKAgq0XHcOkguIeHB+PHj7d4birFoG1QPfdtwIABljLYcOl4VmNSUGrrW1+e3Zo1a0hP\nr0e1xs8AABk8SURBVGU60E5w+LV7UkqrmPyHH34gISEBZ2dnXFxc8Pf3r3V9kzmky8jIYOjQoRb3\nuK4NIBuC+Zx+fn4cP36cgICASzyu2lApBm0Lg8HA559/bllSFR0d3WS/j9r6Xm74AmDChAmkpaVx\n4sQJuwz3WoVImSkqKuL222+nQ4cObN68GaPRSHBwsFX9pup9a44HmOs+XemuK9V/IFDLDI9CgTau\nmZ6eTnx8PNOnT+fPf/5zs16vPpF75ZVXWLduHbt371Yi1dTUVvSusd5MzbsMKHFRNC/1eTYtjVnE\n5s+fr0SqqalNpBrrzahQS9HS2Otvzl5n91qdSDU1qgKBoq1gryKlq79L28Y8M5KQkIDBYLC1OQpF\nm0OJVD2oCgQKhW1R4V492Ov4gULR1Khwz0ExJ1/WJ1Ax5h0c7QR7skfZUjf2Zo89okSqibC3H5s9\n2aNsqRt7s8ceUSKlUCjsGiVSCoXCrnH4gXNb26BQtCbsceDcoUVKoVC0flS4p1Ao7BqbiJQQ4iMh\nxFkhRPxl+rwthDghhDgghBjSkvYpFAr7wVae1MdopYFrpfrmoMBstM1BFQpFG8QmIiWl/AWofadC\nDavNQQE/IURwS9imUCjsC3sdk+oMpFQ7TkXb1kqhULQx7LnGeb2bg6oUBIWiaamZgtCSf2N1pT/Y\nqyfV4M1BpZR28Xj55ZdtboO92qNscQx76qIhNtf172ho38thryL1HfAAQOXmoLlSyrO2NUmhUNgC\nm4R7QogYYCzgJITIBZ4GnMGy/95vQD8hRClgAl6zhZ0KhcL2tLgnJYTQo4VvPQAXIAn4VUq5TFZt\nEPoEsEJK6YoW9s2r3J7dbomMjLS1CVbYkz3KlrqxN3saQm021/XvaEzfumjxZTFCiNHAy1LKKZXH\nzwFIKV+r1mcOMFBK+bgQIhz4UUrZs5ZzyZa2X6FordRW9K6l/sYuV3DPFt5JbekFI2v0+RDYIoRI\nB7yB6S1km0KhsDNsIVINkeUXgP1SykghxDXAT0KIQVLK/JodFy1aZHkdGRnpkO6zQmELYmJiGlR0\nrzn+xhp6bbBNuDcKWFQt3HseMEkpX///9s4+Wq6qPOO/hxCQ8BVp6lcAgxowiFFAwPLVIFhCbKGV\nFkWiYqxaMehaiAShbVylIGorsGCJfMlCadCuQCEpCQghKAtDIJYUBAKJCF3hI4UFIXyVEPL0j71v\nmMyduffMZDLn3Lnvb62z7pw5++zz3JuZN3uf/Zz3rWkzDzjb9p15fwEww/aSur5iuhcEHaKq070y\nLAhLgPGSxknaCvgUyXJQyzLgCID8OMwewKNdVRkEQSXoepCyvY70gPHDwEvAU7YfkvSVfMMc4Bzg\nSEmvAo8Da2w/122tQRCUT9fvSWULwomk0dETwD2SJtTYDwDWATsC422vlDSm2zqDIKgGZUz39gdW\n2H7M9uvAz4Fj6tp8BrjW9koA2892WWMQBBWhjCDVyIIwtq7NeGAnSQslLZH02a6pC4KgUlTVgjAS\n2Ac4HBgFLJJ0l+3l9Q3DghAE7REWhGYXLGZBmAFsY/s7ef9ykut8dl1fYUEIgg7RsxYEJSZLemvB\nU4pYEG4ADpY0QtIokiP9wU3VGgTB0KPlIFW/0pbD7G3AJyXdONj5RSwItpcBNwGPAC8Di21HkAqC\nYUg7I6l+z9HZXmv7ClJQGZA6C8K2wDv7LAh1NoTzSB6pG4H5begMgqAHaCdInSVptqTTJE2StF3N\nsQcKnF/EggBwMjAbeKYNjUEQ9AjtBKkzSUnoXiaNiBZJul/SgGWqahjUgiBpLClw9ZWyirvjQTBM\nadmCYPvH+eWGh30l7QDsB3yjSBcF2pwPnG7bkkT/ogxBEAwTOuKTsr0GWCBpTYHm9UUWdiGNpmrZ\nF/h5ik+MAY6S9Lrt+lXA8EkFQZuET6rZBVMa4IdJRs0nSfnMj7f9UJP2VwJzbV/X4Fj4pIKgQ1TV\nJ9V1x7ntdZKmAzcDI4Ar+iwI+fglA3YQBMGwopTiBrbn56KD5wPTJG3kOAeQdAJwGul+1O6SVti+\nrwS5QRCUSCl197JX6iLSauCewPGSJtQ1exQ41PZE4Czg0u6qDIKgCpRVHHRQr5TtRbZfyLuLSWWw\ngiAYZpQVpIqka6nli8C8zaooCIJKUlbBzcLLBZIOA6aRKh73IywIQdAeYUEY6KIF0rXk9ycC1wGT\nba9o0E9YEIKgQ1TVglDWdG/QdC2SdiUFqKmNAlQQBMODsoLUEcBWpKwJTwK/aFAxZj6wG7BQ0jJJ\nd5ektRBFh67dokp6QktzqqanCI00N/s9WmnbjK4HqRr7wSGkVC0rgeshGTltXyJpCvC47RHAJGC1\n7f27rbUVqvZhq5Ke0NKcqukpQs8HKYqlajkauArA9mJgdC4SGgTBMKOq1WIatQmfVBAMR2x3dQOO\nBS6r2Z8KXFjXZi5wUM3+rcA+DfpybLHF1rmtzO9Ys5hRhk+qSKqW+jY75/c2otmSZRAEnaEK37Ey\npntFqsXMAT4HGzxVq22v6q7MIAiqQNeDVK4WsyJvL9LAfmB7HjBW0lpgIXBBt3UGQVANynKcH0Iq\nZ/VT2x9scHwKMN32FEkHABfY/mi3dQZBUD6lmDlt3wE8P0CTsCAEQQCU5zgfjLAgBEEAlJcFoQj1\nqwr95qU5u2cQBB2i0QPGZV27j6qOpApZEICu+7yabTNnzixdQ1X1hJahoacZRTQ3+z2Kth2Iqgap\nsCAEQQCUNN2TdDspid2WklYDpwAjAZyqxdwNfEDSa8B6UsXkIAiGIWVlQdgZGE9K1/IYsMg5A0Ju\nNh2YZXtr0rTv67leX2WpWkbQKukJLc2pmp4iNNLc7PdopW0zyigO+ifATL+ZlfN0ANvn1rT5CjDR\n9tckvQe4yfbuDfpyt/UHQa9S1cycZYxOGtkLDqhrcxlwm6Qnge2B47qkLQiCilFGkCoSls8Altqe\nJOm9wC2SPmT7xfqGUYghCNojCjE0u2CBIgyS5gFn274z7y8AZtheUtdXTPeCoENUdbpX1SwIy0h5\n0MmPw+xBqmgcBMEwo6wsCFcCD5MeMn7K/YswnAMcKelV4HFgje3nuq01CILy6fo9qWxBOJE0OnoC\nuEfShBr7AcA6YEdgvO2VksZ0W2cQBNWgqoUYPgNca3slgO1nu6wxCIKKUNVCDOOBnSQtlLRE0me7\npi4IgkpRVQvCSGAf4HBgFLBI0l22l9c3DAtCELRHWBCaXbCYBWEGsI3t7+T9y0mu89l1fYUFIQg6\nRM9YECR9VdLhkrbJ+zu12EURC8INwMGSRkgaRXKkP9iq1iAIhj7t3JN6HjgqbwD/LOk4SbtLGrT8\nTRELgu1lwE3AI8DLwGLbEaSCYBjSzj2pkbZPrdl/g7Ri923g34B/GejkghYEgPOAKcBDwPw2dAZB\n0AO0M5IaXbd/TQ5a+wJPFTi/iAUB4GRgNvBMGxqDIOgR2glSYyRtCFS2f5N/rge2LXD+oBYESWNJ\ngevivsu0oTMIgh6gnSB1MTBL0qG1b0raAtirwPlFAs75wOl5WUH0L8oQBMEwoeV7UraflnQScLWk\nHYDbgdeAAylWabi+yMIupNFULfsCP8/34ccAR0l63Xb9KmD4pIKgTYaFTypn2TyQdPN8vu2HC5yz\nJWll73DgSVI+8+NtP9Sk/ZXAXNvXNTgWPqkg6BBV9UltkuPc9iJgUYvnrJM0HbgZGAFc0WdByMfr\nV/mCIBjGlFLcwPb8XHTwfGCapI0c5wCSTgBOI92P2l3SCtv3lSA3CIISKaXuXvZKXQRMBvYEjpc0\noa7Zo8ChticCZwGXdldlEARVoKzioIN6pWwvsv1C3l1MKoMVBMEwo6wgVSRdSy1fBOZtVkVBEFSS\nsgpuFl4ukHQYMI1U8bgfYUEIgvYYFhaEdimSriW/PxG4Dphse0WDfsKCEAQdoqoWhLKme4Oma5G0\nKylATW0UoIIgGB6UFaSOALYipWJ5EvhFg4ox84HdgIWSlkm6uySthSg6dO0WVdITWppTNT1FaKS5\n2e/RSttmdD1I1dgPDiE9kLwSuB6SkdP2JZKmAI/bHgFMAlbb3r/bWluhah+2KukJLc2pmp4i9HyQ\noliqlqOBqwBsLwZG5yKhQRAMM6paLaZRm/BJBcFwxHZXN+BY4LKa/anAhXVt5gIH1ezfCuzToC/H\nFltsndvK/I41ixll+KSKpGqpb7Nzfm8jmi1ZBkHQGarwHStjulekWswc4HOwwVO12vaq7soMgqAK\ndD1I5WoxK/L2Ig3sB7bnAWMlrQUWUiyZXhAEPUhZjvNDSOWsfmr7gw2OTwGm254i6QDgAtsf7bbO\nIAjKpxQzp+07SPX7mhEWhCAIgPIc54MRFoQgCIDysiAUoX5Vod+8NGf3DIKgQzR6wLisa/dR1ZFU\nIQsC0HWfV7Nt5syZpWuoqp7QMjT0NKOI5ma/R9G2A1HVIBUWhCAIgJKme5JuJyWx21LSauAUYCSA\nU7WYu4EPSHoNWA+cW4bOIAjKp6wsCDsD40npWh4DFjlnQMjNpgOzbG9NmvZ9PdfrqyxVywhaJT2h\npTlV01OERpqb/R6ttG1G131SuaDoTL+ZlfN0ANvn1rT5CjDR9tckvQe4yfbuDfpyt/UHQa9S1cyc\nZYxOGtkLDqhrcxlwm6Qnge2B47qkLQiCitFSkJL0VVI2zd/YflXSTrafa/GaRcLyGcBS25MkvRe4\nRdKHbL9Y3zAKMQRBe/RkIQZJnwY+QgpS10n6EXA7sBRYXmRcWKQIg6R5wNm278z7C4AZtpfU9RXT\nvSDoEFWd7rV643yk7VNtX5f33yBl2vwF8M2CfRTJgrCMlAed/DjMHqSKxkEQDDNaDVKj6/avsX0q\nsC/wVJEOnLIgXAk8THrI+Cn3L8JwDnCkpFeBx4E1bUwrgyDoAVq9cT5G0mjbqwFs/yb/XC9p2yId\nZAvCiaTR0RPAPZIm1NgPANYBOwLjba+UNKZFnUEQ9AitjqQuBmZJOrT2TUlbAHsV7KNIIYbPANfa\nXglg+9kWdQZB0CO0NJKy/bSkk4CrJe1Aumn+GnAgxRPTFbEgjAdGSlpIsiBcYPtnrWgNgqA3aNkn\nZfsx4OBsyjyQdPN8mu2Hi3ZRoM1IYB/gcGAUsEjSXbaX1zcMC0IQtEdPWhA6QUELwgxgG9vfyfuX\nk1zns+v6CgtCEHSIXrEg1Hc8QtLO+WZ4UYpYEG4gjdZGSBpFmg4+uClagyAYmrQdpCTtDxwGbAcc\nlkdIg1LEgmB7GXATyd3+MrDYdgSpIBiGtPXsnqTxwO7AEtvLJAHsJ2l1DjADnVvEggBwHjAFeAiY\n347OIAiGPu2OpN5v+2rgbZL+DHh7Xn3bZZDzoJgFAeBkYDbwTJsagyDoATY1n9SjpFW437dwTiML\nwtjaBpLGkgLXxfmtuDseBMOUdlO1rJG0G/AB2+dK+oSkkcDrBc4tEnDOB063baW5ZNNSz2FBCIL2\n6HkLgqRvApfZXpNX4P7O9g8LnFfEgvAobwamMcArwJdsz6nrKywIQdAhqmpB2CSflKQ/Bd4CvGr7\n1wXP2ZK0snc48CQpn/nxth9q0v5KYG5N5oXaYxGkgqBD9GSQahdJRwGXAm8H1gA/APoeWr4ktzkB\nOI10M34V8Cnb99X1E0EqCDpEVYPUJqUPzveLjgTu6suMUJBfkp75251sQ6D/aOpR4FDbL0iaTApq\nhbxYQRD0Di2t7tWnTMkh9jbgWEk3ttDVoDYE24tsv5B3FxNl1oNgWNKqBaFfQQTba21fQXKHF2VQ\nG0IdXwTmtdB/EAQ9QqvTvbMkfYx0s/tukuP8pXzsgRb6KTzJlXQYMI1UTDQIgmFGq0HqTNIDwgeQ\nHm25MD8Ss4SU9+nygv08wcbu9F1Io6mNkDSRVN5qsu3nG3UUPqkgaI+e90lt6CAlv9sP+Ibtowue\nM6gNQdKupPtdU23f1aSfWN0Lgg7Rk6t7ALbXAAskrWnhnHWSpgM3AyOAK/oyIeTjlwD/CLwVuDiP\n1l63vf+m6g2CYGixqc/ubcD2PUXbZkvBefn6l9n+bu7jkppsCK8Az+U206oeoIoOXbtFlfSEluZU\nTU8RGmlu9nu00rYZHQtSRcmpWi4CJgN7AsdLmlDXZgrwPtvjgS/z5oPGlaVqH7Yq6QktzamaniL0\nfJCiWKqWo4GrAGwvBkbnIqFBEAwzyghSRTxSjdqEmTMIhiO2u7oBx5LuQ/XtTwUurGszFzioZv9W\nYJ8GfTm22GLr3Fbmd6xZzNjk1b02KOKRqm+zc35vI5otWQZB0Bmq8B0rY7pXpFrMHOBzsCH/1Grb\nq7orMwiCKtD1kVQRj5TteZKmSFpBqhbzhW7rDIKgGpSSTyoIgqAoZUz3NglJZ0n6b0lLJS2QtEvN\nsW9LWi5pWa5i0w09P5D0UNZ0naQdy9Ij6W8kPSDpDUn71B0r428zOV9vea5K3VUk/UTSKkn317y3\nk6RbJD0i6ZeSRndJyy6SFuZ/n99J+npZeiS9RdLi/B16UNJ3y9JSiG6v7nVgdXD7mtcnA5fn13sC\nS4GRwDhgBbBFF/R8vO86wLnAuWXpAd5PSiS4kJrV0JK0jMjXGZevuxSY0OXPyiHA3sD9Ne99Hzgt\nv57R9+/VBS3vAD6cX29HenZ1Qol6RuWfWwJ3AQeXpWWwbciNpGy/WLO7HfBsfn0McI3t120/RvqC\nbPZHaWzfYnt93q1Nztd1PbaX2W6U16uMv03R+oqbDdt3APXZMzYYhfPPv+ySlqdtL82vXyIVvR1b\nop5X8sutSP+hPF+WlsEYckEKQNLZkv6HlC7mu/ntd7GxlWGwRHqbg2m8mZyvCnr6KENLq4kNu8Xb\n/eZK8SpSnv2uImkcaYS3uCw9kraQtDRfc6HtB8rSMhhl+KQGRdItpOFxPWfYnmv7TOBMSaeTavQ1\nW/3ryKrAYHpymzOBtbZnDdDVJuspoqUgm3vFpPIrMrYtqas6JW0HXEtKbfRizvDRdT159P/hfA/1\n5pxcshQtg1HJIGX74wWbzuLNkUshA+jm0CPpRGAKKT9WH5tFTwt/m1o229+mhWs2TGxYAqskvcP2\n05LeCfxvty6sVED3WuBntq8vWw+AU6GTG4F9y9bSjCE33ZM0vmb3GODe/HoO8GlJWylVVx5PSqa3\nufVMBr4FHGP7/2oOlaKnVlrJWoqYdstgDvD5/PrzwPUDtO0YubLSFcCDts8vU4+kMX0rd5K2IS3+\n3FuGlkKUfee+jVWJ2cD9pNWia4G31Rw7g3RTeBlwZJf0LAceJ/0j3wv8qCw9wF+R7gO9CjwNzC/5\nb3MUaRVrBfDtEj4r15Ayv67Nf5cvADuRngV9hFRabXSXtBwMrM+f277PyuQy9AAfBP4ra7kP+FZ+\nv5S/zWBbmDmDIKg0Q266FwTB8CKCVBAElSaCVBAElSaCVBAElSaCVBAElSaCVBAElSaCVBAElSaC\nVBAElSaCVNAzSNpS0h5l6wg6SwSpHiNn5bw3Z39cKukU1T5q37/9jpK+2oHr3rmpfdT0tbWkX9Xq\nlnSgpB8NcuokYL2kIyTdL+kCSX8r6YeS/jU/u/hrSfG5H0LEP1bv8YrtvW3vRXpw9Chg5gDt3wqc\ntKkXtX3QpvZRwwnAf3rjZ7b2A46R9EcDnLeH7eW2byU94Hyt7cttnwLca3stcAcVSeYWFCOCVA9j\n+xngy8B0AElTc27reyX9OI8ozgXem9/7nqR31+UEP1XSzPx6nFI+90vzSO1mSW/Jx14q0OYfcs7z\nOyTNkvTNJtKPB26o0TAO+B1wNQMH1PV1+7UjyN/ln3Ny/8EQIYJUj2P7D8AISYcCxwEH2t6b9IU+\ngZTL+vd59DWDjb/Y0D953fuAi/JIbTWpInV9u35tJO0HfBKYSBrdfaRB30gaAezljdMgH2F7AXAh\n8OWc+qX+vP2Bewb4OyzNL5cCBzZrF1SPCFLDh0mkxGZLJN0LfAzYrY1+/mD7vvz6t8C7C7QZRwoM\n19te65Tjey79AyLAGGBDHntJOwBrAGyvJE3XpjY4b1/bSwYTb/s1YIu+0V1QfSqZmTPoHJLeA7wB\nPAdcZfuMuuPj6k5Zx8b/eW1Td/y1mtdvAI2+7PVt+vqoDUoDle+uPXY08O81++cBP8lbLY3+w22W\nh0gDHAsqRoykehhJfwz8mDRNWgD8dX6vr8barqRRy/Y1p60C3paPbw38+UCXYOBgU8udwF/klbvt\ngE/QOFA8S6oC1Df12zLf8AbA9j3AC6qpHZhtBw830bfxG+l3eiOPqIIhQIykeo9t8nRuJGlU9FPb\nPwSQ9PfAL/MN89eBk2zfLenOfLN8nu0Zkv6JlF74CeBBNg4m9a/d5P1abHuJpDmkTJCrSNlVX6gX\nb/uNfMN9D2Av4PuSzqprtgPwDVL2SEhT2Sv6Dkr6OPmel6RXcmDrY29gUf11g+oSmTmDriFpW9sv\nSxoF/Ar4Us0N7dp2J5LKK32vYL8n276wYNtzgHts/0cL0oMSiele0E0uzaO83wKzGwWozCzgEwOZ\nUPuQ9C4KVr7JU72DqUqBgaAQMZIKhjSSPkUyfr5ctpZg8xBBKgiCShPTvSAIKk0EqSAIKk0EqSAI\nKk0EqSAIKk0EqSAIKk0EqSAIKk0EqSAIKs3/A7kUXU1rcoP1AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8b3ff60>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "...and the residuals..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# add residuals to sub-panels\n",
      "for axis in [ax_LRes,ax_GRes,ax_VRes,ax_LHist,ax_GHist,ax_VHist]:\n",
      "    axis.axhline(0,color='k',linestyle='--')\n",
      "\n",
      "ax_LRes.plot(x,y_LRes*100,'b.')\n",
      "ax_GRes.plot(x,y_GRes*100,'r.')\n",
      "ax_VRes.plot(x,y_VRes*100,'g.')\n",
      "\n",
      "fig.canvas.draw()\n",
      "display(fig)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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itQQaxRkwVZlYdXwdlcPgCyqA6bi/mYTRuIGYmELmz5/vaBObxIwZ8Pki+2mW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       "text": [
        "<matplotlib.figure.Figure at 0x8b3ff60>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A histogram of the residuals is a nice addition to this type of plot, and contains at-a-glance important information about the quality of the fit. With normally distributed errors (like we have, artificially, added into our example data), one expects a histogram of residuals of a 'good' fit to also be normally distributed, with a mean ideally around zero.\n",
      "\n",
      "There are a few ways of making a histogram, but in this example we're going to use the method built in to matplotlib to generate and plot it at the same time. We use the orientation option to place the histograms horizontally (NOTE - this is a well documented bug in matplotlib versions before version 1.3.0 so make sure you're running an up-to-date version).\n",
      "\n",
      "Let's add these to the plot now:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# check matplotlib version - horizontal histograms are buggy in versions before 1.3.0\n",
      "import matplotlib\n",
      "print 'Running Matplotlib Version: ', matplotlib.__version__\n",
      "\n",
      "# Histograms\n",
      "histrange = (-20,20)\n",
      "ax_LHist.hist(y_LRes*100,bins=25,orientation='horizontal', \\\n",
      "        fc='b',alpha=0.6,range=histrange)\n",
      "ax_GHist.hist(y_GRes*100,bins=25,orientation='horizontal', \\\n",
      "        fc='r',alpha=0.6,range=histrange)\n",
      "ax_VHist.hist(y_VRes*100,bins=25,orientation='horizontal', \\\n",
      "        fc='g',alpha=0.6,range=histrange)\n",
      "\n",
      "fig.canvas.draw()\n",
      "display(fig)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Running Matplotlib Version:  1.4.0\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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MJFIroFE0gLnSzOrj66kYAZ9TDszE/Y1ETKaNGI0FLFiwwNEmNotZs+CzxfbT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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8b3ff60>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now to tidy some things up. First, let's add a legend to the main panel:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax.legend(loc=0)\n",
      "\n",
      "fig.canvas.draw()\n",
      "display(fig)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ocOy8tJP3k7bzZ1tI7N6OqKjWAHToEMfs2bONa1wtMHjwYCwtLfnll1/KPC6l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\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8b3ff60>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The *loc=0* keyword argument places the legend in the 'best' location, i.e. it tries to avoid the legend hitting any data in the panel. This is automatically placed every time the program is run, but can also be manually placed - for a list of location parameters, see <a href=\"http://matplotlib.org/api/legend_api.html?highlight=legend#module-matplotlib.legend\">here</a>."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Next, let's tidy up the y-axis tick labels on the residual plots - at the moment they're all overlapping with each other which looks terrible. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# we only need to do this to one of the axes, since the y axes are shared (with the sharey option when we first made them)\n",
      "ax_LRes.set_yticks([-20,-10,0,10])\n",
      "\n",
      "fig.canvas.draw()\n",
      "display(fig)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ocOy8tJP3k7bzZ1tI7N6OqKjWAHToEMfs2bONa1wtMHjwYCwtLfnll1/KPC6l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\nUg4ODimN/w/MvAVpuYZGEQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8b3ff60>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Finally, let's make sure that the y-axis labels are all aligned. With this figure, it doesn't look too bad, but this is a useful bit of code for other plots where the difference is more obvious.\n",
      "\n",
      "We need to override the automatic placement (so this takes a bit of guesswork...):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# set / align multiple vertical axes labels\n",
      "\n",
      "# position of labels (play with this number until it looks right!)\n",
      "labelx=-0.1 \n",
      "\n",
      "#set label position, relative to axis coordinates\n",
      "for axes in [ax, ax_LRes, ax_GRes, ax_VRes]:\n",
      "    axes.yaxis.set_label_coords(labelx, 0.5)\n",
      "\n",
      "fig.canvas.draw()\n",
      "display(fig)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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bJ2KtWpKX54i9fSYvvzza1KLVCr1798bW1paffvqp0vNSSsaOHcsDDzzAxYsX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\nR0wODg4ZB0dMDg4OGQdHTA4ODhmH/wf31AKluTJ6LAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8b3ff60>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Great - we're all done for this example. When this code isn't running in a notebook, you can save figures either from the matplotlib figure toolbar, or integrate saving into the code, with\n",
      "\n",
      "    fig.savefig('your_filename_here.pdf')\n",
      "\n",
      "Many file formats are supported, including png, pdf, ps and eps. Most journals want a vector graphics format, so either eps or pdf are the way forward. If you don't know what vector graphics is and why it's important, see/run <a href=\"./files/code/raster_vector_demo.py\">this example</a> for an explicit demonstration of the differences."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}