{ "metadata": { "kernelspec": { "codemirror_mode": { "name": "ipython", "version": 2 }, "display_name": "IPython (Python 2)", "language": "python", "name": "python2" }, "name": "", "signature": "sha256:8ae04c848646baddd3d24bcee6087095ca8c39fbd26a67958503af40e0550cef" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Example Two-Dimensional Correlation Spectroscopy Pipeline" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Objective" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will demonstrate a faux pipeline that incorporates" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Environment Setup" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Configure notebook style (see NBCONFIG.ipynb), add imports and paths. The **%run** magic used below **requires IPython 2.0 or higher.**" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%run NBCONFIG.ipynb" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n", "['dti', 'd', 'h', 'm', 'us', 's', 'ms', 'intvl', 'ns']\n" ] }, { "html": [ "" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] } ], "prompt_number": 1 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Load the builitin dataset from [1] that depicts solvent evaporation" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from pyuvvis.data import solvent_evap\n", "\n", "help(solvent_evap)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Help on function solvent_evap in module pyuvvis.data:\n", "\n", "solvent_evap(*args, **kwargs)\n", " Model solvent evaporation dataset graciously shared by Dr. Isao Noda;\n", " used in his 2004 book Two-Dimensional Correlation Spectroscopy. From \n", " page 47:\n", " \n", " 'The system described here is a three-component solution mixture of \n", " polystyrene (PS) dissolved in a 50:50 blblend of metyl etyl ketone (MEK) and\n", " perdeuterated tolune. The initial concentration of PS is about 1.0wt%.\n", " Once the solution mixture is exposed to the open atmosphere, the solvents \n", " start evaporating, and the PS concentraiton increases with time. However,\n", " due to the substantial difference in the volatility of MEK and toluene \n", " coupled with their slightly dissimilar affinity to PS, the composition of\n", " the solution mixture changes as a function of time in a rather complex \n", " manner during hte spontaneous evaporation process. \n", " \n", " The transient IR spectra were collected as the two solvents evaporated, \n", " eventually leaving a PS film behind, as shown schematically in Figure 4.1\n", " (A). The measurement was actually made using a horizontal attenuated total \n", " reflectance (ATR) prism. \n", " \n", " ...\n", " \n", " As expected, the intensities of bands at 2980 and 1720 cm-1 due to violatile\n", " MEK and those of bands at 2275 and 820cm-1 assigned to perdeuterated toluene\n", " gradually decrease, while those of PS bands at 3020 and 1450cm-1 increase \n", " with time.'\n", "\n" ] } ], "prompt_number": 2 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Plot the data, and denote time through a [Two-channel colormap](http://wiki.scipy.org/Cookbook/Matplotlib/Show_colormaps)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "ts = solvent_evap()\n", "ts.plot(cbar=True, colormap='cool_r');" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "WARNING:pyuvvis.core.spectra:Spectrum does not have subtracted baseline; could affect result in specious absorbance data.\n" ] }, { "metadata": 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} ], "prompt_number": 3 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Time and Spectral Slicing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the above colormap, we can begin to see the order of events in the experiment. Let's choose three regions of interest to focus on. **A good approach is to use plotly to interactively zoom in on separate regions in the dataset.** Go ahead and play with it a bit. After doing this, I decided to focus on these regions of the spectrum:\n", "\n", " 1. $1800 \\lt \\lambda_1 \\lt 1650$ : **Sharp peak**\n", " 2. $3150 \\lt \\lambda_2 \\lt 2750$ : **Broad, multipleaks**\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import plotly.plotly as py\n", "\n", "fig, ax = plt.subplots(1,1)\n", "ts.plot(ax=ax, colormap='cool_r', title='Interactive Plot Through Plotly (interact with me)')\n", "py.iplot_mpl(fig, update=_pyupdate); #_pyupdate defined in NBCONFIG" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "WARNING:pyuvvis.core.spectra:Spectrum does not have subtracted baseline; could affect result in specious absorbance data.\n" ] }, { "application/pdf": 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QooooAKKKKACiiigAooooAv6H/wAhKP8A3Jf/AEW1UKv6H/yEo/8A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\nW+aqQmv/AEg6vwp/yUXx0f8Apppv/pNXaVxnhQf8XD8cn/p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FUtns5XUhLuz2PGMfhXo1\neV3TY8bTN2N4R+TD/GvuOCaUatTEJ/yafefG8YaYdz7U6j/CJ6pRRRXw59kFeX/Fs48a/Dv/ALCr\nf+jLevUK8t+Ln/I6/Dr/ALCrf+jbevqODdc4h/gq/wDpqZ9fwLrncP8ABW/9M1CPRJRH+0N4gU/8\ntdNVB+Eds39K0PHbLcap4TMzlg81wGIPPWMVg28wh/aJ1Hn79sV/K0iP9K1fGCtdXvhKC3Zd73M6\nIT0yWjAz+Jr7qjh1HG4Ou9P9npa9v3FZP8kfOcfVZf6yZVhb3jPLJtx6N2pKN16Rkl6s67wbJJJa\nTmVyzmTLE9Se5roa53wsPKuL63/uSsv5MRXRV+dcSpf2lOS2ai//ACVHwvBDayn2Ut4VKq/8qyf5\nNBXJeLP+QxbH+5Ejf+RQK62uK8ZXTQ65bRhQRJCqk+n7wH+n6118IQc8ySj2f6GXHa5spa83/wCk\nTO1ooor5c+zL+h/8hKP/AHJf/RbVQq/of/ISj/3Jf/RbVQoAK5f4jStF4ZYqxBNxCOP98H+ldRXI\nfFKQR+GFJIAN3EP517XDkPaZtho/34/mRU1jb0/MX4Ytu0C4P/T9N/7LXnfw+eF774eMkiNIbjWy\n6gglcoxGR2rvvhRJ5vh25bGMX8o/Ra8t+FcMieIvBs7H5JZb8r9fIlz/ACFfodCgpVc4k3blu/W9\nHEK343+R91wivZ5XjFLfX/1GxZ6Z8PCW8T+Nzn/mLKPySu6rgvhqd3iDxu3/AFG2H5LXe18DxMrZ\ni1/cpf8ApqB83nOmNmu3L/6SgrX+Ev8AyXrSf+xQ1z/0t0qsitf4S/8AJetJ/wCxQ1z/ANLdKrwD\nzD6UooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiuX+Jmval4b8D6nqujypFfARW9vM\n6B1heaVIhKVPDBPM3YPB280AdRXAftBf8kF+JP8A2KGs/wDpFLVrwFqPiCW58W+GtU1yTVZ/D2qp\nZ2uoXUESySJJZW1wvnLCsaFladh8qrlQnfJPmni/UvE1/wDBP4zRaj4qn8S6PaeENQgtdVmtILcT\nXy2d0L1IBCiq1uhEIVjuIfzlLvtzQBylFZ+gX+p6nePb6zoU2jQLEXWeWeOYM+QAm2MkgkEnPTj6\nVv8A2HTf+g1H/wB+JP8ACgCiOo+tXtf/AOQ7qP8A19zf+hmlFjpuR/xOo/8AvxJ/hVzW7PT21m/Z\n9XjRjdSkqYXO07zx0oAw6Kv/AGHTf+g1H/34k/wo+w6b/wBBqP8A78Sf4UAUKKv/AGHTf+g1H/34\nk/wo+w6b/wBBqP8A78Sf4UAUKKv/AGHTf+g1H/34k/wo+w6b/wBBqP8A78Sf4UAUKKv/AGHTf+g1\nH/34k/wo+w6b/wBBqP8A78Sf4UAF9/yDdN/3JP8A0YaoVuXlnp50+wB1eMAJJg+S/wA3zn2ql9h0\n3/oNR/8AfiT/AAoAoUVf+w6b/wBBqP8A78Sf4UfYdN/6DUf/AH4k/wAKAKFFX/sOm/8AQaj/AO/E\nn+FH2HTf+g1H/wB+JP8ACgChRV/7Dpv/AEGo/wDvxJ/hR9h03/oNR/8AfiT/AAoA53xPJ5PhrVpg\ncbLGdvyjauS/ZrXb8OtfX0u1/VbY1W+IF9HZ+O9Y0xdYd7aXwNeSogDiMzB5CPlP8W1CM+9Xf2cF\nhX4Z+JppbwI8d4cKUJOBFbEH05PFfoNLLZ4Xhitd39rKlJfO6X6n6FXwv9i8GYqvWd1P2VXTsr/f\nbUj+Mt1JZaPoV3E2Gh161lB90WRv/ZaT4fyy/wDCYa/9slZ7q4sdPd2bqxi86JiffKj86sfFWCxv\ntI8Lwf2kjTXWrI5QxN8oFvNknjB5IHHrUXha3sofjDqmnnUUSI6TNJvEbkE/bnIGMZ6SZrSlRUeH\nJYdr3+Wo2/KNSD+68ThyvE08w4apypaKpQlNX35fawkv/SNTvqKv/YdM/wCg3H/34k/wo+w6Z/0G\n4/8AvxJ/hX50fFlCir/2HTP+g3H/AN+JP8KPsOmf9BuP/vxJ/hQBQoq/9h0z/oNx/wDfiT/Cj7Dp\nn/Qbj/78Sf4UAN0X/kM2H/X1F/6GKqS/61/941saRZ6curWTJrEbsLiMhfJcZO4cdKrSWWmeY2da\njHzH/lhJ/hQBnUVf+xaZ/wBBqP8A78P/AIUfYtM/6DUf/fh/8KAMy5kENvLKeiIzfkK434RS+b4V\nkOMbbuQD/vlD/Wu21+20+HQdSmTWEZktJmC+S4yQh46VxXwSjs5fDt5b3GoLBJHd79pjZsq8aEHj\n6Gvp8DRvw/i6n9+n+HN/mUuVxlfdNW/8mv8AodrRV/7Fpn/Qaj/78P8A4UfYtM/6DUf/AH4f/Cvm\nCShRV/7Fpn/Qaj/78P8A4UfYtM/6DUf/AH4f/CgA1bpZf9eifzNUK3NTs9PYWe7V0XFqgH7lzkZP\nPSqX2LTP+g1H/wB+H/woAoUVf+xaZ/0Go/8Avw/+FH2LTP8AoNR/9+H/AMKAKFeVXQz4ge6/6iTr\n+HFeyfYdM/6Dcf8A34k/wryCVI2ja880bzrDrtwem0HOa+94GfLUrS78q+/m/wAj4zi/36M6fejX\nf3cn+Z6hXF+MLz7P488DxMx2yXN9kZ4J+zlR/wCh16GLLTCM/wBtx/8AfiT/AArzH4pR2dt8Qfhy\nItSSRZNQuFdhEw2A+SM4I569q8DhqisRj/ZPrTrf+mZn6vwnQWKzF0n1pV//AExUO/ryz4vf8jt8\nOv8AsKn/ANHW1ewR2+jzbvK8QQPsYo22JztYdQeODXlPxkt7OPxz8Nli1FZVfViHYRsNg8+15wRz\n17eldnBqcc5gn/LV/wDTUzr4DaeeU/8ABW/9M1DlZpvL/aDu39VkT/yRH+Fa/iq6+zah4ccMCbae\nWQc9SGjNYV8i/wDC/roJMGQXMyB8H5sWRxx15xWh4gnFp4l8Nl3x9nuS5bHT50Of0r9cWGi/qltW\nsPDTvaE1+tj4Hin22L4u4dn0eWQV/N0sRJ/+kxO/8IXMtxe3U8yqrz7pGA6BiQSP1rqq5Tw75EPi\nKe1a5CQi6ng83YxHDHBx17Cu6+w6Z/0G4/8AvxJ/hX49xVDlxkZpWUoxt+R83wZKMaeLoLeNaX4q\nMr/e2vkUK4Pxp82uoR/yyhQ/+RF/xr0v7Dpn/Qbj/wC/En+FeZ+OJrS38SXNuLnzd1vEkTKhAZt8\nbHryOA35V18Excsybj0j/wC3RX6lcaxc8HTprrKX/pmq/wBDvaKuRWmmvEjnWoxuUHHkyen0p/2H\nTf8AoNR/9+JP8K+Qas7M+upzVSKmtnqGh/8AISj/ANyX/wBFtWZPL5ETSYzjoM1P4M13Qta8Uavp\nFlqJMmiSeRI7QsFlLwliV7/Kcg5A5XjIqv4kisbbTd0WorOWkVSixMCR+I9q9DDYGf12nhq8bXcb\nrykk196dzzeLauKyPKsTWS5KsablG/RyjeDs011i9U/NEgIYAjoea4n4vNt8LRY73sX8mrvtPttP\nnsLeaTV0RniUsphc4OORkCuI+NdvaQ+E7drfUUnY38QKiNlwNj88ivU4Zp+zz6hTfSdvuO/K6/16\nlQr/AM/JL77MrfCA58NXX/YQl/8AQUrzj4ZzOuq+CElACRzXgQ4677eT+teifCprWDwBrGoS6gsL\n29zcyKhjYk7YUOcgYrzrwZKiXXw/jkZYVF9tMm0nh4yOcdeSeBX6Jh6DqV84Vt216fuq3+dvmfdZ\nLeFGvS/5+TaXr9XxP+Z6F8MOda8bN669MPyrv64n4TW1nLq/jkyakkaDxFcKjGNjvGTyMDjt19a9\nF+w6b/0Go/8AvxJ/hX5xxXpms1/dp/8ApuB87nDvj6vrb7tChWv8Jf8AkvWk/wDYoa5/6W6VVG9t\n7S3s57i1v1up4omeOBY2QyuASEDMAASeMngZ5pnwN1DVb/49af8A2noE2mbPCGtbPMuIpfMze6Xn\nGwnGMDr6186eafUtFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVT1jSNN1/SrvRNYs0u\nrG+he3uIXztkjYYI45HB6jkdquUUAc5p/wAPvC+lWQsLGC/RPtMt3JI2qXTzzzSQmFnmmaQyTHy2\n2jzGbbtQrgohXgvih4C8OeAf2ePiTpvhr+1Y7RfBepwRwXmsXl7HDHHYzBEiW4lcRAA4wmMgDOcD\nHsFcB+0F/wAkF+JP/Yoaz/6RS0AeJ0UUUAKOo+tXtf8A+Q7qP/X3N/6GaojqPrV7X/8AkO6j/wBf\nc3/oZoAoUUUUAFFFFABRRRQAUUUUAX77/kG6b/uSf+jDVCr99/yDdN/3JP8A0YaoUAFFFFABRRRQ\nAUUVS1q7On6Nf36nBtraWYH02oT/AEq6cHVmoR3bt95dKnKrNU47t2+88D8SeMLLxh40m1vTBKtr\nJ4ZvrdFlUK4xb3LEEAkdQe9ei/BI/Y/APi+02lXikhcg9QXitv6g18/aYwstElvC5EzTNZxpg5KP\nBMrH85F/OvbvhLeOmhePbWR2LN/Z+zJ/vbcfolf0JxHlFPCYBYbD/wAODpRV99KiV/PSerP2Dxlw\nVHK+E8dhcE/3dLDzjHveE6aXq/3iXqHxBvvI1HwM8xAjS6uZpR/soFB/RjVvRLyBviwt8UaNtS0+\n4hVCwO1l+zyMPwPmD8KxPG95FJrnhywkDPJZx3jt6nfLEo/9BapWu0tviPompyxtGkeozWjoDnbJ\ncrKu3PH3WCivnq2A/wCE/wCHV08R87znKNl5vlPzngmrCrkOWYLXmjgpxfnJe2svnJI7jTfGc198\nRNX8FNaRrFp1nFcJKCdzMQhYHtjEiY+h6546qvKPB0rXPxi13VZXA+0/brFB6m3Nov8AL+Ver1+c\ncRYKlgK1GFJWvSpt/wCLltJ/+BJm/E+AoZfXoQoRtelTcv8AHy2m/nJMKKKK8A+bCiiigC7ov/IZ\nsP8Ar6i/9DFVJf8AWv8A7xq3ov8AyGbD/r6i/wDQxVSX/Wv/ALxoAbRRRQBm+Jjt8N6sw7WM5/8A\nIbVyHwrT7PcanatwTb2EoH+9Dk/zro/HdybTwhqso725j/77IX/2auT8N6zpen+J4p45m+z32l28\nKHbjMiSCHv6FG/AV9zkuHq1+H8VShFvnenrBwlb7r/ic9aTpTjUadneN+ibs1f5J2PSqKy9I1631\ne91KyhXD6bP5L89evP5g/lUniGV7fQNSnjOHjs5nU+hCEivkXgqsMRHDVVyyfL/5NZr8GmbKfPHm\niaFFZ3hyRpvD+mSucs9nCxPuUFaNY16XsKsqTd+Vtfc7FK/Uv6t0sv8Ar0T+ZqhV/Vull/16J/M1\nQrIAooooAR3CIzt0UEmvHBqkhlOmGNNhvjcK/wDFnG0j6cD869Y1ecW+m3Enfyyo+p4/rXkZgAgj\nvuv+mNGT+ANfpXAlKCpVatRfFKKXqk3+p8nnFsVi6+H/AJMPN/8Agbt/7YeywtuiRvVQf0rx/wCP\ns722seD7iNirRzXTKQcEHMNet6e4ksLdx3iX+VeN/tE3CJqPhsHGbcXErE9gXhH9DXncC0bcUUqT\nWi9ov/JJo/UfC2TxuZYST156U/8AyahNfmztvhJcSXPhmaSRiSLxhk/9c0rD+L2B4y+HzngJqZYn\n0Hn2tX/gtJINBvLeRcfv0uF91kiUqfxAB/GsL433vkeIvDB/59C1wD6ZuLf/AOJr1sNQk+NqlOPX\n2n40pW/M7ODKMafENKnQ+H30rbWlCS/UwLyQR/Ga5uNgO/UzCpPYvZlQfwJB/CrHisSSatYm4LMY\nYmkkJPOAwyfyrP1D/SPFi6sz7Yz4rS2d/QGJB/LNL49m8QHX9Pt7awlury4tZ1eGEYLdRkhRwFIy\nfZe1fodKP7+hFNK1Hld3b4U7+W2+umhwYXJp5zmOQV42Ulg+XV2S5cPJ6t6LSb1emp6Jp11INTtT\nLIvmSX6iUf3nJ5P6frXoVeOaZrcVy/hvUCRuv75LhtpyF3FSV/A8V7HX5Fxlh3QnQuraSX3SsfCZ\nflc8sxmKjVVpSlqvRyj+at8iK6kMNtLKOqIzfkK8u8Rs91qrzSElo4Vc5HP3lX+tej67L5Oj3cme\nkZH58V5b4huJ49UCIskayW6iRZFG7aGUjr2yByOtenwHStzVFa7bV/Jcrf5nzedYSePz+Ebu1OjJ\n26e+qivbv7tj1uybfZW7/wB6JD+grP8AFOrzaLo8l1ap5ly7pDAgXJZ2YAADucZ/KrOiy+dpNpJ6\nwqPyGK4PxJrqa54k03wSlwyTyXsl5vH3hHGCFC5BHPz9f7tfPZVlLxuZTU17lKTc77csW27+Wmp7\neGx7hg8NTpwlLnpOd4pu0YU1KTaSb27IZ+zpfzal408Z3lwMSTXKyMMYwStxnj8K7bxFI6W9usSh\nnaddoPToa4T9nplsviJ4u0yaRnnmkklBIA3CN7hGJx3ywrs/Erym50+CCURuzu2444wOvP417/EF\nKP8ArXLkSUVGLXayor8NLH0/i1JV+fkWk44WK+caMXa9ttd+2pbsLny9AW6P/LOFm6/3c/4V554y\n1Yaz8NtNujM0kqXkccpbOd4jfqT1OCDn3rSn8a2ljBF4TaB2ub7T76ZZN2BGI42YAjqScN9MVxNz\ncFvheNrYYa6MjrgeQf8A69epkuSSoY54qtGzdeLj5xbqL7r3+487g7LcUsuy6U1ZOnTSv1teDf8A\n4FTe/Z9zpvB0hi+FPiaUH7iXrf8AkspriNGieD/hA5udrarbKM+uVB/nXW+Fr2A/CjxdGG2+XbXR\nO7jlrYKP1U1k3lsdO0D4fTuoHl6xE5/CSMf0r3sHN0cVjaTWtSq1/wCUZSPuMNU9lXwcJaN4ibfy\nouH51EdL8CCX0rXpm6yatIT/AN8ivT680+A0RTwzqUp/5a6pMQfXAUV6XX5lxvJSz/E27pfdFI8P\nN4KnmOIgtlOa+6TQVr/CX/kvWk/9ihrn/pbpVZFa/wAJf+S9aT/2KGuf+lulV8oeefSlFFFABRRR\nQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFcB+0F/yQX4k/9ihrP/pFLXf1wH7QX/JBfiT/\nANihrP8A6RS0AeJ0UUUAKOo+tXtf/wCQ7qP/AF9zf+hmqI6j61e1/wD5Duo/9fc3/oZoAoUUUUAF\nFFFABRRRQAUUUUAX77/kG6b/ALkn/ow1Qq/ff8g3Tf8Ack/9GGqFABRRRQAUUUUAFYnjlingrxA4\n6rpd2f8AyC1bdec/GO+vZLbTvDVtem0g1NLuW7kUfM0UUY/d9D95nUfXHI5r2OH8JLHZnRoxdtbv\n0j7z/BOy6nscP4f6zmVGLaSi+Zt/ywTnL8Iuy6vQ4SDRdBvNNtVRzHKmmrP5UyB/30cTFsdMZK/U\nZzzWfod/rWh+MDpUd15Ol6uIRdgopDusTmMZwWGCSeMZ711I0S5ezNxPZtFIIQGiUFTgocTewyRn\nHGM1la1pniS5nN9p+nTXCadc2t+5MBVMABFDMAdoYHA65zmv3XCYynU9pSnNSi017zTtK65bP1s1\nfqk1rt3VM/o5rWrZViOWccVTq07Ts1HncbTW/vRkoyi3s4p3Vrkeoz2l14/V43Jj0+KGHkHu7yHj\nr93b+dbPjdJZrvVLjTVKTRiLV7FgOs0bq2fyEhz7Va+H/he8v/EU+p6mpnRXllu5mjaNHmddojQH\nnCrjHcY9xnX8YeGp9Glj1fToZLi3ijZXyu4xdSSQB93GQTzgFvavFxOc4L+0qGD5ryjBLV6Ntp2e\n2r5VutVK258nWli8gzOhisqlzQoUoQUX8Mpw1vZpPklNy07S1Whzng3UJJfGnhyWSxltZ9RvNau7\nhH4KtMA4QjqCFjTr617VXjcWpxQeLvCOotbJHbNqNxBF5a8ZmgEYG4cHb+7GB0weua9kr4HjaFsR\nQmo8qcH1vr7WpdX8ro+s4jx0c1hhMfCPKp05O29n7arK19dlJIKKKK+KPmQooooAu6L/AMhmw/6+\nov8A0MVUl/1r/wC8at6L/wAhmw/6+ov/AEMVUl/1r/7xoAbRRRQBynxLlx4Z+x/8/wBdQW/5vu/9\nlrgbnw9emwtZUt2drOJVd1IXy90cc28qOSMyScDuK6z4tXsMVlpdjMxjWW6a4aQDPlpEh3Nj23g/\nhVH4d65rt1YtJq2nm4vUgiW5gjKq/kMPNhlCnqNkjpgc5jA9x+n5FXr5VklPF0kmuaTabte75Lp9\nlZr1cV1PSlk+MxuS18RhZJOMo25r2vqujTas2m76SlC6ezb8PLq9TxTctqEiZ1WCaYFPuySLMc47\nHHz9O1dn41nFt4S1eQ97SSP/AL6G3+tcxr2pR6fqeia2mnyWlvp8rIpdCN9o52O+3AKkEg4PZ1Pc\nga3xMuok8DahIsqETLEIzu4fLqePXjJrzcfD+0c4wWKUOWNWUY27OM+W2mmkeVvte3Q8nBU8RHlp\nV0lUvqk7q7eiT0vbRXsteiZb8ATm58HaVIT0g8v/AL5JX+ldBXmvg/xjDpWgeHdGWNXuLm+ks51J\n5i/eH/44n61s+BdTuL3WvFFtLKXjg1AtGScjBLrx7YjH5V52dZFiIV8XjJK0Iyk15p1HDT5/ejpq\nwnGpNNbNp/fb5noGrdLL/r0T+ZqhVzXJobaC1uLiVIoorFXd3YKqKNxJJPAAHeuJ8F+Ok8X3mp24\n06S0S1aOW0aTINzavuCSgEAjJRj9CtfO0cFXr0KmJpxvCnbmfa7svXXsb0cBiMRh6mKpxvCnbmfa\n7svXXt6nVUVhX3jbw7p2v23hq5vsXtyyphVLJG7/AOrR2HCs+DtB5OPcZl8UeKNP8KacL+/iuJ2k\nfy4be2j3yzPgkhV9lDEkkAAGqjl+KlOnTVN3qax03XdeXnt1HHLcXKdOmqcr1NY6W5l3Xl57W12G\neLJvLsoIgeZbhRjGcgAk8d+3FefTNaxeF7VPtMTXEupNc7AefLKlN303LitD4ieLwyaS/h8JeTXi\nxtYocqJZJwBH1wRwQ3Y/Q1i6/onibwdaWJ1y6ttSsHnRZb+KAxG2dsDEigkbCTgMABkDPLV+lZDh\nfqOEw1KvJRnOXMovRu3NdbdnHdpt6Ru9vNynh6rmE8ZjG1FzcqUIu6lP2cUppK1tJNqzacnpFOSa\nXqPhq487TEQnmLC49sA15J8crC81zxVBpGn25nuf7HDxICMkm5HTPsprt/C/iGysp5rO4uU+WNC6\nggshAIGR2zg/l65rO0GRPG/xMvPFFrauNM0e0XTUmY5WaYOztj6bhkc9BzyBXPlsJ5HnNbOJR/dw\ng5J7JykkkvNuTasumuxyeG+eYnh+hU9gk8Xg4vljNSt/EjGk5Ws+VxlG9mm4qVvhbVb4LaxHdNNZ\niOQM+m2JBYY/1MKRt+pGK5v4+TN/wksMa/8ALDSFmHtm5H/xNT6TeQ/DHxN/Z15HKVsrp4Zpkj4a\nwlCeXLI2MKquY1B6Fg4yMVp6voh+J+qeLtUskB/sjT7HTIIs8yNK0sxOemQI2H1Ir3cPClhOIVnt\nrYZwj73TVxpr8HFvsnr1P1OjiKeScTVMe4t02vbRdnZ03Vpxunt9taN82u1jEsYDL4am1Voldbbx\n9FPJvOAYwUTk+mWxXSaTaz3fxZ097qZbiW30i4mmlBBSUFwgZccYIZfxzWtYfDqZvhW/gyefy726\njM7ysc/6QXEiljg/xBQcZ6HFM8AeF/EcHiC68VeJtOg02X7GNPgtIZhKpUuJHk44UFgMKP8Aa4HG\nfJx2e4SvRxtSFRXTqQiusoyUYxcdbtP3m7LRJN9DmWKwjw0qsKq/cqrTitE3GVOFODServ7zdr8q\nWtro8m1O71Pwjq9v4Zayka30XUpL0SKpJFuzpnj2IGDnncK9x0j4gaRqWm6Xdgyb9ThnlRSoUr5O\nd4cZ4PBHGRkGtDV/CWi6ybua5tttxd2/2Z5kOGChgwPpkFVOf9kV5jrHwo8Q+GpbrX/Dl819b207\nyx6YqkymBwd+xj1cZ+5/Fg45IWnPN8k4toUqGOfsq0G9XtJyTvr0vNqWtlZa26eJm0sLxH+/w/7n\nGqEopuT9nVm7yvJWajNztaTailKadro73xBrcd54U+0oVVrk7SoPIwef5D8xXhfiHxlNNqNzPp0z\nrZjTzawEco04IJKkjPBb6fL3FdRI3jqy0trO+8EX6vqMJis5YXVyrv8AcEq/8s+eTk5HccGvT9N8\nAeHo/DekaFq2k2d0dMWGQt5fDXCKNz+p3Ecg8EcEV14fMMs4PppyiqsZzlZRlF2Wl3o3tZRV2nuc\nvB2Wx4WrzzHieEa858tKKjKE/cgnzTdtFJytppbnmlsmM+HmsR3fgPTNSu51ytuDNJ23Yyen16V5\nZYzXF340fxjA/lnRr+zsnkfiNY7jz1ZnPYBnjBPo1OWTxB4Wa7+Hlnpmo3DWtzJNBFFEZGntCxKS\njHBGDg84DDB54r0vwJ4KGi+FrjS9eginm1WWS4vYWIkVd4CiPOPmwqqCemc4rnxlXCcOwxeLUlP6\n1JcqVr+zk25O3nFtdtUdWU4GfDWOxGNxVOKoKM6NBK150qk03NJOyi6MYxj2lJ3fQ800PWZ/DGvW\nPixpUjPiiHUrNZVkICSfb2JbOOQVbA6dc13V1run63rNjaMxnkjsvOe3QjzG5wSAe2e59DXR6j8N\nvD/ifw6vgyOCOyhhhb7FKkYZraRFLK4zyckfNzlgW5yc1xU3wE0xEin07xPqdvfgFZbl8SblZCG2\nqcFcud/U4ycdiMlxFkmZR9viJOlVXPFPlb91/C3bVWjaNl1V+py5rkuR8T4+GZ4rGTpSi6nuODlF\nr2lSpRbcfhUfacjspSaium/Fa1crD8QbaaWTCRaXcw5/25ILnb+Zx+dU9bu5NA0n+x79eHuorjah\nz/rIflP5Pk/jWnrHw68YT6NdeLtWtmjv4LmATWsY3u0EKeXJKuDzuJdtvA2jjggVl6jpx8YTHTrS\n6uNQvrSC6klRYHEkSW9tiNHVgG3mQrH35GMnivv8JjsDX5KiqqUIR5Z21tKHvXutLe9LXq7WPv8A\nK8jw9SWBw8ZqVDCpU6ko7LlvVbUtrXqSje+r22Lkt62keEPEnhvEj3OpSW0MDRrlSfNAbJ7ZB4rq\nviVZSaX8PNCvOA1hdwv9Cz7v6Vz3gpJPFF3pGj3trMNQTWRqM7ldu2xijEke+PqAzMgVyMHdwTzX\nrnj7wmfGPhW50GCVIJm2vA7Z2q6njOATjqOK+VznOaGWZrhadbS9T2lR7q3KqV1bo0m+/W2p83nW\nBq5TneDhmLXs6dWVSVrtuM50E3tslRk1bV83oZHwUhC+Aba524N1c3M31/fMP/Za7usrwroEXhfw\n7YaBDL5os4QjSbcb36s2OcZYk4961a/K89xsMxzPEYun8M5ya9L6fgfOZnKjUzDE1cO24Tq1Jpve\n05ykvwdgrX+Ev/JetJ/7FDXP/S3SqyK1/hL/AMl60n/sUNc/9LdKryjiPpSiiigAooooAKKKKACi\niigAooooAKKKKACiiigAooooAKKKKAKGv6vD4f0HUteuI2ki020mvHRerLGhcge+BXifxA8W+LdV\n+DnxT8P+Ml0lrtfhxca3DJpsEkUapdWl4rQMHkcs0bW/+sBUOHHyLg591vLS21C0nsL2FZre5jaG\nWNujowwyn2IJFeO/E34e6f4K+BPxRuIdZ1XVrmfwNf2AuNSkjeSK0trK58iBfLRAVUyyncwLsXJZ\nm4wAeUeG9Yv9cvntNT8Lap4eiSIyC61J7Z4nYEDywLaaV9xBJ5ULhTyDgHo/7Ns/+hgsP++Lj/41\nVCigDQGm2eR/xUFh/wB8XH/xuuD1nxd8U21e+ab4RIshuZS6r4ggIB3HIB211gIByTxVjUb6y1PU\nLnUtNvILu0u5nnt7iCQSRzRsxZXRlyGUgggjgg124PFUsNf2tCNS/wDM5q3pyThv5320sdeExNLD\nNupRjUv/ADOat6cko/jc8/8A+Et+J/8A0SMf+D+3/wDiaT/hLPih/wBEkX/wf2//AMTXc0V2/wBq\nYX/oCpffW/8Alx2/2nhf+gKl99b/AOWnD/8ACV/FDr/wqZPXH9vwZ/8AQaQeLfief+aRgf8Acft/\n/ia7mil/amF/6AqX31v/AJcJZlhk3fB03863/wAtOGbxX8USMJ8JkB7E69AR/wCg1C2rfGkMsg8J\n+HzGB80QvG3nqRhs4HUDoeQexFd/RVxzfDw+HBUv/Kr/ADqP8DKpjcPPVYWC0to6v61Hr5qz+djg\n11L40SDy/wDhG/DcLR/ekku5CsnOPlC8r681Mg+NFwQR/wAIZa5IGJXumx7nah4rtqKf9twXw4Sk\nv+3ZP85szWKoJ3+rw++p/wDLP+G2Whzktl8bms7RJ9X+HKRKjeU5XUcsC3zZxH2OR0H41nXOg/Gi\n54HjHwDCCQcRJqAxj3MBNd7cXtncWlra293DLPaq6zxpIGeIsxZQwHKkggjPUHNVqIZ66cuaOHpX\n/wAF/wA2a1sxp4in7Kphqbj6NX9WpJnKxWPxfiULNrHw/nwAu5TqKknj5j+5x7nH4U9rD4usPk1r\n4dof9r+0j/KGunorF5tFy5vq9P8A8Bf5c1hLH0Y/8w8P/J//AJM5U6d8Yu2v/Dgf8A1P/wCNVQj8\nL/GiO9a+PxB8FNuIzbNDemHAzgD9xuHXqDk4GSa7mitoZ7Kmmo4elrp8Cf53t8jZZvFfDh6X/gLf\n5yZyw8PfFe9O+7+IPgvTTHwqWdheTLKG4JcyRgqVHK7ep4PFV1+EFxqWrDXPFfxOstRuhEIFEWnS\nwJHFu3FFAQ55J5IJ6eldjRSp8Q4zDycsMoU7q3u04J2e6vyuWvr5baHPicyrYmnOilGEZKz5IqDt\ne9uaKUrPr72q0emhRt/hz4TgC+bq9ncsjBlab7ScAYwuBGARx0IrqrDTrNLDUETW7EqYYxlY5wFA\nkXr+7/DjNYdWba9sra1u7W5u4YprtFjt43kCtMwcMVQHliFVmIHYE9q8zE4zEYxp4iblbu729Ox4\nuFwWHwUXHDwUb723fq938yb+zbL/AKGCw/793H/xqkfS7CRGjk17T2VgQymOcgj0P7qqNFcydtUd\nRy+u/B3R9VtEtbPx2liLebzrQqlw/wBmbOcp+7BBB5Bzn61lQeAPixpm23074x6JcW6HI+3Ws8jt\nznljblv/AB6u9or6CnxNj1T9lW5akd7ThCVm93dq9311OvL8bVyuj9Xw9uS7fLKMZpN7tKakl8rH\nKvpHxfkBT/hLPh9EAeHS31EsfqDHiol0r4xJMqnxR4GdZBklra9Cx47cJk5/H8K6+isVnbSt9Xpf\n+AI6nm9Zqyp0/wDwXD/5E89W0+ODXEjnVvDKq6GMIYJPLQnnzFI+YsOgB445HepbKP44EtHLc+Dw\nI4wBJcpcDzSvGf3ecM2cngL8vGOh72itXn91Z4Wj/wCAf8EyWaYm/vKDXb2VJflBP8fU5DSz8dDd\nwTW//CCC4SZDFDILzMj5yBxxgkAckdfqRRivPjlLK4uNK8IxHk5aScDOenBavRLC8tNOvrfUNQuo\nba1tpUmnnmcJHFGpBZ2Y8KoAJJPAAqF2V3Z0YMrEkEHIIqf7bjZp4Wl/4C/0kaf2rN70qb/7civy\nSORn0j4yzxNEnin4d2xcYEsceos6e4DQlc/UUyx8A/EyO2WK4+N2jREErhdHlmKr13b3jBLZ4weM\nV2FFL+3qsY8lOjSj/wBwoS/9LUi5ZxJr93Rpx/7ci/8A0rmOMtvg7dalrUOseN/ibp+srAwAtlsJ\noo5VU/JuATGCeWQDBPHIznqNe+HvhjxFNFd3uu20V3AhSO5tXuoZVGdwG5YxkK4DgHIDAHHXNuis\ncRnmPxNSFWVSzgrR5UoJLslFJK/XTUznnGMlUjUhPkcb25EoJX30ikrvq93pfZHLaj8IbYW/maH8\nRFW8J3TPqX2m8juiFwPMDRjacbVyuBtGCrVy6/BvxxdrHpmpfEHw4mmK/EcT37+SCeWjRocBgCcZ\nPGa9RoruwvFeZ4SLjGSfZuKbXo7dOl72Wi00N4Z9iVJVKsYTmtpSgnJW21trbondJaJJaHmNx8Ev\nElr9otNG8beGGtxj7PcXMd4LlwGDKJCsJAI/vL12jI54teH/AIZePvCV5/aOl+MfB97JcwGO5iuR\nfoquWyCpWE7vXJA6sMdDXolFb1OMszr0ZYetyyjL4k4rXTra3rpby00KqZ9VrqSq0ab5vifJZvrd\nuNmnfW6s+m2hyHifwl8VPGOnxaT4g8QeDNMsHjRHjtvtrSXESsSCWMRCqxAOOGxwcc1Si+GPjHSt\naTxHofjXwt9u8hraZbqO8MU0ZwcNth3ZDAEEHPHJIJFei3t7Z3gtxZ3cM/2eBYZfLkDeXIM5RsdG\nGRkHnmq1c9LifFUKTw9KnTUHe65NHe173u3ey37Lsc0s3xfPB0pKEI3XIox5HzJKXMmnzXSWrbei\n7K3nUvwO1ObTZpZfiXp7atdz/a7iURXKJ5+4MHUiLjBVcYUYwB2FXZPhb4o8R6qureMPiB4fje3i\nMNtHYW920cYONxCyRDDNgZbJPAA4ruKKuXF2Zy1bjzK9nyRvFOyaWlkmklttoZyzTGyhUpyqv322\n9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0v/hZnjUvp0zRmDSgii5wV/dy5ydvOfpXZ/adF/6BVx/4Fj/4ivP/\nAA3/AMlI8Z/9ctM/9FSV2NexnmmKj/17o/8ApmB7fEH+9w/69UP/AExTLN7LYS2VxFYWUlvdPE6w\nzSTeYschB2sU2jcAcEjIz0yOtR/Ay11+2+PWn/25rNrf7vCGteV5FkbfZ/pul5zmR92ePTGO+eIq\n1/hL/wAl60n/ALFDXP8A0t0qvHPEPpSiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK\nKACuA/aC/wCSC/En/sUNZ/8ASKWu/rgP2gv+SC/En/sUNZ/9IpaAPE6KKKAFHUfWr2v/APId1H/r\n7m/9DNUR1H1q9r//ACHdR/6+5v8A0M0AUKKKKACiiigAooooAKKKKAL99/yDdN/3JP8A0YaoVfvv\n+Qbpv+5J/wCjDVCgAooooAKKKKACiiigAq/Yf8g7U/8ArjH/AOjUqhV+w/5B2p/9cY//AEalAFCi\niigAooooAKKKKACiiigC7ov/ACGbD/r6i/8AQxVSX/Wv/vGrei/8hmw/6+ov/QxVSX/Wv/vGgBtF\nFFABRRRQAUUUUAFFFFAF/Vull/16J/M1Qq/q3Sy/69E/maoUAFFFFABWXKf+KjhH/Tsf5mtSsmU/\n8VLD/wBe5/ma9bKFeVb/AK9z/I+Q4wdqWD/7CaH/AKWjWoooryT68Kvw/wDIBu/+vy3/APQJqoVf\nh/5AN3/1+W//AKBNQBQooooAKKKKACiiigAooooAv6H/AMhKP/cl/wDRbVQq/of/ACEo/wDcl/8A\nRbVQoAKKKKACiiigDjfDJ/4uT41/656X/wCipK7KuM8Mf8lJ8bf9c9L/APRL12deznv+9Q/69Uf/\nAEzTPc4h/wB7h/16of8ApimFa/wl/wCS9aT/ANihrn/pbpVZFa/wl/5L1pP/AGKGuf8ApbpVeMeG\nfSlFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFcB+0F/wAkF+JP/Yoaz/6RS13x\n6V4L4iGoRfs4fGiw1LXNS1eSxsvE9sl1qE/mzMi2smATgBRknCqFVc4UAYFAHH0VmeHYfEsF67+L\nNR0y+szEQkenWUlrIJMjDF5JZQVxuGNoOSDnjB6LzdC/58b/AP8AApP/AI3QBRHUfWr2v/8AId1H\n/r7m/wDQzSiXQsj/AEG//wDApP8A43VzW5NFGs34ls70v9ql3FblACd5zgeWcCgDDoq/5uhf8+N/\n/wCBSf8AxujzdC/58b//AMCk/wDjdAFCir/m6F/z43//AIFJ/wDG6PN0L/nxv/8AwKT/AON0AUKK\nv+boX/Pjf/8AgUn/AMbo83Qv+fG//wDApP8A43QBQoq/5uhf8+N//wCBSf8AxujzdC/58b//AMCk\n/wDjdABff8g3Tf8Ack/9GGqFbl5Jov8AZ9hus70rsk2gXKZHznr+75ql5uhf8+N//wCBSf8AxugC\nhRV/zdC/58b/AP8AApP/AI3R5uhf8+N//wCBSf8AxugChRV/zdC/58b/AP8AApP/AI3R5uhf8+N/\n/wCBSf8AxugChRV/zdC/58b/AP8AApP/AI3R5uhf8+N//wCBSf8AxugChV+w/wCQdqf/AFxj/wDR\nqUeboX/Pjf8A/gUn/wAbq7ZSaL9g1HZZ3oXyk3A3KEkeYvT93xzQBh0Vf83Qv+fG/wD/AAKT/wCN\n0eboX/Pjf/8AgUn/AMboAoUVf83Qv+fG/wD/AAKT/wCN0eboX/Pjf/8AgUn/AMboAoUVf83Qv+fG\n/wD/AAKT/wCN0eboX/Pjf/8AgUn/AMboAoUVf83Qv+fG/wD/AAKT/wCN0eboX/Pjf/8AgUn/AMbo\nAbov/IZsP+vqL/0MVUl/1r/7xrY0iTRTq1kI7O9D/aI9pa5QgHcMZHl1Wkl0LzGzY3+dx/5ek/8A\njdAGdRV/zdC/58b/AP8AApP/AI3R5uhf8+N//wCBSf8AxugChRV/zdC/58b/AP8AApP/AI3R5uhf\n8+N//wCBSf8AxugChRV/zdC/58b/AP8AApP/AI3R5uhf8+N//wCBSf8AxugChRV/zdC/58b/AP8A\nApP/AI3R5uhf8+N//wCBSf8AxugA1bpZf9eifzNUK3NTk0UCz8yzvT/oqbcXKDAyev7vrVLzdC/5\n8b//AMCk/wDjdAFCir/m6F/z43//AIFJ/wDG6PN0L/nxv/8AwKT/AON0AUKyJf8AkZof+uB/9mrp\nvN0L/nxv/wDwKT/43WDM+m/8JfAy21yLcQYZDMu8nDdG249O1exk3xV/+vU/yPjeNHalgl/1E0P/\nAEsv0Vf83Qv+fG//APApP/jdHm6F/wA+N/8A+BSf/G68c+yKFX4f+QDd/wDX5b/+gTUeboX/AD43\n/wD4FJ/8bq7FJov9i3RFne7PtUGR9pTOdkuOfL6de3pQBh0Vf83Qv+fG/wD/AAKT/wCN0eboX/Pj\nf/8AgUn/AMboAoUVf83Qv+fG/wD/AAKT/wCN0eboX/Pjf/8AgUn/AMboAoUVf83Qv+fG/wD/AAKT\n/wCN0eboX/Pjf/8AgUn/AMboAoUVf83Qv+fG/wD/AAKT/wCN0eboX/Pjf/8AgUn/AMboAND/AOQl\nH/uS/wDotqoVuaNJop1BPKs70Nsk5a5Qj7jZ/wCWdUvN0L/nxv8A/wACk/8AjdAFCir/AJuhf8+N\n/wD+BSf/ABujzdC/58b/AP8AApP/AI3QBQoq/wCboX/Pjf8A/gUn/wAbo83Qv+fG/wD/AAKT/wCN\n0AeeeF/+SleN/wDc0v8A9EvXaVy3hOTSB8T/AB4ZLW7MZXSvLUXChl/cPnJ2c/kPxruPN0L/AJ8b\n/wD8Ck/+N17Wff73D/r1Q/8ATNM93iL/AHyH/XrD/wDpimUK1/hL/wAl60n/ALFDXP8A0t0qqF8+\nnvZXCabb3EN40Ti3knmWSNJMHazoqqWUHBIDKSOMjrUfwKh8TQ/HrT/+Ej1LS7vPhDWvJ+xWUlvt\n/wBN0vdu3yybs/LjGMYPXPHinhH1PRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUV5/4w+In\ni7R9WudJ8MfDk6hHZJvn1PV9Yh0qwbEYkZYnZZJZSiHLMIhGORvLK4UA9Aorkvh545v/ABtYyz6l\n4Tu9GliSKVJRdQXtjexSbtstrdQMUlT5TkMEccEoAyFutoAa6JIjRyIrIwKsrDIIPUEV5P8AF3wL\n4J8GfAb4pHwf4O0PQjeeEdWNydN0+G187bZzbd/lqN2NzYz0yfWvWq4D9oL/AJIL8Sf+xQ1n/wBI\npaAPE6KKKAFHUfWr2v8A/Id1H/r7m/8AQzVEdR9ava//AMh3Uf8Ar7m/9DNAFCiiigAooooAKKKK\nACiiigC/ff8AIN03/ck/9GGqFX77/kG6b/uSf+jDVCgAooooAKKKKACiiigAq/Yf8g7U/wDrjH/6\nNSqFX7D/AJB2p/8AXGP/ANGpQBQooooAKKKKACiiigAooooAu6L/AMhmw/6+ov8A0MVUl/1r/wC8\nat6L/wAhmw/6+ov/AEMVUl/1r/7xoAbRRRQAUUUUAFFFFABRRRQBf1bpZf8AXon8zVCr+rdLL/r0\nT+ZqhQAUUUUAFYkkg/4SmKPv5Wf0atusCQ/8VjH/ANcP6GvcyON5Yj/r1P8AI+M4z+DA/wDYVR/9\nKZv0UUV4Z9mFX4f+QDd/9flv/wCgTVQq/D/yAbv/AK/Lf/0CagChRRRQAUUUUAFFFFABRRRQBf0P\n/kJR/wC5L/6LaqFX9D/5CUf+5L/6LaqFABRRRQAUUUUAcV4V/wCSl+Ofppf/AKIau1rifCv/ACUv\nx19NL/8ARDV21e3n/wDvcP8Ar1Q/9M0z3eI/98h/15w//pimFa/wl/5L1pP/AGKGuf8ApbpVZFa/\nwl/5L1pP/Yoa5/6W6VXiHhH0pRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV4X+0X4bsfE3ib\nwhp48PeLPEF69nqK3GkaFFpuL3SRcafJdxzSX88KRo7x2kR2PvaOWZQuCWT3SvAf2pLDQ9S1DwjB\n4ksfDVnYImoyjxDr/h681e3sJh9nCWyx208PlPOrSMJHkxi2ZQpLggA9I+DWhT+HPh9Z2F3omqaR\ncTXupX89jqS2izwS3N9PcOu2zlmgVN0pKKkjYQoGO4MB21ef/AQ/8Wp0VRoFto8aPeRxQW1lPZwy\nxLdzBLmOCctLCk6hZxG7MyiYAs2Mn0CgArgP2gv+SC/En/sUNZ/9Ipa7+uA/aC/5IL8Sf+xQ1n/0\niloA8TooooAUdR9ava//AMh3Uf8Ar7m/9DNUR1H1q9r/APyHdR/6+5v/AEM0AUKKKKACiiigAooo\noAKKKKAL99/yDdN/3JP/AEYaoVfvv+Qbpv8AuSf+jDVCgAooooAKKKKACiiigAq/Yf8AIO1P/rjH\n/wCjUqhV+w/5B2p/9cY//RqUAUKKKKACiiigAooooAKKKKALui/8hmw/6+ov/QxVSX/Wv/vGrei/\n8hmw/wCvqL/0MVUl/wBa/wDvGgBtFFFABRRRQAUUUUAFFFFAF/Vull/16J/M1Qq/q3Sy/wCvRP5m\nqFABRRRQAVz0h/4rKP8A64/0NdDXOSn/AIrOP/rlj/x017+QK8sT/wBean5I+K42doYD/sJo/mzo\n6KKK8A+1Cr8P/IBu/wDr8t//AECaqFX4f+QDd/8AX5b/APoE1AFCiiigAooooAKKKKACiiigC/of\n/ISj/wByX/0W1UKv6H/yEo/9yX/0W1UKACiiigAooooA4nwp/wAlL8df9wv/ANJ2rtq4nwp/yUrx\n3/3DP/Sdq7avbz//AHuH/Xqh/wCmKZ7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/6C0//gJ/9nR9m0b/AKC0/wD4Cf8A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\n7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ZkcdK5F8K5JEdXCAAAs4SgPA6yxujgQAINDMC8tiFfMqDrPTKZ7XKR5zZ8ZY6uzDJl0zI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h19snMxJJq7noz1rWnPS3uj9Q47AhSDQQam4/sbgpo58Ibr6QmrF8N7ynk5TIc58F5ioErnWSH9\nEw1ZUSsIO79c+OLi+0ysFPmpr3DLRoN0UXCRDLyWWABWakMGXjOElYHXDPEsXNWaBtLCl2qz5hS9\nDmTtpZl43J2C5KZnNRt3mQS8np8HktgnV+DC1/cyFDiNYdyBJV+ZAZLfHKt5frcMvDabQYBdX+QT\nRy1CMWfsRA6i3iogY+5w7B14f63Kp0mVwEKH+ZuDSFGHpwiN3MPrGtHLbu1stCnwpnIPLzLwr+RO\nDPb9c/uBeyn9tHw26EdZUKeAGq9vJUPndoqVrq7skeBV/KgMhv7QXgqR3HPbxYFXwYG3Lc5AupPm\nUIRqBKr0frjNX4SOEMyuGamDnGHLp7Az8HjiOzzzWWwjhbOcy9nfdWmoNV5iz3Yu5KM1dx0dXN+a\n9nx3T6xeXeSrGAqU2Jxx01qziPsjDPTEtKJFa2HVOblsViGwj86mTOGsw5nfKUh0pSjs/Oog4H08\nQYr80ku45TWddFFgsQy8llwIFm5LBl4zBJWB1wzxLFzVmgbSwpdqs+YcmtUk47p0POZBgTlLrOr1\nnSYBr9/fw0jkq2G4aNNuAK9ZxGZFmrAhmyJXLVS/bN2jM20mjgkd2YOHd+8nBRJPQyWEnVECLqo5\nGXhNmLf/LPIbRQHlIQ0sfG4aSQYV6vIWoCFMYOqofnZne71NgTf5ZzlU0AJIjgyVv78AvGs48A74\nUxrYKeC2i7tJ34ZmcaeXF3vCN4EJ3MN7X1c5JE5MoK5XOPAa+Oa1WAOpTgQhW91ES7ThuM8HQVWY\nil4w3YTP7O21+DqZ5eEY97rm0vgKYGkOPVCkCBSaEiX6bNexB7zFnQl6zS4vxXAAYbNG4G0V8GCI\nRE9Ys3gsaT/PY5NqzD67kOeCIxhllYAUW6oQiw4CXv8/J0lh33oIt76mlS4K2CUD74+4R21VRQZe\nM5SWgdcM8SxcVQbexYKqK11I2h2peNyLMlBJUPmmacAb9MoKEvyPQFy8uRjATYcQRSz2wnyqUuhR\n+Ts7ZQ+vhdfu0Zor/bCAKCXAvqeVMx+nCbvU3t49vL8VC2jqPgZBM7OMUox6fDnwhigBRw/RX7fW\nWHWT0cFzawxpSF2fyxi/HYmKA+8H+zMvGH9r1hew/iBCAzsE3PWzPtp9U4PwqZ8/fdsjGjmBgV3f\nW0szxsfFrpc58BKA3hgd2ZMoCme6VLDPpxPQsC+FtlCBZhq6YUNHh8UhflyhYA86ZNOkar7xb5bh\nPj8EDYmUPte93eJ9GfWwpj0v5MHH7rs9FMP+hHt4EUsow6gt2kDz8osWrYXVZ+axUVfEvrl0Fhlm\nVZBeRqWkXUpxx9cLHt6APyaR0K0e+PY3teQCX+ts4vthnch5eM0zvjLwmqGfDLxmiGfhqtY0kBa+\nVJs151Liboi/P1mYdieAMEGVm3YiU05aC30qlvhv8cUlHxQBczLMP5SjL86nGh1Bpe/vsLj3yGaC\nLLEje/Dwln1YQFUGQM6nVJNQl2GbwdlSpLJ34P0dLqCrKhiE6qdgdkaD+vwoNIYLlK4chftbKq0C\nbIfTzwi8aRx4CWPAc42hsg8PBd6+QO7h5TG8PScN0a47a4T3AoPJx65h/M7FcOlIIzutvw93c+AV\nODD3R0pS1PGlImIU3h1fA5N+EqsLVkAiGoDbWhot/mI0qFTSP6nWssQqhp15WrUBL8zqkgh9ttc6\neWetac+3ta3Se+5xVYzwEBIKQOKrsNgYR0nOcYsPjVhzRj4d8GZsx3VDiEeWQOyXbiRlBwfebxaA\nN/DhZBJS5I7veGuOnO9j3S9gMvAuxTItLisDrxn6ycBrhngWrmpNA2nhS7VZc27fe0uJv48XxrwJ\nE4CiineLTALe8D/ES147vPGej/kDmcc8Gi84ckMe8RzlCdeNf/uJ/OwFeEUJkHhcG43zsrznzhJT\nac/AS3ns6YMon60qZxAOE2hizA0N8k/ZzaECm4kfZ48277Mt8J7KgRc48IoceD9auJcyuIe3KwBo\ncA/ggdxh2v6bauHxFTGkec4H6TCGE0kbXNzTiY0eXpEDb28IpWmnFvKEDwJ7dSQb5ny00BiioL6q\nCfRgQ7XFgbfb0ZE9hTNoYjXDvmSGtTk7s6oUip7rXwjLsMRa+qENa9rz75tT9d57XIQRf/7pSkAk\nrhKJDRx4cw8DvKtPzyd9/ozu2jAqIswgaquLIf1blaLwYOB9IIUEl7hxD+8cuVAGXksuA4u3JQOv\nGZLKwGuGeBauak0DaeFLtVlznp/yzWdPr8BjvoTH/TG0b/MOk4A36ndJxL3cA+35dCEVVsgNuSSg\nk+efPCjhus0Gskwd2QPwVny4P2ZzLquPpAU3yB7eJa4FDovsDyyPcuBFEcIYmhx3g35PYM0RGEYT\nJtmfm8tsC7yn5IIBU4YxQqX/2p/aSuJQnn1KPnQGc+DtBjycNk5bH9kn/CY2iY5NuFGJpyVLF3vR\nhvZmNvhyARa5X7I/mNKUU/fHnD43VABaXy3r8kdM6TLL/lRXYfEx1Wo09HWSxjjwIneqRT1qR1aZ\nwuBvA9Y52c2a9vybpjR90lcasTOE0RlnhmLrMG6KYST7hG2L7q/MUwtYRyilpVcNYpXrDPP9KEjK\n+ooD77cLHt7g+1JJ4F4XfNHfGbnNbXEbS1yyRy0ue3jNU1MGXjP0k4HXDPEsXNWaBtLCl2qz5nz/\nEUqiXgrHI4F6qprFPO/nDsGUkIaVt6dSpzYNKvv3A9l4wf53ZUnhVUqejmch/6TNBrJMHS038BKG\nWM0H+YD5d9eZpFGSEVMpA+8S1wKPPaXP6LIhpQIgQjGEJifcoNdNZC3hCAZTptnjjXuXBIea3R76\nuEfixZJPFnLomnpJ8yENHHglAYgAjH9B2Q+L9RoNuyInDTqMwNvDsBHEW/5Uju+OX0Vmh10Q5cAb\npR5kV7U20tkXcwUMFHr8MF11+jYsIIb+OZZK2914WrIAhHVeBvZc1R6Le3hLXd3oe9oUSKxjyIvN\nQKeDM9vHgfe5oWMPeL+uW21I+c5Z7AoBfrQwhZh6jFuiGclatxhWs07JZ00rKK2+ug+rNDPg/n6E\nlPelQtx+MPD+Oo0E7NPgizYx6Xb3bfKmNVNviGUoJwOvGaLLwGuGeBauKgPvYkEDXouUwt4NFkaD\neNL0SYxK/1loUpaG+OszqGLQAZW9U8Tm6gKYOq1T8Pxduj661Eks/tw6DzgLLweLNLfcwGugAqv/\nIBeMB8XyHMhkbWKpDLxLnNlepYq+PJfFUis4NDoMoAkOvD3OStYUDdCfOMeeaty9JOANejZaH/V+\ngGLr90s/+c4IvKtPzgW9CHxmGd77yf6XxzJ3d3ZTZjK0cQAL7mV4InKaNT1Vii9dlUVdepU8DS9D\nQZphckV7C2PPZ4kYERjx4zl7jy9H7k4z+LXRtWzEDdhQIIVBnq3htYpiiwNvoac3/WIqHuI58PoL\nk6xVdIV9qxg8ewwC71c16YaUrWqxJ4gDrzOwFQ0gtEZx4D1pMfCuPbmANcQRUn9tJ3bwnACHd2J1\nOV+pVCVbFr5+hd6dTnyr1fjSzUS61UU+WniJt6hNi8vAa4bcMvCaIZ6Fq8rAu1jQoOejSfBHAXg0\nRM+chwVU+mGhSSENSRuyKPDz43f9uh3Nlgczt/N3g8dLCTTlCxfhYM+GhafQ7ppbbuCdkVS06ZMs\n414ZmAmdozmrrLsh5sdOgD3H8LY7qek7E+nA8/BCjHPvPPB2OjixlihA7avm6HMNJUsC3pC/xBoi\nPvMVzQFenRKoQuIe3k/3A2+htze7Oy0emsOAhnDgnQ2eY/V/241/np4r+fOsDYiXUnPYuqG5iTg8\nu1rkuYTZiBdjrgUNONhlEL0zuob1aZSsKxCjoQACm/YVWTzOfouPHykcXYni6ygKdhiFRuYFZamM\n/W3YOi/A1rTnX1dm6JMLHRW9gUBn1Twutwnh1ghGM0/ZtmgtZK8rYJWrJLLnjFnsGjDE1J+F0DM+\nUojbvlt44Qn7ZTrxqVPjSzYTcpvGOpv4frg35ZCGH2ul9teTgdcM/WTgNUM8C1e1poG08KXarLmQ\nJ2JJwFe+eDhUx9z6FWjvv7abBLzJF61lEt9Vs+uGAZAG1Ui9pgNcvvUjGe94CwcbepsNZJk6Wm7g\nHdc70e6P1iBjrtA5Lz3Lz7TOMa7mymvPwFun1tAPx9IYB1600rUbTU66QYfgzGN4AVrXaOnz9UtL\nIxXxUKIhZKvnjwbe9JNygWe3og4Ghnd/th94P/Pzo4+krERNEUAD+hgY+DHgtc/tES5PWEs9+wVM\n+KY16jVDf9HUQN3+mioiJWETPNUgW9vB+qc80YhGQE0aDYwG6VlLkEg/KLO8l/ED/yBa3h8FcY0U\nRTv3032GADCGNPx1dDEkmruejPVNseft/CS6sJmZJcP91/uyDMlFKrHXH9isM9DIZhA6+MvGmvVb\nFwFvDgfesgwi7SyQsJPPOHPf4cnO+4d4yPyH3Z5BfBod8aX/lMitTjus+hVGBl7zVpcMvGboJwOv\nGeJZuKopBtLCXdp9c+F/SCQ+hZ54OFLLPDqUaM8npgFv6rk5bIafMV/y8xmENLNM9JkCTY8jzftL\nkPBjPFt2L9QRLnC5gXdgVkNGPkwDgwNwwCE4L3/pcaO20N6egbfUxZV8PbAKEmoAxXt0Ia1WAQ2S\nL20NQ6h+tY691Li0o3H5hk5D0A4Pcfsn2yl1pkvyDhtDGozAO+MA1FG/ALyvhkSQl2NDcH30/hhe\n6qpn/3qvEv0ufBWJ79PiAcEJzXgb6PVNjSTor4kKpJLYpAdjgymTaHjOBSF3LWtzc4RefwadAQJs\nrrJ85oRNQWG0oSeMxTUQHOU8wCqJP1TEIfrU1FarAN5/s+evhIfTL/z84P3ipYdvfF2abUjepRD7\n/PiLpBM/kKQNYeMGtvTTF8N77okFrDhfMuxIFwUHrynkXa5ml2zCRjt44NYKvyWDerU6osv/aZBu\ncZSPFraFzfmxfcjA+2OV4/Vk4DVDPAtX/W8G0sLdHRPNRT6QTDz3uuHhFVrq1eyAd/NPqKZsWku5\nIIdMugAtWz8tikGjDLQqpFHOsLx7o1HRe0VU8jYs6UF/TIh1mItcbuDtmXKX6OZknkIZcCvfOJN7\nvHWS/Js7P/YMvIVuPmRXTxzEN0qQ6NWBxsfcURtz52nJENSs0dONjTuXBGwrf7Ga+tU4o51v7KL6\nEO2S7oN54F2XC9NOQNVahkv+nfHkyciV9L0oP1S/EkggT0uGHQ3wzJZGeMM9GsJGDGhSUBhjc9kN\nzc368KdXqpCjgU1o+PHCSXrGGMIKlznW7OZEB30A1fo7oLcaCpGS7k8naKnfa4ERUldXCI5ppijW\ntZuVzgWjqgREn5he7BW1RJ//zZ7/OjGRdjo5od9XV8NSvbzf7M3RJ5aIigFfI/DyI6fbAXfzeN7V\nZy4eS94JBez7dZJUupphR59JcNvtCZe9A/jgF57Im7KIe4cKX/vBHLleZd2wI9nDa97qkoHXDP1k\n4DVDPAtX/W8G0sLdHRPNRd2zirrVuqDh2FnqW+uES/gnVFOAN+P0PDbEj9osSVRgzYm1TNfki1y9\nRyHzd9Gs6i9l0kzCpPKYEMDMi1xu4G0e9jeoPozhmASoLZyx7HXWiZc0UyawZ+D9ysOfVLXFoFj+\nKT7Bpx2mp12gVeLAGyJA1WqJbWxZ2tG4cddmUu9mB7TnmXI6mzixZOBdw4F3gn9Gd5kBvOuL/XGg\nf4iKp59EeqO6GCAB/YAV2IDu3dnFvlSGQfLIJBoSVTDsjulZ3d1S8pORSqzR0QmlAE3JBuwk6vj7\nKGa9HgoweM6xwiB3eLp9F/bW6SwKvE+GxZLpFl8U3cxQkmc72z0Vhqo58D42szzAe1JeHlvf30/T\nR0ZQ9sjIkjbpfbMrR+L7P4UhH2B80xoNawehh8fzph4OeI8vYF+eaTDUr9EJs9QRR3bryQUvOgo7\n3y2mBj/d/PxHhgUOeQAAIABJREFU3bCWuHUr8ZUf6MjNyuIlvUAt9d6TgXepih1aXgZeM/STgdcM\n8SxcVQbexYJG355GNa3OaChxmgaUOfMHrGnAm3lqPutJmKP7YlTY5cxyNlcZAi6+wyjnjjio+VUD\nHVvXt6QHvYWn2mbNLTfw1g2E6Z0/DhP5UbKolcd3rj3JOnBhrqD2DLyfegZKDc0rcDz3TCb4t8L0\nlAZa9V6sKRhDZTrlwFu4pLUczzd0enWq0L6HaulE7uCS6ho9vGtOzIVx/vXEdXoBeH8Xk0y/DXNH\n1dzD6z/AsBOhcHL9OL9WZ4jp17EZlUDLA5zweR3dJP9PwYIjM0CvH0b1KRILdhmCfkmDBj0EmPGS\nYLefK9w3sBeiZmaWBIH/bQ08HJ4gCY1eeEULQ2m+zWzneBSqikXs/3Tb+HkMxm2Vlv0dzZ6P8FRz\nf1m5knlqDRCincHnd3Utqf9vi3MNCeWCOOQNVOsIENQFuN8faMrPF99fa08qoB+vJ/q+7GmlqNaB\neo8Xu+wNjEte2kO1UTPz87/iurXEpU+JN3yoJbeIS4sJX6pqMvAuVTEZeM1T7KDaMvBaTEqzG5KB\nF8Cl3FWiKgLTcdPzXobo29Mlx04nNJI4CcElLrh4i2nAa0zF07p6htTGCoLrz8tg/J+rwTO7Htbe\nnARtV7TTvkval/SgN3tyl6mB5Qbe8u4VBu8vAkWBAHQFAqw5Y+mpsGwhnT0D7zv+4WSkOhTFtBKU\nGNgCM9MaaJ7zZg0hAqtMY/Bq69LCRJIuy6auvQpUc1e9YfTU/iV96TACbwYH3lE3IG4TIOzasn8+\nfxW7ihQGu+KqOCB+g4A1WoIyWueI46iA/Lsx04sYqsMU+OTeAf3ZD/iKPKxeaI2TWOR5RbhpNJA1\nz/jCqAeA1l1iRQFu6OrpWsgeHl4SBP63dXJf1CriVOuKVrQTtMa3gRYOr8Q1sZjdTwqRIyEW7ct4\nLUez5838lLf7I5IZlhCs1fejm5qbl9T/94V5Ulw1FkY8gHHgZf48jGTQF2jy2YuBt+C4AvbOxcww\nsWZEVKlnAZcEsiteFXDpk/voTNL4fuC9JptoBhT49M8ZeZjIB0/8t7W0nP8ve3jNUF8GXjPEs3BV\nGXgBUi7OMmiGlELh1/s/fcdfm0mUwyrUFzdHI/Y68UMjtpoU0mDcmVxfMC21pM8Krutq0dBzBeB9\nZhlkXpcKPev7aNctDTLwWnj9Hq65Pe1x+oCvfBT8RFPo459fU89ayP1pg+5N7sKegff5oBiiq/SH\n+GYJJ4Y0c+B1hpYpP9YYzEMa0ih7sWNpHt7ki3Ko64CI6m/oIIPnty3p8/U88J6QC8MeQDzHQCjm\n96NR5Fui10ilYU5CRQIQ3yHA7pMMTmycZIoxBXYaUHH/KUP14SLLHBjVX/Ggh0I9y3DTCmBR5+9A\nXdOeUDseDNMelOk48H4b7Anr9c1wXk+3Re/Ru1asljyq1DiyFaG84Er23UACql2J2V2oCLkbDEsC\nTlMW1tHs+V4PD/aEfxxzliQUAlPsvoaaJXmzt23Ll2JqkDDmDkzHgdePh5EM8PuLA+8h7eAJgeT9\nPBe/cRkQXVYvVmtmkG57BL1io4ArH6omEznD+x0LV+YS9YiAz+TA+5AkA68p87tcZX7SwPviiy9C\nWVkZiKIIt956K3CPziHzwHcEwLPPPguVlZV8JwGGG2+8EVJSUg6UkYF3uZbt4n5l4AVYfXYOcR4T\n8Q+ZFBL451c8pUA9QYjE1CFh51eHAu8L4eFkh48Peruk5BBDn8s3auw7c0LqjiCi6xmVaPzjJKZZ\n1UYyb0gVRjNHWcvvqpb0gLGfVbK0K1luD29xU5Ih9DsP0cDPbuKfwVnSOVsRz8lqcbhYmiqLS9sz\n8P5faIKk2euFY9oklBTWyIFXDS0TAawxSITS1cA2diwtrVbK+TlMMyJC88XdpO/q5h8FvINeQHxG\nYP4F1KjmjdFZtCJMhcoT+d+HAXuNMnR2zQgVeKi8YUiDEKaoLQyxlYMz+hse1Cg0cwy3RVPmc1op\nTGN+QMxQBBi8dKzfRYH2+avBy3WcPlxbZVHgvTU6gwTuc4RwvsHr+PAy9lVfMtRFCXCDqgQC5+Ys\nbg+OZs+/9/Iiz/km4LVTA6zaw4Vt3Le0XMrbv8sj0Q0Y85TMbI5nQPEdADzkxe+vcw8FXmWHE8m6\ncg1+7Qp+UEh+B9Y4j7PRrxPgyo0Cqrq7gYyf3LcfeDfkUPWoiM74gkgPGyyfEu7gO04OaTDPWv1k\ngdcIsZs3b4ZHH30U2tvb4fHHH5+H24N/JSUlsGXLFnjggQegp6cHHnroIXjppZdk4DVvzVmltgy8\nxk/eucxpWkA/pMxJumQtY3oF6w5GJK4KxKKvDwXe56Ki6D+CgtB3W7caKeoASGWekk9LzxnVj4RK\nKpeT6tDY5tXMObOFpd+eTGfixqDp9xVLetBbZcJt0OhyA++2utX6yB3Oihm+k5xwxaPO3YGUAv+O\na2c/ewbeh4KTJa+97nhlu4RSIutBq1Wx+qEQaApQwu50RF/tXFparVXn5DI136vW/vN+2v2L+iVB\npdHDm8k9vP3e857c+RdQ41Reu2ItrQ1TorJV3PM7DNSH/9/5FSNogu+ochpRIg2eY+VhTiy6Xyvd\n8ZCD6MQ9vO1RjDmfXIeweo4V98WhOQ8DGFOVVXur0aSfRJ7hxws7UX4osYV+t0ZlkuAyBxTWS9G6\nsDLY0pUKtTGYXuJUimKmpyzWzw+XezR7/mpIGNmmDkC5E0Ow1ccb3t63c0n97/i2QIpsBGGKv0TO\nOgDwlwzg4Q2QeN6hwKsu9ZDS7k4S3rhe4ifzMfD2HGbqYl901SsYNdzQSobO6Zy3gysvzyWOEwI6\n8wsDfUhfZFXbKAOveQv6Jwu8b775Jvj4+MApp5wyr+DVV18NzzzzDKjV6gOKarVaIITM/83476uu\nugo2bdp0CPBedNFF/KT7Q39paWkWeShxrzILDAyEriUG5Zu3JI7N2l5eXmxubg5mfkQi8mNzxIuv\nOjo3iTpMIVzzaS0hQQYh7JQExtN9QncIIonlINaWVoKDgwNzdXWFgYEBtCEyEjpUKninqYkFHPRZ\nMik5CYruGDXM+lGF7ym90L0pHNxWjdDY60P5gtSz7ncbLLK+7V13f39/Njw8DAYrfLI1ZewfFcaQ\nmEKV0BLOYz7HARI21IKbo7QkyDKlH3PLaDQaZvxKNjY2Znfr4jr3aClsh4Owsoug7JRONjXtAJUd\nXqjBT2R7uZl+X1u5pGuOyE9k/B5DfaeP07GHO5cEWkZ7HrMqAfX4g8SzMYj1e/nxb/x3oXssq4hQ\noD2rQfIYBRzWCficpimmGOUHEI8rsY7fwy0BmIWMGvR3PqhQOmsBdUYS0Jw+QL0DJ9H7FVHI4GuA\nOhcH0HkZoN5fQTcONkCYXr+k6zvcOjB+5TT+zvZJYLF7BBreT4TzkpvYuxUxqC4K2CXBrXTN7LTZ\na1KxUyPpE6YAucA8MB7Nnj/sF8yqmYblTE6RzYEeQlFzxZLG+dl7ySS8BQQezm308IL3CBjjqiHn\n+opD1oLjJx5S5IPB4mtXU8rhHvx5WjLNTg267HUE/dcM6Kev7lcZr9X/9CSq4Hb3gq8M0mOqWosB\nb2lp6X7xD/q9++67+Ouvvzb3lv3J1v/JAu/TTz8NmZmZkJGRMT/599xzz3xYQ1BQ0GEXA19o89B7\n5ZVXHvh/Y0hDenr6okV5zTXXWGRBGb9fGuFkfJw/7eTfURVwcnICSZJAr9f/ZJUKXsm38vM7uv/F\nPonkaRU+GeFshm+r6QgCklaKxI7G1vnwHQ69PD3TNOQHhlAXibJHx4dQsk43/9BAQ4iEZ4YLRQ9N\nUxyiB9+8cdzzhQdz9NOzsOt9kEpDYOjbjiVBwrE6IS4uLvM6UbronfbAkH4AAmuM8R1+jGnMDgWu\ni2UsqBtB8DX9NMpn/85we/oZ15Mx5Gt2dnZZL8toL//zd54yhERvF3FsN0Un5Q6wiQkF7K5zQ3U+\nItuTDvC52ApcUJPXs++qcFDOIhg5aY7O/rVvSaBlvLaQmAh+OARIwb0gdjW0zl/uCQ5htDEU410c\neF1n+IlwDSD8tmFM6ppyRI5TgCcpjxn2EJnPCDXc8ygWnXSAOyIYKNdNQkraGHv++1A060dAy09f\n63FBrNZPRI8YBtgJ2tklX9+RJrDAMZykFiMc08VYrNs02q1zRg2RiJ4VNwjr56bN7iciMgK6b5jQ\n6+8emd8IeDR7fp2nPx3inm83noa42leALZOdyHMJG+f+8VIEDekANOMIyJiH13OUhwxx4D3lrtZD\n1oHyZQ/J7zE38bXrGGuKo8zFTQ8exY5wzSuARy6bInO/GZq/FzW54VScRfj8bwl50a3DYvfnK6+8\nsmg69uzZg2Tg/fFmRgZeE4D3iy++gMLCQnj44YfngeGHnxzD++MXnqVryiENAMZTgYxvX/W3N9Hh\n03uE1WflUZ7zE9pDwJBahhQlX2zDB+fhvTIhk86BEm7vq0GZPJ+lcU7UNS4k7dZU/OWtwwzCx5Fj\nUjeaKYlgSCSQ+nQwU2sBSt/bYfYDztLzb432ljuk4bu92VJiiULYsRZISgXg6fUtNNGny2IeJEtp\nZs8hDTeF5pAUfrZEXL8WkldUo7k5NartioDmABXbkwbsicFCrF4CLKXzHNUK7jjtT5+grb8vXxLc\nGEMaso7PhfZA0If0gvKHkIYrI/NoUwRGuzJAUhgAUvm2kj8UNbEW5gGKOYRmudOz1kNDebiDdPef\nkdJpDoB7eNlk6iiKT6hB/6pfSwc9BVB4T6E6V2c67iSgk1grO6e/a0nXd7T1cHlYPkkvBRTdQvix\nHQZod3XATRGIJQY1sjMHe83qx/2TYG3ik5GqltOHSc+d1fPr+2j2/DfRyXRywg35GWag0cuJ3d9T\nuqQ0bCWfF5Dgbg67DoC0PIbX+PVkwgUg7sJDQxr8Xo6WIv8RIGy8lrKxVeO0x+AmxP0/e+cdH1WV\n/v/nnHvvlMxkJsmk9x4SQkhCIAkhBQsKimJb0BV7Q13L2l3Xsuqqa69gQ8UuCGIDFYQEQgkhgZBG\neq+TSZmZTLn3nvM7k6yurICRxIXfl1z/8fVizs3Mc+89932e8zyfTzkiV72NcPtpvaT1zoqR351w\nca7M2xnw/mCXH7BMypJN1LzyR5znpAXe999/HwwG1tG6YMFIXF2Z21dffXVkZfnLY/PmzfDjjz+O\n1PEqFIeq0EwC7x9xSx7bOSeBF8DlCiQxl4KmS1tJx2UN3MxzcqhLeqcpikhztvJCweb/1PDW1NfD\nuam5VG2naKmpli7s7BiBWM/NfnLiE/F4w00myid0I1VcN7KVBVN5SA1JK/0lJqeESz7/fZ3tx3ZF\nj/+o4w28WwrnSFP389z6c6h82g8IdeX1kcyIA6yF7cQ6TmTgXRaSLaft5FBMi4TSppeyLLQaqlgB\nbFWIgpROR+gx0w5kEMUxL+BmnZVLeRHRniQz1D2zd2TcluZkIiAZ5oQevZlzBHjnZgMrURGjGkHY\nvmm0hvfKSAa8URgVZoCECdDZu0G4dVcr5dmmCxU5JDOZhkJPP+rTQeXbX0KCmkrQ7MdJtllGPCWx\ngltfk0WNekz9vLuAKCnZ4B3ITcfd8g0dBydscXRNcI6cug9BdJMMKguPO/0AmDY0DQlvJJd2No/r\n7/iujJXi3w/kWjOHSMM/S0Yg8mjz+VUJ6bLBhFGqtRe+9wmAGwYrUfq/F+xjeTL2fJPncrRDNuUo\n8OqGgDJ5ZhfwHgLuAa9McQZt8OdWXUXQ/kwnDSFDyKPIG65+E6PuLBNpeqCMsziUJP3yDMo5EZq/\nVSJPmbaPKxa/9f0na3h/K0JH//eTFngrKirgvffeg6eeegrq6upGYPeFF14Yqdl1NaixBw5c9Xsu\n0H3uuedAyWod//uYBN7x3XwTOXoSeAFymWakUwm0fUEXbbmlmpt1di7t9iekMUYic79T8vk//gd4\ni5ub4Zr42eDjtJN0Sxe9om30peW7OkQMfycKb75qEClntiBFWB+y1xuI1OkJ8Z/60qAGAe9ijm0T\nee1O1HMdb+DNz8+W4yo5vP4iQk7/GqOGWcPyaQlFk8D7O26YGwJzpXS2FR/dxvRjZ+yBYbaHXVEb\nBzX+SsqaxOCe4d0oxGYb8/3MFpGyC25MccOw4eE2qDSGIQflKccROC/q6M1TPwFvTRQ44+pAsY0t\nQF0/5ZqgXLk6HuGCLBBlDtCpW4G/oKSfxvWbQYNkNAhK2KILpIFtVLr5NSQoJQLdISJtCsc0PbeQ\n21g/i5q0mBpYCljgZbTeO5j4KQfI/e3jXxx1s5p/jpX03O89m6bsJ3RKo4yVLAvd4S7QOlbD6x7X\nATe11owrwxvwVJIUvdELd061yXWv7B65v482n1+WMIf497HmOXMrfOQTCedI9fS89rYxL1qKv8qV\n/LoQOFgljpPlsNzNAMMszxV3yaEZ3tDHkuTA7V74DVbSsCeVoZK3TZ61ScFf/QZGfckuOC/l1tbM\npvc8pJCYOQzMzafo1e5JWbLf8Xj+zz960gKvK9KuGpni4mIQBAFuu+02iGJNPCaTaeT/XU1trrrd\nzz//HDw8WIHPvw9XY9tPWeBJ4P2f369H/IOTwAuQyVyBWDc/Ms420oYHynHauTlymz/IzYkOdMZa\nNe+SK/uppGFLRwfcETkTPEUHRJIhek9t5cgLI+S1ODnoywC86dZeWZnSggV/M3a26+lwUSTElSjk\n6N1qrvDf3eUnztX/Y77J8QbebT/mSNG1mHvvCkou/hDh+kSnfMostj9/gh0ndIbXP1eatQtx0W0E\nMtJ3sgyvG5RXJ8BBBrz7koHeJO3FcWbzmIGXKZgwXVxEB8Nt8NEVkozbtXxjNJUxA95zpx49u+cC\n3tksw8sc1RzTqkDp2nFxXcob/HJJ+TSEtuaCaHEDdO7XwD+8s5YwOEfDrCZJ5hHs99Mi/3bkvGE5\nUihZhrfXj9LOWJEmZu5B+e3JaFCFqbenkXQotVABPpwUYJUe79gzrsVRqacnfToujlxdX08/56ai\n5H0UxTQS5KG00jaVmlSGCAgSe+Ge5tGt/WM9Qu6bKYcVaXB/sEgq3iv8zQzvxQk50rQeCz7L3AJv\n+EyB6biTXNdS/6vnwiFj+au6LHxB7Db4pZxf6Rd5srcREGv/RCzhT7RWZtrByhvi/nwo8EbdN0M2\n7HfHr18DpJQtjixMxixrG8HXv4HQYIyV1j5fjD+pzqGPPsCUstmVzC2UpNc6tv8uM5LfG7PJDO/v\njdihnz+pgXd8oQOYBN7xRnDixp/swKtsVcsZl6dj9sIE89QBWvPUPuxyTKuLBbkx1ULPWqU9BHi/\n7O6mjwXMoL52B1VrbPiJqn0jwBv5eCLxLvRGGy+2Ud3ZZYj3tCF5QE0Hv06CSItVSlrtzf/0op64\nq3dinul4A++ujblSSCvi3rpJkq5YwfNNkZTm5o2aipxIx4kMvDd75cppexGObKUwJ2sbDFvVtKx2\nKhz0doMDiUAXC2Uo3WQac0xdzxQrG6LmQCe8uwQz/VWB6woC6hIAm5d5dGOQEeDNywZmMGFLPgDq\nArbj4rqON/nkkbLpgDblgWj0ofyFaxFs+X4beo3OAl+T7IJp1OzLgXeTglz2DserBCf0eSDaNcVG\nFL5mZNZRxGwRqE5nRvtUPmDkVcju4yBPde0aF4iuCg2VtukCwBuGwdzphVPKCbBSDOTJOumasY66\nanhtU/vpAy1lv3sRxlLZ5GBfoKziCT79wRAUWK1CZh+Zln28beRaHG0+v4LVPM/sN8G8oVZ4wXs6\n8tH304cP/rqcxCyqyDe1GZAZcoCGuff9HIvSdXnEwJQZCANeV0mDG+u1dCgAxSw91Mkw5paZRN/g\nhpZfC6RkBhoxqZi5m6IbVwBnC7HR6leL8OZteeT25wEkHuiCAgd9pmvn747F73mWJ4H390Tr15+d\nBN5xxG8SeMcRvAkeerIDr3uRl5x8XxIe1FHiCLfQg8/v5V2OaeWJIHXMNaIFz3tz+Rvzicagwf7+\n/vDG4BBdpYmn2mGCqa9NfrW8eFRE/a4U4l6lQ9+dJ4HXJUWA1SKSHZiYv5sGvnarNOPtYJ45RP0K\nENRNbnLyTTNgz6qdIBlOPOmsY7ndjjfwFn2ZJxkGAK25yUwufMUdd/kDZJ3xa/vTY/ltEznmRAbe\n23R5cvI+tpBjUl8Ril6q9LDSmoEgXBfIA7PyJfO01XBGT/eYwXD2vDxqV4Bs83fQd/8kUM0AFpgx\nCGXwBDPOGRvwMvkxW/oeULtKjFzX4S+GPLIvBdD3p4CzzweESz8CtPGTPfJy3yTsZhSAeUsjoy8B\nv3pevORDTnB1rbGeLWKd2o/7VRwdZvJaFhVCbjor3e7mD9yAErVFSdIzfYXjyjbeMnUGMRMVtSkJ\nCWgUuGnlrIa3hSCD3gTNkteIW13nLAt9qr54zPH76b7b1pYo99jd2eQD+O4neerTwmGrXkb712wb\nicmR5nM7WzRcF5oN6R2DRHBgWu6v5pCXjb5YOVpP/cuj164lPzakoamBDSTRo+Xn71j2eR5hjWos\nUw+YlTVQpR2YfTPgmMsOBd4p16cTdacarbiR0pJkBrxulM4qRC7gxU4fO618axfez8512fsjGWJY\nUOiUn2/dMa6s+m89l5PA+1sROvq/TwLvOOI3CbzjCN4EDz3Zgdf762B5yovRDHiZhJZOpAfe2Yld\nqg1FM0A2Zpjw2S95oR2fFRJlhHIEeB+3OelmiEBKiQL1tJNXqotGXghTb5hFhC41+mEeAt2fd5L+\nhlDwT6nljG9kU8OUFpr3cAQqXL2d/DfURv99uhSw0xO3LOyizbf+PkH+Cb4VJux0xxt4XVuv+gHA\nH983SC5/VI+7WKNQ2sJDX8oT9mPHcaITFXitPE8eVM0hqSUURfXbkL5dSXRBRtQgeuPGYATl8Zhk\n6mvhvK72MQObaxHJzAqoqBfh03ME1/XhmEuXK1sIqef+NvBmsQxv4SywZe8CtavESESI3KXPpXtn\nAP7uDHCyc3FLPwBu9Sv1UtEUNe40+WM9Z6WtXgrkXadw/vlTpBD0FhggbnRo2gCYvQlyqgk1s1KI\nYQ2TS1MLsmnQg+sMJeSfwwVj/l2Hu/xXJ2XIcYNmMsgrsNSjQ1MY8EYx4A0NbiUHBwPhYIgADTOH\n6QsNRb87q7mmMlvSIYfoEBX8bc+w7HUPwpKKor1fjO5gHGk+72I1xff5prvAGwxgg4NebtQc4CBv\nVez+1XdoNHtLTcWJWBvbLs8Mqv0ZRA+sySOsbpdimQEvy9ryIluwMPiNuvzQZ2vq5bMJPySg5csQ\nLUllNsQeTjJri4K7aTkg4u6k5e/twGVr88jl77FdNSbfv2ibnTzTOqnSMI6p5A8fOgm84wjxJPCO\nI3gTPPRkBd6UpenEFuwgQ3FmFP5xCB7wIpQloNC+jwtRzil5dHs2lQbTjPjs533w7jf3EH46GgHe\nOyRKDtiDsY44aJuBpx9UbR95OSZelyFSswIKLxngbIE2cAxoqG9SA7J/F488Ijpp7kORqOyRA2Qw\n6z9bhK5xyUszidqoJP1Th6D6mZLf/QKc4NthQk53vIG3jL2YlUxW+tv7uugl9/pjZlgASedsAx7L\nY645nZBA/MZJTlTgLdHp5c8cKSh5P4WwXgk8ehEoNHbU4aZh0mAUKqdgGhHYBtc11Y25pGEOW0Ra\nNZQQFUGfn8W56j+5Ll8YqZ2fMUbg3ZQLtnlbRoF3QBDoo8osKEoH+tU5YGcW0moGvPDRP3qljvQB\n1Gf15LolHdg8RBpQrZTP+oLnlcwAwaLGxORFgCSY0JCkIsMagtpUGpyiaYNvLAlg9KXkEWl8wLsk\nPkeO6HUCVTPdNqOaj63GiLm9QbBfB63pC0G1wQItT3fS15p2/C6wHnSoScmOdNnLJFFZ4NClDPDd\nhxDIAkXFX402xB5pPq/RauVntGk0tceMIkk/3qH1JbUs0/xh1Wjt7y+P8r4gp1gQww+kDNC54ft+\n/veKz/IIK2OgvATYznrR2R9kLWeAo5ceumBJumQOIQ4OLb8RsaY1SlWeFjp1sztatgIocnNCxXs7\n+P1r8uSrVgEy6QEWF1jJk817/tC5bzLDO74ZbRJ4xxG/SeAdR/AmeOjJCrzZp+YRh4ZA52k9xO97\nP2w2MGdA5gRVsnYbymOqDd8tkEQxuY9f8KQf2vv8PoJmSyPAeyPhSZ3NBwWIw1DlrYWPqkczKzMu\nzCYWDYWdp0toKMgGasMQEdROJLS5g9rNQmc/Gg0tlzSSrkuaD9m6Szs/m7DXBst8SXj/qp0nFJAd\n6612vIG38tM8VsBJUeWt9STn4WjOyCTmws7eBTqVfcyAdqy//feMO1GBd7V/CNnXGwXJZRTC+5zU\nj+kdyKIStWA9bg0ncnU0RvqwbrirvmrM8XTtmrjKhjhE0dqFGBg4oR5vIK5u/xmLjl5u4qrhzcrN\nphvOoI6zNiJVwcYC0uyhRC+idFSUAbBmieSwqnjFUgZQH//FKqH5tdye4XAmeMZBnUZDE6tk+Yyv\nBF4Z3keHNTLttnghLrcJ9Zk9YEiLgcEyDeL7aU1LFK6LoeQBIf93geh/X/NLYvLklN4B6k/s0G3x\nwD4NAo1o4nAsc8aobIuAmiAF3Zcu0RUto4vlsR7lfaGSc1skbkm0OwIPKoWrViLMqjRcEuK46NvR\nLOuR5vNNPn7kCxIP00xmNIN00g1u4bQqVEAfHfx1bfverminsiCY7wqRyIAncBdO2T5ybvZcEYUT\nZLaY5FkpA1COJehFVvJy5aEZ3mkXZ7OWXg5evxbwngwCke6dsmFTEH/jcmC6GTLs+2zbSEnD1e8C\nGA1AL/1xiDzWUjJZ0jDWG+E4fG4SeMcR9EngHUfwJnjoyQq8TIpMsrHaN1PiEHiWeqK+EAcY2gVU\ntC6fzlk6zDYMAAAgAElEQVQwl35/NROZZI0t8x8LQvv/UU7IGfYR4D2f1xFrvw5PdRppga83fHJw\nNLsxayHT7mVSZsXzbLgLtBCUUQlDrb6gsxBQuQ/TrMfDkSmjj9YzFYhfXsLZrHt92Ie1ogxjbu/n\noy+W/9+P4w281R/nEYsW6OA1VUj5YTxlqlPgflYZBLubfhdg/NHX4UQF3mfDYp2Drf58MtOPDR+w\ng0G2I2uPJxj9Ce0IZOAzhencRvXTR2v2jzmeLq1roydL8LLt8HULMdOxZuaEDHZY0xKafsHRy01c\nwDsnNxu+WEgd532JlNtXF5KSCDV85ExFO7MAfXqNzW4mauXlDHgPXtwm77zYjLtEHWLb7dCmcJf1\n5Vo6fwPm+ZgekJRMpcHoB6pzKlBHVwB0uqmAGKxUIFjq6zHwbcFA79Qee733EM/T2wLnwHmmRrlc\n8Ea+Jko19Roc1IlQQnwlVDRGQ1WwkpTOkOmKjt+nPbu9MsWhrNcLEefuguLyZHLP31QcA04Js7KC\n4q9Gv/OR5vNVweFyqTkcThtqhwRqxKuFKbQ0Qkk/rvn1b93eNE30LDLwHUGE9BkQXjJlNHtc/Uke\nS40DcWfZeabFSyW2JaZi7h7hVx16/WYsyiVDWkRXXA90T4aETlXUkqH8eG7ZcibH5iBo79oC7NqF\nueodQKzciFy3qZ882LJ/Enj/6AlnHOefBN5xBG8SeMcRvAkeerICL8visuwSRaaYYapudUO98RbR\n/6AbV/mvEph5/Uz85eU2ysX0wimPhULd7dWS8zyL4ALe03kDEfoVOMvWQTcYguHVlu3AHKdw5vxc\n2jZFJPuzh7HFRwa/5DrauScOeauHwCWKn/lsCJJ9h2n5ij2HAK8LBDqnWxz6JjVfvHbbH7qtN8G3\nzhFPdzyBl1nm0ZoP82gPgzPdhWXYsjqZ+vSwbvDT6uUEv9YJeanW9TOLVqsnZAaPStId63GiAu/t\n0azLvt6NJB8gKLLfgbx4Kxru8QBLoJ3UuWugMh6YvJRFfr5m7Fk5V5lQjw+VmeMgt/YcRN0Y8Bo9\nWLoPAYq/5LczvNk52fDJBdS+eC1S7Xp3N9k4Q4d+NMbTbXmA3/urxWEf0iquYMDbuqCbfHq1E3lz\nZuiVdagR66n3AXdmQAK8Ipk5UMgYOky+oFlQTjuM/qxMQ4W0voPQOmigkkUNRm9Eb/E6duBtVqvl\nx7zS8dyuPjmNNqPv7QnIv4GHABNFiQlltKwhAWp81FA8C+jyzt/3d/IL58hKM4d2LB6UsIPj3j2D\niQgDkjU24H5ynzvSfP5i5BS5tdsfnW1tRDNoJ32bT4ayIDV6rnMH9fgvA5Et9ami714d32cA2aGk\n3OlzRoG3lj1XLLMrew4C52BPksUdJGY+wYf9F/DOdOmY+wB962oEe3Kd9CZxDxSxlckNrIbXy0xQ\n8foC5ALeqxnwtgYB+euGHnp3a+UfOvdNljQc6yw1Om4SeMcRv0ngHUfwJnjoyQi8XD8vZ58/B7nE\n6geCHRQsAupJtcjRW3Vcxd+qyLRH4/EXS+10WOBgyTsCqr2mUR6+uo9zAW+uwk8KbqGgHybcgWAV\nerR3NwQzAf6MBbny/nSnuCdP4nXYzoVmH0D1G2eSgED2kh1QwYy3AqhGJqj0w1/U7TE4m31mHm2K\nJCS8EXE7Nv7fMKY4nsBrFRVy18ezoTpJJkmZxVxB+Uw6ayeG/gwjmRVdPiHAu7l5umx2atCimKMb\nJvzWo3qiAu/10ZlSRIUCx1cwdQGrBbzUQ8jWpwejWqCt3hzTw8W0Y6oTXq/aOeYMby4DXpYxlD36\nEbd2EQKOlfGw+LhUGlDA1fnIVepwpHi5Mrwu4F15OXFesQoril/eK390qh7ta4uWt54OwspHBqTh\nDg98xQfAnLz65JeWcfwZ7hW03uEDXVRLUakfOXMDh92RHQ1MHZK7B304xTkVUN0ci7oMCOKD60lx\nfxRy7xZoG5NKu9Fv6zHD13Yvg/wpnkoTjVY6BRm5OocfNdQpkL9ZQtMSy6CkLAVqgzlamoLhZePR\nm/VuSUlh1hWAXiktHYnN7o15ZEBLpbDQRtgU5YNWnunOO5QgeQ4A/5P73JHm8ztiZ8h8mxYtthwk\nvtIwWqVMxg0BmD4wuAeFW62HxH5TzUyn/z6NYFez7DGrC0ldNPo9a99nddhuVPY1Is7JnqQ+byr6\n9CIh9OpDM7zp8/NocyiQN64BqMiyw33OXdy3u/PkG1ZQ5M/qwYu+3opb38yTL1gHuDECpIe/6sI3\ntfyxDbuTwPtbs9HR/30SeMcRv0ngHUfwJnjoyQi8mjK9POPWFHABr9UgIpHHtO0UI0n6yI+rvbaJ\nRK0Mx+sucZBhrEDMuAC1ntdBBu7sGClpyAN/GtQCIIgYNQcg+hfrPkgaHMSuTO23FznFwiSFkKRq\nh4DUWtTwXRr1CeimpE2Ppn7hRZg2LN654T9Q61aul9NuTcFVs4eluF1u/PYfTjwlgWO53Y4n8LZb\nvYj4WRIqyXLKC+J3c68NZsvnrwauJdUm5UwfdaMa7/FleY7I2TE+K+33Zej++++eqMB7ZXgOnVKB\nSfxBJrdnHQQvzwHkGNSgXllLmpjqQWUC0NoEgt6r3j7mDLerLr4pUpZ8Ozl+/SKmzsCCwTR4XdiL\nPa8spFpePCI8Cw5Byjozi3vzaiJf+gHG5f/YLz59oYGDomD62cXAv/e4URrs9OKueRujziQrWXm/\nGbNmMYJ4GWx6wF77dfLp63jq2w+4mT3L9qoAznZxBalsi8E4vB+FeHTJRcY40DeouZYwIJdHHvt1\n/SwwlOwyR8Jp5jbS7YJtdp/4NStpUC2HtBor6lBoUV0QB0y9QH5+IJ+h/JFBf8n0LFnFFsnvlrMV\nGzv2fJ0nu5spsRmc1M3CwaUred7kSaWgDjRif+76zJHm8+viM8SAeoG5ng+z0ltK7Xon3RFggEuc\nB9Cc/kNLfb47kCkFVis5JpJIWAjxtItGz127Ko/E1gEwRQxWCw24PZQ4g5uxIuSaQ+ctl+Yy2wWQ\n374SUEOmhdw1XCx8VZwnX7+CGfR0ULzj6220+80ceh4DXvY56dVPW9BlHSwN/gcek8A7vuBOAu84\n4jcJvOMI3gQPPRmB1+s7P2fUi1N4NxtCFr0sD/nJpOn8Fpz1ZDRXt6Rd9Ps6kPtmsRM7BEQWv6vA\nHaf0EtMjzSPAm4GCaVwDQQHETEt8dGQRHETzero41wt95U0OsdFfwUUGtEJEVAPXVRpF1LydoE49\nDipyc07P1yi3//Af4PX7PFSOfj0SHzh7QEpe7/F/xpjieAJvhTFEdF8fxe9eaJUv8t3D363LlW5+\nHnEut7W56Ye6rclMzdQhCdRN4RgzuLkev61bckW1FeH0s48djFznORGB1+UE9qAhncysEFF4HY8i\nh83I4NsL9n4d9Dm1tNlXoFURHNqfAuSjqrHbwWadnkcaIll2sgUJP5wKxKphNaCsnIGyyCsuLSZ+\nbpYjLkYUvSop808Z3IrrWL3n24DL76qR71rmBaEbvcmbN4DwwdOdsqnTF139Ooe6IkSy4ckmXNsc\nCUqFA2HvYaorNpDL3kXEvwv4kkfLnW7vJkLN4g5UpzYoaMAQ9dP3oaFBd9laHQTNbIv94sStx6zD\n+1pwrNRiDOQusVdBJ9USzsojW50/RDTJ7Ps4UadWDY0hlJYnYHoL2QkhDscRQX9xQo5TRhitqdgq\ntLQG2cyFMUJLFAE8rwb2WkOldae5Kdn3FePrQfhJm/iIwBuVRYJreZo4NIgC+CGoE/S0xFePstV1\n9PyutkO+w8aSLCmAWaGPNKWxDO+0C0eBtmllHglrZrtirAnUogHUmCA6IysEhfdV24iKY+mDfx+z\nFuSS0iTk+PBSUPTNMZFlA2XC2n150nVvAIpuALz16+2076059IIvWO3xDJA+eLsOLelqmwTeCX63\nT+TpJoF3HNGcBN5xBG+Ch56MwBvwfoQc8X6Yy9WUZXkptCbbSPsVDSj7pkTUPX1I1ta6c18uFsGB\nebr4Q+b/PrOfdD9bj7UBAfRUGkjj6ymKABMq1nvTBH0r3LytXUq9NoN/5l5CmWkq6MO6CDKrUKBy\ngKN2jnJlfshbMwiz3/ShRR8UyqIfk9x3ZWNeiXV6fx/AlV/RSjJfDuXzmdzSBF/e43K64wm82+un\nOcO2GITtSwbJxdpS7q7QdOmvD6i52hhKc+bmY9YeSAVmSOAKTJvZWy7tjEbpQdXgy5wQxhqs4q/y\nZIUDcNK/QWCs4/77cyci8H4SECLtEiNQwkEZhTViFGm2gk94J9hM7mA0e9AGvRscjANaPp2id+sK\nED5KhvKn34uGsJx7bg4uTwBHWAtRFGbgkVKGQSZJNeLadVGlM8qzhwldHf5wq9OLM69L4V+7DuTr\n3wau+upm+fr7PdCcj/Tkib8B/+nzrVJPjze+4iU17meL19oVO/CXNbOJXjmMGnQaNL0YkaXvIAjo\nAcTUWIh6kEOls0THzouG1TwrKgW1hCwmPXXf74mbwoFkZRVivSge07P4QHSKRJt13ExTP5mtbEBV\ndn8YrAuG6FaJqt2tqFfPQYNORQ/E8XCmRwXKNvUe8b67IiSPmdMBesy4C6EXM8iQhi0ybrYhnkG6\nk3WNfTPNg9ufSKWZe5Gw01UOxZRJjjSfXxKdKyVVAcy2d2APzgp1xJtleD0h2LOT3tZSw0c+Nk0c\nmNVLTfO6FBt354jJpRj3egITIAPFTxnelrfzpOB2wP0eAMxaGB1Md9hj9iiVmkv2UG+19WfgZQo4\ndNMpIH5+AWAus1la3N+o+qSCZXjfAIlliPmNn+8m4nvp/HlrATafAlLB09X4TGPXmMtjjuV5m8zw\nHkvU/jNmEnjHEb9J4B1H8CZ46MkIvKEvxMuBG/1YwwelLIMBB+f3S30XNAtzrk4Bq7dIZCePN15i\nI07CowvfV8Bg7BB0vl6DzMHB5DqzP4rskNAcaCVfaCPA078Xnn3VBFP/FYdfuo3K/TqEsHaYBmv7\nkdLB3IgIwdyOEOQ9tZFk/jMcNy9pJR1XNIxM7nF3pxAtc2fb+/QBknVLEtr+7RZAPBozeE3wrTBh\npzuewLtpf6YYU6LEFYuM4vz+VsXy03zpmQ8FoS5/SmOzS1FZTyScEj5qB729I0Hu7fMhTB8Vz/St\nHfMLt5g5uTF5Ju7/IvA+FJEsOowanNgogW8zy/AO2JDflBawMV3pXubf2+CuRbXR1GUvDI/1FqEg\nVr/+WzeOokUlZ16RgYtTZCm5jOO35I0YFqDmECA6K+Dec1vEpICGI2ZV3UsNYsod0/hXl4Hz+jdB\n0Xh+h3zOcwZ8xQtK6e5nQfji1XrSMWhAS5/wANHDAYXLy6C8NxIGHBqEvayyzy4DXPkOYIOJdXgx\ngYl+b/Zoyxh/9c82AaucxIg1iLS7yx71btDhD8jvnBI81Tx0TM/hrTEZsuGgCuIGzNSDs3Emp4b4\nt3PIv1FggrkI+gMk2u7FQUU8RyMCmsjlnU2HzWwzZzRyq3e2a36iiw/0iLPf8lWsupQ6C85xctd1\n1HDvB0XS/QEaLj8XxNM3gbD7sQrJPrtXONx8zqoq6F/8ckhivYxmQxsKFkzwozUOKvzVIAQNyC+8\n00aSH57Gt+X0Qf3DB/CPBTly4gGMB/QjsnE44aJ8wKzGuv31POLHGkBZOQOTcwPQ2wmxcBjZLq0i\nkfrun3+Hq177g8XUuXke4qOnVdI5lh7+vfo8menwktR9wH+ydr8kfDidX/gl0C8XgNjw9wPC7P6+\nY4r3b917P/37JPCONVKH/9wk8I4jfpPAO47gTfDQkxF4Ix9MIoa9nmwKRy4BfPzdUrNo5zF36mds\nn5WTiVkLaNMZTEiS1bktXKmm9lALbX+nmtsXHiE/1+OF0vv6Ubzch9a5RVMx2Ew+ucOCQtYFYtak\nwRgaoCXJTjMMNainJZgqexXYyyaCe0AvTXkumjjDzFD9r9KR7btpV2cQNKiEspf2otxL02D323uI\nPeI/mZIJvtT/s9MdT+DdvHOONOUAz9FAsxxSreC+fqZRnv74FK6HSWANppmoaUhP50fvxm4KJ/6+\nIkPUNatgKMqK58XtGTPwljAnNwXLfSX+H8zwXh85Ww7vkimz40XRLYCCbcPgN7WBlTS4I2OnH63V\naWhjONM4norIdcNlOGXgtzPjmn0e0ow7krnCOUScXYiF0iQQWbMVPPiERK5bziubzuoR00Mrjwi8\nHtsCxGkPx7mAV7ruLeDbT+slp67wlO95jIdr3wLhq+U1ZFBWk7P/FsJjZoH4zYqD0GtlHsIE0SB9\nL+3ekghL3+ZA65Rl0UNC++YNyTM+8VF8/Fw7xWoJt3DuVGh2J1FlAm5nJQ3dV1fDmX1dx7TFfnNY\nphRSrcSznB3QIekJLyHkwwzpgrspUjML426sgjZ3NaqMwYBjjeTe5vLD/p16tZv8pjyLMvdlev1L\nVLxoLVZ9dAkV1z7Wzd1RV82/HhEhrYoJ49ZeQJ0XrEaKiiXdYv81VYrDzee1Gg15QTkTklvs9BSh\nDoUr+uCTgTRU7asGS6idfPL3Phq9KgSZou2k4o1dwrZNOXJsDcaujLKdOdFFLNoJWuZH3P1aHvEc\noOBUIpdDGoNfyvV5Idp6fpucGlg3ArzIykm5Z2dzL99M5V0ZCJ8dvpMGiA5uZXOefOXbIOXsAMXK\n1TWi+8exPGtaQ+9eBpJ92V48zWIe8/N3LBPZJPAeS9QmM7zji9q/R08C74SEcUJOcjICb+xtaUTX\nqIWWECr6sxfRrnMt3LBVg+Z9gxETxIfGKRLZPlNA6rA+Mu8lA6Y6BzSuOYDWRcbKX7dpcVr/AE0j\nHehDZQJqjZbptj8Pyroib/zGtUz+inWENKfY0WLfIrxjW6YczHIgHoMI3KK6SOyKCNmjD3P7Phl1\nN0peOlsaxjypfbFImHNhBpQ9XE4G5xj/0Il/Qm6a3zjJ8QTegs15JK4WkMqDZdi6eFTxYBWQt1MJ\nYuUrdbEE3C0Ih06rIdEenVz+lhxZb8K030Bgbl7BmAGndG2eqyYCJS7ahsbj3nailTQMcJx0Z/Ac\nlNZsBl2bCoW0YORnkiAsZz/YmSVWV20INBpUtCGUQnkCT09l8HRBe9tvZuY8tvhJ0x6L5zYuANv8\nb0HNpK1cCg2Q2jFIH16mxxWnD0pzY0qPWMPrtTFUnPpMJP/SjVS6/k3E96b3k1Pf0cM/7sXi+V+A\nYuOKChmpZS7zliSqoDJa8XwfTfSth7r+YKzmneAsiCKXrwRsjR+kB/9Vih21viTrNiYPljkk7V1s\n5m06CRx7g+WgVswNeCDIv6ODLGurGfP98MvH4bqQHCm2FuAsWoPDlEaoHQiEwZog8GlVIG2ACcw8\nR+pUeqiKQ2CcaqVPNxzeYewHL18pfyiBi6S9jgeu8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OiUlAoJn/odorGb9SONW9/eagS3\nJjfb3C/ceHa/ceeuA7hEV4yihy2/euYf4PKc08oonFpApIVfcCqWtcapF2WRnrm9UnvmIK/5McSZ\n+q1WOa+1V577isHx12exesWdTvv6bAUKYwnqMh8BPz+cL2wx+EjfDSWg+E6nnCj28S7dcJfPXflA\nOOoS3KQSTz3eeBbgpijJGVfFK7YyLfCaD/OIXaCyYRC5FvqwP90ushUeyvpOyQcNOml9JKIWHYGQ\nXgn1Ug3tYlJusYsKqIonXPKiPGJ1A3rHswANM+z0Xx27RuL3gyLYrlsbLdzxIuBvF0iiZoAnmXtA\nEdYmw+qybX/4nDeZ4T3GSfXfwyaBdxzxmwTecQRvgoeejMCbPj9Xro0hUuN5XYLC14Kc3TrKe5uB\n9LkjTVYtNX6YAW1+PFV7mpH/Hh1d+CWiVhWV9VakuPBrB81CbRDADdAf7PEoc6PC+acPsOKNm4C6\n9zO74akO8n2GEjmjzFLooBVNL1Riv90eWM0ASX9OCa04kAR/fZxnckNENqsR3ntPLbI3+KPcT90R\nUklQtmrHHz75T/At9KvTHa8M748VaWLMTi3XPq9LyvzBTaB/LgP0fAbseroSerYkypENFHp9KR2O\ntCC3XiVRdai4b8+XxMXv8YJTQHjK2YVUzYtHjX+JMcwZsCaC2VCx5rWZVmlO2p5jzk6dSMDrZFqt\nD+MceUqvDc+mLTDQ7U09+jjwCWtH+pBRc4TGTSm0myXFjSESamEy1kOBTloSpMVvHtyJjmbUEHtj\nOtG2qtGr/xyUlj6hRyyTiF2gWnxpl7PW6YfNegT1SU68KPbwDVxxy2ZL2lYF99AjIN36AnCeQwAX\nrCPki4UsU+8DpOXd0YWK6esk5/nPegkfvlcrBQZ38PV1ERRzVNZ+F8pduAahPW8VEVvk8MhnZbOS\nmNamopg6ao/Zp1QwEQpg50VK5r74xk1Oy/wveeWidYh7qT9/5PP5P+ZKXOCQNGdKqWpZVKbk2SZA\nnw+lr7dt+/n6Xxs6R4yr4rgpLU7iNyCjAN9OkvdwOFEzxS3WB6BYf6FEmPEGnbpXwb4WoAVfMVvg\niBZ6WVfjIbsEBCH6wkCuPK1chiWfIBJbO2oskXBrmoiYwkLFil2Cz9oQKW55FPfs3jL6ttcUuSZE\nIaxaAsM/zKNyjMegcrvGA0cFN8ilWgNyr9GjmHoJn+G/j7Y0M1pl967Ku580U09c7O0J35yNcOlc\nm5i2SS3s+LSQVG/Iwj1BsqyL6kSG74JxdQKRmMoJyt6KSW4BcBWz7HLfkiYelwRAI6ejFhWi1TNE\nSYus5L7bPZT10dR57z9BoYjtpXe2VI4AL+vxlIs25cA9T7PGuzTKmj4RSt4PXKpxgL60b1QX+488\nJoF3fNGdBN5xxG8SeMcRvAkeejICr8sJqCidit2pFkEIYVt0HQbi5BD1ch/C6umtyPRFKtu2pVTj\nPQDuxV40uF1yVkQq4coPQPm3F62iRm/nrFY33OKpQvc9SeTQBozeu0EkeFCB6hNkqTkMkSYmTXZW\nZzN09ITwSZs0vOzuJHuzJNZQheR5Hwrk7A0cV3zuAGeJtSNCCcx6PXCka7zsk8JjboKa4FvjmE93\nvIB3054safoegbOf3khDS7WI/qkC4XeTiW1xJVk6NxVOOzAk99i90LxtEqqY309mrPRX5N/Y6Qjf\nGMDFsY+SBXV0itfRLU63diXYY9f7MiVSlsGaKspZc479ep1IwLvT3Uve2psE6VIH4k1Kqh8ApGXm\nZyGZ5UC73Siw+ueO6ijUjTUsI8pqNwOZVa7PgLTJz5+73FKF5hiNR4SWhMtnE549G4897pQGFQoi\nBtjIu+eqVQdmimJNGMeZ1Ri6/QAyj5BhT7h8jqwY4PF9jzHgfRloQBflL/kIEZeTV3k8ES0vFIxk\n2QdKI+Rz7wjDu1cUy7YoK+dkttGV5Yk0fKcbnPc5j/N/yGfdavTnmtyBbxNlu0VFzn1Hi9h6lPaH\nOWCYKCG8CSgzVpCZCQVnXM6225nqQdXqudTkRUjOaQXCzYZcKbZVIq0eAn5QKkDuhDkIMzmGez2y\nSWqDlQY1C1xAM5Vyv+cd0fVY+cOZBM3/GvNl6aLTa4AgQ5MK6S3AL/4ISFeamTzSvvdnaLbyPLkj\nYtZw6o8K5ay9Mv3zxxynwBIq/Xg79tri44x+Lp4r+qqAC1oeI4V8E8Dt+roAPZA4TdoQ7oX3pqJh\nNm8puhaZSLHkyZWkgJORNTd9H+HTDjolzwCToFIPI8JWGxJr2q2SfVC1lwZWL0H0hwWO4dO/UWrL\nH6xwltVNVbTHOcWszB2K1rfyKNMmltkUxeYsVnNbwXSQIwmpv4LVbdd5McUJLdjYb+8JoDZDD+Vv\neB2r1l5ptz91nVJYqKiAuSbjz/PZxsI86cHHgGuIorLAjOzCm4G7v6yGLmSKEsc82Yxx4CTwjjFQ\nR/jYJPCOI36TwDuO4E3w0JMReF3NYj8s7bdbPbASe1qhr8ebOqiAfGwi0p5aRfsK46kpSEQan35Q\nbg8C72EH3RurpHc9i7hVtzjFukQ7z4TYoYnX0WeuV6DOACrvSUf8gAbRxhSH1KlSUqXODvZQM3ex\nsQ6qts7CgofV9bJHNfP76bynArjIxEpKWzw507AetFm1cvyDidhzAKHi9QVjbpKZ4Fthwk53vIC3\nYHOOGNnA+g9n1BKlmUc0txmjL+MIiTHCD3k83ejvz8Q4HCRotwedWSbLziGN0nDhbvpN9wxy5pc8\n7kg2y7mJe49ak/tNY6pz1gadMKwEwuCCZM47ev0hc95l/ib/gaxfBvlEAt7XAmOclpogbuZgL03y\naoaqPcnY0INR7Kl7qLkghhK7Apv9HbRH1gGrc4UhBodavYVs8vPh4lVd8k2NtUdcqE1fnE0k5iF8\n/1MyLUvgINrQQt5NDOXrFnXJB1V6XBmolnkR8KyzD19SMmXpHCaUy8O9TwC54XVAaaXAL/1Iph8u\n5tAPZztF5V93jFwz0aiRspbOxHU31DtKdAaFIa4NgdoBmpdmiHPzQZH/46HW3aLRjZg2J7oa3Wh8\ngRrveLpMbN2bpPDupfZhLYUzN2D1yvy9xL1WR5RfxfD9rPSi9tYaUtIbA3FGZkeucuPdojvkmztq\nFHeGzLIp+5T89EYH9u1k8lsrVGJIE/A/XOB0iAanZt46JbFggbhZgXfpEeiGEDx/Ix3+8BpZ9VLv\nf8omro7Jkjw7OWfmLiSkHKBoyfsYded1y813V438xrQLsyRmvCFq67QK9xY1KvmscAQWw67JFnV9\nWGQ606r37rPbKsKJoi5IIbophslrF7rzSjtwa15uwaHBLcjBDIsb6+MQChiktU2R0nN3geL9ZXZx\nyVsqoe5PbXIRF4xbZ/c5s+MOqKo/zJMtrERB5QR89jcgB3VSjtk3Q9XSTmYbp6ZtnIZW+ykJh2RZ\nT0S67J9uyps+GbBsSdGrX27ZxmlYbclP9/wXpXnSww8DMnpRViOMqGFY5PeuGf3+f/QxCbzji/Ak\n8I4jfpPAO47gTfDQkw14VbVuztSbZvEbrx0AyaFCSGOjQ0qBEIFyui4BVDHdpL/FH2wxQ1TrxT6z\nNQp3T7XI67K1sDkX84Wni2LxEhPvqTJDncObPrHUA397Rb+tT61WsvwJOGZ30EqFJ/ZW9Ft1JoWy\nNZiDKflqbtANpCkNwH14nRN5VerIVd8PsaK4Zjy4OwZ5LCqF0NvTSXA7wru+zZ8E3mO8x/d8nSd5\nGwFHGjoQ9bcAzOxAqN6DQBXrRDq7Bg/xPK3Rasn3/v4kMt9d2nI64l8vKxJe1iaLC17zwC0RhMzN\nLjhqicLXFZnOWflKwSW83+dNyYyzR7e8D3d0WT3I3rY4elbc7sN+5kQC3nsD0qXQUhVKknrRNG07\ntNWFEa0Fcb5eRspseIH0u6FhnpN7wI3JuhHa7cU4xWcYigxeYA10ouUHio4Yh+kX5kgWJtt397ME\nitMwvn5oH1o2dxp0ze6T22Qvoc2bIw4BocgrDq9YkHpuHnFZ2d71NJCLPgbngu9B9fAjkv3hh3jV\nl7eZZN/5ZT9fs/T5ebQ5RpJ2nmd2aeByhoRmGvjPFCmhEnhWFvAruBrYHC+ZhzSyb6UWKe4pEJo+\nmkMGPYAYujAwFQHu4ztM9i4fAUXsdBfsakDPPmqXoksUOKxLRP0aAe1NA6eHaHWITjf1n7oboX/A\nm9M3qOH6Z5TIHGuWi0+zIV1ONRe4MUCMWBFDGWvyHWlmOWyXTrEviYp/eQnQo4587BJRY2YTTleZ\n1LRGmxxdw3Mx1Zies4bn9qzcIYtBTL2YHZ47vZyxTySCU0HAGm9BNY/uG/nt+h0G59RHEjFbXaH9\nM5zizj+ZSeb7nuLaK23kH7drGKoD3XaBydY2laqUWASrn4TV2iEifR0nL/mMU76+TJaueYPjm2ZZ\nJFamwjsz2sSIpDrFru9zJFaqBUrWwHbueqY9baXEyvY3DlzZ6bre1Cwyp0gvD7rj/AH7Df/Q4wXr\nOVVWtdlJdTJ+smr0u/10vNOY43z2Lsy7XEdcJUGISTWWvbvrfzLfTQLvMU6q/x42CbzjiN8k8I4j\neBM89GQCXv0ug+y2z4OErQvhv7+bpSq8rNTR5AO9vBKzIkTZYBYRHhaQUdJQPrUD1F5DyP5NAhSc\nZZerYjn4PlvgOkJl8uPDLdhLNUBDXpgqx+8VuM/uGJJxm5br8eQgLrsUl8gBpEXQOH2sMhcFRtrd\n7c/51ym5AT9JDmBtyU/OieCWVdbL8d12TuzwBPecWux9Z5YUe0DgdjBJpQm+xP/z0x2vDG/5Z8xI\ngjmqJaqZan6oifCYYMf0PozeSab0in3sDcv+Y0eFTkfeDWcNS729/MLOTvytr5+U9Fg87gwAOnPh\n0RvXvtuVLU3fxzGZKaAOFdDp5x/581urUkVtjQ5HzisCL/Vo7egvjxMJeG/zYr9rL4ezdHUgsZp2\nvewAJS8iocmdus1qAGejDxo26amRU2FLsJ0269xIrE8DfKmJQTWhHKyq3XZE4E1fkEdbA4GwDn1a\nPttBXzuwm5vzpww07OukNWki1M3vdPDrE5To4lIxRMO8tv/rmLUgjzSFgPzyk2ZIX+uOL/6USWWF\nEUdgN1bkP1lDdQkdP2eXpzK4dnqJtOqlPVw3M8qQLUo5Z5UX+PYTrmj9qHTWLw/ixHL/5zMkrHVy\nngv38/Vbk0W+QQcWlwb3BsSUCih5/3LgI2sR2JnSwtP3EHtypayO7HOSXg0HddFE6lcqpey+HoUB\n2znJpMGavT7ystcQv/+ZEtGaNPTzjoHvFwG2oDURuPSDHcqk83NlG6t9veZNQvqnm513N+wXHvKY\nI8c1Szimxw6Re9VcZjGIEdWccs+Xh2r1eu72EiNei6Yl7x1q1qFqcJOTbp+FTD7/r70zAY+qOhv/\ne7fZM9kXQkhIAoQ1UPbVoFZrcWm1LtV+1aJ17VdKW/WvVWu1KtrPDZda69IqFcWqFa2K1SoIshvW\nQEgCZCEJkHUy+9zl/N8zCBJI4A4JxJB3nodHk5x77zm/8869v3tWwwgahpZTLQsaKic2ruOUMyY2\npulG5ezy4IgnCpSlD1bI+9Sk6LrgBZtl+eHbITz3KZD3pWvGxkJZ7tdsALtiq/CBnKdnrXfpuGqD\ncM0CkAwXrm3tl6Xiq5pAsoc0Hd9Ulo+2M+seh4GT/oxxq0TLWWWt2h+2b5HdONTjcNZ/9kxV/zTH\nwtf25fODhZYzcF3kWw+0XJ/sDwlv1wiT8HaBHwlvF+B186F9SXinnV2k4h5FuOMWE7+4OMBso3AR\nT4sGlSWDceMiEbLc+5lLDLHqxv5iY27I8IBVGL7BgrsR4SLscZox9xGcmY73+yXzdzDBYzEunZMv\nFo/XItsv9NiEXQlCbabApk9bji1LTFzlz9c8mg28bXF6pupljpJkJa9wq1pfNkj6y6V2GOmqhisX\n65KIE9Xk3CbBNW98ePQG2YJLC52SLr5uDqN2p+sp4S1dOFPHSWn6GWV+JUHxa7YdCUL9n1ZJwuKh\nDPexBZbTdojtdpdLG+bzRUVpn9Vq7HpvMktqErC1vZilO9o6lbdlS4u0QWWC1JjCUIYEGPbjjlsl\n+Xm/WD5DT66XRH3SHrUwp+KoB/u3RXj3WizafGGqMbpEVyZYqjX+HSi6bYgO2Pde8osKQ0kNKEHF\novhK+rPWJMY8uBVznUsBa4LfKElwS9sGWNnPPVtgYmtrh9ymnjuTbRgN6m8eN+DC+K3y5OZmcfiv\nx2qW/TbYNFWTjIu3s/2Lx8Ga6VroolErsO38m0+rohgXFk0TPj0bIjvmlAnW94bI01aAMbgcjFWT\nmKDesUJQnLi2wtefwb+YpLmrbeLyB3FYS/5+wbN9gF40L1sykoJQ8kLHrdBGCAf28llkii4EWx36\nzs/GqokFdYryeTabvoLp1pCg8VUWtgwD8bWbVDXJZ9gz6g0Dl/ISPPkhFicFtGQWFAVd5OsVi5P+\nlqaOXy3aV/3n6JdXQUV1UJgw8DcTw/G7HPKdTwf1DSOtBh7NRu4w1MFtrUpqnUVIqZXks963MiE+\nDCXPrT2q1wFzZOB45KPuFYVzESiuJyxrItT8uFLvv2igUPXDWjWhxK24d8TheAq+zxpoe7N0cddz\nX8oFP5uO5ROF//cgi/zpbgEVHGDFNGAJgFv9JkQMtdkpLB8vaun7BeG6l0FqHtWmJW2JVzb8oFUd\nsCxeqBkbEv23rhNC2OQ88doJooAxs/qfKyFOwwwc8flAyAv86LfZyrhNIOMwYlby4EbNO66VhPdk\n3oy76dwkvF0AScLbBXjdfGhfEd6MRTl6/ku54tbvN4caHHZLbZwFhAyv6Ej1GHX700FwRsCFi7f3\n+04Fq1g1GnbnYB8jLkKZt8HG2uKYkdIGbOIagRVsA+m5h1vghrsTjBUzjNCOaSFberMhtQTioHZU\nwDhr1DcPJw1no+Eynezf1ZMhvxrY/nSVDW4Ow6e5SbBzpk+484mIxLJbdU9jCqRUSvr5C1zRZYG6\nuYpP+el6Qnh1XTBqXi2C+rywNqWxVRm+ODkS1yhKuH4pqH4XbsWKT9hp1Ue/TGAVgEUXXmiaFjl7\nsSLVTmlTZwwrxq2kOv6s+mCmmleFxoBqF3CCOPh/vmnhXbxzkj613xaW6ghEBWz5x0VaOg51bBjj\nM6aNWX9Cwpv61oBIw6U1J1UKHs8ZFgnh0nwjSpk4wlkHV9zaXwtajIjq0IX4ZjROyWDFc7G73pNk\n2Y/DdkJZQdjrsgou3Ft2p5TAtmTY8f/b2L3lWzocDlJ01ky24McQfumOoHBf85poC27il8nakIdG\nCF/9uBnUybXCztrsUHBvojT88i8UCVvmD9JfXlfgvW12P+ff7tD1oWeukDf+q0hPbTGM7B1isLpQ\ndWX95Mt2km2pt6jjfjZV/OR/PCE9NYKDlXR2xrMZQvU1Fdr+y/d0Wq+H17Z/X4LmTG+VS98o0lwt\ngjphLUS0LI+Rvzbe9edfaGE9PWxNqZT1NqcoOiW/r3FUKM6Ks7BcliDYvLJ49h25Oi6dJmx5s/N1\nnZ1b4sIjbh+nPHmXGt41IiwIEZnl1mh6Cu5j4jYillQ5IEx5IVXY/Ng6NTQUFzEz+cnOyNYzxg9U\n9xXt16tuK3WKbaJuuA+0tI67eEa46ie7desG7HHa7FTeub1Z/9EjSVL94JB282M2/c/XC1JqE4gf\nnwuQ7sBxDC1WAwcgiMuKmNqvVYXr7020br11R2TIMwVS+TCNDd8gS2WjNL3+qQNjkMddMFNvHd9k\n7PxDx3EQ/U58MlO942HsacGd2lZ+iJMIlaOl3WRRY0pGLbwx4ToqMQlvF/iR8HYBXjcf2leEd+iv\nxqn2aqe44deVRmtrotBk4DMEt/vEOUXMSAsye5JPDGzNhMSseqN+X6bQNsQr2JQIhLb1M+LamNE2\nUNWGbVLks94DcPoFtvqiVqNisEUKDW0WnF9kCq1OUYgrKhcGJR0947h4X76+rzlFz6kCURixN9K6\nfaDy9lWqfveLfmtbss7iMvcb+uo8uOQlm7T+mWIjMLzzFsZurv6TcrqeEN4tzdmRuMV5EqT4jdyc\n3eLk20biwEOcST+hzaiZs0OS3h8M+gXlIK7G5TfOq4gKAKuJM4TPcw3h3ArxlWH9tJmPDpB25zFW\ndOaB3aE6+mx+e6aeVQOGojOxKUkQBs4+0MK7vS1D8y4dCp58n37OiPVRqduGC/gnt2D3+2DdmPLd\no7v8j9fCm//HkVrGihRj40MbwD/O023SK7fKupbAlf3AZ27mtPCgLbJYWB0Sf/YiTnyqVcTlc/ZK\n7rPKpLiv4kKJG5K01MU5thWX+vQqm1M2hjVBk8NiJCc3GBshQ6lLk42KLJHNadwqDPd6D7H7+4Cc\n0IyHc4NXvgZu7DI3Qhfv0Ge11B+SzoI7CiPWKrew9eImYOftlBr+PtX4/MrWwGWpG92rExLUz1Iy\n2AX/l27g8ADryg98YWZZb1v3xSStX5lNCFmZkZBXz1KmlR/FZehtY1VbmVNtSzcMexP2ylhV+8aF\nK4+5OUZHdd0WUCJVn03Q639cCef66uwFV08P4UBm9u+rQ1JqM1NHfaIYaS2SDZXN2FMQVItvrJOb\ngwnKtXcmC+WX7wu1/qy0XWv1kdcYj4JYOoRFFt/kZ4k+1UgpcykJhj/szGpRxjw9QPQXthi77ikx\nLbv8/Px+3ra/TW8NtR4Vw5JP0nUXXxAOV8Q4d6b+5fdbvTM+SnCv+PfnbFl6ipozb1T4f96CuOfv\n9aoj5b2yUJIuQJNdiNy8zsh8vkDNXZakFL/6JUz70XSx6oxWI3t5grhnUMTYed8mQfTYYNKvR7Et\nf9yo+8d33mq7PNTfO+Ivg8X0n3xpU1PVmOvkRG9YJLwnSu7AcSS8XeBHwtsFeN18aF8R3tFXTtOD\nOKis7PlVSsML0/mYQtWCkmvFXZ9c+fslp8vDqncMEuytMvOkq7An0WLUKg6Wt0sQ4nBAnN0jQopH\nkKwOj5reAFrbrCpLTTBDNmrjNJzcI+xNkIXJZx3YVejIj2aAvmLPaNyb2BpyNUpKeY4iZu8U2ejc\nHWJzY4rkS1LVuJJE8bKXreLuqyq1vVdXdpvcdHO4mDpdTwjvfzZOjYxeZVGS9SBzzigzJs8dLVb/\naI/u/qyfuOkdlM1d8ToszwFhgIe/4zDj7GpFeHeoYYzG8b4rs1nd1Tv0nUum4FaqgpA2q1jAYQ0d\nSm/ZwplGZh2AVTWEhnjR0H66Sc+Oa7F8umlKOB3HaePWxeLUcw4MS6l4daZhVcHgwx++84OjV3M4\nlvDaKu2RMbdMlNuSDE3GtYFxcs8xl6vr97ecNu+oNrtvfMsxJ93l3TMilPZVsrz6w29aH2+3Fmkj\ndui46YQmz3nIxvbkqere+V+2aw0ddsPEkNhs0z+51mdVcz24aIIk1Cp2w5Hgg1JLHKvuZzW2DpKl\nu8s2i9vcbn1lUoqeuDmOLbpCxgXNgOFyYnBL2tJ2ce3Y6dDG3DxRXPHwVk3alCUO/jDBWDIL1Pn/\n1wqhoFO0hwzxz7MtxuiNgjVU0yRs2bJFaGtyqXs+Hac6DFUZeOXKTss6+O6RAUeVW9Sw9b7kNNe6\n8QAAIABJREFUpQOtyt3xKby0KBIC8Is23WbBLpyPzzXEAbsk77BSOc4WAEGzMjWsiMq2N5ce95qD\n5mI5ql3w/sW4j52oSYODbcaQDzFYBF0I5/rFHY9uOO45jiyT2ft5ziMjI5mfpbDac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"prompt_number": 4, "svg": [ "400035003000250020001500100000.20.40.60.811.21.4Interactive Plot Through Plotly (interact with me)inverse centimetersCounts (photons)" ], "text": [ "" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "t1 = ts.nearby[1800:1650] #Slice using ts.nearby\n", "t2 = ts.nearby[3150:2750]\n", "\n", "fig, (ax1, ax2) = plt.subplots(1,2)\n", "t1.plot(ax=ax1, title = 'Early Peak Fades')\n", "t2.plot(ax=ax2, \n", " cbar=True, \n", " fig=fig, #pass fig required by colorbar\n", " title= 'Later Peak Rising'); " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "WARNING:pyuvvis.core.spectra:Spectrum does not have subtracted baseline; could affect result in specious absorbance data.\n", "WARNING:pyuvvis.core.spectra:Spectrum does not have subtracted baseline; could affect result in specious absorbance data.\n" ] }, { "metadata": {}, "output_type": "display_data", "svg": [ "\n", 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\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n" ], "text": [ "" ] } ], "prompt_number": 5 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Let's look at the area vs. time to see at what time points these peaks arise and fade. We can then cut out times that the spectra are not changing." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from pyuvvis.plotting import areaplot\n", "\n", "ax1 = areaplot(ts)\n", "areaplot(t1, ax=ax1, color='r', ls='--')\n", "areaplot(t2, ax=ax1, color='blue', ls='--')\n", "plt.ylim(0,220)\n", "plt.legend(['Total Area', 'Peak 1 Area', 'Peak 2 Area']);" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "WARNING:pyuvvis.plotting.basic_plots:Recomputing area from shape (1711, 13) to (13,)\n", "WARNING:pyuvvis.plotting.basic_plots:Recomputing area from shape (79, 13) to (13,)\n", "WARNING:pyuvvis.plotting.basic_plots:Recomputing area from shape (209, 13) to (13,)\n" ] }, { "metadata": {}, "output_type": "display_data", "svg": [ "\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 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Meanwhile, Peak 1 is done changing at timeunits 9. 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\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n" ], "text": [ "" ] } ], "prompt_number": 7 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Filters and other processing functions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since pyuvvis is build over numpy and pandas, it's very easy to apply numpy compatible operations such as *integration, interpolation, transforms etc...*, many of which are defined in [scipy](http://docs.scipy.org/doc/scipy/reference/index.html). We will demonstrate this using the `scipy.signal` signal processing module. In particular, let's apply a [Weiner](http://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.wiener.html#scipy.signal.wiener) filter. (I don't know much about this filter; just using it \n", "\n", "Any numpy/scipy function can be applied using the `ts.apply()` method. This will ensure a TimeSpectra is returned rather than just a numpy array." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from scipy.signal import wiener\n", "\n", "t1_filtered = t1.apply(wiener, mysize=15)\n", "t1_filtered.plot(title='Weiner Filtered; random parameter of 15 selected');" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "WARNING:pyuvvis.core.spectra:Spectrum does not have subtracted baseline; could affect result in specious absorbance data.\n" ] }, { "metadata": {}, "output_type": "display_data", "svg": [ "\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 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"markdown", "metadata": {}, "source": [ "We can also do arithmetic on numpy arrays. For example, let's generate some random noise and just add it to our data. The noise should have the same dimension as our data to ensure it's added properly. We will use [np.random](http://docs.scipy.org/doc/numpy/reference/routines.random.html) to generate the noise. This module has about any kind of noise we could want (e.g. gaussian noise). For now, I'll just use random numbers from 0-1." ] }, { "cell_type": "code", "collapsed": false, "input": [ "noise = 0.05 * np.random.randn(*t1.shape) #Reduce amplitude to 0.01" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "t1_noisy = t1 + noise # Add the noise (matrix addition)\n", "t1_noisy.plot(title='Noisy Peak 1');" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "WARNING:pyuvvis.core.spectra:Spectrum does not have subtracted baseline; could affect result in specious absorbance data.\n" ] }, { "metadata": {}, "output_type": "display_data", "svg": [ "\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", 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{ "cell_type": "markdown", "metadata": {}, "source": [ "Please refer to the [correlation analysis tutorials](http://nbviewer.ipython.org/urls/raw.github.com/hugadams/pyuvvis/master/examples/Notebooks/correlation_p1.ipynb) for a better explanation of the basics. In this guide, we focus on performing correlation analysis on multple wavelengths and time subsets in the data." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from pyuvvis.correlation import Corr2d\n", "\n", "t1.varunit='s'\n", "cspec = Corr2d(t1) #Since no time unit in dataset, assign one\n", "cspec.plot();" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "WARNING:pyuvvis.core.spectra:Spectrum does not have subtracted baseline; could affect result in specious absorbance data.\n" ] }, { "metadata": {}, "output_type": "display_data", "svg": [ "\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 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\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n" ], "text": [ "" ] } ], "prompt_number": 13 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "We'll make a multplot of the synchronous and asynchronous spectra of the two peaks" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from pyuvvis.plotting.plot_utils import hide_axis \n", "\n", "cspec_2 = Corr2d(t2) \n", "cspec_2.center()\n", "\n", "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2) #2x2 multiplots\n", "\n", "# Remove sideplots to allow for multiplot calls\n", "cspec.plot(ax=ax1, sideplots=False, title='sync peak 1')\n", "cspec_2.plot(ax=ax2, sideplots=False, title='sync peak 2')\n", "cspec.plot(attr='async', ax=ax3, sideplots=False, title='async peak 1')\n", "cspec_2.plot(attr='async', ax=ax4, sideplots=False, title='async peak 2');\n", "\n", "# Remove tick marks from subplots as they are shared and tend to overcrowd figure\n", "hide_axis(ax1, 'x')\n", "hide_axis(ax2, 'both')\n", "hide_axis(ax4, 'both');" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Arithmetic on Correlation Spectra" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The correlation matricies (*synchronous, asynchronous, phase etc...*) all natively support numpy arithmetic. They are stored in an object `CorrFrame2d`, which is essentially a pyuvvis `Spectra`. Let's play with some of these operations. Although we will be customizing the correlation spectra with various arithmetic operations, **we can still pass the new matricies to cspec.plot()**. This saves the hassle of going through a different plotting protocol for this use case. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "print type(cspec.synchronous)\n", "cspec.synchronous" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] }, { "html": [ "

Synchronous Spectrum: (79cm-1 X 79cm-1)        Scaling: False        Span: None\n", "


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1796.2 0.000006 0.000005 4.406443e-06 0.000004 0.000002 0.000002 0.000001 0.000000-0.000000-0.000001... 0.000001 0.000001 0.000001 0.000001 0.000001 0.000001 0.000001 0.000001 0.000002 0.000002
1794.2 0.000004 0.000004 3.578850e-06 0.000003 0.000003 0.000003 0.000003 0.000003 0.000004 0.000004... 0.000010 0.000009 0.000008 0.000007 0.000007 0.000007 0.000007 0.000006 0.000006 0.000006
1792.3 0.000000 0.000001 2.417126e-06 0.000003 0.000005 0.000006 0.000007 0.000009 0.000010 0.000011... 0.000024 0.000022 0.000019 0.000017 0.000016 0.000016 0.000016 0.000014 0.000014 0.000014
1790.4-0.000002-0.000000 1.509277e-06 0.000003 0.000006 0.000009 0.000011 0.000013 0.000016 0.000017... 0.000036 0.000033 0.000028 0.000026 0.000025 0.000025 0.000024 0.000020 0.000020 0.000021
1788.4-0.000004-0.000002 7.657243e-07 0.000003 0.000007 0.000011 0.000014 0.000017 0.000020 0.000022... 0.000045 0.000041 0.000036 0.000033 0.000031 0.000031 0.000030 0.000025 0.000025 0.000026
1786.5-0.000006-0.000003 1.064351e-07 0.000003 0.000009 0.000013 0.000017 0.000020 0.000024 0.000027... 0.000054 0.000050 0.000043 0.000040 0.000038 0.000037 0.000036 0.000030 0.000030 0.000031
1784.6-0.000008-0.000005-4.630557e-07 0.000004 0.000010 0.000016 0.000020 0.000024 0.000029 0.000033... 0.000065 0.000060 0.000052 0.000047 0.000045 0.000045 0.000044 0.000036 0.000035 0.000037
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1780.7-0.000011-0.000007-1.218322e-06 0.000004 0.000012 0.000020 0.000025 0.000031 0.000037 0.000042... 0.000082 0.000076 0.000065 0.000060 0.000057 0.000057 0.000055 0.000046 0.000045 0.000047
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Polystyrene Evaporation  (1711)         [minutes X inverse centimeters]          Iunit: Counts\n", "


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3951.20 0.001779
3949.20 0.001931
3947.30 0.002140
3945.40 0.002372
3943.50 0.002502
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744.70 0.228529
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735.06 0.080638
733.13 0.065893
731.20 0.055776
729.27 0.050166
727.34 0.048000
725.41 0.049635
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709.98 0.137207
708.05 0.193717
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704.19 0.446092
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700.33 0.833778
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1711 rows \u00d7 1 columns

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Polystyrene Evaporation  (9)         [seconds X seconds]          Iunit: Counts\n", "


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120 0.097871
180 0.071567
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540-0.111089
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 21, "text": [ "*Polystyrene Evaporation*\tSpectral unit:seconds\tPerturbation unit:seconds\n", "\n", "60 0.117834\n", "120 0.097871\n", "180 0.071567\n", "240 0.035585\n", "300 -0.003136\n", "360 -0.039637\n", "420 -0.072054\n", "480 -0.096940\n", "540 -0.111089\n", "dtype: float64 ID: 67820432" ] } ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "cspec = Corr2d.from_spectra(ts, col_unit='time') #Since no time unit, pass one\n", "cspec.center()\n", "#print cspec.shape\n", "#cspec.synchronous[-1, :]\n", "#cspec.plot()" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "type object 'Corr2d' has no attribute 'from_spectra'", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcspec\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCorr2d\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_spectra\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcol_unit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'time'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#Since no time unit, pass one\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mcspec\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcenter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;31m#print cspec.shape\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;31m#cspec.synchronous[-1, :]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;31m#cspec.plot()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mAttributeError\u001b[0m: type object 'Corr2d' has no attribute 'from_spectra'" ] } ], "prompt_number": 22 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Covariance of ts.tranpose (pandas)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "ts.transpose().cov()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Shiege" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from pandas import read_csv\n", "\n", "data = read_csv('/media/HP v100w/2D_Data/pskdt_data_synch_color_avg.csv', sep=',', index_col=0, header=0)\n", "data" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "My Correlation" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from pandas import DataFrame\n", "\n", "df = DataFrame(cspec.synchronous, index=ts.index, columns=ts.index)\n", "df.reindex(index=df.index[::-1], columns=df.columns[::-1])\n", "\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
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This notebook is free for redistribution. If citing, please reference as:

\n", "- *Hughes, A. (2012). [A Computational Framework for Plasmonic Nanobiosensing](https://www.researchgate.net/publication/236672995_A_Computational_Framework_for_Plasmonic_Nanobiosensing). Python in Science Conference [SCIPY].* \n", "\n", "\n", "

Questions or Feedback?

\n", "\n", "* hugadams@gwmail.gwu.edu \n", "* twitter:
@hughesadam87\n", "* Mark Reeves Biophysics Group\n", "\n", "\n", "\n", "\n", "

References:

\n", "\n", "* [1] : Noda Isao, Ozaki Yukihiro, *Two-Dimensional Correlation Spectroscopy*, Wiley, 2004\n", "* [2] : Noda Isao, *Scaling Techniques to Enhance Two-Dimensional Correlation Spectra* Journal of Molecular Structure. 2007\n", "\n", "\n", "\n", "\n", "\n", "

Related:

\n", " \n", " * pyuvvis: Exploratory Spectral Data Analysis\n", " * Mid-Infrared Data Analysis Software 2010 A Matlab Package for 2D IR Spectroscopy Analysis\n", " * pyuvvis: Exploratory Spectral Data Analysis\n", " * pyparty: Image Analysis of Particles\n", " * Lorena A. Barba (GWU Engineering)\n", " * xray: extended arrays for scientific datasets\n", "\n", "\n", "\n", "

Notebook styling ideas:

\n", "\n", "* Louic's web blog\n", "* Plotly\n", "* Publication-ready the first time: Beautiful, reproducible plots with Matplotlib\n", "\n", "
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" ] } ], "metadata": {} } ] }