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   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "2.18 Programming for Geoscientists class test - 14:00-16:00 18th December 2014\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Test instructions\n",
      "\n",
      "* This test contains **5** questions each of which should be answered.\n",
      "* Write your program in a Python cell just under each question.\n",
      "* You can write an explanation of your solution as comments in your code.\n",
      "* In each case your solution program must fulfil all of the instructions - please check the instructions carefully and double check that your program fulfils all of the given instructions.\n",
      "* Save your work regularly.\n",
      "* At the end of the test you should email your IPython notebook document (i.e. this document) to [Gerard J. Gorman](http://www.imperial.ac.uk/people/g.gorman) at g.gorman@imperial.ac.uk"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Question 1** \n",
      "\n",
      "* Correct the 5 errors in the following program.\n",
      "* In each case add a comment explaining the error and the fix.\n",
      "* To verify your answer remember that $sin ^2( x ) + cos ^2( x ) = 1$."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from math import sin, cos\n",
      "x = pi /4\n",
      "1_val = sin^2( x ) + cos^2( x )\n",
      "print 1_VAL"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Fix 1: pi needs to be imported from the math module before it can be used in the line below.\n",
      "from math import sin, cos, pi\n",
      "\n",
      "x = pi /4\n",
      "\n",
      "# Fix 2: 1_val is not a valid variable name - it cannot start with a number\n",
      "# Fix 3: raising something to a power is done using **, not ^\n",
      "# Fix 4: when calling sin or cos, it's name must be followed by the arguments in brackets. If we want to represent $\\sin^2(x)$\n",
      "# in Python, we have to write it like $\\sin(x)^2$, i.e. sin(x)**2.\n",
      "val = sin(x)**2 + cos(x)**2\n",
      "\n",
      "# Fix 5: variable names are case-sensitive in Python. val is not the same as VAL.\n",
      "print val"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1.0\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Question 2**\n",
      "\n",
      "* Create a python list containing 101 evenly spaced values between -1.0 and 1.0.\n",
      "* Show how this could be more efficently done using NumPy.\n",
      "* Print out the values using a formatted print statement."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n = 101 # The number of values\n",
      "start = -1.0\n",
      "end = 1.0\n",
      "dx = (end-start)/(n-1) # The distance between each value\n",
      "\n",
      "values = [start + i*dx for i in range(n)]\n",
      "print values\n",
      "\n",
      "from numpy import linspace\n",
      "values_numpy = linspace(start, end, n)\n",
      "\n",
      "for v in values_numpy:\n",
      "    print \"%.2f\" % (v)\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[-1.0, -0.98, -0.96, -0.94, -0.92, -0.9, -0.88, -0.86, -0.84, -0.8200000000000001, -0.8, -0.78, -0.76, -0.74, -0.72, -0.7, -0.6799999999999999, -0.6599999999999999, -0.64, -0.62, -0.6, -0.5800000000000001, -0.56, -0.54, -0.52, -0.5, -0.48, -0.45999999999999996, -0.43999999999999995, -0.42000000000000004, -0.4, -0.38, -0.36, -0.33999999999999997, -0.31999999999999995, -0.29999999999999993, -0.28, -0.26, -0.24, -0.21999999999999997, -0.19999999999999996, -0.17999999999999994, -0.16000000000000003, -0.14, -0.12, -0.09999999999999998, -0.07999999999999996, -0.05999999999999994, -0.040000000000000036, -0.020000000000000018, 0.0, 0.020000000000000018, 0.040000000000000036, 0.06000000000000005, 0.08000000000000007, 0.10000000000000009, 0.1200000000000001, 0.14000000000000012, 0.15999999999999992, 0.17999999999999994, 0.19999999999999996, 0.21999999999999997, 0.24, 0.26, 0.28, 0.30000000000000004, 0.32000000000000006, 0.3400000000000001, 0.3600000000000001, 0.3800000000000001, 0.40000000000000013, 0.41999999999999993, 0.43999999999999995, 0.45999999999999996, 0.48, 0.5, 0.52, 0.54, 0.56, 0.5800000000000001, 0.6000000000000001, 0.6200000000000001, 0.6400000000000001, 0.6600000000000001, 0.6799999999999999, 0.7, 0.72, 0.74, 0.76, 0.78, 0.8, 0.8200000000000001, 0.8400000000000001, 0.8600000000000001, 0.8800000000000001, 0.9000000000000001, 0.9199999999999999, 0.94, 0.96, 0.98, 1.0]\n",
        "-1.00"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "-0.98\n",
        "-0.96\n",
        "-0.94\n",
        "-0.92\n",
        "-0.90\n",
        "-0.88\n",
        "-0.86\n",
        "-0.84\n",
        "-0.82\n",
        "-0.80\n",
        "-0.78\n",
        "-0.76\n",
        "-0.74\n",
        "-0.72\n",
        "-0.70\n",
        "-0.68\n",
        "-0.66\n",
        "-0.64\n",
        "-0.62\n",
        "-0.60\n",
        "-0.58\n",
        "-0.56\n",
        "-0.54\n",
        "-0.52\n",
        "-0.50\n",
        "-0.48\n",
        "-0.46\n",
        "-0.44\n",
        "-0.42\n",
        "-0.40\n",
        "-0.38\n",
        "-0.36\n",
        "-0.34\n",
        "-0.32\n",
        "-0.30\n",
        "-0.28\n",
        "-0.26\n",
        "-0.24\n",
        "-0.22\n",
        "-0.20\n",
        "-0.18\n",
        "-0.16\n",
        "-0.14\n",
        "-0.12\n",
        "-0.10\n",
        "-0.08\n",
        "-0.06\n",
        "-0.04\n",
        "-0.02\n",
        "0.00\n",
        "0.02\n",
        "0.04\n",
        "0.06\n",
        "0.08\n",
        "0.10\n",
        "0.12\n",
        "0.14\n",
        "0.16\n",
        "0.18\n",
        "0.20\n",
        "0.22\n",
        "0.24\n",
        "0.26\n",
        "0.28\n",
        "0.30\n",
        "0.32\n",
        "0.34\n",
        "0.36\n",
        "0.38\n",
        "0.40\n",
        "0.42\n",
        "0.44\n",
        "0.46\n",
        "0.48\n",
        "0.50\n",
        "0.52\n",
        "0.54\n",
        "0.56\n",
        "0.58\n",
        "0.60\n",
        "0.62\n",
        "0.64\n",
        "0.66\n",
        "0.68\n",
        "0.70\n",
        "0.72\n",
        "0.74\n",
        "0.76\n",
        "0.78\n",
        "0.80\n",
        "0.82\n",
        "0.84\n",
        "0.86\n",
        "0.88\n",
        "0.90\n",
        "0.92\n",
        "0.94\n",
        "0.96\n",
        "0.98\n",
        "1.00\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Question 3**\n",
      "\n",
      "* a. Write a python function that evaluates the equation below for input variables $x$ and $t$: $$f(x, t) = e^{-(x-3t)^2} \\sin \\left( 3\\pi \\left( x-t \\right) \\right)$$\n",
      "* b. Generate an array of $x$ values in the range $(-3.0, 10)$.\n",
      "* c. Using this array of x values, plot the function for $t=0.0$, $t=1.0$ and $t=2.0$."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from numpy import *\n",
      "from pylab import *\n",
      "%pylab inline\n",
      "def f(x, t):\n",
      "    return exp(-(x-3*t)**2)*sin(3*pi*(x-t))\n",
      "\n",
      "n = 1000\n",
      "xarray = linspace(-3, 10, n)\n",
      "\n",
      "for t in [0, 1, 2]:\n",
      "    plot(xarray, f(xarray, t), label=r\"$t$ = %.1f\" % (t))\n",
      "xlabel(r\"$x$\")\n",
      "ylabel(r\"$f(x,t)$\")\n",
      "legend(loc=\"best\")\n",
      "show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "WARNING: pylab import has clobbered these variables: ['power', 'draw_if_interactive', 'random', 'fft', 'linalg', 'info']\n",
        "`%pylab --no-import-all` prevents importing * from pylab and numpy\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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wep2xFxMVknh/PECakAgNHoNB3DeqlkXCcewMlkcflXZ/BkNComP4hAQAiouV\nLSoHI0aihpBwHKt+fvIk8J3vAH5/5PVjnfzB9jZXci2SePERQNrOeDVcW8e7j+OGDTdgX8s+3PX2\nXZEX3W4WC6msjHxe6eABlAkJIB4nibcKkhNsf+kl4NlnmZC8Iv30zEyEhETHRO9qDyHFzZ3MYLsa\nbm4pQvLJJyye8tJL7P+ycWPkdaFg+2BZJEJIOSVRDdfW37/4O64YfwWeXf4snv38WTQ7w9Kejx1j\nVT6jU2iVDh5AuZCItVfLnH38ceAXv2BC8utfSz/UKwPhiYbx09vbi7/97W/Yt28fAoEA3G43jEYj\ncnNzcdZZZ+HKK6+E0Ui6pCWUWiRaDLYD0ueCV18FrrqKFau89VZ2yN9ll7Frne5OBLgAimxFEW1y\nLDkIBAOCtawAdoZJgAskHCyPV2dLSnt/0A9/0M/7GvnWfDi9ThaDMfLHYEK8eOBFrL5oNcpyynBJ\n7SV4Yd8LWHXWKnaRz60FSI+RKHFtxcvaktJejfTf7m7g44/Z6sNqBX74Q2D3bnawDxGDJCHZsmUL\nDhw4gEsuuQTf//73I65xHIe9e/fid7/7HS666CJMmzYtKR0l5CMkJINlkSg52CreXCRVSLZtYwtJ\nALj0UiYmnZ2sfcitFR2nMBgM/e6tKksV7+uG6lzFi3EosUjE2rt87Jhfvvc3GozIzcpFj7cnYqNl\nNL3eXuw/tR9nV58NALhuynW46+271BMSrVokUoPtH30EzJo18DpXXQX8618kJAKImhButxujRo3C\nqlWrMHr06JjrBoMB06ZNw09/+lOY+HaSEinB6WT7SMrLY68NhkWitESKUovE4wE++4wdMQywBIPz\nzwfeeIM95nNrhRBzb8WzVgDxGEe8OltS2seLzwDSyqTsPLETUyqmwGa2AQDmj5qPb7q+QX1HPbtB\nSEiKi6WtQpQG24VWIYB4jESNYPsHH7ABE+Kyy5iQkHuLF1EhsdlsGDNmTP/jpqam/t/7+voi7p08\nebKKXSOUwLeHJESqYySDEWw/eJDNg+H/h2XLBmKmjk5HzB6SEGIpwEKVd0OoYZHEy9oSCrSHkHJK\n4s7GnZg9bHb/Y5PRhCW1S7Dhqw3siXhC0tkZP/1W6xaJFCH57LNI62PmTPa6Bw6It81AJAc1Hnzw\nQbzxxht49dVX+5/bv38/3nnnnaR0jFCGkFsLSJ8YidBcJGVRuXcvMHVq5HNLlgBvvsnem688Sgix\nzK0+X5/7Or9EAAAgAElEQVQiiyRena3+9iKurXhCJiXgfqD1ACaWT4x4btm4ZXjly9NKKyQkZjOr\nphnvDyAWbBdbSWgh2P7558CUKQOPDQbmHw2b/4gBJAvJ8uXLUV9fj8cffxxLlizBzTffjM8++wzv\nvvtuMvtHnGb3buBPfwKOHpV2fzwhUermTvY+Eo6TNxcEuSBeO/Qa/rz7z+j2sJ2K0fMAwErFTJwI\nvPNOfItEbC+G2ESu1CIRq7Ul5tqKSQF2OoGnn2aumdOWxMFTBzG+dHxEu4tqLsKepj041XtKWEgA\ncfdWqoPt8d7fbmd54PHad3SwATYianwsXRqb+kcAkCEk48ePx6233or7778fr776Ku677z4MGTIE\nS5YsUb1Tmzdvxrhx4zB27Fg8/PDDvPf86Ec/wtixYzF16lTs2bNH9T5oiYceYqvpTz8FZs9m/4rB\nt6s9hBSLRMnBVqEKGHwVMgDxecDnY1mnQiG3cCHxBry4bP1luPudu7Hp8CbM+fMcdLm7cPgwcMYZ\nsW0vvRTYsOH0OSSF/EIidqaH2EQuKUYSbx+JiBCJubYihNDhAKZPZyLy8MPA9deDCwZxsPUgxpdF\nCondYkddTR3e3P1P9gcqK+N/A7GViBTXVryVhNJ9JPFWIQaDuEl76BAbPNFZqBdcwHymzeLVoTMN\n2fm6CxcuBIB+EZmpchZDIBDA7bffjs2bN+PAgQN4/vnncfDgwYh7Nm3ahCNHjuDw4cNYt24dbrnl\nFlX7oCX+3/9ji8ldu4CnnmJWyfXXx26uiyaeRSLFNaTEtRWaBwSSmkSFSGxBGxISX8CHq1+8GgDw\nyU2f4KWrXsLcEXPx31v/W3APzaWXskXl0Y6jqCmq4X19sWB1si0S0WC7mGsr1P/GRmD+fGDVKvaf\nfvdd4PPP0fHcE8gyZaE0uzSm7bJxy7Djw/Vs8Aj9AcXiDMkOtitJ/wXE+y80eLKygLo64PXXhdtm\nKIo2ftTX12P06NHYtm0b3nzzTVU6tGPHDowZMwYjR46ExWLB1VdfjQ0bNkTcs3HjRqxYsQIAMGfO\nHHR2dqJZh6uEP/0JeOwxYOtWYMgQ9tx3vsMWiv/4R/y2R48KeybEvkccxzY2KxUSIZS4uAEmJB1d\nPlzz0jXwBrxYf8V6WEwWAMAD8x/AiwdexNG2b2I8EwBbaGaXtMPnD6LEXsL/+qeD1Q4H8MwzwLp1\nLJW4p+d0/6RYJHGERNI+kngxEgmuLf+J48BFFwErVwK3384u2O3AI48g6/6HMCHKrRVi8djFOLV/\nB/wjhwu+PgoKwHV24tMTn+KJXU/g2b3PYvfJ3QhypwPwWg62n+5/3C9APHNeqnvL5wPeegtYu5Z9\nWR0O8TZpjOQNiXyMGjUK7733HiqjyygooLGxEdXV1f2Pq6qq8Mknn4jec/z4cVSE10tPMuvv/g28\n3V3gAIADgqGswGAQHNhkzHHMcvD7Ab8vCJ8PCASBbDuQXwBUDgOysweyX8IzC3fuCGLvXmDNCsDx\nIuDovymI/5wIbP9foHoggY514vQLcADGdQNdW4D33g97/vTr9/YBc4wc3v3NwP3h+H1BXD0KeO+3\nkRc5sL729QLzc4B3Hgr1Oxh+E9we4PJK4O0HwhsP/N7dzeGCLODt+4AggvAFfPAGvPAFfDAAMPuL\ncHneELj/Oro/PTW8fXmTB7Ny12Lcv3340ewfIuvv6/uvFQN4+Pg0fFy8CoUbliEGjsPPquuxf0ch\nDE8/HXMNAKYc3YvGTw/gdx88hfHjAZuVw4cngfUN7DiO/HE7cXZhM5qP/QV2G1u4ezwDP4Wtn2Bi\n9wHsfuvPEUVuDac/TLd7F852NWLXu0+wRT/HDSz+DUBh73ac4TmCvW+sgzHgg8nTB6PXBZPHhYA9\nF+6sb1Be5MFrdj84U+xX2LMvgKv+8AS+rPsJDo//LyA8PsxdhDOdHszcmy8QNy7C7M4qfBwwo1Mg\nrlzbYcRz/7wLf/6aw8Tc8+ELuvF13wPo9rdict48/GpHKyxlE3DkxXpkm/Lh57zwBt3wBl3wBPsw\n/3gHjr37KT7msuHnfGGfD/sQLtt3ECe6XfjkmX9GPB/69+oTLTi49QN83meMeB6n//3OgSM4lFWA\nvdaNYdcG+E+3H+/98w0cPch/rsqSt95Fc/VI7Hg29gOwO434yVtb8Nt16+G184vl6C/2YNmTa9Bd\nVIKWyuHIdnZj+OGD8FptqB8/GY5xk3BqWDW6iksQNJpgDAZh8vtg8Xph8Xpg9nlh8Xph9nph8Xlh\nCM+QC7MSuf5fw5+L/f/2PxfeNvyG08/ztZWKbCE5evQohg4dCvtpxVdTRAAIbvKKhovK5xZqd889\n9/T/PnfuXMydOzfRrkVgfOUvKO+NXNWEehD+BzEY2PMGAwAjG9bBIOAPAH1ewG0EsqwGZFkBs4kJ\nTW8vMCYAzC41wLQp8rUBYAIMKDwJGP8eGYcwnH7vYAC4wgMUvDZwLdQnAwArByzpBuyvRD4fIhAA\nlvsMsL8a+96cwQBbAPi2E8jZFPv6AGDzAwv7gLw3WevQgA/dYfcDF3YakPc2e2wymGAymmAymsFx\nHDr7XDjX2IlX/tCBAlshKnLKUZ5TAZvFjk53B/a1HMC8U8W4uvpbMP17W4wLZl6TDVZuC7h/5/FO\nJGd7HbB/ZUCwYFuMG9zrM6D1vW8wo/A4ll36HrIspy+MNcAfANpagQNNR9D8TSfee/1DeHwGgGNF\naS0W9vcI5DpQmX0Kbb3bI1+fAzgYYMn6GiMs3Wh37gi7ZAjdAmtWA8qzmnGy51MEDWZ4TNnwmLLh\nNebB7u+BnfsC/9V5ANV/KMLB4nPwRck8fFn0LfiNFkxpfQd3Of6EP545Cbu7/j9gXfT/3oC9Iyfi\n/NePY93XMR8NAOD6QxXYNKwJ+2LaAk7rYVzm3Iy83gsx0/UyDDDCDmA6AJelAW1529ByYjX2cPX4\nv92vwW/qhpGzwhi0whTMgSmYjcLOEzh0tB5P5x6HkbOc/n+Hvs8cZjQfwQ5jB17Y1TnwvGHg+vTu\nb/Dx131Yb3UAYe1CnHPqEN443ofXdx/m/f+d5WnB+/s34HXvDt7r847uwvP5bXh7N/+Ry9OG5aHz\nHw/gucmxJu+lX53A7e/ux03fno73h4dch6XAuefijHYnzj7ehG+9sx/f6urDMKcLBo4JgsdkhNts\ngttkgtvMfneZTfCYjQjwzG390hk2Dw48F34fx/Nc7O+f93nwuVtkl3A8OJnceuut3DvvvMNxHMe9\n//773AcffCD3JeLy8ccfcwsWLOh//OCDD3KrV6+OuGflypXc888/3//4jDPO4JqammJeK4H/3qDi\n93Pcrl0c97vfcdzSpRw3fTrHXXwxx/3f/3GcxxO/7a23ctxDD/Ff+/BDjps9W7htMMhxZjPHeb38\n1xsaOK6yUri908lx2dnC1+vrOW7ECOHrzc0cV1oqfH3nTo6bOZPj+rx93Lb6bdw979zDzX96Pjf9\n8enclf+4knv14CbOYAxygQB/+xdf5Li8/57AfdzwMe/1+9+9n6tc8d/cSy/F/r/OPZfjLl21jTvv\nyfME+/f4zse5mzfeLHj9ta9e4xY+t1Dw+l/3/JVb8fIKwev/2PcP7vL1lwte/9OOP3E/ePUHHNfW\nxnEvvcRxP/whx33rWxx35pkct3Ilt+nl33JX/uNKwfa3rF3KufNzBAeA9/xzueXfy+WOdx2PeP7r\n9q+56t9Vc7u+v4Tj7r5b8PW5FSs47qmnhK/fcQfHPfKI8PVrruG4Z58Vvn7zzRz3+OPC1xcs4LhN\nm4Sv33ADx/3lL8LXzziD4/bvF76+aRMboMFg5PMvv8xxFRUct3evcNs0Qe7cKTtGMnv2bNTX16O+\nvh7nnnsuWlpaElcxHmbNmoXDhw/D4XDA6/Vi/fr1WLp0acQ9S5cuxTPPPAMA2L59OwoLCwfVraUW\nJhMwYwbw4x+zTKLdu9k+hx/8IL6LGIjcXBfNkSNA2B7SGAwGdkCUkJtYjexNpbFSu51lEV0w8gL8\nau6vsPX6rdi9cjf+ceU/cMm4hcjNMfTHLKJxOIBxhkux4csNvNePdhzFJefU4N57B85X6utj7u/R\no4G7/yv+PgzRDYliMRKF+0j6s7aKi4Hly1lRwY8+AnbsAB5/HIZJk+ImC3xmOgXPqOEs+M6Dpf4Y\npp97JR7+cCBjsr6jHhc+fSH+59z/wYxx8+LHGJQOoMGIkQjtJeG4gd28Qlx8MQsihsdKXn8d+P73\n2b/ReecZgGwhaWhogNVqxe9+9zvMmzcPu3btUrVDZrMZa9aswYIFCzBhwgRcddVVGD9+PNauXYu1\na9cCABYtWoSamhqMGTMGK1euxGOPPaZqH9KBCy4AvvqK//x1MSEB4scbxZJezGb2fRM65E7KPJBo\nFfAQ8faVORzAOaWXYuMh/qDokY4juOLC0SgvZ6L94YdsbqiqAv7yF6A4O34pdtESKRLKyCsp2ij2\n/mLpy45OB4LLlvKvRFwuoKUFP7jsQfzr4L/w+KePY8OXG3DeU+fhv875L9xy5i3iwWo1gu1KViJS\n0v6E+t/czOrp5OQItzeZgP/9XzZ43noL+MMfgBtvZJsVM7QWl+wYSU1NDS6//HJcc801aG1txUsv\nvaR6pxYuXNifZhxi5cqVEY/XrFmj+vumE1lZwKJFbFH0gx9EXjt8mF2LR7y5QMpEHvou880XUrcB\ncBx/hqnYghIYEBK+haPDAdw4/0y88HU7DrcdxtiSsf3XOI7D/pb9mFg+AevXA3fcwQq7XnMNswxN\nJgkbEhVaJFJqbYmVSMmz5glej5e+7PF70OZqQ953rgMuXsAmxPA/wtGjwIgRKMsfgje/+ybu2HwH\n3H431i1Zh0VjTw8qNdJ/hcxJQJ19JGLpv/X1/Nfi5c2HM28esGYNcNddbAXy3nv8G5cyBNkWyVVX\nXYX9+/cDYOm/eky7TReEMhE//xwQK3smJiTxvodA/EWh2DxgNDKrxufjvy5HSPhwOIDRNUYsqV2C\njV9FfkAtvS3gwGFI7hAUFbE9Ort3Az/96cAGyHxrPnq8PQPprNH9GwyLRCz9V6xEioAQNnQ3oDKv\nEqYJE9mqe/fuyBu++AKYNAkAMKl8ErZevxUf3PjBgIgA4hV00zn9V6qQAMDll7Pdwa+8ktEiAkgQ\nEo/Hg9bWgUqoJpMJM2bMAACceeaZ+OUvf9l/7ZtvvklCFwkhFi5kRUrDF3duN1tUjuffJtCPmGtL\nqkXCh9g8AMTflKhESDiOzQUjRgDLxy3HK19Fum++aPkCk8snx80ONBlNyLHkoMfDv2pWHCMR20ci\nZUNigq4tR2fY8cJ8KxG+ImXRDMaGxGRvRFJDSIh+RIXEarVi+/bt+Pvf/w6Xy8V7T0dHB9atW4dj\nx46p3kFCmPx8ViY9fC/ogQMsPhLPxQzEX1QqtUjESiWF2gt5J5QISXs7S8MtKAAuHHUhvmj+IuLk\nv8+aPsOUCvFgaLzCh8m2SMROSOzzxy+RYjfb4Q/6eftwrPPYQI0xPiH57DNA7EyhZMdI1DjYSnRH\nawf/NRKShJAUI/H7/aipqcHvf/97tLS0wO12w+fzwWQyITs7G1VVVbj55ptRUFCQ7P4SUYTmgiuu\nYI8//hg480zxdkqC7YC4RSImZPHaS3l/ISEJ35RsNVuxYMwCvHroVdw04yYAwIcNH+KqiVfFf3EM\nxBmGF8Tu8FYjRlJkLxK8LinYHuf9DQZDv1VSbo48kCai6vHZZwPHjw9kKQWDwM6dLOMgHlIskmRX\n/0201hYQv1aYw8G+VIQsJAnJu+++i2uvvRZ33XUXNmzYgEsvvTTZ/SIksnQpcPfdAym377zD6kmJ\noVawnQ8pri0xIZFikfD1P3pBueyMZXjui+dw04ybwHEcPmr4CI9++9H4L474Z3qIlpFXGiMRKyMv\nUiIFGBDC8pxIIXF0OjBv5Dz2wGQCFi8GXn6ZZR18/jmbZIcNi/va/YNHKFtCjfRfsYOthNoHg6y9\nzSbcvrQUaBU4uIwskoSQJCRLlizBAw88ALfbDZfLhcOHD2Py5MmYNGmS6jvbCXlUVzNPxCuvMAHZ\nto1lI4pRUAA0NfFfS3awHYg/F/T1sQzMeBQWssV0NNHzwOLaxbht021o7G6Eo9OBYnsxqguqYxtG\nUWATznwSs0hsZpuyfSQKD7YCgGJ7MdpdsaXeY85hWbEC+NGPWGHHN95gRR7FsNlYxoRQQbZkV/8V\nW4VYrcIFJ4EBiyRaCKXsISF4kSQkF154IS688EIAwCOPPIJZs2Zh//792LBhA06cOIGqqir88Ic/\nxBkZnrmQKlatAv7nf5jbd9Yslo0oRkEB24fChxYsEr4jgsMpLAT27Yt93uEAamsHHudb83HdlOvw\n63d/DXfAjeumXBf/hUOvHydgrcZRu8ms/gsA5TnlaOmN3SwcEWwHgLlzmSg89xwrMPjii3Fft5+Q\nVRI9UDiO/WHjWQTJDLZL9cva7UB3N/t/hJCyh4TgRfY+kjvvvBMAcMEFF/Q/98ILL2Djxo342c9+\npl7PCMksWQK88ALwk58A//63tDZiri0xi0ANIUlGsN3hABYsiHzu1/N+jbP+chYCwQD+sECCuYb4\ne0nELBKz0QwOHALBAEzG2ENVRPeRKKz+CzAhOdV7KuI5f9CPJmcTqvLDVhoGAxOQ+fOZSTtrVtzX\n7Sc0gEJlqUP09bE/bnQRs3DUCLYrGTwAc2+dOhUpJOTWShhF1X9DZGVlYdy4cWq8FJEABgPw97+z\nneZCh0FFE09Ientj54doxLK2lAbblQhJ9FxQYCvAgVsPgAMHo0Ha1ql4m/rELBJgQAyyjbGrY6kn\nJHIcx5umLMW1VZZdFmORHO8+jvKc8tj3PusstjqXOngA4QHU1ye+oldqkSjNHQcG4iThJSBISBJG\nFSG57LLL1HgZQiFqzAMAExKxuSDedzlV+0jC95BEYzAYeCsBC76+rRCtffwBWZffJTqRh9xTfPeJ\nxUhMRhOMBiP8QX//OSsR7y/RtdXY0xjx3LFO4XPqZQ0eQHgASRk8ySyRIldIwiEhSRhFB1sR6YuY\nkCQ72J4Mi6Stjb1ufn78tlIQ3UciMpHHc0+JWSRA/MwtKa6tspxYi8TR6RAWErkkW0iUxEhISAYd\nEpIMJd7mXqneCSE3tdIYiZSsseLi5M4D8YLtvb5eSRaJ2+/mvSZ2ZjsQP3NLimurPKccp/oiYySO\nTsfAZkSlJEtIgkF2Epw5jrNEDSEpK2MxknBISBKGhCRDUeraSrVFUlDAanX19Q08F++EVLkIxUg4\njkOvtxc5WfE/oHh7SSRbJDztg1wQHr9n4ORIAfiytmJSf5UgVBpBqTnr87HBEy99N17RRikphwBZ\nJCpDQpKhZGez7yxf4USlQiK1RIoSITEYWELAybDTUtUUEqENid6AF0aDUZFrSqzWFiDsGnP73bCa\nraJJA2XZZTFZW2nh2hqMwQPECkkwSHtIFEBCkqHEO9xKjRiJWNaWGok3Q4dGCsmRI8DYscL3y0Eo\nRuL0OkWtESB5FomU+AzAYiSn+k5FHEk9KK4tpVlbapizYoMXYK6t8MrljY1AURHtIUkQEpIMJtVz\ngdKtAHxCInagl1SEXFu9vl7kZuWKto9nkYjtIwGECzdKCbQDbHe91WRFt6cbABAIBtDY08hbOywh\nkmWRqFGoTcrgqaqKLI2g5iokAyEhyWCSORcMhndi6NDIMi9qComQa6vX24scyyBYJALtpQTaQwzL\nG9afAnyi5wRK7CWiQX7JpHLwxIuRSB081dVAQ8PA48OH1Rs8GQgJSQajVSGRGi8dOpR5JID+E2JR\nLV5GSxLZlmx4A96YzKlen3igHRCJkYjsIwm158vakuraAoCRhSPh6HQAAA61HYo4KVIxSgaPycQ2\n/fj9sdcGaxVSVsb6GsrWUHMVkoGQkGQwQpv6BuM8EqEYCcdJnwtqatghXgALtI8YIX9fnRD9pdij\nrBKn1ynNtaWGRaLAtQVECsmXrV9iXImK1SeE8selBNgMBuEBNFhCYjBEurdISBRBQpLB8M0FbjdL\n4Y+Xxg8kL0bi9Up7f4B9748cYb8fPgyMHi3eRg58AXfJri2xGInYPhKBYLsc19bIwpGo72Bnk3/Z\n+iXGlaosJELpv1IC1kIDSMoqxGxmWVaBQOw1qeYswMzX0KmuFCNRBAlJBsM3F0gJtAPJi5dKXVAC\nA0LCcdLOqZdLoa0wJuAu2bWVLItEpmurvpMJycHWgzijVMXq3EJCImcACa0kxITEYBCOk8gZQLW1\nwJdfsoF45Ehk2WhCFiQkGQzfXKB0QQko807ImQeKitj7tLQAn34qvXCtVPgqADu9TunBdgX7SEKF\nG6OR49qaUDYB+1r2IcgFsevkLswcOlNSO0mEH24VjtIBJGUVEq+91PRfgJ1Nv3cvW4WMHSu9HRED\nCUkGk0ohEYqRSInPhDNpEjtm/NNPgZkqzpOAsGtLcvovjxAEggEEuSBMhvjBHKFguxzX1sSyiTjR\ncwIffvMhSrNLUZFbIamdJCwWduaI0xn5vBpCIjZ44rWXsxKZOpWJyM6d6q9CMgwSkgxGSEikTOTJ\nipHImQcA4LzzgEcfZUH2UaOkt5OCoGtLgUXiC/pgNVt5y8NLaS/HtWUymjBr2Czc9959OHf4uZLa\nyCIZKxGlQiJnJTJlCnDwIPCvfwHnny+tDcELCUkGk6wYiZIqF3I8EwBw7bXApk3AD34QvzxTIgi6\ntqSm//JYJFLiI4A6+0gA4LtTvostR7fgpuk3SW4jmWSsRKQMHkCdGEleHrB0KfDuu8CyZdLaELyo\nch4JkZ4k07WVaLBdTtINAIwbx85kyhX3NsmG1yLx9qK4oFi0rZBFIVlIBLK+xE5njObG6TfiiglX\nIN+qQm39aLRokcg1aZ95Bvi//xM/EpSIC1kkGYySeUDpwVZqzQMAmwPUtkaA0xZJdIxEqmtLwCKR\nEmgHhC0SKaczRpMUEQGSY9IqDbbLDbIZjSQiKkBCksEUFgIdHZHPDVaMRMgzIXceSCa8wXY56b8C\nFolYnS1AuNaWXNdWUtGDRUKoAglJBpPu6b/Jhs+1JXlnu9IYiVCJFJmuraSSSiFJh5VIBkFCksHk\n57PvffgG4Z4eaUfVxqveO5jB9mTCF2yXVbRRSYxEyLUlYx9J0okWEo5j6cBSAlZkkegKEpIMJuQe\n7u4eeK6ri+01E0Msa2uwgu3JRNHOdoFguZSCjfHaa9q15XazQaUkxiE1a4uERFOQkGQ40XNBd7d0\ni0TpwVZq7CNJJnwxElk72wVcW1JKuccNtmvVtSV18ADxhWQwg+2EKpCQZDhqC0kwyI7vtVgSa68l\n1xZf9d9uT7ekLCghi0KV9F+tuLaKiiIHj1RzFhBO+5MqJHwrEZ+PDUCxwUeoDglJhsMnJEpcWyEX\nt1g6bjq4tkLpv+HH1Xa5u1BoKxRtG88ikSIk8bK2dG2RKImRhFYhycgFJ+JCQpLhRM8FXV3KLBKl\nngktWSQWkwUWowV9Pnb4UZALosfbo8gikbOPROhgKykxmkGBb/BItUiEsjWUDCAt+UUzDBKSDCfR\nRaXJxBZ+0YfcKc3e1NpcEB5w7/H0IMeSA5NR/PSsuDESCftI1DiPJOloLUaitcGTQZCQZDiJurYA\n/u+yGrFSLc0FJdklaHe1AwA63Z2S3FoAYDPblGVtCaQPa1pI5FokSoUkeiVCgfaUQUKS4UTvbpfq\n2gKUCUlWFouNRh9noSXXFgCUZpeita8VANDl6UKBTdpEGa9EiqSsrTgWiWZiJNFnkgxmjIQvWE8W\nScogIclwiouBtraBx0rnAqmlkgwGllzDt6jU0lwQLiRyLBIhi8IT8EhzbaWDRWI2s82HIatEqTkL\nSF+J2Gxs30o4ZJGkDBKSDKe8HDh1iv3u8bDsSZtNWlshi0TKghLgnws0Z5HYwywSiRlbgIhFIrHW\nVnSwPcgF4fa7tZP+CwBlZeyISkC5OQtIFxK7nQ2WcMgiSRkkJBlOWdmAkLS1ASUl0rMnlbi2gPSY\nC0qySyIskgKrRNdWPIskQdeW2++G1WyF0aChr234AGpvZ3tLpEBCois0NCKJVFBePrCgPHWKzQtS\nSYaQ6Ma1pdAi4RMiTbm1QoSbtKdOscdSUBojERo8WjJnMwgSkgwn3DNx6hRQWiq9rVCMRKprKzub\nfffD0ZxrK7sUrS4mJO2udhTbxQ+1ApJjkbh8Lu0JSfQAkroSUWqRCA0eLa1CMggSkgynpITFSgMB\n+RYJX+KM3lxbpdmlONXLVtxNziYMyR0iqV2WKQu+gC9iVzygQ4sk3LXV0jJ4QkIWiaYgIclwzGaW\naNPWRq4tPoblDUNjTyMA4KTzpGQhMRgMsJgsMQFzqRaJ3WKHyxf54WheSAbTIkmHVUgGQUJCoKIC\naGpSR0j05tqqzq9GQ1cDAHkWCcBvVUi1SGxmG9x+d4RFo0khGToUOHGCDQS3W3n6r5IYCQlJyiAh\nITByJOBwAK2t8oUkeh+IEovE72cuNi0Vby20FSLIBdHl7pIvJDxxDqkWidFgjCncqEkhCQ2e0CpE\nasqf0uq/5NrSFJoSkvb2dtTV1aG2thYXX3wxOqOP8TzNyJEjMWXKFEyfPh2zZ88e5F7qj5oa4OhR\nZpVITboB1HdtabF4q8FgQHVBNRq6G9DkbEJFToXktrwWicQNiQBzb4UKRgIaFZJRo4D6eqC5Wfng\nASjYnqZoSkhWr16Nuro6HDp0CPPnz8fq1at57zMYDNi2bRv27NmDHTt2DHIv9UdoLqivZ79LJRlC\nosV5oDq/Gl80fwGLySKr8i6vRSKxRAoAZFuyI+IkmiqPEqK8nP3hvviCWSdSUVr9lywSTaEpIdm4\ncSNWrFgBAFixYgVeeeUVwXujs2GIxKmpAb7+Wh0hURIj0VqgPURNUQ02HdmEscVjZbVTbJGY7XD5\nI5ruGXEAABH9SURBVIVEcxaJwcAE5O23lQ8egGIkaYqmhKS5uRkVFcx1UFFRgebmZt77DAYDLrro\nIsyaNQtPPPHEYHZRl4wfD7z+Oju/vVjaNgkAyXNtaY2pFVPx3OfPYULZBFntlFok0ZlbmhQSAJgw\nAXjuOWDKFOlt+AYPx1HWVppiHuw3rKurQ1NTU8zzDzzwQMRjg8EAg4Cz/MMPP8TQoUNx6tQp1NXV\nYdy4cTjvvPN4773nnnv6f587dy7mzp2bcN/1Sm0t+/ess+S1yxTX1oIxCwAAi8cultVODYtE8zES\nAFi0CHjxRWDePOlt+AaP3w8YjeywGzHItaUq27Ztw7Zt2xJuP+hCsmXLFsFrFRUVaGpqwpAhQ3Dy\n5EmUCwTvhg4dCgAoKyvD8uXLsWPHDklCQvBjMLAMztxcee2EXFt5edLaZ2dHHmeh1XmgpqgGjlUO\nDC8YLqudKhaJ1l1bALBiBXDuucCIEdLbKF2FWCzMgvH5BtL8tLoSSQOiF9n33nuvrPaacm0tXboU\nTz/9NADg6aefxrJly2Lu6evrQ09PDwCgt7cXb731FiZPnjyo/dQjQ4dKF4AQmWKRAMCIwhGCFrIQ\nVpMVbn9keWM5Fkl0sN3l12CJFIBZEWPlxY8UB9gMhtgBpNWVSAagKSH5+c9/ji1btqC2thb//ve/\n8fOf/xwAcOLECSxezNwKTU1NOO+88zBt2jTMmTMHl1xyCS6++OJUdjtjUVtI9DYP8J3bLssiSRfX\nViKYTztDws9qljN4gPRaieicQXdtxaO4uBhbt26NeX7YsGF4/fXXAQA1NTX47LPPBrtrBA9qCEl4\n1pbTKd+9pmX4zm2Xu48kLVxbiRIaQCFRUSokvb36GkBphKYsEiK9UCP9N3we0J2QqGCRpEXWVqJE\nD6BEhETPK5E0goSESBi1XVt6mwcUWyTpsI9ECdEDSM4qBND/SiSNICEhEoaEJD6CRRtl7GwPj5E4\nvU59C4kS11YgwNpTjCQlkJAQCaP2znbdCYlQ0UY5MZIw15bT60RelszUOi2jppD09gI5Odoq1JZB\nkJAQCUMWSXyiLZIgF0QgGIDZKC3HJTprq8fbgzyrjoQkugKwEiHR2+BJM0hIiIRR+4REp5MtKvVC\ntEUScmtJ3Y+Sm5WLXl9v/+MeT4++LRK5MZLwYDsJSUohISEShtJ/4xNtkchxawFMSJxeZ/9j3Vkk\n0RWA5Vok4cF2vQ2eNIOEhEgYtdN/9bYNQMgikUqeNQ89XlbFgeM4OL1O5Gbp6QNSMUZCQpJSSEiI\nhFHjhESySITJzcpFj4cJicvvQpYpS3J8JS1QKiQ5OWz1Aehv8KQZJCREwvBZJG43YLNJa2+zsaxN\nn4891ttcoNgiycrrd23pLj4CKI+R5OcDp+vu6W7wpBkkJETCKBUSg4HNBd3d7LHe5gKlFkm4a0t3\n8REgdgDJrZWl58GTZpCQEAnDJyRK5oKeHh1mbQUSt0jCXVu620MCxA4gOasQgIREQ5CQEAkjZJEk\nIiQcx84mKSxUt4+pxGa2Rbq25FokUa4tXQXaAeUWSV7egJB0dbHBRKQEEhIiYaLngVC8Q66bu6uL\nxUwtFnmxVq0T49rye5Blkv7h5GblosfbA47jyLXFR7hF0tEBFBWp2z9CMiQkRMIIeSbkVKkIzQWd\nnfqbB2KC7QEPbGbprhur2QoDDPAEPJkRbFfi2iIhSSkkJETChOYBjmOP5c4DwMBc0NGhL7cWEGuR\nuHwu2C3yigrmWZl7q93VjmJ7sdpdTC1kkegGEhIiYYxGwGQaOOQukQPqCgoyxyJx+V2wm+V9QKGA\ne5urDSX2ErW7mFrUFBK9BdjSDBISQhHhc4HcQDswECPR44Iy2iJJ5DyRAmsBujxdaOtrQ0m2zoWE\nXFtpCwkJoYjwucDlSty1pccFZYxF4pNvkZTllKG1r5UsEj5yc1mWRjBIQpJiSEgIRVitbCEJJOba\nyjSLRG6MpDynHC29LUxI9GaR2O0DgweQP4BMJlawradHnwMojSAhIRQRXngxkWB7SQnQ3q7PeYAv\nRiLXtVWWXcaEpE+HFkl29kCtLCCxAVRaCpw8yQZhns6y2tIIEhJCEeF18xKxSMrLgZYWoLWViYqe\n4M3akunaKs8px6neU2hztekvayv6iMxEB9BXX7FVCJ2OmDJISAhFhM8FiQTby8uB5ma2qBw2TP3+\npZJoi0SRa0uPwfacHOVCUlYG7N2rv8GTZuioJjWRCsK9E4kE2ysqgKYmFjfV21wQskg4joPBYEgo\n/bc8pxyOLgf6fH36tEiUuraGDAE+/VR/gyfNIIuEUET4ojKRBWVFBWv/+edAdbX6/UslJqMJFqOl\n372VSIykPKcc249vx8jCkTAadPZ1VcO1VVMDvPOO/gZPmqGzkUkMNuGLykQWlAYDS/vt6wOqqtTv\nX6oJPy43EdfW+NLxcHqdGF08OhndSy1quLbGjmWVfydNUrdvhCxISAhFKLVIAODb3wZmztRnrDQn\nKwe9Xqa0iQTbi+xFGFk4Et8e/e1kdC+1RLu2EhlAF17I/q2rU69fhGwoRkIoIjpGkoiQPPGEun3S\nEjmWnH6LJBHXFgDUr6pXu1vaINy1xXFsc2Ii+eOBAKvXQ6QMEhJCEeFzgdOZWCq/nueAnKwc9PqY\n0ibi2tI14YPH5WLnDyQyGPQ8gNIE+gsQigjfR0KH1MWSm5WryLWla0JCwnGJr0IITUBCQigifFHZ\n00NzQTThrq1EijbqGpOJWSFuNw2eNIeEhFAEWSTxIdeWCKGVCA2etIaEhFCEGjESPZNrGUj/7fH2\nIN9K54pHEEr7I4skrSEhIRRBFkl8Qum//qAfbr8bOZacVHdJW4TS/mjwpDUkJIQiwtN/e3poLogm\nx8JcW6Ez1w163CyjhJwcJiJkkaQ1JCSEIkJH5QLk2uIjtLO929ONAltBqrujPQoL2YE0ZJGkNSQk\nhCIKC9nphgDNBXyEXFvdnm6Kj/BRVMQGEFkkaQ0JCaGIoiJ2KBVAri0+crNy0ePtISERIrQSoVVI\nWkNCQigiNA8EAqzCRTZtk4ig2F6Mdlc7CYkQhYVsJUIWSVpDQkIowm4HgkGgrY3FTSmWHEmJvQRt\nrjZ0ebpISPggi0QXkJAQigiVgf/mG1pQ8lGaXYq2vjZmkWSRkMQQEhKySNIaKtpIKKaoCDhyRH9n\nrqtBSTazSMi1JUBISLq7gWKdnQCZQZCQEIopLCQhESIUI+lyk2uLl5CQtLcDpaWp7g2RIOTaIhQT\nskhoHogly5SFbEs2jnQcQVlOWaq7oz1CQtLaSgMojSEhIRRTXAx8+SXNA0KU2Euwr2UfhuYOTXVX\ntEdpKdDSApw6RQMojSEhIRQzfDjwySfAiBGp7ok2Kc0uxb6WfRiSOyTVXdEew4cDhw+z3HGKkaQt\nmhKSf/7zn5g4cSJMJhN2794teN/mzZsxbtw4jB07Fg8//PAg9pDgY+TIyH+JSGqKagAAo4pGpbgn\nGiTndBHL7GzKHU9jNCUkkydPxssvv4zzzz9f8J5AIIDbb78dmzdvxoEDB/D888/j4MGDg9jLwWPb\ntm2p7oIkzj6b/TtnzsBz6dJ3IdTs//Qh0wFgUC2StPr8i4pizNm06j8P6d5/uWhKSMaNG4fa2tq4\n9+zYsQNjxozByJEjYbFYcPXVV2PDhg2D1MPBJV0G4+TJzDsxKmzBnS59F0LN/v/snJ+h4787VHs9\nKaTV5//ll0BUf9Oq/zyke//loikhkUJjYyOqq6v7H1dVVaGxsTGFPSIAYMyYVPdAuxgNRhTaClPd\nDe1SXk6bEdOcQd9HUldXh6amppjnH3zwQSxZskS0PZ3nQBAEoTE4DTJ37lxu165dvNc+/vhjbsGC\nBf2PH3zwQW716tW89wKgH/qhH/qhnwR+5KDZne1MB2KZNWsWDh8+DIfDgWHDhmH9+vV4/vnnZb0G\nQRAEoR6aipG8/PLLqK6uxvbt27F48WIsXLgQAHDixAksXrwYAGA2m7FmzRosWLAAEyZMwFVXXYXx\n48enstsEQRAZjYGjZTtBEAShAE1ZJMnikUcegdFoRHt7e6q7Iouf/exnGD9+PKZOnYrLLrsMXV1d\nqe6SJNJ5w2hDQwPmzZuHiRMnYtKkSXj00UdT3SXZBAIBTJ8+XVLyitbo7OzEFVdcgfHjx2PChAnY\nvn17qrski4ceeggTJ07E5MmTcc0118Dj8aS6S3G58cYbUVFRgcmTJ/c/197ejrq6OtTW1uLiiy9G\nZ+gs7TjoXkgaGhqwZcsWjEjD+h0XX3wx9u/fj71796K2thYPPfRQqrskSrpvGLVYLPj973+P/fv3\nY/v27fjTn/6UVv0HgD/+8Y+YMGFCWmY4rlq1CosWLcLBgwfx+eefp5Xb2uFw4IknnsDu3bvxxRdf\nIBAI4IUXXkh1t+Jyww03YPPmzRHPrV69GnV1dTh06BDmz5+P1atXi76O7oXkJz/5CX7zm9+kuhsJ\nUVdXB6OR/YnmzJmD48ePp7hH4qT7htEhQ4Zg2rRpAIDc3FyMHz8eJ06cSHGvpHP8+HFs2rQJN910\nU9olm3R1deH999/HjTfeCIDFQwsKClLcK+nk5+fDYrGgr68Pfr8ffX19qKysTHW34nLeeeehqKgo\n4rmNGzdixYoVAIAVK1bglVdeEX0dXQvJhg0bUFVVhSlTpqS6K4p58sknsWjRolR3QxQ9bRh1OBzY\ns2cP5oTXftE4P/7xj/Hb3/62fwGSTtTX16OsrAw33HADZsyYgZtvvhl9fX2p7pZkiouLceedd2L4\n8OEYNmwYCgsLcdFFF6W6W7Jpbm5GRUUFAKCiogLNzc2ibdJvtEVRV1eHyZMnx/xs3LgRDz30EO69\n997+e7W4QhPq/6uvvtp/zwMPPICsrCxcc801KeypNNLRncKH0+nEFVdcgT/+8Y/ITZOzxF977TWU\nl5dj+vTpmhzrYvj9fuzevRu33nordu/ejZycHEluFa3w9ddf4w9/+AMcDgdOnDgBp9OJv/3tb6nu\nliIMBoOk77Rm95FIZcuWLbzP79u3D/X19Zg6dSoAZvLPnDkTO3bsQHl5+WB2MS5C/Q/x17/+FZs2\nbcLbb789SD1SRmVlJRoaGvofNzQ0oKqqKoU9ko/P58Pll1+O7373u1i2bFmquyOZjz76CBs3bsSm\nTZvgdrvR3d2N66+/Hs8880yquyaJqqoqVFVV4cwzzwQAXHHFFWklJJ9++inOPvtslJw+KvSyyy7D\nRx99hGuvvTbFPZNHRUUFmpqaMGTIEJw8eVLSfJn2FokQkyZNQnNzM+rr61FfX4+qqirs3r1bUyIi\nxubNm/Hb3/4WGzZsgM1mS3V3JBG+YdTr9WL9+vVYunRpqrslGY7j8L3vfQ8TJkzAHXfckeruyOLB\nBx9EQ0MD6uvr8cILL+DCCy9MGxEBWHyquroahw4dAgBs3boVEydOTHGvpDNu3Dhs374dLpcLHMdh\n69atmDBhQqq7JZulS5fi6aefBgA8/fTT0hZTsvbBpzGjRo3i2traUt0NWYwZM4YbPnw4N23aNG7a\ntGncLbfckuouSWLTpk1cbW0tN3r0aO7BBx9MdXdk8f7773MGg4GbOnVq/+f+xhtvpLpbstm2bRu3\nZMmSVHdDNp999hk3a9YsbsqUKdzy5cu5zs7OVHdJFg8//DA3YcIEbtKkSdz111/Peb3eVHcpLldf\nfTU3dOhQzmKxcFVVVdyTTz7JtbW1cfPnz+fGjh3L1dXVcR0dHaKvQxsSCYIgCEXo1rVFEARBDA4k\nJARBEIQiSEgIgiAIRZCQEARBEIogISEIgiAUQUJCEARBKIKEhCAIglAECQlBEAShCBISgiAIQhFp\nX7SRINKFQCCA9evX4+jRo6iursaOHTtw5513oqamJtVdIwhFkEVCEIPE3r17cfnll6OmpgbBYBBX\nXnklhg4dmupuEYRiSEgIYpCYMWMGrFYrPv74Y8ydOxdz586F3W5PdbcIQjEkJAQxSOzcuROtra3Y\nt28fRo0ahQ8++CDVXSIIVaAYCUEMEps3b0ZFRQXOOeccvPzyy2l1Ng5BxIPKyBMEQRCKINcWQRAE\noQgSEoIgCEIRJCQEQRCEIkhICIIgCEWQkBAEQRCKICEhCIIgFEFCQhAEQSiChIQgCIJQxP8P6V3G\nhy17d9cAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f81202d9d50>"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Question 4**\n",
      "\n",
      "* Write a program to read the data file *seismic.dat* and store the magnitude of the earthquakes in an numpy array.\n",
      "* How many earthquakes are there above magnitude 6.0?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from numpy import *\n",
      "f = open(\"seismic.dat\", \"r\")\n",
      "\n",
      "magnitude = []\n",
      "for line in f:\n",
      "    s = line.split()\n",
      "    magnitude.append(float(s[-1])) # The last column is the \"magnitude\"\n",
      "magnitude = array(magnitude)\n",
      "\n",
      "f.close()\n",
      "\n",
      "print magnitude\n",
      "\n",
      "count = 0\n",
      "for m in magnitude:\n",
      "    if m > 6.0:\n",
      "        count += 1\n",
      "print \"There are %d earthquakes above magnitude 6.0\" % (count)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 6.6  6.1  5.2  5.   5.1  7.3  5.2  5.4  5.4  5.7  5.2  5.6  7.6  5.7  5.4\n",
        "  5.4  5.5  5.2  6.1  6.1  6.2  6.   5.2  5.6  5.1  5.5  5.2  5.2  6.7  5.5\n",
        "  6.5  5.6  6.   5.7  6.3  5.5  5.5  5.6  6.4  5.2  5.5  5.4  5.7  5.8  5.3\n",
        "  5.4  5.6  5.3  5.6  5.5  5.7  5.9  5.1  5.7  7.   6.4  5.6  5.2  6.1  6.7\n",
        "  5.9  5.4  5.9  5.6  5.8  6.1  5.   5.3  5.4  5.2  5.2  5.9  5.4  5.2  5.7\n",
        "  6.1  5.6  5.3  5.3  6.1  5.4  5.3  5.5  5.4  5.5  5.8  5.4  5.1  5.3  5.2\n",
        "  7.2  5.4  5.5  5.   5.   5.6  6.4  5.2  7.1  5.2  5.1  5.3  5.1  5.4  5.2\n",
        "  4.9  5.4  5.   5.1  5.4  5.1  5.2  5.3  5.   5.1  7.5  6.6  5.1  5.1  5.1\n",
        "  5.3  5.5  6.8  6.1  5.   5.3  5.3  6.6  5.6  5.9  5.2  5.4  5.3  5.1  5.1\n",
        "  5.1  4.9  7.8  5.4  5.1  5.8  5.2  5.1  5.5  5.3  5.   6.1  5.   5.2  5.1\n",
        "  5.3  5.6  5.3  5.   5.2  7.   4.8  4.9  5.   5.   5.   5.6  6.1  5.5  5.6\n",
        "  4.9  5.   5.   5.1  5.2  6.   4.9  5.2  5.6  5.5  4.8  5.2  5.7  5.4  5.8\n",
        "  4.9  5.2  4.9  6.   5.2  5.4  4.9  5.1  6.3  5.   5.   5.7  4.9  6.4  5.5\n",
        "  5.7  6.5  5.1  5.   6.6  5.6  5.9  5.9  5.4  5.2  5.   5.7]\n",
        "There are 34 earthquakes above magnitude 6.0\n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Question 5** The file *human_evolution.txt* holds information about various human species and their hight, weight, and brain volume. \n",
      "\n",
      "* Write a program that reads this file and stores the data in a python dictionary humans.\n",
      "* The dictionary key should correspond to the specie name (e.g., \u201chomo erectus\u201d), and the value should be a list containing the corresponding data. For example, humans[\"H. habilis\"] should give [\"2.2 - 1.6\", \"1.0 - 1.5\", \"33 - 55\", \"660\"].\n",
      "* Write out the humans dictionary in a formatted tabular form."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = open(\"human_evolution.txt\", \"r\")\n",
      "\n",
      "humans = {}\n",
      "\n",
      "for line in f:\n",
      "    # Ignore lines that don't start with \"H.\"\n",
      "    if(line[0:2] != \"H.\"):\n",
      "        continue\n",
      "    \n",
      "    # Get each column and strip out leading and trailing whitespace\n",
      "    name = line[0:21].strip()\n",
      "    lived = line[21:37].strip()\n",
      "    height = line[37:50].strip()\n",
      "    mass = line[50:62].strip()\n",
      "    brain_volume = line[62:].strip()\n",
      "    \n",
      "    # Add to dictionary\n",
      "    humans[name] = [lived, height, mass, brain_volume]\n",
      "f.close()\n",
      "\n",
      "# Test\n",
      "print humans[\"H. habilis\"]\n",
      "\n",
      "print \"%25s %25s %25s %25s %25s\" % (\"Species\", \"Lived when (M. years)\", \"Adult height (m)\", \"Adult mass (kg)\", \"Brain volume (cm**3)\")\n",
      "for human in humans:\n",
      "    data = humans[human]\n",
      "    print \"%25s %25s %25s %25s %25s\" % (human, data[0], data[1], data[2], data[3])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['2.2 - 1.6', '1.0 - 1.5', '33 - 55', '660']\n",
        "                  Species     Lived when (M. years)          Adult height (m)           Adult mass (kg)      Brain volume (cm**3)\n",
        "      H. neanderthalensis               0.35 - 0.03                       1.6                   55 - 70               1200 - 1700\n",
        "       H. sapiens sapiens             0.2 - present                 1.4 - 1.9                  50 - 100               1000 - 1850\n",
        "       H. heidelbergensis                0.6 - 0.35                       1.8                        60               1100 - 1400\n",
        "               H. erectus                 1.4 - 0.2                       1.8                        60 850 (early) - 1100 (late)\n",
        "          H. floresiensis              0.10 - 0.012                       1.0                        25                       400\n",
        "              H. ergaster                 1.9 - 1.4                       1.9                                           700 - 850\n",
        "               H. habilis                 2.2 - 1.6                 1.0 - 1.5                   33 - 55                       660\n"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}