relative efficiency

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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import root_numpy",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "%load_ext inumpy",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "The inumpy extension is already loaded. To reload it, use:\n  %reload_ext inumpy\n"
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "d = root_numpy.root2rec('SP643*.root')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "xs = d.XsMass\nxs2 = d.XsMass**2\nmb = 5.279\neg = (mb**2 - xs2)/(2*mb)\nprint xs[:10]\neg[:10]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "[ 0.93089718  0.77283931  1.27766895  1.39854205  1.35178268  0.97835112\n  0.76633221  0.7948947   0.81622112  1.14152396]\n"
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": "array([ 2.55742311,  2.5829289 ,  2.48488379,  2.45424533,  2.46642613,\n        2.54884195,  2.58387733,  2.57965398,  2.57639933,  2.51607919], dtype=float32)"
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "figsize(12,6)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "hist(xs, bins=200, histtype='step');\nxlabel('XsMass')\nylabel('Yield no Weight')\nxticks(np.arange(0.5,2.5, 0.1))\ngrid(True)\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
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E0QsytiE8TteuaZsnwPodFFvVYRuyI2QcyuiE1glySpRqayIY+rVEHI5YmjhSyTyYRrgphd\nEZtgx7ouvRQmT4a77krPSu4I1+v3v4eTT95yTwaoabrhBvj+95tnLGvXJrcPKCpKthtThGu1tYhN\nELMrYhPE65owAW65BSBWF8S7VlUidkVsgphdEZvALkmKJpe5Tldyq+CUlpbmewhNImJXxCawq5D1\n7w9Ll8JLLyWvb3+7lJdegrFjk+ObN+d3fI0lwrXaWsQmiNkVsQnidg0YEK8r6rWK2BWxCWJ2RWwC\nuyRJNVzJnWJRV3JLaZDGldyRTZqUrOL+139NtptDU67klqTtqVrJPXFisi1JkiSlnSu5t6GyspJH\nHnmEAQMGsN9++9GuXTu6devGeeedx4svvljnORUVFQwePJg99tiDdu3a8ZWvfIU777yTzdtYIjh1\n6lT69u1LcXExnTp1YsCAAcycObPe969bt47S0lJ69uxJUVERXbp0YcSIESxZsiTnZkmSJEmSJElq\nKcJPcl988cV885vfZNGiRZx11ll897vfpXfv3syYMYMTTzyRX/3qV1nvnzFjBieffDLPPfcc5557\nLpdddhkbNmzgiiuuYOTIkXV+j6uvvpqLLrqIDz74gO985ztccMEFLFy4kCFDhjB58uRa71+/fj1f\n+9rX+P73v0+nTp347ne/y6mnnsp//ud/cuyxx/KHP/yhSX4vJEmSJEmSJCmaXfI9gKb0zjvvcN99\n97HPPvvwyiuvsOeee1YfmzdvHgMHDuSmm27i/PPPB+Dvf/87F198MW3atGHevHn07t0bgJtvvpmB\nAwcyffp0HnnkEUaMGFH9ORUVFdxxxx10796dl19+mY4dOwJwzTXXcMwxx3D11Vfz9a9/nYMOOqj6\nnDvuuIOKigqGDx/OI488Ur1/xIgRDB06lDFjxrBw4cLqJfqSpPxatQr++MfsfZ9/np+xSJIkSZKk\nbKFXcq9YsQKA4447LmuCG6CkpIQOHTqwcuXK6n3Tp09n5cqVjBw5snqCG2C33XZj4sSJANxzzz1Z\nnzNlyhQAJkyYUD3BDXDQQQcxfvx41q9fz/3331+9v7KykilTppDJZLj99tuzPuvss8+mf//+LF68\nmPnz5+eSLklqRAMGwKBB2a+zzkqOtW6d37FJkiRJktTShZ7kPvLII9lnn3146aWX+Oijj7KOPfvs\ns3z22Weceuqp1fvmzJkDwBlnnFHrs04++WSKiop44YUX2LBhQ9Y5mUymznPOPPNMAObOnVu9b+nS\npbz77rv06NEja3X31udUjaUlKisry/cQmkTErohNYFeaNFfTu+8mX086KZnw3vL1j5+BNiqvVXpE\n7IrYBHG7nnmmLN9DaHRRr1XErohNELMrYhPYJUmqkalsyOMqU+QPf/gDF1xwAZ988gnf+MY36Ny5\nM0uXLuWJJ57glFNO4cEHH6xe5d2nTx8WLFjAggUL+OpXv1rrs4488khee+01Fi1aRK9evVizZg3F\nxcUUFxfzySef1Hr/ypUr2XvvvenSpQvvv/8+ADNnzmTIkCEMGTKEGTNm1Dpn+vTpnHfeeYwYMYKH\nHnpom225PHG0kGUymXBNELMrYhPsWNell8LkyXDXXcl2GkS8XlVNkybBDTfAv/4rTJrU+N+nc2f4\n+GNYuTLZbmqRr1U0EbsiNkG8rgkT4JZbAGJ1QbxrVSViV8QmiNkVsQnskqRocpnrDH1PboCjjjqK\nCy+8kB/84Af8/Oc/r97fvXt3Ro8enXUbk08++YRMJpN125EtdezYkcrKyuoJ7aqv23o/wOrVq7O+\nx86e09KUlpbmewhNImJXxCawK00iNkHMrohNELMrYhPE7RowIF5X1GsVsStiE8TsitgEdkmSaoS+\nXcnGjRsZNGgQN9xwAxdffDFvvfUWa9euZcGCBXTt2pXzzz+f6667Lt/DzFkmk6n3VVJSQiaTqfef\nO5WVlRXc8a23C218DT1e13sKaXwNOQ4U9Pgaerzq11XHS0vL2LiRrNfmzQBlXHZZ4Y2/vuN1dRbS\n+BpyvLy8vKDH19DjZf/434xCHV9Djkf887etB0QXyvj881eWta+Qx7ezx2+5JQOUMWhQ7fcUwvj8\n+8X2/35RaONryPG63lNI42vo8Yh/v4j452/LrkIdX0OP16dQxudxj3vc47kcr9qu65WL0LcreeCB\nBxgzZgz/5//8H6ZPn551bN26dfTo0YP333+fP//5zxx88ME7dLuSxYsX89prr9GzZ09vVyK1AM8+\nC4MHw5o1dR9P0+1KIot2uxJJ2p6q25VMnJhsS5IkSWmXy1xn6JXc/+///T8ABgwYUOtYUVERffr0\nYfPmzfzxj38EoGfPngC8/vrrtd6/ceNG3n77bdq0aUPXrl0BaN++Pfvuuy+fffYZf/vb32qd8+ab\nbwLQo0eP6n29evUC4I033qhzzHWdIyl/XnihZoK7devs1557wokn5nd8kiRJkiRJLV3oSe5dd90V\ngAt5zN0AACAASURBVA8//LDO4ytWrMh636BBgwCYNWtWrfc+++yzrFu3jn79+tGmTZvq/YMGDaKy\nsrLOc5566ikABg4cWL2vW7duHHjggbz++ussW7Zsh86RlH/XXkutW5asWAF1/KMPSZIkSZIkNaPQ\nk9ynnnoqAPfeey9//etfs4499dRTPP/88xQVFdGvXz8Ahg0bxp577snDDz/MggULqt/7+eefc8MN\nNwAwbty4rM+55JJLAJg0aVLWwyKXLVvG5MmTadu2LRdddFGd51x77bVZy+9nzJjBc889xxFHHMEp\np5ySU7skSZIkSZIktQShJ7kHDx7M0KFD+eCDDzjssMO48MILue666zj77LM566yzyGQy3Hbbbey+\n++4AFBcX87Of/YxNmzZRUlLCxRdfzLXXXsvRRx/Niy++yPDhwznvvPOyvscJJ5zAlVdeydKlSznq\nqKO44oorGD9+PMceeyyrV6/mRz/6EQceeGDWOVdeeSX9+vVj+vTpHHfccVx//fV861vfYtiwYbRv\n35777ruv2X6PClF9N6lPu4hdkZpWrYL/+3/hjDOge/cyzjgj2f7FL/I9ssYT6XpVidgEMbsiNkHM\nrohNELfrmWfK8j2ERhf1WkXsitgEMbsiNoFdkqQaoR88CbB582buvfdefvnLX/Lqq6+ydu1aOnfu\nTN++fbn88surV3tvqaKigkmTJvHCCy/w+eefc+ihhzJmzBguv/zyep/0OXXqVCZPnszixYtp3bo1\nvXv35pprrmHw4MF1vn/dunXcdtttPPTQQyxfvpyOHTtSUlJCeXl59X27tyfqgyczmUy4JojZFanp\n17+GESOqfpX5/9m78/Coyrtx4/ckbC5sWqsWBBUFK2LdEBWEsPiqVRF3WxUsdrGv2hZFK6WaoKi0\nVfraurX6uiJCtVWou68ooEaL1irUjbIICtWfVhSRnfz+GJMQEhBmJpk539yf6zpXJnNm5jx3D0V4\n8nAOULPr+uvhJz9p6FHlVqTzVamyKdqNJyOfq2gidkVsgnhdlTeehFhdEO9cVYrYFbEJYnZFbAK7\nJCmabOY6m+R6MIWmqKiIc889t+oSIZvjsMMO45FHHtmi4wwZMoQhQ4Zs9uu32morRo0axahRo7bo\nOI1BaWlpvodQLyJ2RWpasyb9tU8faN++lDPPrN63zTbw5VWNEi3S+aoUsQlidkVsgphdEZsgblff\nvvG6op6riF0RmyBmV8QmsEuSVC38Su7Ioq7klvJh/Hg44wz4znfSj5UsuVrJPXcuPPoobPjb6s9/\nDsuXN9xKbkn6KpUruUePTj+WJEmSks6V3JIk5cA558Czz9a9r6gImjVr0OFIkiRJkqTN4CS3JElf\n+uST9NfTToMddqi57+CDoWXLhh+TJEmSJEnaNCe5JUlhffEFPPYYrFhR8/l//Qs++wyaN6/5/KJF\n6a+XXgr77dcwY5QkSZIkSdlxkluSFNaYMXDllVv+vlatcj8WSZIkSZJUP4ryPQBpQ2VlZfkeQr2I\n2BWxCexKkq9q+n//L/31oIPgu9+tufXsCZdfnr5R5frbn/4Eu+9e/2PflMZ4rpIqYlfEJojb9fTT\nZfkeQs5FPVcRuyI2QcyuiE1glySpWqoik9tVqiBkc8fRQpZKpcI1QcyuSE3jx8MZZ8B3vgP33Ren\na32RzlelyqarroJf/hJ+8Yv0RHWlH/8YbrkFbrop/TgpIp+raCJ2RWyCeF0jR8LVVwPE6oJ456pS\nxK6ITRCzK2IT2CVJ0WQz1+lKbhWc0tLSfA+hXkTsitgEdiVJxCaI2RWxCWJ2RWyCuF19+8brinqu\nInZFbIKYXRGbwC5JUjVXcidY1JXcUq488ADMnFn7+eJiOPVU2Guv6ufWX8k9fnzDjVG5EW0ltyR9\nlcqV3KNHpx9LkiRJSZfNXKc3npQU0ocfwimnbHz/Sy/BI4803HjUMObOhSefrP5+4cL8jUWSJEmS\nJDUMJ7klhbRsWfpr69YwbFj18/Pnw513wtKl+RiV6ktxcfrrhAnpbUNN/K+dJEmSJElh+dd+SaG1\nbQvrX9Ju2rT0JLdiOe00ePll+Oyz2vu22w6OO67hxyRJkiRJkhqGk9ySGqVPPoGnn67+ftas/I1F\n2dttt/Q12CVJkiRJUuNTlO8BSBsqKyvL9xDqRcSuJDZVXtZi1iwYMKB6u+aa6v1J7NocEbsiNkHM\nrohNELMrYhPE7Xr66bJ8DyHnop6riF0RmyBmV8QmsEuSVC1VkcntKlUQsrnjaCFLpVLhmiBmVyE3\nzZsHu+8Ou+6aflxp1So491x4993a72nWDC67DHr2LNyubBTy+cpUxCaI2RWxCWJ2RWyCeF0jR8LV\nVwPE6oJ456pSxK6ITRCzK2IT2CVJ0WQz1+nlSlRwSte/gHIgEbuS2NSsGdx++6Zfk8SuzRGxK2IT\nxOyK2AQxuyI2Qdyuvn3jdUU9VxG7IjZBzK6ITWCXJKmaK7kTLOpKbikXNraSW5KkCCpXco8enX4s\nSZIkJV02c51ek1uSJEmSJEmSlFhOckuSJEmSJEmSEstrckuSJEkJNX06XHttzee+9jU44wxo2jQ/\nY5IkSZIampPckiRJUsJsvXX66xNPpLcNtW0Lxx/fsGOSJEmS8sXLlajglJWV5XsI9SJiV8QmsCtJ\nIjZBzK6ITRCzK2ITxOv64Q+htBQOPbSMiy6iatt77/T+JUvyO75sRDtXlSJ2RWyCmF0Rm8AuSVK1\nVEUmt6tUQcjmjqOFLJVKhWuCmF2F3DRvHuy+O+y6a/rxlijkrmxE7IrYBDG7IjZBzK6ITdB4uoYM\ngbvvhjvvTD9OosZyriKI2AQxuyI2gV2SFE02c53FZf6IMLFGjRoFxPwpb0lJSb6HUC8idhVq05Il\ncP310KYN/OxnW/7+Qu3KVsSuiE0QsytiE8TsitgEjaProYfgtddg0CDYb7/8jSlbjeFcRRGxCWJ2\nRWwCuyQpkmzmOl3JnWBRV3JLuZDNSm5JkpIqwkpuSZIkNU7ZzHV6TW5JkiRJkiRJUmI5yS1JkiRJ\nkiRJSqwm+R6AJGXr3HPhqadqPrd6dX7GIkmSJEmSpIaVk5Xcffv25e67797ka8aNG0e/fv1ycThJ\nqrJ6NfzhDzB3bs1t4cL0/m7d8js+SZIkSZIk1a+cTHJPnTqV+fPnb/I18+fP59lnn83F4RRcJndQ\nTYKIXYXUVFwM//pX7e3BB7f8swqpK5cidkVsgphdEZsgZlfEJrArSSI2QcyuiE0QsytiE9glSaqW\nqsjkdpUbKCoqoqysjMsvv3yjrxk5ciS/+c1vWLVqVbaH05eyueNoIUulUuGaIGZXITStXg3NmkGT\nJrm7REkhdNWHiF0RmyBmV8QmiNkVsQkaT9eQIXD33XDnnenHSdRYzlUEEZsgZlfEJrBLkqLJZq6z\n3q/JXVFRwbvvvstjjz3GLrvsUt+HUwClpaX5HkK9iNgVsQnsSpKITRCzK2ITxOyK2AR2JUnEJojZ\nFbEJYnZFbAK7JEnVMl7JXVRUVGt2vfL7DVXuHzFiBFdddVUmh1Mdoq7klrZEfazkliQpqSKs5JYk\nSVLjlJeV3L179656PG3aNDp27EjHjh1rva64uJjtt9+eAQMG8P3vfz/Tw0mSJEmSJEmSVEvGk9zr\n30SyqKiIs88+239SI0mSJEmSJElqUDm5JvfcuXNp27ZtLj5KknjjDfjzn2Hdutr79t8fBg5s+DFJ\nkiRJkiSpMOVkknvXXXfNxcdIEgDnnw/PPLPx/Q88AK1bpx+vWdMwY5IkKUkeegjefbfmc7vsAmef\nDRu5jY4kSZKUWEW5+qBVq1Zx//33c8kll/CDH/yAoUOH1rlJX6WsrCzfQ6gXEbvqq2np0vTXc86B\nyy+v3iqdfDIccUR6O/ro9HPFxbk7fsRzBTG7IjZBzK6ITRCzK2ITNJ6ubbdNf33oISgtrbkNHQov\nv9zwY9xSjeVcRRCxCWJ2RWwCuyRJ1VIVmdyucgOLFi1iwIABvPXWW1/52nV1XX9AGcnmjqOFLJVK\nhWuCmF311dS9e/ov4X/7W/pxpdtugwkT6n7PMcfAsGG5OX7EcwUxuyI2QcyuiE0QsytiEzServff\nhzvvhJUra77u7rvTK7uffhr69WvYMW6pxnKuIojYBDG7IjaBXZIUTTZznTm5XMlFF13EW2+9xXe+\n8x1+8IMf0L59e5o0yclHqxGKegPTiF0N3fT976e3+hbxXEHMrohNELMrYhPE7IrYBI2nq107GDmy\n9uuee6725UsKVWM5VxFEbIKYXRGbwC5JUrWcrOTefvvt2WeffZg6dWouxqTNFHUlt7SxldySJCkz\n/fql73eRhJXckiRJapzyvpJ7xYoVHHLIIbn4KEmSJEkNZNkyeOUVqOvvEXvvDTvs0PBjkiRJkrZU\nTia5u3btyrtJ+fePkiRJUiP12muw/lUFBwyA1avrfu2OO6av8Z3LmztLkiRJ9SEnk9yXXHIJZ511\nFv/85z/p2rVrLj5SkiRJUo5UTlRfeGHd+9u3h913r/5++nT44ANYtQq22qr+xydJkiRlI6NJ7mnT\nptX4focddmDgwIH07NmTn/zkJxx00EG0adOmzvf27t07k0NKkiRJytBPfwpr1sDatbX37bMP3Hgj\nfHkJRCA9sb1iRcONT5IkScpGUSZvKikpqbH17duX+++/n88++4zRo0czaNCgWq+pfJ30VcrKyvI9\nhHoRsStiE9iVJBGbIGZXxCaI2RWxCew69tj0jSenTau93XRTzQnufGvs5ypJIjZBzK6ITWCXJKla\nqiKD21Vm+htuKpWitLQ0o/eqtmzuOFrIUqlUuCaI2VVfTd27w8svw9/+ln7c0CKeK4jZFbEJYnZF\nbIKYXRGbwK4tVbmS+4svGv5yJZ6r5IjYBDG7IjaBXZIUTTZznRldrsSfKqo+Rf1BSMSuiE1gV5JE\nbIKYXRGbIGZXxCawK0kiNkHMrohNELMrYhPYJUmqltFKbhWGqCu5pXyv5JYkqbHL50puSZIkNU7Z\nzHVmdE1uSZIkSZIkSZIKQUaXK9lQ3759q2baN6aoqIhWrVrxzW9+kxNOOIGDDjooF4eWJEmSJEmS\nJDViOblcSVHRli0IT6VS/OhHP+Kmm27K9tCNmpcrUVRerkSSpPzyciWSJElqaHm/XMny5cs5/vjj\n6dKlC+PGjWPevHl88cUXzJs3j3vuuYfOnTszaNAg3nvvPZ544gn2228/brnlFm6//fZcHF6SJEmS\nJEmS1EjlZJJ79OjRvPzyy7z44ot897vfpWPHjrRo0YKOHTtyxhln8NJLLzFjxgxuvvlmjjjiCJ58\n8knatm3L//7v/+bi8AqmrKws30OoFxG7IjaBXUkSsQlidkVsgphdEZvArvq2bh2sWlV7W716yz+r\nUJpyLWJXxCaI2RWxCeySJFXLyeVKdt99d4477jiuv/76jb7mpz/9KX/961+ZO3cuAGeeeSYPP/ww\nS5YsyfbwjVbUy5WkUqlwTRCzq76a8n25kojnCmJ2RWyCmF0RmyBmV8QmsGtLbcnlSpYuhW7d4N13\na+8rKoJrr4Vhwzb/2J6r5IjYBDG7IjaBXZIUTd4vV7Jo0SKaNWu2ydc0bdqURYsWVX3fvn17li9f\nnovDK5jS0tJ8D6FeROyK2AR2JUnEJojZFbEJYnZFbAK7MtWtG3TuXHPbcIHhv/5VPcHdtGn1Vlyc\nXuE9ZcqWHdNzlRwRmyBmV8QmsEuSVC1nK7mbNWvGrFmzaNKkSa39q1evZp999mH16tVVK7nPOecc\nHn74YT744INsD99oRV3JLeV7JbckSY3dQQfBK6/Uve9rX4P/9/+qv3/1VTjgANhvv/TjSn/9Kwwc\nCMcem34sSZIkbUreV3IPHjyYd955hwEDBvDcc8+xbt06ANatW8f06dMZMGAAs2fP5qyzzqp6T3l5\nOV27ds3F4SUl2Lp1tTd/biNJUn698AK8/XbNrbw8vc//TkuSJKnQ1F52nYERI0bw8ssv8+ijj9K7\nd2+Ki4vZbrvt+M9//sPatWsBOOqoo/jFL34BwOLFi9l333056aSTcnF4SQl1443w05/Cl79NSJKk\nAtGsWfrSJOv76KP8jEWSJEn6KjmZ5G7evDl//etfGTduHHfddRevvvoqH3/8Ma1ateKAAw5g8ODB\nDB48uOr1O++8MxMmTMjFoSUlwJQp8KMfwYaX4X///erHX/6LlCqdOsFee9X/2CRJkiRJkpRsOZnk\nhvQ1U84666walySRJICHHkrflKouLVqkr9/phLYkSZIkSZIykZNrcku5VFZWlu8h1IuIXVvaNGoU\nLFxYc/vww8Kb4I54riBmV8QmiNkVsQlidkVsAruSJGITxOyK2AQxuyI2gV2SpGqpikxuV6mCkM0d\nRwtZKpUK1wQxuza36Sc/gd//Hq6/Pv240EU8VxCzK2ITxOyK2AQxuyI2gV258NFHsMMOsP32Na/P\n/eqrcMABsN9+6ceV/vpXGDgQjj02/Xhzea6SI2ITxOyK2AR2SVI02cx1ZnS5kqKiIlKpFG+++Sad\nO3eu+n5TKioqSKVSVTeilDamtLQ030OoFxG7IjaBXUkSsQlidkVsgphdEZvArnx6/XU499yaz7Vo\nAT/7Gey6a+3XJ6EpExG7IjZBzK6ITWCXJKlaRiu5S0pKSKVS3H333eyyyy6UlJRs3sFSKZ555pkt\nPZw2IupKbsWTtJXckiSpti1dyf3889Cr18Y/b9gwGDu2/sYrSZKkZGnwldzPPvvsJr+XJEmS1Lgd\ndhhMmgSLFtV8fupUmDABVqzIz7gkSZIUT0aT3JIkSZK0KalU+prcG1q3Lj3JLUmSJOVKzie5ly1b\nxttvv82yZcs4/PDDc/3xkiRJkvLo449hxx2rv1+9On9jkSRJkgCKcvVBCxcu5MQTT6RNmzYcdNBB\nNa7TPX36dPbee28vayJJkiQlVJs2sOee6ccffli9ffJJ+rmDD87f2CRJktS45WSSe/HixRxyyCFM\nnjyZY489lkMPPbTGBcJ79OjBBx98wMSJE3NxOAVXVlaW7yHUi4hdEZvAriSJ2AQxuyI2QcyuiE1g\nVy40aQL//Cf8+9+1tw8/hFtuyc1xPFfJEbEJYnZFbAK7JEnVUhWZ3K5yA+eeey633XYbTz75JP36\n9aOsrIwrrriCdevWVb1m0KBBzJ07l9dffz3bw+lL2dxxtJClUqlwTRCza3ObfvIT+P3v4frr048L\nXcRzBTG7IjZBzK6ITRCzK2IT2FVIbroJzjsPfvzj9OMNJbFpc0TsitgEMbsiNoFdkhRNNnOdOVnJ\n/eijjzJw4ED69eu30dd06NCBRRveWl2qQ2lpab6HUC8idkVsAruSJGITxOyK2AQxuyI2gV2F6NZb\noWXLmlu7dnDOOclt2pQkn6uNidgEMbsiNoFdkqRqOVnJ3bx5c4YNG8aYMWMA6lzJfdFFF3HjjTey\nYsWKbA+nL0Vdya14kraSW5Ik1Z+XXoJ+/eCLL+ref/XVMGJEw45JkiRJ+Zf3ldxt27Zl4cKFm3zN\n7Nmz2WmnnXJxOEmSJEkJ1aMH/Oc/8NlnNbdhw/I9MkmSJCVVk1x8SK9evZg8eTKLFy9m5513rrV/\n9uzZPP7445xxxhm5OJykPFu3Dh57LH2TqQ1tvTUcd1z6qyRJUl2aN09vGz4nSZIkZSInk9wXX3wx\nDz30EH369OH6669n+fLlAHz++edMmzaNYcOGkUqluOiii3JxOEl59vTTcOyxm37N8OHVj597rn7H\nI0mSJEmSpMYrJ5PcPXr04I9//CPnnnsuxxxzTNXzrVu3pqKigqZNm3L77bezzz775OJwkvLso4/S\nX3fdFUpKqp+/887qx9deW/t9rVvX46AkSZIkSZLUKOXkmtwAQ4cOZdasWfz0pz/l4IMPplOnTuy/\n//6cd955vP76616qRJutrKws30OoFxG7ttmmjDvuoGpbtgz+8Af49a9rb7feCqeemu8Rb56I5wpi\ndkVsgphdEZsgZlfEJrArSf7v/8ryPYR6EfFcRWyCmF0Rm8AuSVK1VEUmt6sExo0bx4ABA7yZZB5l\nc8fRQpZKpcI1Qayu++6D734XIE7T+iKdq/VF7IrYBDG7IjZBzK6ITWBXEowYAWPGgH++SI6ITRCz\nK2IT2CVJ0WQz11lcluGPCPfbbz/Gjh3Ln/70J95++21WrVrFzjvvTIsWLTL5OGVg1KhRQMyf8pas\nfw2MQKJ0zZoFf/4zdO0K//3fJfkeTr2Icq42FLErYhPE7IrYBDG7IjaBXYXu6afT9/Ho1w+GDCnJ\n93DqRZRztb6ITRCzK2IT2CVJkWQz15nxSu4hQ4YwZcoU3n///arniouLOeCAA+jfvz8DBgygZ8+e\nNPc26fUm6kpuFb7Kldynn55+LEmSlK3KldxXX51+LEmSpMYlm7nOjG88eddddwHwzjvvMGXKFJ5+\n+mmeffZZZsyYwYwZMxgzZgwtWrTgsMMOo3///vTv35/u3btXDVZSMqxaBZ9/XvO5Db+XJEmSJEmS\n8iXjSe5KnTt3pnPnzpx77rlUVFTw+uuvV016T58+nSlTpjBlyhRGjhxJ69at+eSTT3IxbkkN4MMP\n4ZvfhP/8J98jkSRJkiRJkupWlMsPS6VSfOtb32LYsGE8/PDDfPLJJzz44IN069YNgE8//TSXh5NU\nz955Jz3BXVwMbdvW3HbcEQYNyvcIJUlSNBUVdW+SJEnSxmS9kntDn332Gc8880zVCu5//vOf6QM1\naUKPHj1yfThJDeCQQ9I3gpIkSapvI0emt/Vtv336zyJ77ZWfMUmSJKmwZb2Se/ny5Tz55JP8/Oc/\np3v37my33XaccMIJ3HzzzWy77bb8/Oc/54knnmDJkiVMnz49F2NWcJncQTUJInZFbAK7kiRiE8Ts\nitgEMbsiNoFdSdCnD2y7LUBZrX0ffwwzZjT0iHIr0rmqFLEJYnZFbAK7JEnVUhWZ3K6S9G+6U6ZM\n4aWXXmL16tUUFxez//77U1JSQr9+/ejZsyctW7bM9Xi1nmzuOFrIUqlUuCZIZtdzz8Hhh0PPnnWv\n5E5i0+awKzkiNkHMrohNELMrYhPYlSQbNp11FowbB3ffnX6cVI3hXEURsStiE9glSdFkM9eZ8eVK\nrrjiClKpFCeffDJnnXUWvXv3plWrVpl+nFSltLQ030OoFxG7IjaBXUkSsQlidkVsgphdEZvAriSJ\n2AQxuyI2QcyuiE1glySpWsYrubfddlu++OILAPbcc0/69etH37596du3LzvssENOB6m6RV3JrcLx\nVSu5JUmS6luUldySJEnatLys5P7kk0946aWXqm4wefvtt/OHP/wBgK5du1ZNepeUlNCmTZtMDyNJ\nkiRJkiRJ0kZlfOPJpk2b0qtXLy6//HKeffZZlixZwhNPPMHPf/5ztt56a2688UZOPPFEtt9+ew48\n8EAuvvhiHn300VyOXZIkSZIkSZLUyGW8kntDW221FUcccQRHHHEEAJ9++ilTp05lypQpPPbYY1x3\n3XWMHTuWtWvX5uqQkiRJkiRJkqRGLmeT3Otbu3Ytb7zxBq+//jqvv/46CxYsALx2tFSoKipg2DB4\n7bWazy9Zkp/xSJIkSZIkSZsr48uVrK+iooJXX32V6667jmOOOYbtttuOnj17Vl3KpFmzZhxzzDGM\nHTs2F4dTcGVlZfkeQr0o5K7334frr4dnn625/eMf6f277FL3+wq5KRt2JUfEJojZFbEJYnZFbAK7\nkmRjTQsWpH8gv/72wQcNO7ZsNKZzlXQRuyI2gV2SpGqpigyXV7/11ltVN52cOnUqH3/8cdW+Zs2a\n0bNnT/r160f//v3p3r07xcXFORu00rK542ghS6VS4ZqgsLvefRd23RW+/nWYMKHmvqIi6NEDWrSo\n/b5CbsqGXckRsQlidkVsgphdEZvAriTZsGnwYLjnnrpf26QJzJ6d/nNMoWsM5yqKiF0Rm8AuSYom\nm7nOjC9Xsvfee1c9Lioq4uCDD6Z///7079+fnj170rx580w/Wo1caWlpvodQL5LQ1aIF9O27+a9P\nQlMm7EqOiE0QsytiE8TsitgEdiXJhk1DhsBbb8HKlTVf969/wRdfwPz5yZjkbgznKoqIXRGbwC5J\nUrWMV3J369aNAQMG0K9fP/r06UOrVq1yPbacevrpp7nhhhsoLy9nyZIlbL/99nTr1o2f/vSnHH30\n0TVe+8ILLzB69GhefPFFVqxYwZ577snQoUO54IILKCqq+wovd911FzfeeCNvvvkmxcXF7L///gwf\nPpxjjjmmztcvX76cMWPGMGHCBBYsWECrVq0oKSlh1KhR7LXXXpvVFHUlt+rXPffAM8/UfO7zz+H+\n+6FDh/SqbkmSpEJXUgJTp6b/XFNSku/RSJIkKVvZzHVmPMmdJJdccgnXXnstu+yyC0cffTRf+9rX\n+PDDD/n73//OgAEDGDNmTNVrJ02axEknncTWW2/NaaedxnbbbcfkyZN5++23Ofnkk/nTn/5U6/OH\nDx/O2LFj2WWXXTj55JNZuXIlEyZM4D//+Q+///3vOe+882q8fuXKlfTv358XXniB7t27069fPxYs\nWMD9999Ps2bNmDJlCgcffPBXdjnJrS21bl16tfbq1XXvP+AAeOWVhh2TJElSJpzkliRJisVJ7k24\n9dZb+dGPfsTZZ5/NH//4R5o0qXmFljVr1lQ999lnn7HHHnuwdOlSnn/+eQ444AAgPSndr18/ysvL\nue+++zjttNOq3v/CCy/Qq1cv9thjD2bMmEHr1q0BePfddznwwANZtmwZb731Fh07dqx6zzXXXMPI\nkSM55ZRTmDhxYtXzkydPZtCgQey9997MnDmz6sRujJPc2pQ5c9I3j1zfunXwwx+mH//v/9Z+T9++\nsNtu9T40SZKkrDnJLUmSFIuT3BuxcuVKdtllF7bZZhtmz55da4J7Q7fffjvf//73GTJkCHfc2Rlv\n5wAAIABJREFUcUeNfc888wz9+/end+/ePLvezOHgwYMZN24cd9xxB0OGDKnxntLSUq688kouv/zy\nqrsjV1RUsOuuu/Lee+8xd+7cGpPfAH369GH69OlMmTKFkq/407qT3NqUbt1g1qy69229NSxb1rDj\nkSRJyiUnuSVJkmLJy40nk+Cpp57io48+4qyzziKVSvHII48wa9YsWrRoQY8ePTjkkENqvH7KlCkA\nHHXUUbU+q3fv3my11VaUl5ezatUqmjVrVvWeVCpV53uOPvporrzySp555pmqSe45c+awcOFCunTp\nUmuCu/I9mzvJLW3Kxx+nv552GmyzTc19RxzR8OORJEmSJEmS6kPdd1EMYsaMGQA0b96c/fbbj+OO\nO44RI0YwbNgwDjvsMEpKSvjoo4+qXv/2228D0Llz51qfVVxczG677caaNWuYO3cuAMuWLWPRokVs\nu+227LjjjrXes8ceewDwzjvvbNYx1n/P7Nmzt7g3isofCESTr66xY9OXJll/O/303Hy25ypZInZF\nbIKYXRGbIGZXxCawK0kiNkHMrohNELMrYhPYJUmqFvpyJT/+8Y/5wx/+QHFxMV27duWmm25iv/32\nY+7cuQwfPpwnn3ySPn368MwzzwDpiec5c+Ywe/Zsdt9991qf17NnT8rLyykvL6dHjx4sWrSI9u3b\n0759exYsWFDr9atXr6Z58+Y0b96c5cuXAzB+/HjOPPNMzjzzTO6+++5a73nqqac48sgjOfLII3ns\nscc22Rf1ciWpVCpcE9Rf14svwj33pK+3vb677oLly+H99+Eb38j5YQHPVdJE7IrYBDG7IjZBzK6I\nTWBXkmxuU9IuV9KYz1XSROyK2AR2SVI0Xq5kI9Z9OevXtGlTJk+eTIcOHQDYZ599ePDBB+nSpQtT\np07lpZdeokePHvkcqtZTWlqa7yHUi/rquuQSmD697n1Nm8K229bLYQHPVdJE7IrYBDG7IjZBzK6I\nTWBXkkRsgphdEZsgZlfEJrBLklQt9OVK2rRpA8D+++9fNcFdaauttuLII48E4G9/+xsArVu3pqKi\ngk8//bTOz6t8vvJzW7duXeP5r3p9pu/5KqlUaqNbSUkJqVRqo//cqaysrOD2b/i40MaX6f66XpOL\nz58+PQWUccklcNNNNbczzyyjdev66wMK5n/fXO6v/L5Qx5fp/ro6C2l8mewfNWpUQY8v0/1lX/6e\nUajjy2R/xF9/lasMCnl8/vorq/FcIY8v0/0bO4eFMr5M9sPm/fli/vzCHP/G9ld+X6jjy2R/xF9/\nZWUx/3wR8dff+l2FOr5M929MoYzP/e53v/uz2V/5uK4tGzm9XEl5eTm33XYb//jHP1iyZAmtW7fm\ngAMOYOjQoRx22GG5Osxmu+OOOzjnnHM4+uijeeSRR2rtv/jii7nuuusYM2YMl1xyCWeeeSbjx49n\n/PjxnL7BRYvXrFlD69atWbNmDZ9//jlNmzYFoH379ixevJj333+fnXbaqcZ7ysvL6dmzJ4cffjhT\np04F0jee3HPPPenSpQtvvvlmrTFdc801jBw5kssuu4xRo0Ztsq/y5PvPmBq3gw+GGTPgpZfSjyVJ\nkhqDpF2uRJIkSZuWzVxnzlZyjxw5kp49e3LHHXfw6quvMm/ePP7xj39w++23c/jhhzNixIhcHWqz\n9e/fn1QqxRtvvFHn/zizZs0CYLfddqt6PcDjjz9e67XTpk1j+fLlHHbYYVUT3JXvqaioqPM9ldfU\n7tevX9VznTp1okOHDrz99tvMnz9/s94jSZIkSZIkSapbTia577//fq655ho6duzIbbfdxty5c/ni\niy+YM2cOt912Gx06dODXv/41EydOzMXhNluHDh047rjjePfdd7n++utr7HvyySd54oknaNu2LUcd\ndRQAJ598Ml/72teYMGECr7zyStVrV6xYwS9/+UsgfTPL9Z177rkAXHXVVSxZsqTq+fnz53PjjTfS\nokULvve979X5nksuuaTG5PukSZN47rnn6Nq1K3369Mk2X5IkSZIkSZLCy8nlSnr37s0777zDzJkz\n2WGHHWrt/+ijj+jatStdunRh2rRp2R5ui7z//vscdthhLFy4kP79+7Pffvsxb948HnroIYqLi5kw\nYQInnHBC1esnTZrEySefTIsWLTj99NNp27YtkydP5p133uGUU06pc6J++PDhjB07lvbt23PSSSex\natUqJk6cyCeffMLvf/97/vu//7vG61etWkW/fv144YUXOOigg+jXrx8LFizg/vvvp0WLFkyZMoXu\n3bt/ZZuXKxF4uRJJktQ4ebkSSZKkWPJ+uZLXXnuNk08+uc4JboCvfe1rnHLKKbz22mu5ONwWadeu\nHa+88grnn38+s2fP5ne/+x3Tpk3j+OOP5/nnn68xwQ1w/PHHM3XqVHr37s2f//xnbrjhBpo3b85v\nf/tbJkyYUOcxrr32Wu644w522mknbr31VsaNG0e3bt3461//WmuCG6BZs2Y89dRTXHbZZSxZsoT/\n+Z//4emnn+bEE09kxowZmzXBHdnGLlKfdBG7IjaBXUkSsQlidkVsgphdEZvAriSJ2AQxuyI2Qcyu\niE1glySpWk5Wcm+zzTacf/75/OpXv9roay655BJuuOEGvvjii2wPpy9FXcmdSqXCNUH9deVzJbfn\nKlkidkVsgphdEZsgZlfEJrArSTa3KWkruRvzuUqaiF0Rm8AuSYom7yu5d999dx5++GHWrVtX5/51\n69bx2GOP0alTp1wcTsGVlpbmewj1ImJXxCawK0kiNkHMrohNELMrYhPYlSQRmyBmV8QmiNkVsQns\nkiRVy8lK7jFjxvCLX/yCb3/724wdO5bOnTtX7fvXv/7FxRdfzKRJk7jqqqsYMWJEtofTl6Ku5NaW\n8ZrckiSpMUraSm5JkiRtWjZznU1yMYBhw4bx+OOP8+ijj/L444/zjW98g5133pl///vfvPfee1RU\nVNCrVy8uvPDCXBxOapQeeAAuvRRWr675/OLF+RmPJEmSJEmSVAhyMsndvHlznnzySa677jpuv/12\n5syZw3vvvQdAp06dGDp0KMOHD6dp06a5OJzUKN13H8yZU/e+li1ht90adjySJEmSJElSIcjJJDdA\ns2bNGDFiBCNGjGDp0qV8+umntG7dmpYtW+bqEJKAm2+Go4+u+dz228O22+ZnPJIkSZIkSVI+5WyS\ne30tW7Z0cluqJzvsAB075nsUkiRJkiRJUmEoyvcApA2VlZXlewj1ImJXxCawK0kiNkHMrohNELMr\nYhPYlSQRmyBmV8QmiNkVsQnskiRVS1VkcrvKOjz77LP85je/YcaMGXzyySesW7euxv6KigpSqRRr\n167NxeFEdnccLWSpVCpcE2TfddJJ8Je/pG9AedJJORxYFjxXyRKxK2ITxOyK2AQxuyI2gV1JsrlN\nJSUwdSo880z6caFrzOcqaSJ2RWwCuyQpmmzmOnNyuZJHHnmE448/nnXr1rHLLrvQuXNnmjSp/dGV\nA5U2pbS0NN9DqBcRuyI2gV1JErEJYnZFbIKYXRGbwK4k2dKmtWvT2/qKiqDQ/urhuUqOiF0Rm8Au\nSVK1nKzk7t69O7NmzWLSpEn813/9Vy7Gpc0QdSW3YNkyWLWq5nODB8PDDxfWSm5JkqR8qVzJXZdv\nfhNefRWaN2/QIUmSJCkL2cx15uSa3LNmzeK0005zglvKgfvug1atYLvtam4PP5zvkUmSJBWOo4+G\npk3Tq7bX3wDefBMWLszv+CRJktRwcjLJvc0227D99tvn4qOkRu+ll2DdOmjRAtq0qbnttRcccki+\nRyhJkpR/P/95+l++VV6upHLr1CnfI5MkSVJDy8k1uQcMGEB5eXkuPkrSl665Bn72s3yPQpIkSZIk\nSSpsOVnJPWbMGObMmcOVV17p9aElSZIkSZIkSQ0mJ5Pco0aNomvXrpSWltKpUydOPPFEhg4dWucm\nfZWysrJ8D6FebNj19tvQqxd061Zzu+ee/IwvE43lXEURsStiE8TsitgEMbsiNoFdSRKxCWJ2RWyC\nmF0Rm8AuSVK1VEUOll4XFW3+XPm6deuyPZy+lM0dRwtZKpUK1wS1u667DoYP3/jrH3kEvv3tBhhY\nFhrLuYoiYlfEJojZFbEJYnZFbAK7kiTbpj32gDlzYPbs9ONC4blKjohdEZvALkmKJpu5zpxck3vu\n3Lm5+BgJgNLS0nwPoV5s2FX5/9fBg2tPdrduDR06NNDAstBYzlUUEbsiNkHMrohNELMrYhPYlSQR\nmyBmV8QmiNkVsQnskiRVy8lKbuVH1JXcjcW118LFF8NFF6UfS5IkKXuFupJbkiRJm5bNXGdOrskt\nSZIkSZIkSVI+OMktSZIkSZIkSUqsnFyTW2ps1qyBTz+t/XxREbRt2/DjkSRJ0qaNHQsPP1z3vtNO\ngx/9qGHHI0mSpNxxklvaQitXwje/CfPm1b3/wgvhuusadkySJEnatF/+EpYvr3vfm286yS1JkpRk\nXq5EBaesrCzfQ9ikDz5IT3CnUrDddtVby5bp/eXldb+v0LsyEbEJ7EqSiE0QsytiE8TsitgEdiVJ\nrppuuy19c+/KbeXK9POPPQZPP53eHngg/dzatZv/uZ9+Cu++W3v74INNv89zlRwRuyI2gV2SpGqp\nikxuV6mCkM0dRwtZKpUq6KYFC6BjR9hll/TjSi+8AD17wqGHph9XWrUq/Zes885LcfnlFTVe/3//\nBxddlP6LVxIV+rnKlF3JEbEJYnZFbIKYXRGbwK4kybZp331h5sy69zVvDkuXQtOm6e8//BB23BF2\n2CH9+Ku8/TZ861vVE+Yb+uMf4Qc/qHuf5yo5InZFbAK7JCmabOY6vVyJCk5paWm+h5BTTz0F550H\nUMoVV9TeX7kCPIminatKdiVHxCaI2RWxCWJ2RWwCu5Ik26ZbboEHH4S6/m7Uq1f1BHcm3norPcHd\nogV8/evVz3/2GSxZAq+9tvH3eq6SI2JXxCawS5JUzZXcCRZ1JXeh+6qV3K1bwyGHVD+/eDG8/jrs\nvTecemrNz9pmG/je92D77Rtm7JIkSaq2pSu5J02CQYNg4MD040o33AAXXJBe2HDDDfU3XkmSpMga\nfCV33759qw66paZMmZLR+6RCt/PO6a+ffgpPPFF7/5FHgj+QlyRJkiRJknIro0nuqVOn5nocUuLt\nthu88Ub6xkMbatYs/c9jJUmSVHg+/xwuvrjmc6kUnHYaHHhgfsYkSZKkzZfRJPe6detqfL9y5UpO\nPfVU/vnPf3LZZZdRUlLCjjvuyL///W+effZZRo8eTbdu3Zg4cWJOBi0Vqm9+M71JkiSp8G21FTRp\nAsuX130j8PJymD694cclSZKkLZOTG0+OHj2al19+mVmzZtG2bduq53fddVfOPvtsBg4cSLdu3bjy\nyiu58sorc3FISZIkScpKy5bw2GPw6qs1n3/vPfjd72DZsvyMS5IkSVumKBcfcu+993LSSSfVmOBe\n33bbbcdJJ53Evffem4vDKbiysrJ8D6FeROyK2AR2JUnEJojZFbEJYnZFbAK7kqShmwYMSF+qZP1t\nyJDcH8dzlRwRuyI2gV2SpGqpikxuV7mBFi1acMEFF/Cb3/xmo6+56KKLuPHGG1mxYkW2h9OXsrnj\naCFLpVIF3bRgAXTsCLvskn68uQq9KxMRm8CuJInYBDG7IjZBzK6ITWBXkhRC09//nr4Wd4sWNS9F\nt2QJzJsHAwfCpEnVz99wA1xwAZx7Llx/fc3PKi5Ob4XQlWsRmyBmV8QmsEuSoslmrjMnK7nbtWvH\npEmTWLVqVZ37V65cyeTJk2nXrl0uDqfgSktL8z2EehGxK2IT2JUkEZsgZlfEJojZFbEJ7EqSQmhq\n3x6aN4cVK9KXMqnc5s1L799jj7rfd8st6fetv7VqBVOnFkZXrkVsgphdEZvALklStZys5L7yyisp\nLS3l0EMP5eqrr6ZXr14UFxezdu1apk+fzsiRIykvL2fUqFFcdtlluRi3iLuSu9BlupJbkiRJyfHh\nh+lrc2+oaVPo2hWK1lsu9OqrcNRR8MknNV+7Z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width=732 height=381/>",
       "metadata": {},
       "output_type": "display_data",
       "text": "<matplotlib.figure.Figure at 0x105c09610>"
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "hist(eg, bins=200, histtype='step');\nxlabel('EGamma')\nylabel('Yield no Weight')\ntitle('Relative Efficiency')\nxticks(np.arange(1.8,2.7, 0.05))\ngrid(True)",
     "language": "python",
     "metadata": {},
     "outputs": [
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YAW9vb+Tk5KC6uhoeHh5NewFO5EitDC5cuID333/feFxMTAz69evnrFN3qtLS\nUgBAly5dTLbpdDoAQGZmpiwfas2UrbWqqamBu7v8x6vSM2W4nobXZU56ejoAYMyYMSbbtFothg8f\njrS0NOTm5mL06NEAWlammrpWBmrKlCPUmqn7wUxZn4eZsn8eZkpv586dAIBRo0bJxltKppq6TgZK\nzxPgWK3y8vKwdOlSLFq0CA8++CC+/vpri/uqOVP21MlA6ZlypE63bt3C5s2bcfbsWfj6+qJ///4Y\nMWIENBrTx7W1lDwBTV8rAzVlKi0tDb/++iumT58OSZKwe/dunDp1Cl5eXoiOjsbQoUNNjlFrphyp\nlYHSM+VsbHIrjJeXF1JTUzFt2jT07t0b48ePR9u2bVFYWIidO3dizJgxeP/992XH/PjjjwCAHj16\nmMzn5uYGnU6HvLw8FBUVITIy0inX4QyO1MogLS0NaWlpsrFRo0Zh48aNCA0NdcbpO01QUBAAoKio\nyGSbYaympkaWD7VmytZaFRYWmly3kjNVU1ODTZs2ATD/F5KGrOUDALp37460tDTk5+cbG7ctJVPO\nqJWBmjLlCLVm6n4wU9bnYabsn0etmVq9ejUqKytRUVGBY8eO4fDhw5gzZw5efPFF2X4tIVPOqJOB\nkvMEOFarmpoaTJ8+HREREVi5cuU991drpuytk4GSM+VInSRJQmlpKWbMmCEb1+l02LBhA0aMGCEb\nbwl5ApxTKwM1Zero0aMAAE9PTwwYMADff/+9bPuIESOwZcsW439DA+rNlCO1MlByplzB8q+gqNl6\n4IEHMHPmTNy6dQvr1q3Dm2++iS1btiA0NBTPPPOMyQejoqICkiShTZs2Zudr06YNhBAoLy93xuk7\nlb218vX1xfLly3H8+HGUl5ejvLwcGRkZiIuLw4EDBxAfH4+bN2+66GqaxmOPPQYAePvtt3H16lXj\neHV1NRITEwHAJB9qzZQjtWoJmVq6dCm+//57PProo3jooYfuuX9FRQUAWM0HgBaZKWfUSo2ZcoRa\nM+UIZsq2eZgp2+dRe6beeustrFixAsnJycjKykJ0dDSmTJlicldaS8iUM+rUEvIEOFarFStW4MSJ\nE/jnP/8JT0/Pe+6v1kzZW6eWkClH6jRr1izs378fFy9exM2bN/Hdd9/hueeeQ0lJCR5++GGcPHlS\ntn9LyBPgnFqpMVNlZWUAgFWrVsHNzQ2ZmZmorKzEyZMnMWbMGBw8eBCTJ0+WHaPWTDlSq5aQKZdw\n0TIpqufomsDV1dXGBfmXLFkiiouLRVVVlTh+/LgYO3askCRJvPTSS7JjunfvLjQajSgsLDQ7p2G+\n3Nxch6+nKTmzVpbU1NSIoUOHCkmSRHJysiOX0eQcrdPdu3fFuHHjhCRJIiQkRMybN08sXLhQ9O7d\nWwQGBorw8HAhSZI4cuSI8Ri1ZsqRWlmihEwJIURycrKQJEn07t1bXL161aZj5s6da3ENRCGEePXV\nV4UkSeKNN94wjik9U0I4r1aWtORMNWTLZ1itmWrI0Z93QqgrU7bMw0zd/zxqy1RZWZnYtm2biIyM\nFG5ubuKjjz6SbVd6ppxVJ0uUkichHKtVbm6ucHd3F0uXLpWNW1trWo2ZcqROliglU4312TP4wx/+\nICRJEgkJCbJxpedJCOfVypKWnKl58+YJSZKEt7e3+Omnn2Tbbt68KUJDQ4UkSSInJ8c4rtZMOVIr\nS5SSKVfhndwKs3nzZuTk5GDixIlYvXo1IiIi4OXlhaioKGzbtg2dOnXCW2+9heLiYuMxht+GGe4Y\nbMgw7u/v75RrcBZHamWJm5sb5syZAwA4dOhQU5+6U2k0GuzcuRNvvPEGgoODsWnTJnz00UeIjIxE\nTk4OtFotJElCu3btjMeoNVOO1MoSJWRq7dq1WLRoEfr06YP09HSb//c0/GbennwoPVPOrJUlLTlT\njlBrphqTmjJlyzzM1P3Po6ZMAUBwcDAmTJiAvXv3wt3dHUuWLJFtV3KmnFknS5SQJ8CxWtXU1GDG\njBmIjIzEa6+9ZnYfIYTJmNoy5WidLFFCppri7wfz588HYHrNSs4T4NxaWdKSM2XYLyoqCmFhYbJt\n3t7eGDt2LIC6pToA9WbKkVpZooRMuRKb3Apz7NgxAEBcXJzJNm9vbwwePBi1tbU4ceKEcbzhOsr1\n1dTUoLi4GB4eHmYfpqdkjtTKGsPSJjdu3Gi8k2wm3N3d8dJLL+HkyZOoqqrClStXsHXrVoSFhSE/\nPx9BQUEIDw837q/WTAH218qa5pypNWvWYOHChejXrx/S09Ntatwb9OzZE4D5fABAfn4+APlabErO\nlLNrZU1LzZQj1JqpxqaGTNk6DzPVOPOoIVMNhYWFoVevXvj1119x8eJF47hSM+XsOlnTnPMEOF6r\nyspK5Ofn4/Tp0/Dy8oJGozG+VqxYAQCYO3cuNBoNFi9ebDxObZlytE7WNOdMNdVnz9I1KzVPgPNr\n1djHOEtj/HeMpUavYbyqqso4ptZMOVIra5pzplyNTW6FadWqFYC6NX0aunTpkmw/AIiPjwcAfPXV\nVyb7Hzx4EFVVVYiJiWn2T6+1lyO1siY3NxcAmuUP3KaSkpKC6upqPP3007JxtWbKGku1sqa5ZurN\nN9/Eiy++iKioKKSnp5t9AIY1hl8spaWlmdw9c/36dWRlZcHX11f2FGmlZsoVtbKmpWbKEWrNVGNr\n6ZmyZx61Z6qx5mnpmbLk559/hiRJ0Gq1xjElZsoVdbKmueYJuL9aeXl54dlnn8WcOXNMXlFRUQCA\n2NhYzJkzBzExMcbj1JYpR+tkTXPNVFN+9ixdsxLzBLimVo19jDPcb53i4+MhSRJOnz5t9l9MnDp1\nCoD+YZ31jwHUlylHamVNc81Us+CCJVJI3HuNzOrqapGXl2eyVtHu3buN6wFfuHBBtu2LL74QkiQJ\nHx8fceXKFeP4tWvXRHBwsPD09BTHjh0zjldVVYlhw4YJSZLEJ5980ohX17icWatvvvlG1NbWmrzH\n119/LTw9PYVGo7FpnSRXcLROQghRUVFhMvbtt9+KoKAg0bZtW/HLL7/Itqk1U0LYXyulZWrFihVC\nkiQxePDge64xZq1OhnXv33nnHdn44sWLhSRJ4vnnn5eNKzFTrqqVWjNVny3rTKs5U/XZUiu1Zsqe\neYRQd6bsrZUaM3XmzBlRXl5usv/du3eNz1cYO3asbJvSMuWqOiktT0I0zc9zA2trTasxU5ZYq5PS\nMtUYdcrLyxOVlZUm+xcXF4tu3boJSZLE66+/LtumtDwJ4bpaqTFTQggxfvx4IUmS+Nvf/iYb37Nn\nj5AkSQQGBopr164Zx9WaKSHsr5XSMtVcSELYsUgV3ZfU1FSkpqYCAEpLS7F371506dIFDz74IAD9\nenSrVq0CAJSUlKBLly4IDw83WTN64sSJSE1NhZ+fHxISEtC+fXvk5eVh165dkCQJa9aswYIFC2TH\nbN++HU888QS8vLwwZcoUBAQEYMeOHThz5gwmT56MTz75xAkVsJ2rajVq1CgUFBQgJiYGnTp1AgCc\nPHkS6enpkCQJf/7zn/Hqq686owQ2aaw6RUdHw8fHB3369IGfnx+coJ4zAAAMCElEQVTy8vKwe/du\n+Pr6YufOnYiNjTV5b7Vmyt5aKSlTGzduxKxZs+Dm5oYFCxagdevWJvvodDo888wzAKzXqaioCDEx\nMSgrK8P48ePRs2dPHD58GAcOHEBkZCSys7MREBAgO0ZJmXJlrdSaKXs+wwZqzZS9tVJjpuydx0CN\nmXKkVmrM1Jo1a/DKK68gNjYWERERaNu2LS5evIiMjAwUFxcjPDwc6enpiIiIkM2tlEy5sk5KyhPQ\nuD/PzUlKSsKKFSuwbt06zJ4922S72jJlibU6KSlTjVWnpKQkvPXWWxg5ciTCwsLg5+eHwsJC7N69\nG7dv38ajjz6Kbdu2wd3dXTa3UvIEuLZWaswUAFy4cAExMTE4d+4c4uPjMWDAABQXFyM1NRVubm5I\nSUlBQkKC7Bg1Zgqwv1ZKylSz4uouu5okJSUJSZKERqORvSRJEpIkCZ1OZ9y3uLjYZMzg7t274r33\n3hMxMTGidevWwt3dXbRv3148/vjjIi0tzeL7Z2VliUceeUQEBAQIb29v8cADD4g1a9aY/e2Qq7mq\nVuvXrxePPfaYiIiIEFqtVnh6eorw8HAxZcoUkZmZ2aTX7IjGqtOqVavEwIEDhb+/v/D09BRdu3YV\nL7zwgskd8A2pMVP21kpJmapfI0NdGr7i4uKM+1urkxBCnDt3TsyaNUt06NBBtGrVSkRERIjFixeb\nvYPLQCmZcmWt1Jopez7D9akxU/bWSo2Zsnee+tSWKUdqpcZMnTp1SrzwwgtiwIABIigoSLi7u4uA\ngAAxbNgwsXLlSnH9+nWL56CETLmyTkrKkxCN/3cEc/NrNBqzdygbqClT1ua3VCclZaqx6pSRkSGe\nfvpp0bNnT+Hv7y88PDxEu3btxJgxY8RHH31k9RyUkCchXFsrNWbK4NKlS2LBggUiPDxctGrVSgQH\nB4uJEyeKo0ePWjwHtWXKwJ5aKSlTzQnv5CYiIiIiIiIiIiIixeKDJ4mIiIiIiIiIiIhIsdjkJiIi\nIiIiIiIiIiLFYpObiIiIiIiIiIiIiBSLTW4iIiIiIiIiIiIiUiw2uYmIiIiIiIiIiIhIsdjkJiIi\nIiIiIiIiIiLFYpObiIiIiIiIiIiIiBSLTW4iIiIiIiIiIiIiUiw2uYmIiIiIiIiIiIhIsdjkJiIi\nIiIiIiIiIiLFYpObiIiIiIiIiIiIiBSLTW4iIiIiIiIiIiIiUiw2uYmIiIiIiIiIiIhIsdjkJiIi\nIiIiIiIiIiLFYpObiIiIiMj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width=732 height=391/>",
       "metadata": {},
       "output_type": "display_data",
       "text": "<matplotlib.figure.Figure at 0x105c09410>"
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}