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@dckc
Last active August 29, 2015 14:04
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BS vs RK
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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*This is a digital restoration of a hardcopy of [BS vs RK][1], a Mathematica notebook of mine, dated 8/7/89 2:26 PM. -- [Dan Connolly][dckc]*\n",
"\n",
"[1]: https://drive.google.com/file/d/0BxOfJ7sv3AmqVWhCT2Q3ek5LbEU/edit?usp=sharing\n",
"[dckc]: http://www.madmode.com/"
]
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Performance Analysis"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Data..."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"m = 60\n",
"index = pd.Series(range(1, 11), range(1, 11))\n",
"rktimes = pd.Series([103.13, 109.59, 188.52, 236.11, 348.45, 545.95,\n",
" 13 * m + 15.53, 20 * m + 14.12, 31 * m + 51.09, 49 * m + 57.72],\n",
" index=index)\n",
"\n",
"bstimes = pd.Series([70, 81.51, 137.07, 134.87, 193.85, 198.69, 218.22, 298.71, 298.42, 297.06],\n",
" index=index)\n",
"\n",
"tolerance = pd.Series(index, index)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Table of tolerance versus time"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"both_times = pd.DataFrame(dict(RKQC=rktimes, BSSTEP=bstimes))\n",
"both_times"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>BSSTEP</th>\n",
" <th>RKQC</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> 70.00</td>\n",
" <td> 103.13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td> 81.51</td>\n",
" <td> 109.59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> 137.07</td>\n",
" <td> 188.52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> 134.87</td>\n",
" <td> 236.11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> 193.85</td>\n",
" <td> 348.45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> 198.69</td>\n",
" <td> 545.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> 218.22</td>\n",
" <td> 795.53</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> 298.71</td>\n",
" <td> 1214.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> 298.42</td>\n",
" <td> 1911.09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td> 297.06</td>\n",
" <td> 2997.72</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
" BSSTEP RKQC\n",
"1 70.00 103.13\n",
"2 81.51 109.59\n",
"3 137.07 188.52\n",
"4 134.87 236.11\n",
"5 193.85 348.45\n",
"6 198.69 545.95\n",
"7 218.22 795.53\n",
"8 298.71 1214.12\n",
"9 298.42 1911.09\n",
"10 297.06 2997.72"
]
}
],
"prompt_number": 3
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Graphs of tolerance versus time"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def bench_plot(s, title):\n",
" fig = s.plot()\n",
" fig.set_title(title)\n",
" fig.set_xlabel(\"log(1/eps)\")\n",
" fig.set_ylabel(\"time(sec)\")\n",
" return fig"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"bp = bench_plot(bstimes, \"Bulirsch-Stoer Times\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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ODwPLgXmhddsCU4D3gMlA+9Bjg4BFwEKgd8zZMqahY4yZrjOE+Tju6WMm8DOX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\n",
"text": [
"<matplotlib.figure.Figure at 0x7fd1e777bc50>"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rp = bench_plot(rktimes, \"Runge-Kutta Times\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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x889w9dUwZ45NCdKkSdiJxA+NaYhI1q1cad1Ra9fClCkqGPlMRSNArvZfuphL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\niIiIiIiIiIiIiIiIiIiIiIhkxv8D1CJ22k77fnkAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7fd1e7659090>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"_ = bench_plot(both_times, \"Both Times\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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2Pz5yEai8TVJd6JhLMlkjmaxzJ1f8mXjGrh3L4vsWU7ZUWS0y6crKuaeaoFoU\nNVCXeL0JdVr0dy08d4iLZfPyWf9f5i2vGFx3bwkhxDX5K+MvHl72MO92f5fm1ZrbHUd7VvqxfgNe\nBT5BXUvDD9iFXCNcCFEMvLLmFfYn7ee7wd95/GgpX+CNa4RXAKKc5h1HTwkhhE9bc2AN/971b2Kf\nji2RBaMwrHxP4zTQyGn+ftSRVKIAuvZf6phLMlkjmawrKNfpP08z/IfhLBy4kKoVqmqRyRdYaWk8\nC3yKOsT2GHAQ9f0MIYTwSYZh8Pjyxxnacih3NrjT7jg+xZ32WCCqZZLipSzXSsY0hBCWzIiewfzY\n+USOiKRMQBm749jKG9fTqAw8ivoinqNlYqCu4KcTKRpCiALtOrWLOxbewYbHN9C4SmO749jOGycs\nXAnUQ323Ygvq8NeYwoQraXTtv9Qxl2SyRjJZ5yrXpfRLDFk2hA96fGBLwdD1tXKHlTGNssBL3g4i\nhBDe9vqvr9O0alOGtx5udxSfZaVJ8grqNB4/An85LU/ySqLCk+4pIcRVrdi7gtErRxP7VCyVy1e2\nO442vPE9jTRgIvAW6tTooMY0GrgbTggh7HAi9QRP/PgEX9//tRSMa2RlTONloCFqXKO+eZOCYYGu\n/Zc65pJM1kgm6xy5sowswr8PZ0SbEXSt11WLTL7MStHYB1zydhAhhPCGj6I+IjktmXG3j7M7SrFg\npR/re9R5piLIGdOQQ26FENqLPRFLzy96EvVEFA0qSweJK94Y0/ieK69fIe/OQgitXUy/yJBlQ5jS\na4oUDA+y0j21wMVtoZfyFCu69l/qmEsyWSOZrHto0kO0DW3L0JuG2h0lm66vlTvya2ksBR4g58p6\nzgzUdTWEEEI738d/z+bEzcSPiZez13pYfq9mKOpstktQ19NwXncC8KAXcxWGjGkIIfjlwC88/O3D\n/PDQD3Sq08nuONrz5JiG4/TnNwCH8zzWzL1YQgjhfTOiZ/CP3/7BsgeXScHwkvzGNEahuqaamPeO\n2yHUeahEAXTtv9Qxl2SyRjK5lpGVwTMrnmHG5hlEjoika72uWuTKS8dM7sqvpbEY+Bl4H3idnOZL\nCnDWy7mEEMKS5LRkHlj6AAF+AWwcsZFK5SrZHalYK04jRDKmIUQJsz9pP30X96V3o95M6jWJUv5W\nvkUgnHnj1OhCCKGdiIMRdJnXhTG3jmFa72lSMIqIFA0v0rX/UsdckskayaTMiZnDQ8seYvF9i3mq\n3VMu15FvFW2nAAAWx0lEQVTXyjukNAshfEZmViavrHmFFftWsH74ernyng1kTEMI4RMu/HWBIcuG\n8FfGXyx9YKmc4txDZExDCFHsHDx3kE6fdaLudXX5+ZGfpWDYSIqGF+naf6ljLslkTUnM9PuR3+k0\nrxNPt3uamffMpHRAaS1yFYaOmdwlYxpCCG0tjF3Iq7+8yhf3fsFdje6yO47A+2Ma84B7gFNAS3NZ\nCPA16kqAh1DnsEo2HxsLPA5koq7XscZcfjPq7LrlgJXACy72JWMaQhQTWUYWb659k6VxS/lxyI80\nr9bc7kjFlm5jGvOB3nmWvQH8AjQG1przAM2BweZ9b2AmOT/ILGAE6jxYN7jYphCimEi9nMqgrwex\nMWEjUU9EScHQjLeLxnrgXJ5l/cm5HsdCYKA5PQD4CkhHtUD2Ax1QZ9sNAqLN9T53eo7WdO2/1DGX\nZLKmuGc6cv4IXeZ1oUr5Kvwy7BeqVqiqRS5P0TGTu+wYCK8OnDSnT5rzADWBBKf1EoBaLpYnmsuF\nEMXIpoRN3Dr3VobdNIy5/edSJqCM3ZGEC3YPhBt48NKx4eHhhIWFARAcHEzr1q3p1q0bkFPhi3re\nwa79u5rv1q2bVnkc1q1bp00enX9/us174u/pb/P+xvTo6Xz50pf0bdxXfn9enF+3bh0LFiwAyH6/\ndEdRfLkvDPiRnIHweKAbcALV9RQBNCVnbON9834VMA51LY8Icq7hMQS4HXg6z35kIFwIH5NlZDF+\n3Xi+2PEFyx9aTsvqLQt+kvAo3QbCXVkOPGZOPwZ877T8IaAMUB814B2NKi4XUOMbfsAwp+doLe+n\nHV3omEsyWVOcMl1Mv8jgbwaz9uBaop6I8njBKE6vlU68XTS+AiJRF3I6CgxHtSR6AnuB7uS0LOJQ\nl5aNQ13HYzQ5XVejgbnAPtQA+Sov5xZCeFHihUS6zu9KuVLlWPvoWq4PvN7uSMIiOfeUEKJIxRyL\nYeDXAxndbjRvdHnD0T0ibOLJa4QLIYRHfRP3DaNWjGJ239kMajbI7jiiEOTcU16ka/+ljrkkkzW+\nmskwDN797V1eWv0Sa4auKZKC4auvle6kpSGE8KpL6ZcYsXwEB84dIOqJKEKDQu2OJK5BcepMlDEN\nITRzIvUEA/89kLDgMOYPmE/50uXtjiTy8IVDboUQJcD2E9vpMLcDdze6m6/u+0oKRjEhRcOLdO2/\n1DGXZLLGVzL9EP8DPb7owYQeExjXbZwtR0j5ymvla2RMQwjhMYZhMGHDBKZHT2flwyu5pdYtdkcS\nHiZjGkIIj/gr4y+e+ukpdpzcwfIhy6l9XW27IwkLZExDCFHkTv95mh5f9CDlcgrrh6+XglGMSdHw\nIl37L3XMJZms0THT/O/m02FuB7rW7crSB5YSWCbQ7kiAnq+VjpncJWMaQohCOZ92ng+jPmTy6snM\nGD2DoTcNtTuSKAIypiGEcEtyWjIfRX3E9Ojp3HPDPbx121vcUOUGu2OJQpJzTwkhvCI5LZkPN33I\n9Ojp9G3cl8jHI6VYlEAypuFFuvZf6phLMlljR6bktGTeWfcOjT5qxKHzh9j0xCYWDFyQXTB0fJ1A\nz1w6ZnKXtDSEEC4lpyUzbdM0Po7+mH5N+rHpiU00CmlkdyxhMxnTEELk4lws+jfpz1u3vUXDkIZ2\nxxJeImMaQohCOXfpHNM2TWPG5hn0b9KfqCeipFiIK8iYhhfp2n+pYy7JZI03Mp27dI5xEeO4YfoN\nJFxIIOqJKOYNmGe5YOj4OoGeuXTM5C5paQhRQp27dI6pm6Yyc/NMBjQZQPTIaBpUbmB3LKE5GdMQ\nooRxLhYDmw7kzdvelGJRgsmYhhDCpaRLSUzbNC27WEjLQhSGjGl4ka79lzrmkkzWFCZT0qUk3v7P\n2zSe3pjjKcfZPHIzc/vP9VjB0PF1Aj1z6ZjJXdLSEKKYSrqUxNSNU5m5ZSaDmg5i88jN1K9c3+5Y\nwsfJmIYQxUzSpSSmbJzCrC2zGNR0EG/e9qYUC3FVMqYhRAl19uJZpm6ayqwts7iv2X3EPBlDWHCY\n3bFEMSNjGl6ka/+ljrkkkzWuMp29eJa31r5F448bc+rPU8Q8GcOn/T4tsoKh4+sEeubSMZO7pKUh\nhI86e/EsUzZO4ZOYT7i/2f3SshBFQsY0hPAxZy6eYcrGKcyOmc39ze5n7G1jpViIQvOla4QfAnYA\n24Boc1kI8AuwF1gDBDutPxbYB8QDvYospRCaOJ5ynDfXvkmTj5uQdCmJrU9uZXa/2VIwRJGys2gY\nQDegDdDeXPYGqmg0Btaa8wDNgcHmfW9gJj4wHqNr/6WOuSSTa5lZmazct5J7v76X5jObszt6N1uf\n3MonfT+hXnA9u+MBerxOruiYS8dM7rJ7TCNvk6g/cLs5vRBYhyocA4CvgHRUC2U/qtBsKoqQQhS1\nI+ePMG/bPOZtm0eNijUY2XYknw/8nJiNMdoUC1Ey2Tmm8T/gPJAJzAbmAOeAyubjfkCSOT8dVSC+\nNB+bC/wMLHPanoxpCJ+WnpnOT3t/Ys7WOUQlRjGkxRBGth1Jqxqt7I4mijFf+p5GZ+A4UA3VJRWf\n53HDvF2NVAhRLBxIOsBn2z5jfux8GoU0YmTbkXzz4DdUKF3B7mhCXMHOonHcvD8NfIfqbjoJ1ABO\nAKHAKXOdRKCO03Nrm8tyCQ8PJywsDIDg4GBat25Nt27dgJy+xKKcj42N5cUXX7Rt/1ebd+5X1SEP\nwLRp02z/feWd9+bvb83aNfx+5HciAyLZfnI73YxuvNf4PcIHhuf7fMcyHV6fvFl0yeOY1/H/z7HM\n7t/XggULALLfL31BBSDInA4ENqCOiJoAvG4ufwN435xuDsQCZYD6wAGubE4ZuomIiLA7gks65iop\nmfac3mO8tOolo9qEakb3hd2Nr3Z+ZaSlp9ma6VrpmMkw9MylYybc7LWxa0yjPqp1Aaq18yXwHuqQ\n2yVAXdSA94NAsrnem8DjQAbwArA6zzbNn18IvVxKv8Q3cd/w6dZP2Xd2H8NbD2dE2xE0CmlkdzQh\n3B7TkC/3CeElO07uYE7MHBbvWkz7Wu0Z2XYk/Rr3o3RAabujCZHNl77cV+w592PqRMdcxSVT6uVU\n5m6dS4e5Hbhn8T2ElA9h65Nb+fmRnxnUbNA1F4zi8joVBR1z6ZjJXXZ/T0MIn2cYBjHHY5gTM4cl\ncUvoWq8rf+/6d3o36k2Af4Dd8YTwKOmeEqKQzqed58udXzJn6xyS05J5os0TDG8znJpBNe2OJoRl\nMqYhhBcZhkHk0UjmbJ3D9/Hf06thL0a2HcmdDe7E3096e4XvkTENjejaf6ljLt0znb14lqkbp9Ji\nVgseX/44La5vwd7n9rLkgSX0bNizyAqG7q+TTnTMpWMmd8mYhhBXYRgGEQcjmLN1Div3raRv477M\n7DOTrvW6Oj6dCVHiFKe/fOmeEtfszMUzbDy6kcijkXyz5xvKlSrHyLYjGXrTUELKh9gdTwiPkzEN\nISzKzMok7nQckUcjiUyIZOPRjZz88yQdanWgY+2O3H3D3XSo1UFaFaJYkzENjejaf6ljrqLIlJyW\nzOr9qxkXMY5eX/QiZEII9y25j40JG+lUuxPLHlxG0mtJrBm2hnfueIe0/WnaFYyS+rsrDB1z6ZjJ\nXTKmIYolwzD44+wf2V1NGxM2cij5EO1qtqNTnU481/45vqz9JdUCq9kdVQifotfHqGsj3VMlWOrl\nVKITo1WRSIhkU8ImgsoE0alOJzrW7kinOp24qfpNcgqPYsYwIDER4uJgz56c27598Ndfrp+TX+Px\nao95ermf35XT+T3mzfW2bZMxDVHMGYbBweSDqgVhFom9Z/fSqnqr7CLRsU5H+ZJdMZKRAQcPqoLg\nXCDi4yEwEJo1y31r0gTKl79yO/m9RVztMU8vN4wrp/N7zNvrtWsnRUMb69atyz6fvU50zJVfpkvp\nl4g5HpNdIDYe3Yi/nz+d6nTKLhJtQ9tStlTZIstkl+KeKS0N9u7N3WqIi4P9+6FGDVUQmjfPXSAq\nV3a9reL+WnmKL125TwiXEi4k5GpF7Dq1i+bVmtOxdkcG3ziYaXdNo26lutoNUgvrLlzIXRgct6NH\noUGDnIIwYAC88YZqOVSQCxlqoTj912nX0hBXZxgGqZdTOZ56nOMpx9l2YhsbE9SgdVpGWvY4RKc6\nnWhXs51c+tQHGQacPp27xeCYPncOmja9slupUSMoLcNORUq+pyFslZGVwak/T3Ei9QQnUk9wPOV4\nznRq7mmA0Iqh1KhYgxbXt8juamoU0khaET4kK0u1EPJ2Ke3ZowqHoyA4dyvVrQv+csC/FqRoaETH\n/ktwP5dhGKRcTrFUCJIuJVGlfBVqVKxBaJAqCI7CkHc6qGxQ9j50fK2KS6asLLh0CS5e9M4tKWkd\nVat2y9VicBSI66/P/2glbyouvz9vkzENYZlzqyC/QnAi9QSgWgV5C8ENVW7IVQiqBVajlL9v/1kZ\nBiQnw/HjcOAAZGaqN14d7vftg59/du9N/a+/1JFEFSq4dwsOtrZebCzcfbfdvzVRVKSlUYwZhsHp\ni6eJPxPPntN7iD8Tzx9n/yAxJZETqSeyWwUFtQhCg0KpWKai3T+Ox6Snw7FjcPgwHDly5e3wYfXp\nOCQEAgJUN4ou96VLu//mX66cfZ/2hf6ke6oEyszK5FDyIfac2ZNTIM7GE38mHsMwaFatGU2rNKVZ\ntWY0qdKE2tfVJjQolKoVqvp8q8CV5GTXhcAxffKkOnyzbt0rb/XqqftKlez+KYQoGlI0NOLp/suL\n6Rf548wfqjCYBSL+TDz7k/ZzfeD1NK3alGZVm6n7auq+WoVqVwwq69ivajVTRoZqJeQtBM63rKyc\nN3/nQuC41axp7QgdX36dipKOmUDPXDpmkjENH+eqSyn+rJo++edJGoU0yi4M9za9l6ZVm9K4SmMC\nywTaHd0jzp+/epfRkSNw4oQaXHUuBC1aQJ8+OQWiUiXpjhHCW4rTv5Z2LY38uNOl5GhBhAWHEeAf\nkO92MzLUAG5CgjoMMu/9sWNqHefz0/j7557Pe8vv8Wt5ruNxgDNnVFHIyLiyZeA8X6uWHMcvhCdJ\n95RmPNWlBDkFwVEEXBWGU6egWjWoUwdq11Y3x3SdOjldM87nnjEM1aWTd5nVx6/luY7Hq1ZVRaFy\nZWklCFGUSnTR+HTLp2QamWRmZZKRleFyOtMw511M51o3v8csbj9pTxIpNVNydSk57vN2KeUtCK4K\nQ0EFoXZtCA0t+JO4jv2qkskayWSdjrl0zFSixzSiEqMI8AuglH8pAvwDXE6X8i9F2YCyV10nwN+c\nL+S08/ZiN8XyQJ8HMLICcgrCHvjPUfg8T2G4WkG49Vb3CoIQQnhTsWppOHdPZWXB5ctXv6Wn5/94\nYdd1Xv/PP9UYgqdaCEII4WklunuqUiUj+407KwvKlLnyVrq06+XeWK9cOTVwKwVBCKErd4uGL+kN\nxAP7gNddPG6cO2cYf/5pGOnphpGVZdguIiLC7ggu6ZhLMlkjmazTMZeOmQC3jiDylfNMBgAfowpH\nc2AI0CzvSo5z5ZQqpccROLGxsXZHcEnHXJLJGslknY65dMzkLl8pGu2B/cAhIB34NzDAzkBWJCcn\n2x3BJR1zSSZrJJN1OubSMZO7fKVo1AKOOs0nmMuEEEIUIV8pGvp9a8+CQ4cO2R3BJR1zSSZrJJN1\nOubSMZO7NOj5t+RWYDxqTANgLJAFfOC0zn6gYdHGEkIIn3cAaGR3CE8rhfrBwoAyQCwuBsKFEEII\nh7uBP1AtirE2ZxFCCCGEEEIUd/OAk8BOu4M4qQNEALuBXcDz9sYBoBwQheraiwPeszdOLgHANuBH\nu4M4OQTsQOWKtjdKtmDgG2AP6nd4q71xaIJ6fRy38+jxtz4W9b+3E1gMlLU3DgAvoPLsMqft4ur9\nMgT4BdgLrEH9nRVrtwFt0Kto1ABam9MVUd1qOozBVDDvSwGbgC42ZnH2EvAlsNzuIE4Oov6ZdLIQ\neNycLgXodFFaf+A46gOTncKA/5FTKL4GHrMtjdIC9f5UDvUB6RfsO2jH1fvlBOA1c/p14P38NuAr\nh9zmZz1wzu4QeZxAfaIHSEV9MqxpX5xsF837Mqg/3iQbszjUBvoAc9HvaD6d8lRC/cPPM+czUJ/s\nddEDdbDK0YJW9LILqC8AV0AV1gpAoq2JoCmqlZ8GZAL/BQbZlMXV+2V/1AcSzPuB+W2gOBQN3YWh\nKnuUzTlA/b5jUc3TCFQXh92mAq+iDqHWiQH8CmwBRtqcBaA+cBqYD2wF5pDTctTBQ6iuILslAZOB\nI8AxIBn1e7TTLlTBD0H9zu5BfVjSRXXUewLmfXUbsxSZMPTqnnKoiHrTybdy26ASqnuqm805+gIz\nzOlu6DWmEWreV0MV2ttszALQDvUJ+hZzfhrwf/bFyaUMqqBVszsIqtsnDqiCaml8BzxiayLlcdR7\nwX+BmagPS3YJI/f7Zd6WR749ENLS8J7SwDJgEfC9zVnyOg+sQL0R2akTqml8EPgK6A58bmuiHMfN\n+9OoN572NmYBdeqcBGCzOf8N0Na+OLncDcSgXiu7tQMigbOoLrxvUX9ndpuHynY7qvXzh71xcjmJ\nGocF9WHpVH4rS9HwDj/gM9Qnnmk2Z3GoSs5REeWBnqgjXuz0JmrgtD6qe+M/wKO2JlIqAEHmdCDQ\nC/tbsidQ4wWNzfkeqCOEdDAEVfR1EI86qqw86v+wB3p0w15v3tcF7kWPrjyH5eQcLPAY+n3I9biv\nUH2Xf6H+qYbbGwdQRyVlobo1HIcj9s73Gd7XEtUXHos6lPRVe+Nc4Xb0OXqqPup1ikX1R+vyZdJW\nqJbGdtQnaB2OngoEzpBTZHXwGjmH3C5Etfrt9hsqUyxwh405HO+Xl8l5vwxBjfuUmENuhRBCCCGE\nEEIIIYQQQgghhBBCCCGEEEIIIYQQQnhV6jU+/2uggTn9T9R5j1JcrBcKrL7GfTl7Hhjmwe0JIYSw\nwNUbvFWNgJ+c5tujTsngapvDgTHXsK+8gtDnWh+ihJDTiAiRww+YiPom8Q7gQXO5P+okc3tQ35hd\nAdxnPvYQub/JHo065YcrdwE/m9OvmutuB8aby8JQp8FYhDr1xVLU6TBAXeNgt7n+RHNZCuocSze6\n8TMKIYS4Ro5WwX2oouCHOlfQYVSr4X5UoQB12ugkcq6H8DOuTxyYt6XhuDohqHNZzTan/VFn970N\nVTSygI7mY58BL6NO8xDvtC3n04e8A4zK/8cTwnOkpSFEji6oE8kZqDN9/hd1KvLOwBJzHce1SBzq\nkXNG3Px0QJ2OHlTR6IUqIjGoy6Y2Mh87Cmw0pxeZmc6jLuDzGepkd46LaYE6j1CYhf0L4RGl7A4g\nhEYMrn61Pr+rTLuad+VuYJXT/HvAp3nWCTMzOG/XQF3trT1wJ6rV86w57byOEEVCWhpC5FgPDEb9\nX1QDuqKuuLgB1XXlh+qeut3pOYfJuWBTfrqTcwW51aiL8gSa87XIuYBRXdSpvQEeNjMFos48+jPq\neuqtnLYbChyysH8hPEKKhhA5n9S/Qw2AbwfWogarT6EuppWAGpz+AnWKecf1uX8n98WsJqC6mMqb\n939HFYQ04E9znV9Q3WAbzf0tQV3lEdTFeZ4x91UJmAVchxr32I4qIs5HYLU3lwkhhNCIo1VQBdhP\nzkV1GpAzSH41j6Cu8VCQMNy72NN15FzJTwghhEYiUAPXu7ny6oL/Rl2b+lqFoVoeVj0PDPXAfoUQ\nQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEJ4xv8DxqXZZkqPtq0AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7fd1e7572b10>"
]
}
],
"prompt_number": 8
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Graph of tolerance versus relative speed"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.title(\"Relative Speed: BS:RK\")\n",
"plt.xlabel(\"log(1/eps)\")\n",
"plt.ylabel(\"Percent\")\n",
"_ = (rktimes / bstimes * 100).plot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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zcsXHTKnQmIZIltuwAZ58Em65xabPvvmmps9KdDSmIZKlkqfP7rCDHVBQ02el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\nREREREREREREREREREREREREJD3+P1L3uBXVVZxhAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7fd1e74aae90>"
]
}
],
"prompt_number": 9
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Fit of time data to curves"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from scipy.optimize import curve_fit\n",
"\n",
"import sympy\n",
"from sympy.abc import x\n",
"from sympy.plotting import plot as symplot\n",
"\n",
"sympy.init_printing()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def fit(data, f):\n",
" popt, pcov = curve_fit(f, index, data)\n",
" poly = sum(c * x ** i for (i, c) in enumerate(popt))\n",
" return poly\n",
"\n",
"at = lambda f, s: sympy.lambdify(s, f)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"bfit = fit(bstimes, lambda x, c0, c1: c0 + c1 * x)\n",
"rfit = fit(rktimes, lambda x, c0, c1, c2, c3: c0 + c1 * x + c2 * x**2 + c3 * x**3)\n",
"_ = at(bfit, x)(index).plot()\n",
"_ = at(rfit, x)(index).plot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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AeBF7Oucc4ALOns75DPZ0ztPAk8CyfI/p/BtESqeTp08StyuOeZvnsXDbQlrU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\nAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7fd1e0814250>"
]
}
],
"prompt_number": 12
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Bulirsh-Stoer performance fits..."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"bfit"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$$28.030303030362 x + 38.6733333334155$$"
],
"metadata": {},
"output_type": "pyout",
"png": "iVBORw0KGgoAAAANSUhEUgAAAU8AAAAUCAYAAAAN879yAAAABHNCSVQICAgIfAhkiAAAB39JREFU\neJzt3GusXUUVwPHfLa2FvtBaoUWMpVDwiZSHaKtVkcQQ+aD4QJOKGBOgGh8INUgsKQgIarCoWKPE\nHKyPRgQrQiKa8NJoUREVtIgPLNT4akCLj1Zq8cOa7Z277z7n7nP2bWhy9j85uT1r1qy9ZvY6s2fW\nzC4tLS0tLX0zUvr+QpyH/XAwfogL8IeS3sH4AB7HdMzAZbinxjWPwIexJdWfh1X4c0nvRTgT/07+\nzMCl+Flrb1LtweFYk3R3pr9r8GimUzc2nmjqtrtJDBdM1G8n4TPp2oXO7qz+j3BVn34Pm16ZczFN\n/AaqmIFNOLJL+a/wIdyKf+JYEdfvwuYB9MDR+DaenL7Pwh34CxZmek/FRizIZIekiy3u4nDB/tiK\nFZnsfNyLJ2WyJbgR+2ayddiOo1p7k2YPnoMHsTR9n48HxCBQUDc2nmjqtrtJDBfU6bdzxODc7XNS\nn34Pm16ZZ4qBbE2X8uPEA+nxHjbK9+A/eGcDPXATDivJlqSKGzLZKjH6ljkfl/dwGi4RP7ipmWwu\nHsPKTPbxdN1TM9nJSfaJ1t6k2ZsqBoz3ZrJn4K94TyarGxt7giONbW8v6ra7SQxTv9/WJfk0TMnk\ny/CpAfweNr0yn006a0ryZ4sY7YhZZ6/Bc0uy83V8JNUdSC+/oS/DLTggk92Nv+HETLYIJ1RcbIcI\nkl68AXdiVyZ7GPelsvy621NZwaz091+tvUmzd5qYcXUy2UN4Gq7MZHVjY0/wPrHErkPddjeJYer3\n23+T/DGjy/VZWI33D+D3sOnlnCJisIrNeDVOF7+FXjyAM/BacQ/GLcH71EPkenaKoMj5k5gqF5wp\nRvYv4ylJNh0/0T3PALNTvasqym7G33s5J0b/XXhBa29S7MF38MsJ6lE/NvYEHc1SA1XtHjSGC+r2\nWxXrcHwNvSq/h1VvFq5O/66aeeZ09J553jaBH/3qgZkib5NzUHLk1kw23Whe4Y8i//ZFo/mbbjwv\n1alK9F5vNHFfxSFiQ+SM1t6k2RsRyfrbsRwXY63IBS4p1a8bG3CMmH1dkfyaKzZmLhdxcmgXH7vR\nMfjgWdVuBo9h+uu3MsvEUnBQv4dV7xKjcdN08PyBmPmvxUdFjB7eQK8rl4mlx9KSfDa+ZTSZeiMO\nnMDW0qR7YUXZ+lRWtnGy2PH6uchH5WmG1l4ze/NSnc04K5O/XOwWP7fiOjlVsbFIzIyL63Rwf9JZ\nJpau50xgt0xH/4Nnr3YXDBLDNOu3u4yfvffr97DpHSUevgVNB8/fiDx0wQoxcJcnB3X1KjkM/xBP\n1jLn4XMiz/A7o0/w5/ewd7zuDd+Qyg7qUneqWCptEsHb2mtu78BUZ4c4KpKzVQwm3egWG58Wg1LB\ntSJHSwTix8ROdz90DD7zrGp3wSAxzOD99kr1l/q9/B4mvSm4xtiTJE0Hz/IAvY/Is5Y3qurqjWO6\nOMd3RUXZ2WJnq2Cm2D3brfcZrUW6N/ybqWx2RVnB8qTztdbepNiblr7fW6G7SeQ4q9IAvWKjPKva\nKpZcTeholvMst5vBY5jB++06fL6ey6j2e9j0VuIVJb2mg2cVW9Knsd6ISKRf1KXsEZF/K3OWcLrb\n1v9MEZxXVpTdkuwWPMv4xP2cZH+3SCC39prZI449fbfC3u1Jd0FJ3is2yhyRbNTdjb8GP634PCxm\nbFVlx5Rs1Gl3kxgu6LffpomZercHSd37NUx68/FJ42kyeN6h+r5tFSuJfvXGcbFIlOaclv4eoPvG\nxIjY8e2VNL9LJF7LPGh042GOSMjvMnZzYabR/NT+rb1JsXeD6iMYm0SQlM9X9oqNMivFLGxGJut3\ns4j6M8+67W4aw/Tfb8vSNc9u4Pew6a0QS/mN2eemVH5f+n6K8XR0HzwfFcekyuwQ53b71RvD21TP\nKoodwhFxPOUlFTqzxcicB+ViY/NCF4nX+fLXQg8VjS1O7+8rzsX9WuzUFhyb9O7MZK29ZvbeLPI4\n+T0aEec3v2osE8XGfuK4SZEzvF7saBdMEa8q9ktHvcGzbrubxjD99Ru8NfmwsqKsrt/DplfFQs1m\nntcan3MvXvZYPYDe/zkB28SRjfyzAV/J9E7FL4wN6Dnpgq/PZMvFFPzmTLZABNhbMtnaZC9PCl8q\n3gDJB4n14olwdGtv0uxNEUcyzs1kbxRvyizMZHVi43UiuN4klmX34HuZjQvwYv3TUT/nWbfdTWKY\n+v1WsEr0zdsb+j1semUWi37slUcvNk9nVJQdJ3KqRdkIvoDvG/vArKWXO/+I0XeXy5SXa8vFmx87\nk6P7iCMqt2U6R4jcwXV4RyY/SjT+fvGknyteaXuodM3T8SoxvZ8vcl+rU72c1l4ze3PF5s9MMSMY\nwQfx20ynTmzME+fhtomYuFDsvu8Q7wXfIJZh/dIRM43f19Q/Xb12N4lh6vVbwWtSO07Ejxv6PWx6\nxIPtG+J+LBD37G5xdnijSMWsx9ONHhXbJjb1rsaXMlsvxbtF6mCOeMhfYnwus65eS8teS8fe9Z+P\ntLSg++HVlpa9he3ap31LS0tLS0tLS0tLS0tLS0tLS33+B5aovEQq+OCJAAAAAElFTkSuQmCC\n",
"prompt_number": 13,
"text": [
"28.030303030362\u22c5x + 38.6733333334155"
]
}
],
"prompt_number": 13
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Runge-Kutta performance fits..."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rfit"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$$7.9144832944984 x^{3} - 75.3946561773232 x^{2} + 271.915709013802 x - 141.952333333649$$"
],
"metadata": {},
"output_type": "pyout",
"png": "iVBORw0KGgoAAAANSUhEUgAAAtEAAAAZCAYAAAD37XL3AAAABHNCSVQICAgIfAhkiAAADihJREFU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\n",
"prompt_number": 14,
"text": [
" 3 2 \n",
"7.9144832944984\u22c5x - 75.3946561773232\u22c5x + 271.915709013802\u22c5x - 141.9523333336\n",
"\n",
" \n",
"49"
]
}
],
"prompt_number": 14
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Raw data and curves for both"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data_and_curves = pd.DataFrame(dict(RKQC=rktimes,\n",
" BSSTEP=bstimes,\n",
" bcurve=at(bfit, x)(index),\n",
" rcurve=at(rfit, x)(index)))\n",
"\n",
"data_and_curves.plot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
"<matplotlib.axes.AxesSubplot at 0x7fd1e0894ad0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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lmJKezoOdO9PPz8SA+Xfx/g3vt+mi3xTS42+AUft5RswlmVwjmVzn7lzvZmdT\naLPxbGwsDy9/mGu6X9Po0ygadV01hoz4hRDnhd2lpfzt4EHWDxjAF7sXk5ydzJYHt+gdSxfS4xdC\ntHuVDgdDt2zh0a5dGeFXwSUfXsIPE39gQPQAvaO5hfT4hRDiNE/v309Pf39+2zGCq+ZdxZOXP9lu\nin5TSI+/AUbt5xkxl2RyjWRynTtyrSwsZPHx48xJSOCltS8R5BPEtMum6ZpJbzLiF0K0WwXV1UxO\nT2dunz6kHd3IB1s+YOuDW/Ewnd9jXunxCyHaJaUUt6em0tXXlxldI+j/z/68M+YdxsaP1Tua20mP\nXwghgPnHjpFeVsYnffow5au7GRs/tl0W/aY4v7/vuMCo/Twj5pJMrpFMrmtqrszycv68fz+fJSay\nZOcCdh3fxd9H/l3XTEbiSuH/GMgFdtZZNhM4AmxzTtfXuW06sBdIB0bVWT7I+Rx7gTebnFgIIc7B\n5nAwKS2Nv3brRkDVMf70059YeOtC/L399Y5mGK70hIYDJcAnwIXOZTOAYuD10+7bF1gIDAG6AD8D\nvQEFJAMPOy+XA28BK057vPT4hRDN8resLNZaLCy7IJHhc69g0kWTeOSSR/SO1aJa4tSLa4HC+l6r\nnmU3A4uAaiAL2AdcAkQDwWhFH7QPkXGuhhRCCFdssFh4Nzub+X368Px/ZxIZGMnDQx/WO5bhNKfH\n/wiwHfgICHUu64zWAqpxBG3kf/rybOdywzNqP8+IuSSTayST6xqTq9hmY2JaGu/Hx7MnZx3zUuYx\n9+a5NaNhXTIZVVP36nkf+Jtz/gVgFnCfOwJNnjyZuLg4AEJDQ+nfvz9JSUnAyRXemtdTUlJ0ff22\ndD0lJcVQeYz6/tUwSh4jX2/M+3fHwoXEA1dd1Jv+C37LY50eI3VTKh2TOro1Xw0918/q1auZN28e\nQG29bAxXPwrjgO842eM/221POZe94rxcgbY94CCwCkh0Lp8AXAX87rTnkh6/EKLR/p2Xx18yM9k2\neDCT/30H3czdmD16tt6xWk1L9PjrE11n/hZO7vHzLXAn4AN0R9uwmwwcA6xo/X4TMAn4uomvLYQQ\ntbIrK/l9RgYLEhNZvH0umQWZvHLtKw0/8DzmSuFfBKwDEoDDwL3Aq8AOtB7/VUDNgS9SgSXOy++B\nqWh79OCc/xBtd859nLlHjyGd/vXOKIyYSzK5RjK5rqFcDqWYnJ7O1C5dCK0+yvSV01l460J8vXx1\ny9QWuNKKdiqBAAAgAElEQVTjn1DPso/Pcf//c06n20L9rSIhhGiSN48codRu509dOjH842G8OOJF\n+kb21TuW4cmxeoQQbdLOkhJGbN/OxoEDeW/ts+wr2MdXd3zl9r142gI5Vo8Qot2rsNu5Ky2Nv/fo\nwb6ctSzetZiU36Wcl0W/KeRYPQ0waj/PiLkkk2skk+vOlmv6gQP0CQhgTLAnU76Zwvxx84kIiNA1\nU1siI34hRJvyY0EBX+blkTJoEJOX3srECydyTY9r9I7Vphjte5H0+IUQZ5VfVUX/zZuZn5hI+t6F\nzE2Zy7r71uHj6aN3NF1Jj18I0S4ppXgwI4M7O3YkqjqbO/87k1/v/fW8L/pNIT3+Bhi1n2fEXJLJ\nNZLJdXVzfXzsGJnl5TwTE82EpRN49dpXie8Qr2umtkpG/EIIw9tbVsZT+/ezun9/nvtlOn0i+jCl\n/xS9Y7VZ0uMXQhhatcPBFdu2cXdUFD3LtjN1+VRSHkohzD9M72iGIT1+IUS78sLBg4R5efGbEE8G\nfn4/n9/2uRT9ZpIefwOM2s8zYi7J5BrJ5Lp3li3jXzk5fJQQz5RvpnDfgPu4MvZKXTMZdV01hhR+\nIYQhWW02Xjp0iA8SEvgi5QOKKoqYcdUMvWO1C9LjF0IYjlKK36an4+/hwdSQckZ+OpKN92+kR1gP\nvaMZkvT4hRBtmkMppmZkkFFWxncXxHPVR0N5fdTrUvTdSFo9DTBqP8+IuSSTayTT2dkcDianp5NW\nVsZPF1/M/bPvZmD0QCZeNFHvaLWMsq6aQ0b8QghDqHI4uCs1lWK7ne8vuogf937HpuxNpE9Ll6Nu\nupnR1qb0+IU4D5Xb7dy2ezfeJhOfX3ABaw6s5K5/38U3d37DsJhhesczvNY6564QQrhFic3G2J07\nMXt58cUFF/Dh5veZ9NUklt6+VIp+C5HC3wCj9vOMmEsyuUYynWSx2bhuxw5i/fyYl9Cbx75/hHc3\nvcu6+9ZxZeyVsq5aiPT4hRC6OFFdzXXbt3NZSAjPd41g7MIxeJo8WX/fekL8QvSO165Jj18I0eqO\nVVYycscOxoSHc3+ogxsX3cjoXqP5x6h/4OUh49HGkh6/EMLQDldUcGVKCrdHRnKd6RDD5w5n2qXT\neGP0G1L0W4kU/gYYtZ9nxFySyTXnc6bM8nKuTEnhoc6d6XTiJyb8+04W3rqQhwY/pGuuxjBipsaS\nj1chRKtILy1l5I4dPBXTlX27Xuc/e//D2ilrdTmZyvlOevxCiBa3vaSE63fs4LmYaL5b+zCVtkq+\n+M0XcnhlN5EevxDCUJKtVkZt387T0Wbe+e5Gupm78f3d30vR15EU/gYYtZ9nxFySyTXnU6Y1RUWM\n3bmTx8PtvPjvkfxu8O9474b38Pb01jVXcxgxU2NJj18I0SJ+LCjg7rQ0pvgcYdayR/j0lk+5rtd1\nescSuNYT+hi4ATgOXOhcFg58DsQCWcDtQJHztunAvYAdeBT40bl8EDAP8AOWA3+s57Wkxy9EO/Bt\nfj7379nDqPK1rE+dw3cTvqNvZF+9Y7VbLdHjnwuMPm3ZU8BPQDyw0nkdoC9wh/NyNPBenTDvA/cB\nvZ3T6c8phGgHPj9+nPv3pJN49BMOH/mejfdvlKJvMK4U/rVA4WnLbgLmO+fnA+Oc8zcDi4BqtG8C\n+4BLgGggGEh23u+TOo8xNKP284yYSzK5pj1nmnv0KI9m7CEs4yV6edv4adJPRARE6J7LnYyYqbGa\nunE3Csh1zuc6rwN0Bo7Uud8RoEs9y7Ody4UQ7cS72dk8lbkHlfIYDyZcy4c3fYiPp4/esUQ93LFx\nVzknt5g8eTJxcXEAhIaG0r9/f5KSkoCTn7Stfb2GXq9f3/WkpCRD5amxevVqw+Qx8vtntOvN/X36\n+6FDvPTVF9j3vs2iR99hbPxYef9a8Prq1auZN28eQG29bAxXNwbEAd9xcuNuOpAEHENr46wC+nCy\n1/+K83IFMAM46LxPonP5BOAq4HenvY5s3BWiDVFKMTPrAO9kpRGQ9izLb53PhVEXNvxA4Vat9Q9c\n3wL3OOfvAb6us/xOwAfojrYRNxntA8KK1u83AZPqPMbQTh91GIURc0km17SXTEoppu3dw5uZW+lx\n6E223LPC7UW/vawro3Gl1bMIbXQeARwGnkMb0S9B20snC213ToBU5/JUwAZM5WQbaCra7pz+aLtz\nrnBDfiGEDhxKMWV3Cl8e2cUNFav55O5v8fPy0zuWcJEcq0cI0Sh2pbhl26/8kLObp8wWZg7/s5wM\nXWeNbfUY7d2Swi+EgVU7HFy18Sc2H9/DvN5duavveL0jCeQgbW5n1H6eEXNJJte01UzldjsXrvmK\nbcdTWTP4ilYp+m11XRmdHKtHCNGg/IoSLlzzNVWVBey5+g66mTvrHUk0g7R6hBDntNeSzaB1P9LB\nVMWOEZMI9gnQO5I4TWNbPTLiF0Kc1ZrsbYxM2cZFgb5suPIePD2kO9weyLvYAKP284yYSzK5pq1k\nmr/7W0Zs387IiCiSr7pLl6LfVtZVWyOFXwhxCqUUf137OvcfLmFK1958N3SM7K7Zzhjt3ZQevxA6\nqrRVctfyJ1jml8Sf4hJ4KV4Ov9AWSI9fCNEkeaV5jP7qQdKi7+XlXhfweGwPvSOJFiKtngYYtZ9n\nxFySyTVGzDT3q7kMWDCejC6/4+2+gwxT9I24royYqbFkxC/EecxSYeHNjW/y2upFeI1/hzl9+nFn\nVFTDDxRtmvT4hTgPFVUU8dbGt3gr+V16X/AYe4IvZ25iX26OaPrZsoR+5JANQoizKqoo4vnVz9Pr\nrV5ssBYTNfzfeEWPZtWAgVL0zyNS+Btg1H6eEXNJJtfokaluwU+1HuPK65azvcMtPN29F2v696dw\n8+ZWz+QKef9ahhR+IdqxoooiZq6eSa+3erHfcpCHbv6JleF30zOoA+lDh3JXVJTso38eMto7Lj1+\nIdygqKKINza8wTvJ73BTwk2MGPAErxwrJdrXl7d69SIxMFDviMKN5Hj8QpzHCssLeWPDG7y76V1u\nSriJey95kndO2FhvtfJ6r16Mj4iQEX47JBt33cyo/Twj5pJMrmmJTIXlhcxYNYPeb/fmiPUIa+7d\nQHz/Gdy8L4/eAQGkDR3KrZGRZy36RlxPYMxcRszUWLIfvxBtWGF5IbM3zOa9Te9xc8LNJD+QTIYj\nhHH79pEQEMDGgQPpFSCHURanMtp3Pmn1COGCugV/XJ9x/HX4XzH5RTNt3z52lZbyZu/e3NChg94x\nRSuRVo8Q7VhBeQHPrXqO3m/3Jqc4h+QHknn7hg/4pMjE4C1bGGo2s2vIECn64pyk8DfAqP08I+aS\nTK5pSqaC8gKe/eVZ4t+O52jxUTY9sIk5N85hhy2Yvps2sbusjG2DB/PX2Fj8PD1bJVNrMGIuI2Zq\nLOnxC2FgBeUFzF4/m/c2v8f4PuPZ9MAmuod1J6OsjDE7d5JVUcGc+HiuDQ/XO6poQ6THL4QBFZQX\n8Pr613l/8/uM7zOevw7/K93DulNis/HSoUPMyclhemwsj3Tpgo+cDvG8J8fjF6INO1F2gtkbZvP+\n5ve5NfFWtjy4hbjQOJRSfH7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"text": [
"<matplotlib.figure.Figure at 0x7fd1e7525d10>"
]
}
],
"prompt_number": 15
}
],
"metadata": {}
}
]
}
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