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@saroele
Created June 17, 2014 14:45
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fmu demo
{
"metadata": {
"name": "",
"signature": "sha256:c98a6236c98fb0cb4bc5c5f13ab9139c829422e57d0e12705ffac553e193945c"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import sys, os\n",
"import configure_jmodelica\n",
"configure_jmodelica.main()\n",
"from pyfmi import load_fmu\n",
"import modelicares as mr\n",
"import matplotlib.pyplot as plt\n",
"% matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Using /home/roel/soft/JModelica_sdk_6171 as JModelica installation path.\n",
"Environment configured for JModelica.org\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fmu = 'Modelica_Thermal_HeatTransfer_Examples_Motor.fmu'\n",
"model = load_fmu(fmu)\n",
"options = model.simulate_options()\n",
"options['ncp'] = 7200\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"res = model.simulate(start_time=0, final_time=7200, options=options)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Simulation interval : 0 - 7200 seconds.\n",
"Elapsed simulation time: 1.679768 seconds.\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for x in res.keys():\n",
" if '.T' in x: print x"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"windingLosses.T_ref\n",
"coreLosses.T_ref\n",
"winding.T\n",
"winding2core.port_a.T\n",
"winding.port.T\n",
"windingLosses.port.T\n",
"Twinding.port.T\n",
"core.T\n",
"winding2core.port_b.T\n",
"convection.solid.T\n",
"Tcore.port.T\n",
"core.port.T\n",
"coreLosses.port.T\n",
"environment.T\n",
"convection.fluid.T\n",
"environment.port.T\n",
"der(winding.T)\n",
"der(core.T)\n",
"Twinding.T\n",
"Tcore.T\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Not possible as modelicares can only read .mat files\n",
"# sim=mr.SimRes('Modelica_Thermal_HeatTransfer_Examples_Motor_result.txt') \n",
"\n",
"plt.figure()\n",
"plt.plot(res['core.T'], label='T core')\n",
"plt.plot(res['winding.T'], label='T winding')\n",
"plt.legend()\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
"<matplotlib.legend.Legend at 0x7feb398486d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
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GCpbmxM3h5PmThppP1bXXEewXTLB/sO7fmRM3h9P1p3VvuuGqUC5OXKx7sdPV\nMIWfjx9z4+fqyi93NxSiSyjd9Yz1hFvcmLjmxs2lsK5w3O+OOzZC/ENIjUgd9zsznapsQQRfN8N3\ni/L39Sc1ItW0PjfjCb47C7dGFlTDAsKID4039L5cEbEgvyAyozJ1e/lGUzLtTERIx4idifK+XQ23\nzE+Yz4naE+N6xu5MXMH+wWTFZI07Qbr6N7ezJGkJH1R+MOY57oSmvBERfJ2UNI7MonFnT1NH9Ahy\nTqzrqZl6dolyxGgc39UvzeKkxXxY+aE+Gy4KZXJ48sAmGuPa8PCY8aLERXxY+eG4aZPuTCphAWFk\nRGWMG9Zz1zNenryc9yvet9zGofJD49uQGL4wHGf7wc6KMk/wxytacjeGb6QlgdFMHVe/mIsTFxvz\nvl3MPFmUuIgPq8afWIwuPDsyETaSw5PxsfmMmw7s7kKkHqF01/tePkOfGLvjfesWfAnpCMNxVgmb\nFePe7k129MTws2OyOdNwxlAJv52ypjJDLQnmxc8z3lPFRcHXI5J2G65++fVOLO58+ZckLeFI5ZEx\nU2fttQSu7qxks9lYkbJiTBHr7eulpq3GpWIlO7qE0s12BLptuDGpLJ2xlCOVR8b8zkiWjuCUkkYn\nHn70LE43TExIJ9g/mBlhMwxPMPZ2AUbEcn78fD6qtr751OIkJfh6KjvdjX2PN7G0dLXQ29ere3/W\n4cSGxBIXEjdmIzVXagmGM55nXNNWQ3RQtEvFSgM2kpdzqGJ0G82dzWhoLn9WoP72x2uOj7por2ka\nVa1VJIUluWwjKiiK5PDkMes9xMMXnDJaDN8MD1/vouqChAV8XKO/0yCoDJoQ/xBDGTQLExfySe0n\nuu8mXP3S2H9nvMrOPq2PqhbXv/xLZyzl/fJx4sX9np7e/VmdsSJ5BQfLD45rwx2WJy+33EZeUh4f\nV388qhhXtKgtAd35rEL8Q8iJzRn1zsv+71bPRu9jMdYdUZ/WR3VrtaHqbW9Hj+D7Ag8B+4C3gPlA\nAvAMsBd4E8joP/dW4CDwDnC5yWOdNDRN42zj2REeflZ0FqfrTxvaJWg4TZ1N9PT1EBUUNe658+Pn\nG2otC66JcVhAGKkRqbrb/rpT2bk4cfyF2/Nt5wkPDB9IiTXK/Pj5nG08O1Dg5gwzPL0VyWOHW8yw\nYQ+FjPZvzgwbIf4hZMdkj3qXV91a7ZbnbWesu5Xq1mq3wlJ6bNS31xMeEK57g/SpgB7BvwLoA9YC\ndwE/BH5KRHWqAAAgAElEQVQCPAJsAL4HLACSgNuB1cAW4EfAlPgkq1urCfUPHdFUKzIokkC/QGra\naly+tp6iKzvzE4z1+QbXxTgvKY8jlUd0netO8YqecIu7Iubv68/ipMVjevkT4X1XtlS6LZSJYYmE\nB4SPWvRnlsc6Voy9urXa5XWIybAx2t/ELBvehB7Bfwb4Uv/zDKAeWAOkAa8ANwCvAyuB/UA30AQU\nAovMHe7k4CycY8fd1sVGcuQnysMHyEvUJ/gDLYVdFEs96XnuZoSAjnCLCZ7x0hlLOVp1dNQc9qqW\nKnO81vGEMsQcoTxw7oDT16paqkyxsTJlJe+ee9e5jdYqUyaupTOW8lH1R057EIngj04v8DBwH/BX\nlPDXAZuBs8B/AeFAo8PvNAPOm8N4GcUNxSPCOXZc2fHIEaMbhxTVFxnb6s7FbIq8pDyOVI0v+M1d\nzQCEB4QbtgHqS/9e2XtjnmPGBhUrU1aOKmBgjvcdHhhORlTGmKEQM0RsrM/MLBFbk7aG/aX7R7Vh\nxvtYnLSY0sZS6trrnNowY1IJDQhlfvx8pxNkVWvVtBN8I+kCNwOJwAGUl/9s//HngB8Ah1Cibye8\n/7wh7Ny5c+B5fn4++fn5BoYwOTjLwbdjtOvjcIwIfrB/MGkRaZyqO8W8+Hm6fqeipWLULpxjYQ/p\naJo2ZrjJni7p6gJeVnQWLV0tY4ZUzNigYkXyCr792rdHfb26tZqcmBy3bNjtHDh3gCUzloy00VbN\nqtRVbttYk7aGr+/5utPXqlqrWJiw0G0bCxIWUNlSSU1rDfGhQ1sHV7dWMzd+rts2/Hz8WJW6irdL\n3+aK3CtG2DBLjNfOXMu+s/tYl75uhA0z7risoKCggIKCAtOvq8fDvxGwf1PaUd7+mwwuym4APkJN\nBOuAQJRnP7f/+BB27tw58PAGsQcV0hltcxJXNrF2pLRR/05R0B/HN5oy6WJvdB+bz7gdOt0Nhdhs\nNlamrBw/88RNDz87JpumzqZRK27NEpjVaavH9IzNsLEiZQXHa447bdZmlg1fH19Wp61m39l9I220\nmSjGaWud2zBb8EuttWE2+fn5Q7TSLPQI/pNAHioj5yXgDuAbwOdQMftLUAu5VcD9qEye14A7Afc7\ni3kAY8XwXd2f1Y7RtgcL4hcY2wTajQyavKQ8Xb1I3I2vjxduMSOea7PZWJG8gvfOOQ+FmHV7v27m\nOt46+5ZzGy3m2AjyC2JJ0hLeLRsZ/zYzzdDuGQ/HrPcBsC59nXMbJoZb1qSt4e3St0cUxXmy4FuF\nHsFvBz6D8uRXo0I4Z1FCvwbl6dtj9w+iFm+XA0+ZPdjJYqyQTnZMNiUNJS53zTS6U5TeEn477njg\ny2Ysm5DS9JUpK0cVYjDv1ns0AbPbMOPLPyduDi1dLU43uDdTYNbNdC6UE+UZmxUKWZWyiiOVR0bs\nSmbmxJUYlkhCaMIIR0kEX3DKWB5+oF8gaZFpLhdglTWVkRKhv+3B0hlLOVxxWNe5mqa5leFyQeoF\nTr1IR8xaUD147uCobQnM+mKuT1/PmyVvjjiuaZppNmw2G2tnrh3h5ff29VLfUU9cSJzbNgCnNjRN\nUzF3k7brW5G8go+qP6K1q3XIcTOFMjQglHnx8zh4bmhIz2wxXps28vOajou2Ivjj0NjRSG9fL9FB\n0aOeMyduDp+eN56p097dTlt3G7HBsbp/Z1b0LJo6m6hpHT/3390MmlUpqzhw7sCY/WHM6DaYEJpA\ndHD0qNlOZn35V6WscipgLV0t+Np8DW9ePhrrZ67nrZKh4nK+/TxRQVFutVVwZM3MNRw4d2BIxlZ9\nRz0h/iEuF6gNJ9g/mLykPN4pe2fgWFdvF81dzUQHj/59MMr69PW8UfzGkGNmC35+Rj6vn3l9hI3p\nVGULIvjjYg+5jJWF4mqmjisl6jabjSUzxu/zDYPet6sZNIlhiUQGRY7ZpdOMdEYYPfbd3dtNY2cj\nMcExbtsI9g9mcdLiEXctZovLuvSR78VsG1FBUWRFZw1Z7LZCwC7KvIhXT7868HNtWy1xIXH42MyT\njs2zNvPK6VcGfu7o6aC9u33ULT9d4eJZF/P6mdeHbOwjIR0vR9M0/vbR30y9pp4Yu6sLt66GQ5Ym\n6QvrmOF9jxfWqWo1p5hoffp69pbsHXG8tq2W2OBY0zaZXj9zZFjH7C9+XlIepU2lQ+7CzFzotLN5\n1mZeKRoUSisE7JKsS4aIsRXvY136Oo5UHqG5U92R2t+HO716hjMjfAYpESkDa1JWTCrewJQS/IqW\nCq7/+/Ujbtnd4VzzOV2Cb3QfWHBdkJclL9Mn+Cbs5nNBytiCb5ZXuSF9A3uL947oEWO2iK1PX8+b\nZ60VfD8fPzakbxjiGVuR831J1iXsOb1niA2zxXhVyioK6woHJi8r3keIfwirUlZRUFwwaMOCUMsl\nswYnr5rWGtMnFW9gSgm+fVs+szYXB30e/oKEBRyvOW5oH1hww8PXuXBrRgbNqtRVo2bQ9Pb1Utde\nZ8pCZHZMNn1a34gNZcwWsTUz13Co/NCQUnsrFu+2ZG2xXIzXzlzL0aqjNHQ0DNowoTrVEX9ff/Iz\n8nntzGuDNiwIg2yetZk9RXustZE1GDqajgu2MEUF353eNsPRI/iRQZHEh8YbnmhcrSDNicmhqrVq\n4Is+6vVNyKBZkrSEk+dPDtxuO1LXXkdkYKQpC5E2m40NGRtGhHXM/vJHBEawOHHxkLCOZd530Z6B\nOxYrRCzYP5jVaat544xa8LQi3AJDQ0dWibFj6MgqG+vT13O44jDNnc3TcsEWppjglzSUAOYLvp7d\nooxslm3HVQ/c18eXJUlLRqSyOb2+mzH8QL9Alicvd1o9avYXc0P6SMG3whPbmr2Vl4teHvjZCoHJ\njskm0DdwYP8Cy4Ry1iVDPGNLQiFZl/By0cumpq8OZ3HSYho6GiiqK7LkTgVU6GhN2hr2FO2Zlgu2\nMMUEv7ihmIUJC00VfD0xfDC2P6sdd6pUV6etHpIu5/T6Ju3mk5+RP+BFOmK2GG/M2MjrZ14fEse3\nwvvekrXFcsG32WxKKAuVHatCCFuyt/BC4QtKjE1seeBIbmwu4YHhHCo/ZNn78LH5sG32Np4+8bTq\nKmqR9719znaeOvGUZZOKpzOlBL+ksYSLZ13MqTrXNvt2ht5KWKMVsOCeIK9OW83bpW+PeY5ZKZMb\nMzZSUFIw4rjZHmVubC5+Pn5DdvWyQoyXJS+jurV6oBrWKm9va/ZWXih8YcCGFY265sfPx9/Hnw8q\nP7DUa90+eztPn3ja0oZjO+bs4OlPn7Zs4gLYNnsbL5x6gbKmMvHwvZ3ihmIuyrzINA+/rbuN1q5W\nXYuSLoV03PDwL0y9kHfL3h2zKMosT2lV6iqO1xwfsWOU2V6SzWbj0uxLefHUi0NtmPzF9LH5sHnW\n5gEv38qFyEPlh6hrr7PMhs1mY8ecHTz1yVPWCr6jZ2yRjU2ZmzhWdYxjVccss5EcnkxubC5PHH9C\nYvjejH0bwrUz11LTWuN0wwOjnGs6R0pEiq7UrazoLGpaa2jsaBz3XFAFRfUd9S6XwceHxpMQmjBq\nOmhPXw+NnY2GqnhHI8gviBXJK0ZUj1rx5b80+1JeLLRW8AEuy7mM504+B5i32cZwQgNCuSjzIp77\n9DlLhXLHXOUZm7XBijNWpKygsbOR9yvet+x9BPoFsiV7Cx9WfWip971jzg4qWyrFw/dmqlurCQ0I\nJTIokoyojBHpfa6gd8EW1ELq/IT5HKs+puv8qtYq4kPi3SooWjNzzahhndq2WmKCY0wrWNqYsdHy\n8ndQXt7B8oMDWUFWifEVuVfwxpk3aOhooKGjwZSJ0Rk75uzg0WOP0qv1EhYQZomNC1Iv4FzTORo7\nG3XtjewKPjYfrpp9FYClQrl99nbLbeyYuwPAsr+5JzNlBN9xV6qc2BxTwjp6F2zt6K2ABXO27Vud\nan3vdTubszYPWegEaxYiQwNCuSD1Al4789pAVohZzcAciQqKYs3MNfzlw78QHRRt2sQ4nCtyr+DV\n068S6h9qWZGPj82HNTPXAFhaSHRZzmWASge1iitnXwlYK/i5sbnsmLODnFj3N7zxNqaM4DtuUpId\nnW2K4BttXTxeX3dHzMiRXztzrdPqVDBf8Fckr6CqpWog9dVuwwrve1uuytYwu6nZcK6Zew2/PfRb\nS8UlOjiayMBIzreft8wGwBfyvmDp9UEJ/t6bR7a/MJOwgDC0uzUCfAMstbP7M7stuxvyZKaM4A/3\n8F3pXjkcSwXfhJTJOXFz6Ortchq+MrsIx9fHl63ZW3n+1PMDx6yKS18992qe/fRZyzMprpp9FSdq\nTxAe6Fo3Ub38+4p/J8gvyFIbO+buoO97oy/gm4GPzYf16esttSFYy5QR/JKGQQ9/Xvw8Pqn9xO1r\nGonhgxLgypZKp5syD8eMPjc2m42LZl00UPbuiBXpc5fnXD4hgp8SkcLc+LnsOrbLUsG379U63iYv\n7vL9Td+n/r9GbO9sOtOtL4xgnCkj+MWNxUME/+Pqj52GOoxgNIbv6+PLsuTxd4kCc6pgAS7OvHhU\nwTe9P0z2Ft4qeYu27jbautvo7u12udf+eFw37zp+ffDXlqfOPXD5A9y17i5LbdhsNss9fEHQw9QR\n/IbigV2p4kPi8bH5UN1a7dY1jYZ0AFYmr+S9stG367NjVhXsRbMu4vUzr0/Ifp1RQVEsnbGUV0+/\nOhC/t8qrvGbuNdR31FseZ/3S8i9xd/7dltoQBE9hSgi+PQffHsO32WzMi5/nUstiO9293dS21Rr2\nMFemrORA+fhxfDM2/wZIjUglJjhmRNGXVdWK1827jsc/ftzyXiRpkWkAo+6CJQiCcaaE4Dd2NmLD\nRmTQ4GYG8+LnDSnRN0plSyXxIfGGO0GuTFEe/njhJPtuV2ZwyaxLhlSngnWdE6+ddy3/OPkPShpK\nLC9c2f+F/TxwxQOW2hCE6cSUEHxnoRd3PfzKlkqXPPC0yDRC/EPG3AHLvtm0WYuq22Zv49mTzw45\nZlXPk8SwRJYnL+fhDx+2XPBXp60mLynPUhuCMJ2YEoJf2lg6EAKw467guxNjd9bX3ZGGjgZTN5ve\nkLGBE7UnqGypBLC0jS3A9Quu54VTL0zLboOC4M1MCcEvayojNdxcD9+dwqgN6RtG7JvqiNliHOAb\nwJasLfzj5D8AaO1uxWazWVawdPXcqwFMm7AEQZgYpoTglzaN9PBnhM2gu697yEbSRnAnxm7fkHu0\nOL4VLQm2zd7GM58+A1iXH28nJjiGrOisgUVyQRC8gykh+M5i+DabjUWJizhSecSla7oawwfVORNG\n31vXCkG+LOcy3ix5k6bOJssWbB0p/Gohtyy9xVIbgiCYy5QQ/NKmUtIi0kYcX5q0lA8qP3Dpmu7E\n8G02m9qur9h5HN+KBdWooCg2Zmxk9ye7Ld2kQhAE72VKCP5oBVJLZixxXfDdzJO/KPMi9pze4/Q1\nq0IuNyy8gUePPjpt9+sUBGFsvF7wNU1zmqUDsCRpie52xcNxtxJ2a/ZWXil6hZ6+nhGvWSXIV+Re\nweGKwxypPCKCLwjCCPQIvi/wELAPeAuY7/DaPwOOO3DcChwE3gEuN2mMY9LY2YiPzYeIwIgRr82J\nm0NpYyktXS2Grtmn9bm9PWBKRAozI2c6bbNg1UbQwf7BbJ+znd8c+o0IviAII9Aj+FcAfcBa4C7g\nB/3HlwCOTbiTgNuB1cAW4EeAtU2tcZ6Db8ff15/5CfP5sNLY5uJ17XWEBYS53fDq0uxLeeHUCyOO\nWxly+Xze5wFrN5AQBME70SP4zwBf6n+eAdQDsSjh/xpg7561EtgPdANNQCGwyMSxOmW8BmdLkozH\n8c3qc3NpztD9We1YtXEIqE1RAKd3PIIgTG/0NorpBR4GtgP/BPwR+DrQ4XBOBOC4g3czEInFjJah\nY2fpjKW6NyWxY1YnywtTL6S4oXjEpGSlh2+z2Wi7s03a8QqCMAIjncFuBhKBYqAc+C0QBMwDfg68\nATg2Rw9H3Q0MYefOnQPP8/Pzyc/PNzTg4Yzn4a9MWcmvDvzK0DUrms1pbObv689Vc67iyeNP8rUL\nvgZAV28XLV0tlrb9tXLPUUEQrKegoICCggLTr6tH8G8EUlEx+XagAiXynUA68BjK209ChXkCURPB\nXOCj4RdzFHwzKG0qZU3amlFfX5S4iLONZ6lvryc6OFrXNStbKk3x8AH+ad4/8f23vj8g+PZNuX1s\nXp8gJQiCRQx3hu+55x5TrqtHdZ4E8oC9wEvAHSixBxW/t/cPqATuR2XyvAbcCXSZMsoxKGsqGzOk\n4+fjx/Lk5bxb9q7ua5q1GxWoDUpO1J6gtLEUsL7tgSAIwmjoEfx24DPABlQGznMOrxX3H7PzIGrx\ndjnwlDlDHJvSxtJxd6Vanbqad8re0X1Ns2L4oBqbXTVbhXVABF8QhMnDq+MKmqYpD3+UtEw7F6Zd\nyNulb495jiNmZenYuWHhDfz5wz8PtC22ep9WQRAEZ3i14Dd1NmGz2cZNQbwg9QIOnDtAb1+vruua\nuRsVwMbMjTR1NnG44rDy8KWPvCAIk4BXC355cznJ4cnjnhcXEkdyeDLHqo/puq6Zi7YAPjYfblly\nC384/IcJ6WQpCILgDK8WfCOx9vyMfF4/8/q457V2tdLT12N64dLNeTfz+MePc7rhtAi+IAiTglcL\nvl4PH+DiWRfz2pnXxj2vpq2GhNAEbDbbuOcaISUihfyMfJ458YwIviAIk4JXC76RbQg3ZmzkrZK3\n6OodO1PUyiyab1z4DXq1Xlm0FQRhUvBqwTfi4ceGxJITmzNumwUrY+yr01bzzdXfJCcmx5LrC4Ig\njIVXC77RAqmLMy/m1dOvjnmO1X1u7t18L5FBlrcYEgRBGIFXC74RDx9UHH9PkfNdqOxI2qQgCFMV\nrxZ8oxWx69PXc7zmOFUtVaOeI5WwgiBMVbxW8DVNM+zhB/oFsiV7C8+dfG7Uc6rbRPAFQZiaeK3g\nN3c1Y8NGeGD4+Cc7cNXsq3jm02dGfV08fEEQpipeK/hGvXs7l+Vcxt7ivbR2tTp9XQRfEISpitcK\nvqsNzqKCorgg9QKeP/W809dF8AVBmKp4reC76uGD6l75yNFHRhzv0/qobaslPjTe3eEJgiB4HF4r\n+O70rL967tW8VfIWNa01Q47Xt9cTFhBGgG+AGUMUBEHwKLxW8N3x8MMDw7ki9woe++ixIcclnCMI\nwlTGawXf3V2pPrf4czx05CE0TRs4JoIvCMJUxnsFv7nCZQ8fVNVtW3cb+87uGzgmgi8IwlTGawW/\nvLncrW0IfWw+3L7ydu4/cP/AMWmrIAjeQ1cXONygW8L770Nfn7U2JhKvFXwzNhq/afFNvH7mdUoa\nSgDx8IWpT1UV/OEPcPAg9PSYf31Ng8cegzVr4JZb4NFH4dw5c220t8Ndd0FEBGRmwhe+AH/9K1RW\nmmejrg6+9CW48ko4fdq86042Xin4zZ3N9Pb1ur0rVXhgOLcuvZUf7fsRIIIvTB7d3fDTnyqhvP12\nePxxqKgw7/p9ffD738PChfDyy/D5z0NsLFx2Gfzv/8IHH7jvyZ48CVu3wg9/CN/6FixZAk89BYsW\nwbx58NWvwrPPQlOT6+/hySdhwQIoLFRC/OKLsGwZ/P3vMHcuLF4M3/wm7NmjJgajdHTAr36lxhsQ\nAMePQ3a2a+MVQDODU+dPaZm/zDTlWrWttVrMT2K0oroi7eq/Xa09/tHjplxXmDr09mrao49q2qZN\nmvaVr2jarl2aduaMpvX1mXP9Awc0bfFiTbvkEk178UVNu/deTbvySk2Ljta03FxNu+02ZfPcOePX\n7uvTtD17NG3lSk1btUrTPvxw8LXqak178klN+7d/07TZszUtNlbTrr1W037zG0379FP97+/4cU27\n+WZNi4vTtJ/+VNO6uoa+3tOjaYcOadqPf6xpF1+saWFhmrZ6taZ973ua9uabmtbZOfb1m5s17aGH\nNG3hQk1bvlzTXn7Z+Xnd3Zr29tuads89mrZmjbJz8cWa9pOfaNq776rrjEZRkabdfbemJSdr2rZt\nmvbBB/re+0QBmBK8Mncfv/HpH7t77D+7n/985T9555Z3TBgS3P3G3ZxpOMOZhjN8f+P32ZCxwZTr\nChNDT4/y9mbNAj8/c6/9zjvwH/8Bvb3wX/8FxcXw9tvq4ecHa9cOPhYuBF9f/dc+fhx27oR9++De\ne+GGG8BxZ82+Pjh2DAoK1GPvXoiPh/x89Vi+HDIywN9/5LVra+Hpp1X4prFR2fmnfwKfMe7py8rg\n9dfV47X+3UAvuEB5znPnQmoqxMQoe2Vl6rP5xz+gpESFP772NYjUsdVDezvs3w+vvgqvvAIffwxZ\nWTB/vno/cXEQEqLucA4dgvfegw0b4Lbb1B2J3t1HGxvV5/bKK+rvdeIEJCWpfycJCSokVFOj7m5a\nWuD661UYavFifdefSPq3XHVbr71S8J/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zszTM1ag3Bkpwn56kcXwLtbj8dv/Pz6KKygB+\nC2xHFZEdRQlvRP/zAODrDNYkfAb4pUVjvBn4ef/zaOAs8IwHjvMq4MH+5xv6x+iJ46Tf9lPACdSX\nyhP/7kEowXfEE8eZ3z8ugFDgHjz3727n/6LWGD1tnFuBv/U/vxgl3paOcaJ66awBXup//h6wfILs\nDqcQNfnYZ82lqNkR4EXUh74C50Vkju/hpf5zrcBZsZsnjvMZ4Ev9zzNQabjLPHCcAD9FfXkq+n/2\nxM9zMRCCSoR4DbjAQ8d5CXAM5bQ9hxJ/T/27g9KaeSjnxNPG2Y7KarT1/7/L6jFOlOBHoAZqp3cC\nbTuyG+hx+Nnm8NxeLDZaEZnje7CysGx4sdtdDP2sPGWcoP6OD6Oyuf6KZ36eN6Pag+zp/9nmoeNs\nRU1MW4Avoz5PRzxlnPEoUbq2f5y78MzP086dqLsQ8Lxx7kfd2Z0Afgfcb/UYJ0p0mxhamOUD9E2Q\n7bFwHEME0MDIsYY7OW4/ZhWOxW7/z4PHCUpQZ6M8qCAPHOfngc2oWG4e8GeUaHnaOE8yKPKngPOo\n+hZPG2ctavLs6R9zB0OFxlPGCRCFWrPZ2/+zp32PvoUS/dmof5t/QYVtLBvjRAn+fuCy/ucXoGJQ\nnsAHqPgzwKWoW6kDDDaGi0RVFH/E0PdgP9cKElFfqG+hvGdPHeeNqMU7ULemvagaDE8b5wZU3Hkj\ncAT4HOr219PG+XlU1TpAMuoLvMcDx7kPFXu2jzMEFYLytHGCioW/5vCzp32PQhn00OsBPw8co0vY\nUDHU/f2P3EkcSwaDi7Y5qAKyt1Eeqv12ylkRWTDwOCoj5VVU1ocVOCt2W+SB4wxGLTjt7R/XlXjm\n5+nIG6h/e544Tj8Gs7PeRDlGnjhOgJ842N/sweP8T+CrDj972jijUMkEb6Eyia73wDEKgiAIgiAI\ngiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiBMbf4/mWqay/5t9mAAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7feb3ea90fd0>"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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