{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Scripts for Calibration of Cernox Sensors\n", "---------------------------------------\n", "\n", "Make a copy of this notebook for an other run.\n", "\n", "See also http://samenv.psi.ch:8080/sample_environment/83" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import math\n", "from scipy.interpolate import splrep, splev\n", "from zcalib import read_curve, convert_res, compare_calib, make_calib, logrange, Sensor, CalibRun, nplog, npexp" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('lsdat', 'READ /afs/psi.ch/project/SampleEnvironment/SE_internal/Thermometer_calibs/2012/73027 Cernox 5/X75610.dat')\n", "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2021-05-04_p0_c1.dat')\n", "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2021-05-04_p1_c1.dat')\n", "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2021-05-04_p2_c1.dat')\n", "{'selected': 0, 'averaged': 62}\n", "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2021-05-04_p0_c2.dat')\n", "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2021-05-04_p1_c2.dat')\n", "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2021-05-04_p2_c2.dat')\n", "{'selected': 0, 'averaged': 62}\n", "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2021-05-04_p0_c4.dat')\n", "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2021-05-04_p1_c4.dat')\n", "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2021-05-04_p2_c4.dat')\n", "{'selected': 0, 'averaged': 62}\n", "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2021-05-04_p0_c5.dat')\n", "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2021-05-04_p1_c5.dat')\n", "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2021-05-04_p2_c5.dat')\n", "{'selected': 0, 'averaged': 62}\n", "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2021-05-04_p0_c6.dat')\n", "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2021-05-04_p1_c6.dat')\n", "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2021-05-04_p2_c6.dat')\n", "{'selected': 20, 'averaged': 42}\n", "ERROR: data for X163062 is not monotonic\n" ] } ], "source": [ "run = CalibRun([\n", " Sensor(3, 'X75610'), # the reference sensor must be the first\n", " Sensor(1, 'X161269', 'CX-1050-CU-HT-1.4M'),\n", " Sensor(2, 'X163059', 'CX-1050-CU-HT'),\n", " Sensor(4, 'X163060', 'CX-1050-CU-HT'),\n", " Sensor(5, 'X163061', 'CX-1050-CU-HT'),\n", " Sensor(6, 'X163062', 'CX-1050-CU-HT'),\n", " ],\n", " #t_points = (1.0, 1.2) + logrange(1.4, 310, n=194) + (330,), # the points to be used in the cal file\n", " t_points = (1.38, 1.42, 1.51) + logrange(1.55, 288, n=57) + (302,310), # the points to be used in the cal file\n", " caldate = '2021-05-04', # the first measuring day!\n", " logT = False,\n", " logR = True,\n", " calib_data_file = '/home/l_samenv/sea/calib_scripts/calib_data/calib%s_p%d_c%d.dat',\n", " outputpath='%s/%s.340')\n", "# smooth depends on number of measured points (1e-7 for 60, 0.8e-7 for 48 and 0.4e-7 for 24 points)\n", "run.make(diflim=0.001, smoothref=1e-7, smoothtst=0.4e-7)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('z340', 'READ 2021-05-04/X161269.340')\n", "('z340', 'READ lakeshore/X161269.340')\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# compare known sensors\n", "if True: # set to True when known sensors are measured\n", " plt.figure(figsize=(10, 6))\n", " tmin,tmax,dmax=1.4,310,0.001\n", " testlist = [('X161269', 'z340')]\n", " for sensno, kind in testlist:\n", " r0, t0 = read_curve('%s/%s.340' % (run.outputoptions['caldate'], sensno), 'z340')\n", " r1, t1 = read_curve('lakeshore/%s.340' % sensno, kind)\n", " diff = compare_calib(r1, t1, r0, t0)\n", " plt.plot(t0, diff, '-')\n", " plt.plot([tmin,tmax,tmax,tmin,tmin], [-dmax,-dmax,dmax,dmax,-dmax], '-')\n", " plt.legend([sensno + \".\" + kind[-3:] for sensno, kind in testlist] + [\"window\"])\n", " plt.xscale('log')\n", " plt.yscale('symlog',linthreshy=dmax)\n", " plt.grid(True, axis='y')\n", " plt.axis([1.0,350,-1,1])\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('z340', 'READ 2021-05-04/X161269.340')\n", "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2021-05-04_p0_c1.dat')\n", "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2021-05-04_p1_c1.dat')\n", "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2021-05-04_p2_c1.dat')\n" ] }, { "data": { "image/png": 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\n", 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\n", 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\n", 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5fNty3vE2styZ1pJkUmwCE0eW88lPXEHp4FEAvPVOFd94dBY7Y3DrKT/inBMvCn0M39z7Jjc+N58NezdzUmsbl+3O4UG+wZy/upBPTSrlu//zEk37/pHVRU1cagP5/mUPE817/+9n155Klm94iC21W9nXWE9LSwOJtiZItWCWZIcVsNPyqYtkk4o6sax22ttjJFtGMsDGc95Rp3LFKSdz3NAclr3zAjev/GcaEg1ce8LfcdGYubSnjLEludgbj+LL/oWNeytZUTqGF7PgZRIkIkbUIhxbehyfGPoJ2lJtbKrfxOb6ze8FgmHMGP4pZlW/zV2pzTRGIiy9emO/C4hPAje7+/kdyzcCuPutXfpdA8wHinPG5ZQeffPRPRo/O4ABqTjZZBPBiAARDDMwoI0ULZak2ZK0WhI/yIu6kiSMbytkZDCG0TknMqj0WHJixltv/JB7BjWQ53GuHv5NRuWOe992Vfuaearq12wvfJU9MaMsmcvMYVcxNP+Y9/o0p5r517f+iVSkhf8TnU3x2PPfN8azdY/xUNPjnNXcwjW7BxJEcmi1CE0RpykKeyMpXsyt54W8CFHg/KZmzmvKZ0jWOLJiUWKRgP2RdvZE2tkZaWdzJMHaaILKSILAIObGmPZcsoIULZEEjZEUDdH3h8qfH9OAXHfyA6coCMhLGdlBlKxUFpEgTksEGmNJGmIp9kYDGmLhj+eAVMDwZJIJiQij2nMZ6SUMyxpBdv5QItkFWCyb9miU+kiKPSRojsYZW3gqWZYVOt5z9atYsvduCoMkV3IG44/66+5/md3Y3dpMRf2rbGtawdZINfWxgJg7JzcH5DeOp7r1M1AwmWMHRTm2NMrwfDvkUVdL0MLe5F62ttTx1t4a2va/QRvVbMptoy1iDEgFTG3JYVhqEoXZ04jkjiGWXUhBPEJelpEbg7f3NLNv1xKSrGRDboJN2XEABqdGUT7wfGYUn0DE/nw0s+at9bzWehur8rKYFj+BOUO/RDwSZ39qP6t3r2BT/VNsyG4lETHOaCniioJziA45hfZ4EQDRZAtHbfwFG/dX8M0hgylOTOE7479KXizCxvqAqrdfxOy3vFjUzJ5olJjD0alcpmYNZ0LuZEYOKCMnmsuAxjfJa6iEPZspbnqT0mAX6+JxHssfwOMFBdTFnBgRjsoeD0GStlQTLUELzZ6gmSQpO/Bi5hMtCYY1jqQk/zymTp5GPBa+U72zawOL9v2clkiEr5R+m3GFfz6t5+78sXE5v6t/iIJUkvm79/JGw2fZPuYSZh6VS3bU3uv31JYElfW3saFkK59ucb5Y8nmqmzfySutbvBJtoCoeftrM3Ilh5Acp8gMnzwNyiJIdyWNfJMJb1kKyY3cpTAWMSbazLjubsW0wq3YIjS3jeNNHAHB9fAlTeJv6+AhqJlxO3dAZgDFwxzM0blvMmqx2nh8whDeiCaJkMTAyjLxgONHkMFKtw2jbV8TNLb/g9Oh6rss+m+dGVLLuy+v6XUBcBsx09690LP8NMN3dr+tum2NPOMnXrH6B1l1vUF9dwZaqCup3bWJbkMVbXkJO0SRGjy5j+pTTOGHEsL84nOtO4AFN7U3sa91HQ6KBva17qN62np3b19FQX0VN+zu8ltVGY8c51YmJBKMSAc8W5DA5OoT/+tz9DModFDp2a3uKny5dxduV/8wbA7fQGDEuLD6Ob5777wzJH8qXfjOb19rf4obgJC6/6tehY/xm/b3cWvEjcgNIGKS63K38IMKZkYnMmTyHU06+oEfvezQmGlmzcw0VOyp4ufZlIhZhYM5ASnJKKMkuZhBRctsTNLQ0Ud/cyL62Rhrbmtnf3kJLqpkEzbR6K80k2G9JmswpcBiUilASRBgYxBgYZFHo2RTESynMG0npwPEMH3oMw0ZMomjIKCwa/oR/JFZuWctNT32ZPbEkJyTzGJszmCmDjuKYocczYcQ0igYfSxCJ0JhopGbfblZVb+PVd7azederNCReoD67jsAOhNfJrSlGBRMZOnwuo485mxNGFzO0MCdttW7dtZMnXrqTNTv+wKu2k4bon3+h+e8GbypGJMiiLqeF/REj6nB0dBhnHD2bWVMu4Kji7t+XemHlcp7/01XcPTCXCQXjKMkpZc2u1QTmDE2mODZyFHPOvpHTx54ePoA7vuq/+e8V/8y/lxQytukURrSOJMheyuqCJEkzTouP5MrT/oFpY84iO5p9yPucbNhJ5eqnqV//LMW7KmiLv8OTBbmsyskhxwMKAmdAcODfLMslkhhI1Z7TiU+6mG//1cmMGph3yDn+uHwRN1b+K/EgzqJLf8+4kuHsbtnNjc/M58Xdq5jR3MIXagtZPup7XHXJBYwuCR9zbfVebn7oW7xZ/BLmjpsRc2dsSzajgtFMGXQ6x44/lYljJ1A6aBixaJzou6eQ21thx2tQUwHbVh34t3kP7QVD2FxQwivxOH9KJnkj1cwxbXlc1RQwJllDcWLHe/PXxkbwb4lLeCBxGnnZ2Zw1eTCThw5gZ2Mr9fV7Oav2bi5ueYhGYmxIjaeNHFqI00Y2kew8TrWNjExu5e0zfkrDpNnMe+bzvPy3yz76AdGbb1KnUknWbXmOp954jFV1a3kzVceZA8u49aL/JB6NH3L7ql1NLHr0EVr3/YRni9rBjGPzxvNyy1t8aW8u//D154nEun/CfHrL07yw/QWKsosojBdSGC+kKLuIouwijis9jtxYbjrvbr9UtXMrP3noi2yL7uGdLGjr9AIhLwhoMcNDXvVPaUswLWEcXzyNU07+MoOnlL93+izTkkGSNZuXsm7LH9nbtJN9zbtoSjTQlGpivycYHSuhfMplnHHKVyjI/svTMd1Zt3oFNU//NbcMHkBB4Jy/v5Ex0RM58+KfMXjUhB6N4dvX8v1H/obfdWRjXuDMGngCX/zU9xk76JiDb3wQQeC8Wvk2myqepGXb6+wMiqgOBrMlKKUmWcT+ZISxg/K46cKpnDnp4O+BdPXYQ9/m5n2PUZIq5EvTb+C21T8g6a1cv6eRnPgVTJ09n+PHlBxynL3NCb7xv7ezq2k1Y0o+xQVTPsOnJ42mKC99L2reJ9EMdZXQXAfjZtAaRFixeTdPrd/JH97Yye6mBMV5WQwrzGFoYQ5Ts+u4aO89DEntIJc2sr2NWNCKtbdALAdm/QyOPheA57c9z4xRM/pdQPToFFNnfekqJnc/ojd2X9m6hyd/eys7ow/zZEGcc5qSfOeSJxg2cmwGqvx4ampL8vKWXVRs/BNvbq+gbv8mPLKHQoswLCuLEVkxRmRnMSoeZeyA0ZQe/3kYNe1DC4UPy1uvLGfQb+ewK3sM8Qt/zOgTyg97jPbmOn722DyGFY7h4jP/LwXdHCn3Ge7cf9fn+KFtIjBjUluCrzUOZcKs/2TCxGN7u7ojEgROIhWQk9XzN827MrN+FxAxDrxJfQ6wjQNvUl/h7q93t01fCogPwt1Z+eobvP2HWxk37UpOn3Fub5f0kRYEzp7mRK9drdWr3n1F+XG638k2Hlx4Nu+07+TSk+Yz4sxrPl73P0S/CwgAM/ss8B8cuMx1kbv/8GD9PyoBISIZlmqHVALi+b1dSZ/Q04Do5rqS3uHuS4GlvV2HiHzERLMO3OSwfLROsoqISNooIEREJJQCQkREQikgREQklAJCRERCKSBERCSUAkJEREIpIEREJJQCQkREQikgREQklAJCRERCKSBERCSUAkJEREIpIEREJJQCQkREQikgREQklAJCRERCKSBERCSUAkJEREL1qYAwsylmdoeZPWBmX+vtekREPs7SEhBmtsjMas1sXZf2mWa20cwqzeyGQ43j7hvc/VrgC8AZ6ahNRESOTLqOIO4CZnZuMLMosAC4AJgKzDWzqR3rjjezR7vchnSsmwU8BixNU20iInIEYukYxN2Xm9m4Ls3TgEp3rwIws/uA2cB6d38NuKibsZYAS8zsMeA36ahPREQOX1oCohsjgepOyzXA9INtYGblwKVANt0cQZjZNcB8oLi4uJhly5alo1YREemiRwFhZk8Dw0JWfc/dH05XMe6+DFh2iD4LgYUAZWVlXl5enq7pRUSkkx4FhLufewRjbwNGd1oe1dEmIiL9QCYvc60AJprZeDOLA5cDSzI4n4iIpFG6LnNdDKwEJptZjZnNc/ckcB3wBLABuN/dX0/HfCIiknnpuoppbjftS9HlqiIi/VKf+ktqERHpOxQQIiISSgEhIiKhFBAiIhJKASEiIqEUECIiEkoBISIioRQQIiISSgEhIiKhFBAiIhJKASEiIqEUECIiEkoBISIioRQQIiISSgEhIiKhFBAiIhJKASEiIqHS8o1y6WJmFwMXAoXAne7+ZC+XJCLysZWu76ReZGa1ZrauS/tMM9toZpVmdsOhxnH337n71cC1wJx01CYiIkcmXaeY7gJmdm4wsyiwALgAmArMNbOpHeuON7NHu9yGdNr8po5tRUSkl6TlFJO7LzezcV2apwGV7l4FYGb3AbOB9e7+GnBR13HMzIAfAb939zVhc5nZNcB8oLi4uJhly5al4y6IiEgXmXwPYiRQ3Wm5Bph+iG2uB84FiszsaHe/o2sHd18ILAQoKyvz8vLy9FQrIiLv06OAMLOngWEhq77n7g+nqxh3vw24LV3jiYjIketRQLj7uUcw9jZgdKflUR1tIiLSD2Ty7yAqgIlmNt7M4sDlwJIMziciImmUrstcFwMrgclmVmNm89w9CVwHPAFsAO5399fTMZ+IiGReuq5imttN+1JgaTrmEBGRD5c+akNEREIpIEREJJQCQkREQikgREQklAJCRERCKSBERCSUAkJEREIpIEREJJQCQkREQikgREQklAJCRERCKSBERCSUAkJEREIpIEREJJQCQkREQikgREQkVJ8KCDObYGZ3mtkDvV2LiMjHXbq+cnSRmdWa2bou7TPNbKOZVZrZDYcax92r3H1eOmoSEZEPJl1HEHcBMzs3mFkUWABcAEwF5prZ1I51x5vZo11uQ9JUi4iIpEG6vpN6uZmN69I8Dah09yoAM7sPmA2sd/fXgIvSMbeIiGRGWgKiGyOB6k7LNcD0g21gZoOAHwInm9mN7n5rSJ9rgPlAcXFxMcuWLUtfxSIi8p4eBYSZPQ0MC1n1PXd/OF3FuHsdcO0h+iwEFgKUlZV5eXl5uqYXEZFOehQQ7n7uEYy9DRjdaXlUR5uIiPQDmbzMtQKYaGbjzSwOXA4syeB8IiKSRum6zHUxsBKYbGY1ZjbP3ZPAdcATwAbgfnd/PR3ziYhI5qXrKqa53bQvBZamYw4REflw9am/pBYRkb5DASEiIqEUECIiEkoBISIioRQQIiISSgEhIiKhFBAiIhJKASEiIqEUECIiEkoBISIioRQQIiISSgEhIiKhFBAiIhJKASEiIqEUECIiEkoBISIiodLyhUHpYmb5wC+ABLDM3e/t5ZJERD62MnoEYWaLzKzWzNZ1aZ9pZhvNrNLMbui06lLgAXe/GpiVydpEROTgMn2K6S5gZucGM4sCC4ALgKnAXDOb2rF6FFDd8XMqw7WJiMhBZDQg3H05sKdL8zSg0t2r3D0B3AfM7lhXw4GQyHhtIiJycL3xHsRI/nyUAAdCYXrHzw8Bt5vZhcAjYRub2TXAfKAYaDWz1zNUZxGwL0Njp3uOIx3ncLbrad9D9TvY+oOtKwV292D+vqC/7Dsflf3mYOu134TPMbZHvd39iG/A08C6kNvsTn3GAes6LV8G/KrT8t8At3+QOjJxAxb2lzmOdJzD2a6nfQ/V72DrD7FuVW/vEx/27zXTc3xU9puDrdd+88Hm+EBHEO5+7hFstg0Y3Wl5VEdbXxN6BNNH5zjScQ5nu572PVS/g63/MB7zD0N/2Xc+KvvN4czTl/W5/cY6UiVjzGwc8Ki7H9exHAM2AedwIBgqgCvcPVOniuQjwMxWuXtZb9ch/Yv2mw8m05e5LgZWApPNrMbM5rl7ErgOeALYANyvcJAeWNjbBUi/pP3mA8j4EYSIiPRPupRURERCKSBERCSUAkJEREIpIKTfMbMJZnanmT3Q27VI/2JmF5vZL83sf8zsM71dT1+ngJA+4XA+2NEPfEzLvN6pVPqaw9x3fucHPgz0WmBzNDpQAAAA6ElEQVROb9TbnyggpK+4i8P7YEeRd93F4e87N3Wsl4NQQEif4If/wY4iwOHtO3bAj4Hfu/uaD7vW/kYBIX1Z2Ac7jjSzQWZ2B3Cymd3YO6VJHxe67wDXA+cCl5nZtb1RWH/Sp75RTqQn3L2OA+eQRQ6Lu98G3NbbdfQXOoKQvqy/fLCj9D3ad9JAASF9WQUw0czGm1kcuBxY0ss1Sf+gfScNFBDSJ+iDHeVIad/JHH1Yn4iIhNIRhIiIhFJAiIhIKAWEiIiEUkCIiEgoBYSIiIRSQIiISCgFhIiIhFJAiIhIKAWEiIiE+v/KsYcaDMxtngAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "('z340', 'READ 2021-05-04/X163062.340')\n", "('ERROR:', IOError(2, 'No such file or directory'))\n" ] } ], "source": [ "# compare calibration files from points 0,1,2\n", "# with the optimized (average or selection of best)\n", "\n", "for sensor in run.sensors:\n", " try:\n", " r0, t0 = read_curve(sensor.outputpath, sensor.outputkind)\n", " plt.figure()\n", " dif = [0,0,0]\n", " for j in range(3):\n", " rr, rt = read_curve(sensor.caldat_file[j], 'zdat')\n", " rc, tc = make_calib(run.rref, run.tref, rr, rt, run.t_points)\n", " dif[j] = compare_calib(r0, t0, rc, tc)\n", " plt.plot(t0, dif[j], '-')\n", " plt.xscale('log')\n", " plt.yscale('symlog', linthreshy=0.001)\n", " plt.grid(True, axis='y')\n", " plt.axis([min(t0),max(t0),-1,1])\n", " # plt.axis([240,330,-1,1])\n", "\n", " # plt.legend(['dif1','dif2','dif3','est'])\n", " plt.show()\n", " except Exception as e:\n", " print('ERROR:', e)\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "if False:\n", " for sensor in run.sensors[4:]:\n", " r0, t0 = read_curve(sensor.outputpath, sensor.outputkind)\n", " plt.figure()\n", " dif = [0,0,0]\n", " plt.plot(t0, r0, '.')\n", " for j in range(3):\n", " rr, rt = read_curve(sensor.caldat_file[j], 'zdat')\n", " rc, tc = make_calib(run.rref, run.tref, rr, rt, run.t_points)\n", " plt.plot(tc, rc, '-')\n", " plt.xscale('log')\n", " plt.yscale('log')\n", " plt.grid(True, axis='y')\n", " plt.axis([20,330,80,300])\n", "\n", " # plt.legend(['dif1','dif2','dif3','est'])\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2.7", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.5" } }, "nbformat": 4, "nbformat_minor": 4 }