334 lines
77 KiB
Plaintext
334 lines
77 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Scripts for Calibration of Cernox Sensors\n",
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"---------------------------------------\n",
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"\n",
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"Make a copy of this notebook for an other run"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import math\n",
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"\n",
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"from zcalib import read_curve, convert_res, compare_calib, make_calib, logrange, Sensor, CalibRun, nplog, npexp"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"('lsdat', 'READ /afs/psi.ch/project/SampleEnvironment/SE_internal/Thermometer_calibs/2012/73027 Cernox 5/X75610.dat')\n",
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"('zdat', 'READ calib_data/calib2018-10-25_p0_c1.dat')\n",
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"('zdat', 'READ calib_data/calib2018-10-25_p1_c1.dat')\n",
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"('zdat', 'READ calib_data/calib2018-10-25_p2_c1.dat')\n",
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"{'selected': 2, 'averaged': 46}\n",
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"('zdat', 'READ calib_data/calib2018-10-25_p0_c2.dat')\n",
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"('zdat', 'READ calib_data/calib2018-10-25_p1_c2.dat')\n",
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"('zdat', 'READ calib_data/calib2018-10-25_p2_c2.dat')\n",
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"{'selected': 2, 'averaged': 46}\n",
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"('zdat', 'READ calib_data/calib2018-10-25_p0_c4.dat')\n",
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"('zdat', 'READ calib_data/calib2018-10-25_p1_c4.dat')\n",
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"('zdat', 'READ calib_data/calib2018-10-25_p2_c4.dat')\n",
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"{'selected': 2, 'averaged': 46}\n",
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"('zdat', 'READ calib_data/calib2018-10-25_p0_c6.dat')\n",
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"('zdat', 'READ calib_data/calib2018-10-25_p1_c6.dat')\n",
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"('zdat', 'READ calib_data/calib2018-10-25_p2_c6.dat')\n",
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"{'selected': 2, 'averaged': 46}\n",
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"calibration file has 199 points\n"
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]
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}
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],
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"source": [
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"run = CalibRun([\n",
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" Sensor(3, 'X75610'), # the reference sensor must be the first\n",
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" Sensor(1, 'X133979', 'CX-1050-SD'),\n",
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" Sensor(2, 'X133978', 'CX-1050-SD'),\n",
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" Sensor(4, 'X133928', 'CX-1050-SD'),\n",
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" Sensor(6, 'X133981', 'CX-1050-SD'),\n",
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" ],\n",
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" t_points = (1.0, 1.2) + logrange(1.4, 310, n=195) + (330,),\n",
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" caldate = '2018-10-25',\n",
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" logT = False,\n",
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" logR = True,\n",
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" calib_data_file = 'calib_data/calib%s_p%d_c%d.dat',\n",
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" outputpath='%s/%s.340')\n",
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"# use slightly lower smoothtst as we measured less points (1e-7 for 60, 0.8e-7 for 48 and 0.4e-7 for 24 points)\n",
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"run.make(diflim=0.001, smoothref=1e-7, smoothtst=0.8e-7)\n",
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"print('calibration file has %d points' % len(run.t_points))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"('z340', 'READ 2018-10-25/X133979.340')\n",
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"('zdat', 'READ calib_data/calib2018-10-25_p0_c1.dat')\n",
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"('zdat', 'READ calib_data/calib2018-10-25_p1_c1.dat')\n",
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"('zdat', 'READ calib_data/calib2018-10-25_p2_c1.dat')\n"
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]
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},
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAD/CAYAAAD12nFYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n064x6O7nVCkwiEjhFBF
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"text/plain": [
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"<matplotlib.figure.Figure at 0x5222c90>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"('z340', 'READ 2018-10-25/X133978.340')\n",
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"('zdat', 'READ calib_data/calib2018-10-25_p0_c2.dat')\n",
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"('zdat', 'READ calib_data/calib2018-10-25_p1_c2.dat')\n",
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"('zdat', 'READ calib_data/calib2018-10-25_p2_c2.dat')\n"
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]
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},
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{
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"data": {
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|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x522b250>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"('z340', 'READ 2018-10-25/X133928.340')\n",
|
||
|
|
"('zdat', 'READ calib_data/calib2018-10-25_p0_c4.dat')\n",
|
||
|
|
"('zdat', 'READ calib_data/calib2018-10-25_p1_c4.dat')\n",
|
||
|
|
"('zdat', 'READ calib_data/calib2018-10-25_p2_c4.dat')\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
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|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x5a37450>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"('z340', 'READ 2018-10-25/X133981.340')\n",
|
||
|
|
"('zdat', 'READ calib_data/calib2018-10-25_p0_c6.dat')\n",
|
||
|
|
"('zdat', 'READ calib_data/calib2018-10-25_p1_c6.dat')\n",
|
||
|
|
"('zdat', 'READ calib_data/calib2018-10-25_p2_c6.dat')\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
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|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x5a42b10>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# compare calibration files from points 1,2 and 3\n",
|
||
|
|
"# with the optimized from above. To estimate the precision\n",
|
||
|
|
"# at each point the curve with the biggest difference does\n",
|
||
|
|
"# not have to be taken in to account\n",
|
||
|
|
"\n",
|
||
|
|
"for sensor in run.sensors:\n",
|
||
|
|
" r0, t0 = read_curve(sensor.outputpath, sensor.outputkind)\n",
|
||
|
|
" plt.figure()\n",
|
||
|
|
" dif = [0,0,0]\n",
|
||
|
|
" for j in range(3):\n",
|
||
|
|
" rref, rtst = read_curve(sensor.caldat_file[j], 'zdat')\n",
|
||
|
|
" rc, tc = make_calib(run.rref, run.tref, rref, rtst, run.t_points)\n",
|
||
|
|
" dif[j] = compare_calib(r0, t0, rc, tc)\n",
|
||
|
|
" plt.plot(t0, dif[j], '-')\n",
|
||
|
|
" #n = len(dif[0])\n",
|
||
|
|
" #dd = np.zeros(n)\n",
|
||
|
|
" #for i in range(n):\n",
|
||
|
|
" # # determine second biggest absolute value\n",
|
||
|
|
" # dd[i] = sorted([abs(dif[j][i]) for j in range(3)])[1]\n",
|
||
|
|
" #plt.plot(t0, dd, '.')\n",
|
||
|
|
" plt.xscale('log')\n",
|
||
|
|
" # plt.yscale('symlog', linthreshy=0.001)\n",
|
||
|
|
" plt.grid(True, axis='y')\n",
|
||
|
|
" plt.axis([min(t0),max(t0),-0.005,0.005])\n",
|
||
|
|
" plt.legend(['dif1','dif2','dif3','est'])\n",
|
||
|
|
" plt.show()\n",
|
||
|
|
"\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"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": {},
|
||
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|
"outputs": [],
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||
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|
"source": []
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||
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|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
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||
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|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
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|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
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||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
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||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
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|
"source": []
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||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
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|
"outputs": [],
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"source": []
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},
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{
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||
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|
"cell_type": "code",
|
||
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"execution_count": null,
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"metadata": {},
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||
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"outputs": [],
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"source": []
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},
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{
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|
"cell_type": "code",
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||
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|
"execution_count": null,
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"metadata": {},
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|
"outputs": [],
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"source": []
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},
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|
{
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||
|
|
"cell_type": "code",
|
||
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|
"execution_count": null,
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|
"metadata": {},
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||
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|
"outputs": [],
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"source": []
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||
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},
|
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{
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||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
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||
|
|
"outputs": [],
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||
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|
"source": []
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||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 2",
|
||
|
|
"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": 2
|
||
|
|
}
|