385 lines
137 KiB
Plaintext
385 lines
137 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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"from scipy.interpolate import splrep, splev\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": 8,
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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 /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p0_c1.dat')\n",
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"('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p1_c1.dat')\n",
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"('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p2_c1.dat')\n",
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"{'selected': 0, 'averaged': 25}\n",
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"('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p0_c2.dat')\n",
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"('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p1_c2.dat')\n",
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"('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p2_c2.dat')\n",
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"{'selected': 0, 'averaged': 25}\n",
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"('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p0_c4.dat')\n",
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"('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p1_c4.dat')\n",
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"('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p2_c4.dat')\n",
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"{'selected': 0, 'averaged': 25}\n",
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"('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p0_c5.dat')\n",
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"('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p1_c5.dat')\n",
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"('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p2_c5.dat')\n",
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"{'selected': 0, 'averaged': 25}\n",
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"('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p0_c6.dat')\n",
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"('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p1_c6.dat')\n",
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"('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p2_c6.dat')\n",
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"{'selected': 0, 'averaged': 25}\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, 'X133982', 'CX-1050-SD'),\n",
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" Sensor(2, 'X137461', 'CX-1030-SD'),\n",
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" Sensor(4, 'X133928', 'CX-1050-SD'),\n",
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" Sensor(5, 'X131824', 'CX-1050-CU'),\n",
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" Sensor(6, 'X132254', '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,), # the points to be used in the cal file\n",
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" # 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",
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" caldate = '2018-11-22', # the first measuring day!\n",
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" logT = False,\n",
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" logR = True,\n",
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" calib_data_file = '/home/l_samenv/sea/calib_scripts/calib_data/calib%s_p%d_c%d.dat',\n",
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" outputpath='%s/%s.340')\n",
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"# smooth depends on number of measured 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=0.4e-7)"
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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": 9,
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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 2018-11-22/X133982.340')\n",
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"('z340', 'READ 2018-11-20/X133982.340')\n",
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"('z340', 'READ 2018-11-22/X133928.340')\n",
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"('z340', 'READ 2018-11-20/X133928.340')\n",
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"('z340', 'READ 2018-11-22/X131824.340')\n",
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"('z340', 'READ 2018-11-20/X131824.340')\n",
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"('z340', 'READ 2018-11-22/X132254.340')\n",
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"('z340', 'READ 2018-11-20/X132254.340')\n",
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"('z340', 'READ 2018-11-22/X137461.340')\n",
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"('z340', 'READ 2018-11-20/X137461.340')\n"
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]
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},
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAlkAAAF2CAYAAABd6o05AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nd+/eJJPJNOfdtm0bFRc
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"text/plain": [
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"<matplotlib.figure.Figure at 0x4565c50>"
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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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"source": [
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"# compare known sensors (skip if not applicable)\n",
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"plt.figure(figsize=(10, 6))\n",
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"tmin,tmax,dmax=1.4,310,0.001\n",
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"testlist = (('X133982', 'z340'),('X133928', 'z340'),('X131824', 'z340'),('X132254', 'z340'),('X137461', 'z340'))\n",
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"for sensno, kind in testlist:\n",
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" r0, t0 = read_curve('2018-11-22/%s.340' % sensno, 'z340')\n",
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" r1, t1 = read_curve('2018-11-20/%s.340' % sensno, kind)\n",
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" diff = compare_calib(r1, t1, r0, t0)\n",
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" plt.plot(t0, diff, '-')\n",
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"plt.plot([tmin,tmax,tmax,tmin,tmin], [-dmax,-dmax,dmax,dmax,-dmax], '-')\n",
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"plt.legend([sensno + \".\" + kind[-3:] for sensno, kind in testlist] + [\"window\"])\n",
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"plt.xscale('log')\n",
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"plt.yscale('symlog',linthreshy=dmax)\n",
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"plt.grid(True, axis='y')\n",
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"plt.axis([1.0,350,-1,1])\n",
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"plt.show()"
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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": 4,
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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 2018-11-22/X133982.340')\n",
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"('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p0_c1.dat')\n",
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"('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p1_c1.dat')\n",
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"('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_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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|
||
|
|
"('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p0_c2.dat')\n",
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|
"('z340', 'READ 2018-11-22/X133928.340')\n",
|
||
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|
"('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p0_c4.dat')\n",
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"('z340', 'READ 2018-11-22/X131824.340')\n",
|
||
|
|
"('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p0_c5.dat')\n",
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|
|
"text": [
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||
|
|
"('z340', 'READ 2018-11-22/X132254.340')\n",
|
||
|
|
"('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p0_c6.dat')\n",
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"('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p1_c6.dat')\n",
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||
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|
"('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p2_c6.dat')\n"
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||
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|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x57ae650>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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",
|
||
|
|
" 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.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": []
|
||
|
|
},
|
||
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|
{
|
||
|
|
"cell_type": "code",
|
||
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|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
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|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
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||
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|
"source": []
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||
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|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
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||
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|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"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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||
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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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|
"outputs": [],
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|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
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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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{
|
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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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||
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|
"outputs": [],
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||
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|
"source": []
|
||
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|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
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||
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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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|
"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": 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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{
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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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"outputs": [],
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"source": []
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}
|
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],
|
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|
"metadata": {
|
||
|
|
"kernelspec": {
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||
|
|
"display_name": "Python 2.7",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python2"
|
||
|
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},
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|
"language_info": {
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||
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|
"codemirror_mode": {
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||
|
|
"name": "ipython",
|
||
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|
"version": 2
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|
},
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||
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|
"file_extension": ".py",
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||
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|
"mimetype": "text/x-python",
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|
"name": "python",
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||
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|
"nbconvert_exporter": "python",
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||
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|
"pygments_lexer": "ipython2",
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||
|
|
"version": "2.7.5"
|
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}
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},
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"nbformat": 4,
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|
"nbformat_minor": 2
|
||
|
|
}
|