530 lines
409 KiB
Plaintext
530 lines
409 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": 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 /home/l_samenv/sea/calib_scripts/calib_data/calib2024-12-12_p0_c1.dat')\n",
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"('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-12-12_p1_c1.dat')\n",
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"('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-12-12_p2_c1.dat')\n",
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"{'selected': 0, 'averaged': 60}\n",
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"('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-12-12_p0_c2.dat')\n",
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"('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-12-12_p1_c2.dat')\n",
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"('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-12-12_p2_c2.dat')\n",
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"{'selected': 0, 'averaged': 60}\n",
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"('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-12-12_p0_c4.dat')\n",
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"('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-12-12_p1_c4.dat')\n",
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"('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-12-12_p2_c4.dat')\n",
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"{'selected': 0, 'averaged': 60}\n",
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"('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-12-12_p0_c5.dat')\n",
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"('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-12-12_p1_c5.dat')\n",
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"('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-12-12_p2_c5.dat')\n",
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"{'selected': 0, 'averaged': 60}\n",
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"('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-12-12_p0_c6.dat')\n",
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"('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-12-12_p1_c6.dat')\n",
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"('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-12-12_p2_c6.dat')\n",
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"{'selected': 0, 'averaged': 60}\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, 'X220790', 'CX-1010-CU-HT'),\n",
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" Sensor(2, 'X220943', 'CX-1050-CU-HT'),\n",
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" Sensor(4, 'X149796', 'CX-1050-SD-HT'),\n",
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" Sensor(5, 'X221295', 'CX-1050-CU-HT'),\n",
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" Sensor(6, 'X222481', 'CX-1050-CU-HT'),\n",
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" ],\n",
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" t_points = (1.58,1.59) + logrange(1.6, 310, n=195) + (330,), # the points to be used in the cal file\n",
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" caldate = '2024-12-12', # 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": 26,
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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 2023-11-05/X133982.340')\n"
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]
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},
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{
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"ename": "IOError",
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"evalue": "[Errno 2] No such file or directory: '2023-11-05/X133982.340'",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m\u001b[0m",
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"\u001b[0;31mIOError\u001b[0mTraceback (most recent call last)",
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"\u001b[0;32m<ipython-input-26-fb75a641ebf6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mtestlist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'X133982'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'z340'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'X133928'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'z340'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'X131824'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'z340'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'X132254'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'z340'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'X137461'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'z340'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0msensno\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtestlist\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mr0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'2023-11-05/%s.340'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0msensno\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'z340'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mr1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'2023-11-05/%s.340'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0msensno\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mdiff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompare_calib\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/home/l_samenv/sea/calib_scripts/zcalib.pyc\u001b[0m in \u001b[0;36mread_curve\u001b[0;34m(filename, kind, instance, **filterargs)\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"READ %s\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 121\u001b[0m \u001b[0mcurves\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfilter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 122\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mIOError\u001b[0m: [Errno 2] No such file or directory: '2023-11-05/X133982.340'"
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]
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 720x432 with 0 Axes>"
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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('2023-11-05/%s.340' % sensno, 'z340')\n",
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" r1, t1 = read_curve('2023-11-05/%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": 5,
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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 2024-12-12/X220790.340')\n",
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"('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-12-12_p0_c1.dat')\n",
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"('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-12-12_p1_c1.dat')\n",
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"('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-12-12_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": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"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",
|
||
|
|
" tmin,tmax,dmax=1.4,310,0.001\n",
|
||
|
|
" plt.plot([tmin,tmax,tmax,tmin,tmin], [-dmax,-dmax,dmax,dmax,-dmax], '-')\n",
|
||
|
|
" plt.legend(['dif1','dif2','dif3','est'])\n",
|
||
|
|
" plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 3,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"('z340', 'READ 2024-12-12/X220790.340')\n",
|
||
|
|
"('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-12-12_p0_c1.dat')\n",
|
||
|
|
"('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-12-12_p1_c1.dat')\n",
|
||
|
|
"('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-12-12_p2_c1.dat')\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"('z340', 'READ 2024-12-12/X220943.340')\n",
|
||
|
|
"('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-12-12_p0_c2.dat')\n",
|
||
|
|
"('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-12-12_p1_c2.dat')\n",
|
||
|
|
"('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-12-12_p2_c2.dat')\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"('z340', 'READ 2024-12-12/X222481.340')\n",
|
||
|
|
"('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-12-12_p0_c6.dat')\n",
|
||
|
|
"('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-12-12_p1_c6.dat')\n",
|
||
|
|
"('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-12-12_p2_c6.dat')\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAECCAYAAADkaECYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi40LCBodHRwOi8vbWF0cGxvdGxpYi5vcmcv7US4rQAAIABJREFUeJzt3Xl8VNX9//HXZyaTTPZAIIQskACRXUCQRVGxbFEoiCuIWC1qqUp/tpa61NqqUPWrdasLUqUoIou0KiCKYhsBRWRRlhCWEJYkLAnZl0lmO78/QjHghHVCEvg8H488Hplzz73nk2Qy77n3nntHjDEopZRSx7M0dAFKKaUaJw0IpZRSPmlAKKWU8kkDQimllE8aEEoppXzSgFBKKeWTBoRSSimfNCCUUkr5FNDQBfyPiIQCrwNOIM0YM6eBS1JKqQtave5BiMhMEckTkS3HtaeKyHYRyRSRh480Xw8sNMbcDYyqz7qUUkqdXH0fYpoFpNZuEBEr8BpwDdAFGCciXYAEIPtIN08916WUUuok6jUgjDErgMLjmvsCmcaYLGOME5gHjAZyqAmJeq9LKaXUyTXEOYh4ftxTgJpg6Ae8ArwqIiOAxXWtLCL3AFOAqMDAwBZJSUn1WKpSSp1/duzYcdgY0/Jk/RrNSWpjTAVw5yn0mwHMAOjTp49Zt25dfZemlFLnFRHZeyr9GuJQTi6QWOtxwpE2pZRSjUhDBMRaIEVEkkUkEBgLLGqAOpRSSp1AfU9znQusBjqKSI6ITDTGuIH7gWVABrDAGJNen3UopZQ6ffV6DsIYM66O9qXA0vocWymlTpXL5SInJ4eqqqqGLsWv7HY7CQkJ2Gy2M1q/0ZykVkqphpKTk0N4eDhJSUmISEOX4xfGGAoKCsjJySE5OfmMtqHXGyilLnhVVVVER0efN+EAICJER0ef1V6RBoRSSsF5FQ7/c7Y/kwaEUko1Mn/5y194/vnnefzxx1m+fDkAK1eupGvXrvTs2ROHw0FqaipRUVGMHDmy3urQcxBKKdVIPfnkk0e/nzNnDo888gi33XYbAFOmTKGyspI333yz3sbXPQillGoEpk2bxkUXXcTAgQPZvn07AHfccQcLFy7krbfeYsGCBfzpT39i/PjxAAwePJjw8PB6rUn3IJRSqpYnFqezdX+pX7fZJS6CP/+8a53L169fz7x58/jhhx9wu91ccskl9O7d++jyu+66i1WrVjFy5EhuvPFGv9Z2IhoQSinVwFauXMmYMWMICQkBYNSoxvGROBoQSilVy4ne6V9o9ByEUko1sCuvvJKPPvoIh8NBWVkZixfX+YkH55TuQSilVAO75JJLuOWWW+jRowcxMTFceumlJ13niiuuYNu2bZSXl5OQkMDbb7/N8OHD/VqXGGP8usFzST8PQinlDxkZGXTu3Lmhy6gXvn42EVlvjOlzsnX1EJNSSimfNCCUUkr5pAGhlFLKJw0IpZRSPjWqgBCRdiLytogsbOhalFLqQue3gBCRmSKSJyJbjmtPFZHtIpIpIg+faBvGmCxjzER/1aSUUurM+XMPYhaQWrtBRKzAa8A1QBdgnIh0EZHuIrLkuK8YP9ailFJN1slu97169WoGDBhA165dufjii5k/f3691OG3C+WMMStEJOm45r5ApjEmC0BE5gGjjTFPA/V3E3OllDoP1HW77x07dvDuu++SkpLC/v376d27N8OHDycqKsqv49f3ldTxQHatxzlAv7o6i0g0MA3oJSKPHAmS4/vcA0wBoqKiokhLS/NvxUqpC05kZCRlZWUNWsNzzz3H+++/T8uWLYmPj6dXr16MHz+e1NRUSkpKmD9/Pp999hmLFi3i7bffBqCsrIzw8HBatGjB7t276dChw0+2W1VVdcavk43qVhvGmAJg0kn6zABmQM2V1IMGDToHlSmlzmcZGRk/frbCpw/Dwc3+HSC2O1zzTJ2L169fz4cffsimTZuO3u67f//+2Gw2goODmTBhAuvWrfN5u+/vvvsOt9tNjx49sFh+etbAbrfTq1evMyq7vgMiF0is9TjhSJtSSqkjzvR23wcOHGDChAm88847PsPhbNV3QKwFUkQkmZpgGAvcWs9jKqXUmTvBO/3GpLS0lBEjRjBt2jT69+9fL2P4c5rrXGA10FFEckRkojHGDdwPLAMygAXGmHR/jamUUueD073dt9PpZMyYMdx+++31+glz/pzFNK6O9qXAUn+No5RS55vTvd33ggULWLFiBQUFBcyaNQuAWbNm0bNnT7/Wpbf7Vkpd8PR23741qlttKKWUajw0IJRSSvmkAaGUUsonDQillFI+aUAopZTySQNCKaWUT43qXkxKKaVqbvcdFhZGaWkpV155JUOGDGHlypVMmjQJm83Gxx9/zJgxY/B6vbhcLiZPnsykSSe8jd0Z0YBQSqlGqq7bfTudTlavXk1QUBDl5eV069aNUaNGERcX59fxNSCUUqoRmDZtGu+88w4xMTEkJibSu3dv7rjjDkaOHElxcTELFixg2bJlfPrpp8yZM+foetXV1Xi93nqpSQNCKaVqefa7Z9lWuM2v2+zUvBMP9X2ozuXr169n3rx5/PDDD0dv9927d++jy++66y5WrVp1zO2+s7OzGTFiBJmZmTz33HN+33sAPUmtlFINrvbtviMiIk7pdt+JiYls2rSJzMxM3nnnHQ4dOuT3unQPQimlajnRO/3GKC4ujm7durFy5Uq/39lV9yCUUqqBne7tvnNycnA4HAAUFRWxatUqOnbs6Pe6dA9CKaUa2One7jsjI4MHH3wQEcEYw+9//3u6d+/u97r0dt9KqQue3u7bNz3EpJRSyqdGdYhJRK4DRgARwNvGmM8buCSllLpg+fMzqWeKSJ6IbDmuPVVEtotIpog8fKJtGGM+MsbcDUwCbvFXbUoppU6fP/cgZgGvAu/+r0FErMBrwFAgB1grIosAK/D0cev/0hiTd+T7x46sp5RSqoH4LSCMMStEJOm45r5ApjEmC0BE5gGjjTFPAyOP34aICPAM8KkxZoOvcUTkHmAKEBUVFUVaWpq/fgSl1AUqMjKSsrKyhi6jXlRVVZ3x62R9n4OIB7JrPc4B+p2g/2RgCBApIh2MMdOP72CMmQHMgJpZTIMGDfJftUqpC1JGRgbh4eENXUa9sNvt9OrV64zWbVSzmIwxrxhjehtjJvkKB6WUutDNmjWL/fv3n5Ox6jsgcoHEWo8TjrQppZQ6A+cyIOr7ENNaIEVEkqkJhrHArfU8plJKNTnvvfcer7zyCk6nk379+vH6668zceJE1q1bh4jwy1/+ksTERNatW8f48eMJDg5m9erVBAcH11tNfgsIEZkLDAJaiEgO8GdjzNsicj+wjJqZSzONMen+GlMppfzt4F//SnWGf2/3HdS5E7GPPlrn8oyMDObPn8/XX3+NzWbj3nvvZerUqeTm5rJlS82VA8XFxURFRfHqq6/y/PPP06fPSS+EPmv+nMU0ro72pcBSf42jlFLnmy+//JL169cfvQeTw+EgNTWVrKwsJk+ezIgRIxg2bNg5r6tRXUmtlFIN7UTv9OuLMYZf/OIXPP30sZeHTZs2jWXLljF9+nQWLFjAzJkzz2ldjWoWk1JKXYgGDx7MwoULycuruVa4sLCQvXv34vV6ueGGG5g6dSobNtRcGhYeHn7OrtnQPQillGpgXbp0YerUqQwbNgyv14vNZuOFF15gzJgxRz9v+n97F3fccQeTJk06Jyep9XbfSqkLnt7u2zc9xKSUUsonDQillFI+aUAopZTySQNCKaWomWp6vjnbn0kDQil1wbPb7RQUFJxXIWGMoaCgALvdfsbb0GmuSqkLXkJCAjk5OeTn5zd0KX5lt9tJSEg44/U1IJRSFzybzUZycnJDl9Ho6CEmpZRSPmlAKKWU8kkDQimllE8aEEoppXzSgFBKKeVTowoIEeksItNFZKGI/Lqh61FKqQuZ3wJCRGaKSJ6IbDmuPVVEtotIpog8fKJtGGMyjDGTgJuBy/1Vm1JKqdPnzz2IWUBq7QYRsQKvAdcAXYBxItJFRLqLyJL
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"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",
|
||
|
|
" tmin,tmax,dmax=1.4,310,0.001\n",
|
||
|
|
" plt.plot([tmin,tmax,tmax,tmin,tmin], [-dmax,-dmax,dmax,dmax,-dmax], '-')\n",
|
||
|
|
" plt.legend(['dif1','dif2','dif3','est'])\n",
|
||
|
|
" plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 4,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"('z340', 'READ 2024-12-12/X220790.340')\n",
|
||
|
|
"('z340', 'READ 2024-12-12/X220943.340')\n",
|
||
|
|
"('z340', 'READ 2024-12-12/X149796.340')\n",
|
||
|
|
"('z340', 'READ 2024-12-12/X221295.340')\n",
|
||
|
|
"('z340', 'READ 2024-12-12/X222481.340')\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.legend.Legend at 0x4ae3090>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 4,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1500x900 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.figure(figsize=(10,6),dpi=150)\n",
|
||
|
|
"for sensor in run.sensors:\n",
|
||
|
|
" r0, t0 = read_curve(sensor.outputpath, sensor.outputkind)\n",
|
||
|
|
" plt.loglog(t0,r0,'o',alpha=0.5, label=\"CH{}:{} {}\".format(sensor.channel, sensor.serialno, sensor.model))\n",
|
||
|
|
"\n",
|
||
|
|
"plt.title(sensor.outputpath.split('/')[0])\n",
|
||
|
|
"plt.grid(which='both',alpha=0.2)\n",
|
||
|
|
"plt.xlabel('T (K)')\n",
|
||
|
|
"plt.ylabel('R (Ohm)')\n",
|
||
|
|
"plt.legend()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"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"
|
||
|
|
},
|
||
|
|
"widgets": {
|
||
|
|
"application/vnd.jupyter.widget-state+json": {
|
||
|
|
"state": {},
|
||
|
|
"version_major": 2,
|
||
|
|
"version_minor": 0
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 4
|
||
|
|
}
|