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sea/calib_scripts/calib_cernox.ipynb
l_samenv 46dcf8e77c initial commit
2022-08-18 15:04:28 +02:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"'''\n",
"utilities for creating cernox calibration curves\n",
"'''\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from scipy.interpolate import splrep, splev\n",
"import math\n",
"from glob import glob\n",
"import time\n",
"\n",
"nplog = np.vectorize(math.log10)\n",
"npexp = np.vectorize(lambda x:10 ** x)\n",
"\n",
"class StdFilter(object):\n",
" '''filter used for reading columns'''\n",
"\n",
" def __init__(self, colnums = None, logformat = None):\n",
" if colnums is None:\n",
" colnums = (0,1)\n",
" self.colnums = colnums\n",
" self.logformat = logformat\n",
" self.output = [[] for i in colnums]\n",
" \n",
" def parse(self, line):\n",
" '''get numbers from a line and put them to self.output'''\n",
" values = []\n",
" row = line.split()\n",
" try:\n",
" for c in self.colnums:\n",
" values.append(float(row[c]))\n",
" except (IndexError, ValueError):\n",
" # print('SKIP: %s' % line.strip())\n",
" return\n",
" self.posttreat(values)\n",
" \n",
" def posttreat(self, values):\n",
" '''post treatment, mainly converting from log'''\n",
" if self.logformat:\n",
" values = values[:]\n",
" for c in self.logformat:\n",
" values[c] = 10 ** values[c]\n",
" for out, val in zip(self.output, values):\n",
" out.append(val)\n",
"\n",
"class Filter340(StdFilter):\n",
" '''filter for LakeShore *.340 files'''\n",
"\n",
" def __init__(self):\n",
" self.logformat = None\n",
" self.header = True\n",
" self.output = [[], []]\n",
" \n",
" def parse(self, line):\n",
" '''scan header for data format'''\n",
" if self.header:\n",
" if line.startswith(\"Data Format\"):\n",
" dataformat = line.split(\":\")[1].strip()[0]\n",
" if dataformat == '4':\n",
" self.logformat = [0]\n",
" elif dataformat == '5':\n",
" self.logformat = [0, 1]\n",
" elif line.startswith(\"No.\"):\n",
" self.header = False\n",
" # print('HDR: %s' % line.strip())\n",
" return\n",
" try:\n",
" no, r, t = line.split()\n",
" self.posttreat([float(r),float(t)])\n",
" except ValueError:\n",
" # print('SKIP: %s' % line.strip())\n",
" return\n",
" except OverflowError:\n",
" # print('OVERFLOW:', no, r, t)\n",
" pass\n",
"\n",
"\n",
"def read_curve(filename, kind=None, instance=None, **filterargs):\n",
" '''general curve reading'''\n",
" path = '%s'\n",
" if kind is None:\n",
" kind = filename.split(\".\")[-1]\n",
" try:\n",
" filelist = glob(KINDS[kind][\"path\"] % filename)\n",
" if instance is None :\n",
" if len(filelist) > 1:\n",
" for i,file in enumerate(filelist):\n",
" print(i,file)\n",
" raise ValueError('instance number needed')\n",
" instance = 0\n",
" if len(filelist) > 0:\n",
" filename = filelist[instance]\n",
" except KeyError:\n",
" # print(\"Keep\", kind, filename)\n",
" pass\n",
" try:\n",
" args = KINDS[kind][\"args\"]\n",
" except KeyError:\n",
" args = {}\n",
" args.update(filterargs)\n",
" try:\n",
" filter = KINDS[kind][\"filter\"](**args)\n",
" except KeyError:\n",
" filter = StdFilter(**args)\n",
" print(\"READ %s\" % filename)\n",
"\n",
" with open(filename) as f:\n",
" curves = [[] for c in filter.output]\n",
" for line in f:\n",
" values = filter.parse(line)\n",
" return [np.asarray(c) for c in filter.output]\n",
"\n",
"def convert_res(res1, res2, resc1, log1=True, log2=True):\n",
" '''interpolate (by default in log scale) the curve res1, res2 from sensors 1,2\n",
" \n",
" resc1: input for values with sensor 1\n",
" return nvalues: interpolated values for sensor 2\n",
" \n",
" may also be used for converting resistivity to T or vice versa\n",
" '''\n",
" if log1:\n",
" res1 = nplog(res1)\n",
" resc1 = nplog(resc1)\n",
" if log2:\n",
" res2 = nplog(res2)\n",
" if res1[-1] < res1[0]:\n",
" res1 = res1[::-1]\n",
" res2 = res2[::-1]\n",
" fun = splrep(res1, res2, s=0)\n",
" resc2 = splev(resc1, fun)\n",
" if log2:\n",
" resc2 = npexp(resc2)\n",
" return resc2\n",
"\n",
"def compare_calib(res1, res2, test1, test2, log1=True, log2=True):\n",
" '''compare two curves\n",
" \n",
" the number of points returned is len(test1)\n",
" '''\n",
" calc2 = convert_res(res1, res2, test1, log1, log2)\n",
" dif = (calc2 - test2) / test2\n",
" for i, d in enumerate(dif):\n",
" if test1[i] < 2:\n",
" print(test1[i], test2[i], calc2[i], dif[i])\n",
" return dif\n",
"\n",
"def make_calib(r_dat, t_dat, r_ref, r_test, t_points=(1.0, 1.2, (1.4, 310, 195), 330)):\n",
" '''create calibration curve\n",
" \n",
" r_dat,t_dat: data from known sensor\n",
" r_ref, r_test: resisitvities measured\n",
" t_points: points to be used for the output. Tuples (firstT, lastT, npoints) denote a log scale.\n",
" '''\n",
" t_cal = np.asarray([])\n",
" for t in t_points:\n",
" try:\n",
" t_min, t_max, n_points = t\n",
" t = np.logspace(math.log10(t_min), math.log10(t_max), n_points)\n",
" except TypeError:\n",
" pass\n",
" t_cal= np.append(t_cal, t)\n",
" t_cal.sort()\n",
" r_dat_cal = convert_res(t_dat, r_dat, t_cal)\n",
" r_cal = convert_res(r_ref, r_test, r_dat_cal)\n",
" return r_cal, t_cal\n",
"\n",
"def write_calib340(rc, tc, out_sensor_no, logR=True, logT=False, path='%s.340', caldate=None, model=None, package='CU'):\n",
" '''write a calibration curve in LakeShores 340 format\n",
" \n",
" out_sensor_no: output sensor serial no (a string!)\n",
" logT, logR: use logT/logT for output\n",
" path: for output, might contain %s to be replaced by the sensor serial no\n",
" caldate: date of measurment (default: today)\n",
" model: something like CX-1050 (guessed, if not given)\n",
" package: for example CU or SD\n",
" '''\n",
" try:\n",
" dataformat = {(True,True): 5, (True,False): 4, (False,False): 3}[(logR,logT)]\n",
" except KeyError:\n",
" raise ValueError(\"logT without logR is not possible\")\n",
" if caldate is None:\n",
" caldate = time.strftime(\"%Y-%m-%d\")\n",
" if model is None:\n",
" if float(convert_res(tc, rc, 20.0)) > 50000:\n",
" model = 'CX-unknown'\n",
" elif float(convert_res(tc, rc, 20.0)) > 5000:\n",
" model = 'CX-1080'\n",
" elif float(convert_res(tc, rc, 4.0)) > 8000:\n",
" model = 'CX-1070'\n",
" elif float(convert_res(tc, rc, 1.4)) > 3000:\n",
" model = 'CX-1050'\n",
" elif float(convert_res(tc, rc, 1.4)) > 600:\n",
" model = 'CX-1030'\n",
" elif float(convert_res(tc, rc, 1.4)) > 200:\n",
" model = 'CX-1010'\n",
" else:\n",
" model = 'CX-unknown'\n",
" try:\n",
" path = path % out_sensor_no\n",
" except TypeError:\n",
" pass\n",
" with open(path, 'w') as f:\n",
" f.write(HEADER % (model, package, out_sensor_no, dataformat, tc[-1], len(tc), caldate))\n",
" if logT:\n",
" tc = nplog(tc)\n",
" if logR:\n",
" rc = nplog(rc)\n",
" for row in zip(range(1,len(tc)+1), rc, tc):\n",
" f.write(\"%3d %#10.7g %#10.7g\\n\" % row)\n",
"\n",
"KINDS = {\n",
" # lakeshore 340 format\n",
" \"340\": dict(path=\"/home/l_samenv/sea/tcl/calcurves/%s\", filter=Filter340),\n",
" # markus zollikers *.inp calcurve format\n",
" \"inp\": dict(path=\"/home/l_samenv/sea/tcl/calcurves/%s\", filter=StdFilter),\n",
" # format from sea/tcl/startup/calib_ext.tcl\n",
" \"caldat\": dict(path=\"/home/l_samenv/zolliker/calib_test/%s\", filter=StdFilter, args=dict(colnums=(1,2))),\n",
" # lakeshore raw data *.dat format\n",
" \"dat\": dict(path=\"/afs/psi.ch/project/SampleEnvironment/SE_internal/Thermometer_calibs/*/*/%s\", filter=StdFilter),\n",
"}\n",
"\n",
"\n",
"HEADER = '''Sensor Model: %s-%s\n",
"Serial Number: %s\n",
"Data Format: %d (Log Ohms/Kelvin)\n",
"SetPoint Limit: %.1f (Kelvin)\n",
"Temperature coefficient: 1 (Negative)\n",
"Number of Breakpoints: %d\n",
"Calibration date: %s\n",
"\n",
"No. Units Temperature (K)\n",
"\n",
"'''\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"READ /afs/psi.ch/project/SampleEnvironment/SE_internal/Thermometer_calibs/2012/73027 Cernox 5/X75610.dat\n",
"READ /home/l_samenv/zolliker/calib_test/calib2018-10-25_c1_chan1.dat\n",
"READ /home/l_samenv/zolliker/calib_test/calib2018-10-25_c2_chan1.dat\n",
"READ /home/l_samenv/zolliker/calib_test/calib2018-10-25_c3_chan1.dat\n",
"READ /home/l_samenv/zolliker/calib_test/calib2018-10-25_c1_chan2.dat\n",
"READ /home/l_samenv/zolliker/calib_test/calib2018-10-25_c2_chan2.dat\n",
"READ /home/l_samenv/zolliker/calib_test/calib2018-10-25_c3_chan2.dat\n",
"READ /home/l_samenv/zolliker/calib_test/calib2018-10-25_c1_chan4.dat\n",
"READ /home/l_samenv/zolliker/calib_test/calib2018-10-25_c2_chan4.dat\n",
"READ /home/l_samenv/zolliker/calib_test/calib2018-10-25_c3_chan4.dat\n",
"READ /home/l_samenv/zolliker/calib_test/calib2018-10-25_c1_chan6.dat\n",
"READ /home/l_samenv/zolliker/calib_test/calib2018-10-25_c2_chan6.dat\n",
"READ /home/l_samenv/zolliker/calib_test/calib2018-10-25_c3_chan6.dat\n"
]
}
],
"source": [
"# write optimized calibration curves, selecting the best samples for each temperatures\n",
"#\n",
"# algorithm: calculate pairwise difference of samples 1,2,3 (internal index: 0,1,2)\n",
"# the two samples with the biggest difference are omitted for each temperature\n",
"\n",
"# reference sensor\n",
"tref, rref = read_curve(\"X75610.dat\")\n",
"t_points = (1.0, 1.2, (1.4, 310, 195), 330)\n",
"config = {1: 'X133979', 2: 'X133978', 4: 'X133928', 6:'X133981'}\n",
"# model = None: automatic\n",
"options = dict(logT=False, logR=True, caldate='2018-10-25', package='SD', model=None)\n",
"\n",
"for chan in sorted(config.keys()):\n",
" sensor = config[chan]\n",
" ref = [0,0,0]\n",
" tst = [0,0,0]\n",
" for j in range(3):\n",
" ref[j], tst[j] = read_curve('calib%s_c%d_chan%d.dat' % (options['caldate'], j+1, chan,), 'caldat')\n",
" dif = [0,0,0]\n",
" for j in range(3):\n",
" dif[j] = compare_calib(ref[(j+1) % 3], tst[(j+1) % 3], ref[(j+2) % 3], tst[(j+2) % 3])\n",
" n = len(ref[0])\n",
" refbest = np.zeros(n)\n",
" tstbest = np.zeros(n)\n",
" for i in range(n):\n",
" ibest = 2\n",
" for j in range(3):\n",
" if abs(dif[(j+1) % 3][i]) < abs(dif[j][i]) > abs(dif[(j+2) % 3][i]):\n",
" ibest = j\n",
" break\n",
" refbest[i] = ref[ibest][i]\n",
" tstbest[i] = tst[ibest][i]\n",
"\n",
" rc, tc = make_calib(rref, tref, refbest, tstbest, t_points)\n",
" write_calib340(rc, tc, sensor, **options)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"READ /afs/psi.ch/project/SampleEnvironment/SE_internal/Thermometer_calibs/2012/73027 Cernox 5/X75610.dat\n",
"READ X133979.340\n",
"READ /home/l_samenv/zolliker/calib_test/calib2018-10-25_c1_chan1.dat\n",
"READ /home/l_samenv/zolliker/calib_test/calib2018-10-25_c2_chan1.dat\n",
"READ /home/l_samenv/zolliker/calib_test/calib2018-10-25_c3_chan1.dat\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x33d98d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"READ X133978.340\n",
"READ /home/l_samenv/zolliker/calib_test/calib2018-10-25_c1_chan2.dat\n",
"READ /home/l_samenv/zolliker/calib_test/calib2018-10-25_c2_chan2.dat\n",
"READ /home/l_samenv/zolliker/calib_test/calib2018-10-25_c3_chan2.dat\n"
]
},
{
"data": {
"image/png": 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Z8WdjTL134nVqFRWuK6iiwv2p9Ra1PWvdJGJPPeUeHTrtNNf3OWaM78f96itYt879twsJ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\n+PXo97gm4w9s/Ns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"text/plain": [
"<matplotlib.figure.Figure at 0x3485610>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"READ X133928.340\n",
"READ /home/l_samenv/zolliker/calib_test/calib2018-10-25_c1_chan4.dat\n",
"READ /home/l_samenv/zolliker/calib_test/calib2018-10-25_c2_chan4.dat\n",
"READ /home/l_samenv/zolliker/calib_test/calib2018-10-25_c3_chan4.dat\n"
]
},
{
"data": {
"image/png": 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fgs9HxMBB7Hjyz+QOSmdX8S7yD+YT//ESzvjrW7z0t5sZ1HUQp/Y6lV5Jgf0N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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x5617e50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"READ X133981.340\n",
"READ /home/l_samenv/zolliker/calib_test/calib2018-10-25_c1_chan6.dat\n",
"READ /home/l_samenv/zolliker/calib_test/calib2018-10-25_c2_chan6.dat\n",
"READ /home/l_samenv/zolliker/calib_test/calib2018-10-25_c3_chan6.dat\n"
]
},
{
"data": {
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M/pphiU4mnD0Of5s/+Ss3kPjZdi7LKSQsp5DVIYE4IsM5Kzme0uhQPq4opSgY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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x512c7d0>"
]
},
"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",
"config = {1: 'X133979', 2: 'X133978', 4: 'X133928', 6:'X133981'}\n",
"ref_dat_file = \"X75610.dat\"\n",
"logTemperature = False\n",
"# discrete points or tuples denoting a logspace (first, last, number of points)\n",
"t_points = (1.0, 1.2, (1.4, 310, 195), 330)\n",
"td, rd = read_curve(ref_dat_file)\n",
"\n",
"for chan, sensor in config.items():\n",
" r0, t0 = read_curve(sensor+\".340\")\n",
" plt.figure()\n",
" dif = {}\n",
" for qual in (1,2,3):\n",
" caldat_file = 'calib2018-10-25_c%d_chan%s.dat' % (qual, chan)\n",
" rt, rr = read_curve(caldat_file, 'caldat')\n",
" rc, tc = make_calib(rd, td, rt, rr, t_points)\n",
" dif[qual-1] = compare_calib(r0, t0, rc, tc)\n",
" plt.plot(t0, dif[qual-1], '-')\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": {},
"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",
"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
}
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