From 4b9e6335ebcc98fad35c409ed5cc7b303b019963 Mon Sep 17 00:00:00 2001 From: l_samenv Date: Fri, 19 Aug 2022 15:35:18 +0200 Subject: [PATCH] added new instance of calib_scripts.ipynb --- calib_scripts/calib_cernox.ipynb | 635 +++++++++++-------------------- 1 file changed, 225 insertions(+), 410 deletions(-) diff --git a/calib_scripts/calib_cernox.ipynb b/calib_scripts/calib_cernox.ipynb index 6dc75d3..33c8b16 100644 --- a/calib_scripts/calib_cernox.ipynb +++ b/calib_scripts/calib_cernox.ipynb @@ -1,435 +1,257 @@ { "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." + ] + }, { "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" + "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, + "execution_count": 8, "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" + "('lsdat', 'READ /afs/psi.ch/project/SampleEnvironment/SE_internal/Thermometer_calibs/2012/73027 Cernox 5/X75610.dat')\n", + "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p0_c1.dat')\n", + "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p1_c1.dat')\n", + "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p2_c1.dat')\n", + "{'selected': 0, 'averaged': 25}\n", + "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p0_c2.dat')\n", + "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p1_c2.dat')\n", + "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p2_c2.dat')\n", + "{'selected': 0, 'averaged': 25}\n", + "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p0_c4.dat')\n", + "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p1_c4.dat')\n", + "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p2_c4.dat')\n", + "{'selected': 0, 'averaged': 25}\n", + "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p0_c5.dat')\n", + "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p1_c5.dat')\n", + "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p2_c5.dat')\n", + "{'selected': 0, 'averaged': 25}\n", + "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p0_c6.dat')\n", + "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p1_c6.dat')\n", + "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p2_c6.dat')\n", + "{'selected': 0, 'averaged': 25}\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", + "run = CalibRun([\n", + " Sensor(3, 'X75610'), # the reference sensor must be the first\n", + " Sensor(1, 'X133982', 'CX-1050-SD'),\n", + " Sensor(2, 'X137461', 'CX-1030-SD'),\n", + " Sensor(4, 'X133928', 'CX-1050-SD'),\n", + " Sensor(5, 'X131824', 'CX-1050-CU'),\n", + " Sensor(6, 'X132254', 'CX-1050-SD'),\n", + " ],\n", + " t_points = (1.0, 1.2) + logrange(1.4, 310, n=195) + (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 = '2018-11-22', # 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": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('z340', 'READ 2018-11-22/X133982.340')\n", + "('z340', 'READ 2018-11-20/X133982.340')\n", + "('z340', 'READ 2018-11-22/X133928.340')\n", + "('z340', 'READ 2018-11-20/X133928.340')\n", + "('z340', 'READ 2018-11-22/X131824.340')\n", + "('z340', 'READ 2018-11-20/X131824.340')\n", + "('z340', 'READ 2018-11-22/X132254.340')\n", + "('z340', 'READ 2018-11-20/X132254.340')\n", + "('z340', 'READ 2018-11-22/X137461.340')\n", + "('z340', 'READ 2018-11-20/X137461.340')\n" + ] + }, + { + "data": { + "image/png": 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FWL58OX788Ue4urpi8ODBeO655zBmzJgW96VUKmFsbKzZP3/+PIKCgrB9+3b4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IhnIuLRNtunRMSGiNgYEbO7r0RJkqSep5SerBuByftuiIhewLXF7ccDMyJiVHHfORFxVUQcve8h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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# compare known sensors (skip if not applicable)\n", + "plt.figure(figsize=(10, 6))\n", + "tmin,tmax,dmax=1.4,310,0.001\n", + "testlist = (('X133982', 'z340'),('X133928', 'z340'),('X131824', 'z340'),('X132254', 'z340'),('X137461', 'z340'))\n", + "for sensno, kind in testlist:\n", + " r0, t0 = read_curve('2018-11-22/%s.340' % sensno, 'z340')\n", + " r1, t1 = read_curve('2018-11-20/%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 2018-11-22/X133982.340')\n", + "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p0_c1.dat')\n", + "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p1_c1.dat')\n", + "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p2_c1.dat')\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "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", - "# 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", + "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", - " 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": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "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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- "text/plain": [ - "" - ] - }, - "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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- "text/plain": [ - "" - ] - }, - "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": { - "image/png": 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M/pphiU4mnD0Of5s/+Ss3kPjZdi7LKSQsp5DVIYE4IsM5Kzme0uhQPq4opSgY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\noC0GpZRyjU8kBh1jUEop1/l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- "text/plain": [ - "" - ] - }, - "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", + " 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.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" + " plt.axis([min(t0),max(t0),-1,1])\n", + " #plt.legend(['dif1','dif2','dif3','est'])\n", + " plt.show()" ] }, { @@ -530,13 +352,6 @@ "outputs": [], "source": [] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -547,7 +362,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 2.7", "language": "python", "name": "python2" },