calib_scripts/calib_cernox_2018-11-20.ipynb

{
 "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": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math\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,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('lsdat', 'READ /afs/psi.ch/project/SampleEnvironment/SE_internal/Thermometer_calibs/2012/73027 Cernox 5/X75610.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-20_p0_c1.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-20_p1_c1.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-20_p2_c1.dat')\n",
      "{'selected': 0, 'averaged': 60}\n",
      "('zdat', 'READ calib_data/calib2018-11-20_p0_c2.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-20_p1_c2.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-20_p2_c2.dat')\n",
      "{'selected': 0, 'averaged': 60}\n",
      "('zdat', 'READ calib_data/calib2018-11-20_p0_c4.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-20_p1_c4.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-20_p2_c4.dat')\n",
      "{'selected': 0, 'averaged': 60}\n",
      "('zdat', 'READ calib_data/calib2018-11-20_p0_c5.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-20_p1_c5.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-20_p2_c5.dat')\n",
      "{'selected': 0, 'averaged': 60}\n",
      "('zdat', 'READ calib_data/calib2018-11-20_p0_c6.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-20_p1_c6.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-20_p2_c6.dat')\n",
      "{'selected': 0, 'averaged': 60}\n",
      "calibration file has 199 points\n"
     ]
    }
   ],
   "source": [
    "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,),\n",
    "    caldate = '2018-11-20',\n",
    "    logT = False,\n",
    "    logR = True,\n",
    "    calib_data_file = 'calib_data/calib%s_p%d_c%d.dat',\n",
    "    outputpath='%s/%s.340')\n",
    "run.make(diflim=0.001, smoothref=1e-7, smoothtst=1e-7)\n",
    "# smoothtst depends on number of measured points (1e-7 for 60, 0.8e-7 for 48 and 0.4e-7 for 24 points)\n",
    "print('calibration file has %d points' % len(run.t_points))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('z340', 'READ 2018-10-25/X133928.340')\n",
      "('z340', 'READ 2018-11-20/X133928.340')\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlkAAAF2CAYAAABd6o05AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8+bN6tev317Pe/rpp3X
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3c50dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare known sensors\n",
    "plt.figure(figsize=(10, 6))\n",
    "tmin,tmax,dmax=1.4,310,0.001\n",
    "testlist = (('X133928', 'z340'),)\n",
    "for sensno, kind in testlist:\n",
    "    r0, t0 = read_curve('2018-10-25/%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 2018-11-20/X133982.340')\n",
      "('zdat', 'READ calib_data/calib2018-11-20_p0_c1.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-20_p1_c1.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-20_p2_c1.dat')\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEHCAYAAACgHI2PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nY0fL/815ASNWtUPJjrc
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3c62e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('z340', 'READ 2018-11-20/X137461.340')\n",
      "('zdat', 'READ calib_data/calib2018-11-20_p0_c2.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-20_p1_c2.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-20_p2_c2.dat')\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEHCAYAAACgHI2PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/4auVi1MOjEX01pOQ8/
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3d3f2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('z340', 'READ 2018-11-20/X133928.340')\n",
      "('zdat', 'READ calib_data/calib2018-11-20_p0_c4.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-20_p1_c4.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-20_p2_c4.dat')\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEHCAYAAACgHI2PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGBITEIAfauVB3zr94O7
      "text/plain": [
       "<matplotlib.figure.Figure at 0x44033d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('z340', 'READ 2018-11-20/X131824.340')\n",
      "('zdat', 'READ calib_data/calib2018-11-20_p0_c5.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-20_p1_c5.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-20_p2_c5.dat')\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEHCAYAAACgHI2PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl4jFf/BvD7RBISW4LYSaoo0sSgRTfSKrFUtWjR6qta\npfS1dqHL76Wolra6oVVLadEUrdqqVdVQVUtriX3fSeySyJ65f3/MUGRfZCbj/lxXrivnPGfO+c7k\nyTdnznPyjCEJERFxXW6ODkBERG4uJXoRERenRC8i4uKU6EVEXJwSvYiIi1OiFxFxcUr0IiIuTole\nRMTFuRfkYMYYbwCTACQCWEVyTkGOLyJyKyroGX1HAPNI9gHwaAGPLSJyS8pTojfGTDPGRBljIm6o\nb22M2W2M2WuMGXrNoaoAjtm/T83L2CIikj15ndF/BSD02gpjjBuACfb6QADdjDF17IePwZbsAcDk\ncWwREcmGPCV6kmsAXLihujGAfSSPkEwGEAagg/3YAgCdjTETASzOy9giIpI9N+NibBX8uzwDAMdh\nS/4gGQfguaw6MMbolpoiIjlEMt2VEqfdXknSab+GDx/u1H3ntI+ctM9O26zaZHQ8p/XO8nWz49M5\noXMiO/1n5mYk+hMAql9TrmqvcxkhISFO3XdO+8hJ++y0zapNRsdv5ut6M93suHVOFD7Odk6YrP4S\nZNmBMQEAFpMMspeLANgDoAWAUwA2AOhGclcO+mRe4xLXMWLECIwYMcLRYYgT0TmRljEGvBlLN8aY\nOQDWAqhtjDlqjOlJMhVAfwDLAewAEJaTJC9yo8I6q5ObR+dEzuR5Rn8zaEYvIpIzN21GLyIizk+J\nXkTExSnRi4i4OCV6EREXp0QvIuLilOhFRFycEr2IiItTohcRcXFK9CIiLk6JXkTExSnRi4i4OCV6\nEREXp0QvIuLilOhFRFycEr2IiItTohcRcXEFmuiNMbcZY6YaY+YW5LgiIreyAk30JA+R7FWQY4qI\n3OpyleiNMdOMMVHGmIgb6lsbY3YbY/YaY4bmT4giIpIXuZ3RfwUg9NoKY4wbgAn2+kAA3YwxdezH\nnjHGjDfGVLrSPJfjiohIDuUq0ZNcA+DCDdWNAewjeYRkMoAwAB3s7b8hOQRAojHmcwAWzfhFRAqG\nez72VQXAsWvKx2FL/leRPA+gb3Y6s1gssFgsCAgIgI+PDywWC0JCQgAA4eHhAKCyyiqrfMuWr3y/\nbt06REZGIjOGZKYNMnygMf4AFpMMtpc7AQgl2dte7g6gMckBueibuY1LRORWZIwByXSXxfNz180J\nANWvKVe114mIiAPlJdEbXH9RdSOAmsYYf2OMJ4CuABblJTgREcm73G6vnANgLYDaxpijxpieJFMB\n9AewHMAOAGEkd+VfqCIikhu5XqO/mbRGLyKSMwW1Ri8iIk5IiV5ExMUp0YuIuDglehERF6dELyLi\n4pToRURcnBK9iIiLU6IXEXFxSvQiIi5OiV5ExMUp0YuIuDglehERF6dELyLi4pToRURcnBK9iIiL\nU6IXEXFx7gU5mDGmA4B2AEoCmE7y14IcX0TkVuSQT5gyxvgAeJ/kCxkc1ydMiYjkQL5/wpQxZpox\nJsoYE3FDfWtjzG5jzF5jzNBMungLwMTcjC0iIjmT2zX6rwCEXlthjHEDMMFeHwigmzGmjv3YM8aY\n8caYysaY9wD8RHJLHuIWEZFsylWiJ7kGwIUbqhsD2EfyCMlkAGEAOtjbf0NyCIBOAFoA6GyM6Z37\nsEVEJLvy82JsFQDHrikfhy35X0XyMwCf5eOYIiKShQLddZMTFosFFosFAQEB8PHxgcViQUhICAAg\nPDwcAFRWWWWVb9nyle/XrVuHyMhIZCbXu26MMf4AFpMMtpebAhhBsrW9PAwASY7NRd/adSMikgP5\nvuvmSr/2rys2AqhpjPE3xngC6ApgUR76FxGRfJDb7ZVzAKwFUNsYc9QY05NkKoD+AJYD2AEgjOSu\n/AtVRERywyH/MJUVLd2IiOTMzVq6ERGRQkCJXkTExSnRi4i4OCV6EREXp0QvIuLilOhFRFycEr2I\niItTohcRcXFK9CIiLk6JXkTExSnRi4i4OCV6EREXp0QvIuLilOhFRFycEr2IiItTohcRcXEFmuiN\nMXWMMZ8bY74zxjxfkGOLiNyqHPIJU8YYA9tHDXbJ4Lg+YUpEJAfy/ROmjDHTjDFRxpiIG+pbG2N2\nG2P2GmOGZvDY9gCWAgjLzdgiIpIzuZrRG2PuBxAL4GuSwfY6NwB7AbQAcBLARgBdSe42xjwDoAGA\n90mesrdfSLJDBv1rRi8ikgOZzejdc9MhyTXGGP8bqhsD2EfyiH3QMAAdAOwm+Q2Ab4wxzY0xwwAU\nA/B7bsYWEZGcyVWiz0AVAMeuKR+HLflfRXIVgFX5OKaIiGQhPxN9vrJYLLBYLAgICICPjw8sFgtC\nQkIAAOHh4QCgssoqq3zLlq98v27dOkRGRiIzud51Y1+6WXzNGn1TACNItraXhwEgybG56Ftr9CIi\nOZDvu26u9Gv/umIjgJrGGH9jjCeArgAW5aF/ERHJB7ndXjkHwFoAtY0xR40xPUmmAugPYDmAHbDt\nk9+Vf6GKiEhuOOQfprKipRsRkZy5WUs3IiJSCCjRi4i4OCV6EREXp0QvIuLilOhFRFycEr2IiItT\nohcRcXFK9CIiLk6JXkTExSnRi4i4OCV6EREX57SJXre6ERHJH06b6CdOdHQEIiKuwWnvXlmuHLF6\nNVC3rqOjERFxfoXy7pWjRwPduwNJSY6OxHn1+LEH2sxug6OXjjo6FBFxYk6b6Hv3BipVAkaOdHQk\nzmlL5BYsP7Ac91S9B3d9eRcuxF9wdEgi4qScNtEbA0ydCkybBqxd6+honM9bK9/CG/e/gf81/x8e\nuu0hzNk2x9EhSQE5fOoS3p+7GvHxzrfsKs7JaRM9SVSsCHz+OfDMM0BMjKMjyp3kZGD2bGDz5vzb\nSbQlcgu2Rm1Fn2L3A0eOoFfDXpi6eWr+dF6YJSYipkdXnHl9ABKPHXZ0NDfFsVMJCB0Wit1ft0Xz\npxoh7Cct20nWnDbR3/n5nZi0cRJatIlBSAgweLCjI8q5qCigRQtgwgSgc2fbV36Yv3M+BpQOhUer\n1rDe3Rj3bk3CxYSL2HRqU577njcPaNUKqF4dGDoUOHUq8/aHLhzC1E1TYaU1+4PExACffgp8+CEQ\nGZm3gK8R178v1q3/Hr+unonzwbUQfy4q3/p2BklJwNtPtcDa+VswxbMlFq3ej3OvdoA1By/9VVYr\nEifPgHX33nyPUzJBAl9/Dbz6KrBtW0GOS6f7AsCVB1ey43cd6fueL3v/+F9WteziggUsNC5fJuvX\nJ8e+dISX27RkVJ9n2brGBi5enPe+G4+rzfjqVfhZw+lsWWw1z6EM3/xoEPst6ZenfseMsbJ8i2/5\nzXfRjIggBw0iK1Ykly1Lv/3YNWNZdmxZBnwcwGmbpmV7nOQB/XmyYV3+EVCbPwd05Ndfk1ZrnkIn\n585lVBVfvjqvN8PDyXkNazC8TYM8dupcJj47jidKFGH8+r9IkscPRfCAjxt//+/0nHV07hzPNXqY\nO93r8oxnJZ76ddtNiFbSiI4mQ0LIu+4ihw2z/XJ9/nm+dW9L5xnk1IwOOPLLHjBJ8ujFo3zztzfp\nO6Y8PXu14Fd/LWByanK+vTg3g9VK9uhBDuhwmBcrl+X7DxXj5Ba+PFjWk7VqXWR8fO773nl6J8e1\nLsmjDz3D2rXJhARyR4fXObPcM/QbVz7Xr80HHyWwVI/u9BzpyXdWv3O1ftUqsmpVcuhQMinp2udo\nZcDHAdwauZWbTm5i+ffL83Ts6awH2rWL0SWLMuCFRmzQ9yme93JjSPABvvQSmZqaq9DJ5GQm1Qjg\no71L8pm+p1itGtnrP7t5orgbvxn+dS47dS6Rf+5nVDEPzvxg2HX1z73Wgmc9ijM1Mhuvvd3hzoM5\np2JjlhxRgv0738GTRSr
      "text/plain": [
       "<matplotlib.figure.Figure at 0x43f4190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('z340', 'READ 2018-11-20/X132254.340')\n",
      "('zdat', 'READ calib_data/calib2018-11-20_p0_c6.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-20_p1_c6.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-20_p2_c6.dat')\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEHCAYAAACgHI2PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n3aF1HB07mi/89ALjT8X
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4e92f50>"
      ]
     },
     "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": []
  },
  {
   "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
}