calib_scripts/calib_cernox-2018-11-22.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/calib2018-11-22_p0_c1.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-22_p1_c1.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-22_p2_c1.dat')\n",
      "{'selected': 0, 'averaged': 25}\n",
      "('zdat', 'READ calib_data/calib2018-11-22_p0_c2.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-22_p1_c2.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-22_p2_c2.dat')\n",
      "{'selected': 0, 'averaged': 25}\n",
      "('zdat', 'READ calib_data/calib2018-11-22_p0_c4.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-22_p1_c4.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-22_p2_c4.dat')\n",
      "{'selected': 0, 'averaged': 25}\n",
      "('zdat', 'READ calib_data/calib2018-11-22_p0_c5.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-22_p1_c5.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-22_p2_c5.dat')\n",
      "{'selected': 0, 'averaged': 25}\n",
      "('zdat', 'READ calib_data/calib2018-11-22_p0_c6.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-22_p1_c6.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-22_p2_c6.dat')\n",
      "{'selected': 0, 'averaged': 25}\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 = '2018-11-22',\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",
    "# 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": 3,
   "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": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x438bb50>"
      ]
     },
     "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 = (('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 calib_data/calib2018-11-22_p0_c1.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-22_p1_c1.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-22_p2_c1.dat')\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x4467990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('z340', 'READ 2018-11-22/X137461.340')\n",
      "('zdat', 'READ calib_data/calib2018-11-22_p0_c2.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-22_p1_c2.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-22_p2_c2.dat')\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEHCAYAAACgHI2PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ny5V7fcxbdoSNB7Ti2ro
      "text/plain": [
       "<matplotlib.figure.Figure at 0x44822d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('z340', 'READ 2018-11-22/X133928.340')\n",
      "('zdat', 'READ calib_data/calib2018-11-22_p0_c4.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-22_p1_c4.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-22_p2_c4.dat')\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x4c10810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('z340', 'READ 2018-11-22/X131824.340')\n",
      "('zdat', 'READ calib_data/calib2018-11-22_p0_c5.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-22_p1_c5.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-22_p2_c5.dat')\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEHCAYAAACgHI2PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfe7p6Ye22brVfcwvSvy
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4a45110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('z340', 'READ 2018-11-22/X132254.340')\n",
      "('zdat', 'READ calib_data/calib2018-11-22_p0_c6.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-22_p1_c6.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-22_p2_c6.dat')\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x55bf310>"
      ]
     },
     "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": []
  }
 ],
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