calib_scripts/calib_cernox_2018-11-15.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/calib2018-11-15_p0_c5.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-15_p1_c5.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-15_p2_c5.dat')\n",
      "{'selected': 0, 'averaged': 60}\n",
      "('zdat', 'READ calib_data/calib2018-11-15_p0_c4.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-15_p1_c4.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-15_p2_c4.dat')\n",
      "{'selected': 1, 'averaged': 59}\n",
      "('zdat', 'READ calib_data/calib2018-11-15_p0_c1.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-15_p1_c1.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-15_p2_c1.dat')\n",
      "{'selected': 1, 'averaged': 59}\n",
      "('zdat', 'READ calib_data/calib2018-11-15_p0_c2.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-15_p1_c2.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-15_p2_c2.dat')\n",
      "{'selected': 0, 'averaged': 60}\n",
      "('zdat', 'READ calib_data/calib2018-11-15_p0_c6.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-15_p1_c6.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-15_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(5, 'X133273', 'CX-1030-CU-HT-0.3L'),\n",
    "        Sensor(4, 'X55918', 'CX-1050-SD-HT-1.4L'),\n",
    "        Sensor(1, 'X134003', 'CX-1050-SD'),\n",
    "        Sensor(2, 'X134014', 'CX-1050-SD'),\n",
    "        Sensor(6, 'X133980', 'CX-1050-SD'),\n",
    "    ],\n",
    "    t_points = (1.0, 1.2) + logrange(1.4, 310, n=195) + (330,),\n",
    "    caldate = '2018-11-15',\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=1e-7)\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 2018-11-15/X133273.340')\n",
      "('lsdat', 'READ /afs/psi.ch/project/SampleEnvironment/SE_internal/Thermometer_calibs/2018/X133273_for_He3/X133273.dat')\n",
      "('z340', 'READ 2018-11-15/X133273.340')\n",
      "('ls340', 'READ /afs/psi.ch/project/SampleEnvironment/SE_internal/Thermometer_calibs/2018/X133273_for_He3/X133273.340')\n",
      "('z340', 'READ 2018-11-15/X55918.340')\n",
      "('lsdat', 'READ /afs/psi.ch/project/SampleEnvironment/SE_internal/Thermometer_calibs/2009/51750 Cernox HiT/X55918.dat')\n",
      "('z340', 'READ 2018-11-15/X55918.340')\n",
      "('ls340', 'READ /afs/psi.ch/project/SampleEnvironment/SE_internal/Thermometer_calibs/2009/51750 Cernox HiT/X55918.340')\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x3fc5dd0>"
      ]
     },
     "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 = (('X133273', 'lsdat'), ('X133273', 'ls340'), ('X55918', 'lsdat'), ('X55918', 'ls340'))\n",
    "for sensno, kind in testlist:\n",
    "    r0, t0 = read_curve(sensno, 'z340')\n",
    "    r1, t1 = read_curve(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[2:] 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 2018-11-15/X133273.340')\n",
      "('zdat', 'READ calib_data/calib2018-11-15_p0_c5.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-15_p1_c5.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-15_p2_c5.dat')\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x3fd3f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('z340', 'READ 2018-11-15/X55918.340')\n",
      "('zdat', 'READ calib_data/calib2018-11-15_p0_c4.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-15_p1_c4.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-15_p2_c4.dat')\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEHCAYAAACgHI2PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGRhIxib/uB59lJw+3f0
      "text/plain": [
       "<matplotlib.figure.Figure at 0x40b1bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('z340', 'READ 2018-11-15/X134003.340')\n",
      "('zdat', 'READ calib_data/calib2018-11-15_p0_c1.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-15_p1_c1.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-15_p2_c1.dat')\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x42e8e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('z340', 'READ 2018-11-15/X134014.340')\n",
      "('zdat', 'READ calib_data/calib2018-11-15_p0_c2.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-15_p1_c2.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-15_p2_c2.dat')\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEHCAYAAACgHI2PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n5OBgcsGCnO3fHSQ5OTn
      "text/plain": [
       "<matplotlib.figure.Figure at 0x46e2e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('z340', 'READ 2018-11-15/X133980.340')\n",
      "('zdat', 'READ calib_data/calib2018-11-15_p0_c6.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-15_p1_c6.dat')\n",
      "('zdat', 'READ calib_data/calib2018-11-15_p2_c6.dat')\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEHCAYAAACgHI2PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLntFvXpwa/Ug+kZsxps
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5207410>"
      ]
     },
     "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": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('lsdat', 'READ /afs/psi.ch/project/SampleEnvironment/SE_internal/Thermometer_calibs/2018/X133273_for_He3/X133273.dat')\n",
      "{'selected': 0, 'averaged': 60}\n",
      "{'selected': 0, 'averaged': 30}\n",
      "{'selected': 0, 'averaged': 20}\n",
      "{'selected': 0, 'averaged': 15}\n",
      "{'selected': 0, 'averaged': 10}\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlkAAAF2CAYAAABd6o05AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUMDPzw/p6emws7ODlZU
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5218c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('lsdat', 'READ /afs/psi.ch/project/SampleEnvironment/SE_internal/Thermometer_calibs/2009/51750 Cernox HiT/X55918.dat')\n",
      "{'selected': 1, 'averaged': 59}\n",
      "{'selected': 0, 'averaged': 30}\n",
      "{'selected': 0, 'averaged': 20}\n",
      "{'selected': 0, 'averaged': 15}\n",
      "{'selected': 0, 'averaged': 10}\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x3fbbfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare with less points\n",
    "sensors = ('X133273', 'X55918')\n",
    "steps = (1,2,3,4,6)\n",
    "tmin,tmax,dmax=1.4,310,0.001\n",
    "kind = 'lsdat'\n",
    "for sensno in sensors:\n",
    "    sensor = run.sensdict[sensno]\n",
    "    r1, t1 = read_curve(sensno, kind)\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    for step in steps:\n",
    "        meas0 = sensor.meas.reduced(step)\n",
    "        rc, tc = sensor.make_calib(run, meas0)\n",
    "        diff = compare_calib(r1, t1, rc, tc)\n",
    "        plt.plot(tc, diff, '-')\n",
    "    plt.plot([tmin,tmax,tmax,tmin,tmin], [-dmax,-dmax,dmax,dmax,-dmax], '-')\n",
    "    plt.legend([str(step) for step in steps] + [\"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": 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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  "language_info": {
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   "file_extension": ".py",
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