{
 "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 /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-22_p0_c1.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-22_p1_c1.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-22_p2_c1.dat')\n",
      "{'selected': 0, 'averaged': 62}\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-22_p0_c2.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-22_p1_c2.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-22_p2_c2.dat')\n",
      "{'selected': 1, 'averaged': 61}\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-22_p0_c4.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-22_p1_c4.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-22_p2_c4.dat')\n",
      "{'selected': 1, 'averaged': 61}\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-22_p0_c5.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-22_p1_c5.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-22_p2_c5.dat')\n",
      "{'selected': 1, 'averaged': 61}\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-22_p0_c6.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-22_p1_c6.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-22_p2_c6.dat')\n",
      "{'selected': 0, 'averaged': 62}\n"
     ]
    }
   ],
   "source": [
    "run = CalibRun([\n",
    "        Sensor(3, 'X75610'), # the reference sensor must be the first\n",
    "        Sensor(1, 'X189666', 'CX-1050-CU'),\n",
    "        Sensor(2, 'X174782', 'CX-1050-CU'),\n",
    "        Sensor(4, 'X133834', 'CX-1050-SD'),\n",
    "        Sensor(5, 'X133833', 'CX-1050-SD'),\n",
    "        Sensor(6, 'X199831', 'CX-1050-SD'),\n",
    "    ],\n",
    "    t_points = (1.2, 1.4) + logrange(1.6, 310, n=195) + (330,),  # the points to be used in the cal file\n",
    "    caldate = '2023-06-22',  # 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": 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"
     ]
    },
    {
     "ename": "IOError",
     "evalue": "[Errno 2] No such file or directory: '2018-11-20/X137461.340'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIOError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-e7e8a820ce5b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0msensno\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtestlist\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mr0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'2018-11-22/%s.340'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0msensno\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'z340'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0mr1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'2018-11-20/%s.340'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0msensno\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m     \u001b[0mdiff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompare_calib\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdiff\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'-'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/l_samenv/sea/calib_scripts/zcalib.py\u001b[0m in \u001b[0;36mread_curve\u001b[0;34m(filename, kind, instance, **filterargs)\u001b[0m\n\u001b[1;32m    118\u001b[0m     \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"READ %s\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    121\u001b[0m         \u001b[0mcurves\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfilter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIOError\u001b[0m: [Errno 2] No such file or directory: '2018-11-20/X137461.340'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('z340', 'READ 2023-06-22/X189666.340')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-22_p0_c1.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-22_p1_c1.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-22_p2_c1.dat')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('z340', 'READ 2023-06-22/X174782.340')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-22_p0_c2.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-22_p1_c2.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-22_p2_c2.dat')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('z340', 'READ 2023-06-22/X133834.340')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-22_p0_c4.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-22_p1_c4.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-22_p2_c4.dat')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('z340', 'READ 2023-06-22/X133833.340')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-22_p0_c5.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-22_p1_c5.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-22_p2_c5.dat')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('z340', 'READ 2023-06-22/X199831.340')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-22_p0_c6.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-22_p1_c6.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-22_p2_c6.dat')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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",
    "    tmin,tmax,dmax=1.4,310,0.001\n",
    "    plt.plot([tmin,tmax,tmax,tmin,tmin], [-dmax,-dmax,dmax,dmax,-dmax], '-')\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": [
    "run.rref"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run.tref"
   ]
  },
  {
   "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 2.7",
   "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
}