{
 "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/calib2023-06-16_p0_c1.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-16_p1_c1.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-16_p2_c1.dat')\n",
      "{'selected': 7, 'averaged': 53}\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-16_p0_c2.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-16_p1_c2.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-16_p2_c2.dat')\n",
      "{'selected': 7, 'averaged': 53}\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-16_p0_c4.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-16_p1_c4.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-16_p2_c4.dat')\n",
      "{'selected': 6, 'averaged': 54}\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-16_p0_c5.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-16_p1_c5.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-16_p2_c5.dat')\n",
      "{'selected': 7, 'averaged': 53}\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-16_p0_c6.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-16_p1_c6.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-16_p2_c6.dat')\n",
      "{'selected': 6, 'averaged': 54}\n"
     ]
    }
   ],
   "source": [
    "run = CalibRun([\n",
    "        Sensor(3, 'X75610'), # the reference sensor must be the first\n",
    "        Sensor(1, 'X200047', 'CX-1070-SD'),\n",
    "        Sensor(2, 'X200044', 'CX-1070-SD'),\n",
    "        Sensor(4, 'X200038', 'CX-1070-SD'),\n",
    "        Sensor(5, 'X200048', 'CX-1070-SD'),\n",
    "        Sensor(6, 'X200037', 'CX-1070-SD'),\n",
    "    ],\n",
    "    t_points = (1.4, 1.6) + logrange(1.8, 310, n=195) + (330,),  # the points to be used in the cal file\n",
    "    caldate = '2023-06-16',  # 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": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('z340', 'READ 2023-06-16/X200047.340')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-16_p0_c1.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-16_p1_c1.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-16_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 2023-06-16/X200044.340')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-16_p0_c2.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-16_p1_c2.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-16_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 2023-06-16/X200038.340')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-16_p0_c4.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-16_p1_c4.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-16_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 2023-06-16/X200048.340')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-16_p0_c5.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-16_p1_c5.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-16_p2_c5.dat')\n"
     ]
    },
    {
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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 2023-06-16/X200037.340')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-16_p0_c6.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-16_p1_c6.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-16_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"
   ]
  },
  {
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