{
 "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": 5,
   "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": 21,
   "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/calib2024-11-08_p0_c1.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-08_p1_c1.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-08_p2_c1.dat')\n",
      "{'selected': 4, 'averaged': 57}\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-08_p0_c2.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-08_p1_c2.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-08_p2_c2.dat')\n",
      "{'selected': 0, 'averaged': 61}\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-08_p0_c4.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-08_p1_c4.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-08_p2_c4.dat')\n",
      "{'selected': 3, 'averaged': 58}\n"
     ]
    }
   ],
   "source": [
    "run = CalibRun([\n",
    "        Sensor(3, 'X75610'), # the reference sensor must be the first\n",
    "        Sensor(1, 'X219230', 'CX-1050-SD-HT'),\n",
    "        Sensor(2, 'X219226', 'CX-1050-SD-HT'),\n",
    "        Sensor(4, 'X219227', 'CX-1050-SD-HT'),\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 = '2024-11-08',  # 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[0mTraceback (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": {
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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('2023-11-05/%s.340' % sensno, 'z340')\n",
    "    r1, t1 = read_curve('2023-11-05/%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": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('z340', 'READ 2024-11-08/X219230.340')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-08_p0_c1.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-08_p1_c1.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-08_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 2024-11-08/X219226.340')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-08_p0_c2.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-08_p1_c2.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-08_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 2024-11-08/X219227.340')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-08_p0_c4.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-08_p1_c4.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-08_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"
    }
   ],
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X219219.340  X219225.340  X219231.340\n"
     ]
    }
   ],
   "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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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "cell_type": "code",
   "execution_count": null,
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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