{
 "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/calib2023-06-27_p0_c1.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-27_p1_c1.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-27_p2_c1.dat')\n",
      "{'selected': 0, 'averaged': 62}\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-27_p0_c2.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-27_p1_c2.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-27_p2_c2.dat')\n",
      "{'selected': 1, 'averaged': 61}\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-27_p0_c5.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-27_p1_c5.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-27_p2_c5.dat')\n",
      "{'selected': 0, 'averaged': 62}\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-27_p0_c6.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-27_p1_c6.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-27_p2_c6.dat')\n",
      "{'selected': 2, 'averaged': 60}\n"
     ]
    }
   ],
   "source": [
    "run = CalibRun([\n",
    "        Sensor(3, 'X75610'), # the reference sensor must be the first\n",
    "        Sensor(1, 'X189667', 'CX-1050-CU'),\n",
    "        Sensor(2, 'X189668', 'CX-1050-CU'),\n",
    "        Sensor(5, 'X189665', 'CX-1050-CU'),\n",
    "        Sensor(6, 'X189664', 'CX-1050-CU'),\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 = '2023-06-27',  # 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 2023-06-27/X189667.340')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-27_p0_c1.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-27_p1_c1.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-27_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 2023-06-27/X189668.340')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-27_p0_c2.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-27_p1_c2.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-27_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 2023-06-27/X189665.340')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-27_p0_c5.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-27_p1_c5.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-27_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 2023-06-27/X189664.340')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-27_p0_c6.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-27_p1_c6.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2023-06-27_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
}