{
 "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/calib2022-03-08_p0_c1.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2022-03-08_p1_c1.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2022-03-08_p2_c1.dat')\n",
      "{'selected': 0, 'averaged': 62}\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2022-03-08_p0_c4.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2022-03-08_p1_c4.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2022-03-08_p2_c4.dat')\n",
      "{'selected': 0, 'averaged': 62}\n"
     ]
    }
   ],
   "source": [
    "run = CalibRun([\n",
    "        Sensor(3, 'X75610'), # the reference sensor must be the first\n",
    "        Sensor(1, 'X161269', 'CX-1050-CU'),\n",
    "        Sensor(4, 'X163062', 'CX-1050-CU'),\n",
    "    ],\n",
    "    t_points = (1.0, 1.2) + logrange(1.4, 310, n=195) + (330,),  # the points to be used in the cal file\n",
    "    caldate = '2022-03-08',  # 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": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "(16898.41610758743, 1.2, 0.8213390500515669, -0.3155507916236942)\n",
      "(26617.606475286462, 1.0, 0.6726896920393037, -0.32731030796069627)\n"
     ]
    },
    {
     "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 = (('X161269', 'z340'),('X163062', 'z340'),)\n",
    "for sensno, kind in testlist:\n",
    "    r0, t0 = read_curve('2022-03-08/%s.340' % sensno, 'z340')\n",
    "    r1, t1 = read_curve('../tcl/calcurves/%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 2022-03-08/X161269.340')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2022-03-08_p0_c1.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2022-03-08_p1_c1.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2022-03-08_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 2022-03-08/X163062.340')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2022-03-08_p0_c4.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2022-03-08_p1_c4.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2022-03-08_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": 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": []
  },
  {
   "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,
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}