{
 "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": 8,
   "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/tcl/calib_data/calib2018-11-22_p0_c1.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p1_c1.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p2_c1.dat')\n",
      "{'selected': 0, 'averaged': 25}\n",
      "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p0_c2.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p1_c2.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p2_c2.dat')\n",
      "{'selected': 0, 'averaged': 25}\n",
      "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p0_c4.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p1_c4.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p2_c4.dat')\n",
      "{'selected': 0, 'averaged': 25}\n",
      "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p0_c5.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p1_c5.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p2_c5.dat')\n",
      "{'selected': 0, 'averaged': 25}\n",
      "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p0_c6.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p1_c6.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p2_c6.dat')\n",
      "{'selected': 0, 'averaged': 25}\n"
     ]
    }
   ],
   "source": [
    "run = CalibRun([\n",
    "        Sensor(3, 'X75610'), # the reference sensor must be the first\n",
    "        Sensor(1, 'X133982', 'CX-1050-SD'),\n",
    "        Sensor(2, 'X137461', 'CX-1030-SD'),\n",
    "        Sensor(4, 'X133928', 'CX-1050-SD'),\n",
    "        Sensor(5, 'X131824', 'CX-1050-CU'),\n",
    "        Sensor(6, 'X132254', 'CX-1050-SD'),\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",
    "    # t_points = (1.38, 1.42, 1.51) + logrange(1.55, 288, n=57) + (302,310),  # the points to be used in the cal file\n",
    "    caldate = '2018-11-22',  # 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": 9,
   "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"
     ]
    },
    {
     "data": {
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FWL58OX788Ue4urpi8ODBeO655zBmzJgW96VUKmFsbKzZP3/+PIKCgrB9+3b4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IhnIuLRNtunRMSGiNgYEbO7r0RJkqSep5SerBuByftuiIhewLXF7ccDMyJiVHHfORFxVUQcve8h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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4565c50>"
      ]
     },
     "metadata": {},
     "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 2018-11-22/X133982.340')\n",
      "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p0_c1.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p1_c1.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p2_c1.dat')\n"
     ]
    },
    {
     "data": {
      "image/png": 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BhgBzgbXAjKyGvIiI5Kws9ehzm3r0IiKZk6M9ehERKVgU9CIiAU5B\nLyIS4BT0IiIBTkEvIhLgFPQiIgFOQS8iEuAU9CIiAU5BLyIS4BT0IiIBTkEvIhLgFPQiIgFOQS8i\nEuAU9CIiAU5BLyIS4BT0IiIBLjgvd+acKwWMBxKBhWY2PS/3LyJSGOV1j/4a4CMzuwPokcf7lgIq\nKioqv0sQP6NjInOyFfTOuYnOuR3OuTWnLO/inFvvnIt2zj100kO1ga1p36dkZ99SeOiXWk6lYyJz\nstujnwx0PnmBcy4IGJu2vBnQxznXOO3hrXhhD3DaexsWBLl5kOXEtjO7jcysn5F1z7ZOeo8X1F/e\n3K5bx0TB42/HRLaC3sx+BPadsjgciDGzzWaWDMwAeqY99ilwnXNuHDA7O/vOTwr67K2jX+q8376O\nibzlb8eEM7Ns7dA5VxeYbWYt0trXAp3NbFBauy8Qbmb3ZGKb2StKRKQQMrPTjpTk6aybjEqvWBER\nybxMDd045+50zq1yzv3knKuezmrbgDontWunLRMRkXyQE0M3oXhDN+eltYsAG4COQDywDOhjZuuy\ntSMREcmS7E6vnA4sAsKcc1ucc/3NLAUYAswF1gIzFPIiIvkn2z16ERHxb355MvZUunSCnMo5Vw94\nDChnZtfndz2S/5xzPYHuQFlgkpnNy+eS/EaB6NGnTdHcZ2ZfOudmmFnv/K5J/INz7kMFvZzMORcC\njDazgfldi7/Il6tX6tIJcqosHBMS4LJxTDwOjMubKguG/LpMcaG8dIKcUWaPib9Wy5vyJB9k+phw\nzo0EvjKzn/OyUH+XL0FfWC+dIOnL7DHhnKvonJsA+NTTD0xZOCaG4E3rvs45NyhPi/Vz/nQythb/\nHZ4BiMP7T8XMjgID8qMoyVdnOib2AoPzoyjJV2c6JsYAY/KjKH+nO0yJiAQ4fwp6XTpBTqVjQk6l\nYyIL8jPoHX8/kbYcaOCcq+ucKwb0Bj7Pl8okv+iYkFPpmMgB+TW9UpdOkL/RMSGn0jGRcwrEB6ZE\nRCTr/GmMXkREcoGCXkQkwCnoRUQCnIJeRCTAKehFRAKcgl5EJMAp6EVEApyCXkQkwCnoRUQC3P8D\n1o9vQ3nKStMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x46356d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('z340', 'READ 2018-11-22/X137461.340')\n",
      "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p0_c2.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p1_c2.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p2_c2.dat')\n"
     ]
    },
    {
     "data": {
      "image/png": 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0vAvfGbt+/w6eL+jJXcvOZHrfs1fP8sVnSjO+RHFyxYq0K0dGkp07k/XqMflw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\n/xcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4df4950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('z340', 'READ 2018-11-22/X133928.340')\n",
      "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p0_c4.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p1_c4.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p2_c4.dat')\n"
     ]
    },
    {
     "data": {
      "image/png": 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/n3OIhDJpHAouCw0a0Kz7zVza4kpWLS3P1VfDoEHeOZbXNm6EK6+E2j1GkmZP\nMzUmhIrxe+Guu7wJ3LVqeX/AJkzAPv2Uybe2Z0D5XVSaGs3cH8pRpUruazhw9CCNX+jMjd835tVN\n8wm6rAvcey+Ehf31r1paGqxcCV99BWPGkF65Cl+16kyfGp9Qav0ABl/2KP+4JSD/bsuxYYN3XW7y\nZEhNhUsvJbljOCvTzmXlrppsjC/D2m1l2bgliPhNR7EjR6l5zh4aH1vNBWVWcV7gSlqkr6TmkU2k\n1A6h1EUXENAxzPs627SBc87581A3fXETHet25N4O9wJFJOgzCukGvIk3A2eMmZ3ytpNnCvoNG7wT\nevVqL9gjIyE83LtoAsDAgd5F12+/zbuuzWls2QILFsC6dV5dv//unWvlykG9et7P5pJLvJ5DXvU6\n9+yBt57bR8joB+hhk5hySV3C+j9H267XQmoqqTGL2TH6C8pPHcfXdYN4Lrwu/7j4XR694aI8u56V\nW+OWjePgM09w166mhMbNYuz4IDL+c82cGXZlD4bs/p0FV93E9Mefyu9SAW/e88I/FvL73t9JOpZE\nqaBStK/TnovqXUTpErnormbYvRtuugmOHYOPPvLOnTMxg1Gj4KmnYNw4uOKKXJdxWvv2eT37PdWm\nEXfe7QyvFMV1MUkELF7i/cFq2JCUSy7mydY7+Xj9Ksp++R3zv69MjRp5V8MfB7bT5o1OBP3ck/Hp\ntemyaSxBuxKhdWtvg127YPNmrF49drTqyjuBPXjZPsOazODpFhN46sYu+R0H/2Xm/YX84QeYP9/r\npu/Y4d0I6vBh749A6dJYqVKkl6tAQMtzca1aeV3/li2hWTMoVSrTQ0xfN51/z/8382+fDxSRi7HZ\n+eA0F2OPHfNm9VWp4l20POUFkx9/NKtd27ti4ue2bzd7+4EV9mHIRRZbKciSA7CjAQH2S9Xy9kLH\n0tb+fp89P3W8paXn7kJlfkhPT7eeE66wDe0a2pprB1rz5t413zP6/HPbXqO+lXu4kR08eiTf6yxI\nqanejNqqVc1GjMj82ufu3WbXXGN23nlmq1cXTH3Jyd6EgWrN1lnrly+10LdC7bno52zKqin20ryX\nrO4rIVZ3wLV2Qad9tmNH/tSw4+AO6/heV6v+eEcr1/xnuyE83kZF/Wij+8y2F677za7qesCC6yRY\n5d5PWenBle3uzx6zvUf25k8xhexY6jGr+nJV27Jvi5kVkemV2XGqHv38+dCvnze29c47EBJyihfu\n2AHnn+80XLixAAAMm0lEQVR1by69tEBqLSoOHkpjbuwqNu5bR6PawfjqnFtkb51w3Pak7Vw2rA1L\nRwXycr1RpHTvybPPZvKCXbtIbukjorMj6t4R9Au/ssBqLUirV3tTHEuUgMGDvRmsx/8TO3LEGxUY\nNAiuugqGDj1jxy/PzZsHjz4KWwNnU6PTNxwpG4vb15htP0RyR9dwXnyRfB0aTEtPY9yycQyaPZiS\naVWpeeQSgihFSskd7A5cTmLqOnq36MUz4c9Qv2L+DOsVFXdNu4vGlRvzr4v+VXSGbrLqxKDfs8cb\nZv/mG3jzTbjmmtMMgaSne/+7tm3rXbWUYuHj5R8zbcJAPh6TxKVuFgMnt6bLqW4uaUbqlZGM3HCA\nEbeVZcXj0wu81oKUluYNy4wa5V3TbdXKW7Z8OVx0ETzySD7dMyiLzGDhQu/9H3/8AaGh3lvxT9kB\nyyep6an8vPVnFmxdQEp6ClVKV6Fl9ZZcWOdCSgadHTesm71pNg/PfJildy0tnkGfnm5MnOid0L17\ne3c/rFgxkxe98gp88YV3hTQLb+CRosHMuOmLm7j453j+MW4d3dxMxi5sScOGf9mI9GefY/3wz7jg\n7niWDPiZ0KoFcc/joiE+3uvlBwR413oKeqaUFF1p6WnUe70es/4xi3OrnVv8gv6yy4zERBg5Ei48\n02SGhQu9aQGLFhVsl0LyxMHkg7Qf1Z53915Eh2HTufec0Twx/wqaNQOSk0n+19MkjpvOJdeV4P5b\n+/Jgx3sLu2SRImPAtwOoULICz3V5rvgF/bBhxoMPZqFzvn+/N1zzyiveuI4US7G7Yrnkw0v4tNJd\n+J74mBW7a5PaKJQ28d/xW1oLHr+tBhU67OC7W2boaUQiJ1j4x0L6fNGH9Q+sL35Bn6W6zOCGG6Bq\nVe8KrRRryxKWETEhgicveJhbtjZj8ZfxbKlzLv9p8xH7Atcx46YZVChZobDLFClSzIymw5uy8cGN\npw36Yv1wcEaP9t419eqrhV2J5AFfTR8Lbl/AF5tnELb/Md69fiZP1L+W4CrJfNvnW4W8yCk457ih\n1Q2Zb1Nse/SrV8PFF3tzvY4/vUD8Qrqls2T7Ejbt24Svpo/QKqGFXZJIkbZqxypa1Wjlh0M3114L\nHTvCgAEFU5SISBFWLKdXZlrX5s3e/dA3b/buNSAicpbLLOiL5xj98OFw220KeRGRLCh+PfqkJO/R\nL0uXQn3/fnuziEhW+VePfuxY773fCnkRkSwpXj36tDTvphoTJsDf/lbwhYmIFFH+06P/+mvvzVFh\nYYVdiYhIsVG8gv71173plHoLvIhIlhWfoF+61HtiS69ehV2JiEixUnyC/o034L77dAtiEZFsKh4X\nY+Pjvadvb9yom3GLiJxCkbkY65xr7pwb4Zz71Dl3R5ZfOGIEREUp5EVEcqBQevTOu6H4JDO7/jTr\n/9ujP3LEe4PU3Lnek9FFROR/5GmP3jk3xjmX6JxbftLybs65WOfcOufcY5m8vgcwHZiUpQN+/DG0\nb6+QFxHJoWz36J1znYCDwEdm1iZjWQCwDugKbAcWATeYWaxz7magLTDMzOJP2M9UM4s8zTG8Hr0Z\ntG7tXYi99NKcfH0iImeFzHr0QdndmZnNd86d/GDWDsB6M9uSccBJQCQQa2bjgfEZyy8GrgFKAbPP\neLAffvDmzBfm4+5FRIq5bAf9adQBtp7Q3oYX/n9hZnOAOVne6xtvwEMP6Q1SIiK5kFdBn+d8zZvj\n27yZBm3bEvzGG/h8PsLDwwGIjo4GUFtttdU+a9vHP4+JiSEhIYHMnHGM3jnXH+gLGPB3M0vIGLqZ\ndsIYfRgw2My6ZbQfB8zMhma689Mf0+zuu6F6dXj22ZzsQkTkrJLnT5hyzjXAC/rWGe1AYC3exdh4\nYCEQZWZrcliwWXAwrFkDNWvmZBciImeVvJ5eORFYAIQ65+Kcc7eZWRpwPzATWIU3Rz5HIf+nyEiF\nvIhIHii6t0BYuhR8vsIuRUSkWPC/h4OLiMhfFJl73YiISMFT0IuI+DkFvYiIn1PQi4j4OQW9iIif\nU9CLiPg5Bb2IiJ9T0IuI+DkFvYiIn1PQi4j4OQW9iIifU9CLiPg5Bb2IiJ9T0IuI+DkFvYiInyvQ\nh4M75yKBK4DywAdm9n1BHl9E5GxUKA8ecc4FA8PMrO9p1uvBIyIi2ZDXz4wd45xLdM4tP2l5N+dc\nrHNunXPusTPs5mngneweW0REsi8nY/RjgYgTFzjnAoC3M5a3BKKcc80z1t3snHvNOVcro/0SMMPM\nluWqchERyZJsj9Gb2XznXMhJizsA681sC4BzbhIQCcSa2XhgfMby+4GuQAXnXBMzG5mr6kVE5Izy\n6mJsHWDrCe1teOH/F2Y2HBieR8cUEZEsKNBZN9nh8/nw+Xw0aNCA4OBgfD4f4eHhAERHRwOorbba\nap+17eOfx8TEkJCQQGbOOOvGOdcf6AsY8HczS8gYuplmZm0ytgkDBptZt4z244CZ2dBMd376Y2rW\njYhINmQ26yZH0yudcw3wgr51RjsQWIs3/h4PLASizGxNDgtW0IuIZENeT6+cCCwAQp1zcc6528ws\nDbgfmAmsAiblNORFRCRvFcobps5EPXoRkezJ0x69iIgULwp6ERE/p6AXEfFzCnoRET+noBcR8XMK\nehERP6egFxHxcwp6ERE/p6AXEfFzCnoRET+noBcR8XMKehERP6egFxHxcwp6ERE/p6AXEfFzCnoR\nET+noBcR8XMFGvTOuYbOudHOuckFeVwRkbNZgQa9mW0yszsL8pgiIme7nDwcfIxzLtE5t/yk5d2c\nc7HOuXXOucfyrkQREcmNnPToxwIRJy5wzgUAb2csbwlEOeeaZ6y72Tn3mnOu1okvyWG9IiKSTdkO\nejObD+w9aXEHYL2ZbTGzFGASEJmx/Xgze9jM4p1zlZ1zIwCfev0iIgUjKI/2UwfYekJ7G174/4WZ\n7QHuycoOfT4fPp+PBg0aEBwcjM/nIzw8HIDo6GgAtdVWW+2ztn3885iYGBISEsiMM7NMNzjli5wL\nAaaZWZuMdi8gwsz6ZbT7AB3M7IFs79x7veWkLhGRs5VzDjM75bD4GYdunHP9nXNLnXO/Oudqnmaz\nP4D6J7TrZiwTEZFCltMefQO8Hn3rjHYgsBboCsQDC4EoM1uTo6LUoxcRyZZc9ehPsbOJwAIg1DkX\n55y7zczSgPuBmcAqYFJOQ15ERPJWjnr0+U09ehGR7MnTHr2IiBQvCnoRET+noBcR8XMKehERP6eg\nFxHxcwp6ERE/p6AXEfFzCnoRET+noBcR8XMKehERP6egFxHxcwp6ERE/p6AXEfFzCnoRET+noBcR\n8XMKehERPxdUkAdzzpUB3gWOAXPMbGJBHl9E5GxU0D36a4ApZnYX0LOAjy3FVHR0dGGXIEWMzons\nyVXQO+fGOOcSnXPLT1rezTkX65xb55x77IRVdYGtGZ+n5ebYcvbQL7WcTOdE9uS2Rz8WiDhxgXMu\nAHg7Y3lLIMo51zxj9Va8sAc45bMNi4P8PMnyYt/Z3Ud2ts/Ktmfa5nTri+svb37XrXOi+Clq50Su\ngt7M5gN7T1rcAVhvZlvMLAW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      "text/plain": [
       "<matplotlib.figure.Figure at 0x4c43ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('z340', 'READ 2018-11-22/X131824.340')\n",
      "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p0_c5.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p1_c5.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p2_c5.dat')\n"
     ]
    },
    {
     "data": {
      "image/png": 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3fs39597Pvb3uLfE5giFThtAsphmP/eox1q+HF16Af/yjcgR9L+Bh59ylheUHAXeiUf2P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\ncQp6ERGPU9CLiHicgl5ExOMU9CIiHvf/HzX/4G4mJWwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4c5ee90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('z340', 'READ 2018-11-22/X132254.340')\n",
      "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p0_c6.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p1_c6.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/tcl/calib_data/calib2018-11-22_p2_c6.dat')\n"
     ]
    },
    {
     "data": {
      "image/png": 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en3Mus85pxB3nbKPAFdEqqhW/ufA33J8RR/Etd3N31PtM2phAdHRdt7qObNmC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\nIgFOQS8iEuAU9CIiAe7/A9Ehh/y006kCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x57ae650>"
      ]
     },
     "metadata": {},
     "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",
    "    #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,
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