{
 "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": 5,
   "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/calib2024-11-05_p0_c1.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-05_p1_c1.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-05_p2_c1.dat')\n",
      "{'selected': 0, 'averaged': 62}\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-05_p0_c2.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-05_p1_c2.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-05_p2_c2.dat')\n",
      "{'selected': 1, 'averaged': 61}\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-05_p0_c4.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-05_p1_c4.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-05_p2_c4.dat')\n",
      "{'selected': 1, 'averaged': 61}\n"
     ]
    }
   ],
   "source": [
    "run = CalibRun([\n",
    "        Sensor(3, 'X75610'), # the reference sensor must be the first\n",
    "        Sensor(1, 'X219219', 'CX-1050-SD-HT'),\n",
    "        Sensor(2, 'X219231', 'CX-1050-SD-HT'),\n",
    "        Sensor(4, 'X219225', '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 = '2024-11-05',  # 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": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('z340', 'READ 2024-11-05/X219219.340')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-05_p0_c1.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-05_p1_c1.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-05_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 2024-11-05/X219231.340')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-05_p0_c2.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-05_p1_c2.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-05_p2_c2.dat')\n"
     ]
    },
    {
     "data": {
      "image/png": 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vbiwRZ++/nyb6zl0QLmVrIJeFXa4g0udMTunfmcHd2+33dUfCpVS5OBkpWQ2eGdgbifH9Ob/kP2XPEis/hujuU2jfZQmRtPe4avBVTMqf9IW/7WWbS3nopbfJWv86UV+ckqxOjBnzLS4+vv9+g6KyupJILIKPdEor4yxct5un39/IwrW7GdA1k/uuHE6/LpkAnDdzFDf/bStHp3ej94uf3ajPzP7tnMtvdEO0oYAwMz+wChgDFAOLgPHOuY8barO/gFh++SXsKN3Ms+NzKYmXUuoqKLMwJb4Y0TrvSX2j1fSqhpUhY2swwKhwR3584SP06f7FUzFlVRGef/A6HklbRNiC/On0BxjZbwQAK1YtY3LhZewMBHjhG2/RMavT59r+8IExvBPYwv3H/5GvHn8OALc/PJ5ZvqX8rMfVjB8z6fP1Hyqg0LeTr1cfz63ffoRY+Q6eevxS7s8sIeRSuf/8Jzm2yxc/MZeU7eDqp0azPRDjV71vZPSZ1+9bV7R6Hje+/X12+x3fz7qcqy/93/3ukM45duytYsm2dazY9G82b1vIjorVlMR3U0mEGDESJIgbxMyoxogZVJsRMyPgHKnOkZpwZCUSdIw5MmJBgrEMwrGORH29SMs+jiFHn8GZx+QxoGum95ic4+2nJvLzyn8SI8gdJ9/H6EE1n4oeeOt/+cuG5zh5b4Kzjn2Yy0eN8Hwdf379WWZtvIOoL8aEaA7XnHkbqZ0G8OLb9zN152w2BXx8zXc0t13+KKmp2TUNy7eRWPY8K9b+k1W7N7Ep0Z3ViWFsCo2kS7s0+nRK5yt5OQzobry79XVe/GQ2H+9ejg8YVu04pbyU3tXV5CQSJID1wSALUjJYkJFCuQ/8Do6LBzmpfA/Hhyvwh7vyQOwKfAPGcvGwPM4Y1LnxUy+bP6j5dL19OZWxKt5w5Txk5Xzih2HhMGeW9+OCax6mQ+fPPlCs3/QeV8+dQCgOFx/9CD84c3iD3b/wwWvc+uHNXFoR5LbrF0Gdh88sXLuLm1/5BpG0bTzb+Uzyzr/Hs49IVTlLp/8/hm+bRQlZVPY8ne6xTdiWDyC7J4y5HYZcwp5ICY9+9AKvrHyG7bF1VNf+nZrz0z31GIZ3OpOzeo7h6I6dyStbTODZ70J0L3x9Khxzwb7tFRWtwJ65jqOrPmJlIo+Vvl6E0tawMrWKeaGOrA6FcP4IZkaHUHf6ZvdhUMc+dIzsJbZzJWtL1rDUVbCh9veennAMi6UxNiOffkd9nXb9vkpJ2DFv9XamffRXEjlz6ewK+N6QSfh9Ph6Zv5ZV1U8Q6vBPvjXoWn4y4ibMDOccs95aSEbhrbisZTycncWqUBBXu7/nxFMZ2nUEZ/QrYGCHgXRM6UBR0VKWLHuNNWX/YUVoDxtDcQC6RX2M29KRQLQD1SntGdLvKPL7dSdIDEo2wrZljIou544Z0KfbMHo/9ti+38+hGBAnAbc5586pXf4pgHPuznr1JgKTgJycnJxOzz333Oc7cnHKN/+TwpKXOeX5MhIGv/mmn86xOJ3ijpy4j+x4kHYulSyXTvtAZ3rkXQDZPSkp38E/Nk7h7fSddI45Tqwey2n9zicjaETijuXFO0jZ8mce61KGnyDX9/gRuamfD5HlRS9yX/A1Tq3qyRWDfrKvfP2OVfyl4m5GVLVj3DG/2VdeEank7g01nzB+1OtOUkJZACxY8zee8H/E6WXduezYn372huni7Proz9yTuYZKCzKu/fcY1n7wZy8/Fubx1bewKC3ClbFTGdlv3Bd+13v2buD+rX9gZyDB10oz6J9+DikZfdlSHWZXdCfl1dupSOyiklIqfeWUBSrZE4wQqxOqHWNxusSM1EQIIwUsBb8vhN8XxGdB/BYg4PMTsAAJHFHixFyESGIvZa6cEouw2xcnUScI/M7RNRokM9qZ7qGvMKJ7Ab2yak4fBPZuZl3RvUzJLsWfCHFNl5/QP6v7517XvC2P8/foexSUJwi4Wxg9qDsZwZr+d1fFeWbVTJa3m0/XeJwbfCeT0ffKmk+jtUrLt/Lq+rt4N6uCTrEEp0YzyUqE2cpe3ktLocz/+TfpDjEIVHdiR3UX4oG9+NI2gjkGRGJctLecUVV+SoKDWRLvzbKKLLYnsgim5dArN5eheR1IDyZYH1nP0qqlLKtaxubqzfv6zq2OkRkLsinRjTKyyAz6yQr6yQkFyQoGSfEFSY1VkVH6MS66i0qfj+KUdD7xO2IGnav9DNuZBzaG0cOPIzXwxcDdvu0lflP1Kr0qcji/+20M6PDF01vReJw/fzKZsL+Cn7W/kWCHY75QZ/rKrSwJ3smx1ZXcEhrF5j5X1pzSqFWxrYghy/9EHzbxaup5+IZdQyhUc/ooZ88SjvrkAdZUb+axDt15KwWqzXF0tJoRkQThtFP5d3U/NkeLiaSuwJ+yA5cIECsfApX96e/L4hfVj9Ivvp7/ZJ7KwtBg4hVr+AoL2Row3so8lj2ZIbbG1xN1UfwYgyJRjq6OUUZvVsc6UukvxRfaRXkwTLT2iKJDDDqEM8gItycTIxwqYVVGOeV+OKuikgtLo+yJd+HFbB//bhdmTJWfX+2pwEecWCCTknaDmBMZyl3xdfg7LKSfncapwXPIXPMGQ+057umYwdLUEHmBLpyQOpTcyF4qdn/IFt9uFqemsNv/xQ8EPucYGPExJJpKqoOXMqsIm2Pq9jKGfzqNtVbCAlSm51LQGX43M52OwS7sufmmfevPOOOMQy4gLgPGOue+W7t8FTDSOXdDQ20+dwSxey2r593HU8Uv8kKGn6gZv3/cT/esXI559FECGe0/98mnMW8ueJg/Lv0jG4LG8PJUOob7khnfTWnmBv6RmUKfQHumXTiT7lk9PNt//68jeS9UwZ+G38+o42pmDnx/6unMT93NE6f9jcFHn/S5+g+++Fvu2v0EZ8W6cNeEfzBn3p+5Zc2DDAgHuf/Kd+mQVe8cq3Ms+PtN/Lr0FTYFgpyXeRY/Of9XlGxezq9f+y7/TE9wcWA4v7xyeoOvcUf5FiY/9x0WuuIG66QlEnSNxcmNO3omQvRIzSWv47H071NAXv/TCKRnN+n32ZB4Is7uvVvYtnkhm7b8myWb3mdFZTErAo5Sv59QwjEoYnSPO7YHovwnNYVOsSymXjCTgV28z3ffX3gL962fzVcro3TYfSaxjmewN7GabbzEJxl7OTEKfxxzH+0bmNGRSDjufuZO3t35AqvTKkiYkVEdJMsdw3F5pzN24BBSrYoNK59nycZ3WO/ClPl8dIrHOT4c4YK9lawLD+WR+Nn8KzGEOH66Z6dy5qAuXHxCLvm92zd4xFYWLWPpzqUs3f4hn6x7kx07PyYSr6bKn85Ol8WeRIiEJQj4IqT7qjCiBADzZRDztYdEDhXl3SjfPYAOgQH8z9hjuOSE3EZPozw84yL+FF1D99Ljeeiqv5LXPn3fOuccE6ZPZJFvAT+ygXzn6lmefYSr45x1/x8py36MEVVh/pT1FbKHXUMiFmXtO0/Sefub7CWHraP+yLAzLtnXriRcwmvrX+PplU+xcs8qMpxxYWWYS10GAwd/A0ZeD+kd9tWviFTzzobFvLTmRRbueIuqeFmDr+tTWaEsemf1ZkinIZzS4xRO7HYimRU7Ye4tsGouJGpmdMV9IdbmnMzi7NP4JOckunXqwaBu7Rial01m7XWhnRVl/PX9e3lx/XNUuPC+bXy/OpXrg12xrB7gD0L5Ntj4HkTKiPlTmZzTg7ntYnSIJUh3cYqDQTqntOeH+TdxwVEX4K/zvrR38wpWvvk4W7ctZJtVsSs1g9QORzFowCmcPORCMlKy9tXdXrmdcS+No2dWT6aPeQAL74F4FMwPmV2I4TjhsROY9kJXemT0+Nyp9kPxCKL5ATF8mHv/4cmsn/83/h5ZxVNZmUTMx3E2kJvOvIOOP6u5hFH/GkRTRMIl/OH57/BMeBXVtX/QKc64pt8lfO+knxHyN3yuddWaf/HNtycyIBzkV5e8zoqiBdyyejKjEt256zuve7b54bSzeStlC8dFg6wOROhS7ePXZ83iK0c3fNF15Xsz+cPi21iQHiS99vRF1IyLMkZx+2X3Numi18IN/+K9la+zq3QD3UOZ9G7XlT4d+9K9fT/aZffC0jvU7PRfovietcxb/ASvbpjH0vgOynwJQi6VU3t8nVvOvqnR3z3AE4v+xN3LplNpn+3bGYkE307px3WXPoEvJXO/Y4jE4ry9ejPpgRB9OmV97o1zH+dgwwIoXgixKLTvDX1Ooyq1C5tKqigLV5PXPo0uWanN/h0AUB2GxY/Av+6F0g0AxPxpBOJVxPHxYvBc7klcyrZYJikBH52zUhjULYuzh3Rj9DFdCQX2f23HVZXwi0dP47lU6Fx2Nndd/HOO65lDVTTO5Gf+zJvRRzinyvj9te9iqe0a7OfjzWV8a8YUYh2fxEfNEUCFz8dOv59IbUBlBbPITskm6A9SFatia8VWAAa2H8gVg67gvL7nkR70+D17SLgE60rXUVRSxJ7wHiqrI4SrwvQgTKesHmR3HkRuZi45KTkN/x1U7oatH0FKFnTsD428vrrCsTBvF7+NYQzsMJDe7TxmBcarYd27sGoubvN/WBgt44E0I7PjURx/1Bi+MeAbTX6tjZmxYga/ee83TB09lVNyP38RujRSyqkzT+XB2T3omt71kA+IJp1iquv4vJC7elJ3nmyXRdSMgakj+HnBTzmuWz+g4YvUzRGJR1i97UPMn8KAzoMbncJa17QXb+Se3YV8tTzArlCEdSEfz50zk97dj/Wsv6u8hEmPn88uXxlpiSDXnzyFgq98db/bcdVhZr56Jwu2z4dgFqOP/SYXDLu0Wa/xcLSzcgcvLr6fSMU2uqV04OwTJpKe3bO1h3VgEoma++hsXgxlm6HzIOh3FuQ0bdbQ/sS2LuWm5y7lrfQQqRW9yPSfBrH/sDPzY46NxHj44mdJ7fLFU0v1rd9VwbVPPMe26vm0S/2EtEA7Bvbox4l9jiYaj1ISKaEkUkIsESPkD9G/fX/yu+YztNPQg57Bc6Sqjldz/nPn0z61PTPOm/G53+O/Nv2L773xPR5/uS85KTmHfEAEqLlIfRawiZqL1N90zi1rqE1Gn3R31G39GJh5GneM+hGDO/f73PpkBMTBuPWpS3khvIqOCeOHA67hklNublK7RMLtd3aFSDLFV7/GjFdv5J7MAJU+Hz7nuLIywQ1f+yvpzfiClXOOkspqSqqq6dMxXW/8X4JZq2Zx+/zbeeichzix24n7yn85/5e8uOZFnnplAD7zHVBAtJnvQTjnYmZ2AzCXmmmuDzUWDgApgUxmXfAsAzv2a6xaq7n9G09z3tq5nNDrDFKDaU1up3CQL5u//9l8K3c+l73ze0orthPK7Eb7034Mac37XoiZ0T6jZvqyfDnOP+p87lp8F08sf2JfQCRcgrc2vsWpuafis50H3HebCQgA59wcYE5T6x+V06vNhgOA3+fnpKMP7S+kyREkvQOp59zJAV4xkVaSGkjlsv6X8fCyh9m0dxO5mbks2bmEHVU7OKPnGcDfD7jvtvdddRERaZZxg8ZhGFM/nArAa+teI2ABTs9r/u016mpTRxAiItJ83TK6ce2Qa3lw6YP4zMfzRc9zdu+zyU7JpuQg+lVAiIgcBm444QY+2PEBz65+lmFdhnH7ybcfdJ8KCBGRw0DAF+APo/7Ac6ufY/yg8Un5noUCQkTkMNEprRPXHXdd0vrTRWoREfGkgBAREU8KCBER8aSAEBERTwoIERHxpIAQERFPCggREfGkgBAREU8KCBER8aSAEBERTwoIERHxpIAQERFPCggREfGkgBAREU9tKiDM7Bgzm2pms8zsv1p7PCIiR7KkBISZPWRm281sab3ysWa20syKzGzy/vpxzi13zl0PXA6ckoyxiYjIgUnWEcR0YGzdAjPzA1OAc4HBwHgzG1y7bqiZvVTvp0vtuguBl4E5SRqbiIgcgKQ8Uc45N8/M+tQrHgEUOefWAJjZTOAi4GPn3BLg/Ab6mg3MNrOXgScPZlyVixYBsP6qqw+mGxGRQ1Z4xQpSBw06oLYt+cjRXGBjneViYGRjDcysALgESKGBIwgzmwhMAnJycnIoLCxssL/yUZW+AAAHNklEQVSutf8tKSlp6phFRA4v3bpRMqA/axt5r2xIkwLCzN4AunmsusU590Kzt9oA51whULifOtOAaQD5+fmuoKCg4corlidraCIiR5wmBYRzbvQB9L0J6FlnOa+2TEREDgEtOc11EdDfzPqaWQgYB8xuwe2JiEgSJWua6wxgPjDQzIrNbIJzLgbcAMwFlgNPO+eWJWN7IiLS8pI1i2l8A+Vz0HRVEZFDUpv6JrWIiLQdCggREfGkgBAREU8KCBER8aSAEBERTwoIERHxpIAQERFPCggREfGkgBAREU8KCBER8aSAEBERTwoIERHxpIAQERFPCggREfGkgBAREU8KCBER8aSAEBERT0l5olyymNnFwHlAO+BB59xrrTwkEZEjVrKeSf2QmW03s6X1ysea2UozKzKzyfvrxzn3vHPuOuB64IpkjE1ERA5Msk4xTQfG1i0wMz8wBTgXGAyMN7PBteuGmtlL9X661Gl+a21bERFpJUk5xeScm2dmfeoVjwCKnHNrAMxsJnAR8LFzbglwfv1+zMyA3wKvOOcWe23LzCYCk4CcnJwcCgsLk/ESRESknpa8BpELbKyzXAyM3E+bG4HRQLaZ9XPOTa1fwTk3DZgGkJ+f7woKCpIzWhER+ZwmBYSZvQF081h1i3PuhWQNxjl3N3B3svoTEZED16SAcM6NPoC+NwE96yzn1ZaJiMghoCW/B7EI6G9mfc0sBIwDZrfg9kREJImSNc11BjAfGGhmxWY2wTkXA24A5gLLgaedc8uSsT0REWl5yZrFNL6B8jnAnGRsQ0REvly61YaIiHhSQIiIiCcFhIiIeFJAiIiIJwWEiIh4UkCIiIgnBYSIiHhSQIiIiCcFhIiIeFJAiIiIJwWEiIh4UkCIiIgnBYSIiHhSQIiIiCcFhIiIeFJAiIiIpzYVEGZ2lJk9aGazWnssIiJHumQ9cvQhM9tuZkvrlY81s5VmVmRmk/fXj3NujXNuQjLGJCIiBydZRxDTgbF1C8zMD0wBzgUGA+PNbHDtuqFm9lK9ny5JGouIiCRBsp5JPc/M+tQrHgEUOefWAJjZTOAi4GPn3BLg/GRsW0REWkZSAqIBucDGOsvFwMjGGphZR+DXwAlm9lPn3J0edSYCk4CcnJwcCgsLkzdiERHZp0kBYWZvAN08Vt3inHshWYNxzu0Crt9PnWnANID8/HxXUFCQrM2LiEgdTQoI59zoA+h7E9CzznJebZmIiBwCWnKa6yKgv5n1NbMQMA6Y3YLbExGRJErWNNcZwHxgoJkVm9kE51wMuAGYCywHnnbOLUvG9kREpOUlaxbT+AbK5wBzkrENERH5crWpb1KLiEjboYAQERFPCggREfGkgBAREU8KCBER8aSAEBERTwoIERHxpIAQERFPCggREfGkgBAREU8KCBER8aSAEBERTwoIERHxpIAQERFPCggREfGkgBAREU9JeWBQsphZBnAfEAUKnXNPtPKQRESOWC16BGFmD5nZdjNbWq98rJmtNLMiM5tcZ9UlwCzn3HXAhS05NhERaVxLn2KaDoytW2BmfmAKcC4wGBhvZoNrV+cBG2v/HW/hsYmISCNaNCCcc/OA3fWKRwBFzrk1zrkoMBO4qHZdMTUh0eJjExGRxrXGNYhcPjtKgJpQGFn772eBe83sPOBFr8ZmNhGYBOQAYTNb1oJjbUw2UNrG+z+QPprTpil191ensfWNresE7NzPtlvb4bqPNKddsuodyH6ifaTh/ns3qbVz7oB/gDeApR4/F9Wp0wdYWmf5MuCBOstXAfcezDha4weY1tb7P5A+mtOmKXX3V6ex9ftZ935r7wNH6j7SnHbJqncg+4n2kYPv/6COIJxzow+g2SagZ53lvNqyQ43nEU4b6/9A+mhOm6bU3V+dxta39O+4pR2u+0hz2iWr3uG6n7TpfcRqU6bFmFkf4CXn3LG1ywFgFXAWNcGwCPimc661ThXJIcjM3nfO5bf2OKTt0j5y8Fp6musMYD4w0MyKzWyCcy4G3ADMBZYDTysc5ABMa+0BSJunfeQgtfgRhIiIHJo0lVRERDwpIERExJMCQkREPCkg5JBnZkeZ2YNmNqu1xyJtl5ldbGZ/M7OnzOzs1h7PoUABIW1Sc2706Gpu2zKhdUYqramZ+8nzruZGoNcDV7TGeA81Cghpq6bTvBs9ypFpOs3fT26tXS/7oYCQNsk1/0aPcgRqzn5iNf4PeMU5t/jLHuuhSAEhhxKvGz3mmllHM5sKnGBmP22doUkb4rmfADcCo4HLzOz61hjYoaZNPVFO5EA453ZRc15ZpEHOubuBu1t7HIcSHUHIoeRwudGjtCztJ0migJBDySKgv5n1NbMQMA6Y3cpjkrZH+0mSKCCkTdKNHqUptJ+0LN2sT0REPOkIQkREPCkgRETEkwJCREQ8KSBERMSTAkJERDwpIERExJMCQkREPCkgRETEkwJCREQ8/X+pgncT0JYegAAAAABJRU5ErkJggg==\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 2024-11-05/X219225.340')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-05_p0_c4.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-05_p1_c4.dat')\n",
      "('zdat', 'READ /home/l_samenv/sea/calib_scripts/calib_data/calib2024-11-05_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\r\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"
   ]
  },
  {
   "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
}