reshape_and_compress_ascii_with_WF.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Script to reshape and compress original ascii files \n",
    "```bash\n",
    "#!/bin/bash\n",
    "if [ $# -ne 1 ];then\n",
    "    echo \"ERROR: use 1 argument (run number)\"\n",
    "    exit 1\n",
    "fi\n",
    "\n",
    "nrun=$( printf \"%6.6d\" \"$1\")\n",
    "ascii_dir=\"/data/insudaq/ascii_asa_2021\"\n",
    "new_ascii_compact_dir=\"/home/insudaq/tlktest2011/maroc3_2012/vme14/ana_insulab2021_asa/python/compact_data_WF\"\n",
    "awk '{for(i=1;i<=NF;i++)printf(\"%s \",$i);if(NR%3==0)printf(\"\\n\")}' $ascii_dir/run$nrun*wave*.dat | gzip > $new_ascii_compact_dir\"/run\"$nrun\"_WF.gz\"\n",
    "\n",
    "```\n",
    "The script can be run from here: `/home/insudaq/valerio/bar-fiber-sipm-2021`\n",
    "\n",
    "It creates a `run260158_WF.gz` here `/home/insudaq/tlktest2011/maroc3_2012/vme14/ana_insulab2021_asa/python/compact_data_WF` folder.\n",
    "\n",
    "The gz files contains **1 row per event**, each row consisting of a total of 23 + 512 + 512 = 1047 columns:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.loadtxt('compact_data_WF/run260158_WF.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2708, 1047)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reorganizing data\n",
    "\n",
    "sili     = data[:, 0  : 4]                # 4 coordinates from the silicon track detector [cm]\n",
    "digiph   = data[:, 4  : 4+8]              # 8 pulse heigth of the digitizer [ADC, fullscale=16384]\n",
    "digitime = data[:, 4+8: 4+8+8]            # 8 time of the maximum of the digitizer [2ns, range 0-511]\n",
    "dtime    = data[:, 4+8+8]                 # 1 time from the start of run [s]\n",
    "abstime  = data[:, 4+8+8+1]               # 1 absolute time from 1970-01-01 [s]\n",
    "nevent   = data[:, 4+8+8+2]               # 1 event number\n",
    "wave1    = data[:, 4+8+8+3: 4+8+8+3+512]  # 512 sampling points of 1st waveform [ADC, fullscale=16384]\n",
    "wave2    = data[:, 4+8+8+3+512:]          # 512 sampling points of 2nd waveform [ADC, fullscale=16384]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,2, figsize=(8,8))\n",
    "ax = ax.flatten()\n",
    "for i in range(4):\n",
    "    ax[i].hist(sili[:,i], 100, range=[-1,11])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,8, figsize=(18,6))\n",
    "for i in range(8):\n",
    "    ax[0][i].hist(digiph[:,i], 100, color='red')\n",
    "    ax[1][i].hist(digitime[:,i], 100, color='orange')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}