make_GTIs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# make_GTIs.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reads in a text file list of NICER event lists, gets the generic GTIs in the FITS file generated by the auto pipeline, only keeps the GTIs that are longer than your segment length (like 32 seconds, so that you don't have a bunch of 1-second GTIs hanging around for no good reason), saves those GTIs to a new FITS file, and writes the file names of the new GTIs to a text file for reading in to quicklook_segments.py."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.table import Table\n",
    "from astropy.io import fits\n",
    "from astropy.time import Time\n",
    "import numpy as np\n",
    "import os\n",
    "import subprocess\n",
    "import matplotlib.pyplot as plt\n",
    "from xcor_tools_nicer import find_nearest, clock_to_mjd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Need to have the list of event lists already made"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "homedir = os.path.expanduser('~')\n",
    "exe_dir = os.getcwd()\n",
    "obj_name = \"Swift_J1728.9-3613\"\n",
    "obj_prefix = \"SwiftJ1728\"\n",
    "min_length = 16  ## seconds\n",
    "\n",
    "# exe_dir = \"%s/Documents/Research/NICER_exploration\" % (homedir)\n",
    "exe_dir = \"%s/Documents/Research/%s\" % (homedir, obj_prefix)\n",
    "data_dir = \"%s/Reduced_data/%s/\" % (homedir, obj_name)\n",
    "evt_list = \"%s/in/%s_evtlists.txt\" % (exe_dir, obj_prefix)\n",
    "data_files = [line.strip() for line in open(evt_list)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "print(os.path.isfile(data_dir+data_files[0]))\n",
    "print(os.path.isfile(data_dir+data_files[-1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# For a list of many data files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ni1200550101_0mpu7_cl\n",
      "\tGTI len:  66\n",
      "\tMin GTI:  1.0\n",
      "\tMax GTI:  173.0\n",
      "\tFinal GTI len:  25\n",
      "ni1200550102_0mpu7_cl\n",
      "\tGTI len:  347\n",
      "\tMin GTI:  1.0\n",
      "\tMax GTI:  319.0\n",
      "\tFinal GTI len:  172\n",
      "ni1200550103_0mpu7_cl\n",
      "\tGTI len:  326\n",
      "\tMin GTI:  1.0\n",
      "\tMax GTI:  453.0\n",
      "\tFinal GTI len:  167\n",
      "ni1200550104_0mpu7_cl\n",
      "\tGTI len:  20\n",
      "\tMin GTI:  1.0\n",
      "\tMax GTI:  1121.0\n",
      "\tFinal GTI len:  17\n",
      "ni1200550105_0mpu7_cl\n",
      "\tGTI len:  282\n",
      "\tMin GTI:  4.3779611587524414e-05\n",
      "\tMax GTI:  1108.0\n",
      "\tFinal GTI len:  18\n",
      "ni1200550106_0mpu7_cl\n",
      "\tGTI len:  875\n",
      "\tMin GTI:  4.1604042053222656e-05\n",
      "\tMax GTI:  1071.0\n",
      "\tFinal GTI len:  28\n",
      "ni1200550107_0mpu7_cl\n",
      "\tGTI len:  299\n",
      "\tMin GTI:  3.191828727722168e-05\n",
      "\tMax GTI:  1039.0\n",
      "\tFinal GTI len:  21\n",
      "ni1200550108_0mpu7_cl\n",
      "\tGTI len:  16\n",
      "\tMin GTI:  1.0\n",
      "\tMax GTI:  1024.0\n",
      "\tFinal GTI len:  15\n",
      "ni1200550109_0mpu7_cl\n",
      "\tGTI len:  60\n",
      "\tMin GTI:  1.0\n",
      "\tMax GTI:  797.0\n",
      "\tFinal GTI len:  18\n",
      "ni1200550110_0mpu7_cl\n",
      "\tGTI len:  90\n",
      "\tMin GTI:  1.0\n",
      "\tMax GTI:  509.0\n",
      "\tFinal GTI len:  9\n",
      "ni1200550111_0mpu7_cl\n",
      "\tGTI len:  45\n",
      "\tMin GTI:  0.008965760469436646\n",
      "\tMax GTI:  347.0\n",
      "\tFinal GTI len:  5\n",
      "ni1200550112_0mpu7_cl\n",
      "\tGTI len:  25\n",
      "\tMin GTI:  1.0\n",
      "\tMax GTI:  273.0\n",
      "\tFinal GTI len:  7\n",
      "ni1200550113_0mpu7_cl\n",
      "\tGTI len:  12\n",
      "\tMin GTI:  1.0\n",
      "\tMax GTI:  739.0\n",
      "\tFinal GTI len:  11\n",
      "ni1200550114_0mpu7_cl\n",
      "\tGTI len:  7\n",
      "\tMin GTI:  1.0\n",
      "\tMax GTI:  925.0\n",
      "\tFinal GTI len:  5\n",
      "ni1200550115_0mpu7_cl\n",
      "\tGTI len:  26\n",
      "\tMin GTI:  1.0\n",
      "\tMax GTI:  1254.0\n",
      "\tFinal GTI len:  11\n",
      "ni1200550116_0mpu7_cl\n",
      "\tGTI len:  36\n",
      "\tMin GTI:  1.0\n",
      "\tMax GTI:  795.0\n",
      "\tFinal GTI len:  7\n",
      "ni1200550117_0mpu7_cl\n",
      "\tGTI len:  15\n",
      "\tMin GTI:  1.0\n",
      "\tMax GTI:  431.0\n",
      "\tFinal GTI len:  4\n",
      "ni1200550118_0mpu7_cl\n",
      "\tGTI len:  8\n",
      "\tMin GTI:  1.0\n",
      "\tMax GTI:  519.0\n",
      "\tFinal GTI len:  5\n",
      "ni1200550119_0mpu7_cl\n",
      "\tGTI len:  8\n",
      "\tMin GTI:  27.0\n",
      "\tMax GTI:  783.0\n",
      "\tFinal GTI len:  8\n",
      "ni1200550120_0mpu7_cl\n",
      "\tGTI len:  3\n",
      "\tMin GTI:  1.0\n",
      "\tMax GTI:  451.0\n",
      "\tFinal GTI len:  2\n",
      "ni1200550121_0mpu7_cl\n",
      "\tGTI len:  5\n",
      "\tMin GTI:  1.0\n",
      "\tMax GTI:  458.0\n",
      "\tFinal GTI len:  3\n",
      "ni1200550122_0mpu7_cl\n",
      "\tGTI len:  4\n",
      "\tMin GTI:  3.0\n",
      "\tMax GTI:  256.0\n",
      "\tFinal GTI len:  3\n",
      "ni1200550123_0mpu7_cl\n",
      "\tGTI len:  4\n",
      "\tMin GTI:  267.0\n",
      "\tMax GTI:  319.0\n",
      "\tFinal GTI len:  4\n",
      "ni1200550124_0mpu7_cl\n",
      "\tGTI len:  6\n",
      "\tMin GTI:  1.0\n",
      "\tMax GTI:  314.0\n",
      "\tFinal GTI len:  5\n",
      "ni1200550125_0mpu7_cl\n",
      "\tGTI len:  7\n",
      "\tMin GTI:  1.0\n",
      "\tMax GTI:  808.0\n",
      "\tFinal GTI len:  6\n",
      "ni1200550126_0mpu7_cl\n",
      "\tGTI len:  9\n",
      "\tMin GTI:  1.0\n",
      "\tMax GTI:  334.0\n",
      "\tFinal GTI len:  8\n",
      "ni1200550127_0mpu7_cl\n",
      "\tGTI len:  3\n",
      "\tMin GTI:  213.0\n",
      "\tMax GTI:  256.0\n",
      "\tFinal GTI len:  3\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "gti_list = []\n",
    "for data_file in data_files:\n",
    "    if os.path.isfile(data_dir+data_file):\n",
    "        obsID = data_file.split(\"/\")[-1].split(\"_\")[0][2:]\n",
    "        evtID = data_file.split(\"/\")[-1].split(\".\")[0]\n",
    "        print(evtID)\n",
    "#         ex\n",
    "        gti_file = \"%s/%s_%dsGTIs.fits\" % (data_dir, evtID, min_length)\n",
    "        if not os.path.isfile(gti_file):\n",
    "            hdu_list = fits.open(data_dir+data_file, memmap=True)\n",
    "#             print(hdu_list.info())\n",
    "#             try:\n",
    "            gti_tab = Table(hdu_list[2].data)\n",
    "            gti_length = gti_tab['STOP'] - gti_tab['START']\n",
    "            print(\"\\tGTI len: \", len(gti_length))\n",
    "            print(\"\\tMin GTI: \", np.min(gti_length))\n",
    "            print(\"\\tMax GTI: \", np.max(gti_length))\n",
    "            to_delete = np.where(gti_length < min_length)[0]\n",
    "            del gti_tab[to_delete]\n",
    "            print(\"\\tFinal GTI len: \", len(gti_tab))\n",
    "#             print(gti_tab['START'])\n",
    "            if len(gti_tab) > 0:\n",
    "                gti_tab.write(gti_file, overwrite=True, format='fits')\n",
    "            else:\n",
    "                print(\"\\t\", obsID, evtID)\n",
    "#             except:\n",
    "#                 print(\"Could not read GTI extension: %s\" % (data_dir+data_file))\n",
    "        if os.path.isfile(gti_file):\n",
    "            gti_list.append(os.path.basename(gti_file))\n",
    "        else:\n",
    "            print(\"\\t GTI file was not created.\")\n",
    "    else:\n",
    "        print(\"File does not exist: %s\" % (data_dir+data_file))\n",
    "print(\"Done!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Write the file name to the GTI list, for reading into quicklook_segments.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "gti_out_file = \"%s/in/%s_%dsGTIlists.txt\" % (exe_dir, obj_prefix, min_length)\n",
    "with open(gti_out_file, 'w') as f:\n",
    "    [f.write(\"%s\\n\" % gti_file) for gti_file in gti_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Here be dragons and untested and/or outdated code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For just one data file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'gx3394-3.evt'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-39fb2df8058c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhdu_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfits\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhdu_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\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[1;32m      3\u001b[0m \u001b[0mgti_tab\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhdu_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/nicerpy/lib/python3.8/site-packages/astropy/io/fits/hdu/hdulist.py\u001b[0m in \u001b[0;36mfitsopen\u001b[0;34m(name, mode, memmap, save_backup, cache, lazy_load_hdus, **kwargs)\u001b[0m\n\u001b[1;32m    162\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'Empty filename: {name!r}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 164\u001b[0;31m     return HDUList.fromfile(name, mode, memmap, save_backup, cache,\n\u001b[0m\u001b[1;32m    165\u001b[0m                             lazy_load_hdus, **kwargs)\n\u001b[1;32m    166\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/nicerpy/lib/python3.8/site-packages/astropy/io/fits/hdu/hdulist.py\u001b[0m in \u001b[0;36mfromfile\u001b[0;34m(cls, fileobj, mode, memmap, save_backup, cache, lazy_load_hdus, **kwargs)\u001b[0m\n\u001b[1;32m    399\u001b[0m         \"\"\"\n\u001b[1;32m    400\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 401\u001b[0;31m         return cls._readfrom(fileobj=fileobj, mode=mode, memmap=memmap,\n\u001b[0m\u001b[1;32m    402\u001b[0m                              \u001b[0msave_backup\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msave_backup\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcache\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    403\u001b[0m                              lazy_load_hdus=lazy_load_hdus, **kwargs)\n",
      "\u001b[0;32m~/opt/anaconda3/envs/nicerpy/lib/python3.8/site-packages/astropy/io/fits/hdu/hdulist.py\u001b[0m in \u001b[0;36m_readfrom\u001b[0;34m(cls, fileobj, data, mode, memmap, cache, lazy_load_hdus, **kwargs)\u001b[0m\n\u001b[1;32m   1050\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_File\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[1;32m   1051\u001b[0m                 \u001b[0;31m# instantiate a FITS file object (ffo)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1052\u001b[0;31m                 \u001b[0mfileobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_File\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmemmap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcache\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1053\u001b[0m             \u001b[0;31m# The Astropy mode is determined by the _File initializer if the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1054\u001b[0m             \u001b[0;31m# supplied mode was None\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/nicerpy/lib/python3.8/site-packages/astropy/utils/decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    533\u001b[0m                     \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwarning_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    534\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 535\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    536\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    537\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/nicerpy/lib/python3.8/site-packages/astropy/io/fits/file.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fileobj, mode, memmap, overwrite, cache)\u001b[0m\n\u001b[1;32m    173\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_fileobj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    174\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m)\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--> 175\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_filename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    176\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_filelike\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/nicerpy/lib/python3.8/site-packages/astropy/io/fits/file.py\u001b[0m in \u001b[0;36m_open_filename\u001b[0;34m(self, filename, mode, overwrite)\u001b[0m\n\u001b[1;32m    562\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    563\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_try_read_compressed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mext\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--> 564\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfileobj_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIO_FITS_MODES\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmode\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[0m\u001b[1;32m    565\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_on_error\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    566\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/nicerpy/lib/python3.8/site-packages/astropy/io/fits/util.py\u001b[0m in \u001b[0;36mfileobj_open\u001b[0;34m(filename, mode)\u001b[0m\n\u001b[1;32m    390\u001b[0m     \"\"\"\n\u001b[1;32m    391\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 392\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffering\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    393\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    394\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'gx3394-3.evt'"
     ]
    }
   ],
   "source": [
    "hdu_list = fits.open(data_file, memmap=True)\n",
    "print(hdu_list.info())\n",
    "gti_tab = Table(hdu_list[2].data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "gti_length = gti_tab['STOP'] - gti_tab['START']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.000479280948638916\n",
      "1108.0\n"
     ]
    }
   ],
   "source": [
    "print(np.min(gti_length))\n",
    "print(np.max(gti_length))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "76\n",
      "211\n",
      "192\n",
      "167\n",
      "121\n"
     ]
    }
   ],
   "source": [
    "print(len(np.where(gti_length < 1)[0]))\n",
    "print(len(np.where(gti_length >= 16)[0]))\n",
    "print(len(np.where(gti_length >= 32)[0]))\n",
    "print(len(np.where(gti_length >= 64)[0]))\n",
    "print(len(np.where(gti_length >= 128)[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/nicer/lib/python3.6/site-packages/matplotlib/figure.py:2267: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not compatible \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2700x2025 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bin_edges = np.asarray([2**x for x in range(0,11)])\n",
    "fig, ax = plt.subplots(1, 1, figsize=(9,6.75), dpi=300, tight_layout=True)\n",
    "n, bins, patches = plt.hist(x=gti_length, bins=bin_edges, color='blue', alpha=0.7)\n",
    "ax.grid(axis='y', alpha=0.75)\n",
    "ax.set_xscale('log')\n",
    "ax.set_xlabel('GTI length (s)', fontsize=20)\n",
    "ax.set_ylabel('# of occurrences', fontsize=20)\n",
    "x_maj_loc = bin_edges\n",
    "x_maj_labels = bin_edges\n",
    "ax.set_xticks(x_maj_loc)\n",
    "ax.tick_params(axis='x', labelsize=20, bottom=True, top=True,\n",
    "                labelbottom=True, labeltop=False, direction=\"in\")\n",
    "ax.tick_params(axis='y', labelsize=20, left=True, right=True,\n",
    "                labelleft=True, labelright=False, direction=\"in\")\n",
    "ax.tick_params(which='major', width=1.5, length=9, direction=\"in\")\n",
    "ax.tick_params(which='minor', width=1.5, length=6, direction=\"in\")\n",
    "ax.set_xticklabels(x_maj_labels, rotation='horizontal', fontsize=20)\n",
    "ax.tick_params(which='minor', width=1.5, top=True, right=True, length=6, direction='in')\n",
    "for axis in ['top', 'bottom', 'left', 'right']:\n",
    "    ax.spines[axis].set_linewidth(1.5)\n",
    "ax.set_title(basename, fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_delete = np.where(gti_length < 64)[0]\n",
    "# print(to_delete)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "del gti_tab[to_delete]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Table length=224>\n",
       " name  dtype \n",
       "----- -------\n",
       "START float64\n",
       " STOP float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gti_tab.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "gti_tab.write(\"%s/%s_64sGTIs.fits\" % (data_dir, basename), overwrite=True, format='fits')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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