make_xsm_ltc.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some tests of XSM and NuSTAR lightcurves in comparison to other X-ray data\n",
    "# \n",
    "# 18-04-2023    IGH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "from sunpy.net import Fido\n",
    "from sunpy.net import attrs as a\n",
    "from sunpy import timeseries as ts\n",
    "from sunpy.time import parse_time\n",
    "from astropy.io import fits\n",
    "import astropy.time as atime\n",
    "import astropy.units as u\n",
    "import datetime\n",
    "import pandas as pd\n",
    "import os\n",
    "from pathlib import Path\n",
    "import glob\n",
    "from stixdcpy.quicklook import LightCurves\n",
    "from stixdcpy.net import Request as jreq\n",
    "\n",
    "import nsx_func\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "\n",
    "plt.rcParams.update({'font.size': 18,'font.family':\"sans-serif\",\\\n",
    "            'font.sans-serif':\"Arial\",'mathtext.default':\"regular\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of all the NuSTAR observations, with my naming, \n",
    "# based off first day of observing per campaign\n",
    "dobs=['20140910','20141101','20141211',\n",
    "    '20150429','20150901',\n",
    "    '20160219','20160422','20160726',\n",
    "    '20170321','20170821','20170911','20171010',\n",
    "    '20180529','20180907','20180928',\n",
    "    '20190112','20190412','20190425','20190702',\n",
    "    '20200129','20200221','20200606','20200912',\n",
    "    '20210108','20210429','20210720','20210730','20211117',\n",
    "    '20220224','20220603','20220906','20221209',\n",
    "    '20230318']\n",
    "\n",
    "# Data directory\n",
    "# ddir=str(Path.home())+'/data/heasarc_nustar/'\n",
    "ddir='/Volumes/Samsung_T5/data/heasarc_nustar/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Volumes/Samsung_T5/data/heasarc_nustar/ns_20210429/\n",
      "['20615001001', '20615002001', '20615003001', '20615004001', '20615005001', '20616001001', '20616002001', '20616003001', '20616004001', '20616005001', '20617001001', '20617002001', '20617003001', '20617004001', '20617005001', '20617006001']\n",
      "/Volumes/Samsung_T5/data/heasarc_nustar/ns_20220224/\n",
      "['20621001001', '20621002001', '20621003001', '20621004001', '20622001001', '20622002001', '20622003001', '20622004001', '20623001001', '20623002001', '20623003001', '20623004001']\n",
      "/Volumes/Samsung_T5/data/heasarc_nustar/ns_20220906/\n",
      "['90810201001', '90810202001', '90810203001', '90810204001']\n",
      "/Volumes/Samsung_T5/data/heasarc_nustar/ns_20230318/\n",
      "['20801021001', '20801022001', '20801023001', '20801024001', '20801025001', '20801026001', '20801027001']\n"
     ]
    }
   ],
   "source": [
    "# Find all pointings/IDs for the chosen observation \n",
    "for icamp in [24,28,30,32]:\n",
    "    maindir=ddir+'ns_'+dobs[icamp]+'/'\n",
    "    print(maindir)\n",
    "    # Most start with 20 or 90?\n",
    "    ids = [f.name for f in os.scandir(maindir) \\\n",
    "        if (f.is_dir() and (f.name.startswith('20') or f.name.startswith('80') or f.name.startswith('90')))]\n",
    "    ids=sorted(ids)\n",
    "    print(ids)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24  ---  /Volumes/Samsung_T5/data/heasarc_nustar/ns_20210429/\n",
      "#:  16  ---  ['20615001001', '20615002001', '20615003001', '20615004001', '20615005001', '20616001001', '20616002001', '20616003001', '20616004001', '20616005001', '20617001001', '20617002001', '20617003001', '20617004001', '20617005001', '20617006001']\n",
      "20615001001\n",
      "20615002001\n",
      "20615003001\n",
      "20615004001\n",
      "20615005001\n",
      "20616001001\n",
      "20616002001\n",
      "20616003001\n",
      "20616004001\n",
      "20616005001\n",
      "20617001001\n",
      "20617002001\n",
      "20617003001\n",
      "20617004001\n",
      "20617005001\n",
      "20617006001\n",
      "28  ---  /Volumes/Samsung_T5/data/heasarc_nustar/ns_20220224/\n",
      "#:  12  ---  ['20621001001', '20621002001', '20621003001', '20621004001', '20622001001', '20622002001', '20622003001', '20622004001', '20623001001', '20623002001', '20623003001', '20623004001']\n",
      "20621001001\n",
      "20621002001\n",
      "20621003001\n",
      "20621004001\n",
      "20622001001\n",
      "20622002001\n",
      "20622003001\n",
      "20622004001\n",
      "20623001001\n",
      "20623002001\n",
      "20623003001\n",
      "20623004001\n",
      "30  ---  /Volumes/Samsung_T5/data/heasarc_nustar/ns_20220906/\n",
      "#:  4  ---  ['90810201001', '90810202001', '90810203001', '90810204001']\n",
      "90810201001\n",
      "90810202001\n",
      "90810203001\n",
      "90810204001\n",
      "32  ---  /Volumes/Samsung_T5/data/heasarc_nustar/ns_20230318/\n",
      "#:  7  ---  ['20801021001', '20801022001', '20801023001', '20801024001', '20801025001', '20801026001', '20801027001']\n",
      "20801021001\n",
      "20801022001\n",
      "20801023001\n",
      "20801024001\n",
      "20801025001\n",
      "20801026001\n",
      "20801027001\n"
     ]
    }
   ],
   "source": [
    "# Do all of 2022 and 2023 (so far) but not June (as mosaics and don't have locally)\n",
    "gdir=str(Path.home())+'/data/goes_xrs/'\n",
    "xsmdir='/Volumes/Samsung_T5/data/xsm/'\n",
    "\n",
    "for icamp in [24,28,30,32]:\n",
    "# for icamp in [28]:\n",
    "    maindir=ddir+'ns_'+dobs[icamp]+'/'\n",
    "    print(icamp, ' --- ',maindir)\n",
    "    # Most start with 20 or 80 or 90?\n",
    "    ids = [f.name for f in os.scandir(maindir) \\\n",
    "        if (f.is_dir() and (f.name.startswith('20') or f.name.startswith('80') or f.name.startswith('90')))]\n",
    "    ids=sorted(ids)\n",
    "    # Filter out random other dirs\n",
    "    ids=[f for f in ids if not f.endswith('_new')]\n",
    "    # Remove the mosiac from 20220603\n",
    "    if (dobs[icamp] == '20220603'):\n",
    "        ids=[f for f in ids if f.startswith('2062400')]      \n",
    "    print(\"#: \",len(ids),' --- ',ids)\n",
    "\n",
    "    # # STIX + GOES/XRS + NuSTAR  lightcurves\n",
    "    for nsid in ids:\n",
    "        print(nsid)\n",
    "\n",
    "        df_test=nsx_func.nsrate(maindir=maindir,nsid=nsid,lvt=True,englow=2,enghigh=10)\n",
    "        df10 = df_test.resample('10s', level=0).mean()\n",
    "        lla=df10[\"lvta\"].values\n",
    "        lla=lla[lla>0]\n",
    "        llb=df10[\"lvtb\"].values\n",
    "        llb=llb[llb>0]\n",
    "        lvta=f'({100*np.min(lla):.2f} - {100*np.max(lla):.2f}%)'\n",
    "        lvtb=f'({100*np.min(llb):.2f} - {100*np.max(llb):.2f}%)'\n",
    "        # yrange for NuSTAR plot\n",
    "        alldata=np.concatenate((df10[\"rta\"].values,df10[\"rtb\"].values))\n",
    "        alldata=alldata[alldata > 0]\n",
    "        maxy=1.4*max(alldata)\n",
    "        miny=0.98*min(alldata)\n",
    "        yr=[miny,maxy]\n",
    "        #  Get it per detector quadrant\n",
    "        dft0=nsx_func.nsrate(maindir=maindir,nsid=nsid,lvt=False,englow=2,enghigh=10,det_id=[0])\n",
    "        df0= dft0.resample('10s', level=0).mean()\n",
    "        dft1=nsx_func.nsrate(maindir=maindir,nsid=nsid,lvt=False,englow=2,enghigh=10,det_id=[1])\n",
    "        df1= dft1.resample('10s', level=0).mean()\n",
    "        dft2=nsx_func.nsrate(maindir=maindir,nsid=nsid,lvt=False,englow=2,enghigh=10,det_id=[2])\n",
    "        df2= dft2.resample('10s', level=0).mean()\n",
    "        dft3=nsx_func.nsrate(maindir=maindir,nsid=nsid,lvt=False,englow=2,enghigh=10,det_id=[3])\n",
    "        df3= dft3.resample('10s', level=0).mean()\n",
    "        alldet=np.concatenate((df0[\"rta\"].values,df1[\"rta\"].values,df2[\"rta\"].values,df3[\"rta\"].values))\n",
    "        yrdet=[0.98*min(alldet),1.3*max(alldet)]\n",
    "\n",
    "        # Time range to nearest 5mins\n",
    "        mint=df10.index[0].to_pydatetime()\n",
    "        mint-=datetime.timedelta(minutes=5,seconds=mint.second)\n",
    "        mint-=datetime.timedelta(minutes=mint.minute % 5)\n",
    "        maxt=df10.index[-1].to_pydatetime()\n",
    "        maxt+=datetime.timedelta(minutes=5,seconds=60-maxt.second)\n",
    "        maxt-=datetime.timedelta(minutes=maxt.minute % 5)\n",
    "        tr=[mint,maxt]\n",
    "\n",
    "        # Get the GOES XRS data\n",
    "        gtimf=np.unique([f'{mint:%Y%m%d}',f'{maxt:%Y%m%d}'])\n",
    "        gfiles=[glob.glob(gdir+'*'+g+'*')[0] for g in gtimf]\n",
    "        trange=a.Time(tr[0],tr[1])\n",
    "        gxrs = ts.TimeSeries(gfiles, concatenate=True)\n",
    "        gxrst=gxrs.truncate(trange.start.iso,trange.end.iso)\n",
    "        tg_tims=gxrst.index\n",
    "        tg_x05=gxrst.quantity(\"xrsa\").value\n",
    "        tg_x18=gxrst.quantity(\"xrsb\").value\n",
    "\n",
    "        # Get the XSM\n",
    "        # For initial test just do day time range start on/ignoring past midnight\n",
    "        year=f'{mint:%Y}'\n",
    "        mon=f'{mint:%m}'\n",
    "        day=f'{mint:%d}'\n",
    "\n",
    "        #  First 5 orbits are 29-Apr 20615*, then 5 on 03-May 20616*, then 6 on 07-May 20617*\n",
    "        #  Get the 29-Apr data file\n",
    "        xsmfname=f'{xsmdir}data/{year}/{mon}/{day}/calibrated/ch2_xsm_{year}{mon}{day}_v1_level2.lc'\n",
    "        hdulist = fits.open(xsmfname)\n",
    "        xdata=hdulist[1].data\n",
    "        xhdr=hdulist[1].header\n",
    "        hdulist.close()\n",
    "\n",
    "        mjdref=atime.Time(xhdr['mjdref'],format='mjd')\n",
    "        xtims=atime.Time(mjdref+xdata['time']*u.s,format='mjd')\n",
    "        xdf=pd.DataFrame(xdata[\"rate\"],index=xtims.datetime, columns=['rate'])\n",
    "        xdf_tr=xdf.truncate(tr[0],tr[1])\n",
    "        xdfdata=xdf_tr[\"rate\"].values\n",
    "\n",
    "        # Anything in the GOES flare list? Obviously needs the internet to work\n",
    "        gsflin = Fido.search(trange,a.hek.EventType(\"FL\"),a.hek.FRM.Name == \"SSW Latest Events\")\n",
    "        gsflhk=gsflin[\"hek\"]\n",
    "        # Anything from AIA?\n",
    "        aiaflin = Fido.search(trange,a.hek.EventType(\"FL\"),a.hek.FRM.Name ==\"Flare Detective - Trigger Module\")\n",
    "        aiaflhk=aiaflin[\"hek\"]   \n",
    "\n",
    "        # Control the time axis labelling\n",
    "        myFmt = matplotlib.dates.DateFormatter('%H:%M')\n",
    "        majorx= matplotlib.dates.MinuteLocator(interval=10)\n",
    "        minorx= matplotlib.dates.MinuteLocator(interval=1)\n",
    "        plt.rcParams.update({'font.size': 16})\n",
    "        fig, axs= plt.subplots(4,figsize=(10, 12))\n",
    "        \n",
    "        axs[0].plot(xdf_tr.index, xdfdata,drawstyle='steps-post',marker=None,color='goldenrod',lw=1,label='XSM 1-15 keV')\n",
    "        axs[0].set_ylabel('XSM [count$\\;s^{-1}$]')\n",
    "        axs[0].set_ylim([0.98*np.min(xdfdata),1.3*np.max(xdfdata)])\n",
    "        axs[0].legend(loc=2,fontsize=14)\n",
    "\n",
    "        axs[1].plot(tg_tims,tg_x18,drawstyle='steps-post',\\\n",
    "            marker=None,color='firebrick',lw=2,label='G16 $1-8\\;\\AA$')\n",
    "        axs[1].set_ylabel('XRS [W$\\;m^{-2}$]')\n",
    "        # Only set limits if not all missing/nans\n",
    "        if not np.isnan(np.nanmin(tg_x18)):\n",
    "            yrgs=[0.95*np.nanmin(tg_x18),1.3*np.nanmax(tg_x18)]\n",
    "        else:\n",
    "            yrgs=[0.5e-7,3.5e-7]\n",
    "        mngs=np.mean(yrgs)\n",
    "        gspow = int(f\"{mngs:e}\".split('e')[1])\n",
    "        mngs=round(mngs/10**gspow)*10**gspow\n",
    "        match gspow:\n",
    "            case -8:\n",
    "                gscls=\"A\"\n",
    "            case -7:\n",
    "                gscls=\"B\"\n",
    "            case -6:\n",
    "                gscls=\"C\"\n",
    "            case -5:\n",
    "                gscls=\"M\"\n",
    "        if (mngs < yrgs[0]):   \n",
    "            yrgs[0] = mngs\n",
    "        if (mngs > yrgs[1]):   \n",
    "            yrgs[1] = mngs\n",
    "        axs[1].set_ylim(yrgs)\n",
    "        axs[1].plot(tr,[mngs,mngs],linestyle='dotted',color='grey')\n",
    "        axs[1].annotate(f' {gscls}{mngs/10**gspow:.0f}',(tr[1],mngs),\n",
    "                        color='grey',fontweight=\"bold\")\n",
    "\n",
    "        for gg in gsflhk:\n",
    "            tfl=a.Time(gg[\"event_starttime\"],gg[\"event_endtime\"])\n",
    "            ptim=parse_time(gg[\"event_peaktime\"]).datetime\n",
    "            axs[1].axvspan(tfl.start.datetime,tfl.end.datetime,color='silver',alpha=0.4, \\\n",
    "                label=gg[\"fl_goescls\"]+f' ({ptim:%H:%M})')\n",
    "            # for ax in axs:\n",
    "            #     ax.axvline(ptim,color='black',ls='-.')\n",
    "            axs[1].axvline(ptim,color='black',ls='-.')\n",
    "            axs[2].axvline(ptim,color='black',ls='-.')\n",
    "            axs[3].axvline(ptim,color='black',ls='-.')\n",
    "\n",
    "        for aa in aiaflhk:\n",
    "            #  Only plot it if peak time during NuSTAR time window\n",
    "            if (aa[\"event_peaktime\"] >= tr[0]) and (aa[\"event_peaktime\"] <= tr[1]):\n",
    "                ptim=parse_time(aa[\"event_peaktime\"]).datetime\n",
    "                axs[1].axvline(ptim,color='darkorchid',ls=':')\n",
    "                # axs[1].axvline(ptim,color='darkorchid',ls=':',label=f'AF ({ptim:%H:%M})')\n",
    "                axs[2].axvline(ptim,color='darkorchid',ls=':')\n",
    "                axs[3].axvline(ptim,color='darkorchid',ls=':')\n",
    "        axs[1].legend(loc=2,ncols=5,fontsize=14)\n",
    "        \n",
    "        axs[2].plot(df0.index,df0[\"rta\"].values,drawstyle='steps-post',lw=2,color='royalblue',label='A0')\n",
    "        axs[2].plot(df1.index,df1[\"rta\"].values,drawstyle='steps-post',lw=2,color='darkorange',label='A1')\n",
    "        axs[2].plot(df2.index,df2[\"rta\"].values,drawstyle='steps-post',lw=2,color='lightseagreen',label='A2')\n",
    "        axs[2].plot(df3.index,df3[\"rta\"].values,drawstyle='steps-post',lw=2,color='orchid',label='A3')\n",
    "\n",
    "        axs[2].set_ylabel('NuSTAR [count$\\;s^{-1}$]')\n",
    "        axs[2].set_ylim(yrdet)\n",
    "        axs[2].legend(loc=2,ncols=4,fontsize=14)\n",
    "\n",
    "        axs[3].plot(df10.index,df10[\"rta\"].values,drawstyle='steps-post',\\\n",
    "            lw=2,color='rebeccapurple',label='A >2keV '+lvta)\n",
    "        axs[3].plot(df10.index,df10[\"rtb\"].values,drawstyle='steps-post',\\\n",
    "            lw=2,color='teal',label='B >2keV '+lvtb)\n",
    "        axs[3].set_xlabel(f'Start Time {mint:%d-%b-%Y %H:%M:%S}')\n",
    "        axs[3].set_ylabel('NuSTAR [count$\\;s^{-1}$]')\n",
    "        axs[3].set_ylim(yr)\n",
    "        axs[3].legend(loc=2,fontsize=14,ncols=2)\n",
    "        for ax in axs:\n",
    "            ax.ticklabel_format(axis='y', style='sci', scilimits=(0,0), useOffset=True)\n",
    "            ax.set_xlim(tr)\n",
    "            ax.xaxis.set_major_locator(majorx)\n",
    "            ax.xaxis.set_minor_locator(minorx)\n",
    "            ax.xaxis.set_major_formatter(myFmt)\n",
    "        plt.annotate(nsid,(0.01,0.01),xycoords='figure fraction',fontsize=14)\n",
    "\n",
    "        fig.subplots_adjust(bottom=0.06, top=0.97, left=0.12, right=0.95)\n",
    "        plt.savefig('xsm_figs/ns_'+dobs[icamp]+f'/ltc_{mint:%Y%m%d_%H%M}_'+nsid+'_ngx.png')\n",
    "        # as looping don't need to plot the figures to the notebook\n",
    "        plt.close()\n",
    "        # break\n",
    "    # break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XTENSION= 'BINTABLE'           / binary table extension                         \n",
       "BITPIX  =                    8 / 8-bit bytes                                    \n",
       "NAXIS   =                    2 / 2-dimensional binary table                     \n",
       "NAXIS1  =                   20 / width of table in bytes                        \n",
       "NAXIS2  =                65250 / number of rows in table                        \n",
       "PCOUNT  =                    0 / size of special data area                      \n",
       "GCOUNT  =                    1 / one data group (required keyword)              \n",
       "TFIELDS =                    4 / number of fields in each row                   \n",
       "TTYPE1  = 'TIME    '           / label for field   1                            \n",
       "TFORM1  = 'D       '           / data format of field: 8-byte DOUBLE            \n",
       "TTYPE2  = 'RATE    '           / label for field   2                            \n",
       "TFORM2  = 'E       '           / data format of field: 4-byte REAL              \n",
       "TTYPE3  = 'ERROR   '           / label for field   3                            \n",
       "TFORM3  = 'E       '           / data format of field: 4-byte REAL              \n",
       "TTYPE4  = 'FRACEXP '           / label for field   4                            \n",
       "TFORM4  = 'E       '           / data format of field: 4-byte REAL              \n",
       "TUNIT1  = 's       '           / physical unit of field                         \n",
       "TUNIT2  = 'count/s '           / physical unit of field                         \n",
       "TUNIT3  = 'counts/s'           / physical unit of field                         \n",
       "EXTNAME = 'RATE    '           / name of this binary table extension            \n",
       "DATE-OBS= '2022-02-24 00:00:00.181789000' / Date observation start              \n",
       "DATE-END= '2022-02-24 23:59:59.924674000' / Date observation end                \n",
       "TSTART  =     162432000.180789 / Start Time                                     \n",
       "TSTOP   =     162518400.923674 / Stop Time                                      \n",
       "TIMESYS = 'UTC     '           / Time system                                    \n",
       "MJDREF  =               57754. / MJD reference                                  \n",
       "MISSION = 'CHANDRAYAAN-2'      / Name of mission/satellite                      \n",
       "TELESCOP= 'CH-2_ORBITER'       / Name of mission/satellite                      \n",
       "INSTRUME= 'CH2_XSM '           / Name of Instrument/detector                    \n",
       "ORIGIN  = 'PRLPOC  '           / Source of FITS file                            \n",
       "CREATOR = 'xsmgenlc '          / Creator of file                                \n",
       "HDUCLASS= 'OGIP    '           / OGIP Standard                                  \n",
       "HDUCLAS1= 'LIGHTCURVE'                                                          \n",
       "HDUCLAS2= 'TOTAL   '           / total lc                                       \n",
       "TIMESYS = 'UTC      '          / Time measured from                             \n",
       "TIMEREF = 'LOCAL   '           / reference time                                 \n",
       "TIMEUNIT= 's       '           / unit for time keywords                         \n",
       "TELAPSE =     86400.7428850234 / elapsed time                                   \n",
       "TIMEDEL =                   1. / delta time                                     \n",
       "CHSTART =                    0 / PHA channel start                              \n",
       "CHSTOP  =                  960 / PHA channel stop                               \n",
       "XSMDASVE= '1.20    '           / Version of XSMDAS that created this file       \n",
       "CALDBVER= '20210628'           / Version of XSM CALDB used                      \n",
       "EXTVER  =                    1 / auto assigned by template parser               \n",
       "FILENAME= 'ch2_xsm_20220224_v1_level2.lc'                                       \n",
       "DATE    = '2022-03-25T12:26:58'                                                 \n",
       "HISTORY ************** XSMGENLC **************                                  \n",
       "HISTORY l1file = ch2_xsm_20220224_v1_level1.fits                                \n",
       "HISTORY lcfile = ch2_xsm_20220224_v1_level2.lc                                  \n",
       "HISTORY lctype = standard                                                       \n",
       "HISTORY areascal = yes                                                          \n",
       "HISTORY enesel = yes                                                            \n",
       "HISTORY enelow = -1522905840                                                    \n",
       "HISTORY enehigh = -1522905840                                                   \n",
       "HISTORY eboundsfile = CALDB                                                     \n",
       "HISTORY gainfile = CALDB                                                        \n",
       "HISTORY tbinsizesec = 1                                                         \n",
       "HISTORY hkfile = ch2_xsm_20220224_v1_level1.hk                                  \n",
       "HISTORY safile = ch2_xsm_20220224_v1_level1.sa                                  \n",
       "HISTORY gtifile = ch2_xsm_20220224_v1_level2.gti                                \n",
       "HISTORY abscoeffile = CALDB                                                     \n",
       "HISTORY effareaparfile = CALDB                                                  \n",
       "HISTORY clobber = yes                                                           \n",
       "HISTORY **************************************                                  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xhdr"
   ]
  }
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