RXJ1131-SHERPA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OBSID 12833\n",
    "This example module will simply go through fitting sherpa models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: failed to import sherpa.astro.xspec; XSPEC models will not be available\n"
     ]
    }
   ],
   "source": [
    "import sherpa.ui as ui\n",
    "import numpy as np\n",
    "import os\n",
    "from astropy.table import Table\n",
    "import matplotlib.pyplot as plt\n",
    "from sherpa.astro.ui import *\n",
    "import scipy.interpolate as spi\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "home_dir = '###'\n",
    "repro_dir = '12833/repro'\n",
    "os.chdir(home_dir+repro_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After downloading the data from the chandra website or using $\\textit{download_chandra_obsid #OBSID}$, we reduced the data with the following command: $\\textit{chandra_repro}$. For details on what the reprocessing does visit the following website:\n",
    "http://cxc.harvard.edu/ciao/ahelp/chandra_repro.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reduction of Data\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here I will discuss some important commands that need to be run in order to reprocess the data. Afterwards, I will include my reprocessing bash script.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### acis_clear_status_bits \n",
    "This script clears out several ACIS status bits that are set by the Chandra pipeline and that need to be cleared before using acis_process_events. This is needed to support the bad-pixel/afterglow pipeline. \n",
    "\n",
    "### destreak\n",
    "issue: There is a feature in the serial readout of the ACIS-S4 CCD (CCD_ID=8) such that for a particular frame of data, spurious events (with moderate to small pulse heights) are occasionally reported along a single row of a node.\n",
    "solution: Removing the streaks before creating a new bad pixel file improves the streak detection efficiency and prevents misidentifying pixels with many streak events as hot pixels. \n",
    "\n",
    "### Reprocess badpix\n",
    "There are three steps in creating a new ACIS bad pixel file:\n",
    "\n",
    "    -identify known bad pixels and columns from the calibration database and pixels with bad bias values (acis_build_badpix with a custom bitflag )\n",
    "    -search for afterglows and hot pixels (acis_find_afterglow)\n",
    "    -mark pixels adjacent to afterglows and hot pixels and sort the list of bad pixels and afterglows (a second run of acis_build_badpix)\n",
    "    \n",
    "    \n",
    "### acis_process_events \n",
    "Creates new level 1 event file that has a number of different filters applied. Details on website...\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creation of Pi files\n",
    "We are assuming that you have already done the following  to create a region of interest:\n",
    "    - opened ds9 and picked the region of interest: \"ds9 ...evt2.fits &\" and RofI is called \"simple.reg\"\n",
    "    - Also created background image \"simple_bkgd.reg\"\n",
    "I saved them as physical units\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Specextract\n",
    "After creating the region of interest and background region, I created a bash script which creates all relevant files for creating a spectrum. First I will display my bash script and then I will describe each piece\n",
    "\n",
    "#!/bin/bash\n",
    "\n",
    "#Runs specextract on data \n",
    "\n",
    "punlearn specextract\n",
    "\n",
    "pset specextract infile='acisf12833N002_evt2.fits[sky=region(simple.reg)]'\n",
    "\n",
    "pset specextract outroot=simple\n",
    "\n",
    "pset specextract bkgfile='acisf12833N002_evt2.fits[sky=region(simple_bkgd.reg)]'\n",
    "\n",
    "pset specextract asp=pcadf415060985N002_asol1.fits\n",
    "\n",
    "#pset specextract mskfile=acisf01838_000N003_msk1.fits\n",
    "\n",
    "pset specextract badpixfile=acisf12833_000N002_bpix1.fits\n",
    "\n",
    "pset specextract grouptype=NUM_CTS\n",
    "\n",
    "pset specextract binspec=15\n",
    "\n",
    "pset specextract clobber=yes\n",
    "\n",
    "specextract mode=h"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion of fits files used\n",
    "#### Primary Event File: evt2\n",
    "This is our primary file. It contains all necessary data with regards to counts per second and energy. It was created from the level 1 event file after filtering the GTI (Good Time Intervals give the time periods when the mission time line parameters fell within acceptable ranges).\n",
    "#### Aspect Solution: asol\n",
    "The aspect solution describes the orientation of the telescope as a function of time. The detected position of an event and the corresponding telescope aspect are combined for an accurate determination of the celestial position of that event.\n",
    "#### Mask: msk\n",
    "The mask file records the valid part of the detector element - ACIS CCD or HRC plate - used for the observation (i.e. the portion for which events can be telemetered).\n",
    "#### Bad Pixels: bpix\n",
    "A list of pixels identified as \"bad\"; criteria for flagging a pixel are listed in the Bad Pixels dictionary entry. Any tool that reads this file will exclude the bad pixels from its calculations.\n",
    "\n",
    "For more information see: http://cxc.harvard.edu/ciao/threads/intro_data/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this $\\textbf{specextract.sh}$ file, we now have our .pi file. Pi stands dor Pulse Invariant and is used to calculate the energy in each bin. We primarily use the PHA - Pulse Height Amplitude - which is another indicator of energy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading of Data and Basic Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read ARF file simple.arf\n",
      "read RMF file simple.rmf\n",
      "read ARF (background) file simple_bkg.arf\n",
      "read RMF (background) file simple_bkg.rmf\n",
      "read background file simple_bkg.pi\n"
     ]
    }
   ],
   "source": [
    "events = load_pha('simple_grp.pi')\n",
    "data_sum = calc_data_sum(id=1)  # total counts (or values) in the data\n",
    "#print(data_sum)\n",
    "data_cnt_rate = calc_data_sum()/get_exposure(id=1)  # calculating count rate in cts/sec \n",
    "#print(data_cnt_rate)\n",
    "bkg_sum = calc_data_sum(bkg_id=1)   # total counts (or values) in the background data\n",
    "#print(bkg_sum)\n",
    "bkg_cnt_rate = calc_data_sum(bkg_id=1)/get_exposure(bkg_id=1)  # calculating background count rate in cts/sec\n",
    "#print(bkg_cnt_rate)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "notice(0.5, 7.0)\n",
    "#ignore(1.0,1.0)\n",
    "subtract()\n",
    "p = get_data_plot_prefs() \n",
    "p[\"xlog\"] = False\n",
    "p[\"ylog\"] = True\n",
    "plot_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(powlaw1d.p1 + gauss1d.g1)\n",
      "   Param        Type          Value          Min          Max      Units\n",
      "   -----        ----          -----          ---          ---      -----\n",
      "   p1.gamma     thawed            1          -10           10           \n",
      "   p1.ref       frozen            5 -3.40282e+38  3.40282e+38           \n",
      "   p1.ampl      thawed    0.0002977    2.977e-07       0.2977           \n",
      "   g1.fwhm      thawed          0.1    0.0001168        116.8           \n",
      "   g1.pos       thawed          3.8       0.4964       7.3292           \n",
      "   g1.ampl      thawed     0.106742  0.000106742      106.742           \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<BinaryOpModel model instance '(powlaw1d.p1 + gauss1d.g1)'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set_source(powlaw1d.p1)\n",
    "guess(p1)\n",
    "p1.ref = 5.0\n",
    "get_source()\n",
    "for n in range(1, 2):\n",
    "    ui.create_model_component(\"gauss1d\", \"g{}\".format(n))\n",
    "guess(g1)\n",
    "set_source(p1 + g1)\n",
    "g1.pos = 3.8\n",
    "g1.fwhm = 0.1\n",
    "print(p1+g1)\n",
    "\n",
    "get_source()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: No guess found for apply_rmf(apply_arf((13613.483341262 * (powlaw1d.p1 + gauss1d.g1))))\n",
      "Dataset               = 1\n",
      "Method                = levmar\n",
      "Statistic             = chi2gehrels\n",
      "Initial fit statistic = 4.5268e+07\n",
      "Final fit statistic   = 99.7418 at function evaluation 325\n",
      "Data points           = 179\n",
      "Degrees of freedom    = 174\n",
      "Probability [Q-value] = 0.999999\n",
      "Reduced statistic     = 0.573229\n",
      "Change in statistic   = 4.52679e+07\n",
      "   p1.gamma       1.65337     \n",
      "   p1.ampl        2.42587e-05 \n",
      "   g1.fwhm        0.0307047   \n",
      "   g1.pos         3.97277     \n",
      "   g1.ampl        0.000112052 \n",
      "(powlaw1d.p1 + gauss1d.g1)\n",
      "   Param        Type          Value          Min          Max      Units\n",
      "   -----        ----          -----          ---          ---      -----\n",
      "   p1.gamma     thawed      1.65337          -10           10           \n",
      "   p1.ref       frozen            5 -3.40282e+38  3.40282e+38           \n",
      "   p1.ampl      thawed  2.42587e-05    2.977e-07       0.2977           \n",
      "   g1.fwhm      thawed    0.0307047    0.0001168        116.8           \n",
      "   g1.pos       thawed      3.97277       0.4964       7.3292           \n",
      "   g1.ampl      thawed  0.000112052  0.000106742      106.742           \n"
     ]
    }
   ],
   "source": [
    "guess()\n",
    "fit()\n",
    "print(p1+g1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_fit_delchi()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ignore(0,3.5)\n",
    "ignore(4.5,7)\n",
    "plot_fit_delchi()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset               = 1\n",
      "Confidence Method     = covariance\n",
      "Iterative Fit Method  = None\n",
      "Fitting Method        = levmar\n",
      "Statistic             = chi2gehrels\n",
      "covariance 1-sigma (68.2689%) bounds:\n",
      "   Param            Best-Fit  Lower Bound  Upper Bound\n",
      "   -----            --------  -----------  -----------\n",
      "   p1.gamma          1.65337     -1.49864      1.49864\n",
      "   p1.ampl       2.42587e-05   -9.643e-06    9.643e-06\n",
      "   g1.fwhm         0.0307047   -0.0248046    0.0248046\n",
      "   g1.pos            3.97277   -0.0453472    0.0453472\n",
      "   g1.ampl       0.000112052 -8.79113e-05  8.79113e-05\n"
     ]
    }
   ],
   "source": [
    "covar()\n"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}