{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performing an unbinned analysis"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "<div align=\"right\"><a href=\"../../../_downloads/unbinned.ipynb\" download=\"unbinned.ipynb\"><img src=\"../../../_static/download-notebook.jpg\" alt=\"Download Notebook\" height=\"40\"></a></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**In this tutorial you will learn to fit a parametric model to the event data (unbinned fit) and how to inspect the fit residuals**\n",
    "\n",
    "Now you are ready to fit the models for the source and the background to the data.\n",
    "\n",
    "We start by importing gammalib, ctools, and cscripts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gammalib\n",
    "import ctools\n",
    "import cscripts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also use matplotlib to display the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we add to our path the directory containing the example plotting scripts provided with the ctools installation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "sys.path.append(os.environ['CTOOLS']+'/share/examples/python/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the model\n",
    "\n",
    "First, we will merge the two models we derived in the previous tutorials for source and background."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== GModels ===\n",
      " Number of models ..........: 5\n",
      " Number of parameters ......: 86\n",
      "=== GModelSky ===\n",
      " Name ......................: Src001\n",
      " Instruments ...............: all\n",
      " Observation identifiers ...: all\n",
      " Model type ................: PointSource\n",
      " Model components ..........: \"PointSource\" * \"PowerLaw\" * \"Constant\"\n",
      " Number of parameters ......: 6\n",
      " Number of spatial par's ...: 2\n",
      "  RA .......................: 83.6192131308071 +/- 0 deg (free,scale=1)\n",
      "  DEC ......................: 22.0199996472185 +/- 0 deg (free,scale=1)\n",
      " Number of spectral par's ..: 3\n",
      "  Prefactor ................: 5.7e-18 +/- 0 [0,infty[ ph/cm2/s/MeV (free,scale=5.7e-18,gradient)\n",
      "  Index ....................: -2.48 +/- 0 [10,-10]  (free,scale=-2.48,gradient)\n",
      "  PivotEnergy ..............: 300000 MeV (fixed,scale=300000,gradient)\n",
      " Number of temporal par's ..: 1\n",
      "  Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
      " Number of scale par's .....: 0\n",
      "=== GCTAModelBackground ===\n",
      " Name ......................: Background_023523\n",
      " Instruments ...............: HESS\n",
      " Observation identifiers ...: 023523\n",
      " Model type ................: CTABackground\n",
      " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
      " Number of parameters ......: 20\n",
      " Number of spatial par's ...: 3\n",
      "  1:Normalization ..........: 1 [0.001,1000]  (fixed,scale=1,gradient)\n",
      "  2:Grad_DETX ..............: 0.0881748792818854 +/- 0.0222611841905954 deg^-1 (free,scale=1,gradient)\n",
      "  2:Grad_DETY ..............: -0.000701218995455618 +/- 0.0227202811076142 deg^-1 (free,scale=1,gradient)\n",
      " Number of spectral par's ..: 16\n",
      "  Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
      "  Intensity0 ...............: 0.00167503068442416 +/- 0.000204995398027476 [1.00182541969977e-13,infty[ ph/cm2/s/MeV (free,scale=0.00100182541969977,gradient)\n",
      "  Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
      "  Intensity1 ...............: 0.000275120588592054 +/- 1.41610992132859e-05 [3.21240099713853e-14,infty[ ph/cm2/s/MeV (free,scale=0.000321240099713853,gradient)\n",
      "  Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
      "  Intensity2 ...............: 0.000102369982797547 +/- 5.87189405970882e-06 [1.03007170346199e-14,infty[ ph/cm2/s/MeV (free,scale=0.000103007170346199,gradient)\n",
      "  Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
      "  Intensity3 ...............: 3.43310169853447e-05 +/- 2.52595458342912e-06 [3.30297405342054e-15,infty[ ph/cm2/s/MeV (free,scale=3.30297405342054e-05,gradient)\n",
      "  Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
      "  Intensity4 ...............: 1.04184963555313e-05 +/- 1.01064863096553e-06 [1.05911438600856e-15,infty[ ph/cm2/s/MeV (free,scale=1.05911438600856e-05,gradient)\n",
      "  Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
      "  Intensity5 ...............: 4.1837003769505e-06 +/- 5.25942224806527e-07 [3.39610080039421e-16,infty[ ph/cm2/s/MeV (free,scale=3.39610080039421e-06,gradient)\n",
      "  Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
      "  Intensity6 ...............: 8.6658971981008e-07 +/- 1.76584928413557e-07 [1.08897592165697e-16,infty[ ph/cm2/s/MeV (free,scale=1.08897592165697e-06,gradient)\n",
      "  Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
      "  Intensity7 ...............: 3.41087754372141e-07 +/- 1.23894301613722e-07 [3.49185323889972e-17,infty[ ph/cm2/s/MeV (free,scale=3.49185323889972e-07,gradient)\n",
      " Number of temporal par's ..: 1\n",
      "  Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
      "=== GCTAModelBackground ===\n",
      " Name ......................: Background_023526\n",
      " Instruments ...............: HESS\n",
      " Observation identifiers ...: 023526\n",
      " Model type ................: CTABackground\n",
      " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
      " Number of parameters ......: 20\n",
      " Number of spatial par's ...: 3\n",
      "  1:Normalization ..........: 1 [0.001,1000]  (fixed,scale=1,gradient)\n",
      "  2:Grad_DETX ..............: 0.0153166143265463 +/- 0.0215144304488992 deg^-1 (free,scale=1,gradient)\n",
      "  2:Grad_DETY ..............: -0.00908019181469828 +/- 0.0213167666197536 deg^-1 (free,scale=1,gradient)\n",
      " Number of spectral par's ..: 16\n",
      "  Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
      "  Intensity0 ...............: 0.00126074399446221 +/- 6.18948752992e-05 [9.62519069451052e-14,infty[ ph/cm2/s/MeV (free,scale=0.000962519069451052,gradient)\n",
      "  Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
      "  Intensity1 ...............: 0.000220109488746193 +/- 1.10099106908638e-05 [2.95441986174209e-14,infty[ ph/cm2/s/MeV (free,scale=0.000295441986174209,gradient)\n",
      "  Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
      "  Intensity2 ...............: 0.000104377151461511 +/- 6.26567390995899e-06 [9.068492247571e-15,infty[ ph/cm2/s/MeV (free,scale=9.068492247571e-05,gradient)\n",
      "  Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
      "  Intensity3 ...............: 2.68164289254463e-05 +/- 2.2601490434718e-06 [2.78354314866283e-15,infty[ ph/cm2/s/MeV (free,scale=2.78354314866283e-05,gradient)\n",
      "  Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
      "  Intensity4 ...............: 8.96128027251196e-06 +/- 9.61601396204211e-07 [8.54399193266454e-16,infty[ ph/cm2/s/MeV (free,scale=8.54399193266454e-06,gradient)\n",
      "  Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
      "  Intensity5 ...............: 3.14829249162658e-06 +/- 4.37081753413707e-07 [2.62254954375342e-16,infty[ ph/cm2/s/MeV (free,scale=2.62254954375342e-06,gradient)\n",
      "  Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
      "  Intensity6 ...............: 6.09624701513041e-07 +/- 1.43872314987655e-07 [8.04982748537824e-17,infty[ ph/cm2/s/MeV (free,scale=8.04982748537824e-07,gradient)\n",
      "  Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
      "  Intensity7 ...............: 3.44982419809933e-07 +/- 1.26076763285202e-07 [2.4708674312253e-17,infty[ ph/cm2/s/MeV (free,scale=2.4708674312253e-07,gradient)\n",
      " Number of temporal par's ..: 1\n",
      "  Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
      "=== GCTAModelBackground ===\n",
      " Name ......................: Background_023559\n",
      " Instruments ...............: HESS\n",
      " Observation identifiers ...: 023559\n",
      " Model type ................: CTABackground\n",
      " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
      " Number of parameters ......: 20\n",
      " Number of spatial par's ...: 3\n",
      "  1:Normalization ..........: 1 [0.001,1000]  (fixed,scale=1,gradient)\n",
      "  2:Grad_DETX ..............: 0.018749400342 +/- 0.0217067733288596 deg^-1 (free,scale=1,gradient)\n",
      "  2:Grad_DETY ..............: -0.126640342510319 +/- 0.020455126417247 deg^-1 (free,scale=1,gradient)\n",
      " Number of spectral par's ..: 16\n",
      "  Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
      "  Intensity0 ...............: 0.00109294743570479 +/- 5.37668986080239e-05 [8.7418891142236e-14,infty[ ph/cm2/s/MeV (free,scale=0.00087418891142236,gradient)\n",
      "  Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
      "  Intensity1 ...............: 0.000220103906688583 +/- 1.10283166290448e-05 [2.69181956755491e-14,infty[ ph/cm2/s/MeV (free,scale=0.000269181956755491,gradient)\n",
      "  Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
      "  Intensity2 ...............: 8.06623852687504e-05 +/- 5.23872750218462e-06 [8.28870337932099e-15,infty[ ph/cm2/s/MeV (free,scale=8.28870337932099e-05,gradient)\n",
      "  Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
      "  Intensity3 ...............: 2.85428972580395e-05 +/- 2.37266748207121e-06 [2.55227373106486e-15,infty[ ph/cm2/s/MeV (free,scale=2.55227373106486e-05,gradient)\n",
      "  Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
      "  Intensity4 ...............: 6.46456658892385e-06 +/- 7.36558401693409e-07 [7.85901111449516e-16,infty[ ph/cm2/s/MeV (free,scale=7.85901111449516e-06,gradient)\n",
      "  Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
      "  Intensity5 ...............: 3.20506991372933e-06 +/- 4.41036712274814e-07 [2.4199620497598e-16,infty[ ph/cm2/s/MeV (free,scale=2.4199620497598e-06,gradient)\n",
      "  Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
      "  Intensity6 ...............: 9.17351299053318e-07 +/- 2.02856047440946e-07 [7.45159440158635e-17,infty[ ph/cm2/s/MeV (free,scale=7.45159440158635e-07,gradient)\n",
      "  Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
      "  Intensity7 ...............: 1.93348643577948e-07 +/- 9.48901838749967e-08 [2.29450949990163e-17,infty[ ph/cm2/s/MeV (free,scale=2.29450949990163e-07,gradient)\n",
      " Number of temporal par's ..: 1\n",
      "  Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
      "=== GCTAModelBackground ===\n",
      " Name ......................: Background_023592\n",
      " Instruments ...............: HESS\n",
      " Observation identifiers ...: 023592\n",
      " Model type ................: CTABackground\n",
      " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
      " Number of parameters ......: 20\n",
      " Number of spatial par's ...: 3\n",
      "  1:Normalization ..........: 1 [0.001,1000]  (fixed,scale=1,gradient)\n",
      "  2:Grad_DETX ..............: 0.0290076925442388 +/- 0.0231627042594752 deg^-1 (free,scale=1,gradient)\n",
      "  2:Grad_DETY ..............: 0.165459453396994 +/- 0.0212165299830029 deg^-1 (free,scale=1,gradient)\n",
      " Number of spectral par's ..: 16\n",
      "  Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
      "  Intensity0 ...............: 0.00173543806725313 +/- 0.000221842434989178 [9.64800822522881e-14,infty[ ph/cm2/s/MeV (free,scale=0.000964800822522881,gradient)\n",
      "  Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
      "  Intensity1 ...............: 0.000247432426542881 +/- 1.35075275068334e-05 [3.05783271724063e-14,infty[ ph/cm2/s/MeV (free,scale=0.000305783271724063,gradient)\n",
      "  Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
      "  Intensity2 ...............: 0.000102349095137416 +/- 6.00669028431716e-06 [9.6914727976462e-15,infty[ ph/cm2/s/MeV (free,scale=9.6914727976462e-05,gradient)\n",
      "  Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
      "  Intensity3 ...............: 3.39523135381865e-05 +/- 2.54888013407308e-06 [3.07160834724385e-15,infty[ ph/cm2/s/MeV (free,scale=3.07160834724385e-05,gradient)\n",
      "  Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
      "  Intensity4 ...............: 9.09839000917866e-06 +/- 9.25509371718351e-07 [9.735133179293e-16,infty[ ph/cm2/s/MeV (free,scale=9.735133179293e-06,gradient)\n",
      "  Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
      "  Intensity5 ...............: 3.01613230297962e-06 +/- 4.11382625961797e-07 [3.08544603688196e-16,infty[ ph/cm2/s/MeV (free,scale=3.08544603688196e-06,gradient)\n",
      "  Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
      "  Intensity6 ...............: 9.30049222211062e-07 +/- 1.86843120695054e-07 [9.77899025229569e-17,infty[ ph/cm2/s/MeV (free,scale=9.77899025229569e-07,gradient)\n",
      "  Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
      "  Intensity7 ...............: 3.98006363733031e-07 +/- 1.41569683967725e-07 [3.09934606573553e-17,infty[ ph/cm2/s/MeV (free,scale=3.09934606573553e-07,gradient)\n",
      " Number of temporal par's ..: 1\n",
      "  Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n"
     ]
    }
   ],
   "source": [
    "srcmodel = 'crab.xml'\n",
    "bkgmodel = 'bkgmodel.xml'\n",
    "models   = gammalib.GModels()\n",
    "for inmodels in [srcmodel,bkgmodel]:\n",
    "    for model in gammalib.GModels(inmodels):\n",
    "        models.append(model)\n",
    "print(models)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note how the source found by cssrcdetect was named *Src001*. We will call it *Crab* instead. Also, spectral parameters are set to default values, but you should make sure they are appropriate so that the model fit runs seamlessly. In this case it is best to set the value of the pivot energy for the Crab within our energy range (> 0.66 TeV). We will set it to 1 TeV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "models['Src001'].name('Crab')\n",
    "models['Crab']['PivotEnergy'].value(1.e6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will save this model to disk for later use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelfile = 'crab_models.xml'\n",
    "models.save(modelfile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting the model to the data\n",
    "\n",
    "The model fit is performed by ctlike. We will use the previously selected events."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "obsfile = 'obs_crab_selected.xml'\n",
    "like    = ctools.ctlike()\n",
    "like['inobs']   = obsfile\n",
    "like['inmodel'] = modelfile\n",
    "like.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now look at the results from the optimisation and the fitted model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== GOptimizerLM ===\n",
      " Optimized function value ..: 98167.993\n",
      " Absolute precision ........: 0.005\n",
      " Acceptable value decrease .: 2\n",
      " Optimization status .......: converged\n",
      " Number of parameters ......: 86\n",
      " Number of free parameters .: 44\n",
      " Number of iterations ......: 28\n",
      " Lambda ....................: 0.1\n",
      "=== GModelSky ===\n",
      " Name ......................: Crab\n",
      " Instruments ...............: all\n",
      " Observation identifiers ...: all\n",
      " Model type ................: PointSource\n",
      " Model components ..........: \"PointSource\" * \"PowerLaw\" * \"Constant\"\n",
      " Number of parameters ......: 6\n",
      " Number of spatial par's ...: 2\n",
      "  RA .......................: 83.6231161425766 +/- 0.00240718177620446 deg (free,scale=1)\n",
      "  DEC ......................: 22.0247773403131 +/- 0.00218186675985154 deg (free,scale=1)\n",
      " Number of spectral par's ..: 3\n",
      "  Prefactor ................: 4.80653958161532e-17 +/- 2.64950884427518e-18 [0,infty[ ph/cm2/s/MeV (free,scale=5.7e-18,gradient)\n",
      "  Index ....................: -2.68230589724908 +/- 0.0650730512302727 [10,-10]  (free,scale=-2.48,gradient)\n",
      "  PivotEnergy ..............: 999999.999999999 MeV (fixed,scale=300000,gradient)\n",
      " Number of temporal par's ..: 1\n",
      "  Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
      " Number of scale par's .....: 0\n"
     ]
    }
   ],
   "source": [
    "print(like.opt())\n",
    "print(like.obs().models()['Crab'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The statistical 1-sigma positional uncertainty corresponds to 0.12 arcmin. Systematic uncertainties are not computed. The fitted position can be compared to the values of 83.629±0.005 degrees in Right Ascension and 22.012±0.001 degrees in Declination reported in [Holler et al. (2017)](https://arxiv.org/pdf/1707.04196.pdf).\n",
    "\n",
    "According to [SIMBAD](http://cdsportal.u-strasbg.fr/?target=Crab%20nebula), the Crab nebula is situated at a Right Ascension of 83.633 degrees and a Declination of 22.015 degrees, which is 0.013 degrees (0.82 arcmin) away from the fitted position.\n",
    "\n",
    "The intensity at 1 TeV of the Crab was fitted to (4.89±0.27)×10−11 photons cm−2 s−1 TeV−1 the spectral index of the power law is −2.70±0.07. This can be compared to the values of (3.45±0.05)×10−11 photons cm−2 s−1 TeV−1 and −2.63±0.01 reported in [Aharonian et al. (2006), A&A, 457, 899](https://www.aanda.org/articles/aa/abs/2006/39/aa5351-06/aa5351-06.html) (note that the datasets, calibrations etc. used in our analysis are not the same as in that paper).\n",
    "\n",
    "In the ctlike run above the energy dispersion, which relates the true photon energies to the energies of the reconstructed events, was not taken into account. By default **energy dispersion usage is disabled** since it involves an extra dimension in the data analysis which slows down the computations. We can run accounting for energy dispersion to compare the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "like['edisp'] = True\n",
    "like.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== GOptimizerLM ===\n",
      " Optimized function value ..: 98168.820\n",
      " Absolute precision ........: 0.005\n",
      " Acceptable value decrease .: 2\n",
      " Optimization status .......: converged\n",
      " Number of parameters ......: 86\n",
      " Number of free parameters .: 44\n",
      " Number of iterations ......: 13\n",
      " Lambda ....................: 1\n",
      "=== GModelSky ===\n",
      " Name ......................: Crab\n",
      " Instruments ...............: all\n",
      " Observation identifiers ...: all\n",
      " Model type ................: PointSource\n",
      " Model components ..........: \"PointSource\" * \"PowerLaw\" * \"Constant\"\n",
      " Number of parameters ......: 6\n",
      " Number of spatial par's ...: 2\n",
      "  RA .......................: 83.6217013908939 +/- 0.00262422672788241 deg (free,scale=1)\n",
      "  DEC ......................: 22.0244582493434 +/- 0.00238660922515514 deg (free,scale=1)\n",
      " Number of spectral par's ..: 3\n",
      "  Prefactor ................: 4.56725958702027e-17 +/- 2.41200684919881e-18 [0,infty[ ph/cm2/s/MeV (free,scale=5.7e-18,gradient)\n",
      "  Index ....................: -2.66140151299329 +/- 0.0671763204868478 [10,-10]  (free,scale=-2.48,gradient)\n",
      "  PivotEnergy ..............: 999999.999999999 MeV (fixed,scale=300000,gradient)\n",
      " Number of temporal par's ..: 1\n",
      "  Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
      " Number of scale par's .....: 0\n"
     ]
    }
   ],
   "source": [
    "print(like.opt())\n",
    "print(like.obs().models()['Crab'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can verify that the results are broadly consistents with those obtained ignoring the energy dispersion.\n",
    "\n",
    "## Inspecting the fit residuals\n",
    "\n",
    "Following a model fit, you should always inspect the fit residuals. First let’s inspect the spectral residuals. You can do this using the csresspec script."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ab0eef0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "residuals = 'residuals.fits'\n",
    "resspec   = cscripts.csresspec(like.obs())\n",
    "resspec['stack']       = True\n",
    "resspec['components']  = True\n",
    "resspec['ebinalg']     = 'LOG'\n",
    "resspec['emin']        = 0.66\n",
    "resspec['emax']        = 100.\n",
    "resspec['enumbins']    = 20\n",
    "resspec['proj']        = 'CAR'\n",
    "resspec['coordsys']    = 'CEL'\n",
    "resspec['xref']        = 83.63\n",
    "resspec['yref']        = 22.01\n",
    "resspec['binsz']       = 0.02\n",
    "resspec['nxpix']       = 200\n",
    "resspec['nypix']       = 200\n",
    "resspec['algorithm']   = 'SIGNIFICANCE'\n",
    "resspec['outfile']     = residuals\n",
    "resspec.execute()\n",
    "from show_residuals import plot_residuals\n",
    "plot_residuals(residuals,'',0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The spectral fit looks satisfactory. Finally you should also inspect the spatial residuals. You do this using the csresmap script."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "resmap = cscripts.csresmap(like.obs())\n",
    "resmap['emin']      = 0.66\n",
    "resmap['emax']      = 100.0\n",
    "resmap['proj']      = 'CAR'\n",
    "resmap['coordsys']  = 'CEL'\n",
    "resmap['xref']      = 83.63\n",
    "resmap['yref']      = 22.01\n",
    "resmap['binsz']     = 0.02\n",
    "resmap['nxpix']     = 200\n",
    "resmap['nypix']     = 200\n",
    "resmap['algorithm'] = 'SUBDIV'\n",
    "resmap.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will inspect the residual map with a slight smoothing to suppress statistical fluctuations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ba7b470>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "resmap._resmap.smooth('GAUSSIAN',0.1)\n",
    "ax  = plt.subplot()\n",
    "plt.imshow(resmap._resmap.array(),origin='lower',\n",
    "            cmap='bwr',vmin=-1,vmax=1,\n",
    "            extent=[83.63+0.02*200,83.63-0.02*200,22.01-0.02*200,22.01+0.02*200])\n",
    "            # Boundaries of the coord grid\n",
    "ax.set_xlabel('R.A. (deg)')\n",
    "ax.set_ylabel('Dec (deg)')\n",
    "cbar = plt.colorbar()\n",
    "cbar.set_label('Residuals/Total Counts')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The residuals look flat across the field analysed."
   ]
  },
  {
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}