{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<script type=\"text/javascript\" src=\"../_static/linksdl.js\"></script>\n",
    "<div class='alert alert-info'>\n",
    "**This is a fixed-text formatted version of a Jupyter notebook.**\n",
    "\n",
    "- Try online [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/gammapy/gammapy-webpage/v0.9?urlpath=lab/tree/spectrum_analysis.ipynb)\n",
    "- You can contribute with your own notebooks in this\n",
    "[GitHub repository](https://github.com/gammapy/gammapy/tree/master/tutorials).\n",
    "- **Source files:**\n",
    "[spectrum_analysis.ipynb](../_static/notebooks/spectrum_analysis.ipynb) |\n",
    "[spectrum_analysis.py](../_static/notebooks/spectrum_analysis.py)\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spectral analysis with Gammapy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "This notebook explains in detail how to use the classes in [gammapy.spectrum](..\/spectrum/index.rst) and related ones. Note, that there is also [spectrum_pipe.ipynb](spectrum_pipe.ipynb) which explains how to do the analysis using a high-level interface. This notebook is aimed at advanced users who want to script taylor-made analysis pipelines and implement new methods.\n",
    "\n",
    "Based on a datasets of 4 Crab observations with H.E.S.S. (simulated events for now) we will perform a full region based spectral analysis, i.e. extracting source and background counts from certain \n",
    "regions, and fitting them using the forward-folding approach. We will use the following classes\n",
    "\n",
    "Data handling:\n",
    "\n",
    "* [gammapy.data.DataStore](..\/api/gammapy.data.DataStore.rst)\n",
    "* [gammapy.data.DataStoreObservation](..\/api/gammapy.data.DataStoreObservation.rst)\n",
    "* [gammapy.data.ObservationStats](..\/api/gammapy.data.ObservationStats.rst)\n",
    "* [gammapy.data.ObservationSummary](..\/api/gammapy.data.ObservationSummary.rst)\n",
    "\n",
    "To extract the 1-dim spectral information:\n",
    "\n",
    "* [gammapy.spectrum.SpectrumObservation](..\/api/gammapy.spectrum.SpectrumObservation.rst)\n",
    "* [gammapy.spectrum.SpectrumExtraction](..\/api/gammapy.spectrum.SpectrumExtraction.rst)\n",
    "* [gammapy.background.ReflectedRegionsBackgroundEstimator](..\/api/gammapy.background.ReflectedRegionsBackgroundEstimator.rst)\n",
    "\n",
    "\n",
    "For the global fit (using Sherpa and WSTAT in the background):\n",
    "\n",
    "* [gammapy.spectrum.SpectrumFit](..\/api/gammapy.spectrum.SpectrumFit.rst)\n",
    "* [gammapy.spectrum.models.PowerLaw](..\/api/gammapy.spectrum.models.PowerLaw.rst)\n",
    "* [gammapy.spectrum.models.ExponentialCutoffPowerLaw](..\/api/gammapy.spectrum.models.ExponentialCutoffPowerLaw.rst)\n",
    "* [gammapy.spectrum.models.LogParabola](..\/api/gammapy.spectrum.models.LogParabola.rst)\n",
    "\n",
    "To compute flux points (a.k.a. \"SED\" = \"spectral energy distribution\")\n",
    "\n",
    "* [gammapy.spectrum.SpectrumResult](..\/api/gammapy.spectrum.SpectrumResult.rst)\n",
    "* [gammapy.spectrum.FluxPoints](..\/api/gammapy.spectrum.FluxPoints.rst)\n",
    "* [gammapy.spectrum.SpectrumEnergyGroupMaker](..\/api/gammapy.spectrum.SpectrumEnergyGroupMaker.rst)\n",
    "* [gammapy.spectrum.FluxPointEstimator](..\/api/gammapy.spectrum.FluxPointEstimator.rst)\n",
    "\n",
    "Feedback welcome!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "As usual, we'll start with some setup ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gammapy: 0.9\n",
      "numpy: 1.15.4\n",
      "astropy 3.0.5\n",
      "regions 0.3\n",
      "sherpa 4.10.1\n"
     ]
    }
   ],
   "source": [
    "# Check package versions\n",
    "import gammapy\n",
    "import numpy as np\n",
    "import astropy\n",
    "import regions\n",
    "import sherpa\n",
    "\n",
    "print(\"gammapy:\", gammapy.__version__)\n",
    "print(\"numpy:\", np.__version__)\n",
    "print(\"astropy\", astropy.__version__)\n",
    "print(\"regions\", regions.__version__)\n",
    "print(\"sherpa\", sherpa.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import astropy.units as u\n",
    "from astropy.coordinates import SkyCoord, Angle\n",
    "from astropy.table import vstack as vstack_table\n",
    "from regions import CircleSkyRegion\n",
    "from gammapy.data import DataStore, Observations\n",
    "from gammapy.data import ObservationStats, ObservationSummary\n",
    "from gammapy.background.reflected import ReflectedRegionsBackgroundEstimator\n",
    "from gammapy.utils.energy import EnergyBounds\n",
    "from gammapy.spectrum import (\n",
    "    SpectrumExtraction,\n",
    "    SpectrumObservation,\n",
    "    SpectrumFit,\n",
    "    SpectrumResult,\n",
    ")\n",
    "from gammapy.spectrum.models import (\n",
    "    PowerLaw,\n",
    "    ExponentialCutoffPowerLaw,\n",
    "    LogParabola,\n",
    ")\n",
    "from gammapy.spectrum import (\n",
    "    FluxPoints,\n",
    "    SpectrumEnergyGroupMaker,\n",
    "    FluxPointEstimator,\n",
    ")\n",
    "from gammapy.maps import Map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure logger\n",
    "\n",
    "Most high level classes in gammapy have the possibility to turn on logging or debug output. We well configure the logger in the following. For more info see https://docs.python.org/2/howto/logging.html#logging-basic-tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup the logger\n",
    "import logging\n",
    "\n",
    "logging.basicConfig()\n",
    "logging.getLogger(\"gammapy.spectrum\").setLevel(\"WARNING\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data\n",
    "\n",
    "First, we select and load some H.E.S.S. observations of the Crab nebula (simulated events for now).\n",
    "\n",
    "We will access the events, effective area, energy dispersion, livetime and PSF for containement correction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "datastore = DataStore.from_dir(\"$GAMMAPY_DATA/hess-dl3-dr1/\")\n",
    "obs_ids = [23523, 23526, 23559, 23592]\n",
    "observations = datastore.get_observations(obs_ids)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Target Region\n",
    "\n",
    "The next step is to define a signal extraction region, also known as on region. In the simplest case this is just a [CircleSkyRegion](http://astropy-regions.readthedocs.io/en/latest/api/regions.CircleSkyRegion.html#regions.CircleSkyRegion), but here we will use the ``Target`` class in gammapy that is useful for book-keeping if you run several analysis in a script."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_position = SkyCoord(ra=83.63, dec=22.01, unit=\"deg\", frame=\"icrs\")\n",
    "on_region_radius = Angle(\"0.11 deg\")\n",
    "on_region = CircleSkyRegion(center=target_position, radius=on_region_radius)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create exclusion mask\n",
    "\n",
    "We will use the reflected regions method to place off regions to estimate the background level in the on region.\n",
    "To make sure the off regions don't contain gamma-ray emission, we create an exclusion mask.\n",
    "\n",
    "Using http://gamma-sky.net/ we find that there's only one known gamma-ray source near the Crab nebula: the AGN called [RGB J0521+212](http://gamma-sky.net/#/cat/tev/23) at GLON = 183.604 deg and GLAT = -8.708 deg."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 432x288 with 1 Axes>,\n",
       " <matplotlib.axes._subplots.WCSAxesSubplot at 0x1c16137978>,\n",
       " None)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "exclusion_region = CircleSkyRegion(\n",
    "    center=SkyCoord(183.604, -8.708, unit=\"deg\", frame=\"galactic\"),\n",
    "    radius=0.5 * u.deg,\n",
    ")\n",
    "\n",
    "skydir = target_position.galactic\n",
    "exclusion_mask = Map.create(\n",
    "    npix=(150, 150), binsz=0.05, skydir=skydir, proj=\"TAN\", coordsys=\"GAL\"\n",
    ")\n",
    "\n",
    "mask = exclusion_mask.geom.region_mask([exclusion_region], inside=False)\n",
    "exclusion_mask.data = mask\n",
    "exclusion_mask.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimate background\n",
    "\n",
    "Next we will manually perform a background estimate by placing [reflected regions](..\/background/reflected.rst) around the pointing position and looking at the source statistics. This will result in a  [gammapy.background.BackgroundEstimate](..\/api/gammapy.background.BackgroundEstimate.rst) that serves as input for other classes in gammapy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "background_estimator = ReflectedRegionsBackgroundEstimator(\n",
    "    observations=observations,\n",
    "    on_region=on_region,\n",
    "    exclusion_mask=exclusion_mask,\n",
    ")\n",
    "\n",
    "background_estimator.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(background_estimator.result[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/deil/software/anaconda3/envs/gammapy-dev/lib/python3.7/site-packages/matplotlib/patches.py:75: UserWarning: Setting the 'color' property will overridethe edgecolor or facecolor properties.\n",
      "  warnings.warn(\"Setting the 'color' property will override\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 576x576 with 1 Axes>,\n",
       " <matplotlib.axes._subplots.WCSAxesSubplot at 0x1c1645afd0>,\n",
       " None)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "background_estimator.plot(add_legend=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Source statistic\n",
    "\n",
    "Next we're going to look at the overall source statistics in our signal region. For more info about what debug plots you can create check out the [ObservationSummary](..\/api/gammapy.data.ObservationSummary.rst#gammapy.data.ObservationSummary) class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Observation summary report ***\n",
      "Observation Id: 23526\n",
      "Livetime: 0.437 h\n",
      "On events: 201\n",
      "Off events: 225\n",
      "Alpha: 0.083\n",
      "Bkg events in On region: 18.75\n",
      "Excess: 182.25\n",
      "Excess / Background: 9.72\n",
      "Gamma rate: 7.67 1 / min\n",
      "Bkg rate: 0.72 1 / min\n",
      "Sigma: 21.86\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c16f8f2b0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stats = []\n",
    "for obs, bkg in zip(observations, background_estimator.result):\n",
    "    stats.append(ObservationStats.from_observation(obs, bkg))\n",
    "\n",
    "print(stats[1])\n",
    "\n",
    "obs_summary = ObservationSummary(stats)\n",
    "fig = plt.figure(figsize=(10, 6))\n",
    "ax1 = fig.add_subplot(121)\n",
    "\n",
    "obs_summary.plot_excess_vs_livetime(ax=ax1)\n",
    "ax2 = fig.add_subplot(122)\n",
    "obs_summary.plot_significance_vs_livetime(ax=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract spectrum\n",
    "\n",
    "Now, we're going to extract a spectrum using the [SpectrumExtraction](..\/api/gammapy.spectrum.SpectrumExtraction.rst) class. We provide the reconstructed energy binning we want to use. It is expected to be a Quantity with unit energy, i.e. an array with an energy unit. We use a utility function to create it. We also provide the true energy binning to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "e_reco = EnergyBounds.equal_log_spacing(0.1, 40, 40, unit=\"TeV\")\n",
    "e_true = EnergyBounds.equal_log_spacing(0.05, 100.0, 200, unit=\"TeV\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instantiate a [SpectrumExtraction](..\/api/gammapy.spectrum.SpectrumExtraction.rst) object that will do the extraction. The containment_correction parameter is there to allow for PSF leakage correction if one is working with full enclosure IRFs. We also compute a threshold energy and store the result in OGIP compliant files (pha, rmf, arf). This last step might be omitted though."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Observation summary report ***\n",
      "Observation Id: 23523\n",
      "Livetime: 0.439 h\n",
      "On events: 125\n",
      "Off events: 98\n",
      "Alpha: 0.083\n",
      "Bkg events in On region: 8.17\n",
      "Excess: 116.83\n",
      "Excess / Background: 14.31\n",
      "Gamma rate: 0.07 1 / s\n",
      "Bkg rate: 0.01 1 / min\n",
      "Sigma: 18.74\n",
      "energy range: 0.88 TeV - 100.00 TeV\n"
     ]
    }
   ],
   "source": [
    "ANALYSIS_DIR = \"crab_analysis\"\n",
    "\n",
    "extraction = SpectrumExtraction(\n",
    "    observations=observations,\n",
    "    bkg_estimate=background_estimator.result,\n",
    "    containment_correction=False,\n",
    ")\n",
    "extraction.run()\n",
    "\n",
    "# Add a condition on correct energy range in case it is not set by default\n",
    "extraction.compute_energy_threshold(method_lo=\"area_max\", area_percent_lo=10.0)\n",
    "\n",
    "print(extraction.spectrum_observations[0])\n",
    "# Write output in the form of OGIP files: PHA, ARF, RMF, BKG\n",
    "# extraction.run(observations=observations, bkg_estimate=background_estimator.result, outdir=ANALYSIS_DIR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Look at observations\n",
    "\n",
    "Now we will look at the files we just created. We will use the [SpectrumObservation](..\/api/gammapy.spectrum.SpectrumObservation.rst) object that are still in memory from the extraction step. Note, however, that you could also read them from disk if you have written them in the step above. The ``ANALYSIS_DIR`` folder contains 4 ``FITS`` files for each observation. These files are described in detail [here](https://gamma-astro-data-formats.readthedocs.io/en/latest/spectra/ogip/index.html). In short, they correspond to the on vector, the off vector, the effectie area, and the energy dispersion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# filename = ANALYSIS_DIR + '/ogip_data/pha_obs23523.fits'\n",
    "# obs = SpectrumObservation.read(filename)\n",
    "\n",
    "# Requires IPython widgets\n",
    "# _ = extraction.spectrum_observations.peek()\n",
    "\n",
    "extraction.spectrum_observations[0].peek()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit spectrum\n",
    "\n",
    "Now we'll fit a global model to the spectrum. First we do a joint likelihood fit to all observations. If you want to stack the observations see below. We will also produce a debug plot in order to show how the global fit matches one of the individual observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = PowerLaw(\n",
    "    index=2, amplitude=2e-11 * u.Unit(\"cm-2 s-1 TeV-1\"), reference=1 * u.TeV\n",
    ")\n",
    "\n",
    "joint_fit = SpectrumFit(obs_list=extraction.spectrum_observations, model=model)\n",
    "joint_fit.run()\n",
    "joint_result = joint_fit.result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Fit result info \n",
      "--------------- \n",
      "Model: PowerLaw\n",
      "\n",
      "Parameters: \n",
      "\n",
      "\t   name     value     error        unit      min max frozen\n",
      "\t--------- --------- --------- -------------- --- --- ------\n",
      "\t    index 2.663e+00 7.100e-02                nan nan  False\n",
      "\tamplitude 2.912e-11 1.782e-12 cm-2 s-1 TeV-1 nan nan  False\n",
      "\treference 1.000e+00 0.000e+00            TeV nan nan   True\n",
      "\n",
      "Covariance: \n",
      "\n",
      "\t   name     index   amplitude reference\n",
      "\t--------- --------- --------- ---------\n",
      "\t    index 5.042e-03 7.189e-14 0.000e+00\n",
      "\tamplitude 7.189e-14 3.176e-24 0.000e+00\n",
      "\treference 0.000e+00 0.000e+00 0.000e+00 \n",
      "\n",
      "Statistic: 42.226 (wstat)\n",
      "Fit Range: [  0.87992254 100.        ] TeV\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax0, ax1 = joint_result[0].plot(figsize=(8, 8))\n",
    "ax0.set_ylim(0, 20)\n",
    "print(joint_result[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute Flux Points\n",
    "\n",
    "To round up our analysis we can compute flux points by fitting the norm of the global model in energy bands. We'll use a fixed energy binning for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SpectrumEnergyGroups:\n",
      "energy_group_idx bin_idx_min bin_idx_max  bin_type     energy_min         energy_max    \n",
      "                                                          TeV                TeV        \n",
      "---------------- ----------- ----------- --------- ------------------ ------------------\n",
      "               0           0          32 underflow               0.01 0.6812920690579611\n",
      "               1          33          34    normal 0.6812920690579611 0.8799225435691069\n",
      "               2          35          36    normal 0.8799225435691069 1.1364636663857242\n",
      "               3          37          38    normal 1.1364636663857242  1.467799267622069\n",
      "               4          39          40    normal  1.467799267622069 1.8957356524063753\n",
      "               5          41          43    normal 1.8957356524063753  2.782559402207123\n",
      "               6          44          45    normal  2.782559402207123  3.593813663804626\n",
      "               7          46          47    normal  3.593813663804626 4.6415888336127775\n",
      "               8          48          49    normal 4.6415888336127775  5.994842503189409\n",
      "               9          50          51    normal  5.994842503189409  7.742636826811269\n",
      "              10          52          53    normal  7.742636826811269               10.0\n",
      "              11          54          55    normal               10.0 12.915496650148826\n",
      "              12          56          57    normal 12.915496650148826 16.681005372000573\n",
      "              13          58          59    normal 16.681005372000573  21.54434690031882\n",
      "              14          60          62    normal  21.54434690031882 31.622776601683793\n",
      "              15          63          71  overflow 31.622776601683793              100.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Define energy binning\n",
    "stacked_obs = extraction.spectrum_observations.stack()\n",
    "\n",
    "e_min, e_max = stacked_obs.lo_threshold.to_value(\"TeV\"), 30\n",
    "ebounds = np.logspace(np.log10(e_min), np.log10(e_max), 15) * u.TeV\n",
    "\n",
    "\n",
    "seg = SpectrumEnergyGroupMaker(obs=stacked_obs)\n",
    "seg.compute_groups_fixed(ebounds=ebounds)\n",
    "\n",
    "print(seg.groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpe = FluxPointEstimator(\n",
    "    obs=stacked_obs, groups=seg.groups, model=joint_result[0].model\n",
    ")\n",
    "flux_points = fpe.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=14</i>\n",
       "<table id=\"table120665162528\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>e_ref</th><th>e_min</th><th>e_max</th><th>ref_dnde</th><th>ref_flux</th><th>ref_eflux</th><th>ref_e2dnde</th><th>norm</th><th>loglike</th><th>norm_err</th><th>norm_errp</th><th>norm_errn</th><th>norm_ul</th><th>sqrt_ts</th><th>ts</th><th>norm_scan [11]</th><th>dloglike_scan [11]</th><th>dnde</th><th>dnde_ul</th><th>dnde_err</th><th>dnde_errp</th><th>dnde_errn</th></tr></thead>\n",
       "<thead><tr><th>TeV</th><th>TeV</th><th>TeV</th><th>1 / (cm2 s TeV)</th><th>1 / (cm2 s)</th><th>TeV / (cm2 s)</th><th>TeV / (cm2 s)</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>1 / (cm2 s TeV)</th><th>1 / (cm2 s TeV)</th><th>1 / (cm2 s TeV)</th><th>1 / (cm2 s TeV)</th><th>1 / (cm2 s TeV)</th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>0.774</td><td>0.681</td><td>0.880</td><td>5.755e-11</td><td>1.149e-11</td><td>8.838e-12</td><td>3.450e-11</td><td>0.936</td><td>2.042</td><td>0.147</td><td>0.154</td><td>0.140</td><td>1.260</td><td>12.360</td><td>152.769</td><td>0.200 .. 5.000</td><td>56.8273607734227 .. 227.6608308654835</td><td>5.390e-11</td><td>7.252e-11</td><td>8.466e-12</td><td>8.877e-12</td><td>8.063e-12</td></tr>\n",
       "<tr><td>1.000</td><td>0.880</td><td>1.136</td><td>2.912e-11</td><td>7.506e-12</td><td>7.458e-12</td><td>2.912e-11</td><td>0.923</td><td>0.319</td><td>0.100</td><td>0.103</td><td>0.096</td><td>1.136</td><td>17.742</td><td>314.775</td><td>0.200 .. 5.000</td><td>114.08743116540921 .. 487.9508451019237</td><td>2.687e-11</td><td>3.308e-11</td><td>2.905e-12</td><td>3.004e-12</td><td>2.808e-12</td></tr>\n",
       "<tr><td>1.292</td><td>1.136</td><td>1.468</td><td>1.473e-11</td><td>4.904e-12</td><td>6.294e-12</td><td>2.457e-11</td><td>0.897</td><td>0.027</td><td>0.116</td><td>0.120</td><td>0.111</td><td>1.147</td><td>14.971</td><td>224.124</td><td>0.200 .. 5.000</td><td>78.24025777805804 .. 358.7180829892944</td><td>1.322e-11</td><td>1.690e-11</td><td>1.704e-12</td><td>1.773e-12</td><td>1.636e-12</td></tr>\n",
       "<tr><td>1.668</td><td>1.468</td><td>1.896</td><td>7.452e-12</td><td>3.204e-12</td><td>5.312e-12</td><td>2.074e-11</td><td>1.160</td><td>1.397</td><td>0.151</td><td>0.157</td><td>0.145</td><td>1.487</td><td>14.582</td><td>212.632</td><td>0.200 .. 5.000</td><td>96.25330907996624 .. 228.79477950602728</td><td>8.646e-12</td><td>1.108e-11</td><td>1.125e-12</td><td>1.171e-12</td><td>1.080e-12</td></tr>\n",
       "<tr><td>2.297</td><td>1.896</td><td>2.783</td><td>3.180e-12</td><td>2.850e-12</td><td>6.454e-12</td><td>1.677e-11</td><td>1.146</td><td>0.162</td><td>0.144</td><td>0.150</td><td>0.138</td><td>1.457</td><td>15.219</td><td>231.631</td><td>0.200 .. 5.000</td><td>101.33520129152666 .. 249.87978333130098</td><td>3.644e-12</td><td>4.632e-12</td><td>4.575e-13</td><td>4.757e-13</td><td>4.398e-13</td></tr>\n",
       "<tr><td>3.162</td><td>2.783</td><td>3.594</td><td>1.357e-12</td><td>1.106e-12</td><td>3.475e-12</td><td>1.357e-11</td><td>1.213</td><td>1.338</td><td>0.218</td><td>0.231</td><td>0.206</td><td>1.700</td><td>10.563</td><td>111.582</td><td>0.200 .. 5.000</td><td>52.63650430718775 .. 110.87759715969896</td><td>1.645e-12</td><td>2.306e-12</td><td>2.962e-13</td><td>3.131e-13</td><td>2.798e-13</td></tr>\n",
       "<tr><td>4.084</td><td>3.594</td><td>4.642</td><td>6.863e-13</td><td>7.226e-13</td><td>2.933e-12</td><td>1.145e-11</td><td>0.774</td><td>0.169</td><td>0.208</td><td>0.225</td><td>0.192</td><td>1.259</td><td>6.451</td><td>41.621</td><td>0.200 .. 5.000</td><td>14.685762527878467 .. 107.4946158539693</td><td>5.310e-13</td><td>8.642e-13</td><td>1.428e-13</td><td>1.545e-13</td><td>1.316e-13</td></tr>\n",
       "<tr><td>5.275</td><td>4.642</td><td>5.995</td><td>3.472e-13</td><td>4.721e-13</td><td>2.475e-12</td><td>9.662e-12</td><td>1.046</td><td>1.303</td><td>0.283</td><td>0.308</td><td>0.259</td><td>1.711</td><td>6.764</td><td>45.751</td><td>0.200 .. 5.000</td><td>21.00035180113159 .. 65.13603952172664</td><td>3.632e-13</td><td>5.940e-13</td><td>9.814e-14</td><td>1.071e-13</td><td>8.981e-14</td></tr>\n",
       "<tr><td>6.813</td><td>5.995</td><td>7.743</td><td>1.757e-13</td><td>3.085e-13</td><td>2.089e-12</td><td>8.153e-12</td><td>1.372</td><td>0.537</td><td>0.382</td><td>0.417</td><td>0.349</td><td>2.278</td><td>8.031</td><td>64.497</td><td>0.200 .. 5.000</td><td>25.5946089640872 .. 36.24124073184054</td><td>2.411e-13</td><td>4.001e-13</td><td>6.709e-14</td><td>7.319e-14</td><td>6.125e-14</td></tr>\n",
       "<tr><td>8.799</td><td>7.743</td><td>10.000</td><td>8.887e-14</td><td>2.016e-13</td><td>1.762e-12</td><td>6.881e-12</td><td>0.929</td><td>1.880</td><td>0.390</td><td>0.445</td><td>0.341</td><td>1.922</td><td>4.215</td><td>17.768</td><td>0.200 .. 5.000</td><td>9.045165002975121 .. 34.66794135146158</td><td>8.259e-14</td><td>1.708e-13</td><td>3.466e-14</td><td>3.951e-14</td><td>3.029e-14</td></tr>\n",
       "<tr><td>11.365</td><td>10.000</td><td>12.915</td><td>4.496e-14</td><td>1.317e-13</td><td>1.487e-12</td><td>5.807e-12</td><td>1.001</td><td>0.026</td><td>0.487</td><td>0.562</td><td>0.417</td><td>2.284</td><td>3.944</td><td>15.552</td><td>0.200 .. 5.000</td><td>5.956190625922787 .. 21.36223608777732</td><td>4.503e-14</td><td>1.027e-13</td><td>2.188e-14</td><td>2.525e-14</td><td>1.876e-14</td></tr>\n",
       "<tr><td>14.678</td><td>12.915</td><td>16.681</td><td>2.274e-14</td><td>8.606e-14</td><td>1.255e-12</td><td>4.900e-12</td><td>0.258</td><td>1.183</td><td>0.327</td><td>0.442</td><td>0.231</td><td>1.398</td><td>1.193</td><td>1.422</td><td>0.200 .. 5.000</td><td>1.2180464610959292 .. 24.869917457085705</td><td>5.860e-15</td><td>3.180e-14</td><td>7.430e-15</td><td>1.006e-14</td><td>5.257e-15</td></tr>\n",
       "<tr><td>18.957</td><td>16.681</td><td>21.544</td><td>1.151e-14</td><td>5.623e-14</td><td>1.059e-12</td><td>4.135e-12</td><td>0.863</td><td>0.049</td><td>0.681</td><td>0.855</td><td>0.529</td><td>2.938</td><td>2.303</td><td>5.302</td><td>0.200 .. 5.000</td><td>1.9628925083487547 .. 10.524331494915844</td><td>9.928e-15</td><td>3.380e-14</td><td>7.833e-15</td><td>9.843e-15</td><td>6.092e-15</td></tr>\n",
       "<tr><td>26.102</td><td>21.544</td><td>31.623</td><td>4.910e-15</td><td>5.002e-14</td><td>1.287e-12</td><td>3.345e-12</td><td>0.000</td><td>0.591</td><td>0.000</td><td>0.264</td><td>0.000</td><td>1.056</td><td>0.000</td><td>0.000</td><td>0.200 .. 5.000</td><td>1.3479592650728285 .. 19.507416492967856</td><td>7.631e-30</td><td>5.186e-15</td><td>1.993e-22</td><td>1.298e-15</td><td>7.631e-30</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=14>\n",
       " e_ref   e_min   e_max  ...     dnde_err       dnde_errp       dnde_errn   \n",
       "  TeV     TeV     TeV   ... 1 / (cm2 s TeV) 1 / (cm2 s TeV) 1 / (cm2 s TeV)\n",
       "float64 float64 float64 ...     float64         float64         float64    \n",
       "------- ------- ------- ... --------------- --------------- ---------------\n",
       "  0.774   0.681   0.880 ...       8.466e-12       8.877e-12       8.063e-12\n",
       "  1.000   0.880   1.136 ...       2.905e-12       3.004e-12       2.808e-12\n",
       "  1.292   1.136   1.468 ...       1.704e-12       1.773e-12       1.636e-12\n",
       "  1.668   1.468   1.896 ...       1.125e-12       1.171e-12       1.080e-12\n",
       "  2.297   1.896   2.783 ...       4.575e-13       4.757e-13       4.398e-13\n",
       "  3.162   2.783   3.594 ...       2.962e-13       3.131e-13       2.798e-13\n",
       "  4.084   3.594   4.642 ...       1.428e-13       1.545e-13       1.316e-13\n",
       "  5.275   4.642   5.995 ...       9.814e-14       1.071e-13       8.981e-14\n",
       "  6.813   5.995   7.743 ...       6.709e-14       7.319e-14       6.125e-14\n",
       "  8.799   7.743  10.000 ...       3.466e-14       3.951e-14       3.029e-14\n",
       " 11.365  10.000  12.915 ...       2.188e-14       2.525e-14       1.876e-14\n",
       " 14.678  12.915  16.681 ...       7.430e-15       1.006e-14       5.257e-15\n",
       " 18.957  16.681  21.544 ...       7.833e-15       9.843e-15       6.092e-15\n",
       " 26.102  21.544  31.623 ...       1.993e-22       1.298e-15       7.631e-30"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flux_points.table_formatted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we plot the flux points and their likelihood profiles. For the plotting of upper limits we choose a threshold of TS < 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c18408fd0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "flux_points.table[\"is_ul\"] = flux_points.table[\"ts\"] < 4\n",
    "ax = flux_points.plot(\n",
    "    energy_power=2, flux_unit=\"erg-1 cm-2 s-1\", color=\"darkorange\"\n",
    ")\n",
    "flux_points.to_sed_type(\"e2dnde\").plot_likelihood(ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final plot with the best fit model, flux points and residuals can be quickly made like this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.4, 50)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "spectrum_result = SpectrumResult(\n",
    "    points=flux_points, model=joint_result[0].model\n",
    ")\n",
    "ax0, ax1 = spectrum_result.plot(\n",
    "    energy_range=joint_fit.result[0].fit_range,\n",
    "    energy_power=2,\n",
    "    flux_unit=\"erg-1 cm-2 s-1\",\n",
    "    fig_kwargs=dict(figsize=(8, 8)),\n",
    ")\n",
    "\n",
    "ax0.set_xlim(0.4, 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stack observations\n",
    "\n",
    "And alternative approach to fitting the spectrum is stacking all observations first and the fitting a model to the stacked observation. This works as follows. A comparison to the joint likelihood fit is also printed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FitResult\n",
       "\n",
       "\tbackend    : minuit\n",
       "\tmethod     : minuit\n",
       "\tsuccess    : True\n",
       "\tnfev       : 31\n",
       "\ttotal stat : 30.31\n",
       "\tmessage    : Optimization terminated successfully."
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked_obs = extraction.spectrum_observations.stack()\n",
    "\n",
    "stacked_fit = SpectrumFit(obs_list=stacked_obs, model=model)\n",
    "stacked_fit.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Fit result info \n",
      "--------------- \n",
      "Model: PowerLaw\n",
      "\n",
      "Parameters: \n",
      "\n",
      "\t   name     value     error        unit      min max frozen\n",
      "\t--------- --------- --------- -------------- --- --- ------\n",
      "\t    index 2.667e+00 7.147e-02                nan nan  False\n",
      "\tamplitude 2.909e-11 1.784e-12 cm-2 s-1 TeV-1 nan nan  False\n",
      "\treference 1.000e+00 0.000e+00            TeV nan nan   True\n",
      "\n",
      "Covariance: \n",
      "\n",
      "\t   name     index   amplitude reference\n",
      "\t--------- --------- --------- ---------\n",
      "\t    index 5.108e-03 7.241e-14 0.000e+00\n",
      "\tamplitude 7.241e-14 3.183e-24 0.000e+00\n",
      "\treference 0.000e+00 0.000e+00 0.000e+00 \n",
      "\n",
      "Statistic: 30.311 (wstat)\n",
      "Fit Range: [  0.68129207 100.        ] TeV\n",
      "\n"
     ]
    }
   ],
   "source": [
    "stacked_result = stacked_fit.result\n",
    "print(stacked_result[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " method index index_err    amplitude     amplitude_err \n",
      "                        1 / (cm2 s TeV) 1 / (cm2 s TeV)\n",
      "------- ----- --------- --------------- ---------------\n",
      "stacked  2.67    0.0715        2.91e-11        1.78e-12\n",
      "  joint  2.67    0.0715        2.91e-11        1.78e-12\n"
     ]
    }
   ],
   "source": [
    "stacked_table = stacked_result[0].to_table(format=\".3g\")\n",
    "stacked_table[\"method\"] = \"stacked\"\n",
    "joint_table = joint_result[0].to_table(format=\".3g\")\n",
    "joint_table[\"method\"] = \"joint\"\n",
    "total_table = vstack_table([stacked_table, joint_table])\n",
    "print(\n",
    "    total_table[\"method\", \"index\", \"index_err\", \"amplitude\", \"amplitude_err\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "\n",
    "Some things we might do:\n",
    "\n",
    "- Fit a different spectral model (ECPL or CPL or ...)\n",
    "- Use different method or parameters to compute the flux points\n",
    "- Do a chi^2 fit to the flux points and compare\n",
    "\n",
    "TODO: give pointers how to do this (and maybe write a notebook with solutions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Start exercises here"
   ]
  }
 ],
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