docs/tutorials/ssc_gammapy_fit.ipynb

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    "# Tutorial: fitting a BL Lac broad-band SED using angpy and Gammapy\n",
    "\n",
    "In order to perform a fit of the broad-band SED of a jetted AGN, `agnpy` includes a `gammapy` wrapper.\n",
    "The wrapper defines a custom [SpectralModel](https://docs.gammapy.org/0.20/api/gammapy.modeling.models.SpectralModel.html), representing the emission due to a combination of radiative processes. The `SpectralModel` can be used either to fit flux points or to perform a forward-folding likelihood fit (if the instrument response is available in a format compatible with `gammapy`). \n",
    "\n",
    "Several combination of radiative processes can be considered to model the broad-band emission of jetted AGN. For simplicity, we provide wrappers for the two scenarios most-commonly considered:\n",
    "\n",
    " * `SycnhrotronSelfComptonModel`, representing the sum of synchrotron and synchrotron self Compton (SSC) radiation. This scenario is commonly considered to model BL Lac sources;\n",
    "\n",
    " * `ExternalComptonModel`, representing the sum of synchrotron and synchrotron self Compton radiation along with an external Compton (EC) component. EC scattering can be computed considering a list of target photon fields. This scenario is commonly considered to model flat spectrum radio quasars (FSRQs).\n",
    "\n",
    "In this tutorial, we will show how to use the `SynchrotronSelfComptonSpectralModel` to fit the broad-band SED of Mrk 421, measured by a MWL campaign in 2009 [(Abdo et al. 2011)](https://ui.adsabs.harvard.edu/abs/2011ApJ...736..131A/abstract).\n",
    "\n",
    "[gammapy](https://gammapy.org/) is required to run this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "55c7fc17",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import numpy, astropy and matplotlib for basic functionalities\n",
    "import numpy as np\n",
    "import astropy.units as u\n",
    "import matplotlib.pyplot as plt\n",
    "import pkg_resources\n",
    "\n",
    "# import agnpy classes\n",
    "from agnpy.spectra import BrokenPowerLaw\n",
    "from agnpy.fit import SynchrotronSelfComptonModel, load_gammapy_flux_points\n",
    "from agnpy.utils.plot import load_mpl_rc, sed_y_label\n",
    "\n",
    "load_mpl_rc()\n",
    "\n",
    "# import gammapy classes\n",
    "from gammapy.modeling.models import SkyModel\n",
    "from gammapy.modeling import Fit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c685d482",
   "metadata": {},
   "source": [
    "### `gammapy` wrapper of agnpy synchrotron and SSC\n",
    "\n",
    "The `SynchrotronSelfComptonModel` wraps the `agnpy` functions to compute synchrotron and SSC radiation and returns a `gammapy.modeling.SpectralModel`. To initialise this spectral model, only the electron distribution has to be specified, the remaining parameters (the ones of the emission region) will be initialised automatically and can be modified at a later stage.\n",
    "\n",
    "The `SynchrotronSelfComptonModel` class provides both the `sherpa` and `gammapy` wrappers. You should specify, through the `backend` argument, which package you want to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0855e01a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# electron energy distribution\n",
    "n_e = BrokenPowerLaw(\n",
    "    k=1e-8 * u.Unit(\"cm-3\"),\n",
    "    p1=2.02,\n",
    "    p2=3.43,\n",
    "    gamma_b=1e5,\n",
    "    gamma_min=500,\n",
    "    gamma_max=1e6,\n",
    ")\n",
    "\n",
    "# initialise the Gammapy SpectralModel\n",
    "ssc_model = SynchrotronSelfComptonModel(n_e, backend=\"gammapy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "829853d9",
   "metadata": {},
   "source": [
    "Let us set appropriate parameters for the emission region. The size of the blob, $R_{\\rm b}$, is set by the variability timescale, $t_{\\rm var}$, via\n",
    "\n",
    "\\begin{equation}\n",
    "R_{\\rm b} = \\frac{c \\delta_{\\rm D} t_{\\rm var}}{1 + z},\n",
    "\\end{equation}\n",
    "\n",
    "where $c$ is the speed of light, $\\delta_{\\rm D}$ the Doppler factor, and $z$ the redshift."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "51753947",
   "metadata": {},
   "outputs": [],
   "source": [
    "ssc_model.parameters[\"z\"].value = 0.0308\n",
    "ssc_model.parameters[\"delta_D\"].value = 18\n",
    "ssc_model.parameters[\"t_var\"].value = (1 * u.d).to_value(\"s\")\n",
    "ssc_model.parameters[\"t_var\"].frozen = True\n",
    "ssc_model.parameters[\"log10_B\"].value = -1.3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d4c490b",
   "metadata": {},
   "source": [
    "With the gammapy backend, we can display all the parameters of the model at once"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "20bba34e",
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       "<thead><tr><th>type</th><th>name</th><th>value</th><th>unit</th><th>error</th><th>min</th><th>max</th><th>frozen</th><th>is_norm</th><th>link</th></tr></thead>\n",
       "<thead><tr><th>str8</th><th>str15</th><th>float64</th><th>str1</th><th>int64</th><th>float64</th><th>float64</th><th>bool</th><th>bool</th><th>str1</th></tr></thead>\n",
       "<tr><td>spectral</td><td>log10_k</td><td>-8.0000e+00</td><td></td><td>0.000e+00</td><td>-1.000e+01</td><td>1.000e+01</td><td>False</td><td>False</td><td></td></tr>\n",
       "<tr><td>spectral</td><td>p1</td><td>2.0200e+00</td><td></td><td>0.000e+00</td><td>1.000e+00</td><td>5.000e+00</td><td>False</td><td>False</td><td></td></tr>\n",
       "<tr><td>spectral</td><td>p2</td><td>3.4300e+00</td><td></td><td>0.000e+00</td><td>1.000e+00</td><td>5.000e+00</td><td>False</td><td>False</td><td></td></tr>\n",
       "<tr><td>spectral</td><td>log10_gamma_b</td><td>5.0000e+00</td><td></td><td>0.000e+00</td><td>2.000e+00</td><td>6.000e+00</td><td>False</td><td>False</td><td></td></tr>\n",
       "<tr><td>spectral</td><td>log10_gamma_min</td><td>2.6990e+00</td><td></td><td>0.000e+00</td><td>0.000e+00</td><td>4.000e+00</td><td>True</td><td>False</td><td></td></tr>\n",
       "<tr><td>spectral</td><td>log10_gamma_max</td><td>6.0000e+00</td><td></td><td>0.000e+00</td><td>4.000e+00</td><td>8.000e+00</td><td>True</td><td>False</td><td></td></tr>\n",
       "<tr><td>spectral</td><td>z</td><td>3.0800e-02</td><td></td><td>0.000e+00</td><td>1.000e-03</td><td>1.000e+01</td><td>True</td><td>False</td><td></td></tr>\n",
       "<tr><td>spectral</td><td>delta_D</td><td>1.8000e+01</td><td></td><td>0.000e+00</td><td>1.000e+00</td><td>1.000e+02</td><td>False</td><td>False</td><td></td></tr>\n",
       "<tr><td>spectral</td><td>log10_B</td><td>-1.3000e+00</td><td></td><td>0.000e+00</td><td>-4.000e+00</td><td>2.000e+00</td><td>False</td><td>False</td><td></td></tr>\n",
       "<tr><td>spectral</td><td>t_var</td><td>8.6400e+04</td><td>s</td><td>0.000e+00</td><td>1.000e+01</td><td>3.142e+07</td><td>True</td><td>False</td><td></td></tr>\n",
       "<tr><td>spectral</td><td>norm</td><td>1.0000e+00</td><td></td><td>0.000e+00</td><td>1.000e-01</td><td>1.000e+01</td><td>True</td><td>True</td><td></td></tr>\n",
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       "<Table length=11>\n",
       "  type         name         value    unit   error      min        max    frozen is_norm link\n",
       "  str8        str15        float64   str1   int64    float64    float64   bool    bool  str1\n",
       "-------- --------------- ----------- ---- --------- ---------- --------- ------ ------- ----\n",
       "spectral         log10_k -8.0000e+00      0.000e+00 -1.000e+01 1.000e+01  False   False     \n",
       "spectral              p1  2.0200e+00      0.000e+00  1.000e+00 5.000e+00  False   False     \n",
       "spectral              p2  3.4300e+00      0.000e+00  1.000e+00 5.000e+00  False   False     \n",
       "spectral   log10_gamma_b  5.0000e+00      0.000e+00  2.000e+00 6.000e+00  False   False     \n",
       "spectral log10_gamma_min  2.6990e+00      0.000e+00  0.000e+00 4.000e+00   True   False     \n",
       "spectral log10_gamma_max  6.0000e+00      0.000e+00  4.000e+00 8.000e+00   True   False     \n",
       "spectral               z  3.0800e-02      0.000e+00  1.000e-03 1.000e+01   True   False     \n",
       "spectral         delta_D  1.8000e+01      0.000e+00  1.000e+00 1.000e+02  False   False     \n",
       "spectral         log10_B -1.3000e+00      0.000e+00 -4.000e+00 2.000e+00  False   False     \n",
       "spectral           t_var  8.6400e+04    s 0.000e+00  1.000e+01 3.142e+07   True   False     \n",
       "spectral            norm  1.0000e+00      0.000e+00  1.000e-01 1.000e+01   True    True     "
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     "execution_count": 4,
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   "source": [
    "ssc_model.parameters.to_table()"
   ]
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   "id": "d17c1ba2",
   "metadata": {},
   "source": [
    "or display separately the parameters related to the electrons energy distribution"
   ]
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   "id": "6403829f",
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       "<thead><tr><th>str8</th><th>str15</th><th>float64</th><th>str1</th><th>int64</th><th>float64</th><th>float64</th><th>bool</th><th>bool</th><th>str1</th></tr></thead>\n",
       "<tr><td>spectral</td><td>log10_k</td><td>-8.0000e+00</td><td></td><td>0.000e+00</td><td>-1.000e+01</td><td>1.000e+01</td><td>False</td><td>False</td><td></td></tr>\n",
       "<tr><td>spectral</td><td>p1</td><td>2.0200e+00</td><td></td><td>0.000e+00</td><td>1.000e+00</td><td>5.000e+00</td><td>False</td><td>False</td><td></td></tr>\n",
       "<tr><td>spectral</td><td>p2</td><td>3.4300e+00</td><td></td><td>0.000e+00</td><td>1.000e+00</td><td>5.000e+00</td><td>False</td><td>False</td><td></td></tr>\n",
       "<tr><td>spectral</td><td>log10_gamma_b</td><td>5.0000e+00</td><td></td><td>0.000e+00</td><td>2.000e+00</td><td>6.000e+00</td><td>False</td><td>False</td><td></td></tr>\n",
       "<tr><td>spectral</td><td>log10_gamma_min</td><td>2.6990e+00</td><td></td><td>0.000e+00</td><td>0.000e+00</td><td>4.000e+00</td><td>True</td><td>False</td><td></td></tr>\n",
       "<tr><td>spectral</td><td>log10_gamma_max</td><td>6.0000e+00</td><td></td><td>0.000e+00</td><td>4.000e+00</td><td>8.000e+00</td><td>True</td><td>False</td><td></td></tr>\n",
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       "  type         name         value    unit   error      min        max    frozen is_norm link\n",
       "  str8        str15        float64   str1   int64    float64    float64   bool    bool  str1\n",
       "-------- --------------- ----------- ---- --------- ---------- --------- ------ ------- ----\n",
       "spectral         log10_k -8.0000e+00      0.000e+00 -1.000e+01 1.000e+01  False   False     \n",
       "spectral              p1  2.0200e+00      0.000e+00  1.000e+00 5.000e+00  False   False     \n",
       "spectral              p2  3.4300e+00      0.000e+00  1.000e+00 5.000e+00  False   False     \n",
       "spectral   log10_gamma_b  5.0000e+00      0.000e+00  2.000e+00 6.000e+00  False   False     \n",
       "spectral log10_gamma_min  2.6990e+00      0.000e+00  0.000e+00 4.000e+00   True   False     \n",
       "spectral log10_gamma_max  6.0000e+00      0.000e+00  4.000e+00 8.000e+00   True   False     "
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    "ssc_model.spectral_parameters.to_table()"
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   "source": [
    "and the parameters related to the emission region, the blob, in this case"
   ]
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   "cell_type": "code",
   "execution_count": 6,
   "id": "2a583d09",
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   "outputs": [
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       "<thead><tr><th>type</th><th>name</th><th>value</th><th>unit</th><th>error</th><th>min</th><th>max</th><th>frozen</th><th>is_norm</th><th>link</th></tr></thead>\n",
       "<thead><tr><th>str8</th><th>str7</th><th>float64</th><th>str1</th><th>int64</th><th>float64</th><th>float64</th><th>bool</th><th>bool</th><th>str1</th></tr></thead>\n",
       "<tr><td>spectral</td><td>z</td><td>3.0800e-02</td><td></td><td>0.000e+00</td><td>1.000e-03</td><td>1.000e+01</td><td>True</td><td>False</td><td></td></tr>\n",
       "<tr><td>spectral</td><td>delta_D</td><td>1.8000e+01</td><td></td><td>0.000e+00</td><td>1.000e+00</td><td>1.000e+02</td><td>False</td><td>False</td><td></td></tr>\n",
       "<tr><td>spectral</td><td>log10_B</td><td>-1.3000e+00</td><td></td><td>0.000e+00</td><td>-4.000e+00</td><td>2.000e+00</td><td>False</td><td>False</td><td></td></tr>\n",
       "<tr><td>spectral</td><td>t_var</td><td>8.6400e+04</td><td>s</td><td>0.000e+00</td><td>1.000e+01</td><td>3.142e+07</td><td>True</td><td>False</td><td></td></tr>\n",
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       "  str8     str7    float64   str1   int64    float64    float64   bool    bool  str1\n",
       "-------- ------- ----------- ---- --------- ---------- --------- ------ ------- ----\n",
       "spectral       z  3.0800e-02      0.000e+00  1.000e-03 1.000e+01   True   False     \n",
       "spectral delta_D  1.8000e+01      0.000e+00  1.000e+00 1.000e+02  False   False     \n",
       "spectral log10_B -1.3000e+00      0.000e+00 -4.000e+00 2.000e+00  False   False     \n",
       "spectral   t_var  8.6400e+04    s 0.000e+00  1.000e+01 3.142e+07   True   False     "
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     "execution_count": 6,
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   ],
   "source": [
    "ssc_model.emission_region_parameters.to_table()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aa62617",
   "metadata": {},
   "source": [
    "### Fit with `gammapy`\n",
    "Here we start the procedure to fit with Gammapy.\n",
    "\n",
    "#### 1) load the MWL flux points, add systematics\n",
    "A function is provided in `agnpy.fit` to directly load flux points in a list of `gammapy.datasets.FluxPointsDataset` object. It reads the data from a file, included in the package, containing a MWL SED following [these specifications](https://gamma-astro-data-formats.readthedocs.io/en/v0.2/spectra/flux_points/index.html).\n",
    "\n",
    "The same function allows to add a systematic error on the flux points, this is done with a dictionary specifying the instrument name and the systematic error, expressed as a relative error on the flux. The systematic error is summed in quadrature to the statistical error.\n",
    "\n",
    "In this example, we use a very rough and conservative estimate of the systematic errors ($30\\%$ of the flux for VHE instruments, $10\\%$ for HE and X-ray instruments, $5\\%$ for all the other instruments).\n",
    "\n",
    "Specifying the systematic errors through the dictionary is optional.\n",
    "\n",
    "We can also set the minimum and maximum energy to be used in the fit. We exclude points below $10^{11}\\,{\\rm Hz}$, as they are measured in the radio band with large integration regions. They hence include the extended emission of the jet, while in our model we are considering the emission from a finite region of the jet, the blob."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8b6c0de5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No reference model set for FluxMaps. Assuming point source with E^-2 spectrum.\n",
      "No reference model set for FluxMaps. Assuming point source with E^-2 spectrum.\n",
      "No reference model set for FluxMaps. Assuming point source with E^-2 spectrum.\n",
      "No reference model set for FluxMaps. Assuming point source with E^-2 spectrum.\n",
      "No reference model set for FluxMaps. Assuming point source with E^-2 spectrum.\n",
      "No reference model set for FluxMaps. Assuming point source with E^-2 spectrum.\n",
      "No reference model set for FluxMaps. Assuming point source with E^-2 spectrum.\n",
      "No reference model set for FluxMaps. Assuming point source with E^-2 spectrum.\n",
      "No reference model set for FluxMaps. Assuming point source with E^-2 spectrum.\n",
      "No reference model set for FluxMaps. Assuming point source with E^-2 spectrum.\n",
      "No reference model set for FluxMaps. Assuming point source with E^-2 spectrum.\n",
      "No reference model set for FluxMaps. Assuming point source with E^-2 spectrum.\n",
      "No reference model set for FluxMaps. Assuming point source with E^-2 spectrum.\n",
      "No reference model set for FluxMaps. Assuming point source with E^-2 spectrum.\n",
      "No reference model set for FluxMaps. Assuming point source with E^-2 spectrum.\n",
      "No reference model set for FluxMaps. Assuming point source with E^-2 spectrum.\n",
      "No reference model set for FluxMaps. Assuming point source with E^-2 spectrum.\n",
      "No reference model set for FluxMaps. Assuming point source with E^-2 spectrum.\n",
      "No reference model set for FluxMaps. Assuming point source with E^-2 spectrum.\n",
      "No reference model set for FluxMaps. Assuming point source with E^-2 spectrum.\n",
      "No reference model set for FluxMaps. Assuming point source with E^-2 spectrum.\n",
      "No reference model set for FluxMaps. Assuming point source with E^-2 spectrum.\n",
      "No reference model set for FluxMaps. Assuming point source with E^-2 spectrum.\n",
      "No reference model set for FluxMaps. Assuming point source with E^-2 spectrum.\n"
     ]
    }
   ],
   "source": [
    "sed_path = pkg_resources.resource_filename(\"agnpy\", \"data/mwl_seds/Mrk421_2011.ecsv\")\n",
    "\n",
    "systematics_dict = {\n",
    "    \"Fermi\": 0.10,\n",
    "    \"GASP\": 0.05,\n",
    "    \"GRT\": 0.05,\n",
    "    \"MAGIC\": 0.30,\n",
    "    \"MITSuME\": 0.05,\n",
    "    \"Medicina\": 0.05,\n",
    "    \"Metsahovi\": 0.05,\n",
    "    \"NewMexicoSkies\": 0.05,\n",
    "    \"Noto\": 0.05,\n",
    "    \"OAGH\": 0.05,\n",
    "    \"OVRO\": 0.05,\n",
    "    \"RATAN\": 0.05,\n",
    "    \"ROVOR\": 0.05,\n",
    "    \"RXTE/PCA\": 0.10,\n",
    "    \"SMA\": 0.05,\n",
    "    \"Swift/BAT\": 0.10,\n",
    "    \"Swift/UVOT\": 0.05,\n",
    "    \"Swift/XRT\": 0.10,\n",
    "    \"VLBA(BK150)\": 0.05,\n",
    "    \"VLBA(BP143)\": 0.05,\n",
    "    \"VLBA(MOJAVE)\": 0.05,\n",
    "    \"VLBA_core(BP143)\": 0.05,\n",
    "    \"VLBA_core(MOJAVE)\": 0.05,\n",
    "    \"WIRO\": 0.05,\n",
    "}\n",
    "\n",
    "# define minimum and maximum energy to be used in the fit\n",
    "E_min = (1e11 * u.Hz).to(\"eV\", equivalencies=u.spectral())\n",
    "E_max = 100 * u.TeV\n",
    "\n",
    "datasets = load_gammapy_flux_points(sed_path, E_min, E_max, systematics_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b0e708a",
   "metadata": {},
   "source": [
    "For the gammapy wrapper, we have to define a spectral model and associate it to the list of datasets we obtained from the flux points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "15e60518",
   "metadata": {},
   "outputs": [],
   "source": [
    "sky_model = SkyModel(spectral_model=ssc_model, name=\"Mrk421\")\n",
    "datasets.models = [sky_model]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ee86bb5",
   "metadata": {},
   "source": [
    "Let us plot all the flux points and the initial model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0158bbd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "for dataset in datasets:\n",
    "    dataset.data.plot(ax=ax, label=dataset.name)\n",
    "\n",
    "ssc_model.plot(\n",
    "    ax=ax,\n",
    "    energy_bounds=[1e-6, 1e14] * u.eV,\n",
    "    energy_power=2,\n",
    "    label=\"SSC model\",\n",
    "    color=\"k\",\n",
    "    lw=1.6,\n",
    ")\n",
    "\n",
    "ax.set_ylabel(sed_y_label)\n",
    "ax.set_xlabel(r\"$E\\,/\\,{\\rm eV}$\")\n",
    "ax.set_xlim([1e-6, 1e14])\n",
    "ax.legend(ncol=4, fontsize=9)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "316896e9",
   "metadata": {},
   "source": [
    "#### 3) run the fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "040420d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3min 53s, sys: 28.3 s, total: 4min 21s\n",
      "Wall time: 4min 24s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# define the fitter\n",
    "fitter = Fit()\n",
    "results = fitter.run(datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "83c99973",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OptimizeResult\n",
      "\n",
      "\tbackend    : minuit\n",
      "\tmethod     : migrad\n",
      "\tsuccess    : True\n",
      "\tmessage    : Optimization terminated successfully.\n",
      "\tnfev       : 342\n",
      "\ttotal stat : 270.78\n",
      "\n",
      "CovarianceResult\n",
      "\n",
      "\tbackend    : minuit\n",
      "\tmethod     : hesse\n",
      "\tsuccess    : True\n",
      "\tmessage    : Hesse terminated successfully.\n",
      "\n",
      "  type         name         value    unit   error      min        max    frozen is_norm link\n",
      "-------- --------------- ----------- ---- --------- ---------- --------- ------ ------- ----\n",
      "spectral         log10_k -7.8836e+00      7.206e-02 -1.000e+01 1.000e+01  False   False     \n",
      "spectral              p1  2.0541e+00      2.509e-02  1.000e+00 5.000e+00  False   False     \n",
      "spectral              p2  3.5406e+00      6.162e-02  1.000e+00 5.000e+00  False   False     \n",
      "spectral   log10_gamma_b  4.9905e+00      2.431e-02  2.000e+00 6.000e+00  False   False     \n",
      "spectral log10_gamma_min  2.6990e+00      0.000e+00  0.000e+00 4.000e+00   True   False     \n",
      "spectral log10_gamma_max  6.0000e+00      0.000e+00  4.000e+00 8.000e+00   True   False     \n",
      "spectral               z  3.0800e-02      0.000e+00  1.000e-03 1.000e+01   True   False     \n",
      "spectral         delta_D  1.9745e+01      7.306e-01  1.000e+00 1.000e+02  False   False     \n",
      "spectral         log10_B -1.3288e+00      4.852e-02 -4.000e+00 2.000e+00  False   False     \n",
      "spectral           t_var  8.6400e+04    s 0.000e+00  1.000e+01 3.142e+07   True   False     \n",
      "spectral            norm  1.0000e+00      0.000e+00  1.000e-01 1.000e+01   True    True     \n"
     ]
    }
   ],
   "source": [
    "print(results)\n",
    "print(ssc_model.parameters.to_table())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea762fe8",
   "metadata": {},
   "source": [
    "Now let us plot the final model and the flux points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fea7342b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "for dataset in datasets:\n",
    "    dataset.data.plot(ax=ax, label=dataset.name)\n",
    "\n",
    "ssc_model.plot(\n",
    "    ax=ax,\n",
    "    energy_bounds=[1e-6, 1e14] * u.eV,\n",
    "    energy_power=2,\n",
    "    label=\"model\",\n",
    "    color=\"k\",\n",
    "    lw=1.6,\n",
    ")\n",
    "\n",
    "# plot a line marking the minimum energy considered in the fit\n",
    "ax.axvline(E_min, ls=\"--\", color=\"gray\")\n",
    "\n",
    "plt.legend(ncol=4, fontsize=9)\n",
    "plt.xlim([1e-6, 1e14])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9fe6702",
   "metadata": {},
   "source": [
    "If you want to find more about fitting with gammapy, you can read [this tutorial](https://docs.gammapy.org/0.20.1/tutorials/api/fitting.html). To show the additional capabilities of the fitting with `gammapy`, we illustrate how to visualise the migration matrix and asses the quality of the fit by plotting the likelihood profile of one of the parameters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "976b6ca1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 633.6x514.8 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the covariance matrix\n",
    "ssc_model.covariance.plot_correlation()\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "052e8131",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 34 s, sys: 4.09 s, total: 38.1 s\n",
      "Wall time: 38.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# plot the profile for the normalisation of the electron energy distribution\n",
    "par = sky_model.spectral_model.log10_k\n",
    "par.scan_n_values = 50\n",
    "profile = fitter.stat_profile(datasets=datasets, parameter=par)\n",
    "\n",
    "# to compute the delta TS\n",
    "total_stat = results.total_stat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8feb3ccf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(profile[f\"{par.name}_scan\"], profile[\"stat_scan\"] - total_stat)\n",
    "plt.ylabel(r\"$\\Delta(TS)$\", size=12)\n",
    "plt.xlabel(r\"$log_{10}(k_{\\rm e} / {\\rm cm}^{-3})$\", size=12)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.7 64-bit",
   "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": "3.9.7"
  },
  "vscode": {
   "interpreter": {
    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}