{
 "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",
    " You can contribute with your own notebooks in this\n",
    " [GitHub repository](https://github.com/gammapy/gammapy-extra/tree/master/notebooks).\n",
    "\n",
    "**Source files:**\n",
    "[sed_fitting_gammacat_fermi.ipynb](../_static/notebooks/sed_fitting_gammacat_fermi.ipynb) |\n",
    "[sed_fitting_gammacat_fermi.py](../_static/notebooks/sed_fitting_gammacat_fermi.py)\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Flux point fitting in Gammapy\n",
    "\n",
    "\n",
    "## Introduction\n",
    "\n",
    "In this tutorial we're going to learn how to fit spectral models to combined Fermi-LAT and IACT flux points.\n",
    "\n",
    "The central class we're going to use for this example analysis is:  \n",
    "\n",
    "- [gammapy.spectrum.FluxPointFitter](http://docs.gammapy.org/dev/api/gammapy.spectrum.FluxPointFitter.html)\n",
    "\n",
    "In addition we will work with the following data classes:\n",
    "\n",
    "- [gammapy.spectrum.FluxPoints](http://docs.gammapy.org/dev/api/gammapy.spectrum.FluxPoints.html)\n",
    "- [gammapy.catalog.SourceCatalogGammaCat](http://docs.gammapy.org/dev/api/gammapy.catalog.SourceCatalogGammaCat.html)\n",
    "- [gammapy.catalog.SourceCatalog3FHL](http://docs.gammapy.org/dev/api/gammapy.catalog.SourceCatalog3FHL.html)\n",
    "- [gammapy.catalog.SourceCatalog3FGL](http://docs.gammapy.org/dev/api/gammapy.catalog.SourceCatalog3FGL.html)\n",
    "\n",
    "And the following spectral model classes:\n",
    "\n",
    "- [PowerLaw](http://docs.gammapy.org/dev/api/gammapy.spectrum.models.PowerLaw.html)\n",
    "- [ExponentialCutoffPowerLaw](http://docs.gammapy.org/dev/api/gammapy.spectrum.models.ExponentialCutoffPowerLaw.html)\n",
    "- [LogParabola](http://docs.gammapy.org/dev/api/gammapy.spectrum.models.LogParabola.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "Let us start with the usual IPython notebook and Python imports:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy import units as u\n",
    "from astropy.table import vstack\n",
    "from gammapy.spectrum.models import PowerLaw, ExponentialCutoffPowerLaw, LogParabola\n",
    "from gammapy.spectrum import FluxPointFitter, FluxPoints\n",
    "from gammapy.catalog import SourceCatalog3FGL, SourceCatalogGammaCat, SourceCatalog3FHL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load spectral points\n",
    "\n",
    "For this analysis we choose to work with the source 'HESS J1507-622' and the associated Fermi-LAT sources '3FGL J1506.6-6219' and '3FHL J1507.9-6228e'. We load the source catalogs, and then access source of interest by name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "fermi_3fgl = SourceCatalog3FGL()\n",
    "fermi_3fhl = SourceCatalog3FHL()\n",
    "gammacat = SourceCatalogGammaCat()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "source_gammacat =  gammacat['HESS J1507-622']\n",
    "source_fermi_3fgl = fermi_3fgl['3FGL J1506.6-6219']\n",
    "source_fermi_3fhl = fermi_3fhl['3FHL J1507.9-6228e']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The corresponding flux points data can be accessed with `.flux_points` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=6</i>\n",
       "<table id=\"table4433829392\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>e_ref</th><th>dnde</th><th>dnde_errn</th><th>dnde_errp</th></tr></thead>\n",
       "<thead><tr><th>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>float32</th><th>float32</th><th>float32</th><th>float32</th></tr></thead>\n",
       "<tr><td>0.8609</td><td>2.29119e-12</td><td>8.70543e-13</td><td>8.95502e-13</td></tr>\n",
       "<tr><td>1.56151</td><td>6.98172e-13</td><td>2.20354e-13</td><td>2.30407e-13</td></tr>\n",
       "<tr><td>2.76375</td><td>1.69062e-13</td><td>6.7587e-14</td><td>7.18838e-14</td></tr>\n",
       "<tr><td>4.8916</td><td>7.72925e-14</td><td>2.40132e-14</td><td>2.60749e-14</td></tr>\n",
       "<tr><td>9.98858</td><td>1.03253e-14</td><td>5.06315e-15</td><td>5.64195e-15</td></tr>\n",
       "<tr><td>27.0403</td><td>7.44987e-16</td><td>5.72089e-16</td><td>7.25999e-16</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=6>\n",
       " e_ref        dnde         dnde_errn       dnde_errp   \n",
       "  TeV   1 / (cm2 s TeV) 1 / (cm2 s TeV) 1 / (cm2 s TeV)\n",
       "float32     float32         float32         float32    \n",
       "------- --------------- --------------- ---------------\n",
       " 0.8609     2.29119e-12     8.70543e-13     8.95502e-13\n",
       "1.56151     6.98172e-13     2.20354e-13     2.30407e-13\n",
       "2.76375     1.69062e-13      6.7587e-14     7.18838e-14\n",
       " 4.8916     7.72925e-14     2.40132e-14     2.60749e-14\n",
       "9.98858     1.03253e-14     5.06315e-15     5.64195e-15\n",
       "27.0403     7.44987e-16     5.72089e-16     7.25999e-16"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flux_points_gammacat = source_gammacat.flux_points\n",
    "flux_points_gammacat.table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the Fermi-LAT catalogs, integral flux points are given. Currently the flux point fitter only works with differential flux points, so we apply the conversion here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "flux_points_3fgl = source_fermi_3fgl.flux_points.to_sed_type(\n",
    "    sed_type='dnde',\n",
    "    model=source_fermi_3fgl.spectral_model,\n",
    ")\n",
    "flux_points_3fhl = source_fermi_3fhl.flux_points.to_sed_type(\n",
    "    sed_type='dnde',\n",
    "    model=source_fermi_3fhl.spectral_model,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we stack the flux points into a single `FluxPoints` object and drop the upper limit values, because currently we can't handle them in the fit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stack flux point tables\n",
    "flux_points = FluxPoints.stack([\n",
    "    flux_points_gammacat,\n",
    "    flux_points_3fhl,\n",
    "    flux_points_3fgl\n",
    "])\n",
    "\n",
    "# drop the flux upper limit values\n",
    "flux_points = flux_points.drop_ul()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitter Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We initialze the fitter object with the `'chi2assym'` statistic, because we have assymmetric errors on the flux points. As optimizer we choose the `'simplex'` algorithm and to estimate the errors we use `'covar'` method: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "fitter = FluxPointFitter(\n",
    "    stat='chi2assym',\n",
    "    optimizer='simplex',\n",
    "    error_estimator='covar',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Power Law Fit\n",
    "\n",
    "First we start with fitting a simple [power law](http://docs.gammapy.org/dev/api/gammapy.spectrum.models.PowerLaw.html#gammapy.spectrum.models.PowerLaw)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "pwl = PowerLaw(\n",
    "    index=2. * u.Unit(''),\n",
    "    amplitude=1e-12 * u.Unit('cm-2 s-1 TeV-1'),\n",
    "    reference=1. * u.TeV\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After creating the model we run the fit by passing the `'flux_points'` and `'pwl'` objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_pwl = fitter.run(flux_points, pwl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And print the result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PowerLaw\n",
      "\n",
      "Parameters: \n",
      "\n",
      "\t   name     value     error         unit      min max frozen\n",
      "\t--------- --------- --------- --------------- --- --- ------\n",
      "\t    index 1.950e+00 2.656e-02                 nan nan  False\n",
      "\tamplitude 1.248e-12 1.599e-13 1 / (cm2 s TeV) nan nan  False\n",
      "\treference 1.000e+00 0.000e+00             TeV nan nan   True\n",
      "\n",
      "Covariance: \n",
      "\n",
      "\tname/name   index   amplitude\n",
      "\t--------- --------- ---------\n",
      "\t    index  0.000706 -2.25e-15\n",
      "\tamplitude -2.25e-15  2.56e-26\n"
     ]
    }
   ],
   "source": [
    "print(result_pwl['best-fit-model'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a quick check we print the value of the fit statistics per degrees of freedom as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.5038842921602655\n"
     ]
    }
   ],
   "source": [
    "print(result_pwl['statval/dof'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we plot the data points and the best fit model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1e-13, 1e-11)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+u4DPRlzDFc3eL92uuN7Uys9eyHNqbW+m9nnatOi2hGlHktqy0HY0+vfVrW1p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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10653d6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = flux_points.plot(energy_power=2)\n",
    "result_pwl['best-fit-model'].plot(energy_range=[1e-4, 1e2] * u.TeV, ax=ax, energy_power=2)\n",
    "result_pwl['best-fit-model'].plot_error(energy_range=[1e-4, 1e2] * u.TeV, ax=ax, energy_power=2)\n",
    "ax.set_ylim(1e-13, 1e-11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exponential Cut-Off Powerlaw Fit\n",
    "\n",
    "Next we fit an [exponential cut-off power](http://docs.gammapy.org/dev/api/gammapy.spectrum.models.ExponentialCutoffPowerLaw.html#gammapy.spectrum.models.ExponentialCutoffPowerLaw) law to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ecpl = ExponentialCutoffPowerLaw(\n",
    "    index=2. * u.Unit(''),\n",
    "    amplitude=1e-12 * u.Unit('cm-2 s-1 TeV-1'),\n",
    "    reference=1. * u.TeV,\n",
    "    lambda_=0. / u.TeV\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We run the fitter again by passing the flux points and the `ecpl` model instance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExponentialCutoffPowerLaw\n",
      "\n",
      "Parameters: \n",
      "\n",
      "\t   name     value     error         unit      min max frozen\n",
      "\t--------- --------- --------- --------------- --- --- ------\n",
      "\t    index 1.876e+00 4.388e-02                 nan nan  False\n",
      "\tamplitude 1.932e-12 3.873e-13 1 / (cm2 s TeV) nan nan  False\n",
      "\treference 1.000e+00 0.000e+00             TeV nan nan   True\n",
      "\t  lambda_ 6.147e-02 5.795e-02         1 / TeV nan nan  False\n",
      "\n",
      "Covariance: \n",
      "\n",
      "\tname/name   index   amplitude lambda_ \n",
      "\t--------- --------- --------- --------\n",
      "\t    index   0.00193 -1.35e-14 -0.00178\n",
      "\tamplitude -1.35e-14   1.5e-25 1.73e-14\n",
      "\t  lambda_  -0.00178  1.73e-14  0.00336\n"
     ]
    }
   ],
   "source": [
    "result_ecpl = fitter.run(flux_points, ecpl)\n",
    "print(result_ecpl['best-fit-model'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.001353939388082\n"
     ]
    }
   ],
   "source": [
    "print(result_ecpl['statval/dof'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot the data and best fit model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1e-13, 1e-11)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RCoUmBxcdjkO21KFarTK1VZXH6XTicrnIZkvvojhJnBxf/15WP/6XdA78irHF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\nQKKUUqoqGkiUUkpV5f8HiYPutsUUn7AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109d86ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = flux_points.plot(energy_power=2)\n",
    "result_ecpl['best-fit-model'].plot(energy_range=[1e-4, 1e2] * u.TeV, ax=ax, energy_power=2)\n",
    "result_ecpl['best-fit-model'].plot_error(energy_range=[1e-4, 1e2] * u.TeV, ax=ax, energy_power=2)\n",
    "ax.set_ylim(1e-13, 1e-11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Log-Parabola Fit\n",
    "\n",
    "Finally we try to fit a [log-parabola](http://docs.gammapy.org/dev/api/gammapy.spectrum.models.LogParabola.html#gammapy.spectrum.models.LogParabola) model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_parabola = LogParabola(\n",
    "    alpha=2. * u.Unit(''),\n",
    "    amplitude=1e-12 * u.Unit('cm-2 s-1 TeV-1'),\n",
    "    reference=1. * u.TeV,\n",
    "    beta=0. * u.Unit('')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogParabola\n",
      "\n",
      "Parameters: \n",
      "\n",
      "\t   name     value     error         unit      min max frozen\n",
      "\t--------- --------- --------- --------------- --- --- ------\n",
      "\tamplitude 1.954e-12 2.797e-13 1 / (cm2 s TeV) nan nan  False\n",
      "\treference 1.000e+00 0.000e+00             TeV nan nan   True\n",
      "\t    alpha 2.140e+00 7.377e-02                 nan nan  False\n",
      "\t     beta 4.947e-02 1.757e-02                 nan nan  False\n",
      "\n",
      "Covariance: \n",
      "\n",
      "\tname/name amplitude  alpha     beta  \n",
      "\t--------- --------- -------- --------\n",
      "\tamplitude  7.82e-26 1.79e-15 2.19e-15\n",
      "\t    alpha  1.79e-15  0.00544  0.00112\n",
      "\t     beta  2.19e-15  0.00112 0.000309\n"
     ]
    }
   ],
   "source": [
    "result_log_parabola = fitter.run(flux_points, log_parabola)\n",
    "print(result_log_parabola['best-fit-model'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.5798902196277185\n"
     ]
    }
   ],
   "source": [
    "print(result_log_parabola['statval/dof'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1e-13, 1e-11)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a0c7a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = flux_points.plot(energy_power=2)\n",
    "result_log_parabola['best-fit-model'].plot(energy_range=[1e-4, 1e2] * u.TeV, ax=ax, energy_power=2)\n",
    "result_log_parabola['best-fit-model'].plot_error(energy_range=[1e-4, 1e2] * u.TeV, ax=ax, energy_power=2)\n",
    "ax.set_ylim(1e-13, 1e-11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "\n",
    "- Fit a `PowerLaw2` and `ExponentialCutoffPowerLaw3FGL` to the same data.\n",
    "- Fit a `ExponentialCutoffPowerLaw` model to Vela X ('HESS J0835-455') only and check if the best fit values correspond to the values given in the Gammacat catalog"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What next?\n",
    "\n",
    "This was an introduction to SED fitting in Gammapy.\n",
    "\n",
    "* If you would like to learn how to perform a full Poisson maximum likelihood spectral fit, please check out the [spectrum pipe](spectrum_pipe.ipynb) tutorial.\n",
    "* If you are interested in simulation of spectral data in the context of CTA, please check out the [spectrum simulation cta](spectrum_simulation_cta.ipynb) notebook.\n",
    "* To learn more about other parts of Gammapy (e.g. Fermi-LAT and TeV data analysis), check out the other tutorial notebooks.\n",
    "* To see what's available in Gammapy, browse the [Gammapy docs](http://docs.gammapy.org/) or use the full-text search.\n",
    "* If you have any questions, ask on the mailing list ."
   ]
  }
 ],
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   "language": "python",
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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