{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ec15d163",
   "metadata": {},
   "source": [
    "# Tutorial: fitting a FSRQ broad-band SED using angpy and Gammapy\n",
    "\n",
    "In this tutorial we will show how to use `Gammapy` to wrap `agnpy` functions to perform the fit of the broad-band SED of PKS1510-089, measured during its gamma-ray flaring activity in 2015 [(Ahnen et al. 2017)](https://ui.adsabs.harvard.edu/abs/2017A%26A...603A..29A/abstract). We select the MWL SED corresponding to the period identified in the paper as \"Period B\" (MJD 57164-57166). \n",
    "\n",
    "[Gammapy](https://gammapy.org/) is required to run this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "39abfeed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import numpy, astropy and matplotlib for basic functionalities\n",
    "import pkg_resources\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import astropy.units as u\n",
    "from astropy.constants import k_B, m_e, c, G, M_sun\n",
    "from astropy.table import Table\n",
    "from astropy.coordinates import Distance\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# import agnpy classes\n",
    "from agnpy.spectra import BrokenPowerLaw\n",
    "from agnpy.emission_regions import Blob\n",
    "from agnpy.synchrotron import Synchrotron\n",
    "from agnpy.compton import SynchrotronSelfCompton, ExternalCompton\n",
    "from agnpy.targets import SSDisk, RingDustTorus\n",
    "from agnpy.utils.plot import load_mpl_rc, sed_x_label, sed_y_label\n",
    "\n",
    "# import gammapy classes\n",
    "from gammapy.modeling.models import (\n",
    "    SpectralModel,\n",
    "    Parameter,\n",
    "    SPECTRAL_MODEL_REGISTRY,\n",
    "    SkyModel,\n",
    ")\n",
    "from gammapy.estimators import FluxPoints\n",
    "from gammapy.datasets import FluxPointsDataset\n",
    "from gammapy.modeling import Fit\n",
    "\n",
    "# constants\n",
    "mec2 = m_e.to(\"erg\", equivalencies=u.mass_energy())\n",
    "gamma_size = 400\n",
    "gamma_to_integrate = np.logspace(0, 7, gamma_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffcdbafd",
   "metadata": {},
   "source": [
    "### `gammapy` wrapper of `agnpy` radiative processes\n",
    "Now let us [follow the `Gammapy` documentation](https://docs.gammapy.org/0.18.2/tutorials/models.html#Implementing-a-Custom-Model) and define a model wrapping `agnpy`'s functions to compute the Synchrotron, Synchrotron Self-Compton and External Compton on Dust Torus SEDs. We will assume a broken power-law electron distribution. The thermal SEDs of the Disk and the DT are added to the total flux model.\n",
    "\n",
    "**NOTE:** for the parameters that vary over several orders of magnitude (i.e. normalisation and Lorentz factors of the electron distribution) it is better to provide to the fitting routine a \"scaled\" version of them (e.g. their log10), such that larger ranges might be covered with small parameters variation.\n",
    "\n",
    "**NOTE:** the size of the blob $R_{\\rm b}$ is constrained through the variability time scale, $t_{\\rm var}$, and the Doppler factor, $\\delta_{\\rm D}$, via: $R_{\\rm b} = (c t_{\\rm var} \\delta_{\\rm D}) / (1 + z)$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "90b627f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "class AgnpyEC(SpectralModel):\n",
    "    \"\"\"Wrapper of agnpy's non synchrotron, SSC and EC classes. The flux model\n",
    "    accounts for the Disk and DT's thermal SEDs. \n",
    "    A broken power law is assumed for the electron spectrum.\n",
    "    To limit the span of the parameters space, we fit the log10 of the parameters \n",
    "    whose range is expected to cover several orders of magnitudes (normalisation, \n",
    "    gammas, size and magnetic field of the blob). \n",
    "    \"\"\"\n",
    "\n",
    "    tag = \"EC\"\n",
    "    log10_k_e = Parameter(\"log10_k_e\", -5, min=-20, max=2)\n",
    "    p1 = Parameter(\"p1\", 2.1, min=1.0, max=5.0)\n",
    "    p2 = Parameter(\"p2\", 3.1, min=1.0, max=5.0)\n",
    "    log10_gamma_b = Parameter(\"log10_gamma_b\", 3, min=1, max=6)\n",
    "    log10_gamma_min = Parameter(\"log10_gamma_min\", 1, min=0, max=4)\n",
    "    log10_gamma_max = Parameter(\"log10_gamma_max\", 5, min=3, max=8)\n",
    "    # source general parameters\n",
    "    z = Parameter(\"z\", 0.1, min=0.01, max=1)\n",
    "    d_L = Parameter(\"d_L\", \"1e27 cm\", min=1e25, max=1e33)\n",
    "    # emission region parameters\n",
    "    delta_D = Parameter(\"delta_D\", 10, min=1, max=40)\n",
    "    log10_B = Parameter(\"log10_B\", 0.0, min=-3.0, max=1.0)\n",
    "    t_var = Parameter(\"t_var\", \"600 s\", min=10, max=np.pi * 1e7)\n",
    "    mu_s = Parameter(\"mu_s\", 0.9, min=0.0, max=1.0)\n",
    "    log10_r = Parameter(\"log10_r\", 17.0, min=16.0, max=20.0)\n",
    "    # disk parameters\n",
    "    log10_L_disk = Parameter(\"log10_L_disk\", 45.0, min=42.0, max=48.0)\n",
    "    log10_M_BH = Parameter(\"log10_M_BH\", 42, min=32, max=45)\n",
    "    m_dot = Parameter(\"m_dot\", \"1e26 g s-1\", min=1e24, max=1e30)\n",
    "    R_in = Parameter(\"R_in\", \"1e14 cm\", min=1e12, max=1e16)\n",
    "    R_out = Parameter(\"R_out\", \"1e17 cm\", min=1e12, max=1e19)\n",
    "    # DT parameters\n",
    "    xi_dt = Parameter(\"xi_dt\", 0.6, min=0.0, max=1.0)\n",
    "    T_dt = Parameter(\"T_dt\", \"1e3 K\", min=1e2, max=1e4)\n",
    "    R_dt = Parameter(\"R_dt\", \"2.5e18 cm\", min=1.0e17, max=1.0e19)\n",
    "\n",
    "    @staticmethod\n",
    "    def evaluate(\n",
    "        energy,\n",
    "        log10_k_e,\n",
    "        p1,\n",
    "        p2,\n",
    "        log10_gamma_b,\n",
    "        log10_gamma_min,\n",
    "        log10_gamma_max,\n",
    "        z,\n",
    "        d_L,\n",
    "        delta_D,\n",
    "        log10_B,\n",
    "        t_var,\n",
    "        mu_s,\n",
    "        log10_r,\n",
    "        log10_L_disk,\n",
    "        log10_M_BH,\n",
    "        m_dot,\n",
    "        R_in,\n",
    "        R_out,\n",
    "        xi_dt,\n",
    "        T_dt,\n",
    "        R_dt,\n",
    "    ):\n",
    "        # conversion\n",
    "        k_e = 10 ** log10_k_e * u.Unit(\"cm-3\")\n",
    "        gamma_b = 10 ** log10_gamma_b\n",
    "        gamma_min = 10 ** log10_gamma_min\n",
    "        gamma_max = 10 ** log10_gamma_max\n",
    "        B = 10 ** log10_B * u.G\n",
    "        R_b = (c * t_var * delta_D / (1 + z)).to(\"cm\")\n",
    "        r = 10 ** log10_r * u.cm\n",
    "        L_disk = 10 ** log10_L_disk * u.Unit(\"erg s-1\")\n",
    "        M_BH = 10 ** log10_M_BH * u.Unit(\"g\")\n",
    "        eps_dt = 2.7 * (k_B * T_dt / mec2).to_value(\"\")\n",
    "\n",
    "        nu = energy.to(\"Hz\", equivalencies=u.spectral())\n",
    "        # non-thermal components\n",
    "        sed_synch = Synchrotron.evaluate_sed_flux(\n",
    "            nu,\n",
    "            z,\n",
    "            d_L,\n",
    "            delta_D,\n",
    "            B,\n",
    "            R_b,\n",
    "            BrokenPowerLaw,\n",
    "            k_e,\n",
    "            p1,\n",
    "            p2,\n",
    "            gamma_b,\n",
    "            gamma_min,\n",
    "            gamma_max,\n",
    "            ssa=True,\n",
    "            gamma=gamma_to_integrate,\n",
    "        )\n",
    "        sed_ssc = SynchrotronSelfCompton.evaluate_sed_flux(\n",
    "            nu,\n",
    "            z,\n",
    "            d_L,\n",
    "            delta_D,\n",
    "            B,\n",
    "            R_b,\n",
    "            BrokenPowerLaw,\n",
    "            k_e,\n",
    "            p1,\n",
    "            p2,\n",
    "            gamma_b,\n",
    "            gamma_min,\n",
    "            gamma_max,\n",
    "            ssa=True,\n",
    "            gamma=gamma_to_integrate,\n",
    "        )\n",
    "        sed_ec_dt = ExternalCompton.evaluate_sed_flux_dt(\n",
    "            nu,\n",
    "            z,\n",
    "            d_L,\n",
    "            delta_D,\n",
    "            mu_s,\n",
    "            R_b,\n",
    "            L_disk,\n",
    "            xi_dt,\n",
    "            eps_dt,\n",
    "            R_dt,\n",
    "            r,\n",
    "            BrokenPowerLaw,\n",
    "            k_e,\n",
    "            p1,\n",
    "            p2,\n",
    "            gamma_b,\n",
    "            gamma_min,\n",
    "            gamma_max,\n",
    "            gamma=gamma_to_integrate,\n",
    "        )\n",
    "        # thermal components\n",
    "        sed_bb_disk = SSDisk.evaluate_multi_T_bb_norm_sed(\n",
    "            nu, z, L_disk, M_BH, m_dot, R_in, R_out, d_L\n",
    "        )\n",
    "        sed_bb_dt = RingDustTorus.evaluate_bb_norm_sed(\n",
    "            nu, z, xi_dt * L_disk, T_dt, R_dt, d_L\n",
    "        )\n",
    "        sed = sed_synch + sed_ssc + sed_ec_dt + sed_bb_disk + sed_bb_dt\n",
    "        return (sed / energy ** 2).to(\"1 / (cm2 eV s)\")\n",
    "\n",
    "\n",
    "# IMPORTANT: add the new custom model to the registry of spectral models recognised by gammapy\n",
    "SPECTRAL_MODEL_REGISTRY.append(AgnpyEC)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cd0a5f7",
   "metadata": {},
   "source": [
    "### Fit with `gammapy`\n",
    "Here we begin the procedure to fit with `Gammapy`.\n",
    "\n",
    "#### 1) load the MWL flux points \n",
    "The MWL SEDs included in the default `agnpy` data are automatically readable by `Gammapy`'s `FluxPoints`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9fcd9318",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "sed_path = pkg_resources.resource_filename(\n",
    "    \"agnpy\", \"data/mwl_seds/PKS1510-089_2015b.ecsv\"\n",
    ")\n",
    "flux_points = FluxPoints.read(sed_path)\n",
    "flux_points.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2848f005",
   "metadata": {},
   "source": [
    "#### 2) add systematic errors\n",
    "Currently there is no function in `Gammapy` handling systematic errors on flux points.    \n",
    "Let us manually add different systematic errors in different energy bands.     \n",
    "We assume them to be independent from the statistical ones and sum them in quadrature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f9b35385",
   "metadata": {},
   "outputs": [],
   "source": [
    "# array of systematic errors, will just be summed in quadrature to the statistical error\n",
    "# we assume\n",
    "# - 30% on VHE gamma-ray instruments\n",
    "# - 10% on HE gamma-ray instruments\n",
    "# - 10% on X-ray instruments\n",
    "# - 5% on lower-energy instruments\n",
    "x = flux_points.table[\"e_ref\"]\n",
    "y = flux_points.table[\"e2dnde\"]\n",
    "y_err_stat = flux_points.table[\"e2dnde_errn\"]\n",
    "y_err_syst = np.zeros(len(x))\n",
    "# define energy ranges\n",
    "e_vhe = 100 * u.GeV\n",
    "e_he = 0.1 * u.GeV\n",
    "e_x_ray_max = 300 * u.keV\n",
    "e_x_ray_min = 0.3 * u.keV\n",
    "vhe_gamma = x >= e_vhe\n",
    "he_gamma = (x >= e_he) * (x < e_vhe)\n",
    "x_ray = (x >= e_x_ray_min) * (x < e_x_ray_max)\n",
    "uv_to_radio = x < e_x_ray_min\n",
    "# declare systematics\n",
    "y_err_syst[vhe_gamma] = 0.30\n",
    "y_err_syst[he_gamma] = 0.10\n",
    "y_err_syst[x_ray] = 0.10\n",
    "y_err_syst[uv_to_radio] = 0.05\n",
    "y_err_syst = y * y_err_syst\n",
    "# sum in quadrature the errors\n",
    "flux_points.table[\"e2dnde_err\"] = np.sqrt(y_err_stat ** 2 + y_err_syst ** 2)\n",
    "# convert to \"dnde\" SED type to fit\n",
    "flux_points = flux_points.to_sed_type(\"dnde\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9687dfe7",
   "metadata": {},
   "source": [
    "#### 3) perform the fit\n",
    "Now we create an instance of the model wrapping the non-thermal and thermal emissions.    \n",
    "The parameters describing the electron distribution and the magnetic field are left free to vary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "622dcc03",
   "metadata": {},
   "outputs": [],
   "source": [
    "# declare a model\n",
    "agnpy_ec = AgnpyEC()\n",
    "# global parameters of the blob and the DT\n",
    "z = 0.361\n",
    "d_L = Distance(z=z).to(\"cm\")\n",
    "# blob\n",
    "Gamma = 20\n",
    "delta_D = 25\n",
    "Beta = np.sqrt(1 - 1 / np.power(Gamma, 2))  # jet relativistic speed\n",
    "mu_s = (1 - 1 / (Gamma * delta_D)) / Beta  # viewing angle\n",
    "B = 0.35 * u.G\n",
    "# disk\n",
    "L_disk = 6.7e45 * u.Unit(\"erg s-1\")  # disk luminosity\n",
    "M_BH = 5.71 * 1e7 * M_sun\n",
    "eta = 1 / 12\n",
    "m_dot = (L_disk / (eta * c ** 2)).to(\"g s-1\")\n",
    "R_g = ((G * M_BH) / c ** 2).to(\"cm\")\n",
    "R_in = 6 * R_g\n",
    "R_out = 10000 * R_g\n",
    "# DT\n",
    "xi_dt = 0.6  # fraction of disk luminosity reprocessed by the DT\n",
    "T_dt = 1e3 * u.K\n",
    "R_dt = 6.47 * 1e18 * u.cm\n",
    "# size and location of the emission region\n",
    "t_var = 0.5 * u.d\n",
    "r = 6e17 * u.cm\n",
    "# instance of the model wrapping angpy functionalities\n",
    "# - AGN parameters\n",
    "# -- distances\n",
    "agnpy_ec.z.quantity = z\n",
    "agnpy_ec.z.frozen = True\n",
    "agnpy_ec.d_L.quantity = d_L.cgs.value\n",
    "agnpy_ec.d_L.frozen = True\n",
    "# -- SS disk\n",
    "agnpy_ec.log10_L_disk.quantity = np.log10(L_disk.to_value(\"erg s-1\"))\n",
    "agnpy_ec.log10_L_disk.frozen = True\n",
    "agnpy_ec.log10_M_BH.quantity = np.log10(M_BH.to_value(\"g\"))\n",
    "agnpy_ec.log10_M_BH.frozen = True\n",
    "agnpy_ec.m_dot.quantity = m_dot\n",
    "agnpy_ec.m_dot.frozen = True\n",
    "agnpy_ec.R_in.quantity = R_in\n",
    "agnpy_ec.R_in.frozen = True\n",
    "agnpy_ec.R_out.quantity = R_out\n",
    "agnpy_ec.R_out.frozen = True\n",
    "# -- Dust Torus\n",
    "agnpy_ec.xi_dt.quantity = xi_dt\n",
    "agnpy_ec.xi_dt.frozen = True\n",
    "agnpy_ec.T_dt.quantity = T_dt\n",
    "agnpy_ec.T_dt.frozen = True\n",
    "agnpy_ec.R_dt.quantity = R_dt\n",
    "agnpy_ec.R_dt.frozen = True\n",
    "# - blob parameters\n",
    "agnpy_ec.delta_D.quantity = delta_D\n",
    "agnpy_ec.delta_D.frozen = True\n",
    "agnpy_ec.log10_B.quantity = np.log10(B.to_value(\"G\"))\n",
    "agnpy_ec.mu_s.quantity = mu_s\n",
    "agnpy_ec.mu_s.frozen = True\n",
    "agnpy_ec.t_var.quantity = t_var\n",
    "agnpy_ec.t_var.frozen = True\n",
    "agnpy_ec.log10_r.quantity = np.log10(r.to_value(\"cm\"))\n",
    "agnpy_ec.log10_r.frozen = True\n",
    "# - EED\n",
    "agnpy_ec.log10_k_e.quantity = np.log10(0.05)\n",
    "agnpy_ec.p1.quantity = 1.8\n",
    "agnpy_ec.p2.quantity = 3.5\n",
    "agnpy_ec.log10_gamma_b.quantity = np.log10(500)\n",
    "agnpy_ec.log10_gamma_min.quantity = np.log10(1)\n",
    "agnpy_ec.log10_gamma_min.frozen = True\n",
    "agnpy_ec.log10_gamma_max.quantity = np.log10(3e4)\n",
    "agnpy_ec.log10_gamma_max.frozen = True"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "094d8387",
   "metadata": {},
   "source": [
    "Let us define the skymodel and perform the fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e6f0358a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/cosimo/work/agnpy/agnpy/synchrotron/synchrotron.py:66: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  u = 1 / 2 + np.exp(-tau) / tau - (1 - np.exp(-tau)) / np.power(tau, 2)\n",
      "/Users/cosimo/work/agnpy/agnpy/synchrotron/synchrotron.py:66: RuntimeWarning: invalid value encountered in true_divide\n",
      "  u = 1 / 2 + np.exp(-tau) / tau - (1 - np.exp(-tau)) / np.power(tau, 2)\n",
      "/Users/cosimo/software/miniconda3/lib/python3.8/site-packages/astropy/units/quantity.py:486: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n",
      "/Users/cosimo/software/miniconda3/lib/python3.8/site-packages/astropy/units/quantity.py:486: RuntimeWarning: overflow encountered in expm1\n",
      "  result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n",
      "/Users/cosimo/software/miniconda3/lib/python3.8/site-packages/astropy/units/quantity.py:486: RuntimeWarning: invalid value encountered in multiply\n",
      "  result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# define model\n",
    "model = SkyModel(name=\"PKS1510-089_EC\", spectral_model=agnpy_ec)\n",
    "dataset_ec = FluxPointsDataset(model, flux_points)\n",
    "# do not use frequency point below 1e11 Hz, affected by non-blazar emission\n",
    "E_min_fit = (1e11 * u.Hz).to(\"eV\", equivalencies=u.spectral())\n",
    "dataset_ec.mask_fit = dataset_ec.data.energy_ref > E_min_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "91019b52",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/cosimo/work/agnpy/agnpy/synchrotron/synchrotron.py:66: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  u = 1 / 2 + np.exp(-tau) / tau - (1 - np.exp(-tau)) / np.power(tau, 2)\n",
      "/Users/cosimo/work/agnpy/agnpy/synchrotron/synchrotron.py:66: RuntimeWarning: invalid value encountered in true_divide\n",
      "  u = 1 / 2 + np.exp(-tau) / tau - (1 - np.exp(-tau)) / np.power(tau, 2)\n",
      "/Users/cosimo/software/miniconda3/lib/python3.8/site-packages/astropy/units/quantity.py:486: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n",
      "/Users/cosimo/software/miniconda3/lib/python3.8/site-packages/astropy/units/quantity.py:486: RuntimeWarning: overflow encountered in expm1\n",
      "  result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n",
      "/Users/cosimo/software/miniconda3/lib/python3.8/site-packages/astropy/units/quantity.py:486: RuntimeWarning: overflow encountered in multiply\n",
      "  result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n",
      "/Users/cosimo/work/agnpy/agnpy/synchrotron/synchrotron.py:67: RuntimeWarning: overflow encountered in true_divide\n",
      "  return np.where(tau < 1e-3, 1, 3 * u / tau)\n",
      "Info: VariableMetricBuilder: Tolerance is not sufficient, continue the minimization\n",
      "Info in Current  Edm is : edm = 0.0128911\n",
      "Info in Required Edm is : edmval = 0.0002\n",
      "Info in matrix forced pos-def by adding to diagonal : padd = 0.0041343\n",
      "Info: MnHesse: matrix was forced pos. def. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "┌──────────────────────────────────┬──────────────────────────────────────┐\n",
      "│ FCN = 230.5                      │        Nfcn = 488 (488 total)        │\n",
      "│ EDM = 7.37e-06 (Goal: 0.0002)    │                                      │\n",
      "├───────────────┬──────────────────┼──────────────────────────────────────┤\n",
      "│ Valid Minimum │ Valid Parameters │        No Parameters at limit        │\n",
      "├───────────────┴──────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│   Hesse ok    │  Has Covariance  │APPROXIMATE│NOT pos. def.│   FORCED   │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "OptimizeResult\n",
      "\n",
      "\tbackend    : minuit\n",
      "\tmethod     : minuit\n",
      "\tsuccess    : True\n",
      "\tmessage    : Optimization terminated successfully.\n",
      "\tnfev       : 488\n",
      "\ttotal stat : 230.45\n",
      "\n",
      "      name         value     unit    min        max    frozen   error  \n",
      "--------------- ----------- ----- ---------- --------- ------ ---------\n",
      "      log10_k_e -2.0588e+00       -2.000e+01 2.000e+00  False 5.371e-02\n",
      "             p1  2.0037e+00        1.000e+00 5.000e+00  False 2.341e-02\n",
      "             p2  3.1622e+00        1.000e+00 5.000e+00  False 2.945e-02\n",
      "  log10_gamma_b  3.0111e+00        1.000e+00 6.000e+00  False 1.797e-02\n",
      "log10_gamma_min  0.0000e+00        0.000e+00 4.000e+00   True 0.000e+00\n",
      "log10_gamma_max  4.4771e+00        3.000e+00 8.000e+00   True 0.000e+00\n",
      "              z  3.6100e-01        1.000e-02 1.000e+00   True 0.000e+00\n",
      "            d_L  6.1289e+27    cm  1.000e+25 1.000e+33   True 0.000e+00\n",
      "        delta_D  2.5000e+01        1.000e+00 4.000e+01   True 0.000e+00\n",
      "        log10_B -4.2148e-01       -3.000e+00 1.000e+00  False 1.480e-02\n",
      "          t_var  4.3200e+04     s  1.000e+01 3.142e+07   True 0.000e+00\n",
      "           mu_s  9.9925e-01        0.000e+00 1.000e+00   True 0.000e+00\n",
      "        log10_r  1.7778e+01        1.600e+01 2.000e+01   True 0.000e+00\n",
      "   log10_L_disk  4.5826e+01        4.200e+01 4.800e+01   True 0.000e+00\n",
      "     log10_M_BH  4.1055e+01        3.200e+01 4.500e+01   True 0.000e+00\n",
      "          m_dot  8.9457e+25 g s-1  1.000e+24 1.000e+30   True 0.000e+00\n",
      "           R_in  5.0589e+13    cm  1.000e+12 1.000e+16   True 0.000e+00\n",
      "          R_out  8.4315e+16    cm  1.000e+12 1.000e+19   True 0.000e+00\n",
      "          xi_dt  6.0000e-01        0.000e+00 1.000e+00   True 0.000e+00\n",
      "           T_dt  1.0000e+03     K  1.000e+02 1.000e+04   True 0.000e+00\n",
      "           R_dt  6.4700e+18    cm  1.000e+17 1.000e+19   True 0.000e+00\n",
      "CPU times: user 2min 42s, sys: 49 s, total: 3min 31s\n",
      "Wall time: 3min 34s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# define the fitter\n",
    "fitter = Fit([dataset_ec])\n",
    "results = fitter.run(optimize_opts={\"print_level\": 1})\n",
    "print(results)\n",
    "print(agnpy_ec.parameters.to_table())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cd4282b",
   "metadata": {},
   "source": [
    "#### 4) visualise the fit results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1c6ed4a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/cosimo/work/agnpy/agnpy/synchrotron/synchrotron.py:66: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  u = 1 / 2 + np.exp(-tau) / tau - (1 - np.exp(-tau)) / np.power(tau, 2)\n",
      "/Users/cosimo/work/agnpy/agnpy/synchrotron/synchrotron.py:66: RuntimeWarning: invalid value encountered in true_divide\n",
      "  u = 1 / 2 + np.exp(-tau) / tau - (1 - np.exp(-tau)) / np.power(tau, 2)\n",
      "/Users/cosimo/software/miniconda3/lib/python3.8/site-packages/astropy/units/quantity.py:486: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n",
      "/Users/cosimo/software/miniconda3/lib/python3.8/site-packages/astropy/units/quantity.py:486: RuntimeWarning: overflow encountered in expm1\n",
      "  result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n",
      "/Users/cosimo/software/miniconda3/lib/python3.8/site-packages/astropy/units/quantity.py:486: RuntimeWarning: overflow encountered in multiply\n",
      "  result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n"
     ]
    },
    {
     "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": [
    "# plot best-fit model and covariance\n",
    "flux_points.plot(energy_unit=\"eV\", energy_power=2)\n",
    "agnpy_ec.plot(energy_range=[1e-6, 1e15] * u.eV, energy_unit=\"eV\", energy_power=2)\n",
    "plt.ylim([10 ** (-13), 10 ** (-9)])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b2292986",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1209.6x982.8 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we can use Gammapy functions to plot the covariance\n",
    "agnpy_ec.covariance.plot_correlation()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9721da93",
   "metadata": {},
   "source": [
    "#### Use `Gammapy` best-fit parameters and `agnpy` to plot the best-fit model specifying its individual components\n",
    "We fetch the best-fit parameters for our model and we use them to specify and plot the individual spectral components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ff4cceb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* spherical emission region\n",
      " - R_b (radius of the blob): 2.38e+16 cm\n",
      " - V_b (volume of the blob): 5.64e+49 cm3\n",
      " - z (source redshift): 0.36\n",
      " - d_L (source luminosity distance):6.13e+27 cm\n",
      " - delta_D (blob Doppler factor): 2.50e+01\n",
      " - Gamma (blob Lorentz factor): 2.00e+01\n",
      " - Beta (blob relativistic velocity): 9.99e-01\n",
      " - theta_s (jet viewing angle): 2.22e+00 deg\n",
      " - B (magnetic field tangled to the jet): 3.79e-01 G\n",
      " - xi (coefficient for 1st order Fermi acceleration) : 1.00e+00\n",
      "* electron spectrum\n",
      " - broken power law\n",
      " - k_e: 8.73e-03 1 / cm3\n",
      " - p1: 2.00\n",
      " - p2: 3.16\n",
      " - gamma_b: 1.03e+03\n",
      " - gamma_min: 1.00e+00\n",
      " - gamma_max: 3.00e+04\n",
      "jet power in particles: 2.52e+45 erg / s\n",
      "jet power in B: 2.43e+44 erg / s\n",
      "* Shakura Sunyaev accretion disk:\n",
      " - M_BH (central black hole mass): 1.14e+41 g\n",
      " - L_disk (disk luminosity): 6.70e+45 erg / s\n",
      " - eta (accretion efficiency): 8.33e-02\n",
      " - dot(m) (mass accretion rate): 8.95e+25 g / s\n",
      " - R_in (disk inner radius): 5.06e+13 cm\n",
      " - R_out (disk inner radius): 8.43e+16 cm\n",
      "* Ring Dust Torus:\n",
      " - L_disk (accretion disk luminosity): 6.70e+45 erg / s\n",
      " - xi_dt (fraction of the disk radiation reprocessed by the torus): 6.00e-01\n",
      " - T_dt (temperature of the dust torus): 1.00e+03 K\n",
      " - R_dt (radius of the torus): 6.47e+18 cm\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# define the emission region and the thermal emitters\n",
    "k_e = 10 ** agnpy_ec.log10_k_e.value * u.Unit(\"cm-3\")\n",
    "p1 = agnpy_ec.p1.value\n",
    "p2 = agnpy_ec.p2.value\n",
    "gamma_b = 10 ** agnpy_ec.log10_gamma_b.value\n",
    "gamma_min = 10 ** agnpy_ec.log10_gamma_min.value\n",
    "gamma_max = 10 ** agnpy_ec.log10_gamma_max.value\n",
    "B = 10 ** agnpy_ec.log10_B.value * u.G\n",
    "r = 10 ** agnpy_ec.log10_r.value * u.cm\n",
    "delta_D = agnpy_ec.delta_D.value\n",
    "R_b = (\n",
    "    c * agnpy_ec.t_var.quantity * agnpy_ec.delta_D.quantity / (1 + agnpy_ec.z.quantity)\n",
    ").to(\"cm\")\n",
    "# blob definition\n",
    "parameters = {\n",
    "    \"p1\": p1,\n",
    "    \"p2\": p2,\n",
    "    \"gamma_b\": gamma_b,\n",
    "    \"gamma_min\": gamma_min,\n",
    "    \"gamma_max\": gamma_max,\n",
    "}\n",
    "spectrum_dict = {\"type\": \"BrokenPowerLaw\", \"parameters\": parameters}\n",
    "blob = Blob(\n",
    "    R_b,\n",
    "    z,\n",
    "    delta_D,\n",
    "    Gamma,\n",
    "    B,\n",
    "    k_e,\n",
    "    spectrum_dict,\n",
    "    spectrum_norm_type=\"differential\",\n",
    "    gamma_size=500,\n",
    ")\n",
    "print(blob)\n",
    "print(f\"jet power in particles: {blob.P_jet_e:.2e}\")\n",
    "print(f\"jet power in B: {blob.P_jet_B:.2e}\")\n",
    "\n",
    "# Disk and DT definition\n",
    "L_disk = 10 ** agnpy_ec.log10_L_disk.value * u.Unit(\"erg s-1\")\n",
    "M_BH = 10 ** agnpy_ec.log10_M_BH.value * u.Unit(\"g\")\n",
    "m_dot = agnpy_ec.m_dot.value * u.Unit(\"g s-1\")\n",
    "eta = (L_disk / (m_dot * c ** 2)).to_value(\"\")\n",
    "R_in = agnpy_ec.R_in.value * u.cm\n",
    "R_out = agnpy_ec.R_out.value * u.cm\n",
    "disk = SSDisk(M_BH, L_disk, eta, R_in, R_out)\n",
    "dt = RingDustTorus(L_disk, xi_dt, T_dt, R_dt=R_dt)\n",
    "print(disk)\n",
    "print(dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a467e157",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/cosimo/work/agnpy/agnpy/synchrotron/synchrotron.py:66: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  u = 1 / 2 + np.exp(-tau) / tau - (1 - np.exp(-tau)) / np.power(tau, 2)\n",
      "/Users/cosimo/work/agnpy/agnpy/synchrotron/synchrotron.py:66: RuntimeWarning: invalid value encountered in true_divide\n",
      "  u = 1 / 2 + np.exp(-tau) / tau - (1 - np.exp(-tau)) / np.power(tau, 2)\n",
      "/Users/cosimo/software/miniconda3/lib/python3.8/site-packages/astropy/units/quantity.py:486: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n",
      "/Users/cosimo/software/miniconda3/lib/python3.8/site-packages/astropy/units/quantity.py:486: RuntimeWarning: overflow encountered in expm1\n",
      "  result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n",
      "/Users/cosimo/software/miniconda3/lib/python3.8/site-packages/astropy/units/quantity.py:486: RuntimeWarning: overflow encountered in multiply\n",
      "  result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# define the radiative processes\n",
    "synch = Synchrotron(blob, ssa=True)\n",
    "ssc = SynchrotronSelfCompton(blob, synch)\n",
    "ec_dt = ExternalCompton(blob, dt, r)\n",
    "# SEDs\n",
    "nu = np.logspace(9, 27, 200) * u.Hz\n",
    "synch_sed = synch.sed_flux(nu)\n",
    "ssc_sed = ssc.sed_flux(nu)\n",
    "ec_dt_sed = ec_dt.sed_flux(nu)\n",
    "disk_bb_sed = disk.sed_flux(nu, z)\n",
    "dt_bb_sed = dt.sed_flux(nu, z)\n",
    "total_sed = synch_sed + ssc_sed + ec_dt_sed + disk_bb_sed + dt_bb_sed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "723ffa23",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot everything\n",
    "load_mpl_rc()\n",
    "plt.rcParams[\"text.usetex\"] = True\n",
    "fig, ax = plt.subplots()\n",
    "ax.loglog(\n",
    "    nu / (1 + z), total_sed, ls=\"-\", lw=2.1, color=\"crimson\", label=\"agnpy, total\"\n",
    ")\n",
    "ax.loglog(\n",
    "    nu / (1 + z),\n",
    "    synch_sed,\n",
    "    ls=\"--\",\n",
    "    lw=1.3,\n",
    "    color=\"goldenrod\",\n",
    "    label=\"agnpy, synchrotron\",\n",
    ")\n",
    "ax.loglog(\n",
    "    nu / (1 + z), ssc_sed, ls=\"--\", lw=1.3, color=\"dodgerblue\", label=\"agnpy, SSC\"\n",
    ")\n",
    "ax.loglog(\n",
    "    nu / (1 + z),\n",
    "    ec_dt_sed,\n",
    "    ls=\"--\",\n",
    "    lw=1.3,\n",
    "    color=\"lightseagreen\",\n",
    "    label=\"agnpy, EC on DT\",\n",
    ")\n",
    "ax.loglog(\n",
    "    nu / (1 + z),\n",
    "    disk_bb_sed,\n",
    "    ls=\"-.\",\n",
    "    lw=1.3,\n",
    "    color=\"dimgray\",\n",
    "    label=\"agnpy, disk blackbody\",\n",
    ")\n",
    "ax.loglog(\n",
    "    nu / (1 + z),\n",
    "    dt_bb_sed,\n",
    "    ls=\":\",\n",
    "    lw=1.3,\n",
    "    color=\"dimgray\",\n",
    "    label=\"agnpy, DT blackbody\",\n",
    ")\n",
    "# systematics error in gray\n",
    "ax.errorbar(\n",
    "    x.to(\"Hz\", equivalencies=u.spectral()).value,\n",
    "    y,\n",
    "    yerr=y_err_syst,\n",
    "    marker=\",\",\n",
    "    ls=\"\",\n",
    "    color=\"gray\",\n",
    "    label=\"\",\n",
    ")\n",
    "# statistics error in black\n",
    "ax.errorbar(\n",
    "    x.to(\"Hz\", equivalencies=u.spectral()).value,\n",
    "    y,\n",
    "    yerr=y_err_stat,\n",
    "    marker=\".\",\n",
    "    ls=\"\",\n",
    "    color=\"k\",\n",
    "    label=\"PKS 1510-089, Ahnen et al. (2017), period B\",\n",
    ")\n",
    "ax.set_xlabel(sed_x_label)\n",
    "ax.set_ylabel(sed_y_label)\n",
    "ax.set_xlim([1e9, 1e29])\n",
    "ax.set_ylim([10 ** (-13), 10 ** (-7)])\n",
    "ax.legend(\n",
    "    loc=\"upper center\", fontsize=9, ncol=2,\n",
    ")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "a5cf2102067dc15f2ebf0f2821ba443678c455e87ade8e96a896230febab46ba"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}