example_nb.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **X-ray spectral analysis using Gammapy**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook explains how to perform a simple X-ray spectral analysis using a code based on `Gammapy 0.20`. In this example we jointly model 3 data sets (one for each of the instruments onboard of the XMM satellite) using an absorbed powerlaw function.\n",
    "\n",
    "Note that this notebook can easily be adapted to jointly fit multiwavelength data from other instruments, as explained in the corresponding tutorials on the Gammapy webpage."
   ]
  },
  {
   "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": [],
   "source": [
    "import numpy as np\n",
    "import astropy.units as u\n",
    "from astropy.coordinates import SkyCoord"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gammapy.modeling import Fit\n",
    "from gammapy.modeling.models import SkyModel\n",
    "from gammapy.datasets import Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gammapyXray import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sherpa.astro.xspec import XSwabs\n",
    "from sherpa.models import PowLaw1D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load and inspect the X-ray data sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: hdu= was not specified but multiple tables are present, reading in first available table (hdu=1) [astropy.io.fits.connect]\n",
      "/home/lucagiunti/miniconda3/envs/gammapyx/lib/python3.9/site-packages/astropy/units/quantity.py:611: RuntimeWarning: invalid value encountered in divide\n",
      "  result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n",
      "WARNING: hdu= was not specified but multiple tables are present, reading in first available table (hdu=1) [astropy.io.fits.connect]\n",
      "/home/lucagiunti/miniconda3/envs/gammapyx/lib/python3.9/site-packages/astropy/units/quantity.py:611: RuntimeWarning: invalid value encountered in divide\n",
      "  result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n",
      "WARNING: hdu= was not specified but multiple tables are present, reading in first available table (hdu=1) [astropy.io.fits.connect]\n",
      "/home/lucagiunti/miniconda3/envs/gammapyx/lib/python3.9/site-packages/astropy/units/quantity.py:611: RuntimeWarning: invalid value encountered in divide\n",
      "  result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "filename = \"XMM_test_files/PN_PWN.grp\"\n",
    "pn_dataset = StandardOGIPDataset.read(filename)\n",
    "filename = \"XMM_test_files/MOS1_PWN.grp\"\n",
    "mos1_dataset = StandardOGIPDataset.read(filename)\n",
    "filename = \"XMM_test_files/MOS2_PWN.grp\"\n",
    "mos2_dataset = StandardOGIPDataset.read(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0, 10.0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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X5anBxiLPScewSz4bl2zqnu16GtRzEHY174IQAsXuYgzqmZ6od+kmmYlUaSgAoDPUiV3Nu1DiiZ3YjkeihnPBggXdIthRpmBjoSPf9mx0530BhU6Ru0h5gBZKj0KiXyGUzxE7uwtsLBiGSYlkDFkhD7EVKmwsGNsk23PJtx5Pvumbj+TLEBvTBe+zYJKCI8c5x6yH1mLWQ2udViMl5BBbqbcUJ/c5OeOT20zqcM+CMcTq7drMD1Q682Cyg3RRLrn88stx++23O6hR5lm5ciVOPfVUU/fpTCxsLJik4P0dhYOZu49CZuXKlbjwwgvZWCRA3g9D8XCIM3DkOGfY29SOLY3NWL/nMM6/Zw32NsVu3EsHLS0tGDp0KHbs2AEg4njv4YcfBgA88cQTGDVqFEaPHq04yDNzL75mzRqMGTMGY8aMwdixY9Ha2or9+/dj4sSJmH72dEw7cxr+8Y9/xOS/ceNGnHXWWRg9ejROP/10tLa2wu/3Y86cOaitrcXYsWMV1yLLli3DzTffrFx74YUXKhsCy8rKcMcddyjOAA8cOID3338ff//733HbbbdhzJgx2L17NxYvXqy4+r788sszUqf5Tl73LBqONhgOh6iduHXnoY5Mlj2bbjW6O//2ZAN87m8AAFsam+EPRFxa7DzYhin3rcHoqt7wh/y476rqpORLr7OSX/ziF5g1axbuv/9+XHvttbj11ltx5MgRzJs3D5999hl++9vf4p///Cf69u2r+D+69dZbDd2L33333fjTn/6ECRMmoK2tDT6fD0uXLsXUqVNx5c1XIhQKYYB3gEaf48ePY9asWVixYgXq6+tx9OhRlJSU4I9//CMA4JNPPsH27dsxZcoUfP7553HLduzYMYwfPx6//e1v8e///u94+OGH8ctf/hIXX3yxEtEPAH7/+99jz549KC4uzrir73wlZ4wFEQ0GcAeAXkII26GznBwOSUdo0GyTLgNitr8jkwaqOxt+iTQUZt+TQT0Mpd5gd/755+Nvf/sbbrrpJmzZsgUA8Pbbb+Oyyy5TwolKN9xvvvkmtm7dqlwr3YtPmDABCxcuxFVXXYVLL70UVVVVqK+vx3XXXYeDrQcxZfoUnPz9kzX67NixAwMHDkR9fT0AKO7G33vvPdxySySG+7Bhw/Dd737X0lgUFRUpQY5OO+00vPHGG4bpRo0ahauuugozZszAjBkzLOusO5LRYSgieoyIDhLRp7rj04hoBxHtIqLbAUAI8YUQYm4i8qt7Vsf1Mpqph4tV7AQg/vAYD50xiXDfVdVYMf9MrJh/Jk7pX6YcdxFwSv8y/OX60/C7WQPS7p02HA5j27ZtKCkpUXoQZm64pXvxzZs3K4GIysvLcfvtt+ORRx5BR0cHxo8fj+3bt2PixIl499138Z0TvoOFNy7EE088oZGViKtvAHFdfXu9XkWWdD1uxMsvv4ybbroJH3zwAU477TTTdPmC2mPxwfaDaXnGZLpnsQzA/QCUu4GI3AD+BOB8AI0ANhLR34UQWw0lWODkcIhZr8YsBoV8E09H/Gmme/Lo7Hqcf+9qdAYFTqoswqJLv6PsVwDMXWcks+Ht3nvvxfDhw3HXXXfhuuuuw9q1azF58mRccsklWLBgASorK5Wod9K9+G233QYg4l5czgfU1taitrYWa9euxfbt21FSUoITTzwRl8++HO3H2vHhhx/iJz/5iZLvsGHD8PXXX2Pjxo2or69Ha2srSkpKFFff5513Hj7//HPs3bsXQ4cOxdGjR/HAAw8gHA7jq6++suVcT+3qOxwOY9++fTj33HNx9tln469//Sva2trQu3fvhOssFdIZf0LtTiUYDsaNsWKXjBoLIcS7RFStO3w6gF1CiC8AgIieBvAjALaMBRHdAOAGABg0aJAj7i6snPjNWTUn7vBYMkNnPARjHyfqKpE8U9FvUGUphp0QMQRyjkLvnTZR1xnqOYvjoeOY+IOJWPjThXjkkUewYcMGlJeXY+LEifiv//ov/PrXv8Ydd9yBc845B263G2PHjsWyZctM3Yvfd999eOedd+B2uzFixAhccMEFePrpp/GHP/wBwiVQWlaKZ558RqNPUVERVqxYgVtuuQUdHR0oKSnBm2++iZ/97Ge48cYbUVtbC4/Hg2XLlqG4uBgTJkxATU0NamtrMXLkSIwbN86yzJdffjnmzZuHxYsX4+mnn8bcuXPR0tICIQQWLFiQdUORbvT3QDqG5ynTPlmixuJ/hRAjo98vAzBNCHF99Ps1AM4A8CsAv0Wkx/GIEOJ3xhK7qKurE5s2bUqrPye9rH2t+5Sey5BeQzTzEvHOGRkSGe/a7Fw8eYnonC3yzY9WLrJt2zYMHz7c9LyR63G5IW/F/DMBGLvOOLmPdh7ALqm4Os/lvLob6nvimz3fYMlXS5TnDwAQ0QdCiLpEZDqxdNYoPqIQQjQJIW4UQgyxYyiyhdG8hMQoGp0kXmQ3o3NmcyAMo0fOX0gG9RykjMuz6wwG0N4THpcnLcPzTqyGagSgfl2uAvC1A3ooxHtDTna1VbzhMbNzyebFb/bdm0L2Tus0+dr7Ud8T/Uv7p2WVphM9i40ATiGiGiIqAnA5gL87oIctsrX5zCx+NNO9qelVk3cPKqYwyfTS2acArAUwlIgaiWiuECII4GYAryESMvgZIcRnCcq9iIiWtrS0pF9pHfGGk/I5L4ZhmETI9GqoK0yOvwLglRTkvgTgpbq6unnJyrBLNldbcSAjhmFylbz3DcVklkJwh11oyE2hDJNN2Fio4J3VTHfE7XYrzv6mnz0df773z1nJ18n5mJUrV2rckzDW5IxvqEQgoosAXHTyycmtJTdi++HtaYnRwDD5hplvqEKGXZQnTl72LIQQLwkhbujVq1da5XKMBibXyVbvNxsuyseMGYORI0eyi/I8IS97FplgWMUwHPEfidlZzTBOo56fMPM55g/6cdf370pKvt7dx08X/hS3zLkl4y7K77jjDoRCIbS3a92VsIvy3ISNhQqO0cDkOpno/TrlojwQCGDGjBmaWBoAuyjPVfJyGCoTbN1/FD//a6Op+46t+492u1VBsx5ai637j+Z02bvDai21y30zl/zJ9irikWkX5SeeeCKuueYadlGeJ7CxYJg8IpsbN6WL8qeeekrpCUyePBnPPPMMmpqaAEAxItJFuUT2VKSL8v/4j/9AXV0dtm/fji+//BL9+/fHvHnzMHfuXHz44YeafNUuygGgtbVV8WT75JNPAoDGRXl1dTU2b96suBpPxUX5f//3f6O5uRltbW2pVV4BkpfDUJlYDcUw+UAmNm465aLc6/WirKwspmfBLspzk7w0Ftncwc0whU4oFFI+qx3nbdu2TTl+zz33KJ9nz56N2bNna2T07dsXK1asiJG9ZElsz8foej319fVYt25dzPFly5bFHCMipcehR91DuOyyy5QJ7QkTJmjmWN577724+jB5aiyY3IBdkjgD1zvjBDxngcjkdXsnT2gxDMOYwcaCMcUfCOFYZxCt/iC2NDZjb1O79UVMwmQ6WiXTfUnnvcXGgjFlx4FWhKP3mj8Qxtzl7Pok3fh8PjQ1NbHBYNKOEAL+Fj98Pl9a5OXlnAWvhkoMdcxmffxms3RAxECo+eLQsazoGO+Ynq37j2ZMJzV26zBRqqqq0NjYiEOHDqUk59uObwEA/pLUNumlS06hkM/18W3Ht/g28C0uHHdhWuTlpbFI52qoWQ+tRbvg+QojfF6XxmAM7tfDQW0KE6/Xi5qa1N3KxAsN7IScQiGf60Pqfon3krTIy0tjoScfGzIfGDqgHJ981YKwiBiOR2fXO60SwzAOURDGgskMPq8bPYojt8iIgT0xqLLUYY0YhnEKNhYZJFPj6ekcM08VK12S0bXQfT3lKtL9uT/kx4yVM7Bk8hKNfzSme8PGAkA4UI6Og1OxPnAYpQfOx6ja9zXnOzp64Ns9k3Hg+GGcf88aPDq7vuDfsvc2tWNLY7MyZ7F+T+6VfW9TO451BhEWyDndcg07YVjN3J9bwcPA3QNeOgug4+BUhAO9AQDt7eX4+JOzNOc//uQshI5HAi3tPtTWLZaQzl2+MWY1VK6Vfe7yjcrS3lzTLR/h4F9MPPKyZ5HupbMRQyHtpgvt7eWa85HvkfNhkdklpLmCURlzrexqXXJNt1zDztv/jJUzYoJ/ca+BkeRlzyLdYVVd3mYA8i06jNLSVs35yPfIeRdpl5DKOAOFhtEyWX3ZnUatS67plo9k0/05k3/kZc8i3ZT0fy06FFWB0tLW6JzFlcr5UbXvY+NH4xE63gdD+pXZWkI666G1aN9/A0YM7Gl43o6BSWRyONlJb/11MtDRo7PrMeW+NZqhKFn2eBvoJHo9MjEp/+jseky6+x2EBVDkMV7aKwM4AZEVXYlu+rNLuuo/2XTpeGHJhPtzxjnS3Ybd3lhs3X8UYsBR9DjxWYyg21H63Wdj0pSUHEPfmhdQffznWVmBlAuR6QZVlmJ0VW/TB20uMKiyFD2KPWjvDGJ0VW+e3GaYDJKXw1AMwzBMdmFjwTAMw1jCxoJhGIaxhI1FgSM3163fcxiT/vAONu9rVjbYqeNTyHROxK5Q63j+PWvgD4TgD4Q0x4z0kZvyQgIZ1dluHTJMIcPGIkFmPbQ2bZPPdmUlk6e8Rr2iqaGpHZ3ByGf9Jjb1Jrx0xa5Qr0SKhzrv3YfasONAK3YcaNUcM9Jnyn1rshJvw24dZoJ03m8Mkwp5aSyI6CIiWtrS0pKV/B6f9jiqj/88K3mlG/0ubIl+E5t+Q1s2N7jpN9f5A2GN3mYb7rIVb8NuHTJMIZOXxiLdm/JyiXQbJp/XuIn1m9j0G9qyucFNv7nO53Vp9DbbcKcvW6Z0tluHDFPI5KWxSDdCAKECDWs5dEA5XBT5TNE/ADGbCx+dXa88FLMdu0Kd95B+ZRg6oBxDB5RrjhnpM3RAl1uWTOqsrsPqylIUe+LrxTCFSLc3FmHXtyDPUbhKGvAZ/RItxwqrSmRMinKfB2XRvzNqKvDGwnM0m9jkJrxynyfrG9xk3lIvn9cNn9etOWakj8/rhpsANyGjOss6PKOmAqtvOxdjToqvF1MYSJftmw5swoyVM7CvdZ/TKjlKt9/Bje88BlfRYRAJiKJD+PxwGPU9qp3WimEYC+y4XU+FZF22J0uuu1jp1sZib1M7qOgQiCJDUEQCwtsEoNpRvXIJM99WDFPosMt2LXltLD77OuJDychnkR0HbVPuWwNXVT+4iiMGQwiCCFRg6/6jqF30Gto7g6irrtA42WvvDCry9U7ptu4/GuNDSfp5MnJgJ+Xpz6v9MRmVS+ZjJkutp5rSYk+MHKN8jHxTyWNSdmmxR5O3lCM/z3poLTY1HEZIAJsaDmvyN3M6uH7PYVTf/jLKfR5FliyTPq3UJaSbajLSSV2G2kWvadpIXVZ5jay7uuoKAF36q/PV178es/shkciC+ryMzhvJyoYDymzJNKtHoOtNPFORI51w2Z7OyJPprpfCGqC3wZxVc5Tuqz8QRkfjbIQ7+0EIQrizH4rFwLTk4w+EcKwzmPZNblKuegObU8iNc63+II51BmN0kWsGQiLyFw7bW0Qgy3esM4gCXXfA5AHssl1LXvcsUsXndcEfqET7noUAwvB5PfAOfiQtsnccaI3ZMPbGwnPSKnf3oTYUeVwYXdU7ZbnJ6iL3IIRF5LvX3fX+od+d0GHTsMny2bQtDJMR2GW7lm7Xs1DTtfQyDPI2a5ZipsLepvaUN4yFw0JxMRGJhR150Oo3q0U2sIU0bi/svsGHw0LTS2npCGh6Q0a9lnBYICSAVn8wpoz+QBit/q7rY66NxsrORG/IqKcVDgt8tO+IolOrP4g2f6QHpHdvkopOdlyTGKF3c2J2nVp+qroyTLJ0a2Mhl16WDHoYJSf8DT6vOy1yjVxAJLp5qyMQ0rjf2HEgEr1Pv1nN53XF9GLsvsF3BEKaXsqOb7RyZJ76a+xgtutZuvPIBHrZHYEQjge1hlMg0gPSuzdJRSc7rkmM0Ls5MbtOLT9VXRkmWfLSWEh3HyJHB7SNehGJbt7Sdw7kw0K9QUxuYNM/mO0O36jThUXkQWqUZzKy4+VpZkgksnwuSuwG1cs209UfCMe0kZVO8bDjmsQIuzHEjXpwDJNt8tJYSHcfRGSd2AH0vQif15Xw5i2Xrmhdu6u7Noh1bWBzxb3WTh4u6trdrc/TSi+7+cnrzdxnSGT5ehR7kEgT62Wb6eXzugzbKFnsuCYxwm4Mcb1uqejKMMnCd10GULuvcBGSmgspURkBn9cVV4a6t+HzulBiczitxOvW9lK+o5VjlKdatkyjlqM+J28u9U0me0OZQC+7xOtGkUerGCFSX3r3JqnoZMc1iRF6Nydm16nlp6orwyRLwayGSnRNsV332TKteq29VZ76+NU+r1uRUVrsMd0/IZHr/EuLPcpKp3hxuWVvQ8aitiqXPO9yEXqo9Nm6/yh6RPdCmG3Gc0WtgpuAQDCMT79qQWmxR5Gj3t+wqeEwIBDpHYjINW8sPEdTDrn3QU17Z1BT3/H2nUjOqKlQ0sp0Lhdh7Il9YurD53Vr2ijRjYftnUGNTOmaRHLbs1tsyZE6APHvW738XIjRznQ/CsZYpIqbCCvmn4kzTFbIyQ1lRpvdChWjzWS5hjs3RyIZpuDgYSiGYRjGEjYWDMMwjCVsLBiGYRhLuvWchdyN3LF3Hlze9PlvSgV/IKQ4x5P+ltK1WZBhGCZZunXPoms3sgvhQG/bO28ziXp3rvS3xDAM4zTd2lhod/i6EvbflAl4ty7DMLlItzYW2o1kYcudt3LYSjrsS9X5nNr5n5Sl351b5KGEHd6p9VT/qZ3p2XU2mCzq4TR9zIl0yZf1FxLImHM9WY50tHkyeUtHj+xAkHGabm0sunYjh+HyNlvuvFU70UuH8zkjp33q3bkRFxyUsBM5K2d/wkaaVMn08Jm6/jKZn1putp34md0jDOMEeT/Bnchu1lkPrUVDUTQa2v6jcLkIbgKKTnoYHhdhUOU1qD7+84hcHFXkS8yc+9nVU/2GbTbcJD3hApGNgK3+YEw6dcwINSERycdOpyHTsSIyPXyWreE6o3yytREwlTIaRWhkmFTI655FWAi0+oPYvE/bRbcbJ8CMlo4AWv1BZeimzR9EMGT8QzWKjSDzU8eLOBbd+S3jVJjJUg97HOsMotijbSIClHzW7W5SyimNit64xEMOS+nrT4+6PqV89fBWW1TOR/uOKJ+NCAlgwu/fwuZ9EVkbovKOdQbRGQhq0hkNn8n2XLe7KcbpIQAlHofUs9UfxIY9hxEMhREOC0W39XsOY9If3tGkV18j812/J9bFC6GrbdT1r405ElLiaMi89ja1W96X6vOT/vBOTBnVQ5If7Tui1KNaltm9aNWuyfxOMi1zb1O7YT1mKj+nSeT+sCprJuolr42FpDOo7aLbjRNgxo5vtN39yLCNsbEwio0g81PHi5D/1XEqjGTpV0MJCGUeI+piScnnmsfWp+WNWl9/etTlM0LqdDwoYtyc6/mq2Y/OYFhzXVgAx22Oiu0+1IZrHltvmI86Hodat45ojA/1qYamdsP0Vqh1Vte/ephox4FWTRyNhqZ2zF2+0fK+VJ9vaGrX6EvQDkkeDwqlHtWyzO5FI1L9nWRa5tzlGw3rMVP5OU0i94dVWTNRL3k5DEVEFwG4qOg7JyvH/IGw4pRO/WYbFsDOg23KUJWVb6et+49aPvDU+ANh7DzYpjm282Abyn0ewwdRvIeT0QM58mMRcFPsRHEgjTPH6uEt+SZXu+g1fLJoakz5nCQsgLBJuePVhlG9p1p7+vr3B8LY1HDYcEJfX4fyvgQi/q30962RrtI46FHf4/p8vjh0TOMIs1TlKFKdVsr4qqQJbqIYJ5lGTjPVzjE/WTRVyc+ojOqhYr1DTbOhMqP7bufBNtQuek1xWqn/rcv87ToWrV30Gto7g6irrjAtq3yuqB1OGvlNWzH/TNPPZqjrxag91GmM6lZ9vTpPu7FSEiEvexYynoX6mJmPfzvxE1LBSL46eI9Reit5ZqSrHInqxfETjDGrMqO6tBP3I5XzatfyauKt8Es2Dko80hknxM59qo/JkmhEylzCLIaNRF+2eO1lN1ZKQvqlLCEHkDEKJKnGTzBqAzI5XuJ1a+JJuKhrlZU69oM8bxT7Qcp36a7Ro85HuSbBH7jMQ3+ZVb52sKNKkZtspUsGo/qQ7eaCVj+z9oxHkWpm20XA0O8Y14u+LqsrS5W4HxKjh6j6vF4/2W7qIUl5Xn3P6WObxFvhl2wclHikM06IUT3qj+ljsiQakTKX0D8v9HX36Ox62+1lN1ZKIuS9sXATUObzaFxiEEWOq6PJJYIrer38k8fKfF2jdvKcy0VKPIlyXySug4z5oI79IONEyBgS5b7In9SzzBeJCidXaBkh81HyjsqVcgAocvV1pK4bl4tiyuKKY3Vk/bkJGtlqubIdrFYKjR3UR0mnv94IeVzWk1E6dRvJKHtS/uk1FZElyLr2K/N5lHZW159eL3UeYwf1Uc71KPagV4nXMK26Dd0ErL7tXPi8buV4uc+jiU9hdF2Zz6Po66audhtd1Rvl0XNlvth7Tn0vjq7qHTdCozrq4uiq3nHvAbvIGB1SZiquatT6lPs8WH3buTE6yt+T/K0nGpEyl5DtL58X+robVFmqadt47aVuh3TVS94bC4ZhGCbzsLFgGIZhLGFjwTAMw1jCxoJhGIaxhI0FwzAMYwkbC4ZhGMYSNhYMwzCMJd3aWLRH4yEAQEgI295rmQiZiFPBMExu0q2NBcMwDGOPvHQkaBezWBchIdBu4LRt6/6jlo4GzZAOzfS7pyP5dZ1Xy5dv5nqHaFZv7Orz7Z1BxTmcXr7ZtfprpH5uQowsdf2py2Ek14579GTrOCRg6qTPKA+ZTl5jNwZFIr0lfYySVNDXiVUdGZ2XDvrMMGr7ZJn10FpNrJdkyeW4G+r4N0BXbBu1Q8HuBPcsAPi//jHCgfKYcKQiiQeBjM+Q6bCliZDIA02tdybDlTIMk1+wsQAggr3ReWhaTKjRZCNFhEXmw5ZmCr3eHMqTYRigwIeh7OOCCPROOb6BmhzqWCREKqFjGYYpXLhnAQAIg7zNafHnL0mnrGxi5VOfYZjuCT8JAJCnGcX9VsX4h0+2clwE1J7YK+WJMDN32UaUFlu7B4+Xj6T2xF6ac3bjEdjJO1n9rIhX9tKoS+e66oqY9igt9qRlshdIrK302NFBHVmOYZygIIahwmGBLY3N8AfCkTdjEfH/b5o+oH0AFvVfBZe31TCWhFD2YQDHVCtQhIjMabT6g9jS2Kwcl6uhzPz4y8lmtSFSTyqbrYoKCWD9nsNJ9Vj0IVn1K5fUZVTPUbjJvBx67Ay7KXUYbR+h08nsGqs0gLY9Mtmrkzob5aPWIVP4A6HIAgrVfRTvXmeYdFEQPYuOQEgZWw8L64npzkPTNN+PH5xmklIrS/1AVB9PZlxffUUik+GZmAtJtSyA/XjW6vZJ52yIWTulm3j5ZGN2Z8eBVtP7kGEySUEYi0QfDiLQW/s92NswXbbI18lwJvvwggPGKQrCWCQ67EDeZu13T7NhumyRr5PhTPbhBQeMUxTEnacOZO8i60IV91ul+V7Uf5VJSq0s9UNdfTyZH7D6iniB12Ouy4BhSbUsAGBXLXX7pPPmM2undBMvn2z8mIYOKDe9DxkmkxTEvaYOZN+j2GM54efyajeauTzmG8+kLBlEXX3cTVCCp9tFrppR66gPTK9PKz+fUVOh0SFZpN4SdRkTKYsaOw9oWYcyP3UdmK0kcttII2XJcqWjjuLlAxjno9YhUyu/fF43ekRXf+nvI4bJJDmzGoqIegB4AMBxAKuFEE86rBLDMAwThUQyDpDsCid6DMCFAA4KIUaqjk8D8EcAbgCPCCF+T0TXAGgWQrxERCuEELOs5BcPPEUMnH0fAKDIQzgejC3L4L498HVLB/yBMIo8hACaIYLlUHeqSgY9BCKA9v8Mx44n7qYjulo3Bq8L0M9HuhC7gqVHkdtWvh4XEArHX3kkS5XOaVAPAQZVm7MUewidUYXli7cA4HUDTnhh8RBARAikYSVDsccFFwEdFhPdcq/PkivGYfI97xiWW963RR4CBHA8JNB1d0Vq7pT+ZfjNj0bi2mUb4A+EcUr/Mjw6ux63PPUhtjS2KLK8LsJf5p6B8UMqMeuhtfAHQthxoBX+QBjVlaXY3+JHZ9BYZ5/Xhdf/7RwMqixVjq3b3YTLH16nSTegvAgHWo+b1ssbCyIy9ja1Y8p9azT6qmWb5VHVuwR3/3i0Ulaf1wXfgP/F0QMTEA5o9x9JuQCUvNTlVH8200Gtpxkn9vbh27bjiswvm9pjfv+yHfV5lha54fO6DZ00EtEHQog604wNyLSxmAigDcAT0lgQkRvA5wDOB9AIYCOAKwD8CMCrQojNRPRXIcSVVvLVxsI+AvoRdmks/HvnO7IyyUW8IopJPz6vCyf1KcXOg21Jy3AR4HYRAtFNLi4ChvQrw74j7TEPOa+bsPO3P8Ssh9Yq+57sckr/Mryx8Jyu73e8ouSZqIzz71mjlFnqq5YdLw+vm3THQojuZtGkk3IBWNavmQ5qPdONi4Aijwujq3qnzVhkdM5CCPEugMO6w6cD2CWE+EIIcRzA04gYikYAVVZ6EdENRLSJiDYlp5X5IK9TD2w2FEwm8AfC+OLQsZRkhAU0D8+wAL44dMzQEKjTJbrEV69nooZCLUMtS+prhFEescdiDYVarp36NdMh1baxyjPdy6ydmOA+EcA+1ffG6LHnAcwkoj8DeMnsYiHEUiFEXaJWUSXB9IxTS1h56SyTCXxeFwb365GSDBdF3rbV3wf362G4ak6dLtFVdXo9vUmsEJAy1LKkvkYY5RF7LAyjZ4aUa6d+zXRItW2s8kz3MmsnjIXRXSCEEMeEEHOEED9NZnK7yGN8cw3u23VjF3kI5DkKsxH9RJawqjG7rQeUF8UcM6pwu/l6XNZLVF0meaSCSdXmLMUqhQlddZZk86aMhyJj+umg2ONCiY2HgIsiy2wfnV1vWm6pUZGHUKQ8JNXzFpHhk79cd4byGxrSLzL+rvcZ5nUR/nLdGcr3oQPKlWuqK0tR7DHX2ed1KeP/ErUsidHvSVLs6ZLx6Oz6GH2N0OdR1btEU1af14XeVa/B5Y0N8iTlqvNSl1P92UwH9bVmnNjbp5FpdBfJY/o87fp1s4sTq6EaAZyk+l4F4OtkhcnobiMG9lQiWckoYqXFHvQrL0a/8mIl/VbxUCRS3pfzlWP+r38MEewNF4UMfe3ISGulxR7F74/8bcm81++JjLbJJakjBvbEivlnYsgvXtb4NCKKzOpLuUDs0ll9HjL/075boYk0p9ZLXW4AmpchvW8oo7JIWXXVFUpZ5HcAyjEz9HkYHZf5GkUL1Kez+qym3OfRtDkAdAYj38tU7aEuhz6ioToeu1n56qorlMh78np9W0kdpDw3Abt+N12JLCcj1envGX2d6Nte7Uhw6/6jcJtMFqudEvq8bgyqLMW4QRWaiIF6WerPoQF/gpsII+h2AFDGu+WSavnd53UrepcWe/DJoqkaPXxet+YadcRFGdFP/mZHDOwZM/k7fkil5rdQ7vNg/R3no3bRawCg+b3L71LGoMrSGH2NkHm0dwZRV10RU1YAaCj6Bv0GP4f2L2/Q5KWWa1RO/Wcj1HoC2iiSsn70cvTlVzuXNMs/XTjRs9gI4BQiqiGiIgCXA/h7IgKI6CIiWpouhSLuPly2/EoxDMN0RzJqLIjoKQBrAQwlokYimiuECAK4GcBrALYBeEYI8VkicoUQLwkhbrBOaRdtNahDq1rNs7V3BrGpIf5bt5lsM3n6tFbyko0bboet+4+mJdZysiQT4zqT9ZELWPWCJGYx6K0ICeFom6eLZMvPGJPRYSghxBUmx18B8Eom806MMApkMzvDMExG6PZPSBekY0EegGIYhjEjZ9x9OAUR4DvhbwCAzr3zkxr2YBiGKXTysmeR7gluhmEYJj55aSzSP8HNMAzDxCMvjQXDMAyTXdhYMAzDMJZ0e2PBE9oMwzDW5KWxSPcEt5sIbg45xjAMY0peGgue4GYYhskueWksGIZhmOzCxoJhGIaxhI0FwzAMYwkbC4ZhGMaSvDQW7O6DYRgmu+SlsZCroXoUeTSRwdSoo4ElQ7nPgzNqKjTHRgzsiXKfRxOdTo1R2GAz/dwU+Sv3xeoZL/zwiIE9NTLVkc/qqitQWuxBXXVFTOQ2Izn6aHHxUOsrP8s81PWhPl7ui+iir7PSYo/yp+aMmgo0/H665rg6P3255X91/cn6kbqqWTH/TEWeOr36GjdF9JDX6vOW6fT1okem05fR6r5Uy9fnJcugr0c9skx20Ed9Swcr5p+ZNplmvxE1mSiDU5i1qSSRtk1nOwB5aiwYhmGY7NLtXZTbJSQi8ZtdBE1862QRIjbWN8MwTK7CPYsESVecbg61xDBMPsHGgmEYhrGEjQXDMAxjSV4aC7l0NhgMZj1vF6Wn0vKy4hmG6bbk5TNLLp31eLI3Py+XVPYo9qRlYpontxmGySfy0lgwDMMw2YWNBcMwDGMJGwuGYRjGEjYWDMMwjCVsLBiGYRhL2FgwDMMwlrCxYBiGYSzJS2Ph5KY8hmGY7kheGgsnNuUxDMN0Z/LSWDAMwzDZJa9fzcNC4FhnEGEBbGlshpsILpexHw1/IIQdB1rhD1wPuw7C/YGQ8jkkIt/DYYGQAFr9kSEwF3LDdUdX+cLY0tgMkYaYG/mAiLZ9KFpedZt1V/T3wtAB5U6rxBQAed2z8AdDCCsPiTA64jwo5I8nUmS3Lfk7DrTGfNfnkStxKbrKF6mLXNEr04QBpdxAbJt1R/T3AtcJkw7y2ljo357Dcd6m1Q8UwF5XQHtN9CGco2/sel27K1wPxvctw6RKXhsL/fCPyQgUAMDnVRfV3hNfe03ke7w8nESva3eF68H4vmWYVMnru8jncSsPb5/XhRKv+fDS0AHl0R9NGIC9cW39WO/QAeUxeeRKBXaVL2rUHNYnW7igfRjy+HzsvcB1wqSDvH6muIjQo9iDcp8Ho6t6m05uA4DP68boqt4or34Epd991JZ8n8owuCny3eUiuAko93ngptyY3AZU5YvWRa7olWmIgNFVveGmrjbq7ujvBa4TJh3ktbFgGIZhsgMbC4ZhGMYSNhYMwzCMJXlpLNg3FMMwTHbJS2PBvqEYhmGyS14aC4ZhGCa7sLFgGIZhLGFjwTAMw1jCxoJhGIaxhI1FgowY2BOlxemZWC8t9mDEwJ620yYqG4jsah4xsKeyw9mdxZ3dIwb2xIr5Z2ZMvpsSr5dkSKSdMpF3NsrIMFawsWAYhmEsYWPBMAzDWMLGgmEYhrGEjQXDMAxjCRsLhmEYxhI2FgzDMIwlbCwYhmEYS9hYMAzDMJawsWAYhmEsyUtjwfEsGIZhskteGguOZ8EwDJNd8tJYMAzDMNmlYF/Nw2GBLY3N8AfC8HldGDqgHD6vG+FAOdoPXKBNGygHAISE9nrbeUWThgTQ6g+ixNtlg4OhsOE1Mu36PYdjjsfDHwjhWGdQk+exziD8gRCAyGe1zJAAWjoCMXK3NDbDTaTJMySAzfuaY3R0ESAEIBD5LiFdOr3+WxqbsW53k6JvWzSNAOAioMTrjl/YKEIAYV3eQgBk4BRRXT/q9Jv3NWNvU7tpHuGwUHTX15/RvRAOC3QEQko7uGCsjx3UeR/rDGrqWtaTy0Wa9IC2fJJWfxCb9zVj2HfKNceFwf3is1n/evY2tSu/LbU+iSLbav2ewzj/njV4dHY9BlWWJiUr3fgDIew40Ap/4Fq4i1pQJMLoDIYRFpH7em9Te87omi0KtmfREQgpN7M/EMaOA62R4wenAsFe2rQHp0aO6663i/6n0hEIG35OBzsOtEL/2wyLyHGjcwCw45vWmGP+QNiwjJ3BWH3DIraMgPExfR7XPLZe0UmorgkL+3VsVINmtWpWB53BMOYu32iaRzxdjM6pDUU8feyglq+va6N6sqq3zmDX/W6kn7xfkmXu8o3Kb8uOPmao22r3oba47ZNtIoYiDMCF0PFe6AiEFV39gfj3UqFSuD0L3QNDeQsK9EbXOzFUx+Jfnyv4TYyP2XHA/KGejTIG4nSVMpF/vHr44tAxlBYbv03H08XoXDp1t5Jl9HJghT8Qhtdt/i4Yr56s+OLQsYT1sdIhLGLlOom2fmLrMZd0zRYF27Nw6YYEfNGhIZe3GfrHp8vbHD1ufn2u4PMaN5nP6zI9Z1aUbJTRGyeARibyN6sDABjcr0dSuhidS6fuVrL05+3kHa8e7JyPh74ek60LtQ4uit8+2UZbP7GGNZd0zRYFayxKvG6lweWcBQCU9H8N8LRo0/Z/LXJcd71d9L8V9ZxFSQo/SiOGDig3fHgMHVBueA4AhurGr4FInRiVsdgTq6+LjA2O1TPC53XhL9edoehEqmsSmbMwqkGzWjWrg2KPC4/OrjfNI54uRudKvG5NPqm0slq+vq6N6smq3oo9Xfe7kX7yfkmWR2fXax6mifxW1Kjbaki/srjtk20ic5wuAGG4i1pQ4nUpuvq88e+lQqVgh6FcLsLIgb1ij3tbUXriCrR/OV9zDIhEXpOjJq4EXpdc0euMIrd53C7AoMsv08oIbHJSVb6Im43e+Lxu9Cj2oL0zqMlTTlb2iMrcuv8oWv1BuAnoVeKNiZA3uqo3tu4/qim3m4AxJ/XW6CLL094ZmUwtjeYtUesg85PHR1f1xvghlYq+yUZ8IwLc6MpDHrOqH3V+Iwb2jDsh6XKRontddYWm/ozuBZeLlHxSRZ23un7N6kvqU+7Tto28xiiqHxEAg/slGQZVlir3T3tnMKHfihrZVpmOqJgMPq8bo6t6o6HobgBA+5c3RH7LsL6XCpWC7VkwDMMw6YONBcMwDGMJGwuGYRjGEjYWDMMwjCVsLBiGYRhL2FgwDMMwlpAQObpV2QZE1Apgh9N66OgL4FunldCRizoBuakX62QP1sk+uajXUCFEQptt8n2fxQ4hRJ3TSqghok2skz1yUS/WyR6sk31yUS8i2pToNTwMxTAMw1jCxoJhGIaxJN+NxVKnFTCAdbJPLurFOtmDdbJPLuqVsE55PcHNMAzDZId871kwDMMwWYCNBcMwDGNJXhoLInqMiA4S0adO6yIhopOI6B0i2kZEnxHRrTmgk4+INhDRlqhOv3ZaJwkRuYnoIyL6X6d1AQAiaiCiT4hoczLLCjMFEfUmomeJaHv03nLUlzcRDY3Wkfw7SkT/5qROUb0WRO/xT4noKSLy5YBOt0b1+czJOjJ6XhJRBRG9QUQ7o//7WMnJS2MBYBmAaU4roSMI4P8IIYYDGA/gJiIa4bBOnQDOE0KMBjAGwDQiGu+sSgq3AtjmtBI6zhVCjMmxNfF/BLBKCDEMwGg4XGdCiB3ROhoD4DQA7QBecFInIjoRwL8CqBNCjEQk/MnlDus0EsA8AKcj0m4XEtEpDqmzDLHPy9sBvCWEOAXAW9HvcclLYyGEeBfAYaf1UCOE2C+E+DD6uRWRH/WJDuskhBBt0a/e6J/jKxqIqArAdACPOK1LLkNEPQFMBPAoAAghjgshmh1VSstkALuFEF86rQgiG4xLiMgDoBTA1w7rMxzAOiFEuxAiCGANgEucUMTkefkjAMujn5cDmGElJy+NRa5DRNUAxgJY77AqcrhnM4CDAN4QQjiuE4D7APw7jIIbO4cA8DoRfUBENzitTJTBAA4BeDw6ZPcIEeVS8OfLATzltBJCiK8A3A1gL4D9AFqEEK87qxU+BTCRiCqJqBTADwGc5LBOagYIIfYDkRddAP2tLmBjkWaIqAzAcwD+TQhx1Gl9hBCh6JBBFYDTo91jxyCiCwEcFEJ84KQeBkwQQowDcAEiQ4gTnVYIkbflcQD+LIQYC+AYbAwXZAMiKgJwMYC/5YAufRB5U64BcAKAHkR0tZM6CSG2Afh/AN4AsArAFkSGqvMWNhZphIi8iBiKJ4UQzzutj5ro8MVqOD/XMwHAxUTUAOBpAOcR0f84qxIghPg6+v8gImPwpzurEQCgEUCjqjf4LCLGIxe4AMCHQogDTisC4AcA9gghDgkhAgCeB3CWwzpBCPGoEGKcEGIiIsNAO53WScUBIhoIANH/B60uYGORJoiIEBlb3iaEuMdpfQCAiPoRUe/o5xJEflTbndRJCPELIUSVEKIakWGMt4UQjr4FElEPIiqXnwFMQWQYwVGEEN8A2EdEQ6OHJgPY6qBKaq5ADgxBRdkLYDwRlUZ/h5ORA4sniKh/9P8gAJcid+oLAP4OYHb082wAL1pdkJdeZ4noKQCTAPQlokYAvxJCPOqsVpgA4BoAn0TnCADg/wohXnFOJQwEsJyI3Ii8GDwjhMiJpao5xgAAL0SeM/AA+KsQYpWzKincAuDJ6LDPFwDmOKwPomPw5wOY77QuACCEWE9EzwL4EJGhno+QGy42niOiSgABADcJIY44oYTR8xLA7wE8Q0RzETG2P7aUw+4+GIZhGCt4GIphGIaxhI0FwzAMYwkbC4ZhGMYSNhYMwzCMJWwsGIZhGEvYWDAFCxGFdB5Sc2X3s9TrhOj3NqtroumqiaiRiFy645uJ6PSo59W9RHR/JvRmujd5uc+CYWzSEXV1kjaIyBN1DJcKSeklhGggon0Avo+IYzoQ0TAA5UKIDQA2ENERALnkNZcpELhnwXQ7orErfk1EH0ZjWAyLHu8R9f2/Meq470fR49cS0d+I6CVEnA2WEtEzRPQxEa0govVEVEdEc4noXlU+84jI9m5+IupLRGuJaHp09/1zUV02EtGEaLKnoHW/nRPO/JjCh40FU8iU6IahZqnOfRt1HPhnAD+PHrsDEfcj9QDOBfAHlZfXMwHMFkKcB+BnAI4IIUYB+A0icR2AiK+ri6M+woDIbuvH7ShKRAMAvAzgTiHEy4jEsbg3qstMdLlzfwbAjKgrbgCYFc2XYTIKD0MxhUy84R7p6PEDRPz2ABGfUBcTkTQePgCDop/fEELImABnI/IwhxDiUyL6OPr5GBG9jUigm20AvEKIT2zo6UUkAM1NQog10WM/ADAi6oIEAHoSUbkQ4hsi+gzAZCI6ACAghHDcjxVT+LCxYLorndH/IXT9DgjATCHEDnVCIjoDEffgUKUz4xEA/xcRh422ehWI+DP6AMBUROciEOn1nymE6DBIL4eiDoCHoJgswcNQDNPFawBuiXouBRGNNUn3HoB/iaYZAaBWnoi6Ez8JwJWw/yAXAK4DMEy1Yut1ADfLBEQ0RpX+OUSC6fAQFJM12FgwhYx+zuL3Ful/g8iQ0McUCW7/G5N0DwDoFx1++g8AHwNoUZ1/BsA/E/EyKoQIIdJbOJeIfoZoTOnoJPpWADeq0jYDWAfggBBij908GCYV2OsswyRI1OW7VwjhJ6IhiMw3nCqEOB49/7+ITE6/ZXJ9mxCiLEO6XQugTghxs1VahkkE7lkwTOKUAniPiLYgElXvp0KI40TUm4g+R2Ri3dBQRDmq3pSXLohoAYBfAHA8nC9TeHDPgmEYhrGEexYMwzCMJWwsGIZhGEvYWDAMwzCWsLFgGIZhLGFjwTAMw1jy/wEOM4OuqcjcxwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = pn_dataset.plot_excess()\n",
    "ax = pn_dataset.grouped.plot_excess()\n",
    "plt.xlim(1,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0, 10.0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xRs+4LPxjV/DlGFw9OGz4THcFz0YkDuh1U73z03eCDIbRldOdpSsMOHcFHVOJffmSsJV9KIMgz7e4WmJOs7m5OejcyJEjMXLkSOXYZDLh4YcfxsMPPxwUVt16UOvz8oqXFT2vvPJKXHnllQFhzjvvPGWmkkSvhVBUVIQPPvhAOdarLCsrK4NaH4BvSqxk1KhR2LFjh+IKvrra5w193rx5AXHk4Dzga/mo8yTRG6MA9PNZWFiIt956Kyisw+EIyFcoHdSzriZNmoRJkyYpxxs2bNCNnwjS3ogQ0dkA7gOQL4SYnGp99NBbYJcqg6FHvHRJZD7Sqby6GrKrSgiBH078gJJuJUq3l6ysfzjxA9o8Hf32yRygj0bPVLJo0SIsXboU7e3tynRhJnpSYkSI6HkAVwM4IoQYoDo/HsCfAZgBPCeEeEQIsQfALCJ6LRW6RgN/RTPJRv31KytmoGMtiRxHkYaipFuJsuYk1gH6WLq9wq2XCKenHskycnfffXfGb0KVSFLVElkC4GkAL8oTRGQG8AyAMQDqAGwkoreFENtSoWAsbk34KzqQ06kMIuU1GWURzVoS9QB9qsab0mHNCxN/UmJEhBBriahUc3oIgB/8LQ8Q0SsArgMQlREholsB3ApAd4AtnugtGuwsqax4T6dKP1NQG4J06KqShEs3nfRk4kc6TfHtC0C9wqwOQF8iKiKiBQAGE9FvQ0UWQiwUQlQLIap79uyZUEVjWTTIMIkmlWtJYqGr6MnERjoNrOutGhJCiHoAtyVbmXDozcbqbg9eLcswyUB2Vf3HS7Wwm+1YNuecVKukSzp0qTHxJ51aInUA1H4qigHE5JaSiK4hooWRXAd0divbWBcNMkxXxGw2o6qqChMum4AJl03AI488kmqVEs7y5cuxbVv0w7DaRZztnvYEapeepFNLZCOAc4moDMABADcAuDEWAUKIdwC8U11dPTsB+inozcb6w2d/SGSSTJTw+E78kG5PusJuhEZ11M4m+/urf8eo8aPg6Bt61piaWGecRSKdyzgUKWmJENHLANYBKCeiOiKaJYRwA7gDwPvwLbx9VQjxXSr0i0QmOfpjMoODJ9qx42Ar1u89jjGPr8G++tgXF0ZDY2MjysvLsXPnTgA+h4WLFi0CALz44osYNGgQKisrFceCWjfum77wuYBfs2YNqqqqUFVVhcGDB6OpqQmHDh3CiBEjUFVVhQEDBuCf//xnUPobN27EpZdeisrKSgwZMgRNTU1wOp2495f3Yvyl4zF48GDFxcqSJUtwxx13KHGvvvpqrF69GgCQm5uL++67D1cNvwrX/+R6HD1yFF+u/xIfvvshHvk/j2DCZRPwr73/wpIFSzB26FhceemV+NXMXwXpwzPOUjc7a2qI8ysBrEyyOl2SWFykM12bUF+nU55dp/zevP8E2ty+CmzXkWaMfXINKosLAADL5lxiKF3pxVd20dz/+/sxZcoUPP3005g+fTruuusunDhxArNnz8Z3332Hhx56CJ999hl69Oih+Ie66667Aty4XzHmCqzasAqPPfYYnnnmGQwfPhzNzc2w2+1YuHAhxo0bh/vuuw8ejwctLYGGsL29HVOmTMGyZctQU1ODkydPwuFw4M9//jMA4L3P30PboTaMHTsW33//fdi8nTp1CsOGDcNDDz2E//zP/8QHyz7A73//e/zPdf+Dq6++GpMn+9Y133jVjdi7dy9sNhsaGhpQkF8QIIdnnKVXd1anIaJrAFxzzjnpObBoBDYKTDRIAyJxurwhQkZPqO6sMWPG4B//+Aduv/12bNmyBQDw8ccfY/Lkycq2sNLd+YcffhgwxtDc1IzmpmYMHz4c99xzD2666SZcf/31KC4uRk1NDWbOnAmXy4WJEycqLs8lO3fuRJ8+fVBTUwMAilv3Tz/9FJNn+Cr9888/H2eddVZEI5KVlaVsPnXxxRdj1apVuuEGDRqEm266CRMnTsTEiRODrndmEWemkE4D651GCPGOEOLW/Pz8VKvCMAln2ZxLlL+zenS4DzERcG6vXOVavPF6vdi+fTscDofS4gjl7lzrxn3d9nXIzcvF3Llz8dxzz6G1tRXDhg3Djh07MGLECKxduxZ9+/bFzTffjBdffDFAViwu1QGEdalutVoVWaFcqgPAihUrcPvtt+PLL7/ExRdfHBROzjjLtmZ3ystyVyajjAjDnK48PLkENouvUuzfMxeLp9UkLK0nnngCF1xwAV5++WWl5TB69Gi8+uqrqK+vB9Dh7lzrxn3bN75Wye7duzFw4ED85je/QXV1NXbs2IF//etf6NWrF2bPno1Zs2bpum0/ePAgNm7cCABoampSvPq+9Q+fI8Pvv/8e+/btQ3l5OUpLS7F582Z4vV7s378/KqeEapfqMt6oUaPw3//932hoaNB1Snm6k1HdWenOlGfXoTbrJCr6dIscOEXIfvZEfMF2RbpKeZzZPQvnn+nwrxOJj67aMZFrJ1yLmTNn4rnnnsOGDRuQl5eHESNG4L/+67/wwAMP4L777sPll18Os9mMwYMHY8mSJUFu3KuGVeGhJx7Ck08+iU8++QRmsxkVFRW48sor8corr+DRRx+F1WpFbm5uUEskKysLy5Ytw5133onW1lY4HA58+OGH+OUvf4nPZ36O8ZeOR3ZWNpYsWQKbzYbhw4ejrKwMAwcOxIABA3DRRRdFzPMNN9yA2bNn46mnnsIrr7yCWbNmobGxEUII3H333SgoKIhL2WYSGWVE4jkmsu3QyU7LSKfxjHTSJRRdQcfTCY/HAyB4+uz27R27ljz++OPK72nTpmHatGkBMnr06BHgxl3Kmj8/2FWQXnwtNTU1+OKLL4LOP/rXRwN0BHy7BMpNn7SoWxSTJ09WBtKHDx8eMIbz6aefhtWHyTAjkqx1Ip2htP3XeGF8en/VMl2TJ28qPe1mBjGph8dEMogpz64LmPaZSWRy3himK8NGhGEYhjEMGxGGYRjGMBk1JpKqxYa+bpZb03YGT7QzjKY8uw7bDkU/eywauek6uymeeqVrHrXweAmTCDKqJcKLDZnTmd/983ed8k7NMEbIqJYIwzDxw2w2Y+DAgco6kZ/f9HPMnTs3xVolluXLl+O8885DRUVFqlXpMmRUSySd2Vffgi11DQn3stoZnC4PTrW501rHZNIV7lkikb6zVny6Ais+XZHxBgSIfT8Rho2ILlOeXYeWNn1fOkaZtXSj4hRv99FmzFq6Ma7yw7Ht0MmoFk9+e6ARXr8bomTrmI6k6p5NeXYdjja1RQ6o4sdTP2LXiV3YdHgTJi6fiP1N+yNHMkCmuYKvrKzEsGHDcPjwYXz++ed4++23ce+996Kqqgq7d+/GU089hYqKCgwaNAg33HBDQsq0q5NR3Vnp7MV3z9FTym+vCDxOF7wi8Hc66phM0v2eqcc/th7bijavz/DsbtyN69+6HgN6DABg3BNAIlzBjxs3Dtu3b8evHvtVWrmCX7RoEX7/+9/j2muvDXAF/8gjjwS4gmeCySgjks4r1s/umYNdR3yuFkzkO043TNRhSNJVx2TSFe6ZxOlxhj02gsPhwIYvNwS4Om/3tHfKFfzJkyfR1NSUMa7gmQwzIunM4mk1GPvkGjhd3qi8rOpNG9Wu2DYypTTcdFSH1YxT7T5/SVkWU4COA+e9DwD4dt64gDjbDp3ElGfXKfLkNGEAAVOFO6O7WubAee+jok833XKJdE6rd6i0ZLxw9yzW6dCR0lIfR2L3UZ9he2H8C8rv//3Zz7C7cTcAwAQTyvLLom6BaLeIlQgIxYAAHdu/2kw2fL31a2TZs/DNv76BK8+FYy3H0NjeGCRDuoJ3OAK3jJ07dy4mTJiAlStXYtiwYfjwww8VV/ArVqzAzTffjHvvvRc///nPO/RJkSv4tWvX4u2338aDDz6I7777DhYLV5tqeEwkSZQUZaOyuABDywqx6p7LUVKUnWqVgjCZCGYC8uwWVBYXpKWOyaQr3DPJ/NHzYTfbAfjWg8wfHezg0Ah6278+/8zzOOe8c/Dkc0/iN3f8Bi6XC5defilWvLkioiv4zZs3A2BX8JkEm9ROIL/CGSbV9MvrZ3gMJNQiRGerE5NHTVYMyWVXXIYpN0/BGy+9obiCX79qPV6a/xIeeOABzPs/8yK6gh8xYgQWLFjAruAzCDYiOjhdHpw6PBlNrgKMeXwNFk+rCfkV6nR5sPNwE5wub8SwiUROR9Xqsa++Bafa3PAKYMzja5CdZYbdataNL8OdanPD6fJEnd6Wugbsq28Jyre6bOxWE8p75+mmHQk59dgrfGMTkXSLhLZMOnPP0uX+JwKPx4N2T3vQ9q+dcQUvYVfwmQN3Z+mw83ATvK4CAKaIUztlBQKkdlpsqOmos5ZuDJi2u/NwU8j4MpxXIGQ4vfScLq9uvtVl43R5I8oMxc7DTTHpFgltmXTmnqXL/Qd8LZB478nC278ykciolkhnpviqu6XacBTZZUthsh2Ft60n9h6YHhR+26GTaHK6lAoECD8NNB4DsaF0XjbnEt3pqFOeXafMLpLn1fqq0ertdHkDykSum5GD6Gq5evGljHDH2jyEIho5sRDPqbva+7/rSLNSRl3Fp5YWOVjfv2duSuIzXYuMaonEy3dWdr/nYbIdBZGAyXYUuWe9GBSmtP3XaNs3BybNZJFUTQNVp6uejmq3mgLOq49DxdfG00N7XS/f2jCRZEabllE5klBlZYRoyzdWBELPOmKYzhDv5yqjjEi8oKx6EPkKmkhAWI+EDOuwmpWKw241RZy6mygWT6tR9FBPRy3vnacYuv49c1HeOy9kfBnORAgZTqKWGyrfvjGQjrKJJDOatKLRLRLqvEYz3TqSbupy76xukqMtXtTX17MhYeKKEAL19fWw2+1xk5lR3Vnxgly9AethgPxz7ruFdqFtMhEG9MlXuqpSNagqp6MCgd0ndqsZOTbfbV51z+UhZ5OVFGUjx2ZBS5sb2TZLxAFwtdxQ+bZbzYpOnUGdljzuDDKvgK9MOqubutzjNVtvxe42XHxWE44ePRp0TbpEaT9mC/gdL7Qyj7UeAwA4HdEtYEyETpGIVcdMSdsIdrsdxcXFcZPHRkSPwzOBvo8BcKGsIH5z7hkmWlrcQFmZ/sfLPGWspSrgd7zQypTuVaIdtE+ETpGIVcdMSTsdYCOiYduhk2hpy0d2Wz9k2yxYPvH1oDDqFdR619Sy5EC6T647IJ56sF1ez/Z/IcuBbPUKbb2V4NsOnVTC6Omgp7c6rFyJXtGnG1ra3PAIBDif3FR7XFeWb2KBG2YK1kHKCRVXrYuUoV2Jrs6DlCPLRr0+R1smA+e9H1COeqvpZdgmpxv9f7sC1aU+Fx3aAfApz67DptrjiiytXuqyCJU/qVO0Eyq0z4ckmlaOeiBf717r5U9NNBMA4jVZIB7eF9IJ9TvQ1fMSKzwmooNHAG6vQJPTpVSy4ZAGIBbCGSI1WsPDREdLmzvunpi7KtF6cWYYI3BLRIPXv4Cgdd9skLUB2ZT8gU2vV8AjAo/VC+Q27D0OAd9gtZkIJu0UMRXqhXpf7z8Bl1so+2OoB5SdLo+SpkcA6/ceh91qghD+mUL+cBv2HscXu+uVeB7h+6I3EdDY6sKeY80BcgCAVPElW+oaYNbxg6Snv5TT5OwwCpv3N+D8M/Lg9Qq0ujxKnrxeASEAOfFWbyGkLBOpo9Plgd1qDlqwmZ2lP/bidHmw/ceTaPeXJQEhw4bLl3qR4oPXDVDSVpeRdoGmOt7IRz/BoUYn2tzesAtJGSaRcEtEQ6uyGtoE4SpQHadCh45j9QI5WSE7Xd6I+qkX6rW7hRJXuzBObwGf0+WFF4EGQAC4+fn1QWG9Atj5YxPa3cFGV88MR6N7KL0AoM3tW7zY6vIELBxsdXmgXkWitxBSXSbqNLQLNkOlvfNwYD4Fgu9ZNPlSp3Xz8+t118NodVDHq61vQZs7sr4Mk0gyyogQ0TVEtLCxMdibaLR4A2o8k+Y4OWjTDLcgLpJ+oRbmaWXGsoDP5dFPNNaiiqZsw+nldHmD9kDRk6m3kFLvWLsIMVTaeudjfU60ixRDlWm0Cy3D6cswiSSjjEg0iw33N+3H1mNbQ+4AF9gz5A1aTJgMtGmGWxAXSb9Qi9+0MmNZJGc16ycaa1FFU7bh9LJbTQEyTKQvM9JCSnmsXYQYKm2987E+J9pFiqHKNNqFlvFc6NjViPROJ5oXxr+A0vZfJzXNdCJjx0TUu76p2Xpsq7Jhj3oHuNos38Cj40wHTh2YCsALsjbAQT2TpbKCel8Pebx4Wg1GPvYJvKJjjEGOiYSjvHeesu1tloXg8ndpyUV2E+b/Uwm3pS6wBWe3mtDu8gaMiRCAv80citl/2xQQ1kTAeb3zsOdYc1CXlt6YiNRdnc9Q+mv1AgCbxbd48YcjzUqXVv+euTjY0IrW9o4uLb2FkOoykcdA8J4v2VlmbD0QnHZ57zxlTETmzxHjWER57zyla6p/z1w8eN0ATF+yIaA1obdAUx2vtChbGROR+mYSod5hLaHe6Wg4XaflxpOM/HQJ5wsp0g5wJquvX9lRsgiOM/8RNGgtZ1XJaaxNTjeanB1TWuVMGL3ZMNk2izKtFAg9g0ju6yH/TCZCSVE2CL7jIWWFGFpWiMriAphMhJY2t5I24JuNI4/lQr08uwWD+3VHrt33uzAnK2Cw2W41K+nl2S2KfPJ/3ctrQ8oKMax/kaK3DJ9jsyDfYYXN0lGRyY9rrQExE2A1m0J641XPXNPqJf+q+hVgz7FTcLo8yLFZlD0/TCYCqfTV2xdlz7FTSqvJTB2LF+99bQusZpMiy241I9tmCZqea7eaMbhfd0UXk/8ehUJvhp1cpCjTGta/CJXFBQF5rCwuCBooV8dbfe8oZFlMyLNbFH2jJdrZgV0BvXeaZ6SFZsqz6+K6hUXGtkRCfWFMXD5Rdwc4Zd3BjycB8NRQhkk10bYS9N5p2+Ff+2WcXms2UkFGtkTCkagd4BiGSQ38TneOGe/NiLrrUI+MbYmEojM7wDEMk37ov9N1qVPoNOO0a4kwDMMw8YONCMMwDGMYNiIG4W0eGIZhTsMxET3UPpMkrftmIavXCmgnTTpdHjQ73bqrs+WUXzXr9wZ7svUIn58puUhZHUYbX54b/shHSvgtdQ0oLcoJWpPR7HSjsdWl+Mpqcrqxfu9xmMi3rkLm0US+fHyxu15JT6vD1/tPQGh8eMlwal3UedbmNcQi7KBy8qh0HfnoJ7BbzTjV5g6S1+x0w241oc3tDdJ38/4GvPX1Ad3yl76pvt5/ImgNi/QTBgSuu9lX36KEl3o9cv0gpQzVYaX+al9WWpqcbsXnmXqdj3odiNrPmdRdhlXL1ls/on6GRz76CQ42tKLdIwLKSfpLKynKVtJSp6H14aX1r6b1LSZlhdIjmjDqtPXCdgX0npUXZw7tknkxArdEEOgzqQMz2o9MCAq783BTzO499Nj5Y2x+jg40dMyFd7q8un6qhF+untuUVpdXyaNX+PKh5wNL0u4WCOVFQ61LvKmtb9HNAyB9VHl1r7W5vbhr2WZdmdI3lZ5fLzVqf16zlm4MCF9b3xLg30qGVfvMUvuy0kNo/mt9Y2l9eqnDqmXr+dRSP8O19S1o17Hgan9p6rTU/tTUedT6V9P6FtP6JDMSJpQvt66E3rPSVfNihIxqiRDRNQCuyTrjHGUfCe2eHQBQPawjzpRn12HXkWY9aQDMaGlzBy0wjAedFRMqfrRy09nPUrx7Cr0C8EZ547yiY68ULVr/VvHwq+Z0eZXtBmK5J06XV1nb1NLmDvEMB+IVwK4jzej/2xW6z7G2nGT4H7N9e6ocVqUhfa9p989Rl5uMr91PRW8xsDosAOV9lftzyL1dAKC6tFA5t37vcZgJGHG5fp7Viyq1e6uo6wbtfi3q81Knb+eNC5Ar0Sv7XUeag+Tr6SZ10TvW5kErJx57u0x5dh1qs05Gvd+NHhnVEpG+s2KNp+9zSABIvgffRKFdTx3OXxMTHaF8dXVGntGw0frNipSG9plQh9f6+9Lz56b1ZaanVyg/cF3V95ee3l01L0Y4fXIahvLeeTovlwdZvVYEhY1XgTk68ZDZraaQzg5DyS0/IzCPDqsZf5s51LAOicLnhyr2srFZTPjzlKqwcqOpo02a/6H0sltNcFjNcFjNSrlGkq93XZ2OWpZeXNKEVePbd8QnLctCyNL5QDCRvo8vUl3/28yhihxteHUa0veaFnX4/j1zg8ZuAJ+PMj0jqBe2K1DeOy/g3ki/bqcLbESAAP9SeXZfD5+jZDEsjh+Dwqr9Mkn0fqt9Pal9P0ksZpPueW0cKU99rrK4QPFnJc9r5Uod5F++w4oc1TavJhNhWP8iJb7aZ5NaF21dpM17tNeiJdduCSgbPXl656r6FeC6wX0D9FfHN5FPtrps9JD+LIkC76HUS8aVfstMJlKenVxNWWrv35CywqA8qf1nSll6ZZ6rki/9qamRPrWkj7TBJd0VH2hSlxybJSieWq8cm0Xx4aUXXuvvS2/gWPp9C+fPq6QoW3kWzQTFT1tX3VDLbjUj1//cSb9uXTUvRmAjwjAMwxiGjQjDMAxjGDYiYeAFhQzDMOFhIxIFtVmPAWf8JdVqRMRM6NRUvXQmHuMtDMPEHzYiDMMwjGHYiDAMwzCGYSPCMAzDGCaj3J6oUe+vLPcgB3z7nMvfWkK5NfGdD3ZGqIeeY0K1HG1aZgo83+R0K33/4dKT19Q6ewR095UOdV59XaabrVpLolceUudwOnUGWRZSll5aeums33tc2bs+3jrpyZTlKfXVlpse2w6dDFt+avl6qJ8ZKU+mpdZHusgIJyMSHuELq86X+h2SboWSTahnOd77qctyTAXxcGeSTLglwjAMwxiGjQjDMAxjGDYiYaAUTinN1ri/UHebLJtzieHprtFMldULE60LkmjCGdE922ZRyiRaVyjqrhi9a6HiRDNNuqJPN3w7b1zYNKJBHb+iT7eo0lbrKMuFYVIFGxGGYRjGMGxEGIZhGMNwOzgErfs6tiVpqp2p6xZeYmQGkHpGVShLrna7Ei4co4/exlKnG+otd0+1ueO/4xdz2sNGJCRy92cg1Fa58SLUfnba8+m7FyGTrqi3wY3HTowMoyXtjQgR5QD4C4B2AKuFEC9FE+9Umxv76ls6m7rqf+f3B4jHmoUmpxtjHl+jtFK0TiLVX9966QnhM0ZSjtdAzaJtIakJlUdPmDh6+umlFUmnaCZCSHmhWilNTje21DVEJW9ffUvAV77ehk/q9OK5CyIAeL0CrS4PvCL0hlORttw91eaG05U5O3gCvvuypa4BTpcXdqsJZqKgfVROV9RlM+bxNcjOMgNZnZNJIgWuaonoeQBXAzgihBigOj8ewJ/hq7GfE0I8QkQ3A2gQQrxDRMuEEFMiybf1OVf0mfYkLP7Ne9qDai03HL1X4v2fP4GxT64J86IJBLZIkvMgmgDk2C0p6Y6xWQht7q75ySp3/0tGi40A9C1woK6hNa5ys8x6z2vsyMrTI0RMe7dbzYQzutmx/4Q6X4HPf06WGQP65uPRyZVh35++BXYcbWpT8iPfpNKibNRqPvCyzCa0e0LLOXLSCXUyZ+TZcKy5DR2PqkDeGavx7X88hjGPrwm557xah4MNrYpufQvsONbcjjZ3oA6987JwuKldKZu/zRyKMwscSr7VtYM2DTWlRdl4ceZQZSOvffUtioxze+XiwesGYPqSDYrhK++dh7fuuAz76lvwkydWo92fUSkHQED8xdNqdDcJ06IuG1mbCXiRnd2EHX+c/q3X3T4oohBtflNkREYAaAbwojQiRGQG8D2AMQDqAGwEMBXAdQDeFUJsJqL/J4S4MZJ8aURC49s//dxeBSEftsAXJ7lGBPDtkHbK6eYuLMYwJgIG9s3HlrrGOEiTH1Q+uTWlhTh+qj3M+5NsfO9o7SNXo/9vV8KToHrNaiaUFuUYyve5vXKx6p7LAQRW5iYCzCaCS/XxYLeasOPBK3UN4rm9cgEgIH7/nh2ywxG6bLw4uPhOZ/vRWkes+UrJWK0QYi0Are+RIQB+EELsEUK0A3gFPgNSB6DYHyakvkR0KxFtIqJNkTXwdU/tOXoqZAhHyUJkn7XQv5ag4wVKJqlcp8J0fbwCcdymteNhlD2g4d6f5NOh39k9cxKWissjDOdbHU/92ysQYECAjm5IvbT2HD0VFD9anUKXjQlkttqjEhIUM33oC2C/6rjOf+4NAJOI6K8A3gkVWQixUAhRLYSojpyUryUS7cNmsjaAp7UwWiwmxH2cI55I3eKjY8fzL+UlsrKOnQ79Fk+rgd2amKrNaibD+VbHU/82kU+uGqm/Xlpn98wJih+tTuqyCUzTC+FxOaMSoiGdjIjeoy6EEKeEEDOEEL+IdlBd0rfAjizd5dEeOHqvjPphc/R6H7CE7hLoTCHqaRdOnsNqwiuzh3UixQ5Ki7Lh0OTfZklMrZiMB42SlI5M6++zhiHHZoEJkdupJoqubPWf1/B6qKd/SOxWkzLQrjfgrkb9CFjNhH7dtT0agR9QUl6k90f7/slfpTp991nm8HK0yZyRZ4O2OPPOWA0AKCnKRmVxASqL84P0U+ug1q1vgR02S7AOvfM6Rp3lmIg633p3S+9caVE2Fk+rUY7VMvr3zMXfZg5VjuWYiAyXpcqolKONr5YdDlk2Q8sK8dE9I/0yfGMi7hMHd0UlREM6GZE6AP1Ux8UADsYigIiuIaKFgG9M4bO5ozG4pLviKsNMvvN5pc/D4vgxoEDz7PpuMADAZG1Cdt9lYdKNTj891x8mHTceevKk/hazCcP6FylxtHHV+VC7CMmzW5RrshxW3zsKFtXLayYgy2IOKC9t+qHyoRcuVJ7C1ZN66erprxfPRIHp5NktAdfVZRPqfufZLRhaVhj4vKj+JEPKCjGsf5GSt1yNftoyzLFZkGUxB+VfLXtoWSEGl3TXzZtWd/nM5vr/5G8ZtrK4QJmRZDJRkAx1+hedVahc3/XQVTizwBGQbl7pIvQuf0HJj5Srfn+kPnkqHeT7J/Ud4g+3+t5RSlmaCf58FwTIkL+HlhXis7mjFR2lzl/c9xNcXFqoyOhd/gKy8/cG5M1uNaOyuCBAploHtW6fzR2Nqn4deZF5WH/fGCX+roeuwrD+RQH5HqLKu9RNnlPnYfW9owIGvtUyVt1zOYb1L1KOK4sLlG7IkqJsDO7XPUiONn40g+papIze5UswdMgqCI+rPWYhSC8jshHAuURURkRZAG4A8HYsAoQQ7wghbo0ckmEYhokHKTEiRPQygHUAyomojohmCSHcAO4A8D6A7QBeFUJ8lwr9GIZhmOhIyWJDIcTUEOdXAliZZHV0yaSZUWbyeYiNtMmO9A4batMuJjmYKTGbaTFMIkin7qxOox4TYRiGYRJPRhmRRIyJeATg9oqo3W/EIrerkkjdu3K5pJKWNrey54x2a1ftFsqZzpRn16Vsa9vTkYwyIgzDMExyYSPCMAzDGCajjAiPiTAMwySXjDIivE6EYRgmuWSUEWEYhmGSCxsRhmEYxjAZZUR4TIRhGCa5ZJQRiceYSDROVKN1IKh1hphtC3SAZyagurRQOS8dvVWXFuLbeeNQXRroGFKuKFeTbQt0DCjP6elY0acbsm2BYb+dN05Jo6JPNyybc0lAmHCOKdV5Uudb6qT+k3mRv7UODkM5VZSy1PlW66d1yKgus4o+3QKuVfTppqQNALv/NCHsvcy2WfDtvHEBaes5gAx1X+Sf1F/qE+r+AFDKX12mMr/h4qnTlHn9dt64AHlaQt3zcOjl1Qjqslg255JOy0s1FX26BT2npwsZZUQYhmGY5MJGhGEYhjEMGxGGYRjGMGxEGIZhGMNklBHh2VkMwzDJJaOMCK9YZxiGSS4ZZUQYhmGY5MJGhGEYhjEMGxGGYRjGMGxEGIZhGMOwEWEYhmEMk1FGhKf4MgzDJJeMMiI8xZdhGCa5ZJQRYRiGYZILGxGGYRjGMGxEGIZhGMOwEWEYhmEMw0aEYRiGMQwbEYZhGMYwbEQYhmEYw2SUEeHFhgzDMMklo4wILzZkGIZJLhllRBiGYZjkwkaEYRiGMQwbEYZhGMYwbEQYhmEYw7ARYRiGYQzDRoRhGIYxDBsRhmEYxjBsRBiGYRjDsBFhGIZhDMNGhGEYhjFMRhkR9p3FMAyTXDLKiLDvLIZhmOSSUUaEYRiGSS5sRBiGYRjDsBFhGIZhDMNGhGEYhjEMGxGGYRjGMGxEGIZhGMOwEWEYhmEMw0aEYRiGMQwbEYZhGMYwbEQYhmEYw7ARYRiGYQzDRoRhGIYxDBsRhmEYxjBsRBiGYRjDsBFhGIZhDMNGhGEYhjFM2hsRIjqbiBYT0Wup1oVhGIYJJKFGhIieJ6IjRLRVc348Ee0koh+IaG44GUKIPUKIWYnUk2EYhjGGJcHylwB4GsCL8gQRmQE8A2AMgDoAG4nobQBmAH/SxJ8phDiSYB0ZhmEYgyTUiAgh1hJRqeb0EAA/CCH2AAARvQLgOiHEnwBcnUh9GIZhmPiSijGRvgD2q47r/Od0IaIiIloAYDAR/TZMuFuJaBMRbYqfqgzDMEw4Et2dpQfpnBOhAgsh6gHcFkmoEGIhgIUAYOtzbkh5DMMwTPxIRUukDkA/1XExgIMp0INhGIbpJKkwIhsBnEtEZUSUBeAGAG/HQzARXUNEC+Mhi2EYholMoqf4vgxgHYByIqojollCCDeAOwC8D2A7gFeFEN/FIz0hxDtCiFvjIYthGIaJTKJnZ00NcX4lgJWJTJthGIZJPKkYWE8YRHQNgGuyzjgnLvK8rjwAQOu+2QC8ynkRZtjeo7rm9Z5+4/tCAKfa3PAKwESAw2qGyaQ3lyIxOF0eJf0tdQ1h7xWT+Wifh/LeebBbzalWK6NIe7cnsRDv7qy2o+P9v0zwrYX04dUNHUyryxMvVboMXgDSdnpF8stg5+EmJX2nyxv1vWIyE+3zsPNwU2oVykAyyojEG+EqUB3F/jV9GjZEgkh2GThdbDaYDrTPAz8f8YeNSBjI2qA6ir02TGIvTtqS7DKwW/mRZjrQPg/8fMQfLtEw2Hq+5//lBdDRLRNtoTlOw75XEzoMhxwTSSblvfOU9O1WEz/gpzna56G8d15qFcpAMuodi/c6EZPV13/qKFkER8liVTqh45jJ9wcgqQPK6QIRkGOzIM9uQY7NkvQysFvNyLFZYCagsrgg7L1iMh/5POTZLagsLuBB9QSQUUaE14kwDMMkl4wyIgzDMExyYSPCMAzDGCajjAj7zmIYhkkuGWVEeEwkMi1t7oBV9QzDMJ0ho4xIZ6jo0w3ZNguy/TN7IqENEylOts2Cij7djCsYQodwMmV+jCJ1lnLUssyETsmWVPTpFvdySSXqcuqqeVs255Ko3oFQJOJZZ9IXNiIMwzCMYdiIMAzDMIZhI8IwDMMYJqOMCM/OYhiGSS4ZZUR4dhbDMExyySgjwjAMwyQXNiIMwzCMYdiIMAzDMIZhI8IwDMMYpvNLjtOUJqcbYx5fA7OJAtx8NDndQO0sACace99K9O+Zi3yHFU6XB81Ot7J/YeuBKbD1WgkAcB78XxDuAkWG1m2IUB0HpaX6vX7v8YB4HgF8vf8EXG4BoQq/fu9xDH/kIxxrbkeb27edp9ra76tvUdLZsPd4wJ6LTU63bxMeuc+5V8AjfDLtVlOArlKWzPf6vccx8tFP4PUKCAGcavPpbCLAZjEpumjzLI/l1WZ/PgQ6NqZyujw41eaGVwBb6hogROC+LHpl6BE+WdlZ5oC8qMtVna4sO4JvM2OnyxMgy+nyRNxP4lSbG84w+8LL9GRZybLbUtcAsz9DrS6Pkk+5CZLT5cHOw03w+Ms11EaZ++pblHICOu671ysUuafa3GhsdSnh5O1Wl8uWugbsq29BSVG2cs6r2atYew/VOshyO9XmhsOVhzZ3vlL28jke8/gaZGeZk7ZHh9RXvtvyuRACOLb3p/C05wecN4p8bmQ6Xq9I2r448jlxurwY8/gaLJ5Wk5R0W1tzYO1x1oVG4mZUS0Q7xXf30Wbs/LFJJ6QJAMHlEcr1nYebAt5r4c5H29Hx/t8FCFdUXgRWZLHQ7jcgWg40OAMqbbX8WUs3duipp4+qYm1VVYhOlzdIz1lLNwbIqK1v8VVW6Ngf3SuAVpc3YL90rRz1sVDp5YvrezFkfD09QpWfUOWhVady14sn/Od3Hg6899pjPbwifDh1erX1LUo+nS4vWl0epaKX56QsWTHINELld9bSjbrlrJbrFcDOHzvKU+8ZcLq8Ac+JlBEqL1odlDACaD0yDg1144LC7T7aHFWZxgu1vuq0vQA87fkATHHRSR1/99Fm3ecuUaifk91Hm4PuYaL45ttLQWar3UjcjDIi2im+XhHqg6/jq0JdCWjDCFeB/3d6FdOeo6eiDuvVL4CwsiLFiRWv0Cvf2OKr/0eLNs1odTCqq1cE6yhlRSsz1L3Vyo2mKLSyoi2/oHiuAui9A529r50hOG1T3HRSx9e7p4lEm3Ys73pnaGnJU1eLMZFetWOcMVGocul4KuR1u1VbFAJkbfD/Ts2LEoqze+ZEHTZSK1xPVrxb7ibSK9/Y4qv/R4s2zWh1MKqriYJ1lLKilRnq3mrlRlMUWlnRll9QPGsD9N6Bzt7XzhCctjduOqnj693TRKJNO5Z3vTNkZzdF92WiQ0Ybkf49c1F+Rp7OFS8AAauZlOvlvfMCXkyyNMLW8z3/7waEMyQmGC/ILAvpVgh9C+ywWVQPlOqaup9UL66JOsI7VP3VdqspSM/F02oCZJQWZcNhNfvypKq8HVZTwMuklaM+lmMSHXHNKO+dp8TX0yNU+ZEqDw6dvne9eOQ/L8cjJNpjPUwUPpw6vdKi7ICPEIfV7Cs7VT6lrPLeeUoFob4/WhZPq9EtZ7VcEwHlZ3SUp94zYLeagvrTteUXTgclDAGOXu+joPj9oHD9e+ZGVabxQq2vOm0TAHNWIwBvXHRSx+/fM1f3uUsU6uekf8/cpI2JDBr4OYTH5TQSN2ONSJ7dglX3XI58hxVmgvKXZ7cgr3Qx8koXYddDVyHfYQUA2K1m5NotyLP7XME7+i6DyerrG7Wf+Q84ShYpsqUsCVHHILH6mpQlfw8tKwyIZyZgcL/uyPWHy/OnP7SsEJ/NHY2qfgWKDPUgdElRtpLOkLJC5Nk75kfk2S3IsVmU8CYTwUzA0LJCVBYXBMiRsmT6Q8sKsfreUTCZCERAjs2nS47NAovZhByV63etHFKVca7dopRljs0Ck4lgt5qRY/Od09ODNOUi/+faLcqgpsyLOr/qdGUecu2+/Nut5gBZ0QwA59gsYcORKp3V945S8llZXACTiWAyUUA+pSy71YzK4gKY/eWqzb+kpCgbOf7tCNT3XcqV8fMdViUd7fNjJqCyuCBgUF3K0OYllA4y/RybBSZrE2w5PyppDC0rxNCyQqy65/KkDaqr9ZXvtkybCOhR9iZ6ly+Ji07yuZHpJGtQXaZdWVyglK/2HiYKh+MUXMf+9Z2RuBlrRBiGYZjEw0aEYRiGMQwbEYZhGMYwbEQYhmEYw2SUEeH9RBiGYZJLRhkR3k+EYRgmuZAI5UCnC0NETQB2ploPDT0AHEu1EhpYp+hIR52A9NSLdYqOdNSpXAgR8yKbTHXAuFMIUZ1qJdQQ0SbWKTKsU/Sko16sU3Skq05G4mVUdxbDMAyTXNiIMAzDMIbJVCOSjjO0WKfoYJ2iJx31Yp2iI2N0ysiBdYZhGCY5ZGpLhGEYhkkCbEQYhmEYw2SUESGi54noCBFtTbUuAEBE/YjoEyLaTkTfEdFdqdYJAIjITkQbiGiLX68HUq2ThIjMRPQ1Ef1PqnUBACKqJaJviWiz0SmQ8YaICojoNSLa4X+2LkkDncr9ZST/ThLRf6SBXnf7n/GtRPQyERnaAjbOOt3l1+e7VJWRXl1JRIVEtIqIdvn/d49GVkYZEQBLAIxPtRIq3AD+txDiAgDDANxORBUp1gkA2gBcIYSoBFAFYDwRDUutSgp3AdieaiU0jBJCVKXRvP4/A3hPCHE+gEqkQXkJIXb6y6gKwMUAWgC8mUqdiKgvgF8BqBZCDABgBnBDinUaAGA2gCHw3buriejcFKiyBMF15VwAHwkhzgXwkf84IhllRIQQawEcT7UeEiHEISHEV/7fTfC97H1TqxUgfDT7D63+v5TPsCCiYgATADyXal3SFSLqBmAEgMUAIIRoF0I0pFSpYEYD2C2E+FeqFYFvQbWDiCwAsgEcTLE+FwD4QgjRIoRwA1gD4KfJViJEXXkdgKX+30sBTIxGVkYZkXSGiEoBDAawPsWqAFC6jTYDOAJglRAiHfR6EsB/Ir02tRcAPiCiL4koHfyynQ3gKIAX/N1+zxFRcjbijp4bALycaiWEEAcAPAZgH4BDABqFEB+kVitsBTCCiIqIKBvAVQD6pVgnSW8hxCHA9wEMoFc0kdiIJAEiygXwOoD/EEKcTLU+ACCE8Pi7HooBDPE3s1MGEV0N4IgQ4stU6qHDcCHERQCuhK87ckSK9bEAuAjAX4UQgwGcQpTdDsmAiLIAXAvgH2mgS3f4vq7LAJwJIIeIfpZKnYQQ2wH8XwCrALwHYAt83d5dFjYiCYaIrPAZkJeEEG+kWh8t/q6Q1Uj9WNJwANcSUS2AVwBcQUR/T61KgBDioP//Efj6+IekViPUAahTtRxfg8+opAtXAvhKCHE41YoA+AmAvUKIo0IIF4A3AFyaYp0ghFgshLhICDECvi6lXanWyc9hIuoDAP7/R6KJxEYkgRARwdd3vV0I8Xiq9ZEQUU8iKvD/dsD3su1IpU5CiN8KIYqFEKXwdYd8LIRI6VcjEeUQUZ78DWAsfN0RKUMI8SOA/URU7j81GsC2FKqkZSrSoCvLzz4Aw4go2/8ujkYaTEIgol7+/yUArkf6lNfbAKb5f08D8FY0kTLKiy8RvQxgJIAeRFQH4H4hxOIUqjQcwM0AvvWPPwDA74QQK1OnEgCgD4ClRGSG70PiVSFEWkypTTN6A3jTV//AAuD/CSHeS61KAIA7Abzk7zraA2BGivUBAPj7+McAmJNqXQBACLGeiF4D8BV8XUZfIz3cjbxOREUAXABuF0KcSLYCenUlgEcAvEpEs+AzwP8rKlns9oRhGIYxCndnMQzDMIZhI8IwDMMYho0IwzAMYxg2IgzDMIxh2IgwDMMwhmEjwpx2EJFH43E2LVZ8q/Q603/cHCmOP1wpEdURkUlzfjMRDfF7st1HRE8nQm/m9Caj1okwTJS0+l2+xA0isvgd6nUGQ3oJIWqJaD+Af4PPoR+I6HwAeUKIDQA2ENEJAOnihZjJILglwjB+/HuHPEBEX/n3EDnffz7Hv//CRr/Tw+v856cT0T+I6B34nDRmE9GrRPQNES0jovVEVE1Es4joCVU6s4koag8GRNSDiNYR0QS/t4HX/bpsJKLh/mAvI9DNeVo4QWQyHzYizOmIQ9OdNUV17Zjf4eJfAfzaf+4++Nyw1AAYBeBRlefcSwBME0JcAeCXAE4IIQYBeBC+fTUAny+wa/1+1ADfCvMXolGUiHoDWAHgD0KIFfDtJfKEX5dJ6HCb/yqAiX6X5wAwxZ8uwyQU7s5iTkfCdRtJJ5lfwufXCPD5zLqWiKRRsQMo8f9eJYSQ+zJcBl8lDyHEViL6xv/7FBF9DN8GRNsBWIUQ30ahpxW+zYFuF0Ks8Z/7CYAKvysWAOhGRHlCiB+J6DsAo4noMACXECItdvhkMhs2IgwTSJv/vwcd7wcBmCSE2KkOSERD4XPFDlW4UDwH4HfwObqMqhUCn7+nLwGMg3+sA77eg0uEEK064WWX1mFwVxaTJLg7i2Ei8z6AO/2eYEFEg0OE+xTAv/vDVAAYKC/4Xbf3A3Ajoq/gBYCZAM5XzSD7AMAdMgARVanCvw7fJkfclcUkDTYizOmIdkzkkQjhH4Sva+kbItrqP9bjLwB6+ruxfgPgGwCNquuvAvgsFq+tQggPfK2LUUT0S/j3DPcP3m8DcJsqbAOALwAcFkLsjTYNhukM7MWXYeKE37W+VQjhJKL+8I1nnCeEaPdf/x/4BsU/ChG/WQiRmyDdpgOoFkLcESksw8QCt0QYJn5kA/iUiLbAtwviL4QQ7URUQETfwzegr2tA/JxULzaMF0R0N4DfAkiLrZmZzIJbIgzDMIxhuCXCMAzDGIaNCMMwDGMYNiIMwzCMYdiIMAzDMIZhI8IwDMMY5v8DQVOdAf5BAUMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = mos1_dataset.plot_excess()\n",
    "ax = mos1_dataset.grouped.plot_excess()\n",
    "plt.xlim(1,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0, 10.0)"
      ]
     },
     "execution_count": 9,
     "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": [
    "ax = mos2_dataset.plot_excess()\n",
    "ax = mos2_dataset.grouped.plot_excess()\n",
    "plt.xlim(1,10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a joint datasets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "xmm_datasets = Datasets(datasets=[pn_dataset, mos1_dataset, mos2_dataset])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `~gammapyX.SherpaSpectralModel` is a wrapper that can be used for fitting `Sherpa` and `XSpec` models within the `Gammapy` framework.\n",
    "\n",
    "Let's verify that it yields the same results as the plain `Sherpa` and `XSpec` models (assuming for example a `wabs`*`powerlaw` model):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "**************************************************************\n",
      "The wabs model is obsolete and is only included for comparison\n",
      "with historical results. The tbabs model should be used for\n",
      "the ISM or phabs for general photoelectric absorption.\n",
      "**************************************************************\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lucagiunti/miniconda3/envs/gammapyx/lib/python3.9/site-packages/sherpa/astro/xspec/__init__.py:1179: FutureWarning: calc() requires pars,lo,hi arguments, sent 2 arguments\n",
      "  warnings.warn(emsg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9c6ee7a820>"
      ]
     },
     "execution_count": 11,
     "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": [
    "fig, ax = plt.subplots()\n",
    "energy_grid = np.linspace(0.5, 10.0, 1000) * u.keV\n",
    "\n",
    "plaw = PowLaw1D()\n",
    "plaw.ampl=1e-3\n",
    "plaw.gamma=2\n",
    "\n",
    "abs_model = XSwabs()\n",
    "abs_model.nH = 5\n",
    "\n",
    "# Gammapy wrapper\n",
    "f1 = SherpaSpectralModel(plaw) \n",
    "f2 = SherpaSpectralModel(abs_model, default_units=(u.keV, 1))\n",
    "f3 = f1 * f2\n",
    "ax.loglog(energy_grid, f3(energy_grid), color=\"r\", lw=3, label=\"Gammapy wrapper\")\n",
    "\n",
    "#Plain sherpa\n",
    "plaw_with_abs = plaw * abs_model\n",
    "\n",
    "ax.loglog(energy_grid.value, plaw_with_abs(energy_grid.value), color=\"k\", ls=\"--\", lw=3, label=\"sherpa\")\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assign the model to the joint data set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lucagiunti/miniconda3/envs/gammapyx/lib/python3.9/site-packages/sherpa/astro/xspec/__init__.py:1179: FutureWarning: calc() requires pars,lo,hi arguments, sent 2 arguments\n",
      "  warnings.warn(emsg, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "model = SkyModel(spectral_model=f3)\n",
    "xmm_datasets.models=[model]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define min/max values for the free model parameters and inspect the parameters table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=4</i>\n",
       "<table id=\"table140309837828448\" class=\"table-striped table-bordered table-condensed\">\n",
       "<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>str5</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>gamma</td><td>2.0000e+00</td><td></td><td>0.000e+00</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td></td></tr>\n",
       "<tr><td>spectral</td><td>ref</td><td>1.0000e+00</td><td></td><td>0.000e+00</td><td>nan</td><td>nan</td><td>True</td><td>False</td><td></td></tr>\n",
       "<tr><td>spectral</td><td>ampl</td><td>1.0000e-03</td><td></td><td>0.000e+00</td><td>0.000e+00</td><td>nan</td><td>False</td><td>True</td><td></td></tr>\n",
       "<tr><td>spectral</td><td>nH</td><td>5.0000e+00</td><td></td><td>0.000e+00</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td></td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=4>\n",
       "  type    name   value    unit   error      min      max   frozen is_norm link\n",
       "  str8    str5  float64   str1   int64    float64  float64  bool    bool  str1\n",
       "-------- ----- ---------- ---- --------- --------- ------- ------ ------- ----\n",
       "spectral gamma 2.0000e+00      0.000e+00       nan     nan  False   False     \n",
       "spectral   ref 1.0000e+00      0.000e+00       nan     nan   True   False     \n",
       "spectral  ampl 1.0000e-03      0.000e+00 0.000e+00     nan  False    True     \n",
       "spectral    nH 5.0000e+00      0.000e+00       nan     nan  False   False     "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xmm_datasets.models[0].spectral_model.model1.ampl.min = 0\n",
    "xmm_datasets.models[0].spectral_model.parameters.to_table()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Restrict the fit range for each instrument:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "pn_dataset.mask_fit = pn_dataset._geom.energy_mask(1.5*u.keV, 10*u.keV)\n",
    "mos1_dataset.mask_fit = mos1_dataset._geom.energy_mask(1.5*u.keV, 8*u.keV)\n",
    "mos2_dataset.mask_fit = mos2_dataset._geom.energy_mask(1.5*u.keV, 8*u.keV)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the binned joint-likelihood fit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lucagiunti/miniconda3/envs/gammapyx/lib/python3.9/site-packages/sherpa/astro/xspec/__init__.py:1179: FutureWarning: calc() requires pars,lo,hi arguments, sent 2 arguments\n",
      "  warnings.warn(emsg, FutureWarning)\n",
      "/home/lucagiunti/miniconda3/envs/gammapyx/lib/python3.9/site-packages/sherpa/astro/xspec/__init__.py:1179: FutureWarning: calc() requires pars,lo,hi arguments, sent 2 arguments\n",
      "  warnings.warn(emsg, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 41 s, sys: 27.6 ms, total: 41.1 s\n",
      "Wall time: 41.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "fit = Fit()\n",
    "fit_result = fit.run(xmm_datasets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspect the fit output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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       : 132\n",
      "\ttotal stat : 70.27\n",
      "\n",
      "CovarianceResult\n",
      "\n",
      "\tbackend    : minuit\n",
      "\tmethod     : hesse\n",
      "\tsuccess    : True\n",
      "\tmessage    : Hesse terminated successfully.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(fit_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  type    name   value    unit   error      min    max frozen is_norm link\n",
      "-------- ----- ---------- ---- --------- --------- --- ------ ------- ----\n",
      "spectral gamma 1.8533e+00      2.710e-01       nan nan  False   False     \n",
      "spectral   ref 1.0000e+00      0.000e+00       nan nan   True   False     \n",
      "spectral  ampl 5.7192e-04      2.655e-04 0.000e+00 nan  False    True     \n",
      "spectral    nH 6.1008e+00      1.013e+00       nan nan  False   False     \n"
     ]
    }
   ],
   "source": [
    "print(fit_result.parameters.to_table())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<AxesSubplot:xlabel='Energy [keV]', ylabel='$\\\\mathrm{}$'>,\n",
       " <AxesSubplot:xlabel='Energy [keV]', ylabel='Residuals\\ndata - model'>)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pn_dataset.plot_fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Energy [keV]', ylabel='Residuals (data - model)'>"
      ]
     },
     "execution_count": 19,
     "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": [
    "pn_dataset.plot_residuals_spectral(color=\"r\")\n",
    "mos1_dataset.plot_residuals_spectral(color=\"g\")\n",
    "mos2_dataset.plot_residuals_spectral(color=\"b\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparison with reference results obtained with plain sherpa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "in this section we provide a comparison between the fit results obtained in this notebook, using `Gammapy`, and the results obtained with a standard `Sherpa` fitting with the same assumptions.\n",
    "\n",
    "The reference `Sherpa` notebook can be found at: https://github.com/luca-giunti/share/blob/master/CIAO_sherpa_validation_nb.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit results obtained with Sherpa\n",
    "amplitude_sherpa = (0.000569626, 0.000251357)\n",
    "index_sherpa = (1.85112, 0.261476)\n",
    "nh_sherpa = (6.11597, 0.904329)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "opts = {\"marker\": \"o\", \"lw\": 1, \"capsize\": 3}\n",
    "pars_sherpa = [amplitude_sherpa, index_sherpa, nh_sherpa]\n",
    "pars_gammapy = [fit_result.parameters[\"ampl\"], fit_result.parameters[\"gamma\"], fit_result.parameters[\"nH\"]]\n",
    "titles = [\"PL amplitude @ 1 keV (keV-1 cm-2 s-1)\", \"PL spectral index\", \"nh (x10**22 cm-2)\"]\n",
    "\n",
    "for par_sherpa, par_gammapy, title in zip(pars_sherpa, pars_gammapy, titles):\n",
    "    plt.figure(figsize=(5, 4))\n",
    "    plt.errorbar(0, par_sherpa[0], yerr=par_sherpa[1], color=\"k\", label=\"Sherpa\", **opts)\n",
    "    plt.errorbar(1, par_gammapy.value, yerr=par_gammapy.error, color=\"r\", label=\"Gammapy\", **opts)\n",
    "    plt.xticks([])\n",
    "    plt.title(title)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lucagiunti/miniconda3/envs/gammapyx/lib/python3.9/site-packages/sherpa/astro/xspec/__init__.py:1179: FutureWarning: calc() requires pars,lo,hi arguments, sent 2 arguments\n",
      "  warnings.warn(emsg, FutureWarning)\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'numpy.ndarray' object has no attribute 'unit'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Input \u001b[0;32mIn [22]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mSkyModel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mspectral_model\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mf2\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/gammapyx/lib/python3.9/site-packages/gammapy/modeling/models/cube.py:71\u001b[0m, in \u001b[0;36mSkyModel.__init__\u001b[0;34m(self, spectral_model, spatial_model, temporal_model, name, apply_irf, datasets_names)\u001b[0m\n\u001b[1;32m     69\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_irf \u001b[38;5;241m=\u001b[39m apply_irf\n\u001b[1;32m     70\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdatasets_names \u001b[38;5;241m=\u001b[39m datasets_names\n\u001b[0;32m---> 71\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_unit\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     73\u001b[0m is_norm \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([par\u001b[38;5;241m.\u001b[39mis_norm \u001b[38;5;28;01mfor\u001b[39;00m par \u001b[38;5;129;01min\u001b[39;00m spectral_model\u001b[38;5;241m.\u001b[39mparameters])\n\u001b[1;32m     75\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39many(is_norm):\n",
      "File \u001b[0;32m~/miniconda3/envs/gammapyx/lib/python3.9/site-packages/gammapy/modeling/models/cube.py:124\u001b[0m, in \u001b[0;36mSkyModel._check_unit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    121\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspatial_model \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    122\u001b[0m     ref_unit \u001b[38;5;241m=\u001b[39m ref_unit \u001b[38;5;241m/\u001b[39m u\u001b[38;5;241m.\u001b[39mUnit(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msr-1\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 124\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[43mvalue\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munit\u001b[49m\u001b[38;5;241m.\u001b[39mis_equivalent(ref_unit):\n\u001b[1;32m    125\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    126\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSkyModel unit \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvalue\u001b[38;5;241m.\u001b[39munit\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m is not equivalent to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mref_unit\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    127\u001b[0m     )\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'unit'"
     ]
    }
   ],
   "source": [
    "SkyModel(spectral_model=f2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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