{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<div class=\"alert alert-info\">\n",
    "\n",
    "**This is a fixed-text formatted version of a Jupyter notebook**\n",
    "\n",
    "- Try online [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/gammapy/gammapy-webpage/v0.15?urlpath=lab/tree/spectrum_analysis.ipynb)\n",
    "- You can contribute with your own notebooks in this\n",
    "[GitHub repository](https://github.com/gammapy/gammapy/tree/master/tutorials).\n",
    "- **Source files:**\n",
    "[spectrum_analysis.ipynb](../_static/notebooks/spectrum_analysis.ipynb) |\n",
    "[spectrum_analysis.py](../_static/notebooks/spectrum_analysis.py)\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spectral analysis with Gammapy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "This notebook explains in detail how to use the classes in `~gammapy.spectrum` and related ones.\n",
    "\n",
    "Based on a datasets of 4 Crab observations with H.E.S.S. we will perform a full region based spectral analysis, i.e. extracting source and background counts from certain \n",
    "regions, and fitting them using the forward-folding approach. We will use the following classes\n",
    "\n",
    "Data handling:\n",
    "\n",
    "* `~gammapy.data.DataStore`\n",
    "* `~gammapy.data.DataStoreObservation`\n",
    "\n",
    "To extract the 1-dim spectral information:\n",
    "\n",
    "* `~gammapy.spectrum.SpectrumDatasetMaker`\n",
    "* `~gammapy.cube.SafeMaskMaker`\n",
    "* `~gammapy.spectrum.ReflectedRegionsBackgroundMaker`\n",
    "\n",
    "To perform the joint fit:\n",
    "\n",
    "* `~gammapy.spectrum.SpectrumDatasetOnOff`\n",
    "* `~gammapy.modeling.models.PowerLawSpectralModel`\n",
    "* `~gammapy.modeling.models.ExpCutoffPowerLawSpectralModel`\n",
    "* `~gammapy.modeling.models.LogParabolaSpectralModel`\n",
    "\n",
    "To compute flux points (a.k.a. \"SED\" = \"spectral energy distribution\")\n",
    "\n",
    "* `~gammapy.spectrum.FluxPoints`\n",
    "* `~gammapy.spectrum.FluxPointsEstimator`\n",
    "\n",
    "Feedback welcome!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "As usual, we'll start with some setup ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gammapy: 0.15\n",
      "numpy: 1.17.3\n",
      "astropy 3.2.3\n",
      "regions 0.4\n"
     ]
    }
   ],
   "source": [
    "# Check package versions\n",
    "import gammapy\n",
    "import numpy as np\n",
    "import astropy\n",
    "import regions\n",
    "\n",
    "print(\"gammapy:\", gammapy.__version__)\n",
    "print(\"numpy:\", np.__version__)\n",
    "print(\"astropy\", astropy.__version__)\n",
    "print(\"regions\", regions.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import astropy.units as u\n",
    "from astropy.coordinates import SkyCoord, Angle\n",
    "from regions import CircleSkyRegion\n",
    "from gammapy.maps import Map\n",
    "from gammapy.modeling import Fit, Datasets\n",
    "from gammapy.data import DataStore\n",
    "from gammapy.modeling.models import (\n",
    "    PowerLawSpectralModel,\n",
    "    create_crab_spectral_model,\n",
    "    SkyModel,\n",
    ")\n",
    "from gammapy.cube import SafeMaskMaker\n",
    "from gammapy.spectrum import (\n",
    "    SpectrumDatasetMaker,\n",
    "    SpectrumDatasetOnOff,\n",
    "    SpectrumDataset,\n",
    "    FluxPointsEstimator,\n",
    "    FluxPointsDataset,\n",
    "    ReflectedRegionsBackgroundMaker,\n",
    "    plot_spectrum_datasets_off_regions,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data\n",
    "\n",
    "First, we select and load some H.E.S.S. observations of the Crab nebula (simulated events for now).\n",
    "\n",
    "We will access the events, effective area, energy dispersion, livetime and PSF for containement correction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "datastore = DataStore.from_dir(\"$GAMMAPY_DATA/hess-dl3-dr1/\")\n",
    "obs_ids = [23523, 23526, 23559, 23592]\n",
    "observations = datastore.get_observations(obs_ids)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Target Region\n",
    "\n",
    "The next step is to define a signal extraction region, also known as on region. In the simplest case this is just a [CircleSkyRegion](http://astropy-regions.readthedocs.io/en/latest/api/regions.CircleSkyRegion.html), but here we will use the ``Target`` class in gammapy that is useful for book-keeping if you run several analysis in a script."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_position = SkyCoord(ra=83.63, dec=22.01, unit=\"deg\", frame=\"icrs\")\n",
    "on_region_radius = Angle(\"0.11 deg\")\n",
    "on_region = CircleSkyRegion(center=target_position, radius=on_region_radius)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create exclusion mask\n",
    "\n",
    "We will use the reflected regions method to place off regions to estimate the background level in the on region.\n",
    "To make sure the off regions don't contain gamma-ray emission, we create an exclusion mask.\n",
    "\n",
    "Using http://gamma-sky.net/ we find that there's only one known gamma-ray source near the Crab nebula: the AGN called [RGB J0521+212](http://gamma-sky.net/#/cat/tev/23) at GLON = 183.604 deg and GLAT = -8.708 deg."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LRDwJLAdeWnnho9eDpGnAjcDVEbG8A7W0FMVqyX8BJ1D8iv8qil94gBuAhV0q7WkNapxXN/lQdl8d7bhR/q6jfg96WrfTqw0pPhPYt274v4ETSjzv5exqeRwNbKDY1iGKDYHnd/h91Ncjiq3yn+uBz/cgYP80PB34EXByevwj4Plp+MPAsl6qkWI71v3AILs2mL6gi59ly78rfdTymAi7ap8F3CQJii/L1yLie2NZQESsknQDsIaiKX4X8OV2FzoGiyma2OvTejzAxRHxnS7UcghwZdqN+Azguoj4dpp2PnC1pL0o/knf1IX6mtYo6e3A9yn2vHwlIjZ0qUborb/ruPkIUzPLMiG2eZhZ5zk8zCyLw8PMsjg8zCyLw8PMsjg8zCyLw8PMsjg8JiBJf5S0VtI9kr6V+t1A0nPSwXCtnr99lPGnSlrQ4rl3S/p6XuXtUfZ92vg4PCamHRHx4og4EngYWArFOUARccY4lnsqRX8YDUk6guI7taSbZ9i24X1aCQ6Pie8n7OozYkDSPWl4hqTrJK2TdK2kVZJqPbAh6eOpFXGHpGdJeilwCrAstWoOa/BabwD+A7glzVtb1jsk/TS91jVp3CxJV0han8afnsYfL+knktZIul7SrDR+k6SPpPHrJc1P449N9axNXQPsO+J97lP3OndJOi6NP1fScknfk3SfpE+3+XOf+Lp9co1v7b8B29P9FOB60omCwABwTxp+N/ClNHwkxTk9i9LjAF6Thj8NfDANfxU4o8nr/hx4LnA88M268Q8Be6fh2glsn6LuBDGKbhHmUHQnODONex/woTS8iXSyIvCPpFPtgW8Bi9PwLIrzm+rf50XAFWl4PkWfLftQ9NJ1P/DM9PgBYF63/3b9dHPLY2Kank68GgZmA7c2mOdlFKd/ExH3AOvqpj0B1E5+u5Pin7EpSS8BtkTEA8BtwEJJB6TJ6yhOoDuLIqSg6Abh32rPj4jfUXQduAD4car/HIowqqmdwl5f04+BSyS9gyKYdrK7l1G0hoiIeylC4vA07baIeDQi/gD8dMRrWQsOj4lpR0S8mOKfYS/SNo8Rml1c6MlIP9UUnUqXOfv69cD81CfFL4D9gNPTtJMoguIvgDtTV4+irnvIuppujWJ7zYsjYkFEvLlu+uMja4qIT1J0XD0duKO2OlPyfT5eN1z2fVri8JjAIuJR4B3Au1MnNPVWAmcCpD0of15ikdsoOu7djaRnUPTa9cKIGIiIAYq+Ql+fps2LiBUU3eztT7F6cQvw9rplHADcASyW9Kdp3AxJh9OEpMMiYn1EfIqi5/GR4fFD4I1p3sOBPwF+VuK9WgsOjwkuIu6i6ATnb0ZM+gJwkKR1FNsW1rGrw+jRXAO8J214rN9guoTicgEP1o37IcUqyFzgKknrKfpJ+WwUvX19DDgg7U6+GzguIrZQbIv4eqrrDvYMg5EurFvGDuC7Dd7nlPT611L0iv/4yIXY2Lk/j0kqdZwzLYpr0xxGsZ3i8OiF64FYX/A63uQ1A1iRVmcEvM3BYWPhloeZZfE2DzPL4vAwsywODzPL4vAwsywODzPL8v9RjMCNrrGEkwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "exclusion_region = CircleSkyRegion(\n",
    "    center=SkyCoord(183.604, -8.708, unit=\"deg\", frame=\"galactic\"),\n",
    "    radius=0.5 * u.deg,\n",
    ")\n",
    "\n",
    "skydir = target_position.galactic\n",
    "exclusion_mask = Map.create(\n",
    "    npix=(150, 150), binsz=0.05, skydir=skydir, proj=\"TAN\", coordsys=\"CEL\"\n",
    ")\n",
    "\n",
    "mask = exclusion_mask.geom.region_mask([exclusion_region], inside=False)\n",
    "exclusion_mask.data = mask\n",
    "exclusion_mask.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run data reduction chain\n",
    "\n",
    "We begin with the configuration of the maker classes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "e_reco = np.logspace(-1, np.log10(40), 40) * u.TeV\n",
    "e_true = np.logspace(np.log10(0.05), 2, 200) * u.TeV\n",
    "dataset_empty = SpectrumDataset.create(\n",
    "    e_reco=e_reco, e_true=e_true, region=on_region\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_maker = SpectrumDatasetMaker(\n",
    "    containment_correction=False, selection=[\"counts\", \"aeff\", \"edisp\"]\n",
    ")\n",
    "bkg_maker = ReflectedRegionsBackgroundMaker(exclusion_mask=exclusion_mask)\n",
    "safe_mask_masker = SafeMaskMaker(methods=[\"aeff-max\"], aeff_percent=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.57 s, sys: 84.4 ms, total: 2.66 s\n",
      "Wall time: 2.68 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "datasets = []\n",
    "\n",
    "for observation in observations:\n",
    "    dataset = dataset_maker.run(dataset_empty, observation)\n",
    "    dataset_on_off = bkg_maker.run(dataset, observation)\n",
    "    dataset_on_off = safe_mask_masker.run(dataset_on_off, observation)\n",
    "    datasets.append(dataset_on_off)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot off regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RBbT2b82EZhMIrhZcjq9I3C2MKSmkbPqO1O+/xxAfD3Z2ODaoj/fIUbiH9ECzt8/X3pSbS9r27aSsXkPOhQsogwE7Pz88Bg7Ae9QoHAIDy+mVCGsgSVrcMSZl4re434hIjcCkTFR3rc69Ne/F3jb/l9buqN0sOLKAsU3GsmXIFvyc/QDzFcaZhDN8de4rtoZvZWnPpfi7+BORGsGU3VPoUasHq/uvJsA9IK+vLH0W2yO2M+fQHKa1nsawhsPyzimlOJtwls2XN3Mt8xo2mg1BHkEMbTiUIM+gO/OmiArDlJmJMS0NzckJWy+vQq92UzZuIv7tt3Hv3o1qr7yMQ506KIOR7OPHSF65khvvvkvA4k9xatQIgJwLF4iZNh3HBvXxfWIKrp06odnbo4uJIWXDBiJHjMR79Gj8npxe4PmMGRnk/PEHpqwsbNzccW7dChsnpzvyXog7R1NKlXcMty04OFgdO3asvMMQJTCajKwJXcO3F77Fxd6FVv6t0NC4knqF8JRwHmrwEJNaTMLF3oXf435n1sFZfPbAZzT2aVxof0oplp5eyt7ovSzuuZhx28cxucVkhjYcWmQMMWkxTNgxgVe7vMp9AfcRlhzGy4dfJjU3lWENh1Hfqz4KxR83/2BT2CYa+TRiftf5VHGpUlZvi6gAlNFIxoEDJH+7mqwTJ7B1d8eUnY1dlSp4P/wwnkMGY+vmBkDy+vUkLv2MwOXLcaxb+C95aT/+SNzCN6i94ivQNKLGjafayy/j0ad3oe0NCQnEPD4F1/vvo8qMGQDooqJI/Oor0n7cjlODBti4u2NMTkYXFYXngw/iM34c9lWrlsn7IcpGcHAwx44dK3yMQylVYX/atWunhHXTGXXqmf3PqHE/jlOnbpxSJpMp3/krKVfUrAOz1MitI1VKTooas22M2h6xvcR+TSaTemL3E+rZ/c+q2QdnWxTLgegDaviW4ep8wnl135r71KZLm5TRZCwYs0Gnlp5aqnpv6K3iMuIse6Gi0jGkpanICRNU+NBhKuX775UxJ0cpZf7sZRw9qmJmzFSXunVX2aGhShcXpy526KhywsNL7Ddp3ToVPmy4ihgzRiWtXlNie31Cggq95x6VfTFUZRw5qkI7d1E3PvxI6eLi87XLjYpScW+8oS7de5/KPn/+9l60KBf/y2WF5jm5khZl6q3f3iIqLYoPu3+Ig61DoW2UUrz9+9ucvnGaG1k32DlsJ3Y2JY/E/BT7EzP2z+DzXp/TpmqbEtsbTUb6f9efHEMOczvOpXedwq9e/vR/Z/6Pn2J/YkXfFfn6OB5/nJvZN7GzsaOhd0O5NV4JKZ2O6EmTcahdm2qvvYpma1tou9StW7nxzru49+mDMuip/uqrJfdtNHK5ew+UXk+DQwcLjFEX5uYnn6K7coXMo0epuWgRrp06Ftk2bft24t98izrr1mJfrVqJfYvyV9yVtIxJi9uilOJY/DHWhq7lQuIFco25+Dr70i+oH0PqD8HT0ZOE7AS2XNnC9oe2F5mgATRNY3bwbLqt60aX6l0sStAAge6BGE1GqrpYdmvP1saWBl4NCEsJKzFBA0xsNpF1oes4n3ieOh51WHVhFesvrcfbyZva7rUxKANv/fYWdT3r8kjTR+gW2M2iOET500VHk7ZtG4abN9Hs7XFs3ASPvn3yxnRTt24FKDZBA3gOHIg+NpaEZcuos2aNRc+t2dpiHxiIKSfbogQN4DV8OJdDQvCfObPYBA3g0bcv2adOk7Tia6r+Zw5KKbKPHyf529VknzqFKTcXWy8vPHr3wmvECEnkVk6StLhlcZlxzNg/g1xDLiMbj2R66+k42jpyNeMqm8I20XdjX55q+xTpunR61e6Fp6NniX3a2thSz6se4WnhFseRZcjC3taedH26xY+5knKF+l71LWpra2PL8IbDWXV+FZdTL1PTrSYfdP+Apr5N89rojXr2xuzl3d/f5UzCGZ5q85TFsYg7Lzc8nPg33yLn7Fk8Bg7AIaguSq8nbcd2brzzDl4jR+A3bRrJq77Ff8bTxSboP3mNGsXNDz/C1svr1oIxmixuqtnagMGAR+9eFrX3HjOayJGj8H5kLNdmzcaYlIT36IfxnzkDG2dn9HHxpH73HeGDh+D98Cj8Z8yQZV9WSpK0uCU3s24yYccERjYayYRmE/L9w67hVoP21doTkx7DtD3TMCkTL3R8weK+G3k34rvL31nc3s3eDb1Jj7Ods+XxZ9+kZ+2eFrdv7d+az89+ztAGQ5kVPKvAF5m9rT196vShY7WOPLrzUfyc/Xi48cMW9y/unOxz54h5fAp+U6YQ8MnH2Dg65p3zfXQiupgY4ubPJ2bSZPQ3buB6zz0W9Wvr6QmaRvruPfiMHWNZMLa3to9U5uHDYGuLrbePRe0datXCPiCAqDFj8RwyGP+nn8732bXz98e5RXP8npxOzNSpqOxsqs6de0sxiTtDdhwTt2Th0YUMqDuAic0nFvmbd6B7IMt7LedaxjVSclIs7rtzjc7kGHKwdJ5Eam4qGhoJ2QkWtTeajOQac+lcvbPFMZ1LPIfRZOS54OeKvdLwdvLmvfvfY+nppeiMOov7F3eGMSOD2GnTqfbKK/iMeyRfgv6TQ2AggYsXo/Q6NE2z6CoaQLOxwcbTk5wLFyyOR9NswGT5lXRO2GU0R0dsXF0sfowhMRHH+vWpUsxVsp2PD7WWLSN9334yjxyxuG9x50iSFha7nnGd3+N+59Hmj5bYtpprNaq6VuXHiB8t7r+uZ11sNBuOxx+3qP26S+voUqMLay5aNhZ4KPYQDrYOONtbfuW95coWgjyDit3l7E91verS0Lshu6N25x27knKFXZG7+DH8R07En8CkLP9iFqUndcsWnFu1KvF2sWZnh+/UaRgSEzGmWz6M4tSwITnnz1nUVul05IaFobt6FWNamkWPyT59GofAQItvSRvT0jDEx+P50IMltrX19MRn4gSSv11tUd/izpLb3SKfKylX2BS2iZj0GBSKALcAHmrwEA28G7Dlyhb6BfXDxd6y3+aH1B/CZ398ht6kx96m5Aky2yK2EVw1mFd/eZUVfVfkbWJSmJ2RO/nl6i982fdLpuyawvpL6xnecHiR7aPTollwZAF9g/qy/tJ6WldpXWI8JmUiLCWMd+57p8S2f+ob1Jefr/6MvY09Ky+s5Gr6VVr6t8TWxpbw1HByDDmMbDSSUY1H4Whb8GpO3D5DYiKZv/yCMTUNGxcXXNoH5+3WlbJ6DVVftGzoxaVtW7CxIWnlKvynPlFie6UUxpRk9LFXyT59GudWrYptn/jFFzg1boydvx83Fi2i2quvFpt8s//4g9zQUABM2dnYOJf8S2bymrWgabhZeMvec9Agbr7/AYbkZJReT8qGDWTs3YcxLQ0bZ2dcgoPxHv0wjvUtm88hSo8kaQHAtYxrvHL4Fa6kXuHB+g8yuN5gAC4kXWDK7inU8ayDl6MXXWt0tbjPMU3GsPjUYsKSw/JNtipMjiGHDZc2sDhkMQdiDjBu+zie7/A8XWt0xdbmr9uOKTkprAldw9rQtSwOWUwt91os6bmEKbuncCnpEuOajiPQ469tFLP0WWyL2MbiU4t5qs1TdA/sTv/v+hOVFkVtj9rFxvRz7M9oaDxQ+wGLX7OHgwenbpzifOJ5nmzzJN0Cu+X9gqKU4o+EP/js9Gfsi97H4p6L821tKm5Pbng4CUuWknHgAK6dOmHr54spPYMb77yDU6uW+EyYiC4yEpeOxc+K/pOtmyvObduQvmOHRUk6++QpVK6O6u++Q8y06dR8fxGuHToUaKeMRpK+/JKUdeupvfpbbFxdiXpkHPHzF1Bl9qwCyVcpReYvv3Btzn+o8dZbpKxbR+oPP+A9vOhfRv+UcWA/Tk2aYOvubuFrdsO+WjVuvLeI9F278Ojbl6ovzMXO1xdjZiYZe/cSNXEiLm3aUuPNN7Bxlc/tnSLrpAWx6bFM2DGBMU3GMLbJ2ALbdOpNelZfWM3HJz9mcovJPN7qcYv77rCqAwFuAXzZ58siZ3nrjXrmHJqDo50jb937FgB7ovbw+ZnPSc5NpmuNrnl7d/9y7Re6BXRjauupBLr/lYwTshNYcW4F31/+nrpedfP27j4ef5x2VdsxodkE2lZtC8DGSxtZfmY5yx5YRi2PWoXGdOrGKWbsn0FKTgpHxxzFyc6y7RZn7p/Jsbhj/Dj0RzwcPAptY1Im5v06j/iseBaHLJZZtf9C1vHjxD71ND4TJuA9Yni+Gdam7GzStm3jxnuLMGVn0/jUSYv7Tfz8C25++CF11q/L28KzMKbMTKIeGYfXiBF4jxpJxk8/c/3FF7GvFYj3iBE4BAWh9Aayjh8jZc1a7KpUoeb7i/KWPRnT07n+8itkHTmC5+DBuHTqiObggD4mhpT1GzCmpVH99ddw7dKFrJMniZ3+JLW+/KLYmDKP/kbMlCl4jRxBNQsngymluNSpM/bVqlHryy+w8yk4QU3pdFx/7XV0UVHU+uLzQsf1xe2RHcdEkUwmkxq2ZZhaeX5liW2f3vu06vJtl0J36SpMbHqs6vptV/X20bdV/0391ZbLW1SOISfvvMFoUAdjDqqx28aqp/Y+pXINuQX6OHvzrFp1fpX64swXatOlTSopO6nY58zWZ6tDMYfUlstb1K7IXepa+rVC2629uFZ1+baLmv/rfHUx8aIyGA1KZ9SpE/En1JyDc9Q9q+9RB2MOqim7p6jNYZster2ZukzVakUr9c25b0psqzfq1YBNA9TxuOMW9S0Kyo2JVaFduqr0n34utl32xVB1vlFjlbb/gMV931y2TEU9+pgK7XqPStu3T5mMBT/zOZcvq/Chw9S1V1/Nt5OeSadTqTt2qugnpqrwBx9S4SNGqKsvvqiy/vij2NcSv+h9FTV5soqcMEHFzpqt0g8dKvC8qT/+qEK7dFVJ69YpY1ZWvnOG1FSV8MWXKrRzFxX39jsqetp0i19v8vdb1PkmTVXutcL/veS9NqNRxTz5pLrx4YcW9y1KJjuOiSL9Hvc7C48s5LvB35V4RRefGU+vjb14//736VG7R4l9f3jiQ3KNucxpP4eDMQdZdWEVocmhNPJuhI2NDREpEXg5eTGq0SgG1RuU77b2nRCfGc/GsI1svryZG1k3AKjlUYthDYYxuP5gPB09ORBzgKWnl/Jt/29LnDy2+NRi/u/M/3Fk9JFiN2/50zfnv+FMwplbGvMWf4l/8000BweqPPdciW0jRj2MKTWVettLnsiolCK8X3+qz5+HKSeXG++8g8rNxWPwIOyrVMGUlUX63n3kXrmC76OP4jNxwh29G5J96hQJny0j++RJXO+9F1t3NwxJyWT+8gtu99yD7xNTcKhZk8s9Qgja/B321auX2OeVPn2x9fGmzrffltg298oVosZPoMG+vWgOJX/ORcmKu5KWJH2Xm31wNm2rtrV4be/IH0aSoctg20Pbim0Xkx7D2B/HsqLPCup41sl3PDotGqMyUt21Og28G/yb8EuN0WRE07QCidhoMjJ+x3jaVm3LM22fKfLLOCUnhT6b+tCzVk8W3LPAoudMyE5g0OZB/PLwL/86/ruNKTuby917ELRxA/Y1S64XnnnkCNGPPkbQpk04NS76VjFA+r593Hz/A4K2fI+maeYdu06eJH3Xboypqdg4O+Hcth0evR4o1ySli71K1tGj5ipY7m64de2Knb9/3vn4N9/EkJRMjXfeLn5i2pkzRI4YSa2vV+Davr1Fzx01bjzeY8fg0cuyzVVE8WRbUFGksOQwHm9p+Rjz3A5zmbhjIktOL2FKyymFXl2Gp4Yzbc80praami9Bg3kN9d/Hkq1FUVfxtja2fNTjI57Y/QRzMuYwqcUkGvn89SWvN+k5EHOAj058hIeDB/3r9rf4Ob0cvcjUZ5KWm8bW8K3sj95Pmi4NZztngqsFM7zhcKq5ypaNhck5dw772rUsStAALh07Yh9Qk2uzZ1Nn/boiSzpmnznD9ZdepuaiRXmJTdM0XNq2Nc/6tiIOATVxCHioyPP+M2cSNX4Cca++RrWXXiz0FwpdTAyxM2ZiX6OGxQkawKlpU/QxMQDo4+LQRUWDMmFfs6bUvy5lkqTvAqFJoawJXcO+6H2k5qbiZAZKGv4AACAASURBVOdEu6rtGNloJHqT3uK9sgF8nX3xdfblyLUjbLm8hRGNRtC5RmccbByIzYjlu7Dv+C3uN2YFz+LBBiWv0awIfJx8+KrPV6w4v4Jpe6dR1aUqtTxqYTAZOBl/kkCPQGa2ncnmK5tJ01m27hXMV9/2Nvb02dSHrjW68kjTR/Bz9iNTn8ne6L0M3TKUnrV78mLHFy26fV4Z6ePiSFm/gZyLF1F6HXZ+/ngO6I9Jr8fWzbKZy2BOtG733U/WiRNEDBuGz7hxeA4YgI2LeTlhbngEyWtWk7b1B6ovXFji/tgVgY2zM7W++Jzrc+cS1iMEr6FDcet2PzbOzhji40n5bjOZv/yC58CBeUu8LKZp5EZEED1lCjmn/zAvzfrfMcegILzHjMa9d2+ZFFkK5HZ3JaY36nn919c5cv0IwxsOZ2C9gfg7+5NlyGJf9D7Whq4lIjWCFzq+wOD6gy3q8/DVwyw5vYSV/VZyNuEsay6u4ULSBXRGHT5OPvSv25/+dftX2qVFBpOB3+N+52b2Text7Gng1YD63ua1o2suruG3uN9Y1G2RRX09vutxziacZeOgjVR3KzhumKnP5OXDL5NlyOLjHh9btNa8sjBmZBL3+utkHDqEZ/9+uHTqZJ71HB1DyoYNGNPT0RwcqL9zh8V9Xp09B5f27bGvUYPk1avJOnIEW29vlF6PMhrxGjoU75EjsK9RowxfWfnIDQ8nec0a83KxnBxsvbxw79Mbz8GDMaWmEjF8BPUP7MfGgtv3Siku9whB6fVUeeYZPPr1zVs+pnQ60vfuJWHZcpwaNqD6woVodnItWBKZ3X0XMpqM6rkDz6npe6arLH1WoW1MJpN6et/Tqv3K9io5O9mifmfum6nWXlxbmqFWGum56arzt53V9YzrJbb9KfYn1XpFa7Uval+x7fRGvZqya4pa/sfy0grT6hkzMlT4sOHq2ksvKUN6RoHzJpNJpe3Zo843bqKS1pRcj1kppQzpGepih45Kd/2v/zeGtDSVGxmpdFevKpNOV2rxV0RREx9VKVu2WNT2+sI31PmmzZQuPr7INsasLBU18VF1/fXXSyvESq242d2yLWgltTtqN9Fp0bzX7b0iC1BomsZb976F0WRk3pF5JfYZlhzGb3G/3dK4693EzcGNcU3H8dyB58jQZRTZzqRMzP91PrU8apVY3tLOxo6Z7Way5uIaDCZDKUdsneLeeAPHhg2oNm8etm4F78homoZ7SAgegwcR/8abGBITS+wzdfNmXDt0yFeW0dbdHYfatbGvUcPikpGVlffYMSR89hnGjKI/twCG5GSSv/0W74cfxr5KlSLb2Tg7U/OjD0nbsRNdVFRph3tXkSRdSa25uIbHWjxW4taTznbO/KfDf9gTtYc/bvxRZLvwlHCm7Z3GCx1fqLS3skvDlJZTaOLbhHE7xnEg5gBGkzHf+fOJ55m2ZxrxWfEs77XcojG7xj6Nqe5ancNXD5dV2FbDkJhI+u49VJ09u8T3puqsWWBjQ9z8+cW2yzp+nITFi/GbPq00Q61U3Lp3x6VdMDFTnsCQnFxoG6XXE/PEE9i6u1NlVsnL3mzd3PB66EHzFqXitslgQSV0NeMq4anh9KhV8lpmgBGNRrApbBOP7XqMgfUGMrLRSBp6NwQgLCWMdaHr2B6xnTnt58hVdAk0TePFji/yY8SPLPtjGW8efZNW/q2wtbElIjWCpJwkutToQgu/FlRxKfpK5J+a+zUnMi2S+7m/DKMvf6nffYd7z54W1Wa28/OjxhsLufrcLK7Pm4/PI2NxDArKO6+/cYOU9etJXrmKGu++i1PjxmUZeoWmaRrVXn2Fm4sWcaVPXzz69cVz0CDs/P0xZWSQvnsPKevXo3Q6qs2fV+Ts+H/yGDSI2Cefoup/5pTxK6i8JElXQjeybhDoHnhLE4261OhCh2odcLF34cl9T3Iz6yYaGr7OvjzU4CE2DdpEVdeqZRh15aFpWt4EuguJFwhPDcdgMjC4/mDaV23P8fjjLD69+Jb6tNFsLC7hWZHlhoXh0sHymdUefftybe4L2DjYEzVmLPaBAdj5+mFKTycnNBSPPn2ovWoljnXrlmHUlYNmY0OVWbPwfuQRUtZvIG7efEypqWgu5gIbgcuXc232bBwCAizu087PD5OFlb5E4SRJV0I2ms0tl0Q0KiMejh5MajGJJ1o9gd6oByiwj7e4NU18m9DEt0m+Y9XdqhOZGmlxdTAwzwdwsHXg2QPPEpkWiVKKGm41eLD+g3QL7HZLy+ismTIY0exv7bVo9vb4TZ2K/zPPkH3qNKb0NDRnZ5xbtMDWo/D900XR7KtWxf/J6fg/Ob3AORsXF0yZmRb3ZcrMRHNxxqTTkRsaiikzExs3d5waN5JZ3xaSd6mCO5dwjjWhazhz8wy5xly8nby5t+a9RKRGkK5Lx93BsrWkp26cYlyzcXl/l+RcdgLdAwnyDGJf9D561+ldYvtDsYc4GneUxJxEHm78MFNaTgHgUvIlvjn/DW/99hbv3PdOXgGRikIXG0vq999jiIsDG1sc69XFxsuL3IgIi/vQ37gBRiM27u5oNja4dixYfUqUHpf27UnfvQeX4GCL2qd89x02Lq5c7t4DO39/bD08MCYnY0xLw2v4cLxHP1xoMQ/xF0nSFVRyTjKzD84mNiOW4Q2H80jTR3CydSIuM47vr3xPjjGHl35+iQ+6f1DiBJxLyZeIzYjl/oDKPd5pTUY1HsX/nfk/7gu4r8jZ92DeiOaZ/c9wT817+KTHJ/n+XzbyacTAegM5fPUwzxx4hv/e/1/aV7N816jyoou9Svz8+WSfPo3HoIE4NW8BJiNZJ06SsW8vyqTwnTABW8/Cq6b9XcqGDXgMHIhmI3Ng7wSvkSOJHDYM/xlP520EU5SsU6dJXPoZ7r164T/j6XzzBXJCL5G8ciURw4ZRa9kyqVNdnKLWZlWEn7t1nXRqbqoasnmIev/Y+8pgNBTaZnfkbtV6RWv139//W2xfOqNOTd45WS07vawsQhVFMJlM6vlDz6vHdz2uMnQF1wIrZV4j3eXbLqrX+l4qU5dZbH+/XP1F3b/m/nxVxqxRzpVwdene+9TNZcsKVHFSSil9QoIK7dJVhfUIKXSNdL62SUnq0j33quwLF8oqXFGIq/95XsXMnKlMhsK/e5RSKicyUl1o1VpdGTwkX4Wwf0re9J261K17sWuu7wayTrqSee/Ye7Sr2o6Z7WYWued0z9o9eazFY3xz/ht2RBS+K1NqbirPHngWB1sHJjafWJYhi3/QNI35XecT6B5Iv039eP/4+1xOvkxKTgqx6bGsOLeC3ht6ozfpWTdgHS72xV+1dK7RmcY+jdkVuav0gjSZzD+lROn1xE6div/TT+E3eXLeLlV/Z+frS60vv8CQkEDM5MlF9mVISiLm8Sl4Dhkis7bvsGqvv4YpNY2YqVPJCb1U4LwuOpqYSZOxcXen9oqvir2T5/XgENxDQkj++uuyDLlCk21BK5jU3FT6buzLDw/9gI9TyWM5z+x/hkOxh2hfvT2D6g7C38WfTH0m+2P2sztqN/2D+jOn/RwZgy5HUWlRrAtdx77offkKbCRkJdCzVk9GNRllUT/7ovex4twKVvRdcfvBZNyEk1/DiW8gJdp8zKsWtB1n/nH1u+2u03bsJHnlSmqv/KbEtpnHTxA9diwuHTrg+9ijuHbqBHZ26K9eJWX9BlI2bsRr2DD8Z86Q/aHLgdLpSPz8c5JXr8E+MBDnFi3Me3eHhpJz/jzGrCzqbfvBomIbuqgoIkc9bN6W1LH4fR0qK6mCVYlsC9/GvQH3WpSgAV7p/Ar9NvUjJDCEHZE7SM1NxdnemXZV2rF1yFZ8nX3LOGJRktoetZndfjaz28/Od3zkDyNp5tfM4n6a+jYlJj3m9gM5uxF+eBaaDIBhn0O1Vubjcafh2BfwcTsYsAiaD72t7pPXrMHrYct+4XBt1xbv0Q+ji4nl5kcfEzNtOphM2Hp54TlwALW/+QbHukEldyTKhObggN/UqfhOmkTGoUPoIqNAmXAePgyVqyNt1y6Lq2E51K6NQ506ZB8/jmuXLmUcecUjSbqCiUmPoalvU4vbezt54+vsay592Gh4GUYmysKtXCVqaCiKuDOWnWz+sXMCt6rwz2GS89/Dzhdhwjao1jz/uZrtzD8dp8KqYWBjB03/UZAlIQyij4A+C5x9oH4IuOT/RTLn3DncPnjf4tfj1q0bSV+tIGjDevMacZMJzbbw4R1RPjR7e9xDQvIdS169Gju/W7vjYufvj1HWUxdKkvRdosgvb2G1arrVJDQplOZ+zUtuDIQmh1LT7W/1lU1GuLQDflsOscfMSVOfDXaOEDwR2o43377WZcHWmfDIpoIJ+u+qNYeHV8M3D0H9B8DBBcIPwE+L4MZ5qBcCjm6QHgc/PgeNB8B9s8DHvJGIys1Fu4XbmZqDIyo31/xnTQNJ0BWC5ux8S2upAUwZGYXOURCSpCucQPdA/kgoeo/tf0rJSSEpO4kqzpZvQSmsw4P1H+Tjkx/zUIOHLLqiXn1xNUEeQcz/dT65+kx8In+hd46RZp1mwOi15uQMcO0k/P5/sLgzjFwJCaEQ2BFqtCk5qBptIKC9+da4SQ8H34EH5pmvrO3+loAzE8y3yD/vbX7umm2x9fNFf/Wqxctt9LGx2PrKcExF49KuHTfeehtTTo5F24cakpPJPnMGp5YtUUrJHIN/kNndVs5oMnIo9hCfnf6MT05+Qq4hlwMxB0jKSbLo8Zsvb6Z7re64ObiVcaSitHWt2ZUMfQZ7o/cW285oMvLS4Zc4fPUwN7NvUs8ziHaRx3C0d+MZHxdGx2zmVNKFvx5Qow0M/hSGLIY1o+H3z6HdBMsDazcBjiw2J+iJ26HliPwJGsDVD3XfbHJazCHj3YfJPLAD927dSdm4yeKnSdm4EY/+/SyPS1gFh8BAnFq2IG3bjxa1T1iyBFsfb8L79uNi02ZcbNuO6EmTSd+3D2U0ltxBJSdX0lbKpEysurCKledX4ufsR4fqHbC3sScsJQydUce4H8exvNdyqrtVL7KPhOwEVl5YyaJui+5g5KK02Gg2vHvfu0zdMxVbzZbutboXaGM0GZm8azInb55kXpd5DGkwBM5thlwTjNnCE5rGrqhdPL3vad669y261PzbxJwGD0D3ubDzJfC9hc0k/BpC0hUY+gX4FJy8pXQ6Ur7bTPLq1ZjS07F38Yf33iQnLhuVk4NLxw64d+tW7FNknTiB/upV3HtYViRGWBe/yZOJfeZZnNu0KXKCn9LpiH16BhkHD+I9+mF8J03CrkoVTFlZZOzbR8Jnn3Hzw48IXLoE++pFf89VekUtoK4IP5V1MxOjyajmHpqrxmwbo84mnC1wPjotWt27+l7VYWUHFZYUVmgf1zOuqwe/f1AtPrm4rMMVZeyPG3+onut7qvHbx6sfw39U0WnRKiYtRu2L2qf6beynWn/dWu2P3v/XA77sr9SZDfn6OB53XN235j4VlxGXv3NdtlKveSt1aZflAZ39TqnXfZQqZCMdY0aGipwwQUWOG6/Sf/5ZmYxGpeLPK/VuQ2VISlCRj01S55s1Vzc/+6zI7rMvhqpL99yr0vbssTwmYXWSN25Soffco1K3bVMmnS7fOUNWlgofNlxdaN5CpWzbVujjTSaTSli+XIU90EvpExLuRMjlprjNTGSdtBVacnoJR64d4bMHPsPJrvAxnaScJMZvH09MegxTW02lR60eONmZtwXdfHkz+6P3M7nlZCY0myBjPJWA3qRnf/R+NlzaQHR6NCZlorprdUKTQ1nRZwWNfBqZG2bchE/awazLYOeQr4+FRxbi5eTF9Nb/KJzwYWuo0sQ8KcwSa8bCjXPw9Ml8h5VSxEyZgp2vH9UXzM8/E/uTDjDsc1TV5sS9Po+U9etx79WLai+/lLd3c25EBClr1pL6/fdUfeklPAdIWdSKLvPXX0n4dDG6qCjceoZg6+GJMSmJtO3bUUoRuGQxrh2K3289/s23MGZmUGPBgjsU9Z0n66QrkCx9FqsurGLtgLVFJmgAHycftgzZwoQdE/jp6k9si9hGrsFcYKN3nd7MCp6Ft5P3HYxclCV7G3t61elFrzq98o79EP4DWy5v+StBA2QlgFu1AgkaYGSjkTy++3GmtJySv2pW3fvhwlZQCkr6hU4piDkCdQvehs46cgTD9esELl5ccKmUkyfoMtE0jeqvvYpzq1bEzZvH5YMHsXF1AaN5ZzOvoUOps2EDDgE1C/QvKh7Xzp1x7dyZnEuXyPr1V4yZmTg2aIDDpUv4jh9XYoIG8J08iSt9+2GcPdui/dwrG0nSVmZH5A7a+LfJv5SmCJqm8XjLx/nwxIdsGbLlDkQnrMmFhHN0dqoKZzaYJ25Va2n+ryG70Pb1veujoZGQnUA112p/nfD432ft+JcQ/GjxT3r8S9BswMG1wKnkb1fjPXp0wRKESkHmTchNh10vw5X9eOnSSfEy4d23PS6Dp6P51sbWy0vKF1ZSTg0b4tSwIQD669dJWLIE9549LXqsnZ8frvd0JW3nTrxHjCjLMK2SzO62MifiT9AtsJvF7TvX6Ex4ajjZRXwxi0ooJxX2v0nuiRU4hO2Biz/AqW9hWTf4cTbkpMHN0EIf6mDrgM6oy3/wyj7oNhcOvA1HloDRUPCBRoP53MF3YMhncHErGHLzTiuTifQDB/AYOLDgY6OPmJP0xsmgTDDwQxi7CY9hY8g8exX7zUOxO7IQTau4Q2/Ccvq4OOwDA9EcCt7tKYpjUF0McfFlGJX1kl9brUy2IbvEYgp/Z6PZ4GTnRI4hp9iSh6KSSLsO3wyB6q3wbTGKqw5O0H6O+ZwuC85tMifFrTPh0e35HpptyCYpJwkvJ6+/DsadNe/R3W4i1O8Jm6fBLx+bNzqp3tLc5vofcPwr8K5jXnLlEwRVm5uv4NuMAUBlZ6PZ2mLr9o+lfiYjbHwUPANg0l7zZif/Y9egLcYLsTBzA2x4FDZOgmFfgpSdrNQ0Ozu4xaVVymjExunu3Ndb/jVYGS9HL25k3bC4fZY+i2x9Nm72sg660jPkwrfDzXtnP7SMPs0fYVv4tr+ujB1coM1YGPe9edx4/xv5Hr4jYgfB1YLxcPAwH9BlwdYZ0Gka2NqZk++j282bj2QlwrEvzT9ZiTBmvfncn0uuur8Au1+BqycA0JycUDodSvePq/QNj0FWEozbmi9BA5jSM7BxdgFHd/OmKmnX4GTJxTdExeYQGIguJgZjSorFj8k+cQKHuvXKMCrrJUnayoTUCmHLlS1YOuv+x4gf6Vqzq1Sxuhuc2wzO3nCfuRBHkGcQDb0bsvny5vztAoKh13w49C6cWg1GAzqjji/OfkEj70bsjNzJqdDvUV8PBP/G0Pkfs72rtYB+78CYdeaffu8U3C40sAMM+hhWDYef3kPLTsKlXTvS9/5v45X48/D9k+Zb8YM+AfeCO96l796NS4f25r/YOUK35+H35eYxbFFp2Xp54Xb//aRs3lxyYyA3LIzcyAjcu3cr28CslCRpK5KhyyDIM4gsfRa/xf1WYnu9Sc+3F79lVCPLKguJCu73/4OOT+SbgT27/Ww+PfUph2IP5W/babp5z+zDH7JrcUt6rurMtbQYLl3ZwY4Dr/Dqzy8wyCWXbxp0RGfS3148jfvB+K2QFA6ftMO7ehTJ77+MWnoffPOgOdl61YIWwwo8VBcbS/bJk3gOGPDXwbrdITcDrp+6vXhEheEzdgxJX3yJ/tq1YtsZMzO5+tws7Pz8uTrnP1x/+RXS9+2/q3YikzHpcqY36tkVtYu1oWu5mHQRN3s3UnWpPLH7Caa3mc4jTR/B0bbgWIzBZOC1X16jmks1OtXoVA6RizsqOwXiz0GD3vkON/RuyEc9PmLm/pl0qt6JkY1G0sq/FZqmkdlyBM9c3cExQy5N7N35zLU5bg4e0CQY1ag/J5POseyPZeyPPcDHPT7G1b7gjO3iKJOJzNAEkndCbmgDVG42xjQdsb81osYHW7CN2AHG3ALLukyZmVx7bhY+EyfkL6pgY2MeB0+KsGwfcVFhObdqhc+jE4kaP4GATz/Jm/n9J6UUCYuXkLB0KbZubvg99RS2nh4Yk5JJ+Gwp8QsW4P/ss3fFWnrZzKQcJWQnMG3PNNwc3BjdeDTdArthZ2OH0WTkv8f+y5rQNXg4eLA4ZHFeXWGDycCh2EN8de4rnO2ceb/b+7c00UxUUCkx8HkveO5CoadTc1PZfHkz60LXkZyTjKOdIynZSTihMb/bfwmpFVLopjZGk5HXf32dhOwEPg351OKNb3LDw4l98ilsnJ3xHj0al+B2YGtH9onjXH/tNTAYqTImBJ/6aTD0/wDzF2/W779z4623cWrWjGrzXi/4fOvGQdMh0PyhW3p7RMWUsnETNxYtwqlRIzyHDMauShWMmZncXPQ+uogIPPr1pcYbbxSYCZ59+jRXn3kWn4kT8XlkbDlFX3qK28xEknQ5ydBlMG7HOEJqhTCt1bRCvxwvJl7k9SOvcy7hHPW96uNs78y1jGvUcKvByEYj6RvUF3sbGYu+K2Qnw/vN4fnogrWg/0YpRZoujeScZMZ8P5Q1Pl0IHPBxsV3rTXoe/P5B5nWZR9uqbUsMRRcVReTYsVSZ+QxeQwsmU0NyMnHzF5C+fTuOPuDSbywYDWT+9hso8Bk/Dq/hwwt+5k0m825pQz+HmiXHISoHk05H+s6dpO/ajTE1FWNqKobERGp/uwrHWrWKfJz+6lUiR4+h5vuLcGlbsT8vsuOYFfrmwjc08GpQZIIGaOzbmNX9V/PSzy9hUiZGNBqBr7Mvge6BdzhaUe6cvc2FLcJ2Q6M+RTbTNA1PR092R+6irUER2GhQiV3b29gzstFI1oSusShJX3t+Ln5Tnig0QQPYeXsTsOg9ssePI2rMKNxMKdjVbYVH3744BwcXfbUecQDsXeVW913GxsEBz4ED8Rw4EKUUEYMGU/Pdd4pN0AD2NWviO+VxklZ8XeGTdHFk4lg50Jv0bLi0gUebP2rR7cXHWz7O4WuHaeLbRBL03az9JPjtM4tmP/985Qf6ZuuhXohFXfcN6svPsT+X2C7n/Hn016/jPWpkiW2dW7XC854W2ET/jM+o4bi0b1/0592QC/vfhA6TS96aVFRa2SdPogwGXDpZNs/Gc9AgMo8cQX/D8mWrFY0k6XJwMv4kVZyr5N9zuRi1PGpRz6sev177tYwjE1bLZIS63cxj04c/KL5tehwZ107g1Wq0xRuDeDt6k6HPKHHpX8rGTXgNH2bx9p1eT75MyplMWD8BdJmFN0q9Bp/dZ95U5Y91sGaMeX12boZFzyEqj5wLF3Dp2MHiuRG2bm44NWmC7vLlMo6s/Mjt7nKQkJ1ATfdbKyAQ4BZAYnZiGUUkrFZSOBz7wrztp8lo3lZz7zz4Yy08sBAa/O1K2ZBrLpSx53VcAmqRFmD5beNUXSqu9q4lfjnqY2Nx7dql2DZ/59S4Cfp0I8rJC+395tB6NDR7CJy9zJuc7HwBYn8H79rQewG4+EF2EpzdBHtfh3uegS5Py9X1XULp9be0XSiA5mCP6Z+b6FQikqTLgYOtA7nG3JIb/k2uMRcH21v78IoK7tiX5oTcZixM2mNe9wzmbTq3PQurhpq36qzVCQw5EHnYXHJywCK6GBPZFbmLPnWKHr/+u12Ru+hco3PJDW83WQ7+BFKizAU6tj5tLraRm27exOTRXVDrH9WQmg+F5ChY9whk3IDeC2/veUWFYufnT9aRoxa3V0qhj47Bzt+/DKMqX3K7uxw09mnM6RunyTHkWNReb9Lze9zvNPZpXMaRCatxajX8tMicnHvN/ytBg3kt8aQ98OTvoM8GFDQeABN/hPFboMEDDKg7gKPXjxKXGVfiUxlNRtaGrrVoUxyH2rXIOXvW4peRc+4c9gE10WxszFuKPjAPph6G7i+CRw3za/hngv6Td23zFqeXdprvEIhKz61bN7JOnkQfV/LnFiD72DGwscGpadMyjqz8SJIuBwHuATTza8bOyJ0Wtd8fvZ9A90AaeDco48iEVcjNgJ1zzVty+hazX7FfA3NiDt1uLo7h99fnw83BjUeaPsJzB58jU1/EWDBgMBp4+fDLONo6Us2lWolj0p5Dh5KyfgNKb9kuZcmr1+A9fHjBE0eXQMir5n27i+Psbd4n/MhSi55PVGy2bq549u9H4hdflNhWmUwkLF+O96hRFo9hV0SSpMvJuKbj+OTUJ8RnFl9+LTE7kQ9OfMD4ZuPvUGSi3J1ZD7W7mm9dl8S3HtR/AE6vLnBqSsspNPZuzLjt4zgUewij6a+tFFNyUnjr6Ft0Xt2ZHZE7yDXkMnHXRAZ/P5hVF1YVmdidGjbEoW5dkr7+usTQss+dI2P/fjyHDs1/4top83h0fctmntN4ACRehpuXLGsvKjS/6dPJ2Le/2M+Y7vp1oidMJDfsMiZdLtmnTllc76CikTHpOywsOYy1oWs5ceME2YZs+m7qy4P1H2Rq66n4Ofvla3s+8Tz/OfQfBtYdSI9aPcopYnHHndkAXZ+2vH3bcbDnNeg0Nd9hTdN4qdNL/BD+A5+e+pQ3jr5B6yqtydJn8fPVn7HVbBlYbyCz28/G2c4ZpRTH44+z6sIq1lxcw9IHllLT7a8JjrrYWFI3bcLG1ZWbH39C5uFfqPrqKzjWrl0gpOxTp4h56imqzZ+Hnbd3/pM3L0Jgx2I3ZcnHzgFqtoOEUPBvWHJ7UaHZ+fpS66uviHn8cTIOHMR7zGjc7r8fzc6O7IsXiXv5FXLOncPW2xuP/v0wxMVzdfYcbNzd8Hv8cTz6WDYPo6KQJH2HZOozmfvTXM4mnGVow6Es6LoAV3tXvr/8PasurGJD2AY6VutI/7r9SdOlsTNyJ3GZcUxtNZWhDYeW/ASi8siIN08Is5R3Hci8WegpTdMY4RWLwwAAIABJREFUWG8gA+sN5FziOU7En2DJqSU80vQRnmrzFHY2dvnaBlcLJrhaMCvOrWDK7ims6rcKl9Rc4l59zVwQY/BgPPr2waVjRxKXLSO8bz8cgupQ5T//wb5qNXQREaSsW0dOaCjV58/HvUf3gkGZDJYn6D/Z2JofJ+4KDgE1CfpuE+k7dpC4bDlXZz6D5uiIKSMDhzp1qPHuu3j06Y1ma/4cqRfmknn4MHHzF5AbHo7/tGnl/ApKjyTpOyDbkM2U3VOo71WfnUN35isr+XTbp5neejrbI7bz7rF3iTsTR4fqHZjYfCL3B9yf70tU3CX+n73zjI6q6hrwc2cy6b2RBoGE3gmhd6SIVOlVioAIKCIWRBQU/VSwYQNFVHzpRQi99yot9BpKKiG9T6ad78cAgoTMDZKQhPusNWuZO/ucu2cc7j5nn12sbMzpVHIx5IKMyP8aHjVYdXkVfav05Y36b+QrO7TGUK6kXGHV/p9p+X9bceneHf+vv3qgIYb7S0PIOnyY259/QfTYcWj8/bD2D8DlxR4EdOyI6lGpNM5+kFgA17UQkHgFnAuWtqhQslHZ2ODSvTsu3buji47mer9++P/fbJw7dHhIVlKpcGzRgvJLFnNz4CCsy5bFpWvXp6D1k0c5ky4C5p6ai4+DD9OaTMuz77NapaZLcBfW9liLCROty7bmuXLPKQb6WcW3DlzbLV/+2m5zxLcFMnWZbL6xmUHVBsmadkj1Ifh8thDnfn3wev21BztWYd55OzZpQlDYGsq88w4qewfKzv8Vl65dH22gAcq3hIx4cyqZHKKOgEkP/qHy5BVKHWlrwnBu1z5PA30/Vh4e+Mz4mMQ5c0vNGbVipAsZrUHL6iurmVBvgsUIRBcbF16u+TJLLy4tIu0UiiWhL8Ox+eaGE5YQwtxnusFIi6IHYg9Qz7seXvbyckrL3szGWSsR1dVyXWS3IYPBoCf7iOU+6KitIHQY7J1pucSpyQh7Z5m/E5nV0xRKF8JgIHX5ctwGDpAlb9+gAZJaRfYR+fnWxRnlV1/I7IraZa657Syv5vbzFZ4nPCGc29mltxatggX8Q8DJF/Z9aVn24PegsTNHg1sgVZuKt723bDVSly7lQqtAUnXpFmUlScJ1wABSlspcYDYeB+lxsOkdsyHOC6PBXPhEr4UGL8vWW6F0oYuMQrK1xbaKvDLKkiTh2K4d2X8fLWTNigbFSBcyURlRVPeQn2hvZ2VHoHMgMZkxhaiVQrFGkqDPAnMp0G3TzJW5/o0uC3b9H/w9D/ovklUJzMbKhhxDjmw1tBcuci3YHlsrW1nyDo0bk3vxorzJre1h8CpIuARzmsKRXyDjFqRFQ/Rx2PcN/NTIXG1s4FLzOb3CM4kpOxuVg0OBxqgdHTHlyP+tF2eUQ89C5nHORSRKb2K+gkycysDL22DjJPi6OvjUMte1trIGJLi6zbx7HrkNnHxkTVnHqw7fHv8WnVEnq8SsUZfL5YxbVPOQka8NSBoNoiA1lO1czRXFzofB7s/Mu2oEIIGkMqdpNXoVrB3lz6lQ6lC7OGNMTEQIIbtoiSEhEbWrSyFrVjQoO+lCJsApgAvJF2TLaw1abqTfwNfBtxC1UigRZMSaXb6SZN5NJ16GmBNwaQN4VIKGo2UbaIAKLhWo6FaRbTe3yZJPdhC0UVfD3dZdlrwuMrLgNZQjdsCGSeYFx6sHYVoqTE+FyZFQuw9sfhf+Gg2G0ttAQSF/NAEBqN3cyD58WJa80OtJ37gRx7alo7aEYqQLmbbl2nIu8RxRGVGy5Dff2Ewtz1r4OMh/+CqUQq5uh//1hIpt4c0LMGYfjDsMr5+Ad25Ao1fgr1FwcmGBph1RcwRfH/+a2MzYfOWupFwhLDiFtuEygtfukLpiJc4FSXuJPAx/vQL9FkKXr6FM9X/c9jaOEDoCRu8Bbar5bLqUROsqFAxJknAb0J/kPxbI8kymb96Cdbly2FYuHYVvFCNdyNhZ2dGjYg++P/m9xR9Yhi6D+WfmM6CqvChGhVJK4hWz8eq/yBy1bf2v8zgra6jVG4ZtNHfJur5P9tRN/Zrycs2XGbp5KPui92ES/xhhIQTh8eHMODSDIZuG4N2tJ+qzV9BeumRx3txr18g6eBCX7t3kKSIEbHwbOn8Jgfl037K2N5/PRx0xG3WFZxKX7t3R37pF4k8/5fsczTlzlvjPPsNrYv51AEoSypl0EfBqnVd5ecvLfHL4EyY3nJxnrnSKNoXXdr5GY9/GtPBv8RS0VCg2HPoRGo4yt6DMD8+K5q5S+7+GCvJ/MwOrDcTHwYfvTn7HZ39/Rvty7YnJjOFo/FHSctNwsXEhxDuEkxkXiG5nYuCIwQT99geuVWrkOZ/u5k2iRo2mzOTJqJ0sNMy4S8xxyE2Hat0ty1rbm137R3/N36ArlFpU9vaU/eUXokaNIvfSZdyHD8Oubl0kScKYmUnyokVkbNqM7loE9o2bgBAFOsMuzkglOeE7NDRUHDt27GmrkS9xmXGsuLyCsIiwe2lVnnae9KrUi+fLP0+WIYt1EevYdH0T/ar0Y3y98agkxcHxzKJNg29rwbi/5Z0367XwTQ14eWv+HbPyQAjBqYRTfPH3F8RkxtAioAWDqg16IBvhWuo1dvw8lZBlp/Ho/iK+g4dhE2y+T+7166QuXUZaWBheEyfi1q+v/JtvnmLucNXqbXnyOanwVVV4LwryWOQqPBsYM7NIXbGClKVLkCQJU64Ow+3bIEnYh9TDoVVrMBpJ++svJI2GMu+/j0PjRk9bbYuEhoZy7NixPFcUsnbSkiQ1A6YDgXfGSIAQQgTlN+5Z589zf/Lz6Z/pGtyVn9v9THmX8pxPPM+c03P43/n/Me/0PPwd/eka3JXV3VcXKIdVoZQSdwq8q8sPCNPYQqX2cPNAgY20JEkcvXUUSZLY1GsTDpqH01yCXIOo8M4i5tb9BKv1O2k1dBemzCyQJFR2drj26kX5lSuxDihgyc6sBFlV0u5h52r+rNo0cPC0LK9QKlE7OuAxfBhugwYSOeJlVLlaykx9H4dGjVA7/pMF4DFqJJm7dhEzaRK+Mz7GqQQHkcl1d88HJgLHgUdUHlC4nwXnFrDqyipWdl2Jr+M/kdq1vWszp90cAA7GHmTy3sk082+mGGgFM7osyz2W/42NE+iyC3yrbH02C84vYFmXZXka6LtIksSYDlPppztD1XfG08y9vvm6vf3juxOtrEFfgDxWIcw1ypV8aQUgae5cVLa2lP39NyTNw54VSZJwatsWKy8voka/gu3KFWj8S2btd7l+1TQhxCYhxG0hRNLdV6FqVoKJzYxl3pl5/Nzu5wcM9L9p6teUD5p8wIcHPiw1dWYV/iO2ruYuWAUhM9680ywgG69vJMQ75IF2lI9CkiT6V+3PssvLUTk4oHJw+G/nfX4hELFTvnzkYXODDSVn+pnHpNWSsmQpPh9+kKeBvh+7WrVw6daVlKXLiki7J49cI71LkqRZkiQ1kSQp5O6rUDUrway8vJKuQV3zNdB3aVeuHQDH4ov32bpCEeFf31x567bMyl05KeYGG8HPFfhWh2IP0T6wvWz5DoEdOBR7qMD3yZPafeH6XkjPPxXsHnfrk5eCQCCF/0b65s3Y1qqJdblysuRd+/cn9a+/ClZopxgh10g3AkKB/wO+uvOSUVj42SQsIozelXvLkpUkid6VexN2NayQtVIoEVhZQ8hLcGSOPPnjf0CljuBYwCIiQLYhG2drZ9nyDhoHDMKA3qQv8L0ewsbJ/DnXTTAXbMmPS5vhxn6o0/+/31ehxJN74QIOjeVH+dtUqIBkY43+dsnshyDLSAsh2uTxKrkn8YWISZhIzEmkvHN52WMquFQgPruALk6F0kujMWZX8PE/8pe7st2crtVSZoT0v3DSOJGSmyJbPl2Xjo3aBo3qCURX56SCXz3zTnpea4jOoxmCIReOzoewceac8cdw6SuUPoReb9HN/W9UGmuE7gksLp8CcqO7XYBpQMs7l/YAHwsh0gpLsZKKhIQKFSZhQo1a1hijMCq9oxX+wcETBv8FC3uajVejMeba3XeJPAxbp0LsSfM57drxULG9eWfqVEb2bVoEtGD9tfX0qNhDlvyGaxv+ew5/8nXY/w2cXwMBDaFMTYg5Br+2B0dvc5EW9yBIuQGnloF3VRi6FsrknaOt8Oyh9vREHy2vgiOAKScHQ1ISVu5uhahV4SHXMvwGnAXuJkIOAX4HehaGUiUZSZIIcg3ixO0TNPKVl593Iv4EQS5KNpvCfXhWMpfEPDofFvcDWxewc4eEC5CdDD41oec88KwMOclwdhX82ADqDTEXOFFZXiB2KN+BL499ybXUawS55v/7M5gMLLu0jKmNpz7+Z4o+BksGQOhwGHf0wQVFxi3Y8r758wY2hYAGMGwDeJWO0o4KTw6Xzp25MWAgXm++icrGcrR/+sZN2Nevj9q1ZHpi5J5JBwshpgkhrt15fQQoVuUR9Knch2WX5EUT6ow6Vl9dTZ/KfQpZK4USh727udjHhNPQ7XtAmHeZky7BmP1Qs6fZWFdoCV1nw+vh5jzrsPGy6lzbqG14tc6rTNw9kaScRydrGE1GZhyega+jL6FlQh/vs6RGmQ10t++gzZSHd/xOPtB7PozYAnGnoVIHxUAr5Il1YCC21auTunKlRVlTbi7JCxbgOqDkxjPINdI5kiQ1v/vHneImpaNZZyHQJagLx+OPczjOcq3huafmUt2jOuVdyhe+YgolE7UV3DoNRr15d/kol7a9OwxcDrfPwWl5i8T+VfvTsXxHBm0cRNjVMHKNuffeE0Jw7NYxxu4YS2R6JF+1+urx066OzDVHdFfplL+cX1147gPYq8SlKjyaMpPfJfGnOWRs3/5IGZNWS8ykSVgHBeHYsuUj5Yo7ssqCSpJUF1gAuGCuNpYMDBNCnCpc9fKnOJcFPXrrKJN2T2Jak2m0Ldf2oYebzqjj59M/s/n6Zv7s9Ccedh5PSVOFYo8Q8FMT6PQFBLWyLH9pM+ydCaPk5yHvj9nPwgsLOZ94nhqeNbBSWXEj7QaSJNGvSj/6VO4jqwd1nuhzzKVLR+4A9wqW5XXZ8E11s7vfLfDx7qlQ6sk5c5boceOwD62P24AB2Navj/bECbJPnSInPBxt+CnsQurhN2sWKuvH/O0WEf+5LKgQIhyoI0mS852/05+gfqWSBj4NmFh/IlMPTMW434innSf+Dv408m1Epj6T1VdXU92jumKgFSwTfRRMBrNbWw6V2ps7TN0682DAWT40929Oc//mRGdEcy3tGnqTnjL2ZajhUeO/NymIPQlu5eUZaDA31KjUAa7vAbeX/tu9FUotdrVqErRhPalr1hA98U2MSUnmPHpJQhMQgJWfL5mHDnN75iw8x43Fyq0UBo5JkjRYCLFQkqQ3/3UdACHE14WoW4klJjOGyXsnE58dz/Caw7FWWXMo7hAxmTH8cuYXrNXWTGsyrUCFJBSeYZKvmdOV5BpLldpcFzv5mmwjfZcApwACnAIeQ8l8yM00N9MoCHZukJvxZPVQKHWoHB3RXbmK2ssT3xkzsK1VEytnZ6Q7O2d9bCxJv87nRv/+BP7+Oxo/v6esccGxtJO+W9A3r2LCSh3LPIjOiGbo5qEMrT6UQdUGob4TZTus5jDAfM635cYWPjn8CT72PtTyKthDVOEZRJgKXmlLkszjigM2TuaI9IKQnWROz1JQyIekn38h99IlKixciMrh4frzGj8/fD78gKT584l6ZQwVVq28Z8BLCvkaaSHEz3f+c7sQ4sD9790JHlO4DyEEb+95m+E1hjO4+uA8ZSRJ4vkKz2NrZcsbu95gU69Nj3/Wp/Bs4BoI8efMZ9NyjLXJZJZv/qZl2SeNLgvOrIQb+8xny7YuUKkdpEZCUoS8Tl25mXBlG7SbXtjaKpRgTNnZJP/+O+VXrczTQN+P+4gRZO7ZS8b27Ti/8EIRafhkkBvd/b3Ma880pxNPk6ZLY2C1gRZlW5dtTZBrEFtvbi0CzRRKNOWagD4776pceXF9D2jszS7yosJkgj2zzAFilzdDcFuoOwACQuHIL2DQms/J5XB6KZRvDi5P2O2uUKpI37gRu5AQrAMs/04kScJt4EBSFi8pAs2eLJbOpJsATQGvf51LO4PMclrPECsuraBv5b6oJHlrn/5V+vPn+T/pEtSlkDVTKNGoVObmErs+hUGrzClZj8Kggz1fFG0zCiFg/QRzU5C8IrIbvAyXt5jzpNeMgx4/Pnqu6GOw6/9g8KrC1VmhxJN1+AhO7eXH9Tg915aYSZMQBgOSVcmp8GjJmlgDjpiNudN9r3RAXgeJZ4jradep611Xtnxd77pcT7teiBoplBoajgZJDatHg16bt4wuG1YON1cmCynCqOjjf5iLqLy05tEpU5U7woDFcGoxrBplLm5yP9nJsP9bWNwXeswpWi+AQonElJODyjF/N/f9SBoNKhsbTNpH/Pspplg6k94D7JEk6Q8hxM2CTi5JUlngT8AHMAG/CCFmS5I0C+gK6IAIYLgQIvXOmFlAG2DSnfuXGIzCiFqS72BQS2oMwkIHIAUFALXG3GQibDzMrg0hQ6HGi+amE9nJcHYlnFxoTl3q8o2ssqD5ce7cOTZv3kx6ejpOTk507NiRWrXyCHIUAg7/BF2+BWsLD8zKz0Ord+H0cvi5hTkwzMELctMh6ihUfQGGrlPqdCvIQu3khDFZfoMYU3Y2Jp0OlZ1dIWr15JF7Jp19p5/0RkmSdt59yRhnwGxsqwGNgXGSJFUHtgE1hRC1gcvAewCSJFW9M64lMK5An6QY4OPgQ0RahGz5iLQIfBx8ClEjhVKFxs5cOvOlMNCmwrLB8EtrWDHM3DFq+Cbo8RNYWa5n/Cj27NlD69atad++PTdumIuZREZG0qlTJ1q0aMGOHTseHHDzgHmHH9hU3g0avwrZiTBqFzSbAFU7m13zr5+EF+cqBlpBNo5t2pC2fp1s+bQNG3Bs2RJJXbJOauU65hcBy4AuwBhgKJBgaZAQIg6Iu/PfGZIkXQD8hRD3R0sd5h/XuRrzjltgrmxWougW3I35Z+fL7iq04vIKugd3L2StFEod3tXghVlPfNqlS5fyxhtv8M0339C7d28097UD/Oqrr1izZg2DBw/miy++4KWX7rjTb501B3nJPf+2dQHv6pAeYy66oqDwmDi1bUP8p5+Sc+4cdjXyX9wJo5GURYvxnjSpiLR7csjdSXsIIeYDeiHEHiHECMw7Y9lIklQeqAcc+ddbI4BNAEKIc4A9sB+Q2fW++NAyoCUJ2QkcjDloUTYiNYK90XtlG3QFhcLk2LFjTJgwge3btzNgwIAHDDSARqOhT58+7N69m3feeYeDB+/8xk16syu+IKg1YNQ9Ic0VnlUkjQavNycS88ZE9LduPVJOmEzc+uhjrNzdcGgm0+NTjJC7k77bLTtOkqTOQCwgOz9CkiRHYBXwxv0lRSVJeh+zS3zR3WtCiNfkzlvcsFJZ8WnzT3lrz1t81eorQn3y7hgUkRrBmO1jeK/he7jYuBSxlgrPCrfStGw5d4ukLB02VirqlnWlabBHnmU+Z82axdSpU6lZM/8CIlWqVOHjjz9m5syZrFmzBpx84fo++UqZTOae0k6+Bf04CgoP4dqjB8bkFG70H4DnmFdw6tIFCQmhzUFyckIbfoqkefMQWi0Bc35CUsndlxYf5DbY6ALsA8pizo92Bj4SQqyVMVYDrAe23F9GVJKkoZhd588JIbJlKWt+umTe/dvd3d0+KenRLfaeFodiD/Hu3nepX6Y+9bzrYaO2QZIkhBCcSTzDzqidTG44mW7B3Z62qgqlkGsJmczacomDEUl0qF4GP1c7tAYjey4loDOYGN0yiH4Nyt4z1nFxcVSvXp0bN27g4mJ50ZiVlUW5cuUIDw+nrLebOTf61QPy8pqvbIcdH8Ere4suRUyh1JOxdx8JX39N7uXL5gsqFRiNqOztce7eHe933kZta/t0lcwHDw8PkpOT77eDjuKOcZZlpB+XO0Z1AZAshHjjvuvPA18DrYQQFs+2H0Vx7YJlNBn58/yf/HHuD7L0WRhMBoQQqFVq7DX2DKo6iJG1R6JRFdBNqKBggdPRqYz44yijWwYxsFEgjjb/OMuEEBy7mcIHa87SNNiTD7pUQ5IkwsLCmDdvHuvXr5d9n169etGvXz/69u1rLlIiBHS20F7SaIA/u0PtPlB/2GN+QgWFB9FeukTUmFexq1ULp06dUDs7IfR61M7OaK9cIXXhIjQBAfh/9SUqe/unrW6e/OcuWJIkeQGjgPL3j7lzNp0fzYAhwBlJksLvXJsCfAfYANvurOYPCyHGyNGluKM36nlzz5tk6jL5pvU31POud39DEk4nnuaHkz8wfsd4ZreZja1V8V3dKZQsUrJ0jFxwjE9frEXHGg9nDUiSRIPy7iwf04S+cw+x8EgkQxoHkpWVhaOjY4Hu5ejoSHb2nYV/q8kwv705z7nZhLx3yEY9rH3dXIil7qDH+XgKCg+hi4wkcuRIykyejEvnzg+9bx8SgtuLLxI75X2iJ06k7I8/lqhCJiA/cCwMcy/p7cCG+175IoTYL4SQhBC1hRB177w2CiEqCiHK3netVBhogE+PfArAL+1/IaRMyAPnf5IkUcerDnPazcHZ2plpB6c9LTUVSiHLj0XRopJXngb6fpxtNXzZpw5zd0dgNAk8PDyIjY0t0L1iY2Nxd3c3/+HgYc5vPr0cfn8Bzq4yVz4D0KbD3/NgbnNz04x+iwoeaKag8Ahuz/oSj6FD8zTQd5GsrfH77P8wpqaSvmVLEWr3ZJC7pLAXQrxbqJqUAmIyY9gRuYMtvbagyedBZKWy4pPmn/D8que5lnqNINegItRSoTRiMgkWHrnJDwNCZMnX9HfBy8mGnRdv07JlSwYPHkxERATBwZYbYERFRXHixAnatm37z0UXf3hlD1zcAEd/NVcVU2vMnbiqdIIXvixYqpaCggX0t26R9fff+H72mUVZSaPBY/hwUhYuytegF0fk7qTXS5JUslqHPAVWXFpBt+Bu2Gssn3vYqG3oWaknyy8vLwLNFEo78RlatHoTdcq6yh7TvnoZjt1Ixs7OjmHDhjF79mxZ47777jsGDRr0sItcrYEaPWDYevggEd65DlNvQ98/oUILxUArPFHSN2/GuUN71DJLgzo99xy5ERH5pmsVR+Qa6QmYDXWOJEnpkiRlSJKUbnHUM8bB2IN0KN9BtnyHwA4ciDlgWVBBwQI5OiP21gWrpGRvrSZHbwRg0qRJhIWF8dtvv+U7ZtGiRSxZsoR33nkn/8lVKrC2f8gwG02C/VcSWXTkJgsP32TflQSMJqU1vULBMSYmogkoK1te0miw8vXBkFj8MoLyQ5a7WwjhVNiKlAayDdk4WzvLlnexcSFbLyv7TEEhX1ztrUnO0qE3mtCo5a2949NzcbUzH8v4+PiwZcsWOnXqxJ49e3jttdcIDf0nz//kyZP88MMPbNu2jc2bNxMgoz3g/eiNJn4/cJ3/Hb6Ji52GWv7mVK/lx6JIyjzD4MaBvNy8AtZWJS+PVeHpIFlbI/R6y4L3IXQ6JOuSFRNhqVVlVSHERUmS8jzoEkKcKBy1SiYOGgdSc1Nly6fmpuJoXbCoWgWFiIRMFh6+yY4Lt0nX6nGwtqJJsAdl3e3Zdj6eF2pZLhRiMJoIC4/h16H/GOKqVaty/PhxfvnlF3r37o2VlRWenp4kJyej1Wp55ZVXOHHiBJ6engXSV6s3MmbhcYwmwQ8DQh5yyZ+JTuPLrZc4GJHIvJdCsdWUrNrKCk8Hm2rVSF6wAK/x8to86OPiMCYkyuo/XZzIN09akqR5QohRkiTtyuNtIYRom8f1IqO45Un/cPIH0nXpTGk0RZb818e/xmQy8VaDtwpZM4XSgM5g4sOws2y/cJt+DQLoUdcfdwdrMnMNbDp7i1/2XsNoMrH37Ta42FvnO9faU7EsOHiDVa/mXSbRaDRy8eJF0tLScHZ2pmrVqlg9ZurKpOWnyNEb+K5/Pawescs3mgRvLAtHAr4boLSpVLCMMBi42vY5ys6bh22Vyhblb8+ejSk9A58PphaBdgUjvzxphBAl9lW/fn1RnIjLjBNNFzcV6bnpFmWzdFmi5dKW4mbazSLQTKGkYzSaxNhFx8Xw3/8WWbn6PGWyc/Ui9JNtInTGNpGlzVtGCCFORqaIkI+3ir+vJxWWuve4kZgp6n28VWTnGizK5ugMov6MreJKfEah66VQOkicN09cHzRIGLXafOVyLl4Ulxo3EdqIiCLSrGDcsWV52rl8D4AkSeqZ36tQlhQlGB8HH7oEdWHS7kloDY9uLK4z6nh337u0Ltuacs7lilBDhZLKmvAYopOz+WlQCPbWee9o7ayt2PpGSwwmEx2+3cuui7cfCMqKSs7mi80XGfHHUWb2rk2D8u6FrveiI5H0rh+AnYygNluNmr6hZVl4uMCt6xWeUdxHjEBTxoeoUaPRx8Q89L4Qgsx9+4h8eSQ+H0zFJqjkpbtacnf/ns9YISxXHCtUipu7G8BgMjBl/xSi0qMYW3cszfyboZJUpOWmkaxN5lzSOZZeWIqnvSezWs7KN59aQeEuPX48wLg2FWlfvYxF2bMxqQyYd4RAd3tuZ+Ti62KLVm8iPkNLz3oBvNQkkPKe8tJW/iudv9vHJz1qUq+cmyz5szFpvLk8nK0TWxWyZgqlBWE0kvjjT6QsWoRdSAiObVqjsrPDcPs2qatXg4Aykyfj2KL501b1kTx2WVAhxPDCUan0YqWy4vMWnxN2NYzvTnzH1APm84+03DRzdxYE9hp7mvk3I02XhqddwYJwFJ49rt7OID5dS9uq3rLka/q7Us3HmVEtK1C5jBNJWTpsrdRU8HSQtaN9kmTlGnC2k78QdbHTkJVrLESNFEobklqN1+uv4THyZdI2bCDnZDhCq0XMGp81AAAgAElEQVTt6orPBx9i37BBnp3fSgpya3f/HzBTCJF65283YJIQovidwBcDVJKKjuU7sv3mdnJNuTT2bUxtr9pUcK5AdY/qXE65zLJLy+i1thdftvqSBj4NnrbKCsWY6JQcKno7olbJf9BU9nEkNlVL++o+BHoUza45L5ztNCRn6Qj2kiefmJmLk23Jqq2sUDxQ2dvj1qcPbn36PG1Vnihy/zV0EkLcC1kWQqTcqUCmGOk8MJqMvL33bZxsnFjVdtVD3a6quFfhwyYf0rF8RybtnsSc9nOo4VHjKWmrUNxRSRIFbVZnElAAm15otK3qTVh4jOzz77DwWNpVs+zSV1B4VpBbOUAtSZLN3T8kSbLD3MVKIQ/2RO8hMSeRGc1m5NuOspFvI94MfZOvjn1VhNoplDQqeDpwIS6dXIM8N7AQglNRqVTw/O85+IakJBJ/mUf0xIlEjR9P3LTpZB89Sn6xLPczoGE51obHkpqtsyibrtWzJjyGAY2UYEoFhbvINdILgR2SJL0sSdIIYBvmPtEKebD04lKGVB8iq19056DO3Ei7wdWUq0WgmUJJpKy7PdV8ndl0Rl7N4fCoVDK0BpoGezz2PU05OcROnUrE853Q3byB03PtcOnWDety5Yj7cBrXu3Un+/hxi/OUcbalX4OyjFl4nGyd4ZFyOTojr/x5nOdr+ODlqKz/FfJHkqTHepVE5JYFnSlJ0mmgHSABM4QQJa/nVxGQlpvGmcQz/PDcD7LkNSoNXYK7sPnGZsa7jS9k7RRKKkObluezjRdoU8UbF/tHL/50BhNfbL7IS00CUT2mv9uUk0PkyFFo/PyouG0ratcHK4S5jxhOxvbtRL/2On4zZ+LYvFm+803uVI3Jq07Ta84hXm9bkXbVy9wrXZqRo2fWlossPx6NzmDifFwaf52MoWUlL4Y0CaRFRc/H/hwKCqWBgkRoXAAMQojtkiTZS5LkJITIKCzFSiop2hTcbd2xVudf8el+fB18lZ20Qr5U9XGiio8TL845wLd961I7j25X6Vo9E5eG42yrYXizCo99r/iZM9H4+uL3xedIqoedbZIk4dy+PVZubkS/9jpB69dh5fHoXbtaJTGzd23Wn47jtwPXmb7uHFV9nMnWGThxMxVHWyuGNinPOx2roFaryNYZWBsey2cbL/C7iy0/DgzBwUYJJlN4NpEb3T0KGA24A8GAPzAXeK7wVCuZWKut0RofXcgkL3RGXYGMusKzgRCCzWdvseDQDa7eziTIyxGd3kT3Hw/g7WzDoEaBtK7iRYbWwKazcaw7FUfXOr5M61qjQJHg92NMTSV9w0aCN2/K00Dfj31oKI5t25C66i88R4/KV1aSJLrW8aNrHT+u3s4kPCqFTzdcYEK7irz+3IMlHe2trejfsBy96gcwedUZxiw8zm/DGshuHKJQenjSLupHzSc3xuJpIHd5Og5oCBwBEEJckSRJXtLmM4a3vTcIuJJyhUpulWSN2R+znx4VexSyZgolCYPRxLurznAuNo1xbSrSsYbPvQ5Rt9K0fLrxAj/tusqCgzcI8nSgaUVPtk5sSRln2/9037SwMBxbtcLKXV40ttuAgcS8/joeo0bKfqBW9Hbkp91XGdw48CEDfT8atYovetViwLzDhIXH0rt+yWqMoKDwJJC7NM0VQtwLz5QkyQoovkuPp4iVyopelXux7NIyWfLX065zOeUy7QPbF7JmCiWJTzZc4FZ6Dn+NbUrXOn4PtHD0cbHl+wH1ODzlOXxdbWld1ZuJ7Sv/ZwMNoL10GfuG8vP27WrWwJiaiikrS/aY5Cwd28/HM0KGS95KrWJ0y2ClVKjCM4tcI71HkqQpgJ0kSe2BFcC6wlOrZNO7cm+23dzG0VtH85XLNeby0aGPGFRtkOLuVrjHzaQs1p6KZc7g+o+s0w3mHtLzXgpl7p4I0rIL1lf3UQiDHsmqgKVqNZoC9fXdcu4Wrap44+Yg7zfftqo3Mak53EySvxBQKFk87Wjsp33//JBrpCcDCcAZ4BVgI0ohk0fibe/NzJYzmbR7Emsj1qI3PfgAS8pJYsO1DQzcMBAHjQMja418SpoqFEcWH4mkV4g/zraWjaWvix1tq3qz4njUE7m3xtsb3c0bsuUNyckIvR61k5PsMQkZuQS628uWV6skAtzsSMzMlT1GQaG0IDcFyyRJ0hpgjRAioZB1KhU08m3ET+1+YtbRWcw+PpsuwV0wmowcijtERGoEEhLe9t5c0F7gpU0v0a9KP16o8AJqldLw/lln2/l4vh8ov6fyi/X8mbM7gpEt/nuHH+euXYka/Qpe48YhaSwvElJXrsK50/NIBeg1rVGryMx9dM50XugMJqzVyr8NhWcPS60qJUmSpkuSlAhcBC5JkpQgSdKHRaNeyaamZ00WdFrAnPZzOJd4juWXl+Ni7cLUxlM5MugIW3pvYWvvrYyoOYKlF5cycfdEco3KbuFZJy1Hj5eT/IIeXk42pOU8GXe3bZUqaAICSAsLsyhrTEsjZckS3PoPKNA9avg5s/9KouyI2tsZWqKSswn0lL/7Vij+FDe3cl4UBx0tLX/fAJoBDYQQ1wEkSQoC5kiSNFEI8U1hK1gaOJt4lrisONb1WEcZhwfrEluprGhbri0t/Fvw7r53mbp/KrNazXpKmioUB+xt1GRqDXjL9CBnag3YP0Z3qyx9FhuubeByymX0Jj2edp50rtAZvw8/JHL4cNTu7ji1bZvnWGNqKlGvjsW5Q3vsatUs0H2bV/QkS2fgZFQqITJaWC4/GkXn2r6y3P8KCqUNS2fSLwED7hpoACHENWDwnfcULKAz6ph9Yjaz28x+yEDfj0at4bMWn3Em8QxnE88WoYYKxY1GFTzYej5etvzmc7doFCS/BKjeqOfLo1/SYWUHDsUeIsgliFqetdCb9IzYMoJxN76AWe9za9p0ol97jayDBxEmE6bsbBLnz+dKy1ZcbtyEnFOnyNi5i4SffkJ/+7bs+6tUEiObV+CjtefyLRUK5jadfxy8wUtNysueX0GhNGFpJ60RQiT++6IQIkGSJGVZK4MtN7ZQ1b0qFd0qWpS1UdvQr0o/llxcwqfNPy0C7RSKI0MaBzJu8QlGtQiyWJQkW2dg9ckY1r8mr6G9zqhj/I7x2FrZsqrbKnwcfB54/7W6r7H66mpGhX/Bj4u+wvNgBPEzZ5F76RIIAWo1Ds2b4z97NrbVqpIbEUHq8hVc69oN74kTcevfT5YegxsHcjYmnUG/HuGbvnUp7/lgO00hBAeuJvHm8nCmvFCNar7OsuZVKN4UZ9e2Je7XvSiLn1gy0vm1rrHc1kaBXVG76BLURbZ81+CudFvdrRA1Uiju1CnrSrCXIx+tO8dH3Wo88sFmMJp4e8Vp2lUrQ4CbvPPar49/jb3Gnq9afZVnkKJGraFvlb542nky4fBkwnqG4VujBlGjX8H3008ecn/b1aiB3Uc18BgxnMiRowCBW//+FvWQJInPetZizp4Ies45SICbHZ6O1iDACNxMzEKlMss8p7SuVHiGseTuriNJUnoerwygVlEoWNJJz03Hw06+K9Ld1p1MfSYmYSpErRSKO98PrMepqFReXxrOtYTMh94/H5vOywuOka7V80kPeWfCablprI1YyweNP7CYRdC2XFvqeNdhfcQ6Yt97D59p0x55Pg1gHRhIufm/cvubb2W7vlV3Uqtc7TXEp2mJS9MSl64lPk1LSrYeb0cb2bnUCgqllXx30kIIJefhP2JrZUu2Plu2fI4hB2u1NSpJqVP8LONsq2HJ6MZ8v/MqveccxNvZhlyDQKs3kpVrwGgSDGpcjrc7VpVd03pdxDpa+LeQvWjsX6U/y5ZMpaFkg1PHDhblrcuVw/n550lduRKvsWMtyn+/4wqrTkTzWc/aNA5yf8BjoDOYWH86llELjjGrT23aVlV20wrPJoolKGRCy4SyK2qXbPmdkTsJLRNaiBoplBTsra0IKeeGtZUanUFQzdeJtlW9Gdq0PL3qB7DsaDRvrzhFlsyc43NJ52jq11T2/Rv4NKDS4Rgce/eUfZbo1q8vaavXWJTbcDqOlSeiWTGmKU2CPR6a39pKRc+QAH4dGsrbK07n6U1QUHgWUPq/FTI9Kvbghb9eIFmbjLtt/k0LhBAsvbhUqUCmAMDaU7F8sv48Pw4KoUH5h3877z5flY/WnWPw/CMsHtkYOwtpWDqjDhu1/PxrSZLwzJCQypeVPcY6OBjDrVv5yggh+Gn3VaZ3q2ExH7xeOTcGNSrHgoM3+Kh7wVK9FIoHJTlY7FEUZRCZspMuZFxtXelRqQdT9k9Bb8y/4MT8s/PRmXS0DGhZRNopFFeikrOZvvYcf77cME8DDeBgY8UXvWpT3sOB/9t4weKc7rbuxGTGyNYhRZuCSQLbgiRyGAxgofpYeFQqGVoDrSp5yZpyQKNyrAmPle0xUFAoTShGugh4s/6b2KnteGX7K5xPOv/AezqjjsUXFtN2eVu+O/EdEakRdF7dme9Pfs+trPx3JAqll4VHbtIrxJ+qPvmnHkmSxHsvVCUsPMZi1bEXgl5g9dXVsoMSV19djVVQBXJOhsvWO/tkODZB+ZcnPX4zhTZVvFDJ7Hnt62JHOXd7LsdnyNZDQaG0oBjpIsBKZcWXrb6khX8LJuyawKCNg/jq2Fd8sP8Dmi5pysyjM6nsVpn1L67nwIADzG4zm0xdJr3W9mL+mfnFuiG5wpNHbzSx8lg0gxoFypL3drKlZWUvwsLz3yXX9aqLjdqGXZGWYySy9Fksu7iMysPGkbpyJUInL+MyZckSXC3kSucaTNjl090rL+w0arR6JeNB4dlDMdJFhFqlZnjN4WzuuZnRtUZjEia23NxCj+Ae7O+/n7nt51LOuRx2VnZUca/Ce43e469uf7H+2np+PfPr01ZfoQhJztKhUkkPFfjIj9BAN67EPzq4KikniWWXllHVvSrv7X+P38/+jtFkzFM2S5/FhF0TaOrflJqhz2NbuQqJv8yzqEPm/gPknDqFS+fO+cq52VsTn661ON9dhBDEZ2hxc1DqJyk8eyhGuohRq9S0KtuKM4lneCv0LaY2mYqjtWOesmUcyvBz+59ZeGEh19KuFbGmCk8Lg0mgLmCwjVqtwmB62OMSmxnLu3vfpeuarpxOOI2/oz+tAlox+8Rsmi5pyjfHv7nnqUnXpbPw/EL6rOtDgGMA7zd6HwC/zz8jLSyMhO9/yHNHnXv9OlFjXiVq9GhMWVlEdHye6DcmknXk7zy9QO2qe7PjQjzpWnlNQY7eSEGtkqhSRn47TAWF0oIS3f0UuJR8iZjMGHpW6mlR1tvem16VerHi0grebfhuEWin8LTxcLAmM9dASpZOdjGPiNuZ+LnYPngtNYLRW0fTu0pvpjSagouNy733ErITmHNqDv87/z8Wnl+Ii40LmfpMWge05uOmH1O/TP17EaxWXl6UX7yI2MnvceW553Dt3Rv7kBCQJBLn/kzOyZOoHB3xm/kFji1aYMrKImPnLm59/DFqFxcCfvgeK/d/gt+8nWxpUdmLxUciGdMqON/PJYTg133XGNI4sFRGCSsoWEIqyeedoaGh4tixY09bjQIz8+hMHDQOjKs7TpZ8XGYcvdb14kD/A8qD6hnhzWXhVPN1ZlRLyz2is3UGmn6+k3Xjm1PW3VweNFufTc+1PXm1zqt0r9j9kWMzdZmM2jaKBmUaMLbuWGytbB8pC5AbEUHKsmXkXo0g98oVEALfjz/GsU3rh36bQggSvp1N5s4dBC5ahNrZHASXmWvg72tJvLniFB91rUH3ev553ksIwTfbr7D9fDwrxjTBwUbZU5RESvsz60nY0NDQUI4dO5bnF6W4u58Ct7JuEeya/w7ifnwdfTGajGTpswpRK4XixOAmgfxx8Aap2ZYDtv44eIP65dzuGWiAjdc3UsmtUr4GGsDR2pGZLWey5qrlAiQANsHB+EyZgvvgwahdnKm4ZTNObdvk+SCWJAmvNyZgV7ceCbO/42xMGm+vOEWTz3bwycYLONpY8caycFrM3MnSvyPvjTOZBHsvJzD8j6PsuBDPHyMaKAZa4ZlF+eU/BdSSGpOpYJGqRmG0WG9ZofQQUs6NF2r5MPyPo8wf2gD3PNzeRpPg4/XnWPp3FNV8nRky/wjVfZ0Z2Kgcyy4tY2LIRFn3KutUlhqeNdhyY4tFo36XlMWL8Rg5EpVD/sFtkiThOfZVLnTuxjhjPQa2rsrut1rj4WguYnI9MZMZ68/z/uqzfLLhAmXd7UjM1OHpaMNLTQLpUdffYpEWBYXSjGKknwJBLkGEJ4TzQtALsuQvJl/EzdYNW3X+rkiF0sV7naoxa+sl2n+9h96hAfQLLYu/mx25BhNzd0fw677rSBKMa1OR0PJu6AwmDkYk0WPOLkxlI6jmVl/2vdqUbcPJ2ydlGWl9fDzaM2cI+PEHWXOviNSj8gzif5WzKd/6QQ9SBU9HfhvWkKTMXAb9eoRa/i681rYSAW52pd5NqqAgB8VIPwVerPQivdb24o2QN7DXWG4xuOzSMvpU7qM8tJ4xVCqJd5+vSr/Qsiw6cpMB8w6TlKlDrZIwCcHY1hUZ37biAw02WlfxZngLT7qstmHgvL9Z9kpjnG0tpy7ZWdmRY8iRpZchLg5NuXKobCyXGM3WGZi55SLLWofikJrwSDkPRxsWj2pM2692M6FdZeW3rqBwB+VM+ing4+BDI99G/Bj+o0XZg7EH2XBtA6m5qfx86md2R+1+ZH6rQumkvKcD73euzpEp7TgwuS121mr+erUZE9tXzrMDlqe9K0g6ape1ZXrYOVn3SMhJeCD6O1/UapB5XBMWHkuD8u642aiRLBzXuDtY06OuP0uOROYrp1AykCTpmVhsFfbnVIz0U2Jak2nsi9nHN8e/ybOmd1xmHKO2jmLMtjHU8KiBk8YJrVHLvNPz6PRXJxacW6D0nH4GWfJ3JJ1r+VIr4NEGVaPW0My/GTUrX2P7hXgSMnLznVMIwdqra2kf2F6WDpqAAHSRkRgzLJfp3Hgmjl4hAeScPo11+fIW5XvXD2DjmThZeigoPAso7u6nhIuNCwueX8D7+9+n/cr29KzUk4a+DbGSrDgRf4K5p+dirbJmVqtZdCzf8YGx55LO8fmRz7mQfIFPm32qBJQ9IwghWPJ3JH8Mb/jI9yNSI4jPjqeuV11WXFnE8zWns/J4NK+2fnQ2wcHYgwiE7BapVm5uODRvRtqaMNyHDM5XNiVbh1/aLXKvReDUto3FuX1dbEmWEdGuoPCsoBjpp4ibrRs/tfuJa2nXWHFpBfNOzyPXmMvllMv0rtybdxq8g5Xq4f9FNTxq8GvHXxmzbQxzT8+VnW+tULLJyDWQlWukmu+DTTf0Jj1rr65l2aVlpOamEugciMFkICojikT1JOJjX+ZV8jbSV1KuMPXAVGY0m1Egl537kCHEvDkJp44d0Hh7A+Zo85iUHLL1BpxtNfi62OJs1CHNmIrKzp4bgwajsrXFrl493Pr1ReP/cH50jt6IrZWy6CwN3M0fLu0u78KuNaIY6WJAkEvQvWpif135i52RO5nSaEq+Y2zUNnza/FP6ru/L8BrDZQWgKZRsTCbBvxtH5RhymLh7Ijn6HF4PeZ2mfk1RSeZTrKScJPqEDeVk7ldM3nuWzn4TiUvVoZIk3JxzuJi5leWXlzO54WSa+zcvkC72ISG49e9H5LDhOH09m1W3JBYduYnJJHC0tSIpU0fvmwd47+BKjHb2eH08HWt/P0w5OWTu3s31nr1waNUS348+QmVnd2/enRdvExLo+p+/KwWF0oJipIsZSy8u5fWQ12XJ+jn6Uc+7Hhuvb6R35d6FrJnC08bJVoMQcCtNi4+LLUIIJu+djIu1Cz+0/eEhr4uHnQdtnT5nT+ZaNlxbwcZrO3BS+WLCQJYxCXtdKAOrf0rHwGaPpY/nmDHEG61I7tuP8sE1mNOvJxWrlseUnc3t738mJ/wU54PqMrVmX7Y1aUVZd3NOtUOTJnhNmEDc9OlEjX6Fsr/OQ2VjgxCChYdvMr1bjf/8XSkolBaUwLFihNagJSI1gqZ+TWWPuZvfqlD6UaskutfzY8md6lzhCeFcTb3KJ80+yfNYJDVbx4KDkbgaWvNZo99wsbXl+47T+aPLNxwatJOvnvuIHaclJi4Px5hHcw4wt83M1hnydOlFJmUzLLU8Cb+uoHH/Lrhs38CtT/+P+FlfknvpEhWWLaHX+oUElHGly/cHyLyvoYbK3h6/zz9H7epC4g/mLIfvd17FztqKJkEeT+LrUlAoFShGuhihNWixsbK5566Ug72Vvez8VoWSz5DG5Vl0JJLbGVqWXlxKvyr90KjzzoPu/8thXO2sWTq6CV2q1aOKexXisuKo4l4FB2sHmlX0ZNnoxiRm5vLZxgv3xmXlGlh8JJIXZu+j6gebCZmxjRrTtvDOylOciU67J/ft9ssMaRzICw2DcOvXl3K/zqPCiuXYVq6M1/jx2NWujSRJrBnbFAno98thYlL/+a1KKhXeb79N8oqVfPLXCdacjGHekPql/gxTQaEgKO7uYoSDtQNag5ZsfbbsM+bb2bfl57cqlHiq+DgxuHE5hv52lGTPPbzT4J085X7YeYXL8RlsmtAC9Z2D7C5BXdgdtZvOQf/0e7bVqPlxYAgtZ+7ilVbBxKdrGfXnMWr5u/DeC1VpFuyJSiWRmJnL8mNRvPK/Y7Sp6s2E5yqx/UI8e95+MGLbmJ5Oxq5deE/+p2Obo62GnwbVY8zCE3SevY/Q8u7ULeuCJElcjs+gkV0ZPE4cYtV7o2R3/VJQeFZQjHQxQqPS0MK/BRuub6BP5T4W5YUQrLu2jvF1x5OYk4hGpcHJ2qlAO3GFkseE5yqhkkzMi8rm+21xDGhoTaCHPbkGEweuJvK/Qzc5HZ3K0KblqeLzTyS4h60HGbqHc5td7a3pVNOXObuvsvZULDO616RTLd8HZDwdbRjbuiIvNSnPK/87xvjFJ2hRyesho6qPjUXj44OVm9sD15sEe+LpaMPM3rW5lpjF9cQsTEIQGuhGi66tsbUyKQZaQSEPFCNdzOhXtR+zjs6iR8UeaFT5l3NcdmkZkemRvL33bWzVtuhNepysnehTuQ8vVnoRTzvPItJaoSiRJInXn6vKgv9Zo9HoGPHHURIyc7FRq6jm58zgxoHcTMpiaJPyD4zL1GdiZ2WX55yda/syfvEJJnWo8pCBvh9HGyt+HhJK8y924uWcRy15kwlUDy8SJUnCx8WWHL2RvqFlH3gv4YQVQv9wQR8FBQXFSBc7Gvs2JsAxgA8PfMiMZjPyDAgymoxM2T+FzTc20zWoK2+FvoWrrTlt5XzSeZZfWk7PsJ583uJzmvrLD0JTKP6kalPJ0GVgr7GnoW8DapSP5P1OPR6S+2DNWVzsHlzkbb+5nQY+DfKcNzNXT5bOSP+GZfN8/34cbczBXfefT9/FqkwZ9LGxmLKzUdk/eGSTozdiq3k4Bzr3ylUcmjSxeF8FhWcRxS9azFBJKr5o+QUpuSkM3zycHZE7MJgM995PzEnkpU0vsfXGVqY0nMInzT+5Z6ABqntUZ3rT6Xzb5lve2/+eEvldCsg15rI2Yi2DNgyi01+dGLVtFN3WdCMiNYKfwn8iS/dwn3FHG6sHKnfdzr7NobhDdAvuluc9tpyLx9vJBhuZhUReahJIVHI2GdoHd8BWHh7Yh4aStmHDA9dvZ2iJuJ1JFR+nB64bEhLI3LcP7blzRI4cReSIl4n74ENywsMLvUiEgkJJQNlJF0PsNfb80PYHNl3fxB9n/2DGoRn4O/qjM+mITI9EIJjfcT4hZUIeOUdImRA+bPwhMw7PYFXXVUrEbAklMSeRsdvH4mztzMhaI2kZ0BK1So0QgkNxh3hr91t0WNWBxZ0XE+gceG9cm6perDsVyxvtKpNjyOGdve8woOoAHK0d87zPqahUaudTD/zfNAn2xEotselsHH1Dyz3wntuAAcR/+inOnTqhdjTfb/nRKDrX9nugI5c+Lo4b/QeA0YiVlxdO7Z4DSSL30iVi3nkXtaMjfl98jk2lSgX5yhQUShdCiBL7ql+/vngWiEqPEifjT4qziWfF5L2TxbzT82SNM5lMostfXcSJ+BOFrKFCYZCpyxQ9w3qKH07+IEwmU54yCdkJovWy1qLhwobi9O3T965fiEsTDT7dKg5EHxED1g8QU/ZNEUaTMe85MrSi4pQN4sedVwqkX81pm0Wzz3eI2+naB66bTCYRO326uD5okDCkpooz0aki5OOt4tKt9HsyuVFR4mL9UHGpcROhT0h4aG6T0ShSVv0lLjVtJnIuXCiQXgrFC6BUv54Ed2xZnnZO2UmXAAKcAghwCsAkTOyI3MFboW/JGidJEi9WepEN1zZQz7teIWup8KRZcnEJFVwqMLbO2Ed6QjztPFnTfQ2jto5i2JZhhJYJpYJLBQwmA6qAA7yx3cjEhiPoX7VfnnNk5hp4deFxmlX05HQeZ8yP4kp8BrYaNX3qB9Bn7kG+6FWbhhXc77Xt8/ngA27NnMWFdh3ZElCfmaMGEWxrQn/rFhk7d3J75izU7m5UWLYMK8+HAxwllQrXni+isrMletx4gjdvQtJY7outUPwQ9x1blBaP3v2fqbBRjHQJIlufjYSEh538ikwBjgGcSThTiFopFAZGk5EVl1bwdZuvLT7YXGxc+K7td/QM60mXoC6k5qZipbLiq7btmLdNYs1eE4GaJJoEe9yby2A0sfPibb7edpl65Vx5r1M1WszcRVxaDr4ueUeA38/CwzcZ0KAsE9pVJtDDgfdWn0GjUtGxRhmcbDUkZOay1hRKrV5VGZd5GsdvpnN1WiqSrQ02FYJQOzkRvG0bKnX+Z+DOnTqRvGgRGTt34dyxg/wvUOGpIIQAg0FZUD1BFCNdgtCoNehNeoQQslekOpPukRWpFIovZxLP4GjtSA0PeXWsfcGYwOMAACAASURBVBx8qO1dGxu1DUOqD7l3vdFLJpYcjWL6unMYjILKZZwwCcHZmDR8XGx5tXUw3er4IUkSfeoHMC3sHHMG179XACUvwqNSWXsqlo0TWgDQo54/3ev6cTAiicPXkriVrsXZVsP8YaHU8HMBuj8wPva9KTi2amnRQN/FbcAAUpctVYx0MUUYjWTu20fKkiVkHzqMMBiQbGxwbNUKtwEDsG/Y4N7zqiTvqoty93w/ipEuQdiobQhwCuB4/HFCfeT1/j0UewiVpGL01tHcSL+BURjxc/Cje8XuvFDhBaV7VjElWZuMn4Nfgcb4OfiRrE1+4JqVWsWQxoEMblSO09FpxKTmoJJgUocqD0Vav/18FUb8cZTXlpzgkx61cP9XcREhBLsu3eadlaeZ1bvOAztuSZJoVtGTZhUt5+bnRkTg1q+v7M9lX78+t7+YKVteoegwJCURNXYsGIy4DRyI/1dfo3Kwx5SWRtr6Ddz6+GM0fn74f/MNakeHp61uiUQx0iWMflX6sezSMllG+nDsYdZFrKO2V20GVRtEba/aqFBxJfUKKy6vYPaJ2Xzc9GPalGtjcS6FwiUhO4HozGiEEPg5+mGttkZr1BZojlxjLjZqmzzfkySJOmVdqVP20W0gbazU/DasAZ9uuEDrWbtoV60MLSp7YmulJjolh+XHohDAdwPq0TT48QvlCIMe1PIfPZJajTAYLAsqFCnGzEwih4/AsU0bvN6Y8MDOWO3qivvgQbj170fc9OlEjx1r7nZmrVSVKyiKkS5hdAvuxq9nfmVv9F5aBrR8pNy5xHOM2zGOxn6N+aX9Lw+85+voS8uAlpxJOMPru15HIGhbrm1hq66QB4diD7H44mJOxJ+ggksFJCRupt8k2DWYc0nnSM9Nx9nG2eI8epOew3GHGVpj6H/Sx8ZKzcfdazKxXWVWHo9m18UEcg3/3959h0dVJXwc/55JI70AoZcACggoVSkKIhaqgCgIiKKyYkMQUFlfG/a2Kgq6umsv9O4qRUF91QVlAUWU3hJ6SQ9pM+f9YwY2kDb4EnIDv8/zzJNw78mZM4eZ/HLvPfccN5UjQniyb3PaN4j7f5+mDKpWndxtWwlt0dyv8jnbtuOKjWHf08+QvnQp7pQUXKGhhLZuTezgwYR36ogpYpYzKVuH336HSk2bFgrogkxgIDUmTiTxjpGkTJ9B3LCbju+rCKe+y+sUd6FGVNTHuXIL1snW7F9jO0/rbOdunmvz3HmF9h/IPGAv+fQS23tOb3s072iJda07uM52mtrJpuekl1VzpQgej8e+suoV231Wdztz40ybmZt5fF92frZdsGWBveTTS+zQfw21+e78UutbumOpHfbFsLJs8mmTumix3T5kqF9lPXl5dvNVV9sNrdvY/a++arO3bbPuzEybd/CgPTJ9ut3at5/dNuB6m7t/fxm3WgpyZ2fbjR062uxt2/wqn7Fypd3Ss1extxLigFupinqcKSXdgqU/PyuglvEt+fuVf2f2ptl0n92dKWunsHDrQuZsnsOE/51Ar7m9CHQFMqP3DCoFFjG/cgHNqzTnkuqXsHDbwjPUegF497d3+feefzO111SuP//6E8YGhASE0KdhHyZ1ncT6w+t54scnSqzr0NFD/G3V3xjebHjZNvo0ibyiK3lJSWSuWFFiOWstSfeNJm/3bhIWLiB+zBhCEhJwhYURWKUKsQMHkjB3DhFXdGXXzbeQn5x8hl6BZH7/PSHnnUdIQoJf5cPatQOPh+zffivjlp19FNIVVNPKTfm458dM7jaZ7Pxsvt/9PWsOrKFZ5WZ0rNmRUS1HERpU+q00AAMbD2Tu5rll3GI5JjUnlffWvcfrV7x+wpSuJ7ukxiWMbzOe+Vvns2DrgkKn3qy1/HLwF2758hb6n9e/wlyyMEFB1HzuWXaPG0/W6uKnrU3+bCoZ331H9YlPEFyz6EF0xhiq3n034ZdeysHXJpVVk895npwc3OnpWI8HgLx9+whOqO/3zxtjCK5fn/z9+4vcX/DIsbw5qS2ga9IVXpO4JjSJa3LCtkXbF9E4rrHfdTSObUxSRtLxf2fkZnA0/ygRwRHFrpokf968LfPoXKcz1cOrl1p26AVDWbF3BS///DL/XPdPeiT0IC4kjvS8dJbsWEJ6bjp3tbyr2Dm5nSq8Y0dqPv8cSXffTXjHDsTceCOhF13kmxZ0E8nTp5E6bz7R1/Un9vrrS62vysg72NqrN/HjxxEQGVlqeSmdOz2d1HnzSZ42jbxduzDBwViPh6gePbwT0Ljdp1SfdedDoCLnVKnHzkanOAbDYsHCnM1zmLZhGjvSdhAWGEZGXgat41szqMkgutTuUuSKXHLqlu1axh0X3uF3+bta3sWE7ybweIfH+TbxWzZkbSAsMIxRrUbRqVanCrt+eMRll9Fw6RJS581n/1NPk7tjB9ZagmrVJPLqa3CFhVH9f/7Hr7oCq1YlvH170hcvJsaPUJeSHV27lsRRowhv144aTzxOaNu2GGPIP3KE1DlzOPz+B9jcXKo99hguPyYu8eTkkP3bekIaNToDrT+76LfuWahuZF3WH15Py/iWfpVfnricHHcOy3YtY3Tr0XSo2QGXcZHrzmXJziW8u+5dPvn9E17r+hrRIf4vwnAuS81JZf6W+Xy/+3sy8zIJCwrj0lqX0rdhX9Jy005pre+qoVVJy02jTbU2tKnWpgxbfeYFREYSN+wm4obddPz0ojGGo+vWkfXjj7hCir6lrCghjRqRt3cfnqwsUv/1L7J/W4/NzSWwSmWievWiUpMmpVciZG/YQOLd91Dj2WeIvPzyE/YFxsVRecQIogcNYkvnLiSNuo86b71Z6ujstC+/pFKzZgTXrl3q8xc8zXymRn075dR2USrmn+BSogHnDWD6xul+vfEOZB3g2ZXPcnX9q5ncbfIJR2bBAcH0btCbT3p+QqOYRoxaNoocd05ZN79C81gPk9dMpsecHvxx5A+GNh3KA+0eYNgFw9iUvImec3uSmpNKRl6G33Vm5mWeE5cdjs37DYDLdfz6p788eblkrljBlq5XkPHtt4Q0Pp+wNq0hMJDEu+5mx+AhHF2ngUul2ffU08SPG1sooAsKjIwkfvx4Mn/4gcx//7vE+vIPH+bQlDeJu3lYieWkGMUN+64Ij3P1FqzSeDwee9386+ycTXNKLXvPV/fYth+3PeEWoKK4PW47culIO/WPqaermWcdj8djn/jxCXvTv26yB7MKr+xkrbWHsg7ZK2ZcYfvP61/s7Sgne3fdu/bh/334dDbV8fJTUuyGdhfb/ORkv8p7cnPthkva2639+tvc3bsL78/Lsynz5tmN7TvYjJUrT3dzzxpHN2ywmzp3sZ68wrd2nszj8dhtAwfaje072Nw9e4osk711m93Sq5c9MOn1P9UezrJbrYqjVbDOMcYYXur8Erctvg2XcXFtw2uLPG20fNdyvkv6jpc6v1Tq9KAu4+K2Zrfx3E/PMahx0Ssqneu+2P4Fvx78lY96fER4UNFTIFYOrcxb3d5i4OcDmb1pNtc3Lvn6qdvjZsbGGbzc5eWyaLJjBURHE3F5F1LmzqPyrcNLLb/vySfxZGZS/5OPcYUX7nsTGEh0374Exsez+/6xNFgwn8DK/i9UczZxp6aSOn8BOZs3Y91uAqvFE33ttYQkJJC2cCHR/fth/BjgZYyhzt//zubOXdjWtx/h7dsTec3VBERF4T5yhNSFn5O9fj1V7rmHuJuG/qm2Wgefhj5TFNJnqQYxDXj3mncZ+81YPvr9IwY1HuSdFtS42JK8hRmbZrDpyCZaxbfimoRr/KqzXfV2ZOdnsyl5E9XCqjFvyzyWJy4nLTeNsKAw2lVrxw2Nb6BWRK0yfnXO9Nkfn3Fvy3uLDehjzo87n1bxrXh19av0a9SPwGKmyLTW8tKql6gXVY/mVfybnetsEnfTTSTdO4qo7tcQVKNGseXcaWmkzJ1H7JAhRQZ0QeEdOnjDf/Ycqtzxl9PdZEfzZGWx/8UXSfviSyI6dyasbVsIcJG7fQc7bxpGpcaNMaGViOx2pd91BsbGEhAVRb1PPyHzxx9JX7QYT1YWrshIonr3ovbkN3BVKnmuBimZQvos1jCmIXP7zmXF3hXM2jSL6Run47EeqodXZ3CTwexK30VaTprf9RljqBlRkw/Xf8g3id9weZ3LGXnhSCqHViYjL4Ovdn7FoM8HcXnty3m0w6PFziNdEVlr+Xnfz94/bpI3ke/Jp2poVfo07EPPhJ4kpidy8OjBEqdqLWhyt8l0nt6ZEUtH8Gj7R2kY0/CE/YnpiUxZO4XtqdsLTet6rgi98ELibr2VncOHU2fKlCJHBrszMtg5/FYCoqKo9oB/66zH3jiY3aNHU3nE7d6jytmzSV/6Fe6UFExYGGFt2hA7+EZCGjYsvbIKwpOZya7bbie4fj0afvlFobMIVceMJmXadA787W+END61AXY2P5+A6GjihgwhbsiQ09lsQSF91nMZFx1rdqRjzY6F9n3y+yccch86pfp2pO7gYNZB5vWbR3xY/An72lRrw6hWo3j0h0cZvWw0b3R7gyBXxV8mMzE9kbHfjCXfk8+gxoO488I7CQoIYmfaTmZtmsVrq1+jT4M+NK/SnACXf8svhgeFc3nty3FbNyOWjCAhOoFmlZthjGHTkU38fvh3+jbqy2PtHzunVyqrfOtwXBHh7Bx2M5VaNCemXz8C4+PxZGWRvmwZaV8uIrByZSrfOdLvNYxDWzQnPyODg6+9RvK06UR260bV+8d4683MJH3ZMnYOH05Yy1bUeO65s2L1pr0TJxLcsCE1nnm6yEtVruBg4m4extHff+fI++9TZeQdfo2sz/7jD1xhYQRE666PsqKQPoc1rdyUmZtmYq1/61N/uf1LDh49yPvd3y8U0MeEBYXxQucXuHfZvXy4/kNGtBhxfJ/b42ZPxh6y8rOIDommWlg1x1/b3pOxh+GLhnN789sZ3GTwCe2tF1WPzrU7s/HIRm5bfBv1ouqdUt1BAUFcXvNyXur8Et8mfcuu9F14rIe+jfryWtfXSp3S9VwRe8MNRPfpQ9qXi0hd+Dnu1FRclSoR2qY1DRYs4OCkSbhCT3H0e14eGd98S4PPFxIUf+J7ObRFC6redRf7nnqKxBEjqPvB+44+ZZublETK9Olk/bwKz9GjBERHE3llN6L79SMgKoq8ffvI+PY7Gn39damft+oP/5W0hQtJmTWLuKGlX0dOnjqN2EEDtcBJGVJIn8Nax7fGZVys2r+KdtXblVr+zbVvclHVi6gbVbfEcoGuQMa0HsOoZaMY3mw46bnpzN48m5kbZ2KxhAeFcyT7CPFh8QxqPIheDXqVyy1GOe4cluxYwvrD68l15xJXKY4eCT1OOPX82I+PMbTpUIY0Lf40XuO4xkxoN4FHfnyE/Zn7qRZerdTnttayJXkLA84bQFBAEFfW8/864LnIVakSMf37EdO/X6F9gZUrk7d7t991pS9bhs3Ops47bxcK6GNMcDDVJ05k99hxHJoyhfhx4wDvpBxpX35JxrLluNPScIWFEXZxO2L69z/jR5Oe7Gz2Pf44Gd9+R3TfvsSPG4srPJz8Q4dInb+Ag5OnUPkvI/BkZxPdq5dfZwQCoqKIuOxSDr0xmdjBg0sM36zVq0lfupQGC+afzpclJ1FIn8OMMdx8wc08/9PzfNj9QyKCI4otu2rfKnam7eTJ7k/6VXeTuCZUC6vGtA3TeP+39+lYqyOvdH2FZpWbAd77iVfsWcHHf3zM9I3TebPbm1QNq3pCHR7rYUvKFtJy0ggNDKVhTEO/ji5z3DkYDMEBRa9d6/a4efvXt5m+cToXVL6ADjU6EBIQwu7M3YxYMoIG0Q14oN0DBLuC2Zqylbe6vVXqc/Zq2Itnf3qWKWun8GSn0vto3aF1ZORlnHWTk5SHqF49SRx5J1XvvdevUckHJ71OSNOmBFUveVpW43IRf/8YdgweQpV77yV17lwOTnqdSs2bE92nNwGVK+NJzyD9q6/Y8uZbxA4aSNUxYzABJ17ysB4PR9euJX/fPggMJKTReYQ0KH5hirz9B0iZPYucP/7A5uYRGF+VqN59CLu43fEjYZubS+KddxEYF0ejZV/jCjvxkkhE587k7dlD4t334MnKotpDD5baL8fUeOEFNrfvwO5x46n+2KMExsae+HqsJePrr9n72OPUfPFFAqtWLaYmOR0U0ue4fo36seHIBm5bfBvPX/Y8DWIanLDfWsuq/asY+81Y6kXVo1W1Vn7XXS+qHpPXTubJjk9ydf2rT9jnMi461upIh5odeOuXtxj51Ug+7vEx4UHhZORmMHvzbGZsnIHFUiW0Cll5WRzIOkDfRn0Z3GQwNSNOXHBhX+Y+Zm6aydzNc0nJScFiiasUx4DzBjDgvAHHj27zPfk88O0DpOel80mPT6gTVeeEeka1HMXCbQu5Y8kdXFLjEvo36k9QQOnXOl3GxfXnX89nf3zG4x0eL/HatNvj5q1f3mJQ40EVdkpPJ6nUpAlBtWqR+vnnxPQrfKRdUG5SEjmbN1P7zSl+1R1crx6VmjZlz4S/krNhA/U++bjQgLKo7teQf+gQu8c/wJ4HH6LmSy9iXC48OTkkf/oZydOm4QoJIbhBA6w7n6O//EJI/QTibrmZyCv/ewbFk5nJviefJP2bb4nq2YOoXr0xwUHkJSay/+mnsB5LjYlPENa2LYfefgdXpUre5woo+r0WVLMmdd9/jy2XdyVn6zYiu3Xz6zUfm/s8ICqKrdd0J7JrV++a3cHB5CUlkTJrNiY4mNpvvE5YG/2RWeaKu4G6Ijw0mcnp4fF47Ae/fWC7TOtib190u/3090/tgi0L7Hvr3rPXzb/O9prTy05ZM8Xevuj2U6q3z9w+duSSkX49//3L77cf/PaB3Zux1/ad29eOXT7Wrtm/5oQJP3al7bJ/+/lvtvO0zvanvT8d3z5702zbaWon+9zK5+zW5K3W4/FYj8djNx3ZZJ/691O209ROduHWhdZaa99Y/YYdsXiEzc3PLbFNPyT9YFt91MqvCWGOycnLsc0/aG4f/PbBYuvPdefaR75/xN666Fabk5/jd91Ssqx1v3knKvnhh2LLuLOy7PYbB9sNl7T3eyIZa61NGjfebmjbzuYdOFBiOXd2tt1+42B7+ONPbH56ut0+ZKjddcdIm7XmxPexJyfHpn7xhd1yTXe778UXrcfjse6MDLvthoF298MP2/z0wmu7ezwem/b1MruxQ0ebtvwbu/HSS2325s1+tX/bDQPt1mv7+v16cxIT7cYOHa211uYdOWIP/fNdmzRuvE0cdZ/dO3GizfzPf06p/6R0JU1mYmwFvlm8bdu2dtWqVeXdjLNGrjuXr3Z+xc/7f+Zo/lEigyLpWrcr7Wu0Jyk9iVsW3cKS65f4NWI7OTuZrjO6MrHjRPo26ltq+V8O/sJD3z1ESEAI1za8lttb3F5s2ZV7V/LAtw/wj6v/wcbkjUxZM4W3r3qb+tH1iyy/JXkLI78ayejWo3np55eY2msqtSNLn0P4qllX0bZaW5677LlSy4L3D95WH7WiS50ubDiygRsa30CPhB5EB0eTmpvK4h2LmbFxBufFnscLl71wTo/aLgtZP/9M0pj7iejcmdghQwht4b233J2RQeqCBSR//AmBtWrhSUkmYdYsv+vd0r0HIQ0bUGdK6UffWavXsGfCBILr1Caodh2qP/5Ysdd13Skp7Bx+K9F9+5KzZTNYih19/d/6V5P4lzsIOf986k/9zK/2py1axO5x42m0fFmx1+ALOvDaa3gys6j+Pw/7Vb/8/7Vt25ZVq1YV+R+v091yXHBAMD0b9KRng56F9tWNqkvdyLp8k/gNV9W7qtS6Zm6aSYAJoEdCD7+e+8IqF5KVl0WdyDolBjR411keedFIJq2exC8Hf+GjHh8VG9AAjWIbMfmKydyy6BZax7f2K6AB2sS34ZvEb/BYj1+npTclb6JKWBUmXTGJ9YfXM2PjDIZ9MYyMvAwigiLoVKsTL3d5+ZycmORMCGvXjgYLF5A6Zw67x4zBnZ6OCQnGk5FJRJcuVH/8cYITEth27bV4jh71a0R43v4D5CUlUfkvI0otCxDaqiVYS+72HdR5550SB14FxMRQ+/VJbL/+BrCWRl8tLXX0dVjr1gQl1IdA/271A4i86iqMy0X64sXEDSt5/mx3Sgops2ZT76MP/a5fypZCWvw2uMlg/vHrP7is1mUlDuCy1rJ4x2IaxzUudvBWoZ/BkpWfReda/k0G0r9Rf15b/RoXV7u40EQgRWlauSmxIbHEhMT4VT/AiBYj+GL7FxzMOujXiO0ZG2cw4LwBADSr3IyJHSf6/VxyehxbpSnutttwp6Zic/MIiI464RaqsFatSPviC2IGDCi1vsyfVgIQ3bPwH65FMcaAMVRq0aLYa8UFBdetS2CVKgTExvg9Ojy0xYWkLVzo962TJiCAoLp1OTR5CmFt2lDpgguKLOdOTSXxrru9U4Q2aFBkGTnzNGpF/HZN/WtoENOA0ctHk5mXWWQZt8fNMyufIT03nfNjz/e77s3Jm7FYmsT5N9tRWFAYoQGhhQaQlaROZB1+P/y73+UbxTYi0BXI9I3TSy27JXkLi3YsYsD5pf/il7JnXC4CY2MJqhZf6B7n2KFDOPzOP3CnlTzbnrWW1FmzCaxa9ZTuw87bs4eQxo39Lh8QE4PN9n91ubDWrfFkZWGzsvwq78nNxZ2cTNUxo9l12+3se/oZcrZuPb4/PzmZw+++y7b+/Qm9sAXx48f53RYpewpp8Zsxhqc7PU2dyDr0nNOTV1a9wsYjGzl09BA703by3m/v0WtuL5Iykni609Os2LsCt8ftV92J6Ym4PW4aV/b/l1ueJ++UZjSrFVGLI9lH/C6flZeFy7iYt2UeUzdMLXay/41HNnLnV3cy4eIJxU7yIs4RfumlhF92GYl/uYP8I0W/H2x+Pvuffob8w4fBWr+XzbQeD+TnE1zf/4ltTEAANtf/kI68spv3lPrOXX6VT1+ylEpNGhM7eDAJc2Z7Z3C7ZTibOnRk02WXsbXbleRs2kztV1+l2l//qolJHEanu+WUBLoCeaT9I9x8wc3M3DSTcd+OIz03ndDAUNpWa3vCNdeYkBh+2PODX/NZr9izgojgCKKCo06pPf7cHnVM+xrtWbB1AfmefAJdpb/1/7X9X3Sq2Ynx7cZz//L7mbFxBoMaD6Jd9XYEugLZmbaTmZtmsubAGh6++OEir+WL8xhjqPbwXzn4+uts7d6DqJ49vCtkVY3Hk5VJxrJlJE+fQch5jaj/2afsuu12Mr//nojOpb+P8/bsAfAuXuFve0JDsYcP+13ek5kJAQEc/uADar7wfImnvD1ZWRx+5x2qjLoX8N6WFT9mDFVHjcKdkuKddzsmxq8pQKV8KKTlT6kbVZdxbccxrm3xp8Zub3E7z//0PBdUvoAqoVWKLbfhyAYW71iMBw9J6Ul+Dexye9zke/JP6T7jw9mHiQiOYNGORfRu0LvEsrnuXD774zMeaPcAdSLrMLPPTH7e9zPTN07nsw2fkefOIz4snj4N+2ikdgVkXC7ix4whdvAQUmbNZN/EJ49PNxrWtg11pkw+fu02dvBgDr39DuEdOpQ4P7i1lkN//ztBdeqQsfwbYgcN9Kstnpxs8g8dwubn+zUZS8qsWUT36UP2hg0ceOFF4h98oMijX3d6Okn33UelZs1OuB8bvEfv5+pSnRWNbsGSMvX2L2+zYOsCnuz0JK3jW5/wV3++J5+vd33Nsyuf5ZH2j7DmwBoCXYGMbTO21Hq/S/qOF396kZDAEGb1mVXqABprLdfOu5Zbmt3C66tf55XLX6Ft9aKPdnLduTz03UO4jIuXu7zs+PnFpWzZ/HyS7rkXV3gYNZ5/Hldw4cGQ1loOvfEGaUuWUHX0aA5NnkLC3DmlnjrO27OHbf2vI7h+PWIHDiJmwHUlls9PTmZ7v/7U+ftbBNWoQdKY+8nbu4fYGwcTeWU377SgBw+RumA+qXPmEtWrF9X+OsGvQWxSfnQLlpSbkReNpGZETZ748QmCAoLoVrcbEUERHD56mC+2f0GN8Bq83OVl2lVvR9O4pgz9YiiX1bqsxLnED2Qd4JkVzzC+7Xje/OVN72noJoNKbMfHv3tnMxtw3gBqR9Zm3Lfj6Fa3G4MaD6JxnPc6+NH8o3y5/Us+/v1jEqITePayZxXQggkMpNak19jz0AS29epN7I03En1tH++0oBkZpC9dSvKnn0FgIPU++ICAuDiSP/yIAy+8QPyECcW+h9wZGSSNuZ/Kt95KRNfL2XXrbQTExhJ5Rdciy+cnJ5N4551E9e5FpaZNAaj7/nscXbuW5M+mcuSjj7BZWbhioom88krqT59GcN2S59kX59ORtJwR1lpW7F3Bz/u8E6VEBUfRtW7XQqO5V+5dyYPfPcjdF93NtY2uPWHhDY/18P3u73l25bPc2PhGhjcfTmJaIsMXD+fmC25mSJMhha5R57pz+XD9h8zcNJMPu39IjYgaABzMOuhd9GPTTNweNyEBIaTkpHBxjYsZ1HgQHWt21JSdcgJrLdm//kryZ1NJX74cT3o6JjSU8HbtiB06hPBLLz1+5OxOSWHXiL8QVLs2Ve66k0oFRntbt5uM//1fDr7yKmFt21Dt0UcxxnD0119JuudeQlteROzgwYRdcgm4XOTv3UvKrFkkz5hJTL++VB07VoO7zjIlHUkrpMVxNhzZwJQ1U1h7cC1X1L3i+NzdyxOXEx0SzR0t7qBbvf/OQ7w7YzeP//g4W1O20q9RP5rENcFi+f3w78zfMp+mcU15ouMTVA8vvKCC2+PmcPZhctw5xIbElrjIiEhBpd2n7MnK4vD775MyYyZB1asTnJCAdbs5uno1AXFxxN08jKjevU+ow5OZSern/yJ52jRyNm4ElwtXeDjRffoQp785bgAADPVJREFUe+MgQho1OhMvTc4whbRUSLszdvNN4jfHV8FqU60NLaq2KLb8tpRtzN0yl90Z3mUL60TWoX+j/iXORiZS1mxeHpkrVpJ/YD8mMJDgho0Ibd6s9J/zeLD5+UVeA5ezi0JaRETEoUoKaV3YEBERcSiFtIiIiEMppEVERBxKIS0iIuJQCmkRERGHUkiLiIg4lEJaRETEoRTSIiIiDqWQFhERcSiFtIiIiEMppEVERBxKIS0iIuJQCmkRERGHUkiLiIg4lEJaRETEoco0pI0xdYwxy40xfxhj1htjRvu2xxljlhpjNvu+xhb4mZeMMauMMV3Ksm0iIiJOV9ZH0vnAOGttU6A9cI8x5gJgAvC1tfY84GvfvzHGNPH9XGfgnjJum4iIiKOVaUhba/daa1f7vk8H/gBqAX2BD33FPgT6+b4PADyABUxZtk1ERMTpztg1aWNMfaAVsBKoZq3dC94gB+J9368HwoDvgbfOVNtEREScKPBMPIkxJgKYDYyx1qYZU/xBsrV2VAn1GCDj2L/j4uJOZzNFRETOuO3bt2OMySywKcJaa+EMHEkbY4LwBvSn1to5vs37jTE1fPtrAAf8qct6hR97JCQklE2jRUREzpCEhAQKZtuxgIayH91tgHeBP6y1rxTYtQC4xff9LcD8smyHiIhIRVTWp7s7AcOAdcaYtb5tDwPPAzOMMbcDu4AbyrgdIiIiFU6ZhrS19nuKH6XdrSyfW0REpKLTjGMiIiIOpZAWERFxKIW0iIiIQymkRUREHEohLSIi4lAKaREREYdSSIuIiDiUQlpERMShFNIiIiIOpZAWERFxKIW0iIiIQymkRUREHEohLSIi4lAKaREREYdSSIuIiDiUQlpERMShFNIiIiIOpZAWERFxKIW0iIiIQymkRUREHEohLSIi4lAKaREREYdSSIuIiDiUQlpERMShFNIiIiIOpZAWERFxKIW0iIiIQymkRUREHEohLSIi4lAKaREREYdSSIuIiDiUQlpERMShFNIiIiIOpZAWERFxKIW0iIiIQymkRUREHEohLSIi4lAKaREREYdSSIuIiDiUQlpERMShFNIiIiIOpZAWERFxKIW0iIiIQymkRUREHEohLSIi4lAKaREREYdSSIuIiDiUQlpERMShFNIiIiIOpZAWERFxKIW0iIiIQymkRUREHEohLSIi4lAKaREREYdSSIuIiDiUQlpERMShFNIiIiIOpZAWERFxKIW0iIiIQymkRUREHEohLSIi4lAKaREREYdSSIuIiDiUQlpERMShFNIiIiIOpZAWERFxKIW0iIiIQymkRUREHEohLSIi4lAKaREREYdSSIuIiDiUQlpERMShFNIiIiIOpZAWERFxKIW0iIiIQymkRUREHEohLSIi4lAKaREREYdSSIuIiDiUQlpERMShFNIiIiIOpZAWERFxKIW0iIiIQymkRUREHEohLSIi4lAKaREREYdSSIuIiDiUQlpERMShFNIiIiIOpZAWERFxKIW0iIiIQymkRUREHEohLSIi4lAKaREREYdSSIuIiDiUQlpERMShFNIiIiIOpZAWERFxKIW0iIiIQxlrbXm34U+rUqWKrV+/frk89/bt20lISCiX5z7bqC9PH/Xl6aO+PH3UlyXbvn27PXz4cJEHzRU6pMuTMSbTWhte3u04G6gvTx/15emjvjx91Jd/nk53i4iIOJRCWkSkaKa8G3AWUV/+STrd/ScZY4xV550W6ktxIr0vTx/15Z+nI+kiGGOGG2N6l1RGb7jTR30pTqT35emjvvzzAsu7AeXNGLMDSAfcQL61tq1v10BjTHdgv7X2qQLlA4BVwG5rbW/ftvuBEYAF1gG3WmuzjTHDga7AUWAvEAQ0BwZaa3PPwMtzjJP7zRhTB/gIqA54gHestZPUZ8UzxlQCvgNC8H52Z1lrH/ftiwH+ibevLHAb0Bj1ZSGl9GN3YBIQAPzTWvu83pPFK+5zXGD/yZ/74agvT4mOpL26WmtbFghogMXW2nvxvoEKGg38cewfxphawH1AW2ttc7wf7htPqudOoLO19hHgJ6BZWbwIhzuh34B8YJy1tinQHrjHGHOBb5/6rGg5wBXW2ouAlkB3Y0x7375JwCJrbRPgIv7b1+rLworsR1+gTAF6ABcAg/WeLFVJn2Mo/LkH9eUpUUgXL9X39fhpGmNMbaAX3iOWggKBUGNMIBAG7CmwL8339aDvay7ev+DPGUX1m7V2r7V2te/7dLwf5Fq+3ed8nxXFemX4/hnke1hjTBTQGXjXVy7XWpviK6e+PElx/QhcDGyx1m7zHdVNA/r6yqkfi1DS57iE35fqy1OgkPZ+OJcYY/5jjLmjlLKvAQ/iPa3j/WFrdwMvA7vwnr5JtdYuKavGVlCF+q0gY0x9oBWw8sw1qWIyxgQYY9YCB4Cl1tqVQAO8v/DeN8asMcb80xije1JLUEw/1gISCxRL4r9/OEopivgcl/i5Fz9Za8/pB1DT9zUe+AXvKZiiyvUG3vR9fznwue/7WGAZUBXvX+TzgJvK+3U55VFcvxXYHwH8B7iuvNtakR5ADLAc7+WYtnhPO17i2zcJeKq821gRHif14w14r0Mf2zcMeKO821gRHid/jkv73Ovh/+OcP5K21u7xfT0AzMV7yqsonYBrfQPNpgFXGGM+Aa4EtltrD1pr84A5QMcyb3jFUVy/YYwJAmYDn1pr55RfEyse6z2d/Q3QHe8RX5L1Hg0CzAJal1PTKpQi+rFOgd21OfHSlRShmM9xsZ97OTXndEgbY8KNMZHHvgeuBn4rqqy19q/W2trW2vp4B4Yts9behPc0d3tjTJgxxgDdKDxQ4pxVXL/5+upd4A9r7Svl2sgKwhhT1TeKG2NMKN4/EDdYa/cBicaYxr6i3YDfy6mZjldcPwI/A+cZYxKMMcF4368Lyq+lzlfc57iE35dyis71W7CqAXO97zMCgc+stYtOpQJr7UpjzCxgNd5TjmuAd053Q89CnfCeTlznuzYI8LC19otybJPT1QA+9I1CdgEzrLWf+/aNAj71hcs24NZyamNFUGw/GmPuBRbjvUvjPWvt+vJrZoWgz3EZ04xjIiIiDnVOn+4WERFxMoW0iIiIQymkRUREHEohLSIi4lAKaREREYdSSIuIiDiUQlpERMShFNIi5cwY4zbGrDXG/GaMWVhgNqyavolySvv5jGK29ztp2cCiyvxijJn651p+evj7OkXORQppkfJ31HrXM28OHAHuAe+88tba6/8f9fbDuy5ykYwxTfH+DuhcnqtmnYbXKXLWUkiLOMu/+e96vPWNMb/5vg8zxswwxvxqjJlujFlpjGl77IeMMc/4jopXGGOqGWM6AtcCL/mO0hsW8VxDgI+BJb6yx+q6zxjzu++5pvm2RRhj3jfGrPNtH+DbfrUx5t/GmNXGmJnGmAjf9h3GmIm+7euMMU1827v42rPWt6xm5Emvs1KB51ljjOnq2z7cGDPHGLPIGLPZGPPiae53EUdSSIs4hG8u6W4UvajD3UCytfZC4CmgTYF94cAKa+1FwHfAX6y1P/rqecB3lL61iDoHAdOBqcDgAtsnAK18z3Wnb9ujeNdKb+HbvswYUwV4BLjSWtsaWAWMLVDPId/2t4Dxvm3jgXustS2By4CjJ7Xp2FmEFr42fWiMqeTb19LX5hbAIGNMHUTOcgppkfIX6luc4DAQBywtosyleJf8w1r7G/BrgX25wLGFNv4D1C/tCY0x7YCD1tqdwNdAa2NMrG/3r3gX67gJ76Ix4F0pasqxn7fWJgPt8Z5O/8HX/luAegWe5tiyhQXb9APwijHmPiDGWpvPiS7Fe3SPtXYDsBM437fva2ttqrU2G+8qX/UQOcsppEXK31HfkWU9IBjf0eRJTAk/n2f/u1KOG/9WtxsMNPGt97sViAIG+Pb1whvIbYD/GGMCfc9/8mo8BljqO1Jvaa29wFp7e4H9OSe3yVr7PDACCAVWHDsN7ufrzCnwvb+vU6RCU0iLOIS1NhW4DxhvjAk6aff3wEAA34jtFn5UmQ5EnrzRGOMCbgAutNbW96352xcY7NtXx1q7HHgQiAEi8F63vrdAHbHACqCTMaaRb1uYMeZ8SmCMaWitXWetfQHv6fGTQ/o7YKiv7PlAXWCjH69V5KykkBZxEGvtGuAX4MaTdr0JVDXG/Ao8hPeUdGop1U0DHvANwCo4cKwzsNtau7vAtu/wnrquBXxijFmHd230V621KcDTQKzvNrFfgK7W2oPAcGCqr10rKBy6JxtToI6jwJdFvM4A3/NPB4Zba3NOrkTkXKH1pEUqAN+gsiBrbbYvcL8GzrfW5pZz00SkDOmajkjFEAYs950GN8BdCmiRs5+OpEVERBxK16RFREQcSiEtIiLiUAppERERh1JIi4iIOJRCWkRExKEU0iIiIg71f63G9cW2Qv1/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "_, ax, _ = exclusion_mask.plot()\n",
    "on_region.to_pixel(ax.wcs).plot(ax=ax, edgecolor=\"k\")\n",
    "plot_spectrum_datasets_off_regions(ax=ax, datasets=datasets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Source statistic\n",
    "\n",
    "Next we're going to look at the overall source statistics in our signal region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets_all = Datasets(datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "info_table = datasets_all.info_table(cumulative=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=4</i>\n",
       "<table id=\"table4809730312\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>name</th><th>livetime</th><th>a_on</th><th>n_on</th><th>n_off</th><th>a_off</th><th>alpha</th><th>background</th><th>excess</th><th>significance</th><th>background_rate</th><th>gamma_rate</th></tr></thead>\n",
       "<thead><tr><th></th><th>s</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>1 / s</th><th>1 / s</th></tr></thead>\n",
       "<thead><tr><th>str5</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>23523</td><td>1581.7367584109306</td><td>1.0</td><td>172</td><td>162</td><td>12.0</td><td>0.08333333333333333</td><td>13.5</td><td>158.5</td><td>21.108139320737838</td><td>0.008534922090046502</td><td>0.10020630750165709</td></tr>\n",
       "<tr><td>23523</td><td>3154.4234824180603</td><td>1.0</td><td>365</td><td>384</td><td>12.0</td><td>0.08333333333333333</td><td>31.999999999999993</td><td>333.0</td><td>29.933816690058016</td><td>0.01014448446074527</td><td>0.10556604141963048</td></tr>\n",
       "<tr><td>23523</td><td>4732.546999931335</td><td>1.0</td><td>512</td><td>790</td><td>18.853317811408616</td><td>0.05304106205619018</td><td>41.90243902439024</td><td>470.09756097560984</td><td>37.37127329585745</td><td>0.008854098865790071</td><td>0.09933288797394521</td></tr>\n",
       "<tr><td>23523</td><td>6313.811640620232</td><td>1.0</td><td>636</td><td>1173</td><td>22.325282717179665</td><td>0.0447922659107239</td><td>52.54132791327913</td><td>583.4586720867208</td><td>41.98855362476707</td><td>0.008321649568265832</td><td>0.09240989521021017</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=4>\n",
       " name      livetime        a_on  ...   background_rate         gamma_rate    \n",
       "              s                  ...        1 / s                1 / s       \n",
       " str5      float64       float64 ...       float64              float64      \n",
       "----- ------------------ ------- ... -------------------- -------------------\n",
       "23523 1581.7367584109306     1.0 ... 0.008534922090046502 0.10020630750165709\n",
       "23523 3154.4234824180603     1.0 ...  0.01014448446074527 0.10556604141963048\n",
       "23523  4732.546999931335     1.0 ... 0.008854098865790071 0.09933288797394521\n",
       "23523  6313.811640620232     1.0 ... 0.008321649568265832 0.09240989521021017"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
    "plt.plot(\n",
    "    info_table[\"livetime\"].to(\"h\"), info_table[\"excess\"], marker=\"o\", ls=\"none\"\n",
    ")\n",
    "plt.xlabel(\"Livetime [h]\")\n",
    "plt.ylabel(\"Excess\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": [
    "plt.plot(\n",
    "    info_table[\"livetime\"].to(\"h\"),\n",
    "    info_table[\"significance\"],\n",
    "    marker=\"o\",\n",
    "    ls=\"none\",\n",
    ")\n",
    "plt.xlabel(\"Livetime [h]\")\n",
    "plt.ylabel(\"Significance\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "datasets[0].peek()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally you can write the extrated datasets to disk using the OGIP format (PHA, ARF, RMF, BKG, see [here](https://gamma-astro-data-formats.readthedocs.io/en/latest/spectra/ogip/index.html) for details):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = Path(\"spectrum_analysis\")\n",
    "path.mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in datasets:\n",
    "    dataset.to_ogip_files(outdir=path, overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to read back the datasets from disk you can use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets = []\n",
    "for obs_id in obs_ids:\n",
    "    filename = path / f\"pha_obs{obs_id}.fits\"\n",
    "    datasets.append(SpectrumDatasetOnOff.from_ogip_files(filename))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit spectrum\n",
    "\n",
    "Now we'll fit a global model to the spectrum. First we do a joint likelihood fit to all observations. If you want to stack the observations see below. We will also produce a debug plot in order to show how the global fit matches one of the individual observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "spectral_model = PowerLawSpectralModel(\n",
    "    index=2, amplitude=2e-11 * u.Unit(\"cm-2 s-1 TeV-1\"), reference=1 * u.TeV\n",
    ")\n",
    "model = SkyModel(spectral_model=spectral_model)\n",
    "\n",
    "for dataset in datasets:\n",
    "    dataset.models = model\n",
    "\n",
    "fit_joint = Fit(datasets)\n",
    "result_joint = fit_joint.run()\n",
    "\n",
    "# we make a copy here to compare it later\n",
    "model_best_joint = model.copy()\n",
    "model_best_joint.spectral_model.parameters.covariance = (\n",
    "    result_joint.parameters.covariance\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OptimizeResult\n",
      "\n",
      "\tbackend    : minuit\n",
      "\tmethod     : minuit\n",
      "\tsuccess    : True\n",
      "\tmessage    : Optimization terminated successfully.\n",
      "\tnfev       : 48\n",
      "\ttotal stat : 121.98\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(result_joint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.1, 40)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "ax_spectrum, ax_residual = datasets[0].plot_fit()\n",
    "ax_spectrum.set_ylim(0.1, 40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute Flux Points\n",
    "\n",
    "To round up our analysis we can compute flux points by fitting the norm of the global model in energy bands. We'll use a fixed energy binning for now:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "e_min, e_max = 0.7, 30\n",
    "e_edges = np.logspace(np.log10(e_min), np.log10(e_max), 11) * u.TeV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we create an instance of the `~gammapy.spectrum.FluxPointsEstimator`, by passing the dataset and the energy binning:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpe = FluxPointsEstimator(datasets=datasets, e_edges=e_edges)\n",
    "flux_points = fpe.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a the table of the resulting flux points:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=10</i>\n",
       "<table id=\"table4811176928\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>e_ref</th><th>e_min</th><th>e_max</th><th>ref_dnde</th><th>ref_flux</th><th>ref_eflux</th><th>ref_e2dnde</th><th>norm</th><th>stat</th><th>norm_err</th><th>counts [4]</th><th>norm_errp</th><th>norm_errn</th><th>norm_ul</th><th>sqrt_ts</th><th>ts</th><th>norm_scan [11]</th><th>stat_scan [11]</th><th>dnde</th><th>dnde_ul</th><th>dnde_err</th><th>dnde_errp</th><th>dnde_errn</th></tr></thead>\n",
       "<thead><tr><th>TeV</th><th>TeV</th><th>TeV</th><th>1 / (cm2 s TeV)</th><th>1 / (cm2 s)</th><th>TeV / (cm2 s)</th><th>TeV / (cm2 s)</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>1 / (cm2 s TeV)</th><th>1 / (cm2 s TeV)</th><th>1 / (cm2 s TeV)</th><th>1 / (cm2 s TeV)</th><th>1 / (cm2 s TeV)</th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>0.859</td><td>0.737</td><td>1.002</td><td>4.041e-11</td><td>1.078e-11</td><td>9.179e-12</td><td>2.983e-11</td><td>0.968</td><td>18.393</td><td>0.087</td><td>49 .. 31</td><td>0.089</td><td>0.084</td><td>1.151</td><td>20.687</td><td>427.949</td><td>0.200 .. 5.000</td><td>188.767 .. 680.271</td><td>3.912e-11</td><td>4.653e-11</td><td>3.501e-12</td><td>3.606e-12</td><td>3.398e-12</td></tr>\n",
       "<tr><td>1.261</td><td>1.002</td><td>1.588</td><td>1.489e-11</td><td>8.853e-12</td><td>1.095e-11</td><td>2.369e-11</td><td>0.973</td><td>12.648</td><td>0.091</td><td>31 .. 36</td><td>0.094</td><td>0.088</td><td>1.165</td><td>20.512</td><td>420.723</td><td>0.200 .. 5.000</td><td>172.542 .. 614.494</td><td>1.448e-11</td><td>1.734e-11</td><td>1.350e-12</td><td>1.392e-12</td><td>1.310e-12</td></tr>\n",
       "<tr><td>1.852</td><td>1.588</td><td>2.160</td><td>5.484e-12</td><td>3.152e-12</td><td>5.788e-12</td><td>1.881e-11</td><td>1.196</td><td>9.917</td><td>0.149</td><td>27 .. 12</td><td>0.156</td><td>0.143</td><td>1.519</td><td>15.287</td><td>233.701</td><td>0.200 .. 5.000</td><td>115.920 .. 242.783</td><td>6.560e-12</td><td>8.330e-12</td><td>8.191e-13</td><td>8.528e-13</td><td>7.862e-13</td></tr>\n",
       "<tr><td>2.518</td><td>2.160</td><td>2.936</td><td>2.467e-12</td><td>1.928e-12</td><td>4.813e-12</td><td>1.564e-11</td><td>1.249</td><td>9.185</td><td>0.177</td><td>10 .. 11</td><td>0.185</td><td>0.168</td><td>1.635</td><td>13.945</td><td>194.460</td><td>0.200 .. 5.000</td><td>96.429 .. 175.789</td><td>3.080e-12</td><td>4.032e-12</td><td>4.354e-13</td><td>4.566e-13</td><td>4.150e-13</td></tr>\n",
       "<tr><td>3.697</td><td>2.936</td><td>4.656</td><td>9.086e-13</td><td>1.584e-12</td><td>5.743e-12</td><td>1.242e-11</td><td>0.940</td><td>14.937</td><td>0.158</td><td>17 .. 11</td><td>0.167</td><td>0.150</td><td>1.291</td><td>10.703</td><td>114.552</td><td>0.200 .. 5.000</td><td>60.543 .. 213.272</td><td>8.543e-13</td><td>1.173e-12</td><td>1.439e-13</td><td>1.519e-13</td><td>1.362e-13</td></tr>\n",
       "<tr><td>5.429</td><td>4.656</td><td>6.330</td><td>3.347e-13</td><td>5.639e-13</td><td>3.035e-12</td><td>9.864e-12</td><td>1.134</td><td>8.760</td><td>0.265</td><td>9 .. 5</td><td>0.286</td><td>0.246</td><td>1.746</td><td>8.141</td><td>66.270</td><td>0.200 .. 5.000</td><td>38.226 .. 81.164</td><td>3.794e-13</td><td>5.845e-13</td><td>8.885e-14</td><td>9.562e-14</td><td>8.236e-14</td></tr>\n",
       "<tr><td>7.971</td><td>6.330</td><td>10.037</td><td>1.233e-13</td><td>4.632e-13</td><td>3.621e-12</td><td>7.833e-12</td><td>1.040</td><td>12.425</td><td>0.274</td><td>5 .. 4</td><td>0.297</td><td>0.253</td><td>1.681</td><td>6.855</td><td>46.987</td><td>0.200 .. 5.000</td><td>32.858 .. 80.471</td><td>1.282e-13</td><td>2.072e-13</td><td>3.382e-14</td><td>3.663e-14</td><td>3.114e-14</td></tr>\n",
       "<tr><td>11.703</td><td>10.037</td><td>13.647</td><td>4.541e-14</td><td>1.649e-13</td><td>1.914e-12</td><td>6.220e-12</td><td>0.901</td><td>10.499</td><td>0.429</td><td>0 .. 1</td><td>0.490</td><td>0.373</td><td>2.006</td><td>3.369</td><td>11.351</td><td>0.200 .. 5.000</td><td>15.510 .. 38.333</td><td>4.091e-14</td><td>9.110e-14</td><td>1.950e-14</td><td>2.226e-14</td><td>1.696e-14</td></tr>\n",
       "<tr><td>17.183</td><td>13.647</td><td>21.636</td><td>1.673e-14</td><td>1.355e-13</td><td>2.284e-12</td><td>4.939e-12</td><td>0.310</td><td>7.211</td><td>0.281</td><td>1 .. 0</td><td>0.357</td><td>0.310</td><td>1.184</td><td>0.950</td><td>0.903</td><td>0.200 .. 5.000</td><td>7.394 .. 43.010</td><td>5.186e-15</td><td>1.980e-14</td><td>4.704e-15</td><td>5.976e-15</td><td>5.186e-15</td></tr>\n",
       "<tr><td>25.229</td><td>21.636</td><td>29.419</td><td>6.162e-15</td><td>4.825e-14</td><td>1.207e-12</td><td>3.922e-12</td><td>0.000</td><td>0.524</td><td>0.001</td><td>0 .. 0</td><td>0.278</td><td>0.000</td><td>1.110</td><td>0.003</td><td>0.000</td><td>0.200 .. 5.000</td><td>1.244 .. 18.536</td><td>1.100e-20</td><td>6.842e-15</td><td>8.675e-18</td><td>1.710e-15</td><td>1.100e-20</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=10>\n",
       " e_ref   e_min   e_max  ...     dnde_err       dnde_errp       dnde_errn   \n",
       "  TeV     TeV     TeV   ... 1 / (cm2 s TeV) 1 / (cm2 s TeV) 1 / (cm2 s TeV)\n",
       "float64 float64 float64 ...     float64         float64         float64    \n",
       "------- ------- ------- ... --------------- --------------- ---------------\n",
       "  0.859   0.737   1.002 ...       3.501e-12       3.606e-12       3.398e-12\n",
       "  1.261   1.002   1.588 ...       1.350e-12       1.392e-12       1.310e-12\n",
       "  1.852   1.588   2.160 ...       8.191e-13       8.528e-13       7.862e-13\n",
       "  2.518   2.160   2.936 ...       4.354e-13       4.566e-13       4.150e-13\n",
       "  3.697   2.936   4.656 ...       1.439e-13       1.519e-13       1.362e-13\n",
       "  5.429   4.656   6.330 ...       8.885e-14       9.562e-14       8.236e-14\n",
       "  7.971   6.330  10.037 ...       3.382e-14       3.663e-14       3.114e-14\n",
       " 11.703  10.037  13.647 ...       1.950e-14       2.226e-14       1.696e-14\n",
       " 17.183  13.647  21.636 ...       4.704e-15       5.976e-15       5.186e-15\n",
       " 25.229  21.636  29.419 ...       8.675e-18       1.710e-15       1.100e-20"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flux_points.table_formatted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we plot the flux points and their likelihood profiles. For the plotting of upper limits we choose a threshold of TS < 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11de538d0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "flux_points.table[\"is_ul\"] = flux_points.table[\"ts\"] < 4\n",
    "ax = flux_points.plot(\n",
    "    energy_power=2, flux_unit=\"erg-1 cm-2 s-1\", color=\"darkorange\"\n",
    ")\n",
    "flux_points.to_sed_type(\"e2dnde\").plot_ts_profiles(ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final plot with the best fit model, flux points and residuals can be quickly made like this: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "flux_points_dataset = FluxPointsDataset(\n",
    "    data=flux_points, models=model_best_joint\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sdiLUbZnqSEUkTZXma8J7ZqYEQCTdNO4Ch90AFy2CY0dBg7Yw5Qa4tSs8dER4omDTxpKvIyKRUpoWgIcIScBXwE+AAe7uqkISSQeVc2D3Y8Ky+nOY/XjoInj692FGws7HhS4Cd01KJCKlSgBGAScBc9lWAyAi6ahOMzjgEtjvYlj2digcnDkaPryPGyq1YlL1Q2BD+9CVICKRVJougM/c/Xl3X+Luy/KXpEWWQGY20MxGrlmzJtWhiJSvrCxotT8cc2/oIjj8ZjZbJU5dew/c2A7GnQIfvxaGJhaRSClNC8BHZvYYMJ7QBQCAu6f9UwDuPh4Yn5eXd2aqYxFJmWp1YM8z+OuMjjTf9Cn/2XVuKBxc8CzUbhorHBwK9VqlOlIRKQelSQCqET74+xfYpscARTLQuz/sDIcOhYP/BYsmhu6Bt26CN/8T5h/ofhJ0GBhmMhSRCinuBMDdT0tmICJSfr5YHRtOuFJV6HRkWNZ8AbMfC8nAM8NhQu1QUNj9pDBEsQoHRSqU0gwE9BDwJ3dfHVuvC9zk7qcnKziRqCjPsfkXfLm2mHv2xqruy2715tF34yvsPf0xqk5/gM8qtWRStf68Va0f67JLP1WxJu8RST+l6QLokv/hD+Duq8wsY6YCFom65as2bPvmD0xdshKApnVy2KXutqZ+tywWVu3CwqpdeKD2Oez74xT6bniFU9aNZOi6+5meszeTqvVndtU8tlp2Sn4OESm70iQAWWZW191XAZhZvVKeLyJFKM9vyINHvMfUJStZet3hcZ5xEHAlfL2ASjNH03POWHquegdqNYauQ8LYAvV3TWbIv1IwiRGRHVeaD/CbgHfN7ElC8d/xwNVJiUpE0s9OHWHANXDQFfDxy6FW4J3/wts3Q4teIRHoOAiq1PjNqYnq4ii++6J01C0hUVeaIsCHzWwa0I8wCuDR7r4gaZGJSNI0rZOz4ydXqhKeEOgwENZ+GRtxcDQ8ew5M+AvsfnQoHNxlz4QVDsbbfSEi8SsxATCzmu7+A0DsA/83H/oFjxGR9JewD83ajWG/C6H3BfDZ+yERmPskzHgYGrQPrQJdT0jYt+3Sd1+ISFHiGQnwOTO7ycz2N7Nf2vbMrLWZnWFmLwMDkheiiKQ9M2ixDxx5J1y8GI64PQw89Orf4eYO8PiJ8NEE2LI51ZGKSEyJLQDufqCZHQacBfSKFf9tAhYBLwKnuPtXyQ1TRDJG1VrQ4+SwfLs4TEg0ewwsehFq7gRdTwhdBA3a7tDly9R9ISK/iKsGwN0nABOSHIuIVDQN20H/K+HAf8DHr4Zk4N074J1bodneoYug05EhaYiT+vxFEkOP8YlI8mVXht0OC8u6r7cVDj5/Hkz8P+h0VEgGmu9dIUcczH9qQU8eSDpRAiAi5avWTtD7z9DrT/D5B6FVYP4zMGs01G8TKxwcArV2TnWkIhVaaaYDFhFJHDNo3hMG3RGmKh50F9RoCK9dATd3hMdOgIUvwJZNqY5UpEKK5zHAacA7wERgsrtrGC4RSayqNaH70LB890loDZj1OCyeGJKCLoND4aCIJEw8LQB7A88AfYApZjbBzP5kZu2SGpmIRFODNmG0wQvmw4njoFlPmHoP3NWTK7/7M/02TIQf16Y6SpGMZ+5euhPMGgOHEp79bwO87+7nJiG2hMvLy/Np06alOgwRKa0fvoU5Y/j8jZE02/wZVK4OHY8M9QIt9k37wkEVAUqqmNl0d88rbF+piwDd/UtgFDDKzLIA/YsWkeSq2RD2PZ+L5+xBm02LuLrlLJj7FMx+DOq1hm5DoduJULtJqiMVyRhlKgJ0963u/k6ighERKZYZn1TZDQbeGkYcPPIeqN0U3rgSbukEjx4HC56DzT+nOlKRtKfHAEUkM1WpDt2GhOX7/8Gsx8Iy7mSoXn9b4eBOHVMdqUha0mOAIpL56u8KB/4dLpgHQ5+Elr3hg3vh7n1gZF/48H7YuDrVUYqklbhbAMzstkI2rwGmuftziQtJRGQHZWVD24PDsv57mDsOZjwCL14IL/8VOg6KFQ72hix9/5FoK83/gBygG/BxbOkC1APOMLP/JiE2EZEdV6M+7H0OnPMOnDkpFAouegkeGgi3dYMpN8Ca5amOUiRlSlMD0Abo5+6bAczsbuAV4GBgbhJiExEpOzNo2iMsh1wNC8eH4YcnXQ2TroFd+4VWgd0Oh0pVUx2tSLkpTQLQFKhBaPYn9rqJu28xs58SHpmISKJVrgZdjg/LqqUw89FQOPjkaVCtbqxwcBjs3DnVkYokXWkSgBuAWWY2GTBgf+AaM6sBvJaE2EREfiWhA+nUbQn9Loc+l8Knk0IyMG1UGHWwcbeQCHQ+NiQGIhVQXAmAmRmhuX8CsBchAfiru6+IHfKX5IQnIpJkWdnQ5qCwbFgJc5+EmQ/DhIvh5cuhw8CQDLQ6IJKFgxrFsOKKKwFwdzezZ919D0AV/yJSMVWvBz2Hh+XL2eEJgrnjYN6TkNs8jDbYfSjUaZ7qSEXKrDTp7PtmtmfSIhERSSeNu8LhN8JFi+GY+8NYA1Ouh/92gYcHhZaCTfFPjrp81YYkBitSeqWpAegLnG1mS4H1hG4Ad/cuyQhMRCQtVM4JtQCdj4XVn4VpimeNhqfOgJxc6Hx86CJo0q3Yy3yxWjOpS3opTQJwaNKiEBEpZ/l926W3P5bTm071ZtN3wyvs9eGDVPnwXpZWas2k6v15u1o/fsiq/aszFny5toz3LD312UtJ4k4A3H2ZmfUG2rr7A2bWEKiZvNBERNKTWxbzqnZnXtXu1Nh6Lr02Tqbvhpc5be09DFt7Px/m7MOk6ofw0vp2fL5m0y/nTV2yEoCmdXLYpW71VIUvApRuKOB/AnlAe+ABoDIwGuiVnNBERJInsd+Q+wPXwFdzqTzzUfadM4Z9V77J5bV3gf4ncv7CDoz/vCpLrzs8gfcUKZvSdAEcBXQHZgC4+wozq5WUqEREMtHOneHQ6+Dgf8GiiWHEwTf/w+04Qyp3hNk/hMcKq+jbv6ReaZ4C+NndHXCA2ABAIiKyvUpVodORMOwpuGAeY2qeQotK38Mzw+Gm9vDCBfDFdHBPdaQSYaVpARhnZiOAOmZ2JnA6cG9ywhIRqSByd+GZWkN4tuZgxhyyBWaODsMPTxsFjTqFJwi6HA81GqQ6UomYuFsA3P1G4EngKUIdwD/c/fZkBSYiUpG4ZUGr/eHokXDxYvjdLaGl4OXL4KbdYOxJ8PGrsHVLqkOViChNCwDu/irwapJiERGJhpxcyDs9LF/PD/MQzBkDC5+HWk2g25AwfXH9XVMdqVRgJbYAmNkLiThGREQKsVMnGHANXPgRHP8I7Lw7vH0L3N4DHjg8DDz08/pURykVUDwtAL3N7Pli9hvQMUHxiIhEU6Uq0PGIsKxdAbMfD/UCz54NE/4Cux8N3U+CXfLALNXRSgUQTwIwKI5jfi5rICIiElO7Cex3EfS+ED57LzYp0RMw4yFouFuscHAw1GyU6kglg5WYALj7lPIIREREtmMGLfYNy6HXw/ynQ6vAK3+D166AdgNCMtDmYMguVUmXSOmKAEVEJEVyasMep4blm4/ChESzx8BHL0DNnaDrkNBF0KBNqiOVDFGagYBERCQdNNoN+l8FFy6EwY9Ck+7w7u1wxx4wakBoJfjph1RHKWku7gTAzH7T2WRm7RMbjoiIxC27MnT4HZw4Fi5cAAf9C9Z/C8/9AW5sF/78bKpGHJRClaYF4C0zOz5/xcwuAp5JfEgiIlJqtXaG3n+G86bB6a/A7kfBvGdgVH+4Y094+7+w7utURylppDQJQB/gJDN7wszeBNoBeyUlKhER2TFm0LwnDLozjDg46M4wzPBr/4SbO8BjJ8BHL8KWTSVfSyq0uIsA3f1LM3sJuAzYClzm7upkEhFJV1VrhqcEug+D7z4OsxPOHgOLJ0KNRtD1hLCvoXpzo6g0NQCvAj2B3YHDgFvM7MZkBVbI/Vub2f1m9mRx20REpBAN2sLB/4YLFsCQMdBsL3j/LrhzL7jvYJjxMPy0LtVRSjkqTRfAne5+sruvdvd5wL7AmnhONLNRZvaNmc3bbvsAM1tkZp+Y2aXFXcPdP3X3M0raJiIixciuBO0PhRMeDU8RHHwl/LgGnj8/FA4+ey4se1eFgxFQmi6AZ7db3wxcGefpDwJ3AA/nbzCzbOBO4GBgOfBhbMjhbODa7c4/3d2/iTdWERGJQ81G0OuPsO/5sHxa6CKY9xTMehTq7Qrdh1J3S3tWZddPdaSSBHEnAGa2DshPCasAlYEf3D23pHPd/U0za7nd5r2AT9z909j1xwCD3P1a4HfxxiUiImVkBs32DMuAa2HBc2Esgdf/zV1kMbNqHiw4P4w8WKlKqqOVBClNC0CtgutmdiRlewqgKfB5gfXlhBqDQplZfeBqoLuZXebu1xa2rZDzhgPDAZo3b16GcEVEdszYs/ZJdQjxq1IDup0Ylu//x3OjrueAja/BuJOgeoNthYONOqQ6UimjHR4K2N2fLanfvgSFTWdVZKeTu38PnF3StkLOGwmMBMjLy1OnlohIvOrvypjapzGu1sk83m9D6CKYOgLeuwOa7hGGHt79aMgpsSFY0lBpugCOLrCaBeRRzAd2HJYDzQqs7wKsKMP1REQkCbZaNrTrH5b138GccSEZeOHP8NJl0HFQaBVo0Quy0meE+VteXcwFB7dLdRhpqzQtAAMLvN4MLCW+qYKL8iHQ1sxaAV8AJwAnluF6IiKSbDUawD7nwt7nwIoZoVZg7pMwZwzUbRkSga4nQm7TVEfKra9/rASgGKWpAThtR29iZo8TRhJsYGbLgX+6+/1mdh7wMqHyf5S7z9/Re4iISDkyC90ATfeA/lfDwvGhVeCNq2DSNbBrv5AMtD8MKlUFYPCI98o9zPK8Z0bVehBHAmBmt1N83/wfS7qGuw8pYvsEYEJJ54uISOosX7Wh+AOqVIeug8Oyckl4jHDWY/DEqVCtHnQZHJKBcor1i9U//rI+dclKAJrWyWGXutXLJYZMEU8LwLSkRyEiImmr4Adqieq1gn5/gz6XwaeTQhfBtPth6t2MbdwtJAKdj4NqdZIXMOGb/9QlK1l63eFJvU8miycBeDQ26I+IiGSIRDV9L/hybRmuVwM4i5oNhrDfxjc4devbMOFieOVv0GFgSAZa7p9WhYNREs/f+gf5L2LdARnHzAaa2cg1a+IauVhEJPKWr9rA1CUrWfdj+P43dclKpi5ZWXJ3QCF+yKrNxBpHwtlvw/DJ4YN/8Svw8CC4rStMvh5Wf17SZUqtaZ2chF+zIjEvYbxnM5vp7t1jr2e4e49yiSwJ8vLyfNo09WiIiMQraU3pmzaGaYlnPgKfTgYMdu0bKxw8HCqX7cM7v8Ui0wrzEs3Mprt7XmH74ukC0OA5IiKSWJWrQedjw7JqGcx+PNQLPHk65NSBLseHgYYad0l1pBVWPAnAbmY2hzBy366x18TW3d312xERqcCS3pRetwX0uRT2vwSWTAmtAtMfgg9Gws6dofvJIVGoXi+5cURMPAmABnwWEYmwcnt8LisrdAPs2hc2rAwDDM0aDRP/Aq9cDrv9DnqcBK36qHAwAUpMANx9WXkEIiIi8ovq9aDn8LB8OSd0D8wZC/Ofhtxm2yYsqtsy1ZFmLKVQIiKS3hp3gcNugIsWwbGjoEFbmHID3NoVHhoY5ibYtDHVUWacHZ4NUEREpFxVzoHdjwnL6s9jhYOPwNNnQtXcUCfQ4yRo3C3VkWaEuFoAzKxL7M/OyQ1HREQkDnWawQGXwB9nwynjod0hYQjikX3gnt4cuv5Zam3V2C/FibcL4HQzawuckcxgkkUDAYmIVFBZWdBqfzjm3tBFcPjNkF2ZU9fewz1fD4VxJ8PHr8LWLamONO2UmACY2T9jx70PZJnZP5IeVYK5+3h3H56bm5vqUEREJFmq1YE9z4Dhk7m4wd28XGMgLHkLHj0WbtkdXr8SVn6a6ijTRokJgLv/C3gNGAu85u7/TnpUIiIiZfB55VY8XPus0Cpw3EOwUyd4+2a4rTs8+DuYPQZ+Lv2wxhVJvF0APd39XGDPZAYjIiKSUJWqQKcjYdiT8Od50O/vsGY5PHMW3NQexv8Jlk+HEpzChkcAABeDSURBVIbFr4jiegrA3S+P/fn35IYjIiKSJLlNYf+LofeFsOydUDQ4eyxMfxAadgjzEHQZDDUbpjrScqFxAEREJFqysqDVfnDUPXDxIhh4K1SpEUYbvHk3GDMUFr8MWzanOtKk0jgAIiISXTm5sMepYflmYRhxcPYY+OgFqLkzdBsSJiWqv2uqI004tQCIiIgANOoAh1wNF30Eg0dDk+7wzm1wew8YdSjMfBR++qFcQrnl1cVJv0c8jwFmm9lZZnalmfXabt/fkheaiIhICmRXhg4D4cQxcMF8OOgKWP8NPHduKBx8/nz4/IOkFg7e+vrHSbt2vni6AEYA1YEPgNvMbIq7XxjbdzRwVbKCExERSanajaH3BdDrz/DZ+6GLYO5TMONhaNAuFA52HQI1GzF4xHsJvXWir7e9eBKAvdw9fyjgO4C7zOxpYAhgyQwuUcxsIDCwTZs2qQ5FREQykRm02Ccsh14H85+BGY/Aq/+A1/4F7Qawx495zKq6J1tsx8rrlq/awBerf/xlfeqSlQA0rZOTlCmZ44mySv4Ld98MDI+NBvgGUDPhESWBu48Hxufl5Z2Z6lhERCTDVa0FPU4Oy7eLYdZomPU4l6x/EWruBF1PgG7DoGG7Hbr84BHvMXXJSpZed3iZQx13dtH74ikCnGZmAwpuiI0G+ADQsiyBiYiIZLSG7eDgf8OFC2DIGGiaB+/eAXfuCff3D10FP61LdZSFKrEFwN2HFbH9PuC+hEckIiKSabIrQ/tDw7Lua5gzJtQLPH8+TLwUOh0V6gWa7x26E0rQtE5O0kOO5ymASwq8Pm67fdckIygREZGMVWsn6PUn+MMHcPor0PkYWPAsPDAAbt8D3roZ1n1V7CWS0ee/vXi6AE4o8Pqy7fYNQERERH7LDJr3hCNuD5MSDboLajaC1/8FN3eExwbDwhdgy6aUhBdPEaAV8bqwdREREdle1ZrQfWhYvvvkl8JBFr8ENRqGOQi6nwSNdiu3kOJpAfAiXhe2LiIiIsVp0CYMLnTBfDhxHDTrCVPvgbt6wn0H0W/DRKptXZ/0MOJpAehqZmsJ3/arxV4TW09+lYKIiEhFlF0J2h0Slh++hTljYeYjnLXmVk5dcw88c3QoHGzRK67CwdIqsQXA3bPdvba713L3SrHX+euVEx6RiIhI1NRsCPueB+e+z1/r38pb1fuF+oAHDw9zEbx5I6xdkdBbajIgERGRdGHG/6q0597cP8HFi+GoEVC7KbxxJdzSCR49DhY8B5t/LvOtNB2wiIhIOqpSPYwq2PUEWPlpmI1w1mMw7mSoXj9WODgMduq0Q5ePRAuAmQ00s5Fr1qxJdSgiIiKlV681HPh3uGAeDH0KWvaGD+6Fu/eFkX3gw/th4+pSXTISCYC7j3f34bm5uakORUREZMdlZUPbg+D4h8PYAgOuC90BL14Ypip+6kxY8iZs3VripdQFICIikolq1Ie9z4GeZ8OXs8LshHOfhLnjoE6L0D1QDCUAIiIimcwMmnQPyyFXh6cHZj4Ck64u9rRIdAGIiIhEQuVq0OU4OOV5+NPsYg9VAiAiIlIR1W1Z7G4lACIiIhGkBEBERCSClACIiIhEkBIAERGRCNJjgCIiUqSxZ+2T6hAkSdQCICIiEkFKAERERCJICYCIiEgERSIB0GyAIiIivxaJBECzAYqIiPxaJBIAERER+TUlACIiIhGkBEBERCSCNBCQiIhUGP8ePx932Lt1fQBueXUxazduwsz4x8COKY4uvSgBEBGRCqNm1Urc+9anbNy09Zdt1SpnM3z/1imMKj2pC0BERCqMc/u2oWZO5V9tq5VTiXP67JqiiNKXEgAREakwcipnc8OxXahWORsI3/6vP7YLObF12UYJgIiIVCh92zdijxZ1yTLIa1mXvu0bpTqktKQEQEREKpxrj+5M56a5XHNU51SHkrZUBCgiIhVOs3rVee683qkOI62pBUBERCSClACIiIhEkBIAERGRCFICICIiEkFKAERERCIoEgmAmQ00s5Fr1qxJdSgiIiJpIRIJgLuPd/fhubm5qQ5FREQkLUQiARAREZFfUwIgIiISQUoAREREIkgJgIiISAQpARAREYkgJQAiIiIRpARAREQkgpQAiIiIRJASABERkQiqlOoAREREBP49fj7usHfr+gDc8upi1m7chJnxj4EdE34/JQAiIiJpoGbVStz71qds3LT1l23VKmczfP/WSbmfugBERETSwLl921Azp/KvttXKqcQ5fXZNyv2UAIiIiKSBnMrZ3HBsF6pVzgbCt//rj+1CTmw90ZQAiIiIpIm+7RuxR4u6ZBnktaxL3/aNknYvJQAiIiJp5NqjO9O5aS7XHNU5qfdREaCIiEgaaVavOs+d1zvp91ELgIiISAQpARAREYkgJQAiIiIRpARAREQkgszdUx1DuTGzb4FlcRyaC6zZgVvs6HmSOBXhd5BOP0N5xpKseyXyumW9lt5bMlem/g5auHvDwnZEKgGIl5mNdPfh5XWeJE5F+B2k089QnrEk616JvG5Zr6X3lsxVEX8H6gIo3PhyPk8SpyL8DtLpZyjPWJJ1r0Ret6zX0ntL5qpwvwO1AIiIiESQWgBEREQiSAmAiIhIBCkBEBERiaBIzQXQoEEDb9myZarDEBERKRfTp0//rqjHACOVALRs2ZJp06alOgwREZFyYWZFjn2jLgAREZEIUgIgIiISQUoAREREIkgJgIiISAQpARDZQX369KFPnz6pDkNEZIcoARCRjKGkSyRxlACIiIhEkBIAERGRCFICICIiRVK3S8WlBEBERCSClACIiIhEkBIAyQhqhhQRSSwlACIiIhGkBEBEJMnUgiXpSAmAiIhIBCkBEBERiSAlACIiIhGU0gTAzAaY2SIz+8TMLi1k/1AzmxNb3jWzrgX2LTWzuWY2y8ymlW/kIsHSpUtTHYKIyA6plKobm1k2cCdwMLAc+NDMnnf3BQUOWwIc4O6rzOxQYCTQs8D+vu7+XbkFLbKdZcuWpToEEZEdkrIEANgL+MTdPwUwszHAIOCXBMDd3y1w/PvALuUaoVRIiarGnjVrVsKuN3ny5DJfQ0SkNFKZADQFPi+wvpxff7vf3hnAxALrDrxiZg6McPeRhZ1kZsOB4QDNmzcvU8AiEJr9C37znzJlCgAtWrSgZcuWKYpKRKR0SkwAzKwXMMvd15vZMKAHcKu7l7Xt0wrZ5kXE0JeQAPQusLmXu68ws0bAq2b2kbu/+ZsLhsRgJEBeXl6h15doSdS37T59+jBlyhTc9c9KRDJPPEWAdwMbYgV4lwDLgIcTcO/lQLMC67sAK7Y/yMy6APcBg9z9+/zt7r4i9uc3wDOELgWpwFRwJyKSOPEkAJs9fMUZRPjmfytQKwH3/hBoa2atzKwKcALwfMEDzKw58DRwkrsvLrC9hpnVyn8N9AfmJSAmSWPpWHDXokWLVIcgIrJD4qkBWGdmlwHDgP1j1fuVy3pjd99sZucBLwPZwCh3n29mZ8f23wP8A6gP3GVmEJKRPGAn4JnYtkrAY+7+UlljksSr6AV36vMXkUwVTwIwGDgROMPdv4p9K/9PIm7u7hOACdttu6fA698Dvy/kvE+Brttvl4pHBXciIslRYgLg7l8BNxdY/4zE1ABIBKjgTkQkPRWZAJjZOgqvyjfA3b120qISESmCikFFEqPIBMDdE1HoJ5IwKrgTSM9iUJFMFPdAQLHn7XPy12NdASLlRn3+mauiF4OKZKJ4BgI6ArgJaAJ8A7QAFgKdkhuaiEigYlCRxIunBeBKYG/gNXfvHhuVb0hywxKRikTFoCLpJ56BgDbFRuDLMrMsd58EdEtyXCIiIpJE8bQArDazmsCbwKNm9g2wOblhiUiy5PefZ2ofuIpBRRIjnhaAQcBG4ALgJeB/wMBkBiUiUhT1+YskRjwDAa0vsPpQEmMRySiZ+g1aRATiewqg4IBAVQjzAKzXQECZJ9ObfkUymQYwknQTTwvArwYEMrMjifjUu/ogFZHS0gBGkm7iHggon7s/a2aXJiMYkaIo2ZJES9TgRPFI5ABG8dL/GSlJPF0ARxdYzQLyKHyOgFIzswHArYTpgO9z9+u222+x/YcBG4BT3X1GPOeKiKSaBjCSdBZPC0DBiv/NwFLCkwFlYmbZwJ3AwcBy4EMze97dFxQ47FCgbWzpCdwN9IzzXBGRIpXnN2QNYCTpKJ4agNOSdO+9gE/c/VMAMxtDSCwKfogPAh728L/mfTOrY2aNgZZxnCsiIiJFKG464Nsppqnf3f9Yxns3BT4vsL6c8C2/pGOaxnkuAGY2HBgOUL9+fa644ooyBQ2hWW/16tUJuVZ5ytS4JbHyq9Ez8d9Bpsa+dOlScnNzMy5uyNy/cymZFdUkZWanxF72AjoCY2PrxwHT3f2CMt3Y7DjgEHf/fWz9JGAvdz+/wDEvAte6+9ux9deBS4DWJZ1bmLy8PJ82bVpZwgYytzkvU+OWxMrkp1gyNfZMjRsyO3YBM5vu7nmF7SuyBcDdH4qdfCrQ1903xdbvAV5JQFzLgWYF1ncBVsR5TJU4zv2NRYsWJaQKNxUVvYmgSmQREckXTxFgE6AWsDK2XjO2raw+BNqaWSvgC+AE4MTtjnkeOC/Wx98TWOPuX5rZt3Gcm3CZWtGbqXFL8mhQGhGJJwG4DphpZpNi6wcAV5T1xu6+2czOA14mPMo3yt3nm9nZsf33ABMIjwB+QngM8LTizi3pnu3bt0/IN9JMbUrP1Lgl8TQojYjE8xTAA2Y2kW1Fdpe6+1eJuLm7TyB8yBfcdk+B1w78Id5zRTKVBqURkfJW3FMAu7n7R2bWI7Ypv+q+iZk1yR+QJ6oydUrSTI1byk5dQbKj1GVUMRXXAnAh4fG5mwrZ50C/pESUITL1DTNT467oNCiNpDN1GVVMxT0FMDz2Z9/yC0dERBIhUV08iewyUldQeolnLoDjgJfcfZ2Z/Q3oAVzp7jOTHp2IJIW6gqQk6jKq+OJ5CuDv7v6EmfUGDgFuBO6hiJH3oiBTs9hMjVsSL1PfwPVvOH6J+rtSl1HFlRXHMVtifx4O3O3uzxEG4hEREZEMFU8C8IWZjQCOByaYWdU4zxMRkQpAXUYVUzwf5McTBtwZ4O6rgXrAX5IalYiIpI1M7TKS4pWYALj7BuAboHds02bg42QGJSIiIslVYgJgZv8E/g+4LLapMjA6mUGJiIhIcsXTBXAUcASwHsDdVxAmBxIREZEMFU8C8HNsTH4HMLMayQ1JREREki2eBGBc7CmAOmZ2JvAacF9ywxIREZFkimc2wBvN7GBgLdAe+Ie7v1qWm5pZPWAs0BJYChzv7qu2O6YZ8DCwM7AVGOnut8b2XQGcCXwbO/yvsdkBRaQEGkyn/OnvXNJRXM/zu/ur7v4Xd78YeMPMhpbxvpcCr7t7W+D12Pr2NgMXuXsHYG/gD2bWscD+W9y9W2zRh7+IiEgpFJkAmFltM7vMzO4ws/4WnAd8ShgboCwGAQ/FXj8EHLn9Ae7+Zf6Uw+6+DlgINC3jfUVERITiWwAeITT5zwV+D7wCHAcMcvdBZbzvTu7+JYQPeqBRcQebWUugOzC1wObzzGyOmY0ys7rFnDvczKaZ2bRvv/22qMNEREQipbgagNbu3hnAzO4DvgOax76Nl8jMXiP032/v8tIEaGY1gaeAP7v72tjmu4ErCU8mXAncBJxe2PnuPhIYCZCXl6fZLERERCg+AdiU/8Ldt5jZkng//GPnHFTUPjP72swau/uXZtaYMNJgYcdVJnz4P+ruTxe49tcFjrkXeCHeuERERKT4LoCuZrY2tqwDuuS/NrO1xZwXj+eBU2KvTwGe2/4AMzPgfmChu9+83b7GBVaPAuaVMR4REZFIKbIFwN2zk3jf6wjjC5wBfEaoLcDMmgD3ufthQC/gJGCumc2KnZf/uN8NZtaN0AWwFDgribGKiIhUOCWOA5AM7v49cGAh21cAh8Vevw1YEeeflNQARUREKri4xgEQERGRikUJgIiISAQpARAREYkgJQAiIiIRpARAREQkgpQAiIiIRJASABERkQhSAiAiIhJBSgBEREQiSAmAiIhIBCkBEBERiSAlACIiIhGkBEBERCSClACIiIhEUEqmAxYRkcwwefLkVIcgSaIWABERkQgyd091DOXGzL4FlsVxaC6wZgdusSPnNQC+24F7SeF29HeXTtLpZyjPWJJ1r0Ret6zX0ntL5kqn/5el0cLdGxa2I1IJQLzMbKS7Dy+P88xsmrvnlfZeUrgd/d2lk3T6GcozlmTdK5HXLeu19N6SudLp/2WiqAugcOPL+TxJnIrwO0inn6E8Y0nWvRJ53bJeS+8tmavC/Q7UApBiytJFJBn03iIlUQtA6o1MdQAiUiHpvUWKpRYAERGRCFILgIiISAQpARAREYkgJQAiIiIRpAQgzZhZDTN7yMzuNbOhqY5HRDKfmbU2s/vN7MlUxyLpQwlAOTCzUWb2jZnN2277ADNbZGafmNmlsc1HA0+6+5nAEeUerIhkhNK8r7j7p+5+RmoilXSlBKB8PAgMKLjBzLKBO4FDgY7AEDPrCOwCfB47bEs5xigimeVB4n9fEfkNJQDlwN3fBFZut3kv4JNYZv4zMAYYBCwnJAGg34+IFKGU7ysiv6EPmNRpyrZv+hA++JsCTwPHmNndVMChJ0UkqQp9XzGz+mZ2D9DdzC5LTWiSbiqlOoAIs0K2ubuvB04r72BEpEIo6n3le+Ds8g5G0ptaAFJnOdCswPouwIoUxSIiFYPeVyRuSgBS50OgrZm1MrMqwAnA8ymOSUQym95XJG5KAMqBmT0OvAe0N7PlZnaGu28GzgNeBhYC49x9firjFJHMofcVKStNBiQiIhJBagEQERGJICUAIiIiEaQEQEREJIKUAIiIiESQEgAREZEIUgIgIiISQUoARCLCzLaY2awCy6Uln1U+zOzJ2Jz1U2OxfWZm3xaItWUR511lZlduty3PzObEXr9uZrnJ/wlEMo/GARCJCDP7wd1rJvialWKDz5TlGp2Aq9z9qALbTgXy3P28OM59xt3bFdh2I/C9u19rZmcADdz9+rLEKFIRqQVAJOLMbKmZ/cvMZpjZXDPbLba9hpmNMrMPzWymmQ2KbT/VzJ4ws/HAK2aWZWZ3mdl8M3vBzCaY2bFmdqCZPVPgPgeb2dOFhDAUeC6OOA81s/dicY41sxqxUe5+NLM9YscYcBxhGlxi1z2xLH8/IhWVEgCR6Ki2XRfA4AL7vnP3HsDdwMWxbZcDb7j7nkBf4D9mViO2bx/gFHfvBxwNtAQ6A7+P7QN4A+hgZg1j66cBDxQSVy9genGBm1kj4FLgwFicc4A/xXY/ThjzPv9aK9x9CYC7fwfUMrM6xV1fJIo0HbBIdGx0925F7Mv/Zj6d8IEO0B84wszyE4IcoHns9avuvjL2ujfwhLtvBb4ys0kQ5qA1s0eAYWb2ACExOLmQezcGvi0h9n2BjsC74Us+VYC3Y/seB6aY2SWERODx7c79NnaP1SXcQyRSlACICMBPsT+3sO19wYBj3H1RwQPNrCewvuCmYq77ADAe+JGQJBRWL7CRkFwUx4CX3P2k7Xe4+1IzWwHsBxwF7LHdITmxe4hIAeoCEJGivAycH+tXx8y6F3Hc28AxsVqAnYA++TvcfQVhPvq/AQ8Wcf5CoE0JsbwLHGBmrWOx1DCztgX2Pw7cBix096/yN5pZFtAA+LyE64tEjhIAkejYvgbguhKOvxKoDMwxs3mx9cI8BSwH5gEjgKnAmgL7HwU+d/cFRZz/IgWShsK4+9fAGcBYM5tNSAjaFThkHLA724r/8u0FvO3uW4q7vkgU6TFAESkzM6vp7j+YWX3gA6BX/jdxM7sDmOnu9xdxbjVgUuychH5Qm9mdwDh3n5LI64pUBKoBEJFEeCFWaV8FuLLAh/90Qr3ARUWd6O4bzeyfQFPgswTHNVMf/iKFUwuAiIhIBKkGQEREJIKUAIiIiESQEgAREZEIUgIgIiISQUoAREREIkgJgIiISAT9PzwXLU4A5CcOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "flux_points_dataset.peek();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stack observations\n",
    "\n",
    "And alternative approach to fitting the spectrum is stacking all observations first and the fitting a model. For this we first stack the individual datasets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_stacked = Datasets(datasets).stack_reduce()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again we set the model on the dataset we would like to fit (in this case it's only a single one) and pass it to the `~gammapy.modeling.Fit` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_stacked.model = model\n",
    "stacked_fit = Fit([dataset_stacked])\n",
    "result_stacked = stacked_fit.run()\n",
    "\n",
    "# make a copy to compare later\n",
    "model_best_stacked = model.copy()\n",
    "model_best_stacked.spectral_model.parameters.covariance = (\n",
    "    result_stacked.parameters.covariance\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OptimizeResult\n",
      "\n",
      "\tbackend    : minuit\n",
      "\tmethod     : minuit\n",
      "\tsuccess    : True\n",
      "\tmessage    : Optimization terminated successfully.\n",
      "\tnfev       : 31\n",
      "\ttotal stat : 34.28\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(result_stacked)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=3</i>\n",
       "<table id=\"table4826822192\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>name</th><th>value</th><th>error</th><th>unit</th><th>min</th><th>max</th><th>frozen</th></tr></thead>\n",
       "<thead><tr><th>str9</th><th>float64</th><th>float64</th><th>str14</th><th>float64</th><th>float64</th><th>bool</th></tr></thead>\n",
       "<tr><td>index</td><td>2.600e+00</td><td>5.889e-02</td><td></td><td>nan</td><td>nan</td><td>False</td></tr>\n",
       "<tr><td>amplitude</td><td>2.723e-11</td><td>1.240e-12</td><td>cm-2 s-1 TeV-1</td><td>nan</td><td>nan</td><td>False</td></tr>\n",
       "<tr><td>reference</td><td>1.000e+00</td><td>0.000e+00</td><td>TeV</td><td>nan</td><td>nan</td><td>True</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=3>\n",
       "   name     value     error        unit        min     max   frozen\n",
       "   str9    float64   float64      str14      float64 float64  bool \n",
       "--------- --------- --------- -------------- ------- ------- ------\n",
       "    index 2.600e+00 5.889e-02                    nan     nan  False\n",
       "amplitude 2.723e-11 1.240e-12 cm-2 s-1 TeV-1     nan     nan  False\n",
       "reference 1.000e+00 0.000e+00            TeV     nan     nan   True"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_best_joint.parameters.to_table()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=3</i>\n",
       "<table id=\"table4811576152\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>name</th><th>value</th><th>error</th><th>unit</th><th>min</th><th>max</th><th>frozen</th></tr></thead>\n",
       "<thead><tr><th>str9</th><th>float64</th><th>float64</th><th>str14</th><th>float64</th><th>float64</th><th>bool</th></tr></thead>\n",
       "<tr><td>index</td><td>2.600e+00</td><td>6.334e-02</td><td></td><td>nan</td><td>nan</td><td>False</td></tr>\n",
       "<tr><td>amplitude</td><td>2.723e-11</td><td>1.108e-12</td><td>cm-2 s-1 TeV-1</td><td>nan</td><td>nan</td><td>False</td></tr>\n",
       "<tr><td>reference</td><td>1.000e+00</td><td>0.000e+00</td><td>TeV</td><td>nan</td><td>nan</td><td>True</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=3>\n",
       "   name     value     error        unit        min     max   frozen\n",
       "   str9    float64   float64      str14      float64 float64  bool \n",
       "--------- --------- --------- -------------- ------- ------- ------\n",
       "    index 2.600e+00 6.334e-02                    nan     nan  False\n",
       "amplitude 2.723e-11 1.108e-12 cm-2 s-1 TeV-1     nan     nan  False\n",
       "reference 1.000e+00 0.000e+00            TeV     nan     nan   True"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_best_stacked.parameters.to_table()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we compare the results of our stacked analysis to a previously published Crab Nebula Spectrum for reference. This is available in `~gammapy.spectrum`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11eb813c8>"
      ]
     },
     "execution_count": 34,
     "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": [
    "plot_kwargs = {\n",
    "    \"energy_range\": [0.1, 30] * u.TeV,\n",
    "    \"energy_power\": 2,\n",
    "    \"flux_unit\": \"erg-1 cm-2 s-1\",\n",
    "}\n",
    "\n",
    "# plot stacked model\n",
    "model_best_stacked.spectral_model.plot(\n",
    "    **plot_kwargs, label=\"Stacked analysis result\"\n",
    ")\n",
    "model_best_stacked.spectral_model.plot_error(**plot_kwargs)\n",
    "\n",
    "# plot joint model\n",
    "model_best_joint.spectral_model.plot(\n",
    "    **plot_kwargs, label=\"Joint analysis result\", ls=\"--\"\n",
    ")\n",
    "model_best_joint.spectral_model.plot_error(**plot_kwargs)\n",
    "\n",
    "create_crab_spectral_model(\"hess_pl\").plot(\n",
    "    **plot_kwargs, label=\"Crab reference\"\n",
    ")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "\n",
    "Now you have learned the basics of a spectral analysis with Gammapy. To practice you can continue with the following exercises:\n",
    "\n",
    "- Fit a different spectral model to the data.\n",
    "  You could try `~gammapy.modeling.models.ExpCutoffPowerLawSpectralModel` or `~gammapy.modeling.models.LogParabolaSpectralModel`.\n",
    "- Compute flux points for the stacked dataset.\n",
    "- Create a `~gammapy.spectrum.FluxPointsDataset` with the flux points you have computed for the stacked dataset and fit the flux points again with obe of the spectral models. How does the result compare to the best fit model, that was directly fitted to the counts data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.0"
  },
  "nbsphinx": {
   "orphan": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}