{
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  {
   "cell_type": "markdown",
   "id": "04aca318-c270-4f78-aa09-be36cb96c8c7",
   "metadata": {},
   "source": [
    "# Viewing PHA responses\n",
    "\n",
    "This notebook is intended to show off the \"rich display\" features that allow objects like ARF, RMF, and images to be displayed graphically.\n",
    "\n",
    "First load up the modules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9752074b-8c74-4b19-99d4-10b49ee33273",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from sherpa.astro import io\n",
    "from sherpa.astro import instrument\n",
    "from sherpa.astro.plot import ARFPlot\n",
    "from sherpa.utils.testing import get_datadir"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6fcf729-045c-48b6-97d4-7a846f01647d",
   "metadata": {},
   "source": [
    "For this notebook we shall use some of the data files from the [Sherpa test repository](https://github.com/sherpa/sherpa-test-data),\n",
    "which is normally only installed when testing Sherpa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2808e4c1-8e73-49d3-a943-7323a877e5ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "def datafile(filename):\n",
    "    \"\"\"Access a data file from the sherpa test data\"\"\"\n",
    "    return get_datadir() + \"/\" + filename"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "520363e7-e018-4dac-84e0-8ff5cbefa528",
   "metadata": {},
   "source": [
    "## The ARF\n",
    "\n",
    "First we start with a Chandra ACIS [Auxiliary Response File (ARF)](https://cxc.harvard.edu/ciao/dictionary/arf.html):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "08c2985c-1214-484e-9603-d0f6f549853d",
   "metadata": {},
   "outputs": [],
   "source": [
    "arf = io.read_arf(datafile(\"9774.arf\"))\n",
    "# Hide the full path name to make the plot title look nicer\n",
    "arf.name = \"9774.arf\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1678786-27ae-4bbc-8131-e0c8a6609eb0",
   "metadata": {},
   "source": [
    "The first thing to do is print the structure, which displays the basic components:\n",
    "\n",
    "- the energy grid over which the response is defined (the `energ_lo` and `energ_hi` fields, which are in keV\n",
    "- the `specresp` field which gives the response (the effective area, in cm$^2$, for each energy bin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3fd5a66e-a3ba-40f2-b68d-71ab78e353ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name     = 9774.arf\n",
      "energ_lo = Float64[1078]\n",
      "energ_hi = Float64[1078]\n",
      "specresp = Float64[1078]\n",
      "exposure = 75141.231099099\n",
      "ethresh  = 1e-10\n"
     ]
    }
   ],
   "source": [
    "print(arf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25b5cc28-8bce-4cd2-9d86-8a6162f9b40f",
   "metadata": {},
   "source": [
    "For notebook users we can just ask the notebook to display this object, which displays a plot of the data and some of the metadata stored with it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "91ce94a0-328b-4e28-86ed-8463404e9e4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style>/*\n",
       "Copyright (C) 2020  Smithsonian Astrophysical Observatory\n",
       "\n",
       "\n",
       " This program is free software; you can redistribute it and/or modify\n",
       " it under the terms of the GNU General Public License as published by\n",
       " the Free Software Foundation; either version 3 of the License, or\n",
       " (at your option) any later version.\n",
       "\n",
       " This program is distributed in the hope that it will be useful,\n",
       " but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
       " MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
       " GNU General Public License for more details.\n",
       "\n",
       " You should have received a copy of the GNU General Public License along\n",
       " with this program; if not, write to the Free Software Foundation, Inc.,\n",
       " 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n",
       "\n",
       "*/\n",
       "\n",
       ":root {\n",
       "  --sherpa-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --sherpa-background-color: var(--jp-layout-color0, white);\n",
       "  --sherpa-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --sherpa-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "\n",
       "  /* https://medium.com/ge-design/iot-cool-gray-is-a-great-background-color-for-data-visualization-ebf18c318418 */\n",
       "  --sherpa-background-color-dark1: #EBEFF2;\n",
       "  --sherpa-background-color-dark2: #D8E0E5;\n",
       "}\n",
       "\n",
       "div.sherpa-text-fallback {\n",
       "    display: none;\n",
       "}\n",
       "\n",
       "div.sherpa {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       "div.sherpa details summary {\n",
       "    display: list-item;  /* needed for notebook, not lab */\n",
       "    font-size: larger;\n",
       "}\n",
       "\n",
       "div.sherpa details div.datavals {\n",
       "    display: grid;\n",
       "    grid-template-columns: 1fr 3fr;\n",
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       "    border-right: 1px solid var(--sherpa-border-color);\n",
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       "\n",
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       "div.sherpa div.datavals div:nth-child(4n + 1) ,\n",
       "div.sherpa div.datavals div:nth-child(4n + 2) {\n",
       "    background: var(--sherpa-background-color-row-odd);\n",
       "}\n",
       "\n",
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       "    border-bottom: 1px solid var(--sherpa-border-color);\n",
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       "\n",
       "div.sherpa table.model th.model-odd ,\n",
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       "\n",
       "div.sherpa table.model th.model-even {\n",
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       "\n",
       "div.sherpa .failed {\n",
       "    background: orange;\n",
       "    font-size: large;\n",
       "    padding: 1em;\n",
       "}\n",
       "</style><div class=\"sherpa-text-fallback\">&lt;DataARF data set instance &#x27;9774.arf&#x27;&gt;</div><div hidden class=\"sherpa\"><style>/*\n",
       "Copyright (C) 2020  Smithsonian Astrophysical Observatory\n",
       "\n",
       "\n",
       " This program is free software; you can redistribute it and/or modify\n",
       " it under the terms of the GNU General Public License as published by\n",
       " the Free Software Foundation; either version 3 of the License, or\n",
       " (at your option) any later version.\n",
       "\n",
       " This program is distributed in the hope that it will be useful,\n",
       " but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
       " MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
       " GNU General Public License for more details.\n",
       "\n",
       " You should have received a copy of the GNU General Public License along\n",
       " with this program; if not, write to the Free Software Foundation, Inc.,\n",
       " 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n",
       "\n",
       "*/\n",
       "\n",
       ":root {\n",
       "  --sherpa-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --sherpa-background-color: var(--jp-layout-color0, white);\n",
       "  --sherpa-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --sherpa-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "\n",
       "  /* https://medium.com/ge-design/iot-cool-gray-is-a-great-background-color-for-data-visualization-ebf18c318418 */\n",
       "  --sherpa-background-color-dark1: #EBEFF2;\n",
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       "\n",
       "div.sherpa-text-fallback {\n",
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       "</details></div><details open><summary>Summary (5)</summary><div class=\"datavals\"><div class=\"dataname\">Identifier</div><div class=\"dataval\">9774.arf</div><div class=\"dataname\">Exposure</div><div class=\"dataval\">75141.2 s</div><div class=\"dataname\">Number of bins</div><div class=\"dataval\">1078</div><div class=\"dataname\">Energy range</div><div class=\"dataval\">0.22 - 11 keV, bin size 0.0100002 keV</div><div class=\"dataname\">Area range</div><div class=\"dataval\">0.533159 - 681.944 cm<sup>2</sup></div></div></details><details><summary>Metadata (6)</summary><div class=\"datavals\"><div class=\"dataname\">Mission or Satellite</div><div class=\"dataval\">CHANDRA</div><div class=\"dataname\">Instrument or Detector</div><div class=\"dataval\">ACIS</div><div class=\"dataname\">Object</div><div class=\"dataval\">3C 186</div><div class=\"dataname\">Program description</div><div class=\"dataval\">The cluster around the powerful radio-loud quasar 3C186 at z=1.1</div><div class=\"dataname\">Observation date</div><div class=\"dataval\">2007-12-06T05:30:00</div><div class=\"dataname\">Program that created the ARF</div><div class=\"dataval\">mkarf v0.6.2-0</div></div></details></div>"
      ],
      "text/plain": [
       "<DataARF data set instance '9774.arf'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d9a8dba-0927-44f9-9159-1be7aec49b33",
   "metadata": {},
   "source": [
    "We can also create a Sherpa plot object directly:"
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  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "834d98d0-4458-4761-815d-37a4be8c1a87",
   "metadata": {},
   "outputs": [],
   "source": [
    "aplot = ARFPlot()\n",
    "aplot.prepare(arf)"
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   "id": "482d037a-4840-48a6-8eda-c1b9584954aa",
   "metadata": {},
   "source": [
    "This structure contains the data needed to create a plot (and can be created even if no Sherpa plotting backend is available):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c3b0d049-07be-4784-97f3-a806224e2cc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xlo    = [ 0.22, 0.23, 0.24,...,10.97,10.98,10.99]\n",
      "xhi    = [ 0.23, 0.24, 0.25,...,10.98,10.99,11.  ]\n",
      "y      = [61.9221,71.4127,81.3637,..., 0.5701, 0.5516, 0.5332]\n",
      "xlabel = Energy (keV)\n",
      "ylabel = cm$^2$\n",
      "title  = 9774.arf\n",
      "histo_prefs = {'xlog': False, 'ylog': False, 'label': None, 'xerrorbars': False, 'yerrorbars': False, 'color': None, 'linestyle': 'solid', 'linewidth': None, 'marker': 'None', 'alpha': None, 'markerfacecolor': None, 'markersize': None, 'ecolor': None, 'capsize': None}\n"
     ]
    }
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   "source": [
    "print(aplot)"
   ]
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  {
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   "metadata": {},
   "source": [
    "When a plotting backend is available we can display the data, which shows a plot essentially the same as the \"rich display\" above, but without the metadata:"
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   "cell_type": "code",
   "execution_count": 8,
   "id": "ef9333e0-cfd1-4fb1-936d-0703141e0b03",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aplot.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0b89e38-ae71-4934-bb1c-3f906995bdd8",
   "metadata": {},
   "source": [
    "## The RMF\n",
    "\n",
    "Displaying the [Redistribution Matrix File (RMF)](https://cxc.harvard.edu/ciao/dictionary/rmf.html) is harder, because it is an intrinsically two-dimensional object, as it describes how the physical properties of the X-ray signal (in this case, the energy or wavelength) is mapped onto the detector properties (channel)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d17dcdc8-f884-4000-b2f4-77266cf83548",
   "metadata": {},
   "outputs": [],
   "source": [
    "rmf = io.read_rmf(datafile(\"9774.rmf\"))\n",
    "rmf.name = \"9774.rmf\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "028d7d44-afa4-4e83-9bfa-0666ce6c2f3b",
   "metadata": {},
   "source": [
    "The matrix is stored in a compressed form, and hard to understand from the object display:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0cb8fced-503f-4404-918a-ba1b21c7841d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name     = 9774.rmf\n",
      "energ_lo = Float64[1078]\n",
      "energ_hi = Float64[1078]\n",
      "n_grp    = UInt64[1078]\n",
      "f_chan   = UInt64[1481]\n",
      "n_chan   = UInt64[1481]\n",
      "matrix   = Float64[438482]\n",
      "e_min    = Float64[1024]\n",
      "e_max    = Float64[1024]\n",
      "detchans = 1024\n",
      "offset   = 1\n",
      "ethresh  = 1e-10\n"
     ]
    }
   ],
   "source": [
    "print(rmf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4355621a-6223-4c38-930b-a2f6fcbbccc1",
   "metadata": {},
   "source": [
    "The \"rich display\" picks 5 energies, spaced logarithmically across the energy response of the RMF,\n",
    "and shows the behavior of monochromatic emission at this energy, along with some of the metadata\n",
    "related to the file.\n",
    "\n",
    "We can see why fitting X-ray data can be hard, since 3 keV photons do peak at 3 keV but can also\n",
    "be observed down to 1 keV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2dc5afe0-e714-416f-9881-5dc037d99921",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>/*\n",
       "Copyright (C) 2020  Smithsonian Astrophysical Observatory\n",
       "\n",
       "\n",
       " This program is free software; you can redistribute it and/or modify\n",
       " it under the terms of the GNU General Public License as published by\n",
       " the Free Software Foundation; either version 3 of the License, or\n",
       " (at your option) any later version.\n",
       "\n",
       " This program is distributed in the hope that it will be useful,\n",
       " but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
       " MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
       " GNU General Public License for more details.\n",
       "\n",
       " You should have received a copy of the GNU General Public License along\n",
       " with this program; if not, write to the Free Software Foundation, Inc.,\n",
       " 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n",
       "\n",
       "*/\n",
       "\n",
       ":root {\n",
       "  --sherpa-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --sherpa-background-color: var(--jp-layout-color0, white);\n",
       "  --sherpa-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --sherpa-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "\n",
       "  /* https://medium.com/ge-design/iot-cool-gray-is-a-great-background-color-for-data-visualization-ebf18c318418 */\n",
       "  --sherpa-background-color-dark1: #EBEFF2;\n",
       "  --sherpa-background-color-dark2: #D8E0E5;\n",
       "}\n",
       "\n",
       "div.sherpa-text-fallback {\n",
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       "div.sherpa details div.datavals {\n",
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       "div.sherpa div.datavals div:nth-child(4n + 1) ,\n",
       "div.sherpa div.datavals div:nth-child(4n + 2) {\n",
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       "div.sherpa table.model tr.block {\n",
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       "\n",
       "div.sherpa table.model th.model-odd ,\n",
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       "    border-right: 1px solid var(--sherpa-border-color);\n",
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       "\n",
       "div.sherpa table.model th.model-odd {\n",
       "    background: var(--sherpa-background-color-dark1);\n",
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       "</style><div class=\"sherpa-text-fallback\">&lt;DataRMF data set instance &#x27;9774.rmf&#x27;&gt;</div><div hidden class=\"sherpa\"><style>/*\n",
       "Copyright (C) 2020  Smithsonian Astrophysical Observatory\n",
       "\n",
       "\n",
       " This program is free software; you can redistribute it and/or modify\n",
       " it under the terms of the GNU General Public License as published by\n",
       " the Free Software Foundation; either version 3 of the License, or\n",
       " (at your option) any later version.\n",
       "\n",
       " This program is distributed in the hope that it will be useful,\n",
       " but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
       " MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
       " GNU General Public License for more details.\n",
       "\n",
       " You should have received a copy of the GNU General Public License along\n",
       " with this program; if not, write to the Free Software Foundation, Inc.,\n",
       " 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n",
       "\n",
       "*/\n",
       "\n",
       ":root {\n",
       "  --sherpa-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --sherpa-background-color: var(--jp-layout-color0, white);\n",
       "  --sherpa-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --sherpa-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "\n",
       "  /* https://medium.com/ge-design/iot-cool-gray-is-a-great-background-color-for-data-visualization-ebf18c318418 */\n",
       "  --sherpa-background-color-dark1: #EBEFF2;\n",
       "  --sherpa-background-color-dark2: #D8E0E5;\n",
       "}\n",
       "\n",
       "div.sherpa-text-fallback {\n",
       "    display: none;\n",
       "}\n",
       "\n",
       "div.sherpa {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       "div.sherpa details summary {\n",
       "    display: list-item;  /* needed for notebook, not lab */\n",
       "    font-size: larger;\n",
       "}\n",
       "\n",
       "div.sherpa details div.datavals {\n",
       "    display: grid;\n",
       "    grid-template-columns: 1fr 3fr;\n",
       "    column-gap: 0.5em;\n",
       "}\n",
       "\n",
       "div.sherpa div.dataname {\n",
       "    font-weight: bold;\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa div.dataval { }\n",
       "\n",
       "div.sherpa div.datavals div:nth-child(4n + 1) ,\n",
       "div.sherpa div.datavals div:nth-child(4n + 2) {\n",
       "    background: var(--sherpa-background-color-row-odd);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tbody {\n",
       "    border-bottom: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tr.block {\n",
       "    border-top: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-odd ,\n",
       "div.sherpa table.model th.model-even {\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-odd {\n",
       "    background: var(--sherpa-background-color-dark1);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-even {\n",
       "    background: var(--sherpa-background-color-dark2);\n",
       "}\n",
       "\n",
       "div.sherpa .failed {\n",
       "    background: orange;\n",
       "    font-size: large;\n",
       "    padding: 1em;\n",
       "}\n",
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       "</details></div><details open><summary>Summary (5)</summary><div class=\"datavals\"><div class=\"dataname\">Identifier</div><div class=\"dataval\">9774.rmf</div><div class=\"dataname\">Number of channels</div><div class=\"dataval\">1024</div><div class=\"dataname\">Number of energies</div><div class=\"dataval\">1078</div><div class=\"dataname\">Energy range</div><div class=\"dataval\">0.22 - 11 keV, bin size 0.0100002 keV</div><div class=\"dataname\">Channel range</div><div class=\"dataval\">1 - 1024</div></div></details><details><summary>Metadata (6)</summary><div class=\"datavals\"><div class=\"dataname\">Mission or Satellite</div><div class=\"dataval\">CHANDRA</div><div class=\"dataname\">Instrument or Detector</div><div class=\"dataval\">ACIS</div><div class=\"dataname\">Program that created the RMF</div><div class=\"dataval\">mkrmf - Version CIAO 4.3</div><div class=\"dataname\">The channel type</div><div class=\"dataval\">PI</div><div class=\"dataname\">The minimum probability threshold</div><div class=\"dataval\">9.9999997e-06</div><div class=\"dataname\">Matrix contents</div><div class=\"dataval\">REDIST</div></div></details></div>"
      ],
      "text/plain": [
       "<DataRMF data set instance '9774.rmf'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae786e59-4880-4e40-a492-782a0560cd70",
   "metadata": {},
   "source": [
    "Note that there is no equivalent to the `ARFPlot` class for RMF.\n",
    "\n",
    "New in Sherpa 4.16.0 is the ability to convert a RMF into a 2D image, which shows the relationship\n",
    "between channel (X axis) and energy (Y axis). It is essentially the same as the CIAO tool\n",
    "[rmfimg](https://cxc.harvard.edu/ciao/ahelp/rmfimg.html).\n",
    "\n",
    "We can convert a RMF into a `DataIMG` structure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "02b274d0-bc20-4a2c-9fca-6f1200e949c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_rmf = instrument.rmf_to_image(rmf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bd35adf-5e14-4f0f-a51c-dd6f37a42a30",
   "metadata": {},
   "source": [
    "As always, let's see what is stored in it. Although the data in in 2D, the `DataIMG` structrure flattens it out into 1D arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "98ce8ae9-3b50-4f7e-8ff5-87f3cce2910d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name      = 9774.rmf\n",
      "x0        = Int64[1103872]\n",
      "x1        = Int64[1103872]\n",
      "y         = Float64[1103872]\n",
      "shape     = (1078, 1024)\n",
      "staterror = None\n",
      "syserror  = None\n",
      "sky       = None\n",
      "eqpos     = None\n",
      "coord     = logical\n"
     ]
    }
   ],
   "source": [
    "print(image_rmf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80935537-43bf-408b-a8f4-5420b44a1dad",
   "metadata": {},
   "source": [
    "However, we can use the rich display to show this data. Note that this uses a linear scale for the data, and so all we see is the\n",
    "\"main\" response, which shows the main peaks we saw in the line plot above.\n",
    "\n",
    "Although not labelled, the X axis is in channel space. For the Chandra [ACIS detector](https://cxc.harvard.edu/ciao/dictionary/acis.html)\n",
    "this has 1024 channels. The Y axis is energy range, which depends on how the RMF was built (it maps to the `ENERG_LO` and `ENERG_HI`\n",
    "columns from the `MATRIX` block of the RMF, in this case accessible as `rmf.energ_lo` and `rmf.energ_hi`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fac26bda-8dbd-4e4b-880d-3ec34306f8d2",
   "metadata": {},
   "outputs": [
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       "\n",
       " This program is distributed in the hope that it will be useful,\n",
       " but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
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       "Copyright (C) 2020  Smithsonian Astrophysical Observatory\n",
       "\n",
       "\n",
       " This program is free software; you can redistribute it and/or modify\n",
       " it under the terms of the GNU General Public License as published by\n",
       " the Free Software Foundation; either version 3 of the License, or\n",
       " (at your option) any later version.\n",
       "\n",
       " This program is distributed in the hope that it will be useful,\n",
       " but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
       " MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
       " GNU General Public License for more details.\n",
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       " You should have received a copy of the GNU General Public License along\n",
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       " 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n",
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       "</details></div><details><summary>Metadata (2)</summary><div class=\"datavals\"><div class=\"dataname\">Mission or Satellite</div><div class=\"dataval\">CHANDRA</div><div class=\"dataname\">Instrument or Detector</div><div class=\"dataval\">ACIS</div></div></details></div>"
      ],
      "text/plain": [
       "<DataIMG data set instance '9774.rmf'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_rmf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79066959-ae06-4a39-870f-f5d7f28386e6",
   "metadata": {},
   "source": [
    "The matrix can be retrieved directly with `rmf_to_matrix` rather than `rmf_to_image` (we could reconstruct the data from the\n",
    "`image_rmf` structure, but the following is a lot more informative):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "62db841e-bd8e-430e-80ce-84a5e68a832e",
   "metadata": {},
   "outputs": [],
   "source": [
    "matinfo = instrument.rmf_to_matrix(rmf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "281354a6-e23f-4a0e-928f-1543d79b72ed",
   "metadata": {},
   "source": [
    "This object does not have a \"nice\" string representation, but it contains three fields:\n",
    "\n",
    "- `matrix`\n",
    "- `channels`\n",
    "- `energies`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98c9a8e1-818c-431a-bada-27ba933700f9",
   "metadata": {},
   "source": [
    "The matrix is the 2D data shown above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8672a7a5-3d7a-447a-a627-66b53679420a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1078, 1024)\n"
     ]
    }
   ],
   "source": [
    "print(matinfo.matrix.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4556fa29-02de-44c7-918c-32a12e6dfdab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min      = 0.0\n",
      "min (>0) = 8.81322481660618e-09\n",
      "max      = 0.13163885474205017\n"
     ]
    }
   ],
   "source": [
    "print(f\"min      = {matinfo.matrix.min()}\")\n",
    "print(f\"min (>0) = {matinfo.matrix[matinfo.matrix>0].min()}\")\n",
    "print(f\"max      = {matinfo.matrix.max()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7667a68a-fa8f-41ee-b9cc-2f1f8d2bc115",
   "metadata": {},
   "source": [
    "This can be displayed with a log scale, to show off some of the secondary features we saw in the monochromatic energy response above. The horizontal lines are added to indicate rows which we shall investigate later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a937863c-e150-4153-a638-7febbd2a3e15",
   "metadata": {},
   "outputs": [
    {
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JVYhjTUoTe3NzM9B/n/bB8vPzB37X3NxMXl7eoN8bjUZ8Pt+gY4a6xsGv8UEPPfQQHo9n4FFaWjryNzRKdF0f9j3EQgzXyuWr+N1X/zJ4/voIKAWVpxTxmRs/NSol+F07d/HTZc8R6z1oasDjpre2kO7J4CrooczTic/cSwKN5rCb+s4sEvU2PFtimDfUQTR24NQKFzfcfzUWiyXlsYrxLamrET8yUcasMLn77rsJBAIDj7q6unSHdEi7du6mY7s/3WGIDBYIBPjJl54h1jP8eXVrloXrv/3ZUdkkKRqN8qOvPEl3w0G33JlNRGeVEJhhwlDSR6mviwJrEKNK9q+CD/jo3re7nOMDu8sZrQYuveccqqZWpTxWMf7JHPvQUprYCwoKAGhpaRn0fEtLy8DvCgoKaG1tHfT7eDxOZ2fnoGOGusbBr/FBFosFt9s96DFeRSMR4uFEusMQGSoej/PI7f9Jwzvtw64MKU1x1pdOHrUS/P8+9wc2/HHHQSV4hV5RsK/Heoz83ADFDj8OQ5SeuIW63ixa2zxYdhvxrPej6loO7G+vYNb5k6UEL8QHpDSxV1ZWUlBQwKuvvjrwXDAYZMWKFSxatAiARYsW4ff7WbNmzcAxf//730kmkxx//PEDx7z++uvEYgfKba+88grTpk2TrVaF+Ai6rvP/fvk/rHl+0/BvbdtXgr/ipstHpQS/c8dOfnvvnwfvLufz0FObT+/kJJ6CbsrdnfhMvcSSBpr6PNR3ZKHXWft3l9tUD/EDu8vlz/Lxlce/NGgtjphYEmgjfmSiYb+rnp4e1q1bx7p164D+BXPr1q1j7969KKW47bbbeOCBB/jjH//Ie++9x1VXXUVRUREXXnghADNmzODss8/m+uuvZ+XKlbz55pvcfPPNLF26lKKiIgA+85nPYDabue6669i4cSPPP/88//mf/8ntt9+esjcuRCbatGETv/mPPxELDb8iZPWZue5bV4zKl+dAIMB3v/AjggeX4C1mIjUlBKYZMRX3UZbVRb6lG4Xevwo+kEWoyYF7WxL7u43Qc+Bcg9XAdY98hpycnJTHKo4d+gjn1/UMnWMf9u1uq1ev5vTTTx/4eX+yvfrqq3n66ae588476e3t5YYbbsDv93PyySfz0ksvDbq39Nlnn+Xmm2/mzDPPRNM0LrnkEh577LGB33s8Hl5++WWWLVvG/PnzycnJ4d57782YW93CfeHDHyTEMHV1dfHwNY8Tah3+35fSFGfdejILjk99CV7XdX71w+fY8Vr9gTK6pkhOLiJQ4yReHqUsx0+x3Y/NECMQs1HXk0V7qwfbTgPudW3Q1H5QsHDKF+Zz6hknpzxWcWyR292GNuzEftppp33k/bBKKe6//37uv//+Qx7j8/l47rnnPvJ1Zs+ezRtvvDHc8I4Jb/9tNYlYpvYVEumg6zpPfvtpmtYNf14doPKUQq5YtnRUSvArl6/ilR++hX5wj8zcAz3Wffn9DV68xj4iSSMNfR4aOrxoey14N4YwvN8AiQP/Xgpm+7jhq9dIj3UhDkH2ik+DRCxxYOQiRAr84fk/8fpPVg3/fnXAlmPhum+Nzir4lpYWfv7VZwm1HVRF2N9jfZoBa1E35d7+HusArfsbvDQ48G1NYH23AUIHzjU5jFz3XSnBi34JXSOhH/2X0Uztxy6JXYhj3K6du/jNfX8gEowd/uAPUAbFkltPZuEJCw5/8DDF43F+8s2n2PVG44EnNY3E1GL8s+wkSyMU5/gpsvuxaDE6Yw72dvvoanHj2Llvd7m2zgOnGhWf+OrpnHzaSSmPVRybkiiSI1gAl8zQEVZmLgkUYoIIhUI8ef8zdG47up0WK08p4rM3f3pU+pb//a//ZPkv1g2uThXmEKzNpq8yQXZ+8KAGL2YaQl6a2r0Y95jxvNeDtqNxUPutyaeXcOXNn8ZolPGIEB9F/oWkQTIh8+ti5HRd59dP/pbVz246qnl1e66V6x68YlT2gm9ubuapO35DInLQ6nynndCcIrqrNOxFQco9neSYe9B1RUvE1d/gpd5O9pZ9DV4iB5rDOApsXP+tq3C5XCmPVRy7ZPHc0CSxj7FYLMaGV7emOwyRAf75ymv8730vD16UdoQ0o+LjXzyJ4xYtTHlc4XCYb//bo7RvDRwYrRsMxKeXEKi2QmmYEl8XhbYAZi1Oe9TF3qCPQJML1w5wrm2GzsCBWE0a597+MebOr015rOLYNvI5dinFixTQdZ1oaPjbfApxsPb2dp66+3kigeHPqwNUnFTEVbd8JsVR9fvtU79ny0u7BpXg9eJcgnOyCFfGyR3osR4hlLAcaPCyx4RnfRC1p5mDSxCTP1bMZ/7t8lGJVYhMJCN2IY4x4XCY/7rvZzSv7ziq8+25Vj7/0BWjsu3y5k1b+MO3XyERPWi6ye0kVFtE9xRwFvZQ7ukk29xLXNdoCrup68wiWW/DtzmKaWM9xPq/+CqlsOdbuf6hq3A6nSmPVRz7+hfPHX05fSTnjmeS2IU4hui6zl/+5yXe+tnaoy/B33IiC09IfQm+r6+Pn/zH03TXH7S7nMlIbGYJ/mozWungBi+tERd79jV48WwD+9omCB5o8KKMis99/1IpwYtDSo5wW1hZFS9SIhqNEo9KKV4cnffefY+nb/3d4BHxEVIKyhcVcvWtn015XMlkkmef+A1b/m/X4AYvZfkEZnuIVsTJz/VT6ujCYYjQHbewtzeLtlYP1l1G3O92oupbB8r3SkHtZdM59+KzUx6rEJlOEvsY27Z1O107uw9/oBAfEAqFePz2XxDujB7+4CFYfWZuePjKUVlZvurt1fzhm38b/IUjy03P3AJ6JjPQ4CXLFCKWNNIU7m/wQp0V78Ywxs0NkDjQ4MVT7mTZtz4vDV7ER9q/eG4kj/HooosuIisri0svvfSozh+f7yqDJZPJo9odTExsiUSCx+79Mbv/1XhUt7Zpxv17wad+Ixq/38+Td/6KsP+gLxxmc3+P9ekmTCUhSrM6ybd0oymd9qiDPf4sQo0O3O8nsb3bAL2hgVOVUeOKhy6irKw05bGKzJJEG/FjPLr11lv55S9/edTnj893JYQY5B8v/5N//WwNyfjws/pol+B/+dhz7H27ZdALJicV4q9xES+PUpDrp8Thx2aIEoxb2dvjo6PFg32nAde6dmjuOPhU5i+dwTkXLkl5rCLzJHQ14sd4dNppp42osiaJXYhxrqmpiV/e87uj2jIWwJpl4QuPXD0qJfjVK9bw8g/+NbgKlZNFT20eoclJsvK7KXd14TWGiCaNNIa8NLT3N3jxbOjtb/CSPFC+L16Qx40PfB6LxZLyWIVIhddff53zzz+foqIilFK88MILHzrm8ccfp6KiAqvVyvHHH8/KlSvHNEZZFS/EOPf/fvYCTeuO7tY2g0njyh9czLwFc1McVX+P9Se+/Mzge+mtFiKziwlMM2Au7qEsq5M8SxCloDXsYo8/i0iDg6ytCazv1kM4cuBUr4mrH/wUpaUlKY9VZKbECFfFJ45iVXxvby9z5szh2muv5eKLL/7Q759//nluv/12nnjiCY4//ngeffRRlixZwtatW8nLywOgtraWePzDi6hffvllioqKhv9GPkASuxDj2Dur1/LKD988qm6ASsHsS6Zy/mXnpnwv+GQyydM/+G/qV7YeeFLTSE4pwj/LQbI8Qll2F8W2ABYtTlfMzp5uH13Nbuw7NVxrG6Hdf+BUo+JjXziOU884JaVxisyW1DWSI1gAl9y3YCUYHNxrwWKxHLJqdM4553DOOecc8prf//73uf7667nmmmsAeOKJJ/jzn//ML37xC+666y4A1q1bd9QxHwkpxY8xTdNQ2vic1xHjS09PDz+58xnCHZHDHzyEgtocbnzgWqxWa4oj618F/6ESfJ6PYG0OoUn9DV7KXF24TX2Ek6ZBDV6867vRdjYd2F1OwZQzSrn56/82Kv3ghTic0tJSPB7PwOOhhx46qutEo1HWrFnD4sWLB57TNI3FixezfPnyVIV7WDJiH2NTpk4ma5Kb9i3+dIcixrmXXnj5qFfBW7wmrnrgUionVaY8rrq6eh79/E+J9RxUSrRb6ZtTTHCqAVtRkDJPJ7mW7v4GL2E3e7t8+xq8xDC/VwfRA+V7e46VZd/7/Kh8ARGZLVWl+Lq6ukE7MR7tGo/29nYSiQT5+fmDns/Pz2fLli1HfJ3Fixfz7rvv0tvbS0lJCb/73e9YtGjREZ8viX2MWSwWjGZDusMQ49ye3Xv4zT1/PLpV8AbFJ796JmeefUbK44rH4/z0vl/Q8X7wwBcOQ3+P9cBMG5SFKdlXgjerBO1RJ3uCWQSaXDi3g/OdZvAf2MfBaDXw+ccvp3rmjJTHKjJfEka0sn3/sk232z0qWywfrb/97W8jOl/qXkKMM7FYjB/d9SSBut7DH/wBSsGkU4v57E2j02P9L//7Emue3zS4BF9woMd6Tl6AMmcXTmOYUMJMXa+X5jYvpt0mPO8GUHtbBkrwSsGCz8xkySfPSnmcQqRDTk4OBoOBlpaWQc+3tLRQUFAwZnFIYhdinPnD83/ivRe2HdWCOe8kF1/+8U3Y7faUx7V3bx2//voLxEIf6LFeW0SwSuEoOtDgJbG/wUuXj2SdDe+mCKZN9XDQSmDfVA+f/9rVsrucOGrjbYMas9nM/PnzefXVVw/EmEzy6quvDquUPlJSihdiHGltbeWPP3j5qPaCN1g0Lr/vfKqmVqU8rlgsxpP3P93fY33gBQ/0WFelfZT6OimyBTGqBG1RF3uCProbnXi26djXNkKwZ+BUi9fElQ9eTHl5WcpjFRPHyPuxD//cnp4etm/fPvDzrl27WLduHT6fj7KyMm6//XauvvpqFixYwHHHHcejjz5Kb2/vwCr5sSCJXYhxIh6P89R3//uo7llXmuK4z9bwiUvPHYXI4P9e+CtrfrPxkD3Wi/P8lDn7G7z0JCzs7cmitcXb3+BlXTuqsf1ArAbFaV84jnMvOvQtQ0KMV6tXr+b0008f+Pn2228H4Oqrr+bpp5/m8ssvp62tjXvvvZfm5mZqa2t56aWXPrSgbjRJYhdinHjjH2/y2hOrjqoEXzAnm1u/feOo7Ni2Z8/eD5fg3U5Cc4vorgJ3YTflnk58pl5iuoHGPg91HT7Ya8W7oQ/jlsENXorn5XLdnVePyhoAMbGkqh/7woULMRgMLFu2jGXLln3kOaeddhr6YW5Vufnmm7n55puPOq6RksSeBgaTLG0QgwUCAZ76yq+J9Q6/pa8918qdTy0jOzs75XFFo1F+9s1n6Di4BG80EqsuwT/DjKE0RJmvkwJLEIPSaY042OP3Edq3u5xtXT2E+g7Emm/jzqduISsrK+WxioknVaX4VatWjatV8SMlGWaMmc1mZi2emu4wxDii6zq/+envaH6vc9jnKq3/1raZNTNHITJ46Q8vs/rXGw/qsU5/j/U5HmKVMQpz/ZQ6/Nj3N3jp9tG+r8GLe20btB54T8qguOTes5k+Y9qoxComnv33sY/kkYky812NcyaLKd0hiHHkndVr+b/vvYaeGF4NXimY9+kZXH7dpaNS1t6zew+//voLxPsOKsF79/VYn3Jwj/VeIrqRhj4v9e1ZaHsseNf3oG1vhOSB3eWOv2oWF195gZTghRhlUooXIo0CgQA/++p/E2oLD/tc3xQ3X/jG53A6nSmPKxqN8vMHfzW4BG829fdYn2HCXNJLua+DfEv/HtttERd7unxEGhz4tsSxrK+HyIH+7LnTvVz/9WtwOBwpj1VMXEldkRzJBjXjtG3rSEliH28UaLKX/ITxp+f/wo5/1A9721iz08invn7+qGwZC/DXP77Mymc3HFSCVyQrCwnMdhMvj1KR20WJ3Y9Vi9EVs7O720dnsxvHdoXrnRboPPCFwOQw8ulvXEBZWemoxComruQIy+mpvo99vMjMd3UMMzuNzDsp9S02xfizY/sO/vDtlwfv4nYElKY47abjuXDpJ0clrj179vLcvX8gET6oBO/z0FObR+/kJL7CIBWuTrzG/gYv9aEsGtuyMO0243k3iNp9oMGL0hQLPz2Tcy48e1RiFUJ8mIzYxxmlKRzO1O8aJsaXaDTKE/c8hX9Pz+EPPohSMP3sCr7w1WtHpRNaNBrl5w/8ko6t/gNPWsxEZpcQmG7EUtJNhXdfgxegJeJmT5ePWJ2dnE1RzBvqIRYfiLVkYR5ffuSLGI3yUSNSb+RtWzNzbCv/2oRIg/959g9s+OP2Yd+z7iiwccO3rhq1W3P++seXWfXBEvykQvw1TpLlEUpyuii2+bFocdqjTnYHfQSaXLi2gWNtEwQONHgxu0zc+IOrM+o2IjG+JFAkRnAf+0jOHc8y8+vKOFc8pRBlyMw/KHF47e3t/PF7LxM/uNR9BEx2A5fce/ao3drW0dHBs197YXBcOV665+bRNzlBbkGAcmcnbmPfgQYvrV7Mu0x41nWh6lsHTtNMGku/8wnmHzd/VGIVIpUWLlxIdXU1jz/+eLpDSQkZsafBtJoqNIMicYjbm46m/7Y4NoRCIX78jZ/RviVw+IM/YOZ5k1l63adG5XaxSCTCz779DJ3bgweetFoIzykhMN2AvThIhbeDXHMPCTQawx72dGaT2GvDtyGMcXM9xA98IZj5iclcfOWFozJdIMR+qSrFZ9oGNRmf2Juamujp6SGZTPLbJ/6HnWv2UjyjgEmzyweOad7Tyra3d41ZTOHuyCH7bMdCCR646hFM1oz/TzMh9XSEaF7fMewFcwB161u47fy7RiEqiPTG2Lui+cC99JoiMaUIf40DysKUZndSZA1g0hK0RlzsDvjoaXDieV/HvrYBekKDrtfd1sOfnv8zR1rpVEoxd9EcHM6hb4ezWq34fL6RvEWRgRKMrJw+vJrZsSPjs8eti76GSfVvCBMJxtATOlv/uoe/syLNkQ0tGUuy87WGdIchxqGOrYHB95WPpjwf3XNzCE9KUFjgp9zZidMYoSduYU+Pr7/By04j7ndaofnDTWt2/6uJn/3rt0f+egosrv855BSVzWehaGbe4OfcFuafNQeDwQDAtNlTycnt31bX7XZjtVqP/PWFyCAZn9gjXVESSmrbQhwxm5W+OSUEp2k4igNUeDrJNvcQS2o09HnZ2+GDPVa863sxvN8AyeG3mP0Qvf+L96GEu6J07ej+0POr/3sT0L8C3+wyoZn7S6t5U7PwFrlx+hzMXzwbu9PO7Pk1mEwmPB6P7H6XIWRV/NAyPrELIYZB00hMK8Y/y4Yq66M8u5NCqx+DStIa8bLb76Ov3kHWlgTWd+shHEl3xED/upSDvxjsaW9mD80opXjrybUog8KWbcFkM1J5QgmzT6tm2uwpTKuehsvlkkR/jEpHP/ZjgSR2IcQBBdkEa7OJTIpTnO+nzNGJwxAlELexp8dHe7MH+3YDrncaod2f7mgPa397TT2u09vSv23v2t1bWPf8Fow2I448K1M/VsEpF53AghPn4/P5ZMHfMUQfYdtWPUNvd5PELoTo57ARmltMcJrCVdRNhacDnylENGmkIeSlri0Lw24L3neDaDubjunbN3QdYqE4/t09rNqzgVW/2oi75NdMO72SM5eeykmnnTgqve2FGAuS2IUQYNCITy/BP9OKoTREeXYHhZYgmkrSGPawqyubSJ2D7E0xzO/VQfTQ8+HHGl3v/5/A3l5W/XID7zy/md+f+iKXfOkTkuDHOSnFDy0z35UQYlj0olwCc31EJ8UozOui3NGF3RAhELOzp9tHZ5Mb5zaF853Bu8tlGl2HeDjBtlf28vCFT3D7BV/l73/9B7FY5nyRyST7u7uN5AGyQY0QItO4HITmFtFTBd7CbirdHWSZQoSTJupCWTS0+TDtMvfvLlfXMuxtcI9Fug6JSJJtr9Tx3dd/whufe5sbvvY5CgsL0x2aGAWZtkGNjNiFmMiMBmLVJfhnWjCWhij3dVBg7d99rjniZnenj9heO96NEUybBu8uNxHouk48nOBfP3mHey9/iLffHJ/7X0xUiX1tW0fyyESZ+a6EEEdEL8kjUOslVhmlOK+TMnvXgR7rgWz8jW5c7+vY32mA7t50h5s+ev+mO9++9Ee8/OIrUpofJ1JVis80ktiFmKg8TnrnFdJTpeMrDFLp6sRjChFKmNnbm0VjaxaWnSbc73SimtrTHe240NsS5geX/5zH7vsx0Wg03eEIMSSZYxdiIjIZic4qxV9txlLaQ2VWB3mWILquaIp42N2RTXKvjez3+jBurYdECnaXyxDxvgQvP/Imuq7zxftuxGw2pzukCSuJRnIE49ORnDueSWIXYqJRoJflE5jjJlERpSKnkxJ7FxYtTlvUxa5ANt0NLjxbktjW1kMonO6Ix51kLMkr33sLdJ0vfuMmSe5pktAViRGU00dy7niWmV9XhBCH5nXTM6+A3ik6OYV+KlwduI1hehMWdvf4aGn2Yt1hxP1OG7R1pjvacSsZS/K3HyznpT+8nO5QhBhEErsQE4nZRLSmBH+1Cdu+EnyupZuEvr/BSzbsseF9twfDtkY4ivayE0kimuQXtzzP+nXvpTuUCUkWzw1NErsQE4VSJCsK8M9xo1dEKMvppMTmx6QStEed7Apk01vvxL053t/gJSKLw45EqC3Mj277GS0tLekOZcLR93V3O9qHLjvPCSGOaT4P3fPyCU1JkFfgp8LZgdMQpjthZU+Pj7Ymb3+DlzUt0DlGfd8zgK7Drjcaeeb7z5FITKz7/NMtgRrxIxNJYhdiIrCYicwpITjDiKO4h0pvOznmHmK6gfqQl71tPrTdFrxrg2i7mo/pBi9pocNrT6zk3bXr0x2JOAqypawQ4tiiFMnJRfhnO9ErwpTldFBkDWBQSVrCXnZ15dBX7yR7474GL7L5ylGJ9cT52X/8iu/+z1QcDke6w5kQkjojmiffv4REtpQVQhxbcrMIzsulb3KcgvwuKpydOAwRgnEbu7qz6Wjy4Hxf4VzTCMHMbfAy2nQddvyzgVdefDXdoUwYI5lf3//IRJn5roQQ/WxW+mpLCE7XcBX3UOnpINvcS1Q3UhfKor7Nh3GXGc9aP6q+dUI0eBlNyViSPz76MqFQKN2hiAlMSvFCZCpNIzG1GH+NHVXeR3l2B0W2ABpJWiL9Pdaje+3kbIhg2jjxGryMlmgoRjweT3cYE0ISRXIEC+BGcu54JoldiExVkE1wbjaRyXFK87soc3Zi16J0xe3sCubQ2eDBtZX+Bi89E7jBSwoZbQY+dc/5GTVfO57JznNDk1K8EJnIYSNUW0xwmsJTFKTS3Y7P1Etf0sTeXh8NLVmYd5rwSIOXlCpdmM9Z5y9Odxhigkt5Yk8kEnzta1+jsrISm83G5MmT+eY3v4l+0O0zuq5z7733UlhYiM1mY/HixWzbtm3QdTo7O7niiitwu914vV6uu+46enp6Uh2uEJnHYCA+vQR/jRVjWYiK7IN6rIfd7OrIJr7HTtb6PoxbpMFLKjWsbeWupffxk0d+zl9eeImGhgY6OjoIBGRfgNEgi+eGlvJS/He+8x1+/OMf88wzzzBz5kxWr17NNddcg8fj4Ytf/CIADz/8MI899hjPPPMMlZWVfO1rX2PJkiVs2rQJq9UKwBVXXEFTUxOvvNLf+/iaa67hhhtu4Lnnnkt1yEJkFL0oh8BcH9HKGBV5XZTZO7FqMTpiTnYGcgg0uA80eOmTBi+pFO2Os/GPO9j4xx0oTWHLtqAMCovTRMXCYpTWX/qtOXUGuYXZABiMBmrmzcJisQxcx+l0YjAY0vIejiVJRrYtrMyxH6G33nqLCy64gPPOOw+AiooKfv3rX7Ny5Uqgf7T+6KOPcs8993DBBRcA8Mtf/pL8/HxeeOEFli5dyubNm3nppZdYtWoVCxYsAOCHP/wh5557Lo888ghFRUVHHI/RkMSoZEQiJginjd75eUSmxsgt7GKKuwWfoZdI3ER9t4f2FhfOneBb24yhsx0Msgx+NEU7+1fHR9pg/S7/wPPrn99w4CClcORb0Qz7koyCioXF2NwHEv3Mk6aTW5Q78LPTZWda9TSUOpCYTCYTFvOBc9ImIVsRp1vKE/uJJ57IT3/6U95//32mTp3Ku+++y7/+9S++//3vA7Br1y6am5tZvPjAPJTH4+H4449n+fLlLF26lOXLl+P1egeSOsDixYvRNI0VK1Zw0UUXfeh1I5EIkUhk4OdgsL/0eOl5W7GZ5JuvmAA0jWShj95Jrai8KHlaN75ALxo6XTEbxd2dnBbYhZ0oxukdUCWr4MetxBroOvBj34uw96Bfa0aNnc7BH982twV3nnPgZ2XQKJlchPGgzz+j0UhObs6gLwRKUxgNKUwFPZHDH5Mi+ghXxesyYj8yd911F8FgkOnTp2MwGEgkEjz44INcccUVADQ3NwOQn58/6Lz8/PyB3zU3N5OXlzc4UKMRn883cMwHPfTQQ3zjG99I9dsR4tjhdhAusJL0Jshy9OE29WEgSW/CQlfYTjRowdaWxNgcBNnT/JiWjCcJ+wePjMP+KF17D2wwpIA9KxsHHaMMCovbNCidWZxmvEWeQcdl5XnIyvEO/KwZDOTkZA9MJQAopTBo6R00jbRDW6Z2d0t5Yv/tb3/Ls88+y3PPPcfMmTNZt24dt912G0VFRVx99dWpfrkBd999N7fffvvAz8FgkNLSUv7fn6dhVKZRe10hxgW3g57Tq+iwGsnO6WROXj2FlgChhIVNgQK2NRdgec9CzitNaNsbZC948ZGUpqO0AyUDzajwlEfQDAcWm9mzrEw7eRLqoK8JRZMKKJlSDHx7TOIc6QK4/ecuXLgQg8HAsmXLWLZsWarCS5uUJ/Y77riDu+66i6VLlwJQU1PDnj17eOihh7j66qspKCgAoKWlhcLCwoHzWlpaqK2tBaCgoIDW1tZB143H43R2dg6c/0EWi2XQ4pOB8xIaqMxc+SgEAEYjsRnltFfbMZZ2U+brItsaIoqJPZFsdnTkkdjlxLk2QHJHK8m4ggwtQYoU+WBBJwLhTR+8KynIjtcHf04rTZHQjr3NeWSv+MMIhUJo2uDLGgwGksn+BWyVlZUUFBTw6qsH9lMOBoOsWLGCRYsWAbBo0SL8fj9r1qwZOObvf/87yWSS448/PtUhC3HsUqCX5eOv9ZCsjFCW20GJvQujStAWdbKzK5u+vU68G6KY36uH2LH3oSuOHXpSJxkbu8XK+0vxI3lkopSP2M8//3wefPBBysrKmDlzJmvXruX73/8+1157LdA/L3PbbbfxwAMPUFVVNXC7W1FRERdeeCEAM2bM4Oyzz+b666/niSeeIBaLcfPNN7N06dJhrYgXIuN53PTML6C3Kkl+oZ9KZwcuQ4RA3MquYDbtjV6c7yscaxohIA1eRGaRLWWHlvLE/sMf/pCvfe1r3HTTTbS2tlJUVMQXvvAF7r333oFj7rzzTnp7e7nhhhvw+/2cfPLJvPTSSwP3sAM8++yz3HzzzZx55plomsYll1zCY489lupwhTh2mYxEa0rwV5uwlwaYlNXfYz2qG6gL+ahry8a404z3nQ5UfVu6oxVCjBGl65m5iiYYDOLxeDiNC2TxnMg8SpGsKqF9SRHh+RGmVzRS7WnCbohSH85iXXMJXZuzyX09gv2fW6G3L90Riwkirsf4J38gEAiM2rz1/s/38/76eUwO81FfJ9Yb5c9LfjaqsaaDNIER4liU5aZ7Xj6hqiRFBV1UODtwGiN0xhzsDOTQ2ejBvUXH/k69JHWRseR2t6HJcnEhjjUWM5E5pQSrDbhKupnkbSfb3Es4sb/Biw/LdhOe1W3Q0pnuaIUQY0xG7EIcS5QiOamQrlonqqKPipx2iq0BFDpNEQ87O7JJ7LaRva4Xw7ZGSMp2yiJzyYh9aJLYhTiW5HgJzs8jPCVOWUEnFY4ObIYo7VEnO/w5BOvdZG1OYF1XB+Gx29pTiHSQxD40KcULcaywmumrLSFYreEpDjLJ047PFKIvYWJ3TzZNzVnYthlwrW6FDmkTKsREJSN2IY4FmiIxpQj/HAeG8l4mZbdTaA2gAw1hL7vbs2GXlax3utF2NsqWsWJC0BnZveiZ+q9EErsQx4LcLILzc4lOiVGZ30m5owOrFqM16maHP5eeOhfZG+OY19fJ7nJiwpBS/NAksQsx3lkt/SX4GRq+4i4mudvJMoXoTVjY1Z1NS6MXx/saztX1srucmFAksQ9N5tiFGM80RWJqMf45dszlvUz2tVFoDZDQNer7stjdloO204p3TReqrvXw1xNCZDwZsQsxnuX5CMzPITolypT8DsrsnZhUnMaIl+2dOfTtdZKzPoJpYz3Epce6mFhkxD40GbELMV7ZrITmldA9A3KK/Ux2t+Ex9hGM29gZzKGtMQvnZrCvqYeeULqjFWLMpaq728KFC6murubxxx9P8ztKDRmxCzEeaRrx6cX4Z9uwlnczJauNfHM3Md3A3pCPPa05mLab8axuQzV1pDtaIY5pmdaPXRK7EOOQXpBNYH428SkRJuW1U7qvx3pd2MeOzlyiux3kvRvCuLVBdpcTE5auK/QRlNNHcu54JoldiPHGYSM0v5ie6ZBf5GeSqx23MUxXzM6OYA6d9R7cm5LY3qmHvnC6oxUibaQf+9Bkjl2I8cSwvwRvxVbew+SsNvIs3USSRvb0+qhrzsayzYR7dSu0daU7WiHEOCQjdiHGEb0wB/8CH4nJEapy2ym1daGhUxf2sKMjl/guO/lruzFsl93lhJBV8UOTxC7EeOG00zu/mN5pOgWFXUxyteM0RuiIOtgRyMVf58G7MYFlXR1EY+mOVoi0kzn2oUkpXojxwGAgVl2Cf7YFR1kPVVlt5Jp7CCeM7O7Npr7Zh/V9A65VzeAPpjtaIcQ4JiN2IcYBvSQX//ws9ElhKnPbKLF1oUjSGPaxoz0XfaeNrHeCaHuaM7dzhRDDJKX4oUliFyLdXA565hcRmpakuKiTSa52HIYIbVEX2wK5dNe58L0Xw/yeNHgR4mBSih+aJHYh0sloIDqrlECNCWdZgCneNrJNvYQSZnb2ZNPY6MO+xYBzdQMEetIdrRDjij7CEXumJnaZYxciXRTopXn453tgUphJOW0UW/0A1Iez2NWWi9ppxbvGj6qXBi9i/FEK+MDDnmtJb1BCRuxCpI3bSfeCIvqmJigt7GSSsx27IUpL1M32rlx69rrIXh/FtLFOGryIUaWGGLgaHUasbtOg54pq8nDnOwd+dmU7WXDmXDTtwAUKSgv4y6zfjlqsB9MZ2V2fmbpcRRK7EOlgMhKtKSUwy4i7rIsqbxs+Uy+9CQs7u3NobszCsUXhWN0gDV7EiBjMGlaveeBnpaC4Nh97lm3gOaPJwMKz52K1WweeKywtoLyybNC1nE4nJtPgZP9BweDY3bWRRKFk57kPkcQuxFhTkCzPp2ueG21SH5Nz2ii2dpFEUdeXxa7WHLQdVryr21GN7emOVowTSlNYPCbUQaNjb7mLnPKsQcdVn1xFbnHOwM8uj5OaebNQBw3LPR7PYRO0OHZJYhdirHnddC8sJDwtQUVhB5WODixanKaIh22duYT2uMhZ14dxc700eMlQSoHRbsRg2r/MSSdrkgdvgWvgmIKpuVTNnTzws8lkZN6JczGbDyRkl8uFw+EYq7DHHVkVPzRJ7EKMJbOJyJxSAjMNeEs7meJpI8vUS0/Cwo7uHFobs3Dt77EekgYvxxKlKUx2w8CEdValG3euvf+XmmL2mdNx+w60Bq2eN52cvOyBn71eL06nE3HkkrpCyX3sHyKJXYixohTJygK65joxVoaoym6lyOInoWvsCfnY3ZKLcZsFz6oWVGtnuqMVQ1AGhdGiYXKYyJvmQ2kw6/SpuHPcmC1mjvvY/IESty/bh8vlOswVhUg9SexCjBWfm+CCfCJT40wuaKdiXwm+PpzF9s48wrsd5K3txfB+IyQzdb3usclg1pi6uJyTLzuOuYvmYLFaKSkpBkDT5K7hdNH1Ea6Kz9B/ZpLYhRgLZhPhOaUEZxrILmmnytOG1xgiELexI5hLW70X9yYd29p6CEfSHa34gEmnFvPI/zyA2Ww+/MFizMgc+9Dkq6YQo00pElOK8M91YK7sZUp2GwWWADHdyJ5QNntacjBvM+Fe1Qrt/nRHK4bQsqWDpqbmdIchRsnChQuprq7m8ccfT3coKSEjdiFGW46X4II8IlNjTM1vp8LegUklqAv72N6RS3SXg7x3eqTH+jgWbAjxPz/7A1/65i3pDkUcJFUj9lWrVuF2uw9z9LFDRuxCjCarmb65JXRXK3JL/EzxtOEx9hGI29kWyKWj3otnQwLrunrpsT6e6fDmr9bQ2ipb+44n+7u7jeSRiSSxCzFaNEWiqpiuWgeWyl6qfK0UmANEdCO7erPZ25yDdasR16om6AqkO1pxGN2NId545c10hyEOsn/x3EgemUgSuxCjJddHYEEO8aoIFfltlNs7MKgkTWEP29vzSOy0410dRNvVkrmbVmcQPQH/+v1K9EzNBiJjyBy7EKPBZiU0v4TumYqCki6qXG24DGE6Y07e9+fRtddD1voYlvV1EJce6+OdLdvC/Mtm8ulbLxm0NatIr/5R90jm2FMYzDgiiV2IVNM04tOL6aq1Yi8PUpXVSp45SCRpYmdvDvVN2di2GHCtaoCg9Fgfr5QCq8/M/MtmcdmyC5k+Y5rcsz7OyO1uQ5PELkSK6YXZBBZkk5wSYVJeG2W2TjSlU9/nZXtbLvoOG1mru1B1shBrvDJYNCpPKuLGR66hZk6NjNLFMUUSuxCp5LARml9Mzwyd4qJOJrvacBgitMVcvO/PJ7jXQ/a7UUwb6iEhPdbHG4NFo/LkYi7+0nmccsZJWK3Ww58k0kZnZMtTMrQSL4ldiJQxaMSqS+iaY8FZHmBqViu55h76kmZ2dufS2OjDsUnhWFUPvdJjfTwxWDQmndKf0E8+XRL6sUJK8UOTxC5EiujFufgX+GBymCl5rZTYugCo6/Oxoy0Xtd2Kd1U7qqkjzZGK/Q5O6KeccTIWiyXdIQkxYpLYhUgFl4OeBUWEpicoK+pksrMNuxalOerh/a48ena7yFkbxri5QXqsp5lSCs2sJKFnAqnFD0kSuxAjZTQQnVVCYLYZT3kXU70t+Ey99CQtbO/OpbnBh2uT6u+x3ic91tNFKdDMMoeeUUZYikdK8UKIoeilefjne1GT+5iS00qxzU8Sxd6Qj50tuRi2mfGslB7r6bI/oU86uZiLJKFnFGnbOjRJ7EKMhMdJ94JC+qYnqCzsYJKzHauK0Rjx8n5nPuFdTnLXhKTHehrsvw995jlTOPPTp0rJXUwYktiFOFomI5GaUgKzTWSVdjDV20KWsZfuhJVtwTxa67NwbUhiW1sHkWi6o50w9if0hUtruOymi5g6rUo2lslQsip+aJLYhTgaCpLl+fjnuzFOClGV20qRJUBCN7ArlM2u5hzMW014VjZChzR4GSu2HAvH7UvoVVOnSELPdLoa2Ty5JHYhxACvm+6FBYSnxZlS0M4kRztmLU5dn49t7flEdzrJX92NtrMpcyfyxgmlwJptYeHls7jspouYNn2q7BQnJjRJ7EIMl9lEZE4pgRojOaXtTPW04jH24Y/ZeT+QR3udF+97cSzr6iAmDV5GizIocqZ5OP3aRZx67slMnVYlCX2CkcVzQ5PELsRwKEVyUiFd85yYK3uYmtNCoSVANGlgV282e5pysG0x4lqxFwLd6Y42IymDIne6lyU3fYxPXH4OPp8v3SGJdJH72IckiV2I4cj2EFyYR3RajGkF7VTYOzCqBLv7snm/PZ/EDjs5q/xo0uAl5QxmjewqD0tu+hjnLz2XrKysdIckxLgkiV2II2Ux01dbSnCmRm5JB1XuVtzGPjpiTt4P5OPf48G3Lor5vTqIS4OXVDHaDFSdUcbFt57HnPmzJaGLAbIqfmiS2IU4EkqRqCrCP8+OtbKbadktFFgChJMmdvTkUNeQjX2zhnPVXuiRBi8jpRQYrAaqTi/jki99gkWnnIDZbE53WGI8ytBy+khIYhfiSORmEViYS6wqSlVBG+X2DjR06sNZbG/Lh+02vCs7UI3t6Y702KbAkWul9qIZnH7pyZLQhTgKktiFOByrhdC8ErqrFYUlnVS5WnEZIrRGXWztyie4203O2jCmTfXS4OVo7Uvox18xh0/deBGTJk+SFe7isKQUPzRJ7EJ8FE0jPq0Y/1wbjsog03wt5Jm7CSXMbO/JpbEhG+dGhX21NHg5GsqgyJ+VxbzzZ/HJq86VhC6GR1bFD0kSuxAfQc/3EViYQ6IqwuS8VsptnSh09vb52N6Sh/a+Bc+KVlSLNHg5UkoBWn9CP/fmMzjnkiV4vd50hyWOSWrfYyTnZ55R2W+xoaGBz372s2RnZ2Oz2aipqWH16tUDv9d1nXvvvZfCwkJsNhuLFy9m27Ztg67R2dnJFVdcgdvtxuv1ct1119HT0zMa4QoxNLuN0Pxieqp1ios7qHK1YjdEaY31l+BDu9xkrQlhfL8hc3e6SCGlFCa7kepPTubL//N5fviP7/Dp6y6XpC5EiqU8sXd1dXHSSSdhMpn4v//7PzZt2sT3vve9QbeoPPzwwzz22GM88cQTrFixAofDwZIlSwiHD5Qyr7jiCjZu3Mgrr7zCiy++yOuvv84NN9yQ6nCFGJpBIz6jGH+tFVdFkGlZLeSYeuhJWNgWzKO5ztff4OUdafByOEqBNctM9Scn8R9/uYXv/vabLDn/LEnoYuT0FDyAhQsXUl1dzeOPPz628Y+SlJfiv/Od71BaWspTTz018FxlZeXA/9d1nUcffZR77rmHCy64AIBf/vKX5Ofn88ILL7B06VI2b97MSy+9xKpVq1iwYAEAP/zhDzn33HN55JFHKCoqOuJ4jIYkRiULmsTw6KXZ+I9zY6rqYXpuI+WWDkjq1Pd62dOUjfV9Dd/KRgz+LjDIaH1IClyFVk64Yi4XfO48ysrLMBlNgA4J+TKUscbyv22K5thXrVqF2+1ORUTjQsoT+x//+EeWLFnCZZddxmuvvUZxcTE33XQT119/PQC7du2iubmZxYsXD5zj8Xg4/vjjWb58OUuXLmX58uV4vd6BpA6wePFiNE1jxYoVXHTRRR963UgkQiQSGfg5GAwCcOl5W7GZDKl+myKTmU1EJ/npq9iO191DcciPLRKlO24lqyfErI4WHIkoxpkdMEO+NH6Q0hTOfDuVtSVUVOXi9fZA+HnYmu7IxJjoiRz+GDGqUl6K37lzJz/+8Y+pqqrir3/9KzfeeCNf/OIXeeaZZwBobm4GID8/f9B5+fn5A79rbm4mLy9v0O+NRiM+n2/gmA966KGH8Hg8A4/S0lIAVIYujhCjRGkkc92E8w1YPRGyrT3YDFEiSSMdESe9QSvWFh1jc1BubfsAzajhKnQw75OzOPfqxdQunCPldjG69rdtHckjA6V8xJ5MJlmwYAHf+ta3AJg7dy4bNmzgiSee4Oqrr071yw24++67uf322wd+DgaDlJaWEjnuRt57Yydt27vobgiRjEvZVByaXp5Pe2EZcV8fc4r3MMvViKZ0NvUU8k5bOWq9g9w/16PtasrYW2WGQykwOQzMWDKJC5edx8zZ1bicrnSHJdIpGAS+PSYvJd3dhpbyxF5YWEh1dfWg52bMmMHvf/97AAoKCgBoaWmhsLBw4JiWlhZqa2sHjmltHdxEIx6P09nZOXD+B1ksFiwWy4eev/qWa3De7aStrY3Vb73Dmy+s4N0XthLtlnaa4gPcTrrnldI9HSYVdVHh6sJkTLInnM2mjiL6dnrIX+knuaudZHxUbig5ZihN4Sqyc9zlNZx+8anMWzgXo1HunhWAQXYKTLeUfzqddNJJbN06eDLt/fffp7y8HOhfSFdQUMCrr7468PtgMMiKFStYtGgRAIsWLcLv97NmzZqBY/7+97+TTCY5/vjjhx2Tpmnk5+dz3kXn8MBT93L6TcO/hshwRiPRmhL8s01klQWY7m3Ba+ylK25nqz+fzj1evOuimNdP7AYvmlGRO8PLuf9xKo+++QB3Pnw7xy1aKEldpEeKVsVnmpT/a/zSl77EiSeeyLe+9S0+9alPsXLlSn7605/y05/+FOi/l/W2227jgQceoKqqisrKSr72ta9RVFTEhRdeCPSP8M8++2yuv/56nnjiCWKxGDfffDNLly4d1or4oWiaxuyTZvIP1woZtYt+CpLlefjnezBO7mVabjNFFj8x3cjO3lz2NOZg22TAubJuQjZ4UQqUUaN4bi7n3/pxFn/iDJxOp+wQJ9JvpPPkMsd+ZBYuXMj//u//cvfdd3P//fdTWVnJo48+yhVXXDFwzJ133klvby833HADfr+fk08+mZdeegmr1TpwzLPPPsvNN9/MmWeeiaZpXHLJJTz22GMpifHj5y0m+osoz339Bdo2+zP2W5s4Qh4X3QsLCU+PM7WwjUn2dowqwa6+XLa25pPcZu9v8NIwsRq8KAUmp5FZn6ji/OuXULtgDk6nM91hCSEOQ+l6Zi4fCAaDeDweAoHAIe9PrKur52cPPMOqZ98jFpq45dUJzWQkcuJUWs9ykj2nnRMKd1Fq7aQj5mRlWwW7NhWS87cYjle3QmiC7AWvwFPqYM750zn3yo9TO78Wg0FuGRVH5kg+e1P1GqX/eT+azXr4Ew4h2Rem7tZ7RzXWdJjQE2OlpSXc86M7+etZL/Pbb71I8/oO9ERGfs8RQ1GQrCyka54L8+Qepuc0U2jxE06a2N6TS11DDs4NGvZV9Rmf1JVSKKOiZH4u8z5RwwVXnUdJSUm6wxLio0kTmCFN6MQOYDKZ+MQl53HK4pP5xfd+xd8fX064S3bFmhB8HoIL84lOjzGjoJVKewcGlWRXXw7bWvNR71snRIMXs8tIYU0On7x1CWeeezoOhyPdIQlxZGSOfUgTPrHv5/F4uO0by5g+v4r/vuf3tG3qyth7HAVgMROeU0qwRiO/tJ1p7hZchj5aY262dBbQvctNzpoQxq2Z2+DF4jEx+4KpfOqLFzFtxlRsNlu6QxJCpIAk9oMopTjngiVUz5nON5Z+l/pVLZn6mT6xKUViciFd8x3YJgWZnt1MviVIKGlmW3cejfXZuNbr2NdkXoMXpSm8FU5O/dxCTjnvJGbNninz5+LYJaX4IUliH0J5RTn3/vrfuWPx/fh3dac7HJFqOV6CC/OIT4syvaCVCnsHCtjTl8225nwMWyy4VzRCRyDdkaaMwaxRdkIBZ1x1Eqede+ohN3oS4pgiiX1IktgPoaKygqX3n89Pr/m1bEObSawW+uaV0D0Liko6mOpqwalFaIp62NJRQGini7zV3Rh2NB3zJXilwOI1U3vRdE658AROOfNkKbcLMQFIYv8IJy8+kd+U/BH/7p50hyJSQVMkphbRNc+OY1KAGb5m8szd9CQsbA3m01znw/NuAuvaOogdu5sXKU2RNcnFSVfM54yLTqV6VjWaNrG3wBUZSkbsQ5LE/hFyc3OpOrWcVbs3pjsUkQp5PgILc0lWRZia10KZrZMkil2hHHY05WHebMa1og78x+b0i8FioPKkIk66bAFLLv44ubm56Q5JiNElq+KHJIn9I2iaRun0IlYhif2YZ7MSmldCz0ydspJ2qlwt2A1R6sJZbO4oILLDSd5KP9qelnRHOjwKbFkWZl8wlVMvPpFTzzx50A6OQoiJRxL7YdicVpSm0JMZWrOZCDSN+PRiuuZacVd2McPXTI6pl2DcxtZAPm17fHjXxbEcQw1elKbwVblZePFszvnMx5k+Y5qU28WEo/T+x0jOz0SS2A/juNMX8rzlL8T7jo0PfPFhekE2gYXZMLWPqXktlFo7iesaO3pz2NWYh3WTAdeKPdA9vhu8KAWaWWPSKcUc98lazrv8HCm3i4lN5tiHJIn9MGLRWMb+x58Q7DZC84vpnZmksridKmcrFi3Onr5strQVEN/mIH9FB6qhLd2RfiSDRWPyx0q45Euf4KTTTsRisaQ7JCHEOCWJ/TBW/H018YiM1o9JBo1YdQn+uRa8FZ3M8DbjM/Xij9nZ4i+gc3cWvnfCmDbWQyKZ7miHpJTCU+Hk0nvO4cLPfFISuhDisCSxH0YiFpcR+zFKL8rFvzALVRViem4zJdYuYkkD23tz2d2Qi32DhmPl+G3wojTFpI8Vc9P3rmF27ex0hyPEuKMY4Rx7yiIZXySxi8zkctC7oIi+6iRTituY4mzFrMXZEcpjS2sB+lYb3rdbUa3js8GL2WnkhKvncOuDN2VUO0khUkpudxuSJHaReYwGYjNL8Neaya5oZ4anGa8xREfMyaauAgI7veSs3tfgZZzd7aAU5M7w8rmHl3Lm2afLSnchxLBJYhcZRy/No2uBF+OUXqbnNlNs7e+x/n53PvV1OTjHaYMXg1lj9sVTuf6+q6iaWpXucIQY/2RV/JAksYvM4nHRvaCQcHWc6UWtTLa3YVBJdoZy2NqSj7bFiuftRujwpzvSAfv3dP/sIxfyyaWfkP3chThSktiHJIldZA6TkWhNCYE5RnLL25juacZj7KM16mJTZyE9O9zkrdrf4CXdwfZTBkXFiYXc+P1rqJ03B6Uyc85PCDF2JLGLzKAUyfJ8uha4sUzpoTqnmUJLgFDSzJbuAhr35uBelxxXDV4sXhPn3XkaV91yBQ6HI93hCHHMkZ3nhiaJ/SMkk0nqtzalOwxxJLwuuhcWEJ0eo7qwhUn2NjR0dof6e6wbN5txvz1OGrwoKJjt48pvXsrHz1sso3QhjpaU4ockif0jJJNJGjYcY01BJiKzicicEoKzDeSXtzDD3YzTEKYp6mVTRyF921zkrfCj7U3/f0uzy8iiz9Vy3V1XUVhYmO5whBAZSBK7OLYpRbKygK55LmyTg1RnN5FvDtKTsLIlUEDznmw8a2Npb/CiFBTMyeaz37yUM88+HYPBkLZYhMgYMmIfkiR2cWzzeQguzCc2I8qMghYq7e3owK5QDtsb87FsNOJ6e3daG7yYnUZOvHYu19z5WYqKitIWhxCZRubYhyaJXRy7LGbCtSUEazSKS1uY7m7GoUWpj2Sxsa2Q6DYH+W93ohrb0xKeUlBQm8OV37yU08/6GEaj/HMTQow++aQRxyalSEwupGueA+fkADOzm8gzdxNMWNnkL6R9t4+sNZG0NXgxOYycfP08rrnjszKXLsRokS1lhySJXRybcrMILswjMT3C1IJmKmwdJHTF9p48djbkYXvPgGPF7rFv8KKgsDaHz33rU5x65ikyShdiNMkc+5DkU0cce6wW+uaW0F0D5aVtTHc1Y9Oi7A7nsKmtkMRWBznLW1EtY9vgxeQwcuoXFnDNHZ8lPz9/TF9biIlI5tiHJoldHFs0jcTUIrrm2nBP6qLa10SOqYeuuIONXYV07sw60OBFH6N/tQoK52RzzXc+zSmnnySjdCFEWsknkDi25PkILMhBnxZmen4z5bZOYrqB93vy2F2fh3M92FaNTYMXpRRGm8ap/7aQ675yFbm5uaP+mkKIg0gpfkiS2D9Ca2sr3S296Q5D7GezEppXRM8smFTaxjRXCxYVY3tfHptbCmGzDc9bjaiOwOjHoiB/dhbXffcznHyajNKFSIsRluIlsU9Ane2d9LaO8eIrMbR9JXj/XBveSZ3MzGrEZ+yhI+5kY2cRwR1eclfub/Ayev9alQKjzcApX1jA9Xd/jpycnFF7LSGEOBqS2MWxITeLwIJs1LQQ1fmNlFo7iegmtnbnU1eXg2tdEus7o9vgRWmKvFk+Pv/Ipznl9JNl9zgh0k1K8UPS0h2AEIdltRCaV0xPjU5lSStTna2YVII9fdlsaS7EsMmKe3nTqDV4UQrsuVZO++JC/vPVBzlt8cckqQsxHugpeIwzdXV1nHbaaVRXVzN79mx+97vfDfsaMmI/hK6uLt7869voyXH4X34i0TTi04rxz7Phq2xnVlYjWcZemqMeNnQU0bPNTd5yP9qe0WnwohT4qtzc9dwXmV1bg6bJd2EhxOgxGo08+uij1NbW0tzczPz58zn33HOH1dpZEvsHRKNRlr/xNs/8x2+pX9OKnpDEnlZFuQSOy0FNDzErv5ESSxehhJnNwQIadufgWRPH8u7oNHjZn9S/+ptbmV07O+XXF0KMTCbex15YWDiwW2VBQQE5OTl0dnYOK7HL8GMfXdfZ9v42vnnTd3jo/P+ibmWLJPU0Ux4XvfOL6J2VZHJJC1OdLRhUkh2hXLY2FGLeYML1dgP0pL7Bi9IUk08v5T+ev02SuhBiwOuvv875559PUVERSileeOGFDx3z+OOPU1FRgdVq5fjjj2flypVH9Vpr1qwhkUhQWlo6rPNkxE5/2f0Pz/6JFx54hVBbeMz2NREfwWImOqsUf62ZnEltzPI24jGEaIhk8V5bMZGtTvLf6kA1pL7Bi8GiMfey6dz9oy/jdrtTfn0hxLGrt7eXOXPmcO2113LxxRd/6PfPP/88t99+O0888QTHH388jz76KEuWLGHr1q3k5eUBUFtbSzz+4YW+L7/88kAHyM7OTq666iqefPLJYcc4oRO7ruv8659v8uS/P0vz+g4ZoY8XmoZeUYi/1oVhai+z8hoptnTRk7CyMVBE685sfKvC/Q1ekqlr8KIUWLxmln77fC658kKsVmvKri2EGAUpWhUfDAYHPW2xWLBYLEOecs4553DOOecc8pLf//73uf7667nmmmsAeOKJJ/jzn//ML37xC+666y4A1q1b95FhRSIRLrzwQu666y5OPPHEI3wzB0zYUnxXVxffvetRvn3h4zSva5ekPo6oXB89c3LpmxFnSnELUx0taEpnWyifbfUF2N/VsK+sg3Akda+pKXJnZnH/y3fw6es+JUldiGPA/jn2kTwASktL8Xg8A4+HHnroqOKJRqOsWbOGxYsXDzynaRqLFy9m+fLlR3QNXdf53Oc+xxlnnMGVV155VHFMyBH7po2b+fZV/0nzu52y6n2cUQ47kepCArMM5FW0MsvbiNvQx95INu+1FBPf7CD7rWZUmz9lr6mZNBZeMZNbHvqCNG8R4liTgo/wurq6QdNuhxqtH057ezuJROJDnyP5+fls2bLliK7x5ptv8vzzzzN79uyB+ftf/epX1NTUHHEcEy6xR6NR/vTM/9G8rkPm0scbg4FkZSH+GjumKT3U5DVQZOkimLDxXlcR7TuyyFnRi2F7anaXUwrcZQ4u+/p5fPLyT2Cz2VLwJoQQxxq32z1u1tOcfPLJJEc4xZjxiV3Xdfx+P++uWc87/3yXDX9/n6Z32ySpj0Mqz0ewxkdkeoxZxc1MsbeCrtjak8+OugKc6xS2NXUQjY38tQyK8kUF3PmzW6iaWpWC6IUQY26c7TyXk5ODwWCgpWXwvhotLS0UFBSk9sU+QsYn9u9+5VG2v1ZH185u9HhSEvo4pew2wjMKCFZr5Ff0r4J3GcLsDuewoaUYfZMN91uN0BU8/MUO91oGxYmfr+WOR24d1r2hQojxZbzdx242m5k/fz6vvvoqF154IQDJZJJXX32Vm2++ObUv9hEyPrG/9dN1GDL/bR7bNI1kRQH+GjvmKd3U5DZQaPbjT9hZ31VM5/Ysct/uxrCzOSXfsKecVsKd37sNu90+8osJISaUnp4etm/fPvDzrl27WLduHT6fj7KyMm6//XauvvpqFixYwHHHHcejjz5Kb2/vwCr5sZDxGU/XdVDpjkJ8FOXzEJydTXR6jJp9JfikrtjaXcCuPfm41iSxrq2DIe77PBoFU3OHvIdUCHGMSUMpfvXq1Zx++ukDP99+++0AXH311Tz99NNcfvnltLW1ce+999Lc3ExtbS0vvfTSmC7MzfjELsY5s5lIdRHBmRqF5W3UeBpwGsLs6MtjQ3MxaqMV9/K9EOhJ2Uu++eRati+/k3NvOZPTzz1VVsILcYxKVSl+4cKFGAwGli1bxrJlyz7ynNNOO61/wPgRbr755jEtvX+QJHaRPgr00lz8c5xYqrqpya2n0BygK+5gfUcJgfe95L7lR9vbmtKXTcZ1mtZ18NQXfsfv8l/kuKVzOPn8E6ipnTVuVsYKIcbOqlWrMurfviR2kT4uJz21+USrY8wpbqTK3kpc19jcXcjuPXm4V+9r8JJI3e5yB0vGdYINIf72veX8879Wkj3FQ81ZU7nwuvOpnFSB2WweldcVQqTIOFsVP15IYhfpYTAQm1FMYI6RoopmajyN2A0RtoXy2dBYjHG9BddbO6G3b0zCifclaHmvk9YNb/PGz9dQNr+AhZ+s5cSPn0DV1CnSrlWI8UgS+5AksYu00Ity8C/wYJvazezcegrMfjpjTt7tKKV7q4f8f3WgmjrGPi4dIv4Y216tY/vf63gh+2WmnzWZUy46joUnLyA3N1eSvBBiXJPEPlEolZLd2lLCYaN3QSGRWXHmFjcyxd5CTDeysbuIvbvy8L4d2dfgJb3x6jqE2iOs/fUm1v5mM87C3zL7E9M4+7NnUlM7S+6BFyLNxtt97OOFJPaJYDwldU0jNr0Y/zwzxZVN1HgasGtRtoQK2dBQjGmdCcfb21Pa4GWkdL3/f7obQrz107W8/fR6sqe4WHDRHP7t7utkK1oh0kVK8UOSxD4RjJekDuiF2fhP8GGfFmRObj355iDtMRfr2ksJbXGT96+2lDZ4STVdh2Q0QdfuHlw+x1E3ixBCpIAk9iHJZKEYOw4bPccXE50dY0ZxI5NsbUSSRjYEi6jbmYf37TCmrY3j6ovIBykFngoXd/3hJq655SqZbxciAyxcuJDq6moef/zxdIeSEjJiF2PDYCA6q5TAfDNllQ3Mcjdi1WJs6S1gY0MJlnVG7Ct3QSSa7kgPTYG30sldv7mFeQvmpjsaISa8VM2xy33sQhwFvSQX/wlenNMCzMmpJ9fUTetBJfj8N1tQ7YF0h3lISlPMunAKN3/n80yaPCnd4QghQErxhyCJXYw+t4Pu44uI1USZW9RAhbWdvqSJDYFi6nfmkvV2COM4LsEbrQZqLqziq//1Zbxeb7rDEUKIjzTqE4Tf/va3UUpx2223DTwXDodZtmwZ2dnZOJ1OLrnkkg/1r927dy/nnXcedrudvLw87rjjDmnccSwyGonMLiU4z0B5eSvVribMKs72UB6b6ouwrjVgX5maHuujwWQ38Onvnc83n7pHkroQ48z+UvxIHploVBP7qlWr+MlPfsLs2bMHPf+lL32JP/3pT/zud7/jtddeo7GxkYsvvnjg94lEgvPOO49oNMpbb73FM888w9NPP8299947muGKVFOQLM+n6zg37ql+arPryDZ10xzz8G5rCdFNLrz/akF1jM8SvM1n4dOPfJLPfuHTsvpdiPFIT8EjA41aKb6np4crrriCJ598kgceeGDg+UAgwM9//nOee+45zjjjDACeeuopZsyYwdtvv80JJ5zAyy+/zKZNm/jb3/5Gfn4+tbW1fPOb3+QrX/kK991337D28DYakhjV6Ow1Lg4jy4X/hBwMs3qZU7CXSksb4ZiJjV2FdOzwkrMiiHVnPWjj77+Pb6qbO565idlzatD0OCTSHZEQx4jEOF4AO0GMWmJftmwZ5513HosXLx6U2NesWUMsFmPx4sUDz02fPp2ysjKWL1/OCSecwPLly6mpqRnUTnPJkiXceOONbNy4kblzP7wiORKJEIkc2NQkGAwCcOl5W7GZDKPxFsVHMRiIl2YTqmrA5wtQ0duBpS9GS9SN1gGnde3Glt0B546fjWgAlFLkz/Ax//Qiss1/hs1/TndIQhxbesbw37QsnhvSqCT23/zmN7zzzjusWrXqQ79rbm7GbDZ/aL4yPz+f5ubmgWM+2CN7/8/7j/mghx56iG984xspiF6MnELPchIuNmP1hci3BbFpUQIJGy09HvRWE9a9wXG1uxyAZlAUVOdwynknYjFL6V2I8U7te4zk/EyU8sReV1fHrbfeyiuvvILVak315Q/p7rvv5vbbbx/4ORgMUlpayv/78zSMyjRmcQggx0PXOZMJ50RZVLYN3Q2bkoW80V7Flvoysv8UwvrWNoiPnxK8yWng0w9/kpM+cz4Wq2wRK8RRCwaBb6c7igkt5Yl9zZo1tLa2Mm/evIHnEokEr7/+Oj/60Y/461//SjQaxe/3Dxq1t7S0UFBQAEBBQQErV64cdN39q+b3H/NBFotlyAVO8YQGSnYHGzNWM31zKvDPNjKtrJEqVxtois29RWypK8Gy2oBxdSPxCIyHjQ+VgpwZXq588FKWnP9x2UlOiJEyHPkaqBFLUSl+4cKFGAwGli1bxrJly1IRWVqlPLGfeeaZvPfee4Oeu+aaa5g+fTpf+cpXKC0txWQy8eqrr3LJJZcAsHXrVvbu3cuiRYsAWLRoEQ8++CCtra3k5eUB8Morr+B2u6murk51yCJVNI1EVTFdC+xkT2ljTlY9HmOIPeEc3m0uQX/PhvvNvdDdm+5Igf5NZ3KrvXzt+S8zfca0dIcjhBgm2XluaClP7C6Xi1mzZg16zuFwkJ2dPfD8ddddx+23347P58PtdnPLLbewaNEiTjjhBADOOussqqurufLKK3n44Ydpbm7mnnvuYdmyZXLb0XiW5yNwXA7ajBCz8xsosXQRjNtZ31lC5/s+ct/0o9W1pjtKAJRBcfxVs/i3+6+juLg43eEIIY6GLJ4bUlp2nvvBD36ApmlccsklRCIRlixZwn/9138N/N5gMPDiiy9y4403smjRIhwOB1dffTX3339/OsIVR8JuIzSvmN7ZSapLm5jmbEYHtvQUsH1PAa5VCSzv1kEi/fPqFreJxbeeyPV3XiM91YUQGWdMEvs///nPQT9brVYef/zxj+ykU15ezl/+8pdRjkykhEEjPr2YrvlW8ia3MierHrehj519uaxvLkFbb8X15i7o7UtrmEqBs9DGZx++mIs+fQFKZeqaWCEmkAwddY+E7BUvRq4wF/9CH6bpPczOq6fI7Kcr7mBdZyn+rVnk/asDrbEjrSEqBQVzsvmP525n6rQqSepCZIBUzbFnGknsYmRcDnrnF9FXk2B2aRNTHc0kUGzqLmTXrnw8K6KYN9RDMn0leGVQLLpmNtfdczVlZaVpi0MIIcaCJHZx9IxGYtUl+OeaKJzUwmxvPU5DhG2hfN5rLMH4rhnn8p3Ql76NaCxeE6ffeAI3/sfnsdvtaYtDCDEKZPHckCSxi6OjQC/Jw7/Ag3laD3Py6igwB+iIOVnXUUrPFg95b7SjmjvTE54Ce66V6350OZ+45DwpvQuRgaQUPzRJ7OLoeNz0zC8gMitObUkjU+ytxJIGNgSL2LMzD8/yMKbNDWnpsa4MipIFeXzlqS/KfLoQYsKRbbbE8JlNRGcWE6g1UlTZRo2nAbsWZWdfLhsbSrCsNeFYUQeRse/ypBkVp99yHN/7yzeZNn2qJHUhMlmK2rYuXLiQ6urqj7xT61giI3YxPEqhlxfgn+/GNjVIbW49+eYArTE369pLCW12kf9GC6p97Hus23IsXHzvWXz6+stlIyMhJgDZeW5oktjFsCifh+C8PKLVUeYXNzDZ1kokaeK9QDEN2/PIeiuEcVvT2JbgFWRXubn2kaWc9YmPj93rCiHEOCSJXRw5q4XIrGKCsw2UVrYwy9OIRYuxsaeYzXVFWN/RsK2qg2hszELSTBpTTi/hKz+9TW5lE2KikVXxQ5LELo6MpqFXFtI1z4ljaoA5OXXkmYI0Rz2sayslstFF/hsNqK7gmIVkMGss/tIibv76v41pi2AhxDghiX1IktjTZf+irjSsGj8aKieLwNwc4tUR5hY1MMnWRl/SzHp/CU3bcsh+qxvDzuYx+Yey/1a2a394OWdfcBZm8xi2iRRCjBtyu9vQJLGniTIa0ZNJSCTSHcphKbuNcE0RwdmKiopWZrkbMGtx1neXsqWuCMdqsK2pg/jovxeloPLUYm5+9Dpq5tSM+usJIcSxRhJ7miiTCWIx9PGe2A0GEpML6aq1467qoja7nmxTD41RL+taSom/58D3rzoI9Ix6KJpJY/LHirnn6X+nqKho1F9PCDHOSSl+SJLY00HTUGbz+E/qAPnZBOdmk6wOM6uogQpbG70JC+92ldK6LZucNwMY9rSMehhml5GzvnQyn7/jc9JqVQgBgNJ11AimM0dy7ngmiT0NlNEIJiOkt4vpYSmng76aQrprYHJ5CzNdjRhVkg29BWzdU4hzZRLr2r2j3mPdVWTji09fx8mnnYTRKH+yQgjxUeRTMg2U0dg/WZzGjmeHZTQSryqiq9aKd0ontdn1+Iw97I1ks665FH29Dfebe6BnFL+dKKg6s5TrH7qKufNrR+91hBDHJinFD0kSezoYDaDr6MlxWopXCopyCdZmwfQ+agrrKbd2EEzYWNdZSsdWH7lvdKHVt49aCAaLxuyLpvLvj95CXl7eqL2OEOLYJavihyaJPQ2UpvWXr5Pj869KuZ2EavLpmZVkankz1c5GFDqbewrZvrsQ94o4lvdGr8e6o8DGuV/+GNfeerWU3oUQYpikCcxYUwqU1r9wbjwu3DCbiE8tomu2Bd+ULmp99XiNIeoiPtY3laLeteB8qw5C4dS/tgJPuYO7freMG758nSR1IcRHkyYwQ5JPznRQjM/5daWgOI/AHA/a9BBzCuops3TgT9hZ11GKf0sWua93oI1Cj3VlUMw4p4Iv/edNVFRWpPz6QojMI01ghiaJfaztG7GPx8SuvG56Z+XROyvJjLJmpjub0FFs6i5i564C3MsjmDc1pHwKwWg18Mmvn8ln/u1TeDyelF5bCCEmGknsY00BmkLXx1diVxYLsamF+GebyZncRq2vDrehjx19ebzXWIJxrRnn8u0QjqTuNRV4yp1c+rVzufSqizEYDCm7thBiApBV8UOSxD7GlNL2jdjH0V+UpqGX5BGY7cYwvZfa/DpKLF10xR2sbS8juMlL3hutqDZ/yl5SaYopp5fw+YeuZN6CuSm7rhBi4pBV8UOTxD7WlAL08bNwTvU3eOmpySU0K8Gs0iamOZpJ6BobgsXs3pmP960+TFsaUxaz0hQ1F07hnifvICsrKyXXFEJMQDJiH5Ksih9j/Y1fkuMmsSuXi+j0IvyzTeRN7mROVj1OQ5idfTm811CCeY0R+4q9KeuxrjRFzUVT+NrP7pSkLoQQo0BG7GNN1/s7uo2DxXPKaiVZWYi/xo5xai9z8usotnTREXeytq2M0AY3ea83oTpS02N9IKk/eSderzcl1xRCTGyZWk4fCUnsY03X0RMJ9DSP2JXJBMV5dM/wEJ4ep6a0kWmOZmK6gfWBEuq255H1Zi/G7c2peT1J6kKIVNNHOK05TiqnqSaJPR0SCUjnqnhNQ+X6CFXlEJyhkT+pjTneehxalE29hWysK8G2SsO2ai/E4yN+OZPdwLl3fYzP3vxpSepCCDHKJLGPNV1Hj8fTt2hDKTSvh1hFLoHpZsxV3dTm1VFoCdAac7G2tYzIe07y3qhHpaDHusluYOl3z+ezX/g0miZLOoQQqSOr4ocmiT0d0ridrOZwkCzOpbvKQXRqjNklTVQ5WogmDbwXKKZxWy7ZbwQx7G4d0esoBb4pbj77rYs596JzJKkLIVJPVsUPSRJ7GuhpWjinrFbIzyY0yU33VCioaGeOtx67FmVjTzEb95bgWKFjfaeu/8vH0b6OpvBVufmP39xKzZyaFL4DIYQQhyOJPR3SMFpXJhOaz0u4LItAlRHr5CC1ufUUmv20xtysbS0l/q4D3xt7oCd09K+jKeZePp2bv30DpaUlKXwHQggxmEr2P0ZyfiaSxD4BKIMRzeMmXuyje7KV+JQos4obqXI0E0maeLerhOatueS87h9Rj3WT08jiLy7i83d+LqMaKgghxikpxQ9JEnumMxhQTgd6Xha95Q56JicpLm9ntqcBmxbjvZ4SNu8pxvl2Asu7dUd1f71SYM+38ekHz+eyqy+R+XQhhEgj+QTOYMpoRLPZUD4P4RI3wckG7JO6mZNbT4E5QHPUw9rmMpLr7Lj+VX9UPdaVgtzqLB7861f41OculaQuhBgz+1fFj+QB0o9dHIo2vlqxKqMRZbGg3E5iBR6ClWYSkyPMKG6kyt5CX9LMuq5SWrdkk/taB1pTx/BfQ1Ms+Gw1X7jvGsorykfhXQghxEdI0QY10o9dfIgyGvv7ujA+EvtAUrfbSeZ46C23EZqcpKSsnRpPA1YtxrruUrbuKsL1Vgzzhvphd5uzuE1c+sDZXHbNJTgcjlF6J0IIcWhyH/vQJLGPlKahzGaS4eGXsUeFUiizGWW1oHud9JU4CU7ScFT6mZtbR74pSGPEy9rGcnjHiutfOyAcHc7l8U318OlvXMAnL/sESqlRfDNCCCGGSxL7CGkWS3+2Gw9l+P1J3WIGp4NYgZvuChPJSRGqCxuZbGsllDSztrOMjk0+8v7ZNqwe68qgqDqzlNse+zeqplaN3vsQQogjIavihySJfSSMRpTNSrKnN92RHEjqZjPYrCSyXfSUWQlNSlJe2kaNpwGzFuedYDnbdhbh+Vd4WD3WrV4zJ183j1u+cSN2u32U34wQQhyelOKHJol9BDS7HZRCj6WmV/lRGxipW1AWM7rbSV+xne5KhavCz9ycOvJMQeoiPtY1lGFYbcax/P0j6rF+8Naw5118rpTehRBinJPEfpSUyYRy2NG7u9Pb+k+BMpn7pwTMZnDYieY76C43QmWYmYWNTLK30pOw8k5HGf6NWeT9sxnVdfge65pRMfm0Eu544hYqJ1WOwZsRQohhkLatQ5LEfpSU0wlKpX3RnDKZ0awWsJjBaiHhc9JbaqVvUoLK0jZq3A2YVIJ3e8rYsb0Iz+shjNuaDzu3ZPGYOOX6BXzxGzdis9nG5s0IIcQwSCl+aJLYj4IymVAuB3pPL8SPvlnKiOMwmvobu1j659aTbsdACd5TEWBuTh25pm72hLNZV1+GaaUR+8odH9ljXSnwVjq59vtLOesTH5cNZ4QQ4hgjif0oKJcTNK0/saeLwYCyWVH7krrusBEpcNBdYUJVhphZ0MgkWxvdCStr2svpfs9D3j8bUP5D91jXjIpJp5Zw189vo7y8bAzfjBBCHAVZFT8kSezDZTSiPC4IhdFjR37/d0ppWv9WsVYrymxCt1qIZdvpKbMQmhRnSmkbs9wNGFWCNd3l7NxeiPe1bgy7hu6xrpTC7DJy2k3Hc9M918uqdyHEMUFK8UOTxD5MmtsFRiPJYHDg256n3EHBjFzef3kP+jB3cBs2pdCs1n2jdRO62UzSYyNUYqO7EjzlAeZk15Fr7GZnOJd368qwLFdYVx+6x7otx8LNT32OM5achsFgGN34hRBCjCqZQB0GZbGgsr3ogW70fYvmjDYDl339PLwFLvTRXmGpaWh2G8ph799ZzmxGd1rpK7QTrDRgrOxlVkEjlbZ2/Ak7q9sq6H3Xjee1BlT3IXqsK/j4F0/i4+eeKUldCHFsSeojf2QgGbEfKU1D5eVAIkmys6t/tK5gzsXTcLodrH5u4+jO1+xP6nbHvqRuRLeaieTa6S43EZsUZUZJCzNcTShgfaCEPVsLyPpHAMPeQ/dYL5qby6XXXTiKgQshxCiROfYhyYj9CGlZHpTDRrK1fWBVuSPfxnnXfpzfP/wXEtFR3FJW09DsdpTDgbJZ0M0msJiIZ1npKTUTmpSgoKyD2VkNeIx97OjL5d09Zdje1LGuPXSPdc2kcdnd51FQUDB6sQshxChRjLBta7rfwCiREfsRUDYr5PrQu4LovQdWwoe7Inz3Uz8m3DmKi+gMhn1JfX/53QQmA3GXld5CKz0V4KgIMie3nkKzn9aoi9XNFUTXOsl9bddH9lgvP76AM845ffRiF0IIMeYksR+OwYAqyIV4gmRH56DSTSKSpC8y9kk9YTcTzrPQU6ahKkJUFzQx2d5GOGliXVcpTZtzyf17B1pT5yEvbXGbuPyrF8gKeCHEsUt2nhuSJPbD0LKzwGFD39P4kRu7pNz+pG639Sd1i6l/Nb7VSNRnoafESLQyxpTSVma6GzGpBOu6S9m2owj3GzHM7zUc8o9WaYqTrp3H6WedNnbvRwghUkxudxuazLF/BOV0QH42dAXHdjMag6H/PnW7DWWzolvM6CYjSYuRmNtCb6GJUHmSnLJOZvvqyTL19u8uV1eGcaUZ57/2wEdUErwVDq79ypWyq5wQQmQg+WQ/FJMRVZzfX4Jv6xi7ks3+zWcGRupmdJMB3WQk4TDRl2eipwxsFd3U5DVSYumiI+bknZYy+ta7yfpHI6o9cMjLG8wal3/jk+Tn54/N+xFCiNGip+ABLFy4kOrqah5//PGxjX+USCl+KEqh5eag262wq+GI2pumhKYd2HxmYKRuQDcZSNqMhLPN9JRo6OVhphU2M9XRQkw3sr6rhMYteWT/I4BhR/NHvS1qL5vOeZecMzbvRwghRpHSddQIBl37z121ahVutztVYaWdjNiHoJwO9LwsVIcfPXD49qapeVGFZrHsG6nvS+pm40AJPuo10VtoIFIep6y0jVmeBqxajK09+WzZVYzrzTjWNXsgcejb7qxZFq6881NYrdaxeU9CCCHGXMoT+0MPPcTChQtxuVzk5eVx4YUXsnXr1kHHhMNhli1bRnZ2Nk6nk0suuYSWlpZBx+zdu5fzzjsPu91OXl4ed9xxB/GxWLxmNKAKcyESI9ncNmYleGU2o/bt/65bDyR13Wwg7jIRyjcSKkviK/MzO7ueHFMPdeEs1tWXoa2y4Hp9D/RFDn19TfGJu06nelb1mLwfIYQYdckUPDJQyhP7a6+9xrJly3j77bd55ZVXiMVinHXWWfQedP/3l770Jf70pz/xu9/9jtdee43GxkYuvvjigd8nEgnOO+88otEob731Fs888wxPP/009957b6rD/RAt24dus0JDy5iV4JXJ1D+vbrP0J3WLaV9S14g7jPTlGOktVpjLe5lV0ECFrQN/3M47bWV0v+cl6+/NqDb/oa+vYPJpJVxx41KUytQtGYQQE83+UvxIHpko5XPsL7300qCfn376afLy8lizZg2nnnoqgUCAn//85zz33HOcccYZADz11FPMmDGDt99+mxNOOIGXX36ZTZs28be//Y38/Hxqa2v55je/yVe+8hXuu+8+zGZzqsMGQNlt6Pk+VHsXye5DtzdNKYMBZbOBzYpusYDFtG9eXSNhNRLJMtJbpJEojzC9qJnpzmYSusZ7gWLqt+bj+0c3xm1NH7k1otVn5sZHrsHhcIzNexJCCJE2o754LhDoX6Ht8/kAWLNmDbFYjMWLFw8cM336dMrKyli+fDknnHACy5cvp6amZtDK7SVLlnDjjTeyceNG5s6de8SvbzQkMaojqLcYNLSSbIiFSba3oGlDd0JLKaXQ7CaU04SyG9GtBnSLQpl0khadWBZEC3SSpWHKS1qZ667HSZjNPYW8v6sA71tRXGt3gh6DQ/RvUZrigrs/Rs2sqZBIU5tZIcTEMZafM7JX/JBGNbEnk0luu+02TjrpJGbNmgVAc3MzZrMZr9c76Nj8/Hyam5sHjvng7Vj7f95/zAdFIhEikQNzzMFg/6K3S8/bis10+K5lymEHZ0v/PeszxuYPUxmNKLMZTCYwGdGNGrqmoRsVSbNG1GUg6tOxZIcpowNfZy/dCStmf4IZTe3YzV2oxX2Hvj7gm+Rh8am7UJseGpP3JISY4HoOvdYn5WTnuSGN6qr4ZcuWsWHDBn7zm9+M5ssA/Yv2PB7PwKO0tPSIz1VmM7gcEAqjR8coqWsGlLF/NzmMBnSDBpoGBoVu0ojbNGIu0Dwxcp3deE0horqRppCHcKsN265eVM+h94EHMNqNzF9ci8VsGZP3JIQQY2lEDWBGuGvdeDZqI/abb76ZF198kddff52SkpKB5wsKCohGo/j9/kGj9paWloEuYwUFBaxcuXLQ9favmj9UJ7K7776b22+/feDnYDBIaWkp/+/P0zAq06EDNWqoyRVg0NC37YLYGJTgNYXmdKI5Heh2274FcwaSZgMJm5Gwz0h3qUasKMq0onpOyN5FnZbFan8569+vxPPHCPY3tn1krEpTXPrtJeSdfmX/FwYhhBgLwSDw7XRHMaGlPLHrus4tt9zC//7v//LPf/6TysrKQb+fP38+JpOJV199lUsuuQSArVu3snfvXhYtWgTAokWLePDBB2ltbSUvLw+AV155BbfbTXX10LdrWSwWLJYPj0zjCQ3UoRObys9HtzlQ2+vQwzqjfmu/UmgWG7rZQcJsRzdb++9VNxpImA1EnUZ6cgyEihIUlgaozmrBboqytbeA9XvKMb1lxPT2DuIfEavSFLWXTOPS6y5HM8k960KIMWQYncXNQ5JS/JBSntiXLVvGc889xx/+8AdcLtfAnLjH48Fms+HxeLjuuuu4/fbb8fl8uN1ubrnlFhYtWsQJJ5wAwFlnnUV1dTVXXnklDz/8MM3Nzdxzzz0sW7ZsyOR9tJTTAbk+VEsnerA7Zdf9yNc0mfrn8+02dJsF3WIkaTaQtBiIOY30ZRvoK9JxlnUzO7eBAouflqibdU1lJN+x4/7nLlR36JDXN9kNzPtUNXc+eltG7aQkhBAfpJL9j5Gcn4lSnth//OMfA3DaaacNev6pp57ic5/7HAA/+MEP0DSNSy65hEgkwpIlS/iv//qvgWMNBgMvvvgiN954I4sWLcLhcHD11Vdz//33py5QowFVmAd94bHbiMZgQHM6wGFHt1vRrSaSFiNJs0bMbiCSpdFXAIbSPmYUNDLZ3kYoYWFdRykdG3zkvdqK1thxyMubHEY++4MLufyaSzEYDr9gUAghROYZlVL84VitVh5//PGP3HC/vLycv/zlL6kMbRAtN7t/Jfru+rFpx7o/qTsd4LCi200kzf1JPW41EPUYCOUq4sUxphS1MtPdhIEk64Il7NxWiPf1PkybDt2KdX9SX3rtZdK1TQgxMUgpfkgTsgmMstvB40ZvbEUPHfp2sdS9oEJzOlAuFzjtJOwWkpZ9m9BYNGIujb5sRaQoSV5JF7N9DXiMIXaE8nhvbym25Rr2t3ZD7MNfQJQCX5WbpfddwCc/9QlJ6kKIiUPuYx/SxEvsSvXPrXeOXYMXzW5Hud3gtJFwWEhaDSRNBpImRcxhIOzTCOfr2Iu7qclroNjaRXvMxdrmMmLvOMn7x25U8MP94JWC3Ble/uP526meOWNM3osQQojxbWIldk1Dmc3o3T3okfCYlGGUxYLyuNE9DpI2M0mbkaRJI2lUxO0aEa9GXy6o4v5WrFWOViJJE+s6SmjdmEPuq+1o9e0fvq4kdSHEBJeqtq2ZZuIkdqMBzWwhGQ5DcoyWQhoMaG4XuB0k7eb+1e/G/qSesGhEXRphH8QL4lQUdzDL04hFi7EuUMr2HUV4Xg9j3lAPyQ//8ZmcRr789E2S1IUQE5fMsQ8p4xN7It+LAQuGBCQDgbFL6qp/Exo8LpJOKwlb/5axg5O6IpKfJKvYT42vAZ+plz192ayvK8W8wojjrZ2H7DCnGTW8WZ6xeS9CCCGOGRm/0ioxtQjsFpI9PRAfg13lADQNze1G5WaRyHIQc5uJO4zEbRpxu4GoWyOcrQjn61hLeqnOa6LY6qc16mZVUwXhNW68f6tHdR363vpIIMZ9lz3Mzh07x+Y9CSHEeKMzsl7smTlgz/wRu2X1DlR47Hch0GNRkp1+9J5ulEnDsO8rlG5QGGwGDFNyieTb8Ll7mOVqpMjURV1fFh11XrJe78Kwt3XI6ypNYXYbKVtYyKKL5uPxyqhdCDExyRz70DI+set9EfioveJHQzLZfxtdqA/FEB1V7Tbik/Ix+iLU+BqYam2mI+5kc3MR1jeTWNbVDTmv7pvi5hO3n8nJZy2irLxMNqERQkxsOiOcY09ZJONKxif2ccdoIDazhMBxJqZN3c0i7w6SKF5rm0rrWzlkv7wL1Td020NHjo0lFy8e2D9fCCGE+KCMn2MfV5RCL8un66Qssms6OC1/Cz5jLysDk1i/bhKeF9vQ2vyHPL1uRQtfW/ot1qx6Z+xiFkKI8Wr/qviRPDKQjNjHks9DcFEham6Ik0vfZ7K1le3hPF7fNh3rX6KYNzcdsjSkNEXOdA8nXbaQ3PzcsY1bCCHGoySgRnh+BpLEPlbsNvoWlNGzEE6Ysov5rt10xR38rX4m4b/byXpz2yFX7RutBs7/2hks/cKl+Hy+MQ5cCCHEsUQS+1gwGIjPKKbreCuVM+o4xfc+RpXgjY6p7FlRSPZLdYdsxWq0Grjomx/nuts+J4vlhBDiILIqfmiS2MeAXpxL1wk+XLO7OK1wC/mmACu7J7F6wxQ8L3ZhqB+6FavRauDiB87i2luvlqQuhBAfJDvPDUkWz402t5OehYXo8/o4qXwbM2xN7Ink8I8dMzC+pGN5t/6Qf1wnXT9PkroQQoyyhQsXUl1d/ZGtxI8lMmIfTSYjsVklBBcaWFC1nRM8O+hNWni1qZrga16y/7ENoh9uxWowa3grXZx75cclqQshxKGkaMS+atUq3G53ioJKP0nso8nrJjjLRf6MNk7P3YJdi/JKx0y2rykh6/+aUP6eIU/Lq87iwT/cQ2lpyRgHLIQQxxApxQ9JSvGjRNms9M0tQl8Q5vSyzZSb29nQW8Jbm6fh/HMPxp1DbxnrLXdy8+PXSVIXQghxVGTEnmpKocwm4tNLCJxsZeGMzSx07aI17uHve6rRXzFhXbljyC5znnIHdzx3E8ctWpiGwIUQ4hgj97EPSRJ7Khn6e77rhdn4T8iiYG4zp+VuQUPn723TaX0zB98ru1Dh6ODzFHgrXNzx3zdKUhdCiCMkt7sNTRJ7iiizGWW1oFxOgrW5qONCnFG6mRJTFyu6J/HO+im4/9z64S1jFcw8fzJfevRGyivK0xK7EEIck2SOfUiS2FNAWSxodjtYLYQnZ9N9nOLEqTuode6lIebl1V3VGF9KYN7SPHjL2H1J/d5f3Ck7ygkhhEgJSewjpMxmNKcDZbEQz3PTNc9OSU0DH8veCsDfW6rpeN1H9uuDt4w12gyccsN8vnDPtZLUhRDiaCR1UCMYdQ/RHjsTSGIfAWU0ormcKLuNpNNGsNqNaV4PZ5RsptAU4F/BKtavq8TzlxaUv3fgPKPNwCUPLuGaW66S+9SFEOJoSSl+SJLYj5amoblcKIcd3Wqhr9xF77wkp1ZtZ46jjr3RbP6xvRrzX6KYtrcAoBR4yp1ccs85XHb1JZLUhRBCpJwk9qOkORwolwPdaiGWa8c/20TFrDpO9W0lphv4W3M1gX+68b31PiSSKAXeSS7u+vUtzFswN93hCyFEBhhpT3UZsYt9lMWC8rjQbRYSTiuBqTastUHOLN5EnrGbf/ins2lNOZ6/NKG6+1CaIne6h39/Zhlz59emO3whhMgMUoofkiT24TIY0LwecNhJ2kyESm2Ea+OcOXkbs+wN7Izk8s/3q7G9GMK4pw2lKeZcUsUdP7yVvLy8dEcvhBAiw0liHybN5QS3E91qIpptJTBLY0r1Xk7Jep++pImXG2bR96od78qtkNTJmeHl3x/7oiR1IYRItaTOiMrpsipeKKsVleVBt1mIO80Ep5hx1XSyuHATWYZeXu6cxbZVJWS9VIcKRfBWOLnzVzeTn5+f7tCFECLz6Mn+x0jOz0DSBOZIaRpalgfdaSdpMxIqMhObHeGkSe8z3dbE++EC3tg8HcefghgaujBYNC68ewm18+akO3IhhBATiIzYj5DmcqJ73SStJiJZZoLViurpdZzk2U53wsrLdbOIvWzGs3Y3oDPr/CouufLCNEcthBAZTBbPDUkS+xFQZhMqO4ukw0LCYaR7khHvrA7OLNiEyxDmz+2z2f12Ib5X9qBFYuTO8HLnj27FarWmO3QhhMhcMsc+JEnsh6MUKttHMstJwm6it9BEvCbCKZVbmWptYUOomOUbpuH6Yxdaqx+D1cA1310qi+WEEGK0yYh9SJLYD0PzuEgWZhP3WIh6jHRXQ820PZzo2U5Xws4re2eRfMmAZUMjACdeW8vpZ52W3qCFEEJMWLJ47iMosxm9KI9Yto2o20h3hUb2zA7OLNiMXYvyj/YZ1P8rH9ff96JiCSafVsKNX/+8bBUrhBBjQefAqP2oHul+A6NDRuyHohQqP4dogZuoy0g4R0OvDnFK+VYmW1p5t7eMleurcP+xDa2jG1uulc9/6wpycnLSHbkQQkwMUoofkozYD0E5HcRLsolkmYh6NPomx6mp2s0J7h20x128smsW2p+TmLc2owyKs790CvOPm5/usIUQQkxwMmIfisGAXpJPOM9G1KkRKtLJm97OGXlbsKoYf2mbTctr2fhe3w7xBJPPKOUzN16OUirdkQshxMSRTAIj2GQmmZkb1EhiH4LyeYmUeoh6NCI++P/t3XtQlHXbB/DvLssuu8IeYNldzuARBQRyBVHTp0cmNN8staaMjKzRNCyNhtR8lXmnMXlyxinLtHxHbSaVcl4PpaYPDx4pREVA8YAYIDwooOKynE97vX8Y+7iAqXFY2L0+M/cMe9+/++Z3XTPLxe8+/UTD6jDZJx/+kjvIqvFHVvZQyA9UQFBdB7m3DG+veQ0KhcLa3WaMMfvCp+K7xKfiOxI7os1Pg0a1GM1yAVr8WhA2pBhjXYpQ2SLHvwqDIDrYAsffKwEBMHXpZOgj9dbuNWOMMQaAR+yWBIBAq0aDjzOalAI0agleQyvxN3U+HAVt+FfFKFQdVUKVXgC0meA4SISJU8dbu9eMMWafeMTeJS7sDxAMkqFpuAb1OhEa1YA0oAaTva7BV3wXp41DcCFrMOQHb0FQ2/DHDoBUym+XY4wxq+A3z3WJT8W3Ewph8teh1k+KRjfA5NmMMP9iPOV8AzeblThaEATJ/nqIbtyxdk8ZY4yxh+IR+x8EKgXqhrqiQSNAi7oNg/0rMMmtAAIQUsuDYPynM5Rnrtnsf3iMMTbQEJlA3Zh6tTv79mdc2AFA5IDWAA3qvB3R5EpQeBkxSXcN3uIq/GoYhsuZflD8UgZBQ5O1e8oYY6wdUfcGW3yN3XYJVArUD5ajQQOItI0Y612M8EElKG1yxbGroyDdXwOHm/c67ATIPWVwkbtYp9OMMWbvqJvX2Lmw2yCxI0xaFRrCPGAcIkKreytGeFZivOo62iBEalkwGg5LoTifDxBBKBLCM1wN/zHecPVQ4m8zJsLNzc3aUTDGGGNm9lnYHR0BDzWahrqjdrAM9ToBmt1MUGhqMM79d2hERhyrCsS1X32gOlIMQVMLAEAgBNyHuGLmgukIHh0MoZDvPWSMMasxmQBBN66T2+g1drurTAKJBAJ/TzSO9kad3yA0K4VoHQQ4qhsRpivF6EH/RmGjO9IvjYTzwWoIb1eb921rNiE75SpW/v0f+J+Fn8JgMFgvEMYYs3fdmtmtm8/A92N2V9iprQ24cw/iggpIK5tgciAINY0Y6X0Tk1yvwUQCpJUGofWwCJKLN7u8fNNS34qau/W4XXm77wNgjDHG/oT9nYpvbQXdM0LYZkLraHe0eLbC1/sOnlZfh86xGml3R6E43QOq1EKgsdliV4EAEMlE8BvngVX/mwiVSmWlIBhjjJHJBOrGqXh+3M2WCAVoGa5D9UgRlL73oFffwHCnWyho0OLXC4Fw2X8Hwqoac3OBUADVYBeM/PsQzHznvzA8cBikUqkVA2CMMcZ3xXfNPgu7Sg5jmArCIQ0I0ZUhzLkE9SYJ/lkcDDoAiK+WAwDEziIMmeSDqJljMOX5Z6DRaKzcccYYY+zP2V9hl4jRNMoD9SMJw30qoFcUw8WhAb9UjMatYxqojt+fY13oKIR2lBtWbUuEu7u7tXvNGGOsIxMBAh6xd2RXhV2glKNlhBeq9S5wG3IXEW5F8BXfxZU6L5w7Nwzy/eUQVNfBSSVG3BezMenZiVzUGWOsvyIC0J3H3WyzsNvPXfFyZzSO8cftiUogvB6R3oUIlN5CZYscR66MhuOuOogKK+GkEuOdb1/D7NiZfOqdMcZYnzIYDNDr9QgLC0NwcDC2bNnyxMewjxG7mxL14/xxb7QTWgKaMcLzDsa4FN+f4KU0CDX/J4HL+d/hpHTEO9++humznoNAILB2rxljjP0JMhGoG6fiqR+O2F1cXHDy5EnIZDLU1dUhODgYs2bNeqK3nPbrEfvGjRvh7+8PJycnREZG4syZM09+EGcpGiL9cTfCCQiuh49/JYJUN+EibETGvaEoOO4F5yMlcHQgvP31K1zUGWNsoCBT95d+xsHBATKZDADQ1NQEInrif0D6bWH/4YcfkJCQgKSkJJw/fx6hoaGIiYlBZWXlEx2nOcgbVXoJBgUZEO5/A2FuZVCKG5Bd64f0rJEYtOcOxC3NeGPDTDw3cyoXdcYYGyDIRN1entTJkyfx/PPPw9PTEwKBAPv27evUpruDUoPBgNDQUHh7eyMxMRFqtfqJ9u+3hX39+vWYP38+5s2bh1GjRmHz5s2QyWTYunXrEx2n0UcG4dB6jPUpwlhlMSTCFlyt1eHEreFoO+kASeFtjH8rDLPnzoRYLO6laBhjjNmCuro6hIaGYuPGjV1uf5xBafv1847LzZs3AQBKpRK5ubkoKirCzp07UVFR8UR97JfX2Jubm5GVlYUVK1aY1wmFQkRHRyMjI6PLfZqamtDU9J/50qur77/jvU7TjBGqCoxxuIqWBiFy/j0YxWUaCKoFcDtbhqfmDMOC/56HxsZGNDY29m5gjDFm44xGI4C+uX7dSk3dOp3eivsTfLX3uZ1EIoFEIulyn2nTpmHatGkPPeaDg1IA2Lx5Mw4ePIitW7di+fLlAICcnJzH6p9Wq0VoaChOnTqFl1566bH2AQBQP1RWVkYA6LfffrNYn5iYSBEREV3uk5SU1P4KIl544YUXXqy8lJaW9lqNaGhoIJ1O1yP9dHZ27rQuKSnpsfoBgPbu3Wv+3NTURA4ODhbriIjeeOMNmjFjxmMds7y8nIxGIxERGQwGCgoKogsXLjzWvu365Yj9r1ixYgUSEhLMnw0GA/z8/FBSUgKFQmHFnvUfRqMRPj4+KC0thVwut3Z3rI7z0RnnxBLno7NH5YSIUFNTA09Pz17rg5OTE4qKitDc3Pzoxo9ARJ3urXrYaP1R7ty5g7a2Nmi1Wov1Wq0WV69efaxj3LhxAwsWLDDfNPfee+8hJCTkifrRLwu7Wq2Gg4NDp+sKFRUV0Ol0Xe7zsFMnCoWCv5AdyOVyzskDOB+dcU4scT46+7Oc9MVgysnJCU5OTr3+e/paRETEY5+qf5h+efOcWCzGmDFjkJaWZl5nMpmQlpaGqKgoK/aMMcYY69pfGZT2hn5Z2AEgISEBW7ZswXfffYcrV65g0aJFqKurM9+QwBhjjPUn/WVQ2i9PxQPAK6+8gtu3b2P16tUoLy9HWFgYDh8+3OnaxcNIJBIkJSX95WsltohzYonz0RnnxBLnozN7z0ltbS2uX79u/lxUVIScnBy4urrC19cXCQkJiIuLg16vR0REBD7//PM+H5QK/rizjzHGGGOPcPz4cTzzzDOd1sfFxWH79u0AgK+++grr1q0zD0o3bNiAyMjIPusjF3bGGGPMhvTba+yMMcYYe3Jc2BljjDEbwoWdMcYYsyE2W9h7ZMrXfm7t2rUYO3YsXFxcoNFo8OKLLyI/P9+iTWNjI+Lj4+Hm5gZnZ2fMnj270zOWJSUlmD59OmQyGTQaDRITE9Ha2tqXofSa5ORkCAQCLF261LzOHnNSVlaG119/HW5ubpBKpQgJCcG5c+fM24kIq1evhoeHB6RSKaKjo1FQUGBxjKqqKsTGxkIul0OpVOLtt99GbW1tX4fSbW1tbVi1ahUCAgIglUoxZMgQfPLJJxbvNrf1fDxqhrKeiv/ChQt4+umn4eTkBB8fH3z22We9HRoD0C/fFd9dKSkpJBaLaevWrXTp0iWaP38+KZVKqqiosHbXelRMTAxt27aN8vLyKCcnh5577jny9fWl2tpac5uFCxeSj48PpaWl0blz52jcuHE0fvx48/bW1lYKDg6m6Ohoys7OpkOHDpFaraYVK1ZYI6QedebMGfL396fRo0fTkiVLzOvtLSdVVVXk5+dHb775JmVmZlJhYSEdOXKErl+/bm6TnJxMCoWC9u3bR7m5uTRjxgwKCAighoYGc5upU6dSaGgonT59mk6dOkVDhw6lOXPmWCOkblmzZg25ubnRgQMHqKioiHbv3k3Ozs70xRdfmNvYej4OHTpEK1eupD179nR63zlRz8RfXV1NWq2WYmNjKS8vj3bt2kVSqZS++eabvgrTbtlkYY+IiKD4+Hjz57a2NvL09KS1a9dasVe9r7KykgDQiRMniOj+BAKOjo60e/duc5srV64QAMrIyCCi+19woVBI5eXl5jabNm0iuVxOTU1NfRtAD6qpqaFhw4ZRamoqTZ482VzY7TEny5Yto4kTJz50u8lkIp1OR+vWrTOvMxgMJJFIaNeuXUREdPnyZQJAZ8+eNbf55ZdfSCAQUFlZWe91vhdMnz6d3nrrLYt1s2bNotjYWCKyv3x0LOw9Ff/XX39NKpXK4juzbNkyGjFiRC9HxGzuVHz7lK/R0dHmdY+a8tVWtE9V6+rqCgDIyspCS0uLRS4CAwPh6+trzkVGRgZCQkIsXvwTExMDo9GIS5cu9WHve1Z8fDymT59uETtgnzn56aefoNfr8fLLL0Oj0SA8PBxbtmwxby8qKkJ5eblFThQKBSIjIy1yolQqodfrzW2io6MhFAqRmZnZd8H0gPHjxyMtLQ3Xrl0DAOTm5iI9Pd08Fae95aOjnoo/IyMDkyZNglgsNreJiYlBfn4+7t2710fR2Kd+++a5v6onZtcZiEwmE5YuXYoJEyYgODgYAFBeXg6xWAylUmnRVqvVory83Nymq1y1bxuIUlJScP78eZw9e7bTNnvMSWFhITZt2oSEhAR8/PHHOHv2LN5//32IxWLExcWZY+oq5gdzotFoLLaLRCK4uroOuJwsX74cRqMRgYGBcHBwQFtbG9asWYPY2FgAsLt8dNRT8ZeXlyMgIKDTMdq3qVSqXuk/s8HCbq/i4+ORl5eH9PR0a3fFqkpLS7FkyRKkpqba5MxPf4XJZIJer8enn34KAAgPD0deXh42b96MuLg4K/eu7/3444/YsWMHdu7ciaCgIOTk5GDp0qXw9PS0y3ww22Nzp+L7y+w6fWnx4sU4cOAAjh07Bm9vb/N6nU6H5uZmGAwGi/YP5kKn03WZq/ZtA01WVhYqKyvx1FNPQSQSQSQS4cSJE9iwYQNEIhG0Wq3d5cTDwwOjRo2yWDdy5EiUlJQA+E9Mf/ad0el0qKystNje2tqKqqqqAZeTxMRELF++HK+++ipCQkIwd+5cfPDBB1i7di0A+8tHRz0Vv619jwYSmyvs/WV2nb5ARFi8eDH27t2Lo0ePdjrtNWbMGDg6OlrkIj8/HyUlJeZcREVF4eLFixZf0tTUVMjl8k7FYCCYMmUKLl68iJycHPOi1+sRGxtr/tnecjJhwoROj0Feu3YNfn5+AICAgADodDqLnBiNRmRmZlrkxGAwICsry9zm6NGjMJlMffoO7J5QX18PodDyT5+DgwNMJhMA+8tHRz0Vf1RUFE6ePImWlhZzm9TUVIwYMYJPw/c2a9+91xtSUlJIIpHQ9u3b6fLly7RgwQJSKpUWdznbgkWLFpFCoaDjx4/TrVu3zEt9fb25zcKFC8nX15eOHj1K586do6ioKIqKijJvb3+069lnn6WcnBw6fPgwubu7D9hHu7ry4F3xRPaXkzNnzpBIJKI1a9ZQQUEB7dixg2QyGX3//ffmNsnJyaRUKmn//v104cIFeuGFF7p8vCk8PJwyMzMpPT2dhg0bNmAe73pQXFwceXl5mR9327NnD6nVavroo4/MbWw9HzU1NZSdnU3Z2dkEgNavX0/Z2dl048YNIuqZ+A0GA2m1Wpo7dy7l5eVRSkoKyWQyftytD9hkYSci+vLLL8nX15fEYjFFRETQ6dOnrd2lHgegy2Xbtm3mNg0NDfTuu++SSqUimUxGM2fOpFu3blkcp7i4mKZNm0ZSqZTUajV9+OGH1NLS0sfR9J6Ohd0ec/Lzzz9TcHAwSSQSCgwMpG+//dZiu8lkolWrVpFWqyWJREJTpkyh/Px8izZ3796lOXPmkLOzM8nlcpo3bx7V1NT0ZRg9wmg00pIlS8jX15ecnJxo8ODBtHLlSovHsmw9H8eOHevyb0dcXBwR9Vz8ubm5NHHiRJJIJOTl5UXJycl9FaJd49ndGGOMMRtic9fYGWOMMXvGhZ0xxhizIVzYGWOMMRvChZ0xxhizIVzYGWOMMRvChZ0xxhizIVzYGWOMMRvChZ0xxhizIVzYGWOMMRvChZ0xxhizIVzYGWOMMRvChZ0xxhizIf8P/5yJRyRRPzsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import colors\n",
    "\n",
    "plt.imshow(matinfo.matrix, origin='lower', norm=colors.LogNorm(vmin=1e-3, vmax=0.2))\n",
    "plt.colorbar()\n",
    "\n",
    "for pos in [200, 400, 600, 800]:\n",
    "    plt.axhline(pos, alpha=0.5, c='orange')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44ad304b-26a6-4616-af92-0c97045a979b",
   "metadata": {},
   "source": [
    "We can use this data to try and reconstruct the monochromatic response plot from above. We can pick a row from the matrix,\n",
    "which will be the response for a photon at a fixed energy (well, a photon in the finite energy range given by the\n",
    "corresponding element from the `energ_lo` and `energ_hi` fields).\n",
    "\n",
    "Selecting values along the Y axis selects different ranges (and let's us explore some of the features seen above). One difference\n",
    "to the rich display above is that this plot uses channel number for the X axis rather than converting this to an \n",
    "\"approximate\" energy (as done above), by using the `E_MIN` and `E_MAX` fields from the `EBOUNDS` block of the RMF\n",
    "(available as `rmf.e_min` and `rmf.e_max`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7838f7e0-b8c7-4db8-a1ee-1202e3207076",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for idx in [200, 400, 600, 800]:\n",
    "    # We could use matinfo.energies, but as we have the RMF object we use that instead.\n",
    "    elo = rmf.energ_lo[idx]\n",
    "    ehi = rmf.energ_hi[idx]\n",
    "    plt.plot(np.arange(1, 1025), matinfo.matrix[idx, :], label=f\"{elo:.2f} - {ehi:.2f} keV\")\n",
    "    \n",
    "plt.yscale('log')\n",
    "plt.xlabel(\"channel\")\n",
    "plt.ylabel(\"probability\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "556c799c-3a73-45ca-9d4a-53c37db73eae",
   "metadata": {},
   "source": [
    "## Looking at a different detector\n",
    "\n",
    "The Sherpa test data directory contains a response file for the [ROSAT PSPC-C](https://heasarc.gsfc.nasa.gov/docs/rosat/pspc.html) instrument, which operated\n",
    "in the 1990s, and used a different detector to the CCD detector used in ACIS. We can see how different by viewing the response using the techniques from above: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5b8bf6ee-ef46-4a86-aa3f-b9c385a9e53f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>/*\n",
       "Copyright (C) 2020  Smithsonian Astrophysical Observatory\n",
       "\n",
       "\n",
       " This program is free software; you can redistribute it and/or modify\n",
       " it under the terms of the GNU General Public License as published by\n",
       " the Free Software Foundation; either version 3 of the License, or\n",
       " (at your option) any later version.\n",
       "\n",
       " This program is distributed in the hope that it will be useful,\n",
       " but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
       " MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
       " GNU General Public License for more details.\n",
       "\n",
       " You should have received a copy of the GNU General Public License along\n",
       " with this program; if not, write to the Free Software Foundation, Inc.,\n",
       " 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n",
       "\n",
       "*/\n",
       "\n",
       ":root {\n",
       "  --sherpa-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --sherpa-background-color: var(--jp-layout-color0, white);\n",
       "  --sherpa-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --sherpa-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "\n",
       "  /* https://medium.com/ge-design/iot-cool-gray-is-a-great-background-color-for-data-visualization-ebf18c318418 */\n",
       "  --sherpa-background-color-dark1: #EBEFF2;\n",
       "  --sherpa-background-color-dark2: #D8E0E5;\n",
       "}\n",
       "\n",
       "div.sherpa-text-fallback {\n",
       "    display: none;\n",
       "}\n",
       "\n",
       "div.sherpa {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       "div.sherpa details summary {\n",
       "    display: list-item;  /* needed for notebook, not lab */\n",
       "    font-size: larger;\n",
       "}\n",
       "\n",
       "div.sherpa details div.datavals {\n",
       "    display: grid;\n",
       "    grid-template-columns: 1fr 3fr;\n",
       "    column-gap: 0.5em;\n",
       "}\n",
       "\n",
       "div.sherpa div.dataname {\n",
       "    font-weight: bold;\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa div.dataval { }\n",
       "\n",
       "div.sherpa div.datavals div:nth-child(4n + 1) ,\n",
       "div.sherpa div.datavals div:nth-child(4n + 2) {\n",
       "    background: var(--sherpa-background-color-row-odd);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tbody {\n",
       "    border-bottom: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tr.block {\n",
       "    border-top: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-odd ,\n",
       "div.sherpa table.model th.model-even {\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-odd {\n",
       "    background: var(--sherpa-background-color-dark1);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-even {\n",
       "    background: var(--sherpa-background-color-dark2);\n",
       "}\n",
       "\n",
       "div.sherpa .failed {\n",
       "    background: orange;\n",
       "    font-size: large;\n",
       "    padding: 1em;\n",
       "}\n",
       "</style><div class=\"sherpa-text-fallback\">&lt;DataRosatRMF data set instance &#x27;pspcc_gain1_256.rsp&#x27;&gt;</div><div hidden class=\"sherpa\"><style>/*\n",
       "Copyright (C) 2020  Smithsonian Astrophysical Observatory\n",
       "\n",
       "\n",
       " This program is free software; you can redistribute it and/or modify\n",
       " it under the terms of the GNU General Public License as published by\n",
       " the Free Software Foundation; either version 3 of the License, or\n",
       " (at your option) any later version.\n",
       "\n",
       " This program is distributed in the hope that it will be useful,\n",
       " but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
       " MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
       " GNU General Public License for more details.\n",
       "\n",
       " You should have received a copy of the GNU General Public License along\n",
       " with this program; if not, write to the Free Software Foundation, Inc.,\n",
       " 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n",
       "\n",
       "*/\n",
       "\n",
       ":root {\n",
       "  --sherpa-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --sherpa-background-color: var(--jp-layout-color0, white);\n",
       "  --sherpa-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --sherpa-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "\n",
       "  /* https://medium.com/ge-design/iot-cool-gray-is-a-great-background-color-for-data-visualization-ebf18c318418 */\n",
       "  --sherpa-background-color-dark1: #EBEFF2;\n",
       "  --sherpa-background-color-dark2: #D8E0E5;\n",
       "}\n",
       "\n",
       "div.sherpa-text-fallback {\n",
       "    display: none;\n",
       "}\n",
       "\n",
       "div.sherpa {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       "div.sherpa details summary {\n",
       "    display: list-item;  /* needed for notebook, not lab */\n",
       "    font-size: larger;\n",
       "}\n",
       "\n",
       "div.sherpa details div.datavals {\n",
       "    display: grid;\n",
       "    grid-template-columns: 1fr 3fr;\n",
       "    column-gap: 0.5em;\n",
       "}\n",
       "\n",
       "div.sherpa div.dataname {\n",
       "    font-weight: bold;\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa div.dataval { }\n",
       "\n",
       "div.sherpa div.datavals div:nth-child(4n + 1) ,\n",
       "div.sherpa div.datavals div:nth-child(4n + 2) {\n",
       "    background: var(--sherpa-background-color-row-odd);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tbody {\n",
       "    border-bottom: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tr.block {\n",
       "    border-top: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-odd ,\n",
       "div.sherpa table.model th.model-even {\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-odd {\n",
       "    background: var(--sherpa-background-color-dark1);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-even {\n",
       "    background: var(--sherpa-background-color-dark2);\n",
       "}\n",
       "\n",
       "div.sherpa .failed {\n",
       "    background: orange;\n",
       "    font-size: large;\n",
       "    padding: 1em;\n",
       "}\n",
       "</style><div class=\"sherpa-text-fallback\">&lt;sherpa.astro.plot.RMFPlot object at 0x7c9654612350&gt;</div><div hidden class=\"sherpa\"><details open><summary>RMF Plot</summary><svg xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"460.8pt\" height=\"345.6pt\" viewBox=\"0 0 460.8 345.6\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">\n",
       " <metadata>\n",
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       "   <cc:Work>\n",
       "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
       "    <dc:date>2025-09-04T09:39:51.157316</dc:date>\n",
       "    <dc:format>image/svg+xml</dc:format>\n",
       "    <dc:creator>\n",
       "     <cc:Agent>\n",
       "      <dc:title>Matplotlib v3.10.5, https://matplotlib.org/</dc:title>\n",
       "     </cc:Agent>\n",
       "    </dc:creator>\n",
       "   </cc:Work>\n",
       "  </rdf:RDF>\n",
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       "</details></div><details open><summary>Summary (5)</summary><div class=\"datavals\"><div class=\"dataname\">Identifier</div><div class=\"dataval\">pspcc_gain1_256.rsp</div><div class=\"dataname\">Number of channels</div><div class=\"dataval\">256</div><div class=\"dataname\">Number of energies</div><div class=\"dataval\">729</div><div class=\"dataname\">Energy range</div><div class=\"dataval\">0.054608 - 3.01 keV, bin size 0 - 0.016932 keV</div><div class=\"dataname\">Channel range</div><div class=\"dataval\">1 - 256</div></div></details><details><summary>Metadata (5)</summary><div class=\"datavals\"><div class=\"dataname\">Mission or Satellite</div><div class=\"dataval\">ROSAT</div><div class=\"dataname\">Instrument or Detector</div><div class=\"dataval\">PSPCC</div><div class=\"dataname\">The channel type</div><div class=\"dataval\">PI</div><div class=\"dataname\">The minimum probability threshold</div><div class=\"dataval\">0.0</div><div class=\"dataname\">Matrix contents</div><div class=\"dataval\">FULL</div></div></details></div>"
      ],
      "text/plain": [
       "<DataRosatRMF data set instance 'pspcc_gain1_256.rsp'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rsp_pspcc = io.read_rmf(datafile(\"pspcc_gain1_256.rsp\"))\n",
    "rsp_pspcc.name = \"pspcc_gain1_256.rsp\"\n",
    "rsp_pspcc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cc0d9578-f473-4846-90d8-418c10f76abf",
   "metadata": {},
   "outputs": [
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       "</details></div><details><summary>Metadata (2)</summary><div class=\"datavals\"><div class=\"dataname\">Mission or Satellite</div><div class=\"dataval\">ROSAT</div><div class=\"dataname\">Instrument or Detector</div><div class=\"dataval\">PSPCC</div></div></details></div>"
      ],
      "text/plain": [
       "<DataIMG data set instance 'pspcc_gain1_256.rsp'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instrument.rmf_to_image(rsp_pspcc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dd22aca-a6d9-496c-a76c-122397161e6a",
   "metadata": {},
   "source": [
    "Note that the ROSAT RMF includes the effective area (i.e. ARF) terms, which is why the matrix values are greater than 1 and some of the vertical structure of the plot. The lower resolving power of the instrument - compared to the ACIS CCD - is shown by the fact the line above is not as sharp as the ACIS version above. If we used a Chandra grating RMF the line would be much narrower (but it was not included here as it is harder to see as there's a lot more pixels)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e0fb09f-2e01-41b4-a9f1-1cf0c08a7ba8",
   "metadata": {},
   "source": [
    "## Using the responses\n",
    "\n",
    "How do we apply the response to a model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bc4d431a-4a82-426f-badc-d3d38c353db0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sherpa.astro.instrument import ARF1D, RMF1D, RSPModelNoPHA\n",
    "from sherpa.models.basic import Delta1D, NormGauss1D"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77f88c0d-8865-413d-929b-789ebb1242dc",
   "metadata": {},
   "source": [
    "There are several ways of applying the response. Here we chose to use the \"wrapper\" models `ARF1D` and `RMF1D` to convert the `DataARF` and `DataRMF`\n",
    "structures into \"convolution-style\" models[$\\dagger$].\n",
    "\n",
    "---\n",
    "\n",
    "[$\\dagger$] technically only the RMF needs to be handled as a convolution model, but for historical reasons the ARF is handled the same way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "91b77cc5-3dda-4e1f-8776-269b526db4e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "aconv = ARF1D(arf)\n",
    "rconv = RMF1D(rmf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af0272d8-707c-498c-8d15-b97b82393350",
   "metadata": {},
   "source": [
    "Let's create a model consisting of a delta function, at 2 keV, together with a gaussian centered at 6 keV and with a FWHM of 1 keV:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "40391745-e8fa-4d7e-981a-d8eb1f145ee0",
   "metadata": {},
   "outputs": [
    {
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       "<style>/*\n",
       "Copyright (C) 2020  Smithsonian Astrophysical Observatory\n",
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       " You should have received a copy of the GNU General Public License along\n",
       " with this program; if not, write to the Free Software Foundation, Inc.,\n",
       " 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n",
       "\n",
       "*/\n",
       "\n",
       ":root {\n",
       "  --sherpa-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --sherpa-background-color: var(--jp-layout-color0, white);\n",
       "  --sherpa-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --sherpa-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "\n",
       "  /* https://medium.com/ge-design/iot-cool-gray-is-a-great-background-color-for-data-visualization-ebf18c318418 */\n",
       "  --sherpa-background-color-dark1: #EBEFF2;\n",
       "  --sherpa-background-color-dark2: #D8E0E5;\n",
       "}\n",
       "\n",
       "div.sherpa-text-fallback {\n",
       "    display: none;\n",
       "}\n",
       "\n",
       "div.sherpa {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       "div.sherpa details summary {\n",
       "    display: list-item;  /* needed for notebook, not lab */\n",
       "    font-size: larger;\n",
       "}\n",
       "\n",
       "div.sherpa details div.datavals {\n",
       "    display: grid;\n",
       "    grid-template-columns: 1fr 3fr;\n",
       "    column-gap: 0.5em;\n",
       "}\n",
       "\n",
       "div.sherpa div.dataname {\n",
       "    font-weight: bold;\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa div.dataval { }\n",
       "\n",
       "div.sherpa div.datavals div:nth-child(4n + 1) ,\n",
       "div.sherpa div.datavals div:nth-child(4n + 2) {\n",
       "    background: var(--sherpa-background-color-row-odd);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tbody {\n",
       "    border-bottom: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tr.block {\n",
       "    border-top: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-odd ,\n",
       "div.sherpa table.model th.model-even {\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-odd {\n",
       "    background: var(--sherpa-background-color-dark1);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-even {\n",
       "    background: var(--sherpa-background-color-dark2);\n",
       "}\n",
       "\n",
       "div.sherpa .failed {\n",
       "    background: orange;\n",
       "    font-size: large;\n",
       "    padding: 1em;\n",
       "}\n",
       "</style><div class=\"sherpa-text-fallback\">&lt;BinaryOpModel model instance &#x27;delta + gauss&#x27;&gt;</div><div hidden class=\"sherpa\"><details open><summary>Model</summary><table class=\"model\"><caption>Expression: delta + gauss</caption><thead><tr><th>Component</th><th>Parameter</th><th>Thawed</th><th>Value</th><th>Min</th><th>Max</th><th>Units</th></tr></thead><tbody><tr><th class=\"model-odd\" scope=\"rowgroup\" rowspan=2>delta</th><td>pos</td><td><input disabled type=\"checkbox\" checked></input></td><td>2.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>ampl</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr class=\"block\"><th class=\"model-even\" scope=\"rowgroup\" rowspan=3>gauss</th><td>fwhm</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.0</td><td>TINY</td><td>MAX</td><td></td></tr><tr><td>pos</td><td><input disabled type=\"checkbox\" checked></input></td><td>6.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>ampl</td><td><input disabled type=\"checkbox\" checked></input></td><td>100.0</td><td>-MAX</td><td>MAX</td><td></td></tr></tbody></table></details></div>"
      ],
      "text/plain": [
       "<BinaryOpModel model instance 'delta + gauss'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dmodel = Delta1D(\"delta\")\n",
    "gmodel = NormGauss1D(\"gauss\")\n",
    "\n",
    "dmodel.pos = 2\n",
    "gmodel.pos = 6\n",
    "gmodel.fwhm = 1\n",
    "\n",
    "# Adjust the gaussian amplitude so that it is more visible.\n",
    "gmodel.ampl = 100\n",
    "\n",
    "model_base = dmodel + gmodel\n",
    "model_base"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebfce262-c90f-458b-b1fc-cdab935c9c10",
   "metadata": {},
   "source": [
    "Let's just check that both components have the integrate flag set to `True` (the composite model does not pass through the integrate\n",
    "setting of its components in Sherpa 4.16.0):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a209a01d-0128-4ef9-97c1-3b65117782c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, True)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dmodel.integrate, gmodel.integrate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea2bee8f-1d16-43cb-a613-1e06b67d574d",
   "metadata": {},
   "source": [
    "We can evaluate this to get \"the truth\" (for XSPEC additive models the per-bin value would have units of photon / cm$^2$ / s, but for the Sherpa models we can give the `ampl` parameter (for these two models) whatever units we want, so let's also assume the same units as XSPEC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c90554f4-d159-4c21-a269-bb83411091cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_base = model_base(rmf.energ_lo, rmf.energ_hi)\n",
    "\n",
    "emid = (rmf.energ_lo + rmf.energ_hi) / 2\n",
    "plt.plot(emid, y_base)\n",
    "plt.xlabel(\"Energy (keV)\")\n",
    "plt.ylabel(\"photon cm$^{-2}$ s$^{-1}$\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f16eaa22-1d52-4978-9c9e-a2e160237e6a",
   "metadata": {},
   "source": [
    "The ARF is incluced by \"convolving\" the base model by the ARF model. Note that, as the ARF contains an exposure time, the model automatically includes this, which means that the output\n",
    "is now not a rate. In fact, because the ARF has units of cm$^2$, the model evaluation will calculate the number of photons per bin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6fe0a60b-ce69-4731-84fa-43bfe0ce5b95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>/*\n",
       "Copyright (C) 2020  Smithsonian Astrophysical Observatory\n",
       "\n",
       "\n",
       " This program is free software; you can redistribute it and/or modify\n",
       " it under the terms of the GNU General Public License as published by\n",
       " the Free Software Foundation; either version 3 of the License, or\n",
       " (at your option) any later version.\n",
       "\n",
       " This program is distributed in the hope that it will be useful,\n",
       " but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
       " MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
       " GNU General Public License for more details.\n",
       "\n",
       " You should have received a copy of the GNU General Public License along\n",
       " with this program; if not, write to the Free Software Foundation, Inc.,\n",
       " 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n",
       "\n",
       "*/\n",
       "\n",
       ":root {\n",
       "  --sherpa-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --sherpa-background-color: var(--jp-layout-color0, white);\n",
       "  --sherpa-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --sherpa-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "\n",
       "  /* https://medium.com/ge-design/iot-cool-gray-is-a-great-background-color-for-data-visualization-ebf18c318418 */\n",
       "  --sherpa-background-color-dark1: #EBEFF2;\n",
       "  --sherpa-background-color-dark2: #D8E0E5;\n",
       "}\n",
       "\n",
       "div.sherpa-text-fallback {\n",
       "    display: none;\n",
       "}\n",
       "\n",
       "div.sherpa {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       "div.sherpa details summary {\n",
       "    display: list-item;  /* needed for notebook, not lab */\n",
       "    font-size: larger;\n",
       "}\n",
       "\n",
       "div.sherpa details div.datavals {\n",
       "    display: grid;\n",
       "    grid-template-columns: 1fr 3fr;\n",
       "    column-gap: 0.5em;\n",
       "}\n",
       "\n",
       "div.sherpa div.dataname {\n",
       "    font-weight: bold;\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa div.dataval { }\n",
       "\n",
       "div.sherpa div.datavals div:nth-child(4n + 1) ,\n",
       "div.sherpa div.datavals div:nth-child(4n + 2) {\n",
       "    background: var(--sherpa-background-color-row-odd);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tbody {\n",
       "    border-bottom: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tr.block {\n",
       "    border-top: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-odd ,\n",
       "div.sherpa table.model th.model-even {\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-odd {\n",
       "    background: var(--sherpa-background-color-dark1);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-even {\n",
       "    background: var(--sherpa-background-color-dark2);\n",
       "}\n",
       "\n",
       "div.sherpa .failed {\n",
       "    background: orange;\n",
       "    font-size: large;\n",
       "    padding: 1em;\n",
       "}\n",
       "</style><div class=\"sherpa-text-fallback\">&lt;ARFModelNoPHA model instance &#x27;apply_arf(75141.231099099 * (delta + gauss))&#x27;&gt;</div><div hidden class=\"sherpa\"><details open><summary>Model</summary><table class=\"model\"><caption>Expression: apply_arf(75141.231099099 * (delta + gauss))</caption><thead><tr><th>Component</th><th>Parameter</th><th>Thawed</th><th>Value</th><th>Min</th><th>Max</th><th>Units</th></tr></thead><tbody><tr><th class=\"model-odd\" scope=\"rowgroup\" rowspan=2>delta</th><td>pos</td><td><input disabled type=\"checkbox\" checked></input></td><td>2.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>ampl</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr class=\"block\"><th class=\"model-even\" scope=\"rowgroup\" rowspan=3>gauss</th><td>fwhm</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.0</td><td>TINY</td><td>MAX</td><td></td></tr><tr><td>pos</td><td><input disabled type=\"checkbox\" checked></input></td><td>6.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>ampl</td><td><input disabled type=\"checkbox\" checked></input></td><td>100.0</td><td>-MAX</td><td>MAX</td><td></td></tr></tbody></table></details></div>"
      ],
      "text/plain": [
       "<ARFModelNoPHA model instance 'apply_arf(75141.231099099 * (delta + gauss))'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_arf = aconv(model_base)\n",
    "model_arf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "321d1f53-de90-4663-a556-bf3322a9e833",
   "metadata": {},
   "source": [
    "Each bin is multiplied by the ARF, so - since the ARF is not flat - the relative signal will change. The ARF is\n",
    "shown in orange (see the right axis) to also show the effective area at each energy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fefe70b5-17b3-4fee-af19-489129e901a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lHaxDRESkbfbu3cuVV15JcnIyUVFRDB8+nM8++8x73DAM7rzzTjIyMoiKimLSpEns3LnT5xpFRUXMnDmT+Ph4EhMTmT17NuXl5d39UY7K8mA2Z84cpk2bxqRJk6wuJXh0NpjZWvjPHpHc9LmELHOGpveclIb74WaQjnVUat9NERHpMocOHeK0004jLCyMd999l5ycHP74xz/6NOg8/PDDPPbYYzzxxBOsX7+emJgYpkyZQlVVlfecmTNnsm3bNpYvX86yZctYs2YNP//5z634SC2ytCtz6dKlfP7552zcuLFN51dXV1Nd3bCWVllZWVeVFrjcrvquTCDaj8EsbWLLG14mZJnLY4Bv16kzhjrDjtPmJtyo6FgtIiIirfj9739Pv379WLJkife5gQMHeu8bhsGjjz7K7bffzkUXXQTAc889R1paGq+//jpXXHEFX3/9Ne+99x4bN25kzBhzNYG//OUvnH/++fzf//0fmZkd7YXyL8tazHbv3s2NN97ICy+8QGRk29bBWrBgAQkJCd5bVlZWF1cZgGoOAW6wR0BYfAcvckQASzzJtwvzSFF9mr9vs1HujgYgwuiBIVlERDqlrKyM0tJS761x40tjb775JmPGjOGyyy4jNTWVUaNG8dRTT3mP79q1i/z8fJ/et4SEBMaNG8fatWsBWLt2LYmJid5QBjBp0iTsdjvr16/vok/YfpYFs02bNlFYWMjJJ5+M0+nE6XSyevVqHnvsMZxOJy6Xq8lrbrvtNkpKSry3nJwcCyq3WM1B82tz3Y5tdWTLWFjC0c+PTIHEkeZszag0n0OVrijzEsbhjtcjIiI9UlZWlk+Dy4IFC5o97/vvv2fRokUMHjyY999/n+uvv55f/vKXPPvsswDk5+cDkJbm+zsqLS3Neyw/P5/U1FSf406nk6SkJO85gcCyrsxzzjmHLVu2+Dz3s5/9jCFDhnDLLbfgcDiavCYiIoKIiAjv49LSHjjo3LP4a+OxXu12RB4PbyWYAaSMbfbpCrfZ2hmO1jITEZH2ycnJoU+fhp6Yxr/jG3O73YwZM4YHH3wQgFGjRrF161aeeOIJZs2a1S21dhfLgllcXBzDhg3zeS4mJobk5OQmz0sj1fUtZuGdaTE7IphFpjV/XhtUutViJiIiHRMXF0d8fOvDcjIyMpoMXzrxxBP597//DUB6ejoABQUFZGQ0rO9ZUFDAyJEjvecUFhb6XKOuro6ioiLv6wOB5bMypR0MoyGYdaYrs/EYs8wLWp6l2QaVbvOvG6eCmYiIdJHTTjuNHTt2+Dz3zTffMGDAAMCcCJCens6KFSu8x0tLS1m/fj3Z2dkAZGdnU1xczKZNm7znfPTRR7jdbsaNG9cNn6JtAmKBWY9Vq1ZZXUJgqysDoxZsjo4tLOvROIjZO/dPoMowg1mYUdXKmSIiIh1z0003ceqpp/Lggw9y+eWXs2HDBp588kmefPJJAO/OQffffz+DBw9m4MCB3HHHHWRmZnLxxRcDZgvbeeedx3XXXccTTzxBbW0tc+fO5YorrgiYGZkQYMFMWuEZXxae1KlWLp8WM1sng5k7HAAnCmYiItI1TjnlFF577TVuu+027r33XgYOHMijjz7KzJkzvef85je/oaKigp///OcUFxdz+umn89577/ms/PDCCy8wd+5czjnnHOx2OzNmzOCxxx6z4iO1SMEsmFR5Bv53phsT/7aY1Qczh7va7GptaS00ERGRTrjgggu44IILWjxus9m49957uffee1s8JykpiRdffLEryvMbjTELJt6lMjozIxPfYGZrOvu1PaoNM5jZAFxqNRMREekMBbNg4o8ZmWC2bHl0sivTwE61O8xMZi5NABAREekMBbNgUVcJrvq1wjrblUmjfS072WIGDRMAFMxEREQ6R8EsWHhay8ISOz0uzGfD8c5ei4ZxZgpmIiIinaNgFiz8sRWTR+Ng5geH69cyo06r/4uIiHSGglmw8MtWTB7+DWaeCQAa/C8iItI5CmbBwi8r/tczmm4Q3xl1Rv04NaPOr9cVERHpaRTMgoG7FmpLzPudnZEJfhnw31idUT9OzV3r1+uKiIj0NFpgNhh4Wssc0eCM6vz14k+E8l0QM6Dz1wLqjPp8byiYiYiIdIaCWTCo9tPCsh52J/S90D/XAuo8/4zc6soUERHpDHVlBgN/rfjfRbxjzNSVKSIi0ikKZsHA22KWZG0dLdDgfxEREf9QMAsGnoH/YYmWltEStZiJiIj4h4JZoKs7DO4a835YgrW1tMA7K1MtZiIiIp2iYBboaovNr844sPt3mQt/UYuZiIiIfyiYBboaTzdmYLaWAbg9/4z8vNWTiIhIT6NgFug8LWbhiVZWcVRubN57IiIi0nEKZoHO02IWyMHMqA9mhmFtISIiIkFOwSzQ1QZ+V6ahFjMRERG/UDALZIYbakvN+wEdzBr9M1KrmYiISIcpmAWy2jLADTYnOGOsrqZFPlFMEwBEREQ6TMEskDXuxrTZjn6uhdyGvVE6U4uZiIhIRymYBbKaYvNreOB2Y0LjWZmoxUxERKQTFMwCmWepjADdisnDwEZDNlMwExER6SgFs0AWBIvLmmwNPZga/C8iItJhCmaBrDbw1zDz0JIZIiIinadgFqhcNeCqNO8HfIsZGDbPtkxqMRMREekoBbNA5Wktc0SBI9zaWtpELWYiIiKdpWAWqLxLZSRaWkZbGdrIXEREpNMUzAJVkCyV4eWdlamuTBERkY5SMAtUajETERHpcRTMAlWQtZg1BDO1mImIiHSUglmgqg2WNcxMWi5DRESk8xTMAlFdBRh1gB3C4q2upk28wUxdmSIiIh2mYBaIPN2YYXFgC5b/RApmIiIinRUsv/V7liDrxoRGY8w0K1NERKTDFMwCUU3wbMXkoa5MERGRzlMwC0R1ZeZXZ5y1dbSLZ/C/WsxEREQ6SsEsENWWml+DZOA/NN4rUy1mIiLiX3fffTc2m83nNmTIEO/xqqoq5syZQ3JyMrGxscyYMYOCggKfa+Tm5jJt2jSio6NJTU3l5ptvpq6urrs/SqucVhcgzaitbzELC54Ws4YxZgpmIiLif0OHDuXDDz/0PnY6GyLMTTfdxNtvv80rr7xCQkICc+fO5ZJLLuGTTz4BwOVyMW3aNNLT0/n000/Jy8vj6quvJiwsjAcffLDbP8vRKJgFGlcVGLXm/SDqyvR2YGqBWRER6QJOp5P09PQmz5eUlLB48WJefPFFJk6cCMCSJUs48cQTWbduHePHj+eDDz4gJyeHDz/8kLS0NEaOHMl9993HLbfcwt133014eHh3f5wWqSsz0Hi6MR3RYHdYW0s7aEsmERHpSjt37iQzM5Njjz2WmTNnkpubC8CmTZuora1l0qRJ3nOHDBlC//79Wbt2LQBr165l+PDhpKWlec+ZMmUKpaWlbNu2rXs/SCvUYhZovN2YwTO+DDzBzECD/0VEpK3KysooLS31Po6IiCAiIqLJeePGjeOZZ57hhBNOIC8vj3vuuYczzjiDrVu3kp+fT3h4OImJiT6vSUtLIz8/H4D8/HyfUOY57jkWSBTMAk1d8I0vM9kAQy1mIiLSZllZWT6P77rrLu6+++4m502dOtV7f8SIEYwbN44BAwbw8ssvExUV1dVldisFs0Dj6coMovFloL0yRUSk/XJycujTp4/3cXOtZc1JTEzk+OOP59tvv+Xcc8+lpqaG4uJin1azgoIC75i09PR0NmzY4HMNz6zN5satWUljzAJNkHZlNmzJpK5MERFpm7i4OOLj4723tgaz8vJyvvvuOzIyMhg9ejRhYWGsWLHCe3zHjh3k5uaSnZ0NQHZ2Nlu2bKGwsNB7zvLly4mPj2/Samc1tZgFmiBcKgPAreUyRESki/z6179m+vTpDBgwgH379nHXXXfhcDj46U9/SkJCArNnz2b+/PkkJSURHx/PDTfcQHZ2NuPHjwdg8uTJZGVlcdVVV/Hwww+Tn5/P7bffzpw5c9ocBruLglkgMdxQV27eD7IWs4ZZmWoxExER/9qzZw8//elPOXjwIL179+b0009n3bp19O7dG4A//elP2O12ZsyYQXV1NVOmTOHxxx/3vt7hcLBs2TKuv/56srOziYmJYdasWdx7771WfaQWKZgFkroKzBYnu7lcRjDxDDFTi5mIiPjZ0qVLj3o8MjKShQsXsnDhwhbPGTBgAO+8846/S/M7jTELJI27MW22o58bYAxD65iJiIh0loJZIAnSGZnQeFamujJFREQ6SsEskNQF64xMQJuYi4iIdJqCWSAJ0hmZ0KjFTIP/RUREOkzBLJB4ujKDMJihBWZFREQ6TcEskHi6Mp3B15VpqCtTRESk0xTMAoW7DlyHzftB2GKmwf8iIiKdp2AWKDytZfZwcATWKsRt0TDGTC1mIiIiHaVgFii848uCrxvTpK5MERGRzlIwCxSeGZlBuIYZqCtTRETEHxTMAkVQz8hsvFemWsxEREQ6SsEsUNQG8eKyNG4xUzATERHpKAWzQFEX3F2ZaIFZERGRTrM0mC1atIgRI0YQHx9PfHw82dnZvPvuu1aWZJ26cvNrWKy1dXSQ1jETERHpPEuDWd++fXnooYfYtGkTn332GRMnTuSiiy5i27ZtVpbV/dy14K4x7zuDNJhp8L+IiEinOa188+nTp/s8fuCBB1i0aBHr1q1j6NChFlVlgboK86s9HOxh1tbSQVrHTEREpPMsDWaNuVwuXnnlFSoqKsjOzm72nOrqaqqrq72Py8rKuqu8ruUJZs4Ya+voFE/jq4KZiIhIR1kezLZs2UJ2djZVVVXExsby2muvkZWV1ey5CxYs4J577unmCruBJ5g5gjeYGRr8LyIi0mmWz8o84YQT2Lx5M+vXr+f6669n1qxZ5OTkNHvubbfdRklJiffW0nlBJwRazAy1mImIiHSa5S1m4eHhDBo0CIDRo0ezceNG/vznP/O3v/2tybkRERFERDTsI1laWtptdXapUAhmxpF3REREpL0sbzE7ktvt9hlH1iN4lsoI5mCmWZkiIiKdZmmL2W233cbUqVPp378/ZWVlvPjii6xatYr333/fyrK6Xwi0mNlsCmYiIiKdZWkwKyws5OqrryYvL4+EhARGjBjB+++/z7nnnmtlWd0vBIKZW8tliIiIdJqlwWzx4sVWvn1gcLvAXWXeD+Jg1tArrhYzERGRjgq4MWY9jqu+tczmAEektbV0hrcnU8FMRESkoxTMrFZXaX51RFtbR6dpjJmIiEhnKZhZzVUfzJzBHcy0wKyIiEjnKZhZre6w+TVkWsw0+F9ERKSjFMysFmotZiIiItJhCmZWC7UxZlouQ0REpMMUzKwWci1mGmMmIiLSUQpmVvO2mEVZW0cnafC/iIhI5ymYWc0VYl2ZajETERHpMEtX/u/xDANcnlX/gzuY9ciuzNpSOJwPVQVQW2YuFlxXAe4azKBqA5sNbGEQkQQRKRCeZN6cMeAM7lZSERHxPwUzK7kOYwYZW9B3ZYb84H9XNRzeB6XfgFELNcUNrZ3NMsybARgu87WH9/me4oiGqEyISgebE4w6cNeZXyPTILpP130eEREJSApmVmo8vswW3MtNGLYQbTEz3HDwMyj+kqafzQ6RvSEyHcJ71beCxYA93PPihlbR6gNQcxCqi6C22Azlrkoo/9a8NSciFZJOhpj+Xff5REQkoCiYWSlEZmT6CJXB/64qKPsWyr4xQxWAMx5iBphhzBENkalgb8P/QmGxEJni+5y7DqoKoSoPDhfUd3k6zesZBlTsgupCyHsPInpD0hiI6ef/zykiIgFFwcxKIbOGGRjeeSRBEszcLij/HurKIW4QhMU1HCv7DgpXm12KYLaApU6A2GP99/52J0Rnmrfm1FVC8VdQkgPV+yHvXYjMgJTspiFPRERChmZlWskVGktlmIKkK/NwPuR/CD/8AwpXQtFG2P1vcyC/uxYK10DBCjOUhSdD4knQb4Z/Q1lbOKMhZTwc89+QOAJsDrN1bc+rULCqIdSLiPRADz30EDabjXnz5nmfq6qqYs6cOSQnJxMbG8uMGTMoKCjweV1ubi7Tpk0jOjqa1NRUbr75Zurq6rq5+qNTi5mVXPX7ZIZAV6bPlkyGYd2YObenleuIf9p1lVDwke8AfGcsYIO6Mij6wgxj5d+ZxxJHQvIp1o/9c0SaAS1hGBzcYI5HK/vGbO3rNdIMbW3pThURCREbN27kb3/7GyNGjPB5/qabbuLtt9/mlVdeISEhgblz53LJJZfwySefAOByuZg2bRrp6el8+umn5OXlcfXVVxMWFsaDDz5oxUdpllrMrBRCXZn47JVpUatZxY+w6x+w6zmoyG14vrYM9rxeH8rsEH8i9L0YBvwU0s42zynb0RDKMs6DlLHWh7LGwmIhfaJZd0SqGSKLPoPcl6FyX6svFxEJBeXl5cycOZOnnnqKXr16eZ8vKSlh8eLFPPLII0ycOJHRo0ezZMkSPv30U9atWwfABx98QE5ODs8//zwjR45k6tSp3HfffSxcuJCamhqrPlITCmZWCqHB/+7GWaw7JwDUlptde3vegrwPzKUsjDoo/I85o9JdB/krzLFkYQnQ/zJIPcMcuG+zmctSNO5Kjh8S2LMgI1Oh38WQdo7Z4ldXDvuWwcGNobtUiYhIvTlz5jBt2jQmTZrk8/ymTZuora31eX7IkCH079+ftWvXArB27VqGDx9OWlqa95wpU6ZQWlrKtm3buucDtIH6QKykFrPOcVXDvrehtqThudhBUJlrLvZa8aPZqlRzyBzAnznNbHlqzBPOKn4wH6dkd0/tnRV3nDlD9MCnULodDn0BlXsh/RzfiQwiIgGsrKyM0tJS7+OIiAgiIiKaPXfp0qV8/vnnbNy4scmx/Px8wsPDSUxM9Hk+LS2N/Px87zmNQ5nnuOdYoFCLmZVCqMXMp9uvO1puDLfZElZbAo4YSD0L+lxodvdF17d45S+vD2WRkDGlaSjzSB5rrkOWejbYw7q+dn+xO83ZoumTzOBZXWhOZChrYV00EZEAk5WVRUJCgve2YMGCZs/bvXs3N954Iy+88AKRkZHdXGX3UouZVVw15orwEBKzMt1G44zfgRYzw20Obq+rhN6nmoPej3bu/k/h8B5z7a/M8yAiueF4RBKU19+3OaDvhRCe2PL1whPNLs5gFXusOe6s4COoyq//ut+cNBBI4+RERI6Qk5NDnz4Nu5y01Fq2adMmCgsLOfnkk73PuVwu1qxZw1//+lfef/99ampqKC4u9mk1KygoID09HYD09HQ2bNjgc13PrE3POYFALWZW8bSW2cNDZFZdJ7syD6w31+0q/xb2f3z0c/e9B6U55v20ib6hDOpnW9aLGXj0UBYqwmKhzwXQq/6HVskWyHvf/ANARCRAxcXFER8f7721FMzOOecctmzZwubNm723MWPGMHPmTO/9sLAwVqxY4X3Njh07yM3NJTvbHKKSnZ3Nli1bKCws9J6zfPly4uPjycrK6toP2g6hkAiCk2epjJAYXwY2+xHLZbRH1X4zSHiUf2+uNxbVzF8w5bvMljLskHomxB7T9JzGwSwqo321BDObHZLHmC2GBSvNsXZ734TMqeZWUSIiQSouLo5hw4b5PBcTE0NycrL3+dmzZzN//nySkpKIj4/nhhtuIDs7m/HjxwMwefJksrKyuOqqq3j44YfJz8/n9ttvZ86cOS0GQiuoxcwq3mAWvH3lRqMA5pvF2hnMyr83v8Yea86KBHMR2JoS3/Mq95gzMMFcwyt+cPPXC+8FtjBz7Fnsce2rJRTEHmuOt3NEQU0R7HnD3HRdRCSE/elPf+KCCy5gxowZTJgwgfT0dF599VXvcYfDwbJly3A4HGRnZ3PllVdy9dVXc++991pYdVNqMbOKq8r8GsTBrCk74KbNwaz6IOxdBu5q83HssRDVB6oKzEH7BzdAxrnmscP5ZhcmbnNrol4jW76uIwIGXGGOL3OEt3xeKIvsba55tu8dc4LEnjfM9dmi0lp9qYhIMFi1apXP48jISBYuXMjChQtbfM2AAQN45513uriyzlGLmVVCLJjZbDQMNG9rV+ahLxpCGUB0PzNUpdYv+lq5u2GGZ/EWwA3RAyDz/NbH5Tmjem4o8wiLg74XmRMD3PVLi1T8aHVVIiJyFApmVvEEM3toBDOTZ5xZG5fLOFy/bozNaS5n4VmqIiLZbO0y6qCuwhzA7gkUyWPA7vBr1SHNEWlOCojub34/85ab652JiEhA6lBXpsvl4plnnmHFihUUFhbidvv+Iv7oo4/8UlxIC4EWs6YNY+1oMaurrJ+ZaoOBV/u2gNls5viwulJzOyVXIeA2x44dOQNTWmd3QsZkcxmN8u/N9d36XGhOEhARkYDSoWB244038swzzzBt2jSGDRuGTWsltV8IBLMmbLb64WVtCGaeVpuI5Oa7JcPizGBWV96w8XhUn6bnSdvY7GYXcd1hqMqDfe+aWztptqaISEDpUDBbunQpL7/8Mueff76/6+k53CEYzLw9420IZt6w1bf5457AUH0Qynaa92MGdKq6Hs/uMFvO9rwBtcVmOOtzocbiiYgEkA6NMQsPD2fQoEH+rqVnCYEWsxbjV1u6MmsOmV8jU5o/bqv/m+HwXvOdwpMgWi1mneaIMCdPOKLNpTTyl2vzcxGRANKhYParX/2KP//5zz7rWEk7eYNZ8G/H5GVrY4uZu87c1xHMwNXsteoH+NcUmV8jtcyD34TFmktn2Jxm8C1cbXVFIiJSr0NdmR9//DErV67k3XffZejQoYSF+W783HhBN2mGu86cIQdB3WLWhCeYHa0FprYUflza8Dgsvvnzjhx3FtO/c7WJr8gUSD8X8t4zu4qdsZB8itVViYj0eB0KZomJifzkJz/xdy09h6e1DHvDEhFBqEmLqaeVy7M5e3P2r22474xt1Mp2BNsRS2Jo4L//xfSD1Almi9mhL8z/HgknWl2ViEiP1qFgtmTJEn/X0bOEwPiyZrUWzAwDqurXLrNHQto5R7lWo3+aYQkhstF7AIo/wVyS5NDn5ubxzhi1ToqIWKhTC8zu37+fjz/+mI8//pj9+/f7q6bQ5w7B8WXQqCuzhWBWV2auQG9zwMArj749kP2IYCZdJ3kMxB0PGOYepVX6f1lEpLHDhw+zd2/Txbm3bdvm9/fqUDCrqKjg2muvJSMjgwkTJjBhwgQyMzOZPXs2lZWV/q4x9IRIi1nT9WXrw1RLway21PwaFt9yF+aR1wJzJqF0rdQJ5tIlRh3kvW8uACwiIvzrX/9i8ODBTJs2jREjRrB+/Xrvsauuusrv79ehYDZ//nxWr17NW2+9RXFxMcXFxbzxxhusXr2aX/3qV/6uMfSESDBrorUWs5oS82tbWsAajzGza52tLmezQ/okc3cFV6WW0RARqXf//fezadMmNm/ezJIlS5g9ezYvvvgi0MxYaz/o0MCdf//73/zrX//irLPO8j53/vnnExUVxeWXX86iRYv8VV9oCtlg1kqLWV2Z+dUZ1/q1GndlKph1D0c4pE+GPa9BVQHs/xRST7e6KhERS9XW1pKWZg69GT16NGvWrOEnP/kJ3377bZfsfNShFrPKykpvkY2lpqaqK7MtXIfNr0EezBr/oWAYNLSYFX3efFdYXYX5tS3bAKnFzBrhCZA20bxfmgMl262tR0TEYqmpqXz11Vfex0lJSSxfvpyvv/7a53l/6VAwy87O5q677qKqqsr73OHDh7nnnnvIzs72W3Ehy9NiZg/uYNaEJ0zVlZnrYx3JE9baFMzUYmaZmP6QNMa8v/9jqCq0th4REQv94x//IDU11ee58PBw/vnPf7J6tf8X6O5QV+ajjz7KeeedR9++fTnppJMA+PLLL4mMjOT999/3a4EhKQS7Mm02fFu5qg/4nuCuNTfPhrYFs8YLzx5tXTTpGr1Gmf8NK36AvOXQ7yfgjLa6KhGRbte3bwt7OgOnnXaa39+vQ8Fs+PDh7Ny5kxdeeIHt282ujp/+9KfMnDmTqKgQWwKiK4TIBuZGo3mZhsHRA9TetxvuO2Nbv7gzGmxhYNRCZGrr54t/2WyQehbsed3c8Dz/Q+hzQeuzaUVEeoiqqiq++uorCgsLcbt9J0tdeOGFHb5uh4LZmjVrOPXUU7nuuut8nq+rq2PNmjVMmDChwwX1CKG4TyaAq9r3sdsFdoe5YXl1o+6wsDYEM4ABV0BtiYKZVRzhkDHZDGdV+ebYweQxVlclImK59957j6uvvpoDBw40OWaz2XC5Ot7T06E/f88++2yKioqaPF9SUsLZZ5/d4WJ6BMMI3a5Md5Xvk0at+bXxgqW9z2j7RZ1REJXe6dqkE8ITzTXOwNy26XCBpeWIiASCG264gcsuu4y8vDzcbrfPrTOhDDoYzAzDaHaK6MGDB4mJacP4oZ7MXYN3aVZ7cC+c2mRWZpMWsxrzq2e8WcJw7cUYjGKPhbjBgAEFK83xgiIiPVhBQQHz589vdoWKzmpXV+Yll1wCmM1011xzDRERDcHC5XLx1Vdfceqpp/q3wlDjaS2zhZndfKEkvFfDWmUArhoIo2HF//BEK6oSf0g5DQ7nQV0pHFgHqe1o+RQRCTGXXnopq1at4rjjjvP7tdsVzBISzBXbDcMgLi7OZ6B/eHg448ePbzLuTI4Qgt2YUN+VmXoGFG2C0vq1rzwtZnXl5te2DPqXwOQINycD7FsGpV9DzABtdi4iPdZf//pXLrvsMv7zn/8wfPhwwsLCfI7/8pe/7PC12xXMlixZAsAxxxzDr3/9a3VbdkSIbmBuGJjLYKROMAf7VxU0DWZhbVjxXwJXdCYkjoDir6BwNfS71BwHKCLSw/zzn//kgw8+IDIyklWrVvkM77LZbN0XzDzuuusuAPbv38+OHTsAOOGEE+jdu3eHC+kxQrTFzIet/i8Hd43ZnekJaGoxC35Jp0DlbjN8H/jE3F9TRKSH+d3vfsc999zDrbfeit3u32WEOrwl07XXXktGRgYTJkxgwoQJZGZmMnv2bG3J1JoQDWY+c0Ec9Sv1u2saWsvskb77X0pwsjsg9WzABuXfQ/kPVlckItLtampq+K//+i+/hzLoYDC76aabWL16NW+99RbFxcUUFxfzxhtvsHr1an71q1/5u8bQEqLBzIe9mWDW1rXLJPBFpkCiueMH+z9uOhtXRCTEzZo1i5deeqlLrt2hJox///vf/Otf/+Kss87yPnf++ecTFRXF5ZdfzqJFi/xVX+gJkQ3MwXe5DB/NBTN1Y4aWpNHmdk21xXBgLaSdZXFBIiLdx+Vy8fDDD/P+++8zYsSIJoP/H3nkkQ5fu0PBrLKystm1O1JTU9WV2ZpQ3cC8scbBrLZ++QwFs9Bid0DqmbD3DSj7BmKPg5h+VlclItIttmzZwqhRowDYunWrX6/doWCWnZ3NXXfdxXPPPUdkpBkwDh8+zD333EN2drZfCww5Pa0r07MYqYJZ6IlKg4RhULIV9v8HIi9tGF8oIhLCVq5c2WXX7lAw+/Of/8yUKVPo27cvJ51kjjX58ssviYyM5P333/drgSEnhIJZ403MfXiCmaumYVsmjTELTcljoeJHc2Hhgxsg9XSrKxIR6XILFiwgLS2Na6+91uf5v//97+zfv59bbrmlw9fu0OD/YcOGsXPnThYsWMDIkSMZOXIkDz30EDt37mTo0KEdLqZHcIdOMGuR57O5qxp1ZWoNs5Bkd5pdmgClOVC5z9p6RES6wd/+9jeGDBnS5PmhQ4fyxBNPdOraHV6/IDo6Wqv8t5fhbljTqycEs7rD4Kofc6iuzNAVnQnxJ5o7AhSugf6XamkUEQlp+fn5ZGRkNHm+d+/e5OXlderaHf7puXPnTlauXElhYSFut9vn2J133tmpokKWpxsTW9BvYA5Hm5VZ/9lcFeZXm0MrxIe65HFQkWvupVn0GaSMt7oiEZEu069fPz755BMGDhzo8/wnn3xCZmZmp67doWD21FNPcf3115OSkkJ6enqTrQgUzFrgnZEZccSKrCHmyNZAtZaFPke4uVdq3ntQvAViB5nrnYmIhKDrrruOefPmUVtby8SJEwFYsWIFv/nNbzq9nmuHgtn999/PAw880KnBbT1SCA38Pyp7OGADz+SAqD5WViPdJaa/GcjKvzUXnu17UWj/ASIiPdbNN9/MwYMH+X//7/9RU2MOUYqMjOSWW27htttu69S1OxTMDh06xGWXXdapN+6RQmzgf+OeTJ9uTZsN7GEN4+mi+3ZnWWKllPHmLM3qQnN9s/gTrK5IRMTvbDYbv//977njjjv4+uuviYqKYvDgwUREdH6YUodmZV522WV88MEHnX7zHqentJhBw0bm0DM+r5ic0eauAAAH1mu7JhEJabGxsZxyyikMGzbML6EM2tFi9thjj3nvDxo0iDvuuIN169YxfPjwJlsR/PKXv2zTNRcsWMCrr77K9u3biYqK4tRTT+X3v/89J5wQon9lh3Awa9JjZQ8DV/19R/BPdJB2SBwGZTug5pA5EaD3aVZXJCISNNoczP70pz/5PI6NjWX16tWsXr3a53mbzdbmYLZ69WrmzJnDKaecQl1dHb/97W+ZPHkyOTk5xMTEtLW04BFi2zEZjfovm8zQtDcK6yEwA1XawWaHlNNg3zIoyYG4EzQRQEQ6ZdGiRSxatIgffvgBMNcLu/POO5k6dSoAVVVV/OpXv2Lp0qVUV1czZcoUHn/8cZ/tI3Nzc7n++utZuXIlsbGxzJo1iwULFuB0BtbyPm2uZteuXc0+7/nlbOvAIN/33nvP5/EzzzxDamoqmzZtYsKECe2+XsDzBLMesXREo6SmYNbzRGea+2eWfwcHPoE+F2oigIh0WN++fXnooYcYPHgwhmHw7LPPctFFF/HFF18wdOhQbrrpJt5++21eeeUVEhISmDt3LpdccgmffPIJYG46Pm3aNNLT0/n000/Jy8vj6quvJiwsjAcffNDiT+erQ2PMABYvXsywYcOIjIwkMjKSYcOG8fTTT3eqmJKSEgCSkpKaPV5dXU1paan3VlZW1qn363Yh1mLWWJPfue66hvt2R7fWIgEiZTzYnFBVAGU7ra5GRILY9OnTOf/88xk8eDDHH388DzzwALGxsaxbt46SkhIWL17MI488wsSJExk9ejRLlizh008/Zd26dQB88MEH5OTk8PzzzzNy5EimTp3Kfffdx8KFC72zKgNFh4LZnXfeyY033sj06dN55ZVXeOWVV5g+fTo33XRTh9cwc7vdzJs3j9NOO41hw4Y1e86CBQtISEjw3rKysjr0XpbpKbMyodFiutJjOWMaTQRYp4kAItJEWVmZT4NLdXXrPydcLhdLly6loqKC7OxsNm3aRG1tLZMmTfKeM2TIEPr378/atWsBWLt2LcOHD/fp2pwyZQqlpaVs27bN/x+sEzoUzBYtWsRTTz3FggULuPDCC7nwwgtZsGABTz75JI8//niHCpkzZw5bt25l6dKlLZ5z2223UVJS4r3l5OR06L0sU3fY/Boiweyo3ApmAiQOh7BE899D0WdWVyMiASYrK8unwWXBggUtnrtlyxZiY2OJiIjgf//3f3nttdfIysoiPz+f8PBwEhMTfc5PS0sjPz8fMLdQahzKPMc9xwJJh0a81dbWMmbMmCbPjx49mrq6umZecXRz585l2bJlrFmzhr59W17zKiIiwmc6amlpabvfy1Ih1mLWWJOuzKi+cHgPRPS2pB4JEDa7OStz39vmRID4IRCRbHVVIhIgcnJy6NOnYRHyoy05ccIJJ7B582ZKSkr417/+xaxZs5pMQAwFHWoxu+qqq1i0aFGT55988klmzpzZ5usYhsHcuXN57bXX+Oijj5rsORVS3HVg1K8fESLBrHH3ZZOuzLSzIGkMZEzpzpIkEEX3gdhjAQP2f2J1NSISQOLi4oiPj/fejhbMwsPDGTRoEKNHj2bBggWcdNJJ/PnPfyY9PZ2amhqKi4t9zi8oKCA9PR2A9PR0CgoKmhz3HAskHZ4junjxYj744APGjzc3K16/fj25ublcffXVzJ8/33veI4880uI15syZw4svvsgbb7xBXFyctzkxISGBqKgQm7noGXNlc/guJRGqnNGQdLLVVUigSMk2Nzmvyoey7yDuOKsrEpEg53a7qa6uZvTo0YSFhbFixQpmzJgBwI4dO8jNzSU7OxuA7OxsHnjgAQoLC0lNTQVg+fLlxMfHB9x49Q4Fs61bt3LyyeYv3e+++w6AlJQUUlJS2Lp1q/e81pbQ8LS6nXXWWT7PL1myhGuuuaYjpQUuV/34shCckQlaCUFa4YyBXiPNcWYH1kHMALAH1tpBIhK4brvtNqZOnUr//v0pKyvjxRdfZNWqVbz//vskJCQwe/Zs5s+fT1JSEvHx8dxwww1kZ2d7G48mT55MVlYWV111FQ8//DD5+fncfvvtzJkzx28r9vtLh34yrly50i9vbjTp/wphIbzqv0ibJI6A0u1QVw7FXzbM2BQRaUVhYSFXX301eXl5JCQkMGLECN5//33OPfdcwFwE3263M2PGDJ8FZj0cDgfLli3j+uuvJzs7m5iYGGbNmsW9995r1Udqkf5k7S6hGMx6UK4WP7A7zbXN8j+EQ5vNHQHCYq2uSkSCwOLFi496PDIykoULF7Jw4cIWzxkwYADvvPOOv0vzuw4vMCvt5OnKdITY2DmR9og9FiIzzIkwB9dbXY2ISMBRMOsudRXmV2cI7gEq0h69TwVs5nZNhwNr/SAREaspmHUXV+gFM0N9mdIREcnmemYABz5tZq0VEZGeS8Gsu6jFTKRB8ilgD4fqA1C6w+pqREQChoJZd6nTGDMRL0dkw6zMgxvAFVibCIuIWEXBrLu46zdmDaFZmeqBkk5JGNqwj+ahz62uRkQkICiYdQfDaAhm9nBraxEJFDa7uSMAQPFWqCm2tBwRkUCgYNYdDm5ouG8PrBWGRSwV0w+i+wNuc0cAEZEeTsGsq9WUmKucQ/0+mQ5r6/Gjxj2Z6taUDkvJBuxQmWvupyki0oMpmHU1V2XDfVsP2LxcpL3CEyBxmHn/wFow3NbWIyJiIQWzruZuNNvMFjqtZUfSJubSKb1ONmcs15ZA8RarqxERsYyCWVdzVTfcD7H00ngTenVlSqc4wiF5rHm/aBPUlltbj4iIRRTMupq7uvVzRATijofIdDDqzB0BRER6IAWzruaqavQgtFrMGguxxkCxgs0GvU8H7FDxA1T8aHVFIiLdTsGsq7kOW11Bl9GsTPG7iCRIHGHe3/8JuGutrUdEpJspmHU1nxYzEWlV0sngjIO6cjj4mdXViIh0KwWzrtZDujJF/MburO/SBEq2QtV+a+sREelGCmZdLZS7Mht1X6onU/wqph/EDgIMKFyttc1EpMdQMOtqIRzMRLpU71PBHgk1RVD8ldXViIh0CwWzrmS4j1hgVl2ZIm3miISU8eb9ok3m9mYiIiFOwawrhfjAfwMtMCtdLP54iOoLhgsKV+kfmoiEPAWzrhTiwUykW6ROMPeZrSqA4i+trkZEpEspmHWlJuPL1JUp0m5hsdD7NPP+wc+g6oC19YiIdCEFs66kFjMR/4g/HmIGAm4oXAnuOqsrEhHpEgpmXSnUZ2T6LJehsT/SxVLPAEcU1ByCgxutrkZEpEsomHWlI1vMPItmikj7OSIh9UzzfskWqNxjbT0iIl1AwawreVrMkkbDsddCdKa19YgEu5j+EJ9l3i9YCXWV1tYjIuJnCmZdydNiZo80t5kJMUaLD0S6UMp4CE8y//ApWKUlNEQkpCiYdSVPi5kzyto6REKJ3Qnp54DNCYf3aFcAEQkpodeME0gat5iJiP+E94KUU2H/Gji4kZxDMSzZVMEXu4spq6rlmlMH8vMJx+Kwa4kaEQkuCmZdyRPMHKEZzLSJuVgqYQhG5R5WfLaWN7c9ybslp1JrhAHw+/e2s/GHIh776ShiI/RjTkSCh7oyu4rhBrcnmKkrU6Qr/HFzKm9uKyPGfpgbh+ezeNYY7rggiwinnY+2FzJv6Wbcbv3ZICLBQ8Gsq7iqG+47IqyrQyREfbAtn7+u3s0nZSdx4ci+zB1r55zMQ8w+fSAvXjeOcKedD78u4PFV31pdqohImymYdRXPwH97JNhC89vsu4m5WiWk+xRX1vDb17YAcMmpo8g+5TzzwIFPobqI0QOSuP/iYQD8ecVOdhaUWVWqiEi7hGZiCAQhPr5MxEoL3tnOgfIaBqXG8uspJ0DiCIjqC0Yd7HsHasu5bHRfJp2YSq3L4LevbdEfDyISFBTMuopbwUykK3xbWM7Lm3YDsOCS4UQ4HWCzQfrE+vXNKqFgBTYM7r1oGFFhDjb+cIhVO/ZbXLmISOsUzLpKXX1XZggP/PeZlanGCOkmT6z+DsOAc7PSOOWYpIYDjkjImAL2cKgqgKLPyUyM4ursAQA8+uE3ajUTkYCnYNZV1GIm4nd7iw/z+hd7Afh/Zx3X9ISwuIY9aQ99AYfzuG7CsUSFOfhyTwkff3ugG6sVEWk/BbOu0gNazES62+L/7KLObXDqccmM6t+r+ZPiBkHc8YAB+R+SElnH5WP6AvCPtT92X7Ei4jcLFizglFNOIS4ujtTUVC6++GJ27Njhc05VVRVz5swhOTmZ2NhYZsyYQUFBgc85ubm5TJs2jejoaFJTU7n55pupq6vrzo/SKgWzrtIDWsyMFu6LdIWqWhf//nwPANdNOPboJ/c+vWE/zbzlXDXeDGYffl3A3uLDXV2qiPjZ6tWrmTNnDuvWrWP58uXU1tYyefJkKioqvOfcdNNNvPXWW7zyyiusXr2affv2cckll3iPu1wupk2bRk1NDZ9++inPPvsszzzzDHfeeacVH6lFCmZdRS1mIn714dcFlByuJSMhkgmDex/9ZLsTMiab482qCxlk20L2scm4Dfjn+tzuKVhE/Oa9997jmmuuYejQoZx00kk888wz5ObmsmnTJgBKSkpYvHgxjzzyCBMnTmT06NEsWbKETz/9lHXr1gHwwQcfkJOTw/PPP8/IkSOZOnUq9913HwsXLqSmpsbKj+dDwayr9IAWM5Hu9PJnZmvZpaP7tm0PzLB4SDvHvF/6NdePMhd9XrpxN3Uud1eVKSLdoKSkBICkJHMC0KZNm6itrWXSpEnec4YMGUL//v1Zu3YtAGvXrmX48OGkpaV5z5kyZQqlpaVs27atG6s/OgWzrtIDWswaz3DTZDfpSvuKD/OfneZyF5eO7tv2F8b0g+SxAJyasIPBcaUcKK9m7fcHu6JMEWmnsrIySktLvbfq6upWX+N2u5k3bx6nnXYaw4aZC0nn5+cTHh5OYmKiz7lpaWnk5+d7z2kcyjzHPccChYJZVzAMcNf/41KLmUinvfr5HgwDxg1MYkByTPte3GskxB6H0w4/P/57YuyVvLl5X5fUKSLtk5WVRUJCgve2YMGCVl8zZ84ctm7dytKlS7uhwu7ntLqAkOSuxjscXsFMpFMMw+C1+iUyLhvTr2MXST0TaksY3becCd9+zofboqm6eBiRYQ4/Vioi7ZWTk0OfPn28jyMijr639Ny5c1m2bBlr1qyhb9+G1vP09HRqamooLi72aTUrKCggPT3de86GDRt8rueZtek5JxCoxawrePfJjAjZfTLhiAVmNS9TusjOwnK+219BuMPOlKFprb+gOXYnpE/mmNQUBsRWMdT5Bau2F/q3UBFpt7i4OOLj4723loKZYRjMnTuX1157jY8++oiBAwf6HB89ejRhYWGsWLHC+9yOHTvIzc0lOzsbgOzsbLZs2UJhYcP/+8uXLyc+Pp6srKwu+HQdE7qpwUraJ1PEb97ZkgfAhONTiIsM6/iFwmKxZ05heN9e9Asv4KstK/1UoYh0tTlz5vD888/z4osvEhcXR35+Pvn5+Rw+bDaEJCQkMHv2bObPn8/KlSvZtGkTP/vZz8jOzmb8+PEATJ48maysLK666iq+/PJL3n//fW6//XbmzJnTaktdd1Iw6wqu0B/4L9Jd3t1iDsqdOiyj8xeLSmPAkCkAHC5YT23Jd52/poh0uUWLFlFSUsJZZ51FRkaG9/bSSy95z/nTn/7EBRdcwIwZM5gwYQLp6em8+uqr3uMOh4Nly5bhcDjIzs7myiuv5Oqrr+bee++14iO1SGPMuoJazET84tvCcnYUlBHmsDHpxA52Yx7hxBPGUrBsLWm13/HDtmUMHn0VRCS1/kIRsUxb9rmNjIxk4cKFLFy4sMVzBgwYwDvvvOPP0vxOLWZdoQcGMy2XIV3h3fpuzNMGpZAQ3YluzEYcdhupx5xFfm0yOfuKIO/9hv9nRUQspmDWFdSVKeIX7241uzHP90c3ZiOThmbwSflINu11YdSWQt5yMLTorIhYT8GsK3hbzBTMRDoqr+QwOXml2Gxwzompfr326YNTcDgjeb1wOHtL66AqD/Z/6tf3EBHpCAWzruBtMQvtrkx1X0pXWrndXOl/ZL9EkmP9O2MqMszBGYNTKHXF8u7++mnypTlQkuPX9xERaS8Fs67QA8eYifjbR9vNhR/PGeLf1jKPc7PMyQRvfRvm3baJ/Z/C4bwueT8RkbZQMOsKGmMm0ilVtS4++dbcz/LsLgpmZx7fG4Ate0s4GHYixB4HuM3xZrVlXfKeIiKtUTDzN8PoMS1mjVf7b8tUZpG2Wvf9QQ7XukiPjyQrI75L3iM1PpIh6XEYBnz87QFz26aIFHBXmTM13bVd8r4iIkejYOZv7hq8+2TaQzuYiXSVlfXbJZ09pDc2m63L3ufME8xWs9Xf7Pdu24QjCmqKoGCVBlKKSLdTMPM37z6Z4WDXBski7WUYBh/tMIPZxCH+WVS2JWcONoPZf3YeMFt9w2LNcIYdKnbBoS+69P1FRI6kYOZvnm7MHtBa5ruJuYh/fLe/nN1Fhwl32jltUHKXvtfoY3oRFeZgf1k1X+fVjyuLSoPUM8z7RZ9B+a4urUFEpDEFM3/ztJg5NfBfpCM+qu/GHH9sMtHhXbtrXITTwanHmeFv9Tf7Gw7EnwAJw8z7BSuhuqhL6xAR8VAw87ce1GIm0hU8wWxi/fivrjahfnbmmsbBDCBlPET1AaNO2zaJSLdRMPO3HtRi1rj7UmOkxR8qquvY9OMhAM46oWuWyTiSJ5h99mMRFdV1DQdsdkifBM54qCvTtk0i0i0sDWZr1qxh+vTpZGZmYrPZeP31160sxz/UYibSYRt2FVHrMujbK4oBydHd8p7HJEfTLymKWpfB+l0HfQ86IiBzCtjCtG2TiHQLS4NZRUUFJ510EgsXLrSyDP/qIWuYiXSF/+w8AMAZg1O6dJmMxmw2G6cPMlvNPt55sOkJ4b0gfaJ5vzQHSrZ3S10i0jN17cjaVkydOpWpU6daWYL/uavNrz0gmDVeVNbQvEzxg4+/Ncd5eYJSdzljcAr/3JDrff8mYgZA0ilQtBH2fwwRSRDZPV2tItKzaIyZv9SWQfn39QvMYq5jJiJtVlBaxTcF5dhseGdKdpfsY5Ox2eCbgnIKS1sY5J80CmKOwbttU93h7ixRRHqIoApm1dXVlJaWem9lZQG0n92P/4T8D6HK3HgZe5i19YgEmY/ruzGH90mgV0z3/mHTKyac4X0SzDq+PdDyialnQVgCuCqgYIUmA4iI3wVVMFuwYAEJCQneW1ZWltUltawHBDPNyhR/8gSi0welWPL+p9W/71GDmSMcMiaDzQmH98HBjd1UnYj0FEEVzG677TZKSkq8t5ycHKtLapm6MkXazDAM78D/0wdbE8zO8AQzz/ZMLQnvBWlnmfeLv4Sy77q+OBHpMYIqmEVERBAfH++9xcXFWV2SqbnujB7QYibiL9vzyzhQXk1UmIPRA3pZUsPJA3oR4bRTWFbNt4XlRz859lhIPMm8X7gaqpuZzSki0gGWBrPy8nI2b97M5s2bAdi1axebN28mNzfXyrLar7kVwdViJtJmnvFl445NIsLpsKSGyDAHYwcmAQ3LdhxV8liI6lu/M8AH2hlARPzC0mD22WefMWrUKEaNGgXA/PnzGTVqFHfeeaeVZbVfcz+QbZauRNIttIm5+Mt/LB5f5uF5/0+ONs7Mw2aD9HMadgbI/0iTAUSk0yxND2edddbRx3IEiyODmT3c/KEtIq2qrnOxoX7F/TMGd+/6ZUfyTABY9/1Bal1uwhyt/O3qiDAnA+x5HQ7vMScDpIzr+kJFJGQF1RizgOU+Ipg5umcrGZFQsOmHQ1TVuukdF8HxabGW1pKVEU9STDgVNS427y5u24sikjQZQET8RsHMH45sMesBq/6bGq38HwINn2INTzfmGYO6bxumltjtNu/itm0aZ+YReywkjjTvazKAiHSCgpk/NAlmUdbUIRKEPOO5TrN4fJnHGYPbMc6sseRTGk0GeF87A4hIhyiY+UOPbTET6ZxDFTVs2VsCWLd+2ZE8AXHz7mJKq2rb/kKfyQDlkL9ckwFEpN0UzPyhyeD/CGvq6Ga+3Zfqy5T2+/S7gxgGHJ8WS1p8YPxB07dXNANTYnC5DdZ/X9S+FzsiIPM8cwJQVb654bmISDsomPlDk2CmxWVF2uLjAOvG9DhtkDnO7OOd+9v/4vBESDsHsEHpdije6tfaRCS0KZj5w5GzMhXMRNrk42/N4HNGgHRjepw+yFy246j7Zh5NTD9Irl8248BaqNzjp8pEJNQpmPlDD20x0ybm0hk/Hqxgd9FhnHYb4wYmW12Oj+zjkrHb4Lv9FeSVdHAQf68REHc8YED+h1BT4tcaRSQ0KZj5Qw8NZiKd4WmNOrl/L2IiAmunjISoMEb0TQQatovqkN5nQEQquGtg37vatklEWqVg1lnuOnN6fGM9YDsmkc7yBJ5AmY15JM/2TB3uzgSwO8ydAZyxUFdqLqPhrmv9dSLSYymYdZarspkne8Z2TD57ZaorU9rB5Tb49DtzEdaADWaN1jPr1NZxzmjInFo/U7MAClfpfxgRaZGCWWsqcqG2tOXjdfXBzBnXPfWIhICte0soOVxLXKSTEX0SrC6nWaP6JxIV5uBAeQ3b88s6d7HwXpA+GbBD+fdwcL1fahSR0KNgdjQVuyHvPfhxacvneFrMnDENzzl6xjpmIh3l6R7MPjYZZ2sbhVskwulg3LFJQAd2AWhOdCaknmneL/4KSnI6f00RCTmB+RMxUFTubv0cb4tZtDnQN/EkiEzt2roChNF4r0wtMCvt8J+dgblMxpE848zatW/m0cQPhqQx5v39n5gt8iIijSiYHU1bZlB5gpkjGhJOhJRxXVuTSJArr65j04+HADh9cG+Lqzk6zzizDbuKqK5z+eeiSSdD/BC8y2gcLvDPdUVC2Jo1a5g+fTqZmZnYbDZef/11n+OGYXDnnXeSkZFBVFQUkyZNYufOnT7nFBUVMXPmTOLj40lMTGT27NmUl5d346doGwWzo3FXt36Oq1GLmYi06tNvD1DrMhiQbG59FMhOSIsjJTacw7UuPv+x2H8X7n16ow3P34Xqdm79JNLDVFRUcNJJJ7Fw4cJmjz/88MM89thjPPHEE6xfv56YmBimTJlCVVVDA8vMmTPZtm0by5cvZ9myZaxZs4af//zn3fUR2kzB7GhcbQhmjVvMehjNypSOWPWN2Y151vGB3VoGYLPZvNtF+WWcmffCdnMZjci0+jXO3j76JCORHm7q1Kncf//9/OQnP2lyzDAMHn30UW6//XYuuugiRowYwXPPPce+ffu8LWtff/017733Hk8//TTjxo3j9NNP5y9/+QtLly5l37593fxpjk7B7Gja0pWpFjORNjMMg9U76oPZCcExFtM7zsyfwQzA7oSM8yA8CVyHYe8yqKvw73uI9AC7du0iPz+fSZMmeZ9LSEhg3LhxrF27FoC1a9eSmJjImDFjvOdMmjQJu93O+vWBNUtawexo2tKV2YNbzETa69vCcvYWHybcaWf8sYG1DVNLPOPMtuwppqSy1r8Xd0RA5jRwxkNdOex9W7sDSI9SVlZGaWmp91Zd3Ybfu0fIz88HIC0tzef5tLQ077H8/HxSU33/GHQ6nSQlJXnPCRQKZkfTWjAz3A0bmPfAFjOfrkzrypAgsqq+tWz8sclEhTssrqZtMhKiOK53DG4D1n7v51YzAGcU9LkAHDFQW6xwJj1KVlYWCQkJ3tuCBQusLslyCmad4fJsbmwHu9YuE2nNqm8KgeAYX9aYX7ZnOpqwWOgzDRxRUHOwvluzg5uniwSRnJwcSkpKvLfbbrut3ddIT08HoKDAd4ZzQUGB91h6ejqFhYU+x+vq6igqKvKeEygUzFrSltHs3m7MKLD1jG2YRDqqorqOjbvMZTLOOiHIgln9sh6d2tC8NeGJ0Ge6OSyipgj2LWv4GSMSouLi4oiPj/feIiLa38gxcOBA0tPTWbFihfe50tJS1q9fT3Z2NgDZ2dkUFxezadMm7zkfffQRbrebceMCa5krBbOWNO7GbGlTcg38F2mzT787SI3LTf+kwF8m40jjjk3CYbfxw8FKdhd1YVjyhrMYqDlU33KmcCZSXl7O5s2b2bx5M2AO+N+8eTO5ubnYbDbmzZvH/fffz5tvvsmWLVu4+uqryczM5OKLLwbgxBNP5LzzzuO6665jw4YNfPLJJ8ydO5crrriCzMxM6z5YMxTMWuIzxqOF1rAePvDfZ+V/DTKTVqzaUd+NeUJvbEHWwhwfGcbIfomAn5fNaE54AvSdDs5Yc8zZnjehNvAWwRTpTp999hmjRo1i1KhRAMyfP59Ro0Zx5513AvCb3/yGG264gZ///OeccsoplJeX89577xEZGem9xgsvvMCQIUM455xzOP/88zn99NN58sknLfk8R9NCU5A0jB+DFoe2q8VMpE0Mw2Dl9oZgFoxOG5TCph8P8fG3B7hibP+ufbOweLPlbO8yqCuFvW+ZEwTC4rr2fUUC1FlnnYVxlBYAm83Gvffey7333tviOUlJSbz44otdUZ5fqcWsJW0JZnUKZiJtsW1fKftKqogKc3DqcYG9P2ZLPPt6fvrdQdzubmgiDoszw5kzHurKYM/rULW/699XRCylYNaSxl2ZLaX0nt6V6bNchvoypWUfbDPXCTrz+N5EhgXHMhlHGtkvkZhwB0UVNWzb102r9IfFQt8LGy1C+5Y2PhcJcQpmLWlTV2b9Kt3O4BrILNLdPsgxp7FPHprWypmBK8xh927PtPzrbtx43BkNfS5stLfm+1CS033vLyLdSsGsJT6D/9ViJtJRuQcr2Z5fhsNuY+KQ4NiGqSWTh5rrHXlaALuNIxwyz4O4EwAD9n8MB9Zp1o1ICFIwa4nriMUdj/wBaLgbDf5Xi5l6MqUlH+SYIWbsMUkkRodbXE3nnDMkFYfdxvb8MnIPdvMyFjY7pJ0JSfV7/RV/BfnLwe3nbaJExFIKZi1psiXKEcnDu7aQHRyRiEjzQqEb06NXTDhjj0kCGgJnt0s6GdImgs0BFT+Yy2lo83ORkKFg1pLWWswaL5URZGsyiXSXoooaPvuhCIBzs4I/mEFDwPQETkvEDYLMCxq2cNr9mmZsioQIBbOWNNmn7sgWMw381ybm0prlOfm4DTgxI56+vUJjLKYnYH72QxEHy6tbObsLRaVB34vrZ2xWwt43ofQb6+oREb9QMGuOYYC7ta7M+mCmgf8iLXrzy30AXDAiw+JK/Kdvr2iGZsbjNmDF14Wtv6ArhcWZMzajB4DhgsJVUPixOQZWRIKSgllzmowvo2lXZp0G/oscTWFZFWu/OwjA9BGBtRddZ02pn535XnfPzmyOIxwyJjdMCijN0bgzkSCmYNYcT2uZvfEMsqOMMeuhfPfKVGem+Hrnqzzchrkwa//k0Pr/ZOowM5it+WY/hypqLK4Gc5xr0smQcZ75c6u6EHL/rcVoRYKQgllzPOPLfLopW2gxU1emSLM83ZjTTwqt1jKAwWlxnJgRT53b4J2teVaX0yCmP/S7BCJSzD8w896D/WvB7bK6MhFpIwWz5nhazBxRDc8dOWbDM2uz8TkiAsDuoko+zy3GZgut8WWNXTzSDJxvbN5ncSVHCIuHPhdBwjDzcckW2PMaVBdZW5eItImCWXPskRBzDERl0PAtUlfmkTQrU1riaS0bNzCJtPjQXOdv+kmZ2GywYVcRe4uPnMVtMbsDep8KGVPMn2c1RWY4K/na6spEpBUKZs2JzjQH0yaPaXjOJ4UYDRME1GIm4sPtNnhp424ALh3dz+Jquk5mYpR3sdm3vgywVjOPmAHQ/zKI7m/O2tz/H9j7DtSWWV2ZiLRAwaw1tmZazFxV9Y9tCmYiR1i36yC5RZXERjg5f3i61eV0qYtG9gHgtc/3Bu4EGGeU2XKWPN7cLeDwHsh9BYq3aq9NkQCkYNYqz6r+jYOZZ+B/ZI9e9b/xj3T9fBcPT2vZhSMziQ53WlxN15o2IoMIp50dBWV8nltsdTkts9mg1wjodylEZoBRBwc+NRel1dgzkYCiYNYaT/BqnDw08F+kWSWVtby71Vzb64pTQrcb0yMhKowL6tdoe3F9ECxNEZ4AfS6A3qeDLQyqCmD3v81FaZtbv1FEup2CWauaaTHTUhkizXr1iz3U1LkZkh7H8D4JVpfTLWaO7w/Asq/2UVJZa3E1bWCzQUKWOfYs5hjAMBel/XFpffemdg0QsZKCWauaCWY1h8yvYfHdXk0gaTymRj2Z4nYbPPvpDwD897j+2HpIN/+ofokMSY+jus7Nq1/ssbqctguLNSc5ZV5g7rfprjG7N3P/pYVpRSykYNaa5royPcEsIqn76xEJUB9tL+SHg5XERzqZcXJfq8vpNjabjZnjzFazF9bn4nYH2Z8p0ZnQbwb0PsNcWqO22FyYds9bULnX6upEehwFs1Yd0WJmuKGqfn+8iBRLKhIJRIs/3gXAT8f2JyYitAf9H+niUX2IjXDybWE5K3dYvLF5R9hskHAiDLgCEkcAdqjKg31vw+7Xofx7zfAR6SYKZq06osWs+oDZ5G+PhIje1pUlEkC27i1h7fcHcdhtzDr1GKvL6XZxkWHesWaLVn1ncTWd4AiHlPFwzE/NnQNsDnPfzfwP4ceXoHgbuAJgb1CREKZg1hrbES1mnqnlESk9eqkMOHK5DP013ZM9+uFOAC48KZPMxJ45W3n2aQMJd9j57MdDbPwhyJegcMaYOwcM+G/odbL5h2hdKRz4BH74B+R/BJV71Iom0gUUzFp1RDCrLTa/hidaUItI4Nm6t4QPvy7AboO5EwdZXY5lUuMjmTHaHFv3+MpvLa7GT5xR5g4ox/y3ucRGeC9zB4Hyb2HfO/DDi3BgHRzOV0gT8ZOeNRCkQzxdmfVTyD0D/8N7WVOOSIDxtJZNPymT43rHWlyNtX4x4Vhe2pjLyh37+SL3EKP6h8jPCbvTXGIjIQuq9kPZN1D2LbgqoPgr82aPhJj+EN0XojJ79D7CIp2hYNYa2xGNijXF5lcFM21iLqz97qC3teyGiYOtLsdyx6TEMOPkvryyaQ8L3tnOS78YH3rLhkT2Nm/J46EyF8p3mV/dVfWB7RvzvLBEM6BFpUNkao9fXkikrRTMWmNr1GLmroW6+s1/1ZUpPZzLbXDvshwAZo4bwKDUnt1a5jF/8vG8+eU+NvxQxPKcAiYPDdH9Qu0OiB1o3gy32Z1ZuRsO7zUnSdUWm7fSnPrzI+tDXSpEpJr3HZFWfgKRgKRg1hqbw/xquMwmfABHjH6gSI+3dGMuX+eVEhfp5KZzj7e6nICRkRDF7NMH8viq77j/7a85Y3BvosIdVpfVtWx2cz20aHN7KlzVcDgPDu+DqkKoOWi2qFXuNm8ezvj6oJZirgsZ3suceCDSgymYtaq+K9NwmT9cAKLSrCsnoKgvs6faW3yYBe9sB2DepONJigm3uKLA8v/OHsRrX+wlt6iSRz/8htvOP9HqkrqXIwJijzFvYLaoVR80Q1p1ofm1tsSc6Vleak4m8LCHmzsRhPcyeybCEsyvztimQ0tEQpCCWWs8LWa44XCBeTci1bJyRKxmGAa3vbqF8uo6RvVP5JoeuG5Za2IjnNx30TD+57nPePrjXUwbkcGIvolWl2Udm71hbBpDzedc1VC9v75Frchciqi2xFwnsiq/YSFvL7s5Ts0T1jyBLSzBnD0qEiIUzFrjCWZuF1TVB7NItZhJz7X4412s+WY/4U47f7j0JBz2EBvc7ieTstKYNiKDt7/K44Z/fsGyG04nLjLM6rIChyPCnMEZ3Wj7LrfLDGc1ReYM+NoSc8JVbYnZa+EZt3Yke7g52SAsHsITGoJbWIK5aK5IEFEwa40nmNUcMsdIYNdWTPV8Z2WqL7MnWP/9QRa8a3Zh/u78EzXgvxUPXDyMzbnF/Hiwkltf3cJffzoq9GZp+pPdYY41O3IfYsOAugozlNWU+Aa2ujKzla26vpu0yTUj68NavHlzxjW6ryU9JPAomLXGXh/MqvLMrxEpDc+J9CDf7y/n/73wOS63wUUjM7k6e4DVJQW8xOhwHvvpKC7/21re/iqP41JimD/5BKvLCj42G4TFmrfGLWwA7jqoLTVDWm1JfXCrf+yqNP+grqpq6PHwua6zIaT53BLqx7QpREv3UzBrVf1gU283psaXSc+TV3KYqxZv4GBFDcP6xLPgkuFq+Wmj0QN6cf/Fw7jt1S089tG3pMRFcHX2MVaXFTrszuZb2cBc4sgb2kp9b3UVYNTVd5s2t4WWvVG3aKJvF6nGtEkXUjBrje2I1jEFMy/fvTItK0O62LeF5cz6+wb2Fh/m2JQYnvnZWKLD9aOjPX46tj95JVU8tmInd76xjapaFz+fcJzVZYU+exhEJJu3IxluqC1rJrCVmq1uuM0hLJ7dXnyuG+47ASE80ZxJGhavmaPSafrp2hoFM+nBVu4o5KaXNlNcWcuxKTH843/GkRIbYXVZQemmSYOpdblZtOo7HnxnO7lFldxxQRYRTg2NsIStvkUsPKHpMcOAuvJGXaPNjWnbb9582M1lPjxrsoUn1Qc2jcWUtlMwa03jYGaP1LYi0iNUVNfxyPJvWPzxLgBG9kvk79ecovXKOsFms3HLeUOIjwzj4fe38/y6XDbvLub3M0YwNLOZcCDWsdkgLM68NRnT5mpoVfNMRqg5ZHaHGnXmepeeNS897OGNglr9+mxaTFdaEBDBbOHChfzhD38gPz+fk046ib/85S+MHTvW6rJMjYOZWst8+MzKVFdmSKipc/P6F3v504ffkFdSBcCs7AHcdv6JRIapZccfrj/rOIZkxHHTS5vZureUC//6Cf89tj/Xn3UcmYkauxTw7I76cHXEfsmeVjbPmmyeJT9qiuvXZitoOgHBFtYQ0jxfPct+aAxnj2V5MHvppZeYP38+TzzxBOPGjePRRx9lypQp7Nixg9TUAAhCjccLRPa2rg6RLpR7sJI3Nu/lxQ253kDWLymKey8cxtlDAuD/wxBz9gmpfDBvAve8lcPbW/L4x7ofWboxl+kjMpkxui/ZxyZj1/pwwaVxK1tMoxnLhtsMZ96gVh/WakvBqG2+S9TmaLSAbmL9TNH6ZT4c0T06tAV0Q46fWB7MHnnkEa677jp+9rOfAfDEE0/w9ttv8/e//51bb73V4urwbTE78i8kkSDkchvsLqpke34p63cVsfa7g2zPL/MeT42L4H/OGMjV2ceolawLpcZHsnDmycz87gB//nAn63cV8eoXe3n1i70kx4Rz6qAUTj0umaGZ8QxOjQv9/TZDlc3ewtps7oaxa56wVnPI7B41XEefLRoWZy7n4Yyp/+q5X7+PsyMyJCchBHxDjp9YGsxqamrYtGkTt912m/c5u93OpEmTWLt2rWV1VdbUUVRRA0BYaSXh9fcrK8IxaistqyvQ7C+r9t6vrHWxu6j5782R3ZyNF6NteuzI1xpHOdbyK30Xv+1EPUdZRPdo3bcd/czGEQfb85nrXAaVtS4O15g3834dxZW1FJZVm7fSKnYdqKC6zu3zWrsNThuUwsUj+zBtRIYCWTc69bgUTj0uhc9zD/GvTXtY9uU+DlbU8NaX+3jry32A2UDSt1cUGfFRpMZHkBoXSXJsODHhDqIjnMSEO4mOcBDpdOB02LDbbDjsNpz2hvueW0ttLc01wrR0dnsabHpw404bRABpYEsz70YAhoGtrgx7XQn22mJsdaXY68qw1ZVhc1VgM9xAFXDkxANfhj0cwxGFYY/CcERi2CPB5sSwh4HNgWELA7sTw+asb4BwYNjsZqCzOTAwv2KzExkeRXKC9eOrA74hx08sDWYHDhzA5XKRlua7xVFaWhrbt29vcn51dTXV1Q1hoKysrMk5/vDh14X88p9fAHB8xA+MjtkBwNKiL8x/rNLEmm/2c8bDK60uQ9oowmnnuN6xjOyfyKnHJZN9bDLJmm1pqZP79+Lk/r24e/pQNu8u5uNvD/DZD0XsyC/jYEUNu4sOs7vosNVliiXM1GYjiWh7FTH2w8TYq4i2VxFtP0y0vZpox2EibTVE2GtaDN8d1afvUOb+9//6+aqmsrIySktLvY8jIiKIiGj6syhQG3K6guVdme2xYMEC7rnnni5/H4fNRoTTDGD7XP1Iqq3ih5o+hDuD6tvVLTwtL5FhvoG18V/aR/7FfOQPjcYLlTb5gWJr9m6T1x35Pq2e28Lrjjzaeu2Nj3W8npZe1573cNhsRIU7iA53EBXuJCrMTnS4k/hIJ6nxkaTGRZAaH0n/pGj6J0Vrj8sAFe60M3ZgEmMHNnR9HSiv5vv9FRSUVnlbPosqaqisdVFZXUdFjYvKmjoO17hwG2Z3tfdmGD6Pm3NkSy00ba1tOLeF55t5hSYF+ZudOmIpIZYSN+Bu7hyDcFstkbZqIu01hNtqiLTXEGGrwWlz4cSFw+bCaavzPrbZ3Dhw47C5sWHgwI3d5saOG7vNwGbvut99WVlZPo/vuusu7r777ibntbchJ5hZmjRSUlJwOBwUFPjOVCkoKCA9Pb3J+bfddhvz58/3Pt67d2+T/6j+MG1EBtNGZPj9uiIiHZESG6H14yQk5eTk0KdPH+/j5lrLehpL++XCw8MZPXo0K1as8D7ndrtZsWIF2dnZTc6PiIggPj7ee4uLi+vOckVERMSP4uLifH6vtxTM2tuQE8wsHzA1f/58nnrqKZ599lm+/vprrr/+eioqKryD+0RERKRna29DTjCzfNDUf/3Xf7F//37uvPNO8vPzGTlyJO+9916TfmQRERHpuebPn8+sWbMYM2YMY8eO5dFHHw3JhhzLgxnA3LlzmTt3rtVliIiISIDqKQ05ARHMRERERFrTExpyLB9jJiIiIiImBTMRERGRAKFgJiIiIhIgFMxEREREAoSCmYiIiEiAUDATERERCRAKZiIiIiIBQsFMREREJEAomImIiIgEiKBe+d/tdgOQl5dncSUiIiLSVp7f257f49IgqINZQUEBAGPHjrW4EhEREWmvgoIC+vfvb3UZAcVmGIZhdREdVVdXxxdffEFaWhp2e9t6ZcvKysjKyiInJ4e4uLgurjD06fvpX/p++o++l/6l76d/9fTvp9vtpqCggFGjRuF0BnUbkd8FdTDriNLSUhISEigpKSE+Pt7qcoKevp/+pe+n/+h76V/6fvqXvp/SEg3+FxEREQkQCmYiIiIiAaLHBbOIiAjuuusuIiIirC4lJOj76V/6fvqPvpf+pe+nf+n7KS3pcWPMRERERAJVj2sxExEREQlUCmYiIiIiAULBTERERCRA9LhgtnDhQo455hgiIyMZN24cGzZssLqkoLRgwQJOOeUU4uLiSE1N5eKLL2bHjh1WlxUSHnroIWw2G/PmzbO6lKC1d+9errzySpKTk4mKimL48OF89tlnVpcVlFwuF3fccQcDBw4kKiqK4447jvvuuw8NT26bNWvWMH36dDIzM7HZbLz++us+xw3D4M477yQjI4OoqCgmTZrEzp07rSlWAkKPCmYvvfQS8+fP56677uLzzz/npJNOYsqUKRQWFlpdWtBZvXo1c+bMYd26dSxfvpza2lomT55MRUWF1aUFtY0bN/K3v/2NESNGWF1K0Dp06BCnnXYaYWFhvPvuu+Tk5PDHP/6RXr16WV1aUPr973/PokWL+Otf/8rXX3/N73//ex5++GH+8pe/WF1aUKioqOCkk05i4cKFzR5/+OGHeeyxx3jiiSdYv349MTExTJkyhaqqqm6uVAKG0YOMHTvWmDNnjvexy+UyMjMzjQULFlhYVWgoLCw0AGP16tVWlxK0ysrKjMGDBxvLly83zjzzTOPGG2+0uqSgdMsttxinn3661WWEjGnTphnXXnutz3OXXHKJMXPmTIsqCl6A8dprr3kfu91uIz093fjDH/7gfa64uNiIiIgw/vnPf1pQoQSCHtNiVlNTw6ZNm5g0aZL3ObvdzqRJk1i7dq2FlYWGkpISAJKSkiyuJHjNmTOHadOm+fwblfZ78803GTNmDJdddhmpqamMGjWKp556yuqygtapp57KihUr+OabbwD48ssv+fjjj5k6darFlQW/Xbt2kZ+f7/P/fEJCAuPGjdPvpR6sx+wceuDAAVwuF2lpaT7Pp6WlsX37douqCg1ut5t58+Zx2mmnMWzYMKvLCUpLly7l888/Z+PGjVaXEvS+//57Fi1axPz58/ntb3/Lxo0b+eUvf0l4eDizZs2yurygc+utt1JaWsqQIUNwOBy4XC4eeOABZs6caXVpQS8/Px+g2d9LnmPS8/SYYCZdZ86cOWzdupWPP/7Y6lKC0u7du7nxxhtZvnw5kZGRVpcT9NxuN2PGjOHBBx8EYNSoUWzdupUnnnhCwawDXn75ZV544QVefPFFhg4dyubNm5k3bx6ZmZn6fop0gR7TlZmSkoLD4aCgoMDn+YKCAtLT0y2qKvjNnTuXZcuWsXLlSvr27Wt1OUFp06ZNFBYWcvLJJ+N0OnE6naxevZrHHnsMp9OJy+WyusSgkpGRQVZWls9zJ554Irm5uRZVFNxuvvlmbr31Vq644gqGDx/OVVddxU033cSCBQusLi3oeX736PeSNNZjgll4eDijR49mxYoV3ufcbjcrVqwgOzvbwsqCk2EYzJ07l9dee42PPvqIgQMHWl1S0DrnnHPYsmULmzdv9t7GjBnDzJkz2bx5Mw6Hw+oSg8ppp53WZOmWb775hgEDBlhUUXCrrKzEbvf9VeFwOHC73RZVFDoGDhxIenq6z++l0tJS1q9fr99LPViP6sqcP38+s2bNYsyYMYwdO5ZHH32UiooKfvazn1ldWtCZM2cOL774Im+88QZxcXHe8RAJCQlERUVZXF1wiYuLazI2LyYmhuTkZI3Z64CbbrqJU089lQcffJDLL7+cDRs28OSTT/Lkk09aXVpQmj59Og888AD9+/dn6NChfPHFFzzyyCNce+21VpcWFMrLy/n222+9j3ft2sXmzZtJSkqif//+zJs3j/vvv5/BgwczcOBA7rjjDjIzM7n44outK1qsZfW00O72l7/8xejfv78RHh5ujB071li3bp3VJQUloNnbkiVLrC4tJGi5jM556623jGHDhhkRERHGkCFDjCeffNLqkoJWaWmpceONNxr9+/c3IiMjjWOPPdb43e9+Z1RXV1tdWlBYuXJlsz8rZ82aZRiGuWTGHXfcYaSlpRkRERHGOeecY+zYscPaosVSNsPQ8s0iIiIigaDHjDETERERCXQKZiIiIiIBQsFMREREJEAomImIiIgECAUzERERkQChYCYiIiISIBTMRERERAKEgpmIiIhIgFAwE5Ggd/DgQVJTU/nhhx8AWLVqFTabjeLi4i5931tvvZUbbrihS99DRHoWBTORHuSaa67BZrM1uZ133nlWl9YpDzzwABdddBHHHHNMp69VUFBAWFgYS5cubfb47NmzOfnkkwH49a9/zbPPPsv333/f6fcVEQEFM5Ee57zzziMvL8/n9s9//rNL37OmpqbLrl1ZWcnixYuZPXu2X66XlpbGtGnT+Pvf/97kWEVFBS+//LL3vVJSUpgyZQqLFi3yy3uLiCiYifQwERERpKen+9x69erlPW6z2Xj66af5yU9+QnR0NIMHD+bNN9/0ucbWrVuZOnUqsbGxpKWlcdVVV3HgwAHv8bPOOou5c+cyb948b3gBePPNNxk8eDCRkZGcffbZPPvss94ux4qKCuLj4/nXv/7l816vv/46MTExlJWVNft53nnnHSIiIhg/fnyLn7myspKpU6dy2mmnebs3n376aU488UQiIyMZMmQIjz/+uPf82bNns2LFCnJzc32u88orr1BXV8fMmTO9z02fPr3F1jURkfZSMBORJu655x4uv/xyvvrqK84//3xmzpxJUVERAMXFxUycOJFRo0bx2Wef8d5771FQUMDll1/uc41nn32W8PBwPvnkE5544gl27drFpZdeysUXX8yXX37JL37xC373u995z4+JieGKK65gyZIlPtdZsmQJl156KXFxcc3W+p///IfRo0e3+FmKi4s599xzcbvdLF++nMTERF544QXuvPNOHnjgAb7++msefPBB7rjjDp599lkAzj//fNLS0njmmWea1HLJJZeQmJjofW7s2LHs2bPHO75NRKRTDBHpMWbNmmU4HA4jJibG5/bAAw94zwGM22+/3fu4vLzcAIx3333XMAzDuO+++4zJkyf7XHf37t0GYOzYscMwDMM488wzjVGjRvmcc8sttxjDhg3zee53v/udARiHDh0yDMMw1q9fbzgcDmPfvn2GYRhGQUGB4XQ6jVWrVrX4mS666CLj2muv9Xlu5cqVBmB8/fXXxogRI4wZM2YY1dXV3uPHHXec8eKLL/q85r777jOys7O9j2+99VZj4MCBhtvtNgzDML799lvDZrMZH374oc/rSkpKDOCoNYqItJVazER6mLPPPpvNmzf73P73f//X55wRI0Z478fExBAfH09hYSEAX375JStXriQ2NtZ7GzJkCADfffed93VHtmLt2LGDU045xee5sWPHNnk8dOhQb8vV888/z4ABA5gwYUKLn+fw4cNERkY2e+zcc89l0KBBvPTSS4SHhwPmOLHvvvuO2bNn+3yG+++/36f+a6+9ll27drFy5UrAbC075phjmDhxos97REVFAWZ3qYhIZzmtLkBEuldMTAyDBg066jlhYWE+j202G263G4Dy8nKmT5/O73//+yavy8jI8Hmfjvif//kfFi5cyK233sqSJUv42c9+hs1ma/H8lJQUDh061OyxadOm8e9//5ucnByGDx/urR/gqaeeYty4cT7nOxwO7/3BgwdzxhlnsGTJEs466yyee+45rrvuuia1eLp4e/fu3f4PKyJyBAUzEWmXk08+mX//+98cc8wxOJ1t/xFywgkn8M477/g8t3HjxibnXXnllfzmN7/hscceIycnh1mzZh31uqNGjeL5559v9thDDz1EbGws55xzDqtWrSIrK4u0tDQyMzP5/vvvfQbxN2f27Nlcf/31XHjhhezdu5drrrmmyTlbt24lLCyMoUOHHvVaIiJtoa5MkR6murqa/Px8n1vjGZWtmTNnDkVFRfz0pz9l48aNfPfdd7z//vv87Gc/w+Vytfi6X/ziF2zfvp1bbrmFb775hpdfftk7uL5xK1SvXr245JJLuPnmm5k8eTJ9+/Y9aj1Tpkxh27ZtLbaa/d///R8zZ85k4sSJbN++HTAnNyxYsIDHHnuMb775hi1btrBkyRIeeeQRn9dedtllhIWF8Ytf/ILJkyfTr1+/Jtf/z3/+wxlnnOHt0hQR6QwFM5Ee5r333iMjI8Pndvrpp7f59ZmZmXzyySe4XC4mT57M8OHDmTdvHomJidjtLf9IGThwIP/617949dVXGTFiBIsWLfLOyoyIiPA5d/bs2dTU1HDttde2Ws/w4cM5+eSTefnll1s8509/+hOXX345EydO5JtvvuF//ud/ePrpp1myZAnDhw/nzDPP5JlnnmHgwIE+r4uOjuaKK67g0KFDLdaydOlSrrvuulbrFBFpC5thGIbVRYhIz/TAAw/wxBNPsHv3bp/n//GPf3DTTTexb98+76D9o3n77be5+eab2bp161HDob+9++67/OpXv+Krr75qV7euiEhL9JNERLrN448/zimnnEJycjKffPIJf/jDH5g7d673eGVlJXl5eTz00EP84he/aFMoA3OQ/86dO9m7d2+z3Y1dpaKigiVLliiUiYjfqMVMRLrNTTfdxEsvvURRURH9+/fnqquu4rbbbvMGm7vvvpsHHniACRMm8MYbbxAbG2txxSIi3UvBTERERCRAaPC/iIiISIBQMBMREREJEApmIiIiIgFCwUxEREQkQCiYiYiIiAQIBTMRERGRAKFgJiIiIhIgFMxEREREAoSCmYiIiEiA+P+du3Uvak7bTgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_arf = model_arf(rmf.energ_lo, rmf.energ_hi)\n",
    "\n",
    "plt.plot(emid, y_arf)\n",
    "plt.xlabel(\"Energy (keV)\")\n",
    "plt.ylabel(\"photon\")\n",
    "\n",
    "ax2 = plt.twinx();\n",
    "emid2 = (arf.energ_lo + arf.energ_hi) / 2\n",
    "ax2.plot(emid, arf.specresp, alpha=0.4, c=\"orange\", label=\"ARF\")\n",
    "ax2.set_ylabel(\"cm$^2$\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2efbb0ad-2814-46bc-989f-b2d05b72b45b",
   "metadata": {},
   "source": [
    "We can repeat this for the RMF, noting that the output - because the ARF is not included - will have the unusual units of count / cm$^2$ / s:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b802108b-4fd7-4762-9149-44f21acbeaf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>/*\n",
       "Copyright (C) 2020  Smithsonian Astrophysical Observatory\n",
       "\n",
       "\n",
       " This program is free software; you can redistribute it and/or modify\n",
       " it under the terms of the GNU General Public License as published by\n",
       " the Free Software Foundation; either version 3 of the License, or\n",
       " (at your option) any later version.\n",
       "\n",
       " This program is distributed in the hope that it will be useful,\n",
       " but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
       " MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
       " GNU General Public License for more details.\n",
       "\n",
       " You should have received a copy of the GNU General Public License along\n",
       " with this program; if not, write to the Free Software Foundation, Inc.,\n",
       " 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n",
       "\n",
       "*/\n",
       "\n",
       ":root {\n",
       "  --sherpa-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --sherpa-background-color: var(--jp-layout-color0, white);\n",
       "  --sherpa-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --sherpa-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "\n",
       "  /* https://medium.com/ge-design/iot-cool-gray-is-a-great-background-color-for-data-visualization-ebf18c318418 */\n",
       "  --sherpa-background-color-dark1: #EBEFF2;\n",
       "  --sherpa-background-color-dark2: #D8E0E5;\n",
       "}\n",
       "\n",
       "div.sherpa-text-fallback {\n",
       "    display: none;\n",
       "}\n",
       "\n",
       "div.sherpa {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       "div.sherpa details summary {\n",
       "    display: list-item;  /* needed for notebook, not lab */\n",
       "    font-size: larger;\n",
       "}\n",
       "\n",
       "div.sherpa details div.datavals {\n",
       "    display: grid;\n",
       "    grid-template-columns: 1fr 3fr;\n",
       "    column-gap: 0.5em;\n",
       "}\n",
       "\n",
       "div.sherpa div.dataname {\n",
       "    font-weight: bold;\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa div.dataval { }\n",
       "\n",
       "div.sherpa div.datavals div:nth-child(4n + 1) ,\n",
       "div.sherpa div.datavals div:nth-child(4n + 2) {\n",
       "    background: var(--sherpa-background-color-row-odd);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tbody {\n",
       "    border-bottom: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tr.block {\n",
       "    border-top: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-odd ,\n",
       "div.sherpa table.model th.model-even {\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-odd {\n",
       "    background: var(--sherpa-background-color-dark1);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-even {\n",
       "    background: var(--sherpa-background-color-dark2);\n",
       "}\n",
       "\n",
       "div.sherpa .failed {\n",
       "    background: orange;\n",
       "    font-size: large;\n",
       "    padding: 1em;\n",
       "}\n",
       "</style><div class=\"sherpa-text-fallback\">&lt;RMFModelNoPHA model instance &#x27;apply_rmf(delta + gauss)&#x27;&gt;</div><div hidden class=\"sherpa\"><details open><summary>Model</summary><table class=\"model\"><caption>Expression: apply_rmf(delta + gauss)</caption><thead><tr><th>Component</th><th>Parameter</th><th>Thawed</th><th>Value</th><th>Min</th><th>Max</th><th>Units</th></tr></thead><tbody><tr><th class=\"model-odd\" scope=\"rowgroup\" rowspan=2>delta</th><td>pos</td><td><input disabled type=\"checkbox\" checked></input></td><td>2.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>ampl</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr class=\"block\"><th class=\"model-even\" scope=\"rowgroup\" rowspan=3>gauss</th><td>fwhm</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.0</td><td>TINY</td><td>MAX</td><td></td></tr><tr><td>pos</td><td><input disabled type=\"checkbox\" checked></input></td><td>6.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>ampl</td><td><input disabled type=\"checkbox\" checked></input></td><td>100.0</td><td>-MAX</td><td>MAX</td><td></td></tr></tbody></table></details></div>"
      ],
      "text/plain": [
       "<RMFModelNoPHA model instance 'apply_rmf(delta + gauss)'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_rmf = rconv(model_base)\n",
    "model_rmf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1452229e-744e-43c5-8649-6ea5b7e48134",
   "metadata": {},
   "source": [
    "An interesting part of this is that the RMF converts between physical units (energy or wavelength), which is used to evaluate the \"wrapped\" model (in this\n",
    "case `model_base`), and returns values in channel space. This means that we no-longer supply the convolved model with energies, but with channels.\n",
    "\n",
    "The obvious differences to above are that the relative intensity of the delta function and gaussian has drastically changed, and the blurring of the created by\n",
    "the RMF is visible (well, it is once you change to a logarithmic scale for the Y axis)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "12f1f668-caef-4092-bfe2-34f5cb1dd8fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "channels = np.arange(1, 1025, dtype=np.int16)\n",
    "y_rmf = model_rmf(channels)\n",
    "\n",
    "plt.plot(channels, y_rmf)\n",
    "plt.xlabel(\"Channel\")\n",
    "plt.ylabel(\"count cm$^{-2}$ s$^{-1}$\")\n",
    "plt.yscale(\"log\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6234af8d-357d-4e3f-b296-68ada283f32c",
   "metadata": {},
   "source": [
    "We can use the \"approximate\" energies from the RMF to get a plot more similar to the `plot_data` command (UI) or `DataPHAPlot` (direct use of the plotting classes):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "47b9ac3a-2c73-46fa-a48f-985751be0db5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "emid_approx = (rmf.e_min + rmf.e_max) / 2\n",
    "\n",
    "plt.plot(emid_approx, y_rmf)\n",
    "plt.xlabel(\"Approximate Energy (keV)\")\n",
    "plt.ylabel(\"count cm$^{-2}$ s$^{-1}$\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d56c089-4412-4c06-a6e9-daed7ea22387",
   "metadata": {},
   "source": [
    "We could combine both with an expression like\n",
    "\n",
    "    rconv(aconv(model_base))\n",
    "\n",
    "but we shall also use the `RSPModelNoPHA` class, which takes ARF, RMF, **and** the model as arguments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d59d92e9-f57e-45fa-9b2e-06f1d1d3b1c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_both = RSPModelNoPHA(arf, rmf, model_base)\n",
    "model_check = rconv(aconv(model_base))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9e88573a-4adf-4196-b5f4-5b0e20760d05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>/*\n",
       "Copyright (C) 2020  Smithsonian Astrophysical Observatory\n",
       "\n",
       "\n",
       " This program is free software; you can redistribute it and/or modify\n",
       " it under the terms of the GNU General Public License as published by\n",
       " the Free Software Foundation; either version 3 of the License, or\n",
       " (at your option) any later version.\n",
       "\n",
       " This program is distributed in the hope that it will be useful,\n",
       " but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
       " MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
       " GNU General Public License for more details.\n",
       "\n",
       " You should have received a copy of the GNU General Public License along\n",
       " with this program; if not, write to the Free Software Foundation, Inc.,\n",
       " 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n",
       "\n",
       "*/\n",
       "\n",
       ":root {\n",
       "  --sherpa-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --sherpa-background-color: var(--jp-layout-color0, white);\n",
       "  --sherpa-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --sherpa-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "\n",
       "  /* https://medium.com/ge-design/iot-cool-gray-is-a-great-background-color-for-data-visualization-ebf18c318418 */\n",
       "  --sherpa-background-color-dark1: #EBEFF2;\n",
       "  --sherpa-background-color-dark2: #D8E0E5;\n",
       "}\n",
       "\n",
       "div.sherpa-text-fallback {\n",
       "    display: none;\n",
       "}\n",
       "\n",
       "div.sherpa {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       "div.sherpa details summary {\n",
       "    display: list-item;  /* needed for notebook, not lab */\n",
       "    font-size: larger;\n",
       "}\n",
       "\n",
       "div.sherpa details div.datavals {\n",
       "    display: grid;\n",
       "    grid-template-columns: 1fr 3fr;\n",
       "    column-gap: 0.5em;\n",
       "}\n",
       "\n",
       "div.sherpa div.dataname {\n",
       "    font-weight: bold;\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa div.dataval { }\n",
       "\n",
       "div.sherpa div.datavals div:nth-child(4n + 1) ,\n",
       "div.sherpa div.datavals div:nth-child(4n + 2) {\n",
       "    background: var(--sherpa-background-color-row-odd);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tbody {\n",
       "    border-bottom: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tr.block {\n",
       "    border-top: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-odd ,\n",
       "div.sherpa table.model th.model-even {\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-odd {\n",
       "    background: var(--sherpa-background-color-dark1);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-even {\n",
       "    background: var(--sherpa-background-color-dark2);\n",
       "}\n",
       "\n",
       "div.sherpa .failed {\n",
       "    background: orange;\n",
       "    font-size: large;\n",
       "    padding: 1em;\n",
       "}\n",
       "</style><div class=\"sherpa-text-fallback\">&lt;RSPModelNoPHA model instance &#x27;apply_rmf(apply_arf(delta + gauss))&#x27;&gt;</div><div hidden class=\"sherpa\"><details open><summary>Model</summary><table class=\"model\"><caption>Expression: apply_rmf(apply_arf(delta + gauss))</caption><thead><tr><th>Component</th><th>Parameter</th><th>Thawed</th><th>Value</th><th>Min</th><th>Max</th><th>Units</th></tr></thead><tbody><tr><th class=\"model-odd\" scope=\"rowgroup\" rowspan=2>delta</th><td>pos</td><td><input disabled type=\"checkbox\" checked></input></td><td>2.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>ampl</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr class=\"block\"><th class=\"model-even\" scope=\"rowgroup\" rowspan=3>gauss</th><td>fwhm</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.0</td><td>TINY</td><td>MAX</td><td></td></tr><tr><td>pos</td><td><input disabled type=\"checkbox\" checked></input></td><td>6.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>ampl</td><td><input disabled type=\"checkbox\" checked></input></td><td>100.0</td><td>-MAX</td><td>MAX</td><td></td></tr></tbody></table></details></div>"
      ],
      "text/plain": [
       "<RSPModelNoPHA model instance 'apply_rmf(apply_arf(delta + gauss))'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_both"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3c872963-a5c7-464e-a1a4-ff9cb1446c08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>/*\n",
       "Copyright (C) 2020  Smithsonian Astrophysical Observatory\n",
       "\n",
       "\n",
       " This program is free software; you can redistribute it and/or modify\n",
       " it under the terms of the GNU General Public License as published by\n",
       " the Free Software Foundation; either version 3 of the License, or\n",
       " (at your option) any later version.\n",
       "\n",
       " This program is distributed in the hope that it will be useful,\n",
       " but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
       " MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
       " GNU General Public License for more details.\n",
       "\n",
       " You should have received a copy of the GNU General Public License along\n",
       " with this program; if not, write to the Free Software Foundation, Inc.,\n",
       " 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n",
       "\n",
       "*/\n",
       "\n",
       ":root {\n",
       "  --sherpa-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --sherpa-background-color: var(--jp-layout-color0, white);\n",
       "  --sherpa-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --sherpa-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "\n",
       "  /* https://medium.com/ge-design/iot-cool-gray-is-a-great-background-color-for-data-visualization-ebf18c318418 */\n",
       "  --sherpa-background-color-dark1: #EBEFF2;\n",
       "  --sherpa-background-color-dark2: #D8E0E5;\n",
       "}\n",
       "\n",
       "div.sherpa-text-fallback {\n",
       "    display: none;\n",
       "}\n",
       "\n",
       "div.sherpa {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       "div.sherpa details summary {\n",
       "    display: list-item;  /* needed for notebook, not lab */\n",
       "    font-size: larger;\n",
       "}\n",
       "\n",
       "div.sherpa details div.datavals {\n",
       "    display: grid;\n",
       "    grid-template-columns: 1fr 3fr;\n",
       "    column-gap: 0.5em;\n",
       "}\n",
       "\n",
       "div.sherpa div.dataname {\n",
       "    font-weight: bold;\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa div.dataval { }\n",
       "\n",
       "div.sherpa div.datavals div:nth-child(4n + 1) ,\n",
       "div.sherpa div.datavals div:nth-child(4n + 2) {\n",
       "    background: var(--sherpa-background-color-row-odd);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tbody {\n",
       "    border-bottom: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tr.block {\n",
       "    border-top: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-odd ,\n",
       "div.sherpa table.model th.model-even {\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-odd {\n",
       "    background: var(--sherpa-background-color-dark1);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-even {\n",
       "    background: var(--sherpa-background-color-dark2);\n",
       "}\n",
       "\n",
       "div.sherpa .failed {\n",
       "    background: orange;\n",
       "    font-size: large;\n",
       "    padding: 1em;\n",
       "}\n",
       "</style><div class=\"sherpa-text-fallback\">&lt;RMFModelNoPHA model instance &#x27;apply_rmf(apply_arf(75141.231099099 * (delta + gauss)))&#x27;&gt;</div><div hidden class=\"sherpa\"><details open><summary>Model</summary><table class=\"model\"><caption>Expression: apply_rmf(apply_arf(75141.231099099 * (delta + gauss)))</caption><thead><tr><th>Component</th><th>Parameter</th><th>Thawed</th><th>Value</th><th>Min</th><th>Max</th><th>Units</th></tr></thead><tbody><tr><th class=\"model-odd\" scope=\"rowgroup\" rowspan=2>delta</th><td>pos</td><td><input disabled type=\"checkbox\" checked></input></td><td>2.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>ampl</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr class=\"block\"><th class=\"model-even\" scope=\"rowgroup\" rowspan=3>gauss</th><td>fwhm</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.0</td><td>TINY</td><td>MAX</td><td></td></tr><tr><td>pos</td><td><input disabled type=\"checkbox\" checked></input></td><td>6.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>ampl</td><td><input disabled type=\"checkbox\" checked></input></td><td>100.0</td><td>-MAX</td><td>MAX</td><td></td></tr></tbody></table></details></div>"
      ],
      "text/plain": [
       "<RMFModelNoPHA model instance 'apply_rmf(apply_arf(75141.231099099 * (delta + gauss)))'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_check"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abf610c9-9d74-43da-86e8-f59c998a0e46",
   "metadata": {},
   "source": [
    "The model display for `model_both` is *interesting*, since it does **not** include the exposure time![$\\dagger\\dagger$]\n",
    "\n",
    "This means that the `y_both` output is a rate (count / s), whereas `y_check` has units of count.\n",
    "\n",
    "---\n",
    "\n",
    "[$\\dagger\\dagger$] Please check the [Sherpa issues page](https://github.com/sherpa/sherpa/issues/1895) as this behaviour may change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "069e2a3b-3583-4088-a8ff-fe449a93bd12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_both = model_both(channels)\n",
    "y_check = model_check(channels)\n",
    "\n",
    "plt.plot(channels, y_both, label=\"model_both\", alpha=0.5)\n",
    "plt.plot(channels, y_check, label=\"model_check\", alpha=0.5)\n",
    "\n",
    "plt.xlabel(\"Channel\")\n",
    "plt.ylabel(\"Signal\")\n",
    "plt.legend()\n",
    "plt.yscale(\"log\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a366f96-d379-4387-8730-92e019687b99",
   "metadata": {},
   "source": [
    "So, here we have evaluated a model and passed it through both the ARF and RMF."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}