{
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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",
      "bin_lo   = None\n",
      "bin_hi   = None\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",
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       "\n",
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       "    display: block;\n",
       "}\n",
       "\n",
       "div.sherpa details summary {\n",
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       "    font-size: larger;\n",
       "}\n",
       "\n",
       "div.sherpa details div.datavals {\n",
       "    display: grid;\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",
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       "\n",
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       "div.sherpa table.model th.model-odd ,\n",
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       "div.sherpa table.model th.model-even {\n",
       "    background: var(--sherpa-background-color-dark2);\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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       "</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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  {
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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"
     ]
    }
   ],
   "source": [
    "print(aplot)"
   ]
  },
  {
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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": {
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       "</details><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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       "</details><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": [
    {
     "data": {
      "text/plain": [
       "(0.0, 8.81322481660618e-09, 0.13163885474205017)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "matinfo.matrix.min(), matinfo.matrix[matinfo.matrix>0].min(), 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": [
    {
     "data": {
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2Y3qk7fYi4c3dgNcz5DWG618QC/+LeHOFTOyfHszmJbFfWF4X34m9p6cHJ04MVIXNnj0b69evx+LFi2EwGJCTk4PHHnsM69atw9/+9jcUFRVh7dq12Lx5M44ePQqdTgcA+OlPf4p3330XL774IgwGA+6++250dHSgsrISUqkUAHDxxRejsbERzz//PADgxz/+MXJzc/HOO++MKs5QB1fglWaoUkS4q36+3dEQ+HrRjpfcyeHyxXClsfHGGw7HJfPQPF8GlAws8FKY0A6VxIVmRyIOdqaj8VQKDPukMH/UCHfNGcHiJXf87mJuFTZjoyCJ/ZMDOdBOMLH3dnuxZHqtqBL7mIe7ffbZZwzAkO2GG25gjH0zQY3ZbGZKpZKdf/757MCBA4Neo7+/n916663MYDAwtVrNLr30UlZbWzvI6ejoYNdccw3T6XRMp9Oxa665ZlwT1CzCypCTKATf9t0Xyh3N0KJYipfcyePysfEZb7ht2bwHWNnd61nOi6EXeMl9ZS2bdu96tmTh74aNM5b/F/HoCjnc7aMDuWzr6fwJbR8dyKXhbpOF4UrswbdDEUl3NIS6Yo5WvOTGvstnyZ3veEPFJks3o/XifHTM9iKloBPTjU3IUlvg8kpxqi8F+5sy4D6mg3mnB+q3d4aNMxb/F/HuClkV/9GBXF5K7MunnxFViV0W7QAizUr99ZBx8iEHZ/D9cFVNkXTHQizES27sunwQ6jUnGm+4C47u+TmwTgFU6b3I03fCpLQBANqcOpzqMsLepIXhJKD7qhbuMcQcC/8LcoXDwyTwTHCRUo8Ii7ZxswgMMLRtaLQHaaTcyRYvubHp8lla5zPecLBzZqJzqgzuLAdyDBZkqK2QSzywuDQ43WNAW2sitGekSNnXDXdT6HkrAi8cYul/QS7/fT6GwwsOXkgmuIlvHLvoE3vweuzDlSpGc0Dz4Y6WWImX3Nh1I3Ei5SPecHHJcrPRMV2D3lw30tO6kJvQCb2s37/Gem17MpR1ChirXWC7D4aNMRb/F+RGp9RODEX0id3HMsnQtqLAAzL4dqTcyRYvuZPD5RM+4g2HdV4GbAVAQvrQBV5qugxwN2qgPwFotoefhCbW/xfkCgctAhOauEnsw1UhDfdYJN3JFi+5sePySbj3nUi8ofBeMBuWYilYth25yRakqwaq4LtcGpzuNqCzWY+E0xIY9ljgsVhGjJXPeMnl1xUKXxv7RDexIfpe8cEzz23yvjHkdmBVU6jH+XbHQizES25sunzCZ7yhkBYVoGmZGV0z3MjObxuywMuhhnTIjmiQvs0B+ceVo46Rj3jJ5dcVchz7W/uKoNVJJ/Ravd0efGfmcVH1ip/Qsq2xTKhlW4MJNRYz0u54xhFHM15yY8+d6Bj1SB5nIcerq65h86/6A8v/wxOsfONv2DU7fsDu3fcddu++77BrdvyAlW/8Dct/4g/srKv/wJarrx31ePVIfb/kTswVchz7m/uK2Qenpk1oe3NfsejGsYuvDiIEI5UmAp1Iu2MhFuIlN7ZcPkvrfMcbDte55egqkoDL7Ed+cidMym5IOS86XVqcthlha9ZBd0qC5N2t8NrtgsVLbmRdIfBCAs8EN68IW6TF94lCEHhCHM3fSLujjTlW4iU3dly+8FWf8hVvuPikZVPRUaZEf44LWakWZGm6oJE60eNWorY3GY3tSdDUypByqB+e46eGjTnW/hfkhn5MyGVbiTBEu8ogUgROKetjuGkSh3ucb3e0VaSxEi+5sePyVf3OZ7zh3meF/gds7vVPsLwn/8BmvnMfu/6rG9m9+77DflF1Ofveth+x0rd/ywoee4Kdc8Xvh41VyO+X3Im7QlbFv1ZVyv59cvqEtteqSmOuKn7VqlUsKSmJffe73x3X8+Oi85yMkwMIXx0eqrQRKXcsxEK85MaOGykmGm+4WB0Xz0Pz2XKwkh6UpTehSNc2dIGXKinM79fBXVcvWLzkRtYVckrZDVXl0Eyw81xftwdXzzoYU53nPvvsM/T09OCll17C//3f/435+aJP7MP9s5ZJhp8HORLuaE/Y4U6gQsdLbmy4fCX60cQx1nhDwc0uQ+NiPbrLnZiS24Jp+mYY5b2wuVU4ZE3HsXoTNIdUyNzSA+zYP+5YxxMvuZF13+x6QbDE/sre6bwk9utmH4ipxA4AmzdvxlNPPTWuxB4XbeyhiIUfwViIhXjJjY472ZK6NDUVHbMS0ZPnQarJihytBcnyPji8MjTYk1DbmQx5nRKGI25K6nHqTmY+//xzXHbZZcjIyADHcXj77beHOM888wzy8/OhUqlQUVGBL774QtAYRb8ITDC+k5Gv85CP4U6ofLuTLV5yo+fyWSUf+D58xBuO3rPzYSsEVBnhF3gxngASttXAE+L5sfq/IHd0rpu5hjiRwtezfWKvMbZK697eXsycORM33ngjvvvd7w55/PXXX8eaNWvwzDPPYOHChXj++edx8cUXo7q6Gjk5OQCAiooKOByOIc/96KOPkJGRMb4PEoDoq+J9E9T4CHWQhjtwI+GOtSo+2vGSG32XDyIV7xDOmo7GRTr0ltlRnN2CEn0LkmV9sLg1ONSVjhO1adAdUCLjMyvY3kOCx0tu5F0h29j/umc2L1XxP5izF3V1dYPiVSqVUCqVwz6X4zi89dZbWLVqlX/f/PnzMWfOHDz77LP+fdOmTcOqVauwbt26UcdFVfFjwHdABh6Ym7xvhLwSjZQ72eIlNzouXwh1/MoyM9AxIwG9uR6Y0qz+BV76vArU9yUNLPBSq4ThsGtUSZ2veMkV3p2MZGdnQ6/X+7exJGEfTqcTlZWVWL58+aD9y5cvx7Zt2/gKdUTiLrGHYiylo0i5YyEW4iU3dtyxEMkYbGdlwzYF0KT3ID+xE6mKHgBAuyMhYIEXBs3Wo1GPl9zIukIx0clpAqvy6+rqYLVa/du999475nja29vh8XhgMpkG7TeZTGhuDr0EcShWrFiBK664Au+99x6ysrKwa9euMcURV4k91IG5TDL6Dkt8uGMhFuIlNzpuJIhUvADAFsxEZ4kUniw78gydyFR3Qc4NrLF+qtuIzpZE6GokMFZ2wGOzhXyN4PeLlf8FuWNzhZygxgvAw7gJbd6vXysxMXHQNlI1/HBw3OAV4xhjQ/YNx4cffoi2tjb09fWhvr4e8+bNG9P7iz6xB67HHkjgAem7HS4RR8odC7EQL7nCuXzgex++4g0XnywvBx3TNejLdSM9ZaAXfILUgT6vArW9BjS0J0FVq4DxkAOe6mMjxh1r/wtyx+7GKykpKZBKpUNK562trUNK8ZFE9J3nrFYrvpt0E4DRJdixJOPxuGMhFuIlV1iXz5PjSHHwdfz2XDEfrXMlUE/twoy0JuRpOgAAtf3JONCage7jSUjbDSS+uQfM5RxXrOONl1zhXSE7zz27Zx7UCRMb3NXf48ZP5+waV7zhOs9VVFTgmWee8e8rLS3FypUrx9VuPx5EX2IHBh98oa5Al0muCHmgRsodC7EQL7nCuXyxyfsGr/GG45s11vuR51tjnft6jXWbEV1NidCdkiBpd0vYpD7aGGL5/0bu0NtCEI312Ht6elBVVYWqqioAQE1NDaqqqlBbWwsAuPPOO/GXv/wFf/3rX3H48GHccccdqK2txU9+8hO+P35Y4mIc+zLJ0PahwAMw+CCNpDvaAz9W4iU3Ou5E4DvecHFJiwrQWq5Gf44LuSldyNEMVMH3eJSo7ft6gZczMqTs74PnRI1g8ZIbfVfM7N69G4sXL/bfv/POOwEAN9xwA1588UV873vfQ0dHBx566CE0NTWhvLwc7733HnJzcwWLMW5K7KGuNEOVooVwR0MsxUtu5F0+k3ok4g2GkytgmZuG7nwvkszdyE/sgFHRAxeTosmuR02nAZI6FZKPeSD5sipsrLH4vyA3sscOn3jB8bIBwLx581BaWoqnn3562PdctGgRGGNDthdffNHv3HLLLTh9+jQcDgcqKytx/vnnR/JrGEJcJHZg5AMu3JWpkG4gkyFeciPrjpdIxRuI6/zp6CqSQJLZj/zkDqQpuyEBQ6dLixqbAT1NCUg8Ceh31EUtXnKj6woBn1Xxu3btQnV1NVavXh3lTzVxRJ/YA4deBLcRBbYdBZeaIuWOhViIl1zhXD7hK95QSEuL0VGmhD3HiWzfGusSJ2xuFU73GNDUkoSE0zKkVPXA3dAY8jVi/X9B7sRcoeBzHLuYEN8nGobAK0zfQRq8X0h3ssVLbmTdiRD8OnzEGwqJToeOCiO6C7xIMduQl9CJZHkfXEyKBnsSTncYBhZ4qXaD7Towqlj5ipfc2HGJ6BI3iT34SnK4q0uh3OGIxXjJ5d/lo4QTfDLlI95wvn1hCaxTOMgzepGf1AGT0gYJGNqcCThtNcDeqIX+BKDdenxUsfIVL7mx7UYKL+N42cSG6HvFb7S+PKorycBqpmi5gUyGeMmdmMsnQhyTkpnT0FEqhyPHgWKjBdlqC5QS90AVfLcRLc1JSKyRwrinEx6LJSrxkhs7rhB4eahK94qwfCu+TzQGAk+wIx2kkXLHQizES25k3fES6RikRgM6ZiehJ88Dk8mKPF0HEmV2OLwy1PUn40xH8tezyznh3X9kwjGQO3ldIaeUJUITF4nddzAOV1oKPnAj6Y6WWImXXP5dPgg8yfIVb7j4es+ZAmvRwBrrBfoO/wIvLY5EnLQY4WrQQn/cC+XnB0eMlc94yY1dVwi8TMLLJjbiYkrZwGkCA6uQRqpOiqQ7EqFeK5rxksufy8dJ0HcyFeKY5OaWo2FxInqn21GS3YwyfRP0sn60uxJwwJKBUzUm6PfLkfFhCzzHTgoWL7mx6b7Z9YJgU8r+bueFUE1wSll7jxu/OevTiMYrNOK7VAlipf76QSet4a5Eg0sskXLHQizESy6/biTgI95QSE1paJ+pQ2+eB+lpXchL6BxY4MWjQG1vMurakqGulSPlgD1sUo9UvOTGtjvZGO0ENZOBuCmxBx94gSUIMRyUBBEJ+leeheazpVCWWDHT1IhCbRsAoLbfgKqWTHQfS4ZpF0PCP3eEfH5wTUWoCwjf749ccbhCltgf/GopLyX2++d/LKoSu+gT+yKshIyTRzscgph8nDUdDRfq0F/ej7LsJkxLbIZOakerU4f9nZk4cyoNyVVSpL9fD/eZkWeYixSBF+fhah4CkxO5kXUXc6uwGRsFSey//WopVAkTO7/be1x4SGSJXfRV8QRBjB1ZZgbaZyWgN8+NzNQu5Gk7oJPa0e1R4XSvEXWtyQMLvOzri2pSBwY3b4SqKgZip8d4PLgbrS8P6xKRhxI7QRBDsM3Phq0QSDR3o1DfDqO8d2B2uf4k1HQaIK1TwXjYDW77vmiHGpbAJO9LOr7bw1UtB5dUyR2bK+RwN+oVHxrxfSKCICYEWzgLnVMH1lgvNLQjXWmFlPOiw5mAGpsBvY066I8DCVvDL8Ua64RK+sFtxaF8H+SO7ApBNNZjnwyIfuY5giBGjyw3G00z1OjLcyEv1YIcrQUaqRNWtxqneoxoakmCtkYK414rPG1t0Q6XN4brXOu7H84nd7C70foy9Ho9hIAFLLs6kdcQG5TYCYLw0zU/E7YCIDndhgJdB5Jlff4q+NMdBshrlTAecoHtPRTtUCNK8IiZ4ATn20fuUHcxt2p0XzIRMahXPEEQAADvBbPRcJ4a7rJezMhqQImuBSqJC3X2ZOxrz0Tr8RSk7uZg+Nd+eHt7ox2uYIQqpY5mX7y6Qg53+/m2S6CcYK94R48Lv1/wH+oVTxCEuJBOyUf7dDX6c13ITetErqYTKokLVrfav8CL7pQExt3tcZXUgfDtysEdx0K1McejK2znOf5WdxPTBDVUFU8Q8Y5ECss8E2wFXqSkW/1rrNu98m8WeDmjQMoBBzyHj0c72qgRqpNdqPvDQW7ssmvXLiqxEwQhDtyLZqGrWAJZZh8Kk9thUtoA+BZ4SYGrXovko17INldFN9AYIbgE7yNcx7J4c4Ucx+75etnWiW5ig0rsBBHHSKdOQfN0Jey5ThSndCJbbYFK4oLFpcHpHgPam/RIOilB8lcNcHs90Q43pgjsSBaqqj64o1m8uG7mGs/XOS4Cq9In8hpigxI7QcQpnFKJznkp6Mn3wmTuQmFiO/Syfti9cpzpM+BMqwHq0/KB2eVqzkQ73JhjuHb34P2h9onV9XVsI6KH+OogCIIYFc4LpqOrmIMyq+frNda74WESNNn1OGUxgtVpYDjigeTLqmiHOikILsEG/w1MhGJ2Be08Bwkvm9gQ3yciCGJEpGVT0V6ugDPHiTxjJ7I1Fig5NyxuDU51G2Fp1CPxJKDfEd154CcDwZ3qgid0CZUUxewK2sbOOF42sUGJnSDiDIlOh/a5BvTke5ButqAgYWCBlx6PcmCN9VYDtKdlSNnbA3dDY7TDnRQEV8uH6lQW6jExu0T0oDZ2gogz+s8rgbUY0GT2oFDf/k0VvEOPE50pkNSpYDjsBnYeiHaok5JQpdhARqrinuyuzWYbsj9SUOe50FCJnSDiCMmsUnSUyuHOsaPA2IEsVRfknAcdLi1OWlPQ3aiD/hiQ8OWJaIc66QistvYNiQtOioF/xe4KAeNhZTdGi8AQBDFZkSYno22OHj0FbmSnDayxniB1oMejxOleI+pbkpFwSoaUSgs8HZ3RDndSEtwmHXg7VC/y4AQpJlcIPODgmeAiLhN9fiwivksVgiBC0ntuEaxFgC5j8Brrtf0GnOowQl6rRMpBF7z7Dkc7VFEzliQoZjfWoCllCYKYVHBzy9FRKoM3px8FyZ1IV1oh5zxodCThpNWIvvoEpB4FVJ8fgjfawYqEUD3Kg+8P99hkdoXCyybeRu79ehk0mlKWIIhJg8xsQvtsHXrz3chKtSAvYaAK3uoZWGO9uTkJulPSgQVe+vqiHa4oGK6tOVxSFJMrFBNtX/dtYkN8n4ggiEHYFuTBOgVISrehMLEdKfIeOJgMdX3JqGk3QnVGiZT98b3AS6QINQRsuJncxOIS0YXWYycIEcMWzETDIi2c5X2YmV3vX2O90ZGE3a3Z6DhmRNpOIPG1rwBxngqiRmDiCzdkLBCxuEKux37dZ1dBkaCY0Gs5e5x4ZfE/aD12giBiH1luNtpnatCX50LO12usayROWN1qnLSloK1Jj8STEhi+aqakHiE2ed8ImSTDDSUTgyvklLI081xoKLEThEjpOisDtkIGQ7oVBboOJMv7Bqrg+5NR02aA+rQCKVV9cJ86He1QRUdwNXXg2O9QJWAxuUJOKUuEhvfE7na78etf/xr5+flQq9UoKCjAQw89BK/3m762jDE88MADyMjIgFqtxqJFi3Do0KFBr+NwOHDbbbchJSUFWq0W3/72t1FfX893uAQhSrwXzIalRAppdh8KkzuQoeoCADTak3C8MwXeOi0t8BJBAku4Y22HDiwJi83lG+o8Fxreh7s99thjeO655/DSSy+hrKwMu3fvxo033gi9Xo/bb78dAPD4449j/fr1ePHFF1FcXIyHH34Yy5Ytw9GjR6HT6QAAa9aswTvvvIPXXnsNRqMRd911Fy699FJUVlZCKpXyHTZBiAbplHy0Tlej/+s11nM1nVBybnS4tDjVbURnox6GE4B+6xm4ox1snBGq13i4WduCk2So0nO4YWax4AqBFzxMKSvCCWp4T+zbt2/HypUrcckllwAA8vLy8I9//AO7d+8GMFBaf/LJJ3Hffffh8ssvBwC89NJLMJlM2LBhA26++WZYrVa88MILeOWVV7B06VIAwKuvvors7Gx8/PHHWLFixajjkUm9kHE0MpeIEyQS2M4yor/QiXRzJ4p1LUiW9MLhlqOhV4/GlkToz3Aw7+sCWhsho2vkmOVi+XeH7JNJB+/3/f98+963/yPgMS/gcQ48rroKm1xfP/b1vsGvy6Mb4jmEsPDeK/7RRx/Fc889h48++gjFxcXYt28fli9fjieffBJXXXUVTp06hcLCQuzZswezZ8/2P2/lypVISkrCSy+9hE8//RRLlixBZ2cnkpOT/c7MmTOxatUqPPjgg0Pe1+FwwOFw+O/bbDZkZ2fjuW8XQy2nsxcRH3jzM9CdKwVSnUjX2WBQ9EIKLywuDRp79HB3qJBQ54XsaEO0QyVijOt/e2XI/S8/9M+wj4VyV925EvqFjwrSK/6KT66HXDuxXvGuXifeWPKyqHrF815i/8UvfgGr1YqSkhJIpVJ4PB488sgjuOqqqwAAzc3NAACTyTToeSaTCWfOnPE7CoViUFL3Ob7nB7Nu3bqQCZ8g4gWJ0YCeNCnceg9SNX1IlPdDBi96vQp0OrRw9Cig6QAUZzppdjliCC8/9M9xPXb9b6/0P379b6+ErccR1uUbWt0tNLwn9tdffx2vvvoqNmzYgLKyMlRVVWHNmjXIyMjADTfc4Pc4bvCXyRgbsi+Y4Zx7770Xd955p/++r8T+f/+ZSuPYCdHDKRXo+u4stKkZ0lI7MCelHukKK3o9ShzuNuNgWzo0R9TI/LgL3v1Hoh0uISL+sfEAgGn+227mEuy9+ej85nv+vHnzIJVKsXr1aqxevZqP8KIG74n95z//OX75y1/i+9//PgBg+vTpOHPmDNatW4cbbrgBZrMZwECpPD093f+81tZWfynebDbD6XTCYrEMKrW3trZiwYIFId9XqVRCqVQO2e/2SABOfL0eCSIQ17mz0FYshyzThvzkThiUfXBAjjNOA450msDOJEB/wA3n3mOgUa5EJHFP0l7mNFf8MPT19UEiGfyyUqnUP9wtPz8fZrMZmzZt8j/udDqxZcsWf9KuqKiAXC4f5DQ1NeHgwYNhEztBxCvSsqloL1fAletAYco3a6y3OXU4aU1BX30Cko4Cqs0Hoh0qQfCKryp+opvY4L3Eftlll+GRRx5BTk4OysrKsHfvXqxfvx4/+MEPAAxUwa9ZswZr165FUVERioqKsHbtWmg0Glx99dUAAL1ej5tuugl33XUXjEYjDAYD7r77bkyfPt3fS54gCECi1aJ9rgE9BR5kBKyx7lvgpbEpGYknpTB+1QKP3R7tcAmCV7zgJjxcjYa7jYL//d//xW9+8xvccsstaG1tRUZGBm6++Wb89re/9Tv33HMP+vv7ccstt8BisWD+/Pn46KOP/GPYAeCPf/wjZDIZrrzySvT392PJkiV48cUXaQw7QQTQf34prMWANrMbRUltSFN0w8Wk3yzwclqJ1P12eI6djHaoBEEIBC0CQxCTFMnMaahfnoy+mf2YmVOP0sRmaCRO1DuSBxZ4OWqE6StA9/qOaIdKxBFu5sJmbBRkuNslH/6Ql+Fu/1nxFxruRhBEdJEmJ6OtIgk9BW7kplmQr+2ARuKExa3xL/CSdEKC5G21NLscIVpouFtoJmf3RYKIc3rPLYK1GEjKsKFI3zZ0gZcaBVL39sJdR+srEES8QSV2gphkcHPL0VEmA8vpQ5GxDRmqLkjA0OxIxLGOVLBaLYzVHnDb90U7VIKIKFRiDw0ldoKYREhNaWiZo0NvvgtTTB3+NdbbXQk4YUuFpVEP4zFA98UJeKIdLEFEGErsoaGqeIKYRHQvzIe1CDBkWFGga0eyrA99XgVqeo0402qAtkaG1N1WeNo7oh0qQRBRgkrsBDFZOHsGOqZJIc3pQZGhHRlKK4Cv11jvSIXkjBopB1zwVlVHOVCCEAaGiY9DF+OwMErsBDEJkKWb0TRbi/58J6amDlTBqyQutDp1OG5NRXdDIlKOAOrNh2iBFyJuoKr40FBiJ4hJgHVhLmxTGEwZXZiia4dB1otujwo1vUbUtSQj4aQUKTvb4Onri3aoBCEYlNhDQ23sBBHjsAUz0TlNCnlOL4qTW2FWWuFhEjTZ9TjekQrZaRVS9zngOXw82qESBBEDUImdIGIYWVYmGr+ugi9LbUeO2gI550GTU4+jXWnordPBdJhB9klltEMlCMGhEntoqMROEDFM14Js2Iq8yMjoxBRdG/SyPnR7VDjRnYqGpmQkHpfCsK0x2mESRFTgc3W3efPmobS0FE8//XSUP9XEoRI7QcQo3nNnoXOaBKpsK4qT2pAm74bLK0NtfzJOtqVAVaNE6t4+uE/XRjtUgpj0iGk9dkrsBBGDyHKz0TBbA0e+AzNS2pGj7oRc4kad3YBjljS4arUwV3sg+bIq2qESRNRgjAObYFX6RJ8fi1BiJ4gYpHNBJmxFHmSYLShMaEOC1A6LS4sT3aloaUxC8nEO+i9raIEXIq6h9dhDQ23sBBFjeBbNgaVEAm12N6YmtcIo74XDK0dtvwEnW1OgOalAWmUP3M0t0Q6VIIgYhErsBBFDyAry0DxLBWe+HaXGNmSpLZByXjTaDTjamQbvGS1SDrrBdh2IdqgEEXWoV3xoKLETRAzRcY4Z3VM8yDF3ojCh3V8Ff9yWivYGPYxHAe2WI7TAC0GA2tjDQVXxBBEjuJdUwFLCISFrYI31FHkPHF45TvWl4FRLCrSn5EjdZYHHZot2qARBxDBUYieIGEBaXIimmUq48/sx3diGLJUFANDoSMKxjlRwtWqk7HfBu/9IlCMliNiBquJDQ4mdIKIMJ5OhY34aego9yDUNVMFrpA60u3Q4ak2DpUGP1COA6tP9olyJiiDGC1XFh4YSO0FEGceSWbBMA/RZVhTrW2GQ9aLPoxxYY73FCN0JGVK+aoPH4Yh2qAQRUzAeSuxiTOzUxk4QUURaWoz26Qp48/pRZGxDtr8KXo+j7WmQnlYhhRZ4IQhiDFCJnSCihESlQvtZRvRMcWOKqQP52g4oJS60OhNxzJqG7oZEpB1mkH9MC7wQRCgYADbB9ikxNm9RYieIKNG/eDq6SgBDZhemJrYiRd7tr4KvbTZAd1wK47ZGml2OiCk2ed8I+9gyyRXYaH0Zer1ekFi84MDRzHNDoMROEFFAMqME7TPkQG4vphraYFZa4WUS1NuTcbQ9DfIaFVKr7HCfOh3tUAkREypJL5Nc4d/vux24byR3MbcqojETI0OJnSAERqLToW1eMnoLXJhqakeBtt1fBe9bY91czSDdvCfaoRKTlHBJ2PeY73aohB34+HhdoaBe8aGhxE4QAtO3aBqsU4HUrC4UJ7ZCL+tDn0eJ491pqG8yQH9MiuQvz1AVPDGoNBy8L9z+ZZIrBiXbYIJL2Xy7NptNuKp4xoGjcexDoMROEALCzS5De7kMktwelBhaYFbY4GUS1PYbcLw9BcpTSqTu6YW7viHaoRIRYKTkGW5fqATv2x+LLhFdaLgbQQiENDkZbfMS0VfoRJGpDfmaDqgkLrQ6dTjSlQZ7rQ7Gag+4bfuiHSoxRjZ53/AntsC/gVswY6m2nmyuUDDGzyY2qMROEALRc0ERrMUM5kwLpujaoJf2w+pR43hPGhobDUg+xiHxi1O0wEuMM1LJNFzpdbSldbG4QkBt7KGhxE4QAsDNLUd7mQzy3G6UJLciXWGFi0kHquBbU6E+qUDa7m54WlqjHSoxDMEdxEJ1JgMG9yaPR3cyMm/ePEilUqxevRqrV6+OdjgTghI7QUQYaWoqmufq0F/gxPS0NuSoOyHnPKh1GHDEkgbXGS0yDtEa65OB4TqPBRLcWzxe3UjDZ4l9165dSExM5COsqENt7AQRYWznF8BazJCR2YmihFZ/FfyJ7lQ01xuQdJRDwqe0attkIXgImA9fr/HApBeP7kr99aP/MieIb3W3iW5ig0rsBBFB2Dkz0VEmhSrXipKkVpiVA1XwNX1GHG9JheaUHKk7aY31yUTwpC3hSq++x+PNFXLmOT46v4mx8xyV2AkiQsgyM9A2RwtHgQNFKe3IUXdCCoYGRxKOdJrgPa1F6j4XvPsORztUYoyEGwoWSHCbdLy6hPBQYieICGE5NwfWYi+y0jtRpGuFTmpHp1uLY7Y0tNUlI+kIoNpEQ9smI8MlsFDjvOPRFYKBEjs3wS2qHyEiUGIniAjgPW82Oksl0ObYMC25GWkKG+xeOU73GXGyORUJJ2VI3dYG5nJGO1RiDARWPw837Wqo9ul4coVi4kl94p3vYhFK7ATBM7K8HLTOUcNV0I+pKa3IVlkgBUOjIwmHO9LAnVYjda8DnqMnoh0qMUqCO4sFEmpsN7lENKHEThA807kgA91FHuSYO1GY0A6NxIl2VwKO2tLQWZcEQzWtsT6ZCDV+O7hdObiTWby7QsF42sQG9YonCB5xX1iBzlIO+uwuTEtqQYq8G3avHKf6UlDTnILE4zIYtzbQAi+ThJGSWSjIFQ6aeS40VGInCJ6QFhWgbZYSnnw7pqa0IktpAQDUO5JxuM0E6SkV0vb0w11zJsqREsPhq3YfbrhXqOeQO/C4kOPYidBQYicIPpBI0XGOCd1FbuSb2zFF2waN1IF2lw6Hu0zorkuE8RCDZMveaEdKDMNw7ce+x333yR3eFQSqiw8JVcUTBA+4ls6GZRpgyOrCNH0zDLJe9HhUONmTgjNNRiQekyL5i9NUBR9jBJZAwyWtYC/U88n9xhVyghrw0audquIJgghGOq0IbTMVQF4fphlbkansggcc6uzJONqeBuVJFdIqe+FuaIx2qMTXBCf0wCFdI/0ld3hXyKp4WrY1NFRiJ4gJIFGp0HZ2CnoK3Zhqakehtg1KiQsNjmQctpjRdyYR6QdpjfVYIVQv7lAlU3LH79psNuFK7ERIOMbEeL0C/8G1CCsh4+TRDocQKY5vzUPDBTIkl7djgbkGOcpOdHtU2NuVjX0ns5G8Uw7zG0fh6eiMdqhxTXAVcqg24XDVzOSOzfWde61Wa8RWS/O9R95ffw2JRjWh1/L22XH6Bw9HNF6hoRI7QYwTyYwStM+QQ5LXgxJDC9IVVnjAobbfgGNtX6+xvstKST2KBFe5hyuJBkPuxF1BYNzE28hF2MZOiZ0gxoE0MRGtZyWjt9CFMlMbCjXtUEpcqLMbUG0xwXk6AZn73WCVh6IdalwymmFZfL0WuUSsQZ3nCGIc9F5Qgq4SBnNWJ6bqWqCX9cHq1uBot2lgjfXDHDSb9kc7TOJrAquRQ3UAI5c/lzrPRR8qsRPEGOEqytA+XQZ5bjdKk1tgVlrh8Mpxsi8Fx5pToT0hR9r2dnjs9miHGjeMpl04uOPXcO3u5I7fFXa4GyY+Dl2EiZ1K7AQxBqQpRrTNTUTfFCemprUiX9MOOedBk1OPwx1m4LQWqVVOeKqPRTvUuGCT940Rk81wbcTkRtYlogMldoIYA93nT0HXVIbMjE5MTWxBgtQOi1uLI1YT2uuTkFwNKD7cHe0wRU9gQgmuGg61n9zouJGGlm0NDSV2ghgtZ89AR5kU6rxulBmaYJLbBqrge1NxsjkVuuMypHzZFO0oRU1wL3ffvlDtwYH7yRXOFXyueJpOdgjUxk4Qo0CWbkZjRQLshQ7MSW1BrqoTEs6LBrsR1e0mSGrUSKu0w33qdLRDFR3B1bvB7b7D+eRG1yWiA5XYCWIUdJ2bC2uxFzkZHShOaP1mgRerGV11STAcZJBu3hPtMEVFcPttcOkw2A2E3Oi5G60vD9kXKagqPjSU2AliBNjCWegskyIh14rS5GakyHvg8MpxojcVp5pSkHhUCsPntdEOUzQEJ/Pgqt/hkgy5seEKBq3uFhJK7AQxDLLcbLRWaOAs7Edpaot/jfUzdiMOt5kgP6lG2m5a4IUPwvWuDnU7sH2X3Nh0hYHjaRMXEUnsDQ0NuPbaa2E0GqHRaDBr1ixUVlb6H2eM4YEHHkBGRgbUajUWLVqEQ4cODXoNh8OB2267DSkpKdBqtfj2t7+N+vr6SIRLEGHpXJAJW7EHBRmBa6wnDKyxfkaPlAO0wAsfhEoSgft9BD9Gbmy6RHThPbFbLBYsXLgQcrkc77//Pqqrq/HEE08gKSnJ7zz++ONYv349nnrqKezatQtmsxnLli1Dd3e331mzZg3eeustvPbaa/jyyy/R09ODSy+9FB6Ph++QCSIknkVz0FnGISlnYI31FHk3+jxKHOtJw5mGFOiPSpD42fFohzlp8bWhj1TKGy65kBu7riDwWBU/b948lJaW4umnnxb0I0QC3ld3++Uvf4mtW7fiiy++CPk4YwwZGRlYs2YNfvGLXwAYKJ2bTCY89thjuPnmm2G1WpGamopXXnkF3/ve9wAAjY2NyM7OxnvvvYcVK1aMGIdv9Z+l0stodTdizEin5KHhEjP6KvpwVt4ZlCc0Qs55cKw/DdvrC+Ddm4jsD21ge6qjHeqk4n37P/y3L1Zd5b8feHuk++TGtvt66/PQG1IFWd0t+5kHIFFPcHW3fjvqbnmAVncbjn//+99YsWIFrrjiCmzZsgWZmZm45ZZb8KMf/QgAUFNTg+bmZixfvtz/HKVSiQsuuADbtm3DzTffjMrKSrhcrkFORkYGysvLsW3btpCJ3eFwwOFw+O/bbDYAwH9dchRquZTvj0mIGg6OGd3oyd6PFG03Mnu7oOl3wuZRQd/Th5L2NiR6GGTZDUB2tGOdPFz/2yuBQ2v9969aedh///3K6Xj5e7Nw/W+vxMsP/RPvV5I7WV3b4SdC/fsJAeG9Kv7UqVN49tlnUVRUhA8//BA/+clP8LOf/QwvvzwwBKK5uRkAYDKZBj3PZDL5H2tuboZCoUBycnJYJ5h169ZBr9f7t+xsOuMS48NbkI7+VA6aRDtSVD1QS5xweGXocCTA1q2Guo2D/GRLtMOcFFz/2yv9f19+6J9DHvfte/mhf/odcsXlRhTfsq0T3UQG71XxCoUCc+fOxbZt2/z7fvazn2HXrl3Yvn07tm3bhoULF6KxsRHp6el+50c/+hHq6urwwQcfYMOGDbjxxhsHlcABYNmyZSgsLMRzzz035H1Dldizs7OpKp4YE9LiQtRflgrHrF6clXca5QmNkIKhujcdO+ryIKnSIeedTpoLfgQCq2d9DFeFS654XCGr4rOeepCXqvj6W++nqvjhSE9PR2lp6aB906ZNw5tvvgkAMJvNAAZK5YGJvbW11V+KN5vNcDqdsFgsg0rtra2tWLBgQcj3VSqVUCqVQ/a7PRKAo1F9xMhwSiXa5pthKXSjLKMTeVoLFFIvah0GHOjKQH9NErL2uOE4cAI0UjQ0w3Woet/1pv+22yMBpApyRej6mkGJ6MH72WnhwoU4evTooH3Hjh1Dbm4uACA/Px9msxmbNm3yP+50OrFlyxZ/0q6oqIBcLh/kNDU14eDBg2ETezgCZ0GKiV6cRMziWDwDlhLAlG3BVF0LDLJedLq1OGwzo7nWgOTDgOKDXdEOM+YIHKoGDF2zO9R9csXtCgZNUBMS3qvid+3ahQULFuDBBx/ElVdeiZ07d+JHP/oR/vSnP+Gaa64BADz22GNYt24d/va3v6GoqAhr167F5s2bcfToUeh0OgDAT3/6U7z77rt48cUXYTAYcPfdd6OjowOVlZWQSkfuDOerqgmuXqGxlkQoJOUlqLvEAHdFNxbm1KAsoREecKjuycDW0/lQ7k5A9ttN8JyoiXaoMcNo5gkPTgbkit99s+uFkOdePvFXxf/PQ/xUxf/st1QVPxzz5s3DW2+9hXvvvRcPPfQQ8vPz8eSTT/qTOgDcc8896O/vxy233AKLxYL58+fjo48+8id1APjjH/8ImUyGK6+8Ev39/ViyZAlefPHFUSX1QFbqr4eMkw86CVFyJwKR6HRom5+M3ikuTDe1+ddYb7AbcbA9HTilhanSTkn9a3y/oVAn/8AZyELtI1f8LlXFRx/eS+yxQnCJPdRBShAAYL/0LDRcIIVpegvONZ1ClsKCTrcW2zvycfRoJsxfSJC4YUe0w4wqoU70QPjSHLnx6wpZYs/+f/yU2OtuF1eJPW4SeyDBJQ0ifpHMKkXtt5LA5tiwMLsG07RN8IDDPls2dpzOg3anBln/dwbu+oZohxoVwlXBhvoNkUsuACzmVmEzNgqT2J/kKbGvEVdij4uuvYHJO/j2Ju/IU1oS4kRqNKD1LD36pjhRktaCfPVAFXyd3YDqdhNkJ9Qw7eqLy6Tu+134fi+j+Q2RS+4yyRWCLttK49hDExeJPbiNKHD/SFeihHjpOW8KuqYxZGd2oETXAp3UjnZ3wsAa62eSYDzgBbe1KtphCkrwCdz3uxjLb4jc+HZX6q8HEV1En9gDD7LgtqBAqEo+vuDmTUf7dBk0eTaUG5qQprChz6vA0R4TTjakQn9ECv2nx6MdpiAE1lqF+l0E7hvpN0QuuYJCw91CIvo29kVYic/Y2wDCdwLx4Xuckry4kZlNaLy8ENb5dswtPIMKfS2UEhdO9JnweUMBXHuSkfueDWz3wWiHGlHClbqCCXycXHJHcofr38QX/jb2J37HTxv7Xb+hNnYxENhWFJzIqVpe3FjPzYO1xIP8zHYUJ7RCI3Gi3aXDoS4zek/rkbrfLeqkPtJJ2/d3pAtccskN5VJVfPQRfYk9XK94YGxrDxPigC2YidoVWihnWnBB1gkUqVvR51VgV1cuKk/kwrBdgbRX98Hb1xftUHllNBero/1dkEvucK6gJfY/8FRiv5tK7JOa4KojSt7xgywrE60VWrgK+1GW2oxsVSc84HCqPwWHW03QHFcibZtFVEl9tMf6WH4X5JI7nCtoiZ16xYckbhJ78NXncG3pY7lSJSYPlnOzYSvxYEp6G6YmtEAjcaLVmYhDnelw1OiQWuWC9+CRaIfJC4Ed4sZyrJNLbiRcQljiIrEHH2yBB2Rwb2A6MMWJ97zZ6CjjkJxjwbSkZhhkvbC6Nai2paOh3oDkQxyU7++OdpgTItSF6FiOdXLJ5cMVchw7x/jZxAbvc8XHIuGuJAP30VWneJHl5aC+Qg1PYR/KUpqRoxyogq/pT8GRljQkHFMg9ctmeCZxd5ORjt+xHOvkkjsR181cQ/ZHDD6Gq03en31Y4iKxA0OH9gS2CwUfoOH2E5OTzgUZsE11Y1p6K4q1rVBKXKh1GHGgIx3eUwlIq3TAc/xUtMMcM6M5fsdyrJNLLh+ur2MbET1EXxXv68gx0kEZ6FBCFw+exXPQUc4hNceCMn0TkmW9sLi1OGRNR0utAYaDDLJPKqMd5pgIrhod6fgll1whXRruFn3iarhb8MHpuz3cfUrykxdpUQHqv22GfV4Pzss7hem6eniZBAd6MrG1pgDqnVpkvTG5FngZ6/FLLrlCu0IOd8t97GFIVBMc7ma348wvfk3D3SYj4RK0b3/wwUpMbji5Ah3nmNBd7EJJeisKNW0Da6w7knCwPR3cSQ1MOyfHAi+Bx2YwYzl+ySVXCJeGu0WfuEnswfgO0HDtR8Tkxrl4BjpLAXNOJ6YlNkMv6/NXwXecSUbKfjYpFngJl9THcvySS66QrqCruxEhiYvEHqr6KJhwVU/E5ENaWoy2WQpIC3pQbmhGuqILDq8cR3rMON6YBv1hKfSfHIt2mCMyXHUoueTGuisItAhMSETfK36l/nrIOPmIHpXYxYFEq0XbfCN6ilyYbW7GFE0r5JwHx+0pONRmhuK4GqYdNng6OqMdaljGU4tELrmx5goCDXcLSVx1nhuOcCX1mDuQiWFxfGse6hfLYJ7egvNNJ5CptKDdpcOX7YU4eTATWZ95oX57Z7TDDMtwHZWGg1xyY8UVtPPc2kf46Tz3q/uo89xkInC4m2/z3Q/8G7wv0CUmB5KZ09A2Uw5FfjdmGBuRrrDC4ZUPrLFen4rkag7aD/ZHO8wRGc0xOZbjl1xyhXSF7DxHM8+FRvQl9kVY6a+KD1XFGa6daJOXhrpNJqRJerT+Vyk6FrhQUXQa85NroJE4cbTPjM/qiuDZnYTcjZ0xORd88LE23DEZbh+55MaK+2bXC4KV2PMe5qfEfvrXVGKfVPh6aAYemME9PYHBVUzUeW7y0XveVFhKGXKz2lGW2ASdxI5WVyIOWDLQV5OItL2xucBL8DE3mmOSXHIng0tEj7grsfuuLEOVkqjEPjnhKspQe5Ee0rlduCDrJKZpG79eYz0Pu47lw7hVDuML26MdZkjGe0ySS26sum7mwmZsFKbE/jueSuy/EVeJXfSJPdw/y3flGfw32CFiG2lqKlounwLLOU6cVVSDs/SnIec8ONyXjs1npkCyOxE5bzbH1Fzw4Uo1ozkmySU31t3F3CrBEnv+Q/wk9prfiiuxi74qPpjgq8uRDlYituk+twBd0xjys9owLaEZGokDLa5E7O/IgOukDqZdsbXAS6jjbCzHJLnkxrpLE9REn7gosX836aYRE3eog5dK7DHOWdNRe5EOijkWLM4+jiJ1C3o8KnxlycPeI3lI+0KGpFeiXwUfeOyN5WKSXHInoyvkcLf8B9fyU2K//1eiKrGLPrEvwkp8xt727w+8ugwm+GClxB67yNLNaPxOAWzn9OOcwhrM1Z+GFAwHezOwuaYIyp0JyH49+gu8jOU4I5dcMbiCJvYHeErsD4grsYu+Kj64WmiT9w3/FSeAQX+pOn7yYF2YC+s0D6ZktKEkoRkqzoUmpx772jPBTmph+ir6C7yM9jgbyzFJLrmx7tI49ugj+hL7cFdhIyVzKrHHJmzBTNSu0EIzuwNLso6hUNUKq0eDrZ2FOFCdg/TNEuhe3xG1+IKPqbFcNJJL7mR3hRzHXnA/PyX2Uw+Kq8Qu+rnifQQm6ZGqnCihxy6y7Cw0VGjhntKPmWmNyFF2wsVkONZrwuEGM/SHZUj6+Ag8UYxxuGMq3HFGLrlicYVdthU0V3wI4iKxj7f9iBJ87GFZkAVbiQclGS0o0rRCzrlx0p6G/e0ZkB7XwLy9OyoLvPiqImO9/ZNcciPt+krTgsBHVboIE7vo29hDEdwmBFASnwywhbPQMZ1DSl4npic1wiDrQac7AQe6MtBRk4zUKg/YrgPRDtPPWI4zcskVm0tED9G3sQ8385yPUAcjHaSxhSwrE/XfzUX/2b24oOAEZiTUAwB22XKx9WQh9FtVML20D96+PsFiCneMjPc4I5dcMbhC9oov+PVaSCfYxu6x23HqYXG1scdFid13NRl4HxhcfeTbF+wSsUHXgmzYStwozfCtse7GabsRB1ozoDyqhmmrRdCk7mO4Y2csxxm55IrJFQzG0xZD1NXVYdGiRSgtLcWMGTPwxhtjz0eiT+yBw91CHXSB+wIPTiqtxw7ec2ehfQYHU24nyvWNSJb1ot2tw/6uTNhqkpC2xwXvfmEWeAk8oYU6doL3h9pHLrlidgXtPCdCZDIZnnzySVRXV+Pjjz/GHXfcgd7e3jG9huir4oOrV5ZJaIjbZEI6JR8Nl6aj/+weLC08hhnaOtiZHDu78rHjeAEMXygEXeBltLU5Ix1n5JIrVlfIqvjCX/FTFX9ybexWxc+YMQP/+c9/kJ2dPerniL7E7mOkq1Aqpcce0iQ9OuebYCtxozyjCVPUrZBwDKf6U7G/JQOaI0qkbWkWJJbgk1y4DkWjOc7IJTceXLHy+eef47LLLkNGRgY4jsPbb789xHnmmWeQn58PlUqFiooKfPHFF+N6r927d8Pr9Y4pqQNxVmIPvLL03Y6HA3Gy4lo+F/WL5Uid3YKl6UeRo+hAkysJHzVNQ1OVGTkfOiH7tDKiMQQfL4G3w5VqyCU3nl0hV3eLRon9/fffx9atWzFnzhx897vfxVtvvYVVq1b5H3/99ddx3XXX4ZlnnsHChQvx/PPP4y9/+Quqq6uRk5MDAKioqIDD4Rjy2h999BEyMjIAAB0dHTjvvPPwl7/8BQsWLBjTZxL9OPaV+uvxGXs75IFJST12kcwqRdsMBSSF3ZhtbEC6vAvdXhX22zJRdyYFpv0s4kndR7hjJ1TnInLJjXd3o/VlQcex8zVBjc1mG7RbqVRCqVQO0S+++GJcfPHFYV9u/fr1uOmmm/DDH/4QAPDkk0/iww8/xLPPPot169YBACorhz93ORwOfOc738G999475qQOxEmJ3be6G5XSJweyrEy0Lc1B2wI3KqbV4FzDCWgkThzszcSmmhIotumQ9Y+TcDe3RCyGcCWWcCUXcskld8AVso19yi/5KbGfePRXQ/bff//9eOCBB4Z9Lsdxg0rsTqcTGo0Gb7zxBr7zne/4vdtvvx1VVVXYsmXLiPEwxnD11Vdj6tSpI75/2LjiIbEHH1yU2GMXiUqF/iXT0XCeDKZZLViWfgRZik7UOw3Y1FSC1j0m5P+7F9ixP6JxhDuhBTPcyY9ccuPRFTyxKyeY2B0Dib2urm5QvOFK7IEEJ/bGxkZkZmZi69atg0raa9euxUsvvYSjR4+OGM+XX36J888/HzNmzPDve+WVVzB9+vRRfybRV8UHQgk99mHlU9BeLoessBtzUuqQrhiogj9gy0DD6RRk7PVGLKmPpjYnuCqSXHLJHexO1uFuiYmJvF2IcBw36D5jbMi+cJx77rnwer0Te3+xl9gDZ54jYhtZdhZal2WjfYEL80pqsCD5JDQSJ6p6cvDxyWJotiYg46WD8AS1hfFBYEkk8CQ1EuSSS+5gBC2x/4KnEvtj4xvuFomqeD6gxE7EBJxMhv6L5qDxfCkyZzVhqfkI0uVdqHUa8WHDNHTtTkXeRhtY5SHe33s07Yjkkkvu6FwhE3vRPfwk9uOP85PYAWD+/PmoqKjAM888499XWlqKlStX+jvPRRpK7ERMwFWUoX6ZHmyeFcvyjqJU04gejwqfdxRh/8E8ZG1iUL+9k9f3nAylH3LJnWyu2BN7T08PTpw4AQCYPXs21q9fj8WLF8NgMCAnJ8c/3O25557DOeecgz/96U/485//jEOHDiE3N3dCsY4WSuxE1JGZTWj9VgHaF7owv+QUFiadhFLiQmV3Lj45MRWJX6iR9pdKMJeT9/cO7tEbrhQSWEohl1xyw7tCjmMv+jlPif33o0/smzdvxuLFi4fsv+GGG/Diiy8CGJig5vHHH0dTUxPKy8vxxz/+Eeeff/6E4hwLlNiJqOO4eB4aFsuQNbsRy0xHYJJbUeNIxYf109CzMwX5b3bAc2jk3qQTIfjkBAwtlQSf6Mgll9yh7ptdLwhWYi++m5/EfuwPv0JxcTGkUilWr16N1atX8xRpdKDETkQVSXkJ6i4xAPOtWJF7GKWaRlg9GnzaPhXV+3OQ84EXyvd2CRpTqOrF4U505JJL7je3hayK5zOxx+pc8eMhroa7EbGFRKNBx9xk9BY7sdDcgAJ1GzyQ4HBvOg7Xm2HYL4Hyva8Ejyuw2hEYmuiD74erpiSX3Hh2BYHHmefERNwsAkPEHo4F02ApBQpyWzFd1wCtxIFahxF7WjOhqlbD9GlTtEMEELoqcrQnOHLJjVdXEES4HjsfUGInooK0qABtc5RQFVkxx1AHk9yKTncC9liy0XXCAPNXDrhPnY52mH58J61Q7YuB9wNdH+SSG28uEV0osRNRoeMcE3pKnJhtrkehqhVOJsOhngwcqTXDWMVB9okwC7yMl8ATWuCJLVSbZPDj5JIbD64QcIyfTWxQYicEx31hBTqmA1NyWzBd1wCNrwq+JQuaahVSPzwV7RDHzXBVlSO1SZJLrlhcwaCq+JBQr3hCUGQFeThzZQYkZ1twce5hTFM3otOdgI9ap+HEnmzkb3RA8sXeaIc5JjZ5h64cGOp2YJUmueSK1RVyHPvU2/npFX/0/4mrVzwldkI4OA6WG85G+yInLig5hvmJpyDhGL6yFuCzI1OR9rEc+ld3RDvKMRGc1EOVWsKdFMklV4yukOPY+UzsYhrHTlXxhGA4l1egYwZDcU4zShMaoZK4cNqegt3N2dBWK2H48GS0QxwTvhNb8N/h2hnJJTdeXCHgs419165dqK6unvRJHaDETgiEdFoRWisUSCzswpzkOqTKutHm1qGyMwe9x5Ng3tYHT1tbtMMcE76TWuDfUPvIJTeeXEGXbaU29pDQBDVExJGoVGhdmAL7tH6cbapHnqoddq8cB7ozceyMGaYqBsmXVdEOc1SEqoYM1wYZ+Di55MaL66smJ6JHxNvY161bh1/96le4/fbb8eSTTwIYWHT+wQcfxJ/+9CdYLBbMnz8fTz/9NMrKyvzPczgcuPvuu/GPf/wD/f39WLJkCZ555hlkZWWN6n2pjT12cFwyD7XLpSideQYXphyFQdaDQ32ZeP90KbhtemT/7Qg8HZ3RDnNEwrUpjsYjl9x4cYWcUnbarfy0sR9+ijrPjZpdu3bhyiuvRGJiIhYvXuxP7I899hgeeeQRvPjiiyguLsbDDz+Mzz//HEePHoVOpwMA/PSnP8U777yDF198EUajEXfddRc6OztRWVkJqVQ64ntTYo8NJDNKcGalAeqz2nFx9mFMUbWgxaXHB82lqK/MQP7GPnDb90U7zBEJPoEFl1SGg1xy48kVsvPctNU8JfanxZXYI1YV39PTg2uuuQZ//vOf8fDDD/v3M8bw5JNP4r777sPll18OAHjppZdgMpmwYcMG3HzzzbBarXjhhRfwyiuvYOnSpQCAV199FdnZ2fj444+xYsWKUcchk3oh47z8fjhiVEiTEtGyQAc2tRvz004jT96GfrccB63paKgxIGOvC/Kde4GRr9Oiyvv2fwCeb5aMvVh1FTa5Bu8LB7nkxptrs9lGdInIErHEvnr1alxyySVYunTpoMReU1OD5uZmLF++3L9PqVTiggsuwLZt23DzzTejsrISLpdrkJORkYHy8nJs27YtZGJ3OBxwOBz++76D678uOQq1PMYzh0hxl2TClt+AtMQu5He3Q9HjRqsrEV4Lh4WWeujQAraS/zXW+eT6314JHFoLAHj5oX8CAN6vvBIvf2/WYCcIcsmNV7ff5RniRAxaBCYkEamKf+211/DII49g165dUKlUWLRoEWbNmoUnn3wS27Ztw8KFC9HQ0ICMjAz/c3784x/jzJkz+PDDD7FhwwbceOONgxI1ACxfvhz5+fl4/vnnh7znAw88gAcffHDI/ue+XUyJPQpwGWnoKlZCmdGHKYltSJb2otujxomeVPQ0JiC52gHW3BrtMEck8ETmO4EF7g+1j1xy49m19TigX/ioIFXxpbfwUxVf/Yy4quJ5T+x1dXWYO3cuPvroI8ycORMAQib2xsZGpKen+5/3ox/9CHV1dfjggw/CJvZly5ahsLAQzz333JD3DVViz87OxlLpZdTGLjBSUyqaLi9E7zl9WFJ0FHO0Z9DnVWJrVyF2HypA5kccNBt3RTvMsLxv/weAgapF320fofaRSy653+yz2WzQG1IpsUcR3sexV1ZWorW1FRUVFZDJZJDJZNiyZQv+53/+BzKZDCaTCQDQ3Nw86Hmtra3+x8xmM5xOJywWS1gnGKVSicTExEEbALg9EtoE3joXTEF7KVCS3YJibSu8EgmOOszY3ZAD7X4VFG/tiXqM4bb3XW9imfwaQKqA2yMBpIpBW/A+csklN8Q+oeBxHPu8efNQWlqKp59+Wrj4IwTviX3JkiU4cOAAqqqq/NvcuXNxzTXXoKqqCgUFBTCbzdi0aZP/OU6nE1u2bMGCBQsAABUVFZDL5YOcpqYmHDx40O8QsQlbMBNtMyXIKGjHTH0DdNJ+1DsN2NWWA8nRBKRvbgdidBbjUGPUfYQa6kMuueQOdYWcoIZmngsN74ldp9OhvLx80KbVamE0GlFeXg6O47BmzRqsXbsWb731Fg4ePIj//u//hkajwdVXXw0A0Ov1uOmmm3DXXXfhk08+wd69e3Httddi+vTp/l7yROwhy85CyzwtUNyLipQ6ZCk60e1RY581Ey2nUmDe6YKn+li0wxwR38lqNON5ySWX3KGuYNDMcyERZBGYwDZ24JsJap5//vlBE9SUl5f7n2O32/Hzn/8cGzZsGDRBTXZ29qjek8axC4xEiu4r56FpkRfzyk/ivOQT0Egc2NuTiw+PT0PiZjVS/rQ92lEOYaTxuSOdzMgll9zBCDlBTdnN/LSxH3qe2tjHzObNm/1JHQA4jsMDDzyApqYm2O12bNmyZVBSBwCVSoX//d//RUdHB/r6+vDOO++MOqkTwuM9bwbaZ3DIzGvHjMSBKvhapxE7W3MgP6yB6dPmkV9EYHxV7z58t5dJrgh54iKXXHJH5woKldaHQHPFExNGOiUfTRVqSIu6MTe1FunyLnR5NNjblY32kwbk7nTCc6Im2mEOItRMcqEeWyYZ2rZILrnkDu8KRWAb+UReQ2zQ6m7EhJBoNOg824TuaS7MzGhAkboFHkhwuDcdh+rSYaySQPHh7miHOYjAknrgySlUtSK55JI7MZcQHkrsxIRwnjMNHeUc8vJaMUPXAK3EgVqHEbuac6A6pEbaprpohzgI34lnpBNQuJIKueSSOzpXEKjzXEgosRPjRlpajNY5SiiKbJhjqEOGwoJOdwL2WLJhPZGM9B12uOvqox2mn8BSBjC0zTCwLZFccskdvysUfA53ExOU2IlxIU3So32eET0lTsxOH6iCdzIZDvVk4MiZdKTs5SD9bE+0wwQwcMIJV53ow3fSCt5PLrnkjt0logsldmJc9J9djM5yoCC3FdN1DVBJXDhtT8Hu5mxoq5UwfnAi2iEOS3DJYrjSBrnkkjsxN2LQzHMhoV7xxJiRzChB2yw5NEUWzDXWwiS3os2tQ2VnDvqOJSH/y1542tqiGqOv5BBcbeg7+YymLZFccskdvysEfPaK37VrF41jJ+ITaWoq2ucmo3eqAxXmeuQr22D3ynGgOxPHTpuRuoeB27Yv2mH6CSxFjHQiIpdccifuCjmlLBEaSuzEmOg7Kw+WMobinBaUJjRCJXGhxpGKPU3Z0B1SQP+fQ9EOMWyVYPDJKVRnIHLJJZcfVxCoV3xIBJlSNhrQlLL8w1WUoW6FHrKzLLgk9xCK1c1ocenxQXMpGnZnoODNbrDdB6MWX/DJJvh2KMgll1x+3Te7XhBsStkZ/70WUsUEp5R12rH/RZpSlohDZJkZaJ+TiP6pdsw11yFP1e6vgj9dk4a0Sm9Uk3ogw5U2fBu55JIbWVcIaLhbaKjzXJTgZDIwtzvaYYwKTq5A99wsWMoYSnKaUZbQCAXnxoG+LFQ2ZEN/QI6Et3dGpUYrsCQRWE0YqoRBLrnkCucS0YOq4qOERKMBczonR3I/ewbqliZAdVYHvpVTjSmqFrS49PhPYzladplR8E8LvPuPRCW0cCeU4OpBcsklVxh3MbcKm7FRkKr4mdfzUxW/72Wqiid4gFMqAS72v35ZXg7aZmnhKOlHhakeOcoO9HqV2GvLRl1NKky7PIIn9dGUEAKrB8kll1zh3I3Wl4d1+YRjjJdNbMR+ZhEjHAfIZADzRjuSYZFotbBWpKOr1Iuy7CaUJTRAwblxvN+EvfVZSN4nhXrjTkFjCixBhDrZ+E4uwaUNcsklVxiXhrtFH0rsUUCiVAISLuar4T2zitBRLoWxwILZSXVIkvah0ZmMXW05kBxOgPmTVsFj8p1IAu8H7gs8+ZBLLrnRcwWBhruFhNrYo4A0MRFQKqM+O9twSIsK0LTcjO6FfVhedAQVCadhZ3J81jEVe/YXIuddL5Tv7xI8Ll+V33BtfeSSS270XCGHu82+5hFe2tj3/v0+UbWxU6/4aCDhAK8n2lGERZqcDOvsNFhLPZiV1YBpmiYAwOHeDOyry4KhSgLl+18JGlNwlaDvhBLqxEMuueRGz13MrQIRXajEHgWkyckA88LTZY12KCHxXjAbdUtUSJ7XiosyDiNf2YpaZwrerS+HbVsa8l+th/t0bcTjCFdaCFeCIJdccqPvClpiv5qnEvuG+1BcXAypVIrVq1dj9erVPEUaHaiNPRpIODBPbHack04rQttMNbxFfZiXWossRSesHi12W3LRejwF6dsdgiX1wL++EkNwCSLUc8gll9zouUJ2nuNzgppdu3ahurp60id1gKriowPHAZ7Yq4qXphjRNdMI6zQ3KrIaMFXTDAA43JeOA3UZMFZxkH1SGdEYQpUMxvI4ueSSG/suEVmoxB4NOAngjbESu0QK5/RcdJRzyMhvxyx9PXSSftQ6jfiqOQeqQ2qkfVAT0RBCVesF/iWXXHJj3xVyHDv1ig8NtbFHAakpDd7OLjCXM9qh+JGWFqNhWQrsC3pw8ZRqzNLWosujwcdt03B4Ty4KNjog2bJXkFgCTyKB7XihqgXJJZfc2HLdzCXYzHMV3+Onjb3ydeoVT/AAi6GqeFm6GZ2zDLBNc2NeZgOK1C3wMAkO9WSi+kw60vYgokk9+GQR7rHgfeSSS27sub6kKwh8lLhFWLSlqvho4HbHzHA3aWIi+mZkoaOcQ2ZeO2YlDlTB1zhSsbMpB9qDKhjePRyx9x+uBBD8l1xyyY19l2aeiz5UFR8FpEn6mBjqximVwIxiNJ6vg3N+Ny4qPOyvgv+gpQwnd+eg4P96gJ0HIvL+wdV4PkY6qZBLLrmx6/rOvYJUxV/5CGTyiVXFu112VP5TXFXxVGKPAjEx1I3jIMnLRmdZArqnujArswEl6iY4mQwHurNwtCYdabtZRJJ6cBXeJu/QdrzA++SSS+7kcwWBMX42kUGJPRrEQPu6LCsTPdMMsJQAuXltmJ1YB5XEhdP2FOxsykHiAQV0b+3h/X3D/fhHqvYjl1xyJ5dLRA9K7FGAuaK7+Is0xQhnQSo6p8ogn9KNCmMtTHIrOt0J2NWZC8cRPTI+s0Sk136oqrzAv8A3yZ9ccsmdvK4Q8DlBjZigXvFRIJo94iU6HbzZZliKVegtcuKc9IFe8E4mQ1V3Fk6cMiNrpxfeffx3mAs8Efhuh7rCDz5pkEsuuZPPFQTqFR8SKrFHgyj1iJeoVODS09A9JQFdxUBebhtmJtZDJXHhpD0NlY3ZSNonh+Ytfhd4Gc2PfiwnBnLJJXfyu0TkoBJ7nMDJFZCkGNGfl4yuIimUBVbMMdTBJLeiza3DzvZcuA8nIveTNvB52eGruvP94H1VdcPdJ5dccie3KxScd2Cb6GuIDSqxxwGcTAZJkh6u7BR0FcrRN8WJ2QFV8Pts2Th90gTzDg88h4/z/v7BP/5QhDtRkEsuuZPPFQyaUjYklNhFDieTQZKgBTMZ0Z2vhm0KQ0FOK6brGqCSuHC834Td9dlIrpJC9e5OXt873InAV4onl1xyxekS0YUmqBExnEwGTq2GJNWI3mmpaJ0jh3ROFy7KPYwyTQNaXHr8p7EcrTvSUfhSI9ynTk/4PQN/2IFVd4GEqrYjl1xyxeEKuR77WSsf5mWCmp0bf03rsRMh4LhoRzAITiYDp1RCkqiDMzMZXQVy2AsdmG2uR6GqFXavHHtt2ag7mYr0bc6IJXXfjz/wyn6T9w1yySVXpK6gU8ryOEENrcdODCbGkjokUnBKJTiNBp60ZNjyVOgu8KIw+5sq+AO9WdhTlwXjHikUH068Cj64us53Mgi+0g/cRy655IrP3Wh9WbBFYPgYh07j2ImQSNRqePv6oh2GH4lKCU6lBAx69OVoYSvgoM3vwhxDHVJl3Whx6bGjLQ+ygwkw/ecUJjJdTuDVvO9+qNvhnksuueSKy7XZbMP6ROShNvYJIlGpAKkU3t7eiL3HWOCUSkg0GnB6HRz5KWibqUL3bDsumHoc5+hPwss4fNpZgt17pqDgXy5IN49v2tjAq/Xg6rjAxwMhl1xyxe8KuQjM/Et/x0sb+1fv/kZUi8BQiX2CcFoNPJ2WaIcBYCCpcwoFOI0anpRE2HKV6M73oiirFdMTGqDg3Kjqy8Ge2mykVEow3qQejlBVduSSSy65kYKq4kNDJfYJIE1MBOQyeDo6I/L6Y4FTKiFRKgGlEkhNRndJMtpmSqGe1YmLc6pRom5EvdOIf9dPR/cXach99tC4l46N9gmCXHLJjV1XyF7xZ1/CT4l9x3+oxE58DafVwGuNfnuSP6kr5OASNLCbdbDmSeEq6MdCUz3yVO3o9qqxuysHbUdTULi5N6JJfbhqPHLJJVfcrqBt7HwsuyrCsi0Ndxsn0uRkQCKJeqc5Tq7wl9Q5ta8KXoGefA+KMltRltAIBefG0T4z9p3JQtougNu+b8zvE6pNLRTh2t/IJZfc+HCFHO5Gq7uFhhL7OOESE8D6+qMbg0wGiVo1kNSVCnj1CejN1sBWACTldmGuoRYpMhvqnQZsb86HZr8a+jfG1q6+yfvNWNVN3tBrqQNDr+7JJZdcconoQIl9HPhK6x5LFDvNSaTg1OqBpK6Qg2nVcGQkwJonhTvfjgpTPfKVbQNV8JZcWI4YkPmxddxrrAf+iH0/ZN++4B8zueSSG7/uRuvLEAyaKz4k1HluHMjyc4F+O9zNLby+7liQaLXgNBpwSgWYSgGXWY/OUjUsM7woKavD8rRqJEn7sLs7H+9VlyH9XTkS3hj9cqzB1XCBP+xwj5FLLrnkCtl5bsGKh3jpPLftw9+KqvMcldjHiDQ5GUwui25S12j8SR0KObxJWvRkq9BdACTldGFOch0M0h7UOw3Y1pSHhH2qUSf1wKvzYIJ/wD6fXHLJJTc44RPRg3rFj5WUZMDaHbW395fUVUpALoNXp0Zfpga2fAmQ1zOoCn5HZz66qw0o+rBjVGusj/SjDHycXHLJJTecKxheNrBN9DVEBpXYx4AsNxucxwtPS2tU3l+i1YJL0ILTqMCUcng1KjhMWljzZOgrcKI8ownTtE0AgP09WTh0MhPpOzzwHDo64msHX5UH3w61j1xyySU3nCsI1MYeEkrso0SapAfTqOA+XSf8m3McJDrdQFJXKcEUckCpgNuohi1Hjp58L7KzOzBT34AEqR01jlRsb8xD0l4F1G+PvMDLJu83vVmDr7qDq+LIJZdccodzBR3uBh6GuwkWrXBQYh8t6WngLDbAO5pKbR7hOEgSEiDRasCpVQNJXSGHO1GFnkwluvMBbZ4Vc1LqYJJb0e7SYXt7PhwHk5Dxn4ZRvUXwjzPwqnukHzK55JJLbqAraK94IiTUK34UyArywKQSeI6f4im6USKRQqLVQKLVAL6SulwGr0aB3iwNOkukcJT3Y0HBScxLPAMPOGyzFGLXgULkbWRQfLBrVG8Tqm0s8Eo98G8oyCWXXHJ9rpCLwCxc8gBksgn2infbsfWTB0TVK546z42A1JQGr0YFduSEsG8cLqmrZLCnqNCdLUVfngtlmc0oS2iCnHPjcG829pzJgXG3FIoPto/qbUJdcY8Wcskll9xoQovAhIZK7MPAyWTgSqdA0tkNd/3oqrV54eukzmnUA9XvSgWYXAYml8KVrII1X4GuUgZTWSsWm48jS9GJWocR79eWwrXNgJy/HBnTwjS+K27f7VAEPk4uueSSG85dzK3CZmwUpMR+7oX8lNi//JRK7HGDpCgf8DBhkzrHQaJWhUzqngQF+sxy9OQCyrxuzDY2IEvRCatHg12dueg5nIzCT2yjTuqBP9jg+8M9Ri655JI7nCsYfPRq//r58+bNg1QqxerVq7F69eqJRhZVqMQeBllWJjypSeBO1MLbLdy4df84dc3XSV0hB5NL4dXI0W9SwlIkRV+ZHWcX1uCspBpIwbC1qxBfHZiC7PcB9caRe8EDoX+YPkLtJ5dccskdjSvkzHPnLbqflxL7F5sfFFWJnXrFh4CTyeBJTYLU0i1sUlepwKlVA0PaApO6SgZnkhzdmVL05rlRlNmKcl0jVJwLR/vM2FObDcMe6ZiSeqi/wOAesIH7ySWXXHLH4hLRg/cS+7p16/Cvf/0LR44cgVqtxoIFC/DYY49h6tSpfocxhgcffBB/+tOfYLFYMH/+fDz99NMoKyvzOw6HA3fffTf+8Y9/oL+/H0uWLMEzzzyDrKysUcUxkRK7dFrRwCIvo5jYhS84pRKSBC04tRpM/XVil0vBlFI49Qp0Z8lhnQoklXbgwsxjyFe2od5pwPt102DfloK8v50c9TS3m7zRr6ojl1xyxem6mUuwNvbzzuepxP65uErsvLexb9myBatXr8a8efPgdrtx3333Yfny5aiuroZWqwUAPP7441i/fj1efPFFFBcX4+GHH8ayZctw9OhR6HQ6AMCaNWvwzjvv4LXXXoPRaMRdd92FSy+9FJWVlZBKpXyH7UdqSgNTysEOn4zYewTDyRUD7eoqFZhqYP53JpeCySVwa+ToT5GhN5uDNK8bc9LqkKPsGGhX78iF9YgRhZ/1jCmpj3SbXHLJJXe87kbry9Dr9UP8SMAxBm6CZdOJPj8W4T2xf/DBB4Pu/+1vf0NaWhoqKytx/vnngzGGJ598Evfddx8uv/xyAMBLL70Ek8mEDRs24Oabb4bVasULL7yAV155BUuXLgUAvPrqq8jOzsbHH3+MFStW8B22H2Y2QtJuhdvhiNh7DEIiHUjqajWgVABKBbwKGZhcAo9GDrtRht5MCey5DlSkN2GqpgVeJsGB7kwcO5WOzB1eYMd+XkLxVbOF+hGTSy655I7WJaJLxDvPnThxAkVFRThw4ADKy8tx6tQpFBYWYs+ePZg9e7bfW7lyJZKSkvDSSy/h008/xZIlS9DZ2Ynk5GS/M3PmTKxatQoPPvjgiO/rq6pZKr1s1FXx0qICQCqBR8Ax6xKt5uv539VgKiWYQgavQgKvUgZHshy2HBm6iz3Im9qM81JOIk1uxeH+dGw6WQLN51qkPj/6pVgB4H37P3Cx6iq8b/9HyMcvVl1FLrnkkjtu12azQW9IFaQq/vxzf8tLVfznXz4kqqr4iCZ2xhhWrlwJi8WCL774AgCwbds2LFy4EA0NDcjIyPC7P/7xj3HmzBl8+OGH2LBhA2688UY4gkrNy5cvR35+Pp5//vkh7+VwOAb5NpsN2dnZeO7bxVDLR666l2i1YDoNWEsHwLzj/chjgpPJwCkUgFwOyGVgMgkgkcAr4+BVSuHUSWA3AMqUfuQldsAo60W3V4WTthQ46rVIqrLCa7ON6T2v/+2Vg+6//NA/h+wPtY9ccskldzTuqjtXQr/wUWES+8Lf8JPYt/5OVIk9ouPYb731Vuzfvx9ffvnlkMc4bvDU+4yxIfuCGc5Zt27dqEryIeEkYDotuD47mFBJXSIBJ5MDMhkgk4JJB5I6k3LwyiVwqSVw6gBO74RZa0OSrB8OJkNjvx69HRokn3aNKakH/wiDH3v5oX8O+ksuueSSOx73+d+ELu1HApp5LjQRK7HfdtttePvtt/H5558jPz/fvz9SVfHhSuyjqYqXlhaDcRy8QvWC5zhIdQngdAlgahWYWgGmkMKrkMKjlqHfKEdPrgQ9xU7MKKzHOYaTUHFu7O7OwdbDRTBvkkH3xuiGtvnwVZsF3g61j1xyySV3Iu7rrc8LVhV/wQJ+SuxbtomrxM57YmeM4bbbbsNbb72FzZs3o6ioaMjjGRkZuOOOO3DPPfcAAJxOJ9LS0vDYY4/5O8+lpqbi1VdfxZVXDlwdNjU1ISsrC++9996oOs+NdribLDMDnnQDuIMn4LXbJ/DJR49Eo4EkUQeWoBloV1fJBpK6SgZHsgw9GVLYijzImtqKxeZjSJd34Xi/Ce/XlEK5ORFpT20b0/sFdnYJHqoSbh+55JJL7nhcIYe7XXDOr/lJ7NsfFlVi570qfvXq1diwYQM2btwInU6H5uZmAIBer4darQbHcVizZg3Wrl2LoqIiFBUVYe3atdBoNLj66qv97k033YS77roLRqMRBoMBd999N6ZPn+7vJc8LEim8qUmQtnTBLVBS55RKcFotoFYNSupepRQunRT9Rgn6MhkScmyYk1KHNLkNLS49drTlgR1MRMZ7jXCP8r2Ce6/6erUG3g/lkUsuueSO1/UlXSHgvAPbRF9DbPA+89yzzz4Lq9WKRYsWIT093b+9/vrrfueee+7BmjVrcMstt2Du3LloaGjARx995B/DDgB//OMfsWrVKlx55ZVYuHAhNBoN3nnnHV7HsEuL8sE5XHDX1fP2msMikQ5MQqNVg2lUYGo5vErZQFLXymBPlqIvnYM3px+zTA3IU7XD4ZWjsisHTUfTkLnFDvep02N+28Ar60B8Q1cCHyeXXHLJ5cMlokfczhUvSzeDGfTCzS7HcZDqdOCS9WAJGng1ioGSukICt1oKe7IUPVkS9E5xoayoHucZTyBBasceWy4+O1qMtI8U0P99x6jeKlx1WeDjoa7GySWXXHIn6gq5Hvuis+7jpSp+885HRFUVH7eJXTqtCKy+WbC54KVJenA6HZhOA69WCY9yYGY5j1IKh16KXrME3QVemIrbsNh8HDnKDpy0p+H906WQfq5H+lM7wdwjV8IHXzX77oe6miaXXHLJ5dsVso190TyeEvsucSX2uFwERmY2Ae1dgiV1iVYLTp/4TVL3dZZTSuFKkMBu4NCfzqDO6sbslAZkKi1od+vwVVseXIcSkfl+66iSuo/Aq+zA+5u8Q2eNIpdccsmNhEtEj7grsXNKJeBlYC6nIHFwcgWkaSnwJieCqeVfJ3UJvHIJ3BoJ+g0S9GZycE7px7yCMzhLfxoA8GVnIar2FyDv3x7IP9o94vsEXk2PBnLJJZfcSLhCVsUvnvsrXkrsn+1eSyX2yYpEowFzOARL6gAgSfK1qcvhUUrhlQ/MLOdRcnDoJLAbOdjT3SjIaEe5rhFKiQtH+0zYV5uF1J2SUSf1wL9A+E4uwc8hl1xyyRXK5R3G+NlEhugTu9SQDJnZBGmKEd6+PmHfO0kPJOng1ang0cjhVUrhlXPwKiRw6iRwGDj0m7xIyerCrOR6JMt6UeswYkdjHjRVahjeHP3iLoE/qHDtYOSSSy65kXZX6q8P6RLCIfqq+AVLH4Cutg+e46cEfX9pkh5INcKTrIVbp4BHJRmY3U7BwakdKKn3pTPICnqwILsGZQmN6Pao8EnzVLTsMqNwQwc81cdG9V6+K2bfjyz4hxiqSo1ccsklNxKukJ3nFs+5FzLpBKviPXZ8tmedqKriRZ/YR5p5LqJwHKSGZHAazcA66xIJmFoBp0GN7mwFLGVA5qwmrMqsQobcgq+6C/HWgdnI/j8pVO+OfsrY4MQ+0j5yySWX3Ei5QraxXzj7l7wk9k/3RnbRGqERfVV8VGEMno5OuOvq4Tl+Cp6jJ+CtqoZ860FI3AyeNCdmGetRpGyG3SvH/q5MaA8px5XUA28H7wt1lU0uueSSGylXMBh4aGMXPuxIQ4k9CjjPK0f7HGBO4RmcnXASHibBlq4S1OzNRM6G06N6jdH+iMK1jZFLLrnkRsologtVxQuMpLwEZ1YakHxeM67L+Qp5inZs752CV6rmI/9lDrJPKkf9WqNJ7oHtX+SSSy65kXYFrYqf+QvIpMoJvZbb48Cn+x4TVVU8JXYBkZlNaFpVgN7Fvbi6ZDfma0/itDMFfz65EJK3jDD8dfuoX2uTN/QsUOF+dOSSSy65QriCJvbpPCX2A5TYJwWxltg5pRLd356FxqVeLJ99EJck74OdyfFq0zk4sakA2Q+PfinWwB9RtK/OySWXXHIDocQefSixC4Rn8RycWaHE1LNP46r0nUiS9uJ9y0y8t2MWSp7phOfw8RFfY7ir5MCraN9+cskll1yh3cXcKsGGuy0pv4eXxP7JwccpsU8GYimxS6dOQe130qBc2I7rCnaiVNmA3X0F+NvBc5DxDwVU74yvFzyAIT++wORPLrnkkiu0+2bXC4KV2JeU/ZyfxH7o96JK7NQrPsJIk/RoW5AKx4w+rMg6ghJlE+pcRvynsQwJWzWjSuq+H1DwjytwX+APjVxyySU3VtzJwrx581BaWoqnn3462qFMGErsEab/7GJ0zPbi/IITOCvhFHq9SnzYUYaOnSaYX9gz4vPD/VACq8aG88gll1xyo+EKAo9zxe/atQvV1dVYvXp1dD8TD1BijyCyrEy0T5cjp6QFFyYfhopzYUdPIXYdKkDuuz3w2u0jvkZgVZjvdnDVl28fueSSS24suIJBi8CEhBJ7hJBoNLCcmw377D5cnH4I2fIOHHGk472aUqR/JgV2HhjT6/l+VIGE+yGRSy655MaCS0QJJlKsVisDwBZhJVvK/Zeg2zLVNezC8x9hef/vD+yHu65j75+cxv55fA678NM7WOnP14/6dRhj/r/BtwPvk0suueTGius791qtVhYpfO+xZOpdbEXprya0LZl6V8TjFRpZdC8rRIZEColKCS43E/UL1cgrr8di/RFIOS8+s05Dzd5MTP37KbhH8VIjXfkGVouRSy655MaaKwQcY+AmWJU+0efHIlQVzxOcXAFpghaSVCPaz0qBa3YPLjEfRIbcgn39ufjg+DTkvu+Cu6l52NcZ7scSWAU20o+KXHLJJTcarqDrsVMbe0gosfMAJ1dAkqAFp0tA3zQz2ud5sazgKMpVdWh26/HvxhlI/EI97DzwgT8Y3+3Aq+Nggn9o5JJLLrmx5BLRgxL7BOFkMkgSE8Bp1HBnGtEyT47i0nqcrz8CANhkKUfjnnSk/WXkpB7uajj4R0QuueSSG6vuRuvLIZ2I4GX8bCKDEvsEkSTpwanVYEk6tM9KAGbacJHpENKk3dhnz8EnR6ci751+MJcz7Gv4rnYD/4baRy655JIb6y5VxccA0e69FymE6BW/wvAjdlHOGrZi6i/Ygst/z3Kef5zdtuf77KNTU9nLx+azsz+8h83+4RPDvkYw4XqjkksuueROBlfIXvFLC25nFxXdM6FtacHtousVT3PFjxOJTgeJIQlMqYAzQ4/6xSpMuaAGN2ZshVbiwOvtZ2HbpnLk/Sb0UqzBVVq+q93hHHLJJZfcWHeFXN1tacHPIJNMcK54rwMfn/ofUc0VT8PdxgEnV0CiTwRTKeFNVKOjXAXFTAsuTjsIo7QHX/YWY/OhqZj6tg2hrppCtV0F7g8FueSSS+5kcgWBj6p0EZZtqY19HEiNyWBaNZhaAVuhFl0zXViRcwQlykbUugzYWDsDaZvlYJWHhjx3pM4no4FccsklN5ZdIrpQYh8j0iQ9mE4LppLDbtaifSaH2VNP4zzdUbiYDO91zEDvVylIemVwFfwm7xuDfhiB1VqhOqQEQi655JI7WVxBO89Rr/iQUBv7GOBkMkizM+FN1MCtU6J1ngbSxR34SdEXKFY047OeaXh5+0JM+38WeA4fH/TcwOr3wH3DVV+RSy655E42V8j12Jfm3MJPG3vtM6JqY6cS+xiQmtLg1anhVcthy1fDNsOBb+VUo1jRjJPONLxdMwMZn3BDknogwSX34B9M8A+FXHLJJXeyukR0oMQ+SqSJiWD6BDClHPYUFTrLgfnFNTgn4Th6mQL/aZsOz45kJLzx1aDnBR74wT+I4H3h9pNLLrnkTkY34tA49pBQYh8taUZ4NQq4E+ToLJEhubwdFxkPQss5sbW7GFX7C5D799pBTwk+2Dd5v5l6MdQ+cskll9zJ7lIbe/Sh4W6jQJaZAU+SFh6tHLZcJXqn23F5VjUKFK044sjA2ydnIPtDBnddfcjnB1dVLZOEb7Mil1xyyRWLG3FouFtIqMQ+AtLERHjSDXAlKtGfqkBnOcPZU2pwjvYEujwa/LtlJuRbE6F6Z+eg5wUf6MFXtuSSSy65YnQFnSueCAkl9hFgeRlwGlRw6mWwFEmRWtaG5cZDUHEubLGV4PCeXGS+eDjkc5dJvhkaEu7KNvAHQi655JIrBlcwGHhoYxc2ZEGI7oy2kYOPueIvyr+TLVq6jp3zX79nM1avZ3l/f4T9dv+32Senitn/HF7MCl9/iC1aui7s3O+h5lkO9xi55JJLrhhcQeeKN/+YXZRx64S2peYfi26ueCqxh0Gi0cCZZYAjSQaHXgLrVC8WFp7EfO1JdHk1+HfTTGg/TxiyxnrgFetwV7O+K15yySWXXLG5RHShxB6OKTmwpyrh1EnQk8MhY2orliZXQ8658al1Gk5VZiPtuaFD24IZ7odBLrnkkitWVxC8Xn42sRHtKoNIMZGq+BXmW9h533qMzb3hCVb68/Ws8LXfsd8duIR9cqqYPVG9lOVveIQtOffhUVfBD7cMIrnkkkuumFxBq+JTb2IXmX46oW1p6k1UFR8PuAvSYTdI4dBz6Cl047z8k5ijOY0OTwI2Ns5E0mYVuK1Vg54T2MFkuCvZwCorcskll9x4cQnhoMQehCw/F71ZKjiSJOjLZMib0oILkw9DAi8+6ipDw64MGP88dI113wEerq0p+OAnl1xyyY0HN6LQzHOhiXaVQaQYT1X8Mvn32XnfeozNuHU9K35gPSv+vwfZ2oMXs09OFbNHD61guS+vZcvm3j/kecEMV1VFLrnkkitmV9CqeMON7KKUmye0LTXcSFXxombmVPRkyuAwAPZsJ87PPYGZ6lo0u/V4u34mUj9RhlxjPZixVEeRSy655MajS0QOmlL2a6SpqbAW6tCfysGe5kFxfjPO1x8DAHxoKUfnDjNyXt427Gv4qqF8B3dgm1Oox8gll1xyxegKBWNeMDaxXu0TfX5MEu0qg0gx1qr4C897mJWvWc/yn/gDK9/4G/booRXso1NT2e8OXMJyXniUrSi9N+xzGYudajByySWX3Gi6QlbFL0m6nq1I/uGEtiVJ14uuKp4SOzcwvG3u9U+wKY88wfL+/gj76e5r2H9OlrGXj81nc/7zKzb/+38Y8TUYY4Nu++4H7yeXXHLJFbMraGLXX8dWJN00oW2J/jrRJXaOMTF2CQRsNhv0ej0WYSVknDysJy0qgGVuGqyFEvRnuVFWUodr03dAJ+3H31vOwb53pyFr3fBV8MDgKqjAKqtAQlVXkUsuueSKyX2z6wXo9XpYrVYkJiYOeR4f+M7vS/TXQcYpJvRabubEJ9ZXIhqv0MRtYpcV5KF3aiq6c2ToT+PgMHqRkGfF1YW7MVt9Glt7i/Hy1oUoXdsAd31DyPcIPtiHa4MKhlxyySVXjO5ibhU2Y6MwiV13DT+Jvfvvokrs8dcrXiKFdFoResrS0Jsug1PHwaVl4NLsODvjNKapGlDnMuJfp2Yi+yOETerANwd7qNubvG9gk3fw5A3kkksuuWJ3BV22lcaxhya6LQGRY7g29mXy77PlM3/NFlz+e1Zy33qW+/Ja9p0vf8JePjafvXViBlv5xU9Z6c/XD3leqI0xNuRv8O1AyCWXXHLF7C7CSuHa2BOuZit0/z2hbUnC1aJrY4+/EjsA5naDHa2BI1GCviw3CrPacIHhOFKlNuzomYKqqgJk/+XQiK/jq3oKbmMKZJnkikH7yCWXXHLjwRUC5vXysomOaF9ZRIqResUvvnAtK1z3BJv17q/Yvfu+w/59spw9ffgCNvXNB9j5Fz066tI6Y+GvZkM9Ti655JIrZlfIXvEXqr/Hlmuum9B2ofp7VGIXA9IkPdpmqMDy+jHXVIcZmlrYvQr8u2UmFFsSofhw97DPD9eBBPjmqjX4ypZccsklNx7clfrrh/WIyBOXveLtl52Fuos4zCg/jW+lHUCOvAMfWKfjP5/MQ+F9lWAu55DXGy6Z+/Ad2OSSSy658er6zr1C9Iq/UHklL73iP3X8k3rFT1akiYnwLJ6DtpkypOV34BzDKeTIO3DcYca7R8tR8K/ekEndx3BXrMskg8d6kksuueTGoytoiZ0xgHknuImvbBs3iZ1TKtF33lQ0na2CZ3oPLkg/gamqJrS6dXitrgKp76qAHfsHPcc3xCNwmEcwwVe15JJLLrnkEuOlu7sb8+bNw6xZszB9+nT8+c9/HvNrxEVilxoN6P72LLTMlaNvihNTza2o0NYAAD7omA7rZjMSN+wY9BzfwRp48AbvCz6IySWXXHLj3RVyHDvzMl62WEKj0WDLli2oqqrCV199hXXr1qGjo2NMrxHzq7s988wz+P3vf4+mpiaUlZXhySefxHnnnTfq53MyGawXFqFtjgTI70FBShdm6BuglTiwtbsYO6qKUfTY9hFfJ/BAHukKlVxyySU3Xl03cw3ZHzGYF8AEh6vF2OpuUqkUGo0GAGC32+HxeDDmrnBR7pU/LK+99hqTy+Xsz3/+M6uurma3334702q17MyZMyM+1zcc4pxlD7LCR59g5Rt/w67Z8QN2594r2NqDF7O1By9mU/75EFu0ZF3IIWyMxe4EEOSSSy65seoKOdxtEfcdtlRy5YS2Rdx3xhTvli1b2KWXXsrS09MZAPbWW28NcZ5++mmWl5fHlEolmzNnDvv888/H9PksFgubMWMGU6vV7KmnnhrTcxmL8eFu69evx0033YQf/vCHmDZtGp588klkZ2fj2WefHfVr9GTJgLw+nJd5CvMSz0ApceNYrwkfNJdCuTMBsk8rhzwn+Eo0sBpqpCopcskll9x4dsU+3K23txczZ87EU089FfLx119/HWvWrMF9992HvXv34rzzzsPFF1+M2tpav1NRUYHy8vIhW2NjIwAgKSkJ+/btQ01NDTZs2ICWlpaxBTnmSwGBcDgcTCqVsn/961+D9v/sZz9j559//hDfbrczq9Xq32praxkAlvvL37BVm/6b/XnPHPZM5Vx20fs/Yrl/+Q3L+p8H2QVn/5otwkr/Fvj8wPvDPUYuueSSS+43t+vq6hgA1tXVFbH8YLUOlNjPxbcGncPHs52LbzEArK6ubtBns9vtI8aBECX2s846i/3kJz8ZtK+kpIT98pe/HNdn/clPfsL++c9/juk5MZvYGxoaGAC2devWQfsfeeQRVlxcPMS///77GQDaaKONNtpiYKurq4tYfujv72dms5m3WBMSEobsu//++0eMAxic2MdaIA1Fc3Mzs1oHmgWsVisrLS1l+/btG/V3wxhjMd95juO4QfcZY0P2AcC9996LO++803+/q6sLubm5qK2thV6vj3ickwGbzYbs7GzU1dWJZiKGiULfyWDo+xgKfSeDGen7YIyhu7sbGRkZEYtBpVKhpqYGTmf4eUfGQqi8olQqx/w67e3t8Hg8MJlMg/abTCY0NzeP6jXq6+tx0003gQ0UvHHrrbdixowZY4ojZhN7SkoKpFLpkC+jtbV1yJcGDPwTQv0j9Ho9/RiDSExMpO8kCPpOBkPfx1DoOxnMcN+HEIUplUoFlUoV8fcZD6MtkIaioqICVVVVE3r/mO08p1AoUFFRgU2bNg3av2nTJixYsCBKUREEQRBEaMZaII0UMZvYAeDOO+/EX/7yF/z1r3/F4cOHcccdd6C2thY/+clPoh0aQRAEQQwiVgqkMVsVDwDf+9730NHRgYceeghNTU0oLy/He++9h9zc3BGfq1Qqcf/994+rnUSs0HcyFPpOBkPfx1DoOxlMvH8fPT09OHHihP9+TU0NqqqqYDAYkJOTgzvvvBPXXXcd5s6di3POOQd/+tOfBC+QinZ1N4IgCILgm82bN2Px4sVD9t9www148cUXAQzMmPr444/7C6R//OMfcf755wsWIyV2giAIghARMd3GThAEQRDE2KDEThAEQRAighI7QRAEQYgI0Sb2Z555Bvn5+VCpVKioqMAXX3wR7ZAiwrp16zBv3jzodDqkpaVh1apVOHr06CCHMYYHHngAGRkZUKvVWLRoEQ4dOjTIcTgcuO2225CSkgKtVotvf/vbqK+vF/KjRIR169aB4zisWbPGvy8ev4+GhgZce+21MBqN0Gg0mDVrFiorv1kAKZ6+E7fbjV//+tfIz8+HWq1GQUEBHnroIXi93yzfKfbv4/PPP8dll12GjIwMcByHt99+e9DjfH1+i8WC6667Dnq9Hnq9Htdddx26uroi/OmImJ0rfiJMZLnXycaKFSvY3/72N3bw4EFWVVXFLrnkEpaTk8N6enr8zqOPPsp0Oh1788032YEDB9j3vvc9lp6ezmw2m9/5yU9+wjIzM9mmTZvYnj172OLFi9nMmTOZ2+2OxsfihZ07d7K8vDw2Y8YMdvvtt/v3x9v30dnZyXJzc9l///d/s6+++orV1NSwjz/+mJ04ccLvxNN38vDDDzOj0cjeffddVlNTw9544w2WkJDAnnzySb8j9u/jvffeY/fddx978803h8x3zhh/n/+iiy5i5eXlbNu2bWzbtm2svLycXXrppUJ9zLhFlImd79V1JhOtra0MANuyZQtjjDGv18vMZjN79NFH/Y7dbmd6vZ4999xzjDHGurq6mFwuZ6+99prfaWhoYBKJhH3wwQfCfgCe6O7uZkVFRWzTpk3sggsu8Cf2ePw+fvGLX7Bzzz037OPx9p1ccskl7Ac/+MGgfZdffjm79tprGWPx930EJ3a+Pn91dTUDwHbs2OF3tm/fzgCwI0eORPhTxTeiq4p3Op2orKzE8uXLB+1fvnw5tm3bFqWohMNqtQIADAYDgIHJE5qbmwd9H0qlEhdccIH/+6isrITL5RrkZGRkoLy8fNJ+Z6tXr8Yll1yCpUuXDtofj9/Hv//9b8ydOxdXXHEF0tLSMHv2bPz5z3/2Px5v38m5556LTz75BMeOHQMA7Nu3D19++SW+9a1vAYi/7yMYvj7/9u3bodfrMX/+fL9z9tlnQ6/XT/rvKNaJ6ZnnxgMfq+tMVhhjuPPOO3HuueeivLwcAPyfOdT3cebMGb+jUCiQnJw8xJmM39lrr72GPXv2YNeuXUMei8fv49SpU3j22Wdx55134le/+hV27tyJn/3sZ1Aqlbj++uvj7jv5xS9+AavVipKSEkilUng8HjzyyCO46qqrAMTnMRIIX5+/ubkZaWlpQ14/LS1t0n9HsY7oEruPiayuM1m59dZbsX//fnz55ZdDHhvP9zEZv7O6ujrcfvvt+Oijj4Zd+Slevg8A8Hq9mDt3LtauXQsAmD17Ng4dOoRnn30W119/vd+Ll+/k9ddfx6uvvooNGzagrKwMVVVVWLNmDTIyMnDDDTf4vXj5PsLBx+cP5YvpO4pVRFcVHyur6wjNbbfdhn//+9/47LPPkJWV5d9vNpsBYNjvw2w2w+l0wmKxhHUmC5WVlWhtbUVFRQVkMhlkMhm2bNmC//mf/4FMJvN/nnj5PgAgPT0dpaWlg/ZNmzYNtbW1AOLvGPn5z3+OX/7yl/j+97+P6dOn47rrrsMdd9yBdevWAYi/7yMYvj6/2WxGS0vLkNdva2ub9N9RrCO6xB4rq+sIBWMMt956K/71r3/h008/RX5+/qDH8/PzYTabB30fTqcTW7Zs8X8fFRUVkMvlg5ympiYcPHhw0n1nS5YswYEDB1BVVeXf5s6di2uuuQZVVVUoKCiIq+8DABYuXDhkCOSxY8f8iynF2zHS19cHiWTwqU8qlfqHu8Xb9xEMX5//nHPOgdVqxc6dO/3OV199BavVOum/o5gnGj32Io1vuNsLL7zAqqur2Zo1a5hWq2WnT5+Odmi889Of/pTp9Xq2efNm1tTU5N/6+vr8zqOPPsr0ej3717/+xQ4cOMCuuuqqkENXsrKy2Mcff8z27NnDLrzwwkkzdGckAnvFMxZ/38fOnTuZTCZjjzzyCDt+/Dj7+9//zjQaDXv11Vf9Tjx9JzfccAPLzMz0D3f717/+xVJSUtg999zjd8T+fXR3d7O9e/eyvXv3MgBs/fr1bO/evf4hwXx9/osuuojNmDGDbd++nW3fvp1Nnz6dhrsJgCgTO2OMPf300yw3N5cpFAo2Z84c//AvsQEg5Pa3v/3N73i9Xnb//fczs9nMlEolO//889mBAwcGvU5/fz+79dZbmcFgYGq1ml166aWstrZW4E8TGYITezx+H++88w4rLy9nSqWSlZSUsD/96U+DHo+n78Rms7Hbb7+d5eTkMJVKxQoKCth9993HHA6H3xH79/HZZ5+FPG/ccMMNjDH+Pn9HRwe75pprmE6nYzqdjl1zzTXMYrEI9CnjF1rdjSAIgiBEhOja2AmCIAginqHEThAEQRAighI7QRAEQYgISuwEQRAEISIosRMEQRCEiKDEThAEQRAighI7QRAEQYgISuwEQRAEISIosRMEQRCEiKDEThAEQRAighI7QRAEQYgISuwEQRAEISL+P0R10Pc47BrhAAAAAElFTkSuQmCC",
      "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();"
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    "## 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: "
   ]
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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",
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       "    padding: 1em;\n",
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       "</details><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": [
    {
     "data": {
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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",
       "\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",
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       "</details><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",
       "\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",
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       "*/\n",
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       "    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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",
      "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}