6 extending existing models (and XSPEC).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extending existing models (with a side-order of XSPEC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I was recently asked by a colleage whether Sherpa allowed you to subtract a model. This is \"easy\" - since you can create model expressions using `+`, `-`, `*`, and `/` - but it turned out that what was really wanted fitted into my current documentation obsession, writing user models in Sherpa. It also lets me play Sherpa's support for\n",
    "[XSPEC models](https://heasarc.gsfc.nasa.gov/xanadu/xspec/manual/Models.html).\n",
    "\n",
    "The aim of this notebook is to show you how you can write models in Sherpa that use existing Sherpa models. In this particular case I will create a model that is the result of subtracting two XSPEC `vvnei` models, providing an interface where the Hydrogen abundance is no-longer a parameter (I make no claim that this is a physically-meaningful model), but the approach is valid for all Sherpa models. The notebook ends up with a brief discussion about optimising model, but this is meant more as a primer on the subject than an in-depth discussion.\n",
    "\n",
    "I am going to assume that you have read at least\n",
    "[Writing your own user model](http://nbviewer.ipython.org/github/DougBurke/sherpa-standalone-notebooks/blob/master/user%20model.ipynb)\n",
    "but please feel free to look through\n",
    "[all the notebooks](https://github.com/DougBurke/sherpa-standalone-notebooks)\n",
    "I've written in the series."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Author and disclaimer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This was written by \n",
    "[Douglas Burke](http://hea-www.cfa.harvard.edu/~dburke/)\n",
    "on June 16 2015. This notebook, and others that may be of\n",
    "interest, can be found on GitHub at\n",
    "https://github.com/DougBurke/sherpa-standalone-notebooks.\n",
    "\n",
    "The information in this document is placed into the Publc Domain. It is not an official product of the Chandra\n",
    "X-ray Center, and I make no guarantee that it is not without bugs or embarassing typos. Please contact me via the GitHub repository\n",
    "if you have any questions. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Last run\n",
    "\n",
    "When was this notebook last run?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2024-01-02 11:40'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datetime\n",
    "datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and with what version of Sherpa?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'date': '2023-10-11T08:27:13-0400',\n",
       " 'dirty': False,\n",
       " 'error': None,\n",
       " 'full-revisionid': '1aaa9c60fc6a34d91b502e178ff75383c41cf47d',\n",
       " 'version': '4.16.0'}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sherpa\n",
    "sherpa._version.get_versions()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up for XSPEC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are using the Sherpa provided with\n",
    "[CIAO](http://cxc.harvard.edu/ciao/) then you can skip this section, as the XSPEC extension\n",
    "is provided for you (**note** if you are using the \"binder\" version to view the notebooks\n",
    "then you are fine, since it is packaged with CIAO 4.14).\n",
    "\n",
    "If you are using the standalone version of Sherpa, then you need to have the XSPEC libraries and then tell Sherpa where they are, before building Sherpa. I used to have documentation here about what needed to be done, but we now have a shiny web site describing \n",
    "[how to install Sherpa](https://sherpa.readthedocs.io/en/4.14.0/install.html), including \n",
    "[building Sherpa with XSPEC support](https://sherpa.readthedocs.io/en/4.14.0/install.html#xspec)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the XSPEC models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The XSPEC models are automatically loaded - if available - by the `sherpa.astro.ui` module, but here I am also going to load in the [`sherpa.astro.xspec`](https://sherpa.readthedocs.io/en/4.11.0/model_classes/xspec_model.html) module so that I can access several\n",
    "low-level routines (i.e. ones we don't provide access to from `sherpa.astro.ui`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from sherpa.astro import ui\n",
    "from sherpa.astro import xspec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To check that things are working, we can find out the version of the XSPEC library in use (it's important to compare this to the \n",
    "[XSPEC issues list](https://heasarc.gsfc.nasa.gov/docs/xanadu/xspec/issues/issues.html)):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'12.13.1e'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xspec.get_xsversion()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If this command fails with the message\n",
    "\n",
    "```\n",
    "ImportError: XSPEC initialization failed; check HEADAS environment variable\n",
    "```\n",
    "\n",
    "then check that this variable is set correctly. It should be set to the parent directory\n",
    "of the `lib` directory used for the `xspec_lib_dirs` setting used to build Sherpa. For example\n",
    "\n",
    "```\n",
    "import os\n",
    "os.environ['HEADAS'] = '/usr/local/headas-6.16/x86_64-unknown-linux-gnu-libc2.21-0/'\n",
    "```\n",
    "\n",
    "and then - assuming this *is* the correct setting ;-) - then the `xspec.get_xsversion()` command should work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xspec.get_xschatter()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The XSPEC chatter level - which can be read with `xspec.get_xschatter` and set with `xspec.set_xschatter` can be used to change the amount of output printed to the screen when X-Spec models are evaluated. The default value used by Sherpa, **as of the 4.14 release** - is `10`, which matches the XSPEC default. If you want no outout, set it to `0` and `25` can be helpful for debugging (e.g. to check that data files are being read correctly; although the amount of information provided depends on the model and the version of XSPEC in use)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model I am going to develop is a version of the \n",
    "[`vvnei` model](https://heasarc.gsfc.nasa.gov/xanadu/xspec/manual/XSmodelNei.html) - which\n",
    "is a non-equilibrium ionisation model with variable abundances - which has no Hydrogen. The\n",
    "standard way for XSPEC to model variable abundances is to make them all relative to Hydrogen, but in this case we (or at least, my colleague), does not want any Hydrogren! One solution for this is to evaluate the model twice, once with a Hydrogen abundance of 1, and the required elemental abundances, and then a Hydrogen-only version. The second version is subtracted from the first (hence the original question I was asked) to return the required model.\n",
    "\n",
    "This can be done directly in Sherpa, using two different instances of the `xsvvnei` model,\n",
    "but we can also write our own user model to wrap up this behavior, and provide a slightly-simpler interface to the user.\n",
    "\n",
    "Before we write the user model, let's try and see the individual components. The following creates two instances of the `vvnei` model (Sherpa prepends `xs` to the XSPEC model names), called `mdl_with_elems` and `mdl_h_only`. I then set several of the elemental abundances in the `mdl_with_elems` component - using `ui.set_par` - and then clear out all the non-Hydrogen abundances in the `mdl_h_only` component (I happen to know that the check I wrote is sufficient *for this model type* to select only the abundances I wanted to change). This code shows two different ways of accessing model parameters (by name, with `ui.set_par`, or by iterating directly from the model component), but I don't want to talk too much about this today:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ui.xsvvnei.mdl_with_elems\n",
    "ui.xsvvnei.mdl_h_only\n",
    "\n",
    "# Set some abundances\n",
    "abunds = { 'c': 2.3, 'fe': 1.8, 'o': 3.4, 'si': 2.1, 'ar': 1.2 }\n",
    "for (k, v) in abunds.items():\n",
    "    ui.set_par('mdl_with_elems.' + k, v)\n",
    "    \n",
    "# Clear out the abundances for the Hydrogen-only version\n",
    "for p in mdl_h_only.pars:\n",
    "    if len(p.name) < 3 and p.name not in ['kT', 'H']:\n",
    "        p.val = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This particular model has a lot of parameters!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       " 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",
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       " 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n",
       "\n",
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       "  --sherpa-border-color: var(--jp-border-color2, #e0e0e0);\n",
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       "\n",
       "  /* https://medium.com/ge-design/iot-cool-gray-is-a-great-background-color-for-data-visualization-ebf18c318418 */\n",
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       "    padding: 1em;\n",
       "}\n",
       "</style><div class=\"sherpa-text-fallback\">&lt;XSvvnei model instance &#x27;xsvvnei.mdl_with_elems&#x27;&gt;</div><div hidden class=\"sherpa\"><details open><summary>Model</summary><table class=\"model\"><caption>Expression: xsvvnei.mdl_with_elems</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=34>xsvvnei.mdl_with_elems</th><td>kT</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.0</td><td>0.0808</td><td>79.9</td><td>keV</td></tr><tr><td>H</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>He</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Li</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Be</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>B</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>C</td><td><input disabled type=\"checkbox\"></input></td><td>2.3</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>N</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>O</td><td><input disabled type=\"checkbox\"></input></td><td>3.4</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>F</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Ne</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Na</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Mg</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Al</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Si</td><td><input disabled type=\"checkbox\"></input></td><td>2.1</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>P</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>S</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Cl</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Ar</td><td><input disabled type=\"checkbox\"></input></td><td>1.2</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>K</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Ca</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Sc</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Ti</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>V</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Cr</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Mn</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Fe</td><td><input disabled type=\"checkbox\"></input></td><td>1.8</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Co</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Ni</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Cu</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Zn</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Tau</td><td><input disabled type=\"checkbox\" checked></input></td><td>100000000000.0</td><td>100000000.0</td><td>50000000000000.0</td><td>s/cm^3</td></tr><tr><td>redshift</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>-0.999</td><td>10.0</td><td></td></tr><tr><td>norm</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.0</td><td>0.0</td><td>1e+24</td><td></td></tr></tbody></table></details></div>"
      ],
      "text/plain": [
       "<XSvvnei model instance 'xsvvnei.mdl_with_elems'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl_with_elems"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the 'Hydrogen only' one has been cleared out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "\n",
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       " 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",
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       "\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;XSvvnei model instance &#x27;xsvvnei.mdl_h_only&#x27;&gt;</div><div hidden class=\"sherpa\"><details open><summary>Model</summary><table class=\"model\"><caption>Expression: xsvvnei.mdl_h_only</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=34>xsvvnei.mdl_h_only</th><td>kT</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.0</td><td>0.0808</td><td>79.9</td><td>keV</td></tr><tr><td>H</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>He</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Li</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Be</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>B</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>C</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>N</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>O</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>F</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Ne</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Na</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Mg</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Al</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Si</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>P</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>S</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Cl</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Ar</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>K</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Ca</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Sc</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Ti</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>V</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Cr</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Mn</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Fe</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Co</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Ni</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Cu</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Zn</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>0.0</td><td>1000.0</td><td></td></tr><tr><td>Tau</td><td><input disabled type=\"checkbox\" checked></input></td><td>100000000000.0</td><td>100000000.0</td><td>50000000000000.0</td><td>s/cm^3</td></tr><tr><td>redshift</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>-0.999</td><td>10.0</td><td></td></tr><tr><td>norm</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.0</td><td>0.0</td><td>1e+24</td><td></td></tr></tbody></table></details></div>"
      ],
      "text/plain": [
       "<XSvvnei model instance 'xsvvnei.mdl_h_only'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl_h_only"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The models can be evaluated on the grid 0.1 to 10 keV, with bin spacing 0.01 keV (XSPEC models expect the input grid to be in keV). The line emission from the elements is visible here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading NEI APEC spectral data from 3.0.9\n",
      "and eigenfunction data from 3.0.4\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "egrid = np.arange(0.1, 10, 0.01)\n",
    "elo = egrid[:-1]\n",
    "ehi = egrid[1:]\n",
    "emid = (elo + ehi) / 2\n",
    "\n",
    "plt.plot(emid, mdl_with_elems(elo, ehi))\n",
    "plt.plot(emid, mdl_h_only(elo, ehi))\n",
    "plt.yscale('log')\n",
    "plt.xscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subtracting these two will give the elemental emission for a situation where there is no Hydrogen. I also convert the plot values into those more commonly displayed: the XSPEC models return the flux in units of photon/cm^2/s for each bin, so we need to divide by the bin width to get the flux density."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "de = ehi - elo\n",
    "y = mdl_with_elems(elo, ehi) - mdl_h_only(elo, ehi) \n",
    "plt.plot(emid, y / de)\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.xlabel('Energy (keV)')\n",
    "plt.ylabel('photon/cm$^2$/s/keV');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aside\n",
    "\n",
    "In previous versions of this notebook I used a \"special\" way to evaluate XSPEC models by only providing a single array. This is **not a good idea**™ so I've gone to\n",
    "providing both low- and high- values for the grid.\n",
    "\n",
    "### End aside\n",
    "\n",
    "## Using the existing models\n",
    "\n",
    "Since we can write model expressions using subtraction, then the \"Hydrogen free\" variant of `xsvvnei` could be handled explicitly, by using the model combination\n",
    "\n",
    "    (ui.xsvvnei.mdl_elems - ui.xsvvnei.mdl_honly)\n",
    "    \n",
    "in the source expression. It then requires\n",
    "\n",
    " - linking the `kT`, `Tau`, `redshift`, and `norm` parameters: for example\n",
    " \n",
    "        ui.link(mdl_honly.kt, mdl_elems.kt)\n",
    "        ui.link(mdl_honly.tau, mdl_elems.tau)\n",
    "        ui.link(mdl_honly.redshift, mdl_elems.redshift)\n",
    "        ui.link(mdl_honly.norm, mdl_elems.norm)\n",
    "        \n",
    " - setting the `mdl_honly` elemental abundances to 0:\n",
    " \n",
    "        for p in mdl_honly.pars:\n",
    "            if p.name.lower() not in ['kt', 'h', 'tau', redshift', 'norm']:\n",
    "                p.val = 0\n",
    "                p.frozen = True # this is actually the default, but just check\n",
    "                \n",
    " - fixing the Hydrogen abundances\n",
    " \n",
    "        mdl_honly.h = 1\n",
    "        mdl_elems.h = 1\n",
    "        ui.freeze(mdl_honly.h, mdl_elems.h)\n",
    "\n",
    " - thaw the elemental abundances in `mdl_elems` that you want to fit.\n",
    " \n",
    "This can be done, but if you started using this model a lot, you'd quickly find it annoying (and\n",
    "easy to miss a step). What is needed is a way to create a user model - as described in\n",
    "[previous notebooks](http://nbviewer.ipython.org/github/DougBurke/sherpa-standalone-notebooks/blob/master/user%20model.ipynb) - but \n",
    "instead of writing the function, use the existing Sherpa code to evaluate the models.\n",
    "\n",
    "## Writing a simple user model\n",
    "\n",
    "So, can we simplify the above by writing a simple user model? First of all, let's look at evaluating the model for a given set of parameters. The evaluations above have required an instance of the model - which I created by calling\n",
    "\n",
    "    ui.xsvvnei.mdl_with_elems\n",
    "    \n",
    "but could have also used\n",
    "\n",
    "    ui.create_model_component('xsvvnei', 'mdl_with_elems')\n",
    "    \n",
    "I could use this approach in a user model, but it would involve coming up with unique model names which is messy and inelegant. What I really want to be able to do is evaluate the model\n",
    "without creating a component. This can be done by using parts of Sherpa directly. In this case, the models are defined in the `sherpa.astro.xspec` module, and we can create an object directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "mdl = xspec.XSvvnei()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model can be given a name by sending in a string to the constructor, but this is only used in screen output which we do not care about, so we let the default value be used. The only real difference to calling this, rather than `ui.create_model_component` (or writing `ui.xsvvnei.xxx`), is that this model is not added to the Sherpa session, so it does not conflict with anything (e.g. `ui.list_model_components()` will not include it). This makes it ideal for our purposes, since it is essentially an anonymous model.\n",
    "\n",
    "An anonymous model function is a start, but I also need an easy way to call the model with different parameters: one set with the elemental abundances, and the second time with Hydrogen only. I *could* set one set of parameters, evaluate the model, and then swap in the second set and re-evaluate. However, the Sherpa model class defines the `calc` method, which has the signature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method calc in module sherpa.astro.xspec:\n",
      "\n",
      "calc(p, *args, **kwargs) method of sherpa.astro.xspec.XSvvnei instance\n",
      "    Calculate the model given the parameters and grid.\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    XSPEC models must always be evaluated with low and high bin\n",
      "    edges. Although supported by the XSPEC model interface the\n",
      "    ability to evaluate using an XSPEC-style grid (n+1 values for\n",
      "    n bins which we pad with a 0), we do not allow this here since\n",
      "    it complicates the handling of the regrid method.\n",
      "    \n",
      "    Keyword arguments are ignored.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(mdl.calc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `pars` argument is the array of parameter values (in the order displayed from `print(mdl)` or returned by `mdl.pars`) and the remaining arguments are the independent axes on which to evaluate the model. This is therefore the second part to writing the user model.\n",
    "\n",
    "Since the interface to the models can change over time, let's check the number of parameters (it is unlikely to change for this particular model):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of pars = 34\n",
      " filtered pars = 30\n"
     ]
    }
   ],
   "source": [
    "print(f\"Length of pars = {len(mdl.pars)}\")\n",
    "\n",
    "# filter out H, tau, redshift, norm\n",
    "fpars = [p for p in mdl.pars if p.name.lower() not in ['h', 'tau', 'redshift', 'norm']]\n",
    "print(f\" filtered pars = {len(fpars)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If I wanted the following to be robust I'd actually check the parameter names and orders, but for this\n",
    "notebook I am just going to index directly into the parameter array of `XSvvnei`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def xsvvnei_no_h(pars, *args):\n",
    "    \"pars is kT, list of 29 element abundances (excluding H), tau, redshift, norm\"\n",
    "\n",
    "    # It's a bit excessive to create this each iteration\n",
    "    mdl = xspec.XSvvnei()\n",
    "    \n",
    "    pall = [pars[0], 1.0]\n",
    "    pall.extend(pars[1:])\n",
    "    \n",
    "    phonly = [pars[0], 1.0]\n",
    "    phonly.extend([0.0] * 29)\n",
    "    phonly.extend(pars[-3:])\n",
    "\n",
    "    # Evaluate the \"Hydrogen-free\" model\n",
    "    return mdl.calc(pall, *args) - mdl.calc(phonly, *args)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a check, does this give the same answer as evaluating the separate models?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pars = [p.val for p in mdl_with_elems.pars if p.name != \"H\"]\n",
    "ynew = xsvvnei_no_h(pars, elo, ehi)\n",
    "y = mdl_with_elems(elo, ehi) - mdl_h_only(elo, ehi) \n",
    "np.all(ynew == y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This can be added to Sherpa using `ui.load_user_model` to create a model instance (i.e. component). Since I want to copy over the parameter settings from the `xsvvnei` model\n",
    "(at least, apart from for `H`), I have written a small function that both loads the \n",
    "model - with `ui.load_user_model` - and sets the parameter values - with `ui.add_user_pars`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_xsvvnei_noh_model(name):\n",
    "    \"\"\"Create an instance of the 'xsvvnei' model which has\n",
    "    no Hydrogen.\"\"\"\n",
    "    \n",
    "    # Create the model instance\n",
    "    ui.load_user_model(xsvvnei_no_h, name)\n",
    "    \n",
    "    # Get a copy of the default parameter settings for the vvnei\n",
    "    # model (excluding Hydrogen).\n",
    "    pars = [p for p in xspec.XSvvnei().pars if p.name != 'H']\n",
    "    \n",
    "    parnames = [p.name for p in pars]\n",
    "    parvals = [p.val for p in pars]\n",
    "    parmins = [p.min for p in pars]\n",
    "    parmaxs = [p.max for p in pars]\n",
    "    parunits = [p.units for p in pars]\n",
    "    parf = [p.frozen for p in pars]\n",
    "    \n",
    "    # Set up the parameters for this instance\n",
    "    ui.add_user_pars(name, parnames, parvals=parvals, parmins=parvals,\n",
    "                     parmaxs=parmaxs, parunits=parunits, parfrozen=parf)\n",
    "    \n",
    "    # Return the model\n",
    "    return ui.get_model_component(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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;UserModel model instance &#x27;usermodel.noh&#x27;&gt;</div><div hidden class=\"sherpa\"><details open><summary>Model</summary><table class=\"model\"><caption>Expression: usermodel.noh</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=33>usermodel.noh</th><td>kT</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.0</td><td>1.0</td><td>79.9</td><td>keV</td></tr><tr><td>He</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>1.0</td><td>1000.0</td><td></td></tr><tr><td>Li</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>1.0</td><td>1000.0</td><td></td></tr><tr><td>Be</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>1.0</td><td>1000.0</td><td></td></tr><tr><td>B</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>1.0</td><td>1000.0</td><td></td></tr><tr><td>C</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>1.0</td><td>1000.0</td><td></td></tr><tr><td>N</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>1.0</td><td>1000.0</td><td></td></tr><tr><td>O</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>1.0</td><td>1000.0</td><td></td></tr><tr><td>F</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>1.0</td><td>1000.0</td><td></td></tr><tr><td>Ne</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>1.0</td><td>1000.0</td><td></td></tr><tr><td>Na</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>1.0</td><td>1000.0</td><td></td></tr><tr><td>Mg</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>1.0</td><td>1000.0</td><td></td></tr><tr><td>Al</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>1.0</td><td>1000.0</td><td></td></tr><tr><td>Si</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>1.0</td><td>1000.0</td><td></td></tr><tr><td>P</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>1.0</td><td>1000.0</td><td></td></tr><tr><td>S</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>1.0</td><td>1000.0</td><td></td></tr><tr><td>Cl</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>1.0</td><td>1000.0</td><td></td></tr><tr><td>Ar</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>1.0</td><td>1000.0</td><td></td></tr><tr><td>K</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>1.0</td><td>1000.0</td><td></td></tr><tr><td>Ca</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>1.0</td><td>1000.0</td><td></td></tr><tr><td>Sc</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>1.0</td><td>1000.0</td><td></td></tr><tr><td>Ti</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>1.0</td><td>1000.0</td><td></td></tr><tr><td>V</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>1.0</td><td>1000.0</td><td></td></tr><tr><td>Cr</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>1.0</td><td>1000.0</td><td></td></tr><tr><td>Mn</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>1.0</td><td>1000.0</td><td></td></tr><tr><td>Fe</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>1.0</td><td>1000.0</td><td></td></tr><tr><td>Co</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>1.0</td><td>1000.0</td><td></td></tr><tr><td>Ni</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>1.0</td><td>1000.0</td><td></td></tr><tr><td>Cu</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>1.0</td><td>1000.0</td><td></td></tr><tr><td>Zn</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>1.0</td><td>1000.0</td><td></td></tr><tr><td>Tau</td><td><input disabled type=\"checkbox\" checked></input></td><td>100000000000.0</td><td>100000000000.0</td><td>50000000000000.0</td><td>s/cm^3</td></tr><tr><td>redshift</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>0.0</td><td>10.0</td><td></td></tr><tr><td>norm</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.0</td><td>1.0</td><td>1e+24</td><td></td></tr></tbody></table></details></div>"
      ],
      "text/plain": [
       "<UserModel model instance 'usermodel.noh'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make_xsvvnei_noh_model('noh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "usermodel.noh\n",
      "   Param        Type          Value          Min          Max      Units\n",
      "   -----        ----          -----          ---          ---      -----\n",
      "   noh.kT       thawed            1            1         79.9        keV\n",
      "   noh.He       frozen            1            1         1000           \n",
      "   noh.Li       frozen            1            1         1000           \n",
      "   noh.Be       frozen            1            1         1000           \n",
      "   noh.B        frozen            1            1         1000           \n",
      "   noh.C        frozen            1            1         1000           \n",
      "   noh.N        frozen            1            1         1000           \n",
      "   noh.O        frozen            1            1         1000           \n",
      "   noh.F        frozen            1            1         1000           \n",
      "   noh.Ne       frozen            1            1         1000           \n",
      "   noh.Na       frozen            1            1         1000           \n",
      "   noh.Mg       frozen            1            1         1000           \n",
      "   noh.Al       frozen            1            1         1000           \n",
      "   noh.Si       frozen            1            1         1000           \n",
      "   noh.P        frozen            1            1         1000           \n",
      "   noh.S        frozen            1            1         1000           \n",
      "   noh.Cl       frozen            1            1         1000           \n",
      "   noh.Ar       frozen            1            1         1000           \n",
      "   noh.K        frozen            1            1         1000           \n",
      "   noh.Ca       frozen            1            1         1000           \n",
      "   noh.Sc       frozen            1            1         1000           \n",
      "   noh.Ti       frozen            1            1         1000           \n",
      "   noh.V        frozen            1            1         1000           \n",
      "   noh.Cr       frozen            1            1         1000           \n",
      "   noh.Mn       frozen            1            1         1000           \n",
      "   noh.Fe       frozen            1            1         1000           \n",
      "   noh.Co       frozen            1            1         1000           \n",
      "   noh.Ni       frozen            1            1         1000           \n",
      "   noh.Cu       frozen            1            1         1000           \n",
      "   noh.Zn       frozen            1            1         1000           \n",
      "   noh.Tau      thawed        1e+11        1e+11        5e+13     s/cm^3\n",
      "   noh.redshift frozen            0            0           10           \n",
      "   noh.norm     thawed            1            1        1e+24           \n"
     ]
    }
   ],
   "source": [
    "print(noh)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A quick check shows how the Oxygen abundance changes the results for\n",
    "the default temperature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yo1 = noh(elo, ehi)\n",
    "\n",
    "noh.o = 5\n",
    "yo5 = noh(elo, ehi)\n",
    "\n",
    "plt.plot(emid, yo1, label='O=1')\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "\n",
    "plt.plot(emid, yo5, alpha=0.5, label='O=5')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This (XSPEC 12.8-ish ere) used to be a more-obvious difference, but changes to the models have reduced the visual impact. So let's try subtracting them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(emid, yo5 - yo1)\n",
    "plt.xlabel('Energy (keV)')\n",
    "plt.ylabel(r'$O_5 - O_1$')\n",
    "plt.xscale('log')\n",
    "plt.yscale('log');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "or try looking at the ratio:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kEBERkSYxyGklRHajIiIicguDHCIiItIkBjlERESkSQxyWglWVhEREbmHQQ4RERFpEoMcIiIi0iQGOURERKRJDHKIiIhIkxjktBIcJoeIiMg9DHJaIQ4MSERE5JpHgpzdu3d74rB0FWMcIiIi1zwS5Pzyl7/0xGHbNGZviIiI3GNo6o5333237HJRFHHx4sUmF4hcY7hDRETkWpODnC+//BLvvvsuwsLCJMtFUcS2bduaXTBSxqwOERGRa00OckaOHImwsDCMGDHCYd2AAQOaVSgiIiKi5mpykPPxxx8rrtuwYUNTD0sqMI9DRETkmttBzrFjx7Bv3z4EBgaif//+6NSpkyfKRU6wtoqIiMg11UGOyWTClClTsGbNGmubEEEQMHz4cLz22mvo27evxwpJzN4QERG5S3UX8ueffx7r16/H8uXL8fPPP+PHH3/EqlWrUFVVhSFDhuB///ufJ8tJNkSGPERERC6pzuS8++67+Nvf/oYHH3zQuqxXr16YNGkSXn75Zdx9993Yt28funfv7pGCUiNWVxEREbmmOpOTn5+PYcOGya77/e9/j/vuuw/PPfdcixWM7DCwISIicovqICc6OhqXLl1SXD9t2jR8/fXXLVIoIiIiouZSHeSMHDkSq1evVlwfFxeHkpKSFikUERERUXOpDnIWLFiAN998UzHQycnJQXx8fJMKsWTJEvTt2xcRERGIiIhAZmYmPv/8c6f7bN26FQMHDkRQUBC6dOmCpUuXNuncrRHb5BAREbmmOsjp378/li5dioceegi33XYbNm7ciHPnzqG0tBRr167FnDlz8Otf/7pJhUhKSsKLL76InJwc5OTk4KabbsJtt92GgwcPym5/8uRJTJgwAcOGDcPevXuxaNEiPP7448jOzm7S+Vsb9q4iIiJyza3BAKdMmYLU1FTMnj0b48aNgyAIABrmUho3bhyeffbZJhXilltukbx+/vnnsWTJEuzcuRPp6ekO2y9duhQpKSl49dVXAQA9e/ZETk4OFi9ejDvvvLNJZfB3DGuIiIjc4/aIxyNHjkRubi727t2L3Nxc1NXVYcCAARg0aFCLFKi+vh7/+c9/UFlZiczMTNltduzYgTFjxkiWjR07FitWrEBdXR0CAgJk96upqUFNTY31dVlZWYuU2dtYXUVEROSa6iBn0aJFuP322zF48GAADZNwtuREnAcOHEBmZiaqq6sRFhaGTz75BL169ZLdtqioCHFxcZJlcXFxMJlMKCkpQUJCgux+WVlZTc42+RPGOERERK6pbpNTWFiIX/ziF0hISMBvfvMbrFu3TpIVaa60tDTk5uZi586d+O1vf4vJkyfj0KFDittbqsosbKeaULJw4UKUlpZaf/Lz81um8F4gMn1DRETkFtVBzsqVK3Hu3Dl8+OGHiIqKwu9//3u0b98ed9xxB1atWtXs7uOBgYHo1q0bMjIykJWVhX79+uG1116T3TY+Ph5FRUWSZcXFxTAYDIiJiVE8h9FotPbgsvy0Rgx4iIiIXFMd5AANWZJhw4bhr3/9K44cOYIffvgBQ4YMwfLly9GxY0cMHz4cixcvRkFBQbMLJoqiYqYoMzMTmzZtkizbuHEjMjIyFNvjaAlDHCIiItfcCnLs9ezZE/Pnz8f27dtx5swZTJ48Gd988w3ee+89t46zaNEifPPNNzh16hQOHDiAJ598Elu2bMH9998PoKGayXbOrOnTp+P06dOYO3cuDh8+jLfeegsrVqzAvHnzmvN2iIiISEPc7l2lpEOHDpg6dSqmTp3q9r7nzp3DpEmTUFhYiMjISPTt2xcbNmzAzTffDKChPVBeXp51+9TUVKxfvx5z5szBm2++icTERLz++uua7T5uj7VVRERErrVYkFNRUYFjx46hS5cuiIqKcmvfFStWOF2/atUqh2UjRozAnj173DqPZjgJci5U1MAYoEeYscX+tERERK1Sk6urfvOb31h/37ZtG3r37o2nnnoKAwYMwGeffdYihaNGapI3pVV1GPjnL9H7j194vDxERET+rslf93Nycqy/P/XUU1i3bh3S09Nx5swZ3HrrrQ6jGFPLUZrW4WhRuZdLQkRE5L+a1fDYoqqqyjr9QlJSErs4exgvLxERkWtNzuQcOHAAsbGxEEUR5eXlKCoqQnx8PGpra1FfX9+SZSQwsCEiInKX20HOsWPHsG/fPnz00Ufo378/OnXqJFl/5coV/POf/2yxApIjxjtERESuqQ5yTCYTpkyZgjVr1kAURev0CcOHD8drr72Gvn37AgCioqIUJ9YkIiIi8hbVbXKef/55rF+/HsuXL8fPP/+MAwcOYNWqVaiqqsKQIUPwv//9z5PlJBts80REROSa6kzOu+++i7/97W+SkYd79eqFSZMm4eWXX8bdd9+Nffv2oXv37h4paFtn26OKIQ4REZFrqjM5+fn5GDZsmOy63//+97jvvvvw3HPPtVjBSIrJGyIiIveoDnKio6Nx6dIlxfXTpk3D119/3SKFIke2MQ4DHiIiItdUBzkjR47E6tWrFdfHxcWhpKSkRQpFMkTbXxnlEBERuaK6Tc6CBQtw3XXX4dprr8UDDzzgsD4nJwfx8fEtWjhqxMCGiIjIPaozOf3798fSpUvx0EMP4bbbbsPGjRtx7tw5lJaWYu3atZgzZw5+/etfe7KsbZqkiorxDhERkUtuDQY4ZcoUpKamYvbs2Rg3bpx1rBxRFDFu3Dg8++yzHikkSamJcWzHMiIiImqL3B7xeOTIkcjNzUVubi727t2Luro6DBgwAIMGDfJE+egqd5M3oggwxiEiorasyXNX9e/fH/3792/BopAztgMAquldxRotIiJq61pkFnLyPGmTHIYwRERErjDIaSXcHRuHUz8QEVFbxyCnFWJ1FRERkWsMcloB+6yMut5VnikLERFRa8EgpxVgwEJEROQ+BjmtgH2Mo6a9DRsnExFRW8cgR6OY/SEioraOQU4r4NAmhwEMERGRSwxyWgHGNERERO5jkNMKNCVzw2wPERG1dQxyWgH7RsTqxslhlENERG0bg5xWwD6oYQBDRETkGoMcjWJ1FRERtXUMcloBh0wOp3UgIiJyiUFOK6RuWgeGOURE1LYxyGkF2AaHiIjIfX4R5GRlZWHQoEEIDw9HbGwsbr/9dhw9etTpPlu2bIEgCA4/R44c8VKpvcexukrNtA5ERERtm18EOVu3bsWMGTOwc+dObNq0CSaTCWPGjEFlZaXLfY8ePYrCwkLrT/fu3b1QYu9ymLtKzT6McoiIqI0z+LoAALBhwwbJ65UrVyI2Nha7d+/G8OHDne4bGxuLqKgoVeepqalBTU2N9XVZWZnbZfUFte1rBMF2J8+UhYiIqLXwi0yOvdLSUgBAdHS0y20HDBiAhIQEjBo1Cps3b3a6bVZWFiIjI60/ycnJLVJeT/ipuAL/3VsAUXRskaMU8zB7Q0RE1MjvghxRFDF37lwMHToUvXv3VtwuISEBy5YtQ3Z2Nj7++GOkpaVh1KhR2LZtm+I+CxcuRGlpqfUnPz/fE29B4vWvjuP3H+5zu7fT6Fe2YvYHufjiYJFM8KKmTQ4jHiIiatv8orrK1syZM7F//358++23TrdLS0tDWlqa9XVmZiby8/OxePFixSouo9EIo9HYouV15ZVNxwAA912XgoGd2rm9/6GzZcjs0t7t/ZjVISKits6vMjmPPfYY1q5di82bNyMpKcnt/YcMGYLjx497oGTNV1NX36T9QowG1XNX2bbJYYxDRERtnV9kckRRxGOPPYZPPvkEW7ZsQWpqapOOs3fvXiQkJLRw6VqGO0FHvblx69BAPbb/dEHdORjZEBERWflFkDNjxgysWbMGn376KcLDw1FUVAQAiIyMRHBwMICG9jQFBQV45513AACvvvoqOnfujPT0dNTW1mL16tXIzs5Gdna2z96HM+4EIBXVJuvvIYEGzFizR3osVedjxENERG2bXwQ5S5YsAQCMHDlSsnzlypV46KGHAACFhYXIy8uzrqutrcW8efNQUFCA4OBgpKenY926dZgwYYK3iu0xpVV11t8l3cKv4txVRERErvlFkKMm67Bq1SrJ6/nz52P+/PkeKpFv2QY5JjPDFSIioqbwq4bHWuZOl+7yapsgp95xP6Vj2QaLrK0iIqK2jkGOBzU16KitN1t/rzebHdarq65ilENERG0bgxwPsq1pcifksM3e1MlkcpSIii+IiIjaHgY5HtTUHk4mm+zNC+sPyxxX6XyNv9ezvoqIiNo4BjkeJMnkuBF02DY2lmt4rNgmx2Z5ZtbX+NNnh1Sfk4iISGsY5HiQbdChJsT5saAUk1Z8j/1nSlvk/G9tP9kixyEiImqN/KILuVZJkjcqopz7lu9EWbUJ3xwvUX9cyQrVRSMiItI8ZnI8yN1mMWU2Ix036XzN2puIiEhbGOR4kNm2C7kXQhC2NSYiImrEIMeDJLVVKgIQvU5mDge54zKYISIicolBjgeZ3YxGjIbm/Tk4ACAREVEjBjkeJNoMVqwm3lEb5ChP66BqdyIiojaBQY4HuZtZCQrQqzsugxkiIiKXGOR4kLvTOqgNcpQw9iEiImrEIMeDpBN0KocgNaZ6iKLoRnWV6/MRERG1dRwM0IPUZHLyLlzB8Jc24+6MJBhVV1cpTetAREREFszkeJBkWgeFwGT5NycAAB/mnEFQM3tXERERUSNmcjxIMiu4WX4b29nCVWdy3F5BRKQdZrOI2nqztYpfEJTHGBNFEefLa1BeY8KVmnoY9ALOlVWj3izCoNehY1QQktqFKLaJLL1Sh0tXanHpSi0uV9Wh9EoddDoBdSYzDHoB4UEGdGkfhkCDDvERQdDZjXdWazLDLIo4fq4CZdV1KK82QRAAnSCgtKoO9WYzTGYRAXodQgL16BBmRNfYMLQPM1qPUVljQmFpNSpqTKioNqGipg5l1SaUV5tQVlWHipqG0fJ1QkPbzvjIIHSMCkbvjpHW44iiiAuVtbhYWYsLFbW4UFmDkvIalFTUoqSiBqVVdRAEQICA4EA9YsONSIgKRr+kSPRMiECAXgdRFHHmUhUKS6txsbIWl6/U4uKVWly+UoeLlbW4VFmLGlPD3yUoQI/IkAB0CDPiF30T0D0uvFl/86ZikONBtkGO0pg5Zps6rYBmDgbIcXKI/IfZLOJKXT2u1JoQERTgVscCs1nEzhMXcOlKHarq6lFRXQeDXoeY0EDERgQhuV0wYiOCWrzMpnozfjh5ETtPXsSe05dQVFZtfXCGGQ0IDzIgPCjA+m9SVDB6JUYgLT4cAXodLlXW4tufSrDt2HnsOnURNSYzCkurAQBJ7YIRqNchLiII58qrARFIiQnBy7/qh1MXKtEuJBCp7UMhisB/ducjMjgQY9PjrAGMKIrIu3gF7UIDcevfv8WpC1cAAOFGA+7KSMLgztHomxyF2HAj9uVfxqe5Z7HlWDHKq024fKXO5XsPNOhw/3UpGHFNBxgNemw+WowvD5/DifOVqq9foEGHLu1D0T0uHKntQ5Fz6iJ2nbqIunr3783xEUHomRCOwtJqHCkqd3t/i5ToEMRHBOFYcbmq6yAnKECHLu3DUHC5CqVV7h+jd8dIBjlaZBvYKAU59TZBjkGvLshhyobaolqTGYEqq3Tr6s3Yl38ZJ85X4szlKhgNOnSLDUO/pChEhSgHHLUmM348W4qcUxfx3c8XcL68Bu3DjOjYLhi39ktEj/hwHDpbhmviwyXftC1+Pl+BDT8WITf/MvacvoQLlbXWdYF6HSKCDYgICkDHdsEYmx6P61Kj0S02DIIg4EqtCRcqarFy+ym8tf2ky/c4ICUKU4emokd8OLp2CMPRc+XY8GMReiVEYEx6vKrrZFFYWoWV20/hvR/yUN6EOfRCAvWIjwjCyQuVil/CzlyqAgCcKGkMGk6UVGLgn7+0vh7WvT3uGpiEBdkHAAChgXrkPHUznvrvj8jec0b2uOU1Jqzcfgort59SLJ9OAMKMBsn8gP2SIlFjMlsDiFqTWfE4oYF6RIUEIiokAFEhAagziTAG6FBvFnHwbJn1wV979XhyQUlwgB7J0cEIMzY8duvNIqJCAhGgFyAIAsqr61BvFlFUVo38i1UoKqtGUVm1df/woIbPTniQAaFGA8KMBkQEByAy2IAwYwAEoeE5U1ljQlFpNU5duIKfz1cg7+IV5F1sCAgFAYgMDkB0SCDahxkRE9bwb/swI6JCrh7DLKKyth7FZQ3H2Jt3CWXVJhwqLAPQ8DlOjApCu9BARIcEIiokENGhAVf/DURQgA61JjOqautxuaoOxeU16NohVPFv42kMcjxIzbQOttVVOicpV8lxlTI5jH3Ig8xmEQWXqxAdGohQo7pbR3F5NXaeuIjcvMv4/MdCxIYb8duRXTGmV7xDWt/2PPvOXEbHqGAcPVeOpHYhmLlmDw6eLUN0aCCmDk3FfYNTUFpVh8SoYAQadKirN2PniQvYf6YU6w8U4uDZMsUy6QSgU0worokLQ6+ESHTpEIojRWXIOXUJufmXUWOSr1te832e5PUt/RIxsU88jAY9vvu5BMu/cR6Y1Nabr1YN1OJESSW+OV5iXdc9NgzHiysU953YJwG19WZcrKzF6QtXcKGyBnvzLmPmmr0AgMwuMdiddwm1JjP0OgE/LBqFGJkgzJbZLOKvXxxFaVUt3vsh37q8XUgARlzTAQM7Rzc8nESg2lTfUDVSbUJ5dR0qqk0oq67DifOV+LGgFGXVJmvwck1cGIZ374Ch3dtjy9HzWPXdKQDAH8am4aUvjlrP0zMhAocLpX+nb46XSK5LZW093t+VJxvgdIsNw7UpUfgwRz74sZic2QlPTuyFQIMO1zz1OWqv/n0/nTkUQENgctfS71BZY8KAlHbY/lMJSqvqcHOvONzcKw7DundAZHCA4vGr6+qx7dh5DOkag8uVddh0+BxWfHMCHdsFY3zvBAy/pgN0AtCxXTCMBnXZvIoaEw4XluFIYRkiQwJxfdcY2aDaldKqOuzLv4wLlTXoHhuObrFhbg9VYjaLOFFSgZ+KK5DULgTXxIWr/rLhDxjkeJBtVZRtxsaWbWCiNkhR7kKusmDUZlXX1eO7n0vQtUMYOsUof7sSRREVV78Rni+vQbfYMEx7Jwf7zpRat7mpRyxuTOuAkWmxSI4OAQBU1dZj67HzWLuvAOsPFDkct7C0GtNX7wEAzBrVHbNGdUfB5Spcqa3Hv78/jZ/PV2D7TxcUy3WxshYvfXFU8rDslRBh/ZappHtsGK7U1qPgchXMInCypBInSyrxxcFzDttGhwaiT8dIZHRqh5SYEGw9dh4bD56ztnuw+GzfWXy276zD/oM7R2NMehz6J0ehS4cwRAYH4EptQ4BQVlWH0qo65OZfxvoDhdh/9XoqBThxEUbMG5OGX2UkS5afL6/B8m9OYP2BQpy5VIUdJxqvWb1ZxLoDhXgws7PTa7Lp8Dks3fqzZNlTE3vioes7w6BX/xAzm0X8dL4CBZer0DM+AvGRjdVoxeU11t+HdImR7Dc5sxOe+LghY3NdajQ6tgvGx3sKHI5foZBZ+nVGMqYN74L1B4qsf5tvF9yIn4orEGY04K6lOwAAt/bvaH0or3poEB57by/+eldf63ECDTqsnTkUoihCEATrv2oFBeitmbOIoABMHZqKqUNTVe8vJ8xowKDO0RjUObpZx4kMDsDwazo06xg6nYBuseHoFuub6qbmYpDjQWra5NgGP2rnulJuk0OtwTfHzyM6NBDpiZEovVKHD3PycWv/RMQptLHIu3AFJrMZZhE4XFgGnSBgTHocAq4+iM6X12DtvrNIbheM61JjEBkSgHqziC8OFmHToXMIDtSjqLQax4vLkX+xocqgR3w4FozvgWfXHsQT43tiXO947D59CQs/3o9j55QzCra+PlKMr48UAziI4AA9qurqIQiOn8+0uHB0bh+Ci5W12Hem1PpN+rWvjuO1r46rvm7XpUbjYmWtQ0DgLMD5v1/0wj2DkxESaIDZLOJ8RQ0EAThWVIEjRWXYd6YUeRev4JrYMAzs1A4ZV7MXtg+52/p3RK3JjA925eHpTw86LeNTE3vikWFdHJY3tGMJQMeoYAAND/zpI7ri09wCzHo/V/F43y8aLbu8Q7gRiyb0xKIJPfG7f+/G+gNFCDMacO/gZCz/5iTWfJ8nG+TszbuEo0Xl+PWgZBSVVkvWZf/2egzs1M7p+5Oj0wm4Ji4c18i0uWgfFij7OwB0bt8YZPdMiIBSXGEbKNkKDnTMSCS1C0FSuxDUm0UE6AXU1Yu4Ji7Muv76bu2x++mbZY9n+Zu7E+CQ/2OQ40HSLuTy29RL2u04P15q+1CcLKlsUsMvUq+kogahgQbZmyjQ0Djzp/MVSIsLl9wQt/9Ugi8Pn0NiZDC6xYYhs2sMjAYdNh06h0OFZZg2rAv+uuEI3t5xGiGBehz60zjM/mAvNh89j89/LMQLd/TB//33IFJiQjDimg7YffoS1h0oxHmZm/xjN3XD78ekAQCWbPlZ0obj4RtSkRgVhD+vO6z4HovKqjFl5S4AwDs7TmFc73j8ed0h1QEOAMwbcw22HS/B7tOXUFVXD6Dhc54QGWRtbDo5sxOeva23dZ+Cy1W44cWvFY/ZpX2opM2GxTVxYXj/N0MwffVuSZAzf1wa3t1x2nq+J8b3QL1ZxKDO0YiLMEqyVTqdYA0kY8ODMLR7e9XvNdCgw6TMzhjdKw4jXtpiDdRs9U2KxKTMTqqPCTQEULf174ifistRXFaD+/71PYCGv+/gVHXf4l+6qx+uTWmHod3bIzhAj+XfnMTpq41y7f3yH98BaKg6sbTTsOgWGya3S7MEBzQ+YmLCjIgNN1qDFkvABzRkz6qvfobsvb8rT3Z58NVqF7nhOfQ6AdsX3IQakxnhQcpVTaR9DHI8yKwik2NbpeVqxGLL43TaOzmy6znisWsXKmrw3c8XMLpnnDWIKauuw0c5Z/DR7jOSrMDonrF4+Vf9ERkSgK+PnMOHu86gsKwa+/IvAwCWP5iBm3vFAQBOlVTi/qsPKCV78i5j27HzAIArtQ039M1Hz1vX/d+nB/HDqYv44dRFfLTbeTuDv3/9EwL1Osy8qRvOV0iDIDWNVm0/d6arPT+qauUfMrbuGpiEj3afQa+ECMy8qTtm3tQdpVV1mPeffcjNv4w37h2AwanROHOpCp/sLcAjw6Rp+45RwfjtyK5YsuVn2eM/d3tvJLcLwc8lFdYgDADS4iMgCIKkPcEXs4cjLT4cFytq8a9vG97zwzekerS9QEJkMCKCAlBS4Rh4Ln8wQ3WbC3uW6oANs4dBLwhu9UQJNRqs2aPSq71nqurqnTbUPnG+EoWlVZJlztqdNFVSu8ZAJjRQjzfvvxbT392Np3/RS1KtZRZFa9BiT6lnUojClxALT/Q+o9aHQY4HiSqyNGp6YFm5yKIyxHFOFEVc98JXMJlFzB+Xht+N7AYA+PfOPPxlwxGH7b88XIy/fXkMz9yajj+uPWit6rHYduw8RveMhSjCZVBi2d6ZvXmX3Hg3wMubjiEoQK8qOLFn28skMqTh4aamuvTpX/TC70Z2RbuQxqqHyOAALH8wQ7JdcnQIHh/VXfYYN6bFWoOcDuFGDEiOwsZDDW1jAvQ6pMSEICUmBF/MHo6xr24D0PCABIAgmyAiKKDhAW7bgNkbDSKVpl+xvSZN1SM+oln7hwcZrFWGpVV16BAu31j17OUqxWxPS0qODsGKyRmICgmEIAgY1DkaOU+NdqgSsozNYnF91xh897Ny2ywACHIR5BABHPHYo2wDm3rFNjny2zeFlhI5dfVm5F+8IvuN2aLeLGLXqYs4atNd82JlLe74x3bc8Y/tqKwxYdux88jNv4yfiiuw7kAhTFcv8u5TjQHF5Su1Dse2WPXdKVTUmHD2crXDupKKGjz67m50WbQeq78/LVnXPznKYXuDXW8i+8bodfUi2ocF4plbelmX2X9b/fmFCXjspm7W16u/P21N87/66/6YNES5uuS1e/rjhm4xDsstgYGaz19kcAC6dAhDu9CmP9BtA5HBqdGSQTBth1GwzSxYenNZAhugsbpiXO+GRp+21R+eNG2YfKNSf+hxotMJCL96rUqrlD/X/9x2wtoDTa8TsGrKII+VaVTPOElbH9sA5zfDuyAuwoh7r0uWBDkzb+wGe/PHpUnKGdLMCY2pbWAmx6NcV0W5k8lpS83hbn5lq3Wwr69/PwIJkcEICtBBEASs21+IP687hFCjAT8VV0CvE/DF7GHoFhuOnFMXsSfvMgDgPzn5eOazQ7LH/+pIMT7NLcBt/Tu6zIA9tmZPw+ioOgGv3TMAM9Y09A46deGKtQus/SBbcg88k0NQ49iuo32YUVIlM39sGm7qEYf7V+zEtGFdoNcJSG4XYl1/oaLW+o08OFCP527vjQ9y8mXbjHTtEIY37r0WA57bJFlu+Wy6+vw5C6DcYZsJiQgyoNbUeN5AvWMQAzReT72ucb0lOLo2pR3WzrxBcl08aVJmZ3SNDcODb/3gl18sokICUVZtUt1278U7+mBkWqyHSyVv0YSeWDi+BwRBkPy9G8Z/CZC8h8jgAOsYMwAQEtjw+x9vScf87P14dLhjg28iBjkeJGmTo/A1uSm9q5T54R23iU7ZpNJvenkrAODWfol46hc9rUGGRb1ZxP/2F2L26HDJNVQKcCyy1h/Bbf07Kv5tLCztZuIigjCxbwKSo2/ArW9sd5oBss/ayJEbjyXUaIDRJlsRFKBHSkwIvpl/k3XZHdd2xH9252PXqUuSbs2Wh0RkcIBsY2W9TpANviyfQWcfv0CDDn+0yTA1h22Q09AwtbG6LcAmyAkKbPxdL9Pjxfah2DcpqkXKpoZeJ2BY9w7IfXoMIABfHT4n27PIVywZMLVBjl7lSOueYsns2P49w4MMDoF61w5hkvGZgq9+Pu4elIwburdHYiTb4JAjBjkeJO1CLr+NJJOjML+V3PGasr61W7vvLIrLHauNAFgbA7tzDSy939TuYqkOsQQKzoZqVzPOiFwmJ9RokLQ7sQ14bI+ddUdfjH5lq2S5Jd1vOz2IpWs30DDYpHyQ0/CvsyD7+4Wj3Bo7xRnbMgQH6lBv88EPsKmuss3qyD2HA1SPEO4ZlrZMd1yb5NNy2LMEOWVV6kYu9nWQY2H7WQ8zGlBr9/+jV2KEtWE1AAQHNj6+vFVVSa2P7yuRNUxNVZQ71VX2/+nbop0nLsouLypryFy4E+dZLrfawKjr1S62lmxDrale8iC2pSaTIxfkhBn1kuqqIIXeOhFBjt9PLN+E9TYP/3Cb7XSCfLm+PHwOPxVXKH7+BneOblYbHHu2PZCCA/SSoMc2k2PbdkNu7BKOZyLPEvypvV+oHWnd02y/5IUaDQ5t1uzn//J1kEutg18EOVlZWRg0aBDCw8MRGxuL22+/HUePHnW539atWzFw4EAEBQWhS5cuWLp0qRdKq567gwG6etjKtbOQnE91yfzTp7kFGP3KVhwpUh7czb6baezV3iNFV7vDulPlZ9lS7T5drg5eZglsyqpNig8SNd+O5f6eoYGO1VVy5Mb+sGwbYNNuRRLk6ATFwOCBf32vmElUGi+oqWyDGoNeJ3mt1HjXXx7ErYHls+eqGtZ+e1+zbbNmNOgwfURXh22iQho/91HBLRd4k3b5RZCzdetWzJgxAzt37sSmTZtgMpkwZswYVFYqz/568uRJTJgwAcOGDcPevXuxaNEiPP7448jOzvZiyZ2TZmkUtpH0rmrc6C939nHYVu6bv63WWF0liiK++6kEeReuYNb7ufipuAKvOxkFN9QofeD2TYoEAFy6Uofqunr3qqvcvF439mgYHj1ARbWNTpCvYrElm8kJMkgCG6XuykEy1ViWnli2nyPbYMhZoFBUVi0b7CVHB7dYWxwL2/fUkF2Sz+RI9pF5vyTPErTYN3RX4i8BZIJNmxpBEPD7Mdc4bBOg12Hv0zdjz9M3+0VvNvJ/ftEmZ8OGDZLXK1euRGxsLHbv3o3hw4fL7rN06VKkpKTg1VdfBQD07NkTOTk5WLx4Me68805PF1kV22fGpkPnMKhztMOw6fUy1VVv3nctru0U5XA815kc/49yRFHEK5uOobquHgvH98SXh8/hN+/ulmwT5mTyx4YeFY0Nfrt0CIPxeAlqTGacL69RHDVVoTTWMrmSHB1snbtFTZpcJwjQ6wSYnbTbkWt4HGY0SIIAo0ImRy4jY8ly2bYVsr2Wco13bckFObYNnluKNMgRJMP5G+yu7aMjumDjwXO4d3AKgNbxGfc1ayZHZRTvL5mcfslReO723uh0dR60AL0OXTqE4sT5SlzftXHog5asOiXt88tQuLS0YdK66GjlYc137NiBMWPGSJaNHTsWOTk5qKuT71VQU1ODsrIyyY8n2d6Qd5++hDuXfOewjbR3VcO/RoNO9sajhTY5eRev4O9f/4Tl35zEp/sKUFLh2EMpOlR5tl37cWPahwVal/3ty2P4w0f73S6TmkdBuLExIxKg4hukmi/Hcg2XQ43STI5cxsbi8VHdJQ0ug2SGubcNKFyVqbnjNKllG6DZf87t2zgtHN8Tm+eNtDamtZ/kkRxZutmbnATY0u09WRr3TBrSSTKh5FuTB+HREV3w2j0DfFgqas386OPdQBRFzJ07F0OHDkXv3r0VtysqKkJcXJxkWVxcHEwmE0pKSmT3ycrKQmRkpPUnOTlZdruWouahYftty/Jw0umk7SosnPXmadjfcdmrXx5zXQgv2mkzW/LJkiuy3zadZWNC7bI8USGB1oe73AzGzlhOreYbr0Gh148SQRBcVhfIZebCHaqrlNvDzL35Gnwz/0YM6twOw6/pYA2IbE9rG0S4+sbui2lB9DoBgs0IUK6qAsf0isM/Jw3EN/Nv9HTRWi3LR1VtJsdfqqvkdG4fioXjeyqO3Ezkit8FOTNnzsT+/fvx3nvvudzWPmVvuUkrNa5cuHAhSktLrT/5+fnNL7ATzh4aZrOIR97ehf1nSq3L6m3Kr29CzwG5s736pfpZnr3B9v2Konzlw5Va5a6v9pmcoAC9YuNcVywPATXPAtsHgZo2OYKK48oFOYlRwQiyra5ykTXS6QR8+Ggm3p4yyPq5t3242Zbb1cPMW5kcW/bVVa4CMUEQMDY9HsnR3hn4rzWyZHLseycpb++/QQ5Rc/lFmxyLxx57DGvXrsW2bduQlOR87In4+HgUFRVJlhUXF8NgMCAmRj6lbTQaYTR67xuBs3vMocIyfHm4WLr91WeeThBkMzmt2bmyalz3wleSZWZRlA0Eq+qUq+XsH9RGg85lIOCKmkeBwY2MCKDu23GNyTFjlRQVrNgOR4l9UK+UyXFV7OYPRum+/slR2Hyk2PWGpJolBrfPJCp96XLVVouoNfOLJ6koipg5cyY+/vhjfP3110hNlZ8bxlZmZiY2bZIOT79x40ZkZGQgIKDlZ9NtGsebiqVbp9xD0NLmRic4PkhDVXTj9bdZyI8WlWPKyh9w4Ewp3t1x2mG9WWzMdlyXGo20q6PGVjnJ5Gy1m+TSaNA1OZNjuVpqrpu733bVPDcWb3SsSuzYLljSTb5pafrG92NbDp2L92D5bLa72k23Z0LzJot0ZtsfbsR704agd8dIVdeK1LNkcuy7kCt96XL1uSBqzfwiyJkxYwZWr16NNWvWIDw8HEVFRSgqKkJVVeOszwsXLsSDDz5ofT19+nScPn0ac+fOxeHDh/HWW29hxYoVmDdvni/egiy5m4rl25Vcl9ifiisANARA9oO2ffTb61u+gB42ZeUP2Hz0PO5Yst2hLQ3QkDmwZA86hBvxuxsbxsWocqOHlNGgd9o41xl3BgO07/XjipqtLfNeWUQGByAk0AC9TsDep29GzlOjmxTA2X7uBCfVVYNTpQ37Ldfhn5My8Pio7njrIenM4i0pJSYEmVd7zHBQv5allMlRqr5idRVpmV9UVy1ZsgQAMHLkSMnylStX4qGHHgIAFBYWIi8vz7ouNTUV69evx5w5c/Dmm28iMTERr7/+ut90HwfkH56Wh7qz6gxBkH67CgnUO/1WLYoiBEHwu3FyzpY2TMFQVy8iOtQxuyaKjQ9k2wn6rtSqD3ICDTqHAQLVEpvYJkeNpjTmjAlr7BrbnG6yttVOtqWwVEtsnDMc246dR2r7UPxwsnEEaUubsPiIIMy92XGMEmodLH/n1746jsyuMdYeaUrVkf7c8JioufwiyFFTXbBq1SqHZSNGjMCePXscN/YTcjcVy7crZ+0f7OvIXTUgFEV11SO+VCvTM8xsbmyToxMaR9atciPIaZHqKhWtctRM0yDRhL9HTAuN/6E00q1w9Rv+NXHhuCYuHN8el/ZCtAbgXs7v+vtnt7Wxnan9nmU7cerFiQCU7znM5JCW+UV1lVbJ3VTqrz7snQ25bl9H7ioGtPYS8uOB0upkehLlnL5kDeB0gmDtOeVOdVVQQNODHMvlUtMJRe/mk9/Zt2PLbMn21WwxTsYHcofS58W+TPZvyXIdvP3NvmuHMK+eT+uUOv8pfc7Z8Ji0zC8yOZolc1OxVAk4e7Daf7GqdxHlNDagdaNsHvbPrT9LXstNYZCbfxnnyxsm1hTQOCZMjZPeVfaa1SbH8q/dddPrBIfsmbuZHGdb902KwtnSIgQH6FFt816jw1ook2NbXWXbPds+yFEYgsHbQc6YXnF4amJP9EuO8up5tUopIFfKCGusIyeRBIMcD5JveNzwUHNWBWXfENNVdZU74714S9bnRySvlebdKrjc0Ljc9j27k5Fq6ELezDY5dufTCwLq7Ze5GeQ4CxQsx7L/u7ZUdZXS1bMvkv17aszktEgxVBMEAY8M6+Ldk2qYUiZHsQs5q6tIwxjDe5Dcw7peRZscd79J+1Nwo0SuTY4tQWh83+7MXhHYIl3Ipct/OaCjw7Yt2YW8cW4h6XLL1AXNpbbthf3nzLIfezu1bkqZHFZXUVvETI4Hyd1ULEGOs8DE3S9W1q7Q7u3mMXLfGF1NLqoTGtPm7gxKZzTomzwYYGMXcun5Hh/dHTf26IAakxmz3s8F0ITqKiebW45lMkuvSXhQy/x3VBwPxa5Q9kGP6KNMDrUspaBFubqKf3DSLmZyPEjuYW+50by5+SfF/dzN5DRWV/lHmCM3kahSdZWFTmicw0jtcPRAQyanqel2S6bN/mxBBh3G9U6QDMRnf45oF1VLzrIh1kyO3SUJD2qZTI7t58C2FPaXSemysUtx6+Z2dRX/3qRhDHI8SO6eYjKLqK6rx4aDRY4rr1J6yNwhU40CAE9+csCtbteeJlcWV0FOQ3VVw+/Oep7Z0+uEpgc5CoMBWo5nsEn725/ji9nDnR7bWYksAwt6O5NjH3gpfc4Y5LRuig2P2SaH2iAGOR4k1ybHbBZdZiqUeju8fHc/PHe748zs/809iyVbf/ab6iq5LuDOZhYHGh7Almeru3MoObtJ39QjVnGd5Sz257MGOTajHNufo0O4EZ1ilCeJtA8UxveOd1hn/zFoqUyO2uundN0E3hVaNXe7kLO6irSMtzMPsq+OABoyOa4eQUrfpAVBQJRC49Qzl674TaMcuRGLnU26CTRkPiyZBldd5i0Gd26YlsBZur13x0jlAyi0ZWrM5DQeV65NjrNHg32RfpXROOGsUvseNfOTqaE2RlQKclh90bopNjxWmtaBf2/SMAY5HiR3S6k3i5Jv2nIPPGdfrAIU5lASIPjNYICvfXncYdmhs6VO99EJQmOGQ0XvqoXje+D93wwB4DyT46xRsqgQ5chVV8l923U6NYf9a8E2KyRfpuAWCnImZ3YC0DD+jDNsk6NNStOsKU7rwKcAaRh7V3mQ7IjHZlHyjUqvExwm0nPWaNXQCu5Ia/eddVj28/lKp/vohMbAQE0mx2jQWQOPJgc51hGPHcfJAaTVVbKZHCexgO3f0LZ7vP1xbTVtxnFHiyb2xI09YjE4NRpPfvKj4nbKGcMWKQb5iF6hvkpxgk7+wUnD/P+J2YpZejNkdGqHlOiG9hsmsyipG5fP5CjfdAJUPLRbI8Emk6Omd5U0M6J8vQKdZnKu/quQybE9rlz2xdUkq7bb2RZRrrzfLxrV5EEN7RkNeoxMi0VIoPPvMErXjZmc1k0paFEcJ4dtckjDGOR4kOXhKQiNwUy9XcNjuQeK0+oqJytbcYwDQXAvgyC4CBosnGdy5Ec8tgRQtrObu5vJsf276uwzOXbHMugExEUEKR/MQ5R7V3m5INSilNp8KXUhZ8Nj0jIGOR5ktgY5jd2c7dvkyN12mpLJae1fvgUITt/DQ9d3lm6vMpOTHK3cA8rVnF+hxsZMiFwVmto2ObY9x+T289XfTunhxkxO62b/dzVdHb5BsQs5/96kYQxyPMiSIdAJUA5y5B6eTh7azkbe9ZfqqrgI99uW2Gc77D1zazpeuquv9bXtlnI36Y5RwXjprr5IT3DSu+oqpTS+bW8nubF/nLWdsv0b2r83+6DMk9MoODuy0sONz7zWzb5JTs//24C1+84qNuhndRVpGYMcD7JmctDY3mTniQuS6iq556uzW06A0iAYcG9iS095+7tTOFdW4/Z+Op3zTA4gbV/jqiHvDd1i8KuMZKdjvjQGhfLXzWBzrWtMjkGOs2eDfRDmNMhRPoxHKbVh59xVrZt9+7G6ehGPv7dXuXcV/96kYQxyPMiSpdHpgEOFZQCANzb/JPlGJXffcTaYm9Mgx/cxDv649mCT9hPg+mZr32PJQr5dk2A9rjP/yclXdd3kMjlOy2uzSrBreGyfjfPVQ4bVFNqk3PCYIx5T28Mgx4NEm0yOLVcj0jrrXaTU/bi136YEwf4qyWxj87uzoKHheJbtnB/1Dx/tVzVCcLXMYIbOMznSgMxZGyJfxRp8uGmT0t9VcYJOfgxIwzhOjgdZqo/sH2L1kobHjjce+3FzbAU4GSfHDxI5TaYTlKtJVj406Oo2NoGDTRAh34ZJcNhHieW6dYoJwYJxPWS3kZuqwlm1jn3vL2e9wXyVyWG1lDYpBTlq5zQj0hJmcjzIUi1lfxNxNQFlUrvgq/s5rgswOGks6g/1VU3krAv5jVfnn7K9d9tu6yyTo+b+bblsM2/shgl9EmS3kZt7y9WxLXNbje4Z57QLua8eMczkaJNS5wR354Qj0gJmcjzIclOxv+fUmBqrPuzvOy/d1dc6KJxeEGCy20BpxOOGaR1aL9tpHZRI2+Q4z+QI1u1cn9ty3ZydPyHScRybQZ2jsTfvsuI+Hz6aiS8OFuHOa5Pw8/kK63L7hqE+q67iN3hNUuqd6erLFZEWMZPjQZZbiv0t5z85+Q7bWFwTF279Xe5mpTR3lT84c+mK0/WueiO5SizYrnfVJkdN4GLdVpSvVgSA938zBLf2S8STE3s5rJsz+ho8MV6+egsA4iKC8GBmZ4QaDc4zOZ4MNpwcuhXMEEJNoJzJ8XJBiPwAb3MeZO1dZfcQe3vHaZuNGn8NDdSjX3KU9bXcN21/7V1VUWPC0L9sdrqNs3m3GhoeN613lbOMhJrwwXZkantDusTg9XsHyM4rFRyox/QRXVWcwfkIzb6qNWLXYW1SW13VIdyIV+7u540iEfkMgxwPanx4Kj9MbBse3z0oWbJO7l6l1Luq4XzyUY7S8pZUVFrtchtnbUB0guB0TJuGbRp/tw2IXB3XlcZBGz330Hc2ro+vGn6yukqblOZrsw9yHr+pG+64NskbRSLyGQY5HmR2kiGQY5/JkK2uUmqT4+Qcaia8bC5nIzFbt3ESoAmC66yLTimT4+Tcaq690kiwLUk6l5X3MjnOsmM6nYAZN6rLRFHroRTk2N8HOGcVtQUMcjzIdlqHdY8Pld/G5r5j/0CWe3g7uzEphTLeqItX01PHWSDkaloHQHp91M5dpSZL0tjV35OZnMbfHa+D7x42o3rG+ezc5BlKk9LaJ3RZXUltAYMcD7Kd1iE9MRLdY8Ocbm//7HO3OkGpVsobXUfVnMNVMOLqpms/s7ez47rzlhsHbfQcZ73BfPmFmg867QnU62WXO2Ry+KenNoBBjgfVX539V3+1muZ4cYXDNra3HftMgruZBeVMjueDHDVVYq6qlVy9XUkmx3YwwGY+qN3pidVUzjI5vowz2C5He5TG0rK/D3AQQGoLGOR4UPXV8XCCro57I9dDx5b9LcdJRyrHfX3cJkdNIOWsd5VOcD1Bp1Imx1lbHzUs44d48p7vbIJOTwZX7gSOpA2BCjcO+9sAA1xqCxjkeJBllNyggIbLvPSBgQ7bSHo+2d1zurmo3nJ6LBveaJPjbCoKC6cNj+G8kSxg3yan8ffm3qzrLEFOs47inHScHLvBAD143sGp0U7Xs7pKe9T2ruI4SdQWcMRjD7JM6mgZwbhrh1CHbcySGEf6wFn8q37IWn8ED93QWbJ8zbTrMPeDfSgqc91tG/D8SKcbfizCp7kFLrdz1dVbbrXtQ1rau0pdw2M1TFerFT2ZvpfMmq6zX+e58951bRIC9ToMSImSXc+pHbRHKcix/yLCAJfaAr+J5bdt24ZbbrkFiYmJEAQB//3vf51uv2XLloYB5Ox+jhw54p0Cq1BjkmZyIoICnG5v/7xJiAzG6/cOwLUp7STLr+/aHjsXjbLOcWWhVGNU38Q2OeXVdVjzfR5KKmqcbjd99W58/mORy+M5HbRPoXfViskZ1t+lE3TaHLeZD+o6a5DTrMM4ZdvY2L5qztPnvX1AR3SKcQywG8riuXOTbyhWV5nZJofaHr8JciorK9GvXz+88cYbbu139OhRFBYWWn+6d+/uoRK6z5LJCQpoyOS4GpfC3XuONChQ3rmpDY8XfnwAiz45gCkrdzVpf3uuelfZv//wIAPCbQJD97qQq3/PpnpvVFfZ/i5Aacwfb+ODTnuU/qb2mRy2yaG2wG+qq8aPH4/x48e7vV9sbCyioqJavkAtoMauTY4rrtqkOGxvs/l7P+RhwTj5eZSaOtjdugOFAIADBaVu7zs4NRo/nLwoWeasTY5OcLw5Oxs0z1UXcnfUeqG6yr7RtE4A6mXWeRurq9oO+0wO//TUFvhNJqepBgwYgISEBIwaNQqbNzufO6mmpgZlZWWSH0+yzDZuaZPjSp+kSLeOb/9wVGoX09RMjprdLO1Z7M0bk4YPH82ULHM6d5XlXyfVOEpzV7kbHNqzZHI8OvKwXRZKUKh68zY+6NqO/QWXJa+ZxaO2wG8yOe5KSEjAsmXLMHDgQNTU1ODdd9/FqFGjsGXLFgwfPlx2n6ysLDz77LNeK6N97yoACAnU40ptvWS7jXOG48eCUozp5d7os/a3qOJy+bYznupCvuPnC/hgV57sOoNecGhL5LTh8dV1OkGwtiGy31qp4bHcvdqduM4rbXJkMjly67yNjU/bjtU7pf9XmcWjtqDVBjlpaWlIS0uzvs7MzER+fj4WL16sGOQsXLgQc+fOtb4uKytDcnKy7LbN9fZ3p/DVkWIAjW1yACDMaHAIcq6JC8c1ceHun8TuHqU8Qaf7h1bj3uU7FdfJPTyd3VMtQYuzahx1LZDcZw1ymnjU6SO6YunWnzF7dHe8+uVx2W3sAzTJuXz4rOH8RW0X//TUFrT66ipbQ4YMwfHj8g8ZADAajYiIiJD8eMof1x5sPK9Nl86wIM/FlUqxTFN7VzWHuzdQa3UVlDM0SpNcBrgzaqKMuvrmDQa4YFwatswbiVmjlBu9218P/8nkSF//3y96+aYg5HXM4lFboKkgZ+/evUhISPB1MRwYbTI5d2e0YObILnZR7ELujdEA7cjdQJ3OiH11e+ludpkchfY60aGBmDYsFY+O6NKUotpUVzXtpi8IAjq3D3W6v+06URQVu8N7m205Hrq+Mx4emurD0pA3McahtsBvqqsqKirw008/WV+fPHkSubm5iI6ORkpKChYuXIiCggK88847AIBXX30VnTt3Rnp6Ompra7F69WpkZ2cjOzvbV29BUZBNw+NHhqbixc+9O5ZPZY3Jq+cDFG6gTqurHPdzzH7IZ3IA4MmJDRmIf2494VYZRbGxa623upBbzt24zndPm1Bj4y2gVqERObU+iyb0wAvrnd9n2CaH2gK/CXJycnJw4403Wl9b2s5MnjwZq1atQmFhIfLyGhvO1dbWYt68eSgoKEBwcDDS09Oxbt06TJgwwetld8V2BFJDM6tWnFFqk3NeoUGyJ7n74Lbcb52NH2PbOaslbs8Beh1qTY0Pdm81PAakbWF8+Y06NLAxAC+tqvNdQahF/WZ4VxwoKMNn+84qbsPqKmoL/CbIGTlypOJDGgBWrVoleT1//nzMnz/fw6VqGUojkDaX/dVSunquRiz2BHdvoI0Nj22rcewbHrdsY91AuyDHs7OQS499paax8bkvu/LanruMQY6m2M92b48xDrUFmmqT4698nRb2TSbHve0Fu38BuYbHtr83/5oG6O2DKM+xL65t1ZC/PGuYydEWV/cdZnKoLWCQ4wXObja39EtsuRMppHJaMpOjNPifPbnshLNbqlzDY4cu5G401lXTocyhV5YXq6sk6/zkf2GV3dAG1LrZB/H2fP3li8gb/OT2qm32NxPb592Ld/Rp8nHtq/eURjYub6GGxws/3o9rn9ukKjMkd/909sXR8qB3VnUjyeS0wA3aPsjxbHWV8jrbKjNf+PPtvREaqMcLzfgskv9xncnxUkGIfIhBjhfY143bvrLt3dJcSskLy7QFzfXeD/koqzbh39+fdrmt221y0DgYoHWZk95VLXF/tm0Q3lLHVOLsehSWVnvwzK49MKQT9j8zFoM6R/u0HNSynE2jAnBaB2obGOR4gf03Km/Xhde1cNdgNZkH9xseO+7nWF1l+7vz44sqZiF3CD49+Hdxdujyau938bfHqgvtcdXwmG1yqC1gkOMFzqqrmsOhd5XCc72uhTI5FmqCHLn3qGbaBGcTdDrrXt4U9tVVnrzn81szeZveVZscfiapDWCQ4wWO1VWeubkotclp8UyOiuPJtZlxdk+1FN1Z4+KWHkDPvrqKyQzSEldBDGMcagsY5HiBpzI59hTb5JhbNshREzS5GzCYZWYet89+tHSbHMfeJ7zrk3a4Gumc1VXUFjDI8QL7BoA39YgFAHSKCWnWcdXOu1lnatnqqpomtsmxXXTHtR2x9IGB1teW6bWcjnjciqurAOAXfRPQPzkKfZOiPHsiIgAXrzgf98hfhi4g8iS/GfFYy+xvJi/e2RfXprRr2TFyAMVUTl2LZ3JcB02uAoa7BiZhYKd21teWTI6kd5WTY7bMYIDe60IOAG/cd61Hj09k61JlrdP1bJNDbQFjeS+wz+REBgdg2vAuiI8MatHzKPUoavneVfUw28xs/oexaQ7bqAkYbK+LZcwfSZscJ72rWoJDJqdlD0/kUxddBDnmlk3wEvklBjle4KnuufZBjVL1lTvj5Hz3cwlmrNnjdMC/unpRkh2alNnJYRvZ6irYt7Fp/N1sbXgsv97+mK6CKLlrcXOvODw1saf1tcO0DoxySEMm9Il3ut7Xg1ASeQOrq7zA1XgVTWX/IFcKZdT0hrK4b/n3V49tH0A1vq41mVFv8zUwQKZyX81bts3UiDJBjmNQ1LzraNAJGNIlpvG1QyaHUQ5px2+Gd0W9Gfjbl8dk17tzXyBqrZjJ8QCzXR64JaYgUKNeIf/sKpMjiiKq66TzFuVfrFI8dq3JLGmXI5epkhsXxtkAfY1tcpw1PFbcXTXbKsIAL/V6I/KFQIMO451kc/h5p7aAQY4H1Kvt9tRMqntXufjG9vCqXej77EZJHb59QGKyCXJq6qWZHLlMlVxA4qz9syVTpDTWDyDNtKgZ0VhO+zAjsn97PT6fNcxrXfuJfEUu+xkVEoCx6XHoz15+1AYwyPEApYyKr7jqDbX56HnUmsz4/MdCxW1sg5w6k9k69o5OaMhUzRtzjWR7uZur80xOw7+2GSSHaR3c+LQ6e8cDO7VDz4QIGOzb5LC6ijRG7svG7FHd8c9JGV7LMBP5EoMcD3CWjfAFtb2rJJkSu7fw29W7rb8fKizD37/6CUBjD6kZN3bD70Z2tW4jG+Q4uSxy18zZODlNYX8KhznF+L+BNEbu/0w1GxxTG8Lbugf4WybH1AINDL85XiJ5/e7OhpnILYGCIAhIjAq2rpeLR5xdFblL5ok2Obbsu/Yzk0NaIxfkVNXWy2xJpE0McjzA34IcZ9VVtr2mbO+HapNRtlU+rgbrk/TYsu8ZJnNCh+oqJ5mmpnCchbz5xyTyJ3LZSftOBkRaxiDHA7wV5ESHBqrars5slg0iAGlbG9tnvtp3YBso2AYhclkXUQTG947HNXFhyOgcLVknW11l/7qFgxD7WZq9GeMM697ei2ejtko2k8Mgh9oQBjke4K3eVUntgl1vhIbgQinwUpqHqqzK+bw3FrZjzbjM5ABY8sBAfDF7uMMM4LLFczJBpytq/gSOmRzvhTmrpgx2uAZELU3u/8y9g1N8UBIi3+Bd1gNaeKooRWqDHECasbFVY/Ot7uWNjYOGnS2tktvcQYAkk9NILl4INRqurnNcKdtY225ZU9vkPHR9ZwDArNHdJcsd2uR4MZWj1wmIizB674TUJtn+n/ntyK7Y+/TN6JkQ4bsCEXkZgxwP8FYm51cZyaq3VRrd1DaTU2wzlYPatxAUoLf+LhmtWCZi+PNtvR2WWWZiH9PLcdAy+yxTU3tXPXNrOo7+eZzDzd0hk9Oko8sLMuhdbiM3UjRRS7LtJh6g16GdyipuIq3gXdYD7Ec89pRr4sJxU49YVdsqzVPT3PlrJEGOkzDBoBOQcjWgsbVxznB8v2gUusWGuSxbczItRpmgw75NTkvMQv7UxJ4YkBKFh4d2drltlw6hzT4fkTO2n2nOOk5tEeeu8gBv9q5KjFI3k7lSt9GLV5zPVOxKUIB8mxx7SuuMBj3iIuSzHvaZnJZuM+OJ3lWPDOuCR4Z1UbXtC3f0QfD/DuOB69hGgjzD9iMeanSdXSTSGgY5HuCt6ipAfbWSUo+Ke5ftbNb5pdVVLRuEKDWKBlqqC7lvx8mJDQ/C3+8d4NVzUtsSHhSAbrFhMJtF3Hltkq+LQ+R1DHI8wFvVVYD6rt5XZDI5ZdV1TgMJW+FGA8prTA7LpdVVypoSANWalLu6Jkc7b3StZm4r+8bOzOaT1uh1Ar6YPRxmUUSAnq0TqO1hkOMBSj2ZPEFp/Bt7ttVVPxaUorquXrExspzwIKUgR2V1leozNZILwL5fNArVdfWICml+A8pSu27yDHJIi/Q6AXqO5k1tFIMcD/Bmmxz11VWmq9uL+MXfvwUAh0k1nbF0/waAiX0SsO5Aw2Setr2ImtImxxm5ICcuQl0bJDUuVErbI3lznBwiIvI85i89wN8m6AQaq6vKqhuzMYttxsVxJSSwMZixbd9jdNG7yjJGzcLxPVWfqzkCrvaYGt69g8ttL9kHOR4pERER+QozOR5gm8lZPfU6j55LdSanth4HzpTi09yCJp2nZ0IE9p0pBQDsOnXRurzGpt2MXCLkj7f0wtShqUiOduw+7gnfLrgJB86UqupaP/Ombvj8xyLrayZyiIi0hZkcD7BkclKiQzDUw3MUqc0a5eZfxn3Ld+Jf3550ut2+/xuDdY8PRaBeh18O6GhdPn1EV0wdmop/P3KddQA/ALh8pbFdi1x1jyAIXgtwgIbqrNG94iSDoClJT4zEridHW19X13lpqGoiIvIKvwlytm3bhltuuQWJiYkQBAH//e9/Xe6zdetWDBw4EEFBQejSpQuWLl3q+YK6UG8W8ZfPjwJwHIfFE2zbyjjz7+/zZBsOAw0TfRoNOiya0AORIQFIT4xE7h9vxkt39bVukxgVjKd/0Qs3dGuPl+7qZ11+2WacnYTIlmsv4y3twxobMHMcESIibfGb6qrKykr069cPU6ZMwZ133uly+5MnT2LChAmYNm0aVq9eje3bt+N3v/sdOnTooGp/T9HrBHSKCcGu0xdxV4bnx6V4fFR3HCgoxe7Tl5p8jD4dI7H8wQzJhJEhgQ0fjdz/uxmiCMk62+kRbPNIGZ3aYeH4HujawXH0YrW2zBuJkYu3NHl/dwmCgDXTrkN5tQmx4a0vSCMiImWCqLYPshcJgoBPPvkEt99+u+I2CxYswNq1a3H48GHrsunTp2Pfvn3YsWOHqvOUlZUhMjISpaWliIhouUnrLlbW4vSFSgxIaddix1Rj0ScHsOb7PFybEoU9eZcBAFEhAZIqJYv4iCAUlVUDAMalx2PppIFunevjPWfw8sZj+OekgejdMbLZZbe1eudpPPXfH5GeGIF1jw9r0WMTEVHrp/b57TeZHHft2LEDY8aMkSwbO3YsVqxYgbq6OgQEBDjsU1NTg5qaxkkoy8rKPFK26NBARPtgIrw/3ZqO+69LQc/4CLz21XFs+LEIHz6aifxLVwAA/9tfiAl94nHqwhXc0DUGXx0uxr++PYGFE3q4fa47rk3CHR4aQfW+wSno0j4U6S0cPBERUdvSaoOcoqIixMXFSZbFxcXBZDKhpKQECQkJDvtkZWXh2Wef9VYRvc6g1yE9sSEwmHPzNZhzc8M4OJEhDcssGZe+SVEAgLsHJePuQepnMvcWnU7A9d0822CbiIi0z28aHjeFfW8eS82b0qBuCxcuRGlpqfUnPz/f42UkIiIi32i1mZz4+HgUFRVJlhUXF8NgMCAmJkZ2H6PRCKPR6I3iERERkY+12kxOZmYmNm3aJFm2ceNGZGRkyLbHISIiorbFb4KciooK5ObmIjc3F0BDF/Hc3Fzk5eUBaKhqevDBB63bT58+HadPn8bcuXNx+PBhvPXWW1ixYgXmzZvni+ITERGRn/Gb6qqcnBzceOON1tdz584FAEyePBmrVq1CYWGhNeABgNTUVKxfvx5z5szBm2++icTERLz++us+HSOHiIiI/IdfjpPjLZ4aJ4eIiIg8R+3z22+qq4iIiIhaEoMcIiIi0iQGOURERKRJDHKIiIhIkxjkEBERkSYxyCEiIiJNYpBDREREmsQgh4iIiDTJb0Y89gXLOIhlZWU+LgkRERGpZXluuxrPuE0HOeXl5QCA5ORkH5eEiIiI3FVeXo7IyEjF9W16Wgez2YyzZ88iPDwcgiA0+ThlZWVITk5Gfn4+p4fwMF5r7+G19h5ea+/i9fYeT11rURRRXl6OxMRE6HTKLW/adCZHp9MhKSmpxY4XERHB/zBewmvtPbzW3sNr7V283t7jiWvtLINjwYbHREREpEkMcoiIiEiTGOS0AKPRiD/+8Y8wGo2+Lorm8Vp7D6+19/Baexevt/f4+lq36YbHREREpF3M5BAREZEmMcghIiIiTWKQQ0RERJrEIIeIiIg0iUFOM/3jH/9AamoqgoKCMHDgQHzzzTe+LpLmZGVlYdCgQQgPD0dsbCxuv/12HD161NfFahOysrIgCAJmz57t66JoVkFBAR544AHExMQgJCQE/fv3x+7du31dLM0xmUx46qmnkJqaiuDgYHTp0gV/+tOfYDabfV00Tdi2bRtuueUWJCYmQhAE/Pe//5WsF0URzzzzDBITExEcHIyRI0fi4MGDHi8Xg5xm+OCDDzB79mw8+eST2Lt3L4YNG4bx48cjLy/P10XTlK1bt2LGjBnYuXMnNm3aBJPJhDFjxqCystLXRdO0Xbt2YdmyZejbt6+vi6JZly5dwg033ICAgAB8/vnnOHToEF5++WVERUX5umia85e//AVLly7FG2+8gcOHD+Ovf/0rXnrpJfz973/3ddE0obKyEv369cMbb7whu/6vf/0rXnnlFbzxxhvYtWsX4uPjcfPNN1vnkPQYkZps8ODB4vTp0yXLevToIT7xxBM+KlHbUFxcLAIQt27d6uuiaFZ5ebnYvXt3cdOmTeKIESPEWbNm+bpImrRgwQJx6NChvi5GmzBx4kTx4Ycfliy74447xAceeMBHJdIuAOInn3xifW02m8X4+HjxxRdftC6rrq4WIyMjxaVLl3q0LMzkNFFtbS12796NMWPGSJaPGTMG3333nY9K1TaUlpYCAKKjo31cEu2aMWMGJk6ciNGjR/u6KJq2du1aZGRk4Fe/+hViY2MxYMAALF++3NfF0qShQ4fiq6++wrFjxwAA+/btw7fffosJEyb4uGTad/LkSRQVFUmel0ajESNGjPD487JNT9DZHCUlJaivr0dcXJxkeVxcHIqKinxUKu0TRRFz587F0KFD0bt3b18XR5Pef/997NmzB7t27fJ1UTTvxIkTWLJkCebOnYtFixbhhx9+wOOPPw6j0YgHH3zQ18XTlAULFqC0tBQ9evSAXq9HfX09nn/+edx7772+LprmWZ6Jcs/L06dPe/TcDHKaSRAEyWtRFB2WUcuZOXMm9u/fj2+//dbXRdGk/Px8zJo1Cxs3bkRQUJCvi6N5ZrMZGRkZeOGFFwAAAwYMwMGDB7FkyRIGOS3sgw8+wOrVq7FmzRqkp6cjNzcXs2fPRmJiIiZPnuzr4rUJvnheMshpovbt20Ov1ztkbYqLix2iVWoZjz32GNauXYtt27YhKSnJ18XRpN27d6O4uBgDBw60Lquvr8e2bdvwxhtvoKamBnq93ocl1JaEhAT06tVLsqxnz57Izs72UYm06w9/+AOeeOIJ3HPPPQCAPn364PTp08jKymKQ42Hx8fEAGjI6CQkJ1uXeeF6yTU4TBQYGYuDAgdi0aZNk+aZNm3D99df7qFTaJIoiZs6ciY8//hhff/01UlNTfV0kzRo1ahQOHDiA3Nxc609GRgbuv/9+5ObmMsBpYTfccIPDcAjHjh1Dp06dfFQi7bpy5Qp0OukjT6/Xswu5F6SmpiI+Pl7yvKytrcXWrVs9/rxkJqcZ5s6di0mTJiEjIwOZmZlYtmwZ8vLyMH36dF8XTVNmzJiBNWvW4NNPP0V4eLg1exYZGYng4GAfl05bwsPDHdo6hYaGIiYmhm2gPGDOnDm4/vrr8cILL+Duu+/GDz/8gGXLlmHZsmW+Lprm3HLLLXj++eeRkpKC9PR07N27F6+88goefvhhXxdNEyoqKvDTTz9ZX588eRK5ubmIjo5GSkoKZs+ejRdeeAHdu3dH9+7d8cILLyAkJAT33XefZwvm0b5bbcCbb74pdurUSQwMDBSvvfZadmv2AACyPytXrvR10doEdiH3rM8++0zs3bu3aDQaxR49eojLli3zdZE0qaysTJw1a5aYkpIiBgUFiV26dBGffPJJsaamxtdF04TNmzfL3qcnT54simJDN/I//vGPYnx8vGg0GsXhw4eLBw4c8Hi5BFEURc+GUURERETexzY5REREpEkMcoiIiEiTGOQQERGRJjHIISIiIk1ikENERESaxCCHiIiINIlBDhEREWkSgxwiIiLSJAY5RNTmXbhwAbGxsTh16hQAYMuWLRAEAZcvX/boeefNm4fHH3/co+cgassY5BCRag899BAEQXD4GTdunK+L1ixZWVm45ZZb0Llz52Yf69y5cwgICMDq1atl1z/66KPo27cvAGD+/PlYuXIlTp482ezzEpEjBjlE5JZx48ahsLBQ8vPee+959Jy1tbUeO3ZVVRVWrFiBRx55pEWOFxcXh4kTJ2LlypWy53r//fcxdepUAEBsbCzGjBmDpUuXtsi5iUiKQQ4RucVoNCI+Pl7y065dO+t6QRDwr3/9C7/85S8REhKC7t27Y+3atZJjHDp0CBMmTEBYWBji4uIwadIklJSUWNePHDkSM2fOxNy5c9G+fXvcfPPNAIC1a9eie/fuCA4Oxo033oi3337bWq1UWVmJiIgIfPTRR5JzffbZZwgNDUV5ebns+/n8889hMBiQmZmp+J6rqqowceJEDBkyBBcvXgQArFy5Ej179kRQUBB69OiBf/zjH9btp06dis2bN1urvyw++ugjVFdX44EHHrAuu/XWWz0eJBK1VQxyiKjFPfvss7j77ruxf/9+TJgwAffff781OCgsLMSIESPQv39/5OTkYMOGDTh37hzuvvtuyTHefvttGAwGbN++Hf/85z9x6tQp3HXXXbj99tuRm5uLRx99FE8++aR1+9DQUNxzzz0OGZSVK1firrvuQnh4uGxZt23bhoyMDMX3UlpaijFjxqC2thZfffUVoqOjsXz5cjz55JN4/vnncfjwYbzwwgt4+umn8fbbbwMAJkyYgPj4eKxatUpyrLfeegu33347YmJirMsGDx6M/Px8nD592vWFJSL3eHyecyLSjMmTJ4t6vV4MDQ2V/PzpT3+ybgNAfOqpp6yvKyoqREEQxM8//1wURVF8+umnxTFjxkiOm5+fLwIQjx49KoqiKI4YMULs37+/ZJsFCxaIvXv3lix78sknRQDipUuXRFEUxe+//17U6/ViQUGBKIqieP78eTEgIEDcsmWL4nu67bbbxIcffliybPPmzSIA8ciRI2K/fv3EO+64Q6ypqbGuT05OFtesWSPZ57nnnhMzMzMl5e3UqZNoNptFURTFEydOiIIgiF988YVkv9LSUhGA0zISUdMwk0NEbrnxxhuRm5sr+ZkxY4ZkG0vDWqAhwxIeHo7i4mIAwO7du7F582aEhYVZf3r06AEA+Pnnn6372WdXjh49ikGDBkmWDR482OF1eno63nnnHQDAu+++i5SUFAwfPlzx/VRVVSEoKEh23ejRo9GlSxd8+OGHCAwMBACcP38e+fn5mDp1quQ9/PnPf5aUf+rUqTh9+jS+/vprAA1ZnKSkJIwePVpyjuDgYADAlStXFMtIRE1j8HUBiKh1CQ0NRbdu3ZxuExAQIHktCALMZjMAwGw245ZbbsFf/vIXh/0SEhIk57EliiIEQXBYZu+RRx7BG2+8gSeeeAIrV67ElClTHPaz1b59e1y6dEl23cSJE5GdnY1Dhw6hT58+1vIDwPLly3HddddJttfr9dbfu3fvjmHDhmHlypXW9kNTpkyBTif9bmmpxuvQoYNiGYmoaRjkEJFXXXvttcjOzkbnzp1hMKi/BfXo0QPr16+XLMvJyXHY7oEHHsD8+fPx+uuv4+DBg5g8ebLT4w4YMECxu/eLL76IsLAwjBo1Clu2bEGvXr0QFxeHjh074sSJE7j//vudHnvq1Kn47W9/i9tuuw1nzpzBlClTHLb58ccfERAQgPT0dKfHIiL3sbqKiNxSU1ODoqIiyY9tzyhXZsyYgYsXL+Lee+/FDz/8gBMnTmDjxo14+OGHUV9fr7jfo48+iiNHjmDBggU4duwYPvzwQ2vDXttMTbt27XDHHXfgD3/4A8aMGYOkpCSn5Rk7diwOHjyomM1ZvHgx7r//ftx00004cuQIAOCZZ55BVlYWXnvtNRw7dgwHDhzAypUr8corr0j2/dWvfoWAgAA8+uijGDVqlOw4PN988w2GDRtmrbYiopbDIIeI3LJhwwYkJCRIfoYOHap6/8TERGzfvh319fUYO3YsevfujVmzZiEyMtKhKsdWamoqPvroI3z88cfo27cvlixZYu1dZTQaJdtOnToVtbW1ePjhh12Wp0+fPsjIyMCHH36ouM3f/vY33H333bjppptw7NgxPPLII/jXv/6FVatWoU+fPhgxYgRWrVqF1NRUyX4hISG45557cOnSJcWyvPfee5g2bZrLchKR+wRRrlKbiKgVeP7557F06VLk5+dLlv/73//GrFmzcPbsWWuDYWfWr1+PefPm4ccff3QaaLW0devW4Q9/+AP279/vVtUdEanD/1VE1Gr84x//wKBBgxATE4Pt27fjpZdewsyZM63rr1y5gpMnTyIrKwuPPvqoqgAHaBjX5vjx4ygoKEBycrKniu+gsrISK1euZIBD5CHM5BBRqzFnzhx88MEHuHjxIlJSUjBp0iQsXLjQGiQ888wzeP755zF8+HB8+umnCAsL83GJiciXGOQQERGRJrHhMREREWkSgxwiIiLSJAY5REREpEkMcoiIiEiTGOQQERGRJjHIISIiIk1ikENERESaxCCHiIiINOn/AWhFSQfpH888AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(emid, yo5 / yo1)\n",
    "plt.xlabel('Energy (keV)')\n",
    "plt.ylabel(r'$O_5 / O_1$');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using a class-based approach"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As with the\n",
    "['User models in Sherpa' notebook](http://nbviewer.ipython.org/github/DougBurke/sherpa-standalone-notebooks/blob/master/user%20model.ipynb),\n",
    "we can also create a class for this model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sherpa.models import model\n",
    "\n",
    "class XSvvneiNoH(model.ArithmeticModel):\n",
    "    \"\"\"A version of XSvvnei which has no Hydrogen. The\n",
    "    abundances of the other elements are given relative to\n",
    "    solar abundance of Hydrogen.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, name='xxvvneinoh'):\n",
    "        mdl = xspec.XSvvnei()\n",
    "        \n",
    "        pars = []\n",
    "        for par in mdl.pars:\n",
    "            if par.name == 'H':\n",
    "                continue\n",
    "                \n",
    "            newpar = model.Parameter(name, par.name, par.val, min=par.min, max=par.max,\n",
    "                                     hard_min=par.hard_min, hard_max=par.hard_max,\n",
    "                                     units=par.units, frozen=par.frozen,\n",
    "                                     alwaysfrozen=par.alwaysfrozen, hidden=par.hidden)\n",
    "            setattr(self, par.name, newpar)\n",
    "            pars.append(newpar)\n",
    "            \n",
    "        model.ArithmeticModel.__init__(self, name, tuple(pars))\n",
    "        \n",
    "    @model.modelCacher1d\n",
    "    def calc(self, pars, x, *args, **kwargs):\n",
    "        return xsvvnei_no_h(pars, x, *args)\n",
    "\n",
    "# Tell Sherpa about the model\n",
    "ui.add_model(XSvvneiNoH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This class can then be used to create model components: in the following I show the lines from Carbon, Oxygen, and Argon lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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a8yIFu6IJ6NV6vrjpa4fdu6Hee+89TCYT7du3tz9mtVrRarUUFRXZH3N3d2/SPorzJLxcJyazhTfXHAfgtgERRPjX/kesVincMTCS1348xoqdKdzaPxyVquHzrUJcq9o77ErNi3A8RVEaNXXjCCaTiY8//phXX32V8ePH13pu+vTpfPrpp8TGxjqod22H00wbtTTf7cvgTG4ZXq5aHhjRqd42N8W1x8NFQ1pBBVtP5jVzD0Vb1yQ77ErNi2hjvv/+e4qKinjwwQeJjY2t9XHbbbfx3nvvObqLbYLThJfm2KQut6SqQe3Kq0y88+spAB4a2QkvV2297dz0Gm7pGwbIsmnR/Jpi2khGXkRb89577zF27Fi8vb3rPDd9+nQSEhLYt2+fA3rWtjjNtNG8efOYN28eBoOh3n9U1yq9sILb3thMp2APhncNYni3ILq286p3qufDzWcoKjcS4e/Grf3DL3vd2wdG8PmOFPYmFXIy20B0iFeT912I+lw4WnL1ZxvJ8QCibfnuu+8u+Vzfvn2xWm1fC/Pnz2+uLrVJThNerrejmSWoFDidU8bpnDI+2HSGQE89N3QNYli3QPpF+aPTqMgsquDz7ckAPDahKxr15Qe3QnxcGdk9mPVHsvl8ewp/mdazGd6NEBcvlb7ampezBzNKwa4QohlJeGmgcbHtGNDRn20n89l0LJcdp/LJK61m5Z40Vu5Jw02nZmDnAIorjNSYrfTr6McNXQIbdO27Bkey/kg2aw9lMXdsF/w9G171fjmrEzL4YNMZHhkTLYdAijqatubFitVqbdQmX0IIcbUkvDSQ1WJBv2MrEwYOYFLvUKprzOxNLmTzsTy2HM8lr7SaXxNzAFAUeGJC1wZ/I48N9yE2zJvD6SX8b3cavxnd+Zr7u2pPGv/8LhGABd8coWe4D4FeLbuKXzSvi2teriZ8XHgNixXUkl2EEM3AaQp2rzfDvn28//7/+G76baTePp3qxf+mnyGNZyZ14Zv5I3j/4UHcP7wjPcN9eGRMdKNrV+48u2nd//akUV1zbbsEf7Uz1R5cPFw0lFeb+Nfqo9d0TeF8Lh5tuZrBl1rLrc8eN1BWVcOba45zLNNwTf0TQohLkZGXBtp76Ajq0oFk6wfyndlE9YEsPHa9RVRRCj3bhxExbDgPDBvGw6MHXNXQ+cjuQYR4u5BdUsXidSeY1DuULiGeV6yZudjn25NZ9JNtf5m7h3Rgcp9QZr+1nY1Hc/klMZvRMTJ9JGwuDi8mswW1St3Ia9Qt+t14LJf/bksmu7iSf8zoc839FEKIizlNeLneZxv59umD+dRuLKcVtJV6NEXhWAnnjB6OlVZRsyEZn9Xr6VaeS9dOXdHHx6OLj0fbrSuK5sp/zRq1itsHRvLvtcf5cmcqX+5MRa9V0T3Um57hPvQ8O7Xk53HpephPtiSxeN0JAGbdEMUjY6NRFIVZN0TxwaYzvPrDUfpF+V9y6bZo3Y5llpBrqGZ4t6AGtb94ebPJYqWx1VYXFuqeu15lte1rsMLYss4ZE0I4D6cJL9d7qXRcXG/i4npjsVg4cewUB3YeJvdwPqoULbpqF3TVnaihE4c0sCe3HNOvybiv2UJoSQY9PD3x790HXXw8uvi+qP39673H7QMjsFqt7Esu5HB6MYZKEwkpRSSknN9uOtBLT8dAD6ICPYgK8qBjkAdRge58tSuNt9afBOCBER35zajO9hGg+4Z35JfEHFLyy1m89jh/ukV2f3Q2VquVp/+7n/zSalY9NZwQnyufVH7xxnJXU7RbX9FvjfncCiTZuE4IcX04TXhpLiqVim4xXegWYzuryFRj4sD+QyTuPk5RYgnaDFf0le7oKzsCHcnUQHqVhcrEQtTHfsb3nffpWFNOt85dcBs0CN2ggajDwlAUBZ1Gxb03RHHvDVFYLFZSC8o5lFZs/0jKKyfPUE2eoZqdpwvq7d/DozrzwMjaO/rqtWqevbkHc97fxbf7MpjQqx3xUecDlMVi5ecj2Xy6NRm9VsUt8WGM6RGCi7ZxUwjCcfJKq8kvrQYgv6y6QeGlzsjLVYQNUz3hxSQb1wkhrjMJL9dIo9UQPyCO+AFxAFRUVHL44BFOH0qm8GQRljQFXYUL7oYAIIBKenIE2JdRgemHQ3h/9R0dq/KJieiIx0BbmNF06oRKpdAh0IMOgR5MObsLb1lVDWdyy0jKKycpt4wzeWUk5ZWRZ7D90Jo7NppZwzrW288+kb5M6xfOyj1pLPw2kU/mDsFFqyYhpYg31xwjMeN8ceXB1GLe+OkYN/Zpz7R+4UQEyOFiLd3pnFL7n8sbeEr5xSMtVzfycj7wnAs/NRf9VwghmpqElybm5ubKgEH9GDDIdhS61WolKzOHwwlHSEvMpPx0BdocV/SVbugrozERzQngUG4lNetP4bn6ZzpUZNMrqitew0egHzYMdUAAAB4uWnpF+NIrwrfWPUsrayivNl3xt+1546LZciKX9MIKXlt9lNIqk315t5tOzb03RKFWFFbuTSO7uIrPtqfw2fYUYsO86RbqTZcQT6JDPOkY5IFeRmValNM5ZfY/l1ddXXi5mpGSC2te7CMvZtm4TojrZcOGDYwaNYqioiJ8fHwc3R2HkfBynSmKQmj7EELbh8CNtseqK6tJ2HeIE/tPUXzMgCZTj77KFX1VJ6x0IglIzCmj+qc9eK36nGhVJT27DMBn2Ch08X1RXGuHFE9XLZ4NKML1cNHy+xtjeOaz/Xy7LwMAlQI39w3jN6M62zfHu/eGKHaeyufr3WlsO5nH4fQSDqeX2K+jVin0i/Ljz1NjZe+YFuJM7gXhpVlHXuoW7J4LLVLzIpxZdnY2CxYs4IcffiAjI4OgoCD69OnDk08+yZgxY5rkHiNHjqRPnz4sWrTI/tiQIUPIysq6LrWdrYmEFwfQu+oZOLQfA4faRmeM1TUcTkjk+P6TFCQWo07T4lrugWt5d6A7JxULu0/nY8r+Cp+PXyVaZaFXWC98+w5G1y8edVDDVpcADO8WxMRe7fjpYBaDOgfw2PgudAr2rNVGrVIY0iWQIV0CySmpZH9KESezSjmRXcqJbAMlFTXsPF3A/e/s4J8z+hAb7tOEfzviapzKPT9tVNaAkReLxYr1oqzSVDUv9oJdqXkRTio5OZmhQ4fi4+PDyy+/TK9evaipqWHNmjXMmzePY8eOXbd763Q6QkJkywvZpK4F0Om19B3Ym7vm3Majbz7Eg/+9mwG/74XXcFcsfjWorCr88oIISo5Hl3MrSdm38snpAF5dvZnFf/odq2ZN4eTv5mH4+CNqEo9ivcJy8een9eSb+cNZNDO+TnC5WLC3KxN7hfLYhK78e3Y/fnpmFP+dN5SOQR7kl1bzyAe7+G5felP+dYhGMpktJOeV2z9vyMjLhSMm2rPb4l5N2Ki9z8u5WpfaIUYIZzN37lwURWHXrl3cdtttdOnShR49ejB//nx27NgBQGpqKrfccgseHh54eXlxxx13kJOTY7/GCy+8QJ8+fVi+fDkdOnTA29ubO++8k9JS2y8i9913Hxs3buSNN95AURQURSE5OZkNGzagKArFxcUAfPjhh/j4+LBmzRq6d++Oh4cHEydOJCsry36vkSNH8uSTT9Z6D1OnTuW+++6zf64oCqtWrarVxsfHhw8//BCwBTZFUfjiiy8YNmwYrq6u9O/fnxMnTrB792769etnv3deXl7T/EVfRosbeUlLS2PmzJnk5uai0Wj4y1/+wu233+7objUrnauWuBt6EXdDLwBKc8s4uvMEJ/ecofRUGaoyDd6FflDoB8SQA6RWV1FkzKDs1FsYPbLRelfh4+GHn384A3tMIi5sICrFllVVKoVg7yuvRqmPoih0DPLgPw8N5KWVh9h4NJcF3xzheFYpT0688kGUoumlF1ZgNJ0PCmWNDC96rZoas+mqpo1q7fNiP+dIal7E1bFarZiqHbM/kEavbtAGo4WFhfz0008sWLAAd/e6ixl8fHywWq1MnToVd3d3Nm7ciMlkYu7cucyYMYMNGzbY254+fZpVq1bx/fffU1RUxB133ME///lPFixYwBtvvMGJEyeIjY3lpZdeAiAwMJDk5OQ696yoqOBf//oXy5cvR6VSce+99/L000/z6aefXvXfx6X89a9/ZdGiRURERPDAAw9w11134eXlxRtvvIGbmxt33HEHzz//PMuWLWvye1+oxYUXjUbDokWL6NOnD7m5ufTt25fJkyfX+4+krfAM8mDAlL4MmGI7br08v4Izh5M5cfA0BccLsWSp0Fe5EJIaAakRANRoaygKyuNkSB7b05aidfkHI4lmQq/bCeo16JoP0HPXa1h4Rx8+3HSGd349xVe7Uvl+fwadgj3oHOxJ52APokO86BTk0aB6nJbG8Poiag4dwu/tt1C0Lbv/py+od4HGj7zoNLbAeTXTRvXv8yIjL+LqmKrNvD/jc4fc+4EVd6J1ufKPxFOnTmG1WunWrdsl2/z8888cPHiQpKQkwsPDAVi+fDk9evRg9+7d9O/fHwCLxcKHH36Ip6dtBHzmzJmsX7+eBQsW4O3tjU6nw83N7YrTRDU1Nbz11lt06mTbJuPRRx+1B56m9vTTTzNhwgQAnnjiCe666y7Wr1/P0KFDAXjwwQftozXXU4sLL+3ataNdu3YABAUF4efnR2FhYZsOLxdSFAWPQHd6jepBr1E9ADDXmMk5lUfKwXSyDmdTeLwYqrUEZYQSlBEK9Maor+Z0aDYvZX5KwDcLGG4NIb7HODyHjUQdHHxVfVGpFB4Y2YnOIZ7845vDFFfUcCS9hCMXFPcChPi40DnYk+hgTzqH2P7b3s8NtaplnuJntVopW7wEa1UVNQcPoYvv6+guXda5ZdIqxXY+UUUDwovpgukevca2cuyqRl7qLdi1nL2ehBfhfKxni8Uu9wvg0aNHCQ8PtwcXgJiYGHx8fDh69Kg9vHTo0MEeXMD28y83N7fRfXJzc7MHl2u5TkP06tXL/ufgsz87evbsWeux63XvCzU6vGzatIlXXnmFvXv3kpWVxcqVK5k6dWqtNkuXLuWVV14hKyuLHj16sGjRIoYNG9bozu3ZsweLxVLrH4CoS61VE9o9hNDuITADLGYLhSnFZCfmknEoh4yEDKjSE5oUSWhSJDCIfT7F/FxxANcdK+lSXUL/0HjaDxmHbsAAVK6Nm1Ia3i2IoV1GkV5YwamcUk5ml3Iqp5RT2aVkl1SRXWz72HL8/DyoXquiU9C5URrbEuxOwZ4t4ugCS04O1qoqAEzp6S0+vKTk2+pdOgV7cjK7tEEFu/WOvDR5zYuVY5klVBrNxHXwa/S1Rduj0at5YMWdDrt3Q0RH245dOXr0aJ2ffedc6oT2ix/XXjSqqygKlqsI/fVdx3pBRb5Kpar1OdhGay73mvraXHyvc+/l4seu5j00VqPDS3l5Ob179+b+++9n+vTpdZ5fsWIFTz75JEuXLmXo0KG8/fbbTJo0icTERCIibFMa8fHxVFdX13nt2rVrCQ0NBaCgoIBZs2bx7rvvXrY/1dXVta5lMMhJtiq1ioCOfgR09CP2pm5YzBZyT+STti+T07uTKEkqw6vYB69iHyCGQrWJ/5pzqV79FSGf/h89axR6hPbGrW8/23EGkZFXnGZSqxQiA9yJDHBnTI/zQ5yllTW2IJNTyqmcMk5ml3I6t5TqGguJGYZam+MBhHi70OlsmDk3/RTu796sozSm1FT7n80ZGc1236uVe3aTwo5BHpzMLm3QtNG5oKJWKWjO/t1ec81LnX1eLNz3tq148dvfjSBIltWLK1AUpUFTN47k5+fHhAkTWLJkCY8//nidWYHi4mJiYmJITU0lLS3N/st3YmIiJSUldO/evcH30ul0TXJeX2BgYK0CXrPZzOHDhxk1atQl25w8eZKKioprvvf10uh/JZMmTWLSpEmXfP61117jwQcf5KGHHgJg0aJFrFmzhmXLlrFw4UIA9u7de9l7VFdXM23aNJ599lmGDBly2bYLFy7kxRdfbOS7aFtUahUh3YMI6R5E/3v6UGmoIj0hi2O7TpCVkIu6VHN2eikUGMghbQ3b8vLRrNlM6KcfE5uXS1j3XugHD0I/cCCabl1RVA0rzPV01RLXwa/Wb95mi5WMi0dpcsrIKq60jdSUVLH1xAWjNBoVHYM8iA7xpGs7L7q396bTddwoz5xyQXhJb/krqQrKbOGlw9mdkBtTsKtRK6jPrTa61pqXc/u7XFT7AnAyu1TCi3AaS5cuZciQIQwYMICXXnqJXr16YTKZWLduHcuWLSMxMZFevXpxzz33sGjRInvB7ogRI+jXr1+D79OhQwd27txJcnIyHh4e+Pld3Qjm6NGjmT9/Pj/88AOdOnXi9ddft69WurDN4sWLGTRoEBaLhT/84Q91RnRakiaNuEajkb179/LHP/6x1uPjx49n27ZtDbqG1WrlvvvuY/To0cycOfOK7Z999lnmz59v/9xgMMg00xW4erkQPTyK6OFRWK1WClOKOb0nmRO7T1F2uhJtjRa/nHZAO8qIZ7uflcqKIlQbEgla9R3d8jOJ6B6Ly/Dh6IcPQ9OhQ6Pur1YpRAS4ExHgzugLRmnKqmo4lVPGqbOB5mROKadzyqiqMXM008DRTAOQYb9GuL8bHQLciQr0oEOg7b8RAe7XfCaTKSXl/J/TW/bIi9VqtZ9pFBloCy+NKdi95pGXempe6ivUzS2pavS1hWipoqKi2LdvHwsWLOB3v/sdWVlZBAYGEh8fz7Jly+zLjh977DGGDx+OSqVi4sSJ/Pvf/27UfZ5++mlmz55NTEwMlZWVJCUlXVV/H3jgAQ4cOMCsWbPQaDQ89dRTtUZdAF599VXuv/9+hg8fTmhoKG+88cYVBxocSbFePMnVmBcrSq2al8zMTNq3b8/WrVtrjZj84x//4KOPPuL48eNXvOaWLVsYPnx4raKg5cuX1yoIqs+SJUtYsmQJZrOZEydOUFJSgpeX19W9sTbMYrZQkFLEkYSjnDmcTPmZKnRFdX9jrvAsRKNKpn3ecXpXluI/aCj60aPQDxvW6JqZy/bHYiWjqIKTOaWcyCrlWGYJxzINFFfUnYsFUBRo5+NaK9R0CvakY6AHLrqGhZrCx56g8n//A0DTrSvB639usvdzJT8l/8jPKWv508C/4Ody5d+ySiqMTPi/XwH4dO4Q7lm6DTe9ml/+NPayr0vJL2fGv7fg5aohMsCDQ2nF/PPOPozs3vDibavVyuAX1to//8eM3oyOCeGRD3axP7moVtv7hndkzpjoBl9bOL+qqiqSkpKIiorCxUVG5dqKy/1/NxgMeHt7N+jn93WZXLy4PuJSxUv1ueGGG66q2GfevHnMmzfP/ubF1VGpVQR29GdkxxsYeesNAOTnFbBvVwIpB9KpPGVEX+CKW6kf4Eeeri9rXE1UZWfi9f5n9HjxL0R37o7r+PG4jBmN+hp3glSpFML93Qn3d2d0jO1aVquVXEMVSXnlJOeV1fqvobKGzKJKMosq2XYy334dRYEwPzc6BXnQ6WyRcKdgD9r71l31ZL5g5MWcntGof7/XauXJ/5FVnsm+nL2MjRx3xfbnRl28XLX4uusAqKg2Y7ZYL1sndH7kRWUfeWnsviyXOl6gvutkFVc26tpCCHE5TRpeAgICUKvVZGdn13o8NzfXvqTqerlw5EU0rYBAf8bfOMZ+NlNhfhE7t+0mZV86Ncet6Cr0uBdFYCaCgwFj2G7JR7dmG53ee5tYRY3nwMHohw5FN3gwaj/fy9+sARTFtslesLcrgzoH2B+3Wq0UlRtJzredup2cX05SXhmnc8ooKjeSVlBBWkEFG46eX8bnolXTMcidTkHnA41HdgHnFi9ay8qwlpSgNMMBaAajgazyTADyKxu2Q2X+2XqXQE897vrzX86VRhMeLpeerz63MkitOl/z0tilzRevTrr4YMYLZRS23MI/IUTr06ThRafTER8fz7p165g2bZr98XXr1nHLLbc05a3qkJGX5uMX4Mukm8fDzbbAcPzoSfZvPUje/kJ0Ga64GwKAAFI9B3FSX4UxOwPPz76gw6t/o6OHFwFxg3AZegP6QQNRNeHUnqIo+Hno8fPQ0/eipbkFZdWczinj9NmVT6dzy0jKtdXT1Fn1NOnP+FQUE1GSTfvCDLpuOE7Hnp3pEOBOoJf+uo3CnCg8P62aX5l/mZbnnRt58ffUo9eq0aoVasxWyqquFF4urHm5uqXS5otGWEyX2Zwuo0hGXoQQTafR4aWsrIxTp07ZP09KSiIhIQE/Pz8iIiKYP38+M2fOpF+/fgwePJh33nmH1NRU5syZ06QdFy2Doih0i+lCt5guABTkF7L9152k7MpAOaNBX+2CvroT0Ilkz7EcdS+nqCSbmo1fot30Kl1q1Azx60e7gaNse8y4uV2Xfvp76PH30DOgk7/9MbPFSnphxflAk1PGqfRCMspMFLv5UOzmw8F23fhxfzHs3wOAm05NdIgn3UK96BbqTbd2XkQENM1S7hNF58NLXkNHXs4ukw44eyK4u15DcUXNFYt2L1wqfa7vF4eRKzFdNFJjH3mpJwQVlRsprzLh3sKXwQohWodGfyfZs2dPrSrlcyt9Zs+ezYcffsiMGTMoKCjgpZdeIisri9jYWFavXk1kZGTT9boeMm3UMvgH+HHT7ZPgdjAZTSQeOk7i7mMUHClGla7Ftdwd1yRbmAHI9jLwH2sO6p8+IvrdP9OPQMJ6DbFNM8X3RbmOhXwX7k1zbtVT5Zo1ZDw8j6wBw8kI7UxySi65g0aS4R5AemEFFUYzB1KLOZBabL+Oi1ZN13aexLT3Jqa9N93be9Pe17XRIzTHiy498vLliS9IMSQzP/5p+xlVUHvaCMDDpWHhpdbIy1UezNiYaSOAuR/u5rV7++LvoW/UfYRzu4Y1I6IVaqr/340OLyNHjrzizefOncvcuXOvulNXQ6aNWh6NTkOv+B70ircdY1BTbSLjSBan9yeRdTCX8pRKPAxeeBi8gGgqGcMPvkVUZmTi88Eiuv79DL2DeuA5fgIu48aiDgq67n02p6Tiaqqmh7+eXv5myr76Gvc+vvj88a+YzBZSCyrsK56OZxk4kV1KZT2BxstVS0x7L7qHehMT5k1MqDf+npf+oW2xWmqNvORX5tUqFP7y+AqqzFVMj76dKO+o8+1Ka4+8uJ2te7nSXi/1L5VuXM3LxQW75zenq//7w/EsA2sOZnH3kA6Nuo9wTuf2EKmoqMC1CVcoipbNaDQCoFZf25YWTjOGKyMvLZ9Wr6FD33A69LXtw1NdbiTrSC6n9yWRnJCGKcuCV5EvXkW+QA/O6C3s1+bgvu4Xei15jW5+7XCbMAGXCePRnN2iu6md2+NFHRlpP/Pp3C67GrVts7yOQR5M7tPe9pzFSmp+OYmZJSSml3A0s4ST2aUYKmvYcaqAHacK7NcO9NLTrZ1XrSmnc4EmsyyD8ppytCotNZYaKk2VlNeU46HzoNJUSZXZtk9KSXVxrf5eWPMC2It2GzPyoj5X89LYaSNz/SMvF9e8RAa4465Xk5hhINcg+70IG7VajY+Pj/0cHDc3t2Zb1Sccw2KxkJeXh5ubGxrNtcUPpwkvMvLS+ujddXQYEEaHAWEAVBZXkXQghSO7jpGfWIyqUI1vnm2zvGOBI9nnVYB+51Giv1xBjBnch92Arn9/dAP6ow4La5JvfOeOBtBERqIKsNXHXG6XXbVKISrIg6ggD248G2iMJgunckpJzCjhaEYJiRklJOeXk2eoJs+Qx+YLzngK9NTTLdQLz0DbqEtnn2jSS9MorSklvzIfD50HxRcElhJj7UMv80qrzl7HNr3mcS68XOF8o6bYpO7ikZr6dtYFW53Q2Nh2JGYYKCiteyyIaLvOnZbcHAf5iZZBpVIRERFxzd+vnSa8iNbP1ceFmBFdiRnRFYDCzGK2/bKTlJ3pqNN0uBn8gcGc8R3MEbdSyE4h8JPP6PbS84RqXNH374duwAB0Awag7RFzVV8c5mTbyIsmIgKVr21Zt7mRu+zqNCp7/cs55dUmTmaX1ppySskvJ6+0mrzjeXiUH8AtEPYdd8HT1w00pRzNSaODd4daoy2G6vOroqxWqz0MBDRy5OXcFI/mgqXSTVbzclGo0WpU9v6dq9ERAmwF/+3atSMoKKjeQwCF89HpdKgaeLzM5Uh4ES2WX6gPN907Ae61rWLa8st20nZnojqtw7XCEypiKSOWPR1uocKzEHVlGkHffEvMqy8T5OqJy8QJuE6ciG7gAJQGnNFhNZsxnR1lUUdG2JdxWwoLsVRUXNNKKHe9hj6RvvSJPL/PTaXRxInsUo6kl7Aq+2uqAGNZMOW6IvTe8H8/7WT5OhXdOp/fN6mk+vzIi6Gyxj7Kca4I1sOlsdNGqvNLpRt5tlHdTerqr3nRqVX2/snIi6iPWq2+5hoI0bY4TXiRmhfn5h/gxy133Ah3QEV5JTu37uL0/hQqTlSjzz+/428JvdnW2UqFVwHa1FRC/vlPemelE3DDSFwnjkc/cuQljy8wZ+eA0QgaDerQUBS1GsXTE2tpKeaMDFTRTbu9vatOQ+8IX3qF+/DND4VggtduH8+KoxUcK09Coy/jTGYZmeYkvGwHsrMnNY2xoZWE+LiSdzYIeLtp0WlsAeTcyEtZA6eNNBculW7syEsD93mRkRchRFNzmvAiNS9th5u7K6PGj2DUeNvnhflF7Nm5j5SENCpP1qAvOr9RXqG6Lz9HmqgqScfv/U/p/fyfiejZB5eJE3EdPw7VBTvnnjsWQB0WhnL2t0B1WHtMR49hTktHe43hxWK1sOzAEjx1XsyKmW1/vLCqgEpTJWpFTe/QjiRVdOBY4kbGx7nTc1BPVhw/TOHZtonZ2Ux9fRO9I3yICvQAzk8ZwdUV7F7tUumLa17MFitmi5WLFyPq1CoCzo68VFSbqTSacNU5zbceIYQDyHcQ0er5BfjWOr4gJzuXvTv2k3YgA+MJC7oyF9yLOlBNB3aGTuBXUy7uq34m5s3X6N6lO25Tp+Eydgym1LP1LpER9mtrwsJs4eUyRbuXYrVaMaekoI6MRFEUThefYk3yTwBM7TQVL70tZKeVpgEQ4t4OjUpDgGsgACXGAiYNCCUFN1afPUzWy72GUoVaS7MD6wkvV1oqXe8mdddY82KyWOvdXVerUeGmV+OiVVNVYya/tJpwf/nWI4S4evIdRDid4JAgJk+dAFNtASLxyDH2bTxAUYIBfa4b7iXBQDCJ/sPYXZOP++ff0+3Vl+lSYdt/QHPBhorqMNtKKFNG44p2AcrffY+SF17E++9/w+P++ziYf9D+3KniU/QNjgcgvcwWjMI8bPcKPBte8ipsq5KKq8+f0Bzoa2Hp/BH8fDibtYeyOJZpoH/H8+c7uTe65kXBRWsbZao0Xv41l7rG+c8t9dbN6NQqFEUhwFNHemElBWVGwv3dG3UvIYS4kNOEF6l5EfVRFIUesd3pEdsdgMyMbHb8upOM3TloU1zOTi8N45jPMPa3N6D0SCLc24tB+VmEBrRDHXZ2P5dGjrxYrVbKPvwQK1Dx1Vd43H8fh/MP2Z8/VXzyfHgpPRtePG3h5dzIS35VPharpdZSaUN1CUFeLtw9pAN3D+mA0WSx17vA1S2V9na1FTOXVDRutUd9+7zUt1fMuf75e+hJL6y0700jhBBXy2nCi9S8iIYIbR/CrffeYl/BtGntVtJ3ZqNJ1eNa7gXlvcnbDt/sWEdFUCm64Er8B/Shx7H96D//HJfRoxu0069xz142hxj4dH5PZn+cwo252SQWHLE/f6r4/PlgGWW2aaNzIy/+rv4oKJgsJkqqS2qtMCqtKcVsMaNW2UZLLgwucH7aKL2wgoSUolqrmy50YXjxcdcBUFLZuPBSZ58X86WnjQACzu5FIyuOhBDXymnCixCN5R/gx7S7p8DdUF5awc6tuzm9O5mqUzXoil3wyPGGHG9KmcFWbxM//pKM9pfn6GYtZlC3gXiOn4A2Nrbe/WRyvlnBV9NDsagVvp0STIcNX1GpO3+y8qnik/Y/nx95se08rFFp8HXxpbCqkPzK/FrTRgAGowFfl/pDSadgT4K8XMg1VDHn/V3cN7wjD4/qjOqigyPPBQ+1SnXByIuxUX9/9e3zUl/Rr059LrzYQpKsOBJCXCsJL0IA7p5ujJ44gtETRwCQl5XPvp0HSD2QQfXxGrTlenxzwoAw0oEPq4pRHfmAyJp0BvboReBN0+wb41mrq/nMupNKN08ACgL0fFS8HnTQO7APB/MOkF+ZT1FVIXq1CwVVtiME2p8deQEIdA2isKqQ9NI0ymvKAdAoGkxWEwZjySXDi5erlk/nDuHNtcf5bl8GH246w6mcUhbc3hu99vw+GrWmjdyubtqoztlGlynYBfBxs4WX4vLGhSQhhLiYhBch6hHYLoAJU8fYi36zk3LZt/kA6bszsKar8TD4AH3Joy9fHqggr+Bzitul4RVooiuBbIu3LWOOd+nK3qrjnPGx/cDusTGZ/A46MtyqOVV8Ch+9DwC+el88dB72+0d5d+R40TH25OwGQK2oCXYPIaMsvdY0Un08XbU8d0ssfTv4sfDbI2w5nsfzXx1kwR290ZwdBblwnxcv1/PTRhceBnkldWteLPXXvKhr70FT0cjCYCGEuNi179HbQixZsoSYmBj69+/v6K4IJ6MoCu06BnPj7PH8dvH9zF5+B10fjkSJNWHWmHCtcCPiRFd6bRxL6HejOJXgS2B6GEPzg3h8xJ/R1pz/gR61chdhB7MAW91L8uEtALT3aF/rnl18uwCwL3cvAN56H3vQuVJ4OWdS71BevzcenUbFxmO5/N/3ifYT4esbeTGaLFTVNLzgvb59XupbbXRu5KWhK6GEEOJKnGbkRQp2RXNx9XRh5I3DGHnjMExGM6f3nuHMliQyd+dAtZ7wUx0JP9URtU5hT8FhRpR1Z0PPU+iNVYTmGIlMrWTnIDh+bDNF6/bBuACCTxfCsPP3OBdezk0Zeeu98T67L8zFhzNeTnyUHy/d1os/rUjgu30Z+LjpeGRMdK19Xtx0arRqhRqzlZKKmgZvIFdn2ugSBbsXj7yUV8uKQCHEtXGa8CKEI2h0aroOjqbr4GgsZgvZR/M4s+k0ybszKS+s4sy2VPTEMe5QL1TaDPL/8Dvab1oGGDlReoaiTrajCvzW7aGq42ZchtsSTHvPMFw1rlSabEW+PnofvHS28GJo4MjLOSO7B/OHKT1Y+O0Rlm9JYuWeNPvxAWqVgqIoeLvpyC+tpriihhCf+o9PuFid4wEs1jonSsMFIy/npo1k5EUIcY0kvAjRRFRqFaGxwYTGBjPUaiX/VCFJO9JI2pFKcboBjBHsWpsLTGfkVwZyw7PIDctEZc6lXVYVRY89js//LcRl/HjUKjWdfTpz6OzeMD56X7z1toMiGzPycs4t8WFU1Zh5a/3JWucendtd19tVS35pNSWVRqxWK2+tP0lkgDuT+7S/1CXr2aTOWmcqCahz7pJMGwkhrpWEFyGuA0VRCIz2JzDanwEz+1CUXkLa3gxS9mSSfSQH91IvohK9iErsCjorVa5JZJq2UPPQI+g7dcDt1ml0ig/mEOfCizfejax5udiMQZFM7x/Of7cls/Rn21LtwrMrf7zO1r0YKmo4mV3KR5uTcNOrmdQ79JIFvKaL93mxWOofeTl7dpKEFyFEU5HwIkQz8A3zxjfMm163xGCsMJJ+IJu0PRmk7s2koqiSDE1HMjp3RGOuJrg4kfZvfUNAxxz4je2oAs8yC94+56aNDFfdD41axb1Do1i+JYnSKhPRwbbl3OeWMZdU1JCSb6uzqag2U1RuxM9DT05JJdU1FiICzm/rf2HR77lDGS9XsOumsy3VrjCaG7WqSQghLibhRYhmpnPT0XFwBB0HR2C1WMk9mc+Zramc3pJCeQFk+MeR4R+HxlxOjx0ZZHRKhg/fxOwdCtM0FJflXdMPf5VKYeVTw/nlSA6jYoKpsdRgcjkOijvFFUYMF+y0m1lciVqlMP2NzejUKlbNH4HX2U3tztW86LUqKqrNZwt2r7xU2myxUl1jwUWnrtNWCCEaQsKLEA6kqBSCuwYS3DWQQff1Jed4Pqc3J3NqSzJVJdDhaBc6HO1CiaU/2rSDeOVXUqxLJ+eGYbiMGYPL2DHoBw5E0euvfLMLeLhouTnetineB4ff57j1a9yDB1BS2alWTUxmUSW/HsnBZLZiMps5k1tmP3Lg3MiLXqOmotp8xZEXV50aRQGr1TZ1JOFFCHG1nCa8yMGMorVTVAoh3QMJ6R7IoAfiyTiQxZbVOzAcKMdU40u25wiGfQeV7uW83ycZn/RDdPz713RLN+M5dBiuN07GZdw4VO4NP7HZaDayLmUNAFqPLEoqasgsqkClLcVq0XAgpYjv9p8/UTu9sMIeXs7VvOg15ze+q+94AL3GFlIURcFdr6GsykR5tQl/z8YFLiGEOMdpwovs8yKciVqjIiK+PXfHT6emsobknemc2Z5K0t4UXMvdcS3vAfTglLeRnV0z8Kk6RvSyF+j63B/wvWE0rjdPQT9qJEadCrVKjValrfc+2zK3UlZTBoDGNY+SymrSSvLw6/4J5mofvt13d62poNSz9TBwwcjL2WMHTBYLNaZ6Rl7U56e3LgwvQghxtZwmvAjhrLSuWqJHRhE9MgpTtYnje05xYNNhDIcq0JbrCEmNAqJIdhvPvuE5uFhPUrJjEYkV/6ZGq8JTceVfo9+knWdonWuvTV5j/7NKU0VacS5VqlT06hpUbnmYqARc8HXXUVRuJK2wwt7eXvNywchLvZvUXXDy9fmiXQkvQoirJ+FFiFZEo9fQY2g3egzthsVsIet4Lnt+3UfWnjzUhWoCM0OBUDyyLejzc8jsmEJ2ZDqL3p3Nn4xjcbvtdrSdOgK206wPFxxChQp3rRelNcXkG9PQuuedv59LATXl7bkprj3LtySRVnCZkRdz/dNGWvX58CK77AohmoKEFyFaKZVaRfuYENrHTMZqtVKUVsKejftJ2p6KkqEmMLMdgZntiN1mJjc8g8+L9jLsg/8QEN0Dt+m3sibGtuS6X0g/LGYde/K2oHHNR+N6PryoXQqhKowJvdqxfEsS6YWVWCxWVCrlfM2L9oKal7MjL+cKc6H2yIvs9SKEaAoSXoRwAoqi4Bfhw/iZo2AmlGSVcnpzMic3JlGcbqBdSgQQwS89J1IYkoL7ru/Yoy4DTzUjklxJ7RLMHmx1Lxq3XPt1Na4FRLt50yHAHbVKoarGTH5pNUHeLrVWG8G5aSPbYy5aNZVG2+hKvSMvVRJehBBXT8KLEE7Iu50nfe/oSdztseSeyWftN+sp2VuBtkxHcGo0EM2gHyooDE3C54f/URlZCnM7oPdKQVGf3+dF41JA72AfNGoV7XxcSS+sIK2wnCBvlzo1LyaLxT7ycmF4kZEXIURTa3HhpbS0lNGjR1NTU4PZbObxxx/nN7/5jaO7JUSrpCgKwZ0CmTn/TqwWK2cOpHDgl8Nk78zHpdKN0NM92NatB67GfKL3Z5EVlUqZTw0aM5jUtpGXG7oGAhDh70Z6UQn/3LaEiXmDMVvaAedrXi4ceXHVqSk6Wx5T38iLFOwKIa5Fiwsvbm5ubNy4ETc3NyoqKoiNjeXWW2/F39/f0V0TolVTVAqd4jrQKa4Dxmojh3YkUrirlNRd6VQSQJeEALok9MTgW4zZ/QQJA7Oo8KrA65/zqbzxdoZ0jCGh9Aeq3Xfyv+TDVJy0/VJhH3kxW+11MK7a8xvQ1VptpLc9LiMvQohr0eLCi1qtxs3NDYCqqirMZts5KEKIpqPT64gf0QdGgLGihuRdaZzanEzqvgy8inygaACj0iG/XTbf1yQw4cnH6RPgjf/vw6gG1LpyjKpcUHzIUtajdvHHbA4+P22kO7cx3fmTq+H8yEuZ1LwIIa6B6spNatu0aRNTpkwhNNR22uyqVavqtFm6dClRUVG4uLgQHx/P5s2bG3WP4uJievfuTVhYGM888wwBAQGN7aYQooF0blq6jOzI5L+MZvZHt9P7N11p3ysEgICsEPT5E1kX9yc+GTQJXYmn/XXt/M/gHryHJPMavCLWU1VjZt3hbOD8yItOrap1BtO58PLTwSzuXrKVT7YkUVBa3VxvVQjhJBo98lJeXk7v3r25//77mT59ep3nV6xYwZNPPsnSpUsZOnQob7/9NpMmTSIxMZGIiAgA4uPjqa6u+w1r7dq1hIaG4uPjw4EDB8jJyeHWW2/ltttuIzg4uN7+VFdX17qWwXD1J+4K0da5erkw6Kb+cBMYsktZ/81G0jfloCvT0y6pO+2SulPjksvxPimotFvQtlNRjgqtew4qrYHCMi/g/MiLVlP796POwefDz5ncMhavO8Gy9ScZ1DmAG/uEckPXoFrTTEIIUR/Feg1zMoqisHLlSqZOnWp/bODAgfTt25dly5bZH+vevTtTp05l4cKFjb7HI488wujRo7n99tvrff6FF17gxRdfrPN4SUkJXl5ejb6fEKI2s8nMzq17SN+QTcnBMiwm27cMs8q2f0x65yTywrIYtVWhxGsGJzxDuWtUV/6+6jB+HjpW/35UretlFFZgtljZl1zIDwmZHEortj/n5aplfM923BTXnq7tPK/65GwhROtz7nifhvz8btLwYjQacXNz48svv2TatGn2dk888QQJCQls3LjxitfMycnB1dUVLy8vDAYDgwcP5rPPPqNXr171tq9v5CU8PFzCixDXQWVJFSc3JLFt9S6U7PNFudUuVRSHnKb/oe30OHEadbfuvDJqDr16R/HATXGXvWZKfjmrEzJYfSCTPMP5r+VOwR7c2Kc9E3u1w89DDnEUwtk1Jrw0acFufn4+ZrO5zhRPcHAw2dnZDbpGeno6Dz74IFarFavVyqOPPnrJ4AKg1+vR6/VyqrQQzcDV24Vet3Sn1y3dOXrkOInrT1C6pxJKIDi5B6mePTg2shg9R7j92wV0WJpL3uDBuM+aievECSg6XZ1rRga488jYLjw8OprdZwr4fn8Gm47lcjqnjDfXHGfJuhMMiQ7gprj2DIkOrDMVJYRoe5p05CUzM5P27duzbds2Bg8ebG+3YMECli9fzrFjx665w1fSmOQmhLh2ZpOFvVv2sXftATimQWW2jchYsVLum0mIYReD9u3FzccP97vvwu2ee9C0r3tI5IUMlTX8fDib7/dnkJhRYn/cx03LLfFhTO8fQZC3y3V9X0KI5uWwkZeAgADUanWdUZbc3NxLFtw2FRl5EcIx1BoVA0b2Y8DIfpQZyvj1p82kbE5Hm+qKR1F7ypjGD4MmoOgO0u+L/xLx78W4jBuL+6yZ6IcPR1HVHUnxctVya/9wbu0fTlJuGT8kZPDjgUwKyox8tDmJT7YmM7J7MHcMiqBXuI/UxgjRxlyXgt34+HiWLl1qfywmJoZbbrnlqgp2G0tGXoRoGTLSslj/vw0YtleirbRNF1kUC+W+yUTm7aT/wUO4hEfgPvNe3O6YgdrP97LXM5ktbDmRxxc7UtiXXGR/vEs7T27tF874nu1w07e4rauEEA10XQt2y8rKOHXqFABxcXG89tprjBo1Cj8/PyIiIlixYgUzZ87krbfeYvDgwbzzzjv85z//4ciRI0RGRl79u2ogCS9CtCw1xhp+XbOZ42tOo0tztT9e4WHARdnPwENbCS414jplCh6zZqLtG3fFkZST2Qa+2JnK2oNZVJtsG+O56dSMjQ1hSt8wYsO8ZTRGiFbmuoaXDRs2MGrUqDqPz549mw8//BCwbVL38ssvk5WVRWxsLK+//jrDhw9vzG0a7cJpoxMnTkh4EaIFOnX8NBv/t42qvWY0NbZRErPaRKXvaTpnbCfu8HFcesTiPnsWrlNvQXV2t+1LKS438kNCJqv2ppFWUGF/vEOgO1Pi2nNTXHu83eoWCQshWp5mWyrdEsnIixAtX0V5Jeu//ZXk9Zno8s4X3pZ7FeFhPsDAA9sJtIDbbdNxnzUTbXT0Za9ntVpJSCniu/0Z/HIkh6oaW+2bXqNiQq923D4wkugQz8teQwjhWBJeJLwI0SpYrVYO7z/K9m92YzoE6rMrlSwqM9WeyXTK2kbvo4m4DhqMx+xZuEycgKLVXvaa5VUm1h3OYuWedI5nnd9xu28HX+4YFMmwrkG1zlsSQrQMbTK8yLSREK1bqaGMn7//lbQNWehzzk8XVXgW4mXazZC92/Hx8Mb97rtwv+ce1KHtLns9q9XKwbRivtiRyoajOZgttm91IT4u3NY/gil9ZUpJiJakTYaXc2TkRYjW7+Chw2z7ZheW/SrUJlttjFFfBS6HGXB4I2F5hbiMH4f77Fnohw27YnFuTkkl/9udxqq96ZRU1ADgqlNzc9/23DW4AyE+rpd9vRDi+pPwIuFFCKdQXFzC2q/Xk/NrEbpS2xEBFsVClfcZuqVuJvbECXTdu+H529/iesvN9e7ge6GqGjNrD2axYmcKp3PKAFCrFMbFhnDP0CipixHCgdpkeJFpIyGcl9lkZuPPWzjy/XF0aRdMKXnl0a54K4MSdqMPDMD9nntwv+du1CEhl72e1Wpl1+kClm9NYs+ZQvvjgzr7c/eQKPp39JOl1kI0szYZXs6RkRchnNvhQ4ls/mo7HNSgstgKfKtdy3C37GHo/k14GmtwnTQR9/vvQzdgwBVDyLHMEj7ZmswvR7I5WxZDdIgndw/pwLjYEDRqOUtJiOYg4UXCixBOLzsnhx+/WEf5lmq0VbYpJZOmBlyO0C9xA2G5OWh798JjzhxcJ09C0Vx+992MwgpW7Ejh230Z9qXWgV56ZgyMZGq/MDxcLr/KSQhxbSS8SHgRos0or6jgp2/Wkr4mF5ci25SSFSvVXql0S91Ej1OJaCIj8Hj4Ydxm3IHK9fLFuSUVRlbtSeeLnSkUlBkBcNOruaVvGDMGRUpxrxDXSZsML1LzIkTbZraY2bhhCwe/O4rrGQ/745We+bQv2sjAA3vR+vrifv99uN83G7Wf32WvZzRZWHsoi0+3JpGUVw7YinvH9Ajh3qEd6NJOvr8I0ZTaZHg5R0ZehBCHjx1h41dbUfbpUJtt00UVnsX4GrcydM8O3BUNbnfOwOO3D6OJiLjstSwWKztO5/PfrcnsSTpf3Du8WxAPjewkIUaIJiLhRcKLEALIys3ix//+TOVWExqjrWal0r0MnXYnN+zdir/BiNut0/B47DG0nTpe8XrHMg18ujWJn49kc+4754huQdw/oiPdQr2v51sRwulJeJHwIoS4gKGklNUr1pD/iwFtpW0vmCrXCkye+xi2dxPtc8twmTQJz0fmoOsbd8XrJeWV8cHGM6w7nGUPMUOiA7h/RCd6hvtcx3cihPOS8CLhRQhRD2OVkbVf/0LKj1loSm0jMUZ9NUbvAww89CtRacXoBg7Ac+5c9GNGX3GZdVJeGR9tOsPaQ1n2Zdb9ovy4f0RH+naQvWIutC1zK2aLmWFhwx3dFdFCtcnwIgW7QoiGMteY2fLDDg6vOoGmyBZiTJoayoIS6X3yV2KP5qDr3QvPp57CZeyYK4aQtIJylm9JYvWBTExm27fUnuE+3D+iI4M7B7T5EGM0G7ntu2kA/Hfy53joZCdjUVebDC/nyMiLEKKhLGYLO9btJuHrRNS5thBjVpsoDj1J7Olf6HswHV2vnng+9SQu48ZdMYRkF1fyydZkvt2XjtFkAaBrOy/uH9GR4V2DULXR06zLjGXcvXoGAB9M+Bh/V38H90i0RBJeJLwIIRrBarVyYPMRdq7YC+m2XXstioXidqfpnvwL/Q4mo4uNxXP+k7iMH3/FEJNfWs1/tyXzv91p9g3vurbz4pGx0Qzs5N/mRmIMRgP3rr4LgI8mLsfX5fLL1EXbJOFFwosQ4ipYrVZO7jvDxk+3YTltCxhWrJQEJ9Ml/Vf6HTyJvkcsnk89gcuECVcMIcXlRj7bnsKXO1OoMNpCTFykL3PGRtM7wve6v5+Wori6mFk/3gPA8kn/xVsvK7NEXRJeJLwIIa7RmcPJ/PLJZsxHzwcUQ0AanbJ/pf+Bo+i7x9hGYiZMQFFd/vyjonIjH28+w9e70+zTSUOiA5gzJrpN7BNTWFXIfT/NBCS8iEuT8CLhRQjRRFJOprFu+QZMB0Gx2oJMqV8WUbnrGHDgKLpu3fGa/xQuE68cYnJLqnhv42m+35+B+ezypLGxITw8ujMR/u7X+604TF5FHg+uvQ+Ajyd9io/ex6H9ES2ThBcJL0KIJpaRksVPy3/GuNeKymILKSVBqXTOXEv/A6fRdeuG11NP4TJp4hVDTGpBOe/+eoq1h7IB27EDN/cN48GRnQjw1F/399LcssuzeXjdg4DUvIhLk/Ai4UUIcZ1kZ+Tww/trqdl7fiSmKDiZLpnr6Z9wCl3XrrYQM3nSFUPMyWwDy34+ybaT+QC4aNXcNTiSe4dG4e5y+VOwW5PMsgzm/PwwIKuNxKW1yfAi+7wIIZpTRlImP733CzWHrCjYQkxxcAqdM9czIOEkuq5d8XziCVxvuvGKIWZfciFL1p3gSHoJAL7uOn4zqjM3922PRn3517YGqYZUHv3lEQA+mPAR/q4BDu6RaInaZHg5R0ZehBDNKf10Bms+/JWagxeFmKz1DNh/En2vXni/8Ff0AwZc9jpWq5WNx3JZ9vNJUvJtp1h3DPLg8QldGdS5df+w//OWP3Ew/wAA743/kEC3QAf3SLREEl4kvAghmlnG6Ux++ugXag5cEGKCUuiZ9BO9jybjOuUmvP78HJqwsMtex2S2sHJPGv/59TSGyhoABkcH8PiErkQFelz393E93LzqRvuf3xv/AYFuQQ7sjWipJLxIeBFCOEjG6UzWfPwLxgRbiLEoFsoCjzB83w+EFVXh+duH8Xh0Hir3y68uMlTW8P7G03y1KxWT2YpapTCtXxgPjeyMj7uumd5N07gwvPxn3PsEuwc7sDeipZLwIuFFCOFg6Wcy+eHttXDMVnhr1FehuG1jwuZf8PDyx+upJ3G7604Urfay10ktKGfJ2hNsPJYLgIeLhvuHd+L2gRHoNC2/HsZqtXLLNzfZP39n3HuEuIc4sEeipZLwIuFFCNFC7N92kG3v7kFTYBstKfMuIrh8DcN3HUDfoSNef3gGlxsnX3G33r1JBbzx03FOZJcCEObnyqPjuzKiW1CLPm7AYrUw9Zsp9s/fHvcu7dzbObBHoqVqzM/vlh/bhRCiFYsb0os579xHxIxgTPoaPEp8KTfdyecT5rHNU6Hgt3PIm3Iz1du2X/Y68VH+fPDbwTx3Sw/8PXSkF1byx88TmPvhbk7nlDbTu2k8i9VS63Mn+31ZOEiLDS8VFRVERkby9NNPO7orQghxTdQaFZPuHsfsd+7AbagWi8qCd14YOdoH+fSmBzhQlkv+7XeQP3MWNYlHL30dlcKUvmF8+fgw7h/eEb1Gxf7kIma9tZ0l605QaTQ147tqmIvDihUJL+LatdjwsmDBAgYOHOjobgghRJPx8HFn5jMzmPrGBNQ9bQW9PtnRnPSdy39vuosTxxPIHT+BwsefxJyZdcnruOk1/HZMNJ8/dgMjugVhtlhZviWJu5ZsZcvx3GZ8R1dmQUZeRNNrkeHl5MmTHDt2jMmTJzu6K0II0eTaRQTz0N9nMmbBEKxRJtQWNV7ZvdgX8SQrJk4hZcOP5IwYSenSZViNxktfx8eV/7srjpfviiPE24Xs4iqe/u9+/vDZfnJKKpvxHV1a3ZEXyyVaCtFwjQ4vmzZtYsqUKYSGhqIoCqtWrarTZunSpURFReHi4kJ8fDybN29u1D2efvppFi5c2NiuCSFEq9I5tiO/fX02A57phTnIiLZGh3veYDbF/I5vBvcj7+WF5I6bQNWmy38PHd4tiM8eHcrMG6JQqxQ2Hstlxr+38sHG01TXmJvp3dSvbs2LgzoinEqjw0t5eTm9e/dm8eLF9T6/YsUKnnzySZ577jn279/PsGHDmDRpEqmpqfY28fHxxMbG1vnIzMzkm2++oUuXLnTp0qVB/amursZgMNT6EEKI1kJRFOKG9uKRt+8n5uGOmLyMuFS6oSqZxNdjHme33kTBXXdT+NtHMGVkXvI6rjoN88Z14eM5g+kT6UtVjZm3fznFXUu2svFojsOma+qEFxl5EU3gmpZKK4rCypUrmTp1qv2xgQMH0rdvX5YtW2Z/rHv37kydOrVBoynPPvssn3zyCWq1mrKyMmpqavjd737H888/X2/7F154gRdffLHO47JUWgjRGpmMJr5fvobM1fmoTWosioWKwAOM2/Y9/lVmPB56CM95c1Fd5vub1Wpl3eFs/r32OHmGagAGdPLnmZtiCPNza663AkCpsZR7Vt9p//zNUUvo4N2hWfsgWodm2+fl4vBiNBpxc3Pjyy+/ZNq0afZ2TzzxBAkJCWzcuLFR1//www85fPgw//rXvy7Zprq6murqavvnBoOB8PBwCS9CiFYtNyOPVW+uxnpMDUCVWzkumg2M2boNd3cfPOc/hfvMe1E0lz59utJo4qPNSXy6NYkasxVXnZonJnTllviwZtsbxlBdwr0/3m3//I1R/ybKu2Oz3Fu0Lg7b5yU/Px+z2UxwcO2tn4ODg8nOzm7KW9np9Xq8vLxYvnw5gwYNYsyYMdflPkII0ZyC2gfy8P/Nps9T3ajxqsalwh0MN7Jy5FOsjWlPwV+fJ++mmzEePHjJa7jqNMwZE81nj95AXAdfKo1m/vldIvM/3UeeoapZ3of5omkjixS9iCZwXVYbXZzorVbrVaX8++6777KjLheaN28eiYmJ7N69u9H3EUKIlmrgyH789j+zCLzRG7PWjFdBAFVVd/PZpN+yr6aAvBunUPzCi1gqKi55jTA/N5bM7s/jE7qi06jYfjKfe5ZuZd2hSy/Hbip193WR8CKuXZOGl4CAANRqdZ1Rltzc3DqjMU1tyZIlxMTE0L9//+t6HyGEaG5aFy23PjyFe9+6FY/BeqyKFd/sSE77PMJnkyeTseJjcseOo3rL1kteQ6VSuHtIBz787WC6tvPCUGniL18d5JnP9pNdfP2WVVtl5EVcB00aXnQ6HfHx8axbt67W4+vWrWPIkCFNeas6ZORFCOHsvAI8ueePtzPltbEQaUJj0uCZPZQfBz7Odh8d+TPupOgPz2IpvfRxAR2DPHjvNwN5cGQn1CqFTcdyuXPxVj7dmoTJ3PQrgS5ebSQjL6IpNDq8lJWVkZCQQEJCAgBJSUkkJCTYl0LPnz+fd999l/fff5+jR4/y1FNPkZqaypw5c5q040II0Va179iOhxfNptv9UZj1JjyL/MnW3c+XE6aS/+Xn5I4aQ+X3P1xyebRGreI3ozqz/JEh9I7woarGzL/XnmD229s5mFrUpH29eKSlbpgRovEavdpow4YNjBo1qs7js2fP5sMPPwRsm9S9/PLLZGVlERsby+uvv87w4cObpMOXsmTJEpYsWYLZbObEiROy2kgI0SaUFBj44l+rsCTafhct9y6kU+7/iD90Gv3gQXi/8AK62B6XfL3FYmX1gUz+vfY4JRU1ANzaP5zHx3fFRae+5v5ll2fx8LqH7J+/PPxVuvl1u+brCufTbEulW6LGvHkhhHAGVquVDas3c+TjU2irdFgUC8WhR7ghYTUdU0twnz0Lrz89i8rd/ZLXKC43smTdCb7bnwFAVKA7L93Wm+gQz2vqW2ZZJnN+/o398/8b9grd/WOu6ZrCOTlsqbQQQojmpygKo24czl2Lp6LqYUFlVeGX0ZP94U/w2Y3jyPrqU3LHjLvsMQM+7jqemxrLm7P64e+hIymvnAf/s4MVO1KuaXfeujvsCnHtnCa8yGojIURb5x/ox2/+MYshf4jD4m9CX+WCZ/Yovh/yBBtC9eTfdbetoLe8/JLXGNDJn0/mDmVol0CMJguv/3iMp/+7n6LySx8QeTkXHwdw8eojIa6GTBsJIYQTMpssbPhqM8e/TkJt1GBWmSlpv5ebfvmBQN92+C5ahL5/v0u+3mq18tWuVP699gRGkwV/Dx3P39qTgZ0CGtWPVEMKj/4y1/75P274P2IDYq/6fQnnJdNGQgjRxqk1KsbcOYJ7ltyKtpsKtUWNX9oAfhrwOLs9FfJvnU7Jwn9iveB4lQspisLtAyN57zeDiAp0p6DMyBMf7+Wf3x6hrKqmwf24eLWRjLyIpuA04UWmjYQQoi7vIC/u/+dd9J7TDbPehFeRP2luD/LVuPHkv7OU3BunUJN49JKvjw7x5IOHB3PbgHAAVu1N564lW9l2Iq9B97dYzbU+r7vjrhCN5zThRTapE0KI+imKwqBJ/bh78TSUTmbUZg0uBSP4asyjHDQVkjtpsm0UprL+nXZddGqevjGGpff3J8zPjTxDNfM/3ce/fjhKdY253tecc3FYkfAimoLThBchhBCX5xPkzW/+NYvIu0KwqM345IVyLPARvphwA9nvvUXO2PFUb9t+ydf37eDHJ48M4c7BkQB8tSuV37y3k7SCSxcA1502kvAirp2EFyGEaEMUlcLEO8cy9bWJWNqZ0Bn1uOVM4Osxj7DV30zeHTNsozA19de1uOjUPDmxG6/d0xdvNy0nskqZ/fZ21h2u/5BHmTYS14PThBepeRFCiIZr1yGYhxfPImJqCBaVBf/s9mRrf8PyqZPJ/PAt8m69DVNa2iVfP6RLIMvnDKFPpC8V1Wb+8uVB/vXDUYymi/d1kZEX0fScJrxIzYsQQjSOWqNi0v1juW3Rjag7gMakxS/tBlaOnMP+ijRyx0+k8ofVl3x9kLcLi2f3Y/awjoBtGmnO+7vIuuCU6jrTRjLyIpqA04QXIYQQVycw0p8HX7+HbjOjsKgt+GeHcSxoDiuHRJM357cU/+k5rFVV9b5Wo1bxyNhoXr2nL16uGhIzSpj91na2nl2NdPHSaDmYUTQFCS9CCCFQVAojbhvK1JfHYw0w41LhhrrgVj6edjsp360gb8ot1Jw8ecnXD+0SyEdzhhDT3gtDZQ2/+3Qfb645jtF0+dVIQlwNCS9CCCHs2nUO4cEl9+A7yAPFqiIwpS8/DXyE7ZoScidMovStt7Ca6w8k7XxceeuBgdwxMAKA/25L5tUfj9RqIyMvoik4TXiRgl0hhGgaWhcNdzw7lb5ze2DRmvHJCybV8zd8Ma4/uS8vJH/adEzJyfW+VqdRMX9yd16+Kw4vVw0pFy2jvtqal03pG/k19Zereq1wPnK2kRBCiEsqyCjk679/jzXT9rtubsRJBh9aSUy2Cd/XXsV18qRLvjanpJLff/s5xZ5f2R/7Y/8/MaT90Eb1wWg2ctt30wD4dPLneOo8r+KdiJZOzjYSQgjRJPzb+/Hgm/fQbkIAVqwEpUZzqP1cVg7tQt5vH6b4ry9gNdZ/4nSwtytG39qrlVbsSLnirrwXM1lM9j9XmeovHBZti4QXIYQQl6XWqrl57kQmvDQCq68ZtzIPWzHvrdNI//Jj8qbfjikjs97XVpgqan2ekFrIox/toaCs/gMhhWgICS9CCCEaJKp3BA8svRufge62Yt7kAXwz/DcklCWTN2EiVRs31nmNu9a91ucuWjWH0op58J0dJGaUNOi+Fxb5KopybW9COAUJL0IIIRpM56bljmen0uvBrljUFgKywjkcOofv+7Yj796ZGF5fhNVyPmx08e1a6/VzxnQm3N+N7JIqHn5vJ1/uTLnirrsWZIWSqE3CixBCiEZRFIXBN/fn5n+Ox+pjxr3Mk5qyu1g+bTw5S16nYOYszIWFgK3Y9kL+njo+eHgQI7sHYTJbeXX1MV743yGqLlMHc+HIi5OtMRFXScKLEEKIq9K+Swj3L7kT1x461GYN/ikj+WzSLE6e3EPexMkYExLqhBer1YqHi5aFM/rw5MSuqFUKaw5mMef9XeSUVNZ7n1rhRY4XEDhReJF9XoQQovnpPfTM/PvtdLw1DCtWglO7sjn2EdZ00vHNwgc4WXyiVvtz4UNRFO4c3IE3Z/XDx03LsUwD971dfx3MheFFNrkT4EThRQ5mFEIIx1BUCuNmj2TUc0OwuJjxLvDHYJrNTzf0tbfRq/VA3Wmf+Cg/3n94MNEhnhSVG5n7wW62nT0XCWxLowurCuyfS3gR4EThRQghhGN1HdCJu9+8FVUY6Ix6Bvw8kq57e4FFQVtm25+lvmmfUF9X3npgAAM7+VNVY+b3n+3nu33pANz/0yye3jjf3lbCiwAJL0IIIZqQd7AnDyy6i8CR3gB0PtiDAetG4FloW+JcmZFS7+vc9Rr+dXdfJvcOxWyxsuCbI7y34RTlpouPF5DwIiS8CCGEaGJqrZpbn5pC/KM9MKtNBGa2o/eGm/Aq8CH903ep+uXXel+n1aj4y7RYZg/rCMB/fj1dp42MvAiQ8CKEEOI66TcujttevQnPYA/URk8Grx5HsRJFwez7KH37nXqXPSuKwiNjo/n9jd1R1bMfnYQXARJehBBCXEdBUQFMf20Snt3d0Jg0+KRO4puRkyn6298oevQxLBUV9b5u+oAI/nFH7zqPW2SfF0ELDS8ajYY+ffrQp08fHnroIUd3RwghxDXQe+iZ8fdb0A62fa42DOXrcfeT/9OP5N18C6akpHpfN6xbYJ3HskvqDzuibWmR4cXHx4eEhAQSEhJ49913Hd0dIYQQ10itUfPAH+8l6A5vLIoFt8IurBrxKH+ZqmfVn++stw7GZDXVeWzBN4c4enYvmI+OfMjyxI+ue99Fy9Miw4sQQgjnNO2eKfT5XVdMuhq8CwLpvm0K347oTN7991P+2ee12potdY8MMFQamfvhbn4+epqvT37Jlye+oKJGRmPamkaHl02bNjFlyhRCQ0NRFIVVq1bVabN06VKioqJwcXEhPj6ezZs3N+oeBoOB+Ph4brjhBjbWc0qpEEKI1mvwsIHc8vJ4zH41uJW7M+incfx4w2CKnv49htdetxfy1jfyEhPmSaXRzIsrE+yPWayXPhdJOKdGh5fy8nJ69+7N4sWL631+xYoVPPnkkzz33HPs37+fYcOGMWnSJFJTU+1t4uPjiY2NrfORmZkJQHJyMnv37uWtt95i1qxZGAyGS/anuroag8FQ60MIIUTLFhbVngf/fQ/qLqA2a7CW3chX42+l6PXXKP7jn7CazZgtdcPL3LHRjOvlh/mCVUemekZohHNTrNdwRKeiKKxcuZKpU6faHxs4cCB9+/Zl2bJl9se6d+/O1KlTWbhwYaPvMWnSJP72t7/Rr1+/ep9/4YUXePHFF+s8XlJSgpeXV6PvJ4QQovlYzBY+W/w1Zb9UA1ASfJhbf/4cv3GTMP3fn3low8O12s+Mmc3yxI8IVMeQZ04EoJ/yHH+6aRAatVRCtGYGgwFvb+8G/fxu0v/TRqORvXv3Mn78+FqPjx8/nm3btjXoGkVFRVRX2/4Rp6enk5iYSMeOHS/Z/tlnn6WkpMT+kZaWdvVvQAghRLNSqVXc88TthN8VBIB3TixfjbuP7I1ryX/qqTrtzxXongsuAD8dyuDZFQlU18gITFvRpOElPz8fs9lMcHBwrceDg4PJzs5u0DWOHj1Kv3796N27NzfddBNvvPEGfn5+l2yv1+vx8vJi+fLlDBo0iDFjxlzTexBCCNH8Jt85nu4PR2FVLPhmd+bb4Q+SknG0Qa/Va2Hz8Tx+/9l+qowSYNqC6zLGpii1t0W0Wq11HruUIUOGcOjQIQ4cOEBCQkKtKanLkVOlhRCidRt+41AGzO+NRW3GNyeCAxEPoqt0ueLr/nBzN1x1anadLuCpT/dSUV23VkY4lyYNLwEBAajV6jqjLLm5uXVGY5rakiVLiImJoX///tf1PkIIIa6fvsN7M/rPQzDrTfjmBTD0+3F4FHlf9jWungXcPbEQd72K/clFPLF8L2VVNc3UY+EITRpedDod8fHxrFu3rtbj69atY8iQIU15qzpk5EUIIZxD177R3PrKJCx+JtzKPBjywzgC09tdsv2re17hu7SPuHdSKV6uGg6lFfP4x3swVEqAcVaNDi9lZWX23W8BkpKSSEhIsC+Fnj9/Pu+++y7vv/8+R48e5amnniI1NZU5c+Y0aceFEEI4r5DIYO5/8y78unmjrdHS/+fhRCZGX/Y1pdZUFs/uj7eblsQMA49+uJvicmMz9Vg0p0aHlz179hAXF0dcXBxgCytxcXE8//zzAMyYMYNFixbx0ksv0adPHzZt2sTq1auJjIxs2p5fRKaNhBDCubh46rn175PxHuSGYlURu7MfnRN6XPY1Xdp5sfS+/vi66ziRXcq8D3dTUFbdTD0WzeWa9nlpiRqzTlwIIUTLZ7Va+fb9H8n+thCAo/32c6bnsTrtRoaNYn6/pwFIzivj0Y/2kF9aTVSgO4vv64+/h75Z+y0ax2H7vAghhBBNTVEUbnlwMv43eQDQfU8cHQ91q9NuQ/qvZJVnAdAh0IOl9/cn0EtPUl45j8oIjFNxmvAi00ZCCOHcbvvNVPwneQK2ANNtd2+4aO7gqxNf2P8c4e/OkvskwDgjmTYSQgjRqvy8fCOnv7Ltpp4dkUbC8B2Ytba9XULcQnhn/Hu12qcVlDP3w93kGWQKqSWTaSMhhBBOa+zMEcQ83BGzykxIajiDV49BV2ULI9kV2VSbqmq1D/d3Z+lFIzBFsgqpVXOa8CLTRkII0XYMu3EIk/82imqXKrwL/Rj042j0FbbdePef2VKn/cUB5vGP91BSIQGmtZJpIyGEEK1WZlIWX/7pO1wq3CjzMrBj0i9Uu1Wycvhy1PWci5eaX86cD3ZRWGYkpr0Xb87qh4eL1gE9FxeTaSMhhBBtQmhUO2a8fAsV7uV4GLzsIzAFd96NpaioTvuIAHcWz+6Pz9mN7J76ZJ+chdQKSXgRQgjRqoWEB3Pny1PPB5ifRnOmMIv8O+/GXFBQp33HIA/enNUPTxfbUQJP/3cfVTVyGnVr4jThRWpehBCi7WoXFsysf91OtUcVHiXe7Oj6G04UZ5J/622Ys7LqtO/Szos3ZvXDTa9mX3IRz65IoMZkcUDPxdWQmhchhBBO49SZ06z+86/oy10o8y6mT9IH9KjRE/DF52giIuq0T0gp4onle6iusTAqJpi/3dYLjdppfq9vVaTmRQghRJvUuWMnbl04GZNHDR4lPhyMeIhDLiby77wLc05OnfZ9In15+a44tGqFXxNz+Od3iTjZ7/ROScKLEEIIpxIWGcodL9+MyduIW5kniSEPcUBvIf/ue+ot4h3YKYAFd/RBpcD3+zNY9vNJB/RaNIaEFyGEEE4nsL0/d/9rGjX+1bhUunG03WwO1xSTP3M2lvLyOu2Hdwvij1NsJ1Z/vCWJz7cnN3OPRWM4TXiRgl0hhBAX8g3y5d6Xb8Pka8S13IOEyPs4VphC4f0PYq2qqtP+5vgwHhkTDcCin46z5mBmc3dZNJAU7AohhHBq+ZkFrPj9t2jKtBj88rnhyH+I7tYf//+8jaKvfcaR1Wrl9R+P8cXOVNQqhVfv6cugzgEO6nnbIgW7QgghxFkBof5M+9skzC4mvAoD2NTrfpIO76Dw4TlYq2ufMq0oCk9O7Ma42BDMFivPrkjgcHqxYzouLknCixBCCKcX2jGEG/86GrPOjE9eCD/3u4/UhK0U/nYOVmPtM45UKoXnp/VkYCd/Ko1m5n+yj6S8Mgf1XNRHwosQQog2ITImgnF/GoZFY8YvJ4y1Ax4gY89WCuc9itVce4ddrUbFwhl9iGnvjaGyhqc+2UtBafUlriyam4QXIYQQbUZ0XEdGPTsEi8aMT24YPw18kNyN6yl+9rk6+7u46TW8dk9fwv3dyC6u4vef7aPKKMcItAQSXoQQQrQp3fpFM+LZgZg1Zrzz2/PtyJnkf/05pa/8q05bH3cdr93TF++zBzn+9X8HsVicap1LqyThRQghRJsT068bg+f3waKy4JPTma/G307B0n9T/vHyOm3D/d35vzttu/BuPJrLknUnHNBjcSGnCS+yz4sQQojGiBvam9gHOwPgl9mbLybfSOGf/0zlTz/Vadsn0pc/T40F4NNtyfxvd1qz9lXUJvu8CCGEaNN++vhnUr7OBqAi+CduX7+DwM8/Q1/PL8PvbzjNO7+ekj1grgPZ50UIIYRooAkzx+A/0vbDUp83jh+G9qBg9v3UHDtWp+39IzoyuXcoZouVP32RwKmc0uburkDCixBCiDZOURRuffwmXHtpUVvUmMqn8mv3MPLvuRdTenqdts/e3IO+HXypqDbzu0/3kS9LqJudhBchhBBtnkqt4u6/3IaqkxWNSUuR6g52BXpQcNc9mAsKarU9twdMZIA7OSVVPP3ffVQaTQ7qedsk4UUIIYQANDo1s/8+A0t4DVqjjlSvezmk1FAwcxaWiopabb3ddLx6T1983LQcyzTw168PYZYl1M1GwosQQghxls5Nx6yFM6gJrkJX7cLhsJmcyUmj6LHHsVostdqG+bnx8l1x6DQqNh3LZenPsoS6ubTI8JKUlMSoUaOIiYmhZ8+elJeXO7pLQggh2gh3TzfuXXg7Rq9qXMs92dxrNlnbN2D4x8I6bXtF+PKXc0uotybz08HM5u5um9Qiw8t9993HSy+9RGJiIhs3bkR/0ZHlQgghxPXk4+/NzS+Mx6wz4VUYzA833E3hf96m/LPP67Qd17Mds4dFAbDwmyMczShp7u62OS0uvBw5cgStVsuwYcMA8PPzQ6PROLhXQggh2prITuEMeSoeq2LBJ6cLX0+8iaI/PkvVps112v52dDRDuwRSbbLwxxUJFJUb67miaCqNDi+bNm1iypQphIaGoigKq1atqtNm6dKlREVF4eLiQnx8PJs31/0ffSknT57Ew8ODm2++mb59+/KPf/yjsV0UQgghmkSfIT2JurM9AB7Zg1k9rB+Fv3kY4+EjtdqpVAovTu9JuL8bOSVV/OWrA5jMlvouKZpAo8NLeXk5vXv3ZvHixfU+v2LFCp588kmee+459u/fz7Bhw5g0aRKpqan2NvHx8cTGxtb5yMzMpKamhs2bN7NkyRK2b9/OunXrWLdu3SX7U11djcFgqPUhhBBCNJXxM0bjMUSPgkJN5Y1s6BFBwcxZmNJqHxHg4aLl/+6Mw1WnZs+ZQpatP+mgHju/azoeQFEUVq5cydSpU+2PDRw4kL59+7Js2TL7Y927d2fq1KksXFi32Oli27dv58UXX+Sns2dLvPLKKwD8/ve/r7f9Cy+8wIsvvljncTkeQAghRFMxmyy8/+wnWE6oMOqrCC/7iCFmDwK/WYni4lKr7foj2Tz3xQEA/nFHb0b3CHFEl1sdhx0PYDQa2bt3L+PHj6/1+Pjx49m2bVuDrtG/f39ycnIoKirCYrGwadMmunfvfsn2zz77LCUlJfaPtDQ5LEsIIUTTUmtUzH7xTiyhJnTVLiT73Mux0hyKn/9rnbZjeoRwz5AOAPx91WGS8sqaubfOr0nDS35+PmazmeDg4FqPBwcHk52d3aBraDQa/vGPfzB8+HB69epFdHQ0N9100yXb6/V6vLy8WL58OYMGDWLMmDHX9B6EEEKI+ujcdMz8x+2YfI24lruzq8u9ZK/6kvIVX9Rp+8jYaNsRAkYzf/w8gfJq2YG3KV2X1UaKotT63Gq11nnsciZNmsShQ4c4fPgwr732WoNeM2/ePBITE9m9e3ej+iqEEEI0lIevO7f+7UZMOhOeRUF8O3o6hc/9CeP+/bXaadQq/n57bwK99KTkl/P3VYe5hioNcZEmDS8BAQGo1eo6oyy5ubl1RmOEEEKI1qhdeDD9H7FtTOeT1ZvvhsZTMOs+ak6fqdXOz0PPwjv6oFEr/JqYwydbkx3QW+fUpOFFp9MRHx9fZ3XQunXrGDJkSFPeqo4lS5YQExND//79r+t9hBBCiAGj++E72gMAS/kEdoZ7U3DPvZhzcmq1iw33Yf4kW93msp9PsOdMQZ1ricZrdHgpKysjISGBhIQEwLaVf0JCgn0p9Pz583n33Xd5//33OXr0KE899RSpqanMmTOnSTt+MZk2EkII0Zymz7sZIs1oa3SkeN/LIRcTBQ88iLWqqla7af3CmNwnFIsV/vzlAXJKKh3UY+fR6PCyZ88e4uLiiIuLA2xhJS4ujueffx6AGTNmsGjRIl566SX69OnDpk2bWL16NZGRkU3b84vIyIsQQojmpNaouPel2zH5G3GpcONQ2H2czE+rswJJURSeuSmGLu08Ka6o4bkvZAO7a3VN+7y0RI1ZJy6EEEJcq8KcIj773Uo0pToM/tlM2byU8BcX4H73XbXaZRZVMOut7ZRVmZh1QxRzx3VxUI9bJoft8yKEEEK0NX7Bvkx9cQJmrQmvghDbCqS//AXj4cO12oX6uvGnm3sA8PGWJHaeyndEd52C04QXmTYSQgjhKO07tSduTgwA3tm9WT24P4UP/xZLSe0Tpkf3CGFav3AAXvjfIQpKq5u9r87AacKLFOwKIYRwpMFjB+Az1h0AU+V49rtC0VPzsVpq17c8MbErnYI9KCo38sL/DmGxOFX1RrNwmvAihBBCONrtc2/BElmDxqQlsf2dZO7YTOlrr9dq46JV8/fbe+OiVbP7TAGfbE1yUG9bL6cJLzJtJIQQwtFUahV3/mUqJlcjHiW+/HjDnRT8+w0qv/u+VruoQA9+N7kbAG//coqDqUWO6G6rJauNhBBCiCaWsOMQO/6ZgGJVKAveyp2/rCfoh+/Qdu1qb2O1Wvnr14dYeyiLEG8XPn5kCF6uWgf22rFktZEQQgjhQH0G9STyjnYAuOcO5seBPSmc91itDewUReEPN8UQ5udKdkkV//hGzj9qKAkvQgghxHUw8a4xuA/SoVhVVFXfyCFTMSX/+GetNu4uGv5+e280aoUNR3P5eneag3rbujhNeJGaFyGEEC2Joijc+btbMbc3oq3RsT9qBtmffUzVz+trtesW6s2jZzese3PNcU5mGxzR3VZFal6EEEKI6ygnPY+vn/oBtVGDIWQPd29ZT/BPq9GEh9vbWK1Wnv7vfraeyCMywJ0PHh6Em17jwF43P6l5EUIIIVqI4LBAej9gW1nkmdOXdT0jKXz4t3XqX/4yNZZALz0p+eW8uvqoo7rbKkh4EUIIIa6zoZMG4dZfi2JVUaLczLGSDIr//JdabXzcdbw0vRcqBX5IyOSng5kO6m3LJ+FFCCGEaAYz5k/D7FeDS4UbO7rfQ843X1P+2ee12sR18OPBkZ0A+NcPR8kpqXREV1s8CS9CCCFEM9C56bj5uQmYNSa880P5dvR0ip5/nppTp2q1mz2sI7Fh3pRVmfjbqsNyfEA9nCa8yGojIYQQLV1Y59DaBzgO7EPRI/OwVp8/oFGjVvH8rT1x0arZc6aQr3alOqq7LZbThBc5mFEIIURrMHjcAHzH2A5wrDaO53BVPoZ//l+tNhH+7jw63rZ8esm6EyTnlTV7P1sypwkvQgghRGsx/ZGbMQfXoKt2YXfXO8j9+D2qNm2u3aZ/OAM6+VNtsvDSykOYzJZLXK3tkfAihBBCNDO1Vs0tf5yIRW3GJ689342eQNFTT2EuPH9Ao6Io/PmWWDxdNCRmGPh4s5w+fY6EFyGEEMIB2ndsR+cZkQC4FAxhezt3in/3u1rnGwV5u/D0jd0BeG/jaY5lljikry2NhBchhBDCQcbeMQJ1tBW1WUOS322k7d5C+Ucf1Wozvmc7xvQIxmyx8sL/DlFVY3ZQb1sOCS9CCCGEgyiKwh1/nIrJtQaPEl9+GnoHhX//OzVHEmu1eeamGPw9dCTnlbN03QkH9rhlcJrwIkulhRBCtEZeAZ4Me3IAVqz4ZnfjuxsGUzh3HpbK8xvUebvp+PPUWAC+2JnKjlP5jupui+A04UWWSgshhGiteg3qQfBNvgAohjHs1VVjWPjPWm0GRwdy24AIABZ8c5jSyppm72dL4TThRQghhGjNpj54I9aOJtRmDUfCppP/yUdUbd5Sq82j47oQ7u9GnqGa13885qCeOp6EFyGEEKIFUFQKt/9+CmatCa/CYFaPmEDxU/OxlJxfYeSiU/P8tJ6oFFh9IJNNx3Id2GPHkfAihBBCtBD+of50vjMcAE3JYA55qCh+/oVabXqG+3D3kA4AvPJ9ImVVbW/6SMKLEEII0YKMmz4aS5QRtVlDQtTtFH63ksoff6zV5qFRnW3TR6XVLGmDq48kvAghhBAtiKIo3Pr7mzBrbNNH34y5keI/PIs5//wKIxetmmdv7gHAyj3p7EsudFR3HaLFhZfjx4/Tp08f+4erqyurVq1ydLeEEEKIZhPcPohu93YEwC1vIBs7BlH8hz/W2n23bwc/pvULA+Af3xxpU5vXtbjw0rVrVxISEkhISGDLli24u7szbtw4R3dLCCGEaFajpg5D21tBZVWR5TaN0/u3Ufm/lbXazBvXhUBPPemFFbz76ykH9bT5tbjwcqFvv/2WMWPG4O7u7uiuCCGEEM1KURTueuZWTN5G3Mo9+bXfbRT+9a+Y8/LsbTxctPz+phgAPtuewvEsg6O626waHV42bdrElClTCA0NRVGUeqd0li5dSlRUFC4uLsTHx7N58+a6F2qAL774ghkzZlzVa4UQQojWztXDlTHzbwDAJ6cLm7uEUfLn52u1Gd4tiDE9QjBbrPzjmyOYzBZHdLVZNTq8lJeX07t3bxYvXlzv8ytWrODJJ5/kueeeY//+/QwbNoxJkyaRmppqbxMfH09sbGydj8zMTHsbg8HA1q1bmTx58lW8LSGEEMI5dOvTBZeBagAy3SeT8+saKn9YXavN/Mnd8HLVcDzLwFe7Uuu7jFNRrBdW/zT2xYrCypUrmTp1qv2xgQMH0rdvX5YtW2Z/rHv37kydOpWFCxc2+NrLly9nzZo1fPLJJ5dtV11dTXV1tf1zg8FAeHg4JSUleHl5NfzNCCGEEC1UWXE5H/32CzRVWsqCdnD3js0E/fIz6sBAe5tv9qaz8NsjuOnUfP7oDQR5uziwx41nMBjw9vZu0M/vJq15MRqN7N27l/Hjx9d6fPz48Wzbtq1R12rolNHChQvx9va2f4SHhzfqPkIIIURL5+HjTuzMLgC45fdnU6cgin//TK3VR1Pi2tMz3IcKo5nXf3LuowOaNLzk5+djNpsJDg6u9XhwcDDZ2dkNvk5JSQm7du1iwoQJV2z77LPPUlJSYv9IS0trdL+FEEKIlm74jUPQxILKoibT/VbOHNxJ5cpV9udVKoVnbopBrVL4NTGHbSfyLn2xVu66rDZSFKXW51artc5jl+Pt7U1OTg46ne6KbfV6PV5eXixfvpxBgwYxZsyYRvdXCCGEaOkUReGuP07H5GXErcyTX/rdRtHf/o7FcH6FUXSIJ3cOigTglR+OUmk0Oaq711WThpeAgADUanWdUZbc3Nw6ozFNbd68eSQmJrJ79+7reh8hhBDCUdw8bauPrFjxyenC+s6hGP71Wq02D47sRIiPC1nFlbzzi3Pu/dKk4UWn0xEfH8+6detqPb5u3TqGDBnSlLeqY8mSJcTExNC/f//reh8hhBDCkbrFdcFjqG1mokA3ibT/fYbx4EH78256Dc+c3ftlxY4UjmWW1Hud1qzR4aWsrMy+Ay5AUlISCQkJ9qXQ8+fP59133+X999/n6NGjPPXUU6SmpjJnzpwm7fjFZORFCCFEWzF97s2Y3I24lXmxdvAkip6aj/WClbdDogMZ3zMEixX+9cNRLJarXljcIjU6vOzZs4e4uDji4uIAW1iJi4vj+edtm+bMmDGDRYsW8dJLL9GnTx82bdrE6tWriYyMbNqeX0RGXoQQQrQVrh6u9L0/FgC3/Dh2q6swvL6oVpvHxnfFTafmcHoJPx3MrOcqrdc17fPSEjVmnbgQQgjRWlmtVv7zzMdYT6gp8c/i1o3LiPjqa3RnBxcAPt58hqU/n8TfQ8cXjw/DXa9xYI8vz2H7vAghhBCieSiKwrSnbsSsMeNd0I7vR46h6PfPYDWdX2F05+AOhPm5UVBm5IONpx3Y26blNOFFpo2EEEK0NYGhAXS6IwwAXdFQ9igVlH/0sf15nUbFkxO7AvD5jhRS88sd0s+m5jThRQp2hRBCtEXjbx8NHc2ozRoSw6aR9/qrmHNz7c8P7RLI4OgATGYrb6w57sCeNh2nCS9CCCFEW6SoFKb//ibMGjNehSGs7dOLkr8tOP+8ovDkxG5o1ApbT+Q5xc67ThNeZNpICCFEWxUQ6k/IBD8AqkyjSNm4huqt588UjAxwZ8ZA26rf1386Ro3J4pB+NhWnCS8ybSSEEKItmzJrIiZvIy6Vbvw8aDJFf/gj1qoq+/MPjOiEn4eOtIIKVuxIcWBPr53ThBchhBCiLdO6aBnwQG8APPJ6s9vDimHRG/bn3V00zB1rO5n6/Y2nyS+trvc6rYGEFyGEEMJJ9B8Zj9LNjMqi5li7qeS+/x9qjiTan5/cO5SY9t5UGM0s/fmEA3t6bZwmvEjNixBCCAG3PjkFs9aEV2Ewq28YRfFf/sK5/WhVKoX5k7sBsDohk8NpxQ7s6dVzmvAiNS9CCCEEBLTzo9MdEQBoSoaQUJhG5Xff25+PDfPhxj6hALz2Y+s898hpwosQQgghbMbfNgpLRA0ak5Z90VMoXrAAS2Wl/fm5Y7vgpleTmGFg9YHWd+6RhBchhBDCySgqhcmPjcWqWPHJjWJDez/Kli6zP+/vqefBEZ0AWLLuBGVVNY7q6lWR8CKEEEI4ocgu4bgN0gKQ5TGRnPfexpSaan/+joGRRPi7UVRu5L0NrevcIwkvQgghhJOa+tsbMelMeJT4sbb/YIqfO1+8q9WoeHKSrXj3i52ppBW0nnOPnCa8yGojIYQQojYvX0863NIOAEvlDZxK3EvVjz/Znx8SHcigzv6YLVbeWn/KUd1sNKcJL7LaSAghhKhr4p3jMAUY0VW7sLHfLRQ9/1cs5edHWeaN64KiwPoj2RxJL3ZcRxvBacKLEEIIIepSa1SMmjsEAJ/s7myI8Kfsnf/Yn48O8WJSb9vS6TfWHLdPK7VkEl6EEEIIJxcT3w2XAWoAst1vJOuj9zAXFtqfnzMmGhetmoOpxfx8ONtR3WwwCS9CCCFEGzB93s2YXGpwN/jwc+9+lL75b/tzQV4uzBoWBcDidSeoMpod1c0GkfAihBBCtAEePu50uNk2PWSqHkzqys+pOX7c/vzdQzoQ4uNCTkkV/92e7KBeNoyEFyGEEKKNmHD7GGo8jbhUuvFLv5EUP/dne42Li1ZtP3X6063JlFQYHdnVy3Ka8CJLpYUQQojL0+g09Lg92vbnkv4cyzhJ5df/sz8/tkcI0SGelFebWL4lyVHdvCKnCS+yVFoIIYS4shE3DcUcYkRbo2Nrr5spWrDAvnRapVKYM8YWbr7cmUqeocqRXb0kpwkvQgghhLgylVrFuEdHYsWKb3YXfo5uX2vp9JDoAHqG+1BtsvDBpjMO7OmlSXgRQggh2pjonh3xGKoHoEA3kcyP3sVcUACAoijMHWsbfflmbzrphRUO6+elSHgRQggh2qBbH5mCybUG91Jvfu4zgNI33rQ/F9fBj0GdAzBbrPzn15Z3bICEFyGEEKINcvN0JeqW9gCYqwaR+u2XmJKT7c+fq31ZeyiLUzmljujiJUl4EUIIIdqo8dNHYzq3dDp+OCULFtqf6xbqxZgewVit8Pb6kw7sZV0tMry8/vrr9OjRg5iYGB5//PFWcc6CEEII0dpodBq6T+8MgLq0H6f2baFq8xb78w+PjkatUth8PI9DacUO6mVdLS685OXlsXjxYvbu3cuhQ4fYu3cvO3bscHS3hBBCCKc08uYbMPmfO3V6MsV//StWs+14gMgAdyb3se3Ku+znEy1mMKHFhRcAk8lEVVUVNTU11NTUEBQU5OguCSGEEE5JpVYx5IF+AHjm9GSnrobKlavszz84ohNatcK+5CJ2nSlwUC9ra3R42bRpE1OmTCE0NBRFUVi1alWdNkuXLiUqKgoXFxfi4+PZvHlzg68fGBjI008/TUREBKGhoYwdO5ZOnTo1tptCCCGEaKC4G3qh6m5BZVVxPHQK+W++jrWmBoAQH1em948A4K2fT7aI0ZdGh5fy8nJ69+7N4sWL631+xYoVPPnkkzz33HPs37+fYcOGMWnSJFJTU+1t4uPjiY2NrfORmZlJUVER33//PcnJyWRkZLBt2zY2bdp09e9QCCGEEFc09fHJWNRmvAva8WtIABVffW1/bvbwjrjp1BzNNPDr0RwH9tJGsV5DhFIUhZUrVzJ16lT7YwMHDqRv374sW7bM/lj37t2ZOnUqCxcurOcqtX355Zds2LCBJUuWAPDKK69gtVp55pln6m1fXV1NdXW1/XODwUB4eDglJSV4eXld5TsTQggh2p5PX/+Ssg3VlPrkc8fxzwj/dQOKqysA7/xykvc3niEywJ3/zhuKWqU06b0NBgPe3t4N+vndpDUvRqORvXv3Mn78+FqPjx8/nm3btjXoGuHh4Wzbto2qqirMZjMbNmyga9eul2y/cOFCvL297R/h4eHX9B6EEEKItmrKrImYNSY8iwNYHxlG2bvv2Z+7e0gHvFy1pOSX8+OBTAf2sonDS35+PmazmeDg4FqPBwcHk52d3aBrDBo0iMmTJxMXF0evXr3o1KkTN9988yXbP/vss5SUlNg/0tLSruk9CCGEEG2Vl78nviM8ACjSjST3naX2YwM8XLTMHhYFwLsbTmEyWxzWz+uy2khRag8lWa3WOo9dzoIFCzh69ChHjhzhzTffvOxr9Xo9Xl5eLF++nEGDBjFmzJir7rcQQgjR1t1832RMOhPuBl9+7hNH6Wuv25+bPiCC8T1D+McdfdCoHbdguUnvHBAQgFqtrjPKkpubW2c0pqnNmzePxMREdu/efV3vI4QQQjgzdy83QicGAFBpGk7K919hSkoCwEWr5qXbehPT3tuRXWza8KLT6YiPj2fdunW1Hl+3bh1DhgxpylvVsWTJEmJiYujfv/91vY8QQgjh7G66ZwImTyOuFe6s7z+W0n/Xv8LYURodXsrKykhISCAhIQGApKQkEhIS7Euh58+fz7vvvsv777/P0aNHeeqpp0hNTWXOnDlN2vGLyciLEEII0TS0Llp63d0dAH1RPInbf8GUkuLgXp3X6PCyZ88e4uLiiIuLA2xhJS4ujueffx6AGTNmsGjRIl566SX69OnDpk2bWL16NZGRkU3b84vIyIsQQgjRdG6YNAhTOyMak5YdPUZR+ua/Hd0lu2va56Ulasw6cSGEEEJc2q4Ne9n/+lFqtDX0S32d+P/+D23nztflXg7b50UIIYQQzqP/iL6Ygoxoa7Rs7T0Sw/+97OguAU4UXmTaSAghhGhaiqIQd1sPAPRFfTh2YAfGvfsc3CuZNhJCCCHEZVgtVpbN+QB1jo6S4IPcn3+agK+/atT+bQ0h00ZCCCGEaBKKSmHIfbZZDc+8HuwtyaTawQcmO014kWkjIYQQ4vroM6QnRJtQWdQc6DiZktdex5ETN04TXmSfFyGEEOL6mfTbMVix4pMbxf7RA8HiZGcbCSGEEMK5RESHo+ll+/OhVHdQOcnZRkIIIYRwXhPuG4VVZaG9b3vMZrPD+qFx2J2b2JIlS1iyZIlD/zKFEEIIZxbeKYyZ792Gu5+bQ/shS6WFEEII4XCyVFoIIYQQTkvCixBCCCFaFQkvQgghhGhVJLwIIYQQolVxmvAiO+wKIYQQbYOsNhJCCCGEw8lqIyGEEEI4LQkvQgghhGhVJLwIIYQQolWR8CKEEEKIVkXCixBCCCFaFacJL7JUWgghhGgbZKm0EEIIIRyuMT+/Nc3Up2ZzLosZDAYH90QIIYQQDXXu53ZDxlScLryUlpYCEB4e7uCeCCGEEKKxSktL8fb2vmwbp5s2slgsZGZm4unpiaIo9bbp378/u3fvvux1DAYD4eHhpKWlyfTTBRryd+dIzd2/63W/prrutV7nal7fmNc0tK18PdZPvh6b537y9Vjb9fp6tFqtlJaWEhoaikp1+ZJcpxt5UalUhIWFXbaNWq1u8F+4l5eXfLO8QGP+7hyhuft3ve7XVNe91utczesb85rGXl++HmuTr8fmuZ98Pdbvenw9XmnE5RynWW3UGPPmzXN0F1qtlv5319z9u173a6rrXut1rub1jXlNS//31NK19L8/+Xps2uvI1+N5Tjdt1FRk1ZIQLYd8PQrRcrSEr8c2OfLSEHq9nv9v725eoWvgMI5fEw2bUYaaMhNZiFBqhinKYjYzUSZiK9tZSRZSFv4COyxkY2Gh1FhIaRYnRFFeFpZKTc0k2ZnZCOdePcrj7rndD87LnO+nZjFnTuOy+PW7OsfJ4uKiampq7I4CeB7zCDiHE+aRKy8AAMBVuPICAABchfICAABchfICAABchfICAABchfICAABchfLyDcbGxlRfX6+JiQm7owCes7u7q/b2drW1tWl9fd3uOICnWbUPeVT6GxiGoVKppI2NDW1vb9sdB/CM5+dndXZ2yjAM1dXVKRqN6vT0VMFg0O5ogCdZtQ+58vINEomEAoGA3TEAzzk7O1NXV5fC4bACgYCGh4e1v79vdyzAs6zahxVfXg4PDzUyMqKmpib5fD7t7Ox8OGd1dVWtra2qra1VLBbT0dGR9UEBD/rqfBaLRYXD4bf3kUhEhULBiuhAxXHTvqz48lIul9XT06Pl5eXffr61taWZmRktLCzo8vJSg4ODGhoaUj6ffzsnFoupu7v7w6tYLFr1awAV6avz+bu73j6f70czA5XqO/alZUwPkWRms9l3x+LxuJnJZN4d6+joMOfn5//quw3DMMfHx78aEfCs/zOfx8fH5ujo6Ntn09PT5ubm5o9nBSrdV/alFfuw4q+8/Jenpyedn58rmUy+O55MJnVycmJTKgDS5+YzHo/r+vpahUJBj4+P2tvbUyqVsiMuUNGcti+rLf+JDvLw8KCXlxeFQqF3x0OhkO7u7j79PalUShcXFyqXy4pEIspms+rr6/vuuICnfGY+q6urtbS0pEQiodfXV83NzamhocGOuEBF++y+tGoferq8/OPf98hN0/yr++Y83QD8nD/NZzqdVjqdtjoW4El/mker9qGnbxs1Njaqqqrqw1WW+/v7D+0SgLWYT8A5nDaPni4vfr9fsVhMuVzu3fFcLqeBgQGbUgGQmE/ASZw2jxV/26hUKunm5ubt/e3tra6urhQMBtXc3KzZ2VlNTk6qt7dX/f39WltbUz6fVyaTsTE14A3MJ+AcrprHH32WyQEMwzAlfXhNTU29nbOysmK2tLSYfr/fjEaj5sHBgX2BAQ9hPgHncNM88r+NAACAq3j6b14AAID7UF4AAICrUF4AAICrUF4AAICrUF4AAICrUF4AAICrUF4AAICrUF4AAICrUF4AAICrUF4AAICrUF4AAICrUF4AAICr/AKP3o5cYBmR7AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ui.create_model_component('xsvvneinoh', 'mdl')\n",
    "\n",
    "# Clear out all the elemental abundances\n",
    "for par in mdl.pars:\n",
    "    if len(par.name) < 3 and par.name != 'kT':\n",
    "        par.val = 0\n",
    "\n",
    "# Colors are taken from ColorBrewer2, or rather,\n",
    "# this StackOverflow answer: http://stats.stackexchange.com/a/118183\n",
    "        \n",
    "# Ensure there's some continuum\n",
    "mdl.he = 1\n",
    "\n",
    "# Carbon\n",
    "mdl.c = 1\n",
    "plt.plot(emid, mdl(elo, ehi), c='#e41a1c', label='C')\n",
    "\n",
    "# Oxygen\n",
    "mdl.c = 0\n",
    "mdl.o = 1\n",
    "plt.plot(emid, mdl(elo, ehi), c='#377eb8', label='O')\n",
    "\n",
    "# Argon\n",
    "mdl.o = 0\n",
    "mdl.ar = 1\n",
    "plt.plot(emid, mdl(elo, ehi), c='#4daf4a', label='Ar')\n",
    "\n",
    "# Continuum\n",
    "mdl.ar = 0\n",
    "plt.plot(emid, mdl(elo, ehi), c='#984ea3', label='Continuum')\n",
    "\n",
    "plt.legend()\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Can we make this faster?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE**\n",
    "\n",
    "The following should be taken with a rather large pinch of salt at this time because Sherpa supports model cacheing, whereby repeated calls for the same model parameters and grid will just use the cache, rather than re-run the model, and this support has not **yet** been accounted for in the following..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If I were to be using this for actual analysis, I would probably re-write `xsvvnei_no_h` so that it is sent the model instance to use, rather than re-create it each time the model is evaluated, since the latter feels wasteful. However, it is *really* important to actually measure the time these functions take to run to make sure changes you make are worth it,\n",
    "so I'll do that too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I repeat the original version as a reference.\n",
    "def xsvvnei_no_h_1(pars, *args):\n",
    "    \"pars is kT, list of 29 element abundances (excluding H), tau, redshift, norm\"\n",
    "\n",
    "    # It's a bit excessive to create this each iteration\n",
    "    mdl = xspec.XSvvnei()\n",
    "    \n",
    "    pall = [pars[0], 1.0]\n",
    "    pall.extend(pars[1:])\n",
    "    \n",
    "    phonly = [pars[0], 1.0]\n",
    "    phonly.extend([0.0] * 29)\n",
    "    phonly.extend(pars[-3:])\n",
    "\n",
    "    # Evaluate the \"Hydrogen-free\" model\n",
    "    return mdl.calc(pall, *args) - mdl.calc(phonly, *args)\n",
    "\n",
    "# This version requires a model instance to be sent in\n",
    "def xsvvnei_no_h_2(mdl, pars, *args):\n",
    "    \"pars is kT, list of 29 element abundances (excluding H), tau, redshift, norm\"\n",
    "\n",
    "    pall = [pars[0], 1.0]\n",
    "    pall.extend(pars[1:])\n",
    "    \n",
    "    phonly = [pars[0], 1.0]\n",
    "    phonly.extend([0.0] * 29)\n",
    "    phonly.extend(pars[-3:])\n",
    "\n",
    "    # Evaluate the \"Hydrogen-free\" model\n",
    "    return mdl.calc(pall, *args) - mdl.calc(phonly, *args)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The IPython interpreter provides the \n",
    "[`%timeit`](http://ipython.org/ipython-doc/stable/interactive/magics.html#magic-timeit)\n",
    "command, which is a basic - *but useful*, as I'll show - way to time functions. In this case,\n",
    "I want to evaluate the two functions, which means I need a parameter array, a model (for `xsvvnei_no_h_2`), and I'll use `egrid` for the grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "pars = [p.val for p in mdl.pars]\n",
    "mdl_2 = xspec.XSvvnei()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `%timeit` command takes the function to evaluate, and runs it multiple times, and reports its best estimate for the run time (it's an adaptive scheme, in that it tailors the number of loops to how long the function takes, and reports the time taken in \"sensible\" units)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.63 ms ± 1.12 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit xsvvnei_no_h_1(pars, elo, ehi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "976 µs ± 13.6 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit xsvvnei_no_h_2(mdl_2, pars, elo, ehi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see in this example, the second function is significantly faster than the first one (which surprised me, and the speed-up depends on the version of Sherpa being used). Now, there's **plenty of things**\n",
    "to be wary of when using this, since\n",
    "\n",
    " - I have not actualy checked that the two functions return the same values (which you\n",
    "   really *must do* before trying to work out which is faster);\n",
    "   \n",
    " - it relies on the function always doing the same amount of work each iteration\n",
    "   (it might be that the function loads in data files for particular parameter values,\n",
    "   or sets up memory or loads in data the first time it is run);\n",
    "   \n",
    " - I have only used a single set of parameters and one energy grid and the difference in \n",
    "   run time could well vary depending on these values;\n",
    "   \n",
    " - when used in a fit, there is plenty of other code being run, so there comes a point\n",
    "   at which reducing the run time of a model component is no longer worth it (perhaps\n",
    "   there's another model being evaluated which is significantly slower).\n",
    "\n",
    "So, this is a start, but it's not the end of the story. There are other commands and\n",
    "modules in Python that can help you optimise the run time (or memory usage), of Python\n",
    "code (e.g. \n",
    "[Timing and Profiling](http://pynash.org/2013/03/06/timing-and-profiling.html),\n",
    "[SciPy notes on Optimizing code](http://scipy-lectures.github.io/advanced/optimizing/),\n",
    "[Jake VanderPlas' example of optimizing Python code](https://jakevdp.github.io/blog/2015/02/24/optimizing-python-with-numpy-and-numba/),\n",
    "[SnakeViz for visualizing the cProfile output](https://jiffyclub.github.io/snakeviz/),\n",
    "and plenty of other resources out there on the internet).\n",
    "\n",
    "## Just one more thing\n",
    "\n",
    "The code above uses the generic Sherpa model interface to evaluate a model; that is, it uses the `calc` method of the `Model` class to evaluate the model, and can be used for *all* Sherpa models. I happen to know that$^\\dagger$,\n",
    "for XSPEC models, this method is defined as\n",
    "\n",
    "```\n",
    "class XSModel(ArithmeticModel):\n",
    "    \n",
    "    @modelCacher1d\n",
    "    def calc(self, *args, **kwargs):\n",
    "        return self._calc(*args, **kwargs)\n",
    "```\n",
    "\n",
    "and that the `XSvvnei` class defines `_calc` to be:\n",
    "\n",
    "```\n",
    "class XSvvnei(XSAdditiveModel):\n",
    "\n",
    "    _calc =  _xspec.C_vvnei\n",
    "...\n",
    "```\n",
    "\n",
    "---\n",
    "\n",
    "$^\\dagger$ - actually, it used to be implemented like this circa version 4.9, but in order to better support different versions the XSPEC model library, we now use a slightly-more complicated scheme, but the *essence* is the same, that is there is a field in the class structure that contains the actual function from the XSPEC model library that we can call.\n",
    "\n",
    "---\n",
    "\n",
    "This means that we can call `_calc` instead of `calc`, saving even-more time. Let's see\n",
    "how much this really helps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sherpa.astro.xspec import _xspec\n",
    "\n",
    "def xsvvnei_no_h_3(mdl, pars, *args):\n",
    "    \"pars is kT, list of 29 element abundances (excluding H), tau, redshift, norm\"\n",
    "\n",
    "    pall = [pars[0], 1.0]\n",
    "    pall.extend(pars[1:])\n",
    "    \n",
    "    phonly = [pars[0], 1.0]\n",
    "    phonly.extend([0.0] * 29)\n",
    "    phonly.extend(pars[-3:])\n",
    "\n",
    "    # Evaluate the \"Hydrogen-free\" model\n",
    "    return mdl._calc(pall, *args) - mdl._calc(phonly, *args)\n",
    "\n",
    "def xsvvnei_no_h_4(pars, *args):\n",
    "    \"pars is kT, list of 29 element abundances (excluding H), tau, redshift, norm\"\n",
    "\n",
    "    pall = [pars[0], 1.0]\n",
    "    pall.extend(pars[1:])\n",
    "    \n",
    "    phonly = [pars[0], 1.0]\n",
    "    phonly.extend([0.0] * 29)\n",
    "    phonly.extend(pars[-3:])\n",
    "\n",
    "    # Evaluate the \"Hydrogen-free\" model\n",
    "    return _xspec.C_vvnei(pall, *args) - _xspec.C_vvnei(phonly, *args)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.63 ms ± 2.75 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
      "962 µs ± 1.85 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
      "854 µs ± 1.79 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
      "854 µs ± 1.7 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit xsvvnei_no_h_1(pars, elo, ehi)\n",
    "%timeit xsvvnei_no_h_2(mdl_2, pars, elo, ehi)\n",
    "%timeit xsvvnei_no_h_3(mdl_2, pars, elo, ehi)\n",
    "%timeit xsvvnei_no_h_4(pars, elo, ehi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, in this case, the extra \"optimisations\" do save us some time, but  I would be reluctant to use them, since they rely on internals of the code that could be changed at any time."
   ]
  }
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