11 grouping-spectra.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grouping spectra\n",
    "\n",
    "This is a (hopefully) short notebook about grouping of PHA spectra in Sherpa. I'm not planning to discuss\n",
    "everythng you can do, but let's see how this goes. The problem is that the addition of grouping support\n",
    "for PHA spectra complicates the API (are we working on the grouped or ungrouped data), so it's not as easy as I would like it to be to get at the data we want.\n",
    "\n",
    "## Setting up\n",
    "\n",
    "The notebook assumes you have CIAO 4.16 installed, so you\n",
    "should just have to run the following command from your shell (in the directory that contains `grouping-spectra.ipynb` and the `3c273*` files:\n",
    "\n",
    "    % pip install jupyter  # not needed for ciao-install but is for a conda install of CIAO\n",
    "    % jupyter notebook\n",
    "    ...\n",
    "    ... a lot of screen output\n",
    "    ...\n",
    "    \n",
    "If you already have a web browser running then you will hopefully have a tab opened for you that lets\n",
    "you start up this notebook, otherwise the screen output will show you the web address to use.\n",
    "\n",
    "## Changes\n",
    "\n",
    "- December 2021 - sometime around CIAO 4.12 the [Can I re-create the plot?](#Can-I-re-create-the-model-plot?) section needed a re-write, so I've finally upated it.\n",
    "- March 14 2019 - added a [section on errors](#Errors)\n",
    "- March 12 2019 - original version\n",
    "\n",
    "## When was this last run, and with that version of Sherpa?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Tue Jan  2 14:27:56 2024'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import time\n",
    "time.ctime()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: sherpa\n",
      "Version: 4.16.0\n",
      "Summary: Modeling and fitting package for scientific data analysis\n",
      "Home-page: https://cxc.harvard.edu/sherpa/\n",
      "Author: Smithsonian Astrophysical Observatory / Chandra X-Ray Center\n",
      "Author-email: cxchelp@head.cfa.harvard.edu\n",
      "License: GNU GPL v3\n",
      "Location: /lagado2.real/local/micromamba/envs/ciao416/lib/python3.11/site-packages\n",
      "Requires: numpy\n",
      "Required-by: \n"
     ]
    }
   ],
   "source": [
    "!pip show sherpa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following command doesn't appear to be necessary, but I include it to indicate I expect the\n",
    "Matplotlib plots to appear within the notebook), and then load in NumPy and Matplotlib for later use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up\n",
    "\n",
    "I am going to load up the `3c273` data set up an absorbed powerlaw - with parameter values taken\n",
    "from a fit - just so that we have some data to work with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: systematic errors were not found in file 'data/pha/3c273.pi'\n",
      "statistical errors were found in file 'data/pha/3c273.pi' \n",
      "but not used; to use them, re-read with use_errors=True\n",
      "read ARF file data/pha/3c273.arf\n",
      "read RMF file data/pha/3c273.rmf\n",
      "WARNING: systematic errors were not found in file 'data/pha/3c273_bg.pi'\n",
      "statistical errors were found in file 'data/pha/3c273_bg.pi' \n",
      "but not used; to use them, re-read with use_errors=True\n",
      "read background file data/pha/3c273_bg.pi\n",
      "dataset 1: 0.00146:14.9504 -> 0.4672:9.8696 Energy (keV)\n"
     ]
    }
   ],
   "source": [
    "from sherpa.astro import ui\n",
    "\n",
    "ui.load_pha('data/pha/3c273.pi')\n",
    "ui.subtract()\n",
    "\n",
    "ui.notice(0.5, 7.0)\n",
    "\n",
    "ui.set_source(ui.xsphabs.gal * ui.powlaw1d.pl)\n",
    "\n",
    "gal.nh = 0.03616\n",
    "pl.gamma = 2.0322\n",
    "pl.ampl = 1.969e-4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this setup we can create a plot of the grouped (and filtered) data, along\n",
    "with the best-fit model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ui.set_xlog()\n",
    "ui.set_ylog()\n",
    "ui.plot_fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Can I access the data used to create the plot?\n",
    "\n",
    "We have accessor routines that represent the data used in the plot, but I also hope to show\n",
    "how you can re-create this data (or at least some of the data) manually below.\n",
    "\n",
    "First, with the accessor routines:\n",
    "\n",
    " - [get_data_plot](https://sherpa.readthedocs.io/en/4.11.0/ui/api/sherpa.astro.ui.get_data_plot.html)\n",
    " - [get_model_plot](https://sherpa.readthedocs.io/en/4.11.0/ui/api/sherpa.astro.ui.get_model_plot.html)\n",
    " - [get_fit_plot](https://sherpa.readthedocs.io/en/4.11.0/ui/api/sherpa.astro.ui.get_fit_plot.html)\n",
    " \n",
    "### A note on documentation links\n",
    "\n",
    "I am going to use the documentation from the \n",
    "[\"standalone\" version of Sherpa](https://sherpa.readthedocs.io/en/4.11.0/) rather than CIAO,\n",
    "as they are better integrated with the Sherpa API - that is, you can get to some of the\n",
    "class-based documentation such as\n",
    "[the DataPlot class](https://sherpa.readthedocs.io/en/4.11.0/plots/api/sherpa.astro.plot.DataPlot.html).\n",
    "\n",
    "### Back to the scheduled programming\n",
    "\n",
    "The `get_xxx_plot` routines return an object that contains the data needed to create the plot,\n",
    "and are evaluated at the time they are called (that is, if you call `plot_fit`, then change the\n",
    "energy filter or alter  model parameter, and then call `get_fit_plot` the results will\n",
    "represent the changes you have just made, not the data used to create the on-screen plot).\n",
    "\n",
    "I start with the `fit` plot which is a combination of a `data` plot and a `model` plot. Note that the plot class (the item returned by `get_fit_plot`) has special support for Jupyter notebooks (new to Sherpa 4.12.2) and so shows an actual plot of the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
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      ],
      "text/plain": [
       "<sherpa.plot.FitPlot at 0x7f1a71a43b50>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fplot = ui.get_fit_plot()\n",
    "fplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `dataplot` and `modelplot` attributes contain\n",
    "[`DataPlot`](https://sherpa.readthedocs.io/en/4.12.2/plots/api/sherpa.astro.plot.DataPHAPlot.html)\n",
    "and\n",
    "[`ModelPlot`](https://sherpa.readthedocs.io/en/4.12.2/plots/api/sherpa.astro.plot.ModelPHAHistogram.html)\n",
    "instances respectively (in this case they are the same as calling `get_data_plot` and `get_model_plot` [$*$]):\n",
    "\n",
    "[$*$] Prior to Sherpa 4.12.2 these two fields contained slightly-different plot types, but these have been replaced with the above versions **when** displaying PHA data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sherpa.astro.plot.DataPHAPlot"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(fplot.dataplot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sherpa.astro.plot.ModelPHAHistogram"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(fplot.modelplot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataplot   = data/pha/3c273.pi\n",
      "xlo    = [0.4672,0.5694,0.6424,0.7008,0.7446,0.7884,0.8176,0.8614,0.8906,0.949 ,\n",
      " 0.9928,1.0366,1.095 ,1.1388,1.1972,1.2848,1.4016,1.4746,1.606 ,1.6936,\n",
      " 1.8104,1.898 ,1.9418,2.0294,2.0878,2.19  ,2.2776,2.3944,2.5842,2.7156,\n",
      " 2.8616,3.0806,3.3872,3.5624,3.796 ,4.0296,4.2486,4.7158,5.0224,5.3728,\n",
      " 5.8984,6.57  ]\n",
      "xhi    = [0.5694,0.6424,0.7008,0.7446,0.7884,0.8176,0.8614,0.8906,0.949 ,0.9928,\n",
      " 1.0366,1.095 ,1.1388,1.1972,1.2848,1.4016,1.4746,1.606 ,1.6936,1.8104,\n",
      " 1.898 ,1.9418,2.0294,2.0878,2.19  ,2.2776,2.3944,2.5842,2.7156,2.8616,\n",
      " 3.0806,3.3872,3.5624,3.796 ,4.0296,4.2486,4.7158,5.0224,5.3728,5.8984,\n",
      " 6.57  ,9.8696]\n",
      "y      = [3.7716e-03,5.6355e-03,6.6602e-03,1.0656e-02,1.0656e-02,1.3201e-02,\n",
      " 1.0576e-02,1.3320e-02,6.4805e-03,1.1169e-02,8.8004e-03,6.6003e-03,\n",
      " 1.0064e-02,7.1043e-03,4.6563e-03,3.7142e-03,5.3282e-03,3.6696e-03,\n",
      " 4.4002e-03,3.4623e-03,4.4002e-03,9.4723e-03,4.9124e-03,6.6602e-03,\n",
      " 4.4643e-03,4.7362e-03,3.3301e-03,1.9756e-03,3.1574e-03,2.5922e-03,\n",
      " 1.7281e-03,1.2230e-03,2.3481e-03,1.7311e-03,1.5902e-03,1.6962e-03,\n",
      " 8.5059e-04,1.2847e-03,1.0601e-03,7.4942e-04,5.4269e-04,9.7735e-05]\n",
      "xlabel = Energy (keV)\n",
      "ylabel = Counts/sec/keV\n",
      "title  = data/pha/3c273.pi\n",
      "histo_prefs = {'xlog': True, 'ylog': True, 'label': None, 'xerrorbars': False, 'yerrorbars': True, 'color': None, 'linestyle': 'None', 'linewidth': None, 'marker': '.', 'alpha': None, 'markerfacecolor': None, 'markersize': None, 'ecolor': None, 'capsize': None}\n",
      "\n",
      "modelplot  = Model\n",
      "xlo    = [0.4672,0.5694,0.6424,0.7008,0.7446,0.7884,0.8176,0.8614,0.8906,0.949 ,\n",
      " 0.9928,1.0366,1.095 ,1.1388,1.1972,1.2848,1.4016,1.4746,1.606 ,1.6936,\n",
      " 1.8104,1.898 ,1.9418,2.0294,2.0878,2.19  ,2.2776,2.3944,2.5842,2.7156,\n",
      " 2.8616,3.0806,3.3872,3.5624,3.796 ,4.0296,4.2486,4.7158,5.0224,5.3728,\n",
      " 5.8984,6.57  ]\n",
      "xhi    = [0.5694,0.6424,0.7008,0.7446,0.7884,0.8176,0.8614,0.8906,0.949 ,0.9928,\n",
      " 1.0366,1.095 ,1.1388,1.1972,1.2848,1.4016,1.4746,1.606 ,1.6936,1.8104,\n",
      " 1.898 ,1.9418,2.0294,2.0878,2.19  ,2.2776,2.3944,2.5842,2.7156,2.8616,\n",
      " 3.0806,3.3872,3.5624,3.796 ,4.0296,4.2486,4.7158,5.0224,5.3728,5.8984,\n",
      " 6.57  ,9.8696]\n",
      "y      = [4.7222e-03,5.2629e-03,7.0078e-03,8.0314e-03,8.7168e-03,9.1869e-03,\n",
      " 9.4902e-03,9.6142e-03,9.5730e-03,9.3568e-03,8.9604e-03,8.3861e-03,\n",
      " 7.7104e-03,6.9874e-03,6.0234e-03,4.9688e-03,4.2929e-03,3.7973e-03,\n",
      " 3.6766e-03,4.1604e-03,4.5614e-03,5.0609e-03,5.2456e-03,4.2719e-03,\n",
      " 3.4851e-03,3.7978e-03,2.9962e-03,2.2218e-03,2.0302e-03,1.9139e-03,\n",
      " 1.7334e-03,1.6424e-03,1.5831e-03,1.5460e-03,1.5063e-03,1.4499e-03,\n",
      " 1.3333e-03,1.1510e-03,9.6025e-04,7.1008e-04,4.5858e-04,9.5696e-05]\n",
      "xlabel = Energy (keV)\n",
      "ylabel = Counts/sec/keV\n",
      "title  = Model\n",
      "histo_prefs = {'xlog': False, 'ylog': False, 'label': None, 'xerrorbars': False, 'yerrorbars': False, 'color': None, 'linestyle': 'solid', 'linewidth': None, 'marker': 'None', 'alpha': None, 'markerfacecolor': None, 'markersize': None, 'ecolor': None, 'capsize': None}\n"
     ]
    }
   ],
   "source": [
    "print(fplot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The objects have a `plot` method which will create a plot, so you can access the components separately:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fplot.dataplot.plot()"
   ]
  },
  {
   "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": [
    "fplot.modelplot.plot()  # note: this does not pick up the x/y scaling unlike the data plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or you can plot them together:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fplot.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want, you can access the data directly, such as the data points using the\n",
    "`x`, `y`, and `yerr` fields (here I display the first five values and you can see\n",
    "they match the values used in the plot):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.46720001101493835,\n",
       "  0.5694000124931335,\n",
       "  0.0037716153818110744,\n",
       "  0.0012631612592398642),\n",
       " (0.5694000124931335,\n",
       "  0.6424000263214111,\n",
       "  0.005635473017161331,\n",
       "  0.0018124040722667545),\n",
       " (0.6424000263214111,\n",
       "  0.7008000016212463,\n",
       "  0.006660236700166901,\n",
       "  0.002208979162615874),\n",
       " (0.7008000016212463,\n",
       "  0.7445999979972839,\n",
       "  0.010656375094869093,\n",
       "  0.003159064457840299),\n",
       " (0.7445999979972839,\n",
       "  0.7883999943733215,\n",
       "  0.010656375094869093,\n",
       "  0.003159064457840299)]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(fplot.dataplot.xlo, fplot.dataplot.xhi, fplot.dataplot.y, fplot.dataplot.yerr))[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also get at the model data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.46720001101493835, 0.5694000124931335, 0.004722182522753494),\n",
       " (0.5694000124931335, 0.6424000263214111, 0.005262864199880122),\n",
       " (0.6424000263214111, 0.7008000016212463, 0.007007819942777563),\n",
       " (0.7008000016212463, 0.7445999979972839, 0.008031440560937872),\n",
       " (0.7445999979972839, 0.7883999943733215, 0.008716793887550481)]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(fplot.modelplot.xlo, fplot.modelplot.xhi, fplot.modelplot.y))[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, if you just want access to the plotted data, then the following routines are useful\n",
    "(I would have used `ui.get_*_plot?` but I'm not sure the output would be saved to the notebook\n",
    "so ended up with the following):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['get_arf_plot',\n",
       " 'get_bkg_chisqr_plot',\n",
       " 'get_bkg_delchi_plot',\n",
       " 'get_bkg_fit_plot',\n",
       " 'get_bkg_model_plot',\n",
       " 'get_bkg_plot',\n",
       " 'get_bkg_ratio_plot',\n",
       " 'get_bkg_resid_plot',\n",
       " 'get_bkg_source_plot',\n",
       " 'get_cdf_plot',\n",
       " 'get_chisqr_plot',\n",
       " 'get_data_plot',\n",
       " 'get_delchi_plot',\n",
       " 'get_fit_plot',\n",
       " 'get_kernel_plot',\n",
       " 'get_model_component_plot',\n",
       " 'get_model_plot',\n",
       " 'get_order_plot',\n",
       " 'get_pdf_plot',\n",
       " 'get_psf_plot',\n",
       " 'get_pvalue_plot',\n",
       " 'get_ratio_plot',\n",
       " 'get_resid_plot',\n",
       " 'get_rmf_plot',\n",
       " 'get_scatter_plot',\n",
       " 'get_source_component_plot',\n",
       " 'get_source_plot',\n",
       " 'get_split_plot',\n",
       " 'get_trace_plot']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[n for n in dir(ui) if n.startswith('get_') and n.endswith('_plot')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The source model\n",
    "\n",
    "Before Sherpa 4.12.2 the `plot_model` and `plot_fit` commands drew the model as a line plot, rather than a histogram, but they now use a histogram. This means they now match the behavior of the `plot_source` command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CgoLE6tWrxYEDB8SmTZvErFmzxODBg1947vjf5PCylHVX3bBhw8T27dvFypUrRaNGjZTuqis+h/79+4utW7eWehdaYWGhCA4OFnZ2dmL69Onir7/+Ert37xYxMTEiJCREbNy4Ud62PHfVqfJvperk8PKcT/FdioMHDxa7du0Sa9asEd7e3qJx48ZKn2VISIiQSqXit99+EydOnJD/vC1fvlz+M7Rnzx4RExMjGjZsKN9Hsf79+4s+ffooxfrsXXWhoaFix44dYsmSJcLBwUE4OzuLe/fuKXymJa3j9Pz6VOrG9OWXX4oxY8aIX3/9Vezfv19s3rxZjB07VhgbG4vXXnutxM+6rJ//999/XwAQo0aNkt+V+Pzdic9r2rSpcHFxKfXGifLEOH36dGFsbCz2798vL3v+rrpdu3aJ+fPnCysrK9GqVSuRl5en8LlzcrhhYOJE1cratWvFW2+9JRo3biysrKyEqampcHV1FcOGDZPfnl9MJpOJb7/9Vrz88svC1NRU2Nvbi6FDhyrdwl5UVCTmzJkjGjRoIMzNzYWPj4/Yu3dvqXfVlZQ4CSHE9u3bRefOnUWNGjWEpaWl8PDwEN9++61Cm7Nnz4ohQ4YIBwcHYWpqKurUqSO6du0qFi5c+MJzr2jiJMTTC1vLli2FmZmZsLW1Ff379xcXL15U2n7p0qWicePGwszMTLz88sti+fLlJS4YWVBQIObOnStatWolzM3NhZWVlWjatKkYM2aM+Oeff+TtVL3YqPpvVZ7EqTzns3z5ctGkSRMhlUpFgwYNREREhFi2bJnSZ5mYmCiCgoKEtbW1/IJbbPbs2cLNzU1IpVLRrFkzsWTJEnm8QgiRnZ0tzM3NS1x6ojhx2rVrlxg2bJioWbOm/G7IZz9PIVRPnNSNacuWLaJ79+7C0dFRmJiYCCsrK+Hn5yd++umnEhd9fdHPf/369eV3vj3/Kukux+I7Cr/88kuluorEWHze+/btUyqLj48Xffv2FVZWVsLa2lq8+eabSgt2MnEyHBIhyugbJSIinbBu3Tq8/fbbuHPnDuzs7BTqYmJiMHLkSMTFxcHHx0cnYqoOpk2bhunTp+Pu3bsVmktH+olznIiI9MCQIUNQUFCgUwmKLsZEVNmYOBERERGpiEN1RERERCpijxMRERGRipg4EREREamIiRMRERGRivjIFQ0rKipCSkoKrK2tVX5gJREREWmXEAKPHj2Ck5MTjIxK71di4qRhKSkpZT6pm4iIiHRXcnJymQ+dZuKkYcXPC0tOToaNjY2WoyEiIiJVZGVlwcXF5YXP/WTipGHFw3M2NjZMnIiIiPTMi6bZcHI4ERERkYqYOBERERGpiIkTERERkYqYOBERERGpiIkTERERkYqYOD3j0aNH8PX1RevWrdGiRQssWbJE2yERERGRDuFyBM+wtLTEgQMHYGlpicePH8PT0xODBg1CrVq1tB0aERER6QD2OD3D2NgYlpaWAIDc3FzIZDIIIbQcFREREekKvUqcDh48iL59+8LJyQkSiQSbN29WahMVFQV3d3eYm5vD29sbhw4dKtcxHj58iFatWqFevXqYNGkS7O3tNRQ9ERER6Tu9SpxycnLQqlUr/PzzzyXWr127FuPHj8fUqVNx+vRpdOzYEcHBwUhKSpK38fb2hqenp9IrJSUFAFCzZk2cPXsWN27cwOrVq3Hnzp0yY8rLy0NWVpbCi4iIiAyTROjpWJREIsGmTZswYMAAeVnbtm3Rpk0bLFiwQF7WrFkzDBgwABEREeU+xnvvvYeuXbvitddeK7XNtGnTMH36dKXyzMxMPnKFiIhIT2RlZcHW1vaF12+96nEqS35+PuLj4xEUFKRQHhQUhKNHj6q0jzt37sh7jLKysnDw4EE0adKkzG2mTJmCzMxM+Ss5ObliJ0BEREQ6z2DuqsvIyIBMJoOjo6NCuaOjI9LS0lTax61btxAaGgohBIQQeP/999GyZcsyt5FKpZBKpYiMjERkZCRkMlmFz4F0X1GRwIytCbiekSMve5xXiLDARjA2ksDa3AStXWpCIpHgTlYusp4UIF9WhP1X7kIiAR4+LkBCShYcrKXA/54jKYEEEvn/AxIJcD8nH23da+FJgQxN6lijpoUp6teqgTq25lV/0kREJGcwiVOx559qLIR44ZOOi3l7e+PMmTMVOm5YWBjCwsLkXX1kOOJvPsDig/+iQCZwKTULqZm5Sm1GxsQpvLc0M8bjfPWS6N2X0kutMzWWoKdnXbSqZ4sRAW4wkkhgZKTazzkREVWcwSRO9vb2MDY2VupdSk9PV+qFIiqLEALHb9zHnaynCdJHv50psd28Ia2w8dRt3H2UB2MjCRJS/7sx4Nmk6SVLUzwpkMHc1BhBHo7IyZPBxsIUbrWeLn0hAAgBCAgIARz65y6cbC3wb0YOrt/NhoO1FP/ezVE4doFM4M+zKfjzbAq+2XYJADDcvz583OzQqbE9alqaafATISKiYgaTOJmZmcHb2xuxsbEYOHCgvDw2Nhb9+/ev9ONzqM5wnEl+iDcW/61U7uduh8Ft6gESoH0jezjXtMCgNvXk9Tl5hdiVkCZPmqQmxghq7ggbc9NyHT8ssJFSmRACaVm5KCgUOHA1HQWyp0OGz1px7CZWHLsJAKhhZoycfBk+6NoIDtZSDPByhtTEGGYmBjOtkYhIK/Tqrrrs7Gxcu3YNAODl5YV58+YhMDAQdnZ2cHV1xdq1azFs2DAsXLgQ/v7+WLx4MZYsWYKLFy+ifv36VRKjqrPySbdsOn0LJxMfAACSHzzBwat3YS01QUuXp8OutWpIMa1fc9jV0J2eHCEEMp8U4MLtLCw6+C+u383B7YdPytymrbsdWtazxZt+rnC0MUcNqcH87UREpBZVr996lTjt378fgYGBSuUhISGIiYkB8HQBzDlz5iA1NRWenp6YP38+OnXqVGUxMnHSP9l5hWg5bSeKnvsmBDapjeiRftoJqoIynxTg4eN8/HUhDakPn+CX//VAlcbRRooBrZ3xhp8rnGqaQ2piXEWREhHpFoNMnHTZs0N1V69eZeKk467eeSTvYXqcXyifJ/RRt8YwkkhgbAT0aekEN/sa2gxTI3ILZLj14DG2nE3FX+dT8U96dqltGztYYbB3PbzZ1rXcQ4xERPqMiZOWsMdJP/h8E4uM7HyFMlNjCa58HWzwd6cVFQk8fFKA3+KSsPPiHZxNflhq27fauqLLy7XxioejynenEhHpIyZOWsLEST+4T9kGIYDOL9eG9H8Tprs0ccBbbV21HFnVyy8sQvqjXKz8+yZWH0/Co9zCEtv1aVkX//emFxMoIjJITJyqGIfqdNvauCTE/W9oDgDWx98CAMRN7Y7a1lJthaWTHuTk43TyA2w8dRtbz6Uq1J38vDvsrfh5EZHhYeKkJexx0j3ZeYVoMW0nnv9JNzM2wqkvX4EV7ywrVaGsCFfuPELvnw4DAFrVs8VbbV3xum/165kjIsOm6vWbVwwyeAWFRfKkaVLPJjD631BTS2dbJk0vYGJshOZOtqhra47UzFycvZWJeznXmDgRUbXFqwYZJFmRQGFREQAgX1YkLx/bqaHBT/6uDJvGtcefZ1Mwc/slpD/KwxuLj6FLEwd0aVIbTeuwZ5WIqg8mThrClcN1x817ORgYdRT3c/Jf3JhUUsfWHL1a1sXM7ZeQX1iEv6/fx9/X72P2X5cBAPVrWSL8lZfR+eXafNwLERk0znHSMM5x0r4/z6bggzWnlcrbNbDDmnfa8a4wNZxMvI+DV+9iTVwy7j7KK7XdiAA3DGrjjJb1alZdcEREauDkcC1h4qR9xYmTr9tLWD7CV15uJTVh0qRB+YVFOJX0AL8cTURc4gNkZJecSL3saIWQADf0b+3MOWVEpLM4OZyqPWMjCay5+nWlMTMxQrsGtdCuQS0AwN1HeTh87S5iE+5g+/k0eburd7IxddMFTN10AQAwuoM7RnZwh3NNC63ETUSkDiZOGsI5Ttpz814Odl28A9n/Ok8vpWZpOaLqqba1FAO96mGgVz0UyopwIyMH60/dwqID1xXaLT18A0sP3wDwdFHN97s2QhNHa/YGEpFe4FCdhnGoruq9vfRvHLl2T6m8S5PaiNGzh/Qaqsf5hdh46jZmbruEJwUl/3HRsbE9wl95Ga1dajKJIqIqx6E6qjYynxQAADo0skcdW3MAgImRpFo+PkVXWZqZYGi7+hjarj4KZUWITbiDWX9dQvL9J/I2h/7JwKF/MgA8/bf8pEcTtHKpqaWIiYhKxsSJDMboju7o0sRB22HQC5gYGyG4RV0Et6iLQtnTpQ1mbb+EhGeGWA9fy8Dha0+TqC/6eODttq4wNzXWVshERHJG2g6AiKovE2MjdGhsj+0fdcS1mcFY/U5b+P9vsnmxr7cmoOkXO/D9riul3rlHRFRV2ONERDrBxNgIAQ3tEdDQHgWyImyIv4XJG8/L6/9v7zX8395rMDWW4IOujRHi7wZbS941SURVi4mThvCuuqqR8vAJvt1xGVn/m9cEAIkZj7UYEVUGU2MjvOHnitd9XbA+/hY+WX9OXlcgE5gXexXzYq/CSmqCb19tiZ6edWDMR+kQURXgXXUaxrvqKtfCA//KH/PxvK0fdICns20VR0RV5VFuAdacSMKPu/9BTr7yHyj1XrLA57090L2ZA0yMOQuBiMqHd9WRQSr83wN7/dztMNi7nrzcuaYFmjsxUTVk1uameLdTQ7zbqSHuZOViwf5/EXM0UV5/68ETjF0VD+DpXXlf9PFAkzrWWoqWiAwVEyfSSw1r18AQHxdth0Fa4mhjjmn9mmNav+a4eS8HCw9cx5oTSfL6w9cy0OOHgwCAD7o2wntdGsLSjL/uiEh97M8mIr1Wv1YNRAxqgcTZvbH1gw5oVU9xuPb/9l6Dx5c70T/yCNIyc7UUJREZCv4JRkQGw9PZFn+83wEFsiJsOn0bn244h+JZnGeTH6JdxB68ZGmKWQNbIKg5J5QTUfkxcSIig2NqbIQhPi4Y4uOC5PuPMe7XUzh/OxMA8OBxAd779RSAp3OhvhngCTf7GtoMl4j0CBMn0lkFsiJsP5+K+zn58rL4mw+0GBHpIxc7S/z5QQekZ+Xihz3/YPVxxblQXebuBwB82LUR3unUANbmXBuKiErH5Qg05Nl1nK5evcrlCDRg+/lUjPtfz8DzRgS4YVq/5lUcERmKhJQsTN54DuduZSrV2VuZ4fshrdGxkT2MOJRHVG2ouhwBEycN4zpOmvPr8ZuYuukC6tiYw9fdTl5uYWqEsZ0bokFtKy1GR4Ygv7AIG0/dwmebzqOohN+EPZo74uv+nnCwMa/64IioSnEdJzIYrVxs8X9vemk7DDJAZiZPVyh/w88V97LzMPuvy/g9/pa8fufFO9h58Q4A4Ms+HhjuX5+LaxJVc/wNQEQEoJaVFN+91go3Inphc1h7uNpZKtTP2JqARlP/Qs8fDiIhJUtLURKRtrHHiYjoGRKJBK1dauLgpEDkFsiw7PANfLfzirz+ctoj9PrpEABgQveXMbZLA0hNjLUVLhFVMfY4ERGVwtzUGGGBjZA4uzdiJ3RCs7qK8x7m776KJp/vQNe5+3HhtvJEcyIyPOxxIiJSQWNHa/z1UUfIigRWHkvEtD8T5HXXM3LQ5/8OAwDGdm6I8d0bw9yUvVBEhog9TkRE5WBsJMGI9u5InN0bJz7rhvaNainULzzwL5p+sQMBEXvYC0VkgNjjRDpBCIFr6dl4UiCTl9168ESLERG9mIONOX4d3Q5FRQIbT9/GxN/PyutSMnMVeqHCX3kZZib8W5VI3zFxKsHjx4/RrFkzvPbaa5g7d662w6kWlh9JxNdbE0qsk4CLEJJuMzKSYLB3PQz2rof0R7mYsPYMjly7J69feOBfLDzwL+rXssSCt73h4cQ13oj0FROnEsycORNt27bVdhjVyvW72QAAa6kJrM3/+7E0MzHCwDbO2gqLqNwcrP/rhVoffwuTNpyT192891h+R96U4KYY3bEBHzRMpGeYOD3nn3/+weXLl9G3b19cuHBB2+FUO6Ed3TG++8vaDoNIbUZGEgzxdcEQXxekP8rFsKUncOXOI3l9xF+XEfHXZTRysMKad9qhtrVUi9ESkar0asD94MGD6Nu3L5ycnCCRSLB582alNlFRUXB3d4e5uTm8vb1x6NChch1j4sSJiIiI0FDERERPe6F2TuiE67N64Ys+Hgp119Kz4TtzN9wmb8O+y+laipCIVKVXiVNOTg5atWqFn3/+ucT6tWvXYvz48Zg6dSpOnz6Njh07Ijg4GElJ/z0N3dvbG56enkqvlJQU/PHHH3j55Zfx8svs8SAizTMykiC0w9M78nZN6IT6tRRXJx8ZEwe3ydvw0W+nkfvMjRJEpDv09iG/EokEmzZtwoABA+Rlbdu2RZs2bbBgwQJ5WbNmzTBgwACVepGmTJmCVatWwdjYGNnZ2SgoKMDHH3+ML7/8stRt8vLykJeXJ3+flZUFFxcXPuS3nKZuOo9fjydhfPfGHKqjaiUnrxBf/HEBG0/dVqozkgBb3u8AT2dbLURGVL2o+pBfvepxKkt+fj7i4+MRFBSkUB4UFISjR4+qtI+IiAgkJycjMTERc+fOxTvvvFNm0lS8ja2trfzl4uJS4XMgouqnhtQE84a0RuLs3lg+wkehrkgAff7vMNwmb8MPu6+iqEgv/84lMigGkzhlZGRAJpPB0dFRodzR0RFpaWmVdtwpU6YgMzNT/kpOTq60YxGRYeva1PHpwppTu6Gtu51C3Q+7/0GDz7aj69z9uPXgsZYiJCKDu6tOIlG8tVcIoVSmihEjRqjUTiqVQiqVIjIyEpGRkZDJOC+BiNTjYG2OtWP8IYTA6hNJmLrpvzt8r2fkoMO3+wAA84a0wqA29bQVJlG1ZDA9Tvb29jA2NlbqXUpPT1fqhaoMYWFhSEhIQFxcXKUfi4iqB4lEgrfb1kfi7N7Y83Fn2Jgr/q0bvu4s3CZvw+hf4pCTV6ilKImqF4NJnMzMzODt7Y3Y2FiF8tjYWAQEBFT68SMjI+Hh4QFfX99KPxYRVT8Na1vh3LQeuDYzGG/6uSrU7b6UjuZf7YTb5G18Ph5RJdOrobrs7Gxcu3ZN/v7GjRs4c+YM7Ozs4OrqivDwcAwbNgw+Pj7w9/fH4sWLkZSUhLFjx1Z6bGFhYQgLC5PPyqfSnbhxH2eTHyqUJaRmaScYIj1jYmyEiEEtEDGoBY7+m4G3lhxXqC9+Pt7EoJcxrksjGHFlciKN0qvlCPbv34/AwECl8pCQEMTExAB4ugDmnDlzkJqaCk9PT8yfPx+dOnWqshhVvZ2xusotkKHV9F3IKywqsf7Tnk3xXpeGVRwVkX7Lyi3AsKXHcfaWcm9TYwcrrB3jD7saZlqIjEh/qHr91qvESZc9Ozn86tWrTJxK8fBxPlrPeDqc2r+1E4yembhvbW6C97s2goO1ubbCI9JrQogyH5i9LMQH3ZpV/pxPIn3ExElL2ONUtmcTp39n9eIDTokqyeW0LPT8oeRHTr3p54pvBnjy+0f0DFWv33o1x4mIiFTTtI4NEmf3Rn5hET5Ycwo7L96R1605kYQ1J5JQw8wYOyd0Qr2XLMvYExE9i4mThnAdJyLSRWYmRlg07OmK5BtP3UL4urPyupx8mXxNqIVD26CnZ12txEikTzhUp2Ecqisbh+qItO/2wycImncAOfnKf+i97uOCWYNa8LtJ1Q6H6oiIqETONS1wcUZPFMqKMHnjeayPvyWvW3syGWtPJqOGmTH2TuwCRxverEH0LINZAJOIiMrHxNgIc19rhcTZvfHd4JYKdTn5MrSdtQduk7fhz7MpWoqQSPcwcdIQrhxORPrsNR8XJM7ujUOTAmHy3DDdB2tOw23yNry74iQKZSWvwUZUXXCOk4ZxjlPZOMeJSD8UyIowYe0ZbD2XWmL9kcld4VzTooqjIqo8ql6/2eNERERKTI2N8PNbbZA4uzei3m6jVN9+9l64Td6GDc/MjyKqDpg4ERFRmXq1qIvE2b3x95RukDzXSfzx72fhNnkbQpafQAGH8agaYOKkIZzjRESGro6tOW5E9Ma/s3phQGsnhboDV++i8dS/4DZ5G5LuPdZShESVj3OcNIxznMrGOU5EhmXHhVSMXXWqxLo5g1tiiI9LFUdEVDF8Vp2WMHH6z44Lqdh0+jae/QkrkBVh35W7AJg4ERmS9Ee58Ju5p8S6jo3tET3CFybGHOQg3cXESUuYOP2n05x9SLpfcpd9TUtTnPr8FRgxcSIyKEVFAh+tPVPq2k+HJgXCxY7PxiPdw8RJS5g4/cc/Yg9SM3MRFtgQTs/dtuxd/yU0rVO9Px8iQ1fWMN4Pr7fGAC/nKo6IqHRMnLSEidN/ihOnrR90gKezrbbDISItScvMRbuIkofxujdzxOJh3ux9Jq3jOk5VjHfVERGVrI6tORJn98b1Wb3QramDQt3uS3fQ4LPtcJu8DemPcrUUIZHq2OOkYexx+g97nIioNOvikjFpw7kS6xa83QbBLepWcURU3al6/TapwpiIiIgAAEN8XTDE1wXJ9x+j45x9CnXv/fp0XlSvFnUQ+VYbSJ5fdZNIizhUR0REWuNiZ4nE2b1xbWYwWrvUVKjbfj4N7lOeDuPdz8nXToBEz2GPExERaZ2JsRE2h7UHAKw4logv/7ioUN/m66cL50aP9EVgEwel7YmqChMnIiLSKcP93TDc3w3X0h+h+7yDCnUjo+MAAIO962Hua620ER5Vc0yciIhIJzVysEbi7N4okBUhcO5+3HrwRF63Pv4W1sffAgBcmN4DVlJezqhqcI4TERHpNFNjIxz+tCsSZ/dG+CsvK9V7frUTbpO34fj1e1qIjqobJk5ERKQ3PuzWGImze+PP9zso1b2++G+4Td6GGX8maCEyqi6YOGkIF8AkIqo6LerZInF2b1ya0RNSE8VL2fIjN+A2eRtenvoXcgtkWoqQDBUXwNQwLoD5Hy6ASURV6YvNF7Dy75sl1m37sAOaO/H3EJWu0h+5cubMmYpuSkREpHFfD/BE4uzeWP1OW6W63j8dhtvkbfhpzz9aiIwMSYUTpzZt2sDb2xsLFixAZmamJmMiIiKqsICG9kic3RvnpgUp1c2LvQq3ydvgO3M3ZEUccKHyq3DidOTIEbRp0waTJ09G3bp1MXToUOzbt+/FGxIREVUBG3NTJM7ujcTZvdG9meKimXcf5aHh/x4ufPNejpYiJH1U4cTJ398fS5YsQVpaGhYsWIBbt26he/fuaNiwIWbOnIlbt25pMk4iIqIKWxrii8TZvRH1dhulus7f7Yfb5G1YeSyx6gMjvaPRyeH//vsvoqOjsWLFCqSmpuKVV17B9u3bNbV7vVAdJ4dvPn0b8TcfKJVvOHULj/NlnBxORDonPSsXfrP2lFjXsp4t/ghrz4cLVzOqXr81fldddnY2fv31V3z22Wd4+PAhZLLqdStodUucsvMK0XLaTpQ1VeDAJ11Qv1aNqguKiEhFQgj0+OEgrt7JVqrr/HJt/PSGF2wtTbUQGVU1Va/fGluj/sCBA1i+fDk2bNgAY2NjDBkyBKGhoZraPemo/MIiedL0YbfGeP7vs4YOVkyaiEhnSSQS7JrQGQCw/PANzNj63+KZB67eRasZu/CSpSmiR/qhtUtNLUVJukStHqfk5GTExMQgJiYGN27cQEBAAEJDQzFkyBDUqKG/F0sTExN4enoCAHx8fLB06VKVt61uPU73c/LlTy2/EdGLXdtEpPfuZOXi3ZXxOJv8UKluWl8PhAS48XedAar0HqdXXnkF+/btQ+3atTF8+HCMGjUKTZo0qejudErNmjW5ThURUTXlaGOOP8Lao0BWhJnbLiHmaKK8btqfCZj2ZwKCPetg3pDWsDAz1l6gpBUVTpwsLCywYcMG9OnTB8bG/MEhIiLDYmpshGn9mmNav+bYdi4VYatPyev+upCGvy7sQG1rKda80w6NHKy0GClVpQovR7Blyxb0798fxsbGOHToEIYOHQp/f3/cvn0bALBy5UocPnxYY4EWO3jwIPr27QsnJydIJBJs3rxZqU1UVBTc3d1hbm4Ob29vHDp0qFzHyMrKgre3Nzp06IADBw5oKHIiItJXvVvWReLs3tg/sQucbM3l5Xcf5aH7vANwm7wNG09xGZ7qQO2H/G7YsAE9evSAhYUFTp8+jby8PADAo0ePMGvWLLUDfF5OTg5atWqFn3/+ucT6tWvXYvz48Zg6dSpOnz6Njh07Ijg4GElJSfI23t7e8PT0VHqlpKQAABITExEfH4+FCxdi+PDhyMrKKjWevLw8ZGVlKbyIiMgwudnXwNEp3XD5657o3bKuQl34urNwm7wNk9afRYGsSEsRUmVTezkCLy8vTJgwAcOHD4e1tTXOnj2LBg0a4MyZM+jZsyfS0tI0FasSiUSCTZs2YcCAAfKytm3bok2bNliwYIG8rFmzZhgwYAAiIiLKfYzg4GB8/fXX8PHxKbF+2rRpmD59ulI5J4cTEVUPq/6+ic83X1Aqb2BfA6tGt4VTTQstREXlVekP+S125coVdOrUSancxsYGDx8+VHf35ZKfn4/4+HgEBSk+nygoKAhHjx5VaR8PHjyQ95rdunULCQkJaNCgQantp0yZgszMTPkrOTm54idARER6Z2i7+kic3Rtb3m8Pa+l/U4evZ+QgYPZeuE3ehtiEO1qMkDRJ7XWc6tati2vXrsHNzU2h/PDhw2UmHJUhIyMDMpkMjo6OCuWOjo4q93xdunQJY8aMgZGRESQSCX788UfY2dmV2l4qlUIqlSIyMhKRkZHVbsFPIiJ6qmW9mjg/vQcynxTg3RUncfzGfXndOytOPv1vR3d81qsZe+f1mNqJ05gxY/DRRx9h+fLlkEgkSElJwbFjxzBx4kR8+eWXmoix3J7/gRRCqPxDGhAQgPPnz5f7mGFhYQgLC5N39RERUfVka2GKtWP8IYTA/Nir+GnvNXndkkM3sOTQDbSqZ4uYkX54qYaZFiOlilA7cZo0aRIyMzMRGBiI3NxcdOrUCVKpFBMnTsT777+viRhVZm9vD2NjY6XepfT0dKVeKCIiosokkUgQHtQE4UFNcPifDAxddlxed/ZWJrz+Nz90/Vh/+LiVPrJBukXtOU4AMHPmTGRkZODEiRP4+++/cffuXXz99dea2HW5mJmZwdvbG7GxsQrlsbGxCAgIqNRjR0ZGwsPDA76+vpV6HCIi0j8dGtsjcXZvnPisGxrWVnyyxuCFx+A2eRsWHvgXGn58LFUCtROnVatWAQAsLS3h4+MDPz8/WFk9XQjsk08+UXf3SrKzs3HmzBn5yt43btzAmTNn5MsNhIeHY+nSpVi+fDkuXbqECRMmICkpCWPHjtV4LM8KCwtDQkIC4uLiKvU4RESkvxxszLHn4y74d1YvvOnnolA3+6/LcJ+yHW8t+RtP8jlfVlepvRxBzZo1sWrVKvTp00ehfMKECfjtt9+QmpqqVoDP279/PwIDA5XKQ0JCEBMTA+DpAphz5sxBamoqPD09MX/+/BLv/NOkZyeHX716lcsREBGRSracTcGHa04rlZsZG2HLB+3RtI7hX0t0garLEaidOO3YsQNvvPEGtmzZIk9OPvjgA2zcuBF79uxB06ZN1dm93uFDfpk4ERFVxLX0bAyMOoJHuYVKdd++2gKv+7pqIarqo8rWcerZsycWLlyIAQMG4OTJkxg3bhw2btyIffv2VbukiYiIqKIaOVjh/LQeuPx1T3RpUluh7tMN5+E2eRvG/RrPVcm1TO276gDgjTfewIMHD9ChQwfUrl0bBw4cQKNGjTSxa73BdZyIiEgTzE2NETPSDwCw9NB1fLPtkrxu+/k0bD//F16yNMWfH3RAvZcstRVmtVWhobrw8PASy9evXw8vLy80bNhQXjZv3ryKR6eHOFTHoToiIk07nfQAA6NKfgLGwqHe6OlZp4ojMjyqXr8r1ON0+rTyJDYAaNiwIbKysuT1vIgSERGpz8v1JSTO7o3MxwV4ffExXE57JK8buyoeADCsXX3M6N+c195KVqHEad++fZqOg4iIiF7A1tIUO8Z3ghAC32y7hGWHb8jrVv59Eyv/von6tSyxeVx7rkpeSSo8Ofyzzz7DiRMnNBmLXuMCmEREVFUkEgm+6OOBxNm9sSzER6Hu5r3H8Po6Fm6Tt+H49XtaitBwVXg5gpEjR2Lbtm0wNjZG37590b9/f3Tv3h1SqVTTMeoVznFiFzERkTakPHyC4B8PIfNJgVLd+O6N8VG3xvwdXYZKX44gOjoad+7cwbp161CzZk18/PHHsLe3x6BBgxATE4OMjIyK7pqIiIjKyammBc5+FYRrM4PRs7niZPEfdv8D9ynb0e/nw8gt4N3f6lB7AcxnXbp0CX/++Sf++OMPnDx5Em3btkW/fv3w5ptvwtnZWVOH0WmG2uMkKxK4cDtTaf2QrNwCjIo5CYA9TkREumbNiSRM2Xi+xLod4ztyVfJnVNnK4aW5e/cutmzZgi1btqBjx46YOHFiZRxG5xhq4vT11gSFSYglYeJERKSbLqdloecPh0qs+3qAJ4a1q1/FEemeSkucnjx5gvv37yv1IF28eBHNmzevWLQGwNCfVTf6lzjsvpQOeyszWJubKtV3b+aAqb09tBAZERGp6km+DIMXHsXFlCylusAmtbE0xBfGRtXzD+BKSZzWr1+PCRMmwM7ODkIILFmyBG3btgUAtGnTBqdOnVI/cj1nqD1OxYkTn5dERGQYvt91Bf+395pSuZmxEQ5OCkQdW3MtRKU9lTI5/JtvvsGpU6dw9uxZLF++HKNGjcLq1asBAJU04kdERESV4OOgJkic3Rur32mrUJ4vK0K7iD1wm7wNuxPuaCk63VWuBTALCgpQu/bTBw/6+Pjg4MGDGDRoEK5du8a5LURERHoooKE9Emf3xt1Heejy3T7k5P93193oFU9v/vmsV1O807EBr/UoZ4+Tg4MDzp07J39fq1YtxMbG4tKlSwrlREREpF9qW0txcUZPXJ/VC8HPPftu1vbLcJ+yHaExccjOK9RShLqhXInTypUr4eDgoFBmZmaGNWvW4MCBAxoNjIiIiKqekZEEC4Z6I3F2b0SPUHwaxp7L6fD8aid8vtmNy2nKE8yrg3IlTvXq1UOdOiU/gbl9+/YaCUhf8ZErRERkaAKbOiBxdm8cmdwVLztaycszsvPQ84dDcJu8DevikrUYYdXTyDpOubm5OHfuHNLT01FUpLhAYr9+/dTdvV7hXXVERGSo8guL8MXmC1h7UjlZGuxdD7MGtoCZSYUfSqJVql6/yzU5vCQ7duzA8OHDS3zEikQigUzGpd2JiIgMgZmJEb4d3BLfDm6JdSeTMWn9f/Ob18ffwvr4W6hfyxJr3/U32OUM1E4L33//fbz22mtITU1FUVGRwotJExERkWEa4uOCxNm9sWN8R7xk+d/CyDfvPUa7iD2YtP4sLqUa3jwotXuc0tPTER4eDkdHR03EQ0RERHqkaR0bnP4yCDl5hZj4+1lcSXuE6xk5WHfyFtadvIV2Dewwqr07ujdzhJEBrEquduI0ePBg7N+/Hw0bNtREPERERKSHakhNsGCoN4QQOHwtA8sP38C+K3fx9/X7+Pv6fbjYWSDE3w2v+7qU+OgufaF24vTzzz/jtddew6FDh9CiRQuYmip+GB9++KG6hyAiIiI9IZFI0LFxbXRsXBvX72Yj5mgifj95C8n3n+CbbZcwP/YqXvNxwcj2bqhfq4a2wy03tROn1atXY+fOnbCwsMD+/fsVVhWVSCRMnIiIiKqpBrWtMKO/Jyb2aIJ1ccmIPpKI2w+fIOZoImKOJqJbUweEdnCHf8NaerMqudqJ0+eff44ZM2Zg8uTJMDLSz1sQNSEyMhKRkZGcEE9ERPQcG3NTjO7YACPbuyM24Q6ij9zA8Rv3sedyOvZcTsfLjlYY1d4dA7ycYW5qrO1wy6T2Ok52dnaIi4vjHKf/4TpOREREL3YxJRPLDydiy9nbKJA9TUVqWpri7bauGO7vBkebql3OQNXrt9pdRCEhIVi7dq26uyEiIqJqpLmTLb4f0grHpnTDR90aw97KDA8fFyBy378ImL0XH645jbPJD7UdphK1h+pkMhnmzJmDnTt3omXLlkqTw+fNm6fuIYiIiMhA2VtJMeGVlxEW2AhbzqYg5ugNXLidhS1nU7DlbAq8XGtiZHt39PKsAxNj7U8JUjtxOn/+PLy8vAAAFy5cUKjTl4leREREpF1mJkYY7F0Pg73rIS7xPpYfvoEdF9NwOukhTiedxkwbKYb7u+ENXxfUspJqLU61E6d9+/ZpIg4iIiIiAICvmx183exw68FjrDh2E2tOJOFOVh6+23kF3++6gv0TA+Fay1IrsamdOBERERFVhnovWeKzXs0wvntjbIi/hdUnkpH1pAAudhZai0ntxCkiIgKOjo4YNWqUQvny5ctx9+5dfPrpp+oegoiIiKoxSzMTDPN3wzB/NzzKLdDqVCC1Z1ktWrQITZs2VSpv3rw5Fi5cqO7uiYiIiOS0/bgWtROntLQ01K1bV6m8du3aSE1NVXf3Ve7GjRsIDAyEh4cHWrRogZycHG2HRERERDpC7cTJxcUFR44cUSo/cuQInJyc1N19lRsxYgRmzJiBhIQEHDhwAFKp9mbuExERkW5Re47T6NGjMX78eBQUFKBr164AgD179mDSpEn4+OOP1Q6wKl28eBGmpqbo2LEjgKerohMREREVU7vHadKkSQgNDcW4cePQoEEDNGjQAB988AE+/PBDTJkyRRMxyh08eBB9+/aFk5MTJBIJNm/erNQmKioK7u7uMDc3h7e3Nw4dOqTy/v/55x9YWVmhX79+aNOmDWbNmqXB6ImIiEjfVbjH6bPPPsOAAQPg5+eHb7/9Fl988QUuXboECwsLNG7cuFKGuHJyctCqVSuMHDkSr776qlL92rVrMX78eERFRaF9+/ZYtGgRgoODkZCQAFfXp89X8/b2Rl5entK2u3btQkFBAQ4dOoQzZ87AwcEBPXv2hK+vL1555ZVSY8rLy1PYX1ZWlgbOlIiIiHRRhROn1NRU9OnTB8bGxujbty8GDBiAbt26VeqcoODgYAQHB5daP2/ePISGhmL06NEAgB9++AE7d+7EggULEBERAQCIj48vdft69erB19cXLi4uAIBevXrhzJkzZSZOERERmD59ekVOh4iIiPRMhYfqoqOjcefOHaxbtw41a9ZEeHg47O3tMWjQIMTExCAjI0OTcb5Qfn4+4uPjERQUpFAeFBSEo0ePqrQPX19f3LlzBw8ePEBRUREOHjyIZs2albnNlClTkJmZKX8lJydX+ByIiIhIt6k1x0kikaBjx46YM2cOLl++jBMnTqBdu3ZYsmQJnJ2d0alTJ8ydOxe3b9/WVLylysjIgEwmg6Ojo0K5o6Mj0tLSVNqHiYkJZs2ahU6dOqFly5Zo3Lgx+vTpU+Y2UqkUNjY2WLlyJdq1a4du3bpV+ByIiIhIt2n0kSvNmjVDs2bNMGnSJKSnp+PPP//Eli1bAAATJ07U5KFK9fxqokKIcq0w+qLhwNKEhYUhLCwMWVlZsLW1Lff2REREpPsq7Vl1Dg4OCA0NRWhoaGUdQoG9vT2MjY2VepfS09OVeqGoZHGJ9/H7yWQUCeW6iymc9E5ERGQwz6ozMzODt7c3YmNjMXDgQHl5bGws+vfvX+nHj4yMRGRkJGQyWaUfq7LM/usy4m8+KLONrYV2l7onIiLSJrUTp0WLFmH16tVK5c2bN8cbb7yh0cQpOzsb165dk7+/ceMGzpw5Azs7O7i6uiI8PBzDhg2Dj48P/P39sXjxYiQlJWHs2LEai6E0hjBUl1vwNOkb4lMP7vZWSvX2Vmbo1oy9d0REVH2pnThV5bPqTp48icDAQPn78PBwAEBISAhiYmLw+uuv4969e5gxYwZSU1Ph6emJ7du3o379+hqNw9D1bumEzi/X1nYYREREOkftxKn4WXXu7u4K5ZXxrLouXbpAiBIm4Dxj3LhxGDdunEaPqwpDGKojIiKisvFZdRpiCEN1REREVDa1E6dJkybh/v37GDduHPLz8wEA5ubm+PTTTzX+rDoiIiIibVI7cZJIJFX2rDpdxqE6IiIiw6exdZysrKzg6+urqd3pHQ7VERERGT61HrlS7NChQxg6dCgCAgLkj1dZuXIlDh8+rIndExEREekEtROnDRs2oEePHrCwsMCpU6eQl5cHAHj06BFmzZqldoBEREREukLtxOmbb77BwoULsWTJEpia/reqdEBAAE6dOqXu7vVGZGQkPDw8qvVwJRERkaFTO3G6cuUKOnXqpFRuY2ODhw8fqrt7vREWFoaEhATExcVpOxQiIiKqJGonTnXr1lV4DEqxw4cPo0GDBurunoiIiEhnqJ04jRkzBh999BGOHz8OiUSClJQU/Prrr5g4caJWVvAmIiIiqiwaWQAzMzMTgYGByM3NRadOnSCVSjFx4kS8//77mohRL3AdJyIiIsMnES96+JuKHj9+jISEBBQVFcHDwwNWVlaa2K3eKV7HKTMzEzY2NtoOp1x6/3QIF1Oy8MsoPz7kl4iIqhVVr99qD9U9efIEjx8/hqWlJXx8fODo6IilS5di165d6u6aiIiISKeonTj1798fK1asAAA8fPgQbdu2xffff4/+/ftjwYIFagdIREREpCvUTpxOnTqFjh07AgDWr18PR0dH3Lx5EytWrMBPP/2kdoBEREREukLtxOnx48ewtrYGAOzatQuDBg2CkZER2rVrh5s3b6odIBEREZGuUDtxatSoETZv3ozk5GTs3LkTQUFBAID09HS9mxytDq4cTkREZPjUTpy+/PJLTJw4EW5ubmjbti38/f0BPO198vLyUjtAfcGVw4mIiAyf2us4DR48GB06dEBqaipatWolL+/WrRsGDhyo7u6JiIiIdEaFe5w+++wznDhxAgBQp04deHl5wcjov935+fmhadOm6kdIREREpCMqnDilpqaiT58+qFu3Lt59911s27YNeXl5moyNiIiISKdUOHGKjo7GnTt3sG7dOtSsWRMff/wx7O3tMWjQIMTExCAjI0OTcRIRERFpnVqTwyUSCTp27Ig5c+bg8uXLOHHiBNq1a4clS5bAyckJnTp1wty5c3H79m1NxUtERESkNWrfVfesZs2aYdKkSThy5Ahu376NkJAQHDp0CGvWrNHkYYiIiIi0Qu276p48eQIhBCwtLQEAN2/exKZNm+Dh4YHQ0FCEhoaqHaQ+iIyMRGRkJGQymbZDISIiokqi8WfV+fn5Vctn1XEdJyIiIsOn8WfV1alTh8+qIyIiIoPEZ9URERERqYjPqiMiIiJSEZ9VR0RERKQiPquOiIiISEVqJ07A02fV1alTR6HMz89PE7smIiIi0hkaSZz27NmDPXv2ID09HUVFRQp1y5cv18QhiIiIiLRO7TlO06dPR1BQEPbs2YOMjAw8ePBA4aVPrly5gtatW8tfFhYW2Lx5s7bDIiIiIh2hdo/TwoULERMTg2HDhmkiHq1q0qQJzpw5AwDIzs6Gm5sbXnnlFe0GRURERDpD7R6n/Px8BAQEaCIWnbJlyxZ069YNNWrU0HYoREREpCPUTpxGjx6N1atXayKWFzp48CD69u0LJycnSCSSEofRoqKi4O7uDnNzc3h7e+PQoUMVOta6devw+uuvqxkxERERGRK1h+pyc3OxePFi7N69Gy1btoSpqalC/bx589Q9hFxOTg5atWqFkSNH4tVXX1WqX7t2LcaPH4+oqCi0b98eixYtQnBwMBISEuDq6goA8Pb2Rl5entK2u3btgpOTEwAgKysLR44cwW+//aax2ImIiEj/qZ04nTt3Dq1btwYAXLhwQaFOIpGou3sFwcHBCA4OLrV+3rx5CA0NxejRowEAP/zwA3bu3IkFCxYgIiICABAfH//C4/zxxx/o0aMHzM3NX9g2Ly9PIRHLysp64TZERESkn9ROnPbt26eJONSWn5+P+Ph4TJ48WaE8KCgIR48eLde+1q1bh3fffVelthEREZg+fXq59k9ERET6SSPrOD18+BDLli3DpUuXIJFI4OHhgVGjRsHW1lYTu1dJRkYGZDIZHB0dFcodHR2Rlpam8n4yMzNx4sQJbNiwQaX2U6ZMQXh4uPx9VlYWXFxcVD5eVcrKLcCqv28i60lhifV3snKrOCIiIiL9onbidPLkSfTo0QMWFhbw8/ODEALz5s3DzJkzsWvXLrRp00YTcars+eFBIUS5hgxtbW1x584dldtLpVJIpVJERkYiMjISMplM5W2r2vqTtzBnx5UXtqthZlwF0RAREekftROnCRMmoF+/fliyZAlMTJ7urrCwEKNHj8b48eNx8OBBtYNUhb29PYyNjZV6l9LT05V6oSpDWFgYwsLCkJWVVaU9beWRk/e0p6lpHWt0aGRfYhvnlyzQxvWlqgyLiIhIb2ikx+nZpAkATExMMGnSJPj4+Ki7e5WZmZnB29sbsbGxCg8Xjo2NRf/+/Sv9+PrQ41TMy/UlfN7HQ9thEBER6R21EycbGxskJSWhadOmCuXJycmwtrZWd/cKsrOzce3aNfn7Gzdu4MyZM7Czs4OrqyvCw8MxbNgw+Pj4wN/fH4sXL0ZSUhLGjh2r0ThKog89TkRERKQetROn119/HaGhoZg7dy4CAgIgkUhw+PBhfPLJJ3jzzTc1EaPcyZMnERgYKH9fPCk7JCQEMTExeP3113Hv3j3MmDEDqamp8PT0xPbt21G/fn2NxkFERETVk9qJ09y5cyGRSDB8+HAUFj6dQ2Nqaor33nsPs2fPVjvAZ3Xp0gVCiDLbjBs3DuPGjdPocVWhT0N1REREVDFqP3IlLS0N8+fPx4MHD3DmzBmcPn0a9+/fx7x588p1d5q+CwsLQ0JCAuLi4rQdChEREVUStXuc3N3dkZqaCgcHB7Ro0UJefu/ePbi7u7MHhoiIiAyG2j1OpQ2dZWdnq/TIEkMRGRkJDw8P+Pr6ajsUIiIiqiQV7nEqnpgtkUjw5ZdfwtLSUl4nk8lw/Phx+TPsqgPeVUdERGT4Kpw4nT59GsDTHqfz58/DzMxMXmdmZoZWrVph4sSJ6kdIREREpCMqnDgVP9x35MiR+PHHH2FjY6OxoIiIiIh0kdpznKKjo5k0gXOciIiIqgO176oDgIcPH2LZsmW4dOkSJBIJmjVrhtDQ0Go114dznIiIiAyf2j1OJ0+eRMOGDTF//nzcv38fGRkZmD9/Pho2bIhTp05pIkYiIiIinaB2j9OECRPQr18/hQf9FhYWYvTo0Rg/fjwOHjyodpBEREREukDtxOnkyZMKSRMAmJiYYNKkSfDx8VF390REREQ6Q+2hOhsbGyQlJSmVJycnw9raWt3d6w1ODiciIjJ8aidOr7/+OkJDQ7F27VokJyfj1q1b+O233zB69Gi8+eabmohRL/BZdURERIZP7aG6uXPnQiKRYPjw4SgsLAQAmJqa4r333sPs2bPVDpCIiIhIV6idOJmZmeHHH39EREQE/v33Xwgh0KhRI4VHsBAREREZAo2s4wQAlpaWaNGihaZ2R0RERKRzNJI47dmzB3v27EF6ejqKiooU6pYvX66JQ+i8yMhIREZGQiaTaTsUIiIiqiRqTw6fPn06goKCsGfPHmRkZODBgwcKr+qCk8OJiIgMn9o9TgsXLkRMTAyGDRumiXiIiIiIdJbaPU75+fkICAjQRCxEREREOk3txGn06NFYvXq1JmIhIiIi0mkVGqoLDw+X/39RUREWL16M3bt3o2XLljA1NVVoO2/ePPUiJCIiItIRFUqcTp8+rfC+devWAIALFy4olEskkopFRURERKSDKpQ47du3T9NxEBEREem8Cs9xevLkCcLCwuDs7AwHBwe89dZbyMjI0GRsRERERDqlwonTl19+iZiYGPTu3RtvvPEGYmNj8d5772kyNr0SGRkJDw8P+Pr6ajsUIiIiqiQVXsdp48aNWLZsGd544w0AwNChQ9G+fXvIZDIYGxtrLEB9ERYWhrCwMGRlZcHW1lbb4RAREVElqHCPU3JyMjp27Ch/7+fnBxMTE6SkpGgkMCIiIiJdU+HESSaTwczMTKHMxMQEhYWFagdFREREpIsqPFQnhMCIESMglUrlZbm5uRg7dixq1KghL9u4caN6ERIRERHpiAonTiEhIUplQ4cOVSsYIiIiIl1W4cQpOjpak3EQERER6Ty1n1VHREREVF0wcSIiIiJSEROn58yfPx/NmzeHh4cHPvzwQwghtB0SERER6QgmTs+4e/cufv75Z8THx+P8+fOIj4/H33//re2wiIiISEdUeHK4oSosLERubi4AoKCgAA4ODlqOiIiIiHSFXvU4HTx4EH379oWTkxMkEgk2b96s1CYqKgru7u4wNzeHt7c3Dh06pPL+a9eujYkTJ8LV1RVOTk7o3r07GjZsqMEzICIiIn2mVz1OOTk5aNWqFUaOHIlXX31VqX7t2rUYP348oqKi0L59eyxatAjBwcFISEiAq6srAMDb2xt5eXlK2+7atQsWFhbYunUrEhMTYWFhgeDgYBw8eBCdOnUqNaa8vDyF/WVlZWngTImIiEgX6VXiFBwcjODg4FLr582bh9DQUIwePRoA8MMPP2Dnzp1YsGABIiIiAADx8fGlbv/777+jUaNGsLOzAwD07t0bf//9d5mJU0REBKZPn16R0yEiIiI9o1dDdWXJz89HfHw8goKCFMqDgoJw9OhRlfbh4uKCo0ePIjc3FzKZDPv370eTJk3K3GbKlCnIzMyUv5KTkyt8DkRERKTbNNLjVFBQgLS0NDx+/Bi1a9eW99hUpYyMDMhkMjg6OiqUOzo6Ii0tTaV9tGvXDr169YKXlxeMjIzQrVs39OvXr8xtpFIppFIpIiMjERkZCZlMVuFzICIiIt1W4R6n7OxsLFq0CF26dIGtrS3c3Nzg4eGB2rVro379+njnnXcQFxenyVhVIpFIFN4LIZTKyjJz5kxcunQJFy9exE8//aTytmFhYUhISNDKORMREVHVqFDiNH/+fLi5uWHJkiXo2rUrNm7ciDNnzuDKlSs4duwYvvrqKxQWFuKVV15Bz5498c8//2g6biX29vYwNjZW6l1KT09X6oUiIiIiqogKDdUdPXoU+/btQ4sWLUqs9/Pzw6hRo7Bw4UIsW7YMBw4cQOPGjdUK9EXMzMzg7e2N2NhYDBw4UF4eGxuL/v37V+qxAXCojoiIqBqoUOL0+++/q9ROKpVi3LhxFTlEibKzs3Ht2jX5+xs3buDMmTOws7ODq6srwsPDMWzYMPj4+MDf3x+LFy9GUlISxo4dq7EYShMWFoawsDBkZWXB1ta20o9HREREVU+vliM4efIkAgMD5e/Dw8MBACEhIYiJicHrr7+Oe/fuYcaMGUhNTYWnpye2b9+O+vXrV3ps7HEiIiIyfBWeHN6rVy9kZmbK38+cORMPHz6Uv7937x48PDzUCu55Xbp0gRBC6RUTEyNvM27cOCQmJiIvLw/x8fFlrsGkSZwcTkREZPgqnDjt3LlTYcXsb7/9Fvfv35e/LywsxJUrV9SLjoiIiEiHVDhxEkKU+Z6IiIjI0BjMyuHaFhkZCQ8PD/j6+mo7FCIiIqokFU6cJBKJ0uKQ5Vlo0tBwjhMREZHhq/BddUIIjBgxAlKpFACQm5uLsWPHokaNGgCgMP+JiIiIyBBUOHEKCQlReD906FClNsOHD6/o7vUOlyMgIiIyfBVOnKKjozUZh97jAphERESGT68WwKSyfbfzMhYduI6iUu5wLOKNj0RERGqp0OTwpKSkcrW/fft2RQ5D5bTr4h0UFgkUCZT4AgCJBPByqanVOImIiPRVhXqcfH190a9fP7zzzjvw8/MrsU1mZibWrVuHH3/8EWPGjMEHH3ygVqCkuqi328Cn/ksl1klNjGFraVrFERERERmGCiVOly5dwqxZs9CzZ0+YmprCx8cHTk5OMDc3x4MHD5CQkICLFy/Cx8cH3333HYKDgzUdt87RpcnhNS1N4WBjru0wiIiIDI5EVGDJ73PnzqF58+YoKCjAX3/9hYMHDyIxMRFPnjyBvb09vLy80KNHD3h6elZGzDqteHJ4ZmYmbGxsqvTYr8w7gH/Ss7H6nbYIaGhfpccmIiLSZ6pevyvU4+Tl5YW0tDTUrl0bH3/8MeLi4lCrVq0KB0tERESkDyo0ObxmzZq4fv06ACAxMRFFRUUaDYqIiIhIF1Wox+nVV19F586dUbduXUgkEvj4+MDY2LjEtsUJFhEREZG+q1DitHjxYgwaNAjXrl3Dhx9+iHfeeQfW1taajo2IiIhIp1R4AcyePXsCAOLj4/HRRx9V+8RJl+6qIyIiospRoTlOz4qOjq72SRPw9JErCQkJiIuL03YoREREVEnUTpyIiIiIqgsmTkREREQqYuJEREREpCImTkREREQqYuJEREREpCImTkREREQqYuKkIZGRkfDw8ICvr6+2QyEiIqJKwsRJQ7iOExERkeFj4kRERESkIiZORERERCpi4kRERESkIiZORERERCpi4kRERESkIiZORERERCpi4kRERESkIiZOz5k7dy6aN28OT09PrFq1StvhEBERkQ4x0XYAuuT8+fNYvXo14uPjAQDdunVDnz59ULNmTe0GRkRERDqBPU7PuHTpEgICAmBubg5zc3O0bt0aO3bs0HZYREREpCP0KnE6ePAg+vbtCycnJ0gkEmzevFmpTVRUFNzd3WFubg5vb28cOnRI5f17enpi3759ePjwIR4+fIi9e/fi9u3bGjwDIiIi0md6NVSXk5ODVq1aYeTIkXj11VeV6teuXYvx48cjKioK7du3x6JFixAcHIyEhAS4uroCALy9vZGXl6e07a5du+Dh4YEPP/wQXbt2ha2tLXx9fWFiUvZHlJeXp7C/rKwsNc+SiIiIdJVECCG0HURFSCQSbNq0CQMGDJCXtW3bFm3atMGCBQvkZc2aNcOAAQMQERFR7mOMHj0aAwcORO/evUttM23aNEyfPl2pPDMzEzY2NuU+pjpemXcA/6RnY/U7bRHQ0L5Kj01ERKTPsrKyYGtr+8Lrt14N1ZUlPz8f8fHxCAoKUigPCgrC0aNHVd5Peno6AODKlSs4ceIEevToUWb7KVOmIDMzU/5KTk4uf/BERESkF/RqqK4sGRkZkMlkcHR0VCh3dHREWlqayvsZMGAAHj58iBo1aiA6OvqFQ3VSqRRSqbRCMRMREZF+MZjEqZhEIlF4L4RQKitLeXqnnhUZGYnIyEjIZLIKbU9ERES6z2CG6uzt7WFsbKzUu5Senq7UC1UZwsLCkJCQgLi4uEo/FhEREWmHwSROZmZm8Pb2RmxsrEJ5bGwsAgICKv34kZGR8PDwgK+vb6Ufi4iIiLRDr4bqsrOzce3aNfn7Gzdu4MyZM7Czs4OrqyvCw8MxbNgw+Pj4wN/fH4sXL0ZSUhLGjh1b6bGFhYUhLCxMPiufiIiIDI9eJU4nT55EYGCg/H14eDgAICQkBDExMXj99ddx7949zJgxA6mpqfD09MT27dtRv359bYVMREREBkSvEqcuXbrgRctOjRs3DuPGjauiiP7DyeFERESGz2DmOGkbJ4cTEREZPiZORERERCpi4qQhvKuOiIjI8DFx0hAO1RERERk+Jk5EREREKmLiRERERKQiJk4awjlOREREho+Jk4ZwjhMREZHhY+JEREREpCImTkREREQqYuKkIZzjREREZPiYOGkI5zgREREZPiZORERERCpi4kRERESkIiZORERERCpi4kRERESkIiZOGsK76oiIiAwfEycN4V11REREho+JExEREZGKmDgRERERqYiJExEREZGKmDgRERERqYiJExEREZGKmDgRERERqYiJk4ZwHSciIiLDx8RJQ7iOExERkeFj4kRERESkIiZORERERCpi4kRERESkIiZORERERCpi4kRERESkIiZORERERCpi4kRERESkomqbOA0cOBAvvfQSBg8erFS3detWNGnSBI0bN8bSpUu1EB0RERHpomqbOH344YdYsWKFUnlhYSHCw8Oxd+9enDp1Ct9++y3u37+vhQiJiIhI11TbxCkwMBDW1tZK5SdOnEDz5s3h7OwMa2tr9OrVCzt37tRChERERKRrdDJxOnjwIPr27QsnJydIJBJs3rxZqU1UVBTc3d1hbm4Ob29vHDp0SCPHTklJgbOzs/x9vXr1cPv2bY3sm4iIiPSbTiZOOTk5aNWqFX7++ecS69euXYvx48dj6tSpOH36NDp27Ijg4GAkJSXJ23h7e8PT01PplZKSUuaxhRBKZRKJRL0TIiIiIoNgou0AShIcHIzg4OBS6+fNm4fQ0FCMHj0aAPDDDz9g586dWLBgASIiIgAA8fHxFTq2s7OzQg/TrVu30LZt21Lb5+XlIS8vT/4+KyurQsclIiIi3aeTPU5lyc/PR3x8PIKCghTKg4KCcPToUbX37+fnhwsXLuD27dt49OgRtm/fjh49epTaPiIiAra2tvKXi4uL2jEQERGRbtK7xCkjIwMymQyOjo4K5Y6OjkhLS1N5Pz169MBrr72G7du3o169eoiLiwMAmJiY4Pvvv0dgYCC8vLzwySefoFatWqXuZ8qUKcjMzJS/kpOTK3ZiREREpPN0cqhOFc/POxJClGsuUll3yvXr1w/9+vVTaT9SqRRSqRSRkZGIjIyETCZTOQYiIiLSL3rX42Rvbw9jY2Ol3qX09HSlXqiqFBYWhoSEBHnPFRERERkevUuczMzM4O3tjdjYWIXy2NhYBAQEaCkqIDIyEh4eHvD19dVaDERERFS5dHKoLjs7G9euXZO/v3HjBs6cOQM7Ozu4uroiPDwcw4YNg4+PD/z9/bF48WIkJSVh7NixWos5LCwMYWFhyMrKgq2trdbiICIiosqjk4nTyZMnERgYKH8fHh4OAAgJCUFMTAxef/113Lt3DzNmzEBqaio8PT2xfft21K9fX1shV7rQmDgUFCmvMfWs2w+fVFE0RERE1ZNElLTiI5Xbs5PDr169iszMTNjY2Ghs/y9P/Qv5siKV2u4O74RGDsqPkyEiIqKSFY8Yvej6zcRJw1T94Mtr8+nbKFLhn8rFzhK+bnYaOy4REVF1oOr1WyeH6kjZAC/nFzciIiKiSqV3d9XpKt5VR0REZPg4VKdhlTVUR0RERJVH1es3e5yIiIiIVMTEiYiIiEhFTJw0hHOciIiIDB/nOGkY5zgRERHpH85xIiIiItIwJk5EREREKmLiRERERKQiJk4awsnhREREho+TwzWMk8OJiIj0DyeHExEREWkYEyciIiIiFZloOwBDUzzymZWVpeVIiIiISFXF1+0XzWBi4qRhjx49AgC4uLhoORIiIiIqr0ePHsHW1rbUek4O17CioiKkpKSga9euOHnypErb+Pr6Ii4ursw2WVlZcHFxQXJyMiedQ7XPTBuqOq7KOJ6m9qnOfiqybXm24XeufHT1+wbwO6ep/ZR328por+3vnBACjx49gpOTE4yMSp/JxB4nDTMyMkK9evVgYmKi8j+8sbGxym1tbGyq/S9xoHyfWVWq6rgq43ia2qc6+6nItuXZht+58tHV7xvA75ym9lPebSuzvTa/c2X1NBXj5PBKEhYWVilt6Sld/cyqOq7KOJ6m9qnOfiqyLb9zlUeXPy9+5zSzn/JuW9ntdRmH6vQE14ciqlr8zhFVLX35zrHHSU9IpVJ89dVXkEql2g6FqFrgd46oaunLd449TkREREQqYo8TERERkYqYOBERERGpiIkTERERkYqYOBERERGpiIkTERERkYqYOBmggQMH4qWXXsLgwYO1HQqRQdq6dSuaNGmCxo0bY+nSpdoOh8jg6dJ1jcsRGKB9+/YhOzsbv/zyC9avX6/tcIgMSmFhITw8PLBv3z7Y2NigTZs2OH78OOzs7LQdGpHB0qXrGnucDFBgYCCsra21HQaRQTpx4gSaN28OZ2dnWFtbo1evXti5c6e2wyIyaLp0XWPiVMUOHjyIvn37wsnJCRKJBJs3b1ZqExUVBXd3d5ibm8Pb2xuHDh2q+kCJDJS638GUlBQ4OzvL39erVw+3b9+uitCJ9JKhXfeYOFWxnJwctGrVCj///HOJ9WvXrsX48eMxdepUnD59Gh07dkRwcDCSkpLkbby9veHp6an0SklJqarTINJb6n4HS5rdIJFIKjVmIn2mieueThGkNQDEpk2bFMr8/PzE2LFjFcqaNm0qJk+eXK5979u3T7z66qvqhkhk0CryHTxy5IgYMGCAvO7DDz8Uv/76a6XHSmQI1Lnu6cp1jT1OOiQ/Px/x8fEICgpSKA8KCsLRo0e1FBVR9aHKd9DPzw8XLlzA7du38ejRI2zfvh09evTQRrhEek8fr3sm2g6A/pORkQGZTAZHR0eFckdHR6Slpam8nx49euDUqVPIyclBvXr1sGnTJvj6+mo6XCKDo8p30MTEBN9//z0CAwNRVFSESZMmoVatWtoIl0jvqXrd06XrGhMnHfT8fAkhRLnmUPAOHyL1vOg72K9fP/Tr16+qwyIyWC/6zunSdY1DdTrE3t4exsbGSr1L6enpStk4EWkev4NEVUsfv3NMnHSImZkZvL29ERsbq1AeGxuLgIAALUVFVH3wO0hUtfTxO8ehuiqWnZ2Na9euyd/fuHEDZ86cgZ2dHVxdXREeHo5hw4bBx8cH/v7+WLx4MZKSkjB27FgtRk1kOPgdJKpaBved0/JdfdXOvn37BAClV0hIiLxNZGSkqF+/vjAzMxNt2rQRBw4c0F7ARAaG30GiqmVo3zk+q46IiIhIRZzjRERERKQiJk5EREREKmLiRERERKQiJk5EREREKmLiRERERKQiJk5EREREKmLiRERERKQiJk5EREREKmLiRERERKQiJk5ERJXg3r17cHBwQGJiIgBg//79kEgkePjwYaUed+LEifjwww8r9RhE1RkTJyLSqhEjRkAikSi9evbsqe3Q1BIREYG+ffvCzc1N7X3duXMHpqamWLVqVYn1Y8aMQcuWLQEAkyZNQnR0NG7cuKH2cYlIGRMnItK6nj17IjU1VeG1Zs2aSj1mfn5+pe37yZMnWLZsGUaPHq2R/Tk6OqJ3796Ijo4u8Vi//fYbQkNDAQAODg4ICgrCwoULNXJsIlLExImItE4qlaJOnToKr5deekleL5FIsHTpUgwcOBCWlpZo3LgxtmzZorCPhIQE9OrVC1ZWVnB0dMSwYcOQkZEhr+/SpQvef/99hIeHw97eHq+88goAYMuWLWjcuDEsLCwQGBiIX375RT6klpOTAxsbG6xfv17hWH/++Sdq1KiBR48elXg+f/31F0xMTODv71/qOT958gS9e/dGu3btcP/+fQBAdHQ0mjVrBnNzczRt2hRRUVHy9qGhodi3b5986K/Y+vXrkZubi6FDh8rL+vXrV+mJJ1F1xcSJiPTC9OnTMWTIEJw7dw69evXC22+/LU84UlNT0blzZ7Ru3RonT57Ejh07cOfOHQwZMkRhH7/88gtMTExw5MgRLFq0CImJiRg8eDAGDBiAM2fOYMyYMZg6daq8fY0aNfDGG28o9fRER0dj8ODBsLa2LjHWgwcPwsfHp9RzyczMRFBQEPLz87Fnzx7Y2dlhyZIlmDp1KmbOnIlLly5h1qxZ+OKLL/DLL78AAHr16oU6deogJiZGYV/Lly/HgAEDUKtWLXmZn58fkpOTcfPmzRd/sERUPoKISItCQkKEsbGxqFGjhsJrxowZ8jYAxOeffy5/n52dLSQSifjrr7+EEEJ88cUXIigoSGG/ycnJAoC4cuWKEEKIzp07i9atWyu0+fTTT4Wnp6dC2dSpUwUA8eDBAyGEEMePHxfGxsbi9u3bQggh7t69K0xNTcX+/ftLPaf+/fuLUaNGKZTt27dPABCXL18WrVq1EoMGDRJ5eXnyehcXF7F69WqFbb7++mvh7++vEG/9+vVFUVGREEKI69evC4lEInbu3KmwXWZmpgBQZoxEVDHscSIirQsMDMSZM2cUXmFhYQptiic/A097gqytrZGeng4AiI+Px759+2BlZSV/NW3aFADw77//yrd7vhfoypUr8PX1VSjz8/NTet+8eXOsWLECALBy5Uq4urqiU6dOpZ7PkydPYG5uXmJd9+7d0aBBA6xbtw5mZmYAgLt37yI5ORmhoaEK5/DNN98oxB8aGoqbN29i7969AJ72NtWrVw/du3dXOIaFhQUA4PHjx6XGSEQVY6LtAIiIatSogUaNGpXZxtTUVOG9RCJBUVERAKCoqAh9+/bFt99+q7Rd3bp1FY7zLCEEJBKJUtnzRo8ejZ9//hmTJ09GdHQ0Ro4cqbTds+zt7fHgwYMS63r37o0NGzYgISEBLVq0kMcPAEuWLEHbtm0V2hsbG8v/v3HjxujYsSOio6Pl87FGjhwJIyPFv4GLhzBr165daoxEVDFMnIhI77Vp0wYbNmyAm5sbTExU/7XWtGlTbN++XaHs5MmTSu2GDh2KSZMm4aeffsLFixcREhJS5n69vLxKXTpg9uzZsLKyQrdu3bB//354eHjA0dERzs7OuH79Ot5+++0y9x0aGor33nsP/fv3x61btzBy5EilNhcuXICpqSmaN29e5r6IqPw4VEdEWpeXl4e0tDSF17N3xL1IWFgY7t+/jzfffBMnTpzA9evXsWvXLowaNQoymazU7caMGYPLly/j008/xdWrV7Fu3Tr55Otne5ReeuklDBo0CJ988gmCgoJQr169MuPp0aMHLl68WGqv09y5c/H222+ja9euuHz5MgBg2rRpiIiIwI8//oirV6/i/PnziI6Oxrx58xS2fe2112BqaooxY8agW7duJa4TdejQIXTs2FE+ZEdEmsPEiYi0bseOHahbt67Cq0OHDipv7+TkhCNHjkAmk6FHjx7w9PTERx99BFtbW6VhrGe5u7tj/fr12LhxI1q2bIkFCxbI76qTSqUKbUNDQ5Gfn49Ro0a9MJ4WLVrAx8cH69atK7XN/PnzMWTIEHTt2hVXr17F6NGjsXTpUsTExKBFixbo3LkzYmJi4O7urrCdpaUl3njjDTx48KDUWNasWYN33nnnhXESUflJREkD+kRE1dTMmTOxcOFCJCcnK5T/+uuv+Oijj5CSkiKf1F2W7du3Y+LEibhw4UKZyZumbdu2DZ988gnOnTtXrmFLIlINv1VEVK1FRUXB19cXtWrVwpEjR/Ddd9/h/fffl9c/fvwYN27cQEREBMaMGaNS0gQ8XXfpn3/+we3bt+Hi4lJZ4SvJyclBdHQ0kyaiSsIeJyKq1iZMmIC1a9fi/v37cHV1xbBhwzBlyhR54jFt2jTMnDkTnTp1wh9//AErKystR0xE2sTEiYiIiEhFnBxOREREpCImTkREREQqYuJEREREpCImTkREREQqYuJEREREpCImTkREREQqYuJEREREpCImTkREREQq+n9PsYsvuODvjQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "splot = ui.get_source_plot()\n",
    "splot.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.10000000149011612, 0.10999999940395355, 6.912509117109752e-11),\n",
       " (0.10999999940395355, 0.11999999731779099, 4.368220594867494e-09),\n",
       " (0.11999999731779099, 0.12999999523162842, 8.710116462453073e-08),\n",
       " (0.12999999523162842, 0.14000000059604645, 7.801459479338991e-07),\n",
       " (0.14000000059604645, 0.15000000596046448, 3.999044308067718e-06)]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(splot.xlo, splot.xhi, splot.y))[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Can we create this data manually?\n",
    "\n",
    "Let's start by seeing if we can go from the ungrouped to grouped data. I am going to\n",
    "use a combination of routines from the `sherpa.astro.ui` module and methods on Sherpa\n",
    "objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = ui.get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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;DataPHA data set instance &#x27;data/pha/3c273.pi&#x27;&gt;</div><div hidden class=\"sherpa\"><style>/*\n",
       "Copyright (C) 2020  Smithsonian Astrophysical Observatory\n",
       "\n",
       "\n",
       " This program is free software; you can redistribute it and/or modify\n",
       " it under the terms of the GNU General Public License as published by\n",
       " the Free Software Foundation; either version 3 of the License, or\n",
       " (at your option) any later version.\n",
       "\n",
       " This program is distributed in the hope that it will be useful,\n",
       " but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
       " MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
       " GNU General Public License for more details.\n",
       "\n",
       " You should have received a copy of the GNU General Public License along\n",
       " with this program; if not, write to the Free Software Foundation, Inc.,\n",
       " 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n",
       "\n",
       "*/\n",
       "\n",
       ":root {\n",
       "  --sherpa-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --sherpa-background-color: var(--jp-layout-color0, white);\n",
       "  --sherpa-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --sherpa-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "\n",
       "  /* https://medium.com/ge-design/iot-cool-gray-is-a-great-background-color-for-data-visualization-ebf18c318418 */\n",
       "  --sherpa-background-color-dark1: #EBEFF2;\n",
       "  --sherpa-background-color-dark2: #D8E0E5;\n",
       "}\n",
       "\n",
       "div.sherpa-text-fallback {\n",
       "    display: none;\n",
       "}\n",
       "\n",
       "div.sherpa {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       "div.sherpa details summary {\n",
       "    display: list-item;  /* needed for notebook, not lab */\n",
       "    font-size: larger;\n",
       "}\n",
       "\n",
       "div.sherpa details div.datavals {\n",
       "    display: grid;\n",
       "    grid-template-columns: 1fr 3fr;\n",
       "    column-gap: 0.5em;\n",
       "}\n",
       "\n",
       "div.sherpa div.dataname {\n",
       "    font-weight: bold;\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa div.dataval { }\n",
       "\n",
       "div.sherpa div.datavals div:nth-child(4n + 1) ,\n",
       "div.sherpa div.datavals div:nth-child(4n + 2) {\n",
       "    background: var(--sherpa-background-color-row-odd);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tbody {\n",
       "    border-bottom: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tr.block {\n",
       "    border-top: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-odd ,\n",
       "div.sherpa table.model th.model-even {\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-odd {\n",
       "    background: var(--sherpa-background-color-dark1);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-even {\n",
       "    background: var(--sherpa-background-color-dark2);\n",
       "}\n",
       "\n",
       "div.sherpa .failed {\n",
       "    background: orange;\n",
       "    font-size: large;\n",
       "    padding: 1em;\n",
       "}\n",
       "</style><div class=\"sherpa-text-fallback\">&lt;sherpa.astro.plot.DataPHAPlot object at 0x7f1a695e2e10&gt;</div><div hidden class=\"sherpa\"><details open><summary>PHA Plot</summary><svg xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"460.8pt\" height=\"345.6pt\" viewBox=\"0 0 460.8 345.6\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">\n",
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       "</details></div><details open><summary>Summary (8)</summary><div class=\"datavals\"><div class=\"dataname\">Identifier</div><div class=\"dataval\">data/pha/3c273.pi</div><div class=\"dataname\">Exposure</div><div class=\"dataval\">38564.6 s</div><div class=\"dataname\">Number of bins</div><div class=\"dataval\">1024</div><div class=\"dataname\">Channel range</div><div class=\"dataval\">1 - 1024</div><div class=\"dataname\">Count range</div><div class=\"dataval\">0.0 - 10.0</div><div class=\"dataname\">Background</div><div class=\"dataval\">Subtracted</div><div class=\"dataname\">Grouping</div><div class=\"dataval\">Applied (46 groups)</div><div class=\"dataname\">Using</div><div class=\"dataval\">0.4672-9.8696 Energy (keV) with 42 groups</div></div></details><details><summary>Metadata (11)</summary><div class=\"datavals\"><div class=\"dataname\">Mission or Satellite</div><div class=\"dataval\">CHANDRA</div><div class=\"dataname\">Instrument or Detector</div><div class=\"dataval\">ACIS</div><div class=\"dataname\">Grating type</div><div class=\"dataval\">HETG</div><div class=\"dataname\">Object</div><div class=\"dataval\">3C 273</div><div class=\"dataname\">Program description</div><div class=\"dataval\">IN FLIGHT CALIBRATION OF THE HETGS EFFECTIVE AREA AND CROSS DISPERSI</div><div class=\"dataname\">Observation date</div><div class=\"dataval\">2000-01-10T06:47:15</div><div class=\"dataname\">Program that created the PHA</div><div class=\"dataval\">dmgroup</div><div class=\"dataname\">The channel type</div><div class=\"dataval\">PI</div><div class=\"dataname\">Data stored</div><div class=\"dataval\">TOTAL</div><div class=\"dataname\">Data format</div><div class=\"dataval\">TYPE:I</div><div class=\"dataname\">PHA format</div><div class=\"dataval\">COUNT</div></div></details></div>"
      ],
      "text/plain": [
       "<DataPHA data set instance 'data/pha/3c273.pi'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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    "d"
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   "execution_count": 20,
   "metadata": {},
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name           = data/pha/3c273.pi\n",
      "channel        = Float64[1024]\n",
      "counts         = Float64[1024]\n",
      "staterror      = None\n",
      "syserror       = None\n",
      "bin_lo         = None\n",
      "bin_hi         = None\n",
      "grouping       = Int16[1024]\n",
      "quality        = Int16[1024]\n",
      "exposure       = 38564.608926889\n",
      "backscal       = 2.5264364698914e-06\n",
      "areascal       = 1.0\n",
      "grouped        = True\n",
      "subtracted     = True\n",
      "units          = energy\n",
      "rate           = True\n",
      "plot_fac       = 0\n",
      "response_ids   = [1]\n",
      "background_ids = [1]\n"
     ]
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   ],
   "source": [
    "print(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use this to plot channels against counts (and I throw in the grouping column too), but\n",
    "it doesn't really match the majesty of `plot_data`, does it?"
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  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(d.channel, d.counts)\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(d.channel, d.grouping, 'o');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make my life easier, I am going to switch `plot_data` to use channels rather than energy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset 1: 33:676 Channel\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ui.set_analysis('channel')\n",
    "pdata = ui.get_data_plot()\n",
    "pdata.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our first step is to group the channel data. The grouping channel defines the mapping - a value of 1 starts the bin and -1 continues the bin, so a grouping of `[1, 1, -1, -1, 1, -1]` represents three \"groups\", having lengths of `[1, 3, 2]` channels. We could use some clever NumPy indexing routines, or use the CIAO `group` module. A warning, this has not been optimised for Python/NumPy use, since it is a direct translation of the underlying C code that CIAO uses to do the grouping!\n",
    "\n",
    "There is some basic information on this module via [`ahelp group group`](http://cxc.harvard.edu/ciao/ahelp/group.html): the routines we want here are `grpGetGroupSum` and `grpGetChansPerGroup`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "gcounts = group.grpGetGroupSum(d.counts, d.grouping)\n",
    "gchans = group.grpGetChansPerGroup(d.grouping)\n",
    "\n",
    "# Need to extract the value for each group; this is done by picking the\n",
    "# value from the first channel in the group.\n",
    "#\n",
    "gfilt = d.grouping == 1\n",
    "\n",
    "xwidth = gchans[gfilt]\n",
    "xstart = d.channel[gfilt]\n",
    "xend = xstart + xwidth\n",
    "y = gcounts[gfilt]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, this is getting us closer, but it still doesn't match the data plot since:\n",
    "\n",
    "- no division by exposure time\n",
    "- no division by bin width\n",
    "- no filter by channel number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot((xstart + xend) / 2, y, 'o');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's easy now to scale by the bin width, and the data object has the \n",
    "[exposure time as an attribute](https://sherpa.readthedocs.io/en/4.11.0/data/api/sherpa.astro.data.DataPHA.html#sherpa.astro.data.DataPHA.exposure). If we also add in log scaling we can see it's much closer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xmid = (xstart + xend) / 2\n",
    "yscaled = y / d.exposure / xwidth\n",
    "\n",
    "plt.plot(xmid, yscaled, 'o')\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In fact, let's overplot the data to see how well we are doing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdata.plot()\n",
    "plt.plot(xmid, yscaled, 'o');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apart from the filtering, I'd like to call that a success, except that the new values are larger than the old ones, because I've forgotten about background subtraction! This means we have to deal with scaling the background to the observation for both exposure time (although in this case they are the same) and extraction area (which we do with the `BACKSCAL` values [$\\dagger$]):\n",
    "\n",
    "[$\\dagger$] for this Chandra dataset we don't have to worry about the `AREASCAL` value since it's the same for both source and background."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "bkg = d.get_background()\n",
    "\n",
    "bcounts = group.grpGetGroupSum(bkg.counts, d.grouping)\n",
    "\n",
    "bscale = d.exposure * d.backscal / (bkg.exposure * bkg.backscal)\n",
    "\n",
    "ysub = gcounts[gfilt] - bscale * bcounts[gfilt]\n",
    "ysubscaled = ysub / d.exposure / xwidth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Actually, the `DataPHA` class has a method to calculate the background scale,\n",
    "[`get_background_scale`](https://sherpa.readthedocs.io/en/latest/data/api/sherpa.astro.data.DataPHA.html#sherpa.astro.data.DataPHA.get_background_scale),\n",
    "which returns the same value as I've just calculated manually:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.134920643888096\n",
      "0.134920643888096\n"
     ]
    }
   ],
   "source": [
    "print(d.get_background_scale())\n",
    "print(bscale)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this correction in place, things look a lot better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdata.plot()\n",
    "plt.plot(xmid, ysubscaled, 'o', fillstyle='none');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We still need to work on the spatial filter, for which we use the `mask` attribute of the data object. Note that this has already been grouped for us; ie it's length matches the number of groups not the number of channels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1024, 46, 46)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.channel.size, d.mask.size, ysub.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This means it is an easy filter to apply:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "\n",
    "pdata.plot()\n",
    "plt.plot(xmid[d.mask], ysubscaled[d.mask], 'o', fillstyle='none');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can compare are values to the plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.0)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dx = pdata.x - xmid[d.mask]\n",
    "dx.min(), dx.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try the Y axis, where I expect to see small numerical differences, but hopefully all $\\lll 10^{-6}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 2.710505431213761e-20, 0.0)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dy = pdata.y - ysubscaled[d.mask]\n",
    "dy.min(), dy.max(), np.median(dy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Success!\n",
    "\n",
    "## Can I re-create the model plot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mplot = ui.get_model_plot()\n",
    "mplot.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(33.0, 34.0), (34.0, 35.0), (35.0, 36.0), (36.0, 37.0), (37.0, 38.0)]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(mplot.xlo, mplot.xhi))[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The grouped values match (at least for the first 5 entries):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(33.0, 40.0), (40.0, 45.0), (45.0, 49.0), (49.0, 52.0), (52.0, 55.0)]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(xstart[d.mask], xend[d.mask]))[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I want to be able to evaluate the source model to get the predicted values, but there are complications. The source model, as given by the user, can be retrieved with `get_source`, and the \"actual\" model - that includes the instrumental response - with `get_model`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(xsphabs.gal * powlaw1d.pl)\n",
      "apply_rmf(apply_arf((38564.608926889 * (xsphabs.gal * powlaw1d.pl))))\n"
     ]
    }
   ],
   "source": [
    "smdl = ui.get_source()\n",
    "fmdl = ui.get_model()\n",
    "\n",
    "print(smdl.name)\n",
    "print(fmdl.name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normally I would just pass in the grid on which I want the model to be evaluated to these models,\n",
    "but that doesn't work here because of the extra complications of the response model. Note that the\n",
    "return value is 1024 elements long, even when the input grid is much smaller, for the \"full model\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3,)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smdl([0.1, 0.2, 0.3], [0.2, 0.3, 0.4]).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1024,)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fmdl([0.1, 0.2, 0.3]).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model is ignoring the user's grid since it knows better, and evaluating it over the PI channel space. So we can try and compare to the model plot (in this case we only have to normalize by the exposure time since the bn width is 1 by construction):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "ymdl = fmdl([1])  # just apply an argument to get a return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mplot.plot()\n",
    "plt.plot(np.arange(1, 1025, 1), ymdl / d.exposure, alpha=0.2)\n",
    "plt.xlim(5, 1000);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, other than the filtering, we have the same result. We can group this to match the data using the grouping library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "ymdl_group = group.grpGetGroupSum(ymdl, d.grouping)\n",
    "ymdl_group = ymdl_group[gfilt]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdata.plot()\n",
    "plt.plot(xmid, ymdl_group / d.exposure / xwidth);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Errors\n",
    "\n",
    "Added: March 14 2019\n",
    "\n",
    "I've been asked about how the errors are generated in the data plot. Let's see if we can re-create them too:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdata.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The error values are in the `yerr` field:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.84421547e-05, 2.64611045e-05, 3.22510821e-05, 4.61223373e-05,\n",
       "       4.61223373e-05, 6.45475413e-05, 4.61505429e-05, 6.45021643e-05,\n",
       "       3.23119961e-05, 4.71339817e-05, 4.30316942e-05, 3.22737706e-05,\n",
       "       4.51117488e-05, 3.30542430e-05, 2.20637980e-05, 1.69371558e-05,\n",
       "       2.58008657e-05, 1.57268392e-05, 2.15158471e-05, 1.65568398e-05,\n",
       "       2.15158471e-05, 4.40723240e-05, 2.25945873e-05, 3.22510821e-05,\n",
       "       1.97991726e-05, 2.20361620e-05, 1.61255411e-05, 9.94756339e-06,\n",
       "       1.46907747e-05, 1.29247984e-05, 8.61653228e-06, 6.15801543e-06,\n",
       "       1.10254602e-05, 8.27841988e-06, 8.08664430e-06, 8.62575392e-06,\n",
       "       4.14342804e-06, 6.31687220e-06, 5.39109620e-06, 3.68484212e-06,\n",
       "       2.81559467e-06, 5.76279934e-07])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdata.yerr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Earlier I used\n",
    "\n",
    "    bkg = d.get_background()\n",
    "    bcounts = group.grpGetGroupSum(bkg.counts, d.grouping)\n",
    "    bscale = d.exposure * d.backscal / (bkg.exposure * bkg.backscal)\n",
    "    ysub = gcounts[gfilt] - bscale * bcounts[gfilt]\n",
    "    ysubscaled = ysub / d.exposure / xwidth\n",
    "    \n",
    "What do I need to do to get the errors? Well first, how is Sherpa calculating errors for this dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(None, 'chi2gehrels')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.staterror, ui.get_stat_name()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This means that Sherpa is calculating the errors: `staterror` is `None` and the statistic is\n",
    "[Chi2Gehrels](https://sherpa.readthedocs.io/en/4.11.0/statistics/api/sherpa.stats.Chi2Gehrels.html), which means that the approximation for the variance on bin $i$, $V(i)$, is:\n",
    "\n",
    "$$\n",
    "V(i) = V_S(i) + [A_S/A_B]^2 V_B(i)\n",
    "$$\n",
    "\n",
    "where the suffixes $S$ and $B$ refer to the source and background regions, and $A$ is the area of the aperture. Now, how do I get $V_S$ and $V_B$? Let's try\n",
    "[get_staterror](https://sherpa.readthedocs.io/en/4.11.0/ui/api/sherpa.astro.ui.get_staterror.html): I could\n",
    "have used\n",
    "[get_error](https://sherpa.readthedocs.io/en/4.11.0/ui/api/sherpa.astro.ui.get_error.html) but in this case\n",
    "we have no systematic errors, so it isn't needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(5.2253983239479105, 2.6583123951777),\n",
       " (4.978501373369197, 2.3228756555322954),\n",
       " (5.098895818000656, 1.8660254037844386),\n",
       " (4.978501373369197, 2.3228756555322954),\n",
       " (5.102310727828878, 2.3228756555322954),\n",
       " (4.9750014798134385, 1.8660254037844386),\n",
       " (5.336069698644923, 1.8660254037844386),\n",
       " (5.336069698644923, 1.8660254037844386),\n",
       " (4.978501373369197, 2.3228756555322954),\n",
       " (5.339332919877688, 2.3228756555322954)]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serr = ui.get_staterror()\n",
    "berr = ui.get_staterror(bkg_id=1)\n",
    "\n",
    "list(zip(serr, berr))[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Gehrels approximation - which is taken from “Confidence limits for small numbers of events in astrophysical data”, Gehrels, N. 1986, ApJ, vol 303, p. 336-346. http://adsabs.harvard.edu/abs/1986ApJ…303..336G - is\n",
    "\n",
    "$$\n",
    "\\sigma_X(i) = 1 + \\sqrt{ N_X(i) + 0.75 }\n",
    "$$\n",
    "\n",
    "where $V_X(i) = \\sigma_X(i)^2$, $\\sigma_X(i)$ is the value returned by `get_staterror`, and $N_X$ is the number of counts in bin $i$ for a given component $X$. So we can check the background values as there's no \"correction\" terms:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.0)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_berr = 1 + np.sqrt(bcounts[gfilt] + 0.75)\n",
    "\n",
    "bdiff = check_berr - berr\n",
    "bdiff.min(), bdiff.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yay, success.\n",
    "\n",
    "### An aside: calculating error values\n",
    "\n",
    "Rather than hard-code the formula, we can use the low-level `sherpa.stats` API to calculate errors for us (which lets you change the error approximation \"easily\"). We need two things: an array of counts and a Sherpa stats object.\n",
    "\n",
    "Let's try a few statistics from [sherpa.stats](https://sherpa.readthedocs.io/en/4.11.0/statistics/stats.html):\n",
    "`Chi2Gehrels`, which I've already talked about, and\n",
    "[`Chi2DataVar`](https://sherpa.readthedocs.io/en/4.11.0/statistics/api/sherpa.stats.Chi2DataVar.html),\n",
    "which just implements the approximation $V_i(X) = N_i(X)$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sherpa.stats import Chi2Gehrels, Chi2DataVar\n",
    "\n",
    "stat1 = Chi2Gehrels()\n",
    "stat2 = Chi2DataVar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.6583124 , 2.32287566, 1.8660254 , 2.32287566, 2.32287566,\n",
       "       1.8660254 , 1.8660254 , 1.8660254 , 2.32287566, 2.32287566])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It's about this time of the notebook I realise that I should really have applied the gfilt\n",
    "# filter to the source and background count arrays and created new variables!\n",
    "\n",
    "stat1.calc_staterror(bcounts[gfilt])[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.41421356, 1.        , 0.        , 1.        , 1.        ,\n",
       "       0.        , 0.        , 0.        , 1.        , 1.        ])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stat2.calc_staterror(bcounts[gfilt])[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, we can see that we can switch object to switch approximation (the class has more functionality than\n",
    "just the calculation of an error, otherwise a simple function could have been used)!\n",
    "\n",
    "### end aside\n",
    "\n",
    "I'm sure my diversion hasn't diverted you from the quest to apply this to the data values, so let's see\n",
    "what happens, remembering that we calculated the background-subtracted counts as:\n",
    "\n",
    "    ysub = gcounts[gfilt] - bscale * bcounts[gfilt]\n",
    "    ysubscaled = ysub / d.exposure / xwidth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-8.881784197001252e-16, 0.0)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yvar = stat1.calc_staterror(gcounts[gfilt])**2 + bscale**2 * stat1.calc_staterror(bcounts[gfilt])**2\n",
    "check_serr = np.sqrt(yvar)\n",
    "\n",
    "sdiff = check_serr - serr\n",
    "sdiff.min(), sdiff.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":kermit-flail\n",
    "\n",
    "However, this doesn't show that we can calculate the plot errors, but it *should* just be a simple\n",
    "scaling away:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "check_ploterr = check_serr / d.exposure / xwidth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first thing to remember is that we don't match up on size, so we can't do a direct comparison,\n",
    "although the previous plots have shown it's the first three and last bin of our manually-created\n",
    "data that differs, so we can just filter them out for a comparison:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42, 46)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdata.yerr.size, check_ploterr.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 3.3881317890172014e-21)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta = pdata.yerr - check_ploterr[3:-1]\n",
    "delta.min(), delta.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In case you glory in visualizations rather than the cold, hard, beauty of numbers, we can plot the values,\n",
    "taking advantage of the poorly-documented `clearwidow` option of the \n",
    "[plot method](https://sherpa.readthedocs.io/en/4.11.0/plots/api/sherpa.plot.DataPlot.html#sherpa.plot.DataPlot.plot) to add\n",
    "the Sherpa plot to the Matplotlib layout without destroying the existing setup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "pdata.plot(clearwindow=False)\n",
    "plt.plot(xmid, ymdl_group / d.exposure / xwidth)\n",
    "\n",
    "plt.xlim(10, 1000)\n",
    "plt.ylim(5e-7, 4e-4)\n",
    "plt.subplot(2, 1, 2)\n",
    "\n",
    "plt.errorbar(xmid, ysubscaled, yerr=check_ploterr, fmt='.')\n",
    "plt.plot(xmid, ymdl_group / d.exposure / xwidth)\n",
    "\n",
    "plt.xlim(10, 1000)\n",
    "plt.ylim(5e-7, 4e-4)\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model line is shown in both to help convince you the results are the same!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}