{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple Templates Fitting of a Quasar SED in Sherpa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sherpa can use the template models and combined them with the other models. Here we show a simple template fitting to the SED of a quasar.\n",
    "A set of accretion disk spectral models with the standard parameters (mass, accretion rate, inclination anlge) has been stored in the subdirectory Templates.  table.txt ascii file index these spectra as required for Sherpa. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First import Sherpa packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sherpa.astro.ui import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the optimization method and load the ascii data file ( $\\log {\\nu}, \\log {\\nu F_{\\nu}}$, $1\\sigma$ errors), plot the data and  set the data filter.  The SED covers broad-band and we filter the data to include only the optical-UV part for fitting with the disk models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "set_method('simplex')\n",
    "load_ascii('3098_errors.dat', ncols=3, dstype=Data1D)\n",
    "plot_data()\n",
    "notice(13.5,16.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Input template model defined via an ascii index file table.txt. The model name for Sherpa session is required and given as 'tbl' in this initial step. set_model() assignes the template model to the SED data. get_model() brings the information about the initial model parameters, which are based on the first entry in the table.txt index file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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;Template model instance &#x27;template.tbl&#x27;&gt;</div><div hidden class=\"sherpa\"><details open><summary>Model</summary><table class=\"model\"><caption>Expression: template.tbl</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=3>tbl</th><td>mass</td><td><input disabled type=\"checkbox\" checked></input></td><td>6.0</td><td>6.0</td><td>10.0</td><td></td></tr><tr><td>rate</td><td><input disabled type=\"checkbox\" checked></input></td><td>0.1</td><td>0.01</td><td>1.0</td><td></td></tr><tr><td>angle</td><td><input disabled type=\"checkbox\" checked></input></td><td>0.1</td><td>0.1</td><td>1.0</td><td></td></tr></tbody></table></details></div>"
      ],
      "text/plain": [
       "<Template model instance 'template.tbl'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "load_template_model('tbl', 'table.txt')\n",
    "set_model(tbl)\n",
    "get_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next step the SED is fit with the templates using simplex neldermead algorithm. The chi2 statistics will be used with the measurement errors entered together with the data. The fit returns the screen output with the information about the best fit parameters and related statistical values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset               = 1\n",
      "Method                = neldermead\n",
      "Statistic             = chi2\n",
      "Initial fit statistic = 426180\n",
      "Final fit statistic   = 91.4691 at function evaluation 324\n",
      "Data points           = 7\n",
      "Degrees of freedom    = 4\n",
      "Probability [Q-value] = 6.4175e-19\n",
      "Reduced statistic     = 22.8673\n",
      "Change in statistic   = 426089\n",
      "   tbl.mass       8.98957     \n",
      "   tbl.rate       0.305722    \n",
      "   tbl.angle      0.995458    \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit()\n",
    "plot_fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plot_fit() generates a figure showing the data points overplotted with the line defined by the best fit mode parameters given above. The fit goes over the data points, but the reduced chi2 statistics is large, mainly due to the deviations at lower frequencies. \n",
    "We add a second model component to account for these deviations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset               = 1\n",
      "Method                = neldermead\n",
      "Statistic             = chi2\n",
      "Initial fit statistic = 26.2289\n",
      "Final fit statistic   = 19.3396 at function evaluation 275\n",
      "Data points           = 5\n",
      "Degrees of freedom    = 2\n",
      "Probability [Q-value] = 6.31632e-05\n",
      "Reduced statistic     = 9.66979\n",
      "Change in statistic   = 6.88929\n",
      "   tbl.mass       8.98154     \n",
      "   tbl.rate       0.29325     \n",
      "   tbl.angle      0.980385    \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ignore()\n",
    "notice(14.5,15.4)\n",
    "fit()\n",
    "plot_fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The reduced chi2 is still high. In order to obtain the confidence bounds for the best fit parameters we will set up the options for confidence before running it. 'sigma' defines the confidence level, 'max_rstat' defines the maximum value of reduced statistics allowed by confidence, if rstat is higher than the max_rstat conf() will fail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tbl.mass -: WARNING: The confidence level lies within (8.961713e+00, 8.965722e+00)\n",
      "tbl.mass lower bound:\t-0.0178194\n",
      "tbl.rate lower bound:\t-0.0232344\n",
      "tbl.rate +: WARNING: The confidence level lies within (3.397280e-01, 3.428440e-01)\n",
      "tbl.rate upper bound:\t0.0480356\n",
      "tbl.angle -: WARNING: The confidence level lies within (9.657205e-01, 9.657121e-01)\n",
      "tbl.angle lower bound:\t-0.0146684\n",
      "tbl.angle upper bound:\t-----\n",
      "tbl.mass +: WARNING: The confidence level lies within (9.035475e+00, 9.035465e+00)\n",
      "tbl.mass upper bound:\t0.0539335\n",
      "Dataset               = 1\n",
      "Confidence Method     = confidence\n",
      "Iterative Fit Method  = None\n",
      "Fitting Method        = neldermead\n",
      "Statistic             = chi2gehrels\n",
      "confidence 1-sigma (68.2689%) bounds:\n",
      "   Param            Best-Fit  Lower Bound  Upper Bound\n",
      "   -----            --------  -----------  -----------\n",
      "   tbl.mass          8.98154   -0.0178194    0.0539335\n",
      "   tbl.rate          0.29325   -0.0232344    0.0480356\n",
      "   tbl.angle        0.980385   -0.0146684        -----\n"
     ]
    }
   ],
   "source": [
    "set_conf_opt('sigma',1)\n",
    "set_conf_opt('max_rstat',10)\n",
    "conf()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The screen output contains the confidence levels as well as the print out of the runs. These values can be accessed later as well using get_conf_results()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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;confidence results instance&gt;</div><div hidden class=\"sherpa\"><details open><summary>confidence 1&#963; (68.2689%) bounds</summary><table><thead><tr><th>Parameter</th><th>Best-fit value</th><th>Lower Bound</th><th>Upper Bound</th></tr></thead><tbody><tr><td>tbl.mass</td><td>     8.98154</td><td>  -0.0178194</td><td>   0.0539335</td></tr><tr><td>tbl.rate</td><td>     0.29325</td><td>  -0.0232344</td><td>   0.0480356</td></tr><tr><td>tbl.angle</td><td>    0.980385</td><td>  -0.0146684</td><td>-----</td></tr></tbody></table></details><details><summary>Summary (3)</summary><div class=\"datavals\"><div class=\"dataname\">Dataset</div><div class=\"dataval\">1</div><div class=\"dataname\">Fitting Method</div><div class=\"dataval\">neldermead</div><div class=\"dataname\">Statistic</div><div class=\"dataval\">chi2gehrels</div></div></details></div>"
      ],
      "text/plain": [
       "<confidence results instance>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_conf_results()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.017819382963949693, -0.023234427094558918, -0.014668384427378611)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_conf_results().parmins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_model()\n",
    "plot_fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ruB32q6Jc97m+XEG9Vj56EBcBU4HWAJIOBY4D9oyIlZK2qtogU1TuBg4HZgPjJT0TEf/KQ14zqy2Lp8OoX8JHL0GH78CJf4bt9kk7ldVQTq84kdQJOAYYWmnzucBvI2IlQEQsyNJ0X+CjiJgeEauAv5IUFTMrBuUr4dVb4Z794dO3kgnos8a4OBSZXPcghgCXA5UHGXcGvifpRmAFcFlEjK/SbltgVqXHs4H9sr2ApLOBswG233772kltZhtv+hh49hew6CPY43g48iZo3THtVLYRctaDkNQPWBARE6rsagS0AXoBvwQelb51U5VsN1mJbK8TEQ9ERElElLRv335TY5vZxlo6Hx7/Gfz5uOTmegMfT26P4eJQtHLZgzgQ6C+pL9AMaC1pBElv4ImICOBtSRVAO6CsUtvZwHaVHncCPsthVjPbWBVroHR4cupq+XI45Ao46BJo3DztZLaJctaDiIjBEdEpIroAA4CXI2Ig8BTQB0DSzkATYGGV5uOBbpJ2kNQk0/6ZXGU1s400ZyIMPQxGXQbb7gXnjk2W+nRxqBPSuA5iODBc0mRgFXB6RISkjiSns/aNiHJJg4DnSU5zHR4RU1LIambZLP88uT3G+KHQcis4YRh0P8G34K5jlIz01A0lJSVRWlqadgyzuisC3n8Mnr8Kli1MroDuczU02zztZLaRJE2IiKyLePtKajOrmYXTkrOTPnkVOu4NpzwKHfdKO5XlkAuEma3b6uXw+h3wxhBo1Bz63gYlZ6a+PoPlnguEmVVv2ksw6hewZAZ850Q44gZotXXaqSxPXCDM7Nu+/AxGX5ncQ6ltNzjtGeh6SNqpLM9cIMzsv9aUw9v3J2tBV5Qnd1w94EJo1DTtZJYCFwgzS8x6G0ZeCvPfh25HwNG3wpY7pJ3KUuQCYVbfLVsML10HE/8ErbeFE/8Cux3raxrMBcKs3oqAdx+BF65JLnzbfxD0vtIL+Nh/uECY1UcLpibDSZ++CZ32hX6/gw7d005lBcYFwqw+WfU1vHoLjL076Sn0/z30GAgNcro0jBUpFwiz+uKDUfDc5fDFrKQoHH49bNY27VRWwFwgzOq6zz+F566AD0dB+93gjNHQuX6trWwbxwXCrK5asxrG3pUs/QlJj6HXedCwcbq5rGi4QJjVRTPegGcvhbIPYNd+yZrQW2y3/nZmlbhAmNUlXy+EF66Fdx+GzbeHk/8GuxyVdiorUi4QZnVBRUVyodtL18Gqr+CgS+HgX0KTFmknsyKW83PbJDWU9I6kkZnH10maI2lS5qtvNe0ukjRZ0hRJF+c6p1nRmvc+DD8SRl4MW3eHn78B3/+1i4Ntsnz0IC4CpgKtK237XUTcVl0DSd2Bs4B9SZYlHS3p2YiYltOkZsVk5VJ45WZ46z5o3gZ+cB98d4BvkWG1Jqc9CEmdgGOAoRvYdDdgXEQsi4hy4FXg+NrOZ1aUImDKU3DXvjDuHtj7NBg0Hnqc7OJgtSrXQ0xDgMuBiirbB0l6T9JwSW2ytJsMHCypraQWQF/Ap2CYLZ4OD/0Q/n56cpHbT1+EY4dAiy3TTmZ1UM4KhKR+wIKImFBl173AjkAPYC5we9W2ETEVuAV4ERgNvAuUV/M6Z0sqlVRaVlZWez+AWSEpX5lcz3DP/vDpW8lpq2eNge32STuZ1WGKiNw8sXQzcCrJB3szkjmIJyJiYKVjugAjI2KddwmTdBMwOyLuWddxJSUlUVpauqnRzQrL9DHw7C9g0Uewx/Fw5E3QumPaqayOkDQhIkqy7ctZDyIiBkdEp4joAgwAXo6IgZK2qXTY8STDSd8iaavMf7cH/gd4JFdZzQrS0vnw+M/gz8dBxRoY+Dj86I8uDpY3aVwHcaukHkAAM4BzACR1BIZGxNrTXh+X1BZYDZwfEUtSyGqWfxVroHQ4/OM3UL4cDrkCDroEGjdPO5nVM3kpEBExBhiT+f7Uao75jGQyeu3j7+Ujm1lBmTMRRl4CcydB197Q93Zot1Paqaye8pXUZoVg+efw8g0wfii03ApOGAbdT/Bpq5YqFwizNEXA+4/B81fBsoWw79nQ52potnnaycxcIMxSs3BacnbSJ69Cx73hlEeh415ppzL7DxcIs3xbvRxevx3euBMaNYe+t0HJmdCgYdrJzL7BBcIsn6a9CKMugyUzYM+T4PDfQKut005llpULhFk+fDEHRl8JU5+Btt3gtGeg6yFppzJbJxcIs1xaUw5v3w+v3AQV5dDnGjjgQmjUNO1kZuvlAmGWK7PehpGXwvz3odsRcPStsOUOaacyqzEXCLPatmxxsrLbxD9B623hxL/Absf6mgYrOustEJIGAQ/5Vhdm6xEBkx6GF69NLnzbfxD0vhKatko7mdlGqUkPogMwXtJEYDjwfOTqFrBmxWrB1GQ46dM3Ybv94Jg7oMM6b1JsVvDWezfXiLgG6AYMA34CTJN0k6Qdc5zNrPCt+hpe/BXcdxCUTYX+v4czRrs4WJ1QozmIiAhJ84B5JOs7tAEek/RiRFyey4BmBeuDUfDc5fDFLOgxEA6/PlnlzayOqMkcxIXA6cBCkrWlfxkRqyU1AKaRLClqVn98/ik8dwV8OAra75b0GDrvn3Yqs1pXkx5EO+B/ImJm5Y0RUZFZVtSsfihfBePuhjG3JGckHX499DoPGjZOO5lZTqy3QETEr9axb2rtxjErUDPegGcvhbIPYNd+yZrQW2yXdiqznPJ1EGbr8vVCeOFaePdh2Hx7OPlvsMtRaacyy4ucrUm9lqSGkt6RNDLz+DpJcyRNynz1rabdJZKmSJos6RFJzXKd1ew/Kiqg9A/w+57w/qNw0KVw/lsuDlav5KMHcREwFWhdadvvIuK26hpI2ha4ENg9IpZLehQYAPwxl0HNAJj7XjKcNHs8dD4Ijrkdtto17VRmeZfTHoSkTsAxJGc/bahGQHNJjYAWwGe1mc3sW1YuhdGD4YFDYPEn8IP74CcjXRys3sr1ENMQktNgK6psHyTpPUnDJbWp2igi5gC3AZ8Cc4EvIuKFbC8g6WxJpZJKy8rKaje91Q8RMOUpuGsfGHcv7H06XFAKPU72/ZOsXstZgcicArsgIiZU2XUvsCPQg+TD//YsbdsAxwE7AB2BzSQNzPY6EfFARJREREn79u1r8SewemHxdHjoh/D302GzdvCzl+DYIdD8W3+3mNU7uZyDOBDon5mEbga0ljQiIv7zQS/pQWBklrbfBz6JiLLMcU8ABwAjcpjX6pPylcmSn6/fDg0aJ6et7nMWNPSJfWZr5ey3ISIGA4MBJPUGLouIgZK2iYi5mcOOByZnaf4p0EtSC2A5cBhQmqusVs9MHwPP/gIWfQR7HA9H3gStO6adyqzgpPHn0q2SegABzADOAZDUERgaEX0j4i1JjwETSe799A7wQApZrS5ZOh+evwomPwZtdoCBj8NO3087lVnBUl26c3dJSUmUlrqjYVVUrIHxw+Dl30D5CjjokuSrcfO0k5mlTtKEiCjJts8Drla3zZkIIy+BuZOga2/oezu02yntVGZFwQXC6qbln8PLN8D4odByKzhhGHQ/waetmm0AFwirWyLg/ceSuYZlC2Hfs6HP1dBs87STmRUdFwirOxZOS85O+uRV6Lg3nPIodNwr7VRmRcsFworf6uXJ9Qxv3AmNmif3Tup5BjRomHYys6LmAmHFbdqLMOoyWDID9jwJjrghmXMws03mAmHF6Ys5MPpKmPoMtO0Gpz0DXQ9JO5VZneICYcVlTTm8fT+8chNUlEOfa+CAC6FR07STmdU5LhBWPGa9DSMvhfnvQ7cj4OhbYcsd0k5lVme5QFjhW7YYXroOJv4JWm8LJ/4FdjvW1zSY5ZgLhBWuCJj0MLx4bXLh2/6DoPdgaNoy7WRm9YILhBWmBVOT4aRP34Tt9oNj7oAO3dNOZVavuEBYYVn1Nbx6C4y9G5q2gv6/hx4DoUGuFz80s6pcIKxwfPAsPHcFfDErKQqHXw+btU07lVm95QJh6fv8Uxh1Ofz7OdhqdzhjNHTeP+1UZvWeC4Slp3wVjLsbxtySnJF0+PXQ6zxo2DjtZGaGC4SlZcYb8OylUPYB7NovWRN6i+3STmVmleR85k9SQ0nvSBqZeXydpDmSJmW++mZps0ul/ZMkfSnp4lxntTz4eiE8eS78sS+sWgYn/w0GPOTiYFaA8tGDuAiYCrSutO13EXFbdQ0i4kOgByQFBpgDPJnDjJZrFRXJhW4vXQervoKDLoWDfwlNWqSdzMyqkdMCIakTcAxwI3DpRj7NYcDHETGz1oJZfs19LxlOmj0eOh+U3I57q13TTmVm65HrIaYhwOVARZXtgyS9J2m4pDbreY4BwCPV7ZR0tqRSSaVlZWWbltZq18qlMHowPHAILP4Ejr8ffjLSxcGsSOSsQEjqByyIiAlVdt0L7EgyhDQXuH0dz9EE6A/8vbpjIuKBiCiJiJL27dtvcm6rBREw5Sm4ax8Ydy/sfTpcUArfHeD7J5kVkVwOMR0I9M9MQjcDWksaERED1x4g6UFg5Dqe42hgYkTMz2FOq02Lp8OoX8JHL0GH78BJI6BTSdqpzGwj5KxARMRgYDCApN7AZRExUNI2ETE3c9jxwOR1PM3JrGN4yQpI+cpkyc/Xb4cGjZPTVvc5Cxr6TGqzYpXGb++tknoAAcwAzgGQ1BEYGhF9M49bAIev3W8FbPoYePYXsOgj2ON4OPImaN0x7VRmtonyUiAiYgwwJvP9qdUc8xnQt9LjZYBvxFPIls6H56+CyY9Bmx1g4BOw02FppzKzWuL+v224ijUwfhi8/BsoXwGHXAkHXQKNm6WdzMxqkQuEbZg5E2HkJTB3EnTtDX1vh3Y7pZ3KzHLABcJqZvnn8PINMH4otNwKThgG3U/waatmdZgLhK1bBLz/d3j+ali2EPY9G/pcDc02TzuZmeWYC4RVb+G05BYZn7wGHfeGU/4OHXukncrM8sQFog476f6xAPztnA1cfGf18uR6hjfuhEbNk3sn9TwDGjTMQUozK1QuEPZN016EUZfBkhmw50lwxA3JnIOZ1TsuEJb4Yg6MvhKmPgNtu8Fpz0DXQ9JOZWYpcoGo79aUw1v3wZiboaIc+lwDB1wIjZqmnczMUuYCUYctXbGaL1eUM2HmEnp2znJX9VlvJ9c0zJ8M3Y6Ao2+FLXfIf1AzK0g5X3LU0jFh5hI+mLeU2UuWc8rQcUyYueS/O5cthmcuhGGHw/IlcOJf4MePujiY2Te4B1FHjZu+iIpIvl9dXsG46Yvouf0WMOlhePHa5MK3/QdB78HQtGWaUc2sQLlA1FG9uralgaAioHGjBvRuswj+cAF8+iZstx8ccwd06J52TDMrYC4QdVTPzm3YtUMrVi3/ihE7vUKHZ4ZB01bQ//fQYyA08Oiima2bC0QddggTOXP13bSfvAD2Ggjfvx428x3UzaxmXCDqouWfw+gruWLJI8xq1BnOGA2dN/BqajOr91wg6pqPXoKnL4Cv5vN4y5N5vOWPedjFwcw2Qs4LhKSGQCkwJyL6SboOOAsoyxxyVUSMytJuC2Ao0J1kedIzI2JsrvMWrZVL4YVrYMIfod0uMGAEJ2zbkxPSzmVmRSsfPYiLgKlA60rbfhcRt62n3Z3A6Ij4oaQmQItcBSx6n7wOT58Hn8+CAy6AQ6/x6m5mtslyeiqLpE7AMSQ9gQ1p1xo4GBgGEBGrIuLzWg9Y7FYtg+euhD/1gwaN4MzRyc31XBzMrBbk+lzHIcDlQEWV7YMkvSdpuKQs94CgK8kQ1B8kvSNpqKTNsr2ApLMllUoqLSsry3ZI3TTrbbjvIHjr3mQRn5//E7bvlXYqM6tDclYgJPUDFkTEhCq77gV2BHoAc4HbszRvBOwN3BsRewFfA1dme52IeCAiSiKipH379rUVv3CVr4QXfw3Dj4Q1q5K7rvb9X2iStX6amW20XM5BHAj0l9QXaAa0ljQiIgauPUDSg8DILG1nA7Mj4q3M48eopkDUK5+9A0+eC2VTYe/T4IgboVnr9bczM9sIOetBRMTgiOgUEV2AAcDLETFQ0jaVDjsemJyl7TxglqRdMpsOA/6Vq6wFr3wVvHITPHgYrPgcTnksuSLaxcHMciiN6yBuldSD5NTVGcA5AJI6AkMjom/muAuAhzJnME0Hzsh/1AIwfwo8+XOY916ywtvRt0DzbNM2Zma1Ky8FIiLGAGMy359azTGfAX0rPZ4ElOQ+XYFaUw5v3gmv3AzNNoeTRsBux6adyszqEV9JXYgWTkt6DXNKYffjkjuvbtYu7VRmVs+4QBSSiorktNV/XA+Nm8MJw6D7CSClnczM6iEXiEKx+BN4+nyY+QbsfBQceye06pB2KjOrx1wg0hYBpcPhhWuhQUM47h7o8WP3GswsdS4QafpiNjw9CKa/Al0PhePugs07pZ3KzAxwgUhHRLI29OgroWJNMgldcqZ7DWZWUFwg8m3pPPj/LoJ/j4bOB8Jxd8OWO6SdyszsW1wg8iUCJj8Ooy6D1cvhyJthv597bWgzK1guEPnw9UJ49lL419OwbQkcfx+065Z2KjOzdXKByLWpI2Hkxck60Yf9Gg64EBr6bTezwudPqlxZvgSeuwLe+xt02BNOexq23iPtVGZmNeYCkQvTXoJnBsFXC+CQK+Hgy6Bh47RTmZltEBeI2rTiS3jhapj4Z2i/G5z8CHTcK+1UZmYbxQWitnzyGjx1Pnw5Gw68CHpf5bWhzayouUBsqlVfw0vXwdsPwJY7wpnPw3b7pp3KzGyTuUBsik/HwVPnwuLpyTUNh/0amrRIO5WZWa1wgdgYq1fAKzfAm3fBFtvB6SNhh++lncrMrFblvEBIagiUAnMiop+k64CzgLLMIVdFxKgs7WYAS4E1QHlEFMbqcnMmwJPnwsIPoedP4IgboGmrtFOZmdW6fPQgLgKmAq0rbftdRNxWg7aHRsTC3MTaQOWr4LVb4fU7oOXWcMrj0O37aacyM8uZnBYISZ2AY4AbgUtz+Vo5NW9ysgTo/PfhuyfDUb+F5lukncrMLKdyfae4IcDlQEWV7YMkvSdpuKQ21bQN4AVJEySdXd0LSDpbUqmk0rKysuoO2zhryuG1/4UHesNX82DAw8l9lFwczKweyFmBkNQPWBARE6rsuhfYEegBzAVur+YpDoyIvYGjgfMlHZztoIh4ICJKIqKkffv2G5X1pPvHctL9Y7+5sexDGHY4vHwD7NYPznsLdj1mo57fzKwY5XKI6UCgv6S+QDOgtaQRETFw7QGSHgRGZmscEZ9l/rtA0pPAvsBrOcybqFgD4+6Bf/wmOWX1h8Oh+wk5f1kzs0KTsx5ERAyOiE4R0QUYALwcEQMlbVPpsOOByVXbStpMUqu13wNHZDuu1i2eDn88Bl64BnY6LOk1uDiYWT2VxnUQt0rqQTLHMAM4B0BSR2BoRPQFtgaeVLIEZyPg4YgYnatAXy1fSZ+vn2XNPX+hYcMm8IN7k8loLwFqZvWYIiLtDLWmpKQkSktLN6jNpH/P4OsRP+bABlP4Z+xJ65PuZ8/dd89RQjOzwiJpQnXXmdX79S7fmL2K5dGEwat/yumrruD1+U3SjmRmVhDq/a02eu3Ynh+9eBkVIZo1bkCvrm3TjmRmVhDqfYHo2bkNu3ZozZcryrlzwF707FzdZRlmZvVLvS8QAK2aNaZVs8YuDmZmldT7OQgzM8vOPQjgb+fsn3YEM7OC4x6EmZll5QJhZmZZuUCYmVlWLhBmZpaVC4SZmWXlAmFmZlm5QJiZWVYuEGZmlpULhJmZZVWn1oOQVAbMzPHLtAMW5vg1cqFYc4Ozp8XZ05Hv7J0jon22HXWqQOSDpNLqFtcoZMWaG5w9Lc6ejkLK7iEmMzPLygXCzMyycoHYcA+kHWAjFWtucPa0OHs6Cia75yDMzCwr9yDMzCwrFwgzM8vKBQKQNFzSAkmTs+y7TFJIaldN2y0kPSbpA0lTJeV1ebqNzS5pF0mTKn19KenivIT+b4ZNed8vkTRF0mRJj0hqlvvE33j9Tcl+USb3lHy/55nX/1Z2SddJmlPp30PfatoeJelDSR9JujJ/qf/z+puSvdr/Z/mwsdklbSfplcznyxRJF+UtdETU+y/gYGBvYHKV7dsBz5NcfNeumrZ/An6W+b4JsEWxZK90bENgHskFMwWfHdgW+ARonnn8KPCTIsneHZgMtCBZ8vcloFva2YHrgMtq8O/kY6Br5t/6u8DuxZB9Xf/PCj07sA2wd+b7VsC/8/W+uwcBRMRrwOIsu34HXA5kncmX1Jrkf/qwzPOsiojPcxQzq43NXsVhwMcRkeur0L9hE7M3AppLakTyYftZ7Ses3iZk3w0YFxHLIqIceBU4Pjcps1tH9vXZF/goIqZHxCrgr8BxtRpuPTYh+ya1rQ0b+/oRMTciJma+XwpMJfkjKedcIKohqT8wJyLeXcdhXYEy4A+S3pE0VNJm+UlYvRpmr2wA8EgOI9VYTbJHxBzgNuBTYC7wRUS8kKeI1arh+z4ZOFhSW0ktgL4kvY5CMEjSe5mhkDZZ9m8LzKr0eDZ5+qCqgfVlL2Q1zi6pC7AX8FY+grlAZJH5xb0a+NV6Dm1E0mW8NyL2Ar4G8j4uW9kGZF97fBOgP/D3XOaqYZYaZc/8Eh0H7AB0BDaTNDD3CdeZqUbZI2IqcAvwIjCaZJimPOcB1+9eYEegB0nRvT3LMcqyrRDOk69J9kJV4+ySWgKPAxdHxJf5COcCkd2OJB8+70qaAXQCJkrqUOW42cDsiFhbzR8jKRhpqmn2tY4GJkbE/DzlW5eaZv8+8ElElEXEauAJ4IC8Jv22Gr/vETEsIvaOiINJhhym5TVpFhExPyLWREQF8CDJcFJVs/lmb6cTeR7ay6aG2QtSTbNLakxSHB6KiCfyla9Rvl6omETE+8BWax9nfuFLImJhlePmSZolaZeI+JBkLP9feQ1bRU2zV3IyBTK8tAHZPwV6Zf5qX07yvpfmK2c2G/K+S9oqIhZI2h74HyCvZ75lI2mbiJibeXg8yVBYVeOBbpJ2AOaQDE3+OE8Rq1XD7AWpJtkliWSec2pE3JHPfHmfyS/EL5IPyLnAapK/kn5aZf8MMmekkAxpjKq0rwfJh9N7wFNAmyLK3gJYBGxehO/7/wM+IPmF+gvQtIiyv07yh8S7wGGF8L5n3sP3M/+OnwG2qSZ7X5KzaD4Gri6y7Ov8f1ao2YGDSIby3gMmZb765iOzb7VhZmZZeQ7CzMyycoEwM7OsXCDMzCwrFwgzM8vKBcLMzLJygTAzs6xcIMzMLCsXCLMckbRP5iZszSRtlrmXf/e0c5nVlC+UM8shSTcAzYDmJPftujnlSGY15gJhlkOZu+WOB1YAB0TEmpQjmdWYh5jMcmtLoCXJSmB5XRbVbFO5B2GWQ5KeIVl5bQeSG7ENSjmSWY35dt9mOSLpNKA8Ih6W1BB4U1KfiHg57WxmNeEehJmZZeU5CDMzy8oFwszMsnKBMDOzrFwgzMwsKxcIMzPLygXCzMyycoEwM7Os/n+6DLirNIfeZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ignore()\n",
    "notice(14.5,15.4)\n",
    "plot_fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}