My data do not like me.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "thrown-track",
   "metadata": {},
   "source": [
    "# What happens if I can't get a good fit?\n",
    "\n",
    "Let's try the completely-made-up example from the \"What is Sherpa?\" notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "superior-aquarium",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sherpa.astro.ui import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "functioning-slave",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset               = 1\n",
      "Method                = levmar\n",
      "Statistic             = chi2gehrels\n",
      "Initial fit statistic = 19.355\n",
      "Final fit statistic   = 9.79605 at function evaluation 6\n",
      "Data points           = 4\n",
      "Degrees of freedom    = 2\n",
      "Probability [Q-value] = 0.0074613\n",
      "Reduced statistic     = 4.89803\n",
      "Change in statistic   = 9.55892\n",
      "   mdl.c0         -2.58909     +/- 2.33419     \n",
      "   mdl.c1         1.73737      +/- 0.597081    \n"
     ]
    }
   ],
   "source": [
    "load_arrays(1, [1, 2, 5, 10], [9, -2, 12, 14])\n",
    "set_source(polynom1d.mdl)\n",
    "mdl.c1.thaw()\n",
    "fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "korean-swing",
   "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": [
    "plot_fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "respected-potato",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset               = 1\n",
      "Statistic             = chi2gehrels\n",
      "Fit statistic value   = 9.79605\n",
      "Data points           = 4\n",
      "Degrees of freedom    = 2\n",
      "Probability [Q-value] = 0.0074613\n",
      "Reduced statistic     = 4.89803\n"
     ]
    }
   ],
   "source": [
    "calc_stat_info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prostate-forest",
   "metadata": {
    "tags": []
   },
   "source": [
    "The problem is that the reduced statistic is ~5, which means that some of the error analysis routines will error out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "strong-merchandise",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset               = 1\n",
      "Confidence Method     = covariance\n",
      "Iterative Fit Method  = None\n",
      "Fitting Method        = levmar\n",
      "Statistic             = chi2gehrels\n",
      "covariance 1-sigma (68.2689%) bounds:\n",
      "   Param            Best-Fit  Lower Bound  Upper Bound\n",
      "   -----            --------  -----------  -----------\n",
      "   mdl.c0           -2.58909     -2.33419      2.33419\n",
      "   mdl.c1            1.73737    -0.597081     0.597081\n"
     ]
    }
   ],
   "source": [
    "covar()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "conceptual-valley",
   "metadata": {},
   "source": [
    "Unfortunately this is a problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cultural-pilot",
   "metadata": {},
   "outputs": [
    {
     "ename": "EstErr",
     "evalue": "reduced statistic larger than 3",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mEstErr\u001b[0m                                    Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-a22cf0ef6f8d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mconf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<string>\u001b[0m in \u001b[0;36mconf\u001b[0;34m(*args)\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/ciao413/lib/python3.8/site-packages/sherpa/ui/utils.py\u001b[0m in \u001b[0;36mconf\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m   9901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9902\u001b[0m         \"\"\"\n\u001b[0;32m-> 9903\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_confidence_results\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_est_errors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'confidence'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   9904\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9905\u001b[0m     \u001b[0;31m# DOC-TODO: add a deprecation note?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/ciao413/lib/python3.8/site-packages/sherpa/ui/utils.py\u001b[0m in \u001b[0;36m_est_errors\u001b[0;34m(self, args, methodname)\u001b[0m\n\u001b[1;32m   9573\u001b[0m             \u001b[0mparlist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9574\u001b[0m         \u001b[0mids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0motherids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_estmethods\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmethodname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 9575\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mest_errors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_methods\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   9576\u001b[0m         \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatasets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mids\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9577\u001b[0m         \u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/ciao413/lib/python3.8/site-packages/sherpa/fit.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(fit, *args, **kwargs)\u001b[0m\n\u001b[1;32m     54\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m             \u001b[0mfit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     57\u001b[0m         \u001b[0mfit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mteardown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/ciao413/lib/python3.8/site-packages/sherpa/fit.py\u001b[0m in \u001b[0;36mest_errors\u001b[0;34m(self, methoddict, parlist)\u001b[0m\n\u001b[1;32m   1440\u001b[0m             if (hasattr(self.estmethod, \"max_rstat\") and\n\u001b[1;32m   1441\u001b[0m                     (self.calc_stat() / dof) > self.estmethod.max_rstat):\n\u001b[0;32m-> 1442\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mEstErr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'rstat>max'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestmethod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_rstat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1444\u001b[0m         \u001b[0;31m# If statistic is chi-squared, change fitting method to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mEstErr\u001b[0m: reduced statistic larger than 3"
     ]
    }
   ],
   "source": [
    "conf()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "identical-parts",
   "metadata": {
    "tags": []
   },
   "source": [
    "# What does this mean?\n",
    "\n",
    "\n",
    "The error-analysis routines (well, those other than `covar`) are essentially trying to climb up from the minimum until we are a given height from the minimum. We do have a couple of routines that let us view the search surface. Here we look at \"region-projection\" (2D) and \"interval-projection\" (1D): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "metric-wellington",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NrCHQ18AIVf22qMd4rcCnjIWM2Uj8Mr8+rb352Nfofn49Huja1tep/Geqys4/jvLpph0s2bCNfYnJBAe4aF+vFt2a1OOS2mfhcvrPlIiqQvYXaNpLkLcFnGcjESMgqJOZujH8yskKvG1z8Gq9c6R5fgzwfPnFfJCE3YBmzEHT30SiHvN1OkUqTcf6RIQ6FeKpUyGe29tfzPq9h1i0fiufbNrORxu3ExceypXnnUO3JvWoX7m8zw90iggEt4WgNpC9DE19wepk6aoH4XdZPXFK0x/IKJVsPcgq1mfadUBt4FVV/c7OeKdLnJXQkJ7WxhLhtyDOir5OqVAOEdTtF++JxUpEOP+sypx/VmVGX9maVTt+Y/H6rcz+biPT1/zE2eVi6X5+Pbo2PpfK0ZE+z5XgThDUAbIWo2kvo8dugoDzIfxuJKiFT/MzjJPxykFWEYkGFgJ3qOrmE24bDgwHqF69etM9e/bYng+A5u1DEzpC6CAckQ96Jea/1fLJN+jYoA4PX9Xe16l4xbGMLJZu3sHi9Vv5cc9BAC6sWZXuTerRsWEdv1iJo5pr9aZPe9U6YS7wYiT8HiSwsa9TM8oon8zBF5LEI0C6qk4o6j7emoM/zp08CjI/sjoSOv1vX9ZW4yfRvl4tHu3RwdepeN2+xGMsWb+NReu3sufoMQJdTtqeezbdm9SjZd0aPj/grJoNGbOsg7HuRAhqh4SPQALq+TQvo+zxyRy8iJQDclX1mFgtGzsAT9sV70xI2M1o5gdo+jQk8n5fp/MPDhH8aRmrN1WLjeaWdi24uW1zNu4/zOL12/hk43aWbv6FmNAQrjjvHLo3qUejqhV8MhcuEgRhQyGkr7VRePoU9OhVaHAXJPxOpASdRGeUXnbOwVcC3vHMwzuA91R1iY3x/jVx1UCDr4TM2Wj4jYgj1tcp/Y0ApXAK/l8RERpXq0TjapW4v8tlfL3jNxat38r8tZuY9e16asTH0K3xuXRrUo+qsVHez88RBuG3QOhATwuEd9CsT9GQq5Gw2/x6lZZR+pXJE50K0rydaMKVEHYTjoh7vBr7VNo98yYX1zqLJ3p19HUqfic1K5tlm39h0fqt/LB7PwAXnFWZbk3q0blRXaJCgn2Sl+YftaZtMmYBCqF9kbBbEGd5n+RjlH5+MQd/OnxR4AHcSXdCzldWH3GH90eBRWn/7BSa16zGk707+ToVv3YgKYWPNm5j0U9b2XUkkQCnkzbn1qR7k/q0ObemTxqgaf5hNO01yJwPOCH0GsQPPyUaJZ9P5uBLEgm/BT36KWRMh/A7fJ3OnxwiuP3oDdhfVYmJZHjri7jxsgv5+eAfLFq/lY83bGf5lp1Ui41iyKVN6XFBfUICvddyQJwVkajH0bAb0LSXIWMamjkHDR2ChA0zO0wZXmFOyQNr5UNQezR9OupOO/UDvMQhgvrHuWElgojQoEoFRl/ZhpX338gLA7oSHRrC2MWf0+HZqby64huS0jO9m5OrOo7oZ5H4jyCwpbXxSEJnNPPDMnsA3fAeU+A9JPxW0GTIeNfXqfxJRMr8QdYz5XI66NiwDnNu7s/0G/rQuFpFXv38W9o/O4Wxiz5n79FjXs1HXLVxxLxs7TDlrIAm/w9N7I/mbvJqHkbZYqZoPCSgERrYyloJEToYcYT6OqUyvUyyuIgIzWpWpVnNquz84yhvf72OeWs3Mff7jVzeoDbXt2pGo6reO5NZAhtD7Dyrc2XaBPRobzSkFxJ+L+KM81oeRtlgRvAFSPhtoEmQOcfXqQBmDr641S4fx7ieHfnsvmFc36opa3bupd/rs7luyjy+3LYLt5c+Lok4kNBeSPwyCB0KmR+gCZej6W9ZZ8oaRjExBb4ACbwAAlug6VNRzfJ1OohgCrwNykeGc0+nVqz43zBGXnEZ+xKPccuMD+nx8gwW/riFnLx8r+QhjggckaOQ+MUQcD6aOh5N6I5mf+2V+EbpZwr8CSTsNnAfgYx5vk7FTNHYLDw4iCEtm7L03ut5qncnHCI88P4yOk6YytRVP5Cale2VPMRVC4mZgkRPAnLRpOtxJ92C5u31Snyj9DIF/kSBF0FAUzT9TVRzfJqKIF6bNijLApxOup9fn4V3XMPkIVdTs1wszy39mnbPTOHZT1ZxODnV9hxEBAlui8R/jITfCznfoAldcKdORN3ptsc3SidT4E8gItZcvPswZC7weS5misZ7RISWdWrw1rDezL91IK3Pqck7q3+k44RpjJ7/KTsOJ3ghh0Ak/CYkfikEd4b0NzzLKhebT3PGv2YKfGECL4WA89D0yT496GWmaHynfpUKTOjXhU/vGUq/5uexbPMv9Hh5Bje9s5Dvdu2z/e8izgo4oicgsXPAUQ5NvhdNHIjm/mxrXKN0MQW+ECJizcXn74fMRT7LwyGm2ZivVY2N4oGubVkx8gbu7HAJPx/4g6FT59P3tVl8snE7efluW+NL4AVI3Dwkchzk70aPXo07+SHUnWhrXKN0MAW+KEFtwNUQTXvJZytqzDJJ/xEdGsLNbZuz/H/DePSq9qRl53Dv3I/p8vxbvPvNejJy7PukJ+JEQvt6llVeZ+1EdqSjdea15tkW1yj5TLOxk9Ds79CkwUj4fUj4cK/H7//GbCKCgnhzaE+vxz5dCQeO8sodU/lpxWZiK0UTXyWW+KpxxFf2XFb56zK6fCROH2/UUVzy3W5Wbt3FtK/Xsn7vIaJCghnYojEDWzQhLtzek+Q0byeaMg5y1oCrDhLxABJ0ia0xDf9lmo2dIQlqjga1RdPfgNDeXu8E6M+9aFSVT6asYNL/ppOfm0+7ga3ISM3gyP5ENq3aSsKBRPJPWE/udDmJrRRNuapxxFWJpVyVv78BxFeNJa5yLIFB3msKdqacDgcdGtSmQ4Pa/LjnAFNXreX1ld8x7au19LigAUNaNuWsuGhbYourNsS8BdmfWWvnk4agQR2RiFGm/7zxN2YEfwpWv/iuENwViXrWq7sHXTN5Llm5ecy7daBPdi06mZdum8Li15fSpG0D7p58M5Vr/f10f7fbTfKRFI7sP0rCgUQS9ieScMDz/YFEEvYf5cj+o2Sl/3OteXT5KCrXrkjVupWoVrcyVepWpmrdSlSuVYGgEN/vy1qUXUcSefvrdXz401by3Pl0Oe9c7uxwMdVio22LqZoN6dOsQYi6rRVgYcMQ8f83SaN4mH7w/5GmvYKmvQSh1yERY7xWbN9Z/SNPf/wlD3Zry8AWTbwS83Q9M+QVVn/wPQuOvnXG0y6qSkaKNeo/XvQTDiTyx94EDuw8xP4dh0g8lPTn/UWE8tXjqVK3ElXrVKKqp/BXPacy5avH+830z5HUdGas+ZGZ36wnz+2m30XncXOb5rZO3Wj+YTTlCcheCq56SNR4JKC+bfEM/2EK/H+kqmjqeMh4G8JuxRFxl1fiut3KrTM+5Jtf9zL7pn7Ur1LBK3FPx9K3VzLh+teYvGECNRudZVucjNRMDvxiFfv9Ow56vj/Ivu0HyUj5q/VvQKDrz1F/lTpW4a/ZqDrnXFjbZ59+/khJ49XPv2XBus0EuVwMbdmUIS2bEhYUaFtMzVqKpjxubQQeNgwJv8PaP9YotUyBLwaqiqY8DJlzrc5/4Td5JW5Seia9Xp1JgNPJ/NsGERHsH/9Zf99zhGtq3sqtLwzl6ju7eD2+qnLsSAoHPMV+/45DHPjFujy48zC5Odbqknot6jD4kb4069jYZ4V+95FEXly+mmVbdhIXFsr/ulxGt8bn2paPupPR1Kcg831w1kSinkACC/3/b5QCpsAXE9V8NHkkZC1GIh5CwgZ7Je6Pew5w3ZR5dKhfm4n9r/Sb+fhra99OzUbVeWzhSF+n8jf5+fn8sTeBtUs3MOephfyxN4Fzm9dh8MN9uLBzE5+9fhv2HeKpj75kw75DtKpbg0euak/l6Ejb4mn2ajTlIet8jtBB1sDE7CRV6pyswJt18P+CiBOJegqCOqCpY9GM970S94KzqjDi8ktZuvkX5n6/0SsxT0fjNg3Y+OXP5Od7p/vi6XI6nVSqWYFuN3fk7R0vcdcbw0k6fIwHrnySOy8ew3cf/+iTM4QbV6vEzOF9GX1lG9bu3k/3F6cz57sNtvUbkqBLkbjF1tr5jFloQlc0+ytbYhn+yRT4f0kkAIl+AQJboikPoJkfeSXu9S2b0apuDcZ/9CU/H/zDKzFPpUnbhqQdS2fXhj2+TqVIAYEBXDn8ct7a/iJ3T76ZY38k82DX8dzRYjTffbTO64Xe6XAw+JLz+fDOa2lcrRKPL/qc66bO47eEpFM/+AyIIwxH5ANWywMJQZOG4T42EnUfsyWe4V9MgT8DIoFIzKsQcIG19VrW57bHdDiEp3p3JjYshDvfXUxCmu87DDZu2wCA9Su3+DiTUwsIDKDLDe15a/tL3PPmzSQfSeHBbk9xe/PRfLvE+4W+amwUU4b2ZFzPjvxyOIGrX57B1FU/2Nb6QALPR+I/hLBbIWsJmnAFmvWp6XVUypkCf4ZEQpCYyRBQDz12J5q92vaYMWEhvDyoG4npGdw2YxGZNp4efzriK8dS7ZzKrF9ZcvYVdQW4uGKYVejvnXILqYlpPNT9KW67aBTfLF7r1YInIvRs2oDFI66lVd2aPLf0a/q/MZtth47YFC8QR8RdSNz74Khk/bs9djua7x+fCI3iZwr8fyCOcCRmKrhqosduRXPW2R6zYdWKPNP3CjYfOMzo+Z/6vF984zYN2PzVtn+ctervXAEuOl/fjmlbX+DeqbeSlpTOw1c9zW0X3s+aRT94tdCXiwznxYFdeX7AlfyenEbf12bx4vLVZOfa02dGAuohce8hESMhexWa0AX1YVM9wz6mwP9H4ohGYt4CR0U06UY0d7PtMTvUr83IK1qzbMtOnl/m2+3dmrRtSEZqJjvW7fJpHmfKFeCi89C2TNv6AvdNu5X05Awe6fEMtza7nzUfeq/QiwidGtZl8V3X0bXxuUz64nt6vfouP+05aFM8FxJ2g7VdoKsOmnwf7mP3oe40W+IZvmEKfDEQZzwS+w44otHE69HcHbbHvPaS8xnQvDFTv1rLez5cWXNeG2sefsNK+9/Y7OQKcNFpSFumbX2RkW/fTkZqJo9c/Qy3NB3J1wu/w+22ty3wcdGhwTzZuxOTh1xNZm4u17w5lyeXrCQ9257dxcRVA4mdiYSPgKyP0KNXoTkbbIlleJ8p8MVEnBWRmLdBAtGkoWjeb/bGE2H0lW1oVbcGYxd/zupffLOSJaZ8FDUaVOOnEl7gj3O6nFx+bWum/fwCI9+5naz0bB7rNYFbmo7kqwXeK/Qt69Rg0Z3XMqB5E2Z+s54eL89gzU57/sYiTiT8NiT2XVA3mjgATXsD1ZI17Wb8kynwxUhc1ZHYt0Hz0MTr0PwDtsZzOR1M7H8ltcvHcdfsJfzyu/1byhXm/PaN2PzVVjJSM0995xLC6XJy+eDWTN3yPPdPv4OczBwe7z2BWy4YyVfvf+uVQh8WFMiD3doy48a+BDqd3PDWAh54fxnJmfbsTyCBF1grbYI7omkTrYFK/mFbYhneYQp8MRNXbST2LdB0NHGI7SsUwoICef3aHoQFBnDz9A84kuL9OdTWfS8hJyuX1R987/XYdnO6nHS45jKmbHmeUTPuJDc7l8f7PMfN5/+PVfO/8Uqhb1qjCgtuv4YbW1/IovU/0+2Fd1i+5RdbYokjEol6Hol8CnI3ognd0azPbIll2M8UeBtIQH0kZgq4j1ijIJu3V6sYFcFrg6/iWHomt81cZOvuQoWpf3FdKtYox+ezSu9Zkk6nk/aDWvHm5omMnnknebn5jO07kZua3MeX8+wv9EEBLu7u2JK5twwkPiKMEbOWcNesxRxJLf7zIUQECe2JxC0EZxX02K24Ux712c5mxpkzBd4mEtgEiZ4EeXvRxGGoO9XWeMc3id5y8HdGvvcJ+V6aKwarILTp35IfP9tE0u/HvBbXF5xOJ+0GtuLNTc8xZtZduPPdjOs3kZsa38eX762xvdDXr1yeubcM4K6Ol/LF9t10e/EdPvhxiy2rfcRVE4mbC6HDrFYHR3uhuduLPY5hH1PgbSRBzZGYlyFvh7WE0p1ha7y29WoxqksbPt/6KxM+9e5ouv2gVrjz3Xw57xuvxvUVp9NJ2/6XMnnjczww+y5UlXH9n2f4efeycs5qW/vzBDidDG99EQtuv4ba5eMY8/4ybnpnIQeSUoo9lkggjsj7kZhp4E6yinz6THMGbAlhukl6gWYtRY+NgMDmSMxk2/tzP7FkJe9+s56Hu7ejf/PGtsYq6KYm9xEUGshLa570Wkx/4Xa7+Wr+t8wcO5/ftuyjer0qDHqwN637XmzrRiRutzLn+w1MXPo1CtzTsSUDmjfG4Sj+jpmafxRNGQXZX0JQOyTqSa9vY2n8k+km6WMS3AmJGg8531inh6u9c+SjurSmzTk1Gbd4Jau277Y1VkFtB7Rk67e/cGjX716L6S8cDget+17CpA0TeHDuPTicDsYPepEbG93L57O+sm1E73AIA1s04cM7r6XpWZV5YslKrp3yHruOFP9xH3HGIdGTkYgHIfsr6wBs9ppij2MUH1PgvURCrkYiH4XslWjyfbauMXY6HDzbrwvnVCzHPXM+sq23yYna9r8UgM9n+/bsWl9yOBy07nMxk9ZP4KH37sEV4GT8NS9xY8N7WPGufYW+Skwkk667mvG9O7Hzj6Nc/fJMJn3xPbnFHE9EkLBrkbj5IOFo0lDcqc/aPmgxzoyZovEyTZ+Kpj4NIT2RyCcRse899vfkNPq/MRsRmHPzAMpH2r/Zwz2tHyY5IYUpm5/3m41JfMntdrP6gx+Y+fg8dm3cQ9W6lbj1haFc2Pl822ImpKXzxOKVLN38C+dWKse4nh2pX7l8scdRzURTnoTMuRDQCImaiLjs277RKJxPpmhEpJqIrBSRrSKyRURG2BWrJJGwYRB2O2QuQFPH2XqwqkJUOK8NvorkzGxumf6hbae7F9R2QEv2bj3Arxt+sz1WSeBwOGjVszmv//gMj7x/H+JwMKbLkzwz5BVSjtqzsio+PIznB3TlpYHdSEhNp9/rs5i49Otib14mEoIjaiwS/bK1WuxoDzRzoTkA60fOuMCLyKmGg3nAvapaD2gB3CYiZpt3QMLvgNDrIWMmmjbB1v8Q9SqXZ2K/Lmw/fMQryycv690Cp8vJyllld5qmMA6Hg5ZXN+eNn55l0AO9+HzW1wxrcDer5tu36qhDg9osvus6rjq/PlNW/cDVr8xk3W/Ff3a1BHdC4heBqz6afL81BWmalvmF/zKC//lkN6rqIVX90fN9KrAVqPIf4pUaIoJE3A8hAyD9TTTtJVuLfOtzz2ZM1zas3LaLcYtX2tpiOCo+kgs7N2HFrK84diTZtjglVWBQAEPG9ufVH56iXLU4xvadyNh+E8lMs6fNQ1RIMON6dmTK0J7k5ucz+M33eOqjL8gp5vbO4qyExE5Hwu+CrI/Ro93R3JOWCMMLTjoHLyL3FHUT8ICqntYaKRGpAawCGqpqygm3DQeGA1SvXr3pnj3+u/1bcVN1oykPQOb7ENIPiXwEEZdt8SYu/Yopq9bSrcm5jOvZkQCblu9tXPUzozuPI7ZSDOMWj+Ks+tVsiVPS5eflM2/CIt56cDY1zzuLsYtGUa5qnG3x0rNzeH7ZamZ9u54LzqrM8/2vpJwNx2U05yf02F3gTrKWUoZ0LfYYxl9ONgd/qgKfBTyLNd1yortVNfo0gocDXwJPqOqCk923LBxkPZGqomkTIX0SBLVHoiciEmJbrMlf/sCLy1fT+pyaTOx/JSGBAbbE2vrdLzzS42lysnJ56L17aHq599bjlzQ/fPoT4/o9T3B4MGMX3U/dprVsjffJxu08uHA5YYEBPD+gK01rFP8Ha80/ih67HXLXQdhwJPxuROw7H6AsO1mBtwpMEV/AGqBpEbftO9ljPfcJAJYC95zqvqpK06ZNtaxyp03X/EN1NT+hr7rzE22NNee7DVr/gYk6aNIcTc7ItC3O4d/+0BvPu0c7uvrqoteX2hanNNi1aY8OqnGLXhk6UFe9/63t8XYcPqKdn5umjR58Qaev/lHdbnexx3C7szX/2EOaf6iO5ifeoO78lGKPYagCa7WImnqqEfw5wFFV/UcfWhGpoKpFntEi1hq5d4BEVb3rdN6JyuIIviDrjNd7wVkFiZmKuKraFuvTTTsYOe8TapWLZfJ1V9vyUR0gPSWDJwe+wPcf/0TPEVcyfMJgW8/sLMmSfj/GI1c/w9Zvf2HY+EH0G3mVrUtNU7OyGT1/KZ9v/ZVeTRvyyFXtcTmLf2GdZsxGU8aCsxoS8zriOrvYY5RlZzxF8x+DtgS+AjYBx5dujFHVj4t6TFkv8ACa8wOadAtIsNXWIMC+hUdrdu7hjncXEx8eyptDelI9LtqWOPl5+Uy6bzoLX/qYFl2bMvrdEYRG2DMNVdJlZ2YzYdjrfDFnNZ2HtuXO128kwKZpNLBaHbyy4hve+OI7OtSvzbN9ryAooPiPA2nOD+ixO0BzrWnIoNbFHqOs+s8FXkSWA31U9Zjn5xhgjqp2Ks5ETYG3aO4vaNIw0FQk+jUk6GLbYm3cd5ib3lmIy+ngzSE9ObdSOdtiLX59Ka/cOY0aDasxdtEoyleLty1WSaaqTH/0PWaOnU/jNg14eP69RMZG2BpzxpqfGP/RFzQ/uxovD+pGeHDx90vS/ANo0q2Qtw0Jvw/CbjAnwxWD4jjRKf54cQdQ1SSg+E+NMwCQgDpI3HtWL+6kG9DMJbbFOq9aRWYO70uA08F1U+bZsk76uG63dGLcktEc3v0HdzQfzfYfdtoWqyQTEa57rB+jZtzJz2u2c+fFD7B/hz2bbx83+JLzebpPZ9b+tp+hU+eTmF78nU/FWQWJmwPBV6Bpz6LJ96JaenYB80enW+DdIlL9+A8ichZgTlezkTgrIrGzIKAJmnwPmj7Ntli1yscxc3g/4sNDueGt9/ly2y7bYl3YqQkvrn6CwOAA7m3zCF+9/61tsUq69oNa8cyKR0hLSuPOi8ew4Ysttsbr1qQerwzqzs4/jnLN5Pc4eMyO9sMh1o5R4fd6NvkegObb++ZVlp1ugX8A+FpEZojIDKw17aPtS8sAz/ZpsdMgqDOa+hTulPGo2nMmauXoSGYM70vt8nHc/u4iFq3fakscgBoNqvHSt+Op1aQGj/d5jtnjzentRWl46bm8/O14YipGc3/HsXw67XNb47U+92ymDO3F0bQMBk2ay84/jhZ7DBFBwm9Cot+A/L3o0Z5ozg/FHsc4zQKvqp8CFwBzgfewlk4utTMxwyIShEQ/D6GDIeMtTydKe3rKxIaF8vYNfWhWoyqj5n3KjDU/2RIHIKZ8FM+ueIS2Ay5l2gOzmDDsNXK9vNXgv/H88h0+i13p7Aq8uPoJGrdtwHM3vM6b98+0deeopjWqMP3GPuS73Qye/B4b9h2yJY4Et0Xi5oFEWZvUZ8yxJU5ZdtprolQ1QVWXqOriwpZNGvYRcSIRDyLh/4OsJWji9bbt8xoWFMgb1/bg8ga1Gf/RF7z82RrbRteBwYGMnjmCax/py7K3v+D+jmNta8D1X724wp5Nrk9XeHQYTywZTdebLue9Zz/k8T7PkZlu3x6p51Qsx8zh/YgMCeL6ae+zZqc9Z5iLq5ZV5IMuRVMexp38iG0DmLLovzQb+7E4EzFOzvpYeyMS9RzkbkATetnW6yMowMXE/lfSq2lDXl/5HY8v+ty2JmUiwuBH+jD63RFs+24nd1w8xvYDiiWVK8DFna/dyC0Th7Dmgx+4t80jJBy0b0P36nHRzLyxL9Vjo7h5+gd8usmeTzHiiLSma8KGQ+ZsNHEIml/8U0NlkekHXwJp7iZruZmmIFFPIcFX2BNHleeXfc2UVWvp3KguT/XuTKDLvpOUtqzZzqNXP0N+Xj4Pz7+PJm0b2hbrdDy/fEehI/cR7etw9+V1fZDRX75dso4nBjxPeHQYYxeNovb5NW2LlZKZxa0zPuSnvQd5qJu920Bq5hI0eQw4YqyTomw8D6S0OONWBd7+KsutCv4td94fmp/QzzoNPGWiut35tsWauuoHrTdmot4w7X1Ny8q2LY6q6sFdh3VYg7u0U0A//WTqCltj/Rtn3b/E1yn8w871u3VAtZu0a/ggXf3h97bGysjO0ZvfWaj1xkzU1z//1pbWBse5czZr/u+Xaf7hxurOWmVbnNKCk7QqOOkUjYikikhKIV+pIlL8a6iM0ybOckjsdAjpA+mvo8duQd32zF9f36oZ43p25Jtf9zLsrfc5lmHf2uVKNSvw4upxNGnX0CsHFEuyWo1r8PJ346leryqPXv0s855bbNvxkpDAAF4a1I1uTc7lpc/W8PTHX9rWdloCGljz8s7qaNJNaOZCW+KUBSct8KoaoaqRhXxFqGqkt5I0CicSiESOQyIfsTZBPtoHzbNnk+2eTRvwwsCubDt0hGsmv8fhZPsOhoZFWQcUu93SySsHFE/HiPZ1fBq/KHGVYnjui8do2fMiJv9vOi/cNIm8Yt656bgAp5PxvToz+JLzmb7mJ0a//2mx7/l6nDjLW+eBBF5obSKSNskspT0Dp2o2dtJ+76parEd4zBz8mdPs79BjdwJ5SPTzSNBltsT5ftc+bpu5iMiQIKYO7UWN+Bhb4oA1ffjBy5/wxj1vW2vmP7yf+Cr29UsvydxuN28/NIfZ4xdyfvtGPPTePUTE2NNATlWZ9MX3vPTZGtvbTqvmoMmjIGsJhF6DRDxg2g6f4L/0g9+NdcaqANWBJM/30cBeVS3WIzumwP83mrcfPXYr5O1AIu6D0GG29Pr4+cDvDH/H+tg86bqraVClQrHHKOi7j9bxxIAXCI0MYeyiUdS5wHQjLMqyd77g+eFvUOnsCoxbMprKtSraFmvOdxsYu/hzzq9emdcGX0VkSLAtcVTdaOozkDENgjoh0RMQKf5eOSXVGfeiUdWaqno2Vk/3bqoar6pxQFfgpJt3GN4nrqpI7BwI7oSmPuM5Kar4pzbqV6nAzOH9CA4IYMjU+Xy3a1+xxyio+ZVNeeHrcThdTu657GHWfGjOeixKx+va8PTyhzl2JIU7Woxh01f2nZHcv3ljJvTrwsb9h7luyjyOpKbbEkfEgSNyFBIxGrKXoolDUbfZDvJ0nO46+Au1QJtfVf0EMP0+/ZA4QpGoF5Dwu62Too4ORPOL/0zEGvExvDu8HxWjwhn+9kI+22Jv47CzzzuLl799khoNq/Foz2eZN2GRmZMtwnmX1eflb58kMi6ckR0eY/n0L22LdUWjc3jj2h7sTUzmmslz2Zd4zLZYEjYUiXreOg8k0Z5/16XN6Rb4BBF5UERqiMhZIvIAYM5E8FPWSVG3INGvQ/5uT6+P4p/6qhAVzowb+1G/cnlGzF7Mqyu+sXVD79iKMUxY+SiX9WnB5JEzeH64fQcUS7oqtSvx0jdP0rBVPZ4Z8gpvPTjbttVIl9Q+i2nX9yIlM5tBk+ay/fARW+IASMiVSMxUyD+EHu2L5vquhURJcLoFfgBQDmtaZqHn+/52JWUUDwlu5+n1EeHp9TG32GNEhwYz7fpedGtcj1c//5bbZn5IcqZ9K16CQoIYM+suBj3Qi0+mrmD0FU+QmpRmW7ySLCImnPGfPMAVw9oz68kFPDHgBbIzs22J1bhaJWYM74vT4eC6N+fx4x772k5LUAsk9l1A0cQBaM73tsUq6U63wJ+NdZBVsPZZbY+1kbbh58RV2yrygS3QlIdwpzyKavE29QoJDGB870481K0dq3fuoe9rs2wdxTkcDoaM7c/Id25ny9fbGHHJAxzYaT6uF8YV4OLuyTcx/JnBfDX/W+5r+yiJh5NsiVXb03Y6NjyUG95awJfb7VmyCyAB9ZC4ueAoZ83JZ31qW6yS7HR3dNoO3Ads5q/t91DVYu1AZFbR2Ec1H017DtKnQMCF1lJKZ/Hv2bJ+70HumrWElKxsHujalp5NG9i6a8+mr7byaM9nUVVGvzuCCzs1sS1WSbf6g+956pqXiIgLZ8Lnj9q2wiYxPYOb3l7I9sMJPNWnM13OO8eWOADqTrK2uMz9CYl8GAkdZFssf1UcOzodUauL5G5V3XP8qxhzNGwm4sQRMRKJmgC5m9CEbmjWymKP06R6ZebdNojzqlbkoYXLuemdhRxIsu+k50at6vHyt08SWzGaMVc8wbPXv2qmbIpwaY+LmLjqcVISUpk3YZFtcWLDQnlrWG+aVK/EqPmf2rrKShwxSOzbENQWTXnMlmnIkux0R/DtsebhVwB/TuKparEulTQjeO/QvJ3osXsgbxuEDkYiRhb7umK3W5nz/QYmLv0aBe7p2JIBzRvjcNgzms/JymHm2PnMfeZDostFcvsrN9CqZ3NbYpV0Twx8gXXLNjD34GRbN/ROycxi4KS5JKSmM+vm/pxd7qTnTf4nqjlWA76cr5CoCUhIN9ti+ZviGMEPBZoAnYFunq+uxZKd4XV/zsuHXgcZM9CjvdG84l3m6HAIA1s04cM7r6XpWZV5YslKrp3yHruP2NPeNjA4kOufGMirPzxFbKUYHu89gUd7Pcu+7fYd7Cup2g9sRWpiGuuWbbQ1TmRIMG9c24MAp5Nbpn9gyz6vx4kEIjGvQOBFaPJINGu5bbFKktMdwW9S1UZ2J2NG8N6n2V9Yp4K7M5DIMRDSr9jnzFWVReu3Mv6jL8jKzePWdi0Y2rIpAU57TjnPy81j/nOLmTl2PrnZubQd0JKBD/Si+rlVbIlX0uTm5NKv8nCadWrMmHfvsj3ehn2HGDJlHvUql+et63sTFOCyLZa609CkoZD7MxLzBhLUyrZY/qI4RvDfiohpzFwKSVAbJG4xBDZFUx5Gj92Ouo8VbwwRrjq/Pkvuuo42557NC8tW0+/12fx88I9ijXOcK8BF/1FXM2P3a/S+pxurF37PDQ3uZvw1L7J3mxnRBwQGcFnvi/nmw7VkptnXGfS4xtUq8XSfK1i/9xBj3l9q67kS4ghHYqaAqxaadFuZ3+v1dAt8S2C9iGwXkY0isklE7P18Z3iNOMshMVORiPsh+wvrAGz2d8UeJz48jBcGdOWlgd1ISE2n3+uzmLj0a7JtOlkppnwUNz4zmOm7XqXPfd1Z8+EP3NDgbp4c9AJ7tu63JWZJ0X5QK7IysvlmkXc+MXdsWId7O7fik007eOmzNbbGEkcUEvMWOCujScPRnA22xvNnpztFc1Zh15tlkqWP5m62DsDm74Gwm5Hw2xEp/gNxyZlZPPvJKhas20KN+BjGXn05TWvYO4Vy7Egy859bzIevfkp2Rg6t+17MoAd7U6NBNVvj+iO32801NW+lZqPqPLFkjFdiqiqPfvAZ89ZuZuzVl9Ormb07dmn+YTRxILhTkdgZSMC5tsbzlTPuJultpsD7B3Wno6njIPN9CGiCRD2HuOwpgmt27uGRDz7jQFIKA5s35u5OLQkLCrQl1nHJCSl/Fvqs9Gwu69OCax7qU+YK/Zv3z2T+xMXMPTiZ6HJRXomZm5/PLdM/4Ptd+5k05GourlXd1niat88q8uQhse8irtLXibQ45uCNMkQcYTiixluNnfJ+RY9ehWYutiXWJbXP4oM7BjP4kvOZ/f0Gur80na92/GZLrOOi4iMZNn4QM3a9Sr/7e/D9xz8x/Lx7GdtvIrs377U1tj9pP6gV7nw3X83/1msxA5xOnh/QlRrxMdw1awk7/7C3pZW4qiGx7wBY7Try7O186m/MCN44Kc3bjybfB7k/QnAP62xBhz0bSazfe5AHFyxn15FErjq/Hvd3aU10aIgtsQpKOZrK+88v4YOXPyEjNZNWvVsw+KHe1GxU6MxkqaGqDD/vXsKiQ3nhq3FejX0gKYX+b8wmOMDF7Jv7Ex8eZms8zd2GJg4GR6Q1knfa1yff28wI3jhjVo/5mRB2G2QtQo9ejebac3y9SfXKLLh9EDe3ac5HG7bT7YXpLN28w/a2wJFxEQwdN4AZu19l0AO9WLd0A8Mb38fjfSawa2PpPWFbRGg7oCVbVm/n8G/2rGgqSpWYSF4bfBVH0zK4feYismzuCioB51qra9yJaOIQ1G3P+Rj+xhR445REXDgiRiCxM0Bz0KP90bQ3US3+9rOBLhd3Xn4J7906kIpR4dw9+yNGzFrCkRT72w9ExkYwZGx/q9A/2It1yzdyU5P7eKz3BH7d8Jvt8X2h3cCWAHz53jdej92oakWe6XMFm/YfZtT8T21dPgkggY2RmMmQf7DMbBpipmiMf0XdyWjyQ5D9KQRegkQ9Y0vTMoC8fDfvrPmRVz5bQ6DLxcgrLrO9eVlBqUlpLHjhIxa8+BEZKZlc2uNCrnm4D7WbFOtOlT43vPG9xFSI4ullD/sk/ttfr+OZT1Zxw2XNuKeT/ScmafZXaNLNENAAiZlm25Sjt5gpGqPYiCMKiX4RiRwHuT/Z1rQMwOV0MKxVMxbeMZhzKsbz0MLl3PDWAvYnemfkFRETznWP9WPm7tcY/HAf1q/cwi0XjOSRq59h50/2tcL1tsZtGrD5623kZBdvG+nTdd2lF9D/ovOYsmot837YZHs8CWqFRD9vNd07dqst21r6C1PgjX9NRJDQvkjcAnBWRI/dhDvlMdRtz56cNeJjeHtYHx7u3o6N+w9z1UvTmbHmR/Jt2qHoRBEx4Vz7aF9m7n6Nax/ty8Yvf+aWpiN5uMfT/PLjLq/kYKcmbRuSk5XLtu9+8Ul8EWFM17a0rFODxxetYM1O+497SHBHJOppyPkOPXYHqjm2x/QFU+CNMyauWp6mZUMhYxZ6tBuavdqWWA6H0L95YxbdeS3NalZl/Edfcs3k92xfZldQeHQYgx/uw8zdr3LdY/3YtGortza7n4eueood6371Wh7F7bzW9RERNqzc4rMcXE4HE/t3oVa5OO6atYRffk+wPaaEdEciH4fsL9Fj96Fa+rZ/NHPwRrHQnLVo8gOQvxtCeiERoxCHPSfPqCpLNmxj/EdfkJ6dy81tLmLYZRcS6LKneVlR0pPT+eDlT3n/+cWkJqXTomtTrnm4D+c0q+XVPIrDrc1GEhIRwnMrH/NpHoeOpdL/jdkEOJ3Mvrk/5SLsXT4JoOlvo6lPWsuAo55CpGSNe80cvGE7CWyGxC+CsJsh8wM0oQuatdSeWCJ0a1KPxSOu4/IGtXl5xTf0e30Wm/cftiVeUcKiwhj0YC9m7H6NIWP7s2XNdm6/aBQPdhvP9h+Kt/2y3Rq3acjWb3bYtmfr6aoUHcFrg68iKT2DEbMWk5ufb3tMCRuChN8FWR+gKY/bvizXm2wr8CIyTUT+EJHNdsUw/ItIEI6Ie5C49629Mo/dgTvpDjTfnv1Z48JDmdCvC69c052k9Ez6vzGHCZ+usn1N9YnCIkMZ9EAvZux6laHjBvDzNzu4vfloxlz5JFt9NK/9bzVp24DcnDy2rNnh61RoUKUC43p1ZP3eQ7y43J4pv38IuwXCboTMWWjqM6WmyNs5gn8ba4MQo4yRgPpI3Dwk/F7IXmmN5jMX2Pafpl29WiwacS29mjZg2lfr6PHyDH7Y7f1ukWGRoQwc05OZu19j2JMD2f79Tu68eAxjujzBz9/6vnCeTMNW9XA4HWxY6R/jsSsanUP/i85j2lfr+GKb/QeyRQQJvw9CB0HGVEh/1faY3mDrHLyI1ACWqOpptY0zc/Clj+btsubmc9dBYEsk8nHEVdW2eN/+updHPviMfYnJ9L2wEXdefgmxYaG2xTuZjNRMFr22lHkTFpFyNJVmnRpzzUN9qNeiDg6H/82O3nHxGBxOBy9+7d22BUXJzs1jwKQ5HDqWyoLbr6FSdITtMVXdaMoYyFyARNyPhA2zPeZ/5bNukqbAG2D9pyFzNpo6AVAk/B4Ivca2g1kZObm8/NkaZqz5iZDAAK5v2ZRrL73A9i6VRclMy2TRa8uYN+FDkhNSCQoJpEqdSlSpW4mqdSpRtW5lqta1LiPj7C9iRZk6+l3mPbeYhYlvERJufw+g0/FbQhJ9XptF3QpxvH1DH9t2AStINR9NvgeyPkEiH0NCB9ge87/w6wIvIsOB4QDVq1dvumdP6e39UdZp/gE0+WHI+QoCLkCinkBc9q04+fWPo7y4fA2f/byTuPBQbm3bgt4XNvRKkShMZlomX773Db9t2ceBXw6xf8dBDu36g/y8vw4kRsZFULXu8eL/V+GvXLsiwaHFuzH6idYu28DozuN48uMxXNj5fFtj/RufbNzOvXM/ZlirZtzb2Ttb8KnmoMfugOwvkKinkZAeXol7Jvy6wBdkRvCln6pC1odoypOg6Uj47RA2DBH7Rtfr9x7kuaVfs+63A1SPi+aO9hfTqWFdXE7fT5Pk5eZx+Lcj7N9+kP07DrJ/xyEO/GJdJhz4e0OsctXirNF+nUpUqVOJctXiiK8aR3yVWOIqxeD8j8tEM9Oz6Bk7hJ53deXGp6/5T89V3B77cAVzv9/I69f2oPU53mkVoZqNJt0IOd9bZ28Hd/JK3H/LFHjD72h+Apo6FrI+AedZSMQYJLitffFUWbV9N88vW82O3xOoGhPJtZdeQM+mDQkNLP4dq4pDZlomB3Ye5sCOQ+zfcYj9nsK/f/tB0o79/axhh0OIrhBNuaqxxFeJJb6KVfiPvwEcvyzqU0BKYipv3PsOy9/5ks7Xt+PeKbd441c8bdm5eXR54W3OqRDPa9f28FpcdaejSUMgbxdS7jPEEeO12KfLJwVeRGYDbYB44HfgEVWderLHmAJf9mj2KjTlCesEqaDWVqF32TdCc7uVz7f9yltfreWnvYeICglmYIvGDGzRhLhw3xyM/bdUldTENI7sP0rCgUQS9h/lyP6jHD2QyJEDidbl/qOkJ2f847ERMWHEVYmlXNU44ipbl4mHkliz6AeSE1Lpf38PBj3Yi8Bg3xyvOJn+b8wmMjiIyUN6ejWu5u5Aj3aH0GtxRHpne8N/w2zZZ/g11RzImImmvQyaA2HXIWG32t7l78c9B5j21TpWbvuVQKeTq86vz5CWTakR73+jtDORmZZpvQEcSCRhv3V5ZP9Rjh5M/PMNIen3ZELCg6levyojXr/RrztlDpw0h9DAAKYM7eX12O7kMZD5IRL/CeKyd5vBf8sUeKNE0PwENG0iZM4HRzkk4l7r9HGbTx3ffSSRt79ex4frt5Kbn0+H+rW5vmUzGlevZGtcf5CXm0dGSiYRseFea8N8pq6ZPJdAl5Np1/f2emzN/x1N6AhBbXFEv+D1+CdjWhUYJYI443FEPYnEzgNnZTR5FJrYz7YdpI6rWS6Wx66+nOX3DePGyy7iu1/3MWDSHK6ZPJeVW3+1fSMKX3IFuIiMi/D74g7WyUi+Go+KswKEDoGsj9Fc/z5prSBT4A2/I4GNkdi5VjvX/IPo0d64k0ej+fZ2GCwXEcZdHS9lxcgbGH1law4lp3LbzEV0f2k676/dTE5e6es2WJI4RHzaQkDChoKEoemv+yyHf8sUeMMviTiQkKuR+KUQdgNkLkITOqLpU23v3R0WFMjgSy7g03uG8kzfKwh0OXlo4XI6PDuVN7/8npTM0rtBhD9ziOD2ZYF3REPoQGsUn1cy9gEwBd7wa+IIxxExEolfAoFN0dSnrV2kslfZHjvA6aRr43N5/7ZBTBnak7oV43l+2WraPTOFpz/+koPHUmzPwfiLCD4t8AASej0QhKa94dM8Tpcp8EaJIK6aOGLeRKInAYom3YA76WY0zwu7/4hwSe2zmDK0F+/fNoh29Wox85uf6PzcW9w/7xO2HbKnW6bxd76eogEQZxyE9oesxWjeXp/mcjpMgTdKFAlui8QvQcL/Z223ltAFd+oE27YLPFG9yuV5pu8VfHrP9Qxs0ZjPfv6Vnq/M5Ma3FrBm5x6fF6DSzJqi8XUWIGE3AE40fZKvUzklU+CNEkckEAm/0ZqfD74S0iejCZ3QzEVeK7BVYiIZdWUbPh95A3d1vJRth49ww1sL6P3qLD7asI28fO/sF1u2+HYO/s8snOUhtI+1sU3+QV+nc1KmwBslljjL44h+Bol9Dxzl0eT70MQBaK73eppHhQQzvPVFfHbfMB7v0YGs3Fz+994ndJ74FjPW/Eh6dunczNkX/GGK5jgJGw6Apk/2cSYnZwq8UeJJYBMkbj4SOR7y96BHe+FOfhB1J576wcUkKMBF7wsbsXjEdbxyTXcqRoUz/qMvaf/sFF5cvpqENO9MIZVmDj84yHqcOCtByNWQMR/N/93X6RTJFHijVBBxIKG9kPhl1gkpmQvQIx3QtFdRd5rX8nA4xDoIO7wfs27qx0U1qzH5y+/p8OxUHlm4nN1HvPemU9qIj5dJnsgaxeej6SdtseVTLl8nYBjFSRwRSORoNLQvmjoBTXsR0qdD+HAIHYRIsNdyaVK9Mi8NqsxvCUm8/fU6PvjpZ+at3cxFNavSrUk9OjasQ0SwvT3eSxN/mqIBEFd1NLg7ZMxBw26yVtj4GTOCN0olcdXCEfO61fYgoL61fv5IBzRjlu0nSp2oRnwMj/bowGf/G8Yd7S/m95Q0Hlq4nMvGT+KeOR/xxbZd5Obnn/qJyjiHw3etCooi4TcDOWjGNF+nUigzgjdKNavtwVtozvdo6kQ05VFInwLht0PwVYh4b3en+PAwbmnXgpvbNmfj/sMs/mkrH2/azqebdhATGsIV551D9yb1aFS1QonoDeNt/jZFA9b5GRrcBTLeRcNu8Lt+8abAG2WCBF4EsbMhZxWa+gKaPArSJkPECAjqZHvHyr/lIkLjapVoXK0SI7u0ZvUvv7Fo/Vbmr93ErG/XUyM+hm6Nz6Vbk3pUjY3yWl7+ztetCooiYbegWUvQ9HeQiLt8nc7fmAJvlBkiAkGtIfAyyF6Gpr2IHhsBrnoQfhcEtfH6yDnQ5aRtvVq0rVeLlMwslm3+hcUbtvHyim94ecU3XHBWZbo3qUenRnWJCvHe8QN/5G9z8MdJQB00qBNkTEfDrkcckb5O6U+mH7xRZqnmQ9YSNO0lyN8HAecj4XcjQS18nRoHklJYsmEbi9dvZdeRRAKcTtqcW5NuTepxWd2aBP7H/VdLov+99wmb9x/mk3uG+jqVf9Dcn9GjPZDwEUj4bV6NfbJ+8GYEb5RZIk4IuQqCu0Dm+9aSyqRr0cCLrUIf2MRnuVWJieSmNhcxvPWF/HzwDxat38rHG7azfMtOokKCuaJRXbo1qUeT6pXKzHy9P62DP5EE1EeD2qHp70DodbbvRna6TIE3yjyRAKuBVMjVkDEbTX8DTeyLBrVFwu9CAur5MDehQZUKNKhSgf91vozVO/ewZP1WPvjpZ+Z8v5FqsVF0a1KPbk3qcVZctM/y9AZ/naI5TsJvRY/2hoxZ1rJcP2AKvGF4iARB2BAI6WPNp6ZPRY9ehQZfYX30dp3t0/xcTgetz6lJ63NqkpaVzfKfd7J4/VZeX/ktr33+LY2rVaJ7k3pccV5dokNDfJqrHQT/aDZWFAk4Dw1sZS2ZDL0Gcfh+E3czB28YRVB3inWWYsY7oFkQ0gMJuw1xVfN1an9zODmVjzZsY9H6rfzy+1FcTgeX1a1JtybnclndmoQEBvg6xWLxwPvL+ObXvXw+8gZfp1IkzVmHJg5AIsYgYUO8EtNsum0Y/4HmH7Vaw2bMAvIh+EokbDgSUNfXqf2NqrL9cAKL1m/low3bOJKaTnCAi0trn0WHBrVpfc7ZRIeWvJU4qspHG7fzxOKVVIwKZ+Edg32d0km5E3qCOHHEzfNKPFPgDaMYaP5hNP0tyJwLmgFB7ZCwm5DA832d2j/ku918v3s/K7bsZMXWX/k9JQ2nQ7iwZlXa16tN+/q1qBgV4es0T+lwciqPf7iCL7bv5rxqFRnXsyO1y/tfS4CC3AldwFkDR8xrXolnCrxhFCN1J0HGTDR9BugxCLgICb8JAlv65YoWt1vZcvB3Pvt5Jyt+/pVdnoZnjapUoH2D2rSrV4tqMdYJVVb6gog153381zn+vbd+P7dbmbd2ExM+/Qq3282Iyy9l0MVNcDr8u7uKah76e2MIG4Ij4n9eiWkKvGHYQN0ZkPmeNU/v/h1c9a0Og8GdvNoC4d/adSSRFT/v5LOff2XT/sNn/DwF3wSEP98JClz31xuCeB5w4nXiuY4T3kjcbiUlK5sWtarxWI8OVIuNPuM8vUnz9qAJlyOR45HQXl6JaQq8YdhINQcyF6Hpb0L+bnDWsLZ1C+mBSKCv0zupw8mpbNh3iL1Hj6GAKn8uRfzz0vO9FriO4/dF/2wA9tf3/7zun4/9+/Mdj02B65tUr0S3JvX88lNRUTRrJXrsJiR2DhJ4gVdimhOdDMNGIoEQ2ttaR5+9HE2bhKY8CGkvQdj1ENIPcYT5Os1CVYyKKBFz8SVG/m7r0lXTt3l4+PeElmGUICJOJLgzErcAiXkLXGejqU+hR9rgTn3Rmrs3SjXN2w0S7TddJU2BN4xiJiJI0KU4Yqdb+8UGNoP0V61Cn/IkmrfX1ykadsnbDT4+Ia4gU+ANw0YS2MTaeCTuIwjqCBkz0IQOuBOHoFmfoprr6xSN4pRvCrxhlDkSUAdH9LNIuZVI+AjI240euxM90hp36nNo3j5fp2j8R+pOBfcRxOkf8+9gCrxheJU4KyLhtyHlPkeiJ0HAeZD+pmdUPwzNWmZG9SVV3i7r0k8OsIJZRWMYPiHihOC2SHBbNP8QmjEPMuehx24HR3k0pBcS2hdxVvF1qsbp8rMVNGBG8Ibhc+KshCPiTmv6Jvp1CKgP6W+gR9rhTrwBzfoM1Txfp2mcgubtBpzg9J9mdGYEbxh+QsQFwe2R4PZo/sECo/pbwVEBDentGdVX8nWqRmHydoOzml+d3GZG8Ibhh8RZGUfECKTcF0j0q+CqC+mvoUfa4k66yTpjUvN9naZRkJ+toAEzgjcMv2aN6i9Hgi9H8/ajme9Z2wtm3wSOShDaB0J6I86Kvk61TFPNh7zfILClr1P5G1tH8CLSWUS2i8hOERllZyzDKO3EVRVHxD2eUf3L1pmyaS9ZJ1Al3YJmf2FG9b6SfxDIRvzoACvYOIIXq53eq8DlwH7gBxFZpKo/2xXTMMoCkQCrY2VwJzRv71+j+qQV4KjiGdX3QpwVfJ1q2eGHK2jA3hH8RcBOVd2lqjnAHOAqG+MZRpkjruo4Iu5Dyn2JRL8Irupo2gueUf1taPZXqLp9nWbpl+cp8M6yMwdfBSh4et5+oPmJdxKR4cBwgOrVq9uYjmGUXiKBEHwFEnwFmvebNarPeB/NXg7OqtZG4iG9EWc5X6daKllNxiLBEevrVP7GzhF8YU2c/9F8XlUnq2ozVW1Wrpz5x2cY/5W4auCIGImU/wqJmgjOqmja81ZbhKQ70OzVZlRf3PJ3getsv+tdb+cIfj9QcMV/VeCgjfEMwyhAJBBCuiIhXdG83WjGXMhcgGYvBWd1COnrmav37z1OS4S83RB4ia+z+Ac7R/A/AHVEpKZYK//7A4tsjGcYRhHEVRNH5CjPqP4568SptAnokctwHxuBZi5B8w/5Os0SSd1p4P7d71bQgI0jeFXNE5HbgaWAE5imqlvsimcYxqmJBEFINySkG5q3E814DzI/QLM+AUAdlSHwAiSwKQRcAK66fr2/rF/I/826LEsFHkBVPwY+tjOGYRhnRly1kcgxaMRIyNsGOT+iuesg53s0a4nnTiGoqz4ENEICGkFAI3Ce5Xdzzb6i+X+gaS9aP7hq+zaZQpgzWQ2jjBNxQUBDCGiIcK21EXb+Acj9Ec3dCLmbIWMOytueB0SiAQ0KFP3zwFGxTBV9VbWOZ6SOB81GIkYhrlq+TusfTIE3DONvRARcVcFVFQnpDmB1s8z7BXI3obmbIXcTpE9D8XS5dMShxwu+qyEEnFdqD95q3j405SHIWQMBFyJR4/xy/h1MgTcM4zRYo/x6EFAPoS8AqtmQu81T9DdB3mY0+0uOr4ZWR2XPKL+hNbUT0BBxRPrwt/hvVLMg4z007TnAgUQ+BiH9EPHfno2mwBuGcUZEgiCwMQQ2/vOkF3WnQ97PkLvxz5G+Zi/98zHqrFFgaqchuOojjlCv5KvqBk0Fd3Khl+pOBk0Bt+dLT7gkx3qiwMuQqLElom2zKfCGYRQbcYRB4IUQeGGBon8Mcrd4iv4myPkBzVrsudWBuur8faTvOqfInuqqWX8vuoUW6aKKeBqFnGtZgBMkAhyRnrNSIyGgkue6KEQirLbNQW1LzPEGU+ANw7CVOKIh6FIIuvSvop//B+Rutgp+7ibIWoFmzvfcGoC6aoBEA7mFj6KLDBb6Z0FGIsBZCaTunz+LI+qv4l2wkEskSFiJKdynyxR4wzC8TpzlwdkOCW4H8NfKnTxP0c/bC+6DVuENqPRnIRYppDA7Ij0FPNyvdlPyB6bAG4bhc39buRPc2dfplBr+e/jXMAzD+E9MgTcMwyilTIE3DMMopUyBNwzDKKVMgTcMwyilTIE3DMMopUyBNwzDKKVMgTcMwyilTIE3DMMopUyBNwzDKKVMgTcMwyilTIE3DMMopUyBNwzDKKVMgTcMwyilTIE3DMMopUyBNwzDKKVE9WR7FHqXiBwB9vg6DyAeSPB1Ev+Sydk7SlrOJS1fMDn/W2eparnCbvCrAu8vRGStqjbzdR7/hsnZO0paziUtXzA5FyczRWMYhlFKmQJvGIZRSpkCX7jJvk7gDJicvaOk5VzS8gWTc7Exc/CGYRillBnBG4ZhlFKmwBuGYZRSZbbAi0gfEdkiIm4RaXbCbaNFZKeIbBeRTkU8PlZElovIL57LGO9k/mf8uSKy3vP1m4isL+J+v4nIJs/91nozx0JyeVREDhTIu0sR9+vsee13isgob+dZII9nRWSbiGwUkYUiEl3E/Xz+Gp/qNRPLS57bN4rIBb7Is0A+1URkpYhs9fw/HFHIfdqISHKBfy8P+yLXE3I66d/a315nVLVMfgH1gHOAL4BmBa6vD2wAgoCawK+As5DHPwOM8nw/Cnjah7/Lc8DDRdz2GxDv69fbk8ujwH2nuI/T85qfDQR6/hb1fZRvR8Dl+f7pov7Gvn6NT+c1A7oAnwACtAC+8/G/hUrABZ7vI4AdheTcBljiyzz/7d/a317nMjuCV9Wtqrq9kJuuAuaoaraq7gZ2AhcVcb93PN+/A/SwJdFTEBEB+gKzfRHfBhcBO1V1l6rmAHOwXmuvU9Vlqprn+fFboKov8jgNp/OaXQVMV8u3QLSIVPJ2osep6iFV/dHzfSqwFajiq3yKkV+9zmW2wJ9EFWBfgZ/3U/g/vAqqegisf6xAeS/kVphWwO+q+ksRtyuwTETWichwL+ZVlNs9H12nFTGtdbqvv7ddjzUyK4yvX+PTec389XVFRGoA5wPfFXLzxSKyQUQ+EZEG3s2sUKf6W/vV6+zyVWBvEJHPgIqF3PSAqn5Y1MMKuc4na0lPM/8BnHz0fqmqHhSR8sByEdmmqquKO9fjTpYz8DowFuv1HIs1tXT9iU9RyGNte/1P5zUWkQeAPODdIp7Gq69xIU7nNfObf9cFiUg48D5wl6qmnHDzj1h9VtI8x2s+AOp4OcUTnepv7Vevc6ku8Kra4Qweth+oVuDnqsDBQu73u4hUUtVDno9gf5xJjidzqvxFxAX0BJqe5DkOei7/EJGFWB/nbSs+p/uai8ibwJJCbjrd179YnMZrfB3QFWivnknWQp7Dq69xIU7nNfPq63o6RCQAq7i/q6oLTry9YMFX1Y9F5DURiVdVnzUiO42/tV+9zmaK5p8WAf1FJEhEamKNGL4v4n7Xeb6/DijqE4GdOgDbVHV/YTeKSJiIRBz/Huug4WYv5ndiPgXnIq8uIpcfgDoiUlNEAoH+WK+114lIZ+B+oLuqZhRxH394jU/nNVsEXOtZ5dECSD4+xegLnmNHU4GtqjqxiPtU9NwPEbkIq14d9V6W/8jndP7WfvU6+/yotK++sArMfiAb+B1YWuC2B7BWJWwHrihw/RQ8K26AOGAF8IvnMtYHv8PbwM0nXFcZ+Njz/dlYKyo2AFuwph18+ZrPADYBG7H+I1Q6MWfPz12wVlX86sucsQ6w7wPWe77e8NfXuLDXDLj5+L8PrKmDVz23b6LAyjEf5dsSa+piY4HXt8sJOd/ueU03YB3kvsTHORf6t/bn19m0KjAMwyilzBSNYRhGKWUKvGEYRillCrxhGEYpZQq8YRhGKWUKvGEYRillCrxhFMHTOTDe833av3icf3UUNMosU+ANo/hdgXWCXB1gOFaLBsPwOlPgjTJBRGqI1dt9iohsFpF3RaSDiKwWq6f/RSISJyLLROQnEZlE4X1FTnzeziLyo6ch1grP1X7VUdAou0p1LxrDOEFtoA/WqPoHYCDWGZXdgTHAXuBrVX1cRK703K9IIlIOeBO4TFV3i0is56aiOgr67pR1o0wyBd4oS3ar6iYAEdkCrFBVFZFNQA3PV08AVf1IRJJO8XwtgFVq7RuAqiZ6rverjoJG2WWmaIyyJLvA9+4CP7v5a7DzbwqxFHF/v+ooaJRdpsAbxl9WAYMAROQK4FT77H4DtPZ0HaXAFI1/dRQ0yiwzRWMYf3kMmC0iPwJfYs3J/4OIrFfVJqp6xLOrzwIRcWDtCXA58DFWZ8SdQAYw1CvZG8YJTDdJwzCMUspM0RiGYZRSpsAbhmGUUqbAG4ZhlFKmwBuGYZRSpsAbhmGUUqbAG4ZhlFKmwBuGYZRS/weI+xbCVHdgHgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg_proj(mdl.c0, mdl.c1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bronze-ordering",
   "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": [
    "int_proj(mdl.c0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "academic-rings",
   "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": [
    "int_proj(mdl.c1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prerequisite-convention",
   "metadata": {},
   "source": [
    "The search surface here is actually quite \"nicely behaved\" so these don't actually look bad - the 1D values look somewhat parabolic and the 2D version is nicely bounded.\n",
    "\n",
    "The problem here is that we have a \"bad\" point, that just keeps us away getting to a \"good fit\". The residual plot doesn't make it obvious of this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "blond-beverage",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_fit_resid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "exciting-oriental",
   "metadata": {
    "tags": []
   },
   "source": [
    "We can try ignoring values, such as the second point:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "electoral-inside",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ignore(1.5, 2.5)\n",
    "\n",
    "plot_fit_resid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "revised-vermont",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset               = 1\n",
      "Method                = levmar\n",
      "Statistic             = chi2gehrels\n",
      "Initial fit statistic = 7.40466\n",
      "Final fit statistic   = 0.0196631 at function evaluation 6\n",
      "Data points           = 3\n",
      "Degrees of freedom    = 1\n",
      "Probability [Q-value] = 0.888482\n",
      "Reduced statistic     = 0.0196631\n",
      "Change in statistic   = 7.385\n",
      "   mdl.c0         8.68041      +/- 4.29407     \n",
      "   mdl.c1         0.558286     +/- 0.706193    \n"
     ]
    }
   ],
   "source": [
    "fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "sublime-dining",
   "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": [
    "plot_fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "retired-amendment",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset               = 1\n",
      "Statistic             = chi2gehrels\n",
      "Fit statistic value   = 0.0196631\n",
      "Data points           = 3\n",
      "Degrees of freedom    = 1\n",
      "Probability [Q-value] = 0.888482\n",
      "Reduced statistic     = 0.0196631\n"
     ]
    }
   ],
   "source": [
    "calc_stat_info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "stretch-communist",
   "metadata": {},
   "source": [
    "For reference we can use `get_stat_info`, which returns a structure for each dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "preceding-warrior",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Statistic information results instance>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sinfo = get_stat_info()\n",
    "sinfo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "original-kazakhstan",
   "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;Statistic information results instance&gt;</div><div hidden class=\"sherpa\"><details open><summary>Statistics summary (7)</summary><div class=\"datavals\"><div class=\"dataname\">Dataset</div><div class=\"dataval\">1</div><div class=\"dataname\">Statistic</div><div class=\"dataval\">chi2gehrels</div><div class=\"dataname\">Value</div><div class=\"dataval\">0.0196631</div><div class=\"dataname\">Number of points</div><div class=\"dataval\">3</div><div class=\"dataname\">Degrees of freedom</div><div class=\"dataval\">1</div><div class=\"dataname\">Reduced statistic</div><div class=\"dataval\">0.0196631</div><div class=\"dataname\">Probability (Q-value)</div><div class=\"dataval\">0.888482</div></div></details></div>"
      ],
      "text/plain": [
       "<Statistic information results instance>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sinfo[0]  # rich text interface"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "right-choir",
   "metadata": {},
   "source": [
    "This looks better, and we can now use `conf`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "wooden-basics",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mdl.c0 lower bound:\t-4.29407\n",
      "mdl.c0 upper bound:\t4.29407\n",
      "mdl.c1 lower bound:\t-0.706193\n",
      "mdl.c1 upper bound:\t0.706193\n",
      "Dataset               = 1\n",
      "Confidence Method     = confidence\n",
      "Iterative Fit Method  = None\n",
      "Fitting Method        = levmar\n",
      "Statistic             = chi2gehrels\n",
      "confidence 1-sigma (68.2689%) bounds:\n",
      "   Param            Best-Fit  Lower Bound  Upper Bound\n",
      "   -----            --------  -----------  -----------\n",
      "   mdl.c0            8.68041     -4.29407      4.29407\n",
      "   mdl.c1           0.558286    -0.706193     0.706193\n"
     ]
    }
   ],
   "source": [
    "conf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "sublime-parish",
   "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>mdl.c0</td><td>     8.68041</td><td>    -4.29407</td><td>     4.29407</td></tr><tr><td>mdl.c1</td><td>    0.558286</td><td>   -0.706193</td><td>    0.706193</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\">levmar</div><div class=\"dataname\">Statistic</div><div class=\"dataval\">chi2gehrels</div></div></details></div>"
      ],
      "text/plain": [
       "<confidence results instance>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_conf_results()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "expensive-commander",
   "metadata": {},
   "source": [
    "# So is it just a case of ignoring a single point?\n",
    "\n",
    "Unfortunately no. For this dataset we could ignore different points and still get a \"good\" fit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "greater-austria",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset               = 1\n",
      "Method                = levmar\n",
      "Statistic             = chi2gehrels\n",
      "Initial fit statistic = 39.9838\n",
      "Final fit statistic   = 2.50522 at function evaluation 6\n",
      "Data points           = 3\n",
      "Degrees of freedom    = 1\n",
      "Probability [Q-value] = 0.113469\n",
      "Reduced statistic     = 2.50522\n",
      "Change in statistic   = 37.4786\n",
      "   mdl.c0         -5.83601     +/- 2.62572     \n",
      "   mdl.c1         2.25876      +/- 0.627529    \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "notice()\n",
    "ignore(None, 1.5)  # ignore the first point this time\n",
    "fit()\n",
    "plot_fit_resid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baking-baptist",
   "metadata": {},
   "source": [
    "This is not as good, but it passes the \"magical\" reduced-statistic < 3 condition, so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "prompt-illness",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mdl.c0 lower bound:\t-2.62572\n",
      "mdl.c0 upper bound:\t2.62572\n",
      "mdl.c1 lower bound:\t-0.627529\n",
      "mdl.c1 upper bound:\t0.627529\n",
      "Dataset               = 1\n",
      "Confidence Method     = confidence\n",
      "Iterative Fit Method  = None\n",
      "Fitting Method        = levmar\n",
      "Statistic             = chi2gehrels\n",
      "confidence 1-sigma (68.2689%) bounds:\n",
      "   Param            Best-Fit  Lower Bound  Upper Bound\n",
      "   -----            --------  -----------  -----------\n",
      "   mdl.c0           -5.83601     -2.62572      2.62572\n",
      "   mdl.c1            2.25876    -0.627529     0.627529\n"
     ]
    }
   ],
   "source": [
    "conf()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "painted-update",
   "metadata": {},
   "source": [
    "# Okay, can I just ignore the limit?\n",
    "\n",
    "Well, there is an option you can do this with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "hundred-reply",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sigma': 1,\n",
       " 'eps': 0.01,\n",
       " 'maxiters': 200,\n",
       " 'soft_limits': False,\n",
       " 'remin': 0.01,\n",
       " 'fast': False,\n",
       " 'parallel': True,\n",
       " 'numcores': 12,\n",
       " 'maxfits': 5,\n",
       " 'max_rstat': 3,\n",
       " 'tol': 0.2,\n",
       " 'verbose': False,\n",
       " 'openinterval': False}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_conf_opt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "surgical-puzzle",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset               = 1\n",
      "Method                = levmar\n",
      "Statistic             = chi2gehrels\n",
      "Initial fit statistic = 11.8131\n",
      "Final fit statistic   = 9.79605 at function evaluation 6\n",
      "Data points           = 4\n",
      "Degrees of freedom    = 2\n",
      "Probability [Q-value] = 0.0074613\n",
      "Reduced statistic     = 4.89803\n",
      "Change in statistic   = 2.01704\n",
      "   mdl.c0         -2.58909     +/- 2.33419     \n",
      "   mdl.c1         1.73737      +/- 0.597081    \n"
     ]
    },
    {
     "ename": "EstErr",
     "evalue": "reduced statistic larger than 3",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mEstErr\u001b[0m                                    Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-1260df7b13a3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mnotice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mconf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<string>\u001b[0m in \u001b[0;36mconf\u001b[0;34m(*args)\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/ciao413/lib/python3.8/site-packages/sherpa/ui/utils.py\u001b[0m in \u001b[0;36mconf\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m   9901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9902\u001b[0m         \"\"\"\n\u001b[0;32m-> 9903\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_confidence_results\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_est_errors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'confidence'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   9904\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9905\u001b[0m     \u001b[0;31m# DOC-TODO: add a deprecation note?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/ciao413/lib/python3.8/site-packages/sherpa/ui/utils.py\u001b[0m in \u001b[0;36m_est_errors\u001b[0;34m(self, args, methodname)\u001b[0m\n\u001b[1;32m   9573\u001b[0m             \u001b[0mparlist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9574\u001b[0m         \u001b[0mids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0motherids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_estmethods\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmethodname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 9575\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mest_errors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_methods\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   9576\u001b[0m         \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatasets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mids\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9577\u001b[0m         \u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/ciao413/lib/python3.8/site-packages/sherpa/fit.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(fit, *args, **kwargs)\u001b[0m\n\u001b[1;32m     54\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m             \u001b[0mfit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     57\u001b[0m         \u001b[0mfit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mteardown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/ciao413/lib/python3.8/site-packages/sherpa/fit.py\u001b[0m in \u001b[0;36mest_errors\u001b[0;34m(self, methoddict, parlist)\u001b[0m\n\u001b[1;32m   1440\u001b[0m             if (hasattr(self.estmethod, \"max_rstat\") and\n\u001b[1;32m   1441\u001b[0m                     (self.calc_stat() / dof) > self.estmethod.max_rstat):\n\u001b[0;32m-> 1442\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mEstErr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'rstat>max'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestmethod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_rstat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1444\u001b[0m         \u001b[0;31m# If statistic is chi-squared, change fitting method to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mEstErr\u001b[0m: reduced statistic larger than 3"
     ]
    }
   ],
   "source": [
    "notice()\n",
    "fit()\n",
    "conf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "unusual-witness",
   "metadata": {},
   "outputs": [],
   "source": [
    "set_conf_opt('max_rstat', 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "level-baker",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mdl.c0 lower bound:\t-2.33419\n",
      "mdl.c0 upper bound:\t2.33419\n",
      "mdl.c1 lower bound:\t-0.597081\n",
      "mdl.c1 upper bound:\t0.597081\n",
      "Dataset               = 1\n",
      "Confidence Method     = confidence\n",
      "Iterative Fit Method  = None\n",
      "Fitting Method        = levmar\n",
      "Statistic             = chi2gehrels\n",
      "confidence 1-sigma (68.2689%) bounds:\n",
      "   Param            Best-Fit  Lower Bound  Upper Bound\n",
      "   -----            --------  -----------  -----------\n",
      "   mdl.c0           -2.58909     -2.33419      2.33419\n",
      "   mdl.c1            1.73737    -0.597081     0.597081\n"
     ]
    }
   ],
   "source": [
    "conf()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "brazilian-pleasure",
   "metadata": {
    "tags": []
   },
   "source": [
    "Please, **do not do this**. I actually think that the `max_rstat = 3` limit is too lax and we really should use a more-stringent limit, but I don't think this would be popular."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "pacific-receptor",
   "metadata": {},
   "outputs": [],
   "source": [
    "set_conf_opt('max_rstat', 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "subtle-medline",
   "metadata": {},
   "source": [
    "# So what can I do?\n",
    "\n",
    "- tweak the range of the independent axis\n",
    "- we may be stuck in a local, rather than global, minimum\n",
    "- modify the model expression\n",
    "- give up and eat ice-cream\n",
    "\n",
    "If we can't tweak the data being fitted then we are probably in a situation that the used model does not represent the data, and **could** be because we are in the wrong area of the search space - so we can try\n",
    "\n",
    "- changing the optimiser ([set_method](https://cxc.cfa.harvard.edu/sherpa/ahelp/set_method.html)), particularly to [\"moncar\"](https://cxc.cfa.harvard.edu/sherpa/ahelp/montecarlo.html) which is likely to be slow, but can search complex surfaces,\n",
    "- or change the model. In this case you should look at [calc_ftest](https://cxc.cfa.harvard.edu/sherpa/ahelp/calc_ftest.html) and [calc_mlr](https://cxc.cfa.harvard.edu/sherpa/ahelp/calc_mlr.html) which let you identify whether one model is \"better\" than the other (**but** there are significant restrictions on how they are used).\n",
    "\n",
    "This is much easier (unfortunately this does not mean it is easy) in an interactive session than in scripts where you are fitting many data sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cheap-intranet",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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