notebooks/SherpaQuickStart.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sherpa Quick Start\n",
    "==========\n",
    "\n",
    "This tutorial shows some basic Sherpa features. More information on Sherpa can be\n",
    "found in the [Sherpa documentation](https://sherpa.readthedocs.io/).\n",
    "\n",
    "Workflow:\n",
    "\n",
    "- create synthetic data: a parabola with noise and error bars\n",
    "- load data in Sherpa\n",
    "- plot data using matplotlib\n",
    "- set, inspect, edit a model to fit the data\n",
    "- fit the data\n",
    "- compute the confidence intervals for the parameters\n",
    "- explore the parameter space\n",
    "\n",
    "First of all, let's activate the inline matplotlib mode. Sherpa seamlessly uses matplotlib to provide immediate visual feedback to the user. Support for matplotlib in Sherpa requires the matplotlib package to be installed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's create a simple synthetic dataset, using numpy: a parabola between x=-5 and x=5, with some randomly generated noise (the form for `y` is chosen to match the model selected in step 6 below to fit the data, and the random seed used by NumPy is set to make this notebook repeatable):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "np.random.seed(824842)\n",
    "\n",
    "x = np.arange(-5, 5.1)\n",
    "\n",
    "c0_true = 23.2\n",
    "c1_true = 0\n",
    "c2_true = 1\n",
    "\n",
    "y = c2_true * x*x + c1_true * x + c0_true + np.random.normal(size=x.size)\n",
    "e = np.ones(x.size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's import Sherpa:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: imaging routines will not be available, \n",
      "failed to import sherpa.image.ds9_backend due to \n",
      "'RuntimeErr: DS9Win unusable: Could not find ds9 on your PATH'\n"
     ]
    }
   ],
   "source": [
    "from sherpa.astro import ui"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Depending on how you installed Sherpa, certain special features may be enabled or disabled. Sherpa prints some warning messages when it cannot find some of its modules, as shown above. These warnings are benign. You can refer to the Sherpa documentation to find out what additional features you can enable in Sherpa and how to enable them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's load and plot the data we just created. Notice we are assigning the ID `mydata` to the dataset we are loading. We will use this ID to refer to the same dataset in the rest of the tutorial. Sherpa can deal with multiple datasets, fit them simultaneously with the same model, and even link parameters between models. Sherpa can read ASCII table and FITS files (provided the `astropy` package is installed)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ui.load_arrays(\"mydata\", x, y, e)\n",
    "ui.plot_data(\"mydata\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data can be retrieved with the `get_data` routine:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name      = \n",
      "x         = Float64[11]\n",
      "y         = Float64[11]\n",
      "staterror = Float64[11]\n",
      "syserror  = None\n"
     ]
    }
   ],
   "source": [
    "d = ui.get_data(\"mydata\")\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The object - in this case a [Data1D object](https://sherpa.readthedocs.io/en/latest/data/api/sherpa.data.Data1D.html) - has support for Jupyter notebooks and will display a summary of \n",
    "the data (in this case a plot, but other objects will display differently, as we will see below):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>/*\n",
       "Copyright (C) 2020  Smithsonian Astrophysical Observatory\n",
       "\n",
       "\n",
       " This program is free software; you can redistribute it and/or modify\n",
       " it under the terms of the GNU General Public License as published by\n",
       " the Free Software Foundation; either version 3 of the License, or\n",
       " (at your option) any later version.\n",
       "\n",
       " This program is distributed in the hope that it will be useful,\n",
       " but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
       " MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
       " GNU General Public License for more details.\n",
       "\n",
       " You should have received a copy of the GNU General Public License along\n",
       " with this program; if not, write to the Free Software Foundation, Inc.,\n",
       " 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n",
       "\n",
       "*/\n",
       "\n",
       ":root {\n",
       "  --sherpa-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --sherpa-background-color: var(--jp-layout-color0, white);\n",
       "  --sherpa-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --sherpa-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "\n",
       "  /* https://medium.com/ge-design/iot-cool-gray-is-a-great-background-color-for-data-visualization-ebf18c318418 */\n",
       "  --sherpa-background-color-dark1: #EBEFF2;\n",
       "  --sherpa-background-color-dark2: #D8E0E5;\n",
       "}\n",
       "\n",
       "div.sherpa-text-fallback {\n",
       "    display: none;\n",
       "}\n",
       "\n",
       "div.sherpa {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       "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;Data1D data set instance &#x27;&#x27;&gt;</div><div hidden class=\"sherpa\"><style>/*\n",
       "Copyright (C) 2020  Smithsonian Astrophysical Observatory\n",
       "\n",
       "\n",
       " This program is free software; you can redistribute it and/or modify\n",
       " it under the terms of the GNU General Public License as published by\n",
       " the Free Software Foundation; either version 3 of the License, or\n",
       " (at your option) any later version.\n",
       "\n",
       " This program is distributed in the hope that it will be useful,\n",
       " but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
       " MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
       " GNU General Public License for more details.\n",
       "\n",
       " You should have received a copy of the GNU General Public License along\n",
       " with this program; if not, write to the Free Software Foundation, Inc.,\n",
       " 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n",
       "\n",
       "*/\n",
       "\n",
       ":root {\n",
       "  --sherpa-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --sherpa-background-color: var(--jp-layout-color0, white);\n",
       "  --sherpa-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --sherpa-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "\n",
       "  /* https://medium.com/ge-design/iot-cool-gray-is-a-great-background-color-for-data-visualization-ebf18c318418 */\n",
       "  --sherpa-background-color-dark1: #EBEFF2;\n",
       "  --sherpa-background-color-dark2: #D8E0E5;\n",
       "}\n",
       "\n",
       "div.sherpa-text-fallback {\n",
       "    display: none;\n",
       "}\n",
       "\n",
       "div.sherpa {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       "div.sherpa details summary {\n",
       "    display: list-item;  /* needed for notebook, not lab */\n",
       "    font-size: larger;\n",
       "}\n",
       "\n",
       "div.sherpa details div.datavals {\n",
       "    display: grid;\n",
       "    grid-template-columns: 1fr 3fr;\n",
       "    column-gap: 0.5em;\n",
       "}\n",
       "\n",
       "div.sherpa div.dataname {\n",
       "    font-weight: bold;\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa div.dataval { }\n",
       "\n",
       "div.sherpa div.datavals div:nth-child(4n + 1) ,\n",
       "div.sherpa div.datavals div:nth-child(4n + 2) {\n",
       "    background: var(--sherpa-background-color-row-odd);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tbody {\n",
       "    border-bottom: 1px solid var(--sherpa-border-color);\n",
       "}\n",
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       "</details></div></div>"
      ],
      "text/plain": [
       "<Data1D data set instance ''>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can set the model we want to fit to the data using the `set_model` call. There are different ways to instantiate the models: in this case, we just use the string `polynom1d` to refer to a 1D polynomial. The name of the model will be `poly`, and will be accessible as a Python variable. One can use more object oriented patterns to access and instantiate built-in models. Also, new models can be added by the user as Python functions or from tabular data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ui.set_model(\"mydata\", \"polynom1d.poly\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Several Sherpa commands can be used to inspect the model. In this case we just use a simple `print` to get a summary of the model and its components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "polynom1d.poly\n",
      "   Param        Type          Value          Min          Max      Units\n",
      "   -----        ----          -----          ---          ---      -----\n",
      "   poly.c0      thawed            1 -3.40282e+38  3.40282e+38           \n",
      "   poly.c1      frozen            0 -3.40282e+38  3.40282e+38           \n",
      "   poly.c2      frozen            0 -3.40282e+38  3.40282e+38           \n",
      "   poly.c3      frozen            0 -3.40282e+38  3.40282e+38           \n",
      "   poly.c4      frozen            0 -3.40282e+38  3.40282e+38           \n",
      "   poly.c5      frozen            0 -3.40282e+38  3.40282e+38           \n",
      "   poly.c6      frozen            0 -3.40282e+38  3.40282e+38           \n",
      "   poly.c7      frozen            0 -3.40282e+38  3.40282e+38           \n",
      "   poly.c8      frozen            0 -3.40282e+38  3.40282e+38           \n",
      "   poly.offset  frozen            0 -3.40282e+38  3.40282e+38           \n"
     ]
    }
   ],
   "source": [
    "print(poly)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also display it directly in a Jupyter notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
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       "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",
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       "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;Polynom1D model instance &#x27;polynom1d.poly&#x27;&gt;</div><div hidden class=\"sherpa\"><details open><summary>Model</summary><table class=\"model\"><caption>Expression: polynom1d.poly</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=10>polynom1d.poly</th><td>c0</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>c1</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>c2</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>c3</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>c4</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>c5</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>c6</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>c7</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>c8</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>offset</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>-MAX</td><td>MAX</td><td></td></tr></tbody></table></details></div>"
      ],
      "text/plain": [
       "<Polynom1D model instance 'polynom1d.poly'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, only the first component (the intercept) is __thawed__, i.e. is free to vary in the fit. This corresponds to a constant function. In order to fit a parabola, we need to __thaw__ the coefficients of the first two orders in the polynomial, as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "ui.thaw(poly.c1, poly.c2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to fit the dataset using the default settings. However, Sherpa has a number of optimization algorithms, each configurable by the user, and a number of statistics that can be used to take into account the error and other characteristics of data being fitted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset               = mydata\n",
      "Method                = levmar\n",
      "Statistic             = chi2\n",
      "Initial fit statistic = 12642.8\n",
      "Final fit statistic   = 15.643 at function evaluation 8\n",
      "Data points           = 11\n",
      "Degrees of freedom    = 8\n",
      "Probability [Q-value] = 0.0477837\n",
      "Reduced statistic     = 1.95538\n",
      "Change in statistic   = 12627.2\n",
      "   poly.c0        23.1367      +/- 0.455477    \n",
      "   poly.c1        0.0537585    +/- 0.0953463   \n",
      "   poly.c2        1.04602      +/- 0.0341394   \n"
     ]
    }
   ],
   "source": [
    "ui.fit(\"mydata\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that Sherpa used a Levenberg-Marquadt minimization strategy (`levmar`), and the $\\chi^2$ error function (`chi2`). These options can be changed with the `set_method` and `set_stat` functions. Note that when using the `levmar` optimiser the fit results include approximate error estimates, but you should use the `conf` routine (shown below) to get accurate error estimates.\n",
    "\n",
    "The best fit values of the parameters are close to the ones defined when we generated the dataset:\n",
    "\n",
    "| Parameter | true value | best-fit value |\n",
    "| --------- | ---------- | -------------- |\n",
    "| $c_0$     | 23.2       | 23.1367        |\n",
    "| $c_1$     | 0          | 0.0538         |\n",
    "| $c_2$     | 1          | 1.0460         |\n",
    "\n",
    "The `get_fit_results` function will return the information on the last fit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datasets       = ('mydata',)\n",
      "itermethodname = none\n",
      "methodname     = levmar\n",
      "statname       = chi2\n",
      "succeeded      = True\n",
      "parnames       = ('poly.c0', 'poly.c1', 'poly.c2')\n",
      "parvals        = (23.136688550821013, 0.05375853511753547, 1.046015029290237)\n",
      "statval        = 15.6430444797243\n",
      "istatval       = 12642.832375778722\n",
      "dstatval       = 12627.189331298998\n",
      "numpoints      = 11\n",
      "dof            = 8\n",
      "qval           = 0.04778369987400388\n",
      "rstat          = 1.9553805599655374\n",
      "message        = successful termination\n",
      "nfev           = 8\n"
     ]
    }
   ],
   "source": [
    "fitres = ui.get_fit_results()\n",
    "print(fitres)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perhaps unsurprisingly by now, it too can be displayed automatically in a Jupyter notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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;Fit results instance&gt;</div><div hidden class=\"sherpa\"><details open><summary>Fit parameters</summary><table class=\"fit\"><thead><tr><th>Parameter</th><th>Best-fit value</th><th>Approximate error</th></tr></thead><tbody><tr><td>poly.c0</td><td>     23.1367</td><td>&#177;     0.455477</td></tr><tr><td>poly.c1</td><td>   0.0537585</td><td>&#177;    0.0953463</td></tr><tr><td>poly.c2</td><td>     1.04602</td><td>&#177;    0.0341394</td></tr></tbody></table></details><details><summary>Summary (11)</summary><div class=\"datavals\"><div class=\"dataname\">Dataset</div><div class=\"dataval\">mydata</div><div class=\"dataname\">Method</div><div class=\"dataval\">levmar</div><div class=\"dataname\">Statistic</div><div class=\"dataval\">chi2</div><div class=\"dataname\">Final statistic</div><div class=\"dataval\">15.643</div><div class=\"dataname\">Number of evaluations</div><div class=\"dataval\">8</div><div class=\"dataname\">Reduced statistic</div><div class=\"dataval\">1.95538</div><div class=\"dataname\">Probability (Q-value)</div><div class=\"dataval\">0.0477837</div><div class=\"dataname\">Initial statistic</div><div class=\"dataval\">12642.8</div><div class=\"dataname\">&#916; statistic</div><div class=\"dataval\">12627.2</div><div class=\"dataname\">Number of data points</div><div class=\"dataval\">11</div><div class=\"dataname\">Degrees of freedom</div><div class=\"dataval\">8</div></div></details></div>"
      ],
      "text/plain": [
       "<Fit results instance>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fitres"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model object has also been updated by the fit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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;Polynom1D model instance &#x27;polynom1d.poly&#x27;&gt;</div><div hidden class=\"sherpa\"><details open><summary>Model</summary><table class=\"model\"><caption>Expression: polynom1d.poly</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=10>polynom1d.poly</th><td>c0</td><td><input disabled type=\"checkbox\" checked></input></td><td>23.136688550821013</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>c1</td><td><input disabled type=\"checkbox\" checked></input></td><td>0.05375853511753547</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>c2</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.046015029290237</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>c3</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>c4</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>c5</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>c6</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>c7</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>c8</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>offset</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>-MAX</td><td>MAX</td><td></td></tr></tbody></table></details></div>"
      ],
      "text/plain": [
       "<Polynom1D model instance 'polynom1d.poly'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to get immediate feedback of the fit results, we can plot the fit and the residuals. Again, Sherpa facilitates the creation of the plots, but users can harvest the power of matplotlib directly if they want to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ui.plot_fit_resid(\"mydata\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now compute the confidence intervals for the free parameters in the fit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "poly.c0 lower bound:\t-0.455477\n",
      "poly.c1 lower bound:\t-0.0953463\n",
      "poly.c0 upper bound:\t0.455477\n",
      "poly.c1 upper bound:\t0.0953463\n",
      "poly.c2 lower bound:\t-0.0341394\n",
      "poly.c2 upper bound:\t0.0341394\n",
      "Dataset               = mydata\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",
      "   poly.c0           23.1367    -0.455477     0.455477\n",
      "   poly.c1         0.0537585   -0.0953463    0.0953463\n",
      "   poly.c2           1.04602   -0.0341394    0.0341394\n"
     ]
    }
   ],
   "source": [
    "ui.conf(\"mydata\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datasets    = ('mydata',)\n",
      "methodname  = confidence\n",
      "iterfitname = none\n",
      "fitname     = levmar\n",
      "statname    = chi2gehrels\n",
      "sigma       = 1\n",
      "percent     = 68.26894921370858\n",
      "parnames    = ('poly.c0', 'poly.c1', 'poly.c2')\n",
      "parvals     = (23.136688550821013, 0.05375853511753547, 1.046015029290237)\n",
      "parmins     = (-0.4554769011263424, -0.09534625892453424, -0.03413943709996614)\n",
      "parmaxes    = (0.4554769011263424, 0.09534625892453424, 0.03413943709996614)\n",
      "nfits       = 6\n"
     ]
    }
   ],
   "source": [
    "conf = ui.get_conf_results()\n",
    "print(conf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or, if you want a fancy display, you can get the Jupter notebook to display it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>/*\n",
       "Copyright (C) 2020  Smithsonian Astrophysical Observatory\n",
       "\n",
       "\n",
       " This program is free software; you can redistribute it and/or modify\n",
       " it under the terms of the GNU General Public License as published by\n",
       " the Free Software Foundation; either version 3 of the License, or\n",
       " (at your option) any later version.\n",
       "\n",
       " This program is distributed in the hope that it will be useful,\n",
       " but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
       " MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
       " GNU General Public License for more details.\n",
       "\n",
       " You should have received a copy of the GNU General Public License along\n",
       " with this program; if not, write to the Free Software Foundation, Inc.,\n",
       " 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n",
       "\n",
       "*/\n",
       "\n",
       ":root {\n",
       "  --sherpa-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --sherpa-background-color: var(--jp-layout-color0, white);\n",
       "  --sherpa-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --sherpa-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "\n",
       "  /* https://medium.com/ge-design/iot-cool-gray-is-a-great-background-color-for-data-visualization-ebf18c318418 */\n",
       "  --sherpa-background-color-dark1: #EBEFF2;\n",
       "  --sherpa-background-color-dark2: #D8E0E5;\n",
       "}\n",
       "\n",
       "div.sherpa-text-fallback {\n",
       "    display: none;\n",
       "}\n",
       "\n",
       "div.sherpa {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       "div.sherpa details summary {\n",
       "    display: list-item;  /* needed for notebook, not lab */\n",
       "    font-size: larger;\n",
       "}\n",
       "\n",
       "div.sherpa details div.datavals {\n",
       "    display: grid;\n",
       "    grid-template-columns: 1fr 3fr;\n",
       "    column-gap: 0.5em;\n",
       "}\n",
       "\n",
       "div.sherpa div.dataname {\n",
       "    font-weight: bold;\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa div.dataval { }\n",
       "\n",
       "div.sherpa div.datavals div:nth-child(4n + 1) ,\n",
       "div.sherpa div.datavals div:nth-child(4n + 2) {\n",
       "    background: var(--sherpa-background-color-row-odd);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tbody {\n",
       "    border-bottom: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tr.block {\n",
       "    border-top: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-odd ,\n",
       "div.sherpa table.model th.model-even {\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-odd {\n",
       "    background: var(--sherpa-background-color-dark1);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-even {\n",
       "    background: var(--sherpa-background-color-dark2);\n",
       "}\n",
       "\n",
       "div.sherpa .failed {\n",
       "    background: orange;\n",
       "    font-size: large;\n",
       "    padding: 1em;\n",
       "}\n",
       "</style><div class=\"sherpa-text-fallback\">&lt;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>poly.c0</td><td>     23.1367</td><td>   -0.455477</td><td>    0.455477</td></tr><tr><td>poly.c1</td><td>   0.0537585</td><td>  -0.0953463</td><td>   0.0953463</td></tr><tr><td>poly.c2</td><td>     1.04602</td><td>  -0.0341394</td><td>   0.0341394</td></tr></tbody></table></details><details><summary>Summary (3)</summary><div class=\"datavals\"><div class=\"dataname\">Dataset</div><div class=\"dataval\">mydata</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": [
    "conf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sherpa allows to inspect the parameter space. In the cell below we ask sherpa to show us the projection of the confidence regions for the `c0` and `c1` parameters. The contours are configurable by the user: by default they show the confidence curves at 1, 2, and 3 $\\sigma$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ui.reg_proj(poly.c0, poly.c1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also directlty inspect the parameter space. For instance, in the plot below Sherpa displays the Interval Projection of the `c0` parameter, i.e. a plot of the error for each value of the parameter, around the minimum found by the optimization method during the fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ui.int_proj(poly.c0)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}