5 plotting using the lower-level routines.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting using the \"low-level\" interface\n",
    "\n",
    "In the last two notebooks I've created, I've shown how to create a user model (using `add_model` rather than `load_user_model`) and use the \"low-level\" interface$^\\dagger$ to fit a data set (that is, without using the routines in `sherpa.ui` or `sherpa.astro.ui`). One thing I haven't done is show how to create plots when using the \"low-level\" interface, so I thought I'd show how here.\n",
    "\n",
    "The previous notebooks, which probably need to be skimmed for the following to make any sense, are:\n",
    "\n",
    "1. [User models in Sherpa](http://nbviewer.ipython.org/github/DougBurke/sherpa-standalone-notebooks/blob/master/user%20model.ipynb)\n",
    "2. [Writing an \"integrated\" user model](http://nbviewer.ipython.org/github/DougBurke/sherpa-standalone-notebooks/blob/master/an%20integrated%20user%20model.ipynb)\n",
    "\n",
    "$^\\dagger$ we haven't come up with a better name for this API yet.\n",
    "\n",
    "## Author and disclaimer\n",
    "\n",
    "This was written by \n",
    "[Douglas Burke](http://hea-www.cfa.harvard.edu/~dburke/)\n",
    "on June 5 2015. This notebook, and others that may be of\n",
    "interest, can be found on GitHub at\n",
    "https://github.com/DougBurke/sherpa-standalone-notebooks.\n",
    "\n",
    "The information in this document is placed into the Publc Domain. It is not an official product of the Chandra\n",
    "X-ray Center, and I make no guarantee that it is not without bugs or embarassing typos. Please contact me via the GitHub repository\n",
    "if you have any questions. \n",
    "\n",
    "## Follow up\n",
    "\n",
    "The notebook has been updated to reflect changes in Sherpa."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Last run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When was this notebook last run?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2024-09-30 11:47'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datetime\n",
    "datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4.17.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sherpa\n",
    "sherpa.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What data am I using again?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start with the data set I've been using recently:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = np.random.default_rng(1)\n",
    "k_orig = 1.1\n",
    "theta_orig = 2.6\n",
    "ysim = stats.gamma.rvs(a=k_orig, scale=theta_orig, size=1000, random_state=rng)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I am going to concentrate on the \"CDF\" version of the data, but will also use the \"PDF\" version to show how to plot `Data1DInt` data sets. The \"CDF\" refers to the cumulative distribution function and \"PDF\" the probability density function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "xcdf = ysim.copy()\n",
    "xcdf.sort()\n",
    "ycdf = np.arange(1, xcdf.size+1) * 1.0 / xcdf.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "(ypdf, edges) = np.histogram(ysim, bins=31, density=False)\n",
    "xlo = edges[:-1]\n",
    "xhi = edges[1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is stored in objects; here the `Data1D` and `Data1DInt` classes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sherpa.data import Data1D, Data1DInt\n",
    "\n",
    "dcdf = Data1D('cdf', xcdf, ycdf)\n",
    "dpdf = Data1DInt('pdf', xlo, xhi, ypdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can take advantage of the new support in Sherpa 4.12.2 that can display objects directly in Jupyter notebooks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "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",
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       "Copyright (C) 2020  Smithsonian Astrophysical Observatory\n",
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       "\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",
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       " This program is distributed in the hope that it will be useful,\n",
       " but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
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       " You should have received a copy of the GNU General Public License along\n",
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      "text/plain": [
       "<Data1DInt data set instance 'pdf'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dpdf"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To plot these, I need the `DataPlot` class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sherpa.plot import DataPlot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A `DataPlot` object is given the data to plot via the `prepare` method, and then actually plotted with the `plot` method. Note that `prepare` takes an optional statistics object, which is used to determine how to estimate error bars if none are given explicitly. For this example I do not want any error bars, so leave it at its default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "plotcdf = DataPlot()\n",
    "plotcdf.prepare(dcdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With all this I can create a plot of the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotcdf.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normal `matplotlib` commands can be used to adjust the display; for instance, to increase the Y range to better show the behavior of the data at large `x`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotcdf.plot()\n",
    "plt.ylim(-0.1, 1.1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The approach for the \"binned\" data set is the same: call `prepare` and then `plot`. However, new in Sherpa 4.12.2, is the addition of\n",
    "a separate plot class for histogram data. I also change the preferences for the plot - `histo_prefs` is a dictionary - so that the Y axis is drawn in a log scale:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sherpa.plot import DataHistogramPlot\n",
    "\n",
    "plotpdf = DataHistogramPlot()\n",
    "plotpdf.prepare(dpdf)\n",
    "plotpdf.histo_prefs['ylog'] = True\n",
    "plotpdf.plot()\n",
    "plt.ylim(0.8, 400);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up the models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I use the same models as in the previous notebooks, but this time I cut out the documentation, to save space. Note that as I'm using the low-level interface I do not need to call `add_model` to use either the `GammaCDF` or `GammaPDF` models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sherpa import utils\n",
    "from sherpa.models import model\n",
    "from sherpa.models.parameter import tinyval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_gamma_cdf(x, k, theta):\n",
    "    tval = theta * 1.0\n",
    "    kval = k * 1.0\n",
    "    x = np.asarray(x)\n",
    "    out = np.zeros_like(x)\n",
    "    for i,xi in enumerate(x):\n",
    "        out[i] = utils.igam(kval, xi/tval)\n",
    "\n",
    "    return out\n",
    "\n",
    "def calc_gamma_pdf(x, k, theta):\n",
    "    tval = theta * 1.0\n",
    "    kval = k * 1.0\n",
    "    norm = utils.gamma(kval) * theta**kval\n",
    "    return x**(kval-1.0) * np.exp(-x / tval) / norm\n",
    "\n",
    "def calc_gamma_pdf_int(xlo, xhi, k, theta):\n",
    "    return calc_gamma_cdf(xhi, k, theta) - calc_gamma_cdf(xlo, k, theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GammaCDF(model.ArithmeticModel):\n",
    "    \"\"\"A Gamma CDF.\"\"\"\n",
    "\n",
    "    ndim = 1\n",
    "    \n",
    "    def __init__(self, name='gammacdf'):\n",
    "        self.k = model.Parameter(name, 'k', 5, min=tinyval, hard_min=0)\n",
    "        self.theta = model.Parameter(name, 'theta', 2, min=tinyval, hard_min=0)\n",
    "\n",
    "        model.ArithmeticModel.__init__(self, name, (self.k, self.theta))\n",
    "\n",
    "    @model.modelCacher1d\n",
    "    def calc(self, pars, x, *args, **kwargs):\n",
    "        (k, theta) = pars\n",
    "        if len(args) == 1:\n",
    "            x = (x + args[0]) / 2.0\n",
    "\n",
    "        return calc_gamma_cdf(x, k, theta)\n",
    "        \n",
    "class GammaPDF(model.ArithmeticModel):\n",
    "    \"\"\"A Gamma PDF.\"\"\"\n",
    "\n",
    "    ndim = 1\n",
    "    \n",
    "    def __init__(self, name='gammapdf'):\n",
    "        self.k = model.Parameter(name, 'k', 5, min=tinyval, hard_min=0)\n",
    "        self.theta = model.Parameter(name, 'theta', 2, min=tinyval, hard_min=0)\n",
    "        self.norm = model.Parameter(name, 'norm', 1, min=0) # allow -ve if they realy want it\n",
    "        \n",
    "        model.ArithmeticModel.__init__(self, name, (self.k, self.theta, self.norm))\n",
    "\n",
    "    @model.modelCacher1d\n",
    "    def calc(self, pars, x, *args, **kwargs):\n",
    "        (k, theta, norm) = pars\n",
    "        if len(args) == 0:\n",
    "            return norm * calc_gamma_pdf(x, k, theta)\n",
    "        elif len(args) == 1:\n",
    "            return norm * calc_gamma_pdf_int(x, args[0], k, theta)\n",
    "        else:\n",
    "            raise ValueError(\"Expected x or xlo/xhi grid.\")\n",
    "            \n",
    "    def guess(self, dep, *args, **kwargs):\n",
    "        norm = dep.sum()\n",
    "        self.norm.val = dep.sum()\n",
    "        self.norm.frozen = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To plot a model, I need an instance of the model to plot (in this case it's just a single component, but it could be some more-complicated expression)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "mcdf = GammaCDF('cdf')\n",
    "mpdf = GammaPDF('pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sherpa.plot import ModelPlot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The approach is similar to the data case: create an instance of the `ModelPlot` class, call `prepare` and then `plot`. The difference here is that `prepare` needs both the data set (to know what the independent axis is) and the model expression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "mplotcdf = ModelPlot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The help for the method is not particularly illuminating - it is on our list of things to improve! - but at least the argument names are somewhat meaningful! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method prepare in module sherpa.plot:\n",
      "\n",
      "prepare(data: 'Data1D', model: 'Model', stat: 'Optional[Stat]' = None) -> 'None' method of sherpa.plot.ModelPlot instance\n",
      "    Create the data to plot\n",
      "\n",
      "    Parameters\n",
      "    ----------\n",
      "    data\n",
      "        The Sherpa data object to display (it is assumed to be\n",
      "        one dimensional). This defines the grid over which the\n",
      "        model is displayed.\n",
      "    model\n",
      "        The Sherpa model expression to evaluate and display.\n",
      "    stat : optional\n",
      "        This parameter is unused.\n",
      "\n",
      "    See Also\n",
      "    --------\n",
      "    plot\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(mplotcdf.prepare)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mplotcdf.prepare(dcdf, mcdf)\n",
    "mplotcdf.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, the `plot` method also accepts optional arguments, and I am going to take advantage of `overplot` to combine the data with the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method plot in module sherpa.plot:\n",
      "\n",
      "plot(overplot=False, clearwindow=True, **kwargs) method of sherpa.plot.ModelPlot instance\n",
      "    Plot the data.\n",
      "\n",
      "    This will plot the data sent to the prepare method.\n",
      "\n",
      "    Parameters\n",
      "    ----------\n",
      "    overplot : bool, optional\n",
      "       If `True` then add the data to an existing plot, otherwise\n",
      "       create a new plot.\n",
      "    clearwindow : bool, optional\n",
      "       Should the existing plot area be cleared before creating this\n",
      "       new plot (e.g. for multi-panel plots)?\n",
      "    **kwargs\n",
      "       These values are passed on to the plot backend, and must\n",
      "       match the names of the keys of the object's\n",
      "       plot_prefs dictionary.\n",
      "\n",
      "    See Also\n",
      "    --------\n",
      "    prepare, overplot\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(mplotcdf.plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using this we can see that the current set of model parameters is *not* a good fit to the data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotcdf.plot()\n",
    "mplotcdf.plot(overplot=True)\n",
    "plt.ylim(-0.05, 1.05);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot may also look a bit like the output of the `sherpa.ui.plot_fit` command, which is not-too surprising as it essentially is! I'll show below how the \"fit\"-style plot is created if you do not want to do it manually, like this.\n",
    "\n",
    "A number of Sherpa classes, in particular the plot classes, support rich display in a notebook environment. For instance, if the `prepare` method has been called then we can display the plot data just by saying the following (but note this does not support setting attributes in the `plot` call like `overplot` or `alpha`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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",
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   "source": [
    "However, before worrying about fits, I'd like to just repeat the above for the \"pdf\" data set, since it is binned (\"integrated\" in Sherpa parlance). Note that \n",
    "\n",
    "- I have had to use a different model-plot object to support the histogram nature of the data;\n",
    "- it uses a log scale for Y axis due to the settings of `dpdf`."
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   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sherpa.plot import ModelHistogramPlot\n",
    "\n",
    "mplotpdf = ModelHistogramPlot()\n",
    "mplotpdf.prepare(dpdf, mpdf)\n",
    "plotpdf.plot()\n",
    "mplotpdf.plot(overplot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To look at fit results, I want to first fit the data (so that the models are a better approximation to the data). For this we need the `Fit` object. I am not going to explain this in depth since it is a repeat of the previous two notebooks, and I really just need this\n",
    "done so that the models can be plotted!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sherpa.optmethods import LevMar, NelderMead\n",
    "from sherpa.stats import Cash, LeastSq\n",
    "from sherpa.fit import Fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the CDF data, for which I am going to use a least-square statistic, I use the \"default\" Sherpa optimiser, which is the `LevMar` class. For the PDF data set, which I am using a Maximum Likelihood estimator, I use `NelderMead` (which is the Nelder-Mead Simplex algorithm), since, although it is often slower than `LevMar`, is less-likely to stop in a local minima (this is a statement of my experience, depends on the data and model being fit, and comes with no warranty).\n",
    "\n",
    "When using the \"standard\" Sherpa UI for fitting data, only one optimiser and statistic can be in use at a time. This restriction does not hold here, since you have to be explicit in your choices!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "fcdf = Fit(dcdf, mcdf, LeastSq(), LevMar())\n",
    "fpdf = Fit(dpdf, mpdf, Cash(), NelderMead())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that creating a `Fit` object does *not* run the fit, so at this time the model parameters\n",
    "are unchanged. The `Fit` class comes with some useful methods, such as calculating the statistic for the current model and data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "238.93586941729743"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fcdf.calc_stat()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More context is provided by the `calc_stat_info` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name      = \n",
      "ids       = None\n",
      "bkg_ids   = None\n",
      "statname  = leastsq\n",
      "statval   = 238.93586941729743\n",
      "numpoints = 1000\n",
      "dof       = 998\n",
      "qval      = None\n",
      "rstat     = None\n"
     ]
    }
   ],
   "source": [
    "print(fcdf.calc_stat_info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name      = \n",
      "ids       = None\n",
      "bkg_ids   = None\n",
      "statname  = cash\n",
      "statval   = 11725.158639582614\n",
      "numpoints = 31\n",
      "dof       = 28\n",
      "qval      = None\n",
      "rstat     = None\n"
     ]
    }
   ],
   "source": [
    "print(fpdf.calc_stat_info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the same output as the `ui.calc_stat_info` routine returns, except that some concepts - such as data set ids - are not relevant here. As neither of the chosen statistics are based on (i.e. derived from) the Chi Square class (`sherpa.stats.Chi2`), the `qval` and `rstat` fields are not filled in.\n",
    "\n",
    "So, what happens when the data is fit?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>cdf.k</td><td>     1.12593</td><td>&#177;      0.33594</td></tr><tr><td>cdf.theta</td><td>     2.53022</td><td>&#177;     0.997753</td></tr></tbody></table></details><details><summary>Summary (8)</summary><div class=\"datavals\"><div class=\"dataname\">Method</div><div class=\"dataval\">levmar</div><div class=\"dataname\">Statistic</div><div class=\"dataval\">leastsq</div><div class=\"dataname\">Final statistic</div><div class=\"dataval\">0.0284038</div><div class=\"dataname\">Number of evaluations</div><div class=\"dataval\">26</div><div class=\"dataname\">Initial statistic</div><div class=\"dataval\">238.936</div><div class=\"dataname\">&#916; statistic</div><div class=\"dataval\">238.907</div><div class=\"dataname\">Number of data points</div><div class=\"dataval\">1000</div><div class=\"dataname\">Degrees of freedom</div><div class=\"dataval\">998</div></div></details></div>"
      ],
      "text/plain": [
       "<Fit results instance>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fcdf.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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></tr></thead><tbody><tr><td>pdf.k</td><td>     1.11618</td></tr><tr><td>pdf.theta</td><td>     2.55147</td></tr><tr><td>pdf.norm</td><td>     1002.41</td></tr></tbody></table></details><details><summary>Summary (8)</summary><div class=\"datavals\"><div class=\"dataname\">Method</div><div class=\"dataval\">neldermead</div><div class=\"dataname\">Statistic</div><div class=\"dataval\">cash</div><div class=\"dataname\">Final statistic</div><div class=\"dataval\">-6922.65</div><div class=\"dataname\">Number of evaluations</div><div class=\"dataval\">355</div><div class=\"dataname\">Initial statistic</div><div class=\"dataval\">11725.2</div><div class=\"dataname\">&#916; statistic</div><div class=\"dataval\">18647.8</div><div class=\"dataname\">Number of data points</div><div class=\"dataval\">31</div><div class=\"dataname\">Degrees of freedom</div><div class=\"dataval\">28</div></div></details></div>"
      ],
      "text/plain": [
       "<Fit results instance>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpdf.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.028403826316123718"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fcdf.calc_stat()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-6922.653307548397"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpdf.calc_stat()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As can be seen, the fits have reduced the statistic value for both data sets. The model parameter values, or the return value of the `fit` call, can be viewed for more details, but let's look at the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Looking at fit results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To display both model and data in the same plot, I could do it manually, as I did earlier, but I want to try out the `FitPlot` class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sherpa.plot import FitPlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "fplotcdf = FitPlot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method prepare in module sherpa.plot:\n",
      "\n",
      "prepare(dataplot, modelplot) method of sherpa.plot.FitPlot instance\n",
      "    Create the data to plot\n",
      "\n",
      "    Parameters\n",
      "    ----------\n",
      "    dataplot\n",
      "        The Sherpa plot object representing the data (normally a\n",
      "        DataPlot instance).\n",
      "    modelplot\n",
      "        The Sherpa plot object representing the model (normally a\n",
      "        ModelPlot instance).\n",
      "\n",
      "    See Also\n",
      "    --------\n",
      "    plot\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(fplotcdf.prepare)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, prepare takes \"plot\" objects, which are the `DataPlot` and `ModelPlot` instances I have already used. However, I need to \"refresh\" the model plot contents, since the model parameter values have changed (it may well be safer to always re-create these objects so you are sure what is being plotted, but for now I want to save some typing): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "mplotcdf.prepare(dcdf, mcdf)\n",
    "fplotcdf.prepare(plotcdf, mplotcdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fplotcdf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mplotpdf.prepare(dpdf, mpdf)\n",
    "fplotpdf = FitPlot()\n",
    "fplotpdf.prepare(plotpdf, mplotpdf)\n",
    "fplotpdf.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, the fits have worked!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How about the residuals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The residuals could be calculated directly from the `DataPlot` and `ModelPlot` instances, but Sherpa also provides a `ResidPlot` and - new in `4.16.1` the `ResidHistogramPlot` class (there's also `DelchiPlot` and `DelchiHistogramPlot` for when you want ``(data-model)/error``): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sherpa.plot import ResidPlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "rplotcdf = ResidPlot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method prepare in module sherpa.plot:\n",
      "\n",
      "prepare(data: 'Data1D', model: 'Model', stat: 'Stat') -> 'None' method of sherpa.plot.ResidPlot instance\n",
      "    Create the data to plot\n",
      "\n",
      "    Parameters\n",
      "    ----------\n",
      "    data\n",
      "        The Sherpa data object to display (it is assumed to be\n",
      "        one dimensional). This defines the grid over which the\n",
      "        model is displayed.\n",
      "    model\n",
      "        The Sherpa model expression to evaluate and display.\n",
      "    stat : optional\n",
      "        This parameter is unused.\n",
      "\n",
      "    See Also\n",
      "    --------\n",
      "    plot\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(rplotcdf.prepare)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unlike previous plots, the `stat` argument is not optional, so I use the same statistic as in the fit (but a different instance). This is only used for calculating the error bars, which I am going to turn off anyway, so the exact choice is unimportant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: The displayed errorbars have been supplied with the data or calculated using chi2xspecvar; the errors are not used in fits with leastsq\n"
     ]
    }
   ],
   "source": [
    "rplotcdf.prepare(dcdf, mcdf, LeastSq())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the plot prepared, can create it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rplotcdf.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the defaults are not sensible here, I can change them by\n",
    "\n",
    " - turning off the error bars\n",
    " - use a line to connect the points rather than a symbol\n",
    "   (I know the data is sorted, so a line works well here)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prefs = rplotcdf.plot_prefs\n",
    "prefs['yerrorbars'] = False\n",
    "prefs['marker'] = ''\n",
    "prefs['linestyle'] = 'solid'\n",
    "rplotcdf.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Earlier I mentioned that the residual plot could be created manually from the data and model\n",
    "plots. This is shown below, and only holds if the `prepare` method has been called on each\n",
    "object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(plotcdf.x, plotcdf.y - mplotcdf.y);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combining the fit and residual plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I *can* use `matpotlib` commands to create a combined fit-and-residual plot (this time I\n",
    "have to change the `clearwindow` option to `False` so that the subplots are not obliterated\n",
    "by the `plot` method):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2,1,1)\n",
    "fplotcdf.plot(clearwindow=False)\n",
    "plt.subplot(2,1,2)\n",
    "rplotcdf.plot(clearwindow=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perhaps unsurprisingly, given the presence of the `sherpa.ui.plot_fit_resid` function, Sherpa\n",
    "provides a class to handle displays with multiple plots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sherpa.plot import SplitPlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "splot = SplitPlot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calling `addplot` with a plot object will add that plot in the next available element in the grid (which defaults to a 2-row, 1-column display)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "splot.addplot(fplotcdf)\n",
    "splot.addplot(rplotcdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Larger grids can also be created, as shown below, but for that I need to create a residual plot of the \"PDF\" case:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: The displayed errorbars have been supplied with the data or calculated using chi2xspecvar; the errors are not used in fits with cash\n"
     ]
    }
   ],
   "source": [
    "from sherpa.plot import ResidHistogramPlot\n",
    "\n",
    "rplotpdf = ResidHistogramPlot()\n",
    "rplotpdf.prepare(dpdf, mpdf, Cash())\n",
    "rplotpdf.histo_prefs['yerrorbars'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multiple data sets can be displayed. Here I chose to switch to a two-by-two grid\n",
    "and plot up the data using the `plot` method which requires you to specify the column and\n",
    "grid location of each plot (in this example I could have stuck with `addplot` since the order of the plots matches that used by `addplot`, but I wanted to show the direct approach here):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "splot.reset(rows=2, cols=2)\n",
    "splot.plot(0, 0, fplotcdf)\n",
    "splot.plot(0, 1, fplotpdf)\n",
    "splot.plot(1, 0, rplotcdf)\n",
    "splot.plot(1, 1, rplotpdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the two residual plots use different styles because of the different preference settings for each object (and because, as of `4.16.1`, the PDF version is now drawn \"as a histogram\")."
   ]
  }
 ],
 "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": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}