3 user model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# User models in Sherpa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I was recently working on a way to characterize the draws from a \n",
    "[MCMC chain](http://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo) - in this\n",
    "case generated with\n",
    "[pyBLoCXS](http://cxc.harvard.edu/sherpa/ahelp/pyblocxs.html) but that's only relevant\n",
    "here because we are interested in Poisson-distributed data, hence the Gamma function -\n",
    "and came up with a simple user model for\n",
    "[Sherpa](http://cxc.harvard.edu/sherpa/). I thought it would be make a good example case,\n",
    "and decided to include an example of using the \"low-level\" API of Sherpa to fit data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Follow ups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have written a follow-up to this, available at\n",
    "[an integrated user model](http://nbviewer.ipython.org/github/DougBurke/sherpa-standalone-notebooks/blob/master/an%20integrated%20user%20model.ipynb). This was written on May 4 2015.\n",
    "\n",
    "I then had a brain wave and decided to talk about\n",
    "[plotting in Sherpa when using the low-level API](http://nbviewer.ipython.org/github/DougBurke/sherpa-standalone-notebooks/blob/master/plotting%20using%20the%20lower-level%20routines.ipynb). This was written on May 5 2015.\n",
    "\n",
    "The code has since been updated to match new Sherpa releases (either standalone or as part of CIAO, which you can now\n",
    "install with conda which makes using it in a notebook a lot easier).\n",
    "\n",
    "The standaone version of Sherpa now has its own [web site with documentation](https://sherpa.readthedocs.io/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Author and disclaimer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This was written by \n",
    "[Douglas Burke](http://hea-www.cfa.harvard.edu/~dburke/)\n",
    "on May 26 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. "
   ]
  },
  {
   "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 11:38'"
      ]
     },
     "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": [
    "## Setting up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make my testing easier, I fix the random seed. It's the same value as used by \n",
    "[Jake's AstroML book](http://www.astroml.org/book_figures/chapter3/fig_flux_errors.html),\n",
    "but you may want to use a different value yourself ;-)\n",
    "\n",
    "Prior to January 2024 this used `np.random.seed(1)` and did not send the `rng` value to the `rvs` call below, which resulted in a different-set of values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = np.random.default_rng(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulating some data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this example, I am going to simulate values from a Gamma distribution using\n",
    "[`scipy.stats.gamma`](http://docs.scipy.org/doc/scipy-0.15.1/reference/generated/scipy.stats.gamma.html).\n",
    "Following the \n",
    "[Wikipedia page](http://en.wikipedia.org/wiki/Gamma_distribution) I use the names `k` and `theta`\n",
    "for the parameters of the distribution; fortunately they map onto the `a` and `scale` values used\n",
    "by `scipy.stats.gamma`.\n",
    "\n",
    "The mean of the distribution is just `k * theta`, so `2.86` here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "k_orig = 1.1\n",
    "theta_orig = 2.6\n",
    "ysim = scipy.stats.gamma.rvs(a=k_orig, scale=theta_orig, size=1000, random_state=rng)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a quick look at the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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(ysim);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(ysim);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the work I was doing, I was interested in the \n",
    "[Cumulative Distribution Function](http://en.wikipedia.org/wiki/Cumulative_distribution_function),\n",
    "so let's start by creating this for the data (`ysim`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xcdf = ysim.copy()\n",
    "xcdf.sort()\n",
    "ycdf = np.arange(1, xcdf.size+1) * 1.0 / xcdf.size\n",
    "\n",
    "plt.plot(xcdf, ycdf)\n",
    "plt.xlabel(r'$\\Gamma$')\n",
    "plt.ylabel('CDF');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "My aim is to fit this data, so I need a functional form for the CDF of\n",
    "a Gamma distribution. Fortunately Sherpa provides the `igam` routine\n",
    "which calculates what we need, so I can write the following (I could\n",
    "have also used `scipy.stats` routines, but I wanted this to be usable\n",
    "from Sherpa in CIAO):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sherpa.utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_gamma_cdf(x, k, theta):\n",
    "    \"\"\"Return the CDF of a gamma distribution.\n",
    "\n",
    "    The cumulative distribution function (CDF) of the gamma\n",
    "    distribution is defined in [1]_ as::\n",
    "\n",
    "       cdf(x;k,theta) = incomplete_gamma(k, x/theta)\n",
    "                        ----------------------------\n",
    "                                  gamma(k)\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    x : array\n",
    "       The values at which to evaluate the CDF.\n",
    "    k : number\n",
    "       The shape parameter, which must be greater than 0.\n",
    "    theta : number\n",
    "       The scale parameter.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    cdf : array\n",
    "       The CDF evaluated at each element of x.\n",
    "\n",
    "    Notes\n",
    "    -----\n",
    "    The mean of the distribution is given by ``k * theta``,\n",
    "    and the rate is ``1 / theta``.\n",
    " \n",
    "    References\n",
    "    ----------\n",
    "\n",
    "    .. [1] http://en.wikipedia.org/wiki/Gamma_distribution\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    # Unfortunately igam does not accept a Numpy array as the\n",
    "    # second argument, so need to map over the input array.\n",
    "    #\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",
    "        # igam is the \"regularized\" incomplete gamma (lower)\n",
    "        # form, so already has the normalization by gamma(a)\n",
    "        # included.\n",
    "        out[i] = sherpa.utils.igam(kval, xi/tval)\n",
    "\n",
    "    return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I can use this to plot the actual distribution on the simulated one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Fv3Wl2/nbkz+n/vHz6HlwNVUmkgftbxI25W9cdM4wFREREQdoy4h0H4117H32f7l57wKwYB1DeCvnR3z76s8THxXudDoRkW5LZUS6hyO7aPj7V8g88AG2sXgx/mauv2suY9wqISIiTlMZkZD19taD/HfLQZL3L+PLu39AjKnisInhrsY7uOfGb2KpiIiIBASVEQk575Uc4UcLN7B21yHuDnueW8NeBGCdPYDvR32Xb0/N59w+ic6GFBGRFiojEjL2Ha1l7n8284/Ve4ijiqcjHucC13oAtvSdwtHxD/DnAWnERmqLiIhIIFEZkZDweNEWHl2yGYBsaz/PxvyCtMY9EB4Nk37FoFFfYpDDGUVE5MRURiSoGWN4bMlmHl+6FYBLIzfy67C5eBq9ENcbbnoW0nIcTikiIqeiMiJB7dl3d7cUkf9Lfoc7auZhNTVB73Ew+W8Qm+pwQhEROR2VEQk6NQ1NPF28i1fX7+f9PRWA4bvRL3F71QL/Ajn/A9c+DuGRjuYUEZG2URmRoFLX6OOax5ezvbwaABc2v4r7G9c0vOpf4MJ74ZIHQeeTEREJGiojEjSq6pv4xtOrW4rIzMv6MnXfD4na9ipgwVU/g/G3ORtSRETaTWVEAt5/t5SzbFM5L72/j/LKesLdFr/8QjZXrb8Hdq8EtwdueBKGf8HpqCIicgZURiSgvfDeHu5Z8H7LdIwnjKf+J5vxb90CZevBEw9T/g7ZEx1MKSIiZ0NlRAJWZV0j9/3TP2jZwJQYvvX5AeT3hrjnboDyjdAjBaa+CKkjnA0qIiJnRWVEAtYDL3xIfZNNZkIUi++6kLCq/fD0tXBoK8RmwLSXIFlDmYmIBDuVEQlI7+48zEvv78PtsvjVlHMI8+6GP0+Co7sgvo+/iCT1czqmiIh0AJURCTiNPpsHXvB/PfM/Y3uTm1gLTzUXkcR+MO1lSMhyOKWIiHQUlREJKLZtePCFD9lcVkV0hJtvT0yEP1/bXESyYfoiiMtwOqaIiHQgl9MBRI73+NKtLFi9G4CfXNmbpH/+DxzaAvFZ/i0iKiIiIiFHZUQCxrw3tvKL//jPvHvXxBQmffAtOPAxxKTB1H9DQh+HE4qISGfQ1zTiuNoGH0/+dzuPLWkuIp/L5O7934H970N0sn9n1Z4DHE4pIiKd5Yy2jMybN4/s7GwiIyPJy8tj1apVp1x+7ty5DBkyhKioKLKysrjnnnuoq6s7o8ASWvZX1HLj/BUtReTqEb245+iPsXa/A5Hx/nFEeg1xNqSIiHSqdpeRBQsWUFhYyOzZs1m7di2jR4+moKCAAwcOnHD5Z555hvvuu4/Zs2ezYcMG/vCHP7BgwQLuv//+sw4vwW3NriNc+8TbfLTPS88eEfxy8mieiP8rbH4VwiJhygJIy3E6poiIdLJ2l5HHHnuM2267jenTpzN8+HDmz59PdHQ0Tz311AmXX7FiBRMnTuSmm24iOzubyy+/nClTppx2a4qEtpXbDzH5t8WUV9YzNC2WF2dM5AsVf8Va+2ewXHDD76HvBKdjiohIF2hXGWloaGDNmjXk5+d/8gAuF/n5+RQXF59wnfPPP581a9a0lI/t27ezaNEirrrqqpM+T319PV6vt9VFQstzq/fQZBty+yby/O3nk7XjH7Bsjv/Oq34GwyY5G1BERLpMu3ZgPXjwID6fj9TU1FbzU1NT2bhx4wnXuemmmzh48CAXXHABxhiampr45je/ecqvaebMmcPDDz/cnmgSRKrrm3j5/X0AFF42mJhdRfDKPf47L7wXxt3qYDoREelqnX5o77Jly3jkkUf49a9/zdq1a/nXv/7FwoUL+cEPfnDSdWbOnElFRUXLZffu3Z0dU7rQr5dtpcFnkxwTwfmxZfD818HYMOZmuORBp+OJiEgXa9eWkeTkZNxuN2VlZa3ml5WVkZaWdsJ1HnroIb761a9y663+v3ZzcnKorq7mG9/4Bg888AAu12f7kMfjwePxtCeaBIlNpZX89s3tANyRl4j19y9DQxVkXwiTfgmW5XBCERHpau3aMhIREUFubi5FRUUt82zbpqioiAkTTryzYU1NzWcKh9vtBsAY0968EsRs23D3gnU02YahvTxM2/0gHC3xn2/mf54Gd7jTEUVExAHtHvSssLCQadOmMXbsWMaPH8/cuXOprq5m+vTpAEydOpXMzEzmzPHvjDhp0iQee+wxzjnnHPLy8ti6dSsPPfQQkyZNaikl0j0s2VDGhv1ewPBs5nNYG4vBEwc3LYDoJKfjiYiIQ9pdRiZPnkx5eTmzZs2itLSUMWPGsHjx4padWktKSlptCXnwwQexLIsHH3yQvXv30qtXLyZNmsSPfvSjjvspJOBV1jXyf/94H4CfZ60gYeOz/kN4b3xKg5qJiHRzlgmC70q8Xi/x8fFUVFQQFxfndBxpJ2MMU55cycrth5kYvom/hv0Qy/ig4BGYMMPpeCIi0kna+vmtE+VJp/vPhgOs3H6YdHcFT/X4tb+I5HwJzvuW09FERCQAqIxIp7Jtww9e+Rg3Pv4SNx9PXTn0GqYjZ0REpIXKiHSqZZsPUHK4hu+EPcvA2vchIhYm/wUiejgdTUREAoTKiHSailr/TqsFrlX8v7CF/pnXzYPkQc4GExGRgKIyIp2irtHH/S+sJ6Z2D4+G/9Y/c8IdMPwLzgYTEZGA0+5De0Xa4vGlW3jtg908H/EEMVYtZJ0H+d9zOpaIiAQgbRmRDnewqp7fvbWde8KeZ4xrGyYyHm74vUZYFRGRE1IZkQ634N3djDUfcnvYywBYk34FCVkOpxIRkUClr2mkQxlj+PeKD3g6/Ne4MHDuVBhxndOxREQkgGnLiHSozaWV3Fv3BGnWEeyeg+CKHzsdSUREApzKiHQYn21Y+c9fcrl7DY2E47rxKY0nIiIip6UyIh2maOVqvlg+D4CtI++C9FEOJxIRkWCgMiIdoqnJR9qybxNr1bIreiTDvni/05FERCRIqIxIhyj6648Z1fAetSaCqit/BS6305FERCRIqIzIWSte/S4X7PglAB+PKGRETq7DiUREJJiojMhZqaprIPzlO+hh1bM5agzn3vgdpyOJiEiQURmRs7J3yTzGWhupJpK+X/8jlr6eERGRdlIZkTNmKvaStfanACzNvB1Pr/4OJxIRkWCkMiJnrPy5u4k2NbxnD2Tw1Xc7HUdERIKUyoicmQ2vkLLndRqNm/8MuJ8hGQlOJxIRkSClMiLtV+el8oV7APid72omFVzmcCAREQlmKiPSbnv+dT+xDQfYaaeyecjtDE2LczqSiIgEMZURaZ+9a8jY/FcAHo+ewS9unuBwIBERCXYqI9J2ts2R5+/GheFfvgu4/eu34HJZTqcSEZEgpzIibbfubyQe+YBKE8Xb2f/LwJQYpxOJiEgIUBmRtqk9SsNrswD4ZdMX+d7NlzgcSEREQoXKiLSJeeMRIuoPs8XOpPbc24iNDHc6koiIhIgwpwNIECj7CN79PQA/sabzy2tyHA4kIiKhRFtG5NSMoeHle7GMj1d944gZlk8PjzqsiIh0HJUROaXD7y4gYs8K6kw4T0bfyneuGOp0JBERCTEqI3JyTfU0Ne+0+lv7Wh64qYCMhCiHQ4mISKhRGZGT2vzKY6T4yigzCZz/lYfJ7ZvodCQREQlBKiNyQqbmMBnvPw7Af7O+ybghWQ4nEhGRUKUyIie081/fI8ZUs8HOIu+6O5yOIyIiIUxlRD7r8A6ytv4NgCWZd5CVHOtwIBERCWUqI/IZ3oUPEUYTb/ly+NxVX3Y6joiIhDiVEWltz2ritr2MbSyejrmVkRlxTicSEZEQpzIirdS86j+U91/253jgli8R5tZ/ERER6Vz6pJEWjVuWEb33bRqMm1eTv0a/5B5ORxIRkW5AZUT8jKHs3w8C8Hf7Uq66YLzDgUREpLvQSUYEgMaNi+ldtZ5aE0H8ZfdxXW5vpyOJiEg3oS0jArbN4Zf9+4o8a11BwXljnM0jIiLdisqIcHTt86TWbKbSRBF5USFREW6nI4mISDeiMtLd2T58RT8CYGGP65l80Rhn84iISLejMtLN1b/3LD1rd3LExGCdfwcul+V0JBER6WZURroz20fd0p8B8CdzDZeMHuhwIBER6Y5URrqxfcULiK/ewVHTA3vcbfSK9TgdSUREuiGVke7KtjFv+reKPBd2DVM/n+NwIBER6a5URrqpw+teIrNhO5UmiqHX3qutIiIi4hiVke7IGA6/6j+C5sXwKxk/YoDDgUREpDtTGemGNix/kYGNm6k1EeTceD+eMI0rIiIizlEZ6W6MIWLFzwF4wX05OYN1BI2IiDhLZaSbWbd8IQNqP6TehJF+5bdxa1wRERFxmMpId/PfRwF40bqE8aNGOhxGREREZaTbqGv08fvn/s2YhrX4jEXulNn08OikzSIi4jyVkW7ij2/vpOcHvwXgnajPMXCItoqIiEhgOKMyMm/ePLKzs4mMjCQvL49Vq1adcvmjR48yY8YM0tPT8Xg8DB48mEWLFp1RYDkz27ZuYJKrGICcyQ85nEZEROQT7d5Ov2DBAgoLC5k/fz55eXnMnTuXgoICNm3aREpKymeWb2ho4LLLLiMlJYXnn3+ezMxMdu3aRUJCQkfklzbYc6SGoTv/SliYzcFeeST3G+d0JBERkRbtLiOPPfYYt912G9OnTwdg/vz5LFy4kKeeeor77rvvM8s/9dRTHD58mBUrVhAeHg5Adnb22aWWdnn+7Y+41f0GAEn59zqcRkREpLV2fU3T0NDAmjVryM/P/+QBXC7y8/MpLi4+4TovvfQSEyZMYMaMGaSmpjJy5EgeeeQRfD7fSZ+nvr4er9fb6iJnxrYN5t2niLHqOBIzENfgy5yOJCIi0kq7ysjBgwfx+Xykpqa2mp+amkppaekJ19m+fTvPP/88Pp+PRYsW8dBDD/Hoo4/ywx/+8KTPM2fOHOLj41suWVlZ7Ykpx9l54Ag34d8/J+6SQrA0roiIiASWTj+axrZtUlJS+N3vfkdubi6TJ0/mgQceYP78+SddZ+bMmVRUVLRcdu/e3dkxQ9bR1c+Tah3lsCsR96gvOR1HRETkM9q1z0hycjJut5uysrJW88vKykhLSzvhOunp6YSHh+N2f3L+k2HDhlFaWkpDQwMRERGfWcfj8eDx6CyyHSF1wx8BWJNyA5eFffbfWkRExGnt2jISERFBbm4uRUVFLfNs26aoqIgJEyaccJ2JEyeydetWbNtumbd582bS09NPWESk45jd75JZ/TH1JgzfmKlOxxERETmhdn9NU1hYyJNPPsmf//xnNmzYwO233051dXXL0TVTp05l5syZLcvffvvtHD58mLvuuovNmzezcOFCHnnkEWbMmNFxP4Wc0JFljwOwyEzkgnNGOJxGRETkxNp9aO/kyZMpLy9n1qxZlJaWMmbMGBYvXtyyU2tJSQku1ycdJysri9dee4177rmHUaNGkZmZyV133cV3v/vdjvsp5LO8+0nYvhCA9b2/zPUa+l1ERAKUZYwxToc4Ha/XS3x8PBUVFcTFxTkdJzgs/RG89VNW2UN456K/ceelg5xOJCIi3UxbP791bppQ1FgHq58C4E9NBYzKSnA2j4iIyCmojISij/4FNQfZZ5JYYsYxune804lEREROSmUk1BiDvdI/hstfmi7n5gkDSIjWUUsiIhK4tFdjqNm7Flfp+9SbcJbFXMlzBUOcTiQiInJK2jISYo685d8q8oqdx7evn0CMjqIREZEApzISSmqPEr3l3wAU9biGi4ekOBxIRETk9FRGQkjtmmfwmHo22ln8v5u+jKWT4omISBBQGQkVxuBb9QcAXgorYHSfRIcDiYiItI3KSIiwdxUT491KjfFQN/xGp+OIiIi0mcpIiPjopbkAvGyfzzcuO8fZMCIiIu2gMhICqg6XMvjQUgCscV8nLT7S4UQiIiJtpzISAg6v+DMeq5EN9OfGayY5HUdERKRdVEaCnTFEfvA0AEtjrsHl0hE0IiISXFRGgtxb/3mRlIY9VJlIMi/8itNxRERE2k1lJMjVr/JvFdnQ8zK+MH6ww2lERETaT2UkiG3atZeJDW8DkHD+dA1yJiIiQUllJIjtWf4M0VY9e929GXjuxU7HEREROSMqI0Esfcc/AdjX/wYsl15KEREJTvoEC1L/XfE2w5s20GRc9L/kVqfjiIiInDGVkSBU3+Rj19InAdgUex490/s4nEhEROTMqYwEoZfWlnB54xsA9L/sGw6nEREROTsqI0Fo58p/k2IdpTYsgagRVzsdR0RE5KyojASZkkM1jChfCEDDiC9BWITDiURERM6OykiQefujrVzqWgtA/HlTHU4jIiJy9lRGgkz8jlfxWE2URfaHtByn44iIiJw1lZEgM2C//yuaXZnXgEZcFRGREKAyEkyO7mZI3fsAeM6d7HAYERGRjqEyEkTq3lsAQLFvOAMHDnU4jYiISMdQGQkWxlC/5hkA3vB8nh6eMIcDiYiIdAyVkWBRup74qm3Um3A8o653Oo2IiEiHURkJEk3rngXgP/Y5jBnU1+E0IiIiHUdlJBjYPhrX/QOAZZ5LOK9/T4cDiYiIdByVkWCw4y2i6ss5YmLwDC3Q/iIiIhJSVEaCwfrnAFjkyyM6OsrhMCIiIh1LZSTQNdVjb3gZgH/7zufcPokOBxIREelYKiOBbttSXPVeykwCe2JGccnQFKcTiYiIdCiVkUD30QuA/yua2z4/iIgwvWQiIhJa9MkWyBrrYOMiAF72TWBs3ySHA4mIiHQ8lZFAtnUJNFSyzySxjoEMSOnhdCIREZEOpzISyJq/olnoO4/+veKIjtAhvSIiEnpURgJVQw1sWgzAK77zGJYe53AgERGRzqEyEqi2vAaN1ew2vXjfDODL47KcTiQiItIpVEYC1XFf0QxJjWOChoAXEZEQpTISiOqrYPPrALzsO4//vXQQLpflcCgREZHOoTISiDYvhqZadtqpfGSy+dzgZKcTiYiIdBqVkQDU+OGLACy087g6J4PYyHBnA4mIiHQiHSsaaBprsbb+B4AVERP59fU5DgcSERHpXNoyEmi2LSXMV8sek8yocRcRH62tIiIiEtpURgJM5Xv+o2he943lipHpDqcRERHpfCojgcTXSPi21wBYH/c5RvWOdziQiIhI51MZCSQ7lxPZ5OWgiSP3wiuxLB3OKyIioU9lJIDUfvAiAEt8uVw0JM3ZMCIiIl1EZSRQ2Db1H74MwAexn6N3YpTDgURERLqGykiAaCh5hwTfIbwmisuv+ZK+ohERkW5DZSRAlLz9DwBWuHK5cGimw2lERES6jspIADC2TeTWRQDUDLiKMLdeFhER6T7O6FNv3rx5ZGdnExkZSV5eHqtWrWrTes8++yyWZXHdddedydOGrOKV/6W3KaXOhDPh8slOxxEREelS7S4jCxYsoLCwkNmzZ7N27VpGjx5NQUEBBw4cOOV6O3fu5N577+XCCy8847ChyGcbPljyVwA2xYwjPUUnxRMRke6l3WXkscce47bbbmP69OkMHz6c+fPnEx0dzVNPPXXSdXw+HzfffDMPP/ww/fv3P6vAoWZjqZcLfP4tS4MvmuJwGhERka7XrjLS0NDAmjVryM/P/+QBXC7y8/MpLi4+6Xrf//73SUlJ4ZZbbmnT89TX1+P1eltdQlXJji2MdO3ExiJqxFVOxxEREely7SojBw8exOfzkZqa2mp+amoqpaWlJ1xn+fLl/OEPf+DJJ59s8/PMmTOH+Pj4lktWVlZ7YgYNYww7i/8FwP7YHOihr2hERKT76dTDNiorK/nqV7/Kk08+SXJy2z9oZ86cSUVFRctl9+7dnZjSOR/v9zKk4m0AEsZc63AaERERZ4S1Z+Hk5GTcbjdlZWWt5peVlZGW9tnhy7dt28bOnTuZNGlSyzzbtv1PHBbGpk2bGDBgwGfW83g8eDye9kQLSq+u2cadro8A6JFzjcNpREREnNGuLSMRERHk5uZSVFTUMs+2bYqKipgwYcJnlh86dCjr169n3bp1LZdrr72Wiy++mHXr1oXs1y9tYYzhyIev47EaqY7KhF5DnY4kIiLiiHZtGQEoLCxk2rRpjB07lvHjxzN37lyqq6uZPn06AFOnTiUzM5M5c+YQGRnJyJEjW62fkJAA8Jn53Y23romc6mIIg/DhV4GGfxcRkW6q3WVk8uTJlJeXM2vWLEpLSxkzZgyLFy9u2am1pKQEl0sjiJ7O5tIKLnW/B0DEcB1FIyIi3ZdljDFOhzgdr9dLfHw8FRUVxMXFOR2nQ/xxwXNM33ArtVY0UQ/sgrAIpyOJiIh0qLZ+fmsThgPqm3w0bngVgMPpF6iIiIhIt6Yy4oDibYe4wF4NQK/c65wNIyIi4jCVEQfs3bWZ4a5d2LiIGHqF03FEREQcpTLiAN/G1wAojcuBHj0dTiMiIuIslZEu1uiz6Xt4OQC+AZc5nEZERMR5KiNdrKT8COOMf9TVlHMnnWZpERGR0Kcy0sUOf/Qm0VY9B60kPL1HOx1HRETEcSojXSxs+38A2BI7XqOuioiIoDLS5dIO+PcXOZJ+kcNJREREAoPKSFc6WkJ64y58xqLXGB3SKyIiAiojXerIB/5RV9eaQQzr38fhNCIiIoFBZaQLla99BYCPoscT42n3OQpFRERCkspIV2lqIKviXQASR+ksvSIiIseojHSV3SuJMrWUmziSBo51Oo2IiEjAUBnpKluWAPCWPZrk2CiHw4iIiAQOlZEuUtd8PpplvtEkx3gcTiMiIhI4VEa6QsUeIg9vwmcsqrM+R3JMhNOJREREAobKSBcwW4sAeN8MYOol52Bp5FUREZEWKiNd4OgHiwBYwRjO7ZvocBoREZHAojLS2WwfPfatAOBQ2gXERYY7HEhERCSwqIx0tv3riGj04jXRVCblOJ1GREQk4KiMdLZtbwBQbA9nZFZPh8OIiIgEHpWRTtawxV9G/mvncPmINIfTiIiIBB6Vkc7UUEPY3ncAKIkfR0aCBjsTERH5NJWRzrRrBS67kT0mmR7pQ5xOIyIiEpBURjpRw5alACz3jSR/uL6iERERORGVkU7UuMU/2NkHEefwhTEZDqcREREJTCojnaXqAD2ObASgoc+FhLn1Ty0iInIi+oTsLNvfBOBDO5vB/bOdzSIiIhLAVEY6idnevL+IPZJcDQEvIiJyUiojncEYfM3ji6xkFCMy4h0OJCIiErhURjrDwS2EVe+n3oRTlz6OyHC304lEREQClspIZ9ju3yryrj2YnGwd0isiInIqKiOdYfsyAJbbOZzbR/uLiIiInIrKSEezfZidywF42x7JqKwEZ/OIiIgEOJWRjrb/fax6L14TzYEeg8mIj3Q6kYiISEBTGelgvu1vAfCOPZQ78odiWZbDiURERAKbykgHa9jqH+xspT2cG87NdDiNiIhI4FMZ6Ui+RsL3rgRgR+y5REeEORxIREQk8KmMdKR96whrquGIiSEx+xyn04iIiAQFlZGOtPPY/iLD6N0zxuEwIiIiwUFlpAOZHf8FoNgeTsEIDXYmIiLSFiojHaWpAVPi31/kPfdIhqTFOhxIREQkOKiMdJS9a3A11XLQxBGVMRK3S4f0ioiItIXKSEfZ6f+KZqU9jDEaAl5ERKTNVEY6yg7/zqsr7eEM6KWdV0VERNpKZaQjNNZhdq8C/DuvjsiMcziQiIhI8FAZ6Qh7VmH56jlgEvAlDmRERrzTiURERIKGykhHOO6Q3oGp2ioiIiLSHiojHaBhm/98NMX2cKZPzHY2jIiISJBRGTlbDTW49q4BYH/iWCb07+lwIBERkeCiMnKW7N3vEkYT+00Skz5/AS6NLyIiItIuKiNnqXKz/5De1WYoXzgn0+E0IiIiwUdl5GztehuArVGjCHfrn1NERKS99Ol5Npoa6HHgPQDKEs91OIyIiEhwUhk5G/vfJ8yu44iJISZzhNNpREREgtIZlZF58+aRnZ1NZGQkeXl5rFq16qTLPvnkk1x44YUkJiaSmJhIfn7+KZcPKiUrAHjXHsL4/skOhxEREQlO7S4jCxYsoLCwkNmzZ7N27VpGjx5NQUEBBw4cOOHyy5YtY8qUKbzxxhsUFxeTlZXF5Zdfzt69e886vNN8O/z7i7xjDyUzMcrhNCIiIsHJMsaY9qyQl5fHuHHjeOKJJwCwbZusrCzuvPNO7rvvvtOu7/P5SExM5IknnmDq1Kltek6v10t8fDwVFRXExQXICKe2TcOcvkQ0evl6+E+Y/91vEBGmb71ERESOaevnd7s+PRsaGlizZg35+fmfPIDLRX5+PsXFxW16jJqaGhobG0lKSmrPUweeAx8T0eil2ngYNe5zKiIiIiJnKKw9Cx88eBCfz0dqamqr+ampqWzcuLFNj/Hd736XjIyMVoXm0+rr66mvr2+Z9nq97YnZJXw7l+MG1tiD+fywDKfjiIiIBK0u/XP+xz/+Mc8++ywvvPACkZGRJ11uzpw5xMfHt1yysrK6MGXbHN3gH+xsfdgIRmXqLL0iIiJnql1lJDk5GbfbTVlZWav5ZWVlpKWlnXLdn//85/z4xz/m9ddfZ9SoUadcdubMmVRUVLRcdu/e3Z6Ync8YPPtW+m9mTdAQ8CIiImehXWUkIiKC3NxcioqKWubZtk1RURETJkw46Xo//elP+cEPfsDixYsZO3bsaZ/H4/EQFxfX6hJIGsu3EtN4iHoTRt/RFzodR0REJKi1a58RgMLCQqZNm8bYsWMZP348c+fOpbq6munTpwMwdepUMjMzmTNnDgA/+clPmDVrFs888wzZ2dmUlpYCEBMTQ0xMTAf+KF1n7/tLyQY+ZCD5OdkOpxEREQlu7S4jkydPpry8nFmzZlFaWsqYMWNYvHhxy06tJSUluFyfbHD5zW9+Q0NDAzfeeGOrx5k9ezbf+973zi69Q7yblgFQmnAOuRFuZ8OIiIgEuXaPM+KEQBtnZO/Dg8k0Zbx93nwmXjHF6TgiIiIBqVPGGRHYtWMzmaYMn7EYmVfgdBwREZGgpzLSToc3vAnAzvABxCcG+cBtIiIiAUBlpJ2ams9Hszd2jLNBREREQoTKSDv1OrIOAM/Aic4GERERCREqI+1g11bQp2knAJH9znc2jIiISIhQGWmHgxtX4MKw26QwZNAgp+OIiIiEBJWRdqjetgKADWHDiAzX+CIiIiIdQWWkHUyJ/3w01SnnOpxEREQkdKiMtJXtI61yPQAJQy5wOIyIiEjoUBlpo8bSj4g2tVSZSHoNOMfpOCIiIiFDZaSNSt5fBsAHZiAD0xIczSIiIhJKVEba6ODHbwFQ2etc7bwqIiLSgVRG2uC9kiOkVnwAQJ8xFzucRkREJLSojLTBouIPyHaVYWMxbOwlTscREREJKSojbWDtXQVAVdxAiEpwNoyIiEiIURk5jdKKOpKbz0djZeU5G0ZERCQEqYycxnOrd3OOtRmAmIE6H42IiEhHUxk5jeLN+xhl7QDA6nOew2lERERCj8rIaSRVbsBjNdLoSYKk/k7HERERCTkqI6fQ6LPp3TwEfGPGWLAshxOJiIiEHpWRU/hwbwWj2QRAVH/tLyIiItIZVEZOYclHpeS6tgDaX0RERKSzqIycQvWB7aRYR/FZYZAxxuk4IiIiIUll5CSMMVi73wGgKmkEhEc5nEhERCQ0qYycRGV9E/3rPgYgWvuLiIiIdBqVkZOorGsi1+Uf7Cw8W/uLiIiIdBaVkZPYtns/Q60S/4SGgRcREek0KiMnseuDt3BbhiPhaRCX7nQcERGRkKUychLuve8CUJ2a63ASERGR0KYycgJ1jT56V30AQI8BEx1OIyIiEtpURk5g/Z4jjLH8g50lDFEZERER6UwqIyew/eM1xFm11FmRWKkjnY4jIiIS0lRGTqBxZzEAh+JzwB3mcBoREZHQpjLyKcYYEg69B4Dde7zDaUREREKfysinrN51hOFNGwFIGHKBw2lERERCn8rIp5SUlNDfVQpA7MAJDqcREREJfSojn1L68VsAlEf2g6hEh9OIiIiEPpWRT+lRthoAq4/ORyMiItIVVEaOc7i6geE+//4icYM1voiIiEhXUBk5zo7Sw4y2tgMQka39RURERLqCyshxjmxbg8dqpNIVBz0HOB1HRESkW1AZOd6edwDYG5MDluVwGBERke5BZeQ4ic2DnVWn6Ey9IiIiXUVl5Bhj6FuzHoCwbB1JIyIi0lVURprVH9pJsjlCo3GTMkw7r4qIiHQVlZFme99/A4BNVj/SkjTYmYiISFdRGWlWs20FAGUJY7C086qIiEiXURlplnxkHQDeXtp5VUREpCupjADUeUmp3QaAnTne4TAiIiLdi8oIYPa8iwubErsXgwcOcjqOiIhIt6IyAlRsWg7AewxhSFqsw2lERES6F5URwLdrJQC7Y0YREaZ/EhERka6kT15fEzEH1wFQpZ1XRUREupzKyIGP8dg1eE0U547TYGciIiJdrduXkSMb/wvAe/Yg8gakOJxGRESk++n2ZcS7xV9G9sTmEB8V7nAaERGR7qd7lxFjSDqwCoDG3uc7HEZERKR7OqMyMm/ePLKzs4mMjCQvL49Vq1adcvnnnnuOoUOHEhkZSU5ODosWLTqjsB3NV76Z2KZD1JtwUodd4HQcERGRbqndZWTBggUUFhYye/Zs1q5dy+jRoykoKODAgQMnXH7FihVMmTKFW265hffee4/rrruO6667jg8//PCsw5+tveteB2CtGcQlOVkOpxEREemeLGOMac8KeXl5jBs3jieeeAIA27bJysrizjvv5L777vvM8pMnT6a6uppXXnmlZd55553HmDFjmD9/fpue0+v1Eh8fT0VFBXFxce2Je0of/OJ6RlUs5cWEaVx396867HFFRESk7Z/f7doy0tDQwJo1a8jPz//kAVwu8vPzKS4uPuE6xcXFrZYHKCgoOOnyAPX19Xi93laXztCr8mMAeo68tFMeX0RERE6vXWXk4MGD+Hw+UlNTW81PTU2ltLT0hOuUlpa2a3mAOXPmEB8f33LJyuqcr1CWF7zKU0N+S99Rn+uUxxcREZHTC3M6wInMnDmTwsLClmmv19spheRLef0hr3+HP66IiIi0XbvKSHJyMm63m7Kyslbzy8rKSEtLO+E6aWlp7VoewOPx4PF42hNNREREglS7vqaJiIggNzeXoqKilnm2bVNUVMSECSceSn3ChAmtlgdYsmTJSZcXERGR7qXdX9MUFhYybdo0xo4dy/jx45k7dy7V1dVMnz4dgKlTp5KZmcmcOXMAuOuuu7jooot49NFHufrqq3n22WdZvXo1v/vd7zr2JxEREZGg1O4yMnnyZMrLy5k1axalpaWMGTOGxYsXt+ykWlJSgsv1yQaX888/n2eeeYYHH3yQ+++/n0GDBvHiiy8ycuTIjvspREREJGi1e5wRJ3TWOCMiIiLSeTplnBERERGRjqYyIiIiIo5SGRERERFHqYyIiIiIo1RGRERExFEqIyIiIuIolRERERFxlMqIiIiIOCogz9r7acfGZfN6vQ4nERERkbY69rl9uvFVg6KMVFZWApCVleVwEhEREWmvyspK4uPjT3p/UAwHb9s2+/btIzY2FsuyOuxxvV4vWVlZ7N69W8PMBwm9ZsFHr1lw0usWfALxNTPGUFlZSUZGRqvz1n1aUGwZcblc9O7du9MePy4uLmBeOGkbvWbBR69ZcNLrFnwC7TU71RaRY7QDq4iIiDhKZUREREQc1a3LiMfjYfbs2Xg8HqejSBvpNQs+es2Ck1634BPMr1lQ7MAqIiIioatbbxkRERER56mMiIiIiKNURkRERMRRKiMiIiLiqG5dRubNm0d2djaRkZHk5eWxatUqpyPJSXzve9/DsqxWl6FDhzodS47z1ltvMWnSJDIyMrAsixdffLHV/cYYZs2aRXp6OlFRUeTn57NlyxZnwgpw+tfsa1/72mfed1dccYUzYQWAOXPmMG7cOGJjY0lJSeG6665j06ZNrZapq6tjxowZ9OzZk5iYGG644QbKysocStw23baMLFiwgMLCQmbPns3atWsZPXo0BQUFHDhwwOlochIjRoxg//79LZfly5c7HUmOU11dzejRo5k3b94J7//pT3/Kr371K+bPn88777xDjx49KCgooK6urouTyjGne80Arrjiilbvu7///e9dmFA+7c0332TGjBmsXLmSJUuW0NjYyOWXX051dXXLMvfccw8vv/wyzz33HG+++Sb79u3ji1/8ooOp28B0U+PHjzczZsxomfb5fCYjI8PMmTPHwVRyMrNnzzajR492Ooa0EWBeeOGFlmnbtk1aWpr52c9+1jLv6NGjxuPxmL///e8OJJRP+/RrZowx06ZNM1/4whccySNtc+DAAQOYN9980xjjf1+Fh4eb5557rmWZDRs2GMAUFxc7FfO0uuWWkYaGBtasWUN+fn7LPJfLRX5+PsXFxQ4mk1PZsmULGRkZ9O/fn5tvvpmSkhKnI0kb7dixg9LS0lbvufj4ePLy8vSeC3DLli0jJSWFIUOGcPvtt3Po0CGnI8lxKioqAEhKSgJgzZo1NDY2tnqvDR06lD59+gT0e61blpGDBw/i8/lITU1tNT81NZXS0lKHUsmp5OXl8ac//YnFixfzm9/8hh07dnDhhRdSWVnpdDRpg2PvK73ngssVV1zB008/TVFRET/5yU948803ufLKK/H5fE5HE/xntL/77ruZOHEiI0eOBPzvtYiICBISElotG+jvtaA4a6/IlVde2XJ71KhR5OXl0bdvX/7xj39wyy23OJhMJHR9+ctfbrmdk5PDqFGjGDBgAMuWLePSSy91MJkAzJgxgw8//DAk9p/rlltGkpOTcbvdn9m7uKysjLS0NIdSSXskJCQwePBgtm7d6nQUaYNj7yu954Jb//79SU5O1vsuANxxxx288sorvPHGG/Tu3btlflpaGg0NDRw9erTV8oH+XuuWZSQiIoLc3FyKiopa5tm2TVFRERMmTHAwmbRVVVUV27ZtIz093eko0gb9+vUjLS2t1XvO6/Xyzjvv6D0XRPbs2cOhQ4f0vnOQMYY77riDF154gaVLl9KvX79W9+fm5hIeHt7qvbZp0yZKSkoC+r3Wbb+mKSwsZNq0aYwdO5bx48czd+5cqqurmT59utPR5ATuvfdeJk2aRN++fdm3bx+zZ8/G7XYzZcoUp6NJs6qqqlZ/Me/YsYN169aRlJREnz59uPvuu/nhD3/IoEGD6NevHw899BAZGRlcd911zoXu5k71miUlJfHwww9zww03kJaWxrZt2/jOd77DwIEDKSgocDB19zZjxgyeeeYZ/v3vfxMbG9uyH0h8fDxRUVHEx8dzyy23UFhYSFJSEnFxcdx5551MmDCB8847z+H0p+D04TxOevzxx02fPn1MRESEGT9+vFm5cqXTkeQkJk+ebNLT001ERITJzMw0kydPNlu3bnU6lhznjTfeMMBnLtOmTTPG+A/vfeihh0xqaqrxeDzm0ksvNZs2bXI2dDd3qtespqbGXH755aZXr14mPDzc9O3b19x2222mtLTU6djd2oleL8D88Y9/bFmmtrbWfOtb3zKJiYkmOjraXH/99Wb//v3OhW4Dyxhjur4CiYiIiPh1y31GREREJHCojIiIiIijVEZERETEUSojIiIi4iiVEREREXGUyoiIiIg4SmVEREREHKUyIiIiIo5SGRERERFHqYyIiIiIo1RGRERExFEqIyIiIuKo/w/oeUOv0NeprgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(xcdf, ycdf)\n",
    "plt.plot(xcdf, calc_gamma_cdf(xcdf, k_orig, theta_orig));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [Quantile-Quantile plot](http://en.wikipedia.org/wiki/Q%E2%80%93Q_plot)\n",
    "also looks good (as it should here ;-):"
   ]
  },
  {
   "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": [
    "plt.plot(ycdf, calc_gamma_cdf(xcdf,k_orig,theta_orig))\n",
    "plt.plot([0,1],[0,1], 'k--')\n",
    "plt.title('Q-Q plot');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using `calc_gamma_cdf` I can now create a Sherpa model that can be used to\n",
    "fit the CDF data. Rather than use the\n",
    "[`load_user_model` function](http://cxc.harvard.edu/sherpa/ahelp/load_user_model.html),\n",
    "I have decided to use a class-based approach. The advantage is that this model then\n",
    "behaves in the same manner as existing Sherpa models - so that you can create multiple\n",
    "instances of it directly - at the expense of a little-more set-up work.\n",
    "\n",
    "To do this, I need to import the Sherpa `sherpa.models.model` module.\n",
    "In this case I am being explicit about the module names, to make it easier to see where\n",
    "symbols are defined, but normally I would say something like\n",
    "\n",
    "    from sherpa.models import model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sherpa.models.model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this, I can create the `GammaCDF` class. The model has two parameters, which \n",
    "I name `k` and `theta`, and set initial parameters and limits (the limit is inclusive,\n",
    "which is not ideal here because both should be `> 0` rather than `>= 0`, but let's\n",
    "see how this works). The important parts are `__init__`, which sets everything\n",
    "up, and `calc` which is used to evaluate the model at a set of points. For this\n",
    "example I am going to assume that if it is used with an \"integrated\" 1D data set - that\n",
    "is, a data set where the signal is integrated between bin limits, rather than just\n",
    "a value at a point - then the code should just use the mid-point of the bin. The\n",
    "`modelCacher1d` function decorator tells the system that the results of the model can\n",
    "be cached when all the parameter values are frozen (e.g. during a fit)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GammaCDF(sherpa.models.model.ArithmeticModel):\n",
    "    \"\"\"A Gamma CDF.\n",
    "\n",
    "    The cumulative distribution function (CDF) for the Gamma\n",
    "    distribution, as defined by [1]_, is::\n",
    "\n",
    "       cdf(x;k,theta) = incomplete_gamma(k, x/theta)\n",
    "                        ----------------------------\n",
    "                                  gamma(k)\n",
    "\n",
    "    The model parameters are:\n",
    "\n",
    "    k\n",
    "       The shape parameter, which must be greater than 0.\n",
    "\n",
    "    theta\n",
    "       The scale parameter.\n",
    "\n",
    "    Notes\n",
    "    -----\n",
    "    The mean of the distribution is given by ``k * theta``,\n",
    "    and the rate is ``1 / theta``.\n",
    " \n",
    "    References\n",
    "    ----------\n",
    "\n",
    "    .. [1] http://en.wikipedia.org/wiki/Gamma_distribution\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    # The Sherpa 4.12.2 release adds support for checking model dimensionality\n",
    "    ndim = 1\n",
    "    \"A one-dimensional model\"\n",
    "    \n",
    "    def __init__(self, name='gammacdf'):\n",
    "        # It would be nice to force > 0 rather than >=0.\n",
    "        # Perhaps should just use a small value, e.g. \n",
    "        # sherpa.models.parameter.tinyval?\n",
    "        self.k = sherpa.models.model.Parameter(name, 'k', 5, min=0, hard_min=0)\n",
    "        self.theta = sherpa.models.model.Parameter(name, 'theta', 2, min=0, hard_min=0)\n",
    "\n",
    "        sherpa.models.model.ArithmeticModel.__init__(self, name, (self.k, self.theta))\n",
    "\n",
    "    @sherpa.models.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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model can be used to fit the data. I'll show this two ways\n",
    "\n",
    " 1. using the low-level API, where data management is not automatic\n",
    " \n",
    " 2. using the high-level UI which most users of Sherpa are used to."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Managing the data directly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The UI that is explained in the \n",
    "[CXC Sherpa documentation](http://cxc.harvard.edu/sherpa/) deals with \n",
    "data management and state. However, this can be handled directly by\n",
    "dropping down to the individual models and - for some cases, such as this\n",
    "one - is very easy to do. Let's start with importing the symbols I need\n",
    "(and to make it look easy, I'm not going to use the fully-qualified name approach I've\n",
    "used so far):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sherpa.data import Data1D\n",
    "from sherpa.stats import LeastSq\n",
    "from sherpa.optmethods import LevMar\n",
    "from sherpa.fit import Fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this example - which is a 1D un-binned data set - we just need a name, and the independent and dependent data arrays. The name is only used to label the results in output structures, so here I just use the label `cdf`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = Data1D('cdf', xcdf, ycdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model to be fit is - in this case - just a single component, the newly-created `GammaCDF`\n",
    "model, so I need an instance (the name need not be the same as the variable name, but it makes tracking\n",
    "things a bit easier!):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "cdf = GammaCDF('cdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With these, I can now create a `Fit` object, selecting the statistic - I use the least-squared\n",
    "version (`LeastSq`) since I have no errors - and the optimiser, for which I use the \n",
    "Levenberg-Marquardt method (as provided by the `LevMar` class):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = Fit(d, cdf, LeastSq(), LevMar())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fit can then be \"run\" and the output stored away. For this case the optimiser converged."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = f.fit()\n",
    "res.succeeded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output of the `fit` method returns a lot if useful information, including the best-fit parameter values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>     1.12594</td><td>&#177;      0.33594</td></tr><tr><td>cdf.theta</td><td>     2.53021</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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, we can also access these values directly from the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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;GammaCDF model instance &#x27;cdf&#x27;&gt;</div><div hidden class=\"sherpa\"><details open><summary>Model</summary><table class=\"model\"><caption>Expression: cdf</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=2>cdf</th><td>k</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.125938433055331</td><td>0.0</td><td>MAX</td><td></td></tr><tr><td>theta</td><td><input disabled type=\"checkbox\" checked></input></td><td>2.5302092204268933</td><td>0.0</td><td>MAX</td><td></td></tr></tbody></table></details></div>"
      ],
      "text/plain": [
       "<GammaCDF model instance 'cdf'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and compare them to the input values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.125938433055331 2.5302092204268933\n",
      "1.1 2.6\n"
     ]
    }
   ],
   "source": [
    "print(cdf.k.val, cdf.theta.val)\n",
    "print(k_orig, theta_orig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One down side to the low-level API is that plots have to be manually\n",
    "created, but it's quite easy to create a residual (i.e. fit - data)\n",
    "plot for the CDF (after writing this section I added a new notebook,\n",
    "[Plotting using the \"low-level\" interface](http://nbviewer.jupyter.org/github/DougBurke/sherpa-standalone-notebooks/blob/master/plotting%20using%20the%20lower-level%20routines.ipynb),\n",
    "which shows how you can use the Sherpa plotting infrastructure to\n",
    "make these plots):"
   ]
  },
  {
   "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": [
    "plt.plot(xcdf, ycdf - cdf(xcdf))\n",
    "plt.axhline()\n",
    "plt.ylabel(r'$\\Delta$ CDF')\n",
    "plt.xlabel(r'$\\Gamma$');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The QQ plot looks good, as might be expected from the residuals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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(ycdf, cdf(xcdf))\n",
    "plt.plot([0,1], [0,1], 'k--')\n",
    "plt.xlabel('Measured')\n",
    "plt.ylabel('Predicted')\n",
    "plt.title('QQ plot');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Letting Sherpa do all the work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The high-level API of Sherpa manages data and settings for users. In the\n",
    "following I'm going to use the version provided by the `sherpa.ui` module,\n",
    "but the \"Astronomy-specific\" module `sherpa.astro.ui` can also be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sherpa.ui"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model needs to be added to Sherpa using `add_model`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "sherpa.ui.add_model(GammaCDF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's select the least-square statistic, by name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "sherpa.ui.set_stat('leastsq')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rather than create a `Data1D` object, here I use the default data set - which is\n",
    "labelled `1`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "sherpa.ui.load_arrays(1, xcdf, ycdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default behavior in plots is to display error bars, even when using a statistic like `leastsq`,\n",
    "so I change this behavior before displaying the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pprefs = sherpa.ui.get_plot_prefs('data')\n",
    "pprefs['yerrorbars'] = False\n",
    "sherpa.ui.plot_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The source model, used to describe the data, is set by `set_source`. Since `add_model` was used,\n",
    "I can create an instance of the `GammaCDF` model using the syntax `modelname.cptname`, where\n",
    "`modelname` is in lower case. To allow comparison with the eariler fit, I name the component\n",
    "`cpt2`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "sherpa.ui.set_source(gammacdf.cdf2)"
   ]
  },
  {
   "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;GammaCDF model instance &#x27;gammacdf.cdf2&#x27;&gt;</div><div hidden class=\"sherpa\"><details open><summary>Model</summary><table class=\"model\"><caption>Expression: gammacdf.cdf2</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=2>gammacdf.cdf2</th><td>k</td><td><input disabled type=\"checkbox\" checked></input></td><td>5.0</td><td>0.0</td><td>MAX</td><td></td></tr><tr><td>theta</td><td><input disabled type=\"checkbox\" checked></input></td><td>2.0</td><td>0.0</td><td>MAX</td><td></td></tr></tbody></table></details></div>"
      ],
      "text/plain": [
       "<GammaCDF model instance 'gammacdf.cdf2'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The defailt optimiser is `LevMar`, so I do not need to set it - which would have been the following\n",
    "call\n",
    "\n",
    "    set_method('levmar')\n",
    "    \n",
    "Instead I can just go straight to fitting the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset               = 1\n",
      "Method                = levmar\n",
      "Statistic             = leastsq\n",
      "Initial fit statistic = 238.936\n",
      "Final fit statistic   = 0.0284038 at function evaluation 26\n",
      "Data points           = 1000\n",
      "Degrees of freedom    = 998\n",
      "Change in statistic   = 238.907\n",
      "   cdf2.k         1.12594      +/- 0.33594     \n",
      "   cdf2.theta     2.53021      +/- 0.997753    \n"
     ]
    }
   ],
   "source": [
    "sherpa.ui.fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and view the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sherpa.ui.plot_fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately it's not that obvious what is going on here, so let's look at the residual plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sherpa.ui.plot_resid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can turn off the error bars for the residual plot too:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sherpa.ui.get_plot_prefs('resid')['yerrorbars'] = False\n",
    "sherpa.ui.plot_resid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results of the two fits should be the same (since the data, model, and starting points are the same). Are they?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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;GammaCDF model instance &#x27;cdf&#x27;&gt;</div><div hidden class=\"sherpa\"><details open><summary>Model</summary><table class=\"model\"><caption>Expression: cdf</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=2>cdf</th><td>k</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.125938433055331</td><td>0.0</td><td>MAX</td><td></td></tr><tr><td>theta</td><td><input disabled type=\"checkbox\" checked></input></td><td>2.5302092204268933</td><td>0.0</td><td>MAX</td><td></td></tr></tbody></table></details></div>"
      ],
      "text/plain": [
       "<GammaCDF model instance 'cdf'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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;GammaCDF model instance &#x27;gammacdf.cdf2&#x27;&gt;</div><div hidden class=\"sherpa\"><details open><summary>Model</summary><table class=\"model\"><caption>Expression: gammacdf.cdf2</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=2>gammacdf.cdf2</th><td>k</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.125938433055331</td><td>0.0</td><td>MAX</td><td></td></tr><tr><td>theta</td><td><input disabled type=\"checkbox\" checked></input></td><td>2.5302092204268933</td><td>0.0</td><td>MAX</td><td></td></tr></tbody></table></details></div>"
      ],
      "text/plain": [
       "<GammaCDF model instance 'gammacdf.cdf2'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Phew. So, I hope you enjoyed this whistle-stop tour through Sherpa user models, the\n",
    "`add_model` command, and direct access to the low-level API provided by Sherpa."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A follow up (May 4th and 5th, 2015)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have written a follow-up to this, available at\n",
    "[an integrated user model](http://nbviewer.ipython.org/github/DougBurke/sherpa-standalone-notebooks/blob/master/an%20integrated%20user%20model.ipynb), and then a notebook about\n",
    "[plotting in Sherpa when using the low-level API](http://nbviewer.ipython.org/github/DougBurke/sherpa-standalone-notebooks/blob/master/plotting%20using%20the%20lower-level%20routines.ipynb)."
   ]
  }
 ],
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