Fitting-Truncated-Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "from scipy import special\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "from IPython.display import Markdown\n",
    "plt.style.use('bmh')\n",
    "plt.rcParams.update({'axes.labelsize': 20})\n",
    "plt.rcParams.update({'legend.fontsize': 20})\n",
    "plt.rcParams[\"figure.figsize\"] = (12,10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the lab, we will have to probably truncate the data. Perhaps there are regions where the background is important, and we want to avoid it, or perhaps the feature we want to fit, like the guassian of the first photoelectron peak, is limited to a region. Slicing the data is very simple however limiting the fitting limits of a distribution forces a change in the normalization. The general formulation of a truncated distribution $f(x| \\alpha < x < \\beta)$ that comes from a distribution $f(x)$ if given by the following expresion:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$f(x| \\alpha < x < \\beta) = \\frac{f(x)}{F(\\beta)-F(\\alpha)}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Where $F(x)$ is the cummulative distribution of the density distribution $f(x)$. For an exponential, this formula can be calculated easily as there is an analytical solution. For the case of the normal distrubution, however, the cumulative is based on the error function.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gauss(x, mu, sigma, norm):\n",
    "    return norm / np.sqrt(2*np.pi)/sigma * np.exp(-0.5*(x - mu)**2/sigma**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's calculate the cummulative distribution of a gaussian by making use of the erf function in scipy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def F(x):\n",
    "    return 1/2 * (1 + sp.special.erf(x/np.sqrt(2)))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trun_gauss(x, mu, sigma, norm, alpha, beta):\n",
    "    return norm / np.sqrt(2*np.pi)/sigma * np.exp(-0.5*(x - mu)**2/sigma**2)/(F((beta - mu)/sigma) - F((alpha - mu)/sigma))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's try these truncated function. For that let's generate a Gaussian with the Hit&Miss method and then we are going to slice it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from argparse import Namespace\n",
    "\n",
    "truth = Namespace(mu = 200, sigma = 50, norm = 1)\n",
    "\n",
    "\n",
    "xmin = truth.mu - 5 * truth.sigma\n",
    "xmax = truth.mu + 5 * truth.sigma\n",
    "mc_events = 10000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "rnd1 = np.random.rand(mc_events)\n",
    "rnd2 = np.random.rand(mc_events)\n",
    "y = truth.norm * rnd1\n",
    "x = xmin + (xmax - xmin)*rnd2\n",
    "yprime = gauss(x, **vars(truth))\n",
    "\n",
    "mask = (y < yprime)\n",
    "data = x[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "hist, binsedges, patches = ax.hist(data,bins = 50, density=False)\n",
    "ax.set_xlabel(\"x\")\n",
    "ax.set_ylabel(\"counts\")\n",
    "ax.set_xlim(xmin, xmax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's slice the data. For that we are going to use even an assymetric slice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "xmin = 170\n",
    "xmax = 250\n",
    "data_slice = data[(data > xmin)&(data < xmax)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "hist, binsedges, patches = ax.hist(data_slice,bins = 15, density=False)\n",
    "ax.set_xlabel(\"x\")\n",
    "ax.set_ylabel(\"counts\")\n",
    "ax.set_xlim(0, 400)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to fit in 2D using both a truncated Gaussian and a normal gaussian."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "mumin = 195.\n",
    "mumax = 210.\n",
    "\n",
    "sigma_min = 20.\n",
    "sigma_max = 60.\n",
    "mu_grid    = np.linspace(mumin, mumax, 100)\n",
    "sigma_grid = np.linspace(sigma_min, sigma_max, 100)\n",
    "\n",
    "tL = []\n",
    "L = []\n",
    "\n",
    "for m in mu_grid:\n",
    "    L_m = []\n",
    "    tL_m = []\n",
    "    for s in sigma_grid:\n",
    "        tP = trun_gauss(data_slice, m, s, truth.norm, xmin, xmax)\n",
    "        P = gauss(data_slice, m, s, truth.norm)\n",
    "        \n",
    "        #we put the logL calculated with both the truncated gaussian and a real gaussian\n",
    "        tL_m.append(-np.sum(np.log(tP)))\n",
    "        L_m.append(-np.sum(np.log(P)))\n",
    "\n",
    "    L.append(L_m)\n",
    "    tL.append(tL_m)\n",
    "    \n",
    "# a list of lists => a 2d array\n",
    "L = np.array(L)\n",
    "tL = np.array(tL)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best fit results with the gaussian: {'mu': 208.03030303030303, 'sigma': 22.02020202020202}\n",
      "Best fit results with the truncated gaussian: {'mu': 199.0909090909091, 'sigma': 51.91919191919192}\n"
     ]
    }
   ],
   "source": [
    "i_best = np.where(L == L.min())\n",
    "ti_best = np.where(tL == tL.min())\n",
    "\n",
    "fitted  = Namespace(mu=mu_grid[i_best[0]][0],  sigma=sigma_grid[i_best[1]][0])\n",
    "trun_fitted  = Namespace(mu=mu_grid[ti_best[0]][0],  sigma=sigma_grid[ti_best[1]][0])\n",
    "\n",
    "print (\"Best fit results with the gaussian: {}\".format(vars(fitted)))\n",
    "       \n",
    "print (\"Best fit results with the truncated gaussian: {}\".format(vars(trun_fitted)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getMinMax(cs):\n",
    "    \"\"\" This functuon returns the min max y-axis and x-axis of a contour object\"\"\"\n",
    "\n",
    "    p = cs.collections[0].get_paths()[0]\n",
    "    v = p.vertices\n",
    "    x = v[:, 0]\n",
    "    y = v[:, 1]\n",
    "    return np.min(x), np.max(x), np.min(y), np.max(y),\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$\\\\mu$')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1224x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,2,sharey=True, figsize=(17,8))\n",
    "mesh1 = axs[0].pcolormesh(sigma_grid, mu_grid, L, shading=\"auto\")\n",
    "mesh2 = axs[1].pcolormesh(sigma_grid, mu_grid, tL, shading=\"auto\")\n",
    "\n",
    "cs = axs[0].contour(sigma_grid, mu_grid, L, [L.min() + 0.5],colors='white')\n",
    "tcs = axs[1].contour(sigma_grid, mu_grid, tL, [tL.min() + 0.5],colors='white')\n",
    "\n",
    "cxmin, cxmax, cymin, cymax = getMinMax(cs)\n",
    "\n",
    "axs[0].axhline(y=cymin,  lw=3, color=\"white\", linestyle=\":\")\n",
    "axs[0].axhline(y=cymax, lw=3, color=\"white\", linestyle=\":\")\n",
    "axs[0].axvline(x=cxmin, lw=3, color=\"white\", linestyle=\":\")\n",
    "axs[0].axvline(x=cxmax, lw=3, color=\"white\", linestyle=\":\")\n",
    "# p\n",
    "\n",
    "cbar = fig.colorbar(mesh1, ax = axs[0])\n",
    "cbar.ax.set_ylabel('-$\\log \\mathcal{L}$')\n",
    "\n",
    "tcbar = fig.colorbar(mesh2, ax = axs[1])\n",
    "tcbar.ax.set_ylabel('-$\\log \\mathcal{L}$')\n",
    "\n",
    "cxmin, cxmax, cymin, cymax = getMinMax(tcs)\n",
    "\n",
    "axs[1].axhline(y=cymin,  lw=3, color=\"white\", linestyle=\":\")\n",
    "axs[1].axhline(y=cymax, lw=3, color=\"white\", linestyle=\":\")\n",
    "axs[1].axvline(x=cxmin, lw=3, color=\"white\", linestyle=\":\")\n",
    "axs[1].axvline(x=cxmax, lw=3, color=\"white\", linestyle=\":\")\n",
    "# p\n",
    "\n",
    "axs[0].plot(fitted.sigma, fitted.mu, marker='+',color='r')\n",
    "axs[1].plot(trun_fitted.sigma, trun_fitted.mu, marker='+',color='r')\n",
    "\n",
    "axs[0].set_xlabel('$\\sigma$')\n",
    "axs[1].set_xlabel('$\\sigma$')\n",
    "axs[0].set_ylabel('$\\mu$')\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2, ax2 = plt.subplots()\n",
    "\n",
    "ax2.hist(data_slice,bins = 20,density=True, label=\"Data\", alpha=0.5)\n",
    "x = np.arange(xmin, xmax, 0.1)\n",
    "\n",
    "# If the histogram is NOT normalized (normed = False) I need to rescale the PDF by the number of mc event and the binwidth\n",
    "#  binwidth = np.diff(bins)[0]\n",
    "ax2.plot(x, gauss(x, fitted.mu, fitted.sigma, truth.norm), lw =2 , label=r\"Guassian\")\n",
    "\n",
    "ax2.plot(x, trun_gauss(x, trun_fitted.mu, trun_fitted.sigma, truth.norm, xmin, xmax), lw =2 , label=r\"Truncated Gaussian\")\n",
    "ax2.set_xlabel(\"x\", fontsize=15)\n",
    "ax2.set_ylabel(\"density\", fontsize=15)\n",
    "ax2.set_xlim(0, 400)\n",
    "ax2.legend(loc=\"upper right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'scipy' has no attribute 'stat'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/lx/66msvxgn7jbds1ygvq14q5z00000gn/T/ipykernel_92645/450265326.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoisson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: module 'scipy' has no attribute 'stat'"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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