{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "iminuit version: 2.8.2\n" ] } ], "source": [ "# stave.py\n", "# G. Cowan / RHUL Physics / August 2021\n", "# Computes Student's t average, with the number of degrees of\n", "# freedom nu related to the relative uncertainty r in the\n", "# standard deviations of the measurements, nu = 1/(2*r**2), see\n", "# G. Cowan, Eur. Phys. J. C (2019) 79 :133, arXiv:1809.05778.\n", "# Uses iminuit version 2.x (not compatible with v 1.x).\n", "\n", "import numpy as np\n", "import scipy.stats as stats\n", "from scipy.stats import truncexpon\n", "from scipy.stats import truncnorm\n", "import iminuit\n", "from iminuit import Minuit\n", "import matplotlib.pyplot as plt\n", "plt.rcParams[\"font.size\"] = 14\n", "print(f\"iminuit version: {iminuit.__version__}\") # should be v 2.x" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "mu = 10. # initial value for fit\n", "y = np.array([8., 9., 10., 11., 12.]) # measured values\n", "s = np.array([1., 1., 1., 1., 1.]) # estimates of std. dev\n", "v = s**2 # estimates of variances\n", "r = np.array([0.2, 0.2, 0.2, 0.2, 0.2]) # relative errors on errors" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "class NegLogL:\n", "\n", " def __init__(self, y, s, r):\n", " self.setData(y, s, r)\n", " \n", " def setData(self, y, s, r):\n", " self.data = y, s, r\n", "\n", " def __call__(self, mu):\n", " y, s, r = self.data\n", " v = s ** 2\n", " lnf = -0.5*(1. + 1./(2.*r**2))*np.log(1. + 2.*(r*(y-mu))**2/v)\n", " return -np.sum(lnf)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Initialize Minuit and set up fit:\n", "negLogL = NegLogL(y, s, r) # instantiate function to be minimized\n", "parin = np.array([mu]) # initial values\n", "parname = ['mu']\n", "parstep = np.array([0.5]) # initial setp sizes\n", "parfix = [False] # change these to fix/free parameters\n", "parlim = [(None, None)]\n", "m = Minuit(negLogL, parin, name=parname)\n", "m.errors = parstep\n", "m.fixed = parfix\n", "m.limits = parlim\n", "m.errordef = 0.5 # errors from lnL = lnLmax - 0.5" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# do the fit, extract results\n", "m.migrad() # minimize -logL\n", "MLE = m.values # max-likelihood estimates\n", "sigmaMLE = m.errors # standard deviations\n", "cov = m.covariance # covariance matrix\n", "rho = m.covariance.correlation() # correlation coeffs." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "par index, name, estimate, standard deviation:\n", " 0 mu = 10.000000 +/- 0.525104\n", "\n", "free par indices, covariance, correlation coeff.:\n", "0 0 0.275734 1.000000\n" ] } ], "source": [ "print(r\"par index, name, estimate, standard deviation:\")\n", "for i in range(m.npar):\n", " if not m.fixed[i]:\n", " print(\"{:4d}\".format(i), \"{:<10s}\".format(m.parameters[i]), \" = \",\n", " \"{:.6f}\".format(MLE[i]), \" +/- \", \"{:.6f}\".format(sigmaMLE[i]))\n", "\n", "print()\n", "print(r\"free par indices, covariance, correlation coeff.:\")\n", "for i in range(m.npar):\n", " if not m.fixed[i]:\n", " for j in range(m.npar):\n", " if not m.fixed[j]:\n", " print(i, j, \"{:.6f}\".format(cov[i,j]), \"{:.6f}\".format(rho[i,j]))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Make scan of -lnL\n", "if not m.fixed[\"mu\"]:\n", " plt.figure()\n", " m.draw_mnprofile('mu', band=False, bound=(8.5, 11.5), size=200)\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.12" } }, "nbformat": 4, "nbformat_minor": 4 }