{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GENERATING DATA FOR: Correlation and uncertainty - Part 1\n",
    "\n",
    "#### Introduction\n",
    "Consider a simple experiment in which the goal is to measure what is presumed to be a constant signal in the \n",
    "presence of a background.  For this example, assume that there is Gaussian noise on both the signal and the \n",
    "background.  In the first part of this notebook assume that you make several measurements of  the 'total' of 'background + noise', and then you make measurements of the 'noise' alone. The 'signal' will then be the \n",
    "difference between the 'total' and the 'noise.' For class on Tuesday execute the cells in this notebook, and then \n",
    "determine the value of the signal, with an uncertainty.  (There is nothing new here -- I just want you \n",
    "to become familiar with the 'experiment.'\n",
    "\n",
    "Marty Ligare, September 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy import stats\n",
    "\n",
    "import urllib  # For importing file from URL\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Following is an Ipython magic command that puts figures in the  notebook.\n",
    "%matplotlib notebook\n",
    "        \n",
    "# M.L. modifications of matplotlib defaults\n",
    "# Changes can also be put in matplotlibrc file,\n",
    "# or effected using mpl.rcParams[]\n",
    "mpl.style.use('classic')\n",
    "plt.rc('figure', figsize = (6, 4.5))    # Reduces overall size of figures\n",
    "plt.rc('axes', labelsize=16, titlesize=14)\n",
    "plt.rc('figure', autolayout = True)     # Adjusts supblot parameters for new size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generate noisy background and noisy signal.  Add them to get measured data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_trials = 100\n",
    "s = 20\n",
    "alpha_s_gen = 1\n",
    "b = 10\n",
    "alpha_b_gen = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "values_sig = stats.norm.rvs(s, alpha_s_gen, size=n_trials)\n",
    "values_bg = stats.norm.rvs(b, alpha_b_gen, size=n_trials)\n",
    "values_tot = values_sig + values_bg\n",
    "#np.savetxt('correlation_1.dat', values_tot)\n",
    "#np.savetxt('correlation_2.dat', values_bg)"
   ]
  },
  {
   "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": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}