{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Hughes and Hase Problem 3.9\n", "\n", "NOTE: In this notebook I use the `stats` sub-module of `scipy` for all statistics functions, including generation of random numbers. There are other modules with some overlapping functionality, e.g., the regular python random module, and the `scipy.random` module, but I do not use them here. The `stats` sub-module includes tools for a large number of distributions, it includes a large and growing set of statistical functions, and there is a unified class structure. (And namespace issues are minimized.) See https://docs.scipy.org/doc/scipy/reference/stats.html." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from scipy import stats\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "tags": [] }, "outputs": [], "source": [ "# M.L. modification of matplotlib defaults\n", "# Changes can also be put in matplotlibrc file, \n", "# or effected using mpl.rcParams[]\n", "#plt.style.use('classic')\n", "plt.rc('figure', figsize = (7, 4.5)) # Reduces overall size of figures\n", "plt.rc('axes', labelsize=16, titlesize=14)\n", "plt.rc('figure', autolayout = True) # Adjusts supblot params for new size" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'pdf')" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "n = np.linspace(0,60,61)\n", "x = np.linspace(0,60,601)\n", "p = stats.poisson.pmf(n,35)\n", "g = stats.norm.pdf(x,35,np.sqrt(35))\n", "plt.scatter(n, p, c='red', label='Poisson: $\\\\bar{x} = 35$')\n", "plt.plot(x, g, label='Gaussian: $\\\\bar{x} = 35, \\\\sigma = \\\\sqrt{35}$')\n", "plt.xlim(0,60)\n", "plt.axhline(0)\n", "plt.legend();\n", "plt.xlabel('x');\n", "plt.ylabel('pdf')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Version information\n", "`version_information` is from J.R. Johansson (jrjohansson at gmail.com); see Introduction to scientific computing with Python . If not already installed on your machine, run `pip install --upgrade version_information` from the terminal" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "%load_ext version_information" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "application/json": { "Software versions": [ { "module": "Python", "version": "3.11.5 64bit [MSC v.1916 64 bit (AMD64)]" }, { "module": "IPython", "version": "8.15.0" }, { "module": "OS", "version": "Windows 10 10.0.26100 SP0" }, { "module": "numpy", "version": "1.23.2" }, { "module": "scipy", "version": "1.11.1" }, { "module": "matplotlib", "version": "3.7.2" } ] }, "text/html": [ "
SoftwareVersion
Python3.11.5 64bit [MSC v.1916 64 bit (AMD64)]
IPython8.15.0
OSWindows 10 10.0.26100 SP0
numpy1.23.2
scipy1.11.1
matplotlib3.7.2
Sat Feb 08 14:28:04 2025 Eastern Standard Time
" ], "text/latex": [ "\\begin{tabular}{|l|l|}\\hline\n", "{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n", "Python & 3.11.5 64bit [MSC v.1916 64 bit (AMD64)] \\\\ \\hline\n", "IPython & 8.15.0 \\\\ \\hline\n", "OS & Windows 10 10.0.26100 SP0 \\\\ \\hline\n", "numpy & 1.23.2 \\\\ \\hline\n", "scipy & 1.11.1 \\\\ \\hline\n", "matplotlib & 3.7.2 \\\\ \\hline\n", "\\hline \\multicolumn{2}{|l|}{Sat Feb 08 14:28:04 2025 Eastern Standard Time} \\\\ \\hline\n", "\\end{tabular}\n" ], "text/plain": [ "Software versions\n", "Python 3.11.5 64bit [MSC v.1916 64 bit (AMD64)]\n", "IPython 8.15.0\n", "OS Windows 10 10.0.26100 SP0\n", "numpy 1.23.2\n", "scipy 1.11.1\n", "matplotlib 3.7.2\n", "Sat Feb 08 14:28:04 2025 Eastern Standard Time" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "version_information numpy,scipy, matplotlib" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 4 }