{ "metadata": { "name": "trapezoid_rule" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Basic numerical integration: the trapezoid rule\n", "===============================================\n", "\n", "A simple illustration of the trapezoid rule for definite integration:\n", "\n", "$$\n", "\\int_{a}^{b} f(x)\\, dx \\approx \\frac{1}{2} \\sum_{k=1}^{N} \\left( x_{k} - x_{k-1} \\right) \\left( f(x_{k}) + f(x_{k-1}) \\right).\n", "$$\n", "
\n", "First, we define a simple function and sample it between 0 and 10 at 200 points" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n", "For more information, type 'help(pylab)'.\n" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": true, "input": [ "def f(x):\n", " return (x-3)*(x-5)*(x-7)+85\n", "\n", "x = linspace(0, 10, 200)\n", "y = f(x)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Choose a region to integrate over and take only a few points in that region" ] }, { "cell_type": "code", "collapsed": true, "input": [ "a, b = 1, 9\n", "xint = x[logical_and(x>=a, x<=b)][::30]\n", "yint = y[logical_and(x>=a, x<=b)][::30]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot both the function and the area below it in the trapezoid approximation" ] }, { "cell_type": "code", "collapsed": false, "input": [ "plot(x, y, lw=2)\n", "axis([0, 10, 0, 140])\n", "fill_between(xint, 0, yint, facecolor='gray', alpha=0.4)\n", "text(0.5 * (a + b), 30,r\"$\\int_a^b f(x)dx$\", horizontalalignment='center', fontsize=20);" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "display_data", "png": 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Hx1NTU0O/fv3UKU4I0S7Fx8cTHx9v8us1RqPR2NIX5ebmMnbsWNLS0gD46aef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\nj8mEXkJ1HTLc9+9XvlxcQLo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} ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute the integral both at high accuracy and with the trapezoid approximation" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from scipy.integrate import quad, trapz\n", "integral, error = quad(f, 1, 9)\n", "print \"The integral is:\", integral, \"+/-\", error\n", "print \"The trapezoid approximation with\", len(xint), \"points is:\", trapz(yint, xint)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "The integral is: 680.0 +/- 7.54951656745e-12\n", "The trapezoid approximation with 6 points is: 621.286411141\n" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": true, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }