bradmkjr/lab 4 graph.ipynb

## lab 4 graph.ipynb
{
 "metadata": {
  "name": "lab 4 graph.ipynb",
  "signature": "sha256:b46a7bb0a69ac9a29e4d9775a76a857d966d88f2b7cd2c967e00207638662fac"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "PHSX 216 Projectile Motion Plotting code"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Import our numpy, matplotlib (for plotting), and math (for standard deviation)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "import math"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "We are plotting y_mean vs theta.  Will need the error in y_mean, for each point we plot as well to help with our fitting."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First, let's create arrays for each of our sets of y data at each theta.  "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "CHANGE THE FOLLOWING TO USE YOUR DATA"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y1 = np.array([74.2, 75.1, 75.9, 81.0, 84.0])\n",
      "y2 = np.array([77.5, 79.0, 82.7, 84.8, 90.4])\n",
      "y3 = np.array([71.0, 71.4, 73.5, 74.0, 78.7])\n",
      "y4 = np.array([64.2, 67.3, 70.0, 70.5, 73.5])\n",
      "y5 = np.array([54.3, 57.4, 58.8, 55.9, 62.3])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Find the mean for each of these values and divide by 100 to convert to meters"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y1mean = np.average(y1)/100.\n",
      "y2mean = np.average(y2)/100.\n",
      "y3mean = np.average(y3)/100.\n",
      "y4mean = np.average(y4)/100.\n",
      "y5mean = np.average(y5)/100."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Find the error in the mean for each value.  error(y_mean) = [standard deviation/sqrt(number of samples)].  You can use the numpy standard deviation function:  np.std(value) and the numpy square root, np.sqrt(value)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#calculate error here\n",
      "y1_err = np.std(y1)/np.sqrt(5)\n",
      "y2_err = np.std(y2)/np.sqrt(5)\n",
      "y3_err = np.std(y3)/np.sqrt(5)\n",
      "y4_err = np.std(y4)/np.sqrt(5)\n",
      "y5_err = np.std(y5)/np.sqrt(5)\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we can define arrays for y_mean, error y_mean, and theta so we can plot"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ymean_array = np.array([y1mean, y2mean, y3mean, y4mean, y5mean])\n",
      "yerr_array = np.array([y1_err, y2_err, y3_err, y4_err, y5_err])\n",
      "theta_array = np.array([38.4, 41.4, 44.4, 47.4, 50.4])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To keep our code generic and reusable, assign values to x, y, and dy"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = theta_array\n",
      "y = ymean_array\n",
      "dy = yerr_array\n",
      "\n",
      "#size the plot\n",
      "plt.figure(figsize=(15,10))\n",
      "\n",
      "#create scatter plot\n",
      "plt.scatter(x, y, color='blue', marker='o')\n",
      "\n",
      "#create labels\n",
      "plt.xlabel('$\\\\theta$ (degrees)')\n",
      "plt.ylabel('$y_{mean}$ (m)')\n",
      "plt.title('Height on wall vs Launcher Angle')\n",
      "\n",
      "#fitting to a 2nd degree polynomial\n",
      "c,b,a=np.polynomial.polynomial.polyfit(x,y,2,w=dy)\n",
      "\n",
      "#Create fit line\n",
      "xnew = np.linspace(x.min(), x.max(), 300)\n",
      "fit = a*xnew**2 + b*xnew +c\n",
      "\n",
      "plt.scatter(xnew, fit, color='red')\n",
      "\n",
      "print \"C = \",c , \"B = \",b, \"A = \",a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "C =  -3.64530951106 B =  0.217846567792 A =  -0.00266377714355\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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uxeDB0qBBZu4T20YABffjj6Yj/+9/m658fv877e9vguGrr0oVKxZvjQAAlBKE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\nS5cgCDZVWQke+5Z2DIWSJElSOurTB6ZPh5ycxlpNDZx5Jpx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66aejQ4YMiW7ZsiXkrlSv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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x81414a8>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    }
   ],
   "metadata": {}
  }
 ]
}