BrianChevalier/Untitled1.ipynb

## Untitled1.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "nbpresent": {
     "id": "6247e680-5f75-4f80-9dd0-5f494a1c564b"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Plotting in python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "nbpresent": {
     "id": "33c29eae-a04b-45c2-8ef8-b015ba4ec501"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "#Import the necessary packages and modules\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true,
    "nbpresent": {
     "id": "ba821c52-38f7-4c54-b173-401bbdf5042a"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The following equation will be plotted\n",
    "$$f(x) = x$$\n",
    "This is a linear equation where the input is the same as the output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "nbpresent": {
     "id": "a79af47f-1db3-47bb-8bea-9ca38a33aaa4"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Prepare the data\n",
    "x = np.linspace(0, 10, 100)\n",
    "\n",
    "def f(x):\n",
    "    return x**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "nbpresent": {
     "id": "83c20a28-219e-4436-9a2f-e41e555248d5"
    },
    "scrolled": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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X9uLCvkmaRExE/o2CPkgVllayZs9hVu8uZOWXhWzeX0RNrSM60hjctTX3XNyH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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110c834e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the data\n",
    "plt.plot(x, f(x), label='linear')\n",
    "\n",
    "# Add a legend\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "nbpresent": {
     "id": "6e393d78-ded1-4879-a13c-68fa5b412e64"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11475a828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.html.widgets import interact\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy import pi, sin\n",
    "\n",
    "def pltsin(freq, amp):\n",
    "    plt.plot(x, amp*sin(2*pi*x*freq))\n",
    "    plt.ylim(-10,10)\n",
    "    plt.show()\n",
    "    \n",
    "interact(pltsin, freq=(1,10,0.1), amp=(1,10,1));"
   ]
  }
 ],
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