{ "metadata": { "name": "", "signature": "sha256:26db0f5f1ed3a99111cb5e24362174f350a65ed7e111205860812b7edec2951d" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "from ipywidgets import StaticInteract, RangeWidget" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [ "k = 1\n", "w = 1\n", "x = np.linspace(0,1,11)\n", "#t = np.linspace(0,1,11)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "t = 0.6" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "y = np.sin(k*x-w*t)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "plt.plot(y)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 28, "text": [ "[]" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEACAYAAABfxaZOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n", "text": [ "" ] } ], "prompt_number": 28 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "x = np.linspace(-np.pi, np.pi,21)\n", "def plot(t):\n", " fig, ax = plt.subplots(figsize=(4,3),\n", " subplot_kw={'axisbg':'#EEEEEE',\n", " 'axisbelow':True})\n", " ax.grid(color='w', linewidth=2, linestyle='solid')\n", " ax.plot(np.sin(k*x-w*t) +np.sin(2*k*x-w*t), lw=5, alpha=0.4)\n", " #ax.set_xlim(-50, 50)\n", " ax.set_ylim(-2, 2)\n", " return fig" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 59 }, { "cell_type": "code", "collapsed": false, "input": [ "StaticInteract(plot,t=RangeWidget(0, np.pi, np.pi/4.))" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", " \n", " \n", "
\n", " \n", "
\n", " \n", "
\n", " \n", "
\n", " \n", "
\n", " \n", "
\n", " \n", "
\n", " \n", "
\n", " \n", "
\n", " \n", "
\n", " \n", "
\n", " \n", " t: \n", "
\n", " " ], "metadata": {}, "output_type": "pyout", "prompt_number": 61, "text": [ "" ] } ], "prompt_number": 61 }, { "cell_type": "code", "collapsed": false, "input": [ "x = np.linspace(-2*np.pi,2*np.pi,41)\n", "def plot(t):\n", " fig, ax = plt.subplots(figsize=(4,3),\n", " subplot_kw={'axisbg':'#EEEEEE',\n", " 'axisbelow':True})\n", " ax.grid(color='w', linewidth=2, linestyle='solid')\n", " ax.plot(np.sin(k*x-w*t) +np.sin(3*k*x-w*t) +np.sin(5*k*x-w*t) +np.sin(7*k*x-w*t) +np.sin(9*k*x-w*t), lw=5, alpha=0.4)\n", " #ax.set_xlim(-50, 50)\n", " #ax.set_ylim(0, 1)\n", " return fig" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 49 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }