VPython Performance Test-2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.notebook.set_autosave_interval(1000000000)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Autosaving every 1000000 seconds\n"
     ]
    }
   ],
   "source": [
    "%autosave 1000000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test VPython-Jupyter Travelling Wave\n",
    "\n",
    "This notebook is set up to test the visualization of a QM travelling wave. The arrows represent phasors (y and z are the real and imaginary parts of the complex wavefunction respectively). The first next cell just sets up the arrows and the following cell does the animation, while tracking in the time between calls to 'rate()'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import display\n",
    "\n",
    "int_range = widgets.IntSlider(min=20, max=100, description='Arrows per row')\n",
    "row_range = widgets.IntSlider(min=1, max=10, description='Number of rows')\n",
    "display(int_range,row_range)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div id=\"scene0\"><div id=\"glowscript\" class=\"glowscript\"></div></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "require.undef(\"nbextensions/glow.1.0.min\");"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "application/javascript": [
       "require.undef(\"nbextensions/jquery-ui.custom.min\");"
      ],
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       "require.undef(\"nbextensions/glow.1.1.min\");"
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     "metadata": {},
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     "data": {
      "application/javascript": [
       "require.undef(\"nbextensions/glow.1.2.min\");"
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       "require.undef(\"nbextensions/glowcomm\");"
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     "output_type": "display_data"
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    {
     "data": {
      "application/javascript": [
       "require.undef(\"nbextensions/ivglowcomm\");"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "require([\"nbextensions/ivglowcomm\"], function(){console.log(\"ivisual glowcomm loaded\");})"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
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   ],
   "source": [
    "\"\"\"\n",
    "Execute this cell to set up the arrows\n",
    "\"\"\"\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as pl\n",
    "import numpy as np\n",
    "from ivisual import *\n",
    "#from vpython import *\n",
    "\n",
    "scene = canvas(title = \"Travelling Wave\")\n",
    "\n",
    "L=6.0 * (int_range.value / 20.0)                          # range of x is 6 units\n",
    "x = np.linspace(-L/2, L/2, int_range.value) # from -3 to +3\n",
    "k = -3*pi/L                    # set up the wave number\n",
    "psi = np.exp(1j*k*x)           # set up the initial wave function\n",
    "rows = row_range.value\n",
    "\n",
    "alist = []                     # an empty list for our arrow objects\n",
    "\n",
    "omega = 2*pi                   # 1 rev/sec\n",
    "t=0.0                          # start t at zero\n",
    "dt=0.01                        # 1/100 of a second per step\n",
    "\n",
    "def once(r=30):  #\n",
    "    global t\n",
    "    rate(r)\n",
    "    t+=dt\n",
    "    psi_t = psi*np.exp(-1j*omega*t) # evolve the wf in time\n",
    "    for r in range(rows):\n",
    "        psi_t = psi*np.exp(-1j*(omega*(1+(r+1.)/(rows+1.0))*t)) # evolve the wf in time\n",
    "        for i in range(len(x)):\n",
    "            SetArrowFromCN(psi_t[i], alist[r*int_range.value + i]) # move the arrows to match\n",
    "\n",
    "def SetArrowFromCN( cn, a):\n",
    "    \"\"\"\n",
    "    SetArrowWithCN takes a complex number  cn  and an arrow object  a .\n",
    "    It sets the  y  and  z  components of the arrow s axis to the real \n",
    "    and imaginary parts of the given complex number. \n",
    "    \"\"\"\n",
    "    a.axis.y = cn.real\n",
    "    a.axis.z = cn.imag\n",
    "    \n",
    "for r in range(rows):\n",
    "    for i in range(len(x)):\n",
    "        xi = x[i]                              \n",
    "        a = arrow(pos=vector(xi, (r-(rows-1)/2)*2.0, 0),       # on the y,z axis at location 'x'\n",
    "                    axis=vector(0,1,0),       # pointing in the 'real' direction\n",
    "                    color=color.red)          # make it red. ;->\n",
    "        alist.append(a)                       # add to list\n",
    "        SetArrowFromCN( psi[i], a)            # set up arrow from wave function\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n",
      "average :  0.0333359999657\n",
      "median :  0.0329999923706\n",
      "mean :  0.0333359999657\n",
      "std dev :  0.00264028752232\n",
      "variance:  6.9711182005e-06\n"
     ]
    },
    {
     "data": {
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AbuCQiPirPP5IYHlEvKuyjuhU/s3MxitJRMR2J+3t6uSlqmuBfSQtkTQVeCNwYWWeC4Gj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaf23668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Execute this cell to evolve the WF in time\n",
    "\"\"\"\n",
    "from time import time, sleep\n",
    "\n",
    "    \n",
    "tlist=[time()]\n",
    "for i in range(500):\n",
    "    once(30)\n",
    "    tlist.append(time())\n",
    "    \n",
    "print(\"Done!\")\n",
    "\n",
    "tarr = np.array(tlist)\n",
    "dts = tarr[1:]-tarr[:-1]\n",
    "pl.plot(range(len(dts)), dts, 'b.')\n",
    "pl.title(\"time between renders vpython-jupyter\")\n",
    "pl.xlabel(\"iteration number\")\n",
    "pl.ylabel(\"time(s)\")#\n",
    "pl.ylim([0.0,0.12])\n",
    "\n",
    "print (\"average : \", np.average(dts))\n",
    "print (\"median : \", np.median(dts))\n",
    "print (\"mean : \", np.mean(dts))\n",
    "print (\"std dev : \", np.std(dts))\n",
    "print (\"variance: \", np.var(dts))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 5.37 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "req = commcmds[:int_range.value*row_range.value]\n",
    "baseObj.glow.comm.send(req)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 6.11 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "req = []\n",
    "for item in commcmds[:int_range.value*row_range.value]:\n",
    "    req.append(item.copy())\n",
    "baseObj.glow.comm.send(req)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 3.04 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "req = commcmds[:int_range.value*row_range.value]\n",
    "send_base64_zipped_json(baseObj.glow.comm, req)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 3.37 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "req = []\n",
    "for item in commcmds[:int_range.value*row_range.value]:\n",
    "    req.append(item.copy())\n",
    "send_base64_zipped_json(baseObj.glow.comm, req)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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