{
 "metadata": {},
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# from http://pythonprogramming.net/3d-bar-charts-python-matplotlib/\n",
      "from mpl_toolkits.mplot3d import Axes3D\n",
      "import matplotlib.pyplot as plt\n",
      "import matplotlib.patches as mpatches\n",
      "from matplotlib import animation\n",
      "import numpy as np\n",
      "import pylab as pylab\n",
      "import math as math\n",
      "from decimal import *\n",
      "from savedWeights import *\n",
      " \n",
      "fig = plt.figure()\n",
      "ax1 = fig.add_subplot(111, projection='3d')\n",
      "colorNumber = -1\n",
      "ypos = []\n",
      "yposNeuronOrder = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]\n",
      "weights = retreiveTestW()\n",
      "colors = []\n",
      "neurons = [None]*len(weights)\n",
      "numberOfWeights = len(weights)\n",
      "numberOfPixels = 15\n",
      "xpos = np.ones(numberOfPixels*numberOfWeights)\n",
      "num_elements = len(xpos)\n",
      "zpos = np.zeros(numberOfPixels*numberOfWeights)\n",
      "dx = np.ones(numberOfPixels*numberOfWeights)*.9\n",
      "dy = np.ones(numberOfPixels*numberOfWeights)*.5\n",
      "timeDelay = 24#3\n",
      "\n",
      "for weightIndex in range(numberOfWeights):\n",
      "\tcolorNumber += 1\n",
      "\tif colorNumber == 4: colorNumber = 0\n",
      "\tif colorNumber == 0: colors = np.append(colors, ['r']*15); neurons[weightIndex] = plt.Rectangle((0, 0), 0.1, 0.1,fc='r');\n",
      "\tif colorNumber == 1: colors = np.append(colors, ['g']*15); neurons[weightIndex] = plt.Rectangle((0, 0), 0.1, 0.1,fc='g');\n",
      "\tif colorNumber == 2: colors = np.append(colors, ['b']*15); neurons[weightIndex] = plt.Rectangle((0, 0), 0.1, 0.1,fc='b');\n",
      "\tif colorNumber == 3: colors = np.append(colors, ['y']*15); neurons[weightIndex] = plt.Rectangle((0, 0), 0.1, 0.1,fc='y');\n",
      "\typos = np.append(ypos, yposNeuronOrder)\n",
      "\txpos[(15*weightIndex):(15*(weightIndex+1))] *= (weightIndex+1)\n",
      "\n",
      "colors = colors.tolist();ypos = ypos.tolist();xpos = xpos.tolist();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a id='generateGraph'></a><div style='font-size:1.7em;text-decoration:underline;font-weight:bold'>generateGraph</div>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def generateGraph(timePoint):\n",
      "\ttimePointScaled =  math.floor(Decimal(format((timePoint), '.1f'))/Decimal(format(timeDelay, '.1f')))\n",
      "\tax1.clear()\n",
      "\tax1.grid(b=False)\t\n",
      "\tax1.set_axisbelow(True)\n",
      "\tdz = []\n",
      "\tfor weightIndex in range(numberOfWeights):\n",
      "\t\tif weightIndex == timePointScaled: dz = np.append(dz, weights[weightIndex])\n",
      "\t\telse: dz = np.append(dz, [0]*numberOfPixels)\n",
      "\tdz = dz.tolist()\n",
      "\txLabel = ax1.set_xlabel('\\nOutput Neuron', linespacing=1.2)\n",
      "\tyLabel = ax1.set_ylabel('\\nInput Neuron', linespacing=1.2)\n",
      "\tzLabel = ax1.set_zlabel('\\nWeight', linespacing=1.2)\n",
      "\tax1.bar3d(xpos, ypos, zpos, dx, dy, dz, color=colors, alpha=0.95)\n",
      "\tif timePointScaled==5 or timePointScaled==10 or timePointScaled==15 or timePointScaled==20 or timePointScaled==25: print 'processing timepoint:\\t',timePointScaled\n",
      "\n",
      "ax1.view_init(elev=30.0, azim=40)\n",
      "\n",
      "line_ani = animation.FuncAnimation(fig, generateGraph, frames=(numberOfWeights*timeDelay), interval=1, blit=False)        \n",
      "ext = 'mp4'\n",
      "fps = 30\n",
      "codec = {'mp4': 'libx264', 'webm': 'libvpx'}.get(ext, 'mpeg4')\n",
      "line_ani.save('26CharTrainedWeights.mp4', fps=30, extra_args=['-vcodec', 'libx264'])\n",
      "print 'Completed saving the video'\n",
      "\n",
      "#plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}