{
 "metadata": {
  "name": "",
  "signature": "sha256:8c1bec06b26b2a1f314b2fdd7fb247289c24adecec9a33591513423daef46f19"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pylab as plt\n",
      "from matplotlib import cm\n",
      "from matplotlib.colors import LogNorm\n",
      "%matplotlib inline\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "file = 'art_cleaned.csv'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = np.recfromcsv(file)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "z = plt.hist(data['year'], bins=np.arange(1500,2012,5),histtype='step')\n",
      "plt.yscale('log')\n",
      "plt.ylim((1,1e4))\n",
      "plt.xlabel('Year')\n",
      "plt.ylabel('# Pieces')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#How about Size vs Year?\n",
      "\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "h0 = plt.hist2d(data['width'], data['height'],bins=100)#,norm=LogNorm())\n",
      "#plt.colorbar()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "h = plt.hist2d(np.log10(data['width']), \n",
      "           np.log10(data['height'])/np.log10(data['width']),\n",
      "           bins=(200,200),norm=LogNorm())\n",
      "plt.xlim((1.5,3.5))\n",
      "plt.ylim((0.5,1.5))\n",
      "plt.colorbar()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "h, xi, yi = plt.histogram2d(np.log10(data['width']), \n",
      "           np.log10(data['height'])/np.log10(data['width']),\n",
      "           bins=(200,300) )\n",
      "\n",
      "plt.figure(figsize=(10,7))\n",
      "plt.imshow(np.log10(h+1).T,interpolation='nearest', origin='lower', aspect=2,\n",
      "           extent=(np.min(xi),np.max(xi),np.min(yi),np.max(yi)),cmap=cm.BuPu)\n",
      "plt.xlim((1.5,3.5))\n",
      "plt.ylim((0.6,1.4))\n",
      "plt.xlabel('Width')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Now for something completely different... Art -> Data -> Art"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# now let's work on drawing squares\n",
      "\n",
      "x1 = np.array([-1, 1, 1, -1, -1], dtype='float')\n",
      "y1 = np.array([-1, -1, 1, 1, -1], dtype='float')\n",
      "\n",
      "# set up the figure\n",
      "plt.figure(figsize=(6,6))\n",
      "plt.xlim((-5000,5000))\n",
      "plt.ylim((-5000,5000))\n",
      "\n",
      "# need to do this for ALL the length of data, not just first 100\n",
      "for i in range(0, 100):\n",
      "    plt.plot(x1*data['width'][i], y1*data['height'][i],'k',alpha=0.1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}