{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Plots.jl] Default backend: immerse"
     ]
    }
   ],
   "source": [
    "using Plots\n",
    "n = 1000\n",
    "a = rand(n)\n",
    "y = Float64[i*a[i]+j*a[j] for i in 1:n, j in 1:n]\n",
    "y = float(y .> mean(y));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[Plots.jl] Initializing backend: immerse"
     ]
    }
   ],
   "source": [
    "spy(y, nbins=(20,100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# get the indices (I,J) and the values (V) of non-zero values in y\n",
    "I,J,V = findnz(y);\n",
    "# plot the J's vs the I's in a heatmap to recreate the spy call\n",
    "heatmap(J,I)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pyplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "histogram(randn(1000), "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
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