mwcraig/plot_demo.ipynb

## plot_demo.ipynb
{
 "metadata": {
  "name": "plot_demo"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
        "For more information, type 'help(pylab)'.\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot([0,1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "[<matplotlib.lines.Line2D at 0x330ee30>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD9CAYAAABHnDf0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAFAxJREFUeJzt3F1om9cBh/G/uuSiWcFpKnclEES9mNmmc5vVthySuNqa\n2i0iuakvrNVZl90oHcMpLGMrjDUOBHoT5swdRr1amZOrZSzJxRZshqyW1B+pY9o68cKaOIXSizhm\npuB1UZuzC7ey5A9Jll5J78fzA4He6FQ6HMLh6dEb+YwxRgAAV3mg0hMAAFiPzR0AXIjNHQBciM0d\nAFyIzR0AXIjNHQBcKOvm/rOf/Uzf+c539P3vf3/dMa+99ppqamr09NNPa2ZmxvIJAgA2Luvmfvjw\nYf3jH/9Y9/Xx8XG98847unLlio4dO6Zjx45ZPkEAwMZl3dz37dunhx9+eN3Xx8bG1NnZqW3btikS\niej69euWTxAAsHGbivmPx8fHdejQodR1dXW1Pv74Y333u9/NGOfz+Yr5GADwrEJ/RKCoL1SNMas+\neL2N/JuxXn+8/vrrFZ+DXR6sBWvBWqx+/O9/Rr/7nVF1dXG/DFPU5h4MBnXt2rXU9Z07d1RTU1PU\nhADAq6ampJYWaXJy6Xkxit7cz507p7t37+rs2bOqr68vbjYA4EH37kmvvy61t0u//KV04YK0fXtx\n75n1zD0SiWhkZERzc3PasWOHent7lUwmJUnRaFQtLS3au3evmpqatG3bNg0ODhY3Gw8IhUKVnoJt\nsBbLWItlXluLqSnppz+VduxYel7spv4NnzGm5D/56/P5VIaPAQDHuHdPOnlSGhiQTp2SurullV9Z\nFrN3FnW3DABg40pV6+n4+QEAKJNSnK2vh3IHgDIoR62no9wBoITKWevpKHcAKJFy13o6yh0ALFap\nWk9HuQOAhSpZ6+kodwCwgB1qPR3lDgBFskutp6PcAaBAdqv1dJQ7ABTAjrWejnIHgA2wc62no9wB\nIE92r/V0lDsA5OCUWk9HuQNAFk6q9XSUOwCswYm1no5yB4AVnFrr6Sh3APia02s9HeUOAHJHraej\n3AF4mptqPR3lDsCz3Fbr6Sh3AJ7j1lpPR7kD8BQ313o6yh2AJ3ih1tNR7gBczyu1no5yB+BaXqv1\ndJQ7AFfyYq2no9wBuIqXaz0d5Q7ANbxe6+kodwCOR62vRrkDcDRqfW2UOwBHotazo9wBOA61nhvl\nDsAxqPX8Ue4AHIFa35ic5Z5IJFRfX6/a2lr19/evev2///2vXn75Ze3atUvPPPOMzp8/X5KJAvAm\nar0wOcv96NGjisViCgQC6ujoUCQSkd/vT73+9ttv69vf/rauXr2q27dv60c/+pEOHjwon89X0okD\ncD9qvXBZy31hYUGS1NbWpkAgoPb2do2NjWWMqaqq0ueff65kMqn5+Xlt2bKFjR1AUaj14mUt94mJ\nCdXV1aWuGxoaNDo6qnA4nPqzSCSiixcvyu/368svv9R777235nsdP3489TwUCikUChU3cwCu5OVa\nj8fjisfjlrxX0V+ovvnmm9q0aZM+++wzffjhhwqHw7p9+7YeeCDzfwrSN3cAWOnePenkSWlgQDp1\nSurulrx2CLAyfHt7ewt+r6zHMs3NzZqZmUldT09Pq7W1NWNMIpHQSy+9pC1btigYDGr79u26ceNG\nwRMC4D1TU1JLizQ5ufT80CHvbexWy7q5V1VVSVrawGdnZzU0NKRgMJgx5tlnn9XFixd1//593bx5\nU/Pz8xlHOQCwHs7WSyfnsUxfX5+i0aiSyaR6enrk9/sVi8UkSdFoVF1dXbp27ZqamppUXV2t06dP\nl3zSAJzPy2fr5eAzxpiSf4jPpzJ8DAAH4Gw9f8XsnfwLVQBlQ62XD78tA6DkOFsvP8odQElR65VB\nuQMoCWq9sih3AJaj1iuPcgdgGWrdPih3AJag1u2FcgdQFGrdnih3AAWj1u2LcgewYdS6/VHuADaE\nWncGyh1AXqh1Z6HcAeRErTsP5Q5gXdS6c1HuANZErTsb5Q4gA7XuDpQ7gBRq3T0odwDUugtR7oDH\nUevuRLkDHkWtuxvlDngQte5+lDvgIdS6d1DugEdQ695CuQMuR617E+UOuBi17l2UO+BC1Dood8Bl\nqHVIlDvgGtQ60lHugAtQ61iJcgccjFrHeih3wKGodWRDuQMOQ60jH5Q74CDUOvJFuQMOQK1joyh3\nwOaodRSCcgdsilpHMXJu7olEQvX19aqtrVV/f/+aYyYmJtTc3Kz6+nqFQiGr5wh4ztSU1NIiTU4u\nPT90SPL5Kj0rOInPGGOyDdi1a5dOnz6tQCCgjo4Ovfvuu/L7/anXjTFqbGzU73//e+3fv19zc3MZ\nr0uSz+dTjo8BoKVaP3lSGhiQTp2SurvZ1L2smL0za7kvLCxIktra2hQIBNTe3q6xsbGMMVeuXFFj\nY6P2798vSas2dgD5odZhpaxfqE5MTKiuri513dDQoNHRUYXD4dSfXbp0ST6fT/v27dPWrVv1i1/8\nQh0dHave6/jx46nnoVCI4xvga9Q6vhGPxxWPxy15r6Lvlvniiy80NTWl4eFhLS4u6rnnntNHH32k\nBx98MGNc+uYOYAl3wiDdyvDt7e0t+L2yHss0NzdrZmYmdT09Pa3W1taMMbt379YLL7ygxx57TDU1\nNWpqalIikSh4QoAXcCcMSi3r5l5VVSVp6Y6Z2dlZDQ0NKRgMZoxpbW3VyMiIFhcXNT8/r6tXr2rP\nnj2lmzHgcJytoxxyHsv09fUpGo0qmUyqp6dHfr9fsVhMkhSNRvXII4/o8OHDampqUnV1tU6cOKGH\nHnqo5BMHnIazdZRTzlshLfkQboWEx6WfrcdiHMEgPyW7FRJAcThbR6Xw2zJAiXAnDCqJcgcsRq3D\nDih3wELUOuyCcgcsQK3Dbih3oEjUOuyIcgcKRK3Dzih3oADUOuyOcgc2gFqHU1DuQJ6odTgJ5Q7k\nQK3DiSh3IAtqHU5FuQNroNbhdJQ7sAK1Djeg3IGvUetwE8odELUO96Hc4WnUOtyKcodnUetwM8od\nnkOtwwsod3gKtQ6voNzhCdQ6vIZyh+tR6/Aiyh2uRa3Dyyh3uBK1Dq+j3OEq1DqwhHKHa1DrwDLK\nHY5HrQOrUe5wNGodWBvlDkei1oHsKHc4DrUO5Ea5wzGodSB/lDscgVoHNoZyh61R60BhKHfYFrUO\nFI5yh+1Q60DxKHfYCrUOWCNnuScSCdXX16u2tlb9/f3rjpuYmNCmTZv017/+1dIJwhuodcBaOcv9\n6NGjisViCgQC6ujoUCQSkd/vzxjz1Vdf6de//rWef/55GWNKNlm4E7UOWC9ruS8sLEiS2traFAgE\n1N7errGxsVXj+vv71dnZqerq6tLMEq5ErQOlk7XcJyYmVFdXl7puaGjQ6OiowuFw6s8+/fRTnT9/\nXv/85z81MTEhn8+35nsdP3489TwUCikUChU3czgatQ6sFo/HFY/HLXmvor9QffXVV/XGG2/I5/PJ\nGLPusUz65g7vundPOnlSGhiQTp2SuruldXoA8JyV4dvb21vwe2Xd3Jubm/WrX/0qdT09Pa3nn38+\nY8z777+vrq4uSdLc3Jz+/ve/a/PmzTp48GDBk4I7UetA+WQ9c6+qqpK0dMfM7OyshoaGFAwGM8bc\nvHlTt27d0q1bt9TZ2amBgQE2dmTgbB0ov5zHMn19fYpGo0omk+rp6ZHf71csFpMkRaPRkk8Qzkat\nA5XhM2W4d/Gb83h4B2frQPGK2Tv5F6qwHLUOVB6/LQPLcLYO2AflDktQ64C9UO4oCrUO2BPljoJR\n64B9Ue7YMGodsD/KHRtCrQPOQLkjL9Q64CyUO3Ki1gHnodyxLmodcC7KHWui1gFno9yRgVoH3IFy\nRwq1DrgH5Q5qHXAhyt3jqHXAnSh3j6LWAXej3D2IWgfcj3L3EGod8A7K3SOodcBbKHeXo9YBb6Lc\nXYxaB7yLcnchah0A5e4y1DoAiXJ3DWodQDrK3QWodQArUe4ORq0DWA/l7lDUOoBsKHeHodYB5INy\ndxBqHUC+KHcHoNYBbBTlbnPUOoBCUO42Ra0DKAblbkPUOoBiUe42Qq0DsArlbhPUOgAr5Sz3RCKh\n+vp61dbWqr+/f9XrZ86c0ZNPPqknn3xSP/7xj3Xjxo2STNStqHUApeAzxphsA3bt2qXTp08rEAio\no6ND7777rvx+f+r19957Tw0NDaqqqtLbb7+t4eFh/fnPf878EJ9POT7Gk9JrPRZjUweQqZi9M2u5\nLywsSJLa2toUCATU3t6usbGxjDG7d+9WVVWVJCkcDmtkZKSgiXgJtQ6g1LKeuU9MTKiuri513dDQ\noNHRUYXD4TXHv/XWWzpw4MCarx0/fjz1PBQKKRQKbXy2LsDZOoD1xONxxeNxS97Lsi9Uh4eHNTg4\nqMuXL6/5evrm7kX37kknT0oDA9KpU1J3t+TzVXpWAOxkZfj29vYW/F5Zj2Wam5s1MzOTup6enlZr\na+uqcR988IGOHDmiCxcuaOvWrQVPxq2mpqSWFmlycun5oUNs7ABKK+vm/s1ZeiKR0OzsrIaGhhQM\nBjPGfPLJJ3rxxRd15swZ7dy5s3QzdSDO1gFUSs5jmb6+PkWjUSWTSfX09Mjv9ysWi0mSotGoTpw4\nofn5eR05ckSStHnzZo2Pj5d21g7A2TqASsp5K6QlH+KhWyE5WwdglWL2Tv6FqoWodQB2wW/LWICz\ndQB2Q7kXiVoHYEeUe4GodQB2RrkXgFoHYHeU+wZQ6wCcgnLPE7UOwEko9xyodQBORLlnQa0DcCrK\nfQ3UOgCno9xXoNYBuAHl/jVqHYCbUO6i1gG4j6fLnVoH4FaeLXdqHYCbea7cqXUAXuCpcqfWAXiF\nJ8qdWgfgNa4vd2odgBe5ttypdQBe5spyp9YBeJ2ryp1aB4Alril3ah0Aljm+3Kl1AFjN0eVOrQPA\n2hxZ7tQ6AGTnuHKn1gEgN8eUO7UOAPlzRLlT6wCwMbYud2odAApj23Kn1gGgcLYrd2odAIpnq3Kn\n1gHAGrYod2odAKxV8XKn1gHAehUrd6/Wejwer/QUbIO1WMZaLGMtrJFzc08kEqqvr1dtba36+/vX\nHPPaa6+ppqZGTz/9tGZmZnJ+6NSU1NIiTU4uPT90SPL5Nj55J+Iv7jLWYhlrsYy1sEbOzf3o0aOK\nxWIaHh7WH//4R83NzWW8Pj4+rnfeeUdXrlzRsWPHdOzYsXXfy6u1DgDllnVzX1hYkCS1tbUpEAio\nvb1dY2NjGWPGxsbU2dmpbdu2KRKJ6Pr162u+l5drHQDKzmQxNDRkurq6UtcDAwPmt7/9bcaY7u5u\nc+nSpdR1MBg0//73vzPGSOLBgwcPHgU8ClX03TLGGC3t38t8K5J85esAgNLKeizT3Nyc8QXp9PS0\nWltbM8YEg0Fdu3YtdX3nzh3V1NRYPE0AwEZk3dyrqqokLd0xMzs7q6GhIQWDwYwxwWBQ586d0927\nd3X27FnV19eXbrYAgLzkPJbp6+tTNBpVMplUT0+P/H6/YrGYJCkajaqlpUV79+5VU1OTtm3bpsHB\nwZJPGgCQQ8Gn9WsYGRkxdXV1ZufOneYPf/jDmmN+85vfmMcff9z84Ac/MNevX7fy420l11oMDg6a\nxsZG09jYaCKRiPnXv/5VgVmWRz5/L4wxZnx83HzrW98y586dK+PsyiuftRgfHzdNTU2mrq7OPPPM\nM+WdYBnlWovFxUXzk5/8xDz11FOmra3N/O1vf6vALEvv8OHD5tFHHzVPPPHEumMK2Tct3dyfeuop\nMzIyYmZnZ833vvc9c+fOnYzXx8bGzJ49e8zdu3fN2bNnTTgctvLjbSXXWly+fNn85z//McYY86c/\n/cl0d3dXYpplkWstjDHmyy+/ND/84Q9NOBw2f/nLXyowy/LItRb37983TzzxhBkaGjLGmDXXyi1y\nrcXAwIB55ZVXjDHGzM7OmpqaGnP//v1KTLWkEomEmZycXHdzL3TftOznB6y8J97p8lmL3bt3p77T\nCIfDGhkZKfs8yyGftZCk/v5+dXZ2qrq6utxTLJt81uLKlStqbGzU/v37JUl+v7/s8yyHfNaiqqpK\nn3/+uZLJpObn57Vly5ZVd+K5wb59+/Twww+v+3qh+6Zlm/vExITq6upS1w0NDRodHc0YMz4+roaG\nhtR1dXW1Pv74Y6umYBv5rEW6t956SwcOHCjH1Moun7X49NNPdf78eb3yyiuSVt9K6xb5rMWlS5fk\n8/m0b98+HThwQJcuXSr3NMsin7WIRCL66quv5Pf7tXfvXp05c6bc07SFQvfNsv4qpMnjnnivGR4e\n1uDgoC5fvlzpqVTMq6++qjfeeEM+n2/NvyNe8sUXX2hqakrDw8NaXFzUc889p48++kgPPvhgpadW\ndm+++aY2bdqkzz77TB9++KHC4bBu376tBx6wxS+Vl02h+6Zlq8Q98cvyWQtJ+uCDD3TkyBFduHBB\nW7duLecUyyaftXj//ffV1dWlxx9/XOfOndPPf/5zXbhwodxTLbl81mL37t164YUX9Nhjj6mmpkZN\nTU1KJBLlnmrJ5bMWiURCL730krZs2aJgMKjt27frxo0b5Z5qxRW6b1q2uXNP/LJ81uKTTz7Riy++\nqDNnzmjnzp2VmGZZ5LMWN2/e1K1bt3Tr1i11dnZqYGBABw8erMR0SyqftWhtbdXIyIgWFxc1Pz+v\nq1evas+ePZWYbknlsxbPPvusLl68qPv37+vmzZuan5/POMrxikL3TUuPZbgnflmutThx4oTm5+d1\n5MgRSdLmzZs1Pj5eySmXTK618JJca/HII4/o8OHDampqUnV1tU6cOKGHHnqowrMujVxr0dXVpWvX\nrqXW4vTp0xWecWlEIhGNjIxobm5OO3bsUG9vr5LJpKTi9k2f8fIBJwC4lLe+mQAAj2BzBwAXYnMH\nABdicwcAF2JzBwAXYnMHABf6P+rM+9lpSt1jAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "title('My plot')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "<matplotlib.text.Text at 0x34b62d0>"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ndwAwEHEHAAMRdwAw0P8Btt2hk4AIWZAAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}
	{
	"metadata": {
	"name": "plot_demo"
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"%pylab inline"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"output_type": "stream",
	"stream": "stdout",
	"text": [
	"\n",
	"Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
	"For more information, type 'help(pylab)'.\n"
	]
	}
	],
	"prompt_number": 1
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"plot([0,1])"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"output_type": "pyout",
	"prompt_number": 2,
	"text": [
	"[<matplotlib.lines.Line2D at 0x330ee30>]"
	]
	},
	{
	"output_type": "display_data",
	"png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD9CAYAAABHnDf0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nABdicwcAF2JzBwAXYnMHABf6P+rM+9lpSt1jAAAAAElFTkSuQmCC\n"
	}
	],
	"prompt_number": 2
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"title('My plot')"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"output_type": "pyout",
	"prompt_number": 3,
	"text": [
	"<matplotlib.text.Text at 0x34b62d0>"
	]
	},
	{
	"output_type": "display_data",
	"png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ndwAwEHEHAAMRdwAw0P8Btt2hk4AIWZAAAAAASUVORK5CYII=\n"
	}
	],
	"prompt_number": 3
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [],
	"language": "python",
	"metadata": {},
	"outputs": []
	}
	],
	"metadata": {}
	}
	]
	}