All of 'em

Graphing

{
 "metadata": {
  "name": "Three new matplotlib plots"
 },
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 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": "Sharing with Plotly: matplotlib gallery, prettyplotlib, seaborn, Software Carpentry, and Stack Overflow"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Plotly's matplotlib support lets you make matplotlib plots into interactive, online, and collaborative projects. It's free, online, you own your data, and you control the privacy. It's like a GitHub for data and graphs. You can use the public key below or [sign up](https://plot.ly).  That means you can use your libraries and code, then use Plotly to make your graphs interactive, shareable, and drawn with D3. "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "%matplotlib inline\nimport matplotlib.pyplot as plt # side-stepping mpl backend\nimport matplotlib.gridspec as gridspec # subplots\nimport numpy as np",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import plotly\nplotly.__version__",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": "'0.5.9'"
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from matplotlylib import fig_to_plotly\nusername = \"IPython.Demo\"\napi_key = \"1fw3zw2o13\"",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": "I. Plotly for Teaching: Software Carpentry Notebook"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "You can use Plotly, matplotlib, and IPython to interactive plots. These can be shared, forked, and easily edited as a learning process. These are drawn from the [SWC repo](http://nbviewer.ipython.org/github/swcarpentry/notebooks/blob/master/matplotlib.ipynb). We'll start off with a histogram. "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig1 = plt.figure()\n\nx = np.array(range(20))\ny = 3 + 0.5 * x + np.random.randn(20)\nz = 2 + 0.9 * x + np.random.randn(20)\n\n#plot the data\nplt.plot(x, y, 'bo')\nplt.hold(True)\nplt.plot(x, z, 'r^')\n\nfig_to_plotly(fig1, username, api_key, notebook = True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3080/600/450\" width=\"650\"></iframe>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": "<IPython.core.display.HTML at 0x10df10b90>"
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "We've left plot.show in so you can see both plots. For the rest, we won't show both. One special Plotly feature is that you'll get a URL for your call. Non-coders and coders can work together. You can also edit the data, fork it, and make new graphs. The data always lives with the code, so for that graph, the graph is here:\n\nand the data is here: \n\n. "
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Plotly also reads sup-plots. "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig2 = plt.figure()\n\nplt.subplot(1, 2, 1)\nplt.plot(x, y, 'rs')\nplt.subplot(1, 2, 2)\nplt.hist(data, 10)\n\nfig_to_plotly(fig2, username, api_key, notebook = True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3081/600/450\" width=\"650\"></iframe>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": "<IPython.core.display.HTML at 0x10e433950>"
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": "II. matplotlib Gallery graphs"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "For matplotlib experts, you'll recognize these graphs from the [matplotlib gallery](matplotlib.org/gallery.html). Let us know if you find others you like or need translated. \n\nYou can also use Plotly's [APIs](https://plot.ly/api) for Python, MATLAB, R, Perl, Julia, and REST to write to graphs. That means you and I could edit the same graph with any language or the GUI. Then it goes to a profile, like this: https://plot.ly/~IPython.Demo."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig3 = plt.figure()\n\nfrom pylab import *\n\ndef f(t):\n    'a damped exponential'\n    s1 = cos(2*pi*t)\n    e1 = exp(-t)\n    return multiply(s1,e1)\n\nt1 = arange(0.0, 5.0, .2)\n\n\nl = plot(t1, f(t1), 'ro')\nsetp(l, 'markersize', 30)\nsetp(l, 'markerfacecolor', 'b')\n\nfig_to_plotly(fig3, username, api_key, notebook = True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3082/600/450\" width=\"650\"></iframe>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": "<IPython.core.display.HTML at 0x10e4242d0>"
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "You can also plot with Plotly with pandas, NumPy, datetime, and more of your favorite Python tools. We've already imported numpy and matplotlib; here we've kept them in so you can simply copy and paste these examples into your own NB. "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig4 = plt.figure()\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# make a little extra space between the subplots\nplt.subplots_adjust(wspace=0.5)\n\ndt = 0.01\nt = np.arange(0, 30, dt)\nnse1 = np.random.randn(len(t))                 # white noise 1\nnse2 = np.random.randn(len(t))                 # white noise 2\nr = np.exp(-t/0.05)\n\ncnse1 = np.convolve(nse1, r, mode='same')*dt   # colored noise 1\ncnse2 = np.convolve(nse2, r, mode='same')*dt   # colored noise 2\n\n# two signals with a coherent part and a random part\ns1 = 0.01*np.sin(2*np.pi*10*t) + cnse1\ns2 = 0.01*np.sin(2*np.pi*10*t) + cnse2\n\nplt.subplot(211)\nplt.plot(t, s1, 'b-', t, s2, 'g-')\nplt.xlim(0,5)\nplt.xlabel('time')\nplt.ylabel('s1 and s2')\nplt.grid(True)\n\nplt.subplot(212)\ncxy, f = plt.csd(s1, s2, 256, 1./dt)\nplt.ylabel('CSD (db)')\n\nfig_to_plotly(fig4, username, api_key, notebook = True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3083/600/450\" width=\"650\"></iframe>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": "<IPython.core.display.HTML at 0x10e5d6590>"
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Another subplotting example. Note if you double-click, Plotly auto-sizes the plot. We initially draw it based on matplotlib defaults, but you can change the zoom and then save and share it how you'd like."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig5 = plt.figure()\n\nfrom pylab import figure, show\nfrom numpy import arange, sin, pi\n\nt = arange(0.0, 1.0, 0.01)\n\nfig = figure(1)\n\nax1 = fig.add_subplot(211)\nax1.plot(t, sin(2*pi*t))\nax1.grid(True)\nax1.set_ylim( (-2,2) )\nax1.set_ylabel('1 Hz')\nax1.set_title('A sine wave or two')\n\nfor label in ax1.get_xticklabels():\n    label.set_color('r')\n\n\nax2 = fig.add_subplot(212)\nax2.plot(t, sin(2*2*pi*t))\nax2.grid(True)\nax2.set_ylim( (-2,2) )\nl = ax2.set_xlabel('Hi mom')\nl.set_color('g')\nl.set_fontsize('large')\n\nfig_to_plotly(fig5, username, api_key, notebook = True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3084/600/450\" width=\"650\"></iframe>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": "<IPython.core.display.HTML at 0x10dca3c10>"
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "A favorte from the gallery is [Anscombe's quartet](http://matplotlib.org/examples/pylab_examples/anscombe.html). You  might also like Plotly's [blog post](blog.plot.ly/post/68951620673/why-graph-anscombes-quartet) on the subject."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig6 = plt.figure()\n\nfrom __future__ import print_function\n\"\"\"\nEdward Tufte uses this example from Anscombe to show 4 datasets of x\nand y that have the same mean, standard deviation, and regression\nline, but which are qualitatively different.\n\nmatplotlib fun for a rainy day\n\"\"\"\n\nfrom pylab import *\n\nx =  array([10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5])\ny1 = array([8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68])\ny2 = array([9.14, 8.14, 8.74, 8.77, 9.26, 8.10, 6.13, 3.10, 9.13, 7.26, 4.74])\ny3 = array([7.46, 6.77, 12.74, 7.11, 7.81, 8.84, 6.08, 5.39, 8.15, 6.42, 5.73])\nx4 = array([8,8,8,8,8,8,8,19,8,8,8])\ny4 = array([6.58,5.76,7.71,8.84,8.47,7.04,5.25,12.50,5.56,7.91,6.89])\n\ndef fit(x):\n    return 3+0.5*x\n\n\n\nxfit = array( [amin(x), amax(x) ] )\n\nsubplot(221)\nplot(x,y1,'ks', xfit, fit(xfit), 'r-', lw=2)\naxis([2,20,2,14])\nsetp(gca(), xticklabels=[], yticks=(4,8,12), xticks=(0,10,20))\ntext(3,12, 'I', fontsize=20)\n\nsubplot(222)\nplot(x,y2,'ks', xfit, fit(xfit), 'r-', lw=2)\naxis([2,20,2,14])\nsetp(gca(), xticklabels=[], yticks=(4,8,12), yticklabels=[], xticks=(0,10,20))\ntext(3,12, 'II', fontsize=20)\n\nsubplot(223)\nplot(x,y3,'ks', xfit, fit(xfit), 'r-', lw=2)\naxis([2,20,2,14])\ntext(3,12, 'III', fontsize=20)\nsetp(gca(), yticks=(4,8,12), xticks=(0,10,20))\n\nsubplot(224)\n\nxfit = array([amin(x4),amax(x4)])\nplot(x4,y4,'ks', xfit, fit(xfit), 'r-', lw=2)\naxis([2,20,2,14])\nsetp(gca(), yticklabels=[], yticks=(4,8,12), xticks=(0,10,20))\ntext(3,12, 'IV', fontsize=20)\n\n#verify the stats\npairs = (x,y1), (x,y2), (x,y3), (x4,y4)\nfor x,y in pairs:\n    print ('mean=%1.2f, std=%1.2f, r=%1.2f'%(mean(y), std(y), corrcoef(x,y)[0][1]))\n\nfig_to_plotly(fig6, username, api_key, notebook= True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "mean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\n"
      },
      {
       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3085/600/450\" width=\"650\"></iframe>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": "<IPython.core.display.HTML at 0x10f3b3810>"
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "[damped oscillation](http://matplotlib.org/examples/pylab_examples/legend_demo2.html)"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig7 = plt.figure()\n# Make a legend for specific lines.\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\nt1 = np.arange(0.0, 2.0, 0.1)\nt2 = np.arange(0.0, 2.0, 0.01)\n\n# note that plot returns a list of lines.  The \"l1, = plot\" usage\n# extracts the first element of the list into l1 using tuple\n# unpacking.  So l1 is a Line2D instance, not a sequence of lines\nl1, = plt.plot(t2, np.exp(-t2))\nl2, l3 = plt.plot(t2, np.sin(2 * np.pi * t2), '--go', t1, np.log(1 + t1), '.')\nl4, = plt.plot(t2, np.exp(-t2) * np.sin(2 * np.pi * t2), 'rs-.')\n\nplt.legend( (l2, l4), ('oscillatory', 'damped'), 'upper right', shadow=True)\nplt.xlabel('time')\nplt.ylabel('volts')\nplt.title('Damped oscillation')\n\nfig_to_plotly(fig7, username, api_key, notebook= True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3086/600/450\" width=\"650\"></iframe>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": "<IPython.core.display.HTML at 0x10f3dc050>"
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "[histogram](http://matplotlib.org/examples/statistics/histogram_demo_features.html)"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig8 = plt.figure()\n\nimport numpy as np\nimport matplotlib.mlab as mlab\nimport matplotlib.pyplot as plt\n\n\n# example data\nmu = 100 # mean of distribution\nsigma = 15 # standard deviation of distribution\nx = mu + sigma * np.random.randn(10000)\n\nnum_bins = 50\n# the histogram of the data\nn, bins, patches = plt.hist(x, num_bins, normed=1, facecolor='green', alpha=0.5)\n# add a 'best fit' line\ny = mlab.normpdf(bins, mu, sigma)\nplt.plot(bins, y, 'r--')\nplt.xlabel('Smarts')\nplt.ylabel('Probability')\nplt.title(r'Histogram of IQ: $\\mu=100$, $\\sigma=15$')\n\n# Tweak spacing to prevent clipping of ylabel\nplt.subplots_adjust(left=0.15)\n\nfig_to_plotly(fig8, username, api_key, notebook= True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3087/600/450\" width=\"650\"></iframe>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": "<IPython.core.display.HTML at 0x10fd3f0d0>"
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": "III. Stack Overflow Answers"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "[histogram](http://stackoverflow.com/questions/5328556/histogram-matplotlib)"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig9 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nmu, sigma = 100, 15\nx = mu + sigma * np.random.randn(10000)\nhist, bins = np.histogram(x, bins=50)\nwidth = 0.7 * (bins[1] - bins[0])\ncenter = (bins[:-1] + bins[1:]) / 2\nplt.bar(center, hist, align='center', width=width)\n\nfig_to_plotly(fig9, username, api_key, notebook= True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3088/600/450\" width=\"650\"></iframe>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": "<IPython.core.display.HTML at 0x10feeb310>"
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "[Density plot](http://stackoverflow.com/questions/4150171/how-to-create-a-density-plot-in-matplotlib/4152016#4152016)"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig10 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.stats import gaussian_kde\ndata = [1.5]*7 + [2.5]*2 + [3.5]*8 + [4.5]*3 + [5.5]*1 + [6.5]*8\ndensity = gaussian_kde(data)\nxs = np.linspace(0,8,200)\ndensity.covariance_factor = lambda : .25\ndensity._compute_covariance()\nplt.plot(xs,density(xs))\n\nfig_to_plotly(fig10, username, api_key, notebook= True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3089/600/450\" width=\"650\"></iframe>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": "<IPython.core.display.HTML at 0x10e43f110>"
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "[different lines for different plots](http://stackoverflow.com/questions/4805048/how-to-get-different-lines-for-different-plots-in-a-single-figure/4805456#4805456)"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig11 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nx = np.arange(10)\n\nplt.plot(x, x)\nplt.plot(x, 2 * x)\nplt.plot(x, 3 * x)\nplt.plot(x, 4 * x)\n\nfig_to_plotly(fig11, username, api_key, notebook= True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3090/600/450\" width=\"650\"></iframe>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": "<IPython.core.display.HTML at 0x10e496d10>"
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig12 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nnum_plots = 20\n\n# Have a look at the colormaps here and decide which one you'd like:\n# http://matplotlib.org/1.2.1/examples/pylab_examples/show_colormaps.html\ncolormap = plt.cm.gist_ncar\nplt.gca().set_color_cycle([colormap(i) for i in np.linspace(0, 0.9, num_plots)])\n\n# Plot several different functions...\nx = np.arange(10)\nlabels = []\nfor i in range(1, num_plots + 1):\n    plt.plot(x, i * x + 5 * i)\n    labels.append(r'$y = %ix + %i$' % (i, 5*i))\n\nfig_to_plotly(fig12, username, api_key, notebook= True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3091/600/450\" width=\"650\"></iframe>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": "<IPython.core.display.HTML at 0x10ff04710>"
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "[variables as subscripts in math mode](http://stackoverflow.com/questions/23276918/writing-variables-as-subscripts-in-math-mode)"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig13 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport matplotlib.mlab as mlab\n\nmean = [10,12,16,22,25]\nvariance = [3,6,8,10,12]\n\nx = np.linspace(0,40,1000)\n\nfor i in range(4):\n    sigma = np.sqrt(variance[i])\n    y = mlab.normpdf(x,mean[i],sigma)\n    plt.plot(x,y, label=r'$v_{}$'.format(i+1))\n\nplt.xlabel(\"X\")\nplt.ylabel(\"P(X)\")        \n\nplt.legend()\n\nfig_to_plotly(fig13, username, api_key, notebook= True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3092/600/450\" width=\"650\"></iframe>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": "<IPython.core.display.HTML at 0x10ff17e10>"
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": "IV. Prettyplotlib graphs in Plotly"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Some lovely [examples](http://nbviewer.ipython.org/github/olgabot/prettyplotlib/blob/master/ipython_notebooks/Examples%20of%20everything%20pretty%20and%20plotted!.ipynb?create=1) from [prettyplotlib](https://github.com/olgabot/prettyplotlib)"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig14 = plt.figure()\n\nimport prettyplotlib as ppl\n\n# Set the random seed for consistency\nnp.random.seed(12)\n\n# Show the whole color range\nfor i in range(8):\n    x = np.random.normal(loc=i, size=1000)\n    y = np.random.normal(loc=i, size=1000)\n    ax = ppl.scatter(x, y, label=str(i))\n    \nppl.legend(ax)\nax.set_title('prettyplotlib `scatter`')\n\nfig_to_plotly(fig14, username, api_key, notebook = True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3093/600/450\" width=\"650\"></iframe>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": "<IPython.core.display.HTML at 0x10e49f310>"
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig15 = plt.figure()\n\nimport prettyplotlib as ppl\n\n# Set the random seed for consistency\nnp.random.seed(12)\n\n# Show the whole color range\nfor i in range(8):\n    y = np.random.normal(size=1000).cumsum()\n    x = np.arange(1000)\n\n    # For now, you need to specify both x and y :(\n    # Still figuring out how to specify just one\n    ppl.plot(x, y, label=str(i), linewidth=0.75)\n    \nppl.legend()\n\nfig_to_plotly(fig15, username, api_key, notebook = True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3094/600/450\" width=\"650\"></iframe>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": "<IPython.core.display.HTML at 0x110ab2b10>"
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": "V. Plotting with seaborn"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "You may need to [import six](http://stackoverflow.com/questions/13967428/importerror-no-module-named-six), which you can do from pip."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import seaborn as sns\nfrom matplotlylib import fig_to_plotly",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "def sinplot(flip=1):\n    x = np.linspace(0, 14, 100)\n    for i in range(1, 7):\n        plt.plot(x, np.sin(x + i * .5) * (7 - i) * flip)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig = plt.figure()\n\nsns.set_style(\"dark\")\nsinplot()\n\nfig_to_plotly(fig, username, api_key, notebook = True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3095/600/450\" width=\"650\"></iframe>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": "<IPython.core.display.HTML at 0x10f5736d0>"
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig16 = plt.figure()\n\nwith sns.axes_style(\"darkgrid\"):\n    plt.subplot(211)\n    sinplot()\nplt.subplot(212)\nsinplot(-1)\n\nfig_to_plotly(fig16, username, api_key, notebook = True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3096/600/450\" width=\"650\"></iframe>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": "<IPython.core.display.HTML at 0x10f5765d0>"
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Finally, it\u2019s also possibly to plot each individual observation with a point, rather than joining them. This is more useful for the gestalt it presents than as a quantitative visualization but you may prefer it. [Visualizing the data for each sampling unit](http://stanford.edu/~mwaskom/software/seaborn/tutorial/timeseries_plots.html#specifying-input-data-with-multidimensional-arrays)"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import numpy as np\nnp.random.seed(9221999)\nimport pandas as pd\nfrom scipy import stats, optimize\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nsns.set(palette=\"Set2\")",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "def sine_wave(n_x, obs_err_sd=1.5, tp_err_sd=.3):\n    x = np.linspace(0, (n_x - 1) / 2, n_x)\n    y = np.sin(x) + np.random.normal(0, obs_err_sd) + np.random.normal(0, tp_err_sd, n_x)\n    return y",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "sines = np.array([sine_wave(31) for _ in range(20)])",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig17 = plt.figure()\n\nsns.tsplot(sines, err_style=\"unit_points\", color=\"mediumpurple\");\n\nfig_to_plotly(fig17, username, api_key, notebook = True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3097/600/450\" width=\"650\"></iframe>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": "<IPython.core.display.HTML at 0x110e59bd0>"
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "[plotting regressions](http://stanford.edu/~mwaskom/software/seaborn/tutorial/quantitative_linear_models.html#plotting-simple-regression-with-regplot)"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig18 = plt.figure()\n\nx, y = np.random.multivariate_normal([1, 5], [(2, -.8), (-.8, 2)], 80).T\nax = sns.regplot(x, y, color=\"seagreen\")\nax.set(xlabel=\"x variable\", ylabel=\"y variable\");\nplt.show()\n\nfig_to_plotly(fig18, username, api_key, notebook = True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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psJ/JkA/NHiIY0sizOrPid2lb4zU8tu85AtEQVpOFrYuvPu/jqwsqZlS7c1hs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\nzrabre0AfG1N9zeWY39jOQAgIzqlZ812NjJiRgRkfgCDmoi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       "text": "<matplotlib.figure.Figure at 0x110eada10>"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "/Users/matthewsundquist/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlylib/renderer.py:448: UserWarning: Dang! That path collection is out of this world. I totally don't know what to do with it yet! Plotly can only import path collections linked to 'data' coordinates\n  warnings.warn(\"Dang! That path collection is out of this \"\n"
      },
      {
       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3098/600/450\" width=\"650\"></iframe>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": "<IPython.core.display.HTML at 0x110ea6c90>"
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig19 = plt.figure()\n\nsns.residplot(x, y);\n\nfig_to_plotly(fig19, username, api_key, notebook = True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "/Users/matthewsundquist/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlylib/renderer.py:363: UserWarning: Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n  warnings.warn(\"Bummer! Plotly can currently only draw Line2D \"\n"
      },
      {
       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3099/600/450\" width=\"650\"></iframe>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": "<IPython.core.display.HTML at 0x111a90650>"
      }
     ],
     "prompt_number": 38
    }
   ],
   "metadata": {}
  }
 ]
}

What about adding a sidebar it? So it would be easier to navigate through the notebook.
Mine is like this:
https://nbviewer.jupyter.org/github/cqcn1991/Wind-Speed-Analysis/blob/master/GMM.ipynb