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msund/Graphing

Last active June 6, 2016 12:08
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All of 'em
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Graphing
{
"metadata": {
"name": "Three new matplotlib plots"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": "18 Interactive Plots from matplotlib, prettyplotlib, Stack Overflow, and seaborn"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "In this Notebook, we'll create interactive Plotly graphs from different Python libraries. Plotly is collaborative, makes beautiful interactive graphs with a URL for you, and stores your data and graphs together. You can also always access the data from your graphs or any public Plotly graph. And it's free.\n\nFor a full walk-through and documentation, check out our [getting started Notebook](http://nbviewer.ipython.org/github/etpinard/plotly-python-doc/blob/1.0/s0_getting-started/s0_getting-started.ipynb). Let's set up our environment and packages.\n\nFor best results, you can copy and paste this Notebook and key. Just run `$ pip install plotly` and start up a Notebook. "
},
{
"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": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": "import plotly.plotly as py\nimport plotly.tools as tls\nfrom plotly.graph_objs import *\npy.sign_in(\"IPython.Demo\", \"1fw3zw2o13\")",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": "# tls.set_credentials_file(\"IPython.Demo\", \"1fw3zw2o13\")\n# tls.get_credentials_file()",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": "import plotly\nplotly.__version__",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": "'1.0.0'"
}
],
"prompt_number": 8
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": "I. matplotlib Gallery graphs"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "For matplotlib experts, you'll recognize these graphs from the [matplotlib gallery](matplotlib.org/gallery.html). \n\nIn addition to matplotlib and Plotly's own [Python API](https://plot.ly/python), You can also use Plotly's other [APIs](https://plot.ly/api) for MATLAB, R, Perl, Julia, and REST to write to graphs. That means you and I could edit the same graph with any language. We can even edit the graph and data from the GUI, so technical and non-technical teams can work together. And all the graphs go to your profile, like this: https://plot.ly/~IPython.Demo.\n\nYou control [the privacy](http://plot.ly/python/privacy) by setting `world_readable` to False or True, and can control your [sharing](http://plot.ly/python/file-sharing)."
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Plotly also reads the label types in this [damped oscillation](http://matplotlib.org/examples/pylab_examples/legend_demo2.html) graph."
},
{
"cell_type": "code",
"collapsed": false,
"input": "fig3 = 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.xlabel('time')\nplt.ylabel('volts')\nplt.title('Damped oscillation')\n\nplt.show()\npy.iplot_mpl(fig3)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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11ajXqPU3pxk7BVJGmbzRCT4hNy+X4hPFMBT7lxlrT/cQcBkiPo+gfZv2XFpzibprLDtG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\nCB4jSkMQBEHwGFEagiAIgsf8f6/NeKkjAey4AAAAAElFTkSuQmCC\n",
"text": "<matplotlib.figure.Figure at 0x106257450>"
},
{
"html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3389\" height=\"525\" width=\"100%\"></iframe>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x106316390>"
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Notice the difference. You can hover, zoom, and pan on the figure. You can also strip out the matplotlib styling, and use Plotly's default styling."
},
{
"cell_type": "code",
"collapsed": false,
"input": "fig = tls.mpl_to_plotly(fig3)\nfig['layout'].update(showlegend=True)\nfig.strip_style()\npy.iplot(fig)",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3390\" height=\"525\" width=\"100%\"></iframe>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x106332f10>"
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Next up, an example from [pylab](http://matplotlib.org/examples/pylab_examples/arctest.html)."
},
{
"cell_type": "code",
"collapsed": false,
"input": "fig4 = 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\npy.iplot_mpl(fig4)",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3391\" height=\"525\" width=\"100%\"></iframe>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x1063d4950>"
}
],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Here's where this gets special. You can get the data from any Plotly graph. That means you can re-plot the graph or part of it, or use your favorite Python tools to wrangle and analyze your data. Check out our [getting started guide](http://nbviewer.ipython.org/github/etpinard/plotly-python-doc/blob/1.0/s0_getting-started/s0_getting-started.ipynb) for a full background on these features."
},
{
"cell_type": "code",
"collapsed": false,
"input": "tls.mpl_to_plotly(fig4).get_data()",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": "[{'name': '_line0',\n 'x': [0.0,\n 0.20000000000000001,\n 0.40000000000000002,\n 0.60000000000000009,\n 0.80000000000000004,\n 1.0,\n 1.2000000000000002,\n 1.4000000000000001,\n 1.6000000000000001,\n 1.8,\n 2.0,\n 2.2000000000000002,\n 2.4000000000000004,\n 2.6000000000000001,\n 2.8000000000000003,\n 3.0,\n 3.2000000000000002,\n 3.4000000000000004,\n 3.6000000000000001,\n 3.8000000000000003,\n 4.0,\n 4.2000000000000002,\n 4.4000000000000004,\n 4.6000000000000005,\n 4.8000000000000007],\n 'y': [1.0,\n 0.25300171651849518,\n -0.54230030891302927,\n -0.44399794031078654,\n 0.13885028597711233,\n 0.36787944117144233,\n 0.09307413008823949,\n -0.19950113459002566,\n -0.16333771416280363,\n 0.051080165611754998,\n 0.1353352832366127,\n 0.034240058964379601,\n -0.073392365906047419,\n -0.060088587008433003,\n 0.018791342780197139,\n 0.049787068367863944,\n 0.012596213757493282,\n -0.026999542555766767,\n -0.022105355809443925,\n 0.0069129486808399343,\n 0.018315638888734179,\n 0.0046338880779826647,\n -0.0099325766273000524,\n -0.0081321059420741033,\n 0.0025431316975542792]}]"
}
],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Or you can get the figure makeup. Here, we're using 'IPython.Demo', which is the username and '3357' which is the figure number. You can use this command on Plotly graphs to interact with them from the console. You can access graphs via a URL. For example, for this plot, it's:\n\nhttps://plot.ly/~IPython.Demo/3357/\n"
},
{
"cell_type": "code",
"collapsed": false,
"input": "pylab = py.get_figure('IPython.Demo', '3357')",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": "#print figure\nprint pylab.to_string()",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "Figure(\n data=Data([\n Scatter(\n x=[0.0, 0.2, 0.4, 0.6000000000000001, 0.8, 1.0, 1.2000000000000...],\n y=[1.0, 0.2530017165184952, -0.5423003089130293, -0.44399794031...],\n name='_line0',\n mode='markers',\n marker=Marker(\n symbol='dot',\n line=Line(\n color='#000000',\n width=0.5\n ),\n size=30,\n color='#0000FF',\n opacity=1\n )\n )\n ]),\n layout=Layout(\n xaxis=XAxis(\n domain=[0.0, 1.0],\n range=[0.0, 5.0],\n showline=True,\n ticks='inside',\n showgrid=False,\n zeroline=False,\n anchor='y',\n mirror=True\n ),\n yaxis=YAxis(\n domain=[0.0, 1.0],\n range=[-0.6000000000000001, 1.2],\n showline=True,\n ticks='inside',\n showgrid=False,\n zeroline=False,\n anchor='x',\n mirror=True\n ),\n hovermode='closest',\n showlegend=False\n )\n)\n"
}
],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Now let's suppose we wanted to add a fit to the graph (see our [fits post](http://blog.plot.ly/post/84402951992/contour-plots-error-bars-chocolate-beer-meat) to learn more), and re-style it a bit. We can go into the web app, fork a copy, and edit the image in our GUI. No coding required."
},
{
"cell_type": "code",
"collapsed": false,
"input": "from IPython.display import Image\nImage(url='https://i.imgur.com/RusH4k2.png?1')",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<img src=\"https://i.imgur.com/RusH4k2.png?1\"/>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": "<IPython.core.display.Image at 0x106332810>"
}
],
"prompt_number": 15
},
{
"cell_type": "markdown",
"metadata": {},
"source": "I can now call that graph into the NB. And if I want to see the data for the fit or access the figure styling, I can run the same commands, but now on this graph."
},
{
"cell_type": "code",
"collapsed": false,
"input": "tls.embed('MattSundquist', '1307')",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~MattSundquist/1307\" height=\"525\" width=\"100%\"></iframe>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x106250c10>"
}
],
"prompt_number": 16
},
{
"cell_type": "markdown",
"metadata": {},
"source": "And Plotly graphs are interactive. "
},
{
"cell_type": "code",
"collapsed": false,
"input": "from IPython.display import HTML\nHTML('<br><center><iframe class=\"vine-embed\" src=\"https://vine.co/v/Mvzin6HZzLB/embed/simple\" width=\"600\" height=\"600\" frameborder=\"0\"></iframe><script async src=\"//platform.vine.co/static/scripts/embed.js\" charset=\"utf-8\"></script></center><br>')",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<br><center><iframe class=\"vine-embed\" src=\"https://vine.co/v/Mvzin6HZzLB/embed/simple\" width=\"600\" height=\"600\" frameborder=\"0\"></iframe><script async src=\"//platform.vine.co/static/scripts/embed.js\" charset=\"utf-8\"></script></center><br>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": "<IPython.core.display.HTML at 0x106332b90>"
}
],
"prompt_number": 17
},
{
"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": "fig5 = 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\npy.iplot_mpl(fig5)",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3392\" height=\"525\" width=\"100%\"></iframe>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x1063f5310>"
}
],
"prompt_number": 18
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Another subplotting example using Plotly's defaults. "
},
{
"cell_type": "code",
"collapsed": false,
"input": "fig6 = 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\npy.iplot_mpl(fig6, strip_style = True)",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3393\" height=\"525\" width=\"100%\"></iframe>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x1068c4650>"
}
],
"prompt_number": 19
},
{
"cell_type": "markdown",
"metadata": {},
"source": "From the gallery here we're shwoing [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": "fig7 = 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\npy.iplot_mpl(fig7, strip_style = 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 id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3394\" height=\"525\" width=\"100%\"></iframe>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x1068fed50>"
}
],
"prompt_number": 20
},
{
"cell_type": "markdown",
"metadata": {},
"source": "And a final [histogram](http://matplotlib.org/examples/statistics/histogram_demo_features.html) from the matplotlib gallery. "
},
{
"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')\n\n# Tweak spacing to prevent clipping of ylabel\nplt.subplots_adjust(left=0.15)\n\npy.iplot_mpl(fig8, strip_style = True)",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3395\" height=\"525\" width=\"100%\"></iframe>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x1068bf090>"
}
],
"prompt_number": 21
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": "II. Stack Overflow Answers"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "We love Stack Overflow, so wanted to show answers to a few questions from there, in Plotly. If you want to plot data you already have as a [histogram](http://stackoverflow.com/questions/5328556/histogram-matplotlib) and make it interactive, try this one out."
},
{
"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\npy.iplot_mpl(fig9, strip_style = True)",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3396\" height=\"525\" width=\"100%\"></iframe>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x1063e12d0>"
}
],
"prompt_number": 22
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Here is how to create a [density plot](http://stackoverflow.com/questions/4150171/how-to-create-a-density-plot-in-matplotlib/4152016#4152016) like you might in R, but in matplotlib."
},
{
"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\npy.iplot_mpl(fig10, strip_style = True)",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3397\" height=\"525\" width=\"100%\"></iframe>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x10e6b6a10>"
}
],
"prompt_number": 23
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Drawing a simple example of [different lines for different plots](http://stackoverflow.com/questions/4805048/how-to-get-different-lines-for-different-plots-in-a-single-figure/4805456#4805456) looks like this..."
},
{
"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\npy.iplot_mpl(fig11, strip_style = True)",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3398\" height=\"525\" width=\"100%\"></iframe>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x10eaa6450>"
}
],
"prompt_number": 24
},
{
"cell_type": "markdown",
"metadata": {},
"source": "...and can get more exciting like this."
},
{
"cell_type": "code",
"collapsed": false,
"input": "fig12 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nnum_plots = 10\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\npy.iplot_mpl(fig12, strip_style = True)",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3399\" height=\"525\" width=\"100%\"></iframe>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x10ed9c450>"
}
],
"prompt_number": 25
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Plotly also lets you draw [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\npy.iplot_mpl(fig13, strip_style = True)",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3400\" height=\"525\" width=\"100%\"></iframe>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x10ef9afd0>"
}
],
"prompt_number": 26
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": "III. Prettyplotlib graphs in Plotly"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "The gallery of [examples](http://nbviewer.ipython.org/github/olgabot/prettyplotlib/blob/master/ipython_notebooks/Examples%20of%20everything%20pretty%20and%20plotted!.ipynb?create=1) we really like from [prettyplotlib](https://github.com/olgabot/prettyplotlib) can be a fun one to make interactive. Here's a scatter; let us know if you make others. You'll note that not all elements of the styling come through. "
},
{
"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=800)\n y = np.random.normal(loc=i, size=800)\n ax = ppl.scatter(x, y, label=str(i))\n \nppl.legend(ax)\nax.set_title('prettyplotlib `scatter`')\nax.legend().set_visible(False)\n\npy.iplot_mpl(fig14)",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3401\" height=\"525\" width=\"100%\"></iframe>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x10eaaa990>"
}
],
"prompt_number": 27
},
{
"cell_type": "markdown",
"metadata": {},
"source": "And another prettyplotlib example."
},
{
"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 # Specify both x and y\n ppl.plot(x, y, label=str(i), linewidth=0.75)\n \npy.iplot_mpl(fig15)",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3402\" height=\"525\" width=\"100%\"></iframe>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x10f00ba90>"
}
],
"prompt_number": 28
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": "IV. Plotting with seaborn"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Another library we really dig is [seaborn](http://stanford.edu/~mwaskom/software/seaborn/index.html), by Michael Waskom. You may need to [import six](http://stackoverflow.com/questions/13967428/importerror-no-module-named-six), which you can do from pip. The styling isn't yet translated to Plotly, so we'll go to Plotly's default settings. "
},
{
"cell_type": "code",
"collapsed": false,
"input": "import seaborn as sns\nfrom matplotlylib import fig_to_plotly",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 29
},
{
"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": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": "fig16 = plt.figure()\n\nsns.set_style(\"dark\")\nsinplot()\n\npy.iplot_mpl(fig16, strip_style = True)",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3403\" height=\"525\" width=\"100%\"></iframe>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x10ef99e10>"
}
],
"prompt_number": 31
},
{
"cell_type": "markdown",
"metadata": {},
"source": "You can also run subplots like this."
},
{
"cell_type": "code",
"collapsed": false,
"input": "fig17 = plt.figure()\n\nwith sns.axes_style(\"darkgrid\"):\n plt.subplot(211)\n sinplot()\nplt.subplot(212)\nsinplot(-1)\n\npy.iplot_mpl(fig17, strip_style = True)",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3404\" height=\"525\" width=\"100%\"></iframe>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x110a99ed0>"
}
],
"prompt_number": 32
},
{
"cell_type": "markdown",
"metadata": {},
"source": "And a final example, [combining plot types](http://stanford.edu/~mwaskom/software/seaborn/tutorial/plotting_distributions.html#basic-visualization-with-histograms)."
},
{
"cell_type": "code",
"collapsed": false,
"input": "import numpy as np\nfrom numpy.random import randn\nimport pandas as pd\nfrom scipy import stats\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport seaborn as sns",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 33
},
{
"cell_type": "code",
"collapsed": false,
"input": "fig18 = plt.figure()\n\nsns.set_palette(\"hls\")\nmpl.rc(\"figure\", figsize=(8, 4))\ndata = randn(200)\nsns.distplot(data);\n\npy.iplot_mpl(fig18, strip_style = True)",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3405\" height=\"525\" width=\"100%\"></iframe>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x11095bc10>"
}
],
"prompt_number": 34
}
],
"metadata": {}
}
]
}
@cqcn1991
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cqcn1991 commented Jun 6, 2016

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

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