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@msund
Last active August 29, 2015 14:00
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MPL gallery
{
"metadata": {
"name": "Three new matplotlib plots"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"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": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": "import plotly\nplotly.__version__",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": "'0.5.9'"
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": "from matplotlylib import fig_to_plotly\nusername = \"IPython.Demo\"\napi_key = \"1fw3zw2o13\"",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 18
},
{
"cell_type": "markdown",
"metadata": {},
"source": "A blend of Plotly and matplotlib plotting for interactive plots, drawn from the [SWC repo](http://nbviewer.ipython.org/github/swcarpentry/notebooks/blob/master/matplotlib.ipynb)."
},
{
"cell_type": "code",
"collapsed": false,
"input": "fig1 = plt.figure()\n\n#generate some data\nx = np.array(range(20))\ny = 3 + 0.5 * x + np.random.randn(20)\n\n#plot the data\nplt.plot(x, y, 'bo')\nplt.show()\n\nfig_to_plotly(fig1, username, api_key, notebook = True)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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5/WvaHXNjcwsA6AqrGN4HS9kCcANL0QKAwViKFgCSCCEOAAYjxAHAYIQ4ABiMEAcAgxHi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\nAGCw/wMzO6rghpcXMAAAAABJRU5ErkJggg==\n",
"text": "<matplotlib.figure.Figure at 0x10bc02450>"
},
{
"html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3037/600/450\" width=\"650\"></iframe>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 19,
"text": "<IPython.core.display.HTML at 0x10c653b10>"
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": "fig2 = plt.figure()\n\n#generate some random numbers from a normal distribution\ndata = 100 + np.random.randn(500)\n\n#make a histogram with 20 bins\nplt.hist(data, 20)\nplt.show()\n\nfig_to_plotly(fig2, username, api_key, notebook = True)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": "<matplotlib.figure.Figure at 0x10c50f2d0>"
},
{
"html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3038/600/450\" width=\"650\"></iframe>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 20,
"text": "<IPython.core.display.HTML at 0x10bd00250>"
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": "fig3 = plt.figure()\n\nplt.hist(data, 20)\nplt.axis([90, 110, 0, 100])\nplt.show()\n\nfig_to_plotly(fig3, username, api_key, notebook = True)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": "<matplotlib.figure.Figure at 0x10d11e3d0>"
},
{
"html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3039/600/450\" width=\"650\"></iframe>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 21,
"text": "<IPython.core.display.HTML at 0x10d132f90>"
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": "fig4 = 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^')\nplt.show()\n\nfig_to_plotly(fig4, username, api_key, notebook = True)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": "<matplotlib.figure.Figure at 0x10d19b510>"
},
{
"html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3040/600/450\" width=\"650\"></iframe>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 22,
"text": "<IPython.core.display.HTML at 0x10d19bd50>"
}
],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": "fig5 = plt.figure()\n\nplt.subplot(1, 2, 1)\nplt.plot(x, y, 'rs')\nplt.subplot(1, 2, 2)\nplt.hist(data, 10)\nplt.show()\n\nfig_to_plotly(fig5, username, api_key, notebook = True)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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"cell_type": "code",
"collapsed": false,
"input": "fig6 = 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\nshow()\n\nfig_to_plotly(fig6, username, api_key, notebook = True)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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CSNFd1pUEgGYA1QAaEf4+KQC+wNSp5/DQQwsc3+rlyl+SFXVTKPtqa2sTeXklYsKE9UJR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\nzcgBflGEB+2eEoA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"text": "<matplotlib.figure.Figure at 0x10d562650>"
},
{
"html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3042/600/450\" width=\"650\"></iframe>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 24,
"text": "<IPython.core.display.HTML at 0x10d7b3650>"
}
],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": "fig7 = 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)')\nplt.show()\n\nfig_to_plotly(fig7, username, api_key, notebook = True)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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bzb9vaWnhSPQIO9btwD/Vr8kBn/+8H70ezjijBYtFHJ9OQyzWgqLAG2/46d3R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\nIAiCYBOiSARBEASb+H8X4EvXoY0w3wAAAABJRU5ErkJggg==\n",
"text": "<matplotlib.figure.Figure at 0x10da9a890>"
},
{
"html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3043/600/450\" width=\"650\"></iframe>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 25,
"text": "<IPython.core.display.HTML at 0x10dcbcad0>"
}
],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": "fig8 = 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\nshow()\n\nfig_to_plotly(fig8, username, api_key, notebook = True)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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O7N8vDoLZ2WKh2ZAhwKBB4mqEdU1JCXDmjGgofvlFTCjo00c0uKNGGe/YkNGM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\nvPEGsHOnAqtWASNHGlZ8vF319oQJQG6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"text": "<matplotlib.figure.Figure at 0x10d7cfe90>"
},
{
"html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3044/600/450\" width=\"650\"></iframe>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 26,
"text": "<IPython.core.display.HTML at 0x10e4a6b50>"
}
],
"prompt_number": 26
},
{
"cell_type": "markdown",
"metadata": {},
"source": "[Anscombe's quartet](http://matplotlib.org/examples/pylab_examples/anscombe.html)"
},
{
"cell_type": "code",
"collapsed": false,
"input": "fig9 = 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\nshow()\n\nfig_to_plotly(fig9, 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"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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i4uJq1wOBAKtXr27weKyWCNMpAMePH6djx460bNmSI0eOkJSUVOk9+fn5PPDAA2RnZ1NQ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\n",
"text": "<matplotlib.figure.Figure at 0x10e558e50>"
},
{
"html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3045/600/450\" width=\"650\"></iframe>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 27,
"text": "<IPython.core.display.HTML at 0x10e411b50>"
}
],
"prompt_number": 27
},
{
"cell_type": "markdown",
"metadata": {},
"source": "[damped oscillation](http://matplotlib.org/examples/pylab_examples/legend_demo2.html)"
},
{
"cell_type": "code",
"collapsed": false,
"input": "fig10 = 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')\nplt.show()\nfig_to_plotly(fig10, username, api_key, notebook= True)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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zLsZHxSv5ozIzwWCgU3EB2dWlSNXKrpJ6qq5WcpicOwfnzmEoKEBKTQWdDik7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\n+QOUtA22BAcowkN3991IKSkNCo+mIKmmzIgIpJQ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"text": "<matplotlib.figure.Figure at 0x10d7cff50>"
},
{
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"metadata": {},
"output_type": "pyout",
"prompt_number": 28,
"text": "<IPython.core.display.HTML at 0x10dcb7e10>"
}
],
"prompt_number": 28
},
{
"cell_type": "markdown",
"metadata": {},
"source": "[Drawing a line](http://stackoverflow.com/questions/12864294/adding-an-arbitrary-line-to-a-matplotlib-plot-in-ipython-notebook)"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "[histogram](http://stackoverflow.com/questions/5328556/histogram-matplotlib)"
},
{
"cell_type": "code",
"collapsed": false,
"input": "fig11= 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)\nplt.show()\n\nfig_to_plotly(fig11, username, api_key, notebook= True)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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AAoQ9YDluemcHHksIWI7HFdqBnj0AWICwBwALEPYAYAHCHgAsQNgDgAUIe8sx7Q43w2cj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\n",
"text": "<matplotlib.figure.Figure at 0x10d7a99d0>"
},
{
"html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3047/600/450\" width=\"650\"></iframe>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 29,
"text": "<IPython.core.display.HTML at 0x10ee0ec90>"
}
],
"prompt_number": 29
},
{
"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": "fig12 = 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))\nplt.show()\n\nfig_to_plotly(fig12, username, api_key, notebook= True)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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KCwOSk4HmzWVHah5//zvw5pu1l8aMYNs2+obVrJnsSJTFI3gTc4XyTEX16gH/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\n",
"text": "<matplotlib.figure.Figure at 0x10d1ac610>"
},
{
"html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3048/600/450\" width=\"650\"></iframe>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 30,
"text": "<IPython.core.display.HTML at 0x110442350>"
}
],
"prompt_number": 30
},
{
"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": "fig13 = 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)\nplt.show()\n\nfig_to_plotly(fig13, username, api_key, notebook= True)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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LK7w3BxGJpanh3SkiiYpC2L59XZ5UI3ccwt0cRKQEzQzvDhFJVBRGbN3qKNW4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\neBMRaRCHNxGRBv1/2Bt54Ht0uroAAAAASUVORK5CYII=\n",
"text": "<matplotlib.figure.Figure at 0x10e921f10>"
},
{
"html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3049/600/450\" width=\"650\"></iframe>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 31,
"text": "<IPython.core.display.HTML at 0x10ee28f50>"
}
],
"prompt_number": 31
},
{
"cell_type": "code",
"collapsed": false,
"input": "fig14 = 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\nplt.show()\n\nfig_to_plotly(fig14, username, api_key, notebook= True)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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bKZfIz/Gvsk2kXCLUQz77FmUin7tUHw+52bJKsrcbhi3CsgeQ+AJIHWHs9VUQ\n7EzZjovPApwpsz3OlCGmnkmSuoRmJDqV61EDFxYMWgGZd8cRDAotlxiQ6V9lm0i5RCQP+exbIF2H\nlKGZskqyy5RdjcoE7p3GgxZhLVTkFZMdKok6U8yALqS+cuVK3n33XYYOHcq+ffsAePLJJ/nTn/7E\nkCHKT6lf//rXLFmyBIDnn3+etWvXkpaWxgsvvMCNN94Y18EkzEEiU7keNXCBjUG+YNAKSJsQ4x9E\nrVzCS+TxlkuEesgvnNV3D7mZBcsRy5Q9ThWj7YYRF2F5pnGDF2GpHsnnTFE84n5nyg0BzpQEMgg6\noptWmjnAYNv1iZP6li1byMrK4qGHHvKR+i9/+Uuys7N57LHHgp577tw55s2bx8aNG6mtreXRRx9l\n9+7d4ReVpJ5UJDKVq9bALbExfLb2GriwYNAyT2NQrMEgI8oljPaQmymrqJYpe0g8Z76x2w19i7C8\nNsPkLsLS5kyZhI1U084U+axuWjnJZfb7PtpoIIuxzLT9sUfu7PFPMHfuXOrq6sIvrPLGO3fuZPHi\nxRQUFFBQUIAQgpaWFrKzk/sri4QCtal8wA+yNU3l3hq4ug1uQKmBu/W/7Jpr4IIag6rAOQf6fRsG\nxxMMUiuXeOU78ZdLRNpD/vvX9fGQR5JVFv5MX1klmt2w9FXInGicpCFcirWw/YPwRVgDfmX6Iiy/\nM8Wb1NwBZHhklNk4WI2d63R1psSLLlq4zJceAv+CZg6QRn9ymEh/JjCC28miGDtpwB97fL+4fyyt\nXbuW119/neXLl/Pwww+TnZ1NTU0NpaWlvueMHz+empoaFi5cGO9lJBKE2lSeuSBd01QeVAP3hVID\nd/3/lcLgido85ZGCQTm/i8O8oFYu8edvxF8uoeYhX7gc/rQ58T3kkWSVmd+Fb7yir6wSZjfMUkh8\nxE+MtRsKAV37/Tc22z9WFmGlL4SsH0PuX01dhBXNmZLG7aTzG8OdKdrO6eIKtQFT+Jd0cI7+lJDD\nREZxF/2ZgJP4f4uKi9RXrVrFz3/+c5qbm3niiSdYt24dq1evVp3eI/3jf/LJJ32PKysrqaysjOco\nEhGQyFSuVgN3wy/spGZom2rUgkFDNsYRDFIrl1h7b/zlEpH2kP/wycQ95JFkleX/qq+sEs1uOPKf\njLUb+hZheSQVe5ZyYzPzfhj0gqmLsBRnSk2AR9zvTHHwHVJ4IWhnSrLQyaWAKXw/zRzESS45TCCH\nSRTwdfqRprxyAAAgAElEQVRRiD0CFVdXV1NdXR3TNTW5X+rq6li2bJlPUw/E3r17efjhh9m2bRtv\nv/02VVVVPPfccwCUlZWxZcuWMPlFaurGIBGtPNEaON2CQXqXSxi5h9wst0qy7IauM/5Nhu2bQbQn\nbRGWm0aPM0WZxN0cspwzxU03VzjGZb7wTeFdXKY/peQw0fMxgTTiXdivjTvjmtQbGhrIz8+nu7ub\nl19+maVLlwIwc+ZMnnjiCU6ePMnx48ex2+1STzcB8U7lajVwU1bZNdfABQWDtinBoOwn4ggGnWtR\nPOSB5RI/XxJ/uYTaHvKl98NvXkrMQ26WrBKxTPlWKPqdcXbDoEVYm6H7NKRXKiTe/zFTF2G5OeUh\ncK8z5QwpzCSV2aTzVMw7U4xAB40+HfwyX9LCEdLJZwATGch0Cvkm/RiNDXNdPT1O6vfffz8fffQR\njY2NDBs2jF/+8pdUV1fz2Wef4XA4mDdvHmvWrGHQIEUDeu6551i7di0Oh4N169Yxd+7c8IvKST1h\nJDKVt5xU5JUTm2KvgYsYDFoG9lg4Te9yiUAPee2hYA95InvIzXCrJGu7oUUWYSnOlCO+G5rdbENx\npszxTeKKMyU5S7kA3HTSzGGPhPIlTXyBmw5ymEB/JjKAifSnlFSMXZkgw0d9EPH6yruuCuo/VNwr\n8dTA+YJBfwX7wDiDQXqWSwR6yKveCN5DPrMS0hKQJMyQVZJRpmyRRVjBzpTAnSmzDduZEtv5BO2c\nDbIUXuE4/RhFDpM8evhEMhhp+hklqfcRhE3lo1NxTut5Kg+qgdsqGFKu3PTMq9BWA6caDIq1MUjP\ncomIHvLlMPWG+D3kZoSAklGmHLYIayukjjV9EZbfmeIN+tR4nCmzfRZDOyMNP0ckuGinmYNBJA4E\n6OAT6c94UkheN6kXktR7OVSn8ikO7NnRp3JfDdx6NynpsdXA6RIMau2Ed/bpUy4RyUO+6K7EPORm\nyCpdFzx2w03mbDe0yCKsyM6U2QE7U5LjTBEI2vgqiMCvcpIsioKm8HTyLOFhD4Uk9V6IeKdyV4fg\nqy3KIq2mI7HVwKkFg2JuDNKzXKKjHXZs8njI34YRRf6JPBEPudGySjLKlC2wCCuyM8W7vTB5zhQl\nXv9lkK3QjtPnRMlhEtmMIyXJN13V0EEzZ/icM+yhgc84wx5W2fZIUu8tiGcqF0Jw6SDUro+9Bk6X\nxiBvucSru+DtfYmVS4R5yKcq1sNFy+P3kJshq5hdpmyBRVjRnCnxtPnohWjxeuVG5kTPFG7uZkUt\nUCPwZr5iKBPJo5x8ysijnDzbZEnqVka8U3n7RcGJjcpNT1cnFC62U3irjcxhPf9jTrgxSM9yiUuN\nyiSup4fcaFnF7DLlJC/CCt6ZYi1nSmC8vtnjCw+M1w9gUkC83jrQSuBDKAkLJUn5xaKIZyp3dwsa\ndig3Pc9/Jhh+o+JeyZ1Cj57yhINBoeUSRYM95RLTYFSM5RJqHvJFd8G82+L3kBspq5htN4y4CMtD\n4gYvwoq+MyV5zhR/vP5Lny88MF7vJfJE4vVGIBECV4MkdQsh3qn88nFlIo+1Bi7hxiC1cokV05Tl\nWUUx3uQ6edTvWDlxRPGQL1wev4fc6CYgM+2G0RZhpS80fBGW+s6U4Z4p3Jw2HzVEj9dPJIdJUeP1\nyYDeBK4GSeoWQDxTeWANXFsjFGqsgUu4MUivcgk1D3mie8gDZZWDGyBrqEdWWQqjZiYmq5hZpiyE\nYvrv8O5QCViE5VwI6fMMXYQVvc1nTlKcKf54vT+d6Y/XKzcz+1OKA/MWhPUEMwhcDZLUk4R4pnLh\nEpzdrUT2z+xUauAKl9jI01ADF9YY9HXIvEdjMEivcgkj9pBHklVKlsCgotjfLxCRthsaYTf0LcLy\n7FGx9/MQ+ALlJqeBi7Cs2OajxOv9MkoLh0knL8gXnox4fSQki8DVIEndZMQzlV/5ylMD977AmaPI\nKwUaauASCgbpVS7R1eX3kH/wt8Q95EbKKmaWKbvOQPuHfoeKaPdr4gYvwvK3+SgecX+bj9eZMs3U\nNh83nbRwJMiR4qKN/gEySg4lpGJ8CEoLrETgapCkbgLimcq72wSnPlLCQZfrYPQibTVwYcGgryk6\nueZgkFq5xIrpsZVLhHrIvXvI4/WQGymrRN1uOAvsOtnuQhdhuRrAeZM/uZlaYojNUFubz2TTnCkC\nQQfnaPJsKWzmS1o4RiajAiyFE8hklCWCPVYncDVIUjcQsU7lkWrg8m+wkeKIEvXvUnIlra9C+yYl\n2R1TMCiwXOL8FQ+RT4utXCLSHvKFd8bnITdKVjGrTDniIqyFSuDHoEVY4c4U784UrzNltqltPkq8\n/lBIvN4dJKNkM55UEliophN6I4GrQZK6zohnKm87L6jzeMptKPLK6FtsZORGIfLQYFCR54an1mBQ\naLnE3WXKRH5jMaRoJHI9PeRGySpmlSknaRFWtDYfs50pSrz+tE8HV+L1Jzzxej+JWyFe31cI3Iur\nrouc7tjHVx2fM3/QjyWp64FYp3JXp+D0duWm54X9Sg1c0VI7gyZEj+yHBYO+rnxoCgZ91aQ4Vl7d\nDUfOwfKpCpFXxlAuoaeH3ChZxYwy5bBFWNs8i7AWGLoIS31nynUhO1PMSUN62+sDp3AlXj8hYAq/\nLunx+r5E4C7RxdnOw5zu+JyvAj7a3ZcZ7pjMcOcUHsj/d0nq8SKeqfzSEUUnP7lZkFNko2ipjRFz\nbVFr4BIKBp1t9pdLfNEAt09WiHxRDOUSenrIjZBVfHbDTXBpc4jdcJE+Zcqqi7CGBJD4TYYsworu\nTPHuTIm/JUcrguP1iiulldNkMy5gR8pE0kmwKSpB9CUCb+4+6yNtL4mf7TzEwNRRjHBOYYRzCsM9\nnweljcbuuWkm5Zc4EOtU3tEkOLlZUPuem64rSg3c6FvtZA2PIq+oBYNWKL/N9xgMaryipDpf3QW7\n6pVyiRXTld3kWsol9PSQGyWreO2GlzZB02Zj7IZJWIQVvjPF60zxesTNcaZ00eIpegiM12d7/OAT\nPFP42KTG6/sKgXe5OzjTeSBs+naJTkY4p/oIfIRzCvnOiTjs0QcpSeoaEetU7u4WnP1E2Yh4bpdS\nA1e42MbQaZFr4IQbOrd6dPJ3wDEVMu7TGAxqaoU3PS1BO2pjL5fQ00NuhKxiht1QdRHWAr8urvMi\nrMg7U2aH7EwxlpCC4/WKjOKP10/wEXky4/V9gcCFEDR1fxU0eX/V8TmNXcfITSv2ELefxHNSh/e4\nPVUNktR7QKxTua8GbqMgc6iySGvUgug1cHEHg5rb4K19CpF/dBQWxlguoece8kBZ5cQ/FFml9LbE\nZBWjy5RNXoQVeWfKbB+Rm+FMUY/XDw7aj5LFmKSRY18g8E53Kw0d+4Mm79Mdn2O3pQVN3sOdU8hz\nlJKml3UWSeqqiHUqV6uBK1xsJ6coik5+2hMMei3GYNDVDnjnC4XINx9S3Cr3TddeLtHRDjuqPB7y\nt+LfQ26ErBKpTFkvu6HJi7AiO1MC23yMdaYEx+uVDyvF63s7gQshuNBVFzZ9X+quJ89R4tO8FQKf\nTP9U45LBXkhSD0AsU7mvBm6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tsA88NLTjvXGphHZKwmyDyQrt9Mb93VrgtQKq3ZispZFc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\nvsetnW1cbb9Ka2eb8rzONlo72jS9d/Iz5gEQAi6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"text": "<matplotlib.figure.Figure at 0x11045b050>"
},
{
"html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3050/600/450\" width=\"650\"></iframe>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 32,
"text": "<IPython.core.display.HTML at 0x1106afcd0>"
}
],
"prompt_number": 32
},
{
"cell_type": "markdown",
"metadata": {},
"source": "[histogram](http://matplotlib.org/examples/statistics/histogram_demo_features.html)"
},
{
"cell_type": "code",
"collapsed": false,
"input": "fig15 = 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)\nplt.show()\n\nfig_to_plotly(fig15, username, api_key, notebook= True)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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RLFbjoiLYvx8CAtSOIqpZpS2JUaNGsXz5cq5du8a1a9f4+9//zqhRoyo9cG5u\nLgAhISG4uLgwYMAA0tLSjLZJS0tj9OjR2NnZERERQWZmpuG9Pn368Nhjj1V47DuTDQpREzzSTnBC\n6wH3GTdU39nc0sMf/gClpWpHEdXMZJFo2bIl1tbWLF++nNdeew0HBwccHBx49dVXWb58eaUHTk9P\nx9PT07Ds7e1Namqq0TY6nQ5v79/6Vzs4OJCVlVXpsVesWEFwcDCLFy8mLy+v0u2FeBQeaSc5Huyh\ndgyLdtm2FdjaQiUzNYjax+Tlpvz8fLN/uKIo5VoF9xvlDRAVFcUbb7zBzZs3mT17NnFxcURHR1e4\nbUxMjOHrsLAwwsLCHjWyqGeaFxTieOoSZ3q4qh3F8g0ZAtu3yyUnC5CYmFhtA6KrPAXNkSNHuH79\numH53kea3iswMJDZs2cbljMyMhg4cKDRNlqtlqNHjxIeHg7A5cuXcXNzu+9x27RpA4CNjQ3Tpk1j\n6tSpVSoSQjyMrmcucKa7C8WNZbamSg0eDPPnwxtvqJ2k3rv3j+I7QxoeRqX3JDZu3Iifnx99+vRh\nxowZhIWF8Ze//KXSA9/pIpuUlER2dja7du1Cq9UabaPVavniiy+4evUq8fHxeHlV/hCXixcvAlBc\nXEx8fDyDBw+udB8hHtaJtg7sndhX7Ri1Q58+8NNP8MsvaicR1ajSIrFixQoSExNp3749hw4dIjk5\nucpjJJYtW0ZkZCT9+/dn6tSp2NvbExcXR1xcHABBQUH07t2bgIAA3n33XZYsWWLYNyIigieeeILj\nx4/Tvn17Pv74YwDmzJlDt27dCA4OpqioiKioqIc5byGq5Fqrllxyd1I7Ru3QuDH8+c/w669qJxHV\nqNI2dG5uLq1ataJNmzZcu3aNXr16MWXKlCodPDQ01KjHEkBkZKTRcmxsLLGxseX2Xb9+fYXH/Ne/\n/lWlzxZCqOCVV9ROIKpZpUXC2dmZ69evM3r0aMLCwnBwcKBnz541kU0IIYTKKi0SGzduBMp6FYWH\nh3PhwgV695ZBRUIIUR9UqcvGlStXSEhIQKPRGHoiCVGnFRVBQ+nRJESlPwWffvopMTExhuIQExPD\nm2++yXPPPWf2cEKoZvVqOHIEeSpn1Rw4cIAJMycYrXOydSI2pvz9RlG7VFoklixZQnJyMk5OZT08\nLl26RHh4uBQJUbdt3Qpjx0KqTCBZFfoSvdET/EbEbuKTx93VCySqTaVdYO3s7NDr9YZlvV6PnZ2d\nWUMJoaqPJtx2AAAcpklEQVRff4WkJLhn8KeoOk2pQrfs82rHENXAZEvilf91ZXNwcMDf399wszol\nJYUnn3yyZtIJoYbdu8umlrC1VTtJrXW8Zyd6/PM7tWOIamCySPj7+xvmURo0aJBh/ciRIyudX0mI\nWu2rr2DoULVT1GonAzvyh0WbID8fWrZUO454BCaLxIQJE4yWc3JyAAz3JoSos65elSLxiApbNiWr\nrQNdvv4aRo5UO454BJXekzh06BDBwcE8+eSTPPnkk/Ts2ZPDhw/XRDYh1PHll+AuN10f1SE3Z9ix\nQ+0Y4hFVWiQWLVrEu+++y5EjRzhy5Ajvvvsub7/9dk1kE0LUYkk+7vD++2rHEI+o0iJx6tQp/P39\nDct+fn6cOnXKrKGEELVfUcOGZZP+iVqt0nESY8aM4bnnnuO5555DURQ+++wzxowZUxPZhBBCqKzS\nIjFz5kx27NjBtm3bABg/frxMzSHqpLkxc8m5kWO07sDhA0aDxISob+5bJIqLi+nevTtHjx5l2LBh\nNZVJCFV4Je2j9cu9uGX3W5fNFF2KiomEUN9970k0bNgQLy8vDh06VFN5hFDHrVs8k7JfHlNqDlev\nwo8/qp1CPKRKfyKuXbtGQEAAPXr04PHHHwdAo9GwZcsWs4cTosbs2sVpR3sKWzZVO0ndk54Of/kL\npEirrDaqtEjExMSgKIrROhlxLeqcrVs57NZe7RR1U9++MGYMXL4MDg5qpxEPyGSRKCoqIiEhgZSU\nFMLDwwkNDaVBg0p7zApR+5SWwrZtHA7vSXO1s9RFTZrAk0/Ctm1wz0wOwvKZ/K0/f/58Vq5ciYOD\nA2+99RbLli2ryVxC1JzvvgN7e36xbaV2krpr2DCQS9S1kskisXfvXjZt2sRrr73Gxo0b2bx5c03m\nEqLmdO4Ma9aonaJuGzwY9uyBggK1k4gHZLJIlJaW0qhR2WO5bG1tuXnzZo2FEqJG2dtDYKDaKeo2\ne3t4/XXIy1M7iXhAJu9J/PDDD1hbWxuW9Xq9YVmj0UjREELcV4WPNH3/XXmkaS1jskiUlJTUZA4h\nRB1z7yNNAbI3ZauSRTw8GTkkhKgxFbUuAJxsnaSFYaGkSIj669atsu6ZDeXHoKZU1LoAaWFYMvnp\nEPXOnYn8hqcepnFxCf/pXTYVvkzmJ0R5MjpO1Ds5N3JwHeFKz8s5XBofgOsIV1xHuKK/rVc7Wr3w\nh/e24nzkrNoxRBWZtUgkJSXh5eWFh4cHK1asqHCbefPm4ebmhr+/P8eOHTOsnzRpEo6OjnTt2tVo\n+7y8PIYPH46zszMjRowgPz/fnKcg6ii7n6/R8no+53xkKo6adtOhFT7/zVA7hqgisxaJGTNmEBcX\nx+7du3n//fe5cuWK0fs6nY7k5GT2799PdHQ00dHRhvcmTpzIzp07yx1z5cqVODs7c+LECdq1a8eq\nVavMeQqijvJKOkpmH08UK2lM17SMUG+8kjPRlCqVbyxUZ7afkNzcXABCQkJwcXFhwIABpKWlGW2T\nlpbG6NGjsbOzIyIigszMTMN7ffr04bHHHit3XJ1Ox+TJk2nSpAmTJk0qd0whqsI7KZPMEG+1Y9RL\nV53t0bdqRvuMc2pHEVVgtiKRnp6Op6enYdnb25vU1FSjbXQ6Hd7ev/2gOjg4kJWVVeXjenp6otPp\nqjG1qA8aFpdw06EV2d1d1I5Sb2WEeuOdKJecagNVezcpivLA05Dfu/39xMTEGL4OCwsjLCzsQeKJ\nOqq4oRX/fkue066mo2E+jPzrl2rHqLMSExNJTEyslmOZrUgEBgYye/Zsw3JGRgYDBw402kar1XL0\n6FHDM7MvX76Mm5tbpcfNzMzE19eXzMxMAu8z587dRUIIYTmuONvz4coX1I5RZ937R/HChQsf+lhm\nu9xkY2MDlPVwys7OZteuXWi1WqNttFotX3zxBVevXiU+Ph4vL69Kj6vValmzZg16vZ41a9YQHBxs\nlvxCCPNSGsjDy2oDs15uWrZsGZGRkRQVFTF9+nTs7e2Ji4sDIDIykqCgIHr37k1AQAB2dnasW7fO\nsG9ERAT79u3j6tWrtG/fnrfeeouJEycSFRXF2LFj6dy5M35+fixevNicpyBquTsD5+4mg+aEqDqz\nFonQ0FCjHktQVhzuFhsbS2xs+Tlb1q9fX+Exra2t5dkWosruDJy7W4pOnrUsRFVJJ3FRb9idv0r/\nf+xWO4YQtYoUCVFvdP/6B6yKitWOIe6iKSklYMt+NA/Qa1HULCkSon5QFLrt/oEf+ndTO4m4i2LV\ngKCNOjwuXFI7ijBBioSoF9pnnKe4cUMudmqrdhRxj8Ph3el99P6DaIV6pEiIeqHbru/LWhGVDNYU\nNe+HJ7vhn3W27PkewuJIkRB1nqakFM9vfuJI/66VbyxqXH5ra062dYAvZQS2JZIiIeo8xaoBf//n\ny9xwslU7ijAhxcsd/vlPtWOICsiT6US9UNiiidoRxH2syb3MtXYuZN31/Gt57rVlkCIhhFBdrlJI\nSVQQrnetk+deWwa53CSEEMIkKRJCCCFMkiIh6q6ff8b77AW1UwhRq0mREHXXxx8TcPKM2inEA7LN\nuUHDwiK1Y4j/kSIh6qaSEvjwQ/b5dFI7iXhAQ5Zuwyv5mNoxxP9IkRB107Zt0LYtZxxbq51EPKDD\n4d3pkXBY7Rjif6RIiLrpgw9g2jS1U4iH8FNvT9oev4hdnkzTYQmkSIi658QJOHgQnn5a7STiIRQ3\nbkhGmDd9Mk6oHUUgRULURe3awfbt0LSp2knEQ9KNCKLvkeNQWKh2lHpPioSoe5o1g4AAtVOIR3C5\nQxs2a7tBQYHaUeo9KRJCCIv0326eYGOjdox6T+ZuEnXG3Ji55NzIMVp34PABXEe4qhNIiDpAioSo\nM3Ju5JQrCCm6FHXCCFFHyOUmUWd0+jmHhreL1Y4hRJ0iRULUDb/8wswte2lUINM51EkXZA4utUiR\nEHXD6tXsd3dB36qZ2klEdTt/Hrp1k2dgq0SKhKj9Cgth5Ur2dPNUO4kwh3btoE8f+Ne/1E5SL0mR\nELXf2rXg7S3zNNVlM2bA8uVQWqp2knpHioSo3W7fhkWL4M031U4izCk0FBo3hl271E5S75i1SCQl\nJeHl5YWHhwcrVqyocJt58+bh5uaGv78/x44dq3TfmJgY2rVrh6+vL76+vuzcudOcpyAsXaNGsG4d\n9OypdhJhThoNzJwJS5aonaTeMes4iRkzZhAXF4eLiwvh4eFERERgb29veF+n05GcnMz+/ftJSEgg\nOjqarVu3Vrjvs88+S+vWrdFoNMyaNYtZs2aZM7qoLTQa6N1b7RSiJowdCzdvlj0rxMpK7TT1htla\nErm5uQCEhITg4uLCgAEDSEtLM9omLS2N0aNHY2dnR0REBJmZmSb3TU1NNeynKIq5YgshLFWjRmX3\nJqRA1CiztSTS09Px9Pytt4m3tzepqakMGTLEsE6n0zFu3DjDsoODA1lZWZw+ffq++65YsYL//Oc/\nPPXUU0ydOhVra2tznYawQBVNvwEyBUddc+DAASbMnGC07tiPx/DsYtyLzcnWidiY2BpMVr+oOi2H\noijlWgUajea++0RFRfHGG29w8+ZNZs+eTVxcHNHR0eaMKSxMRdNvgEzBUdfoS/QVTrNy77rsTdk1\nlqk+MluRCAwMZPbs2YbljIwMBg4caLSNVqvl6NGjhIeHA3D58mXc3Nyws7MzuW+bNm0AsLGxYdq0\naUydOtVkkYiJiTF8HRYWRlhYWHWcmlBZg9JSgj9PJe2pIBQr6aAnxL0SExNJTEyslmOZrUjY/G+K\n36SkJJydndm1axdv3tNNUavVMmvWLMaPH09CQgJeXl4A2Nramtz34sWLtG3bluLiYuLj4xk8eLDJ\nDHcXCVF3BP90Gs9L50gdHax2FKEWRaHTd8c5qfVQO4lFuveP4oULFz70scx6uWnZsmVERkZSVFTE\n9OnTsbe3Jy4uDoDIyEiCgoLo3bs3AQEB2NnZsW7duvvuCzBnzhwOHz5M48aNCQkJISoqypynICxN\ncTHDdD+w840RaicRKuv5eSotrt/iFHZqR6nTzFokQkNDDT2W7oiMjDRajo2NJTa2/E2nivYF+JcM\nza/fPviAay2bk93DVe0kQk0aDXsm9+Pptz7nq6eHqZ2mTpMLuqL2uHgR/vxnPumrLRsfIeq18z7t\nyXF3IuzH42pHqdPkoUPCot3d3XXAoaO0cnVi69ksRtFD5WTCEuyd1I9np6+F69fhscfUjlMnSUtC\nWLQ73V1dR7hyfOFgDsQ+hf62Xu1YwkJc6uhIursLvPuu2lHqLCkSolaRLq/iXht6B8Abb6gdo86S\nnzghRK12u1HDshlihVnIPQkhRK1W0fQdINN1VBcpEsKi2ebLIyvF/VU0fQfIdB3VRS43Ccu1cSOv\nbdoDMuuveABN8guwKipRO0adIS0JYTHu7u7qkJvHgn9vZ67n43jImAjxAMI/SCDX0ZYsGxe1o9QJ\n0pIQFuNOd1f3we2Y9c23fDMljLSmUiDEg0mc2JegjTqcL19TO0qdIEVCWJxBy7dz9Xet0T0VpHYU\nUQvddGjF9umDmP7VXvjlF7Xj1HpyuUlYFNucGzidvMQ/3xsvU2+Ih5bRrwvX1yYR6tuVd0YOoOR/\nT7OTHk8PToqEsCg3nGz56IMpKA2kQIhHs+R31vRp2JgBDrfJDCl7DIH0eHpwUiSExZECIaqDotGw\nIeZpGaX/iOS7J4Sos6RAPDr5Dgp1yRgIISyaFAmhnsJCGDkSqulZvEJUSYkMtHsQUiSEOgoL4emn\ny3ow9eqldhpRTzQqLgY/P9Dp1I5Sa8iNa1HzCgs52q0Lv5bcZuWgUEpmvwDAgcMHKpyDR4jqUtSw\nIfz1r/CHP0B8PPTvr3YkiydFQtSsvDyIiODXkttsXzWe9g2tDG+l6FJUDCbqjT/8AT7/HEaPhpUr\nYdQotRNZNCkSomZlZ4OLCys72BkVCCFqVEgIJCTAkCFljz6dMkXtRBZLioSoWV27wvvvU1LB/P9C\nmNu9z55wfFKL/9aNPCNFwiQpEsKs7p7Z9W5y/0GooaJnT2zflM0z6sSpFaRICPM5cMAws+u95P6D\nsBTyZLv7kyIhqt8PP8CcOXD8OC37B6qdRoj7qqh14ZF6gpv7visbU2FVv++dyTgJUW0WvTqVFG93\ncntqWae/xuTBT7Dvpwy1YwnxwIqaNuLJw5ll99DWr6/XA/CkSIjqsW8fr6xaQ6n/43ywYSYnYwbR\nflRH9Lf1aicT4oFl93DlqY5tWNL5d5x4bQYXHFqzamAI896co3a0GieXm0T18PVlwXPDsB3rrXYS\nIaqFvrSAWzN68anyBG4HTvFEwvekVtAJo64za0siKSkJLy8vPDw8WLFiRYXbzJs3Dzc3N/z9/Tl2\n7Fil++bl5TF8+HCcnZ0ZMWIE+fn55jwFcbezZ2HNmrIpNe7VqhU3Wjav+UxCmJtGw6mAjnz5p5EV\nPwhLr4fS0prPVUPM2pKYMWMGcXFxuLi4EB4eTkREBPb29ob3dTodycnJ7N+/n4SEBKKjo9m6dWuF\n+z777LO0bt2alStX4uzszIYNG3jttddYtWoV0dHR5jyNGpWYmEhYWJjaMcqcPw9pabBnD+zeXTbo\nqH9/GDKEuSuXGnVtzTmfw89Xfq513VqzD2fj2sNV7RgPpbZmr825K+oJ5bNzH1OzL3CibRuO/64N\nP/3OkYJOPvz1z0vUCVrNzFYkcnNzAQgJCQFgwIABpKWlMWTIEMM2aWlpjB49Gjs7OyIiIliwYIHJ\nfVNTUxkyZAg6nY4FCxbQpEkTJk2axKJFi8x1Cqqo8SJRWFg2XXfTpuXGNEza9Q0NL13mrJc7RwM9\nOWf/GIpGA4vmcODwAUbF/DadQfbabPQXat/9h9r6Cwtqb/banLuinlBxuhQ0b7+C85GzuHx/hj4H\nD2H35dfg3gWef16dsNXIbEUiPT0dT09Pw7K3t7fhF/0dOp2OcePGGZYdHBzIysri9OnTJve9+7ie\nnp7o6tBsjiUlJZSWllJcXGxYp9FosKqsC15padkve70e8vOhVSuwtS2/3YYNsGsXXL0KP/9cdvno\n2jVYuxYiIsqNadg7wpV189cx9s9/oAHgctehZJyDEL/Jt2vJ0VBvjoaW3ZO7uOEEcSNHlttubsxc\nBn3+JZ0uXOJ6i+bcaNmcEtvW9BkyrGxW5M6dyx/8xg1o0ACaN4eGNX8bWdUb14qioNzz0BlNRdf8\n7lp/7/Z1xe3bt9k5eAC5afs5/2EcGkWhgaLQQIFWf/kr1i++WH6nWbNgxQooLoYmTdADvzay4vNe\nfnzn2dGw2bEfj+HZxROfMxdwuJlHftMmNOjSialffglOTvW+H7gQ1e3bIz8w4fVXyq0/cPgANotH\n8ePlm1hfzcP68k1O/TuVG5s3kJap47yDHfDbzyzAlK9T8D95hm+83dk9YnjND/BTzOTGjRtKjx49\nDMsvv/yysnXrVqNtli9frrz33nuGZTc3N0VRFOX69esm9x05cqRy8OBBRVEUZf/+/cqoUaMq/PyO\nHTsqgLzkJS951ftXx44dH/p3udlaEjY2NkBZLyVnZ2d27drFm2++abSNVqtl1qxZjB8/noSEBLy8\nvACw/d+lkor21Wq1rFmzhnfeeYc1a9YQHBxc4eefPHnSXKcmhBD1hlkvNy1btozIyEiKioqYPn06\n9vb2xMXFARAZGUlQUBC9e/cmICAAOzs71q1bd999AaKiohg7diydO3fGz8+PxYsXm/MUhBCiXtMo\nSh29yC+EEOKR1fppOc6dO0ffvn3x8fEhLCyM+Ph4oPYMuispKcHX15ehQ4cCtSf3rVu3eP755+nU\nqRPe3t6kpaXViuwffvghTzzxBP7+/sycOROwzO/5pEmTcHR0pGvXroZ198u5fPlyPDw88Pb2JiVF\nvZ5nFeWePXs2Xl5e+Pn5MXPmTPT637pKW0puqDj7He+++y4NGjTg2rVrhnWWkt1U7o8//hgvLy98\nfHyYM+e36UQeOPdD382wEBcvXlQOHTqkKIqiXL58WenQoYNy8+ZNZfHixcrLL7+sFBQUKNOmTVOW\nLFmictKKvfvuu8qzzz6rDB06VFEUpdbkfu2115QFCxYoer1eKSoqUm7cuGHx2a9evaq4uroq+fn5\nSklJiTJo0CBl586dFpk7KSlJOXjwoNKlSxfDOlM5L126pHTu3Fk5c+aMkpiYqPj6+qoVu8LcX3/9\ntVJSUqKUlJQoU6ZMUT766CNFUSwrt6JUnF1RFOXs2bNKeHi44urqqly9elVRFMvKXlHuI0eOKMHB\nwcrx48cVRVGUX375RVGUh8td61sSTk5O9OjRAwB7e3t8fHxIT09Hp9MxefJkw6C7tLQ0lZOWd/78\nebZv386UKVMMXXtrQ26A3bt3M3/+fJo2bUrDhg2xsbGx+OzNmjVDURRyc3PR6/X8+uuv2NraWmTu\nPn368NhjjxmtM5UzLS2NgQMH4uzsTGhoKIqikJeXp0bsCnM/+eSTNGjQgAYNGhAeHs6+ffsAy8oN\nFWcHmDVrFu+8847ROkvKXlHuHTt2MHnyZDw8PICyMWjwcLlrfZG428mTJ8nIyCAoKKhWDLp79dVX\nWbJkCQ0a/PbPUBtynz9/noKCAqKiotBqtSxevBi9Xm/x2Zs1a8bKlStxdXXFycmJXr16odVqLT73\nHaZypqWlGXoGAnTu3Nliz+HDDz80XFrV6XQWn3vz5s20a9eObt26Ga239Oxff/01P/74IwEBAUyZ\nMoWjR48CD5e7zhSJvLw8xowZw9KlS2nZsqXFD7rbunUrbdq0wdfX1yirpecGKCgo4Pjx44waNYrE\nxEQyMjLYsGGDxWe/fPkyUVFRHD16lOzsbL777ju2bt1q8bnveJCcpgalqumtt97C2tqap59+Gqj4\nfCwp96+//srbb7/NwoULDevuZLb07AUFBVy7do3k5GSGDx/Oyy+/DDxc7jpRJIqKihg1ahTjxo1j\n+PDhAAQGBpKZmQlAZmYmgYGW9YS0b7/9li1bttChQwciIiLYu3cv48aNs/jcAO7u7nTu3JmhQ4fS\nrFkzIiIi2Llzp8Vn1+l0BAcH4+7uTuvWrXn66adJTk62+Nx3mMqp1WoNfykCHDt2zOLOYe3atSQk\nJBh1c7f03FlZWWRnZ9O9e3c6dOjA+fPn8ff359KlSxafPTg4mDFjxtCsWTOGDh3KsWPHKCgoeKjc\ntb5IKIrC5MmT6dKli6G3Cvw26E6v19930J1a3n77bc6dO8fp06f57LPP6NevH5988onF577Dw8OD\ntLQ0SktL2bZtG/3797f47H369GH//v1cu3aNwsJCduzYwYABAyw+9x2mcgYFBZGQkMDZs2dJTEyk\nQYMGWFtbq5z2Nzt37mTJkiVs2bKFpk2bGtZbeu6uXbty6dIlTp8+zenTp2nXrh0HDx7E0dHR4rP3\n7NmTHTt2oCgKaWlpdOzYkaZNmz5c7uq4u66m5ORkRaPRKN27d1d69Oih9OjRQ9mxY4dy8+ZNZdiw\nYUr79u2V4cOHK3l5eWpHNSkxMdHQu6m25P7pp58UrVardO/eXXnttdeU/Pz8WpH9448/VkJCQpSA\ngABlwYIFSklJiUXm/uMf/6i0bdtWady4sdKuXTtlzZo19825bNkypWPHjoqXl5eSlJSkeu5GjRop\n7dq1U1avXq24u7srzs7Ohp/PqKgoi8utKBV/z+/WoUMHQ+8mRbGc7BXlLi4uViIjIxVPT09lxIgR\nik6ne+jcMphOCCGESbX+cpMQQgjzkSIhhBDCJCkSQgghTJIiIYQQwiQpEkIIIUySIiGEEMIkKRJC\n3MeHH35IaGgo3bp1w9fX1yzz87z99tvVfkwhqouMkxDChAsXLjBw4EBSU1Np3ry5YaR227Ztq+0z\nSktLsbGxUXX2UyHuR1oSQphw/Phx2rRpQ/PmzQGws7Ojbdu2uLq68uc//9nwoKvTp08zcOBAunXr\nxpdffglAfn4+/fv3x8/Pj8GDBxumx87OzsbLy4sXX3yRrl27MmXKFPR6Pb6+vowbNw6AiRMn4ufn\nR9euXdmwYYM6Jy/EHeYaKi5EbVdaWqr07dtXcXZ2Vl555RXlxIkTiqIoiqurq/LWW28piqIoEydO\nVNzd3ZVLly4p2dnZhge/FBcXKzdv3lQURVHOnDmjhIWFKYqiKKdPn1Y0Go2yadMmw+e0bNnS8PXe\nvXuVsWPHGpZzc3PNe5JCVEJaEkKYoNFo2Lt3L59//jnNmjWjV69ebN++HYDnnnsOKJtIrWfPnrRp\n0wYXFxeuX7/OrVu3sLKy4m9/+xtPPPEEQ4cOJT09ndzcXABat25tmK34Xl5eXuh0Ol577TWOHDlC\nq1atauZkhTChodoBhLB0gYGBBAYG4uXlxfr16wGwtbUFoHHjxtjY2Bi2bdSoEYWFhaSnp5OcnExC\nQgItWrSgTZs2hiLh5ORk8rOcnJz4/vvv2bBhAy+88ALjx49n6tSpZjw7Ie5PWhJCmHD8+HFOnDgB\nQHFxMWlpafTs2dNoG6WCfh+KovDzzz/zu9/9Dmtraz777DOuXbtm8nMcHBz49ddfAbh48SIA48eP\nZ8aMGRw6dKi6TkeIhyJFQggT8vPzmTBhAj4+PvTq1YsmTZrw/PPPG22j0WiMnux1Z3nEiBHcuHED\nLy8vUlJS8Pb2Ntrmbq+88gp9+vRh7NixHDlyBK1Wi5+fH59++ilz5swx70kKUQnpAiuEEMIkaUkI\nIYQwSYqEEEIIk6RICCGEMEmKhBBCCJOkSAghhDBJioQQQgiTpEgIIYQwSYqEEEIIk/4/pZJAa6Be\n7f0AAAAASUVORK5CYII=\n",
"text": "<matplotlib.figure.Figure at 0x1106b81d0>"
},
{
"html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3051/600/450\" width=\"650\"></iframe>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 33,
"text": "<IPython.core.display.HTML at 0x111397410>"
}
],
"prompt_number": 33
},
{
"cell_type": "markdown",
"metadata": {},
"source": "[simple plot](http://matplotlib.org/examples/pylab_examples/simple_plot.html)"
},
{
"cell_type": "code",
"collapsed": false,
"input": "fig16 = plt.figure()\n\nfrom pylab import *\n\nt = arange(0.0, 2.0, 0.01)\ns = sin(2*pi*t)\nplot(t, s)\n\nxlabel('time (s)')\nylabel('voltage (mV)')\ntitle('About as simple as it gets, folks')\ngrid(True)\nsavefig(\"test.png\")\nshow()\n\nfig_to_plotly(fig16, username, api_key, notebook= True)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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SduYMqw1aWsIAgNat2VyNGzd4R0K0RUnDAgQFASdOALm5mo+lNmNxaVOeUVFA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\nz5CoT4NI3pAhbOVb3ktS//QTMGIEJQz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"text": "<matplotlib.figure.Figure at 0x11048a550>"
},
{
"html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3052/600/450\" width=\"650\"></iframe>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 34,
"text": "<IPython.core.display.HTML at 0x11139b190>"
}
],
"prompt_number": 34
},
{
"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": "fig17 = 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()\nplt.show()\n\nfig_to_plotly(fig17, username, api_key, notebook= True)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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M3SUl2MTHRiFyGqCvj/9zdMSbVKsgPKJEoeNUrVHwnSiU7qe4cwe4cgWYPRsT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\nZEZvgqrMvQAAAABJRU5ErkJggg==\n",
"text": "<matplotlib.figure.Figure at 0x111725fd0>"
},
{
"html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3053/600/450\" width=\"650\"></iframe>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 35,
"text": "<IPython.core.display.HTML at 0x111716910>"
}
],
"prompt_number": 35
},
{
"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": "fig18 = 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(fig18, 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/3054/600/450\" width=\"650\"></iframe>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 36,
"text": "<IPython.core.display.HTML at 0x10c516410>"
}
],
"prompt_number": 36
},
{
"cell_type": "code",
"collapsed": false,
"input": "fig19 = 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(fig19, 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/3055/600/450\" width=\"650\"></iframe>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 37,
"text": "<IPython.core.display.HTML at 0x10cb12590>"
}
],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": "",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 37
}
],
"metadata": {}
}
]
}
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