plotly-pydata-lighting

{
 "metadata": {
  "name": "",
  "signature": "sha256:e887e6ca7fda0fe7f2781998ff822b9a90470ddd5e8cde57abf469809375cd26"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Sharing and Streaming Data from IPython with Plotly\n",
      "\n",
      "To run this NB:\n",
      "\n",
      "```bash\n",
      "pip install plotly\n",
      "pip install numpy\n",
      "pip install matplotlib\n",
      "```\n",
      "\n",
      "###imports"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import time\n",
      "import numpy as np\n",
      "import plotly.plotly as py\n",
      "import plotly.tools as tls\n",
      "from plotly.graph_objs import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We'll use the 'PythonAPI' credentials so you can check this out later..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tls.set_credentials_file(username='pythonapi', api_key='ubpiol2cve', stream_ids=['cegp0qdli7'])\n",
      "tls.get_credentials_file()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "{u'api_key': u'ubpiol2cve',\n",
        " u'stream_ids': [u'cegp0qdli7'],\n",
        " u'username': u'pythonapi'}"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###just for fun"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stream = dict(token=tls.get_credentials_file()['stream_ids'][0], maxpoints=1000)\n",
      "fig = Figure()\n",
      "fig['layout'].update(title='Sierpinski Triangle',\n",
      "                     showlegend=False,\n",
      "                     autosize=False,\n",
      "                     width=600,\n",
      "                     height=600,\n",
      "                     xaxis=XAxis(showline=False, zeroline=False, showgrid=False, ticks='', showticklabels=False),\n",
      "                     yaxis=YAxis(showline=False, zeroline=False, showgrid=False, ticks='', showticklabels=False),\n",
      "                     annotations=[Annotation(text=\"(1) randomly pick point<br>\"\n",
      "                                                  \"(2) randomly pick vertex<br>\"\n",
      "                                                  \"(3) get/plot midpoint<br>\"\n",
      "                                                  \"(4) loop: (2)\",\n",
      "                                              x=1, y=1, xref='paper', yref='paper', xanchor='right', yanchor='top',\n",
      "                                              align='right', showarrow=False)])\n",
      "fig['data'] += [Scatter(x=[], y=[], mode='markers', marker=Marker(symbol='triangle-up', size=2), stream=stream)]\n",
      "py.iplot(fig, filename='sierpinski')  # all credentials stored in .plotly directory"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~PythonAPI/304\" height=\"525\" width=\"100%\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x1056b6810>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "time.sleep(5)\n",
      "def get_midpoint(pt1, pt2):\n",
      "    return [(pt1[0] + pt2[0])/2., (pt1[1] + pt2[1])/2.]\n",
      "\n",
      "verticies = ((0,0),(2,0),(1,2))\n",
      "\n",
      "my_stream = py.Stream(tls.get_credentials_file()['stream_ids'][0])\n",
      "my_stream.open()\n",
      "pts = [[0,0]]\n",
      "for iii in range(1000):\n",
      "    vertex = verticies[int(3*np.random.rand())]\n",
      "    pts = [get_midpoint(pts[-1], vertex)]\n",
      "for iii in range(20):\n",
      "    for jjj in range(100):\n",
      "        vertex = verticies[int(3*np.random.rand())]\n",
      "        pts += [get_midpoint(pts[-1], vertex)]\n",
      "    my_stream.write({'x': [p[0] for p in pts], 'y': [p[1] for p in pts]})\n",
      "    time.sleep(.2)\n",
      "my_stream.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###entry points\n",
      "####matplotlib"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt\n",
      "mpl_fig = plt.figure()\n",
      "ax = mpl_fig.add_subplot(111)\n",
      "num = 800\n",
      "x1 = np.linspace(-0.5,1,num) + (0.5 - np.random.rand(num))\n",
      "y1 = np.linspace(-5,5,num) + (0.5 - np.random.rand(num))\n",
      "x2 = np.linspace(-0.5,1,num) + (0.5 - np.random.rand(num))\n",
      "y2 = np.linspace(5,-5,num) + (0.5 - np.random.rand(num))\n",
      "x3 = np.linspace(-0.5,1,num) + (0.5 - np.random.rand(num))\n",
      "y3 = (0.5 - np.random.rand(num))\n",
      "ax.scatter(x1, y1)\n",
      "ax.scatter(x2, y2)\n",
      "ax.scatter(x3, y3);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAEACAYAAABMEua6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8V8y/Mn9Q1r3dAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1082f0050>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "py.iplot_mpl(mpl_fig, strip_style=True, filename='mpl_converted')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~PythonAPI/305\" height=\"525\" width=\"100%\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x10835acd0>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####embedding"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "http://www.wired.com/2014/03/can-find-1964-silver-quarters/"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tls.embed('rhettallain', 76)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~rhettallain/76\" height=\"525\" width=\"100%\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x108c5d6d0>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####examples"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print py.get_figure('rhettallain', 76)['data'].to_string()  # use to_string(pretty=False) to get an executable string"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Data([\n",
        "    Scatter(\n",
        "        x=['age', '87', '57', '47', '42', '37', '32', '27', '22', '17', '12...],\n",
        "        y=['coin probability', '0.004494382022472', '0.002247191011236', '0...],\n",
        "        name='Nickels and Pennies',\n",
        "        mode='markers'\n",
        "    ),\n",
        "    Scatter(\n",
        "        x=[2, 3.7346938775510203, 5.469387755102041, 7.204081632653061, 8.9...],\n",
        "        y=[0.24413413010545373, 0.22305737175286025, 0.20370810517749433, 0...],\n",
        "        name='Fit',\n",
        "        line=Line(\n",
        "            color='rgb(55, 126, 184)',\n",
        "            width=4\n",
        "        ),\n",
        "        opacity=0.5,\n",
        "        xaxis='x',\n",
        "        yaxis='y'\n",
        "    )\n",
        "])\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print py.get_figure('rhettallain', 76).get_data()  # returns the data list"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[{'y': ['coin probability', '0.004494382022472', '0.002247191011236', '0', '0.002247191011236', '0.029213483146067', '0.026966292134832', '0.089887640449438', '0.112359550561798', '0.175280898876404', '0.112359550561798', '0.161797752808989', '0.28314606741573', '0', '0.006802721088435', '0.006802721088435', '0.006802721088435', '0.054421768707483', '0.020408163265306', '0.040816326530612', '0.115646258503401', '0.108843537414966', '0.142857142857143', '0.136054421768708', '0.360544217687075'], 'x': ['age', '87', '57', '47', '42', '37', '32', '27', '22', '17', '12', '7', '2', '87', '57', '47', '42', '37', '32', '27', '22', '17', '12', '7', '2'], 'name': 'Nickels and Pennies'}, {'y': [0.24413413010545373, 0.22305737175286025, 0.20370810517749433, 0.1859447418205052, 0.16963729799448743, 0.1546664437258282, 0.14092262955577112, 0.12830528491054954, 0.11672208217464972, 0.10608826108205016, 0.0963260084816573, 0.08736388893836336, 0.07913632200314002, 0.07158310232708327, 0.06464895910783731, 0.0582831516446395, 0.05243909804245396, 0.04707403434823076, 0.04214870162501484, 0.037627058674064996, 0.0334760183028218, 0.02966520520886195, 0.02616673370815082, 0.02295500368111604, 0.020006513243374865, 0.017299686770329926, 0.014814717017200928, 0.012533420179203147, 0.01043910283127347, 0.00851643977367294, 0.006751361889599615, 0.005130953194207638, 0.003643356321687533, 0.0022776857588075305, 0.0010239481900013794, -0.00012703062887359923, -0.0011836730032106085, -0.0021537109300039693, -0.003044242676857274, -0.0038617847236668817, -0.004612319447064847, -0.005301338896552858, -0.005933884982659829, -0.006514586371200537, -0.007047692353609679, -0.007537103941197933, -0.007986402410862882, -0.008398875511138349, -0.008777541520345388, -0.009125171332890796], 'x': [2, 3.7346938775510203, 5.469387755102041, 7.204081632653061, 8.938775510204081, 10.673469387755102, 12.408163265306122, 14.142857142857142, 15.877551020408163, 17.612244897959183, 19.346938775510203, 21.081632653061224, 22.816326530612244, 24.551020408163264, 26.285714285714285, 28.020408163265305, 29.755102040816325, 31.489795918367346, 33.224489795918366, 34.95918367346939, 36.69387755102041, 38.42857142857143, 40.16326530612245, 41.89795918367347, 43.63265306122449, 45.36734693877551, 47.10204081632653, 48.83673469387755, 50.57142857142857, 52.30612244897959, 54.04081632653061, 55.775510204081634, 57.51020408163265, 59.244897959183675, 60.97959183673469, 62.714285714285715, 64.44897959183673, 66.18367346938776, 67.91836734693878, 69.65306122448979, 71.38775510204081, 73.12244897959184, 74.85714285714286, 76.59183673469387, 78.3265306122449, 80.06122448979592, 81.79591836734694, 83.53061224489795, 85.26530612244898, 87], 'name': 'Fit'}]\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####simple, declarative syntax"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = tls.get_subplots(2,2)  # returns a Figure object\n",
      "x_data = np.random.exponential(1, (4, 2000)) + np.random.randn(4, 2000)\n",
      "y_data = np.random.exponential(3, (4, 2000)) + np.random.randn(4, 2000)\n",
      "for i, x, y in zip(range(1, 5), x_data, y_data):\n",
      "    fig['data'] += [Histogram2d(x=x, y=y, xaxis='x{}'.format(i), yaxis='y{}'.format(i), showscale=False)]\n",
      "py.iplot(fig, filename='hist2d-subs')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~PythonAPI/306\" height=\"525\" width=\"100%\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x108c40a10>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####GUI editing"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The above graph links to the following page. There, we'lll make some edits in the GUI.\n",
      "\n",
      "https://plot.ly/~PythonAPI/306/"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####sensor streaming"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tls.embed('streaming-demos', 6)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~streaming-demos/6\" height=\"525\" width=\"100%\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x1095eced0>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###more graphs"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tls.embed('plotlyimagetest', 0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~plotlyimagetest/0\" height=\"525\" width=\"100%\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x10835add0>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tls.embed('plotlyimagetest', 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~plotlyimagetest/1\" height=\"525\" width=\"100%\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x108c61690>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tls.embed('plotlyimagetest', 6)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~plotlyimagetest/6\" height=\"525\" width=\"100%\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x108c61450>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tls.embed('plotlyimagetest', 12)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~plotlyimagetest/12\" height=\"525\" width=\"100%\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x108c61850>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###and a double pendulum simulation..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tls.embed('streaming-demos', 4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~streaming-demos/4\" height=\"525\" width=\"100%\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x1095ece50>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "have a look: https://plot.ly"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}