{
 "metadata": {
  "name": "",
  "signature": "sha256:62c9fb2a2551a3c634fb9597cfe3aaa55608c02bdb9235440621cd9098a64944"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.display import HTML\n",
      "\n",
      "with open('creative_commons.txt', 'r') as f:\n",
      "    html = f.read()\n",
      "    \n",
      "name = '2014-01-13-tides_mpld3'\n",
      "\n",
      "html = \"\"\"\n",
      "<small>\n",
      "<p> This post was written as an IPython notebook.  It is available for\n",
      "<a href=\"https://ocefpaf.github.com/python4oceanographers/downloads/\n",
      "notebooks/%s.ipynb\">download</a> or as a static\n",
      "<a href=\"https://nbviewer.ipython.org/url/ocefpaf.github.com/\n",
      "python4oceanographers/downloads/notebooks/%s.ipynb\">html</a>.</p>\n",
      "<p></p>\n",
      "%s \"\"\" % (name, name, html)\n",
      "\n",
      "%matplotlib inline\n",
      "from matplotlib import style\n",
      "style.use('ggplot')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I usually use IPython notebook in my classes, they are a great tool for\n",
      "teaching code and science or even both at the same time.\n",
      "\n",
      "However, when I am using the notebook to teach, I miss interaction with\n",
      "matplotlib figures.  One can drop the `%matplotlib inline` magic to force the\n",
      "figures to pop-up in the screen and use the interaction, but that's confusing, force\n",
      "students to deal with multiple windows, and breaks the linear flow of the\n",
      "notebook.\n",
      "\n",
      "I've looked at some alternative to create interactive figures in HTML documents like:\n",
      "\n",
      "- [Bokeh](http://bokeh.pydata.org/)\n",
      "- [d3po](http://d3po.org/)\n",
      "- [d3py](https://github.com/mikedewar/d3py)\n",
      "- [glue](http://www.glueviz.org/)\n",
      "- [plot.ly](https://plot.ly/)\n",
      "- [vispy](http://vispy.org/)\n",
      "\n",
      "Some of those, like Bokeh, even have some sort of [integration](http://bokeh.pydata.org/quickstart.html#example-ipython-notebooks)\n",
      "with the IPython notebook.  The problem with them is that they all require you to learn a new tool!\n",
      "\n",
      "So [here](http://jakevdp.github.io/blog/2013/12/19/a-d3-viewer-for-matplotlib/) comes\n",
      "[Jake](http://jakevdp.github.io/) to the rescue! (Like he already did\n",
      "[before](http://jakevdp.github.io/blog/2013/12/05/static-interactive-widgets/),\n",
      "and [before](http://jakevdp.github.io/blog/2013/06/01/ipython-notebook-javascript-python-communication/),\n",
      "and [before](http://jakevdp.github.io/blog/2013/05/19/a-javascript-viewer-for-matplotlib-animations/)...)\n",
      "He is developing a [D3 Renderings of Matplotlib Graphics](https://github.com/jakevdp/mpld3).\n",
      "That means we can keep using the familiar matplotlib interface to make nice d3 interactive plots.\n",
      "\n",
      "As an example I will re-write my tidal harmonic analysis using his `mpld3` module so my student will be\n",
      "able to explore the plot, zooming-in and -out at any point.\n",
      "\n",
      "For that I will use sea level data from the\n",
      "[GLOSS Station 194](https://www.bodc.ac.uk/data/information_and_inventories/gloss_handbook/stations/194/).  Here is the data format:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%bash\n",
      "sed -n 71,83p ./data/gloss_hourly.fmt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "          The data records are coded as:\r\n",
        "\r\n",
        "          field          bytes   length             comment\r\n",
        "     -----------------  -------  ------  ------------------------------------\r\n",
        "     station number        1-3     3     exactly 3 digits\r\n",
        "     station version         4     1     letter from A to Z\r\n",
        "     station name          6-9     4     Abbreviated if necessary\r\n",
        "     year                12-15     4\r\n",
        "     month               16-17     2     numerical value\r\n",
        "     day                 18-19     2\r\n",
        "     day record count       20     1     either 1 or 2\r\n",
        "     sea level data      21-80    60     12 sea level values,\r\n",
        "                                         hours 00-11 (1); hours 12-23 (2)\r\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To read that data let's define some handy functions and load the data directly from the zipfile into a pandas Series object."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os\n",
      "import numpy as np\n",
      "import numpy.ma as ma\n",
      "\n",
      "from zipfile import ZipFile\n",
      "from datetime import datetime\n",
      "from pandas import Series, date_range\n",
      "\n",
      "def basename(fname):\n",
      "    return os.path.splitext(os.path.basename(fname))[0]\n",
      "\n",
      "\n",
      "def make_date(data):\n",
      "    date = str(int(data))[:-1]\n",
      "    return datetime.strptime(date, '%Y%m%d')\n",
      "\n",
      "\n",
      "def load_gloss(fname):\n",
      "    data = np.loadtxt(fname, skiprows=1, usecols=range(2, 15))\n",
      "    elev = ma.masked_equal(data[:, 1:].ravel(), 9999)\n",
      "    start = make_date(data[0, 0])\n",
      "    dates = date_range(start, periods=len(elev), freq='H')\n",
      "    return Series(elev, index=dates)\n",
      "\n",
      "series = Series()\n",
      "with ZipFile('./data/h281a.zip', 'r') as ziped:\n",
      "    for fname in sorted(ziped.namelist()):\n",
      "        if not fname.endswith('/'):  # Skip directories.\n",
      "            if False:\n",
      "                with ziped.open(fname) as f:\n",
      "                    print(f.readline())\n",
      "            name = basename(fname)\n",
      "            series = series.append(load_gloss(ziped.open(fname)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we can convert the data from millimeters to meters, print a data description, and produce a quick plot to inspect the data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series /= 1e3\n",
      "print(series.describe())\n",
      "ax = series.plot(figsize=(10, 3))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "count    457699.000000\n",
        "mean          1.689822\n",
        "std           0.404013\n",
        "min           0.140000\n",
        "25%           1.400000\n",
        "50%           1.690000\n",
        "75%           1.980000\n",
        "max           4.880000\n",
        "dtype: float64\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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99dUhfPRbAg6dtK/Rf/HnCfy1Tzl4dTfxTeqNzw64ZJ0f/5aAGYtj8PiKHdifcAWzl8XY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\nPx/W1OJvvvkGo0aNwpgxY7B161YA3E9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       "text": [
        "<matplotlib.figure.Figure at 0x7f2e99a02090>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The data need some more pre-processing to fill-in gaps, and to remove outliers\n",
      "that passed the initial QA/QC.  That is beyond of my scope here so I'll\n",
      "just slice a clean and continuous 2-month period from 2004, remove the mean, and move ahead."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obs = series.ix['2004'] - series.ix['2004'].mean()\n",
      "obs = obs.ix['2004-07':'2004-08']\n",
      "ax = obs.plot(figsize=(8, 3))\n",
      "print(obs.describe())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "count    1488.000000\n",
        "mean       -0.031990\n",
        "std         0.425528\n",
        "min        -1.213540\n",
        "25%        -0.339540\n",
        "50%        -0.009540\n",
        "75%         0.283710\n",
        "max         0.960460\n",
        "dtype: float64\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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5UJ6y5IeirSrxjqgxQg0kHmPuXPLz3mvXIowifPEvX8UEV6MVYKDXbA+aLGt6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\nMBMEQRCEjfj/Fhq9MclXFjMAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f2ec41cbd90>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Looks good to me. The next 3 cells are just a copy-and-paste from my previous post on calling [t_tide via oct2py](http://ocefpaf.github.io/blog/2013/11/04/t_tide/)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from matplotlib.dates import date2num\n",
      "\n",
      "dates = date_range(datetime.now(), periods=72, freq='H')\n",
      "tim = date2num(dates.to_pydatetime()) + 366  # Matlab Argh!!!\n",
      "elev = obs.values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext oct2py.ipython"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%octave -i elev -i tim -o tidestruc -o pout -o yout\n",
      "\n",
      "pkg load all\n",
      "\n",
      "addpath(genpath('./t_tide_v1.3beta'))\n",
      "\n",
      "[tidestruc, pout] = t_tide(elev, 'interval', 1, 'latitude', -25, 'starttime', [1998, 1, 1, 0]);\n",
      "\n",
      "yout = t_predic(tim, tidestruc);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "   number of standard constituents used: 35\n",
        "   Points used: 1487 of 1488\n",
        "   percent of var residual after lsqfit/var original: 27.26 %\n",
        "   Greenwich phase computed with nodal corrections applied to amplitude\n",
        " and phase relative to center time\n",
        "   Using nonlinear bootstrapped error estimates\n",
        "   Generating prediction with nodal corrections, SNR is 2.000000\n",
        "warning: isstr is obsolete and will be removed from a future version of Octave, please use ischar instead\n",
        "   percent of var residual after synthesis/var original: 33.93 %\n",
        "-----------------------------------\n",
        "date: 02-May-2014\n",
        "nobs = 1488,  ngood = 1487,  record length (days) = 62.00\n",
        "start time: 01-Jan-1998\n",
        "rayleigh criterion = 1.0\n",
        "Greenwich phase computed with nodal corrections applied to amplitude \\n and phase relative to center time\n",
        "\n",
        "x0= -0.0365, x trend= 0\n",
        "\n",
        "var(x)= 0.1812   var(xp)= 0.11915   var(xres)= 0.061482\n",
        "percent var predicted/var original= 65.8 %\n",
        "\n",
        "     tidal amplitude and phase with 95% CI estimates\n",
        "\n",
        "tide   freq       amp     amp_err    pha    pha_err     snr\n",
        " MM   0.0015122    0.1571    0.183    64.59    77.12     0.74\n",
        " MSF  0.0028219    0.0256    0.164   231.18   210.70    0.024\n",
        " ALP1 0.0343966    0.0019    0.006    16.85   171.73    0.091\n",
        "*2Q1  0.0357064    0.0109    0.007   235.60    44.29      2.2\n",
        "*Q1   0.0372185    0.0473    0.008   212.37     9.84       36\n",
        "*O1   0.0387307    0.1614    0.007   203.26     2.58  4.8e+02\n",
        " NO1  0.0402686    0.0029    0.005   240.58    99.45     0.35\n",
        "*K1   0.0417807    0.1032    0.008    29.44     3.80  1.9e+02\n",
        " J1   0.0432929    0.0021    0.006   295.19   161.03     0.14\n",
        " OO1  0.0448308    0.0051    0.008   227.13   101.19     0.44\n",
        " UPS1 0.0463430    0.0086    0.010   230.15    78.72     0.73\n",
        " EPS2 0.0761773    0.0050    0.016   227.96   161.74      0.1\n",
        "*MU2  0.0776895    0.0356    0.019    65.92    34.86      3.5\n",
        "*N2   0.0789992    0.0650    0.019   190.48    18.93       11\n",
        "*M2   0.0805114    0.3383    0.021    83.78     3.94  2.7e+02\n",
        "*L2   0.0820236    0.0488    0.026    44.37    29.43      3.5\n",
        "*S2   0.0833333    0.2356    0.023   210.99     5.78    1e+02\n",
        " ETA2 0.0850736    0.0336    0.031   143.32    60.92      1.2\n",
        "*MO3  0.1192421    0.0596    0.011   152.47    10.36       30\n",
        "*M3   0.1207671    0.0703    0.008    42.64     6.71       70\n",
        "*MK3  0.1222921    0.0397    0.009    16.25    14.31       21\n",
        "*SK3  0.1251141    0.0489    0.010   298.89    11.84       25\n",
        "*MN4  0.1595106    0.0241    0.006   166.80    13.51       18\n",
        "*M4   0.1610228    0.0526    0.005   182.36     5.57       97\n",
        " SN4  0.1623326    0.0054    0.005   125.25    61.09      1.1\n",
        "*MS4  0.1638447    0.0270    0.006    49.39    12.76       20\n",
        " S4   0.1666667    0.0029    0.005   325.77   113.83     0.28\n",
        " 2MK5 0.2028035    0.0005    0.003    17.45   225.26     0.03\n",
        " 2SK5 0.2084474    0.0031    0.005   312.31    93.55     0.44\n",
        " 2MN6 0.2400221    0.0004    0.002   319.96   174.12    0.061\n",
        " M6   0.2415342    0.0012    0.002   246.25   100.47     0.44\n",
        "*2MS6 0.2443561    0.0054    0.002   333.73    24.77      7.7\n",
        " 2SM6 0.2471781    0.0029    0.002   100.65    51.16      1.6\n",
        " 3MK7 0.2833149    0.0025    0.003   181.48    67.60     0.81\n",
        " M8   0.3220456    0.0023    0.002   216.09    47.06      1.9"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now I'll use mpld3 to plot the observed data, tide prediction from the analysis, and the residue.\n",
      "The main difference from my previous post is that, while the original plot was just a static `png`\n",
      "figure, this one allow us to interact with the graph."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import mpld3\n",
      "mpld3.enable_notebook()\n",
      "\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "fig, (ax0, ax1, ax2) = plt.subplots(nrows=3, sharey=True, sharex=True, figsize=(8, 4))\n",
      "\n",
      "ax0.plot(obs.index, obs)\n",
      "ax1.plot(obs.index, pout.squeeze(), alpha=0.5)\n",
      "ax2.plot(obs.index, obs-pout.squeeze(), alpha=0.5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f2e8336b110>]"
       ]
      },
      {
       "html": [
        "\n",
        "\n",
        "<style>\n",
        "\n",
        "</style>\n",
        "\n",
        "<div id=\"fig_el255471398380973438884809636830\"></div>\n",
        "<script>\n",
        "function mpld3_load_lib(url, callback){\n",
        "  var s = document.createElement('script');\n",
        "  s.src = url;\n",
        "  s.async = true;\n",
        "  s.onreadystatechange = s.onload = callback;\n",
        "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
        "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
        "}\n",
        "\n",
        "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
        "   // already loaded: just create the figure\n",
        "   !function(mpld3){\n",
        "       \n",
        "       mpld3.draw_figure(\"fig_el255471398380973438884809636830\", {\"data\": {\"data01\": [[731763.0, -0.43153950364298943, -0.37776091867930345, -0.05377858496368598], [731763.0416666666, -0.5445395036429894, -0.47130222181729764, -0.07323728182569178], [731763.0833333334, -0.5565395036429894, -0.43420518120151974, -0.12233432244146969], [731763.125, -0.3765395036429895, -0.23325407714288915, -0.14328542650010034], [731763.1666666666, -0.08553950364298935, 0.08659445522435659, -0.17213395886734595], [731763.2083333334, 0.18246049635701067, 0.3943588893515315, -0.21189839299452085], [731763.25, 0.3204604963570108, 0.547677129598979, -0.22721663324196817], [731763.2916666666, 0.20146049635701058, 0.485264890887308, -0.28380439453029743], [731763.3333333334, 0.014460496357010522, 0.24329275450478793, -0.2288322581477774], [731763.375, -0.30853950364298943, -0.09452932930799703, -0.21401017433499242], [731763.4166666666, -0.5775395036429893, -0.43455256966392036, 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        "   }(mpld3);\n",
        "}else if(typeof define === \"function\" && define.amd){\n",
        "   // require.js is available: use it to load d3/mpld3\n",
        "   require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
        "   require([\"d3\"], function(d3){\n",
        "      window.d3 = d3;\n",
        "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3git.js\", function(){\n",
        "         \n",
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[2004, 7, 31, 23, 0, 0, 0.0]], \"xlim\": [731763.0, 731824.95833333337], \"axesbgalpha\": null, \"ydomain\": [-1.5, 1.0], \"texts\": [], \"collections\": [], \"bbox\": [0.125, 0.67205882352941182, 0.77500000000000002, 0.2279411764705882], \"images\": [], \"paths\": [], \"lines\": [{\"data\": \"data01\", \"xindex\": 0, \"id\": \"el25547139837746697616\", \"zorder\": 2, \"color\": \"#4C72B0\", \"yindex\": 1, \"dasharray\": \"10,0\", \"alpha\": 1, \"linewidth\": 1.75, \"coordinates\": \"data\"}], \"zoomable\": true, \"id\": \"el25547139838097346192\", \"markers\": []}, {\"ylim\": [-1.5, 1.0], \"axesbg\": \"#EAEAF2\", \"yscale\": \"linear\", \"sharex\": [\"el25547139838097346192\", \"el25547139837746824272\"], \"sharey\": [\"el25547139838097346192\", \"el25547139837746824272\"], \"axes\": [{\"position\": \"bottom\", \"tickvalues\": null, \"grid\": {\"alpha\": 1.0, \"color\": \"#FFFFFF\", \"dasharray\": \"10,0\", \"gridOn\": true}, \"tickformat\": \"\", \"fontsize\": 10.0, \"scale\": \"linear\", \"nticks\": 9}, {\"position\": \"left\", \"tickvalues\": null, \"grid\": {\"alpha\": 1.0, \"color\": \"#FFFFFF\", \"dasharray\": \"10,0\", \"gridOn\": true}, \"tickformat\": null, \"fontsize\": 10.0, \"scale\": \"linear\", \"nticks\": 6}], \"xscale\": \"date\", \"xdomain\": [[2004, 6, 1, 0, 0, 0, 0.0], [2004, 7, 31, 23, 0, 0, 0.0]], \"xlim\": [731763.0, 731824.95833333337], \"axesbgalpha\": null, \"ydomain\": [-1.5, 1.0], \"texts\": [], \"collections\": [], \"bbox\": [0.125, 0.39852941176470591, 0.77500000000000002, 0.2279411764705882], \"images\": [], \"paths\": [], \"lines\": [{\"data\": \"data01\", \"xindex\": 0, \"id\": \"el25547139837746959184\", \"zorder\": 2, \"color\": \"#4C72B0\", \"yindex\": 2, \"dasharray\": \"10,0\", \"alpha\": 0.5, \"linewidth\": 1.75, \"coordinates\": \"data\"}], \"zoomable\": true, \"id\": \"el25547139837747071376\", \"markers\": []}, {\"ylim\": [-1.5, 1.0], \"axesbg\": \"#EAEAF2\", \"yscale\": \"linear\", \"sharex\": 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\"el25547139837746622736\", \"zorder\": 2, \"color\": \"#4C72B0\", \"yindex\": 3, \"dasharray\": \"10,0\", \"alpha\": 0.5, \"linewidth\": 1.75, \"coordinates\": \"data\"}], \"zoomable\": true, \"id\": \"el25547139837746824272\", \"markers\": []}], \"plugins\": [{\"type\": \"reset\"}, {\"type\": \"zoom\", \"button\": true, \"enabled\": false}, {\"type\": \"boxzoom\", \"button\": true, \"enabled\": false}], \"width\": 640.0, \"height\": 320.0});\n",
        "      });\n",
        "    });\n",
        "}else{\n",
        "    // require.js not available: dynamically load d3 & mpld3\n",
        "    mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n",
        "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3git.js\", function(){\n",
        "                 \n",
        "                 mpld3.draw_figure(\"fig_el255471398380973438884809636830\", {\"data\": {\"data01\": [[731763.0, -0.43153950364298943, -0.37776091867930345, -0.05377858496368598], [731763.0416666666, -0.5445395036429894, -0.47130222181729764, -0.07323728182569178], [731763.0833333334, -0.5565395036429894, -0.43420518120151974, -0.12233432244146969], [731763.125, -0.3765395036429895, -0.23325407714288915, -0.14328542650010034], [731763.1666666666, -0.08553950364298935, 0.08659445522435659, -0.17213395886734595], [731763.2083333334, 0.18246049635701067, 0.3943588893515315, -0.21189839299452085], [731763.25, 0.3204604963570108, 0.547677129598979, -0.22721663324196817], [731763.2916666666, 0.20146049635701058, 0.485264890887308, -0.28380439453029743], [731763.3333333334, 0.014460496357010522, 0.24329275450478793, -0.2288322581477774], [731763.375, -0.30853950364298943, -0.09452932930799703, -0.21401017433499242], [731763.4166666666, -0.5775395036429893, -0.43455256966392036, 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+/8qqLofBxo4DWWSLKn75l81Vf0fe/U8v78TnfvZP7O8sMHPiVxedjF1ZYQvw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\nIlGWd5cTQvBVowNyiQjhKBdkF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       "text": [
        "<matplotlib.figure.Figure at 0x7f2e981e4590>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now I can tell my students to zoom-in at the observed high water level event that occurred around July 14$^{\\text{th}}$ and start quizzing them about what is happening there."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "HTML(html)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "<small>\n",
        "<p> This post was written as an IPython notebook.  It is available for\n",
        "<a href=\"https://ocefpaf.github.com/python4oceanographers/downloads/\n",
        "notebooks/2014-01-13-tides_mpld3.ipynb\">download</a> or as a static\n",
        "<a href=\"https://nbviewer.ipython.org/url/ocefpaf.github.com/\n",
        "python4oceanographers/downloads/notebooks/2014-01-13-tides_mpld3.ipynb\">html</a>.</p>\n",
        "<p></p>\n",
        "<a rel=\"license\" href=\"https://creativecommons.org/licenses/by-sa/4.0/\"><img\n",
        "alt=\"Creative Commons License\" style=\"border-width:0\"\n",
        "src=\"https://i.creativecommons.org/l/by-sa/4.0/88x31.png\" /></a><br /><span\n",
        "xmlns:dct=\"https://purl.org/dc/terms/\"\n",
        "property=\"dct:title\">python4oceanographers</span> by <a\n",
        "xmlns:cc=\"https://creativecommons.org/ns#\" href=\"https://ocefpaf.github.io/\"\n",
        "property=\"cc:attributionName\" rel=\"cc:attributionURL\">Filipe Fernandes</a> is\n",
        "licensed under a <a rel=\"license\"\n",
        "href=\"https://creativecommons.org/licenses/by-sa/4.0/\">Creative Commons\n",
        "Attribution-ShareAlike 4.0 International License</a>.<br />Based on a work at <a\n",
        "xmlns:dct=\"https://purl.org/dc/terms/\" href=\"https://ocefpaf.github.io/\"\n",
        "rel=\"dct:source\">https://ocefpaf.github.io/</a>.\n",
        " "
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "<IPython.core.display.HTML at 0x7f41c828a910>"
       ]
      }
     ],
     "prompt_number": 10
    }
   ],
   "metadata": {}
  }
 ]
}