Sediment Thickness EDA (Stats, Plots, Clustering).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Conduct a brief exploratory analysis on a 18500 point random sample of sediment thicknesses from NOAA's Crustal Sediment Thickness dataset. Further, explore the spatial structure of the data using cluster analysis.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-29T23:23:54.492185",
     "start_time": "2016-11-29T23:23:54.403982"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Load the data into a pandas dataframe:\n",
    "\n",
    "fpath='/Users/paulp/GoogleDrive/projects/ContinentalMarginMapping/data/'\n",
    "hdr=['easting','northing', 'id','thickness','depth', 'X','Y']\n",
    "\n",
    "df = pd.read_csv(fpath+'SedThkness_region03_18500randpts_xyz.csv', header=0, names=hdr)\n",
    "\n",
    "# drop the first x, and y fields (they're redundant):\n",
    "del df['X']\n",
    "del df['Y']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Some Basic Statistics:</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-29T23:23:58.546000",
     "start_time": "2016-11-29T23:23:58.494067"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>easting</th>\n",
       "      <th>northing</th>\n",
       "      <th>id</th>\n",
       "      <th>thickness</th>\n",
       "      <th>depth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.850000e+04</td>\n",
       "      <td>1.850000e+04</td>\n",
       "      <td>18500.000000</td>\n",
       "      <td>18500.000000</td>\n",
       "      <td>18500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-1.086693e+07</td>\n",
       "      <td>2.814588e+06</td>\n",
       "      <td>9250.500000</td>\n",
       "      <td>1738.411782</td>\n",
       "      <td>-3522.316531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.483999e+06</td>\n",
       "      <td>7.437839e+05</td>\n",
       "      <td>5340.634326</td>\n",
       "      <td>2901.823687</td>\n",
       "      <td>1744.307914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.486150e+07</td>\n",
       "      <td>1.668500e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.861584</td>\n",
       "      <td>-8384.296000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-1.333050e+07</td>\n",
       "      <td>2.199250e+06</td>\n",
       "      <td>4625.750000</td>\n",
       "      <td>100.966271</td>\n",
       "      <td>-4788.435250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-1.051950e+07</td>\n",
       "      <td>2.729500e+06</td>\n",
       "      <td>9250.500000</td>\n",
       "      <td>429.657440</td>\n",
       "      <td>-4072.972500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-8.488500e+06</td>\n",
       "      <td>3.339500e+06</td>\n",
       "      <td>13875.250000</td>\n",
       "      <td>1822.712829</td>\n",
       "      <td>-2640.011750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-6.847500e+06</td>\n",
       "      <td>4.502500e+06</td>\n",
       "      <td>18500.000000</td>\n",
       "      <td>16676.121094</td>\n",
       "      <td>-0.000017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            easting      northing            id     thickness         depth\n",
       "count  1.850000e+04  1.850000e+04  18500.000000  18500.000000  18500.000000\n",
       "mean  -1.086693e+07  2.814588e+06   9250.500000   1738.411782  -3522.316531\n",
       "std    2.483999e+06  7.437839e+05   5340.634326   2901.823687   1744.307914\n",
       "min   -1.486150e+07  1.668500e+06      1.000000     10.861584  -8384.296000\n",
       "25%   -1.333050e+07  2.199250e+06   4625.750000    100.966271  -4788.435250\n",
       "50%   -1.051950e+07  2.729500e+06   9250.500000    429.657440  -4072.972500\n",
       "75%   -8.488500e+06  3.339500e+06  13875.250000   1822.712829  -2640.011750\n",
       "max   -6.847500e+06  4.502500e+06  18500.000000  16676.121094     -0.000017"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### quick and simple statistics:\n",
    "\n",
    "#df.head()\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Exploratory Plots:</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-29T23:43:02.843489",
     "start_time": "2016-11-29T23:43:01.503846"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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mgm6jmGZqtK9t+vu2ARuAIyKiFzgUuLVp33XAXOCgSfZJkiR1uapB5uVAAuc2bbsJuKec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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116828208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "### exploratory 2-D plots:\n",
    "\n",
    "#boxes=df.boxplot(column='t', return_type='axes')\n",
    "#sp = df.plot(kind='scatter', x='easting', y='thickness', s=5, c='blue', edgecolors='blue')\n",
    "h = df['thickness'].plot(kind='hist', bins=136, xlim=[0,3000])   ## bins=square root n where n = 18500\n",
    "#kdp = df['thickness'].plot(kind='kde', xlim=([0, 30000]) )\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-29T23:43:48.126827",
     "start_time": "2016-11-29T23:43:47.862474"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## exploratory 3-D plots:\n",
    "\n",
    "\n",
    "#plt.switch_backend('nbagg')\n",
    "%matplotlib notebook\n",
    "\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "ax3d=plt.axes(projection='3d')\n",
    "ax3d.scatter3D(df.easting, df.northing, df.thickness, s=5, cmap='Greens')\n",
    "\n",
    "ax3d.set_xticklabels([-1.6,-1.4,-1.2,-1.0,-0.8,-0.6], fontsize=9)\n",
    "ax3d.set_yticklabels([1.5,2.0,2.5,3.0,3.5,4.0,4.5,5.0], fontsize=9)\n",
    "ax3d.set_zticklabels([0,5000,10000,15000,20000], fontsize=9)\n",
    "\n",
    "ax3d.set_xlabel('Easting (x10^7 m)', fontsize=9)\n",
    "ax3d.set_ylabel('Northing (x10^6 m)', fontsize=9)\n",
    "ax3d.set_zlabel('Thickness (m)', fontsize=9)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Clustering Experiments: k-means</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-29T23:44:52.551833",
     "start_time": "2016-11-29T23:44:50.212225"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x12114ecf8>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gq2PkcBOyLBFlk+jawYgsS7Roqic5UWFgHyurP6knJ6vxE3nn9kY+XN/AxYOsXDzIyoRb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fcICAhk26ZP8fTyZMDggYydNJ6xk8Y/dA4+eONdkj7PRoKE+SVvs2bnWrLzMjn5xRnkopwSaQGt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WfOb5INZ6p4MiVVU3K4qyG4WdmXmAAO4BfrmTtTY7wp4Uip3fqvDpAsCkquptb3NumjcHMHb+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\nNMZc0wBVFBERkShr9EmRMSYX+BQ4FiisFesOHAOcb61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fzrmG/P9j6ojVrmuojn1qi9nJ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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1219659b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "X = np.array([df.thickness, df.depth]).T\n",
    "\n",
    "#plt.scatter(X[:,0], X[:,1], s=5)\n",
    "\n",
    "kmeans = KMeans(n_clusters=2).fit(X)\n",
    "y_means = kmeans.predict(X)\n",
    "centers = kmeans.cluster_centers_\n",
    "\n",
    "plt.scatter(X[:,0], X[:,1], c=y_means, s=5, cmap='viridis')\n",
    "plt.scatter(centers[:,0], centers[:,1], c='black', s=200, alpha=0.5)\n",
    "\n",
    "plt.xlabel('Crust Thickness (m)')\n",
    "plt.ylabel('Elevation (m)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Clustering Experiments: Fisher-Jenks Natural Breaks:</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-29T15:42:34.094978",
     "start_time": "2016-11-29T15:42:26.759268"
    },
    "code_folding": [
     6,
     17
    ],
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trying 2 class breaks...\n",
      "Trying 3 class breaks...\n",
      "Trying 4 class breaks...\n",
      "\n",
      "Number of class breaks: 4\n",
      "Bins: [   737.345154   2863.395996   7017.302246  16676.121094]\n"
     ]
    }
   ],
   "source": [
    "### compute 1-D natural breaks using the Natural Breaks module in PySAL\n",
    "\n",
    "from pysal.esda.mapclassify import Natural_Breaks as nb\n",
    "\n",
    "\n",
    "## FUNCTIONS:\n",
    "def ComputeNaturalBreaks(pc_array, klasses):\n",
    "    '''\n",
    "    partitions data into classes using PySAL pysal.esda.mapclassify, Natural_Breaks algorithm. \n",
    "    input: array of values to classify\n",
    "    returns: upper bounds for classes\n",
    "    '''\n",
    "    breaks=nb(pc_array, k=klasses, initial=20)\n",
    "    return(breaks)\n",
    "\n",
    "\n",
    "\n",
    "def goodness_of_variance_fit(array, classes):\n",
    "    # get the break points\n",
    "    #classes = jenks(array, classes)\n",
    "\n",
    "    # do the actual classification - assign values based on class breaks\n",
    "    classified = np.array([classify(i, classes) for i in array])\n",
    "\n",
    "    # max value of zones\n",
    "    maxz = max(classified)\n",
    "\n",
    "    # nested list of zone indices\n",
    "    zone_indices = [[idx for idx, val in enumerate(classified) if zone + 1 == val] for zone in range(maxz)]\n",
    "\n",
    "    # sum of squared deviations from array mean\n",
    "    sdam = np.sum((array - array.mean()) ** 2)\n",
    "\n",
    "    # sorted polygon stats\n",
    "    array_sort = [np.array([array[index] for index in zone]) for zone in zone_indices]\n",
    "\n",
    "    # sum of squared deviations of class means\n",
    "    sdcm = sum([np.sum((classified - classified.mean()) ** 2) for classified in array_sort])\n",
    "\n",
    "    # goodness of variance fit\n",
    "    gvf = (sdam - sdcm) / sdam\n",
    "\n",
    "    return gvf\n",
    "\n",
    "\n",
    "def classify(value, breaks):\n",
    "    for i in range(1, len(breaks)):\n",
    "        if value < breaks[i]:\n",
    "            return i\n",
    "    return len(breaks) - 1\n",
    "\n",
    "### END FUNCTIONS\n",
    "\n",
    "X = np.array(df.thickness)\n",
    "#X = np.array(df.depth)\n",
    "\n",
    "klasses=2\n",
    "gvf = 0.0\n",
    "gvf_acceptance=0.80\n",
    "    \n",
    "# compute F-J natural breaks and goodness of fit:\n",
    "while(gvf < gvf_acceptance):\n",
    "    print('Trying', klasses, 'class breaks...')\n",
    "    # classify data using PySAL Natural Breaks Lib.\n",
    "    breaks=ComputeNaturalBreaks(X, klasses)\n",
    "\n",
    "    # compute goodness of variance fit\n",
    "    gvf = goodness_of_variance_fit(X, breaks.bins)   #(data, nclasses)\n",
    "    klasses+=1\n",
    "    \n",
    "#print results:\n",
    "print()\n",
    "print('Number of class breaks:', klasses-1)\n",
    "print('Bins:', breaks.bins)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-29T16:09:19.878366",
     "start_time": "2016-11-29T16:09:18.743910"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/paulp/anaconda/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x114f78e48>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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AQDgGAAAADIRjAAAAwEA4BgAAAAyEYwAAAMBAOAYAAAAMhGMAAADAQDgGAAAADIRjAAAA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\nTcxrVSYnDnMc8Ji3IxZOGwCBPsLUZnFai4JjuVx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      "text/plain": [
       "<matplotlib.figure.Figure at 0x114f53668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#h = df['thickness'].plot(kind='hist', bins=136, xlim=[0,3000])   ## bins=square root n where n = 18500\n",
    "#kdp = df['thickness'].plot(kind='kde', xlim=([0, 3000]) )\n",
    "\n",
    "sns.distplot(df.thickness, kde=True, rug=False, bins=136)\n",
    "#plt.plot((745,745),(.00045,.0032))\n",
    "plt.plot((2897,2897), (.00025,.0032))\n",
    "\n",
    "plt.xlabel('Crustal Sediment Thickness (m)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on findings:\n",
    "\n",
    "Number of class breaks (from Fisher-Jenks Natural Breaks): 4\n",
    "Bins: [   745   __2897__   7067  16676] all in meters<br>\n",
    "\n",
    "What about a limiting maximum depth? \n",
    "\n",
    "Number of class breaks (from Fisher-Jenks Natural Breaks): 4\n",
    "Depth Bins: [ -4,481  __-3,015__  -1,220  0]\n",
    "\n",
    "\n",
    "So, starting bounds will be as follows:\n",
    "\n",
    "- bottom slope to define the flat or planar surface: 0.69°  (from Wright et al., 2001 and Paris, Walsh, and Corbett, 2016)\n",
    "- limiting depth down to 3,015 meters\n",
    "- limiting sediment thickness, >= 2,897 meters\n",
    "- geomorphon type(s): slope, ???"
   ]
  }
 ],
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