jGaboardi/Millstream Loop.ipynb

## Millstream Loop.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This is largely built off @jlaura's code that @dfolch and I reworked last summer.\n",
    "\n",
    "## Representation and distance calculation of loop roads in `pysal.Network`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import IPython.display as IPd\n",
    "import networkx as nx\n",
    "import pysal as ps\n",
    "from collections import OrderedDict\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib notebook\n",
    "mpl.rcParams['figure.figsize']=7,7\n",
    "# Local path on user's machine\n",
    "path = '/Users/jgaboardi/Desktop/Loop/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Waverly Hills, Tallahassee FL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Instanitate network of Waverly Hills\n",
    "ntw = ps.Network(path+'Waverly/Waverly.shp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mill Stream Road $ \\Longrightarrow $ self-looping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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qq6aqswuoreYL5s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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Millstream Road\n",
    "Millstream_Rd = IPd.Image(path+'/Millstream.jpg')\n",
    "Millstream_Rd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Instantiate graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 'Actual' Roads and Nodes\n",
    "g = nx.Graph()\n",
    "# 'Simplified' Roads and Nodes\n",
    "g1 = nx.MultiGraph()\n",
    "# Same start and end nodes...\n",
    "g2 = nx.MultiGraph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Draw Graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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": {
      "text/html": [
       "<img 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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Annotation at 0x1164ef590>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Draw Graph of Actual Nodes and Roads\n",
    "for e in ntw.edges:\n",
    "    g.add_edge(*e)\n",
    "nx.draw(g, ntw.node_coords, node_size=5, alpha=0.25, edge_color='r', width=2)\n",
    "\n",
    "#Draw only unique edges in graph\n",
    "for e in ntw.graphedges:\n",
    "    g1.add_edge(*e)\n",
    "    # highlights cases where start and end node are the same\n",
    "    if e[0]==e[1]:\n",
    "        g1.add_node(e[0])\n",
    "for node_id in g1.node:\n",
    "    g1.node[node_id] = ntw.node_coords[node_id]\n",
    "nx.draw(g1, ntw.node_coords, node_size=50, alpha=0.5)\n",
    "\n",
    "# highlights cases where start and end node are the same in the simplfied network\n",
    "for e in ntw.graphedges:\n",
    "    if e[0]==e[1]:\n",
    "        g2.add_node(e[0])\n",
    "for node_id in g2.node:\n",
    "    g2.node[node_id] = ntw.node_coords[node_id]\n",
    "nx.draw(g2, ntw.node_coords, node_size=500, alpha=0.2, node_color='b')\n",
    "\n",
    "# Legend (Ordered Dictionary) from collections\n",
    "LEGEND = OrderedDict()\n",
    "LEGEND['Network Nodes']=g\n",
    "LEGEND['Roads']=g\n",
    "LEGEND['Graph Vertices']=g1\n",
    "LEGEND['Graph Edges']=g1\n",
    "LEGEND['Graph Vertices of loop']=g2\n",
    "plt.legend(LEGEND, \n",
    "       loc='lower right', \n",
    "       fancybox=True, \n",
    "       framealpha=0.5,\n",
    "       scatterpoints=1)\n",
    "\n",
    "# Title\n",
    "plt.title('Waverly Hills\\nTallahassee, Florida', family='Times New Roman', \n",
    "      size=40, color='k', backgroundcolor='w', weight='bold')\n",
    "\n",
    "# North Arrow and 'N' --> Must be changed for different spatial resolutions, etc.\n",
    "plt.arrow(-84.281, 30.49, 0.0, 0.005, width=.0003, head_width=0.001, \n",
    "          head_length=0.0015, fc='k', ec='k',alpha=0.75,)\n",
    "plt.annotate('N', xy=(-84.2815, 30.498), fontstyle='italic', fontsize='xx-large',\n",
    "            fontweight='heavy', alpha=0.75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ZOOM IN on the blue node above to see that the 'graph edge' is not being visually represented as the `.shp` is.\n",
    "\n",
    "#### This edge in the simplified edge represented with the same start & end \n",
    "\n",
    "#### $ \\Longrightarrow $ {(-84.258739, 30.498577): 244}\n",
    "\n",
    "#### but has the actual distance as an attribute \n",
    "\n",
    "#### $ \\Longrightarrow $ 0.00344029294389 decimal degrees\n",
    "\n",
    "#### $ \\Longrightarrow $ 0.237295581922 miles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "**********************************************************\n",
      "Loop edge start and end:      [(244, 244)]\n",
      "Loop edge length in degrees:  0.00344029294389\n",
      "Loop edge length in miles:    0.237295581922\n",
      "Loop node coords:             (-84.258739, 30.498577) 244\n",
      "**********************************************************\n"
     ]
    }
   ],
   "source": [
    "# highlights cases where start and end node are the same in the simplfied network\n",
    "print '\\n**********************************************************'\n",
    "loop = []\n",
    "for e in ntw.graphedges:\n",
    "    if e[0]==e[1]:\n",
    "        loop.append(e)\n",
    "print 'Loop edge start and end:     ',  loop\n",
    "for i in loop:\n",
    "    print 'Loop edge length in degrees: ', ntw.graph_lengths[i]\n",
    "    print 'Loop edge length in miles:   ', ntw.graph_lengths[i] * float(10000/90) * 0.6214 # into miles\n",
    "    \n",
    "for i,j in ntw.nodes.iteritems():\n",
    "    if j == 244:\n",
    "        print 'Loop node coords:            ', i,j\n",
    "print '**********************************************************'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This shows us the 3 edges that use node 244 as a start and 1 as an end.  All these edges are Millstream Road."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(244, 244)\n",
      "(244, 539)\n",
      "(244, 690)\n"
     ]
    }
   ],
   "source": [
    "for e in ntw.graphedges:\n",
    "    if e[0] == 244 or e[1] == 244:\n",
    "        print e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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koh+zxLHDF12Qosa/koApHu2l2faIYJzH9xpoUkZfoWBIqrRRzy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\n4UsFrq+pf6iP7LEf+Wko+Y/Rf8a6aw0LT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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Millstream Road Length\n",
    "Millstream_Rd = IPd.Image(path+'/Mill_loop.jpg')\n",
    "Millstream_Rd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### It is critically important when modeling spatial network-constrained events for statistical analysis and optimization to be able to maintain as accurately as possible the true structure of the road network. Altering the structure of the simplified graph will alter how events are snapped to the network and will, therefore, alter the cost matrix calculations. This will likely leasd to various errors in decisions (Current and Schilling 1987)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}