diff --git a/.gitignore b/.gitignore
index c6b06b2e7712d68327292afe000be8f8736c8d07..ead4351e3696c8fcfc89ca47cdab2509c05fa565 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,4 +3,4 @@
 node_modules
 *.pyc
 coverage.xml
-.ipynb_checkpoints
+.ipynb_checkpoints*
diff --git a/docs/algorithms/.ipynb_checkpoints/AdjustedPhase1GenerationTool-checkpoint.ipynb b/docs/algorithms/.ipynb_checkpoints/AdjustedPhase1GenerationTool-checkpoint.ipynb
deleted file mode 100644
index 4a3820d7938cd32b89f8210e0a0ef759e430b49f..0000000000000000000000000000000000000000
--- a/docs/algorithms/.ipynb_checkpoints/AdjustedPhase1GenerationTool-checkpoint.ipynb
+++ /dev/null
@@ -1,6365 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Adjusted Phase 1  Generation Tool\n",
-    "Below are packages imported during development, most of which are used below.\n",
-    "Read through the worksheet, and enter any values in the cells headed 'Enter' below.  Then click 'Cell' in the menu above, and 'Run All.'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#%matplotlib inline\n",
-    "%matplotlib notebook\n",
-    "\n",
-    "import matplotlib as mp\n",
-    "\n",
-    "import pandas as pd\n",
-    "\n",
-    "import numpy as np\n",
-    "\n",
-    "import scipy as sp\n",
-    "\n",
-    "import scipy.linalg as spl\n",
-    "\n",
-    "import glob\n",
-    "\n",
-    "import json\n",
-    "\n",
-    "import urllib2\n",
-    "\n",
-    "from datetime import datetime \n",
-    "\n",
-    "import dateutil.parser as dp\n",
-    "\n",
-    "import matplotlib.pyplot as pl\n",
-    "\n",
-    "import re\n",
-    "\n",
-    "import obspy\n",
-    "\n",
-    "from obspy.core import UTCDateTime\n",
-    "\n",
-    "import geomagio\n",
-    "\n",
-    "from geomagio.edge import EdgeFactory\n",
-    "\n",
-    "#from geomagio.Algorithm import DeltaFAlgorithm"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Example url for baseline web service"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "https://geomag.usgs.gov/baselines/observation.json.php?observatory=BOU&starttime=2016-01-01&endtime=2016-10-07"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Enter the Observatory in the cell below as a string, similar to the following example:\n",
-    "\n",
-    "```python\n",
-    "obs_code = 'BOU'\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "obs_code = 'BOU'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Enter the start date and end date for which you'd like to request baseline measurements\n",
-    "If you choose a year's worth, that will result in the mean delta F for adjusted data being closest to 0, but will amplify the daily variation.  If you choose a shorter time period closer to the present, the daily variation will remain small, but the mean delta F will be biased by seasonal variation.  If the baseline service is called without dates, it will return the last one month's baseline measurements.\n",
-    "\n",
-    "NOTE: the datetimes used to index sets of Absolutes in the database are not necessarily associated with the datetimes of the actual measurements made, although they seem to be reliably on the same day at least. A github ticket was submitted to change this web service behavior, but for now, assume the start_date and end_date below are somewhat fuzzy, or just limit the search to whole days."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "start_date = '2015-01-01T00:00:00Z'\n",
-    "\n",
-    "# convert to unix epoch time (seconds since 1/1/1970)\n",
-    "start_epoch = (dp.parse(start_date, ignoretz=True) - datetime.utcfromtimestamp(0)).total_seconds()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "end_date = '2016-01-01T00:00:00Z'\n",
-    "\n",
-    "# convert to unix epoch time (seconds since 1/1/1970)\n",
-    "end_epoch = (dp.parse(end_date, ignoretz=True) - datetime.utcfromtimestamp(0)).total_seconds()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# pull baseline info from USGS Geomagnetism Program's web service\n",
-    "baseline_url = 'https://geomag.usgs.gov/baselines/observation.json.php'\n",
-    "full_url = baseline_url + '?observatory=' + obs_code + '&starttime=' + start_date + '&endtime=' + end_date\n",
-    "response = urllib2.urlopen(full_url)\n",
-    "parsed_response = json.load(response)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# extract only complete and validated baseline sets; also,\n",
-    "# filter on times to partially address issues with database time stamps\n",
-    "h_abs = []\n",
-    "d_abs = []\n",
-    "z_abs = []\n",
-    "h_ord = []\n",
-    "d_ord = []\n",
-    "z_ord = []\n",
-    "h_t = []\n",
-    "d_t = []\n",
-    "z_t = []\n",
-    "\n",
-    "for datum in parsed_response['data']:\n",
-    "    for reading in datum['readings']:\n",
-    "        if (reading['H']['absolute'] is not None\n",
-    "           and reading['D']['absolute'] is not None\n",
-    "           and reading['Z']['absolute'] is not None\n",
-    "           and reading['H']['baseline'] is not None\n",
-    "           and reading['D']['baseline'] is not None\n",
-    "           and reading['Z']['baseline'] is not None\n",
-    "           and reading['H']['valid'] is True\n",
-    "           and reading['D']['valid'] is True\n",
-    "           and reading['Z']['valid'] is True\n",
-    "           and reading['H']['end'] >= start_epoch\n",
-    "           and reading['D']['end'] >= start_epoch\n",
-    "           and reading['Z']['end'] >= start_epoch\n",
-    "           and reading['H']['end'] <= end_epoch\n",
-    "           and reading['D']['end'] <= end_epoch\n",
-    "           and reading['Z']['end'] <= end_epoch):\n",
-    "            h_abs.append(reading['H']['absolute'])\n",
-    "            d_abs.append(reading['D']['absolute'])\n",
-    "            z_abs.append(reading['Z']['absolute'])\n",
-    "            h_ord.append(reading['H']['absolute'] - reading['H']['baseline'])\n",
-    "            d_ord.append(reading['D']['absolute'] - reading['D']['baseline'])\n",
-    "            z_ord.append(reading['Z']['absolute'] - reading['Z']['baseline'])\n",
-    "            h_t.append(reading['H']['end'])\n",
-    "            d_t.append(reading['D']['end'])\n",
-    "            z_t.append(reading['Z']['end'])\n",
-    "\n",
-    "# perhaps a separate pier_correction  should be associated with each datum...\n",
-    "# revisit and fix if necessary -EJR\n",
-    "last_datum = parsed_response['data'][-1]\n",
-    "pier_correction = float(last_datum['pier']['correction'])\n",
-    "\n",
-    "# convert unix times to Python datetimes\n",
-    "h_dt = [datetime.utcfromtimestamp(ut) for ut in h_t]\n",
-    "d_dt = [datetime.utcfromtimestamp(ut) for ut in d_t]\n",
-    "z_dt = [datetime.utcfromtimestamp(ut) for ut in z_t]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-22.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "print pier_correction"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plot of H absolutes, ordinates, baselines\n",
-    "Absolutes represent the field magnitude as measured by the overhauser (with the pier correction applied), and field direction measured by the theodolite.  Ordinates represent the variometer's reading of the field corresponding to the nearest times absolute measurements were taken. Baselines, or the difference between absolutes and ordinates, are presented in the bottom plot. All vertical axis units are nanoteslas."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\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",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\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 backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\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 * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\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'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\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 * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\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",
-       "    var width = fig.canvas.width/mpl.ratio\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 + '\" width=\"' + width + '\">');\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 width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\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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wyDjcj+jqBAIUSr2npwI83MaSXtHp28gzNzFY0QNc9GM7Aq5uK3m/en72Kx+pp+a2P2c3ARD+eHDdUuKFCMQIglXy1v5c3jJ7S5DRv9LXi/eTgXf7yVpo7zFY6T4hoxG7iHcunWrbdq0iZLxsm3bNueTaCI9OU28CXwz9pTds/Bl3yA8beHL9T40NIMyi3ZU8/5smCGjZvVfXAXp4bolRYoRCBGnHvLC4MuSDvL5fn5Rmc8X3HawiheVGSVpbe5n5fWUt0XFabmv84vK7FJwn0vVfVGZe0qef65qtKjM9u3brb293VpbWykNUJYsWWKbN28uewJNbIGEJr/waLbFVu5e8JLvRfM9C+ofCJnXGI8Fq9fbhNlttmD1+nofStkGqGrenw3V+93EDXUo4bolRYoRCBGn38vbO/B4SfsUlL4F97lWXo/gCfK2nZibK3nvlbdv4O/lzQc8TNLk3M/dN3efAfKGfk7Kff9L8noQC7edOE7ethMX5H7mONVw24l8GFy3bp11dnbWvYeLUnnZuHGjLVu2zF588UXbvn176Ak00dbueq/qhsSkdVhdM/bWJuF7tz5TVNffu/WZ+h5QmUWsFqxe73vecwmzaX1vV6TJG+pQoAEWfiMQIk7d5t/lyjcK7pPfmH69vBA3VV5oLPQJeYvRdOTuN0s7ehDzjtCOjelfkjTa53i+IOkFefMWn1WNNqbfunVrVxhEY+jo6LDW1lbbsmVL6P2quTCq7MCabG+zJjZl3iobnns/DR9d//DVMNsFpEzi5xAXZXq+qmkIa6gho2aRG+roYW9gGd8aikAIuPMNhJs2bbLW1lbr7OQE3yg6OzuttbXVNm3aFHq/hru4QWwquRCMY6hspRekaX6vZ/kie9xdi31f15/e9Wz9DqpMz1c1jQMN2bDg2FBXTQ/7zNa19qOpCxt2D9aGkbG9BwsRCAF3oYGwXHhAdrjWabNtnI7KTJm3qmuY3LA69vZVc0Ga1qF+WR/GOu6uZ/0D4d21C4QVBeYyPV/jH23ret0PGDM90uue9TqLIv/aL1i93jkIl84nPfWax4oed+o1jyX9a8QqVfNnmxiBEHDXdIFw5cqVJsmeeSa+OS0jR460k08+ObafXwnXOh13d2UXc1nu0UA0adk6o9qemTQuBpLGY3JR+Pmvdj5eufNIVeEroOer8GcOGz3Nxj/S5v4zLT0NJHEr/D2DSmkPe+l80v/439mZbHR0/RuXuvmzCUvTtQCBEHBHIIxBJYFQkt1xxx0xHVGEQBgw3GtcyHCvZrkYgmfS4yt93yPn/vnpRI8jypBPv4uUNA4ZTWp7jkqH+/o9Zsq8VTYsH6ZyAa2Si2KXoBfH8Mygn7lg9Xqn16ghh4z68GusKBfsghoH/Mr5U5I9f0Th2ghRrjEkyucuTcHKVdp6ygmEgDsCYQyyHAijtu6nddgd4vOb+5/3fY8kXe+uw5uDLlIqvZB3uVCr9GIuie05/AJcuWMPew2D3gcLVq+362cvr3ylTp+e0apDfMdL3gIzBT2EQT8zfzzlLmyTbFioZ0gIem+WlutnL+96zITZbc6B8OqHlib+O7mI0mt/+QNLfH+3Kx5YEqnhNEoATcvQ1DQ2jBAIAXeJBMKk/4jde++99uEPf9gGDBhggwYNss985jPW1uYNAcoHwj//+c927LHHWu/eve2www6zRx55pOvx69evt9NOO8323HNP69Onjx144IF2ww03dH1/0aJFdsIJJ1ifPn1s0KBB9q1vfcvefPPNru+XBsKhQ4fab3/726JjPOKII2zs2LFd31fBqrVDhw7tut+dd95pH/zgB6137942fPhwGzdunL3zzjtd3x87dqwNGTLEevXqZfvuu6+NGjXK9zWJUqdRWvfTvL9cpbLYMpuksFb/JHvXgi42Cy9Iy12kTJm3yg4YM73r9nIt2i4XatW0kse9QmdYgAs69rDXcNJc/97iyY+vjHRcP7x9oe/PyfeMVjJvrZunJu5YWGbc7l1zCP1+v26vUciw3aQuhMsF+bgF9V6HvVej9BCm9Xwb9HtPfnxlt78TE+eu8L3vVbOWOjecugbQtA1NTeOICwIh4C72QBh0cbT5nW325qYttvmdbVU/R6nbbrvNbr/9dlu6dKk988wzdtJJJ9n73/9+27ZtW1cg3H///e22226z1tZW++Y3v2m77bZb1zYb55xzjn3gAx+wJ5980lauXGkPPvig3X333WZmtnHjRttvv/3sc5/7nC1evNhmzZplw4cPt5EjR3Y9f9RA+Oqrr5oku/HGG23t2rX26quvmpnZX//6V2tpabGbbrrJli9fbg888IANGzbMxo0bZ2Zmf/nLX6ylpcVmzJhhq1atsieeeMImTJjg+5pErVPX1v2rZi3NVGtvOVPmrSr6Peo95CWtzpo8v1udJ90a7BKeXC5SXFc+dbnwrzYcxD1kNCzABV2IBh3T3LZ1NZlzHDQndehor2Gp9G9I6YIkThfCZVYZLWwYCBoWGVYHURsWXOV7gIJ6w9PweStXF6XBpbTUYppBnI14QY2efj3IQe/loGH2fg2nLueANG7t4nfuGz7aO3/UK+wTCAF3sQbCoIuj1vYOW7TmDVu45g1btOYNe/2tt51/ZiVBMh+4Fi9e3BUIf/nLX3Z9/5133rH999/ffvWrX5mZ2UknnWRnnHGG78+aMGGCDRw40N56662u26ZPn2477bSTvfLKK2YWPRCa+Q8Z/fjHP26XXnpp0W2TJ0+2fffd18zMLr/8cjv44IPL7i1oFt8w4KDhg5ffv6Smz5OEtCyWkhWX3fd8V49FLS+CoyjXSl7L3huXcFltK3mlPe6uQ8fCAlxYT0jQaxjUIzJp7srAYygNeJdMey7wgnvB6vVl5605LbpTZh9Csx0NA1Eu3gvVekuVckFq6OhkG96C3tuT5q4s23i4YPV6++aN83wff0WVfytqMW8tLFC6zJ0sHXVQOjQ06D3l15Pucg4I+rtb7WtZrcK9ZoeNdh92HRcCIeAu1kAY9Afk5sdftIW5QJgvLgHv9bfedgqSS5cutS9/+cs2fPhw22233axfv34myaZPn94VCB999NGix5xyyin2jW98w8zMZsyYYX379rUjjjjCLrzwQpszZ07X/c4//3z76Ec/WvTYjo6Oop9Zq0CYH7Lar1+/rtKnTx+TZBs3brTVq1fbkCFDbP/997dvfvObNnXq1KLhpIXiCoRpbKmsVCUXt80uDfsKluvNrtXQKpehXNV+HiqZkxvl9wt7j4d9L+g5ojaiuC5MMnT0NLvsvuedhymW7UEt00NYKM6td/ze2363uQ61THIhlmrni8fxt6IWDT6FgXLo6Gl2zs3zux1TadDx+z3CRh1E6fl3eZ1rNVS71sJWoa3HfEICIeAu8R7C4WOm2aznX+kWCN/YuDm092/zO9u6PSYoSB5yyCH2yU9+0mbOnGmtra327LPPdgWusEBY2Cv46quv2o033minn3669enTxy644AIzMzvvvPPshBNOKHpsPhDOnu21NpcGwuHDh9sVV1xR9JhDDz20bCDs06eP/epXv7Jly5Z1K9u2eb93Z2en3XXXXTZq1CjbZ5997Nhjj/XtMYxzoaC0zWWoVNiCAEhGrVepq2UPocuFWi2GfBZefA4fHf4aVLIIVFCAC7rIvPqhpWXnYbouluEa8PKvmetCJk4XwmX2IcxzmZtaCb85gEFD1INWe44aUmu5mmyUxVWC1PpvRVivpUuPeVgDhd9og7lt6wIbDMKey/W1y7/24x9tCz0HlJsLXA8ujT1JzyckEALuEplDWDi34obHVvgGuzXrN4b2/r2xcbPv497YuLnofuvWrSsKZ2beXDyVBML88FAzb8jokCFDim4rNH78eNttt93MrLIho0cddZRdeOGFXV9v2LDB+vbtWxQId9llF7vtttuKnve4446zM888M/T1LbRkyRKTZE899VS378W9cmyUFQWT5npR1Ei9nVlUiwvOUrVc6MDlZ9VqkSXXnteom8D7teDnLzaDjv3y+/0bSiqZhxnUCxn0mrn2lDm/vgH7EBaKqyfL9fdu7+gMrNfCUm4z90oaV8IeU6vPUi3/VrgsCBQWOss1UPgdY6WvQ7n6KP3++EfaQj9TadviwaXxJum/pQRCwF1iq4zmT2wbN7/jG+zK9f6te/Nt3/use7M4OG7bts322GMP++pXv2rLli2zWbNm2ZFHHtktEL773e+2qVOn2vPPP2/f/va3rX///vbaa6+ZmdlPfvITu/POO23ZsmX27LPP2oknnmhHHXWUmXmLyuy77772+c9/3hYvXmwPPfSQHXDAAaGLyowZM8b22Wcfmz17ti1atMhOOeUU69+/f1EgPOigg+zss8+2tWvX2vr13knzvvvus549e9q4cePs2WeftdbWVvvzn/9sP/7xj83M7MYbb7Trr7/eFi9ebMuXL7eLLrrI+vbt27U4TqFG3kokTNRFYkoXrCh30YXaiWNBlVr2ELoEhWqH1UUV1JP0U599Q4Na8PPHFnTsVa3sWSKoFzIoGAVdfMf9+ta6JyvK7z1p7srA99qER9vsojsWOfUMutaZ6wqu9dpWoFyDnsuCQEFBJGhuX7749QpX8zoENZxU2hhWqyH7tViUx6X3nx5CIL0S34fwzU1bnANhYe+faw+hmdmDDz5oI0aMsN69e9vhhx9ujzzySLdAeMstt9hRRx1lvXr1skMPPdQeeuihrsf//Oc/txEjRljfvn1t0KBBdvLJJ9uKFSu6vh9124kNGzbYF7/4RWtpabEhQ4bYTTfd1G0O4d13320HHnig9ezZs2jbifvuu8+OO+4469u3r7W0tNhRRx3VtZLoHXfcYUcffbS1tLRYv3797JhjjrGZM2f6vu7NGAgrmd+Utn2UmklcW5jUagVI156BKEM+qxWlN8slcAcde60CkusQ0PzFsN9nsmixihhf31r2ZF34lwXOgTA/RL2a1zzSe3XMjte13GPiWk01iGsvWD4YBQ37DxruWy4QBtV9rV+HuFcXDhOlpzFs8apyQ0aZQwikW+KBcPM727qGhkYJhFHmEKK7ZgyEUReJSeM+Ss0kzt61WrSkR+11qfViO0Fcg4PrSoelx17LobxRFpXJf+78Lr6TfH1r0XsSJRCW7uNXSSh1WRgn6uqZhY9L4rWv5H0XdUGgsAaKcgG8lq9DvRY0i3JOcznPlDYw1HsVagIh4C7xQGiWXy20IzdfsMNe/ccmp7DnusooumvGQBh1kRh6COsvyd61SiTdQ+LKJThEWfq+UK17L0rr+Nf3+i+hXzoUN6kAWHqshcdUaX0HXfCffPVfI4UQVy4L47gM8Ttr8vyaHE8lKnnfRW3UCwrF//dksp/revUQur5eUUYiFH5W6/W5zSMQAu7qEgjN8vsJvtMV+lzDXunj4KYZA2Eli0Ok9YK/mdT7IqKctB9fkEqH5MYxlLfwNYxrqLCvjpe8PQlDFpUpPEa/46q0ZzToZ8WxIJfLuc91gZ/xj7bV7LiiqOR9UUmjXmEDRb4kvUhLvRY0c+1RjWvl3bgRCAF3dQuEfgh78UlrIHTdULtSlczDyeoFPxCm0iG5cS+Uk1jvyFMTd+xFOG73wG0n8n436wXf47pqVmWbwUfZnqMWyp37XLcAGV7lSr+VqvR9V0mj3oLV67uGN7oGyVqq13QF16CX1RW4CYSAu1QFQsQnjXWa1P6Fad4SA0hSpUNy4xzKG8d2I91E2Jg+77wpT/teBH/v1so3g0+6sSns3FfJfM6kVfq+i/o613v+eL2mK0QJelncb5hACLgjEDaJtNVpVlscgayrNJTEGWZi31NtxaPFYTBfVswOfEjUBUqyyGWV0TRseB53iE7D/PF6TVeIEvSy1rhKIATcEQibRNrqNKtzEgDEI9YL/wp6CM2aY1/S/OseNIS03oEwKWmYP16v6QpZC3quCISAOwJhk0hbnTZD6zuAFHlqohcC82GwzBzCvJmta502g8+6sCGkzYiMPucAACAASURBVLL1DvPHu5vZutZ+NHVhJt//BELAHYGwSaStTus9ZwNA83llTZstfuwee2VNfVbOTLvxj3QfucHWO80r6z3kBELAHYGwSaStTtMwZwNA84h9nmKDOPvm+ZlbPAS11wijeAiEgDsCYYixY8faEUcc0fX1yJEj7eSTT67jEVUujXWahjkbABofDVBueJ2Q96OpC30D4UV3LKr3oTkjEALuCIQhSgNhR0eHvfHGGzV9jhtvvNEGDBhQ05/pJ611ypwNAHFjiLobXifk0UMINJemD4Tbt2+3d955x/d7pYEwDs0eCAEgbmxz44YeQhRiDiHQPJIJhB0veftAlVniu1befvttGzVqlO21117Wu3dv+/CHP2zz5s0zM7OHH37YJNmMGTPsn/7pn2yXXXaxhx9+2MzMfvGLX9i73vUu69+/v5155pk2evTo0CGjxx9/vI0aNcouvPBCGzhwoO299942duzYomO5/PLL7X3ve5/tuuuutv/++9vZZ59tb775ZtGxFJb8499++2274IILbL/99rNdd93VjjrqqK7jNDN78cUX7cQTT7Tdd9/ddt11Vzv00ENt+vTpga8JgRBAs6Lnyx1D+VEoy6vsEggBd/EHwqcm7tj/adzuzkt9V+N//ud/bL/99rMZM2bYc889ZyNHjrSBAwfa66+/3hXCDj/8cHvggQesra3NXn/9dbv11lutd+/edv3119uSJUvsxz/+se22225lA2FLS4uNGzfOli5dahMnTrQePXrYAw880HWf3/72t/bQQw/ZihUrbNasWXbIIYfY2WefbWZmmzdvtiuvvNJaWlps7dq1tnbt2q6w+M1vftOOO+44mz17trW1tdlll11mvXv3tqVLl5qZ2Wc+8xn7xCc+YYsWLbLly5fbPffcY48++mjga0IgBNCs6PmKhqH8aAQEQsBdvIGwws2Aq/HWW2/ZLrvsYn/605+6btuyZYvtt99+9utf/7orEN55551Fjzv22GPtO9/5TtFtRx99dNlA+C//8i9FjznyyCNt9OjRgcf3l7/8xfbYY4+ur/2GjK5atcp23nlne/nll4tu//jHP24//OEPzczs/e9/v40bNy7weUoRCAE0M3q+gOZCIEScfijpSUlvSnpV0p2SDim5Tx9J10h6XdJbkm6XtHfB97+hkmGCBeVdBff7qKSnJW2W1JZ7XKlzJL0o6W1JT0g6KuLvE28gXPFocRjMlxWzq/u5IRYuXGiS7MUXXyy6/ZRTTrEzzjijKxC+9FJxKN19991t4sTi3svzzjuvbCAsDZGf/exn7Ywzzuj6+sEHH7SPfexjtt9++1n//v2tT58+JsneeustM/MPhNOmTTNJ1q9fv6LSs2dP++IXv2hmZtddd5317NnTjjvuOLv44ott4cKFoa8LgRBAs6PnC2geBELE6T55wewwSUdImi5plaR+Bfe5VtJqSR+T9CFJj0uaU/D9vpL2KSn3SXqk4D7DJW2UdLmkEZK+K2mrpE8V3OdL8sLiGZIOlTRB0hsqDpXlNFwP4YIFC0ySrVpV3Pp78skn25lnntkVCEtXC919991t0qRJRbe5BMJzzz232/OMHDnSzMxWrlxpvXv3tvPOO88ef/xxe+GFF+yPf/xj0fP7BcIpU6bYzjvvbEuWLLFly5YVlbVrd4zjX716tV177bV26qmn2i677GK/+93vAl8XAiEAAGgWBEIkaS95b7aP5L4eIGmLpP8suM97c/c5JuRnbJH0tYLbfiXp2ZL7TZEXHPOekHR1wdc7SXpZ0hj3w09qDuHAHWEw5jmEb731lvXq1avbkNHBgwfbZZddFhgI/YaMHnPMMVUFwttuu8169uxp27Zt6/r+z3/+86Ln/9Of/mT9+/cv+hkvvPCCSbLZs917UseMGWPvf//7A79PIATQ7No7Om1O22v0EAJNgECIJB0o7832vtzXH8t9vXvJ/VZJOj/gZ1wgqUNez2HebElXltzvDEkbcv/vJa/H8JSS+0yUdJfjsUuJrjI6O7FVRs8991zbb7/97N577y1aVGb9+vWBgXDKlCnWp08fu+GGG+yFF16wiy++2GlRmbBAmO+tvPLKK2358uU2adIkGzx4cNHzz5kzxyTZzJkz7bXXXrONGzeamdnpp59uw4YNs9tvv91WrFhhTzzxhF166aU2bdq0rt/xvvvusxUrVthTTz1lRx99dNdwUj8EQgDNbMq8VV0LywwfM405hECDIxAiKTtJmibpsYLbTpM3jLPUPHm9fn6ek/T7ktuWypuvWOg/5L2x+0raL/f/Y0vu82t5PYdBesv7YOTLYDXgPoSbNm2yUaNG2Z577hm47YTfBvOXXHKJ7bnnnta/f38bOXKk/eAHP6gqEJqZXXHFFbbvvvta37597VOf+pRNmjSp2/OfddZZtsceexRtO7Flyxa7+OKLbdiwYbbLLrvYPvvsY6eeeqotWrTIzMy++93v2nve8x7r3bu37bXXXva1r33N1q0LXkI963UKAJVilVGg+RAIkZRr5S3osn/BbUGB8ElJv/S5/Vh5b9YPldzuFwg/k7tvHwUHwssk/S3kmMfJZzGbRguE6I46BdCs2IcQaD4EQiThaklr5C3+UijqkNE/SnrG5/a4how2RQ8huqNOATQregiB5kMgRJx6yAuDL0s6yOf7+UVlPl9w28HyX1Smv7ztK77r83N+JWlxyW23qPuiMlcVfL2TpJeUtkVlkArUKYBmxj6EQHMhECJOv5e3AMzxKt42onBBmGvl9QieIG8o6NxcKfVfkjZJGujzveGSOuXNCXyvpO8oeNuJkfK2pviDvG0n9pY7AmGToE4BNDv2IQSaB4EQcQraUP4bBffJb0y/Xt5eglPlhcZScyX9KeS5TpA3nHSzpOXy35j+u/LC52Z5PYZHu/4iOQTCJkGdAgCAZkEgBNwRCJsEdQoAAJoFgRBwFxoIOzsZVtMoOjs7CYQAAKApEAgBd76BcOvWrdba2hq6rx2ypaOjw1pbW23Lli31PhQAAIBYEQgBd76B0Mysvb29KxR2dnbapk2bKBktGzdutGXLltmLL75o27dvr8NpGQAAIDkEQsBdYCDcvn17VyikZL8sWbLENm/eXIdTMgCkQ3tHp81pe41VRoEmQCAE3AUGwrytW7fWvYeLUn3Ztm1bgqdhAEiXKfNWdW1OP3zMNPYhBBocgRBwVzYQAgCQZe0dnV1hMF8OGDOdnkKggREIAXcEQgBAQ5vT9lpRGMyXuW0snAY0KgIh4I5ACABoaPQQAs2HQAi4IxACABrelHmr7IAx07vCIHMIgcZGIATctUiyNWvW2IYNGygUCoVCadjywupX7MFnVtoLq1+p+7FQKJR4y5o1awiEgKPB8j4sFAqFQqFQKBRKo5XBAhCqh7wPSksMJR824/r5FOqnWQp1la1CfWWvUGfZKdRVtkq962uwvGtdAHXSIu8k0FLvA4Ev6ic7qKtsob6yhzrLDuoqW6gvoMlxEkg36ic7qKtsob6yhzrLDuoqW6gvoMlxEkg36ic7qKtsob6yhzrLDuoqW6gvoMn1ljQu9y/Sh/rJDuoqW6iv7KHOsoO6yhbqCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADc9ZA0WFILhUKhUCgUCoXSQGWwvGtdACEGSzIKhUKhUCgUCqUBy2ABCNUiydasWWMbNmygUCouL6x+xR54ZoW9sPqVuh8LhUKhUCiU5i5r1qzJB8KWOl9rA6nXIsk2bNhgQKWmzFtlw8dMs6Gjp9nwMdNsyrxV9T4kAADQxDZs2EAgBBwRCFGV9o7OrjCYLweMmW7tHZ31PjQAANCkCISAOwIhqjKn7bWiMJgvc9vW1fvQKtbe0Wlz2l4j1AIAkFEEQsAdgRC+XENRo/UQMvy1thasXm8TZrfZgtXrnR9DIAcAVItACLgjEKKbKfNW2bBcuBs2unwomjJvlQ3P3X+4w/0Lpeniv9HCbb1979Znil7L7936TNnHEMgBILvS9DedQAi4IxCiSHtHZ1cYHFoQCsNO7lPmrbJhuYv4YREu4it9XFwacfhrnML+8C9Yvd73tZzZujbwMQRyAMiutDXoEQgBd3ULhJW2IqWp9akR3b3gJd8L+XsWvOx7f98AOSY8QOYfV/oc5YJn3CoJw9U8V5bfx4V/+IeNnmbjH2kr+v6E2W2+76PC17X0YoFADgCerP2NqPRaIE4EQsBdXQJh1CGJhY9LU+tTI5o0d6XvRfnkx1f63v+ehS/73n/aQv8AWenzVMN1HptfSB0aQyDM+vvY7w//0NHTbPyjO0JhUA9hWNgOekyU+YcAkHVZ/BtR6bVAnAiEgLvEA2FYL0xYixjDyZJx6YznfE/qv5jxnO/9Jz1eWbD7zf3P+z7uivuX1PT3iTKPLWrvaCUa4X0c9Id/eElr8KnXPFY2FBa+tpX2EGatJR0AgmT1b8TEuSt8z9+T5q6s2zERCAF3iQfCoIvuH96+KLRFjOFkyThvytO+r/P5U572vX+lISqJ3qCoz5FEC2cjvI+D6rzw9/C7qCn32lZyIVTpaAMAqEZcDVFZ/RtBDyGQbYkHwqCTRmmvYemFYFZbzbJmZuta3/qZ2brW9/7VzLurZBXKKC5/YInv73LFA/69kEnMa0xqWGqcgn6Hwh7CoIuaotfWZ37JlHmr7IAx07s+32EBL8p7L+leRHotgcYV55DOelzr1OJ8xRxCINvqMmS03IViUItYFsfVZ1HpUL9Tr3ks9P7VbDuxYPV6u3728ljmiUUdwpLEojJRA3caucwh9LuoGZb73Jb7/LZ3dNrctnVlX3fX3uly541ah7danKcIlEA6JRHY4m4sLVTLURal577xj7TV9TxGIATcpWIO4dCCC8WwE2ySJ8lmN7N1rV10xyLnoOJ6EZ+kqL1xSQzV+dHUhb7PcdEdi2r2HHFzfZ38evtq+T5xGaJU7uKt1o1MtWglp+ELSFaUBphq/k6Ue572jk67e8FLifUQxjFiJX+OH/9oW92H8xMIAXeJB8Kgk+nXrv9baNhjBUJUIkrrZxItv43SQ+j6OsXZUOASvsIu3uKo72rn0VR6TPQoApWJ2gBT6We03PMU/q2Ku2EyL+qicFFW7E5qC6cwBELAXV16CEtPpsNHl+8hDNrX7PrZyxM7dmRTlFASZQ5bpaIOyU2jJF4n1+MoNxw06NwSR49wtSvVVnJM9CgClak03EUdrVSu8arcVJq4egiDVvu+vGC17/y58uyb5zv/zkms2O2CQAi4q9s+hIUXk5dM89/qoPAiiB5CJCWJ4a9Rh+SmUVqGCZc7jqDwGsc5pdohWFGHnLLYFlC5ShpgKvnMlRs5ENRTF3eDW7lz4JR5q2xYyGrR9Vyx2wWBEHBXl0BoVnwR53qCZQ4hgEr4hcY4egirHSoV9fFZXaIeSIOgQBTWUFfJZ65cj9nYuxb7fv+syU+GnjtqMVQ86LrKZQHAoBFaaVlxlEAIuKtbICzlOgQtzlUpATSPOObrVRvQoj6eHkKgcmHbYJVeg+Tnz81sXRv5M1du5EDUuXxmtR0q7nddNWlucK9luR7CWh9fpQiEgLvUBEKz9AxBA5Ad1bSSR50LWc2cRdffJerj0zKfM0ksohOfRnhtXX+HoK2JSnu0SnvRTr3msdDPXOnzVzqHsJ5DxYPmF5b2JIap9zUdgRBwl6pACABR1GrPP5eLFteLsGoDWiVD46NeeFVy0Z+WoJCGnod6ibsOKnlt0/K+yIvyOwT1EObLtIUvhw4r9fvM+T2/S89/lBWxkxgqHvR7//TuZzMzQotACLgjEALIpKSHS0a5CKu0ZTyJ36mSi/60hLBmHiIbdx1U2judhvdFXtTfwa/nrrDcs+DlSCucBz3/gtXrnY7L9byR1CJ7WV+3gUAIuCMQAsikpBdUSWIPybh/p0ou+tMUwtK8iI5LT1mlvWlJ7E/ZCPNXK922JSgQLli9PlL4Cnv+Wg7tTvJzkOV1GwiEgDsCIYBMSvqCNIm9UOP+nSq5kExTCEtjCDFz6ymrpjctif0pXVeGzIfMoOGW9XpfzGl7LbDRplyYKfe7uPaUlQuPtZpTxzZcbgiEgDsCIYDMSnJBlTQP03LtCXLp5Yy6IEatuP4OaVtExyWkVtvD5zrksJrnc1nYpDBkBhW/HvPCui38nWox97DwmIKGf5YLqS6vl0tPWVKNJ2lqpEkzAiHgjkAIINOSXMku7jk1cc/jOufm+aG9nH4/K+reiJWI2pu1YPV6uy4lw9hcLs5r0cN36jWPOb/3arlX3g9vX2Rm5efblb6X/H6PYT6hrZq5h36fl0obbWrR0JBUo1Fae8rThkAIuCMQAkAEcc6piXMeV1APUP6CNehnuQwNrGalyagXt2lcyKRcD2pYUPB77VzCV9hrVEkwCarn4bnfpdyKnH7P4RrYyvV2Rt33M0oQLn2uahqXkuy5S1tPeRoRCAF3BEIASImo4SjKBWjQfb92/d9Cv3/PgpdDewirDWhRV29NW8+Iy1DLoN/xkmmtRV/nXzvX8BUUNIJ6++5Z8HLk3yP/PGF79uVLaa+la2AL+l0q2fczStistaTfn/Xe5y/tCITIuo/KewP7lSML7ne4pL9KelvSGkk/qOC5CIQAkCJRWv6j9AQF9TrlLyaD5hf+35OrAnvAKr0Antm61n40daHNbF0beFE//tG2bo+rtgdmwer1NmF2W2BPWbnv+3EJX0H1FBQkJz2+0um+QSvcBgXKaQuDA6GZ2a/v9d+MfMHq9YHH9IsZzwX2mLsOM/V7z7rOXS39vHzv1mfq2nNGz116EAiRdb0k7VNSrpO0UlKP3H1aJL0i6WZJh0n6sqROSd+O+FwEQgBIGdeW/yg9QeV6soJWUb1o6qLAEFZJQCudC3fqNY/Z+Ee6P7ffxX81c7TKzf8s/f5Zk+c7DYN1CV9B9eRXJs1d6Xz/oBVuK10IKKw+KwmZUQLh5MdXRn5dC5+n8PNS756zej8/PARCNJpdJP1d0k8Kbjtb0np54THvl5KWRPzZBEIAyKgoF83lwmNQ2JrZujawFzBqQAvqhfz6H//m9HtUMhQy7HfLH2e5Hrxzbp4f+Dv5Be3SRXdch4AOHT3NrnhgiXOPYlgQrmQob7m5jlFDZpQho5ffv6TosZXWNZBHIESj+bykbZKGFNw2SdKdJfc7Qd4bf2CEn00gBICMinKR7hIeg3rRgobBhfUo+Q2//NHUhc4Bwe/iv9KhkL+5338o5BW5EBLUO1pa/Fb2dFmFNcqQ0fx2DJUci9+xRempKtfjW8nehi6LyviF2yRWt0VjIxCi0czIlUIPSPpDyW2Hynvjjwj5Wb3lfTDyZbAIhACQWa4X6eXmEObNbF1rP75jUbf5aX7hImgO4dkl21vkw8v/PbnKORgVXvzn5xyGzWcMc9Wspb7PcfVDS80semArVM22E1+7vrhnNP86Bb2uM1vXxrbCbdjzltZ5lJBZ2JgQNdxOmbfKhufuM3x0/VeURbYQCJFWv1TwYjH58t6Sx+wvr3fw8yW3+wXCwwJ+RqFxfs9LIASA7HK9SC/tARxacpFdyTDD0t7DsIVJogwhzO9/Vzrn8F9/NSvyMZbrWQxbYbO0XPFA8dBG103Ng16ToG1MklqcpLQnN47nbe/otHsWvNztdRo2OnhhnMLHMh8PlSAQIq32khfWwkqvksf8RNKr8uYRFqp0yCg9hADQhIJ6CIdXuWKoWfFG8ePuWuwbfsbd9azvcwQtOrJg9frQlU+jhIRyQ2ujBNWrZi3t9vPLhahKV0eNOwwFDRGO63lZgRNJIhCiUfSQtELSb3y+l19UpjAoXioWlQEA+AgLPZWuGGrWvVfxtAlzfX/OT+9+tuv+haHgkmnPBT5v0JzDi+5YFPn3D+v9jDLXLWi+YliISuP+idWs2FoNevyQFAIhGsXHFTwncIC8bScmyRsq+iVJG8W2EwAAH3H0EEbt8St8XD4UhD1vUA9huWGGYccbFEZc5rq5zFcMkrbescsfWOL7O5YOiQWyikCIRnGLpDkh3z9COzamf0nS6Aqeg0AIAE1iyrziRVmGjek+hzAotLR3dHbbly/qYilhxxX0vH77FsYlHxhLF8Xx61Ws5uenoXds4twVvnU3ae7Keh8aUBMEQsAdgRAAmkh+gY9pC18OHN5YGlqChluG9e4FLZYSdlxBYWlm61q7yGfl0zjlj39m69rUhLhaClpIp9F+TzQvAiHgjkAIAAhUbihpJSuTIh0Ke4yHjabu0FgIhIA7AiEAIJDLZuW1CBV+Q1IRvzQNYwVqiUAIuCMQAgAChfUQ1mr1THoZAdQagRBwRyAEAIQKWvSl0q0qCqVxSwYA2UcgBNwRCAEAZfkNLaxFmKtFqASAUgRCwB2BEABQsWr316OHEEAcCISAOwIhAKAq1S5MkrZN2wFkH4EQcEcgBADUXb1Xu2SVU6CxEAgBdwRCAEBTY5VToPEQCAF3BEIAQNNiDiPQmAiEgDsCIQCgabHKKdCYCISAOwIhAKBp0UMINCYCIeCOQAgAaGqscgo0HgIh4I5ACABoevVe5RRAbREIAXcEQgBA3bHtA4BaIhAC7giEAIC6YtsHALVGIATcEQgBAHXDoi4A4kAgBNwRCAEAdcO2DwDiQCAE3BEIAQB1Qw8hgDgQCAF3BEIAQF2x7QOAWiMQAu7KBsKtW7fapk2bKBktW7duTfD0CwCVYdsHALVEIATcBQbC7du3W3t7u7W2tlIyXtrb22379u11OB0DAAAkj0AIuAsMhPkwuG7dOuvs7Kx7Txcleuns7LR169Z1hUIAAIBmQCAE3PkGwq1bt3aFQWRfPhQyfBQAADQDAiHgzjcQbtq0yVpbW62zk7kcjaCzs9NaW1tt06ZN9T4UAACA2BEIAXehgZAA0RioTwAA0EwIhIC7hguExx9/vJ177rl1e/6RI0faySefnJrjMct2fQIAAERFIATcEQhrrDQQvv766/aPf/yjbsdjlu36BAAAiIpACLgjENZYaSBMgyzXJwAAQFQEQsBdQwbCc845x8455xxraWmxPfbYwy666KKuffgmT55sH/rQh6x///62995721e+8hX7+9//3vX49evX22mnnWZ77rmn9enTxw488EC74YYbur6/evVq+8IXvmADBgywQYMG2Wc/+1lbuXJl1/fLDRkdOnSoXXLJJXbGGWdY//79bciQIfaHP/yh6Hco9xwPP/ywHXnkkbbrrrvagAED7LjjjrMXX3wx8DXJcn0CAABERSAE3CUSCNs7Om1O22vW3hH/qqXHH3+89e/f384991xbsmSJ3XzzzbbrrrvahAkTzMzsj3/8o82YMcOWL19ujz/+uB177LH26U9/uuvx55xzjn3gAx+wJ5980lauXGkPPvig3X333WZmtmXLFhsxYoSdeeaZtmjRImttbbXTTjvNDjnkENu8ebOZuQXCQYMG2TXXXGPLli2zX/ziF7bTTjvZ888/7/Qc77zzjg0YMMC+//3vW1tbm7W2ttpNN91kq1atCnxNCIQAAKCZEAgBd7EHwinzVtnwMdNs6OhpNnzMNJsyLzi41MLxxx9vI0aM6OoRNDMbPXq0jRgxwvf+Tz75pEmyN99808zMTjrpJDvjjDN87zt58mQ75JBDin725s2brW/fvnb//febmVsg/OpXv9r19fbt2+1d73qXXXvttU7P8frrr5ske+SRR5xfEwIhAABoJgRCwF2sgbC9o7MrDObLAWOmx9pTePzxx3cLdHfeeaf17NnTtm7davPnz7cTTzzRhgwZYv3797ddd93VJNlzzz1nZmYzZsywvn372hFHHGEXXnihzZkzp+vnfP/737edd97Z+vXrV1R69Ohhv//9783MLRD++te/Ljq+ww8/3H760586P8c3vvEN6927t5144ol25ZVXWnt7e+hrQiAEAADNhEAIuIs1EM5pe60oDObL3LZ1Vf3cMGGB8K233rI99tjDTjvtNJs9e7Y9//zzdv/995ske+aZZ7ru/+qrr9qNN95op59+uvXp08cuuOACMzM766yz7KijjrJly5Z1Kx0dHWbmFgh/+9vfFh3fEUccYWPHjnV+DjOzp59+2i699FI79thjrX///vb4448HviYEQgAA0EwIhIC7huwhLB0eOmbMGBsxYoTNnz/fJNnq1au7vjd58uRugbDQ+PHjbbfddjMzswkTJtjAgQO7vV6Fqg2ELs9R6phjjrFRo0YFfp9ACAAAmgmBEHCXyBzCA8ZM7wqDScwh7N+/v51//vm2ZMkSu+WWW6xfv342fvx4e/XVV61Xr1524YUX2vLly+2uu+6ygw8+uCgQ/uQnP7E777zTli1bZs8++6ydeOKJdtRRR5mZ2caNG+2ggw6yj370ozZ79mxbsWKFPfzwwzZq1Chbs2aNmVUfCMs9x4oVK2zMmDE2d+5ce/HFF+3++++3PfbYo2s4qR8CIQAAaCYEQsBdYquMzm1bl9gqo9/5znfsrLPOspaWFhs4cKD96Ec/6lqk5ZZbbrFhw4ZZ79697dhjj7W77767KBD+/Oc/txEjRljfvn1t0KBBdvLJJ9uKFSu6fv7atWvt61//uu25557Wu3dvO+CAA+xb3/pW12tYbSAs9xyvvPKKnXLKKbbvvvtar169bOjQoXbxxRfbtm3bAl8TAiEAAGgmBEI0goMl3SVpnaR/SHpM0kdL7vNuSdMldUp6VdJlknpGfJ6G24cQ3VGfAACgmRAI0QiWygt7h0s6SNI1kjZK2if3/Z0lLZb0oKQPSPq0pNckXRrxeQiETYD6BAAAzYRAiKzbU94b+F8Lbtstd9u/5b7+tKRtkvYuuM9ZkjZI6hXhuQiETYD6BAAAzYRAiKzrIWmJpOsk9ZM3DPT7kv4uZbIazgAADzhJREFUaWDuPj+TtKDkccPlvfE/GPKze8v7YOTLYBEIGx71CQAAmgmBEI1gf0nzJW2XtFVSu4qD3gRJ95c8Zld5b/xPh/zccbn7FBUCYWOjPgEAQDMhECKtfimfMFZS3iuvh/AuSTMkfVjSP0n6vaSXJO2b+1lhgfDfQ46BHsImRH0CAIBmQiBEWu0lL/CFlV6SPi5vfmDpG3iZpDG5/1c6ZLRU6BzCzs74t4lA/Do7OwmEAACgaRAIkXUnyQuE/Utuf0HSj3L/zy8q866C739b3qIyvSM8l28g3Lp1q7W2ttq6devq9DFGLa1bt85aW1tt69at9T4UAACA2BEIkXV7ytt/8HZJR8jbk/AySVtyX0s7tp24P3fbp+TtRViTbSfMzNrb27tCYWdnp23atImSsdLZ2dkVBtvb2+twOgYAAEgegRCN4J/lhb3X5W1M/7i6LxYzVN48w055exD+RjXamN7MbPv27V2hkJLt0t7ebtu3b6/D6RhIh/aOTpvT9pq1dzAMHgCaAYEQcBcYCPO2bt1a954uSuWFYaJodlPmrbLhY6bZ0NHTbPiYaTZl3qp6HxIAIGYEQsBd2UAIAFnV3tHZFQbz5YAx0+kpBIAGRyAE3BEIATSsOW2vFYXBfJnbxoJZANDICISAOwIhgIZFDyEANCcCIeCOQAigoU2Zt8oOGDO9KwwyhxAAGh+BEHDXIsnWrFljGzZsoFAolIYsL6x+xR58ZqW9sPqVuh8LhUKhUOIva9asIRACjgbL+7BQKBQKhUKhUCiNVgYLQKge8j4oLTGUfNiM6+dTqJ9mKdRVtgr1lb1CnWWnUFfZKvWur8HyrnUB1EmLvJNAS70PBL6on+ygrrKF+soe6iw7qKtsob6AJsdJIN2on+ygrrKF+soe6iw7qKtsob6AJsdJIN2on+ygrrKF+soe6iw7qKtsob6AJtdb0rjcv0gf6ic7qKtsob6yhzrLDuoqW6gvAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEB67SWpR70PAs6oKwAAaqxn7t+d6noUCLKXpOHasecPF0PpdoCkqyX9c70PBGUNkzRD0u25rzkHpt+ekt6lHX+3OB+mG1sTZAd1BTSx/5V0f70PAr56SPqdpBclPS1pqaQD63lACNVD0rWStku6QdKu9T0chOgh6Q+S3pH0kqTXxMVQFlwl6WVJcyTNlvS++h4OyvitvLrau94HgrKoK6BJjZA0XV7Y2C7p9NzttJCnw8ckPStprqSPSvp3eRdAhPd0+qyk1+UF9w+VfI8ejHS5QNIGSU9I+oCkEyU9p+71hvToLekWSX+VdJykT0q6R9JKSZ+q43HB3wGS7pDUKu/6YnR9DwchqCugyX1O0vWSTpDXMrRW0i51PSIU+oGkSyX1L7jtQkl3S9o59zVBIz0myLs4zYeKD0j6Yu7f3ep1UOimn6T7JJ1RcNvh8noK80N8aRRLn/fJu2D9t5LbN8prJHtv4keEMMdL+r2kD2tHAwyjW9KJugKaTOlFzh7yegklbx7Ny5J+EXBfxK/0NR8k6d0FX+8lr0fjCnm9Uaiv0vo6SNKjkm6UNFVeOHxGXq/hTEkDEj06FCqtqx4l3xsk6XnRMp4mpXX2r5K2SepTcNve8urteUkXJXRcCJevtxZJ78n9v4e8OrqpHgeEbnqWfD1A1BXQNC6Wd6H6E3lBsNTOkr4rabN2hBB6n5JTrn6+KO9i6DFJt8kLGRMlDU3qAFGktL7yF0E/lNQu6S+SPiivlfUYSa9KGi/mqNVD0Gdr54L/D5L0lKSfJ3hcCOZXZ3vLa2SZIK+XV/LmE/5ZXg/hdNHoUi9fkXRomfucJGmrpI/EfzgI8TN5DZZXyesQKA2HEnUFNKQh8i50Fslb9XCtpCcl/Wfu+4Whb8/c9+5I8gCbXLn6yfu4ik/OH5K0Sd2HTyFeQfX1pdz3+8sb1ntQyeO+IKlTTNZPkuu5Lx/m75IXKgq/h2T51dl8Safkvv85SVty339T0jJ5Yf5jkt7WjlWYkYyPSFosb97ZJZL6lrn/DHlzQPuUuR9qby95DcqL5DW4LJG0QNL5ue+XnvOoK6DBjJQ3bC3fctpP3oXPXyUdkbutsIXoRHkn93z4+KSkg+M/zKblUj9+dpUXCL8Z69GhVFh9fTB3m998wX+VFwj/Ne4DRBeXz1ZhL+FPcvffM6kDRDdBdfaYdtTZByV9Wd7fprzPSFoub2seJGN/SX+Ut/7AJZLWyxsNEeYweYH+a/LWKzhR3rw1xO8keXNwh+S+7i2v7lbIW6RJKr4WpK6ABjNW0jwVf9A/Im9O058Kbsu3DvWVNwRnpaS/ybuIPSr+w2xarvVT6kx5y0PvF9+hwUel9fWz3H38hucgHlHPfefJazXfW/QQ1ktQnc2St8JokGu0Yx9JJKNF0ue1Y9uPF+RdO+xe5nFXyBtCv0Beo+Yn4jpAFDlT0mpJvQpuO0TeInVzAh5DXQEN5Bfyti0oDQ7fl9cSmx9ymB82daCkB+T1El4nVkeMm2v9SN4wxAPlDaX6u6TviQvXpJWrr48X3HawvIn6V8tbsGlk7nbqLBmun618+HivvPPe4YkcHfxEOR++R968tWvlDS3NDyvl81Uf/ybv83OyguvgPfKC+3Z5e4ByfZGcs+UNmT+y5PbPSVolb50Cace1IHUFNIj8hzp/kXNKyfePkNcDWLiq3iHyWmeflTdcAPGJWj8DJf0/ecM7CodPIRlR62uQpF/LW2DmMREyklTJuU+SjpZ34bOPCBVJq6TOTpO36vLj4vNVb/n6myGvnvb3uc9+8hqbl4jriyTlz2XvlrcY3bkq3l5sqLxh2X8ouO++oq6ATHmfvCEbO/t8r3DIzf/J2yy7dG7M3yT9ruDr3UTQqKVa1M9VBV8fqh1j/VF7ta6vw8UqbXGp5bmPLXaSUeu/Vy3aMVwRtedaX4Vfv1veatijtONzNTj3b1+xt11cDpLXi36Iz/cK6+pqSS/K2xu30O3yhvvm9RZ1BWRCL3mTubfL6zUqbM3eueR+B8o7SXfKm/idn6zfU96eaT+N+2CbEPWTLdRXdlBX2UOdZYtrffXUjn2MC793ubyRLF+Rtx3IZJVffRSV2Vne/Nm35dXZvxR8r7Chq4+8xZh2lrRG0vUq3rLqdnlDrwFkyCh5y23PUXhv3v9I2ijpB7mvvyVvie775W1qfoW84Wyl48lRHeonW6iv7KCusoc6y5ao9XWBdgw/zAfHIfLC5HZJ0+QNn0c8LpQ3JeHoktsLQ/z/SPqHpMtyX39e3lDrxZL+S9KVkl5T8fx3ACnXIm9p51kFt71X3uTf/ITfHvI27v27pK+quJXoRHn7bM2VN7m49CSC6lA/2UJ9ZQd1lT3UWbZUUl+lc21Pk/SOvLUISoclonZ6yNuSZa52bD11rKRvy+sl7J+77dfy6vR0FX+2Dpe30vL9uZ9RbrsQAClReNI9Q97E4E/Im2vRJq8l9QlJ38jd5xAVb9BbOkeGzbFri/rJFuorO6ir7KHOsqXa+srbSd6Kld+O60BR5CBJr8hbwOdyeT3oc+SF9Vny9ireU8Wrg5YGeFYOBTIiv/9f4R/IHvJOztvljQP/iKT/yP2/XdLHcvfzmwiO2qJ+soX6yg7qKnuos2yhvrLDr676ylsR/iZJt8mbzzlI3mJAb0j6jVgtGci8U+TtW/a6pGG52wpPwB+St1/THgW3DZM0Vd4QG8SL+skW6is7qKvsoc6yhfrKjrC6Gihvr+h/yFsUZiftCIxnSOoQi/kAmXaavPH3f5b0V0njfe7TQzvGiBeaLG8foH6xHR2on2yhvrKDusoe6ixbqK/scKmrr0haJ+nO3Nf5sHi0pFcl/VPMxwggBoUf5F/IW3r7Qnkbg3605D5++kqaqR0rSaG2qJ9sob6yg7rKHuosW6iv7HCpq165f/tJ+q28Ib7/VvAzLpC3sTx7rAIZcpC6j/PObyR6mKS7VDxMo/S+A+Qt8/xHSa2iRajWqJ9sob6yg7rKHuosW6iv7IhaV/ngOFzSRElvyRs6eou8Iab5xX2YRwik3BclrZTX6vOEpDMLvle66tdzuX+l4haff5f0e3lDBh6Wt6kvaoP6yRbqKzuoq+yhzrKF+sqOSuuqp4r9t7xtJm6QtxIsgAz4hLwTwHckfUreksFb5G3Gm58EnP+wD5a3wtc87RjXnx8uMDT3M9hQtLaon2yhvrKDusoe6ixbqK/sqLaudknsSAHUVL6152JJ81X8Yb5G3ua7p/o87jO5742Tt6HoNHnDOFBb1E+2UF/ZQV1lD3WWLdRXdtSqru4RdQVk2hRJt+b+nz8RDJT0mKQbJe2Tuy0/RnxXeSeJ7ZLekXSfpN6JHGlzon6yhfrKDuoqe6izbKG+soO6AprEJyT9TtJ52rGxqOQNB/iHdnzIdym4/QXtWElK8laROk/SVnnj+N8f3+E2HeonW6iv7KCusoc6yxbqKzuoK6BJ7SuvK//vkm6WtEjeJqH5E8HBkl6S9LPc170KHrtW3oc+71BJf5P0tRiPt9lQP9lCfWUHdZU91Fm2UF/ZQV0BTWxXSTfJGwowvOD2J+QNAZCk3ST9WFKndoz/zq/u9Yik6+I+yCZG/WQL9ZUd1FX2UGfZQn1lB3UFQH+Qt2SztGOFqLHyWnfyk4mHyxsn/ri81bwkbwPSVnkThxEf6idbqK/soK6yhzrLFuorO6groMkVrhiVb+35k6QJJfcbLGmZvGWH/yLpZUmzJO0d9wE2OeonW6iv7KCusoc6yxbqKzuoKwDdPCZpZO7/O2nHyeFASV+SdEXB95E86idbqK/soK6yhzrLFuorO6groIkdIOkVSR8quK1XwH2RPOonW6iv7KCusoc6yxbqKzuoK6BJ5ceIf11SW8HtYyVdK+ldiR8RClE/2UJ9ZQd1lT3UWbZQX9lBXQGQJF0t6Vfy9qJZKW8J4k/W9YhQiPrJFuorO6ir7KHOsoX6yg7qCmhifeRNFt4u6W1Jo+t7OChB/WQL9ZUd1FX2UGfZQn1lB3UFQA9K+r28EwLSh/rJFuorO6ir7KHOsoX6yg7qCmhyO9f7ABCK+skW6is7qKvsoc6yhfrKDuoKAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQCX+P4hs7z9EsSFcAAAAAElFTkSuQmCC\" width=\"900\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.figure(figsize=(9,3))\n",
-    "pl.subplot(2,1,1)\n",
-    "pl.plot(h_dt,h_abs,'.',h_dt,h_ord,'.')\n",
-    "pl.legend(('absolutes','ordinates'))\n",
-    "pl.subplot(2,1,2)\n",
-    "pl.plot(h_dt,np.asarray(h_abs) - np.asarray(h_ord),'.')\n",
-    "pl.legend(('baselines',))\n",
-    "\n",
-    "# re-formats dates for better presentation\n",
-    "pl.gcf().autofmt_xdate()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plot of D absolutes, ordinates, baselines\n",
-    "Absolutes represent the field magnitude as measured by the overhauser (with the pier correction applied), and field direction measured by the theodolite.  Ordinates represent the variometer's reading of the field corresponding to the nearest times absolute measurements were taken. Baselines, or the difference between absolutes and ordinates, are presented in the bottom plot. All vertical axis units are degrees from surveyed geographic north."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\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",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\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 backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\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 * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\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'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\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 * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\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",
-       "    var width = fig.canvas.width/mpl.ratio\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 + '\" width=\"' + width + '\">');\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 width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\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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\" width=\"900\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.figure(figsize=(9,3))\n",
-    "pl.subplot(2,1,1)\n",
-    "pl.plot(d_dt,d_abs,'.',d_dt,d_ord,'.')\n",
-    "pl.legend(('absolutes','ordinates'))\n",
-    "pl.subplot(2,1,2)\n",
-    "pl.plot(d_dt,np.asarray(d_abs) - np.asarray(d_ord),'.')\n",
-    "pl.legend(('baselines',))\n",
-    "\n",
-    "# re-formats dates for better presentation\n",
-    "pl.gcf().autofmt_xdate()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plot of Z absolutes, ordinates, baselines\n",
-    "Absolutes represent the field magnitude as measured by the overhauser (with the pier correction applied), and field direction measured by the theodolite.  Ordinates represent the variometer's reading of the field corresponding to the nearest times absolute measurements were taken. Baselines, or the difference between absolutes and ordinates, are presented in the bottom plot. All vertical axis units are nanoteslas."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\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",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\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 backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\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 * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\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'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\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 * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\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",
-       "    var width = fig.canvas.width/mpl.ratio\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 + '\" width=\"' + width + '\">');\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 width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\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": {
-      "text/html": [
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toof2ixeaK/4oK2ADqyrvMnCuyT9W9KY0lYHGWifeKG94oO2ih/aLF5or/igrQDoOUl/kveGgPJD+8QL7RUftFX80GbxQnvFB20FdHC7l7oCCEX7xAvtFR+0VfzQZvFCe8UHbQUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgF/8fdfOdNBhO5BYAAAAASUVORK5CYII=\" width=\"900\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.figure(figsize=(9,3))\n",
-    "pl.subplot(2,1,1)\n",
-    "pl.plot(z_dt,z_abs,'.',z_dt,z_ord,'.')\n",
-    "pl.legend(('absolutes', 'ordinates'))\n",
-    "pl.subplot(2,1,2)\n",
-    "pl.plot(z_dt,np.asarray(z_abs) - np.asarray(z_ord),'.')\n",
-    "pl.legend(('baselines',))\n",
-    "\n",
-    "# re-formats dates for better presentation\n",
-    "pl.gcf().autofmt_xdate()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Note about averaging\n",
-    "The baselines have up to four values, corresponding to the up to four sets measured by the observer.  Pre-averaging these does not improve the transformation matrix calculated by the least squares solver in scipy/numpy."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Calculate $(h,e,Z)_{variometer}$ from $(H,D,Z)_{ordinate}$ and $(X,Y,Z)_{absolute}$ from $(H,D,Z)_{absolute}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# convert to NumPy arrays for convenience\n",
-    "h_abs_n = np.asarray(h_abs)\n",
-    "d_abs_n = np.asarray(d_abs)\n",
-    "z_abs_n = np.asarray(z_abs)\n",
-    "h_ord_n = np.asarray(h_ord)\n",
-    "d_ord_n = np.asarray(d_ord)\n",
-    "z_ord_n = np.asarray(z_ord)\n",
-    "z_t_n = np.asarray(z_t)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# convert to Cartesian coordinates\n",
-    "x_a = h_abs_n*np.cos(d_abs_n*np.pi/180)\n",
-    "y_a = h_abs_n*np.sin(d_abs_n*np.pi/180)\n",
-    "z_a = z_abs_n\n",
-    "h_o = h_ord_n*np.cos(d_ord_n*np.pi/180)\n",
-    "e_o = h_ord_n*np.sin(d_ord_n*np.pi/180)\n",
-    "z_o = z_ord_n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Calculate Transform matrix"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This cell contains code to constrain M to impose a uniform scaling factor. \n",
-    "Its contents may be copy-pasted into an actual **code** cell if this is desirable.\n",
-    "\n",
-    "---\n",
-    "\n",
-    "```python\n",
-    "\n",
-    "# LHS, or dependent variables\n",
-    "abs_st = np.vstack([x_a,y_a,z_a])\n",
-    "abs_st_r = abs_st.T.ravel()\n",
-    "\n",
-    "# RHS, or independent variables\n",
-    "# (reduces degrees of freedom by 6:\n",
-    "#  - 2 for the common scaling factors, and\n",
-    "#  - 4 for the last row of zeros and a one)\n",
-    "ord_st = np.vstack([h_o,e_o,z_o])\n",
-    "ord_st_r = ord_st.T.ravel()\n",
-    "ord_st_m = np.zeros((10, ord_st_r.size))\n",
-    "ord_st_m[0] = ord_st_r\n",
-    "ord_st_m[1,0::3] = ord_st_r[1::3]\n",
-    "ord_st_m[2,0::3] = ord_st_r[2::3]\n",
-    "ord_st_m[3,0::3] = 1.\n",
-    "ord_st_m[4,1::3] = ord_st_r[0::3]\n",
-    "ord_st_m[5,1::3] = ord_st_r[2::3]\n",
-    "ord_st_m[6,1::3] = 1.\n",
-    "ord_st_m[7,2::3] = ord_st_r[0::3]\n",
-    "ord_st_m[8,2::3] = ord_st_r[1::3]\n",
-    "ord_st_m[9,2::3] = 1.\n",
-    "\n",
-    "# regression matrix M that minimizes L2 norm\n",
-    "M_r, res, rank, sigma = spl.lstsq(ord_st_m.T,abs_st_r.T)\n",
-    "\n",
-    "M = np.zeros((4,4))\n",
-    "M[0,0] = M_r[0]\n",
-    "M[0,1] = M_r[1]\n",
-    "M[0,2] = M_r[2]\n",
-    "M[0,3] = M_r[3]\n",
-    "M[1,0] = M_r[4]\n",
-    "M[1,1] = M_r[0]\n",
-    "M[1,2] = M_r[5]\n",
-    "M[1,3] = M_r[6]\n",
-    "M[2,0] = M_r[7]\n",
-    "M[2,1] = M_r[8]\n",
-    "M[2,2] = M_r[0]\n",
-    "M[2,3] = M_r[9]\n",
-    "M[3,:] = [0,0,0,1]     \n",
-    "\n",
-    "print np.array_str(M, precision=3)\n",
-    "```\n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This cell constrains M to rotate about the z-axis only, with a uniform \n",
-    "scaling in the x,y plane. Its contents may be copy-pasted into an actual \n",
-    "**code** cell if this is desirable.\n",
-    "\n",
-    "---\n",
-    "\n",
-    "```python\n",
-    "\n",
-    "# LHS, or dependent variables\n",
-    "abs_st = np.vstack([x_a,y_a,z_a])\n",
-    "abs_st_r = abs_st.T.ravel()\n",
-    "\n",
-    "# RHS, or independent variables\n",
-    "# (reduces degrees of freedom by 11:\n",
-    "#  - 2 for making x,y independent of z;\n",
-    "#  - 3 for making z independent of x,y\n",
-    "#  - 2 for not allowing shear in x,y; and\n",
-    "#  - 4 for the last row of zeros and a one)\n",
-    "ord_st = np.vstack([h_o,e_o,z_o])\n",
-    "ord_st_r = ord_st.T.ravel()\n",
-    "ord_st_m = np.zeros((6, ord_st_r.size))\n",
-    "ord_st_m[0,0::3] = ord_st_r[0::3]\n",
-    "ord_st_m[0,1::3] = ord_st_r[1::3]\n",
-    "ord_st_m[1,0::3] = ord_st_r[1::3]\n",
-    "ord_st_m[1,1::3] = -ord_st_r[0::3]\n",
-    "ord_st_m[2,0::3] = 1.\n",
-    "ord_st_m[3,1::3] = 1.\n",
-    "ord_st_m[4,2::3] = ord_st_r[2::3]\n",
-    "ord_st_m[5,2::3] = 1.\n",
-    "\n",
-    "# regression matrix M that minimizes L2 norm\n",
-    "M_r, res, rank, sigma = spl.lstsq(ord_st_m.T,abs_st_r.T)\n",
-    "\n",
-    "M = np.zeros((4,4))\n",
-    "M[0,0] = M_r[0]\n",
-    "M[0,1] = M_r[1]\n",
-    "M[0,2] = 0.0\n",
-    "M[0,3] = M_r[2]\n",
-    "M[1,0] = -M_r[1]\n",
-    "M[1,1] = M_r[0]\n",
-    "M[1,2] = 0.0\n",
-    "M[1,3] = M_r[3]\n",
-    "M[2,0] = 0.0\n",
-    "M[2,1] = 0.0\n",
-    "M[2,2] = M_r[4]\n",
-    "M[2,3] = M_r[5]\n",
-    "M[3,:] = [0,0,0,1]     \n",
-    "\n",
-    "print np.array_str(M, precision=3)\n",
-    "```\n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[  9.878e-01  -1.461e-01   2.521e-02  -1.262e+03]\n",
-      " [  1.682e-01   1.000e+00  -1.532e-02   4.615e+02]\n",
-      " [ -1.663e-02  -5.521e-03   1.007e+00   6.099e+02]\n",
-      " [  0.000e+00   0.000e+00  -0.000e+00   1.000e+00]]\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/erigler/local/lib/python2.7/site-packages/scipy/linalg/basic.py:1018: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n",
-      "  warnings.warn(mesg, RuntimeWarning)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# No constraints, allow all degrees of freedom for M\n",
-    "\n",
-    "# LHS, or dependent variables\n",
-    "abs_st = np.vstack([x_a,y_a,z_a,np.ones_like(x_a)])\n",
-    "\n",
-    "# RHS, or independent variables\n",
-    "ord_st = np.vstack([h_o,e_o,z_o,np.ones_like(h_o)])\n",
-    "\n",
-    "# regression matrix M that minimizes L2 norm\n",
-    "M, res, rank, sigma = spl.lstsq(ord_st.T,abs_st.T)\n",
-    "\n",
-    "# clean up a bit by applying a threshold...this is mostly for aesthetics\n",
-    "tol = 1e-9\n",
-    "maskM = np.abs(M) > tol\n",
-    "M = maskM * M\n",
-    "\n",
-    "# transpose matrix to operate on data whose vector components are stored\n",
-    "# as column vectors (this is NOT a universal standard, but common enough)\n",
-    "M = M.T\n",
-    "\n",
-    "print np.array_str(M, precision=3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Enter path to save adjusted statefile"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "path = './'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = {\n",
-    "            'M11': M[0,0],\n",
-    "            'M12': M[0,1],\n",
-    "            'M13': M[0,2],\n",
-    "            'M14': M[0,3],\n",
-    "            'M21': M[1,0],\n",
-    "            'M22': M[1,1],\n",
-    "            'M23': M[1,2],\n",
-    "            'M24': M[1,3],\n",
-    "            'M31': M[2,0],\n",
-    "            'M32': M[2,1],\n",
-    "            'M33': M[2,2],\n",
-    "            'M34': M[2,3],\n",
-    "            'M41': M[3,0],\n",
-    "            'M42': M[3,1],\n",
-    "            'M43': M[3,2],\n",
-    "            'M44': M[3,3],\n",
-    "            'PC':  pier_correction\n",
-    "        }\n",
-    "with open(path + 'adj' + obs_code + '_state_.json', 'w') as f:\n",
-    "            f.write(json.dumps(data))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Enter Start and End Times for Test month(s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "start2=UTCDateTime('2015-03-01T00:00:00Z')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "end2=UTCDateTime('2015-03-31T23:59:59Z')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# pull raw data from Edge server\n",
-    "factory = EdgeFactory()\n",
-    "\n",
-    "hezf = factory.get_timeseries(observatory=obs_code,\n",
-    "        interval='minute',\n",
-    "        type='variation',\n",
-    "        channels=('H', 'E', 'Z', 'F'),\n",
-    "        starttime=start2,\n",
-    "        endtime=end2)\n",
-    "\n",
-    "dt_test = np.array([(hezf[0].stats.starttime + second).datetime for second in hezf[0].times()])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# apply affine transformation matrix to raw data to generate Adjusted Data\n",
-    "raw = np.vstack([hezf[0].data,hezf[1].data,hezf[2].data,np.ones_like(hezf[0].data)])\n",
-    "adj = np.dot(M,raw)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# generate averaged static baselines, then estimate an alternate Adjusted Data stream\n",
-    "# (this amounts to the traditional method for [Quasi]Definitive Data processing)\n",
-    "h_pqqm = np.mean(h_abs_n - h_ord_n)\n",
-    "d_pqqm = np.mean(d_abs_n - d_ord_n)\n",
-    "z_pqqm = np.mean(z_abs_n - z_ord_n)\n",
-    "\n",
-    "def_h = (raw[0]**2 + raw[1]**2)**0.5 + h_pqqm\n",
-    "def_d = np.arctan2(raw[1], raw[0]) * 180./np.pi + d_pqqm\n",
-    "def_z = raw[2] + z_pqqm\n",
-    "def_f = (def_h**2 + def_z**2)**0.5\n",
-    "def_x = def_h * np.cos(def_d * np.pi/180.)\n",
-    "def_y = def_h * np.sin(def_d * np.pi/180.)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plot of $\\Delta F$ Over the Test Period\n",
-    "The left plot (black) shows adjusted delta F, the cyan shows adjusted delta F using average baselines over the period (instead of the transformation).  The blue on the right shows raw delta F."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\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",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\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 backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\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 * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\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'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\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 * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\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",
-       "    var width = fig.canvas.width/mpl.ratio\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 + '\" width=\"' + width + '\">');\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 width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\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": {
-      "text/html": [
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\" width=\"900\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.figure(figsize=(9,3))\n",
-    "\n",
-    "pl.subplot(1,2,1)\n",
-    "\n",
-    "# plot \"definitive\" delta-F\n",
-    "def_delta_f = (def_f - hezf[3].data )\n",
-    "def_delta_f_med = np.nanmedian(def_delta_f)\n",
-    "pl.plot(dt_test, def_delta_f,'c')\n",
-    "\n",
-    "# over-plot Adjusted Data delta-F\n",
-    "adj_delta_f = (adj[0]**2 + adj[1]**2 + adj[2]**2)**(0.5) - hezf[3].data\n",
-    "adj_delta_f_med = np.nanmedian(adj_delta_f)\n",
-    "pl.plot(dt_test, adj_delta_f,'k')\n",
-    "\n",
-    "pl.ylim(adj_delta_f_med - 20., adj_delta_f_med + 20.)\n",
-    "pl.title('adjusted')\n",
-    "\n",
-    "pl.subplot(1,2,2)\n",
-    "\n",
-    "# plot raw delta-F\n",
-    "raw_delta_f = (((hezf[0].data)**2 + (hezf[1].data)**2 + \n",
-    "                (hezf[2].data)**2)**(0.5) - hezf[3].data)\n",
-    "raw_delta_f_med = np.nanmedian(raw_delta_f)\n",
-    "\n",
-    "pl.plot(dt_test, raw_delta_f,'b')\n",
-    "\n",
-    "pl.ylim(raw_delta_f_med - 20.,raw_delta_f_med + 20.)\n",
-    "\n",
-    "pl.title('raw')\n",
-    "\n",
-    "# re-formats dates for better presentation\n",
-    "pl.gcf().autofmt_xdate()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plots of Adjusted X using Static Baseline, Affine Transform, and '$\\Delta x$'\n",
-    "\n",
-    "This is not quite the same comparison presented for $\\Delta F$, because $\\Delta x$ is a comparison of two derived quantities."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\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",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\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 backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\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 * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\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'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\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 * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\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",
-       "    var width = fig.canvas.width/mpl.ratio\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 + '\" width=\"' + width + '\">');\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 width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\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": {
-      "text/html": [
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hJBIxHj8SAgpIByAEEIiEeMBCO0jISQStI+EEGInxvaREFJAOAAhhEQixgMQ2kdCSCRoHwkhxE6M7SMhpIBwAEIIiUSMByC0j4SQSNA+EkKInRjbR0JIAeEAhBASiRgPQGgfCSGRoH0khBA7MbaPhJACwgEIISQSMR6A0D4SQiJB+0gIIXZibB8JIQWEAxBCSCRiPAChfSSERIL2kRBC7MTYPhJCCggHIISQSMR4AEL7SAiJBO0jIYTYibF9JIQUEA5ACCGRiPEAhPaREBIJ2kdCCLETY/tICCkgHIAQQiIR4wEI7SMhJBK0j4QQYifG9pGQouMxAKMArAGwBMDXAA4w6jQB0BnAcgBrAXwJYGetvDXUF9aWmmr1WgEYC6AKwAznPJO7AcwGUAlgBIDjQtwLByCEkEgYAxDaR0IIcYjxBI32kRASiRjbR0KKjj5QE6WDARwOoDeAOQC20Op0AfA7gNMBHA1gGIAhWvlmAHYxUh8A/bU6ewEoA9ABwIEA7gFQC+Acrc5VUJO3mwEcBKAbgJXwTvKC4ACEEBIJYwBC+0gIIQ4xnqDRPhJCIhFj+0hI0bMT1JfvFOd4GwDVAC7X6rRw6pwQ0EY1gBu0vOcBTDbqfQI1kXMZAeA17bgxgPkAHs2x7xyAEEIikWUAQvtICNlgifEEjfaREBKJGNtHQoqefaG+fIc4x6c7x9sa9eYAeNCnjTYAVkF5HrgMBNDRqHczgD+cvzeB8ji42KjzPwA9c+w7ByCEkEhkGYDQPhJCNlhiPEGjfSSERCLG9pGQoqYxgG8BDNbyroVatmIyEsprwMYUAK8beaVQ8WR0zof6om8GYDfn75ZGnRegPA9sbAplJNzUDByAEEIiEDAAoX0khGzQxHiCRgGLEBKJGNtHQoqaLlABgnfX8vwmaKMAPGfJbwn15T3ayLdN0C5w6jaB/wTtRQDDffpbAktgZA5ACCH5EjAAoX0khGzQxHiCRgGLEBKJGNtHQoqW1wDMhQomrBN2iczbAMZZ8guxRIYeBoSQesVnAEL7SAjZ4InxBI0CFiEkEjG2j4QUHY2gJmfzAexnKXeDFF+m5e0Pe5DiLaG2m7/H0s7zACYZeR8hM0jxq9pxYwDzwCDFhJB1hDEAoX0khBCHGE/QaB8JIZGIsX0kpOh4HSqg8KnwbvOuBxjuAuVRcBrU0pehTjK5BUAFgO0sZXsBKIeK2dICwF3w3yb+Jqit5LtCbRO/c473wgEIISQSxgCE9pEQQhxiPEGjfSSERCLG9pGQoiMjRoqTWmt1mgDoDGAFgDIAX0FN4kyGAvgw4FqnQS2fqQIw07iGyz1Qk8EqKI+D43O9EXAAQgiJiDEAoX0khBCHGE/QaB8JIZGIsX0khBQQDkAIIZGI8QCE9pEQEgnaR0IIsRNj+0gIKSAcgBBCIhHjAQjtIyEkErSPhBBiJ8b2kRBSQDgAIYREIsYDENpHQkgkaB8JIcROjO0jIaSAcABCCIlEjAcgtI+EkEjQPhJCiJ0Y20dCSAHhAIQQEokYD0BoHwkhkaB9JIQQOzG2j4SQAsIBCCEkEjEegNA+EkIiQftICCF2YmwfCSEFhAMQQkgkYjwAoX0khESC9pEQQuzE2D4SQgoIByCEkEjEeABC+0gIiQTtIyGE2ImxfSSEFBAOQAghkYjxAIT2kRASCdpHQgixE2P7SAgpIByAEEIiEeMBCO0jISQStI+EEGInxvaREFJAOAAhhEQixgMQ2kdCSCRoHwkhxE6M7SMhpIBwAEIIiUSMByC0j4SQSNA+EkKInRjbR0JIAeEAhBASiRgPQGgfCSGRoH0khBA7MbaPhJACwgEIISQSMR6A0D4SQiJB+0gIIXZibB8JIQWEAxBCSCRiPAChfSSERIL2kRBC7MTYPhJCCggHIISQSMR4AEL7SAiJBO0jIYTYibF9JIQUEA5ACCGRiPEAhPaREBIJ2kdCCLETY/tICCkgHIAQQiIR4wEI7SMhJBK0j4QQYifG9pEQUkA4ACGERCLGAxDaR0JIJGgfCSHEToztIyGkgHAAQgiJRIwHILSPhJBI0D4SQoidGNtHQkgB4QCEEBKJGA9AaB8JIZGgfSSEEDsxto+EkALCAQghJBIxHoDQPhJCItGA9vEUAN8AWOBc/2KjvBGA9gAWAqgA0BfAfiHap30khEQixuNHQkgB4QCEEBKJGA9AaB8JIZFoQPt4HoCnAVwKu4DVFsAqJ/8wAD0B/AagSY7t0z4SQiIR4/EjIaSAcABCCIlEjAcgtI+EkEgUiX00BaxGUJ5XD2l52wCoBHB1jm3SPhJCIlEk9pEQsp7BAQghJBIxHoDQPhJCIlEk9tEUsPZ28o4w6g0A0CnHNmkfCSGRKBL7SAhZz+AAhBASiRgPQGgfCSGRKBL7aApYJzp5uxr1PgPwqU8bm0Ldg5uagfaREBKBIrGPhJD1DE7QCCGRiPEAhPaREBKJIrGPuQpYnwP4xKeNEuccT6J9JITkS5HYR0LIegYnaISQSMR4AEL7SAiJRJHYx/pYQkgPLEJIvVIk9pEQsp7BCRohJBIxHoDQPhJCIlEk9tEviHsbLW9rMIg7IWQdUiT2kRCynsEBCCEkEjEegNA+EkIi0YD2cUsoD6sjnOs/6Py9p1PeFsBKABcCOBTA1wB+A9Akx/ZpHwkhkYjx+JEQUkA4ACGERCLGAxDaR0JIJBrQPraCJWYVgO5OeSMA7QEsgvK86gtg/xDt0z4SQiIR4/EjIaSAcABCCIlEjAcgtI+EkEjQPhJCiJ0Y20dCSAHhAIQQEokYD0BoHwkhkaB9JIQQOzG2j4SQAsIBCCEkEjEegNA+EkIiQftICCF2YmwfCSEFhAMQQkgkYjwAoX0khESC9pEQQuzE2D4SQgoIByCEkEjEeABC+0gIiQTtIyGE2ImxfSSEFBAOQAghkYjxAIT2kYSmvLxcunbtKnPnzm3orpAigPaREELsxNg+EkIKCAcgJDT9VqyQCyZMkDkVFTJk1Sq5aOJEmVle3tDdIg1EjAcgtI8kNA888IAAkJ122klERBYuXCjJZLKBe0UaCtpHQgixE2P7SAgpIByAkNAgkRAkEnLauHGpv48dPbqhu0UaiBgPQGgfSWgOOOAA9/sg33zzjQCQq6++uqG7RRoI2kdCCLETY/tICCkgHICQ0Lii1b7Dh6f+3nHw4IbuFmkgYjwAoX0koTnwwANTAtYJJ5yQ+ptsmNA+EkKInRjbR0JIAeEAhITGFa32HjYs9fcOgwY1dLdIAxHjAQjtIwnNwQcfnBKtWrZsSQFrA4f2kRBC7MTYPhJSVDwGYBSANQCWAPgawAFGnSYAOgNYDmAtgC8B7GxpqzWAiQAqnbY6G+WHARjklM8F8IiljSsATHPqTAJwfsj74QCEhMYVrZpTwCKSMQCJk42kfSShOfTQQylgkRQxnqDRPhJCIhFj+0hIUdEHalJ1MIDDAfQGMAfAFlqdLgB+B3A6gKMBDAMwxGjnHwDmA7gWwD5QE7ELtfKtASwC8IFzrasBlAO4XavTEkAtgIcBHAigPYBqAIeEuB8OQEhoXNFKTxSwNlyMAUicbCTtIwnNYYcdlhKtdtllFwpYGzgxnqDRPhJCIhFj+0hIUbMT1BfvFOd4G6gJ0uVanRZOnROc4+2gJlpnBLR7J4AVADbR8p6D8iRw+RTAt8Z5wwG8kXv3OQAh4aGARXSyDEDWZxtJ+0hCc8QRR6REKz2RDZMYT9BoHwkhkYixfSSkqNkX6ovn/qJ/unO8rVFvDoAHnb+vhFrOciOAXwDMA/AZgD20+u9BLb3ROc1pezvn+HcADxh12gGYEKL/HICQ0FDAIjpZBiDrs42kfSShOfLIIylgkRQxnqDRPhJCIhFj+0hI0dIY6tf9wVretQCqLHVHAnje+ftRKA+EaQDOgfI66Oscu94EPwLoarRxENSX/EDnuBrANUaduwAsDujzplBGwk3NwAEICQkFLKITMABZ32wk7SOJzNFHH00Bi6SI8QSNAhYhJBIxto+EFC1dAMwGsLuW5zc5GwW1vAUA/gn1ZT1bK98JQB3UZA2wT84Ods5r4RzbJmd3Q8WF8aMEloE1ByAkDBSwiE7AAGR9s5EloH0kETn22GMpYJEUMZ6gUcAihEQixvaRkKLkNahdr/Yy8nNZHnOzU2d3o85iALc5fxdqeQw9DEhkbALW9hSwNlh8BiDro42kfSSROeaYYyhgkRQxnqBRwCKERCLG9pGQoqIR1MRsPoD9LOVugOLLtLz94Q1Q7B7rAYq3h/IucD0O3ADFG2t1/oPMAMXfGNcfCgZxJwXGJmAhkWjobpEGwhiAxMlG0j6S0MAiXoEC1gZLjCdotI+EkEjE2D4SUlS8DmAVgFMB7KKlzbQ6XaC8CU6D2iJ+qJN0vgYwGcCJUMGNvwEwBenJ2DZQy1zeg1oWcxWAMni3iD8Raov4NlBLZkoQbot4gAMQkgcUsIiOMQCJk42kfSShAQUsohHjCRrtIyEkEjG2j4QUFX6D09ZanSYAOkN5B5QB+ApqAqezNYC3AawEsNyps4dR53AAg6B245oHoK2lP1cAmA4VU2YygPND3g8HICQ0FLCIjjEAiZONpH0kofH7DpANkxhP0GgfCSGRiLF9JIQUEA5ASGgoYBGdGA9AaB9JaEABi2jQPhJCiJ0Y20dCSAHhAISEhgIW0YnxAIT2kYSGuxASHdpHQgixE2P7SAgpIEU/AKlLJhu6C0REaurqUn9TwCI6MR6AFLV9fO+992TrrbeWefPmNXRXNnhefPFF+fLLL0VE5IQTTqCARVLQPhJCiJ0Y20dCSAEp2gFIMplMCSOra2oaujsbNGNXr5ZN+/eXZ2bPFhEKWMRLjAcgRWsfRdJL1TbddNOG7soGzciRIz0iVcuWLSlgkRS0j4QQYifG9pEQUkCKdgAyo7w8JYy8tWBBQ3dng+bEMWM8IhUFLKIT4wFI0dpHEYm1ODJkyBCZO3duQ3fDStLwCv7mm2887+Gkk06igOVQwx+faB8JIcSHGNtHQkgBKdoByNS1a1PCyCMzZjR0d+qVZdXV8vjMmTK9rKyhu5JBTV2d9FuxQipqa1N5jQyRigKWYn5lpbyzYIHnWW2IxHgAUrT2USS+AtaoUaOK9r769OkjAOTYY4+VX3/9Vf7zn//IBRdckOpvWVmZ3H333RSwRGTgwIECQG699daG7kqDQvtICCF2YmwfCSEFpGgHILqA1fqXXxq6O/XKZZMmCRIJ2WzAgIbuSgbuM/9T//7Sae5cuWbKlAyRigKWwr3vQ0eObOiuNCgxHoAUrX0Uia+A1bFjx3q7r/Lycnn66aelvLy8HnrmfeaNGzfOEKkWL14sL730EgUsie/nMyy0j4QQYifG9pEQUkCKdgAyWROwrp86taG7U6/sOmRIvYk+/541S5BIyIz6mqD5iFN6f08ZO5YClsgGfe86MR6AFK19FImvQFCfAlZ9PyNYhCk9vfTSS/LKK69QwBLvs6qurm7o7jQYtI+EEGInxvaREFJAinYAUlpWlhIHHp05s6G7U6/oAlZdMimz8hSf9ED39SGi1Brt2VJNXZ00GTAgI78YvckKDQUsRYwHIAWzj1VVVbIgYmw/NJA4UltbK9ddd5288sorBWm/U6dOqfsaNmyY7LPPPvL++++Hbqeqqqpen1EymcwqYB199NG+ZRsa+r0X6rOyPkD7SAghdmJsHwkhBaRgA5CPFi2SL5csyfv8XzQPrMsnT67HnmVnwpo10mzIkMjB4yvr6qSyri4jfzdNwDpt3DhBIiEfL1oUuv32jvdVfYko7y9cmFXA8ktNNnABq7QI45mtK2I8ACmYfWzRooUAkMkRbBsaSBz59ttvU9c1A5rnSk1NTaqNFStWeMp0ASvKPU6fPj3S+bVGbLvq6uqsAlZQ2tDQ7/3CCy9s6O40GLSPhBBiJ8b2kRBSQAoyAFlcVZWa2FdbBJxcmLRmTYN5uBwzenTquivyWPpQl0zKF0uWeGsjtr4AACAASURBVLysdHQBK8o9/nXixLzP/628XPqvXOnJe3zmzLwFrA3RC0m/93GrVxf8emtra2XSmjUFv05YYjwAKdgEDc7E/qmnnorcBnzEkXnz5snjjz9e77v5ffrpp55r//jjj6HOr6yslD//+c+p85s0aeIp/+9//1svAtCIESMCz584caJsvvnmMnbs2IyyLl26yBZbbCGDBw9O5f3++++xEbBqamrksccek759+xbsGuv6/t944w0BIN27dy/4tcJA+0gIIXZibB8JIQWkIAMQffmfzQMpF8atXp1VHOk6f760HDNGllZVRehtJkeMGuW5dlhPrHcWLPCcv9joX30JWNsNGpQ6d0dtomWWDzKEKpG0+DJGE16iiFfFJmANWrlSmg8bJt8uW1awa+j3/tg6WObqXutvRbapQYwHIOu1gNWyZUsBIIcddlhO7dXW1srw4cOlKos9/eKLLzLEif79++d0jZEjR0rnzp0zlt3pvPrqq1YBaOnSpTldw2X33XcPfEZ6mekJ5+bvueeeqbw77rgjNgJW165dC96v0047bZ3ef31f68cff5SnnnpK6vIcw7jQPhJCiJ0Y20dCSAHJewAStHREF7AqjGUYuTLqjz+yiiNu2d3Tp+fU5oQ1a+Tfs2ZJmdYn230cZQhYSCRkVU1N1var6+qsws4Lc+Z46jXzEbBMT61s6OeeO2GCp2yk8fymG0vc3Pwnfvst9QziJGBt2r9/ql/5LnPKxrq+//q8VjKZlBunTpX7S0sjtxXjAUhe9nHIkCFy/vnny/QAu4SIAlZtbW3WCbutvKKiImNpnMsTTzyRqv/BBx9I7969BYBMNTbR+OqrrzLEmQcffDBrn4M8mKqrq6W8vFy6devmu4Tw2muvDfGE7B5AkyZNkpdeeknq6uoCBSY9f/vtt5eamhq54YYbikbAKisri7T8tG3btql+3XjjjfXYszT33Xffei1guW19+OGHqbxJkybJl19+Gaod2kdCCLETY/tICCkgeQ1AHpoxQ/YeNkxW+iyv+7UeBKzuRjwmG2Z5bTIp3y1bJst8+qXX/2rJEmk6eLAgkZD5lZWeesdpSwjdtMCoY2MHzSPKJm68vWCBNAoQgL7PwVsomUzK6pqajADu5zsC1uWTJ8vJY8dK72XLAgUWs6yitraoBKySWbPkngjiit6vqwoUQ21dClj1HbBf/47WOgJfMpmU26dNk3azZoVqK8YDkLzsI5yJ70EHHZS1Tr4ClukFFXQNt3zt2rWp47Vr1wbWN5NOz5498xKwevXq5dv+HXfcIY0bNw7swzHHHJP1GqNGjZJjjjlG+vbta70H9+/LLrssZwELgLRu3VquuOKKdS5g+YnvBx10kACQ7777Lq92DzjggIKLSw8++KDvNWpqauSnn36S1fW49Fq/1qpVq+qtvXbt2mXkvfjiizm3Q/tICCF2YmwfCSEFJL8JmjPxfWb2bGv5jPLyyAJWLuKAWd7h998DvZn8hJemxvK7lmPG5CVgBYk7/VeuzCoA5RL0/tZp0wSJdPB3N+0+dKiUaSLUyWPHhhKw3pw/f50LWANWrpRhlomGLtZMyzM4+roQl4KuMWHNGnloxoy8YqjZMHeIzHdprst0yzLfMdqy3dkVFTm3FeMBSCQBa4sttshaJ18B629/+1soAau6ulpKSkpSx0888YRUVFTIsGHDZPTo0Rn1zaQLAq5nlp5yEbDatWsXSQBy73Px4sVSXV0tEyZMkCeffFLKNBuR7dxs5aNHj5Y2bdpYy88777x1KmC1adNGdt99d+vSSbfNsF5pfs+pEDz00EO+13jmmWcEgJx66ql5td2jRw+54447PAKffq2g716uuG1ttdVW1mvkCu0jIYTYibF9JIQUkEgC1tM5CFjlBRKwTI8h85zSsjLpu2KF/N/EifLT8uXWNvWke23ZxJ85PhP61TU1KQ+WoPZtopiZPli0SMpra+WzxYtlZXW1XD1lihwycmSqfb8likgkZM+hQ2WW9txtyVyWqaeHZsxYpwLWqpqa1HlmoH9drJmYZ9Dyhhaw3LybjOVXYdreZciQ1LH57tv8+muUrstM7bPyvLPE9TvNa29CiOce4wFIJAELAZNctzxfAQtZBAg9xpF7D/rxBRdc4DkeMmRIoPiy7bbbptr+4YcffAWsSk3o15fpVVVVBba/+eab5yQC9enTJyOvTZs2IuII3wHnZit/6aWXAssvvPDCdSpguec98cQTvmXXXXed9dxkMinvvvuuTDCWlpvn59s381o2HnnkEc819FhSe+yxRyr/k08+keHDh+d8rTFjxqTOfe6553zvaUHEXYT1thYvXiyrVq1KHR966KE5t0P7SAghdmJsHwkhBSSSgOUXP0cXsMryELDM5VI2AcJWrh8PMjyeKgPEHyQScvSoUam2WxneTUgkZEZ5uZTV1sr/Fi5MiXKfL16cKrf1WU9/++WXrAJQx7lzU39vNmBA6u/X5s0TEZHn5swJPH9EgEAVtLwRidwEtvoUsGZXVKTOW23EF9PFGr9d934rL5drpkyRsT5LUOpLwJpdUZHXNfT8+0pL5dccPcl+154LEmkPRtvnNwpzjOvUJpNyiibcTrYsMfMjxgOQyALWckc896vz5JNP+razevVq6dChg8wylnSuXr06UIAYPHhwRvmHH34YKK6YYoMtiYisWrVKrr76aquA5e5OuP/++2c8h4cffjiw7QMPPDCSONStW7cMkc5MM2fOtOY3atQop2vsv//+DSJgtW3b1rfMT8Dq0aNH4HWj9s3FjVdmCmULFizIWBJaof0ItMMOO2T0oaamRjp16iQfffSR7/U+++wzzzk33XST7z399NNPed1TTU2NrFixIqM9XfQ97rjjcm6P9pEQQuzE2D4SQgpIJAHLbxKte3esCQh+/v2yZXLW+PEZ3k3PG0LNSWPGpMrmVVbKvsOHZ0zmJ6xZEyiuzDYm7H4CzECfpX7TtCVXB40YkSFYLaisDGy7xYgRWa/fOkDkGr16tfxXE7hs6Yfly33L/jx0aNbrH2B5rrakx0/KV1CZrz2v5cYyO927boqPkKLvFGmjvsQe/f3rvGXsNBkkYLlpTkWF7Dd8uPx37tys13OTu3lAuSVGWb70WrpUSmbN8rS1sRb0HomETKWABdSDgNWhQ4fAOkEC1l133ZWqpwdev/nmm30FiKOOOiqSyBKUREQuvvhia9n999+fUVc/fuGFF6RFixa+bW+99daR+zdu3LiMPH3Zop+XWdRrv/POO3LJJZekjnv27JmxA5/+jsJ+jh566CHfsuuvv9567j//+c/A65p9y3enPb/7O+KIIzKuoS9DtT3HSZMmZX1W5jmfffaZb9kPP/wQ+Z780oknnphze7SPhBBiJ8b2kRBSQAouYJneNbm0EySO+Ikqh40cGSi67Oqz8595Db+ynwxxKLFihefY9PgqRDpz/PiMvG5O7KpN+/eXTzWPsHzS7j4i17jVq+VFTVQUEdly4MBIgoou+JlB99fmIGBtogku2T5buhAUlnw+o37l106ZkvVZmecsrqoSEZGV1dWRnrfL/CxCq5t+oYAF1IOAZS7/qq2t9XhQBQlYejt9+vSx5rupV69evmX1lcK0f9ttt3mOb7/9dqvwVp/pzTffTP293XbbyZQpU0REZJdddhEA8tFHHxXsuVRUVMhee+0l5513noiInH766dZ6+XyO3CWStrIbbrjBeu6jjz7qe12/pZS2eqWlpdYlghMnTpS//OUvvufb2l+0aFFguR54v8bHXpvn6N5aZlmrVq1kxowZ1nb8qKioyOmd655f2aB9JMROba3INtuIXHqpSIghB4kRMbaPhJACUu8C1ozychm6alWqfKFP8POlVVWedtw4SH5L8SatWZPz5DvfZN5bmPTuggWyp0UAMpf9md5j22ZZ2qcnd9dEJBLyyeLFIiKpuFdNBgyQrvUQiD1ImJlbUZGazGwdUcBaqL1LV6Rx+UOLj+XnCeQnYM2tqPC0HdS/3ysqpPeyZdYJ2uvz5gWen619W/klkyYFPivbTpCud+JnFnHyqsmTM3bQzMaLWZahuokCFoA87KMeJweAlJSUeMqPOeaYnIQNv13y9J0E9dSyZUsRyV1gsqXNNtsssLy2tjbvtk8//XQ58sgjM/Jffvllz/HGG2/sOR47dmzO17j00kutz9Rd+vf8889nnOOKW1GSjTPPPDOnekG459kC5LtlfgLWY489Zr3ubrvtlvN9uEHYn3nmmYyybbfdNvB8W/tznFh7IiJnnXVWRnnbtm1Tf5eXl0v//v3lrrvukjXaMnLznHfffTfwmltuuaX1+fgJZJ07d/Z9Ptttt13q7zfeeMN6vg3aR0LsPPGECKDS1ls3dG9IQxBj+0gIKSChByA1RiwenSWGKIWEN7aUjlnvjHHjRETkvtJS64T6+2XLZLS2S1qhhJp8z3105kxr/pdLlniOL5w40XOcryjnskzzzPnP7NkZ9cbWwzOzYRPewqB7YJk7PK7Q7slPSNnYImDpgeH97qOitjbl8eXmf+qIgTrZnkM+5Rdp7z6ZTMpNU6fKX8aOlSpHvJ1hCcI/de1aqamrkxumTrW2eYElSPP8ykr5rbzc+txyfedhYtfFeAAS2j62bNnSM+n9/PPPvc8/RwHEr54tH1BxnKqrqz15W2yxRSghZptttgksLy8vz1vkOeSQQ6z51dXVsskmm6SOzdhICxcutApPtrTzzjtbn6m7nO3BBx/MOOfrr7+2ioVhko2zzz47p3pBuOc98MADvmU33nij9Vw95pjfTn22/p1xxhkCqCWfepkp8vudv3btWo8nlZ5KS0vltdde873+nXfemfq7ffv2nrJEIiE1NTWp46ZNmwoA2XPPPeXAAw+UpUuX5vR+9I0FBhu7D4uI/Otf/8rpndfSPgIUsEhE2rQRAdKJbHjE2D4SQgpI6AHIkVrsIXPS/rOxrC5I2LDVW2vxQHHTD8uXZyzj+2Xt2sjijNnPsOecO2GCIKEEODfP9Q564NdfZbDmjYZEQq7RlpG51zRFLb0NN+kxrE53xD4RkTLtmd1rEf9W1dTI/gGxrd7MwWvLxvYRBax5moA114iBpnvn+QlYtuu+miVGmH7ev40YUEHtI5GQg0eMEBGRH5Yvl/bGuW5KJpNygfN5sCW97MrJkz1lvZYulSHOZ2Xj/v093nZ7DRvm6znVwumXS6+lS1Nloy3B53OJc3bzL7+EeJOxHoCEto8wJrp9+/YNLIcxaneFAr96tnw36TGg3Am2TbTxS9l2Ahw9enRocefZZ58VANKsWbNUXpcuXeTNN9+UJUuWiIjXK6h58+Zy6KGHeu751VdfzelartfRhRde6HmmrqhoCz7/888/S5s2bXzbbN68edbr2jjnnHNyqpfLZ+m+++7zLfPzwNKXMLobCejiTdjP1xpjMw2/883dLfU0ceLEwOuXlJQEluu7ROredgDk5JNPzun9mLtzduvWTUpKSlIxwHLZzCDsu6R9JMTOBx+IgALWBk2M7SMhpICEGoCY3lfmxL9fFgErmUymlhTa6k0OEKS+MjyZXLJNxsOkg3MItG4md+dAN0h744Ta8dDdxc28p1unTUv9fdb48SIicoUhZtjSWePHy1+cXeI+1zyG6rQll5dpS9TcVJtMykkBOwxOyhL83ibuiIjsqAksQfX8mKsF1Z9tCFiLNAHLbwmh7bpXZXmOQZ+XoPaRSMilkyZ5dke0pXFZvN3ODRC3kEh4xGHbclRb2t/Yft4sv3zyZGmv7WJ3hCFA29LG/fuHepcxHoCEso82zxNdwPLbJc8NRD127FjZZZddfEUnv9hFADJiLrn069cvpwl5odKMGTMEUOLYfvvtJwBk4MCBnud22GGHpeofeuihUlpaKhdccIEMHTpURES6deuW07XuueceATJ37XOfTatWrTLOGT16dKBw8vbbb2fk9e7dO6uYcd5550USPUQkdd69997rW+YnYOnXnetsGmHbWc/sn1+ZLmAtW7bMWmfx4sWB7f/000+B5a1btw4s33333QWA/OlPf8pa1/bcgwTYfv36iYjI5ZdfHqrNXFgP7OPdAGYDqAQwAsBxOZ5HAYtE4qOPRKAJWHlsWk7Wc9YD+0gIKUJCDUD+ZtklT+cen+V/by1YICIibX79VZBIyDmWYORIJGT4H394jo8dPTr19zHa3/p1D8xDdMo1NbWINGbqvnChIJFIBTXf1xAUzCWC95eWyqQ1a+Tp2bNTy7T8lofp6S9jx8rhTqD6Ps4v6i6bOt5aLS1ClYgECie23e30ZY/3lJZaPwu2ZxOGOZqA1XnePE+ZvrxwcggB6xKLgGfWCypzscWbQiIh//RZJuom2y6Qep90Lz1b0uOjBXnNmefYnomZah3Pnu1yjLkWhhgPQELZx3fffTdjovvPf/4zVR7k6SMicvTRRwdOmm+99VbP8dKlS1NLrS688ELrBNsUvRo3bpzzxD9b2nLLLbMuO9TFDjdu0qRJkzzPTd+xz7a7W/fu3XPqzxVXXCEA5Omnn/ac78Yd22OPPTLOGTNmjHTo0MG3TZto+Ouvv2YVM2yeSLmyYsUK+eWXXzznVhsbXbj5frsQ6uc+++yzIiIyd+7crKKMX9muu+6a8g40vZ/cdOqppwa2f9FFF2Xk6bs35vJ5cz9H+nLDbOnjjz/OKt4ddNBBIiIpkTPbstIwFLl9vApAFYCbARwEoBuAlQCa5nAuBSwSiY8/FoEmYBmbPZMNgCK3j4SQIiXUAET3HnLT2444JRI8gc5WjkRCntFiOH3meBld7uNV42LGeMrFwyTXlG1nwZd//116GJ5hh48c6XlmZYZA9M+ZM3N6rmY6ctQo2XvYMEEiIUO07cj159rcKTefk7lcLdt70ZeC3ucjYO1i2dUxV36vqMiIZ6aje2f1X7nS2obt3GxB7P02CEDCuywz38+LuSwRiYSMMkTZoOR6yt0wdaocmmVXTTc1SigvQFsAeD11X7hQ6pJJaeQct7aI0W5qG3LnrhgPQELZx//973/Wye6XX34pIsHLq0TEN06ULblBqJ944omsE+ygdqIEMR83blyggHXCCSd44ha5yfUIcnGFJwBy9tlnZzzXb7/9Vg4++OCs/TnhhBMEgHTq1Cnn+6+qqsrw8Lryyis9z7FXr16ectPTyMZf//rXjGvZNouwsdFGG2Wc+/333/ve0zvvvJPRhl5+zjnniIjI1KlTs36mgsq7dOkS+DxPOeWUwPPff//9jLxrr7029Odujz32CLU0Npe+ue/R/fu7776T0tJS6zO59NJLc3qPLkVuH0cAeE07bgxgPoBHcziXAhaJxIcfikATsIzfZskGQJHbR0JIkRJqAOLnKbTKmUwFTaCzleup5ZgxqWu64s5GWvnd06enyifksAQuKPl5jTUbMiRQ8EBC7Z5nxv06Ueu7iPKA0ONZPTN7dsZz7Tp/fmDsJDft5Hg9TTLjkWR57rcYYsVW2g6CIpmeS4u1JXz3+whYu+UpYOkB2v3Ona0JWEikd1z0u+dS52e7LpadA/VkWwKrp2GOMJjvZ+mjRYsy8sIE0d/LESBvnzbN432YS3rl998Dy2+dNs2zu2O54/1nex9hifEAJJR9fOqpp3KaHNtSVVWVHHXUURn5L774YkbeZpttlrqmn1eX57tilP3444+pv92g3UHJJh75ta0nN0i2GUzejKeke9MEiQMHHnhgTmKFKegE1RUR+fjjjz15egB0EZHVq1d7yvVg9uedd561r6ZHHIBUnKVs2PrZq1evwDoiKsbV9OnTpaqqylqeLYbZmjVrAst33XXXwOdp21lQTzaBd+bMmTm9UzPpuyzWR9pvv/08u2x++umnqWd9yy23eOq6S1tzpYjt4yYAagFcbOT/D0BPS/1Noe7BTc0AClgkfz7/XASagDV/fkP3iKxritg+EkKKmFATNL/J8Q6DBgWWI+Hv/VKr5btxlc5xYkOJpJcd6umUsWNT5TZRwm9HQFvKFrcr6FwRkTGGQHG21ndbGw/++qv12S4MsRuhGTMqWx8f0J7h3sOGyZnOEs5NnVhHpoeW3uZgw9vLZXdLjKZcGGoEtbedO9PYjW9b5/M1cc0a6Tp/fkb5COfzm+29T8widr42b55MKyvL+T2YqbMhoJ06dqz8ZtlZMFs6fvRoOdmJd5ZrMpfYmunKyZNT8dqQSHi8QVoYy3DDEuMBSCj7+MADD/hOkLN5tzRr1kyaNGmSkT9q1Cj56KOPPHlNmzZNXXPAgAHW9nR0DycAsmjRotTf559/fmC/7rzzTk+MKrP9oHNdAUsP4L7RRhtleCLpO7/57aonItYlgLb0/vvve87ba6+9POXusssdd9xRRJSHl14+b948ueiii6Rz584iIhnvLplMyuLFi6VTp06pAOkmtuVyue5cZ7unr7/+OrCOSDpgv+kVd/HFF4uIv7eem4J28gPULo977rmnb7nf8lT3vf33v//15Jc5Pzzk8k7NpC87rY+04447yoIFCzzfEZf777/fU3e85f/4IIrYPu4G1a+WRv4LUJ5ZJiWwPDsKWCRfvv5aBJqAZVmgQNYDkkmR558X6dMn/LlFbB8JIUVMqAmauQOhn+hhS/ruaOZ5/zBEqssnT05d07Ysy5xkm2V6DKNsy/Mqfbxy/No26/xqCB6XGPFdzDb0gNo6ZjtuMncjRCIhy814KEb5p879v7dwoYiIPPHbb6myI0aNkoWVlXJvaWkqvpQp4omIrK2t9d0BUESsQcZzYVgOAlap5VnY7tNNblD7oHe1z7Bh8rXPZ9BNnbLsYpgt6Z9VV6hco3k9NWQ6e/x4uU/zNtQxvb3CEuMBSCj7+Pnnn/tOkL/44ousk2hbPKDa2lrp06ePJ2+fffZJXXP8+PHWtnTMmE2rVq1K/W1b6qanQYMGpTzD3FhSxx9/fKrtIK8oN9bVQQcdlMrbZpttMp7brrvumiq/8847fZ+vG0MrWzJFMFewctPbb7/tKTdFwEpnoxEdv2frh+1duss+s2G7p549ewbW8Tsv13LAu4ulLe20006e42xxogDI7bffbs0/99xzU/fiBt9fl+nYY48VIB1Xa6ONNvIE3tfRBVZAxUALQxHbRz8B60UAwy3115kH1po1yjvHcNYkMeOLL0SgCVhHHhm+jRzNKonAH3+I3Hefillmo3fv9DsMmDb4tF209pEQUsSE8zCweEO5Kdtyu70t8ZncybIe+wqJhNz8yy+pa75mWRZmejmZ/dLFsv8YbZtJ38XPDcT+ofbra9C5IiJLtOV2SHi9x2xtfOVsHW/iLjVrMmCAp/7GFgGryliKcp6x/HBWebmn/IU5c1JlJ2veay7LtWVkZnwtP/zibWXDDNRvO/cXy26UItnfRTYRJ9sugLcZYucbWWJqIeH1INSD6Ouxu7Yw3mmU5Pc9MlPbGTMEiYT838SJgkQiYydKndOM4PJhifEAJJR97NGjh++Eebfddss6qbYFxxYRGTp0qCdPF7BmzZrlK1jo6GWVlZU5T/SXLl2amuy76Ywzzki1O2fOnEDRRESkZcuWgX176KGHUmUPP/yw9dnW1NTkHID+rbfe8pz7yiuveMq/+OILT/nYsWNTZZtuuqn1+u3atZPDDjtMVvrE5DM5+eSTM/plBmL3w3ZP2QSs6urqyKLOSSedFKq+SPadDd944w1r/hLt/8F27dqF7quft+PFF1+c0/l77bWXTJw40Tewvc7zzz/vKVugxf3MhSK2j2GXEJrUWwysIUNEttlG5JNP1PHll6vJ8OWXq+O6OpGpU9W/GyoVFUpIKATJpMj774tooUA9VFWJvPOOyG+/2cv/+EPkrrv8xYtkUuSzz5QoqfPBB+o968l2rt+w9KWXRDbfXMTYO0lWrRJ59VURbSi/3lNTU9hdGtu1EzH+m0nRtWv6/RjTCxER2W67dLm2+jonitg+EkKKmFADENtyPjflG4tKRDKWXukxrp62CFCPGX7GehyfCWvWeHaD8wsCr1+/sZH3lPa/9PSAJWUiIlWGB5cZxF3EK2D0XbHC+my/W7ZMkFCeQrk8Mx1TnFhpTJR0IeaCCROs1y8tK5O5xtLEIHLpl40ROQhYky0CVjaBNJdk82bT00aWPk3K8rnO5Vmc7bPrJhIJq7j16MyZspmP6PVyllhXbhq9erWU19ZKH8vOiGb/tjd2JgxLjAcgoeyjGew7bDr77LOtE+kpU6Z48vbcc8/UNW0CQqNGjTL65i4jnDp1asbOev369fPt07JlyzIEKLdfLn7nupjCk4ku0LVr1876bN3lXbmIWKuM2c59993nKe/bt6+nXPdQ22mnnbK85dyw9auqqirvc3v16iUVFRXSsWNHmT59ekZ50AYBuSZzqVy2FNRfN61cudK6NFYnaBdI2yYBnTt3lnfeecda//rrr8+p7w8++KCIZC4PtfWvU6dOgZ+vbBS5fRwB4FXtuDGAeVjHQdzPOEMEzgR49er03+6r2Hdf9fc990S+VN707Zv/ErfKSpFEQokxfnzwgcibb9rLqqpEDj9cZKutRCxhVHPGdv26OpH33lPPd4cd1LVMXn1VlR96qF1EvOkmVX7wwfbruu0DaZFk7VqRt97yvmvLfw+p/Oee8+YPG5Yu08JCiojINdeo/COOsPcnF5JJJRpdd51IyFXD9U4yKXL66SLbby9i7IGSM337irzyir3s22/Tz9ImULVpky7/6afMcv39/fvf4fpV5PaREFKkhBqAuF4dtnTtlCl5iQoikhH8Wo8T9abFC8a2k1/fFSvkI+fnlgHa7oG7DhkiCZ84V+71TWGj+bBhXuNsOW97Jy6TWX6PJei5/txG+jzrD51ncGoOsY9M9LJGCeVVZmsbiYRcM2WK9fphyaVfNvwErGQyKaVlZfLwjBnyk0V0eV7zIss3ZfPA0tPjzmdsIFzFlQAAIABJREFUmU/Qef2esz2LS40g+Xr6s2Up5uqamozA+27KxSsMibSX3uAclmzm8x51YjwACWUfv/vuO98Jc1DsIDfZPGBExOol4qIHnraV6+ixp8y6tiVvAGTFihVWbyIdv/uxlZ944okZ/dIFpA4dOlj7/ttvvwkA2XzzzTPEmrvuuivw3s04X6NGjfKU6zHBdHEwCrbnYVuamOu5PXv2tAb0r8+ke8JlS5O1Zf5+dW677TYREauopNO1a9eM8kceeUQAZMQnA9SOjB988EGke9U/A5tuumlg/8x4W7l60rkUuX28CkAVgJsAHAigK4CVAHbO4dy8BaxVq9JiSjIpAm0CrC9HApTwgwCBQz1jkVtvFRk4MJ338ccizZuLjB6dvT+ffabavuoqe7l+fZsI1L17unz1apW3YoXIkiVK8HHL/Pao6NEjXccIdyci3qV2HTva21i92t9Dau3a9Pm6l9W0aSLbbuu9v2+/zTz/wguD+6ef369fZvmWW3rrdOrkPQ56v3qZvoRtxx39z2vSJPjzYmPt2vS7nTAhe7/Ccv/9qh1dgJwxQ3mJVVSkr2MTkKZNS5c72nsoBg4MvpcHHkiXffNNZvnFF6fLS0oyy489Nl1uionZKHL7SAgpUkINQP4ZECT7dGMZkpnutez25wZj7+14H7npUU2gsgkeNgFLRz/nwokTRUTkdZ8d6kQyl3i5wc1dbOedpf0ko+dfZomB1V6LjTTNCVxr8s6CBYJEQs6fMEEOGD7c9znaPLz08m30EZyDvqTS3CUxX7KJIn74CVg1dXXS1Ani7wbzr+/0SIAAi0RCjtZivNXqk31L3U3795fvly0TkczllDdNneq55xuN3TufmzNHrp0yRfYZNkw+1+K16de+c/p063VtglfQuxjnswti0LsMS4wHIKHs4w8//JD3hPrKK6+Uww8/3JP3jTOSNHfBgzECNcv+9Kc/Ze2rW/eUU04RESVulZSUyN577+1pa9WqVdKqVavA69s8tPQ6egws81wRkZUrV6bKHn30UWt/p06dKgBk++23z1gCaQaZN7n22ms95WYMo7KyssDz88H2PCpy8HC1vWtABXG3BYavz3TjjTfmXFfnyiuvDCy37a6p8+GHH3rK+vbtK8lkUpY59rVnz56e8gEDBsinn37q2zczWLwtjdH+H9xhhx0C+3fTTTdF+nysB/bxHgBzoISsEQCOz/G80ALWTz+JvPaamuhee63IrFlq5zlok+vjjvMem8mmHx5wQLr8559Vnnu8zz5qiaJ77IQGTZFMekUc00l95Ejv9d95Ry23+vprtcRxyRJv+SabqDabNrX33xbo+oIL0uWXXy6yfLnIv/6l+j5unEjr1unyc85xP1civ/6qnl9Zmchhh6nyd9/NbN995oDy5HL5v//L7N9116XL6+qUF1KLFuny/fdXebffrjyoFizIbOPww9W/vXqJLF4c/D7NZOJXvtFG3vzHH0+Lh9tsk84vLxd5+23191Zbpc8fPVrk6afVsrxvvknXHztWZIstMq/bs6f6t23bzD66Iuz113vzly8XufdetazOvIdZs9LHl13mLa+oUOe6zp66B9uuu9pF1B9+UO/ZFhPsoYcy7+fKK0WeekqVn3RSOr9168zzDzkkXX788Zki2667etsO46S6HthHQkgREmoA8qQWDDxssgVjX+ss6DaDiP9Dm2D0tATefvn33wP7OV5b9jXG/R9NRGZYdoQTEdnWWEJ1vSFA2O7nfG2Uo+cfYhGY2mn3vsDnV/gujsB2yaRJnrhKO1vEHBO9bEuLgKV7oO3o7AwWlWyiiB9+IlJ5bW0kcSqXdJMhJPkl8x3uNmSIp/z/HFHU71lsZbwDU4yqqauTZDKZSncZ5SIid/sIWO8vXGhdVuv3LmybAzQy3pVetp3mWZgrMR6AhLKPQUvxsqUTTjhB9tlnH+tEua6uLnCCbZb16tUra1/durag6XpblZWVcuaZZwZe/7HHHrPek8ujjz7qe66IeJY03uOzRkgPMC4i8vHHHwsAadOmTYbwZ2LGYVpixCE0l1TWB7bnUW77ad1g/vz51nO/+uorXy+5+kqnnnpqznV1rrnmmlCfT7PcFKgGWv4P04XVt956yzfenPt5Dur7UUcd5dkR0vSObG3M4MwYdGGhfVQsXOid5Lrp3HPt+X7pq6+UF8vBB6vjs8/2lrdtq5bs6XnNmnmPBw0S6d8/+DoXXaT+3WGHcP0DRL7/Prh8zRol7gwcqLxwzjoruL7Z/2zp738X6dYtfXzllZl13OdXDOmgg9S/5m8v1dWZdSsrRc47z7+tOXNEdt45fXzjjd5yxzE0dXz99eH7O2GCEmlsZWefne7/44/b6/ztb7lfq3//zHbMhRQzZ9rPnT9fld9yi3/7NTXKa8rMf+QR9fzr6rwebXo6/3zludaokTdfFwo3YPtICCkgoQYg7SwiVK7JjNN0+7RpqXYHakv+kEjIflpERjNIuit2BDFNm7TPNCYNYw2PFBGRHQwB63HDw+tMSwyjizQR42Rt2d8Lc+Zk9GdBZWXWvt/sLBn7i+OV5gam72uIe5sNGJBxbrYg3GO0e35kxozAZ5cr+QpY5ufATavWwW59u+fovWTei7njork7o3nuv42dJh82RLuk8fOZ+RkXETne2BnQTa4HX1DfP3V2ZRQRWah99tw01Ph5TPcg+ziPqKcxHoCEso/9+/d3n0O9JJ2gsueee863zA+37sUXX+xbBkCSyaScc845gdcvKSkJ7P9VV12VtX8PP/ywNG3aNOV1Y6KLHC6LFi2SZDIpt912W2D7poeVLRZV2OeXDdvzKPPxvtWZPXu29dxcdrEsRDriiCOyfjZvuOEGT9kJJ5yQ9VnomMKvTcByd8AEINOnT5fevXtb2z3S2cbMr9z2fk0PQdM+m15xYaF9VMyY4T+JZsot5SOmhUm6p5UtnXlmcLkumtmSuVzUTXoMNB1b3Q8/zH4fe+wRXD5iRMO/y3WVmjVTscv8yp9/Xv1rE7GAtGdf48Yim27q385WW6Wf+3bb0T4SQgpLqAFI+wgClpn0WFHmJHt/Y0uRjnPnpso+zGGCPUvztJpneDzZBKydDC+nCcbezedbYiddrscA0fK/WbrU2qcfly/3DeAukvbSOXDECBFRXgGrHF/gbALWJ8YyNJNSTdC7Uut3FMIIWJV1dbLQeQ8tRoywnmsTKtdF8otPpWPu/GfGGDPP7er+7OVgCr8mFYb3mYhXFNWTK8iaO3f6tb/aEAYHW3y79WevC8u5EuMBSCj7OHjwYPc51EvS6dKli2+ZiFq+eOihh8pIiweoDbedIyxRbs3rmDGnnnnmGU/9Z599NrD/et4//vGPnPpnMnXqVNlqq63kzDPPzCjTlyDank0uHlbZysNiex5rfbbomjBhgtx9992yaNEiefXVV63nBi2ZK2RyPd2CPps333yzp+ztt9/O+ix0RowY4Sn7yRIl+JRTTkmVL1++XH788cfU8dNPP536+9hjj02d4wb+D7q2iHh22ezUqVPW9xkW2kf9OfonfZlbIVI2L6Y//9kbrNpMRx8dvQ977+1fttFG6aV3fmnZsuDyDh2Cy8eNU8vjrrvOuyzMTStXZsbEctNRR/kLUIBajigi8sIL9nI9+LgZA8sN0g+oJZkiKph8IT8PthQk0NRHmjcvuPy554LLw3ri2d5/UPnPP/uX7buvKr/iCnv5kUcqz0Lj99sN2T4SQgpIUQhYIt5g0ydZ4jSNWb1aOs+bl/HrqI35miC2xPi1XfdG2tiJdbWLsUTM5MKJEzP6rwdD1/MTASJVEKtraqTvihVSavmVXvfgscX/0peU7WMEoBfxCnpLc9wJKxthBCzXw2emZQmnmwYZXnjrKvl5Fersb8Qky/YsHjK83DoYOweamDssivgHfp+leRTaNicwv1e1RtumoGvewxuG+JYLMR6AhLKPw4YNc59DvaSMdxRQFha3nUceeSTrdczYSz169PDUf+mllwL7v+2226byWrZsGbnvQffj92xyLe/atWu998dNa4wfRsy6F1xwgbzyyivWcz/66KO8PkO6OOOmI488MufzBwwYkPWzaXrA1RhBWLKdb+6y+dVXX2U8oz322MPzHHVvx9deey3198knn+w5z9zkwLZkVg/S/sknnwS+o7P19UE5QvuoP0f/NHZscHm2pAc6tyX9Y9mzp/IYmTIlXb5yZXA/KyvVLoFB13j55fTf22+vYgUdeaQ6HjrUG+x98mSR0lLv+SbJZDq+kBtgvrpaxSFy4y2ddpr3fL0PZrLFD3Of24svup9XFUPqiSfSdczz3PY6dFBLNo8+Ol1H30Xyu++UEGUO2//6V/8+/vKLquPubOgmP/Hvww/VNfR3mW9KJoOXFGa7Rvv2weUiXi810zOqtlb1obJSiUGmYDl7dvrzZEvuBu4//WQvLy9XsdVsZUcdlX4/o0eLPPywN5bYeed536EZu86y+XFOxNg+EkIKSKgByKMBQdzDprtdS6uRixiSC4s0j5Jlxv+8o7Qg4o85YpAZ48jkQIvXkB6oW8/vYcRXqQ+max5Uz1j2UJ5bURHY/5q6Otl3+HA5dOTInATAXAgjYLnl/9U86cxkLpXMNZ2RZfOA+hCwDjLef7ZnYQbq72rsHJjteYqIXKIJWKdo3ljzLQKUfu5n2vJBW3mFzxLWx2bOlJZjxuT1+YjxACSUfazvZV4Z7zGgLCxPPvmk7LXXXhnxoGzXufTSSz15Pd190B30IN677babAJAnn3wyVa6LL+3atYvcdxtu+w888EBgud+z++abb+S+++4LvcNctv7oabUWj9FlxYoVnmfn54H19ttv5/UZevPNNzOWcF5xxRU5n7948eKsn80777wz588tAOlnbFNmLpu02SC9vLKyUgYNGpQ6vueee1J/l1i2yNLPPeaYYzLKL7vsslT5Dz/8YH2fy5cvl/feey9DnMsF2sc0jRv7T75ratK7AdpSjx6ZcbRE1HIwy7BI1qxJxwAKy+efi+y0k4jp8G4KCmaMJve6fv+NVler4Oe6Tu6ee9ll9nPc5W6WIbOIKMGpfft0eV2dyKhRIi1bZnpk1Rfz5qlYZM5mx6F5911vv7bZJi1QDRmi6pjv3xY3asstve3efrv/5wdQMbb048ceS/990UWqjRUrvHUSCSUaffllZr/KykSGD08f24KXf/CBKjMcl1PstFPw+7G9v6oqJYDqXmo335wuX7w4M8i9fv706Zllfl/j668X2XxzEVv4XD3W3BZb2M/PRoztIyGkgIQagNSXeIVEQu4KELB2c/8Hy5NkMinnTpgg50+YkDEYHmkRsMzYSEH3vbmzY+FobTJSX8JbEIeOHClIqADgJjV1dVn7UFNXZz03X3IVsH5ZuzZV/mqAgNVjyZK8Pkdm7KxmhhgZlM6bMMHqVfj6vHmB95rtWZh8uGhRquwBYwc0WxsiYg3ej0Rm/Czz3PYW/+1Cfz5jPAAJZR/NZX5RUlvLVkcHHHCAAJCrr766vl+hB70fIpm7zH1r7LPeuHHjVNkNN9wgM2bMyPicuuUrdTeHeqSiokLGjx/vK8Ca91RobO/U9jnSy5s1ayaff/659Vz9GYdJ/fr1y1hCGWa3weXLl2fkXXjhhZ57uPfeewOf70MPPeQpHzVqlKd82bJlqTLTg8r2nJLJpEc069q1a+rvDz74IONc89mZ6B5kuS7BDQPtYxoz2DMcrxNdFzTLx4wR6d07Xb5qlQrCXk+/xYWmslIFuHY9hWbOFLn4YvVvPkyYoALZ56GN5oT7HC2rxRsMfSndV1+pd+kun+zZU4mA5ufg668z877/3tuu7sVXWqranTNH7dw4fbp6Bm75m2/a+1ZTk67jvmMd/frukPqKK5QYVgjca1mcpQNZulQJbF27qvP/9z9vudv3bN+j2lrVjh977qna9wlfmZUY20dCSAEJNQAxPVGiJDOQtIikxIc5OWw3ni8jNAHrUWfE8ecQAtay6uoMr641johyihOAvSFw+2fbBbGQ1wt6bpM18QqJhHR2dlq0pbMtgfJzSSLiWX6417Bh0jjE+aZ3FBLeIOjmvW7iLDsNehYm+k6aT9t+Kjba8Hu+fgKUXj7eskyJAlbehLKPM2fOzEtkMNOoUaM8u6S5rF27Vvr165eXF0gY9L6IiFx77bWevPmGW4Ne1rFjR2ubq1at8g3Qvi4w72ldXs9No0ePDqy3++67y8iRI+vlMwRAzj333JSgp+ebuzIGpWQyKccdd5wn78orr/Tcw2effRb4fGtqajzlZpD2ysrKVNkpp5yS9XmKeONm6V5iiy0eqJMmTUqVd+/ePaO8bdu2qfLpfm4uEaB9TGN6hWy+eWYdPQ7VIYfU44vYgGkosc+PZFItibz66nTf3Hdu2x3x+OMzd/1r0iSz3WnT0uW2/27cgOTZ/hto317kwQftZZ9+mlsb9UVVlchvv62ba+VDRYUSHPMlxvaREFJAQg1Anp8zp17Eq1995Py6ZFLWFHhyNkOLw+QKFdliYBVaAKgP3P4FBYovxPWCns0rRuyn1+fNy1iu6Sazbi7pC2f5k7mE8i8+AdBtaXl1dUbel8ayqmO0HQEvmTQp4z5XGG2Y6MsjO+qRTC3Pc4dBg0RE5JopU1J5z2oxzmz8VYvRlu1dFYIYD0BC2Ue/oNFhU0PTqVMnAZASnMyd6Ez0stdff31ddzcn3KV5vXr1WifXs73XH3/80VPH9IzaZJNNZNSoUaE/L1dffbU1/+9//7u1PytXrpSOHTvm/Fmsq6vz5F3vBt9xqKioyPr51ctNLzn9ObRo0cJ6frdu3TLEsy5dukifPn1ERKRdu3bSvn1767l6+7ZA+voumovy2IU1G7SPacwlXO+8k1knmRTZf39V7uxpQzYAmjTJFK6gLW2rq/PmP/54ZhtLlqTLbVOJ779XZffem38/f/993QpYcSfG9pEQUkBCDUCCPGjCpIbmrunT5TxteWG2/hVT3/34o6bG6n1TKHJ5r52MJYP3l5bKAUZA9FyS33I6d0nkSkNAmm/sapnts7jGWIbYy9hJss/y5amy8ydMyPo8TIZrXn9tjQDvLu1mzZItBw6USc47rKyrk7cWLJDfKyokmUzKm/Pny0Sf91tdVyfbDhqU8/LE+ibGA5BQ9nHJkiWxELBMsvVPj2FUV4/LlNdnbO/VFVtcTGEIQF4Clt+SwFnacmI9v7y8XP/OStOmTbN+FvVA6TfccIPnPvRdIM8999yszyOf8urqakkkEnnHKFuxYoWvOPX3v/89de2KAnh/0z6mue229OS/RQsRn405RUQtXSIbDs8/nyleNW/uraPvnOi3oXaPHiLGbwUeli2L7pE2Zkxxe0WtT8TYPhJSdDwGYBSANQCWAPgawAFGnSYAOgNYDmAtgC8B7GzUsQ0YrzbqtAIwFkAVgBkAWlv6czeA2QAqAYwAcFyIewk1AHk9JgKWSbb+Laqqkh0HD5bBtgiNGyi5vNculs9LWAFrtyFDZA9jiad5vWpLDDB9w4Ebp061nn+stqRHF2e/M3zPv9aWAPp5CAY9B30p5bM+SwhF1I6BhWBXzeutEBgDkA3WPuoT+Sip2Cj2/hUjtvf6/fffy9tvvy2DHC/L6urqjDr5LCG85ZZbsn6O9Pza2lqPgPXee+/l9Fl082688UZPvr4LZb4xyNyyXXbZJd9Hnjcvv/xyQT/bMZ6ghRawyspUUOvlywvyqMl6TElJpoBlDrn1+FRB4idZf4ixfSSk6OgDNVE6GMDhAHoDmANgC61OFwC/AzgdwNEAhgEYYrQjTju7aKmJVr4XgDIAHQAcCOAeALUAztHqXAU1ebsZwEEAugFYCaBpjvcSLkjxBiBg3VNa2tDdWS/I5b12X7gwo87+eXhg7TNsWNbrmfmPaQLWm5Y4V0gk5DhNwPpV2+nxJ2N07eZvY8RuCbq+zhJtV8xBBQpiHcR+2jMvBMYAZIO1j2vWrIm9gHX22Wc3dHfWC2zv9fHHH/e8Yz32U5Rk7gJo+xyZ+bqA1bNnT+v5V111lbWNm266KbDtbM/DxiWXXCIA5PPPPw/7qCMzdOhQClj5EVrAIsSPL7/MFLBsrFghUoCVvqSBiLF9JKTo2Qnqy3eKc7wNgGoAl2t1Wjh1TtDyBMDFAe0+D2CykfcJ1ATRZQSA17TjxgDmA3g0x76HGoDYAl7HTcCq4hKYnLC915q6Ovl22TJZ4Szz+HTx4siflWNGj5YDLZsHvDBnjm9/RLwCll8/Bmhi0iwtNlrCiCOWy2c3Wx23rLIBPl+uB9m9BRJnswxANhj76CdING/ePDYCVrHGuCo2bO9V381RRKSsrCyyeFVaWir33XdfRv5f//pX3/6IqA0B3OOBAwf6tm1ro3Xr1oFtZ3seNlavXi3jx48P/Zzri27dusnPP/9ckLZjPEGjgPX/7d17mBx1ne/xdwATEIVVuSggEkUM4RJRVuC4x0UWFpCV4x51UZb7EV12UWQXFY6ioMCKXPRRgUW8AIuCiig3g7puAgmQcEkCBA4gF80FEkIghBATmEydP37dmZqe6pmerqnuql+/X8/TT6ara3p+31TPZ3717eoqjZnTTx/cvEpfeVLxijgfpdLbkfDLt2vt/n61+3/RsN6fgJNT9xPCztSzwF3AccC41OO3Ad9qeI5jgRdqX48nHHHQuJN3BXB9i2Mf1QTke00aWPvNndtyQ+KuEk520uNr9hEIDZa1bb/25JMJ06YlU2pXQrzp2WdzNa9OeeyxIeenqt/ubDi2vL58t9rP/uFTT61fNrXJONLSJ4Jv/KjoaBpYlyxalP8/twCL16wp7LU9wgSkZ/Kxr68vsxFQP9fR2WefnZx22mnDNiQmTpxYyDbKIz2+Sy+9tNvDqYSsbfvRj350UBOnlSP2mp3fCgbO13TKKacMeeyQQw4ZNJ45c+Ykm2yySXLNNdesX3byyScnJ5xwwqCr+AHJtddem1x//fVNa2psYF122WUjNrAWL16cHHroocncuXPb/j+tqoh30Gxgacz893+PfPSV4hNxPkqltgFwEzAztexwwsdWGt1FOGqg7nTgvcAewBcI52j5TOrxRwnnk0n7AOEXfRNgm9rX+zSs8w3CkQdZJhBCon7bllFMQL6fago07tSvqp1xc/e77hq2KTG/hB9cL/PRYWWVtW0npY6USpIk+XULDazhjtIa7mc1NpleeOWVZOqzz64/wqmvvz8588knk2nPPZfcvmLFoO/95oIFyS0NHxN8OnXi99kNvw+H164IuM+99zb9/1i7bl3y4KpVPdkAHWYC0lP5mCTZjYu0vr6+ZNasWcnFF1+cAMlmm2027PplkB7b1KlTuz2cSsh6HTQ2sJ577rkRG1iXXnpp08eG+1kHH3xwy2NtPJn8SDUde+yxg5bXz9u1/fbbj/J/qTdEvINmA0tj5vnnB5pX731vt0ejTok4H6VSu4RwguDtUsua7aDdDXx9mOf6KrAwdT9rB+0Qwi/6xjTfQTsPmNXkZ5xBxmS31QnIDzIaWO9t2Klfu25d8oeXXkpuWLYsec899yRvbTh/0WOrVxeRgbnYwBq9rKbSzg0NrF8+88yIDax97r13xAbWe+65Z8hjozmX1AMvvrj++w5uchXBZanzVN2zcuWgx85fsCBh2rTkiIceGvX/Uy8YZgLSU/mYJEObCTvvvHPmev39/cm8efOS3/72t4PWv/LKK8dqs4yZ+lUG99tvv55s0LYj63WUbmCtXbt2xOYVMOj8TI234X5Ws6sBtjLekdY57rjjBi2fMWNGAt05AXsVRLyDZgNLY+bFFwcaWP/2b90ejTol4nyUSuu7hB2qiQ3LW/2ITKP0zhcU8xGZXEcY/DCjgfXTpUuH/Z6D7ruv9A2i+atWJZ96+OFkydq13R5KZYzUwHru5ZdHbF5NbHJydqZNS7acOXP9z/q7++8f8vito2hg/Sn18cBdZs/OXCc93nsbGlhn//GPCdOmJR9/8MH2/rMi12QC0nP5mCRDmwl77bXXsOtPnz59/bqf+tSn8m4KlcS555475LXwkY98ZP3Xp5566ojNqyRJkptvvnnEBtYFF1ww5LHRnmy//n1bbrnliOs0foTwyCOPHLH51csi3kGzgaUxs3r1QAPrlFO6PRp1SsT5KJXOOMLO2WLg7RmP109S/OHUsp0Iv6B7Z6xf90XgudT9c4EHGtb5CUNPUvyd1P0NgEUUdJLi3y1fPqSR8Mtnnhn2ew6cN6/UzSu1Z8L06cM2sEa6bXv77Ul/f/+wza26rMcbT7Q+nHRz6g8vvZS5zsrUubbmNDSw6stffeut7f1nRa5hAtKz+ZgkSfK1r31tUCNh7733Hnb9adOmrV93nReQiMbjjz8+bAOrlVuSJMncuXNHbGBdccUVQx7bf//9RzXe+tFh999/f9N16s995JFHDlr+8Y9/3AbWMCLeQbOBpTGzdu1AA+tzn+v2aNQpEeejVDoXAyuAv2bwJd43Sa1zCeGIgvcTLhN/R+1W90Hg/xAuNb8jcALhkvBnptaZCKwmnLNlEvDPNL9M/NGES8lfSrhM/NYt1jKqCUh/f39yVu1olPrt+mXLhv0eG1hxyjoab/IoGlj118PBDUfo1W87jNDA+q9RNLD6+/uTQ++/P/ng/fc3/QjU6r6+9c89t0kDy9dwtoYJSM/mYx2pRsLdd9897LrpBpbi8cQTTwxpKn35y18edQMrfX6qK664Itl5552HvF7SVzes30444YRRjbevry959tlnh12n/tyHH374oOVXXXVVAiSbbrrpqH5mr4h4B80GlsZMX99AA+vzn+/2aNQpEeejVDrNJpzHpNbZGLiIcMTAS8B1hJ24uoOAucCLwCpgHvApwhECae+vrbcWeLzhZ9SdSNgZXEs44mCvUdTS3g5aaod+2Qgfu7OBFaexTkxZAAAgAElEQVTLn356TBpYa9atS/afNy+5asmSQVccfMsdd6z/WVnfO5qPELbi5XXr1j/3vBdfHPTY6U88kTBtWnL2H/84pj8zFg0TkJ7Px0984hMtN6Vuu+02G1gRqp83LH1rPDpvpFuWXXfddcjjRxxxxJDm1fNjnI9JMtDA+tjHPjZo+bp165IbbrghWbx48Zj/zBhEvINmA0tjpr9/oIF16qndHo06JeJ8lFSgXA2sA+bNG3HdD6SOsFE8rshoYO3SRgOrUSsNrP/9wAPJujE+mfS61McZGxtY/f39yROrV3sC6yYinoC0vYO2YMGCZM2aNSOuN3PmTBtYEVqwYMGQhtRZZ52Vu4G12267DXn8uuuuG/H7xsL48eMTIPnhD39Y2M+IkfkotabewDrttG6PRJ0ScT5KKlBbE5BX1q1L7lixIulrYYc+fQJuxeM/C25gbd+kgbVxgeehesOMGQnTpiWr+/oK+xkxingCUvgO2p///Odk8803T3bbbbfCfoY6b9GiRYU0sHbfffchj8+fP78jDawFCxYk1157redqGyXzUWqNDazeE3E+SipQ4ROQO1asSJg2LTnqoYcK+xnqvKuWLBnUWNpp1qxk17vuGrMG1ptTDawPNlyFsChr1q1LVtm8GrWIJyAd2UFbs2ZN0ufrLipPPfVUxxpY6fNkNfs+dY/5KLWm3sD64he7PRJ1SsT5KKlAHZmArHjlFT9+FZkfNzSw3jFrVrLbGDawtks1sNJXCPRIvvKJeALiDpra8vTTTxfSwJoyZUrm4yN9n7rHfJRaU29gfelL3R6JOiXifJRUICcgasvVGUdgFdXASi+3gVU+EU9AzEe1ZenSpR07AitJbGCVmfkotabewDr99G6PRJ0ScT5KKpATELXlmqVLBzWV3j5rVrK7DayeFPEExHxUW5YtW1ZIA2vy5MmZj5933nk2sErKfJRaU29gHXxwt0eiTok4HyUVyAmI2vLThgYW06aNqoH1P+fMyXze+uM73HnnoOWXLV6cMG1acuOyZZ0oT6MQ8QTEfFRbli9fPqQhdfbZZ+duYO20006Zj/f39yczZ85Mli9f3onyNArmo9SaegNr/Phuj0SdEnE+SiqQExC15ec5G1h3N3nN1R/fcdasIY95HrVyingCYj6qLc8//3whDay3vvWtHmlVMeaj1Jp6A2vChG6PRJ0ScT5KKpATELXl2meeGdKUmjKKBtaclSszn7f++KTZsztckdoV8QTEfFRbVqxYkauBNW7cuMzn/fGPf5wAyXHHHdfhitQu81FqTb2B9Vd/1e2RqFMizkdJBXICorZcl7OBdd+LL2Y+b/1E8F978snOFqS2RTwBMR/VlpUrV7bUwPr0pz+d2cDaYIMNmj73woULPRq1QsxHqTX1Bta3vtXtkahTIs5HSQVyAqK2/DKjgfXOu+8esmynWbMyG1gPrlqV+bzLX345uX7ZsuTldes6XJHaFfEExHxUW1atWtVSA+uiiy7KbGAdeOCB3S5BY8R8lFozdWqSnHRSkqxd2+2RqFMizkdJBXICorZkNbD2yGhgHTZ/fmYD65GXXup2CRojEU9AzEe1ZfXq1S01sG644YbMBtZDDz3U7RI0RsxHScoWcT5KKpATELWl1QbWnJUrk1dNnz5k+eq+vm6XoDES8QTEfFRb1qxZ01IDa9asWcmsWbNaOoG7qsl8lKRsEeejpAI5AVFbss6B9a6MBlaShI8FZi1XHCKegJiPasvatWtbamDNmTMnSZIkmThxog2sSJmPkpQt4nyUVCAnIGrLL0bRwEqSZNCy7e64o3sD15iLeAJiPqotr7zyypBm1TnnnDNk2fz585MkSZK3ve1t65fdeOONXR69xpL5KEnZIs5HSQVyAqK25Glg3bNyZfcGrjEX8QTEfFRb1q1b11ID6+GHH06SJEl22mknj76KlPkoSdkizkdJBXICorZcm9HAeneLDSzFJeIJiPmotvT397fUwHrssceSJEmSSZMm2cCKlPkoSdkizkdJBXICorac+OijNrCUJEnUExDzUW2jhQbWokWLkiRJksmTJ9vAipT5KEnZIs5HSQVyAqK27Dd37pBm1Z733GMDqwdFPAExH9U2Wmhg1e222242sCJlPkpStojzUVKBnICoLVkfIbSB1ZsinoCYj2obo2hgTZkyxQZWpMxHScoWcT5KKpATELVl6rPPjtjAOvHRR9evbwMrXhFPQMxHtY1RNLD22GMPG1iRMh8lKVvE+SipQE5A1JasBlbj7bTHH1+/fn3Z62fM6OKoVYSIJyDmo9pGQ7PqhBNOaNrA2nPPPW1gRapL+fhF4A5gNbCiyTrbAzfX1nkGOA/YaBQ/w3yUlEvE80dJBXICorbcsnz5iA2srz755Pr168veYAMrOhFPQMxHtY2GZlXj7bDDDlu/7j777GMDK1JdysczgZOBC8huYG0IPAD8DngncDCwDDhnFD/DfJSUS8TzR0kFcgKitrTSwLry6afXr7/JrbcmTJuWHHLffV0ctYoQ8QTEfFTbGKGBNXv27PXrPvDAA8kWW2yRfPvb3+7iiFWELufjMWQ3sA4G1gFbp5b9E/ACML7F5zYfJeUS8fxRUoGcgKgtrXyEsK+/f/36D7/0UvL5xx5Lnlm7toujVhEinoCYj2obIzSw5syZM2j9/lReKh4lbWB9FZjXsGwiYZx7tPjc5qOkXCKeP0oqkBMQteXmFhpY6g0RT0DMR7WNERpY8+fP7/YQ1QElbWB9D/hNw7JXE8Z5cJPnmkCooX7bFvNRUg4Rzx8lFcgdNLXlhmXLbGApSZKoJyDmo9rGCA2sRx55pNtDVAeMYT5+faTXFDCp4XuOYfQNrIOa/Pwzsn6m+SipXRHPHyUVyB00teXJ1attYClJkqgnIOaj2nb++ecnRx11VNNmw7x587o9RHXAGObjloQG1XC3xvNXHcPYfYTQI7AkjamI54+SCuQOmto2Z+XK5Bt/+pMNrB4X8QTEfFRuNGlgzZw5s9tDUweU9COE9ZO4b5Va9knCSdwntPjc5qOkXCKeP0oqkBMQ5XLN0qU2sHpcxBMQ81G50aSBtWjRom4PTR3QpXzcHngn8GXgxdrX7wReU3t8Q+ABwscIpwAHAs8A54ziZ5iPknKJeP4oqUBOQJTLT21g9byIJyDmo3KjSQPLqw72hi7l4+VNXnf7ptZ5C/BrYDWwDDgf2GgUP8N8lJRLxPNHSQVyAqJcfm4Dq+dFPAExH5UbTRpY6g3moyRlizgfJRXICYhyufaZZ2xg9biIJyDmo3Ijo3n1s5/9rNvDUoeYj5KULeJ8lFQgJyDKpVkD66olS7o9NHVIxBMQ81G5kdHAuv3227s9LHWI+ShJ2SLOR0kFcgKiXH7RpIHl+V16R8QTEPNRueEVCHua+ShJ2SLOR0kFcgKiXH7ZpIGl3hHxBMR8VG7YwOpp5qMkZYs4HyUVyAmIcvnVsmU2sHpcxBMQ81G5kdHAmjFjRreHpQ4xHyUpW8T5KKlATkCUy/U2sHpexBMQ81G5YQOrp5mPkpQt4nyUVCAnIMrlBhtYPS/iCYj5qNzIaGDddttt3R6WOsR8lKRsEeejpAI5AVEuNz37rA2sHhfxBMR8VG5kNLBuvfXWbg9LHWI+SlK2iPNRUoGcgCiXm21g9byIJyDmo3Ijo4E1ffr0bg9LHWI+SlK2iPNRKp3TgLuBF4FngF8B72hYZ2PgImA5sAr4BbB1k+d7A7CI8Av8Fw2P7QvMAdYCjwHHZHz/vwB/BNYAs4H3tF6KExDl82sbWD2vYQJiPkopZDSwHnnkkW4PSx0S8Q6a+Sgpl4jzUSqdWwg7SrsAU4CbgT8Bm6bWuQRYAOwHvBu4E7i9yfP9Cvg1Q3fQJgIvARcAOwMnAn3Agal1DiPsvB0LTAa+BzwPbNViLU5AlMtUG1g9r2ECYj5KKWQ0sNQ7It5BMx8l5RJxPkqltyXhl+99tfubAy8DH0mtM6m2zt4N33sCMJ2wI9e4g3YuML9h/WsIO4h1s4Hvpu5vACwGTm1x7E5AlMtvli+3gdXjRpiAmI/qadjA6mkR76CZj5JyiTgfpdLbkfDLt2vtftbOFoSjEE5O3Z8MPA1sT/goTOP33AZ8q+E5jgVeqH09nnDEwYca1rkCuL7FsTsBUS42sDTCBMR8VE/DBlZPi3gHzXyUlEvE+SiV2gbATcDM1LLDCR9baXQX4agBgAnAfcARtfv7MnQH7VHC+WTSPlBbbxNgm9rX+zSs8w3CkQdZJhBCon7bFicgymHxmjU2sHrcMBMQ81E9j4bm1SWXXNLtIamDIt5Bs4ElKZeI81EqtUsIJwjeLrWs2Q7a3cDXa19fSPi4S92+tLaDdkhtvY1pvoN2HjCryXjPIOPdYCcgateadeuGNK9+smRJt4elDhpmAmI+quc1vp6uvvrqbg9JHRTxDpoNLEm5RJyPUml9F1hIOJlwWisfkZkHrCN8xKWv9nVS+/rM2jpFfETGIww0prIaWOotTSYg5qOUDG1gXXPNNd0ekjoo4h00G1iScok4H6XSGUfYOVsMvD3j8fpJij+cWrYTg09S/DbCOWHqt2MZOFqgfoWsc4EHGp77Jww9SfF3Uvc3IFxy3pMUqyNsYKlhAmI+Sik0NLCmT5/e7SGpgyLeQTMfJeUScT5KpXMxsAL4a+CNqdsmqXUuIRxR8H7CZeLvqN2a2Zfsy8SvJpyzZRLwzzS/TPzRhEvJX0q4TPzWLdbiBES52MBSwwTEfJRSaGhg9ff3d3tI6qCId9DMR0m5RJyPUulkXlUIOCa1zsbARcBzwEvAdYSduGb2JftjNe8H5hJ2wh5v+Bl1JxJ2BtcSjjjYq/VSnIAoHxtYapiAmI9SSsPvQbeHow6LeAfNfJSUS8T5KKlATkCUS39/vw2sHhfxBMR8VG7YwOpp5qMkZYs4HyUVyAmIcrOB1dsinoCYj8oNG1g9zXyUpGwR56OkAjkBUW42sHpbxBMQ81G5YQOrp5mPkpQt4nyUVCAnIMrNBlZvi3gCYj4qt6233np98+qzn/1st4ejDjMfJSlbxPkoqUBOQJRbunn186VLuz0cdVjEExDzUbkdddRR6xtYfX193R6OOsx8lKRsEeejpAI5AVFuHn3V2yKegJiPyi3dwOrv7+/2cNRh5qMkZYs4HyUVyAmIcrOB1dsinoCYj8rtiCOO8PxXPcx8lKRsEeejpAI5AVFuNrB6W8QTEPNRudnA6m3moyRlizgfJRXICYhys4HV2yKegJiPyu2kk06ygdXDzEdJyhZxPkoqkBMQ5bbbXXfZwOphEU9AzEflduutt9rA6mHmoyRlizgfJRXICYhy+8B999nA6mERT0DMR+V222232cDqYeajJGWLOB8lFcgJiHI72AZWT4t4AmI+KjePwOpt5qMkZYs4HyUVyAmIcvvHBx+0gdXDIp6AmI/K7ZFHHrGB1cPMR0nKFnE+SiqQExDl9tSaNcm+c+cm1yxd2u2hqAsinoCYjxoTl19+eTJ16tRuD0NdYD5KUraI81FSgZyASMol4gmI+SgpF/NRkrJFnI+SCuQERFIuEU9AzEdJuZiPkpQt4nyUVCAnIJJyiXgCYj5KysV8lKRsEeejpAI5AZGUS8QTEPNRUi7moyRlizgfJRXICYikXCKegJiPknIxHyUpW8T5KKlATkAk5RLxBMR8lJSL+ShJ2SLOR0kFcgIiKZeIJyDmo6RczEdJyhZxPkoqkBMQSblEPAExHyXlYj5KUraI81FSgZyASMol4gmI+Sgply7k4w7AD4AngT8DjwNnAuMb1tsdmAGsARYCnx/lzzEfJeUS8fxRUoGcgEjKJeIJiPkoKZcu5ONBwI+AvwXeChwKLAXOb8i2JcBVwC7Ax4DVwCdH8XPMR0m5RDx/lFQgJyCScol4AmI+SsqlJPn4OeCJ1P0TgOcYfFTW14GHR/Gc5qOkXEqSj5IqZjMgWbhwYfLCCy948+bN26hvCxcujHUCYj568+Yt160k+XgWcE/q/pXArxrWeT9hnK9r8hwTCDXUb9tiPnrz5i3HrST5KKlitiUEhzdv3rzlvW1LXMxHb968jdWtW/m4I/ACcHxq2W+BSxvWm0wY585NnucMuv9/6M2btzhvsc0fJRVoHCE0NivJrb7DWKYxWaP1WV9r3zeOuJQtH339Vf9mfdW+dTMfv87IO4GTGr5nW+Ax4PsNy7MaWLs0eY66xiOwNiOcML7b28TXXzw366v+rZ0aY5w/SuohmxGCb7NuD6RAsddofdUWe31VF/v2sb5qs77ibEloLg13S5/TahvgUcLHBTdoeK52PkJYBb7+qs36qq8XapSkQXoh+GKv0fqqLfb6qi727WN91WZ95bAtoXl1NbBhxuP1k7i/KrXsHEZ3Evcyqsr2aZf1VVvs9UFv1ChJg/RC8MVeo/VVW+z1VV3s28f6qs36um8b4A/A7wmNrDembnWbA0sIR2LtAhwGvAR8sqMjHXtV2D55WF+1xV4f9EaNkjTIBMKJQid0eRxFir1G66u22Ouruti3j/VVm/V13zE0P0dW2hRgBrAGWAR8oXNDLEwVtk8e1ldtsdcHvVGjJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSAMZ1ewCSVFLmoyRlMx8lSVLh3gBcDBxau79BF8dShM2ArWtfx1YbwBuBLwLHAvvUlsU0idwGmA38W7cHUpDXAJvXvo5pu8XCfKy22PMRzEh1j/lYbeZj9ZmPknrSN4B+4BeEP9YQTwh+CXgG+Fq3B1KQrwCrgN8ADwKLgL+sPRbDNvwW8DJwA2GiFZszgD8Cx3R1FBqO+VhdsecjmJHqLvOxuszH6jsD81FSj7qF8Afs98Bnasuq/sfrNYR3Be8G7gKmAu+tPVb12uoOBuYAB9Xu7wpMA87q2ojGziRgMfAwsGeXx1KE1wPfB+4lTD5+Bry99lgsr89YmI/VFHM+ghmpcjAfq8l8rDbzUVLPaAy1jYAJwDXA+4DLCZOQnWuPb9ixkY29VwGnAh8D9iZMRM4HNqk9HkPAf4cwAUn7PfA/UverWudewP8Dvlm7/y7Cu4XH1r6uovS22Irwru4hwH6EidaJhNetusN8NB+rxIxUJ5mP5mOVmI+SFIHxhMlGXToI5wITCe/E3Eb4wz0eeFPHRpdfvZ70pGmz1NdfBe4E/r5jIxpb9frS52A4CVgIHABsB/wUeAH4HXAJ8LpODjCnjRj8mpwAfJJwePv1hHeYflf7dxnwuc4OL7fG37+NCBOQuh8BtzNw+L46y3w0H8vOjDQju8V8NB/Lznw0HyVF5gzCYdC3EAI9/YfpHYRDUOvB/w3gScI5DT5NNU5a+WngzCaP1evaCphOeJdwm4bHyq6xvvq4dwSuAH4NrCW8e7Y/YWIyn/BHO71+WZ0G3AhcDfwvwuH7EA6HvhaYCbwbeG1t+YXAHVRnMnkGg3//Xp96rP77tS1hYnU28Be1ZWXfbrE4A/PRfCw3M9KM7JYzMB/Nx3IzH81HSRHZCLgS+ANwFPATwh+mG1PrbA38tvb1wYQTVr4I3Ero+EN5Q3AKIdD7gfsJh9HC0ElT/f4nCJOtf0o9VtbaoHl9GzWs9yHgvxg8sfxrYA3w5oLHmMd7CO/ePkCYNE0nHNL+r7XHxxHOO7Fn7ev6u6Pb1b7nlA6OtR3Nfv9ualivXteXCIe8H5R6rMyvz6ozHwffNx/Lx4wMzMjOMx8H3zcfy8d8DMxHSVGZSAizD6aWHQCsZiDgDwGWEN6NWEk4/PsrwAzgiNo6ZQ3AfyW8S3QUcDMh6Ot/nNNjTn99HfBLYA/gw5T76jIj1Vev61TCRCXtE8ATDJzUsWy2AC4FvsfAu2UQzqdxKQOT38bJVr3mJZT/ZKPD/f6dnFpWnyCPA+YRTsw5kfBO4onFD7NnmY9DvzYfy8OMHGBGdp75OPRr87E8zMcB5qOkqOxEePdl+4blpwErCIecbgE8RDiUeMfa49sTrkbycwZOWllGbyScPBTCuy+zgKNr9xsnTfWA35/wbsazhEvrnl7wGPNotb6zCO88fYjwx3pHwruiP6S8h/BvQZjo1k+mWT/x5HnA7BG+91DCu2yTixnamBnu9+95Br+7WX8H7aOEd7H/BLxCOPxfxTAfB5iP5WNGmpHdZD4OMB/Lx3w0HyVFamfC4bWNJyvcjPDuyvm1+9sx9A/VPgx+V6PstiNcRvYmwmHtMLSmtxDemekn/HF+PdWRVV/9D/ZkwmTxZcI7aS8QDjV+LeWWfmesPqG6nHB+gka7Ek5O+U3C5PEchr6zVjYj/f5dULtfn3i8hXDy1Cq+PqvIfBzMfCwfMzIwIzvPfBzMfCwf8zEwHyVFZVPC4bTXAjvUltX/KJ9C6NBv3PA9ZT3cezj1mj5OOJT9/zZZ70uEdyaqdpWOVup7E+FdpX8B3tmhceUxruHfupnAkRmPfYpwEss7CJdHroLR/v5dCDxN9V6fVWU+DmY+losZaUZ2k/k4mPlYLuaj+SipgrYkfMa5fqnf9LtF6XcVPgbcB3y+4fuPJ5zEsKwnaWy1vvT9TYD/IBwOvXtt2bsop7Gqb8+CxpfXaOqrv3u0A+Hdv11Tj9Wv+PNaBmoug+2AfyT7UtOj+f1LHxpepXesy858HHrffCwXMzIwIzvPfBx633wsF/MxMB8lRWEc8G3C5VLnAI8ycO6BDVPrbUgIRwiHlN4J/F3q8dMIJ9pMf08ZtFrfOAY+zw8Df9zeT7iiyjXAVMKhtG8qbrijZn0D6x3NYCcQDpeGcH6NnxLeMSvbYdBvBpYR/u8PSC2P4fev6szHgfV6KT+qUh+Ykemvq/g7WGXm48B6VcyP2OsD8zH9dRV/ByVpiP0Il1C9A9iXcKnUGcBvGtY7HlhaW/4qYBLwA8IJ/S4GvkM4CWf96hRlOex7tPXdDGzV8NhWtefoJ1wx5i3FDXfUrC9I17dlavmFwLcIf5hXA78nTELK5o3ArwgTrKkM3UZV/f2rOvMx6KX8qFJ9YEbWVfV3sMrMx6Cq+RF7fWA+1lX1d1CSMn2ecOLB9GGinwNuYOAdlqOBBcBxDD3M9hTCSShvIfyhKJvR1tf4zsPewHLCZWffW+hI22N9zevbBHiSMLF6hMHvSpXNgYRLaW9PGO8xDFyy+RhgIdX8/as687F38wPKXx+YkWBGdov5WO38iL0+MB/BfJQUgcaroLyewZ933pJwudgLCZfArWv8HHRZu/NjVV/dpsARYza6/Kyv9fpeD1wJHD6WA8xpgyZf7024ag/AVYTLiL+agav6NF46vKy/f1VnPpofaWWrD8xIMCO7xXysdn7EXh+Yj2A+SorMl4EfAacDb8h4/B+AdYQrblxLeGflPynfIcHNjHV9ZQt462u9vrLVBsPX9xnCoet1qwl1Lgb+qiOjk/lofqSVrT4wI83I7jEfq50fsdcH5qP5KCkqbwbuBe4Hvku4JOrdwEca1vsb4H2p++8G/lxbXmbWF1hfObVS3+kMnHvgg8BKoI9weLeK5esvsL7yir1GM7K8fO0F1ldesddoPkrqSUcTrqKxee3+psD1hBMZThnm+15NCPfjCx1dftaXzfrKYbj66pfU/iYwDbgVeA44iXDuhctS36di9PLrz/rKXx/EX6MZWV69/NqzvvLXB/HXaD5K6klfIVz2NX3yvvcRLn3742G+7zjgdmCb4oY2Jqwvm/WVQyv1fZlwCeTLgLfVlv0d4WScf9uZYfYsX3/ZrK88Yq/RjCwvX3vZrK88Yq/RfJTUk/6dcAnZxpA+hdDV3z+17O3AjoTDVJcC/0o5PwueZn0DrK98hqvvPmAvwiWPJzO0lhOBCUUPsMf18uvP+spfH8RfoxlZXr382rO+8tcH8ddoPkrqKfUrVEwidOE/1PD4FGAW8IXa/dcBZwFPEE7+N9yht2VgfdZXZq3UN5twmedGjZer1tjz9Wd9ZRd7jWZkefnas76yi71G81FStHYFPkx2WKUPN/0ZMAfYomGdWcB3UvcnA/9jLAeYk/UF1hfEWN+3a1+X/V3AKvL1F1hfULb6IP4azcjy8rUXWF9Qtvog/hrNR0k9ZTzwA0JX/iwGB9eGDevtCGxPuKzq2Qyc0G8jwgn/zix6sG2wvoH1rK98Yq+v6mLfPtY3sF4V64P4a4y9viqLfdtY38B6VawP4q8x9vokaYhPAy8STj443CGwnwFeYuDw0uOBPwC/AQ4FLgSeAv6ysJG2x/oC67M+jV7s28f6gqrWB/HXGHt9VRb7trG+oKr1Qfw1xl6fJA2xGeESqb9PLZtEuOrEa2v3xwHfI5yg8AgGPkcN4aoUNxNOCHg34eR/ZWJ91md9alfs28f6ql0fxF9j7PVVWezbxvqqXR/EX2Ps9UnSIOnDS48FlgMHED4X/RihKz8bOKa2zjsIQVmXDkCArQsZZfusz/rSrE+jEfv2sb5q1wfx1xh7fVUW+7axvmrXB/HXGHt9kjTIe2r/psNrHCHo+oHvA+8DPlD7+ilgv9p6VbgKhfVZX5nFXl/Vxb59rK/a9UH8NcZeX5XFvm2sr9r1Qfw1xl6fJA3yIWAxoUu/Q21ZOszeDfw78IbUsh2A6wiHl5ad9VlfmcVeX9XFvn2sr9r1Qfw1xl5flcW+bayv2vVB/DXGXp8kDXE4cBdwNTAD+I+MdcYBr8lY/p/Ar4FNCxtdftZnfdandsW+fayv2vVB/DXGXl+Vxb5trK/a9UH8NcZenyQNUu/O70XozG8PfA54GNi3YZ0smwD/BZxX0Pjysj7rsz61K/btY33Vrg/irzH2+qos9m1jfdWuD+KvMfb6JGmQtzP4BH8AG9X+3QW4nsGHlDauuznwZuAHwEPAuwoYYx7WZ33Wp3bFvn2sr9r1Qfw1xl5flcW+bayv2vVB/DXGXp8kDfIPwJOE7lQbmNAAAAPUSURBVPxs4LjUY41XrHiw9i8MPhngQcDFwLPANGDHogbbBusLrM/6NHqxbx/rC6paH8RfY+z1VVns28b6gqrWB/HXGHt9kjTEAYTg+2fgQOAC4GXgeMJhpDDQwd+WcHWKuxj4zPT42r9vqT3H3xQ/5FGxPusD61N7Yt8+1lft+iD+GmOvr8pi3zbWV+36IP4aY69Pkgapd+W/DNwDvCr12EXA3cDfZ3zfIbXHzgB2B24iHHJaNtZnfdandsW+fayv2vVB/DXGXl+Vxb5trK/a9UH8NcZenyQN6xrgp7Wv6wH4OmAm8CPgjbVl9RP+vZoQjv3AK8AtwISOjLQ91hdYXznFXl/Vxb59rC+oan0Qf42x11dlsW8b6wuqWh/EX2Ps9UnqcQcA3wY+C7wntfx4YCUD4faq1PJHGLhiBYTLqX4W6CN8Rnq34oY7atYXWJ/1afRi3z7WF1S1Poi/xtjrq7LYt431BVWtD+KvMfb6JGmQNwE3AkuBq4D7gRUMBOBOwCLgq7X741Pf+zQh7OomA7OAIwsc72hZn/XVWZ9GK/btY33Vrg/irzH2+qos9m1jfdWuD+KvMfb6JGmIVwOXEw4xnZhaPptwaCnAa4EvAqsZ+Bx0/coU04HLih5kDtZnfdandsW+fayv2vVB/DXGXl+Vxb5trK/a9UH8NcZenyQ1dSnh8qgwcCWKrxC68PWTAE4kfF76TsKVKAC2Bx4inPCvzKzP+sos9vqqLvbtY33Vrg/irzH2+qos9m1jfdWuD+KvMfb6JClT+soU9a78j4HvNay3LfAHwuVYfw4sBn4PbF30AHOyvsD6yin2+qou9u1jfUFV64P4a4y9viqLfdtYX1DV+iD+GmOvT5JaNhM4uvb1BgyE4o7AYcCFqceryPqsr8xir6/qYt8+1lft+iD+GmOvr8pi3zbWV+36IP4aY69PkoZ4K7AEeHdq2fgm61aR9VWb9ambYt8+1ld9sdcYe31VFvu2sb7qi73G2OuTpEHqn5U+CngstfwrwCXAVh0f0diyvmqzPnVT7NvH+qov9hpjr6/KYt821ld9sdcYe32SNKzvAucCBxA+K70U+NuujmhsWV+1WZ+6KfbtY33VF3uNsddXZbFvG+urvthrjL0+SRpiY8JJ/vqBNcAXujucMWd91WZ96qbYt4/1VV/sNcZeX5XFvm2sr/pirzH2+iSpqd8BFxOCMEbWV23Wp26KfftYX/XFXmPs9VVZ7NvG+qov9hpjr0+SMm3Y7QEUzPqqzfrUTbFvH+urvthrjL2+Kot921hf9cVeY+z1SWPi/wMxIb9kQaqejAAAAABJRU5ErkJggg==\" width=\"1200\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.figure(figsize=(12,3))\n",
-    "pl.subplot(1,3,1)\n",
-    "pl.plot(dt_test, def_x,'c')\n",
-    "pl.title('static baseline')\n",
-    "def_x_med = np.nanmedian(def_x)\n",
-    "pl.ylim(def_x_med - 200., def_x_med + 200.)\n",
-    "\n",
-    "\n",
-    "pl.subplot(1,3,2)\n",
-    "pl.plot(dt_test, adj[0],'k')\n",
-    "pl.title('affine transform')\n",
-    "pl.ylim(def_x_med - 200., def_x_med + 200.)\n",
-    "\n",
-    "\n",
-    "pl.subplot(1,3,3)\n",
-    "pl.plot(dt_test, def_x - adj[0],'b')\n",
-    "pl.title('$\\Delta x$')\n",
-    "pl.ylim(- 20.,  20.)\n",
-    "\n",
-    "# re-formats dates for better presentation\n",
-    "pl.gcf().autofmt_xdate()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## ## Plots of Adjusted Y using Static Baseline, Affine Transform, and '$\\Delta y$'\n",
-    "\n",
-    "This is not quite the same comparison presented for $\\Delta F$, because $\\Delta y$ is a comparison of two derived quantities."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\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",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\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 backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\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 * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\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'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\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 * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\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",
-       "    var width = fig.canvas.width/mpl.ratio\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 + '\" width=\"' + width + '\">');\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 width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\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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\" width=\"1200\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.figure(figsize=(12,3))\n",
-    "pl.subplot(1,3,1)\n",
-    "pl.plot(dt_test, def_y,'c')\n",
-    "pl.title('static baseline')\n",
-    "def_y_med = np.nanmedian(def_y)\n",
-    "pl.ylim(def_y_med - 200., def_y_med + 200.)\n",
-    "\n",
-    "\n",
-    "pl.subplot(1,3,2)\n",
-    "pl.plot(dt_test, adj[1],'k')\n",
-    "pl.title('affine transform')\n",
-    "pl.ylim(def_y_med - 200., def_y_med + 200.)\n",
-    "\n",
-    "\n",
-    "pl.subplot(1,3,3)\n",
-    "pl.plot(dt_test, def_y - adj[1],'b')\n",
-    "pl.title('$\\Delta y$')\n",
-    "pl.ylim(- 20.,  20.)\n",
-    "\n",
-    "# re-formats dates for better presentation\n",
-    "pl.gcf().autofmt_xdate()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plots of Adjusted Z using Static Baseline, Affine Transform, and '$\\Delta z$'\n",
-    "\n",
-    "This is not quite the same comparison presented for $\\Delta F$, because $\\Delta z$ is a comparison of two derived quantities."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\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",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\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 backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\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 * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\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'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\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 * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\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",
-       "    var width = fig.canvas.width/mpl.ratio\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 + '\" width=\"' + width + '\">');\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 width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\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": {
-      "text/html": [
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\" width=\"1200\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.figure(figsize=(12,3))\n",
-    "pl.subplot(1,3,1)\n",
-    "pl.plot(dt_test, def_z,'c')\n",
-    "pl.title('static baseline')\n",
-    "def_z_med = np.nanmedian(def_z)\n",
-    "pl.ylim(def_z_med - 200., def_z_med + 200.)\n",
-    "\n",
-    "\n",
-    "pl.subplot(1,3,2)\n",
-    "pl.plot(dt_test, adj[2],'k')\n",
-    "pl.title('affine transform')\n",
-    "pl.ylim(def_z_med - 200., def_z_med + 200.)\n",
-    "\n",
-    "\n",
-    "pl.subplot(1,3,3)\n",
-    "pl.plot(dt_test, def_z - adj[2],'b')\n",
-    "pl.title('$\\Delta z$')\n",
-    "pl.ylim(- 20.,  20.)\n",
-    "\n",
-    "# re-formats dates for better presentation\n",
-    "pl.gcf().autofmt_xdate()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "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": "2.7.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/docs/algorithms/.ipynb_checkpoints/Average_Algorithm-checkpoint.ipynb b/docs/algorithms/.ipynb_checkpoints/Average_Algorithm-checkpoint.ipynb
deleted file mode 100644
index 03fef5b73ce73c1dbfe9161c45996278743f4875..0000000000000000000000000000000000000000
--- a/docs/algorithms/.ipynb_checkpoints/Average_Algorithm-checkpoint.ipynb
+++ /dev/null
@@ -1,291 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "\n",
-    "Average Algorithm\n",
-    "-----------------\n",
-    "\n",
-    "Algorithm Theoretical Basis for \"Average Algorithm\"\n",
-    "\n",
-    "## Summary\n",
-    "The average algorithm creates an averaged stream from each of the traces in the timeseries that is passed to the algorithm. There are checks to ensure that the traces contain the same channels with the same timestamps but come from different observatories. This is used primarily for creating DST (Disturbance Storm Time) event data.\n",
-    "\n",
-    "## Math and Theory\n",
-    "The timeseries that is passed to the average algorithm process is looped over each observatory. The data related to each observatory is compiled into a 2D numpy array. As the data is input to the numpy array it is multiplied by a correctional value that defaults to 1 but can be changed by specifying an average_observatory_scale parameter. The correctional value is a weighting scale that varies from 0-1 in order to place a stronger reliance on certain observatories. The data in the 2D numpy array is then averaged into a single array using numpy's mean function. A new stream is created with this new data using a get_trace function that resets the stats to a new channel, station, network, interval, and location.\n",
-    "\n",
-    "## Practical Considerations\n",
-    "The averaging function can be called from the command line using the geomag.py script and adding in the optional call to --algorithm average. The algorithm can also be used from your own python script as shown below. This algorithm will take streams of multiple observatories but only one channel may be average at one time. When initializing the algorithm three parameters may be set, observatories, scales, and channel. The observatories and channel may be used as a sort of check to ensure that only the observatories and/or channel you specified are used to create the averaged stream. The scales is used to set the correction factors and must be set in the same order that the observatories are set. Sometimes when using the command line it is necessary to specify the --outchannels argument as some factories, like the iaga writer, will try to set a different outchannel. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from geomagio.edge import EdgeFactory\n",
-    "from geomagio.algorithm import AverageAlgorithm\n",
-    "from obspy.core import UTCDateTime, Stream\n",
-    "import matplotlib.pyplot as plt\n",
-    "import datetime as dt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialize variables and Plot the Input Timeseries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "channels = ['MSD']\n",
-    "observatories = ['SJG','HON','KAK','HER']\n",
-    "start = UTCDateTime('2018-08-02T18:00:00Z')\n",
-    "end = UTCDateTime('2018-08-03T12:00:00Z')\n",
-    "stream3 = Stream()\n",
-    "stream4 = Stream()\n",
-    "\n",
-    "input_factory = EdgeFactory()\n",
-    "for obs in observatories:\n",
-    "    stream4 += input_factory.get_timeseries(\n",
-    "        starttime=start,\n",
-    "        endtime=end,\n",
-    "        channels=channels,\n",
-    "        observatory=obs,\n",
-    "        type='variation',\n",
-    "        interval='minute')\n",
-    "for obs in ['HON','SJG','GUA']:\n",
-    "    stream3 += input_factory.get_timeseries(\n",
-    "        starttime=start,\n",
-    "        endtime=end,\n",
-    "        channels=['MDT',],\n",
-    "        observatory=obs,\n",
-    "        type='variation',\n",
-    "        interval='minute')\n",
-    "    \n",
-    "# # These lines will plot each trace in the stream so that you can see which\n",
-    "# #  observatory is contributing to different features\n",
-    "# stream4.plot()\n",
-    "# stream3.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialize the Algorithm factory, run the averaging process and plot the output of the averaged stream"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c140b9f10>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c15224d10>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "algorithm_factory = AverageAlgorithm()\n",
-    "dst4_stream = algorithm_factory.process(stream4)\n",
-    "\n",
-    "algorithm_factory = AverageAlgorithm(scales=[0.92959,0.994381,0.9999])\n",
-    "dst3_stream = algorithm_factory.process(stream3)\n",
-    "dst4_stream.plot()\n",
-    "dst3_stream.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Set observatory scale/corrections and re-run the process. The Plot shows the scaled version of the averaging function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c152034d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "scaled_factory = AverageAlgorithm(scales = [.2, 2, .5, .5])\n",
-    "averaged_scaled_series = scaled_factory.process(dst4_stream)\n",
-    "averaged_scaled_series.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a0ad39c90>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c15617650>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# This obtains the currently running (old version) of DST data\n",
-    "dst_factory = EdgeFactory()\n",
-    "chan = ['MGD','MSD',]\n",
-    "OG_DST = input_factory.get_timeseries(\n",
-    "        starttime=UTCDateTime('2018-08-02T18:00:00Z'),\n",
-    "        endtime=UTCDateTime('2018-08-03T12:00:00Z'),\n",
-    "        channels=chan,\n",
-    "        observatory='USGS',\n",
-    "        type='variation',\n",
-    "        interval='minute')\n",
-    "\n",
-    "# This just renames the channels for clarity in comparing the old and new DST\n",
-    "OG_DST.select(channel='MGD')[0].stats.channel = 'DST'\n",
-    "OG_DST.select(channel='DST')[0].stats.station = 'old'\n",
-    "OG_DST.select(channel='MSD')[0].stats.channel = 'DST3'\n",
-    "OG_DST.select(channel='DST3')[0].stats.station = 'old'\n",
-    "dst3_stream[0].stats.channel = 'DST3'\n",
-    "dst4_stream[0].stats.channel = 'DST'\n",
-    "\n",
-    "# # Plots each stream separately\n",
-    "# OG_DST.plot()\n",
-    "# dst3_stream.plot()\n",
-    "# dst4_stream.plot()\n",
-    "\n",
-    "# For easier comparison\n",
-    "all_streams = OG_DST + dst3_stream + dst4_stream\n",
-    "all_dst3 = all_streams.select(channel='DST3')\n",
-    "all_dst = all_streams.select(channel='DST')\n",
-    "all_dst3.plot()\n",
-    "all_dst.plot()\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Comparison"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x1c15351f50>"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c1560e250>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "dst3_difference = all_dst3[0].data - all_dst3[1].data\n",
-    "dst_difference = all_dst[0].data - all_dst[1].data\n",
-    "times = all_dst3[0].times(reftime=start)\n",
-    "plt.plot(times,dst3_difference)\n",
-    "plt.plot(times,dst_difference)\n",
-    "plt.legend(['dst3','dst'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The difference between the 3 station dst is quite large but the 4 station is near identical. The large spike in the 4 station dst is actually indicative that the new method is working. In the previous version, if a channel had a gap then the dst formula would average only 3 stations instead of 4. Now if even 1 channel is missing a gap is created because the dst requires 4 stations. The much larger difference in the 3 station could be due to different correction factors."
-   ]
-  }
- ],
- "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": "2.7.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/algorithms/.ipynb_checkpoints/SqDistValidate-checkpoint.ipynb b/docs/algorithms/.ipynb_checkpoints/SqDistValidate-checkpoint.ipynb
deleted file mode 100644
index 99f3a04953525420162839922ed801a9177f85ac..0000000000000000000000000000000000000000
--- a/docs/algorithms/.ipynb_checkpoints/SqDistValidate-checkpoint.ipynb
+++ /dev/null
@@ -1,1419 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# This is always required for inline plot rendering in IPython Notebooks; might\n",
-    "# as well do it first, even before the markdown sections, just to be safe\n",
-    "%matplotlib inline\n",
-    "#%matplotlib notebook"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# SqDist Algorithm - Contents:\n",
-    "\n",
-    "- [Theoretical Basis](#Theoretical-Basis)\n",
-    "  - [Motivation](#Motivation)\n",
-    "  - [Simple Exponential Smoothing](#Simple-Exponential-Smoothing)\n",
-    "  - [Holt's Linear Trend Forecast](#Holt's-Linear-Trend-Forecast)\n",
-    "  - [Holt-Winters Seasonal Forecast](#Holt-Winters-Seasonal-Forecast)\n",
-    "  - [Prediction Intervals](#Prediction-Intervals)\n",
-    "  - [Spike Detection and Adaptive Baselines](#Spike-Detection-and-Adaptive-Baselines)\n",
-    "- [Putting it All Together](#Putting-it-All-Together)\n",
-    "  - [Programming Interface](#Programming-Interface)\n",
-    "- [Functional Tests](#Functional-Tests)\n",
-    "  - [Imports](#Imports)\n",
-    "  - [Test Configuration](#Test-Configuration)\n",
-    "  - [Test 1](#Test-1) | [Test 2](#Test-2) | [Test 3](#Test-3) | [Test 4](#Test-4) | [Test 5](#Test-5)  \n",
-    "- [Synthetic Data Demonstration](#Synthetic-Data-Demonstration)\n",
-    "  - [Construct synthetic time series](#Construct-synthetic-time-series)\n",
-    "  - [Apply SqDist Algorithm](#Apply-SqDist-Algorithm)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# [Theoretical Basis](#SqDist-Algorithm---Contents:)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## [Motivation](#SqDist-Algorithm---Contents:)\n",
-    "\n",
-    "Exponential smoothing of time series has been employed in countless research, engineering, economic, sociological, political, etc., applications. While its utility has been empirically demonstrated time and again over the last half century or more, it has only been in the last couple decades that it has normalized in form, stood up to rigorous mathematical scrutiny, and been tied directly to well-known statistical time series models. A major contributor to this recent maturation of this subdiscipline of applied mathematics is R. J. Hyndman. We largely follow notation used in his free Online textbook (http://www.otexts.org/fpp), and related literature, to provide a very brief overview of exponential smoothing that culminates in an algorithm that can be used to decompose a time series into a trend, a repeating “seasonal” pattern, and a residual."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## [Simple Exponential Smooting](#SqDist-Algorithm---Contents:)\n",
-    "\n",
-    "Exponential smoothing is a form of causal time series filtering; a way to estimate the most likely observation at time $t+1$, given observations up to time $t$. In its simplest form, it is a weighted average of the most recent observation, and the previous weighted average: \n",
-    "\n",
-    " <a name=\"eq01\">(1)</a>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;\n",
-    " $\\hat{y}_{t+1|t}=\\alpha y + \\left(1-\\alpha\\right)\\hat{y}_{t|t-1}$\n",
-    "\n",
-    "where\n",
-    "\n",
-    "$\n",
-    "\\begin{array}{rcl}\n",
-    "y_t       & \\text{is} & \\text{observation at time } t            \\\\\n",
-    "\\hat{y}_t & \\text{is} & \\text{predicted observation at time } t  \\\\\n",
-    "\\alpha    & \\text{is} & \\text{forgetting factor between 0 and 1} \\\\ \n",
-    "\\end{array}\n",
-    "$\n",
-    "\n",
-    "Equation [(1)](#eq01) is a recursive formulation, which is preferred for algorithmic implementation, but if expanded into an infinite series, it becomes clear why we refer to this as “exponential” smoothing:\n",
-    "\n",
-    "\n",
-    " <a name=\"eq02\">(2)</a>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;\n",
-    " $\\displaystyle\n",
-    " \\hat{y}_{t+1|t}=\\alpha y_t + \\alpha \\left(1 - \\alpha \\right)y_{t-1} +\n",
-    "                               \\alpha \\left(1 - \\alpha \\right)^2 y_{t-2} +\n",
-    "                               \\alpha \\left(1 - \\alpha \\right)^3 y_{t-3} + \\ldots\n",
-    " $\n",
-    "\n",
-    "\n",
-    "While $\\alpha$ is constant, the term $\\left(1-\\alpha\\right)$ changes exponentially for older and older observations. Since this term is by definition less than one, older observations are weighted exponentially less than newer observations. As $\\alpha$ approaches unity, the memory of the algorithm disappears.\n",
-    "\n",
-    "An alternative, but equivalent formulation decomposes [(1)](#eq01) into two steps:\n",
-    "\n",
-    "<a name=\"eq03\">(3)</a>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;\n",
-    " $\n",
-    " \\begin{array}{r@{}l}\n",
-    "  \\hat{y}_{t+1|t} & = & l_t \\\\\n",
-    "              l_t & = & \\alpha y_t + \\left(1-\\alpha\\right)l_{t-1}\n",
-    " \\end{array}\n",
-    " $\n",
-    "\n",
-    "where\n",
-    "\n",
-    "$\n",
-    "\\begin{array}{rcl}\n",
-    "l_t       & \\text{is} & \\text{level, or instantaneous baseline at time } t\n",
-    "\\end{array}\n",
-    "$\n",
-    "\n",
-    "By rearranging [(3)](#eq03), we obtain the so-called error-equation formulation:\n",
-    "\n",
-    "<a name=\"eq04\">(4)</a>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;\n",
-    " $\n",
-    " \\begin{array}{r@{}l}\n",
-    "  \\hat{y}_{t+1|t} & = & l_t \\\\\n",
-    "              l_t & = & l_{t-1} + \\alpha\\left(y_t - l_{t-1}\\right) \\\\\n",
-    "              l_t & = & l_{t-1} + \\alpha e_t\n",
-    " \\end{array}\n",
-    " $\n",
-    "\n",
-    "where\n",
-    "\n",
-    "$\n",
-    "\\begin{array}{rcl}\n",
-    "e_t       & \\text{is} & \\text{1-step prediction error for time } t \\text{, or } y_t - \\hat{y}_{t|t-1}\n",
-    "\\end{array}\n",
-    "$\n",
-    "\n",
-    "The value of this formulation will become clearer when we present a numerical algorithm that includes not only simple exponential smoothing, but additional terms to model trend and seasonal variation in order to better match realistic observations. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## [Holt's Linear Trend Forecast](#SqDist-Algorithm---Contents:)\n",
-    "\n",
-    "Equation [(3)](#eq03) can be augmented to include a linear trend:\n",
-    "\n",
-    "<a name=\"eq05\">(5)</a>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;\n",
-    " $\n",
-    " \\begin{array}{r@{}l}\n",
-    "  \\hat{y}_{t+h|t} & = & l_t + h b_t \\\\\n",
-    "              l_t & = & \\alpha y_t + \\left(1-\\alpha\\right)\\left(l_{t-1}+b_{t-1}\\right) \\\\\n",
-    "              b_t & = & \\beta^*\\left(l_t-l_{t-1}\\right) + \n",
-    "                        \\left(1-\\beta^*\\right)b_{t-1}\n",
-    " \\end{array}\n",
-    " $\n",
-    "\n",
-    "where\n",
-    "\n",
-    "$\n",
-    "\\begin{array}{rcl}\n",
-    "h       & \\text{is} & \\text{forecast horizon } \\\\\n",
-    "b_t     & \\text{is} & \\text{slope at time } t \\\\\n",
-    "\\beta^* & \\text{is} & \\text{slope forgetting factor between 0 and 1}\n",
-    "\\end{array}\n",
-    "$\n",
-    "\n",
-    "In words, a forecast is the level plus a slope multiplied by the number of discrete time steps. The level is still a weighted average of the observation at time $t$, and the 1-step prediction from time $t-1$ to $t$. The slope itself is the exponentially smoothed 1-step difference in the baseline from time $t-1$ to time $t$.\n",
-    "Just like with simple exponential smoothing, there is an error-correction formulation:\n",
-    "\n",
-    "<a name=\"eq06\">(6)</a>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;\n",
-    " $\n",
-    " \\begin{array}{r@{}l}\n",
-    "  \\hat{y}_{t+h|t} & = & l_t + h b_t \\\\\n",
-    "              l_t & = & l_{t-1} + b_{t-1} + \\alpha e_t \\\\\n",
-    "              b_t & = & b_{t-1} + \\alpha \\beta^* e_t\n",
-    " \\end{array}\n",
-    " $\n",
-    "\n",
-    "A naïve implementation of Holt’s linear trend method tends to over-forecast, especially for larger forecast horizons $h$. One way to address this is to dampen the trend, or in other words, force the slope toward zero for larger forecast horizons. A minor tweak to [(5)](#eq05) gives:\n",
-    "\n",
-    "\n",
-    "<a name=\"eq07\">(7)</a>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;\n",
-    " $\n",
-    " \\begin{array}{r@{}l}\n",
-    "  \\hat{y}_{t+h|t} & = & l_t + \\left(\\phi+\\phi^2+\\ldots+\\phi^h\\right) b_t \\\\\n",
-    "              l_t & = & \\alpha y_t + \\left(1-\\alpha\\right)\\left(l_{t-1}+\\phi b_{t-1}\\right) \\\\\n",
-    "              b_t & = & \\beta^*\\left(l_t-l_{t-1}\\right) + \n",
-    "                        \\left(1-\\beta^*\\right)\\phi b_{t-1}\n",
-    " \\end{array}\n",
-    " $\n",
-    "\n",
-    "where\n",
-    "\n",
-    "$\n",
-    "\\begin{array}{rcl}\n",
-    "\\phi       & \\text{is} & \\text{dampening factor between 0 and 1} \n",
-    "\\end{array}\n",
-    "$\n",
-    "\n",
-    "if $\\phi=1$, [(7)](#eq07) is identical to the traditional Holt linear method; if $\\phi=0$, [(7)](#eq07) is identical to simple exponential smoothing. As before, rearranging terms leads to an error-corretion formulation:\n",
-    "\n",
-    "\n",
-    "<a name=\"eq08\">(8)</a>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;\n",
-    " $\\displaystyle\n",
-    " \\begin{array}{r@{}l}\n",
-    "  \\hat{y}_{t+h|t} & = & l_t + \\left(\\phi+\\phi^2+\\ldots+\\phi^h\\right) b_t \\\\\n",
-    "              l_t & = & l_{t-1} + \\phi b_{t-1} + \\alpha e_t \\\\\n",
-    "              b_t & = & \\phi b_{t-1} + \\alpha \\beta^* e_t\n",
-    " \\end{array}\n",
-    " $\n",
-    "\n",
-    "\n",
-    "While we do not anticipate a need to forecast far into the future, data gaps are likely to occur, some of which may be long enough that if a constant slope is used to extrapolate across the gap, the algorithm may become unstable as the forecast increases/decreases without bound.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## [Holt-Winters Seasonal Forecast](#SqDist-Algorithm---Contents:)\n",
-    "\n",
-    "Finally, it is common for time series to exhibit a repeating pattern over a fixed interval. Seasonal variations are among the most familiar form of repetition, but a “seasonal” correction can be applied at any interval, even a 24-hour day. Holt and his student Winters share credit for formalizing a powerful seasonal correction tool that has stood the tests of time, and is still used frequently today. Augmenting Equation [(5)](#eq05) gives:\n",
-    "\n",
-    "\n",
-    "<a name=\"eq09\">(9)</a>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;\n",
-    " $\\displaystyle\n",
-    " \\begin{array}{r@{}l}\n",
-    "  \\hat{y}_{t+h|t} & = & l_t + h b_t + s_{t-m+h_m^+} \\\\\n",
-    "              l_t & = & \\alpha \\left(y_t-s_{t-m}\\right) + \n",
-    "                        \\left(1-\\alpha\\right)\\left(l_{t-1}+b_{t-1}\\right) \\\\\n",
-    "              b_t & = & \\beta^*\\left(l_t-l_{t-1}\\right) + \n",
-    "                        \\left(1-\\beta^*\\right)b_{t-1} \\\\\n",
-    "              s_t & = & \\gamma^* \\left(1-\\alpha\\right)\n",
-    "                                 \\left(y_t-l_{t-1}-b_{t-1}\\right) +\n",
-    "                        \\left(1-\\gamma^*\\left(1-\\alpha\\right)\\right)s_{t-m}\n",
-    " \\end{array}\n",
-    " $\n",
-    "\n",
-    "where\n",
-    "\n",
-    "<!--- this is unnecessarily complex to get exactly the formatting desired\n",
-    "<table id=\"where\" style=\"width:100%; border-style: hidden\">\n",
-    "<tr><td style=\"width:5%; border-style: hidden\">\n",
-    "    $m$\n",
-    "    </td>\n",
-    "    <td style=\"width:5%; border-style: hidden\">is</td>\n",
-    "    <td style=\"text-align: left; border-style: hidden\"> \n",
-    "      number of discrete time steps within a repeating interval\n",
-    "    </td>\n",
-    "</tr>\n",
-    "<tr><td style=\"width:5%; border-style: hidden\">\n",
-    "    $m$\n",
-    "    </td>\n",
-    "    <td style=\"width:5%; border-style: hidden\">is</td>\n",
-    "    <td style=\"text-align: left; border-style: hidden\"> \n",
-    "      modulo of $(h-1)$ with $m$ (i.e., $(h-1)\\mod{m}+1$)\n",
-    "    </td>\n",
-    "</tr>\n",
-    "<tr><td style=\"width:5%; border-style: hidden\">\n",
-    "    $m$\n",
-    "    </td>\n",
-    "    <td style=\"width:5%; border-style: hidden\">is</td>\n",
-    "    <td style=\"text-align: left; border-style: hidden\"> \n",
-    "      number of discrete time steps within a repeating interval\n",
-    "    </td>\n",
-    "</tr>\n",
-    "<tr><td style=\"width:5%; border-style: hidden\">\n",
-    "    $m$\n",
-    "    </td>\n",
-    "    <td style=\"width:5%; border-style: hidden\">is</td>\n",
-    "    <td style=\"text-align: left; border-style: hidden\"> \n",
-    "      number of discrete time steps within a repeating interval\n",
-    "    </td>\n",
-    "</tr>\n",
-    "</table>\n",
-    "-->\n",
-    "\n",
-    "$\n",
-    "\\begin{array}{rcl}\n",
-    "m         & \\text{is} & \\text{number of discrete time steps within repeating interval } \\\\\n",
-    "h_m^+     & \\text{is} & \\text{modulo of } (h-1) \\text{ with } m\n",
-    "            \\text{ (i.e., } [(h-1)\\mod{m}]+1 \\text{)} \\\\\n",
-    "s_t       & \\text{is} & \\text{seasonal correction for time } t \\\\\n",
-    "\\gamma^*  & \\text{is} & \\text{seasonal correction forgetting factor between 0 and 1}\n",
-    "\\end{array}\n",
-    "$\n",
-    "\n",
-    "\n",
-    "In words, the appropriate seasonal correction is added to an $h$-step prediction. The seasonal correction for time $t$ is removed from the current observation before updating the level for time $t$, and the slope is updated using these seasonally corrected levels. Finally, the seasonal correction is updated using yet another exponential smoothing parameter, $\\gamma^*$. Rearranging terms, and including a trend dampening factor, leads to the error-correction formulation that will ultimately be implemented algorithmically:\n",
-    "\n",
-    "\n",
-    "<a name=\"eq10\">(10)</a>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;\n",
-    " $\n",
-    " \\begin{array}{r@{}l}\n",
-    "  \\hat{y}_{t+h|t} & = & l_t + \\left(\\phi+\\phi^2+\\ldots+\\phi^h\\right) b_t + s_{t-m+h_m^+}\\\\\n",
-    "              l_t & = & l_{t-1} + \\phi b_{t-1} + \\alpha e_t \\\\\n",
-    "              b_t & = & \\phi b_{t-1} + \\alpha \\beta^* e_t \\\\\n",
-    "              s_t & = & s_{t-m} + \\gamma^*\\left(1-\\alpha\\right)e_t\n",
-    " \\end{array}\n",
-    " $\n",
-    "\n",
-    "\n",
-    "A very astute and careful reader may have noticed that the 1-step prediction error will be the same if an arbitrary offset is added to the level and subtracted from the seasonal correction. This means that, while the final predictions will not be impacted, the separation of level from seasonal component is not unique, and may lead to equal/opposite drifts in both that are not desirable.  This can be mitigated by assuming that the sum of seasonal corrections is zero over a repeating interval. To enforce this, a re-leveling adjustment can be calculated at each time step, added to the level, and subtracted from the seasonal correction:\n",
-    "\n",
-    "\n",
-    "<a name=\"eq11\">(11)</a>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;\n",
-    " $\n",
-    "  \\displaystyle\n",
-    "  r_t = \\frac{\\gamma^*\\left(1-\\alpha\\right)}{m}e_t\n",
-    " $\n",
-    "\n",
-    "Note that this adjustment can be applied with each iteration, or simply accumulated over all iterations, then applied at the end to obtain the same result. The latter may prove to be more efficient for certain interpreted programming languages that are known for slow loops, but which have vector-optimized functions for relatively simple mathematical operations.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## [Prediction Intervals](#SqDist-Algorithm---Contents:)\n",
-    "\n",
-    "Every forecast has an associated prediction error, or residual, $e_t$. Assuming a real measurement is available for time $t-1$ before time $t$, this residual allows forecasts to be made using the error-correction formulations described above. This formulation is convenient for causally smoothing a time series, but it also allows simulation beyond 1 step if $e_t$ is treated as a statistical value with its own distribution. By performing many simulations, each using $e_t$ drawn randomly from its distribution, so-called prediction intervals (PIs) can be constructed for any number of steps into the future. Useful percentiles can then be estimated from these PIs, providing a measure of confidence in the point forecast (i.e., the forecast that assumes $e_t = 0$).\n",
-    "\n",
-    "Any number of distributions for $e_t$ may be assumed. The most accurate might simply be to store every $e_t$, then draw a random sample from this distribution during a simulation. Such “bootstrapping” is not especially convenient in real-time processing, and drawing from a distribution that is not adequately populated can be problematic. For the formulations presented above however, analytic expressions exist if $e_t$ is assumed to be normally and independently distributed with a known variance $\\sigma^2$. For the damped Holt-Winters forecast given by [(10)](#eq10), the variance of the PI h steps into the future is:\n",
-    "\n",
-    "\n",
-    "<a name=\"eq12\">(12)</a>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;\n",
-    " $\n",
-    " \\begin{array}{r@{}l}\n",
-    "  v_h & = & \\sigma^2\\left(1+\\sum_\\limits{j=1}^{h-1}c_j^2\\right) \\\\\n",
-    "  c_j & = & \\alpha\\left(1+\\phi_{j-1}\\beta^*\\right) + \n",
-    "                          \\gamma^*\\left(1-\\alpha\\right)d_{j,m}\n",
-    " \\end{array}\n",
-    " $\n",
-    "\n",
-    "where\n",
-    "\n",
-    "$\n",
-    "\\begin{array}{rcl}\n",
-    "\\phi_{j-1}  & \\text{is} & 1+\\phi+\\ldots+\\phi^{j-1} \\\\\n",
-    "d_{j,m}     & \\text{is} & \\text{1 if } j\\bmod{m} \\text{ equals 0, otherwise 0; in words, bump } c_j \\\\\n",
-    "            &           & \\text{ each time seasonal corrections are recycled after } h \\\\\n",
-    "            &           & \\text{ grows longer than the number of seasons}\n",
-    "\\end{array}\n",
-    "$\n",
-    "\n",
-    "Here it is assumed that $\\sigma^2$ is known, which may not be the case. Since the variance is nothing more than the standard deviation squared, and the standard deviation may be thought of as a magnitude of an expected residual, one way to estimate and track this is using simple exponential smoothing. All that is required is to define a forgetting factor for the expected residual magnitude:\n",
-    "\n",
-    "\n",
-    "<a name=\"eq13\">(13)</a>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;\n",
-    " $\n",
-    "  \\sigma_t = \\left(1-\\alpha\\right)\\sigma_{t-1} + \\alpha\\left|e_t\\right|\n",
-    " $\n",
-    "\n",
-    "where\n",
-    "\n",
-    "$\n",
-    "\\begin{array}{rcl}\n",
-    "\\alpha            & \\text{is} & \\text{magnitude of expected residual forgetting factor} \\\\\n",
-    "                  &           & \\text{(may be identical to baseline forgetting factor)} \\\\\n",
-    "\\left|e_t\\right|  & \\text{is} & \\text{magnitude of residual}\n",
-    "\\end{array}\n",
-    "$\n",
-    "\n",
-    "In practice, $\\sigma$, and therefore $\\sigma^2$, should update via [(13)](#eq13) when 1-step prediction errors are available, and using [(12)](#eq12) for any forecast beyond 1-step. Note that [(13)](#eq13) is not an error-correction formulation, since $\\left|e_t\\right|$ is a random variable to be predicted, and linearly independent of the actual 1-step prediction error. It is possible to rearrange Equation [(13)](#eq13) into an error-correction formulation, but the notation might be confusing.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## [Spike Detection and Adaptive Baselines](#SqDist-Algorithm---Contents:)\n",
-    "\n",
-    "Imagine a threshold residual is defined as a multiple of the standard deviation. Any $e_t$ with an absolute value exceeding this threshold is treated as a bad data point. If this bad data point is treated as a gap (i.e., forecast is made, but level, slope, and seasonal correction parameters are not updated), $\\sigma^2$ will grow with the prediction interval simply due to the dynamical nature of the prediction equations. If the threshold residual is exceeded repeatedly, eventually the standard deviation will grow large enough that $e_t$ no longer exceeds the threshold residual, and the sequence of observations previously treated as bad data will once again be used to update the level, slope, and seasonal correction parameters. In effect, a large DC shift in observed data, while initially treated as bad data, will eventually be recognized as a new baseline, and the adaptive algorithm will converge on it according to [(10)](#eq10).\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# [Putting it All Together](#SqDist-Algorithm---Contents:)\n",
-    "\n",
-    "The purpose of all the preceeding mathematics is to specify a potentially time-varying baseline, and update a potentially time-varying set of seasonal corrections. In the geomagnetic time series context, these represent so-called secular variation (SV) of Earth's internal main field, and the solar-quiet (SQ) variation, or daily magnetic tides caused by quasi-stationary geospace electrical currents fixed spatially relative to the Sun. So-called magnetic disturbance (DIST) is what remains, or the $e_t$ term.\n",
-    "\n",
-    "We developed software to implement these mathematics, and decompose geomagnetic time series (or any time series really) into its SV, SQ, and DIST constituents. It is integrated with the USGS' [Geomag-algorithms](https://github.com/usgs/geomag-algorithms) software packageg as a new algorithm class [geomagio.algorithm.SqDistAlgorithm](https://github.com/usgs/geomag-algorithms/blob/master/geomagio/algorithm/SqDistAlgorithm.py). This is mostly a wrapper for the additive() method, so-named because the theory presented above assumes that the seasonal corrections are independent of the baseline level, and can simply be added.\n",
-    "\n",
-    "The additive method can be extracted and used in a stand-alone mode, but to encourage and facilitate interaction the USGS' robust geomagnetic data ingest and analysis framework, it is recommended that the full class be used as much as feasible. For those users not interested in working directly with Python, geomag-algorithms provides a command-line tool that can perform most geomagio.algorithm.SqDistAlgorithm actions, and generate output data in several standard formats. A users' guide for both is described in [SqDist_usage.md](SqDist_usage.md)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# [Functional Tests](#SqDist-Algorithm---Contents:)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The purpose of this section is to demonstrate that the algorithm works as expected, and to a lesser extent, demonstrate its utility with realistic usage examples. While some material here might be extracted to generate unit tests for the algorithm, these are primarily *functional* tests, and may be more complex than one might want to incorporate into an automated testing framework. Explanatory markdown, inline comments, or both, should tie different tests to the Algorithm Theoretical Basis above as much as possible."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## [Imports](#SqDist-Algorithm---Contents:)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# standard imports\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ImportError",
-     "evalue": "cannot import name ChannelConverter",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-3-d27d693f820e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# import SqDistAlgorithm class from geomag-algorithms package\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mgeomagio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malgorithm\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSqDistAlgorithm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# import some mid-level classes from ObsPy to construct test data objects\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mobspy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mStream\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTrace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mUTCDateTime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Users/arigdon/anaconda2/lib/python2.7/site-packages/geomagio/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m__future__\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mabsolute_import\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mChannelConverter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mStreamConverter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTimeseriesUtility\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mImportError\u001b[0m: cannot import name ChannelConverter"
-     ]
-    }
-   ],
-   "source": [
-    "# import SqDistAlgorithm class from geomag-algorithms package\n",
-    "from geomagio.algorithm import SqDistAlgorithm\n",
-    "\n",
-    "# import some mid-level classes from ObsPy to construct test data objects\n",
-    "from obspy.core import Stream, Trace, UTCDateTime"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## [Test Configuration](#SqDist-Algorithm---Contents:)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# define assert_almost_equal\n",
-    "assert_almost_equal = np.testing.assert_almost_equal\n",
-    "\n",
-    "# configure to test zero-step predictions of 4 \"season\" cycles\n",
-    "m=4\n",
-    "t=np.linspace(0,2*np.pi,m+1)[:-1]\n",
-    "hstep=0\n",
-    "\n",
-    "# initial slope is 0; average age is infinite\n",
-    "b0=0\n",
-    "beta=1/np.inf\n",
-    "\n",
-    "# initial trendline is 0; average age is 12 steps\n",
-    "l0=0\n",
-    "alpha=1/12.\n",
-    "\n",
-    "# initial seasonal correction is sinusoid; average age is 12 steps\n",
-    "s0=np.sin(t)[0:4]\n",
-    "gamma=1/12.*m\n",
-    "\n",
-    "# standard deviation of unit-amplitude sinusoid\n",
-    "sigma0=[np.sqrt(0.5)]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## [Test 1](#SqDist-Algorithm---Contents:)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "PASS\n"
-     ]
-    }
-   ],
-   "source": [
-    "# predict three cycles ahead given l0 and s0, no inputs,\n",
-    "# and assume PI only grows with trendline adjustments\n",
-    "\n",
-    "# instantiate SqDistAlgorithm object\n",
-    "SqDist_01 = SqDistAlgorithm(alpha=alpha, beta=0, gamma=0, m=m, hstep=hstep,\n",
-    "                            s0=s0, l0=l0, b0=b0, sigma0=sigma0)\n",
-    "\n",
-    "# define input stream\n",
-    "yobs_01 = Stream()\n",
-    "yobs_01 += Trace(np.zeros(12) * np.nan)\n",
-    "\n",
-    "# process input stream\n",
-    "SvSqDistStream = SqDist_01.process(yobs_01)\n",
-    "\n",
-    "# test process outputs\n",
-    "assert_almost_equal(SvSqDistStream[0].data, \n",
-    "                    [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan,\n",
-    "                     np.nan, np.nan, np.nan, np.nan, np.nan, np.nan])\n",
-    "assert_almost_equal(SvSqDistStream[1].data, \n",
-    "                    [0, 1, 0, -1, 0, 1, 0, -1, 0, 1, 0, -1])\n",
-    "assert_almost_equal(SvSqDistStream[2].data, \n",
-    "                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n",
-    "\n",
-    "# also test certain states within SqDist_01\n",
-    "assert_almost_equal(SqDist_01.yhat0, [])\n",
-    "assert_almost_equal(SqDist_01.s0, [0, 1, 0, -1])\n",
-    "assert_almost_equal(SqDist_01.l0, 0)\n",
-    "assert_almost_equal(SqDist_01.b0, 0)\n",
-    "assert_almost_equal(SqDist_01.sigma0, 0.73361737)\n",
-    "\n",
-    "print 'PASS'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## [Test 2](#SqDist-Algorithm---Contents:)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "PASS\n"
-     ]
-    }
-   ],
-   "source": [
-    "# predict three cycles ahead given l0 and s0, no inputs,\n",
-    "# and assume PI only grows with seasonal adjustments\n",
-    "\n",
-    "# instantiate SqDistAlgorithm object\n",
-    "SqDist_02 = SqDistAlgorithm(alpha=0, beta=0, gamma=gamma, m=m, hstep=hstep,\n",
-    "                            s0=s0, l0=l0, b0=b0, sigma0=sigma0)\n",
-    "\n",
-    "# define input stream\n",
-    "yobs_02 = Stream()\n",
-    "yobs_02 += Trace(np.zeros(12) * np.nan)\n",
-    "\n",
-    "# process input stream\n",
-    "SvSqDistStream = SqDist_02.process(yobs_02)\n",
-    "\n",
-    "# test process outputs\n",
-    "assert_almost_equal(SvSqDistStream[0].data, \n",
-    "                    [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan,\n",
-    "                     np.nan, np.nan, np.nan, np.nan, np.nan, np.nan])\n",
-    "assert_almost_equal(SvSqDistStream[1].data, \n",
-    "                    [0, 1, 0, -1, 0, 1, 0, -1, 0, 1, 0, -1])\n",
-    "assert_almost_equal(SvSqDistStream[2].data, \n",
-    "                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n",
-    "\n",
-    "# also test certain states within SqDist_01\n",
-    "assert_almost_equal(SqDist_02.yhat0, [])\n",
-    "assert_almost_equal(SqDist_02.s0, [0, 1, 0, -1])\n",
-    "assert_almost_equal(SqDist_02.l0, 0)\n",
-    "assert_almost_equal(SqDist_02.b0, 0)\n",
-    "assert_almost_equal(SqDist_02.sigma0, 0.78173596)\n",
-    "\n",
-    "print 'PASS'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## [Test 3](#SqDist-Algorithm---Contents:)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "PASS\n"
-     ]
-    }
-   ],
-   "source": [
-    "# smooth three cycles' worth of zero-value input observations,\n",
-    "# assuming only the trendline varies\n",
-    "\n",
-    "# instantiate SqDistAlgorithm object\n",
-    "SqDist_03 = SqDistAlgorithm(alpha=alpha, beta=0, gamma=0, m=m, hstep=hstep,\n",
-    "                            s0=s0, l0=l0, b0=b0, sigma0=sigma0)\n",
-    "\n",
-    "# define input stream\n",
-    "yobs_03 = Stream()\n",
-    "yobs_03 += Trace(np.zeros(12))\n",
-    "\n",
-    "# process input stream\n",
-    "SvSqDistStream = SqDist_03.process(yobs_03)\n",
-    "\n",
-    "# test process outputs\n",
-    "assert_almost_equal(SvSqDistStream[0].data, \n",
-    "                    [          0,         -1,  0.08333333,  1.07638889, -0.01331019, -1.012201,\n",
-    "                      0.07214908, 1.06613666, -0.02270806, -1.02081573,  0.06425225,  1.0588979])\n",
-    "assert_almost_equal(SvSqDistStream[1].data, \n",
-    "                    [0, 1, 0, -1, 0, 1, 0, -1, 0, 1, 0, -1])\n",
-    "assert_almost_equal(SvSqDistStream[2].data, \n",
-    "                    [              0,               0, -8.33333333e-02,  -7.63888889e-02, \n",
-    "                      1.33101852e-02,  1.22010031e-02, -7.21490805e-02,  -6.61366571e-02,\n",
-    "                      2.27080643e-02,  2.08157256e-02, -6.42522515e-02,  -5.88978972e-02])\n",
-    "\n",
-    "# also test certain states within SqDist_01\n",
-    "assert_almost_equal(SqDist_03.yhat0, [])\n",
-    "assert_almost_equal(SqDist_03.s0, [0, 1, 0, -1])\n",
-    "assert_almost_equal(SqDist_03.l0, 0.0293435942031)\n",
-    "assert_almost_equal(SqDist_03.b0, 0)\n",
-    "assert_almost_equal(SqDist_03.sigma0, 0.61505552)\n",
-    "\n",
-    "print 'PASS'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## [Test 4](#SqDist-Algorithm---Contents:)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "PASS\n"
-     ]
-    }
-   ],
-   "source": [
-    "# smooth three cycles' worth of zero-value input observations,\n",
-    "# assuming only the seasonal adjustments vary\n",
-    "\n",
-    "# instantiate SqDistAlgorithm object\n",
-    "SqDist_04 = SqDistAlgorithm(alpha=0, beta=0, gamma=gamma, m=m, hstep=hstep,\n",
-    "                            s0=s0, l0=l0, b0=b0, sigma0=sigma0)\n",
-    "\n",
-    "# define input stream\n",
-    "yobs_04 = Stream()\n",
-    "yobs_04 += Trace(np.zeros(12))\n",
-    "\n",
-    "# process input stream\n",
-    "SvSqDistStream = SqDist_04.process(yobs_04)\n",
-    "\n",
-    "# test process outputs\n",
-    "assert_almost_equal(SvSqDistStream[0].data, \n",
-    "                    [  0,               -1,  0,                1,\n",
-    "                       0,  -6.66666667e-01,  0,   6.66666667e-01,\n",
-    "                       0,  -4.44444444e-01,  0,   4.44444444e-01])\n",
-    "assert_almost_equal(SvSqDistStream[1].data, \n",
-    "                    [ 0,                 1,  8.33333333e-02,  -9.16666667e-01,\n",
-    "                      0,    6.66666667e-01,  5.55555556e-02,  -6.11111111e-01,\n",
-    "                      0,    4.44444444e-01,  3.70370370e-02,  -4.07407407e-01])\n",
-    "assert_almost_equal(SvSqDistStream[2].data, \n",
-    "                    [  0,                0, -8.33333333e-02, -8.33333333e-02,\n",
-    "                       0,                0, -5.55555556e-02, -5.55555556e-02,\n",
-    "                       0,                0, -3.70370370e-02, -3.70370370e-02])\n",
-    "\n",
-    "# also test certain states within SqDist_01\n",
-    "assert_almost_equal(SqDist_04.yhat0, [])\n",
-    "assert_almost_equal(SqDist_04.s0, [0, 0.29629629629629639, 0, -0.29629629629629639])\n",
-    "assert_almost_equal(SqDist_04.l0, 0)\n",
-    "assert_almost_equal(SqDist_04.b0, 0)\n",
-    "assert_almost_equal(SqDist_04.sigma0, 0.70710678)\n",
-    "\n",
-    "print 'PASS'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## [Test 5](#SqDist-Algorithm---Contents:)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "PASS\n"
-     ]
-    }
-   ],
-   "source": [
-    "# smooth three cycles' worth of sinusoid input observations,\n",
-    "# assuming only the seasonal adjustments vary, starting at zero\n",
-    "\n",
-    "# instantiate SqDistAlgorithm object\n",
-    "SqDist_05 = SqDistAlgorithm(alpha=0, beta=0, gamma=gamma, m=m, hstep=hstep,\n",
-    "                            s0=s0*0, l0=l0, b0=b0, sigma0=sigma0)\n",
-    "\n",
-    "# define input stream\n",
-    "yobs_05 = Stream()\n",
-    "yobs_05 += Trace(np.concatenate((s0,s0,s0)))\n",
-    "\n",
-    "# process input stream\n",
-    "SvSqDistStream = SqDist_05.process(yobs_05)\n",
-    "\n",
-    "# test process outputs\n",
-    "assert_almost_equal(SvSqDistStream[0].data, \n",
-    "                    [ 0,               1,               0,              -1,\n",
-    "                      0,  6.66666667e-01,               0, -6.66666667e-01,\n",
-    "                      0,  4.44444444e-01,               0, -4.44444444e-01])\n",
-    "assert_almost_equal(SvSqDistStream[1].data, \n",
-    "                    [ 0,               0, -8.33333333e-02, -8.33333333e-02,\n",
-    "                      0,  3.33333333e-01, -5.55555556e-02, -3.88888889e-01,\n",
-    "                      0,  5.55555556e-01, -3.70370370e-02, -5.92592593e-01])\n",
-    "assert_almost_equal(SvSqDistStream[2].data, \n",
-    "                    [ 0,               0,  8.33333333e-02,  8.33333333e-02,\n",
-    "                      0,               0,  5.55555556e-02,  5.55555556e-02,\n",
-    "                      0,               0,  3.70370370e-02,  3.70370370e-02])\n",
-    "\n",
-    "# also test certain states within SqDist_01\n",
-    "assert_almost_equal(SqDist_05.yhat0, [])\n",
-    "assert_almost_equal(SqDist_05.s0, [0, 0.70370370370370372, 0, -0.70370370370370372])\n",
-    "assert_almost_equal(SqDist_05.l0, 0)\n",
-    "assert_almost_equal(SqDist_05.b0, 0)\n",
-    "assert_almost_equal(SqDist_05.sigma0, 0.70710678)\n",
-    "\n",
-    "print 'PASS'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# [Synthetic Data Demonstration](#SqDist-Algorithm---Contents:)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## [Construct synthetic time series](#SqDist-Algorithm---Contents:)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x11365b390>]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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rse4RodH3HtUTIvJHxphHReRjIvI71toPzX6T1mDGGHdzS+FsbOiIIKc5tG5s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\nqPivlQT51Mfx73cAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11358fed0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# first 50 \"days\" at 100 samples per synthetic \"day\"\n",
-    "t000to050 = np.arange(5001)\n",
-    "syn000to050 = 10. * np.sin(t000to050 * (2*np.pi)/100.)\n",
-    "plt.figure(figsize=(16,4))\n",
-    "plt.plot(t000to050/100., syn000to050)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1159d2610>]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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Ld3+TiJ6x1v6q/4Ix5mjh+3+biJ6u+mV0ef4Yc+QM0c5Ix1LHQXOFxsnRufad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\nqTsWInXe5Cpm3xmPZfaytHGOcr1Q5+5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K86QkJ8d7hC+AWJxYCHm3e67O0a4XVQE1KEVq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\noaGhoaGhoaGhoaExWPz/aRqqgW+kGgwAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11367ee10>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# next 50 \"days\"\n",
-    "t050to100 = np.arange(5001,10001)\n",
-    "syn050to100 = 20 + 10. * np.sin(t050to100 * (2*np.pi)/100.)\n",
-    "plt.figure(figsize=(16,4))\n",
-    "plt.plot(t050to100/100., syn050to100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x115bb0250>]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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Roa5F7euTVEodAZ0UR1cEKeI2Gk6cyB+PlBYn6m9mEyfqfJqb9UX01Knx7mah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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x115a73f90>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# next 50 \"days\"\n",
-    "t100to150 = np.arange(10001,15001)\n",
-    "syn100to150 = 20 + 10. * np.sin(t100to150 * (2*np.pi)/100.) + 20*np.sin(t100to150 * (2*np.pi)/5000.)\n",
-    "plt.figure(figsize=(16,4))\n",
-    "plt.plot(t100to150/100., syn100to150)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x115e34b50>]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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WRrnOdmCS6RTHRkymN40TvwfVe+IWOOPPhMlzBYzu+bYZRAYAP/85fc//8i/m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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x115ae9a90>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# next 50 \"days\"\n",
-    "t150to200 = np.arange(15001,20001)\n",
-    "syn150to200 = 20 + (10. * np.sin(t150to200 * (2*np.pi)/100.)) * (1*np.cos(t150to200 * (2*np.pi)/5000.))\n",
-    "plt.figure(figsize=(16,4))\n",
-    "plt.plot(t150to200/100., syn150to200)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1160f79d0>]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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G4B5XUViYOc/RhKDumjYLuiCnVbsiu3dHx0giPThqRqsmbH6sqcsKpMaJeN93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\nYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x115d91c50>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# next 50 \"days\"\n",
-    "t200to250 = np.arange(20001,25001)\n",
-    "syn200to250 = 20 + ((10. * np.sin(t200to250 * (2*np.pi)/100.)) * (1*np.cos(t200to250 * (2*np.pi)/5000.)) + \n",
-    "                  20*np.sin(t200to250 * (2*np.pi)/5000.) )\n",
-    "plt.figure(figsize=(16,4))\n",
-    "plt.plot(t200to250/100., syn200to250)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x116345310>]"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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uoGleXkrRevnlfmVIF9C8epoTO+wQt9/229vXhQhF05znPBZe2H/bGPd113bq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\nREREVFoMWomIiIiIiKi0/h+wfkK3QHBrCgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11367e5d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# next 50 \"days\"\n",
-    "t250to300 = np.arange(25001,30001)\n",
-    "np.random.seed(123456789)\n",
-    "syn250to300 = 20 + ((10. * np.sin(t250to300 * (2*np.pi)/100.)) * (1*np.cos(t250to300 * (2*np.pi)/5000.)) + \n",
-    "                  20*np.sin(t250to300 * (2*np.pi)/5000.) ) + 5 * np.random.randn(5000)\n",
-    "plt.figure(figsize=(16,4))\n",
-    "plt.plot(t250to300/100., syn250to300)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x116895fd0>]"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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GeGGMUE6rmxsb8rpKO6koE++roijZ41OfAk46iewk0drV5RetLBY5XBewlX2/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CGEEEIIIbmFopUQQgghhBBCSG6haCWEEEIIIYQQklsoWgkhhBBCCCGE5Jb/D+7n5/Es2k4lAAAA\nAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x115a82a10>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# all 300 \"days\n",
-    "t000to300 = np.concatenate((t000to050,t050to100,t100to150,t150to200,t200to250, t250to300))\n",
-    "syn000to300 = np.concatenate((syn000to050,syn050to100,syn100to150,syn150to200,syn200to250, syn250to300))\n",
-    "plt.figure(figsize=(16,4))\n",
-    "plt.plot(t000to300/100., syn000to300)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## [Apply SqDist Algorithm](#SqDist-Algorithm---Contents:)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# set up Holt-Winters smoothing parameters\n",
-    "m = 100 # length of \"day\"\n",
-    "alpha = 1./100./3. # average age of level is 3 \"days\"\n",
-    "beta = 0 # slope doesn't change\n",
-    "gamma = 1./100.*100./3. # average age of \"seasonal\" correction is e \"days\"\n",
-    "phi = 1 # don't dampen the slope\n",
-    "\n",
-    "# initialize states for HW smoother\n",
-    "l0 = None # this uses algorithm's default initial level\n",
-    "b0 = 0 # this is NOT the algorithm's default initial slope\n",
-    "s0 = None # this uses algorithm's default initial \"seasonal\" correction\n",
-    "sigma0 = [np.var(syn000to050)] # this is NOT the algorithm's default initial standard deviation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### First 50 \"days\"\n",
-    "\n",
-    "Items to note:\n",
-    "\n",
-    "- SQ+SV (green) gradually grows from zero to match the repeating signal;\n",
-    "- SV (red) has non-zero values because SQ has not yet adjusted to its full amplitude;\n",
-    "- SQ is adjusted by exponential smoothing with a lag of length m; HOWEVER, \n",
-    "- the SQ is also adjusted to enforce a zero-mean seasonal correction, which is why\n",
-    "  there is obvious non-zero SQ in the first m steps"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x116e51650>]"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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CnTMoi2N85SHNz2KZozCv8Udf+QYUxg2ynEbB+MLbI6jJKd53doxYoSdirz9hmN/tH8PP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tb/Z2Lha86OIcU554daNH6COnAI4mLuvJXtGG60kPExyc2JCGxzolLjnAH0IKqxN/GQ4ck4dx7y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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x115d73c50>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# first 50 \"days\"\n",
-    "\n",
-    "# instantiate SqDistAlgorithm object\n",
-    "SqDist_syn = SqDistAlgorithm(m=m, alpha=alpha, beta=beta, gamma=gamma, phi=phi,\n",
-    "                             yhat0=None, s0=s0, l0=l0, b0=b0, sigma0=sigma0)\n",
-    "\n",
-    "# define input stream\n",
-    "yobs_syn = Stream()\n",
-    "yobs_syn += Trace(syn000to050)\n",
-    "\n",
-    "# process input stream\n",
-    "SvSqDistStream = SqDist_syn.process(yobs_syn)\n",
-    "\n",
-    "plt.figure(figsize=(16,4))\n",
-    "plt.plot(t000to050/100., yobs_syn[0].data, color='blue')\n",
-    "plt.plot(t000to050/100., SvSqDistStream[1].data + SvSqDistStream[2].data, color='green')\n",
-    "plt.plot(t000to050/100., SvSqDistStream[2].data, color='red')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### next 50 \"days\"\n",
-    "\n",
-    "Items to note:\n",
-    "\n",
-    "- SV (red) gradually adjusts to the new baseline level\n",
-    "- SV does not adjust for a brief interval after the change in baseline level because\n",
-    "  the standard deviation of DIST had shrunk to a point where the new offset data points\n",
-    "  were treated as bad data; once the standard deviation of DIST increased to a critical\n",
-    "  level, new data were treated as valid, and the baseline level adjusted\n",
-    "- the \"hiccup\" in SQ is expected; it is ~1/3 the residual at the point the data became\n",
-    "  valid again; it gradually smooths out because it doesn't exist in the actual data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x11700f5d0>]"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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dxgEl8yxiONkKDAIWeubVjnXupGR+MyaBq9sR8NgBAOZKGJdpmG9FYaurr+Xk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lpwV8UlVn+3oce8nLP35GiNEmDehyVUy5SYZyf2Kn1rHKikX/mswdwW69InUJljbK36YqzLJKNj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\nbvAM05nJ7WSzQ82XJrmWmO5Mc+0M37g6yVJumrODc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dbx3nGmklOk8ilMmkl4kJNCoVAo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\nbev/ARCNi67IzI67AAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x115e50c10>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# next 50 \"days\"\n",
-    "\n",
-    "# define input stream\n",
-    "yobs_syn = Stream()\n",
-    "yobs_syn += Trace(syn050to100)\n",
-    "yobs_syn[0].stats.starttime = SqDist_syn.next_starttime\n",
-    "\n",
-    "# process input stream\n",
-    "SvSqDistStream = SqDist_syn.process(yobs_syn)\n",
-    "\n",
-    "plt.figure(figsize=(16,4))\n",
-    "plt.plot(t050to100/100., yobs_syn[0].data, color='blue')\n",
-    "plt.plot(t050to100/100., SvSqDistStream[1].data + SvSqDistStream[2].data, color='green')\n",
-    "plt.plot(t050to100/100., SvSqDistStream[2].data, color='red')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Next 50 \"days\"\n",
-    "\n",
-    "Items to note:\n",
-    "\n",
-    "- SV (red) tracks changing baseline level on which SQ variation is superimposed with a lag\n",
-    "- SQ (orange) remains more-or-less steady"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x11715e9d0>]"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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5M/W6TG+ZkLaa0NATMnKJ7fgUG7Z0G9GZaBkdFXuqsOPvAh0eL+Pp8pHWqdkp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IAAAg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GEk0KvaEumt0p+keXga19PqJRaKmLkiqV3QHga6bXF+T52TEKGa3Ds1He6K9Scmbv8EMoMsb4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\nGo1Go9Foti3/PwSWzGT2DN+RAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x116d8c050>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# next 50 \"days\"\n",
-    "\n",
-    "# define input stream\n",
-    "yobs_syn = Stream()\n",
-    "yobs_syn += Trace(syn100to150)\n",
-    "yobs_syn[0].stats.starttime = SqDist_syn.next_starttime\n",
-    "\n",
-    "# process input stream\n",
-    "SvSqDistStream = SqDist_syn.process(yobs_syn)\n",
-    "\n",
-    "plt.figure(figsize=(16,4))\n",
-    "plt.plot(t100to150/100., yobs_syn[0].data, color='blue')\n",
-    "plt.plot(t100to150/100., SvSqDistStream[1].data + SvSqDistStream[2].data, color='green')\n",
-    "plt.plot(t100to150/100., SvSqDistStream[2].data, color='red')\n",
-    "plt.plot(t100to150/100., SvSqDistStream[1].data, color='orange')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Next 50 \"days\"\n",
-    "\n",
-    "Items to note:\n",
-    "\n",
-    "- SQ+SV (green) tracks sine wave modulated signal with lag\n",
-    "- SV (red) is mostly the expected steady value, but erroneous oscillations tend to\n",
-    "  amplify when the relative amplitudes of SQ and the carrier signal differ substantially"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1174b81d0>]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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azuafEM6NlQosdk8w5lXUQ5IkEewaJLIodk9JJoGeCIGLvBH85n4ipd8y0ro8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s39upmMBP4ulxg79R/Evy6+XPzo3X555q5ceaCYOWPXPHGNWHnjleLb84+UWEwWot1uEX7fJ8S/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\nNFj/AP9ay0adLD1FAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x116f3dd90>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# next 50 \"days\"\n",
-    "\n",
-    "# define input stream\n",
-    "yobs_syn = Stream()\n",
-    "yobs_syn += Trace(syn150to200)\n",
-    "yobs_syn[0].stats.starttime = SqDist_syn.next_starttime\n",
-    "\n",
-    "# process input stream\n",
-    "SvSqDistStream = SqDist_syn.process(yobs_syn)\n",
-    "\n",
-    "plt.figure(figsize=(16,4))\n",
-    "plt.plot(t150to200/100., yobs_syn[0].data, color='blue')\n",
-    "plt.plot(t150to200/100., SvSqDistStream[1].data + SvSqDistStream[2].data, color='green')\n",
-    "plt.plot(t150to200/100., SvSqDistStream[2].data, color='red')\n",
-    "plt.plot(t150to200/100., SvSqDistStream[1].data, color='orange')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Next 50 \"days\"\n",
-    "\n",
-    "Items to note:\n",
-    "\n",
-    "- changing baseline level is tracked well by SV"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x117816b50>]"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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3r+EPO/5A0FtBbMrYxKr5q7C0sGz9HB8fVQBduFCtdm5pgYcfhqeeAkdH077O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\nqGXb2VXdV1oDnAMoO1tGXWM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supjRauRs60wvy14MGlXJ7gtNdmlqUqsV3YO7389qdHHS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dklNrRpWll+UpxZTM9ID9qiybB9rd3dYChu5tv1aWhqakSc8tat8OCDQsb1qzXoNsuS34EBSMsf\nJCU+JfRBhfjIkNYVZeU4NG0zGQ9OJ0yltbOgsExRnaockfp7/fXih+FOb6c8uzyqAKQLKi/g7fa3\n+fOhP3N+xfmKn69ChQoVKv712LRJLELOiDKsPZi0+qzBSm4jPqV1o3Ejx/qO8aMdP+KymssUpRD7\niK8xK1AVNDvN5Kfm02tLmnd0Tny86J3KS5JXJy1OC/lJBtLSIDlZpoAXOh1kaLShNWJixBv+wgt8\n+1fn4UrUc8+hi+Dss+Hvfw9opioqgmT3/CrpxPQEjnEHnY0FAaQ1MVG8r5HU8H99hgwDkgSXXipG\nIjkcUJqlfN5rQ19DoCpSsIwjPUcU1VChQkX0eP55cZ3629+ie34wadVolKut/krrdS9fR+69uTT1\nN5GVJQKWgsZch62RrXPgnHAG9KPGaGKozKnEk9UcUepvsk7eGgyid1+bpiWz2BJYS5KEJ/pXv0L3\nuU3sqVvHx5s88Je/iGjlZctCahVnFNM9asblgomJwMe6uiC3tAtdWgRz3ObBR4a0VueXE1/QNrNL\n0dICqYZWqnKUDdnTp+sZnhzGOeEEROpvpcIaPuSl5PH55Z9nj2UPn1/++ahqqFChQoWKjxZKSkTY\nxvS0+FqpNRhmCWdSXBKPfPIRLl94OVcsvkJRCnFamlhUlWVUBqTZ+9JwlQQ6ZcbK95JanBYymN9m\nrNNBimfuftRjCQ6eP+0mHrilHb76VfjhD8XMoT//GSYm0Oshfnx+lbR7uBttmpYTx2Oorg58rKIC\nCJOoLAcfaX3nHWENXL1anE5RepHiea/BVr6F+Qs5PnBcUQ0fJt2T3Pj6jTxZ/2RUz1eh4lSDJMH3\nvge33gp33BHdfNVg0gpQXS2uDZHCp7Qe6jrEm21vclPtTdy96240msjH3nR1AfmN1OTVhIhqVTlV\njCW3zKu0dnVBXE540gqidz+hoINe06hg+l/+smi+veQSaGvjyNnf4FOf+y33/+ACOPPMsHX06Xp6\nR3op1E2FjIPr6oKMIpEc/0HxkSGtxiwjZLfR7L0vNzRAbH4LFTkViupoNBqqc6s51nsMgPqeepYW\nRDHzwIvfXvRbum7pIjcl8gRjFSpUqFDx0UVioliU+NTWhgYCFL9IkJsrFL2pKbi05lJ+fcGviYuJ\ni2rea2F8JS2Ds1PrWwdbqciuiLiWTgcp7vCjc5InDfPW0enmJ5w2l42RLj360ni46ioR3/y738Gz\nz0J5OddYf0Fyb+a8hNPqsqJP12MyhYZfVVTA5ID87FrZWk4xyuGZZ0Ru1A03wNNPi/F4kdbwoam/\niZq8mtlzya6Ielbr43WPs7NjJze+fiP9o2FSUlSoUDGDw4eFcePOO2F4WHmAEsiTVqMRTKbIa/iU\n1r8c+QvXrbyOb679Jq8ef5XhyeGIw5hsNphKa2FBbugozcqcSuwaobTORcy7u0HKMM9JWs/qT+Nr\nm7/Hzffq4P77xSbiW2+Jm9vvf88+3SeZ1A7OOxY0PjaegtQCcstsIQpwVxekFHTJ9tUqxUeGtBZn\nFjOd2MOBw2JuWmMjjKe0RqWSnqY/jX02MXipvveDkVYQScIqVKhQoeLUwYoVYpEEYnG0RGF4fGys\n6IHt6ws8Hi7EIxy0WsjyVNFinyWtLYMtlKZX4HJF1mer1UL8hIy1F7C4LMSMzK+06vUgDcvXAJAk\nCZvLhsPsF8Sk0cC558KWLfD66xhHj/K7X93OJX/YMefKzuq0ok0tYnAwtM+2shKcNgVKq0sorTt3\nwgUXiHCuhgZIkyInviBSlm0uW8ACsTy7PGrS+pcjf+GejfewqXwTrx5/NaoaKlScSti5E847T3Qk\nXHABbN+u7PmSJFyclUG0oqxM9MpHCt9m4Vvtb3Fx1cVkJ2ezRr+GHaYdFBcLl8586OqC0cR2IdgF\noSqnis6RZjQa0SoZDt3dMJHUKTvuhhMn4OKLueWX79BWms5Xzm0TZPXmm4W07FV3u7pASrOGjruR\nQXFmMekGc0h/rM0Gsdli3NkHxUeGtMbFxJGXUMLO+lYADtS7mNYMR+WRri2qZZ9tH5Iksc+2j5W6\nKAb1qVChQoWKUxYrV87Oe62vV05aQaikwVaqzk5k03XDQauFlLFKTgycmDnW2N9InqYanU4s4CKp\noRmRJ3pWpxW3PTJ78PSgju4ReXuwfdxOYmwiXZ2p8q9v+XLqb32Mr170LCNTIyIKeONGEfjR0xN4\nTi4rGRRhMAjy74+KCuhvV9bTmqkx0N4uWrUSEsSMxmMHMplyT0U0M9Z3Tto0LQmxCTPHClILGJ0a\nnWlHihSjU6McsB3gXOO5XFR5EZtbNit6vgoVpyLee09MYAHhZH33XWXP7++HpCTIyAg8XlYWudLq\ncgm1dyymB4vTwirdKgA2lW/i7fa3MRgiI602Gzg07ZRllYU8VpVbRctgy7xjb7q7waUJsge7XHDb\nbbB+PZx7Ln975V6eubiI4/3ybtGuLphItM6Ou5kDxRnFJBWGktaODtBk2E4tpRVgeeEyDtnq8Hhg\nd3MjlTkLogpQqi2q5d3Od2nqbyIhNkF2J0OFChUqVKgIBx9p7esTO+uLF8//nGDILWCimfeaOLSU\no71HcXvcABzpOUL2xPKIFVudDqbt4e3BYz2R2YNHe8OTRatTWHrN5vBKsl4PzfaV3HI+4k39xjfg\nnXfEzv+558Kf/gSDg9hcNhLGiygrC61hNIK1OQ/nhJOJ6YnQb5B5fT3NBlatEqFUIGb3HjqkUWQR\nNjlMIQtMjUZDeXa54hTiPZY9LNcuJzUhlTNLzuR96/uKnq9CxakIf9K6dq1IiFcCi0V+w1AJafWp\nrHus77HOsG4mtbe2qJb9tv0UFc1PWt1ucV/pnTDJ8pPKnEqaB5vnHXvT3Q2Dbq89WJLgqadg4ULB\nROvr4ZZbKM6vYMBtCkt+bTZwaeQT1oNRkllCTJY55JxMJphO6jq1eloB1lcsZzTjMC+8AEllhzmt\neEVUdZYULCEuJo5vbvkmF1ZcGBXxVaFChQoVpy7OOAPefx9efVXs6sdFMfEsOIXYbhe79Dk5kdco\nLARXXxa6NB1N/U0Mjg0yND6EZ9AYcaCTVgvjfaEhSh7JI+bHWvQRKa1DVm1YW67FaUGXUoxGE6pk\n+FBUBL3t+QxNDDGZECvifJ94Qiyyvv51eOMNMBr5r9ufYPGefspKPCE1iovBaomhILWAnpEemf9l\nFq4JF9OeadobM1m+fPb4kiXC8q1Pj9wibHKYKM0qDTkejUX4UNch1ujWAFCRU4F9zM7A6MA8z1Kh\n4tSF3S7++Ky9lZWCtA1HZpQAREdCsYyT1mAQ5M0XvDcXfOF3h7sPs1I76+JcrVvNoe5D6Irc85LW\nvj6R6G4akldaC1MLGZ8eJ8/gCEtaJUlcNrvHOik73iNcK/feC888A3/960xfRWlmKd3jHfT2CrIc\njK4usE9HaA/OKMaT3kl70B5dRweMxJxiQUwAK3UrKFx2hCuvhJLaI6wojI60ajQa7txwJ439jdyy\n/paTfJYqVKhQoeI/HTk5cNZZImzxs5+NrkZ5OQE3+GhG5/hSiNfo17DPto/d5t2cVnSa4nmvrq7Q\nftQuVxc5yTl0WxIjUloHOrT0jvTikULJpMVpITNGWHrDvT6dDrq7vIRz2I9wJifDpz8Nzz0HFgtv\nLk/n0y//jZ+/tkSor34zFvLzhQOuMHV+i7DVJWxv7e0akTrsxdKlQohQSlrLMstCjpdnldNqb42o\nhg/1vfUsLRRZGzGaGFbqVnKo+5CiGipUnEpoahKjaXzXlthY8XVDQ+Q1wimtCQlic3C+tF6YVVoP\ndx9mhXaWo2QnZ6NN0zKV2TQvae3qAq1+mq7hLoozQ1m0RqOhMqeSZH1rWIXUaR7imtg/8NLDLjKu\nvR6uvBL27w+Z81aSWYLNZSUz201/UN6bJIGt283gRG9ErZjFmcVMJJpp9bvcSZIgrfapU2zkDYib\nsiPtfX7562lGC3ayrnhd1LWuXno15m+ZZZO5VKhQoUKFivnw4IPwwANwzTXRPT/cvFcl8JHWM0vO\nZFvbNra2bmWTcZOiFGIf4RwYGwiw1LbaW6nIqYhodI5WC71dCWQmZsqm3VpdVpKnDHP26yYnizE+\n+UlzBCmlp/PICjc3Xfh3Dn7xfjGYsbIS7rsPRkaIiRGvOyt2/jAm37ib4JFFNTUikEWbUoTVGdnY\nm46hjhlV5PnnhSgMYlFoHoogLtQPdT11AQGRKwpXcLj7sKIa/hgaH0KKZv6HChX/IoyNwW9+Iz53\n0aCxUThf/bFkidh8ihTBSqv/tTBSi7DvWnmk5wjLtcsDHltWuIzBuKPzkl+bDbLLzBSmFgb0yPuj\nPLucmLxWUcvhEClU990nItAXLSK9Rs8Vcc/w6llaNC0tYsRYcAAAkBiXSG5yLvnloam/LhdIKT3k\nJOcQHxs/72svzijGIZkDfoa9vZCa5qFr5BRUWvXpekqzStCe+ww9I12s1q3+d5+SChUqVKg4RWEw\nwH//t+xaICIYjYGkta1NOWktLBQ5RVcsuoKXml7i0cOP8umFn1ZEWrVa6OmKpzijGJPDNHs+9jbK\nMsoZHISCgrlrJCZCejrkJ4dJIXZaiBudm7SCWPBlxoRXSSVJwuq00m8ykHjRRpE8/NJLovfVaIS7\n76am0C5G+MyjtIYjrcnJ4mebOKlQac0q47334Gtfg6uvFuqPIcOgaN6r2+Omsb+RxQWzTdLVedUB\nc3iV4O32t8n6RRY3bb4pquerUPGvwF13iU3Ayy6Lbr5qU1MoaV28WJDZSOGvtN629TaS7kni8brH\nAUFag22vc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8KJ+yHCpD35OvfBou/C8d9EX8M9KRYZK38JzQ9GX2e8F7q2QPXN0Ppw9HVG\nrVD/Y7C+DD1vR1/HMw3bzhE3VZX8qlChQkUo3JPw/pcEIWp+KPo63dug6JOCbPZsj/JcJsBRB7lr\nILcWBg+GXLurq0XysA+yo3MGDwgCHRMLWcsDbL0wq7Revuhy9lj2sFK3kgU5C+nq8ib6+mA/DNmr\nxL8zF4NzlgTpdNBti6M4o5h2R/vM8TZ7GwkjFYGk1VEPWUvFv7OWwpCQFOPiID8f8pLkLcIWp4VB\nkx9pddRBjneRmbMaBg8BgrSma+ZIIXZa0aYWMTDgfX3dW4UKrT0HNB2w+21ePP2XLG8c4jMXfAvO\nOgt++EPYvFmwyKBzmhwwUFUFOI5B/3tQeA6atkdZsgTcjshIa/dwN1lJWSTGJvOznwkF68ILBTEo\nySxRZA9uGWxhQU7g2A2fCqIUuzp2UZBawC3rb6Eip4I3Wt9QXEPFfx4kCX76U3j8cfjUp0TOmVLU\n1YkeVn9UVwvSGukStaNDKK0eycNlz1xGU38TFzx+AU6vzb+9fd4SmM2QrO0gKymL7OTsgMdW61dz\nqPsQxSWeeZXWmLwga/DwMNx6q9iZvOYanAd285vlQ3SO5DI2FlrDaoVkfbv8jNYgVORUMJbUGqK0\nWq2QVRpZCJMPpVmlZBs7OO7tPmlogOJlkZ2HP7KTs0mNT5253jX2NbIwb6GiGnPhQxHEBIhFQvc2\nqPkWpJTAQJQpd2NdMGKCJT8EVzOMy1uD5kX/e5BeBcYvQN+ugLAJReh6U+wCl1wO3W9FVwPAthl0\nF0DV10UfULTo3gqTA2JXeVBB00AwPFMi9dH0ZPQ1VKhQoeJkY9QmiGZQ0JAidG0RiuTq+6DzA9gh\n+9+F/DME2XQ2wZSCSEwfnE2QVgZxqUJJRBL3OT+UlYm1UW8vTE4KO1zwzEKcxyHDu3hIMYBnUqT3\neuEjrRmJGZi/ZealK1+iq0v0oAakELtOQEa1+HdmjagreQCRvhkfD2UZgaNzWu2tePrnIK2ZC2Fo\nlkzp9ZARow3pjR2ZHGFsegxbW04gac3ySjXZK2ZaeoqLIWlqbqU13WOgpERYtLEfEpbn2CTIXAKO\nI7jWX8DPPvU1bn/8C3DHHTA9Db/6lUi5qqqCz30O7r+f3KNtDHfqqaxEbCwbLgPjtWD5G0uWgMta\nFBFpNTlMlGaV8t57kJHuYc3EVfzyU1fy9FMexaS1zd5GRU5gL5xPBZkvDTkYr554lU9VfwoQI0C2\ntGxR9HwV/5k4ckQEKa1fD1/8Ijz9tPIaJ07AwiBO47vmdEcWuD2jtL7R8gZT7imeu+I5Nho38of9\nf8BojExpNZthPLOepQVLQx7LSc4hIzGDdEPnnKTVaoWpdD+l9fXXxe5hV5dQWm+4AW2GnonpCbTG\nQfleVCvE5kZIWrMrcGjaQuqYTJBeFNm4Gx9KM0vJLO2Y6SVuaIBsYzvGbGWkFWBxwWLqe4XS3dj/\nn0paHXWQXgHxGYLk9e6Irk7/e8JqFJcs/u77R/R18j8GSXley1GUhv2+d6HgbMhZE/2iBaDnLbFb\nX7gBeqN8TSACNMq/BCVXgO0D2J47noHenbDv6zDljL4OiOerqq8KFSpOBvZ8XgTzNMzfkRIWPW+D\n7kJxD3DURXfdnh6F4XZBqGITIb0ahhrmf14w/MmmRuNVSQPvRxqN6AM7dEj0UBqNMvPmXScgY8Hs\nE9KrxTEvtFohIE5MQHJ8MrExsbS1yWSAuE5AurdOfAYkZMHorGdOp4PC+EpaBoXPzO1x0zrYypi1\nMpC0Dh2dJa0ZC8E1S371ekjxhBJOYek1YB/UUFjoPehPWrOWiroIpTVmdO5Ap7ixolkbtf2weG/B\nq9gewGiEsZ4iTFN9YtTET38qop7tdhGQcvbZeA4f4t4nernlFxv5TPuvwLZdWLkLz4HBAyyumcDe\nYYho3muHQ9gC33oLbr7yDTRD9eQnNFCWuIXY8UKGxocYm5KRZ2TQZm+jPDvQI+5TQSI5F3/sNu/m\n7LKzATi77Gx2dexS9HwV/5l45RXRiqnRiPRfs1nws0ghSWKDTS5nqKYmMouwJIk+09JSePjgw/z3\nmv9Go9HwldVf4bG6xygqEtc1OVXTHxYLDMTVs6Qg2KIisKRgCe7co/Mqra74Zk7rjRepejfdBA8/\nLKRo7wVLo9FQnVdNduVx2QRhqxWm0yMji+XZ5fRMtmG2BK6f29shoaBTsT04Nsc0M1/14EFI0oUh\nz4562LIGuuTnPq8zrGO3WbR77Lftn0kwPxn48JDWgb1iNxogZ9VMdL1i2Osg23fjWRl9HUc9ZHtv\nhDlrxC5sNBjy1olNFFaqaMmv/bA4j+xVYtEwPRJdnb7dgvgWbIjergbQ8ZSwcuetCwn0UIT+PfBc\npugfVqFChYoPAucJcByFc94U7RjRbob1/gMKzhKbn9kronP+OJsgvVL0foK4/nstsIrr+JRNEMTT\nFdpjuWoV7NsnZh6edZZcneOCqPqQXgHDs2poXJwgeibT7Le0tsr0xjr9yC8IwjkUaBHO8VRzrE+8\n1jZ7G3kpefR0ZlDic6u5J4VanFomvk7IFAR4VKzi9HqIHw8lnOYhM3kJBnQ6rzo6MSDs08le/3Ja\n5cxrKi4Gt2NupdVt95LWKaeYPZvuVUiyl4OjHqMRHGaZIKbYWKGgfPnLdP3vXWy6Tccdlc9gHNwH\nbW/DK/XgiYP0BawoO0J3s6jh8ZLycDA5TJRmlrJrF5xX8xxUfBlN1Vf42sefZvvbMRgyDJidczTV\n+aHV3jpLWgcPwIhYbVfnVXNi4MQczwzE+PQ4x/qOsUonLOGrdKswOUwMjs0Ro6rilMA778yO6YqL\ng3POgW3bIn9+d7dwZ2RkhD5WU8OMVXUu9PZCSgrEJY2zrW0bly+6HIAzS87EPm6n2d5EcbGwEIeD\nxyPIomXyqKzSCrAkfwnDKV7SKklivs2xYyJI6dln4f77+dT+O3jgN69z7k3/C2efLXYQZdL1qnOr\nSTKcCKu0jsRHprSmJ6aTlpBKW083Hr9LS3s7eNLDpP6GuScas4w4YlppaYGREfjHPyAmnOJb/2NI\nyAkYVeaPM0vO5J3Od5AkiXfN7wakMX9QfHhI6+ABQcpA9N1ESxIdRwKtQo4oSetQkHXJqaAr3AdJ\nEguoTHkLVMRwjwvLc0YNxCaIhZDM2IN5MTkEo53C+pR3unjPo1nUedzQ9w5ozwPtuR/M9tz0v7D0\nLmj7M0zOPQtvXkSrYqtQoeLfj95/iN3bD7KZ1rUFij4u7iGauOiUTc+06NPM9obYZK+IbrNxqBEy\nFs1+nbVE3A+UwtkUSDbTKmE4lLReeCG89JJoudy4UaaOK4hsplXAcKBvrrIyNIU4QAWZHoHJQUjx\n28FPrwyoo9NB7sQaDnQJn1l9bz1LC5diNjOrtI6aBdH0EXoQhNFLxouKQHKFEs6OoQ4yKZ6tM9wO\naeWzCVYpxaIlaHoMgwHGenV0j8h7DK1OKyPdXtI61CDuzzGxfufSjNEIva1zpwebnWYMGQZe6a7F\n/fD/QlYWvLJLeB7t2VRm7aW9OYnMxEz6RuZuVzI5TBSnl/H++1AUtx30F4H+ItYUb+e99yLva532\nTGNxWsS814H9sPVjsPUMmB7FmGWk3R5Bk58Xh7sPU51bPZNCGhcTxyrdKg52KYh39cPQ+BDb2rbN\nS+BVfLjhdovxoevWzR4780xxLFK0tIhrjhx8fa3zwZcc/I+Of7C0cOlMP2qMJobzy89nW9s2ysvn\n7mvt7xfkuXEgvNK6JraYM155mvsOnClm9FRWwuWXi/mpzz0HjY04XLH86Bw3Y82N8K1vQbJ8+FB1\nbjXkhldaBz3tEQUxAVTklJNiCAxjam8HV3wrlTlBb67jKDyTBEfuCKmzuGAxTQMNfOxjwlASGwu2\n8eMsyA3si2d6VLQZrn8SxmwivT0I6wzrONh1kF0du0iJT1HUWzsfPjyk1eVngcpYIHYFo0ltdPgp\nrZlLRCiCUnimRD+sb8GRESXZHLVAbLKwGPvqREN+hxrFAiM2wVtnUXSLMUe9eE9i4iAxB+LTAmxd\nEcPZAEmF4nXlnxky7y9iuCdFr271jZC3/oORX9OT8FyGSFhWoULFRwuSBPu/Lsjmvq9Fr5D2bIfC\ncwWJ0Z4bXZuJq0UQqrhU8XXWsuhIq7MBMv1Ia9RK6/FApTW9QlZpPeccYYM7fBg++cmgBz3TMGad\nVTZB3FNcgbMgKiqYSY8EGaV1pEMQQ43f0iG1RGyGeqHTQYJ9OY19jYxPj7PPuo+V2pWBpHXEFHgu\nACmlM3X0epge1IfYWFsHW0mb8uuNHWmHND8lICZW9P8Ot1FcDE7b3Eqro1Mk/jLcJjYDfEhfAK4T\nGAzQbxK9teFIlsVpQZtiYGgItAmHoOA02PIGPPIIvGui8KUfsbb9acqSdPP2tXYMdeAZLGPlwj5i\np+2CPKdVkJQwgbmpM2LSah4yo03TkhiXCM2/h2V3i3u/+QVBWh2Rk9a91r2sLVobcGxZ4TKOdCsX\nBDyShwufuJCrX7iaH23/gFMiVPxb0dAgHK95sxOVWLOGGXtpJJiLtEaqtPpmtG5p2cKFFRcGPLbR\nuJG329+eN4zJbIaikkla7a0szA/qv3Q44KabuOLyH5JsM/Nj9w+YaukQxxsbhbXlueeY/u0D3J5w\nA+8tziAzPU/+P/KiOq+asVR50trZPcy4Zxhtmnb+F48IY8qragu4bre1S/ROtVCRHeS7bvwVVH8D\nTvwuZCxoWVYZA2MDXPuVIX76U/jqf3s41neMxQWLA2sM7BUcJCnPOzot1CKcmZTJJxZ8ggufuJDP\nL/88mrCx+MrxISKtzWLHFiAmXtwYR0zKargnBFFM8/6g0oziJqg0iXjY5F20eHdJMmqiI5vOJrF7\n60PmwoCkxYjh3/vzQeq4ghY/GYuiW0QNHpxVxTMXi0VVNGnPgwfETTkh2zvKQYGvxB+SBEd/ArV/\nhGP3iE0HFSpUfHTgOCJUvNo/is9vtA6ZwQMiTAdEX2I0jp2heqGK+pC1TLSdKIWzSdw7fEivCrDj\nRozRTkj1m5MXRmmNjRX24KNHZfpZx3sgMS9I2awIOZ/gmYbNzUFK65gNUooCnkNKyYz1FARpHehO\nYUHuAg52HWS7aTu1+Rtxu4UICYh7e1pZYJ3Ukplde70exrsqaLUHnl+rvZU4Z0Wg0hpMfr3vj8EA\nfe3yPa0eyYPNZaPrhN6PtPqx82Q9TLmIk5wYdAmkx2eFVUktTgupbgMVFRAz7Lf5vmEDfP8BNOtK\n+Gr8n9j6vUYKbvg2/P73oi/2xAkYHQ2oZXKYGLaU8on1+8Q9VhMDGg2avLUkje6jOKM0ItI6088q\neZhsf4m1V/4Xr9RfA+YXKc8uV0Raj/UeY2nhUnGfNb8EI50sL1xOXa/yz8RrJ15jyj3FwRsO8sC+\nBxgaH1JcQ8XJgd0ulNENG2Aoih/D++/D6acHHlu5UuQNTUe4HJyLtFZViY/IfPAprbs6d3GO8ZyA\nxzYaN7LDtIPSMvecpNVigeyq45Rllc2Od5EkMSh50SKYmmLiWB2fu2iUuqJzsY1lh9To7oYMY3Oo\nMimD6txq7DHypNUyYqI0syyQ6E2PwM5PwbHQnIaK7ApSDK00eydqejzQ2tVLUnxiYAqyZwosL0P1\ntyD/rJBMmxhNDIvyF1Fe20BjI1z5lQ6yk7PJSsoK+D5xj/Wu/3NrYVB+l+K+C+/j3k33ctsZt837\nfijBh4O0Tjlhani2LwUEgZXZTZ4TI52QXDR7Y45NEoqgUjVxpB1S/XZv0yvFwkFpGuWIKfBGGC35\nDd4Fzlj4AQI9/Ehr5uIo6zTNKgjxaeLn5opiBm3/e0JhBbHQjLpvuAHco1DxZbGA6XsnujoAPTth\n16XKf/dUqDhVYa+DLadB66PR17D+XYyG0WjE35ZXldeYGICpodlrbvbKmfEniuA4KlQpHzIXiU1C\npervSGcgoUotFfciJUn07gnxmpIKZo+llYuNVRnlLy8PCgpCDouRaf73VxDnFmTtqq4W4gEI+19D\nQ9DonLEumTolAXUMBrEIvLTmUn72zs84MXACnft0iov95tAOm0LJZuos+dXrwdFWQctgS0DSbau9\nleneCgwG74ERU+C9GrzvTxu5uTDRL5TW4LTc/tF+0hPSMbUkYTTivcf61dFoxCaD8wRlZZAVG94i\nbHFaiBspFuNuXC2zm+YgNpgT+rnzjG3c/N3P0rK8WCSc/OQncPHFkJMj/ixbhnTNNWx8qw1XfQZr\nq/wWhkB83hJqq48RP1pCx9AczXl+71N5Vjlj3ccw9+Tx7e8X8cMHNjJt24kxq5Q2ewRxql4cHzgu\n7IytD8O+r8Jb57I8f1FUSutjdY/xldVfwZBhYEPZBl5sfFFxDRUnB9//vnCwl5aKX0elOHo0dL5q\nejqUlASO35oL/qR1YHSA72z9Dn89IuZZG43iOjI5z7LbZAJ96RgNfQ2s1q0OeEyXriM3JZeEoqZ5\nldb4Ij9rcGur6Lf42c/ghRfgoYdILSpDn64nv7pFNozJZoP00qBxN2FQlVtF71QrZkvgvWBoCKSM\ndipyg65pzQ/C9LBopwtyx5Rnl6PJaZ2xUptMkFbSSlVusDW4HlL04o/+QtnRmUsLllLXU0dNDTT0\nH2Vx/uKQ7wlo5fQG1smhILWAm9bedNLms/rw4SCtrhZBDP13FtIrlROhYKsQePt2FO5uB+8Cx8RD\nkhbG5o+sD6kTsmixKB+fE3w+6RVih1kpgoM4fIsxxXUaZ3eTQajAjnrldYbqZ63cWcvEYjEaxdbf\nEqi/KHrF1uMWsxnRwP5vRFdDhYpTDfu/LpJSD/0PjPdHV6PvHREQB1DwsehaDnzprz7ratYyca1S\nutk43D7r+gGIT4e4NBiPcP6CD6MWMVrGh9hEoXaOhe+PDMFYl7j3+Ntx45JFaNF4r4I6tlCymVQo\nxp/5OVNWrRKcSpJEP2t+flBIilydlEB7cHm5WPN9edWX2W3ezW1n3Ia1IylQsQ1nD/aS36Ii6OnM\nJDU+NWDsTetgKyOWIKU1+J6fUgyjFjQaKNamkBybRu9I4HvV4ehAn1pCUpJoTwtRWkF8PdKO0Qgp\n7rlJ68yM1uHWwN+dlBKYGGBxtYv+hIVsPadEDLPcvl2s2MfGhJz0f/+H6/RVnN+q4Y6HlrJm4P9g\nLGe2TuZiahccY8QamT3YN+7mwBu7MY2s58or4errdPS7cqmKnVLU03pitT75dwAAIABJREFU4ATV\nuQug8ddw5vOQomfpVAvHB44z5Y7c1eT2uNnWto1LFlwCwCULLmFzy+aIn6/i5MHpFCLi3XfDPfeI\nX8n50nWDcewYLJbhNCtXihTzSOAjrR7Jwyef/iT9o/3cvetunqh7goQEsQE234zVjg6YzjvIovxF\nsgSptqiWobS98yqt0zlHqU0oF2x+7VrYtAkOHAho2l1SsISUsvqwpDWu8ETIbGRcrTASKJylxKeQ\nl1IQsgHV2go5FTL9rG1/EcGnJZ+FjsC5QhXZFUyktM3Yso8dA+2ilpBxV/TvgVyvNJ67VjZccH3x\nev7RKaaT7LOJto4Q2A+KsFwQ99ihY7IbqP8sfEhIa3PghR6EsqhU7ZK7gaWH9u3MX8ckswtcGmCB\niqpObJKwwo73hHtGZHX8bu6K4D9fD8R7FU2dYNtb5qLoFGTHMaH2glgYJuujC5jq3y0WuiDsCgP7\nlNcA0f8WnykazPvfC5mFqAiSFF1PtgoVHyUMt4nryvKfiTnS0cw0lSRvery3by53LQy8r1zZHDoW\n2EYRlyKcN8ORq0pAqB0XZEOL5oR7UhDCpMLA46llytpexqziNQQjxTCTtBtZHRmyGRMHiQUB1zm9\nHhISxELw4EFYvjyozqgNknVB51Ik7mle8ltZKRajRekG+m/t57Yzbwu1AcqRVr/e2NxckWBZnlVJ\n86DYvB4YHcAtueluyw/saQ0hv7PvjcEABQllmBymgG9pd7STG2MUKivMBjoFn8+IGaMRYkbmJq0u\ni2FWafVfy8TEQlo5q6ramOiXmdWq0Qh5fNUqmi47k5/cuJT1hW0kasfhpnvg+uvFijpzMRX5xxg0\nRU5ay7PLGTPvIbNcLLqvvRZ2N60ie8zG0MQQo1Oj81QB54QT54QTvccu5vrmnwHGz5Noew19ul6R\nYnuk5wiFaYXo0nUw3seFFRewrW0bbqWb+Co+MF58UdiCtVrxGVm5ErYoHL179Kg8aV28eNatMRck\naZa0PnP0GaY90/zpk3/i8cse57tvfZcp9xQLFjBjew0Hkwl6E/ZwetHpso+vLVqLZfp9kpvrRGDS\nH/8Ijz4qWPvTT8MTT7Byy8/48VN/5uZr7hepTAcOwK23iqHTflhasBSpQH7sTWcneLKDlNbhNtiy\nGrasDFlPLsyvZij+eECHQGsrJOmDelHHe4VDJ289GD4JXW8E1KnIqaBvupWDB4Ut++BBSC1pDu1n\nHXgf8rz32Kxl4loVNIVkQ9kGdnbsRJIkdnbsnBlzNQPPtOAjvpT1+HTvyDNlI7Q+CD4kpLVl9k3w\nwbvLqQjBtl5fHaWLlnA3VKWkVXY3uUQ5UQxWkBNzxQ1EyXxUSfLa1fwWY6ll4hdQCTxT3l9avxtz\nND8ryRMaVJK9bGbGniI4jswmfeacJjz20QS52DaD4VNCydBfKL6OFnu+CM+mCruxChUfRrgnoOHe\nDzb3ufM5MHxaLM5LrhA9M0rhaoG4dEj2Bk+k6CEmSfl1W04t86bAKsJIh7hO+0PpfWTcq5D60mhn\n6hiVuWRGraE9pDCjJkYMOdIKonZQnTVrYO9eIQb6xlnMYFzGHhwTL8i5V0HOyRFcbGCAmb6sENIa\nrELD7D1W8gjTjB70iVUc7xcbmfW99SzJX0J7m0b0ocrd08D73ghlo7gYMjwypNXeTupkuajjnhQq\nun8isl8doxGm7XOT1t4WAwsqJ71hV0Hnk1pKtaEDp9kwZxCTyWGiKLWUlsFsYrNH4LVDgtAuXw4/\n/wu58S10NeowD5nnTd5ts7ehSyonJ/YY1acLD6dWCz0TK7AdPUJpZmlEamvzgFiEx/TuELNnZ9xM\nb7Iwd4Gi0Tnb27ezoXQDtPwJXixA13AH+an5NPRF0aKE+Bm+3f52iPVbxfzYvBkuuWT26898RiSP\nR4rBQbGpVFwc+tjixZHZg/v7xZic7Gy4f9/9fO/M7xGjiWGtYS0V2RW80PgCVVVzk1ZJEqS1dXIP\npxtkSGtPD595bD+/+vIjPDp8OVOPPS0a/3ftgpdfFrOW//53pP4Bnl3uoePITvjDH4RnWgZLC5Yy\nkiqvtJpMMJp0gqocPy7T9BtYcCMUXy5+7/1Qk1dNTtXxgNfX1gbu7OOBYVB974jNophYMWLSfjBA\nENGmaRl3j1FYNjAzgUfKrw8d3eOohyzvOjk2QWzEOgM/vxXZFcRoYthu2s7h7sOho2pGOsR9OtYv\nNMEbWvevwoeDtMrtbCvdSYbwVqExhbsAIyaZOtGQTZN82MSoAvLrmfb2Efnd4DUar/Kr4HymhkAT\nK3ZGAs7FrEzaH+uCpHxhdfMhqo2BTojPErs0PviNPIgY7nHxf/vsysmFwsoXTeBJz3YxxgfEjMZo\ne2P79wrVdu2fw86xUqHi344j3xMkc9elIfaliNH9ttjgAfGZGdij3OLvqJvddPIhmjEzsk4bhaTV\n4/aGDQURqrRyZY6dEXNoDfj3Kq2y5NcQcn+8+GIxdnDzZuGQC6kjS36LZzZ1NZrQQKcA0ipJgiQG\nK7ZxqSIjYVwEHhmNoJVWzYzOqeupY0HWMjQasdBlakgQ5vi00Nfkp7QmjskrrZohoyCtYxZxLv4h\nVTBzrzYaYbRHnrS6PW66hrvoPKZnQVGH+L9j4oPqlFKS10FPsyEkDdkfJoeJ1Oky1q3qQxMTB4Xl\noqeuvh5cE8Q4pri67lbWDaTQOzy3W6vV3oql3shCfSPpRbML4EzjSiZ7DmPMjixB+PiAd+RFz9tQ\n6J2jlKyDhFzOzMxTRFp3W3ZzdvFaOPJduGAfdG/lSl0l71sVzEjxwjxkZu2f1nLdK9fx2/d/q/j5\npzLcbjFL9fzzZ4+de64IwY0UPmuwXCjsokWRkVbfNaGhr4F2ezsfX/DxmceuX3U9T9Q/MS9pHRiA\nxEQ40BNEWt1uuP9+WLKEQimVjV+O49KlRzh21wvw8MNCaX36aXjmGXjqKb6b8mP+vHgYY5mfHbZ/\nj8ha8MPSwqX0auRJa7vJzSBts2NmJAnML4Lx81D+Reh4KuD7q/OqSSk+HhA21doKroQmavL8nIy9\n74gpHSDW72mVYJ/tJ4/RxLBSt5JlFx7k4YeF0mrzHGG51s8mI3lkMm1CA101Gg1fW/M1zn/sfC5f\neDnpiekBj4ue/SBXbPqC6BySUeJDQlotgaQMAnZLI8awjNKaHLqTHFmdssBjSsmme0IEgyQF3ZiV\n2oxHLWIX2zfuJqCOAtI6ZhMKhj98dmUlNthRmUVUqjEK0tourNv+CJOKOSeGjomFqT+JzlysfESR\ne8I7m9F70co/M3rS2vGUuEgZrxHKRDTjknzwTKnzZ1WcfEw6oPUR+NiLUHGd2BFWCsnjtRx5e34S\nc8V1aVDh/EZXc+D8UIguIV3OaZNepWwXeLxbDE33v56AN0dAwTVu1BKq3IG3JeNkKK0GZRkL4chm\ncij5vfJKQVgrKmBp0Ga9rD0YRG2/+4jPIuxDQArxlANiEoV9W7aOIIdGI6S7atlnE+0eB7sOomU5\n5b6xrGFfk178HD1uiotBspeF9I612duY6PaS1lFbmI2B4hl7sL1Tj204lLT2jPSQmZjFiDORwlSZ\ndQNAahn5KR3Yjgt7cDhlsMPRgcZRxoZVx8VC0Ae9Hh58EIpXE5c6xOOPjZBXXA3r18MnPgFXXQVf\n+hLceCPceiuj997Dqo5JbIddTGsyAjaGy1cuITf2KOVZ5RFZe4/3e0OYBvZB3jpGfG7CvLXUJkmK\nSGtdTx1nxAyKvvPcNVB5A5cnDfO+RTlpvXvX3Xxl9VfY+rmt3LXzLpwTClxn/wFwu5X3oPpw8KBI\n+Db4Lbmrq2F8XKiFkSBcPyuInnabbf7z85HW5xue57+W/BdxfptGn1jwCXaadqI3Ds1JWk0m0NdY\nGZ8eF2nZIGKNa2uFFXjHDuIeeJCEhUvIXnRQtq/V4wHrZANVuQuI9TljnMdh5ydg7w0BoYBVOVXY\np6102EZC6pzoMZOdmEtqgndU2tAxcZ1LrxTBRePdAdfI6txq3NnHA8b6nGgfYVjqFfOVfXDUza5L\nwdtHGuhIXK1bTdn6AzzwAFx+jYOBsb5Ae/CoFRIyxR8fwozy/Pa6b/PsFc9y34Uy4yNdzaGu2Izq\nU1FplbkxJ+aCeyzEcz0nxiyCXPpD6Y70TC9S0IwkxWTTNzw9yB6mVLGVsxhDSGLj/OcTZsde8c6/\njK0rxSB890p6OEfMoYu6aBKjh5pm5+n6kFEtxvsogaNefBh9Y44yF8NYN0wMKqsjSWB+QVglNTFg\nuBQsCnw3/pgehTdq4W866NsdXQ0VKuTQ8bQYM5VcKEhrx1PKwxSGGkSwkH+ybd56QWSVQPZGqHA2\ntiSdHKVVzm4KXjeJAqVV7joJM0Qo8jr/xJ5W8NqDA9W/7GzRn/X660HfK0lee7AcadWFkFafguB0\nQm+v37zXsTA1QGzyeuuUl8O0ZQWNfY2MTY2x3bSdgtENfnXCvKbYBLHxMN6NwQATvfJKq8PkU1pl\nkpVhJmCqsBDGevWYHaEqabu9ncIEI5WVoBkLs1GRWkr8hIn8jHRiicMx7gj9HsA0ZGLEVsrqKpnP\nA6ApWEzD8k187p6Ps/nV38C998INNwif5xlniMGW+fkMNxzhwVc8XP/aWmIdSWK2iRcr1ulJinNR\nlqSLyB58YvAESzN1SNMj/NeXS0lPh9tuA3LXsgA7xwciu8+OTI5gcVooGtoLpVeJgyWfpWaskfet\nykLXXBMunmt4jm/WnE+l6UH+X+lqnj769PxP/A9Bb69I7c3LEw5Xpdi9W4y68YdGA2edFbnaOhdp\njYsTn/+meWJOfKT19ebX+XjVxwMey0zKZEPZBjqTXpuXtKYt2EttUS2anh647jq47DK4+Wbhk/We\nZK2+lphi+TCmvj5IKj7GskK/mPTjv4UFN8Ga+8UYRS/iY+OpzF6AaTRUSu4cCRp30/0W6M4Tb25M\nrHAi+bWLLchdgDMhkLTW2U5QnlU1S55BENRMvzdbJvh0tW415un9dHfDVd8+xLLCZYE1nE2BIawg\n7rEyG8PxsfF8euGnQ1VWEKJSSP6QUfl40g+ADwdplbvBazSyu8Bh4ZmGif7Q4IuUInFTirTvYbxb\nhFPIkU0lSqucsgnKFdtwO+1KSXS4G3xambK+VrlepJg4cUwRie4M7RuLasyRKXShGs1oocEDIr7b\nB02MV7FV2GM70iH6jX12Ze0m6I2yr7X5QbFYXvsI7L8xuj5dFf95cByDV6rgrY3KNvX8YdsMhsvE\nvzOqRRqtXeEIi/49syqrD9nLlc9YlSOtSpPNJwZAExfYbgCQoZS0doRufII3+V3B9TacPdhPSYwI\ncymtJ4W0ytfJzZWZ9TqjkKaG1knWCULrxYoVswmi9fVi7Rjru6WOhVFrg+qUl4O5LZl1xev4+Ts/\nx+1xM26pniWt4VRfmOn5LS4GZ0dZgBXW7XFjHjJjayz1Kq1h3uOkQpi0EyNNUJxRQocj9Off7mgn\nw20UIUxy90aYcUVVVUFOXPi+1g5HB73NpVToOsNsVJezoqqN+NESmuOGBPO45BK4+mqxWL/xRvjO\nd9h56xXc/uuLeazkCyQMJ4mBl/fcA8PDJCXHYHNVkd6bFJk9uP84SxM8dI8vw2zW0NUFzz8Pe1tX\nkT9hjVhpbehrYEFuFTG9O0F7rjiYXkV8XDK4TjAxHfmG9+aWzZxtqCV/37UwMcDP4+p5sf6xiJ//\nUcdNN8FFF4me8+uvFxZZJZCbrwpi32NPhPsH/qRVkiR2mnaypWXLTKhWJBbhlhbIL+ujqb+Jj5V+\nLOTxi6supr39Rc60PsPUz38trPJ//KNIkdq1CxoaGH33EBsdf+Gn/2cR83uysuDAFjgv8LpQW1TL\ncJY8aTWbIaXMb7yLe0IECpZ/AYouEZuVfjkEK3RLmc6pD5htOzQEkxmNLNb6EcPBAyIY1Iec00Q/\nqhfFmcWMY2dfnXDS9fSIGkv8a4z3C/HO/7qStSSEtJ5ddjY7TDvIy3ezo+MtNpRtCHyRzuOB4akg\n3E1K2+jkQnOD5nT/s/HhIK1Tw2LHPhhKbszjvZCQG9qXEpcqQj0mI1TM5ObQzZyLkh3yrlBrMCgP\n0JALvoAoSPRc5NcUeZ1Ri/zOv9K+1lEzpAbtSifrRZ/S1HDkdeSU6Ixq5R57Z1PgbhZ4d7QUkta+\nd4W12NfskbdeJBErTUiUJGj5A9T8j1Btp4ZkI8pVnGKQJNjzeaj5lvgcHvmB8hqeKdFzrfVrWNSe\np3xUlP3w7Lw2H7KWKye/4ZRWZ1Pk6q/cuDMQSevj3ZG7QOQ200Bcy8d7Iu/XDau0FinLWAinAiYr\nuB+5x0WLQWKuTB0F5zMahvhCgEIKwp23d6/4dT18OCiFONy9EQIU2/JyMe7ixtNu5K5dd3FT7U20\nt2tmSWu4eyPMrB0MBuhtqsDkMDHpHX3Uam9Fm6ajvytZBMmMhbEHx8SK+qMWKvX5jE+Ph9hQ2+3t\nxA3PR1rLYKSDykpIccv3tUqSRLujnY7DRvSZnfIbJ2nlVGnbmB6YO0G41d5KTkw5mUXDxF/xNSGt\n1dcL8vrAA4zHVhJnm57XHixJwv5b4rbz5t7l/PKXUFgo+O+Pf7OYhJEWhsbtuCbmb1+p763n/LxS\nYbv3fU41GjSF53BFTi6N/ZFvUL3R8gbfzM8SNsl1/0diXi2Lh/dGbREemxqLKEn5w4ATJwRZvftu\n8Rm7+GIxrkYJ9uwRE12CsWYNM2NT5oOPtEqSxM1bbub6V6/n9rdu57PPfxaP5ImItDY3Q2/GFjYa\nN5IQ3P62YwfX/s/j3Hnz/2fvvMPjqq6v/d5R771ZLpIt996xjXHFmGZj02uoAQIJHRJCAiQQQkJI\nQkggkNBLqAab3sHdMu7dlixZLuqS1fv9/thzpRnNLecaJR+/xOt5/IBnRsczo3vPPmvvtdd+m8vD\nXuXozkPCDNevhxdflLE0ixcz+8XLmbN7JcGTpwgL/vUtsPoUWHs17O6St07KnMRhzZy05uWBlrq9\na0ZrxVppM4nqJ3yi12l+jr2j0kYRk7PVrwJcWAiR2VsYneYzuLZqAySM6/p74ji/1hmP5mFYyjAK\nG4UAb94MCcM2Mibdx9/h6HaZGe7bPBw3Qh73Qe/Y3vSK6cWKAyt4f+/7zBswz+95mfjRrdIaleV+\ndGZ9YWALjttC3HfE94O0RmT4z6Ez4IbgWWWSwR35tZIuhcTKAUqVUFmt001G5QjFHiK1dSwOP64z\n/xbZZDekvt7kcKh53MvwTEnrMVRauw+FB9OMliPKVwlRNRCeIt+7W1OZ6q2gt0PyCfK99LtImvqP\nBc2V8NlM+HCce7fo4/h+4fAHsg8NvA7GPAz5z0JLlfPP+aJ8rfSPh6d0PZY+W4zI3ODoNrlHfBE/\nUmTDquSutUb+dN+bQuPEUVjVSr8u35y0GsRDlZjVWxCGoFBJiqqOK7PqaQ2J9/apK8QRXbcxUPLK\nelXUF2azXjvXcRMb7Sqk6X7xyHAVPXBADtknndTt/ZipkCCAtObnw6Khi9j3433cOe1O9uzxMXSy\nI9HeJHNSErQ3R9A7ui97K+SUua10G9mRI+nXT6SMlokB6PTW6J+tkaBlB0hq91fvp6VU5MGWpDU8\nFdpqGTqoAU+deaW1tL6U8KAIqktiifZYJE6i+5MRm8/RA/0CenR9kV+Vj14xgBFZ3h7vQYPEdOaD\nD+C99xiS+ynjd31AxM596C0+M4zb26VsV1QEBw5wuDSP6NBoavL3Unh0NFO9Ye3ss2HDtnjatHhm\nJGd1jiSyw9aSrcyIieyaE2kg9SRmRYeypUQtPuq6zsd5HzOleRsMllnqwUNv5UcJoXyx/wulNXyx\n7tA6sv6cRZ8/9mHFgWP0sPgP4oknpKge4e1iuvFGMbtVFWGVlYnz7+DBgc+NGSNk1PeSsFqjuVla\nrd/c8SYf531M7jW5rL16LUdqj/D4uscZNkzWsoKuC2nd1vwBpw08reuJoiJpqr/8ciKvupZpv+rH\nr099gBWLHoXf/lYqrUuWwPLlsGsX103ZyMKLW0n96a9FHrLjd+IlMvsz2PbrTj+QwcmDadAr2HMw\ncI54fj40RG9jeKq3aFHyVdfMcIDUGVD6TedfR6aNJChjqx8p37sXSNvCKIO0tjVITPIthCSMEyLr\n88ua0ucEMiau6XRr9/TJZWLmxK6f6S4NBtn7W6sDYsj1E67nwrcupK6ljul9u1WuzZK6oQlylmgx\nb1cwRYOJgigsRRRfx6r6convB2k12+iNx1WJkF1A7YnArGnuCKcVaQ1PExmzauXNKpvslvxa9ka5\nlKtZVRDcGl41mPS0gnvZc11BoENzeLpUVtwc5uvyAo2h4kfCUZektfusSBDZcZXitG0DRz6GjPld\nGbbM04WwHAs23i6Vq77nwOpLj8uM/3+iudJdkOiO/S9Czg+FgET2kuDqdsyM71xjA0mToXKd+rWh\n65JYietGWkNiZG9SleTW7pP7zoxQxQxQNy0yM+Ez4MZHwMzJvnMdF0lUKwKjaeoS4ZZKazlucKQ8\n3hx4CAuAXULXeC+q5NdynQypaHuhaTB7tnihfPGFuJP6vR+FSmtysswdLC+XWYSgsW0bjBjhs44d\naW08hKYJye0TPoJtpaKa2VqylaT2kVIdNdYxSwyA39ibiKbsgOrk/ur9HC3IZtAgrGOj5oHIPozM\nLqS1wmRWq3ed1NBshg4FrcG60hrryefILvtKa35VPkcL+tMvqdtBdexY+PBDmhfdQnR4KS/9qxli\nY2VGUWKi2LAOHAhTpsC0aaT3HUbuIzVE5H3C2OyIzmVCQuCSS6CwajjT4xOVJMJbS7cyNKTdJDZO\nYIinga0lanF2b+VeMoM6CGsugdSZ8mDKdNKDNDbseUNpDQNtHW1c/PbFfDZ+HnmDknj2vUXf64qr\nrour9+WXdz02frzI7jcqHi/WroWJE8Fjst1GRYlZ2jYHcdn27XIPNrc3cfPHN/PMwmeIC48jNCiU\nfyz4Bw988wB9B9bYVlorK6GDNr4++ImQ1sZGePABmDhGerN37IBLL2Xm4Pkw4BPLvtY95fuIDY0n\nJSpFiOL+F2DwLSJhTZ3ROTfco3kYnzGBwrbcgK1uR34VLUHVZMVnyQOlX8vPGkg9SR7z/uDI1JHU\nRW1h2/auhbZua6c+ckdXtbZ6i5y5fCvIEWngCfVLoE7pPYWIQatZuhQ+/Kid8uANjM/waVMzMyns\nLO7470VXj7uaO6fdyZLzl/j3s4K3QtotrmmanJ1VlZatNZKMDk0IXMetV8N3wPeDtJqRKRD56Pel\n0goBfTvO65i8H08whCVCc6niOlaV1gyXZNPGadFVpfVgz/RYmcmDwZ3sWe/wkt/uFVvN3TxEvcOb\nieo24zFuhPQPuiF5NbvE+dQXCWPcSyaLP+savwPSD9F4WKoLblBXAIeWwpiHYOhd0vtX+pW7NY6j\nZ1DwKiwdAEv7w6FjSEC01sKRD0UubqDvuTIr1Q0q1vn32oDc055Q9QDWVCzB09eEyUDccKm2qsBM\nGmwgykXLgdn927mOi0SYmQLEgGoStaMVmssCzfw611GU5Fr1Wvqto7AfWPkrgBjPBUWqtc/Yxdjw\nwCTqD38Id9whJre9fH/MNsZ2KYg0TQxntnr5TIm3yJ1m2FbYVn57de6VOTmQ2DqSraWy0NbSrYRW\nj+girQ12lVaJa9nZQHX/gD7Q/VX7ObQ9WypXZlMQOtfpS06vImoOmlda91ftJ7q1P8OH69YJ3fBU\ngmik9XCCaX+tgfyqfIo2ZxEfat4bGz31FBr6xLL4nnGs2/6JlIn27pXyWWUlHDwIRUU8/fUfeean\npxAWV81pz98MCxZ06kcXLYJVO4YzLiKsc46uHbaWbqVXR1Unae0MqXHDiO+oYV+pmuP4moNruDo9\nA63XaV2tYJ4g6tPmEFPmrtL6r23/4qLYUEbWrSV+/MM8Gl/Du2secLXGseBYc8abNkFkpH+VVNPk\nd6E6Y9Wqn9WAikTYkAa/tOUlRqeNZmqfLmXZsJRhnDzgZFbv+y2jC96l9W9Pi5x3+XK5trzYuxcy\nJq5lWGgmvV58B0YNhqjH4I81cEm6fFBg3oB5lMeZk1ZdhwPt65jc2xvLij8VCa6x13WbGz6l7ySC\n+q3t3EcMbC3fwKCYsXg0j+zdFWu7RsyAKPA6Wjr32l4xvQgPCSV3X1dsWrcvn7iQZOLCve681dsC\nEzQQ0Lo2re80yiKW87cnOqgI3UTvuAySIn3aOOryAxWAxnvqpkgMCQrh5hNu7iLOvl+UGWkFr0S4\nIPBxMzQclDO72Zwjt+2K3wHfD9JqVWl107djm0120bfjMjDbr+PskOi8jkX/T0icyEdV5cpWB6CI\nXuoSPL3Dmoy7+Y5bjspaIfGBz0W5GHXUeESyPsERgc+5cUVuOORdp1tFIywZ0IXoqaClSiQS3ZMw\n8S7NaXQdqr6FZJ/GE09QV3+sGxS8DH3PE4MaT5DISvP+6W4NA22NsPNR2P2YO6fo45CK4rc/gZOX\nw4z3Ye0V7p2piz8Xsunbm9jrNJEuufl9VORKEqQ7EifKcyqo3hbYa2PAjXu3HWl10ydv5hxswI3T\nupURE6gn5hqPeM38gs2fV91zrdQxvuuokFY7GS2oq21sY2OK9N23d2kLZ84UN9KXXuq+jnpieNQo\n6fUCOqusnZecbeW3Kx4NHAjhlRNZVbQKXddZfmA5+oEpQlrtJNjg/Z0fIisLmo/4V1qb2pooritG\nq+lLSkK9VHrM+oYBovrSK/4AFfv7mBLO/Kp8qMpmwogymTNuNg5I09Cis5g+pJ7a5lrTqmBreyuH\nag9RtTMELTTePDbGDCQnbQ8RTdnkNRwUWWVSko9TlmB39T5aM8fQEhRG0I5COOUUYUhnnMEJQbls\n3D+C3q3Njg7CpfWltLS3EF63lz2lIxgxQgq8jz0GeIJpjx2GVrXgOPMgAAAgAElEQVTJdg0Daw+u\nZWqEB1L97W8Ts85luF5m6cxshpe3vMjtUZUw6Snos4iK7OtIyXvMciTRd0VVlXyF4eFi+tza6u7n\n338fTj898PGFC2HpUrU11q2TXlgrqJLWocM6+P2q33PntDv9n1y1iicf2c0PLnuYG8P+Ts1na+Gj\nj+DOO2WGVXIyTJxIv6vnsmTj2Xz+s12ii310KkycA2fuhi33diY9Z2fPpqB9FbvyAufnVFSA3iuX\nqf28sezgu5C5sOsFGfNF6tsmPzspcxKhWYF9rQWt65mY6fVmqNkte4evoZ+mycgZ7zWqaRqTMqax\n7ejKzpdsrchlZLJP/2qtifEReFvXuu6XrPgs0mJSePqj1Zz782WcMegM/9fX5ZsnY01IqyVaqwHN\n4ryd5eKcbJOUi/rPmTF9v0mriR2/Jez6ZHqy0qpKNq1GA7hdx+pzuZErd7RLZdcs8x+eLs+pyJWb\nyoQsB3W3lcTdd2xkkq0yNspVkQJzl0VwV2mtywscmAzy/mIGqs+OPbpTNqXunyvBa06jGgwbD4EW\nFPj7Spkq0k43OPB614gBkN7Yg0vlgOUGug4rL5Ah84c/gJXnH5cZu8GWe2HwTdIDmjJFRiHtfMTd\nGiWf+5sngQTX2KHqyYzGEmirDXQABEiaCJWKpNWsn9WAGyO0niStVvJgVeff1hrJqIcmmj+vusdZ\nSYMNRChWSJ0qrRGK8dHKaKhznV5qCg672Kh5pLepW8/vSSeJoWfg+7FLDBd37i2jR8MWb7vjhg1C\nYoEusmlbae0ire15J5F7OJc1B9cQExpD8a4sIa2t1aIwMJNgg7eaLZXWqv3+s013l+8mPaw/QwaG\noDV6vR7MYhpAZB/C2opIYAB7ygMPm/ur91N/sD9jBhVaV/oBorKYOuoA8Z4+FB0NTO4WVBeQGp7J\nmOyDeGIt7oewJIKDIKK8l60Z0+6K3YQdiKSqbaBUvm64QcxuTj8dz7lnc2P5P0gtO0DhYR9VRVOT\nvOaLL+DZZ+FXv6Lm/ru57VAKNFVy6rlZ3HKLJCIeewxefRVCU6Yy1NNAeYOz1H3tobVkt5dLO4MP\ngjPmclIErCtSi4/1LfVElH1DZERqpxQ0e8JDjAluYsveN5XW8MXm4s1c/s7l3PPFPabJhI4O6QUe\nPFhmmO7bB/ff7+7fsCKtkyfL6JdSB/GergshnWiSrzQwYQLkOoSA7duhJuNd4sLimNHPK6PdvRsW\nL4bzzyfmmhs47Q/j+Pm5P+Ljc/4BL78Mq1dDdbXIfh97jE/G3MXNZ8Xw7eaP4IXHofkTGPdH2fMH\n/wR2/gGQ0Tcjksewo/6bgPexezeEZK9icu/J8uEOfwi9z+x6QViiuNB7R7BNypxEY+I68vO7zi3N\nzVATvZ6Zg7yktWqTKOO6I2FMJ2kFOHnIVKpjV1JVJcrm4tDlzB/q03Jz1MT4CGTsTDe/lctGXcYr\nh+/lzbxnuHCkz1lN1+1Ja60iaTWqrGZ7k5tzspU6Ety7638HfE9Iq0VA7akKqRsJbE+STcu+nXS1\ndVprpSIZbDIvCdQ/V3OpVBK7O7SB12AkQeRsTmi0kAaDO9JqZXYC7pzIrCQP4C6DZPTVmSEmR70/\nr2ZX16gbX4SnCtFX/VyVGyWz132TSZ4q7sSqaCqT78jPGCpZJDQlLk0rDrwh3+dJS2DGMvkdFnQv\nofwXozZPPUh0R10BFH8spNXAkNsg7x/i7KqK4s8hbU7g4+lz1H+flbni+GsWwBLGQJWiYZhRaTVD\nzGCoURw23hOktaNdAmr33nYDqj2t9UWy/1gSDzek1SK4g3oytqcqrXaxsUfXUYiPum5PfoMjZK/0\n+hFMnCjmtyBTLjoNnVqq5HVmFUnokk7rOjk5ULg3hllZs1jwrwWcO+xcdu70yiztpMHQOXYvMRG0\nsmFsLelyl9letp1kfThDhuCcqPDGtcGpWRypO9jpZGwgvyqfkt3ZDMq0iY0AUVmM7F9AeFNfUzOm\nfZX7iO8YyEnjbZI4mkZbxACiSyMdSSsHOgiK87k/w8Lg+uth714ax59LYthhPr9tM3pCAsTFSQl1\n3jz41a9kTmZLC9WH8rmwoAZ9byP/ajuXqwavoH9/eO01GafZED6BmbExjmZMTW1NHKrYQURLWaA5\nTXgqdSGJ7M9/y3YNA18WfMkNKbEEDbq+837XQiLZGzuZ4m2/U1rDwJaSLVzw8hzuZiM/Lv4Tr7w6\njNY2/9/vs89CfT388Y9S1H7lFTFVylfMyZWVCd/zMzTzIjgYpk+Xr9sO+fmSe0j35sJXFa1i5nMz\nifpNFKOfHM3S3UsZNaQFdu6kefk6sdVt9y9m6Dps3abzbsXD3DXtLrSiIvjlbPjjGJieKfbGV1zB\nNZN/RGm/J/zNmDQNUlNhyhSWBA1idd9qJuXMgIIXZfxahPeN5VwHRW92migtGDaf6uSPqO/m87Np\nZw3NsTuZlDlJCg+e4MBrPmVa55mpV0wvwoOiWLmnizTu2QNBfXKZ3Mcgrd6zV3ckjPXzJTmp34mE\nDvyK5ct11q6FkJxvmJ3jQ1prd5uT1m6VVoAbJt1AREgEF4+8mAm9fNz4m4pFdRFicv6PcVFp7alz\ncr1F2wK48434jvh+kFarknOn5EhB+mbb0+qCtNpmgdP9zCYs0d4kMlErqZCqPNgI7laHKFUS3dBT\nMjMbeUBoAnQ0q8mVrXp2wF1Dd32B9UG1JyqtIAdqZdK6M7Cf1YBRbVWBVbYvaaKY37QFSmVMUbZC\n5mh2b8rPPBMOvae2Bkik2v4bGPNb8ITInwl/keqhqktsd7S3qH+O/59orYNvFsGnJ8Kn02DNFdL3\n4gb7n5dqt2/wiR0kPS+q5lqNR2TvMQuoabNEBqWCinVyHZnBjfHY0e2SxTZD7CAJ2iqVeFvSmq1G\nWhsPS1bdTAEC6j2tdiZM0GnK47yOxYxWA6puxo49rYrr2PW0Gu9HxavBaZ3wDOf42FYr8czsINb5\nfrri2ujRIqvcvRtWrpTDeed7sSObITFS/W09ypAhsHMnPDb/L1w19iquGnQ3ra10jbux/Y5FHqxp\n0D8xm8qGSqoahVBvL91O2NHhXf2sdr9zb1wbnBNKnCeTwmr/A15e5X6aj/QnJcqmpxogKosBaQW0\nV/YzNWPaW7kXrSqHMTk2cnkgIiWHlHrYW24eI5vbmjlUc4jQiqMk9ze5P8PC6P/bWyhtTGPag4kU\nb1wucz+am4UdffUVPP88PPAAT5yXTf51c3i95GL6XzUbfvADmD6d8WUfcfppOs+9O5YRIW2OZkwb\nj2xkYWomWuJYU+l9Y+IJMgdWAR/teZ8pwfXQy790mTbiTkbWb0RXNMrUdZ1r3r2cr/vHM6jPLFJO\nXcm04KOsfn9m52taWuC+++Avf+lSYKelSeH6wQeV/hk++kiMzcLCzJ+fMwc+/9x+jdzcrirr098+\nzeLXFnP1uKs5ctsRnuj7I7jwQjyZcbyjL6T12htk0YQE+e/dd8M771D+xRaGJD3HlPWFLHroHfjF\nYOi3Da6+A/q+BnVC7M4fcT7FQWtZt9f8+trY8B4npp0mhkFFb0sLk4HwZOkpPfQ+AKcOnE/Q4A/Z\n103w9lXeSnp7JhAeHA6ly/1HDRrwIa0AExJOZvnhTzr//tm3+wkKa2JgovcaV6y0js0YS0hkI69+\nvo03P9sP0aVdo2raWyTemPWixgVOtogMiWTZhct4aO5D/q+1qrKCnFf/06TVymgO3PkPfUd8P0ir\nVdDQPEIUlQheD1RaO9qkzyzMxFwE1EliY7G4BNuRTdVDgl1gDlc0Y1LJtKtk/q3G3YBXrtxb8TB2\nwJq0RmTKd6NChuoKrOXBUdnqzqNm424MxAyU51VwdJd5HwN4CYHizNeqjRBvsnEGRwoprlIzraB0\nuTjfdUfmGUJalX3yl0NHE2Sc0vVYyjTZqApfU1vDQEc7bLwT3kyAt1Ng7TX/Mat01+hog28WSEJm\nYSEsyJd50N/erL6G3gH5z0H/KwOf63c+FL6utk7xF+KW2T0BASKVq9qgRqYrvw2crWogsq/8Lpx6\nuHVdEjRmqgIQmaiuOzvbtlRJgs/KsCgiQ5KWTtdHvU1VCbrUG07Xe72DNPM/LQ92rLRmKsp6FSqk\nTusYFVIr9ZCxjlN8dOqv7VxH3o/HI7LK+fPlwN1lwmTTz2rAW21NSYHoaOio6sdv5/6WvJ0xjBnj\nDc9OldbwNGipgPYW+md76B06srMiuLV0K81FI4S02h3owJvwOEBODkQ155BX1XXgNHpjR/Tti8fK\nOdhAdBbpMQXUHOwbQHxBKq01+wcyIM2etHpiB3BCvyb2lJknhfKq8kgO6cv4nHzCkweZviYyEkqa\nhjI2tBc79VLRgZucebaWbiX0YDPtCWNJ+uUNkoG4/nq4/XaeWD+BDY/sJLmjjl3FPnGtvFwaNe+5\nB844A0aNYuDMxdz/bRnkaaYlyuSsxfRtLqDN4eyg6zpFhe/iiUgPkDtmZy+gQQ9i9x41FdGXBV+y\nyHOElNgsGPdHPAmjSZy/koE1azi092VAerqHDg3sJb3pJnjrLenNNFDbXMuu8l00t/kXaqykwQbm\nzBFFth1yc2HCBJ2Hlj/EQyseYvkVy7kkeByxl13D1Cvv5aTFtzLhpwlc+cPf8OKPc0VzvH8/3H67\nsOWnniL86ot5tvzH3LE3Bc/UeFiQAJfshAm/gslPw+rLoL2FyJBIFg+4jPX6UwHvQ9fhcPQyzhl1\nptzHNbsk8eqLPmdDkRgMjkkfgxZRxYrt/ue5TUe/ZkKKV55cthxSujniAyRPk9YZ77zvs0acwj66\nZq5+vOdzBofOQdM0r5fIJvPEcMxA2Y+9iXaP5uH8Eefz1v5n+OeG5zk9+xyCjURKXb7c70EmGYbI\nfqJ8VGnPsjJhAq8c96DaOdmWtLppx7MpNkW4NGL9Dvi3k1ZN0+ZrmrZL07Q9mqbdZfoiK6kQqEmE\nO1rlABSWYv58WLL0KzlVbJtK5LVmB0PjfSoT6O8Y3MFeRgWS9VYiv069US4qrU5ulirk15DhmSEo\nVH6PKt+PbU9rlpBaFWJmNu6mcx038mCbg3zcSJFUqqBqEySabJwgBKVindo6Zd+Yb+Qxg2RDVZ1B\nm/88DLgmcCzJkFth71/V1jCQez1UroeFBXDWQbknP5/bKQX6XmHbA4AGk56W6zI4Eqa+AoeWiVRX\nBSVfSR+42e+z9yJxA1Yh7SWfQ9ps8+dC4+Q+UJkFXLVR5OFm0DSvY7bDddF4RPoAw5Ot14kdDLUO\nEuEab5XVKrmnedTcDe1MmED6FYOjnFsg7NoWoKsi6VSFcay6uehFddy3FZON31Ue3FIFQRHmxj6d\n6yjENbv2GwPdlEj33w9nngmPP+7zGiciDn49v+PHw7ffysObNslMyq51bL5jT5AQ16YjZGdDYuto\nNhZvRNd1Vh9cTfmmExg6FHsVEnSOSxo0UIeqAeyr7EqE7i7fTZI2gDEjQ5yr/VFZROoFNBf3Z1dp\nYKVlT/leinfkkBzhkMiJyWFCVhkVzcUBUmWAnWU7iagfyrC+NkoIIDhhCFlN0ZYOwh16BzvKdhBe\nWcLI6V431eBguOgi2LKFsAfv5afhjxNU0MZPf/eaNGj27SuzV/76V8laXHklvPACv//RGDwTEmkt\nDKduxGQ+iz+Hm6bm8s47EuZjMk/lhHCdLcUW8190HUpLycv9hDlaPWG9FwS8RNM0CmLGUb67i2zV\n1MAvfyn9njNniqzZwFNr/8hNMXVoEx7r3MfSEoexIvUC2tffQHtrO7/9rRQquyMpSfj4Cy9IZfvm\nj24m89FMznz1TFIfSeXWj2+loqGCtjb45BM47bTANaipgT/+kRFXTWJDXiwdkVHCkC+9VGbzVncZ\nU63L7WB98i28uu1V1kz8OwN/8nM4fQaMHwd5ecTf82teuOp91qVcz8eG+1lSEpx6Ktx7L3zwAffd\n+Dzjb4wn/fO10O9rmPS3LkVh74VCsvY/B8Ads66jOvsZqmv9z91b91XQnrGGc8bOE/OkjFMDW9cy\nz5QJCh2teDQPOczno3x/dVhR+HssHDZf/lK2InCMG8iYmdD4zsLDRSfMpSllJQXFNQBsrPuQWX29\nPhENRRLXItIC1/EES9XTJ6bdf8pthE18Ec8Jj/HQmbd1vdZKGgyyn0T3VztT1uZZV1qDQmVfUlH+\n2JHW0ETQW4UfOcEuMReZKe/lP+Bz8m8lrZqmeYDHgVOA4cCFmqYFlqI8IdaLqNj6NxZ73RotyKZq\nxVYlI61EEh2ywK7Ir1OlVUUe3EM9TY6HMdVKhE3GBtRleHakNSRW5IJOB1Vd9/a0OsiDnW7G9iY5\nQFqR3/gRapXWlmrJxFnJlZMmqZHW1lrJYJpJQTVNnPWOfBz4XHe0t8DBd/zlOwZ6nS4boir5LXxN\n5p2dtFSk/6HxMOV5iB8uJk/HKjXujrZ6yVKWr4GSr+X/3W6mJV/Dvr/DlBf995XQOBj/J5l9683e\n2iL/Weh/hflz4Sny+zz8kf0aui4kOd2kn9VA8hQoczBjajwiSQK7amL8SOffp11yxkDMIGczJjtp\nsAGVvlY7EyYDKj03DQ7SzKAwqbp3MxsKXMdhfzPiiNP149TWoRIbW2tBb5PEieX7Udj/naTBoBYf\nnaq1JuukpIhpzyDfgp9TYhj8Ptf48V0GM8uX+4z9cErogreKcIicHIgsncnn+z9nZ/lOIoOjKc/v\nLe/LKTaGRENQOKOGVFCzP8dvtun2su2E1w5j5EjsRy4BRGWh1RfQP3YIWw7vCnh6R8k+ekfmENzk\nkMiJHkB28n5Cm3qbVmx3le+irmAwqRF7Idb6Hu01dCjpDbDLgrTmV+UTHZTIoOSdjDyxWw+8xwML\nFqCtWMEbxefzr/nttD36iFhOV1bCxx9Lb+zixTBmDG+F7CE2upa5nz7NT8/bz8Arp/ObveeQcfEs\nfjP9Q6oaU2kMimF3nk9fa2WlDAv+4Q+hf38YMoSUsy7kxtIatHvfgp//HIr8zxpJg6+mT8160HV2\n7JAKaUGBXIO33gqP/OIoz5z+FhWPPMDCTZ8QEjE+YD88fdbTHG5u5KNX7yU5GWbMwB9tbfDuuzzc\ndBMj7l3A2il9mP7khxwecgN7h/alclA0N1a/wp+f78c9r/+Bflnt/mOjCgvhttsgO5u2NavZcNN5\nLL7sYe7+9QtUPPc3mDZNSrx9+8KkSdRfdw1zjg7n/NdeZ8PrraS+swBOWwIPN8PIf0GDJDzHZozl\np6P/woexi6hoCFTcvFH2S06Nu52wI+/LDOneC/1fMPKXsONh0DsYnj6IqLqRPPHV234veXLlq6Qd\nPZ3YsFgoWgJ9FgVeOOEpsvd73ezn9bqYVfUvdD69/chemoMqWDRpspgLNpn0ORtIHNfZj5oUlUBK\nzTx+9+HLlNZWUBr9OdfP8v77VZvNpcEGYoeK2aYX6dHp7L55Cztv2kROos95rcbChMmAasuZnTwY\n1Fvg7Eirpqmbn9pVWkNihPB7vQj+nfh3V1onAXt1XS/Udb0V+Bew0OFn/KEiXVXKuqoEZocscGfF\nNjAz6Wqdnqy09oTMTHVuYKONPBi8VXEV0mojDwY1yYLe4a2M2GelHas0zeWSRes+MNlAWBKgOUsd\na/fKwdkqARM7VF7jJOGs2gzxo6wTMKqktXw1JIwzl6iAl7Q6kCWQ2WexQ8xd4zzBMOAq2BcoAQpA\nW4MQvROelQOcAU2DiU+Ia+u3N6uTy452KFsF2x6EVZfCJ1Ph3f7wejS8lQyfz4Zvb4It90g/6tIB\n4tarIstpKofVl8AJz5gf1HsvAi0EDji4TLYclaps1sXWr+m9CA4usV+nLk+yoVbSc4DkE6Bijf06\nld4qq1VlE9RI61Gb3m0DKg7CPUVa6x0O6ODta3UgrU77CaiNYXMiMEHhklRrskmotTeLs63ZHFwD\nYakyX9VuTzESqHa/c1XS6hRjlRPDKmSzB9bxiWuzZwsHamkR0jrLUCOqfK7ITGg4yLhxUL72ZL4p\n/IYn1z/JuOjTGT3a26fopEICiOxLduoBGgtHseFQlypie+l2Wg8NF9LqJA8OS4b2JqZlZVBYt8dv\nPEtreytH6g9w0vBMSazYxdiYHOKD99Fa2p+8ysD7a/PhnYQezCQoJNQ6NgKJWUMZEVfLujzze31r\nyVZ61QwiLAw0swoWMke3KWYqqSmR7BucIqNRuo3fqWioILSplLqjHk4/ry+PPxdNv0dvIurwPsb9\n9Wou3/0zWlIzCd0eyuglz8AVV0hDdL9+4oI0bBi89x5UVHDZb0fQPiQC7nlJ3JFGj4bzzpMLQ9cZ\nNfgHePQ2Xnn5U2bMgLvughf+VMnUPc+x4KkzWFfSl0Er/smK195kUV+d4D/vkwbpe+6BA3JuiQyN\n4lDOrYxtf4R7flrfdfsdOiTSgaws+P3vyZiUyQvj6vh6VDaL58YSffiP8NA6gj6dQv+Im7h1wDRu\nb/sZZ/+gD5/vXELrpx/Tfu45tI0ZTe7h9Vx0zzASxnzIj5vepnb4Sp4reYIBXy6if/PvuOiKWH7x\n6g/59cIEHjzwMtGtcZy9eBrB1x6BS38B51XAudUw/G74ZmGnz8Xt8y+gY+u5nPPaeX5S6yU7l1DW\nvo8fTbgOtt4PI+8zN4sMju40BhzbegNPb3uUDm+CrkPvYMmBp5gSebkQnPLVchYxQ9ocqbYCl504\nh5qOI2wulgrwo188T1LxuURFeiTuJZ8QqAQzkDAWKruk54t73cwLBQ9y/svXkHjwIgb389qbH91q\nPlvVQOxQSdj6oFdML/rGdbtfa2wqrSAJXRXSWp9vXQQB9RY4O9IKauft1hpAt09+Rv5n+lr/3aQ1\nE/CN8Ae9j6lDhVApZYF7gLRqHjkoOJlNNDlkkw3DCqcDutP7UTZ0UulpVTVicjatsIXe4e2Ntau0\nKjgINxZLpc5OrhatcFPX7rOuaoJ37E2Oc1+rMe7GCsER8rmcXFWrNjpk+wZLz6HdgRdkdqdZP6uB\ntFlCfp2Mswpfk95LKwy4GgpecSaDu/8s0uaUqYHPeULgxDeh9CvY/Sf7dfQOeU8fDIfc6yTwpc+B\nMQ/D7E9g0RE4r0Hkx6eslbmoZx2C6W9IgPxgtJBdu/VXXyrGSb1ONX+NpsHoB2HrL+2logdek/dm\nJaMFGX1z6H37RJjhGmxHPJKnOI+9qdogiQw79FSlVWVWqzJpdTCccJIHg5oZk92M1s51+tjHo442\nUUo4EjOHKmnjESGBVgcxkMRWWKrshZbrKPaQNhXbV36VKqQqZLNY4btRiGsqPa0+Ce8TToCSEinc\njR4t4yIB52o2dCqIxoyBvVsSuXTEFTy+7nGGHL2Jccbt1OgQ0wAi++BpKmJ44lg2FW/sPMhvLtlC\n+c4RjBzWJEobu0SFpkF0FieNrMbTHs2h2q5rMa8qj4jW3sydVCKf3WpOMEBEBkHtNSS19GHV7sDY\n9u2BnczPCUdzuj9jhzAsudiy0rruwGbSi1MJTrKY6ezFSQvGMkwPIveAeYvDukO5TGnrx4H6Sdxx\nh886ISGEXH4xmWWb2PrX5SzdNIv25Go6pkyDv/9dGkY/+ABuvpn6rOEs+7SWyLJcDjZN56um6dQ/\n+Ccpo554Ilx1FYwZQ8fd97E/P4PwVXex6YLfcsVLc4RIL1sGF1+MVlRExta3+PvC/dQmDILPiiQj\nUlsLY8eKnPbZZ0nafSlrakMYpv1AZvuceSaMHCkX4gcfwIoV/HNOAp/OLmbyoLFoQyLhmmJ4bgcM\nPhEefo/4c1ZTdW8Ot5XUMOvLxZQ8NZ87m95m2j0ZPH/pSM479TYO3nKQVVet4vXzX6Ljuc8ov6OS\n9y96n3kD5hERm0j6/HPIWLiaGVdnEpy9G+Yvh5F3S+JM80Dfc2HGe2IyWLWFsDAYWfobGuuDufLd\nKznadJRvCr/h2veuRX/nGaakL5VWmV4memVNg5wfwr6nAVgweCH19Rovbn4RgDe2v0FLYyinD5kr\ncS9tln8S2xfpc6UtBhg5PAjP2lu57aO7KK4r5vW8p5gZeYO8rny1kFYrJIzz8wG55expeL75BYfz\nkvhBbx/zo2oH0hoXSFpNUbNbHPSt8J+stLY1COEMN08YAWrn7foi+5Fe4G6CyHeAza7WIzD7hAFM\n7b777uv8/5kzZzJz5syuJyMynV1XGw/3UEBVNYkotj/cNB7xHzPSHcER0h/UUmntMKzyfnwMK2zh\nOO9PgdDrupo82JsZs0RTqUgJ7MhmZB/nm9pOGmxA5aa262c1EDNQehlSpli/xs452IAhEY63kLGA\n9LOmTLN+XvOIkU5FLmSaNbl4UbYchpk00hgIiRbpcOlXYsxkho5WOPw+jPmN9TpRfSVgFL4GAyxk\nsE1lsOsPcLINqQqNg5nvS8U0LBmyL/V/XtclE7zlFyJDGf8YpJ9sv4ka0DRIHA/T3xKnwuWLhWyP\nvDewMr7ll7LJj3awdUyfK73Xha9C9iXmr9n3FIx6wH6dyF6S7Cj5EnqdYv6aks8DXC4DEDvEm8wo\ntT70Vm4wl3n7In6kOAPrHdaEqWYnZAb2g/m/H8VK66Ab7F8T3d+5f9jJiAmc5cEdbd5+SwWpqJ27\neVOx1xfBpuUFfLwaLHrXGx36UDvX8fa1Ws3PU4lpQWFygG2uEFme1Toq8mAVspkw2mGdHqr8RmTK\nfYUU7n79a/jRj6Q/0G8dp8/lPYxFRIhE+dKUP/GnXzzCOYuDOe88pCruRDZBfkf1B5gwbCH5ejz5\nVfkMSBjAqsI19Op4goRQ42DoUEuIymLCkAKC3hvM7vLd9I6VmLylZAuUjGbS/AJodrgfNA9E92dG\nWgord+8AnxDQoXdQ1LCb00a2ihO4HSJ6ERnSiodDNLU1iZOrDz7etJlLe8cSmmpDBoDscaNI3VPH\nvZ9s5NJxgXvU39/LZXZbNMNnTLbc8udeO4BDMx/Cs+YNJkPGFk8AACAASURBVD1/ChfW9iFyI+za\nJWNCt2+HPvM+5965SeQeOI0/PSUzgEeNimXGjJ+QfeuN8M03NP71U06alMjYM7cSUzRLHJNmzxY3\nLy++yXuWO3tH8NCrN/GjiRo5w4fDn/8MDz0E776L/vbbDF52P4kpLYT84i30T2vRzr1YyKt3ndxD\nudz9xd2sm3kNTeve5eiYlcSFxkOfeJkFdPPN7FtXyU2n5/HeQvAk15I57A88UrUJLXMGJI2HmGRo\nr4b2MPr2DSchAbZt9TBmzFCGpgwV0pL/HFUhp1KUdAmc8rK5y3ryJJmTuuJcmL+eCeNjGKS/yQ7P\nTaT/IZ2kiCTuGf4Cz0ROJnzfNTD2EevYm3UxbP45NJUzdUoySb98ktvjTqGguoC/5v6V+DVLGH22\nJgojM2mwgdTpsOIcaK0jKCSaSdxIceUS+v+5P0Oqfsrs0V5iWL5GqsVWSBwnsU/XQdMYPBjOz7mW\nd567ljt8cyTVW2GYue0O4JUHP2T9vAG7nlaQ8+T+F6yfB/m9tVTZ73FR2c4tXvVeVaPdnqJSaXVq\neQHvPhkYG7/66iu+MuYxqUyCccC/u9J6EPBld72BgGh03333df7xI6yg2NOqIvHpgUorqPft9EQ2\n2cm0IjRBLgInIxelOX0OFdLWasnc2o0qsLho/dBQZN+zA163T4d1VEirinzCqdIKanIOqxmtvogb\n4WzGZGW57gsniXB7s7jE2pFsEFmOXT9l6ddirGCXqAAYeAPsecxaObDtV9DvItveKECqYbM+EWKa\ne6MY9TSWCCH+eDJsvhtG3S8V1Ix5aoS1O/oshlM3yffzybTOnhnaGmDDbTIfbvrbzqRD02Dk/fLZ\nzHpxKzcKWU8/WeE92UiE9Q6RWtn1s4IEpaTJEsCtULXB2oTJQGiCEBg7gqciD47OkYSRVZ+yrveM\nPLi9WYi60zUa1Q8abD5T4xFJQpjNsvZbx6Hfvr7I3pDHgJMZk1Oi0Xcd24qtQmwEZ6KoJA9Okyqz\nnfpAxYhJudKqkKj2+Y6vuUbkwZ3S4I528Tywq0KAnyv+rFle0tsRzNdfC5eRpLlDVRy8FY0ixo2D\nmJqJrCpaxd7KvdAWwYyxvZ17qg1EZZHTq4CmoiFsPtJV+VlbuJmmwlFkpex3jo0A0QOYkxXBzgr/\nmHSw5iB6UywTcw47x0ZNIyhhCMNa01ixI1ARsaNqE/OH1FvPdDYQEktLSDLFB1fT2G0S2o4d8MHm\ndSweUkto+iTzn/cic1A2ISFhLL5kCYWFsHEj9O4Nf/iDGBJPv/x9zkiu57yb57NqFZSWwgMPQEgI\n5H7r4WDOTGatepDhH6+lPrmD4tvPggUL/Airruu8mvsoUyPqGXLKBSxeTNcM0chIuPBCll7yBnMH\nFKDlN/JaWBo7Lm+Eyy7rXKe0vpRz3jiHZSdeTXbF8/x58wf86634gM/z9leJ9D17Ip7JE2HAbLSZ\n76PN/kzOJCVfwsY7pP3ljTh4PZbldw4keeOJ8PUCefztNPTDH3Lp0+8SMfV31mPBQJKvqdMh9wam\nToXcFTE8s/AZan9Wy4FbDhBcMJ8bz3wFQuKtJb0gCrjMM6DgZcaNg4LV43j77A+oaKzgmVNfpyR3\nGqOGN0qBI/NM63WCo6RKWrYCgHlzQpl78Et23rCLsjd/KfdeR5vE8iSb6yIiXRLdPvv200/L7z7D\n2ELaW6SAYXeGix0MdfvsvTeaKyTZb7enqFRa67z3sN2eoqIkdDJ2A7XWGaeCFVhWWmfOnNnF8c6x\naHtzgX83ac0FcjRN66dpWihwAbDU1QqqPa3/CXkwKAZUlb4d1fEAdnJlzfu5bORhbY3QVifZfyuE\npzrPw1WVUTmR3/oD1lUBA1EqcgUHnT70cKXVYZNxkgeDt9JqI71sb5aKrlOAT54MFWutn69cL/KU\nkFj7dZz6Wq1MErqj13wxoir9KvC5mj1SjRzxC+d1QKrQp6yXBMkXc+D9YZD3Txj+Mzhtsxg/HAtZ\n9UVEOsz8QPpxl58DSzJhSYZsuCevNHcPNEPaLDmsFrwS+NzeJ2R9q95kX/ReJC6KZvLMqk1y7zoF\nDJCKd7mF9Lm5Uv5YGY75In6ktRNxS7XM2nTKugZHyN5ldf81VwC6/b4EQlrr91tLV+sLZV+yk0KC\nc2BWCe7g3LfTeNB5fwNnV3wnH4LOdRxaOxocHIgNOI1PU4lpQaHS82TX/69qxNR42DoJZozfUepp\n9f9MfltHc6m4ZzolqLw9rSAKzzfekBEkQ4ZAejr2o+D81pFK64wZULvhNJbtXsaSnUtIrjqNKVNQ\nq2YARPUjtKWAzKCxfLbt286Hv9m9haGJowhuKnBWHgBED+Ck7A5K2ebXG/vxpq14KoaTEa2QVAK0\nuCGcEJ3My5/5x7Y1m47SElzG4PgDEvscEJIymrF9t3LnnV2PlZfDosU6UQPW0UsvtJ4x3flmNEoj\nB5GdtIzHHoOnnoI77pD5vuHhOvsKlxIeGi2kD+GYs2eLbPzpp+W/o0ZBaEgE28IGs3/zbwP+idUH\nV3OKdpiQ7Iu45kcxjB0rCRHjKywvhx//WAqvoSEexp+8hMTKlWzd8mdACOv8l+bzs6Enc8Lhf8BJ\n77Lwon7885/+/46uy6jbiy7q9gbihsLQW2HqC3DKajirCM5vgrMOsCXhPR79/CEZsTbqQTjrIPv7\nLuPbgonkKGz9jP8zVK7nzBEv8MUX0NEBwZ5gPJqH9etaOHfIfTDmIecY3P9KyH+GiHCdYcOAQxN5\n7NTHCCqaycSJEFrxiaif7JSGIMlar0R4wQJ4+81gDm7vS3g4MmqqequcF0MDCb8fjGqrD/w+Qs0u\nuWesPEBAJNHh6fZnypo9cvZy8hBorbV37K3fby8NBm9C1+F8q3JOVpEHNxxUan9w9LRR8c5xwL+V\ntOq63g7cCHwCbAf+peu6gijcB0Zwt+v/VJr9pig5cgqoKrNRm1T6dhzkym31Yk4T4nAzOkmEjcy2\n3U2keSQzZPd+VDItYane2Yt25FdFZqAgV6gvkLE2dohSIK12M1oNOJFWvUOedyKtTmNvju6QjchO\nOg1dlVare8Kpn9VA/EhobzDv19U7xDW4twJp1Tww5BbYHhjg2XQXDL3DWnZohvBkceg96wCcUyG9\nqn0WOVcy3EDTYOC1sHA/zFsFC/bDia85B9Hua4y6H7b92v+ary+Eordg4I/U1okdKP+uWZXU6GdV\nQcqJfoPU/WD0Sqt8hwljrFsyjOSMSuLArq+1do/9uBsDwVFChKz2JpV+VnAmrU4zWg04qUmcRp90\nrtNDlVYnlYxqpdVJiaTSQwrOSiQVshkSA1qQ9aGupUoqRsGRzu/FrvKrkogFvwrCzJldE1tuNsY1\nq8RG6FQQDRoEYQUL+TTvc3636neUfnop8+bhPHKpc50sqC9kWtZkvi0WxY2u62yvyuW0seO890SW\n8zoxOQxMLKa93cPeI12J7zdWfsuw+AlotfbOwZ2IHcqc3pEsy93gF5Lue2ILvUOG46nd4ZyIBSLT\nTmRsbD3vf32EH/9YRsuceCKcfHYRo2Pa0SIzbU2hDISmzyWqOnCW+abiTZwc0UFI5ulK+1fMwKtJ\nKf/Sj9AD/GnVH7gmTkfLuRZNgyefFAny1VfL3NU5c2TizFzvJJVBvaZwaPiDpG2+hZ+8MIHhfxvO\nrf2GcW3dMpj8D0iawLx50uq61icXvW6dKAOmm0xyCYCmQWg8k08ezD+XTac84ixImwGhcSxdKuNy\nlHK9wVFw4mskFd7G2Jw9bPXmIXQdRmgPEpQ4Qu1skTZT7t+qjZx6qrQDA3z5pffzHHgD+pyjsM6c\nzvaQ0aNFnj9/PvzkJ97PU74akmz6WQ1062sNgFM/qwETMyY/OEmDwccnxeZMWZfvnHiKyBAFZFuj\n9WtU9hRlefCxVVr911FoZ3TAv31Oq67rH+m6PljX9YG6rpucah0QEi2Z0Naj1q/pKffgpiPfvW+n\no00qGmEO/S1Kwd3B9RGcP5dqYHYyBlEJzJ4gZ1LvZOkPXofERnuToLoChZ7WLLnZ7ORqdXnO1adY\nh7E39YVCOqwMBQzE5MgB00rOXbXRfLB1d0RkyIHNSjZZutx8Zll3aBpknGLeF1GRK5XaOAcibiD7\ncpFfHlzW9Vjh69IfOegnamv8/4DmEUITlnhsP582S6rDW++Vv+u6yIwHXm9vwNQdfRabS4QPLbU2\nhOqO5ClyDbU3BT5XsVZ6oVWQMEYqvGZQMWEyEGPT11q7p7Pa4Qg7ibCKczB495Rm63nAqoTBaRyX\nKoHpqUprT8qD7Q4UrmTGFnGtrVGuTQXiYRtnVYgvyLkhNEGIq+k6Lj6T16jK45Eq61tvieGsrONg\nUGjAe+1oGiyYm8Ti9te4otcjZAdPo18/nJ2DDURlQX0Bl50ygvK2Qo42HWVPxV5aGsO48NQsb+uM\nWqU1qCGfxLYR/GNZV5V0XdF6zhg7TqSQTvJggLihjEtspSnxWz78UB7auROW7/uWC4fnSDU71MZ1\n1AtPwlimx8Zwz1Or0DR4+WXpQ5583tecn5GF5lRl9aLfwEsYQTXFdf4KtHd2vcO58VH20lYfTBx9\nE6G0s2V7lzv+3oq9RJV+SlRsTmerRUSEkLHUVPjnP4VQPdDNymDCuJ8SPu0lfheRT3G2ziUtq9FO\n/FfnyJigIDEf/tnPuo4ZDz4IN9zgTlgUEyM+UG+80fXYkiWwSCH33In4kTD6QV68+gxWfFwAQMGK\nd7n0hL8TNeMJtTU0D2T/APKfZdEiuV/a2+V9nX1Wo5gw9TnbeZ2kSZJUb5bxO6+/Dq+8It8LIIle\nOxMmAyaVVj9Ub1EjrU5mTDW71eJazEBpf7KCkwkTyHcc2VfudyuoVFojMr3zx21kz8ryYIe2vu97\npbXH4CQRViWtdkG5o1160Bz7WxwqpE0lXiMOB1mgk/OvamDuiV4kcHZpVj5EOWRbVCqtmubc1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T6uxxcPB7+F+SB9v2orokrVZmE66zwD2UTbaUQCmuY2cw1VwhVUAVjX1ILGBR+VU1iACpijSX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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1170e1450>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# next 50 \"days\"\n",
-    "\n",
-    "# define input stream\n",
-    "yobs_syn = Stream()\n",
-    "yobs_syn += Trace(syn200to250)\n",
-    "yobs_syn[0].stats.starttime = SqDist_syn.next_starttime\n",
-    "\n",
-    "# process input stream\n",
-    "SvSqDistStream = SqDist_syn.process(yobs_syn)\n",
-    "\n",
-    "plt.figure(figsize=(16,4))\n",
-    "plt.plot(t200to250/100., yobs_syn[0].data, color='blue')\n",
-    "plt.plot(t200to250/100., SvSqDistStream[1].data + SvSqDistStream[2].data, color='green')\n",
-    "plt.plot(t200to250/100., SvSqDistStream[2].data, color='red')\n",
-    "plt.plot(t200to250/100., SvSqDistStream[1].data, color='orange')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Next 50 \"days\"\n",
-    "\n",
-    "Items to note:\n",
-    "\n",
-    "- tracks reasonably well when signal-to-noise ratio is relatively large"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x117625810>]"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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KUY8YrgcAbd8JL/e7zr6tiTKZp6fDngLVjo6CKdJibaWU3Qq8MvAdf2/92y7a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YxjnE7U76ILw6x/rQF2rmulkdhFVD0RO+MM2EENzuIHhnR7keojdxEgwrw34c1xcMpqDL6s6uSs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\n7Gx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89Kpgok4tJ5ifb9ZCBIIsOhPZZjGP1F7nc12x/ER6rb+iNiHbs1DQEigIuO3a2LIj\nXdIjmjquTFjRIjlUcdzn4dQsu84AOjnUDPNLSQ9ghOSFplxUAgBGvWtEKWQCDfsDU6qBvCbh22DM\n2MWNFMnC7AZeGKOsqJ0IpY+DrIL4ktJkhocQYupzg1CMlgmzcRFXOTEVQYWrkjlAU3N5ifVby5Bq\nHGLBGIMBb4QpGpT3HR/I219eDgxSRCiCJYOni/S/XdzHJvwsmojgaU0mDSGnmmp1lMVjjwnlYS9U\nK0bG/IXLWunTwVutXL8Ex2nBJVOnAn/+swhv1mnRIHgaSNDw4O6W5aN1avCaKnr3FkrNffsG80w7\noRuYUbyjALB+azm6Xr7B1OamTUCez2Ac/e+ev+xgfPptX/QOq+fX/XJgks/6pW5YHQ1h5oZkvvc5\nYRkfJO3RZVAF3fx0mn/a/sa5bnsAIhmtnPPlnPOVsMeiHQPgCc55Ned8NYCVAFRTbDDKRgI/zxPh\nYevfBspGBW8jrpAjVZ5MmFC4xpYQA135K3CeovQTLAq5BWX9zO+eEB7fHjOjtaMz+MHgfbKG84W9\nyK13jsEPAYMfCNeWvGgK25/es4DmWpXzMR+Ha4MlgGZaxMDxm4Ah/nfqiQOYskPCee5rkzg2mg59\nS/yvG62THIouBiXOnPAw5JQglrqvViM1zD5xl4vtnogwizHVnGbJ4QpNrxBq8h3PNj2tSSXDBaYE\nDQF0INH/Zq0fEcaeagM4zB+VUwKU9kwLboXNlQWEMVKTEte6Hh4chNkh0vk8qdcROS2914q6UXT3\n3ebj6fDmI75Ch4F/D/zxfn+SKEJMMgsXCk98XOgGZhx5qDurG4Zuq502LX21pjv6//XT8J1IJM3p\nHQeFiOUGgOIK8/NQm3uWa6TJIeH6oqIkiNKyyzVfGlPVkKgCWBq1dbduDkCOA12rHYtGYWsgvzmw\n7BZgz/p4wjKBcINWZfyGMWCcEqeD3nxk78PP7wbvB2B33a99SYhdBTX0JyuEJnrMDKdya/WqhN10\nsG1PxuTZiLKIKT8UmLghWji5Tk6pCDchiAMVq9Bd1JBaQAirxXEfaR1SfGj8YnO0RBRaSDGXA+4K\n3071TvPzMHOuyms44tXg7ahUuaP87k1GaA9YOKPVasD3ujZcP7T0n4sfuxFVvUIqPzcbCwx/Nv30\n+55LA22cq/7+6+f+Cf9ZGn7hnExKRqtDeLAbcuhvbMEHR60AunuXEBk9GujqVrWkftdQ10KfPsCk\nSd7nOeW0BvW0xk1cnlYAyM01txmEQYNq6W9vEyKWW0dO4wu7Ns0qMh43PzJ8X6LgNm8Uto7nM6yC\nqiHxHDqMsTcZY19I/xZr/x/l9jbFsXiGW7fLgIUXAd0uDz9IrII15Yepz3MjrjyfuNS+DnrMfmxs\nwHpNva+LR3BJZchZS034Ze8Gy4GY9lkizX7SdxRVmbI2BVQI4kCisA3QdGz0usj5Wqjg1BQw6h3g\n0IC5jTJh7h0yJd21gvQx3B5lj3G9CufzvKjZ6X1OGMJsqsXlndWRan6GMlq1+8aC5Zq4UzcfiX4q\nmNiAufHRUcju9odwbQDGgrewDVp1j65g/ufHr8fmneGFt3JygGrNw5oK6EWeORM44YTQHx2ZsWOB\nr76Kv90GDYCnnvI+7+OPgf8plmx1bbTq10kckhnZ2r5jXagH1wpyGl/Y6KbxUhLz+nfDtZEf0Sc4\n9KmA4cQhaDY2lmY8V9yc8zB35TUA5LtNCwA/Op181VVXpR+PGDECI0aMcG651URg8k6RBxSWoXOA\nlyXXeRhvmarsShj3t8potSZ4+6FshP1Y/R7B24pLzOfob4GXpNyooPUUdbr/VeTCLdVi4cOE8tXv\nbt/5j5Knq+dFt5wQvIYhQRDhyCoARr4KvHUIsGtt+HZaTQaajtES5CKWzyrS5rioK8m4V6IlEZLZ\nbH0JeU/IawLs+dl4XhNigzYu9Wkd6R4d5Va3o3gcgP+Gb0CLGkgWqAWCfKMvmo/5Nvhba0G379xz\ngUEDk8A64Nbbgt2rr7jC/PzXpivolKO5r41UK3Ebrbm5B6jRGhZ5LRo2iuS4NcDO74AX23ieqiS/\nTPyrBd5dAry7sgBYETIqxUKcBczkIf0SgMcYYzdDhAV3AOCYuCcbrb6IutCwFnUP4zVViUGEiWlX\nGa1xXAiAEKDo+ifv82oDOeQBCC/MUa8D0PqEaEbreE1xeuuXQnV66FPRcuySucC0Or6TEMSvlZGv\nR1vJMRYuVUFFPb3WdtT5QHp/1HI69SqibaaNeAWo2gq80gM47L/hvZ3W+2qYBZm+OdzxHGDlnTHk\nfEU0WrXvdeyxTYGPInSDMbHpKZeICENxp+DCVLVIYSEwYmQCmAOcflq0turaWCPMRDVa77pLlAM6\nNEMEc2MhbiElaxnDILAYxO96XgN8cXn0diRGdAVG9C0GJlwFAJg5M6BGjoWoJW+OZYz9AGAwgLmM\nsVcBgHO+BMBTAJYAeAXAOZxn0BRkNXrDeFqtRhkQMqdVMUgzIWQ0N6IHUTYKs2Oqqwog1K4/Y+Jf\nSQ+RAxSX+jBBEPueZF7m5G93/iMwaXv0NAHZaD1qVbSmogpLFbYUYcvTOND4oPCrVTkaaRoHCkKE\nsJX0EJsUA+4Qzw/5d7i+6Eie1lDenkEPiLJt7X4HHBvB2w/Ecy8qaAFM3hHqrbXqyew0fZ+USsqg\nVSXhwVlnZZjB2jRiDXIAGPZ09DZkoqQLxqHYXlsXVIxih1HVg1/gnLfknOdzzptyzsdJr/0f57wD\n57wL5/yN6F2Nme7aboLPmmY2ktqELC9WwniAVYM0r3bc9IGIqrKse4snbQeOc4wMD9HugRJXQhDE\nfg9LANlFQL/ZwOEfhm9HXixE8ZIO+Vc0EaY4aTkxehuMAU21Mu/TeAziW4alFspoyy4ShiJLAAXe\nZVQymVo1Wvvd9OuL791HkKGeQRTGnHPf7vfh3yuvjQeELcwbj8KvLZo1bIqJgl+vBdB5utjpjGIE\nTePAxF9Ezafefwf63Rq8Dd1olZUfw6p1DX0i3PtURN4l1b7XrMLo4dzygCejlSCITCOvEdBoUPj3\nl48WOaCHLYjWj7YnqfUN6oLes7R75Ma67olB+WjsHDIfANlUTvVO770X+OabfdsXgthvySuLHpkI\nAF0vBVoeF6EBeZ0cMoIjrh2RI5cCFedJB2IyhvFrNlpzSkVOSeR2SkQOUtdLjB3hIDQ/Gig7FBj2\njHh+6JtA/S7h+tI6Rum9yPHx2uCPY2Vgyvv59Q5ZgiAOUAbfD0xYDzQeWtc9iZ/c8Gq0sTKNA8UV\nSDUaDoCM1qFDgfnz7ccbNgTatrUfJ/YdF13k/Bp5WoHWcVRhaX8K0Hqa93lejPvcrAAchl7XAj2u\njNZGTikw6H7xOOwgaeFWFCYAyTyg781A299G648CsgDqmubjgVFvi3DcfrcBZd5FsPcJyYie1qyi\n+MLUClsBh38gaiq2Oj6eNgmCIIhfHXou66/daE0kgOHDzcduuknUKyUyA9UYrSuj9YC7XlodDwxV\nlIoMShzKu93+El0pnTFhiAMILQzYoB8w8J5o/dBJJIFv9Trb8Q3aONWDiah0Oreue2CghweHza9l\nDOh4Vnz9aTQYGBMhZ4wgCIL41RNnCY8DjenT67oHBuRRJJygseFFFHV9KbT4yBXRulF+GPDTm9H6\nY4GMVkKNHqM/4ae67QdBEARBxAQZrUSm42aU3XUXUBWhMkpY6HrZj4hUEk6u/KGokhKE/rcBczvH\nustARuuBRhx1lhK5wMB/AjyksjJBEARBZCBktO4f0O8jvgPrev+0iPVvwzB1KtCmzb7/XCdobNQi\nstEa1disV6E3FK0dCcppPdDo/tfoKsJT9gBFbYB67WPpEkEQBEFkArTg3T+gENDMGatz5gD5GVIa\nm/BDhIunxbGGHg2vjtaN9AAmo5Vwo9Vk4IQ9dd0LgiAIgsgoMsUQIKJzoBq25eXi/y5dgPbkOyCC\nEuXCyK5n6NGkYopDp/BgwhXGgGRuXfeCIAiCIDKKnBxgQcRyuERmkDhA3S7TpwMnnQQUFR24hjlR\ni+SURm+j1/+Jyh1RGf6cMIRjgvE6viIYY7yu+0AQBEEQBEFkBrm5QGWls9E2b56oNZsTsTofsX/B\nmKjTunp1XfckQ9m1FshvlrEhJYwxcM5Dd448rQRBEARBEETG8Mgjwmh1YuTIfdcXgthvKGhe1z2o\nVcjTShAEQRAEQRBERsOYUDL+9tu67gkRhqie1gM0I4AgCIIgCIIgiAMJ8nP9eiGjlSAIgiAIgiAI\ngshYKKeVIAiCIAiCIIiMpm1bYMCAuu4FUVdQTitBEARBEARBEBlNdbVW1TFZ1z0hwkDqwQRBEARB\nEARBHNBkkdXyq4ZyWgmCIAiCIAiCIIiMhYxWgiAIgiAIgiAIImOJZLQyxq5njC1ljC1ijD3LGCuW\nXpvBGFupvX549K4SBEEQBEEQBEEQvzaielrfANCNc94bwEoAMwCAMdYVwGQAXQCMA3AnYyx04i1B\n7A+8++67dd0FgogMjWPiQIHGMnEgQOOYIASRjFbO+Vuc85T29EMALbTHRwN4gnNezTlfDWHQDozy\nWQSR6dCNhTgQoHFMHCjQWCYOBGgcE4QgzpzWUwC8oj1uDuAH6bW12jGCIAiCIAiCIAiC8I2neDRj\n7E0AZfIhABzAZZzzf2vnXAaginP+uHSOFSrGShAEQRAEQRAEQQSCcR7NlmSMnQzgDACHcs73ascu\nBcA553/Xnr8G4ErO+UeK95MxSxAEQRAEQRAEcQDDOQ+tcRTJaGWMjQXwDwAHc843Sse7AngMwCCI\nsOA3AXTkUS1kgiAIgiAIgiAI4leFZ3iwB7cByAHwpiYO/CHn/BzO+RLG2FMAlgCoAnAOGawEQRAE\nQRAEQRBEUCKHBxMEQRAEQRAEQRBEbRGnerANxlgLxtg7jLEljLHFjLHztONXMsbWMMY+0/6Nld4z\ngzG2kjG2lDF2eG32jyD8ohjL50uvnccYW6Ydv046TmOZyDhc5uUnpDn5W8bYZ9J7aCwTGYXTnMwY\n68UY+4AxtpAx9jFjbID0nlu1cbyIMda77npPEAYeY/l9xtjnjLEXGWNF0ntoTiYyCsZYLmPsI23u\nXcwYu1I73oYx9iFjbDlj7HHGWJZ2PEdbd6zU5uxWnp9Rm55Wxlg5gHLO+SLtYvsUwDEATgCwnXN+\nk+X8LgDmABgAUfP1LVAuLJEBuIzlcgB/ATCec17NGGvEOd9AY5nIVJzGMud8mXTOjQC2cM7/RmOZ\nyEQU4/h/AI4DcAuAf3DO32CMjQNwCed8JGNsPIA/cM6PYIwNAjCbcz647v4CghC4jOWHAfyRc76A\nMfY7AO0451dIujE0JxMZBWOsgHO+izGWBPBfABcA+COAZzjnTzPG7gKwiHN+D2PsbAA9OOfnMMZO\nAHAc53yKW/u16mnlnP/EOV+kPd4BYCmMeq0q9ahjADzBOa/mnK8GsBLAwNrsI0H4wWUsnw3gOs55\ntfbaBu0tNJaJjMRjXtaZDGGoAjSWiQxEMY6XAWgGIAWgvnZaCUSdeAA4GsC/tPM/AlCfMVYGgqhj\nHMZycwAVnPMF2mlvAZioPT4aNCcTGQjnfJf2MBdCN4kDGAngWe34wwCO1R4foz0HgGcAjPJqv1aN\nVhnGWBsAvQHoZW/+oIXo3McY028wzQH8IL1tLeyLKYKoUyxjuQLAwVrowzzGWD/tNBrLRMajmJfB\nGBsO4CfO+TfaIRrLREZjGcfTAdzIGPsewPUAZmin0TgmMh5pLH8I4EvG2FHaS5MhvKoAjWUiQ2GM\nJRhjCwH8BFE55muIqK2UdsoaGGM1PY455zUAtjDGGri1v0+MVi3c4RkAF2i7SHcCaM857w3xh/1D\nP1Xxdgp3IDIGxVjOAlCihZldAuBp/VTF22ksExmDYizrTAXwuHyq4u00lomMQDGOz9Yet4IwYB/Q\nT1W8ncYxkTEoxvKpAM5ljH0CoBBApX6q4u00lok6h3Oe4pz3gdhgGQigi+o07X/rOGbwGMe1brRq\nCbfPAHiEc/4iAHDOf5Fi7++FEdawBkBL6e0tAPxY230kCD+oxjLELtFzAMA5/wRADWOsIcRYlpPK\naSwTGYPDWIaWhzIBwJPS6TQvExmJwzg+mXP+AgBwzp+ByPsDaBwTGYzDWnk553wM53wAgCcgvFYA\njWUiw+GcbwPwHwCDAZQwxnR7Ux6r6XGsrT2KOeeb3drdF57WBwAs4ZzP1g9oSec6EwB8qT1+CcAU\nTVGqLYAOAD7eB30kCD/YxjKAF6DF4TPGKgDkcM43QozlE2gsExmKaiwDwGEAlnLO5QUQzctEpqIa\nx2sZY4cAAGNsFES+HyDG8W+144MhQtbW78vOEoQLqrVyY+3/BIC/Arhbe4nmZCLjYIw10tM9GWP5\nAEYDWAJgHoBJ2mknA9A3GF/SnkN7/R2vz8iKs8NWGGNDAZwIYLEW48whlFanaXLzKQCrAZwJAJzz\nJYyxpyD+yCoA55AaGpEJuIzlBwE8wBhbDGAvtEURjWUiU3Eay5zz1yCU3eXQYBrLREbiMiefDuBW\nbed+D4AzAIBz/gpjbDxjbBWAnQB+Xzc9JwgzLmO5gjH2B+35c5zzhwCak4mMpSmAh7VNlgSAJ7V5\ndymAJxhj1wBYCOB+7fz7ATzCGFsJYCMAV+VgoJZL3hAEQRAEQRAEQRBEFPaZejBBEARBEARBEARB\nBIWMVoIgCIIgCIIgCCJjIaOVIAiCIAiCIAiCyFjIaCUIgiAIgiAIgiAyFjJaCYIgCIIgCIIgiIyF\njFaCIAiCIAiCIAgiYyGjlSAIgiAIgiAIgshYyGglCIIgCIIgCIIgMpb/Byi9oXsvsR8BAAAAAElF\nTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x116fca710>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# next 50 \"days\"\n",
-    "\n",
-    "# define input stream\n",
-    "yobs_syn = Stream()\n",
-    "yobs_syn += Trace(syn250to300)\n",
-    "yobs_syn[0].stats.starttime = SqDist_syn.next_starttime\n",
-    "\n",
-    "# process input stream\n",
-    "SvSqDistStream = SqDist_syn.process(yobs_syn)\n",
-    "\n",
-    "plt.figure(figsize=(16,4))\n",
-    "plt.plot(t250to300/100., yobs_syn[0].data, color='blue')\n",
-    "plt.plot(t250to300/100., SvSqDistStream[1].data + SvSqDistStream[2].data, color='green')\n",
-    "plt.plot(t250to300/100., SvSqDistStream[2].data, color='red')\n",
-    "plt.plot(t250to300/100., SvSqDistStream[1].data, color='orange')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "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": "2.7.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}