diff --git a/docs/algorithms/AdjustedPhase1GenerationTool.ipynb b/docs/algorithms/AdjustedPhase1GenerationTool.ipynb
deleted file mode 100644
index adaf279553131718a2adb44528128fb6560a88db..0000000000000000000000000000000000000000
--- a/docs/algorithms/AdjustedPhase1GenerationTool.ipynb
+++ /dev/null
@@ -1,6401 +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": {
-    "collapsed": false
-   },
-   "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": {
-    "collapsed": false
-   },
-   "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": {
-    "collapsed": false
-   },
-   "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": {
-    "collapsed": false
-   },
-   "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": {
-    "collapsed": false
-   },
-   "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": {
-    "collapsed": false
-   },
-   "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": [
-       "<img src=\"data:image/png;base64,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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(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": {
-    "collapsed": false
-   },
-   "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": [
-       "<img src=\"data:image/png;base64,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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": {
-    "collapsed": false
-   },
-   "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": [
-       "<img src=\"data:image/png;base64,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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(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": {
-    "collapsed": false
-   },
-   "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": {
-    "collapsed": false
-   },
-   "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": {
-    "collapsed": false
-   },
-   "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": {
-    "collapsed": false
-   },
-   "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": {
-    "collapsed": false
-   },
-   "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": {
-    "collapsed": false
-   },
-   "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": {
-    "collapsed": false
-   },
-   "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": {
-    "collapsed": false,
-    "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": [
-       "<img src=\"data:image/png;base64,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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": {
-    "collapsed": false,
-    "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": [
-       "<img src=\"data:image/png;base64,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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_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": {
-    "collapsed": false
-   },
-   "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": [
-       "<img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABLAAAAEsCAYAAADTvUpQAAAgAElEQVR4nOydd7gURdbGX8SAYRETK0YMiBnTYmbBRcW4xhVdFVwTa9bVVdxPBAOorGIA8yLmLGLEOGRQEFBBMlxyzhdu4N57vj+qe251dXX3zPTMDe37e556YPpUVdd0z5xb/c6pUwAhhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQkhdozsAyVNfAwAU56mvQlAENUaXtlDvvW3ND4UQkgMtAHwNYC3Ud/c85/ifAIwCsME5fgTy69uSzB8BfABgBdT1uq12h0MIIYQQQgipz1yGeA8V20A9zLW12LqDAlbbmh8KISQHRgFYDOAmAJcD2APAFlDf7akArnOO74DaFbDuRbW4Vtd5C8B6AHdCXbsDa3c4hJDfMTdA+e0fansghBBCCMmdz6Ae0HJlZ6gJQXeLbXMAjWL0rTMA9UvA2gzqvW9WG4MhhGRFIwBVAB4yjh8I5d+uMY7n07dlSzG8vqYuswTAG7U9CEIIATASQBmUT9+/lsdCCCGEkBwppICVTwagfglYhJD6w15QfuxO43gb5/hFNT6iYLIRsLYt4DgyoQpA3zz2xx8FCCG5sA+UL78PQDmA+2t3OIQQQgix8QcAT0KJK2UAlgH4BsBRjn0I1B90vRQ5ti0BPADgJ6icMBsADAfQTuu/uaW9LmZ1h32ZzeUAfgSwEcBqAMMAnBbxXgZAPbjtC+ArZzyLAHQD0MCoeyfUcqCVAEqc92B7AD0VwAgAa5y+pwHoadTZCkAPADOhruF8AI85x3WKEL2EcAiASQAOBpCCev8LAfzbMrZMz0sICWZvAM9CfbdLoHzC+1C+y6U77H5wgOX4EKONjkCJNedBfc/LAEwG0MEyrt0B9AewVKt3dQbvx+ZvBxhjOhhq6d5qABMc2+FOvdkASqEio/oD2Mno3+1jf6f+Gij//wrUcnGdMP/ZOWCsLvtC3YdVUH5wDICzjP7bOm06QkXGLYASxJpo/Z8E4GkAy51xvAD1t6sJgNec/ldD+U7z7wQh5PfD/wGohMrL9zmA6ZY6X8D+g24DKF86vFCDI4QQQojiTaiHo8ehHo7+DWAQgL879lOh/igvhxKVLkd1bpWdoQSixwF0AXAXVB6YcqgkxoD6db8L1IPER1ofhzv27vA/5N3vHBsJJTTd4ozzkYj3MgDqAXQ61IPJjQA+dfp6wKg7H0A/p87tUPkOBN4HpEOgrs1YZwzXA+gNYKhWZzNUi2V9oHLgPANgE4CPjXMWITMBayGAeVDC4j8BfOfUOyPH8xJCgrkIwEQoMfhaAA9DiRpFqBZkDofKAyhQwo/rB4936guAp5zjpzptusMuYE2E8pv/B+BWALOgvsc7a/X+COWj5kFFA3SB8suZJDm/HEqAGoZqf3u8MabJUH7in1A5XwDgX06b+5zr8CSUcPQDvMKO28d4AB86fbzkHHtUqxflP/d1xiZQifHdsbrvfwmAdVDC1O1Q160KwPnaOdpq72eCU+8eqPvW2bFNAPCl8z5f08Y5HOrvyj9R/XfiSt/VJIT8XpgC4Hvn/52gfMKfjDo9nOM7GscvdY6fWMgBEkIIIUT9Ih21fCNoCWFDqF+ydZpAPXj8TzsWtoSwO7wPeftD/QL2EfzLQKJ+HR/g9PW00eYzqAcp/QFxa6PtFgB+hRKLXNwH1p0RzOXOeE8yjl/vtD1BO1aEzAQsAXCFdmxLqGv6QY7nJYQEY/oCADgO/u9hc9iXELaFfQlhd9gFrDIA+2nHDneO36QdexlK5DKjn96G8tm2MesELSF0x/S2xWbrs6NT/2RLH/8z6n4EtZOgSyb+E6iOStPpg+roKZftoKLD5qD6b0Nbp94sy/g7O7bB8P7tGAUlhD2rHWsIJRgOiRgrISSZHA3lL653Xm8P9UPAk0a9c5x6p2jHtoCKhP+kwGMkhBBCCJSo8iOA3ULqZJIDazOoX6R2dupP0GzZCFh3onob+mwZ4LQ9wDjeAdXLTGzs4IzxWailJC6dnXZXIzinyiCopUA7G6WF0/Y/Wt0iZCZgFcMv1g2CinjI5byEkMzYAko02hkqCquPZmuO/AhYn1vOuxbAE87/G0D5oRfg/353Rma/8kcJWG0i2jdyztfcqX+rpQ8zMuF253hj57U71jD/CdgFrGmw7wJ2j1P/UOd1W+d1N0td9/wXG8ddcewY4/hAqIg3QsjvjyegIth1wX0Q1I+HDbVju8L/d+BGqB8UDwUhhBBCCs7foJbdVUIJWd2hlnbohAlYnQD8ArVsUM9jMlurk42A9ZwzFjOyKxMGOG03N47v65zjbu3Y2VA5VUqNcVdpdbaGyt8iUEso34G6XvrD2G9Ge7M8pdUtQmYC1pSA9zYnx/MSQoLZGmqJ8Xyo77/+Peqv1WuO/AhYz1nGUASVQwoAmiL8uy3wLqOzESVg7Wmx7QjlN5ZazqcLRG4ffzTad3aO7+28zsR/AnYBqxRquZ/JX+Fd6t0W/kg5czzHGseDxj8AwHpLP4SQZNMQKuL1K+P4ZVC+wsy/ugBq+TGg0mQsAfBqIQdICCGEEC/NoPKDfAyVi6UE3nxLQQKWm79kINQDxOkA2kMtw9PrZyNgPY/8C1j7wStgnQz1oDoEwD+g3mt7qAmJ+cC5GYC/QP0654pG36H6F7mpUAJe+4DSUuurCJkncbe9tyLtdTbnJYQE8zKU33gcSoQ6Feo7tALe72tz5EfAsi3ZLtLO5f7C/zqCv99NI95TlIBlW9b3LVTOqx5QAtmpUD7d9N1BfXR2jjfXjkX5TyA/ApZtAw53PGakVdD4B6Bu72JLCCkMp0L5hH8Yx7eD8ommODUQyp8BKmdgGaqFe0IIIYTUME2hfl0aoR37FHYB62Oo3CPmcreRRv2dULeWELrJic3d+mwClsm9Tp32zuvPoa5XJrtXFSF/AlY25yWEBLMG3kgrQC2hq0DtCFgNoZKXvxU66nDWIzsBawfYl+K5S5K7Z9BHZ/gFLBPTfwLZLSG8G/YlhBSwCCG5MgBqFcEOFtsHUP5Yz7F3L9SPHntBLf8282QRQgghpEA0hEpUafIj1M5RLu/AmxvK5UOoxJX6kpBjoaKbirRjW0M9MNj+yHeH9yGvUEncywHs4hx7HCrSTN/yvblzTB+LucsMAJwJbwRAJ+f1dZa6W0OFl7sUIX8CVjbnJYQEsxLVy/dc7oL6fg3QjjVHzQhYcMZTBntOlV0sx0yWwL4bqTsmU7xp7By/3zjeD7kLWJn4T8B+Tdw8Vcdrx7aF+sFkDvxJ3ClgEUJyYWsogcqWmxBQy54FwCXasdOcYyOctpn4ZEIIIYTkgSaoXmpyO9TW6e9C/WG+Q6vnPsw9AbVV8DnO8auc44OghJReUELXJPgjtiYDWAy1ZXlHVD+YdYf/Ie8B59hIqK3db4IK4e4V8X4GQC1/nA61/OQGVG+P/rBWr51zbBjU9vTdoPK+/GyM5UmoxOkPArgG6le3BVC5clzhbzOoiU8V1M5eN0ElPH4O6sFYf3gqQv4ErGzOSwgJ5lWoaKsnofzYK1Df8dpaQgio/ExFUKK6O657ALwHlVw+is+hfPsdUP7WzQPljsm2hHCoc76HoPz0QFT7xO5avaA+OsMrYGXiPwH7NfkjlAi3BurvwW1QG4NUwZv/qy0oYBFCcucSKH/wOZSPNYs7H9V3GNwR1fkBTdGfEEIIIQVkSwCPAZgI9StSsfP/fxr1toVaXrca6g92kXO8AYCuzutSqIeVs+AXWwD1S/o4qKgC/YGoO+zL9q5y+iuFemAbAu+yExsDnPewL1Qyzg1QD0Hd4Y/m+geU0FUKlTS9s2Usp0BFMSx0xr0QallPC6OvLQD8G0p4csc7DkoYa6zVK0L+BKxszksICaYJ1BLC5VBL7wZD5ZArQu0JWIBazt0Xame8cqgfAL6F+qEhipZQgtRGeCPJ3DHZBKzdoSJfV0MJR+9B5UfMVcDK1H8GXZN9AbzvjKcEaknhWUadtqCARQjJnU/g37TCVsqh0mG4zAGwDCpPFiGEEEIIIYQQQgghdYp9oaJ2b6ntgRBCCCGEEEIIIYQQYuNdqPyvueyWTQghhBBCCCGEEEJIQWgClQP2BahcfKfX7nAIIYQQQgghhBBCCPFyIVQurPnILA8hIYQQQgghhBBCfid0BTAWanOFZVCbArQ06jQC0A9q991iAB9C7WJJCCGEEEIIIYQQQkjBGQy1m+QhAFoB+BzAXKidkV2eg9qp8xQARwMYDWBkjY6SEEIIIYQQQgghhBCHXaCWcbVxXm8PoBzARVqdA506x9Xs0AghhBBCMqcBgN0BNGZhYWGJUXaH8idJgv6RhYUlH6W2/eP+UOLUoc7rU5zXTYx6cwHcHtDHVvC/r+aWYywsLCzZlNr2j4SQesbuUJMYFhYWlrhldyQL+kcWFpZ8ldryj5sB+AzACO3YZQDKLHV/BPBoQD/dUfvXkIWFJZklafNHQkgBaQxA5s+fL2vXrmVhYWHJusyfP9+dgDSuZX+Wb+gfWVhYYpU64B+fA1AEYA/tWJCANRbAIwH9mBFYu4P+kYWFJUapA/6REFIPaQxA1q5dK4QQkgtr165N6gSE/pEQEota9o99AcwHsI9xPJclhCb0j4SQWCR4/kgIKSCcgBBCYpHgCQj9IyEkFrXkHxtAiVcLAbSw2N0k7hdqxw6AGmemSdzpHwkhsUjw/JEQUkA4ASGExCLBExD6R0JILGrJPz4LYA2APwPYVStba3Weg4q4agfgaACjnJIp9I+EkFgkeP5ICCkgnIAQQmKR4AkI/SMhJBa15B+DEiV31uo0AtAPwCoAGwB8BCVyZQr9IyEkFgmePxJCCggnIISQWCR4AkL/SAiJBf0jIYTYSbB/JIQUEE5ACCGxSPAEhP6REBIL+kdCCLGTYP9ICCkgnIAQQmKR4AkI/SMhJBb0j4QQYifB/pEQUkA4ASGExCLBExD6R0JILOgfCSHEToL9IyGkgHACQgiJRYInIPSPhJBY0D8SQoidBPtHQkgB4QSEEBKLBE9A6B8JIbGgfySEEDsJ9o+EkALCCQghJBYJnoDQPxJCYkH/SAghdhLsHwkhBYQTEEJILBI8AaF/JITEgv6REELsJNg/EkIKCCcghJBYJHgCQv9ICIkF/SMhhNhJsH8khBQQTkAIIbFI8ASE/pEQEgv6R0IIsZNg/0gIKSCcgBBCYpHgCQj9IyEkFvSPhBBiJ8H+kRBSQDgBIYTEIsETEPpHQkgs6B8JIcROgv0jIaSAcAJCCIlFgicg9I+EkFjQPxJCiJ0E+0dCSAHhBIQQEosET0DoHwkhsaB/JIQQOwn2j4SQAsIJCCEkFgmegNA/EkJiQf9ICCF2EuwfCSEFhBMQQkgsEjwBoX8khMSC/pEQQuwk2D8SQgoIJyCEkFgkeAJC/0gIiQX9IyGE2EmwfySEFBBOQAghsUjwBIT+kRASC/pHQgixk2D/SAgpIJyAEEJikeAJCP0jISQW9I+EEGInwf6REFJAOAEhhMQiwRMQ+kdCSCzoHwkhxE6C/SMhpIBwAkIIiUWCJyD0j4SQWNA/EkKInQT7R0JIAeEEhBASiwRPQOgfCSGxoH8khBA7CfaPhJACwgkIISQWCZ6A0D8SQmJB/0gIIXYS7B8JIQWEExBCSCwSPAGhfySExIL+kRBC7CTYPxJCCggnIISQWCR4AkL/SAiJBf0jIYTYSbB/JIQUEE5ACCGxSPAEhP6REBIL+kdCCLGTYP9ICCkgnIAQQmKR4AkI/SMhJBb0j4QQYifB/pEQUkA4ASGExCLBExD6R0JILOgfCSHEToL9IyGkgHACQgiJRYInIPSPhJBY0D8SQoidBPtHQkgB4QSEEBKLBE9A6B8JIbGgfySEEDsJ9o+E1Dv+CeAXAOucMhrAGZr9BQCzAJQAWA5gEIADjT72AvA5gI0AlgHoDWBzo05bAOMBlAGYCaBzDmPlBIQQEoscJiD1xUfSPxJCYpHgBzT6R0JILBLsHwmpd5wD4EwABzjlYQDlAA5x7NcBaAOgOYCjAHwCYB6Aho69IYBfAXwD4AioB7vlAHpq59gHwAYAjwM4CMBNACoAnJ7lWDkBIYTEIocJSH3xkfSPhJBYJPgBjf6REBKLBPtHQhLBKgBXB9gOh/ry7ue8PgNAJYA/anW6AFgLYEvn9aMAJhn9vANgcJbj4gSEEBKLPE1A6qKPpH8khMQiwQ9o9I+EkFgk2D8SUq9pCKAj1BKWgy32bQH0ATAb1Q9eDwCYaNTbB+oLfqTzehiAJ406V0E9wGUDJyCEkFjEnIDUZR9J/0gIiUWCH9DoHwkhsUiwfySkXnIYgGKoJStroJbL6Nzg2AXAFFRHFgDAiwC+Mupv49R188RMB9DVqHOmU2frkHFtBeUk3LI7OAEhhMQgxwlIXfSR9I+EkLyS4Ac0CliEkFgk2D8SUi/ZEsD+AI4B0AsqP4seXbA9gBZQeV4+AfATgEaOLezhrIPz2vZwdpZTpxGC6e7U8RROQAghuZLjBKQu+sjuoH8khOSRBD+gUcAihMQiwf6RkETwLdTOWja2hEo2fKnzupDLYxhhQAjJK3magNQFH0n/SAjJKwl+QKOARQiJRYL9IyGJ4HsAAwJsW0FtBd/Zee0mKG6q1bkO6sFrK+f1o1C7cOm8BSZxJ4TUMHmagNRFH0n/SAiJRYIf0OgfCSGxSLB/JKTe0RPAyVBbwB8GtTymCsCpAPaFWtZyNIC9AJwAtTxmJaofxtwt4r8C0Apq2/dl8G8RvxHAYwAOhMoXk+0W8QAnIISQmOQwAakvPpL+kRASiwQ/oNE/EkJikWD/SEi9438AiqB21VoGtTTmVMe2G4AvACwFUA5gPoA3AbQ0+tjbqbcRKjfMfwFsbtRpB2CCc55ZqI5OyAZOQAghschhAlJffCT9IyEkFgl+QKN/JITEIsH+kRBSQDgBIYTEIsETEPpHQkgsatE/tgHwKYBFzvnPM+wNoPIJLgZQAvVDQoss+qd/JITEIsHzR0JIAeEEhBASiwRPQOgfCSGxqEX/eAaAhwBcALuAdTeANc7xwwEMAjAb4TtZ69A/EkJikeD5IyGkgHACQgiJRYInIPSPhJBY1BH/aApYDaAir+7Ujm0PoBRAxwz7pH8khMSijvhHQkg9gxMQQkgsEjwBoX8khMSijvhHU8Da1zl2hFFvKICnMuyT/pEQEos64h8JIfUMTkAIIbFI8ASE/pEQEos64h9NAesE51gzo957AN4N6GMrqPfglt1B/0gIiUEd8Y+EkHoGH9AIIbFI8ASE/pEQEos64h8zFbDeB/BOQB/dnTaeQv9ICMmVOuIfCSH1DD6gEUJikeAJCP0jISQWdcQ/5mMJISOwCCF5pY74R0JIPYMPaISQWCR4AkL/SAiJRR3xj0FJ3P+lHWsMJnEnhNQgdcQ/EkLqGZyAEEJikeAJCP0jISQWtegft4OKsDrCOf/tzv/3cux3A1gN4FwAhwH4GMBsAI0y7J/+kRASiwTPHwkhBYQTEEJILBI8AaF/JITEohb9Y1tYclYBGODYGwB4AMASqMirbwEckEX/9I+EkFgkeP5ICCkgnIAQQmKR4AkI/SMhJBb0j4QQYifB/pEQUkA4ASGExCLBExD6R0JILOgfCSHEToL9IyGkgHACQgiJRYInIPSPhJBY0D8SQoidBPtHQkgB4QSEEBKLBE9A6B8JIbGgfySEEDsJ9o+EkALCCQghJBYJnoDk7B83btwomzZtKsDVJoTUJ+gfCSHEToL9IyGkgOQ0AVlWViZ/mTBBPl6+vEAujRBSX0jwBCQn/7hhw4b0jl+E1EcWLFggFRUVtT2MRED/SAghdhLsHwkhBSSnCUi7CRMEqZQglSqMRyOkgAxbvVraTZggk4qLa3soiSDBE5Cc/OMXX3yRFrBWrVpVoKtOSGEYPHiwAJBzzjmntoeSCOgfCSHEToL9IyGkgOQ0AXHFqzABa1V5uZTwF1xSB3E/u81Hj67toSSCBE9AcvKPH3/8cVrAWrRokbXO0KFD5aWXXsrH5Sckr5x22mmMIMwj9I+EEGInwf6REFJACiJgrSovF6RSssPw4Xlwb4TkF/ezu+WQIbU9lESQ4AlITv5x4MCBaQFg4cKF1jqufcSIEfm4BYTkDQpY+YX+kRBC7CTYPxJCCkjWE5D/zpsXKWANXrmSSwxJnYUCVn5J8AQka//Yu3fv9MM/MhCwXnvttXzdBlLHqKqqqu0h5AQFrPxC/0hIZlRViZSU1PYoSE2SYP9ICCkgWU9AdPGKAtbvl9kbN8o1U6fKlHqYR4oCVn5J8AQke/+oiVcAZMGCBaH1Xn311XzdBlKHWLx4sey2227StWvX2h5K1lDAyi/0j4RkxoUXigAi8+fX9khITZFg/0gIKSAUsEhOHPTDD4JUSnaqh8tEKWDllwRPQGILWFERWBSwkskdd9xRb0WgU089td6OvS5C/0hIZgCq9OhR2yMhNUWC/SMhpIBQwCI5kUkifxtrN22Sp+fPl0WlpYUZWAZQwMovCZ6AFDwCi0sIk8ltt92WkwhUVVUlb7/9tkyaNKlAI4uGAlZ+oX8kJDNcAev++2t7JKSmSLB/JIQUEApYJCdyFbAunTxZkErJ/mPGFGZgGeCOeysKWHkhwROQ2ALW/IC1EKCAlWhyFbAGDx5c6+IRBaz8Qv9ISGa4Ala3brU9ElJTJNg/EkIKCAUskhO5CljbDRtW658NClj5JcETkNgC1tKlS0PrUcBKJrfeemtOIlDPnj1rXTyigJVf6B8JyQxXwLr44toeCakpEuwfCSEFhAIWyQkKWMQlwROQ2ALW3LlzQ+sxB1YyOeiggyhgERGhfyQkU1wBa999a3skpKZIsH8khBSQ2ALWtA0bfHUoYCWf+iBglVVWWo9TwMovCZ6AxBawrrrqqtB6FLCSSbNmzeq0gLV27Vrp16+fLF682GejgOVl2bJlUhojZyP9IyGZ4QpYTZrU9khITZFg/0gIKSCxBaw3lyzx1aGAlXxyFbD+UEMC1n2zZwtSKRlr+WxTwKpm6oYNcuy4cfLJ8uU595HgCUhsAQsBIgAoYCWaXXfdtU4LWJdddpkAkAMPPNBno4BVzfz58wWANG/ePOc+6B8JyQxXwKLr+f2QYP9ICCkgsQWsVxYt8tX5igJW4qnrApZ7jj+PHx9oazR0aEHHUB9oPW5c7PuR4AkIBSySE3V9CeF2220XeJ727dtTwHJ4+eWXY18L+kdCMsMVr7beurZHQmqKBPtHQkgBiS1gvUwB63dJfRGwTq4DAtaK8nLZFLCcsbZpMWYMBaxgKGCRnLjvvvtyEj569er1uxOwxowZI999912NnCtbXnnllfS1WLhwYU590D8SkhmugHXLLbU9ElJTJNg/EkIKSGwB63nLpK6+CVhfr1wpA5ctq+1h1CsaDR0aeY9LKytleVmZ51iuwle2uOc48aefAm01IWDN2bhRkErJYT/+WPBz5YJ+P34rLs6pjwRPQChgicinn34qV1xxhaxbt662h1Jv6N69e0YikJlbSRewpk+fXrDxhQlYNb2E0D1X0I6dtUnfvn0jv8tR0D8SkhmugNWrV22PhNQUCfaPhJACElvA6rdgga9OfRKwqqqq0mNdFCNR6++NrYYMibzH+zvRPXNLStLHfm8CVq+iovT5qqqqCn6+bNHvxwBLQudMSPAEhAKWVI/13//+d20Ppd6QiYDVrl07ASB9+vRJH9MFrE8++aRg46uLAtbzzz9fI+fLhueff54CVjAUsEjeqKioFrB6967t0ZCaIsH+kRBSQLKagOhij1uenj/fV08XsOriQ7tOpfaeJq5fX9vDqTdkImC59uc0kbOmBazW48YF2moiifuN06alz1daB5cR6vejr0WMzoQET0AKLmD169cvp2tek7hj7dixY20Ppd6QiYBl+4z83gSsqqqq9LnOO++8gp8vWz766CMKWMFQwCJ5Y+PGagGrU6faHg2pKRLsHwkhBSSrCUilRcB6KkLAOnn8eOm/aJHM3rgx334vlEyFswrtPf3EJTIZk42A9WwNC1iznWV7QSJqTUZg3TZjRvp8GyoqMm5XVFIid8+cKSUVFbK4tFRGrFkTWDe1apV0nzNHKgI+88vLymTqhg1Wm36dHioqynh8OgmegBRcwAIgzz77rIwdOzana58rVVVVUpzhklF3nBdffHGBR5Uc7r///owFrD322CN9rCYErE2bNnk+f2PGjPHYa1LAqqioSJ9r9913z7jdvHnzBIB8+eWXsnTpUnnhhRcCl7iOGjVKTjvtNJk8ebLVvmzZMnn//felzFjuLiIycOBACljBUMAieWPNmmoBq4aCP0kdIMH+kRBSQLKagFRYBKwnLQLW15qAVVMRNzqVVVVy4k8/ydm//BIpZG2qrEyP78ff6UTs/aVL5Y0lS7Jqk42A1a+GBaw/aTvr2SKfalLAOuvnn9PnG7xyZcbtbN+fIatXh9btb9lQQURkM8c+yyIi5+M7muAJSI0IWGH1CkWHDh0EgMy3+G8Td3wXXHBBDYys7tGnTx857LDDssrRlI2Ateeee6aP1YSA9dZbb3k+d/fdd5/HXpNJ3E0xLVNs35/WrVuH1m3evLnVfsghhwgAeeCBB3y2jz/+mAJWMBSwSN5YtqxavDr44NoeDakpEuwfCSEFJKsJiC72uOWJefN89WpbwJq6YUP6nGURy7bWbdqUrjsqJMoljDFr18qwAHFBRGTCunXy3IIFOS+nLK+sLNgudmXaPTUTrlGI/DEAACAASURBVIehC1glAZFFhYjAWlBaKqMj7tP22k6HtsinmlxC2Gb8+PT5/hcgMNmwfX+Os+Tz0uveO2tWqP01S46rzbT+tx82LOPx6SR4ApJYAcs9Z68MsuW6ddu1a5fTuX766Sd57LHHZNOmTVb70KFDpWnTplKUYwRgeXl5QROAu+//liy2xtIFrPGWnVD1fnUB65FHHoklYFVUVMiUKVNC/9b069fP87nr1q2bx16TAlZZWVneBKxMvmth9hYtWvhsgwYNooAVDAUskjfef79awLr66toeDakpEuwfCSEFJKsJSLlFwOo9d66vXm0LWFOKi9PnXBvw0OTywJw56bofL1+e9bn0qLRV5eXWOlERMmGUV1ZK0xEjZN/RowuST6y4oiI9vjlZLPPU7+s2Q4dKpWVsrj2bHFjDVq+Wo8aODRQT3bbjQ5Z77jR8uOc880pKpOusWTLPSSafbwHrqilTZPthw2SN5bN26sSJ6fONibFZAlIp2d9Y6mPWvXHaNJ9tlrac8k1LlJ0uYD04Z07G49NJ8AQk8QLWY489lnHdXMfotn3qqacK0n+rVq0EgEyZMiWn9lG4Y7v++uszbqMLWABkxIgRgf3utdde6WNRAlZpaancf//9vmV/Lh07dhQgPLfac8895xOwbrzxRvnmm29EJP8C1q233hp4DUpLS3O6/02aNMmLgKXn4LLZdQFrp512ynh8OvSPhETjildu4Z5Kvw8S7B8JIQUkqwlImUXAerQOCljDV6+OFJVcrps6NTLCJYyV5eXp9lMsOWX0vGFH5ZDnZloG0WRVVVWyLkSo6z5njpw2caLVpkegFWm7BUZh3ttiSxSWazt5/HhZ6kR3RX0eXFvDCLtt90uXpiNGWD9/7jnzKWAtKi1N93ezZdv7T5cvT9vfySJKJGz8QXVPsHx+e2gC7RcrVvjsjbVota4BEVxRJHgCkngB6/HHH8+4bq5jdNt26NChoP2ff/75VntZWZkMHz5cygP+Flx99dUCQEaOHBnaf5cuXQLHMHHiRFmiCcSmgHXPPfcE9rv11ltLieN7owQs3R42Vj2vlom5s555/fOdAyvs/paUlKRtf/zjHzPu86ijjsqLgKWf/9hjj/XZ9STuzZo1y3h8OvSPhERjClhvv13bIyI1QYL9IyGkgGQ1ASm1CFiPzJ0rk4qLpVwTV2pbwPpx7dr0OaOWxXXQchQd8sMPWZ+r02+/pdt/bhEI1moC0Yk5CGQDFi9Ot39x4UJrncudMUyyCGj6zpH/N3u2z75KE+DmBghYszZulNMmTpRvV61KHzPv7XqLgKbbWzoRA5mKMVH2Z0MErGYjR2YkYGWaA2vEmjUyLiDi63Lt/p/7yy8+++Pz5uX0HchUwNIjAG07Lg5ctixtH7hsmc/eRdsl8VaLAJcJCZ6A5EXAmj59ukfcCKo3MUBkLgTuOZ988snQelERKlGsW7culsCQCVHtu3TpIgDkpptuyqm9a/vnP/9ptf/666++9qaAddJJJ4We1xWcogSsK664IqOx6ssSTV544YVQAatt27ZZ3Y9x48bJyy+/bI0Qjvr8LFy4MKf7f8wxx+RFwFqxYkXadvPNN/vser6wI444IuPx6dA/EhLMkiUimzb5Baw33qjtkZGaIMH+kRBSQLKagJRoy83CHq7zKWCt2bQp66Vzo9esSZ9zSYSApe8SZ4smi2IXLdrn7pkzffb5JSVpe1COos9XrJBH5861vs++Cxak23cPWOLl2v9hWUJjLvs0GaVdq6AIrBN++snXPlsByxSP3NflAQnWowSs50IErD1GjQr8jM7QItoyicBaoQl8tvvTaOjQ0DFn8h0wr4Gtna19cUWFdJ4yJVRAe3vJkrT9bcsSQr3va6dOtV+ECBI8AcmLgOWWZZqAaLMPHDgw62s/evRomW0RpjMd59NPPx1aL9ck2/r4otpH2QcPHizt27eXOUH+L2b/YfZVq1albUE5wPQleS7dunXz9HvCCSeEntdtawpY69ev9/idK6+8MraA9dJLL4UKWNneb328JlE5pN55553I85WWlvp2CGzdunWkgFVVVSWvvvpqaP/uboYA5IYbbgh8bwDkgAMOCL0OQdA/EmJn4kQlVh13nF/Aeuut2h4dqQkS7B8JIQUkqwnIxggBq8KZaOdLwPpyxQpBKiW3ZBkZMkRbQriwtFSWlZV5Eo3P3rhRpm/YICJeAeuZDHbkMtEjWGzv8QktAucxTSDTlwO69he0CKuZGzdKz6IiOU4Tjw4MiBBz7Z1++81n26DdswMseVP0/rsFPAjvM3p0pIB1lyHe/W/RokgB62Wnzida7rF8CFh7BwhYOw4f7oloy0TAmqzlU1tpWYIU9RmPss8vKZEmw4fLP438VZkIWLp4hZR9ieLL2n0YYCRxrzJ2Fb3c8vnJhARPQPIqYOlL0Gz2Dz/8MKvrPmXKlJyFJbdd3759Zc6cOfKTFh26cuVKGTVqlFRVVeWco8hFj06ytV+6dGnatv/++4uI+lyOHz9eNjg+2tZ+zZo10q1bN/n2228LKmBlIsDpEU0u9913n+/+6kns9aVreltdwHL70D83nTp1ii1g/e9//yuIgHXZZZf5bC1atAjtz9wR0aSyslL23Xdfadasmef6HX/88ZEClrmDoK1//Tt0tSVztN527733zuRy+KB/JMTO7bf7hSu3vPlmbY+O1AQJ9o+E1Dv+CeAXAOucMhrAGY5tRwDPAJgGYCOAeQCeBrC90cdeAD536iwD0BvA5kadtgDGAygDMBNA5xzGmlcBy83DpOf9iSNgHfbjjzm11QU0PcJIxLvkav2mTZ4k7m9YIlSieHL+/IwFDDeCqp8TVfWZs+TQ1v4Pxk56Qf3rO0MeY1lCpkcQHeQIYBVVVXLZ5Mnyrxkz5KixY339z9iwQZ6YN082OqLffhkIWEilZJn2K3nQ2G3HNwvo10YmApYuuJkCliuKmgKWLYJMxLshwCWTJgWOJ1cB677Zs622TASshobtQ8sSwT6agPq8sQTVzGlne3+ZkMMEpL74yLwKWHqEh83+/vvvZ3Xd9Yf/MMzIwQ0bNqTb6dFDcx2BfaeddhJARdTodaPOY+PHH38MbW+zv/HGGwJU5ySytXfzVpnFRi72q666SgD4RBARFbVzxRVXyDjH3+oRTS42AUtPrP7kk09ax/7oo4+Gvq/OnTtn9F70xPAm/fv3D/yMVlZWWs8blD8s6vpG3Z8333wz1K5HwOmRhieddFLk/f/Pf/7jsdlybOmfv7///e+h42cOLB8UsEgs7rwzWMDK8s8hqack2D8SUu84B8CZAA5wysMAygEcAuBQAB86dfYDcAqA6QA+0No3BPArgG8AHAH1YLccQE+tzj4ANgB4HMBBAG4CUAHg9CzHmtUEZEOEgLXY2TYkk4dvl8qqKpkdsPvdAWPGpNtuCkhg/ltxsewxapRnB0FdQNtyyBDP+edqS/qeXbBA9tSidV7JYZfABzUBzPYeexUVpW3uEkP39bZODiaz/bKysoyvob5E0WZfYEky/qa2rMzWfwNtvGaUjout7TxtCWI2AlZQvzaCxBidfQMErMbDhnkErC0cAcvNY3bmzz/7+pquLTm0RcDFFbB0W7klKi+sfQPDZkZYiXiTuD9lRBiu1sRNpFJy/q+/2i5nJDlMQOqLj8yrgPXee++JiMioUaOs9s6dO/v6q6yslJEjR0qxJb/ds88+m267NGCDgLVr18ree+/tiS755JNP0u3cHeIAyODBg31LBj/88MP0/w866KCMr4PL999/HypQTJw40Wc3czCZ9vXr1wdeYxu52MP6P/nkkz2vDz/88IwErBtvvDFtv+2223ISsFxhLeq9hEULDRgwIPD92QTL8ePH53x9XTE0yB4lYI0cOTJt06ME27RpE3n/u3btGvn50CP4LrrootD3xl0IfVDAIrH497/9wtVmm6l/Bwyo7dGRmiDB/pGQRLAKwNUBtouhIgTc6IEzAFQC+KNWpwuAtQC2dF4/CmCS0c87AAZnOa6sJiDFEQLWTEeIykbAcpOoH/rjjz5bJu1t9g+0xNWm/Vpt10Gz3BSwVHFhaWl6yaHJ3TNnptvblmD9V4uAcQUk9/U2Q4d6RIR9Ro8OvX62a/Do3Lmhdj3n0/VOjqM+RmJxt+zm7MKlHzt14sSMBay5BRKw3lm6VG6bMcOzo6O+3LK0slKGr16dFjn314RPvWw9dKhHwGpgGZOJHoEVV6CKsv9bW4ZpG78pMAXdP51/heR4W6iJm0il5CyLgJcJeZqA1EUfmVcBa4AzI2/WrJnV3qBBA19/ffv2DXz4NtvbcrTpIpeLLkrp5csvv/TkBAIgZ5xxRvr/egRKeXl5+nyrVq2SoUOHWs+vi2Vbb721z66LeVtuuaWIiLRr1y59zBYRFHaNo+5JJnYz8bhp33HHHdOvdTFL798mYOlJ4HXhUG9rO673+49//MN3bO7cudKnTx9PwnxdwKqqqpJff/1VNjp/n1977bXA97dy5crQ653t9Y26P1EClm7bbLPN0sd1kdMtpsCUiYCl7zJ49tlnh56/cePG1vcfRYIf0ChgkVh07eoXsNzSqJGqM3q0qhfwOzep5yTYPxJSr2kIoCPUw9fBAXWugYoecHkAwESjzj5QX/AjndfDADxp1LkK6gEuG7KagKzXdtSzlZ/XrxeRYJHC9oCj2/UIFJtYFpUo3OWtgAijsLHpdSauXy+H/PCDDHKiulybbUfDG7UcWDYBS1+ieI0jIOkC1h2awIBUyiPSmKWFJYeV/l6Ps+xyOFzLB3bZ5Mki4s3Lle01sl13t7gClhm1lUnf7vI3/di4deuklbaMVBcm/zFlilRUVcnt2vX714wZIiLSMkDA2nzIEBls5GcL+gy5/DMix1nQ9Ylrj7r+QXVMrtEE2x5GEmw9ugwpJVbmQswJSF32kXkVsNwlZGF1wvr7VRMwbSKLLQm1LoCVON/PVCplPfcXX3whDz30kOeYmWtIRGT58uXyhz/8QS699FIREdlvv/0EgLz++uu+8+vLHJs3b+6zf/fddz77Kaeckj6m5yhyz5/N9YvaBa+srMxnN6PQTLsuYAWdP0rAuvnmm61t9V0G9XLHHXeIiFfAWr9+vScS6ZprrvH19+WXX6ZfH3300SJSvUTTVhYsWBA6JpMZM2Zk/PkF4EvG/tlnn3ns5hwh6Pr+5S9/sY6/UptD3HPPPZGfDz0a7dRTT40cfy4k+AGNAhaJxRVXKLEqqIhU/797dxWxxaWFySLB/pGQeslhAIqhlqysgVouY2NnAHOhltC4vAjgK6PeNlBfcDdPzHQAXY06Zzp1tg4Z11ZQTsItuyOLCci6CAFr9Jo1IhL88O0KVN+tWmWN1vpp3br0uW43hB2kUvKptkzQxfbw/oolgXgm4oxbx8z55P5/5Jo18vKiRXL02LGy0FkueaWz/AyplFxoWYJ1z6xZPgHJfb310KFy2eTJnvOH7fR4kSVH0berVqXttzkCjs52Wi6tC5zx6WPO9hrZrrtbXAFrYEgEXFTf+us9jWTsnYxxP2PkH3P7OOiHHwLPka2A1Vi7fuYufTahzuR+LceVzZ7N9TXbm0sIbRGEf5s0KW03d8Ecv25d5PgzIccJSF30kbH8o4iEiiuPPfZYZB0Rkfnz58sUZ0dR3XbVVVelzzN8+PCMBJxnnnkmbevfv7+IiIwbN87aVo/WChvff//7X89r9/9nnnmmTJ48Wc4880z50Ymo1ROc23IQ6RFaTZs2FRGvgPXLL7/4zh80Nlv/5eXlabttWd3TTz/t69+WYF2350PACmobJGC5dl3Aevzxx0PvlRlN5/ZhRj3p5bfffvPVv/vuuwM/X0OGDAn9/Jn9/2r8jTTblzp/V11OPPFEa/+nnXaadfz6Tp9mBNaOO+4Yev9PPvlkj80mEq9atcrXRxQJfkCjgEViAUOw+te/vK9tdQCRHL6GpI6SYP9ISL1kSwD7AzgGQC+o6AEzuqAxgB8AfAlgC+142MNZB+e17eHsLKdOo5BxdYdl0pfpBGRthID1rfNXJci+oaJCftIems26oxwBTETkmHHjfO1tAo3t4fuFhQtzElDcOjsOH55+rUeCDdOimRo6dc/79df0sbN/+SV0fO4SMPf11kOHSkdNwJqwbl2oSGhb4qUvibvB2cmu/6JFcsmkSb4k3e74Xgq4PplcI9v7cosrYP03IMJr5saNkX3rr4MS2Uf1cXAWApa5MUHY/RtjfE++0K59UHtdwGxpiaDL5vqa/Zs2U6ASqV6ii1RK7jC+P7ZcYbmQ4wSkLvrI7ojhH0UkVFC4//77Q+u4S6Tc1/oOfQDkuOOOS58naBmgybnnnpu2uRFgY8aMsbbNVMDq3bt3+vX06dPT/z/99NOlefPmnrq62NWkSRPf+Hbbbbe0fbvtthMR7xJCPUfWf/7zn9Drt/322/v6Ly4uTtvdCK/hw4dLhw4dZNq0aZ5IKHcJo97G9v532WWXULuIyLXXXuuzuQKWuSxSbxu0u55r13Ng9ezZM/RemRFZbh9vv/12YBszP1tFRYXceeedgZ8vPcLLJhCa/c8wfNCnn37qsa/R5gAiImeffbb1+nbo0ME6fl1g6tGjh8dmClQi4ok4bN26tcf22GOP+fr/+uuvfX1EkeAHNApYJBYwhKlOnUTGjat+XVHhrwNN3LKxcqVqZ+O330TmzSvIWyE5kmD/SEgi+BbAC9rrPwAY5Rw3H6YKuTwmVoTBmggBy1xyZ5a1mzZ5xCWz7rDVq9Pnam0RsM6zRDjpdnf5wdOWyBykUr6k1UECwQ6agKWXlBbt5NY9ZcKE9GvbEiy9fgdHgHJfbzN0qJylCQwzNmyQlSFj/MuECb7+P9ES1l9rLFF81tnt0Bzfq4sXh77/qOsTVKfIEbCCcmw1CbiuSCkxL+rcmRQRkUO1ZYdmCdohM0jAaaEtR7xLy1ElYo8SNNGXmJpLQMMioGxja2bkuDLt/7IIvMf/9FPafqMjcIadIxfyNAGpCz6yoBFYd911V2idP/3pTx77iBEjPPbDDjssfZ6BAweGCii28TzzzDMiEhy91aVLl9Dxu/3rApZe2rdv76urRyLZcmDp9Rs0aCBVVVWe/Ea62BIVwbbVVlv5+l+9enXavs8++3jat2zZ0iM0ue21z7P1/e+6666B9oqKCqmoqLDaXAEraImiLgYGnVsXsLbffvvI+2Xr49133w20f/HFF57XBxxwgOf1JmMZv76L36GHHhr5fdB3EhTxi2lLtJ2A58+f72u/wclFedZZZ1nHv8LZ2VdE5LjjjvPYTIFKROSuu+5K2w8//PDQsQOQTz/91NdHFAl+QMtawHrlFZHWrUVCNhGW8vJgAYIki7//XQSaKPX3v4ts2lT9+oEHvHZECFjTpyvbX/7itz37bHjbuJSWinzzDXN1ZUuC/SMhieB7AAOc/zeG2jZ+CFTUgImboLipduw6qAevrZzXj0LtwqXzFgqcxD1KAHrLmXwG2VeUl3uif8y6X2iTz6YjRvjad3SW4OnodneJYm8jsblbRq5ZEymAzAqJEvrWImAdPXZs+nWb8eN949MFENce1P/MjRtlsZFYWy8nWnJc6XmhOrvLjpzXeoJ5pFLyZ+f8rwUIWKc7AleUwPNRwBJBV8B6MkBA3MtYEmiW/wUs/XTL5SFLH/XxtQoRsF4PEe90AadXUZEcqd1b0y4icrqR4B4pf543fbnmnqNGBX52zf5tNl0Asy1fNAUqEZFDtGi0q53PRybnz4Y8TUDqoo/Maw4sN0dVkL1t27Yeuylg7bfffunz6Mmn9RI2nm7duomIf2dAtzRt2jRSACkuLpZzzjnHatMjp9yx3HLLLenXm222me/70a1bN0+bkpKSwB3mevbsGXmNK43davUoNlPAAuBZsudGwK1atcra9zbbbCMi3qgxs5SUlMgjjzxitbkCVmlpqdXep0+f0Pd26623egSsXIqIyPvvvx9oD4vOAiDrnGX+gwcPloMOOkiuv/76wM+fbQneBONHmBdffNFjn6ttNNGqVStfe3eJoL65gF50Acy0tWrVyvf90MffsmXLwO+OW9ydRLMhwQ9oOfhHVa6/3m5fvLi6jiVlaiS5tCG1R5cu1fcbEOndWx1HgGiFCAEryD53rtdmpAPNC/fco/rWVoqTDEiwfySk3tETwMkAmkPleekFoArAqVBRBWMA/AK1RfyuWmnotHe3iP8KQCuobd+Xwb9F/EYAjwE4EMANyH6LeCDLCciqCAHrpYULAxN4I5WSd5cuDRUpPtDyV9jsDxcV+cak29c5vw4/XFRkbZ+JgBVWTOFGxJsv69hx43zjO1GLgPmTYw/qf/bGjTKvpCQjgcXlbS2Ju5ljyyxukvcgAatjRHv3/EE2dwlhUAScbclaNsUW8WQbn014ckvQ2Mzrm8n172/5LH+pibAi3nxcTUeMCPzsZnL+fZ1dKoPs1xg5ukTEk0fM3GQgk89XJuQwAakvPjKvAlanTp0i64TZ9V3SzEgZQOWgMtlqq618/X/99dfW/sNyO7nl4IMPDrTpywfdc3Xq1MlzrLy83DM+Pb8S4N8FTy8PPPBA5PVb72wk4qJH8ey1116+9jfddJPn9aZNm2TZsmXWvnfffXcREdlzzz0Dz79u3TrZf//9rTZXwNq4caPVrucLCyqdO3eOrBP1+QoTsKKWkQ4aNCj0HujYItHMPFRmnqppmghv63+Ws0z6yiuvtNoXOrvT2j7jBx54oO/7cemll3o+vzq2/m0bFUSR4Ae0nAUsQOSrr7y2117z2s1u16xR0TlBXHWVanfEEVncnDzijru4uHbOXx+5/nrvPf/vf9VxBIhWZunSpbqv997z2h56qHq54Hff+duauNFeAZuRRxLWdz5YvlzkrbfCvwOZUlTk/z4VF4ssXRq/72xJsH8kpN7xPwBFULtqLYNaAnOqY2uL4Mlhc62PvQF8AfUAthzAf1G9hbxLOwATnPPMAtA5h7FmNQEJW96GVEqemj9fJq5fH2g/YMwY60O/W15bvFhEREoqKuRUS3RLVy3Hzw9r1/p28FvkJIDdduhQa/9DtBxWuZTDjMgeEa8I0MpJXCwisqK8XEYZgtkhP/zga6OXORs3ZpQnSkfPseQmeQ9qe+TYsSIi8mhAhNoFRo6uoPMH2Xo6AqMtuborwMS5/rdOnx5ZR8QbFWeWB7VdIcOubybX/xbLeHoZIqtuazxsWPr4NGMHQLN/M4E9UinZW4vgsrW9wrILpm43NwHI5POVCTlMQOqLj8yrgHXRRRdF1gnKjwRArrvuOhFRkS22nQT1HD8LFiyQG2+8UQ455JC0/fjjjxcRv2jglm222SZ0bNkW23t1Babi4mIZOnSoz25bNuaWq6++OvL6mUu8JkyYkLY1a9Yssv2GDRusyc8ByC677CIiInvvvXdg+++++y5QwDrvvPNERGT9+vVWuxmNZCv5ELDClhD26tUrtP3DDz8ceg0rtLVfQfcy7Pvyi5ZH0tbW3RwgKNn9POeJ1WZzI/B09GTwu+66a+R3+eOPP7Z/8UNI8ANadrtYrxdBgIhQVeW3ASLbby9y4YVqyaF7rE8fe/+2fvNJaamI8xud1Vbo8yeNd9/132/36/X++/bPg62IiHz/vd3matY2my7eVFZ6bRUVIiNHKjHr9NNFvvxSZPDg8Psb9/43aKDarlxpt7dqpez/+pfdPmuWyMUXiwwfbrfPny/y2Wcqys28PiIibdqoY+3a2SMZR41S9ssvz+59RZFg/0gIKSBZTUBWRAhYPYuKZERIlNOAxYsDE6wjlZLnFy70JdXWi77Lms3+yqJFgTakUvKVkcA727KdkVR8k5Ek/UBHoPqnlvdIL24ETVD/C0tLZUpxcUYCi4j4diw815n8B7WNEtDOjmjvnj/K3tfIvYWUij6LK2DdnKGA9SdL/jS33Gksqwy6vlH2oEjDW53P6HMLFsiZWn4zpFKy5ZAhIiKyKGSZqLsMNmi5Zdj4/mYIVOZSTnOTgajPV6YkeAKSVwHLjZAKsjdq1Miza55ZLndmjhdddJHVfuSRR6bHYUsGfu2114aev2HDhqHjz7bYInBWrFjh23lOLzNnzgy03X777ZHXWF+iZi5hcwWosParVq2Sv/3tb1Zb48aNRURk3333DWz/7bffBgpYcJ5sgnJsvfTSS5HX1Ixoy7aIhCdxv+eee0LbRyXS/+ijj9LXP+g+i4gMGzbMt+QUqBaoLrjgAmtbV0C67LLLAj8/QePbbbfdPN9VPT8aANlhhx0iv8tjnR+BsoH+UTFjhggMEeGKK1Tkx2+/+W1h5aij1AO3u6p+/nyv3caIEcrWrp3dnkqJWFbhi4jIhAnVfQ8cKHLllSLaggEZP957flMAmDKl2haU3yvu8seePUXOOy+4n9deEzn4YCV02Lj0UjU+SyYMEVH3ybIReM7Y7qs79hUrMv8sPPJIuH3QIPvxJk2UcCWi7rtuu+++6PO6eypNmiRy5plemy2SaeDA4M9nUZG3vXmPTIG3eXP/fT76aGXbf39//0ECsTuWNWu8x5580tt+0yaRXXapttuE3M8+E7n/fpXDLhsS7B8JIQUkqwnI8rKy0If/e7VoIFvpGmF/Yt48X9SSXtwcT78GRHndPmOGLAgRB76JKWCZ0Tvmjnb7jB7tE5X04ibhDrKvKC+Xn0Mi2LbTInhs/ZhJ4s2yvzPbC3uPUfbKkCWibvt+FgHrrJ9/9iy3zKXcECAMmue3bQDglmumTo1sH3YNouzmTpNmqaqqkttClkKudv767xEgYFU6s5ao8dnq6JsM6ALcOCeZ/GZG+0xJ8AQkrwLWKaecIgMGDAi0t2zZMjQK54ILLgg9R4sWLUREQkWwoATjAOToo48OHX+2ZcOGDb5jCxcuDG0zadIkueGGG6y2rl27Rl5jPYLnpFUljgAAIABJREFU2GOP9djcXRDD2i9ZsiTUXlVV5ROomjVrlv7/sGHDQgWsqqqqwBxbNSVgvfnmm4H2qET+Tz31VOg1fOmll0REQiO5wtoPHz489B589tlnIiLSsWNHq71Hjx6h/Yd9V81NBk466SQBIP3790+LlqOMPIaZUA/8441QEbGlULu+ts6wXVb+UY+i0su22/qTeWdTREQuuMB7bOlSkTfeEDnsMCWcLVzob6Pz44/VNieQ3yMQtG9vP/fkyXZxYN48NYYFC0TOP99r+7//U4KYzqpV1XZnP5usmDixuv2rr/rtevSbJRDRlyPKZNMmdZ8AkaZN7WP4/nsVAWRsJGpl1qzge+lisx9ySO6fk6AyebJIhw757fOgg7xC5dCh/jo33aSEs7IyvwBm7kdiE38BkY4dlX3RIu9xZzFLmjFjgsdaUaGSz5vHhw+v/g7oAi6gotN09AjEY4+Nvv869cA/EkLqIFlNQJZFCFhxI2wenDNH3lu6NNB+YQZL3MLyH31vJGHPtrwUEj2GVEp2Gzky1L79sGGhAtC6TZsCE6AjlZJG2szGFsXTzpkVBbXfy5l8h42x1IgqQyrl2X1yQ4hA5wootuNn5kHA6pKhgLX5kCGB9osmTQptX1VVFXqPlpeVhe5keP3UqaG7dZZUVPgErGumTk2PeYEze27mfJbumDFD3tG+E+WVlfJyyDJcHdN2svbTqn6f9WWrlTn8DJzgCUheBSxbVFQ2pUOHDtbE2G5xl0C99957gXXCRJL7778/1vjOPffcyDp///vfQ+3jxo2Txo0bW2333nuvL2rG1l4kWMSLukfz5s0LjURbvHixtU93qeZXX30l++23X2D7yspKWbFiRdb3xi1xBaylS5fKyy+/HGgPEobcknJ9fIC9TZs2oSJp1D345ptvfNd4zz33lMMPP1wAyLvvvisiko6S22GHHTxLFTt37uxZNmqWsrKywO+qucmA+zn87LPP0rsxDjN+REqAf7wEaon1VQAOBvAigNXwbpARRNb+cY89RJBnAeK228Ltf/6zSqytHzM3tNZtb72ldpJzX4uInHFGcP+25W533BE9bjeCR0Tk7LO9NhvDh6s8X5a9jKSqSmSnnVTbVq3Uez7llGoR5cknvf27H/MFC0ROPVXkxBO99gULvNFWv/7qH//cudX96DsHNmigBI9zzlHCnI2ga6Kz995+e5CQ6JZ99w23H3NM/j9/+SiPP24/7u5oGLakUsS/HLN/f5FHH1X/X71a5IADws9/8MHq32OP9dtWrRJ5+WV7u3POUec3BS5nr4+MqOP+kRBSR8lqArI0QsAyl9hlW8IiU5BSu+QtCRlD2NIxpFLSKYNd7MLKc5bIIr3sMHx4qH3LEGHFFTfC7JulUqHixQlOkvYgu5tEfGsjR9h1WlSSKTJd/ttvUqEJOqkIEXB2QA6vM37+WfbXdmS0FVveJ71kuoQwzG7LraaXsF0gkUpZc5QdrCVqj0o0v7q8XG40hDgRke2d7840Z5t41/bz+vXysSaY2a6/3p/LQ9pGBj2cyMHW2iYDepSc/p0qM3Zx+51PQPIqYNl2VcumBO3O55btttsuVOACELj8MB/l4osvDrT94Q9/yKiPSy65JND20ksvyQMPPBDaPmonv02bNoXap02bJvfdd5/n2HfffZf+/6OPPuprI1K9ZPPDDz8M7b+ioiIwyikTASsoeXmmxSbA6SVodz+3fP311zJ48ODQOj169Ai1hyWK//TTT2XRokW+6+uOq3///iIi0qRJEwEgDz74oOd7Z1ta2Lt37/T/Nzj+dc2aNeljrjgGVG8yoOeie+utt9IC5ffff580//gDgL7a680ALARwTwZts/aPu+4qghgP+h98EK99XSwTJoj87W/B9hdeUMuzzOOtWimh6cEHVcTN0qX29q1bi2y+uf/4+ednJuiUlKioriB7ebnIp58G212CRBBbXRGVe8q060KZrUybppYU3nuv3X7KKeHto4Seo44KtwcJUXHLTTepf6+9tnCfQ0Dll7MddyPfDj3Ub/v2W5G33/Yec1aaJ8E/EkLqKFlNQMLEI6RS0iBCXIgqf5kwIdR+grajn63cE7FEMar8ccSIUHv3gATgY9aujXVepFQC9TJL9FM25Zhx4+TVgB0GkUrJH4YNs+Zu+sEZ/04WAc5NSu6+fjckQg6plEwNSE7eIQMBy0ySn21pNnKkLy+ZWaJEzqDcU24ZGrERwBcrVviO3T97dvq7YX6HOjmJ192Iq4nr18tmmv1/ixbJ51qfL1qiAL9zRC03x5kpsn3mtD9c22RAt68zIuyyJcETkLwKWGFLyzIpxxxzTKi9QYMG8vjjjwfa871E0CxBeYt69+7tWWZnK4ceemhk/1HiUyZl8uTJofaJEyf6jkWJgiIiZ555pgAIjW6Keg+ZCFhREWxRZfbs2aH2qCjBq6++OtT+zDPPWI/vsMMOAigB1fYe3OV6H3zwgU+EFBG58MILBYD069dPHnzwQZ/d/b9NoC0rK0v/f/Xq1b772a1bt/T/3U0GdBGuT58+afH5K3PrvPrtH7eE2pn1POP4qwAGWepvBfUe3LI7kJ1/3HlnEYQ8QC9Y4M0VZJbSUpGLLgq2u3mugkr37uH2ulq22y66zksvibRoEe88O+5oP37kkepf2/075hiRq68O7tPNB9W6dfi5nU1603z1lTp+6aXe5ZxB7U86ydtez2s1cKCKZpo8WS2hDOpjxQqRE04Itg8cGP4ZclY4B9r79FFJ2sOuw623Bttee00thw0Sgt94I/oem0ta9TJqlIqesglVgMjnn4t06+Y/bu4mef75ifCPhJA6TFYPaEHRKadFRLVkWo4LEKiemDcvI4Hj3F9+iXX+XCPIwvJuZVpeXrRIxju5iMxydobv69CI69MwlbImWHfPa1sC+djcueoPcobvIygJ/ekTJ0YKWLmW9x1Rrcnw4dJt9uxYfe0YEUVnK9sOHZre+fDDZct8dhGRRk7U22Tj+nzoZIJ1l9+aUWbvLF0q32pRV7b8YsMdUa1FQI6zYY79AMe+ytiMQY/8W5PDHs0JnoDkRcD605/+JAAiRZyocvDBB1uP33TTTbH6zVf5y1/+Yj0uIrLPPvuEtnWvUVgJur5h18YsUQLMoEGDrOfddtttQ8flRuhE7eQYlp8sSvwCwqPcgkrfvn1lp512EiBawGvevHmsz0DLli2tx2+99VYBYN0BU0Skffv2AkDeeOMNj+3QQw8VEZHLL79cAG80le1zYVvGqgtWy5YtkxtvvNFj16Oxli9fLlVVVZ6dJteuXZsWfz///PMk+cfdoMZ1vHH8MajILJPusNzbbPxjkyYiCHm4rvaj/uLmbqqoUDmmXnnF3t7M52TaX3op2H7eeeHj03Nl2cr8+dW7ydlKaakSALp0EfnkE7/dFALMcvnl4fa1a8PtUbv7ffFFuP2VV9SOfGF1ci0vvOD/vDgBkx6C2pvZD6qqRBo3VtFDtswI+rU+7zyV7D3qHKtX+xP2AyLHHSfStm31eaZMUZ91Uwxz7RUVIsOG+ftxd2EcOdJ+/jlzqsf4zjt+e2WlP9+bXsaOrf7+lJT47WZy9gEDvHZ3SaOIyndmtt9jD3X822/91zuIOuwfCSF1mKwe0IJ2T7vw118zetA/IELA2CcgR1J/Z9lcc80eNyG7XqKibsJKVVVV5O6MmZS3lyzx5OhaWlYmk4uLZcSaNRlHZgVdP1OwMMskR1TZ2RKB9l93W/IM30fULoqFKO7ukltFLNHMpITlzwoqfxwxIp04/nzLd0FEpIkjjF1uLGO90onAOkRbhqiXsspKKdfufx9HzNWLGwHY3LLLZfuJE9MRdntbcqB1nzPHs0TULUu0XDG/4wlIXgSsU045RYDMl9EFlSBxoUePHtKgQYOc+z3vvPMi62TSv02EcnelixKYTj755FD7Tjvt5BF/HnzwQVmyZIkMGjRIKioqMhLAgsoLL7yQFqAOO+wwn11E0hFEtiIicuCBB2Z0Lj0ayCxnn312ZHtdWMm09OvXT3bffXcB4ufQOuKII3Jqd8cddwigdpK0Xb+zzjorfV9t9muvvVYA+HaIfOuttzzfu379+lnbb7755gJAFixYEGk3d2IUqd4QwBUCf3CiXeu5fwwSsHoDGGOpHzsCy00ErhdXENE3eNTtf/2r98FZZ/Xq6np6TimzD0BkyRKvfdYs7w52TgYGa1tApFEjr80WCSZiT+r+4IP2fFB68mu3vYjId98pUcTsp7hYRcHYxgeoZO1z5qixmULdfvsFXzu3uAnsJ01SObBMu/uxDzp/v37BNkCkWbNg29df2++xSXm5yNSpIs8/r9rtvHNm7bLBtqnAjTcq208/VR8LS1jvZmPYZhtVd8AAfx3b58fF3c0vyC6i7odr04Pnn37afo1N9Pdis4uIHHGEsv3tb97j69b5+2/Txt5HGHXYPxJC6jBZPaAtDBCwrpoyJaMH/agIoW2dKBUz0sqNatGXKEbli8qmvGZZdmcTtXQBrb22o1tUYnOkUukIHLf8efx4z+v3ly6Vgc77PF6fSYlYl/1N3bDBtytkkIATJYBNd5b9bWFp/0Q9ELBeCckLVhNllxEj5PiQ5a0iIrsGJPg/zrnX7tJGPUptH0eQEhFp6Bx7bO5cXx96BJ2ZgP61xYtlorOz5a4jR/o+S+75zeW/3wVlX/19TUDyImD99a9/FUAlibbZ3RIVpeTm/QG8QsZDDz3kE8ei8hS5YwKQjm4JKhdffLE0atTId8ysZ0bf6EQtXzSjt0wh46ijjpKlS5emX1cYS11PPPFET/1LLrkk8H6Y5ZtvvgkVwEREmjZtGmq3CS+2UlpamlG9fJYmTZrEXr4at9x5550CQPbYYw/r9XOXCF533XVW+y233CIA0snUzc+Be/9tGxiIiGy99dYCQObMmeOxXX311SJSHWE3c+ZMa3t3iaNbDj744CT4x2yXEJpk7R/POksEEQ/V6ntbXVxRJVvGjavuQ9uc1MesWX77xIkiN9+sdoh78021/M2NTpk3T2Tw4OpoGvccjRtXt3/vPZGuXUUaNlTtw5g1S0VkBf1mZF4rfUdBPfdUw4b+tqWlaindwoXB53eXB9ruhSnGucm533vPfx8BJYotWlSdUN5WbFFg115rj5KqLS67rHpsHTp4bfqOfpn+zheUWnTLLav7cgVSk6jvio3Fi1X9nXdWecPOOy/4MzB6tKrr/Mn0sXq1SK9e9mi4jh2947v88szH6FKH/SMhpA6T3TbIAQLWrRkk10YqOkm3W0746SdPXqnvLImrRaoTUf/N2FnuwB9+kLtnzhSkUtI5A3FtuJHXaIshQ6SlJVqssbbEsER7gDJFg5ZjxngEq+VlZb4E78UVFXK9ljx96oYNaSHmDPOnRBGPuNTM2cM2bLe7yqoquXbqVHl2wQKpqqpKCyC2Micg8TpSKueViGS8jPEXRyypyTIjIO9WnDJ45crITQvcstPw4XKSIUi65SznXu7tfPbbG8tt/2/2bBERaWO0f2DOHNmkzXrcz1NPLTm7W351rvkuI0bIz8b1H7J6tfzmiIo7WJZYuon/TfEym2TuCZ6A5EXAihKI3GJ7uA8qejTSww8/LLvttpvHPn78ePnhhx8EqI4A04ubmLxFixaBuavcMmDAAN+xxx57zHds5513Tv9/qLEXvC4wbb755mlBA4A88cQT6Qght4iIZ1fEHj16yNSpUwWANNafFB3atWvnab/Y2Uc86D1NmjRJunfvLp06dZLKykr585//HFhXRELvjYjIc889l9F9qw0By/xs5KM0adLEmhss6Fx333136PVzk6/b8mOJiC8qqn379rJQeyJzBdC33nrL2t7dUXD69OkeWxsnZGDHHXcUQCWRt7Vv27at59jrr7+esU+o4/7xBwDPaK83A7AABUrivmyZyH33iSBDAat584y7tjJmjBKbCklJiTqHufwqnyxa5BVBFi3yChLLl8cTgML+3Lu72en3qqpKLWGbMcN+L92x2JLPm3magqLrahNdaJ0+3Wv7+efwz262fP+9yHPPBduffVada8GC7PrNYT+enNDvZRaB+2nquH8khNRRspqAzC8psT6kZ5p36A8Z5pg6YuxYGasJWOMsuaF0XjciqPovWiQikhaZ/i9ifF8aibcP//HH0HxNXaZN810bfWe/k8ePl0+1neOqqqo8+aXaTpiQbjeluFi+d6Jd3OVhHS37JOvi2Unjx4tIcOTXcUYEl4gEXvsBixcHRtYhlZL1Tk6kTKPsHrFECMUtZrSaWXKNxrsiZFfKjRUVsjrDpaHbDxtmHeNT8+fLSicO3BVEzSjEn52kwR1+/tlz/IsVKzz3z83P1sOykcA0R8Dbftgw3y6LmyorZZYjUNqWR/7inH8bI0IwGxI8AcmLgHXNNddkJAqELVPTS0Pnp3b3dc+ePX2RKatXr06Pafny5R7b3nvvLSIikyZNkuLiYhk2bFjo+cy8RIASnYLqt2jRwndd3BxHgFpKqY//3Xff9eWYclm4cKG8/vrrUlZWJqNHj/aMX0ffPW+LLbaQSmf2HjTGMmOmHbT7Xgfn5/eg6Lhrr71WREQ++OCDjO5dSUlJRvWyKXpknq3ccMMNeT+nK+Bk+hm+9957fcd69Ogh45ydUf/xj3+kr7de57DDDhMR8e1s+Oijj3ru3+mnny4A5LXXXvOdR0TS4qor6rrFzWm16667Bl478/Pr9pMpddw/XgKgDEAnAAcBeAHAagB/zKBt1v7RBc4D71572e1uomw3KTapXcLEscWLRR54QIlqQbj3u29flSjcfW0u66wr6LvxmZFLVVUi99yjhCWi8l0BSojLhTruHwkhdZSsJiBzAwSs/1ry8sQtumhli7DR+fvkyR7boOXLPfZn5s8PPZe5FK/1uHGB+aSQSskpmgDloueP2mLIEPlYE7BExCOI/WfWLOv1dXc5tAlkevSamzepPGBpoC2Cy9xhcWV5uVQ5s5JlxvvXl3C6ImCmUXa25Zhxy11ONN2WAUskRbwCYqYlaFfNbZ3okeIshLF2xg6aLxuzuVYBy2fdpOlm7qx1RjJ1N4fWfRYxdrYjUJki1HtO9JwtcnK7YcM8UYTbGwJnNiR4ApIXAeuggw7KSBSIWmKoF/1cvXr18i3Rq9R+ft24caPHdtttt3nGPH78+NBzvf32275jQbvN6ePTMfM76eN/77335Prrr0+/bhOQSOPLL78UANKqVSufrWPHjun2usBlG9u2227ra+8uYXPLf/7zH9m4cWPaR+oCoS62PfzwwyIiMmTIkIzum3kv8lHM5W1m6dq1qzW3VzafMbOMcTeMyLCv++67z/P6wgsv9Fz/IJHNFarMiL9PPvnE097NodW/f3/r+3A3UDCTvLvstddevnZff/11eomiKXD+ErYmzaAe+MebAMyFErJ+AHBshu1yFrC2314EEQJVWH4hUr+AIQa9/np1Pq26SHl59Zjr0tLGJFIP/CMhpA6S1QSkKEDAen7hwlgCxbFO/h+96Dvy2R7AdQYaO7+NNGY+L0fkSCo1hKCTx48PTezu5oXSMevru9GJeAWMxy3tRURumzFDkErJ3TNn+mx65M6/HbstNxZSKblk0iRfe12Q227YMI/NXIp46eTJcvP06Z5xZBpl96MWOZev0nXWrFC7iMgeOSTityUvR6o6GXqQQIiUPxLKXBroikcurY3PeKOhQ2WmFjtvirAmrkB6j3EtNh8yJP39MJcBupgCJVJqkwCdnbQlro2M5V9RJHgCkhcBq1WrVnkXLfRzPfroo3LooYdaH8xFxLfU68EHH/TYp0yZEnqu999/X4488kjPsWeffTawftOmTX3XxcyZpY9/4MCBctddd6Vf//Wvf7VeW3d5WNu2bX22Ll26pNu3bt069H40a9bM195c5vmTEcWqX9/dd99dHn74YTnqqKPSn41JkyZldN82bNiQ989CmzZt0snFbWXatGk+gS7bz5hZZjp/G4LspmCpLwcF1M6ZOrbk7g8++GA6Uq5v374e20hnGb2Lm9PtxRdftL4PW/L7Ndo8oUWLFh7bkUce6en/nHPO8dhXGBGyYdA/+lm2TGToUIoDvxeWLFG5uOoTVVX8fNYECfaPhJACktUEJChX0htLluQsThz3008yYs0az7Gbp0+XCZqAtdgQsPob0S1mhNY0I9vgmxHjM4Wg9hMnepb8mWXgsmW+a3OgtovcjsOHyweGgKUvMTPH7+Lm6+pVVOSznaglCe89d276uG18106d6muv73K3p7MbnYu5FPF6S/tMo+wKIWDdHyGemdc/k/KRcw8/MMRPpFJyjLOsJUggRColLxii7UHG+b80HnDMHFfuuF30fGhbDRniu/5uEvgjxo719LHFkCGBubpc1hoC5b5acnizf6RScnCWP40meAKSFwGrdevWWUVXmWXo0KGh4oIZnXLooYeGjutZY+3D3LlzQ8//0UcfyX777Zd+veWWW/oEBb3cfPPNvvN37tw5cPyffPKJJwl6p06drNf2+eefF8AucOk5lk477bTQ+9GyZUtfezN5+GwnN53LLrvskrYddNBBvvZ6gvmwUlxcnPPnIKi0adMmcAkcAFm6dKlceeWVWfcrItKzZ0+rzRV/gtqaS1ofeOABz+t77rnHc/26du1qPb+LGVm1zs0m7eAKdEG7ENqS2Ou4u1C6ZZ7xI9P555/vsZubCIRB/0gIIXYS7B8JIQUkqwnI7AAB6yOLCJBpGbN2rQw1kqj3XbAgvXMaUilfBMkbxsJ5c+e7Fe7+sw5mhJbtQd88Zi6508sQLb+My9GasPDCwoWysaJCWo4ZI52c5X4XaEvEbAKYSPUysucs2RrP0HIk6cvTbOO70xLB9SctAsgUMDYZkUa2JZIvZhhlN6YAAlZ3S94nfTmhiMhR2vU3l9KFiTu2a3iqtsOke6yp8XmYFxCN6JZnjXto5qYyx+Auk0QqJUfp+4k7BEWYbTlkiCdXl5us/23tO2JGGB6RQf/ZkOAJSF4ErHbt2qV3Qcu2nHvuub5+zR32TAHr0ksvDR3Xu+++67EtW7YsdAyDBg3yHXvyyScD63fr1s13/ptvvjltd/NKnXDCCbLtttvKunXr5KmnnkrbzSWOLr179xZAJcU36dWrV7r9xRdf/P/t3XmcHEX9//EXScglBIkccgqIKKCACooCIYBBgqio/ABPIoKCiqAih/KVAHITgpwCIgIGgyCKcoQjBggC4YYokEQgEELug9ybPer3R03P1vTUXNvTO9M17+fj0Y/sTM/M1md69pPqz1RXlT0ee+yxR9HzTzrppILHLInl+PhrxLW3t1d1PJcvX96jz0G5bc899zRbbbVVyf3vvPOOOf7442t6zUlODojv69u3b/7SylLP/+AHP1gwb9hOO+1UsH+33XYreP9Gjx5d9j2+/fbb8/evs846+d8fOeKIIwyUngTefa7v9T/xiU+UPf6HH3542eNfjvKjiIhfwPlRRFJUUwfkfyUKWPfEJkGvtLnFmCnvvls019Dezz1XUMBaGJtM+/9i347HJ3nviHVu71+0qGIxI37fRmUKWNHE2y53hNX43OVjbif7WGeEja8A5o44u9Uzs+WRziVm7uVpvvad/cYbZdvnK1C4+3wFlC84x8wdDRbfnoiNpqvHFr+8jkn2ss+tH3/cXDFrljGmcIRaNatdui6OTTx/mHMJZnTfd2ITvrv7fBP+vxZbWie+iuNNuVXSIvFLNOO2KTEnW/+HHy74+4lWu3zMuTwmvkrmPrlFAFzxOd9qEXAHpC4FrB/84Admgw02qLp44K54Fo02cvdvvPHGBfddcsklFQss7r6HHnqoYF+leZnuueeeovvGjBlT8vGXXnpp0e93J/E+/vjjjTHGdHZ2mjVr1hhjCkfYjB492vveRu9LNLG2y10F8Jhjjil7PA444ICy7YPCOcR8r+NTzbFdtmxZTYWkajbfJarnnntu/ueuri7z85//vKbXdC+hdIuPYCdqj8f84Q9/uOAxDz74oHfUU7RFk99H3AKk7z1+4IEHyr7/vsKV+9j4fWeddVbB8/fcc8+C/e2xOQijVRLLHf9SlB9FRPwCzo8ikqKaOiC+ydSZNMlMXLy4poLE5c6k6k+9+27RCfbRr7xSMAfWkrVrzZedAsCjsQJQfC6juEpFFWOKC0EbOnMCxbfZuZMul7s/voKcMaZgHqeXPAUwdxJw3/PdS8wmLFrk/b3RdlmuqOMaGVvlLq6vsy8+ws0YY8Y4lxD+scxE7f923uvzZs40pSZer2Xzfb7i3BFO8cvsSh1zlzvC6uhXXsnff+/CheaPc+Z4C4DRz74CVnwS9q/FJmmPjyCoNAKqVBwDHn646O+HSZPM1NhnzJ0f62DPJP87ODFs9NhjRfvLCbgDUrcC1iabbFJ18eCwww7L//yVr3zFGFN8gu3+rjFjxphJkyaVPcF2902ePLlgX1dXl+nXr1/J9kyYMKFoFb5oNJRv+8Mf/lD0+90iy6mnnlq0313F77e//W3R/uuvvz6/P375mTGmYKL5n//852WPR/SeutxLGH3vnzvKLRoVV+o9Xm+99Uq+N87fSt22+PxiYOctc51xxhk1veaC2EIo0eWbgNlmm23y90+YMMEcdthhRZe5Ll68uOCy00rti69qeXdsdu/4JPlx++yzT8nf5fscjBkzpuD5w4YNy+8bNGhQ0evHL8GshfKjiIhfwPlRRFJUUwdkuqeAdejUqWZWhcup4pt7idfTud8dLwA96xSwlrW3F4yS8Sl38v/f2CWG0fatl182l+eKPfGJ5IfEVmVzt1We+S/cAtRjnuVz3DmkZq1eXbT/DGcEziOeEVqnOJeYPeUcL1/7fHNsHfaf/+T3X+jMoeV7nYmLFxftd+eDKrUaJZMmmcnO5aC3zp1rrnn77R4dJvAuAAAgAElEQVQXrphkJ6z3TaZerv0HxFYEZFLxKK5yzz9x+vSi/fHLUN3n+ApY8QLVN5zfv7GnQOSOsPvZjBll2xcvYBljTLxQ+GbsM7ae83n2TfK/U2wOr1oE3AGpSwHrkUceKXuJV7ntq1/9qjHGmH333Td/32c/+9mC33XppZeaxYsX52+PHTu2qF1Dhw7N758TG/0X3x9tgwcPNjvvvLNpa2srusTuggsuKNnmG2+8sej1v/e97+X3n3322UX73QLcTTfdVLT/0Ucfze//5S9/WbQ/WqEQ7KqM5Y6Hb44td0TZDjvsULTfXeXx9NNPL9pvjDGf/vSnDWBmzZpV8r1ZunRp/ueNNtrIPPTQQz36XLjb3Llzi+6Lj2JzL9GrZj62eP5yC4wf+9jHimJfsmRJwfM7OjrKjsC67777Cp4fn7tqbuxLlAcffLBsAalcLMYYs99++xXcFy9gjRgxIr/PtwjB0UcfXfb3l6P8KCLiF3B+FJEU1dQBmRYrYB2Tm+zbt8pZue3I//7X7Pnss2brxx83bblLNdz9Dy1eXHBZ4IqODnOFM2rLJ9q3qac4UKrA5to7NsJmvTIFLJ/v5iZgZ5L/EsGPOasIrvAUwA5yRkjNiE1Cb4wxX3JGoL3uXJ7ma99fPXNsuZfA3eI5gXWf/3rs8re4cgUsdxs/b17VBaw9S1yWeHduNNp7YvNalWv//3OKddEWnwg+zv39P/Isl7M8NhG6McZ8MXdMrvTEGOd+Pj4Qm0TfmMLLXK+L1pp2lBoRGK0YGC+4Lo2NAHNXGTzGM0m/u0qmClh5iQtYr+be63In876tb9++BjDXXHNN0etGc0xFt8eOHWvWrFmTv/3SSy+VbZfPlltuWdSGJ598Ml/IOO200wr2uZeoxTdfgWrcuHH5/YceemjR/qlTp+b3X3XVVUX7p0+fnt8/bNiwov1PPPGE9/m+9vkmmXcvQRw1alTRfjd+3wiyuFLvjVvo2Xjjjc2cOXOq+jy4o/Lcba+99vL+vltuuaWgPZtttll+n2/FwgEDBpT9jEyYMCG/zzeHWLwNxnRPzL799tubIUOGFH22XO4IO7AjuFxtbW35ffvvv3/R744XqOJtGTlyZMF95513XsHzv/CFL+T3ffCDHyx6/R/84AcqYBVTAUtEEgk4P4pIimrqgMQnS49OhOOr2FXalre3m86uLtPuzDPi7n94yRLztDMZ+OqODrOyo8Ps//zzZkxsdaD4830rrC2JzaE1dflyMyd2GaC7//rZs83+ziieL8fmLyr3+5k0ybzjucTQXakx/u22MYVFNh+3gDM/t7R4/PdG24POJYYRdw6u8c4cWpFoou9PPv206aywdnC1BayVHR11K2BVKhDtXGEVQreAFRV9XD+bMSO/f50Kx/iG3Ai3ts5O8/yyZd7VCuOOmzYtv2/72Mlb/D31jaDzTQLPpElmUC6W+CTzcZs5qwz6Rnjt7oxAPNezCmY5AXdAEhewSt1fbvvQhz5k3nzzTTN+/Pj8amfu/qhAFN3+7W9/a7q6uszgwYMNYFZ5CtDuKnU+7iTbY8aMMeecc07B/jPPPLOgDWPHjs3/HC9OTPMUgN1L0HxzZLlzQ915551l9//HM4LwlVdeye93LxH0vb+/+tWvip5/0003FRSL4rq6uvL7n/PMIRdX6ti6I+U23XRT7+gp3+a7TBAwe++9tzGmeIRQ/P+Y+P74VqmANXny5IoFnPj+NWvWmHHjxpm5c+eaW265pWD/jFgOuvnmmwv2L49dAu2+/0OHDi363b/+9a/LFrDiqwi+EZsn0t0fn2DeGGN+9KMfqYBVTAUsEUkk4PwoIimqqQPyUGwuomNzBSzfCXy5zcfdP3nJEjPFKWCtjU2oW+75O02ZUrQvfglapd//+NKl5p01a8yJ06ebV1asKNpf6fmrSyyxfc/ChWa6Z3RVNdz5xxY5qyz63l/fHFoHOgWQePGuVtUUsD6cK9JUW8B6sMRE+/dWWcCq9Pqj33jDnJNbzTC+SqUxxqxTxWf0wUWLzPkzZ3oLkJXad+L06WX3u/O4+VahjBdRo21wroC1tTOHlm8VSncS+DNjiyAYUzgC7dnYEvWVBNwBaUgBa9y4cWVf99xzzy247/LLLzfGGLNixQqz1HP5sjHGbLrppmVPvjfffPOSxQNjjPna176W3z9s2DCzYsUKM3z4cDNmzBjT0dFR0D5fAc0tYFxxxRXeNlx99dXeCdoj06dPNzNLFFc7Ozvzr3/yySfn7/e9v745sP72t7+V3W+MMUuXLi0qvJRS6ti6c21ddNFFZt68eVV9Jn74wx96799nn32MMcb8+Mc/LltgcecI820DBgwwBxxwgAHMLrvsUvT8SpfwGWPMX/7yF7PddtsVTAAfiU+yHh9hddtttxXsb3O+pIm/p9ttt13RvnKXtBpjzJFHHpm/vdVWWxU9/+tf/3rRe+o68cQT8/svvPBCb/ylKD+KiPgFnB9FJEU1dUD2j80tdKxzKVI9C1gTFy82TzoFrEojgtzn+1bQi79+pf2+OaiiS/y2+Pe/vc93i3tpmOtcpvmuc3nYhzzzL03xHM9ocvVdn3oqcVtmVjkCyxhjrq6ygGWMMYvWrjXTVq40/3Ley/s8BaxBnhFU7v5zZ84sev2zPCszuvZxLiG90lNAqsS9hO8nnjm0fuHMYVbpMzjWM8rwSM9KjEyaZN6Tey8+7HwOfLG6+y/xvL4bf3wFxUoC7oDUlB9XrlxZlwLWilzR3OXuj4/AKlUQcrmTyPv42lxqv+8x0fxPpZ7vXuL4u9/9rmJ7eyJ6/VNOOSV/30EHHWQA079//7Ltf/XVV/P7fHOE9bQt5baFCxea+fPnV/XYW2+91Zx++ulFl/9FxZZvf/vbZeNzR5j5toEDB5rOzk7z1FNPeYtHlVYBrOTwww8veH58lUe3gAjFI8jc93TzzTcv2nf55ZeXLWCNGjUqfzsateZy52g7+OCDi/affPLJ+f3XXnttTbErP4qI+AWcH0UkRTV1QOInzz0pYFVTYBrz1lsFKwf6OrNxn8mNIPmdZ/6g+OtX2j/JM4l5Nf6xYIF5osQIiHr44bRpRROMr+roMK+vWlWwil0pc9vaqhrNVsnba9akUsCKLHPmm6q2gPWXefPy+6/y/M5KBay7Fy7MP/YFzwiUSlZ1dJifzZhh3vJM0G9M4ST9lT6Dvjmwjnbm0HK39R591BhTOIfV9z1zXO3i7L/W8/puAcs3Qq2cgDsgNeXH+EpsOCf6H//4x6suYPmcd955+f1DhgwxxnSf0F955ZUV23bwwQcbsHNr+VT6/fFVCOM6OjrMlVdeaV73jO6LnHLKKWaPPfbIXxZZb7vvvruBwjnAli1bZu68805z4YUX5tt+1113eZ9/1113Fa1+11PVHOdFixaZBQsWVPXY8ePH51/bLQZG84FFc6aVOj7uc3zbwIEDy8bjjrJbf/31a34/3OLua6+9VrT/3nvvrfgZjPb5LiGMz6EVf63jjjuu7Ov/5Cc/ye874ogjiva7c6D9+c9/ril25UcREb+A86OIpChRAcs9US41yXS0/W3+fLP3c8+ZN0qM7nAfe9Xbb+dHDJUryLiWtbebR5YsKTlaq5YClm+VwGY3cfFiM+iRR7zFiTQc8+qr5oTYZXHVFrDWe/RR89yyZeYOZ2U/1wpnTrUJufm83OdHK++52p3LRK+fPducN3NmwWWTZ1coYLm/c2oPCliVRJcvMmmS2e/5572PufLtt80XX3rJewlq/BLEeAHLXUXTN0+cu0jBrbEVvowx5pNPP53fX2uRM+AOSG35sUwx6rOf/WzZAsJZZ51loHuFwTh3DqnodaOffZOex82ZM8ccf/zx5sUXX6zYdh/3EjTfJNrNYO3atSVHT7mTvPeGU045peLKk0uWLDELFy707ttll10KLru77bbbCl4/un/fffc1xhRPwu/j7j/mmGMKbg8aNKhiTNFjDz/88MTvT9zEiRMrtv+oo44ygLn44ouL9t16661lC1jxVTTj3ALVMcccU7T/jDPOyO+fMGFCTbEpP4qI+AWcH0UkRXUrYG3ljACKb6VGXbm+OnVq/vHz29rMq86cT/VQSwErq9rrMLqqVj0tYBljzN8XLPC+5+6iAPd7Clj9PAUsY7rneXJHQVVbwOrq6jJ7Pvus2eWpp1J5Hy968818W/Z45pmanx8fwRV/L/d1ClSjXnml6PkHO6tcHu3Zv4NziWGtAu6A1K2AVe4SJ8C0t7eb+++/3ywrMf/YW2+9lX9sfASWb86sWvna7Lrhhhvy+//1r38l/n2NcO+995r/eeaHS4s7sb5vW7p0qVm0aFHZz010+/bbby947ej+4cOHG2OKV+HzifYdcsghpqurq+AzVUsBy51jrF6qmSR+zZo15sknn/SO4Lv77rsLnj9s2LCC13ILVCNHjix6/tlnn53f36dPn6L9UYEZMFM882yWo/woIuIXcH4UkRSlcgmhOxrk16+/XtUlSe4k3tEIlCvfftvcMX9+z7JiibZv8thjFWOT6vWkgDV08mRjjDF/KzECa7VTwHogV8Aa5KzC6LuE0Bh7GV98BcjoOedUKGAZY0xnV1dV8631xJXO+zDOMwKqEneVRHdbP1fAcidpn+i5BPbw//wnv/9pz997ks9/wB2QmvKjO0E3sRPxcqvAxVf883EnKb/sssuMMcZceuml5tBDDzVra7zk08fXZtfVV1+d3+9bZVD8yhWwli1bVrAqoe8YRLfvuOOOgteN7t9vv/2MMcYMHz684jF8/PHHzTe/+U3zjrPKafT4wYMHV4xl/Pjx5qtf/WrJImsSzzzzTL4tvknWK3n00UfLFrBGjx6dv+2bBP6SSy4p+/65f9u1fv6VH0VE/ALOjyKSopo6IM8uW5Y/yd34sce8I12iAlT0c7Wr7k1esiT/nDRGwESv7Vul0N2vAlZtKhWwfPNRfTo3AsktWrrWOJcDPpgrYE1zRuQNLlHAKte+agpYaRr71lv5ttzjWSWykl+99lr++f9YsCA/Z9UJuTnR3Pd3uTPJf+Qwp4Dl+/tSAcurpvz4z3/+s+AkeI899uh+f5373fmsjj766Krf5+g51113Xc3HqJJddtmlbPFj7Nix+f3TPYsUiN+6665bsoC1fPlys2TJkqoKWH/9618LXje6P7qcMz4HVLWix1dTwErTyy+/nGiE1wsvvFAQfzTnW/Re7L///mVHYF1zzTX5/TfddFPR/u222y6/f26NX0AoP4qI+AWcH0UkRT3ugMRHquw4ZUr+BNgtQFRbwHpx+fL8c6qZtL1W9y1caD71zDPmP54Vvowx5vhp01TA6oGeFLD2zC2z3tnVZb77yivm0ticTW3O5+chZzTRT3OjkK6qYZXA6HV+M3Nm8mATcON/2LPKZS3Pf+rdd83S9nZz5/z5+dGKfSoUoL700ktl96uA5VVTfuzq6jJnnXWWufzyy80555xTMB/TRRddlD8BnjFjRqICVjWTttcqWqUumhQ8zi1gtXsKpOIH/uIV2NUmly5dWraAtddee5kBAwYUfQajx0UFrM7OTtOnT5/MFrBee+21RAUs9/mAef31182uu+6aL0Ydeuih+X0TJ04ser67SuNTnpV6o9UsAbOyyj5NRPlRRMQv4PwoIimqWwfEPUHuSQGrq6vLnDh9urnozTcTt6UnVnR0mBvfecfM9ywhLqX1pIBVqQDVXqKA1dXVVXIRgErtO7fBBax/OPN9+S7hq+TmOXPyz1/lmQOmUgFqN2eSdh8VsLzqlh+ffvrp/AnwzJkzExWwSk3EnkRXV5d54YUXzOoSC1gsWrTIbLrppt4JrqU0KF3AWrVqlfu34y1gdXZ2mjWxy6Ld1z3ggAPy9/33v/81Q4cONRdeeGHN7VtvvfWSBZrQvHnzevQ3EZkzZ07++YccckjRfneSd99KmbfffntBgTnOPTa1fsGm/Cgi4hdwfhSRFNWtA/IVZxL2th4UsCSbKhWw3Lmffvnaa2bS4sUV55nq6OrKP8c3n1NP2nd+gwtYC9ra8m15rgdzyHR1dZnx8+aZl0uMIKxUgDrbWQWxJ88vJ+AOSN3y49SpU/MnwLNnz+7Ryfrs2bO9o0N6S2cDFonIOjzFqWhbvXq1WbZsWdH90cqC1bzuiBEjCu6v9RhFrxMtDNBIUVuOOuqomp/rTpjvGyEYrWBIiUsA3ct/fXPKucenVsqPIiJ+AedHEUlR3Tog7hw7bgFrhgpYQaulgHX97NlVvWZXCgWsCxo0si/S6cT00vLldX/9ts5O897Jk82/ly717l/e3m7GvPWWea3ECLZ5bW3mfZMn9+jyxoA7IHXLj4888oh3BNb3vve9xK8tzQtKF7Da2tqKClj33XdfVZOkR4+PF7B62r4NNtgg0evUQ9SWalZErJU70s1n9erVZtiwYebEE0/s0fOr/N3KjyIijoDzo4ikqG4dEHe1uTUqYLWMWgpYtay+Fz3nX3UqYP21TqtZJvH7d94xoxs8mXwaAu6A1C0/Pv744/kT4AULFqiA1SIoU8Bau3atWb58eY9G91DnAtaGG26Y6HXqYccddzSAeazESsGN1tnZ2aP5OZUfRUT8As6PIpKiunVAOrq6zE1z5pjXVq1SAauF1FLAaqvh8pboOZMSFrAeXrLEXPzmm6ksDCBWwB2QuuXH9vb2giIFKmC1BMoUsNrb282KFSvyt3ffffeaXzdpAeuaa64xffr0MQ888ECi16mXEC9TVX4UEfELOD+KSIpS6YCogNU6ailg9eR1kxawJH0Bd0Dqmh/dERyogNUSKFPA6uzsNCtXrszffuihh2p+3QMPPDBxG31zPkn9KD+KiPgFnB9FJEWpdEDcSbjf1ZLrQUu7gNWTOZmkdwXcAUntBI1cAeK0006r+2tL86BMAaurq8usWrUqf/uJJ56o+XU///nPp9h6qQflRxERv4Dzo4ikKLUOyKsrV5qpKUxWLc0l7QLWIypgNb2AOyCp5cfx48ebL3/5yzr5CxxlCljG2MnDUQEraMqPIiJ+AedHEUmROiCSiApYEnAHRPlREqFCAWvNmjWJClgHHXRQWk2XOlF+FBHxCzg/ikiK1AGRRCoVsK6YNUsFrMAF3AFRfpREqFDAamtrS1TAGjlyZFpNlzpRfhQR8Qs4P4pIitQBkUTSKmB9+MknzboPP2xWdHTUv9FSVwF3QJQfJREqFLDWrl2bqIB10kknpdV0qRPlRxERv4Dzo4ikSB0QSSStAlZ7Z6dZreJVJgTcAVF+lESoUMBqb2/P337yySerft3HHnvMnHDCCWbZsmVpNV3qRPlRRMQv4PwoIilSB0QSqVTAuryHBSzJjoA7IMqPkggVClgdHR3521OmTGlwayUNyo8iIn4B50cRSZE6IJKIClgScAdE+VESoUIBq7OzUwWswCk/ioj4BZwfRTLneOAlYFluewIY6ez/PvBwbp8B3ut5jaHAuNxjlgI3AOvFHrMLMBlYA8wCTulBW9UBkURUwJIaOyDKj9IyUAGr5QV8gqb8KCKJBJwfRTLni8DBwA657VxgLbBzbv9JwGm5rdQJ2n3AC8Cngb2BGcCtzv4hwFzgT7nXPRJYhT35q4U6IJKIClhSYwdE+VFaBhUKWF1dXSpgBS7gEzTlRxFJJOD8KBKExcD3YvcNx3+CtmPu/t2d+w4CuoDNc7ePz71mf+cxFwCv1tgudUAkERWwpA4dEOVHCRIVCljuY1TAClPAJ2jKjyKSSMD5USTT+mK//W8DdortG47/BO1oYEnsvn5AB/CV3O2bgb/HHrNf7vU2rKF96oBIIpUKWL9VASt4CTogyo8SNFTAankBn6ApP4pIIgHnR5FM+hiwAntStRR7yUzccPwnaL8EpnkePx87sgDgAeDa2P6dcq+3Y5l2DcAmiWjbAnVAJIFKBazLVMAKXg86IMqP0hJQAavlBXyCpgKWiCQScH4UyaT+wPbYy1zOBxZQ/QiDUidoC4Djcj/7TtB2zr3eR8q0azSejrQ6INJTpYpX2z7xhDHGmImLF6uAFbgedECUH6Ul+D5PqIDVUgI+QVMBS0QSCTg/igThIYpPqIbT+5fIaISB1FWpAtabq1fnHzNu7lzz/LJlDWylpKkOHRDlRwkSNRSwZsyY0cCWSloCPkFTAUtEEgk4P4oE4V/AH2P3Daf8JMWfdO47EP8kxes6jzkPTVIsvaxUAWtFR0ejmya9pA4dEOVHCRJVFLDGjRtnLrvssga2UtIU8Ama8qOIJBJwfhTJnPOAfYBtsHO9nI89uRqR2/9+YDfgGOwf7T6520Od17gPeA74FLAXMJ3CZeI3wC4TfzP20pgjgJVomXjpZeXmv5LWUGMHRPlRWgYlilff+c53Gt006SUBn6ApP4pIIgHnR5HMuQGYiV1Zaz728pgRzv7R+Du1o5zHDMWekC0H3gX+AKwX+z27ApOBNcDbwKk9aKs6IJKIClhSYwdE+VFaRonPsjn11FMb3TTpJQGfoCk/ikgiAedHEUmROiCSiApYEnAHRPlREqFEAWvOnDmNbpr0kgblx18BjwOrsCu9+mwN3JN7zHzgYux8gtVSfhSRRALuP4pIitQBkURUwJKAOyDKj5IIJQpYy5cvb3TTpJc0KD+eBfwUGIO/gNUXmAo8iL1EeyR2Jdfzavgdyo8ikkjA/UcRSZE6IJKIClgScAdE+VESoUQBa8WKFY1umvSSBufHUfgLWCOBTmBT577jsJdk96/ytZUfRSSRgPuPIpIidUAkERWwJOAOiPKjJDJw4EAVsFpckxawzgZeiN23LbadH6/ytZUfRSSRgPuPIpIidUAkERWwJOAOiPKjJKICljRpAes64P7YfYOx7RxZ4rUGYGOIti1QfhSRBALuP4pIinSCJomogCUBd0CUHyWRUgWslStXNrpp0kvqmB8v8H2WYttHYs8ZRe0FrINK/P7Rvt+p/CgiPRVw/1FEUqQTNElEBSwJuAOi/CiJqIAldcyPG2MLVOW2+PxVo6jfJYQagSUidRVw/1FEUqQTNElEBSwJuAOi/CiJlCpgrVq1qtFNk17SpJcQRpO4b+Lc933sJO4Dqnxt5UcRSSTg/qOIpEgdEElEBSwJuAOi/CiJqIAlDcqPWwO7Ab8Glud+3g1YL7e/LzAVexnhrsDngfnAeTX8DuVHEUkk4P6jiKRIHRBJRAUsCbgDovwoiZQqYK1evbrRTZNe0qD8+Eff5w4Y7jzmA8C9wCpgAXAJ0K+G36H8KCKJBNx/FJEUqQMiiaiAJQF3QJQfJREVsET5UUTEL+D8KCIpUgdEElEBSwLugCg/SiIqYInyo4iIX8D5UURSpA6IJKIClgTcAVF+lEQGDRrkLWCtWbOm0U2TXqL8KCLiF3B+FJEUqQMiiaiAJQF3QJQfJREVsET5UUTEL+D8KCIpUgdEElEBSwLugCg/SiKlLiFsa2trdNOklyg/ioj4BZwfRSRF6oBIIipgScAdEOVHSUQFLFF+FBHxCzg/ikiK1AGRRFTAkoA7IMqPkkipSwhVwGodyo8iIn4B50cRSZE6IJKIClgScAdE+VESKVXAWrt2baObJr1E+VFExC/g/CgiKVIHRBJRAUsC7oAoP0oiKmCJ8qOIiF/A+VFEUqQOiCSiApYE3AFRfpREShWw2tvbG9006SXKjyIifgHnRxFJkTogkogKWBJwB0T5URJRAUuUH0VE/ALOjyKSInVAJBEVsCTgDojyoySiApYoP4qI+AWcH0UkReqASCIqYEnAHRDlR0lEBSxRfhQR8Qs4P4pIitQBkURUwJKAOyDKj5KIClii/Cgi4hdwfhSRFKkDIomogCUBd0CUHyWRwYMHewtYHR0djW6a9BLlRxERv4Dzo4ikSB0QSUQFLAm4A6L8KImUKmBpBFbrUH4UEfELOD+KSIrUAZFEVMCSgDsgyo+SSKlLCKV1KD+KiPgFnB9FJEXqgEgiUcFqz2efNbs/84wKWC0o4A6I8qMkogKWKD+KiPgFnB9FJEXqgEgiUcFq/+efVwGrRQXcAVF+lETcSwgvvvhiFbBakPKjiIhfwPlRRFKkDogkogKWBNwBUX6URNwC1h133KECVgtSfhQR8Qs4P4pIitQBkUSigtV+KmC1rIA7IMqPkogKWKL8KCLiF3B+FJEUqQMiiUQFq+EqYLWsgDsgyo+SiApYovwoIuIXcH4UkRSpAyKJuCOwvvPyyypgtaCAOyDKj5KIClii/Cgi4hdwfhSRFKkDIom4c2AtWrvW/HDaNDNFn6eWEnAHRPlRElEBS5QfRUT8As6PIpIidUAkEbeAJa0p4A6I8qMkMmjQIBWwWpzyo4iIX8D5UURSpA6IJBIVsA5QAatlBdwBUX6URNwC1qOPPqoCVgtSfhQR8Qs4P4pIitQBkURUwJKAOyDKj5KIW8Dq6uoyo0ePNuPHj290s6QXKT+KiPgFnB9FJEXqgEgiUQHrcy+80OimSIME3AFRfpREBg4cqFFXLU75UUTEL+D8KCIpUgdEElEBSwLugCg/SiIqYInyo4iIX8D5UURSpA6IJBIVsEaogNWyAu6AKD9KIgMGDFABq8UpP4qI+AWcH0UkReqASCIqYEnAHRDlR0mkf//+KmC1OOVHERG/gPOjSOYcD7wELMttTwAjnf0DgauARcAK4K/AprHX2Bq4B1gFzAcuBvrFHjMceA5oA/4HjOpBW9UBkUSiAtaBKmC1rB50QLKSI5UfJREVsCTgEzTlRxFJJOD8KJI5XwQOBnbIbecCa4Gdc/uvAd4C9gc+iT15+7fz/L7AVOBBYDfsid0C4DznMdsCK4ExwI7Aj4EO4PM1tlUdEElEBSzpQQckKzlS+VESUQFLAj5BU34UkUQCzo8iQVgMfA/YAHuidpiz7yPYP949c7dHAp0Ujjg4DngX6J+7fSHwn9jvGA9MqC0gWMoAABXESURBVLFd6oBIIipgSZ06IM2YI5UfJREVsCTgEzTlRxFJJOD8KJJpfYEjsZew7IQdUWCA98Ye9ybw09zPZwMvxPZvm3vex3O3HwUuiz3mu9gTuFqoAyKJRAWsz6uA1bISdkCaOUcqP0oi6667rgpYLS7gEzTlRxFJJOD8KJJJH8PO3dIBLMVeLgPwDeyJWtxT2BEDANcB98f2D8b+gUfzxEwHTo895uDcYwaVadcAbJKIti1QB0QSUAFLetgBacYcqfwoddWvXz8VsFpcwCdoKmCJSCIB50eRTOoPbA/sDpyPnZ9lJ0qfnD0NXJD7udzJ2UG5276Tsy/kHjOwTLtG5x5TsKkDIj0VFbAOevHFRjdFGqSHHZBmzJGjUX6UOlIBSwI+QVMBS0QSCTg/igThIeBaGn95jEYYSF1FBayRKmC1rDp1QJohRyo/Sl2pgCUBn6CpgCUiiQScH0WC8C/gj3RPUPw1Z98O+Cco3sR5zPexJ14DcrcvxK7C5boVTeIuvUwFLKlTB6QZc6TyoyTSt29fFbBaXMAnaMqPIpJIwPlRJHPOA/YBtsHO83I+0AWMyO2/BjuaYD/sEvGP57ZItET8/cCu2GXf51O8RPwq4CLsCl0/pPYl4kEdEEkoKmAdrAJWy+pBByQrOVL5URJRAUsCPkFTfhSRRALOjyKZcwMwEzuPy3zspTEjnP0Dgauwy8avBO4E3h97jQ8A92JPwBYAlwD9Yo/ZD3g+93teA0b1oK3qgEgiP5o2zTBpknlKn6GW1YMOSFZypPKjJNKnTx8VsFpcwCdoyo8ikkjA+VFEUqQOiCS2sqOj0U2QBgq4A6L8KIl85jOfMYDZYIMNGt0UaRDlRxERv4Dzo4ikSB0QEUkk4A6I8qMk8vbbb5sTTzzRvPrqq41uijRIA/LjNthRrm8Aq7GjT8/Crvzq2gWYDKwBZgGn1Ph7lB9FJJGA+48ikiJ1QEQkkYA7IMqPIpJIA/LjQcCNwIHAdsCXgHnYy6zd3DYX+BOwM3Ak9nLs79fwe5QfRSSRgPuPIpIidUBEJJGAOyDKjyKSSJPkx18Arzu3j8fOMeiOyroAeLWG11R+FJFEmiQ/ikjGDAHMrFmzzLvvvqtNmzZtNW+zZs0KtQOi/KhNm7ZEW5Pkx98Azzi3bwb+HnvMfth2bljiNQZgY4i2LVB+1KZNW4KtSfKjiGTMFuRWSNKmTZu2hNsWhEX5UZs2bfXaGpUftwfeBY517nsAuDb2uJ2w7dyxxOuMpvHvoTZt2sLcQus/ikiK1sEmjSFNskUnjM3UJsWo+BRfdc9bh7A0W37U5y/7m+LL9tbI/HgBlU8CPxJ7zhbA/4Dfx+73FbB2LvEakfgIrCHYCeMbfUz0+QtnU3zZ33oSY4j9RxFpIUOwiW9IoxuSotBjVHzZFnp8WRf68VF82ab40rMxtrhUbnPntNocmI69XLBP7LV6cglhFujzl22KL/taIUYRkQKtkPhCj1HxZVvo8WVd6MdH8WWb4msOW2CLV38G+nr2R5O4r+vcdx61TeLejLJyfHpK8WVb6PFBa8QoIlKgFRJf6DEqvmwLPb6sC/34KL5sU3yNtzkwA5iILWS939kiGwBzsSOxdgaOAFYC3+/VltZfFo5PEoov20KPD1ojRhGRAgOwE4UOaHA70hR6jIov20KPL+tCPz6KL9sUX+ONovQcWa5dgcnAGuBt4NTea2JqsnB8klB82RZ6fNAaMYqIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiAjAOo1ugIhIk1J+FBHxU34UERGR1L0PuBr4Uu52nwa2JQ1DgE1zP4cWG8D7gV8B3wU+k7svpE7k5sAU4OeNbkhK1gM2yP0c0nELhfJjtoWeH0E5UhpH+THblB+zT/lRRFrSRUAX8Ffsf9YQThI8A5gPnNPohqTkTGAFcD/wX+BtYI/cvhCO4WXAWuAf2I5WaEYDM4FRDW2FlKP8mF2h50dQjpTGUn7MLuXH7BuN8qOItKgJ2P/AJgI/yd2X9f+81sN+K/g08BRwH7BXbl/WY4uMBJ4DDsrd/igwCfhNw1pUPx8BZgOvArs3uC1pGAr8HngW2/n4C/Ch3L5QPp+hUH7MppDzIyhHSnNQfswm5cdsU34UkZYRT2r9gAHAeGAY8EdsJ2TH3P6+vday+lsXOA04EtgT2xG5BBiU2x9Cgr8C2wFxTQQ+69zOapyfBl4BxuZufwL7beF3cz9nkXssNsF+q/sFYH9sR+vH2M+tNIbyo/JjlihHSm9SflR+zBLlRxGRAPTHdjYibiJ8HtgW+03Mo9j/uPsDm/Va65KL4nE7TUOcn88GngC+0mstqq8oPncOhhOBWcAIYEvgNuBd4EHgGmDD3mxgQv0o/EwOAL6PHd5+F/Ybpgdz/y4AftG7zUss/vfXD9sBidwI/Jvu4fvSu5QflR+bnXKkcmSjKD8qPzY75UflRxEJzGjsMOgJ2ITu/sf0YewQ1CjxXwS8gZ3T4ASyMWnlCcBZJfZFcW0CPIz9lnDz2L5mF48vavf2wE3AvUAb9tuzz2E7Jv/B/qftPr5ZnQ78E/gz8GXs8H2ww6HvAB4DPgmsn7v/UuBxstOZHE3h399QZ1/097UFtmN1LvDe3H3NftxCMRrlR+XH5qYcqRzZKKNRflR+bG7Kj8qPIhKQfsDNwAzgO8Ct2P+Y/uk8ZlPggdzPI7ETVi4HHsFW/KF5k+Cu2ITeBbyEHUYLxZ2m6PYx2M7Wcc6+Zo0NSsfXL/a4Q4GHKOxY7gusAbZKuY1JfAr77e1UbKfpYeyQ9p/l9q+DnXdi99zP0bejW+aec3IvtrUnSv393R17XBTXGdgh7wc5+5r585l1yo+Ft5Ufm49ypKUc2fuUHwtvKz82H+VHS/lRRIKyLTaZfdG5bwSwiu4E/wVgLvbbiGXY4d9nApOBb+Ue06wJ8GfYb4m+A9yDTfTRf85um92f7wT+Bnwc+BrNvbpMpfiiuE7DdlRcxwCv0z2pY7PZCLgWuI7ub8vAzqdxLd2d33hnK4p5Ls0/2Wi5v7+fOvdFHeR1gBewE3Nui/0m8cfpN7NlKT8W/6z82DyUI7spR/Y+5cfin5Ufm4fyYzflRxEJyg7Yb1+2jt1/OrAUO+R0I+Bl7FDi7XP7t8auRnI73ZNWNqP3YycPBfvty5PAUbnb8U5TlOA/h/02YyF2ad3/S7mNSVQb32+w3zwdiv3Penvst6J/oHmH8G+E7ehGk2lGE09eDEyp8NwvYb9l2ymdptVNub+/JRR+uxl9g/b/sN9ivwm0Y4f/SzqUH7spPzYf5UjlyEZSfuym/Nh8lB+VH0UkUDtih9fGJyscgv125ZLc7S0p/o/qMxR+q9HstsQuI3s3dlg7FMf0Aew3M13Y/5yHkh2++KL/sHfCdhbXYr9Jexc71Hh9mpv7zVjUofojdn6CuI9iJ6cci+08nkfxN2vNptLf35jc7ajj8QHs5KlZ/HxmkfJjIeXH5qMcaSlH9j7lx0LKj81H+dFSfhSRoLwHO5z2DmCb3H3Rf8onYyv0A2PPadbh3uVEMX0dO5T9lyUedwb2m4msrdJRTXybYb9V+hGwWy+1K4l1Yv9GHgO+7dn3A+wklo9jl0fOglr//i4F5pC9z2dWKT8WUn5sLsqRypGNpPxYSPmxuSg/Kj+KSAZtjL3GOVrq1/22yP1W4UjgReCU2POPxU5i2KyTNFYbn3t7EPA77HDoXXL3fYLmVK/4dk+pfUnVEl/07dE22G//Pursi1b8WZ/umJvBlsA38S81Xcvfnzs0PEvfWDc75cfi28qPzUU50lKO7H3Kj8W3lR+bi/KjpfwoIkFYB7gcu1zqc8B0uuce6Os8ri82OYIdUvoEcIiz/3TsRJvuc5pBtfGtQ/f1/ND9n9t+2BVVxgP3YYfSbpZec2um+LofdxSFjscOlwY7v8Zt2G/Mmm0Y9FbAAux7P8K5P4S/v6xTfux+XCvlj6zEB8qR7s9Z/BvMMuXH7sdlMX+EHh8oP7o/Z/FvUESkyP7YJVQfB4Zjl0qdDNwfe9yxwLzc/esCHwFuwE7odzVwBXYSzmh1imYZ9l1rfPcAm8T2bZJ7jS7sijEfSK+5NVN8lhvfxs79lwKXYf9jXgVMxHZCms37gb9jO1j3UXyMsvr3l3XKj1Yr5Y8sxQfKkZGs/g1mmfKjldX8EXp8oPwYyerfoIiI1ynYiQfdYaK/AP5B9zcsRwFvAUdTPMz2ZOwklBOw/1E0m1rji3/zsCewCLvs7F6ptrRnFF/p+AYBb2A7VtMo/Faq2Xweu5T21tj2jqJ7yeZRwCyy+feXdcqPrZs/oPnjA+VIUI5sFOXHbOeP0OMD5UdQfhSRAMRXQRlK4fXOG2OXi70UuwRuJH4ddLNW5+sVX+Q9wLfq1rrkFF/18Q0Fbga+Uc8GJtSnxM97YlftAfgTdhnxwXSv6hNfOrxZ//6yTvlR+cPVbPGBciQoRzaK8mO280fo8YHyIyg/ikhgfg3cCPwf8D7P/sOBTuyKG3dgv1m5heYbElxKveNrtgSv+KqPr9lig/Lx/QQ7dD2yChvnbGDvXmmdKD8qf7iaLT5QjlSObBzlx2znj9DjA+VH5UcRCcpWwLPAS8CV2CVRnwYOiz3uAGCYc/uTwOrc/c1M8VmKrzlVE9//0T33wBeBZUAHdni3pEufP0vxNa/QY1SObF767FmKr3mFHqPyo4i0pKOwq2hskLv9HuAu7ESGu5Z53mBscj821dYlp/j8FF9zKBdftKT2WGAS8AiwGDgRO/fC9c7zJB2t/PlTfM0fH4Qfo3Jk82rlz57ia/74IPwYlR9FpCWdiV321Z28bxh26dtxZZ53NPBvYPP0mlYXis9P8TWHauL7NXYJ5OuBD+buOwQ7GeeBvdPMlqXPn5/iax6hx6gc2bz02fNTfM0j9BiVH0WkJZ2PXUI2nqRPxlb1P+fc9yFge+ww1XnAz2jOa8Fdiq+b4ms+5eJ7Efg0dsnjnSiO5cfAgLQb2OJa+fOn+Jo/Pgg/RuXI5tXKnz3F1/zxQfgxKj+KSEuJVqj4CLYKf2hs/67Ak8CpudsbAr8BXsdO/ldu6G0zUHyKr5lVE98U7DLPcfHlqqX+9PlTfM0u9BiVI5uXPnuKr9mFHqPyo4gE66PA1/AnK3e46V+A54CNYo95ErjCub0T8Nl6NjAhxWcpPivE+C7P/dzs3wJmkT5/luKzmi0+CD9G5cjmpc+epfisZosPwo9R+VFEWkp/4AZsVf43FCauvrHHbQ9sjV1W9Vy6J/Trh53w76y0G9sDiq/7cYqv+YQeX9aFfnwUX/fjshgfhB9j6PFlWejHRvF1Py6L8UH4MYYen4hIkROA5djJB8sNgf0JsJLu4aXHAjOA+4EvAZcC7wB7pNbSnlF8luJTfFK70I+P4rOyGh+EH2Po8WVZ6MdG8VlZjQ/CjzH0+EREigzBLpE60bnvI9hVJ9bP3V4HuA47QeG36L6OGuyqFPdgJwR8Gjv5XzNRfIpP8UlPhX58FF+244PwYww9viwL/dgovmzHB+HHGHp8IiIF3OGl3wUWASOw10X/D1uVnwKMyj3mw9hEGXETIMCmqbSy5xSf4nMpPqlF6MdH8WU7Pgg/xtDjy7LQj43iy3Z8EH6MoccnIlLgU7l/3eS1DjbRdQG/B4YBB+d+fgfYP/e4LKxCofgUXzMLPb6sC/34KL5sxwfhxxh6fFkW+rFRfNmOD8KPMfT4REQKHArMxlbpt8nd5yazTwLnA+9z7tsGuBM7vLTZKT7F18xCjy/rQj8+ii/b8UH4MYYeX5aFfmwUX7bjg/BjDD0+EZEi3wCeAv4MTAZ+53nMOsB6nvtvAe4F3pNa65JTfIpP8UlPhX58FF+244PwYww9viwL/dgovmzHB+HHGHp8IiIFour8p7GV+a2BXwCvAsNjj/EZBDwEXJxS+5JSfIpP8UlPhX58FF+244PwYww9viwL/dgovmzHB+HHGHp8IiIFPkThBH8A/XL/7gzcReGQ0vhjNwC2Am4AXgY+kUIbk1B8ik/xSU+FfnwUX7bjg/BjDD2+LAv92Ci+bMcH4ccYenwiIgUOB97AVuenAEc7++IrVvw39y8UTgZ4EHA1sBCYBGyfVmN7QPFZik/xSe1CPz6Kz8pqfBB+jKHHl2WhHxvFZ2U1Pgg/xtDjExEpMgKb+H4IfB4YA6wFjsUOI4XuCv4W2NUpnqL7mun+uX8/kHuNA9Jvck0Un+IDxSc9E/rxUXzZjg/CjzH0+LIs9GOj+LIdH4QfY+jxiYgUiKryvwaeAdZ19l0FPA18xfO8L+T2jQZ2Ae7GDjltNopP8Sk+6anQj4/iy3Z8EH6MoceXZaEfG8WX7fgg/BhDj09EpKzxwG25n6MEuCHwGHAj8P7cfdGEf4OxybELaAcmAAN6paU9o/gsxdecQo8v60I/PorPymp8EH6MoceXZaEfG8VnZTU+CD/G0OMTkRY3ArgcOAn4lHP/scAyupPbus790+hesQLscqonAR3Ya6Q/ll5za6b4LMWn+KR2oR8fxWdlNT4IP8bQ48uy0I+N4rOyGh+EH2Po8YmIFNgM+CcwD/gT8BKwlO4EuAPwNnB27nZ/57lzsMkushPwJPDtFNtbK8Wn+CKKT2oV+vFRfNmOD8KPMfT4siz0Y6P4sh0fhB9j6PGJiBQZDPwRO8R0W+f+KdihpQDrA78CVtF9HXS0MsXDwPVpNzIBxaf4FJ/0VOjHR/FlOz4IP8bQ48uy0I+N4st2fBB+jKHHJyJS0rXY5VGheyWKM7FV+GgSwG2x10s/gV2JAmBr4GXshH/NTPEpvmYWenxZF/rxUXzZjg/CjzH0+LIs9GOj+LIdH4QfY+jxiYh4uStTRFX5ccB1scdtAczALsd6OzAbmAhsmnYDE1J8luJrTqHHl3WhHx/FZ2U1Pgg/xtDjy7LQj43is7IaH4QfY+jxiYhU7THgqNzPfehOitsDRwCXOvuzSPEpvmYWenxZF/rxUXzZjg/CjzH0+LIs9GOj+LIdH4QfY+jxiYgU2Q6YC3zSua9/icdmkeLLNsUnjRT68VF82Rd6jKHHl2WhHxvFl32hxxh6fCIiBaJrpb8D/M+5/0zgGmCTXm9RfSm+bFN80kihHx/Fl32hxxh6fFkW+rFRfNkXeoyhxyciUtaVwIXACOy10vOAAxvaovpSfNmm+KSRQj8+ii/7Qo8x9PiyLPRjo/iyL/QYQ49PRKTIQOwkf13AGuDUxjan7hRftik+aaTQj4/iy77QYww9viwL/dgovuwLPcbQ4xMRKelB4GpsIgyR4ss2xSeNFPrxUXzZF3qMoceXZaEfG8WXfaHHGHp8IiJefRvdgJQpvmxTfNJIoR8fxZd9occYenxZFvqxUXzZF3qMoccnUhf/H4TMfqG/YgvXAAAAAElFTkSuQmCC\" 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": {
-    "collapsed": false
-   },
-   "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": [
-       "<img src=\"data:image/png;base64,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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": "2.7.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/docs/algorithms/AdjustedPhaseOneFunction2.ipynb b/docs/algorithms/AdjustedPhaseOneFunction2.ipynb
deleted file mode 100644
index 1d1cdbe9d68e0e73c50148eca6ed0173480f1a17..0000000000000000000000000000000000000000
--- a/docs/algorithms/AdjustedPhaseOneFunction2.ipynb
+++ /dev/null
@@ -1,6317 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Adjusted Data Phase One, Function-based"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "import applicable libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\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 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": [
-    "Set up edge factory"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "factory = EdgeFactory(host='137.227.224.97', port=2060)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Class used as a container for absolute $[ H, D, Z ]^T$, baseline $[ H, D, Z ]^T$,  and start times of absolute measurements"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class Baselines:\n",
-    "\n",
-    "    \n",
-    "\n",
-    "    def __init__(self):\n",
-    "\n",
-    "        self.absH = []\n",
-    "\n",
-    "        self.absD = []\n",
-    "\n",
-    "        self.absZ = []\n",
-    "\n",
-    "        self.baseH = []\n",
-    "\n",
-    "        self.baseD = []\n",
-    "\n",
-    "        self.baseZ = []\n",
-    "\n",
-    "        self.begin = []\n",
-    "\n",
-    "\n",
-    "\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Class containing absolutes ($[ X, Y, Z ]^T$) and ordinates ($[ h, e, z]^T$).  Conversion of coordinates happens in <pre>get_ord_abs_from_baselines</pre>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class AbsOrds:\n",
-    "\n",
-    "    \n",
-    "\n",
-    "    def __init__(self):\n",
-    "\n",
-    "        self.ordp1 = []\n",
-    "\n",
-    "        self.absp1 = []"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Function that reads a json file output from the <pre>geomag-baseline-calculator</pre> webservice (currently requires login, url <pre>https://geohazards.usgs.gov/baselines/baseline_data.php?observatoryId=3&startTime=1419630615&endTime=1453326615</pre>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def get_baselines_from_file(filename):\n",
-    "\n",
-    "    f = open(filename)\n",
-    "\n",
-    "    json12 = f.read()\n",
-    "\n",
-    "    parsed_json12 = json.loads(json12)\n",
-    "\n",
-    "    f.close()\n",
-    "\n",
-    "    bns = Baselines()\n",
-    "\n",
-    "    for i1 in range(len(parsed_json12)):\n",
-    "\n",
-    "        if ((parsed_json12[i1]['horizontal_intensity_valid'] == 'Y') & (parsed_json12[i1]['vertical_intensity_valid'] == 'Y') & (parsed_json12[i1]['declination_valid'] == 'Y')):\n",
-    "\n",
-    "            if ((parsed_json12[i1]['absH'] !=  None) & (parsed_json12[i1]['absD'] != None) & (parsed_json12[i1]['absZ'] != None) & (parsed_json12[i1]['baseH'] != None) & (parsed_json12[i1]['baseD'] != None) & (parsed_json12[i1]['baseZ'] != None)):\n",
-    "\n",
-    "                bns.absH.append(parsed_json12[i1]['absH'])\n",
-    "\n",
-    "                bns.absD.append(parsed_json12[i1]['absD']*np.pi/180)\n",
-    "\n",
-    "                bns.absZ.append(parsed_json12[i1]['absZ'])\n",
-    "\n",
-    "                #bns.baseH.append(parsed_json12[i1]['baseH'])\n",
-    "\n",
-    "                #alternate d undoing small angle approximation in web absolutes\n",
-    "\n",
-    "                db1 = parsed_json12[i1]['baseD']*np.pi/180\n",
-    "\n",
-    "                da1 = parsed_json12[i1]['absD']*np.pi/180\n",
-    "\n",
-    "                do1 = da1 - db1\n",
-    "\n",
-    "                do2 = np.arctan(do1)\n",
-    "\n",
-    "                if (do2 >np.pi/2):\n",
-    "\n",
-    "                    do2 = do2 - np.pi/2\n",
-    "\n",
-    "                if (do2 < -np.pi/2):\n",
-    "\n",
-    "                    do2 = do2 + np.pi/2\n",
-    "\n",
-    "                db2 = da1 - do2\n",
-    "\n",
-    "                bns.baseD.append(db2)\n",
-    "\n",
-    "                #alternate h undoing small angle approximation in web absolutes\n",
-    "\n",
-    "                hb1 = parsed_json12[i1]['baseH']\n",
-    "\n",
-    "                ha1 = parsed_json12[i1]['absH']\n",
-    "\n",
-    "                ho1 = ha1 - hb1\n",
-    "\n",
-    "                ho2 = ho1/np.cos(do2)\n",
-    "\n",
-    "                hb2 = ha1 - ho2\n",
-    "\n",
-    "                bns.baseH.append(hb2)\n",
-    "\n",
-    "                #bns.baseD.append(parsed_json12[i1]['baseD']*np.pi/180)\n",
-    "\n",
-    "                bns.baseZ.append(parsed_json12[i1]['baseZ'])\n",
-    "\n",
-    "                bns.begin.append(parsed_json12[i1]['begin'])\n",
-    "\n",
-    "    return bns"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Function that gets baselines between a given date pair"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def get_bns_between_dates(a,b,baselines):\n",
-    "\n",
-    "    d = next(idx for idx, value in enumerate(baselines.begin) if value > a)\n",
-    "\n",
-    "    e = next(idx for idx, value in enumerate(baselines.begin) if value > b)\n",
-    "\n",
-    "    baselines.absH = np.asarray(baselines.absH[d:e])\n",
-    "\n",
-    "    baselines.absD = np.asarray(baselines.absD[d:e])\n",
-    "\n",
-    "    baselines.absZ = np.asarray(baselines.absZ[d:e])\n",
-    "\n",
-    "    baselines.baseH = np.asarray(baselines.baseH[d:e])\n",
-    "\n",
-    "    baselines.baseD = np.asarray(baselines.baseD[d:e])\n",
-    "\n",
-    "    baselines.baseZ = np.asarray(baselines.baseZ[d:e])\n",
-    "\n",
-    "    baselines.begin = np.asarray(baselines.begin[d:e])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Function that removes baselines above and below one standard deviation from the mean"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def remove_outlier_baselines(baselines):\n",
-    "\n",
-    "    baseHstd = baselines.baseH.std()\n",
-    "\n",
-    "    baseHave = baselines.baseH.mean()\n",
-    "\n",
-    "    maskH = (abs(baselines.baseH - baseHave) < baseHstd)\n",
-    "\n",
-    "    baseDstd = baselines.baseD.std()\n",
-    "\n",
-    "    baseDave = baselines.baseD.mean()\n",
-    "\n",
-    "    maskD = (abs(baselines.baseD - baseDave) < baseDstd)\n",
-    "\n",
-    "    baseZstd = baselines.baseZ.std()\n",
-    "\n",
-    "    baseZave = baselines.baseZ.mean()\n",
-    "\n",
-    "    maskZ = (abs(baselines.baseZ - baseZave) < baseZstd)\n",
-    "\n",
-    "    maskbase = maskH & maskD & maskZ\n",
-    "\n",
-    "    baselines.absH = baselines.absH[maskbase]\n",
-    "\n",
-    "    baselines.absD = baselines.absD[maskbase]\n",
-    "\n",
-    "    baselines.absZ = baselines.absZ[maskbase]\n",
-    "\n",
-    "    baselines.baseH = baselines.baseH[maskbase]\n",
-    "\n",
-    "    baselines.baseD = baselines.baseD[maskbase]\n",
-    "\n",
-    "    baselines.baseZ = baselines.baseZ[maskbase]\n",
-    "\n",
-    "    baselines.begin = baselines.begin[maskbase]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Function that converts $[ H, D, Z ]^T$ in ordinate, absolute (absolute = ordinate + baseline) to $[ h, e, z ]^T$ and $[ X, Y, Z ]^T$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def get_ord_abs_from_baselines(baselines):\n",
-    "\n",
-    "    absolutes_ordinates = AbsOrds()\n",
-    "\n",
-    "    ordH = baselines.absH - baselines.baseH\n",
-    "\n",
-    "    ordD = baselines.absD - baselines.baseD\n",
-    "\n",
-    "    ordZ = baselines.absZ - baselines.baseZ\n",
-    "\n",
-    "    ordh = ordH*np.cos(ordD)\n",
-    "\n",
-    "    orde = ordH*np.sin(ordD)\n",
-    "\n",
-    "    ordz = ordZ\n",
-    "\n",
-    "    absh = baselines.absH*np.cos(baselines.absD)\n",
-    "\n",
-    "    abse = baselines.absH*np.sin(baselines.absD)\n",
-    "\n",
-    "    absz = baselines.absZ\n",
-    "\n",
-    "    absolutes_ordinates.absp1 = np.vstack([absh,abse,absz])\n",
-    "\n",
-    "    absolutes_ordinates.ordp1 = np.vstack([ordh,orde,ordz])\n",
-    "\n",
-    "    return absolutes_ordinates"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Function that computes the affine transform matrix from the absolutes using numpy's least squares function of the linear algebra library.  More robust than, but similar to the matrix multiplication method that would compute the 2-norm-minimizing solution $[M]$ of:\n",
-    "$$ \\left[\\begin{array}{cccc} X_1 & X_2 & \\ldots & X_n \\\\ \n",
-    "                          Y_1 & Y_2 & \\ldots & Y_n \\\\\n",
-    "                          Z_1 & Z_2 & \\ldots & Z_n \\\\\n",
-    "                          1   &  1  &  \\ldots & 1 \\end{array}\\right] = [M] \\left[ \\begin{array}{cccc} h_1 & h_2 & \\ldots & h_n \\\\ \n",
-    "                          e_1 & e_2 & \\ldots & e_n \\\\\n",
-    "                          z_1 & z_2 & \\ldots & z_n \\\\\n",
-    "                          1   &  1  &  \\ldots & 1 \\end{array}\\right] $$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def get_transform_from_abs_ords(abs_ords):\n",
-    "\n",
-    "    tol = 1e-15\n",
-    "    \n",
-    "    ones = np.ones(abs_ords.ordp1[1].shape)\n",
-    "\n",
-    "    ordp2 = np.vstack([abs_ords.ordp1,ones])\n",
-    "\n",
-    "    absp2 = np.vstack([abs_ords.absp1,ones])\n",
-    "\n",
-    "    M, res, rank, sigma = np.linalg.lstsq(ordp2.T,absp2.T)\n",
-    "\n",
-    "    maskM = np.abs(M) > tol\n",
-    "    \n",
-    "    M = maskM * M\n",
-    "    \n",
-    "    return M.T, res, rank, sigma"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Function that applies the first equation (matrix-multiplies $[M]$ by the array of $[ h, e, z, 1]^T$ vectors\n",
-    "in the previous markdown comment block)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def make_adjusted_from_transform_and_raw(M,raw):\n",
-    "\n",
-    "    h = raw[0]\n",
-    "\n",
-    "    hd = h.data\n",
-    "\n",
-    "    e = raw[1]\n",
-    "\n",
-    "    ed = e.data\n",
-    "\n",
-    "    z = raw[2]\n",
-    "\n",
-    "    zd = z.data\n",
-    "\n",
-    "    raws = np.vstack([hd,ed,zd,np.ones(hd.shape)])\n",
-    "\n",
-    "    adj = np.dot(M,raws)\n",
-    "\n",
-    "    return adj"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Function that saves the computed matrix, as well as the pier correction, in json format"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    " def save_state(filename, M, PC):\n",
-    "        \"\"\"Save algorithm state to a file.\n",
-    "        File name is filename.\n",
-    "        \"\"\"\n",
-    "        \n",
-    "        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':  PC\n",
-    "        }\n",
-    "        with open(filename, 'w') as f:\n",
-    "            f.write(json.dumps(data))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Example Problem Demonstrating the Adjusted Data Algorithm"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Example function.  x1 takes the place of time, x2, y2, and z2 take the place of magnetic measurements in the three axes.  Chosen to show how different affine linear transformations effect orientation, location, stretch, etc."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "x1 = np.linspace(0,np.pi, 5000)\n",
-    "\n",
-    "x2 = (2*x1 - np.pi)/np.pi"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "y2 = np.sqrt(1 - x2**2)*np.cos(24*x1)\n",
-    "\n",
-    "z2 = np.sqrt(1 - x2**2)*np.sin(24*x1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0xcc923c8>]"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEACAYAAABVtcpZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nPOoY/x/0yQ7mo8ZZ+AAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xcbc72b0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(x2,y2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0xcee4b70>]"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEACAYAAABVtcpZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xc82ef60>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(x2,z2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0xd02fe10>]"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEACAYAAABVtcpZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb94e4e0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(y2,z2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<mpl_toolkits.mplot3d.art3d.Line3D at 0xcdbd0b8>]"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADtCAYAAAAcNaZ2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIAD4P4iwz5yW0mmLAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xcbc3080>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from mpl_toolkits.mplot3d import Axes3D\n",
-    "\n",
-    "fig = pl.figure()\n",
-    "\n",
-    "ax = fig.add_subplot(111, projection='3d')\n",
-    "\n",
-    "ax.plot(x2,y2,z2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Function to rotate axes.  Makes a rotation matrix $M_{rot}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.80473785,  0.50587936, -0.31061722],\n",
-       "       [-0.31061722,  0.80473785,  0.50587936],\n",
-       "       [ 0.50587936, -0.31061722,  0.80473785]])"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "variations2 = np.vstack([x2,y2,z2])\n",
-    "variations2e = np.vstack([variations2,np.ones(np.shape(x2))])\n",
-    "\n",
-    "def make_axis_rotation_matrix(direction, angle):\n",
-    "     \"\"\"\n",
-    "     Create a rotation matrix corresponding to the rotation around a general\n",
-    "     axis by a specified angle.\n",
-    "\n",
-    "     R = dd^T + cos(a) (I - dd^T) + sin(a) skew(d)\n",
-    "\n",
-    "     Parameters:\n",
-    "\n",
-    "         angle : float a\n",
-    "         direction : array d\n",
-    "     \"\"\"\n",
-    "     d = np.array(direction, dtype=np.float64)\n",
-    "     d /= np.linalg.norm(d)\n",
-    "\n",
-    "     eye = np.eye(3, dtype=np.float64)\n",
-    "     ddt = np.outer(d, d)\n",
-    "     skew = np.array([[    0,  d[2],  -d[1]],\n",
-    "                      [-d[2],     0,  d[0]],\n",
-    "                      [d[1], -d[0],    0]], dtype=np.float64)\n",
-    "\n",
-    "     mtx = ddt + np.cos(angle) * (eye - ddt) + np.sin(angle) * skew\n",
-    "     return mtx\n",
-    "    \n",
-    "Mrot = make_axis_rotation_matrix(np.array([1,1,1]),45*np.pi/180)\n",
-    "Mrot"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Matrix to change the scale (stretch) of the data (think scale value, but nanotesla to nanotesla scale).  An entry of 1.5 means the sensor reports 1.5 nT for a field value of 1 nT  $M_{scale}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 1.5 ,  0.  ,  0.  ],\n",
-       "       [ 0.  ,  0.95,  0.  ],\n",
-       "       [ 0.  ,  0.  ,  1.2 ]])"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Mscale = np.array([[1.5, 0, 0], [0, 0.95, 0], [0, 0, 1.2]])\n",
-    "Mscale"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Translation vector, or offset.  This says that at a field of 0 nT, the sensor reports a non-zero value of the field strength.  $V_{trans}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 5],\n",
-       "       [10],\n",
-       "       [ 7]])"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Vtranslate = np.array([[5],[10],[7]])\n",
-    "Vtranslate"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final matrix combines these operations.  Here I've taken the shortcut to put the translation in, and multiply the 3x3 matrices $M_{3x3} = M_{rot}*M_{scale}$, then this is augmented by $V_{trans}$ on the right, and $[0, 0, 0, 1]$ on the bottom.  this is the form of an affine transformation matrix in 3 dimensions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  1.20710678,   0.75881905,  -0.46592583,   5.        ],\n",
-       "       [ -0.29508636,   0.76450096,   0.4805854 ,  10.        ],\n",
-       "       [  0.60705524,  -0.37274066,   0.96568542,   7.        ]])"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Maugmented = np.hstack([np.dot(Mscale,Mrot), Vtranslate])\n",
-    "Maugmented"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  1.20710678,   0.75881905,  -0.46592583,   5.        ],\n",
-       "       [ -0.29508636,   0.76450096,   0.4805854 ,  10.        ],\n",
-       "       [  0.60705524,  -0.37274066,   0.96568542,   7.        ],\n",
-       "       [  0.        ,   0.        ,   0.        ,   1.        ]])"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Maffine = np.vstack([Maugmented, np.array([0,0,0,1])])\n",
-    "Maffine"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here I apply the transformation the sensor might (the rotation is a bit much but it's illustrative)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "hez2 = np.dot(Maffine,variations2e)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Below is a visual representation of the true field values (blue) and the sensor's field values (red)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADtCAYAAAAcNaZ2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfb/7MMGzomvMxSp+pLPpvqmF329DXxn7yq8SAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xce3ca58>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig2 = pl.figure()\n",
-    "\n",
-    "ax2 = fig2.add_subplot(111,projection='3d')\n",
-    "ax2.set_xlim3d(-2,12)\n",
-    "ax2.set_ylim3d(-2,12)\n",
-    "ax2.set_zlim3d(-2,12)\n",
-    "ax2.plot(hez2[0,:],hez2[1,:],hez2[2,:],'r')\n",
-    "ax2.hold(True)\n",
-    "ax2.plot(x2,y2,z2,'b')\n",
-    "pl.savefig('hezftrial.png')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 3.79289322,  3.8146376 ,  3.82363296,  3.83043869,  3.83607063])"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hez2[0,0:5]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here is the sensor data viewed from the true axes:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0xd480898>]"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEACAYAAABbMHZzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmDyCmMLfxMTE5BHEFP4mJiYmjyD/HzPcYVkR6CScAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xd2465f8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(hez2[0,:],hez2[1,:],'r')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0xd67acc0>]"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEACAYAAABWLgY0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsXXd4FGX3vftTPxULJY0OYujSexUQUEAEBD56UaQrCIgC\nIkWKIEiNFEFEunSkCNJ7L0kooQUSkkCAVFK3zPv74zDfbjZbprwzSJzzPHkI2d2Z2Sn3vffcc+81\nMcbIgAEDBgzkTPzfsz4AAwYMGDCgHQwjb8CAAQM5GIaRN2DAgIEcDMPIGzBgwEAOhmHkDRgwYCAH\nwzDyBgwYMJCDIcnIm0ymYSaT6bLJZAoxmUyrTSbTf5xef9dkMiWaTKYLT3/GanO4BgwYMGBADl70\n9gaTyVSQiL4gojKMMbPJZPqDiDoT0Qqntx5hjH2kwTEaMGDAgAGF8Grkn+IFInrNZDIJRJSLiGJc\nvMfE7agMGDBgwAAXeKVrGGMxRPQTEUUSUTQRJTLG9rl4ax2TyXTJZDLtNJlM5TgfpwEDBgwYUACv\nRt5kMuUhojZEVIyIChLR6yaTqavT284TUVHGWGUiCiKirbwP1IABAwYMyIcUuqYpEYUzxuKJiEwm\n02YiqktEa8Q3MMZSHH7/y2QyLTCZTPnEz4gwmUxGoxwDBgwYUADGmCJKXIq6JpKIaptMpldMJpOJ\niN4jomuObzCZTAEOv9ckIpOzgXc40Bz7M378+Gd+DMb3M77fv+27/Ru+nxp49eQZY2dMJtNGIrpI\nRBYiukBEv5hMpv54mf1CRB1MJtPAp6+nE1EnVUdlwIABAwa4QJK6hjE2kYgmOv15scPrPxPRzxyP\ny4ABAwYMcIBR8coRjRo1etaHoCmM7/f8Iid/N6Kc//3UwKSW75G1M5OJ6bk/AwYMGMgJMJlMxDRM\nvBowYMCAgecUhpE3YMCAgRwMw8gbMGDAQA6GYeQNGDBgIAfDMPIGDBgwkINhGHkDBgwYyMEwjLwB\nAwYM5GAYRt6AAQMGcjAMI2/AgAEDORiGkTdgwICBHAzDyBswYMBADoZh5A0YMGAgB8Mw8gYMGDCQ\ng2EYeQMGDBjIwTCMvAEDBgzkYEiaDGXAwL8OKSlEly8TPXhA9Pgx/v/ii/h54w2igAD8FCtG9Oab\nz/poDRhwC8PIG/hnwmwmCg0lunCB6O5dopgYothYIquVSBDwnty5ifLlw0+RIkRvv00UGAjD+5//\nyNsfY0QnTxJt2EC0axfRvXtEZcsSFS5M5OtL9Npr2K/FQpScjGOJjSWKiICRL1OGqHx5otq18VOi\nBJFJ0YwHAwa4wpgMZeCfA5uNaMcOopUrifbuJSpalKhaNRjvggXhOf/nP0T/938wyklJRPHxRHFx\nRJGRRLdv4+f+fRjdqlXxU7MmUeXK8MJd4cABoq+/hrfetSvRRx8RvfOO+/c7QhCIoqOJwsKIQkKI\nTp3CT2YmjH3TpkQtWmDxMYy+AYVQMxnKMPIGnj0YI9q2jWjkSHjl/foRtW5N5O+vbHvp6YgCzp9H\nJHDiBFFUFFH9+kSNGhG99x5RlSow0CNGEG3eTDRrFtHHH2MB4YGoKKLjx7FY/fUX0SuvEH3wAVGr\nVjD8ciKNpCSiO3cQOaSm4hhffhmLXv78OE9SFiQDzy0MI29AXzBGlJAAQ/b4MSgUqxV/z52bKG9e\nojx5YHxeesnzttLSiPr3Jzp3jmj+fBhALfDoEdGRI0SHDhHt2QOv/f59vHb/PoylVmAM/P7u3UR/\n/kl05QpRmzZE//0vvq+rc3ThAtGKFVggoqNB/wQEEL3+OraXnk708CFyBgkJiBQqVMBPrVpEdeoQ\n5cql3XcyoCsMI29AW2RmEh08CCN1+jRoiZdesvPVL71k9ySTkogSE2F44uOJChWCgSpRAgaoShWi\nSpWQvExPh3dbqBDR0qX6GqWBA4kWLQKVExZG1KQJUefOiCC0Po7oaHD/69cT3biBCOLTT2Gco6OJ\nhg4lOnMGf2vXzjt1lJGB7xAaimtz4gRRcDDOc6NGiB5q1SJ64QVtv5cBzWAYeQPa4N49ojlziH77\nDUnIDz8kqlsX/Hbu3N4/bzaDKw8PJ7p1C4bn4kV4soULw8AR4TU9E5UPHxKVKwfuPDAQnP6OHURr\n1sC4tm5N1K0baB2taZDISOz3119xHoiIBg0imjmT6NVXlW83LQ2J5P378d1iY3H92rQhev990D0G\nnhsYRt4AX1itRNOmwcD37k30xRdQrPDc/owZRGPGwPCcPQuOumlTeJ3NmmkrS/zhByRoly7N/tqD\nB0R//EG0ejUMcNeuyBGUKaPd8RCB9vLzg/F95RWcg88/J2rYkM/id+cO0fbtyD+EhhJ16EDUvTtR\nvXr88hAGNINh5A3wQ3w8DMCLLxItWcLXuItgjKhGDaKxY4natsX/b94EV75zJxKWNWpgAWjfnv8x\n1KxJ9OOPoDI84eZNRDHLlhGVLo3cQfv22njBQ4bgPMyfD8pr9Wr8/vLLeK1rVxh/HoiMJFq7Fiqm\nlBTQQp99BgWTgX8k1Bh5Yozp9oPdGfjH4skTxmrUYGzIEMasVu32ExLCWLFijNlsrl9PSWFs2zbG\n+vRhzMeHsVq1GPvpJ8YiI9XvOzOTsZdfZiw9Xd5nNmxgrGlTxvz8GBsxgrGbN9Ufi4i0NMby5mUs\nKirr3202xnbvZqxFC8b8/Rn79lvGYmL47VcQGDt/nrH+/RnLk4exjz9mbO9e99fFwDPDU9upyO4a\nnnxOAmNQvISFoXgoIQE/ZjOSo//5D7xBUXqXPz+KiPLkwed79IAHv2yZtvz4tGk4vnnzvL/XYoGO\nff16oq1bwaV/8glRx45I3srF7dvg2u/elf9ZIvDmv/wCD79+faLhw/GvmvN1+DDRqFHg0N3h+nWc\nr7VriTp1gty0RAnl+3RGcjKih4ULcc5HjACdwyt6MKAKajx5SWScyWQaZjKZLptMphCTybTaZDJl\nE/maTKZ5JpPppslkumQymSorORgDChAXB5VIu3ZQutSsSTR1KhJud+7A+LzxBpQVGRmQC/79N9H0\n6URdusDIFyiA961aBQXMpUvgzbVCcDDoGCl46SUkCn/9Fcc+YgQ09UWLwtgfOYLFTSpSUtTx/YGB\noHru3gVvLqpi1q1Tfs6uXoWCxhNKlyb6+Wcs4Pny4fx17w5pJg+8+SYUR8HB2M/mzURvvYV7KSGB\nzz4MPBt4c/WJqCARhRPRf57+/w8i6un0nhZEtPPp77WI6JSbbWka0vyrEBbGWLdujOXOzVjnzoyt\nWaMslBcExu7dY4yIsTp1GPvkE8bKlWPstdcYq1+fsZEjQRmkpvI79tq1GTt+XN027t9nbMYMHGtg\nIGNTpzL28KH3z4WGMla2rLp9O8JqZWzrVsYaNmSsaFHGZs0C3SQHM2cyNny4vM8kJjL2ww+MBQQw\n1qYNYxcuyPu8FISGMtarF6ikoUOz00kGdAOpoGukptVfIKLXTCbTi0SUi4hinF5vQ0Qrnlrx00SU\n22QyBSheeQy4R2Ym0bffgiIoWxa9U9auhVdeoID87ZlM0Kvnzw+veNkySByjo4kmTEDPlilTUNjU\nuDF+v3RJnvfsjORk9eqZ/PmJvvoKnuzq1aBRSpWCd3/hgvvPFShgL4LigRdegCzx8GGiTZuQNC5R\nApTUkyfStvHii6DU5CB3blA84eHQ+LdqhYT5lSvyv4M7vPMO0fLl0N6/8AKivKFDPZ+/uDhEA0eP\nosAtPFzbqNCAd0hZCYhoCBE9IaJYIlrp4vXtRFTX4f/7iKiqi/dpvN7lcMTHw7v+8EN4srwwezZj\nfft6fs+TJ4zt3MnYl18yVqIEEqdDhjB24ABjFou8/VWowNilS4oP1y0eP2Zs2jR41HXrMrZ2LWNm\nc9b3CAJjr77KWFIS//2LCA1FdOXry9jEiYwlJHh+/8aN8MbVIDWVsR9/RGK4Wze+iWER9+/j+ufN\ni8jjwQP8PSODsblzGStfnrE332TsnXdw/qtWZaxIESS6S5dmrGdPxn75BVGoIPA/vhwMUuHJSzHw\neYhoPxHlI3j0W4ioKzOMvL5IS2OsenUYVt7qh549GVu6VPr7BQGGbNIkHJOPDxQahw9LO7Z69fBe\nrWCxMLZ5M2ONGzNWsCBj06dnNep16jB28KB2+xcRFoZzmy8fY2PHujf2oaGgnHggKYmx77/HNfns\nM8aio/ls1xHR0Yx98QW+16BBMOAtWjB27JhrVVZaGmPBwYwtXsxY9+6MFS7MWMmSoAJPnDAMvgSo\nMfJe1TUmk6kDEb3PGOv79P89iKgWY+xzh/csIqKDjLE/nv4/jIjeZYzFOm2LjR8//n//b9SoETXy\nplU2AHz6KaiaVav4K1/q1kUitkEDZZ+PiEDicc0aJOm6dEHFaMWKrt/ftSs6M/boofyYpSIkBN9t\nzx4UNQ0disSpjw+KsfRAeDjR5MkoRvrqKxSXObZOEAQkza9e5ddDJz4e33vpUhRVjRyJvjc8ERWF\nxD0R0ezZqNSV0niNMVBqW7eC4jKbcX/37m1o9Z/i0KFDdOjQof/9f+LEiYrVNVI8+ZpEFEpErxCR\niYiWE9Fgp/e0JHvitTYZiVe+OHIE3s+TJ9psv1w5eJM8EBrK2JgxCNNr1GBsyZLsxz1+PN6jJ8LD\nGRs8GFRD4cKgdPTG1auMtW+P6GLBgqxUUrt2jC1bxn+fd++CvilQAFSJXGrNEzIzGXv9dURFLVsy\n9vbbqCeQ45kLAmOnToEuzJOHsVatGNuzx/DunUBa0jXYPo0nomtEFPLUyL9ERP2JqJ/De4KI6BYR\nBZMLqoYZRt6OxESEqcuXQyEyahRjAwci9B0yBHznxImMLVrE2JYt4DlnztTueEqVYuzaNb7btFoZ\n27WLsbZtYVj79mXszBk8vDt3gkp5FoiNxfklYqx5c1AqeuPsWcaaNUNuY9UqUFxr1oDy0HKf774L\n3vyvv/hsMywMhl3E3r2MVaoEOuzECfnbS0lh7NdfwelXrMjYihVYSAwYxVD/eDCG/izr1hHt24fw\nvUwZ/Pj7gzoQC5LEtr2JiWgqdfYs1Cwvv4xwu0wZqGqqV4cm/p13vLfz9YZq1YgWL8Y2tcD9+yge\nWroUqpqePYm++Qa90eVOcOKFli1Bj6Sm4vdx4zCcRE8cPEg0ejToikmT7Lr3QoW02R9jdsqoTBlQ\nLGq+87VrUBaJjeaIMPhl1SoowJo2BWUUIFNoxxjotZkzURfw1VdEAwb8qwuzjLYG/1TYbAhfK1RA\nYm3CBISmckLmpUsZ69oVHvD9+4wdOsTYzz8z1ru3Xc9ety7oj/375ZXri3j/fcZ27JD/OblwLNMn\ngmfJUyUkBzt3ImmcmIjr4uODNgp37uh7HILA2Lp1UCsR4VprjYwMqJB8fJAQVloDkZrK2CuvuL6f\nk5MZ++orKIzmzVNOE124gGiwUCFQXP9Sz560pmt4/fyrjHxEBGONGjFWpQoMilKOsU8fGHV3SEpi\nbN8+9DWpXRsc6XvvgQa6fl3aPgYPZmzOHGXHpxTduuH2y5MHBTcXL+q7f6uVseLFQWMwBnnq2LFQ\njPTvjwIxPZGWxliPHjgnAwd6l13ywL17kHoWLSqfSxdRuTJyRu5w5QqouUqVoL5RijNnGPvgA1yz\nZcu07a30D4Rh5P9pOHMGlYg//KD+ZmzSBIkoqUhMRHOv/v2RbCtThrFvvgFH6k7eGBSExURPhIUx\nlj8/qlSnToWn1qQJohG9km4//ADD6ohHjxj7+msY+9GjcT71RP36eCz9/XFdeCZK3eHgQfDgTZsy\nduOGvM9OmAA5pScIAmoWChXCgv7okdIjZezoUZyjihVRo/EvgWHk/0m4fBkFKX/+yWd7agqHbDbG\nTp+Gl1+uHBQvX38Nr9nRkJ4/j9f1RpUq9gXMbGbst9+QBK5dm7Ht27U39gkJoCxu387+2r17oMT8\n/UE36EUTiPfPsWOIBCtXVt8CQgosFrRk8PFhbPJk6d83IgILopTisuRkFFMFBCDRrPT6CgIij+LF\noUq6dUvZdp4jGEb+n4K0NBip5cv5bbNcOTz4PBASAu+0WDH0b5k0Ce17LRYoeMQKRr2weDFjH32U\n9W9WK2N//AFPrVIlxtav1zY0HzsWRUPuEBwMmuDtt3EsekQZ3box9t132NeaNZBcfvKJtN48anH3\nLmSM5cpJp1c6d2ZsyhTp+zh1CiqfDz9U1z46PR3RmI8PnBe5PYOeIxhG/p+CSZOgg+aJKlUYO3eO\n7zYFAfTNwIGQN7ZsydhLL0GyqSdSUuAFukp2CgK8+Vq1UFG5cqU2xv7xYxgJb20A9u7FtahVS5k8\nUA4iI3FexGNKSmJs2DB4+AsWaM9HCwIWtIIFGevXz3t+4OZNnMPYWOn7yMyETNjXF99JTRX3/fug\n3YoVwz2TA2EY+X8C0tKkGQu5eP99JG61QmoqIg8I16DZ1zP8HTYMP+4gCDCwderA+9uyhb83PXUq\nBmZ4g80G7XahQijP16JlgIhp06BCcvyuISGMNWjAWLVqyPtojYQExgYMgLHfutXze4cO9RwRucPl\ny6DnGjRQ/+zs2wcVW/v2Oa5jpmHk/wlYtw7FNbzx2WfwdLRGQgJuhz594DG2aKFP5WFUFKIJb1SR\nIEDmWakSKmn//pvfsaWlQWEitZ/OkyegvXx8YIwzMvgchyMyM5E037gx698FAQtN/vzgt7WqgnbE\nkSOgq7p1YywuzvV7kpJwDvfulb99qxVN8nx9EU2qua7p6aC6fH2RuM4hU64MI681BAGKgNOnYcwX\nLYLkcNo0SBUXLUJnw1atUNafnMxv33PmgFbRA//9Lx6M9HTI1CpWBDe7eDHffvLO+PxzaKqlwGbD\nNShVColJXknJNWuQ5JSjZrl5k7HWreE9alFncOwYjLkrGuTxYzQ/K1aMXwWrJ6Smwlv35NXv3o1k\nqFJF0tWriFJatFA/5vDKFUQITZogz/CcwzDyWiAxEWqPjh3xoOXOjdap7duDpxwyBF30hg9HyT4R\nvIcyZWDw8+YFf9uvHwzn0aPKEkOHDmE7emD/fqh5RE9KECBT++gjfLfRo7UJg0VvXk5hlMWCEvii\nRXF8atsTCAIkhD/+KP+zf/1l78QoV4LoDV9/DSrJnXe7Zw8Ma7du+iRmvXn1gwZ5Pl5vMJvR28jf\nHwl4NbBYkJj19cW98hz3wzGMPE/cvQseMnduVNotX47mVt5ukDffREENY3jvw4d4IObPxyJQowZj\nuXIxVrMmFoYtW9yHvo5IS0NVK8/owB1sNnilrhKLN29iYcubFxQS79zDiBHKON2MDERTvr6ICNRo\nsG/dci+p9IbMTByHjw9a/fKicDIykItYscL9e1JScP4CApCg1tqYOXr1zvmijAxUEv/0k7p9nD6N\nhbNrV/WFYSEhSJq3bKltHkVDGEaeB2w2u0549Gh5SgGzmbEXXvD+cKWlgfedMgWyvDfeQEJx4kQk\n0tzxh40a8dPde8P8+Vjc3OHxY8bGjYNR7dqVX/fKhAQYKaWVr48ewcj7+sLYKjWy06ejeZhSQxkR\ngciidGl+PesvXcL38hatnD2LnIVexuzQIURSgwZlpfPu3IEnfuiQuu2npqIau3hxxk6eVLetzEzc\ntwEB+j1LHGEYebXIyGCsSxfQIko8VLOZsRdflP+59HQkqoYPh249IAD8+4EDWWVys2ejMEcPpKXh\nOLwZ76Qk5CTEGaM81B4LFqAEXo0neu0aePK33kK4L3dbZjO4+SVLlB8DY4jUihRRX+EpYtEiUGne\nciOiMfPzQ4dLPQrKunbFouYo9d27F4aeR3fTrVuxrWnT1CdSjx3DwjRkiDYJc41gGHk1EARobFu3\nhoFTApsNRt551Jxc3LwJOV+VKripBwyAwY+IAE2iV9XlDz+Ac5WC1FRUhBYpguhEjabfYoEhW7tW\n+TZE7N8PY12vnvyK4cuX4Tmr5deTkyEP9fdHIluNwRUEOCJS20+cOweap107eVGpUqxejYVl6lS7\ng/Lbb1hseRTZRUTgWjZvrn578fHIG1SpIr2/0zOGYeTVYPFiJFTVqkcKFODb1Eo0+JUrQ5dNBI9e\nDyQl4YGVU2mbmYlGagUL4gFSWqV78iQS3VLyFd5gtcID9vcHlSOH250/H3kUtQs3Y2gbUb06ohQl\nfL+I5GQk9n/5Rdr709ORuM2fP7sUUwtERKCzaIMG+J0xRBXVq/OZqWuxoEK5YEFlUk1HCAIiR19f\nz/mOfwgMI68UCQkwZsHB6rdVsyYUNFogJAT8PRFjDRsiGax1CfesWSg7l4vUVHDi/v4oGFJSWPX5\n53wbpj1+jIZtAQFQWUgJ+QUB3Pa33/I5BosFyh0fHwy9Vko7XL+OcytnRu6JE5ip2qWLXRygFaxW\nRIL+/miUJwiISOvX53fP7tsHQ//dd+qrf4ODIccdOPAf3cbYMPJK8dNP/Pp3f/aZ55bAapGaCgXP\nTz/B+ObJgxvzyhVt9peRgVBbafIwKQkJZR8fyEjlSC+TkjCib/9+Zft2h7NnsRjXrg3v2hsePECE\nxvM4wsJAO9Svr5wq2LMH3rmc3vepqVg85RR9qcHx49DwDxuGiKJ3b2jWlVKiznjwAFHD++9jEVeD\nxETklerU+cdWyhpGXimqVuWngPj5ZzSR0hIjRkCbzxjUE+PH42Fv2hSKAd49TdauRXGKmmTX48f2\n1r3jxkmv0Ny1CwaJd6tfmw2DWMQkt7ft79uHc6ymkZYzrFZ48z4+iHqUXLc5c5C/kEuD7NyJ7zNm\nDB8qyhPi4mA8q1dHfqNLF+RteBl6iwXPRPHi0hZtT7DZ0H2zYEF9FkGZMIy8Ejx+DM+Y141+5Qo8\nFy3VDLdvwzA4GqaMDKgoataE5/3TT/wGTthsmDrFo3GZOFC6YEEoV6QYtgEDUNWpBeLiUL9QqBBj\nmzZ5fu+0aVBe8VZj3LoFeWzNmqj2lANBwCL13nvyj+vBAxRu1azJv97BGYKABc3PD1XFXbrAA+fB\n0YtYvx7cOo9B6Lt3g2qaO/cfVTxlGHklOHIEYTsvCAJCe94Vj87o0QPdLl3h1CnQT/nyweNXWxrO\nGPIBfn782hCfOYPEXIUK3oehpKSgOEvLpOHhw5D/tW3rPnEuCFCpaNFewmZDAlAJV2+14rg6d5Yf\nbYnG19cXKhitDdrZs6iUHTwYEW+NGuppFkdcvYrr2K+f+sU4PBxDVPr31z7akQjDyCvBypV4OHhi\nwAAknbTE9et4MD3RDBER9urUfv3Ue2sjRyKJyguCwNjmzTDgH3zgOa9w8iQ8K1GtoQXS00El+fi4\nb2qVmIjkJQ9v0RVu3EC00LSpPJVWWhr4/eHDle03JARSy06d+HrXrpCQAKly3bqI6sqX56tIS0qC\nsqtOHfVOSVISop1mzfQZxegFhpF3BUFAIcbSpXgAWrQAN1iyJDxusbVupUq4kL17g6f89Vd4xEra\nCBw6hO1pjR49UDrvDY8e2TvydeqEochKkJKivMOgJ2Rm2rsPDh/u3shMn46oS2v1w5UrSIrWqeO6\nGOzKFUQ1WnG2Fguuq58fmrBJRXw8GsnNnKlsv2lp8FoDA5XfI1LhyH23bIkEu9LJZ+62P24cqFO1\n27VYMNqwbFl10lcOMIy8I6KjcZGLF0eBTo8e4FS3bYPxvnYNGfQxY+ABXbiABlNLl0IN0qMHErK5\ncuFG+e9/keQ6fdq7kbFacfPK5VflQhzSILWhV3IyDEDBgkiEKbn5d+/WJhHKGLyuTz7B8bnqvWKz\nQVGk1FuVA5sNOQhfXxhc53D977+RtNWSyz5zxt63RarkMTIS12fxYuX7XbMG33vhQu3pm927cR5r\n1cI+d+3iu/1167DdLVvUbysoCMlqPcYwuoFh5BkDDzdmDPjowYOzzzF1xoYNngdFWK2gRn7/HTRM\npUpI1H74IQpl3D3k33zjeQgGLwwfLr+hV1oaPOf8+Rnr0EG+/LJfP8Y+/VTeZ+Tg5EkssPXrZ1+I\n4uKw6G7erN3+HREZierKqlWze/ULF8IIa6k5FyWPRYpIH1h98yY8YzXjJ8PC0GK6c2ftm+LduQP1\nVuHCjL3+Os4rT5w5g8T61KnqF62//kKEtW0bn2OTCcPIR0WhRLldO+kcn5LE6+PH8BA++QSUT4kS\nCOcOH7arRe7exUKj9TCHhARw1UoaeqWkgALx94e3KFWvnZyMCEmL3ukinKtUHQ3p6dN40PQKnQUB\nSiBfXxgKx17zw4ahglVrCmn3btxrY8dK63V/7RoiojVrlO8zLQ3Ko5Il+VIpriBq6F97jbFXXoGD\nxvOcRkVhIeneHfuSi4QEJKjv38eikT+/dnkZD/h3G/nYWHCJclfrR4/QTljNxPiQEChdKlbEg/jF\nF1g82rbVZ5rTggWQoyn9DsnJ4Ed9fbFwSVkgDx6EEdG6d7lYpVqgQNZGY/PnQ5mjR+tlERERSIjW\nqGGPfqxWdJvs1k376UMPHiCqqFdPWgI6NJRPK4NVq+y92LWEICDCFOcw1K3Lt4tmairmQjRoIL9d\nxr17kPHmyYNr8P33iA6mT9dVYvnvNfKCgKTp6NHKPh8QwG9qTFgYDH6FCux/SV0eHfg8wWIBnfDb\nb+q2k5CAc5gvHygvbyqLUaOQyNZjtNrx41BhtGqFayUIoKnatNF3tJsgIMLw8cEDbrXCeNSrB69e\n6wfeZrNHX1Ioq4sXcX97qwHwBlGa2L+/9l0bd+/GolKsGBwJnm1CbDZ7t1clz3xqKooDGzRg7D//\nwfP9xRe63YP/XiO/YQNoGjkj2xzRoYM2zYkuX7Yb+gYNtO01c/48KAwenQYjI9EaNyAAySZ3GmGz\nGVSX2sEQUpGZiYjDxwceX1oaziuvvjJycOcOCpjq18fv8fHQVE+bps/+T54EZTZokHf64eJFePQr\nV6rbZ1ISFtXatbXvU3/9OnrJBAbC6Zgzh+8COns2PHGlcwsYQ7viatXwfBcvzr/S3AX+vUa+WjV1\n/HBQkHZ92vfvxw2wYYO918yAAdr0mhkxQnprYCm4dAkRUqlS8BpdPWR37mBx4dFHXiquXwcPXq2a\nfeydGu4VlArbAAAgAElEQVRZKWw2NBsTe7ZHReFY9OJqExJAP1Ss6D1avHIFRk2N6oYxfOdJk/h7\n2K6QkICeNCVK4B5s1YovPbh+Pa6dGkmwIGDxFJ05jRsGamrkiagUEV0kogtP/00ioiFO73mXiBKf\nvucCEY11sy1+3/r6dfC1albRu3cRHmpR1SYI6Bj5++/4v5a9ZlJSYGR4y9D27IEhadjQdafOjRvx\nIPJoCywVggB6yt8fhuD11/VdaBxx4QLC/y5dkBTOn18/9Y8gwHD7+nqfhXrzJiiQWbPU73fXLpz7\noCBtKSpRoy52zyxYEPJVXjh8GN9DbZQTFWU39OHhfI7NBXTz5Ino/4gohoiKOP39XSL6U8Ln+X3r\npUv5eK81a/Iv8hFx5Ai0y44NmTIycGNVrw4DOXs2nyTigQPw2HiWijOGhWjhQjwQgwdnN+jDhqFq\nVYeQNQtiY+HNig/YsypWEUfUFSuGa+nvr636yBnnz6Nn0ZdfenZWIiJAgXz/vXrjfOsWck+9emnP\n08+ZAwM/bRru75Ej+e3z8mU8n2rnNKSm2u/Ds2f5HJsT9DTyzYnoqIu/v0tE2yV8nt+3/vJLhMxq\n8eOPygZIS0WHDq6rUwUB/GqnTvDGxo5VH5IOGwbDp4WHFRcHHtjfHwlI0ahbLOCox4zhv08pWL/e\n/oDxLJGXix07EFk2aQJqzltfHp6IiwOlUbeu51a5MTGIzAYPVr8op6Tg3q5bV/vJU1u22FU+bdog\nB8LLmEZEIFpQu/ilpdnvwxMn+BybA/Q08r8S0SAXf3+XiB4T0SUi2klE5dx8nt+37trVToWoQXQ0\nHkqt+nbcuYOEoaeH79YtqBfy5kWIqrRPS3o6yttXrVL2eSm4dAn0TeXKdm724UN4RHpMH3KF2Fj7\nA8ardbTS42jVyn4sUouYeMBmw4B4b/3vExOR1/j4Y2W6ced9ii0EeAze8YTTp/Hd5s/HqEF/fyjC\neHj19+8jMvnqK3WGPj3dfu051xfoYuSJ6CUiekREfi5ee52Icj39vQUR3XCzDTZ+/Pj//RxU80B2\n6OCdi5SzraAgPttyhTFjpFFL0dG40fLmRSispKPlhQtIKskZKCEXggA5WeHC9kHV587B21I69o/H\nMb38Mm5pniG9kuOYOdP+sGudpHTG3r3IDfzwg3t5X0YGIsgGDfhU7a5dq081aHg4ciDDhyMqadsW\nTs2pU+q3HRcH6rZ/f3VRTkqK/dqriMwPHjyYxVbqZeQ/IqLdEt97h4jyufi74i+dDd268fHkGYP3\nV7asdomkJ0/g7Ujl/uPj7VOVPv1UvsH+6Sf0BNG6GjM5GbRZQABkor/9htBXz0SsIzIz0X6CCNp6\nras1PeHkSfvDzjNhKAWRkZA7tmvnvvLaZgO9V64cn4Eop0/rUyQUH4/ahO7dkYNYuxb334AB6hes\n5GQUF3btqk6MkZBgv/acclV6Gfm1RNTLzWsBDr/XJKK7bt7H5QszxrCa8+DkGcNNWbEiY9u389me\nK+zahUSrnIHh8fHQgufLh5tYKucsCGjpqkdDL8aQ/KtWDdz8hx+CznlWnnRysr0fiq8vvGo9i6Yc\nER+PfkdEfAavyEFGBhyEd97xnJSeORPG+dw59fu8dw91K1onZFNTcZ+1aAHPOT4evf4DAiBjVXO9\n09LQHbNdO3WG/v59XPdixZRvwwGaG3kiyvWUqnnD4W/9iajf098HE9HlpxLLE0RUy8121H1Tsxk3\n0unTSMAEBsI7PnMGN7KafjEbNqBsXUsvpEsX+/g+OXj0CJ/Lm5exoUOlJbrEhl5bt8rfnxKII+3y\n5sVtpVUCWAoePYIn37cvvL733nt2SVlBsKuAtB4P6Wrf8+fD+Hni6TdtwoK4YYP6faakwEA2aqRt\nH3azGYtJnTr2yPHcOVAu9eqpK3bKyEBupUMHdYb+wAFc9zlzlG/jKXJuMZQgIIs+YgToh1dfhZyq\nWjV7aXGTJijtL1YMrwcEoBqxTx9I/86fl3ahbDYYBt5ac0fExiJhpFQZcP8+ErM+PkiyeYsKxIEb\nGup3s+HePejXiWBcnxViYuAEzJqFIh5/fyhxnhWCgnBOihTRf9rQ/v14LubNc7/wnj+PY5s8Wf3i\nbLXCGSlfXtthL4IA56dcOfsibrMx9ssvyEv07Kl8/xkZiBT++1/lFfWMoaiQSHWb4pxp5HftgoKj\nRAlk8A8fzlpVlpaGnu+OGnSbDSqWgwftg7XLl8f76tbFdo4dc3/R1q2DN69leL9mDWNlysijbZxx\n8ya8jCJFwIV74v3mzAFPrXFFXjYsXIjbK29e/fct4u5dqH6WLkX0FxgI70/rCUjuIHp2RHxGM8pB\neDgUJJ9+6p5KiY5G/Ua3buqVN4xhgS1cWJ1XLQU//ggnz1GokJwMWXK+fBgkrySqSE+Hw9Kli3JD\n//gxrvdLL6kaDZqzjHxmJnTrb78NqsGTV1GrFmP79nnfZnIy3vfNN1g4cudGZv6337Ima2w2RAla\nl8p37gyPXC1OnMDiVamS++SeIMCj6dRJf/rk6FG7UTt0SN99i7h+3d5698kT3FslSjy7KtnQUPs5\nOXZM330/eQLpZO3a7sfjpabCe61Th89CtGEDlDda1w0sXYrcgnPbkOhoXHM/P3SqlbvAp6XBG+/W\nTXkStUsXPKOBgYpFCTnHyFut4No/+khaFeiUKeg5LhcPHqDqtF07JMZatEChRVyc6ypV3oiPhxfO\n48YXBOjTAwPBI7ryFtLS4KFNn65+f3Kxf7/dqA0erH2ffVcICUH4Lo7UEw3P7NnPJm9w9679nMyd\nq+8xiNr24sXdy10FARRXoUJ8FqKjR0EXqe2W6g0rV+I6u1JVXbkC1YyvL5Rrcjz71FTQwr17K4vy\nV6xA5D1sGNoVK1gsco6RnzwZEiapnOXVqwgH1TwkycmQYbVvDw+/QwfQOxMnKt+mFOzbh4fo0SM+\n28vMRNjq44NWwM7GNDISxSR//cVnf3KwaRMGQtSrB+MiJfrijeBgGAAxSrt9GwvfRx89G8nnw4eg\nEojg6amh75RgxQosdJ7knWKfmvnz1S9EYWFovzBliraL2oYNOGZ3kdr166DsfHxQv+KpSNERKSmI\nmpW0lb50CbSxxYLFYtQoeZ9nOcXIx8SAv5WbKHnnHX5VjgkJ4JJ9fHBqunZFNapWGDmSf1/26Gho\niAsXhkFzvCGPHsWD7WpItdZYsgQGfskSHNvQodpGS64QGoqFTmxKlZkJnX+xYpqUonvFkydY+IjQ\ns13v/juHD8PD/uUX9++5dQvy4h491C9EMTF4XocP19bQ//kn7nNPUcjt22jTkTcv6NMTJ7wfU3w8\n8hqTJ8s7nqtXUT/CGJy64sVliwByhpGfOhUzROVi3jxcJN7o0gWnx9cXNMiePfwTsmYzvIMffuC7\nXcZwg1eujKpGx/B15UoYNalDwHli+nQoIW7eRI6gXDlU6OqJK1fA0TsW0m3bBu9v+nT9NfWZmTgX\nRIy9+KL+uYsbN0D1ff21+++emgpOunJl9Uqt+Hjw/Z98ok614g27d8PQezufiYmg7UqUQD5u0SLP\nRVUxMXivnMlv69dDey/iwgXYlatXJW8iZxj5OnWUhfEJCeg9w7tJUmYmQqzff4f3WbEivK2gIL6j\n5+7dgzd1+DC/bYoQO0j6+aFdgqhy+f57UBXPQvXy9dfQMj95gh4kYkJMzy6W166BKnPs/373Lu7B\nli31p29sNpwXkadX2/tdLh4/hjPQvr17b10Q4FD5+anvUZSSAm66bVs+Kh532L/fu0cvwmZjbOdO\nJJ1z58a52LLF9fm4fRuOgpjj8QSrFfeVc0tj0aZI/P7Pv5G32aBxV2o8+/RhbMIEZZ/1hGPHcDET\nEnCTHzoEdYKvL2PffcdvkMGuXTA6WnXzi42FJ1asGG5kQUASqU0b/VsEi+P7GjfGAxQRgd/r1dNX\nzx8WBtrIsWeR2Qwq4a239I8wGINxFw39F19o6+k6IyMDNF+tWp7zRGfOwJMdPFidgc7MRJFYkyba\nzuvdsweGXk5/m4QEUFiNGzP2xhuorl28GNSVSOkEB2O7npwzcbjL++9nj5IEAfk/iSq759/Ix8XB\nG1eK69dheLW4WQYPBh/piBs3UE2ZNy9jQ4bwKfgYOxbtALQslPn7b0hTO3aE59qkCThpvWG1wqA0\nbQpe3mZDeb2vLxQYeqlNwsNxPiZNyrrPdetwLMuX63Mcjti9G5pqIpwfHg3EpEIQ0NmxdGnPc1AT\nE3EPVa6MZ08prFY8RzVq8J+D4IidO5UXIcbHI7clDi4JCIAqb+JEUE5EEDM8fAgq5/Jl0DODB+Me\nGjDAfe4pPh6O159/ej2M59/Ix8Tg5KlB587aSARTUpA0cRWiOnaN/OQTdZ0fbTZ4DIMHK9+GFKSl\nQVXg6wuapEwZLmXXsmG14sF5/327RxgSAoqsa1dtvTtHxMQgmeacDLx8Gdd9wAD9+/AEB8OgEOEY\nwsL03f+cOYhyQkLcv0ccbO7rq661tVi1WrEi3xF/zti6FYZeTWGWIMChW7MGz5Dj0Boi2LAyZaDY\nmjpVWmR67Bg+5+W7P/9GPikJY9zUICQEJ0sL43DyJLbtLlkZHw/6Jl8+NEqSKstyRmIivKilS5Uf\nq1SEhqIopnhx3AZaDDT3BosFD0rLlnZDmpoK7y4wEKX2eiAuDufi00+zUiSJieCNa9XSv/dNdDTa\ndRCBI9Zbdrp2LYyit1zRpUu4Z7t3V96rRhDQiK98efdFWjywcSOeY97tsL/9FvePUrXYV1/B4fGA\n59/I22zQUastlOnZE7SHFhg7FiobT1SCYyOxYcOUcexhYeD69JD0Wa1oSyx6Ino1M3OE2YwkV+vW\nWVsjr1uH86BXsdCTJ6hs/PjjrJ67zQavLH9+fYeAMIYo8uOP7ddHb/po715cg02bPL8vJQXOTdGi\n6uTMEyfCE9ay5cPq1YhSeM5bEAQYaaVV5ampoA09dMF9/o08Yyj7VVtqHhkJb1oLryszE9yhlGHI\nMTFIqOTLB44zMVHevnbuhJ5by8Efjrh+3d7wTY8owhlmMzzmtm2zGvpbt+wFS1pytiIyMrDgNGmS\n/Zr9/Te8wJ9/1v44HGGzQQ0lGvoJE/StkD1/HvfiwoXe37trF2imESOUU1xTpjBWqpTyaFgK5s0D\nDcZT6KC2qvzAAVTBu2m7kDOMfI8efKRjY8bAo9cC4eHwbE6flvb+iAhQAAEB0NXKUUvMm4dBJlq2\na3WE1QpvmgjXQm/VTWYmjHzLllnD3sxM8OVFiujT68VqhVdaoUJ2Z+HWLVyTAQP07yS5bZvd0Pfu\nrf1AGEfcugVFzbRp3t/76BESkxUqKB8J+OOP8Gy17GA5bhx63/NsVqe2qrxPHzAALpAzjPyyZdCo\nqkVyMsIxLXTnjCF0LV5cnvG9eBHeYbly8loZf/klZFx6PtA//sj+l0TSK5IQYTYj7G3cODt1t2MH\nOGI9+s0IAgxakSLZq4OTkkDbNWrEryWFVIiFXERo/yE3QlSDqChQKWPHej//ggCVlK8vqkOVLIiz\nZkHKqlUuRBBQ8fruu3y1+mJVuZKOk7GxqLZ3oVjKGUY+Kgr0Bg8PctMmeFxaGcfPP4e3IsfYCAKk\nUqVKQVEipbWA1QrvtmdPfUP0JUvsXuOKFfru22qFR1OnTvaFNDwcycj//lcf9Y1YrOXMxVutKF4q\nUUL/mbYJCfYe5QUK8BndJxUPH0I2+eWX0u6JiAjc65UrK1O1zJyJ50Wr6myrFTx627Z8I9eFC+HQ\nKckx/vgjImon5Awjzxg4LV6dGVu1Ar+nBTIyULWpZPtmM5KJfn6gBbzpoFNTkQvQotjLE8R+8K++\nCgWMnlWgNhvqD6pWze4tp6djEShbVlZZuGIcOIBrtXp19tdWrIC3KkHnzBVWK9pmiwuxngtNfDyU\nJH37SjOMolfv5wcFmlyu/vvvobrRKmrKzESUPWQI3+327q2MNs7IQARz5EiWP+ccIz9vHjTSPHDn\nDkIfrQxBVBRC5x07lH0+Ls4+l3L5cs+e0YMHuPBat2p1RlAQjq9tW1BgUgeR84BYmFO+vGu1xdKl\nMLB//KH9sYSEgLqZOjX7dTp5EvfBjz/q37Z4yxa7odezwVpyMuiqrl2l55liYlBhXa6cvOpTQUDX\nxipVtMtPJSTAaZg7l982U1KwTSWKqKVLs01VyzlGPjwch3TxIm4KtXTLggXwgrUqDz9+HB6KmmKV\nM2cQwTRo4JnCuXYNMj69ZY5z52KBEYcyDBumb3HQ5MlIwrnqBnr+PI5t6FDtE6FRUWhg1aNH9u8f\nGYlingED9G1FwBj6qIiGXs/xhmlp6KDqrIjyBEGANDYgAJSn1JyCIMDTrl1bO5ruzh3QXzyjstBQ\nOCLOg0y8wWxG3s9BaPD8GvmYGEiOmjSB150rl/2GDQhAV77cue0P17Rp6B8jtehAEMBfTpok7f1K\nsGQJikHUJMGsVixIvr6Qn7m7kc+dc80Ra41Zs2Bog4PhjVWrpm0LZmcsXAhv2VU/mbg4GBs9EqGp\nqeg3UrdudvldUhLutZYt9R+Mkp4OCTKRol7liiEqotq0keeQPX4MuqdgQRRdSYmABAGfadhQu977\np0/jGeRZhLd0KaJRuce8aBGkw0/x/Bn5tDR4hHnz4sL9+SeSK4KACrqCBXHT2Gy4IU6exOSmL78E\nF54rFxJzEybgwfd0k0RG4sJp2XBq0CC0JFDbpjY2FgMNCheGZM4VDh6EoVc6DFwpZsxAFWpkJGg1\nX188oHph0yZ87/37s78mJkLfestzKT4P2GxQmBQvnn1fZjPyBVWqoGJVb4wahUe6alX99pmZCWPU\nrp38aOr4cURATZtKU6NYraCIWrfWLmLatAkRK6+EtiCg5crQofI+l5ICIcpTGenzZeSTk1Em3rGj\n+34NzZt7HmSQkoIy7+HD4WEWKYKeL4cOuTa0q1YhS69VqGc2w5McPpzP9g4dgkHt3Nn1Odq2DZGO\nHolHR8yYAUN6+za8ncBALNJ6TTU6dAiG3h0Pv2oVFp/Nm7U/FlF545yTEQRQTEWLPpvhLDt32qNh\nvQaWZ2bCyWnfXr6ht1hQde3jw9j48d7vpcxM2Ic+fbTLgUybBgqV11CbuDg4rnIj8CFDUPfDnjcj\n37EjWs16ukAnT8KblXKSBQHG7ocfMHWmeHEUOty8mfV9ffqg3a5WN0Z8PBIt8+bx2V5qKqibgADX\nIe2KFVjcPHUL1AI//4xrc+0ajEjXrghH5fKOSnHpEjwtd+f5zBkc38SJ2g8AOXECPO6MGdmvz6pV\nWASexahDMbdFhGdJD2RkgKrq2FGZlx0ZCSqsaFHw9p6e0ydPYIS1amEiCJBW9urFz17s2AHbJMfR\nDA3F+RCE58zIFy8uzXi3aQOdrBwIAjzMoUPxgDVogGSUxQKj+c472pbt37mDFZtncvT0aRjRjz7K\nTgHMm4dIRu/mWcuXw7gFB+Oc//qr+m6EchAejrL0MWNcP4QxMUjStW+vPT8eEYEcRefO2YewHDqE\nAi5xrqyeiIuzG/pJk/SpYE5PZ+yDD3AulNIphw5BV1+/PnJQ7hAbi3tg/nxl+/GGlBRQSbycNsbg\naPbtK/39goB835kzz5mRlzrq7vJlGGqlPUvMZhj4Bg3g2U2dCu2pr6+2YfTZs/JaH0hBRgaiEz8/\nVAY7GrYZM3Cz680Br18PAyZ+z+BgLDiff65Phe7DhzDknTu7rljMyED758qVte2Dwhicll69UMrv\nPKc1JMRz5KElEhPhCRLBaGrZJkBEejrolG7dlEdSVisEDfnz4xq6K4YSJzRppSoKD0ckzWuGdFIS\nrocc2mb0aMZGjXrOjLwcjewXXzDWv7/097vDhQu4WfLkQdL2lVe05Sv//BOeLu/BzMHBUFG0bZuV\nq582DTkHLbv3ucL27Vmn4yQkIOKoU0ef6CItDdWvdeu6zl2I7QkKF1bXR1wKBAF1Bf7+GPzhiDt3\nsBB/953+WvrkZBh4IvDeesgs09KQoxo4UN33TUpCQl3k6109sxcv4h50Kh7ihr17sdjwWiC3bEF7\nCKmO0KFDjNWq9ZwZeTn9ohMScII9hW1yEBuLlVEMY7VMXP78M/9Od4zBQ/36aywijkm/qVMR2uk9\noHvfPjxkO3fi/3q35rXZQNu8/bb7eoX16xHBiceoJY4cwbVxLpyKjYXqZcAA/Zu/paSguCYwEHRp\n797a971JSgKN9TRxqArh4ZBQBwRgoIlzncKePXhNK1nvtGlwJHjUYojV+FOnSnt/aipjuXI9Z0Ze\n7vSXZctws/CUTMXG2g19p05IImqBceNAF2hRqXfoEEaH9e9v54InTULyV8vBC65w6hQeMseK3L17\n8bfp0/XxXn/9FV60u9D6xAksPHq0Co6KgoLs44+zGtOkJDRf69hR/2lTYvGSqEwpUoRPCxFPePQI\nXuuMGXy2FxwMFU+xYoz9/nvWxXLBAuxLi2fNZkMPntGj+WwvPBzRidSZxu+8o62RJ6JSRHSRiC48\n/TeJiIa4eN88IrpJRJeIqLKbbcn3nsWCJqkrn1SIHd9q1IAn2qcP/2ZPggDKqX59bWSGiYnojxEY\naKfBJk5E6bhWQ8HdISwMD9+0aXajHhmJ89u5sz4ySzGqcFdKfvs2DMGwYdp70xkZoCsCA7NSRenp\nMP5Nm+o34tDxmFq3RkJ61y4Y+v79tT2Oe/cQPSxZwm+bR49i8Hv58pDTitdyyBDYCi2qn2Njwf//\n/Tef7U2ejOsgBW3a6OfJE9H/EVEMERVx+nsLItr59PdaRHTKzefdF/l4QkSENgnT8+ex3cOHsUrn\nywfZIs8BFTYbDPEHH2iXkNywAV7s5MnY34QJoG70Vt1ERUHBNHSoPemWlobRcFWr6tMx8epVdIf8\n+mvXhjw+Hnxxmzb6LDxr1+IeW7zYvvg5dtrUs10wYzD0H3wA6WtcHOYdFC+uLbV24wYoLJ75AEEA\n/VarFqLXVavw3Vq0ACWmRfS4bx++Bw9KNDUVCXkpAo2+fXU18s2J6KiLvy8iok4O/79GRAEu3ofu\neUqweDG0sbwr3dauxU1+/z4SlwMH4qH86Sd+RtliQbK0Y0ftPMioKCiJmjUDXeNYuKQnEhJQet65\ns52SEAQ08CpYUJ9GWo8egRZp0cJ1+J6ZCfVHvXreu4DyQFgYlDddu9olnTYblEjVq+vb4ZMxLLxN\nmoCbt9mQ2ylUCFGnswyUFy5dgiPCu25AEOBd16+PHNi8eXBwtBpOP3YsojAeNRhLlsDh8LYgDRmi\nq5H/lYgGufj7diKq6/D/fURU1cX7cFMrgUjbjB+v7POeMHEieH/xAbx2Dd5O6dLyhnx4Qno6HqzP\nPtOuSMdiwU1YsCDK/xcsgLJE78rY9HSUub/3XlZFxM6doFP06KZpNsOIli7tOiFrs4G2KV9ee4kl\nY/Dc+vQBXSRGpIKAIc6VKsnPValFSgqcgv79cRxxcYi43n7bdesIHhArlrWQMAsC8jGNGzNmMsG0\neZiZqhgWC6S7PPT5FgvuB2+TpD79VB8jT0QvEdEjIvJz8ZpkIz8+Vy42fsgQNn78eHZQrv40JgbJ\nM166VRGCgLC1ZcuskcLOnZAmtmyprtOkiORkZOkHDNC2GvPvvxFWjhuHxHX+/NpLCJ1hteJ7VqyY\nlaa5ehU8tR68OGPwlvz8XC/WgoDEcLFi2iXfnbF8OSLFpUuxf0HAwlyunP4S2ORkGKwhQ+ze5J9/\ngqvv3VububqrV0MrrmVdx4kTaGxIhIiNN20ZFoZ8nnNVvRJs3AjH18mbP3jwIBs/fjx+3n5bNyP/\nERHtdvOaM10T5pauGTQIgwCUYvduhJa8PR+zGRn0fv2ynvDMTFTe+vriYVQ7KiwpCVysWg2xN8TE\nIHJo1Aheh7+/fiXuIgQBtFGhQlllsPHx8PJbt9anY+OxY1j03Cl9fvsNSiA5NRxqcOUK6JuOHe10\n0aRJoBv0zqMkJCCKHTnSfm6Sk2H4AwLAdfO+T6dOhepM68Rz374wca+9hiiFlxSbMXRmrV9fvbNm\ns8Gbdxc9CQJjvr66Gfm1RNTLzWstHRKvtT0mXkNC7F0mleKbb8C38vaGk5Nx87lS8kRHo7dGYKD6\n4RlJSfCgtDb0ViuoqAIF0KHQ1/fZ9FLZtAn7dmz3kJmJArWqVfXxYCMjYcy6dnXNO2/fjmPkRc95\nQ3o6DGnRovZCHjGPonc/osePQVtNn57176dPIxJr3pxvbkdsG/zBB9rOARAE5MK6dIHqq2hR3AO/\n/KLeubBaYeRnz1Z/nMuWgYp2hVOnGCtZUnsjT0S5nlI1bzj8rT8R9XP4fxAR3SKiYFdUDRONPGNI\nXKxYofykmM3whqW2SJCD6GiE7+5keNu34/Xu3dVFE4mJUAYMGqS9jnzPHnjy7duDutCzRbCIM2ew\nuM+aZf++jh0btW4RzBgSjj17QgHkYlgyO3EC50mPaVMiduwAnTZuHKjCuXOhDnoWyqhixVBv4Aiz\nGcbfxwfJc17CB4sFRt5bs0K1SExEhCTq6nfuRFV23rygE9XQmDdv4rwoGdrtiMxMPBuuchW9ejE2\nZcpzVgzFGBIN5cqp42Tv3cOJ0cLzEqcwbdrk+vUnTyC19PcHx6j0JhUN/eDB2hv68HAk+CpVglSU\nhwciF3fvwmMcODCrsVizBosPLw2yJwgClFp+fq6vb3AwIh8lY9uUIiYG3nKdOrhOM2fCMOnN0YeF\n4b53JXO+dQve5jvv8MuJiZEzr2IpdxAnNDk6EvfuIcotUgTPxPTpyiS+M2fi2ql9fr/7Lvuc2TNn\ncD0SE59DIy8ICHXUqiyOHcPD6sorU4vz57FtT1WBZ85gsWrXTnmVaWIiBqFonYxlDAqPbt3gxbz8\nMrTkevdSSUzEQ/HBB1k14keOgAPWskuoI86ehef61VfZvdNr16BKWrBAn2NhDNf+p5/sxVxTpkD/\nrS9NDhYAACAASURBVHdRm9hgT+xH5AhBQKKwaFFUivOINiIiYMic+/3wxm+/wcFw7oBrs2HR6tsX\nzk/DhnACpCadzWbkV9RGf3fvIioQjy82FtLup7UFz5+RZwxTYYoUUd+Yf/FiPAxaNBw7ehQegMOs\nxWxITwffHRCg/EInJUHO1r279jNCBQGUwIsv4vL37Kn9fFRnmM2gqUqXzrpAX78OqmLSJH0Wn8eP\nsdg0bJi9wOX2bTxkcttdq8WlSzAabdti4a9QQfuxhs7YuxeGPjjY9eupqfA8fXyQv1LbouHIEUTF\namkPTxAnNA0c6P49GRnIG3XsyNibb8IRnTYNiXJP9+PRoxAXqLVBzZrBhty8iUjOYWzp82nkGUN5\ntxqljYgBA6DU0EKSt3s3bnhv4wNPnYLR6thRWXFLaioMTrt2+vQ1OXzYLjP74AP955IyZqdNHCm3\nmBiEz59/rn1kw5i9QrhQoeydDCMjIaH9/nt9I56MDDgO+fNj/5Ur61O05Yg//gAdeueO+/fcvg1+\nOzBQffO3RYu0c9ZEJCYisS1lclh6OmjlQYPgjJYoAVp1/XrXUfsnnyAqVIP589n/uoUuWpTlpefX\nyEdE8EtcNG4sf46iVGzciAfu8mXP70tLwxzaIkWU8ZYZGUiONm+uT8n93bt4sIhgWPXmgBmDF1Sg\nAJJ6oiFNTLRXzerRm54xPNABAUgEOzoL9++Dhx41Sn9q6/hxGFAieHZ697qZMwd0pLfWC7t24fha\ntVJXeCc6a1ou7idPImqQUwAnCIhqfvwR3zF3bsgeP/sMDe9OnEDOIl8+6U3HHLd96RLk2S+8gGvt\nIoJ6fo08Y+Ah33tP/QOUkIAbUquE4qpVMEbeDD1juOkLFGDs22+Vzbzs2ROhoh59TZKTUexFxNgb\nb7gP0bVERASklF272um79HRQFs2a6WfcoqIYe/dd1Bc4LniPHmERHD1af0OfkgJvUuyaqmf3SkGA\n9yql6VdGhr2epH9/Zf1dMjOxuH/7rbLjlYoJExC9Kr2WVitydkFBMPTVqjH26qv2a/Tpp4j+goIY\nW7kSBWbbtyPRv2YNFs8RI/Dc+fkhuhgxAonhunVdSrSfbyNvsWC6PY+E2927CLvdqWLUQo6hf/AA\nxVW1a8vXGNtseLCrVdOHj7VaUYEq3qR6acUdkZoKPXO1avaEnsWCh6h6df3K/q1WGIH8+bOWmz96\nBH78u+/0OQ5n/PWX/fpoUYnqDhYLalLE9gfeEBeHgfb58kG9IrcXTmwsImHnAek8YTbzszmO27x4\nEdfns8/gEAwYAMelVSv8tGuHITeDB4Pr37wZ97rjeR09GnJaJzzfRp4xrGC+vnya/oudJbWq7pRj\n6EXFhK+v/DmfggCPplQp+SGgUixaZDckQUH67NMRYpuBAgXsdJcgYPBE2bL60kmHDkFhM3KknTKK\njUW0OHGifsfhiIcP7ddnyxb99puUhAVOThI6PBx0W8GCaC0hJ1927BgoFS2LwkSbw3sk4qJFcO6U\nYtcuUM9OeP6NPGMokqlTh4+6ZOdO8KtXrqjflivIMfSMIWkbGIgVXC7HHBSEB8Vb4pcX9u61G5Kh\nQ/WfYsQY9PLOA0emTME51GNOqYhHj+CB1axpj8YePAAfO3myfsfhiKQk+/Xp2FG/ATEREbgP5S4u\np0+DgilfHvp7qRTJjBmoIdEyJzN1KgozeVJwGRmIRJTOeI6JAYXjhJxh5G02cPMTJsg+Ly6xciU8\nMa28YNHQS63UTEhAD/PateXrizduxIVX205BKi5fBj9PhEk8eif8GINRqVkTvLyYm5g9G7JGrca8\nuYIgwAHx9UWVtiDgQSxVSpuKaymIjcW9/dpr8Hh//12fXMHZszgPch0OQQAvXbEihsj89Zf34xUE\nJGG//FL58XqDxYJckJrqe1cICsJzowSCwNjrr2dTU+UMI88YWgoUKMCv8nH+fLRO1SrMX7sWHqfU\nVdtx/qncdq6HD+OBlkv7KMW9ezBkRKAo9DSsIjIyEP0EBtoTwosWwcDp1TVSxIULOA+iRDYqCsel\nVd9ybwgPR/5p1ChILN9/X5/ZAX/8gYVWSa7IZsPny5RBL39vg0ri47EvJYOGpOLMGTzDPHv6p6cj\n6rl0SdnnK1TItpDmHCPPGLjYgAB+YfmkSThpWg1m2L4dXrYcyeS+fTD0cuefhoTAwOlVoBMfD5UP\nEWN58ugXSThj1Sp4kL//jv8vXw5nQG8lUFoaKKzChXEu7t5FaC4el964eBHn5cgRe3+ZSZO0V+B8\n/TUUSEqpVasVkXZgIPhnV9W1Io4dw7Oi5YD6wYPRfZYnpkzB/AAlqFcvW81GzjLyjOGGrVmTz80q\nCMj2166tHe1w4AAMvZwhBffugXPs0EGeAiEyEh7lkCH68OVpaVAFvPACY7lyZW0wpidCQxFZ9OsH\nT2ndOjz8ejQ2c8bff8PQDx0KjysgQFtv0xPEBmfh4Vh0PvoIRXlaDf5gDPdd8+aQ/amBxYKGaG+/\nDWdi1y7X99Z336mTPHpDYiL/qWWxsXCMlDiXzZplGyTy/Bv5jAxUP7ZpA86uVi32v+TSsWPqk7GC\nAAlYvXraGfrTp/Gwy+nwmJEBTXzVqvJ4+vh4eFKtWunDl4sDQPLkgQfds6f6vvpKkJQEuqRiRdA1\noqHXKsHuCXFxkMOVK2cfTMJ7mI1UzJsH9ZE46nDbNvTl6dZNu8RsXByqQFevVr8tiwXPTYUKoJ7W\nr8/qwJjNcPp4TGNyh1WrYHt4FmJ1766s+VqO8+QzM7GKN2uGh/bUKRh2Rzlf3rzQnp48qXw1t9nQ\nhKh+fe0Mo9grf/Fi6Z8RZYNSh/qKMJtxTpwnL2kFQYB+t0gR3IQ1augzNs/VcSxeDJrit9/wcBYs\nqD9HLx7LypU4lsaNsQjyHEwhB0OGQLggFi2lpIBW8fVFVaYWUV9wMLYvVWXmDYKAaLh2bURtS5bY\ni+Nu3AAdpdWCbrPhnuaxaIk4dgyLr1ybVbp0tu/5fBv5hQuRNHJ1IqKjYVTmzIGSoWRJlJivWKGs\nqZbNBsNYv752vVpu3oSHM3GivIu7bRseGDmRgOPkpbNn5R+rEkyfjgq9fv1gXI8e1We/zggNhRfd\nrRs8vEKFtG1w5QmRkbiHRaeEx6hIubBaoejo0yfrfRcaiireihW1iTSWLcN14DkAXJzX+uGHEBuM\nHQvxxMKFiPK1oimPHkWHTbVNE0UIAiIqOQlYsxnVs0726fk28t264UZxhwsXYPxOn7ZPZm/UCIZm\n8WL5VI4ehv7+fVAwffvKO77gYNwUkyfLWyA2b8Y50qrS1xnz5+NhmDsXD+FPPz0bnj41Fec4MNDe\nSEoPhYkrCAL4ZdHQP4so58kT0B2zZmU/tg0bcG916MC3yEgQQN/17s1vm464fh3XNk8e0B9vvpn9\n+/HExx+7ngynFN98AwWUVJw7B+/fCc+3ke/SBSGvJ2zdCi7YcXDu8eMIkcuV8z7t3Bk2GzyeevW0\n6w+TnIzkVOvW8pqNxcTgQe3fX94Cce4cvNlp0/QxuL/+Ck9+xw60ImjfXtsOgp6wbh048TJlYMj0\nnqrkiHv37IaeZyJPKu7eRZ7C1ZjHtDT0VPHxQTKTl/f95AnOvZaDVuLjEUWK53bcOH4etyNEWoiX\nXbhwAYllqRg3DkIRJzzfRn78eGkr3eLF8N4dp7wLAmiOkiXRX8NTW1Rn2GxoZ1u1qnb9YTIzGevR\nAxyjnH0kJSFHIXeBuHfP3uhLjy6Wa9bAoJw4gUWpVKlno3ZhDB58jRq4pfPl07e/izNsNrsxGjtW\nv06aIg4cgAjA3fMQGQnnqkgRcNA8ko3i9CU1XSilwGKxy3rz5UMugjdP36NHll7uqiAIiHalFGWa\nzbgmLvI6z7eRP3YMPLsU73PqVJRHO8uSMjPxmo8PlAZSOTtBQEOgcuWyLh48Ie5Dbg+azEyEwbVq\nyWvOlZoKI1+lij4tADZvxk184QL04mJl6LOA2QyjKhrYZ9EjX0Rmpr2YrHx5RJ56YvZsRISeFvuj\nR9H8rXp174VJUrBoEZwMrRc1qxUL+oQJ6O9UoAAM/9KldoWRGoSF4T7mFZl27YoksjcsWuSybw1j\nz7uRt9kQzkh5CAQB2tzatV2HmmFhuNh168ozqD/8gGPQshFYUBBuRjmN08TmXKVLy1PQCAIKpvLn\n91xowgsbN8JzDAnBT8mSkFzq2RbXEceP2w39s/To4+PBr9arh2s/cKA+7aMZwz3QvTs8dk8OlM2G\nZP9bb6H1rath0nL22aoV7lmt4TD/lJnNcDY+/hicfceOiPDVLDZdu6KgiQd+/RXXwRMiIz1O43q+\njTxjSOS1aeP5JIgQBExhad7ctVbbcVbmunXStskYZGaFC2sbbu7YgeOSo6BhDN9HSc+WPXvgZQcF\nac/Tr1sHQ3blCh689u0RTWgxf1cKEhLshl6Phc4d7tzBeVm50q5I2rhRn7xJWhr64C9c6P29GRlQ\nsfn5oR+60rzG/ftY8D2NzOSFvn2zDwqKiwO126CBvbf9nj3yDX5ICK4bj6jk0iWwBe6QmIi8lgdN\n/fNv5NPSoNaQ+jBaLChEadHCfVHOuXNQXXz2mfTCnRUrcINqmTATFTQTJsh70BcvRmJVrib51i3Q\nBX36aO9Zr1wJIxYWhu+2YMGzpW/S0+2GfswY/blxESdOwHheuwaKpGxZ5Fv0oNNu3JA2vlJEQgJy\nZPny4V8lFZtbtkBGrHWh3qNH+G7u8kB37iBZW7s2am26dcMCKzXh3Lgxn15RGRmMvfKK6+cvJgbU\n0+DBHu3B82/kGcPJrFxZuqLEYkFY5snQJydDMlarlvQmZbt24cb5809p71eC+/dRwdeli7zK0dWr\nsQjJLbhJTkZrglq1tDcsy5Zl7f4ZHAzlRc+ez4YjT0xENEMEvvhZJYaXLAHtlpiIh11Uucyerf3w\n9rVrQUfK4ZgjI+Eg+fhAHCGXZvrkE1B2WuPnn1H97c1hio6G09GsGTqsNmuGBeDCBfeJ5y1bsEDw\nQJkyWe89QQDFVKCApBnCOcPICwJ6O8vp0202w9C3bOneSxUEZMoLFwaPJwUi3yclWaIUaWmIRurU\nQZ8Lqdi6FUZLbr9qsbI2IADDybVEUBCiKLGkPiUFFECpUvr1xXdEZCSioBYtEFl8/72yYjq1GDQI\nnLVoVK5fR5VqhQraU0oDBuB+k0sT3brFWK9eOG+TJ0v3zuPjEdU5D0fnDYsF99WePdI/k5iI52jw\nYCy8vr6MdeoE2vjMGXvEZ7Ui6uZRxfzeezhGmw0O5LvvgsI5dEjSx3OGkWcMXqavL7rrSYXZDG/d\nk6FnDKuyr6/00XZi5apcWkUObDbolYsXl/edd+yAoVdy8x06hIdv/HhtG5xNmIDIzNEDXLMG12Du\nXP2Lp8SJYRs3otlVlSrKW8EqhdmcfYapWKhUpAjoBK1UXunpuB5yWm44IiwMkae/PwZaS5HobtoE\nI6p1n6MNGxClKZWCRkQgAu3bF5XBuXLBgx8yBM9KzZrqutgmJuLe8/HBolGtGtpxyHA0co6RZwxf\nvmRJeeGhaOibNfPMt504gZvUW/GViAcPcPN89pm2nt8ff8gfEbhtm126KBcxMTA2zZtrVyMgCKhD\naNgwa9HKrVvgID/8UL+pRiK2b0d4fPs2HmpfXyxGenL1sbHIP23YkPXvKSnIG/j4IAGnxf129Sq+\ns5r2D6GhSKoHBEC27O05bddO+8HcggAZqByhhSc8eQJnaMYMGHsiGH4fHyyUH36IhO7o0VDgzJsH\nKmjuXKjaxo3DgvHhh3AUX3sN2xDnIihwcHKWkWcMUrO2beWdDIsFpdV162abqpIFly/Da5KiOGAM\n4WnLlqCSeGhw3SE4GDK2kSOle9ibN+NhU+KRWiwouS5SRLt5uDYbvL/WrbPyzpmZeEDy59e/RW9Q\nEPjRxEQoSFq2hAJFTiSlFmJU4Up5dOMGIo2yZV1XrarFvHnIzajNA4SGQqLp44PFyR3lGB2N76pG\nmikF+/bBOdQiOq1aFe1UHjxA9Lx1K3IBU6YgOf355zD6n3/O2LBhMPILF+J9167hmHr0QEM9hch5\nRj4jAyGSXJ2qzYZxYRUrevYSw8Nh3Dz1zHGExYLQrWxZbXujPH6MxaRZM+nh4fr1MJZKuzBu24ZE\n8+zZ2lAomZmIGAYMyL79o0exsPXpo++IwUGDsPDYbDim33/HOfj2W21K5V1h4ULcp672JwgwEMWL\nQ/vt2M5DLWw2NFPjNWYzPBxOWd68jH3xhevE/vz5UKpoTdHVq4eomDemT8d3VINWrVSJOTQ38kSU\nm4g2ENE1IrpCRLWcXn+XiBKJ6MLTn7FutiP9W0VFwRDL1ZQLAjpABgZ6bsR0/Tr4NjkUSVAQDKqW\nGmCLBQVfJUpIV4IsXw4KQKm2WWwJ8OGH2tA3SUlILrqaaJWUhKRsiRL6VYVmZkJHPW6c/W/R0aD8\nAgO18aCdIQhI9nmaSJSWhkI9Hx94iJ4iVDmIjpY3tlIKYmLQ2jhfPnit58/bX7NYUNW+cSO//bnC\n9u3ItfBeTIKD5fWfcYUqVaQLP1xADyO/nIg+efr7i0T0ptPr7xLRnxK2I++bhYTAw1KiPJg7F4uE\np74WoaHgtSVmuBljaIbm58e377QrrF4tj6efMQORhtIKz8xMUEWFC/MpcXeGqHBx1ylzyxYYHr30\n7A8e4P7YvDnr37dvx4LZs6d2+QoRSUmgGLzdS7GxiIT8/FCwxOP8rF2L+gne51psJFakCPIxmzeD\nrjhwAElHLSMlmw2LCW/1mCDAuVM659hsBqevoiGcpkaeiN4kotte3vMuEW2XsC35327vXhhiJZze\nypX4rKc+2nv3wrjICYlDQxFKjxmjrULl0iV7G10phUwjR7pv+SAVu3fjhh47lr9++9w5e9toV7h/\nH2Ft1arac7iMoQe/n192R+DJE3jO/v6IkrSkGcQ5rVIMyOXLkIEGBmJRVHNcggDKihdt4wyzGdRJ\nnTp4VmbNAm03caI2+xOxYgXkirzRo4dyZdKJE4hkVUBrI1+JiE4T0W9PqZhfiOhVp/e8S0SPiegS\nEe0konJutqXsG65dC1WEkmEM+/d797yDgsCPypF6xcZC69qypbYJ2cRE8LLVq3vvsikISD63bKnO\nQN+/j7xAvXp8e48zBl6yYEH3vXgEgbFffrHrsrXWs//+O7xpV4VC584hzG7SRNuBJLNmwRhKvWa7\nd8NjbdhQXXX2vXt8Jzu5w6lTjHXuzP5XfSxH0y4XmZlYnHlfr6AgKGaUYORI1QojrY18NSKyEFH1\np/+fQ0QTnd7zOhHlevp7CyK64WZbbPz48f/7OShnUs1vv4FKUBIyhYYiBJ861bX3IwiQhX35pbzt\nms1IyJYsqe2cUUGAIfD39z4s3GyGgR4yRN0+bTaE3X5+/LnUadOQA/C0qEZEwPOrUsVt0yZuGDAA\n/Lire8NiQe8gsQe7Fi2cbTZ4n3La24pDsIsUgUeu9BwtXIjoT4+h8FFRdkNfvToWcy0S7iNH4ocn\nTpxAhCkX6elgCmT2xDp48GAWW6m1kQ8gonCH/9f3Rs0Q0R0iyufi7/JPkiMWLgSvJ6dvvIjoaGhc\n+/Vz7THFxWERUTLObvlyGMMtW+R/Vg6OH8dDPWqUZ68vIQEywZ9/Vr/PU6eQdOrVi18HRUFA9WXv\n3p4pB3HaktZ69rQ0RHK//OL+PZGROOZixcAz86Zw7t3DPSR3jGN6OpRRAQHonCjXCbLZ0LlVKRUh\nF/fvM5Y7N85127aY+NSvn33yGw+I/Xp49mpKTITeXe4xzp0LClIl9Ei8HiaiUk9/H09E051eD3D4\nvSYR3XWzHdVflgUFwdApoW6Sk6FBbt7cNcWydi0WAiVezZkzOK5x4/hOfHfGw4c4/oYNPatpbt3C\ng88jNH7yBA9isWLyktSekJICnnL+fO/vjYrCg1KxonZtEa5dw2LiTdG0bx8S3M2b85/lunat8grR\n5GR7P5wBA+RVzl68iChRTVWnHIwahQJDxnCckybBkShVCpy90gSnIxo14j8OM08eecKGqCjcUxyi\nfD2MfCUiOvuUc9/8VFLZn4j6PX19MBFdJqKLRHTCWWLJeBp5xqBvL1BAeRHQ0KG4oZwfUkGAPt9Z\ncSEVDx5Amte6tbY8vdWKGgJ/f+ip3eHIEXg0vNr9ihWjI0fy8ZJu38ZCJEU95axn16JU/vffYcC9\nJa7NZjuF8803fBuvtW+PQjGlePyYsa++gm592DDpjfkGDkQxjx6Ii8PxOeZlBAFR4+ef4xrXrg0H\nQM7AHEcsWIA2ETxRvrx0WiwjA9+BU0/6nFcMJQXr18PIKa3WXLoUN5Oz3OqPP+AFKEVmJopCSpTI\nqhXWAidOQLkweLB7adqiRUjS8Zrn+fAhStUrVODDlf/1F6SVUh/mmBgkokuWRFKdN3r2RHGW1GPp\n0QM039q1fOiG+/dxX6qtwI2OhjOTNy8Mp7caisePPQ6t4I4vv0Q9iCuYzegx1bUrhoA0agTpqBya\nNiYGnjdPyqZWLWmJ7vR0iB+UNIRzg3+nkWeMsZ07EQ4prSQ7ehSe6U8/2S9GejpakaotPFm/Hsf2\n88/aSvASEpA0rFDBdVgoCDBEPXrwOw5BQCLc1xfJWbVJu2++gTRQDs21bRvosV69+OrZk5OxcO7Y\nIf0zR48iQVynDp9ZBMuWIcnHQ8J6/77dsx8wwLNaKigIFdd6ICIChVPe8jxpaXi+P/0Ui1DlysjP\neGoRLKJePdgIXmjUyLtjEREBD75zZ67KsH+vkWcMCZsCBRCeKUFEBHqX9OplpwCqVeNTDXjjBrbd\nqRO/eZGu4JigXLw4uzFPTYU3v2gR3/3euQMZaZ06ytsqMIaHoU4dj5NxXCI5GZREQABfPfuBA4gu\n5HDUNhuOoVAhXG81oyTFtttyz4cnPHxoHwby2WeueW+zGdy4FhGSK3TrBidBKqxWUJDDh6NWwN8f\n3v6yZa4jlWnT1KvMHFGrlnvmwGLBIunnh+/EOS/37zbyjIHbLVUKZdWeTm5MjOvwLSUFoVXVqng4\ny5bll+BLS0PSsmRJ7VvbXrsGT6ddu+z0x/XrWAR4Jy5tNkQrPj4owVfqfd69iwfk1Cn5nz13Dteu\ncWN++YcvvlDG6aakIHmYLx/uR6WKpJs3cU55tx6Oi4MU1NcXbRycz/eaNchL6dEK+tw5RE1KDeKd\nO1DpdOyI8x0YCGdt8WLIpk+cQLKeF8qUyV5TkJQEBzMwEPefRkNpDCPPGDjFevVw47rjn0eNgnyr\nfXus/rdu2W8wQQBtI+p4eVd7rlqFB+uXX7R9gDIykBgtUCC7pn7lSgwq0CJpeecOvM/q1ZXf6Js2\n4WFRkj+wWCAl9PFBOK+2fD4lBceiNAkfHQ2Kwd8fi6CS+2n0aHR61AJPnkDeV7w4xALbtuFZsNkQ\nfWotB2YMz0Hlyqg6VwurFffdokXIqwQGopUAEaiTRYsgQX74UNnzZ7Mx9uqriB6vX8dC0q4dcgbt\n2oGy0/C5VmPkTfi8PjCZTEzT/WVkEA0YQHTxItHWrURvvZX9PQ8fEu3ahZ+TJ4kSE4mKFyd66SWi\nu3eJEhLwvu++Ixo/nuiFF/gd37VrRB07ElWoQLRwIVGePPy27YwjR4h69SJq2pRo1iyiN97A8tWp\nE1HhwvgbbzBG9OuvRKNHE33xBf596SV52+jRgyhfPqK5c5UdQ2Qk0YgRROfOEc2ZQ/TRR0Qmk7Jt\nHT9O1KED0ZUrOCYluHSJ6KuviO7dI5o0Cdv7v/+T9tmUFKIyZYj++IOoXj1l+/cGq5Vo0yaiGTOI\nnjzBucuXj+j774mCg5WfO6kICsJ5XruW/7YfPyby8yNq2xbfKSSE6PZtIosFz/xbbxH5+xP5+OD1\nPHmIXnwR39lkIsrMJIqPx8/x40SnThG99hre/+67RE2aELVpQ5Q3L/9jd4LJZCLGmKKLkbOMPBEM\nzbx5RD/8gBuncWPP709MJIqIwIUvXJgof36i2Fiizp1hoNasIfL15Xd8aWlEX39NtH070cqVRA0b\n8tu2M5KTiYYPJzpwgOj334kaNCCKiyOqVIloxQrcpFrg3j2i/v2JYmKIli0jqlpV+mfj44kqViRa\nvRoPklLs24eFpnhxLBilSinbzqBBRIJAtGiR8mNhjGjvXqIxY7CtKVOIPvhAmgFds4Zo5kwsWlIX\nB6XHeOQIjP2ZM0SPHhEtWEA0cKB2+ySCU/XWW0Th4coXUk8YNAgL5ZAh9r8lJRHduQOn7tEjPBPx\n8bAFNhuuEWNEL7+MY8qXD07h3btYtHPn5n+cXqDGyOccusYZ+/cjITdvnrIwynGohhZzKrdvRyOw\nb7/Vvj/Ltm3Y19dfg87ZvRttHrQcrC3q2v39kRyVs6/t29FnXu3xZWaivbEaPXtCAs6dklyBMwQB\nLSLKlAFFIqVltVi7Iacltlpcv478CBHaT//1l7YFfu3aIWmtBebNQ4M/tWjfHrLrZwQyOHk3CA9H\n4qVbN+UGY8cOLBYTJ/Lv7/HgAaSDNWpo2wCLMTRUa9sWBR2nT4O3dKdT5omHD7GvIkXkTYHq1Yuf\nMkKtnn3lSkgkeeVpLBZIUIsVg57amyZ+/36oXvQcPm42I4fUrx+++9tvY8HUoip2+XIYei2waRNj\nbdqo28bDh8jl8WrroQCGkfeE1FQUt5QurbzQIzoanQgbNnTfPVEpBAGVfb6+kEFqmZQVBHiE/v4w\nom+8od8w6/37oTBq107acJNHj/gUBTni2DEk+urVk+eZCwKkolJHRkpFRgY8zfz54Sl6+q5Nm/KX\nwHrD7NkY3ygIkA527w5j16kTvHteTs+jR0hgatFr/sQJREJqMGqU5+EuOsAw8lKwcqV7HbkUWK3o\nYunvr43yIDQUBU0ff+x+XiYvxMZCMioqifSQyzEGVc/48aBP5s71biQWL8bMXp5UgdUKZVWhHvzE\nMAAAG+5JREFUQlBdSK2iPH8exliLrokpKegyWqAAWmK4miB05gyOWQtllDvEx8OoO/ZriYuDZLBm\nTbSM/uYbdTUSIho25Fu4JOLSJXUyytu3Ic+MiuJ3TApgGHmp+P/2zjw6iipt48+dj31kCdmIhIAQ\nFRE0LMqSBD4XljAsLiPoICIeZREFPW6gIHKGcRf5UGcw6GHEcQREBRQVFQgCYwQ0EQfCEkIkQBKS\nkLBk7XTd74+n245NJ11dXdWhm/s7p07S6UrXvX2r3nvvu2ZlMSjorruMP6zOVAIPPmh+2tnKSro/\nRkczYtZq1qzhLXDppdbq593JyuLK2FvQmd1OYaK3Fq8vnDtHV0tf/NknTmQxFauoqOCuLjaWifTc\nyyGOHCnlsmXWXd8TEyZwQvbE3r387mJiGOX51luMsDXC/PlcMZvN3r20gRihpoYBUK+9Zm6bDKCE\nvC9UVDD5f9euxmu1lpZyorjiCnPrZDr5/nuql8aNM56gSS8rV/I2iI01x19ZL07DbEwM/cnr273s\n3s1Jz6pC305/9uhoRiw2pPd2huJbvaqrqqJqpnNn5pnfsoXfV1oa77lA5H53smULF0YN7fZsNq7C\nJ0xgvpjBgzlZ6U2OJiXvvaQkv5t7Hrt2GcsDb7dzUncWfG9klJA3grOm6Jw5xvOUr1pF9c28eebn\nOq+oYM6RDh2sL4A8bBhv5s6d+aAWFFh7vbqUlTFMPSKCSag8GTfvvtu6MnVOMjOp977ySn7f9Qm1\nxx5jNGwgqKmhnebyy2mcX7mSux+jAVpG0DTuXPXasyoraWCfOJH5cpKTaXfwVmHs9GkGL5k9gW3c\n6Hs5wMpK3nPJydYUiTGAEvJGKSigcEtIMF5T9MQJbqN797amLumOHVy9jR9vXWFppwqqrIzqoshI\n6sMDuYLZt49CtmfP82vyOvWiVu9qNI0Gxd69KUw3bjxf2OfnU3j5skr1F7udKaWTkuRvdpRAqtce\nfdRYbdaqKrrDTprEe6pHDy5cNm/2vCjq1IljbSZLl0o5ebL+87OyqCJsKHK+EVBC3h80jXpOZ0ZF\nI25yzrqk4eFMKmX2aqSigqvdmBgpP/rIGkPp4MGuOrg//0wd66BBluXi8IjTj7xzZ6qq6noyzZhB\nYRMI7HZ+z1deSduBu1pv1iyOR2OwbZtL0M+ZE5jJZutWLoT8wW6n8fi55yhE27alD/4rr1ClYrNx\nR+mtvKWvPPQQjdreOHWKMSvh4dx5XAAqmrooIW8GOTlcSfbpY9xt7/BhbvEGDLCm5uuOHUyeNmoU\n9cNmsmEDV7BO7HaugiIjaVwL5KqmvJwVttq354N35kzjrKBtNhp94+JYmcp5Xxw/zrYEqpKSO3Pm\n0Pd7xgzqwCdM4G7MKi+p2lreB2YWdT95khPpjBmM3WjbVv5mG9qwwTwPs169Gk7//PPPnLTDw+lq\nbfZzZRJKyJuFpvGhjozkg2TEXc1up4uZVXVJq6pcZd4WLzZv12C3U5i5Z6ksKKAQ6dzZul1EfRw9\nykCqDh044UyZ4l/VJKPU9WcfN467m4kTzU0F7AvZ2a4apiUl9P6Ij+dqOzXVmgl5/HgGcFnFyZPc\nOQFcbLVrR/XNmDF001y+nA4JvtR5yM3lc+J8RjSNLrNr1tCuEh/Pa8yda6xudADxR8iHXu4aMygo\nYN6TPXuYs8Rb/htP5OUxb8aRI8A77wADBpjbxoMHmR/m3DkgNRXo3dv/z3zuOebwWLLk/PfS0pj/\nIzKS7199tf/X08tPPzHJ13/+w6RRZ84w4VqgKS9nPpdFi5i47vhxJvgyM4mdXgYPZg6kUaP4WtOY\nH+fvfwe2b2eit+nTgSuvNOd6b73FcXj3XXM+zxMvvMC8Mi++SIXU4cPMFXPgALB/v+uQErj0Uh4x\nMUws1qoVj5YtmX+mqgp4/nl+bkoKn8ejR5lgrG9fYNAg/v2aa6zNCWQSKneNVXz6KVe3d95pzG1O\n0xhGHx3NLaHZxjLnziMqigYtf90MDx/mZ9Wnj7TZ6BoXGcmUA1bWsXVH06ivdeqjzYyE9ZXycu6i\nnG0xI6+NryxaVH+ZwtxcKZ9+mmM5ZAhXwf7eexkZVBVayQsvUDXYEJpGT5ysLEZR/+tf9M9/9VXu\ncGfP5sr8qac4NrNm8b7JzGw89ZoJQKlrLKS83GWQeeUVY/lDioq4vY+Lo/ub2SqPwkKqNcyoNdq9\nOw1hDVFUJOXUqZy8li0LrN/2hg28baOjWRXo0KHAXdudN95gW+LipBw61JpEdvWRk8PJtqHvvqqK\nuVtGj6bOe9IkerYYMSpWVUnZooW5NVPd+etfOTmZwVNP8ZkIEZSQDwQHDzIK8aqrpPz6a2OfsWkT\nhWhKiufya/6yfTsLPtxww/kVbPTy6KNcEenhxx/pgdO3LwN1AoHTdrB1q8s28cAD5ucU0kNJCXOu\nFBczQ2G3bsyL88kngZn4rrnm/KjY+igo4Oq/Vy/aV+bN8z0pXvfu1npbPfwwd0j+sn07dzGBNNJb\njBLygULTqMKJj6fAN3LDV1fTVTM8nKHcZidlqltr8tFHfa8t+/HH9N7RizPpWZcuNJKZkcfEG/Pm\nudwpS0q4amvfXspHHrE+7487o0czcldKfverV9NFMD6eagQrg2lmzmQdU1/QNBrXZ87kbighgTmZ\n9OyIUlJ8K3DuK7fe6n86jyNHXB46IYQS8oGmupr5PKKiqBc1Uofz6FEGXHTtas2DU1jIcP1LL6VO\nVu8W/fBhPiS+UllJdVZEhJTTp1srbDMy+L3VVUvl53Ml6HS7DJS94L33mFSuLppGf/ZbbuFkO3eu\nNVHEq1ZxkjFKbS0Dz6ZPp8Dv04d68b17Pav8Jk+2Nqd6ly5S7t9v/P+zs/kZb7xhXpsuEJSQbyxK\nS2koat+eD7IRwfLVVwxbT0mxxrc+PV3KgQP5AOtRqdhsUjZpYjx3enExV9Th4VIuXGjNSlbTqHLw\ntJPKzeXkFh7OFX/dDIpWcOIEfebrU88cOCDltGl0CZw8mSouszh2jP00w8ZTW0t9/YMP0q3wsss4\naW7c6NLDP/aYlC+/7P+1PJGfT7uB0SCkDRu46Ap0OuYAoYR8Y5ObK+W997qKSPtaXKC6mrm7IyP5\nkJkdvq9pXPV16cItsbeteXi4/yvx7Gwp77iDu4K33za/4MXUqfzOGrr+/fe7skxamY+nZ0/vHjZF\nRVL+7W+0J/Tvz0IZZqjqIiKMZ36sD03jBPr881wgtGnDYLAmTehpZkU06NKlTPrnK/n5zDMTF2c8\n4WAQoIT8hcKhQ7ToR0TQU8BXfXhxMXWl4eFcMZntyVBZSR1ueDh12vUFlkREmKduSU9ncEu3bszp\nb5ZBcsUKTiLe+PVXhraHhfG71VOwxFceeogufHqorZVy/XradCIi6PrqjxF+0CAaoa2kqIi6cqfL\naHg4i5wsWkTBasZklZhIe5BeDh1yRfw++WRgc/k0AkrIX2js388o0chIqgx8FZj791PXetllXIGb\nvXIqLKQKITKSRuC6KhW7na5yZkdNbtlCgdSjByMO/e3ToUO+2Q5OnKC6ISyMuwAzXS+XLTPmrped\nzYRwERFSDh/OsfY1yvquuzjhBYL77mNfjx3jhP3gg/SsatmS6sBp02hsTkvzbTe6Ywfv9YZUhHY7\nfd1fe407oagoCvcQ8qBpCH+EvIp4tZKDBxkduWoVMH488NhjwOWX6///TZsY1QgACxcCI0YAwljQ\nm0f27wfmzWMk6bPPAvfdB+TnA/3786fZSAl89RUwdy5/X7iQUYdG+qRpjHA8dYo/9VJcDCxeDLz9\nNpCUxDFJTPTve925E5g2jRGhRqiqAtasAf75TyAjAxg3Dpg0iePgrV0PP8x7auZMY9f2hZEjGUU7\nevTv/15VxXbv3An897/Avn3A3r1As2aMuI2LA2JjgU6d+DMsjBHLbdowAjUxkf29915GNBcV8f7L\nzwdychh5npkJtG8P3HQTMHYscPPNQNOm1vf5AsGfiFddQl4I0RbAOwB6AtAA3Cel/MHtnCUAUgCU\nA7hXSpnp4XMuLiHv5ORJ4M03mSIhKYkP5JAh+gSLlMAnn1AwRkQwVDs52dz27doFPP00kJtLgdGs\nGbB2rbnXqIuzT88+y4d87lyG5/saXt69Oz+nRw/f21BeTqH6+utAeDiF/W23AU2a+P5Zp09TeJ09\n6/v/unP0KPD++8B77/H+mDSJKQo6dfJ8/jPPAC1acLK2mrg4prfo2tX7uVJSSB88yJQCeXnAsWM8\nysr4XZ05QyEOANHRvO+aNWPqjJgYHp07M/XANdcwjcFFiuVpDQD8E8Bkx+9NALRxez8FwAbH7/0B\npNfzOdbsZYKFc+fow96jB5Mxvf66/lDr2loa67p0oT53927z27dpk0vvum6d9cnI7HaqbhISGKTz\n4Ye+6eyHDKFHiD/U1jJ4KTGRHjuLFvluS9E0KZs3N1fFpWnMnjh1Ko3HSUlMkuburjt7Ng26VpOf\nT/23marDTZvoumlFYGCIASt18gDaADjs5ZylAMbXeZ0FINrDedZ+E8GCpjEE/i9/odvYPffQeKbn\nAaqupt4zJobBR2aWH/zuOxaLXrWKgjchgcYwq3Nraxpd4AYOZIGU5cv1eeMMG8YiH2aRnk5jbrt2\nFK6Zmfr/NybGurKAzuIbEyeybUlJjNPIy6MBXa/R1x+WLWMGTrPYtIm2CH8n6YsEq4X8tQB+ALAc\nwE8AUgG0dDvnMwCD6rz+FkAfD59l/bcRbBQVMYioZ0/6Jz/5pL5Sa86iz506MW9KWpp/K+/CQhq/\n1q7la03jar5vX66yV68OjLDfvJnl2jp3piBrKOnaiBHWBJIdO8ZKSB07cuJZscK7QTQ2NjCpFZwC\n/557uMIHGBj2/ffWplK48UbutPxF06T8xz9o9A9UKowQwGoh3xeADUA/x+vFABa4naOEvBns2cMQ\n/bg4qnRmz6bnQUMPb3U164DGx1Pl8MUXvgv73FwK8nnzzn9P0yhIr7+ebfrgA+OBUr7gXFWHh9MD\nxZMA7dPHmkLqTmw2prEYNszl7lhfvpfGyJVis/ERTk7mIiEykj7jy5ebW+AjM5OR0/7WRsjOdpXb\nPHDAnLZdJFgt5KMB5NR5nQTgM7dz3NU1++tT18yfP/+3Y4t7LU8Fsdu5Mnv6aQrfyEiu3JYvZ9oB\nT0K8tpYrrZ49WeFpxQrvfvY1NSxwEhXFLX9Dk4OmMTo3OZmr7MWLA+ObfOQIVRJhYXQXdGbI1DS2\n2wq/d08cOkQhHxVFV9ClS11xBnY7A4XMDvjyRmUli187bQG5uVwljxvHe6ZrV6bdeP99ClgjOz1N\no3vna68Zb+fBg/RpDw9ngJWRYjwXGVu2bPmdrLRUyEsK560ArnD8Ph/AS27vj6xjeB2gDK8mc+QI\nhfH48axOFBvLyMOXXqJO+tgx1wNst3PlPXQo9cQLFvzeT99m4+5gzhyuzm6+2ffc7OnpDIaJiGCe\nGCujSZ2UlVHQxMVxonn1VdozAlmpSkoK8s8+Y96hNm2421iyhAbEQPPtt1Jed53n9zSNmUiXLOFY\ndepE9c6IESytuH49FwzeVHAffMDyfL5OYMePc8K58UZOOM88Y35k7kWEP0JerwvltaALZVMAOQAm\nA7jTceFUxzlvAhgBulBOllKe5zR80bpQmomUQHY2sGMH/YedR3W1yw85Koq+40eOAF9//fv/b9GC\nvsvDhtE1r1cv423JzmYcwIcfAnfcQZ9tfz5PD7W1dJscP56v58wBpkwBunSx9rqeKC1lDMT06Xz9\n8MP8HgYNCky1qClT6PL6xBP6zs/Pp7vszp3Ajz/Sn72oiPfDVVcB8fG8fzp25M/iYuDWW4GtWz1X\nHpOScQoFBbzXsrLoJ79jB/8+fDi/j5QUVmxSGMZyP3mzUELeQkpLXX7IJ08ClZVARQWFTXU1sHo1\nH+xrrwVmzWLAzR//aM61i4pYdi41lUJnxgzgllusC1aRkgE0Y8YAhYX0K+/fnwFJI0cGvhzfAw/w\nmrGxwEcf8fu/7TYKuORka9pTXEzhvGcPhbJRzp1jUNy+ffRZP36c99BXX7nOadIEuOQSLhzsdqCm\nBrDZGATVujXQoQN96K+6isegQYxdCIKyesGCKv+n0IfNRo+ZUaOo4542zdysiDU1dL8cPJieKQsW\nWLNF/+YbZu50GqQrKhhDMGAAVVmzZ0u5b5/51/VEZSX19HV9vQ8coO96794ue8qqVeamP378cbp5\nWsHnn7NPTm+a6mraH/LyOJ4lJfR6CrQN4iIGKneNwmfy8lhZqXNnGndffJHJvMxizx4KoXbtmG/9\ns8/M8co5e5aeROvW1X/dJ56gPaJfP7phmp3Vsy7vvMM00fWRk8MAuJQUKVu3ZgDXSy8xmM2oy+Pu\n3RTCZk+gJSWuVMMhnNExGFFCXmEcu53+ylOm0PshOZmeI2YJxrIyKVNT6W8eE8M4AKPVo2w2BoDd\nf7/3c2trmQt9wgQaaEeNYgUrX6NZG+L0ae5Y9JbgKy/nKnnGDLqjOtv16qv0GtKzMi4oYDzDypX+\ntb0uJSXceURFUcgHccHrUMUfIa908goXNTXUxf773/zZqxeTQY0d61titfrIymK+mBUraCi9+27g\n9tup0/XGmTM0FNtszKvTrJn+6549S2Pt6tXAtm3A4MHUmY8dy7w1RpASmDyZ+vZ33zX2GYWFwHff\nAVu20LiZm8scLdddx6NvXxpDnX0tLKQRc/RoYMECY9d0Ul4OfPstDcdffkkbyuOPA1df7d/nKixB\nGV4V5lNVReGzdi2wfj0zB44YAdx4I4VkmzbGP7u2Fti4EVi5Evj8cyAhgYbg22+nZ5A7aWnMkDl0\nKPDGG74JeHdOnwY2bKDQ/+YboF8/epCMGEGBqpcXX+Rk+P335hmwz56lcXzXLh4ZGUzs1aULx+PX\nXzkhpqbS2NqxI5PWNWTYramh98uxY67skOnpwC+/cCJp6HtXXDAoIa+wFk0Ddu+mUNy0iS54vXoB\nN9xAr5brrjOeIbCqigJ/9WoK33796DUzfDgzMr7yCnDoELBkyfkpbv2looIupuvWsQ0tW/K6w4ez\nb54mMk0D5s/nCnjzZnrUWElpKb2GVq+m50piInDiBL1gjh8HSkqA5s050bRqRU+Y6urfH1FRnBC6\nd+dKvV8/YMAA31I0KxoVJeQVgaWykjno09Jcq84WLSjse/UCrrjCdYSF6ftMu52ufPPnAx9/7Pr7\nJZdQoN50E4WZVUjJ1e3GjTzS0+mimJzMIymJk8LUqRScq1bpUzMZpbQUWL6ccQiJicDLLzPtrqd2\nV1VR/VJeTnVW8+auo3Vr5coYAighr2hcpKSP9a5d1LsfPMjV98GDfD8qikdYmCtnOEAf7XPnuDLN\ny2Me8f796Wf9pz9RqH7xBVf4v/zCFahT6A4cyAnAKqqruXvZto2r6IwM13vz5nFCu/ZaruTNEqIF\nBdwdfPop9eUjRwKPPMJrKS5qlJBXXJhISYPpyZM8SkupI66p4futW1PN0KED9c4tWtT/WadPc/ew\nbRuNlRkZVD307ctozIQEoGdP81QQRUUUuCtX0ig6YQJ198XFvHZGBieeU6dYRCM+HujWjX1xTmoR\nEexTs2YMDBOCenfncfQocPgwj8xMflZyMg3CY8Zw0lMooIS84mKkspIr7Z9+ooDMzKS6p0MHl8B1\nCt3ISB5hYRS2TZtSd11ZyUno9GkaJrOzeaSnU9+dlAT8+c8U7u3aeW5HeTmFdHY2fxYWckIrKuJR\nXe2KENU0TmzOIzbW1c6ePalzV6oVhQeUkFcoAArSX391CeucHApdp8AtK+M5Nhs9fFq1onG1dWsa\nJp2TQ79+dGU0UgpQobAAJeQVCoUihPFHyKu9oUKhUIQwSsgrFApFCKOEvEKhUIQwSsgrFApFCKOE\nvEKhUIQwSsgrFApFCKOEvEKhUIQwSsgrFApFCKOEvEKhUIQwSsgrFApFCKOEvEKhUIQwSsgrFApF\nCKOEvEKhUIQwSsgrFApFCKOEvEKhUIQwSsgrFApFCKOr9I0QIhfAaQAaAJuU8nq394cAWAcgx/Gn\nT6SUC01sp0KhUCgMoHclrwH4Xyllb3cBX4fvpJR9HMdFKeDT0tIauwmWovoXvIRy34DQ758/6BXy\nQse5hkpThRKhfqOp/gUvodw3IPT75w96hbwE8I0QYpcQ4oF6zhkohMgUQmwQQvQwqX0KhUKh8AO9\n5egTpZT5QohIUNhnSSm313n/RwBxUsoKIUQKgLUArjC7sQqFQqHwDSGl9O0fhJgP4KyUclED5xwB\n0FdKecrt775dTKFQKBQAACmlIZW415W8EKIVgD9IKc8JIf4IYBiABW7nREspCx2/Xw9OHqfcP8to\nIxUKhUJhDD3qmmgAnzpW4U0AfCCl/FoIMRWAlFKmAvizEGI6ABuASgDjLWuxQqFQKHTjs7pGoVAo\nFMGD6RGvQojmQogfhBAZQohfHDp893OGCCHKhBA/OY65ZrfDSoQQf3C0e3097y8RQhxyeBslBLp9\n/tJQ/0Jg7HKFED877s+d9ZwTtOPnrX8hMH5thRAfCSGyhBB7hRD9PZwTzOPXYP+MjJ9e7xrdSCmr\nhRA3ODxt/gfADiHEl1JK9xvuOynlGLOvHyBmAdgHoI37Gw7vom5SyssdA7QUwIAAt89f6u2fg2Ae\nO2dgX6mnN0Ng/Brsn4NgHr//A/CFlPIOIUQTAK3qvhkC49dg/xz4NH6W5K6RUlY4fm0OTiSedEJB\naYQVQsQCGAngnXpOGQtgBQBIKX8A0FYIER2g5vmNjv4BQTp2DrwF9gX1+CGEAxeFEG0AJEsplwOA\nlLJWSnnG7bSgHT+d/QN8HD9LhLxju58BoADAN1LKXR5OC9bgqdcBPAHPExcAdASQV+f1ccffggVv\n/QOCd+wA74F9wT5+oRy4eBmAYiHEcoeqIlUI0dLtnGAePz39A3wcP6tW8pqUsjeAWAD9PTTEGTyV\nAOBNMHjqgkcI8ScAhVLKTHA2DcoVUX3o7F9Qjl0dEqWUfcDdygwhRFJjN8hkvPUvmMevCYA+AN5y\n9LECwOzGbZKp6Omfz+Nnaaphx1ZjC4ARbn8/51TpSCm/BNBUCNHeyraYRCKAMUKIHAAfArhBCLHC\n7ZzjADrVeR3r+Fsw4LV/QTx2AAApZb7jZxGATwG4J9wL5vHz2r8gH79jAPKklLsdr9eAQrEuwTx+\nXvtnZPys8K6JEEK0dfzeEsBQAPvdzomu83u9wVMXGlLKp6WUcVLKrgDuBLBZSnmP22nrAdwDAEKI\nAQDKnIFiFzp6+hesYwcwsE8IcYnjd2dg33/dTgva8dPTv2AeP8c45AkhnClTbgIdBOoStOOnp39G\nxs907xoAMQDeE0L8AZxEVkkpvxAhHDxVt2+Ovo4UQmQDKAcwuZGb5zchNHZeA/uCfPwuhsDFmQA+\nEEI0BetXTA6h8QO89A8Gxk8FQykUCkUIo8r/KRQKRQijhLxCoVCEMErIKxQKRQijhLxCoVCEMErI\nKxQKRQijhLxCoVCEMErIKxQKRQijhLxCoVCEMP8PQ7sFkjKK7okAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xd49b668>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(hez2[0,:],hez2[2,:],'r')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0xd9aef98>]"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEACAYAAACwB81wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRBWBJHwJCQmJKgJJ+BISEhJVBP8PF1fjK+Bx4LgAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xd8bdb00>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(hez2[1,:],hez2[2,:],'r')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here the time series are shown, true data in blue, sensor data in red"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0xd7c0dd8>]"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEACAYAAACwB81wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+QAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xd7c0e10>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(hez2[0],'r')\n",
-    "pl.plot(x2,'b')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0xdd3b470>]"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEACAYAAACwB81wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xdd3b4a8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(hez2[1],'r')\n",
-    "pl.plot(y2,'b')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0xdea5198>]"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEACAYAAACwB81wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xdea5080>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(hez2[2],'r')\n",
-    "pl.plot(z2,'b')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we are making the absolutes and ordinates by sub-sampling, similar to taking absolutes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "#ex_bns  = Baselines()\n",
-    "#ex_bns.baseH = x2[:250:] - hez2[0,:250:]\n",
-    "#ex_bns.baseD = y2[:250:]\n",
-    "ex_abs_ord = AbsOrds()\n",
-    "ex_abs_ord.ordp1 = hez2[0:3,::250]\n",
-    "ex_abs_ord.absp1 = variations2e[0:3,::250]\n",
-    "ex_abs_ord.absp1\n",
-    "irange = np.linspace(0,4999,5000)[::250]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Below we make the transform from the ordinates and absolutes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "Mex, resex, rankx, sigx = get_transform_from_abs_ords(ex_abs_ord)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.5364919 , -0.32696549,  0.42156614, -2.36376755],\n",
-       "       [ 0.33725291,  0.84709248, -0.25884768, -8.34525556],\n",
-       "       [-0.20707815,  0.53250459,  0.67061488, -8.98395935],\n",
-       "       [ 0.        ,  0.        , -0.        ,  1.        ]])"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Mex"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This transform should be close to the inverse of the example scaling, translating and rotating we did earlier.  Changing to orthogonal coordinates is also a linear affine transformation, even though we did not include it in our example."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 1.20710678,  0.75881905, -0.46592583,  5.        ],\n",
-       "       [-0.29508636,  0.76450096,  0.4805854 , 10.        ],\n",
-       "       [ 0.60705524, -0.37274066,  0.96568542,  7.        ],\n",
-       "       [ 0.        ,  0.        ,  0.        ,  1.        ]])"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Mexi = np.linalg.inv(Mex)\n",
-    "Mexi"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we can see that to within floating point math error, we have essentially un-done the transformation the sensor applies to the field"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ -1.33226763e-15,   1.22124533e-15,  -1.05471187e-15,\n",
-       "         -8.88178420e-16],\n",
-       "       [ -1.27675648e-15,   7.77156117e-16,  -4.44089210e-16,\n",
-       "         -1.77635684e-15],\n",
-       "       [  1.11022302e-16,   1.22124533e-15,  -5.55111512e-16,\n",
-       "         -3.55271368e-15],\n",
-       "       [  0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
-       "         -2.22044605e-16]])"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Mdifference = Mexi - Maffine\n",
-    "Mdifference"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we apply the inverse transform we found to make adjusted data from the sensor data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "h2Trace = obspy.Trace(data=hez2[0])\n",
-    "e2Trace = obspy.Trace(data=hez2[1])\n",
-    "z2Trace = obspy.Trace(data=hez2[2])\n",
-    "hez2Stream = obspy.Stream(traces=(h2Trace,e2Trace,z2Trace))\n",
-    "adj_ex = make_adjusted_from_transform_and_raw(Mex,hez2Stream)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we visualise the absolutes (blue), ordinates(red), and adjusted data (black).  absolutes are taken at the times shown by stars in the plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0xe4ec400>]"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAEACAYAAACnJV25AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xdf52400>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.subplot(1,2,1)\n",
-    "pl.plot(hez2[0],'r')\n",
-    "pl.plot(irange, ex_abs_ord.ordp1[0],'r*')\n",
-    "pl.plot(x2,'b')\n",
-    "pl.plot(adj_ex[0],'k')\n",
-    "pl.plot(irange,ex_abs_ord.absp1[0],'b*')\n",
-    "pl.subplot(1,2,2)\n",
-    "pl.plot(ex_abs_ord.ordp1[0] - ex_abs_ord.absp1[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we plot the error in the x component"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0xe711e80>]"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEGCAYAAABmXi5tAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xe03b2e8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(adj_ex[0] - x2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0xe85ae80>]"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEACAYAAACwB81wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmczdX/B/D3ubNYhlmMGWMda/Z1xMg2diEkRWQrUspa\nCvGlnyIiSURkSUml1ZJERotEZQ0hWbJMjAmNaYx7X78/zr2auXOXz3Iuw+f9fDw8zNz7ufd972fu\nfX/O55z3OR8BgBhjjN3+bDf7BTDGGLsxOOEzxphFcMJnjDGL4ITPGGMWwQmfMcYsghM+Y4xZhOaE\nL4R4SwiRIoTYk+226UKIA0KIXUKIj4QQ4YF5mYwxxszS08JfQkTt3G7bQETVAdQhosNENFbVC2OM\nMaaW5oQP4DsiSnO7bSMAh/PXbURUSuFrY4wxppDKPvyHiegLhc/HGGNMISUJXwjxHBFlAVih4vkY\nY4ypF2z2CYQQ/YmoAxG19LMdL9rDGGMGABAqnkdvC184/8lfhGhPRKOJqDOATH8PBsD/AJo4ceJN\nfw155R/vC94XvC98/1NJT1nmCiLaSkR3CCFOCCEGENEcIipERF8JIX4RQsxT+uoYY4wpo7lLB0Av\nDzcvUfhaGGOMBRDPtL0JkpKSbvZLyDN4X/yH98V/eF8EhlDdR+Q1kBC4UbEYY+x2IYQg3KRBW8YY\nY7coTviMMWYRnPAZY8wiOOEzxphFcMJnjDGL4ITPGGMWwQmfMcYsghM+Y4xZBCd8xhizCE74TLu0\nNKLU1Jv9KhhjBnHCv1Xt2EHUsiVRrVpECxYQBXLZCoBo0iSismWJypcnevbZwMYjIlq8mCg+nqhm\nTaItWwIbi4goM5No/Xqi/fsDH4uxm4QTvipff01UuzZRWBhRz55EFy4ELtZPPxF16EDUvz/RG28Q\nvfYa0YwZgYv3+utEn3xCdPgw0R9/EG3eHNh4775L9MILMuaUKUTduxPt3h24eGfPEiUkEE2eLA+i\n06cHLpZLejrRl18SnTwZ+FiMudzARfxxw127Bhw/Dvz7b2DjrF8PxMYCq1cDFy4Aw4YBtWsD//yj\nPlZ6OlCxIvDBB//d9uefMv7PP6uPd+wYEB0NHDr0321//CFvO3JEfbwzZ4CYGGDnzv9uW7wYqF9f\n/j1Vs9uBli2BceMAhwM4fRooUwZYu1Z9LJcDB4D4eKBJE7kf33svcLHYLc+ZO9XkYVVP5DfQjU74\nS5cCcXFA8eJAkSLArFnyC63aqVNAsWLAN9/8d5vDAfTtCwwapD7eSy8B3brlvn3hQiApSf17HDQI\neO653LdPmgQMGKA2FgCMHCkPmNnZ7UDDhoFJjCtX5j6YfPEFUKFCYBoK6elA1arA/Pny9717cx/g\nVLtyBRg6FKhSBRgyJDANERYwnPD9mTgRqFQJ+OUX+fuhQ0DdusDo0epj9ekDjBmT+/ZLl+TB5qef\n1MVKSwOKFgUOHsx9X1aWfM+bNqmLd+IEEBUFnD+f+74LF2Tr9NgxdfH++kvGO3Uq932ffQbUq6f2\ngHbtGlC5MrBhQ+772rSRZxaqvfgicN99OW9bsAC4667ANEjsduDuu4EePeQZYO/eQOvW8vPCbgmc\n8H1ZtgwoX14mj+xSU4E77gBWrFAX68cfZVK/dMnz/QsXyu4CVV57DejZ0/v9CxcC99yjLt6kScCT\nT3q/f9gwYPx4dfHmzgUefNDzfXa7TM7ffqsu3urVQIMGnhPthg1A9epqk7DrgP3bbzlvt9tlLE8H\nHrPmzAEaNwauXpW/Z2XJM8GZM9XHcjl8GGjXTn43Bg+WZzXMME743hw6JFud+/Z5vv/nn+Xp8+nT\nauJ16AC88Yb3+69elf3B27ebj+VwALVqARs3et8mPV2+/6NH1cSrVEke1LzZuxcoWVJda7FZM+DT\nT73fP20a8MgjamIB8uDorRXvcMiuF5UHmJkzgV69PN+3dCnQqpW6WABw8aIc29m9O+ftBw/KA09q\nqtp4gBxPKllSvtejR+X7bd06MOMvFnFrJ/w9e4ApU4CnngLefFN2Dahgt8uEMWuW7+1Gj1bT97xn\njxwjyMjwvd2rr+Y+hTdixw6gXDn5Pn0ZNcpzF5Ne27fLfmx/LdzERGDNGvPx/vxTduf46jc/dUpu\no6LFePKkfC5f/dkvvaRuHMbh8H2Gkpkpk/Phw2riAcDUqcBDD3m+b8AAYPJkdbEA+R7bt5dnhi7X\nrskzipdeUhvLPe4PPwCffw5cvhy4ODfJrZvwe/QASpQARoyQrbUHHpAt0jffNH/qPG8e0KiR/5bE\n33/LRG22ouWhh+QXyp/Ll+Wg8YkT5uI99pi2L+iePUDp0v4PDP4MHw7873/+t5szR45jmDVrFtCv\nn//t2rVTM3j78sv+k7nroHDlivl4mzcD1ar5/pyPHOl5gNyIrCx5duntc/7rr/IA46/BoseaNbJr\nytV95PL77/J7fuaMulgu6elAp07ybLRlS9mNtHWr+jg30a2b8IcPz90627cPqFFD9gcbTVJ//ilP\nUX/9Vdv2c+bID4lRx4/LJJ6Wpm37IUNytnr0Sk+XiefkSW3b164tE4xR167Jg6J7X7Mnp08DkZHm\nE0dioqyO8WfRIqB7d3OxXPG+/NL/dq1aAatWmY83cCAwY4bvbXbvlgdrFd0fn3wi36MvrVvLKiUV\nHA45qP7RR57v91R9pSJmly6yweE6yKxZIw9kWj67t4hbN+F7k5YmB5aGDTPW0u/eHZgwQfv2GRmy\nn9FoBc2oUbJLSqtdu4BSpYx/kZctk+MFWr38srm+7g0bZKmiVklJvvve/fnjD3nAdm8ZenLuHBAe\nbq7VffKkPGBriTdvnvd+d62uXpXvT0tFU+3aQHKyuXiAbNAsXep7mxUr5BmTCq4zGG+NNleXnaou\nXEAWKdSpI7vDsnv9dTkYf5uMG9yUhE9EbxFRChHtyXZbFBFtIKLfiOhLIorw8Xjf7yotDahZU3b1\n6LF2rexr1psAZs8GOnfW9xhAVuQUKaK/HLFhQ+N93c2aeW85eeL6chlNiv36Aa+8on37efO8V9do\nMW0a8Oij2rdv0UK2YI2aPRvo31/btqdPy33pnlT0+OormYC0mDxZ1sybkZoqD4oXL/re7soVNd2N\ngPzM+Kv86dNHWzeoFpcuyfkvnuYv2O1yUtu8eWpiZedwyLOiPn1kN+u2bepjuLlZCb8JEdVxS/jT\niOgZ58/PEtFLPh7v/539+afsd3znHW174p9/gLJltZ2au7tyRfb3uWr1tZo9G7j/fv3x3nrLWMnk\nb7/JD7behNOqFfDhh/rjXbkiu2j0VDKlpAAREcYPMHXr6ps/8Prr5sYNmjaVJZlaNWokZ1MbNXgw\nMH26tm3375fjXGbGYBYt0l4oMHiw+QHVy5fl3//sWd/baS080GLiRO8D0oD8XhcvrrYk9MoVeeZU\nv74cd5w+Xf6tnnsuMHMonG5alw4Rxbsl/INEVMz5cxwRHfTxWG3vbt8+2Qfnq/zQ5dFH5YxWo159\nVV8r/9o1WeP//ff6Y/3zj2wp6m1NPfss8PTT+uMtXgx07ar/cR98IPt29WrZEvj4Y/2PO3RIHtD0\nnH67qnWMtLrPnJEHND2zaKdP13cGkt21a/Lz/Pvv2h9Ttaq5gcc2bbQf7L/+Wva9m7FkibbGjKu0\n2OzkQFchhL992q2b/3ETrex2+R579sxZhvzXX/IAMG6cmjge5KWEf8Ht/gs+Hqv9HSYny3p59/rh\n7D74QCZff6etvly5oq8v/9NPvU/U0WLIENky0erqVTl4euCA/lh//y1P6/XWWnfpYmyG6RtvGOvW\nmTzZ9+Qub4y2uo30yR8+rP+g5GIkoU6YoG+MKDvX2ZbWlu21a/K9mSkHbd5ce5fj7Nnmx0Tmz5ef\nU3/27JHvTUUl0owZchDc07jPX3/J9a3efdd8HA/ycsJP9fFYTJw48fq/zf6qSN5/XyZjT310ycly\nEEzFYmFz5gAdO/rfzuGQScZMVcPu3fomKn3yiRzMNqp7dzltX6sLF+RB4u+/9ccy2q1TvbqxyU0v\nv2ys1d2qlb7xEJeaNYHvvtP/uCFD5LwTPXbtkl2VRhoWc+fK5RP0GDJELvlgxO+/y++i1rOt8+fl\n58To4K3DIav6tPQAAHJZiUWLjMVycZWV/vGH921++UXuBz1ncl5s3rw5R67MSwn/gFuXzgEfj9X/\nzj/4QO7EmTPloO7Fi/JIGxOj/Q/uT0aGrKDxNaMUkOMEVauaH/lPTJTrwmjRvr2s0DHqk0/kgK9W\nb75pruSxRQt93Tp798p9b6RP9/ffZVeJnr/H+fPygGakX3fiRFmdpYervDX7SqNaOByyxWikikzv\n+AQAbNkiq4OMmDhR/yDzAw/IA5MRmzfL76HWg+FXX/mf/+DPAw9omwPzyivy+614naKbmfDLEtHe\nbL9PI6JnnT+bH7T1ZP9+2ReXPz9QoABw772eFw8z4403ZLLy9qFQuVrj0qXaSixdSxCbKT/891/Z\n13n8uLbtmzY1V/2it1pn/Hj9STS7OnVyrlLqz+LFnlca1WL3bv2t7m++kX3WRowZo3/GtJbZyp5c\nuyYHOPV+r+x2uU/0nmmvXw8kJOh7jMt99+k7WLjGDbTM8fDkhx/kWbmWRoLdLs8gX3jBWCwvblaV\nzgoiOk1EmUR0gogGOMsyNzrLMjcQUaSPx5t71w5H4EbCs7JkpYi31vSSJcCdd6qp671yRdsqk+PG\nyRnJZj36qLYqjN9/l2dOZsoPz57V3q3jWqvHzDpD//d/+vZRp07G+1kdDln+q6eqa/hw4PnnjcX7\n6SfZytfzmX/1Ve3lpu6GDtX/Wjdvll1der+X167JCWa7dul73IkTsgGjd/mEpUuNFSI4HLJLdckS\n7Y85cUJ+j8x0N2/dKidqvvgicOLEbTjxKi/46SfP1RTHjsnbd+xQF2vUKJkMvLl8WX5oVJzJbNki\nv5T+TJpkvv4bkJOwtJwlaF2rx5d9+2QZr5bnuHgRKFzY2PiEy+jR2lcHdThkUvO2kJ+Wx5ctqy8p\n3nUXsG6dsXhbt8r18vX8PbTU3nszYYLv74An48YZm62bmSnLJ/Vec+Cjj2RXl96G3jvvyG4kvWfn\nFy7IRkm5csDYscATTwAlSnDCD5g5c+QCV67BmT//lMlSVWmXi2syj7da95kz1SwfAMjTzNKlfX/Y\n7XZZ8aTioDZ3rrYqjGHD9FUseeJwyCWvtbzu997TN1vZkx9+kF9kLX78UX6WzBzQnn5a+wHG1fo1\neobmcMjPgNaWqdbae29cg71au58yMswtmfDyy3ItL60yM+UZlpElqx0O2e+v5+zz0CH5WR42LGcl\n0LBhnPADatYsWafdvLn8Ak2dGpiupBEj5Ew9d2lpcqBP5RWQJk8GHn7Y+/1r1qi7uMiZM/67dbKy\nZLmc3sFMT8aMka0hf7p2lZPfzLDbZUtRS5nsM8+Yr83etk22urV45RXff2Mtxo+Xa95osXix+Wsv\ntGihfb7AkiWy4saoS5fkAUZr+ens2bJowqjUVNn3r6W4ZONGeTDzUlHHCT/QUlLkkV3VuvmeXLgg\nk4f7oOPgwcYn+Xhz/rw8o/C2WmHr1sDy5eritWvnu7pozRo5CK7Cjh3+u4bOnZMHITNzNlyeeMJ/\nCaPdLrua9PZRu3N1C2lZFLBBA+MDky4HDsjBWy1dGI0ba68282b5cm1J3OGQY2xGu6tcJkzQttx1\naqrsUt2711y89etlFZq3yZYOhzwjLlZMztfwghP+7WLNGvkF27lT/vFnzpSnkVpX4dRjyBDPFTHf\nfy+TipnBWnerV8tBbm/atdM3EOaLwyFr+X3N63jtNfOTfVy++cZ/WeCmTcbLHN2NGOG/W2ffPtl4\nUFEOmJAgSxl92b9ffm7NxtO6ls+338oBfrNLMpw7Jxs+vurpAdmt4uns24hXXpENEvfxuEuX5AB7\njRp+zzo44d9OVq6UVTvFiskvm4qrVXly9qyMs3//f7dlZcmWoZlaf0+uXZMDTz/8kPu+/fvVr8M+\ne7b3clCHQyZfI+steXu+ypV9T8Lq08f/hXi00jJZb+RIbd1aWrz2mux/9uWpp9RcZAeQA7f+lg65\n5x45vqbC//2f73WGdu+WXT/ul0g148035Xdv2DBZMfTcc/IA/fDDmiqOOOHfbjIyZDVQABdgAiCn\npFerJls6Dof8srVurWYxK3dvvCHX13F/T92765956s+FC94XfNu0SbbIVb7Hl1/2Xv547px8LSkp\n6uIlJnpffjojQ3Y/qLpS1sWLvlvdFy+qu4wmIONERcluR0927JAHPFUNhCtXZPWTp+6hzEzZOAjE\nxeuPH5eVcL17y/WxdHT3ccJnxjgcsnUREyMTf2KiTFCBkJUlE232i8Z/+aXsPlJxBSl3o0blXpPH\n4ZAHHbNT69399ZdMUqdO5b5v0iS1190FZKuwTRvP982fr21pED2GD5eDzp5Mn25uKWxPBg6UJa/u\nHA7ZIFHVunfZvFmeUWfv2nE4ZJlpt26Bb3jpxAmfmXPkiKwACUTLPjvX+iLvvScH+GJjzV2Jy5eU\nFNnyzD7QtmqV50vuqTBypBzAze78eXkwNbLYnS///gvEx+decygzU5ZSqrzQOiDPNosUyX2FtQsX\nZKL0taihEWfOyM/Jnj05b1++XLa4A/H3mzdPDqh++KH8LnTpIgeifV3j+CbhhM9uHd9/L5ds0HpJ\nQTMWL5b960eOyIlEMTHGlrLWIiVFPr/rAhgOh7wwuPtBQJUlS+R4S/a+/BdfNHepTl/GjpXdb9lb\nu4MGySqyQFiyRBYsuLrltm5Vt0CiN198IUtDa9WSZ2Z6l6S4QTjhM+aJwyEHS8PD5aCY2bJBfz76\nSMZ5/33ZDVKtmprST0/sdlnd9NhjMum7zpj8VZwYdeWKLIV85hlZNTZ5spwYZGamsj9Tp8ok36aN\n/N9sGeZtQmXCF/L5Ak8IgRsVizECiIQIfJwvviCaM4eoVCmiKVOIihYNXKy0NKLevYm++UbGWbmS\nKDExcPFSUogGDSLasIGoWTOiJUuISpYMXDwioqNHiX79lahxY6IiRQIb6xYhhCAASj7MnPAZu9Wk\nphJFRhIFBd3sV8JuAE74jDFmESoTvk3FkzDGGMv7OOEzxphFcMJnjDGL4ITPGGMWwQmfMcYsghM+\nY4xZBCd8xhizCE74jDFmEZzwGWPMIjjhM8aYRShJ+EKIkUKIfUKIPUKId4UQoSqelzHGmDqmE74Q\nogQRDSWiegBqEVEwEfU0+7yMMcbUClb0PEFEFCaEcBBRQSI6reh5GWOMKWK6hQ/gNBHNJKITRHSK\niP4GsNHs8zLGGFPLdAtfCBFJRF2IKJ6ILhLRKiFELwAr3LedNGnS9Z+TkpIoKSnJbHjGGLutJCcn\nU3JyckCe2/R6+EKI7kTUDsAg5+99iKghgCfdtuP18BljTKe8th7+CSJKFELkF0IIImpFRAcUPC9j\njDGFVPThbyeiVUS0k4h2E5EgojfNPi9jjDG1+BKHjDGWh+W1Lh3GGGO3AE74jDFmEZzwGWPMIjjh\nM8aYRXDCZ4wxi+CEzxhjFsEJnzHGLIITPmOMWQQnfMYYswhO+IwxZhGc8BljzCI44TPGmEVwwmeM\nMYvghM8YYxbBCZ8xxiyCEz5jjFkEJ3zGGLMITviMMWYRnPAZY8wiOOEzxphFcMJnjDGL4ITPGGMW\nwQmfMcYsQknCF0JECCE+FEIcEEL8KoRoqOJ5GWOMqROs6HlmE9E6APcLIYKJqKCi52WMMaaIAGDu\nCYQIJ6KdACr42Q5mYzHGmNUIIQiAUPFcKrp0yhHReSHEEiHEL0KIN4UQBRQ8L2OMMYVUdOkEE1E9\nInoCwE9CiFeJaAwRTXTfcNKkSdd/TkpKoqSkJAXhGWPs9pGcnEzJyckBeW4VXTrFiOgHAOWdvzch\nomcB3OO2HXfpMMaYTnmqSwdAChGdFELc4bypFRHtN/u8jDHG1DLdwiciEkLUJqJFRBRCREeJaACA\ni27bcAufMcZ0UtnCV5LwNQXihM8YY7rlqS4dxhhjtwZO+IwxZhGc8BljzCI44TPGmEVwwmeMMYvg\nhM8YYxbBCZ8xxiyCEz5jjFkEJ3zGGLMITviMMWYRnPAZY8wiOOEzxphFcMJnjDGL4ITPGGMWwQmf\nMcYsghM+Y4xZBCd8xhizCE74jDFmEZzwGWPMIjjhM8aYRXDCZ4wxi+CEzxhjFsEJnzHGLEJZwhdC\n2IQQvwghPlf1nIwxxtRR2cIfTkT7FT4fY4wxhZQkfCFEKSLqQESLVDwfY4wx9VS18GcR0WgigqLn\nY4wxpliw2ScQQnQkohQAu4QQSUQkvG07adKk6z8nJSVRUlKS2fCMMXZbSU5OpuTk5IA8twDMNcqF\nEFOI6CEiukZEBYioMBF9DKCv23YwG4sxxqxGCEEAvDakdT2XyiQshGhORE8B6OzhPk74jDGmk8qE\nz3X4jDFmEUpb+D4DcQufMcZ04xY+Y4wx3TjhM8aYRXDCZ4wxi+CEzxhjFsEJnzHGLIITPmOMWQQn\nfMYYswhO+IwxZhGc8BljzCI44TPGmEVwwmeMMYvghM8YYxbBCZ8xxiyCEz5jjFkEJ3zGGLMITviM\nMWYRnPAZY8wiOOEzxphFcMJnjDGL4ITPGGMWwQmfMcYsghM+Y4xZBCd8xhizCNMJXwhRSgjxtRDi\nVyHEXiHEMBUvjDHGmFoCgLknECKOiOIA7BJCFCKin4moC4CDbtvBbCzGGLMaIQQBECqey3QLH8BZ\nALucP/9DRAeIqKTZ52WMMaaW0j58IURZIqpDRD+qfF7GGGPmBat6Imd3zioiGu5s6ecyadKk6z8n\nJSVRUlKSqvCMMXZbSE5OpuTk5IA8t+k+fCIiIUQwEa0hoi8AzPayDffhM8aYTir78FUl/LeJ6DyA\nUT624YTPGGM65amEL4RoTETfENFeIoLz3zgA692244TPGGM65amErzkQJ3zGGNMtT5VlMsYYuzVw\nwmeMMYvghM8YYxbBCZ8xxiyCEz5jjFkEJ3zGGLMITviMMWYRnPAZY8wiOOEzxphFcMJnjDGL4ITP\n8pTDh1Np+vSNdPr05YDHcjhAzz+/jtq2nULJyUcDHo+I6Ouvf6d5876jq1ftNyQeY9lxwmc+vfLK\nZqpVayiNHPkBORyBXQtp0aJtVLlyVZo8+X9Upkx1+vLLQwGN167dFHrxxeGUknKWWrZMpI8+2hvQ\neD17zqPWrRPpqaeGULFiren8+SsBjXf8+N/UuvUL1KjRWNq160xAY7FbBIAb8k+GYmYdO5aGoUPf\nwzvv/BzwWI8//g5sthLo0GE6QkOrokWL5wMW6/jxv2GzlcT48Z8DAHr2nId8+WogPf1qQOItXvwj\nbLY47N59BgAwaNAy5MtXDRkZWQGMVwxbthxFZuY1lCnTE9WqPRaQWABw5sxl5M9fG+XKPYQ6dYYj\nKKgMdu48HbB4AHD5cibGjfsMU6duQFaWPaCxrMSZO9XkYVVP5DcQJ3zTVq/eD5utBGJjO8FmK4lW\nrSYHLNbWrcchRDQ+/ngvAGDv3rOw2eIwf/73AYlXv/7TqFx54PXf7XYHihRpjfvvfz0g8cLDm2Lg\nwKU54kVGtkCfPm8qj2W3OxAWdicee2z59duOH/8bQsRi1ao9yuMBQM2aT6B8+T6w2x0AgObNJyIm\npkNAYgGyIRIWdifCw5sgf/6aKF68a8AOnlbDCT8POXjwHHr1mo8hQ97FxYv/BixOauoVhIRUxCOP\nLAEgE3BwcLnrLWLVypV7CE2bTshx26BByxAe3uR6ElHl6NELECIK27adyHH78uU/ISiolPLEsWjR\nNgQHl831vK++mozQ0MrK39+MGZsQGlo5V6u3c+eZKF26h9JYALBp0xEIEY2jRy9cv+3y5UyEhFTC\nlClfKo8HAKVK3Y+qVQfDbnfg8uVMREe3RWLimIDEctm27QTuu+81jB376W19RsEJP49YuXInbLbi\niI/vhcjIFggLa4Djx/8OSKymTSegVKn7c9w2c+bXCAoqrfxAI1v3RXDq1KUct2dkZCEkpBJee22L\n0ngdOkxH+fJ9PN5XuPBdeOaZj5XGK1u2N7p0eSXX7Xa7A/nz18TUqRuUxouN7YS+fRfmul228iOV\nd7VUrjww18EaAAYPfhtRUa2UxgKA6dM3IiSkItLSMq7ftm9fCoQoitWr9yuPB8iDqBDRqFTpYRQs\nWB8xMR0C2uC6mTjh5wEHD55DUFAZDBu2EoBMFjVqDEFcXGflLcRjx9IgRBF8++0fue6LibkbPXrM\nVRovMXEM6tYd4fG+e+99FfHxDyqLZbc7kC9fda8HkSFD3kV0dDtl8c6cuQyiCOzbl+Lx/vvvfx3x\n8b2Uxdu3LwVEEUhJ+cfj/VWqPIrWrV9QFu/YsTQQhePgwXO57rt8ORM2Wwml3Uh2uwOFCiXiySdX\n5LqvffuXlO5Ll23bTkCIopg1azMAID39KooX74qqVQcrj5UXcML3IS0tA2PHfoqBA5cGdJCqSpVB\nqF17WI7bLl/ORL581TFy5AdKY7Vu/QLKl+/r8b75879HcHAFZae0GRlZECIGGzYc8nj/0aMXQBTh\nMaEYsWLFLwgOLuv19aek/AOiCOzf/5eSeI8//o7PvmxXgj53Ll1JvPvuew3lyj3k9f65c79Fvnw1\nlMQCgN69F6Bkyfu83t+06QQkJIxSFm/Bgq0ICano8e8nz2CK4LvvjimLBwCxsR1zjV+dOnUJQUFl\nlJ99Zrd48Y/o23fhDSmYyI4TvhefffYrQkIqISKiOUqX7gEhoj22PMx6//1dsNniPHbfzJixCSEh\nlZCZeU1JLLvdgeDgslix4hev9+fPXwvTpn2lJN6sWZtRoEA9n9uUKdMTDz74hpJ49eqNRJMm4/3G\n69VrvpJ4MTEdMGTIuz63KVKkDUaN+lBJvLCwBnjhhfVe78/KsiMoqNT1wXGzChVqhAkTVnu9f926\ng7DZiiv7fFao0A8dO77s9f6EhKfQoMEzSmIBsoETFBSPy5czc903cuQHKFCgjvIz7LS0DJQt2xvB\nwWVRoUK+3WDkAAAWmUlEQVQ/BAWVQfnyfXN0YQUSJ3wPNm06AputeI7Ki1Wr9sBmK6F8YLNSpQFo\n3/4lj/fZ7Q6Ehzfxm1S0WrBgK0JDq/r8EPfoMTdX/75RtWsP81v988wzHyMqqqXpWPJgVg4ffrjb\n53ZjxnyCyMgk0/Hk2UIhv+MsffsuROnSD5iOt2PHnxAiyu+gc0LCU34Pelps2XIUQsT4LWUNC7tT\nyeCtlrO9tWsPwGYrrmzgPTq6rddKKrvdgQIF6ij9vtvtDsTHP4jixbsiNfUKAODcuXQUL94VJUp0\nU3bg9IUTvpu0tAwUKFAX9933Wq775s//HkLEYseOP5XEOnToPISI9PkhnzhxDQoWrK8kXs2aT/pN\nwPKLF46TJy+aimW3OxAUVBqfffarz+1SU6/47AfX6rPPfkVQUGm/LTIZr3COqhMjxo//HJGRLfxu\nt3v3GQgRaXoOQJ8+b6JMmZ5+t5s//3vkz1/LVCwA6NZtNipVGuB3u65dZ6FSpYdNx+vXb5HP7iOX\nQoUSMXHiGtPx1q//DTZbMY+te5eRIz9AWFhDZa38Pn3eRIECda8ne5eLF/9FeHgTtGs3VUkcXzjh\nu2nUaCxKlOjm9Y98113jlA0edegwHRUq9PO5TWbmNQQHl8WyZTtMxZL96bHYtOmI321jYzvmqPM2\nYtmyHQgJuUPTl0VFN0v79i+hRo0hmraNje14fYDcqKpVB6NTpxmati1YMOH6oKBRcXFd8Pjj7/jd\nLjPzGoQoiq1bj5uKFxXVCmPGfOJ3u2+//QNCxJpunRYt2l7T30SOK3Q3FQuQ32N/4w/yu1cOixf/\naDre/v1/QYgYr4Pcspotxmt3qyqc8LNZt+4ghIjGzz+f8rpNSso/CAoqZXrSkCuRL1263e+2bdtO\nwR13PGIq3gsvrEdYWANN2w4e/DZiYzuZiteo0VjNtdMjRrxveiJPeHgTPP/8Ok3bPvjgGz4HP/1x\nnb1oLRNs1ux/qF//acPx0tIyQFQYhw6d17R9+fJ90LPnPMPxjh//G0SFvVYDucufvzbmzv3WcDzX\nWaV76a4nR46kgigcZ85cNhwvM/MagoJKaaowuvvuaahYsb/hWC7Vqj3mtVrNpW/fhShUqFFA5wHk\nuYRPRO2J6CARHSKiZ71so3xHyNmYbTzWVLvr128RihRpbSre+PGfa07Ae/eeBVGEqQ95+fJ9ce+9\nr2ra9uTJiyAKN9XtERpaRXPLSJb/FTZczXLo0HkQhWse+JKtqaKGW6WrVu1BcHA5zaf6ixf/iNDQ\nqoZiAfJgXbjwXZq3HzHifcTGdjQcb9iwlboOwM2bT0RCwlOG4w0atAzFi3fVvH1MzN0YOvQ9w/Gm\nTt2AggUTNG178OA5CBGJI0dSDceTpZ9F/FajZWXZERbW4PqEyEDIUwmf5AJsR4gonohCiGgXEVXx\nsB3KlXsINltJhIZWRsOGz5rukx016kPN661cvpyJoKB4U6386Oh2GDz4bc3bx8R0MNzNkpp6Rfek\nnOLFu2LAgMWG4sllG0rqaqlERib5rAjxZfDgt3UlDADIn78WFizYaihe+/YvoWbNJzRvn5VlhxCx\n2Lz5d0PxatUairZtp2je3uwBND6+l64utpUrdyI4uILhvu5ixe7R1F3l8sgjS1C8+L2GYgFAfPyD\n6N59jubty5V7SHP3nSc1az6hubpo2bIdsNnicOxYmuF4rucpUaIbbLYSyJevGjp0mI709Kt5LuEn\nEtEX2X4f46mVT0Ro3nwitmw5ipUrd6JKlUEICiqlqXvEk9TUKwgKitfVz9qr13wULdreULz163+D\nELG6SrGefHKF4UlDo0Z9qPuMZMiQdw23Etu0eRE1az6p6zGdOs0wPNmldOkH0K/fIl2PadRoLO66\na5yheOHhTTV3H7lUrNhfV5JxkdVH5f1WH7mLjGxhqMIkPf0qhIjSVZggu7jiDZWDyrPJwrpmlZsp\nLJAHwwjN3WOA7/kB/riqq/QUJVSpMgh16gzXHQuQf4u2bafAZotDjx5z8d13x7B06XZERrZAxYr9\n81zCv4+I3sz2+0NE9JqH7XK90bFjP4UQMXj11WTdO6lFi+d1lyJevPgvbLaSWL78J93x6tYdgUaN\nxup6zLlz6Yanzhcvfq/u1rrri2ikpVGwYH3MmLFJ12NkTXdJ3a1EmaAir69UqdW8ed8hf/7auh4D\nuJJN4VyVFv6MHPmBoQbC2rUHNFUfuevceaahA+jMmV8bqgrTUoLriWxY6B8vio3tqOuswMXIoK+c\nn1Lb0DIZCQmj/Pbdu5PdSN4HeL2x2x1ISHgKBQrUw/btJ3PcJ/NH9K2b8CdOnHj93+bNmwG41sSI\n0dUakv1r0YZm8HXtOktTKVl2Z85chhBFDFVRVKjQT9MYQ3au6fFG1uWJi+usq9sJ+G9lTL210rIl\nWwErV+7U9TijCUpWs0Tn+mL4M2zYSkNnPnIgtJDubhajZz6y7FD/AbRu3RFo2fL/dMebNWuz5n7x\n7IoXv9dQn/XgwW8jLq6z7scZLevs1Wu+7m4k1zIm7gv5adGjx1xERDTX9fdr0eJ55M9fK8d4w+bN\nm6/nyYYNu+e5hJ9IROuz/e61S8ebESPeh81W0uNaMZ6ULHkfmjX7n6Zt3aWk/AMhYnUt6iQ/OPr6\nm12mT9+IAgXq6npM//5voUSJbobiPfbYchQrdo+ux3TrNttwVUOdOsN1rwVTv/7TaN58oqF48fG9\n0Lv3Al2PKV++r+H1hoyMUxjtmgGAkJBKusr8jB50AVfZb7Suhoxci8hYcYA8gIbrOgOVE7fiDE3c\nkg21KF0NhHbtpnpdyM+fzMxryJ+/tuby4d69FyAkpCL27j3rc7u8lvCDsg3ahjoHbat62M7nm+re\nfQ5CQu7wu2bKiBHvIzS0qqlpzb7WpnHnWtxr+vSNhmJlZl6DzVZSV19pVFQrw1P7XeV5evpKIyKa\nG05Q06Z9hbCwhroeExpa1fDYzZAh7+pqJcrB1xjNjQl3HTpMR/Xqj2ve3tWtprU80l29eiN1tda1\nTl7zpkKFfh4nLHozcuQHiI5uaygWIM8O+vd/S/P2iYljTJXH1qz5hObGYVpaBmy24qYWl5s791vY\nbCX9VghNmrQWNlscNm487Pc581TCl6+H2hPRb0R0mIjGeNnG7xtr3Pg5FCxY32sp48aNhyFELBYt\n2ub3uXzxtfqku2nTvkK+fDVMzdxLTByjecGq7dtPQogoUwe02NhOmvtKXYuF6e3fdrl8OVPXOIWc\n/h9ruG7ZVdOtdf8sXvwj8uWrZigW4EqoZTT//UeP/sjU6p4zZmzSXPoL6Ju85snYsZ9qmn3sUrp0\nD91nWNmNHv2R5mUyMjKyYLOVwKef7jMc79NP98FmK6Gpkq9v34WIibnbcCyXhIRRiI3t5PUzvnDh\nDxCiqOaKszyX8DUF0pDw7XYHKlceiPDwJrla+jt2/InQ0MqmJqdk16jRWE0tt9jYjrqrSdxt2HAI\nQsT6nBLucvfd03Jc+cmIgQOXau6C6t17gabp/77Exz+ouSSwe/c5fmcq+1O4cGOfC5JlZ3YCld5q\nlkqVHka3brMNx3MdQP2d5rvo2ReenDuXDqJwTRUwcjKZuSU10tIyIESUpm6kCRNW6z579CQiopnf\nM+b09KsICaloena167kKF26MWrWG5moouMYsJ01aq/n5btuED8hT8EaNxsJmK4kHH3wD77+/C/37\nvwWbraSuumZ/5AUaony2TD/+eC+EiDXc+s0uIqI5nn56lc9tZPdRDdNLvOqZBVmkSBu/r8sfeZqv\nrVVbtGh708tHt207BbVqDdW0bcGC9TFz5tem4tWoMQR33z3N73Z2u0PzabovpUrdr6lCa//+v0AU\nYfrCH8WL35tj0UFvxo37DBERzU3FAmQJo7fFB3O+rq4eLxyj1zPPfIwCBer5PEvr23ehkgUBXY4f\n/xuFC9+F6Oi2mD59IxYt2oZatYbCZovT/Xm8rRO+y/z536Nkye7Il6864uK6BGSd69q1h/msnY2L\n64LOnWcqifXYY8v9lvi9887PCAqKVzJNW8vMxoMHz4Eo3PTa76dOXdJUl+2qPjK7yNuHH+5GcHB5\nv90srtnOZhdBmzRpLSIimvndbvnynxASUslULEDOYtUyaP/II0sMD+67x4uL6+J3u/j4XobmJbib\nM+cb5MtXzeffb/fuMyCK0NRo8UeuolnX65XTzp1LR1BQaSxc+IPpWNmlp19F166zUKhQIxQoUBd3\n3jla85lbdpZI+DeCbOXHeryggevygarWvJYzZ6N9LoRWsWJ/ZWcxAwYs9lt+qmpRK0B2fflbElpr\nYvFHdrOUwtq1B3xu9+CDbyi5Zqzs9vA/vyExcYyS67i6Wu7+ziyjo9uZWq7AxbXMsa/KG1kMoObC\nN1lZdoSGVvHZ0k1KmoQqVQaZjuXy/PPrEBJSyWPj5s47R5vu1gwkTvgKDRy4FPnyVc/ROj106DyC\ng8spWdI1uyZNxnutz5YHn0hdswl9cQ1u+mpNh4U1UPYe+/d/y++4gZaDglY1agxBmzYv+twmMrKF\nsuvhxsZ28rlMhmt2raqrIRUp0hojRrzv9X5/l07Uq3TpHj5b7337LlRyNuHSu/cCr5O3ZOl0DNat\nO6gsHiBXeHWfTT5t2lew2YqZXuo7kDjhK2S3O1Cz5hMIC2uIjz/eiw8/3I2CBROUXqXHRc7Gi/I4\nqSMh4Sld5X9alC79gNfF1+TFYUoquzCFLEeM8HrK6losTcUpOiC7/EJDq3jtFnB156gYfwH8L5Px\nzjs/m1qbxp1c/dT7ZDHV196dOnWD1/kidrsDYWENlV5YRJ7xxnqccNmx48um1t3x5siRVOTLVx0J\nCaOwe/cZDB36HoQoamim/43ECV+xrCw7OnWagaCg0ggKKoPOnWcqv0yaS7Nm/8vVUpLlpkWUX4N3\nwYKtCA6u4HGFycqVBxqe/OSNr8vddeo0w/CEFk9cE468XXOga9dZppZTdudaJsPbMtx1645A48bP\nKYvnuti6pwOoq0/aTHWOu6wsO4KDy2POnG9y3Td37rcICamo/OpOPXrMRWRkUo7vmrz4TLTf7jqj\n9u1LQZkyPSFEFAoVSjS8GN+NxAn/FpaWloHQ0KrXT59PnryIggUTTJXyeWO3O1CoUKNcSy3IpRSK\nKOs+cpGJIff1fLOy7AgJqWT6egTukpImeTwrstsdCA2tbGq9d08qVXrY4xWOXGueGJ3c5U2VKoM8\nThqS+/kO5Wuw9+u3CFFRrXLcZrc7EB3dVtk1jLPLyMhCWFiD6xPNzp1LR0REM+UNkVsdJ/xb3JYt\nRxEcXA5RUS0RFBSPmjWfDNgZxYIFW2Gzlbg+IGe3OxAX1yUgXyq73YHChe/KNenrySdXKL3snIts\nDeZe1XDy5C9MT5bzRF68vkSugfxeveabvhiMJ3L+RtFc3WBFi7Y3vFSEL7IW/Y4cZbpPP70KoaFV\nTVc6efPzz6cQGloVERHNEBJSEeXL970h14m9lXDCvw2kpPyDsWM/NbRyp151645AeHhTrFq1BwkJ\no1CgQD3TtdveyOqm+OuD4KdOXUJwcDndK3FqVb364zlWNszKsqNgwQTTtf7exMZ2zDEucu5cOmy2\nkqZnf3tToUK/HKXD48d/jpCQipom8RmxaNE2CBGDCRNWY8KE1RAiRnm5orvU1CuYMGE1Fi78IWAN\nn1sZJ3ymS2bmNTRtOgHBwRVQqtT9Aa9IqFFjCKKiWmH+/O8RHd0WVao8GrBY+/f/BZut2PUDSqtW\nkxEe3iRgl5xbu/YAhIjGxx/vhd3uwB13PIKyZXsHJBYgB7uDgkqjQ4fpzuXEYwMyJyW7adO+QoEC\n9VCgQD3Da0gxdTjhszwtIyMLTZqMR/78tZGYOEZZJZA3s2ZthhDRCAtrgJCQSoaWttXjySdXQIgi\nyJ+/NsLCGhpaxlqPTZuOoFixe1C48F1KB2rZrUFlwhfy+QJPCIEbFYtZz4ED52jdur00YEAjKlKk\nQMDj/fjjSdq+/Q8aNOguyp8/OODxmHUJIQiAUPJcnPAZYyzvUpnwbSqehDHGWN7HCZ8xxiyCEz5j\njFkEJ3zGGLMITviMMWYRnPAZY8wiOOEzxphFcMJnjDGL4ITPGGMWYSrhCyGmCyEOCCF2CSE+EkKE\nq3phjDHG1DLbwt9ARNUB1CGiw0Q01vxLuv0lJyff7JeQZ/C++A/vi//wvggMUwkfwEYADuev24io\nlPmXdPvjD/N/eF/8h/fFf3hfBIbKPvyHiegLhc/HGGNMIb/rugohviKiYtlvIiIQ0XMAVju3eY6I\nsgCsCMirZIwxZprp5ZGFEP2JaBARtQSQ6WM7XhuZMcYMULU8sqkrNwgh2hPRaCJq5ivZE6l7wYwx\nxowx1cIXQhwmolAiSnXetA3AEBUvjDHGmFo37IpXjDHGbq6Az7QVQrQXQhwUQhwSQjwb6Hg3gxDi\nLSFEihBiT7bbooQQG4QQvwkhvhRCRGS7b6wQ4rBz0lrbbLfXE0Lsce6rV2/0+1BBCFFKCPG1EOJX\nIcReIcQw5+2W2x9CiHxCiB+FEDud+2Ki83bL7QsiIiGETQjxixDic+fvltwPRERCiGNCiN3Oz8Z2\n522B3x+qrobu6R/JA8oRIoonohAi2kVEVQIZ82b8I6ImRFSHiPZku20aET3j/PlZInrJ+XM1ItpJ\ncvykrHP/uM60fiSiO50/ryOidjf7vRnYF3FEVMf5cyEi+o2Iqlh4fxR0/h9Ecq5KAwvvi5FE9A4R\nfe783ZL7wfnajxJRlNttAd8fgW7hNyCiwwCOA8giopVE1CXAMW84AN8RUZrbzV2IaJnz52VE1NX5\nc2ciWgngGoBjJGcoNxBCxBFRYQA7nNu9ne0xtwwAZwHscv78DxEdIDkhz6r744rzx3wkv7AgC+4L\nIUQpIupARIuy3Wy5/ZCNoNw9LAHfH4FO+CWJ6GS23/903mYFsQBSiGQSJKJY5+3u++SU87aSJPeP\nyy2/r4QQZUme+WwjomJW3B/OboydRHSWiL5yfjmtuC9mkazoyz5oaMX94AIi+koIsUMIMdB5W8D3\nh6myTKaLpUbHhRCFiGgVEQ0H8I+HeRiW2B+QS4/UdS4s+IkQojrlfu+39b4QQnQkohQAu4QQST42\nva33g5vGAM4IIWKIaIMQ4je6AZ+LQLfwTxFRmWy/l3LeZgUpQohiRETOU6+/nLefIqLS2bZz7RNv\nt99yhBDBJJP9cgCfOW+27P4gIgJwiYiSiag9WW9fNCaizkKIo0T0HhG1FEIsJ6KzFtsP1wE44/z/\nHBF9SrL7O+Cfi0An/B1EVFEIES+ECCWinkT0eYBj3izC+c/lcyLq7/y5HxF9lu32nkKIUCFEOSKq\nSETbnadwF4UQDYQQgoj6ZnvMrWYxEe0HMDvbbZbbH0KIoq5KCyFEASJqQ3JMw1L7AsA4AGUAlCeZ\nA74G0IeIVpOF9oOLEKKg8wyYhBBhRNSWiPbSjfhc3IDR6PYkKzUOE9GYmz06HqD3uIKIThNRJhGd\nIKIBRBRFRBud730DEUVm234syZH2A0TUNtvtCc4//GEimn2z35fBfdGYiOwkK7J2EtEvzs9AEavt\nDyKq6Xz/u4hoD8n1p8iK+yLb+2hO/1XpWHI/EFG5bN+Pva68eCP2B0+8Yowxi+BLHDLGmEVwwmeM\nMYvghM8YYxbBCZ8xxiyCEz5jjFkEJ3zGGLMITviMMWYRnPAZY8wi/h8VJLVsnt8UIwAAAABJRU5E\nrkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xdf3b7f0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(hez2[1],'r')\n",
-    "pl.plot(y2,'b')\n",
-    "pl.plot(adj_ex[1],'k')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0xed96dd8>]"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEACAYAAACwB81wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/wPXofRt7ZTjeAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xe820240>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(hez2[2],'r')\n",
-    "pl.plot(z2,'b')\n",
-    "pl.plot(adj_ex[2],'k')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we show the absolute error as a function of how many absolutes are taken"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.text.Text at 0x10509f60>"
-      ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAERCAYAAABVU/GxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xf027f28>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "a = np.arange(30)\n",
-    "max_error_x = np.ones_like(np.float64(a))\n",
-    "max_error_y = np.ones_like(np.float64(a))\n",
-    "max_error_z = np.ones_like(np.float64(a))\n",
-    "max_error_fc = np.ones_like(np.float64(a))\n",
-    "\n",
-    "for n_abs in a:\n",
-    "    skip = 5000/(n_abs + 1)\n",
-    "    ex_abs_ord = AbsOrds()\n",
-    "    ex_abs_ord.ordp1 = hez2[0:3,::skip]\n",
-    "    ex_abs_ord.absp1 = variations2e[0:3,::skip]\n",
-    "    ex_abs_ord.absp1\n",
-    "    irange = np.linspace(0,4999,5000)[::skip]\n",
-    "    Mex, resex, rankx, sigx = get_transform_from_abs_ords(ex_abs_ord)\n",
-    "    adj_ex = make_adjusted_from_transform_and_raw(Mex,hez2Stream)\n",
-    "    max_error_x[n_abs] = np.max(np.abs(np.float64(adj_ex[0] - x2)))\n",
-    "    max_error_y[n_abs] = np.max(np.abs(np.float64(adj_ex[1] - y2)))\n",
-    "    max_error_z[n_abs] = np.max(np.abs(np.float64(adj_ex[2] - z2)))\n",
-    "    max_error_fc[n_abs] = np.max(np.abs(np.float64(adj_ex[0]**2 + adj_ex[1]**2 + adj_ex[2]**2 - x2**2 - y2**2 - z2**2)))\n",
-    "\n",
-    "#pl.ylim(0,0.00002)\n",
-    "#pl.xlim(3,20)\n",
-    "pl.semilogy(a+1, max_error_x, 'r', a+1, max_error_y, 'g', a+1, max_error_z, 'b', a+1, max_error_fc, 'c')\n",
-    "pl.xlabel('number of absolutes taken')\n",
-    "pl.ylabel('maximum absolute error')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([  2.06799438e+00,   1.02560749e+00,   2.14203655e-14,\n",
-       "         3.68281794e-13,   4.55191440e-15,   2.63261635e-14,\n",
-       "         2.55351296e-15,   5.77315973e-15,   2.55351296e-15,\n",
-       "         5.74540415e-15,   3.38618023e-15,   4.86208296e-14,\n",
-       "         4.10782519e-15,   3.44169138e-15,   3.88578059e-15,\n",
-       "         1.06581410e-14,   4.77395901e-15,   2.99760217e-15,\n",
-       "         5.55111512e-15,   3.55271368e-15,   2.88657986e-15,\n",
-       "         5.32907052e-15,   3.05831749e-15,   8.24340596e-15,\n",
-       "         3.10862447e-15,   3.21964677e-15,   3.23352456e-15,\n",
-       "         3.10862447e-15,   3.99680289e-15,   3.55271368e-15])"
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "max_error_x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "$$\\alpha = 1$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x10a48390>]"
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEGCAYAAABmXi5tAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBBZ8hmGYgsCCzzAMUxBY8BmGYQrC/wN/aeOYDi7DTAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xed4e4e0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(adj_ex[0] - x2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.5527136788005009e-15"
-      ]
-     },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.max(np.abs(adj_ex[0]-x2))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here the matrices are calculated for different observatories.  One thing we have not done in phase one is correct for the effect of temperature on scale and offset (part of the physics of fluxgate magnetometers)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "bou_bns = get_baselines_from_file('/users/aclaycomb/Documents/boujson12.txt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x10f204e0>]"
-      ]
-     },
-     "execution_count": 81,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEACAYAAABfxaZOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmYVNW19/HvYlJBRIyKIqKAqOCMiiZObXjFKc7Gq4lx\nTJyHxBiH3CiQ5DpkMBrvJXGORhOiiQPJNUpQOlGDgjSiTAoiiqiAV5EghmZY7x/rlFXdVNPVXUNX\nV/0+z9NPV53adc7eXdWrdq2z9z7m7oiISHXo0NYVEBGR0lHQFxGpIgr6IiJVREFfRKSKKOiLiFQR\nBX0RkSqSU9A3sx5m9oiZzTKzGWa2n5mdbGbTzWyNmQ3JKLudma0ws7rkZ3Txqi8iIi3RKcdytwFP\nuvtXzawT0BVYCpwA3JGl/Fx3H5Jlu4iItKFmg76ZbQIc5O5nAbj7amBZ8oOZWbanFbCOIiJSILmk\nd/oBH5rZfUm65k4z26iZ52yflJ1gZgcWoJ4iIlIAuQT9TsAQ4H+SlM0K4Nr1lH8P6JuU/S7wOzPb\nOO+aiohI3nLJ6b8LLHD3l5P7fwSubqqwu68CPk5u15nZm8COQF1mOTPToj8iIq3g7q1OoTfb03f3\nRcACM9sx2TQMmNmo2OcVMLPNzaxDcrs/sAMwr4l9V+zPiBEj2rwOap/aV43tq+S2ueffV8519M5l\nwENm1jkJ4Geb2fHA7cDmwF/M7BV3PxI4GPihmdUDa4Hz3X1p3jUVEZG85RT03X0asG+jzY8nP43L\nPgo8mn/VRESk0DQjt0hqamraugpFpfa1b5XcvkpuWyFYIXJErTqwmbfVsUVE2iszw4t5IldERCqH\ngr6ISBVR0BcRqSIK+iIiVURBX0Skiijoi4hUEQV9EZEqoqAvIlJFFPRFRKqIgr6ISBVR0BcRqSIK\n+iIiVURBX0Skiijoi4hUEQV9EZEqoqAvIlJFFPRFRKqIgr6ISBVR0BcRqSIK+iIiVURBX0SkiuQU\n9M2sh5k9YmazzGyGme1nZieb2XQzW2NmQxqVv9bM5iTlhxen6iIi0lKdcix3G/Cku3/VzDoBXYGl\nwAnAHZkFzWwQcAowCOgDjDezge7uhau2iIi0RrNB38w2AQ5y97MA3H01sCz5wcys0VOOA8Yk5eab\n2RxgKPBSAestIiKtkEt6px/woZndZ2Z1ZnanmW20nvLbAAsy7i9MtpXUZ5/B2rWwciUsXVrqo4uI\nlKdc0judgCHAxe7+spndClwLXJ/vwUeOHPn57ZqaGmpqavLdJQAffQRf+hLstht88gkMGAC/+lVB\ndi0iUlK1tbXU1tYWbH/WXKrdzHoBE929f3L/QOBqdz8muT8B+K671yX3rwHc3W9O7j8FjHD3lxrt\nt2hp/tNPh549YcYMmDIFTjwR7ruvKIcSESkpM8PdG6fVc9ZsesfdFwELzGzHZNMwYGbjemTcHguc\namZdzKwfsAMwqbUVbI2FC+Gkk2DcOLjllkjziIhI7qN3LgMeMrPOwDzgbDM7Hrgd2Bz4i5m94u5H\nuvtMM3uY+GBYBVxU6pE7a9dChw7QqRN06aKgLyKSklPQd/dpwL6NNj+e/GQrfyNwY35Va701ayLo\nQ/xes6ataiIiUl4qckZuqqcP0LGjevoiIikVH/TV0xcRSauKoK+evohIqPigr/SOiEhaxQd9pXdE\nRNIqNuh37Bi31dMXEUmr2KCvnr6IyLqqIuirpy8iEio+6Cu9IyKSVvFBX+kdEZG0qgj66umLiISK\nDPqZa+8ovSMiklaRQV/pHRGR7Co+6KunLyKSVvFBXz19EZG0qgj66umLiISKD/pK74iIpFVs0E+t\nvaP0johIWsUGffX0RUTWVfFBXz19EZG0qgj66umLiISKD/pK74iIpFV80Fd6R0QkLaegb2Y9zOwR\nM5tlZjPMbD8z62lm48zsdTN72sx6JGW3M7MVZlaX/IwubhPWpZ6+iEh2ufb0bwOedPdBwB7AbOAa\nYLy77wQ8C1ybUX6uuw9Jfi4qaI1zkLngmnL6IiJpzQZ9M9sEOMjd7wNw99Xu/glwHHB/Uux+4PjM\npxW6oi2h9I6ISHa59PT7AR+a2X1JuuZOM+sK9HL3RQDu/gGwZcZztk/KTjCzA4tQ7/VSekdEJLtO\nOZYZAlzs7i+b2S+I1I43Kpe6/z7Q190/NrMhwONmNtjdlzfe8ciRIz+/XVNTQ01NTctbkIV6+iJS\nKWpra6mtrS3Y/sy9cexuVMCsFzDR3fsn9w8kgv4AoMbdF5nZVsCEJOff+PkTgO+6e12j7d7csVvD\nPZ3HN4PFi2GXXWDJkoIfSkSk5MwMd291Cr3Z9E6SwllgZjsmm4YBM4CxwFnJtjOBJ5IKbW5mHZLb\n/YEdgHmtrWBLuUewt+RPovSOiEhaLukdgMuAh8ysMxHAzwY6Ag+b2TnA28ApSdmDgR+aWT2wFjjf\n3Zdm2+nEifDppzBsWDpI5ytzsTVQekdEJFOz6Z2iHdjMt9nG2XBDOP10yEjv56W+Hrp3h5Ur4/6y\nZdCnT/wWEWnv8k3v5NrTL4qnnoItt4yc+ze+AQMG5L/PzJO4oJ6+iEimNg36u+4av7feGpavM7an\ndbIFfeX0RURCWay9U8iTrY2Dvk7kioiklU3QL1QKRukdEZGmVVzQz1x3J7Vv9fRFREJZBP1C9sYb\n9/TNYux+Gw1SEhEpK2UR9IuZ009N1FJvX0SkjIJ+sXr6qf0r6IuIlEnQL2Z6p9D7FxFpz8oi6Bcz\nvVPo/YuItGdlE/QL2dPPXHsHNEFLRCSlLIK+0jsiIqVRFkFf6R0RkdIom6Cvnr6ISPFVRdBXT19E\nJJRF0C9FTl9BX0SkTIJ+IXvijdfeAaV3RERSyiboK70jIlJ8ZRH0NWRTRKQ0yiLoF3vIpnL6IiKh\nbIK+0jsiIsVXFUFf6R0RkVAWQb+Q6Zdsa++opy8iEnIK+mbWw8weMbNZZjbDzPYzs55mNs7MXjez\np82sR0b5a81sTlJ+eHP7V09fRKQ0cu3p3wY86e6DgD2A2cA1wHh33wl4FrgWwMwGA6cAg4AjgdFm\nZuvbeSmCvnr6IiI5BH0z2wQ4yN3vA3D31e7+CXAccH9S7H7g+OT2scCYpNx8YA4wdL2VKPKQTaV3\nRERCLj39fsCHZnafmdWZ2Z1m1hXo5e6LANz9A2DLpPw2wIKM5y9MtjWpFEM2ld4REYFOOZYZAlzs\n7i+b2S+I1I43Ktf4frNGjhwJwIsvwu671wA1Ld3FOtTTF5FKUltbS21tbcH2l0vQfxdY4O4vJ/f/\nRAT9RWbWy90XmdlWwOLk8YXAthnP75NsW0cq6H/2GfTs2fLKZ9PU2jsK+iLSHtXU1FBTU/P5/VGj\nRuW1v2bTO0kKZ4GZ7ZhsGgbMAMYCZyXbzgSeSG6PBU41sy5m1g/YAZi0vmMovSMiUhq59PQBLgMe\nMrPOwDzgbKAj8LCZnQO8TYzYwd1nmtnDwExgFXCRu6839aMZuSIipZFT0Hf3acC+WR76f02UvxG4\nMddKaJy+iEhplM2MXPX0RUSKryyCvlbZFBEpjbIJ+oXs6Tdee0fpHRGRUBZBX+kdEZHSKIugryGb\nIiKlUTZBXwuuiYgUX1kEfaV3RERKoyyCvtI7IiKlUTZBv1BBOdvaO+rpi4iEigv6yumLiDStLIJ+\nsXP6Su+IiISyCPrFzukrvSMiEsom6KunLyJSfGUR9DVkU0SkNMoi6Bc6vZNt7R0FfRGRMgr6Su+I\niBRfWQR9pXdEREqjLIK+ZuSKiJRG2QR99fRFRIqvKoK+TuSKiISyCPqFTL9kW3tH6R0RkVAWQV8z\nckVESqNsgr6GbIqIFF+nXAqZ2XzgE2AtsMrdh5rZHsCvgG7AfODr7r7czLYDZgGzk6e/6O4XrW//\nGrIpIlIaOQV9ItjXuPvHGdvuAq5w9+fN7CzgKuD65LG57j4k10qUYsimgr6ISO7pHctSdqC7P5/c\nHg+c1Kh8zpTeEREpjVyDvgN/M7PJZvbNZNsMMzs2uX0K0Cej/PZmVmdmE8zswOZ2rnH6IiKlkWt6\n5wB3f9/MtiCC/2zgHOB2M7sOGAvUJ2XfB/q6+8dmNgR43MwGu/vyxjsdOXIkAAsXwtKlNUBNPm0B\nIrh37txwm9I7ItJe1dbWUltbW7D95RT03f395PcSM3sMGOrutwCHA5jZQODopEw9yQeAu9eZ2ZvA\njkBd4/2mgn5dHUyZkm9TQlPpnVWrCrN/EZFSqqmpoaam5vP7o0aNymt/zaZ3zKyrmW2c3O4GDAem\nJ71+zKwD8APg18n9zZNtmFl/YAdg3vqOofSOiEhp5NLT7wU8ZmaelH/I3ceZ2WVmdjGR73/U3X+T\nlD8Y+KGZ1ROjfs5396XrO4CukSsiUhrNBn13fwvYM8v2XwK/zLL9UeDRllRCM3JFREqj4mbkNrX2\njoK+iEiZBH2ld0RESqMsgr7SOyIipVE2QV89fRGR4quKoK+evohIKIugX4qcvoK+iEiZBH1dGF1E\npDTKJugXsqffseO6+1dPX0SkTIK+0jsiIqVRFkG/FEM2ld4RESmjoK+efn7+8Q+4+ea2roWIlLuy\nCPqakZu/OXNiiWoRkfUpi6Bf7LV3quFEbn19/IiIrE/ZBH1dGD0/K1fGj4jI+pRN0Fd6Jz/q6YtI\nLsoi6JvF70L0xqt1GYaVKxX0RaR5ZRH0oXCBuVp7+gr6IpKLsgr6hQjMTfX0Fy6EK6+EBx6ozOCo\n9I6I5KJsgn6heuNN9fRfeQVWrIDf/hb22ituVxKdyBWRXJRN0C9keqfx2jv77Qd/+hOMHg3jxkG3\nbjBlSv7HKifq6YtILsoq6Berp9+zJ5x4Ytw2g6FDYfLk/I9VTpTTF5FclE3QL2Z6p7F9900H/bvu\ngueey/+4bU1BX0RyUTZBv5g9/cb23RcmTYIZM+Dii+H3v8//uG1N6R0RyUVOQd/M5pvZNDObamaT\nkm17mNk/k+1PmNnGGeWvNbM5ZjbLzIbncoxiDtlsbKedYMkSOOooOO20plM9S5fmX59S0YlcEclF\nrj39tUCNu+/l7kOTbXcBV7n7HsBjwFUAZjYYOAUYBBwJjDZLTb9qWqF6+tnW3sl2rJtugt/8Bn71\nq+jxL14MU6emy6xeDf37w+OPx4fCvHnwn/8Jhx8Oc+fmX89CU09fqtHixdk7OytXwoQJ4F76OpW7\nXIO+ZSk70N2fT26PB05Kbh8LjHH31e4+H5gDDKUZpczpA1x0ERx6KHTtCgMHRq//S1+CQw6BH/0o\nAr17lDvqKNh7b/jf/4U994RLLsm/noW2ciWsWqU3uVSG1ath/vz1f3v95S+hb9/ovGV67z3YYYfo\noL3xRjFr2T51yrGcA38zszXAHe5+NzDDzI5197FEz75PUnYbYGLGcxcm29arlOmdxvbdF8aOjTdZ\nXR2cd178Pvdc2GijCPoDB0KXLrBoUbyZyk3qn2PVqqinSLnI9X/ypZeiEzZ5MowcGd9ct9gCfvc7\n2G23hmWXLIERI+Cyy+Dll+H889OPXXMNnH46vP02/POfkc6VtFyD/gHu/r6ZbUEE/9nAOcDtZnYd\nMBZocXJh5MiRn9+ur69hzZqalu5iHa0J+uedB1/9KvTqBUceGSd3r70W/vpXOOKIhmU33DBm965a\nBZ07513dgkmldlauVNCX8vHWW/Et+Te/gWOPbbrc7NkwbBhssw0MGgQPPggHHQR33gnDh8f2b30r\nHdzvuiuGYZ9wAlx6aWxbvDg+CCZMgJkz4f77I+iffXbRm1lUtbW11NbWFm6H7t6iH2AEcEWjbQOB\nF5Pb1wBXZzz2FLBflv14pgED3N94w/O2337uEyfmt48PP3Tfc0/35cuzP77ddu5z5+Z3jELbdVd3\niLqLlIsbb3Q/7DD33r3dzz7bfeed3f/61/TjY8e6n3uu+8iR7pdfnn0f9fXuzz7r3qOH+6JF8bPV\nVu6vvBL/oxtt5D51qvu227p/5zvxuLv7lCnugwfH8d57z33NGvcVK4rf5mJLYmeLY3fqp9k+sZl1\nTY3MMbNuwHBgetLrx8w6AD8Afp08ZSxwqpl1MbN+wA7ApOaO05bpnca+8IU4qdutW/bHBwyAN9/M\n7xiFlurp62SutKUnnmg472XMmBgAMW0abLVV9PZvuCEemzYtUqgTJsDPfgannJJ9n507x/m3E0+E\nW26JEXfnnAN77BH/o9ttB8cfD1ddFY9vuWU8b/fdI8Xz9a/H8w8+OB6vdrmkd3oBj5mZJ+Ufcvdx\nZnaZmV1M5PsfdfffALj7TDN7GJgJrAIuSj6d1quU4/TzVY5BP5XTV9CXtjByJPTpA1dcEYMd/vEP\nmD49cu8HHhj/3zfcECdoBwyIvP1998F3vgP77w+XXx6/1+eii+CLX4wUz6hR6e177RWdtAsuaFi+\nU6dIAx1yCDz6aKQ9zz234E1vdyyHeFycA5s1+CzYddeYJNX4hE1LDRkC99wTb4RiufnmeDP/7GfF\nO0ZLbb01LFsWC8sNHNjWtZFKtGZNdKoan8tatiwC/qabwllnwd13w/jxcOONkZ///vcblr/lljhp\n+8IL8OyzsOOOMeqs+YHdcaxNNmm4bdKk+FDZe++8mtdumBnunsNfK7tcT+QWXXvq6ffvDy++2PTj\nn3wCf/5zvLGnT4cnn4xRQMVUXw/du2uClhTH1KmRJlmyBL75TbjwwhguCfDUU9Gbf+KJ6F1DpGAW\nLoT//u9193XuudFT7907Aj7kFvBh3YAPsZaW5K6slmFonNNfvDiGXrVEKYL+HntED+Waa+L+u+82\nrPuFF0ZvZ/PNY7G3H/yguPWBCPbduyu9I4VRXx9BG6IXftZZ8O1vR+/8s8/im/S558ZjTzwRufrO\nnSN4X3cd/PjHsYx5jx7r7rtHj0gDnXFGSZskKfmcBc7nh0ajd/bZx33SpIZnqceMiREpM2fmfmZ7\nl13cp0/PvXxrvfOO+/bbx8iA7t3djz3W/Yor3C+4wL1Pn/TInw8/TI86cHf/059iJEGhde7svtde\n+Y9ckuqydu262z7+2H2LLWJUzPjx8R7fbbeGZZctcx80yP2OO9w326w472nJjmKP3imVbDNyX3gh\neq+PPpr7fkrR0wfYdtsYz3/KKdHL2X336MH07h0jFlIjf77whZjM9cQT8L3vRfl77ilsXdaujXkD\n3bqppy+5+/a3YzLUffc13P7cc/Ft9re/jff4RRfFvJXMFEz37tGjP//8uCLd1luXtu7SemWV02+c\n3nnhhUih/PGPMewrF7msvVMo55wTJ6Wuvz6dm8zmpJPg6qujfb//Pdx6a2FTPqlZuBtsoJy+5O6x\nx+Ik6x/+AF/5CvzXf0W6pkuXGN544okxnPLLX05fjyLTKafA//3fuqNmpLyVTU+/8Yncf/0rZul9\n+9uR23/tteb38d578SYs1YzUzTaDBQvWH/AhZvkuWhTB/phjoi0XXhi9rK5d153121IrV0bA79JF\nPX3JzWefxXvy0kujc/X1r8NHH8W31DFjYpijWZyIzRbwIf5nL7kkffJW2oeyCfodOsR1a1evjvtT\npsR4365dY5mE0aPXfc748XHC6K23YsjnoEFxgmj77UtX78aXZsyme/eYJHLCCbGMwyGHxIngefPi\nQ+O55+Df/259HVJLL2ywgYK+5GbuXOjXL4ZZHnxwvBfvvDPSOUuWaERMJSubz+iOHeHMM2HjjSOX\nOGNGBHKINTcGD45x8alc+ezZ8LWvRW/k0UfhuONi8aVevdquDeuzxRbp27fcEkM4t9oq7u+8cwyJ\n++IXW7fv+nr19KVpa9dGxyBz2PAbb6S/of74x/F7ww3jf6hv37gtlalsevodO8ZXzjPOgP/5H5g1\nK3ruECeJ+vaF11+P+x9/HEH+xhtjPfwVKyJHXq4Bv7GBA2MyS8r++8e4/9WrIxVUV9ey/Sm9I43V\n10d+/u23oyc/dGj6vbFsGcyZkw76e+2VnsyYmmAllausevpnnBHB/NRTY1W9Y45JP96/f3wFHTIk\npnx/+cvpKdXHH1+6k7fFsP/+8Je/xMifujr4xjdiudhcJ3TV10fA79JFJ3Lbq5/+NOZ1bLxxjJxp\n7jxRUz74IDo/w4fHt8tp0yKIT5wYJ1w32ijSOLvtFueVpPqUTag8++wY0jh4MLzzTqQ7dt45/Xjm\nejdPPtnwDdueAz7AAQfAuHGxYNTdd8eCUU89lfvzUz39Sszpn39+XOeg0v385/HaX3UV/PrXzZfP\nZuXKuHjIFVfEea4NNoiZsd//PtxxR6RsunWLY02d2voPFmnfyqan/x//kb69++7w6qsxFj6lf//o\ntcybFyN78l2jp5z07w/PPBMpnqOPjl7+lClx4jcXlZrecYdHHolvf/menH/yyUid5Trdv5Q++CCG\n3T7/fNTz9tuzl3vyyVgtsqlvgBMnxoiy22+P9aG++930Y717pwdDuEeKdMiQwrZD2oey7CPvvXf0\n8jN78Kme/rhx8dW1HP958zFkSEyC6dAB9tknAn+uMtM7lRT0P/wwgtOiRfntZ8WKSBW+805h6lVo\nr7wSI9XM4pvujBnrllm0KD78/vjH9LaFCxteDnD8+EgNPvPM+lM3ZnGxke7dC9cGaT/KMujX1Ky7\nzGoqpz92bHlerrCQ9t47gr579Piffnr95Su1p58KaPkG/enTYwTLggX516kYUkEfYm34jz+ORftS\n3nsvZs326hVj6CEuIbjrrvHtZdky+NOf4pvAsGExJLhr19K3Q9qHsgz6J58cI3gybbddLGw2eXLu\naY/2qnfvCOAzZsRs3tSQuqZU6onc1GitfIP+tGnxu1Q9/fp6+MlPYPnyhtvXrIlvHY1NnZoO+h06\nxLfcWbPi/s03x8i1666LSw4+/3ysVX/ZZfD3v0cOf7fdYnDD0qXwpS8Vs2VSCcoy6GfTpUsEw3PP\nrY5ezL77xj/wQQdFUPjss6bLVuqJ3DfeiGG7hQj63boVvqe/alXMLWm8NtStt8ZonAMOiCUOUoF+\n1KhIqZx4YnwALF0ac0/Gj2+4Fvwuu8QH/oQJ0fmZNy8m8A0bFhcbOfXUODm7++7x/GOOiVFf8+Zp\nfL00r2xO5Obi6qtjeGY1uOOOCBb9+sWkrRdfjDXLMy9gMW5c9BArNb3z+usxxjwzb90a06bBYYcV\nvqd/ySXxzXPmzAjkCxbElZpGj46TqpMnw733xgikhx+OlMwLL0TArqmJ3vxRR8Vjgwen97vLLpHS\nW7w4vuX17Zteu/6HP4yflN12y75mvUhT2k1PH+LkVLWs5rfVVnEewywCxIUXxhC7NWvSaxRdeWX0\nJCv1RG4q6Le0p//aazHqByKX/+qrsaBYS4L+3LmRTnnrrYbbU2vMv/deBOsXXkgvOrbXXjGybOzY\nmID3ta9F8H7wwTjBCrDffvG8s86Kb3APPBA9+Ezf+ha8/358iHztay1ru0hz2lXQr1bHHRc9vU02\niRN622wTwWjmzMjrZvb0yymn/+abrU+prFgRs0kPOqjlQf+xxyLIvvRSpAOHDo2A3Fxd3norUi5v\nvx3fqu6+O1ZQTZkzJ4aOptapOfXUuEjOiBExm/qll+AXv1g3r3700dGTP+20+BDffPOoV+aQ5Ew9\ne0bK54UXtJiZFJ7eUu3AF78YqZzRo6NH2alT5I232CJO6h16aPnk9DOvdXrrrXH+5eabW76furoI\nlL17x0iWVavWvTZrU956K4a9DhsWPfzHH49zIo17+qtXR13Xro1vUo89Fn+/lSvhppviqm077hgn\nZDfeOH2JzAsuiF56bW3cP/PM+GmKWcy4bsnqrxtskF6bSaSQ1NNvR047LU7a/ehH0Qs96qgIqq++\nmnt6Z9KkmPVZLLvsEj1liBTFnDmt28+kSZEK6dgxLkSzZEn6sc8+i5TJPffENQ1Sxxs9OoLxW29F\n3vtf/0pf0OYLX4iVTFMjau66KyY5HX54pFhmzYqZv8uWRbrmiitiZvQBB8Tfe/bsqNP3vhffYO67\nL9qaq27dcv/QEikmBf12pGfP6I0ee2ykP4YMiaD10EMxaiMz6D/zTIz4iCtTpk2cGKt8tiTt8umn\nuZWrr4/gmFow7v334yTsm2+2/GphL70UQR9ifHpmimfMmBgJM25cBPMDDoiAf+mlMXlp3rw4AZ45\ngc+s4aJ9994bufcPP4xrNtxwQ4ysMWs4aen666MNhx0WwyWPOira85WvtKw9ImUjn2st5vNDo2vk\nSu7WrnXfdlv3F190X7nS/e9/j2v2/vnP7kcf7f7ss+69erkPGOB+1VUNr2166aXuPXu6X311bsea\nPdu9Sxf3m25qvuzcuXFN41Gj4n6/fu4bbOB+ww1xzPr6+Hnqqbju8V13ub/+evZ9bbddHNvdffhw\n9wcfTD82fLj7H/6Qvv/kk+5bbeX+1a+6H3po1HfVqnX3+eMfu59+elyvuEeP+Ns995z7N77RfNuG\nD3fv2NH900+bLytSTOR5jdxcA/R8YBowFZiUbNsTmJjaBuyTbN8OWAHUJT+jm9hn8f86FWzx4nUv\nav300xH0DjrI/YEH4qLsO+zg/swz6TJHHeV+223um2/uPn9+88e5+GL3b37Tfcst3efMSW+/4w73\nkSMblh0/Pt5RJ58cddtwwwj2u+8egfjOO6M+e+8dF48/5piox513NmzLBx+4b7qp+5o1cf/Pf44P\nsfPOc//HPyJgNw6+a9a4v/tuBOZ+/bK35ZNP4nhnnBF1bIl//tP9+ONb9hyRYihV0J8H9Gy07Wlg\neHL7SGCCp4P+qznss4h/lur00kvxih56aLqne/PN7hdckC6z007u06dH7/vww9f94Ej59NPoiW+6\nqfvChe4nnOA+Zkz68ZNOcu/d23316vS2e+6JgL7TTu5Ll7p37+5eUxN1uvJKd7M4bqaZM9333DMC\n6t13u//yl+5jx0bPOtNHH7lfdpn7rru6X3999jqvXRsfDl/+ctN/o/HjI+A//XTTZUTKWb5BP9ec\nvrFu/n8t0CO5vSmwsFF5KbF9941c/7PPpof6nXBCjF5Jje+fPz/y3VdeGXny3/624T4iRMeEooce\nilx8794wW2fXAAAIZUlEQVRx/mDKlHS5yZNj9Mtzz8X9Vati38OHx4nVefNi9MmOO8bxrroqlgq4\n+uqGxxs0KEbF7LhjjF+/7rrYZ+PL9fXsCbfdFmPwR43K3n6zmNnav3/Tf6Nhw2IM//DhzfwxRSpU\nrkHfgb+Z2WQz+1ay7TvAz8zsHeAnwLUZ5bc3szozm2BmBxawvrIeZusuuztwYAztrK2NCUWbbRYj\nfjp3joD+ve/F2PSUm26K+QBTpsQww9RFsffeOx30Fy+OYZRXXBGB+JFHYm2kN96I4w0cGOPMt946\nhl0efHDU4frrs1/7YIMNYljn00/HiJg77kifxG2po4/W+jMi65PrOP0D3P19M9sCGGdms4GTgcvd\n/XEzOxm4FzgMeB/o6+4fm9kQ4HEzG+zuyxvvdOTIkZ/frqmpoaamJr/WSFajRsUM0BtuiCWqU4YM\ngSOOiMC9eHGMYpk2Lcqdc076esQQQb+uLr4FvPxyjIO/5BL461/jimd9+8Y3ivPOi1UeH3wwevEX\nXtiyuQPHHgv//GfrL8x90UWte55IuaqtraU2NSmkEFqaDwJGAN8FPm60/ZMmyk8AhmTZXpyEl2R1\n663unTu7n3lmw+2zZsXJzw4d3H/968jJv/hi9n306RPnDa680v2aa2Lb8uXudXVxMhbc33wzRtOA\n++WXt7yeb7zhvv/+LX+eSLUgz5x+sz19M+sKdHD35WbWDRgOjALeM7ND3P3vZjYMeCMpvznwkbuv\nNbP+wA7EiWBpQ5dfHmPMO3ZsuH3nnSM988orsbLj3LmxXG82P/lJfDPYZBP4299iW7duscTB9tvH\n4mB9+kRaZ8MNW7dO0sCBMZdARIojl/ROL+AxM/Ok/EPuPs7MzgNuM7OOwL+B85LyBwM/NLN64mTv\n+e6+NNuOpbQGDsy+/YQT4lzAtdfGeYHNNste7rTTInc/YEBMmMrUs2d6ZizEidJ8L3EoIoVn8W2h\nDQ5s5m11bFnXO+9EQN9nnxiZk69//ztmCLf3i9aLlBszw91bPUJSC64JECs+brxxwxO9+dDFPETK\nk/phAqQvyt1UPl9EKoOCvnzusMNaPz5eRNoH5fRFRNqRfHP66umLiFQRBX0RkSqioC8iUkUU9EVE\nqoiCvohIFVHQFxGpIgr6IiJVREFfRKSKKOiLiFQRBX0RkSqioC8iUkUU9EVEqoiCvohIFVHQFxGp\nIgr6IiJVREFfRKSKKOiLiFQRBX0RkSqSU9A3s/lmNs3MpprZpGTbnmY2MbXNzPbJKH+tmc0xs1lm\nNrxYlRcRkZbJtae/Fqhx973cfWiy7WZghLvvBYwAfgpgZoOBU4BBwJHAaDNr9fUc26va2tq2rkJR\nqX3tWyW3r5LbVgi5Bn3LUnYt0CO5vSmwMLl9LDDG3Ve7+3xgDjCUKlPpbzy1r32r5PZVctsKoVOO\n5Rz4m5mtAe5097uA7wBPm9nPiQ+FLyVltwEmZjx3YbJNRETaWK5B/wB3f9/MtgDGmdls4GTgcnd/\n3MxOBu4FDitWRUVEJH/m7i17gtkIYDnwA3fvmbF9qbtvambXAO7uNyfbnyJy/y812k/LDiwiIgC4\ne6vPkzbb0zezrkAHd19uZt2A4cAo4D0zO8Td/25mw4jcPcBY4CEz+wWR1tkBmFTISouISOvkkt7p\nBTyW9Mw7AQ+5+zgzOw+4zcw6Av8GzgNw95lm9jAwE1gFXOQt/TohIiJF0eL0joiItF9tMiPXzI4w\ns9lm9oaZXd0WdSi0Jiaw9TSzcWb2upk9bWY9mttPOTCze8xskZm9mrGtyba0t8l4TbRvhJm9a2Z1\nyc8RGY+1t/b1MbNnzWyGmb1mZpcl2yviNczSvkuT7e3+NTSzDczspSSOvJacQy3sa+fuJf0hPmjm\nAtsBnYFXgJ1LXY8itGse0LPRtpuBq5LbVwM3tXU9c2zLgcCewKvNtQUYDEwlUn/bJ6+ttXUbWtG+\nEcAVWcoOaoft2wrYM7m9MfA6sHOlvIbraV9FvIZA1+R3R+BFYp5TwV67tujpDwXmuPvb7r4KGAMc\n1wb1KLRsE9iOA+5Pbt8PHF/SGrWSuz8PfNxoc1NtaXeT8ZpoH8Rr2NhxtL/2feDuryS3lwOzgD5U\nyGvYRPtSc4Ha/Wvo7iuSmxsQwdwp4GvXFkF/G2BBxv13qYzJW6kJbJPN7JvJtl7uvgjijQps2Wa1\ny9+WTbSl8evZnifjXWJmr5jZ3Rlfn9t1+8xse+JbzYs0/X5st23MaF9qSHi7fw3NrIOZTQU+AP7m\n7pMp4GunVTYL5wB3HwIcBVxsZgcRHwSZKumseSW1BWA00N/d9yT+2X7exvXJm5ltDPyRmES5nAp7\nP2ZpX0W8hu6+1mNNsz7AUDPbhQK+dm0R9BcCfTPu9yG9bk+75e7vJ7+XAI8TX7EWmVkvADPbCljc\ndjXMW1NtWQhsm1GuXb6e7r7EkyQpcBfpr8jtsn1m1okIiL919yeSzRXzGmZrX6W9hu6+DKgFjqCA\nr11bBP3JwA5mtp2ZdQFOJSZ0tVtm1jXpdZAxge01ol1nJcXOBJ7IuoPyZDTMjzbVlrHAqWbWxcz6\n0cRkvDLUoH3JP1LKicD05HZ7bd+9wEx3vy1jWyW9huu0rxJeQzPbPJWWMrONiKVtZlHI166Nzk4f\nQZxxnwNc09ZnywvQnn7EKKSpRLC/Jtm+GTA+aes4YNO2rmuO7fkd8B6wEngHOBvo2VRbgGuJUQOz\ngOFtXf9Wtu8B4NXkdXycyKG21/YdAKzJeE/WJf9zTb4f21Mb19O+dv8aArsl7Xklact/JtsL9tpp\ncpaISBXRiVwRkSqioC8iUkUU9EVEqoiCvohIFVHQFxGpIgr6IiJVREFfRKSKKOiLiFSR/w8+y8nH\nnKPecgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10d727b8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(bou_bns.baseZ)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "585.5477061"
-      ]
-     },
-     "execution_count": 82,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bou_bns.baseZ[81]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4"
-      ]
-     },
-     "execution_count": 83,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = UTCDateTime(bou_bns.begin[50])\n",
-    "\n",
-    "a.month"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "a = UTCDateTime(2015,7,11,0,0,0)\n",
-    "\n",
-    "b = UTCDateTime(2015,9,15,0,0,0)\n",
-    "\n",
-    "get_bns_between_dates(a,b,bou_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x115b1eb8>]"
-      ]
-     },
-     "execution_count": 85,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEACAYAAACwB81wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOuH/Ayp2CtviY4TPAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1134b6a0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.figure()\n",
-    "pl.plot(bou_bns.baseH)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "remove_outlier_baselines(bou_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x117cec50>]"
-      ]
-     },
-     "execution_count": 87,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1145a5c0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.figure()\n",
-    "pl.plot(bou_bns.baseH)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "bou_abs_ords = get_ord_abs_from_baselines(bou_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "remove_outlier_baselines(bou_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 20819.0225    ,  20822.6725    ,  20835.5725    ,  20830.505     ,\n",
-       "         20834.92      ,  20838.195     ,  20839.1875    ,  20841.3375    ,\n",
-       "         20842.5775    ,  20855.4525    ,  20856.925     ,  20853.8875    ,\n",
-       "         20831.53      ,  20832.8425    ,  20826.38      ,  20828.645     ,\n",
-       "         20843.1925    ,  20832.9675    ,  20837.4525    ,  20846.0975    ,\n",
-       "         20830.6325    ,  20834.1125    ,  20769.14      ,  20794.5625    ,\n",
-       "         20859.625     ,  20864.105     ,  20833.81      ,  20833.975     ,\n",
-       "         20839.245     ,  20840.4875    ,  20816.2625    ,  20812.6225    ,\n",
-       "         20812.0275    ,  20856.985     ,  20866.515     ],\n",
-       "       [   -66.05552143,    -69.56509653,    -69.58531975,    -70.39066131,\n",
-       "           -77.32957443,    -98.58777987,    -60.17731356,    -66.21031332,\n",
-       "           -73.53311902,   -101.7679456 ,    -99.69692926,   -103.47704148,\n",
-       "           -75.13988255,    -80.45152894,    -55.17681569,    -65.26578657,\n",
-       "          -111.69252708,    -74.91308838,    -86.73110189,   -106.15381897,\n",
-       "          -107.58356323,   -108.28667639,   -104.51786586,   -102.19585427,\n",
-       "          -107.14591108,   -100.2047143 ,    -96.81981031,   -106.12597753,\n",
-       "          -108.38802327,   -114.06326124,    -90.660629  ,    -93.93875212,\n",
-       "           -96.43670871,    -99.84246126,    -98.05981425],\n",
-       "       [ 47390.9975    ,  47391.1575    ,  47388.755     ,  47385.1925    ,\n",
-       "         47381.635     ,  47375.7125    ,  47370.3       ,  47369.2175    ,\n",
-       "         47369.5525    ,  47371.2225    ,  47374.06      ,  47378.91      ,\n",
-       "         47375.375     ,  47375.2475    ,  47379.9475    ,  47377.25      ,\n",
-       "         47383.3625    ,  47377.49      ,  47374.835     ,  47377.4675    ,\n",
-       "         47377.0675    ,  47379.205     ,  47372.7525    ,  47376.0625    ,\n",
-       "         47385.835     ,  47388.29      ,  47366.6525    ,  47366.8075    ,\n",
-       "         47367.555     ,  47369.59      ,  47374.9675    ,  47375.1025    ,\n",
-       "         47376.87      ,  47387.67      ,  47388.185     ]])"
-      ]
-     },
-     "execution_count": 90,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bou_abs_ords.ordp1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "Mbou, resbou, rankbou, sigbou = get_transform_from_abs_ords(bou_abs_ords)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  9.83427577e-01,  -1.54730742e-01,   2.73849863e-02,\n",
-       "         -1.27616468e+03],\n",
-       "       [  1.66801730e-01,   9.87916201e-01,  -4.98683323e-03,\n",
-       "         -8.45819258e-01],\n",
-       "       [ -6.72505308e-03,  -1.18093515e-02,   9.96186901e-01,\n",
-       "          9.05380089e+02],\n",
-       "       [ -0.00000000e+00,  -0.00000000e+00,   0.00000000e+00,\n",
-       "          1.00000000e+00]])"
-      ]
-     },
-     "execution_count": 92,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Mbou"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([  8.12387327e+00,   6.62182726e+00,   2.38802764e+00,\n",
-       "         1.24385440e-37])"
-      ]
-     },
-     "execution_count": 93,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "resbou"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4"
-      ]
-     },
-     "execution_count": 94,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rankbou"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  1.49668069e+10,   2.29654469e+09,   3.49755539e+10,\n",
-       "          7.29213470e+05],\n",
-       "       [ -6.45768758e+07,  -9.89864120e+06,  -1.50900417e+08,\n",
-       "         -3.14617119e+03],\n",
-       "       [  3.40342627e+10,   5.22230716e+09,   7.95338758e+10,\n",
-       "          1.65822030e+06],\n",
-       "       [  7.18360022e+05,   1.10227043e+05,   1.67871880e+06,\n",
-       "          3.50000000e+01]])"
-      ]
-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "m2 = np.dot(np.vstack([bou_abs_ords.ordp1,np.ones_like(bou_abs_ords.ordp1[0])]),np.vstack([bou_abs_ords.absp1,np.ones_like(bou_abs_ords.absp1[0])]).T)\n",
-    "m2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\aclaycomb\\AppData\\Local\\Continuum\\Anaconda2\\lib\\site-packages\\geomagio\\edge\\EdgeFactory.py:520: ObsPyDeprecationWarning: 'getWaveform' has been renamed to 'get_waveforms'. Use that instead.\n",
-      "  edge_channel, starttime, endtime)\n"
-     ]
-    }
-   ],
-   "source": [
-    "hezfbouJan16 = factory.get_timeseries(observatory='BOU',\n",
-    "\n",
-    "        interval='minute',\n",
-    "\n",
-    "        type='variation',\n",
-    "\n",
-    "        channels=('H', 'E', 'Z', 'F'),\n",
-    "\n",
-    "        starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n",
-    "\n",
-    "        endtime=UTCDateTime('2016-01-31T23:59:59Z'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "bouJan16adj = make_adjusted_from_transform_and_raw(Mbou,hezfbouJan16)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 3127.49144074,  3131.6261902 ,  3135.43180642,  3137.51476505])"
-      ]
-     },
-     "execution_count": 98,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bouJan16adj[1,1:5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "bouh_pqqm = np.mean(bou_abs_ords.absp1[0] - bou_abs_ords.ordp1[0])\n",
-    "\n",
-    "boue_pqqm = np.mean(bou_abs_ords.absp1[1] - bou_abs_ords.ordp1[1])\n",
-    "\n",
-    "bouz_pqqm = np.mean(bou_abs_ords.absp1[2] - bou_abs_ords.ordp1[2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-30, 30)"
-      ]
-     },
-     "execution_count": 100,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nERFZ/H+0CVRDKgo6mQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x117ec630>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.figure()\n",
-    "pl.plot(((hezfbouJan16[0].data+bouh_pqqm)**2 + (hezfbouJan16[1].data+boue_pqqm)**2 + (hezfbouJan16[2].data+bouz_pqqm)**2)**(0.5) - hezfbouJan16[3].data + 23.1,'c')\n",
-    "\n",
-    "pl.hold(True)\n",
-    "\n",
-    "pl.plot((bouJan16adj[0]**2 + bouJan16adj[1]**2 + bouJan16adj[2]**2)**(0.5) - hezfbouJan16[3].data + 23.1,'k')\n",
-    "\n",
-    "pl.ylim(-30,30)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "save_state('/users/aclaycomb/Documents/adjbou_state_.json', Mbou, -22)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "hezfbouJan16[0].stats.channel = 'H'\n",
-    "file1 = open('/users/aclaycomb/Documents/BOU201601vmin.min','w')\n",
-    "a = geomagio.iaga2002.IAGA2002Writer()\n",
-    "a.write(file1,hezfbouJan16,['H', 'E', 'Z', 'F'])\n",
-    "stats1 = hezfbouJan16[0].stats\n",
-    "stats1.channel\n",
-    "stats1.channel = 'X'\n",
-    "x = obspy.core.Trace(bouJan16adj[0], stats1)\n",
-    "stats1.channel = 'Y'\n",
-    "y = obspy.Trace(bouJan16adj[1], stats1)\n",
-    "stats1.channel = 'Z'\n",
-    "z = obspy.Trace(bouJan16adj[2], stats1)\n",
-    "stats1.channel = 'F'\n",
-    "f = obspy.Trace(hezfbouJan16[3].data - 22, stats1)\n",
-    "adjbouJan16 = obspy.Stream([x,y,z,f])\n",
-    "a = geomagio.iaga2002.IAGA2002Writer()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 113,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "file2 = open('/users/aclaycomb/Documents/BOU201601adj.min', 'w')\n",
-    "a.write(file2,adjbouJan16,['X', 'Y', 'Z', 'F'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 115,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "brw_bns = get_baselines_from_file('/users/aclaycomb/Documents/brwjson12.txt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 116,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x12035b38>]"
-      ]
-     },
-     "execution_count": 116,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBIWBEEJIEP8PdorL7Pr0LX4AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xf025048>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(brw_bns.baseH)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1231ed30>]"
-      ]
-     },
-     "execution_count": 117,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEACAYAAABVtcpZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njkn666oAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1226b898>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(brw_bns.baseD)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 118,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x12461400>]"
-      ]
-     },
-     "execution_count": 118,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwYLBGGNMhP8PxUUJNmE2bfsAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x124f6f98>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(brw_bns.baseZ)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 119,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "a = UTCDateTime(2015,7,1,0,0,0)\n",
-    "\n",
-    "b = UTCDateTime(2015,9,1,0,0,0)\n",
-    "\n",
-    "get_bns_between_dates(a,b,brw_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "remove_outlier_baselines(brw_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x125e3cc0>]"
-      ]
-     },
-     "execution_count": 121,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEACAYAAABRQBpkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11f91048>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(brw_bns.baseH)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x128345c0>]"
-      ]
-     },
-     "execution_count": 122,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEACAYAAACznAEdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIIiICAD/D3bH5j9kRkceAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x126f5160>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(brw_bns.baseD)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x129ade80>]"
-      ]
-     },
-     "execution_count": 123,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEACAYAAACgS0HpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIjkRAFDRERyooAhIiI5+f+3SSe5pr3XvgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x12850c88>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(brw_bns.baseZ)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "brw_abs_ord = get_ord_abs_from_baselines(brw_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "Mbrw, resbrw, rankbrw, sigbrw = get_transform_from_abs_ords(brw_abs_ord)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  9.34857870e-01,  -3.26009140e-01,   1.26923853e-02,\n",
-       "         -6.01772088e+02],\n",
-       "       [  3.29840807e-01,   1.08450379e+00,  -4.38199362e-02,\n",
-       "          1.96667652e+03],\n",
-       "       [ -9.26036364e-04,  -5.46591946e-05,   9.98670684e-01,\n",
-       "         -6.15282786e+01],\n",
-       "       [  0.00000000e+00,   0.00000000e+00,  -0.00000000e+00,\n",
-       "          1.00000000e+00]])"
-      ]
-     },
-     "execution_count": 126,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Mbrw"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([  5.04178763e+01,   8.74894816e+02,   8.79660086e-01,\n",
-       "         1.34495954e-37])"
-      ]
-     },
-     "execution_count": 127,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "resbrw"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4"
-      ]
-     },
-     "execution_count": 128,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rankbrw"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([  2.99444530e+05,   9.36118621e+02,   1.18394564e+02,\n",
-       "         2.08605176e-03])"
-      ]
-     },
-     "execution_count": 129,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sigbrw"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "hezfbrwJan16 = factory.get_timeseries(observatory='BRW',\n",
-    "\n",
-    "        interval='minute',\n",
-    "\n",
-    "        type='variation',\n",
-    "\n",
-    "        channels=('H', 'E', 'Z', 'F'),\n",
-    "\n",
-    "        starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n",
-    "\n",
-    "        endtime=UTCDateTime('2016-01-30T23:59:59Z'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "brwJan16adj = make_adjusted_from_transform_and_raw(Mbrw,hezfbrwJan16)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "brwh_pqqm = np.mean(brw_abs_ord.absp1[0] - brw_abs_ord.ordp1[0])\n",
-    "\n",
-    "brwe_pqqm = np.mean(brw_abs_ord.absp1[1] - brw_abs_ord.ordp1[1])\n",
-    "\n",
-    "brwz_pqqm = np.mean(brw_abs_ord.absp1[2] - brw_abs_ord.ordp1[2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-30, 30)"
-      ]
-     },
-     "execution_count": 133,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nA8MwDKOBhYFhGIbR8P8ByZeMebDgmxgAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x129f4e10>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(((hezfbrwJan16[0].data+brwh_pqqm)**2 + (hezfbrwJan16[1].data+brwe_pqqm)**2 + (hezfbrwJan16[2].data+brwz_pqqm)**2)**(0.5) - hezfbrwJan16[3].data + 1,'c')\n",
-    "\n",
-    "pl.hold(True)\n",
-    "\n",
-    "pl.plot((brwJan16adj[0]**2 + brwJan16adj[1]**2 + brwJan16adj[2]**2)**(0.5) - hezfbrwJan16[3].data + 1.0,'k')\n",
-    "\n",
-    "pl.ylim(-30,30)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "save_state('/users/aclaycomb/Documents/adjbrw_state_.json', Mbrw, -1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "cmo_bns = get_baselines_from_file('/users/aclaycomb/Documents/cmojson12.txt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1313c6d8>]"
-      ]
-     },
-     "execution_count": 136,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEACAYAAABfxaZOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x129ebbe0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(cmo_bns.baseH)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "a = UTCDateTime(2015,7,1,0,0,0)\n",
-    "\n",
-    "b = UTCDateTime(2015,9,1,0,0,0)\n",
-    "\n",
-    "get_bns_between_dates(a,b,cmo_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 138,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x13409d68>]"
-      ]
-     },
-     "execution_count": 138,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEACAYAAACznAEdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nHMdxHAD+Px/HiMyUwjW5AAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x132e84a8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(cmo_bns.baseH)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 139,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "remove_outlier_baselines(cmo_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 140,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x12de5c18>]"
-      ]
-     },
-     "execution_count": 140,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEACAYAAACznAEdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n5xzgCcE551zAE4JzzjkA/h/dgGrbp54v5gAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x135b8a58>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(cmo_bns.baseH)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 141,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "cmo_abs_ord = get_ord_abs_from_baselines(cmo_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "Mcmo, rescmo, rankcmo, sigcmo = get_transform_from_abs_ords(cmo_abs_ord)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 143,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  9.39880017e-01,  -3.08098629e-01,   8.11979835e-03,\n",
-       "         -2.01433612e+02],\n",
-       "       [  3.22786162e-01,   9.68250360e-01,   1.65168860e-02,\n",
-       "         -8.65385130e+02],\n",
-       "       [ -6.29637516e-03,  -4.05108544e-03,   9.91887061e-01,\n",
-       "          4.72928486e+02],\n",
-       "       [  2.57918802e-15,  -3.27337577e-15,  -0.00000000e+00,\n",
-       "          1.00000000e+00]])"
-      ]
-     },
-     "execution_count": 143,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Mcmo"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([  4.43079439e-01,   5.71490493e-01,   2.15290921e-01,\n",
-       "         2.83800382e-38])"
-      ]
-     },
-     "execution_count": 144,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rescmo"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 145,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4"
-      ]
-     },
-     "execution_count": 145,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rankcmo"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 146,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([  2.34191239e+05,   6.16124599e+01,   4.15099818e+01,\n",
-       "         6.83659777e-04])"
-      ]
-     },
-     "execution_count": 146,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sigcmo"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 147,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "hezfcmoJan16 = factory.get_timeseries(observatory='CMO',\n",
-    "\n",
-    "        interval='minute',\n",
-    "\n",
-    "        type='variation',\n",
-    "\n",
-    "        channels=('H', 'E', 'Z', 'F'),\n",
-    "\n",
-    "        starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n",
-    "\n",
-    "        endtime=UTCDateTime('2016-01-31T23:59:59Z'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 148,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "cmoJan16adj = make_adjusted_from_transform_and_raw(Mcmo,hezfcmoJan16)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "cmoh_pqqm = np.mean(cmo_abs_ord.absp1[0] - cmo_abs_ord.ordp1[0])\n",
-    "\n",
-    "cmoe_pqqm = np.mean(cmo_abs_ord.absp1[1] - cmo_abs_ord.ordp1[1])\n",
-    "\n",
-    "cmoz_pqqm = np.mean(cmo_abs_ord.absp1[2] - cmo_abs_ord.ordp1[2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-30, 30)"
-      ]
-     },
-     "execution_count": 150,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSGAQQgihI4FBCCGEzv8HghbAP31tLx4AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x12a3b240>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(((hezfcmoJan16[0].data+cmoh_pqqm)**2 + (hezfcmoJan16[1].data+cmoe_pqqm)**2 + (hezfcmoJan16[2].data+cmoz_pqqm)**2)**(0.5) - hezfcmoJan16[3].data - 7.9,'c')\n",
-    "\n",
-    "pl.hold(True)\n",
-    "\n",
-    "pl.plot((cmoJan16adj[0]**2 + cmoJan16adj[1]**2 + cmoJan16adj[2]**2)**(0.5) - hezfcmoJan16[3].data - 7.9,'k')\n",
-    "\n",
-    "pl.ylim(-30,30)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "save_state('/users/aclaycomb/Documents/adjcmo_state_.json', Mcmo, 7.9)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 152,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "frd_bns = get_baselines_from_file('/users/aclaycomb/Documents/frdjson12.txt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 153,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1312c438>]"
-      ]
-     },
-     "execution_count": 153,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEACAYAAABRQBpkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xe655a90>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(frd_bns.baseD)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "a = UTCDateTime(2015,8,1,0,0,0)\n",
-    "\n",
-    "b = UTCDateTime(2015,10,1,0,0,0)\n",
-    "\n",
-    "get_bns_between_dates(a,b,frd_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 155,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "remove_outlier_baselines(frd_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 156,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x138281d0>]"
-      ]
-     },
-     "execution_count": 156,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZkAAAEGCAYAAAC3lehYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOeeyxoOMc865rPn/XwKuKrpmMxsAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11ceecc0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(frd_bns.baseD)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "frd_abs_ord = get_ord_abs_from_baselines(frd_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 158,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "Mfrd, resfrd, rankfrd, sigfrd = get_transform_from_abs_ords(frd_abs_ord)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 159,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ -9.82142394e-01,  -1.83524785e-01,   1.28706175e-02,\n",
-       "         -5.26638206e+02],\n",
-       "       [  1.79292243e-01,  -1.00656618e+00,   1.13500368e-01,\n",
-       "         -5.12726686e+03],\n",
-       "       [  3.98204985e-03,   6.04692528e-03,   1.01122495e+00,\n",
-       "          9.93776528e+01],\n",
-       "       [  0.00000000e+00,   0.00000000e+00,  -0.00000000e+00,\n",
-       "          1.00000000e+00]])"
-      ]
-     },
-     "execution_count": 159,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Mfrd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 160,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([  1.65574426e+00,   1.93399773e+01,   2.49780320e-01,\n",
-       "         4.92759318e-39])"
-      ]
-     },
-     "execution_count": 160,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "resfrd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 161,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4"
-      ]
-     },
-     "execution_count": 161,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rankfrd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 162,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([  2.53434501e+05,   8.05438116e+01,   1.80397682e+01,\n",
-       "         5.10446006e-04])"
-      ]
-     },
-     "execution_count": 162,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sigfrd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 163,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "hezffrdJan16 = factory.get_timeseries(observatory='FRD',\n",
-    "\n",
-    "        interval='minute',\n",
-    "\n",
-    "        type='variation',\n",
-    "\n",
-    "        channels=('H', 'E', 'Z', 'F'),\n",
-    "\n",
-    "        starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n",
-    "\n",
-    "        endtime=UTCDateTime('2016-01-31T23:59:59Z'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 164,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "frdJan16adj = make_adjusted_from_transform_and_raw(Mfrd,hezffrdJan16)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 165,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "frdh_pqqm = np.mean(frd_abs_ord.absp1[0] - frd_abs_ord.ordp1[0])\n",
-    "\n",
-    "frde_pqqm = np.mean(frd_abs_ord.absp1[1] - frd_abs_ord.ordp1[1])\n",
-    "\n",
-    "frdz_pqqm = np.mean(frd_abs_ord.absp1[2] - frd_abs_ord.ordp1[2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 166,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-30, 30)"
-      ]
-     },
-     "execution_count": 166,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAQEBATGEiSEgICAgIIb/AdrMvwzEX9WaAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x138e45c0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(((hezffrdJan16[0].data+frdh_pqqm)**2 + (hezffrdJan16[1].data+frde_pqqm)**2 + (hezffrdJan16[2].data+frdz_pqqm)**2)**(0.5) - hezffrdJan16[3].data + 4.6,'c')\n",
-    "\n",
-    "pl.hold(True)\n",
-    "\n",
-    "pl.plot((frdJan16adj[0]**2 + frdJan16adj[1]**2 + frdJan16adj[2]**2)**(0.5) - hezffrdJan16[3].data + 4.6,'k')\n",
-    "\n",
-    "pl.ylim(-30,30)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 167,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "save_state('/users/aclaycomb/Documents/adjfrd_state_.json', Mfrd, -4.6)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 168,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "frn_bns = get_baselines_from_file('/users/aclaycomb/Documents/frnjson12.txt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 169,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x13ffb320>]"
-      ]
-     },
-     "execution_count": 169,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x12df0be0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(frn_bns.baseH)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 170,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "a = UTCDateTime(2015,5,1,0,0,0)\n",
-    "\n",
-    "b = UTCDateTime(2015,6,10,0,0,0)\n",
-    "\n",
-    "get_bns_between_dates(a,b,frn_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 171,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x142bc0b8>]"
-      ]
-     },
-     "execution_count": 171,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEGCAYAAABmXi5tAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x12df0f98>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(frn_bns.baseH)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 172,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "frn_abs_ord = get_ord_abs_from_baselines(frn_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 173,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "Mfrn, resfrn, rankfrn, sigfrn = get_transform_from_abs_ords(frn_abs_ord)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 174,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  9.68565555e-01,  -2.00310287e-01,  -2.76807061e-02,\n",
-       "          1.17417624e+03],\n",
-       "       [  2.42370058e-01,   1.02706749e+00,  -1.21082597e-01,\n",
-       "          4.70604971e+03],\n",
-       "       [ -2.33058122e-02,  -1.65549754e-02,   1.02128327e+00,\n",
-       "         -3.03542290e+02],\n",
-       "       [  0.00000000e+00,  -0.00000000e+00,  -0.00000000e+00,\n",
-       "          1.00000000e+00]])"
-      ]
-     },
-     "execution_count": 174,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Mfrn"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 175,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([  3.27533547e-02,   4.12836189e-01,   2.23735118e-02,\n",
-       "         1.61860062e-39])"
-      ]
-     },
-     "execution_count": 175,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "resfrn"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 176,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4"
-      ]
-     },
-     "execution_count": 176,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rankfrn"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 177,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([  1.45113868e+05,   3.65063445e+01,   2.10145476e+01,\n",
-       "         1.23018540e-04])"
-      ]
-     },
-     "execution_count": 177,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sigfrn"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 178,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "hezffrnJan16 = factory.get_timeseries(observatory='FRN',\n",
-    "\n",
-    "        interval='minute',\n",
-    "\n",
-    "        type='variation',\n",
-    "\n",
-    "        channels=('H', 'E', 'Z', 'F'),\n",
-    "\n",
-    "        starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n",
-    "\n",
-    "        endtime=UTCDateTime('2016-01-31T23:59:59Z'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 179,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "frnJan16adj = make_adjusted_from_transform_and_raw(Mfrn,hezffrnJan16)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 180,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "frnh_pqqm = np.mean(frn_abs_ord.absp1[0] - frn_abs_ord.ordp1[0])\n",
-    "\n",
-    "frne_pqqm = np.mean(frn_abs_ord.absp1[1] - frn_abs_ord.ordp1[1])\n",
-    "\n",
-    "frnz_pqqm = np.mean(frn_abs_ord.absp1[2] - frn_abs_ord.ordp1[2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 181,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-30, 30)"
-      ]
-     },
-     "execution_count": 181,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1401c2e8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(((hezffrnJan16[0].data+frnh_pqqm)**2 + (hezffrnJan16[1].data+frne_pqqm)**2 + (hezffrnJan16[2].data+frnz_pqqm)**2)**(0.5) - hezffrnJan16[3].data + 6,'c')\n",
-    "\n",
-    "pl.hold(True)\n",
-    "\n",
-    "pl.plot((frnJan16adj[0]**2 + frnJan16adj[1]**2 + frnJan16adj[2]**2)**(0.5) - hezffrnJan16[3].data + 6,'k')\n",
-    "\n",
-    "pl.ylim(-30,30)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 182,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "save_state('/users/aclaycomb/Documents/adjfrn_state_.json', Mfrn, -6)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 183,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "gua_bns = get_baselines_from_file('/users/aclaycomb/Documents/guajson12.txt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 184,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x14b648d0>]"
-      ]
-     },
-     "execution_count": 184,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEACAYAAABfxaZOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1SD/D0KaEsPCygenAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1402feb8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(gua_bns.baseH)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 185,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "a = UTCDateTime(2015,10,15,0,0,0)\n",
-    "\n",
-    "b = UTCDateTime(2015,12,15,0,0,0)\n",
-    "\n",
-    "get_bns_between_dates(a,b,gua_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 186,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x14d9c898>]"
-      ]
-     },
-     "execution_count": 186,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEGCAYAAACevtWaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIC7ujuM4CeT/A1ZVyZoNLfOyAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x14b80438>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(gua_bns.baseH)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 187,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "remove_outlier_baselines(gua_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 188,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x14ff91d0>]"
-      ]
-     },
-     "execution_count": 188,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEGCAYAAACJnEVTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x14db9f28>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(gua_bns.baseH)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 189,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "gua_abs_ord = get_ord_abs_from_baselines(gua_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 190,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "Mgua, resgua, rankgua, siggua = get_transform_from_abs_ords(gua_abs_ord)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 191,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  9.98443843e-01,  -1.93733450e-02,  -4.25390474e-03,\n",
-       "          6.52567915e+02],\n",
-       "       [  2.02166513e-02,   1.00429017e+00,   7.01363090e-03,\n",
-       "         -6.73172625e+01],\n",
-       "       [  6.93024175e-04,  -4.44958958e-03,   1.00187292e+00,\n",
-       "          2.19933652e+02],\n",
-       "       [  0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
-       "          1.00000000e+00]])"
-      ]
-     },
-     "execution_count": 191,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Mgua"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 192,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([  1.38786609e-01,   8.45265521e+00,   1.15363688e+00,\n",
-       "         7.95410191e-37])"
-      ]
-     },
-     "execution_count": 192,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "resgua"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 193,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "hezfguaJan16 = factory.get_timeseries(observatory='GUA',\n",
-    "\n",
-    "        interval='minute',\n",
-    "\n",
-    "        type='variation',\n",
-    "\n",
-    "        channels=('H', 'E', 'Z', 'F'),\n",
-    "\n",
-    "        starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n",
-    "\n",
-    "        endtime=UTCDateTime('2016-01-31T23:59:59Z'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 194,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "guaJan16adj = make_adjusted_from_transform_and_raw(Mgua,hezfguaJan16)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 195,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "guah_pqqm = np.mean(gua_abs_ord.absp1[0] - gua_abs_ord.ordp1[0])\n",
-    "\n",
-    "guae_pqqm = np.mean(gua_abs_ord.absp1[1] - gua_abs_ord.ordp1[1])\n",
-    "\n",
-    "guaz_pqqm = np.mean(gua_abs_ord.absp1[2] - gua_abs_ord.ordp1[2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 196,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-10, 10)"
-      ]
-     },
-     "execution_count": 196,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x15123630>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(((hezfguaJan16[0].data+guah_pqqm)**2 + (hezfguaJan16[1].data+guae_pqqm)**2 + (hezfguaJan16[2].data+guaz_pqqm)**2)**(0.5) - hezfguaJan16[3].data - 7.9,'c')\n",
-    "\n",
-    "pl.hold(True)\n",
-    "\n",
-    "pl.plot((guaJan16adj[0]**2 + guaJan16adj[1]**2 + guaJan16adj[2]**2)**(0.5) - hezfguaJan16[3].data - 7.9,'k')\n",
-    "\n",
-    "pl.ylim(-10,10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 197,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "save_state('/users/aclaycomb/Documents/adjgua_state_.json', Mgua, 7.9)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 198,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "hon_bns = get_baselines_from_file('/users/aclaycomb/Documents/honjson12.txt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 199,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x13c403c8>]"
-      ]
-     },
-     "execution_count": 199,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEACAYAAABbMHZzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ni4hUIQV/EZEqpOAvIlKFFPxFRKrQ/weiYsc0OMVbqQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x14b9f048>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(hon_bns.baseH)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 200,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "a = UTCDateTime(2015,10,15,0,0,0)\n",
-    "\n",
-    "b = UTCDateTime(2015,12,15,0,0,0)\n",
-    "\n",
-    "get_bns_between_dates(a,b,hon_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 201,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1199fcc0>]"
-      ]
-     },
-     "execution_count": 201,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEACAYAAACwB81wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x13a02d30>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(hon_bns.baseH)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 202,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x150b9400>]"
-      ]
-     },
-     "execution_count": 202,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEACAYAAABbMHZzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZjaE/h+ajd9d4R/wjgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x12a78518>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(hon_bns.baseD)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 203,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x13cedf28>]"
-      ]
-     },
-     "execution_count": 203,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEACAYAAAC9Gb03AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIipyDPREREWOgZ6IqMgx0BMRFbn/B0Q9uuSZiSIiAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x139e9208>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(hon_bns.baseZ)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 204,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x14944240>]"
-      ]
-     },
-     "execution_count": 204,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwFQFAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x152362b0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "remove_outlier_baselines(hon_bns)\n",
-    "\n",
-    "pl.plot(hon_bns.baseH)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 205,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "hon_abs_ord = get_ord_abs_from_baselines(hon_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 206,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "Mhon, reshon, rankhon, sighon = get_transform_from_abs_ords(hon_abs_ord)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 207,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  9.99678600e-01,  -1.74029673e-01,   2.63717245e-02,\n",
-       "         -9.82568114e+02],\n",
-       "       [  1.69668389e-01,   1.01995732e+00,  -5.55170451e-03,\n",
-       "          1.74349733e+02],\n",
-       "       [  1.23732740e-03,  -1.69453292e-03,   1.05273966e+00,\n",
-       "         -1.02140068e+03],\n",
-       "       [  0.00000000e+00,   2.98072927e-15,  -1.09299776e-15,\n",
-       "          1.00000000e+00]])"
-      ]
-     },
-     "execution_count": 207,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Mhon"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 208,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([  9.16388677e+00,   4.33161926e+00,   3.69640761e+00,\n",
-       "         4.59177481e-41])"
-      ]
-     },
-     "execution_count": 208,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "reshon"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 209,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "hezfhonJan16 = factory.get_timeseries(observatory='HON',\n",
-    "\n",
-    "        interval='minute',\n",
-    "\n",
-    "        type='variation',\n",
-    "\n",
-    "        channels=('H', 'E', 'Z', 'F'),\n",
-    "\n",
-    "        starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n",
-    "\n",
-    "        endtime=UTCDateTime('2016-01-31T23:59:59Z'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 210,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "honJan16adj = make_adjusted_from_transform_and_raw(Mhon,hezfhonJan16)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 211,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "honh_pqqm = np.mean(hon_abs_ord.absp1[0] - hon_abs_ord.ordp1[0])\n",
-    "\n",
-    "hone_pqqm = np.mean(hon_abs_ord.absp1[1] - hon_abs_ord.ordp1[1])\n",
-    "\n",
-    "honz_pqqm = np.mean(hon_abs_ord.absp1[2] - hon_abs_ord.ordp1[2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 212,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-30, 30)"
-      ]
-     },
-     "execution_count": 212,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x150d3c18>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(((hezfhonJan16[0].data+honh_pqqm)**2 + (hezfhonJan16[1].data+hone_pqqm)**2 + (hezfhonJan16[2].data+honz_pqqm)**2)**(0.5) - hezfhonJan16[3].data - 14.5,'c')\n",
-    "\n",
-    "pl.hold(True)\n",
-    "\n",
-    "pl.plot((honJan16adj[0]**2 + honJan16adj[1]**2 + honJan16adj[2]**2)**(0.5) - hezfhonJan16[3].data - 14.5,'k')\n",
-    "\n",
-    "pl.ylim(-30,30)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 213,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "save_state('/users/aclaycomb/Documents/adjhon_state_.json', Mhon, 14.5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 214,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "new_bns = get_baselines_from_file('/users/aclaycomb/Documents/newjson12.txt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 215,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x15931048>]"
-      ]
-     },
-     "execution_count": 215,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEACAYAAABfxaZOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4+6cAAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x13cfde10>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(new_bns.baseH)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 216,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "a = UTCDateTime(2015,10,15,0,0,0)\n",
-    "\n",
-    "b = UTCDateTime(2015,12,15,0,0,0)\n",
-    "\n",
-    "get_bns_between_dates(a,b,new_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 217,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x15b24a58>]"
-      ]
-     },
-     "execution_count": 217,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEACAYAAACznAEdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x158e7080>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(new_bns.baseH)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 218,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x15d0f550>]"
-      ]
-     },
-     "execution_count": 218,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEGCAYAAACevtWaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nD21pvl8bAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b41748>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "remove_outlier_baselines(new_bns)\n",
-    "\n",
-    "pl.plot(new_bns.baseH)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 219,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "new_abs_ord = get_ord_abs_from_baselines(new_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 220,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "Mnew, resnew, ranknew, signew = get_transform_from_abs_ords(new_abs_ord)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 221,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  9.70381886e-01,  -2.61357358e-01,  -5.88339193e-03,\n",
-       "          3.31498500e+02],\n",
-       "       [  2.78701300e-01,   9.69226963e-01,   4.60085193e-02,\n",
-       "         -2.56585110e+03],\n",
-       "       [ -1.91365970e-02,   6.40280253e-03,   9.26542509e-01,\n",
-       "          4.80701668e+03],\n",
-       "       [  0.00000000e+00,   0.00000000e+00,  -0.00000000e+00,\n",
-       "          1.00000000e+00]])"
-      ]
-     },
-     "execution_count": 221,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Mnew"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 222,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([  2.77548347e-02,   5.00419296e-01,   2.95001807e-01,\n",
-       "         1.51528569e-39])"
-      ]
-     },
-     "execution_count": 222,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "resnew"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 223,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "hezfnewJan16 = factory.get_timeseries(observatory='NEW',\n",
-    "\n",
-    "        interval='minute',\n",
-    "\n",
-    "        type='variation',\n",
-    "\n",
-    "        channels=('H', 'E', 'Z', 'F'),\n",
-    "\n",
-    "        starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n",
-    "\n",
-    "        endtime=UTCDateTime('2016-01-31T23:59:59Z'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 224,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "newJan16adj = make_adjusted_from_transform_and_raw(Mnew,hezfnewJan16)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 225,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "newh_pqqm = np.mean(new_abs_ord.absp1[0] - new_abs_ord.ordp1[0])\n",
-    "\n",
-    "newe_pqqm = np.mean(new_abs_ord.absp1[1] - new_abs_ord.ordp1[1])\n",
-    "\n",
-    "newz_pqqm = np.mean(new_abs_ord.absp1[2] - new_abs_ord.ordp1[2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 226,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-30, 30)"
-      ]
-     },
-     "execution_count": 226,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nICIiAQoMIiISoMAgIiIB/w+MbZKWF/bP+gAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x15cbd278>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(((hezfnewJan16[0].data+newh_pqqm)**2 + (hezfnewJan16[1].data+newe_pqqm)**2 + (hezfnewJan16[2].data+newz_pqqm)**2)**(0.5) - hezfnewJan16[3].data + 7.1,'c')\n",
-    "\n",
-    "pl.hold(True)\n",
-    "\n",
-    "pl.plot((newJan16adj[0]**2 + newJan16adj[1]**2 + newJan16adj[2]**2)**(0.5) - hezfnewJan16[3].data + 7.1,'k')\n",
-    "\n",
-    "pl.ylim(-30,30)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 227,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "save_state('/users/aclaycomb/Documents/adjnew_state_.json', Mnew, -7.1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 228,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "shu_bns = get_baselines_from_file('/users/aclaycomb/Documents/shujson12.txt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 229,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x166727f0>]"
-      ]
-     },
-     "execution_count": 229,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJI8Sg4iI5FFiEBGRPEoMIiKS5/8BBsUfFKPK6REAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b41630>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(shu_bns.baseH)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 230,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "a = UTCDateTime(2015,10,25,0,0,0)\n",
-    "\n",
-    "b = UTCDateTime(2016,1,1,0,0,0)\n",
-    "\n",
-    "get_bns_between_dates(a,b,shu_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 231,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "remove_outlier_baselines(shu_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 232,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1682f470>]"
-      ]
-     },
-     "execution_count": 232,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEACAYAAABGYoqtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nohkZyuIAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b41710>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(shu_bns.baseH)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 233,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "shu_abs_ord = get_ord_abs_from_baselines(shu_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 234,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "Mshu, resshu, rankshu, sigshu = get_transform_from_abs_ords(shu_abs_ord)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 235,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  9.88588472e-01,  -2.08072460e-01,  -6.35567167e-03,\n",
-       "         -1.47139029e+01],\n",
-       "       [  2.10268162e-01,   9.69217999e-01,  -1.82802376e-02,\n",
-       "          5.17502020e+02],\n",
-       "       [  1.51436210e-02,   4.47899422e-03,   1.01739086e+00,\n",
-       "         -1.48503633e+03],\n",
-       "       [  1.99237003e-15,  -2.49062245e-15,  -0.00000000e+00,\n",
-       "          1.00000000e+00]])"
-      ]
-     },
-     "execution_count": 235,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Mshu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 236,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([  3.73523464e-01,   8.51217195e-01,   1.34919961e-01,\n",
-       "         1.48084738e-39])"
-      ]
-     },
-     "execution_count": 236,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "resshu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 237,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "hezfshuJan16 = factory.get_timeseries(observatory='SHU',\n",
-    "\n",
-    "        interval='minute',\n",
-    "\n",
-    "        type='variation',\n",
-    "\n",
-    "        channels=('H', 'E', 'Z', 'F'),\n",
-    "\n",
-    "        starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n",
-    "\n",
-    "        endtime=UTCDateTime('2016-01-31T23:59:59Z'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 238,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "shuJan16adj = make_adjusted_from_transform_and_raw(Mshu,hezfshuJan16)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 239,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "shuh_pqqm = np.mean(shu_abs_ord.absp1[0] - shu_abs_ord.ordp1[0])\n",
-    "\n",
-    "shue_pqqm = np.mean(shu_abs_ord.absp1[1] - shu_abs_ord.ordp1[1])\n",
-    "\n",
-    "shuz_pqqm = np.mean(shu_abs_ord.absp1[2] - shu_abs_ord.ordp1[2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 240,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-30, 30)"
-      ]
-     },
-     "execution_count": 240,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x167dd400>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(((hezfshuJan16[0].data+shuh_pqqm)**2 + (hezfshuJan16[1].data+shue_pqqm)**2 + (hezfshuJan16[2].data+shuz_pqqm)**2)**(0.5) - hezfshuJan16[3].data - 13.1,'c')\n",
-    "\n",
-    "pl.hold(True)\n",
-    "\n",
-    "pl.plot((shuJan16adj[0]**2 + shuJan16adj[1]**2 + shuJan16adj[2]**2)**(0.5) - hezfshuJan16[3].data - 13.1,'k')\n",
-    "\n",
-    "pl.ylim(-30,30)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 241,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "save_state('/users/aclaycomb/Documents/adjshu_state_.json', Mshu, 13.1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 242,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "sit_bns = get_baselines_from_file('/users/aclaycomb/Documents/sitjson12.txt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 243,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x15828fd0>]"
-      ]
-     },
-     "execution_count": 243,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIhIHgWDiIjkUTCIiEie/w8QZ3sE1bQ1TwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x167d2128>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(sit_bns.baseH)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 244,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "a = UTCDateTime(2015,10,1,0,0,0)\n",
-    "\n",
-    "b = UTCDateTime(2016,1,1,0,0,0)\n",
-    "\n",
-    "get_bns_between_dates(a,b,sit_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 245,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x144972e8>]"
-      ]
-     },
-     "execution_count": 245,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEACAYAAACwB81wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x15e97828>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(sit_bns.baseH)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 246,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "remove_outlier_baselines(sit_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 247,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x16a71d30>]"
-      ]
-     },
-     "execution_count": 247,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1443bf60>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(sit_bns.baseH)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 248,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "sit_abs_ord = get_ord_abs_from_baselines(sit_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 249,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "Msit, ressit, ranksit, sigsit = get_transform_from_abs_ords(sit_abs_ord)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 250,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  9.59545352e-01,  -3.66675295e-01,  -1.06533425e-01,\n",
-       "          5.29374275e+03],\n",
-       "       [  2.40650589e-01,   1.03242483e+00,  -2.66118481e-02,\n",
-       "          2.86265553e+03],\n",
-       "       [  2.24111073e-02,   2.01999547e-02,   1.00408997e+00,\n",
-       "          2.41162824e+02],\n",
-       "       [  1.08442241e-15,  -0.00000000e+00,  -0.00000000e+00,\n",
-       "          1.00000000e+00]])"
-      ]
-     },
-     "execution_count": 250,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Msit"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 251,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([  1.18381961e+00,   5.83420751e+00,   6.22840607e-01,\n",
-       "         1.57555273e-39])"
-      ]
-     },
-     "execution_count": 251,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ressit"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 252,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "hezfsitJan16 = factory.get_timeseries(observatory='SIT',\n",
-    "\n",
-    "        interval='minute',\n",
-    "\n",
-    "        type='variation',\n",
-    "\n",
-    "        channels=('H', 'E', 'Z', 'F'),\n",
-    "\n",
-    "        starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n",
-    "\n",
-    "        endtime=UTCDateTime('2016-01-30T23:59:59Z'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 253,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "sitJan16adj = make_adjusted_from_transform_and_raw(Msit,hezfsitJan16)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 254,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "sith_pqqm = np.mean(sit_abs_ord.absp1[0] - sit_abs_ord.ordp1[0])\n",
-    "\n",
-    "site_pqqm = np.mean(sit_abs_ord.absp1[1] - sit_abs_ord.ordp1[1])\n",
-    "\n",
-    "sitz_pqqm = np.mean(sit_abs_ord.absp1[2] - sit_abs_ord.ordp1[2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 255,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-30, 30)"
-      ]
-     },
-     "execution_count": 255,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1443b6a0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(((hezfsitJan16[0].data+sith_pqqm)**2 + (hezfsitJan16[1].data+site_pqqm)**2 + (hezfsitJan16[2].data+sitz_pqqm)**2)**(0.5) - hezfsitJan16[3].data - 3.2,'c')\n",
-    "\n",
-    "pl.hold(True)\n",
-    "\n",
-    "pl.plot((sitJan16adj[0]**2 + sitJan16adj[1]**2 + sitJan16adj[2]**2)**(0.5) - hezfsitJan16[3].data - 3.2,'k')\n",
-    "\n",
-    "pl.ylim(-30,30)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 256,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "save_state('/users/aclaycomb/Documents/adjsit_state_.json', Msit, 3.2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 257,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "sjg_bns = get_baselines_from_file('/users/aclaycomb/Documents/sjgjson12.txt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 258,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x16f84ac8>]"
-      ]
-     },
-     "execution_count": 258,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEGCAYAAACAd+UpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAEB5JREFUeJzt3X+sZOVdx/HPZ7MgLZvKVtuFcIGWYG3B1nX/INRaM/4A\nt00o1RAC/UPrj6QmFkykFRCSvZombUnaxpigsWJTUIJBEwqt4O6C06R/QDdZtrvoAlvlx+5WFjBr\nYmNjKvv1jzlwp9s5z8zcc2Zmv3Per2RyZ849c84zz577uc/9Ps/MOiIEAFhOGxbdAADA7BDyALDE\nCHkAWGKEPAAsMUIeAJYYIQ8AS2yuIW/7TtvHbO+fYN/zbD9qe6/tfbY/MI82AsAymfdI/kuSfmXC\nfW+T9HcRsU3SdZLumFmrAGBJzTXkI+Ibko4Pb7N9oe2HbO+x/XXb76i+dULSm6r7Z0k6OsemAsBS\n2LjoBkj6S0kfi4h/s32ppD+X9EuS/ljSTts3SHqjpF9eYBsBIKWFhrztMyX9rKT7bLvafFr19TpJ\nX4qIL9i+TNLfSLpkAc0EgLQWPZLfIOl4VXc/2W+rqt9HxGO2z7D94xHxylxbCACJtVKTt73d9lO2\nn7F907jdq5si4r8lPWv76qFjvae6+7yqEo3td0n6EQIeAKbjpp9CaXuDpGc0qKN/R9IeSddGxFMj\n9r1HUk/Sj0k6JmmHpEcl/YWkczT4y+LeiPhUFexflLRJg0nYT0bEI40aCwAd00bIXyZpR0R8oHp8\ns6SIiM+20D4AQANtlGvOlXR46PGRahsAYMH4WAMAWGJtrK45Kun8occrGvHGJdv8F1QAsA4R4fF7\njdbGSH6PpItsX2D7dEnXSnpg1I4R0drthRdCKyuT73/sWOgtb2nv/E1uO3bsWHgbTpUbfUFf0Bfl\nW1ONR/IR8artj0vaqcEvjTsj4mDjlo1hSydOTL7/iROD5wBAl7TyZqiIeFjST7ZxrElt2CBN80su\nYvAcAOiStLGXeSTf6/UW3YRTBn2xhr5YQ1+0p/E6+YlPZEeb5zp2THr3u6WXXpps/8OHpfe+Vzpy\npLUmAMDM2VYseOJ1ISjXAMB4aWMvc7kGAOYlbchv2DBdyDOSB9BFaWNv2nLNiROEPIDuSRt7lGsA\nYLy0Ic/EKwCMlzb2GMkDwHhpQ37aiVdq8gC6KG3sUa4BgPHSxh7lGgAYL23Is04eAMZLG3uskweA\n8dLGHuUaABgvdchLk4/mKdcA6KK0sTdtyDOSB9BFaUNemm7ylZo8gC5KHXvTTL5SrgHQRaljb5rJ\nV8o1ALoodchPU65hJA+gi1LH3jTlGmryALoodexRrgGAstQhz8QrAJSljj1G8gBQljrkWScPAGWp\nY49yDQCUpY49yjUAUJY65FknDwBlqWOPdfIAUJY69ijXAEBZ6pBn4hUAylLHHiN5AChLHfKskweA\nstSxR7kGAMpSxx7lGgAoaxTytq+2/aTtV21va6tRk2KdPACUNY29A5J+VdLXW2jL1FgnDwBlG5s8\nOSKeliR7MYUQyjUAUJZ6bMvEKwCUjR3J294lacvwJkkh6daIeHCak62urr5+v9frqdfrTfP0EW1j\nJA9gufT7ffX7/daO55h0KFw6iP3Pkm6MiL2FfaKNcw27+GLpvvukSy4Zv+9dd0m7dkl3391qEwBg\npmwrItY9RG2zgDH3cTLlGgAoa7qE8sO2D0u6TNJXbT/UTrMmPT/lGgAoabq65n5J97fUlqmxTh4A\nylLHHuvkAaAsdexRrgGAstQhz8QrAJSljj1G8gBQljrkmXgFgLLUscfEKwCUpY49yjUAUJY65CnX\nAEBZ6tijXAMAZaljj3INAJSlDnnKNQBQljr27OnKNYzkAXRN6pBnJA8AZaljj4lXAChLHXtMvAJA\nWeqQp1wDAGWpY49yDQCUpY49yjUAUJY65CnXAEBZ6thjnTwAlKUOeUbyAFCWOvaYeAWAstSxx8Qr\nAJSlDnnKNQBQljr2KNcAQFnq2KNcAwBlqUOecg0AlKWOPdbJA0BZ6pBnJA8AZaljj4lXAChLHXtM\nvAJAWeqQp1wDAGWpY49yDQCUpY49yjUAUJY65CnXAEBZo9izfbvtg7b32f4H229qq2GTnZ918gBQ\n0nRsu1PSJRGxVdIhSbc0b9LkGMkDQFmj2IuI3RHxWsw+JmmleZMmx8QrAJS1GXu/JemhFo83FhOv\nAFC2cdwOtndJ2jK8SVJIujUiHqz2uVXS9yPinpm0sgblGgAoGxvyEXF56fu2Pyrpg5J+cdyxVldX\nX7/f6/XU6/XGPaWIcg2AZdPv99Xv91s7nmPSlBz1ZHu7pM9J+vmI+M8x+0aTc41y443SOedIn/jE\n+H2vu0668krpIx9ptQkAMFO2FRHrLjY3Hdv+maRNknbZ3mv7jobHmwrlGgAoG1uuKYmIn2irIevB\nOnkAKEs9tmUkDwBlqWOPiVcAKEsde6yTB4Cy1CFPuQYAylLHHuUaAChLHXuUawCgLHXIU64BgLLU\nscc6eQAoSx3yjOQBoCx17DHxCgBlqWOPiVcAKEsd8pRrAKAsdexRrgGAstSxN025JoJyDYDuSR3y\n05RrGMkD6KLUscc6eQAoSx3yTLwCQFnq2KNcAwBlqWOPcg0AlKUOeco1AFCWOvYYyQNAWeqQZyQP\nAGWpY4+JVwAoSx17lGsAoCx1yFOuAYCy1LFHuQYAylLHHuUaAChLHfKUawCgLHXsMZIHgLLUIc9I\nHgDKUsceE68AUJY69ijXAEBZ6pCnXAMAZaljj3INAJSljj3KNQBQljrkKdcAQFmj2LP9J7a/ZfsJ\n2w/bPruthk12fkbyAFDSdGx7e0T8dET8jKSvSdrRQpsmxkgeAMoaxV5EfHfo4ZmSJozcdjDxCgBl\nG5sewPanJP26pP+S9AuNWzTVuSnXAEDJ2LGt7V229w/dDlRfr5SkiLgtIs6X9LeSrp91g4dRrgGA\nsrEj+Yi4fMJj3SPpHyWt1u2wurr2rV6vp16vN+GhR6NcA2DZ9Pt99fv91o7nmLTeMerJ9kUR8e3q\n/vWS3h8R19TsG03ONcru3dKnPy098sj4fc86S3r2WWnz5labAAAzZVsRse5ic9Oa/Gdsv0ODCdfn\nJf1uw+NNhXINAJQ1CvmIuLqthqwHE68AUJZ6bMtIHgDKUsceE68AUJY69ijXAEBZ6pCnXAMAZalj\nj3INAJSljj3KNQBQljrkKdcAQFnq2JtmJB/BSB5A96QO+UlH8q/9IiDkAXRNJ0KeSVcAXZU6+iYt\n1zDpCqCrUof8NOUaRvIAuih19FGuAYCy1NFHuQYAylKHPOUaAChLHX2M5AGgLHXIM5IHgLLU0cfE\nKwCUpY4+yjUAUJY65CnXAEBZ6uijXAMAZamjb9JyDZ9ACaCrUoc8I3kAKEsdffbkIc9IHkAXpQ75\nDRsmL9cwkgfQRamjj3INAJSljj7WyQNAWeqQZ508AJSljj7KNQBQljr6WCcPAGWpQ56RPACUpY4+\n1skDQFnqkGedPACUpY4+yjUAUJY6+lgnDwBlqUOedfIAUNZK9Nm+0fYJ229u43iTolwDAGWNo8/2\niqTLJT3fvDnTnpt18gBQ0sb49guSPtnCcab22uqacUHPSB5AVzWKPtsfknQ4Ig601J4pzz/4OknI\nM5IH0EUbx+1ge5ekLcObJIWk2yT9kQalmuHv1VpdXX39fq/XU6/Xm7ylte0bH/JMvALIot/vq9/v\nt3Y8xyRF7VFPtH9K0m5J/6NBuK9IOirp0oh4acT+sd5zlWzcKH3ve9Jpp9Xv8/jj0g03DL4CQCa2\nFRHrrkWMHcnXiYgnJZ091JBnJW2LiOPrPeZ6TDKSp1wDoKvaLGKExpRrZmGSZZSUawB01bpH8ieL\niAvbOtY0Jgl5VtcA6Kr00TfpxCvlGgBdlD7kGckDQL300TfJZ8oz8Qqgq9KH/CSfKc/EK4CuSh99\nlGsAoF766GOdPADUSx/yrJMHgHrpo49yDQDUSx99rJMHgHrpQ56RPADUSx99rJMHgHrpQ5518gBQ\nL330Ua4BgHrpo4918gBQL33Is04eAOqljz7KNQBQL330sU4eAOqlD3lG8gBQL330sU4eAOqlD3nW\nyQNAvfTRR7kGAOqljz7WyQNAvfQhzzp5AKiXPvoo1wBAvfTRxzp5AKiXPuQZyQNAvfTRxzp5AKi3\ncdENaOqMM6RrrpHe8Ib6fY4fH+wDAF3jGFfQbutEdsziXK+8Ir388vj9zjtP2rSp9dMDwEzZVkSs\nuxaRPuQBYJk1Dfn0NXkAQD1CHgCWGCEPAEuMkAeAJUbIA8ASaxTytnfYPmJ7b3Xb3lbDAADNtTGS\n/3xEbKtuD7dwvKXX7/cX3YRTBn2xhr5YQ1+0p42Q5wMDpsQFvIa+WENfrKEv2tNGyH/c9j7bf2X7\nR1s4HgCgJWND3vYu2/uHbgeqr1dKukPShRGxVdKLkj4/6wYDACbX2sca2L5A0oMR8Z6a7/OZBgCw\nDk0+1qDRp1DaPjsiXqwe/pqkJ+v2bdJIAMD6NP2o4dttb5V0QtJzkj7WuEUAgNbM7VMoAQDzN/N3\nvNrebvsp28/YvmnW5zvV2H7O9rdsP2H7m9W2zbZ32n7a9j8t66ok23faPmZ7/9C22tdu+xbbh2wf\ntH3FYlo9GzV9UftmwiXvixXbj9r+l2ohxw3V9s5dGyP64vpqe3vXRkTM7KbBL5FvS7pA0mmS9kl6\n5yzPeardJP27pM0nbfuspD+s7t8k6TOLbueMXvvPSdoqaf+41y7pYklPaFBCfFt13XjRr2HGfbFD\n0h+M2PddS94XZ0vaWt3fJOlpSe/s4rVR6IvWro1Zj+QvlXQoIp6PiO9LulfSVTM+56nG+uG/mK6S\n9OXq/pclfXiuLZqTiPiGpOMnba577R+SdG9E/F9EPCfpkAbXz1Ko6Qtp9JsJr9Jy98WLEbGvuv9d\nSQclraiD10ZNX5xbfbuVa2PWIX+upMNDj49o7QV0RUjaZXuP7d+ptm2JiGPS4B9Z0lsX1rr5e2vN\naz/5Wjmqblwro95M2Jm+sP02Df7CeUz1Pxed6I+hvni82tTKtcGnUM7e+yJim6QPSvo92+/XIPiH\ndXn2u8uv/eQ3E35uwe2ZK9ubJP29pN+vRrGd/bkY0RetXRuzDvmjks4ferxSbeuMiPiP6uvLku7X\n4E+rY7a3SIP3Gkh6aXEtnLu6135U0nlD+y39tRIRL0dVaJX0Ra392b30fWF7owahdndEfKXa3Mlr\nY1RftHltzDrk90i6yPYFtk+XdK2kB2Z8zlOG7TdWv6Fl+0xJV0g6oEEffLTa7TckfWXkAZaD9YO1\nxbrX/oCka22fbvvtki6S9M15NXJOfqAvqiB7zfCbCbvQF38t6V8j4k+HtnX12vihvmj12pjD7PF2\nDWaMD0m6edGz2fO8SXq7BiuKntAg3G+utr9Z0u6qX3ZKOmvRbZ3R679H0nck/a+kFyT9pqTNda9d\n0i0arBY4KOmKRbd/Dn1xl6T91TVyvwY16S70xfskvTr0s7G3yonan4tl7Y9CX7R2bfBmKABYYky8\nAsASI+QBYIkR8gCwxAh5AFhihDwALDFCHgCWGCEPAEuMkAeAJfb/p7qPpnR6XqkAAAAASUVORK5C\nYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x15e7d940>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(sjg_bns.baseH)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 259,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "a = UTCDateTime(2015,10,1,0,0,0)\n",
-    "\n",
-    "b = UTCDateTime(2015,12,1,0,0,0)\n",
-    "\n",
-    "get_bns_between_dates(a,b,sjg_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 260,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x17180e10>]"
-      ]
-     },
-     "execution_count": 260,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEACAYAAABRQBpkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n3e843eR4UJ0AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x16e660f0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(sjg_bns.baseH)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 261,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "remove_outlier_baselines(sjg_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 262,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1738a278>]"
-      ]
-     },
-     "execution_count": 262,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEACAYAAACgS0HpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x171de198>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(sjg_bns.baseH)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 263,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "sjg_abs_ord = get_ord_abs_from_baselines(sjg_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 264,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "Msjg, ressjg, ranksjg, sigsjg = get_transform_from_abs_ords(sjg_abs_ord)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 265,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  9.90536307e-01,   2.04242590e-01,   3.87150502e-02,\n",
-       "         -1.50422264e+03],\n",
-       "       [ -2.27532793e-01,   9.62960561e-01,   5.14425894e-03,\n",
-       "          8.62310605e+01],\n",
-       "       [ -6.93279286e-03,   2.77582644e-02,   9.44616720e-01,\n",
-       "          1.70847335e+03],\n",
-       "       [  0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
-       "          1.00000000e+00]])"
-      ]
-     },
-     "execution_count": 265,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Msjg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 266,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([  1.76264961e+01,   7.71329740e+00,   1.34822012e+01,\n",
-       "         3.73081703e-39])"
-      ]
-     },
-     "execution_count": 266,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ressjg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 267,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "hezfsjgJan16 = factory.get_timeseries(observatory='SJG',\n",
-    "\n",
-    "        interval='minute',\n",
-    "\n",
-    "        type='variation',\n",
-    "\n",
-    "        channels=('H', 'E', 'Z', 'F'),\n",
-    "\n",
-    "        starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n",
-    "\n",
-    "        endtime=UTCDateTime('2016-01-31T23:59:59Z'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 268,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "sjgJan16adj = make_adjusted_from_transform_and_raw(Msjg,hezfsjgJan16)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 269,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "sjgh_pqqm = np.mean(sjg_abs_ord.absp1[0] - sjg_abs_ord.ordp1[0])\n",
-    "\n",
-    "sjge_pqqm = np.mean(sjg_abs_ord.absp1[1] - sjg_abs_ord.ordp1[1])\n",
-    "\n",
-    "sjgz_pqqm = np.mean(sjg_abs_ord.absp1[2] - sjg_abs_ord.ordp1[2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 270,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-30, 30)"
-      ]
-     },
-     "execution_count": 270,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x171d0dd8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(((hezfsjgJan16[0].data+sjgh_pqqm)**2 + (hezfsjgJan16[1].data+sjge_pqqm)**2 + (hezfsjgJan16[2].data+sjgz_pqqm)**2)**(0.5) - hezfsjgJan16[3].data + 55,'c')\n",
-    "\n",
-    "pl.hold(True)\n",
-    "\n",
-    "pl.plot((sjgJan16adj[0]**2 + sjgJan16adj[1]**2 + sjgJan16adj[2]**2)**(0.5) - hezfsjgJan16[3].data + 55,'k')\n",
-    "\n",
-    "pl.ylim(-30,30)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 271,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "save_state('/users/aclaycomb/Documents/adjsjg_state_.json', Msjg, -55)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 272,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "bsl_bns = get_baselines_from_file('/users/aclaycomb/Documents/bsljson12.txt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 273,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x17e9fb70>]"
-      ]
-     },
-     "execution_count": 273,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEACAYAAABfxaZOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEYW+iEgV+f/4vS4iKUHaYwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x172ec080>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(bsl_bns.baseH)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 274,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "a = UTCDateTime(2015,10,1,0,0,0)\n",
-    "\n",
-    "b = UTCDateTime(2016,1,1,0,0,0)\n",
-    "\n",
-    "get_bns_between_dates(a,b,bsl_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 275,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x180ddd68>]"
-      ]
-     },
-     "execution_count": 275,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEACAYAAABVtcpZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x17f5d278>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(bsl_bns.baseH)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 276,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "bsl_abs_ord = get_ord_abs_from_baselines(bsl_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 277,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "Mbsl, resbsl, rankbsl, sigbsl = get_transform_from_abs_ords(bsl_abs_ord)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 278,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  1.05069413e+00,   1.10714328e-01,  -2.11533688e-01,\n",
-       "          8.16799353e+03],\n",
-       "       [  2.79831733e-02,   1.06881857e+00,  -1.29195735e-01,\n",
-       "          4.37112281e+03],\n",
-       "       [ -3.85811324e-02,  -5.23226381e-02,   1.12178759e+00,\n",
-       "         -4.01891646e+03],\n",
-       "       [  0.00000000e+00,  -0.00000000e+00,   0.00000000e+00,\n",
-       "          1.00000000e+00]])"
-      ]
-     },
-     "execution_count": 278,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Mbsl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 279,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([  2.28382379e+00,   2.91445223e+00,   7.70382134e-01,\n",
-       "         3.85709084e-39])"
-      ]
-     },
-     "execution_count": 279,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "resbsl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 280,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "hezfbslJan16 = factory.get_timeseries(observatory='BSL',\n",
-    "\n",
-    "        interval='minute',\n",
-    "\n",
-    "        type='variation',\n",
-    "\n",
-    "        channels=('H', 'E', 'Z', 'F'),\n",
-    "\n",
-    "        starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n",
-    "\n",
-    "        endtime=UTCDateTime('2016-01-31T23:59:59Z'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 281,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "bslJan16adj = make_adjusted_from_transform_and_raw(Mbsl,hezfbslJan16)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 282,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "bslh_pqqm = np.mean(bsl_abs_ord.absp1[0] - bsl_abs_ord.ordp1[0])\n",
-    "\n",
-    "bsle_pqqm = np.mean(bsl_abs_ord.absp1[1] - bsl_abs_ord.ordp1[1])\n",
-    "\n",
-    "bslz_pqqm = np.mean(bsl_abs_ord.absp1[2] - bsl_abs_ord.ordp1[2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 283,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-10, 10)"
-      ]
-     },
-     "execution_count": 283,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1809aeb8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(((hezfbslJan16[0].data+bslh_pqqm)**2 + (hezfbslJan16[1].data+bsle_pqqm)**2 + (hezfbslJan16[2].data+bslz_pqqm)**2)**(0.5) - hezfbslJan16[3].data + 4.2,'c')\n",
-    "\n",
-    "pl.hold(True)\n",
-    "\n",
-    "pl.plot((bslJan16adj[0]**2 + bslJan16adj[1]**2 + bslJan16adj[2]**2)**(0.5) - hezfbslJan16[3].data + 4.2,'k')\n",
-    "\n",
-    "pl.ylim(-10,10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 284,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "save_state('/users/aclaycomb/Documents/adjbsl_state_.json', Mbsl, -4.2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 285,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "tuc_bns = get_baselines_from_file('/users/aclaycomb/Documents/tucjson12.txt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 286,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x18c8ba20>]"
-      ]
-     },
-     "execution_count": 286,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nD7zGBEZOJPFkAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x18105630>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(tuc_bns.baseH)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 287,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "a = UTCDateTime(2015,12,1,0,0,0)\n",
-    "\n",
-    "b = UTCDateTime(2016,1,1,0,0,0)\n",
-    "\n",
-    "get_bns_between_dates(a,b,tuc_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 288,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x18e459e8>]"
-      ]
-     },
-     "execution_count": 288,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEACAYAAACgS0HpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xu8lXPe//HXpxJNlEJ0UAmlE8kUP8KW23nCMAw/MzRm\nDJkZ5s7NdLixc88YmTsGv8mtYRrHcTufRkbRDkMapZOSSJHkVCHpuD+/P77XZrVbe+913Nc6vJ+P\nx3q01rWv61qfnaz3+n6/1/d7mbsjIiLSkCZxFyAiIsVBgSEiIilRYIiISEoUGCIikhIFhoiIpESB\nISIiKckqMMzsejNbaGazzexhM2sVbR9gZq8nPE5NOOZsM5sbHfO0mbWt49wjzWxxdP5js6lTRESy\nZ9nMwzCzfwOed/dqM7sOcHcfaWY7ABuj7XsAc4D2gAErgP3cfbWZjQW+cvdrap23J3AfMADoBEwB\n9nVNGhERiU1WLQx3n+Lu1dHL6YQPd9x9fcL2FkDNc4v+3MnMDGhFCJDaTgHud/fN7r4UWAwMzKZW\nERHJTi7HMM4HJtW8MLOBZjaf0Lq4yN2r3X0zcDEwD1gO9ATuSHKujsD7Ca8/iLaJiEhMGgwMM5sc\njTnUPOZFfw5J2Gc0sMnd76vZ5u4z3L0PoVtplJk1N7NmwDDgAHfvSAiOUTn/rUREJOeaNbSDux9T\n38/NbChwIjC4juMXmdlaoA8hoDzqZgJ4APhNksM+APZMeN0p2pbs/TWuISKSAXe3hvf6VrZXSR0P\nXA6c7O4bErZ3NbOm0fMuQA9gKeFDv5eZ7RLtegywMMmpnwDOilolewH7ADPqqsPdi/Zx9dVXx15D\nsddfXe0sX+48/rhz1VXOSSc57ds7bds6xxzjjBjhPPig8847Yd+a45591tlzz/jrL/a//3KsvRTq\nz0SDLYwG3AI0ByaHMWymu/vFwCBghJltJAx4D3P3VQBmNgZ4MfrZMmBotH0IcJC7V7r7AjN7AFgA\nbAIu9kx/Qykp7vD++zBzJsyaFf6cOTNsP+ig8Dj/fPjTn6BzZ7B6vj8deiisXAlffQUtWzbe7yBS\nrLIKDHfft47t9wD31PGzCcCEJNufBJ5MeP174PfZ1CfFzR2WLfs2FGoComnTb8Phwguhf3/o1Kn+\ncEimZUvYYw/45z/hWM30EWlQti0MyVJFRUXcJWQlk/rd4csv4cMPYcWKbx/JXu+8cwiG/v3hF78I\nzzt0yF39Rx5ZwdSpxRsYxfzvp5hrh+KvPxNZTdwrBGam3qoCsnZt/QFQ8xzCB3+HDtC+/bfPE1+3\nbw877ZTfep9/HkaNgunT8/s+IoXGzPA0B70VGJKxF16A227bOhA2b4aOHesOgcQgSLcLKR++/hp2\n2y3Unu9wEikkmQSGuqQkY3/+M7RtCz/96beB0KpVYQRBqlq0gO9+F158EU48Me5qRAqbAkMyNns2\n3HlnGF8oZkcdBVOnKjBEGqLlzSUj69fD229Dr15xV5K9msAQkfopMCQjCxbAPvvADjvEXUn2Dj4Y\nFi2CNWvirkSksCkwJCNz5sABB8RdRW5sv30IjRdfjLsSkcKmwJCMzJ4N/frFXUXuqFtKpGEKDMlI\nKbUwQIEhkgrNw5C0uYfLaRctgnbt4q4mNzZtgl12gaVLw+8mUuoymYehFoak7b33wvyFUgkLgO22\nC4sRTpsWdyUihUuBIWkrtfGLGhUV6pYSqY8CQ9JWauMXNTSOIVI/BYakrVRbGAcdFLrbPvkk7kpE\nCpMCQ9JWqi2MZs1g0CCoqoq7EpHCpMCQtHzxRbhL3b5Jb51V/NQtJVI3BYakZe5c6NMn3PWuFCkw\nROqmwJC0lOr4RY1+/UILauXKuCsRKTwKDElLqY5f1GjaFI44QuMYIskoMCQtpd7CAHVLidRFgSEp\n27w5LGvet2/cleSXAkMkOQWGpOytt8JtWEv93td9+8KqVfDBB3FXIlJYFBiSslIfv6jRpAkceaRa\nGSK1KTAkZeUwflFD3VIi21JgSMrKpYUBWohQJBkFhqSsnFoYvXvD2rWwbFnclYgUDgWGpGTlSti4\nETp1iruSxmGmVoZIbVkFhpldb2YLzWy2mT1sZq2i7QPM7PWEx6kJx5xtZnOjY542s23ub2ZmXcxs\nnZnNih7js6lTsjdnTmhdWFr35ypuGscQ2Vq2LYxngd7u3g9YDIyMts8DDnL3A4ETgNvMrImZNQX+\nCBwZHTMP+GUd537b3ftHj4uzrFOyVE7jFzWOOirM+NYdgEWCrALD3ae4e3X0cjrQKdq+PmF7C6Dm\nec33053MzIBWwIo6Tl9G32ULXzmNX9To0SN0w737btyViBSGXI5hnA9MqnlhZgPNbD4wB7jI3avd\nfTNwMaFlsRzoCdxRx/m6Rt1RU81sUA7rlAyUYwvDTN1SIokaDAwzmxyNOdQ85kV/DknYZzSwyd3v\nq9nm7jPcvQ8wABhlZs3NrBkwDDjA3TsSgmNUkrddAXR29/7AZcB9ZrZjVr+pZOzrr2HJEujVK+5K\nGp8CQ+RbzRrawd2Pqe/nZjYUOBEYXMfxi8xsLdCHEFDu7kujHz8A/CbJMZuA1dHzWWb2DtAdmJXs\nPSorK795XlFRQUVFRX0lS5reeAO6d4fmzeOupPEddRRUVoZxjHIa8JfSU1VVRVWWyzCbZzGiZ2bH\nA+OAI9z9s4TtXYH33X2LmXUB/gnsD2wPvAbs7+6fmdk1QAt3v7zWeXcFVrl7tZl1A6YBfd19TZIa\nPJvfQRp2++3w4otw551xV9L43KFzZ3juuRCaIqXCzHD3tL4GNdjCaMAtQHNgchjDZnp0RdMgYISZ\nbSQMeA9z91VRkWOAF6OfLQOGRtuHEK6sqgSOAK5JOP7CZGEhjaMcxy9qJI5jKDCk3GXVwigEamHk\n3+GHw5gxMDhpp2PpmzgR/vEPuP/+uCsRyZ1MWhgKDKlXdTW0aRMGvXfZJe5q4rF0KRxyCHz4ocYx\npHRkEhhaGkTqtXQptGpVvmEB0LUr7LADLFwYdyUi8VJgSL3KefwikS6vFVFgSAPKcYZ3MjXLhIiU\nMwWG1EstjKAmMKqrG9xVpGQpMKReamEEe+4JrVuHSYwi5UqBIXVaswY++wz23jvuSgqDxjGk3Ckw\npE5z5kDfvtBE/0oABYaIPgqkThq/2NpRR8G0aRrHkPKlwJA61dxlT4L27aFdu/D3IlKOFBhSp9mz\n1cKoTd1SUs4UGJLUpk1hZnPfvnFXUlgUGFLOFBiS1KJF4VLSli3jrqSwVFSEpd43b467EpHGp8CQ\npDR+kVy7dtCpE7z+etyViDQ+BYYkpfGLummZEClXCgxJSi2MulVUaBxDypPuhyHbcIfddw/dLh07\nxl1N4fn00zD7/dNPYbvt4q5GJDO6H4bkxMqVITQ6dIi7ksK0667hHhkzZ8ZdiUjjUmDINmrGL3R3\nubrp8lopRwoM2YbGLxqmwJBypMCQbegKqYYdcQS88gps3Bh3JSKNR4Eh21ALo2Ft2kD37jBjRtyV\niDQeBYZsZd06WLYM9tsv7koKn7qlpNwoMGQr8+eHsNDlog1TYEi5UWDIVjR+kbrDDw9dUuvXx12J\nNDZ32LIl7ioanwJDtqLxi9S1agW9e8Orr8ZdiTQG9/AFYcQI6NEjLM75zjtxV9W4FBiyFbUw0qNu\nqcxs2RLuXvjxx3FXUr+aOi+5BDp3hnPPhaZN4W9/g6uvhuOOCxNdy4WWBpFvVFfDzjuHQe82beKu\npjj84x9w7bXhQ0VS8957cN55sGIFfPRRWFGgogKOPDI89tgj3vo2bIDnn4dHHoHHHw+rE592Wnj0\n6rX1vr/9LTz0UPjv37p1PPVmKpOlQRQY8o2334ajjw6BIalZuzZ8wH3yCbRoEXc1he9vf4NLL4Xh\nw+Hyy8O2OXPCB25VVbjXyG67bR0gjbGe2VdfwTPPhJB4+unQ1XjaafD978Nee9V9nHtofcybF47f\nYYf815orjR4YZnY9MATYALwD/MTdvzCzAcCEhF3HuPtj0TE/BEYRusOecveRdZx7JHA+sBm41N2f\nrWM/BUaOPPww3HVX+FYlqTv0UPiv/wph21i+/hpGjYIhQ2Dw4MZ730ytWQO//CW89hrcey8cdFDy\n/aqrw4dvTYC88EJo9SYGSOfOualp9Wp46qkQEs89B4ccEkLilFPC/dtTVV0N55wTLn548EFo1iw3\n9eVbHIHxb8Dz7l5tZtcB7u4jzWwHYGO0fQ9gDtAe2Bl4HTjQ3VeZ2UTgLnefWuu8PYH7gAFAJ2AK\nsG+yZFBg5M6VV4b1o665Ju5Kisvo0eHv7be/bZz3++qrEBQtWsAbb8DBB8N//3cYhC1E06aFLqiT\nToI//AG+853Uj62uhgULQnhMmxYeLVtuHSBdu6a+7tlHH4UvRI88Ai+/HML2tNPge9+Dtm0z+OUi\nGzeG/yadO8OECcWxDlujr1br7lPcvTp6OZ3w4Y67r0/Y3gKoed4NeMvdV0WvnwNOT3LqU4D73X2z\nuy8FFgMDs6lVGqYrpDLTmAPfX3wBxx8PXbrAE0+ED9P99gv/3X7/+9D/Xig2bgxXFJ19NowfD3/6\nU3phAdCkCfTpE1onDz4YPvCffhoGDgxdQIceGv4uzj0X7rgjdKvW/v64bBn88Y9hOZcePcJ/q5/+\nNIyhPPZYODabsABo3jy00OfMgf/8z+zOVchyNoZhZk8QPuTvi14PBP4CdAZ+7O6Pm9nOwFxgELAC\nuB/Yzt1PqXWuW4BXEs51O/C0uz+S5H3VwsiRzp3D/0x77x13JcVl3bpw69aVK2HHHfP3PmvWhLDo\n1y98ADdJ+Lq3ZAn8+7+HALn5ZjjhhPzVkYqFC0M3TadOcPvt4e8nH9xh8eKtWyDuoQWy114hVJYt\ng5NPDi2Jo4/O7zjDJ5+E+TnDhoWxmkKWSQujwd42M5sM7J64CXBgtLs/Ge0zGthU8wEP4O4zgD5m\n1gO4y8wmufsaMxsGPABsAV4Gsv54qqys/OZ5RUUFFRUV2Z6y7KxaFT6Q6hvgk+S+8x3o3x/++c9w\nmWU+rFoFxx4bvlHfdNO2XR7duoWulqefDoOwvXrBjTeG7Y3JPYTZ1VeHq8cuuCC/3TNmYU2v7t3h\n5z8P779kSQiQt98OXWCHH9544wq77RaunBs0KNw35ZxzGud9U1FVVUVVtvcWdvesHsBQ4J/A9vXs\n8xzQP8n2C4DrkmwfAfwm4fUzwMF1nNsle88/737YYXFXUbyuusr9N7/Jz7k//th9//3d/+M/3Kur\nG95//Xr3a691b9s21PXVV/mpq7YPP3Q/4QT3AQPcFy1qnPcsVPPnu7dr5z5pUtyV1C367Ezr8z6r\nMQwzOx64HDjZ3TckbO9qZk2j512AHsDS6PVu0Z9tgIuB25Oc+gngLDNrbmZ7AfsAWhc0jzR+kZ18\njWN8+GHoXjn5ZLj++tS+rW+/PYwcGSZhvvlmaG08+ui2ffu59PjjcOCB8N3vhpZW9+75e69i0Lt3\n+Ds/99wSWwkg3YTxrb/dLwaWAbOix/ho+4+A+dG214AhCcfcB7wR/fyMhO1DgMqE1yOBt4GFwLH1\n1JCvAC4r553nPmFC3FUUr6+/dt9xR/fPP8/dOZcvd+/e3f2aa7I7z3PPuffq5X7sse5vvpmb2mp8\n+aX7BRe477WX+0sv5fbcpeCpp9x33919wYK4K9kWGbQwsu6SivuhwMiNfv3cZ8yIu4ridtRR7n//\ne27OtXSpe7du7mPH5uZ8Gze6jxvnvssu7ldc4f7FF9mf89VX3ffd133o0NwGZam58073zp3d33sv\n7kq2lklgaC0pYeNGWLQoXL4omctVt9Q774T5BZdcAldckf35ICxXP3x4mBT34YfQs2eYdZ1JN9Xm\nzWGi4pAhYWB74sSwEKMkd+654Yqp446Dzz6Lu5rsKDCEN98Mk5+0tEV2chEYixaFMYsRI/JzWWb7\n9mE2//33w9ix4b3mzUv9+CVLwnyGF16AWbPgBz/IfY2laPjwELAnnRQmXhYrBYZohdocGTgwfOCv\nWZPZ8QsWhJnHY8bARRfltrbaBg2CmTPhhz8McxMuvbT+ut3hr38Ns8rPPDNcOtoYazyVkuuuCxcg\n/OAHsGlT3NVkRoEhukIqR5o3D+sRvfBC+sfOnRs+uMeOhfPPz31tyTRtChdfHIJq/frQTTVxYliO\nI9Fnn8EZZ8ANN4RVXH/9660nDUpqzMKyIc2bw09+su3fczHQf3ZRCyOHMumWmjkzTMq76Sb40Y/y\nU1d9dt0VbrsNnnwy/HnooWGRQIDJk8O/jS5dws2D+vZt/PpKSbNmoTtw2bLQTZXPS53zQcublzn3\nMDt1/vz470NQCl55JSwLMXt2avtPnx7mWEyYAKeemt/aUlFdHbqeRo0K3SeLF4fXjbkSbzlYvTpc\n2HD22WHOTBx0PwxJ2/LlYanpjz6Ku5LSsGkT7LILvPtu+LM+L70U1jeaODEMhhaSNWvCN+Ezz8x+\nYT5JbsUKOOywsNrxz37W+O/f6KvVSvHT+EVubbdd+BBo6A58U6eGm/Pcc0/hhQWEe1BcdJHCIp86\ndAgXD1x5ZVg1txgoMMqcxi9yr6FxjGefDVcnPfhgGLuQ8tW9e7iJ089/Xhy3+VVglDm1MHKvvsB4\n6qkwsP3oo2EOhMhBB4VJlGecEf5/LGQKjDKnFkbuHXhgGBv6+OOttz/6aLhxz5NPhm4rkRpHHx1u\nMHXiiWFyZKFSYJSxtWvhgw/CXcgkd5o1C/dgSOxi+N//DVdPTZoUJr+J1HbGGeFufcceW7gXoSgw\nyti8eWGyVrHctL6YJHZL3X13uBve5MnhRksidRk2DH7843Bnxc8/j7uabSkwypjGL/KnJjDuuCOs\nCzVliia9SWquuipMnrzwwrgr2ZbmYZSxiy4KN3r51a/irqT0VFeHCZEtW4awKPcbCkl6tmwJ3VId\nOuTvPTQPQ9KiFkb+NGkSBjGrqhQWkr6mTfMbFplSC6NMbdkCrVuHQe/WreOuRkQam1oYkrJ33oF2\n7RQWIpI6BUaZ0vwLEUmXAqNMafxCRNKlwChTamGISLoUGGVKLQwRSZcCowx9+mlYFqRLl7grEZFi\nosAoQ3PmhO4oS+uCOhEpdwqMMqTxCxHJhAKjDGn8QkQyocAoQ2phiEgmtDRImdmwAdq0gVWrYIcd\n4q5GROLS6EuDmNn1ZrbQzGab2cNm1iraPsDMXk94nJpwzA/NbI6ZzTOz39dx3i5mts7MZkWP8dnU\nKd9asAC6dVNYiEj6su2Sehbo7e79gMXAyGj7POAgdz8QOAG4zcyamFlb4HrgKHfvC+xhZkfVce63\n3b1/9Lg4yzolovELEclUVoHh7lPcvTp6OR3oFG1fn7C9BVDzvBvwlruvil4/B5xex+l10WceaPxC\nRDKVy0Hv84FJNS/MbKCZzQfmABdFAfI20MPMOptZM+BUYM86ztc16o6aamaDclhnWVMLQ0Qy1eDd\nnM1sMrB74ibAgdHu/mS0z2hgk7vfV7OTu88A+phZD+AuM5vk7mvMbBjwALAFeBnYO8nbrgA6u/tq\nM+sPPGZmvdx9bbIaKysrv3leUVFBRUVFQ79WWXJXC0OkXFVVVVFVVZXVObK+SsrMhgIXAIPdfUMd\n+zwHXO7us2ptvwDY291HNPAeU4HLah8f/UxXSaXovffgkENgxYq4KxGRuMVxldTxwOXAyYlhYWZd\nzaxp9LwL0ANYGr3eLfqzDXAxcHuS8+5qZk2i592AfYAl2dQqal2ISHYa7JJqwC1Ac2CyhYWJpkdX\nNA0CRpjZRsKA97CEge6bzOwAQrfWGHd/G8DMhhCurKoEjgCuSTj+Qndfk2WtZU/jFyKSDU3cKyOn\nnw5nnAFnnRV3JSISN93TW+qlFoaIZEMtjDLxxRfQvn34s2nTuKsRkbiphSF1mjcP+vRRWIhI5hQY\nZUJXSIlIthQYZULjFyKSLQVGmVALQ0SypUHvMrB5M7RuDStXwk47xV2NiBQCDXpLUosXhyukFBYi\nkg0FRhnQ+IWI5IICowxo/EJEckGBUQbUwhCRXFBglAG1MEQkFxQYJe6jj2DDBtizrvsaioikSIFR\n4ubMCa0L0x3SRSRLCowSp/ELEckVBUaJ0/iFiOSKAqPEqYUhIrmipUFK2Pr10KYNrFkD228fdzUi\nUki0NIhs5Y03oHt3hYWI5IYCo4S99JLGL0Qkd9QlVaIWLYJBg+CZZ+Cgg+KuRkQKjbqkBIC1a+G0\n0+DaaxUWIpI7amGUGHf40Y9gu+1g4kRN2BOR5DJpYTTLVzESj1tvhfnz4ZVXFBYikltqYZSQV1+F\nIUPg5Zdhn33irkZECpnGMMrYp5/CmWfChAkKCxHJD7UwSsCWLXDiiWFG99ixcVcjIsVALYwydc01\nYQnz3/0u7kpEpJRlFRhmdr2ZLTSz2Wb2sJm1qvXzzmb2pZkNT9jW38zmmtlbZvbHes490swWR+c/\nNps6S9mkSXD77XD//dBMlzCISB5l28J4Fujt7v2AxcDIWj8fBzxda9utwE/dvTvQ3cyOq31SM+sJ\nnAn0BE4Axpvpmp/ali6FoUNDWOyxR9zViEipyyow3H2Ku1dHL6cDnWp+ZmanAEuANxK27QHs5O7/\nijbdBZya5NSnAPe7+2Z3X0oIo4HZ1FpqNmyAM86AK66Aww+PuxoRKQe5HMM4H5gEYGYtgSuAMUBi\ny6AjsDzh9fJoW20dgfcTXn9Qx35l69e/hs6dYfjwhvcVEcmFBnu9zWwysHviJsCB0e7+ZLTPaGCT\nu98X7VMJ3Oju6xqjJ6mysvKb5xUVFVRUVOT9PeN0113w/PPwr39pcp6IpKaqqoqqqqqszpH1ZbVm\nNhS4ABjs7huibS/wbfdUG2ALcBXwCDDV3XtG+50FHOnuw2qdcwTg7j42ev0McLW7v5rk/fN2We3K\nlfD44/Czn0HTpnl5i7TNmweDB4fA6Ns37mpEpFg1+mW1ZnY8cDlwck1YALj7Ee7ezd27AX8ErnX3\n8e6+EvjczAZGg9jnAo8nOfUTwFlm1tzM9gL2AWZkU2smxo6F0aPhyCPh3Xcb+9239fnncPrpcOON\nCgsRaXzZjmHcAuwITDazWWY2PoVjfgHcAbwFLHb3ZwDMbIiZVQK4+wLgAWAB4Sqrixt7dt6aNXDn\nnTBzZlj5deDA8DquOYLucP75cPTRYXFBEZHGppnedfjDH2D2bLj33vB6zhw45xzo1Qv+53+gbduc\nv2W9xo0Ll8++9JLuoCci2dNM7xzZtAluvhkuu+zbbQccAK+9Bh07hudTpjRePS+8EALsoYcUFiIS\nHwVGEg88EBbw699/6+077BDGD/7ylzBh7rLLYP36/Nby4Ydw9tnw179Cly75fS8RkfooMGpxD90/\n9c1vOOaY0EW1dGkY25g3Lz+1bN4MZ50FF1wAxx+fn/cQEUmVAqOWadNg3To46aT699tll9BFNHx4\nuMz1xhuhurr+Y9I1ahS0aAFXXpnb84qIZEKD3rUMGQLf+x5ceGHqxyxZEq5catkydB11zMGc9Ecf\nDbO5Z86EXXfN/nwiIok06J2lN9+EGTPg3HPTO65btzAwfcQRYdzjoYeyq2Px4hBYDz6osBCRwqEW\nRoILLwyrvo4Zk/k5ZswIl98OGgQ33QStWjV8TKJ16+CQQ2DYsPAQEcmHTFoYCozIJ59A9+6hlbH7\n7g3vX5+1a8PYxpQpcPfdcNhhqR3nHq6+2rIlHKd1okQkX9QllYVbbw3LbmQbFgA77hjurX3jjeGc\nV14Z5nY05M9/DmMWt92msBCRwqMWBmEuRdeuYUG/Xr1yU1eNlSvDkh6ffgr33BNaMcm89hqccEKY\nyd2jR25rEBGpTS2MDN1zTxisznVYQBgT+fvfQ1fTYYeFlkftfPvsM/jBD0IrR2EhIoWq7FsY1dXQ\npw/ccktY2C+fFi4MA+KdOoX7cLdrF97/e9+Dnj3DhEERkcagFkYGnnkGmjcPk+/yrWdPmD4deveG\nfv1Cy+N3v4Mvv4Trrsv/+4uIZKPsWxhHHw3nnZf+3ItsTZsW3nPjxjDQ3aFD476/iJQ3XVabptmz\nwxIg774bWhmN7fPPYfXqMOAuItKYMgmMBu/pXcpuuAF+9at4wgKgdevwEBEpBmXbwvjgg3Cb03fe\ngTZt8lCYiEgB06B3Gm65JSwYqLAQEUlNWbYw1q4N4wYzZoSFA0VEyo1aGCmaOBGOPFJhISKSjrJr\nYWzZEpbnuPtuOPTQPBYmIlLA1MJIwWOPhRnWCgsRkfSUXWCMGweXXRZ3FSIixaesAuOVV8Lqsd//\nftyViIgUn7IKjHHj4NJLoWnTuCsRESk+ZTPovWQJDBgAS5fCTjvlvy4RkUKmQe963HQT/OxnCgsR\nkUyVRQtj9WrYe2+YOzfci0JEpNw1egvDzK43s4VmNtvMHjazVrV+3tnMvjSz4Qnb+pvZXDN7y8z+\nWMd5u5jZOjObFT3GZ1PnhAlhVVqFhYhI5rLtknoW6O3u/YDFwMhaPx8HPF1r263AT929O9DdzI6r\n49xvu3v/6HFxpgVu3BjWjRo+vOF9RUSkblkFhrtPcffq6OV04Jvv8GZ2CrAEeCNh2x7ATu7+r2jT\nXcCpdZw+raZSXR54IMzsPvDAXJxNRKR85XLQ+3xgEoCZtQSuAMaw9Qd/R2B5wuvl0bZkukbdUVPN\nbFAmBblrop6ISK40eAMlM5sM7J64CXBgtLs/Ge0zGtjk7vdF+1QCN7r7OrOMGgorgM7uvtrM+gOP\nmVkvd1+bzkmmToX16+GEEzIpQUREEjUYGO5+TH0/N7OhwInA4ITNBwOnm9n1QBtgi5mtBx4B9kzY\nrxPwQZL33ASsjp7PMrN3gO7ArGQ1VFZWfvO8oqKCiooKILQuhg+HJmVz8bCISHJVVVVUVVVldY6s\nLqs1s+MJA9tHuPtndexzNfClu98QvZ4OXAL8C/g7cLO7P1PrmF2BVe5ebWbdgGlAX3dfk+T8SS+r\nXbgQKirCRL0WLTL+FUVESlIcE/duAXYEJqdx+esvgDuAt4DFNWFhZkPMrDLa5whgrpnNAh4ALkwW\nFvW54QYvGh8FAAAGdElEQVQYNkxhISKSKyU5ce/jj6FHD1i0KCxlLiIiW9PSIJHx4+GMMxQWIiK5\nVHItjK+/DvfrnjYN9tsvvrpERAqZWhiEW68OGKCwEBHJtQYvqy0m1dVhsPvWW+OuRESk9JRUC2PS\npHBVVDQNQ0REcqikAqNmGZDMJpeLiEh9SmbQ+/XXYcgQePdd2G67uKsSESlsZT3oPW4cXHKJwkJE\nJF9KooXx/vvO/vuH+3bvvHPcFYmIFL6ybWHcfDP8+McKCxGRfCqJFkbbts5rr8Fee8VdjYhIcSjb\nFsbgwQoLEZF8K4nA0B31RETyryS6pIr9dxARaWxl2yUlIiL5p8AQEZGUKDBERCQlCgwREUmJAkNE\nRFKiwBARkZQoMEREJCUKDBERSYkCQ0REUqLAEBGRlCgwREQkJQoMERFJiQJDRERSosAQEZGUZBUY\nZna9mS00s9lm9rCZtar1885m9qWZDU/Y9lsze8/Mvmjg3CPNbHF0/mOzqVNERLKXbQvjWaC3u/cD\nFgMja/18HPB0rW1PAAPqO6mZ9QTOBHoCJwDjzSytdduLRVVVVdwlZEX1x6uY6y/m2qH4689EVoHh\n7lPcvTp6OR3oVPMzMzsFWAK8UeuYGe7+UQOnPgW43903u/tSQhgNzKbWQlXs/+hUf7yKuf5irh2K\nv/5M5HIM43xgEoCZtQSuAMYAmbQMOgLvJ7z+INomIiIxadbQDmY2Gdg9cRPgwGh3fzLaZzSwyd3v\ni/apBG5093VRT1JJdieJiJSTrO/pbWZDgQuAwe6+Idr2At92T7UBtgBXufv4hOO+cPdWJGFmIwB3\n97HR62eAq9391ST76obeIiIZSPee3lkFhpkdTxjYPsLdP6tjn6uBL939hlrbv3T3neo4phdwL3Aw\noStqMrCvZ5tuIiKSsWzHMG4BdgQmm9ksMxvf0AFmNtbM3gdaRJfXXhVtH2JmlQDuvgB4AFhAuMrq\nYoWFiEi8su6SEhGR8lDUM73N7Hgze9PM3jKz38RdTzrMrJOZPW9mb5jZPDO7JO6a0mVmTaKW5RNx\n15IuM2ttZg9GE0PfMLOD464pHWb272Y238zmmtm9ZtY87prqY2Z3mNlHZjY3YVsbM3vWzBaZ2T/M\nrHWcNdanjvrrnbhcSJLVn/Czy8ys2szaNnSeog0MM2sC/D/gOKA3cLaZ7RdvVWnZDAx3997A/wF+\nUWT1A1xK6DYsRjcBT7t7T+AAYGHM9aTMzDoAvwL6u/v+hKsdz4q3qgZNJPy/mmgEMMXdewDPs+3E\n30KSrP6GJi4XkmT1Y2adgGOAZamcpGgDgzCRb7G7L3P3TcD9hAl/RcHdV7r77Oj5WsIHVtHMNYn+\noZ0I3B53LemKvgke7u4TAaIJovUuVVOAmgItzawZ8B1gRcz11MvdXwJW19p8CnBn9PxO4NRGLSoN\nyeqvb+Jyoanj7x/gRuDyVM9TzIFRe3LfcoroAzeRmXUF+gHbXDZcwGr+oRXjINhewKdmNjHqUptg\nZi3iLipV7r6CcHXie4RJrWvcfUq8VWWkXc2qD+6+EmgXcz3Z+GbicrEws5OB9919XqrHFHNglAQz\n2xF4CLg0amkUPDM7CfgoaiEZxTcxsxnQH/iTu/cH1hG6R4qCme1M+HbeBegA7Ghm/zfeqnKiGL98\nJJu4XPCiL0ijgKsTNzd0XDEHxgdA54TXnaJtRSPqTngIuNvdH4+7njQcBpxsZkuAvwFHmdldMdeU\njuWEb1avRa8fIgRIsfg3YIm7r3L3LcAjwKEx15SJj8xsdwAz2wP4OOZ60hZNXD4RKLbA3hvoCswx\ns3cJn58zzazeVl4xB8a/gH3MrEt0hchZhJVwi8lfgAXuflPchaTD3Ue5e2d370b4e3/e3c+Nu65U\nRd0g75tZ92jT0RTX4P17wCFmtkO0ivPRFMegfe3W6BPA0Oj5eUChf2naqv5o4vLlwMk1q1wUuG/q\nd/f57r6Hu3dz970IX6IOdPd6Q7toAyP6ZvVLwpUKbxBWty2G/2kAMLPDgHOAwWb2etSXfnzcdZWR\nS4B7zWw24Sqpa2OuJ2XuPoPQKnodmEP4EJgQa1ENMLP7gJeB7tGE3Z8A1wHHmNkiQuhdF2eN9amj\n/rQnLseljvoTOSl0SWninoiIpKRoWxgiItK4FBgiIpISBYaIiKREgSEiIilRYIiISEoUGCIikhIF\nhoiIpESBISIiKfn/+VRr6z775yAAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x18cbcf60>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(tuc_bns.baseH)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 289,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "tuc_abs_ord = get_ord_abs_from_baselines(tuc_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 290,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "Mtuc, restuc, ranktuc, sigtuc = get_transform_from_abs_ords(tuc_abs_ord)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 291,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  9.83650649e-01,  -2.14063605e-01,   8.68640018e-02,\n",
-       "         -3.67288828e+03],\n",
-       "       [  2.12187743e-01,   8.89843829e-01,   2.22513804e-01,\n",
-       "         -1.02132303e+04],\n",
-       "       [ -1.15735139e-02,   3.83529111e-02,   8.93747080e-01,\n",
-       "          4.91693773e+03],\n",
-       "       [  0.00000000e+00,  -0.00000000e+00,  -0.00000000e+00,\n",
-       "          1.00000000e+00]])"
-      ]
-     },
-     "execution_count": 291,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Mtuc"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 292,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([  9.16746608e-01,   2.68686591e+00,   4.59761366e-01,\n",
-       "         1.91204373e-39])"
-      ]
-     },
-     "execution_count": 292,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "restuc"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 293,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "hezftucJan16 = factory.get_timeseries(observatory='TUC',\n",
-    "\n",
-    "        interval='minute',\n",
-    "\n",
-    "        type='variation',\n",
-    "\n",
-    "        channels=('H', 'E', 'Z', 'F'),\n",
-    "\n",
-    "        starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n",
-    "\n",
-    "        endtime=UTCDateTime('2016-01-31T23:59:59Z'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 294,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "tucJan16adj = make_adjusted_from_transform_and_raw(Mtuc,hezftucJan16)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 295,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "tuch_pqqm = np.mean(tuc_abs_ord.absp1[0] - tuc_abs_ord.ordp1[0])\n",
-    "\n",
-    "tuce_pqqm = np.mean(tuc_abs_ord.absp1[1] - tuc_abs_ord.ordp1[1])\n",
-    "\n",
-    "tucz_pqqm = np.mean(tuc_abs_ord.absp1[2] - tuc_abs_ord.ordp1[2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 296,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-30, 30)"
-      ]
-     },
-     "execution_count": 296,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl8VOW9+PHPMzOZ7AkJgQAJm+yLssqqErQutCq2Vq/X\n1opt7aq2/WmvS1uFtrdae73WYvW2dlHrvlRFbRXUBFCJIEsA2WULWxJCyL5MZp7fH3PO4ZzJJDOQ\nmSTo9/165ZXJyZNznpw55/k+6xmltUYIIYQwubo7A0IIIXoWCQxCCCEcJDAIIYRwkMAghBDCQQKD\nEEIIBwkMQgghHDodGJRSiUqpj5RS65VSm5RS9xjbs5RSS5VS25VSbyulMjufXSGEEPGmYrGOQSmV\norVuUEq5gQ+AW4ArgUqt9f1KqduBLK31HZ0+mBBCiLiKSVeS1rrBeJkIeAANzAeeMLY/AVwRi2MJ\nIYSIr5gEBqWUSym1HjgCLNNarwFytdZlAFrrI0DfWBxLCCFEfMWqxRDQWk8C8oFpSqlxBFsNjmSx\nOJYQQoj48sRyZ1rrGqVUEXAJUKaUytValyml+gHl4f5GKSUBQwghToHWWsVjv7GYlZRjzjhSSiUD\nFwJbgSXAAiPZ9cBr7e1Da93jvu65555uz4PkSfL0ecxXuDwBXHDrrQBc9NOf9og8dfdXPMWixdAf\neEIp5SIYaJ7XWv9LKVUMvKCU+iawD7g6BscSQggRZ50ODFrrTcDkMNuPAV/o7P6FEEJ0LVn53I6C\ngoLuzkIbkqfoSJ6i1xPzJXnqfjFZ4NapDCiluzsPQoieTSnFBbfeyrsPPMBFP/0pb99/f3dnqdsp\npdA9dfBZCCHEZ4sEBiGEEA4SGIQQQjhIYBBCCOEggUEIIYSDBAYhhBAOEhiEEEI4SGAQQgjhIIFB\nCCGEgwQGIcRpQam4LPIVYUhgEEKcHiQwdBkJDEKI04M8U63LSGAQQgjhIIFBCCGEgwQGIYQQDhIY\nhBBCOEhgEEII4SCBQQghhIMEBiGEEA4SGIQQQjhIYBBCCOEggUEIIYSDBAYhhBAOEhiEEEI4SGAQ\nQgjhIIFBCCGEQ6cDg1IqXyn1nlLqE6XUJqXULcb2LKXUUqXUdqXU20qpzM5nVwjxeSUf1NN1YtFi\naAX+n9Z6HDAT+KFSajRwB/CO1noU8B5wZwyOJYQQIs46HRi01ke01huM13XAViAfmA88YSR7Arii\ns8cSQnx+afmgni4T0zEGpdQQYCJQDORqrcsgGDyAvrE8lhBCiPiIWWBQSqUBLwE/MloOoeFdwr0Q\nQpwGPLHYiVLKQzAo/ENr/ZqxuUwplau1LlNK9QPK2/v7hQsXWq8LCgooKCiIRbaEEOIzo6ioiKKi\noi45VkwCA/A3YIvW+iHbtiXAAuC3wPXAa2H+DnAGBiGEEG2FVpoXLVoUt2N1OjAopWYDXwM2KaXW\nE+wyuotgQHhBKfVNYB9wdWePJYQQIv46HRi01h8A7nZ+/YXO7l8IIUTXkpXPQgghHCQwCCGEcJDA\nIIQQwkECgxBCCAcJDEKI04I8Qq/rSGAQQgjhIIFBCHFakMdudx0JDEIIIRwkMAghhHCQwCCEEMJB\nAoMQ4rQgH9TTdSQwCCGEcJDAIIQQwkECgxBCCAcJDEIIIRwkMAghhHCQwCCEEMJBAoMQQggHCQxC\nCCEcJDAIIU4L8gi9riOBQQghhIMEBiGEEA4SGIQQpwf5PIYuI4FBCCGEgwQGIYQQDhIYhBBCOEhg\nEEII4SCBQQhxepAP6ukyEhiEEEI4xCQwKKX+qpQqU0pttG3LUkotVUptV0q9rZTKjMWxhBBCxFes\nWgx/By4O2XYH8I7WehTwHnBnjI4lhBAijmISGLTW7wNVIZvnA08Yr58ArojFsYQQQsRXPMcY+mqt\nywC01keAvnE8lhBCiBjxdOGx2p1SsHDhQut1QUEBBQUFXZAdIYQ4fRQVFVFUVNQlx4pnYChTSuVq\nrcuUUv2A8vYS2gODEEKItkIrzYsWLYrbsWLZlaRwPjJ9CbDAeH098FoMjyWEECJOYjVd9RngQ2Ck\nUmq/UuoG4D7gQqXUduAC42chhBA9XEy6krTW17bzqy/EYv9CCKHksdtdRlY+CyGEcJDAIIQQwkEC\ngxBCCAcJDEIIIRwkMAghhHCQwCCEOK1o+VyGuJPAIIQ4LZgBQQJD/ElgEEKcVnQg0N1Z+MyTwCCE\nOL1IiyHuJDAIIU4r0pUUfxIYhBCnBfOBGBIY4k8CgxDitBKQMYa4k8AghDitSIsh/iQwCCFOKxIY\n4k8CgxDitCJdSfEngUEIcXowPo9BWgzxJ4FBtOuYz4cqKqLJ7+/urAhhkcAQfxIYPocmrFnDVzZv\njphuT1MTAP86dixi2gNNTZS1tERMp7Xmz4cORc5kD3G0pYXq1tbuzkZMLT12jJ9++ml3ZyNqoYFA\nAkP89ajAoLXmV3v3dnc2PvM21tfzytGjEdM1G3250dyGo1avZvratRHTbWto4Ls7dvD9HTui2Gv3\n6/Phh/R6//3uzkZM/W9pKf9TWtpme3VrK0eam7shR+3b09iIa/lyxzZ5JEb89ajA0KI1d+/daxVI\np5PLNm1CFRXFdJ+b6upQRUW0RnE+Zq5bx1WffBLT4y80gnSVzxcxbUMgwL4oCpXf7N8PwP+dRq2G\nfl5vd2fhlAW05vYoWwdXbt5M/1Wr4pyjk1N4/Hibbe1VVDbV1fF+mPRdZfa6ddy6a1dUaYuqqqK6\nr7tLjwoMfqOJ2HAa9mm/UVkZddq7du9mZRQX8BdKSgB4oqwsYtrimhpeqqiIOg/R2FJfD0B1DN+P\np4z/5XsDBkRMu6amhpw41dZbTuKmzHC745KHaNz+6aeooqJT7j5pDAS4v7Q07PUWWjC928lC9ZjP\nx66GhojpWgIBVFERjVFcVxvq6qzX9UaXZnuzks76+GPO3bAhytzG3oc1NfzvgQMR0/m1Zm5JCd/p\nwa3mHhkYmnpIJNVac/POnTHv07x3/37Os13AK9q5IcuNmvqrUXT7AJxM8dU3ISFimkt79wagPg6B\nOpoWw/Ljx6lsbeV4FC2Wk6GKikhcsSLq9BkeT1Tp5m/axOEYd8VsMoLzZuO73btVVQQiXJs+4156\noaKiTUt8f4zzOrS4mBGrV0dMZ/5PX42ihbuuttZ6fczo/orF/Vjp88VlUkVOFPeVWYkck5IS8+PH\nSo8JDEdbWlhn1A4ae0hgWFNby8MHD1IVw8FH80YdbVwUjX4/czZsCDvAeXNeHgBzMjOj2neSK/q3\n061UxDQrq6uDeYzh+zE8OTnqtM+VlwOQ9cEHEdPub2riYIwLOrPl6o3iXNX7/SyprIz52Mm7VVUA\n7DMmAth9oaSE1TU11s8HwqTxGYXowwcPcsO2bQC8bezzWeP8dmRLfb3jGB2pibKgfcVo2c6O4rr+\nwHZs1cF01ZPtfs754AOu2rLlpP4mGr2iqEQsMSp6R6Oo8Cw/fpyHomiFxFqPCQzT162jwKhFn0pB\n9JdDh1gc4xO4w2gWH4lito3JH6E2U2FcDJPS0oATF/QPwhQoCsh0u6mMEJjMGyWas+Y7iXM7KS2N\nKWlpEbv2jp1Ejf4LWVlc1adPVC2WtUZF4dERIyKmHVxcTP5J9o9HqnkeMAJNNAWeWbOdkp4e1XH3\nNDZGkUOYauyvvW5C+70ysLiYO3fvdvzeZ/sfny0v55e2yR0/37Mn4vHHrVnD9HXr2mwPvc5Ppvv3\nv41xpp1RdDsBzO3VK/iig8BQY9wjmSfR7Rc53MOgVauiGjs0uyajqUTMyMgAiKrC+eXNm/lxlOMW\nsdRjAsNuW23H7Eq6eedOvrhxY1R/f+OOHdwS4xNoFkxbwjTjQ6W73biA48ab3V4zdadRIJgBwvxf\nw9Ue/nDwINV+P/cZN1J76v1+EpSiJRCIGJi2GjdjNN1DT5eXU+v3UxshMJr/syeKm6IlECA/MTGq\nJveE1FQgtmMcltraiF1Upc3NDElKsgqdjhw2zlE0tcBLNm7kjI8+ijior7VmVU0NnvnzKbr++rYJ\nmpo4f8UKfLb9hF4roS2N56NoJZis6cd79tBqOweb6urwhMwUOt7SAq+9Bi0tPP300x3v+NgxmDuX\nx8eO5ayzzoqYj+qXXw6+MCtAYSo3Zuu21u+PeA+UGuckmkL8i9nZABHHQ8z3vSGKitdRn49hSUlR\nVahi2VtxMnpMYLAz34SHDx7k31HMoY+X3xstEG+ELpqa1lYCWjMoKYkK42ZKXrky7NQ/80Y1b7qm\nDqaEzu/dm6v69OGavn07PH5layu5Xi/pHo9VSLfHHMyr+Z//4VCEfn4XcPiqq/j7+PEdplty9Cho\nTWtBAZu3bu0wbYvW1G7axOEnn3Rsn71uXZtCbExqKtmPP84dgwd3uM/WQADq6mDuXI5GGI9p9Pvx\nVlfD5ZeTnZjYYauhtKkJ/fvfs3/WLK699toO9/ut7dvB7+eVm26izjZgGs7SsjJYsIA//N//0dJO\n0NVa86P770fPnUtrTQ371qxBKeX4Yt48uOIKvF5v8Oe5c2HuXL773e9a+5m1fn3whd8P3/gGW6ZP\nt9Ixdy5KKbTWFFVVwapVsHw53/nOd1BK0S8xMZjum98kISGBFKP7c0djIzQ10dTUxLhx41BKkZeS\nAr//PVx8MV//+tetPL788sts27aNefPmncj3lVda+du0aZO1feLEicyfP5/8/PwTaefOZd2vfw1A\nqlFIlzz1lPX7GTNmMHLkSG6bM4e0u+8mMHcuHperw/e11u+H6mrevuqqDt8ngFSjBRKp16DC5yM3\nIYHSr3+dmghdbxU+H94lS9izZk3E45uiGaiPKa11t34Fs6A1hYXW17LKSq211m7j52hQWKhVlGm/\nu22bPtDUFNU+01es0E8ePtxhuu319Xp4cbGmsFD/cPt2629LamvbpP3dvn16VHGx7vP++zoQCOit\ndXWawkI9d/36Nmnnrl+vCcYM3dLSorXW+sH9+/WglSt1IBDQ7777rn7mmWf0w0VFVjpAb9q0SWut\ndSAQ0A0NDbq1tdX6OfU3v3GkDQQCYf+n1tZWPfCvf7XSvfzyy+3+/0uPHnXssyO8+qqVbu3atSe2\nFxbq58rKHGnPfe01K+1DDz3U7j5XHTzoOH5jY2ObNE1NTbqwsNCRzvzy+Xx6165d+rbbbtOAfuGF\nF3ReXl7YtOb5CgQCurS0VK9YsUK/8cYbbdLV19db5/Gxxx4Lu69ov6Zdfrn+ou1cOL4uuKDdv5s3\nb55m0qROHRvQGOcl4teUKRrQ02fMiJj2stWrNYWF+tZbb406H9O/9rXO/y9hvurq6tpcL4FAQB84\ncEBnjx0b8V7RWutXdu+O6h6oq6tzpPvRj37UblqttWbq1Hb3aWyLT7kcrx1HnQHQzX6/IzAsqagI\n/uNRBgZ/IKApLNSZK1ZETLuyqkpTWKjzP/wwYtoRxcX6gvXr9eLS0g7T/WL3bs1772lAL3ztNV3W\n3KwpLNQ7jcLBjsJCzYABzovz3HP1pBdf1MuWLYvLhd9VX94JEzSgs7KydE1Nja6oqNBLlizp8OY3\ngxZvvqn/vnevXrx48Skf33PddVGnnfm3v+lp8+ZFlTalnYAS7mvE449Hle6Cjz7Sfygp6TDNfRs3\nagoL9YqqKj3LFkSt68j4qmtt1W8sXap5+WWdtXJl+/u8/Xbrb/62d6+es26dfuihh5xpnnlGT165\nUl+0YYO+d+9e/V+7dukRxcW6uLpa33HHHSfS5eRopk/XNTU1WmutZ61da+17dXW1lc8thw7pQ4cO\nWYXqkeZm3ff99/W9e/dGvLf3NjbqgR9+qF8sK9OAnnbttZpx4/RXn31Wa6213+/XPp9PNzQ06CdK\nS/Wc5cv1pDVr9APPPRfVe5CzaNEpXWc333yzzsrKCv/73r3jeo/ZgxPELzDEvStJKXWJUmqbUmqH\nUur2cGlC+/CbAgFrUDbBaOp2pM7vx6sU1X5/xEUjN2zfDsAF5oBWB2r9ft6dNImbBw5s0+TXWtPY\n2Mjvfvc73v/FL+D88wFYOH8+uUYTfM3Gjbzzzjv86le/cjSNCe3CWbmS9VddxYUXXtgmDwNGj2bU\n8887N555JrfddhurVq3i9ttv544XXuCajRuZV1LC6xUVPProo8yYMYMFCxbw/PPPc8cdd1h/es2r\nr/Lg/v2csWoVr0SxRmB5VRWz167luuuu6zDdxS+/zNA//Ynp551HVVUVGRkZ9OnTh8svv5wHHnjA\nuc9t23D/8Y8AeDye4Hn50pe4YcgQbr75Zkfa+SUl/KexnqMjM4qLcX/rWxxup+tx0KBBbNu2jd/t\n28ePduxg9MyZ3PiXv3DgwAEWLVrE2rVrrZvivvvuo7y8nHklJSypqKCX10tpYyNltvUkBQUFvPTS\nS1RWVlJZWcm0NWt49sgRGkeOpLm5Ga+xKO7SSy+ltrbW2nd9aysUFjKpd28aevU6UUEqLLS+1lRX\no7VmaG4uAAMSE/mwg+6JpkCAs+fMgexshiUno7UmEAhQcugQvPceLFsW3Pcll1h/M6pXL2r9fm65\n5RbHsenfn3WtrSytqqLo+HHS3W4GG+Ms9957L1prbtmxA158Ee67j91KkbxiBVuMsas5mZnUGd0e\nm+vqGLt9O/369UMpRZPfz+b6enp5PJzXqxfTQwbqdzY0ONZbrK2tpbS5mQnGRI3QbleXy4XH4yE5\nOZnrd+1ieSBAtsfDmRde2GGh99t9+7hx2zaa586looO1P1ddcw28+y69Vq7kW4sXW9sXL15MlTGz\ny+67mzeT8cor7e7PNKmoiLcrK0mPZsp0YiIF69cz4+KLrf+5K0Q3QfsUKaVcwMPABcAhYI1S6jWt\n9TZ7ul0hMzQaAwGOtLSQ6XbTojX1fj9pHUwDK21upsWIcmZ/e3vOSEpiV2Mjr/zkJ4wuKHAUmnYt\nfj9HZs+2fk5MTIz073Lu7bez8re/tX6+dubMdtOeuXo1T44ezdK33uL2bdu4Mj2dJ268EZfLxT//\n+U/OvfJKBhcX89SECZxfUmIFx6s/+YQXKyr4wfTpLKmsZPyPf8wfDx2iprGRnIQESurr+dn3vsf1\nN97I0qoqfr1vH89eein33XcfAHvy8znP5Qre8OPGobWmtrWVAatWMSo5mY+nTgWMqYEjRpDmdlMf\nCPDkk0/y5JNP8vrRo1xuPGdJFxQE0xYV8TYw1eNh8euvM7ChgTvvuosDs2bx9NVXU3nwIENHjiR5\n5UoApg0fjuvIEd5Ytox5tmA4+UtfYukTT9CrVy9eOHqUa7dupcrv5zWjoAxnxfHjvHr0KFsbGsjx\n+/EZBWOoT+rr2dvcTOGBA8zLzqa0uZkKn4+8wYO5++67HWlvvz1Yfzmwdy8DExPJ8Hio8fsZ27dv\nu/ko276d0SkpHG9txev10tzO1NlKn488r5c8r9cx4cLu7HXrKJ48mYDWXNq7N/2N61lrbU3ZBHhm\nzJjgOfL5SDAKDHOgXCnF9w8fDs7kCbl3JqSmku3xsM5YWd+et6uquCQ7mwy325qZVd7Swh8OHrTS\nTPz4YwDGpaQwJjWVer+f/c3NfNrYyJnG78xHWtzYvz+PHT7MtPR0MtzuYF8/wZlyXlshaV5X5hhf\nutHPb58wEXoubsrLY3djIylud8QB29t37+b8Xr1o8PvJzM5u9z3dWl/Pi2vWcEO/fmRfein6ppsI\nBAL4/f4TFRrDwj170EBadTVbmprI66C8GFpczBlJSTQGArT4/dZ7V+Xzcc2WLSytquI/+vTha7m5\nXL55MzkJCXz1ySfJXLeOxYsXt/nf4yHe4WcasFNrvU9r7QOeA+aHJgqd+9sUCLChro5qv5+chARr\nBk97zjUG2DIffpg9IYOPfr+fyspKamtr+fKXv8zSiRNh7lxq3n6bO++8E6UUN910U5uBvUQjTwnp\n6SQ89libY9544408/PDD+P1+vEZt64LvfY9f7N7N+1VV8MYb8L//y/mFhWzdupU/l5by5tGjfGXT\nJl4sK6Of18uRlhZeHz4czj2XlydOJG3NGvLWrmXnrFnW9L/pxtQ2swB/0ajhnPHRR/x41y6u27aN\n4poaRqak0Ko1a43aacrKlVyxeTMf19byR1sL5aPaWlLcblLdbuqNFlbG++9T5/dbs7DsUlwuKy9N\nfr8VFCD4bJ3/sC1S+ri2lq0NDfTr14+nrr+ed4YPJ3fdOoaPGuUYFE9yu8lwu5l63nlorfnq5s1Q\nWMi6224jOzsbXC6uNQaxf5Kf75iCeKS5mUV791oLu+Zs2MCDBw7w1rFjZCUkOI5T2tRkTQsdv2YN\nl2zcyL+OHaOv10sf23Wltea9qipUUZFVUGqt2d7QQH5iIplud4cP0qvy+djX3Myw5GQaIrRaV1RX\nc7ClhQGJiRwygkeRUfvcO2OGlW7GunX859atvFFZSYrbTaZR4Jl5THa5+EJWFgAjVq+mwe8nw5bP\nlkCg3VZGSX09KWGmdb4eZpLBrMxM0t1uK+Dkfvhh2H2uratjZkYGrxw9yoJt21iwbVubNI8dPgzA\n6tpa0j0eq5UROsW2JRAgoDWZHg8DExNJDykfXq6owLV8uaNAT3G5OK9XL7I8HiswfNrYiCoqCvve\nZXo89GpnssbRlhZqW1s50NzMBb16kZeYaE0Zd7lcJCQktCmYj/p89E5IIM3ttlpM7Tnq89HX6yXT\n7baO7wsEWLR3L0uNayHF7WZpVRUepejt8VDp83HJJZfw5ptvxj0oQJxbDEAeYH9a1wGCwcJhb1MT\nX8rO5k2jG6AxEEARXHlbWlvLodpahoZZGHXs2DFuuOEGqpYsAaAKmGlObYugz/z5VLz2GgB/NLo1\n7IaPG0f6ww9zx9ixvFhRwYvGRTisuJgnx4xxLM5pAfp7vWS43exragpOWUtNhUmTeA8YNWoUY5Yv\nJ83tZmZGBukeD7leL2U+H+8b0+xMVa2tLNq3j8tycpiclkaK202Sy0WjUYC3Z0G/fmypr6e4pobK\nkED6+5D1HfOys3muvJwav5/bQp6js/jAAWvab4bHQ6rbbU3BM2v8JvvD5SpmzeKaLVvIcLvRWpNu\nq7mtq6trUwNJN2rhm6qqHHP0sz/4gFbbDT8wKYkzjPf+QFMTA4uLgeBznIYkJTnzYysUPqyuZrY5\nIyfE2enpNAYClNTXt6mtAty4fTt/MQqx3gkJZLZTgGiteeLIEat7Mt3jsVoX2S4X39q2jRXV1Xw8\nZQqZRuGW6nIxKS2NAV4vh4zuyblGV9ngkP/Hrn9iolUpgOA9km2b8tsYCNDP66XUCDaLbOsVNkyd\nyoHmZi7dtMnaNiCkVf3wiBFcmpPDW2edxSW2KeIjk5Ot/yncAjq7Or+fh0eMYFV1tdUayvN6eXX8\neM4OWQthtgLeP36cB4zrs2TqVCZ8/LFjVfr1ubmkGDXq0Hq9a/ly6s89lxS3m/tLS/l6bi4DvF5r\nGvDwjz4CTlynJVOnMsqYWfXQ8OGcX1JCVWsrfULOxejVqxmVksKOxkampKWRk5DgeDRHOIdbWpjT\nq5cjMNT7/fxs925+OXSotXq+we+nJRAg3e0mOyGBqtZWchIS2lyDfz9yhO/278+t+fl4lIpqGnQs\nxTswROXGu+4CoKBXL9Zs28axRx6horaWN4w5zucAl112GU899RSPv/IKP1qwIOx+Jtx1FyW/+U2b\n7Qt+9zt+/uUv07tPH7KMCzQjKYnyV18FoLKpibUNDdZiouyEBF6uqOCpsjI8SlkFl1mbPGf9eq7I\nyeEVo4Y1PjWVuwcP5vnycl4+epRZISs6zaZ0nd/PsqoqfjF4MP28Xg43NzMoMTHsownKW1qsGz/b\nqDGY/HPm4A6ZR56oFD/fs4cAwSeYTk9Pp3jKFP573z5rIZMnJ4dWoK/XS5LLRYXPx0NGt0D1OeeQ\n+f77jrUgNa2tpLjdbRYv9fZ42iy6652QQF+vl4ZAgJK6OkdzfoatUDALJLMr4b/37XPsJ7QATnW5\nrC4EMyiY9toKqmfHjOHp8nLr78MFhefHjuXLOTkkuFxsbWigoqXF8Ujx63Jz+UdZmRUUINgl099o\n3dW1tpIeYVzGbF08cvAgfztyBDhRMJ2VmsqktDSGJCU5WgwAucZ7PS87O+wU7W0NDXxvx47g+TAC\ntX31emlzM70TEvi0sRFfIGAVzF/PzWVCWppVWC0ePpyv9umDx9ZX3c/rtVofFxtTQk29EhJYbFwj\nLxhrIP4yahRjUlLanOMf5uXx+JEjPF1ezrV9+7Kqpobt06eT6nbz8ZQpTF27lilpaXw8darV4jML\n+0dGjOCstDSSjUqQ6St9+lg15IDWJLlc/HrYMG4zfp9qq6zsbWpiXEoKVa2tYVdCTzC6tiBY4cj2\neDjm8wUHW4376eERI6hsbbVaWyuqq7kpL8+6/wqrqoIBZfZsetkC8ytHj3LbwIGkud3UtraitSbN\nyNtDBw9SOGECBVlZfGf7dlqMrqAs4/iPh1QOTSX19fxs0CB2NDayv6mJoqIiimL8oM72xLsr6SAw\nyPZzvrHN6dpr4YknKHroIerffptfDhvGf48d60jy+uuvk5mZ6QgKkydP5m87dnDeunX86eBBZnzj\nGzxy4IA1yHTc54PCQh6fOpVhw4bxgfHmHp4503oOEUBOcTEXb9zI0OJizjYeHX2wuZk8r5czkpLo\nY1wA/W01C/P5RRUtLTQHAoxNTbVaEeYism/16xf2pOxtaiLL4+Hne/awv7k57JvwxU2beMdoVuZ6\nvVazu/acc3ApxXsTJjjSX9K7NzunTycnIYE/HjpkLSC7zhjABLAXuUd9PmvQ/6+jRpHh8VA+a5ZV\nkzOlGAWz2Wz/YNIk3p80icuN5ygBBObMQSlldTvVdtCUNrsNMzwealpbrRouwMVG4WRndnnZnwnU\neO65jjTPjhnD5Tk5bboGens8VMyaxZLx49k/YwZX9+1r9eeaXUn/rKggNyEBXVDAk2PGcMA2LuSf\nMwcIDv4eamnh1yFBzG6iMUBqti6er6hgcloateecY6XZWF/PE2VlvHL0KP29Xg63tPBro2b/rHG9\nvzxuHF/fvb4fAAAY0ElEQVS3vWf7jO6lYUZr4pGRI9l89tmkhgxCXrF5MykuFxkeD7V+v1WQmY9e\nmZ2ZSemMGdyUnx9cn2BzpKWlw8epmIPE5r6+1b+/o/JzlrEQMdXt5htG3p8pL+e18eOtdQBT0tPR\nBQXWGJbLKOxfMCpdV/XpA8Dxc86h8dxz0QUF6IICLs/JsY6T4naT4HKR4nZz3HZeTcsnTiTLqIWb\n45aBOXOs9zGUWWOfbHtc/E07dzrSHJg5k5yEBKvGfr7Rusv64AM21tWxrrY2+JBDIC8x0Qps9soF\nBFuFVT4fT9sWGJrH/6bR4mydM4cd0050qBTX1DA6JYXeCQlUtrZSUFDAwoULra94indgWAMMV0oN\nVkp5gWuAJW1SGSPuDnfdxd0lJfxw+3b+UFrK3//+d9xZWfDoo9YMinUPPMA3Dx7kg+pq+iYkON5A\nVVTk6OpoDgSspnSu10tzIECT3+9YCVrj97O7qQlVVMSPdu3ij4cOkZ2QQKLLRUBrDre0cGDmTFZP\nngzA2NWr6fvhh+xsbGRIUhJfNS5u83HVDw4fTtmsWW3+ta/n5vLXw4cxi89/jh9P+axZVq3NZN5w\n/b1ePjYeuWAOwp9lFER2eYmJHPX5eK683OqWG5SUdKLv2FbD/FJ2tvUIjVnGOEYfr9dZqCtFkstF\ns9bWeZ2RkcHo1FS+3b8/ECykzRqd2e1kDlQ+PWYMa6dMceTRLIDSjBbDDuMGzvZ4+LdtFez1ubnM\nyMgg1e3mSEuLFUACc+aQ5HZTZUwM+PPIkVyTm0uK2211Jf3RqOGWz55NjtfLZTk5DAzppjEDQ5LL\nxY/z8x3n8Bu5ufw4P98qvAZ4vRxqbqbM52PRkCHW4ChA03nn4VHKKjQ31tfz2OHDKODPo0aR5vFY\nhZypdMYMko2JFWb30DTjPUh2u63FTLqggEFGvm8wzve41FTGpaZSd955APxs0Il6V4oxdlPd2mot\noDzXVoDnd9BVNcgWLIonT+Y3Q4daT8C9e8gQAP525IhjxfovBg9m0ZAhjtq5fTzAHB/ryMPGe5Vj\nVLq8LhdJ7T3WwlY5yDTO64VZWXiUosaoMJljDNsaGpjfuzdKKVxKoQsKWDtlCpdkZ9NgVCyyPB6O\ntbbSqjUepdg2zdnLrQsKyA4pVwB+aJyXCR9/zBRbUBmUlGR1+7ZozXf790cXFPC7M84Agt2kEAwA\n9uNDsKXiVsoatDavp2HJyWRFsXA11uIaGLTWfuAmYCnwCfCc1rrt0tjJkynZuDE44FdfHyz4L7wQ\nV0YGZT4fr1dWsmDBAvz//CeMHt3mz/0EBz77eL1U+Hw8E+Yx1Um2PjylFH29XsqNWQADExP5eZjV\ntU+NGWPVQrcbNfa8xETONi74rbZnvaS63Qyw3Vy/GDyYdGOAK5RSil8NHWr9nJ+YSB+vt83TFjca\nNfqPa2vbPNcm0+22WjKmRFutzz6QealZ61KKTUaNbXhyMqtrakh3uxltBKBQCeYUW4IPc5udkWEV\nlmbLwn7MFKN2b95E52RmWv//22a3oFFQpRg30LXGqu5Xx48/EWBcLh4fM4ZVkyeT6nbjJ1irnZKW\nZqUxj5tpO79ZHg9VPh+tWjMyOdnKazjmtfLnw4dZbXuCJ8ATY8bw4PDh1s9mi2Fzfb0VvM3CPtHl\n4tDMmTxuXJcDvF76eb3sbGxs835uOfts3j7rLEcBbb7Hybbz+H8jR/LSuHGOvy02ujZGh+xzmG3s\nzWwxnPHRR9Z+z4tiWjbgGNCcnpHBnYMH8+jIkQBcZKuwZNnO9y+HDuXuIUPafbZZR7MDAa6wtQZO\n1dIJE/DNmWMFJPMaeK68nOSQADM5PZ1/n3WWtT3bSJvt8fDvM89kVEqKI4CbshMS2NPUxLeMAfX7\nhw1j1aRJ1u83TJ1q/V2y201TIMCrR4+SZhznJwMHWmnXTJ5sdQGax89NSLDORYrbjS4osIKyUsox\nqaCrxH1SrNb6La31KK31CK31fWETPfAAZ515JgAjbRf+pb17MyQpyXqYmemlceMYGNIcrjQGcY76\nfHzNmNHyWyNS2/3bOM6B5mbr8b+vjh/Pr4YOpW9CAj8fPJh/jB6NLijga7m5pBl97PuamjjfdpP9\ny9gPnKgB2Pt8zQHfBNu2I7NmWReQ2XSG4AUCcO8ZZ/CYcTOa+YJg/z1g1dIBPC4X5Uat+a5B9t66\noNDzA4BSjDdaGuNTU1lRXd2m2+cJW+C1P/voP7dudQzAmY8JsRcoKS4XDYEAFS0tjElJYWBiohUY\nzBreQ8YD8cyxC7OrwTxP41NTHQVGostFqsvFPXv2OGZNecMEBgg+E7+2tdVqvbUn1e22HqL2u2HD\nOkzb3+tlX1MTW+rrrVacXR+v1wpU1/frx6eNjfTzetvM/BmTmspFIX34AGNTUhxBLMfr5cqQ/P/K\nqLWnhuzTPgCd7HY7zsdDtuDWGfbxiMlhWqrf7t+f79iuTV1QQKCd7hu7GVG0KE5WWUsL7x4/zksV\nFeHvAZt0j4fS5mZWVFe3CbhPjRljvTaD4d+OHGFcSgopbjczMjOte3WC7ZyYXUmNfr/Vveg2Wiy6\noICptv85OyGBwy0tHGttdXRTA1xm66rtnZDQZkJJvPWIwedQ9qh9uLmZT+rrHVPTvpKTw5V9+jjm\nYD86ciRLjx2jwufjqj59uLJPH/6jb1/+yyg0zbSXGCc80+22upbMN7DMtm7B5FKKdLebTfX1jlkj\n83r3Zvf06QxJSgo7fczsq7b/zl6DSrDdbGbzMdXtti7QM5KSuNCoqV3Tty/37N3b5uIBuDU/3xEw\nbsrL4+GDB8PXlm3b2nv8dXIHfc1TbQuSpqSntyl4Utxujjc34wsEuC4316rtQNsPuzGDiBmYzCBU\nYrRo7NI9HsalpmIPYW5b68JU3drKsqoqdjQ08N0oPggo0biJz+igiwWCLYb1dXWMS0npcD0NBFsM\nv92/ny/Zbuz2XNWnDy9WVET1KPKJRh99qMt79+bRESP4/s6dpLhc1rO6oG3rIlSCUvi0pjBkvCqc\nP44YwcS0tDYTKwB+YQQtu2imVP504EDu2L076tpppIWuAHOMylue1+uofIVTdPy41RILXXeQYruu\nlFIcnT2bnA8+cDyt9tsDBvDtkOss2eWi0e/HD9ZsuvZkeTy8WVlJqtvd5n49IznZer/7GmOMXbF+\nwdQjH6Jn19+YvWF+uEXTeedZJ8fsSjFPYJbHwztVVexrampTWwjMmWPV7OFEX3d+YmKHXQ4QHCh9\n+9ixNl03Q5OT27xRtxnNxmg/3AWcD+kza55vnHmmVeM0L/BwgeF/hg93TOVdPGJE2AIEcPyfHpeL\nh4YPt/pbTcc6aLIutRUgiS4Xt9j65sHWYvD5rL5os7YZOriZYjSPnysv55a8POtx1S6jT9guw5j6\nOjFMbdW+33nZ2VyUlcW+5mZrznxHzH7bSDeb+dGen0TxmOhkt5sAzi6e9pjdT6HTbk+GUsrqwkx2\nudhuWyyaHeEa3Dh1KtunTaMgzKB/qB/k5YUNCp1h9v3727leT4V5Lg62tDC+nS5S0x+Mis209PQ2\n10Do55WYrfZITxA2WwyHm5sjfiRsnd/Pu8ePMyrCtWI+ov5Ac7PVnRVvPT8weL2U1NezxugHtvdp\nF02cyJazz7Z+HmdcCFsaGtrUlpRSYT+cxt5kbE+mx8O7x49HfJwvBLslQgvb9jSdd16btOb/Z+8y\nGJOayvT0dL4TRS24IyqkcL4lP79NP2xKmBbDl4zuj0hPmU01uocqfD5HEN04dWqbdSjJLpc1oD4q\nJaXDwjnd7ebFigpHt5zJPhjayzatd5UxQSAWzPfkwQhdTnBiFlg0rYAUt5vyWbMidmVFm7/Qrqus\nCI82H52a6ui67elaQsaCwrHf46HXdihzrDB0jCl0PyZdUMAn09osw3JINiox+6IIDD81KpHrI6yR\nUEpxdno6k9eutaZAx1uP7EqyM6Plr/bt486QvvSxITUC88ao8/sdfa/hHJo1iwdKS6Pq5zT7GC+L\ncrAs0gVpSgxT0Jof+JEWso/ikNk9pySKZuh/9u3L5PR07GtgXxo3rsMpqKYUl4tav5/C48f5L9uA\n25lhavopLpe1uC9St0+Gx0Od399mgVP5rFmOxUmZHo81DhFp4BOCYzhTo/hgHaDdVlgor8sVdVqg\nzeKqzkh2udg3YwaDjfUeWSfRaj0dfPrWW6RH0e31zX79IvYCmBYPH94mOH6tb982z3GKVrbHw+tG\n70a41eV2SW43Dw4bxlURHqsPwRbomigCY6z0iCsntMC3sw98hft4w1PlUoqfdnBcO7NJPijCYFZ7\nDs2c2WEXjZ05WBsaGGIhtMUQjsflslpepiS3u/0phDYpbjevGOs7ItWWUtxu68NtIn3MqPl7+wAn\ntC1UzXM3M8pBzfkxmBXTE5jdG8kuF4OSkmidMwfP8uVt1qR8Xvw1zMzF9twU0h0K8FTIGqqT0dfr\nZWV1NfOjGGMC+LGtAtWRk/mUxFjoEYFhZJSfA3xPmEGuUCdTW4vWa0YN4FT7gvsnJtI/yqAyPCWF\nT6dPj9ht0xPZC/hIA28dDXKHMhf6RZplYp7jVVF+RvFnhXkmzfEWcxaM6Hp9EhJo1bpT40bhfCEr\ni6VVVbxhmw0ZTz2i9Lm+nRXCpgeHDeMn+fnd1h/6/NixDE5M7LIZAZEK1VMVTYuhMy60DWJGasr/\n3egrvTvCp7PZeU7DYNkVzHN9OlYmPmvMmYsfxbjbx5wNFbriPV56RIshUoEbbXMrXq7u25ero+gH\n7PHi/aheY//fi2KQ/HsDBrBm+3bHwwjbUzl7tuNBex05OHNm2Nlbn2W9zScBd1HFRbTP7P34k209\nUizclJfHz/bsiTh2Gis9IjCILtIFBUe0XRhfy83l53v2cEEUUyWzExKinpE14BTHgU5n5qLFSFMp\nRfypOHXjZRgLRLuKBIbPkZ5UbCS6XBwK8xwpceqkxSBiRTolP0+k4PhMS5AxBhEjciUJ8RkRbnGi\nEKdCupI+R7pqVpXoejI9VcSSVDGEEEI4SGAQQgjhIIFBCCGEgwSGzxEZYRBCREMCgxBCCAcJDEII\nIRwkMAghhHCQwCCEEMJBAoMQQggHCQxCCCEcJDAIIYRwkMAghBDCQQKDEEIIBwkMQgghHCQwCCGE\ncOhUYFBKfVUptVkp5VdKTQ753Z1KqZ1Kqa1KqYs6l00hhBBdpbMf1LMJ+DLwJ/tGpdQY4GpgDJAP\nvKOUGqG11p08nhBCiDjrVItBa71da72Ttg/unA88p7Vu1VrvBXYC0zpzLCGEEF0jXmMMeUCp7eeD\nxjYhhBA9XMSuJKXUMiDXvgnQwM+01q/HIhMLFy60XhcUFFAgn18rhBAORUVFFBUVdcmxIgYGrfWF\np7Dfg8BA28/5xraw7IFBCCFEW6GV5kWLFsXtWLHsSrKPMywBrlFKeZVSQ4HhwOoYHksIIUScdHa6\n6hVKqVJgBvCGUurfAFrrLcALwBbgX8APZEaSEEKcHjo1XVVr/Srwaju/uxe4tzP7F0II0fVk5bMQ\nQggHCQxCCCEcJDAIIYRwkMAghBDCQQKDEEIIBwkMQgghHCQwCCGEcJDAIIQQwkECgxBCCAcJDEII\nIRwkMAghhHCQwPA5Is8xFEJEQwKDEEIIBwkMQgghHCQwCCGEcJDAIIQQwkECgxBCCAcJDEIIIRwk\nMAghhHCQwCCEOG3UlpR0dxY+FyQwCCGEcJDAIIQQwkECgxBCCAcJDEIIIRwkMAghhHCQwCCEEMJB\nAoMQQggHCQxCCCEcOhUYlFL3K6W2KqU2KKVeVkpl2H53p1Jqp/H7izqfVSGEEF2hsy2GpcA4rfVE\nYCdwJ4BSaixwNTAGmAc8opRSnTyWEEKILtCpwKC1fkdrHTB+LAbyjdeXA89prVu11nsJBo1pnTmW\nEEKIrhHLMYZvAv8yXucBpbbfHTS2CSGE6OE8kRIopZYBufZNgAZ+prV+3UjzM8CntX42LrkUQgjR\nZSIGBq31hR39Xim1APgicL5t80FgoO3nfGNbWAsXLrReFxQUUFBQEClbQgjxuVJUVERRUVGXHEtp\nrU/9j5W6BHgAOE9rXWnbPhZ4GphOsAtpGTBChzmYUircZhFjSikShw2jadeuqNMnjx5Nw9atcc6Z\nEJHZ56488tZbfP/ii7sxNz2DUgqtdVwm9URsMUSwGPACy4w3rlhr/QOt9Ral1AvAFsAH/EBKfyGE\nOD10KjBorUd08Lt7gXs7s38hhBBdT1Y+CyGEcJDAIIQQwkECgxBCCAcJDEIIIRwkMAghhHCQwCCE\nEMJBAoMQQggHCQxCCCEcJDAIIYRwkMAghBDCQQKDEEIIBwkMQgghHCQwCCGEcJDAIIQQwkECgxBC\nCAcJDEIIIRwkMAghhHCQwCCEEMJBAoMQQggHCQxCCCEcJDAIIYRwkMAghBDCQQKDEEIIBwkMQggh\nHCQwCCGEcJDAIIQQwkECgxBCCAcJDEIIIRw6FRiUUr9USpUopdYrpd5SSvWz/e5OpdROpdRWpdRF\nnc+qEEKIrtDZFsP9WusJWutJwJvAPQBKqbHA1cAYYB7wiFJKdfJYXaqoqKi7s9CG5Ck6kqfo9cR8\nSZ66X6cCg9a6zvZjKhAwXl8OPKe1btVa7wV2AtM6c6yu1hMvBMlTdCRP0euJ+ZI8dT9PZ3eglPo1\n8A3gODDX2JwHrLIlO2hsE0II0cNFbDEopZYppTbavjYZ3y8D0Fr/XGs9CHgauDneGRadoHV350AI\ncRpQOkaFhVJqIPCm1vospdQdgNZa/9b43VvAPVrrj8L8nZRWQghxCrTWcRm77VRXklJquNZ6l/Hj\nFcA24/US4Gml1IMEu5CGA6vD7SNe/5gQQohT09kxhvuUUiMJDjrvA74HoLXeopR6AdgC+IAf6Fg1\nTYQQQsRVzLqShBBCfEZorbvtC7iEYPfTDuD2LjjeXqAEWA+sNrZlAUuB7cDbQKYt/Z0Ep9puBS6y\nbZ8MbDTy/fuTzMNfgTJgo21bzPIAeIHnjL9ZBQw6xTzdAxwA1hlfl3RxnvKB94BPgE3ALd19rsLk\n6ebuPldAIvARwWt6E8GxvJ5wTbWXr+6+rlzGcZf0hPMUkq/1tnx173mKNuOx/jJOxC5gMJAAbABG\nx/mYu4GskG2/Bf7LeH07cJ/xeqzxRnmAIUZezRbWR8DZxut/ARefRB7OASbiLIRjlgfg+8Ajxuv/\nILie5FTydA/w/8KkHdNFeeoHTDRepxG8cUd357nqIE/dfa5SjO9uoJjgmqFuvaY6yFd3n6ufAE9x\nogDu9vPUTr669zxFm/FYfwEzgH/bfr6DOLcagD1A75Bt24Bc43U/YFu4/AD/BqYbabbYtl8DPHqS\n+RiMsxCOWR6At4Dpxms3UHGKeboHuDVMui7LU8hxXwW+0BPOVUieLugp5wpIAT4Gzu5h58mer247\nVwRbfMuAAk4UwN1+ntrJV7deU935EL08oNT28wHivwhOA8uUUmuUUt82tuVqrcsAtNZHgL7t5M9c\npJdn5NUUi3z3jWEerL/RWvuB40qp7FPM101KqQ1Kqb8opTK7K09KqSEEWzTFxPb9OuV82fJkTsHu\ntnOllHIppdYDR4BlWus19IDz1E6+oPvO1YPATwmWA6ZuP0/t5Au68Zr6vD1ddbbWejLwReCHSqlz\naftmhP7cHWKZh1OdDvwIcIbWeiLBG/uB2GUp+jwppdKAl4Af6eAjWOL5fkWVrzB56tZzpbUO6ODz\nyvKBaUqpcfSA8xQmX2PppnOllPoSUKa13tBROrr4PHWQr269prozMBwEBtl+zje2xY3W+rDxvYJg\nN8A0oEwplQtgPB223Ja/gWHy1972zohlHqzfKaXcQIbW+tjJZkhrXaGNtifwGCeeddVleVJKeQgW\nwP/QWr9mbO7WcxUuTz3hXBn5qAGKCE7q6DHXlD1f3XiuZgOXK6V2A88C5yul/gEc6ebzFC5fT3b3\nNdWdgWENMFwpNVgp5SXYJ7YkXgdTSqUYNT2UUqnARQRnSywBFhjJrgfMAmgJcI1SyquUGoqxSM9o\nblYrpaYZT4z9hu1vos4OzqgdyzwsMfYBcBXBWTQnnSf7I9SBrwCbuyFPfyPYb/qQbVt3n6s2eerO\nc6WUyjG7GZRSycCFBGerdOt5aidf27rrXGmt79JaD9Jan0GwrHlPa30d8Hp3nqd28vWNbr//ohkc\nidcXwZrNdoLTqO6I87GGEpz5ZE6fu8PYng28Y+RjKdDL9jd3Ehz1D50WNsXYx07goZPMxzPAIaAZ\n2A/cQHDKXEzyQHCa4AvG9mJgyCnm6UmCU982EGxd5XZxnmYDftt7ts64XmL2fp1svjrIU7edK+BM\nIx8bjDz8LNbX9Sm+f+3lq1uvK+Pv5nBikLdbz1MH+erW8yQL3IQQQjh83gafhRBCRCCBQQghhIME\nBiGEEA4SGIQQQjhIYBBCCOEggUEIIYSDBAYhhBAOEhiEEEI4/H8owhE8gvArSgAAAABJRU5ErkJg\ngg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x18cc81d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(((hezftucJan16[0].data+tuch_pqqm)**2 + (hezftucJan16[1].data+tuce_pqqm)**2 + (hezftucJan16[2].data+tucz_pqqm)**2)**(0.5) - hezftucJan16[3].data + 4.7,'c')\n",
-    "\n",
-    "pl.hold(True)\n",
-    "\n",
-    "pl.plot((tucJan16adj[0]**2 + tucJan16adj[1]**2 + tucJan16adj[2]**2)**(0.5) - hezftucJan16[3].data + 4.7,'k')\n",
-    "\n",
-    "pl.ylim(-30,30)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 297,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "save_state('/users/aclaycomb/Documents/adjtuc_state_.json', Mtuc, -4.7)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 298,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.5707963267948966"
-      ]
-     },
-     "execution_count": 298,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.pi/2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 299,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "ded_bh = np.array([-137.8, -137.7, -140.1, -139.2, -138.2, -140.5, -138.4, -139.4, -137.4, -138.7])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 300,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "ded_bd = np.pi/180/60*np.array([1265.5, 1266.3, 1265.7, 1265.6, 1266.3, 1265.7, 1265.1, 1265.1, 1265.9, 1265.8])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 301,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "ded_bz = np.array([22.4, 22.6, 22.8, 22.5, 22.7, 23.1, 22.6, 23.1, 23.0, 22.7])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 302,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "ded_ah = np.array([8931.4, 8922.9, 8970.2, 8880.4, 8992.0, 9038.4, 8943.7, 8985.0, 8944.5, 8960.9])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 303,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "ded_ad = np.pi/180/60*np.array([1121.7, 1127.8, 1121.7, 1143.9, 1128.7, 1130.9, 1132.0, 1123.5, 1127.0, 1119.4])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 304,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "ded_az = np.array([56850.0, 56825.0, 56840.4, 56834.9, 56856.0, 56816.0, 56832.4, 56831.0, 56809.0, 56834.3])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 305,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "ded_bns = Baselines()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 306,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "ded_bns.baseH = ded_bh\n",
-    "\n",
-    "ded_bns.baseD = ded_bd\n",
-    "\n",
-    "ded_bns.baseZ = ded_bz\n",
-    "\n",
-    "ded_bns.absH = ded_ah\n",
-    "\n",
-    "ded_bns.absD = ded_ad\n",
-    "\n",
-    "ded_bns.absZ = ded_az"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 307,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x18fcd9b0>]"
-      ]
-     },
-     "execution_count": 307,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEACAYAAABGYoqtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xt4VOW1+PHvChdFRUVE8QaICERRFAUp16StHu2RiqfW\n6s/T1lO1Alqt0GoVPdBa9dhWW7XeTvXY2tZWWtt6AxSBWEArKDcJyEWgXBQVg9dUMWT9/nj3wBAy\nZCZ773n3nqzP8+RJZs/MnhVIsmav933XK6qKMcYY05gy3wEYY4xJLksSxhhjcrIkYYwxJidLEsYY\nY3KyJGGMMSYnSxLGGGNyCpUkROQcEVkiIttEpF/W8f4isiDrY2RwfJ/g9vzg8zsicnuOc18rIitF\nZJmInBYmTmOMMc0jYdZJiEgvoB64H/ieqs4Pju8JbFXVehHpDCwCDlHV+gbPfxm4UlXnNDheDjwC\n9AcOB54DjlZb1GGMMUUV6kpCVZer6kpAGhz/JCshtMMlkp2ISE+gU8MEETgL+KOq1qnqWmAlMCBM\nrMYYYwoX25iEiAwQkSW4q4hRDa8igK8Bj+Z4+mHA+qzbG4Njxhhjiqh1Uw8QkWnAwdmHAAXGq+qT\nuZ6nqnOBPkFJ6mERmaKqW7Mech7wn80L2xhjTDE0mSRU9dQwL6Cqy0XkI6APkBmzOB5opaoLcjxt\nI3BE1u3Dg2O7EBEbpzDGmGZQVWnqMVGWm7a/mIh0E5FWwdddgV7A2qzHng/8YTfnegI4T0TaisiR\nQA9gbq4Hq2riPiZMmOA9BovJYmqJcVlM+X3kK+wU2JEish4YCDwlIlOCu4YAi0RkPvAYMFpVa7Ke\n+lUaJAkRGSEiEwFUdSkwCVgKTAbGaCHflTHGmEg0WW7aHVX9G/C3Ro7/Dvjdbp7Xo5FjTwJPZt2+\nBbglTHzGGGPCsRXXMamoqPAdwi4spvxYTPlLYlwWU7RCLaZLAhGxSpQxxhRIRNAiD1wbY4wpMZYk\njDHG5GRJwhhjTE6WJIwxxuRkScIYY0xOliSMMcbkZEnCGGNMTpYkjDHG5GRJwhhjTE6WJIwxxuRk\nScIYY0xOliSMMcbkZEnCGGNMTpYkjDHG5GRJwhhjTE6WJIwxxuRkScIYY0xOliSMMcbkZEnCGGNi\n9Prr8PbbvqNoPtvj2hhjYnTGGbD//vCHP/iOZGf57nFtScIYY2JSWwsHHwxt2sDChdCli++Idsg3\nSYQqN4nIOSKyRES2iUi/rOP9RWRB1sfI4Pg+we35wed3ROT2Rs7bVURqg8fNF5F7wsRpjDE+zJgB\nJ58MF14Id97pO5rmCXUlISK9gHrgfuB7qjo/OL4nsFVV60WkM7AIOERV6xs8/2XgSlWd0+B4V+BJ\nVT0+jxjsSsIYk0ijR8NRR8FXvwr9+sGaNbDvvr6jcopyJaGqy1V1JSANjn+SlRDa4RJJwwB7Ap0a\nJojsh4SJzRhjfFKFyZPhS1+Crl3h1FPhgQd8R1W42GY3icgAEVmCu4oY1fAqAvga8OhuTtEtKDXN\nFJEhccVpjDFxqK6GsjIoL3e3x42DO+6Aujq/cRWqdVMPEJFpwMHZhwAFxqvqk7mep6pzgT5BSeph\nEZmiqluzHnIe8J85nv4G0EVVtwRjHX8TkWNU9aPGHjxx4sTtX1dUVFBRUdHUt2WMMbF6+mn4938H\nCWoi/fu7K4o//xnOO6/48VRVVVFVVVXw8yKZ3SQiM4FxmTGJRu6fDnw/a8zieGCSqvYOe34bkzDG\nJNHw4XDNNa7clPH44/DjH8PcuTuShy9FGZNo+JpZL95NRFoFX3cFegFrsx57PpBz1rCIHCgiZcHX\n3YEewOoIYzXGmNhs2QILFkBl5c7HzzwT3nsPZs/2E1dzhJ0CO1JE1gMDgadEZEpw1xBgkYjMBx4D\nRqtqTdZTv0qDJCEiI0RkYnBzGLA4eP4k4FJVfS9MrMYYUyzPPgvDhkG7djsfb9UKrroKbrvNT1zN\nYYvpjDEmYt/8JpxyCowZs+t9tbVubOKFF+Doo4sfW4atuDbGGA/q66FzZ5g3zyWDxlx/PdTUwD0e\nlwn7GJMwxpgWb948OOig3AkC4PLLXS+nd98tXlzNZUnCGGMilFlAtzudO8PZZ8O99xYnpjAsSRhj\nTIQy6yOaMnYs3H03fPJJ/DGFYUnCGGMi8uabbv+IQYOafmyfPtC3LzzySPxxhWFJwhhjIjJ1quvR\n1KZNfo8fOxZuv931eUoqSxLGGBORfEtNGaee6vo7PftsfDGFZVNgI6QKb70FmzbBCSf4jsYYU0xb\nt7pZTStWuM/5+vWvXcmp2InC1knErKbGdXlcsmTHR3W1SxQff+zqkocdVvSwjDGezJzpejXNnVvY\n8z79FI480pWqjm9yB53o5JskmuwC29J99BEsXbojCWQSwgcfuIGnPn3g2GPddLY+fdxWhWefDbNm\n+en0aIzxo9BSU8Yee7h1E7ff7q4qksauJAKffgrLl+98ZbBkiSsd9eq1IyFkPrp0yd3F8fbbYeXK\ndMyBNsZEo7wcfvtbt11poWpq3A521dVw6KHRx9YYKzflUFfnSkENk8Hate6Sr2Ey6N4dWhd4vfXK\nK/CNb7j/cGNM6Vu92k17feMNNxDdHJdf7rY2vfnmaGPLpcUnifp6WLdu5/GCJUvc1cIhh+xcKurT\nx10t7LFHNDHV1UHHjrBqFXTqFM05jTHJ9ctfujeHDz3U/HOsWgUDB8I//wl77x1dbLm0qDGJTZt2\nvTKorob99tuRCD7/ebjiCndJuM8+8cbTurV7VzF7thufMMaUtqefhosuCneOHj1g6FA3LnHZZZGE\nFYmSuJLo2FF3KRMdeyx06OAvrltucdNhf/ELfzEYY+JXW+smrGzY4N6YhjF7Nlx4oat4tGoVSXg5\ntagriXfe8b8VYEPDh7saozGmtM2Y4QarwyYIgMGDXan6iSeSU4UoiRXXSUsQ4H5oVqyA99/3HYkx\nJk7NnfraGBEYNy5ZO9eVRJJIorZtYcAAmDPHdyTGmLio5tcavBD/8R+wcSO89FJ05wzDkkSMhg2D\nv//ddxTGmLhUV7spr+Xl0Z2zdWu48srkXE1YkojRsGHw/PO+ozDGxCVTaoq65H3RRTB9OqxZE+15\nm8OSRIwGDoTFi10vJ2NM6Ym61JTRvr1LFHfeGf25C2VJIkZ77eW6wf7jH74jMcZEbcsWWLAAKivj\nOf93vgO/+Q28914858+XJYmY2biEMaXp2Wfd73e7dvGc/4gj4Iwz4Fe/iuf8+QqVJETkHBFZIiLb\nRKRf1vH+IrIg62Nk1n3ni8hiEVkoIpNF5IAc575WRFaKyDIROS1MnD7ZuIQxpSmuUlO2ceNcyemz\nz+J9nd0JteJaRHoB9cD9wPdUdX5wfE9gq6rWi0hnYBFwCCDAG0BvVd0iIrcCH6vqjxqctxx4BOgP\nHA48BxzdWJOmJG061JgPPnBdHd99N7reUMYYv+rroXNnmDcPunaN97UqK+Hii+GCC6I9b74rrkNd\nSajqclVdifvjn338E1WtD262wyUSsh7XXkQE2BeXNBo6C/ijqtap6lpgJTAgTKy+7Lsv9O7tfpiM\nMaVh3jy3+1zcCQJ2LK7z9V44tjEJERkgIktwVxGjVLVeVeuAMcCrwAagHHiwkacfBqzPur0xOJZK\nNi5hTGkpRqkp40tfcv2hqqqK83oNNdm7SUSmAQdnHwIUGK+qT+Z6nqrOBfoEJamHRWQK7opiNNBX\nVdeKyF3AdcBNIb4HJk6cuP3riooKKioqwpwucsOGwX33wXXX+Y7EGBOFp58u3mK3sjIYO9ZtZhZm\nJlVVVRVVzcg0kXSBFZGZwLjMmEQj908Hvo+7crlFVU8Njg8FrlHVMxs8/geAquqtwe2pwARV3WWh\netLHJAA2b3abF9XUFL6BkTEmWd58E445Bt5+G9q0Kc5r/utf0K2bmwTTu3c05yzKmETD18x68W4i\n0ir4uivQC1iLKxsdIyIdg4eeCixr5FxPAOeJSFsRORLoARS4vXhyHHigq10uWOA7EmNMWFOnwqmn\nFi9BgJtmO3o0/PznxXvNjLBTYEeKyHpgIPBUUFICGAIsEpH5wGPAaFWtUdU3gR8Cs0RkIdAXuDk4\n1wgRmQigqkuBScBSYDIwJvGXC02wcQljSkOUXV8LMWYMTJrktkYoppLYdCgN38Ojj8Ijj8Djj/uO\nxBjTXFu3ullNK1a4z8V2ySVw+OEwYUL4c/koN5ndGDYMZs1y86uNMek0Zw707OknQYAbwL7nHjdG\nUSyWJIrkkEPc2MSSJb4jMcY0l69SU0Z5udvQ7He/K95rWpIoIhuXMCbdfCcJcIvrfv7z4lUlLEkU\nkSUJY9Jr9WrX+bVfv6YfG6fKStfiZ+rU4ryeJYkiyiSJFIyzG2MamDzZdWUt8/xXs9j7YFuSKKKu\nXd3e1ytW+I7EGFOoJJSaMs49F5Yvh4UL438tSxJFJALDh1vJyZi0qa2F2bPdIrokaNvWbUpUjKsJ\nSxJFZuMSyVZTA6tW+Y7CJM2MGW5W0X77+Y5kh29/213dbNgQ7+tYkigySxLJdsklMGqU7yhM0iSp\n1JTRoQN8/etw113xvo6tuC4yVbdm4qWXitOL3uRv+nT41rfc1cTmzbZJlHFUXXO9KVNcY78kWbMG\n+vd3n9u3L+y5tuI6oURsS9MkqquDK690889793ZJ3BiA6mo3o6m83HckuzrySDcl9qGH4nsNSxIe\nWMkpee69121HefbZ7pdu5kzfEZmkyJSapMn33H6MGwe/+AVs2xbP+S1JeGBJIlk2b4Ybb4Q77nB/\nCCxJmGzF3IWuOQYOdCXsv/41nvPbmIQH9fWuj1N1tfvPNX6NGuWmFN55p7v94Yfu/+Wdd1wff9Ny\nbdnixg7feivZPwt/+Qv89Kfw4ov5P8fGJBKsrAyGDLGriSRYuNC9A/vhD3cca98ejjuusF84U5qe\nfdZd+Sc5QQCcdZbbKe+FF6I/tyUJT2xRnX+qcMUV8KMfuemE2azkZCD5paaMVq3gu9+NZ3GdJQlP\nbFzCv0cfdaWliy/e9T5LEqa+3k17Tdr6iFz+67/crMnXX4/2vJYkPDnxRPjnP+Hdd31H0jJ9/DFc\nfbUbh2jVatf7Bw92paiPPy5+bCYZ5s1zmwulZT3TPvu4xaB33BHteS1JeNK6NXzuc64fjCm+W291\niWDo0Mbv32svl8jjqPGadEhLqSnbd77jNiTasiW6c1qS8Gj4cFtU58OaNXD33fCTn+z+cVZyatmS\n2IqjKYceCiNGwP33R3dOSxIe2biEH9/7Hlx1FRxxxO4fZ0kiN1X42c/g0099RxKPN990tf1Bg3xH\nUrixY10/p61bozmfJQmP+veH116DDz7wHUnLMX06zJ/vVqk2ZeBAePVVN7htdlZdDd//vlvpW4qm\nTnVtwdu08R1J4fr2dT2m/vjHaM5nScKjPfZw7Yet7l0cmf5Mt92W37z3du3c/4+NG+3qmWfgtNPc\nAq433vAdTfTSWGrKNnas+zmPYp1xqCQhIueIyBIR2SYi/bKO9xeRBVkfI7PuO19EFovIQhGZLCIH\nNHLeriJSKyLzg497wsSZZNbsr3iy+zPly0pOjXvmGbdS/ZJL4Ac/8B1NtLZuheeec1uVptXpp7s3\nRdOnR3AyVW32B9ALOBqYAfTLOr4nUBZ83Rl4C5eQWgVfdwjuuxX470bO2xVYnGcMmmbPPac6aJDv\nKErfO++oduqkumRJYc97/nnVk0+OJ6a0+vhj1X32UX3vPdUPPlA99FDVF1/0HVV0ZsxQ7d/fdxTh\nPfCA6hln5L4/+NvZ5N/YUFcSqrpcVVcC0uD4J6paH9xsB2S+zjyuvYgIsC+Q62I1oT0XozVwoJuP\nX1vrO5LSdv31cN55cOyxhT3vlFPcuNH778cTVxr9/e9wwglul7b27eF//sdNvayvb/q5aZD2UlPG\nBRfAggWwdGm488Q2JiEiA0RkCbAIGKWq9apaB4wBXgU2AOXAgzlO0S0oNc0UkSFxxenb3nvD8cfb\n/gVxaqw/U7722MMlCpuFtsMzz8C//duO2xdc4AZ4f/1rbyFFqlSSxJ57wpgxcPvt4c7TuqkHiMg0\n4ODsQ4AC41X1yVzPU9W5QB8R6QU8LCJTcFcUo4G+qrpWRO4CrgNuavD0N4AuqrolGOv4m4gco6of\nNfZaEydO3P51RUUFFRUVTX1biZKZCltZ6TuS0rO7/kz5yoxLjBgRbWxp9cwz8Jvf7LhdVuZWro8Y\nAV/5SrL2gS7U6tVuIVq/fk0/Ng1Gj4aePeGmm2DZsiqqqqoKP0k+NammPoCZZI1JNHL/dKAfcDIw\nLev4UOCpMOcn5WMSqqpPP61aWek7itL0hz+onnCCal1d888xZ447h1Fdt061Y8fG/z2/9S3VceOK\nH1OU7rpL9cILfUcRrVGjVG+4YdfjFGNMooHtYwgi0k1EWgVfd8UNcK8FNgLHiEjH4KGnAst2OZHI\ngSJSFnzdHegBrI4w1kQZPBjmzo1u8YtxmurPlK/+/d3Cqpqa6GJLq2eecesHGvv3vPlmd4Xx2mvF\njysqpVJqynbVVXDffc0f9ww7BXakiKwHBgJPBSUlgCHAIhGZDzwGjFbVGlV9E/ghMEtEFgJ9gZuD\nc40QkYnB84cBi4PnTwIuVdX3wsSaZPvt5y4JX37ZdySlpan+TPlq08atvLWpym6RWfZ4RLaDD4Zr\nr3V/lFK2Dxjg/ojOnu2SYCnp2dP1iXv44eY933amS4jvftfN4S+1Oee+rFnjFsItXNh0+4183Hor\nbNy4Y/e6lqiuDjp1crNlcu2ouHWrm4jxs5/BmWcWN76wnnrKLUArxXUxzz8P3/42LFvmxpDAdqZL\nHVtUF618+zPlyxbVuZJoly6733K3bVvXquO7301fX6dSLDVlDBvmpis//XThz7UkkRBDh7r2HHV1\nviNJv0L6M+WrXz9Yv97te91SNZz6msvpp7veQWnq66Saztbg+RJxvw/N2bnOkkRCdOoEhx8Oixb5\njiTdCu3PlK/Wrd2+5C35ai/fJAFubn6a+jpVV7syTHm570jic845borvK68U9jxLEglircPDa05/\npnxVVLTcklNNjRuLGJLnstYePdLV1ylTapIS7vPQps2ON1CFsCSRIJYkwtm8GW680W3fGMcve0se\nl3juOVcS3WOP/J9z3XWu9PePf8QXV1RKudSU7eKL3RXhunX5P8eSRIIMGwazZpVOD5xiu+GG5vVn\nytcJJ7jNaDZtiuf8SVZIqSkjLX2dtmxxPY5aQseD/faDCy8sbJaeJYkEOeww2H//8A25WqKFC+Ev\nf2lef6Z8tWrlEnlzOhukmWrzkgSko6/Ts8+6/9cox7CS7Ior4KGH8n+8JYmEsZJT4aLoz5Svllhy\nqq52A/c9exb+3Exfp/Hjk9tJt6WUmjK6di1swaAliYSxJFG4SZPcFqMXXxz/a7XEJPHMM25aa3PH\neU4+2f0RvvHGaOOKQn09TJlSuusjcrn22vwfa0kiYTKL6kpgEXlRfPyx22s5bH+mfB13nJvps3Fj\n/K+VFM0tNWVLal+nefPgoIPcu+uWpG/f/B9rSSJhjjzSXdqvWuU7knSIqj9TvsrKYPjwlnM1UVsL\nL74In/98uPMkta9TSys1NYcliYQRsZJTvtasgbvvhp/8pLiv25JKTtm70IV1+eXu/6w5rSHiUsqt\nOKJiSSKBLEnkJ+r+TPlqSUkiilJTRtL6Or35pmsBP2iQ70iSzZJEAlmSaNqMGdH3Z8rXMce4sZB/\n/rP4r11sUSYJSFZfp6lT3SyfNm18R5JsliQSqHdv90eokFWRLUldnZvyGnV/pnyJtIwWHevXw9tv\nR7+VZ1L6OlmpKT+WJBLIxiV2L87+TPmqrCz9RXW724UujCT0ddq61bUaOeMMfzGkhSWJhLIk0bi4\n+zPlK3MlkaSZOlHb3S50Yfnu6zRnjlsceNBBfl4/TSxJJJQlicbF3Z8pX716uXeja9b4jSMudXXu\nj3hcScJ3XycrNeXPkkRCHXccvPVWy2wml0sx+jPlS6S0ZznlswtdWD77OlmSyJ8liYRq1cr17p81\ny3ckyVDM/kz5KuUkEfWspsb46uu0erXr/Br1gHypsiSRYFZy2qGY/ZnylUkSpTguUYwkAX76Ok2e\n7Aasy+yvX17snynBLEk4xe7PlK+jjnJ/aFau9B1JtArdhS6sYvd1slJTYSxJJFi/fm5gtKbGdyR+\nFbs/U75KdVyiObvQhVHMvk61tTB7dmGtslu6UElCRM4RkSUisk1E+mUd7y8iC7I+Rmbd9zURWSQi\nr4rILbs597UislJElonIaWHiTKs2bWDgQPdD3VL56s+Ur1JMEsUqNWUrVl+nGTNciSuKXlQtRdgr\niVeBs4HnGzl+kqqeCJwB3C8iZSJyAPAToFJVjwM6i8gumwaKSDlwLlAePP8ekVLeojy3ll5y8tWf\nKV+ZRXWlMi4RZhe6MIrV18lKTYULlSRUdbmqrgSkwfFPVDUz+7kdkPm6O7BCVTMFlOnAVxo59VnA\nH1W1TlXXAiuBAWFiTauWnCR89mfKV7durjXIsmW+I4lGmF3owoq7r5OqtQZvjtjGJERkgIgsARYB\no4KksQroJSJdRKQ1MBJo7D3iYcD6rNsbg2MtzoAB7hf3ww99R1JcvvszFaKUSk5hd6ELK86+TtXV\nbqJBeXn05y5lrZt6gIhMAw7OPgQoMF5Vn8z1PFWdC/QRkV7AwyIyRVXfE5HRwCRgG/ACcFSYbwBg\n4sSJ27+uqKigoqIi7CkTY8894aST4IUXil8C8CkJ/ZnyVVkJjz8Ol13mO5LwnnkGRo/29/rZfZ0e\nfjjac2dKTS2zcA1VVVVUNaPhmGgExVQRmQmMU9X5Oe6fDny/4f0icglwlKr+oMHxHwCqqrcGt6cC\nE1T1pUbOrVF8D0l2ww2udcFNN/mOpDg2b3Zlh5kz/bffyMeGDW5jnrffTvfc+9paN9Nowwa/A7sf\nfug6IT/2mJu4EZXhw+Gaa6zclCEiqGqTKTPKH+ntLyYi3USkVfB1V6AXsDa43Sn43AEYAzzQyLme\nAM4TkbYiciTQA5gbYayp0tLGJZLSnylfhx/uVoFXV/uOJJwod6ELI46+Tlu2wIIF7qrPFCbsFNiR\nIrIeGAg8JSJTgruGAItEZD7wGDA6a7D6DhGpBmYBN6vqquBcI0RkIoCqLsWVpJYCk4ExJX+5sBuf\n+5wbwP3Xv3xHEr8k9WcqRCnsL+FjVlMuUfd1evZZ92Yr6eNbSRRJucmnllBuAjjlFLdWYPhw35HE\nR9V9fxdcAJde6juawjzyCPzpT/DXv/qOpPmOOcatfO7f33ckzssvw4gRbiV22Kubb37T/Q6NGRNN\nbKXAR7nJxGj4cHi+4WqUEpPE/kz5qqx0/z8+2l5HIa5d6MKIqq9TfT1MmWLrI5rLkkRKlPq4RFL7\nM+XrkEPcBjaLFvmOpHni2oUurCj6Os2b5/5vunaNLq6WxJJESgweDC+95Da6KUVJ7c9UiDSvl0jS\neES2KPo62QK6cCxJpESHDq7r6PxGJxmn29q1ye7PlK+0Jom6OtfUL4lJAsL3dbJWHOFYkkiRYcNK\nc1wi6f2Z8lVR4TaJqqvzHUlhirELXRhh+jq9+Sa8/joMGhRPbC2BJYkUGT689MYlZsyAV15Jdn+m\nfB10kFszsWCB70gKk9RSU7bm9nWaOtWNtbRpE09cLYEliRQZOhTmzIFt23xHEo26OrjyynT0Z8pX\nGktOaUgS0Ly+TlZqCs+SRIocdJArCSxe7DuSaNx7rxuYTEN/pnylLUkUexe6MLL7OuVj61Y31nLG\nGfHGVeosSaRMqUyFXbfOraq+667Sarg2fLi72vvsM9+R5KfYu9CFdd11MH06/OMfTT92zhzX8vyg\ng+KPq5RZkkiZUlhUpwqjRrnB6lJr29yxI3Tv7lYLp0FaSk0ZhfR1slJTNCxJpMzQoe5KIs2dSH7/\ne9i4Ea6+2nck8UhLycnXLnRh5dvXyZJENCxJpMwRR8C++6Z3J7S333Yzmf7v/0p3xklamv0tXepv\nF7owysrcyvzx4+H99xt/zOrVrvNrktqMpJUliRRK87jElVfChRe6jZRK1bBhrmae9NXxU6f63YUu\njKb6Ok2e7Aas07y/R1LYP2EKpXVR3RNPuFp91kaCJalDB/fufG7Cd0BJY6kp2+76OlmpKTrWKjyF\nXn/dJYoNG9LzLvD996FPH/jd70q73XnG977n2lvfcIPvSBqXlF3owrr9dpg2zV05ZH4XSuV7i5u1\nCi9h3bu7z6tX+42jEFdf7d7ZtYQEAckfvE7KLnRhNdbXacYMV45K+/eWFJYkUkgkXeMSM2e6d3q3\n3uo7kuIZOtSVmz75xHckjUt7qSmjsb5OVmqKliWJlEpLkqitdatk77mnZb2z23dft0d3Pou+fCiV\nJAE793VStdbgUbMkkVJpWVQ3YQIMGOC2oWxpklpySuIudGFl+jo995yb0VRqizR9siSRUuXlbqvP\n9et9R5LbvHnw29/CHXf4jsSPpCaJpO5CF0amr9O557pSU1omdKSBJYmUEnF171mzfEfSuK1b4aKL\n3Du8Tp18R+PH4MFuk6jaWt+R7KyUSk3Zxo+HvfeGs87yHUlpsSSRYkkel7j1VreRzfnn+47En332\ngb594YUXfEeyQ9J3oQtjn31g5Up3lWSiY0kixZK6qG7pUtc24d577bI/aSWnpO9CF1ap7EuSJKGS\nhIicIyJLRGSbiPTLOt5fRBZkfYzMuu9rIrJIRF4VkVtynLeriNSKyPzg454wcZaqvn3d9oxvv+07\nkh22bXNlphtvTP92pFFIWpIo1VKTiU/YK4lXgbOBhu9nXwVOUtUTgTOA+0WkTEQOAH4CVKrqcUBn\nEanMce5Vqtov+BgTMs6S1KqVq3snaVzil790c9e//W3fkSTD5z7nNon66CPfkTiWJEyhQiUJVV2u\nqisBaXD8E1XNdHtvB2S+7g6sUNWa4PZ04Cs5Tt/CCxX5SdK4xJo17griV7+yxmoZe+3lpprOnu07\nknTtQmedeT4vAAAPQklEQVSSI7ZfZREZICJLgEXAqCBprAJ6iUgXEWkNjARyFSW6BaWmmSJiP9Y5\nJCVJqMKll7r2G2lrPR23ykqoqvIdRfp2oTPJ0LqpB4jINODg7EOAAuNV9clcz1PVuUAfEekFPCwi\nU1T1PREZDUwCtgEvAEc18vQ3gC6quiUY6/ibiByjqo1etE/MaitaUVFBRUVFU99WyTjpJFi1yvXO\n79DBXxy/+Q28+y6MHesvhqSqrIRrrvEdhZWaWrqqqiqqmvFuJZIusCIyExinqvNz3D8d+H7D+0Xk\nEuAoVd3t1ua7O39L7ALb0Be/6HrXnHmmn9fftAmOP9514+zb108MSfbJJ3DggfDGG65dhw+qbiLB\n9OnQq5efGEyy+OgCu/3FRKSbiLQKvu4K9ALWBrc7BZ87AGOAB3Y5kciBIlIWfN0d6AGkqOdpcfku\nOV1+uVvtagmicXvu6VqT+JxgkNZd6Ix/YafAjhSR9cBA4CkRmRLcNQRYJCLzgceA0VmD1XeISDUw\nC7hZVVcF5xohIhODxwwDFgfPnwRcqqrvhYm1lPlMEo89BkuWJHffhKTwPRU2zbvQGb9s06ES8K9/\nudYXmza5VafFsmWL63T6pz+5qbgmt9mz3datr7zi5/VPOw1Gj4azz/bz+iZ5bNOhFqRdOzjxRHjx\nxeK+7rhx8JWvWILIx4ABsGKFS6zFVlvrfjY+//niv7ZJP0sSJaLYJadp09wOYDffXLzXTLO2bd3C\nOh9lwVLZhc74YUmiRBQzSXz0kVtRfd990L59cV6zFPgal7CpryYMSxIlYtAgV+8uxnaZ11/vktLp\np8f/WqXEkoRJI0sSJaJ9e7eF49y58b7Oiy/Co4+6fSJMYU46Cdauhc2bi/eapbgLnSkuSxIlJO6S\n06efug6vd94JHTvG9zqlqk0bN8hfzPbupbgLnSkuSxIlJO4kcdNNbrXuOefE9xqlrqKiuCUnKzWZ\nsGydRAmpqYFu3VwPpTZtoj334sWu/cfChXDoodGeuyWZNw8uvBCqq+N/rbo6t35m6dLS3WTINJ+t\nk2iBDjjAJYkFC6I9b12dKzPdcosliLBOPBE2bizORlGlvgudKQ5LEiVm+PDoa9533OEa033rW9Ge\ntyVq3dq16y5G63ArNZkoWJIoMVGPS6xa5a4gfvUr6/sTlWJNhbUkYaJgYxIlZtMmKC930yzDzmhR\nda0cvvxluOqqaOIzrhx4/vnw2mvxvUZmfOqdd2yTIdM4G5NooTp3hoMOcp1Zw3rgAdf354orwp/L\n7NC3rxuTeOON+F7DdqEzUbEkUYKiGJfYuBGuuw4efNDm2EetrMz9H8U5LmGlJhMVSxIlKOy4hKpr\nK33ZZdCnT3RxmR3iHJdQtSRhomNJogRlkkRzh2omTYLVq+Haa6ONy+wQZ5KwXehMlCxJlKAuXWCv\nvWD58sKfu3mz2y/7wQetnh2nY4+F9993vZWiZrvQmShZkihRzS05XXWVm3lzyinRx2R2KCuLr0WH\nlZpMlCxJlKjmDF5Pngxz5sCNN8YTk9lZHCUn24XORM2SRIkaNswliXzHJT74AEaNcovm9t473tiM\nE0eSsF3oTNQsSZSoHj1g2za3f0E+rr0WTjsNvvCFWMMyWXr3dptErVkT3Tmt1GSiZkmiRInkPy4x\naxb87W/ws5/FH5fZQST6cQlLEiZqliRKWD7jEp98AhdfDHffDfvvX5y4zA5RlpxsFzoTh1BJQkTO\nEZElIrJNRHb50RSRLiLyoYiMzTrWT0QWi8gKEfnFbs59rYisFJFlInJamDhbqnyuJH70I9cmYuTI\n4sRkdlZZ6VZeR9F+zHahM3EIeyXxKnA2kOv96m3A5AbH7gUuUtWeQE8R2eXiWETKgXOBcuAM4B4R\nm/VdqGOOgS1bXIuNxixY4NZD3HVXceMyOxx9NNTXw+uvhz+XlZpMHEIlCVVdrqorgV3+gIvIWcBq\noDrrWGegvarOCw49DDT2HvYs4I+qWqeqa4GVwIAwsbZEZWWuydusWbve99lnbn+In/4UDj64+LEZ\nRySaklNdnWvqZ0nCRC2WMQkR2Ru4GvghOyeQw4ANWbc3BMcaOgzIXou6McfjTBNylZxuu80lh69/\nvfgxmZ1FkSRsFzoTl9ZNPUBEpgHZ7zUFUGC8qj6Z42kTgZ+ram0xqkQTJ07c/nVFRQUVFRWxv2Za\nDB/uSkrZli93M5leecVaNyRBZSVcf70bl2ju/4eVmkxTqqqqqGpG6+FINh0SkZnAOFWdH9z+O3B4\ncHcHYBvw38BfgJmqWh487jxguKqObnC+HwCqqrcGt6cCE1T1pUZe2zYd2o26OujY0e0w16mTq38P\nHw7nngvf+Y7v6Ay45NCtm/tD37t3884xcCDcdJOtczH587Hp0PYXU9VhqtpdVbsDvwBuVtV7VHUT\n8L6IDAgGor8BPN7IuZ4AzhORtiJyJNADmBthrC1G69YwaBDMnu1u33efSxRjxviNy+wQdlyipsZ1\nfh0yJNq4jIHwU2BHish6YCDwlIhMyeNplwEPAiuAlao6NTjXCBGZCKCqS4FJwFLc7KgxdrnQfJlx\niXXrYMIEt+OcTZNMljBJwnahM3GyPa5bgBdegMsvd1ubDh4M48f7jsg0tG4dnHwyvPVW4eMSF13k\n1rrYNrOmEPmWmyxJtABbt7rV1EcfDS+/DG3a+I7INOaoo+DxxwvbDVAVjjgCpk+HXr3ii82UHh9j\nEiah2raFsWPh17+2BJFkzenjZLvQmbhZkmghfvxjOPFE31GY3WnOuITtQmfiZknCmISorHQNGevr\n83+OrY8wcbMkYUxCHHaYW9OyeHF+j7dd6EwxWJIwJkEyXWHzYbvQmWKwJGFMghQyLmGlJlMMliSM\nSZCKCneFsG1b04+1JGGKwZKEMQnSubPr5Lpw4e4fZ7vQmWKxJGFMwuRTcrJd6EyxWJIwJmHyTRJW\najLFYG05jEmYzZtdi45333WrqRuqq3Nt35cutU2GTPNZWw5jUurAA6FrV7cpVGNsFzpTTJYkjEmg\n3ZWcrNRkismShDEJZEnCJIWNSRiTQDU1ruT07ruui2/28W7d4J13bJMhE46NSRiTYgccAD16wLx5\nOx+3XehMsVmSMCahGis5WanJFJslCWMSqmGSULUkYYrPkoQxCTVsGLz0Enz6qbttu9AZHyxJGJNQ\n++0H5eUuUYDtQmf8sCRhTIJll5ys1GR8CJUkROQcEVkiIttEZJd+lCLSRUQ+FJGxWcf6ichiEVkh\nIr/Icd6uIlIrIvODj3vCxGlMWmWShO1CZ3wJeyXxKnA28HyO+28DJjc4di9wkar2BHqKSK73RqtU\ntV/wMSZknEVXle/2YkVkMeUnSTENGQIvvwy3316VyF3okvRvlWExRStUklDV5aq6EtilSioiZwGr\ngeqsY52B9qqamf39MDAyx+lTXXlN4g+FxZSfJMXUvj0cdxz88pdViSw1JenfKsNiilYsYxIisjdw\nNfBDdv5jfxiwIev2huBYY7oFpaaZIjIkjjiNSYPKSnjrLRuPMH40mSREZFowhpD5eDX4PGI3T5sI\n/FxVa5sZ1xtAF1XtB4wDHhGRfZp5LmNSrbIS2rWzXeiMH5H0bhKRmcA4VZ0f3P47cHhwdwdgG/Df\nwF+AmapaHjzuPGC4qo4u5PwN7rPGTcYY0wz59G5qZEuTZtv+Yqo6bPtBkQnAh6p6T3D7fREZAMwD\nvgHcucuJRA4EalS1XkS6Az1w4xu7yOebNMYY0zxhp8COFJH1wEDgKRGZksfTLgMeBFYAK1V1anCu\nESIyMXjMMGCxiMwHJgGXqup7YWI1xhhTuNS3CjfGGBOfVK+4FpHTReS1YGHeNQmI50EReUtEFvuO\nJUNEDheRGSJSHUw6uCIBMe0hIi+JyIIgpgm+Y8oQkbJgVt0TvmPJEJG1IrIo+Pea6zseABHZT0T+\nJCLLgp+tUzzH0zP495kffH4/IT/rVwULjheLyO9FpG3Tz4qfiFwZ/O41+TchtVcSIlKGK1l9ATcb\nah5wnqq+5jGmIcBHwMOqeryvOLIFa1M6q+rCYIbYK8BZPv+dgrj2UtVaEWkFzAGuUFXvfwBF5Crg\nJGBfVf2y73gARGQ1cJKqbvEdS4aI/Bp4XlUfEpHWwF6q+oHnsIDtfxs2AKeo6nqPcRwKzAZ6q+pW\nEXkUeFpVH/YVUxDXscAfgP5AHTAFGKWqjY77pvlKYgBuTOOfqvoZ8EfgLJ8BqepsIDG/yACquklV\nFwZffwQsI/falKLJmh69B24Chfd3KyJyOPAl4AHfsTQgJOh3VUT2BYaq6kMAqlqXlAQR+CLwus8E\nkaUVsHcmkeLe0PpWDrykqp+q6jbg78B/5HpwYn7wmuEwIPuHYHcL8wwgIt2AE4CX/EayvayzANgE\nTMtahe/Tz4Hvk4CE1YAC00Rknohc4jsY4Ehgs4g8FJR3/ldE2vkOKsvXcO+UvVLVN3CtidYBG4H3\nVPU5v1EBsAQYKiIdRGQv3BujI3I9OM1JwhQgKDX9GbgyuKLwSlXrVfVE3HqaU0TkGJ/xiMi/A28F\nV11CstrCDA4Wln4JuCwBHQhaA/2Au4O4aoEf+A3JEZE2wJeBPyUglv1x1Y2uwKHAPiLy//xGBUGp\n+VZgGq633gLcWrZGpTlJbAS6ZN0+PDhmGggudf8M/FZVH/cdT7agTDETON1zKIOBLwf1/z8AlSLi\ntXacoapvBp/fAf6KK7X6tAFYr6ovB7f/jEsaSXAG8Erwb+XbF4HVqloTlHX+AgzyHBMAqvqQqp6s\nqhXAe7jx3UalOUnMA3oEbcXbAucBSZiRkrR3oQD/ByxV1Tt8BwJusaSI7Bd83Q44FfA6kK6q16lq\nF1XtjvtZmqGq3/AZE7gB/kxLmqAn2mm4coE3qvoWsF5EMnvkfQFY6jGkbOeTgFJTYB0wUET2FBHB\n/Tst8xwTACLSKfjcBdfJ+5Fcj41yxXVRqeo2EbkceBaX7B5UVa//ASLyCFABdBSRdcCEzOCex5gG\nAxcArwZjAApcl1nE6MkhwG+CWShlwKOq2rClvHEOBv4atJ9pDfxeVZ/1HBPAFcDvg/LOauC/PMdD\nUF//IvBt37EAqOpcEfkzrpzzWfD5f/1Gtd1jInIALq4xu5t4kNopsMYYY+KX5nKTMcaYmFmSMMYY\nk5MlCWOMMTlZkjDGGJOTJQljjDE5WZIwxhiTkyUJY4wxOVmSMMYYk9P/B9iNxsVsFOSbAAAAAElF\nTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x18b850b8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(ded_bns.baseH)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 308,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x17834358>]"
-      ]
-     },
-     "execution_count": 308,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEGCAYAAACZ0MnKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+/91jDENqZDz7QAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x18c38cf8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(ded_bns.baseD)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 309,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x16ebcf28>]"
-      ]
-     },
-     "execution_count": 309,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEACAYAAAC9Gb03AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x17ad3fd0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(ded_bns.baseZ)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 310,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "ded_abs_ord = get_ord_abs_from_baselines(ded_bns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 311,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "Mded, resded, rankded, sigded = get_transform_from_abs_ords(ded_abs_ord)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 312,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  9.17300296e-01,  -3.73211099e-01,  -5.14226627e-03,\n",
-       "          2.98482986e+02],\n",
-       "       [  3.53096520e-01,   9.06733279e-01,   6.61541037e-04,\n",
-       "         -2.98943984e+01],\n",
-       "       [  3.68482513e-03,  -3.82402473e-04,   9.90805880e-01,\n",
-       "          5.11440783e+02],\n",
-       "       [  0.00000000e+00,   1.61017236e-15,  -0.00000000e+00,\n",
-       "          1.00000000e+00]])"
-      ]
-     },
-     "execution_count": 312,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Mded"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 313,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([  4.44991815e+00,   1.17540091e+01,   1.07493093e-01,\n",
-       "         1.63926361e-38])"
-      ]
-     },
-     "execution_count": 313,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "resded"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 314,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "hezfdedJan16 = factory.get_timeseries(observatory='DED',\n",
-    "\n",
-    "        interval='minute',\n",
-    "\n",
-    "        type='variation',\n",
-    "\n",
-    "        channels=('H', 'E', 'Z', 'F'),\n",
-    "\n",
-    "        starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n",
-    "\n",
-    "        endtime=UTCDateTime('2016-01-31T23:59:59Z'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 315,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "dedJan16adj = make_adjusted_from_transform_and_raw(Mded,hezfdedJan16)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 316,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "dedh_pqqm = np.mean(ded_abs_ord.absp1[0] - ded_abs_ord.ordp1[0])\n",
-    "\n",
-    "dede_pqqm = np.mean(ded_abs_ord.absp1[1] - ded_abs_ord.ordp1[1])\n",
-    "\n",
-    "dedz_pqqm = np.mean(ded_abs_ord.absp1[2] - ded_abs_ord.ordp1[2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 317,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-30, 30)"
-      ]
-     },
-     "execution_count": 317,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYRgdrBgYhmEYHf8PC7KB4QT7HCsAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x16ce82e8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.plot(((hezfdedJan16[0].data+dedh_pqqm)**2 + (hezfdedJan16[1].data+dede_pqqm)**2 + (hezfdedJan16[2].data+dedz_pqqm)**2)**(0.5) - hezfdedJan16[3].data + 0.5,'c')\n",
-    "\n",
-    "pl.hold(True)\n",
-    "\n",
-    "pl.plot((dedJan16adj[0]**2 + dedJan16adj[1]**2 + dedJan16adj[2]**2)**(0.5) - hezfdedJan16[3].data + 0.5,'k')\n",
-    "\n",
-    "pl.ylim(-30,30)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 318,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "save_state('/users/aclaycomb/Documents/adjded_state_.json', Mded, -0.5)"
-   ]
-  },
-  {
-   "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": "2.7.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}