rover_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Benthic rover current meter analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.signal import welch\n",
    "import os\n",
    "import matplotlib.dates as mdates\n",
    "import utide\n",
    "import physoce.tseries as ts\n",
    "\n",
    "from scipy.stats import chi2\n",
    "from statsmodels.stats.multicomp import (pairwise_tukeyhsd,\n",
    "                                         MultiComparison)\n",
    "\n",
    "import datapath"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load Rover data and select time range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tomconnolly/programs/anaconda/lib/python3.5/site-packages/ipykernel_launcher.py:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/tomconnolly/programs/anaconda/lib/python3.5/site-packages/ipykernel_launcher.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "# Load data\n",
    "rover_csv_file = datapath.rover()\n",
    "df_all = pd.read_csv(rover_csv_file,parse_dates=[5])\n",
    "\n",
    "# Datetime index\n",
    "df_all = df_all.set_index('Date_time_R')\n",
    "\n",
    "# Create u and v components with units m/s\n",
    "df_all['u'] = df_all['Easting (cm/sec)']/100\n",
    "df_all['v'] = df_all['Northing (cm/sec)']/100\n",
    "\n",
    "# mask erroneous points \n",
    "# abrupt shift near end of deployment (recovery)\n",
    "bi = ((df_all.index > np.datetime64('2015-06-18T12:00')) &\n",
    "    (df_all.index < np.datetime64('2015-06-20T00:00')))\n",
    "\n",
    "df_all['u'][bi] = np.nan\n",
    "df_all['v'][bi] = np.nan\n",
    "\n",
    "# select time range\n",
    "t1 = np.datetime64('2014-10-11T00:00:00')\n",
    "t2 = np.datetime64('2018-10-17T00:00:00')\n",
    "df_all = df_all.loc[(df_all.index >= t1) & (df_all.index <= t2)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tidal analysis\n",
    "\n",
    "Use UTide to extract tidal currents that are phase-locked with astronomical forcing cycles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "solve: matrix prep ... solution ... diagnostics ... done.\n"
     ]
    }
   ],
   "source": [
    "# tidal analysis\n",
    "time = mdates.date2num(df_all.index)\n",
    "\n",
    "coef = utide.solve(time, np.array(df_all['u']), \n",
    "         np.array(df_all['v']),\n",
    "         lat=35+8.4585/60,\n",
    "         method='robust')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select tidal constituents based on signal to noise ratio and frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "crit = (coef['diagn']['SNR'] > 2) & (1/coef['aux']['frq'] < 33)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resample to hourly values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prep/calcs ... done.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tomconnolly/programs/anaconda/lib/python3.5/site-packages/scipy/signal/signaltools.py:459: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return correlate(volume, kernel[slice_obj], mode)\n",
      "/Users/tomconnolly/programs/anaconda/lib/python3.5/site-packages/scipy/signal/signaltools.py:179: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  in1zpadded[sc] = in1.copy()\n"
     ]
    }
   ],
   "source": [
    "# resample and interpolate\n",
    "df_all_r = df_all.resample('1H').mean()\n",
    "\n",
    "# Calculate tidal currents at hourly points\n",
    "time_r = mdates.date2num(df_all_r.index)\n",
    "tide_r = utide.reconstruct(time_r,coef,constit=coef['name'][crit])\n",
    "df_all_r['u_tide'] = tide_r.u\n",
    "df_all_r['v_tide'] = tide_r.v\n",
    "df_all_r['u_detide'] = df_all_r['u'] - df_all_r['u_tide']\n",
    "df_all_r['v_detide'] = df_all_r['v'] - df_all_r['v_tide']\n",
    "\n",
    "# interpolate over gaps in detided data\n",
    "df_all_int = df_all_r.interpolate(limit=int(24*3.5))\n",
    "\n",
    "# add tides back in\n",
    "df_all_int['u'] = df_all_int['u_detide'] + df_all_int['u_tide']\n",
    "df_all_int['v'] = df_all_int['v_detide'] + df_all_int['v_tide']\n",
    "\n",
    "# low pass filter hourly data \n",
    "df_all_int['u_f'] = ts.pl64(df_all_int['u_detide'])\n",
    "df_all_int['v_f'] = ts.pl64(df_all_int['v_detide'])\n",
    "\n",
    "# high-pass filtered data with tides removed\n",
    "df_all_int['u_super'] = df_all_int['u'] - df_all_int['u_f'] - df_all_int['u_tide']\n",
    "df_all_int['v_super'] = df_all_int['v'] - df_all_int['v_f'] - df_all_int['v_tide']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subset with no gaps\n",
    "November 2015-October 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# select time range\n",
    "t1s = np.datetime64('2015-11-11T13:00:00')\n",
    "t2s = np.datetime64('2018-10-14T07:00:00')\n",
    "df_sub = df_all_int.loc[(df_all_int.index >= t1s) & (df_all_int.index <= t2s)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write dataframe to csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "csv_file = 'data/rover_processed_2015_2018.csv'\n",
    "df_sub.to_csv(csv_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time series plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "\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 overridden (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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t1p = np.datetime64('2016-12-01')\n",
    "t2p = np.datetime64('2017-12-01')\n",
    "\n",
    "t1b = np.datetime64('2017-04-21')\n",
    "t2b = np.datetime64('2017-05-11')\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.subplot(211)\n",
    "h1 = plt.plot(df_sub.index,df_sub['v_f'],'-',color='gray')\n",
    "h2 = plt.plot(df_sub.index,df_sub['u_f'],'k-')\n",
    "plt.ylim([-0.06,0.06])\n",
    "plt.xlim([t1p,t2p])\n",
    "yl = plt.ylim()\n",
    "plt.plot([t1b,t1b],yl,'k--')\n",
    "plt.plot([t2b,t2b],yl,'k--')\n",
    "xl = plt.xlim()\n",
    "yl = plt.ylim()\n",
    "plt.text(xl[0]+0.01*np.diff(xl),yl[1]-0.1*np.diff(yl),'a)')\n",
    "plt.ylabel('[m/s]')\n",
    "plt.title('Benthic rover\\nlow-pass filtered velocity - $u_{l}$,$v_{l}$')\n",
    "plt.legend((h2[0],h1[0]),['u','v'],loc='lower left')\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.plot(df_sub.index,df_sub['v_super'],color='gray')\n",
    "plt.plot(df_sub.index,df_sub['u_super'],color='k')\n",
    "plt.ylim([-0.06,0.06])\n",
    "plt.xlim([t1b,t2b])\n",
    "monthyearFmt = mdates.DateFormatter('%B %d')\n",
    "plt.gca().xaxis.set_major_formatter(monthyearFmt)\n",
    "xl = plt.xlim()\n",
    "yl = plt.ylim()\n",
    "plt.text(xl[0]+0.01*np.diff(xl),yl[1]-0.1*np.diff(yl),'b)')\n",
    "plt.ylabel('[m/s]')\n",
    "plt.title('high-pass filtered velocity, coherent tidal motions removed - $u_{hn}$,$v_{hn}$')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('figures_paper/rover_tseries_examples.png',dpi=600)\n",
    "plt.savefig('figures_paper/rover_tseries_examples.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spectral analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "u = np.array(df_sub['u'])\n",
    "v = np.array(df_sub['v'])\n",
    "\n",
    "N = len(u)\n",
    "window_width = N/3\n",
    "fu,Su = welch(u,fs=24.,nperseg=window_width,window='hanning')\n",
    "fv,Sv = welch(v,fs=24.,nperseg=window_width,window='hanning')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.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 overridden (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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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SUlBq1atULx4cfz9998azykBUNd9dq1bt9Y7a9ecIPXnn3+CiBAYGCgfy8jI0DiU4GxMAFSGQTdu3AgAaNKkid7Aakm9s9u9ezecnJzQsWNHrXsDHR0d0bdvX63XKAFQ33C2oQAYGxuLhg0bwtfXF8HBwTh8+DDmz58v79vMyMgw63MwxnIfB0DGCrCcAVCtVsPPzw/VqlVDenq6VgD8/vvvUaxYMZ3nqlatGlq1aiV/joqKAhEhKCgIALBr1y6tiRkvk5qairZt28LFxUXnciS5NQSsS2ZmJtzc3NC5c2f5WM778ZYvXw7AvCHg6tWro2XLlgbrYEkADA0NhYuLC9q3b4+0tDSt53NjCPiXX36Bo6Oj1vI94eHhICKsWLHC5M/BGMsbHAAZK8ByBkAACAsLAxFh7ty5Jl8B7NKli8bjjRo1wjvvvAMA6NixIypUqKBx35khSvhzdnZGaGiozjJHjx7VuGKpyMjIsGgSiC6ZmZkoXry4xmeMjIzUOJShbXMmgeTmFUAl/LVp00bv0OuHH34IHx8frccnT54MIsK9e/d0vs5QAGzTpg0qV66s9XhCQgKICD///LMJn4Ixlpc4ADJWgOkKgEBWGChbtizeeustjQC4Z88erWFQANi0aROICIsXL9Z4XLnPLSIiAs7Ozhg1apRR9UpNTUW7du3g5OSEnTt36i2nDCu3bdtW4/F169aBiBASEqLzdeYEKeU+x5kzZ760rLIMTOXKlXUuaaMsAzNlyhT5mHIPYPZJODnFx8eDiDBixAij671nzx64uLigVatWSElJ0VtOGZLOPsyekZEBX19fGeJ1MRQAv/vuOxQrVkxrBvHevXuN/i4ZY7bBAZCxAkxfADx9+jQcHBxARDpnATs6OkIIgbCwMLmwb/ZZwIq4uDi4urrCy8sLRITLly8bVa+PP/4YRITRo0drLcacc7h39erVICL07t0bBw4cwKJFi1CyZEmthaCBrHvgNm3ahGXLlsnJCJs2bcKmTZuQlJQEALh58yaaNm2K2bNnY/fu3QgJCcHIkSPh4uICX19fg8O62eVcCPrgwYPYsWMHvvnmG50LQSuzgEuUKIEJEyZg79692L59O4YOHYrw8HBZztvbG7Vq1cKePXsQGRmJmJgYvXWIiIiAq6srKleujPDwcK3v8sWLF7JsamoqfH198frrryM4OBhhYWHo0KGDzoWgb968Kb+3tm3bgojkz9nb0smTJ+Hk5ITatWtj5cqVCA8Px+zZs1G2bFmUK1cOT548Meq7ZIzlPQ6AjBVg+gIgALljha51AH/55Rd4e3vD0dER5cuXR79+/TTWAdR1nmbNmhldL13r3SlHixYttMqvXbsWb7zxBpycnODp6YlBgwbpnK3r7e2t97xKkIqNjUWHDh1QuXJluLq6wsnJCTVq1MCIESPkhBZjZd8KzsnJSe5Eom8ruOfPn2Pw4MGoVKkSHB0dUbZsWbRv3x6XLl2SZfbt24c333wTzs7OL10HMCAgwOB3mXNLuYcPH6J79+4oXbo0XFxc0LhxY52zkrPvIPOydQlPnz6NDh06wMvLC87OzqhatSp69eqF27dvm/RdMsbyFgdAxhhjjLFChgMgY4wxxlghwwGQMcYYY6yQ4QDIGGOMMVbIcABkjDHGGCtkOAAyxhhjjBUyHAAZY4wxxgoZDoCMMcYYY4UMB0ALZGZm4s6dO4iLi8OLFy/44IMPPvjgg498cMTFxeHOnTsau/UUNhwALXDnzh2Dq/DzwQcffPDBBx/2e9y5c8fWUcJmOACaYe7cuahduzZq1KghG5Ct/zXDBx988MEHH3wYdygXcEzd/rEg4QBogRcvXoCI8OLFi5cXZowxxphd4P6bA6BFuAExxhhj+Q/33xwALcINiDHGGMt/uP/mAGgRbkCMMcZY/sP9NwdAi3ADYowxlh+p1Wqkp6cjJSWlQB7p6elQq9V6Pz/33xwALcINiDHGWH6TlpaGmzdvIjo6ukAfN2/eRFpams7vgPtvDoAW4QbEGGMsP8nMzMSlS5dw9epVxMXFITk52eZX66x9JCcnIy4uDlevXsWlS5d0LvbM/TcHQLMo6wDWrFmz0Dcgxhhj+UdKSgqio6ORlJRk66rkuqSkJERHRyMlJUXrOQ6AHAAtwg2IMcZYfqIEQF2hqKAx9Fm5/+YAaBFuQIwxxvITDoBZuP/mAGgRbkCMMcbyEw6AWbj/5gBoEW5AjDHG8hMOgFm4/+YAaBFuQIwxxvITDoBZuP/mAGgRbkCMMcZ0UavVOHjwIK5evWrrqmjIzwHQ29sbf/zxh8Zj9evXR0BAgM7yHAAN4wBoAW5AjDHGdLl48SKEEBBC2LoqGnSFIrVajcTERJschnbryIkDoHVxALQANyDGGGO6HD9+PN8EwMTERBCRTY7ExESj684B0Lo4AFqAGxBjjDFdOAByALR3HAAtwA2IMcaYLvkpAOaXIeAqVaogMDBQ47E6depwADQTB0Az8FZwjDHGDMlPATC/aNSoEYYPHy5/fvHiBVxdXTkAmokDoAW4ATHGGNOFA6D1jRw5Ep6enjh8+DDOnz+Pzz//HCVKlOAAaCYOgBbgBsQYY0wXDoDW9+LFC3Tu3BmvvvoqXn/9daxYsYLvAbQAB0ALcANijDGmCwdA2+MAaBgHQAtwA2KMMaYLB0Db4wBoGAdAC3ADYowxpgsHQNvjAGgYB0ALcANijDGmCwdA2+MAaBgHQAtwA2KMMaYLB0Db4wBoGAdAC3ADYowxpgsHQNvjAGgYB0ALcANijDGmCwdA2+MAaBgHQAtwA2KMMaYLB0Db4wBoWKEOgLdv30aLFi1Qu3Zt1KtXDxs3bjTp9dyAGGOM6fL3339zALQxDoCGFeoAeP/+fZw5cwYA8OjRI1SsWBGJiYlGv54bEGOMMV04ANoeB0DDCnUAzKlevXq4ffu20eW5ATHGGNOFA6DtcQA0zK4D4KFDh/Dxxx+jfPnyICJs3bpVq8y8efNQuXJlODs7w8/PD4cPHzbrvSIjI+Hr62vSa7gBMcYY04UDoO1xADTMrgPg7t27MXr0aGzevFlnAFy/fj0cHR2xePFiREdHY/DgwXBzc8OtW7dkGT8/P/j6+mod9+7dk2WePn2K2rVr4+jRoybVjxsQY4wxXTgA2h4HQMPsOgBmpysANmrUCH379tV4zMfHByNHjjT6vKmpqXj33XexatUqk+vEDYgxxpguHACtKygoCBUqVEBmZqbG45988gm6d++u8zUcAA3LtwEwLS0NRYsWxZYtWzTKDRo0CM2bNzfqnGq1Gl26dEFAQIBR5VNTU/HixQt53Llzp9A3IMYYY9ryUwBUq9VIS0uzyaFWq42q97Nnz+Dk5IR9+/bJx2JjY+Hk5IQ9e/YY/VkVHADzcQC8d+8eiEhr2HbixImoWbOmUeeMiIiAg4MD6tevL49z587pLR8QEAAi0joKcwNijDGmLT8FwLS0NFnXvD7S0tKMrvunn36Knj17yp8XLlwIT09PqFQqoz+rggNgAQiAx44d0yg3YcIE1KpVK1fqwFcAGWOMGYMDoPUD4IYNG+Du7o7U1FQAQPPmzTFkyBCTPquCA2A+DoDWGAI219y5c1G7dm3UrFmz0Dcgxhhj2vJTAMwPQ8AAkJycjFdeeQWbN2/G7du34eDggJMnT5r0WRUcAPNxAASyJoH069dP47HatWubNAnEEtyAGGOM6ZKfAmB+4u/vjy+++AJTp0596WgfB0DD7DoAJiQk4MyZMzhz5gyICIGBgThz5oxc5kVZBmbp0qWIjo7GkCFD4Obmhps3b+ZJ/bgBMcYY04UDYO7Yu3cvnJ2dUatWLYwfP95gWQ6Ahtl1ADxw4IDOSRf+/v6yzLx58+Dt7Q0nJyf4+fnh0KFDeVY/bkCMMcZ04QCYO1Qqldwc4vr16wbLcgA0zK4DoL3iewAZY4wZwgHQ9jgAGsYB0ALcgBhjjOnCAdD2OAAaxgHQAtyAGGOM6cIB0PY4ABrGAdAMPATMGGPMkF27dnEAtDEOgIZxALQANyDGGGO6TJkyhQOgjXEANIwDoAW4ATHGGNOFA6DtcQA0jAOgBbgBMcYY08XeA2BycrKtq5LrkpOTOQAawAHQDHwPILO127dv49y5c7auBmNMD3sNgCqVCtHR0Xj69Kmtq5Lrnj59iujoaKhUKq3nOAByALQINyBmC5mZmahSpQqKFCmCI0eO2Lo6jDEd7DUAAsD9+/dlCExOTkZKSkqBOpKTk2X4u3//vs7vgPtvDoAW4QbEbOGff/6Ru+LUrVsX6enptq4SYywHew6AarVahsCCfNy/fx9qtVrnd8D9NwdAi3ADYrYghNDYGnHKlCm2rhJjLAd7DoAKlUpl86t1uXXoGvbNjvtvDoAW4QbEbKFhw4YgIrRq1QpEBFdXV9y4ccPW1WKMZTN58mS7D4CFGfffHADNwpNAmK08fPhQXvm7d+8e3n//fRAR2rVrp3eogzGW9zgA2jcOgBwALcINiOW1FStWgIjg5+cHALh06RKcnJxARNi4caONa8cYU3AAtG/cf3MAtAg3IJbXOnXqBCLCb7/9Jh8bM2YMiAjly5dHXFycDWvHGFNwALRv3H9zALQINyCWl9LT0/Hqq6+CiHD8+HH5eEpKCmrUqAEiwoABA2xYQ8aYYtKkSRwA7Rj33xwALcINiOWlgwcPgojw2muvac1w279/P4gIDg4OuHnzpo1qyBhTZA+AvXv3xo4dO2xdJZYN998cAC3CDYjlpeHDh4OI8O233+p8vmXLliAivuLAmB3IHgCViVvMfnD/zQHQLDwLmNlCnTp1QERYt26dzufXrFkDIoK3tzcyMzPzuHaMsew4ANo3DoAcAC3CDYjllZiYGBARihQpgtjYWJ1lkpOT4e7uDiJCWFhYHteQMZYdB0D7xv03B0CLcANieWXevHkgIvzf//2fwXL9+vUDEaFr1655VDPGbE+tVmPHjh24deuWrasicQC0b9x/cwC0CDcgllc++ugjEBEmT55ssNzJkydBRHB2dtZ7pZCxgmbz5s12F7KyB0BHR0e7qhvj/hvgAGgRbkAsLyQlJcHFxQVEhHPnzhksq1ar8cYbb4CIMHfu3DyqIWO2NWDAALsLgBMnTpQBUAiBr776ytZVYtlw/80B0CLcgFhe2LVrF4gIXl5eRm33NnPmTBAR3nzzzTyoHWO2lx8CIM/Oty/cf3MAtAg3IJYX+vfvDyJCnz59jCr/9OlTuT3c6dOnc7l2jNme8jvCAZAZi/tvDoBm4WVgWF5Rq9Xw9vYGEZm0kKyyZdzAgQNzsXaM2Qdl8hMHQGYsDoAcAC3CDYjltvPnz8tJHYmJiUa/LjQ0FESEUqVKISUlJRdryJjt9e3blwMgMwn33xwALcINiOW2nj17gojw8ccfm/Q6lUoFLy8vEBGCg4NzqXaM2QcOgMxU3H9zALQINyCWm65du4aiRYuCiHDs2DGTXz9mzBh5FfDChQu5UEPG7EOfPn04ADKTcP/NAdAi3ICYqYKDgxEQEID09PSXlvX39wcRoW3btma9V2JiIho3bgwiQoUKFRATE2PWeRizd71797a7ADhhwgQOgHaM+28OgBbhBsRMERISIjupIUOGGCx75coVFClSBESEEydOmP2ez549g6+vL4gINWrUwKNHj8w+F2P2igMgMxX33xwALcINiBnr4cOHKFu2rOykiAgbNmzQW75bt24gIrRv397i9757966cSezn58ftlRU4P/zwAwdAZhLuvzkAWoQbEDOGWq1Gu3btQESoW7cufvrpJxAR3NzcEB0drVX+0qVL8upfZGSkVepw+fJleHh4gIjg7+9vlXMyZi84ADJTcf/NAdAi3ICYMWbNmiWXcjl//jwyMjLQsmVLEBF8fHwQHx+vUf7rr78GEeHTTz+1aj2UHUUqV65s1fMyZmu9evXiAMhMwv03B0CLcANiL3P27Fm5K0f2vXkfPXqEihUrgojQuXNnucVbdHQ0HBwccmUXj5mQemkAACAASURBVPv374OIULRoUaMmoTCWX3z//fccAJlJuP/mAGgRbkDMkKSkJNSuXVuu45dzH99jx46hWLFiICL88ccfAICvvvoKRIQOHTpYvT5qtRouLi4gIly/ft3q52fMVpT1MjkAMmNx/80B0Cy8FRwzhrI4raenJx4/fqyzzOzZs0FEKFasGIKCguTVv7Nnz+ZKnZRAGhYWlivnZ8wW7DEAjh8/ngOgHeMAyAHQItyA7FNsbKzN/59s3bpVdkh79+7VW06tVqNr164as4M7duyYa/Vq3749iAiLFi0y+xzHjh3D0aNHrVgrxizz3XffcQBkJuH+mwOgRbgB2Z9169bBzc0NJUqUwKRJk2yyD+7du3dRunRpEBGGDx/+0vIJCQmoU6cOiAgODg44f/58rtVt4MCBICKMHDnSrNffuXMHjo6OcHZ2xtOnT61cO8bM06NHDw6AzCTcf3MAtAg3IPuRmpqKH3/8UeNKGhHB29sb69ev17r/LreoVCq8//77cs29tLQ0o1536dIl+Pj4YNiwYblav8DAQDnxxBxCCPndrlu3zsq1Y8w89hgAx40bxwHQjnH/zQHQItyA7MOtW7fQqFEj2QH8+uuvWLlypZxlS0Ro0qQJ/v7771yvy5w5c0BEKF68OC5fvpzr72eqbdu2gYjQsGFDk1+bkZEBLy8v+Z3yeoLMXijbJnIAZMbi/psDoEW4AdleaGgoypQpAyJCyZIl8ddff8nnEhMTMXbsWBQvXlx2Dl9//TVu3bqVK3V5/vy5rMu8efNy5T0sde7cORARypQpY/Jrd+zYASKSi1SXK1cOmZmZuVBLxkzDAZCZivtvDoAWURpQ165d4e/vr3V0797d4NGtWzeDR48ePbBmzRqkpqba+qPaHZVKhTFjxshZs2+99RZu3Lihs+zdu3c1OggXFxeMHj0aCQkJVq3TiBEjQESoU6cOMjIyrHpua0lISJDfQ1xcnEmvVSaQDBw4EG5ubrmyViFj5ujevTsHQGYSDoAcAC2iNKDcPsqWLYvRo0fj9u3btv7IduHx48f48MMP5ffTt29foyZ7nDx5Es2bN5ev8/T0xJIlS6BSqSyuU0xMDJydnUFE2LVrl8Xny03KlnBnzpwx+jW3bt2SV/4uX76MTz/9FESESZMm5WJNGTOOsnc2B0BmLA6AHAAtojSgcePGYdq0aUYd06dPN/oYOXIkKlSoIP+wFS1aFF988QX279+fZ5Ma7M2xY8fkfWjFixfHqlWrTHq9Wq3G5s2bUbVqVfm9NmjQAOHh4RbVS1nK5YMPPrD7/zfvvPMOiAhbtmwx+jW//fYbiAjvv/8+AGD+/PkgIrz77ru5VU3GjPbtt99yAGQm4QDIAdAiedGA0tPTsWnTJrRo0ULjqmDt2rUxd+7cQtN41Wo1Zs2aJXfOqFWrlkXLpaSmpmLGjBlwd3eX3+nw4cPNCm8nTpyQS7iYclXNVrp06QIiwowZM4wqr1Kp5D9E1q9fDwC4ceOG/EeJqUPJjFkbB0BmKg6AHAAtktcN6Pz58+jXr5+8/4qIUKJECfTv3x9RUVF5UgdbiI+PR+fOneVn7ty5M+Lj461y7idPnqB///7y3CNHjjQpBKrVarz77rsgIvTo0cMqdcptv/76K4gI/fv3N6r8lStXQERwdXXVWNamVq1aICL8+eefuVVVxozyzTff2F0AHDt2LAdAO8YBkAOgRWzVgOLi4jB79mzZASvH+++/jz///NNuJyCY48KFC/JzOjo6Yvbs2bkyxDp37lz5PY4ZM8bo1yk7fri6uuLOnTtWr1diYiKio6OtFngBYMmSJSAitGvXzqjye/fuBRHB19dX4/HBgweDiNCrVy+r1Y0xY+zfvx916tTBkSNHAABff/01B0BmEg6AhTwAxsfH4+2330b9+vVRt25dk7fHsnUDUqvV2LdvHz7//HN5gz4RoWLFihg/fjwePnxok3pZy+rVq+USLl5eXjh+/Hiuvt8ff/whv8Px48e/tHx6ejpq1KgBIsL//vc/q9RBrVbj0aNHOHz4MJYsWSI7jqVLl1rl/EBW56kMoxtj0aJFICJ89NFHGo+HhoaCiFCpUiWr1Y0xY2S/LxrgAMhMZ+v+2x7Yz2+LDahUKiQlJQEAkpKSUKVKFZO2t7KnBnTr1i38+uuvcoancsWsa9euOHr0qN1PTMguJSUFffr0kZ+jdevWePLkSZ6897Rp0+T7Tp061WBZZdHnsmXLWnyFLjMzE2FhYZg5c6ZWp6Ecjx8/tug9FDExMSAiODk5GbWOn74h47i4OPld2cPvACs8so98ANDYT9tecAC0b/bUf9uK/fy22NizZ89QqVIlk4KGPTag1NRUrF69Go0bN9b4I9mgQQMsXrxYBl57lJKSgk2bNsHPz09OqggICLDKMi2mmDBhgvze/vjjD51lsi/6HBQUZPF7Xrp0SXYS48ePR3BwMCIjI/HixQusXbsWQgjs27fP4vcBsnb0UCbT3L17Vz4eGRmJpUuXaoVZ5erKtGnTtM6l/IPj1KlTVqkbY8bgAMgsZY/9d16zn98WHQ4dOoSPP/4Y5cuXBxFh69atWmXmzZuHypUrw9nZGX5+fjh8+LBJ7/H8+XO88cYbcHV1xdy5c016rb03oFOnTqFnz55wcXGRfxxLliyJoUOH4urVq7auHoCsIc/jx4+jb9++KFmypKxnmTJlEBoaarN6jRkzRtZFV7tQFn2uXbu2Ve65DAkJgRACmzZt0to/OCoqCkIIBAYGWu1KrrIMjvL7kpqaikmTJkEIgUOHDmmUbdq0KYgIGzdu1DpPkyZNQETYsGGDVerFmDFyBsAuXbqgZs2acHd3t3HN/sMB0L7Ze/+dF+w6AO7evRujR4/G5s2bdQbA9evXw9HREYsXL0Z0dDQGDx4MNzc3ja2+/Pz84Ovrq3Xcu3dP41wPHz5E06ZNTbpvLr80oKdPn2L69Okaa98REdq2bYudO3fm+RU2IGvIeuLEiahZs6ZGnby8vDBq1CiNK1O2oFarMXLkSFmvhQsXyueyL/q8c+dOq7xfUFAQhBA6l7bJyMjAlClTIITQu9uJqVq1agUiwooVKwBkLWWjdFJLlizRKKssAfPPP/9onUfZgWHChAlWqRdjxsgZAHv16mV3IYsDoH3LL/13brLrAJidrgDYqFEj9O3bV+MxHx8fjBw50qz36Nu3r86rHIrU1FS8ePFCHnfu3MlXDSgzMxO7du1Cu3bt5BZqRIQqVapg2rRpJt3/aI6EhASsXLkSLVu21Hj/4sWLo1u3bggLC7NJGNVHrVZj6NChsp7Lli0D8N+QqLUWfU5OTpYdhL57Cf/66y8IIbBt2zaL3w8AevfuLWc8q9VqzJkzR6OjSkxMBJDV5pX/V7ruQRw/fjyICP7+/lapF2PGyBkABw0aZHchS9d9vMx+cADMxwEwLS0NRYsW1drNYNCgQWjevLlR53z48KH8n//ixQvUqVMHZ8+e1Vs+ICBA4w9Pfr4B/tq1axg2bJjGsKuLiwu+++47nDx50mrvk5mZifDwcPj7+2usX0hEeO+997B8+XKrLnFibWq1GgMHDpT3JP7yyy/yv6216LNy/9+cOXP0lrl9+zaEEJg0aZLWELGxYmNjsXLlSkRERGDKlCkgInTr1g3Xrl2T5547dy6EEPj3338BAFevXpUhXVfYXb9+PYgITZs2xYsXL+wqwLOCiwOgfYuPj8fgwYNRqVIluLi4oEmTJhojCNn3ZleOd955R+McP/30E0qVKoXXX38d69at03huw4YN+Pjjjy2qIwfAfBwA7927ByLC0aNHNcopw4rGOHnyJOrXr4833ngD9erVw/z58w2Wz+9XAHVJSkrCkiVL0KBBA41fxsaNG2P16tVITU0167yXL1/G6NGjUalSJY3zVq9eHePHj0dMTIx1P0guUqvV6Nu3r8bnsOYVr9DQUAghsGPHDoN1mDVrFoQQBv+Roo9KpcKiRYtkR7RgwQIQEZo1ayYnmezevRvh4eEQQsgr4WFhYfJeR11OnTolJxkJIWx63yYrPDgA2rfOnTujTp06OHToEK5evYqAgAC8+uqr8tYef39/tG3bFg8ePJDHs2fP5Ot37NiBcuXKITIyEmvXroWLi4scoXr+/DmqV6+ucauXOTgAFoAAeOzYMY1yEyZMMHp9M0sVpAakVqtx9OhRfP3113B0dJR/XD08PDBq1CijftliY2OxYMECOTFAOdzd3dG7d+98txxNdpmZmejZs6e8UmrNRZ8XLlwIIQTOnTtnsNyBAwcghMDq1atNfo89e/ZACCG3pxo7diyqV6+OmjVrys7pyZMnuHv3LoQQmDx5MlQqFRYvXgwi/YtGK78D7du3hxAC8+bNM7lujJkqZwAcOHCg3YWswhoAk5OTUbRoUa37o+vXr4/Ro0cDyAqAn332md5zTJ06FV999ZX8uWzZsvIK4g8//IDAwECL61mQ+m9z5dsAaI0hYHPNnTsXtWvXlhMYCloDevjwISZMmAAvLy/5R7ZIkSL4/PPPERYWphHiMjIysHPnTnTq1ElOjCDKWqD1o48+woYNG5CSkmLDT2M9KpUKy5YtQ0REhNXOmZKSIm8Wf1k7io2NleHNlDZ3+fJl2QFdvHgRW7duhRACo0aNwldffaURKtVqNaZPny4nnIwePRpEhH79+uk9f9myZTFgwABZt4K0Ew2zTxwA7Vd8fDyISGvZqsaNG6NFixYAsgKgu7s7PDw8UKNGDfTq1QuPHj2SZUNDQ1GtWjXExsbi5MmTeOWVVxAbG4uIiAi89dZbVrnVhANgPg6AQNYkkJwdU+3atc2eBGKqgt6AMjIysHnzZrRs2VLjD26tWrUQGBiIoUOHoly5chrP1atXDzNmzMCDBw9sXf18QQlns2fPNqr8smXLIISQW2C9zIsXLzB16lQ5xAtkBdnly5drdEyXL1+Wr9m2bZsczlX2WDW0KPZ7772nca78vgMNs3/2HgDj4+MLbQAEspaHatGiBe7duweVSoXVq1fDwcFB3p61fv167Ny5E+fPn8eOHTtQv359+Pr6atxyFBAQgGrVqqFu3brYsmUL0tLSULduXZw8eRJz5sxBzZo10bRpU1y4cMGsOhb0/tsYdh0AExIScObMGZw5cwZEhMDAQJw5c0YORyrLwCxduhTR0dEYMmQI3NzccPPmzTypX2FqQFFRURgwYABKlCihdfOuh4cHhgwZgjNnzuTbIV5b2bt3L4QQ2L59u1HlT548KYdaX/ZdZ2ZmyqAXFBSkcWUuOTkZP//8sxzuTUlJwdSpU/Htt98iMjJSTkpp1qzZS9f569+/v0YnZ849ioyZwt4D4JYtWwp1ALx27RqaN28uR4MaNmyIb775Ru+9xPfv34ejoyM2b96s95wBAQEYMmQIzp49i3LlyuHx48dYtmwZ/Pz8zKpjYeq/9bHrAHjgwAGds26z34A/b948eHt7w8nJCX5+flqL2OaGgj4EbEh8fDzmzZuHd999Fx07dsSOHTuQnp5u62ppSE5ONnumbF5TJmYYG5pSUlIwfvx4CCFw//59g2WVIDdx4kSdS/x06dIFnTp1Qr9+/eDr6yt/v2bMmCHvFaxTpw6ICCdOnND7PkpZ5QgLCzPqszBmLnsPgL/99luhDoCKxMRE+Xeqc+fOWvuJZ1e9enVMmTJF53MXL15E9erVkZCQgFmzZqFTp07y/Ob2wRwA7TwA2jtuQPbn/v37mDx5MqZNm2a1vXNzS2pqqrz/Ly4uzujXbdq0CUIIhISEGCy3ZMkSCCG0Zsorhg0bptGRFilSRK5vuGrVKggh5IQeQ8O6kydPhhBCdsJr1641+rMwZo6cAfDHH3+0q5A1YsQIDoDZxMbGwt3dXWNB/eyePn0KZ2dnrFy5Uus5tVqNd999V46SBAYG4vPPPweQNSOYiPD8+XOT68T9NwdAi3ADsi9xcXGYMWOG/GP7+++/IzY21tbV0uvKlSsQQmDWrFkmvU65b3D69Ol6b4bOPmFE3zqLixYtkp1oz549cfz4cRARHB0dcfDgQQgh8O2338LFxUXvcHNSUpL8vt955x0IIfTun8yYtdh7ABw+fHihDoChoaEICQnBjRs3sHfvXtSvXx+NGjVCeno6EhISMGzYMBw7dgwxMTE4cOAAmjRpgooVK+r8W7Vw4UJ8+eWX8ucTJ07g1VdfxfHjxzFmzBjUqVPHrDpy/80B0CyFeQjYXqWkpGD+/Pny/rh58+ZBCIGZM2fa7f+fsLAwCGH67h4qlUrO1NW3dExERASEEDr/Ra1QblRXbptQq9WoVq0aiAhr1qyBEAJDhgyBj4+P3nNER0dDCIF+/frB1dVVdnTmrh/JmDE4ANq3DRs2oGrVqnBycoKnpycGDBggRzmSk5PRunVreHh4wNHREZUqVYK/vz9u376tdZ6HDx/C29tba+vWsWPHonTp0vDx8TF4e4ohHAA5AFqEG5B9UKlUcshyxowZiIuLQ3x8vFw4ee7cuUhKSrJ1NbUoQ7Tm7Chy6NAhOblD19U5ZW9hU3d1UXY96du3L4QQGDNmjN41AIGs/bqFEPjyyy9BRHI4WNcfcyArZF68eBGZmZkm1Yux7Ow9APIQsP3j/psDoEW4AdmeWq2Wy5ZMnDhRY2JEbGwsfv/9dwghsHDhQrtajzAtLU3e/2fO/SvJycmYOHEihBC4du2axnNPnjyBEFmLPpsafENCQkBE8PLyQkBAAIQQGDBggN7yCxYs0AiA06ZNMxg858yZAyLiYWJmEXsPgIX9CmB+wP03B0CLcAOyPeVK2NixYzXWslM8efJEhpJly5bZzYxlZf9dS4JQSEiIzmFeZceQ4OBgk8+ZnJwMFxcXEBF++uknGax1SUxMlB3b999/DyKSPytrDubUsGFDEGnv+2ms4OBg9OrVy67CPMt7HACZpbj/5gBoFr4H0D6cO3dO/mHNvtF4TsrMYCEE1qxZY5VV5C21b98+CCG0Fjc3RVxcnLyKqNwjo1arMWfOHIvW4/voo49ARHKXkAULFugsd+TIEQghsGjRIkyaNAlEJNcEXLFihVb5hw8fasw4fvLkicl1U+5R5JnGhZvSjhwcHAAYFwBVKhVGjRqFXbt25Xr9OADaPw6AHAAtwg3Idm7evCnXw9uzZ89Ly9+6dQsTJkyAEAIbN260+T1oS5cuhRACp0+ftug8yoKzmzZtAgA8ePAAQghMmDDB7IkYc+fOBRGhdevWEEJg+fLlWmUyMzMxc+ZM+RlWrVoFIkKHDh0ghMC0adO0XrN8+XLZcXt5eWHcuHEIDQ01qW6vvPIKiAg9evQw67OxgiH7PyQAyK0IDYWs1atXa1w1zE0cAO0f998cAC3CDcg2njx5gilTpkAIgQ0bNhi9+8jVq1flosXbtm2z2a4laWlpsh6WLlPz8OFDOQT+7NkzObPY0M4dL3P9+nUQEd5++229AVBZwmbKlClIT0/H1q1bQURo1qyZ7OwSEhI0XqPcJ1i8eHH06dMHQgjMnz/f6HqlpaXJDrxChQq860whZk4AnDhxIgdAJnH/zQHQItyA8l5iYqKc3btkyRKT7+mLioqSw6YhISE2CRHXr1+HEAKBgYFWeX9lyZa//voLf/zxB4QQiIqKsuictWrVQrVq1eSyOjkFBwdDCCGv4IWFhYGIULduXcyePRtCCFy/fl2WT0tLk1fvxowZIztEXVcK9Xnw4IHGvV/nz5+36DOy/IsDILMU998cAC3CDShvpaenY/HixRAia/HkxMREs85z5swZ+Qc5PDzcyrV8uf3790MIgS1btljlfDExMRqdzKRJkyye7PLTTz+hVKlScjg5e1BVFpkWQsgt5v7++28QEby9vWU4jIyMlK/ZsmULWrdujTZt2sirt8ph7HD8+fPnNQLg77//btFnZPmX0gaKFi0KgAMgMx333xwAzcKTQPKeWq3Ghg0b5LCjORMIsjtx4oT8o3zs2DEr1dI4y5YtgxACp06dssr51Gq13FPYWsHy1KlTcHFxkUvBZG/je/fuhRACq1evlo9FRUWBiFC6dGns3LkTQgjs27dPPp9zb9Rhw4bJc+ccKtbn4MGDGgGwdevWFn9Olj/lDIDK5CNDIUuZqMQBkAEcAAEOgBbhBpR39uzZAyEExo8fj5s3b1rlnMoSMtYMYy+Tnp4uJ688e/bMaueNioqSn0XXcjjmiIuLkxM9lO88MzMTU6dOhRACFy9elGVv374NIoKTk5OcHbx582YAWQFVCYCBgYFYt24dhBCykzS0z3B2f/75J4gIr776qgybrHAyJwBOnjyZAyCTuP/mAGgRbkB5459//pF/QPVtfWYOtVotr2YJIRAREZHrS8TcuHEDQmTtU2zN+w8zMzOxevVqLFu2zKqfQdlhRZmtfPfuXXkVNvvQrbIpOxHJIfZly5YB+G/NwzFjxsgh47Nnz8pOe//+/QCy1iA0VPeFCxeCiODn5yf3LGaFU8mSJdGmTRuULFkSgHEBcMqUKRwAmcT9NwdAi3ADyn2XL1+WkzYOHz5s9fOr1Wr89ddf8g/0nDlzcPXqVau/jyI8PFzj6pi9U74bJaQpV/fWrVunUS4jI0N2rhcuXJBX+4D/hryHDx+u8Zphw4ZBCIGePXvi9OnT8PDwQJMmTfTWRRnC69Chg3yvtLQ0K39ilh8MHjwYQgj07dsXgHEBcOrUqRwAmcT9NwdAi3ADyl3379+X251t374912bsqtVqnD59GtOnT5d/qNetW2fxEi26LF++HEKYvkevrRw9ehRCCPz5558A/pv9q+u+SWUHEWU4euzYsVCpVBgyZAiEEPjf//6nUV5ZC7FJkyYoVaqU7Jz1DQkPHToURIQhQ4bIstYcRmf5R85QlV8CIC9dZD+4/+YAaBFuQLknLi4OM2bMgBACq1atypPdO1JSUhAaGirX6Bs/fjz2799vtatMFy9elB1Bfgku0dHRECJrt4/MzEy5o0r2PZcVHh4eICKcO3dO3uf4/PlzfPHFFxBCYPbs2Rrlla3sWrVqpTG5IyQkRGdd/P39QUSYPHkynJ2dQUS4detWrnxuZt/MCYDTpk2zeQC09QL07D/cf3MANAvPAs5dKSkpmD9/PoTIWig4r/d9ffz4sbz3TRnKvHDhgkX/eo+Li5OTJ0zd/cKWlIWmp06dinv37kEIgcmTJ+vsyKpWrQoiwrFjx+RagKdOncInn3wCIbT3B46IiIAQAp06dUK9evXQunVrEBEmTZqksy7t27cHEWHx4sUoU6aMvNrICp+cAbBfv35GBcAWLVqgatWquV4/fQHQHrahZFk4AHIAtIjSgA4fPozTp0/rPU6dOvXS4+TJk1rHqVOncOnSJdy/fx+JiYkFfvggJSUFN27cwMqVKyGEwIwZMxAXF2eTuqjVakRHR8tZsEJk7W9r7IzV7DIzM+V9cAsXLsxXnUBaWpr8/Mr9i8HBwTrLvvHGGyAi7NmzR/4/XLhwIbp16wYhBM6cOaNR/vTp0/J7TUtLk1doOnbsqPP8jRs3BhFhy5Yt8Pb2BhHhxIkTVv/MzP6ZcwVQuXqdF/fi6QuAGRkZuf7ezDgcADkAWkRpQCNHjtT5y27tY/z48Zg1axZWrFiBLVu2YN++ffjnn39kSExKSso3ITEhIQFXrlzB4cOHsWHDBo2gJYTAxIkTdQ4z5rX09HQcPHhQ7iM8duxYhIaGmvSHXAlOkyZNyjdDv9kp90YGBgZCCIEjR47oLNesWTMQEf78809s27YNQgj8/PPPGDhwIIQQiImJ0Sh/+fJlCCEQFBQE4L/dRKpVq6bz/DVq1JD/4PL19QUR2WQhb2Z75gTASZMmcQBkEgdADoAWURrQ4sWLERwcLI+1a9cadaxbt87gsXbtWixatEjeC2fMMWHCBMyePVsrJF6+fBkPHjzI85CoVqvx/PlzREdHY//+/QgODjb4eWbOnIkNGzbg9u3beVZHYzx//hwbN26U9QwKCjJqMersu3RYcwmbvKRM1lCOu3fv6izXtm1bEBFWrFiBgwcPQgiB7t2743//+5+8HzA7ZUj5999/h0qlwqpVq9CmTRsQkc4rv8pEkaioKLzzzjsgIuzYsSNXPjOzb/k1AFq6Qw+zHg6AHAAtkpcNSKVS4fnz57h58ybOnTuHI0eOYPfu3Vi/fj0WLVqkMYPVlJC4detW7Nu3D5GRkTIkJicnmxUSMzMz8eTJE5w7d04OA+bc9iv7MXfuXGzevBlHjx7FjRs3kJycnAvfnHVdvnwZ06ZNk1cpT58+rfe7SkpKwu+//w4hBLZt25bHNbWeLVu2yP9nkyZN0nsje6dOnUBEmDNnjlwLUOmYx44dq/W6uLg4CCEwbtw43L59W76Hq6srDh48qFE2IyMDDg4OcpZwy5YtQURYu3Ztrn1uZr9yBkBj7gG0hwDIyxbZDw6AHAAtYm8NSKVSITY2VobEiIgI7Nq1C+vWrcPChQtNDolz5szBypUrsXXrVuzfv1+GxIcPHyIpKQkPHjzA6dOnsWvXLixdulQu2ZLzGDduHIKCgrBt2zacOHECt2/fztd/COPj4+U9bkJkLZGSc6KKWq2WS6bMmTMnX3/eAwcOyM+affu3nL777js5iSPn/sR//PGHVvmMjAz5fPZdWWrUqKFV/vHjx3IGZ3p6Oj799FMQERYtWmT1z8vsnzkB0B7uAUxNTc3192bGsbf+2xY4AFogPzagjIwMGRLPnj2rFRKVq1vmHhMnTsSSJUuwa9cunDp1Cvfv389Xkx6MlZmZiYiICLlI9cyZM3Hnzh35/PHjxyFE1n2bDx48sGFNLXf27Fn5/9fQYtyDBg0CEWHUqFGIjY3VaBfLly/X+RrlCnH2YeaWLVuiW7duGuUuXrwIIoK7uzsA4OuvvwYRycWmLRUeHo6SJUti06ZNVjkfy12WBsDcXo5FXwDM6xUNmH75sf+25xfZfgAAIABJREFUNg6AFiioDSgjIwPPnj1DTEyMDIk7d+7EunXrEBQUJEPilClTsGLFCuzZswfnzp3D48ePC906V3fu3JETWMaNG4eIiAjcu3dPriX4zz//2LqKFss+PGvo3szRo0eDiDBw4ECoVCqNjm/r1q06XzNnzhw5RKyU7d69O3x9fTXKHTlyRGOCSO/evUFEGDduHICsyTpr167FvXv3zPqMDRo0yLM14pjlzAmA2W9HWbBgQa7WT18AzA+3uRQWBbX/NgX/tTMDrwOYNdycX2Yc57aUlBRs2rRJY8hbCIH169cXiO8oJSUFkyZNwvTp0w1ezVX2Wu3RowcAzU76wIEDOl+jLI+T/fj1119RtGhRJCQkyHLbtm0DEaFRo0YA/tsV5JdffgEAbNq0CUSEr776yuTPd//+ffzwww9wdXUFEZkdIlneMScAKutwCiHg6emJsLCwXKsfB0D7xwGQA6BFuAExhbKdnHIf5B9//FGg/tg/evTopUvYzJs3D0SEL7/8EgDw22+/yY4v5xqAiuwzqydPniyH6Tw9PTVC49KlS0FEaNeuHQBgzJgxICIMGDAAQNbwHhHBz8/P5M+mvL+y0LSyLA2zX5YGQOVqb25dodcXABMTE3Pl/ZjpuP/mAGgRbkAspydPniA0NBSPHz+2dVXy3MqVK0FEaN26NQBgxIgRsuPLuQagYteuXRr3CSo7sDRs2BCTJ0+W5ZR9XJV7A5VFo/39/QFA7g9cvnx5k+utvP/3338PIkKHDh1MPgfLW+YEQGV7wuwBcOHChblSP30BMPtVbWZb3H9zALQINyDG/rNlyxYQEZo2bQoAGDBggOz4YmNjdb5GWS9QCIGQkBC5aHaHDh3w6aefynIjRowAEWHIkCEAtK82du3aFUSEIkWKmDTpKD09Xb7/t99+CyJCs2bNzP0KWB4xJwA2a9ZMllF2kunevXuu1E9fAIyPj8+V92Om4/6bA6BFuAEx9h9lJ4969eoBAL755hvZ8ekLZSdPnpRl/v33X1y9ehVCCAwePBhly5aV91Aq4Uy5KqhcbWzTpg0AyHUBicikHWSUvY6VySdEhDfeeMOSr4HlAUsDoBACZcuWBRHlyn26+gKgrba2ZNq4/zYjAG7fvt3koyDdC5UdNyDG/vP333+DiFC5cmUAQJs2bSBE1uLR+ly8eFF2jg8fPkRqaqrcdq9ixYq4ceMGAKBJkyYgImzYsAEAsHnzZo2rdcrWcESEU6dOGV3n8+fPy/cfMGCARv2Z/bJGAFT2rs6Ntfl+/vlnnQEw5244zHa4/zYjADo4OJh0FClSBNevX8+NutscNyDG/nPhwgUQEcqUKQMAqFatGjp27Ghwbb07d+5AiKz1EpWrhMrOI+3bt5c7fShXa06ePAkA2LNnD4gIDRo0AAC89tprMgD+9ddfRtc5+yLXI0eO1Kg/s1+GAqC+K3r6AmBuhDJ9VwD13QrB8h7332YGwEePHhldvkSJEhwAGSsEbt26BSKCs7MzAMDd3R1EhIsXL+p9jUqlwpo1azRm/F6/fl0GsoEDByI+Pl6GO6WzPnr0KIgI1atX19gmztTdQbIv3yOEgKOjIxwdHQvE8j0FmTUDoCm3DBhL3xXAl82kZ3mH+28zAmCPHj1MupG1b9++ePLkialvky9wA2LsP7GxsTKEpaSkyP825R+MQNaSOsow8Keffop///0XRITSpUvLMmfPngURwdPTEw8ePJDvRUQGhwFzWrBggUYHXbp0aVl/Zr8MBUB9i9E3bdpUZwBUbjOwJn0B8OnTp1Z/L2Ye7r95EohFuAEx9p/09HQZwq5evaqxd6+plGHgbt26ITg4GESEhg0byuevX78OIkKJEiVkQFSOPn36AACSkpJeetN9zv2xldmhhXEZn/zEmgHw6tWrVq+fviFgblf2g/tvKwVA5b6cwoJ3AmFMN2dnZxAR9u/fDyKCm5ubWed5+vQphBAYM2YMvvzySxARunTpIp9/+PAhiAgODg4IDQ3VCICffPIJAOCNN96Aq6ur3nu81Gq13LVl0KBBEELg7bffBhHh2rVrZtWb5Q1DATAjI0Pna3IGwKZNm770FgVzcQC0fxwArRQAX3/9dWucJt/hBsSYJmUyxvLly0FE8PLyMvtcykLSjRo1AhFh9OjR8rnExEQZ+BYsWCDDIBHh7bffhkqlks+vWrVK5/nT0tJkx9ylSxcIIdCmTRsQkd6dS5h9sEYAVIb8L1y4YPX66RsCfvjwodXfi5mH+28TAmCnTp10Hh07djT7X/n5HTcgxjRVqVIFRIRx48ZprAloDuU+wG7duoGIsGzZMvlcZmamDHzDhw8HEaFWrVogIlSsWFFj4siwYcN0nj8uLk5eZfzwww9lECQiHDp0yOx6s9xnKACmpaXpfE2TJk20Atm7776Lf//91+r10xcAHzx4YPX3Yubh/tuEAFiqVCns3LkTBw8e1DgOHDiAsmXL5mYd7RY3IMY0KfdV9erVC0SE5s2bm32ukJAQGdCKFy+uFcpKlCgBIsIXX3whh4iJCEWLFsW9e/dkAGzZsqXO8z948ABCCPz6669yhqiyHZwpS8mwvGetAPjll1/myi1M+oaAc2PGMTMP998mBMAOHTrg4MGDOp9TVuMvbLgBMaapWbNmcj9gIsLnn39u9rkSExPRu3dvCCHg5+eHu3fvajzv6ekJIkL9+vVBRJg4caK8KqgsE6NMFNG1E8mNGzcghMDPP/8MPz8/CCEwcOBAEJFcf5DZJ0MBUN8M7m7dumkFsq5du+LEiRNWr5++K4D37t2z+ntZIjY2ttD2X9x/8yxgi3ADYkyTcg+dj48PiAjfffedRefr1KkThMjapzfn7M7q1avLgEdEWLFiBcqVKwciklvFKYeuYb6oqCgIIfDjjz+idu3aMgwSEYKCgiyqN8tdpgbAZcuW6QxkQghs3rzZ6vXTFwBz/iPGlrIv1aRv5nRBxv23BQGQ72XgBsRYTh07dgQRoXjx4iAiDB061KLzKbNzf/nlF63nGjRooBHyQkJCULt2bRARJk2apPHcvHnztF6v7EP8/fffo0qVKnI4mIgwffp0i+rNclfOANi3b1/5s66tR5X1IXUdRYoUsXr99A0B37lzx+rvZa4rV67I34/c2A7P3nH/bUEAtOTm7oKCGxBjmr777juN4DV+/HiLzrd582YEBARACIGEhASN55ThZuU4ffq0nDE8aNAgjecGDRqkde6IiAg5DOjp6QkhBH777TcQEX777TeL6s1yl6EAmJiYqFXeXgJgTEyM1d/LXJcvX+YAWMj7b7MDYN26da1Zj3yJGxBjmnIGr7lz51p8zmnTpkEIobVgrzLc/Mknn6B379549uwZPvjgAxAROnXqpFEPXfci7t27F0IIfPzxx3B3d4cQAgEBASAiDB482OJ6s9xjaAjY1ABYrFgxq9dP3xDwe++9Z/X3Mlf2AKhv4kxBxv03XwG0CDcgxjQpQ6jKYY3JFH/++SeEEDh8+LDG419++SUcHR3lFcKIiAh06NABRIR3331Xox5vvfWW1nm3b98OIQSaN28OZ2dnjUDQs2fPl9YrIyOjUN47ZQ8MBUBdW5UaCoBOTk5W3/tZXwBU9sm2BxwAuf/mAGgBbkCMaZo8ebLWfXmWOnLkCIQQ2Lhxo8bj/v7+qFixouxcp0+fDn9/fxARatSoASJCpUqVQEQ6l6rasGEDhBBo2LAhiAhjx46FEAKvvPIKOnbsaLBOiYmJ8Pb2xgcffGDx52Oms2YAdHFxsXoA+umnnzgA2jnuvzkAWoQbEGOa5s6dqxEA//77b4vPee3aNQghMHv2bI3H33//fbl8i3IMGzZMY2awshwNEWnNDl2xYgWEEKhbty4cHBzkULOHh8dLl7Zas2aNPC/Le4buAdS1/7OhAFi8eHGt+0st1adPH71h015cunSJA2Ah77/N/uula0glv0pKSkKlSpX07higDzcgxjTlXH7l8v+zd97xURT9H98kJAGCIAalSqLEQAgIxEfQR6WIIioCIk1RAZEOoUgJfanSLRARQxOkSEcU7BQFKWJAeYKoSCAGlA6hhZB8fn/kt+Pu3d5l73avhPu8X695cbs7OzvZm+T7ZnZn5vBh02VeuXJFBFD1y+pNmzbF008/DVmW8eabb2oGcSipS5cuYkSy7TuEc+bMgSzLqFKlCkqUKIF3330XsiyjcuXKePjhh53WafTo0QE9hYavcdYD6KoAlihRAmfOnLG0fsrKMv4sgOoBWzdu3PB1dbwO4zfnAQSQ/95SmzZtKICEmGTNmjUaATt16pQl5c6cOROyLCM9PV3s+/XXX/HGG29AlmUsX74csixj7NixdqN/lSXivvnmG90yK1SogHLlyiElJQWyLKNq1aqIj493Wh9l1RFJknSnHSGexZkAnjt3zi6/MwG84447LJ+g2d8F8OzZs5rfE/YABiYBL4C//fYbWrVqhYULF1IACTHJl19+qQksOTk5lpS7bNkyyLKMnTt3in15eXmi52/Hjh2QZRnjx4/XXH/48OF48sknxUTRaiZOnAhZllG6dGnExMTgo48+gizLqF27NqKjo0W+kydPasQTABISEsQ1zp8/b8nPSIxjpQBGR0fjzz//tKxuOTk5DgWwWLFill3HDCdOnKAAMn67LoADBgwwnMyybds2NGvWDOXLl4ckSVi3bp1dnuTkZERHRyM8PBwJCQl2IwULonnz5jh8+DAFkBAL+OGHH0RQue222ywrd+fOnZBlGSkpKWLf+fPnIcsyxo0bh/T0dMiyjAkTJmgC28SJE/Haa69BkiSMGzdOnJuTk6Pplaldu7YYbfzQQw+hTJkyAIDc3FyRT5leJDc3VzxWliSJk+L7AGcCqPc415kA3nvvvXavB5ghOTmZAlgIYPx2QwAbNmyoSbfddhuKFy+OOnXqoE6dOoiIiEDJkiXRqFEj05XbtGkTRowYIR4r2QrgihUrEBoaipSUFKSlpaFfv36IiIjAsWPHRJ6EhATEx8fbpczMTKxfvx6DBg0CAAogIRZw8OBBEVQqV65sWbmXL1/GuHHjIMsy/v77bwD5j4BlWcacOXPwzz//6PYAvv3225BlGZIkoWvXrqK8rKwsEZSDgoLw6KOP4tNPP4Usy2jUqJEI1GfPnhX5/vnnHwD2wdOfJvcNFKwUwJiYGBw6dAgbN27Ejz/+aLpur7/+ukMBLF68uOnyrYACyPgNmHwEPGPGDDz33HOaLvdz586hRYsWmD59uunKqdETwLp166JHjx6afdWqVUNSUpKhMpOSklCpUiVERUUhMjISJUuWxNixYx3mv379Oi5evChSRkZGwDcgQtSkp6eLoFKrVi1Ly165ciVkWcZnn30GANi6dStkWcbatWtx4cIF3XcAU1JSMH/+fEiShKZNm4qyTp06JXoPlWPffPMNZFnGM888IwZ3KJIpy/8u47V//37NNQ4dOmTpz0kKxpkA6r136kwAY2NjsXr1astGdXfv3t3vBTAzM1PThrkSSGBiqrVXqFABBw8etNv/yy+/oHz58maKtsNWALOzsxESEoK1a9dq8iUmJqJ+/foul2+kB1BZJcA2BXIDIkSN+uVyq1c9UKaDmTx5Mm7cuIEFCxZAlmXs2bMH165dE0E2JCRE1GHZsmXivcSYmBhR1rFjxzQC2Lp1a/GYuVWrVpAkCVlZWWIOQln+dyUS2/cc9+/fb+nPSQrGmQAqPbVqgoKCHApgXFwc+vbtK77Pxo0b200Z5Aq9evUqFAIYFhaGIkWKUAADOH6bEsASJUrYjawDgG+++QYlSpQwU7QdtgKo/A9mx44dmnwTJ05EbGysy+UbEUD2ABLinBs3bohA+vzzz1tadl5eHt566y3Isozdu3eLiZvPnTuneVdP/X7ehg0bcPbsWYSFhUGS8tcLBv59fKz0GHbq1AmpqamQZRkvv/yyeLdv/fr1olzlP7vqOQAlyZq5Dokxbt68qXksryeAyisCaoKDgx0KYJcuXez+U//ee++5Vb/ffvsNkiThiSee0L1WRESEqZ/fKpT/AI0cOZICGMDx25QAvvLKK6hcuTJWrVqFjIwMZGRkYNWqVYiOjsarr75qVR0BOBZA9ahAAJgwYQKqVq1q6bUdwQZEiD3h4eGQpPw5+KxGeew7adIkyLKMWbNmiWPqUb1KIP/6668BAO3atYMkSejTpw8ACNlT5vPr06cPjh49ClmW0b9/f0iShD/++ENMDSPLsng/7K233tLIwtatWy3/OYk+jz76qN3jXCC/182ZAIaEhDgUQOUdUXWaNm2aW/VTzvd3ATx48KCoU3BwMAUwQDElgFeuXEHPnj0RHh6O4OBgBAcHIywsDD179tRdkNsMnn4E7AqzZ89GXFwcYmNjA74BEWJLmTJlIEmSGGBlJRcvXhQ9f7IsY9OmTeLY9OnTIcsyypUrJwLxDz/8AAD44osvIEkSSpcujWvXronHvcraxUlJSWJgyJgxY1CkSBHs379fiKYsy+Jpw7BhwzSy8Pnnn1v+cxJ9lHvuTAD1RmUXKVLE4wK4c+fOAgXQ6idj7pKWlibqFBoaauqRd2GFAmjRPICXL1/GgQMHsH//fsvFT8HRIJCePXtq9sXFxRkeBGIWNiBC7ImOjhZTsHgCZU5AWZbx22+/if3qlTyUQPzzzz8DyH90qIjh9u3bxYCPQYMGQZIkTJgwAXl5eZg8eTJkWUbZsmWxZcsWTfBWXnexfWS4YcMGj/ycxB5HAti7d2+xrTepc2hoqEsCOHXqVJfr9vrrr4vzx4wZQwH0cxi/LZwIOi8vD3l5eVYVByB/qobU1FSkpqZCkiTMnDkTqampYpoXZRqY+fPnIy0tDf3790dERITdpK2egg2IEHtq1qxp6j2qglDe3xs/frxm+oq5c+dClmXcf//9IhAfOXJEHH/iiScgSRLmz58vpnzp06cPJCl/uhgAmDdvHmRZRnx8PNasWaMJ3kpv43PPPaeRhY8//tiujr/99ptlq6CQf3FXAMPCwrwmgMWKFXN4HasF0N0OF/Xodgpg4MZv0wI4b948xMfHIywsDGFhYYiPj9dM1mqGLVu26I667dixo8iTnJyMqKgohIWFISEhAdu2bbPk2s7gI2BCHPPYY49BkiSsXLnSI+Xn5ubi22+/tRt9u2jRIsiyLN4TkyRJ8z5Yr169xONeZdJnZZLoefPmAYAY9NGgQQMxylhJyhOIevXqaf4effjhh5p6/P3335AkCcHBwYZ+HqNLye3atQvt27fH8ePHDeW/FXEkgH369BHbf/31l915RYsW9bgAKj3DNWrUcHgdKydHnzFjBiRJwooVK1w+99ChQ6JOYWFhFMAAxZQAjhw5EhEREUhKSsKGDRuwYcMGJCUloUSJEhgxYoRVdfRb2IAIsWfTpk149dVXcenSJa9eV1kP+OmnnxaBPCsrSxyfMmUKnn76abRu3RpLliyBLMto0qSJJogq07688MIL4pGykpYvX45Tp06JaWbuvPNOSJKEuXPnauqxfft2cX1l7kAAuk9IBg4cCEmSsG/fvgJ/PqXMBg0auHmHCj9GBFB9zxWKFy/uVACDgoI0AjhlyhSX66YI4CuvvOIVAVTqGhYW5vK5tgIYiOtZM36bFMDIyEgsW7bMbv+yZcsQGRlppuhCARsQIf6D8si2bdu2Ijjm5uaK48oo4aZNm2LOnDmQZRn33XcfJEnCp59+CuDfR2Pdu3fHlClTIMv5I41lWcaCBQsQFRUlylbWA3733Xc19fjuu+9EntWrVwPIn8S6TJkyGD9+vCavku+ZZ54p8OdT8irL1AUiRgTQtoc0JycHJUqUcCqAwcHBGgFs0aIFfv31V005f/31l92sE2qMCGCFChUsvxfh4eEun0sBZPwGTArg7bffrnkJW+Hw4cMoVaqUmaILBWxAhPgPynt9nTp1Eu9iqRk/fjxkWUa7du3E6N5KlSpBkv6dyuXkyZOQ5fzBIUr+VatWQZZlDB48WATdiIgItG7dWvdxoTJRdEhICAYPHox//vlHIxjqaWOUfS1bttSUoddbqOQNhL+tjnAkgH379hXb6qVAASAjIwMlS5Z0KoBPP/00IiMj7R4F61177969mv1r167Fs88+ixYtWhQogEp9rbwXRYsWdflctQCGh4dTAAMUUwLYp08fDBgwwG7/G2+8gV69epkp2q/hO4CE+B9fffUVZFlGz549xSNahZs3b4qAN3z4cIwaNQqyLCMyMhKxsbHid1iZCmb06NEi/9dffw1ZltGvXz9IkoTmzZvj8OHD6NatGyRJwrhx4zT12LBhA+666y6MGDECXbp0EUIYHByM22+/XbPcpBLE27RpI/Zt2rQJZcqUwfr16zXlKnn9ZTUJX2BEAG0HAaalpeH22293KmVKu3jyySfxwgsvIC4uDp07d8b58+ftrq0MGLLdryRvC6Dtf3SMQAGkAAIWCGDJkiURHx+PLl26oEuXLoiPj0fJkiWFHCrpVoQNiBD/Ydu2bZBlGQMGDIAkSYiOjhbHrly5ohuMbUfw5ubm2k3hsXjxYsiyjCFDhkCSJBw4cABA/pyjkiRh+PDhmjKWL1+Odu3aifOXLFkCSZLw4osvQpZldO7cWeRVgniHDh3s9gUFBWnKVfaHhoZads8KG44EMDExUWwfPXpUc87Bgwdxxx13FCiAemnJkiV2137nnXd061SYBfDKlSuW1auwwPhtUgAbNmxoKDVq1Miq+voVbECE+A+7du2CLMtISkqCJEmIj48Xx86dO6cbjE+fPm1XjloAx40bh+7du4tewVWrVol8ihD269cPly5dwuHDh5GXl4cFCxagdevWooxp06ZpeqDatWsHIP8xrxLEO3XqJMpV9oWEhGjq5ejRZCDhSAD79esntv/880/NOb/88gvKlSvnlgDOmTPH7tr+JoDu9AhTABm/AQvnAQxE2IAI8R+U5d1GjRoFSZJQt25dcUx5t0+dRo4cqVuOeqWRd955B9HR0WJbPe+gch11evPNN5GcnIyWLVuKcwYNGoSHHnpIbD/yyCO4efMmLl++LM7r2rWrKFfZZztggALongD+/PPPGDFihFsCmJycbHdt20E/tm1APSBFLy1fvtzSe+HO8nJqASxatCgFMEAJ3L8kJuA7gIT4H+rVDSRJ0jx5SE9PhyzLePfdd8VoYEdzvSnHZVnG/PnzIUmSeCfwwoULmny2wb9UqVIYOXIkmjVrJspo1aoVHn/8cY0AZmRkiPXMJUlCt27dAEAjhXXq1NHUiwJoTAD/+OMPzTkHDhxwS/5kWbvWtBEBbNKkSYFlWvX9Kdd0Z3JpCiAFEDApgJMmTcL8+fPt9s+fPx+TJ082U3ShgA2IEP/hjz/+gCznrxCiPIrbtWsXgH+nd0lJScHmzZshyzLef/993XKmTp0qguOCBQs0vTrK2sLAvxPxSpKE5557DnFxcZAkCTExMWjevLkoo3bt2njmmWfEdv369fHrr7/i0KFDmseGSj2VffXq1QMAbNy4ETNnzqQAwrEA9u/fX2z//vvvmnOUuR3dSerHvcq11VKo3m9bL28JoDtzC9oKoKeWcPVnGL9NCmBUVJRYIF3Nrl27NC9g36qwARHiP2RkZIievWrVqokAd+HCBdELtHjxYpw6dQpvvvkmvvrqK91y1BNAv/fee5Ck/FG6ilwqgwySk5PFoIxhw4bhlVdeEY/klPyynD/VzPPPPy+2H3/8cezfvx+7du0SQbx169YAgH379ol91atXB2D/iJECaC+AAwYMENu2U5Nt2rTJbQF866237K7tbwJYsmRJl89VC2CxYsUogAGKqZYYHh5u974FABw5csStySkLG2xAhPgPp06dgizLmDJlCo4ePSoCXHp6Ovbs2QNZ/nfUr3qCaFvmz58vzlVW6ujQoQMmT54MWZYxduxYXL16Fe+//z5uu+02PP3005Dl/HcKlaD88ssvizKqVq0qRgDLcv7qIz/88IOYHkaSJDz77LMAtJNI33333QDyA/1jjz2GN954A6VKlaIAFiCAaWlp2LJli1je7LPPPnNbAGfOnGl3bX8TQHfmhaQAMn4DJgUwJiZGM0xeYfHixbjnnnvMFF0oYAMixH+4ePEiZDl/5K4yJYwsy/j555+xfft2yLJsN7eeHitWrND03klS/lQvimDKcv5AA+WzWj6UoNylSxexLyEhAZ07dxbbzzzzDL799lusWrVK5H/88ccBQCOFpUuXRm5urp1Y3HXXXZ6+lX6LIwEcOHCg2O7cuTMkScJLL70EwJwAqqf4Ua49e/Zs3TqFh4f7RABvv/12l89Vvy9LAQzc+G2qJU6ePBmRkZFYsGAB0tPTkZ6ejvnz5yMyMhKTJk2yqo5+BweBEOJ/XLt2TQS1Dz/8UHz+7rvvxCTRmzdvLrAc5R1BWZZRokQJSJKElJQUAMA777wDWZaxZcsWkWfo0KF2AtirVy+xr379+ujZs6fYfuONNzB+/HjMmjVL5H/44YcBAOvXrxf7ihQpIgaFqAWiR48eHr2P/owRAbzrrrs0j8rNCGD//v1x6NAhzbXVI4PVU/m0atWqUApg8eLFNWtmBwoUQJMCmJeXhyFDhqBo0aIIDg5GcHAwihcvrpnp/laGDYgQ/yE3N1c34H722Wdimbhvv/22wHL27t0ren+UIPv1118DAObOnQtZlkV5sixr5g1U8qt7BZs0aaIZpGArA5IkoXbt2gDyJ5FW3iMsU6YMMjIy7IRn0KBBHr2P/owjAXzjjTfEdtmyZZ0K4IoVK3D8+HFNj64zAXz55Zc111YL4COPPCL2Dx8+3LAAOnsFwdV7Ubp0aZfPVf5DRAEM7PhtyX9FsrKysGfPHvzyyy+4fv26FUUWCtiACPEv1FO4KGn58uVYs2YNZFnGzp07CywjNzcXI0eORN++fUWQPXLkCACInsWVK1fP7BUEAAAgAElEQVQ6FbqkpCSxr1mzZppttTgq+atWrQoAYtoZde+lngDm5OR49D76K1YIoHouRyMC+NRTT2l6+vTmBnRVAG1XoDFzL+644w6Xz42MjKQAMn5zHkAzsAER4l9Mnz5dBLZx48ZBlmXMnTsXy5YtgyzL2Ldvn+Gy8vLy0KZNG7Ro0UL02Hz88ceQZRkLFy50GNyDgoI0vYKtWrWyW15OlmWMGDHCbsDHrFmzUKRIEZFn2bJlugIYiO9sAY4FcNCgQWK7XLlyTgVQTUGyNmDAADRu3BivvfYa6tevj86dO1sigI8++qhl9yIyMtLlc9Ur01AAAzd+UwBNwAZEiH/x1VdfYdq0aZg/fz5++OEHyHL+tDALFiyALMs4ePCgqfI3bNgAWZbFu4B6wT0sLEyz79VXX9XNq4wwliQJZcqUAZA/B+Gdd94p8rz//vt2wjN48GCcPXvWittV6HAkgIMHD9YVwOzsbHzyySemBLB69eqaa86YMcOuPpIkYdiwYYYF0Ir3AG3bjiuoBTAiIgKXLl0yXZ/CBuM3BdAtOAiEEP/nypUrIsjNmjULsmw/SbCrfPHFF5reRdsUFBSEkiVLavY5WhpMecQcGhqKhg0b4vTp05BlGbGxsSLP9OnT7YRnyJAhyMzMtOgu+QZ334HTE8CsrCzNIJDy5cuLfKtXr0blypVNCaDtNWfMmIETJ07gs88+c0sAy5Qp41cC+OSTT1IAAxQKoAnYgAjxX/Ly8jBhwgRN8M3IyDBVpnp6Gb0UGhqKu+66S7PPkRj07NkTkiRplokbMmQI6tWrJ7Zr1KhhN73IkCFDdOdfLSycOXMGkyZNwsCBA8XoaqPoCeC0adM07wBWqFBB5FuxYgWioqLcFkB1L62yTz04yB0BVHoBzaJc984773T53NKlS2vqc/78edP1KWwwflMATcEGRIh/o/T8KenUqVOmylMmlHaUIiIiULVqVUMS0L17d0iSpHlE3LdvX7v1ZNUjihUBTEtLs+gOeR/1PIuKCOXk5GD06NHYtm2b03MfffRRdO/eXZyvvFupfgfQVgCjo6PdFkBZlsWTHmW7bt26ugKoN9DHmQBeuHBB0xOak5ODf/75x/B9VK7rzryQtvfk3LlzLpdR2GH8dlEAr169ir/++stuv9n3agorbECE+Dfq+QBlWTb9u/rzzz87DeylS5fGQw89ZEgCunXrBknKn7BY2delSxfNsnF6adiwYUhNTbXoDtlz48YNrFmzBv/73/88Uv6SJUvsBDAlJUUjbXrk5eXZ3YsxY8Zg9OjRmncAK1asqCnrnnvuMSWAtunBBx+0RAAlScKTTz4p6lK/fn1IkmT4u1XKKFu2rMvfgXpiclmWA/KdUsZvFwRw1apVqFSpEu6//37UrFlTLLIOAHXq1PFI5fwdNiBC/Jv169drAp3Zaap+++03p4G9bNmyeOaZZyDLMt58803NMdvH0UoPYNu2bcW+F198ER06dChQIH744Qen9czIyBATGLuK+jG3J1CPoFYE0Paxqh43b97UvRcjR47EkCFDdAVw+fLlmnWhrRDA//znP5YJoPpnVbZ79+5t6D4q+cuVK+fyd2D7XioFMDAxLIC1atUSj0/27t2L6tWrY+nSpQAgJjENNNiACPFv9u/frwl0eXl5pso7fvy408B+9913i3WAZ8+erTk2c+ZMzXavXr0QHByMF154Qexr2bIlunbtWqBAqCe0vnHjhl09FTlw55G3MtLZUwL4wQcf2AnghAkTChTAnJwc3XsxYsQIjQAqy/dJkqQ7XY8aXwlgUFCQQwHs2rWrofuo5C9fvjyA/Hcrx48fj/T09ALPpQAyfgMuCGD16tU122fOnEH9+vUxduxY9gAGcAMixJ+x7TUyizJS11GqUqUKEhMTIcv2k0Urq4goKTExEZUrV9YIYJMmTdCvX78CBWLTpk0AgJ49e6JUqVKawS1Xr14VcvDjjz/a/Qyvv/46WrZs6VCG165d61EBfO+99+wEcObMmQUKYHZ2tu69GDZsmGY5vrvvvluU89Zbb1kugA888IAoX+mtbdWqlWUC+Oqrrxq6j0r+ChUqAACaN28OSZJQsWLFAs+lADJ+Ay4IYMOGDXHgwAHNvuzsbLRv3x4hISGWV6wwwAZEiP+jXvbKLFlZWU4De/Xq1TFq1CjIsowdO3ZojqnffZPl/MEdDRo00Kwh26BBA4cTCqtHuq5duxbAvxIwePBgUcejR4+K/bbvZ+fm5opjv/76q+7PuGrVKo8KoLpnVBGgOXPmaARQb6UT9VrPShozZgySkpI08qUWQNv5GseNG6cp0x0BrFevHiRJshudrZeU+SdtU82aNS0TwIoVK+L69esFCrSa3r17UwAZv40LYEZGBk6ePKl77Pvvv7esQoUBzgNISOHh5s2b+OqrrywZOevoMaSSHn74YfH5999/1xxTlqNT0uDBg9G5c2e0adNG7GvZsqXDsvv27Ss+L1u2DMC/EjBs2DBRxx9++EHstx1QoO4ddHQ/1KN0PYERAdT7m6qe11EtgEOHDtVMwVK5cmWHPYDjx4/XlOmOACrr/9pO+K2X9u3b5/CYWtYuXbpkSgBfe+01lwSQPYAUQMDENDCOZDCQYAMiJPB49913Cwz8SUlJ+Pvvv0VvoCznP7a1zTN48GBDgz5kWUaPHj0wevRoyHL+UnTAvxIwatQoUb/169eL/Xv27NHU/cKFCwX2AC5dutROAP/++29cuXLFkvunFsDQ0FDMmTMHU6ZMgSRJiI6ORsmSJXXnpdPrfR0zZgwaN26s6TVVC6B6aUA9AbQdJW4kvfLKK4iLizMkgLm5uYYEsGXLlm4LoPqdR6MCqP7PhCzLOHPmjKFr3kowfpsQwJo1a1pZj0IJGxAhgYftyGK91L9/f1y8eFHzaHLr1q2aPCNHjsTw4cPRqVMnQ+Lx+uuvY8SIEZDl/CXi8vLyRMAfO3asqN/cuXPFftunM//88484dvjwYd2fTy1FQP5/9vXkyV3UAlirVi3xWT1/ol6P1MWLF3UFUJIkcV9kWUZUVJT4GSdPnqzJP2HCBE2ZelPLGE1GHgEDjnsZlTq+8MILGnl75ZVXDN1HvYEoavF1hq0Anj592uC3d+vA+G1CAGvUqGFlPQolbECEBB4//fRTgYG/d+/euHHjhmaJMr1JpI8fP47XX39dV/Zs93Xq1Ek86nznnXc0731NnDgRAHD27FnRmyZJkma0MJD/Kk9Bj4DV760B0LzLqOb69eu4cOGCy/cvOTlZlHf//ffr3j89Iblw4YJDARw5cqTYFx0dLX7GZs2aORVAwL3HwLIso1GjRpYIoG3SE8ABAwagfv36mhHfQUFBuPvuuxESEoIiRYogJiYGRYoUMdQLqAxUogAGdvxmD6AJ2IAICTz0RgLbvuzfvXt3ANqelgMHDtidl5OTg549e9rtf+WVV+z2xcTEiB7FqVOn4syZM5qeLuBf2VAk6PPPP9fU/Y8//hDnKIP6PvzwQyxatEjkmTdvniEBVKZDMTrVTHZ2No4eParpAaxevbquHG3evNnu/HPnzunmbd68uXg0biuAtquqbN261a5cdwXQSHJWfkECqF4VRDm2fv16sa9x48aQZRmtW7cWA4k6dOhgSACVFVQogIEdvymAJmADIiQw+f777zFp0iQRQNPS0jTz53Xr1g0ANMuWHTp0CPv27dPMTXf9+nW7x3GyLGtWB1ELgzLdyYQJE5Ceni6CvdKzpeR99NFHIUkSNm7cqKl3WlqaOGffvn04e/as2Fbe8VPP0wcAO3fu1BVA5bwFCxYYumd6c/LFxsY6lCPb5cnOnDljSLruueceUTf1I2ZZ1p8H0h8FcPr06WJ727Zt4vPq1atFvdXSq07PP/+8GBzy5Zdf4vjx4wX+zGaXSCyMMH5TAE3BBkRI4LJ9+3YRQI8ePYpff/1VbCuT+arf7/vzzz8BaN87u3z5st1av7KcvyKInjAoEx6PHj0aBw8eRFhYGMqUKYMRI0YAsBfANWvWaOq8f/9+IRO7du3CwYMHxbbyOFc9XyFQsAAafTdQT1buvfdeh3K0d+9ezfmnTp1yWQDV7wba1t9ZvXwpgJGRkQ6PqQXQthfPWdkF/cwUwMDEbQF84IEHrKxHoYQNiJDARS1GmZmZmkfDyiNg9SNI9Trq48aNgyznP3rT68mxFcAWLVpoegBbtmyJXbt2iXe5KlWqhN27d4v8ylQly5cv19R59+7dmgEi3333ndhWHgO+//77opz9+/cXKICJiYmG7peeqFSpUsWhwHzxxRea8//++29D0hUfHy/qpidkRurlSwHUSyVKlMAjjzyC5cuXY9++fZgzZ47T67oqgOrHzYEC47cJASRsQIQEMupBHadPn9ZMVKys56p+xKnuZVEeH9tOU6IngMqL/pIkiUEglStXxrJly0Sep556SiM8igAuXrxYU2e18G3ZsgXr1q0T28rUXrZyoRbA3NxcUZZyXt++fQ3dL72fMyYmxqHArFu3TnN+ZmamYfFS3gM0IoCLFy/2iQCGhobaiVpERASCg4Pt9ivviU6dOtWh3JoRwL///tvQd3grwfhtUgCvXr2qmRsqPT0db731lt3/3G5V2IAICVxSU1NFAL148SJyc3PRokULJCUlibncihYtqsmjMGXKFIfBe8SIEWI9YdtgrghjdHQ0unTpIvI8/fTTCA4OFtv//e9/IUkS5s2bp6nz119/LcpSVkhRtpV3xdRLtY0dOxY//PCD2FaPQrVCAJ29A2grgBkZGYbFa8SIEYYFcPPmzT4RQFmW0a5dO3Ef77zzTshy/ghwW4HTkzsKoDkYv00K4JNPPok5c+YAAM6fP4+yZcuiUqVKKFq0KN577z1LKujPsAERErjs379fBNBr164ByJeikJAQdOrUSWwnJCTYLUE2Y8YMh8F76NChmncHIyIiRCBXljarUqUKKlasKPL06tVLMzHxww8/DEmS7P4Ob9q0SZSVkpKikYQjR44A0E7TMn78eI0AXr16FYB2STlbATxy5AhatWqFXbt2AchfieXzzz/X/VkfffRRhwKjLHcHADdu3MDRo0cNi9fIkSMNC+Bnn33mMwGUZRlhYWF46qmnNN+5JwVQb3JqCmBgYkoAIyMjxVqTKSkpuP/++5Gbm4uVK1eiWrVqllTQH+FScIQQ9XyAN2/eBPBvr5gyCGTnzp1ISkoSgqjw9ttvOwzeo0eP1swDeNddd4lylSlU7rvvPjsJUI94VXoA3377bc111Y9827dvr5GE1NRUzTrAsixj4sSJmkfAly5dQtu2bTWTF/fu3RufffaZeMexXr16eOCBBxAREQEgf17BgoTFmQAOGzYMxYsXx8qVKw2fP3ToUMMC+Mknn/hUAJ966qkCBc5KAVy7di0FEBRAwKQAFitWDMeOHQMAsZ4lABw/fhzFihUzXzs/hw2IkMDlxx9/tBML9TtbzlDPhaeXevToIT7ffffdolxlgEa1atXsJKBt27bi82OPPQZJyp8IWf3Y9uOPPxZl9ejRQyMJ6tG/SnrzzTfx3Xffie3Dhw/byYUy6jYsLAwA0Lp1a8hyfq8kALRq1cotAVRGvCrXMbLyhjoVK1bMkAAqj8J9JYB6U/4EBQU5FMAiRYogKirKaZlVqlRxKIB6+QNxaVfGb5MCWLNmTbzzzjs4fvw4SpYsiZ07dwLI/8NYtmxZSyroz7ABERK4qAeBKCgBt6CRsSkpKU4DeK9evcRn9cTGyiTN1atXdyqAjRo1EucMGjRIXHfx4sVif7du3TSSMG3aNLt6TJkyRbOEnXoaGb2Ul5cnRior96Vv375uCeDHH3+suad6Qucs6U2TokdaWppPBbBdu3Z2+5RBP3oC+MILLxi+h0YF8MSJE67/AhRyGL9NCuCqVasQGhqK4OBgPPnkk2L/pEmT0LRpU9OV83fYgAgJXI4cOeJQAN944w2n5+7bt89p8H7xxRcxaNAgNGrUCDExMaLcRYsWQZZl1KxZ004MlKcwsizjiSee0AjAunXrMHfuXM17f6+88kqBAjht2jR88803YnvixIl20leqVCnx+cKFCxg0aJDmvgwfPtwtAVy2bJnmnhYvXtwSIbPlf//7n0fkT5kfsaB8anE3IoCu3EMKoGMYvy2YBubkyZP46aefNNMD7N69G4cOHTJbtN/DBkRI4JKXl4c9e/aI12CAf2Vl6NChBZ7rLHir5axq1ari85IlSyDL+e/7hYSEOBTApk2bQpIk1KlTRzPVyowZM0RZ6vf4JEnSnZJmyZIl+PLLL8V2uXLl7ATwzjvvFJ9PnjypmdgagFiv1xV5KVeuHJYsWaK5pyVKlPCIAB48eNAjAqisZFJQPr1HwBUrVvSIACpLxtmmzMxMU78LhRHGb84DaAo2IEKImuLFi0OS8pfvKgjbIPzaa6+Jz127dhXBW3ncq/SKybKMhIQEzahfWdb2JDVr1gzly5e3u8abb74pynr66acLFMB169bh008/FdsVKlSwE8Dbb79dfD569Cj69eunEa4JEya4LDF9+/YV6xMXLVoUJUuWRMmSJU0JWXp6uu738Msvv1guf0899ZQo//fff9c8FndH4NwVwIiICBQtWhQHDx7U9ORSABm/AQqgKdiACCFq/vnnH+zYscNQXtu5AO+44w40b94cFStWFO/NSZIkHvdKkoQVK1ZAlvMHedg+Eu3QoYP43LJlSzzwwAN2gX7cuHGirAYNGojPpUuXxtSpU+3yr127VjMyuFKlSnYCqEivJElYunSpZv1jAJg8ebLLEvPGG28gPj4ef/75J7p27QpZdjxptJHUpEkTh9/DoUOHLBfAqKgozTXckTizAhgaGqopy1leCmBgQgE0ARsQIcRd1GsHDxw4UDPyc9CgQeJz7dq1xWdFxiZNmqTpMZRlbQ9i69at8fjjj9sF+lGjRomyHnzwQQQHBzsVq9WrV+PDDz8U2+oRyZIkoXnz5mjZsqVmn3oKGwDisbMr8jJgwABIkoQyZcqIfS1atHBbyNQ9crZkZ2fj/fffxxdffOFRARw1apRLZTRp0kR89+7UQX3v9HqD1Um9TGGgwPhNATQFGxAhxAwXL15ETk4OlixZIgQqKChIDJyQJAkPPPCA+LxmzRoRtG177NRTx7Rv315XmF588UVRVnx8vO4ABHVatmyZ5p2+qKgo3XfywsPDxX71CiUAMH36dJclpl+/fqhTp45mQEnLli3dFjKjgxI9KYDqn8WVpNeTayQ1aNBAfC7oe6YABiYUQBOwARFCrODgwYNCoEJDQzU9dXXr1hWf169f7zCIJyYmis8vv/wynn/+ebs8L730kijLyCPVSZMmoX///mJbPSWNuoepWLFiDgVQWb/WFXlR/yxWCOAzzzxj6HuwSgCjo6M15ZoRQCtS+/btnR7ft2+f5W3a32H8dlEAr169qvs/BWU1kECDDYgQYgWZmZlCoIoWLYqxY8eKbWVVD0mSnK5aoRaMzp076wpThw4dND2ARuRh+PDh4vO9994rzr/77rvF/hIlSjgUQHfeAdQbNKEntEbTs88+a+h7sEq4KlSooCnX1wL44osvOj0eERGBgwcP4sSJE1i6dCmys7Mtb+P+BuO3CwK4atUqVKpUCffffz9q1qwp1nkEgDp16nikcv4OGxAhxAouXbokBKpIkSJi5KwkSWJVD0mSNCNynYla//79daf8UAtgtWrVChQH2/fW1CtMxMbGiv3quQDV7yLevHkTjzzyCIKDg01LjBkB9HYPoHrqFSBfAAcPHuwzASwoRUREYPTo0WKanzFjxujen5s3b4rpbQo7jN8uCGCtWrVw6tQpAMDevXtRvXp1LF26FABQu3Ztz9TOC4SEhKBWrVqoVasWunTp4tK5bECEECvIycnRDKSYNGmS7mhdo+vWDh06VCzJpk4vv/yyKKtv374FljNy5EjNtrIGsTI4Rdl/xx136Arg7Nmz8cILL2hk0d3kaA47I+npp5829D14SgC7dOni1wJYokQJlC5dWnyHCQkJuvfnP//5DyRJwp9//mm6zfsaxm8XBLB69eqa7TNnzqB+/foYO3Zsoe4BjIyMdPtcNiBCiFWoBVDdAzhv3jzxzt6sWbMMBfSRI0fqvvivnmDa0blNmzbFE088AVm27wGMjY3VPJpW9qsng1YLYM+ePS2TFDMC2LhxY0PfgacEMC8vz27aH39K6kf4kiQ5jOnK8cmTJ5tu776G8dsFAWzYsCEOHDig2ZednY327dsjJCTE8op5CwogIcQfUAdg9Xx9V69exQ8//IALFy44ncxXncaMGaP73pcigEFBQQ7PLV26NOLi4nSPNWjQQIz4bdy4sdhfvnx5XQHs1q2bZZJidA1cvdSwYUND34EV9SxbtqydAALQnWfRX9Jtt92maX9ly5bFW2+9hfPnzwMANm7ciISEBHF8ypQplrZ9X8D47YIAZmRk4OTJk7rHvv/+e8sqpGbbtm1iRntJyl/P0pbk5GRER0cjPDwcCQkJ2L59u0vXCA0NRUJCAh555BFs3brVpXPZgAghVqEOwMryaZIk4ebNmyJPdna2YQlUTwytpFdffRWSJNktI6dOpUqVQtWqVR0e79OnDyRJwnPPPSf2DRw4ENWrV7ebnNrIY2ajqaCpTJylRo0aGfoOrJgLUPnebPHnHsBnnnkGzz77rKYNSpKEli1b2rVNJf3999/WNHwfwfjt59PAbNq0CSNGjMCaNWt0BXDFihUIDQ1FSkoK0tLS0K9fP0RERGjW5kxISEB8fLxdUmY+V/795ZdfULlyZZcaAxsQIcQq1MF15MiRDkXi7NmzhoL6q6++6lAAixUr5vC8kiVLFvjOniRJuo9kmzRpotm28r23gqYysUIA09LSAlIAlWS71nORIkXs2qaSOnToYK7B+xjGbwsE8Nq1a9i9ezc2btyIDRs2aJKV6Alg3bp10aNHD82+atWqISkpya1rNG3aFHv37nV4/Pr167h48aJIGRkZAd+ACCHWoA6uw4YNcygSly5dMhTM1StyKKljx474z3/+4/CcRYsW4bbbbitwjkBJktCmTZsC62Bk9Yt+/fo5fSStJL0eTaPJ6CPg//3vfwEtgHpL/Y0fP15XAJ988knTbd6XUABNCuDmzZtx5513IigoyC4FBwdbVUcA9gKYnZ2NkJAQrF27VpMvMTER9evXN1TmuXPncP36dQD5j7grV66Ms2fPOsyvfiyjToHcgAgh1qD+mzJkyBCHInH16lVDwbxPnz4uBf/ExERkZWWhRIkSuPfee53mtWJaFyW1adMG06ZNKzCfXo+m1QJotHfVHQGcPHmyzwXPHQF0lCiAhR9TAlilShX06tXLK+8C2AqgMnGq7cLrEydORGxsrKEyd+zYgRo1auD+++9HrVq1dN8xVMMeQEKIp1AHV/VawLbk5ORogva4ceN0g7mra88++uijAICIiAhER0c7zVurVi1LBXD//v1i29Fj486dO7t9DaOPgAHgr7/+ClgBbN26NQUwgDAlgLfddhv++OMPq+riFEcCuHPnTk2+CRMmoGrVqh6ty+zZsxEXFyemRAjkBkQIsQZ1cB0wYIBDkcjLy8PYsWNF0DbSe2YkPfTQQwCA4sWLa1b50EvqKWCsEED1o1f10nPqpPdI22gaOHCgS99FoAqgLMuoWrUqJCl/ree6detq1nlWp3vuuQd//vkn0tLS8MUXX9i1UX+HAmhSADt37ox58+ZZVReneOIRsFnYgAghVhEUFCSCa2JiokORAIBJkyaJgP3uu+9aEvj/85//AACKFi2KChUqeFUADx8+LLbV08uoU/fu3d2+RlZWlkvfhVkBbN68uV2ZhUUAZVnGI488gl69eonvx1b+ypYtazdgZP/+/QCAwYMHo1KlSjh9+rSLvwHehfHbpABeuXIFzzzzDDp27Ijp06fjnXfe0SQrcTQIpGfPnpp9cXFxbg8CcRU2IEKIVYSFhYlg2rt3b6cCqO71++CDDzTBe9iwYW4FfWVFp/DwcJQtW7ZAQbBKNtq2bYsjR46I7ccee0w3nyIkrqY9e/a4/F3Y3lNX0i+//IIbN27Ylfnmm2/6XOzcSaNGjdKInvr9T3Wb/eijjwD825P96quvunzfvQnjt0kBTElJQUhICEqUKIGoqChER0eLdM8995iuXFZWFlJTU5GamgpJkjBz5kykpqaKaV6UaWDmz5+PtLQ09O/fHxEREUhPTzd9bSOwARFCrKJ48eIiePbo0cOpAM6ePVsE4aVLl2oC9rx589wK9DVq1ACQPzdqmTJlnOZ99NFHLRXAY8eOFVj2wIED3SrfHbZt2+b2z+OIW0UAw8LCxLGSJUuK/QsWLACgfZXBn2H8NimAZcuWxcSJE5Gbm2tVfTRs2bJF992Djh07ijzJycmIiopCWFgYEhISsG3bNo/URQ3fASSEWE2pUqXE37glS5Y4DaILFiwQQXjDhg2agG0rhEbTfffdh2vXrkGSJJQuXdprgtG2bVucOHFCbFvZu+iuAG7dutWtazlbc7iwCqAs54/61hNAdZuVpPwl4tTbX331lVv33xtQAE0KYOnSpb02CMQfYQMihFhFZGSkCJy5ublYsmQJfv/9d928H3/8sQjC3377rSZYr1u3zq0gHxMTgzp16kCSJJQsWdLjUqH06MXHx+PUqVNiv5XvF7orgHv27HHrWs5mkijMAvjEE084FcA6deogMTFR04YlSULTpk1178WSJUu80lnjDMZvkwLYv39/TJw40aq6FDrYgAghVqF+qb4gNm7cKILw3r17NcH6888/dyvI33vvveL6ERERmmOuTiljJBUtWhR33303JEnCuXPnxP6HH37Y5wJ48+ZNrFmzxuXJm52hHrhT2FJSUhIkScK9996LBx98UOx//vnnIUmS2O7UqZNGAPV6RJVXunz9iJjx26QA9illlwkAACAASURBVO3bF6VKlUL9+vXRp08fDBgwQJNuVfgImBBiNYoMGQmM6vWAf/31V02w3rlzJyZOnOhykL/nnnvE9YsWLao55u7AEmdJLQrq1U1q1KjhcwFUSE1NpQD+f+ratavufnVvcZcuXXQF8NSpU0hJScHmzZuxdu1aTTvfunUrUlNTTX1P7kABNCmADRs2dJhcmXizsMIGRAixCnUPXEHs2rVLBN3jx49rAnJOTg5mzpzpcoCPiooS1w8NDdUcs3JNXz0BvHbtGqpVq4YmTZoYWhbOWwKYl5eH3bt34+TJkwEvgI5Su3btxOfXXnsNkiQhKCgIMTExKF68OID818WU73rgwIHis/q+ehvGbwvWAg5k2IAIIVahrP4RFRVVYN6MjAyMHTsWH374IXJzc0UQfeKJJwAAc+bMcTmQV65cWQTm22+/XXPM3RG4RgUwJydHs220jBUrVnhUANWYFUB3emULQ1Iv0de5c2dIkoTHH39cc0/U3+0jjzwiPv/5558UQB/isgAeOHDApVG/Bw8eRE5OjquXKRSwARFCrOLatWuYP38+MjMzDeW/fv26WHFBCaLKS/eLFi0S+xwtFWeb1I+go6OjMWbMGHEsMTHRowKYl5fnsgBOmjRJ87NTAH2TOnbsqNkuUaKEZtv2u1UL4NGjRymAPsRlAQwODsapU6cM57/ttttw5MgRVy/j1/AdQEKIP5GUlARZlhEZGQlAO0rYqHhUrFhRE6hHjBghjinle0oAAbgsgFOmTAGgL2bKPImLFi2y7B5TAPVTp06dNNtvvPGGZvvIkSMUQD/FZQEMCgpC9+7d7QZ8OErh4eG3nAAqsAERQvyBNWvWIDw8HKGhoQCgmQrG2UjW0aNHi88VKlTQBGq19C1cuNBSaejQoYNpAZw5cyYAfTFbuHAhfvrpJ5w9e9aye0wBNCaAtkn9vVIA/QuXBbBBgwZOB3/opRMnTnii7j6HDYgQ4g/k5eXh888/F4+PN2/eLALrjBkzDAmgehqabt26aQZ+LF68WPf8uXPn4pNPPjG8zu1jjz2GqlWrapYQc1cAlZUn9I59//33lt9jR/V4++23Icsytm7d6vT8CRMm+FzWPJE6d+7skgCqEwXQt3AQiAnYgAgh/sixY8cwffp0rF27FrNmzTIUyMuWLSsC84EDBzQDP1atWqV7jvKIdefOnYaucd999+mKAPCvAN53332Gylq1ahUAfTHzBOry1cvtffHFF7h+/XqB59+qAuhoehgjAvjzzz9TAH0IBdAEbECEEH9HvW6ws3TnnXeKwHz8+HH069dPHEtPT0dKSopDAczLyzN0jfr16zsUwJSUFERGRhpehWPlypUA/EMAjXCrCmBByZkAqv+T4W0YvymAbsFBIISQwkJycrIIsocOHXIYqMuUKSMCc1ZWliY4nz59GoC9bL333nviOsq+iRMnagRJnb777juHAgjAblSzEQE8evQoPvnkE817ip7ArAB++OGHkGXn72TeismZAKofH3sbCiAF0BRsQIQQf0c9JyAAZGVl6QbqO+64A0FBQWjbti0ArTieO3cOgL2YTZ06VVznxx9/xKRJk5Ceno6lS5fqXuP48eNOBVDBiFh8/PHHmnNOnTqF8ePH4+uvv/bIfVRfW31PP//8c0PnZ2Vl4csvv8SZM2d8LmX+IoBRUVEi3/nz5z3yvTmC8ZsCaAo2IEKIv2MrgIC+YPXt21f0wAHA3LlzxTHlb5ztOUovnIIyR+z8+fN1r3Hq1CkR/NXrxtpiRCxWrFhhd54rc9S6ivraP/74o8sC6KisWz09+OCDKFKkCCIjIzWTjUuShGrVqmlE8cCBAx745vRh/KYAmoINiBDi77z//vsOBVD9XpptD4z6nb8rV65ozpPl/AmmHQnX2rVrdWXg4sWLSEtLwy+//ILevXtb2gPoadTXVr/zSAEsOKlXBrnjjjsgSZLdkn+SJOHFF18EkL/W9YQJE3Djxg2rv0YB4zcF0BRsQIQQf+eDDz5wKIDqASK2f8fUAqiMclW2x44di5s3bzq85uXLl7Fx40akp6drgvzly5dFnp49ezoUQCPzDvpKAN955x3NNgXQtRQbG4saNWogNjbWTgAlSUJmZqb4nJKSAiA/1i5YsAB//fWXZd8n4zcF0BRsQIQQf0ctcgrK9qeffio+X7p0SXOeWhwV2VO2lVU4CiInJ0cT5NXTpfTo0cOhAF67dg27d+/2SwF89913NdvuCODBgwexbds2n8uYL1K9evV09yttoUWLFuLzqFGjAACtWrWy22cWxm8KoFtwFDAhpLCgHrGqcPToUXz55Ze4ePGiOJaVlaU5Ty2AtqNzp02bZujaN2/e1AR59brwXbt2dSiAQP6I4NWrV9+SAmhbZiClxo0bOxVA27Ro0aICBw25AwWQAmgKNiBCiL+jHpBhi3pEsPrxLKAdBKKgbCvLsBVEbm6uJsirB5ksWbLEUEBftmwZBfAWSo4EsGTJkk5HDLsigEYGAzF+UwBNwQZECPF3FixYYLkAvv3224aubTtBtJrc3FysWLGiwLXi/U0Alcmvle3NmzebLtOqVNCqHOrUu3dvvxLA5557TgwOMSOAixYtQvHixfHNN984zcf4TQE0BRsQIcTfcTZBsrsCOGvWLEPXdiaARvEXATxy5Ag++ugjMVpaqYeZeQddlacHHnjA6XFXyvzoo498IoCOxPO5557D/fffj6SkJERFRdlJX3x8PGJiYiBJEg4fPoznn38eO3bssLunSv4SJUo4vfeM3xRAU7ABEUL8nd9++82hMN24cUMEYNspN5wJ4Nq1aw1fXzlnz549btXf0aTS3hZAW3bt2oV58+bh2rVrbpfhqjwVL17cMgFUv//pD2no0KHi87BhwyBJEpo1awZZllG6dGlxrKDeQAqgcSiAJmADIoQUBi5duqR5/05NZmYmTpw4Ybf/7bffthPAkydP4vPPP8fVq1cNX1spY+/eva5XHFoBXLRokfhsOwl1YcSXAujO9b2Z1PMEDh48WCOAwcHBCAsLs1tGUD2ynAJYMBRAE7ABEUJuVVasWAFZlvHWW2+ZKkcJ3Fb0AC5ZsuSWF0BH+wNNAGvXrq27X5IkJCYmQpZlhIeHi3t58OBBu97Bli1b4qeffrK774sXL8aQIUMCPn5TAN2A08AQQm51zp8/jy+++ML03zezAqh+V039OVAEcP369RoBrFu3bkAI4MCBAx0KoPI5JiYGbdu2xeLFi/Hjjz/qDhgpUqSI3X1XHw/k+E0BNAF7AAkhxDlWCqC6NzBQBHDDhg0aAZQkCZ999pndeV26dHFYpm1q2LCh4bz+ltQCeN999wmRcySAzt4TDPT4TQE0AQWQEEKcY6UAqkcE32oC2LlzZ6SlpQEATpw4IfZ/8skndgJ44MABOzFq3769XZl6qV69ekKKfC1zVgrgvn37KIAuQgE0AQWQEEKcY6UAKu8l3ooCWLt2bd1jRgVw2LBhdmU6EsDMzExDef0xqQWwadOmGDhwIGJiYjBq1CgKoItQAE1AASSEEOeYFUD1wI+PP/444ARQvV6zngCuXr0akyZNEms5GxFAvesXlhQWFuZQDAsSwBs3buDVV1+lAP4/FEATUAAJIcQ5VgrgqlWrAk4A1e/7qQXw0KFD2Ldvn9Myb0UB7NChg9sCOH/+fLtjgRy/KYAmoAASQohzrBTA1atXB5wAbtq0SVcAjZSpl5o3b244b2FK6hVDnnjiCV0BnDx5MgVQBQXQBBRAQghxjlkBXLx4sShjzZo14vOqVassrqn3MSKAmzdvtlQAe/ToYThvYUqK0Cnb8fHxGgFMSkrS7R0M5PhNATQBBZAQQpxjpQCuW7cu4ATwyJEjSExMRK9evRAUFARJkvDzzz87LPODDz5wKElFixZFt27dRN7ly5f7XNw8JYDqfQB05S/Q4zcF0AQUQEIIcY6VAqieFPlWEED1cnu2AnjmzBn8+uuvAIBy5coJ+ZMkCZcvX3ZYZk5ODk6fPu1Qkrp37y7yfvnllz4XNysFsEaNGroC2KBBAwqgDhRAE1AACSHEOWYF8MMPPxRlqCdFvhUE8L333nMogGrKlSsnhOXcuXOGyg5EARw1apSuADpLgRy/KYBuwKXgCCHEGEow3r17t1vnqwVQPSdeoAqgUSiAMooUKYJ27dohISEB4eHhaNSoEe68804K4P9DATQBewAJIcQ5VgrguXPnAlIAH3zwQcsEUD0I5FYSwIiICLt99erVE5+bN2+uOVa5cuWAj98UQBNQAAkhxDlmBfCLL74QZajLCyQBPHLkCJo2bYotW7YYLlspV3nPsGvXrpAkCV999ZXIc/z4cZ+LmydTgwYNxOcePXpojimjggM5flMATUABJIQQ5ygB9/fff3fr/OzsbHz77bc4efKkprxAEkB3UMpNSUlBRkaGGERy5swZTT715Nq3WmrYsCEF0AkUQBNQAAkhxDnp6enYvXs38vLyLCnvVhXAxx9/3NKy1QL4999/i0fItgK4detWn4uaN1LPnj0pgDZQAE1AASSEEO+iBPDVq1f7uiqmUQvgkSNHLC1bLYDnzp1zOOjhxIkTPpczCqBvoACagAJICCHe5VYVQKtRCyAATJo0CZMnT3aaN5ASBZACaAoKICGEeBcKoDFsBdBI3kBKFEAKoCkogIQQ4l0ogMagAFIAC4ICaAIKICGEeBcKoDEogBTAgqAAmoACSAgh3uVWEsDvvvsOsizjvffes7xs5T598MEHhvMGUqIAUgBNQQEkhBDvcisJYG5uLn7//Xdcu3bN8rIpgBTAggh4Afzzzz/RsGFDxMXFoUaNGrh8+bLhcymAhBDiXW4lAfQkrgjgxYsXfS5kFEDvE/ACWL9+fWzfvh0AcPbsWeTk5Bg+lwJICCHehQJoDFcE0PacQEgUwAAXwIMHD6Jx48Zun08BJIQQ70IBNIbVArhp0yafSxsF0Fr8WgC3bduGZs2aoXz58pAkCevWrbPLk5ycjOjoaISHhyMhIUH05hlh3bp1aNGiBZ577jnUqVMHEydOdKl+FEBCCPEuSgBfs2aNr6vi11gtgDt27PC5tFEArcWvBXDTpk0YMWIE1qxZoyuAK1asQGhoKFJSUpCWloZ+/fohIiICx44dE3kSEhIQHx9vlzIzM7Fy5UrccccdOH78OK5fv46GDRviyy+/NFw/CiAhhHgXCqAxKIAUwILwawFUoyeAdevWRY8ePTT7qlWrhqSkJENl7ty5E0899ZTYnjp1KqZOneow//Xr13Hx4kWRMjIyAr4BEUKIN6EAGkO5T8uWLXP5HApgYFBoBTA7OxshISFYu3atJl9iYiLq169vqMycnBzUrl0b586dQ25uLpo1a4aNGzc6zD9mzBixoLY6BXIDIoQQb0IBNMbvv/+OpUuXuhSfKICBRaEVwMzMTEiShB07dmjyTZw4EbGxsYbL3bRpE2rUqIH4+HgMGDDAaV72ABJCiG+hAHoOCmBgUegFcOfOnZp8EyZMQNWqVb1SJ74DSAgh3oUC6Dl++uknvP322wUKYLdu3XwucBRA8xRaAbTiEbC7zJ49G3FxcYiNjQ34BkQIId6EAuh5KICBQaEVQCB/EEjPnj01++Li4gwPAjELewAJIcS7UAA9jxEBrF69us8ljgJoDr8WwKysLKSmpiI1NRWSJGHmzJlITU0V07wo08DMnz8faWlp6N+/PyIiIpCenu6V+lEACSHEu1AAPY+eMH3//fcaAZQkyecSRwE0h18L4JYtW3RH3Xbs2FHkSU5ORlRUFMLCwpCQkIBt27Z5vF58BEwIIb5BCeC2r/8Q66AABgZ+LYD+DnsACSHEu1AAPQ8FMDCgAJqAAkgIId6FAuh5ChLAOXPm6Arg0KFDfS52FEDjUADdgI+ACSHEN1AAPU/Hjh2dCuDcuXN1BfDFF1/0udhRAI1DATQBewAJIcS7UAA9j54AqkcBf/DBB7oC2L59e5+LHQXQOBRAE1AACSHEu1AAPU9BArhkyRI7AaxTpw7atWvnc7GjABqHAmgCCiAhhHgXCqDncfQI+NChQ1i4cCHOnz+vEcAWLVpAkiS0bdsW27dvx/bt230ueBTAgqEAugHfASSEEN9AAfQ8L730kq4AqnEkgAq+FjwKYMFQAE3AHkBCCPEuFEDPU7t2bbzyyiuoVauWIQHs1KkTJEnC119/LY77WvAogAVDATQBBZAQQrwLBdDzxMfHi4UXjAjg2rVrxQpdCr4WPApgwVAATUABJIQQ76IEcNu14Yl16AngwYMHNXnUx9avX29XxtixY30ueRRA51AATUABJIQQ76IE8J07d/q6KrcsagH8448/sGXLFuTl5WnyFCSA58+fx/79+5GVlSXy6Q0uqV+/PgXQR1AA3YCDQAghxDecOHEC33//PW7evOnrqtyyVK9eXQigIwoSQD3q1auHIkWKiPP69u3rsyXlKIAUQFOwB5AQQsithhEBrFu3rlsCqDd4hALoGyiAJqAAEkIIudUwIoBnzpyhABZyKIAmoAASQgi51fjoo48gSRLatGnjNJ+rAvjQQw9RAP0ICqAJKICEEEJuRY4ePYrc3FyneTzRA9ijRw8KoJegAJqAAkgIISRQ8YQA9unThwLoJSiAbsBRwIQQQgIdTwjgypUrKYBeggJoAvYAEkIICVRcFUBH7wA+9dRTYt+qVasogF6CAmgCCiAhhJBAxSoBVE8GTQH0HhRAE1AACSGEBCobN26ELMs4ffq0ofyOBLBGjRoUQB9AATQBBZAQQkggk5OTYzjvww8/rCuAQUFBmDRpEo4ePUoB9CIUQBNQAAkhhBBjOOoBlCQJM2bMAAAOAvEiFEATUAAJIYQQY9gKYLdu3YQATp8+HQAF0JtQAE1AASSEEEKMYSuACxcupAD6EAqgG3AeQEIIIcQ1bN8BpAD6FgqgCdgDSAghhBjDiAAqI4spgJ6HAmgCCiAhhBBiDCMCeOXKFSxevFgjaxs2bKAAegAKoAkogIQQQogxnAngtGnTNHnVsgYA48ePpwBaDAXQBBRAQgghxBhmBHDSpEkUQIuhAJqAAkgIIYQYw5kATp06VZN37NixkGUZ7777LgAKoCegAJqAAkgIIYQY47///S8kSULTpk0hyzKOHTvmUABPnjyJlStX4syZMwCsF8CIiIiAj98UQBNQAAkhhBBjKAIoSRKys7MBQGzPmjXL6bkTJ04U8tamTRvTAqhcN5DjNwXQBBRAQgghxBhqAVSYMmUKGjRogKtXrzo9Vy2A6s8UQPehAJqAAkgIIYQYY8aMGZAkCZUrV3b5XLX0HThwwJT8jR49mgIICqBbcCUQQgghxDVu3ryJjRs34vTp0y6f60gAR48ejR49ergkgM2aNWP8BgXQFOwBJIQQQjyPIwEcPny4ZmSxkbRhwwbGb1AATcEGRAghhHgeRwL4wAMPuCyAn3zyCeM3KICmYAMihBBCPI8jAVTe5aMAug4F0ARsQIQQQojncTYIRJIkNG7cWFf2/vvf/1IAHUABNAEbECGEEOJ5HAkgACxZskS3F/D1119HxYoVKYAOoACagA2IEEII8TzTp093KIAKFEDXoACagA2IEEII8TyZmZlITk7G4cOHXRLAqVOnchSwAyiAJmADIoQQQryLIwH8+eef0atXL3Gsd+/eyMjIoAA6gAJoAjYgQgghxLs4EkAgf23hvn37QpZlbN++nQLoBAqgCdiACCGEEO9SkAAWK1YMDRo0QG5urkYAX3rpJciyjBMnTjB+gwJoCjYgQgghxLsUJICSJCEmJgYANAIYFBSEokWLAmD8BgJcAH/99VfUqlVLpKJFi2LdunWGz2cDIoQQQryLuwKoHAMYv4EAF0A1WVlZiIyMxOXLlw2fwwZECCGEeBd3BbB8+fJYsWIFAMZvgAIoWLp0Kdq2bevSOWxAhBBCiHcxIoBVqlQBoBXAvLw8kY/x288FcNu2bWjWrBnKly8PSZJ0H88mJycjOjoa4eHhSEhIwPbt2926VosWLbBmzRqXzmEDIoQQQryLKwJ448YNyLKMadOmafIxfvu5AG7atAkjRozAmjVrdAVwxYoVCA0NRUpKCtLS0tCvXz9ERETg2LFjIk9CQgLi4+PtUmZmpshz8eJFlClTBteuXXOpfmxAhBBCiHdx5REwkC+BN2/e1ORj/PZzAVSjJ4B169ZFjx49NPuqVauGpKQkl8pevHgxOnToUGC+69ev4+LFiyJlZGQEfAMihBBCvImrAqgHBbAQC2B2djZCQkKwdu1aTb7ExETUr1/fpbKbNWuGTz75pMB8Y8aMEY1LnQK5ARFCCCHehAJoDYVWADMzMyFJEnbs2KHJN3HiRMTGxhou98KFC7jrrruQnZ1dYF72ABJCCCG+hQJoDYVeAHfu3KnJN2HCBFStWtUrdWIDIoQQQrwLBdAaCq0AWvkI2FVmz56NuLg4xMbGBnwDIoQQQrwJBdAaCq0AAvmDQHr27KnZFxcX5/IgEHdhAyKEEEK8y/79+ymAFuDXApiVlYXU1FSkpqZCkiTMnDkTqampYpoXZRqY+fPnIy0tDf3790dERATS09O9Uj82IEIIIcS7GBHAuLg4p2Uwfvu5AG7ZskV31G3Hjh1FnuTkZERFRSEsLAwJCQnYtm2bx+vFR8CEEEKIb3AmgEuWLEHlypWRmprqtAwKoJ8LoL/DBkQIIYR4F2cCaBTGbwqgKdiACCGEEO9CAbQGCqAJ2IAIIYQQ70IBtAYKoBvwHUBCCCHEN1AArYECaAI2IEIIIcS7UACtgQJoAjYgQgghxLtQAK2BAmgCNiBCCCHEu5w8eZICaAEUQDfgO4CEEEKI7/j9999x6tQpt8+nAFIATcEGRAghhBQ+GL8pgKZgAyKEEEIKH4zfFEBTsAERQgghhQ/GbwqgKdiACCGEkMIH4zcF0C04CIQQQggpvFAAKYCmYAMihBBCCh+M3xRAU7ABEUIIIYUPxm8KoCnYgAghhJDCB+M3BdAUbECEEEJI4YPxmwLoFhwEQgghhBReKIAUQFOwARFCCCGFD8ZvCqAp2IAIIYSQwgfjNwXQFGxAhBBCSOGD8ZsCaAo2IEIIIaTwwfhNATTFhQsXIEkSMjIycPHiRSYmJiYmJqZCkDIyMiBJEi5cuOBrlfAZFEATKA2IiYmJiYmJqfCljIwMX6uEz6AAmiA3NxcZGRm4cOGC+F9FbGysx/7HYkXZrpZhNL+RfAXlcXRcb78i3/7c++qptuDP7cDM92zkuO2xQG4HVpR9q7SDwtAWbqV24Mo5ZmODp9rBhQsXkJGRgdzcXF+rhM+gAFpMXFycX5ftahlG8xvJV1AeR8f19l+86P/vb3iqLfhzOzCS18xx22OB3A6sKPtWaQeA/7eFW6kduHKO2dhwq7UDf4ICaDGzZ8/267JdLcNofiP5Csrj6Lje/sLwS+6ptuDP7cBIXjPHbY8FcjuwouxbpR0A/t8WbqV24Mo5ZmPDrdYO/AkKICmU8JecAGwH5F/YFgjAduAKFEBSKLl+/TrGjBmD69ev+7oqxIewHRAFtgUCsB24AgWQEEIIISTAoAASQgghhAQYFEBCCCGEkACDAkgIIYQQEmBQAAkhhBBCAgwKICGEEEJIgEEBJLc8x48fR4MGDRAXF4eaNWti5cqVvq4S8REtW7bE7bffjhdeeMHXVSFeZOPGjYiNjUVMTAxSUlJ8XR3iI/j7r4UCSG55Tpw4gdTUVADAP//8g4oVK+Ly5cs+rhXxBd9++y0++eQTBoAAIicnB/fddx/++usvXLp0CTExMTh79qyvq0V8AH//tVAAScBRs2ZNHD9+3NfVID5iy5YtDAABxI4dO9CyZUuxnZiYiGXLlvmwRsSX8Pf/XyiAxOds27YNzZo1Q/ny5SFJEtatW2eXJzk5GdHR0QgPD0dCQgK2b9/u1rX27t2L+Ph4s1UmHsBb7YABoHBhtl2sWrUKvXv3FttTp07FtGnTvFJ3Yh1W/X3g7/+/UACJz9m0aRNGjBiBNWvW6P5ir1ixAqGhoUhJSUFaWhr69euHiIgIHDt2TORJSEhAfHy8XcrMzBR5zpw5g7i4OOzYscNrPxsxjrfaAQNA4cJsu1i5cqWdAE6fPt2rPwMxjxV/HwD+/quhABK/Qu8Xu27duujRo4dmX7Vq1ZCUlGS43OvXr+Oxxx7D4sWLLakn8SyeagcAA0Bhxp12ofcIeOnSpZ6vLPEYZv4+8Pf/XyiAxK+w/cXOzs5GSEgI1q5dq8mXmJiI+vXrGyozLy8P7du3x5gxY6ysKvEgnmgHCgwAhRd32kVOTg5iYmI0g0DOnDnj1XoTazHz94G///9CASR+he0vdmZmJiRJsntsO3HiRMTGxhoq87vvvkNQUBBq1aol0s8//2xpvYm1eKIdAECTJk1QpkwZFCtWDBUrVsSePXssqzPxPO62iw0bNuC+++5DlSpVMHfuXK/Vl3gGd9sBf/+1UACJX+HoF3vnzp2afBMmTEDVqlW9XT3iJdgOiB5sFwRgO7AKCiDxKzz56I8UHtgOiB5sFwRgO7AKCiDxKxy93NuzZ0/Nvri4OJdf/ieFB7YDogfbBQHYDqyCAkh8TlZWFlJTU5GamgpJkjBz5kykpqaK4fvK8P758+cjLS0N/fv3R0REBNLT031cc2IlbAdED7YLArAdeAIKIPE5W7ZsgSRJdqljx44iT3JyMqKiohAWFoaEhARs27bNdxUmHoHtgOjBdkEAtgNPQAEkhBBCCAkwKICEEEIIIQEGBZAQQgghJMCgABJCCCGEBBgUQEIIIYSQAIMCSAghhBASYFAACSGEEEICDAogIYQQQkiAQQEkhBBCCAkwKICEENPk5eWha9euKF26NCRJQmpqqq+r5JcsXLhQrGDQr18/j1+rVKlSPrk2IcT/oQASQkyzadMmhIaGYseOHTh58iRyxArbHAAABvhJREFUcnJ8XSW/ZOHChShZsiROnjyJS5cuefxaagG8evUqTp48iYcffpgCSAihABJCzDNr1ixUrlzZaZ7s7Gwv1cZ/sZUyX1yrQYP/a+/eQqLo+wCO77rtqrtqkuWpXV2oyEOkN1lWFFJQWZZdZKUXWmQkYmShEYIdoNROalEUtVHQhQYqlYGYJQhWKELYha6ZS4FJB0uMyKz8vhey0zvpquVz0fP4+8CCO/OfmXUu5MuM/9mVEoBCCAlAIcTkpKamqr6cPTQ0FBgOjczMTLKzs/Hz82PFihUA9PX1kZ6ezqxZs/D29iYuLo6nT5+q9llQUIC/vz9eXl7s3LmTgwcPEhUVpawfLWI2bdqk+mL4r1+/kpOTQ3BwMEajkZiYGOrr65X1zkCqqakhLCwMk8nEmjVreP36tWq/NpuNiIgIDAYDgYGBZGZmArBjxw7Wr1+vGvvt2zcCAgKw2WyjnitXUTYwMEBOTg5msxmDwcDcuXO5evUqAPX19Wg0Gqqrq1m4cCHu7u7ExMTQ2to6Yt8WiwVPT08SExM5ffq0BKAQwiUJQCHEpPT19XHs2DHMZjM9PT28ffsWGA4NLy8vcnJyaG9vp62tjaGhIZYtW0ZCQgLNzc10dHRw4MAB/Pz86O3tBaC8vByDwcCVK1dob28nLy8Pb2/v3w7A5ORkli5dSkNDA52dnZw6dQp3d3c6OjqA4WDS6/WsXr2a5uZmWlpaCA8PJzk5WdnHxYsX8fDwoKSkBLvdTlNTE8XFxQA0Njai0+lUwXj79m1MJhOfPn0a9Vy5CsCkpCQsFguVlZW8ePGCuro6ysrKgJ8BGB4eTm1tLa2trWzYsAGr1crg4CAAT548QavVUlBQgN1up7S0FF9fXwlAIYRLEoBCiEkrLi5Wrvw5rVy5kujoaNWyBw8e4OPjw8DAgGr5nDlzuHz5MgCxsbHs2bNHtX7x4sW/FYCdnZ1otVq6u7tVY1atWsWhQ4eAn5MiOjs7lfUXLlwgICBAeR8cHExeXp7L3zsiIoKioiLlfWJiImlpaS7HjxaAdrsdjUbD/fv3R93GGYDOIATo7e3F09OT8vJyALZv387atWtV223dulUCUAjhkgSgEGLSXAXgrl27VMtOnjyJm5sbJpNJ9XJzcyM3NxcAX19fbty4odpu3759vxWAt27dQqPRjDjOtGnTSEpKAoZjzGg0qvZRWVmJVqsF4M2bN2g0Gh4+fOjy9z579ixhYWHKeL1eT0NDg8vxowVgeXk5Op1OuZr3K2cAvnz5UrU8OjqaI0eOKD8fPXpUtb6kpEQCUAjhkgSgEGLSXAXgr6FRWFjI7Nmzef78+YjXu3fvgIkFYFxcHHv37lWNiY+PVwKwrKwMnU5He3v7iOP09PQAo8dYVVUVGs3wn8X+/v5xA/D9+/cYDAYePXrEmTNnmDdv3pjnabRj3rlz548D0Bl9UVFREoBCiN8iASiEmLSJBmBtbS06nQ6Hw+FyX7GxsWRkZKiWLVmyRBWASUlJbNmyRXn//ft3QkJClAB03lb93atx/x+AAFardcxbwM7Pkp6ezoIFCzhx4sSYY0c7psPhQKvVjnsL2Hm7F+DDhw8YjUbVLeB169apttu2bZsEoBDCJQlAIcSkTTQAh4aGWL58OVFRUdTU1OBwOGhsbCQvL4/m5mZg+Oqdu7s7NpsNu91Ofn7+iEkgly5dwmg0Ul1dTVtbG7t378bHx0c1CSQlJQWr1UpFRQVdXV00NTVRWFjIvXv3gIkF4PXr1/Hw8KC0tJSOjg5aWlo4d+6capva2loMBgM6nW7E/xz+ytUkkLS0NCwWC1VVVXR1dVFfX6/EnTMAIyMjqaur49mzZ2zcuJGQkBDl0TqPHz9Gq9VSVFSE3W7n/PnzMglECDEmCUAhxKRNNABh+NZqVlYWwcHB6PV6LBYLKSkpvHr1Shlz/PhxZs6ciZeXF6mpqeTm5qoCcHBwkIyMDGbMmIG/vz8FBQUjZgEPDg6Sn5+P1WpFr9cTGBjI5s2blcenTCQAYTg258+fj16vJygoiKysLNX6oaEhQkNDiY+PH/c8uQrAL1++kJ2dTVBQkPIYmGvXrgE/A/Du3btERkZiMBhYtGjRiEfn2Gw2zGYznp6eJCQkyGNghBBjkgAUQvz1Dh8+rArAv8nnz5+ZPn06FRUV4479kwdBOwPw48ePf/oRVSQAhRAgASiE+Bf4GwPwx48fdHd3s3//fkJCQib09XfOR8+YTCZl1vN4/qkAvHnzpjLjWgJQCCEBKIT46/2NAehwONBoNJjNZurq6ia0TX9//4hZz+P5pwLwT44thPjvkgAUQgghhJhiJACFEEIIIaYYCUAhhBBCiClGAlAIIYQQYoqRABRCCCGEmGL+B70b2CzLNCsEAAAAAElFTkSuQmCC\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.loglog(fu,Su,'k-')\n",
    "plt.loglog(fv,Sv,'-',color='gray')\n",
    "xl = plt.xlim()\n",
    "plt.xlim(xl[0],np.max(fu))\n",
    "\n",
    "# confidence intervals (Emery and Thomson)\n",
    "dof = 2*window_width\n",
    "edof = (8/3)*dof\n",
    "lower = edof/chi2.ppf(0.975,edof)\n",
    "upper = edof/chi2.ppf(0.025,edof)\n",
    "fplot = 5\n",
    "fac = 0.0008\n",
    "plt.plot(np.array([fplot,fplot]),fac*np.array([lower,upper]),'k-')\n",
    "plt.text(fplot+0.5,fac*np.mean([lower,upper]),'95%',verticalalignment='center')\n",
    "\n",
    "plt.legend(['u','v'])\n",
    "plt.xlabel('frequency [cpd]')\n",
    "plt.ylabel('[m$^2$ s$^{-2}$ cpd$^{-1}$]')\n",
    "plt.title('Benthic Rover\\nNov 2015-Oct 2018')\n",
    "\n",
    "plt.savefig('figures_paper/rover_spectrum.png')\n",
    "plt.savefig('figures_paper/rover_spectrum.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Descriptive statistics, 60-day means and standard deviations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_all_2M = df_all_int.rolling(24*60,min_periods=24*60,center=True).mean()\n",
    "df_all_2M_mon = df_all_2M.resample('1M').last()\n",
    "df_all_2Mstd = df_all_int.rolling(24*60,min_periods=24*60,center=True).std()\n",
    "df_all_2Mstd_mon = df_all_2Mstd.resample('1M').last()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.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 overridden (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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"650\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6.5,6.5))\n",
    "plt.subplot(311)\n",
    "plt.plot(df_all_2M_mon.index.month,df_all_2M_mon['u_f'],'ko')\n",
    "plt.plot(df_all_2M_mon.index.month,df_all_2M_mon['v_f'],'^',color='gray')\n",
    "plt.ylim([-0.011,0.011])\n",
    "plt.ylabel('[m/s]')\n",
    "xl = plt.xlim()\n",
    "yl = plt.ylim()\n",
    "plt.text(xl[0]+0.005*np.diff(xl),yl[1]-0.1*np.diff(yl),'a)')\n",
    "plt.gca().set_xticks(np.arange(1,13))\n",
    "plt.gca().set_xticklabels([])\n",
    "plt.legend(['$u$','$v$'],loc=(0.6,0.7))\n",
    "plt.title('60-day mean $u$, $v$ velocity components')\n",
    "\n",
    "plt.subplot(312)\n",
    "plt.plot(df_all_2Mstd_mon.index.month,df_all_2Mstd_mon['u_f'],'ko')\n",
    "plt.ylim([0,0.022])\n",
    "plt.ylabel('[m/s]')\n",
    "xl = plt.xlim()\n",
    "yl = plt.ylim()\n",
    "plt.text(xl[0]+0.005*np.diff(xl),yl[1]-0.1*np.diff(yl),'b)')\n",
    "plt.gca().set_xticks(np.arange(1,13))\n",
    "plt.gca().set_xticklabels([])\n",
    "plt.title('60-day standard deviation - $u_{l}$')\n",
    "\n",
    "plt.subplot(313)\n",
    "plt.plot(df_all_2Mstd_mon.index.month,df_all_2Mstd_mon['u_super'],'ko')\n",
    "plt.ylim([0,0.022])\n",
    "plt.ylabel('[m/s]')\n",
    "xl = plt.xlim()\n",
    "yl = plt.ylim()\n",
    "plt.text(xl[0]+0.005*np.diff(xl),yl[1]-0.1*np.diff(yl),'c)')\n",
    "plt.gca().set_xticks(np.arange(1,13))\n",
    "plt.gca().set_xticklabels(['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'])\n",
    "plt.title('60-day standard deviation - $u_{hn}$')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('figures_paper/monthly_stats.png',dpi=600)\n",
    "plt.savefig('figures_paper/monthly_stats.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test for statistically significant differences between months\n",
    "\n",
    "ANOVA with Tukey HSD post-hoc test to account for multiple comparisons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi = np.isfinite(df_all_2Mstd_mon['u_f'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "MultiComp = MultiComparison(df_all_2Mstd_mon['u_f'][fi],\n",
    "                            df_all_2Mstd_mon.index.month[fi])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multiple Comparison of Means - Tukey HSD,FWER=0.05\n",
      "============================================\n",
      "group1 group2 meandiff  lower  upper  reject\n",
      "--------------------------------------------\n",
      "  1      2     0.0005  -0.0106 0.0115 False \n",
      "  1      3     0.0016  -0.0095 0.0127 False \n",
      "  1      4     0.0016  -0.0095 0.0127 False \n",
      "  1      5     0.0028  -0.0083 0.0139 False \n",
      "  1      6     0.0072  -0.0048 0.0192 False \n",
      "  1      7     0.0077  -0.0042 0.0197 False \n",
      "  1      8     0.007    -0.005 0.019  False \n",
      "  1      9     0.0065  -0.0055 0.0184 False \n",
      "  1      10    0.0056  -0.0079 0.0192 False \n",
      "  1      11    0.0031  -0.0089 0.0151 False \n",
      "  1      12    0.0025  -0.0086 0.0136 False \n",
      "  2      3     0.0012  -0.0099 0.0122 False \n",
      "  2      4     0.0011   -0.01  0.0122 False \n",
      "  2      5     0.0023  -0.0087 0.0134 False \n",
      "  2      6     0.0067  -0.0052 0.0187 False \n",
      "  2      7     0.0073  -0.0047 0.0192 False \n",
      "  2      8     0.0065  -0.0054 0.0185 False \n",
      "  2      9     0.006    -0.006 0.018  False \n",
      "  2      10    0.0052  -0.0084 0.0188 False \n",
      "  2      11    0.0026  -0.0093 0.0146 False \n",
      "  2      12    0.002   -0.0091 0.0131 False \n",
      "  3      4      -0.0   -0.0111 0.011  False \n",
      "  3      5     0.0012  -0.0099 0.0123 False \n",
      "  3      6     0.0056  -0.0064 0.0175 False \n",
      "  3      7     0.0061  -0.0058 0.0181 False \n",
      "  3      8     0.0054  -0.0066 0.0173 False \n",
      "  3      9     0.0048  -0.0071 0.0168 False \n",
      "  3      10    0.004   -0.0095 0.0176 False \n",
      "  3      11    0.0015  -0.0105 0.0134 False \n",
      "  3      12    0.0009  -0.0102 0.0119 False \n",
      "  4      5     0.0012  -0.0099 0.0123 False \n",
      "  4      6     0.0056  -0.0064 0.0176 False \n",
      "  4      7     0.0062  -0.0058 0.0181 False \n",
      "  4      8     0.0054  -0.0066 0.0174 False \n",
      "  4      9     0.0049  -0.0071 0.0168 False \n",
      "  4      10    0.0041  -0.0095 0.0176 False \n",
      "  4      11    0.0015  -0.0105 0.0135 False \n",
      "  4      12    0.0009  -0.0102 0.012  False \n",
      "  5      6     0.0044  -0.0076 0.0164 False \n",
      "  5      7     0.005    -0.007 0.0169 False \n",
      "  5      8     0.0042  -0.0078 0.0162 False \n",
      "  5      9     0.0037  -0.0083 0.0156 False \n",
      "  5      10    0.0029  -0.0107 0.0164 False \n",
      "  5      11    0.0003  -0.0117 0.0123 False \n",
      "  5      12   -0.0003  -0.0114 0.0108 False \n",
      "  6      7     0.0005  -0.0122 0.0133 False \n",
      "  6      8    -0.0002   -0.013 0.0126 False \n",
      "  6      9    -0.0007  -0.0135 0.0121 False \n",
      "  6      10   -0.0015  -0.0158 0.0128 False \n",
      "  6      11   -0.0041  -0.0169 0.0087 False \n",
      "  6      12   -0.0047  -0.0167 0.0073 False \n",
      "  7      8    -0.0008  -0.0135 0.012  False \n",
      "  7      9    -0.0013  -0.0141 0.0115 False \n",
      "  7      10   -0.0021  -0.0164 0.0122 False \n",
      "  7      11   -0.0047  -0.0174 0.0081 False \n",
      "  7      12   -0.0053  -0.0172 0.0067 False \n",
      "  8      9    -0.0005  -0.0133 0.0123 False \n",
      "  8      10   -0.0013  -0.0156 0.013  False \n",
      "  8      11   -0.0039  -0.0167 0.0089 False \n",
      "  8      12   -0.0045  -0.0165 0.0075 False \n",
      "  9      10   -0.0008  -0.0151 0.0135 False \n",
      "  9      11   -0.0034  -0.0162 0.0094 False \n",
      "  9      12    -0.004  -0.0159 0.008  False \n",
      "  10     11   -0.0026  -0.0169 0.0117 False \n",
      "  10     12   -0.0032  -0.0167 0.0104 False \n",
      "  11     12   -0.0006  -0.0126 0.0114 False \n",
      "--------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "print(MultiComp.tukeyhsd(0.05).summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "MultiComp_std = MultiComparison(df_all_2Mstd_mon['u_f'][fi],\n",
    "                            df_all_2Mstd_mon.index.month[fi])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 60-day running means and standard deviations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.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 overridden (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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"700\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,8))\n",
    "plt.subplot(311)\n",
    "plt.plot(df_all_2M.index,df_all_2M['u_f'],'k-')\n",
    "plt.plot(df_all_2M.index,df_all_2M['v_f'],'--',color='gray')\n",
    "plt.title('60-day running mean')\n",
    "plt.ylim([-0.011,0.011])\n",
    "plt.ylabel('[m/s]')\n",
    "plt.gca().set_xticklabels([])\n",
    "yl = plt.ylim()\n",
    "xl = plt.xlim()\n",
    "plt.text(xl[0]+0.01*np.diff(xl),yl[1]-0.1*np.diff(yl),'a)')\n",
    "plt.legend(['$u$','$v$'])\n",
    "\n",
    "plt.subplot(312)\n",
    "hl = plt.plot(df_all_2Mstd.index,df_all_2Mstd['u_f'],'k-')\n",
    "hh = plt.plot(df_all_2Mstd.index,df_all_2Mstd['u_super'],'-',color='darkgray',lw=2)\n",
    "ht = plt.plot(df_all_2Mstd.index,df_all_2Mstd['u_tide'],'-',color='lightgray')\n",
    "plt.gca().set_xticklabels([])\n",
    "yl = plt.ylim()\n",
    "plt.ylim([0,yl[1]])\n",
    "plt.ylabel('[m/s]')\n",
    "plt.title('60-day running standard deviation - $u$')\n",
    "yl = plt.ylim()\n",
    "xl = plt.xlim()\n",
    "plt.text(xl[0]+0.01*np.diff(xl),yl[1]-0.1*np.diff(yl),'b)')\n",
    "plt.legend([hl[0],ht[0],hh[0]],['$u_{l}$','$u_{hct}$','$u_{hn}$'])\n",
    "\n",
    "plt.subplot(313)\n",
    "plt.plot(df_all_2Mstd.index,df_all_2Mstd['v_f'],'k-')\n",
    "plt.plot(df_all_2Mstd.index,df_all_2Mstd['v_super'],'-',color='darkgray',lw=2)\n",
    "plt.plot(df_all_2Mstd.index,df_all_2Mstd['v_tide'],'-',color='lightgray')\n",
    "plt.xticks(rotation=30)\n",
    "plt.ylim([0,yl[1]])\n",
    "plt.title('60-day running standard deviation - $v$')\n",
    "plt.ylabel('[m/s]')\n",
    "plt.tight_layout()\n",
    "yl = plt.ylim()\n",
    "xl = plt.xlim()\n",
    "plt.text(xl[0]+0.01*np.diff(xl),yl[1]-0.1*np.diff(yl),'c)')\n",
    "\n",
    "plt.savefig('figures_paper/runnning_stats.png',dpi=600)\n",
    "plt.savefig('figures_paper/runnning_stats.pdf')"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:stationm]",
   "language": "python",
   "name": "conda-env-stationm-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}