CSTR_dynamic_optimal_control.ipynb

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    "## 連続撹拌反応槽 (CSTR) の最適制御\n",
    "[JModelica.org Users Guide - version 2.10](https://jmodelica.org/downloads/UsersGuide-2.10.pdf) 6.5.2.1 を実習します。ソースコードは$JMODELICA_HOME/Python/pyjmi/examples/files/CSTR.mop にありますが、実習に必要な部分のみを切り取って解説します。本文書は Users Guid の例題を解説するものであり、モデルのソースコードやオペレーションで使用する Pythonのコードなどの著作権は Modelon AB にあります。\n",
    "\n",
    "### 連続撹拌反応槽(Continuously Stirred Tank Reactor, CSTR)の概要\n",
    "\n",
    "容積 $V$ の容器に、入口から濃度 $c_0$、温度 $T_0$ の物質が流量 $F_0$ で流れ込みます。容器内で連続的に撹拌されて化学反応した結果、\n",
    "出口で濃度 $c(T)$、温度 $T(t)$ となって流出します。化学反応の速度はアレニウスの式に従って濃度 $c(t)$ や温度 $T(t)$ に依存します。また、容器には冷却装置がついていて冷却温度$T_c$で冷却されています。\n",
    "\n",
    "[Derivation of Stirred Tank Reaction Optimal Control, Lorenz T.Biegler](http://cepac.cheme.cmu.edu/pasi2011/library/biegler/hicks.pdf)\n",
    "\n",
    "のモデルが参考になります。\n",
    "\n",
    "#### 状態変数 (state variables)\n",
    "* $c(t)$ 濃度 (concentration)\n",
    "* $T(t)$ 温度 (temperature)\n",
    "\n",
    "#### 制御変数 (control variable)\n",
    "* $T_c(t)$ 冷却温度 (cooling temperature)\n",
    "\n",
    "#### 方程式 (equationns)\n",
    "濃度 $c(t)$ の変化は次の方程式で表されます。右辺第１項は流れによる物質の出入り、右辺第２項は化学反応による濃度変化を表しています。\n",
    "$$\n",
    "\\dot{c}(t) = \\frac{F_0 (c_0 - c(t))}{V} - k_0 c(t) e^{-\\frac{E_{div} R}{T(t)}}\n",
    "$$\n",
    "\n",
    "温度 $T(t)$ の変化は次式で表されます。右辺第１項は流れによる熱の出入り、第２項は化学反応による発熱、第3項は冷却部との熱伝達をあらわします。\n",
    "$$\n",
    "\\dot{T}(t) = \\frac{F_0(T_0-T(t))}{V} - \\frac{dHk_0c(t)}{\\rho C_p}e^{-\\frac{E_{div}R}{T(t)}}+\\frac{2U}{r \\rho C_p} (T_c(t)-T(t))\n",
    "$$\n",
    "\n",
    "冷却部の熱伝達率を $U$ とし、伝熱面積を $A_c$ とすると、熱流量は $U A_c (T_c(t)-T(t)$となり、容器内物質の熱容量は $\\rho C_p V$ となります。温度変化率は、\n",
    "$$\n",
    "\\frac{U A_c (T_c(t)-T(t))}{\\rho C_p V}\n",
    "$$\n",
    "となり、これと上の温度の変化式の右辺第3項を比較すると $r = 2V/A_c$ は長さの次元の量になります。\n",
    "\n",
    "#### パラメータ\n",
    "* $V$ 容積 (volume), $r = 2V/A_c$ 長さの次元の量 (length) \n",
    "* $F_0$ 流量 (flow rate), $c_0$ 入口濃度 (inlet concentration), $T_0$ 入口温度 (inlet temperaturea)\n",
    "* $k_0 $ 速度定数 (rate constant), $E_{div}$ 活性化エネルギー (activation energy), $dH $ 反応熱 (heat of reaction), $R$ 気体定数 (gas constant)\n",
    "* $\\rho$ 密度 (density), $C_p$ 比熱 (specific heat), $U$ 熱伝達率 (heat transfer coefficient)\n",
    "\n",
    "### 最適化制御問題 (Optimal Control Problem) の概要\n",
    "\n",
    "微分代数方程式系(DAEs)の動的最適化問題 (Optimal Control Problem)では、初期化したモデル方程式の微分が全てゼロになるべきです。このような点を停留点(stationary point)といいます。\n",
    "\n",
    "ここでは、低温で反応速度が小さい停留点 A から高温で反応速度が大きい停留点 B まで状態変数が移動し、\n",
    "$$\n",
    "\\int_{t_s}^{t_e}{ q_c (c_{ref}-c)^2 + q_T (T_{ref}-T)^2 + q_{Tc} (Tc_{ref}-Tc)^2 } dt\n",
    "$$\n",
    "を最小にするように冷却温度 Tc を制御する問題を解きます。\n",
    "ここで、$c_{ref}, T_{ref}, Tc_{ref}$ は、停留点 B の濃度、温度、冷却温度です。$q_c, q_T, q_{Tc}$ は重み定数で、ここでは、\n",
    "$$\n",
    "q_c = q_T = q_{Tc} = 1\n",
    "$$\n",
    "とします。\n",
    "\n",
    "次のような手順を行います。\n",
    "1. CSTR モデルを作成し、動作を確認します。\n",
    "2. CSTR_Init モデルを使って、2つの停留点 A, B を求めます。\n",
    "3. CSTR_Init_Optimization モデルを使って、初期推定値の軌跡(Initial Guess Trajectory)を求めます。\n",
    "4. CSTR_Opt2を使用し最適制御シミュレーションを行います。"
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    "### 使用するモデル\n",
    "\n",
    "\n",
    "#### CSTR\n",
    "連続撹拌反応槽のモデルです。微分代数方程式の初期値問題です。\n",
    "* 初期条件として濃度 c_init と温度 T_init を設定します。\n",
    "* 制御変数として冷却温度 Tc を設定します。\n",
    "* 状態変数である濃度 c(t) と温度 T(t) が時間とともに変化します。\n",
    "\n",
    "#### CSTR_Init\n",
    "停留点(stationary point)を求めるためのモデルです。CSTRを継承して初期化方程式として\n",
    "$$\n",
    "\\frac{dc}{dt} = 0, \\ \\frac{dT}{dt} = 0\n",
    "$$\n",
    "を加えています。\n",
    "冷却温度 Tc を設定してモデルを初期化すると、停留点の状態変数 c と T が得られます。\n",
    "\n",
    "#### CSTR_Init_Optimization\n",
    "direct collocation 法では、よい初期推定値(good initial guesses)を与えるとロバストに収束することがよくあります。問題が非凸(non-convex)である場合には初期値はより重要になります。初期推定値は全ての状態変数に対して最適化制御を行う時間に渡って必要となります。\n",
    "このモデルは、初期推定値の軌跡 (initial trajectories) を得るためのモデルです。\n",
    "変数としてCSTRモデルを持ち、次の方程式を含んでいます。\n",
    "$$\n",
    "\\frac{d (cost)}{dt} = q_c (c_{ref}-c)^2 + q_T (T_{ref}-T)^2 + q_{Tc} (Tc_{ref}-Tc)^2\n",
    "$$\n",
    "これにより、最適化の目的関数\n",
    "$$\n",
    "cost = \\int{ q_c (c_{ref}-c)^2 + q_T (T_{ref}-T)^2 + q_{Tc} (Tc_{ref}-Tc)^2 } dt\n",
    "$$\n",
    "がどのように変化するか確認できます。\n",
    "\n",
    "このモデルは、オリジナルの例題から一部変更しました。コメントアウトしている部分\n",
    "```\n",
    "input u = T_ref\n",
    "parameter Real T_ref = 320\n",
    "cstr.Tc = T_ref\n",
    "```\n",
    "のようなっていましたが、u はCSTRモデルの制御変数、T_refは目的関数のパラメータとして分離して考えるようにしました。\n",
    "```\n",
    "input u\n",
    "parameter Real T_ref = 320\n",
    "cstr.Tc = u\n",
    "```\n",
    "Modelica で記述した部分 CSTR1.mo と、最適化の部分 CSTR2.mop （後述）を別々のファイルにしました。"
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     "text": [
      "Overwriting CSTR1.mo\n"
     ]
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   "source": [
    "%%writefile CSTR1.mo\n",
    "package CSTR1\n",
    "\n",
    "  model CSTR \"A CSTR\"\n",
    "\n",
    "    // Parameters\n",
    "    parameter Modelica.SIunits.VolumeFlowRate F0=100/1000/60 \"Inflow\";\n",
    "    parameter Modelica.SIunits.Concentration c0=1000 \"Concentration of inflow\"; \n",
    "    // Modelica.Blocks.Interfaces.RealInput Tc \"Cooling temperature\";\n",
    "    // parameter Modelica.SIunits.VolumeFlowRate F=100/1000/60 \"Outflow\"; \n",
    "    parameter Modelica.SIunits.Temp_K T0 = 350;\n",
    "    parameter Modelica.SIunits.Length r = 0.219;\n",
    "    parameter Real k0 = 7.2e10/60;\n",
    "    parameter Real EdivR = 8750;\n",
    "    parameter Real U = 915.6;\n",
    "    parameter Real rho = 1000;\n",
    "    parameter Real Cp = 0.239*1000;\n",
    "    parameter Real dH = -5e4;\n",
    "    parameter Modelica.SIunits.Volume V = 100 \"Reactor Volume\";\n",
    "\n",
    "    // Control Variable\n",
    "    Modelica.Blocks.Interfaces.RealInput Tc \"Cooling temperature\";    \n",
    "    \n",
    "    // Initial State\n",
    "    parameter Modelica.SIunits.Concentration c_init = 1000;\n",
    "    parameter Modelica.SIunits.Temp_K T_init = 350;\n",
    "  \n",
    "    // State \n",
    "    Real c(start=c_init,fixed=true,nominal=c0);\n",
    "    Real T(start=T_init,fixed=true,nominal=T0);\n",
    "    \n",
    "  equation \n",
    "    der(c) = F0*(c0-c)/V-k0*c*exp(-EdivR/T);\n",
    "    der(T) = F0*(T0-T)/V-dH/(rho*Cp)*k0*c*exp(-EdivR/T)+2*U/(r*rho*Cp)*(Tc-T);\n",
    "\n",
    "  end CSTR;\n",
    "  \n",
    "  model CSTR_Init\n",
    "    extends CSTR(c(fixed=false),T(fixed=false));\n",
    "  initial equation\n",
    "    der(c) = 0;\n",
    "    der(T) = 0;\n",
    "  end CSTR_Init;\n",
    "\n",
    "  model CSTR_Init_Optimization\n",
    "    CSTR cstr \"CSTR component\";\n",
    "    Real cost(start=0,fixed=true);\n",
    "    // input Real u = Tc_ref;\n",
    "    input Real u;\n",
    "    parameter Real c_ref = 500;\n",
    "    parameter Real T_ref = 320;\n",
    "    parameter Real Tc_ref = 350;\n",
    "    parameter Real q_c = 1;\n",
    "    parameter Real q_T = 1;\n",
    "    parameter Real q_Tc = 1;\n",
    "\n",
    "  equation\n",
    "    // cstr.Tc = Tc_ref;\n",
    "    cstr.Tc = u; \n",
    "    der(cost) = q_c*(c_ref-cstr.c)^2 + q_T*(T_ref-cstr.T)^2 + q_Tc*(Tc_ref-cstr.Tc)^2;\n",
    "  end CSTR_Init_Optimization;\n",
    "\n",
    "end CSTR1;"
   ]
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   "source": [
    "### 準備\n",
    "必要なモジュールをインポートします。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "from pymodelica import compile_fmu\n",
    "from pyfmi import load_fmu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CSTR モデルの動作を確認します。\n",
    "デフォルトの状態で CSTR モデルのシミュレーションを実行します。\n",
    "制御変数として、冷却温度を $Tc=0$ とし、初期条件として濃度 $c_{init} = 1000$、温度 $T_{init} = 350$ とした場合の\n",
    "濃度 $c(t)$、温度 $T(t)$、冷却温度 $Tc(t)$ をプロットします。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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     "text": [
      "Final Run Statistics: --- \n",
      "\n",
      " Number of steps                                 : 72\n",
      " Number of function evaluations                  : 92\n",
      " Number of Jacobian evaluations                  : 2\n",
      " Number of function eval. due to Jacobian eval.  : 4\n",
      " Number of error test failures                   : 0\n",
      " Number of nonlinear iterations                  : 88\n",
      " Number of nonlinear convergence failures        : 0\n",
      "\n",
      "Solver options:\n",
      "\n",
      " Solver                   : CVode\n",
      " Linear multistep method  : BDF\n",
      " Nonlinear solver         : Newton\n",
      " Linear solver type       : DENSE\n",
      " Maximal order            : 5\n",
      " Tolerances (absolute)    : [ 0.001    0.00035]\n",
      " Tolerances (relative)    : 0.0001\n",
      "\n",
      "Simulation interval    : 0.0 - 150.0 seconds.\n",
      "Elapsed simulation time: 0.00426888465881 seconds.\n"
     ]
    },
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       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        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=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fmu = compile_fmu(\"CSTR1.CSTR\", \"CSTR1.mo\")\n",
    "model = load_fmu(\"CSTR1_CSTR.fmu\")\n",
    "res = model.simulate(final_time=150)\n",
    "t = res[\"time\"]\n",
    "c = res[\"c\"]\n",
    "T = res[\"T\"]\n",
    "Tc = res[\"Tc\"]\n",
    "\n",
    "plt.figure(1)\n",
    "plt.subplot(311)\n",
    "plt.plot(t, c)\n",
    "plt.ylabel(\"Concentration\")\n",
    "plt.xlim(0,150)\n",
    "plt.grid()\n",
    "\n",
    "plt.subplot(312)\n",
    "plt.plot(t,T)\n",
    "plt.ylabel(\"Temperature\")\n",
    "plt.xlim(0,150)\n",
    "plt.grid()\n",
    "\n",
    "plt.subplot(313)\n",
    "plt.plot(t,Tc)\n",
    "plt.ylabel(\"Cooling Temperature\")\n",
    "plt.xlim(0,150)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CSTR_Init モデルを使って２つの停留点 (stationary points) を求めます。\n",
    "まずモデルをコンパイルしてFMUを作成し、ロードします。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_fmu = compile_fmu(\"CSTR1.CSTR_Init\",\"CSTR1.mo\")\n",
    "init_model = load_fmu(init_fmu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stationary Point A を求める\n",
    "冷却温度 Tc_0_A = 250 を設定して、CSTR_init モデルを初期化(initialize)します。\n",
    "初期化方程式(initial equations)の評価の結果、停留点濃度 c_0_A と停留点温度 T_0_A が得られます。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stationary Point A\n",
      "Tc = 250.000000\n",
      "c = 956.271352, T = 250.051971\n"
     ]
    }
   ],
   "source": [
    "Tc_0_A = 250\n",
    "init_model.set('Tc', Tc_0_A)\n",
    "init_model.initialize()\n",
    "[c_0_A,T_0_A] = init_model.get(['c','T'])\n",
    "print('Stationary Point A')\n",
    "print('Tc = %f' % Tc_0_A)\n",
    "print('c = %f, T = %f' %(c_0_A, T_0_A) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stationary Point B を求める\n",
    "同様に冷却温度 Tc_0_B = 280 を設定して、停留点濃度 c_0_B と停留点温度 T_0_B を求めます。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stationary Point B\n",
      "Tc = 280.000000\n",
      "c = 338.775781, T = 280.099198\n"
     ]
    }
   ],
   "source": [
    "init_model.reset()\n",
    "Tc_0_B = 280\n",
    "init_model.set('Tc', Tc_0_B)\n",
    "init_model.initialize()\n",
    "[c_0_B,T_0_B] = init_model.get(['c','T'])\n",
    "print('Stationary Point B')\n",
    "print('Tc = %f' % Tc_0_B)\n",
    "print('c = %f, T = %f' %(c_0_B, T_0_B) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CSTR_Init_Optimization モデルを使って初期推定値の軌跡 (Initial Trajectories) を求めます。\n",
    "停留点 A の状態からスタートし、冷却温度をステップ的に Tc_0_A から Tc_0_B に変えた場合の状態変数の変化を求めます。\n",
    "\n",
    "冷却温度の制御信号を作成します。時刻0に Tc_0_A から Tc_0_B に変化するものとします。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stepf(t):\n",
    "  startTime = 0\n",
    "  if t <= startTime:\n",
    "    return Tc_0_A\n",
    "  else:\n",
    "    return Tc_0_B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CSTRモデルの初期状態として停留点Aの状態変数を設定し、目標関数のパラメータとして停留点Bの値を設定します。\n",
    "そして上で定義した制御変数の関数を設定して、シミュレーションを実行します。\n",
    "シミュレーション結果 init_res に含まれる初期推定値の軌跡 (initial trajectories) をプロットします。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Run Statistics: --- \n",
      "\n",
      " Number of steps                                 : 30\n",
      " Number of function evaluations                  : 33\n",
      " Number of Jacobian evaluations                  : 1\n",
      " Number of function eval. due to Jacobian eval.  : 3\n",
      " Number of error test failures                   : 0\n",
      " Number of nonlinear iterations                  : 32\n",
      " Number of nonlinear convergence failures        : 0\n",
      "\n",
      "Solver options:\n",
      "\n",
      " Solver                   : CVode\n",
      " Linear multistep method  : BDF\n",
      " Nonlinear solver         : Newton\n",
      " Linear solver type       : DENSE\n",
      " Maximal order            : 5\n",
      " Tolerances (absolute)    : [  1.00000000e-06   1.00000000e-03   3.50000000e-04]\n",
      " Tolerances (relative)    : 0.0001\n",
      "\n",
      "Simulation interval    : 0.0 - 150.0 seconds.\n",
      "Elapsed simulation time: 0.00321006774902 seconds.\n"
     ]
    },
    {
     "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 overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        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>"
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     },
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     "output_type": "display_data"
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S/HC/Ap/bn+ar6eT5emM6k6O2MvFP7IfVaxUeH9+3dMU/uy3KnxNblT4vXmHolbFx+zvHb/XVinuVOxU+LU7FH5ftSm+rjhX8bSiVPHn6t8VzfkG+zS9S9GtOKxYpLhS4X36vPxalyo2KIoVf098T2EnF19rv/cfU/iYnqfw+/lTij9QDFRxMsP2pysOKLoUtpqiuE7h7zyXToU/R29QFJZ/1YLX5Be+Q9O/Vvh8ypV3Jiu/oLrff/0pl2ijbyjs7tKjiNfFCVR/5y1Q+DPk469l+TftzOdtt9sUfv/Y84UK+41Q+P23VOHP680Kfw85IX2LYmN+3u39ORyn+JbiTIU/M0Op1OrzvEYn9Yn8ib0nOcG4LFmUVf157WsZqQ2+qXhFsmH6/efPld/bLYpJiiUKv8//SVGpNKmB9+1r5ut+h+Iphb/Hnq/wd6vL9YqPKn7XMxQEEEAAAQQQQAABBIazQJcO3j/wHIsVxUqHFsY2/rHs+j2KsxRp8Y+k9yliW0+vSRsU1NN2Bat6Z7tUi+06epeWr3RpdaVtLlebDyucLChVZmjFlxRxX06ilCrtWhHbdZdq1Iflp6itf+jEffqH8GkF2/uH8p/n28Tr4vZdimKlUwvj/pYUa1CwrF3zsX13wbp09oNJOydJXq/wD7fC8lot8A/muE+3Kyx/oAVx/aOq+wd0seL3mpMyn1T4R2BanIQ6qPB+DinepnD7YmWWFv6+4p3FVvZhmX+APqSIx+6EwEuKbO9lm5J296seE1GFzbuSdk6AeN//oHCCIS2zNfNTRXxtvxcKP5uxfWfSbklcWGE6X+v/WXGZoth19eYTFH+viMfgJEmptlrV287tS5VurYj78zntVrxZUXgtn6NlqxWx7edVL1Xcdo8itv1H1Z3gKSyna8FtitjuxsIG+Xlfi8eTdk+r7kRWWny8fl8fVsTr6P12K060LNYO4jHGff9Yy/y+TkurZnyusa2nb08b5Os+1hWK2O66Im3SRU4Mx9f1+c1LV/ax/h21j6/rRGBMWBbuxuYvU3y6cEV+vlPTuJ8l+WWFkw4tiG3i8d+iZYXHP1HLfpa0dT0epz8TYxVp8efOibG4779IVxbUYxtPqyldahS36SixwZKkTWeJNvO1/GR8ngsPJ55LufP/39oobfd3mi/8/pupZTcl7fzd7z/fi5VOLYz783eK6/5sO5mbFv/Zmn6fHdF8e9qAOgIIIIAAAggggAACw1GgSwcd/0K8uMQJdCRt3NY/7t3DoVT5T62I+/xUqUZJG7ctVbq0Iu6ro1SjguVdmu/rNgW7OG7WPxDi/s4+bs2xmfakTfexxf2upT8+Vmovk8rs6f9pXTw+T7tKtO3U8thuSYk26eJ2zcT23emKpN6uupNsbrdTUSrxpFVZeYH+H/f5mOojcot7/+8f/nH9i3qX9q3y8mQfX+nbpv1u/fbkNd2b5OIye3LiMiYofa5vLdG2S8ujhaflPkttWv9w0v7rqhcrnVoY97mkWIMTXOZjjPv/lTL7im08LVW6tSK28w/w60s11PJfTdruUt0/4IuVNFH6gWINkmVjVfd7NB5DsaTCbyfr96p+hqJU+WOtiPvytLtUwz4sX6y26T4f0LyTfaXKZ7UitneiqpiTXWKbSp8fJzZj2x+qfiJlszb2vpyYKffnS6XX6FSDeExLSjTuSNq4ra+zP0PFylwtTD+vbv+5Yg3zy35D0/j6j5dpF9t4Wk3pUqO4TUeJDZYkbTpLtOnL4lp+ngtfN55LqfP3Pyr4sxzb+R8/ShUnBe9RxLZO1BYrnVoY23h6q6LYZ0CLsz+b0n3680tBAAEEECgjUO5ffstsxioEEEAAgSEu8Cc6PvfGKVU+n6xwsmO4lyXJCbwoqQ9U1X9+vi3Z+WLVtyfzhdW/rLC+sH0t593j07dSuTgR+URWK/0//zBzbw0XJ1MvymrH/ucffbFsipU+Tmuxjz6+ZPgfyQafVt09+0qVe7XCiZhYfjdWykz9Q7jcD9Aerf/DZPtXqe6ekINdvpC8YC0/K9/Tfn+Y7LuweqMWrM8vdPKoWCL6Ai13AtrF/4jxt1mt9P/cU/CDyerfTOqx+q5Y0fRjiuXJfGHVCYynCxfWeN7vgX1l9vm/tM7n5TJb4cRpYfH3t5P6Lq9RlPvHh3dkrXL/K5cUS5qVrMbPrROp5f58KbmDE1jxJ9rWn6Fi5RktvCNZ4R6D/yeZL6x+WwvcxuVMxfisNjz/N1Cf52o03qRGMRG8QfX/W2Yje/9esv461W1fqbxfDeJ7vbDtUS1Iz78e/i5TeI7MI4AAAjUVKPUvKjV9EXaGAAIIIDCoAv5x6R/j5cqDycr2pD5Uq2N0YFcozlNMV/gHW0xqqXrc7UEXesEAl3O0fx+Hi3uefCOrlf6ff9B/R9FZusmArXlZsuf/SOrlqk4CvjTf4HmaPpA0XpXU3616mlhLVpWtpvtwAuPvFDE5VHbDfq70j9RLkm3TBHiy+LiqkyUx8ecflu5t5utYqnxXK9zDslxx77bVijkK31ZsW78valm838sVTqbNVPizkv59L0121PKz8p96nXLFP9YfVviYXNoVv3QlKel71fvzLauVit+rsdgzLT7Xi5MFX07qxaqHtPCrig8UW1mDZU5UdVXYz3at93vJyRUXJ0o8n5Z1mvF3/KsUrQq3/aSisPh9e15+4WZN/6uwQR/n/bldoJis8GvaajCKE38/qPBCfi9dk29zq6abyrT3n5FPKfw9PkLRrviFYiiWk/V5rsbiBUkj/9lSKkEbm92jip3je9Lv7aVxZZHpCi27v8jydNFw+7tMeuzUEUAAgUEXSP9COOgvzgsigAACCAyIgP9CfaDCnrck6ycm9aFWnaID+qDirYrxVR7ctCrbnUizNHHyuHa0q4qd3as2nVW0q2WTqdrZomSH71fdiZhKxT+MY/HtdWn5mmbemV/w25o6yfBFxU2KJxTVlLvVaKVivsL7f1Thffy34i5FpR+SatKn4mRYU36L3Zo+UsXWD6mNE35jFd7W+7hDUar4uCsV2/vcnQB0uUjxnax24v9r0y7+VPE7imo/A9W2q+bo/MO+Uqn0vXNlsgPXP57Ml6qOSFYUvlfP17qR+fVOzvqzWqlUcx0r7aPUel/7aj5/d6qdE2wufo8UK5/VQicAXfx5LJYAjJ9Tt/mSotKfC25XrnxNK/0ec/mK4o0KL3MS1knJgSrLtGP/Q0u5si1Z+VhSL1VN28eejaXanozlJ/vzXM05p+/N26vZQG3c7rx82+dW2KYW3ykVXoLVCCCAQGMJkABsrOvN2SKAQGMI7KjiNNMfU0P1zwInh25VzKvifNIm1SYK0236Wk8TJ6ur3HhNle1q2WxWwc7eXTBfzezkgkY/1vw/Kt6fX+4fgQ4X9zLyD7wuxTcVzyiKFb//3qz4vsI/vp3o9f4cTlK4x6GvvdffpqgmaaJmJcv0ZI2P6UgyX6rqNm57Vr5Bes2LbbOq2MIiy1KT9LiKNK160WS1dBImTUxXs3EtPys7qnjB9HunuUj72cmyF6ju6EuxQ1pSX39Oq3kfpdcn3Vct6rV8j/xQB+T9+fvRiRQnqB9WxOIEkhN0sfxbrJzA9MPa9lrF1QonXn8tH5pkt07fpulPFN9VOOFaq1LNe8u9N2Ppa/ti78W4r5MxnawXPdmf52rOO/18+R90qindSaNK36nVXMdK3ynJy1FFAAEEEBgJAQIIIIBA3QlU8yN3OJz0v+sg/ePWxT8m/0HxUsVpCt/S2aTwj1DHdYpYBuPPNr9+LH4eVjVlTzWNatxmYg32VyxB/D+131coCnvE+QfdKxVOEK5UOAk4X1Gs/FwLnbT4vCK1adH8FYr/rbhF8YTi1YoTKen1Sl+r0j7TtpWSZf15H1TaZ6Xji+s/oUpM/u1X/TMKX58zFH4NX0N/ThynKWKp5WelFt87J/p+9XdCWtLr3p/rk+6rFvX+HEOp98gRHVCa1HtnwQG+VvPR0z0KHytY359ZH/91ivcrnirYgd9Xb1V8SeHegH+vcBKyFqWv762+tq/FMdZyH0Ph81zN+aSfr/S7sty2abtS7+24/XC/jvE8mCKAAAJDRqCWf/EbMifFgSCAAAIIDHuBq3QG7mXisktxueKPFD9SdCv8I8I/gGOp9EMitqvVNP0RM6bKnfpW0lqXSn+Op8e5XS/uBFBfo7PEQft2XV8j387q3nxOOqVJBr/OryvuVyxSFCvdWujExXTFixQfVNysSG8B9rbfUjjp2N+yO9mwL9chbev3YbnSn/dBpX2We7247lRVYk+vw6q/ROHbgH19nlT43L08lsH+rMTXrWaavl99e2tf36tun5b0uvfn+qT7qkW9P8dQ7j3iBGC8tr+p+ujkINOEoNvVqhzUjv5JsVBxnuI9iv9QrFHE4vP097U/y7VKAsZ9D+Vppe/jao59OH2e089X+l1Z7jzTduXe2+X2wToEEEAAgX4K1OIPqn6+NJshgAACCCBQUuCFyZolqrsXWLkyv9zKAVi3OdmnE2DVFP+wq1T84zoW99qqVGIPn1LtNiQrJqnuRFuti3/4u7emk07PUcxV/JkiJnOmqv5RRbnihN9PFX+heIHC27xG8bAilr9RpRrD2D6dbkpmfHyFiaJkdW/Vf0dy21jSax6XpdN56UyZetqu0j7L7KZ3lb3i+dyo+q29a4pXBvuzUvwoii9N36/uvXiipT/XPb3mJ/r6hdun175wXTqftiv3HvFn7wf5DadoGnvKLlD9mvxyJ2m+lq/XevJL7fCTit9Q+HvwIsW/KWLxP9y8J84Mw+mh5Jhr8X2c7K5kdTh9ntPPV/qeLXlyWpF+/5R7b5fbB+sQQAABBPop4L/cUhBAAAEEEDgRgaMnsnGJbdNngT1aok26OP7YTZcNZP2hZOdnqz4umS9VvbTUimR52iPCSbBKxT1wyhXfircqaeDeYQNdVusF/krxW8kL+XXT3knJqqJVJwTd6+86xfp8ixZNX5qv93XiROLh/Ea+VpXc3NS3J491RcXbpsnIbGHB/64smC8260SdkyKxPBArybSvn6eh/llJTq1i9e6kRX+vdbKLbLCXI/kF7vl4TrqyRN23nw9U8b5jsrbca6THUOw9km77r8lM7PX3juR1nPxLe2olzWte9ffiuxTpMb2i5q8yeDvsy/exv58W1eDQhtPn+cHkfK9K6uWqVycrK723k6ZUEUAAAQRqIUACsBaK7AMBBBBobIF9yek3J/UTqcYf7d5Hpdvm/INpsH9kOikZez/4nF+nKFecSHpVuQb5dU8nbZyAqpQseH3SvlT1+8mKP6hin0nzE6p+L9naRu6h1NeyTRvckWx0SlLvS9UJkPuSDTqTeqnq25MV96i+J5kvVvV7cGKxFcmyF6keezG6t+ftybpY7evnqS+fFX+W3hpfaAhO0/eMewFXk6gtdxpO4NyfNHhzUi9WHaWFbyq2okbL3Euuo8K+Jmn9K5M2Nyf1YlX3+lyTX2Ez9/57W37ek7RHXrJ4QKvpdezvZ3ZAD7DKnaffxxdW2Maf/9YKbapZPVCf575+r1RzrD9LGr1R9Urn/1y1OT/ZptJ7O2lKFQEEEECgFgIkAGuhyD4QQACBxhbYkpx+TG4ki/pVXZFslf4YThZn1Sb9/18VfeldVriP/sz7R9qXkg0Xq+4f7qXKX2hFufVxu8dVib1OZqn+kriiyPRXtcxRqfyDGsTeb5eo7mOptsws0nBakWXFFs1LFtprazJfTe/G2DzdT0y6xnV9mfoZhbG8R5X0h2hcHqe+ldG3NMfy6VgpMx2vdX9bZn2b1n0kWf9d1YudT18/TyuSffr94CRWqeL3wlBOyDjR2pU/eCe/v6KYkJ+vNGlRg8lFGqUJsPdq/RlF2sRFf6jKaXFmgKYf1X7LJUr+XuvH5l97naZpAj+/+LiJP9ufzy+x2ZcV8Xv4MdXvzK870Ym/Y6vp6ezXqdVn9kSP+US39/sxls5YKTL1e7TcZ7/IJiUXDdTnua/fKyUPMFnxVdXTP6/K/dniz+e/JNs6+bc0maeKAAIIIIAAAggggAACEuhSHM3HYk2LlQ4tjG26ijUosiy297RUqabNH2vj2M49P/wjtFLpUoO4TUeRxmdp2ZGkjRMnTqCkxcmp7yi8n935qetdimKlXQvja3YXa9DHZU7QuYda3OddqhcmD5yM+bN8m31J2y7VSxUnLOI+V6l+TkFD+75F4R5p6T67NV+qLNaKuE9PlyjcG6lYcVL1RQonONcWabBcy25QvEzhH3XFytlaeL8ivuaPChot1vxDincr7FisjNfCv1PEffh5XHOLNaxymY/Vrxn35+TKdUW2faGWbUja+Tyai7Tzoi5F3N/+fN3vVSdL0uJz/IkibVt4XWN7J+hiux7V02RKbJNOnfTyeyFu46RZYbLZCQonyt0m/ax0a75UifvztFTp1orYrr1Uo2T5EtVj+85keVo9VzNOKsR2T6ju92OpslArPqBwL7iXF2nkZNtSRdyfEyyXFLTzZ+p9isOKeB3dvltxomWxdhBfO+7bnwe/J9Li94yTg7Gtp+9MG5Sp+z3iY0+3df1EBs4pfLl2LfD33T8oLlMUK3a8XrFFEY/lT4s07EzWLymy3os6FHEfXapXKovVILZ3vVLpUoPYvqNE4+cnbdz294q0859V9+Xbpd/HHUXaetESRXzdTtULy0B9nn+gF4qv+78LX7TEfGzvaanifaXtPqT5wj8X/J3mRHZsd1D1yxXFSqcWxnZLijUoWNau+di+u2AdswgggAACCCCAAAIIDDuBLh1x/Avu4hJH35G06SrRpnBx3KenpUo1bc7Qxmmy7hea/4ziI0kU/uDu0rq47w7Vi5UvamFs4+lahXtNfVbxY0X8Mb1T9f+hiG27VC9W2rUwtuku1qAfy96gbdJz9w+bnyp8jF9TrFf4NQ8o/ihf9/zPFKVKu1akSRpv6/O16Q2KlQrvwwkxJwiqOSf/MF+StI3b36tlX1Z8SvHvip8r0uTLZs0Xlm4tiK+5V/W7FF9VfFrhc44/htM252t5WhZrJq6333LFtxV2W6JwgiQ1cNu/UpxoOVs72KiIr+2pk4JL8pEmCL3OicBFilKlSyvivnx943vSCZD/VPyr4oeKNDHg9u9VlCu+DnG/Trr4ujs5FD9Tv6t6Wv5SM7G9p379GxX2/J4iWvo981ZFbNuteqkS23haqnRrRWzXXqpRsnyJ6rF9Z7K8sPpyLUiTmt7GyfBvKvw5+ILC5/WMIu7PU29XrFyhhen+/J67Q/FvCr/vve+4nz9I6t2qn2hZrB3Eff+j6ivy835P+L3h98g3FGnSzO29rC/F1zu+jqd+L07ryw4qtG3X+nT/Pl4ntf097XP4jiKeW2y3VMtib0ZVe0unarHNkt6lx1c6NBvbdB2/qujcYi2N7V2vVLrUILbvKNPY77PYztPHFV9QfF5xuyImXr2sSxHbdqherCzRwtims1gDLRuIz/O7ktf1+/9nCvfI+0gShT1o43F6WqqM1Ir/UqRtN2s+fv/9QPXC7z9/xkqVTq2I+1pSqlGyvF312L47WU4VAQQQQAABBBBAAIFhKdClo45/wV1c4gw6kjZdJdoULo779LRUqaaNt/W/+qdtC+udbpSULtVjm45keVodo5mbFLFdsakTAFcrOpJ2XaoXK+1aGPfRXaxBP5e9TdulyYX4GnG6Q+tfqXixIi5zIrNccS+aSvv8dbVpV8R9dqteqfyeGmxVxG3KTf0jsdhxOsFbbrt03Qq1vUpRWP5QC9J25epOZPx54Q5OYN4JvQcU5V7T6+5XLFCUK11aGffToforFNuTZXFdnPpc3qeoVJ6rBn7fxO0Kp10FO2jSvBMxhe3SeScSX6VoT9p1q16qpNuWatOtFbFde6lGyfIlqsf2ncnyYtULtPA+RWxfafq02l5YbEf5ZS/Q1AndUvs5oHXukdqetOlW/UTLYu0gvqbr7jHmJFJcVmz6Ja1vVvSl+Nqm+3ICppblVO2sMJGTvl5h/Wa1n1niADq1PLZfUqJNR9Kmq0SbdPFizcR9ul6pdKlBbN9RpvEUrbs3aRu3SadOJLsHZ1fSrkP1YmWJFsZtO4s10LKB+DyP0n6d9IuvXWzarvVpSdukywvr3reTif4HhnSbwrq/GzsV5UqnVsbtlpRrmF/Xrmls351fxgQBBBBAoISAv7ApCCCAAAIInKjAn2kH7g3xDsUlilMUTuCdSHHvsl9RvEnxNsVFigkK9y5wYsm9gZYonNjoUJys4sTLLYr3Kl6mmKs4qHhG8d+KTytWKd6giMU/hMqVH2qlEwV/pHipwvs8rPB+vM9P5evtmvalfFyNfbxvUTgh6STLdEWrYpditeJRRZfCPYp8DoXFSZYrFNcpLlOcqZit8PX2NVuveEjhXiFfVzjpVVj+QQt8/XwMThCep2hX+PoeUdjHSRL/YP2SYqWiVmWZduT36GsVr1H4HGYoXDYq7lZ8Q+Hj8w/LvhSf8/mK31G8XOHr5sSAXZ3Mtv9SRaXygBp4P7+veIHidMU4hRMDxYrfG/6MOOnz24rLFZMV/mz4PeNE7ucVaxXtiuFQHtZB+jq9RPEqhRP9fp9NUvg9tUnha3mXwrZ3KspdL7+XzlbY9NUKm45U+Nr8VOHP1C8V7YqBLE9o55cq3qF4vWKhwtfK7707FP+q8PH0tXxfG9jF7zeXz+UmNfv/Gu1pquIFiucrLlb42P390aLw94c/p06WfU3h3oH1ULbqJK5SvEvxG4rnKPxZXKfwufp6/VhRyzIQn2cn516q8PvO33v+znVy09fuRIv37c+V/6zz/l+omKsYr7CfP6c3Kj6r2KKgIIAAAggggAACCCCAAAJ1LfBhnZ0TFI4/qesz5eQQQGCwBTr0gvH7ZZXqIwf7AHg9BBBAAAEEEEAAAQQQQAABBBpdYIQAliriD3T3kKAggAACtRL4inYUv18W12qn7AcBBBBAAAEEEEAAAQQQQAABBKoXeJ+axh/na1UfVf2mtEQAAQTKCszW2n0Kf8f4dsw5CgoCCCCAAAIIIIAAAggggAACCNRIwM+G8jOg/Cy9YsXPilqs8DOdYgLwf6lOQQABBGoh4OdC+lmb8fvlq7XYKftAAAEEEEAAgfoT8C1JFAQQQAABBBDon0CHNrs5v6kfgO/BL/wwf/8o90PQnSAcq4jlVlVeoHBCkIIAAgj0R+AN2sgDifgfGK5VnKVw8SAgHjxmmWcoCCCAAAIIIIAAAggggAACCCBQG4EO7Sb2vKk0vUFtx9TmZdkLAgg0sMASnXux75v3NLAJp44AAggggAACCCCAAAIIIIDAgAl4pM3rFH+vuEXhnjdbFQcVmxXuEfjPCvfWoSCAAAK1EFiincQE4E7Vb1b8moKCAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAALHBEYcq1JDYFAFRuvVzsu/4iZNDw/qq/NiCCCAAAIIIIAAAggggAACCDSGQJNOc3r+VH+h6f7GOG3OMhUYlc5QR2AQBZz8u3cQX4+XQgABBBBAAAEEEEAAAQQQQKDRBS4VwH2NjtCI5z+yEU+ac0YAAQQQQAABBBBAAAEEEEAAAQQQQKBRBOgB2ChXeuidp2/7zco999wTZs2aFWdDT09PuPXWW7P5a665JrS1tfWuo9JYArwXGut6lztb3gvldBpvHe+Hxrvmpc6Y90IpmcZczvuhMa97sbPmvVBMpTGX8V7IXfd169aFyy67LL4Jen+LxwVMG0OABGBjXOeheJa9z/xz8m/OnDm9x+gv6WnTpmXzXk4CsJem4Sq8Fxrukpc8Yd4LJWkacgXvh4a87EVPmvdCUZaGXcj7oWEv/bNOnPfCs0gadgHvhaKXvve3eNG1LKxbAW4BrttLy4khgAACCCCAAAIIIIAAAggggAACCCAQAglA3gUIIIAAAggggAACCCCAAAIIIIAAAgjUsQAJwDq+uJwaAggggAACCCCAAAIIIIAAAggggAACJAB5DyCAAAIIIIAAAggggAACCCCAAAIIIFDHAiQA6/jicmoIIIAAAggggAACCCCAAAIIIIAAAggwCjDvgSEl8JGbloaP3/ykjin/1rzzZxWPb8SIEPRfGKFKbur53MJ0vrBdvkm23Ug1HOntFbHuaTavNLnX5dbHepH22qBJ2zRp6rajNOOp55vi1Ovy86OSupd53m2btd2oppGaKjw/amS2LpvXMu+3JT8dNXJkaBnlbfPte7f1fnLLvN+W/D6832Pb5s63IjANEEAAAQQQQAABBBBAAAEEEEBgWAuQABzWl4+Dt8DRo4pY6SXJlvTOUSku4GRja3NTGNPiGBXaVB87uim0qT4mLtd8XJe1G31sXVt+u9z2+XbZsqYsAVn8VVmKAAIIIIAAAggggAACCCCAAAKDKUACcDC1eS0EhpjAwcNHw8HDh8KufYd0ZPtrenTuaZhLECYJxnxyMCYcPc3aNI/KJx5zbdvy801HD4c1e0JoUS/Mjbv2h6kjm7MkpXtKUhBAAAEEEEAAAQQQQAABBBBAoDoBEoDVOdEKAQT6KHDg8JFwoOdI2NFzsI9bFjbPfU391UO3964YrVua0yRiTCSOdS/G3iRjrj42SzKq12KyPLb3NN2mdVRTdot27wtRQQABBBBAAAEEEEAAAQQQQKAOBEgA1sFFrKdT6Ly6Pbz07KnhjttzyZ4rr7oqtLa29p6ib/dNy1Hd/JvdApzdBpxbGdvkbg3Or9dGR7XALXLr0+W5dUe8D6309Eg2zbXJ1XPLjl8f22ubIyEc1oaHtZ2n3uaQetd56vlDXpas722rNt7G69zGy90r75B2eFAJtKyen+bmj2Ttcj33VE/WefuDh7SNtvVre75ey36dp2Pb3hNNLj5b6Nht0E1h3OjmMHVsS5iimDpOkU1HZ/PTND9l7Ohs+XjdFu3nRVIQQAABBBBAAAEEEEAAAQQQGIoCJACH4lVp4GOaNm50GNt0JKwYk0M4Y8a40NbW1sAi/T/1mFR04tAJQScGe+tJ4rDn4OGw98Dh0HPgUNizX3XNu+5lucjVezS/J98u197zx9bVS8LRHo5c6anqAvhZilOVDCyXKPS6XNKwRYlFEoZVwdIIAQQQQAABBBBAAAEEEECgJgIkAGvCyE4QGHoCHlm4JT8C8GAc3QH1yNubJA6dMEznXXdizUnG3gSj5vfuzycRXXeSUfMxKZlbd0g9N4d27zr3yFy/c18W1Vh7VOZcb8JcL8Jpvb0M1aMw39swlzDMJRV9qzI9DKuRpQ0CCCCAAAIIIIAAAggggEAxARKAxVRYhgACfRZwUqtlVEuYlO+92ecdFNmgp6cn3HTTj9R7MYSrr+0IRzQISOyJmCUUsyTj8YnG3HolEXvXHVufLTuohKN7Omr9sZ5+RV58ABc5Wbpux74sqnmZ1mYnDHO3G2e9DPP1eHuye84e6304OnsOYjX7pQ0CCCCAAAIIIIAAAggggEBjCDRKAvBsXc7fVjxfcbpivELjipYtfoBao/iUhWAlAidTwI/WUwe4MHlMS81vB/dt0vsO5ZKBWVLQyUEnBhXuibhToyNv3bM/bNlzIGzZfUB1T4/ND1YCcZ8yoGu292RRzbVwj8FcQjDfo9A9DPXMwmnH3aZ8LKHY2ixgCgIIIIAAAggggAACCCCAQN0KNEKC63/q6v2Nwuc6tO8jrNu3GSeGwNAU8G3SYzRysCOM6/sxuhdib2LQiUIlCZ0sdKJwsxKFuYThsXkPXDIYxUnMvQd6wupt1T3D0M8kPNaD0IOdqEdh76An+fnsGYa5nobu7UlBAAEEEEAAAQQQQAABBBAYPgL1ngC8XpfiI/nL4R59dynuV2xVDM4vcb1QQfGQtu9QvEZxvmKiYpPiIcWXFF9TlCodWnFzqZUFy/9S84sLlhWbdVL01xVvVFyimKlw1mCDwlY/Vfi44qgIqlIQQMACWfJwyqgwd0rl+549grQTc7kkYS5ZmCUKlTjcmk8c5noaHkscHtBgLYNRdqu3o2PV1r1Vvdz41lHZswpnTmwN83TucyePCfOmjglzPNW8BzvhmYVVUdIIAQQQQAABBBBAAAEEEBgUgXpPAP5BXnGbpq9Q3D4oqqVf5Eyt+q7C07TM1ozjZYpOxWsVexQDXebpBf5d8byCF3KScrLiLMVvKr6t2K6gIIBAPwWcEBurnnYOJ8sqFScMnZRLexWmtx+ntyZvcRJRvQ49GMlglF26NdrRvWVvuGuF/z3l+NKmW4rnTmnLEoNOjmYxuS07bycLbUBBAAEEEEAAAQQQQAABBBAYPIF6/xXmHm3+RfxBxclO/k3XMfxYMVfh8p+KLyrWKpz8e5vidYrrFTconLAsV9yL8N4yDTaWWedVPo4uxWkKdzP6D8V3FN0KZyfmK5wYfLWCggACgyzghOH41uYs2qeNrfjqThjmnll4/HMKnSjcHJ9fWHCb8mE9A3Egip+NuGzD7iyK7d+Dl8xRYjDXe1CJwXyS0NNZ6lU4qmlksc1YhgACCCCAAAIIIIAAAggg0E+Bek8Axm42P++nTy03+wvtLCb/Cm/PfVDrvq/w8j9X/JrCt+V+S1GqPK0Vvyy1ssLyEVr/FYWTf7sUTjZ2KQqLb/19t4LbfwtlmEdgiAk4YTixrTmL06pIGHoAlJ37DiYDnCSJwmSgk+w5hvkehrXKF2a3OqvH4sPPPLtjcZOey+gkYC45GG8tbgunjG0Kuw7qUY31/qfWEHtfcTgIIIAAAggggAACCCBQHwL1/lNqjS7T6YqWk3y5PMSmb6V1Wan4UFZ79v8+qEWdCt+a+38U5RKAWt3v4mO5Jr/172vala8XmxwqtpBlCCAwvAU8AMokjazsWOD+yRWKE4bbew7qVuNcotCDnHiQkWf03EA/O9D11dv2nvBtyO6VmNuXH0W6peCoRoWWkUfDJ1bcHeZPHZfdZhwThblbjdtyA7oUbMUsAggggAACCCCAAAIIINDoAvWeAPxvXeD3Ka5W3HkSL/YZeu1J+df/saaletR5ude/U+Hbl9sV3Ypal9/L79C9CN3Lj4IAAgiUFXDC0CMFOxbOKN7UybsNO/dlCUEnBrNIkoQbd+0vvmEflh44MiIs37gni2KbeQCS3DMH87cY+1mEvsVYzx7k9uJiYixDAAEEEEAAAQQQQACBRhCo9wSgRwB+q+IPFV9RrFecjDIleVGPrluupOvdS6+7XON+rHPvwsvz231D0/gQsNGqn6o4oLATPf+EQEEAgeoFfPvu7EltWVxx+tRnbbhPzwZ0L8Fntvb0Jgnde/CZfJLQg56caPHzDh0Prnr27cWj8sfnAUrcczCOWuwEoecnj2lm9OITvQBsjwACCCCAAAIIIIAAAkNSoN4TgGul/kqFR7G9Q+GebzcqBrvsSV5wYlIvVk3Xn1OsQX7ZX2vqZJ774nj/3YouxacUyxSlSkz+ef2dikUK78vPHWxRuOxW/EDxl4pHFRQEEEDghAVaNTrwwhnjsyjcmQcx2b73oJKBuVuKY5LQCUMnCdcoSXjoBB9C6O29L8ftz7q9OISxLR69ODdqce7W4mMjFztZ2Kb1FAQQQAABBBBAAAEEEEBgOArUewLwZ/mLslVTJ7p8S7C7hSxX7FWUK+4Z98JyDfqw7km1PahoVrhXX7mSrneCr1S5Mlnh24svzMd7Nf2Qwsm72LtP1d6SJhW9f/eMjIOlxEbjVHmdwoODvEXhEYv7WuZU2GBmXN/T0xMcsezbty9WQ1rvXUilYQTS65/WGwagwU60dWQIZ0wdnUUIk487+z17e8J///S2sGXfiDB1/qKwYbd6E273cwcV2/dlvf6O26AfM3sOHA5PrN+VRbHNfXvxnEmtup24LZyqno5zJ7eqF2FbFqeMHx3cA5IyOALp90FaH5xX51WGkkB6/dP6UDpGjmXwBNL3QFofvCPglYaKQHr90/pQOT6OY/AE0uuf1gfvCIbGK6W/t4fGEXEUJ0Og3n+tHBFqTIJVe65u77ae1rK7h3vUXa9weZPihqx2/P9+Q7NfTRZ9T3X3zIulQxWv9+AgP1esUPieOSfy3M7JOicZXf5G8adZ7fj/fUyzThK6+IFcoxVfUPh2aScq3aPQ+1mscI9At3GvwYcVfSnRveI2n/vc58K0adMqtqMBAgggUEpAubuwVd9Wm/ePCFv1bwiF0/16duBAlqYRR8NkfZtOHX1UEcK01qNhpv5pZdYYLdc3qQZppiCAAAIIIIAAAgggcFIENm/eHN71rnfF156ryuo4w7RxBEbV+aneqvOrOhE1wBZ/of2/SGHzLyoWKDwAxzrFLMVbFX+u8DP4nHhzactNev9/r2rzFe5NmJYHNPMdxWcUP1L4NuI/UXxd8ZAiLWOTGSf/PqHwrdGx+IvgbxTdCicb3ebDipcrKAgggMCQFPDduU64zVTCLVfiVH8IqLpH/1SyRYnBLUoQblGi0D0J43Sb5o9k/+7T/1M7fHRE2OzEo/ZbWFqbjioRqC/6Nk9z9dmajo3/XFO4AfMIIIAAAggggAACCCCAQI0Fnv1LpcYvwO6OE3CS77OKmOA7bqVm1Icl/IHiX/IrnNR7db5e7eQ31fAr+caf0/S3Cjb8uObfk1/m+25nK7bn5wsnTjheojii8EAmOxTVljkVGs7Ueu8/LFu2LMyZc6y5u2bfeqtzt7pf+pprQmtra1bnf40nwHuh8a55qTMeyPfCoSNHNHrx/mwwktXb9vXeVuzBSdboNuMtewr/zaXUUfZt+XTdUnzGjLFh0Yxxuekp48KC6WNDm56VSCkvMJDvh/KvzNqhJsB7YahdkZN7PLwfTq7/UHp13gtD6Wqc3GPhvZDzX716dVi0aFG8GPQAjBINNnVvNMrgCbjHn2+l/YDCtwOPV7g4wXazwss9oEcs22KlD9Ovqa179bkX4LVFttuVLLtL9VLJPze7SeEE4EjFxYqfKaot7klYVWlrawuOYsXJv1LrirVnWf0K8F6o32vb1zMbiPfC+LFjw8JZxY9kj0Yn9rMGsxGLs1GLPZLxsdGMezS6cX/KJo1W7LhjxbGvet8qPF8DkSw6ZXw4a+b4sEjhafvUsWFUk7+KKYUCA/F+KHwN5oeHAO+F4XGdBusoeT8MlvTQfx3eC0P/Gg3WETbye4Hf1IP1Lhvar0MCcPCvjxOAr1e4i4d/brp721pFHJTkTarH8lis9GGqG92yUYAv1fTUIts9kyyrlKRL2/rZgBQEEECg4QTGjh4VzlQSzlFYPHrxlj0HjiUH86MMr9i0JyzVgCK7lDzsS/Htyt1b9mbxo8c29G7aouTfAvUUzJKCSXJw9sRWPV+Qzvy9UFQQQAABBBBAAAEEEECgqEAjJgD9S+l0hW9pddmqeFrhXniDWdxlpFgC7nnJQdyd1PtSLfdr8NFkR5XuM0vX9+1XbPIiVBFAAIF6FXDybdq40Vk8d97k407TycF1O/ZlicClG3blpkoKPrlxdzhwuG9/5Lj94+t2ZpG+yPhWJSeVEIw9BWPPwUljSj1pIt2aOgIIIIAAAggggAACCDSKQCMlAF+qi+rBLjoUehz7ccW973wLrp+P50E0TlbxL7bX5l98jaZ39ONAfE0X5bdbW2R7P3fPz/7zPbcLiqxPF6XrfTwUBBBAAIEqBZwcnD2pLYvrzjrWifqQknnu5ecegkvX7wwxObhSvQfdA7AvZde+Q+G+lduySLebMX50rteikoPuuXjWzAlhoXoQtnm0FAoCCCCAAAIIIIAAAgg0nEAjJACdVPui4vX5q1usd9xYrfvVfHxN007FAcVgl/fpBafnX/TTmvbnwVJv1HYT8vu4JT9NJ052/lDxaoWf7+cHgD6jKCx+2NQr8wu9zf2FDZhHAAEEEOi7gJ/l52Sc41fPP/bgwZ4Dh8Pyjcd6CsbE4MZd+/v8It7Gcdvyzb3b+k5hP0twkQYbOVMJQfccdHKwfeoYni/Yq0QFAQQQQAABBBBAAIH6FGiEBOBXdemc7HLiz7ex/ljhW2vXK7zsFMVlihcrmhVvUNglJgxVrVmZpz2tKrG3X9PyD+fXLdf0IwXtfG/ZBYquguXprM8jjiDsfiROIhYrf6uFNnFXkE/m64W3+HpAkgUKly8oTkZCNHtx/ocAAgg0goB7550/Z1IW6flu0zMGYzIwTpf18/mCT2/eExw3Pbqh9yVaRikhOT3/fMH8sw6dHJzF8wV7jagggAACCCCAAAIIIDDcBeo9Aehefb+ucDLsZsU7FCsVxYqTc59XvEDxGsXLFDcqall+qZ3dqfhPhZ/F56Rau+J1CiceXTwcpOv7PJOUiar7HB5RfEfhHnnrFO4l6GN3AvEtCicxXZxAvC+rPft/92iRE3/vVrxccYvinxRPKWYovJ83KVzcO3CxKxQEEEAAgcEXmDy2JVxx+tQs4qv7+YJr9XxBJwKfUCzTMwY9fao/zxc8dCQ8pucLOtISny8YB0CJPQZ5vmCqRB0BBBBAAAEEEEAAgeEhUO8JwM78ZXhY0+sVB/PzxSbumfcrCvcOdE+7tytqnQAcqX2+JB+aPKs8piVvVjz4rDXHFpyvqqNUcULwQ4oPlmqQX/5eTccp3qq4Kh+aHFee1JwThJuPW8oMAggggMBJFfDzBU/V8wUdz36+oEcg3p09XzAmB2v5fMFTJvj5gr6F+NitxGeo3trsTuUUBBBAAAEEEEAAAQQQGIoC9Z4AvELo7v33D4pyyb94bdzmI4qvKLxtrcu7tEMnAC9T+MFPTsBtUrhX3zcUX1aUOs61WueeglcqvP2pimmKVsUOxVJFl+Jzim5FpeJE4dsUNyh8XD5fP39wt8K9E7+p+IyisCeij+mUvAAAQABJREFUFlEQQAABBIaiQO75guP1fMHxxz1fcO+BQ9now1lCUD0F463E/Xm+4Iad+8OGnZvCrcv8x1eujBwRwnw9XzAdkdg9B+dP4fmC0YgpAggggAACCCCAAAInU6DeE4BOaLm4Z1215Yl8QyfXal3+Qzt09Kf4dmEnCR21LD/UzhwUBBBAAIE6FRjTMmpAny94RP/UFp8v+MNH1/cq+vmCZ2iwk3j78KJsROLxYeaE1uBejBQEEEAAAQQQQAABBBAYHIF6TwDuEeMkxdQ+cE7Jt93bh21oigACCCCAwLATKPd8waXrd/beSrx0w+5+P1/w0bU7gyMtk8c0hwvm5gY8uXDuxCw5OW3c6LQJdQQQQAABBBBAAAEEEKihQL0nAH1b7OUKD6rxkyrd3phv520pCCCAAAIINJRA+nzBF5x1Su+5Hzx8JKzcsic36Egy+Eh/ni+4be/B0LV0UxbxBfw8wwuVFLwgnxA879SJYezoev9rSjx7pggggAACCCCAAAIIDKxAvf/N+r/Ed4XCA3rcrliiKFc6tdJt/dxAj7RLQQABBBBAAAEJNDeNzJ4t6OcLpkNR+fmCy9VDMD5XMI5IvGnX/j65rdneExzf/4UHuA/BzxU8Q68VE4JODvq5gj4OCgIIIIAAAggggAACCPRNoN4TgP8ijt9XzFT8m+J1is8rPNLvBoWLuze4l+A7FS9V+KFEaxQfV1AQQAABBBBAoIyAny/o23kdadm654BuId4VYkLQ02Wa37X/UNqsZN3PFcySitru6/etztr5mYLPmT0hnDtTY2htHRHmjTsajhz1v9lREEAAAQQQQAABBBBAoJxAvScA/QzAlyt8++9kxfX50KRocfJvm8Lb8AzAokQsRAABBBBAoLLAlLEt4coFU7OIrY8qWbd6W094ZPWO8PDq7eGhZ7aHX67ZEfYe8MD0lcuBQ0fCg6u2ZxFCU7bBvzxxW5Z8PH/OxHDBnEnZbcQzNMgIBQEEEEAAAQQQQAABBI4J1HsC0Gf6oOI8xccUr1LkfjGoUlD86+Pbivcr3AOQggACCCCAAAI1FPDzBedOGZPFr54/K9vzYXX1e3Lj7vCwkoEPKSn4iOKJdbvCIXcBrKLs3Hco3LZ8cxaxuUcZ9q3D7pV4oZKC5yo5OKG1Oa5migACCCCAAAIIIIBAwwk0QgLQF3Wtwrf/+lbg6xTnKuJov1tV/6WiS5F78JAqFAQQQAABBBAYeIEmPezPz/ZzvP7SudkL7jt4OBs52MlAJwYfVo/Bpze7U391Zf3OfWH9o/vCTY/Gp32EsGD62Cwh6F6CTgyePWt8GD2q1L8JVvc6tEIAAQQQQAABBBBAYLgINEoCMF6P9arcEGeYIoAAAggggMDQE2htbgoXz5+cRTy6HRo5+JE1uYTgAyu3hvtWbAo7D/rJHdWVpzbtCY5vPZDr5N/cNEJJwAnZbcPZMwzVS3DB9HFhpEcfoSCAAAIIIIAAAgggUGcCjZYArLPLx+kggAACCCDQGAITxzSH558xPYuenp5w000/CjsOhDBp4YXhiY09WU9BP1twd5WDjBw8fDR7FqG3+fJdKzPEcaNHhfNOnRjO1+3DvnXYicFZE1uDb12mIIAAAggggAACCCAwnAVIAA7nq8exI4AAAggg0KACzslNGh3CS86eEV753LZM4YieG7hCtwrnbhvO9RZ8XM8TPHD4SFVKTh7euWJLFnGDaeNGa2CR3AAjTgh6sJFJY1riaqYIIIAAAggggAACCAwLgXpJAF6TaN+a1NPlyeKqq+m+qt6IhggggAACCCAw+AK+fXfhjHFZvObiOdkB7D90OBtUxKMOP/xMbvThpzbtDhqQuKqyeff+8JPHN2YRN2ifOibrHXh+NurwxPCc2RODb1umIIAAAggggAACCCAwVAXqJQHYJWD/Vd6RnlNcrsV9LoX76vMO2AABBBBAAAEETq6AB/rInvGn3nvhytyx7Np3MPxijZKBTghmg4xsD+t27Kv6QLu37A2O7z7kMcZCyAYyOWV89jruLejE4BlKRI5qGln1PmmIAAIIIIAAAggggMBACqTJsoF8ncHYd6kH9JRaPhjHxGsggAACCCCAwBATGN/aHK5aMC2LeGgbNXKwRxuOCUFPd+47FFeXnR7WrcePrduZxQ335Jq2qUdg9jxB3TLsBOSFijmT23ieYFlJViKAAAIIIIAAAggMlEC9JACvKwFUanmJ5ixGAAEEEEAAgUYUmDGhNbz4HMcp2ekf1T3C7uX3iG4dfsi9BBWPrt0Z9h+q7nmCPQcPh3u6t2YRPaeMbcmeIXhBdutw7nmCU/WMQQoCCCCAAAIIIIAAAgMtUC8JwFtKQJVaXqI5ixFAAAEEEEAAgZD11Dtt2tjgeOWFp2YkBzWYyNL1u9RTMJcQ9AjCyzbsCuoAWFXZuudA6Fq6KYu4gXsFZrcou6egEoPnahTisRqNmIIAAggggAACCCCAQC0F+BtmLTXZFwIIIIAAAgjUrUCznunnBJ3jNy+fn53nHo0c/Es9T9DJwIfyicHV23qqNnBbx/cfWZdto3FMwjmzJ4RL5k8Jl502JVzaPiVMH08vwapBaYgAAggggAACCCBQVKDeE4B/nj/rT2q6uajAsxdO1qLfzy/+4LNXswQBBBBAAAEEEMgJuLfe5adPzSKabNHIwVlCMD/AiOvu/VdNcW/CX67ZmcWSO7qzTdwL8dL2yVky0AnB+RqFeMQIZQopCCCAAAIIIIAAAghUKVDvCcDFcvCNOd9QVJsAnKK2ixXejgSgECgIIIAAAgggUL2An+t33VkzsvBWfp6ge/n5WYJ+pqBHH/YoxH5OYDXl6c17guPr963Oms9Qj0AnArOkoHoJnjVzQjYScTX7og0CCCCAAAIIIIBAYwrUewKwMa8qZ40AAggggAACQ0bAvfXmThmTxa9dMDs7rkN6nuCTm3Zng4s8pISgBxlZqucJekThSmXjrv3h+79Yl4XbjlcvxOfOn9x7y/D5ep5gq0YhpiCAAAIIIIAAAgggEAVIAEaJY9PmfPXgsUXUEEAAAQQQQACB2gmM0vME3XPP8YZLc/vtOXA4G2Dk3qdzowc/sHJb2KNllcouPYfwlmWbsnDbFu37grkTwyXqJXiZwsnBiW3xrzeV9sZ6BBBAAAEEEEAAgXoUIAH47Kt6YX7RpmevYgkCCCCAAAIIIDAwAm0tTeEKPU/Q4eJegk9o1OF7lBC8tzsXm3dXfpbgAW13b/e2LD4VntLzAkOWaLxMzxHMkoK6bfiUCa0DcxLsFQEEEEAAAQQQQGBICtRbAvCtJZRfqeWXlFgXF3uIvQWKdyh8/829CgoCCCCAAAIIIHBSBNxLMI46/I7nnZY9S7B7y94Qewg6KbhS85WKHkEYHl+3M4sv3rkyaz5PtyT7OYKXnZZLCp6ugUYYWKSSJOsRQAABBBBAAIHhK1BvCcAluhSFD8/xMHl/1YdL5PZHFB/rwzY0RQABBBBAAAEEBlTACTqPCOx4/aVzs9fasHNf1jvwPvX4c0/Bx9fvVKKw8mGs2ro3OL75wOqs8bRxLeGS+RpYRL0Dfdvw2bPGBycgKQgggAACCCCAAAL1IVBvCUBfFSfwCkuxZYVtPO/7atzz728UtygoCCCAAAIIIIDAkBXwrbwvP392Fj7InfsOhvv17ED3EnRS8CGNOnzgkP9ds3zxrcU/fHR9Fm45Vrcj+9mBudGGp4SL5k1iYJHyhKxFAAEEEEAAAQSGtEC9JQBPS7Sd9Fuh8L+Dv1SxXFGquM0+xRZF5adtl9oLyxFAAAEEEEAAgZMoMKG1OVx35owsfBj7Dh4Ov1izo/c5gvcrKehBQyoVDz5y2/LNWbhtc9OIcN6pE3sTgpfoeYKTxrRU2g3rEUAAAQQQQAABBIaIQL0lAFeWcF2r5aXWldiExQgggAACCCCAwPAWaG1u6k3a+UwOHzkalmpgET8/8B4PLKKeght37a94kgcPHw0PrNqexWdu9b+vhnDmKeN1y/CxXoKzJ7VV3A8NEEAAAQQQQAABBE6OQL0lAAsVeXhNoQjzCCCAAAIIINCwAk0jR4RzZk/I4m1XtWcDizyztac3GejE4IrNe6ryWbphV3B85a5VWftTlQC8TM8QzN02PDksnDGOgUWqkqQRAggggAACCCAw8AL1ngAceEFeAQEEEEAAAQQQGKYCHlhk3tQxWbz24jnZWWxSj8D73DtQtws7Ifjo2h1BHQcrljXbe8K3H1yThRtPHtMcLvFIwwoPLvIcJR6bGVikoiMNEEAAAQQQQACBgRAgATgQquwTAQQQQAABBBAYpgLTx48Ov3LerCx8Crv1zMAHPLBIlhTcGh7UrcD7qxhYZNveg+HHj23Iwvtp0+3IHkzEPQTdU9D1MS38VdQ2FAQQQAABBBBAYKAFGulvXQuE+QrFBYppCj+oxgOFlCr+t+4XllrJcgQQQAABBBBAoBEExo0eFa5ZND0Ln69HFfbAIk4Ixp6CO3oOVqTo0YAkdzy1JQs39u3I56pXYHbLcP7W4SljGVikIiQNEEAAAQQQQACBfgg0QgJwjFw+oXiLojDh5/nCm1pim8LlakpBAAEEEEAAAQQaW6Bl1Mhw8fzJWYRrF4Qjuj94+cbdxz1HcN2OfRWRPCDJw6t3ZPG5nz+dtfdzA+MzBD2dM7mN5whWlKQBAggggAACCCBQWaDeE4BO5n1b8SKF65sVqxUXKpzgu00xWXGmolnhZUsV6xUUBBBAAAEEEEAAgQoCI9WT78yZ47N4yxXzs4FF/DxA9xC85+ncrcNPKkFYTXE7xw335AYWmTWxNekhODksmjE++PUoCCCAAAIIIIAAAn0TqPcE4OvE8WKFE3t/qfiQ4hzFIwqXa3OTMFbTdym8foritxQ/V1AQQAABBBBAAAEE+iDggUXmTB6Txasvyg0ssnXPgfztwkoKanCRR3UL8aEqRhZxT8L/enhtFj6ESRpY5MrTp4arFkwNVy6YFmaPG9mHI6MpAggggAACCCDQuAL1ngB8U/7S3qmpE4AuTgYWlj1a8DHFHYpbFN9SuJfgWgUFAQQQQAABBBBA4AQE/Gy/lzxnZhbezd4Dh7LBROLAIg+s3B78jMBKZbsGFvnBL9dn4bbTx7WEea0jw6KJR8Nz1OtwYZsf8UxBAAEEEEAAAQQQKBSo9wTgJTphJ/w+W3jiJebv1fJPKd6veK/iTxQUBBBAAAEEEEAAgRoKePTfqxdOy8K7PXj4SHh07c5w79Nbc4OLaNRh9xqsVDbtPhA27R4Z7tdDXm742J1h3pQx+d6B7iE4NcwY31ppF6xHAAEEEEAAAQQaQqDeE4Ae7ddlRW6S/T8dps7/TNyTrHP1+wonAF+uIAEoBAoCCCCAAAIIIDCQAs1NI8OFcydl8VvXnJ49R/CpTRpYJP8MwXuUGPRzBSuVVVv3Bsd/3PtM1nTRKeOUEJyWJQOvOG1qmKhbiCkIIIAAAggggEAjCtR7AvCQLqr/prcrubhpfaaWP52sc3VHfn5uwXJmEUAAAQQQQAABBAZBwM8RXKgBPxxvunxe9opOAN751JZwx1Obwx1Pbgnrd1YeaXjZht3BseSObo0mHMK5syeGqxb6GYLTNLjI5OCeiBQEEEAAAQQQQKARBOr9bz1+ht8CxfTkYnqEX/8Tsu8Jea6iMAG4UMtc6t0md5b8HwEEEEAAAQQQGAYCp05qC6+9eE4WR48eDU9v3hNueWJ9+O5dj4flO0aEPYeU4StTtEn4hQYfcXzmlhWhuWlE1uPQg4l4UJGL5k0Ko0c1ldkDqxBAAAEEEEAAgeErUO9Jrod1aZwAPE/x4/xl8jMB71Zcq3i34puKWOzh239dlucm/B8BBBBAAAEEEEBgKAm4h+Dp08eFWeNODVO2Pho8oHD7BVeG+1fvznoJ3q1bhnfv940gpcvBw0f1vMFtWfzzT5eH1uaR6hU4Jbtd2D0Ez509IYzSrckUBBBAAAEEEECgHgTqPQH4M12k1yiuV3w0uWCfV70jHx719+uKMYo3Ki5SOEnoZRQEEEAAAQQQQACBIS4wUp3/zpo5Plx02ozwruefHg5pUBH39Lsjf8vwfUr07T90pOxZ7Dt4JNy2fHMWISwN40ePCpef7oSgByuZGhbpduSRfiEKAggggAACCCAwDAXqPQH4bV2TjyuuU5yuWKFw+YriTQonBp+XD016y0OqpQnD3hVUEEAAAQQQQAABBIa2gHvuXTRvchbvuW5h2HfwcHhw1Xb1DtTzA5UUfOiZ7eGQuw2WKbvUg/Anj2/Mws2mjm0JV+hWYd8u7B6C7VPH6LmCJATLELIKAQQQQAABBIaQQL0nAP28Pw8C4r+dHS5wf7Xm/6/inQoPBuKyXfHvig8oKj9ZWo0oCCCAAAIIIIAAAkNboLW5Kbu190ol7/6nDnWPknv3dG/tHVTk0bU7NfJw+XPYsudA+P4j67Jwy9kTW7PegVlCUD0EZ01sK78D1iKAAAIIIIAAAidRoN4TgKYtdb/Hfq37s3xM0dQWmxQV/vqnFhQEEEAAAQQQQACBYSswVrf3XnfmjCx8Etv3Hgh3rXBCMNdDcPnG3RXPbe2OfeGbD6zOwo1PmzY2//zAqeHK06eGqeNGV9wHDRBAAAEEEEAAgcESqPcE4Lw8pP8Wt7UMarl1ZTZjFQIIIIAAAggggMBwF5g0piVcf+7MLHwuG3fuC3eu2BLueFKxYnN4ZmtPxVP0qMSOr969KmvrZxL6VmH3ELxMzxKc0OqbUigIIIAAAggggMDJEaj3BGC3WN2j7/cVn1RQEEAAAQQQQAABBBAoKzBjQmt45YWnZuGGz2zdq2cH5noH+hmCm3b5RpLy5Yn1u4Lj87c/HTx2yHlzJoWr888PvHj+5NDW0lR+B6xFAAEEEEAAAQRqKFDvCUD/c22r4t4amrErBBBAAAEEEEAAgQYSmDtlTHjDlHnhDZfO07MCj4anNu3OjTCsHoLuKbij52BZDY838rAGHnF8suup0JINUjIp10NQzw+8QMnBllEjy+6DlQgggAACCCCAwIkI1HsCcI1wFij4J9YTeZewLQIIIIAAAggggEAm4JF/F84Yn8Vbr2wPh5Xde3zdzt4egvc8vTXsPVA49tzxeAcOHwl3q53jH38Swhj1Bry0fUp2u7BvGz5n9oTQ5G6DFAQQQAABBBBAoEYC9Z4A/JGcflfxPMVdNTJjNwgggAACCCCAAAIIZAJO1J176sQsfvuaBeGgknuPrN4ebvfzA3Xb8AMrtwcn/MoVJwxvWbYpC7eb0DoqXKGBRK5emHuG4MIZ44ITjxQEEEAAAQQQQKC/AvWeAPyYYDoVf6S4QeEegRQEEEAAAQQQQAABBAZEoFm39148f0oW733hGWHfwcPh/pXbensIPrJ6R9ZrsNyL79x3KPzosQ1ZuN00jSjswURyMS3Mmzqm3OasQwABBBBAAAEEniVQ7wnA5TrjNym+onAPwD9WfENxQEFBAAEEEEAAAQQQQGBABVqbm7KefO7N57Jz38Fwr2799WAiDt8+XKls3r0//NfDa7Nw2zmT23qTgVcqMXiKBi2hIIAAAggggAAC5QTqPQH4s/zJb9L0NMWXFf+mcGJwm6LcA1o8evALFRQEEEAAAQQQQAABBGoiMKG1Obzw7FOy8A637jkQ7tJAIrc/uTncqYTgis17Kr7O6m094ev3rc7CjRdMH6uE4LTwvDOmBScE/RoUBBBAAAEEEEAgFaj3BGCHTtaJvFj88JTRinPjgiJTt3e7dLsizViEAAIIIIAAAggggMCJCUwZ2xJedt6sLLyndTt6skRg1kNQScG1O/ZVfIGnNu3RyMR7wpfvWpkNHvLceZPC88+YrpgWztcIwwwoUpGQBggggAACCNS9QL0nAG/VFSSRV/dvY04QAQQQQAABBBCoD4FZE9vCrz93ThZHjx4NK7fszd8unOshuEU9BssVj0p8b/e2LD7642XZgCLuGRgTgnMm8/zAcn6sQwABBBBAoF4F6j0B2FGvF47zQgABBBBAAAEEEKhvAY/82z5tbBZvunxecEJw2Ybd2e3C7iF4t24d3rX/UFkEDyhy4y/WZ+GGp2t/7hnohKBvFx47ut5/DpTlYSUCCCCAAAINI8Cf+A1zqTlRBBBAAAEEEEAAgeEs4ITgmTPHZ/GO550WDh0+Eh5duzPc/tTmLCl479PbwgEtK1f8jEHHF+9cGZqbRoTnzpscrlmUu1343NkTw8iRfhIOBQEEEEAAAQTqTaDeE4Dz8hdsjablBvxIr2uTZk7NL1iVrqCOAAIIIIAAAggggMBQERjVNDJcMHdSFu/uWBh6DhwOdz+9Jdy2fHO4ddmmsHzj7rKHevDwUbXfmsXf37Q0TB7TnI1YfI2fH7hoWvDtyBQEEEAAAQQQqA+Bek8Adusy+Z9Bz1c8pqimtKuRRwn2dvXuo1OkIIAAAggggAACCNSDQFtLU+g4c0YWPh8PKOJkoOPnyzeFbXsPlj1Nr//eI+uycMMzZozLPTtQycDLT5sSxrTwV+OygKxEAAEEEEBgCAs0wp/i/b2Pob/bDeHLzaEhgAACCCCAAAIINIqAe/C9/pK5WRzR4CC+XfhWJQJvU9y/cltwD8ByxT0IHZ+//enQot6Gl7RP7h1M5JxZE7hduBwe6xBAAAEEEBhiAo2QAOwreUz8lX+ASl/3SnsEEEAAAQQQQAABBE6SgJ/td96ciVm857qFYbcGD/EgItntwkoIrti0p+yR+dmCHnjE8Xc/DGHauJbwvIXHRheeMaG17PasRAABBBBAAIGTK0AC8Nn+s/KLdj17FUsQQAABBBBAAAEEEBj+AuM0+u8Lzz4lC5/N6m17dZtw/nbhJzeHHT3lbxfevPtA+M5Da7Pw9mdpcJI4uvBlul24tdmP1aYggAACCCCAwFARaJQEYPn7G3JXo1mTBYoP5C/O0vyUCQIIIIAAAggggAACdS0wZ/KY8MbL5mVxWLcLP7J6e/75gZvCA6u2By8rV55Yvys4Pnvb02H0qJHBScA4mMiZp4wPHsGYggACCCCAAAInT6DeEoDFRvr13zZ+2Udi/w3nG33chuYIIIAAAggggAACCAx7gSbdLnzRvMlZvPeFZ4Rd+w6GO3Xrb25AkU2he8vesue4/9CRfNvNIdwYwvTxo7PegU4IXq3bhj1PQQABBBBAAIHBFai3BGCpf1ostbyU9te14p9KrWQ5AggggAACCCCAAAKNIjC+tTm85Dkzs/A5r1ICMA4mcseTW8IuPU+wXNm0a3/41gNrsnA7DyDyfI0sfK0SghdrYJHRo7hduJwf6xBAAAEEEKiFQL0lAP+yAOUvNO/efJ9WbCxYl866zT7FOsUdiqcUFAQQQAABBBBAAAEEECgQmDd1THjz1PnhzVfMD4c0OMjDul341mV+fuCm8NAz20OFu4XDY+t2ZvGZW1boWYEjwxWnT81GF77mjGlh4Yxx3C5c4M0sAggggAACtRBohASgnT6heKwWYOwDAQQQQAABBBBAAAEEcgKjmkaGi+dPyeL9L16UDR5y51Obwy1KCN66bFNYs72nLNW+g0dC19JNWbjhTI0m7MFErlmUu114ytiWstuzEgEEEEAAAQSqE6i3BGDhWb89v2B14QrmEUAAAQQQQAABBBBAoLYCE9uaw/Xnzsri6NGj2fMC3TPQPQSdGNxzoNgju48dw/qd+8J/3r86C48bct6pE3tHF36unkvYogFGKAgggAACCCDQd4F6TwB+se8kbIEAAggggAACCCCAAAInKuCRf0+bNjaLt17ZHg7qduEHNaJwlhBcvjkbaVg5wpLF6x5ZvSOLT9z8VBjT0hSuzG4XzvUQ9L4ZXbgkHysQQAABBBA4TqDeE4DHnSwzCCCAAAIIIIAAAgggcHIEmnW78GWnTcniD19yZti250C4Xb0Cb/PtwuoluG6HH8lduuxV78GfPrExC7c6dVKbbhWelj0/8OoF00KLegxSEEAAAQQQQKC4QCMlAKeK4ErF6YrximqGG/ug2lEQQAABBBBAAAEEEECgxgKT9Xy/l58/OwvfLvzUpj1Z78Db1Dvwzqe2hJ6D5W8X9vMFb7jnmSxGKvl37uwJYVYYGc6cdCTrbdhW4+NldwgggAACCAxngUZIAM7QBfpHxWsVfT1fEoDD+d3NsSOAAAIIIIAAAggMCwHfyusRgB1vv/q0sP/Q4XD/ym1KCOZGF/7lmp1lz8MjDz+iNo8oAXjTmpHh80/eFq5Sr8DnazARjy48f+rYstuzEgEEEEAAgXoX6GtCbLh5TNYB/1yxQMFNAcPt6nG8CCCAAAIIIIAAAg0pMHpUU5bAcxLvj68/K2zZvT/8/EknA3MJwQ0795d12b3/cPjRYxuycMN5U8aEjjOnZ3Hl6dNCm54nSEEAAQQQQKCRBOo9AfgnupgL8xf0R5p+VHG/YquizCOHtZaCAAIIIIAAAggggAACQ0Jg6rjR4ZUXnpqFbxdevnG3RhbW6MJKCN69Yot6DB4pe5yrtu4NX7pzZRYeSfhyPYuw48wZWULwdAYTKWvHSgQQQACB+hCo9wTgK3WZnOj7vuIV9XHJOAsEEEAAAQQQQAABBBpXwLcLLzplfBbvev7pYZ+eFXhf97bws8fXhZseWhnW7C1/488BJQtzPQk3hw99L4S5U9pCx6JcMvDKBVM12nC9/0Rq3PcOZ44AAgg0skC9/+k2L39xP9HIF5lzRwABBBBAAAEEEECgXgVam5vC8/Scv4vnjA0XHF0Rdh4IoWXe+eHu7h1ZD8HNun24XHlma0/48l0rs2jRSMWXnz4lXKtnB7qH4ILpY4MTjhQEEEAAAQSGu0C9JwB36wKNVmwY7heK40cAAQQQQAABBBBAAIHKAhNaQnjJ+TPDGy4/LRzR6CCPr98ZupZuCrco7l+1LRz2iCElyoHDx3oH/tX3Hw9zJqt3oJ4deK16CF6l3oFjR9f7z6cSMCxGAAEEEBj2AvX+J9gvdIU6FPMVDykoCCCAAAIIIIAAAggg0CACI0eOCM+ZPTGL91y3MOzoORhu12AiXUs3hlv0DMFKg4ms3tYTvnLXqizcO/DS0yb33i7sEYvpHdggbyROEwEEEKgDgXpPAH5G1+g6xVsU362D68UpIIAAAggggAACCCCAQD8FJrY1h5edNysLDyby+LpdoWvZxqyH4P0rK/cOvP3JLUogbgkfvvHxcOqktnCtRxbW7cJXL5xG78B+XhM2QwABBBAYHIF6TwB+XYweCOSNCo8I/LcKCgIIIIAAAggggAACCDS4gHvvnTN7Qhbv7lgYdu5T70CNKuyegb5leP3OfWWF1mzvCV+9e1UWzU0jwqXtHlk49+zAM+gdWNaOlQgggAACgy9Q7wnAa0T6OYVvAf6w4tcVX1U8odirqFRurdSA9QgggAACCCCAAAIIIDD8BSa0NodfUe9Ah3sHLt2g3oFKBPp2YY8yfKjMswMPHj4a7nhqSxZ/feMTWe/Aa7KBRHK9A8fx7MDh/wbhDBBAAIFhLlDvCcAuXZ/0Kb8Xa95RTfF29e5TjQNtEEAAAQQQQAABBBBoKAH3Djxr5oQsfufaBWGXewfq1t9b8rcLr9tRuXfgDfesCg73Drxk/rHegYtO4dmBDfVm4mQRQACBISLQCAmuEUPEmsNAAAEEEEAAAQQQQACBYSgwXr0Drz93ZhbuHbhsw+6sZ6B7CN63cmtwD8BSxevuXLEli7/5wRNh9sTW7NmBHln46oVTg/dNQQABBBBAYKAF6j0B6AFAKAgggAACCCCAAAIIIIBATQTcO/DMmeOz+B/qHbh7/6H8yMKbwi26XXhthd6BXn/DPc9kMUqjFF/SrpGFz5yRPT/wzFPGM7JwTa4SO0EAAQQQKBSo9wTgLYUnzDwCCCCAAAIIIIAAAgggUCsBP9/vpc+ZmYV7By7feKx34L3d5XsH+rmCd63YmsXfqnfgzAnqHRifHXjGtODnElIQQAABBBCohUC9JwBrYcQ+EEAAAQQQQAABBBBA4P+zdx4Achxl2i5tXmmVV1myV5IVnXFATrIcscFgcj4wYO7g5zjCke8AwQHHAcfBmYw5RDiSwYANZxwVnC05I6GcrLwrraTVavPqf7+e7lVrND07ox3tzvQ8H7zqmqrq7uqnyrMz33xVBYFeCVh04ExF8Zn+fn4iOvCR9Q1uiXYWXqrpwrZzcDqznYd/veIFTxYd+KJTLTpQOwtruvCcCUQHpmNHGQQgAAEIpCeAAzA9H0ohAAEIQAACEIAABCAAAQicEAGLDrxW0YEmiw5c70UHamdhbSbyxKbeowOtjukrf1njxg2r9KMDx7pLiQ48of7gJAhAAALFTKCYHIDD1NGvlS6SxkuDpXdKW6TAJioxQrJtvTYGmRwhAAEIQAACEIAABCAAAQj0hYBFB85QZKDp3fOnuWatHfjIhr09m4n0Fh24+2Cb+82KbZ5KFR143ikjvc1ELEJw7oRhrB3Yl87hXAhAAAJFQKBYHIDvU19+URrq96ntDGxbdQ3xXweHy5X4X8kcgJOlfRIGAQhAAAIQgAAEIAABCEAgpwSGKDrwmrnjPFl04IZ6WztQ0YGSRf21d3VH3q9Lawc+ofUFTV+9e40bO/TY6MDh1awdGAmPAghAAAJFSqAYHIAL1beflszp1yY9L50vpbJfK/NrkkUIvkb6oYRBAAIQgAAEIAABCEAAAhA4aQQsOvC0sUM93XxZIjrwUYsO1FRhcwhua0y/duCepjZ325PbPFl04ItOGeHtLGwbipw+kejAk9ZxXBgCEIBAARGIuwPwXPWFOf/Mfi69XzogRf2cZvm3Sf8kXSPhABQEDAIQgAAEIAABCEAAAhDoPwIWHXi1ogNNiejAZm+q8FJtJvK4dg3uLTpw+eZGZ7LowDE90YFj3GWnjXHDBxMd2H89yZ0gAAEI5A+BuDsAzeFnkX+PSG/LEPujqmcOwDMzrE81CEAAAhCAAAQgAAEIQAACJ4VAIjqwRtGBNc6iAw+3dzovOtCmCytC8IV96aMD6xUd+FtFB5oUHKjoQH9n4VljvbUDSywTgwAEIACB2BOIuwPQ1vSztf6+lUVPbvbrTsriHKpCAAIQgAAEIAABCEAAAhA46QQGV5S5q+aM82TRgZsaLDrQnIH17rGNe117Z9RkJ02D0jejFVsaPX3tnrWutiaxduAVsxUdOEPRgawdeNL7jxtAAAIQGCgCcXcATvDBrskCsK0TaFaZOPAvBCAAAQhAAAIQgAAEIACB/CNg0YHTxtR4euelU11Le5fnBFyyRmsHyiG4Ze/htI1uONTmfvfUNk+2duD5p450V84e68kiDu36GAQgAAEIxINA3B2A7eomc+Rls9BF4DTcH48u5ikgAAEIQAACEIAABCAAgWIgUF1R6q6QA89klogOTGwkYtGBbWmiA21n4ce1+7Dp3+9a7SaPrPYcgXati6aNdlXlpcWAkGeEAAQgEFsCcXcAblPPzZFOl57IsBev9eutz7A+1SAAAQhAAAIQgAAEIAABCOQdgam1Q9zU2qnuHZf40YGb9rqlNl1YEYKbe4kOtJ2Hf/roFk9V5SXukum1nmPRIgQnjqjOu2elQRCAAAQgkJ5A3B2AD+jx50rvkH6cHoVXOk3/vkuydQPv9XL4BwIQgAAEIAABCEAAAhCAQIET8KIDtfHHFZLFR2z21g7c4xbLIfhoL2sHtnZ0u/tX7/FkGGaPH9rjDDx3yghXVlpS4HRoPgQgAIH4E4i7A9A2/3iPdIm00JcOKe185f5KqpFape9LGAQgAAEIQAACEIAABCAAgdgRqFN04E2KDrxJ0YG2s/DD6/e6B+Tks+jAnQfs61C0rd7V5EzfXbLB2zjk8pljvOnCdhw5pCL6REogAAEIQGDACMTdAbhWZP9N+pz0ael66XdSYNcp8XLJpv0ukMws+u8T0k57gUEAAhCAAAQgAAEIQAACEIgzAdtZ+Jq54zzZzsJ/29mkyMA9nkPw6a2N3u7BUc9/oKXD3fHsDk/aR8Sde0piIxGLNJwzYSgbiUSBIx8CEIBAPxOIuwPQcJoD0DYB+ZR0gWSRfubkM/tq4uD9a1tcWf7npf/2cvgHAhCAAAQgAAEIQAACEIBAERGwnX/nThzm6X1XnOYam9vdUu0obNGBdjSHX5RpHxH35JZGT1+9e42bMLzKLZAj0NYNvOS00c4cjRgEIAABCAwMgWJ5B/6M8N4hWWSfRf0NlsJmuwXfL31ReiRcQBoCEIAABCAAAQhAAAIQgECxErApva88d5Knzq5u9/QL+z1n4GI5BG0acDqzqcS/fGKrp4qyEjdPuwlfOcumC49zp4xO/kqW7kqUQQACEIBAXwkUiwPQOK2QXivZM9vGILb6re1lv1daKbVIGAQgAAEIQAACEIAABCAAAQikIGCbfVxQN8rTx6+b7bbvb3HmCDQ9vKHB2WYhUdbe2e2WKYLQtPDOVW76mCFeZOAVig60a5azkUgUOvIhAAEI5IRAMTkAA2CdSjwXvOAIAQhAAAIQgAAEIAABCEAAAtkTmDSi2r113qmeWju6vN2EzRlo04W3NaaPr9hQ3+w21G9yP3xwkxtaWeYum1nr7VBsU4bHDK3MvjGcAQEIQAACaQkUowMwLRAKIQABCEAAAhCAAAQgAAEIQCA7AlXlpZ4Dzzb/+Nwrjrj1ew55jkBzBq7QuoBdtkBghDW1dbr/e36XJy1B6M6aNNxZZKCtHXjGxOGuxHYXwSAAAQhAoE8E4u4ArBKd1/uE7tKxvhdaY1RuOwWb/UKyaEEMAhCAAAQgAAEIQAACEIAABDIkYBuJzBg31NM/XD7d2zjkwXX+RiJr6t1ebSwSZdqE2D277YCnb9y3zosGXDBzjLtqzlh36YwxrkbRghgEIAABCGRPIO7vni8VkkXSdskcer1ZoyrYRiATpX3SnyQMAhCAAAQgAAEIQAACEIAABE6QwPDqcnfDWRM9dSsS8Nlt+711Ax9Ys8f9dfvBtFetb2pztz25zVN56SB34dRRXqShRQdOG1OT9lwKIQABCEDgKIG4OwBf5z/qr3XMJJrP6vxS+ohkkYM4AAUBgwAEIAABCEAAAhCAAAQgkAsCNp333FNGevrwtbPc7oOtbokcgTZV+KF1Da65vSvyNh1dR9zD6/d6+sKf/+bqtJNwMFXYHIOVZbbHIwYBCEAAAqkIxN0BeKYe2habWJbq4SPyHlS+OQDPjignGwIQgAAEIAABCEAAAhCAAARyQGDcsCr3hgtO8dTW2eWWb2r0nIGL5RTc1NCc9g6b9x52P354s6fBFaXu0tNqvXUDL6oblvY8CiEAAQgUI4G4OwAn+536Qhadu82vOymLc6gKAQhAAAIQgAAEIAABCEAAAn0gYBF8l86o9fSZl8/1HIAWGWg7Cz++aa+zCMAoO6zIwXtW7fZkdSYPKXVzRxxxY7We4IXTq1wpG4lEoSMfAhAoEgJxdwAGz5fNPvIVft8PLpIxwGNCAAIQgAAEIAABCEAAAhDIOwJTa4e4d1061dMh7RRsU4TNGWjRgXu0NmA629Y8yJnu+dGTbtSQCmcbidh04fk62pqEGAQgAIFiIxA4yOL63Lv1YHXSGdJjUiZm04bNetsxOFGLfyEAAQhAAAIQgAAEIAABCEDgpBKw3X+vO2O8J9tIZNXOg95UYYsQtE1FbPfgKNunXYdvf3q7J4sEPO/Ukd5UYdtIZMbYGme7FmMQgAAE4k4g7g7AR9SBU6V3S7dm2Jn/oHr25yNTh2GGl6UaBCAAAQhAAAIQgAAEIAABCPSVgG0kcsak4Z7+6aoZruFQm1u6pt7ZrsLL1ta7ptbo/R+75Dx8YtM+T1++a7WbNKK6xxl40fTRrqqcjUT62j+cDwEI5CeBuDsAfyHsb5HOl74pfVCK+m3Ifvb5hnSeX8fOxSAAAQhAAAIQgAAEIAABCEAgjwnU1lS615w32VNHV7d7ZO0u95N7n3SrGge5XS3po/u2729xP3tsi6eq8hJ38fTanp2FzTmIQQACEIgLgbg7AO9SRz0gXSn9o3Sx9N+S7Qq8UzKbIM2X3i8Fzj8r/6OEQQACEIAABCAAAQhAAAIQgECBECgvLXEX1o10+0/tdjee6tzcCy5zj25JTBd+ZMNe197ZHfkkrR3dPdOKP61as8YN9ZyBV88Z6849ZSQbiUSSowACECgEAnF3AFofvF5aItk6gC+SFklRZj8PPS+9JqpCDvKrdI13+vc4S8fhkq03+Iz0U+nXUpQtUMHiqMKk/M/p9cKkvN5eWnuelIJx8ROlb5IwCEAAAhCAAAQgAAEIQAACBUdg8shq97aJo9zbLqpzh9s73SPr93pThW0zkZ0HWtM+z5rdTc70vaUb3MjB5e6KWWPdlXIG2kYiw6rYSCQtPAohAIG8IxA4evKuYTls0D5d68XSlyRbCzBqd99mlX1fsh97WqSTYbN0UYsstGPYJuqF6aXSTdJrJWtPf1qJbvYDqRjGRH9y5V4QgAAEIAABCEAAAhCAQB4QGFxR5q6eO87TEe0asnpXkxfxZ87Ap7Y2Oi0PGGmNhzt6NhIp0xqEF04d5a0dePWcca5OuxVjEIAABPKdQLE4e8yh9yHJouKukM6VaiWzBukpySLrDkgny8bowvdKU/wb3KajRdjtkMz593bpddJ10i+lV0jpzKIIl6epsCdNWaoimyJtjlI7b2yqCuRBAAIQgAAEIAABCEAAAhCIAwHb+XfOhGGe3nfFaa5ROwUvW6eNROQMXKqNRPbL4RdlnfIU2nRi0xf+/Dc3bcwQZ45A21X4fO0wXKZpyBgEIACBfCNQLA7AgPt+JX7vK8jrr+NndaPA+WeOyIWhGz+t9J8ly/+M9HLp1dLtUpRtUsFfowqzzJ+s+l+QjkgflcwxiUEAAhCAAAQgAAEIQAACECgKAiOHVLgbz5nkyXYKfloRgeYMNFmkYDrbWN/sflC/0f1g2UZNDS5zCzRV+CpNFV4wc6wbrqnDGAQgAIF8IFBsDsCBYm57ydtuxGZbpH/zUsf/83ll3SSdIn1SSucAVHHO7Nu60lDpx9KynF2VC0EAAhCAAAQgAAEIQAACECgwAqWa4nt+3ShPH7tutnth32G3eM0ed//f9rhHbSMR7TQcZQdbO90dz+7wZNc5TxGBtonIlbPHuemKFLTIQwwCEIDAQBDAAdg/1GfoNiP8W9k04K6I21q+lb9LOl+qkzZLJ9NsvUGbbrxX+phUI2EQgAAEIAABCEAAAhCAAAQgIAJTRg32NhGxjUSa2zrdQ+sb5AzcrejAetdwqC2SkUUSPrFpn6cv/d9qVzd6sOcItOjAC+RgrChjqnAkPAogAIGcEygmB+Bo0btImiZZtJtF5fVmFpGXCxsVusjuUDpVMlw+XxU2p6qUozzbgfi//WuZ88/WQ8QB6APhAAEIQAACEIAABCAAAQhAIExgSGWZe8np4z11y8H33PYD7gE5A+/XVOGVOw6Gqx6X3rz3sPufhzd5Gqrr2G7C3lRhTRkepSnIGAQgAIGTSaAYHIC2ocV/SRbplu3z5soB2BzqRHO6pbNw+dw0FW1XY5sqbM9n198sLZG+K62VMrH/UKUJ0kOSTf/FIAABCEAAAhCAAAQgAAEIQCADAiWa4nvOlBGePnztLLfzQIu3ZqBNFX5YUYJtndFThZsUSfjn53d60mXci04Z6a5UZOBVmio8c1wNU4Uz4E8VCEAgOwLZOsSyu/rA1x6pJphza7o0kIstrNf9bRspWwHWovrSWbjcHHxRZtGMgdn04nN8/ZOO/yZ9TrJNPaLsEhX8vWTteo+Urq6KszbbWCSdjQ8KW1panCmw1tbWIOnC6Z5MEkVDINz/4XTRAOBBewiE+z+c7qlAoqgIhMdAOF1UEHhYj0C4/8Np8BQngfAYCKeLk0ZxP3W4/8Ppk01lhIL4Xn3WWE8tHV3usY2NbsnaBrdkXYPb09QeeXsFEroVWxo9feUva9ykEVVuwYxad8Ws0e4CrSHIVOFIdL0WhPs/nO71xJhVCH/fjtmj8ThZEBhIp1gWzTzhqhbhZrvamt0jfV16Uton5drhpUumtbtUep1f4806/jJF7Tcp7xeh/D8p/fLQ6wVKW/ntkjk2N0qdkjkKrd7fScE2U/+u9KekVKY/Te5pySIMjdEnpMDqlNjkv/iJjjf56WwPGfO99dZbXW1tbbbXpz4EIAABCEAAAhCAAAQgAIG8J3BE34y2ac7WysZB7q+NJe6F5sy/hleWHHGzRhxxp49MaGjwbS/vn5oG5hOBhoYGd/PNNwdNmqLEtuAFx+IhUBbzR71Rz2eOqD9LrxjgZ/2s7n+1ZMzNsWZRiT+Vdko2Dfdt0mck+2nIHHRm1YlDz7/LlTpVsqi9sD2lF3+Qvi+Zo9OmEZtT7zfSM1KyWZk5/7ZIuZrmnHwPXkMAAhCAAAQgAAEIQAACECh6Arbx7xSttD6l5oi7bkqXO6BvfKvkDDSH4JoDg1x7d7RDsE1lz+0z2ZS2I+4UXef0kd3uDDkEJw5WXvSpRc8dABCAwLEE4v52cViPWym9VLr72EcfkFfm5PuhFDj4khthuwB/ULrFLzCn3qv8dKaHt6jiz/3Kt+r47qQTZ+n1s5JxsahBizIMW51ebPIzfqLjTX4620MmU4DNoenWrl3rJk8+Wt1Cs5ctW+bdb/78+a6qqirbe1M/JgQYCzHpyBw8BmMhBxBjdAnGQ4w6s4+PwljoI8CYnc54iFmH9uFxCmkstGqq8BOb9yemCmu68M6DbRk/+YRhle7ymbVuwczRbt7Uka6yrDTjc4ulYiGNhZPZJ9u2bXMzZ84MbkEEYECiyI5xjwA8pP40R9fuPOlXi/gz59u/SDYdeKhk1i0tlizfNvQIrDFIZHH8tep+W7IowMuTzjOHr0UJGpPfS8nOP2XlzLZleqXq6mpnSmXm/IsqS1WfvPgSYCzEt2+zfTLGQrbE4l2f8RDv/s3m6RgL2dCKf13GQ/z7ONMnzPexYF+DXnJWjTTZHdFc4dW7mtz9/q7Cz7ywX3nRT2rOwl+t2O6purzUXXJarber8JWzx7pxwwiiSCaX72Mhub25fM136lzSLNxrxd0B+Ly6ZoF0qpRqKqyy+93MAfh6yX6emSDZO/MOyaIVzd6cOHj/rgqlM03amoBrpQukSUknzdPrwCn4iNJvTCq3l2NCeVOVDur8VWkTBgEIQAACEIAABCAAAQhAAAI5JjBI83nnTBjm6R+vnOHqm9rckjV75BDc4x5cV++a223CWGqzTUfuk+PQZHbmpOGeM9B2FT5j0jB2FU6NjVwIFBWBuDsALdrtCunvpD/mWc/au3eqKLlLQ+18PJTOJmmRfqnMIv8C+2qQSHOcrzKT2eckHIAeCv6BAAQgAAEIQAACEIAABCBwcgmMGVrpXnf+FE9tnV3u8Y373AOr93hOvm2NLWlv/vz2A870jfvWKRqw0llU4JVyBl6qKMHqCqYKp4VHIQRiSiDuDkDbBONGyaLYPiF9Wcpns7UBX+s3cLuOFqWXrVmfzvRP2pHtydSHAAQgAAEIQAACEIAABCAAgfwiYOv7zZ85xtNnXz7XrdtzyIsMtOnCT21tdN1ppgrv1lThXz7xgqfKshJ38fTRig4c5zkFJ45IvRRTfj09rYEABHJBIO4OQIteu1WyKcBflF4t/UJaLQVTbpWMtMROFJHFOS/4gK4YTMH9ntLRMd7RtzZn5zC/eGlStSV6HRUdGFStU2KT/+InOt7kpzlAAAIQgAAEIAABCEAAAhCAwAATsKnCM8cN9fTeBdPdvuZ2t3StRQbuccvW1LumNlsVKrW1dXa7xapjMpurKcdXzbHowLHu7MkjXElJb18XU1+XXAhAIP8JxN0BuERdEP4t5Dy9NmVidl6u+Zyia26NuLntyGtOSrN10te81NF/Rip5trTkaNZxqQuVE+wgbO03JyIGAQhAAAIQgAAEIAABCEAAAjElMGpIhXvVuZM9dXR1u+Wb9rn7NVXYogM3700f97Jq50FnuuWB9a62psJdMWusFx142YxaN6Qy11+HY9oBPBYECoRAMfwXnU8/Ydgaeo9Kt0krpXapTnqd9AbJrFGydKu9CNlwpRdLz0l/kJ6UdkoWJWiORXMg2lqH5ZKZORBXeCn+gQAEIAABCEAAAhCAAAQgAIHYEygv1RRfrfNn+teXzXEbG5rdA4oMtM1BVmxpdF1p5go3HGp3tz25zVOFrjPPpgp7aweOdVNGDY49Ox4QAnEnEHcH4BV51oElas+1vlI1zXb9fav0dKpCP+8sHU1RZg7Bf5M+H1WBfAhAAAIQgAAEIAABCEAAAhCINwGbKjx9TI2nd8+f5g4c7nBLNFXYNhJZoinAB1o6IgG0K5Jw2dp6T5+9Y6WbpSnHV2qq8NXSOVNGulKmCkeyowAC+Uog7g7ApXkG/ma1xxyANlV3glQj2eILFtX3W+lnUtS78A6VWaTgRZKdP0mqlaqkA9IaaYlkax5uljAIQAACEIAABCAAAQhAAAIQgIBHYPjgcnfjOZM8dcrB96QiAoOpwhvqm9NSWrO7yZm+u2SDG6nrBFOF58+sdUOrgkloaS9BIQQgMMAE4u4AHGC8x93+V8oxnYjZdGFzEppOpm3WxfNp2vTJfFauDQEIQAACEIAABCAAAQhAoOgIlGmK74unjfb0qZfOcZs1VdicgQ+s3u0e37jPdaaZKtyoSMLbn97uqbx0kHvx1NFeZKDtLMxU4aIbSjxwARHAAVhAnUVTIQABCEAAAhCAAAQgAAEIQAACuSZQVzvEvevSqZ4Otna4B9c2eJuILF6zx5nDL8o6uo64h9Y3eFp45yo3e/xQb1dhcwaew67CUdjIh8CAECg2B6DtAHy1dIY0yie+T0fbnOM+yTbWwCAAAQhAAAIQgAAEIAABCEAAAkVJYJim9L7srAmebNOQZ15o1CYiig6UbBpwOlu9q8mZvr14g3YVrnRXzh6j6MBx7lLtKjy4otjcD+lIUQaB/idQLP8Fnim0P5Bs7bwo+5IKHpf+QXo+qhL5EIAABCAAAQhAAAIQgAAEIACBYiBgm32cd+ooTx+/brZ7Yd9hbxMR21X4sY17nUUARlnDoTb3mxXbPFWWlbhLtDPxVdpE5KrZ49z44baUPQYBCPQngWJwAFrE351ShRSsbWcxzHv91xYJGKxaOk/pJ6QbpPslDAIQgAAEIAABCEAAAhCAAAQgAAERsDX+3n5xnadDbZ2aKlzv7pUzcLHWD0w3Vbits9tzHNoOxP+iCXhnThruOQMtOvD0icOc7ViMQQACJ5dA3B2AtkvubVKl1C39SPqh9LTUKZmVSudK75beKVldO2eGZE5CDAIQgAAEIAABCEAAAhCAAAQgAIEQgZrKMnf9mRM82VThp7baVOHdWjtwj1u/51Co5vHJ57cfcKZv3LfOTVA0oBcZKGfgRdqYpKrcvqJjEIBArgnE3QH4AQEbLtkOujdKd0vJ1qWMFb5+p6NFC9o5du5nJAwCEIAABCAAAQhAAAIQgAAEIACBCAI2VfiCulGePnl9Yldhcwaalm9udOYgjLKdB1rdzx/b6mlwRam7TOsF2iYiV84e660jGHUe+RCAQHYE4u4AfJlw2DvNt6RUzr9kWvco4xbpw5KdiwNQEDAIQAACEIAABCAAAQhAAAIQgECmBGxX4Zsvm+bpgHYRXrJ2j7eRyBLtKtzUGkzGO/5qh9u73N0rd3uyWcHnThnhrp47zttIZMbYGqYKH4+MHAhkTCDuDsCpPok7MibinNU1B+C0LM6hKgQgAAEIQAACEIAABCAAAQhAAAJJBIYPLnc3njPJU0dXt1u+aZ/nDLTowK3aVCTKjiiU56mt+yg/oW4AAEAASURBVD195S9r3Claf9CmCtu6gRdOHeXKS0uiTiUfAhBIQSDuDsBga6HmFM8elRW8A9lagBgEIAABCEAAAhCAAAQgAAEIQAACOSBgTruLtRuw6dM3zPHWCrxPawaaM9DWEDSnX5SZs/DHD2/2NLSqzF0+c4y7RtGBC2aOdeZkxCAAgfQE4u4A3KXHP0WyTT6eTI+ip9Tqmu1OHPgXAhCAAAQgAAEIQAACEIAABCAAgVwSsJ1/Z4wb6um9C6a7hkNt3m7CtonIsnX1zqYDR5lNI/7Tczs92fqD55860nMG2tqBUzX9GIMABI4nEHcH4IN65LdKn5B+Ix2U0tkwFX5cst8d7FwMAhCAAAQgAAEIQAACEIAABCAAgZNMoLam0r3u/CmeWju63GMb93o7Clt0oG0UEmW2wcjjmlZs+sKf/+amjxniTRO2tQPnjAkmBUadTT4EiodA3B2A31dXmgPQ1gJcJr1bWi6lsguV+QPJ1v4zB6Cdi0EAAhCAAAQgAAEIQAACEIAABCDQjwSqykvdglljPX3+xtPdqp0H3X2r9rj7V+92z207kLYlG+qb3Yb6je77yza6EdXlbkZNiTtj5BF3SVunq65OeyqFEIg1gbg7AB9W731H+n/SmdJj0irpccmm+Jqjb7z0YmmuFJidY+diEIAABCAAAQhAAAIQgAAEIAABCAwQAZsqfPrE4Z4+cPUMt/tgqxcZeL8iAx9a3+DaOrsjW7a/pcMtbylxy+ud+/lXHnTzpo/2ogNtM5HJIwdHnkcBBOJIIO4OQOuz90uHJdvZ17YJOl0KO/v00mmDcc/sneNrkk0ZxiAAAQhAAAIQgAAEIAABCEAAAhDIIwLjhlW5N7/4FE8tWifQnIDmDLTNRGwdwSjr0FThB9c1ePrsHSvd7PFDe9YNPGvScFeitQQxCMSZQDE4AC3K72PST6X3SldLM6SwrdOL+6TvSn8NF5CGAAQgAAEIQAACEIAABCAAAQhAIP8IVFeUek482w24Ww6+57Yf0FRhcwbudqt3NaVtsJWbbnlgvRsztNJdNXuss01ELtUOxXZdDAJxI1AMDsCgz8yx9z7/RYWOI/10o47tfpoDBCAAAQhAAAIQgAAEIAABCEAAAgVGwCL4zpkywtNHXjLLbWs87P7y3Hb320dWu3UHB7muI9ERfvVNbe5Xy1/wVFlW4jkBzRloU4Ut4hCDQBwIFJMDMNxf5vCzNQAxCEAAAhCAAAQgAAEIQAACEIAABGJGwNb4e8uFk92Y/atca6dzlXXnuGXrG93iNXtc4+GOyKe1NQXvX20bjuxx7vfOnTV5eM+6gXMnDHO2JiEGgUIkEDcH4PXqhC/6HWFr+f0ii055i+r+s1/fpgzblGAMAhCAAAQgAAEIQAACEIAABCAAgQImUCXPx7Vzx7pXnneq69JU4ae2NvZMFbZdg9OZ7Tps+vq9a93E4VXeNGGLDLxIG4pUljFVOB07yvKLQJwcgOaG/y/J1vd7QMrG+afqXv2bdLxK+k/pbAmDAAQgAAEIQAACEIAABCAAAQhAICYESjVV+IK6UZ4++dI5blNDs7+JyG63fHOj5yCMetQdB1rdzx7b4mmI1gm8bMYYd7XWH7xi1hg3uqYy6jTyIZAXBOLkALxSRGdKXdIHT4CubRbyAelZ6QxpgbREwiAAAQhAAAIQgAAEIAABCEAAAhCIIYGptUPczZdN87T/cLtburbe3auNRJauqXdNbZo7HGHN2oH4Lyt3ebJZweedMtKLDrxa0YGnja1hqnAEN7IHjkCcHICv8THeq+PKE0S6SufdLdlUYrveEgmDAAQgAAEIQAACEIAABCAAAQhAIOYERgyucDeeM8lTu9YCXL55n7ejsO0q/MK+lsinP6JwohVbGj39x19Wu1NHD/bWDbxaG4mcXzfSlZeWRJ5LAQT6i0CcHIAXCppF8d3ZR3h/0vkvleb18TqcDgEIQAACEIAABCAAAQhAAAIQgEABEqjQbsCXnFbr6TM3zHXr9hzynIH3/22Pt4agOf2ibMvew+5HD23yNEwLEF4xe6znELxcU4WHVZVHnUY+BE4qgTg5AE/1Sa3pI7G1/vl1fbwOp0MAAhCAAAQgAAEIQAACEIAABCBQ4ARs59+Z44Z6+n8LTnMNh9rcYu0SbJGBD65rcIc1HTjKDmoL4j8+s8NTmdYfnDdttJyBY73pwlNGDY46jXwI5JxAnByAw306+/pIKTh/WB+vw+kQgAAEIAABCEAAAhCAAAQgAAEIxIxArTb8eN35Uzy1dnS5Rzfu9TYSsejAndooJMo6tQPxQ+sbPC28c5WbPX6ou0abiFylqcJnTRruSuQgxCBwsgjEyQF4UJBGSiP6CCs4v6mP1+F0CEAAAhCAAAQgAAEIQAACEIAABGJMoKq8VLsAj/X0bzcecSt3HOyZKvz89gNpn3z1riZnuuWB9W7M0EovMtDWDbSpx3ZdDAK5JBAnB+AegTEH4FxpiXSiNsc/0a6HQQACEIAABCAAAQhAAAIQgAAEIACBXgnYVOEzFMln+uDVMxUN2KLIwMRU4UfW73XtXd2R16hvanO/fOIFT1XlJe6yGWPcNXIG2vqB5hzEINBXAnFyAD4hGLOlV0jf6QOYG3WuLee5vA/X4FQIQAACEIAABCAAAQhAAAIQgAAEipjAhOHV7q3zTvXU3Nap9QLr3b2r9rjFa/a4fc3tkWRaO7pVb7cn+RTdOVNGeJuI2HThGWNrnDkaMQhkSyBODsC79PBvk66R5kvLpGzNzrtWMgegXQ+DAAQgAAEIQAACEIAABCAAAQhAAAJ9IjCkssxdd8YET11aC/DprY3uXm0icp8cfRvqmyOvbbsNP711v6ev3r3GnaKNQ2ya8NVzx7oL6ka58tKSyHMpgECYQJwcgL/Tg22Upkm/kS6XstkReKZ/njn/Nku/lTAIQAACEIAABCAAAQhAAAIQgAAEIJAzAqXa7ON8Oe9Mn7x+jttYf8ibKmwOwRWb9zn5ByNt677D7n8e3uRpWFWZN0XYHIKXzxrjhlWVR55HAQTi5ADsVHf+s3S7NEZaIX1G+qF0SIqyGhXcLH1esrT9p2bXsethEIAABCAAAQhAAAIQgAAEIAABCEDgpBGYNqbGmd49f5pr1NRgmyJ8n5yBS9fUu+b2rsj7HmztdH98ZoenMjkVXzxtVCI6UA7BKYoUxCAQJhAnB6A91x+lf5W+KNlo/5r0OelB6Slpt2SxtUOkcdKLpMv818Ek+s/q9R8kDAIQgAAEIAABCEAAAhCAAAQgAAEI9BuBkUMq3KtfNNlTW2eXe3zjPs8ZaFOFdxxojWxHp8IGH9ZGI6bP3bnKzR4/1J8qPM6NsnnEWNETiJsD0Dr036Vtkm0EYo4+i+q7zpcOx1ng+Duskn+UFh1XgwwIQAACEIAABCAAAQhAAAIQgAAEINCPBCrLSt38mWM8fe4Vp7tVOw9qzcBEdODz2w+kbcnqXU3O9K3F693w7oNp61JYHATi6AC0nvuZdJ/0Yeltkk0JjrIGFSySviHtkDAIQAACEIAABCAAAQhAAAIQgAAEIJA3BGzn39MnDvf0gatnuJ0HWrx1A22q8CMb9rr2zu7Itu49FL3jcORJFMSOQFwdgNZRO6WP+pqr49lSrTRUapLM8festErCIAABCEAAAhCAAAQgAAEIQAACEIBAQRCYMLzavXXeqZ6a2zrdg+savKnCD6ze4/ZpHUEMAskE4uwADD+rOflw9IWJkIYABCAAAQhAAAIQgAAEIAABCECg4AkMqSxz150x3lOX1gJ8emujsx2Fbd3ADfW2DQIGAeeKxQFIX0MAAhCAAAQgAAEIQAACEIAABCAAgVgTKNVuwOfXjfL0yevnuI31h9xvlz7rPv7dWD82D5cBgZIM6lAFAhCAAAQgAAEIQAACEIAABCAAAQhAoMAITBtT496sqcIYBHAAMgYgAAEIQAACEIAABCAAAQhAAAIQgAAEIBBjAjgAY9y5PBoEIAABCEAAAhCAAAQgAAEIQAACEIAABHAAMgYgAAEIQAACEIAABCAAAQhAAAIQgAAEIBBjAjgAY9y5PBoEIAABCEAAAhCAAAQgAAEIQAACEIAABHAAMgYgAAEIQAACEIAABCAAAQhAAAIQgAAEIBBjAmUxfjYeLb8JlAbN27lzZ5D0ji0tLa6hocFLb9u2zVVXVx9TzoviIcBYKJ6+7u1JGQu9ESqucsZDcfV3uqdlLKSjU3xljIfi6/OoJ2YsRJEpvnzGQqLPk75z93wXL74RUdxPPKi4H5+nH0AC5+veywfw/twaAhCAAAQgAAEIQAACEIAABCBQbAQu0AOvKLaH5nmdYwowowACEIAABCAAAQhAAAIQgAAEIAABCEAAAjEmQARgjDs3zx+tUu07029jvY5dofaOVzqIDrRfJ3aFykgWFwHGQnH1d7qnZSyko1N8ZYyH4uvzqCdmLESRKc58xkNx9nuqp2YspKJSnHmMhUS/27TfMf4QeF7HNj/NAQIQgMCAEpisux/xZWmseAkwFoq375OfnLGQTKS4XzMeirv/w0/PWAjTIM14YAwEBBgLAQmOjAXGAAR8AkwBZihAAAIQgAAEIAABCEAAAhCAAAQgAAEIQCDGBHAAxrhzeTQIQAACEIAABCAAAQhAAAIQgAAEIAABCOAAZAxAAAIQgAAEIAABCEAAAhCAAAQgAAEIQCDGBHAAxrhzeTQIQAACEIAABCAAAQhAAAIQgAAEIAABCOAAZAxAAAIQgAAEIAABCEAAAhCAAAQgAAEIQCDGBHAAxrhzeTQIQAACEIAABCAAAQhAAAIQgAAEIAABCOAAZAxAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAJFQuAUPefXpL9JzdI+6QnpI9JgCStcAi9S0z8l3SW9ILVJh6S10iLpMikbu06Vb5e2SXYtO9pry8cKl8BX1PQjIS3I4FEYCxlAKpAqtWrnx6SHpV2S/be9Q3pc+qp0kdSbWZ2fSZulVmmn9BfpjRJWGAQq1Mx3SdZv1n82DuzvxRrpf6R5UibGe0MmlAamzljd9gbp85J9LmiQgvf+RUpna7no6zLd9B+kZVK91CKtl74nzZWwk0MgF2OhSk27UbpFsr8X9v2hwz8+quNCaYKUqdl3jo9KT0h2LXv/se8m9h3FvqtgJ49ALsZDVOusXzdKwXvN5qiKSfmMhyQgvIQABCCQCwIv00X2S8GbcvJxtcqm5eJGXKPfCSzVHZP7M9Xrn6qeffFLZ4NU+H0p1flBnpVbPaywCJyt5toH9qAf7bhAijLGQhSZwsx/nZoddgKEx0GQ/kMvj/YZlXdJQf3k4x0qsy+KWP4SmKKmPScl913y66+rTtT7PO8N+du/QcuS+zP8elFQKYNjrvp6tO71mBRuRzjdqrJ3ZtAeqmRPIMw5Ob0og8udpToHpeRzk19bnddncL3pqmPfOZLPD17bd5WXZnAdqpwYgYBzquOiE7tkz1nmwA1fd3NPSXSC8RDNhhIIQAACJ0zAvvhbxJ+9KTdJFil2kXSl9AMpeLO2X99qJKywCNgv6NaH26VvSK+RLpDmSR+SLHov6ONfKJ3OvqjCoO5TSr9RsmvZ0V4HZV9QGiscAiVqqv3Sbv232z9aeoEUZYyFKDKFl/82NTlw3Fn/L5Sull4k2Ret90v3SLdJUXazCoL//u09x76s23uDRYU8IAVlP1cay08CZWpW2Pn3rF6/XbK/FddIn5MsEifoy48qncp4b0hFJb/ygj6041bpbinIW6R0ppaLvi7VzZZKwf1/p7RFFF4o2XtP8DepU+mXSFhuCQTcT3QsXKrmBNd4SOlPSFdL50rXSt+TrO+sjh2vl6LMvmPYd43gevYd5ErJvpPYdxP7jmJlzZI5HrHcEwjY27Ev7w3JLbPxYP1vkb2Bw3iz0umM8ZCODmUQgAAE+kBgsc61N3qL/rE/sslmH/KDPwgW4YEVFoE/qbn2q6t9yE5ltcpcIwV9HDUd+DTVCSLElitdLYXNQvQtPxhL9qsdVhgEPqhmWr/ZB+8v+Wl7vUBKZYyFVFQKM2+Omt0qWX8vk4ZLURYVITxCJzRKdo0tkr2nhM3ee+6QrNw0X8Lyj4D9OBT00SNKp/qbcZ7y2/16+3Qsk8LGe0OYRv6mzZl7gzTOb2KdjkHfL/Lzejvkqq9v0o2Ce387xU3tPgf8Omt1TB5zKU4hKwsCfR0LF+tev5bmprmn/RDULVk/r5cGSalsoTKDsZDqBwb7jhJ8DrUflrDcE+jreEjVIvtbskKyvv20tNlP2zGdLVQh4yEdIcogAAEInACBC3RO8OZqv9KlshJlrpKsnn3gL5eweBGwLwLBOPhmxKPZB/OgzryIOpYf1Lklog7Z+UXApvwFv6ovUHqhFPShvU5ljIVUVAoz7z412/q7Xkp23GX6RPZFLRgzb4w4abLy7dd/q3dnRB2yB5bA13X7oB9fnqYpt4fqnZFUj/eGJCAF8rJO7Qz6flGGbc5VX6/0722fL+2HxFT2CWUG7XtNqgrk5YxAna4UsF6Us6s699vQdS0aLNnsu0XwQ5J957DvHqnse8oM2ndeqgrk5ZRAna4W8F50glf+sH+N1TraD4mb/dd2jDLGQxQZ8iEAAQj0kUB4+saL01wr/OHrmjT1KCpMAjVqdvAH3iIGk81+rd0mWR2LEktn9gfe6r0gRf3Km+58yvqXgDljrL8W+bddqGMwFhb4eeEDYyFMo7DTs9X8oK8/24dHedi/jkXpREUJ2uX/4tdr1dHec7D8IvAtNScYD6enadpXQ/XCX8B5b0gDLc+L6kJ9uiiDtuaqr2eE7vvdNPcdH6r3v2nqUdR3AnW6RPA+sKjvl+u5wvtC131tT+7RxDWh8o8fzT4uNS9U74vHlZKRawJ1umBfxsOpOv+Qf40rdDTbLNk17RhljIcoMuQXNIGoXzYK+qFofMERCKZ7NqvlT6Zp/dJQ2aWhNMl4EAh/abdpGsk2VRmT/MzwWEiuZ6+Dcov4qbMMLG8J2NTwGySLvPhohq1kLGQIqgCqvS7UxttC6ZFKz5BGh/KikvbecaFf+KiONj00yoL3hkpVuCCqEvkDRsCmVwY2LUikOE738+wL3LpQOe8NIRgxT+aqr4PPoIYreH9IhW6XMoPxyWfQVITyP8/e9wNL9Tkz07GwQhex7yxmjIUEh3z+9ztq3BDpZ9LiLBrKeMgCFlULhwAOwMLpqzi31NZ/MlsvdXqp1P+sDmUH54SySBY4gctD7Q/3dZAd7vNU5UE9O4bLw+eF65AeeAK2blsw3dt+ba/PsEnhPg33darTw+Xh81LVJa//Cczzb2mRexbZ+xbpWckcwvZlu0HaKH1WiorYM0dhsCZXuL+VfZyFyxkPx+EZ8IxfqgW2OLuZvSfYuk3Jdq4yXuZn/krHoL5lhfs03Nd+9WMO4fLwecdU4kXeEgj3WbgvUzU4XB4+z+qGX4frpbvOFBWaQwErLAK5+pxp31U2+I8eHj+FRaM4WvtGPaZtJGZTuz+S5SOH+zbdewPjIUuwVB9YAjgAB5Y/d3euShBqfRA2vTOd2Zt38IubffjC4kPA3os+EXqc34TSQTLc572NFZv6G1j4vCCPY34Q+IqaMV56RPpRFk0K9yljIQtweVh1rt+mzTreIv1cOksKm0X6LJQsum+ilGyMh2QihfvafgS4SWqRLpGWS2+T5klXS+YItigti/p8RrJ1ncLGWAjTiHc6V319ItcZJLST4403dk93tp4o+OFgpdK2xl+yBWPBvmvsTy5Meh18zhyj/HBkYVI1Xg4ggZG69zf8+9t3jD1ZtoXxkCUwqhcGARyAhdFPcW7l0NDD2foMvVngAIyKBOntfMrzk8CH1KxgCt/vlbbpFcmWzVgJxoldg7GSTDI/Xl+qZtws2S+n75GOSJkaYyFTUvlfb5TfxNk6vk+yL102HsZK9gPRBdJdktkZ0m1S8mcXxoPRiY/Z34DzpR9J50g/kcz5e6+0UDosmePP3kN2SWFjLIRpxDudq77O1XXiTbuwn84cdLdKQUTxpyIeJxgL2XwfsUvxOTMC6ABnf1X3HyfZ348fnkBbGA8nAI1T8p9A8ofo/G8xLYwbAfuCF1h7kEhzbPPLqtPUoaiwCNiUjC/7TbZf594b0fxsxkowTuxSjJUIoAOYbdE7P5AsiuK/pOelbIyxkA2t/K47xG+efUHrkq6Xvi/VS/bfsf0YcIMUOAEvVvrVUtgYD2EahZ8u1yO8WXq5ZO8RyWZf6N4kLUgu0GvGQgooMc3KVV/n6joxxRyLx/qWnsJ+VDCzHxTu8FLH/xOMhWy+j9hV+Jx5PMuBzpmvBrxTOpEfmYO2Mx4CEhxjRQAHYKy6syAfpjXUanMK9Gb2JdGsJXHg3wIncLrab9EeZZJ92X+9tFtKZdmMlWCc2HUYK6loDmzep3T7OdJW6XMn0BTGwglAy9NTwn15m9r4WIp22mLtHw3lm/MnbOFr9PZ3hPeGMLn8S5tD+D7pXyTbAMaWCbD3Cuu34dK10kOSRYbeKX1AChtjIUwj3ulc9XWurhNv2oX7dJ9U0222gdmT0vu8VOp/grHQ298RO5u/JakZ5kOu9c0PJPsB6ZvSc9KJGOPhRKhxTt4TwAGY910U+wY2hZ4wkxB6+3Jglkl4fqIm/+Yrgalq2D2SrdFhkT/2pX6pFGXZjJVgnNi1GCtRRAcmf7Zuax/Izd4vhadre5kZ/MNYyABSgVQJ92UQ5Zeq6SuVud0vMOdP2MLX6O3vCO8NYXL5l7YfBCxyw+xd0sel1ZJF5ByUbBrwFdJiaZD0dSm8ZiRjQUCKxHLV17m6TpFgL6jH/Ae19kt+i9foeL2U7jNHMBZ6+ztil+RviVHIT7MfkGZJtk7jQulEjfFwouQ4L68JWNQNBoGBJGC/rjRItdLkXhpijqLgD26w+G4vp1CcpwQmql0W5WFHW/vNwvQtEjCdbQsV9jZWgoV77RTGSghcHiRtvUf7dX2jNFh6o5RsZ4QyrlR6vP/6Th3twztjwQcSg4P99xn0b7hfUz2a1Z0k2fqAYQufx3tDmExhpc2h9w6/yWt1/ElE8zuV/2npIcl+yLZz7H3FjLGQ4FAM/+aqr5OvY59Joyz4bGGfW8LnRdUnf+AI2I/K3/Fvv0XHq6V6/3XUwfr0xZJ91xgh2Zq0URaMBbtmW1Ql8geEgP1wZGbfM27wUsf/E3yftGPwOdSWIXogVJXxEIJBMj4EyuLzKDxJARP4m9p+mXSaZGPSPtynMoscCszOwQqTgDl7LYpjmt98iwL7qZ9Od1gVKgyPhVB2TzJczljpwZIXiWDajPX/LzNokX3RD2yqEuYAZCwERAr/uFKPcIH/GKW9PE5Qnvw3wpxFXZKVh//bT3W5cDnvDakIDVzeON16lH/7p3tphk3lCyzcp7w3BFTif8xVXydf55k06IKxZj9G2N8iLD8JvELNss+VJdJO6SrJnDm9mY2F1/iVrK8fizjBvqtM98v4OxIBaQCzgync9uOQKZ3Zd5Lgs+hSpcMOQMZDOnKUFSwBe2PEIDDQBB7yG2C/wpyXpjG2WURgDwcJjgVFwNZwulua67f6Ezp+20/3dtikCjv8SuGxkOq8YAqZTRncnKoCeQVNgLFQ0N13TOOXhV4FX6hCWcckp/mvgqnAQWG7Ek/4Ly7SMfjwH5SHj8F7h0VsrAgXkB5wAmHHrn3BTmflocLwebw3hMDEPJmrvg4+gxqu4P0hFTqLVJ7pF/AZNBWh/MgzZ99vJHsP2StdI22QMrFMx8L5ulgQQcZYyIRsYdZhPBRmv9FqCECgAAhcqDYe8fW9iPaas9p+ibF6jVL4w79eYgVAYLDaaH9Mg77+wgm0+Tuh8+dFnG/5wT0ydS5GXIrsASKwUPcN+nBBRBsYCxFgCix7tNprDjzr73vStP1yv47VuzVFvY+Fyt+YotyyJkvmLLJr/FnC8ouA/Z0/IFn/bJfKpCizaV1Wz/TfSZV4b0gCUiAv69TOoE8XZdjmXPX1Kv/e5jCyzyqp7BPKDNr3ulQVyMsZgTpdKWC9KIurXqy6h/xz7b0kXVBBqsvaj0c27dfubWNikJTKvqfMoH0XpKpAXk4J1OlqAe9FObryZv+adowyxkMUGfIhAAEI5ICARYHYm3uHZBEcyfZRZQRv/guTC3md9wTsj6hF/gV9+I0TbLH9+m5jxK6zXKqWwmavLT8YSzPChaQLhsBCtTQYKwsiWs1YiABTgNnhL/GpnHdD9Uw2JTQYE6m+cNnU0eCL22alzbEYtlK9uEMKrnFFuJB03hD4RaiPPhvRqpHKXxmqd21SPd4bkoAUyMs6tTP473NRhm3OVV+/M3Tvb6W4t0UnB87p9Uqnc06nOJ2sLAnUqX62Y+EcnWMBAnaeOQEvkU7EPq+Tgnvbd49ks+8owefQJcmFvD4pBOp01aBPFuXoDpv9a9oxnTEe0tGhDAIQgEAfCJyrcw9L9gbfJH1SmifZl7TvS8Eb/xql7csgVlgEfqfmBn14v9JnSmekkX2oj7J/V0FwraeUfoNk0zHsaK+Dsi8pjRUmgYVqdtCPC9I8AmMhDZwCKhqjtm6RrM/ti9Utkr33W/TGTZKtsRSMB3MWRtk/qCCoZ1/S3yHZe4OtB2Xr+gRlv1Aay08Ctu5WsxT0lTltXyPZZwT74v0hKRgrVuc+KZXx3pCKSn7lXarm3BTSR5QO+t1mC4TLLB1luejrUl3c7hnc/7dKv0S6UPpHabdkZbbWqO0ki+WWQF/HwnQ1J+gj66cPSuk+Y1rZWCmV2XcM+64RjAX7DmJ/j+w7yScl+45iZfadxZyOWO4J9HU8ZNKizapk/WjHdMZ4SEeHMghAAAJ9JPBynR/8whr84Q0f7Q/yaX28B6cPDIFwP2aS3pymmSUq+5GU7jq3qtzqYYVJYKGaHfTvgjSPwFhIA6fAiuaoveukoN9THe2/+96Wf/ic6nSnuc6fVVYlYflL4Go1rV5KNQbCefZj0siIx+C9IQJMHmUvUlvC/dlbOqrpuerrWt3A1hKNakebyt4d1Qjy+0Rgkc6O4p4qP/lmN2V5vl1zoRRl9l1jrZTq3pZn31VukLCTQ2CRLhvFPlX+ibRis38PO/ZmjIfeCFEOAQhAoA8ETtW5X5fM2WdRABbOb1M6PyZFrcuiIizPCaT6g50ub3MGz/NS1fmDZGtF2QdzO9prfp0XhAK3hWp/MD4WZPAsjIUMIBVAlSFqo0UB2c6LeyX77/oF6VeSRWBkarYO1P9KWyW7hkWG2PqCb5KwwiAwWs20v/uLpT1Su2QRNxulX0s3SoOk3oz3ht4IDVz5It06eJ/P5NhbS3PR12W6yXulB6UGqUXaIP1AOl3CTg6BRbpsJmMgqJPcipuyPN+us1BKZ/b3yN6D7DuIfRex7ySrJfuOcqqEnTwCi3TpoK8zOZ5ISzb797BjJsZ4yIQSdQqCQCYfngriQWhkwRGoVIvP9Ftdr2NXwT0BDYYABCAAAQhAAAIQgAAEIAABCOQ/AVvuYIzfzOd1tB9JsSIjYL90YRAYCALm/LNf1TAIQAACEIAABCAAAQhAAAIQgAAE+ofABbrNiv65FXfJJwK2bgbWfwRepFt9SrpLsmlN5nU/JK2VFkmXSZmahZ9/WXpS2i/Zwun7pEekT0uBd1/JtDZYpR+VnpDsfGuPLbj+NekUCYMABCAAAQhAAAIQgAAEIAABCEAAAhAoYAJMAe6/zluqW83P4HY/U52bJVvvJsrerIIfSua8izJbP+n10gNRFZQ/XfqzNCuiji1ya/f6v4jyvmSbA3OzXeCJJ55wEyZMsKRnLS0tbtmyZV56/vz5rrq62i/hUGwEGAvF1uPRz8tYiGZTjCWMh2Ls9dTPzFhIzaVYcxkPxdrzxz83Y+F4JsWaw1hI9PzOnTvdhRdeGAyDOiW2BC84Fg8BpgD3X19P8m+1Q8fbpAclW6C8VLpI+mfJ6vydZP1ijrdUZnV/Ktl53dJPpD9Kdl2L2Hu79HLJFtC+QzpD2iwlW40y/iQFzj9zKNpC67bg8RXSJ6XhkrXV7vmclEvrWfPPnH+TJ0/uuba9SdfW1nqvLR8HYA+aokswFoquyyMfmLEQiaYoCxgPRdntKR+asZASS9FmMh6KtuuPe3DGwnFIijaDsZCy63u+i6csJTO2BHAA9l/X2s5Rn5J+JyX/B2c7Hv5MeliaKb1J+q5kTsJks2uY88/s/dJ3vFTiH1tTz67/n9KHpSH+8Z90TLaPKGO2n/kxHb8aqvCo0oslC8OzKMNvSFdKGAQgAAEIQAACEIAABCAAAQhAAAIQgECBESgpsPYWcnNvUON/IyU7/4JnalDCogADe22QSDpe4r/eq2PY+Reu9vnQi4tD6SBZrsQH/Be23p85DJPNnIA/8jMtIvC85Aq8hgAEIAABCEAAAhCAAAQgAAEIQAACEMh/AjgA86uPloSaMz2UDicr/BebwplJaVu7zxyKZpWJwzH/LtCrEX6OTSG2qcSpbFEo89WhNEkIQAACEIAABCAAAQhAAAIQgAAEIACBAiGAAzC/Oipw7lmropxya/0mT03T9GEqSyyil9hhOLnqZaGMpaF0cnKFMpr9zEuTC3kNAQhAAAIQgAAEIAABCEAAAhCAAAQgkP8EcADmVx9dHmqOrRmYyr7vZ9omH+9JVUF5nw7lB/VDWW5O6EXUfaxKp7TBrxs+x8/iAAEIQAACEIAABCAAAQhAAAIQgEB/EThy5Ijr6j7iOrq6XVtnl2vt6HIt7V2uua3TNbV2uAMtHW7/4XbX2Nzu9h5qc/VNba6hqbW/msd98pgAm4DkT+eYM/YToebYeoGp7FZlWgTfW6RvS7Y2n+32u1OyXYDfKr1KMvsP6R4vdew/U/yXFt23/9ii4169oJyzpDGSTSdukzKxyb1UGh+U285MpsBaW4++OYXTQTnH4iEQ7v9wungI8KQBgXD/h9NBOcfiIhAeA+F0cVHgaY1AuP/DaegUJ4HwGAini5NGcT91uP/D6eKmkv7pu32nkjmWupTu1ny0TqWPye8pO+KXadqaV9fqabF7pfX/nnMsncjzy1UpyLPzvLSXd+z51oae8lC9Y/PMCZa4V/ia1u5wvY6OTrdte4nXvgf/uNINKik9ptzqJp8TzrPn8l4ntSPI9+7ttzfI845+npXrSb37B+0KjlaWuLbq2PXVReHXdh3vfP0TXNvqnIh1HgxWCDuRszknLgQGxeVBYvActgHI1/zn+L2Ova2593rVMYfhuf454cNivfiylMr5Z/VWSnOl3VKPI07pVPZrZdq9zGxasW0+koll/N506623utraYMZyJpemDgQgAAEIQAACEIAABCAAgewJmEOlKyRzrPS8NueRLmmOpSAv4Xix/EG+EyZRpydf51ta//fOCa5vr4+p0/Pav07Pa/98ndClPDu/57ykOta2njKvnq4VyrN7WruzuobOsfpH9HxYfAmYA3D7d28KHtACgrYFLzgWDwEiAPOjry9XM8xhZ7ZHeq+Xiv5ntoreLJ0ZUeUi5b9Nel6yyMBkq/Iz2pMLUrwOR/xVpygnCwIQgAAEIAABCEAAAhCIKQHPmaRnS0RbJRxMYedYkA6O5qBKONAG9TjRrMzLt7JAvpPtaH5Sfb885bn+NbqP+OdkUje4H46umI5UHgsCEOiNAA7A3gid/PLTdQuL+LO+MGebRdtZZF6U2fRfm/Jru/hukf5VulfaJ42TXiF9QbIpwpdL10p/k8IWzLENbzoSLg+nw7sIH52nG66ROh1MM05dmog8XG6F8+fPd5MnH50xbGH6y5Yt886zsqqqwF8ZdSny40qAsRDXns3+uRgL2TOL8xmMhzj3bnbPxljIjlfcaxf6eLApgO3ynLV3Bkdb30uvldfhHZXvv7ZjUGbpDnnJgjIvv6deYp2wxHWjz+nU+R2aB2lHm/aZOCZed5iHDoMABCAAgYIngANwYLtwqm5v03RHShbx/SZpqRRl5oz7pWTOv13SPP+og2fb9O93JLuG7eBrXrWfShdIYWvyX9SEMyPSQ0L5h0Lp3pLWloysurramVKZOf+iylLVJy++BBgL8e3bbJ+MsZAtsXjXZzzEu3+zeTrGQja04l83k/FgDjfPcSYHW5sW0Q+cZMnOtR6HmudUUz07SpbfU+Y57oJ8v46fF67T1pFw6AXXCO5pC/lbWzAIFBKBkkHOleqfQYMGuVLJXpfYUYlwmZdWvtUr0cr3ibpH63nn+GU96dC1g2tZWeJex97n6D2tHYkyr57u1d3V5XZs367znDv1lCmuorw80cYU1z/mObxnSDzH0esffd7gmRJtSuSH63lp3cN7Znu2nnYFeUE7/Wfxns3SQfnRc2xMJK4d1E3UUVX/WYyL0l6bVab/Hb1Oos7OHdvd6d8tpNFFW08GARyAJ4NqZtecqGr3SXa0v/bvlCwSMJ1dp8JJfoVbdDQnYCqzNf5+Lt0snS+dLT0rBWbOuRdL5twzZ2K6jUCCSL561QtPB9ZLDAIQgAAEIAABCEAAArkl0CnH2WE55Fq1q2WLjof9o70O0pZvu16Gj02H29yGLSWuXVM9b294Vg4913Mdc7AFTjvv6DvncttyrhYnAuXyJJXJW1UmT0qp0ua0MmfXMUqVZ3WUb84Y79zgnFR1U+X55x93T6ubRTvs3uY0Oqa9/rXLdB0rs+fzHHLKD+oHecHRniVIH3P0HVX53ue22eQ999i+lpoad+2sog0uaa4uz/euon39QAAHYD9ATnEL2/HCpu1O88ver+NP/XS6w5xQ4VOhdKrkk8o0B6DZbCnsAFyl16+xApmVPealjv/Hxsd0Pzt5GvHxtcmBAAQgAAEIQAACEIg1Aduds9Wcb0kOOHPMBfmBk+4Yh117Z+IcRcC1BGnfoRc+z9J9i4RTyI/Z3kz3rUtU59++EzAHkjmWys1ppmNZacJ5dkyeyjzHml9WbkerG8ov969j5yfSiTp2XXNmpTo/fE87p7urwz33zDNeNNiLLzzf1Qyu8u7htSV0b7teuH1ee5Rn+Ra5hUEAAhCIEwEcgP3fm8N1y7sl24XXzHby/baX6v2fzlCV3vou7OIPn2eXeCh0HVsnMMoBaNGDwRTgh0PnkIQABCAAAQhAAAIQyDMCNqXVppsGDjjP0aZwuEQUXeexDjrfYRfUNcebl1b+Mc69JEefXR/rfwIVclpVlJW4SsmOnvy8IF1ZXuqsXk+dVOfYuX6dyjLV12tzeiWcaglHXLKTLLWD7qhTLlGeXw4zi/rq3JKYUn1h3ciijfrq/5HKHSEAgXwm0JsTKZ/bXohtG6xG/1l6kd/4L+r4H346k8OmUKXLlP5T6HVy0hx7gYXPs7wl0gHJnJFvl74iHZGS7aZQRm/Tk0NVSUIAAhCAAAQgAAEIZEPAHHCH2jpdU6upo+d40Ht9bF6iztE8q9Osc81xh+WGgEWz9TjSjnG4lfbke+W+k+2oE84cbAnHmuUFdSrLE443r57vhEuUJ+r23CuV0055RKPlpl+5CgQgAIFiJoADsP9633bcNSfaJf4tv6njv/rpTA/3q+JhyRyJ75V+Lj0vJdv1yniVn7ldx2eSKrTr9X9Ln5ZsWvFHpK9KYbtIL97lZyzVcXm4kDQEIAABCEAAAhCAgH5BVdRdq6a1NrUFTrujjrnAkZdw4gXlwTFcr9PbgAKeqQnYTMxqRbd5qog+lqvenh0vuArNAj599mlumKZ9Vqt+lc61aDdzwlWGHXa+Iy6InAs77Gz9NgwCEIAABCAQJwI4APuvN2333mv92z2g44+kM/zXqQ7mpFubVGCbdXxZ+rw0VHpEss1AbD3BRmmcdKP0bslfAMWbYpxqroY5/N4gzZQsAvA06VdSi3SF9CnJxoe9/qCEQQACEIAABCAAgVgRMOedTXvtibpLEYEXRNsdDEXlBXnm4LOovb6tWVf4SC16bbDvmKvSsSctx1uQNkdcdXmZHHIlviNPab/cHHRWHtS110Ha8u36mUTAJRb73+IBvfbSOqZ9Fv7Q4gkgAAEIQCCHBHAA5hBmL5d6daj8SqWfC71OlbRPL3UpCr6gvFHSB6Qa6ZO+dDjGOvTKnHg/Pyb36IsmJV8m/Z80Q/p7Xzr02EGl3iIlRxD2VCABAQhAAAIQgAAEBoJAtzajMOdbY5t+rexy7qmt+127azrqzDtmKm0i2i55Oq2db5taxNlsbbcgcm5wRZkXDVetSLietDnefAdc2PHmOfL8/OD8VA67KkXWES0X5xHEs0EAAhCAQFwI4AAsvJ60T6kfksyxd7N0qXSqZNOCD0nrpaXS96XkCEJlHWNW91zpfdLrpNOkCsn2STfHoE1TTvyMqgQGAQhAAAIQgAAETgYBc8I1Hm53+5rbXcOhNu+491C726vXe4PXSh9sSUyftWg8c94pgE/mf5x99qmT0bQBuaY57YZWlUtlCVUG6cRxmJd/bN6QyjI3pNKfHhty6NkGDxgEIAABCEAAAhDwPzHFGoQ5uN4h2Zp246VqydbhWyMFdoESFgVnUXF3Bpk5Pg7K8fWe1PVMfbVmXcCmAJswCEAAAhCAAAQg0GcCFp13QM66vc1tcuD5jrywM8/LO1pmzr+EM6/Ptx7wC9h0VXPemZOuJsKBZ469YT0OvsCRZ86+RDrTKa8D/rA0AAIQgAAEIACBgiEQZwegRbJ9W3qn3xuBA85+Ky7184KDraf3c0kTSLwoOKLeAjIcIQABCEAAAhAoegK2Vt7Bls6EQ89z5JlTT5F6KZx7Dcozh14hTq21Ka49UXe+M84cdTWKrkvkH+usOzYSL+HAs40kMAhAAAIQgAAEIJBvBOLsAPyJYL9eMsffaukh6WYpldmmHFZnlmRTYb8mYRCAAAQgAAEIQCCWBMyhZ1Noe6LzQtNsE3mJabjmzNtnjj45/fJ9owvbNOJoVN3RaLogGs8ceAlHXuDESxyDcyxaj+mysRzuPBQEIAABCEAAAiIQVwfg9Xo22+HWov0+JgUOvSgHoKq430n/Ii2QgvpKYhCAAAQgAAEIQCC/CQS72aZbQ69BTjxz5nkOPjn22ru68/KhhleXu9FDKtzomgo3yjtWeq8tPXJwxTHReBWDOt3yRx50VZrbcd1LrmTX17zsURoFAQhAAAIQgEA+EIirA/BdPlxz6mXqzFvunzM3HzqGNkAAAhCAAAQgUNwEWju6IjfECCLzEptkJKbjtnbkp0NvqKbPhp15tYFjb4gce0qP1tGce5Y/UsdsovBaWlrcyrh+mi3u4c/TQwACEIAABCCQYwJx/cg0T5ws+u9nWfDa4dcdm8U5VIUABCAAAQhAAAJZEbBovf2HO9z2/S1uW2OLd9zuHQ+73QcVoedH6R1ut6WJ88+GaKrtKN9xF0Tqja5JROklHH3hdIWrLEteejn/nokWQQACEIAABCAAgbgTiKsDsNbvuK1ZdGDws3lcmWSBgqoQgAAEIAABCJwoAdsBd09Tmxx7h5McfHL2+Q6/fHLuVZWXeFF4PZF5yc48z9lnU3ITjr0qbZSBQQACEIAABCAAAQgUFoG4OrsOqRtGSjVZdEedX3dvFudQFQIQgAAEIACBIiPQ3tntdh5IOPO2KYovcOoFRysbyA0zbBfaWk2lTY7SG9Uz5faoM88i9gZXxPXjYJENTB4XAhCAAAQgAAEIpCEQ1098G/XM50nnSw+nef5w0Sv9FyvDmaQhAAEIQAACECguAs3aHdem55pD71gH32Ev36L7NIu336ysZFDPWnmJNfNsc4xg/byEMy9YQ8+OttPtoEGD+q193AgCEIAABCAAAQhAIP8JxNUB+BehN+ffe6RvS51SOrtMhW+S7OP8/6WrSBkEIAABCEAAAoVLIN36e8F6fLY+X3+Y/Hpu/LAqN2lktZs0QtJxwvBqbYZR6W2IYc48m3Y7rAqHXn/0B/eAAAQgAAEIQAACcSYQVwfgLeq0D0ozpV9I75CapWQrUYaVfV2yBW1s+u8PJQwCEIAABCAAgQIkkE/r71WUlriJIxIOvskjBh/j6DOH3/jhVVnteFuA3UGTIQABCEAAAhCAAATyhEBcHYD14nuz9EvpNdJV0t1SYOYcHCpdIY2R9Bu8s01A3i6lchQqG4MABCAAAQhAYKAJ2Pp7W/cddmsPDHL72pxbu2Sj231IU3b9zTX6c/09m2obRO4lHyfLwWeRfCUW5odBAAIQgAAEIAABCEBggAnE1QFoWH8jdUm3SrYhyBukYMWedyltFnwqt01DzPnH9F+jgkEAAhCAAAQGiEDm6+/5O9Fu2HzSWjpaU3B7puf6U3QDR59F9A2rZmruSYPPhSEAAQhAAAIQgAAEckogzg5AA/U7abH0/6RXSOdI4Wdeqdd3SN+U9kgYBCAAAQhAAAInkcD+w+3uhX3aYGP/YResuRdE79nGGwO5/t6k8DRdOfyqK3wn40nkwaUhAAEIQAACEIAABCDQHwTCzrD+uN9A3GOfbvoFX7bm3yjJPtFbfv+s8q0bYRCAAAQgAIFiInC4vdOt233IrdnV5FZLa3cnjg2HNG+3H6yirCQxPdci95Ki9+w16+/1QydwCwhAAAIQgAAEIACBvCEQVwfgWT5hc/JtC9G2df4aQq9JQgACEIAABCDQBwKdXd1u895mz8lnzj5PcvbZOn1HgoU3+nD9qFOrSo+4kZXOzZpc604ZXXOck692COvvRbEjHwIQgAAEIAABCECg+AjE1QH4jLrSvna8R2JX3+Ib1zwxBCAAAQjkmMARefN2Hmg9LqJvw55Drl1OwFxbuvX3aqsHuceW2Qofzl177dmuuro617fnehCAAAQgAAEIQAACEIgVgbg6AG0n38HS07HqLR4GAhCAAAQg0A8EbJ2+IJIvHNXX1NqZk7vbxrjjh1Ud3WBjpE3THXz0dS/r77W0tOSkHVwEAhCAAAQgAAEIQAACxUIgrg5Am/Y7U9LkIAwCEIAABCAAgVQEWju63HpF8NkafWt2HXRrvDX7DrrdB3OzTt8gOfrqRg9xs8YNdTPHD3WzpVnSKaMGu/JSW5YXgwAEIAABCEAAAhCAAAT6g0BcHYB3CZ45AC+XHu4PkNwDAhCAAAQgkK8EurqPuC1ap8+i+YINOSxta/epKCc2dmil59wzJ99MOfxmjx/mThtbw066OaHLRSAAAQhAAAIQgAAEINA3AnF1AH5DWN4p/bP0a2mDhEEAAhCAAARiTcDW6bPovTXahMMi+gJnn+3G29aZm3X6hlaWedF8FslnkX3BceSQiliz5eEgAAEIQAACEIAABCBQyATi6gDcqk55tWTOv0elhdIvpUYJgwAEIAABCBQ8gQMtHW6dHH2J6bty+HlOvyZn+bmw8tJBbvqYGn/a7jA5+mqkYW7i8Co3yOb2YhCAAAQgAAEIQAACEIBAwRCIqwPwKb8HWnWcJN3ia7uO5gTskqLMJkOdF1VIPgQgAAEIQKA/CbR1drkNezR9d7cf0eet19fkdmhH3lyZrclnkXxHp+8OdXW1Q1inL1eAuQ4EIAABCEAAAhCAAAQGmEBcHYDniKs58oIQheA4WXmmdJaj1ZDS3YIyCEAAAhCAwLEEurUY39Z9h3si+YKIvk0Nzc7W8MuF1dbYOn2K5Bt3NKJvhtbpG6JpvRgEIAABCEAAAhCAAAQgEF8Ccf3Ef4e6LDffluLb9zwZBCAAAQgMAAFbp6/+UJtbu8t239XOu4roW6vpu2u1Tl+LduXNhQ2uKPU34ji6Rp9F+I2WAxCDAAQgAAEIQAACEIAABIqPQFwdgK8svq7kiSEAAQhAIN8IHGrr9Jx75uTrkZx9+5rbc9LUspJBbtqYId7afOHpu5NGVLsSlWEQgAAEIAABCEAAAhCAAASMQFwdgPQuBCAAAQhAoF8JtCp675kX9rvHNu51f91+wNucY1tjS87aMHlk9dFdd20HXmlabY2rKCvJ2T24EAQgAAEIQAACEIAABCAQTwI4AOPZrzwVBCAAAQicZAK2OcczW83ht889urHBPaV0e2d3n+86cnC5vyGHrdM31JvKO3NcjRtaVd7na3MBCEAAAhCAAAQgAAEIQKA4CeAALM5+56khAAEIQCBLAubce3abHH4b9srht9c9uaXRtfXB4Vddbuv01XgOPnP0BRqjdfoGDWL6bpbdQ3UIQAACEIAABCAAAQhAIA2BuDoAX5HmmTMpsk1EMAhAAAIQKGIC5vB7fvt+96gcfhblt2LLPtfakX2EX6nW4ptaq3X6xh118ln6lFGDWaeviMcXjw4BCEAAAhCAAAQgAIH+JBBXB+AfBPFEdwG28+LKpT/HFveCAAQgUFAEOrrM4XfAd/jtdSs2N2a9K68F7p0xcbi7cOood8akYV503/QxNa5K0X4YBCAAAQhAAAIQgAAEIACBgSIQZ0cX86cGalRxXwhAAAIFQKDTd/hZdJ9t3LFi8z7X3N6VVcvN4Td3wjA3b9pod5F0gRx/w6tZqy8riFSGAAQgAAEIQAACEIAABE46gbg6AM/NgNwQ1ZklvUm6WnpE+pDULmEQgAAEIBAzAubwW7njoOfsszX8lm/K3uFnSObI4WfOvnnTRnmRfiMGV8SMFI8DAQhAAAIQgAAEIAABCMSNQFwdgM9m2FHm9Pux9B7pO9Knpb6uH6hLYBCAAAQgMNAEurqPuFVy+NkOvRblZw6/prbOrJs1Wxt0WISf6cWK8Bs5BIdf1hA5AQIQgAAEIAABCEAAAhAYUAJxdQBmC/V7OuEy6Y3SOyRzCmIQgAAEIFBABMzh97ediQg/m9L7uDn8WrN3+NnOvIkIPzn85PQbhcOvgEYBTYUABCAAAQhAAAIQgAAEUhHAAXiUyi+UtOnA75RwAB7lQgoCEIBAXhLoNoffLnP4Jdbwe1xOv4Mn4PCbMbbmaISfpvXW1lTm5fPSKAhAAAIQgAAEIAABCEAAAidKAAfgUXLb/eTco1mkIAABCEAgXwjI3+d2HnbuZ4+/4FZsPehF+B1o6ci6edPHDPEcfhdNtym9o92YoTj8sobICRCAAAQgAAEIQAACEIBAQRHAAXi0uyb6yaqjWaQgAAEIQGCgCBw5csSt3X3I27TjoXV73CPrSl1zp7bdfW5dVk2aVjvEm8prDr95WsNv7DDe5rMCSGUIQAACEIAABCAAAQhAoOAJ4ABMdGGJDrYDsNmmxIF/IQABCECgPwmYw2/9noTDz3bpfVxTe/c2hzdml/MvA6sbPdh5zj5/445xOPwyoEYVCEAAAhCAAAQgAAEIQCDOBOLqAByWQaeZ02+kdL70AeliSRPM3G0SBgEIQAACJ5mAOfw21Ddrl969XpSfreHXcCjs8MusAaeMksPPnH3TR3lTeycMr87sRGpBAAIQgAAEIAABCEAAAhAoEgJxdQA2nmD/PafzvnaC53IaBCAAAQikIWAOv00NgcMvsXFHfVNbmjNSF00eUaUIv9rExh2a1jtpBA6/1KTIhQAEIAABCEAAAhCAAAQgkCAQVwdgZvPvzaIxAABAAElEQVTEjo4CLSvvfip9Umo+mk0KAhCAAAROlIA5/DbvPexF9z26IRHlt+cEHH7m4Lvg1OFuyKHtbvqwI+5Nr7jYVVfj9DvRfuE8CEAAAhCAAAQgAAEIQKD4CMTVARis55euR7tV2CTZmn/LJXMCYhCAAAQgcIIEzOG3dV/Y4bfP7TrYmvXVJgxXhJ83pXe0d5yiKb4tLS3unnu2ZX0tToAABCAAAQhAAAIQgAAEIAAB5+LqAPwmnQsBCEAAAieXgDn8tjW2uCC67zGt4bfjQPYOv/HapCOxaccoOfxq3ZRR1W7QoGwDuU/us3J1CEAAAhCAAAQgAAEIQAAChUwgrg7AQu4T2g4BCEAgbwm0d3a7JWv2uLtX7vam9m7f35J1W8cOrezZpdci/U7Vrr04/LLGyAkQgAAEIAABCEAAAhCAAAQyJhBXB+DXfQJf1nFPhjRGq96/SLYT8D9neA7VIAABCMSegEX6PbvtgPv9U9vcHc/ucI2HO7J65tqawOFnEX6j3dTaITj8siJIZQhAAAIQgAAEIAABCEAAAn0jEFcH4AeFxRx5t0qZOgBHqG5wHg5AwcAgAIHiJmDRfX94erv7nRx/G+sz3x+ptqbCvViOPm8dPx2nj8HhV9wjiaeHAAQgAAEIQAACEIAABAaaQFwdgAPNlftDAAIQKEgCTa0d7q6/7nK3y+n32MZ9GT3DqCEVbt60RHTfPDn8ThtbQ4RfRuSoBAEIQAACEIAABCAAAQhAoH8I4AA8yrnST7YfzSIFAQhAIP4EOru63cMb9npOv7tX7nKtHbZJeno7/9SR7mVnTXAXT691M8fh8EtPi1IIQAACEIAABCAAAQhAAAIDSwAH4FH+F/jJTKcMHz2TFAQgAIECJPC3nQc9p98fntnh6pvaen0C25331edOdq86d5Kr0zp+GAQgAAEIQAACEIAABCAAAQgUBoG4OAD/KQL3W5W/K6IsyLbIv+nSmyRbN/AxCYMABCAQSwJ7Dra6P8rhd7vW9jMHYG82tKrM3XDWRPeaF01y5ynqj916eyNGOQQgAAEIQAACEIAABCAAgfwjEBcH4DeE1px3YRukFx8PZ/SStvqdUrCDcC/VKYYABCBQGARa2rvcPatsXb/t7sF19a47+d0y6THKSga5BbPGKNJvsrtqzlhXVV6aVIOXEIAABCAAAQhAAAIQgAAEIFBIBOLiADTm5sALLPh6G84LylIdG5X5iPQf0uOpKpAHAQhAoJAIdMvL9/imfd4UX9vU41Cb/b6R3s6cNNy9WpF+Lz97oqutCZZFTX8OpRCAAAQgAAEIQAACEIAABCCQ/wTi4gAcGUJtTj/butKcgBdJq6UoszqtEht/RBEiHwIQKCgCG+oPud8r0u/3muK7fX9Lr22fMLzKvVJr+r1amjFuaK/1qQABCEAAAhCAAAQgAAEIQAAChUcgLg7AA0nobWErc+7tl5LLkqryEgIQgEBhE2hsbnd3PrfD/U6Ov2dfsLe99Da4otRdf8YEL9pv3rTRrlRTfjEIQAACEIAABCAAAQhAAAIQiC+BuDgAk3toRHIGryEAAQjEiUBbZ5dbvHqPt67f4jV7XEdXsPJB6qccJB/fpafVek6/l5w+3g2uiOvbf+rnJxcCEIAABCAAAQhAAAIQgEAxE+AbYDH3Ps8OAQgUFIEjR464pxXhd/tT29ydz+50B1o6em3/zHE12sF3srvxnEluvKb7YhCAAAQgAAEIQAACEIAABCBQfARwABZfn/PEEIBAgRF4Yd9hb00/W9dvU0Nzr62vranwHH6v0rp+p08c5gZZ+B8GAQhAAAIQgAAEIAABCEAAAkVLoBgcgOeqd18pnS3VStVSum/DNo/uPAmDAAQgMGAEDrZ2uLue3+mt6/eEdvPtzSrLStw1c8d50X6Xzah1ZaUlvZ1COQQgAAEIQAACEIAABCAAAQgUCYE4OwBtHcAfS69I6suw8y9YNCtVXtJpvIQABCBwcgl0dnW7B9c1yOm3zd27ardr6+zu9YYXTh0lp98kd/2ZE9ywqvJe61MBAhCAAAQgAAEIQAACEIAABIqPQFwdgKXqyjuliyVz7m2StkmXSeb0+5M0UrKowKGS5T0vWT0MAhCAQL8RsHX9Vu446E3x/eMzO1zDobZe7103erA285jsbIrvlFGDe61PBQhAAAIQgAAEIAABCEAAAhAobgJxdQC+Wd16iWSOvQ9J35ROl8zJZ3Zj4uDs+d8i/adUJ31culvCIAABCJxUArsPtro/aE2/25/a7tbsbur1XsOry93Lz57gOf7OnTKCdf16JUYFCEAAAhCAAAQgAAEIQAACEAgIxNUB+Ab/AZfoaM6/KOtUwU+kJ6VHpV9J50hbJAwCEIBATgkcbu90d6/c5Tn9Hl7f4LrtJ4o0Vl46yF0xa6ycfpPcFbPHusoyC27GIAABCEAAAhCAAAQgAAEIQAAC2RGIqwPwXGGwr9Y/zhDHX1XvFukT0gekD0sYBCAAgT4T6JaX77GNe73NPP7y152uub2r12uerQg/W9fvhrMmulFDKnqtTwUIQAACEIAABCAAAQhAAAIQgEA6AnF1AI72H3pT6OE7QmlbNOtw6LUlbeqvOQCvl3AACgIGAQicOIH1e5o8p98fNc13x4HWXi80aUS1t6bfq+T4mz6mptf6VIAABCAAAQhAAAL/n737gJOivP84/hy9qzSVLk3ERlQUC4i9x6jRWGLXaGKMGmuKkfiPMZZYorEkas4SjT2xK4qKXbArTUWQIggCghQPjvt/f8cMzO3NtmOP23n287xe35vZZ57dnXk/w3L73BQEEEAAAQQQyFXA1wFAO7XXbocZHeRbGEHZWPOfRx7b7OLgcdeUeh4igAACOQl8oxt4PP7BTPeIBv0+nP5t1ue0ad7E7b/lRhr46+Z20N18GzWyexZREEAAAQQQQAABBBBAAAEEECisgK8DgNPE1F/pHOGapXkb5LOj/wYrqQOAA1RnpdGqCT8RQACB7ALLlle6URO+1nX9pruXJs5xK7Jc2M/G+Ib261R9Xb+9B27kWjbjun7ZlWmBAAIIIIAAAggggAACCCCwNgK+DgC+LxQbANxaid7V9zU93ls5U3lQCS/G1VLz5ylWJq6a8BMBBBCIF6iqqnLvTJ1ffaTfEzrib+EyO+g4c9ls43bV1/X74dZdXOd2LTI3ZikCCCCAAAIIIIAAAggggAACBRTwdQBwlIzsTsB2Pb8rI163ad4GAIcoY5WHFDsi8DDFBgztxiH3KhQEEECglsCX3yzRoN9096hO8Z2q+WylU9vm7keDulSf4juwS7tszVmOAAIIIIAAAggggAACCCCAQL0I+DoA+Ii0rlLsbsB9lPB0Xxvws/xY2SqIJi688Nbrmr/eKigIIICACXy7dLl78sOvNOg33Y2ZMj8rSoumjdw+m2+kU3y7uZ37dHBNGnNVgaxoNEAAAQQQQAABBBBAAAEEEKhXAV8HAL+R2vpp5I5S/VvKKYod9WeDf3bNwLuUy5Ts5/KpEQUBBPwVsFN87Xp+D70z3Y0cP9tVrFiZdWN37N2h+rp++26xkWvbwu5BREEAAQQQQAABBBBAAAEEEECgOAR8HQDMpGvX/ftrELv6vqVCoSCAAALu9c/nuiuemeg+mLYgq0bvTq11Xb9u7mCd5tttA7uaAAUBBBBAAAEEEEAAAQQQQACB4hPwdQDQTu+1Mk+ZXj0X/8MGA8MbgcS3oBYBBEpCYNzMhRr4m+BenjQn4/Zu0Kqpsxt52Cm+W3Vbz5WVhVcQyPg0FiKAAAIIIIAAAggggAACCCDQYAK+DgDaXYDthh6nK/9sMF3eGAEEil5g2rwl7pqRk9x/35/hdOZvbGmm6/jtsVnn6kG/Xft3cs2acF2/WCgqEUAAAQQQQAABBBBAAAEEilLA1wHAxdK28/HeK0p1VgoBBBpcYN7iCnfjqM/cPW9OdRWV8df4692xtTtxl03cQVtt7NZv1azB15kVQAABBBBAAAEEEEAAAQQQQKAuAr4OANppv3aDj+Z1QeE5CCDgr8CSihXujle/cLe+PNkt+j7+nj+d2jZ3Z+/Zzx2xXXfXlLv4+rszsGUIIIAAAggggAACCCCAQIkI+DoA+LT6zwYAd1VeK5G+ZDMRQCCDwHId5ffA2Gnuuuc/dXMWfR/bsk3zJu70XXu7k3TUX6tmvn48xm46lQgggAACCCCAAAIIIIAAAh4L+PoN9zr12UnKucr9yucKBQEESlCgShf2e+bjWe6qZye6yXPt6gC1S9PGZe7YIb3cL3fv69q35lTf2kLUIIAAAggggAACCCCAAAIIJFnA1wHAL9Uphyo2+PeGMkK5T5mvUBBAoEQE3pz8jbv86Qnug2kLYrfYbuD7o0Fd3a/36u+6t7fLhlIQQAABBBBAAAEEEEAAAQQQ8E/A1wHAd4OuWqZpV+WGIDM0tUHASiVdsfuAbptuIfUIIFD8AuO/WuiufGaCe3HinLQra3fzvWDfTd3mXdZL24YFCCCAAAIIIIAAAggggAACCPgg4OsA4CB1jg3k6fie6hJOu+mRJVOx51EQQCCBAtPmLXHXjpzkHn1/htOZv7Flq27ruYv2HeB26tsxdjmVCCCAAAIIIIAAAggggAACCPgm4OsA4GPqqDRf/33rQrYHAQTmLa5wf3/xM3f3G1NdhW72EVc26djanbf3pm7/LTdyZXbuLwUBBBBAAAEEEEAAAQQQQACBEhHwdQDwRyXSf2wmAiUtsKRihfvXa1PcLS997hZ9vyLWomOb5u7sPfu5nwzu7po2bhTbhkoEEEAAAQQQQAABBBBAAAEEfBbwdQDQ5z5j2xAoeYEVOsrvgbHT3XXPT3JfL/o+1qNN8ybutGG93Um7bOJaa56CAAIIIIAAAggggAACCCCAQKkK8K24VHue7UYggQJVurDfs5/Mclc+O9FNnrM4dguaNi5zPx3S0/1yt76ug47+oyCAAAIIIIAAAggggAACCCBQ6gKlMgBoF/waqGyktFJGKfGjB1pAQQCB4hMYM3W+u+aFL9z70xakXbkfDeriztV1/rq3t3/mFAQQQAABBBBAAAEEEEAAAQQQMAHfBwDtNp8XK8cp7ZSwbKmZceEDTW35T5VvlcMVCgIIFInATA3VP/5lIzfujffSrtGw/p3cBfts6rboul7aNixAAAEEEEAAAQQQQAABBBBAoFQFfB4AtEG+p5WNlegtP+PuDvy82tymNFZ2V+wIQQoCCDSgwPT5S9zVz4x3//uwsW7pHf0nvGalttSA30X7DXA797WxfgoCCCCAAAIIIIAAAggggAACCMQJ+DoA2Fob+4TSRZmnXK2MVl5V4spMVT6n7KccqDAAKAQKAg0hMH9xhfv7i5+5u96Y6ip0s4+a4/er1qhnh1bufB3xt/8WG7tGjeIHBxti3XlPBBBAAAEEEEAAAQQQQAABBIpRwNcBwDOE3V2xi4XtpExSspVn1WB/ZYdsDVmOAAKFF1haUenueO0Ld8tLn7tF36+IfYOObZq5s/bo547cvodr2rhRbBsqEUAAAQQQQAABBBBAAAEEEECgpoCvA4AHazPtVN/rlFwG/0zlY/uh0nfVhJ8IILAuBFboKL8H35nurnt+kpu98PvYt2zeqMqdOqy3+/lu/V3r5r5+bMVuOpUIIIAAAggggAACCCCAAAIIrLWAr9+kBwQy+ZzKOz94zvprrcoLIIBAVoGqqir37Cez3ZXPTnCT58TflLupTu/dsXOl27vbSnfYrpu4lgz+ZXWlAQIIIIAAAggggAACCCCAAAKpAr4OANo1AK0sWjXJ6WeroFX8IUg5vQSNEEAgF4G3v5jnLn96vHvvSztLP74cPKiLO2NYTzd+zCvxDahFAAEEEEAAAQQQQAABBBBAAIGcBHwdAJyrrbe7/3ZRPshJwrlBQbtZObanGQII5CkwcdYid+UzE9wLE75O+8yh/Tq6C/cd4LbQHX6XLl3qxqdtyQIEEEAAAQQQQAABBBBAAAEEEMhFwNcBwHe08XY3372Up3OAsNuInqLYdQPfyKE9TRBAIA+BGQuWumtHTnIPvzvd6czf2LKlBvxs4G8XDQBSEEAAAQQQQAABBBBAAAEEEECgcAK+DgA+KKKDlJ8ptyoTlUzlr1poRwDa0MR9mRqyDAEEchdYsKTC3aS7+pa/PsVVrFgZ+8SeHVq58/be1B2w5cauka75R0EAAQQQQAABBBBAAAEEEEAAgcIK+DoA+G8xnaP8QHlJOU95RAmLDfS1VPZUbNkuitWNVp5RKAggsBYCSysq3b9e/8LdrMG/RctWxL5SxzbN3K/26OeOHNzDNWvSKLYNlQgggAACCCCAAAIIIIAAAgggsPYCvg4A2mDewYrdPaCncpdSrli9lZeVDZRw1MEOO/pc+YlCQQCBOgqsqFzpHnpnurv2+Ulu9sL4++m0btbYnTqstztlaG/Xhrv61lGapyGAAAIIIIAAAggggAACCCCQu4CvA4AmMF2xIwBvUI5UGithiV5kzAYFH1B+rswPGzBFAIHcBap0Yb/nxs2uvsHH53MWxz6xiU7vPWaHHu6Xu/dzndo2j21DJQIIIIAAAggggAACCCCAAAIIFF7A5wFA01qgHKv8TrGbgmyndFZsMPAb5T3lcWWSQkEAgToIjJkyz13+1Hj37pf2zy2+HLR1F13nr7/r2aF1fANqEUAAAQQQQAABBBBAAAEEEECg3gR8HwAM4b7UzE3hA6YIILD2ApNmL6o+4u/58V+nfbGhuqPvBfsMcFt2Wy9tGxYggAACCCCAAAIIIIAAAggggED9CpTKAGD9KvLqCJSQwMwFS921Iye5h9+d7laGV9VM2f4turZzF+47wA3t1yllCQ8RQAABBBBAAAEEEEAAAQQQQGBdC5TaAGAHAbcPkOdpaqcBUxBAIAeBBUsqqu/q+6/Xp7iKFStjn9GjfSt33j6bugO33Ng10jX/KAgggAACCCCAAAIIIIAAAggg0PACpTAAuJOY7QYfeygbppDbuYsjlVuU11OW8RABBCSwbHmlK9eg300vfuYWLlsRa9KhdTP3qz36uaO27+GaNQlvrh3blEoEEEAAAQQQQAABBBBAAAEEEFjHAj4PADaT5T8UuwmIlbjDkWxA8Jggd2l6mlKhUBAoeYEVlSurT/O9duSnbtbCZbEerZo1dqcO7e1OHdbbtWnu88dJ7OZTiQACCCCAAAIIIIAAAggggEAiBHz+xv4/9cDeig382ZXKXlPeVmYrVmd3Ax6s7Bw8Pi6oO0BTCgIlK1BVVeVGjpvtrnx2ovvs6+9iHZro9N6jd+jhzty9n+vUtnlsGyoRQAABBBBAAAEEEEAAAQQQQKA4BHwdADxJvPsoNvBnp/aerExU4kp/Vd6m7KLsq5yo/EuhIFByAmOnzHOXPz3BvTN1ftptP3Crjd15e2/qenVsnbYNCxBAAAEEEEAAAQQQQAABBBBAoHgEfB0AtEE8Kx8puyuZTuudpOV7KnZ04FaKDR4yACgESukIfDp7kbvimYnu+fF2gGx82alPB3fRfgPcVt3Wj29ALQIIIIAAAggggAACCCCAAAIIFKWArwOAW0jbjv67Ssk0+Bd2irWxtncr9lwKAiUh8NW3S921Iye5h96Z7lbav5iYMnDjdtUDf0P7dXRlZXb2PAUBBBBAAAEEEEAAAQQQQAABBJIk4OsAYOOgE8bl0Rlh2/C5eTyVpggkS6BixUp346hP3a2jJ7vvNR9XurdvWX2q70FbdXGNdM0/CgIIIIAAAggggAACCCCAAAIIJFPA1wHAyeqOLZUOeXRL+6DtF3k8h6YIJE7Abuxx9v3vuY9nLIxd9/atm7lf7d5XN/no6Zo1aRTbhkoEEEAAAQQQQAABBBBAAAEEEEiOgK8DgA+qC+x6fkcqz+fYHUepnZ0Eac+tj7KNXtRuMjJUsdOM7S7Ey5WZit2o5HblFSWfsr0an6DspnRVbLTGLuI2QXlBuVuZo6Qr1v8nK8comyltlBmKmf1NCY+K1Cwl6QJ2d9973pzqLntqvFu2vPZRf62aNXanDO3tTh26iWvbomnSN5f1RwABBBBAAAEEEEAAAQQQQACBQMDXAcBrtX1HKycodnOPfyiZyqlaaDcOGa9ck6lhHZe9rOcNi3luM9X1C3K8pjZgd4qS7bqFzdXmRsUG71LPzeytOsv+ih0J+V8lrtjRkU8qO6Qs7KPHlhOUXyh3KJSEC3y9aJm74KEP3UsTa48HN9HpvUdt38OduUdf17lti4RvKauPAAIIIIAAAggggAACCCCAAAKpAr4OANp2Habcqdys2GBguTJG+VqxI/02VAYrNvBmg3NjleMUe247Ja7EnzMZ17JmnR2dZ8WO9rMjDF9RvlQaKzsq5yrW5ljF3t/WN12xQcNHlf2CBvZadyk2eLlC6alsrRyupCv2vo8o4eCfzf9TmRfU/V5TO0LRBk5nKM8qlIQKPPfJLHfRIx+5eYtrjyv37dzGXfeTQW6LrusldOtYbQQQQAABBBBAAAEEEEAAAQQQyCbg6wDg/MiG2xFydtqtJV2xNtspmU55tUHDunrZKbm/VR5WKpVoeVMP7lZeU/ordiqyDVrawF5cscG5cPDvPM3/NaXRW3r8gPI7pWnKsvChDTTaoKeVm5QzqudW/Xhbk6eVdxQbCL1BGajY4CIlQQJLKla4/3tinLvv7Wmxa338jj3db/bfzLVoauPBFAQQQAABBBBAAAEEEEAAAQQQ8FWgrgNaxe6Relps6uO49c+lTdzzcqk7MEujuVpuRwE+HrT7saZxA4B2au9FQZtyTVMH/4JFqyfLV8/VnDk/eGgDpeF8tMVnenB5kH6aHqzY4CUlIQLvT1vgzrn/fffF3MW11rhT2+buyh9v5Xbb1A7ypCCAAAIIIIAAAggggAACCCCAgO8Cvg4AnpPAjnspss59IvPR2Z/pgR3VZ0cjXhpdkMe8DejZEX1W7leWVM/V/lGuKhsEtHKowgBgNUVx/1hRudLd9NLn7voXPnWVK203qVn2Hrihu/zQLV2HNnYZSQoCCCCAAAIIIIAAAggggAACCJSCgK8DgNcnsPPs2n5hWRnOpEzD6/rZ9Qq/CJY10rSLYgODs5SlSqYSPRX65QwN7bUmKXZa8i4Z2rGoSASmfrO4+qi/d79cUGuN7A6/lxw00B2xXXdXVlafB7vWemsqEEAAAQQQQAABBBBAAAEEEECggQVs8IhSHAK7RlbDrhmYWjqpwk4BtvKGYtfnu06x04ftIm+TFbtJiQ3qHaCkK5tFFsS9T2SxC5d3V2Xr6ALmi0egqqrKPTB2mtv/+ldc3ODfoO7ru6d+NdT9ZHAPBv+Kp9tYEwQQQAABBBBAAAEEEEAAAQTWmYCvRwCuM8ACvZENxIbX9rOXtJt4pJbwtF2rt6P83lVSTxW2/rSbe1iuVX6tpBYbzAvL9HAmzdQGFq3YIWPdlIn2IMdi7TOVjcKFS5cudZawLFu2LJx10fnVlcysFpi/ZLm75IkJbuT4OavrwpnGOtLv58N6udOG9XRNGjWqYRy2KfZptP+j88W+3qxf4QWi/R+dL/w78YpJEIjuA9H5JKw761hYgWj/R+cL+y68WlIEovtAdD4p6896Fk4g2v/R+cK9A6+UFIFo/0fnk7L+hVrP6PftQr0mr5M8Ac4FLI4+sxuAXB2syqOa2jX3UsshqngkqPxeU7uI2+vKb5QxSgvF7g5sr7OxYuXnyi3Vc2t+PKnZ/YOHLTVdM9q2pk04d4VmLgge2F2S7c7AuZbaF6BL88zbbrvNdezYMc1SqtMJTFhQ5v79WSO3cHntf8Ydm1e5Y/tVul5t0z2begQQQAABBBBAAAEEEEAAgVIQmDt3rjvllFPCTbWDgrIdDBS2ZeqRgB0x5nuxU2vtund2+qwNhzRWMhUbuDosU4MCL7P1+0vwml9raoN2cSV6Cq4N/tlg3B5KOIBnh9Ddq9hg4HuKtb9UuVNZc3jdqoFCVVWXinAmzdQGGsNig4WUIhCoqHTuiS8buZdn2YGjtcuOnVe6Q3qtdM2z7em1n0oNAggggAACCCCAAAIIIIAAAgh4KODzAKAdsVaubJZHv9mhVDkfuZbH66ZrurkW2BF/1g822HaEMluJK+FAX7jsd5pJrbNlnyo3K+cpdt3APZXHlbBEn2M3Hok+DtuE0+itYqODiOHyTNPoqcZx7TZSpQ1WumHDhrlu3dacMWyHZo8ePbr6ObasRQs7uJFiAhNmLXLnPzLOfTZncS2Q9Vs2df/3wwFuzwHW7X4U9gU/+rEQW8G+UAhFf16D/cGfvlzbLWFfWFtBv57P/uBXf67N1rAvrI2eX89lX1jVn9OnT/erY9maOgn4OgBog36jFDsKLhzUsz3ebo+6UimGsolW4jllA0XHdLmjlJeVdGVRZIEdufdi5HHq7LOqsAFAK4OV6ABg9HXaaFmmAUDzC8t34UyO05w/YVq2bOksccUG/9Iti2vva93KlVXutlcnu6ufneQqKmvvwsP6d3JX/3gr17mdv4Ol7Au+7t35bxf7Qv5mPj+D/cHn3s1v29gX8vPyvTX7g+89nPv2sS/kbuV7y1LeF/hO7fvendv2+ToA+Edtvg1urVAuUW5R5ivFUrpoRZ5XbGpHHJ6k2JGAmUp4Qw5rY0cJ2iBguhJt2zmlUXRgzg67m5uyPPowPIrP1jH6vGgb5utZYOaCpe7cBz5wb0z+ptY7NW/SyP12/83ccTv25A6/tXSoQAABBBBAAAEEEEAAAQQQQAABE/B1AHC4ts0Gra5QLleKqXTUyoxUegcrdaamdwXzmSZ2au9ypamS7epu0eU2CBot4yIPBmj+/cjj1FlbbsUGFGufc1q9iB/1KfD4BzPd7x79yC1cltqNzg3cuJ27/shBrt+GdmlLCgIIIIAAAggggAACCCCAAAIIIBAv4OsAYHjqavTU13iBdVu7nt7OTs8dGLztRZr+PZjPNrHBvzeUYcqGim1jukG5PloWlhnhTDB9NfJ4V83/J/I4OmvX6OsfVLwWXcB8/QssXLbcXfK/T9yj76V2n85p10ntpw3r4369V3/XTEcAUhBAAAEEEEAAAQQQQAABBBBAAIFMAr6OHnwZbLSGSoqmtNKaPKlsE6zRZZraEYr5lIeDxnaE38EZnnhoZNkrkXmbnaSMD+rspiO2XnHlhEhlttOTI02ZXVuBt3Sq737XvRI7+Nd1/ZbuvlOHuIv2G8Dg39pC83wEEEAAAQQQQAABBBBAAAEESkTA1wHAJ4L+26lI+tHutmuDaDsH63O9pr8P5vOZ3KHGXwdP+LOmdiRgahmuimODyo81jTt67+pgeXtNrwzmoxM7gvA3QcXnmjIAGNWpp/mKFSvdFc9McEf+8003Q9f9Sy0/GtTFPXXWUDekd4fURTxGAAEEEEAAAQQQQAABBBBAAAEE0gr4egrwtdri45ULFDvF9SulIct9evO9gxUYpentyhbB47iJ3eDDjtRLLXYn3l8p9no9lTHKX5S3lRbKfso5ih0haBeNO12xayGmljtVcZJiA5JnKHa67z+V+cr2ysVKO8VuN2vXKKx9ATpVUgonMGXuYvfL+951H89YWOtF27Zo4v70oy3cwYO61lpGBQIIIIAAAggggAACCCCAAAIIIJBNwNcBwJna8IMUuwbgm8q5yv+U5UpDlOgpubtrBT7MshJTtbxXmjb3q95uJHKN0l2Ju4agDRT+VIk7+k/VrlL5kfKUMlg5LIgmq4sNQv5SeXp1DTP1IjBqwmx31n/ed4tibvQxpHd799cjBjk79ZeCAAIIIIAAAggggAACCCCAAAII1EXA1wFAs3hLsaPZbGqDZjagZQNrS5RMxY6Y2zZTgyJYZoN+Lyl29N5eih0aZoN6k5VnlOuUbEc9zlWbnZRTlaOVzRS7sYgNnr6g2GnKnyiUehJYubLK3fjiZ+7a5ye5qpTjNJs2LnPn7b2pO3Vob9eoUVk9rQEviwACCCCAAAIIIIAAAggggAACpSDg8wCgnXJ7l2JHy9kISnOlv5KtpAzFZGue0/L6GMGxwblf5PTu6RvZqb03B0nfiiUFF7C7/J77wAdu5LjZtV67b+c27vojB7nNu6xXaxkVCCCAAAIIIIAAAggggAACCCCAQL4Cvg4AbiOIx5Smig2+zVNswGyBYte1oyDQYAKffb3I/ezud9zkOYtrrcNBW3dxVxy2pWvVzNd/mrU2mQoEEEAAAQQQQAABBBBAAAEEEKhnAV9HGS6WWzPFRlh+ptgpwAz8CYHSsALPfDxLR/697xZX2Bnba4qd5fub/TZzpwzdxJWV1ccBo2veizkEEEAAAQQQQAABBBBAAAEEECgtAV8HAHdQN9qpvJcqdsdcCgINKlCp6/1dO3JS9TX/Uldkg1ZN3Y1Hb+N27mtnq1MQQAABBBBAAAEEEEAAAQQQQACBwgr4OgDYLmAaVVguXg2B/AW+XbLcnXX/e+6liXNqPXmLru3cLT/d1nXboFWtZVQggAACCCCAAAIIIIAAAggggAAChRDwdQDwS+FsqtiNPygINJjAhFkL3Wm63t/Ub2rffPrQbbq6Px+ypWvRtHGDrR9vjAACCCCAAAIIIIAAAggggAAC/gs08nQT/xts1+6ebh+blQCBxz+Y6Q75++u1Bv+a6IJ/f/zh5u6vh2/N4F8C+pFVRAABBBBAAAEEEEAAAQQQQCDpAr4eAXi1OuZo5VzlYWW8QkFgnQisqFzprnx2ovvH6Mm13q9jm2bupmO2ddtv0r7WMioQQAABBBBAAAEEEEAAAQQQQACB+hDw9QjAecLaS5mmvKGcr/RQKAjUq8C8xRXu+H+9HTv4N6j7+u6JM4cy+FevPcCLI4AAAggggAACCCCAAAIIIIBAqoCvRwDaAKAV2742yl+CLNe09sXYVBkpdvfgDpHHzCKQk8DHM76tvt7fjAVLa7U/avvuboRO+23ehOv91cKhAgEEEEAAAQQQQAABBBBAAAEE6lXA1wHA9VPUyoLHzTS1ZCo2AEhBIC+Bh9+Z7n776Efu+xUrazyvWeNG7o8Hb+6O2p4DUGvA8AABBBBAAAEEEEAAAQQQQAABBNaZgK8DgNevM0HeqKQFVq6scn9+ary77dUvajls2K65u/mn27ptemxQaxkVCCCAAAIIIIAAAggggAACCCCAwLoS8HUA8Jx1Bcj7lK7AsuWV7uz/vO+e+WRWLYTBvTZwfz9mG9e5bYtay6hAAAEEEEAAAQQQQAABBBBAAAEE1qWArwOA69KQ9ypBgW+++96dctdY996XC2pt/fE79nS/O2Cga9bE13vs1NpkKhBAAAEEEEAAAQQQQAABBBBAoIgFGAAs4s5h1YpT4Iu5i90JutPv1G9q3k+maeMyd9khW7ojtutenCvOWiGAAAIIIIAAAggggAACCCCAQEkKlMoAYEf17g7KRkor5R5lvkJBIC+BsVPmuVN15N/8JXZD6TWlbYsm7lZd72+nvrarURBAAAEEEEAAAQQQQAABBBBAAIHiEfB9ALC/qK9SDlDCOwGb/gtKdADwND0+V/lWGaJUKhQEagg8+eFX7pwH3ncVKXf67bp+S/evEwe7/hu2rdGeBwgggAACCCCAAAIIIIAAAggggEAxCPh8kbLdBDxWOVCx7bQBwOggoB6uLvdrrquyjbLf6lpmEJBAVVWVu/Xlz90Z975ba/Bvi67t3KO/2InBP/YUBBBAAAEEEEAAAQQQQAABBBAoWgFfBwA7SPwhpY0yRTlKyXRhNruTwxOKlf1XTfiJgHMrKle6i//3sbv86Qm1OHbbtJO7/2c7us7tuNNvLRwqEEAAAQQQQAABBBBAAAEEEECgaAR8PQX4bAlvoMxUdlS+VrKVUWpwuDI4W0OWl4bAkooV7sx733MvTKi9+xyzQw/3xx9u7po09nUMvTT6mK1EAAEEEEAAAQQQQAABBBBAoBQEfB0AtKP4qpRrlNqjN/E9Oz6o3iR+MbWlJPD1omXu5PKx7qMZdlnImuWi/Qa404b1dmVl6c4or9meRwgggAACCCCAAAIIIIAAAggggEBDCvg6ANg7QH09D9xwpKddHs+hqYcCn85e5E741xg3Y8HSGlvXrEkjd80RW7sDt+pSo54HCCCAAAIIIIAAAggggAACCCCAQDEL+DoA2DxAX5YHfjjwtySP59DUM4HXP5/rTrv7Hbdo2YoaW7Z+q6bun8dt5wb3al+jngcIIIAAAggggAACCCCAAAIIIIBAsQv4egGz8LTfnnl0wPZB2xl5PIemHgk8+t50d/wdb9ca/OvRvpV75Oc7MfjnUV+zKQgggAACCCCAAAIIIIAAAgiUkoCvA4BvBJ14UI6d2UztTlfsuoGv5PgcmnkiUFVV5f72wqfunPs/cMsrbRdYUwZ1X9898oudXO9OdkNpCgIIIIAAAggggAACCCCAAAIIIJA8AV8HAP+trrA7NByn7JylW2zw7y6lT9CuPJgyKQGB5ZUr3YUPf+iuGTmp1tbus/mG7r5Th7iObcIzyms1oQIBBBBAAAEEEEAAAQQQQAABBBAoegFfBwCfkPxIxa5x+IzyO6W/EpammtlUOVP5WDlcsUO/HlbeVCglILC0otKdetdY98DY6bW29qSdN3E3HbOta9msca1lVCCAAAIIIIAAAggggAACCCCAAAJJEvD1JiDWB0cozyvbKpcGCc/vfFePo8WOFrSBvxOjlcz7K/Dd9yvcyeVj3FtfzKuxkWXaE/5w4EB3ogYAKQgggAACCCCAAAIIIIAAAggggIAPAr4eAWh9862yk3KZslCxQb64LFX9lcpwZbFC8Vzg2yXL3U9ve6vW4F+Lpo3cLT/dlsE/z/ufzUMAAQQQQAABBBBAAAEEEECg1AR8PgLQ+nK5crHyF2VXZTuls2LndX6jvKfYUYI2WEgpAYG5333vjr39bTf+KxsTXlPatWjiyk/a3m3TY4M1lcwhgAACCCCAAAIIIIAAAggggAACHgj4MAC4Uv1g2UoZl6ZP7Mi+p4KkaUK17wKzvl3mjrntTff5nJoHenZo3czdffIObmCXdr4TsH0IIIAAAggggAACCCCAAAIIIFCCAj4MAFq32am9FATSCkybt8QdrcG/afPsjO81ZcN2zd2/Txni+nZus6aSOQQQQAABBBBAAAEEEEAAAQQQQMAjAV8GAD3qEjal0AKfz/mu+pp/X+kIwGjptkFLd68G/3p0aBWtZh4BBBBAAAEEEEAAAQQQQAABBBDwSoABQK+6k41JFbBr/R17+1tu7ncVNRb17tja/fvUHdzG67WsUc8DBBBAAAEEEEAAAQQQQAABBBBAwDcBBgB961G2Z7XAB9MWuOPueNt9u9TuBbOmDNiobfU1/zq1bb6mkjkEEEAAAQQQQAABBBBAAAEEEEDAUwEGAD3t2FLfrDFT5rkT/zXGfff9ihoUW3dbz92pu/2u36pZjXoeIIAAAggggAACCCCAAAIIIIAAAr4K+DQAWOVrJ7Fd+Qm88ukcd+pdY92y5XZz6DVlcK8N3B0nDHZtWzRdU8kcAggggAACCCCAAAIIIIAAAggg4LmATwOAz6mvap7rWbfOs4HEPnV7Ks9qaIGR42a7M/79rquorDn4N7RfR3frsdu6Vs182uUbWpv3RwABBBBAAAEEEEAAAQQQQACBJAj4NBrStUDgHElYIMh1/TKPfTDTnXP/+65yZc0u3HOzDd2NR//AtWjaeF2vEu+HAAIIIIAAAggggAACCCCAAAIINLiATwOAM6VZiCMAG7xTWIH8BR4YM81d+MiHrqrm2J87cKuN3bU/GeSaNm6U/4vyDAQQQAABBBBAAAEEEEAAAQQQQMADAZ8GAPdWf4zzoE/YhDwF7nx9irvksU9qPevwbbu5vxy2lWvcqKzWMioQQAABBBBAAAEEEEAAAQQQQACBUhHwaQCwVPqM7YwI3PzS5+6KZyZEalbNHr9jT3fJQZu7Rgz+1bKhAgEEEEAAAQQQQAABBBBAAAEESkuAAcDS6m9vtrZK5/peO3KS+9uoz2pt0+m79nEX7rupKyvjyL9aOFQggAACCCCAAAIIIIAAAggggEDJCTAAWHJd7scGX/v8p7GDf+fu1d/9cve+DP750c1sBQIIIIAAAggggAACCCCAAAIIFECAAcACIPIS61bghhc0+Keklt8fsJk7ZWjv1GoeI4AAAggggAACCCCAAAIIIIAAAiUtwABgSXd/8jbervn3V536m1ouO2QLd8wOPVOreYwAAggggAACCCCAAAIIIIAAAgiUvECjkhcAIDECt70yOfaGHwz+JaYLWVEEEEAAAQQQQAABBBBAAAEEEGgAAR+OANwkcJvRAH685ToSKH/tC/enJ8fXerdLD96cI/9qqVCBAAIIIIAAAggggAACCCCAAAIIrBHwYQBw6prNYc5HgXvenOpGPD6u1qZdfOBAd9yOvWrVU4EAAggggAACCCCAAAIIIIAAAgggsEbAhwHANVvDnHcCD7470/3h8Qm1tuu3+w9wJ+8SHvxZazEVCCCAAAIIIIAAAggggAACCCCAAAKBAAOA7ApFK/D212Xu3jdqD/6dv8+m7mfD+hTterNiCCCAAAIIIIAAAggggAACCCCAQDEJcBOQYuoN1mW1wLtzNfj3eSNXtbpm1cw5e/Z3Z+zWN6WWhwgggAACCCCAAAIIIIAAAggggAAC6QQYAEwnQ32DCYyaOMfd/akN/pXVWIczd+/rztqzX406HiCAAAIIIIAAAggggAACCCCAAAIIZBZgADCzD0vXscArn85xZz/4sVuZMvh3+q593K/36r+O14a3QwABBBBAAAEEEEAAAQQQQAABBJIvwABg8vvQmy2YMnex+9ld77jllTVP/D1x517uwn03dWVlNY8I9GbD2RAEEEAAAQQQQAABBBBAAAEEEECgHgUYAKxHXF46P4GeHVq543bqWeNJP/7Bxu4PBw5k8K+GCg8QQAABBBBAAAEEEEAAAQQQQACB3AUYAMzdipb1LGBH+F207wB31m69q9/pBx1WuhEHDmDwr57deXkEEEAAAQQQQAABBBBAAAEEEPBboInfm8fWJU3ABgFPH9bLLZ75qRu4fpVr3IjTfpPWh6wvAggggAACCCCAAAIIIIAAAggUlwADgMXVH6xNILBl+5rXAQQGAQQQQAABBBBAAAEEEEAAAQQQQKBuApwCXDc3noUAAggggAACCCCAAAIIIIAAAggggEAiBBgATEQ3sZIIIIAAAggggAACCCCAAAIIIIAAAgjUTYABwLq58SwEEEAAAQQQQAABBBBAAAEEEEAAAQQSIcAAYCK6iZVEAAEEEEAAAQQQQAABBBBAAAEEEECgbgLcBKRubjxr7QUahy/x1VdfhbPV06VLl7q5c+dWz0+fPt21bNmyxnIelI4A+0Lp9HW2LWVfyCZUWsvZH0qrvzNtLftCJp3SW8b+UHp9nm6L2RfSyZRePfvCqj5P+c69+rt46e0Rpb3FZaW9+Wx9Awpsp/ce04Dvz1sjgAACCCCAAAIIIIAAAgggUGoCg7XBY0tto9le5zgFmL0AAQQQQAABBBBAAAEEEEAAAQQQQAABjwU4AtDjzi3yTWuu9dsyWMc5mlZG1ncjzYdHB9pfJ2ZFljFbWgLsC6XV35m2ln0hk07pLWN/KL0+T7fF7AvpZEqznv2hNPs9bqvZF+JUSrOOfWFVv9tpv52CXeAjTb8P5pkggAACDSrQTe9eFcTmKaUrwL5Qun2fuuXsC6kipf2Y/aG0+z+69ewLUQ3m2R/YB0IB9oVQgin7AvsAAoEApwCzKyCAAAIIIIAAAggggAACCCCAAAIIIOCxAAOAHncum4YAAggggAACCCCAAAIIIIAAAggggAADgOwDCCCAAAIIIIAAAggggAACCCCAAAIIeCzAAKDHncumIYAAAggggAACCCCAAAIIIIAAAgggwAAg+wACCCCAAAIIIIAAAggggAACCCCAAAIeCzAA6HHnsmkIIIAAAggggAACCCCAAAIIIIAAAggwAMg+gAACCCCAAAIIIIAAAggggAACCCCAgMcCZR5vW7hp7TTzY2VHZSOllXKSMlUJSxfNrK8sUyaHlUwRQAABBBBAAAEEEEAAAQQQQAABBBBAoLgFztDqLVAqg6wMpgM1jZaj9MCWLVHaRxcwjwACCCCAAAIIIIAAAggggAACCCCAAALFKTBCq2UDfzawt1R5O5i3utQBQDsVeoZiy05VKAgggAACCCCAAAIIIIAAAggggAACCCBQxAI/0LqFR/3dpfn1gnVNdwSgLb5OseUP2AMKAggggAACCCCAAAIIIIAAAggggAACCBSvwB1aNRvMezVlFTMNAP4keM74lOfwEAEEEEAAAQQQQAABBBBAAAEEEEAAgcQK+HoX4F3VI1XKjXn0zJSgbdc8nkNTBBBAAAEEEEAAAQQQQAABBBBAAAEEilrA1wHAjQP1iXnofx+0bZ7Hc2iKAAIIIIAAAggggAACCCCAAAIIIIBAUQv4OgBYEag3zUM/HDS0uwZTEEAAAQQQQAABBBBAAAEEEEAAAQQQ8ELA1wHA6UHvbJ5HL+0dtP0sj+fQFAEEEEAAAQQQQAABBBBAAAEEEEAAgaIW8HUAcJTUy5QTc9TvrXYnK3bdwJE5PodmCCCAAAIIIIAAAggggAACCCCAAAIIINBAAv31vnYacKUyQglL3F2At9NCO+rPli1RwlOBNUtpAIEees+rFbsb82JlnvK2cp7SSqEkV2AbrfpvlaeVaYpdd/M7ZZJSrgxV8in7qvEjih3xa69lU3ts9ZTkClypVbc/xoQZnsOmsC/kgJSQJh21nhcorymzFPu3PVN5S7lK2VHJVqzN3coUZZnylfKMcqRCSYZAM62m/WHW+s36z/YD+/9ionKHMkTJpfDZkItSw7TprLc9ULlUsd8L5irh53655vMthejrJnrT05TRyhxlqWLfEW5RBiqU+hEoxL7QQqt2sHKDYv9f2PeH5cH0DU1HKBsruRb7znG+8rZir2WfP/bdxL6j2HcVSv0JFGJ/SLd21q+TlfCzZkq6hin17A8pIDxEoBgFLtZKrVRsEND+I7AvFOHjX2v+QuUFxZaH+ZXmKQ0ncIDe2q7BGH4op04naJkdrUlJnsDLWuXU/ox7fJfa2Re/TMWO7r1ViXt+WGfLrR0lWQJba3XtF/awH206XElX2BfSySSz/nCtdnQQILofhPP/zbJpf9By+z89bJ86fUzL7IsipXgFumvVPlRS+y718TVqk+5zns+G4u3fcM1S+zP6uDxslMO0UH3dQe/1phJdj+j8Mi07KYf1oUn+AlHn1PnyHF5uK7VZqKQ+N/WxtTkih9frozb2nSP1+eFj+66yfw6vQ5O6CYTOcdPyur3k6mfZAG70daesXpJ+hv0hvQ1LECg6gUu1RiuUcOAvHOiLTsNllxTd2pfWCtkXfzvizz6UFyl2pNiOyu7KP5Tww9r++tZGoSRLwP6Cbn04Q7lOOUwZrAxRzlHs6L2wj+/VfKZymRaGbd/V/JGKvZZN7XG47E+apyRHoJFW1f7Sbv03O5ja/HAlXWFfSCeTvPrjtMrhwJ31/whlT2Ubxb5onak8pzyopCunaEH4798+c05S7LPBjgoZpYTL7tE8pTgFmmi1ooN/H+jx8Yr9X7GX8kfFjsQJ+/J8zccVPhviVIqrLuxDm36pPKuEdeWaz7UUoq8b681eVsL3f1jzdkTh9op99oT/J9l3in0USmEFQve67gu7aHXC13hV8xcpeyo/UPZWblGs76yNTfdT0hX7jmHfNcLXs+8guyv2ncS+m9h3FFu2WLGBR0rhBUJ7m67NZ0Pqmtn+YP2/VAkHjKdoPlNhf8ikwzIEilRgO63XQ4r9wmiDfdEs0+MnlZ0USsMKvKi3tw96O/rH/pNNLfZLfvgfwh9SF/K46AWe0BraX13tl+y40lGVE5Wwj4fGNVJdXyU8QmyM5lumtLND9K0+3Jfsr3aUZAicrdW0frNfvP8czNvj4UpcYV+IU0lm3WZabfv/2Pp7tLKekq6kO0J4fT1hvmKvMVWxz5Rosc+exxRbbhmmUIpPwP44FPbR65qP+z9jW9VXBO3madpEiRY+G6IaxTtvg7kHKhsGq9hL07Dvy4O6bJNC9fUJeqPwvf8e86b2Pt8GbSZpmrrPxTyFqjwE1nZfsO9x9ysDM7yn/SHIvgNaP3+mlClxZYQqw33BvnukFvuOEv4eOip1IY8LIrC2+0PcStj/JWMV69uLlSnBvE0zlRFayP6QSYhlCBSxgP1nbX+p2VOxv95tp6QOHqiK0gACg/We4Yer/ZUurjRS5TjF2tkv/E0Vil8C9kUg3A+uT7Np9ot52GZImjZWH7a5IU0bqotLoLtWJ/yr+nDNj1DCPrTHcYV9IU4lmXXPa7Wtv+coqQN3uW6RfVEL95kj0zypm+rtr//W7vE0bahuWIFr9PZhPx6UYVUeibTbIqUdnw0pIAl52EvrGfZ9eY7rXKi+/iR4b/v90v6QGFcuUmW4fofFNaCuYAK99EqhdXnBXnXVASHh69rRYKnFvluEf0iy7xz23SOu3KLK8HW2jWtAXUEFeunVQu/yOr7yr4PXmKCp/SFxSvDYpukK+0M6GeoRQACBtRSInr6xQ4bXiv7ytVeGdixKpkAbrXb4H/wTMZtgf62dHrSxo8QyFfsP3l5rmpLur7yZns+ydStggzHWX+XB247QNNwXhgd10Qn7QlQj2fMDtPphX1+yFpvyWvA6dpROuqME7eWfCdot09Q+cyjFJXCjVifcHzbPsGpXRdpFv4Dz2ZABrcgX9Yr0aXkO61qovu4Xed+bM7zvRpF2/87QjkVrL9BLLxF+DpSv/cutfoUzIq/749W1a2b2iiy/cE11rbkhkXaX1VpKRaEFeukF12Z/6Knnfxe8xm6aWpmi2GvaNF1hf0gnQ32iBdL9ZSPRG6WVt0O87a/8FyR9Q0pk/cPTPRdre9/JsM0vR5btEpln1g+B6Jd2+zecWjZRRdegMrovpLazx+FyO+Knl1VQilbATg23oz/tyIvzc1xL9oUcoRLQ7PDIOj4Ymd9A8/2UDpG6dLP22bF9sPANTe300HQl/GxorgaD0zWivsEE7PTKsPQOZ2KmfYI6+wL3aWQ5nw0RDM9nC9XX4e+gxhV+PsTRzVJluH/yO2icUPHX2ed+WOJ+z8x1XxirF7HvLFbYF1Y5FPPPm7RyrZW7lRfzWFH2hzywaJocAV8HAO2X/zLlleR0RUmvqV3/ycpnyorqufgfEyLV4XMiVcwmXGDXyPpH+zqsjvZ53PKwnU2jy6PPi7ZhvuEF7Lpt4ene9tf2OTmuUrRPo30d9/To8ujz4tpSt+4FhgRvaUfu2ZG9xygfKDYgbF+25yqTlUuUdEfs2UChXebDSrS/V9XU/Bldzv5Q06YYHt2nlbCLs1uxz4TG1XM1f/xADw8Iqv6jadjeqqJ9Gu3roHmNSXR59Hk1GvGgaAWifRbty7gVji6PPs/aRh9H22V6ne5aaAMKlGQJFOr3TPuu8nmw6dH9J1kapbG2R2oz7UZidmr3eXlucrRvM302sD/kCUvzhhXwdQBwZsBa2bC8vHsOAi3UpmPQzk7vzFTswzv8i5v98kXxR8A+iy6KbM4DkflwNtrn2faVaeGTNI0+L1LNbBEIXKl12Eh5Xbk9j/WJ9in7Qh5wRdh0YLBOUzS9QblH2UqJFjvSZ4RiR/d1UVIL+0OqSHIf2x8BTlCWKjsrY5TjlCHKnooNBNtRWnbU5/uKXdcpWtgXohp+zxeqr+vyOnaQQTe/eb3buq21ReEfDj7R/LiYLQz3BfuusSBmebQq/D2zkyqjRxZG2zDfddCI4AAAQABJREFUsAIb6O2vC1bBvmN8nefqsD/kCUbzZAj4OgA4OuDfJhndUNJr2Tay9XZ9hmwlHABMdyRItuezvDgFztFqhafwPap5O70iteSzr4T7ib0G+0qqZHE83kWrcYpifzk9XalSci3sC7lKFX+79sEqDtD0DMW+dNn+0FmxPxANVp5WrGyhPKik/u7C/mA6/hT7P2A75XZlkHKnYoO/I5URyhLFBv7sM2SWEi3sC1ENv+cL1deFeh2/tZO9dTZAd5sSHlH82zSbE+4L+XwfsZfi98w0oA1cfZXef0PF/v/4Zx3Whf2hDmg8pfgFUn+JLv41zm0N7SiCSuU8pV1uT6FVAwnYF7yw2Knb2cr3QYOW2RqyPDECdkrGX4K1tb/O/TzNmuezr4T7ib0U+0oa0AastqN3/qHYURTXKh8p+RT2hXy0irtt62D17Aua/b+9n3KrMkexf8f2x4ADlXAQcCfNH6pEC/tDVCP58021CUcrByn2GZFa7AvdUcrw1AV6zL4Qg+JpVaH6ulCv4ymzF5t1o7bC/qhgxf6g8Fj1XO0f4b6Qz/cRexV+z6xt2dA1w7QCJyl1+SNzuO7sD6EEU68EfB0AfEe9dKbSU3lZsS8MlOIUWBZZLRsUyFbsS6KVpasm/Ey4wOZafzvao4liX/aPUGYrcSWffSXcT+x12FfiNBu27rd6+82UL5U/1mFV2BfqgFakT4n25YNaxzdj1tMu1n5+pN4Gf6Il+hrZ/h/hsyEqV3zzNiD8vPI7xW4AY5cJsM8K67f1lL2VVxU7MvRx5SwlWtgXohp+zxeqrwv1On5rJ3frfqNVt7MNrNj3wzOq5+J/hPtCtv9H7Nn8XxJvWAy11jf/UOwPSNcrHyp1KewPdVHjOUUvYF+6fSx3BBs1UdOtlVcUu1aDfQDMV+wog3TFTkM7Od1C6gsusCjyirkcQm9fDqzkcnj+qpb8LFaBTbRizyl2jQ77N2lf6m3APl3JZ18J9xN7LfaVdKINUz9Ab2u/kFuxP9RET9eurszhB/tCDkgJaWJ9Gf57DY/yi1v1T1Q5Q+mq2OBPtLA/RDWSPW9/ELAjN6zY72J3Vs+t+lGhiZ0G/KJi/3fsplwTPA6/4LEvCKRESqH6OvV1wi/9cYzhZ5Ut43eLOKHiqjtNq/PnYJXsO+F+SqbfOcJ9IZ/vI/by7AumUDzF/oC0qWLf/UcodS3sD3WV43lFLeDrAOAJUreBPCs2LVN6KN2VTMXaMQCYSajwy+wXrblKR6Vblpe3gaLwly/7UKckV6CLVt2O8rCp/Zuzw/QfVTKV6ZGF2faV6L919pUIXBHM2vUe7a/rk5VWypFKatkiUrG75jcKHj+uqf3yzr4QgHgwsX+fYf9G+zVu06ytDQDa9QGjJfo8PhuiMsmat9/BTgxWeZKmd6ZZ/RWqv1h5VbEzWew59rlihX1hlUMp/CxUX6e+jv1Omq6Ev1vY7y3R56VrT33DCdgflW8K3n6qpnsqc4LH6SbWpzso9l1jfcWuSZuuhPuCveb36RpR3yACFwbvat8zDkyzBuH3SZuGv4faZYhGRdqzP0QwmPVHoIk/m1JjS+y0MvvPmZIMgfFazaFKX8X2SfvlPq7YkUNhsedQkilgg712FEfvYPXtKLC7gvlMk3GRhdF9IVK9eja6nH1lNUtRzISnzVj/35fDGtkX/bBsohkbAGRfCEWSP/1EmzA42IzGWTYnXJ76f4QNFlUqtjz6bz/u5aLL+WyIE2q4ug311u2Dt38vy2rYqXxhifYpnw2hiv/TQvV16uu8n4Eu3NfsjxH2fxGlOAV+qNWy3ysbKV8peyg2mJOt2L5wWNDI+vrNNE+w7yp9gmX8P5IGqQGrw1O47Y9DlkzFvpOEv4u+rPnoACD7QyY5liGAAAJrIWCH59uArWWHDK9zUaTd3hnasah4BdbTqtkXt7C/L8xjVcvU1k4BtOdm+4XLlls7+4XPnkcpHoFyrUrY//lOewWbwb4QQHgwOVHbEO4Hp2fZHjsyx9raqVyp5XVV2LJvlfCX/9Q29vgZxdotU9oqlOIRsC9i4b7wUJbVsr4L29qRwWHhsyGUSN60l1Y57NPyHFa/UH3dP/K+N2d4340i7e7N0I5Fay/QSy+Rz74QfUcb7LPPd3u+/Z+xuZJr2VsNw/fN9PvpkEi7P+f64rSrs0AvPTPsl/IcXiVsm+/0pZTXZn9IAeEhAgggUCiB7fVC4Yf0LWle1P6KZ3+JsXZ2HUe7SyAlWQJ2uuerStjXf6rD6t8Ueb79AhZXor+Y/T2uAXVFLzBCaxjuJ8PTrC37QhqYhFV30PpWKNbfz2VY912DNtbutph2F0SWHxmz3Kq6KXb0oL3GkwqluATs/3kbwLX+sT/22FE26Yqd1mXtLH9LacRnQwpIQh720nqGfVqe4zoXqq/HBe/9jab2u0pcuUiV4fodHteAuoIJ9NIrhdblebzqTmr7XfBc+yzZNo/nWtNmip32a+9t+4QNMseVW1QZrt/guAbUFVSgl14t9C4v0CtPCV7TpukK+0M6GeoRQACBAgiM1mvYh/tyZceY1zs/WG5tRsQsp6q4Bew/0WeV8D/w6+q4uv31PNtH7HXGKC2VaLHHVh/uS/2iC5lPjMAIrWm4rwxPs9bsC2lgElgd/RIfN3hnR3vZKaHhPhH3hctOHQ2/uE3RvA0sRktjPXhMCV9jt+hC5otG4N5IH12SZq02UP0nkXZ7p7TjsyEFJCEPe2k9w3+f5Tmuc6H6+qTIe98Y8952umc4OP2Z5jMNTsc8nao8BXqpfb77wiA9xw4QsOfZIODOSl3KpXpS+N723SO12HeU8PfQl1IX8rheBHrpVcM+KS/QO0wJXtOmmQr7QyYdliGAAAJrIfADPXeJYh/wi5TfKEMU+5J2qxJ+8E/UPKdtCSFh5WGtb9iHL2h+S2WLDLFf6tOVy7UgfK13Nf8TZbtgao/DZX/WPCWZAiO02mE/Ds+wCewLGXAStKiT1nWqYn1uX6xuUOyz347eOEEJT+m35TZYmK6cpgXhfmNf0k9U7LPBrgdl1/UJl92reUpxCth1txYrYV/ZoO1hiv2OYF+8z1HCfcXaPK/EFT4b4lSKq24Xrc4JkZyn+bDfX43Uh21UFVsK0df2BwJ7z/D97RT0fZTtlV8qsxVbZtcatTvJUgorsLb7gg3Qhn1k/XS2kul3TFvWWYkr9h3DvmuE+4J9B7H/j+w7yW8U+45iy+w7iw06UgovsLb7Qy5rNEWNrB9tmqmwP2TSYRkCRSTQQ+uyNimiTSmpVTlIWxv+hTX8jzc6tf+Q+5aUiD8bG+3HXOanZNj0Rlp2u5LpdW7TcmtHSabACK122L/DM2wC+0IGnIQt2kzr+6kS9nvc1P7dZ7v8wx/VZmWG13lSy1oolOIV2FOrNkeJ2weidfbHpA3SbAafDWlgiqi6XOsS7c9s8+lWvVB93VFv8HaGdfpey05NtxLUr5VAuZ6drf+jy1Pf7IQ8n2+vNUJJV+y7ht1cKvqe0Xn7rnJguidTv9YC5XqFqHe2+bq84ZTgPWyarbA/ZBNiOQJFIGB/oatrVhTB+pfyKvTUxl+j2GCfHQVgh/PbKZ0XKOmuy6JFlCIXyPafd+ryKTlsz/5q81/FrhVlv5jb1B7z13khJLyM0PqH+8TwHLaFfSEHpAQ0aa11tKOA7M6L3yj273qa8h/FjsDItdh1oP6tfKnYa9iRIXZ9waMUSjIEOmg17f/9F5WvlQrFjriZrNyvHKyUKdkKnw3ZhBpuebneOvycz2WabU0L0ddN9CY/V15R5ipLlc+VfyibK5T6ESjXy+ayD4RtUtfihDyfb68zQslU7P8j+wyy7yD2XcS+k0xQ7DtKT4VSfwLleumwr3OZ1mVNpgTvYdNcCvtDLkq0SYRALr88JWJDUlbS/vpf12IfNHYqAKV+BZrr5bcM3mKOpjZgS0EAAQQQQAABBBBAAAEEEEAAgcIK2BhHp+AlP9LU/khKKTEB+0uXj+XEHDbKRvI3VezaMl2U15V/KpR1I2CDf/ZXNQoCCCCAAAIIIIAAAggggAACCKwbgcF6m7Hr5q14l2IS8HUA8M48kO10o78pP1NsENAO966vso1eeF9lqBJegNYueD5Tsfe+XbHTDnIpdvi5naawl9JHsQFNuzCtHZ7+tHKLYkfWZSut1OAM5XClr9JMmaY8qZiLnUJFQQABBBBAAAEEEEAAAQQQQAABBBBIqICvpwDXpTte1JOGKXYNkWfr8gJZnvOyltvrZyt3q8Epil3vJl05WgvsaEUbvEtX7PpJRyij0jVQvQ0c2kCfHQkZV+wit/ZeT8UtXMs6G8CcYq/x9ttvu4033thmq8vSpUvd6NGjq+eHDRvmWrZsGSxhUmoC7Aul1uPpt5d9Ib1NKS5hfyjFXo/fZvaFeJdSrWV/KNWer73d7Au1TUq1hn1hVc9/9dVXbvvttw93g16amRo+YFo6Ar4eAViXHrxZT9pVOVOpjwHArsFK2dF+DyqvKHZ0XWNlR+Vcxdocq1i/2MBbXLG2dyn2PLvWoR3t+D/FXreHcrxykNJBeUyxIw2nKKmljSqeUMLBPxtQtAut2wWPd1N+o6yn2Lrae36oFLKsvuafDf5169Zt9Wvbh3THjnYzNlddzwDgapqSm2FfKLkuT7vB7AtpaUpyAftDSXZ77EazL8SylGwl+0PJdn2tDWdfqEVSshXsC7Fdv/q7eOxSKr0VYABwTdd+Gsxut6aqoHN2au5vlYeV1H9wdsfDu5XXlP7KUYoNSNogYWqx17DBPys2WHlT9dyqH2M0sdf/q/JrpXUw/ZWmqcVOfR4QVF6g6VWRBm9o3o6ItMPw7CjD65TdFQoCCCCAAAIIIIAAAggggAACCCCAQMIEGiVsfetzde1oNyvhdNWjwv08UC/1gJI6+Be+w1zN2FGAYflxOJMy3Tl4/I2m0cG/aLNLIw92isyHs001c1bwYLymNmCYWmwQ8Pag0o4I3Da1AY8RQAABBBBAAAEEEEAAAQQQQAABBIpfgAHANX1kp85a+WrVpEF+vhR51z6R+eis3aTDyherJrE/7dp9NqBopfmqSY2fw/Vo/aDGTiG2U4njSnmk8tDIPLMIIIAAAggggAACCCCAAAIIIIAAAgkRYADQuX7qK7tjrg0AVilPKQ1VwsE9e/90g3KTgpXbJMNKttOyVRfRcy5sH20+NPLg5ch86uxYVSwOKndJXchjBBBAAAEEEEAAAQQQQAABBBBAAIHiF/D1GoCTc6C3wU87Cq5tpO3Xmr8s8nhdz+4aeUO7ZmBcuVWVNmBpN/k4PZjXpEa5OPLI2qeWzSIV6d7HmqxQPle2UqLP0cP6LZUail2qd5+3uMK1qGScun61i/fVly2rcN8tX7V+7AvF20/rYs3YF9aFcnLeg/0hOX1V32vKvlDfwsl6ffaHZPVXfa4t+0J96ibrtdkXVvXX/MXfJ6vjWNt6ESirl1dt+BdNd/RcpjWzG3GcqEzM1Kgel9kol113b/vgPQZrakfgpRa7AcidyjGKbecdit3t105d7qH8VDlEsXKFclH1XM0ftq07KHZ0X5uai2o9ekI1BwS1LTTN9ZOjW61XqlmxkR7aTUvcpEmTatwF+OF3prnLnp7kllb6unvWhOARAggggAACCCCAAAIIIIAAAvUlsGLhXDfj5hPCl++umenhA6alI+DrEYA2QJat2ODZIuUL5WXlfaUhyzl683Dw71HNxw3+2fpVKjbIZ4N+Nrh3ShBNVpcXNfcX5bnVNTVnwqMev6tZHfsoPAXYFtpgYa4DgNNiXy2mcvTo0a5jx1VnLNuRf5eNaczgX4wTVQgggAACCCCAAAIIIIAAAggggEBdBHwdADyxLhgN+Jxd9d42YGfFTkP+efVc+h8DtOhoZcs0TXZU/XHKR0rcTU3sSD4rFasmGX9GB/xaZmxZgIV22i9H/hUAkpdAAAEEEEAAAQQQQAABBBBAAAEEAgFfBwCT1MGba2XtiD/rCxtsO0KZraQrQ7XAjv6z6xdOVX6vjFTmKRsqP1T+pNgpwjawuLcyXomWZcGD6E1Hosuj89G7CC+NLsgyb4cVZyqrTwEeNmzY6lOA7TpvbuyrmZ7HMgQQQAABBBBAAAEEEEAAAQQQQACBPAR8HQC0o9+s/FdZWD2X/Yed3npo0Oyu7M0L0mITvYqdpruBYqf2HqXY6cjpig3G3afY4N8sZUgw1aS6TNfPmxR7DTuF2K7DZ9ti1xOMFjv12Yptc7bSOtIgl1OGw+a2LjmVli1bOouVuBt+jDxnmGvfOpexypzejkYJEli2bJl76aWXqtd4+PDhrkWL8ODVBG0Eq1oQAfaFgjB68yLsD9505VpvCPvCWhN69QLsD15151ptDPvCWvF59WT2hVXdOXPGdLfVzV51LRtTBwFfBwDLZaGryVUPgo3L0cWOnitX7NqA62IAsIve53nFprauJyl2JGCmsq8Wdg0a3KCpDQLGlU9UeY9yirKdsrXygRIWG5zbQbHBPRtMXKCkK+GRfHPUIHo6cLr2Ba+3wb8ObaIHIhb8LXjBIhVY2nila9N01crZftCyJftBkXZVva8W+0K9EyfqDdgfEtVd9bqy7Av1ypu4F2d/SFyX1dsKsy/UG23iXph9YVWXLW3N96jE7bz1sMKN6uE1k/6SZetgA+yOF3babu/gvc7UNJdBx82C9jZ5NzIfN/tOpHJAZN5mo4OiqcuiTW2AuE9QMT66gHkEEEAAAQQQQAABBBBAAAEEEEAAgWQIMAC4pp9ssMuKbkNRr2U9vfqzysDgXexOvn8P5rNNousWrm+65wTHTVUvjj7PKl6NPMmuE5iu2NGD4SnAr6VrRD0CCCCAAAIIIIAAAggggAACCCCAQPEKMAC4pm82DWbtZhr1VVrphZ9Utgne4DJNrwjmc5l8EWlkNwPJVKIDe9Hn2XNeUr61GZXjlXRHPZ5gDYKS7fTksB1TBBBAAAEEEEAAAQQQQAABBBBAAIEiEsh2FFkRrWrGVRmWZqnd/MJOt81U7GR4O831PKVKeV+pj2J3sbBBtJ2DF79e098H87lOXlDDJYoNJP5cuUf5SEkt+6nikKByhqap26Rb7bq/KRcrdlqxbftVSrTsqAcnBxV2U5Ex0YXMI4AAAggggAACCCCAAAIIIIAAAggkQ8CXAcCXxG2Dd9FiR7XdEa3IMm/t7TVuzdKurovv0xP3Dp48StPblS2Cx3ETG6SblLLAbtbxF+VSpa3yumI3A7HrCc5X7EYmByunKuHRnXaKsd3YJLXYgN9PlP7KlUpf5T/KUmU35beK7R/2+GyFggACCCCAAAIIIIAAAggggAACCCCQQAFf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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(-1, 150)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compile the optimization initialization model\n",
    "init_sim_fmu = compile_fmu(\"CSTR1.CSTR_Init_Optimization\", \"CSTR1.mo\")\n",
    "# Load the model\n",
    "init_sim_model = load_fmu(\"CSTR1_CSTR_Init_Optimization.fmu\")\n",
    "\n",
    "# Set initial and reference values\n",
    "init_sim_model.set('cstr.c_init', c_0_A)\n",
    "init_sim_model.set('cstr.T_init', T_0_A)\n",
    "init_sim_model.set('c_ref', c_0_B)\n",
    "init_sim_model.set('T_ref', T_0_B)\n",
    "init_sim_model.set('Tc_ref', Tc_0_B)\n",
    "\n",
    "# Simulate with constant input Tc\n",
    "init_res = init_sim_model.simulate(start_time=0., final_time=150, input=(\"u\", stepf))\n",
    "\n",
    "# Extract variable profiles\n",
    "t_init_sim = init_res['time']\n",
    "c_init_sim = init_res['cstr.c']\n",
    "T_init_sim = init_res['cstr.T']\n",
    "Tc_init_sim = init_res['cstr.Tc']\n",
    "\n",
    "# Plot the initial guess trajectories\n",
    "plt.figure(2)\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.plot(t_init_sim, c_init_sim)\n",
    "plt.grid()\n",
    "plt.ylabel('Concentration')\n",
    "plt.title('Initial guess obtained by simulation')\n",
    "plt.xlim(-1,150)\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.plot(t_init_sim, T_init_sim)\n",
    "plt.grid()\n",
    "plt.ylabel('Temperature')\n",
    "plt.xlim(-1,150)\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.plot(t_init_sim, Tc_init_sim)\n",
    "plt.grid()\n",
    "plt.ylabel('Cooling temperature')\n",
    "plt.xlabel('time')\n",
    "plt.xlim(-1,150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CSTR_Opt2 を使用して、最適化制御問題を解きます。\n",
    "\n",
    "#### 最適化モデル CSTR_Opt2 のソースコード\n",
    "最小化する目的関数\n",
    "$$\n",
    "\\int_{startTime}^{finalTime}{q_c (c_{ref}-c(t))^2 + q_T (T_{ref}-T(t))^2 + q_{Tc} (Tc_{ref}-Tc(t))^2}dt\n",
    "$$\n",
    "の被積分関数 (objectiveIntegrand)と、動的最適化の開始時間 (startTime)、終了時間 (finalTime) を設定します。\n",
    "被積分関数は $10^{-4}$を乗じてスケーリングしています。\n",
    "\n",
    "制御変数 u と最適化するモデルのオブジェクト cstr、目的関数のパラメータのデフォルト値などを定義します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting CSTR2.mop\n"
     ]
    }
   ],
   "source": [
    "%%writefile CSTR2.mop\n",
    "optimization CSTR_Opt2(\n",
    "  objectiveIntegrand=1e-4*(q_c*(c_ref-cstr.c)^2 + q_T*(T_ref-cstr.T)^2 + q_Tc*(Tc_ref-cstr.Tc)^2),\n",
    "  startTime=0.0,\n",
    "  finalTime=150)\n",
    " \n",
    "  input Real u(start = 350,initialGuess=350,min=230,max=370)=cstr.Tc;\n",
    "  CSTR1.CSTR cstr(c(initialGuess=300),T(initialGuess=300, max=350),Tc(initialGuess=350));\n",
    "\n",
    "  parameter Real c_ref = 500;\n",
    "  parameter Real T_ref = 320;\n",
    "  parameter Real Tc_ref = 300;\n",
    "  parameter Real q_c = 1;\n",
    "  parameter Real q_T = 1;\n",
    "  parameter Real q_Tc = 1;\n",
    "end CSTR_Opt2;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最適化モデル CSTR_Opt2 をロードします。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyjmi import transfer_optimization_problem\n",
    "op = transfer_optimization_problem(\"CSTR_Opt2\", [\"CSTR1.mo\",\"CSTR2.mop\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "目的関数のパラメータとして停留点Bの変数を設定し、初期値として停留点Aの変数を設定します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "op.set('Tc_ref', Tc_0_B)\n",
    "op.set('c_ref', float(c_0_B))\n",
    "op.set('T_ref', float(T_0_B))\n",
    "op.set('cstr.c_init', float(c_0_A))\n",
    "op.set('cstr.T_init', float(T_0_A))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最適化モデルのオプションを設定し、シミュレーションを実行します。\n",
    "init_trajとnominal_trajに初期推定値の軌跡(initial trajectories)を含む init_res を設定しています。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Initialization time: 0.11 seconds\n",
      "\n",
      "Total time: 0.21 seconds\n",
      "Pre-processing time: 0.00 seconds\n",
      "Solution time: 0.09 seconds\n",
      "Post-processing time: 0.01 seconds\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Set options\n",
    "opt_opts = op.optimize_options()\n",
    "opt_opts['n_e'] = 19 # Number of elements\n",
    "opt_opts['init_traj'] = init_res\n",
    "opt_opts['nominal_traj'] = init_res\n",
    "opt_opts['IPOPT_options']['tol'] = 1e-10\n",
    "opt_opts['verbosity'] = 1\n",
    "# Solve the optimal control problem\n",
    "res_opt = op.optimize(options=opt_opts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最適化制御シミュレーションの結果をプロットします。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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 overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        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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\" 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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract variable profiles\n",
    "c_res=res_opt['cstr.c']\n",
    "T_res=res_opt['cstr.T']\n",
    "Tc_res=res_opt['cstr.Tc']\n",
    "time_res = res_opt['time']\n",
    "c_ref = res_opt['c_ref']\n",
    "T_ref = res_opt['T_ref']\n",
    "Tc_ref = res_opt['Tc_ref']\n",
    "\n",
    "# Plot the results\n",
    "plt.figure(3)\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.plot(time_res, c_res)\n",
    "plt.plot(time_res, c_ref, '--')\n",
    "plt.grid()\n",
    "plt.ylabel('Concentration')\n",
    "plt.title('Optimized trajectories')\n",
    "plt.xlim(0,150)\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.plot(time_res, T_res)\n",
    "plt.plot(time_res, T_ref, '--')\n",
    "plt.grid()\n",
    "plt.ylabel('Temperature')\n",
    "plt.xlim(0,150)\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.plot(time_res, Tc_res)\n",
    "plt.plot(time_res, Tc_ref, '--')\n",
    "plt.grid()\n",
    "plt.ylabel('Cooling temperature')\n",
    "plt.xlabel('time')\n",
    "plt.xlim(0,150)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 再び CSTR_Init_Optimization モデルを使って最適制御シミュレーションを検証する\n",
    "最適化シミュレーションで得られた最適化した冷却温度を CSTR モデルに入力して、最適化の効果を検証します。\n",
    "\n",
    "最適化シミュレーションの結果から入力信号を抽出して opt_input オブジェクトを生成します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get optimized input\n",
    "(_, opt_input) = res_opt.get_opt_input()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "例題ではCSTRモデルを用いますが、ここでは目的関数を計算する機能をもつ CSTR_Init_Optimization モデルを使用します。\n",
    "モデルをロードします。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_model = load_fmu(\"CSTR1_CSTR_Init_Optimization.fmu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "初期値、目的関数を計算するためのパラメータ、ソルバーのオプションパラメータを設定してシミュレーションを実行します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Run Statistics: --- \n",
      "\n",
      " Number of steps                                 : 228\n",
      " Number of function evaluations                  : 343\n",
      " Number of Jacobian evaluations                  : 5\n",
      " Number of function eval. due to Jacobian eval.  : 15\n",
      " Number of error test failures                   : 32\n",
      " Number of nonlinear iterations                  : 342\n",
      " Number of nonlinear convergence failures        : 0\n",
      "\n",
      "Solver options:\n",
      "\n",
      " Solver                   : CVode\n",
      " Linear multistep method  : BDF\n",
      " Nonlinear solver         : Newton\n",
      " Linear solver type       : DENSE\n",
      " Maximal order            : 5\n",
      " Tolerances (absolute)    : 1e-08\n",
      " Tolerances (relative)    : 1e-06\n",
      "\n",
      "Simulation interval    : 0.0 - 150.0 seconds.\n",
      "Elapsed simulation time: 0.0509309768677 seconds.\n"
     ]
    }
   ],
   "source": [
    "# Set initial and reference values\n",
    "sim_model.set('cstr.c_init', c_0_A)\n",
    "sim_model.set('cstr.T_init', T_0_A)\n",
    "sim_model.set('c_ref', c_0_B)\n",
    "sim_model.set('T_ref', T_0_B)\n",
    "sim_model.set('Tc_ref', Tc_0_B)\n",
    "\n",
    "sim_opts = sim_model.simulate_options()\n",
    "sim_opts['CVode_options']['rtol'] = 1e-6\n",
    "sim_opts['CVode_options']['atol'] = 1e-8\n",
    "\n",
    "# Simulate with constant input Tc\n",
    "res_sim = sim_model.simulate(start_time=0., final_time=150, input=(\"u\", opt_input), options=sim_opts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最適化シミュレーションの結果と検証用モデルによるシミュレーションの結果をプロットします。"
   ]
  },
  {
   "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 overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        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=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract variable profiles\n",
    "c_sim=res_sim['cstr.c']\n",
    "T_sim=res_sim['cstr.T']\n",
    "Tc_sim=res_sim['cstr.Tc']\n",
    "time_sim = res_sim['time']\n",
    "\n",
    "# Plot the results\n",
    "plt.figure(4)\n",
    "\n",
    "plt.subplot(311)\n",
    "plt.plot(time_res,c_res,'--')\n",
    "plt.plot(time_sim,c_sim)\n",
    "plt.legend(('optimized','simulated'))\n",
    "plt.grid()\n",
    "plt.ylabel('Concentration')\n",
    "plt.xlim(0,150)\n",
    "\n",
    "plt.subplot(312)\n",
    "plt.plot(time_res,T_res,'--')\n",
    "plt.plot(time_sim,T_sim)\n",
    "plt.legend(('optimized','simulated'))\n",
    "plt.grid()\n",
    "plt.ylabel('Temperature')\n",
    "plt.xlim(0,150)\n",
    "\n",
    "plt.subplot(313)\n",
    "plt.plot(time_res,Tc_res,'--')\n",
    "plt.plot(time_sim,Tc_sim)\n",
    "plt.legend(('optimized','simulated'))\n",
    "plt.grid()\n",
    "plt.ylabel('Cooling temperature')\n",
    "plt.xlabel('time')\n",
    "plt.xlim(0,150)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "冷却温度 $Tc$ をステップ的に変化させた初期推定値の場合と最適化制御を行った場合の目的関数\n",
    "$$\n",
    "\\int_{startTime}^{t}{q_c (c_{ref}-c(t))^2 + q_T (T_{ref}-T(t))^2 + q_{Tc} (Tc_{ref}-Tc(t))^2}dt\n",
    "$$\n",
    "をプロットします。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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 overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        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=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0, 150)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "init_cost = init_res['cost']\n",
    "sim_cost = res_sim['cost']\n",
    "plt.figure(5)\n",
    "plt.title(\"cost\")\n",
    "plt.plot(t_init_sim, init_cost, time_sim, sim_cost)\n",
    "plt.legend(['Initial Guess', 'Optimiezed'])\n",
    "plt.xlim(0,150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}