{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <center> Qubit Demo Notebook with ZCU216"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "try {\n",
       "require(['notebook/js/codecell'], function(codecell) {\n",
       "  codecell.CodeCell.options_default.highlight_modes[\n",
       "      'magic_text/x-csrc'] = {'reg':[/^%%microblaze/]};\n",
       "  Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n",
       "      Jupyter.notebook.get_cells().map(function(cell){\n",
       "          if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n",
       "  });\n",
       "});\n",
       "} catch (e) {};\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "try {\n",
       "require(['notebook/js/codecell'], function(codecell) {\n",
       "  codecell.CodeCell.options_default.highlight_modes[\n",
       "      'magic_text/x-csrc'] = {'reg':[/^%%pybind11/]};\n",
       "  Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n",
       "      Jupyter.notebook.get_cells().map(function(cell){\n",
       "          if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n",
       "  });\n",
       "});\n",
       "} catch (e) {};\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "# Import the QICK drivers and auxiliary libraries\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "\n",
    "# Import the QICK drivers and auxiliary libraries\n",
    "from qick import *\n",
    "from qick.helpers import gauss\n",
    "import time\n",
    "\n",
    "import os\n",
    "import sys\n",
    "sys.path.append('../../PythonDrivers/')\n",
    "from setattens_remote import setatten\n",
    "#from setYoko_remote import setyoko\n",
    "#from setSignalCore_remote import setsignalcore\n",
    "from scipy.optimize import curve_fit\n",
    "from datetime import datetime\n",
    "from pylab import rcParams\n",
    "from tqdm import tqdm\n",
    "\n",
    "from q3diamond import *\n",
    "import scipy as sp\n",
    "from scipy import fft, ifft, fftpack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Q3DiamondSoc(QickSoc):\n",
    "    def __init__(self, bitfile=None, force_init_clks=False,ignore_version=True, **kwargs):\n",
    "        QickSoc.__init__(self, bitfile=bitfile, force_init_clks=force_init_clks, ignore_version=ignore_version, **kwargs)      \n",
    "            \n",
    "        # Initialize the configuration (this will calculate the frequency plan)\n",
    "        #QickConfig.__init__(self, self.cfg)        \n",
    "        \n",
    "        # Instantiante IQ constant IPs.\n",
    "        self.iqs = []\n",
    "        self.iqs.append(self.axis_constant_iq_0)\n",
    "        self.iqs.append(self.axis_constant_iq_1)\n",
    "        self.iqs.append(self.axis_constant_iq_2)\n",
    "        self.iqs.append(self.axis_constant_iq_3)\n",
    "        \n",
    "        # Configure IQ constant IPs.\n",
    "        for iIQ,iq in enumerate(self.iqs):\n",
    "            self.iqs[iIQ].config(1, iIQ, self.dacs[self.gens[0].dac]['fs'])\n",
    "            \n",
    "        # MR Buffer for full-speed data from readout.\n",
    "        self.switch_mr_buf = self.axis_switch_readout\n",
    "        self.mr_buf = self.mr_buffer_et_0\n",
    "        self.mr_buf.config(self.axi_dma_readout, self.switch_mr_buf)\n",
    "        \n",
    "            \n",
    "        self.cfg['fs_proc'] = 349.99\n",
    "        \n",
    "    def freq2reg(self, fs, f, nbits=32):    \n",
    "        k_i = np.int64(2**nbits*f/fs)\n",
    "        return k_i        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "adc_offset = 180 #clock ticks\n",
    "cavity_ch = 1 # 4\n",
    "cavity_nqz = 1\n",
    "cavity_SN = 27786\n",
    "cavity_RF = 7.1922e9 #11.08311e9 #11.08335e9\n",
    "cavity_LO = 7e9\n",
    "cavity_IF = (cavity_RF-cavity_LO)/1e6\n",
    "cavity_atten = 0\n",
    "\n",
    "qubit_ch = 7\n",
    "qubit_RF = 4.601e9/1e6 #4.676e9/1e6  #MHz\n",
    "qubit_nqz = 2\n",
    "qubit_SN = 27796\n",
    "qubit_atten = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# setatten(atten=qubit_atten, serial=qubit_SN) # Qubit\n",
    "# setatten(atten=cavity_atten, serial=cavity_SN) # Cavity\n",
    "# setatten(atten=squeezing_atten, serial=squeezing_SN) # Squeezing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load bitstream with custom overlay\n",
    "#soc = QickSoc(bitfile='./test_axis_sg_int4_v1.bit',force_init_clks=False)\n",
    "soc = Q3DiamondSoc(bitfile='./q3diamond.bit') #,force_init_clks=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "soc.set_nyquist(ch=cavity_ch, nqz=cavity_nqz); # Cavity tone: 11.08335e9. IF = 1.08335 GHz\n",
    "soc.set_nyquist(ch=qubit_ch, nqz=qubit_nqz); # Qubit tone: 3.362463e9. IF = 3.362463 GHz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Time of flight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LoopbackProgram(AveragerProgram):\n",
    "    def __init__(self,cfg):\n",
    "        AveragerProgram.__init__(self,cfg)\n",
    "\n",
    "    def initialize(self):\n",
    "        cfg=self.cfg   \n",
    "        r_freq=self.sreg(cfg[\"res_ch\"], \"freq\")   #Get frequency register for res_ch\n",
    "        self.cfg[\"adc_lengths\"]=[self.cfg[\"readout_length\"]]*2          #add length of adc acquisition to config\n",
    "        self.cfg[\"adc_freqs\"]=[self.cfg[\"pulse_freq\"]]*2   #add frequency of adc ddc to config\n",
    "        ch=0\n",
    "        avg_buf = soc.avg_bufs[ch] # the buffer we want to acquire with\n",
    "\n",
    "        \n",
    "        if self.cfg[\"pulse_style\"] == \"const\":\n",
    "            self.add_pulse(ch=self.cfg[\"res_ch\"], name=\"measure\", style=self.cfg[\"pulse_style\"], length=self.cfg[\"length\"])  #add a constant pulse to the pulse library\n",
    "        if self.cfg[\"pulse_style\"] == \"flat_top\":\n",
    "            self.add_pulse(ch=self.cfg[\"res_ch\"], name=\"measure\", style=self.cfg[\"pulse_style\"], length=self.cfg[\"length\"], idata = self.cfg[\"idata\"]) \n",
    "        if self.cfg[\"pulse_style\"] == \"arb\":\n",
    "            self.add_pulse(ch=self.cfg[\"res_ch\"], name=\"measure\", style=self.cfg[\"pulse_style\"], idata = self.cfg[\"idata\"]) \n",
    "        \n",
    "        freq=soc.freq2reg(soc.gens[4].fs, cfg[\"pulse_freq\"],nbits=soc.gens[4].B_DDS)  # convert frequency to dac frequency (ensuring it is an available adc frequency)\n",
    "        self.pulse(ch=cfg[\"res_ch\"], name=\"measure\", freq=freq, phase=0, gain=cfg[\"pulse_gain\"], t= 0, play=False) # pre-configure readout pulse\n",
    "        self.regwi(0,1,(1 << 0) + (1 << avg_buf.trigger_bit),\"p, $r, imm\") # set up the trigger bit\n",
    "\n",
    "        self.synci(200)  # give processor some time to configure pulses\n",
    "    \n",
    "    def body(self):\n",
    "#         self.trigger_adc(adc1=1, adc2=1,adc_trig_offset=self.cfg[\"adc_trig_offset\"])  # trigger the adc acquisition\n",
    "        self.seti(7,0,1,0,\"ch, p, $r, t\")\n",
    "        self.seti(7,0,0,100,\"ch, p, $r, t\")\n",
    "\n",
    "        if self.cfg[\"pulse_style\"] == \"const\":\n",
    "            self.pulse(ch=self.cfg[\"res_ch\"], length=self.cfg[\"length\"], play=True) # play readout pulse\n",
    "        if self.cfg[\"pulse_style\"] == \"flat_top\":\n",
    "            self.pulse(ch=self.cfg[\"res_ch\"], name=\"measure\", play=True) # play readout pulse\n",
    "        if self.cfg[\"pulse_style\"] == \"arb\":\n",
    "            self.pulse(ch=self.cfg[\"res_ch\"], play=True) # play readout pulse\n",
    "        self.sync_all(soc.us2cycles(self.cfg[\"relax_delay\"]))  # sync all channels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'regwi', 'args': (1, 23, 672164102, 'freq = 672164102')}\n",
      "{'name': 'regwi', 'args': (1, 24, 0, 'phase = 0')}\n",
      "{'name': 'regwi', 'args': (1, 26, 32000, 'gain = 32000')}\n",
      "{'name': 'regwi', 'args': (1, 28, 0, 't = 0')}\n",
      "{'name': 'regwi', 'args': (1, 27, 589924, 'stdysel | mode | outsel = 0b01001 | length = 100 ')}\n",
      "{'name': 'regwi', 'args': (0, 1, 17, 'p, $r, imm')}\n",
      "{'name': 'synci', 'args': (200,)}\n",
      "{'name': 'regwi', 'args': (0, 15, 0)}\n",
      "{'name': 'regwi', 'args': (0, 14, 0)}\n",
      "{'name': 'seti', 'args': (7, 0, 1, 0, 'ch, p, $r, t')}\n",
      "{'name': 'seti', 'args': (7, 0, 0, 100, 'ch, p, $r, t')}\n",
      "{'name': 'regwi', 'args': (1, 27, 589924, 'stdysel | mode | outsel = 0b01001 | length = 100 ')}\n",
      "{'name': 'regwi', 'args': (1, 28, 0, 't = 0')}\n",
      "{'name': 'set', 'args': (4, 1, 23, 24, 25, 26, 27, 28, 'ch = 4, out = $23,$24,$25,$26,$27 @t = $28')}\n",
      "{'name': 'synci', 'args': (100,)}\n",
      "{'name': 'mathi', 'args': (0, 15, 15, '+', 1)}\n",
      "{'name': 'memwi', 'args': (0, 15, 1)}\n",
      "{'name': 'loopnz', 'args': (0, 14, 'LOOP_J')}\n",
      "{'name': 'end', 'args': ()}\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "115bae9dc17f4e989d528fb9e64bc7bc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/300 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "config={\"res_ch\":4, # --Fixed\n",
    "        \"reps\":1, # --Fixed\n",
    "        \"adc_chs\":[0,1],\n",
    "        \"f_mixer\":0,\n",
    "        \"relax_delay\":0, # --Fixed\n",
    "        \"res_phase\":0, # --Fixed\n",
    "        \"pulse_style\": \"const\", # --Fixed\n",
    "        \n",
    "        \"length\":100, # [Clock ticks]\n",
    "        # Try varying length from 10-100 clock ticks\n",
    "        \n",
    "        \"readout_length\":200, # [Clock ticks]\n",
    "        # Try varying readout_length from 50-1000 clock ticks\n",
    "\n",
    "        \"pulse_gain\":32000, # [DAC units]\n",
    "        # Try varying pulse_gain from 500 to 30000 DAC units\n",
    "\n",
    "        \"pulse_freq\": 1500, # [MHz]\n",
    "        # In this program the signal is up and downconverted digitally so you won't see any frequency\n",
    "        # components in the I/Q traces below. But since the signal gain depends on frequency, \n",
    "        # if you lower pulse_freq you will see an increased gain.\n",
    "\n",
    "        \"adc_trig_offset\": 100, # [Clock ticks]\n",
    "        # Try varying adc_trig_offset from 100 to 220 clock ticks\n",
    "\n",
    "        \"soft_avgs\":300\n",
    "        # Try varying soft_avgs from 1 to 200 averages\n",
    "\n",
    "       }\n",
    "\n",
    "###################\n",
    "# Try it yourself !\n",
    "###################\n",
    "\n",
    "prog =LoopbackProgram(config)\n",
    "iq0, iq1 = prog.acquire_decimated(soc, load_pulses=True, progress=True, debug=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img 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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xffff707d5430>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot results.\n",
    "plt.figure(1)\n",
    "plt.plot(iq0[0], label=\"I value; ADC 0\")\n",
    "plt.plot(iq0[1], label=\"Q value; ADC 0\")\n",
    "plt.plot(iq1[0], label=\"I value; ADC 1\")\n",
    "plt.plot(iq1[1], label=\"Q value; ADC 1\")\n",
    "z= iq0[0]+1j*iq0[1]\n",
    "plt.plot(abs(z), label=\"amplitude; ADC 0\")\n",
    "z= iq1[0]+1j*iq1[1]\n",
    "plt.plot(abs(z), label=\"amplitude; ADC 1\")\n",
    "plt.ylabel(\"a.u.\")\n",
    "plt.xlabel(\"Clock ticks\")\n",
    "plt.title(\"Averages = \" + str(config[\"soft_avgs\"]))\n",
    "plt.legend()\n",
    "#plt.savefig(\"images/Send_recieve_pulse_const.pdf\", dpi=350)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Plot results.\n",
    "# plt.figure(figsize=[4.5,3])\n",
    "# plt.plot(iq0[0], label=\"I value; ADC 0\")\n",
    "# plt.plot(iq0[1], label=\"Q value; ADC 0\")\n",
    "# plt.plot(iq1[0], label=\"I value; ADC 1\")\n",
    "# plt.plot(iq1[1], label=\"Q value; ADC 1\")\n",
    "# z= iq0[0]+1j*iq0[1]\n",
    "# plt.plot(abs(z), label=\"amplitude; ADC 0\")\n",
    "# z= iq1[0]+1j*iq1[1]\n",
    "# plt.plot(abs(z), label=\"amplitude; ADC 1\")\n",
    "# plt.ylabel(\"a.u.\")\n",
    "# plt.xlabel(\"Clock ticks\")\n",
    "# plt.title(\"Averages = \" + str(config[\"soft_avgs\"]))\n",
    "# plt.legend()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# mux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#############################\n",
    "### Mux4 SG Mixer Setting ###\n",
    "#############################\n",
    "# Mux4 Signal Generator is connected to channel 6 of tProcessor.\n",
    "# This block is driving DAC 0 of Tile 228.\n",
    "\n",
    "# Set parameters.\n",
    "ch = 6\n",
    "f_mixer = 0\n",
    "nq = 1\n",
    "\n",
    "# Get Tile and Block for mixer.\n",
    "tile = soc.dacs[soc.gens[ch].dac]['tile']\n",
    "dac = soc.dacs[soc.gens[ch].dac]['block']\n",
    "\n",
    "soc.mixer.set_freq(f_mixer,tile,dac)\n",
    "soc.mixer.set_nyquist(nq,tile,dac)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for ch in range(4):\n",
    "#     avg_buf = soc.avg_bufs[ch] # the buffer we want to acquire with\n",
    "#     print(avg_buf.trigger_bit)\n",
    "#     print(avg_buf.fullpath)\n",
    "#     print(avg_buf.readout.fullpath)\n",
    "\n",
    "# thegen = soc.gens[6]\n",
    "# thero = soc.readouts[0]\n",
    "# print(thegen, thegen.fstep)\n",
    "# print(thero, thero.fstep)\n",
    "# print(thegen.fstep/thero.fstep)\n",
    "# adcf = int(600/(thegen.fstep*5)) *(thegen.fstep*5)\n",
    "# print(adcf/(thegen.fstep))\n",
    "# print(adcf/(thero.fstep))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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 / mpl.ratio, fig.canvas.height / mpl.ratio);\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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N = 86\n",
      "2457.6\n",
      "2457.6\n",
      "2457.6\n",
      "2457.6\n"
     ]
    }
   ],
   "source": [
    "plt.figure()\n",
    "\n",
    "#############################################\n",
    "### Mux4 Readout SG and MR_BUFFER Capture ###\n",
    "#############################################\n",
    "# This example creates 1, 2, 3 or 4 outputs tones muxed using SG Mux4 V1.\n",
    "# This drives DAC0 of Tile 228.\n",
    "# This output is fed-back to ADC0 on Tile 226.\n",
    "# MR_BUFFER captures data syncrhonized.\n",
    "\n",
    "def adcfreq(f,gen):\n",
    "    return int(f/(gen.fstep*5)) *(gen.fstep*5)\n",
    "\n",
    "gen_ch = 6\n",
    "gen = soc.gens[gen_ch]\n",
    "\n",
    "freqs = [adcfreq(f,gen) for f in [100,200,400,600]] #we'll mask the 4th output\n",
    "\n",
    "# adcf = int(600/(gen.fstep*5)) *(gen.fstep*5)\n",
    "\n",
    "for iF, f in enumerate(freqs):\n",
    "    gen.set_freq(f=f, out=iF)\n",
    "\n",
    "\n",
    "# Signal duration (uSeconds)\n",
    "T = 0.2\n",
    "N = T*gen.fs/gen.NDDS\n",
    "soc.tproc.single_write(addr=100, data=N)\n",
    "print(\"N = %d\" %N)\n",
    "\n",
    "# Set data mux.\n",
    "buf_chs = [0,1,2,3]\n",
    "for buf_ch in buf_chs:\n",
    "    readout = soc.readouts[buf_ch]\n",
    "    readout.set_out('product')\n",
    "\n",
    "    fs = soc.adcs[readout.adc]['fs']\n",
    "    print(fs)\n",
    "\n",
    "    # Set dds frequency.\n",
    "    readout.set_freq(f=freqs[buf_ch]+f_mixer)\n",
    "\n",
    "# Enable capture on buffer.\n",
    "soc.mr_buf.enable()\n",
    "\n",
    "########################\n",
    "### Start of program ###\n",
    "########################\n",
    "# Simple QickProgram to set mr_buffer trigger.\n",
    "prog = QickProgram()\n",
    "\n",
    "# Register for PMOD0 and mr_buffer trigger bit.\n",
    "prog.regwi(0,1,0x101,\"p, $r, imm\")\n",
    "prog.regwi(0,2,0x100,\"p, $r, imm\")\n",
    "\n",
    "# Registers for SG on channel 6.\n",
    "prog.memri(1,1,100, \"nsamp\")\n",
    "prog.regwi(1,2,0xf,\"mask\")\n",
    "\n",
    "# Initial sync.\n",
    "prog.synci(1000)\n",
    "\n",
    "# Set SG channel 6.\n",
    "prog.set(gen_ch, 1, 1, 2, 0, 0, 0, 0, \"ch, p, $ra, $rb, $rc, $rd, $re, $rt\")\n",
    "\n",
    "# Trigger.\n",
    "prog.seti(7,0,1,0,\"ch, p, $r, t\")\n",
    "prog.seti(7,0,0,100,\"ch, p, $r, t\")\n",
    "\n",
    "prog.end()\n",
    "\n",
    "soc.tproc.load_bin_program(prog.compile())\n",
    "\n",
    "soc.tproc.stop()\n",
    "\n",
    "soc.tproc.start()\n",
    "\n",
    "#################################\n",
    "### Capture and transfer data ###\n",
    "#################################\n",
    "# Disable capture on buffer.\n",
    "soc.mr_buf.disable()\n",
    "i,q = soc.mr_buf.transfer(0)\n",
    "\n",
    "# plot time-domain\n",
    "plt.plot(i)\n",
    "plt.plot(q)\n",
    "# plt.plot(i[500:600])\n",
    "\n",
    "#####################\n",
    "### Plot spectrum ###\n",
    "#####################\n",
    "# x = i + 1j*q\n",
    "# x = x[500:]\n",
    "# w = sp.hanning(len(x))\n",
    "# xw = x*w\n",
    "# Y = sp.fftpack.fftshift(sp.fft(xw))\n",
    "# F = (np.arange(len(Y))/len(Y)-0.5)*fs\n",
    "# plt.plot(F,20*np.log10(abs(Y)/np.max(abs(Y))))\n",
    "# plt.xlim(-50,50)\n",
    "# print(len(Y))\n",
    "## plt.plot(np.real(x))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N = 430\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 / mpl.ratio, fig.canvas.height / mpl.ratio);\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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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NnAqZI/ctzdmqdr8Z0S14RGVYPthlqBfOmtXu7+W6y7xlmJC34uACLSagOZvCcBWB8veKiqAgsS3+j148QzoMhIu+iRII2rW4QlM3+1Gkes273lDT4d3Xg6KgAi0JQHN3xKAIcWXAihIfKvehpd+bt/I8OuPgjSiZh2egLlZ5f6d2zZ+txySWv+e7g7PRg6KgAiEREDztwSgAigkAkE2XvE/eOVcyDkcfvV+kEbULCwI3NIFGnbA5YsgNTcsXJaTIiACbU9AAlACMKQoUwAFiW/56/YNE93HwQXvBGlEzcKCwG059h7N/1sAab3CwmU5KQIi0PYENH9LAIYUZQqgIPEtfQ3+/Sv7LtRfvBWkETULCwK3d7dvCvrtXMjoGxYuy0kREIG2J6D5WwIwpChTAAWJzzxm5T8X2o9rOe+/QRpRs7AgcGdP+1Ezl34NmQPCwmU5KQIi0PYENH9LAIYUZQqgIPEtehHe+A30OgbO/U+QRtQsHAjU396bqLpiPj/6DY48svmzP8OBgXwUARE48AQ0f0sAhhRVCqAg8ZmHE//3t9BnEpz9apBG1CwcCGz5ay6dHKWcVH8Lb992WTi4LB9FQATagYDmbwnAkMJMARQkvvnPwpuXQ9+fwM9fCtKImoUDgc1/7UW2Yzsn19/E/267PBxclo8iIALtQEDztwRgSGGmAAoS37yn7Few9Z8MZ/4rSCNqFg4ECqb3oSvbmFZ/I6/fdmU4uCwfRUAE2oGA5m8JwJDCTAEUJL5vHod3rrbf6XvGc0EaUbNwIJB/Y39yAkWcVv8XXrvtqnBwWT6KgAi0AwHN3xKAIYWZAihIfHMehXevgUHT4GfPBGlEzcKBQN7fBtHdX8AZ9Tfw8m1Xh4PL8lEERKAdCGj+lgAMKcwUQEHi++pheP86GHwanPZkkEbULBwI5N00hO6+TZzl+RNP33gV0ZGucHBbPoqACLQxAc3fEoAhhZgCKEh8sx+ED/4MQ8+AUx4L0oiahQOBDTcNJ9e3gXM813HHNVeQnRwTDm7LRxEQgTYmoPlbAjCkEFMABYnvy/vho7/CsJ/DtEeCNKJm4UBgzd9G0Me/nvM913LVpZcytGtyOLgtH0VABNqYgOZvCcCQQkwBFCS+L+6Bj/8GI86BKQ8FaUTNwoHAyhtHMSCwll96rubc8y9mYr/McHBbPoqACLQxAc3fEoAhhZgCKEh8n90Fn9wMI8+Hk/8epBE1CwcCS6YfylC+5ULP7zn+1F9x2qiu4eC2fBQBEWhjApq/JQBDCjEFUJD4Pr0dPr0NDvklTL4vSCNqFg4E5v/1MEY5VnOx50qGHncuv53YOxzclo8iIAJtTEDztwRgSCGmAAoS38xb4PM74dBfw0n3BGlEzTo6Ab8/wNzphzPauYpLPZdTmH0Cb/x2XEd3W/6JgAi0AwHN3xKAIYWZAihIfGb/n9kHeNjFcOKdQRpRs45OoMbjZdFN4xnrWsH/eS7j7cBYVvztBD0KpqMPvPwTgXYgoPlbAjCkMFMABYnvvevg64fh8EvhhNuCNKJmHZ3A9up6VtxxDEe6lnI9/8cLdWN487IjGNI1qaO7Lv9EQATamIDmbwlAHn74Ye666y6KiooYNGgQ999/P+PHj29V6CmAWoVpz0obv4RnTrI/G381HHNDEEbUJBwI5JfWsO6+E5jgWswjyVdzx5aRXHNCPy6doH2A4TD+8lEE2pKA5u8wF4Avv/wy5557riUCx40bx6OPPsoTTzzBihUr6NatW4uxpwBqEdGeFbz1cPNuj/E4djoc8bv9NKLq4UJg9ZYqCh76Kce4FvL14L9x5rzeRDgdvHX5EfTvlBguGOSnCIhAGxDQ/B3mAnD06NGMHDmSRx7Z9TDiAQMGMHXqVG67reWlyTYLIK8H5j8Dh1wArkgCZXmsrYmlu7sa98rXKOg+jZROPYhzu6BkLaT2gk2zYetySgaci8MVQWqcm0AgQF5JDd1SY3E6HZTu8PDCnDyO6JPB8Jy9P1C3qKKW5Bg3Mca2KeWbICoRYuz6Pn+Aovx1ZHfqhKOhFso2QvZI+//xmRCVgNm8v7FkB7mJARzmvb8Dfkp9Sm+8L51P3No3d53KJ94Nh13YqlP7ng9Ws3RzBf/4+UjioyL22qayroHSag++QICe6XE4HI5W2d5bpYKyGjISooiK+P5Xj1XUNOD1+0mLj6KwvNbi3tLrygzD6/6zhB31Ph44czgRLmeL/axr8FFW46Fz0q43YWzYvoPMhCjiduNhxtx83j0tDpfT9n9lUaXFrGtKDK/OLyDOHcGm0hqOG5jFgk1lPDt7o3V37YlDOu+zH5vLa0mIjiAxOpKS6nq8/gBVdQ30zkygbIeHBp+fzMTopvbmMzMO6fFR1mczV21la2U9I7ul0Dcr/nvHpt7rY9nmSmo9Pmr/eTrHuebjPel+Tp/blwWbyi17fzpxABcOjWRBfgWzvy3Cm9SdnJRYpo3ItmK9pVK9ZR0lgUTKvJF0SYq2+15XATWlkJprNV9XXM2mkhpWFFUyZXgXuqbEYpalX51XwAmDO5GbHtfsMIbDc1/lUef1kRrr5ui+aSQsf54YfzX+qCTejDyBxBg3E/tnwvY1kJQDkTY3s+exumgtmdm5EBFln3sxKdY5RSAAC5+z/84abNUvIg23O8qKvYraBjxePxm+bVBXbtXZWFJDl+QY3DRAWR5k9GVbZR0vzc1nVPcUlm2uYEK/THpmxBFpYtD8QKssbPJ/d+dMXP17/ia6BrZw+CGH2f0pWsTWilpKkwcT447A622gu2s7kZFRbK6LIM1bTHF9BJ275uLdvp4id3dyM+J3ma3YbPvjjoUty2D9J3DIr+y/TTH9KVwEGf2arj/W5/XVsGMbn3+7jdLILKYO7cyilatZWJXIuaNziChZjWfjHMqjOpPZ73Aw19OELNtmXSV4qiGxi/Xn598Wk1daw88P64aregtExtjHWvU2eHbgG/wz/jUnj5FZTpJdDXTJ6UlD6UZeWFLBqL49GJriBRwQl2YzWfGGfRzznvPSDZDQybJpridvLynilJFdretKUzHX0G8eY1HUofxpto/rfjKAI/qk23Foru1dRoLPw6b8PDLT04le9Tq+hc/zv25/pF+3zgwcMNiaI5qKiZnlb7C+6xTeXd/AL8b2oL7BR2DrctJy+sOWJbbvi1+CoadDSg/rPP52azWd/Vt4Y4ODc8b2ss7z95dvoa6ulik5dWxw5FjnyLaqeqLqthEbE0dyXJTdT3P8JS9Dr2MgrZcVr+Y82JK/jpxOWcxau52EJU/T9ZCTiEjvSWJsNOVlJdTEdLLic8eOasq35pPdcwD4/TDvSWqTelKWNdb6vq1Km83fbdXhNrDrCJgzOwyLx+MhNjaWV199lWnTpjURuOKKK1i0aBGfffZZMyr19fWY/xqLCaCcnBwqKipITDxwGYmy//2JlAX/+N5R+dB9LEN9y8nyFTWrN9/fhyER+bj9dRQE0ily59LLu5aNvjSSqSba4cGNjwRHLVF42EgXkp211BPJNm8smc5KAs5I0v3bicBn2a90JlFLNFn+rc2O10AEkZgL4a6y2t+VHo4tRDm8lAfiiKHe+vceZeoMGH7WrmtXjYffPD+flUVVREU4rYuNefWXCdHCiro9mvbLSuAvPx3IvxcU8MmqbZTVNDTrV3JspHUhM3rghMGd6ZMZz7y8UjZur7HEZHW9l5TYSGsiN+LEXLSO7p9pibhHPltHQlQElXVe0uLcDO2aZF0kjTgxNqvqvbgcDrZU7tkvc3H/5bhcPlixhd4Z8UwZns3Dn65l9roSS2jUNPgo/05fD+uRSrTbxaqiSsvnxmKEUlF5nXWs3YthssPjteykx7vJTollcb4tjPZVjBY2zFZtqdpnHSOa/YGAJSQOy03lWCMQ88p5ataGfbYxorCqzkus28VlR/fmyzXbLeFkeJrijnAyvGsyc/NKrfmxsRhheMLgLAZ1SWLhpjLr/z8Z0onr/7OMj1buirEZkfdxgmsunHQvX6VO5eePz+Ys1yeMdq5kimt2k71nvccxzLmOJGcdWZTQ4IiiPqUvkb4aUipWUO1KYl3cSPpUfkUs9pgVBlJZ4u/FCOcaAhExpPu2EoGfWmcs9f4IkqlkcyCN+kAkCwN9rIm80uOgm2MbfR0FlCUNICo+hboGL8kli6gKRIPfR7qjgupAjHWedXVs34OdOTe3BFI4yfVN0+dlJLLJlUO6dyvZju34Aw58OIl0+KghmryofgyoX9z8vAu42BTIZK2jG7O8/bnA9R7dHdtwOmzQlYEYPvQfYmVQzXlvyiJ/Txb6+3CYcxXRePi370gGJPtI9JVyVN3MZsfY7MgiydVAftwQtpeWMN61jI3RA6jxRzDQs7SpfiOnns4t+4yVokAq0Q4vKVTuUWebM4NMf3HTZ5/7hpDjLCbX0dyWuZ7tzjTPn2lxzqCCB31T6ecosOPlO6XWlYDPFU28p9hiuy5xNGtrEyiojWS8c6l1vHhqv/ccMl+WE9/EsiyQQIrDPp+qAjFEOPzWdc6UbaSQSZn17/URvXiq9khrbBf7e9EtK4Ve1Qs5KjCXTt7NTcdc5u9BcSCJia49x9qLq+k6vLcOGq4vRv2MMlc6N9Xe0lSlOJCIxxVHtr/5HNFYaT1d6UkBDQGXFW9lgXgWuIZyiH8xG/ydSKKaXGfza74H8yPciRvPHl0y8fKG63hGe77mEOe3e43ZmshUkrzFmLGLdHjp4ii16pUG4vHhIsNRYfXnbf9oBkSXkZjWibopT5DbOb3F8dmfChKAYZwBLCwsJDs7m1mzZjF27NimuLn11lt59tlnWb16dbNYmj59OjfeeGOzzw+0AHzqsfs4ZfNdJDt27E88/+jqLhn3D+vZbqZ8u7WKk/7+BQ2+sPw98qMbu/bq8D8iH2Cyaw785C4YfRFlnzxIymd/bq/D6zgiIAIHAYEFXc5i5EUzDmhPJAAlAJk9ezZjxoxpCqxbbrmF5557jlWrVjULtvbKAJql2jtfmcngsg/oVzGLru4dpMZGElWx3urTCld/FnhycCRlM7X6JeKo403f4RQEMrgkwl5izYvoQWniACJ2bOHrHV2sJbJB6S5G1s0hbutcCtPHsajr2XQrfIfIio10bthEhK+eOEcdda4E5mafxwfrdtDTUURscqb1K6yyopRBdQvJqs8jOVDBZndPlnc+heVrN7Ao0Mv6hRtFA0c6lzA2aj3x3jLyMo/B72uge8kXfNb1ElyVeRxZ+bbVx4dz7ubMM8/n18/ObVraa4Tudjmt7NaI7inW0onJXpmsV0FZ81/pZgnr1JFdOWZAJimxbubnlVlLXWYp8LZ3VlHbYGcxTbbquAH2sqdZHjNlTM80juybQVq8mxWFlTwze6P1efe0WGuZ74tvt1tLgLsXkxEs2bHnL99eGXFWxuzr9aW4XQ7mbrR//X+3jO2V1rSEbZZo312294xJpMthieHE6AirfoM/QHFVPUf1zWB8n3RrOXJQl0Qe+HgNr80vsA5jtgTs8PgY2DmR0T1TLVE9oFMifTslYPbTzdtYamU7TebTLOeYDGdiTCTnjenOo5+t5+lZG4iPjmjK3iXFRFpLi98thrVZQnzok7WYpWFTTMa23uu39uiZJSyTEcxJjbXsmjJ5aGfrLR63vrPSWqLumR7P6q27spEm62ri/rvl75EPcrLrKzjhdmu5ijcuhdqdGYOILAqTRzGo6gsCvgZKfXHE+yuJdjTv83ftPu0/kX4DhkJ9Feu3VXLSjtcpCqTxqvN4zo/4iB5eu9+NZY6/P4c6Vjdl18znb0RPtTLjUVWbmBC9hqSkJCJL1+Ly15Mf1Zs8fwbDfMvZ4XeT50+3nmfYWJ72Hk9nR2lTtsrYT4yNYbl7MIsiR3JawnKGb3zCqr6FdFb4ulIYSKPKnclk/0ySopzE+iqJ8O76kbjVkc5DznPYGtubs8sfte6eXuDvzUe+kQxzrud41zwrU1XnjOP9hmHUO6Ksh2wPd65r6tf2QCLpjko3zJsOAAAgAElEQVQqA7EkOuxzZI0zlx6+TeQ5c1jp7WRl4GqjM1k79GqiS1fjX/MhfSKLKUnoh7d0E50cpQxzbqAivhdfJ0/my5quVERmcG7R7RzuXL4H1xpHLLGBGqqcSWzxxtPHudnyM9NRTn7CcGY7RzGwZi5dPBswmbBNaeOtJcby8nKScwaSHO1kYUGllT3NLF9orWJsCaRSFd+DCdHr8Zul8sSheEoLqGqAyog00pMSGVv4tHVcb1xnKmvqmO/tSVlEOlP8M4lyNHBPw2mY9ZFj4zdweL2dad7gz6KTo4z1gc4Mcubhc0SSlzaOOfU9SChfyfHOeawO5JCRkkhWxZI9/DRZZGO3sWwgm0pHPH38GzEZvA/8hzAuN4mP1tdZmewK4ih3pXE+bzE/bjz9aheR5C9nvmMQ9Uk9ObziPevzlY5e9KpbxriGryzT+a4copwBMhsKKIvrxTe+vgxqWM76hiS8ARerYkcyKW49C90jcG6eSzqVHOXa1df1jhy6BzbjsshhZc5nZZzJcSXP4WyooSjlEFalHsPGsnq2VDWQW7ucMyM+tdiUOVMZyUo2+rOslaeIhAyLcWVEKpWuFHrWraR/3aJdDKIHkFu3Eo/DzfWeC6zPT3N9zrbYvqT1GkFNaRHxhbMY41jGp4c8zITJZze7PoTygQRgGAvAYJaAvxts7R5AZi+LwwmuCGsp0uxPKy8uYu289xlx3Nl8W1xLQ005Q2u+sfbdWfuIwNpL1bSnz3xg9tbs/G53n8zSX6S/Dof5zumyjlHX4McIgWbF2HC5weGw9i7d9NZKRuQkW0uZQ7KT7P1RPq9lx9Qx+2lwx0F5Ptxv72G6OvEeEvuMbVpiNCLiP5eOpVdGPEYANu7lMv0wf5u9c0Y0mb195rMPlm+1hMj37RNp5GTEhRFTZr+dWVI2e9hMsfY+7VZMfbNUafrSuIfQ8PP4/MREuiyBY/a2zVy1zVp6vmHyQKuusba7LbOXcupDsxiRk8IDZw23xsDs1fvu8cyhzTHNPpkBnROtPXONdRr73tg9sw9w9341fl5e4yHWHWEtt5o2kc5d7IK9QDYe2+wTM0vjI7qZpc49j2/2ehoujXsem8WZmZDMnbzF1ZZwNTwNexNTxo+PV22zltaN+DNczfK3iTVjb0tFHYff9jH3RT7ENNcsGHwaLHvNdidzIFz0GUS47b9NnJnluXo/X60r4Rj3ctxmWKPiqU0fSkxsrL0/y9dgxWLAac6fXf02bXfs2GHFc1y0Hev++hoCvnpceV9C18NoiM1gS3mttRdr7NB+eL1eIt32+dUYn1a8mPPC4bLOUduQn43bq3h45ir+WHwdqd5iuOAdviqJpVNSNF0THJTXO1mYX84xA7Ka9m1abU2fV71NXeYw3s93cuyALCuGmuLC77PreGvt64I5v3YWU8dM2J+sq+awnmlWbJiQb9w/a8aycdzMfk2Hr57kxAQaGhqssTD7iKnaCvlf4+/3UzyeOiq9TqY9NJtDeqTwwJkjmo7l9flxOhzW+WpipWk/4XeuMT6fz7IfHR1tc3JGgtNpj1/AT43PgddTQ2JC88f87B6H+4pp84PHnOeH9ki14ux799aaa6nZu+ZwWNcVcw6ZvZQNnno+/rbM2iNt4tJwK85fzRsffEK/8ada1zbzo4mGWlxm76bpP1jbJr5YsYkRPTuTava9mr14mxdAn0nWcSzeUVHgrbPHbOc+x9cX5DF7bSl//ulgK/bNnkQTF2bftjU+jdfq7WuhPA96H2O393t37f0zf+8othgSl2nvcVzzAfSf3LS/1PTPhOd3rz/meN2jquk+92Y8Q8/G3fdoex+er94+ttkTacbR7FV0mXlhz+vl6oJteJe/SV76kRwzrKd13kRExVJd7yMlbuf5ufuArXwTVr8Lk26G2FQ7XgINrC31WD88zfW82R7qxrkj2IvZPtq1+/x9gPt/IMyF7R5AA8/cBDJq1CjrLuDGMnDgQKZMmfLD3gRyIEb2YLVRWw53dLd6d5r/NjbH9KOooo6rjutrZdz6ZCUcrD1Xv9qZgLk5Jf7dyzjV9QUkdIaqnXuZfr+yaRN/O3cptMOZidXMwiHcnBRaB9RaBESgkYAEYBhnAE0QND4GZsaMGdYy8GOPPcbjjz/O8uXL6d7dFinfVxRALRHay/cma/G3VOuLE+tvZUWghzUfLp1+/D7v7g3iKGrSAQiYDGPpCxeSvvbVXd5MuB4mXNsBvJMLIiACPyQBzd9hLgBN8Jns35133mk9CHrw4MHcd999HHnkka2KSwVQqzA1q+R/80pmfrOICxuuIoCT3pnxfPT7o4IzplYdm8D/LocFz+7y8ayXod8JHdtneScCItDmBDR/SwCGFGQKoODx9fvzu9Y+LFMumdCLa0/oH7wxtey4BN76Hcx7apd/v18Fift+XmHHBSHPREAEDiQBzd8SgCHFkwIoeHwjb/qw6a7PGeeMsvb/qYhAMwJvXw1zH7c/jsuAq9doD53CRAREIGQCmr8lAEMKIgVQ8PiOuGNm0yNdXrhwNGN7HdiHfAbfM7U8qAi8ey3M2fn8r97Hwjn/Pqi6p86IgAj8OAlo/pYADClyFUDB4zv+vs+bngP39uVHWG+CUBGBZgTeux6+fsj+2Lw32rw/WkUEREAEQiSg+VsCMKQQUgAFj888I2/RzteXfXntROvBxioi0IzABzfA7L/bH0+6BcZeJkgiIAIiEDIBzd8SgCEFkQIoeHynPTKbeXn22zKWTJ9kvV9XRQSaEfhoOnx5n/3xiXfDYRcKkgiIgAiETEDztwRgSEGkAAoe35SHZrF4ZwZw/a0nNr31I3iLatkhCXx8E3xxt+3ayf+Akfa7o1VEQAREIBQCmr8lAEOJHxRAweM798k5fLFmu2Vg4+0nBW9ILTs2gc/ugk9utn089UkYclrH9lfeiYAItAsBzd8SgCEFmgIoeHxz1pdwx3urOHFIZ349vmfwhtSyYxP46iF4/3rbxzOet99xrSICIiACIRLQ/C0BGFIIKYBCwqfGItAygXlPw1tX2vXO/jf0ObblNqohAiIgAi0Q0PwtARjSSaIACgmfGotAywSWvAL/2Xnjx/lvQe74ltuohgiIgAhIALYYA45AIBBosZYq7JWABKACQwTamMDKt+Dls+2D/OojyDm0jQ8o8yIgAuFAQPO3MoAhxbkCKCR8aiwCLRNY9wk8N9Wu95svodOQltuohgiIgAgoA9hiDCgD2CKifVeQAAwBnpqKQGsI5H8DTx5n17xsHqT3aU0r1REBERCB7yWg+VsZwJBOEQVQSPjUWARaJrBlGcwYZ9e7chkk57TcRjVEQAREQBnAFmNAGcAWESkDGAIiNRWB0AiUrIMHR9o2rl4L8Rmh2VNrERABEQA9xxdlAEM6EZQBDAmfGotAywTK8uCBoXa9P+ZDdGLLbVRDBERABJQBbDEGlAFsEZEygCEgUlMRCI2ApwZu7WzbuKEEXBGh2VNrERABEVAG0IoBCcAQTgVlAEOAp6Yi0FoCZhnY4YTU3Na2UD0REAER+F4Cmr8lAEM6RRRAIeFTYxEQAREQARH4QQho/pYADCnwKioqSE5OJj8/n8RE7U0KCaYai4AIiIAIiEA7ETACMCcnh/LycpKSktrpqAfXYbQEHMJ4FBQUWAGkIgIiIAIiIAIi8OMjYBI4Xbt2/fF1/AD0WAIwBIh+v5/CwkISEhJwOBwhWGretPHXibKLBxRrM2Pi3LZ8G62Lszi3D4H2O4piun1YtxVn8xbcqqoqunTpgtPpbB9nDrKjSAAeZAOy+4Rp0tJmmVnLy203SNoH0nZsd7cszuLcPgTa7yiK6fZhLc5tx1kCsO3YhmRZQR8SvlY3FudWowqpojiHhK/VjcW51ahCrijWISNslQFxbhWmoCpJAAaFre0bKejbnrE5gjiLc/sQaJ+jKJ7bh7OuHeLcfgTa7kgSgG3HNiTL9fX13HbbbVx33XVERUWFZEuN901AnNsnOsRZnNuHQPsdRTHdPqzFue04SwC2HVtZFgEREAEREAEREIGDkoAE4EE5LOqUCIiACIiACIiACLQdAQnAtmMryyIgAiIgAiIgAiJwUBKQADwoh0WdEgEREAEREAEREIG2IyAB2HZsZVkEREAEREAEREAEDkoCEoAH4bA8/PDD3HXXXRQVFTFo0CDuv/9+xo8ffxD29ODskrl7+j//+Q+rVq0iJiaGsWPHcscdd9CvX7+mDpunwN9444089thjlJWVMXr0aB566CGLd2Mxd59dffXVvPjii9TW1nLMMcdgxiZcXxv0faNtmF9//fVcccUVVryaIsYH7vzYvHkz1157Le+++64Vi3379uXJJ59k1KhRYn2AMHu9XqZPn86//vUvtmzZQufOnfnFL37Bn//856Y3RSimg4P9+eefW3Pa/PnzrXnt9ddfZ+rUqQf8emyu5Zdffjn/+9//LNsnn3wyDz74IMnJycF1vIO3kgA8yAb45Zdf5txzz7WExrhx43j00Ud54oknWLFiBd26dTvIentwdueEE07gzDPP5NBDD8Vc1P/0pz+xdOlSi2FcXJzVaSMIb7nlFp555hlrMr355psxF6nVq1dbr/Yz5ZJLLuHNN9+06qSlpXHVVVdRWlpqXcRcLtfB6fwP0Ku5c+dy+umnW2+smThxYpMAFOMDMxhmUhsxYoTF1sRkZmYm69ato0ePHvTq1UvxfGAwW9eD++67j2effdb6IThv3jwuuOAC69pgftjouhE8aPPDZdasWYwcOZJTTz21mQA8UNeKn/zkJxQUFFg/7E256KKLrPPEXMdVmhOQADzIosJkosxJ8sgjjzT1bMCAAdavJZNlUdl/AsXFxdak+dlnn3HkkUdamSnz/scrr7zSyqqYYrJ9WVlZljC8+OKLrVfwZWRk8Nxzz3HGGWdYdcx7n3NycnjnnXc4/vjj978jHbBFdXW1Fa/mB4uZKIcPH24JQDE+cIP9xz/+0Zo8v/jii70aFesDw3ry5MnWNcBkVhuLESuxsbHWdUCcDwxnh8OxhwA8UFxXrlzJwIED+frrr60VHVPMv8eMGWOtBu2+AnRgPPnxW5EAPIjG0OPxWBebV199lWnTpjX1zPz6XLRokSVgVPafwNq1a+nTp4+VBRw8eDDr16+3MicLFiywMiuNZcqUKdZSgckAzJw501ryNRm/lJSUpjrDhg2zxLhZPlaB888/n9TUVCtzMmHChCYBKMYHLjrMpGZ+cJjMhrkGZGdnc+mll3LhhRdaBxHrA8P69ttvZ8aMGXzwwQfWqsDixYuZNGmS9YPmrLPOEucDg5nvCsADFb9PPfUUv//97ykvL9+jp+aabq5PJpursicBCcAQIsLv91tZIbNkaII61GL2RvTv39+6ADX+gjE27777bl544QVLsKjsHwHz69IsB5uLwvvvv281njNnjnVhN78KzT6fxmL2juTn51u/Tl955RVrkt2+ffseBzTir3v37jzwwAP715EOWPu1116zYvPTTz8lOjqaE088kSFDhlhZVDE+cANuMtGmXHbZZdaPD7MFwWQFTQwaYSLWB4Z14/4+IxbMFg+fz8df/vIXa+uHrhsHhrGxkpSUZO2zNBnXA8nVXIuM3YULF+7RWfMj/5xzzmkax8YvzXhXVVVZq0FOp/PAOfgjsiQBGMJgmV/kZklQRQREQAREQARE4MdHwPzoD9cb+36UArC97ihqKZTNPjGTXjYBZDbAq4iACIiACIiACBz8BCorK60EjlkdMlnJcCw/SgHYXncUtRQQJoBM4BghKAHYEq29fF9TCkWLIfcoCNMUfBDU1EQEREAERCBEApq/4UcpAHcf97a6o6g1saUAag2l76nzyBGwdSlMexSGnRmiMTUXAREQAREQgdYR0PzdAQXggbqjqDUhpABqDaXvqTN9Z9q950Q4740Qjal5hyTg9ys73CEHVk6JwA9LQPN3BxSAs2fPth6gbJ6cb+7uaSzmgZB5eXnWnaDmjlpzS7h59tvuxdwZmpubaz18eW/F1N+9TeMeAi0BB3kiSwDuG1zxakjqCm77wdVhWTbPh2enwNF/hsN/E5YI5LQIiEDbEJAA7MAC0DyeZfdHfJhnZpmbNd577719CsDjjjvOej6ceRbU3op5TdDenv8mARjECerzwk1pdsPcIyF7FPgaYNApkNYTyvMhJhmSu9mfl+XBupnQ/0RwOCEy1v7elJVvQkQMpPSA9N72Zw21sGUp1FfZ7UrXw4hzYdty2LIMsgbbxzWfJ+dAUg6kdN/liNmfuOYDqCyETkNh4T/tOpkDIDYddmyzj2f+vekr6DURnBF2X03/UnP3hFJZBLWlkNoLKjdDWi9Y9Q6UrIEhp4NnB/gbID4Lti6HZydDdBKc/k+o2Axr3oeBU8BbD4NPhR3F4I63GZhjGsHojoW4TAj4YNGLEBEF6z+BCdeB8SejP0QlQHme3bfSDbDoeRh1gd3/woXwxT02G+NrzmFQbfzsbh9j3ccw51Gb28jzIGuInZ379n1Y/BJkDrSX9E3//D4YNA2Wvgr1lVBbDluX7RzfXrD6HRg4bdd4lW2EZf+BZf+G9L626PvHIRDw232dXhFEkKmJCIiACOydgARgBxSAbbkErAzgAbyUGEF0b//QDLqiwO+1Bc/BWGJSbMHWUNO8d9HJULfnA0t/UBeSu9vC1PDcnxIRDd66/WmxZ92UXCjb0HJ7CcCWGamGCIhAqwlIAHZAAdj4Wpnf/e53XHPNNVYwmDdsmFeBffc1X88//7z1DlNTzEOYzbOA9uc1XwqgVp9rzSuaLNyMcSEYaIemCTsfEl1VZB/MZPtq9nwwdJv2wmQ59yYe2/Sg3zEeGQcNO9rziHs/lgTgDz8G6oEIdCACmr9/pALQvH/UvN7LFPOU73vvvdd6Ubp5JVW3bt0soWfem/v0009brwC79dZbrbcVrF692nprhynmpepvvfUWzzzzjNXu6quvpqSkxHrKvnkKfGuKAqg1lPZRp2A+PHG0/aVZvp02w17uNcueZpnSLG8WLYJFL0DWIEjMtsXQkpfBZJ0GToXlr0PeLHv5cuT5YLJYUfH2EurGL+Cwi+1lUJOJK1lrL1Oau43Teu9cNl0J9dX2cqoRdmaplQDUVdr77w6/xO7fmg+hy3B7WbSuAkrWQYQblr8BLjf0nGDbMG3NEu7mBXY/c8fbPiX3gOyRtj/rPtm53LrIXl7udjhEJdrLySYjWLwK3rsecg6Fn78K+XPs5WKzxGuWmsf81l7aNiwyBti+mraJXWx7S16y+2l8Nnvouh5i9/fQX0HBPAgEdu4rDNiZu6otMPdJiE2DkedCr6Nh4yx7udYsYxvOpt95s+1l9bH/BxUF4PPYtkxfjN8V+bD2Yzji9+B02f3busK2Mfg0ezndU23bMeNnPjdLzqbvJutYU2Iv9444B7avgdcv3hU4JhauN2OjIgIiIAIHhoDm7x+pADRizgi+7xbzXlIj6Bpf6WNu5igrK7Neq/bQQw9Z74FtLHV1dfzhD3+w9gPW1tZa7301L7Tfnzd7KIBCOBGNyHjmRDBLgL/50hZuKjYBsx/QiCQjpMK1mD2iW5bA4xPtfY1/WBOuJOS3CIhAGxDQ/P0jFYBtEAtBmVQABYXNbmRuzHhumn3DwSWzQjCkph2WgLlJ5e/D7Rt+/rRzGb7DOivHREAE2pOA5m8JwJDiTQEUAr7V78KLZ9p3/144MwRDatphCezYDnf1st37S2l4Z0Q77CDLMRH4YQho/pYADCnyFEAh4DP75149H7qNhV++G4IhNe2wBMwd1Ddn2u5dm7frsT8d1mE5JgIi0F4ENH9LAIYUawqgEPAtfhlevwj0FpAQIIZB07+l289HPPlB+9mDKiIgAiJwAAho/pYADCmMFEAh4Jv/LLx5OfT9Cfz8pRAMqWmHJnBL512PwrlhO7giO7S7ck4ERKB9CGj+lgAMKdIUQCHg++ZxeOdq++0W5m0XKiKwNwKNrws0311XYD8iSEUEREAEQiSg+VsCMKQQUgCFgG/2g/DBn2HoGXDKYyEYUtMOTeD502Dth7aLf1gPcTtfH9ihnZZzIiACbU1A87cEYEgxpgAKAd/nd8PMm+x9XWZ/l4oI7I3AjhK4q6f9ze9XQeLOt7OIlgiIgAiEQEDztwRgCOEDCqAQ8M28BT6/Ew69EE66OwRDatrhCdzcCby1cMVi+80hKiIgAiIQIgHN3xKAIYWQAigEfB/+BWY9AGMug+NvCcGQmnZ4Ard3s1/B99u5kNG3w7srB0VABNqegOZvCcCQokwBFAK+d6+FOTNg/FVwzF9CMKSmHZ7AXb3tdy3/ZhZ02vU6xw7vtxwUARFoMwKavyUAQwouBVAI+N68EuY/DRP/BEddE4IhNe3wBO4dBJUF9htjzJtjVERABEQgRAKavyUAQwohBVAI+P59ISx9BY67CcZdHoIhNe3wBP4+AkrXwy/fh26Hd3h35aAIiEDbE9D8LQEYUpQpgELA9/SJkDcLTn0ShpwWgiE17fAEHhoNxavgvP9Bz6M6vLtyUAREoO0JaP6WAAwpyhRAIeC7bzBU5MOvPoKcQ0MwpKYdnsCM8bBlCZz9b+hzbId3Vw6KgAi0PQHN3xKAIUWZAihIfLXlcEd3u/FVqyGhU5CG1CwsCDx+DGyeB2e+AP1PCguX5aQIiEDbEtD8LQHIww8/zF133UVRURGDBg3i/vvvZ/z48a2KPAVQqzA1q1T+4V0kz7oZErrA75aD0xmcIbUKDwKN2wV+9gwMmhYePstLERCBNiWg+TvMBeDLL7/Mueeea4nAcePG8eijj/LEE0+wYsUKunXr1mLwKYBaRNSsQiAQ4OUbpnJmxKfUjbmK6OP1CJj9pxhmLf45BdZ/ar8xxrw5RkUEREAEQiSg+TvMBeDo0aMZOXIkjzzySFMoDRgwgKlTp3Lbbbe1GF5tGkCBgH18s/k9LtN6B6oRTw5vHZRthMwB4PfD1qWQOQgcTjuT1tjO4Wix/40Vvl5fQtkODz8ZspfXbJlj7MzQba2ss5pkJUbbTc2xzHFK1rHDmUChJ4Y+WQnNjrutsg5/ADolRlFZto2i+4+mn7OALUfdSaeJF7e6n6oYpgT+dTqsed92fuT5cPLfWw+iMUZb02Iv587KwnJy0+OJdkfsivnaMlj0AvSZtOeDqc25Ys6Hneeezx/A6bBPE6f5x27FnMumWJ/urB8o3rkdoqEWAn4ciV2sOv6aMpzmvP/ua/CqtoDfB/WV9vWgNcXvZ3NlPZFOB5nmPN4LH78/gIMAjiUvQUwq/t6TcLrsLH2Nx0theS290+NCy9zvdl2xrms7GdQ1+HhhziaO7p9JD3OM75Qmbo3MAgGbrwHpcFjXSBupg9VbquiWGkuky8GqLVUM6pLYdJzdj9kabAG/D4fT1XJVn5dAwTc4UnKbxsu0NSPtcDr36F+jsW+32v2MjnTZ1/h9XbsNs6oicEawccs20jKzSUhKtcawzutnw/Yd9O+UgMPvhfxvrGdm+t2JLCusoG9WgmX/e0tj/HqqwcRWep+9V/fUwPbV0GnYrhgwMWvu1He4ICkbohJoiiPzuXmDj8s+h5YWVLC8sIKpI7L37JOJ/+TuELlzfgFKquutNmnxUS2z388abTp/72dffqjqjkDjGfND9eAHOq7H4yE2NpZXX32VadN2LStdccUVLFq0iM8++6zFnrVVAPmrt1Px1KmklC5q6kNe3FBW1iRyQuBL67OyiAz8DfWkOSqpIYaoQB01rgQcEVFEe0opSBxBbN1WvnUPZH23UxgeX4lzzXsk7djAEk828ezgKBY02f/cN4SMlEQaqsuo9foZ7VzV9F2hIwt/1lA+KIpmZGAFw53rrO+qnQmUurvQrW41ZYF43vSN4S3f4YzrFs24lHI2ltRQ5EvCt20VE5yLm9o1Gs4//ilyxpzaImdVCHMCL58DK99sgrA6ehiVdQ0cygrrsy8ZTpkvmo2BTkSYH0oEyEyKJdddSY/Cd8DvxR8ZQyx1FCcM4iv3GOqLVnKe422cBPhP4jkc5vmGrLr1FAcSecc3mqGJNaR7NpPaUERFII61jm70idxOmq+YiEADUXia+rPB1YOMQAnx/irrszw6U+DoxLcNmVQSRxoVVMd1o2d6LO7aYtwV6ynxxdCXAro5i4nxVzfZ2ujPIt1RSayjjk1Jh1JW62WExz5PN2QdT5EnmtrtmygOJHFKxCzcNDS1rSeKhog4NsYMJOBtINLtpjwqm6zoBjpt/oAYXxV+h4sCfxqpVBLt9ON0BHD6G3g/fiqljmQG1Mwn27uJDEdFk91v/dkUJo2gT2QxNSWb6eMowI+DDe5+5PkzSPeX0DUlhpWu/nwZGMzgbhlkl35DevkSSmO7U5IygiUFZRxb9jK5ji2WWHPhpyymG58yig11CZyS9C19yj5vOubrvnHW2IyJ2kiSr5QqYlnt7E2DP8ACT3ciHF4Oc28i01tInKOeVOcO1scM5vqaM0n1bGGsczmZjjIe8062jnW4cwWR+IhxeDgt4gtb4LoiSfVtt45ZEdsDV1oP/uuaxNvfVjPFOZuRCaUkRLvpVDaPBiLwRsYT4au1fux6skawtdZJael2cmK9uB0+ttS56ONdQ5KjhlpHDAu6XUCX8nlkVyxknb8TtUTRy1HIR/5RzIsdj6fBywkNH1ssva5oytJG8db2LM5OWU5y9QZKnKn0iCynIqm/9SP7qPpPml0ItkV0IcVfSsDv5RtfP3y4OCRyPXH+akpI4lrPrxnnXEZPdxndEyCioZqkugIaHG5mxx9PlLeCYd5lZNZvbLJtbLjwsTqiL/2831IUP4jkmo0URPVmvq8XJ/k+JsFXQUl8X5Zlnkx1eTETSl8hjlrLRokjlZVxhzGs6jMSHPZnpmzOmkidO5WNG9fjx4kvMp663ieStWekZ3gAACAASURBVO5VxvjmNdUrcPdkkzeZF/zHMaeuO1HuSB656HiGdE06oBfCtpq/D2gn29hY2ArAwsJCsrOzmTVrFmPHjm3CfOutt/Lss8+yevXqZujr6+sx/zUWE0A5OTlUVFSQmJh4wIZqyT0/ZWjVrovhATN8kBlaOfm/DDhkwkHWK3XnYCJQXe+l9Nlz6Fb47sHUrbDpS30gkijHLpEZNo7L0YOGQGUghkdyH+TaX/zsgPZJAjCMl4AbBeDs2bMZM2ZMU2DdcsstPPfcc6xatSsD1vjl9OnTufHGG5sF4YEWgPMXLabX6yeS7Khmvr8PdzacyVWRr+DGS14gy/oVOdhp/2J7z3co85OPp3NmBvGFs1lWGc0xzoWMcn5LvMNest29FJNCbSCCldEjGNUw3/r13lhe9k6gp3MLhzpX4SGSwsThbHL3pq6mikDNdrZ540lxVJFKFc6YRHpTwBZHOi/XHMKk9BLGl72+SxwHYq1ftlWOBCrcWby5Y6D1KzrXUcS5ER9Z9eac8hWjhw48oCe1jHUsAje9tYIBc67lNFfrfhDVBSKtzE6Uv8YCUUE8syMO4xjv51Q7E2kIOKn2mVyQkwpnMsMi8nB77Qzcs97j2OGMJye6ji01DiubkhET4BjHXNy+Wl50nEhxvYsRyXVs63EyaSXzmLj5USurZMqXvkG85j8Kf8DB0RFLODS1ltroTJJqNuHET5UvgjqiiA3sINHtoDIQy0ZXNzpt/5q+zs2WjVqiiaGOJf5edHKUkOis5xtvb97xj6afI59Bzo0md0VNYi+2ksam8nqujHwdd8Duw+ZAGvP9fdkaSOHCiHesz8pIJIVK69+fB4bj6jGOHhVzyC63sy6bnZ3J9hdR74zldfdJDEmDxNgYtqcMpSz9ULpvm8nmNQtZUxnJtMBHpGJnB2ud8XzlHEGcp5iBjjxKnal0D2ymIJBOcSCZQHQSPb0bSPaX7hGURlR+7BxLD99GBjrzrDH61DeUIY711vWnnAQq/dEsDPSxrhlTXV/iDAT40D+KEe580v3bmesezfwd6XR1ljDZMZvNpLMtvj+jdnxhX1v8/Ylx+hjKGuvvWb5BVob4OPcSMv3Fe/SnKhDDK74JVjb1dNdnluB933cIH/lH0tdRwMTIFWz1mvyWm/HOpUQ5vFZ7M85LArlsiBpAP89ytviTSXdUWFnOSLxURaSxPP04/HGdmLDhXpwBn/VZIOAnzldhZSb3VrZE92RzIJ3udausbPDuxfR196ya+e4Z7yTOivjE5H+bqpbF9aQ0sjMZDYVExyVQVu8kq8JeUapzRBMdqLPYmoyxibkU7zYanDGUxeVS7MpiSPnMPY5r4spkLqNcUOKNtmLsZ67PON45j/WBztQ44/ncO4C5/v6c5vqMwVHbcGSPoq7LaCo2zGf0lhcte5/5h9M5xku8q4H0mnXWnLZ72RyRQ5nXzWDsVabG4hs4Ddfpz+yVV7AfSgCGsQAMZgm4vTKAJqAraz0kRgb4amMlHrO1yGx/6p5Cg9dPhMuBu3ITUcVL8OQegzvWzj42+Px8uXY7h/ZIJS7SiaeumsilL+MJOFmSfhKjeqTjiti5l2nnWfP56q2ULf+ISceeSHRC8j73n5j9TMa+2Z4S4XTi2m1Pk8frxx3htPcjbfoaskdSE4gktnHfFLBscwX1Xj9/fmMZ3bZ+TIKjhkk//z2TBukRMMFewMKh3YS7PuGCioc4P+JDy90FJ75J727dSFzyJPXDz6MhOo34OQ/g7X8yvoz+RLhjrdj0e2qpqt5BUlISuCKbUNV6fCwpKLfOEa8/gDtQDxu/hJ4TqPM7iYpwWueAiWlTrLj2NezcY+uyPrc+ayx+Pw0+LwUVDdR7ffTvlGjt46pr8BPjbsWesaYZbucxqrdCTQl1aQOt/sVHRVj77ubnldE7M54NxTs4vGda055Cqz+mOz6PtXfK7KGLcDosH6xztKGu6fNIR4AGP7v2M+bNhvS+EJsGm+dDbCqk9mwxrIrKqiksr2FUj4ymfXemf307JVhCeW5hvcW3ac9Z0WKIiIbkbvb/Dc8IN16vD6ffg9MdQ0VtA3Fulz0mLueeeybN3jSfB4/DbbPf6ZPXZ5awHTh8ddQHIoh2R4I5ViCAJ3OotffPUbyauvo6onOG236ZPYLm+Jj9yy4I+MAZSUW9j1lrt5PUsI3RKVWQM8b6AWsQRrjsvXuVtV6SYiMJeOup9Tmora0lNcneV2i+N9c3Y96Mu6d2B+6omF175LYuB5d71766yiIoXkld13G4I9046ysgIgq89RCT3NTXJfnbSYl2kZOZCj4vy4qqSV/1TzLT0nGm9YKuh1rzgxlzZ8MOa+8o+XOgxxEQGbPnWBq/nRH2nlPzKC53fNO+vGaDXr4JqrdB10PA6yHgjKDeF7DG1HA3xYzV1pJy64fN4Oxdy7PNzhFrcqqjzsjinTFtPvLU1xEREWltQbD2/Zn5Y7d9lr6SDQS2ryFi05cw4fo99ga2GKStqCABGMYC0MSHuQlk1KhR1l3AjWXgwIFMmTLlh78JpBUB/GOsYi6UudfZmYkHzhzOlOHZP0Y31Od2InDZCwvosfxhro581T7iFUsgZeczJNupDzqMCIhAxyMgARjmArDxMTAzZsywloEfe+wxHn/8cZYvX0737i1PMgqg4C4Kv352Lh+t3MZtpwzhrMNaftxOcEdRq45A4MY3l9Pw9ePcHPm07c41G+xMlYoIiIAIhEBA83eYC0ATOyb7d+edd1oPgh48eDD33XcfRx55ZKvCSgHUKkzNKv3fiwt5c3EhN0weyK+OyA3OiFqFBYHp/1tO1df/5B73DNvfG0r2vWwVFkTkpAiIwIEgoPlbAjCkOFIABYfv2teW8PK8fK6e1JfLjt7Hs6aCM61WHYzADW8so/ibV5nhvt/2bPqux5N0MFfljgiIQDsS0PwtARhSuCmAgsNnsjrPzN7IpRN6cc0J/YMzolZhQeC6/yzl9W/W8I77Ota6cpn01/fCwm85KQIi0LYENH9LAIYUYQqg4PDd8d4qHvl0Hb8cl8tffqrHwARHMTxaNWaLzV2bCdGRLJ1+fHg4Li9FQATalIDmbwnAkAJMARQcvns/WM3fZ67lvDHd+duUwcEZUauwIHDVK4v594ICy9fjBmbx+HmHhIXfclIERKBtCWj+lgAMKcIUQMHh+/vHa7j3w2+tO4DNncAqIrAvAle+tJA3FhVaz+eb/cej2+SdoKIvAiIQfgQ0f0sAhhT1CqDg8D30yVruen81px/SlTtPGxacEbUKCwLmOYBvLSnirz8dyAXjdMd4WAy6nBSBdiCg+VsCMKQwUwAFh++xz9dx6zurOGVENveesfMJ/cGZUqsOTuDSf83nnaVb+NuUQZw3pkcH91buiYAItBcBzd8SgCHFmgIoOHxPfbmBv721gp8O68KDZ40IzohahQWBi/45jw9WbOWWaYM5e3TLD2cPCyhyUgREIGQCmr8lAEMKIgVQcPie+2ojN/x3OT8Z3IlHzhkVnBG1CgsCv3pmLh+v2sYdpw7hjEP11piwGHQ5KQLtQEDztwRgSGGmAAoO3wtzNnH960s5dkAWT5yvuzqDoxgerX7x9Dd8urqYu382jNNGdQ0Pp+WlCIhAmxPQ/C0BGFKQKYCCw/fKvHyueW0JE/tl8PQFhwVnRK3CgsC5T87hizXbue+MYUwbIQEYFoMuJ0WgHQho/pYADCnMFEDB4Xt9YQG/e3kx4/uk89yvRgdnRK3CgsBZj33NV+tL+PtZIzh5WJew8FlOioAItD0Bzd8SgCFFmQIoOHxvLi7k/15cyOE9U3npojHBGVGrsCBw+oyv+GZjKQ+fPZITh3QOC5/lpAiIQNsT0PwtARhSlCmAgsP33rIifvP8Ag7pnsJrl4wNzohahQWBUx+Zzfy8MmacM4oTBncKC5/lpAiIQNsT0PwtARhSlCmAgsP34YqtXPjPeQzPSeaN344LzohahQWBKQ/NYnF+OU+cdwjHDswKC5/lpAiIQNsT0PwtARhSlCmAgsP3yeptXPD0XAZnJ/LW/40PzohahQWByQ9+wbLNlTx9waFM7JcZFj7LSREQgbYnoPn7RyoAb7nlFt5++20WLVqE2+2mvLy8WbRs2rSJ3/72t8ycOZOYmBh+/vOfc/fdd1v1G8vSpUu57LLL+Oabb0hNTeXiiy/mhhtuwOFwtCr6FECtwtSs0pdrtnPOk3Po3ymB9648MjgjahUWBH7ywBesLKrkn788jCP7ZoSFz3JSBESg7Qlo/v6RCsC//vWvJCcnU1BQwJNPPtlMAPp8PoYPH05GRgb33HMPJSUlnH/++Zxyyik8+OCDVmSZwe/bty8TJ07kT3/6E99++y2/+MUvMLavuuqqVkWfAqhVmJpV+np9CWc+9jW9MuL4+KoJwRlRq7AgMOm+z/h2azUv/Ho0Y3unh4XPclIERKDtCWj+/pEKwMbQeOaZZ7jyyiubCcB3332XyZMnk5+fT5cu9qMjXnrpJUvgbdu2jcTERB555BGuu+46tm7dSlRUlFXn9ttvtwSiEZatyQIqgII7SedtLOW0GV/RPS2Wz/4wMTgjahUWBI6+51PWF+/g5YsOZ3TPtLDwWU6KgAi0PQHN3x1UAP7lL3/hv//9L4sXL26KorKyMmuZ1ywJm6zfeeedR0VFhVWvsSxcuJCRI0eyfv16cnNzm0VgfX095r/GYgIoJyfHsmNEpUrrCCzKL2fqQ7PITo5h1h+Pbl0j1QpLAkfd9Ql5JTW89psxHNIjNSwZyGkREIEDT0ACsIMKwIsuuoiNGzfywQcf7BE1JtNnsoZnnXUWkyZNokePHjz22GNNdQoLC8nOzmb27NmMGdP8+XTTp0/nxhtvbBaJEoD7d3Iu21zB5Ae/JCsxijnXH7t/jVU7rAgcccdMCspqef3SsYzolhJWvstZERCBtiMgAXgQCcB9iav/Z+88oKI6ujj+3116LwIKiKgoduy9xm7sXb8YNcZu7IndWKNGE1tiixpbYo2994YNe6EoTQEFpEqH3eU7M4+lCMiyC4jLnXNyIrtz78z83t03/zftZb78bm5uqF8/492xuU0BMwH4+vVrnDt3Lkv0sA0gu3btwsCBA7kAZKN8mzdvTs8TFBQEe3t73L59G40bN6YRwEL67XkFx6DjmuuwNNTBg3ntC6kUcqsJBJosu4R30Yk4MaE5atqbakKTqA1EgAgUAwIkAIuRAAwLCwP771OJjdjp6emlZ8lNABbWFPDHdaMAUu1X7PM+Fm1/uwYTPS08XdBRNSdkVSIINFh6Ee9jknBqYnNUtyUBWCIuOjWSCBQBAeq/i5EAVOV657UJhG3mKFNGeH3U/v37+U7gzJtAZs+ezTeBKI6GWbFiBdatW0ebQFS5GPmweR0eh1Yrr8JARwL3RZ3yYUlZSxqBeosvIDwuGecmt4RzaeOS1nxqLxEgAoVEgATgFyoA2Rl/EREROH78OFauXIkbN27wEHFycoKRkREUx8DY2Njw71letgO4Z8+e6cfAsHV7zs7O+Oqrr8CE4KtXr3geNnpIx8AU0i8uzW1QVAKaLb8MHS0xXi7pXLiFkfcvmoDLwvOITkjBxamt4GRt9EW3hSpPBIhA8SFAAvALFYBMqO3cuTNbJF25cgWtWwvnyjGROG7cuGwHQSuOfGF52EHQ7LBodhC0ubk5xowZwwWgMkfAMHsKINV+zKEfEtHwl0sQiwDfZV+r5oSsSgSBGj+fQ2ySFFemt0b5UoYlos3USCJABAqfAPXfX6gALPzQUK4ECiDlOH2cKzIuGXUWX+Afv1raGdoSsWqOyErjCVSZdwaJKXJc/7ENHCwNNL691EAiQASKhgD13yQA1Yo0CiDV8Mnkqag05zTkqcC92W1hbZKxsUc1j2SliQRSU1NRcbYQJ3dnt4UNxYkmXmZqExH4LASo/yYBqFbgUQCpjk+xu/PkD81Rw452d6pOUnMtk6VyVJ57hjfwyfwOMDXQ1tzGUsuIABEoUgLUf5MAVCvgKIBUx6d4xVcDR3McHNNUdUdkqbEEPiSmoNYC4TB3z8WdoKct0di2UsOIABEoWgLUf5MAVCviKIBUx+c481S6sd+yLkpvvFG9RLL80giExiSi4dJLELHNQr9QjHxp14/qSwSKMwHqv0kAqhWfFECq4xuz+wHOvgjmDmgdoOocNdmy78ZbuP86kjfRfzntFtfka01tIwJFTYD6bxKAasUcBZDq+MJik1B/yUXu4K9v66N9NRvVnZHlJwmwzRQsKXu80efGyep76EEgfjz0NL0qJAA/91Wh8omAZhGg/psEoFoRTQGkFj58t8MNlz1Doaslxn9jm/LNIG/C43Hq2Tu0q2rNBUuFUobwfh+LC+4h6FyjNOzNDfDibTQMdbVgZqCN00/foZqtKfzD4nDHNxwzu1QBG118+CYKhjoSpMhT0aeuPeZ1rYrw2GToaotx2SMUDhYGOPH0LZ4ERKOitRHGta4I3/dxCIqKh+e7GLRytkIzp1I49yIYbDNCZHwK/39FK0O8jUpEZRsjlLM0xOOAKFgZ6+LM83d8jZqZvjbYLueBDR1wySMEW677omP10uhZxw6+72Nx2zec15XV31Rfm/usZGOEHrXtUNnGGO+iE3DJIxQxiVLentbOVqhpZ4oLHiHQ15agjbM1HxVjZbIDkiUiEXrXteNlM3Zmhtp4/CYKSVI5ylro45rXe14H9jaNho4W+LZpOdSyM0NgVDzc336Ad2gsqpQ25uUz7m2rWnMm7H3NhroSiEUilDbVg394HHS1JJwp243rFRIDtn6zXjkLsGN9fj3nxf/P2smuD/N75nkwv0YjW1SA+7sPqFbGBP3rl8WbiHj8e/cN4pOlnAE74Pl1eDwPJlYWY8qud+ZEAlC93xpZEwEikJUA9d8kANX6TVAAqYWPi4Rx/zzAy5BY9Rwpac3WkqUNhilpUbTZbEx004VmfkpmB2rXLmvGRa+6Kb+MmCCUiEX4kChVt+hP2pMALFS85JwIlDgC1H+TAFQr6CmA1MLHjdlOz34bb/MRJWWTnraYj5yx8+HUTWxULSFFlm83THRlLp+NolW3NUV0QjIueoTm219OBmz0LCo+JUdfTHSVMtLh37PRvtySjkSMmvamiIpPhs/7OKXr1ai8BeKTZfALi+PlmBno8JE5lhh/djhz5sRe68cEbEBEQrYychOVlayNoK8jQUBEPCyNdPm7oVm7EpJlMNHXxj2/iHRfJACVvnSUkQgQASUIUP9NAlCJMMk9CwWQWvjSjUM+JGK7qx9q2JpyEVDO0gB77wXgZUgMngREwbm0Maa0q4zXEfFcILCpYDY9HJ8k5VOh72OS8OLdB7jYm2HhiReIS5Lhj8F1oCUW4Z5/BDzexfDpTufSRlycta1ijZaVraAtEcHJ2hhszVnIhyQ+XekZHAMTPS0+HcymaWvZm2LPndd8etbWTB9HHgVhSJNysDDQQbJMzqev2ZQvy6tIbGpzxy1/2BjroVcdO14HJhANdLT4jlY2rSoCEJWQAgtDHaTI5LjoHoJHAVFc2H7fojz/nOVPTJHh4ZtIPsLHprBZnuZOpfh0MBNdbBR1v1sAylsZ8inWa16hXJjWczRHdVsT7sPoo7qxKWy+w9bRgo847rjlx8XeiObl+fQv45/T2YysLqy9zP/Jp2+x9JQHQmOSUNfBDAu6V+dT1Wyq2ZK3KRVSuZxzNNHT5m/xiI5PwW3fMD7lW9pUP0u9cooktkaUrRVliQRgwfzWyAsRIAICAeq/SQCq9VugAFILHxkTgU8SaLrsEt5GJ5IApDghAkSgwAlQ/00CUK2gogBSCx8ZE4FPEmi18kr65hAaAaRgIQJEoCAJUP9NAlCteKIAUgsfGROBTxIYuet++m5gEoAULESACBQkAeq/SQCqFU8UQGrhI2Mi8EkCbJ3ikpMe+KZxOTQsb0G0iAARIAIFRoD6bxKAagUTBZBa+MiYCBABIkAEiMBnIUD9NwlAtQIvOjoaZmZmCAgIgImJiVq+yJgIEAEiQASIABEoGgJMAJYtWxZRUVEwNTUtmkKLWSmiVMV7oopZxb6E6gQGBvIAokQEiAARIAJEgAh8eQTYAI69vf2XV/ECqDEJQDUgyuVyvH37FsbGxgX+nlXF0wmNLqpxgZQwJc5KQCqALMS5ACAq4YI4KwGpgLIQ6wICmYebwuLMxr5iYmJga2sLsVhcNI0pZqWQACxmF0RRHVqfUDQXhjgT56IhUDSlUDwXDWdWCrEuGtbEufA4kwAsPLZqeaagVwuf0sbEWWlUamUkzmrhU9qYOCuNSu2MxFpthEo5IM5KYVIpEwlAlbAVvhEFfeEzpqf4omFMnIlz0REoupLoHl00rIlz4XEmAVh4bNXynJSUhGXLlmHWrFnQ1dVVyxcZ506AOBdNdBBn4lw0BIquFIrpomFNnAuPMwnAwmNLnokAESACRIAIEAEiUCwJkAAslpeFKkUEiAARIAJEgAgQgcIjQAKw8NiSZyJABIgAESACRIAIFEsCJACL5WWhShEBIkAEiAARIAJEoPAIkAAsPLbkmQgQASJABIgAESACxZIACcBieFk2bNiAlStX4t27d6hevTrWrFmDFi1aFMOaFs8qsd3Thw8fhqenJ/T19dG0aVOsWLECzs7O6RVmp8AvXLgQW7ZsQWRkJBo1aoQ///yT81Yktvts+vTp2Lt3LxISEtC2bVuwa1NSXxv0qavNmM+ePRuTJk3i8coSMS6430dQUBBmzJiBM2fO8FisXLkytm3bhnr16hHrAsIslUqxYMEC/PPPPwgODkaZMmUwbNgwzJ07N/1NERTTqsG+fv0679MePHjA+7UjR46gZ8+eBX4/ZvfyiRMn4vjx49x39+7dsX79epiZmalWcQ23IgFYzC7w/v37MWTIEC40mjVrhs2bN2Pr1q1wd3eHg4NDMatt8axOp06dMHDgQDRo0ADspj5nzhw8e/aMMzQ0NOSVZoJw6dKl2LFjB+9MlyxZAnaT8vLy4q/2Y2ns2LE4ceIEz2NpaYlp06YhIiKC38QkEknxbPxnqJWbmxv69+8PExMTtGnTJl0AEuOCuRisU6tTpw5ny2LS2toaPj4+cHR0RMWKFSmeCwYzvx+sXr0aO3fu5A+C9+/fx/Dhw/m9gT3Y0H1DddDswcXV1RV169ZFnz59sgnAgrpXdO7cGYGBgfzBnqVRo0bx3wm7j1PKToAEYDGLCjYSxX4kGzduTK9Z1apV+dMSG2WhlH8C79+/553mtWvX0LJlSz4yxd7/OHnyZD6qwhIb7bOxseHCcPTo0YiOjoaVlRV2796NAQMG8Dzsvc9ly5bF6dOn0bFjx/xXRAMtYmNjebyyBxbWUdauXZsLQGJccBd75syZvPO8ceNGjk6JdcGw7tq1K78HsJFVRWJixcDAgN8HiHPBcBaJRFkEYEFx9fDwQLVq1XDnzh0+o8MS+3eTJk34bFDmGaCCacmX74UEYDG6hsnJyfxmc/DgQfTq1Su9Zuzp8/Hjx1zAUMo/AW9vb1SqVImPAtaoUQO+vr585OThw4d8ZEWRevTowacK2AjA5cuX+ZQvG/EzNzdPz+Pi4sLFOJs+pgQMHToUFhYWfOSkdevW6QKQGBdcdLBOjT1wsJENdg+ws7PDuHHjMHLkSF4IsS4Y1suXL8emTZtw/vx5Pivw5MkTdOjQgT/QDBo0iDgXDGZ8LAALKn63b9+OqVOnIioqKktN2T2d3Z/YaC6lrARIAKoREXK5nI8KsSlDFtTqJrY2okqVKvwGpHiCYT5XrVqFf//9lwsWSvkjwJ4u2XQwuymcO3eOG9+9e5ff2NlTIVvno0hs7UhAQAB/Oj1w4ADvZMPCwrIUyMRfuXLlsHbt2vxVRANzHzp0iMfm1atXoaenhy5duqBmzZp8FJUYF9wFZyPRLE2YMIE/fLAlCGxUkMUgEybEumBYK9b3MbHAlnjIZDLMnz+fL/2g+0bBMGZeTE1N+TpLNuJakFzZvYj5ffToUZbKsof8b775Jv06Kr5k1zsmJobPBonF4oJr4BfkiQSgGheLPZGzKUFKRIAIEAEiQASIwJdHgD30l9SNfSQA1YhXtk6MDS+zAGIL4CkRASJABIgAESACxZ/Ahw8f+AAOmx1io5IlMZEAVOOqswBigcOEIAlANUCSKREgAkSACBCBIiRA/TdAAlCNgKMAUhHekbHAhyBgyFGghK69UJEcmREBIkAEiEABEKD+mwSgWmFEAaQCvtRUYGHaoZyjrwNlXFRwQiYlhkDQQ+D4D0D7RYBT2xLTbGooESAChUuA+m8SgGpFGAWQCvhSEoGlNoLhmJtA6ZoqOCGTEkNg3/8Az5NCcxdEl5hmU0OJABEoXALUf5MAVCvCKIBUwBcfAfxaXjAcdwewrqqCEzIpMQROTAIe7BCaO/stoCO8yYUSESACREAdAtR/kwBUJ35AAaQCvg9vgd/TRB8JQBUAljCT83OBW+uFRk9/BRhZlzAA1NwCI+C2DfA6DfTbAegKr3ukVHIJUP9NAlCt6KcAUgFfuA+wvi439O59Fk61mqjghExKDIFzc4DbfwjN/eEhYCm8+5YSEcgXgcxrj7/+DWjwfb7MKbPmEaD+mwQgf4fpypUrwd7CwV4Azl7706JFC6Wi/YsJoDd3IL25FvOSh+KDjjV+6+cCPW2JUm1UZGKnpv92/iXEImBqB+ccbf+9+waewR8gEYtQ2kQPo1vl0FkHPwc2NeP2/xMtxz8/j81XPShzCSNwdhZwZ4PQaJfBQK+Md2QrSyIiLhmzDz9Dv/r2aFs1bf2pwlguBy7MA8wcgEajlXUJMEHB6mZqDzSdkNUu9j1wcjJQbxgg0QHubgK6rAJM7bL5Z7+rVee9YKqvjVEtC1jc3lwNsCUXbANN2puKZPJULDvtgdKmevi+RQXg7azW/AAAIABJREFU3l9ApD/QfnGh7ci/5xeBnbf9sbB7dZQy0k1nwK7LwhMv0KuOHVo7539k988r3ohOSMHMTlUgZjcmsMuSiiWnPPj9Z2TLChm8WRvXpm0467AUsG8gXJfOvwJGwptW8kqM3dJTHrA1S2MHQC4XyitfygBDmjjm6OKPy69w/VUYzPS1sahHDc5epRT2Cri4AGg1AyhTK93Fg9eR2O7qhwXdqsPKOI2vz2Xg+iqg9mCgzje5FnfRPQSnn73D4p41oK8twdLTHnC0TGsLi/FLCwFtA0Q1mIz5x15gQIOyaFbeFDg6DkiJB3puBPSynoHL2nvTOwwWhjqY3aUq7M0NVGpuYRt9Mf13IYIo0cfA7N+/H0OGDOEisFmzZti8eTO2bt0Kd3d3ODg45Im9SAPo+krA5wpQ8SvA3BHwuwbUHyGMjrSdL3RgaSlFJseC4y/QsLwF6ugFw2HfV/ybQ7KWmJ4yBvu7G8DJcyNCwyJg1/JbmDT+NmtbH+0BmFBjHYeWDk49fYc/rnjD41005mvtxuDy8QgWW2Of+Rj82K0uJBfnQ2pWHk5HhNeq1Rd5YrvOShg4NoDW4L2ArhHuvQxC0unZsDA1RvXXu3m+3kkLcHjZlDw5U4YSTOD0T8C9zRkAav8Pj6L0YRH5BA7mehDV6APIpXjy/Clcy0/EuDaV0oXA6ouvYKgjQUBkPPbceYOKoiBcqncHaD0LKCXkg78rsKML/2f05NcwNTODm38E1l58BQMdCX7pXTOLaDnyKBCsw/25vhTaW1tzu1WV/8EP9j6IjgzDL/G98EPM76j49kTWi1ahNfDtMf7ZbZ9wHLwfgO+al8fUA4/xMiQWhkjA44aXoe3cHrCoAFxcCJlcBu/3CTil3xXnA7Xxs8VFvKz1I9xjDDC/WzUY6mrlHhiZHrRm2+/C2N7tUdbCABdvuiLu3BKsl/bEuYZPIXmyR/DRdCIQ/BT3zTpiS1RD6GpLMLV9ZZQvlX3N5fEnb3H8cRC0JWL88FUlVLM1AR7sBJ4dBKLeIAYGWFVqKSxKO3CBxsQJS1/XLINFZW7h4fPncOi7HAtPecLO/z9UFb1BfKufMb5dVZx+FozbvmGY06Ua9HXSHlKjA4FT04XR3w5LuJgNi01C/SUXud+hTcohJkmKxa1NITs/HwNfNEZgqhVu1r4Ik3r9gErtAa8zwN6B2Xmx++mQI0r9wI4+CsLk/Y95Xq8lnaAjEaPvpts8Hvj9rI4dFvWsASMJe6iYD4R5QRruh9jIUPwveQ5epDpiftkn+K58pNCOS4uEOGR1YHGeqX2ZK3TFKxTnX4RgScxcSPyuCl+lbYj667ovF20GSMQ2i11oUskW6LYW8q1tIQ5+yrP2sTmDmZ2roIGjBZAYLTy42NcH/K7j4lN/nJU3xCnxV2htFoKOUfuwStof15YOgyTKH1hXm/tYV/Ev/P5CiAX/MWbpvxm0mYNXz92gI41BOQsDhGuXQZPH7VFD5IfhWmex02gEDs3onyvfJKkMC467o2lFS3RzseX5YhJTsOiEO76qYo3ONTNe1anURcpHpiLtv/NRr6LMWqIFIHvfbt26dbFxY8aoQtWqVfn7NpctW5bndSjsAGJPs/wdwyEvgI1Nc62P3Kg07mg1xNbQypA5dURdB3OsvviS51+htQUDtISbxlN5eXRPXoqX+sOhk5qU7s9HpwpiJWZITZXjsX5jDItcx79LFBtgjd3vsPc7CFtROPSQjKYS9yz1SNY2gU7KB/5Zs8S1eAtL+OllfeKUNhyL6Lt7YCmKyWI7IHk+9v8ivGeTEhHIkcCpaYDbVqXguMvLwey7g7B1dMbKfWdR8cV66CMJydBGWKopXMQ+qC8Wfhf3DZoj3sAeMLJBS3/hvc7hqcZwM+2Eje9r4lutC1ieMgg9mtfBnK+rYt0lbzx89Rrd365GUqo2nomrYpk4bWQyU+3aJ/2Kddp/oKr4TbY6h7VcihC3I4iMTUBzyQsclLbEXOl3GC45i5Fap7L9PjI7iE3Vg5EokX90VeYCM7vKqD1yIyDRRszD//Do5hnodPkFjZ2seQd6b8dPaBu8necflDwHFojBDNtHcAi78UmW8am6+FU6AOaiWJw0H4qjbSPx5MYJONjbwz7JB6kOTRB7bgnOyBrCL7UMEqCDBJdhWOEuPGRmTpdltaEFGeKgh+XSQfwavNAbwbPslLaHgygUbSRP+N8/pwzFDfPe8A2L43+PalEeP+kdxlXfWLR5tx0SuXC/Ol9xNmzD70Auk2JFeDO4ymtCD0lYrPU3+mld53keyZ3gn2qDXhJXoTqjbwCbc57VkYu0If7hHqJPzIZ/SCSSUrVgppUMHW0d+LyPwzvLRrhp3AmDIzdBFhcFt2QHbJD1wMWprRAT7Au/g7NhhARckdfGXllbQcgkHseg8D+zsPCS22Nw8hw80Eub8ajWE3A/mv1a1PkGEe1W81Ha8QYX4BjzCKdfhEIKCbpLbqfnfzfOB6e8PiDk7Eo0EHvBJ9UWY7U+euhIy71b2g7HbSfj4LgWQC6/p/qJG7FVZyVqi30RmmqGlJE3YLe3LRAXyr38Ke2OlVJBQL8cnAidw9/lGkeBqaVgL8p4h3qSSA8RVf+HUj2W4OGWMXCJOAdXw/Y4YPUDKpYyhMXtJSgjCkcZYx1U1wpEtFQLj2NMcUDWGilOnVDHwQyT21X+ZNyq8mVh99+q1KmobUqsAExOToaBgQEOHjyIXr16pXOfNGkSHj9+jGvXrmW7FklJSWD/KZLiVTIF/SYQNix/9MJldI79D49SymEulOsAWb2cE3cgCTpoJ36A3pIb6CK5l6Ud7GYwREt4ci7o9EJeDpGpRrxzUyZ9j/nYuoAEoDKsSmyeE5OBB3/nq/mJIj3opQpiSZ10RtYAkWJzOGh/QEJSMlzEvrAWRX3S5W1ZNdiIIlBBHKxO0UrZppqXxwftUjANdeP5v0+ehjGjJmDIpivw0Mu9g1bKOYBXcjtUEgflmT0uVReGooz7Ym4GwanmKC0SRstySgtThiAotRTqiV/Cw6w11sT+mGfZTAy3ThORuWUOl1jBUvY+V18xWhYwlkbk+v1ZWQN0kgiMWWJcttn+jOXBo7LYsHiJhy76SG7mWe9PZdgo7QYbUSR65+HnibwCj0ll0vDkHzHFKRi13gizLx8n9lCjK0rJ1dVzuSNCUs1RRfwGuiI5SiH366hMfVieuSnDsUT707/tKol/IxG6ODyuKR/YKMhEArAErwF8+/Yt7Ozs4OrqiqZNM0bXfvnlF+zcuRNeXl7ZYm3BggVYuHBhts8LWgCuOOuJQbe6wkGc9ablIy+DiuJ3n/wN3JFXhS3CstkW5A+noHxN0Z6P1XNIABYUT430ww6BfrhLI5uWU6NCUs1gk4fI/BSMickTsFR7G4xFCSWGGTVUswnMSxmGmJrDsGZgnQJtKAlAEoC4desWmjTJ2Im6dOlS7N69G56entmfkopoBDA4OBClN1XPUv7/kmfBReSDn7QP8M9lOiaQJAtTr3kl1qlY6EugnRieLStbC6RtYoMx8cI6q0v24/E8Whcnw0rjlM4s6Ihk/Ik3xqAs6ibeyasoMJG6Xt4H3uYtsP/Dt+kjA9JUMbRE8iz2c/TnYemM6Xn6pAwlmMCx8QBbk5qWyifuwYra4RDrGGKDWxQcRCEYITmDFpLnSkH6eDRHYVSYI+OsjB9TRkECOb5rbIdQ3XJwCruI0i//zVJnNnXWJul3dBS7wS+1NHbrLIOFKDZbu6J1y0A7MQIGSoy6KYyXpwzETO192XwdkTXLmCrNgyAb3WwicQebJs5P2ZndJhqUxpNYMzQSZ7+/5lZ8gNwK/8raYkam+q+V9sIkLeXW7ikVGJkyfZc8nc+c9JUIU8qqpKBUK9iJhAf4vdI2GKR1JUc3s1JGQBcpYMsXPFMd8FRvZL6LSzR2xO8JXeATrw8ptLBTZ0W6D39DFzjGCdPsLH18vZcZz+bHmS0Tpe20/6j0P6Q90EV8N8cRbW+5LZzEb7kFi29Zqhi/62zKd/0/NmAjg331HqC2TFjDyFL0D14wtSyttu/MDkgAlmABqMoU8MfRV2gB9OYusL0DL86r3s+IsWuJ2dfiUPn9Bfyhk3YmWs9NfDF08IlFKC0Nwt3y49GoTm1EvLgEC6+sN/pbRu3Q1FYbeHkm2w/oUHtXNKtREVE+9xEUnYzmzVtDLBLxnWHaQXdRSScc78p2QUuzcIjS1tFcEDXBzqTWSE7V4lMeFkb62Gm8EaIIbwR23IIoh06wNtGF90t3eD69j+evfOBUrTbqvlyLxmKP9DqsNJ+PHyfRCGCB3tU0zRl7b/QTQSjtkrbHzcozsXlIPf53rw238DggCkOra2Ghj7DQvE3Sb3AUBWPqV44wNbOA7PhklBeHpFNhnUs78UM+bZh5RD30B18EP7uKqrbmuP8uBXVLa0E32hcpcVFYcCkUrjJnHOokh3nZKpDs6sb9PbXugRTnrqh3I+9O2zHxX9yd3RY2Jmk7QNnuY7Zp4ogwjZjYYBy63KwA31RhIfzfwxvgx78vYqfuClQX+We5qv5NlmLC1VS+ztDXoDZWWJ6EU/DpbFd+dPJkVDIT4VqkJZ6lVsBkrUOYrHUYfpYtMetdS7xLteCi9LKu8BD2odls+EscUat6TWBjxkMxE80zU76HHCKMdniHKnVboe1ZYQNMZKl6MA97kKXsC21PoXWpD9B+45pxhI8ix+ADWHX1Laa/nZxu86zyDyjv0gKyyDfwDXyLOp6/p3933HEurGq0QcUKTrAOuQnPKBH0Le3xJMEKZRO8cOvWNex974i/rA6i6oebCDOqjCiYwCn2PjILFFTtjuOiNvj9kRwbtddydkyYiZpPwqtbx/Faao7qZimYPHQQyq/2gzakuNg1EQ4RtyB6uAPxRuVg8PVSviNWuqc/tCCFh9wBFb7fCd1n/2RbpxrddStMT36PRN1SeNzrKizfXcPiC2/4Wurl2sKSHh/brnCtuRQxiVLUsDPF7tuvoet1DH/qCGuwWZKnivC/lNnQghzzWhjD6K0rbANOZb3Ww04j0LQOmq8QROYcrT0YqXUafrZdUXbYdsB1NbSuLUNCqg5aJK3FfBtXdI/egyCbr2Ax4hDfUKN1cR7KuGddahSUaolmSevQXfs+1klWc9+edn1RJegQ//dqg4lo1LITJClxkNnWxeC/7sBf73/Z4rB64jZc1Z0CK1HOAxY7dQejcvxj/nDhKquOyXqLcGNSPYjX1kpfX44RF4CyDbP5VueDQuu/1alUEduW2DWAjDPbBFKvXj2+C1iRqlWrhh49enzeTSCP/wWOjgXKtwSGCgt7Y5OkuHTTFT1udBeqmvZWhMj3b+Htdg51OwyBRCttV6DfdcDAErJUETzvnkHZZoNhoi0HPE8BbDdiyDNAx0g4wsIm60hjrvGXHAf8InROb8d44GaADD/9Jzyhrerngr4VpECoB+DcOYuLZKkc514Eo5WzFQasOIBNsgUoJxYWFm+0noex42gEsIh/819WcYdHAU/3g613Gpq6EDfmdIaxnjZvQ3B0Ih69iUSnGqUR/vIOvt95D4/lTvipkzPGtXbieR4+94Bt1D2UvjiR/90u6VfEwgA3hphjwn0bBHnc4bsPRw/skyuXZ4HRSJHLhTVIqamQL7GBWJYEfHcOcGgMLDDltkwQKDZ/pE64D9Ef9dN9bvnqYc7HvLx7ArDXIzo0QptVV+EXFoe1A2ujR207uHqHwfntMZS6PFXw881hICUBiRU7ocr8c/yjC1NaolIpPaQ8OQA/z0cQG5aCGT5AWqoqLoiao3MtWzwPiubC83VoNJqk3IGfWUP03CqMmK7uVRm9zqTVc9IT4YQBNuLy8ibcPPyQKkuBiUt3DPjrrsBzXntYGGhnvM97wn1gQxNAngK26eO541BMHJG2/lAmFV7jV7EN4H8TSI4HavblO3afuZ5CI3t9SCQSwKldFvZ+L+5B/vIcAk3qoXGrjtDVyv3Iquj4FNzyCUOHajaQvDwFODQFUuV4cvMEQu3aw8DrCFxstGDUaChStQ1w3j0EOvIEHNy7HZdS68FjaQ9+jAvbqTysqSMWdK8O3/exCP6QiKYVSwn1YjuI2RuL0thcvHUPN07twSO9Rjg+739AgBuwLVMb2HFFPTcAHseB0rUAC+HNR++iE+Dn7YWmJ1oJftlDfO1B6W2Pik/GNa9QdNV9DMmNlcC7xzip1xUTogZjdpcqQvwkRAK+1xCaKEFQVALqVCgt9BMAjx3/8DiU0kqEceB1ODYfwDcJsRTw5ArG7XvOHwTcF7SFgc8ZYfexnhC7kCYB7seAq8uACF9Em1VDSv998E82hq5EjJrxtwHrasJxQmuE13emTPeFtpFlev1ZfQJfPUEjsxggKUboYywqwEtqA90Hm+HotljI22YOYGKHwNev8MygEdq06YCr7m9RP/E27klc8JWLk3BMGd/J3pwd7pONVZaAUfEPEoAleASQxYziGJhNmzbxaeAtW7bgr7/+wosXL1CuXLk8w6rQAujSYuDGKqDecKDbmqz1eHURMCmjvHDLsxX5yMBudOzHWLYhP/+qwmxh1OHytFaoYGWUp6OAiHi4B7xHxyPCGVYbLWdh7A8z87SjDCWYwKERwPNDWJQyBA9sB+HYeOEMyZzSFc9QBEbGY0ADB+hoibNmYZ1JzDvcEteBoY4WXMqagR1BwY44YsdNmBnoKA/57WOhI2bCBsCOwydx+74bEss0xM76rwHHZkAZF2BPX8D7AgLsu8L2uz38fMxPJZ/3sfAOjUXH6pmmuljHzB4IjUtnebhiwjcxRY4mFTM6YOUbALCjRezN9FHJxhhgMw4sOTTK1QU76kQqk6NRhbTy3j0FEqME8RHuA2mAG47LmqGVszUsM531l586FWXeW95hMNLTQi17MySmyHDy6Tu0r2bDz2PMK7HTGc4+D+ajduxoHX4m5PP/AGkiINYGnNoChmni8WNnbOR3UdpmhgkPgFLCg0q2JE0GXhxGWJnWcH0rQ7datulnHeZVv9y+v+oVyh8EqpbJem5flvzRQYDvFaBWhnjM5s/3KqBrAtgJB/orlZ4fBg4NF7LOCuJHgymVFJvAWv4IfDVXKRNlMxVa/61sBYpBvhI9Asj4s9G/X3/9lR8EXaNGDaxevRotWwpPVHmlQgug17cA9iOzqwdU7phXNT7b93d9w/k5Xx0yd1hK1CZkw9ewCb2JTeY/Ysykgv1RK1E8ZfmSCBwcBrw4ggUp3yK61gisHiCcS1acEjt38/DDQHxVxSbjIF5WwbhwwP0IUKMvoG9WnKpMdfmcBAIfCOKZCcWSkkI9gQ1pDxhpZxgq1fSX54CgB8LMVbncj0JTytdHmQqt/1alMp/JpsQLQHW4UwCpRi9kU3fYBF/DJtPJGDMl+65q1bySlUYSOPAtn5piOwEt24wvlPPANJIbNYoIFDcCzw4J7/JOm7L+3NWj/ruETwGrG4AUQKoRDNnSBzZvL2KT8Q8YM22Jak7IqmQQ2Pc/vo5sTsp3qNFjCgY1zPsNPSUDDLWSCBABdQhQ/00CUJ344dvnTU1NUdDnAKpVqS/AOGTbQNgEnMFmwzEY/WPGkQVfQNWpikVNYO8gwOs034X61eAf873coKirS+URASLwZRCg/psEoFqRSgGkGr6Qv4fA5vVxbNH/HqNm/KaaE7IqGQT+6Q+8OsfPGRs0enaBvw2gZECkVhIBIvAxAeq/SQCq9augAFINX+iuYbD2PYK/9IZh5EzhPayUiEBOBFL39IHI+yKmp4zGpGkLhF2XlIgAESACahKg/psEoFohRAGkGr7Qf0bC+tUBbNX5Bt/PzvrSdNU8kpWmEpDt7AGJ31VMSR6LJT8vgaFu2lmXmtpgahcRIAJFQoD6bxKAagUaBZBq+N7vGwcrz3+wTXsgRswRXkFHiQjkRCB5e1fovLmBicnjsWbJUrXPQiPKRIAIEAFGgPpvEoBq/RIogFTDF3ZgEkq578Dfkr4YPm+bak7IqkQQSNraGbqBtzBJOhFrl6S9SaBEtJwaSQSIQGESoP6bBKBa8UUBpBq+8P+mwfLZVuwQ9cSwn3eq5oSsSgSBxC0doPf2LqbIp2D1ogUlos3USCJABAqfAPXfJADVijIKINXwRRydCYvHG7ELXfHtgn9Uc0JWJYJAwqa20A++j2mi6fjt53klos3USCJABAqfAPXfJADVijIKINXwRZ2YC7MH67E7tQuGLNyrmhOyKhEE4je0hkHoI/yoNQMr584uEW2mRhIBIlD4BKj/JgGoVpRRAKmGL/rUzzB1W4M98o74ZtEB1ZyQVYkgEPdHCxiGPcUM3dlYMWtGiWgzNZIIEIHCJ0D9NwlAtaKMAkg1fLFnF8PozirskbXHN4sPqeaErEoEgdh1zWAU8Ryz9Odj2YxpJaLN1EgiQAQKnwD13yQA1YoyCiDV8MWdXwrDW7/iH2lbDF78H0QikWqOyErjCcSuaQyjKA/MMVqEpdMnaXx7qYFEgAgUDQHqv0kAqhVpFECq4Uu4tBz6N5Zhr7QN+i08DC2JWDVHZKXxBGJWN4Bx9EvMN12KRVMmaHx7qYFEgAgUDQHqv0kAqhVpFECq4Uu6sgq61xbjgLQVeiw4Al0tiWqOyErjCcT8VhfGMT5YaL4CP08ao/HtpQYSASJQNASo/yYBqFakUQCphi/5+mroXF6AQ7KW6DLvCAx06PVeqpHUfKuYlS4wjvPHYqtVmDd+pOY3mFpIBIhAkRCg/psEoFqBRgGkGr6UG2uhfWk+Dsuao/2cozDW01bNEVlpPIGYX2vCOP4Nfim9BrPHDNf49lIDiQARKBoC1H+TAFQr0iiAVMMnu/UnJOdn45isKVrNOgYzAx3VHJGVxhOIWVENxglBWGH3B2aMHKLx7aUGEgEiUDQEqP8mAahWpFEAqYYv9c4miM7OwElZYzSZcRyWRrqqOSIrjScQs9wZxonBWOWwEdO/G6zx7aUGEgEiUDQEqP8mAahWpFEAqYjv3l/A6ek4JWuIBj+dgLWxnoqOyEzTCcT+UglGyaFYXeEvTPm2v6Y3l9pHBIhAERGg/psEoFqhRgGkIj63bcCpqTgrawCX6SdQxlRfRUdkpukE4pZWgGFKONZX/hs/DO6t6c2l9hEBIlBEBKj/JgGoVqhRAKmI78EO4MQkXJDVQ9WpJ2FvbqCiIzLTdALxSxxhII3Ehqq7MW5Ad01vLrWPCBCBIiJA/TcJQLVCjQJIRXwPdwPHJ+CSrA6cJp9COUtDFR2RmaYTiF/iAANpNDZV/xdj+n2t6c2l9hEBIlBEBKj/JgGoVqhRAKmI7/G/wNGxuCJzQbmJp1HBykhFR2Sm6QQSFttDXxaDzbX2YXTvzpreXGofESACRUSA+m8SgGqFGgWQivie7AOOjMZ1WU3Y/nAGTtbGKjoiM00nkLSoDHTl8fir9kGM7NlB05tL7SMCRKCICFD/TQJQrVCjAFIR39ODwOHvcVNWHaXGn0WV0iYqOiIzTSeQvMgGOvJEbKt3BCO6faXpzaX2EQEiUEQEqP8mAahWqFEAqYjv+X/Aoe9wW1YNJmPPorqtqYqOyEzTCUgXloJWagq2NziJ775uoenNpfYRASJQRASo/yYBqFaoUQCpiO/FUeDgUNyVV4H+qHOoZW+moiMy02gCqanAQiE2tjc5j+86NtLo5lLjiAARKDoC1H+TAFQr2iiAVMTnfhw4MARu8srQ+v486jiYq+iIzL4oArf+AMRaQOMxylVbmgQsseZ5/25+DcPb1VbOjnIRASJABPIgQP03CUC1fiQUQCri8zwF7BuMh3InpI64gHrlLFR09BnMwrwBt7+AZpMAE9vPUIFiVuSrC0CoO9B0IiAS5V65hEhghaPw/TQvwLh03g1J/AAsLysIwNa3Mbx1tbxtKAcRIAJEQAkC1H+TAFQiTHLPQgGkIj6vs8DeAXgsr4CkYRfRqIJlhiM26uO6FkiOA0ztgQbf5ywsfK8Cr28BrWYAYknWirCpw1vrAYvyQNVuKlbyI7OUBOD6SuDGb8IXlTsBZVyAyh0Bu3oFU8anvLDyr/0KVOkK2BdBeZnrws5t1DfLzlIuBxaljd4OOQJU/GiThlwmXAeb6oBZOeDPBoLXgf8CVZQ40y8uDFhZkZvsaPcQw5oL/6ZEBIgAEVCXAPXfJADViiEKIBXxsVGjf/riqbw8YodeRNOKpTIcXVwA3Fyd8ffgg0DlHI7/WJC2caTfTqB6z6wVCXADtrUTPmszF2g+BZBoKV/ZwPvAmztAozEZdq7rgAvzcvYxLzy7/yf7AR1DIMwLKN8KsK+fYctGtpgwchkIWCopapj4u7JU8LEgGghxB16dB5pMAOLDgQd/C/V99wQIeSH8WyxWvs2ZczLfXqcF31FvMoTb7LdCm1jbgp8KIu7vtLP5uq0F6g0TvHhfBKICAG19ftwPTwP3AvsGCf9uPlUQlE7tBHHIEhPzL44I/uMjgErthYeAI6MhTRVjb+fHGNK4nGrtISsiQASIwEcEqP8mAajWj4ICSEV8TCDs6cONn7bdjVotMr3ia00tIOp1huPa/wO6rhZEIRNODUYIIuE3ZyFPi2nC33WHAU/2AmVqAWdnASHPM3xYVACGngA8TgBeZ4BSlYSRRasqwL0tABOkTm0F0cQOqT42TrBtPRtoPUP495GxwJN/c27wiAtA2YbCdz6XgbtbgJdnsub9+neg3nDg9nrgwvyM78o1F4RS+ZaC4PpYtLHRTLetwOnpGTZMAC40B1LlQIXWABsNZYkJTb9rwr8tKwni0sBSEGaK+j3cJYysMqFo3wAIuCuw0DMV2Ly5DbAp+nBvof1mDsDRtDV7w04Lgm1FmhBj3zGBqEht5gARfjlzYmUFumXn12ebIPROTMz6nUQXkCWlf7a3yzMMauiQM3/1tiTKAAAgAElEQVT6lAgQASKQTwLUf5MAzGfIZM1OAaQiPp8rwO5Mo3ZNfxAc2dYFTk0F2Hqx9CQSRIhCFJqWFUaPgp+pWHiamU0NoOMvwK483i9brQdg3xA4Pyf38qyqAsNOAfrmwqYFeUrOeUvXEkbOckusbQ1HAYnRwuiYLBl4cRhg707OnNi097UV+Wt/3W8Bf1cgwie7HROOgw8AS22yfqdjBCTHZnzWcLQw2vj8UP7KLoDcB7o+R//6wnpASkSACBABdQlQ/00CUK0YogBSEZ/fdWBnHmvz2KjVx8JHxeKKzIwJpHubi6y4XAsyLw+Yl8sYGVSmRlW7Ax7HlcmZvzx1hwojrwkR+bP7KPfh7i/Qu669Wj7ImAgQASKgIED9NwlAtX4NFEAq4mMjUTu6ZBhLdITRLkVi03/zQoE9fQHvC8KnLX8Ewn2EETF1EvPDBCib+iyqxNYgZl7XyMplbZz0RBhNS4rJ/4heTnVnO3ENrYDG44Q1iWlvXCmQZmobAilxObtiG1M8T+b8Hdvx+/YRsHeg8tXosAQ4PzdL/mM93dGjtp3yPignESACROATBKj/JgGo1g+EAkhFfK9vA393yjBm68P67wJ+ryp8Vn8E0PV3YX3Zvb+E9XFsU0BMMHBnI+C6RsinZyZspNC3AJJjAHZEi2LtHVvzF/QAYJtKFIlNwY65Abx/mbGxgX3HhBg7nFokBuRSYQr61jrBik3LVmgF1OwPlK4pbLa4/zcQzTY5GAjHwVxdlhXEV/MASycg6QNQyhnQNQY2NhHyjL0N+N8Q1u5Zpa1jZJ/7XgN29wJSZVl9mdgDHRYBCVHAw53C2j2WbGoCIWnT4Kxd/zuY89Eqis0yzKb9YkFos13TbCd2wJ2sZbG1khG+wmcNRgKGpQC2IYZNH7MRRbb5g5VpW0dYP3l0rJCXbdRhbf1vhPB31zVCXRhLxS5s70tA3HtAS48fAs4TY8fWHrIp9A9BgCwFMLAAag0Q1nBuap5ev1O9PfF1rTJZ60t/EQEiQARUJED9NwlAFUNHMKMAUhFfwD1gW/sMY7YrtN3Pwgjfs4PCZghdo9ydh3oCr84Bjcdn3X0bHQisTttVOvmZsHaQCStWHhNWbLMDEzUsPU9bW9d4LOCctpM1c4l+N4BIf6DukOz1CPUQRGC17oBjcyDyNXBmhiBmmFhybJbd5tkh4TibGsLmlxwTq//tP4FyTQVxy0QQm1ZWbAxJjgcuLRRGDNstFOrHRtcajsz9DL5j44FHewBzR0HoKhITlExMOzQCPE8L6xfZ6GhsiJC/yXhA7xPvaGZHwDAhzkQb24jDRCWbsmcikgnm3JJiUws7QkexMSW3vL9WENYcAjjb1wudaihxdqCKIUlmRIAIlCwC1H+TAFQr4imAVMQX+ADYmunMuNnvAB0DFZ19ZOZxUhAN9dJGmQrG65frhZ0fyI6cYecVMtH1JaVVzkBsMK/xxf4v0a7aR5tUvqS2UF2JABEoVgSo/yYBqFZAUgCpiI+NWm1pzY29nEbA+ZvfVXREZhpN4PfqwIdA3sQrg16hjbPwWjhKRIAIEAF1CVD/raEC0NHREa9fZzpLDsCMGTOwfPny9Jh58+YNxo8fj8uXL0NfXx+DBw/GqlWroKOjo3RcUQApjSprRraObXNL/tmzKpNQc+AiFR2RmUYTyHQm5I3/eaNFJSuNbi41jggQgaIjQP23BgvAESNGYOTIkenRZGRkBPYfSzKZDLVr14aVlRV+++03hIeHY+jQoejduzfWr1+vdARSACmNKmvGYLbAX1gn96D6LNTrN1NFR2Sm0QTW1xMOpAZwa4hP1jfGaHTDqXFEgAgUNgHqvzVYAE6ePBnsv5zSmTNn0LVrVwQEBMDW1pZn2bdvH4YNG4bQ0FCYmHxi8XsmhxRAKv5E2SaKDY258e2ai9CkzyQVHZGZRhP4d2D6ru57Q/3QsLyFRjeXGkcEiEDREaD+W4MFYFJSEpKTk1G2bFn069cPP/74Y/r07vz583Hs2DE8eZKxKzIyMhIWFhZ8SrhNmzZKRSEFkFKYsmfKdAzLdZdf0bJX2vtiVXRHZhpKICYYl9cMx+b4tvhpzAjUK2euoQ2lZhEBIlDUBKj/1lABuHr1atStWxfm5ua4d+8eZs2ahR49emDr1q08xkaNGgV/f3+cP38+S8zp6upix44dGDQo7aX1H0UkE5XsP0ViAcQEZnR0tNKjhkUd5MWyPHZe3x/1eNUu1F6H9j1px26xvE7FoFLNll9GUFQCjo1vBpeyZsWgRlQFIkAENIEACcAvSAAuWLAACxcu/GTcubm5oX79+tny/Pfff+jbty/CwsJgaWnJBSDbJHLu3LksedkGkF27dmHgwJzfWpBbHUgA5vN2wA4bXleHG+2tthGD+g/OpwPKXlIINPrlIkI+JOHkD81Rw860pDSb2kkEiEAhEyAB+AUJQCbe2H+fSmz3r56eXrYsQUFBsLe3x507d9CoUSOoOgVMI4AF9ItkByevrcWddUtagqXjh6CWPY3uFBBdjXJTf8kFhMUm4+zkFqhSWrm1uRoFgBpDBIhAoRAgAfgFCUB1IuDkyZPo1q0bH/VzcHCAYhNIYGAgypQRXi+1f/9+vhOYNoGoQ1pJ20xv7GibtBJd2rTCtA6ZXoumpBvKpvkEai86j6j4FFyc2hJO1saa32BqIREgAkVCgASgBgrA27dv85E+tpHD1NQUbFp4ypQpfGqYbfxgSXEMjI2NDVauXImIiAi+A7hnz550DExR/PQ+vE1/72/TxHXo+1VjTC2BAjAxRQY9bUmhEE+RyaElFkHEXj9XzNOnONT8+RxikqS4Or01HEsZFvOWUPWIABH4UgiQANRAAfjw4UOMGzcOnp6efMNGuXLl+Jq+n376CQYGGa8bYwdBs3wfHwTNNoIomyiAlCX1Ub7oIGB1Nf5h7cTN6NywOpb1rsn/lstTIRarJlpSU1PBXjWrqn1O5edUH3XqqCBx7HEQpux/jF/7uqBvPfscQbL2SOWp/DttiVhp2PHJUnRdfxM6EjFOTWwBiYo8lS5QxYxMpO685Y9lZzyxfVgDtKqccdAzY8xS9Z/PISFFhhs/tUFZiwJ6XaCK9SUzIkAENIcA9d8aKACLMjwpgFSkHeEHrKvNjZ0Td6BVtbLY8m19rL/0CuuveGP/qMao4/DpIz+YOGKjW4r/M19jdj/ALZ8wHBnfDBWtjPh3iqQYCYtLkuLhm0iM2HEfv/SumUV8HXALwLxjz/HH4Lpo7WwFNjLV4w9XlDLSxf7RjcE0Sa8NrkiRpeLIuKbZRu8y14WVq/g7SSqDlliM8LgkdF13E22r2uC/h4FIlsp59fyXf50jyIFbbuOObwT/7u/hDdA6TSCxtkhlcl6fVKRy35lF3t57bzDr8DNu9/HuWYWdjlaGoGTt9AqOwYAttzGzUxUMa1Y+zwu76ZoP1lx8iUNjmvLNGcyHrpY4zxHHDVe9sf6SN6Z3dMaKM55IlgkMWBrezBEzOlVJF4WZK/FoXnuYGyr/lp48G0AZiAARKNEEqP8mAajWD4ACSEV8cjmw42uEpuiiod/3AESwNdXD2+hE7pCNiK3q55LunI0Gjdr9AO5vo/HfuKb470Egtt70w7Cmjthy3RcLu1eHc2ljdP/DNd3mt34uePAmEmefB8NYTwuGOlr8IOEdt/yzVDqz+Ko+/yzikmX8ezMDbfSsbZeen41AJUnlaPf7tXT7zDtTX4fHof/m2+hcowzmd62G97FJ6L3hFhdmwR8S08WewthUXxvRCSn8T79lXSCTp0JLIubC7l10Ilr8eiUb3LIW+oiMS8H4Nk7Ydduf52OpjKkeTk9swQUSs5979Dn2uQWk2/+vkQNaO1tj5K77/DM9bTEOjG6Cpac8cNdPEJiZ0+VprXhb2lW14SOzCpGmq5UxXe048xQ3qeNghpEtKmDi3keoZW+K/aObZButZG1z9Q7DuH8eIjZJ+smgYdPWilHPzBl9f+mi1siuipFKZkSACGgoAeq/SQCqFdoUQGrhw5vweLRcmV3oDGxQFqExSXgblYCN39RD13U30oWZeiVmt/Ze2pkLr7F7HuDM8+Bc3WtLRHzkL3Pq5mKL0A+JOYooVepZ2cYIvu/jchRAefljI2exSSnYeNWHjwxmTrmJqrx8su+tjHXxPkY4+7JKaWOEfEhEZLwgXHNLgxo6wD8sDmyKlwn53htvISIuWZnics2T2yipWk7JmAgQgRJLgPpvEoBqBT8FkFr4uPHM/55mGa1in7HpScX0aH5LaOZkCVfv8PyaUf5iSMBYV4tvAGGJBGAxvEBUJSLwBROg/psEoFrhSwGkFr5048ueIfh+5320rGyFq17vc3TKRsdehsRm+e7B3HZo+/s1fkyIIl2Y0hLtV1/PtWI17EzwMjg2y9qzT7Xi8LimfCpXkdjGCjYFu+bSS77hRJlkY6KL8NhkLOxRHUceBuH+60hlzJTKw6ZgH72JyjHvi4UdUW/JBSSmZKyzyykjmyJngptNceeURresgGdB0bjlk1VYlzLS4Wf0qZI+NSrJRlvXDazDN4e8iYgnAagKYLIhAkTgkwSo/yYBqNZPhAJILXxZjNk6P9bZt151Nf3zzCLh6PhmWHjiRbrYYZs0dgxviB2uflhwwp2/JuzouKZ8E8L2m35YdNIdbZytcOUjQfnn4LpoU8UK1eZnfQtMTi35vnl5zO1aDfOOPseB+wF83VxNO1O+Fm3Urvs47x7CzaZ3qIxdt1/zaevMia3t+5AohYmeVvru5BE73HDJMzRbcU0qWOK2bzjYer1Tz96hroM52FT4mD0P8HO36rx9bF3e8KaOfE3jbxdeYt/Ixvz9uJ3X3sCr0KzieFzrivipUxX8dOgJDtwPTC+PrUkc0bw8XzvJ0sgW5TG7S1UuEi96hOCHvY/450zcMWFtqKuF81Na4uijIC7IFOmXXjUxqGFZ3i62SzcqIQU9/3RNny7+VGSw9ZW3Z7ZF1flns2VjAv34+Oac8T2/CAz+6w6mtK/MRTclIkAEiEBBEaD+mwSgWrFEAaQWvmzGbNds+Vmn+efsSJCtQ4XX+jGRwaaFFUeDRMYnw9xAJ31TQHhsEswMdLLshGUjWmy378Atd9LLYWvYTvzQnG9SYILup0NPs9Th2ybl+E7W7a7+2DeqMeqXM+eCkpXLNkJkPrOP1bXnhlvwex+Li9NawUxfB2zk6sarMHy7/R4WdKuW425aNtLJhBZLszpX4aJq29D6aONsjYj4ZL7jmNWdCTX2H/u3Yscu20yhONEl83E3bK1dTKKUbySxNNRBeFwyX7snsEvFscdvMXn/Y77DdnAjB7Cp1V4bbyEoMh6nJ7WAtXHG23OYD8UhPHHJUr7DWF9HglchMeiy7gb/+9zklnCwzH4kC6vH08Bo9Nt0C0a6Wtg8pD6G/X2P1+vIuGbo9sdNXicDHQncF3XC8Sdv+eaRtQNr45rXe1zwCOG7qzMf+FyYZyUWbPSSNyJABL4kAtR/kwBUK14pgNTCl6Px3KPPcMAtEMcmNEPVMuq9+ouJHzZq5hkcg0vTWoHtvFXsZGXfBUQkcFHDdqayUbrzU1qBTdeyv431tPNsHBM88tTUdJ8Kg08difI8KJoLIbZRgo2ifUhMgYkSZeVZmTwyfFwnJixZ3fNzEHVUfDIX3XnVl234YKKVicCYxBQuAJlA/+u6L5ae9gAbhf26lvAGHkW9hH/LudikRASIABEobALUf5MAVCvGKIDUwpejMTvGJFEq5+KhIBI7g4+dOpObsGCHJosg4ufpGegUTJl51TsyLpmLUXUOrM6rjOL4PRPdTBxaGil/2HpxbAfViQgQgS+fAPXfJADVimIKILXwkTERIAJEgAgQgc9CgPpvEoBqBV50dDTMzMwQEBAAExP1pivVqggZEwEiQASIABEgAkoTYAKwbNmyiIqKgqmpqdJ2mpRRlJr5fVma1LIiaEtgYCAPIEpEgAgQASJABIjAl0eADeDY2+f8PvYvrzX5qzEJwPzxypJbLpfj7du3MDY2zvMdqPktRvF0QqOL+SWXv/zEOX+8VM1NnFUllz874pw/XurkJtbq0FPetrA4s7GvmJgY2NraQizOeDe68jX78nOSACym15DWJxTNhSHOxLloCBRNKRTPRcOZlUKsi4Y1cS48ziQAC4+tWp4p6NXCp7QxcVYalVoZibNa+JQ2Js5Ko1I7I7FWG6FSDoizUphUykQCUCVshW9EQV/4jOkpvmgYE2fiXHQEiq4kukcXDWviXHicSQAWHlu1PCclJWHZsmWYNWsWdHXp3DS1YH7CmDgXFtmsfokzcS4aAkVXCsV00bAmzoXHmQRg4bElz0SACBABIkAEiAARKJYESAAWy8tClSICRIAIEAEiQASIQOERIAFYeGzJMxEgAkSACBABIkAEiiUBEoDF8rJQpYgAESACRIAIEAEiUHgESAAWHlvyTASIABEgAkSACBCBYkmABGAxvCwbNmzAypUr8e7dO1SvXh1r1qxBixYtimFNi2eV2O7pw4cPw9PTE/r6+mjatClWrFgBZ2fn9AqzU+AXLlyILVu2IDIyEo0aNcKff/7JeSsS2302ffp07N27FwkJCWjbti3YtSmprw361NVmzGfPno1JkybxeGWJGBfc7yMoKAgzZszAmTNneCxWrlwZ27ZtQ7169Yh1AWGWSqVYsGAB/vnnHwQHB6NMmTIYNmwY5s6dm/6mCIpp1WBfv36d92kPHjzg/dqRI0fQs2fPAr8fs3v5xIkTcfz4ce67e/fuWL9+PczMzFSruIZbkQAsZhd4//79GDJkCBcazZo1w+bNm7F161a4u7vDwcGhmNW2eFanU6dOGDhwIBo0aAB2U58zZw6ePXvGGRoaGvJKM0G4dOlS7Nixg3emS5YsAbtJeXl58Vf7sTR27FicOHGC57G0tMS0adMQERHBb2ISiaR4Nv4z1MrNzQ39+/eHiYkJ2rRpky4AiXHBXAzWqdWpU4ezZTFpbW0NHx8fODo6omLFihTPBYOZ3w9Wr16NnTt38gfB+/fvY/jw4fzewB5s6L6hOmj24OLq6oq6deuiT58+2QRgQd0rOnfujMDAQP5gz9KoUaP474TdxyllJ0ACsJhFBRuJYj+SjRs3ptesatWq/GmJjbJQyj+B9+/f807z2rVraNmyJR+ZYu9/nDx5Mh9VYYmN9tnY2HBhOHr0aERHR8PKygq7d+/GgAEDeB723ueyZcvi9OnT6NixY/4rooEWsbGxPF7ZAwvrKGvXrs0FIDEuuIs9c+ZM3nneuHEjR6fEumBYd+3ald8D2MiqIjGxYmBgwO8DxLlgOItEoiwCsKC4enh4oFq1arhz5w6f0WGJ/btJkyZ8NijzDFDBtOTL90ICsBhdw+TkZH6zOXjwIHr16pVeM/b0+fjxYy5gKOWfgLe3NypVqsRHAWvUqAFfX18+cvLw4UM+sqJIPXr04FMFbATg8uXLfMqXjfiZm5un53FxceFinE0fUwKGDh0KCwsLPnLSunXrdAFIjAsuOlinxh442MgGuwfY2dlh3LhxGDlyJC+EWBcM6+XLl2PTpk04f/48nxV48uQJOnTowB9oBg0aRJwLBjM+FoAFFb/bt2/H1KlTERUVlaWm7J7O7k9sNJdSVgIkANWICLlczkeF2JQhC2p1E1sbUaVKFX4DUjzBMJ+rVq3Cv//+ywULpfwRYE+XbDqY3RTOnTvHje/evctv7OypkK3zUSS2diQgIIA/nR44cIB3smFhYVkKZOKvXLlyWLt2bf4qooG5Dx06xGPz6tWr0NPTQ5cuXVCzZk0+ikqMC+6Cs5FoliZMmMAfPtgSBDYqyGKQCRNiXTCsFev7mFhgSzxkMhnmz5/Pl37QfaNgGDMvpqamfJ0lG3EtSK7sXsT8Pnr0KEtl2UP+N998k34dFV+y6x0TE8Nng8RiccE18AvyRAJQjYvFnsjZlCAlIkAEiAARIAJE4MsjwB76S+rGPhKAasQrWyfGhpdZALEF8JSIABEgAkSACBCB4k/gw4cPfACHzQ6xUcmSmEgAqnHVWQCxwGFCkAQggDd3gAg/oPYg1am+9wJ8rwH1hwMSbeX9hHoC/jeAesxOS3k7ylm4BNh18bsO1P/u810XaTLw4G+gYluglFOe7T37PBh62mK0drbOmlfV2MyzROUyHHkUCDszAzQsb6GcQWHkksuA+9sBx+aAddXCKOGz+Tz2OAg2JnpoXMHys9WBCi46AtR/AyQA1Yg3CqCP4C1Ie4r6/jJgL5xPlu+k8NH5V6DRaOXNFXadVgCNxyhvRzkLl4DiunRcBjQZV7hl5eb9+irg8mLh2wXRn6xDWGwS6i+5yPP4/NIFEnGmtb3pMbYcaDy2SNvi8e4DOq8VdgH7L/+6SMvOUhgTfyenKMXy81Uy/yV7Bceg45rrn59v/qtOFioSoP6bBKCKoSOYUQB9hE/RQfbZBtTsqxpbhQ+XQUCvTcr7UNhV7QYM2KO8HeUsXAKK6+L8NTDo38ItKzfvu3sDPpeUEi0vQ2LQYbUgBF4u6QwdrUyLwxVtqdEH6Lu9SNty1SsUw/5242X6LetSIJvOVGrA0fHA47TfVx5iWiX/n8noonsIvt91nwTgZ+L/OYql/psEoFpxRwGUiwBknSPrJFVJik62zjdAjz+V96CwY9N8Qw4rb0c5C5eA4rpUaAN8e7RwyyoAAej+9gO6rBNG2jwXd4KedqYDvz+jALzrG44BW+7kXK+ipKqhAvCCewhGkgAsykj67GVR/00CUK0gpADKTQD+DdTorRpbRSdbbxjQLR9HrSjsHJoC351RrWyyKngC6delCfDd2YL3r4zHPX0Ab2FaN68p4KeBUej+hyvP6r6oIwx0Mq0n/YwC8HFAFHr+KdTLbU47WBnrKtPygs9zbDzwSPNGAM+/CMao3Q84r886xV7wV4w85kKA+m8SgGr9OCiAchGA/XYC1TPe85gvyIpOtsH3wNe/KW+qsCvjAowWpvAoFQMC6delNjD6Mx1kng8B+OB1JPpsvMXBPVvQAcZ6mTYifUYB+PBNJHpvEOp1ZXprlC8lvNKwyJOGCsBzL4IxmgRgkYfT5yyQ+m8SgGrFHwVQJnxyObAo7Y0Z/XcB1XqoxlbRyTYaC3RerrwPhZ1lJeAHYS0PpWJAQHFdSjkDE+59ngrlYw3gPb8I9N98m9fzyfwOMDXIQQBW7w30+7tI23LfPwJ9Nwn1Oj6hGWrZf6aX22voFPDZ5+8wZo9w0L7vL10gzrz5p0ivNBVWVASo/yYBqFasUQBlwseO2lgivLEA/XcD1bqrxlYhGJpMADouVd6Hws7EHpj6Qnk7ylm4BBTXxdQBmPKscMvKzXs+BOAtnzAM/usu9/RwXntYGOpkeFW05TMIwDu+4RiYtgbw3+8boalTqc/DsgQIwGybfz4PaSq1kAlQ/00CUK0QK+4BlCyV47JnCD/XyswgU0eWFAP4XAac2gM6BvlmcO3le5S3NIRDii/AfJVrAiTHAb/YCr7YLly2GzeXxI60eB4UjS41y8BQ96Mz+xSdbPMpQLsFYMdysGm5tlWsoSXJ+roe79AYvI9JRpOKloDCTt8cmOGf7zblZBCXJMXpZ+9Q3dYU1WwL4KDvuDDhrMTKnfJ3Jp7/TcDQCrByzlLNgIh4eL+PRZuPz6v7qDFJUhmueIaiSYVSWUe08kMpNRV4eRYoXQswtctumRApnPfH2qaVaX2a4rqw+v/orXSJ11++RzlLA5Sz/GiqU5oEvDwnnENnYAGlGGQSgA+/80edsma47BnKr2kZU33A35X7YufasXInbL+CJuIXWPLjNFhJg4HYYKB8y4wYc+4CuAwE2MYWvaxx8T4mCWy0jr0Zsr6jBUoZfWKtHntocj8GmDkADsLL63NLrt5h+N9WQZhuHlIPHauXVpol2LV7dQGwriKU9VGKSUzBzVdhaFPFOuuml5xKyCQA737ri0afODOP/T5DY5LQtGLOYvWKVyicrIxQ1iLtHsTreR6wqQ6Y2meULksRYq9cM+E6FUI68+wdxv4jjAB6LOoEfR0JwGP6BlC5Y5aYvvHqPRwscohNJeqVIpPjkkcIGpW3hCw1ld/b2lW1yThuiP/OzgGla+b8O1OijMLK4h0aCxbf/H4LgLXlzPNglDbRSz+bskDuNYXVgI/8Fvf+uygw0DmAalAu7gG04qwnNl71QV0HMxwe1yyjpf/0E260+d1pyxag+0egX9pUlL/eYMHnVA9A2wBYUU74e8A/QFXhPY85pcpzz4CJ04lfOWFqh6yiJl3ItfwR+Goumq+4jMDIBMzvWg3fNS+fxZ3jzFP874tTW8FpQ5oo0dID5oaocVUzTH+/8BLrLr2CtkTEjwRR+33Pa12ASH+g/WKg2UTl6hjuA6yvK+T96NgNRft3j2iIFpXSRl9z8LrsjAc2X/OFS1kzHBufKQ6Uq4GQ68UR4OAwQKILzAvNbrm9M/DmFtBsEtB+Ucb3CgGoYwzMDlSqxMzr3bItyL+0GLixCrCtA4y6CgWDPSMaoXmlXEbFMq0BdEz8F8t618Ssw8+4SPP7qQawtlY6X3YciNG+Hmgs9kBc3VEwfLhF+G7sbWBjk6z1d2oHfPNfls8a/3IJwR8S+WdlTPVwe1bb3Nv8eC9wNO3MylmBgK5xrnmZMP12uzCF/ls/F/Spl0kg5UXV+xKwJ21TVg5Htwz/+x6ueL3HoIYOnM0nU6Y1gIzljZ/aZAi4jwwV1+bytFaoYGWU5dtb3mEYnCZo06+x52lgX9oh8pnreXU5cHUZYF0dGCesgyzodOrpO4z/VxCAzxd2hBF7MN3eCXhzG2j6A9BhCf/uk7GpRKXWXHyJNRdfoVqZ/7N3FcBRJH/3rSSbkITgwQluAYK7u7u7c7i7Hnq4++Euhx7u7gR3DwkOQcJu1r76TY81WiAAACAASURBVM/MzqzH+H9VR1ddHdnp6e7p6Zl+85P3EuK7zoCXnyIxsmZOdCqdiZ1NHwRb2ojr0Y0mf1kV6/u592YYem5gurtCuISw5+RL649dPUv9srHFpKP/7/t3TK4puuf8BoDRnTFJ/f/vC6jA+MP49COKG7FsIxU2ZToQTS6vxSefYMr++wDMeO7Vks1G271AilzANP4l1mwDkMM+Wa3JZEam4fu40+rkS425zfNbZpRUBv7kv/DLDQPKDRU3+OKZkmJjl2KyuyW8kGY3DUa9XbnYMZUnMOp9LO6q5dT+m0Pwz/XX3A82lCAx6UGY97SFgU58Vqqrdh4eAjY0trshCNf/R7nMGFIth8OWik46grdfdbbrwFXf0uM7ugE3NtodB/ejcG0JkgKDn1rOjAEA/PvMM4zfe9f+eGfnBb68EMchzEG3spkxtLqDOZBYAAm0FM2YBBeffWLtd04ArOUTlsZGcBbfGttZO2a1NxSGn6wvoiQi8GNdHIByoZrTjNIzs4EjY1jVfnedWnzIgtt+FeMBnNIgD5oVsbXkObydxyYCp6Y6vHfCHHLz4YpkWmIBpLlc0a4QKuQIsOnabDYj4zD2nC9uVRDVguQWywXHH2PawQfye3xoJHBunu045xYAPj1xvPYcXrj7B+yBGXFNe/kDQ19yjS0//RQT/r1nf2260Z3wQSutKgNLu3sD11bH67W6MUy7VYR1IligV519hrF72HMqfAgUn3wU4RHsA8jlWorpQOLovP/v+3ccXabTZn4DwFjM8v/3BRQ05iD3lWnzMMYBAPSEHg+92rLZIytIQBAwg7fmObEAavVG5BjF6EDqBafG7GYSABgVCUxKxdqsMBIoM0gEgBVypMCKdoVld0t4IZHVovk+3nKhVAOjP8birlpOHbztBrZcYVarKyMrOXfnudNjTAAg0ZeQBYuKA7DRsVRGjKrFA2A744iTl/LW9sAdnl/R3keDKwCoSQgMe+XOLMEpAFxQDHjPNmCaD2ENtCsRiLF1cttv3xkA7OILrOHjVcd8wc6QMMvHhLS1GtOBfQNt248NADw1DTjGLEvocwNIHOhwfqRExePrBaF1Md7a7s6MnpwGHOf7sXPvYgMAF7YswIVyWBfpc24PANq9x8cnAyf5xC/pOOcEA5+f2X0G3Ll8d+rsuRGGXhuZNUuM/RTWtNobGPmGO7bs1FNM3BdzAEgqMxTWIi3ZAnxxqF9Z9tP/4xhLawAovYcnBpZDYDIflJxyDK+/sI+m3wDQnZX3v63zGwDGYv7/vwPAbCP2I8posn0Y4wAA+iISt706sdlrtpHFrMwOYn87oYGJ+KlHvnGHuGoN8qfBzKbBljtAMTd/8ZtgpbFAqX7iBl8jT0osbCmXlxNeSAR+Oh7h21GogDHMuhPb0m9zCHbwFsA4od4Q5j1NIaAzr0zhapBPjlssVKM+ymIHhesnMECgwFGJk5fyppbA/b2sC6cAMBkwmLfWcHV5eUCNPzCMWVFcFacAcGk5IIxt1FIA2KRQWkxtlM9+084AYNeEwGo+XGHEW2wJeY8m/9qZy0rjLNY6aS+xAYBSwNPzqlOdYtIn7raO8dSNqZ0L7UvKwyGczunZucDhUazKmC/gfN+SEhsAOLNJPjQoYOuOlkrqLWpZANWtQOL6iy8wYsdtbhSisonUIiqd19l5gC/82ommx8LVWhOOkw5wn00h3J+XRlRECj8vy9pVKIExn7ljS04+wWTOAxIzgJNnzEF84z/Khb7TJvbGmSEV2J+uLO3uXlAc1zOazMjMe24EC+DSU08waR+bi6MDyiJzcl8xZCem8xPHw3ba3P/3/ftXzMVvABiLWf7/voAcvtjjAAAmwVdc8+LjlxqtBFIHA3N5a54TJRAKIi48kbk/GxRIg5lNJADw2xuLFZHiyEr2EQGgTV1APDawSjb0PFWI3UnJyzoWt5Y7tcf6a/j3Vjj37z09SyFPWh7MxLThmABACkIXAMrwMMDTkhQh3F+n4AdA6anH8OpTLL/K1zUCHh9mVx4TAChxo7maPqcAcGUN4AUjRJYCwFp5U2F+Cz5W0roDKwBYMENiLvie26S6JQJW1WBnDH2JtSFf0PoAHxMobafsEODkX7ZDjw0AFOIZqdXuF1mShoNCrunufJLCiBo50bkMH27hajLp+MUlwP7BrOaIN4CHt+ysmAPA9ZhYPw9aFrW1Rj778APlp5/g+pnWKC8aF0on6/Ofa6Hov+UG99udcVVZMtilZRYrq/RjZ1YQEMFbj+MJAG67GoqBW9l4LgyriJT+EgAoWfMLTzzG1ANWrmt37gFfJ8vwfTCYzLIzkvp44uqoyuy37Z2BW1vYv+PpWqMxXLHqzygjco5mnhsBAErn4nC/Msga4IcyU49zcY1UflsAYzLTv/ac3wAwFvP9XwaAKfERF7x6sdmrMx9IXwyYz4OwBsuAvE3sziy5B8giRaVucGrMkbqAP7+wBORXHAOU7i+CvBZF02NSfUuAujTGqEf5zBh0XsiiVABjv8TirlpO7bT6Co7cYwklGzoXdZjN6HZnMXEBvzgHrKzOuhj8TJYFKWzcNq50qwGVm3Yczz/G8qW8ujbL8nW0MdlzAVNG4zier84rETCUj91zMWFSAGjDyWZF6izMQaWcKbC8rTxEQOzGCgDmSeOPW68j2Cb1RxJgZTVWdeBj/B3y3WJNlo6TaInOz7cdeWwA4OHRwFle7abbGWZFd1B23whDb95FSfGeFPfpdrn8N/Bvf/4aHwG+KWSnxhQAZtauxbCaQZYEBkmrt0IjUHv+Ge4XexZL6fVcHF4RAQm9mMKIEGdJmfyU0U9lZi7gK4vFjS9QtPnySwzZzmiKzgwpj7SJE1gsgJJ+7cYuun0jLB+t0lMSeKpw909+DboKtYhGX3FZNSJSj3x/Ms+NAADnH3uE6Ycecr8d6FsaOVIm5EA/gX8qvwFgXN6B+GnrNwCMxbz+lwFgesVbnNL0Y7NXdTKQqZwlS7LeYiCYz+azml+pZaBmnlRY0FJitfnwyAIiK4yErkR/ZB/Jvjqt49ykLon2JQMx5moJS09x9OVMWZeUfUllaeuCqBId6g176ypGAPC8BaAMeAD4sWB6KQC25x6Xdl9h+gk8je1L+e+qwCumRevcAihJAiH6jvF8Zq53EmAIH8fl4pmTAkCb5BsrV7QAXkpkTooNneVJQmI3VgAwR0o/3H/zjW1SUhdwvztYdF2HP07YsSQWaGsJzpeOPzYA8MBw4AKvd935OJDGgQWTQsOuv0bfzcxFOaByNvSqmNX9N9eFxcCBIax+r2tAUjl4FLLy3dq0JTFq2bWr0LtqHvQon8VmLFI+RXvjlVoAuSz+FL5AyAZg5x+srb63LJQ1M3IA35glPr4A4LoLLzByJ3NJnxpUHumT2geA844+wozDDPTEBOBIwbZ00sS2KAOYMoHj8VrdXziWmu++aVFkIgtbWdyqAKoFpYKQ0Uy//du7FEeXVXHGCTx5/xsAxmSO/xfn/AaAsZj1XwYAIz8BP97b8MDZHTrFytDGq/uGwLmhSIEv0CiicHpyB0R8/oBPYU+RcSvvbiAgMeYLyCNBvHyZlWF4/UWPbElVUKQMAgw6vLp/Gf4+CZAwVRaO84xiYP45cAg5FS8x23MhGwJl7GavDiwpw/4mi2CB1hyP1qc3LxDhl1WUrnrw5huqzmaWpCq5ArC0DbMa6j+/QuidC8h4hI8rLDccHwv1RcEJ5C42489CUWhTNrc4B0QjQxsXlWKZkmBTGP8FTQkb9U4hOOFXqAN5yhMCLn6pgCQZOeB06+FjJFZGIl1W+zFjj16GIaX5Hboe+A7zi3N4bU6G0ZXToVL5Six+iqhZqFBgerpiQORHNudR3/HRLzu0T89BBSOUGUogub8Pbr/+iqwBvvCayDKczWkKQeEsBpC4FT89YxYhcneu4jOq+9yEIWE63An7CpXhJ4Ys/Qf3zOnRPlMERnVowjIl1Rp8CX2Iu5o8UHl4oXBgEjScsQdfP73BE3MaHOlfBkl9NEjsreYoLrS+afEy/C1SpA7EzY8KFE38HRqFET/ePUFoglzInkTFOB4F6y4A7YhPcr44owEYz7jBuCLEmUmSekzeSfGy4y2kMr7Gi08/kTWRAopUvKuV2ie6DYqN/PQEK5/4Ydy+R9x9P9DACzkyZ8TLj9/w9u1b5AvfDM+7PPXKyPcIHHkYSpjQIOUHTO/VClBJlDvIokxJQbt7AU/Y5hWoXU9xAuJQL9cIR/JjA7i/v3W+hBX3lOhyugS8FSx7XiyJMliyj6W/D5Rb1CoOWwo//ESImUCWAs8H5UAkvPBM54tcfj+h0Eaw+UwVzEDZJUYz87HZv0iaoxTow+b2qw9I8vU+NOkLIIU/c/lLXZS9K2ZF/8rZ8PJjJDQeSqTw04hrTPHpCZ5GKJAjaxbxuQ56vgqqoyzb+GPjnfiuSY7wSAVSpQmEVm9C48Xn8FXLJ4tNqo6Pjy/he6JsyJCCz8gnlZ/wEMbPt7c/EMK0gLtG9UP20o1Rq0B6BPh5yTgmpdq6XcpkwvAaOfElMgof375COh8zFhx7gHUhEfgIf+zsURLB6RIB19aSzAmb3W5nAXoH0bMw2RJjeKPjC5AF16FSxxdyFZtt+A7Jhfnk/XfkTp2Qo+kxGM2MvubNLXyMUmP9hReYec3IdX28Rx58//gGeXZa3pP44xyQLDs27d6LFZfe4K05Mdb2rIpsAX7wMuuADw9hCsiL2+FfkT2ZJ949vg6fwEKgCOyvP/UiDY4jAHiqW3aozQakvjjeJtaW3ll3w79y473z+isCEmoQGWXkroWy+4mPT+RSlK7Nt3eBhKksltQPj/BN6YvXUT6ctQ6fngLf3wHpinLvtdDPkVAqFEjt74Wvz67hvSYtMqdhGd6v34Rj6NxVuGzKjr+aFUXd4DSYeegB5h5j3J4T6weheeH03Lv90bvv3G8iqKX1Q893ovR4pEvEJdO9+hzJ6WyT9ZP6M4XdwJ2oFMiWLgAatYp7T9N7kz4MOE7GeCi/bP+Oh7HHVZO/AWAsZvKXLaBxiQGzCeh5BUjm5MtfrwUmWigZ+kd1w0zPxewKh7zAh7/yIRnk7tEN1W9xMRt7Tl7AWa8+ltnocBDfT86B7xNG5cARyPa9hfUHTqHlBSuSZ3KPkTrCchbI/KnCNCQp0wWYmokDRzV0k7BqeEcusFrqGhIze6UycsIIyg7Bi7x9UHbaCRRUPMB2zTh2pP997qUmjUnhXjYCJyGASLMGCRQ6oO5CRuC6vSNA/ICDn+HU8x8os4FZLCL+uAX/ADmdRsirL0i2vCDSKj7gsmcRFI6SyJdRdjNRuMzIZpkn4oKjTF2+HDYWRGUVC9YfrW+LoPqDMHjbTZTMkhTrQ6tyv39MlBdJ+552vPLmF+Y2FLTczpI+1vCyej2vYOJFPZadfoadnqMQrHyCO6YMyK18wbKw3zILBpW1hkoYZejAccbV25MHKphQVjcTL8wp4alS4mHDD5bNlu6Z2RcldXNxz6uD2MYFU06OD8+6tEi1Hxu6Siyu0uQdqky0QBlLAwR2prD5/WBOiBq6ybjkJaFSabuHESxv6wjc3iZ28yhlTVR+3hKVlFex3FOuB/1BlRzJjDzNz+BnCPzzPHqp/sEAj21AcEugHv9RIs0oJ+v0UxaPllW7Bnow8nFZJjtlpZtnoGSJUuh4riKSKNgm5lYRrIC0mU5nz2ezqJF4aEorxskSZYp0jaLccGbVusok5RrrRmPLpP6Yd+wxkh0fhBbq45hvqIueE9Zwx7dcfoXB229y/+5eLjO6ls0sJlNNb5yPi1+rkl6Bpe+Y5X1Lzdt4/vEHFp54gqUZjqHK2+Xc7/dN6ZBDyeLpaEzW5Xm9VxwwPWgshGLDDjBQR25qclcHNQQoI5YHgHTuVp+WGPSxJhJ6qXFzLFvfVLZfDcUAPqZO4BekBIhbCnloCI1B5LG8shLY25c10J6In4sDC4oC71migTDmQVWz27U6QqpGNOIt4OElnld3/hncCI3AnGbBYrLHvXYaeG/iM+wBFNYuwHsklt8n6QSRzng4ixOMMquQTbcW5bInxyrTKM46fjJoItpeyYiNvnNQ3HARY/VtsMrIPkzPD6sAIh23DwAtlFqG9KWgfslc54K1kwiz269kFEDSMq95fjFzWeQuFCqEhQBLywICLRN9UM5l8dY05zs750XwunyA2cgR92uz1BDZGZ60NkG1tRXumdLDq/cF7uP959Kq8A67gG3GMjDXXcjFdE49cJ9bX0KhWOw9N8Lx4C1vXRcohXjXvkntjWzfl8DAP3/CeU9a66Ha2hYhpsxYmn0Zl+wnrB979F82ExHDH37Z/h3D8f2K034DwFjM8i9bQILrsNZsoFB7xyOOeA3MstCB0ANFIIErnY8By/hMM0kL1RLt4dxhdZTnMNdTEuNEhL5CfJJQf2wEdvyzEfVv8skfwu+FOwN5GgMrqnC/3Mg3GvnqDxBjaKbqm6JU+0mcfJVU07RMtuRY06EIIN2shTZLD8TtHL1Ra94Z1FOesVgbeVcZ0dsQzQ0VBUx45tXKdl7KDGYWoBOT2LE+N7D+AdDyALP8hdZaj7SF5ITVRP7c/6yDWLKsVYGiXSy0LC7WzkpDVexM2ZvbeKgIAOCBOjuyj3SiiyvcbyLqJmAtkPh2O4vA2SyOTgYm7IzjtTkpSurmMXfhaWZlHaLvjM3G8uz8SpeBM7NkZ5bUzpF/BDi4PnL9PZhS33JUGrtJvxLwzt8SIMv1VJat+t6cEIP0XbHKc5rlPHKr1pkrj7Xij9Im1UG1H6M91jqe5X53ETg5BE81LaFU8IH1AhiLCAVm8bQwaQoCrxkoz6FdCS2YOkcAPuGiF29xAlBTNxElS1dEx4vVEKCIRhyp0Ofra8AyNr+D9F04y/EGT7b2yPIo8mbSD0SoTXGy19n1NY8agZXjBiDX6AN4quEJ1ukA3/aGiy8xfAeLUetaJhPq5U+D6nPYRwSRCpOFqJTyFtZ5TuZ+q5FoD+7ybu4B6i3opd5pM48Ztetghlxd51nyAVDw7ta7XV4xBRz+Q45rIF8L4IYFOJ415kZL/Qi2piQcgqvPPceY3UySUcj2zzR0D55aPad0n8Us4cvLgX+ZNRZtdrGwEmnCGg9eNGolHkzg42KlVyVZb9a8igLwKpQhMa7wCUA3gnfD//4msYX2UYNw3JTf5bMlnCAAaOFZvKLMg0aRw8Tzn5sCUC6KPWNCCIk9AOgNrfjhFeWfEZ4Rcsqb/ltC8M81PgZScr0Ejs4/ZZRXB/uWQfaUEiLx45MsSUu0hm5sBnZ04dfiBgwr5oWuITw5eJWJeJG9PfexTeVx3vVQP2Qk+/Qh0aRwOvE+/DBrsKP6ZbQqlgGT993DklMWzk+y5pFCihheIawHSbJTHu1yfINcfepR7lXweMLiC7kPpSk10XDROUuilituSpuV7d4Pv2z/dm84/5NavwFgLKb9ly0gtwGgZNOz+trnvqiFYHfJNQsAsInqOKZ6LLMcIfAkkMcKv46NwD+bV6HBPYmlkI4VbA9D7oZQr2Fg6mae4cjbcIgMAFboPIWTxpIqAIhxW9YWJGqkVH+cz9gTzZddQCPVSUz3WMJG0fEIkK4wpEHJahjw2Itnz5feT5KTI9esELzf9TTWP/FEyyMsYeRZne3IWKCSbAVQkHePk3K6GbFCYGmgVF+3AeBWQxmsTzUUZFWkEm0AWKQLk+sTiKA7H0PgPMZH5i4A7FspK/qeYYBWapGIDQCkl/itKTw5NTX85jawWKIwUnsuULAt8O2taC0lC+AA/R9Y7SnJpM3XHKi/2CEA7KbajaEelg3a5lHtfR2BU+/J50IAY1/DgZl8Vm2y7MAHlrkp3YAyKN7gpIZPjiCgohuL4BJV0elKTaRWRINKSHB5k8zfCmYFG6HvAALhAuDlQLNXO8slKD2API1Ecu02UUMwfVh/Ls5Kdm/561l7/jlG7WKAqkPJjGhRNB0qzWShFOmSeHNZ3lIA2DDRFtx6b+JooIap16Ormm3o0kJj0kEiEQngqf8fUOrYB8vj7qHIksIPmJET+BbGTqUPvVtbxWaumbKgQRRTfpECQGmyRM28qbCgRQHkHboFN706y8aQTbsaExoVRBPKEpZmKxO3KFnX7QBA677EBiXrzTrWUQBe0gSga/l2I8kDy/rqHtUb+0xF5UDdZtYsP1gDwPOqwmj+o594/8iCVj2K8RpaA0BPtZJTQ6KSAp9Fy3hUoizw/MJLJvL3fsSOW1h/0ZZCqVSWZDjz+APXhg1LweExwNnZbLB2AOCIoh7ofIN/hiuOxr0sncUPikf5NsLjwR7uVGsA+M3sja2Vz3OqTBP23sXyM5a4XgKAZC2kEBXZPSKuS+K8BBCsXYIvkCve3AtaD+/HbH0KALD50gsiuI1JrKWT2yYe+mX7tzuD+R/V+Q0AYzHxv2wBuQsAJWZ+7gE0BSBQycui0Re14EqUXLMAANuqDmKcB89AT8eJiFkgqRXqj43AtvVL0OgRTykh/J6/Fb5nrQ/fLcydcitoKPI0GCyqepAFsFLXv1AgfWJI3RlFApNgS7fiMqAgDq1kHxxO0wOd11xBM9UxTPFgLizBNUQKJ6R0QkWDKPnmKjRSrAcQ9Q24xtxoaLcP659o0PI0k+d6VHc3subnCVj5c4jotfMxCTm1dH2kLgAQHcjGpm6tmgPGwlicclzMAWCJ3kD64hZ5rPYHELiIV7CQuLztDUawAPaqkAUDzhXhqvylb4ZFRkZ6/LzCBeDcXNmpFXXTcFQzyOW1FdIuwpUpEivVC0miCp1dcwZQuBMgscJ9Nvuil76XaKHiOsldH2i8ChBCHCQ900bQW/UP+pNr11HpfhGBM5/YB4AUC0v8cVT8UosAJr92MT6D6ffmVjzDvxpmvaJCbtssRaqh67X6SKeMhprMqA8s9vDpSZFUepy+NcLMybDEk1mACmgXW2iT6Afiq6Tr513fZH0a1a8vKsw4Kbse4+gvnE6sVHWhTfEMnGybYAFMnMADnyP1KK+8jpW8hbW1/yrciEjAxfaNUa9GezWzlkuLPWvMswTtoDCx+Md7XV8hZ6qEgCBhKNwzkgXky11TBtSIYlZHkc8PgCA/SL9XzhWAZW0KocTQ1TjnJZdApHnpWasYk3k8vwA4OJy13HwzkL1a9ACg9J53OcHkAvkiAEAiXX74lrn3L+fZieSPeMoVSq6J6oZ9piKyMAibSZP8YA0AT3mURJtvPcT7JwXHRIZdPkdyManN39sDxIlKJbPitfjc6f3SweObnPKGVHEoMcq6kAdFSFLb2q04F+8rlgPDgAt8OIQdADiqiAIdb/KJemWH4mqmbpzVjcrDfJvh+YAlolgDwAhzAmyscAakvDN29x2sOmfRXScASFyAYoa9HQsgvTs+QE6ndTNoCxI+ZhZqAQC2XXEJpDlP5TcAdLYKY3fsNwCMxfz9EgAopdKoPQcoKLEiWI/9/QNgAdvsqbwxJ0ZKBeM7Q/NNwMZmNlcrAMCuqj0Y5sFLfVEt0qsVyGOFs8ZGYOvquWj8jCeVFX7P2wzvM9ZG8l1MGu5GjgHIV6+PGP9FALBMx8kolikpDt55g65rmTsuf/pE2EEaxdIXt9Bm8Z7YkeIP9Nt8A61UhzHBg8VKod2/QGApSLPSEkCLu5LYNfEiCYRQgoawYTXfjI1PNWh+kUl/3a+5HTkKyy2A5Lpqe8gBoXDynECZgSym0I1yxpgb01NOizkAJLBJiSCbefd2m10IXMpn2LkJAClebPAFZvGcY2iAWYZG3L+flT0NxcVFsqugOLStGomOr4NrLKGdi3NTeBUYqiOVq6O/q/0FFOvGdI8JPFAgvNkb3fT9RJco92OOWkCz9cCfSQETS0IQCm0EA9Wb0VPNZ0TaGYu5y0lknPvaPgCUaiiTTrWe0eAU1i7EezBqmmLKu9jkyStkkNcxaggSBlXDwPvNLB9ObtxnDHsNaHyBR4eB9Wx+J+ubI8ycFPP4sIoyulmWrHmuhgLIVUfM+OwS1Q89u/dFnflnZdcjJNxIJcgI/DUrnA51FzA+RNKq1hvNqK08J/bXNeEi3NAGcAkPk9TL0ULNqJekpaB2EZeEIS1S6yMlXJB+NOYXES2o3D0TCMEBPDGlQsUoFqcp8vmRBVRitSqbLTlWdyiCKsMW45CGz0bmO6V5aVixNPpUygpICaubrmPrQ6AR4uuLoMueW/DDY2A+b70X4kuF83jd8AxJE+AFT4l0PtcOpHpqsWaS1ZZieGVxqk7uvzUAPKapiA4RHcX7R89/K949ToTZ5bKnED9aKXHn3TemCJJP8Ri7NKO5fxsSpIA6ktfa5i2A0w7ex4LjEnJ1fkwUf3jiAQNJK9sXRvnsEnofSta58jerSe3c3AL8w6yvRN8zqjDQ7lZrdrxUP5zJ0BOt/r7I/fkgeCs09xnItwaA9CG3tswJUCLS6F23sea8hdrJx1PFJW2IIS/CPToyVgw3KaJdgHfg6X3467iSZweSPWL3QQCA9OF/+C4zXvwGgE4WYSwP/QaAsZjAXwIApYHNrgBg+E1gSWnxir6YfZBIwQCDuf5SKPgYEOklCwCwj2o7+nlIhO2rT7WQxwonEAD8exoav7JsmtyhoIYITVsTaQ+wBILLWfqgcO0uYgzWbEMDFGgzFfTFKpVcypvWH7tJMFz64hb6KtYda/27cm6v9qr9GCPEgvGxQW8itCg2mWV2+iEStwRVEunF5W/NAu2FJI0Gy7DluTeaXGOA6m61zchVzJI9TL+RQkHL/XaIgOkgJcKU7GvhVHOxdkJMmTAu5XxcfxkNF7A0kYe4EJNkBLbyoL/FVgSuYFYDVy7gUHMylNLN5eLFhl1i9CiLDbUwxcAsd09KHYPqCm9V5a+ja1RfLPHk3UZOrq2cbgZOTOaztane1+0YzQAAIABJREFUrW1yUFxlAlCil+y+UuxQR/0gGeDi3NuttgHjUwBGuTwWbQTD1evRxY7rUhiaof0hZFn0wT4AfP8QWGAby1lcOw/hYBnL1kkmnaIGQJupKsa9bIvMSp52xJ33g8DPeG8vsJl9BM3QN8IbJME0D5blW1M3Cf9qeOuW0KYETJH7sVWHPmix/KLsen4M+8iRJEtVF4j4m4LwGy8+Lxtdc9VRTPZgm34/vxm4Yc7C0f9M91iMRiqew1FyhnQuhJ+l6+pyu2fMqrS4FJcty5Vs1YCHjJqJSpg5CUroWOywyOdH4babrmNXCHMbU6wa8Wg2Gj7LkszFn0/zUqJUeYyomQs4PRM4yid7Ebl8tqrApNSya3QKAN/eARbxyUlWeuSCBZAyaAVd7FPZtyP9C8s7b7y+FU6Y8rllBadBCTGUwpwd8q6JLp9bivePwGRnPYtpHFcnN5cwIsTZUSasIAlXUnkL6/nYTaNnQqiimAtViP+UUs9IJ4OS6I7dZ2CREkJq55PMFfEpUvKF0I4EAObUrsCwwkq0ucW/V4r1wIG0vUWlmTv5tsHnAZN93FLrNnPP814oCuVYVeIIBlbNzsWkUmyqUAgAZkvpJ77vRIvwoVGit4E+HsPAU0PxJ54N2os0j1lcKRcrO6UWem64hr032TP4GwDKHoE4/eM3AIzFdP4SACilQXAFAEOvAMuZe5OKzuwBjYIBBn21afA4YOveEwDgUPVGdFOzuA+uELffwWHy2SEAuHQiGofxwvLC0Vx18TigGrIcZxxeZzP8gZI12wILGfBYbqiOwBZzUClXgCw7kNxL+/uUBqQvbqHNIl2xwLsLJxjfSfUvRnoQfQfpDv8DZKnIURaU+us491MifEOIV1fbO5m3KbNCvWJftuSa3P7CBw1vsySW25XWIqgUrwPLn+3UApggGQM2RxilhqtC1pGBKf+OHgD8/h6YzvOq0T0g0l7B4th0HQJXs6B9dwFg55LpMeJqKe6c1YbKGGNgSUSPih+Cx/VVsksgC8hEjxWuLgtVdH9h/8SunGuSK1dWAHt5Tkj6m5fxw7t74hqgtdhOPxgbPSda2qcMYLLUTEwlWuiEg7TRj1WvQjs1Cw63V3St9iD78m/2AaC9NUWqKLpZeGVmmfL1lacxy9NiBSUQ9iJlFcz80A3ZlUwD2q3CZ6bj9nZgG/sImmeohy9mX4zyYJtwU90obNaMlzdHSUWPmGu2d1QP1GjBNmHpvY0Y/IHLxJWqLlBSRcOCadFyOb+u+Va7qPZgOG/FH+E3HiEe+bl4rLke81BHJQeLdIqQFS4dlLTvs62eoGSWZMDySkAon4WauaJIqUPnkUUov46BXKIY4mIGKVR31WUc5cEJKa8QAOw6egpWecrfHTQvmQpXweQGeVmcmBB2QmTytD4EfXF+kE4BoCQJB1ZcpAIATJTAA18i2TvxaJZtyBzK61uTYom+Cc6YgkRrnKt7TzGUZihETfR9vg3R/UND8f7tMpZAHz1LMqLMZbKEUlIbFek4qikvYTH/4WVSaaAUPoZ4C6BUf1g6pko5A0SieiLJJ7J8sUgVRUZ/ZqEGvAUwn3YpBhdSouVt/iOucCdsDeiLQdtYlvnNvNuR8CEDxhwALJhWtMS+MyfC8qIHOFqfodtvYtNli743AUB6pwtJNg8nVAfFOuLgCDEOu5RuNkLNciLyY7kPINMTFqZD4PTelIaQJr48nlgdapU8WcnVvXHn+C/Zv90ZyP+wzm8AGIvJj68F9O6rlnMPBCZNAMPzc0i0iYGU72XGwLdcXxi+v8dXcwJE/vgGg6c/J8LNFXJ1CtYiq+vSlhoCrzO2UlZNE67FxXcqm83WVHEslEfHyloxj/6Mw7Pao8o3eUahNk1xPE3bALkuMoB5LVEV5ClaGR4H2d97jcUQXnY66hXNhv23wzGaD2Ynd8HGzsXg//kmPFfIXbE/M1XBPE1X7Al5iVrKixjCJwNoK03Cm5QVYEiYlguCpwy65IoIK/caP2wrd5Uhf3v8G5kTdR+wGMab5VYgd6m6ePHqFTJkCIT2cxjmnHiB4bfsZBgKM0GJCzckrnIX66e89zZ8+fyBC7YX3NRvlCmRst9JfFMngVqpgIf2I55pfeAd9QmJ1Vr4LGFufFOJvlAG5AR2MHBrrL8MmTf6QKbD7KB/wQLYqVgqjAxhmakXTTnQN6oH/BU/sKvAdWjubJadPUvfUG4FdtA2bdpzu9ZEQLKkDKAKNCF8/R8lh8Kn8jBAYpE2mJUYqO9myeamD5SEGRDW+hwCl2aDQs8s1UIhS8Bk9XI0VzOQb698rrYIVXea5C47IQlEoMGwOrGCbjqempmlpLXqEMZ7WEDwAkMdbPRpixU/eyOb0jbr0uFA+tyA1jcdzCEb4P0v2/CXGmrCGzq0VjOKoMH6zvIkK/oxcwXgCXPNEmVT/gqNMeZIuCxT9uEfoUicwBObLr0UCYhb5fJA0YyJMX3fTe49QAkrFO7RVn0Qvfls34k+Q3HHpxievnyFvz2nM6ogq1JdN5k7T4iJpMNSALi26g3UyqSC/5b6UH4kXkbAnLYIFKGWDPafZk/k1LE53NklP4L9f+KzVzo0mH8KXz6/59oOSmLCqs6lMW/6aHmcMYCR+vZ4lboa/qwfjAx3FomJC6FlZ0CRthDSrJfH6FIWdxQ88HRUCbwz+XB8lJ5KQPXpEUJfhyLjHpbY8KP4IPiU7w+tNhJRnv7IO9byIUHvjBSKL5ie8igKf7YkxxDtzn1Tesz3nOfwVksPUAylv+I7zmgYdc0J3xro8qGpCAg3GcphqKEL0ineony2ZKhTNAf+WENzp8AHhT8Smn9wdFNllTcw2EP+LFJ77/q/gcZDjTXnnmPu4bvIqgjFK3MKjveS1laq9Jlx/+Ub7lpaVi/HJQcR9U9mf8CwqQ08nvFu//73WOIOUfnwdDf9CqjQ4i7P5pCvOTYk6oLhB8OREN9xNtNq+IWx8ILVRf9FjcLZkHw+ozei9TYv306MqFcIYzafweob3+GDnzARF4M6AUjXWCCC5jKTfSJhPDAcqjsslpe8B8/NqcRppP7WZjyCfOEsFpNiYUf06IKJh57jOO/ePje0AlInkssXunWDXFSKr/07Lsb2q9r4DQBjMdPxtYCIimTu0UcYqt6Abuq9shG+TFYG6T9Y3DkURD2ySWk08L0DbLAvv0YN/MzfEd7X+ZgQq2uupZuAVqojaKZmNABU9idrhyqfNkDFB4TTb9tS9EKjd+69HK2nVWv2QG7dChhhS+pZSHEf25zEnlE2XU6lPAuun3IodkYG4a6mgy1pr5N7ei9JJeT8xDblkNJLoA5Zi6BvZ3AhUU0U+2KbKRmL5eHyVLKMplB+Rx3laWwwlOe432wK0YXwFoG5fv2w8H1e3PdyQgXENyC45joWToZRtxg9T7wU4iokom0+y4/6mGtsgPr9FiDdz3t2qYek41hmqIGmquNIqGBaxUIhS8AEjxVoqOI50dwYvIEoTUZ9ZNYCK2u4cDpZLx+amS5td9Uum413q7kCCpjvRc8F3OMycs97ijrGQ6ILlmIera/J5hIoq/w5o3KhtdBJvR8UN1ZKxbJ9qVhz9eVVPMFujVUMLs9Ld9RUANVVzFI33asXukWtgq+JcbLZKwTeiPBaahWVAkCKzzvu2R8qgWLHQTsUU0bP9d0Uo5Dg6xOMNnVBDZzmOCRr6yZgj2akG3dPXoUoi0orb6GWilef4Q/rzSpcM2dFUeV9VNJNxWNzWkzVrEATxRF5zLOkObmr24wTnv25GE/6KFErWCYulWPGYFRQMbUVd0pR7XwZjRCdQ7Q4Jfn7RzRQl0w5sMiTl/uTNLrdWApVlFfhZ7Xupf1m0q6DiafpWeMxGWVUt2A0M6s73ZP6unGY5bGQu5b9WUZju7EMjt8Lx33vjvAwWxGZSxoupZuDnsEqNLtnoUCiw1V1U7DNc5zTMYnN8JZgypzf4vknBwBz6FaL46V6GRXhOKYZCAURc/OFEs2IkJ5KYnzFdUFPXjK+h6Y0qBIloYuijPR4sALG1/7tztr5/1LnNwCMxZ2IrwUkUCiEaDqLMXyOhklxW2lLNMWo0K7AG2bCt1d+ZKkDn8e77R7bbqS4QbNssyVrSEevk/AyMEoIKkeN+VFRdT3GM2Yv6JwaK6G8LU8OcKMHirH621jDfvKHk/O/qZPCz8C4s66XWID85yTExG70+7+sMlTfCbdNgdjrxoZKvHuFdYvRoWAijL5TI/6GTZmWpCRwkSccB0BrJ3frGSineWhRMXEyAsos9FewJA2hECkvxX1aAwBnF0LkvD8Gv0FiH09AQskiPaee7k+EmJmLvZ96G/qoLS5AoZ4se96NmdN3PoWs80Jhk0nv6tz0JYCXLPNSJC+3OscaAMookazqPjMFICOf9T9b1R59jXzilItxUJLWQiMjG5cCQEpMWcpnMTtrgsD6T2hE+pQdxpKor2IWpKumrCioZNbD6BSyDFZWXkVZleN32iJDbfxlaO4yHIIyslcamVWfrFV3vNxL4hLGS6DTQ8FUQqSFwM8/GrmXRHqcAOAPeDlNZHI2J0SRQ9ZObh41XZFUIQfzROs01oO5Tq+kaIhGLxsiOb7gsld3p1NNIKxrPk80uS+n89piKIsm6pPRuU04ZczDAVMq5FqOgK94fnXlRRvwK/0Aq6q8LGbJW3eaRbtGRhh9fVRl9lzHYYmv/TsOhxjvTf0GgLGY4vhaQILO6w1NJ5uN0Xq49JJOXKAB/vrS3xKnY+eavqcqAd9wttlYFwKAGuhlmy1ZZpr5XIWfjqeRIaugsbBoYYjJtEktDdLzyymvywmC3Wh8iaEmFhrq4oYXIzd1VhrpRqOa6jJnYZGWq0Vmo+AlXnnAVSMujlNwdDIFH7wNgOJ/6qrsz3dMu6JNkWg3nG06QtvkqsmrW44Owb4YfV8e5+hu/wTCiFjYaTwcAUDKjpaQA9O9ydh8JqqorlkobJx0SnFkia2UN8hVO8xjEyorr7g7XM468mHAGwQk9AKenQZWy0m+qaFWUcNwxsToYQapN6GH2vajiNzn5Jpzt0S0Ooh8yz+is2ovRnjYqms4bIdUZfjYuu9mL/gqtDZVrQFgC9VRTOITPawrU3wWuQOpLDE3QFeFLbi1NxZHAFCmJORkMvJql3EA8BHPxSkFgPTBEqS0UIW4O6cEbmqrznPg0VFsqpDV7ioedqq+CRYaWea/Nfm3q/EIH1JnNL1t1kS7qEFO31tHfGujUq5Uotyfq76sj0tJyx9o2kCjkGfKU5ypQPB9PnkTNH9VDzKNdgcdkuu/Qx4PNH44UFaDrJLRsbjTyYeNBVCZnnP6kLdK8GisOiEmQQkdkSrUXXMg92eXVI8w/LP9eGprvsDLIyohuR8jcI+rEl/7d1yN71e08xsAxmKW43MBZRuxH5fVHVwCwI5RA+CVuxYW6EeL7iR7l/TDPyt8Iux/iRMApEzaKryEGZ1PCQO1/R4iyU9L7NBeY1HUUskDz6MzfdKHX3qesy9BR+1vMFTALENDXJZKizmoXEc3HhVU19HXytpzpeBUFLpqxWkYnQuS1CW3hTRujDJurd33MWxaPI0sGffN6eWJFA4aJZc7uWQ65NVg9EOL3FV0xkBf9JS0kctO/JjYDgHAhGk4ahCd2g8awzf8baiOgCYzUct8WlQfcNavPQtgXd2fGKT5B6XgvkuO+njVO5zpolJs3VqJWgk/gG5RfXHAxGIsHREkk/s8OkTQbxvsQNENP9FDtRODPCy8ci7nmvSASWOXj61KaGUFpd+tAWA71QHR6mPdvhRE0vNhj/rF3pgcAUCSMvxTyg3q4IIoDCUSGjE0YaexBOrxHz9Sq6TL+ZBUGK9viSaqk9zHByXnLPSUc1ZSVWHcrgAgAaUZBhYeI+Xcc2c8NP7yUbNwzLM/MikZCbtQiDdwhiC1aaexE761UC6zX7RihqXNBGmX4zsS2EgWCnWk9/h80gZo/roRciue22abW42Nnq0WOT3Q9Kk8yU9639yZG6ojBYBS6x4ds6fkQ+FGt82ZuOb/SP8KQ97JaYGEfq2TRc4OrYA0cRwHGJ/7t7vz97+u958HgAsXLsS0adMQHh6O3LlzY/bs2Shd2kKl4uwGxecCIpmzs2jnEgDSV6gxc2WsJYUFXvDe3pi1mqTw0jHXp3UhAJgUX1FOxXQuqWw0lEeFhKEIiLSARunDHpOFS8kDF805bU6V8pe52+4eYzHO/XNGY6VKYqeBaropKKm8LWZkClUuB09E4RALEbC7fdurJ9XNNZkVmGJoJmZkxqZd6bkT9C3xxJxaJPt11S4lUnTIrcLoJ7b8j67OpePk2iOevHxKJvckxIzJziVybI0f8OwkvnilRSJtKPfx4NdgNhoY9gP75FYGe/0SRYwPaTdLSsuoYein2YNCZou+sTtjftz9NcdFZsNNyJ9MEm1bjeW4v0ap16KjlVWYfpda0tzp81n1dSi/Q4l+6q3oo7aQJLs8V6LfLKVskp5nDQClmb7O2ict36oq96ynMoJwCb8kWc4GuwFoyVJM8noCFZMUSNgD9y7nBcAUfTO0Vh9GGsVHTiZPlj3ON0Ag8W9jTZcuYPogGW9gnHfBisfYyXPuuTOOm6aMqBM1EQc8h4gaysJ5k/TNnT7jZ/yqo1RqJfAgZrHFebVL8RW+SIKvchJxfgBENE+eDSrnEtdBi/BmKKq4Z5ttbnWhxPfZIJsKzV/IrW/0Tq1tFXPpao6k2ucNdWNw1ZxdPMWGWoz0tqUhGJlC0SfM/ge4tbHg+MBynMpIXJb43L/jcpzx2dZ/GgBu3rwZrVu3BoHAkiVLYsmSJVi+fDnu3r2L9OklKfUO7kB8LiBSuThuaOMSALaNGoKvactiR+L5wIN9DtcK0RVIg3GlFQkApsJHlFDdFX8md0ApnzAEaC1aj+eMuWR1orswyVp51GQrs9ZQecrpl7S9fo4b83EvdQoydlXInVhI+cAmC/NijqEoep9JNcW27DMWQQ0Vy44kV+QoQweHrrqY9kUbNSl8zPVc4FYTWbVr0DqnEqOf2dFJdqMFCu6nAG8hhsvazc01QQCQStg1hPnkQuofd0GWCWWdOWim22bhdXPSn5SuSKhGsa29vA8gyHjfjZFaqoj6tff32XU//6lvjRV8PNg49Uq0VTM1GWmxe51ORnG/wjJU2+cDGyolVyOXSNR9MvsiiZUbnE63BoA9VTsw0MNCXuyoi8umbCisfOhqBNxxyv6eY2RWYqk1bbGhtpwaykFr5Pr7CU8xoF/qAnZrAHYqTdc3Rhf1Xi6RhlyW+zVWlFQAyEK5zljZRl/YujlKsBpuYCTIpZU3sdbT/Wee3nkt9COx23ME8irlahwU6mBPYk/o/3zCaiie5IdTz4yz+SHL6ickdOjWld7jcwmro8W71jbclvbap4+rGhnVaPlaTksUkxhvqVGgXdRgnDAFi12OUK9DZ7V8T6K4yWvmbFydkVleoFOo7X2lY010o3BJYiyw0TqO6cKSnBef+3ccDO+XNPGfBoBFixZFgQIFsGiRhQssZ86cqFevHiZPZvJGzkp8LqBik47ikK4V7LmFpGMi9YLwZCVxON1Ki+KFq4FbHf/HWArpFe9QSLJhkLu3sHc4AqIs2bchpswIVtoy0rvbXZ+o7thlYpx00uIsrslR2/TyG6XvgAOaoS67p6w3yp60diOdT98FxV8yDrPYFgLRDVUso5MAYD99D8zlVSBi27ZwPiW+0IbgDlcfnZNb+zeaZldh9Asn6jFOBkfgY7Pnn1zGJZWXpuRIby2RRgCQuCo/PsIDv2LI/u0CKJj8Z425aBu5SlQAcDYHNF/WmaYD9V3RzesIshijt95COr5AMKlX3N0FbLHVh56pb8RlKVNxpJDhyBrn6BpulpiHOseSOpRcc3jtSTIDn9j1OcoatgaA/dVbRKoXZ3NKHJTukllTvO9EA/tIkALAdYaKaKVmZOvOCmULk3VYCMfYbSxul3fQVTvS43MM9dFLtRNKhRnFtPNwwauXzekUEkGZr9b6wtYVySLZl+fjs5eY4Gxch4wF0UU/ANs8x8rej3TONmMZuwTbQnuXE1ZGYZ/3ops/OtdPdQXVGkduXek9PudbGS0+tEc95RkZ1ZK9PolqpWIGFVqFy4FwTBJ2pKCxZ1Qv7DUVF7ucrF5mQ+MkBXaTsj9Fixf2M8Q7RA3EMRP/cWlP6zi6k2mnfnzu33EwvF/SxH8WAEZFRSFBggTYunUr6te3xAr16dMHISEhOHnSdTZUfC6gMlOPY++PFi4BIFnVbvqUwKWcW6C4acsl5c4qIndRWsV7GVcYAaz0Ht8QYLQoIkRnU7HXL7lMVhmrIYGXJ75qjVxsC7mOOqr+xSiB6NmdAfMKBH9E9XWLtJVcVDmUr2y+/C8kq49iH6LhsnMyNimNCwGajvqB0U5scXXplF37w+zllluO2iILQp2MZowNZwTd0S0EPtZ7TBRpLezR8ZgC8gLf30D54x3OJqiIkpFHQR8Pn8pNQZvINcBV9zJRrcdG1s4W3ueRzmDLX+fsOo43uYsSWZJBc3+XSHwrrU+ZmX8ZmnHrbobHQrtB7yR474yew7r/S8GT0ORCICaq/0ZLNwCTeD4py5AMIkmAWVGSCHUEihXhb0dxi9ZjojAEAk/SojOrbRIJ6PhWQxkMMnQjJjc887JYi911I1PsVxTUOKFhqhenjUEorYqe6956/JsN5dCUp6XiyIHtSD2SwtAOYymc1PR3urQpU5VUaIg/jz42RVUhNx4I8oQM0HfHBo8JNt4PaQasvaZu+pVBXkqAiZBTWLnRLVelpHYOpyZDdDjWBNp0nOJ8vXii/7OeJdHha2c0Vp20yGY66Iis68UDTGj3WR5XGZN4TWnoyzB9R2wxloMHDPBCFEfjZB0zTu7886Zc8IEWE3K+Qv1n9rOoyVhwxFSQiy31xw+s6FwaBTIz+pi4KvG5f8fVGOO7nf8sAAwLC0OaNGlw9uxZlCjByweRVWDSJKxevRoPHjywmXudTgf6Tyi0gNKlS4eIiAgkTMgE5uOqVJp5Etsjmrl0AVN/JGHVKMF1VDPY6n3G1Xjiox3akCYbWjgManfVp7sZhpTMkEkRjh0a9xQ8XPVr7/gqQxVRtYLinjpGDXTKbRiTPqJ7jiP6CnfbIQC42mOKSMXhykKwwlANHdQWmTB3+/nV9WhenplTOiR7dkTJ4myctFn3VW9HY7Wt5JrD8/xSA9+YXJqjQpbB9cZKXLwrlTHq1WivZuoh0S3OLJuU8UvWm4deEo3n6HYQD/UJyGbSrRMpZuKhC6dN0nM91tAOKz3+QnlJjPSvGkd04lHpw2W/sYhLKheK73ZGsB6da3P3HSy0SVn4DVSn0UB1Bm8TZENApHthCi/z9kH6Bq51yqMz9t8AEPjPA8Bz586heHGL2XrixIlYu3Yt7t+3jT0aO3Ysxo3jtSolKy0+AGCNOaex8VMTtwAgDSU+aEei8zBZ16WsWHJfh5mTooDycWyaivW5ZEGg8q/ncJtMvug0TnJdYz3XYZq+Ef5Q7RbdoeT+6qnvxcXK/eG5HxsTdsT6t+ldZuNFp+9fXZfcuIMNXbHcYxoq8dyPziweN0yZOGuMwEsW0/HaizONrkUupn0L5zmylDlrd62hEhIrvkUvS94rEaBltC2uiuAKduS2dnU+Hf9o9rPhkpOel0u7Itq8mu70G9s6dO2uMn1j24ej80kdZLqhKZZ4zHQ7qSa+xuJOu09NKWP1jnOnD2md6HJmUpygPWum0KajDPznubsjsLHrsKzojP83APwPA8CYuIB/pQWw3oKzWPOukUsXsLDghYww60xN0reMFjUFUUJTduK7u1CYLSz50gfLHddQ76ie2G0qjoo5ArC8TQGQxm66gx2dEkkfTdocB9/4iska783+nMybdYnuNZEbrVyOVDh6/y2UMHOxX1LVE3dfGoW0i6D0TY5334lln9xrCs6tRP8yQ4m9vUohKJUfoFRi1oZd6PdQHoMW3dgyd8ZFZLH3NO3EGLryuhk4zrvi7J2/KuVwFAzbgDxW3GzSRAxyNU8zNEOnUhlR5FJvVFGyTMNbfqWR5xuLc6QYrcWKptDpGTcZKQG09zqJMXAdU0nuvSGGLrjt1RG+sCiACMS3Mz0WchYCoVCM2R/1KiBjyFQUC+c1od2ZHDfrOErAEE/3SwV8s4RCCGDqqLGAaG2heLl0ivd2iYvJ7dWyaUvU/LkbigOuY1btDZtAkK9GjVvBO6BwJUVYsg+T57MqX+CLRPjO/UoqE1WUV0QNWvqNsk5v2uHVpESz1cQyEBcld30xVtnc8QgUHx8DO3lJMgftuwMAqU7xjP5I/OKgXcqYmA6dJDGb3SmG1q/HOsyQJa7A5BIO0Jj25eo8e3yZ1ue8NSdCAM8F6ao9Ov4zRwN4ZysP7LaNsVyduBeqth2B4lOOwBdaMctb2m50E6Yotm+F53S7QyOWiFfm5Djn1Vt2nD7ITjS+hapBTMIxrspvAPgfBoC0iCgJpGDBglwWsFBy5cqFunXr/s+TQJosOY/lYQ3cBoCCjFG4OQlSKT6J1zNe34pTPLBHMuvwQUqVD/jwCNDL1RmE+pQ0It2g7bVD3F37TMVQNzg15jTLj42XXsJ7T1eRH8zeOftTdMaRUJWYEfzClAIZlO9sqpIixhSP5W6/B2hzaFwwLbZeDeXOmaD+225wOylJeNph/Bc6InJSoyYxvunkhKzC8UP9yiBbgB/35+wtB9D3blPZGGMbQ2nvgonq5Z6mvSiHRxmZ1i9Q6Xkb0o1F9hfrbdQZpC9yISu0e7nMCL7QB1XA5LhCEldF8GfmfiRKmuXGmrIhdU14HsOiXEsFElgaaejIZY2SHJRQMmrXcUB6knqZTBYvv3YxhjUqhUxXJ6FQeDSIlt1cIZRZTXQjDotPCuAWQa71AAAgAElEQVSHfB3SfO00lhTJxemaSK7QnuoFZT72ad8KZSOPuAQ7jsZAazi1vxfOZV0P3N4uq2aTQFJ+JHB8gk1T35CA4/ukwgEm5R0ZvQrFjF6zI81FkmNxFj5RvCdwfj4bW7czLAZyUwund8pdAFgmW3KoHx90CDDcXA7yajVnoOm13GgSOlFM8rJux9F7Kkb9OTnJ3vvD+t67JUEo6eN7jibwzVnJLl/n6qT90KjLSOQecxAaROGBl20ymb0MfmfX/UdUH7vSeHQOPSePzWlsknrIEHChwQXUzvcbAMb1mvrPuoBpIgUamMWLF3Nu4KVLl2LZsmW4c+cOMmTI4HKu4/MLovnSC1j8ur7bLmBBQ/SBKa1MuYG4sigVP6Xis8vrESsQue/Hp4DO1vpGddzJDiSFkkOmwmhZND0m1s+D7VdD8XNHb6dZhbtT98Ox5zoxi+2+KZ0N9xb1765ElXA9mXQbOKH05WcYjYOjOCpXliBSPPim8IFZHlsvTtuJgeUQyHNVzd1xAr1vMHktoVwyZUcRpW1sqfs3xrYmbY43NZ3EDwWyUl7xcpz0sTXzZKR9uBbFJZQ/1KrUlSPwwvWtlBW5z/ZGZfN5ruMryeqjEJ80QyB8k7GCbEA9klzFoMgZLi+HEjHGGdriklcvpAADXpQ4k1nHrHtj1avEeEr6myyDfzUthMyXxyJvmGsKFJcDsKrgEph7JwF+Wj6q6HQCgBSbJ0jJ0TNRWPnArmpKTd1E/NmtJQpGngU2t4zu8Lj6dJ+zBfjiUKqlHOm2tNhkZ1edBBwcbtPPD3hzUmhCe3kUT2U6vUW0C3DJDrE6ae4e0cQNYTqqTgYO8tQfPS5zCURYXdvpnLgLACvlDID2wRFOvcZlcSP+kmujwTI0PZ8OdV/9ZV+rG4C95CiX/ceggr33h/W9t5f846yriJzN4Z+7MrCtg0211ckHo0334cg4bB9UMOKJF+NSjE0hneg/lfa9BETYf9uc0YbWh57PkLpH0LBg2th0bXNufO7fcTrQeGzsPw0AaV7J+jd16lSOCDooKAizZs1CmTJl3Jry+FxABACXvK7vtgVQeDlYvySILZ+CbrMonQebyy44TSHg8zMg0r5VZLmpFjop5ZuQ9YQR1cBxU350LZsJw6rnxJ4bYXi9dbBTXrHt6Ufi5OMvIn2KI9oZIhzdrrGNxXR00/JiC1oXz4AFxxnlhj1+KvrdlQQYxUhFwsvh2jg3tAJS82z1C/ZeQI8rVWV1paSpwgFHEmBuLUAeGEg1owmkOqPF2JVzBhLdXm3jqpQGcwtceYOrZUf2U71Q0cQk7S6maomivAu2R1Rv/GsqJhtmn5S30O+L681XoB05590fqc1MXYEsCdl1q7l/D1evRxc1I88Vfp/XPD+yXBiOnOFxk7UtHbj1R5PN3Hv5A1r5xxABwNWGKhjgsY2rTgCQ1GbsKYhU1k3Fgr4tkO3HNWBNzGT5iNMxV9qk2JVoNvBYzl1I8ZcCWTc3mNpzgD22BOk/4QVvMLk5AlWBinAxc5d+o0SWs1625xXVzsdFr57uLknn9ZqssdDz9LkB/PwMLGXE3I6KuwCwZp5UeH/7GLZo5Px2dtsl+qIwJmHmtDTfjKYn/FH91UzZR4n0nCumbDYUMa6ajclxe1KcNvc+mg1/zNkaSfNUtkuZROEi7boNQeBQehbNcZKIM1/ZEj1N9sM4auom4Y450Cbek67xbq1daF7ENTdvdC4/Pvfv6Izjf1n3Pw8AYzP58bmACAAufV3PbTqKEFMmBCufyqR56NpIL7Os8kb0+PvSFgG+vAC+W3SApfO0wNwIPRRs43NUWkcNxWlTXgyqmh09ymfBwTtvcHPDKKfxiFszjseJB++xgJd9Om/MZWOpov6IdmKv53Cn7lrpuEp67UCTQukw6wjLOBui3og/1Htshu4KAGbXroIOjgXJr4yshGS+TK9y8cHr6HZevrHtNRaT6S1TPYrrSQAdNDydQ3TXI22O1zVdRC1dGqM9V43Q7oHgBVBdXYHKEtk/OialcyDNYSLYHVkzJ7Kc6IFyRgYAz6XrghKv2Nc7xYWdNOWTDXdQugfo8d41MF9kqM1ltZ5KMATpTa+4NggIB+lYss5A9Wb0VO/i/k1Aq5BuMRa3KoCsZwcgc3jMVBWczesdUwYZBZJNXU9fIIrFzgnFGgBSZmVN1QWOuNi6lNXNxKahLZDq211gudxq6u79pvCDrIHpsdV7MvBMnml80phXDugb/g1s72jTNK1dcuVRoXWTDBEyazHFWp7S9LM5j/gk73jZtufu2GX1Oh0FlldkPw14AET9AOZZ+N7stekuAKyfPw2ehpx0ixoK6UsAL93Q6m63D00PKlHx1Tzxo8R6jK7oYGI0T3ZOklJNCYdt7n00O3ubsx0C8lW1S5q+KvUYtOvSnweAco7IaHYjVt/kUR/N9PY/4gQpOeuEH9oHHlbfiLYlmIZwXJX43L/jaozx3c5vABiLGY7PBdRi2QWsCK0l8jy5GqbghrAW9F5oqIMgxTOUUd1y1YTlePriwJdXwFcWM2ddpptbYaBindP2GN9Tboyrk5t7cI8/eIcTayZgnBNt0X+yTMShu+/FwHQhrtG6I9pQ//Ec4zSjUXpOtUR7UDc4Df46wDK7B6i3iCLq0noEAMmCQ4kd9koW7RoYoHZ43bfGVoGflwd3fOnxe+hyUm4hsye2TpQxeqiRLIZB5NYAkOLopFxu1oM9UXQZvp9bbpOtesSYX8z2FeTSxtcLQoYj3VDGwDbKs5n6ouTT2dy/pYz+Qh8jMj9D59eupfUEbdZjviORycCUZihBJli3jPt3L9U/omVN0GJd1qYQsp3uhQzhMaNAcbZYhY8nh3XU3oBBDuysAeC/xiKorrxsw71HbZKu6f4xreBHWtyLLIwD7j+QrI0MmXNivWIM8IrFZArFRsO1+WZgozz+lOrSOiOONiq0bqzjuirqpuGoZpDNsFytqehcB7pfABbyz8XgZ4DJCEzP4rQJdwFg00LpcPPqGbuqITYduAsAu55G090/UOrVYrvvDGpXKskWrbmIZmUKnbCmAIot+0NYzo5IXaA6sL6RzWhWpRuPdh17xykAPKipiqo6+88wKTY9Nae2sQCeNebG3crr0LkM0xCOqxKf+3dcjTG+2/kNAGMxw/G2gMxmXPmrGgppLS96d2M7rF8Syw3VkVrxUZQpc+tyM5Ri4O/zc7vV/0RnjAbbrB0V0pu8bM6BGY3zcbEb5x5/wLYV0zDTiXj6rqyTsO/OeyzxnMU1S5tqTV5eTdpPce08bPIcbzdBxN54GgXsR408qfDnXiZ1Z0+jkn6neJrUHt+gNjI3mXURkhQcXfODCdWgUau4wytOP0WHo/llVe19wRPFyUdzQgQq7VtbXd0vawDoarO8UGYNXh9fakOALJ1rQbFlasO8SHW4G0pHsYzcM9mGodRD5uK1Fn6n38bleo22T20BhDWtipBkcthvHLLqWUykYOmjf3dV7cEwj43c77dMgagdNQkr2xdG9mNdkfpt3HNdunThefgA+h+yW0HZlusMlUSgKlUzeWNOLIu5JfB2amI7KL88A+bK14R1XVf3295xKQcld7ztHrtxdQaooIaRq8JoZdxz67laU9Eac69rFovfsNeAUg1MDHDahDv9U502xTPg7IVzdkGsTQfuAsDeIWi27Q2KvliGfh7y5BuhTXsfdtGaEzcrk8a2tXShPVDoZnNcNTr/U9qKGPDGNsZzdeAUtG33R4wAoHUyojtjKq2bhVfmABsASPHtIeVXo2eFrO4043adeNu/3R7B/77ibwAYi3sQbwso8hMwNaNsZD/MGvgoLCTUjoZNlrcVHtPErNDGngvR+OdmNFG5VjYR2+xwCNjVg5P3slcGK/phqpmBNEeFMgevm7NiSeuCqJo7Ja48/4SlS+diKQ/u7J03v+hxXD+9F397skQC0iJtqDplQwVDWaEU6J1bKVeJIIJfKh/BMjS7qfeCXs57Mo0BBYiP3MnUCbqrdmGwh61qCiU/NMiTGFnvM2lAij8za3zhFcUSaKxluayv4dnkGlAoFNzPH77rkGx6ClkVetkSN6IgGUcHiXT4qTkVgiS0LKSD2ku906Vb2JilKjLfboubmo6i65HG2MjrEqaDWeqsy5tGu3F80ywZEexFUw6Or7G+6ixXfZByAHbqCmFH95I4umkO+nybgeumLAgrNgY1r7SH1iMR8kdMBcWUScuk/F/Q4l53mz6tqYmm6JthsbEOLvoMEJVmyALZSc/AY3vVflGtgdw/zfUjsa5jUaTWPkSm7dU5reGKqmswaJIgTZRFq9re9QrAliwlZ0xBmOZhG4BO1y/I3QltEKD7x1iaETuXHgicllNX0D3aaixrExtHsZQqmLiMYKE0SbEHW7qXAYwG/JyaA9669+Ixsm6UVN2xe6/c+fEJ0uCwoYA8trbbWWBxSe50KTB9Vm4uMp7ojcn65lhiZIkX/3qNRG44nkPafFvpR6CX/xkM0FnYEtwZm02d7DWAJmuB2UGAhzfQ8ypAz8u4RA6bE9gEnPEA9o3qjp2mUhxtUabESlQ6VBUpnFChRKl84Nn9NIwLS+CnAY4ZEvzTAb2u4lpYJOYuXshlF9vzDlDM7FDNNniabN3/ji6MkvNI/chd8EhWRpLYm+85D2ZNIui0kZy2+0hDB8zwWGy3G61CAy+z8z2DsnI/ww+bPG2zxldnmom2bTpi/N67+PvMM6zxmOy2J4neF/mjyf9KDAZhSIY5ibei7k+Lm3i4viOSle2K/lWyx2jZOTop3vbvOB1l/Db2GwDGYn7jbQGR5W2OPL7KFYmrcBnldDMQbk7KuXtIai1DQDJsDdyFhDdc06ZQ7NfHzA2xqENpYEFR4L0tGTb100U5FktNTMJHX3cxcmz24eKJcnt/xAozU9uorZuAW+ZM2NCpKCfNdTP0CyYvXGqhnag4GpEFOsM76hO0an94e3lh7qlQXD26VeQco012mbEmbo8si71/tRIByoKS51HodHubTZskqCiO8ODDCC5WLzm+4D38UStvahBFxOBtN7mxWUvP0Xn99d3xHomwvE0hvHjxFBNPfuDcZVeSjoHfDwY0XQHA51PktCgY6y9bXfqSA9HySUXcfP4G973ac8donOSCFAAI6RaHmpMjKJkCK9sVhr+vD0zX1sLrEP+F3mYXQC56bQR0msTIPuoQHmjaimDxVe9wJPX1xLfN3RDwhGXMkhW4k3o/G0unY9i0ZCKaqZjG68PGx1Ft7WtMVi8XpbeiGm/Az0xV4O/tgWZLz+PR02cgDrkJDYLRPI8fDjz4gm6bmDVVWv4qYULTaxYpMeGYTukNjWRzHKHvwGXQXvPpjSTGD1w1wbpKmcfvjy8S9Y4pcaazfgA2di6G4pmTYs2puxi97xm3tgdUzYmuJ4uIQ/jQ+xleb+qLfO9Y/CAV0k+mNaTlYjcVaKs6aBOGIGTQS6+FXPP5dMtwuU9eJE+VHsK9JJd0k6gx3FqZ0ywYwQGeqD3nuJisRZaP7Z5juHhcKp86nId/2pxQKdmHwd7rL7Boy16RJJziQsn9LWjuUob797RlsFbXD6oI+xZ4pMwLdDgIKFUcgDEcmwS/SzzgL9ELKNAWmF+I608GvoeHoePqKzj6hMUz7utdGtlTJIBqQlLZfTT7pcbJ8tvRdcsDTuJtT68yCI/Q4uGGgeih3s3qDg/D++WNkPydG3F0VL/ldiBLRQb4DFHs/yoWLmH9nIiDqbsQ77I0RJGJRx0SQRM42GBkMYVEWzS4Wg78jPwB76kWypDSHpvx4dsPmKFAFDwwuFJGdKsUhKuPw9Bo+VXkUzzFTs1osVtzm91QpMwDePoAahbT+zPKCG9DBLRKb/TbcAUJHu8V6ap0DddCk60Cfur08FbqgZfn7SZVSCeZ3iVe0OFOh4RQbWjIHeoV1ROHTIVwS9NRjG/eVO4Exh54xq3fv9sWRslUgJdfEu4aPdVKGMJvQbOmhuz+0R8EMEOztcXJe685oGiECp1U/4oykuSheWJOzWmLF1Q8sJtUtyrrfLRryTJ/I6MMpBoB78NDgCt/2/RHH+vdCvgANzdxx4zZakH10H6ioMkvNRTF/oDi8ChZOyTZuX9kYyTyUkP98z3gkxyf3ofhh0dSJErgIYbX2HQewx/ibf+O4Xj+F6f9BoCxmPV4W0BvbgGLS8lG5pKrjK9NNCAfYAEe+dIlwpasR6E555qeY6C+K8IzNsD6TsWAhcU5Mmh7paVqGtYbeVdf03UIXK3kqhXzfYtNBhZEXl03GffMGbCnZynkSeuPB2++YeCcVRbaifpLgHzNZM0vPPEYZw9tw3qexoEymBca64Isa7tG1xA5BJdXvI4MhzqiskqexUcB0bvzzMf2a/LYxeZF0qFYpqTosymE66+16hDGe6wS+z5uzIf2+iHc32s6FMGdsK9ivODtpMPgG0cAEJXHo/X9ojj96IO4oRFQv2bKIgJAgQw5c3IfHB3AJ5Hc3GLRtSXqjOTZ2EvWZEbm4fvkm+NYlq0avmUAUt1loJ+A/QQPXpO36ynsWToatc0nuGOPuj5H5TnnMF69Aq3VR9ictPqHbdYA2qy4hFMPmcVqZpN8aFAgLY7cfYtOa67YLI2ZlfzQ4IwtpYdW6QMvk8WFSooqu00lcStBN/iZGA+gAK5H18qFe/sXipY6QYt1W7fiKBSYBAduv0G3dVe5cybUC0Krg8GAQFg+NgLnVw5B8RcWi8g4fWusNFYXx1pPeUakGRJ+PGHMh3JWMl8UD1pKNxfXR1VGYh9PEaQQJUXFKPYsrWhXCEUyJkXQGHlM0xbPcRa6nwEPAT+Li5OSocatPShyNVJYAIHJPmpm8ain+xP+WYtjdWRP4IMDyiAK0WgvSYY5MQU4wWdfVxoL5KoHzA3m2pORj498j3ZrQ7hEKyqnBpVH+qQJbAFYkkw4UumAeI9DRlfGjdAIXFw93KJDPTYC7xfXQfI3bnoWOh4B0hW2WTPcD1YfSmKlZhuAHDU5F6QjC6A0G50+HvpWYs+GtE1KAnv9xWKdG1Y9B7qWzcy9k6rOPoXMitdyt3GXk0BqNn/2Sre1VxF57yDWCOTYLbcBWStbqj45Bqy16Mvba0NY7897JAf+ZueSSsYJU7DMor+xxi0M+4fFbx/sWwbZUzKeUbGEXrEk1Uh+JpomXd7W2HH9tfir1LJOYQmhZuahyKd4bDdxZlXOpWjX1CqW9PwCuxRDlGzYp3RqC8djwfaOdcAzVwCIENyKfJo8O9enMMnDX1Hibf/+FYOPoz5+A8BYTGS8LaAX54CVlk2LhuiSq4y/DutM1aIZk2Bj0GUorb627F02xX69SlsL/3QvCSwsAbxjrimp6Dj9XV+9ADsMPVgTzTcjcCWLKyqW6As2aZkLkLjDHpvT4vjAcsiYzAfPPvxA+xkbLbQTjVexl4CkLDv1FEf2/4PNPI2D4Koiy9qhURVQhc9cXVv1Bnz3dRctgkIT/9fedUBXUbzfm5BGJ/QioffeQxWkIxCUHyAoXUCKNOk1IF0FFOkoICgof3oREJDee++9hJ7Q0//nm9ndt6+Fl30vCZFvzuGc8N7O7Oydebt3v3K/rZGlsL7YVKw4arrp0XftKuUE4dD1d0kYWyT5FxM9TTGMevfj0s7+OH0nBGPWnxPHnkk/BMlfSEuMsxZAfDwZHc8Ux9bzD7QHGsVlUY1d1QKonsOMAJLwr6rTRZmTKTOL+URHRwuNLrOHo0oAVwxGlpPSZUd1XrXYy677sGlWP9SNlu7eG1/fxYffbcdwj0XoqFoJ260HcsoXkC8XHsaWczI+8edWpYQ1lQghEUPL9nNAdjTcVNXq89dJUiJp5HPtc1Ui6FzSL5E02iRMTAcQqTu4ZjZ+8poujqe4p5ER7bGqe2WUzJ4Gey4/wufzDojvyAIXsKG8KUM3MAT7F42A/xVTFYxRSbpj/kvpDqXWwH2/VaWIfyJLW71MnIvKjvphE3F2dF0k8/LQCAWV2voobLIYa1HH8uLFIt9QxbqqnIPiU/3d5f7BwOtAUl/t/JQM1Wv+dk2qh+L37kSnx1BPKXBNiRj5CpfBrJe9AHoRtNUsCeDO74FtivTJxz8A+esBU4qInma1ZEcGo8PCw9h2Xopa7x5YAx/42iCAafywufY/6LxIEu1zo+vhxO1g7PhlCAZ6SgsPAkPwaG5TpL+jvDTYnqnp0y47ARKYt9XsEUCyGuarFSMB7BTWF/9ESWsnyRZ1q64klOjGrJpsJW49MRFAym7/smpuQQorT9gGP7f75tnP5ELPXNTuFXX7/QiCTu/ECm/pBUGbNUDuD03HU5a2A9qG1OF639xaUsznYYOxJ6oYDnt/pSWFrWh8Bn3/OiHG1stMaSe7exyYozu38gW9zHuVbY0/DphCEdok2YTRShIeZZUHQ5LJIm7XsN7bOnlrQbGFaNe0iTkOR3+zWTVkUnhzDKicBjiohFhU6Qvslr8Tq1Y4QN77l5kLSxd7Mw+nJjR7205y2fdx9vx22QzjfiAmgE5gHGcb6OIm4I/mZjN7q1SFcjRVhiBXl9qqF8iABcXP2tQFs7z0HmFf40qmuvi7V1VgZmXgvoyZsxRIruc5HxvDpQsTrZYh56/h4k//dK+w9OWX4m9yRV+PzoJDQ2shQ0pvcbMNmLDSJDuhvN3r5zB/zzWsXbdKu7Gq1SaIAO4aXhlVk8j5/PnxKYSt7mOyWCmDkLtwXZEfsPq4ueYhEcCq+dIL9xe1T9x3YYqXjPOjRqXtuoT3FX+v6FYJJ24FY9Raaf08m2Eokj2XAtJOE8CAGeh6piD+Ph2kkTYqoXYoqkDMBPD4ElMFiSF3pWtKaVbWEZUArhmFLEflDfirsN6mkl/d9mP7zJ6oHi0J3L0+Qag4fpu5NA5JdXwgH6rd/ziK9SdlGbQ5rcugTpHM2H/1MT6bY56JSt/PblkMdVcW0y+p+PuVR2okizDp6KkJQpeStoVntNw7KraT/lcc21bM0+arlqVb2a0SSvn5ilCCxj9L8koWuI/WVgZeKjF1RACXjIX/hUnaHCamHISZD4tr/6/lfgTzlBhT9UNbWZyHovKjWVggLo+tD48k7jYJIMUlVs6bTpBwfTMjgEPuAV7JtK8pGeqLefs0sVsigBeis2O8p3SrkRhz5VJFMSW4J3BPPvitWo7KQHvdOXdPBbbI0At8Og/IVRX4QcZLmUkbBYaYEfp9gz9CltRJrS1wyTNgY/1d+GqxfGG6Mq6BIICbZg/WknOIAD7+tQXS3dxoe46Wn3Y7AGQsaPtYewSQkllyVYuRAOrliFRiJ06iG7NGitXiBVRtIxsVRvvKuRDyOhwlRm1GNjw01z/sfhDIYD/ejH4TF04dMoljkzveT5fxf30PsMDaLau/eM0COLiERtY/CxuG/VGFsde7h6YnqSeAJwPrIJWiMqCNdf8MMLOSFa70Mp+xcmvM3SXvXdTIBTzMU2rw6RUNCrjdxCZv6xKFC0suQdsmFtdxZhWwrK3V+ehePaxCEoAIIrXqQ4Dt42yvd6kvgCKfAos/NfuearafmyDd4fHR4uz5HR+Td9E5mAA6AWScbaBT/2el4/XWTEXlOixJSr0imTGr1HWbSu+Wl94lrDcupa2Bbf2qAzOrAPelBcLS/Vzd+y9sD1UIavNFyPmbTL7wzxSNpSGy0oGa0XX+23rw8UyCh89DUWXsBpNGXbOFQBHzt8tF+65j2Zo1WOMtY0OojN0vkQ1ABPDwiHKa2OrKgDMIWj7ISsuPhFLXF5qEdQphUa+PCGDNQhnR+hdJeiytQJQo0D28t/iO6vkeuxWM4UrCyLmMQ5H0mYsIYNNf0PtMHqw6ftfMaqdPQrBpATz8K7BO0WcbGSzjp9T1tnSPKQQwaMMEZD4o3YJtwgaa3FXd9mPvrG6oFCUf7o/7PUCZMVvQ2+P/0NtjhRy1yy4giyRNff88jhWKG2lB+3KoXiAjjt58ik9nWMd+UaZujSXWmXqvPH2RLNxUiUYND9BbLtXrJqveqr/mY77Xd+L8alWS5V0roUwOX1x9+AIf/SDdjn91qYjyqz4EQhQrR2AIDiz7ARXOjNbwmZp+FKbeNs2pqvtJLPKaYLb110VWsJLFUcMCtLhOhVDoXcBEAKvkS69lSaqDmhHAEU9ErJ7ajtx4gqYz92nrTwSQxG/V5BQSGw8onw/jg7rYDcGAJQHUu+VIAuaDcsB3UjKDYhZzqRnmgSHosugwNp2RFt0DQ2oiUyofawLolQIbGh1GN8ViThiQVXzVjMEagSAC+GThF0h7zVpPU7tYz2SmcpI9jwNpzRPbtOPsEUCFWMXkAqa9vVPRo/w2oAhaV1S04nRj1kq1BpcfmHQc1ePUEIqMeGpeAYUyldPlMdsj+v/0XHIMB0+cwn4fpX5up3+BbDotw5sHgF/r2O1PX2gEMLAKMEEKHLcKG4K9UUWx3auPpgqwtMEpDFJcwETE1VhSbfCHF4DppjhY9XN6mc9Tow1+3GpK5NMnv+mfE1YucGWQBWVWol0jC93Ky1utiBsdPiK8LUaXDQPUOtUUirBFsZBaIuHfTRLAX2qZfUNi55cmmFdPihFEJ7+Ms+e3k/OKz+5MAJ1AO842kP6Br8zPVqC6ralbEkASR51S+qFNnSfL/h3DvsG5lJWxd3BNYFZVIEgmTVyOympWSYRcKrteKe7bT+ci5x/SIlUxmxeWPJZ6UiQqezM6k4jfo8xY+ba9yaQm/+lcoLi5lZPqBS9euVYLkA8Mb4MFkfUEATw7ojgKK1m/G5qex+mlI60yeelBvqHgeGw4JatLqI0IIMnAUH1lapZWoLWR/vg6XBYg39K3Gg5ff6rddM9nHAofVxHAFosx4Iwf/jp8O3YEcN8MU/ksheCp12bPAhi0aQoy75M3YLPKKd324/CszigbJdf2+QvMF7MAACAASURBVKBHKBa42TwzWmcBGbT8JJYekmLNf3SqgEp50uPQ9SdoNktiqW/i+0XWD86XXumQPMxUVcaW3IO6b2d+XhqLlyzU4kBVUWo1BvDB8zciKYDa+p5VUGRFbVOsXGAIDq36GeWOm9xZUzNPwNTrpgoCFdzOaSEG6tw3R5bRwgvUz9Q9ERMB/P1LsgBaE8AlnmNMAuYW63Xqdgga/bzbLgEkCz5Zp0Zeaw08kZVrrJqlC/jAHOBvJSa33QbpvlRIhVnoSGAIyH2p/j5U67xVDJ5bEqz75DR6/HFMnJowuHT/OZb8NBgjPBfJ6QSG4Oni9vC9rLw02JpnGj9Z65da33NAKju1XO1aANcJa2ZMBPDLsG+wJaqMOAXJFjUvl12ZnykOmnRAzweZQhDGflIUn1eQpT5p7LR4Zl4DufcpgOZup/X58zj+OXYJp32ktwOWBNBOXJ5+OI0AjqsPjJYhAh3C+mFbVGls9hqA/O4yjvm3uicwYrUMxbFKMqMPH1+xKaRNL/Nl67XF2A1KKAJg9pKnf05YucCVif5afh06NLAI6bh1yIq40eGiPnvJJ8AZZT/UmwBstLYqiqE/HCRdwDMqmCEs9CYnxFwW0O6iGPgizp7fBuaSUF2YADqBfJxtIL1LR5mfo3IRlgSQEiDGl3391jdSOg0FIZ/wKYdjI+oAs6tpLijSYiumkyn5MPkq7HipWO8a/YicyzKIWVbKmQJ/BMlSVzVCf8C16CzaTetNeCQKDt9oIj6NfwZKm9eWXHb4FuYuX4/N3jIhY3h4OyyKrCPGuDqiAHK7S2K3pflF7Ph9nFkiB31OUh+rco/Cv0qQu7q0RACblMqGJtOl6/DjZGcxPcoke7AysjL6hMuYRgqMP3j9Cfotk+63C5mGwTtEZnQ67QJutQzDzmbB4v0330oA82ZMgS19ldieXT8AWxWrloME8P62Gci0U9ZcbRA6Dhu8ldqwXffhxOwOKBElHwyhw56gwLCN5pnROmvNiNWn8ds+mQW9vGtFlMmR1iwOT//zEd/Pt7byvPTOgOQ66ZNyb2aIxAdbFsBf2pbFrN8WY5m3vF5Vk/DPzv6okDsd1H1E323sXRUFVzc0uUoDQ3B4/VyUPdRPm9b3WX7Az9eyaP8v5XYJK70Vd6nyqa1KDiQ1MyTiS9NDVyEp+hhAewTQzAJosV5q4oF67SQNdDY6h2YBpD3WtXoeDDzXzGTZ1INMf1sSQP0L41e7gbS5gXGSbF2KyoZ87kpMbGAIuv9+FOtPSZf+0eG1kVaX4KI/zeomZ7WkKfr93Xj8EvMnD0Kgp+LiCwxB8NLOSHPeWk5JG4dqit+VJBJ9zwOpTOtgdkn2COAXlDkccwygKhND401tUVL8zkXTjdnAdx3O3pPJRtRI4Ly1v4kApsJL8/KJMZFVupS/jmPl0VsmwXVLFzBdswPl7WgugtQpc1UTWjb4DEVhSK8DJbyp8cg2CSAR7KnWYRf0Ml+jcVtN+orGGuCxFN2ULG79vczKBa7gNLv8JnRpYC5mDzsWRxFnXPy2qU71x5OB9TKsxqrVGQsUbGCliSn0Hi2VFGyP4JJP4+z57ZLZxc8gTACdwDnONtDWb610x0jfb4nXWKvZvnRPiX/DCwsX1i7vamgd8pU4pvgHqXHydghE7FTaCER9n19oWD3I2RgZrytSDhajURByvcYt5c2RspCVIHR9DBk9sCLrTkDDLR9JsdteJ9Bp7SP8c/Y+5rUug1rLZOxMwTfzUdgvk0woARAVFY3cQzbggHc3ZCKNrl4nAV95E1bbqmN38NNfG7DNWz7ASS7Ep2InDG9YGD/9MAo9n0/GqfQNEFJ3GpbP/94sjo+Op4zRLG0XopWSJFAsW2qcuhMiMpE9PdxQb+ouMW7LjDcw/plSkJ7cvm7V0eN1Z/HdwSE1cevpK+GmI3fL6Sp7kfTgNIRnLYd8V63LZAWUzCpiDlUJCrMLmt8AuCFJp2j9r2L0tvv4dc81M/JDb/6kMYaCDdHXrb9wuYoEh5LKw+zBefm2nDYP0NM885ksGNqNndwuVeQcH+78BRm2yRswxWNu9/5GzmHIXQybPA1j3owXsY91Rm8RMWzV3Y9hAbldSfS4/yUtznDchnOYs1MSYDWj++nLMJT6VtajJaJ65eELREcDIkbp12rCdRmRrx48Lm0EBYf3TL0LPq8k6RB9lIoq83JuQ62gediRpT3aXpOZkGSVajj2L+zz/lpU1VDrg1ImappkXlriCx17ZlRdJL+yXkpuFGoMtFiEyyd2I+9KkxzPwqo7sPT0c5y79wy1C2fCsbMXcdCnB9wVQWQax1ZtacoeXuMTgCPDlezO5V8Cp5bhm7CvsDxK1gpXM4TLjtkidB/VFhMBJEt4ubFb8Kf7MKGTRnJJj6JTY5/P1yBRaP/Q6SIGt9DJiaaMStMOkn+1XAoU0CWJXd0hawwn8QL6XQLI9TpGvpQtia6Nlm7/AFTObsgdMwJ4YkQdpE7mCWwaan6uEi0R9NFU+I/fipTeHjg1qq6QAfl0zGJsdO+FN0kzwWfgRYSu7g3vYzK7/E7hTsh21kIcXl+RZNgDTVLF8nKwbQywU7r8zVr/K0Dy9Cg/dgsOhptiw964JcXrqCSi/CHFTD6AtKBRucB6RRWSGUjagtHi88mVDuKnbZe1odXqRPRBtUn/4v6TYPPyif0uAykkfrbawWtPhDfhis8XQvPRMtEHlnF5pH9IcjJnZKb3vVQlUPHBQK1OOibkAN4Eo2b0LFwJTYUemc+gX/BYkc19tcZ0EfLglzYZdg6oYT2dsFfAd3mthMq/dh+Kek1aixhetRV2uy5eBKlqVP0wUxiElQtc6UC6rD3qW5TpC30hz2dRGWdF7m/xaeWiMvs5dw2g8U82iakYmpJ7KMnMQgC8dfbNWNTR3CpodxFc8EWcPb9dMLf4GoIJoBNIx9kGCrkja/FGRaLk7DtIjje4gwxIhxAcaZfGVLcxby1ENpmN26+SwOfRGfjmKYsXEe4gybGkXjLuTmT5kV7f01t49CQYWXIXBZ5ctek2uPfJcmQuXlOKGU8rAzyWN016MyM3AVURuBadGX/3/hAp3MKQyes1PH2zIyIyCneD30hJiddPERkRgdPBnsiXKYXMoFQakRXSvvraPx26N7HOXFt38i4mLtmIXUo90kc1vkPaqp3g7u4mHkA3Lp1BgQJFcPLeC0yf+SPmeplnmb0s1BzJW8zFk5dhVhjcfvoKVSb+K2bS3u8BRj6QMX/Ujvg2QNN7Ur9OzfokUpPcywOZU3oB944hOEUelBwvyVyfWvnRtEw2IcNCN+Ybj18hR7pkmgi0NnBkOPDsrswCDX8t5EAm/H0es3ZcMSOAt3vdQ4aIe/BOmwORbklAc82RzpToIcaj0nzJMwCe5uLLaqH2/J4PsXl0Oy0+8NnBP5BqQ1fRdVTeZRjySTl4EulJnh4f/bAd4Y+u4m50elyZ0FiLYRtZwQ3ta5cFUpgErH/aegmT/5E1lPUyFA+evRFu/expkyEsMgqh4VEi2Qfhb2RSRuoP8PjWeTz2yob8Sypr1qzgHhdR8nuZjNPW3w+jqibDqD2vMX+vtDJSyMDDF6FI9uoeUnhE4XlyP7xRx1aADXkVjvCoKK3ustjPqf2AJHKv3b58GuFhr/HGwxcF8+VBaESUIGjZ0iSVa+XxBG4RoVh9JRJjVh7CQ/hidescSJ0uM5K9vIWMSd1wP3kBpPDxRHJvZf9SybLgG7iFzEjh7SHOnzGlXAvSiCs0wpQMERMBpOPvP3uDJyHPse/EOYzeLV2T0z7OhCpF8+CVm4+YpyiRdvc4bnvmQKao+/CkH3VkGOCT2nYs3cOLgHdKaWUjNq6IK7+sMgTJy7eR33mnMCOAWunCqCjg6TWxZgg6LeM/k3iKeab08dB+w3Q/ef7gOnJnzy4TW/4ZCexR9Ad7nUS1SVvxKtoHkXDDsQH+cp4kak/zSW6uNaj9RugP+p5UCvZOkx+TZEya7Fq2u9DgG5dW6xI64CYuBL1AId9ozD3xBpM2SrkcNSZT/GdcNi07PGpEsEhi+USJW6UXyo5VpKWaLMpPXoQi648666RF5rbZXJX/3HryCpm8w+AVFWom8yN/qzqrXOk2wMdT5O+S7uluSRCdMjNuBEeY7hlEqkKf4aFbOpGsUjRbKiR7cVPb03eDXwtLLcVS22wvHgCvHsva7b/JGLrnzZfjhGdJfPGLzJjvUSMvGpbIAj/3xyg1+bjQH6WXVnrJfB0chJJLZdKXvk2psBt96ltbF/H8vrjPh/1fZ3g9kJ6S6BaL4VaokawelSqb1Hike9+bEFPpv/rfiaQeNRnowOkLuLJ0EFp5yOo+EcOfyoSreGpx9vyOp/m74jRMAJ1AMT42kHzAm9r1HplMuk9VvwFqmgRMY3UpttwuVAHET3kDm1wYeCZdR5auT3JNkuUntk29ls7VcmNIg0JW3TeevofRizdrGmmw4SamTkTOhk6Zaa1eX/wz4NPZNqelZvzRlx1yh2DEXUmOqB1IUx8tgqQ7Wl/NQz/Qi9AITe9tWstSaFTCTjzTW0AhMkWkypZ0S2zxFGuj2x9690nYyRXwWiEztX8stQG9AkxSKLUm79CC4qmPOoZwPdYzz9SkzGw1I1qV9In1PKcWlw8/Xcwh/U2lu0YHFMWotWcwf4+U2olPF5A+o3h198ogzUyjLffg9YiSBidRplCTgbFwAevH12saqvGVRs9v1U/9fdccCVQ1ueL0LmBN4sboSfXyM99cQM6xUjbG0Drq5UV6HAbSWyQT6e9XOkw3nwnS5GrM1vC7fMBLKXdD8Yr638rQBoWs68qOyWyyalGJOu/Y39+0i38dDExUvBs1hgEfWpdHNAp5jP3IcDClsDyk3XocT1JUC3tRM/hDIyJFyAe1GZ+XFrHR0M9Xd4JJFfZjQH3r+7R2CEndkOQNNUstRPWgZ/eAyco9RacuQF+TmsDRX3trbml1neIEGxuDxsfzO76uxeh5mAAaRQ5AfGwgKwLYKzswWwnMpWDaGiZXZqwuxRYB/HIb8IEMqMbEXMDrJ/LGKeqGmtr2ftWRM72FhcqBk6vXQiWbhjVUblS6fiQwPPi3f3DIR9EYDJgBlJJZxfpG1qf24+dZa1cVawY0tV3xRM34o3Ha5HmN0Xc6akMeSFkbLR5KsmSPgOhjz7QbpwPXbHnI9H8v47tNF+KcAEafXQ03co2SMaT4Jgz51BTLU2fKDly8L7Mi9QTQFjGnuMz+SgUVmzpkjmDwY0lpYSKLy9AnIhZUrEMCE8DzQc+0sADhds2SypGrsXmM/nfqKAHUaxqq8ZWGJ2DZUf191x4NVO6lfasngGqGvuFz6pOTBlxDztGmxKBYE/ljvwOrlTKCtjKG7RDAbefvo8MCaVHe+s2HyJNBIW66lw5LAkgvOfSyY9Ym5hRWLdGG3reytMcKI7LejlYsls68pMfqpABePgK+U66rwyZcSVoUNZWMeaoqVKNgRk08noamhKv6RADJAjleCTfRnXNChQMYVN+OdA8dt/RzU8yfItljNWV9WVMLAkjJZIfm9WICGNt1duHxTACdADNBCOA3eU1p/x8NA6oZfLu0RQD1Yq1js2gSDpYEUBOQjSV26kOyQ+VcGNHImgBuv/AAfeZvxTEfGceIJjOBkq2s7ylhEag/ciF2eFsEGVNmGQlM22nq+VvmjcT426YElH3JP0LLxzKjz96Di9zceRXBX7NYo1hiMG/XVRHUHdcWQJxdA/wlr3Fo4S0Y29xUhaHe1J1aVqSeAJJbjNxj+vb3qXuagPbhYbVMbtfYXLcunCB8+FNNOFklgKPXnhVxkTHhH5vTOXqsXlLGjDw4OoDuOCMEkPQmA5TEJJIfKprNvHSggWmYuqi/bwq4r9RD+1xPAC+OqS/KiRluh+YB602xpTlHyOoyhtbxxJ/AShmHiz5npDta3+wQwB0XH6KtIkpO8bsZSdaGmr6SkYUFsH/dAuheQxGMVs/xQyHguaIfaiHdYwgfdb7+3YF6dvTwDA0cQ6c3z4AJShZ0+4144FsK5cfJjHmqcETlMKmpe1UjgBGhwBjzuuV03Pjy+zHYhqdGm8HKr0yyL3rvkX6KYS+1hCRRvlCnl0hyUvvn9GQC6Op9EIvxmADGAizLQxOEAA4orJV5gi7oP9aXYYsAdt0HZFIIgO57SwKo6YfF8qTqjYeycgMby0oF+kYWka7ztpky8uxYAKkCRtnBS3DEx+TGFeNQDEqLxXZnpZ6/eX53TLppKkO3L+mHaPm0i+hnjwCqVTfomNmty6BuEVmNI7btt33XhaxDnBPAc2uBP2VcY+8C2zC1pWLZpSzon3aJcnfq9ca0Lv+ef4D2Cw6JY20K0ToCwM/lNamW6JHBmnBy24o5MCqgKBKKAOrjQncNqCHiGY02PQGMSQZGPz6FMqgWms19qiF/JosyX0YnQ/3U32/d8UBFxbJGwt66LGCbunKxOeexxcBqxVo//DFyDjWVxIu1BVCvfaqrdqNNxw4B3HruvibwbubSnlsTuKOUK7QggH1r50fPmhYu5p9Km2R3YnDbOwyPOt+yHYCGUxzu5tSBeiLXbj1eZfVH4RFyTYRoekFZklDdq5ong2JAFSka/fnHlN1n01OjHbNhAHBQCbnpvB2grG/LFhkBfKvEf+oqDNFh9AK0d3YPk56rK3CPBYDx8fyOxXQS5FAmgE7AHh8byMoFPKQUMFmJy7B4u4/VpdgigLo6s3oZBUsCqOmHxeqEphuPavmx7E4xIe3nbMc5nw7yKzsxgPRV/kGrcNHHQpGesu1aLrE7KxXLJvm9MfWmKatwr3dltAqRD7KYHlxq/7ltyoqMUiNt6cGbQmMwPglg59xbMaeNKci70bTdIjtavV71uij7myQy9E3vpjTsMpzuDzxU9MgCQ7QHUEJbAPWagmbWIwMLa4QA6s9vOL7S3lzV33e9iYC/YlG3IID24l0dvvyTy4AVihaebl3f9juyOb6eAPa/ap00YocAUuKYqldodj36+DQLAkjkj0igWdMJ37skFk2db4mWwCem2tQOY2vkQF3yD9quRXTOqtrL1vRWpfFxcZnoYkUA6UMbz4PRZfbZ9NRoU9OrVeiNB5ZzV8dusxrIrdQ3B4S4+K6Z3ZkAGllrF/VhAugEkAlCAEdUBCYpWmsWb/exuhRbBLDnMakhZnFDcDUB/MLfD2OaWGeXUZWEz2buwiUfGbuGRj8BZazLDqk3MasC8fnqAp//ZRcG9cYXkD8pfrxpqkO819MfrZ5LIWhHCKD+bTpWmFOpuaO3RW1PVxHAXIPXiyRKq7nrLICW8grkdqS3b7WPikurCn4Y94n5uuhFn6+OayAysmPddGUF6eGqni+hCeDjF6GiCgo1oy81KhZGCKA+e5gEyPNmjAMLYIPvgfKdtCXTWwBjbaWzXPizq6UEj7hfmNb1bb8jm/tHTwAH3ZTZzvpmhwDqY1TNruePFsBFJTPbggBSRmy/uhal3ubVAm5LS7dLCSDVvW2uaCfG+odjoIMF2VL35ffNSuB/ZaRb3coFLK7ZOvwgsPRem54abVZ6vdqYqqeoYyu6jmp/kmb6d3p3dgEbWGZXdWEC6ASS8UEAqVYlZbBS88+dFkvbFjMF7DpjAVRdJH4VgZtK8Lb+xru2F3BkAe7maYFKZwKQ1DMJXodHinkYtQSVHL0Zwa/CoQYkW0JPpKTJ9F0mgdWGU4GySs1hi4PzD/sbFz1MblzxdZ6aQGv7lQlq/rAdVx6+xLQWRdBotakw/c30VVHtdldRaoxKjtlr6o1zz6CPpFSHgabW0R3nMU/KH1BdzIDpBkaSXVRZmQbFMmPG5yY3r5Bj+FFe418NT6N5WSU2CMCi/TdEqTuSm1j3dVWU/vYfIZ1DIsw1C5lbNvUkyTBh2D8L2DgQyFYW6LTVigDuvvRIyFWomnOGwYhlR31GpNE9rZ5y6MpT+P2ArHrRPMm/mOQ5F8hYBOhmXTJP7aMPKzgxsg5SJ/WM5RXEcLj60CVh6MwmUq/K+qTy8cDJwLrOne/pDeBHpc5yYAgGrzgFqubTomx2TPyfqf6yQyfRE0CL+smi//p+wKG5QLHmQFOT3iBVKKk9RWaimu3P08tl+Ut6oaUXW/IaDPsbYRFRWNrZH/65LWRpNg+TMjSpPgD6ysobTjU18anpL0AxWR0pXpq67n3OAqmzab+1f/pUQz4lxEC9j+3oX90kN2WDAO5odRkfKnGDNueufwEYeANIaieLXh2792kp76M0khXqMW6aqMwT5e4J9xGP4gUi9STx8fyO1wsycDImgAZAi88NRA9muslFRkejxAdpkNwjGvg2vZyCRYZfrC4l9LnQGRNBuSQkmzKT2YMCFE9ycz+is1fAkTuvkDtDCjx9FSaEeI1aKkhA+PLDFyibw9daMw8QYr31f9xlso6Rmnw5U7au/vro5nHv5iUU940A5iiaguReIDeDnUb6ceeCnqFCrrRwoxJKP0vCFJ2nFvZXmoNiH6QWGm/2GsWMERkvktV4sD7ht+/KYyT3iESJqPMSfxKJNdjCI6Nw6NoTlM7ha6UT9uL6YVx85oWSRYuZWe5IlJuqnRTJmgopfTzhyLqQJqLQeTTSKMaIBLGzlhR6dOoDSI0BpCEPX3+CXOmTI10K41gYmZoqYm1E1kh/PiIWdA3enu7ImtobWYKPyd+TpSXLYpLXH73Em4hIFMxsPAPZ5nWT/AbpsKlZ/cpBRHq3X3iIQplTGV9P/QnvnZA6l2n8QGMfuf4UZXL6wtvDjmadvUU6+RewQrFUDnsIeHiZHxkRJl9Us1ewytClF0eqaZw5tU4jk8ziN/bKmGaaHyC0IKmiCVWzsWokqHx5i9yjMZSBc3iPUUbx/bNAjkpmtbsd7m/0QNIgJFkXpZZ3UMgboeeolzgiXUF6rpglHRGZf3Ef14IeI9rdA8+S+aFEwfw279Pa1Cjb+cq/1s8Oy7mThikpSmQxvXSrh1BpxMib+1G8WCm4p7RORDEKgyP9mAACTAAd2Sl2jkmQDaSP83AmCcSJ646rrmpQvOYetXBf2T2v+oaZsyrQbp3j01P7vYU4Oj4gH+kIArYIoCP9+Jj/MAL6LOCRwfFLmv7DsPKl2UcgQZ7f79iCMAF0YkESbAOpxOWj4UA1U91TJy7lnehK6vpVJ/1rsgCScnwFRRoiphmqePhVAjr87fi1GCWOjp+Bj7SBgGUMIIPECODEUmClzMR3SQweQ8oIvAWBBHt+v0MrwwTQicVIsA2kEpf4VJl3AidHu6pZkZoFsP4koILyUHCEAJJ7qONmR09nCnzOURlov8HxfnykUwgwAXQKvv9m5+NLgFVKtnI8y4H8NwHlq3obAgn2/H7bxOLxeyaAToCdYBtIJYDVhwDVBzpxBe9WV4rRKzF6s8kCWG8C4G+h9Wdryioe2coAnWRdSYea2i+2xNGhwfkgewiwC5j3hhUC+kogTAB5g8QDAgn2/I6Ha3P0FEwAHUXKxnEJtoFU4uJMKTgnrjuuuqqyGJoF0NEsZxUPCjKmaiaONqPE0dHx+TibCDAB5I1hTQB1otJMAHmDxAMCCfb8jodrc/QUTAAdRepdJIBUBo7Kwf1HmlqvVyOAtb8FKkt9vhibSuQyFQW67nnb0abvjRJHx8/AR9pAgF3AvC2sEDj6G7Dma/kxE0DeIPGAABNAzgJ2apsl2AZSictnS4CCDZy6hnetM5EDjQBaKMfbnevkwsCzO0DVfkDN4Y5f0vcFgBdBwIcDgRpDHO/HRzqFgEoAf/ysJAJKWhehd2pw7pw4Ebh9BJj3ERPAxLl6iXLWCfb8fofQYgugE4uRYBvo0SUg6BRQ5JP/nFwCkYO8brfRKtcrdOjcx7HVeR4EXN0u8YiNpp7Rfo7Nio+ygwBp3524HYzGJbLGrDPGCL5fCJxfD6TJAWQ2L0f4foHAVxtfCCTY8zu+LtCB8zABdAAke4fwBnICPDtdtXJtJbPix89sFBd3/Sl5REaAEWAEGIH3DAF+frML2KktzxvIKfhsdmYC6HpMeURGgBFgBBgBcwT4+f0eE8CcOXPixo0bZjti4MCBmDBhgsO/E95ADkPl8IEqAST34E8t2QLoMHB8ICPACDACjIDDCPDz+z0ngB07dkSnTkr9SQApUqQQ/xxtvIEcRcrx45gAOo4VH8kIMAKMACNgDAF+fr/nBLB3796gf0YbbyCjyNnvxwTQ9ZjyiIwAI8AIMALmCPDz+z0ngKGhoQgLC0P27NnRrFkz9O/fH15eXg7/TngDOQyVwwcyAXQYKj6QEWAEGAFGwCAC/Px+jwnglClTULp0afj6+uLgwYMYPHgwAgICMG/ePLvbiQgj/VMbbSAijyEhIUiVKpXBbcjd9AioBHBw/YLo8mEeBocRYAQYAUaAEXA5AkwA/2MEMDAwEKNGjYpxoxw6dAhly5a1Omb58uX43//+h0ePHiFdunQ2x7A3PhNA1/02T98Jwc5LD/Flldzw8nB33cA8EiPACDACjAAjoCDABPA/RgCJvNG/mBpl//r4+FgdcufOHXzwwQfYv38/KlSoYHMItgDyvYMRYAQYAUaAEUj8CDAB/I8RQGe25Lp169CoUSMhDePn5+fQUGT5S5MmDW7dusUuYIcQ44MYAUaAEWAEGIGER0AN4QoODkbq1KkTfkIJMIP3shLIvn37hKWvRo0aYuHJLdynTx/hGl69erXDy3D79m0RA8iNEWAEGAFGgBFgBBIfAmTAIe/f+9jeSwJ49OhRdOvWDefPnxdJHTly5MBnn32GAQMGIFmyZA7vg6ioKNy9excpU6Z0eU1T9e2ErYsOL4ehAxlnQ7DFuhPjHGvIDHVgnA3BZqgTY20Itlh3iiuco6Oj8fz5c2TNmhXu7u9nvPl7SQBjvQMToAPHJ8QP6Iwz4xw/CMTPWXg/xw/OJ3AtAQAACtxJREFUdBbGOn6wZpzjDmcmgHGHrVMj86Z3Cj6HOzPODkPl1IGMs1PwOdyZcXYYKqcPZKydhtChARhnh2AydBATQEOwxX0n3vRxjzG/xccPxowz4xx/CMTfmfgeHT9YM85xhzMTwLjD1qmRKTZx/PjxQqDa29vbqbG4s30EGOf42R2MM+McPwjE31l4T8cP1oxz3OHMBDDusOWRGQFGgBFgBBgBRoAReCcRYAL4Ti4LT4oRYAQYAUaAEWAEGIG4Q4AJYNxhyyMzAowAI8AIMAKMACPwTiLABPCdXBaeFCPACDACjAAjwAgwAnGHABPAuMOWR2YEGAFGgBFgBBgBRuCdRIAJ4Du4LDNmzMB3332He/fuoUiRIpg6dSqqVq36Ds703ZwSZU+vWLFCVHpJmjQpKlWqhIkTJ6JAgQLahEkFftSoUZgzZw6ePn2KChUqYPr06QJvtVH2Wb9+/bBkyRK8fv0aNWvWBK3N+1o2KKbVJsyHDBmCXr16if1KjTF23e/jzp07GDhwIP7++2+xF/Pnz49ffvkFZcqUYaxdBHNERAQCAwPx+++/IygoCFmyZEG7du0wbNgwrVIE72ljYO/cuVM8044cOSKeaytXrkSTJk1cfj+me3nPnj2xZs0aMXbjxo0xbdo0pEmTxtjE/+O9mAC+Ywv8559/onXr1oJoVK5cGbNnz8a8efNw9uxZ+Pn5vWOzfTenU69ePVHar1y5cqCb+tChQ3Hq1CmBYfLkycWkiRCOHTsWCxYsEA/TMWPGgG5SFy5cEKX9qHXt2hVr164Vx6RLlw7ffPMNnjx5Im5iSZIkeTcvPgFmRbW0mzdvjlSpUon62ioBZIxdsxj0UCtVqpTAlvZkxowZceXKFeTMmRN58uTh/ewamMX9YMqUKVi4cKF4ETx8+DDat28v7g30YsP3DeNA04vLnj17ULp0aTRt2tSKALrqXlG/fn3cvn1bvNhT69y5s/id0H2cmzUCTADfsV1Blij6kcycOVObWaFChcTbEllZuMUegYcPH4qH5o4dO1CtWjVhmaL6j7179xZWFWpk7cuUKZMghl26dEFISAgyZMiARYsWoUWLFuIYqvucPXt2bNiwAXXr1o39RP6DPV68eCH2K72w0IOyZMmSggAyxq5b7EGDBomH565du2wOyli7BuuGDRuKewBZVtVGZIXqw9N9gHF2Dc5ubm5mBNBVuJ47dw6FCxfG/v37hUeHGv1dsWJF4Q3Se4BccyWJfxQmgO/QGoaFhYmbzbJly/DJJ59oM6O3z+PHjwsCwy32CFy+fBn58uUTVsCiRYvi6tWrwnJy9OhRYVlRW0BAgHAVkAVg27ZtwuVLFj9fX1/tmBIlSggyTu5jbkDbtm2RNm1aYTmpXr26RgAZY9ftDnqo0QsHWTboHpAtWzZ069YNnTp1EidhrF2D9YQJEzBr1ixs3rxZeAVOnDiBOnXqiBeali1bMs6ugRmWBNBV+/fXX39F3759ERwcbDZTuqfT/YmsudzMEWAC+A7tCLIw0c2d3vYpbk1t48aNE6SE3JPcYocAvV0SsSM3mmpB2bt3r3CvU1wVWQLVRu6CGzduYNOmTfjjjz/EDYMsg/pGD4RcuXIJ1/z73pYuXSrcZuQC9vHxMSOAjLHrdgdhS40ebs2aNcPBgweF9Zr2YJs2bcBYuwZruldQHCt5ASjEIzIyUuxvqsZEjXF2Dc6WBNBVuNJzksJ1Ll68aDZRIvN0L1fX0TVX8d8YhQngO7SOKgGkHwSZrdVGNyFyQZAZm1vsEOjevTvWr1+P3bt3a8kb6g2H8KZAb7WRReXWrVvYuHGjXQJYu3ZtYT0kS8H73AinsmXLCmsJWUWp6S2AjLHrdoeXl5fAmjBVGwW6E/Het2+fRkx4PzuHOb3Q9O/fXyQrUAwgeV2IaE+ePFlYunlPO4ev2tseAXR2/9ozlJD3p2PHjqBQCm7mCDABfId2BLuAXbsYX3/9NVatWiWSO8hqpzZXuRxcO9vENRrhSmEK+mQYspjQzd3d3V1Yq/Pmzctudhcsa44cOUAvHpQMpjaKEaaYS7Ji8352AciAiO8lkkAvjWojjBcvXixevhln1+DMLmDX4OiKUZgAugJFF45Bwask7UBB9WqjGCByY3ISiGNAkyuHyB9JDWzfvl3E/+mbGnTcp08fDBgwQHxF5JsSRSyTQOjmTxmu1Ei+gCRgOAkEeP78uXCX6xu5WQoWLCgSa8iCQu51xtixPRvTUa1atRKWaX0SCOF64MABYZXi/ew8xjQCZfoT4aNMa7XRPXf+/PnCrcg4uwZne0kgzt4r1CQQ+l2UL19eTJb+9vf35yQQO0vHBNA1e9plo6gyMORiJDcwpbPPnTsXZ86cAVkCuL0dAQqQpxi+1atXm2V+pU6dWugCUiOip97ciSCS+4DIoqUMzLp160RcCSU6kCbg48ePWQbGzhLoXcCM8dv3qaNHkKuXYoIp8YheRigGkMIV6N7w+eef8352FMi3HEeaf1u2bBGxlfQCc+zYMSEj0qFDB3G/4D1tHGhSC6BkPGqUeEdudZI1ovsqyZu56n5MMjDkSlZjtGn96LnJMjC2144JoPE9HWc9yfo3adIkYXGirFXKYCL5Em6OIUBvmLYavcnTTZ6aKuhKNwq9EDThrbY3b96ImCAik3ohaHIVcbNGwJIAMsau2yX0IkJB7JcuXRLhDJQQomYB8352Dc5k1R4+fLjwHDx48EBYsCn7d8SIEaA4TMbZOM70ck2Ez7JRbCW9YLvqXkGqDZZC0D///DMLQdtZOiaAxvc092QEGAFGgBFgBBgBRiBRIsAEMFEuG0+aEWAEGAFGgBFgBBgB4wgwATSOHfdkBBgBRoARYAQYAUYgUSLABDBRLhtPmhFgBBgBRoARYAQYAeMIMAE0jh33ZAQYAUaAEWAEGAFGIFEiwAQwUS4bT5oRYAQYAUaAEWAEGAHjCDABNI4d92QEGAFGgBFgBBgBRiBRIsAEMFEuG0+aEWAEGAFGgBFgBBgB4wgwATSOHfdkBBgBRoARYAQYAUYgUSLABDBRLhtPmhFgBBgBRoARYAQYAeMIMAE0jh33ZAQYAUaAEWAEGAFGIFEiwAQwUS4bT5oRYAQYAUaAEWAEGAHjCDABNI4d92QEGAFGgBFgBBgBRiBRIsAEMFEuG0+aEWAEGAFGgBFgBBgB4wgwATSOHfdkBBgBRoARYAQYAUYgUSLABDBRLhtPmhFgBBgBRoARYAQYAeMIMAE0jh33ZAQYAUaAEWAEGAFGIFEiwAQwUS4bT5oRYAQYAUaAEWAEGAHjCDABNI4d92QEGAFGgBFgBBgBRiBRIsAEMFEuG0+aEWAEGAFGgBFgBBgB4wgwATSOHfdkBBgBRoARYAQYAUYgUSLABDBRLhtPmhFgBBgBRoARYAQYAeMIMAE0jh33ZAQYAUaAEWAEGAFGIFEiwAQwUS4bT5oRYAQYAUaAEWAEGAHjCDABNI4d92QEGAFGgBFgBBgBRiBRIsAEMFEuG0+aEWAEGAFGgBFgBBgB4wgwATSOHfdkBBgBRoARYAQYAUYgUSLABDBRLhtPmhFgBBgBRoARYAQYAeMIMAE0jh33ZAQYAUaAEWAEGAFGIFEiwAQwUS4bT5oRYAQYAUaAEWAEGAHjCDABNI4d92QEGAFGgBFgBBgBRiBRIsAEMFEuG0+aEWAEGAFGgBFgBBgB4wj8P6XoyIYLDqTUAAAAAElFTkSuQmCC\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This example creates 1, 2, 3 or 4 outputs tones muxed using SG Mux4 V1.\n",
    "# This drives DAC0 of Tile 228.\n",
    "# We read out using all four of the regular avg_bufs.\n",
    "\n",
    "def adcfreq(f,gen):\n",
    "    return int(f/(gen.fstep*5)) *(gen.fstep*5)\n",
    "\n",
    "freqs = [adcfreq(f,gen) for f in [200,400,600,1400]] #we'll mask the 4th output\n",
    "gen_ch = 6\n",
    "buf_chs = [0,1,2,3] # which channels to read\n",
    "sg_mask = 0x7 #0xf means enable all outputs, 0x7 means enable 0,1,2\n",
    "\n",
    "gen = soc.gens[gen_ch]\n",
    "for iF, f in enumerate(freqs):\n",
    "    gen.set_freq(f=f, out=iF)\n",
    "\n",
    "# Signal duration (uSeconds)\n",
    "T = 1\n",
    "N = T*gen.fs/gen.NDDS\n",
    "soc.tproc.single_write(addr=100, data=N)\n",
    "print(\"N = %d\" %N)\n",
    "\n",
    "trig_bit = 0\n",
    "for buf_ch in buf_chs:\n",
    "    # Set up the trigger bit\n",
    "    trig_bit += (1 << soc.avg_bufs[buf_ch].trigger_bit)\n",
    "\n",
    "    # Enable capture on buffer.\n",
    "    soc.config_buf(ch=buf_ch,length=1000,enable=True)\n",
    "\n",
    "    #set up readout.\n",
    "    readout = soc.readouts[buf_ch]\n",
    "    readout.set_out('product')\n",
    "    readout.set_freq(f=freqs[buf_ch]+f_mixer)\n",
    "\n",
    "########################\n",
    "### Start of program ###\n",
    "########################\n",
    "# Simple QickProgram to set mr_buffer trigger.\n",
    "prog = QickProgram()\n",
    "\n",
    "# Register for PMOD0 and trigger bits for the four avg_bufs.\n",
    "prog.regwi(0,1,trig_bit,\"p, $r, imm\")\n",
    "\n",
    "# Registers for SG on channel 6.\n",
    "prog.memri(1,1,100, \"nsamp\")\n",
    "prog.regwi(1,2,sg_mask,\"mask\")\n",
    "\n",
    "# Initial sync.\n",
    "prog.synci(1000)\n",
    "\n",
    "# Set SG channel 6.\n",
    "prog.set(gen_ch, 1, 1, 2, 0, 0, 0, 0, \"ch, p, $ra, $rb, $rc, $rd, $re, $rt\")\n",
    "\n",
    "# Trigger.\n",
    "prog.seti(7,0,1,0,\"ch, p, $r, t\")\n",
    "prog.seti(7,0,0,100,\"ch, p, $r, t\")\n",
    "prog.end()\n",
    "\n",
    "soc.tproc.load_bin_program(prog.compile())\n",
    "\n",
    "soc.tproc.stop()\n",
    "soc.tproc.start()\n",
    "\n",
    "#################################\n",
    "### Capture and transfer data ###\n",
    "#################################\n",
    "fig,ax = plt.subplots(4)\n",
    "for buf_ch in buf_chs:\n",
    "    i,q = soc.get_decimated(ch=buf_ch,length=1000)\n",
    "\n",
    "    # plot time-domain\n",
    "    ax[buf_ch].plot(i)\n",
    "    ax[buf_ch].plot(q)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def adcfreq(f):\n",
    "    return int(f/(soc.gens[6].fstep*5)) *(soc.gens[6].fstep*5)\n",
    "\n",
    "class LoopbackProgram(AveragerProgram):\n",
    "    def __init__(self,cfg):\n",
    "        AveragerProgram.__init__(self,cfg)\n",
    "\n",
    "    def initialize(self):\n",
    "        cfg=self.cfg   \n",
    "        # Set parameters.\n",
    "        nq = 1 #nyquist zone\n",
    "\n",
    "        self.ro_chs=self.cfg[\"adc_chs\"]\n",
    "        \n",
    "        self.cfg[\"adc_lengths\"]=[self.cfg[\"readout_length\"]]*len(self.ro_chs)          #add length of adc acquisition to config\n",
    "        self.cfg[\"adc_freqs\"]=[adcfreq(f) for f in self.cfg[\"ro_freqs\"]]   #add frequency of adc ddc to config\n",
    "        \n",
    "        trig_bit = 0\n",
    "        for i,buf_ch in enumerate(self.ro_chs):\n",
    "#             # Enable capture on buffer.\n",
    "#             soc.config_buf(ch=buf_ch,length=self.cfg[\"adc_lengths\"][i],enable=True)\n",
    "#             #set up readout.\n",
    "#             readout = soc.readouts[buf_ch]\n",
    "#             readout.set_out('product')\n",
    "#             readout.set_freq(f=self.cfg[\"adc_freqs\"][i]+f_mixer)\n",
    "            # trigger bit\n",
    "            trig_bit += 1<<soc.avg_bufs[buf_ch].trigger_bit\n",
    "\n",
    "        self.regwi(0,1,trig_bit,\"p, $r, imm\") # set up the trigger bit\n",
    "        \n",
    "        gen = soc.gens[self.cfg[\"sg_ch\"]]\n",
    "        \n",
    "        # Get Tile and Block for mixer.\n",
    "        tile = soc.dacs[gen.dac]['tile']\n",
    "        dac = soc.dacs[gen.dac]['block']\n",
    "        soc.mixer.set_freq(self.cfg['f_mixer'],tile,dac)\n",
    "        soc.mixer.set_nyquist(nq,tile,dac)\n",
    "\n",
    "        gen_chs = self.cfg[\"pulse_chs\"]\n",
    "        sg_mask = 0\n",
    "        for i, gen_ch in enumerate(gen_chs):\n",
    "            gen.set_freq(f=adcfreq(self.cfg[\"pulse_freqs\"][i]), out=gen_ch)\n",
    "            sg_mask += (1 << gen_ch)\n",
    "\n",
    "        # Registers for SG on channel 6.\n",
    "        self.regwi(1,1,self.cfg[\"length\"], \"nsamp\")\n",
    "        self.regwi(1,2,sg_mask,\"mask\")\n",
    "\n",
    "        self.synci(200)  # give processor some time to configure pulses\n",
    "    \n",
    "    def body(self):\n",
    "#         self.trigger_adc(adc1=1, adc2=1,adc_trig_offset=self.cfg[\"adc_trig_offset\"])  # trigger the adc acquisition\n",
    "        self.seti(7,0,1,self.cfg[\"adc_trig_offset\"],\"ch, p, $r, t\")\n",
    "        self.seti(7,0,0,self.cfg[\"adc_trig_offset\"]+100,\"ch, p, $r, t\")\n",
    "\n",
    "        # Set SG channel 6.\n",
    "        self.set(self.cfg[\"sg_ch\"], 1, 1, 2, 0, 0, 0, 0, \"ch, p, $ra, $rb, $rc, $rd, $re, $rt\")\n",
    "\n",
    "#         self.pulse(ch=self.cfg[\"res_ch\"], length=self.cfg[\"length\"], play=True) # play readout pulse\n",
    "        self.sync_all(soc.us2cycles(self.cfg[\"relax_delay\"])+self.cfg[\"length\"])  # sync all channels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'regwi', 'args': (0, 1, 112, 'p, $r, imm')}\n",
      "{'name': 'regwi', 'args': (1, 1, 200, 'nsamp')}\n",
      "{'name': 'regwi', 'args': (1, 2, 7, 'mask')}\n",
      "{'name': 'synci', 'args': (200,)}\n",
      "{'name': 'regwi', 'args': (0, 15, 0)}\n",
      "{'name': 'regwi', 'args': (0, 14, 0)}\n",
      "{'name': 'seti', 'args': (7, 0, 1, 100, 'ch, p, $r, t')}\n",
      "{'name': 'seti', 'args': (7, 0, 0, 200, 'ch, p, $r, t')}\n",
      "{'name': 'set', 'args': (6, 1, 1, 2, 0, 0, 0, 0, 'ch, p, $ra, $rb, $rc, $rd, $re, $rt')}\n",
      "{'name': 'synci', 'args': (549,)}\n",
      "{'name': 'mathi', 'args': (0, 15, 15, '+', 1)}\n",
      "{'name': 'memwi', 'args': (0, 15, 1)}\n",
      "{'name': 'loopnz', 'args': (0, 14, 'LOOP_J')}\n",
      "{'name': 'end', 'args': ()}\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ac681e43f02341cd9650683ef7bde602",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "config={\"sg_ch\":6, # this needs to be the sg_mux output\n",
    "        \"f_mixer\":0, #you can try nonzero but the frequency conversion may not be quite right?\n",
    "        \"pulse_chs\":[0,1,2], # which sg_mux outputs to drive\n",
    "        \"pulse_freqs\": [200,400,600], # [MHz]\n",
    "\n",
    "        \"adc_chs\":[0,1,2], # list of inputs to read\n",
    "        \"ro_freqs\": [200,400,600], # [MHz]\n",
    "\n",
    "        \"reps\":1, # --Fixed\n",
    "        \"relax_delay\":1.0, # --Fixed\n",
    "#         \"res_phase\":0, # --Fixed\n",
    "#         \"pulse_style\": \"const\", # --Fixed\n",
    "        \n",
    "        \"length\":200, # [Clock ticks]\n",
    "        # Try varying length from 10-100 clock ticks\n",
    "        \n",
    "        \"readout_length\":500, # [Clock ticks]\n",
    "        # Try varying readout_length from 50-1000 clock ticks\n",
    "\n",
    "#         \"pulse_gain\":32000, # [DAC units]\n",
    "#         # Try varying pulse_gain from 500 to 30000 DAC units\n",
    "\n",
    "        \"adc_trig_offset\": 100, # [Clock ticks]\n",
    "        # Try varying adc_trig_offset from 100 to 220 clock ticks\n",
    "\n",
    "        \"soft_avgs\":100\n",
    "        # Try varying soft_avgs from 1 to 200 averages\n",
    "\n",
    "       }\n",
    "\n",
    "###################\n",
    "# Try it yourself !\n",
    "###################\n",
    "\n",
    "prog =LoopbackProgram(config)\n",
    "iq0, iq1, iq2 = prog.acquire_decimated(soc, load_pulses=True, progress=True, debug=True)\n",
    "# print(prog)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        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 / mpl.ratio, fig.canvas.height / mpl.ratio);\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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"900\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results.\n",
    "plt.figure(figsize=[9,6])\n",
    "plt.plot(iq0[0], label=\"I value; ADC 0\")\n",
    "plt.plot(iq0[1], label=\"Q value; ADC 0\")\n",
    "plt.plot(iq1[0], label=\"I value; ADC 1\")\n",
    "plt.plot(iq1[1], label=\"Q value; ADC 1\")\n",
    "plt.plot(iq2[0], label=\"I value; ADC 2\")\n",
    "plt.plot(iq2[1], label=\"Q value; ADC 2\")\n",
    "# plt.plot(abs(iq0[0]+1j*iq0[1]), label=\"amplitude; ADC 0\")\n",
    "# plt.plot(abs(iq1[0]+1j*iq1[1]), label=\"amplitude; ADC 1\")\n",
    "# plt.plot(abs(iq2[0]+1j*iq2[1]), label=\"amplitude; ADC 2\")\n",
    "plt.ylabel(\"a.u.\")\n",
    "plt.xlabel(\"Clock ticks\")\n",
    "plt.title(\"Averages = \" + str(config[\"soft_avgs\"]))\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'regwi', 'args': (0, 1, 112, 'p, $r, imm')}\n",
      "{'name': 'regwi', 'args': (1, 1, 200, 'nsamp')}\n",
      "{'name': 'regwi', 'args': (1, 2, 7, 'mask')}\n",
      "{'name': 'synci', 'args': (200,)}\n",
      "{'name': 'regwi', 'args': (0, 15, 0)}\n",
      "{'name': 'regwi', 'args': (0, 14, 99)}\n",
      "{'name': 'seti', 'args': (7, 0, 1, 100, 'ch, p, $r, t')}\n",
      "{'name': 'seti', 'args': (7, 0, 0, 200, 'ch, p, $r, t')}\n",
      "{'name': 'set', 'args': (6, 1, 1, 2, 0, 0, 0, 0, 'ch, p, $ra, $rb, $rc, $rd, $re, $rt')}\n",
      "{'name': 'synci', 'args': (549,)}\n",
      "{'name': 'mathi', 'args': (0, 15, 15, '+', 1)}\n",
      "{'name': 'memwi', 'args': (0, 15, 1)}\n",
      "{'name': 'loopnz', 'args': (0, 14, 'LOOP_J')}\n",
      "{'name': 'end', 'args': ()}\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "85e98859674c4d25ad572461215120d9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-4.400000e-04]\n",
      " [-2.233514e+01]\n",
      " [-8.366860e+00]]\n",
      "[[12.53996]\n",
      " [12.99672]\n",
      " [-5.68778]]\n"
     ]
    }
   ],
   "source": [
    "config['soft_avgs']=1\n",
    "config['reps']=100\n",
    "prog =LoopbackProgram(config)\n",
    "i, q = prog.acquire(soc, load_pulses=True, progress=True, debug=True)\n",
    "print(i)\n",
    "print(q)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CAVITY TRANSMISSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ResTransmission(AveragerProgram):\n",
    "    def __init__(self,cfg):\n",
    "        AveragerProgram.__init__(self,cfg)\n",
    "\n",
    "    def initialize(self):\n",
    "        cfg=self.cfg   \n",
    "        \n",
    "        r_freq=self.sreg(cfg[\"res_ch\"], \"freq\")   # Get frequency register for res_ch\n",
    "        freq=soc.freq2reg(soc.adcfreq(cfg[\"res_freq\"]))   # convert frequency to dac frequency (ensuring it is an available adc frequency)\n",
    "        \n",
    "        self.cfg[\"adc_lengths\"]=[self.cfg[\"readout_length\"]]*2          # add length of adc acquisition to config\n",
    "        self.cfg[\"adc_freqs\"]=[soc.adcfreq(self.cfg[\"res_freq\"])]*2         # add frequency of adc ddc to config\n",
    "        \n",
    "        self.add_pulse(ch=self.cfg[\"res_ch\"], name=\"measure\", style=self.cfg[\"pulse_style\"], length=self.cfg[\"readout_length\"])  # add a constant pulse to the pulse library        \n",
    "        self.pulse(ch=cfg[\"res_ch\"], name=\"measure\", freq=freq, phase=0, gain=cfg[\"pulse_gain\"], t= 0, play=False) # pre-configure readout pulse\n",
    "        \n",
    "        self.synci(200)  # give processor some time to configure pulses\n",
    "    \n",
    "    def body(self):\n",
    "        self.trigger_adc(adc1=1, adc2=1,adc_trig_offset=self.cfg[\"adc_trig_offset\"])  # trigger the adc acquisition\n",
    "        self.pulse(ch=self.cfg[\"res_ch\"], length=self.cfg[\"readout_length\"], play=True) # play readout pulse\n",
    "        self.sync_all(soc.us2cycles(self.cfg[\"cavity_relax_delay\"]))  # sync all channels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "threshold=0\n",
    "pi_gain=0\n",
    "hw_cfg={\"res_ch\":cavity_ch,\n",
    "        \"qubit_ch\":qubit_ch,\n",
    "       }\n",
    "readout_cfg={\n",
    "    \"readout_length\":soc.us2cycles(4.6), # [Clock ticks]\n",
    "    \"f_res\": cavity_IF, # [MHz]\n",
    "    \"res_phase\": 0,\n",
    "    \"adc_trig_offset\": adc_offset, # [Clock ticks]\n",
    "    \"res_gain\":32000,\n",
    "    \"cavity_RF\": cavity_RF/1e6,\n",
    "    \"threshold\": np.int64(threshold)\n",
    "    }\n",
    "qubit_cfg={\n",
    "    \"qubit_relax_delay\":50,\n",
    "    \"f_ge\": qubit_RF, # [MHz]\n",
    "    \"qubit_gain\":32000,\n",
    "    \"sigma\":soc.us2cycles(0.140),\n",
    "    \"pi_gain\":np.int64(pi_gain),\n",
    "    }\n",
    "\n",
    "atten_cfg={\n",
    "    \"cavity_atten\": cavity_atten,\n",
    "    \"qubit_atten\": qubit_atten,\n",
    "    }\n",
    "expt_cfg={\"center\": cavity_IF, \"span\":0.3, \"expts\":75}\n",
    "expt_cfg[\"step\"] = 2*expt_cfg[\"span\"]/expt_cfg[\"expts\"]\n",
    "expt_cfg[\"start\"] = expt_cfg[\"center\"]-expt_cfg[\"span\"]\n",
    "fpts=expt_cfg[\"start\"] + expt_cfg[\"step\"]*np.arange(expt_cfg[\"expts\"])\n",
    "\n",
    "config={\"res_ch\":cavity_ch, \"cavity_relax_delay\":20, \"pulse_style\": \"const\",\n",
    "        \"pulse_gain\":readout_cfg[\"res_gain\"], \"reps\":500, **readout_cfg, **atten_cfg} \n",
    "config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "amps=[]\n",
    "start = time.time()\n",
    "now = datetime.now(); dt_string = now.strftime(\"%Y_%m_%d_%H_%M_%S\")\n",
    "for f in tqdm(fpts):\n",
    "    config[\"res_freq\"]=f\n",
    "    prog =ResTransmission(config)\n",
    "    avgi,avgq=prog.acquire(soc, load_pulses=True)\n",
    "    amp1=np.abs(avgi[0][0]+1j*avgq[0][0])\n",
    "    amp = amp1\n",
    "    amps.append(amp)\n",
    "print(f'Time: {time.time() - start}')\n",
    "amps=np.array(amps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=[4.5,3])\n",
    "plt.subplot(111,title=\"Resonator Transmission\", xlabel=\"Resonator Frequency (MHz)\", ylabel=\"Amp. (adc level)\")\n",
    "plt.plot(fpts+cavity_LO/1e6, amps,'-o')\n",
    "\n",
    "max_freq=fpts[np.argmin(amps)]+cavity_LO/1e6\n",
    "plt.title(\"Cavity freq = \" + str(max_freq))\n",
    "plt.tight_layout()\n",
    "\n",
    "x_pts = fpts+cavity_LO/1e6; y_pts = amps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cavity_RF = max_freq*1e6 #11.08311e9 #11.08335e9\n",
    "cavity_LO = 7e9\n",
    "cavity_IF = (cavity_RF-cavity_LO)/1e6\n",
    "print(cavity_IF)\n",
    "\n",
    "readout_cfg[\"f_res\"]= cavity_IF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=[4.5,3])\n",
    "plt.subplot(111,title=\"Resonator Transmission\", xlabel=\"Resonator Frequency (MHz)\", ylabel=\"Amp. (adc level)\")\n",
    "plt.plot(fpts+cavity_LO/1e6, amps,'-o')\n",
    "\n",
    "max_freq=fpts[np.argmin(amps)]+cavity_LO/1e6\n",
    "plt.title(\"Cavity freq = \" + str(max_freq))\n",
    "plt.tight_layout()\n",
    "\n",
    "x_pts = fpts+cavity_LO/1e6; y_pts = amps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=[4.5,3])\n",
    "plt.subplot(111,title=\"Resonator Transmission\", xlabel=\"Resonator Frequency (MHz)\", ylabel=\"Amp. (adc level)\")\n",
    "plt.plot(fpts+cavity_LO/1e6, amps,'-o')\n",
    "\n",
    "max_freq=fpts[np.argmin(amps)]+cavity_LO/1e6\n",
    "plt.title(\"Cavity freq = \" + str(max_freq))\n",
    "plt.tight_layout()\n",
    "\n",
    "x_pts = fpts+cavity_LO/1e6; y_pts = amps\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=[4.5,3])\n",
    "plt.subplot(111,title=\"Resonator Transmission\", xlabel=\"Resonator Frequency (MHz)\", ylabel=\"Amp. (adc level)\")\n",
    "plt.plot(fpts+cavity_LO/1e6, amps,'-o')\n",
    "\n",
    "max_freq=fpts[np.argmin(amps)]+cavity_LO/1e6\n",
    "plt.title(\"Cavity freq = \" + str(max_freq))\n",
    "plt.tight_layout()\n",
    "\n",
    "x_pts = fpts+cavity_LO/1e6; y_pts = amps\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=[4.5,3])\n",
    "plt.subplot(111,title=\"Resonator Transmission\", xlabel=\"Resonator Frequency (MHz)\", ylabel=\"Amp. (adc level)\")\n",
    "plt.plot(fpts+cavity_LO/1e6, amps,'-o')\n",
    "\n",
    "max_freq=fpts[np.argmin(amps)]+cavity_LO/1e6\n",
    "plt.title(\"Cavity freq = \" + str(max_freq))\n",
    "plt.tight_layout()\n",
    "\n",
    "x_pts = fpts+cavity_LO/1e6; y_pts = amps\n",
    "\n",
    "# dataFolder = '/home/xilinx/jupyter_notebooks/KerrCat/qick_demos/TAFC1_B3_halfFlux_thruCL/'\n",
    "# x_pts.tofile(dataFolder + 'cavTrans_x_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "# y_pts.tofile(dataFolder + 'cavTrans_y_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "# plt.savefig(dataFolder + \"cavTrans_\"   + dt_string + '.pdf', dpi=350)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PULSED SPEC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PulseProbeSpectroscopyProgram(RAveragerProgram):\n",
    "    def initialize(self):\n",
    "        cfg=self.cfg\n",
    "        \n",
    "        self.q_rp=self.ch_page(self.cfg[\"qubit_ch\"])     # get register page for qubit_ch\n",
    "        self.r_freq=self.sreg(cfg[\"qubit_ch\"], \"freq\")   # get frequency register for qubit_ch    \n",
    "        \n",
    "        f_res=soc.freq2reg(soc.adcfreq(cfg[\"f_res\"]))            # conver f_res to dac register value\n",
    "\n",
    "        self.cfg[\"adc_lengths\"]=[self.cfg[\"readout_length\"]]*2   #copy over adc acquisition parameters\n",
    "        self.cfg[\"adc_freqs\"]=[soc.adcfreq(cfg[\"f_res\"])]*2\n",
    "        \n",
    "        # add qubit and readout pulses to respective channels\n",
    "        self.add_pulse(ch=self.cfg[\"qubit_ch\"], name=\"qubit\",style=\"const\", length=self.cfg[\"probe_length\"])\n",
    "        self.add_pulse(ch=self.cfg[\"res_ch\"], name=\"measure\",style=\"const\", length=self.cfg[\"readout_length\"])\n",
    "        self.f_start =soc.freq2reg(cfg[\"start\"])  # get start/step frequencies\n",
    "        self.f_step =soc.freq2reg(cfg[\"step\"])\n",
    "        \n",
    "        # pre-initialize pulses\n",
    "        #self.pulse(ch=cfg[\"qubit_ch\"], name=\"qubit\", phase =0, freq=self.f_start, gain=cfg[\"qubit_gain\"], play=False)\n",
    "        self.pulse(ch=cfg[\"qubit_ch\"], name=\"qubit\", freq=self.f_start, gain=cfg[\"qubit_gain\"], t=0, phase=0, play=False) # pre-configure readout pulse\n",
    "        self.pulse(ch=cfg[\"res_ch\"], name=\"measure\", freq=f_res, phase=cfg['res_phase'], gain=cfg[\"res_gain\"], play=False)\n",
    "    \n",
    "        self.sync_all(soc.us2cycles(1))\n",
    "    \n",
    "    def body(self):\n",
    "#         self.pulse(ch=self.cfg[\"qubit_ch\"], play=True)  #play probe pulse\n",
    "        self.pulse(ch=self.cfg[\"qubit_ch\"], play=True)#, mode=1, outsel=1)  #play probe pulse CW\n",
    "        self.waiti(self.cfg[\"res_ch\"], self.cfg[\"cavity_wait\"])\n",
    "        #self.sync_all()# soc.us2cycles(0.05)) # align channels and wait 50ns\n",
    "        self.trigger_adc(adc1=1, adc2=1, adc_trig_offset=self.cfg[\"adc_trig_offset\"])  #trigger measurement\n",
    "        self.pulse(ch=self.cfg[\"res_ch\"], play=True) # play measurement pulse\n",
    "        self.sync_all(soc.us2cycles(self.cfg[\"qubit_relax_delay\"]))  # wait for qubit to relax\n",
    "    \n",
    "    def update(self):\n",
    "        self.mathi(self.q_rp, self.r_freq, self.r_freq, '+', self.f_step) # update frequency list index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "config['readout_length'] = 3268"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "config['readout_length'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "qubit_cfg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "readout_cfg['readout_length'] = 3268"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "probe_length = 40\n",
    "cavity_wait = 35\n",
    "expt_cfg={\"reps\": 20,\"rounds\":200,\"probe_length\":soc.us2cycles(probe_length)}\n",
    "expt_cfg[\"center\"]= qubit_cfg[\"f_ge\"]; expt_cfg[\"span\"] = 10; expt_cfg[\"expts\"]= 101;\n",
    "expt_cfg[\"center\"] = 4601\n",
    "expt_cfg[\"step\"] = 2*expt_cfg[\"span\"] / (expt_cfg[\"expts\"]-1)\n",
    "expt_cfg[\"start\"] = expt_cfg[\"center\"]-expt_cfg[\"span\"]\n",
    "fpts=expt_cfg[\"start\"] + expt_cfg[\"step\"]*np.arange(expt_cfg[\"expts\"])\n",
    "qubit_cfg[\"qubit_start\"] = fpts[:1][0]\n",
    "qubit_cfg[\"qubit_stop\"] = fpts[-1:][0] \n",
    "\n",
    "print(qubit_cfg[\"qubit_start\"], qubit_cfg[\"qubit_stop\"])\n",
    "\n",
    "config={**hw_cfg,**readout_cfg,**qubit_cfg,**expt_cfg,**atten_cfg} #combine configs\n",
    "config[\"cavity_wait\"] = soc.us2cycles(cavity_wait)\n",
    "\n",
    "\n",
    "config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "now = datetime.now(); dt_string = now.strftime(\"%Y_%m_%d_%H_%M_%S\")\n",
    "qspec=PulseProbeSpectroscopyProgram(cfg=config)\n",
    "expt_pts, avgi, avgq = qspec.acquire(soc, load_pulses=True,progress=True, debug=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#40 dB attenuation on middle peak closeup \n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "plt.subplot(111,title=\"Qubit Spectroscopy\", xlabel=\"Qubit Frequency (MHz)\", ylabel=\"Amp. (adc level)\")\n",
    "amp=np.abs(avgi[0][0]+1j*avgq[0][0])\n",
    "phase = np.arctan2(avgq[0][0],avgi[0][0])\n",
    "max_freq=expt_pts[np.argmax(amp)]\n",
    "plt.title(\"Qubit freq = \" + str(max_freq) + \", qubit atten = 50 dB, cavity atten = 35 dB\")\n",
    "plt.plot(expt_pts[:-1], amp[:-1], '-o')\n",
    "plt.show()\n",
    "\n",
    "x_pts = expt_pts; y_pts = amp;\n",
    "\n",
    "dataFolder = '/home/xilinx/jupyter_notebooks/ZCU216_Shared/qick_demos/3QDIAMOND/'\n",
    "x_pts.tofile(dataFolder + 'qubitSpec_x_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "y_pts.tofile(dataFolder + 'qubitSpec_y_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "plt.savefig(dataFolder + \"qubitSpec_\"   + dt_string + '.pdf', dpi=350)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#10 dB attenuation on qubit\n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "plt.subplot(111,title=\"Qubit Spectroscopy\", xlabel=\"Qubit Frequency (MHz)\", ylabel=\"Amp. (adc level)\")\n",
    "amp=np.abs(avgi[0][0]+1j*avgq[0][0])\n",
    "phase = np.arctan2(avgq[0][0],avgi[0][0])\n",
    "max_freq=expt_pts[np.argmax(amp)]\n",
    "plt.title(\"Qubit freq = \" + str(max_freq) + \", qubit atten = 50 dB, cavity atten = 35 dB\")\n",
    "plt.plot(expt_pts[:-1], amp[:-1], '-o')\n",
    "plt.show()\n",
    "\n",
    "x_pts = expt_pts; y_pts = amp;\n",
    "\n",
    "dataFolder = '/home/xilinx/jupyter_notebooks/ZCU216_Shared/qick_demos/3QDIAMOND/'\n",
    "x_pts.tofile(dataFolder + 'qubitSpec_x_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "y_pts.tofile(dataFolder + 'qubitSpec_y_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "plt.savefig(dataFolder + \"qubitSpec_\"   + dt_string + '.pdf', dpi=350)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#20 dB attenuation on qubit\n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "plt.subplot(111,title=\"Qubit Spectroscopy\", xlabel=\"Qubit Frequency (MHz)\", ylabel=\"Amp. (adc level)\")\n",
    "amp=np.abs(avgi[0][0]+1j*avgq[0][0])\n",
    "phase = np.arctan2(avgq[0][0],avgi[0][0])\n",
    "max_freq=expt_pts[np.argmax(amp)]\n",
    "plt.title(\"Qubit freq = \" + str(max_freq) + \", qubit atten = 50 dB, cavity atten = 35 dB\")\n",
    "plt.plot(expt_pts[:-1], amp[:-1], '-o')\n",
    "plt.show()\n",
    "\n",
    "x_pts = expt_pts; y_pts = amp;\n",
    "\n",
    "dataFolder = '/home/xilinx/jupyter_notebooks/ZCU216_Shared/qick_demos/3QDIAMOND/'\n",
    "x_pts.tofile(dataFolder + 'qubitSpec_x_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "y_pts.tofile(dataFolder + 'qubitSpec_y_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "plt.savefig(dataFolder + \"qubitSpec_\"   + dt_string + '.pdf', dpi=350)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#30 dB attenuation on qubit\n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "plt.subplot(111,title=\"Qubit Spectroscopy\", xlabel=\"Qubit Frequency (MHz)\", ylabel=\"Amp. (adc level)\")\n",
    "amp=np.abs(avgi[0][0]+1j*avgq[0][0])\n",
    "phase = np.arctan2(avgq[0][0],avgi[0][0])\n",
    "max_freq=expt_pts[np.argmax(amp)]\n",
    "plt.title(\"Qubit freq = \" + str(max_freq) + \", qubit atten = 50 dB, cavity atten = 35 dB\")\n",
    "plt.plot(expt_pts[:-1], amp[:-1], '-o')\n",
    "plt.show()\n",
    "\n",
    "x_pts = expt_pts; y_pts = amp;\n",
    "\n",
    "dataFolder = '/home/xilinx/jupyter_notebooks/ZCU216_Shared/qick_demos/3QDIAMOND/'\n",
    "x_pts.tofile(dataFolder + 'qubitSpec_x_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "y_pts.tofile(dataFolder + 'qubitSpec_y_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "plt.savefig(dataFolder + \"qubitSpec_\"   + dt_string + '.pdf', dpi=350)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#30 dB attenuation on qubit\n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "plt.subplot(111,title=\"Qubit Spectroscopy\", xlabel=\"Qubit Frequency (MHz)\", ylabel=\"Amp. (adc level)\")\n",
    "amp=np.abs(avgi[0][0]+1j*avgq[0][0])\n",
    "phase = np.arctan2(avgq[0][0],avgi[0][0])\n",
    "max_freq=expt_pts[np.argmax(amp)]\n",
    "plt.title(\"Qubit freq = \" + str(max_freq) + \", qubit atten = 50 dB, cavity atten = 35 dB\")\n",
    "plt.plot(expt_pts[:-1], amp[:-1], '-o')\n",
    "plt.show()\n",
    "\n",
    "x_pts = expt_pts; y_pts = amp;\n",
    "\n",
    "dataFolder = '/home/xilinx/jupyter_notebooks/ZCU216_Shared/qick_demos/3QDIAMOND/'\n",
    "x_pts.tofile(dataFolder + 'qubitSpec_x_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "y_pts.tofile(dataFolder + 'qubitSpec_y_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "plt.savefig(dataFolder + \"qubitSpec_\"   + dt_string + '.pdf', dpi=350)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#40 dB attenuation on qubit\n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "plt.subplot(111,title=\"Qubit Spectroscopy\", xlabel=\"Qubit Frequency (MHz)\", ylabel=\"Amp. (adc level)\")\n",
    "amp=np.abs(avgi[0][0]+1j*avgq[0][0])\n",
    "phase = np.arctan2(avgq[0][0],avgi[0][0])\n",
    "max_freq=expt_pts[np.argmax(amp)]\n",
    "plt.title(\"Qubit freq = \" + str(max_freq) + \", qubit atten = 50 dB, cavity atten = 35 dB\")\n",
    "plt.plot(expt_pts[:-1], amp[:-1], '-o')\n",
    "plt.show()\n",
    "\n",
    "x_pts = expt_pts; y_pts = amp;\n",
    "\n",
    "dataFolder = '/home/xilinx/jupyter_notebooks/ZCU216_Shared/qick_demos/3QDIAMOND/'\n",
    "x_pts.tofile(dataFolder + 'qubitSpec_x_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "y_pts.tofile(dataFolder + 'qubitSpec_y_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "plt.savefig(dataFolder + \"qubitSpec_\"   + dt_string + '.pdf', dpi=350)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#50 dB attenuation on qubit\n",
    "plt.figure(figsize=(8,4))\n",
    "plt.subplot(111,title=\"Qubit Spectroscopy\", xlabel=\"Qubit Frequency (MHz)\", ylabel=\"Amp. (adc level)\")\n",
    "amp=np.abs(avgi[0][0]+1j*avgq[0][0])\n",
    "phase = np.arctan2(avgq[0][0],avgi[0][0])\n",
    "max_freq=expt_pts[np.argmax(amp)]\n",
    "plt.title(\"Qubit freq = \" + str(max_freq) + \", qubit atten = 50 dB, cavity atten = 35 dB\")\n",
    "plt.plot(expt_pts[:-1], amp[:-1], '-o')\n",
    "plt.show()\n",
    "\n",
    "x_pts = expt_pts; y_pts = amp;\n",
    "\n",
    "dataFolder = '/home/xilinx/jupyter_notebooks/ZCU216_Shared/qick_demos/3QDIAMOND/'\n",
    "x_pts.tofile(dataFolder + 'qubitSpec_x_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "y_pts.tofile(dataFolder + 'qubitSpec_y_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "plt.savefig(dataFolder + \"qubitSpec_\"   + dt_string + '.pdf', dpi=350)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "plt.subplot(111,title=\"Qubit Spectroscopy\", xlabel=\"Qubit Frequency (MHz)\", ylabel=\"Amp. (adc level)\")\n",
    "amp=np.abs(avgi[0][0]+1j*avgq[0][0])\n",
    "phase = np.arctan2(avgq[0][0],avgi[0][0])\n",
    "max_freq=expt_pts[np.argmax(amp)]\n",
    "plt.title(\"Qubit freq = \" + str(max_freq) + \", qubit atten = 50 dB, cavity atten = 35 dB\")\n",
    "plt.plot(expt_pts[:-1], amp[:-1], '-o')\n",
    "plt.show()\n",
    "\n",
    "x_pts = expt_pts; y_pts = amp;\n",
    "\n",
    "dataFolder = '/home/xilinx/jupyter_notebooks/ZCU216_Shared/qick_demos/3QDIAMOND/'\n",
    "x_pts.tofile(dataFolder + 'qubitSpec_x_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "y_pts.tofile(dataFolder + 'qubitSpec_y_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "plt.savefig(dataFolder + \"qubitSpec_\"   + dt_string + '.pdf', dpi=350)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "plt.subplot(111,title=\"Qubit Spectroscopy\", xlabel=\"Qubit Frequency (MHz)\", ylabel=\"Amp. (adc level)\")\n",
    "amp=np.abs(avgi[0][0]+1j*avgq[0][0])\n",
    "phase = np.arctan2(avgq[0][0],avgi[0][0])\n",
    "max_freq=expt_pts[np.argmax(amp)]\n",
    "plt.title(\"Qubit freq = \" + str(max_freq) + \", qubit atten = 50 dB, cavity atten = 35 dB\")\n",
    "plt.plot(expt_pts[:-1], amp[:-1], '-o')\n",
    "plt.show()\n",
    "\n",
    "x_pts = expt_pts; y_pts = amp;\n",
    "\n",
    "dataFolder = '/home/xilinx/jupyter_notebooks/ZCU216_Shared/qick_demos/3QDIAMOND/'\n",
    "x_pts.tofile(dataFolder + 'qubitSpec_x_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "y_pts.tofile(dataFolder + 'qubitSpec_y_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "plt.savefig(dataFolder + \"qubitSpec_\"   + dt_string + '.pdf', dpi=350)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "plt.subplot(111,title=\"Qubit Spectroscopy\", xlabel=\"Qubit Frequency (MHz)\", ylabel=\"Amp. (adc level)\")\n",
    "amp=np.abs(avgi[0][0]+1j*avgq[0][0])\n",
    "phase = np.arctan2(avgq[0][0],avgi[0][0])\n",
    "max_freq=expt_pts[np.argmax(amp)]\n",
    "plt.title(\"Qubit freq = \" + str(max_freq) + \", qubit atten = 50 dB, cavity atten = 35 dB\")\n",
    "plt.plot(expt_pts[:-1], amp[:-1], '-o')\n",
    "plt.show()\n",
    "\n",
    "x_pts = expt_pts; y_pts = amp;\n",
    "\n",
    "dataFolder = '/home/xilinx/jupyter_notebooks/ZCU216_Shared/qick_demos/3QDIAMOND/'\n",
    "x_pts.tofile(dataFolder + 'qubitSpec_x_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "y_pts.tofile(dataFolder + 'qubitSpec_y_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "plt.savefig(dataFolder + \"qubitSpec_\"   + dt_string + '.pdf', dpi=350)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plt.figure()\n",
    "fig, axs = plt.subplots(2,figsize=(8,4))\n",
    "fig.suptitle(\"Qubit freq = \" + str(max_freq))\n",
    "axs[0].plot(expt_pts, avgi[0][0], label=\"I\")\n",
    "axs[0].plot(expt_pts, avgq[0][0], label=\"Q\")\n",
    "axs[0].plot(expt_pts, amp, label=\"amp\")\n",
    "axs[1].plot(expt_pts, phase, label=\"phase\")\n",
    "axs[0].legend()\n",
    "axs[1].legend()\n",
    "axs[0].set_xlabel(\"Frequency (MHz)\")\n",
    "axs[0].set_ylabel(\"Amp. (adc level)\")\n",
    "axs[1].set_xlabel(\"Frequency (MHz)\")\n",
    "axs[1].set_ylabel(\"Phase. (adc level)\")\n",
    "plt.show()\n",
    "plt.savefig(dataFolder + \"qubitSpec_\"   + dt_string + '_withphase.pdf', dpi=350)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "qubit_RF = max_freq\n",
    "#qubit_RF = 3726.18\n",
    "print(qubit_RF)\n",
    "qubit_cfg={\n",
    "    \"qubit_relax_delay\":100,\n",
    "    \"f_ge\": qubit_RF, # [MHz]\n",
    "    \"qubit_gain\":32000,\n",
    "    \"sigma\":soc.us2cycles(0.4),\n",
    "    \"pi_gain\":np.int64(pi_gain),\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AMPLITUDE RABI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class AmplitudeRabiProgram(RAveragerProgram):\n",
    "\n",
    "    def initialize(self):\n",
    "        cfg=self.cfg\n",
    "        \n",
    "        self.q_rp=self.ch_page(self.cfg[\"qubit_ch\"])     # get register page for qubit_ch\n",
    "        self.r_gain=self.sreg(cfg[\"qubit_ch\"], \"gain\")   # get gain register for qubit_ch    \n",
    "        \n",
    "        f_res=soc.freq2reg(soc.adcfreq(cfg[\"f_res\"]))            # conver f_res to dac register value\n",
    "        f_ge=soc.freq2reg(cfg[\"f_ge\"])\n",
    "        \n",
    "        self.cfg[\"adc_lengths\"]=[self.cfg[\"readout_length\"]]*2   #copy over adc acquisition parameters\n",
    "        self.cfg[\"adc_freqs\"]=[soc.adcfreq(cfg[\"f_res\"])]*2\n",
    "        \n",
    "        # add qubit and readout pulses to respective channels\n",
    "#         self.add_pulse(ch=self.cfg[\"qubit_ch\"], name=\"qubit\", style=\"arb\", \n",
    "#                        idata=gauss(mu=cfg[\"sigma\"]*16*4/2,si=cfg[\"sigma\"]*16,length=4*cfg[\"sigma\"]*16,maxv=2**15-1), \n",
    "#                        qdata=0*gauss(mu=cfg[\"sigma\"]*16*4/2,si=cfg[\"sigma\"]*16,length=4*cfg[\"sigma\"]*16,maxv=2**15-1))\n",
    "        self.add_pulse(ch=self.cfg[\"res_ch\"], name=\"measure\", style=\"const\", length=self.cfg[\"readout_length\"])\n",
    "        self.add_pulse(ch=self.cfg[\"qubit_ch\"], name=\"qubit\",style=\"const\", length=4 * self.cfg['sigma'])\n",
    "\n",
    "        # pre-initialize pulses\n",
    "        self.pulse(ch=cfg[\"qubit_ch\"], name=\"qubit\", phase =0, freq=f_ge, gain=cfg[\"start\"], play=False)\n",
    "        self.pulse(ch=cfg[\"res_ch\"], name=\"measure\", freq=f_res, phase=cfg[\"res_phase\"], gain=cfg[\"res_gain\"], play=False)\n",
    "    \n",
    "        self.sync_all(soc.us2cycles(500))\n",
    "    \n",
    "    def body(self):\n",
    "        \n",
    "        self.pulse(ch=self.cfg[\"qubit_ch\"], play=True, phase=deg2reg(90))#, mode = 1, outsel = 1)  #play probe pulse\n",
    "#         self.sync_all(soc.us2cycles(0.05)) # align channels and wait 50ns\n",
    "        self.waiti(self.cfg[\"res_ch\"], self.cfg[\"cavity_wait\"])\n",
    "        self.trigger_adc(adc1=1, adc2=1, adc_trig_offset=self.cfg[\"adc_trig_offset\"])  #trigger measurement\n",
    "        self.pulse(ch=self.cfg[\"res_ch\"], name=\"measure\", play=True) # play measurement pulse\n",
    "\n",
    "        self.sync_all(soc.us2cycles(self.cfg[\"qubit_relax_delay\"]))  # wait for qubit to relax\n",
    "    \n",
    "    def update(self):\n",
    "        self.mathi(self.q_rp, self.r_gain, self.r_gain, '+', self.cfg[\"step\"]) # update gain of the Gaussian pi pulse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#setatten(atten=qubit_atten, serial=qubit_SN) # Qubit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "readout_cfg['readout_length'] = 3268"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "expt_cfg={\"start\":0, \"step\":300, \"expts\":100, \"reps\": 20,\"rounds\":200}\n",
    "config={**hw_cfg,**readout_cfg,**qubit_cfg,**expt_cfg,**atten_cfg} #combine configs\n",
    "config['sigma'] = soc.us2cycles(40)\n",
    "config['cavity_wait'] = config['sigma'] * 2\n",
    "config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "now = datetime.now(); dt_string = now.strftime(\"%Y_%m_%d_%H_%M_%S\")\n",
    "rabi=AmplitudeRabiProgram(cfg=config)\n",
    "x_pts, avgi, avgq  = rabi.acquire(soc,threshold=None, load_pulses=True,progress=True, debug=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=[10,4])\n",
    "y_pts = avgi[0][0][1]-avgi[0][0]\n",
    "amp=np.abs(avgi[0][0]+1j*avgq[0][0])\n",
    "ax.plot(x_pts[:-1],amp[:-1],'o-')\n",
    "# ax.plot(x_pts,y_pts,'o-')\n",
    "\n",
    "def sinRabi(x, a, T, phi, c):\n",
    "    return a*np.cos(2*np.pi/T*x + phi) + c\n",
    "\n",
    "guess = (12, 70e3, 0, 0)\n",
    "popt,pcov = curve_fit(sinRabi, x_pts, y_pts, p0=guess)\n",
    "#ax.plot(x_pts, sinRabi(x_pts, *popt))\n",
    "pi_gain = popt[1]/2\n",
    "print(\"pi_gain = %.0f\" % pi_gain)\n",
    "ax.set_title(f\"Amplitude Rabi, $\\sigma={soc.cycles2us(config['sigma'])*1000}$ ns, Pi gain = \" + str(pi_gain)); ax.set_xlabel(\"Gain\"); ax.set_ylabel(\"Amp. (adc level)\")\n",
    "\n",
    "\n",
    "dataFolder = '/home/xilinx/jupyter_notebooks/ZCU216_Shared/qick_demos/3QDIAMOND/'\n",
    "x_pts.tofile(dataFolder + 'ampRabi_x_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "y_pts.tofile(dataFolder + 'ampRabi_y_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "plt.savefig(dataFolder + \"ampRabi_\"   + dt_string + '.pdf', dpi=350)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pi_gain = 15000\n",
    "qubit_cfg={\n",
    "    \"qubit_relax_delay\":1000,\n",
    "    \"f_ge\": qubit_RF, # [MHz]\n",
    "    \"qubit_gain\":32000,\n",
    "    \"sigma\":soc.us2cycles(0.8),\n",
    "    \"pi_gain\":np.int64(pi_gain),\n",
    "    \"pi2_gain\":np.int64(pi_gain/2)\n",
    "    }\n",
    "print(qubit_cfg[\"pi_gain\"])\n",
    "print(qubit_cfg[\"pi2_gain\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modified single shot readout to optimize readout for pulsed spec\n",
    "pi_gain = 32000\n",
    "qubit_cfg={\n",
    "    \"qubit_relax_delay\":500,\n",
    "    \"f_ge\": qubit_RF, # [MHz]\n",
    "    \"qubit_gain\":32000,\n",
    "    \"sigma\":soc.us2cycles(10),\n",
    "    \"pi_gain\":np.int64(pi_gain),\n",
    "    \"pi2_gain\":np.int64(pi_gain/2)\n",
    "    }\n",
    "print(qubit_cfg[\"pi_gain\"])\n",
    "print(qubit_cfg[\"pi2_gain\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SINGLE SHOT READOUT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold = 0\n",
    "class SingleShotProgram(RAveragerProgram):\n",
    "    def __init__(self,cfg):\n",
    "        AveragerProgram.__init__(self,cfg)       \n",
    "        \n",
    "    def initialize(self):\n",
    "        cfg=self.cfg\n",
    "        \n",
    "        cfg[\"start\"]=0\n",
    "#         cfg[\"step\"]=cfg[\"pi_gain\"]\n",
    "        cfg[\"step\"]=cfg[\"qubit_gain\"]\n",
    "        cfg[\"reps\"]=cfg[\"shots\"]\n",
    "        cfg[\"expts\"]=2\n",
    "        \n",
    "        self.q_rp=self.ch_page(self.cfg[\"qubit_ch\"])     # get register page for qubit_ch\n",
    "        self.r_gain=self.sreg(cfg[\"qubit_ch\"], \"gain\")   # get frequency register for qubit_ch    \n",
    "        \n",
    "        f_res=soc.freq2reg(soc.adcfreq(cfg[\"f_res\"]))            # conver f_res to dac register value\n",
    "        f_ge=soc.freq2reg(cfg[\"f_ge\"])\n",
    "        \n",
    "        self.cfg[\"adc_lengths\"]=[self.cfg[\"readout_length\"]]*2   #copy over adc acquisition parameters\n",
    "        self.cfg[\"adc_freqs\"]=[soc.adcfreq(cfg[\"f_res\"])]*2\n",
    "        \n",
    "        # add qubit and readout pulses to respective channels\n",
    "        #self.add_pulse(ch=self.cfg[\"qubit_ch\"], name=\"qubit\",style=\"arb\", \n",
    "        #               idata=gauss(mu=cfg[\"sigma\"]*16*4/2,si=cfg[\"sigma\"]*16,length=4*cfg[\"sigma\"]*16,maxv=2**15-1),\n",
    "        #               qdata=0*gauss(mu=cfg[\"sigma\"]*16*4/2,si=cfg[\"sigma\"]*16,length=4*cfg[\"sigma\"]*16,maxv=2**15-1))\n",
    "        self.add_pulse(ch=self.cfg[\"res_ch\"], name=\"measure\",style=\"const\", length=self.cfg[\"readout_length\"])\n",
    "        self.add_pulse(ch=self.cfg[\"qubit_ch\"], name=\"qubit\",style=\"const\", length=self.cfg[\"probe_length\"])\n",
    "\n",
    "        # pre-initialize pulses\n",
    "        self.pulse(ch=cfg[\"qubit_ch\"], name=\"qubit\", phase =0, freq=f_ge, gain=cfg[\"start\"], play=False)\n",
    "        self.pulse(ch=cfg[\"res_ch\"], name=\"measure\", freq=f_res, phase=cfg[\"res_phase\"], gain=cfg[\"res_gain\"], play=False)\n",
    "\n",
    "        self.sync_all(soc.us2cycles(500))\n",
    "    \n",
    "    def body(self):\n",
    "        \n",
    "        self.pulse(ch=self.cfg[\"qubit_ch\"], play=True)  #play probe pulse\n",
    "        self.sync_all(soc.us2cycles(0.05)) # align channels and wait 50ns\n",
    "        self.trigger_adc(adc1=1, adc2=1, adc_trig_offset=self.cfg[\"adc_trig_offset\"])  #trigger measurement\n",
    "        self.pulse(ch=self.cfg[\"res_ch\"], name=\"measure\", play=True) # play measurement pulse\n",
    "\n",
    "        self.sync_all(soc.us2cycles(self.cfg[\"qubit_relax_delay\"]))  # wait for qubit to relax\n",
    "    \n",
    "    def update(self):\n",
    "        self.mathi(self.q_rp, self.r_gain, self.r_gain, '+', self.cfg[\"step\"]) # update frequency list index\n",
    "        \n",
    "    def acquire(self,soc, load_pulses=True, progress=False, debug=False):\n",
    "        super().acquire(soc, load_pulses=load_pulses, progress=progress, debug=debug)\n",
    "        return self.collect_shots()\n",
    "        \n",
    "    def collect_shots(self):\n",
    "        shots_i0=self.di_buf[0].reshape((self.cfg[\"expts\"],self.cfg[\"reps\"]))/self.cfg['readout_length']\n",
    "        shots_q0=self.dq_buf[0].reshape((self.cfg[\"expts\"],self.cfg[\"reps\"]))/self.cfg['readout_length']\n",
    "        shots_i1=self.di_buf[1].reshape((self.cfg[\"expts\"],self.cfg[\"reps\"]))/self.cfg['readout_length']\n",
    "        shots_q1=self.dq_buf[1].reshape((self.cfg[\"expts\"],self.cfg[\"reps\"]))/self.cfg['readout_length']\n",
    "        return shots_i0,shots_q0,shots_i1,shots_q1\n",
    "        \n",
    "    def analyze(self, shots_i, shots_q):\n",
    "        plt.subplot(111, xlabel='I', ylabel='Q', title='Single Shot Histogram')\n",
    "        plt.plot(shots_i[0],shots_q[0],'.',label='g')\n",
    "        plt.plot(shots_i[1],shots_q[1],'.',label='e')\n",
    "        plt.legend()\n",
    "        plt.gca().set_aspect('equal', 'datalim')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#setatten(atten=26, serial=cavity_SN, printv=False) # Cavity\n",
    "#readout_cfg[\"f_res\"] = 999\n",
    "#readout_cfg[\"readout_length\"] = soc.us2cycles(4.6)\n",
    "expt_cfg={\n",
    "        \"shots\":5000, \"res_phase\":0, \"probe_length\":soc.us2cycles(40)\n",
    "       }\n",
    "config={**hw_cfg,**readout_cfg,**qubit_cfg,**expt_cfg,**atten_cfg} #combine configs\n",
    "config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "now = datetime.now(); dt_string = now.strftime(\"%Y_%m_%d_%H_%M_%S\")\n",
    "ssp=SingleShotProgram(cfg=config)\n",
    "di0, dq0, di1, dq1 = ssp.acquire(soc, load_pulses=True,progress=True, debug=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "fid, threshold, angle, ig_new, ie_new, x_pts = hist(data=[di0[0], dq0[0], di0[1], dq0[1]],  plot=True, ran=1.5)\n",
    "readout_cfg[\"res_phase\"]=deg2reg(-angle*180/np.pi)\n",
    "readout_cfg[\"threshold\"]=np.int64(threshold)\n",
    "print(threshold)\n",
    "print(np.int64(threshold))\n",
    "plt.tight_layout()\n",
    "\n",
    "x_pts = np.array(x_pts); ig_new = np.array(ig_new); ie_new = np.array(ie_new)\n",
    "x_pts.tofile(dataFolder + 'blobRabi_xData_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "ig_new.tofile(dataFolder + 'blobRabi_y_g_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "ie_new.tofile(dataFolder + 'blobRabi_y_e_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "plt.savefig(dataFolder + \"blobRabi_\"   + dt_string + '.pdf', dpi=350)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loop over cavity frequency and readout power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "attens = np.linspace(20,30,11)\n",
    "print(attens)\n",
    "center_freq = readout_cfg[\"f_res\"]\n",
    "freqs = np.linspace(center_freq - 5, center_freq + 5, 11)\n",
    "print(freqs)\n",
    "fid_array = np.zeros((len(attens),len(freqs)))\n",
    "thres_array = np.zeros((len(attens),len(freqs)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "now = datetime.now(); dt_string = now.strftime(\"%Y_%m_%d_%H_%M_%S\")\n",
    "for iii, atten in enumerate(attens):\n",
    "    print(\"Cavity atten = %.0f\" % atten)\n",
    "\n",
    "    cavity_atten = atten\n",
    "    setatten(atten=cavity_atten, serial=cavity_SN, printv=False) # Cavity\n",
    "    \n",
    "    for jjj, freq in enumerate(freqs):\n",
    "        print(\"Cavity freq = %.2f MHz\" % (freq))\n",
    "        readout_cfg[\"f_res\"] = freq\n",
    "        expt_cfg={\n",
    "                \"shots\":5000, \"res_phase\":0\n",
    "               }\n",
    "        config={**hw_cfg,**readout_cfg,**qubit_cfg,**expt_cfg,**atten_cfg} #combine configs\n",
    "        ssp=SingleShotProgram(cfg=config)\n",
    "        di0, dq0, di1, dq1 = ssp.acquire(soc, load_pulses=True,progress=False, debug=False)\n",
    "        \n",
    "        fid, threshold, angle, ig_new, ie_new, x_pts = hist(data=[di0[0], dq0[0], di0[1], dq0[1]],  plot=True, ran=50)\n",
    "        plt.close()\n",
    "        fid_array[iii,jjj] = fid\n",
    "        thres_array[iii,jjj] = np.int64(threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(6,4))\n",
    "\n",
    "ax.plot(freqs, fid_array[1])\n",
    "ax.set_xlabel(r\"Cavity frequency (MHz)\")\n",
    "ax.set_ylabel(r\"Fidelity\")\n",
    "ax.set_title(\"Cavity attenuation = %.0f\" % attens[1])\n",
    "ax.xaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
    "print(fid_array[1])\n",
    "print(freqs)\n",
    "x_pts = freqs; y_pts = fid_array[1]\n",
    "\n",
    "# x_pts.tofile(dataFolder + 'optReadout_x_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "# y_pts.tofile(dataFolder + 'optReadout_y_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "# plt.savefig(dataFolder + \"optReadout_\"   + dt_string + '.pdf', dpi=350)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(6,4))\n",
    "dx=freqs[1]-freqs[0]; dy=attens[1]-attens[0]\n",
    "xedge = np.arange(freqs[0]-0.5*dx, freqs[-1]+dx, dx)\n",
    "yedge = np.arange(attens[0]-0.5*dy, attens[-1]+dy, dy)\n",
    "p = ax.pcolormesh(xedge,yedge, fid_array, vmin=fid_array.min(),vmax=fid_array.max())\n",
    "ax.set_xlim([freqs.min(),freqs.max()])\n",
    "ax.set_ylim([attens.min(),attens.max()])\n",
    "ax.set_xlabel(r\"Cavity frequency (MHz)\")\n",
    "ax.set_ylabel(r\"Cavity attenuation (dB)\")\n",
    "ax.xaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
    "cb = fig.colorbar(p, ax=ax)\n",
    "# plt.savefig(\"optReadOut_freq_atten_11_afterAddSqz.pdf\", dpi=350)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# T1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class T1Program(RAveragerProgram):\n",
    "    def __init__(self,cfg):\n",
    "        AveragerProgram.__init__(self,cfg)       \n",
    "        \n",
    "    def initialize(self):\n",
    "        cfg=self.cfg\n",
    "        \n",
    "        self.q_rp=self.ch_page(self.cfg[\"qubit_ch\"])     # get register page for qubit_ch\n",
    "        self.r_wait = 3\n",
    "        self.regwi(self.q_rp, self.r_wait, cfg[\"start\"])\n",
    "               \n",
    "        self.cfg[\"adc_lengths\"]=[self.cfg[\"readout_length\"]]*2   #copy over adc acquisition parameters\n",
    "        self.cfg[\"adc_freqs\"]=[soc.adcfreq(cfg[\"f_res\"])]*2\n",
    "        \n",
    "        # add qubit and readout pulses to respective channels\n",
    "        self.add_pulse(ch=self.cfg[\"qubit_ch\"], name=\"qubit\",style=\"arb\", idata=gauss(mu=cfg[\"sigma\"]*16*4/2,si=cfg[\"sigma\"]*16,length=4*cfg[\"sigma\"]*16,maxv=2**15-1))\n",
    "        self.add_pulse(ch=self.cfg[\"res_ch\"], name=\"measure\",style=\"const\", length=self.cfg[\"readout_length\"])\n",
    "               \n",
    "        # pre-initialize pulses\n",
    "        self.pulse(ch=cfg[\"qubit_ch\"], name=\"qubit\", phase =0, freq=soc.freq2reg(cfg[\"f_ge\"]), gain=cfg[\"pi_gain\"], play=False)\n",
    "        self.pulse(ch=cfg[\"res_ch\"], name=\"measure\", freq=soc.freq2reg(soc.adcfreq(cfg[\"f_res\"])), phase=cfg['res_phase'], gain=cfg[\"res_gain\"], play=False)\n",
    "    \n",
    "        self.sync_all(soc.us2cycles(500))\n",
    "    \n",
    "    def body(self):\n",
    "        \n",
    "        self.pulse(ch=self.cfg[\"qubit_ch\"], play=True)  #play probe pulse\n",
    "        self.sync_all()\n",
    "        self.sync(self.q_rp,self.r_wait)\n",
    "        self.trigger_adc(adc1=1, adc2=1, adc_trig_offset=self.cfg[\"adc_trig_offset\"])  #trigger measurement\n",
    "        self.pulse(ch=self.cfg[\"res_ch\"], name=\"measure\", play=True) # play measurement pulse\n",
    "        self.sync_all(soc.us2cycles(self.cfg[\"qubit_relax_delay\"]))  # wait for qubit to relax\n",
    "    \n",
    "    def update(self):\n",
    "        self.mathi(self.q_rp, self.r_wait, self.r_wait, '+', soc.us2cycles(self.cfg[\"step\"])) # update frequency list index\n",
    "countIdx=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "expt_cfg={ \"start\":0, \"step\":25, \"expts\":int(350/25), \"reps\": 4000,}\n",
    "config={**hw_cfg,**readout_cfg,**qubit_cfg,**expt_cfg,**atten_cfg} #combine configs\n",
    "config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "now = datetime.now(); dt_string = now.strftime(\"%Y_%m_%d_%H_%M_%S\")\n",
    "t1p=T1Program(cfg=config)\n",
    "x_pts, avgi, avgq = t1p.acquire(soc, threshold=None, load_pulses=True, progress=True, debug=False)\n",
    "countIdx = countIdx+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#% matplotlib inline\n",
    "y_pts = avgi[0][0]-np.mean(avgi[0][0][expt_cfg[\"expts\"]-2:expt_cfg[\"expts\"]-1])\n",
    "multiplier = 1/y_pts[0]\n",
    "y_pts = y_pts*multiplier\n",
    "\n",
    "fig = plt.subplots(figsize=(4,3))\n",
    "ax = plt.subplot(111, title=\"T1 Experiment\", xlabel=f\"Time ($\\mu s$)\", ylabel=\"Qubit Population\")\n",
    "ax.plot(x_pts, y_pts,'o-');\n",
    "\n",
    "def T1expo(x, a, T1, c):\n",
    "    return a*np.exp(-x/T1) + c\n",
    "\n",
    "guess = (1, 500, 0)\n",
    "popt,pcov = curve_fit(T1expo, x_pts, y_pts, p0=guess)\n",
    "perr = np.sqrt(np.diag(pcov))\n",
    "ax.plot(x_pts, T1expo(x_pts, *popt))\n",
    "plt.tight_layout()\n",
    "T_1 = popt[1]; T_1_sigma = perr[1]\n",
    "ax.set_title(\"$T_1$ = %.2f $\\mu$s $\\pm$ %.2f $\\mu$s, wait=%d us\" % (T_1, T_1_sigma, config[\"qubit_relax_delay\"]))\n",
    "\n",
    "dataFolder = '/home/xilinx/jupyter_notebooks/ZCU216_Shared/qick_demos/3QDIAMOND/'\n",
    "x_pts.tofile(dataFolder + 'T1_x_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "y_pts.tofile(dataFolder + 'T1_y_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "plt.savefig(dataFolder + \"T1_\"   + dt_string + '.pdf', dpi=350)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# T2 Ramsey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RamseyProgram(RAveragerProgram):\n",
    "    def __init__(self,cfg):\n",
    "        AveragerProgram.__init__(self,cfg)       \n",
    "        \n",
    "    def initialize(self):\n",
    "        cfg=self.cfg\n",
    "        \n",
    "        self.q_rp=self.ch_page(self.cfg[\"qubit_ch\"])     # get register page for qubit_ch\n",
    "        self.r_wait = 3\n",
    "        self.r_phase2 = 4\n",
    "        self.r_phase=self.sreg(cfg[\"qubit_ch\"], \"phase\")\n",
    "        self.regwi(self.q_rp, self.r_wait, cfg[\"start\"])\n",
    "        self.regwi(self.q_rp, self.r_phase2, 0)\n",
    "        \n",
    "        f_res=soc.freq2reg(soc.adcfreq(cfg[\"f_res\"]))            # conver f_res to dac register value\n",
    "        f_ge=soc.freq2reg(cfg[\"f_ge\"])\n",
    "        \n",
    "        self.cfg[\"adc_lengths\"]=[self.cfg[\"readout_length\"]]*2   #copy over adc acquisition parameters\n",
    "        self.cfg[\"adc_freqs\"]=[soc.adcfreq(cfg[\"f_res\"])]*2\n",
    "        \n",
    "        # add qubit and readout pulses to respective channels\n",
    "        self.add_pulse(ch=self.cfg[\"qubit_ch\"], name=\"qubit\", style=\"arb\", idata=gauss(mu=cfg[\"sigma\"]*16*4/2,si=cfg[\"sigma\"]*16, length=4*cfg[\"sigma\"]*16, maxv=2**15-1))\n",
    "        self.add_pulse(ch=self.cfg[\"res_ch\"], name=\"measure\", style=\"const\", length=self.cfg[\"readout_length\"])\n",
    "        self.add_pulse(ch=self.cfg[\"qubit_ch\"], name=\"qubit_pi\", style=\"arb\", idata=gauss(mu=cfg[\"sigma\"]*16*4/2,si=cfg[\"sigma\"]*16, length=4*cfg[\"sigma\"]*16, maxv=2**15-1))\n",
    "        # pre-initialize pulses\n",
    "        self.pulse(ch=cfg[\"qubit_ch\"], name=\"qubit\", phase=0, freq=f_ge, gain=cfg[\"pi2_gain\"], play=False)\n",
    "        self.pulse(ch=cfg[\"qubit_ch\"], name=\"qubit_pi\", phase=0, freq=f_ge, gain=cfg[\"pi_gain\"], play=False)\n",
    "        self.pulse(ch=cfg[\"res_ch\"], name=\"measure\", freq=f_res, phase=cfg['res_phase'], gain=cfg[\"res_gain\"], play=False)\n",
    "    \n",
    "        self.sync_all(soc.us2cycles(0.2))\n",
    "    \n",
    "    def body(self):\n",
    "        \n",
    "        self.pulse(ch=self.cfg[\"qubit_ch\"], name=\"qubit\", phase=0, play=True)  #play probe pulse\n",
    "        self.mathi(self.q_rp, self.r_phase, self.r_phase2,\"+\",0)\n",
    "        self.sync_all()\n",
    "        self.sync(self.q_rp,self.r_wait)        \n",
    "        self.pulse(ch=self.cfg[\"qubit_ch\"], name=\"qubit\", play=True)  #play probe pulse\n",
    "        self.sync_all(soc.us2cycles(0.05))\n",
    "        self.trigger_adc(adc1=1, adc2=1, adc_trig_offset=self.cfg[\"adc_trig_offset\"])  #trigger measurement\n",
    "        self.pulse(ch=self.cfg[\"res_ch\"], name=\"measure\", play=True) # play measurement pulse\n",
    "        self.sync_all(soc.us2cycles(self.cfg[\"qubit_relax_delay\"]))  # wait for qubit to relax\n",
    "    \n",
    "    def update(self):\n",
    "        self.mathi(self.q_rp, self.r_wait, self.r_wait, '+', self.cfg[\"step\"]) # update the time between two π/2 pulses\n",
    "        self.mathi(self.q_rp, self.r_phase2, self.r_phase2, '+', self.cfg[\"phase_step\"]) # advance the phase of the LO for the second π/2 pulse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "expt_cfg={\"start\":soc.us2cycles(0.0), \"step\":soc.us2cycles(1), \"phase_step\": deg2reg(360/8), \"expts\":64,\n",
    "          \"reps\": 10, \"rounds\": 200\n",
    "       }\n",
    "fringefreq = 0.125\n",
    "fitted_freq = 1.04\n",
    "freq_corr = fringefreq - fitted_freq\n",
    "print(\"Freq corr = \" + str(freq_corr) + \" MHz\")\n",
    "qubit_cfg[\"f_ge\"] = max_freq #+ freq_corr\n",
    "qubit_cfg[\"qubit_relax_delay\"] = 1000\n",
    "config={**hw_cfg,**readout_cfg,**qubit_cfg,**expt_cfg,**atten_cfg} #combine configs\n",
    "config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "now = datetime.now(); dt_string = now.strftime(\"%Y_%m_%d_%H_%M_%S\")\n",
    "t2p=RamseyProgram(cfg=config)\n",
    "x_pts, avgi, avgq= t2p.acquire(soc, threshold=None, load_pulses=True,progress=True, debug=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "x = soc.cycles2us(x_pts); y_pts = avgi[0][0][0:-1]\n",
    "x = x[0:-1]\n",
    "\n",
    "fig = plt.subplots(figsize=(8,4))\n",
    "ax = plt.subplot(111, title=\"Ramsey Fringe Experiment\", xlabel=\"Delay time ($\\mu$s)\", ylabel=\"Signal (a.u.)\")\n",
    "ax.plot(x,y_pts,'o-')\n",
    "\n",
    "def T2Ramsey(x, a, T2, T, phi, c):\n",
    "    return a*np.exp(-x/T2)*np.cos(2*np.pi*x/T + phi) + c\n",
    "\n",
    "guess = (1, 50, 10, 0, 0)\n",
    "popt,pcov = curve_fit(T2Ramsey, x, y_pts, p0=guess); perr = np.sqrt(np.diag(pcov))\n",
    "T_2 = popt[1]; T_2_sigma = perr[1]\n",
    "ax.plot(x, T2Ramsey(x, *popt))\n",
    "plt.tight_layout()\n",
    "ax.set_title(\"Ramsey Fringe Experiment. $T_2$ = %.2f $\\mu$s $\\pm$ %.2f $\\mu$s\" % (T_2, T_2_sigma))\n",
    "\n",
    "print(\"Fitted freq = %.2f MHz\" % (1/popt[2]))\n",
    "#print(popt)\n",
    "\n",
    "dataFolder = '/home/xilinx/jupyter_notebooks/ZCU216_Shared/qick_demos/TATPRBOEA1ReRecool/'\n",
    "x_pts.tofile(dataFolder + 'T2R_x_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "y_pts.tofile(dataFolder + 'T2R_y_'+ dt_string + '.csv', sep=',', format='%10.3f')\n",
    "plt.savefig(dataFolder + \"T2R_\"   + dt_string + '.pdf', dpi=350)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If you get a runtime error, run this cell instead of clear the kernel! Then run your program again.\n",
    "soc.streamer.stop_readout()\n",
    "soc.streamer.poll_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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