tclab/Frequency domain.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TCLab in the frequency domain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "tau_p = 150\n",
    "K_p = 0.33\n",
    "theta = 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "omega = numpy.logspace(-3, -2)\n",
    "s = omega*1j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = K_p/(tau_p*s + 1)*numpy.exp(-theta*s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's choose 5 logarithmically spaced points around the corner frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "freqs = numpy.logspace(-2.8, -2, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotfreqs(ax):\n",
    "    for freq in freqs:\n",
    "        ax.axvline(freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bode(omega, G, gainax=None, phaseax=None, phasecorr=0):\n",
    "    if gainax is None:\n",
    "        fig, (gainax, phaseax) = plt.subplots(2, 1, sharex=True)\n",
    "    gainax.loglog(omega, numpy.abs(G))\n",
    "    angle = numpy.angle(G)\n",
    "    phaseax.semilogx(omega, numpy.unwrap(angle) + phasecorr)\n",
    "    return gainax, phaseax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gainax, phaseax = bode(omega, G)\n",
    "plotfreqs(gainax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Direct frequency domain tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "Qbar = 50\n",
    "deltaQ = A = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How long does one sine wave take to repeat? \n",
    "\n",
    "$$P = 2\\pi/\\omega$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "P = 2*numpy.pi/freqs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll do a couple of repeats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "nperiods = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "switch_times = numpy.concatenate([[0], numpy.cumsum(P*nperiods)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([    0.        , 11893.26574888, 19397.409123  , 24132.20349893,\n",
       "       27119.65678502, 29004.61237718])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "switch_times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tclab import runexperiment, labtime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def send_sine_wave(t, lab):\n",
    "    print(f'\\rTime: {t} Last sleep: {labtime.lastsleep} ', end='')\n",
    "    step = numpy.max(numpy.nonzero(switch_times <= t))\n",
    "    lab.Q1(Qbar + A*numpy.sin(freqs[step]*(t - switch_times[step])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29004.612377178186"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totaltime = sum(nperiods*P)\n",
    "totaltime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Experiment will take this many hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.056836771438386"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totaltime/60/60"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like we'll be running it overnight."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TCLab version 0.4.6dev\n",
      "NHduino connected on port /dev/cu.wchusbserial1410 at 115200 baud.\n",
      "TCLab Firmware 1.3.0 Arduino Uno.\n",
      "Time: 4872.0 Last sleep: 0.8694100379943848 TCLab disconnected successfully.\n",
      "CPU times: user 15.5 s, sys: 28 s, total: 43.6 s\n",
      "Wall time: 1h 21min 16s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "experiment = runexperiment(send_sine_wave, connected=True, \n",
    "                           plot=False, twindow=1000,\n",
    "                           time=int(totaltime), \n",
    "                           speedup=1,\n",
    "                           dbfile='sinetest.db')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tclab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "h = tclab.Historian((('Q1', lambda: [0, 0, 0, 0]), \n",
    "                     ('Q2', None),\n",
    "                     ('T1', None),\n",
    "                     ('T2', None)), dbfile='sinetest.db')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# h = experiment.historian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(2, '2018-03-06 18:45:27', 13710),\n",
       " (12, '2018-03-07 14:55:43', 2001),\n",
       " (15, '2018-03-07 18:43:39', 7526),\n",
       " (25, '2018-03-08 05:34:09', 5523),\n",
       " (27, '2018-03-08 07:10:23', 4873),\n",
       " (28, '2018-03-08 12:59:29', 55),\n",
       " (29, '2018-03-08 13:00:31', 116),\n",
       " (30, '2018-03-08 13:02:35', 1001),\n",
       " (31, '2018-03-08 13:25:17', 2001),\n",
       " (32, '2018-03-08 14:30:19', 0),\n",
       " (33, '2018-03-08 14:30:32', 891),\n",
       " (34, '2018-03-08 14:46:08', 536),\n",
       " (35, '2018-03-08 14:55:18', 132),\n",
       " (36, '2018-03-08 15:02:28', 2001),\n",
       " (37, '2018-03-09 04:37:56', 0),\n",
       " (38, '2018-03-09 04:39:17', 0),\n",
       " (39, '2018-03-09 04:45:49', 0),\n",
       " (40, '2018-03-09 04:46:44', 0),\n",
       " (41, '2018-03-09 08:22:48', 0),\n",
       " (42, '2018-03-09 08:23:20', 0)]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h.get_sessions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# h = experiment.historian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "sine_sessions = [2, 15, 25, 27]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sinefit(session, freq, Tbar=40, inphase=0, gain=0.4, phase=0):\n",
    "    h.load_session(session)\n",
    "    t = numpy.array(h.t)\n",
    "    Q1 = numpy.array(h.logdict['Q1'])\n",
    "    Q1_sine = A*numpy.sin(t*freq + inphase)\n",
    "    T1 = numpy.array(h.logdict['T1'])\n",
    "    T1_sine = A*gain*numpy.sin(t*freq + inphase + phase)\n",
    "    plt.plot(t, Q1 - Qbar, color='blue', alpha=0.4)\n",
    "    plt.plot(t, Q1_sine, color='blue')\n",
    "    plt.plot(t, T1 - Tbar, color='red', alpha=0.4)\n",
    "    plt.plot(t, T1_sine, color='red')\n",
    "    plt.ylim(-A, A)\n",
    "    print(f'Gain={gain}, Phase={phase-inphase}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gain=0.3, Phase=0\n"
     ]
    },
    {
     "data": {
      "image/png": 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HvXvzXp07A+vXh3+/aEZ1NaOtCwsjx2+vXhq/GzaEf79oRm0tM+JGsv/26sXfq7AwPvndsoU7iVNOoV1x40Zg8GD/57drxyDGmprgsucOGkQBZL0uRYMjJHxARZUOGQJcemlkgpF69OC9SkqYiuLgwfDvGa1QK6XBgyPP7ymnUMib4ABnW6xdy0nGCH4HDmQup8oWLiixjaYmLkCGD6fjSVNT60JCCOC88xi7E8yEP2gQ54Y9e8JucsTgCAkfUDrXSKzAvKEGrN22lGZC8RuJdCfecPjV+O3VK/L3VvzGm13C2x4BtC4kFFyu4OwSgwbx1U791xESPrB6NaMo8/Iif29nEuMga9+ehuZIQ02M8czv6tU04BcURP7e8cqvt/tr+/aBCeFgCxA5QiJKsGoVXQcj4frqjdxcbkHt1AnMxurVNFobwW92NnPzx9tKVw/Vf43INpydTY+eeOPXW0gMHMgo/7YQrJDo1IlqLTvx6wgJL0hJnW6k/Pe94XJ5FoGJN0hJnW7Xrsbx27Vr/PILMJjQKH5VbEu88asXEmvWaCv+thCskAB4bzvx6wgJL+zezXQRhYXGD7JgdJWxgn37aLg3il8lJNati09+q6qYLqJzZ+P6r+K3sTHy97crVF3rAwfI8YABgV0XqpD44Qf7pO9xhIQXlHtqYaEx6hAh6MtfXc38L/EGlZLESH47d6YbaDCuh7EC1X+NFBKFhczfFE/8qp2E4jdQIRGs4RqgKqu+nnEZdoAjJLygFxJGrXQLC/k+Hv3NjeZXTWKAw68RQljff+Mp3idUIRHKTqJ/f77ahV9HSHhh/Xoa5jIzjZvElNeJXTqBmVi/nhHAHToYJ4SV11S8Com0NJYeNar/Kn7jqf8qddOGDQyUC7QQWThCwi791xESXli6lB5IJ08atxJLSeFAjsf0EUuWGMuvEBzEGRnxy29eHtVBRvXf1NT44nfmTKp/1E6iX7/AuXW5aOdcuDDw52Vl2Suy3fDcTdGGLVuoEywpiUxtYG/07UuDX2EhQ/vjDVu2cKVkFL99+lAAxSu/mzczCHTAAOP4ra+nzSNe+N2xg699+1JInHFG4NcOGUIhEWwE9YAB9tmpOTsJHQ4d4l9xMTBqFH3CI41OnXjvoiKt88ULjhyhd4iR/GZna/xu3x75+9sZ1dX0vOneHTj7bCCnRVWW8NGxI/ktLmb/tVOOISMxbBh3qNu2BW6PAOjJWFQUmsppwwZ78OsICR3U9q5bN+Of1bWrJpTiBWbze/QosH+/8c+yCxS/Xbsa/6xu3TShFA8QgjsnKTWbQaAIxcNpwAAuqvbuDe46I+AICR3U9s6sSQywj97RDDj8GgvFrxlCQj3DLioRo6Am91A8mxSi3XjtCAkd1q8HkpKMydnkDTVRxvog0+OHH5gqwoicQt5Q/NphkJmF9euZKqJzZ+OfVVSkPTOWoSZ3l4vf1eUKPjFlKEJCCSI79F9HSOiwfj0D3ZKSjH9WQQEnTDt0ArNgJr+qCmCsT2J6rF9Pg70Z/Obm0ssp1vuvmtzVTkJV+wsGoaibOnemB5kd+q8jJHRYv964nDfeSEzkhGmHTmAWzOQ3IYH8xvokpsf69cblHPOG2rHEev/1VjcFq2pS1wa7kxBCM15bDUdIuHH8OFBebt4kptJz2KETmIH6evLbpYt5/HbtGvuTmMLJk3Qv7tLFmPgIb6gcWbHOr5rcGxvpXmyWkAAoJOzAr+HdSQhRIYRYI4RYKYRY6uNzIYR4WgixRQixWggxzOg2+YIqT2hUzhtvKCFRVsbAp1hHWRlXZUal4/CG4reiAqirM/55VqO8nBOZmf23sJBusLW1xj/PKqjJfc8eCuJ+/YK/R6hCYsAA5nc7diz4ayMJs3YS50sph0opS318dgmAPu6/yQCeM6lNHti8ma8FBeYMMpUDp6nJPom8jIQV/BYUcHA6/EYe+vQyW7ca/zyroNRNKqYplGqKQjCja7CCQgVDbtkS/DMjCTuomy4H8KokFgLoIIQoNLsRy5fzNTk5sGIi4cLlYnUrgKkUYh3LlvHVTH7bteP7RYuMf57VMLv/JiQwtQwALF5s/POsgJTAO+/wvRISoZQ0TkigtmDWrOCuU89SCwCrYIaQkAC+EEIsE0JM9vF5FwD62OOd7mMeEEJMFkIsFUIsrTIggmfTJnoTnH46UOprvxNhlJayUDpgD72j0di0iULxzDPN43f0aL6PB7vPpk0UimecYQ6/w4YBY8bwfaz238ZGqpjS0xn0mp4eWsldVaDo8OHgrlNCIh52EmdJKYeBaqWfCSHO8frc1+a4xcZMSjlVSlkqpSzNzc2NeCMrKhgfccopTJ1hNLKzOaDT0qhPjnVYwe/IkRzYFRXGP89qlJeT35ISc/jt2BEYMYILq1jlV6maBg6kyrJPn9BUeZmZvDZYdVNaGm1MMb+TkFLudr/uA/AhgBFep+wEoI/B7Qpgt9Ht8sa2bRxkZuhzFYSgv3k8FG9RQsJsfvPy4iOHk+q/Zng2KSh+Y7X/6gPpNm8OzR6hEKrxuk+fGBcSQog0IUSGeg/gQgBrvU77GMBNbi+nkQCOSCmDzJkYHmprgcpKTtjOIIs8jh9nDhqzhYTLFR/8njjBTKMOv5GFmtQbGrjICUdIhBJQB8SBkACQD2CBEGIVgMUAPpNSfi6EuEsIcZf7nBkAygBsAfBfAD81uE0toHR+Vg2yvXs5kcYqtm7lgMvPt2YnsXdvbLtpKvdiq3ZqVVVM9hdrUJP67t20T1ixk+jdm3Xhjx4N/dnhwtB6ElLKMgBDfBz/t+69BPAzI9vRFpSktmISy89n59m6lfr6WITi1yp1CEB+lQEx1qDn14r+C3ChNXSoec82A2pSD8f9VSEcdRPA33j48NCfHw7s4AJrOawcZGoSs3pLaSTsMIk5/EYesd5/1aSubFpWCgkrPZwcIQH+AJ06MWGZFYZrIDYHmcLmzfSGad/efHVevPCbmUlPLjN3arHOr5rUt20jv+EUcQpVSPTqxVcr+XWEBPgDqNTSZqtD0tKADh2s94U2Elby264d3WFjcRJT2LJFS91t9iInNZWTZyzyqxcSobq/KoRquG7fnjmyHCFhMTZv1nLwm73SBdgJNm0y77lmY9Mm5lECzJ/EAOsHmdHYvNl6fmOx/6qcX0pIhAOVmuPkyeCvtdrDKe6FRHU1k3clJXHSNnu77nJRTbBmjXnPNRM1NfQOUfyaLYRV+ohY5ff4cRpWFb9mpORQ0PO71tuxPcohJTB9Oif2XbvCFxKqxseMGcFf27u3IyQshVLzlJQAEyaYP4mNH89OcOBAbLppKn4HDCC/ZgphgPz26cO0CjU15j7bDCj34v79+V3NFBIAn9m3L100rXTTjDSUqklKqonCFRIDB1JQhJKRuE8f1moPNq1HpBD3QkJJ6P79zSlb6o38fC1HSyxm01T89utnDb95efbJpmkE9PyGklcoXOj5jSWVnrIfNDbyNVwhkZwMdO8enR5OjpBwd2wzisf7gzI6xtIgU1DfqVu31s8zEuq3dfg1BrHYfyPp/qoQiVgJK+AIic1AVhbtAlZBDbJYXOlu2aK5Z1oFNYHGIr+bN5PbrCzr2qD4jaVElfpAug4d6CEXLkL1cLLaDdbQiOtowJYtEcjZtG8f8NFH3Hvv29f6uUVFTJ+p63UZGRzosVgcp7yc/AZt66ms5EUVFcDKlYFfl5oKXH65x6yZnk6OY5HfsrIQg+ikJLdr19JzAwhsqXvxxdqqxo327bkQiCV1qb7YUEDurw0NzEm/cKGnJDjzTBokEPpOIjVVq2JpBeJ+JxHyJKbHRx/xtS0BAXD/+t57HodcLvqax+oklpMTJL8NDXQt+eij4AQEQHcfVSnGDYdfH1i7FvjyS01AAIHNYJ9/Dhw86HFIBYXGEr+Kil27gJ49A7jg22+B775ruVX47rvmt6EKCYC7CauEcFwLiePH2QlCGmQRhBCxOYmdPMmVWKdOQe7UQtmTt4JYnMQAytLt20PgFwivcLJXUfZYFMLKq2n37gCFxKFDbZ4SqroJcISEZVDFUuwiJLZt48CPFWzfzkFhF363bw8tmMmu2LGD3jch8RuOftXrYYrfHTuA+vrQb2snSMl5v6EhQCERAJ/h7iT27LHGTT6ubRJq5RPSIJs6NbyHq+tvuw1CJCA3lx1yxw6gR4/wbm0XKH6DUueFy6v3fXr1gqvTGOTkcELdvl0zBEY7gua3qQl4/vnwH/zxx3zt2hW49NLmnVpTU2Sik+2ApibGJgCtjMdA+XT3xYQ+10PKtJDaowRVebn52aLjeiehH2Qhq0MOHgQ2bqQFPJRoospKj0R/seSBo75LWDsJxe/mzaHxu3VrzCZSVOqHTp0C5NdXNFY4/O7cCSA2E1UeOqQJCb87CXVCWzfatAnYvBkd532IhobQgjrVwsYKlVNc7ySWL2cUZGYmg10CxsmTwIIFwFdfUWmpxxlnAL/6FXDFFS1Hrq9VclNTc5I0AFi0CBg7NqivYVusWMEI4A4dgJSUAC7QRzAtXAjMnt2S35Ejye+VV/qeGX1wnJKiTWKLFtFBJxawYgUXNx07BsivWgm1xu/pp5Pfq67y5Hf3buDTT33eNiVF678LFwKXXhr8d7EbvviCxZQSElqJQfGnO2pspEvSQw/R6OlGHwBZ3Z/GhrJfYfj/Xh3UyskREhZh2zZ27vHjgcLCAC9asAC44QZeXFwMXHMNswNKyZnopZc4wMaNA154oe0w4xkz0K97D1z/k/Px4IOJMVVUfts2rnInTGiD38OHgWnT6Eu5bx95q6igq+V992nK3Lw84OWXgR/9iDPRiy8GFGbcb+5U3FJ6Jh5MHBiT/I4fryX484u1a+lpU1VFfsvLOfv9+Me8OCeHQuTll4GrrwYuuYT8FhS0ft/XX0efkWfgxht74cEHY6eUaVMTU2gUFQGJvmbJ9euB+fNbHvfm9+qrqZaTEk3btqPdt6sw/PFrgJUXca4IcOLJzqZXtyMkTIbybApYHbJgAXDhhRxMkycDw4Z5Xjh5MvDrXwPPPAP89rfcVcycqeUt8JPOUVSUo2DHHuTk3NRcBSsWEDC/06bxdc0acgcAt98OlJZSGMyZQ1c0xe+zzwK/+Q13FTNnMqdKKxAC6LTpO+Rl98fOnbHT5XfuJLdtqpu2b6eAKCsD/vlPHrvtNuC007QLR41iArMHHgCee46vit8BAxho4gu1tRBff4Wc8VnIyclRGqiYwIEDflRNTU2+BUR5OfltagJef51bPB0hrpISbOtyIRqr6zDk3Qc1fktK2myLENxNWOFBZtiIEUJ0A/AqgAIATQCmSimf8jrnPADTAahYzQ+klI8Y1SY9pOTvN2JEgAJi3jzqKTIzubrt1Il6oZQUFi1QgygxEZgyhUE048YB555LH+qePYHzz+fArKujC+Ls2c23d504jpwcxNQg27kTGDIkQH63bAGeeoq6qTff5MSUlsbl0/XXa+clJAD33ksBfNllGr8qAdatt1JX7HIx1sINAaAgpyHmhMTAgQHwW13NJehTT7H/vv8++UpIoL61XTuqRwAeu+ceTmD6/tunD3DLLbzX0aPUx+ggaqqRk5MTE4scfYzEyJGtnKAwdiywahXwy19SmzBjBvuvlLT57N8PzJ0LABCJLmw7+wYMufdcz/6rFpKtoGdPYPXqML9cCDDScN0A4JdSygEARgL4mRDCl8icL6Uc6v4zRUAAXCXU1AS4i9i4kTaG4mLg//0/CgiAbg+dO3PF4L0nHTEC+OYb+gReeCG3oQDDf3NzWy5R3Ma/WBESR47wLyB+KyuBf/2LPP7qV8DZZ5NXFTWdkNAyvelpp1FwNzaSXxXImJTELbyXGkoIILdTE3buDN0N0U44dow2UaUlahW7dnH3lZWl8VtYSPVdx46agNCjtJQTm5Tkt7KShrvsbGaq84ILTcjN5aOinV8puXE9eDBA99emJu5y8/M54Q8YwONCcK7o16/5VKHOHzaM/VcICpm9e9t8TK9e3KyopINmwTAhIaXcI6Vc7n5/DMB6AG1pTk2DyjPTpmfTsWNcUblcwI03ciUWKEpKgM8+48x/ww2t/roCwMSjU5F0dH8gcTnJtUUmAAAgAElEQVS2h+K31Ulsxw7g6aepYhIC+PnPyW+g+a779+eqbc8e4LrrWudXAFfWvo7Tqr/2DhiOSuj5bVUIP/00+y1AfoNJ8tSvH9UhlZXAtde2zu9XszHx6FScqG0IyOnHzpCSi0jAh/vrihW0OSicOEGjUGMj+6KqXuYHQgCdl0znnNCnD/mtqgImTmwzSGrUKG6qzY6VMMUFVgjRHcCpABb5+PgMIcQqIcRMIYRfD2AhxGQhxFIhxNIqtSoPAwHHSNxzD0+eNMmzyG2gg23kSOopv/gC+NOfPD/T9UAhuMkYg69iIlFaQD78M2cCb7/NQXLnnTz5lFOCK4owYgRXyV99BTz8sOdnOl2v4rc3tsREZHDA/XfaNO6y7ryTO4e0IP30S0tpo5gzB/jDH7TjEyZ4nKb4zUVV1OdwkrIV99clSzz/nz+fOZveead1ldH48QB0AXWq+tDw4cB//sNd20MPtdquceNo6/ZnHjIKhgsJIUQ6gPcBTJFSejtiLwdQLKUcAuCfAD7ydx8p5VQpZamUsjRX+TOGATXIWjX6vfUW8OqrwP/8j6bzBqib/clPAn/Y7bdzCfDoo/S7VRg7lttUsA0ZGYCAjPpBBgQ4iS1ZAnz/PY3TffuSi7POCv5hkyYBN90E/PnPnoN41KhmfgEgI92zbdGMgGJ83nuP6o+LL+auoE8fT/tOoLj5Ztp6/vd/gcWLeayggNx6JaoUkFHPb1OTph1uVd2UlQW8+y6N/Bdc0PpNCwvJl6+o6xtv5Bzx+OP0IbYZDBUSQogkUEC8IaX8wPtzKeVRKWW1+/0MAElCiBzv84xAWRn7d7t2fk44cIDb8zPOoKeSHj594lqBENxN5OVxsPnIXaBWYpk4CtenH9NAGMUoL+ek4XPh2tTEldTbb3M3ddll4T/w6ac5ECdN8psbotm34OMPor5MXXk5uW3f3o8QrqigB1NxcfMqNqycL//4B1UpkyZ55m7S7frS04Fx+BRHFm0I/Tk2gNpJpKW1kiK8tpYOKsOGAY8EbkoVAJplhF6v/H//R5fZSZNoELERDBMSQggB4AUA66WU/+fnnAL3eRBCjHC354BRbdKjvJxzit9V7q9/Tcvr1Kmai6ZCKHlvOnYE/v1vuif87W8+T0lOBtq3A+rK99LDJ4pRVsY5xSe/S5dyVVpbS1tNQkL4BRGyssjv2rVckflAUhKQ1h6o2bYfeOON8J5nMcrKGN4ghB+Ob76Z9rQbb9Qm8nDykWRlcSysWwc89ph2XGeUTUzkbq3dknktkgBGE5SQ6NrVi1t9kM306VTjTZ0aVCSu0EuJd9/VPsjI4L3Wrwf+8pcwWh95GLmTOAvAjQBGCyFWuv8uFULcJYS4y33O1QDWCiFWAXgawEQpzfGN2Ly5FVXIggU0Tv3iF/Qx1FuKJk0K/aETJjDQ7i9/8YjExO230/DqVjkF4Ohge7TK77ffkuMxYzgSb76ZQYnhYtw4Boc99pinm9jttwM33kiVXmYc8LtoEXXlo0dzdXrxxVTHBeSq0wouuYRq1sce00q2lZSQ35tualaZ7t2LqM6kuHcvhUSLSGu1u6+ooA3hnntoUwjm3uNux4kTQK2vWtcXXcR54IknYKeoTyO9mxZIKYWUcrDOxXWGlPLfUsp/u895Rkp5ipRyiJRypJTyu7buGwk0NHAOad/eR8W0pibGQXTrRluEN4JVNXnjiSc4gPQqLJcLSE9HqjvkItonMZXoLS3NB79ScsXfoQMndYCxJpFKE/vXv9LT5De/0Y65XCQ2LxftUoG9lZF5lFVoauIc4pff++7jyl8Zlzt08O3mGgr++le+/vrX2jGXC0hNRUoqaa6sRMTTvZuJBQsoJHx4+pLfd96hu+ujjwZ974wsTrkV/pxTHnuMY+GBB4K+t1GIywR/O3awD5eUMGreA9Om0bj85z9zFEYqK6lCz57cobz2mmYEdGP4cCAvnwuWhkZE/tkmYfduCuL+/b34bWqil82mTaweF6mJS4/u3RnL8sYbNIrrMPLcVOTnAzXVbvX81KlRmZt9716uM/r397EB+/BD7iQmTDCG36IiTmBvv80doQ7DTuXcWVsLnHztbfJrtlN/BHD4MPkdOlR3cOdORq2vWkVd3yOPBOcO78app/JnaZah3mO8WzcK4HffZRyFDRCXQkJ5X3Tt6uVtWV8P/P73DBP25QUyZkxkGvC739GI/bvfeRx2nTMKHTsATRI4FMW+/Irfbt28+K2tZbW5zp21UNYLL4x8A37zG3rf/Pa3Hq4kCRddgI4dqRJWfvAhZZa1GH77b0MDv3NhocZvZmZIk1mreOABPsOLX9eEccjOJr/7Fb/hFDeyCCou00M7N2cOBd6HH5L4W28N+f77R13ResDhr35Fg5MXv1YhroVEi7iXqVP54WOP+TZOR6oQQUYGO8BXXzEqW6GkBLsuo8tmNAckqTiPFvy++CJ9C6+6Sst/5XNPHybS0ymA584Fvv5aO56UhN3jJuMoMjV+wypubg1a5XfTJmbIVdJj4sTINyAtjYup+fNZAlWhc2d06MC3+1UoUxSqnSrd6kgPISEld6Z799KTLgy188mOeWh16m/fHnjwQe5cZs0K+TmRQvSNkAigvJxjyCNzQ10ddYznn08DEsABZxTuuourhYce8lgtqGyezfGCb7wRWO1sG6GsjGpVjwSix44x2K1Pn+bC8Ibijju44vPBbxNcqFJCwktlEg3wye+JE/yuvXsDgwcb34jbb6fqyYvfDh352tx/Z8+OOk8n1fbiYt3BY8eYKr1HD6boCQMeHk6A793spElcQHnxawXiUkio4vEei4Hnn+dk/PDD/BWPHvVc5UcaqalcjS1Y4JEsLS8PcAndTqKmhiqaKEJ5Of3LPTwDf/pTJsMZP96cWqYqn//33wOff958OCcHqEjso01iu3Yx+2wUoazMR8qlJ55g/x03zhx+U1LI7+LFHnUmEgb0Q2KCrv8ePgy88orx7YkgqqoYpNjM7/LlDHI7dIi2njD5bVHG9O23W56UnEzHmaVLtUqAFiGuhUTzb11fT6+Ns8/mH9AyQUo4rq/+oIKdHn64udckJgKzcm/AvioLi0KHibIyr0jg+noGz/XqpaUuuOkm4xty661c+f3xj838JiQAO3NPxQG9Oi/K3MnKy71yYp08ydQZPXpoadNHjQpLbx4Qbr6Zv6mu/9aUnoulORdHtbp03z6v+hw7dnCh0b27Z4xIiBAC2Db29rZPvPFG7rx1/FqBuBUSHjmFXn2V3gsPPuj/onBdX30hOZlGwO+/544C7sjrvPZYXt126mC7ooUQfv117iIuvVQ7aITnjTeSkugpsnixlqpZsG0RSP9lGVr03zffpEuZnt+SEn5/I6H4Xbas2fYjBFBfUKSp86IQVVVe9p7587k1uuSSiPRbl4sqT49ccL6QmEgnjBUrPG0/JiPuhMTRo/RsaZ7EGhoY/VtaqtUNra83b4t3660c8e4ViprEduzxEkpR4k5YV8ekrB78/va31F+bXcEd4Go3P7+ZX5d7bFbtF9rirLy8RX0Eu+L4ca2YkxBgn/jDH2h/GTTI/AbddBM9nbz67/4qr8VvZXQEp5w4QQ1Z805iyxbaBbt0iZitp1ndpK8568/d/frrKbEisIMJFXEnJFqksH7nHS7NHnxQW4V512DUJ/eLNNq1Y/DTjBnA6tXNk9jsYyNapheKguWvChT14HffPq7CFL+jR5vXoNRU5tiZNQtYsaJ5Ept6/EZPfm0U4doaVNfs1MnN73vv8aB+F2EmUlLI7+zZwLJlzf33tfprPL1fdXYhO2PbNk7gXbu6DzzxBNWRl1yi5cAKE81CIhCX+pQUxlXNmdMirsosxJ2QUNVDs7OZsRJPPMEVrr4D6JdAV11l/KT205/SbfPxx5vrlDQgCevOmux5XhTsJFrw+/e/0w1HRSZNnmys0PWFu+9mrICb35wc4ARSseaMyW1fazMofjt1cvP7t79xp3TqqdpJk03+XnfdxQhvN7+5ucARdMDqkbp2REmaDjUPN+8kvvyS3iTDhwdQSDwwuFzUXh2sDVB1NXkyo+b95CQzGnEnJFT5v4ICoMvmbxhBef/9nv7yepdTM/zoO3bkQHv7beRVlzW7NuqzigNgASObG1n1/HbeOp/61DFjrI1HyMoiv+++i/zqrf75/eILy90N24IHvxXf0fvFan4zM7nQee895B3Z3Oxa7sFvUxMrPNocbtMgNaMzZ1L1MHp0RPlVpoh167w+8OXlBDCu6mc/YyDfBvMz7MadkNi2jbEqEycCnac9yV/MO7r6hx+098EWaQkVU6YACQko+ujpZscfb60XAMvd4dpCRQXt8ddfD3T51+/Jn4r+jdBKLCS4+e32/pPN/Kocdc2oqGiRysNu2LaN9uJrrwW6TPsHO7PPQswm4777gKQkdHm3FX7nzvXTqe2DvXvZf0eMAF1827VjuYAIYtgwDoumJnhayI8e9bFycePee6l6+j+fCbUNRdwJie3bKRcSyrcAn3xCVYQ/j4WJEz2NS0aiSxcm4nnxRXRwHUVamnuQ3XKLjwRT9oXiN3F7GQPVzj6bo+7ii6k3twqFhZxZX3oJWfIwMjJ0/F51lXaezQ2sit+kXRVcWZ59ttZHr7mGQW5WID+fK4OXX0ZGwyFkZbn59XbDtXmdlMpKfhXXzu3cBY8axfkhwrw22yXGjQN+9CPtA3+agrw8usS++qrptsm4ExI7dnCQtfvvU3Qxu/tu/yebtYtQmDIFOHYMKW+9RA+nHdCKz0cJmvl94Rlu0c87jx9kZlpjWNXjvvuAmhqkvPEicnLck1hyctuuiDaCEhLtX3TXBVf8AtRbW6l2uu8+oLYWKa89r/Hr7YZr8zQolZXuSPZnn+Usfv75/CDC7Xa5dBlL9OO7tTQmv/oV65eq3Ccmwd6/WITR1MRJrDjrMFLfeokry8JCz5P0Vc2CqbUcCZSWAmedhdSpTyOvU2PL7brC1Kn007MZpCS/Q9ptQupLz9HY17Gj1c3SMGwYcM45SJ36NPI7Nfjmt6rKttl3Fb9ds44h9dWp/D52WkAMGQKcdx5S//tPFOQ0cJHjjfnzbc3v3r1Aj9xq4JlnyG+nToY8yyPqWr942r3bsxaKHn36cM4yOv7FC3ElJPbsoQz40eHnIWpquHL3htVuplOmIKGiDJfJT7F7t86hyTsR3ooVZresTRw4wCwid+x5GOLEcU8XP5NXP34xZQpcO7bhssaPsWdPdGUKP3iQKYSurn4ZrppjnvyqFa/VmDIFrl07cNnJj5rHW6RcR43GoUOM87ni6KvMuKD4vfzyiD+rRWoOPWbMiPjzwkFcCYmyMiABDbhkyz/RcNa5nm6DCla76l1xBZq6FeHHu5/EyZO6AnbeKbWtbqcPlJUBLjRiSNVsNPbopQk2s10yW8OECWgq6o4f73oSjY3QVrvedh8bZi8tLwcEmjCu7Ck0FvdgGg6A/PbpY23jFMaNQ1P3nvjRzichpdtOXVhorz7gB2Vl5Hfs+qfYd3v2ZBoZj0ygkYGHugnwKl5hL8SVkCgvB67Eh8g+th31P/WxiwCsN6wlJuLknT9HSeU3GIxVzWnNW8CGwV9lZcB1eAOZdfvQcG6Eam9EGgkJOHn3vei3bz5OxXKNX2+h8MMPaBnNaC3KyoAf4X3kHd2KhnMvsLo5vuHmt+++b1GKJf77rw3dYcvKgEswEzkHNnEX4beAePhosZOw2l7XCgwXEkKIi4UQG4UQW4QQv/HxeYoQ4h3354uEEN2NaktZGTAFT+JITk80XeZnC/ydu4JqXp5RzWgTDTffhvrkNNyHp/wPMsCHj6G1KC8HHsSfUde+E+Rg+66MGm6ahBPJ6Z78tmvnedJ33zEdg41QVgY8iodwvH1HNA7xsQu2CRpuvBUnUjJwH55qznDQAnPn2q4gUVkZ8Ev8HSfTOmi1q1sU7YgMWggJ7/nGRtwYKiSEEAkAngVwCYASANcKIUq8TrsNwCEpZW8A/wBgXFjh4sU4C99h3eh7IRLbMEpHqgpdCBDZHbFp5M24Hm+gcrXOJXPiRM/C61bbT7xwcvEK9MMm7O5/vsbvbbdZ2ygfEB2ysOnMW3Et3sK+1W6Xw7Q0upBGuopbBHFy6Sr0x0bs7ns+hHKqCLO2gREQWZnYeNZt+Anewf5Vu7QPbr7Z80TvTMsW48SytRiNOTh86nma04pBarwWQqK4GDjtNO1/G+1ijd5JjACwRUpZJqWsB/A2AG8r0OUAVML59wCMEcKYvdfIRU/imCsTG8+a1PbuzmzPJh2EANaNuRcpqEfxrP9oH2RmegoJm+nNT/v2SZwQKdjbaxSg+LWQR39Q/CaiAT1mPad90KEDMGCAdQ1rA8PnP4l6JGNv71Fa/zXI+yYcuFzAutE/RwIa0esLHb/eMUc2SzMzfP6TqBPtcGTw2YY/Swjaww4d0h3U71psFPlvtJDoAkDvCLfTfcznOVLKBgBHALTo+UKIyUKIpUKIpVWhrKCbmpDWVI1ZXW9Hfu8M3/Fzx49r7y30505JATJP64dv0i7D2M3/8l/Za8UK+7gTbtyIsVVvYE3GmcjMb4ckAzKrRwopKUDW8N6YmzEOF5U95/m7ewu1qVPtIYwrK3HBvjexJuMMpBekaQWdbBh3kJwMdBjWE3MyL8dFFf+my5AvfPop+bXDhFhVhbGVr2N92mnI7Jxu+OO6deOrRwC6/rf85BPD2xAojO5hvtbr3j0ikHMgpZwqpSyVUpbm5uYG3xKXCzVvfQzX35/ApZf6GVv6dBxm1DvwA5eLSSdn9J2C3MZK/zldbISmPzwMF5qwqetoDBrk5nfcOKub5ROK35n97kenxirWY1BQRXv0sEFMSuO//o1k1GNTl9EYrPhNTLSlwVPx+8WAKchuOgD52uvah9dc0/ICG+jfG5/7D1JwAhu7no88Nb385CeGPU85VnrIR5sGdRotJHYC6Kb7vyuA3f7OEUIkAsgCcNCwFrW28lLbX5uszvaUjMEaDETj3//h2ZsM8NsOC8ePQ86YgTUYBKEMcJMnG2b0ixQqB5yHVRiMxr8/qfGbmMj0B3pYvZM4cQJ49l9Yg4FoyndnJ8zONqZaYgSxv+QcLMepaPw/Hb8dOrR0h7Wa3/p6yGeexVqUoCnP3WcvuYSJIQ2Cku0eX10IraaNjWD0bLgEQB8hRA8hRDKAiQC8M9R9DEBZtK4G8LWUFu0/lSHNiCp0IaCgUOBJTEHCmlXNldUAtNyez5hhbVTYSy8h4dgRzMYYO9t9W6CgUOAfuB8JP6xprqwGoOWk9dVXnpH4ZuPtt5FwYB9mYwyyFL92UNG0AdV/Ezf+0HplNZXa1iq89RYSq/biK33/NVhwKSHR4mfU7wyXLDG0DYHCUCHhtjHcA2AWgPUApkkp1wkhHhFCTHCf9gKATkKILQB+AaCFm6xpULp/mxgv8/OBN3EdTmTmAP/4h/ZBupfOdOdO4MUXzW2cgpTAX/6Cmg5dsBH9kGnc4iviKCgA3sZEHM/KA558UvvAO+3BkSPAyy+b2rZmSAk89RRqsgqxHgO0SaxXL2vaEwQKC4F38BMcz8r35NcbGzZ4WXBNhJTAo4+iNrMAP6BE49fAXYSCz6hrfRqbFSsYZm8xDNerSClnSCn7Sil7SSn/7D72P1LKj93vj0spfyyl7C2lHCGlbC0ywFioyff00y1rgh6FhcBxtMOKkXfTkLVlCz9IT6c7bHP5LAsxdy6wcyc2dB2DBJcAevexpdurLxQWsvjQ8hF304i6aRM/SE5uqXKyCvNZk2NTtzFwCYGMDABXXsm8QjZHQQFQjxQsGfEz1mZYv1778JZbPD31/Bm3jcZ33wFbt2JTt9EQwkV+r73WlDQyLpcPIeEtnKwO7kWcRVwDbdj5GhrMz/zaCrKyGOP1ade7qQL75z+1DzMzLQ34a8aTTwLp6ViVPALpGcDJwiJbur36QkYG5e3HXe6mYHj6ae1D7+A6q/Dkk0CnTliRfDoyMtzUhuK4YQHS0thNpxfcSZcyPb/JyS2Ta1qBJ58E2rfHypSRSE93myMzMkx5tBABaLWsttcgDoVEq9iwwV5BLC6qnFZWFnLn8OKLVH3oT9DDbHfYb79lEaRzz8XeA0nIzEDUCAiAgzQvD1i1Nx+47jqmYW5N7WE2v2VlwEcfAaedhspDyZy77OfM5BdCsP+u2pMH3HAD8MorzAKpoO+/yh3WzNiJbduA998Hzj4bew+lmJ7NvtUkfwpffGFKW1qDIyQUbCCxvaEGWVkZmLG2uhp44QXthMGDLWsbAOB//ocD/dxzsX8/V43H84utbVMQUEK4md/aWuD557UTvJMqmo1ntJoR+6sUv92tbVMQcLkohMvKwFoTdXXAf/+rneArcZ6ZOng9v+7+e+iUUaY93q+QuO4609oQCOJKSLQqtW2YM1qtdMvLAXkqayHg6ae1tiYmWpdd88gR6nNPOw11yVmorgGWDZ8M4Yqepa7it6ICaBo0hOm2//lPjd/u3a3j9+hRLgiGD8fxdh1xrBpYN2giDgy3WHAFAcXvtm1Aw4BBTHXzzDNaBmMhWlZ8M2spX11NgTVsGE6kZePoMWDpsMmoKfbOGmQcWmSCVUhP97TXWIy4EhKtwoapt10uqkePH2ctEtx/P0fc9On+L/r6a/fJBuPJJ9mwMWOwfz8PpafbMrbLL4Tg6rG+3l3nZcoU5kr44AP/F33/vTkqyWeeoaC44IJmftunu6KS38ZGd2Tx/fcz9/1773mepMfatfzeRmPqVC50LrgAVW5+09LM778//BBArOaSJZbOT46QUJg3z+oWtEDfvlpJhg0bwOItPXq0Xgx9yxbqd43cGR0/zjaUlABFRdi7F2iCC8XFQL9+xj020ujbl3nVADe/l11G19J//MP/tnPNGuOzw544Afz1rySze/fmsseduydHHb+q5MWGDWCAWt++nvx6z8qbNjHDgJGp8Ovr2X/dDdznzqFZVGRu/1Vj22eWIb1zwooVtJdZBEdIKKjqM0OGWNsOHbp3Zx12gConJCRwNfbdd3SNVLj22pb6XSNXHq++ytXexRcDAKoOJeIl3IpzztEmhWiAX34XLvQMXrzuOkMKz/jFq69ylevm98AB4FOMw6jRyVHFb1ERNaSA2y7hcpHfJUuAOXO0E3/0I2jJqNxQ2ycj8MYb3NGo/ntA4H38CKNGmRt+4jM1h0JRkW0KSTlCwhsW5mzyhS5duNhqzst/221U9P75z9pJGRnaaFQwKiK3sZGr3O7duRIDsLJxEFLbJ9iqnHWg6NzZi99JkygQ9Pymp7eMnTHK0aGxEfjb3zhJuIM69+8HDqV2tlU560DRuTNlQ/PG4JZb6Pr6pz9pJ3XqZF46iqYm4PHHWQmuhPaHRcnnoCalk+kJdX2m5tCji3cuVGvgCAlv2MyFMzWV8RLNg6x9e+AXvwBmzfIM2/cWCq+/HnmXQimBu+8Gtm4FLrqouZfv2utCp07RZY9QSElhGqRmIdGuHfD//h8wezZ3FAre/D7/vDEZTP/zH6pcdPweOMDcbzZJKRYUEhMpA5r5TU0FfvUr7iS+/VY70bvzLF8OfP555Bs0fTqr4pWWav13p0SnTubz6zc1h4L3BxYlQozCbhce2pzIfGUBtRBCcILwqPB1990M39evdv0t43ft8n08FOzfz4Gbn+9Rk/ebA4ORmxudQsLlIr8eKZvvuouSQ8+vP3VTJI3YUnKVm5vrEVH93onxyMmJTn5V//Xgd/JkHtTzW1DQ8uLt2z3TuIcLKbmDycnx4Pf7qt7IzbWhkOjd2/P/NWsMbY8/xJ2QaBM2Se6nIATnDI9BlplJv/Pp07WOIwQHn/e2PZJqkc8+o+1Gl2u96fbJ2Lo9KeonMQ87aXo6PZ0+/RRYuZLHXC7y653+PJL8Tp/OifGSS5r5lXdMxtJdhVHNb26uF79pabRNzJzJHQOg8euNSPL76ad83iWXNGsM5M23oGx7oiU7YSWU/AqJhARPTiIpMIOAIyRsDrXS3b3bq4/8/OeczB55pPUbzJkTvqdIQwOwYAGD5woKgBEjmj/as4fOONE8ieXkAJWVXtU077mHth5vfr1H9Acf8MJwa07s2UM1Yl4eMHJk8+HKSsagRTu/Bw96JgvAz35GPerDD7d+g9dfj0w9j6Ym4Le/pcQ644zmw1VV3AxasRNucyfhjS1bgGXLTM8AHFdCwi+3+/aZ2o5goAYZ4DXXZ2dzUnnvPU/bhHdispMnGdofTqrrF19kFtQdO+iGq5ZA/fo1q8GiVWeu59dDpdexI/DLXwIffuhpm/DO61Nfz4ls2rTQk7FVVlIAl5eTX2UXKy6mVxCil1+1yAG8+M3Kom3i44/prdcapk0LvyEffACsW8edoM7uWL6d7620SQS1WVq2zPTsEFHY7QyADTIt+oPfSQzgJJabC/z615oE7NjRd/WvcIREYyOz0HbtSl3uyJGczM45x2MSi9aVrnJJV9+lGWpl/8ADGr+ZmcDVV/u2XYWqDjh0iPx27kyDKsDJ7MILm3/zaLX56Plt0X+nTOHOVM/vpElUZ0YSDQ0UwoWF2i548GDguutQto1Cwgp+21Q3KXin6TC5oY6QAGy9RHO5WpnEMjOBhx6iSmnWLO24rzTH4aw+5s7lbuvyy9mgwYM54IRAWRn7bHZ2dE5i+pVuC34zMoA//IExKTNmaMezs4GBA1veLFQ1wFtvcTeh+AWafXNVm6LVe0y/yGnBb1oa8Mc/0stJ1XROTIx8Cvz//pdpyidM0PgtKQHS05sFlxX8Bqxu8q4fYzLsZaW1CkYG7oQJl4uyICmJgZctcOedTJHxwAPABRf4N7x718m+9FLuoI4caRkDUFZGF1CA53zyCX32Bw1qcdvVq7l5SUqytaz1C5eLYzA1VbNRe+COOxgh/MADTNt62qkAACAASURBVPjnXZBIjw8/1N4PG6btCvxh9WpWvfv73xnq6yOQc/Vqyvxo5rd9e/757L+TJjH6+Te/oUHZH7/6DLx33NH2jC4lq+GtW8eFVN++WvSaahjIb2YmXaGtUjctXgz07InAqzo6OwkLsGEDX82OpgkA+fns2zk5Ws0hDyQnA088QS8nfb2JthKEzZjBVCSrVrX8TAkIgAKiro4qLCE8XF8BqhBycji/+fJitDv0/G7d6uOEpCQGt/3wA/DUU9rxtiLblNdOa1i4kB43tbUavwDw4x83n1JWFt385uWxy7TJ7/r1ntUXL7jA/00DySZw/DiNeJ991pJfoHl1XlbGYT94sPkl2V0uLcK7zXRVRUXae0dIWACV58gORVC8kJRENWpubishD1deybxDDz2kpRcZPpzuc+FU2Covp6rp3HO1EaTzbALYppwcbka8MytEAxS/OTmt8Hv55bTB/PGPdFFVCDdDbEUF8M03wNlnayqWyZM9Yl6ind/ERPKbl9dK3snx44ErriC/yjujZ09ycccdLc8PNEB0+3aqYkeNArp1047rfrdduzi2rOJXbc7bVDldfDHbbUFWYkPUTUKIJwCMB1APYCuAW6WULfzYhBAVAI4BaATQIKVsY39uEJRR18b7+dxcJiCV0sdCQgjuIk45ha6xH36onRRIor+pU6lvSUnRykiePMkcQllZHMA+UFdHF8Izzwz9e9kFublczPrkFyC/JSV0jZ0+PbDV3CuvaHXTheDkX1fHv4QE8puZSSHvA/X1NAV5yeWoRG4uN62NjX6SGjz1lMbvJ59o/Pri+bXX2C+VT21Skra76NCBy/L6evKfnu6X35MnaQoaMsQ6e0/QbrAWwKhZ8UsAA6WUgwFsAvDbVs49X0o51DIBoYeNk+Pk5nLXrC/s5YEePejTP306XVYVSgLMj3/8OAedEpiffcal3w03aKU8vcpmqgA/O1RRDRd5eZzPKyv9nFBczGjdTz7xLEzUWhoXJSAAzgIHD2pC+IMPuIy9/noq7H1g2zZeFiv8njzZym6iqIgR2J99xtQkbUEfdKFXPx0+TCeNmTOZ//366z1LEuv68I4dFFpWVoMNyQ3WZBiyk5BS6mvuLQRwtRHPiThsXDtYTRRKR+0Tv/gF02bcey9w1ll00xw6lOH9dXWccWbPbtvld9063ufMMxmAlJ5OQeE1ISpvFRvTFjD0bpp+df/33cfJ5777qMIYMAC49VZGYx07xhnn88/bXhb+8APvM3IkleHjx3Mn5xWDEUv8qv5bXu6p+fHAz39O/u6/nyq4U07h8ZtuonNJY6OnF58/rF9PYXP66ez/48dzlwx4qPL07sVWIWA3WAthhn5lEoCZfj6TAL4QQiwTQrSqbBNCTBZCLBVCLK3ymYA9tqFSB7VwI9TD5aIKo317qohUKcj0dI6EvLy2c3nv38+KaJ07AzfeyFflXuWljlNtiYWVbsD8KhWG4ldVhurcmbOfKlDhDwcOkN/CQs3/vbCQk5eXZ5qaxOKK35dfZn+74grN6zA1lTabtrgF+Ju88AIlvZ7f7OwWftp26L8xrW4SQswWQqz18Xe57pzfA2gA4K9Ky1lSymEALgHwMyHEOX7Og5RyqpSyVEpZmhuG6G9V92jjX0q/k2gVnTuzuHt5OXDVVZp6IxAcPUrdsJQ0kCk1kx+Ul3OBFo5t3C7wG4vijcJCqooqKjiReeTyQOvqp2PHyG9jI12X1erWD8rKaEyNBX5VxHib/Obnk98dO3zz2xqqq+kOfvIk+28baf/Ly/lzWenUGA3qppCFhJTyAinlQB9/0wFACHEzgHEArpfS9+wrpdztft0H4EMA1pjolFuDjQsipKayeS2iVn3hnHNYyWrePMZD6P3rhg2jz7i3v9/Bg3RBPHSIxsOCAm7TW0FZGedMG9v7A0ZKCieyNicxgKqmV15hPquLL/bUj/uz4h86RH4PHtT4BVpdHSvVl82y14eEpCQK4oD4Pess7oi/+44p0/W5m9ri98AB5oVS/fuqq/w+pqyMMslKfqNhJ2GUd9PFAH4N4Fwppc+lgBAiDYBLSnnM/f5CAG1kqzMIaWnczto4pFUITsgBDTJA22rffDN1s2+8QQGRkgKcd57nuZ9/zsji2lrqfM8/P6BHKCFhY9oCRtD8TpzI1xtv1PgdPpy7L283xS++YCK76mraIsaMCegRsSSEXa4g+VWpZW64ge5db77J4MSBA1tGu8+ezWJGR48y/ifAAkbl5VrRKasQDULCqO73DIAMAF8KIVYKIf4NAEKIzkIIld8gH8ACIcQqAIsBfCalNKDKiAa/P0RNje1ShHsj6EkMoKCYNYuD57TTOOBmzaKLyfbt3NZfdhkjXbOzmR4hQAEhZewJiYKCAHdqChMnMqq3upoT2fXXU+Aqfj/8kDmYLrqILpsLFgQsIADyW1AQW/wG1X+vuYYCoK6O/F53HYXsrl1UR330EXe7Y8fSTjR/flAV7uzAr1oAGFmSPlwY5d3U28/x3QAudb8vA2CPgtL19fb+lUAZlpJCt8iTJ1vPDuGB0aOZe+Cxx4DnnuOKVw9V/OUXvwiqdOuBA5wbExJiQx2i+N25k56rbZgLNJx3Hvl9/HHg2We54tUjJ4eus7/8ZVD8HjpELUtSUmzwm5BATisruSbTe6W2inPO8eT3rbc8P+/UCXj0UfLbhg1Nj8OHaRdPTraWX/XsBQtYEdiPN7SlsPfy2Qwo4WBz6+A551BFO2MGF6lBFWzv1ImpOx55hKutsjLOiv360Q0zYImjQa0I+/ZlQHa045xzmCVDSgpid/nuwJCdzUnsj3/U+E1IIL9nnBEWv336xAa/Z5/NHEUAbf7KuzUgdOzIRY7id+tW8tu3L/kNIVRapQjp2zfgzbMhaNeOweVlZdwwOULCjlBCIohVnhUoKOCcA1AlEpSQUGjXjknqIgA1yAYMsGU2k6BRUKBl/y4rC1JIKESQX5Wnq6QkNvjNz9fiOsvKghQSCqmpVCcFoVLyB33/NTtnkzf69CEndrVLxIBJLEyoGgA2t0kAWnqfoPS6BkFNYn4Do6IQ6rvYgV81icUivz4T/ZkMO/Vfu7vBOkJCCYkoUPzm5bGZdpjEtmyhFiAINbDtkZNDzVBQxmuDsGULNaB2VD+Eio4daZewS//NyrIHv3b3cIo7IdHCk0GpmwJO5m4dkpJoXrDLIIvWamn+kJAQRKyEwYhFflUBLTvwu3Wrffh1hITdoZKwRYG6SVX5ssMg27KFO5tY8OFXcPg1FqoKoJ34tYOQsHv+phjqgiEiitRNahKzWh1y9ChTWNtlJRYp6IWElQO2pgbYsyd664b7gxDcCVdUWMtvbS1DWezCr7OTsBF8/gjqFwrYMd46qEns4EHPTAVmQxke7bISixTUSvfoUS03ohWIVX6F4MKirq6VlOwmQJ/Yzw47NcdwbXcom0QUqJuUThewdjehPENyc+0xyCIFJYQBe/Brl0ksUlBCGLBW5aTvv3YQwo66ye5QVXyiSN0EWDvINm/mq10GWaSgVrqAtW6adpvEIgW78KuebRd+VRvaKvNiFRwhocR4FCzZkpI0IbFokXXtWL6czmCpqSEFE9sWynsMsJ7fjAy6F8cqvyr62gosW8a0IGlp9uBXtWH+fK0wpJ1g/5nRaDQ00GE6CtC7NzBhAn27VelQK1BRQVXI2LFsU6xA8ZuRwe9oFcrLucodMya2+O3Rg/kOO3Swtv/q+VVZDKxEhw6MugYcIWFP1NVFhT0CoEasqIi7iR07rGvH9u0cZEVFUUNdQNDzu2uXde3YsSP2+d2507p2KH67dbMPv1268NWOdglHSNTWslJYlEAI5sGxSkjU1ABVVbHneaOgnAOs4vf4cWDvXvvoyyMNZVezSgjX19O92G5OAXZ2g7URTRYhISGqcksIwQ6+e7cWB2gmlME81jyb9MjL40StQmjMRHk5J4pYFcIAv9u+fcFV1o0UKiroamo3IWxnN9gYHeZBoKHBHglcAoTLxZ1EU5M1HiJ6H/5YRUEBJ2rlZWQm9O6vdprEIon8fL5awa/dPJsU7OwG6wiJI0fso5gMEGqQbdpk/rPVwFZtiEWo1NxW8hvLOzVV3nvjRvOfbVch7Kib7IzERHu6FLQCNcismsQ6dAiislgUwmohnJHBapyxCiv53byZioOMDHsKibhSNwkh/iiE2OWucb1SCHGpn/MuFkJsFEJsEUL8xqj2aM/zOtDYaPuqdN5IS2PaZasmsa5d7TXAIo20NPrzW8Vvly6xu4sAyG9OjjX8btxIDyu7xc7Gs7rpH1LKoe6/Gd4fCiESADwL4BIAJQCuFUKUGNwmDU1N/FWiTN0kBDu6FYNs06bYn8SEoHuklZNYLAthxa8V6ia78uuom/xjBIAtUsoyKWU9gLcBXG7a02tr+RpFLrAAJ+iCAmDDBnOfW1ND19D8fPsNskhCCGv4ra1lkFm88Gu2kKirY4yPHflV7dm719p2+ILRQuIeIcRqIcSLQoiOPj7vAkDvkb7TfawFhBCThRBLhRBLq6qqQmpMCyl98iRfo0zdlJrKLXtVlbnZYNXKOjU1KpLmhgzF74ED5NgsqJxYKSm2L7keFhS/hw6Zmw12yxbOAXbkV7Vn4UJrXK9bQ1hCQggxWwix1sff5QCeA9ALwFAAewD83dctfBzzueGSUk6VUpZKKUtzVZawcKEywNohgUsQuOIKoLSU781cja1fz9czz2QbYhUTJgAjRvC9mSonxe9ZZwFXXWXec83GhAnA6afzvZm7NcXvGWfYj9+cHGDoUL5X05JdEJaQkFJeIKUc6ONvupSyUkrZKKVsAvBfULXkjZ0A9KXIuwLYHU6bgoKK5okym0RqqpZzxsxJbONGbov79bPfSiySSE0F+vfne7P5BYC+fR1+jYDit39/e/KrFBp2s0sY6d1UqPv3SgBrfZy2BEAfIUQPIUQygIkAPjaqTS2gfo0otMIq45vZgyw7O6oC1ENGt27sFlbwG0WxnSFDOT+YzW/Hjvbl165usEYuof8qhBgKqo8qANwJAEKIzgCel1JeKqVsEELcA2AWgAQAL0op1xnYJk+ofZ0dlxVtICXFfDfNjRtpcLSb0c8IpKSY76a5aRONqlG4ZgkaKSkMGFR2GDOwebP9cjbpYVcPJ8OEhJTyRj/HdwO4VPf/DAAt3GNNgTJcR5m6CdByOJk1yJqaOImNHBkfQsJsfqWkEC4tjR9+8/PNE8JS8llDhtiXX7vGSthUphoHjw5y9Chfo8xwDWg5nDZvNqdT7dpFF8142UmoSWzrVnO2/3v2sDJZvPFbVmaOB7ryBLSj+6uCXXcScSckPKDCLqPQn1MNsupqc3yrldGvoMC+2/VIQvFbW8uMu0Yj3vhVi5wTJxi7YDQUv3ZW59nVJmFTukxCQwMFhV2XFq1ATWKAOW6E+kEWhXQFDaVuAhx+jYC+/5qhctILYbvy6+wkbIAW5B84YL8kLgFCCC1b6Q8/GP+8DRuYdC4ry76DLJJwuYDOnfle+dcbiY0b6XXToUN88Gv2ImfTJioMsrPty68jJOyIxET7Ra4ECJeLE0p6OrBihfHPW7eObqFC2He7HkkIAWRm8m/5cuOft3YtEye6XPadxCIJPb9m9N81azS3W7vyq8aVFTmtWkMcDPdW0NhIP8coRHo6O1VeHrBkifHPW7eO6ZXVs2Mdit/cXHP4XbOGEyag8RzLyMgwt/+uXMkYCfVsO0Kl31+/3tx0O20hvoVEbW1Uur8CVDXdeivQsyeTwhmJY8dYbrJXLz5TqWFiGfn5nvwaqQKormYOo96944ffvDx+1169jOe3pobOHXbvvx07AqNH872dco7Gt5Coq4tadRNA+VZUxOJ6RiaiUzaP4uKo9BYOGYmJ/M5qEjcKyuYRr/zW1NAF2CgofouK7M+vap+d7BLxLSQSE6M+x0RREV+NNF6vdSdUKS427hl2RTd3ZjGHX2OgvvM6A/MsqHursWJn2NENNr6FRFNTVMZI6GHGIFu7FkhO1sqmxhO6d+er0ZNYvPJrlpBITLSvmkkPO3o4OUIiyl11cnLoOmn0SreoKGrNN2EhO5tGbKOFcNeu8clvx440JBstJKKFX0dI2A0xICRcLg4AoweZHUs+mgHFr5FCON757dbN6b8KdszfFN0zZLiIASGh6gUbNcgOHKBRUcVIxBv0/BoxcA8fBnbu1FKTxxv0ixwj+D12jN5T0dJ/nZ2EDeDRUU6ejPqRKQQHWVWVMR5OSvhEyyCLNNRK9+BBYzyc1A4lXvlV/ffoUWNyZOn5jYah7hiu7QYpo9oFFtBWYoAxuwnleRMt2/VIQwhG6gLG8hvvQgIwhl+9Z1M08Ouom+wEFa0S5d5NQjAqGGAR9Uhj+XIabpOTo2MlFmno+V20KPL3X7aMjgepqfHJr8ulJT0wit/kZHIcDUJCtXHRImbItQPisFu6oXYQUR4nkZfHpHuZmcCCBZG//7JljD6WUsuKGk/Iy6P3TYcOwPz5kb//0qVaort45Dc3V+P3228jf/9Fi+j6Gi39V6W8OXiQNVzsgLgSEh5buJoaHwejD0OGALfcwu10pBODqWp0RUV8xtChkb1/NEDPb6Sr1KlqacXF8cvvoEH87sXFkU8ZrvhV/Xf48Mje3wi0awdccw3f22VqMkRICCHeEUKsdP9VCCFW+jmvQgixxn3eUiPa4hfLlvF19WpTH2sUiouBigqgvj5y9ywrY3orFVAWz1D8Hj8euXuWlzPlRzxGWnuje3cWH6qri9w9t22jd1M0RFrrYTcPJ0OEhJTyJ1LKoVLKoQDeB/BBK6ef7z631Ii2+IWqb61SL0Y5ioupQYuk8U+lyHYmMXLQ2BhZflWKbIdfConGRmbDjRSilV+7eTgZqm4SQggA1wB4y8jnhAQlJKIhDDMAqIGw0ueeLTSsWMGaTMr7JJ6hdlORrH2g+FX5oeIZRvC7cqXmwhxNsJuHk9E2ibMBVEop/WlzJYAvhBDLhBCTDW6LJ5TTu0riH+UoLKSHTCSFxPLlQI8e9A6Jd+Tl0UMm0kK4qCjqHewigry8yBfQUvympkbunmbAbuqmkJfRQojZAHylJPu9lHK6+/21aH0XcZaUcrcQIg/Al0KIDVLKeX6eNxnAZAAoCkPJ2MINLtqWGX6QkMB8+ZEaZFLyXqWl8ema6Y1I8wvwXoMGOfwC5MAIfgcMiA7XVz3sJiRC7p5SyguklAN9/E0HACFEIoCrALzTyj12u1/3AfgQwIhWzp0qpSyVUpbmKsf1SCBGRqgQHGQrV0ZGl7l7NyO4+/SJvkFmBIRgUaBVqyLDb2Ul05307u3wC3AY9u5NP5JIxLfu3890J717R98QjxkhEQAuALBBSrnT14dCiDQhRIZ6D+BCAGsNbI9vxMgIFYIT+rFjkXHVVEZrZxIjlJCoqYmMq7HDryfUIuf48cjwu9TtK9m3b/TxG0+G64nwUjUJIToLIWa4/80HsEAIsQrAYgCfSSk/N7A9vtGpk+mPNAIuF9C/P99HInJ18WLes2fP6BtkRsDlAvr14/tI8LtoEe/p7NQItcgBIsevENEpJOLGcC2lvEVK+W+vY7ullJe635dJKYe4/06RUv7ZqLZoz/dxMCHB6MeaAiG09ANffhn+/b75hkbrQ4eib7tuBIRgacm0tMjwO2cOPdIOH3b4BTR+09OBWbPCv98335DfQ4eiT0io9m7YwNgcq+F0zxhBz54cYMXF4edwamykOqRbNwqdnj0j08ZohuK3e/fw+W1qYiynw6+Gnj2ZnqNHj/B3ElJq/LZrRzVWNCExkTwcPRr5KPRQ4AiJGNlJDB8OXHUVUFLCSGmVdSQUrF/PSOuBA3nPUnPDHG0JPb8VFYyUDhUbN/L3cfjVcOqp5OKUUxh5feRI6PfavJm2uZIS3nOEX3cYe0IIYOxYVu2zg10ifoWEUuAnJVnbjgijXz9tpRoqvv9eu5cDTyh+l4aRREbtRBx+W6J/f+4EFi8O/R6KX2XjiFYIYQ+7RPwKCZcr+qJsAoCaeMJRiSxcyBjDaCgcbzb69uWrEqSh4Pvvqbpy+G2Jfv04OYbD78KFVONFe6YAR0hYjcOHo8+iFQCysjj5hJN2+dtvOVgdg2pLZGZy8okEvzGSESaiSE9nlHQ4QmLePO5Iop1fR0hYjeRk+1T1iCCEYBTv3LlaXaVgsHs3deaDBsWkDA0bQtCWMG9eaEFflZUsqenw6xtCMEp6wQItvVow2LePSRiHDIl+fh0hYTUaG2MmRkIPl4uT2JEjWsBWMJgzh6/OJOYbLhcweDANo6HYfRS/gwc7/PqCWuRUVwNLlgR//Tff8HXgwOjfCbtcjuHaWtTVRf9+1AfUIAOAr78O/vo5c1glrLg4+geZERCCHjgA8NVXwV//9ddUCfbo4fDrC2qnJkRo/H7zDV1pYyGS3dlJWI1jx0LTx9gcQnASGjgwNCHx9dfAeedxAov2QWYEhKBdYvDg0IXwuefyPg6/LSEEAxaHDg1NSMyZA5x9dmz0X0dIWITmjpOUFJPeTS4XO9bo0azJHIzZpayM1dJGj+Y9on2QGQGlAhgzhgboYCrVVVQAW7YA55/v8OsPqv+OGUPjdW1t4Ndu28Yo5TFj+BtF+07NUTdZDSnpJxdjEIJF1NPSqFH7+OPAr33nHe0eBw5E/yAzAkIw1UO7dhQQH30U+LXTpvE1IYH8OkKiJYSg42FqKkvxhsJvYiKzwEY7v6qvffghx7RViD2lfKBoaor+XuQD/fvza3XqxGI2778P/PjHgV07fTrdZ3v25EBTMQEONCh+c3M5kb3/PjBxYmDXTp8OFBQwTURiYvQHexmBfv24fsvL0/i97rrArv3kE17Xuzf57d3b2LYajf79qRHfuZNCLzvbmnbElZDw0O/Fwn7UB4qL+VddTQPr118H9lUPHWIU8dix1JnHSOnviEPxq9KWzJnDgdxWdpcjR+itc/75wKhRMVMQMeIoKuJfbS0dMObMoatxWz4m1dUMojv33Njht7iYi70337RW7RR7s2SgkDImhYSCEDT+VVUFljDt44852cWCf7kZcLnI74EDgQV+ffIJ/f6HDInpbhcxuFzM53ToUGCBi9Onxya/dihAFEN0BokYVTcpKFfYlBTgtdfaPv/VV5k1s0ePmKYlYlCumqmpgfPbtatTnyNQKFfjdu24km4Lr73GOu+x4PqqhyMkrESMqpsUXC7a5S+4gIOsrs7/udu3c1s/frzjmhkohOAENnYs8NZbrXvh7NoFzJ5NfmPBNdMMCEEBPHYs+29rWXf37GGNj1jk1w4FiGJ3lmwLMS4k1EC56irqw997z/+5//kPXy+7jK8xTEvEoPi94gqG3CjPGl9Q/I4f73mtA//Q99/qauDtt/2f+5//cDhPmOB5bSzADqVM43M6UGI5hmdD1bmGDWNe/ccf993RqquBf/0LuPJKoEsXz2sd+Ifi6NRTqXZ6/HHfsZk1NcCzz3IC69bN81oH/qE4GjyYf3/7m29+6+rYf8ePp6FXf20swFE3WYD/397Zxlhx1WH897ArIOsLUKrdArGgTRMw0SIRQWMaUy3F7QsWGxqToq2hsY0FCV0h25L6pQ2tYbdNBdpYKSq2vFitQZHWpka/uBWQ5UV2YQuoWxtbomjiB9vq8cP5z97Zy53du7sz9869e37J5J4558zc5z4zZ87MOWfOlShcLcdAJSHBvff6Sc92774w36ZNflx6a2vhRKynQpYV8VPnvvv8S1yl7nbb2/0Y99bWMXHapUbcow0b/KSTO3ZcmK+93Q/OuOee+rz3q/nmJklflHRc0v8kzS9KWy+pV1KPpGsStp8lqVPSKUk7JY0fjZ6yiaaXfPPNinxdNYjfgdx8sx+Js3q1H40T0d0NDz7o0xcsqM9ClhVxf5ct809sa9b48ewRJ0/CAw/ATTfBokWhEh4OcX+XLvX/3rd2rZ/lNSLy98Yb/VQc9ehvPTxJHAO+APwmHilpDrAcmAssBjZLKjWSfCPQ7py7HPgHcPso9ZRHNMdzPQymTiB+B9LQANu2+Qriuut8R+qJE9DS4i3YtKmQF+qrkGVFvK143Djv7/nz3tO+Pl8Bt7T49006Onze4G/5FPv71FP+P58jf3t6/Lk8cSI88kghb3zbeiAPfRKjepnOOXcCQBcelRuAZ5xz/wHOSOoFPg70jyiX3+gzQPQ+5XbgfmDLaDSVRVRJ1OEssBHRITl4EA4f9uGVK30nX/SPXU1N/uni+ef9etTmW0+FLEvGjfPeHj3q1++4A7ZsKfQ9NDXBqlV+5A0U/A1PauVRyt/Nmwv+TpoEd99dmAiwHv2NyuKhQ9DV5cO33jr0y5upanApPMdI+jWw1jl3wNYfA37nnPuhrT8J7HPO7YltM83yfMjWZ1qeDyd8x0pgpa1eAfSMUO404NyQufJDremF2tNca3qh9jQHvdlTjuYPOOcuHs5Oh7yVlvQr4JISSW3OueeSNisRV1wblZOnkODcE8ATSenlIumAc27+0DnzQa3phdrTXGt6ofY0B73Zk5XmISsJ59zVI9hvHzAztj4D+GtRnnPAZEmNzrm3E/IEAoFAoIpk1Xr3M2C5pAmSZgGXAy/HMzjfzvUSsMyiVgBJTyaBQCAQqAKjHQK7VFIfsBD4uaT9AM6548Au4I/AL4G7nHP/tW1+IelS28U3gTXWsX0R8ORo9JTJqJusKkyt6YXa01xreqH2NAe92ZOJ5lQ6rgOBQCBQn9TRYLFAIBAIpE2oJAKBQCCQyJipJCQttilCeiWtq7KWmZJeknTCpjVZZfFTJb1g05S8IGmKxUvSo6b9iKR5sX2tsPynJK3IWHeDpD9I2mvrJadVsQELO01vp6TLYvsYcrqWlLROlrRHUrf5vLAG/P2GnQ/HJD0taWKePJb0PUmvSzoWB58+HgAABFFJREFUi0vNU0kfk3TUtnlUGv1rnQmaH7bz4oikn0iaHEsr6V3S9SPp+KSpN5a2VpKTf8esch475+p+ARqAV4DZwHigC5hTRT3NwDwLvxs4CcwBHgLWWfw6YKOFlwD78O+WfALotPipwGn7nGLhKRnqXgP8CNhr67uA5RbeCnzNwncCWy28HNhp4Tnm/QRglh2Thoy0bge+auHxwOQ8+wtMB84A74x5++U8eQx8GpgHHIvFpeYpfgTkQttmH3BtRpo/BzRaeGNMc0nvGOT6kXR80tRr8TOB/cCfgGmV9DiTi0neFjNlf2x9PbC+2rpiep4DPot/i7zZ4pqBHgs/DtwSy99j6bcAj8fiB+RLWeMM4EX8VCp77SQ7Fyts/R7bybzQwo2WT8W+x/OlrPU9+AuuiuLz7O904C9WsBvN42vy5jFwGQMvuKl4amndsfgB+dLUXJS2FNhh4ZLekXD9GKwMpK0X2AN8BDhLoZKoiMdjpbkpKoARfRZXdayZ4EqgE3i/c+41APt8n2VL0l/J39UBtALRVGMXAeedfxGy+Lv7dVn6Py1/pfTOBt4Atsk3j31XUhM59tc59yrwbeDPwGt4zw6SX48j0vJ0uoWL47PmNvwdNUNoKxU/WBlIDUnXA68657qKkiri8VipJIY1BUilkPQu4MfAaufcvwbLWiLODRKfKpJagNedcwfL0DRYWqWOQyP+kX2Lc+5K4N/4ppAkqq0Xa8u/Ad/McSnQBFw7yPdXXfMQDFdfxXVLagPeBqJ/qsidZkmTgDZgQ6nkYeoakd6xUkmUM01IRZH0DnwFscM596xF/01Ss6U3A9Hs+Un6K/W7PglcL+ks8Ay+yakDm1alxHf367L09wJ/r6DePqDPOddp63vwlUZe/QW4GjjjnHvDOfcW8CywiPx6HJGWp30WLo7PBOvMbQG+5KztZQSa+6cWylDzB/E3Dl1W/mYAhyRdMgK9I/M4rbbKPC/4O8vTZnbU8TS3inoEfB/oKIp/mIGdgA9Z+PMM7KB62eKn4tvep9hyBpiasfarKHRc72Zgp92dFr6LgZ2quyw8l4Edg6fJruP6t8AVFr7fvM2tv8AC4DgwyXRsB76eN4+5sE8iNU+B31veqFN1SUaaF+Nng7i4KF9J7xjk+pF0fNLUW5R2lkKfREU8zuxikrcFPxLgJH6UQluVtXwK/5h3BDhsyxJ8G+eLwCn7jA6sgO+Y9qPA/Ni+bgN6bflKBbRfRaGSmI0fLdFrhWWCxU+09V5Lnx3bvs1+Rw8pjF4ZROdHgQPm8U+tsOTaX+BbQDf+z7x+YBer3HgMPI3vL3kLf1d6e5qeAvPtt78CPEbRwIMUNffi2+yjsrd1KO9IuH4kHZ809Raln6VQSVTE4zAtRyAQCAQSGSt9EoFAIBAYAaGSCAQCgUAioZIIBAKBQCKhkggEAoFAIqGSCAQCgUAioZIIBAKBQCKhkggEAoFAIv8H3KgL51HRW3YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109f29a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sinefit(2, freqs[0], 40, 0, 0.3, 0, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipywidgets import interact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f49bbe28dfba4cc69e7f11d4cd7f6567",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "interactive(children=(Dropdown(description='session', options=(2, 15, 25, 27), value=2), Dropdown(description='freq', options=(0.001584893192461114, 0.002511886431509582, 0.003981071705534973, 0.00630957344480193, 0.01), value=0.001584893192461114), FloatSlider(value=40.0, description='Tbar', max=50.0, min=30.0), FloatSlider(value=0.0, description='inphase', max=6.283185307179586), FloatSlider(value=0.4, description='gain', max=1.0, step=0.01), FloatSlider(value=0.0, description='phase', max=0.0, min=-6.283185307179586), Output()), _dom_classes=('widget-interact',))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.sinefit>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interact(sinefit, \n",
    "         session=sine_sessions, \n",
    "         freq=freqs, \n",
    "         Tbar=(30., 50.), \n",
    "         inphase=(0, 2*numpy.pi),\n",
    "         gain=(0., 1., 0.01),\n",
    "         phase=(-2*numpy.pi, 0),)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "gains = [0.3, 0.32, 0.25, 0.24, 0.19]\n",
    "phases = [-0.28, -0.38, -0.58, -0.88, -1.28]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11a430ba8>"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1188a3e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gainax, phaseax = bode(omega, G)\n",
    "gainax.scatter(freqs, gains, color='red')\n",
    "phaseax.scatter(freqs, phases, color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FFT based bode diagram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "next_values = {'time': 0, 'value': +A}\n",
    "def random_noise(t, lab):\n",
    "    if t > next_values['time']:\n",
    "        next_values['time'] += numpy.random.uniform(50, 300)\n",
    "        next_values['value'] = -A if next_values['value'] == +A else +A\n",
    "    lab.Q1(Qbar + next_values['value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'time': 0, 'value': 10}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TCLab version 0.4.6dev\n",
      "NHduino connected on port /dev/cu.wchusbserial1410 at 115200 baud.\n",
      "TCLab Firmware 1.3.0 Arduino Uno.\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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kjKRs1oEEEAAAQQQyApwmKkKu0Jw2jf8nD/u9q3CQPCTCCCAAAIIpFSAw00VHvio077c7VvhQeDnEEAAAQQQSLkACWAFd4B8p31ntDVXcCv4KQQQQAABBBBIuwCngCu0B3Dat0LQ/AwCCCCAAAIIDCvAEcBhicb+AU77jt2QNSCAAAIIIIBA6QRIAEtnGbkmS/4uuWeFW7Np2+By3u07SMEMAggggAACCFRBgFPAZUbfuLXXS/6427fM4KweAQQQQAABBIYV4AjgsETFf8CO/q3r6hlcwXVnHe4O2KvVNdTXDbYxgwACCCCAAAIIVFqABLBM4pb8XXT3s27t5t0J4JSJTSR/ZfJmtQgggAACCCAwcgFOAY/calSf3NC93Uv+7MtTWppGtQ4+jAACCCCAAAIIlEOABLAMqva8v/Pv+JW35mvOOJSjf54IFQQQQAABBBColgAJYInl7dTvObf+cshaD5y+x5A2GhBAAAEEEEAAgWoIcA1gidXt1G/4Hb/3XnCsa2yod03jyLVLTM3qEEAAAQQQQKBIARLAIuGivpZ76tee9zdxPMRRVrQhgAACCCCAQPUEOCxVIvvcU78TGhvctNbxJVo7q0EAAQQQQAABBEonQAJYIsvcU793n38MN32UyJbVIIAAAggggEBpBTg/OUZPO+27o8+/69dO/XLN3xhh+ToCCCCAAAIIlE2ABHAMtJb8nXHTE94aOPXrcVBBAAEEEEAAgRoU4BTwGAbl5bXvDvk2p36HkNCAAAIIIIAAAjUmQAJY5IBs6+1zV//gN963OfXrcVBBAAEEEEAAgRoVIAEsYmDsjt+zbn7S+2ZzYz13/XoiVBBAAAEEEECgVgVIAIsYGbvjN1waG+rcPecfy12/YRTmEUAAAQQQQKBmBbgJZBRDY0f+LPkLv+fX3vF70Mw2kr9ROPJRBBBAAAEEEKiuAAngCP0t+bvknhVuzaZtg9+wO35J/gY5mEEAAQQQQACBmAjE/RRws5wfVLyieEHxqGKOwsp0xSOKVxUvKk5QFF3syF9u8scdv0Vz8kUEEEAAAQQQqKJAEo4ALpbfjxT9ii8qblWcqLhG8ZSiQzFP8QPFexQ7FKMque/4/eaZh7n3zdiD076jUuTDCCCAAAIIIFArAnE/AtgjyIcVlvxZsYRvts2onKX4TmbOuWc0fVPx4Wx9xJPcd/zaF+vqHMnfiAX5IAIIIIAAAgjUmkDcE8Bcz8vU8EPFnopGxVpFUFZpZr+gMpKpJX8r13e7bTv6vI8fMK3Vq1NBAAEEEEAAAQTiJKBjWYkpV6snH1N8RDFBYUf8xiuCcp9m7JrA24KG7HShphaZ0tLSMuve+x9wW3c6d8tLfa53V7DEuQsPbnCTtcZx9Uli292/qLmenh7X3NwctSi1bZj4Q48HHr6AX2P/8D2sholvgofv0dHRsUYt+/itpa8l4RpAU7lccbriJMXWbCiFczMVwVHA2Zp/Q5FbrlODRabsHDehf/HvBo7wTdDEMkkrsyZPcJ889cjUnfrt7Ox07e3tAwj8mRHAxN8R8MDDF/Br7B++h9Uw8U3w8D0qVUtCAmhH785WWPK3KQR3v+YvUixS2E0gsxQ/U4yq2KNerv/UEZm3fDSk6MjfqJD4MAIIIIAAAgjESiDuCaAdIr1WsVLxWFbeXtNxjOJLirsU9hiYXsU5ilHdAXzdWYe7A/ZqTd1RPzlREEAAAQQQQCDBAnFPAFdrbPJdkLdOy04uduxmTmom+SsWj+8hgAACCCCAQE0LxD0BLDluq+4dvvUzR3PKt+SyrBABBBBAAAEEakWABDBnJOr1kL8Zbdz1msNCFQEEEEAAAQQSJJC05wAmaGjoCgIIIIAAAgggUB4BEsDyuLJWBBBAAAEEEECgZgU4BZwzNJs2bXL77FP25y/m/GrtVrdv3+7Gjw8/T7t2t7VSW4aJL40HHr6AX2P/8D2sholvgofvoZo9tq7sJd8dtGX/4Vr9gWnTpvWvX7++Vjev4tvFAzqHkmPim+CBhy/g19g/fA+rYeKb4OF71NXV2ftny36AjlPAvjs1BIYV2Kl3RPfuDL0jcNhv8AEEEEAAAQRqS4AEsLbGg62pcQFL/L6xos+dcdMTJIE1PlZsHgIIIIBAfgESwPw2LEFgiMDKDd2DbeH5wUZmEEAAAQQQGJvA7n9oxraegt8mASzIw0IEEEAAAQQQQKCiAiSAI+C2JzY/qHhF8YLiUcUchRW7wWWRwpb9RhG8K1izFAQQQAABBBBAIL0CZb/LpAK0i/UbP1L0K76ouFVxouJvFIcpDlH0KmYqKAiMSiC42aOhvs5t6N7u3u7aPvj9P3b3uj7dEGLLKAgggAACCMRJIO4JYI+wHw6BP6X5y7P1KzT9c4Ulf1bWDkz4E4GRCVjyZzd7WNmztclZwhcu1/zot+72BfMy740OtzOPAAIIIIBArQsk7RrAywT+Q0WbYobirxS/zMYnNKUgMGKB8E0eucnfiFfCBxFAAAEEEKhBgSSdu7pavh9TfERh1wb+UfFlxf9RzFbYoZy/UNi1guGyUBWLTGlpaZm1bNmyoJr6aU9Pj2tuNs70lTXd/W7Jb+15nH7p12nfOp32Pe+gBrf3ROfq65L0fyO/ryOppXkfifLBw1fBw/ewGia+CR6+R0dHxxq1lP2VZEn5l8tO+35ScZJik8LKu4rDFSutonK/olNh1wjmLbwJxKdJ0xPa7Xq+jVt73cSmcW5Xf7/77dp33aKHXvJBVNvctdlNapvE6d+sTJr2kSE7Q0QDHj4KHr6H1TDxTfDwPfQmkIokgHG/BtDU7Ojd2Ypw8mft31N0KP5VMVXxQcU/KSgIDBGw5O+Se1a4NZu2DVlGAwIIIIAAAkkTiPs1gHaI9FrFZIU95uV5hV3zZ+UqhSWALyp+rviG4mkFBYEhAnbkbzTJ36zJE9yUlqYh66EBAQQQQACBOAjE/QjgaiHnO41t1wCeGodBYBvjI/CV0w5xrz7/lDv9L4/k8S/xGTa2FAEEEEAgRyDuCWBOd6gi4AuEn+NnR/namhsz1/mFPxU84y/clm++ubHetTXVkfzlA6IdAQQQQCAWAiSAsRgmNrIYgfBz/KbvMd69/e7uhzgXsz6+gwACCCCAQFIE4n4NYFLGgX6UQSD8HL+xJH/2EOigHDCtNZhligACCCCAQGwFOAIY26Fjw0slcNUpB2VW9fWHfzu4yoX/7X1u7sw9Mqd6p7WOz7zyzRY2jeO/mQaRmEEAAQQQiK0ACWBsh44NL5XA1Im7j/AF69x7crP7E93pGxTe9xtIMEUAAQQQSIIACWASRjHFfQhu8jACe5bflt6dgw9y7tmxK8UydB0BBBBAAIH8AiSA+W1YUuMC4Zs8anxT2TwEEEAAAQRqSoAEsKaGg40ZjUD4Jo/RfC/3s1E3dkS15X6POgIIIIAAAnEVIAGM68ix3aMW+NrHD3V2bZ+96zd8TV9wY8cDFx/vdvTtco0N9dzsMWpdvoAAAgggECcBEsA4jVaKttVO74aTMauHr/Ezine29I5KpHFcnbM7evMVSwSDZDDfZ2hHAAEEEEAgCQIkgEkYxYT1IffavnsvONZ9cvFTCesl3UEAAQQQQKB6AjzUrHr2/HIegdxr+37yX+vyfHJ0zVzXNzovPo0AAgggkFwBjgAmd2xT17Mr2ue6Yw/YM/OuXztdbNf52bt/d/X3c11f6vYGOowAAgggUEiABLCQDsvKKmCneq3YdXeWsG3c2ptJ2N7u8t/Zu6F7ZNf6TW8bn1nXjLbmsm43K0cAAQQQQCDuAiSAcR/BmG5/+Dq/+y48zv3d9593azZti+zNg8+tiWynEQEEEEAAAQSKE+AawOLc+NYYBcLX+a14Y2Pe5K/Qz+zZ6r/CjWv8CmmxDAEEEEAAgd0CHAHcbcFcDARuPveowWf42SNd7NRx+HExMegCm4gAAggggEDVBeJ+BNAu9npQ8YriBcWjijmKcPlzVfoUfxtuZD6eAna9oF3jZ2E3eVh94vhxPL8vnsPJViOAAAIIVEkg7gmgsS1WzFUcrvih4lZFUCZp5hrFw0ED08oL2PV+wQ0fdsRuXVePe2tTz+CGjPaBzoNfZAYBBBBAAAEEihKI+ylgyyLCyZ09LfjykMQNmv+q4vRQG7MVFMi92eNv7n3Ord28O/mzTVn885UV3CJ+CgEEEEAAAQSScAQwPIqXqWJHAa2cqbDnjDxkFUp1BHJv9shN/kayVTP0eBcrsyZPcFNa/Bs/RvJ9PoMAAggggAACvkCdX4117Wpt/ccUH1G0KR5RnKjYpFiieF7xbUVuWagGi0xpaWmZtWzZsqCa+mlPT49rbi7+uXpruvvdkt/aJZg6DHtAvVu2cuDZf7mwJ+9b7378h93Lzpnb4GZNHPiULvVz3Tuca210rr6u+rvsWE1y+x73Oh7+COKBhy8wtMY+4pvg4Xt0dHTYs8/28VtLX4v7KeBAxE772mnekxRbFfMVeyss6bMyTXGqYi/F/1KEy3WqWGSKEsD+9vb2oJr6aWdnpxuth532tWv9tvTudDM2bnM/ePPFjOOsA/d3kza8Hmk6//iD3C9/9NvBZSf82WHuoJltg/VaminGpJa2v9TbgocvigcevsDQGvuIb4KH71GpWhISQDt6d7bCkj872mfl3xQzMnMDfyzRJN8RwNDHmB2rQPiav9x13flkdPKX+znqCCCAAAIIIFBegbgngHaI9FqF3UXwWJbK3iN2THaeSYUFwtf8jfSn7dq+o/af4n2chzp7HFQQQAABBBAoqUDcE8DV0hjJRWHnlVSNlY1J4CunHeIOnN6auZ7PThPbjR32TL8HLj6ehzqPSZYvI4AAAgggMDKBuCeAI+slnxqzQPAcP3vwspVw3a7329C9PXPdX/j5fvl+tLlx4OHNtnxCU8Pgx2zdwfoHG5lBAAEEEEAAgZILkACWnDR5Kwxf12dH6ayccdMTmel9Fx7nop7tl1nIHwgggAACCCBQkwIkgDU5LLW1UeHr+sLztpUr3tg45MHOw2091/cNJ8RyBBBAAAEEyitAAlhe39Su/Yr2ue7YA/Z0G7f2Zgzamhvdrv5+19jAad7U7hR0HAEEEECgZgRIAGtmKJK1IdP19g67nm9GW/EPkU6WCL1BAAEEEECgdgSS9iq42pFN6Jb8sbvX9ezY/caONzdtS2hP6RYCCCCAAALJFaj2EUB7yeuXFLMVDyoeUgTles1cGlSY1obANaG3ddgW8XDn2hgXtgIBBBBAAIHRCFT7COCN2tj3K15SfEPxLUVQ/jSYYRo/AW70iN+YscUIIIAAAukRqPYRwA+K+nBFv+I7inuz04s0HckDnvUxSi0JLDr1YHforEk8z6+WBoVtQQABBBBAIEeg2kcALQG15M/KFsXpiumKWxSUKgrYs/+29fa5rt5+t2NnMETDb9DE8Q0kf8Mz8QkEEEAAAQSqKlDtI4Dr1ftDFC9mFXZoepbi+4rDsm1MKiwQfvDz5q4+N2nVbyq8BfwcAggggAACCJRToNoJ4CXqXE9OB3eqbkngJ3LaqVZIIPdhz6P5Wa79G40Wn0UAAQQQQKA6AtVOAP+Xun12RNf71LY0op2mKgpcc8ah7sDpe0Q+3DnYLN7lG0gwRQABBBBAoHYFqp0AHlS7NMneMjvN27er33X17MhMG+rrnIWVt7u2R3beHurMw50jaWhEAAEEEEAgVgLVTgBHfndBrFhre2PD1/jV9paydQgggAACCCBQDoFqJ4B2o8c7ER2zQ1GWHE6NWEbTGAWKucZv1uQJbkpL0xh/ma8jgAACCCCAQC0IVDsBfFkIp9QCBNvg3FWnDJyR//rDv81wXNE+1737u2fdMR+al0n+glPEWCGAAAIIIIBAvAWqnQDaxWavx5swOVs/daJ/hG9623i3TdcFTmu1N/ZREEAAAQQQQCApAtV+EPRY3/bRrIGwdwi/onhB8ahijsLK7Yqg/XHNz7PGtJXggc4burdnHuxsD3d+Z0tv2hjoLwIIIIAAAgiEBKp9BPCI0LYUO7tYX/yRwq4Z/KLiVsWJih8oPq+w5wp+VHG/YrYiNYWbPVIz1HQUAQQQQACBUQlU+wjgqDY24sP2EOmHFcHdxE9pfrbCykMKS/6sWPssRbUTXtuWipXR3uxhD3EOP8g5PF+xjeaHEEAAAQQQQKDsAklLiC6T2A8j1KzdEsUgIYz4SLqavnnmYc6u+WtrbnS7+vtdY0P94Dt8H7j4+AwGD3VO1z5BbxFAAAEE0iMw1mvwaknqam3MxxQfUWwNbdg5mv8HxQmKdaH2YHahZiwypaWlZdayZcuCauymO/VwZ/3PbVWq++aWfveDlbsi+3DpYQ2urWn44e/p6XHNzXapJSUQwCSQGJjigYcv4NfYP3wPq2Him+Dhe3R0dKxRyz5+a+lrw2cApf/Ncqzxcq30k4qTFJtCP2DvE/6qwpLCN0LteWenTZvWv379+rzLa3nBaK75u33BvBHd3dvZ2ena29trudsV3zZMfHI88PAF/Br7h+9hNUx8Ezx8j7q6uookgEk4BWxH7+x9wrnJ31lqs+TP2keU/OlzsS4jveZv5qRmHuoc65Fm4xFAAAEEEBibQNwTQDtEeq1ipeKxLIU9W/AYxT2KtYrwNYF2JPCPitSVm889avBdv/ZcPx7qnLpdgA4jgAACCCAwKBD3BHC1epLvNHbjYC8TONOnC/3s2X5W7BVtG7f2urc22U3R0cVu6OCBztE2tCKAAAIIIJA2gbgngGkbr0x/Lfm76O5n3drN+RO+VMLQaQQQQAABBBAYkUDcnwM4ok4m7UN2tG80yd+syRO45i9pOwH9QQABBBBAYAwCHAEcA16tfvWK9rlu3uypmc3b0rszk/xxzV+tjhbbhQACCCCAQOUFSAArb172X5zeNt5NaGrI/E4wLfuP8gMIIIAAAgggEBsBEsAYDJVd82enfe2tHTZ9Z0tvDLaaTUQAAQQQQACBWhUgAazVkclulyV/l9yzwq3ZtK3Gt5TNQwABBBBAAIG4CHATSI2PlB3xG23yd8C01hrvFZuHAAIIIIAAAtUU4AhgNfXH8NtXnXKQ23dKi/uCjg4G5cZPHensLR/2zD8KAggggAACCCCQT4AEMJ9MhdqD6/vsYc5W7IjfxKaBYenq2ZH3er+pE5sGb/QINrVlfAPJX4DBFAEEEEAAAQTyCpAA5qUp/4Lw9X125M7KaJ7vV/4t5BcQQAABBBBAIIkCJIBVHNXw9X2jTfzsOj97tp895NmuEeRhz1UcSH4aAQQQQACBmAmQAMZgwOx6v6P3n+rslLCdHrbEL7jO78ZPH5k5bWynkHnYcwwGk01EAAEEEECgBgRIAGtgEIbbBLvezxK+aa3jh3zUkr6o9iEfpAEBBBBAAAEEEMgKkABWcFfo3blr8Nd29O1y23r7BuvMIIAAAggggAAClRIgAayQtCV/Z9z0RIV+jZ9BAAEEEEAAAQTyC/DAuPw2JV2yckN30evjwc5F0/FFBBBAAAEEEIgQIAGMQKmlpq99/NDBGz5qabvYFgQQQAABBBCIrwAJYBnHzp7zt6F7u7PTv293bS/qlxrH1RX1Pb6EAAIIIIAAAgjkE4j7NYD29OR7Fe9XbFO8rbhY8ZpiuuJOxXsVln19QfFzRUVK+CHPFflBfgQBBBBAAAEEEBihQBKOAC5WX+cqDlf8UHGrwso1iqcUByoWKJYqGhUVKeGHPI/lB7n+byx6fBcBBBBAAAEEogTifgSwR516ONQxS/guz9bP0nROdv4ZTd9UfFjxk2xbzUxuPveovM/yCx74XDMby4YggAACCCCAQOwF4p4A5g7AZWqwo4B7Kuxo31pFUFZpZr+gUmgaPK/Pkq9g3j5vz+6rr6vLvJHD6m3NjW5Xf3+mbUvvTmenfYP20Tzjz36HRC9Dxx8IIIAAAgggUAGBJN1hcLW8Pqb4iGKCwo74hV+dcZ/qjyhuU4TLQlUsMqWlpWXWUVfbR537Hx9ocNc+X/6HNV96WINra6rNoejp6XHNzc0ZD/4YEMDE3xPwwMMX8GvsH76H1TDxTfDwPTo6OtaoZR+/tfS1pBwBtNO+pytOUmzNxk5NZyqCo4CzNf+GIrdcpwaLTBnfPKF/Utukgcqs97hJK3+fXVKeyazJE9zpf3lkzb7Ht7Oz07W3t5en8zFdKyb+wOGBhy/g19g/fA+rYeKb4OF7VKqWhATQjt6drbDkb1MI7n7NX6RYpJinmKX4maLi5cq/OMgdud8U73SxnT7u6tnhprQ01WzyV3EofhABBBBAAAEEKiIQ9wTQDpFeq1ipeCwrZo98OUbxJcVdilcVvYpzFDsUBcvut/XqmTJFPrsv9wf2bG1yE5oaBpuD+Wmt4TPUg4uZQQABBBBAAAEEyioQ9wRwtXTyXTy3TstOHq3eNjtxnC0PvWCXEVIQQAABBBBAAIFkCSThOYA1MSL19fnyUOd4ll9NDBEbgQACCCCAAAJZgbgfAazYQF7RPjfzmJfrHn0l8jcX61l+dj2fPQrGru2zYtf5NSgx5BEvkWQ0IoAAAggggECVBEgARwg/va3w9XrhRC+4xm+Eq+ZjCCCAAAIIIIBARQU4BVxRbn4MAQQQQAABBBCovgAJYPXHgC1AAAEEEEAAAQQqKsAp4BFyR93IMUOnhdfpUTH2MGe7/o+CAAIIIIAAAgjEQYAEMGeUWvUG4aWfP8aNHzfw3D57/29jw+539T5w8fGZdwJbm133t3FrLw9zzjGkigACCCCAAAK1LUACmDM+9XV1bg/dvRuU3Dt4rR5u42HOgRRTBBBAAAEEEIiLANcAxmWk2E4EEEAAAQQQQKBEAiSAJYJkNQgggAACCCCAQFwEknAK2B7QZ+8Dblf0KF5Q2Ht/D1TcoZim2Kw4T/GSomDZtGmT22cfe8UwxQS2b9/uxo8v/AzEtElh4o84Hnj4An6N/cP3sBomvgkevodqs4a0lKEhCQngNXLpV7wvO52ZdbpZ08WKJYozs9N5mhYskydPdqtXry74mTQt7OzsdO3tlltTAgFMAomBKR54+AJ+jf3D97AaJr4JHr5HXV1dn99SnlrcE8CJYvmcwg7ZWRJoZa1iuuJoxckKKw8oblDMUbymyFt29fdnXudmd/gmrdhr6uyu5dGUrt5+t6F7u/cVe+RNJXyK2V5vQ8tUCZtUyqJMXWG1CCCAAAIpFYh7Avhejds7iqsVJym2KRYpNineUuxUWLHk8A3FfoqCCWC3XuNrSVLS7u61ZOqSe1a4NZuMaORlc1efu3PVM94X7LmHN376yLImgcVur7ehZaqETW5fMC9x+0qZ2FgtAggggEANCcT9MNeRsnxW8RnFnYojFI8q/jJbn6tpUJ7WzJWKnwYN2elCTS0yZVzrlFlLly51bU1xpwl6NDC1o1bX/3r0R5X7lTjWRRwNvfSwhrIaFbu9fq/LUwublNuhPD0o7Vp7enpcc3NzaVca47Xh4Q8eHr6H1TDxTfDwPTo6Ouy+hcl+a+lrcT8CaEf1dinuydI8p+nvFfsr9lZY/+wooGVzdvTPPp9brlODRabU19f3z58/P3FHdew0bnAk77qzDndTJo7szSWPPfaYMw8rG7f0uoX32T02Tm3lPfJV7PZmNq7Mf4RNOAXM9Uy5uxvXM/kiePgeVsPEN8HD91Cte0hLGRringBukMm/K+wuhYcV78nG45quUJyjWKI4Q7FaUfD0r5anoljyN9JT3HYkdKSfLRfeaLa3XNsQXm8tmIS3h3kEEEAAAQRGKxD3BND6e5Hiu4pvKOxo4IWKNdnpEk3t+sAuxQIFBQEEEEAAAQQQSL1AEhLAlRrFgXOU/nC+rOpxfhM1BBBAAAEEEEAAgXoIEEAAAQQQQAABBNIlQAKYrvGmtwgggAACCCCAgCMBZCdAAAEEEEAAAQRSJkACmLIBp7sIIIAAAggggAAJIPsAAggggAACCCCQMgESwJQNON1FAAEEEEAAAQRIANkHEEAAAQQQQACBlAmQAKZswOkuAggggAACCCBAAsg+gAACCCCAAAIIpEyABDBlA053EUAAAQQQQAABEkD2AQQQQAABBBBAIGUCJIApG3C6iwACCCCAAAIIkACyDyCAAAIIIIAAAikTIAFM2YDTXQQQQAABBBBAgASQfQABBBBAAAEEEEiZAAlgygac7iKAAAIIIIAAAiSA7AMIIIAAAggggEDKBEgAUzbgdBcBBBBAAAEEECABZB9AAAEEEEAAAQRSJkACmLIBp7sIIIAAAggggAAJIPsAAggggAACCCCQMgESwJQNON1FAAEEEEAAAQRIANkHEEAAAQQQQACBlAmQAKZswOkuAggggAACCCBAAsg+gAACCCCAAAIIpEyABDBlA053EUAAAQQQQAABEkD2AQQQQAABBBBAIGUCJIApG3C6iwACCCCAAAIIkACyDyCAAAIIIIAAAikTIAFM2YDTXQQQQAABBBBAgASQfQABBBBAAAEEEEiZAAlgygac7iKAAAIIIIAAAiSA7AMIIIAAAggggEDKBEgAUzbgdBcBBBBAAAEEECABZB9AAAEEEEAAAQRSJkACmLIBp7sIIIAAAggggAAJIPsAAggggAACCCCQMgESwJQNON1FAAEEEEAAAQRqOQG8g+FBAAEEEEAAAQQQKL1ALSeA80fY3VX63MuK57PxCU2tHKh4QvGK4hnFwQoKAggggAACCCCQeoFxVRZ4J8/v16l9jzzLopot6bMEMFxuVmWxYonizOx0nqYUBBBAAAEEEEAg1QLVTgD7pP8RxeacUbAE8PGcttFUp+vDRytOzn7pAU1vUMxRvJZtY4IAAggggAACCKRSoNoJ4LNSn6r4dYT+hoi2fE13aoEljU8rrlTsq3hLsVNhpV/xhmI/BQmgECgIIIAAAgggkF4BS5qqWSbqx3sVOxR7KbYpuhWjKZbUWXLXqPiq4lDFPyiWKuYqghIkhz8NGrLThZpaZMq41imzli5d6tqaqk0TbFFppl29/e76X9sBV+cuPaxhxP3r6elxzc3Nme8Vu47Ml0f5RyV/a5Sb5sImo/1uEj+Phz+qeODhCwytsY/4Jnj4Hh0dHWvUso/fWvpatY8AblGXLlT8o2JvhR2pe0lhCdlPFJMVmxSFiiV/ViyJ/LbCbvr4g8LWZ/2zo4CWzQWJoma9cp1qFplSX1/fP3/+fDetdXzQlIjphu7t7s5Vdi+Mc/Pnzxtx/zo7O117e3vme8WuI/PlUf5Ryd8a5aa5sMlov5vEz+PhjyoeePgCQ2vsI74JHr5HpWr1lfqhPL/zWbVfpjhfMVWxp8JO4V6rsOv3/l1RqNgRREsSg3K2Zp5TvK1YoThHYeUMxWrFa1ahIIAAAggggAACaRao9hHAvxV+hyI4imdj8bDiPxV2JG/wyJzmo8oMNdoNHg0KO8q3UvHXCit2ZHGJ4mpFl2KBgoIAAggggAACCKReoNoJoCVu4eQvGJBVmrGwo4GFiiV8R+T5wMtqPy7PMpoRQAABBBBAAIHUClT7FHCT5AfuMPCHYIKq1d42f4uoIYAAAggggAACCRGodpK1TI53KcLX8U1R/U6FndqlIIAAAggggAACCJRYoNoJ4N+rP3b3rt2gYTdvWNgdvHbnri2jIIAAAggggAACCJRYoNrXAFry9ynFHEVwLZ8lgdytKwQKAggggAACCCBQDoFqJ4BBnyzhI+kLNJgigAACCCCAAAJlFKj2KeAydo1VI4AAAggggAACCEQJkABGqdCGAAIIIIAAAggkWIAEMMGDS9cQQAABBBBAAIEoARLAKBXaEEAAAQQQQACBBAuQACZ4cOkaAggggAACCCAQJUACGKVCGwIIIIAAAgggkGABEsAEDy5dQwABBBBAAAEEogRIAKNUaEMAAQQQQAABBBIsQAKY4MGlawgggAACCCCAQJQACWCUCm0IIIAAAggggECCBUgAEzy4dA0BBBBAAAEEEIgSIAGMUqENAQQQQAABBBBIsAAJYIIHl64hgAACCCCAAAJRAiSAUSq0IYAAAggggAACCRYgAUzw4NI1BBBAAAEEEEAgSoAEMEqFNgQQQAABBBBAIMECJIAJHly6hgACCCCAAAIIRAmQAEap0IYAAggggAACCCRYgAQwwYNL1xBAAAEEEEAAgSgBEsAoFdoQQAABBBBAAIEEC5AAJnhw6RoCCCCAAAIIIBAlQAIYpUIbAggggAACCCCQYAESwAQPLl1DAAEEEEAAAQSiBEgAo1RoQwABBBBAAAEEEixAApjgwaVrCCCAAAIIIIBAlAAJYJQKbQgggAACCCCAQIIFSAATPLh0DQEEEEAAAQQQiBIgAYxSoQ0BBBBAAAEEEEiwAAlgggeXriGAAAIIIIAAAlECJIBRKrQhgAACCCCAAAIJFiABTPDg0jUEEEAAAQQQQCBKgAQwSoU2BBBAAAEEEEAgwQIkgAkeXLqGAAIIIIAAAghECZAARqnQhgACCCCAAAIIJFggSQngAo1Tv+K07HhN1/QRxauKFxUnKCgIIIAAAggggEDqBZKSAM7WSH5e8VRoRK/J1g/U1JLDpYpGBQUBBBBAAAEEEEi1wLgE9N6S2FsVlyquDfXnLM3Pydaf0fRNxYcVP8m25Z1s3NKbd1lcF5S6T6VeX65rudef+3vUEUAAAQQQSJNAEhLAhRqwxxXPhgZuT83b0b61obZVmt8vVM87u/C+F/IuY8GAAEbsCQgggAACCMRXoC6+m57Z8kP05y0Ku75vh2K54tuKXyjsiN94RVDu04xdE3hb0JCdWgJpkSnjWqfMOu7Ku4Jq4qZ7Nte5Cw6ud/V1Ixv6np4e19zcnHHY1d/vFr+0y/2xxy61rEwZ7fZWYqvCJpX4vVr/DTz8EcIDD19gaI19xDfBw/fo6OhYo5Z9/NbS10aWBZT+d0u1xou1on9UbM+ucKamXYovK/5Z8V5FcBTwac1frSh4Cnjqnnv2v7zK7JNZprQ0uYb6kQ97Z2ena29vH8To29XvNm6t3Cny0W7v4IaWcSbXpIw/FYtV4+EPEx54+AJDa+wjvgkevkddXV1FEsC4nwK+SWwWQVmuGTsC+KDiGMVFikWKeYpZip8pChY7MjatNXzgsODHU7fQkkd8UjfsdBgBBBBAIGECcU8ACw3Hl7TQzuXaY2DskNU5CjtNTEEAAQQQQAABBFItkLQE8MTQaK7T/MmhOrMIIIAAAggggAACEqjlBPBAbd8dimmKzYrzFC8pcsvn1HClwh4H81PFFxR2pO9ExY8ULyuCcpxmtgWVqOmmTZvcPvuU/drLqJ+uybbt27e78eM5JR4eHEzCGroAl33EA8HD42D/8DkyNfYRHwUP30M1u2St7KWWE8Cb1fvFiiWKM7NTu5YvXN6jylcURyrsiN8PFRcoblRYseTvA5m5Ef4xefJkt3r16hF+Ovkf4+LcoWOMiW+CBx6+gF9j//A9rIaJb4KH76GbQPr8lvLUavVNIPYat6MVd2e7/YCm+yrmZOvBxBLDhxR2p689m+Q7irMVFAQQQAABBBBAAIE8ArWaAFqy95ZiZ3a7Lbl7Q7Ffth5MrP56UNF0lSL8mfeqvkJhbwKxU8MUBBBAAAEEEEAg9QJ1NSpwlLZrqWJuaPvsOX52rZ9d5xeU6zVjD3z+erbh/Zo+orAksE1h/bPrB+2ivocVX1XcpwgXewi0Raa0tLTMWrZsWVBN/ZQHdA7dBTDxTfDAwxfwa+wfvofVMPFN8PA99CBoy1sm+62lr40r/SpLssY/aC17K2z77CigJXKW1NlRwHCxuh3lC8pszQSf6QoaNV2t+J7iQ4rcBPA6tVlkihLA/vCDj4P2tE65NmPoyGPim+CBhy/g19g/fA+rYeKb4OF7qNY9pKUMDfVlWGcpVvm2VrJCcU52ZWdoaknca9l6MLFrA09VzFRYkmgPfr5XYcUSyKB/e2j+o4rnFBQEEEAAAQQQQCDVAkGCVIsIF2qjLF5R2KnfBQortyos6bOyUmGvfXtcYcnhesXNCiuWNP5G8YLiKcWjitsVFAQQQAABBBBAINUCtXoK2AblZcVxEaNzfk7bLapb5JYb1GBBQQABBBBAAAEEEAgJ1PIRwNBmMosAAggggAACCCBQKgESwFJJsh4EEEAAAQQQQCAmAiSAMRkoNhMBBBBAAAEEECiVAAlgqSRZDwIIIIAAAgggEBMBEsCYDBSbiQACCCCAAAIIlEqABLBUkqwHAQQQQAABBBCIiQAJYEwGis1EAAEEEEAAAQRKJUACWCpJ1oMAAggggAACCMREgAQwJgPFZiKAAAIIIIAAAqUSIAEslSTrQQABBBBAAAEEYiJAAhiTgWIzEUAAAQQQQACBUgmQAJZKkvUggAACCCCAAAIxESABjMlAsZkIIIAAAggggECpBEgASyXJehBAAAEEEEAAgZgIkADGZKDYTAQQQAABBBBAoFQCJIClkmQ9CCCAAAIIIIBATARIAGMyUGwmAggggAACCCBQKgESwFJJsh4EEEAAAQQQQCAmAiSAMRkoNhMBBBBAAAEEECiVAAlgqSRZDwIIIIAAAgggEBMBEsCYDBSbiQACCCCAAAIIlEqABLBUkqwHAQQQQAABBBCIiQAJYEwGis1EAAEEEEAAAQRKJUACWCpJ1oMAAggggAACCMREgAQwJgPFZiKAAAIIIIAAAqUSIAEslSTrQQABBBBAAAEEYiJAAhiTgWIzEUAAAQQQQACBUgmQAJZKkvUggAACCCCAAAIxESABjMlAsZkIIIAAAggggECpBEgASyXJehBAAAEEEEAAgZgIkADGZKDYTAQQQAABBBBAoFQCJIClkmQ9CCCAAAIIIIBATARIAGMyUGwmAggggAACCCBQKgESwFJJsh4EEEAAAQQQQCAmAiSAMRkoNhMBBBBAAAEEECiVAAlgqSRZDwIIIIAAAgggEBMBEsCYDBSbiQACCCCAAAIIlEqABLBUkqwHAQQQQAABBBCIiUAxCeB49e0fFbcpTs3p5/U5daoIIIAAAggggAACNSZQTAJ4o/rwfsVLim8ovqUIyp8GMyWYHqh1PKF4RfGM4mBFVPmcGl9V/E5xi6JREZRCy4LPMEUAAQQQQAABBFIlUEwC+EEJna24VnG0Yo7iOwordQOTkvx5s9ayWPE+hSWaSxS55T1q+IriQwrbjhmKCxRWCi0b+AR/IoAAAggggAACKRQYV0Sf7Tv92e9t0fR0xfcVdvStVGW6VmTJ5cnZFT6g6Q0KS/Jey7bZ5EzFQ4q1VlGxRPRqhR2lLLRMi6PLrv5+t6F7e/TCFLZ29eKRO+xhkyktTa6hvpT/3ZP7a9QRQAABBBAovUAxCeB6bcYhihezm7ND07MUlgQelm0b62RfreAtxc7siizhfEOxnyKcAFr9dUVQVmnG2qwUWjbwiYg/u9WbBbfbGWeKCWzu6nN3rsIjvDeETW5fMM9Na7XLYikIIIAAAgjER6CYBPASdW9btot7Zee7NbUk8BPZ9jhNFmpjLTJl165dSno2B9XUT/t39eORsxeETR577DHX1pTuI4A9PT2us7MzRym9VTz8scfD97AaJr4JHr5HpWrFJIB25O8ixc8Veyvs6JzdEGJJ1FLFZMUmxVjKH/RlW7dtnx0FtH9h7YieHQUMF6u/N9QwW/PBZwotC33FXaeKRaa0ja/vX3bZSUE19VNLcObPn596hzBA2IRTwC6T/LW3t4eJUj1vyTAeu3cBPHZbBHOYBBIDUzx8j0rVikkAP6uN+xvF+Yonsxt6vKZ2U8gViq8rjlKMpbytL69QnKNYojhDsVoRPv2rqrNrA/9DsUixTmGJ6b0KK4WWDXwi4s/6ujpO6YVc7OgWpzhDIJrFxPeghgACCCAQP4FxRWzy3+o7HYrgSJut4mHFfypeVVgiWIpyoVayRGE3dXQpFiis3Kp4KBsrNf2y4nGFleWKm21GpdCygU/wJwIIIIAAAgggkEKBYhLABjmFk7+AbZVmfq+4MmgY4/Rlff+4iHWcn9Nmdx9bRJVCy6I+TxsCCCCAAAIIIJB4gfoietik7zRHfG+C2opZX8SqaEIAAQQQQAABBBAol0AxCdsybcxdCrvZIyhTNHOnwq67oyCAAAIIIIAAAgjUsEAxCeDfqz87FKsVz2XD7trdqbBlFAQQQAABBBBAAIEaFijmGkBL/j6lmKM4Its3SwRfy84zQQABBBBAAAEEEKhhgWISwKA7lvCR9AUaTBFAAAEEEEAAgZgIFHMKOCZdYzMRQAABBBBAAAEEogRIAKNUaEMAAQQQQAABBBIsQAKY4MGlawgggAACCCCAQJQACWCUCm0IIIAAAggggECCBUgAEzy4dA0BBBBAAAEEEIgSIAGMUqENAQQQQAABBBBIsAAJYIIHl64hgAACCCCAAAJRAiSAUSq0IYAAAggggAACCRYgAUzw4NI1BBBAAAEEEEAgSoAEMEqFNgQQQAABBBBAIMECJIAJHly6hgACCCCAAAIIRAmQAEap0IYAAggggAACCCRYgAQwwYNL1xBAAAEEEEAAgSgBEsAoFdoQQAABBBBAAIEEC5AAJnhw6RoCCCCAAAIIIBAlQAIYpUIbAggggAACCCCQYAESwAQPLl1DAAEEEEAAAQSiBEgAo1RoQwABBBBAAAEEEixAApjgwaVrCCCAAAIIIIBAlAAJYJQKbQgggAACCCCAQIIFSAATPLh0DQEEEEAAAQQQiBIgAYxSoQ0BBBBAAAEEEEiwAAlgggeXriGAAAIIIIAAAlECJIBRKrQhgAACCCCAAAIJFiABTPDg0jUEEEAAAQQQQCBKgAQwSoU2BBBAAAEEEEAgwQIkgAkeXLqGAAIIIIAAAghECZAARqnQhgACCCCAAAIIJFiABDDBg0vXEEAAAQQQQACBKAESwCgV2hBAAAEEEEAAgQQLkAAmeHDpGgIIIIAAAgggECVAAhilQhsCCEJ0ei0AAAw9SURBVCCAAAIIIJBgARLABA8uXUMAAQQQQAABBKIESACjVGhDAAEEEEAAAQQSLFCLCaBt0/WK3yleU3xRka8cqAVPKF5RPKM4WBGUVZp5WfF8Nj6hKQUBBBBAAAEEEEi9wLgaFDhH2/R+xfsUkxTPKR5TvKTILTerYbFiieLM7HSepkGxpM8SQAoCCCCAAAIIIIBAVqAWjwBa0naLok/xjuL7irMVuWW6Go5W3J1d8ICm+yrmZOtMEEAAAQQQQAABBCIEajEB3E/b+XpoW1dp3tpyiyV7byl2Zhf0a/qGIvzZO1X/jeK7ir0UFAQQQAABBBBAIPUCdVUQeFK/adfuRZUj1Piw4gKFfc7KFxTHKv7aKqFylOaXKuaG2p7W/JWKnyosEbSEsFHxVcWhilMUuWWhGiwypaWlZdayZcuCauqnPT09rrm5OfUOYQBMwhrO4YGHL+DX2D98D6th4pvg4Xt0dHSsUcs+fmvpa9VIAIfrxb/pA3cp7s1+8Jua9ir+PlsPJnYK+DXFVIUdBbS+2BHBP1NYe7jsrYrdKLJHuDFqftq0af3r16+PWpTKts7OTtfe3p7KvufrNCa+DB54+AJ+jf3D97AaJr4JHr5HXV1dRRLAWjwFfL8oPq9oUFhyZ9cE2nWAueVtNaxQnJNdcIamqxWW/E1UTFYExa4htJtJKAgggAACCCCAQOoFxtWggB39szt5X1XYdX3XKew6PiunZuP8TM25CzVdorha0aVYoLAyQ2E3hVgSaUcGVypyTyGriYIAAggggAACCKRPoBYTQLv795I8Q/GQ2i2C8rJmjgsqoaklfHY9IQUBBBBAAAEEEEAgR6AWrwHM2cSKV3fqF9dW/Fdr9wdbtWndtbt5VdkyTHx2PPDwBfwa+4fvYTVMfBM8fI+ZqtbiATp/KxNYW53APo2lS3gM1cPEN8EDD1/Ar7F/+B5Ww8Q3waMKHrV4E4jPQA0BBBBAAAEEEECgpAIkgCXlZGUIIIAAAggggEDtC9hdspShAk8ObUp1Cx5Dhx8T3wQPPHwBv8b+4XtYDRPfBA88fAFqCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIjF7gQH3lCYW9M/gZxcGKpJdmdfBBhfX5BcWjijkKK8sVv1c8n42/0zQo9h7mRxT2tpYXFScoklJWqSP2gPGg3/YqQiuF9o9Cywa+Hc8/99RmBw42tf3EnpNpr2hcrkjD/vEv6ucqhb2V6AOKoBQa82KXBeuu5WmUR6G/R6wvyxVJ3leiTKzfqxRRf5fYsrTtI4X+LjGP5Yqk7iOF/v9R6N/SYpeZJ2WUAj/V58/LfudMTS0JTHqxHfMURfBA8C9qfrnCynLFaTYTUW5T26Jsu722z57h1Jitx32ySh0I/0Mf9KfQ/lFoWfD9JEwvVyf+b7YjyzVNw/5xgvq5j2KVIrxfFBrzYpfpJ2q+RHkU+nvEOrRckeR9JcrE+r1KEd5nrC0oadtHgn4H0/DfJda2XJHUfaTQ/z9uU78XKazk/lta7LKBtfHniAUs0+5SjMt+wxKitYo52XpaJkero6uynV2uab7/Q3ZrmT2pPChPa+akoBLz6Sptf+5f2oX2j0LLYk4xZPP/Sy3BPrE8NJ/7wSTuH6vUyWC/KDTmxS7LNaz1etgjd1vDf4/YsuWKYL+xergkaV9ZpY4F+4j1MbdubVbYR5wL/11iJssVadhHrK/h/38U2v+LXWa/MaLCcwAHmPbV5C2Fnd6yYqd73lDsZ5UUlcvU1x+G+nuN5n+j+L7igGy7Hcq3o32WIAdllWaSZHWn+mP9/q5iL0Wh/aPQMn01MeV49WSK4v+FepTW/aPQmBe7LMQa+9ncv0esQ2ndV3L/LjGLtO8jUX+XpGkfCf7/Uejf0mKXmeOIS/2IP8kHky5wtTpoRzyvynb0XE0PUhym+IUi/A+/qoktJ6hn1ucjFRsUdygozn1OCPaPWfAfSWndP9gXCgvk/j1in07rvsLfJdH7Su7fJWnaR6L+/xGtRGvFBAodkq/YRlTxhy7Xb/9KMbnANvRomf1XiZUtiqSeAs50MPvH3pq+qyi0fxRaFl5XnOftRe3mYP9BkK8kff9YpY4Hp/cKjXmxy/K51mp72CPYxpH8PWKfTeq+EmUS2AR/l1g9zfvISP4uMaMk7iNR//8o9G9pscvMjzJKgeX6/HnZ75ypqSVEaSgL1clnFXZ6Lyh2LeSMoKLpGYrXQ/Ulml+Urc/TdI0iCTeBTFQ/wkmw2fxcYWW54jyFldz9Y7nazlNYyV020BrvP+2/2P8j1IU07h+r1P8gATSK5YrzFFZyx3y52s5TWBnNsoFvxOPPVdrMsEfU3yPWkzTtK2GTQn+XmMtyxXkKK2nZR6yvuX+XWFsa9pF8//9Yov4vUljJ/bd0idoWKayMZtnAN/hzVAJz9eknFa8oLPk7VJH0Ync32vWOv1M8n41famp/eZnBbxQvKP5dcbgiKJYc/lhhj4F5STFfkYRygDrxnOLXCuu7XQ85W2Gl0P5RaNnAt+P95xPa/AWhLqRp/7hZ/V6tsFPf6xSvKawUGvNilw2subb/jPLI9/eI9SQN+0qUSaG/S8wlbfuI9dlK7t8l1pb0faTQ/z8K/Vta7DIzpSCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAJlEggeU/SfWn+fIqjbawqPVti03MV+50cRP7Kv2h5S2KODLGzb/lxR7fLP2oBF1d4Ifh8BBGpTYFxtbhZbhQACCHgCH8jWZmtqCVZQzza7TwQzZZx+XOt+MGL9N6nNnpV5anbZNE1bsvNMEEAAgZoUqK/JrWKjEEAAgZELnKiPWlJoZbZik+IrihUKe1j5nyq+pbDPvKg4RBGUczVjDz+3z/5cEX7guapesQTPHg6eW+xBr/Y2nKBs0Mwb2Yq9IecaxdMK+/37FFMUViYpblXYNr2guE1hpVVh89Zu8WVFUJZrxo7s/ULxO8V3FEGx1411Kuwo6U8Utl1B+Zhm7AHngcFfBQuYIoAAAggggAACtS4wWxtoCV64nKiKJTZWZivs7TanKazYq6e6FfOtonKF4v7M3EBi+LDmx2frH9LU3mwTVQ5U4+NRC9R2tuLd7PJrNT1BERR7+fs/BJXs/I3Z+u2a/qsi+A/xvbLt39D0nmy7vSXhOUVwhHO55n+gsLM3ExS/VxynsGL9ssTXyizFesUihRVLMIPP2e9NtkYKAggggAACCCAQB4HZ2sjhEsBtoY4crvm1ofqHNR8ki9/U/Jps3dos3lJYYpVb/qcaLHnMV+yo3ukKSwA3KoLP2pG/lxXB+u3onCWdVixBOyAz5//xrKonhpr+TvN2pNDKcsUnbSZbLBk8Jzv/jqb7Z+dt8l3FIptRuU5h22L9+ICCggACKRfgGsCU7wB0H4EECmwP9alP8z059eDvvTq136Gwo3TDldP0gc8U+JAlfcuy8Yymts5/UthvXKqwd2cXW/pzvpivPzkfyxwJDdoWauZghR0JtT7bEUZLgCkIIJBSgeDUQ0q7T7cRQCDFAnbnrh092y9rYH8fHp2dD0/2VsWuy7PrCaPKR9XYkl1gCd8Rit9l6w9qakfwguU2tUTMiv3+5Yrg7+HgFPBP1Ganrm1dExXnKkaSQNr3PquwYtts1ywG5SDNvKS4QWE3rRyroCCAQIoFgv8STjEBXUcAgZQK2I0UdkrUTqPa34VNin9T/EoRLn+liiVr+YqdVrajfTsVlrTZKd8vKqzY9Xx2jeEvFcGRPGuzZMwSw28p7NExOxTPKD6vsOv4/kVh7VbuV9yXmSv8x2VavERhp5nt1PZPFUH5mmbmKnoVWxUXKygIIIAAAggggAACeQQeUXvUkcE8H6cZAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgVgL/HzAeLIcPR/QTAAAAAElFTkSuQmCC\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TCLab disconnected successfully.\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "Labtime clock lost synchronization with real time. Step size was 1 s, but 3.22 s elapsed (2.22 too long). Consider increasing step.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/Documents/Development/TCLab/tclab/experiment.py\u001b[0m in \u001b[0;36mrunexperiment\u001b[0;34m(function, connected, plot, twindow, time, dbfile, speedup, synced)\u001b[0m\n\u001b[1;32m     99\u001b[0m     \"\"\"\n\u001b[1;32m    100\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mExperiment\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconnected\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtwindow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdbfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspeedup\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msynced\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexperiment\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mexperiment\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclock\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    102\u001b[0m             \u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexperiment\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlab\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mexperiment\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/Development/TCLab/tclab/experiment.py\u001b[0m in \u001b[0;36mclock\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     78\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m             \u001b[0mtimes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtimes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     81\u001b[0m             \u001b[0;32myield\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/Development/TCLab/tclab/labtime.py\u001b[0m in \u001b[0;36mclock\u001b[0;34m(period, step, tol, adaptive)\u001b[0m\n\u001b[1;32m     98\u001b[0m                            \u001b[0;34m'Step size was {} s, but {:.2f} s elapsed '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     99\u001b[0m                            '({:.2f} too long). Consider increasing step.')\n\u001b[0;32m--> 100\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0melapsed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0melapsed\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    101\u001b[0m         \u001b[0mlabtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlabtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m         \u001b[0mnow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: Labtime clock lost synchronization with real time. Step size was 1 s, but 3.22 s elapsed (2.22 too long). Consider increasing step."
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "experiment = runexperiment(random_noise, connected=True, \n",
    "                           plot=True, twindow=2000,\n",
    "                           time=8*60*60, \n",
    "                           speedup=1,\n",
    "                           dbfile='sinetest.db',\n",
    "                          )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "h = experiment.historian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "h.load_session(36)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x118ee7a20>]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1136375f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(h.t, h.logdict['T1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "startcut = 900"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1 = numpy.array(h.logdict['Q1'][startcut:])\n",
    "T1 = numpy.array(h.logdict['T1'][startcut:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1176f1278>]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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o96gMI2m0NFUoMSMW6gU9FNJbDz3EbNEFyuNDY8UjpNpU0YIuInUiMk9EHoqOfyEib4rI/Oh1WHrBLI2A0nE3NvW/PNg4faMaqY9x7RXAImBw1rl/cc7dl2yQkkW5Q7Wb1JbPDST+iiWE6NBiY4i1y6I8dBEZC5wJ3JZucJIlRC/TZoqWCaXRozHPhPQsF9vkcgPwDaA95/x3RWSBiFwvIg3JBi0ZQiylDaMnaMkzOqyIR7eCLiJnAc3Oubk5X80GDgKOAoYBVxX4/SwRmSMic9auXdvT8BoFSK1TNJ3b1ixKtK4gGn1ZjbWOQhTjoR8PnC0iy4C7gVNE5E7n3Crn2QncDkzL92Pn3C3OuSbnXNOIESMSC3gxhFTVypCUzSFlglKw6DGqkW4F3Tk32zk31jk3HrgQ+JNz7lMiMhpAfP3sHGBhqiEtEeUOlWEkjpY8o702lY84o1xyuUtERuDTfz7w+WSCZJRCalP/A8wUXaFlnZNCaJxdrc+iwsQSdOfc08DT0edTUghPoih8NrvFpv6XhxCfLaP60T9TVLdDtZv0POlAIrBIgqixKLFRe20qH6oFPUQnKjEP3VzQLtHa4a7RqpAeZdWCDmGW0obRE7TkmCBqUzmoF/RwsKn/5UB9dCj0ZrXWpvKhW9DDScfdJDYOPZG76CWkarxRO+gWdMzDNJKngRbqNr1V6WCkhpap/yGiXtBDwab+lwcRuLH3fzLq9qOhrbXSwUkcjc0TIdWmVAu6xoezOxJ7eMOLuqL5YF20rNF771Y2IIaRQ09mitYEoXiY6c0UDSQGnYN1r8HAkfD28+DaoXd//137Lv8uvRj57saO3yx+CBr3g9YWqOsNrTth7FEwaFT5w58gWlI8lEc3G9WCHlJVK2mCq93Mvwse+BIMHgubVxS87OTsg4evzH/RNZsSDVo5sTxT26hucoEwS2mjBFa84N/ziflF98Jlj+8+/Nauz9B8/oP+3EW/LlMAy4eWPBPiHBTVHnpIpNU0UtNZYucWaG+Dlq2+CaX/cGjZBrve8y/noP8w2LbOvwox6VSo68gqz7RP4fNjjoLG/vk7Rle/BANG+nvX9U7BsPTQ6KGHNOtZtaCHk4wdJDf1P5n7VJRrxyZzn4yYjzwEml+m2TV2xE9dniz00xP8+wFnwEX3JBMGwygC1YIOYVa7DOINKRz/AZg2y3v0fQdD43jYvMq/u7aO6z51H088N48tT+b8/vJ5UNfHN9u0t8EzP4GVL8JrjyRgSPnRkme0NB3FQbWgB1HVipoU6lq300ALfTa8Bn2Hehd70CjoO6Sk22airqoyxfaN0DAY2luhrQUaBsLmldCvEbY2Q686qO8HW1d33YSSS+N4mHJ253N7v3/P6waPYUNjG7Cg8/lhE/37kKhGsPA3Hd+tedm/DxjhR9BUOSo6w7dv8M/EllWM5l2GbX0d1kbbIbdHBX19XxgwHOoaoHffyoU1YVQLehA89i149r84BXi1L5ClJew1Gb4yp0IBS5gta+CHB8CpV8Obf4E3noJ/eQOuO7jn9x66X8/vkc1e+3d8vum4js9feAZGTUn2v4zOvPIg3Ptp+PTv6P3Lc3imL/Bi9MrHoDHw9UVlDGC6qBf0qvIw02DuHYW/e3dJj29fNdXvDW/698UPwztRIbVxWf5rxx0Ly5/xn8+5CfoM8J5ZpoN04N4w/ADfUbp6ARz44aKDUVTn80lXwaj3+Q7RXnVw78X+fPMrNSHoNZ1nXn/Cvy/6ff7vP/h/fXPaczf54y0ryxOuMqFa0Gu+8tiyDXr19k0Iu7Z7AXIO6ht8U8q2dbBrW9f3WLUAGgb5Jor6Bn+PvkO9sA0dV/BnZY+71p1ecEVgyyrvNb+7FHr3g9Yd0Bx5UZuzMuAbf85/r4nTvaDX94PDLur6f0ccUFJwu2zNaxgIUz++5/lNK2Dtqz49Wnf6JpoqGwVTU62U2zf6fNGy1R+37epoalv/Rv7fHHIurHi+Q9AB1i2BgaN8WvTul26YU0a1oNc83xvjh9q9V3x78I4hk+i7aWnHiZs/UPjiix+EiSf1IIAJcsvJ0Pwy7H+a97ImnQJL/7Tnddke1ZP/lv9e+xzp3ydVyS6JfYfAjk3wxNX+lWHccXDpHysXrlpm8yq47qDC37/xVP7zA0bs2cR2Y5N/7zMI/nfhSWW1gHpBr9naY8ZVyhXz6bPh6Ws7js+7HXZu4ZXm7fz0r8v5woyPc/Dgnd4Tf2+9f7CfvyX/f6x/o1tBL1v1uznqPFw5z7/nivkRM+HFrOal918AB53pR5e07/Le/aDR3lubdKpvr25MuG2cEp+ny+fDksfh/lmdzy//nySClCg146BvXJ7npMD5v/CFZ98htPUdysyfPcvxJ87gCwdug/57+Q7QsU3wyfu8N/+7rL3tW7aUK/SpoVrQa6b6uHOLr4JLL/8w9qr3ozbycdBZnQX9fecCsHrxGh788xwuHbwf7Du04/uWrYUFfdU/fCTlUe2yjBByDja+5W3P0NqS/9oxh3UW9BO+1nV7dMpt1bFGg/Qf5ptgcgUdfLNSn4FeaEockaSanVt8npBe3klpb/NDSdcu3vPaIWPhkHM6jtsdf2vfxlH1g2DCEZ2vnfxB//7sT/xEsAzrlnjh7z8seVvKgGpBB6q/h8e57ifA1PXxbeB1DQWHvhXsvOxqBMfc230mOPGfiwxswrxwG/wh578LeUmNEzofD9o7nTB1Q48ep169Oxb6yvCfWUIz62kYc3gP/iAZqmZBttaWeJPDxnduXizKinHHdRb0G5t8ATt7RfVrRx70C3q1s3NzgS8Ezv6xHy87bKIfM9u7vxf0S/7gVwacOH2PX+3hWY872lcvGyf439T1hk1vw0Nf898vuCevoJelcpOdkQCG7Aunf9d3UG1e6TttW3f4zs2xR8IVC/ySta07K+5BlVSBufIV73GufxN2bIRXftd5NMbK+RUX9Kqq1W4rUEvNcNb1Pk8MHuPnJ4zI36beZW1qxnfgwA/5GnGmBtWy1dcK+g0t/LsqxQS9nOzY5KuMOzb6at22dX5ERz5GHQJHXJz/u/HH+1exZKqXw7PGR7/yALzxNGxZDete9yMvevfzMyWTonWnb8dv3+Uz3o5oFcLM5I7t6ztff9CZMOWjhe/XuF8q7eJlY+BI/9prkj9+d2lnQc/bLhwwuQV+Lk2X9vw/6vvApGgNzewmsRUv+Alnvfv5pjDnks0bKaFe0Kum0rRzC/x74WGCezDumHj3j2vo2KO8oO/cDDce2XH+2+s6DaXrUa3zOzFnRo4MbIx27pDJv13nR/nEKaxToCryzNZm+NWFPbpF7LTq1+hnmQLcdd6e31/6aPx8WWbUCnrVTfsv1MkJMOFEmHKO994Hj/Ee7YFnlvQ3RVt90jf95Jq2XfDAFzvOb1sHg0enX/U+7+f+vWEwjDnCj7gZ25TynyZHItEz5Ry47Ak/l2DpU/D3G2D90goLepXkmw3Rnq0HnAGHfwqQjv6HhsEwsvgZwkU/y1+e48e2r1/qm13uy6kBrJxvgh40zvmJD20tsGFZ4es+8PW87eGpUlcPh17gw9hJ0Jth8Oj499u1w49CaCswSiWbhsHwvn/qfG7AXvH/s9YRgX2P8p/3PdoL+sa3oeU9H499BuZfzVEj7W3++Wnd4T9vjAT9pKtgnyO6/m1SDBjuX5mmyVxBb9/lF31rb63a9V/UPy0V66he+yr8ZFpx1w4qQUBzKNnM3Ai6+USYfDpM/XF0327u/N56+I8JXV+Ty+ip8a6vIlJbCqF3Pz+D9y//4V/gO0hnPZ3O/xWgwCjWdLmmi+GaPcgbmdE6idU5HvsW/PU63/dzyrcrNzqsC9QKesVbXN78S8fnugY47Ro/9buuHkYfBsufhf2O9UOzhpc2/TwfJdl96WPw1Hc6wrzkUZha5I0ya6xkGDoOjv6C/9yyFTa/4zNl6w4/hr5la00LeoZUmvQuuMNPqPr7j/zx6oUVUtgykj0HIcPp0TyLQXuXVltMii8+Cwvu9WPg//oDfy7Tkf/Sr03QK0HZF5dq2+WrZK07Os6NOACO/WLn66IJQUnRo7HD446GC38F1+7Tcc61M5D3kMwsTNcOUge9ol0L29t8s8CW1Z3vtfehe9qqiFS1deJ0GH5gh6C37/JDTPsOjTqqpSxV/bKVH+1t+Zsiq+X5GXkwnHY17NzaIegZ2lthRzTkuL7Bv6oAtYJeVgf93aWdJ4jkMiKBJV6LpkTLGwZ2OjzjN1M4oy9wW9bJgz8CH7/Tf/7ZjI5VD7PJrA2unNSerwEjOh/fkGdd9sYJcMX8VP6+LPnm8X/tKLTKQU9rU73773nu3dfh3/ftfO7qjRWvTakV9LKSWao1myM/40Vy2CR4//nlD1MpXPRrWPx7ePH/5f8+e8x0tpgf+2W/BrgITP1EumHUTl09zPy9HwO95DHf2bzwN34HpAy5zVy1RraYDxrtV0AcNMqPsDo0zyqVlaZXr85p0rLN11bBj4iZE43Yyux4VUHUC3rqBWZ7mx/JksuRM8s66y8RMw+Y4V+L/1B4hcfMujMZ+g7xszuN5Jhwon8/6rP+fePyzoIO/pmTOj8xJmFSbabM3Rpw9FQ443up/V1i+T83TTKs+keHoG9YBiMOrGjzi1pBL9s49DvP9RN0culXmanpiZg95nB4/fH83+WurbH3oQn8Ye1R1k73fOvWfzday+YjP4IjL0nsr1LPN7ee3Pk4e3enlEjVouxROJmlqgeNhq/nWTysDKgV9LKxaoHfIScz0aFxgp/aXctT1P/pNpY8+H2+MX8UPz25nVHta/343Pq+vnMU/MidQXvD+BMqG9YQOOYLfmmG9lbvEQ4Y0dFJt+TxRAU9VdrbYM1C7+2OOBj69PfLQdcyA0fCubfCbz/Xca7Qch5loDYEvb3dT8ntPyx2HSrxyqNzHduZtbf6YUwTT4bpVyX9T7FItGmp31CWTLmcefNeZOOhJzJq70EJ3ry2qchKhL3qfBNeBuc6BL291Y/C6HR9fcmjYRwpNFM659udt631DsHBZ8O0z3X/uwQQylCbOvSCzoIOPk3qevs/L+MkpNoQ9Ae/AvPv9J0n599e1E8SS8OuJj1kqNBSrvlIyu6Kj+OveioYQdmK+9ojnYebZnPubXBoBTvk7/88vPaoHxTw/M0d56sov6RGvjT51w0dw35TojYEPZN5Xv5t0YKeIXWH6oSvwSEfS/lPuietjizNc1pKoWqi45KH4e3n/Brr2Wzf4Bf5Ar95QwmCnpiN//iVf1/1Dz/a68hL/IzY/U9L6h+6pWw1qi/P8bWQtYv9kMZlf9tz9Nv2DakvcVG0oItIHTAHeMc5d5aITADuBoYBLwKfds4VsZBHKcRPlEQ8zJb3ur/mqM9WfKhSNkl51rF25AmQitdgxp+Qv/9ix6YOQS+BVOza+JbvZzr+8hRu3j1leZaHT/bvYw7z749fvaegb2uuHkEHrgAWARn1+j5wvXPubhH5KXAZcFOhH/eIAcM7Pn8vqsq0bN3zuisW9Lwz8pHZ8Ox/xQjbiO6vMYxy0ZDlXKyclz+/HPpxOLfAtoSl0rYLfnoC7HecXzly29qO77as8puWhET/PMK9tTnWKpGlUJSgi8hY4Ezgu8CV4usxpwAXRZfcAVxDWoJ+0lUdmyJkeObGPa9b/PAe04ZjV7nm3dn5+KCz/PTrzOiOkYf4Tqfhk/32bVUy5TetmmXVNDFUCVXfBCUCH7sZ5twO+xzpj7dv9H1QGRbc07Wgl2LkllW+uSGz1+eBZ/r1xbet9UtfHDGz69+nRMWS6+jP+60j+w+D33/Vj08vQ6FWrId+A/ANIDPcYS9go3Muo7IrgAI9Mz3nzhfX8vTqczqdu409Bf3uF5bzxGsvANBeai0rt8554V0l3qgyfP+RxTT27939hd2wevOO7i8KmG8/sJCBDdXaBTUJ6r8Da/zRwLaN3EBnR+Wzd7yQ95cL39nE2GF5prp3x9a1nY+Pv7xq1g5/eMEqXl1dYK/aVIk2jhn7AABXtIwmz0IOidLtEykiZwHNzrm5IjI9czrPpXklVERmAbMAxo2LsWNPFpt37GLVpu4F5tz1t3Hh+qxKQl94s+VauCaK2IEZ4EHJAAAHTUlEQVSjYPIMmPfLzj88+COdp7XXIJNHDmTa+GFsa2ll1aa2RO55/P57sU9jv0TupYX3jRnCkfs1smVHK1t2tHb/gypA3J6zSZs3bqVN9sz+ew/py+mHxPAkn7/Vb/Sdu95Jgc3My805h+/DolWbi9KPtGlpSyZfdoV0NzNMRK4FPg20An3xbej3A6cDezvnWkXkWOAa59zpXd2rqanJzZmTZ0GnUljzCrxwa7R5xFt+KvS+0+K1fxdixnd9s8rYppraRccwCvL8rfDqH+Ht56FlC1y5OJmlaTPDesdO8xPq+jV6MT/+qzXQPlU7iMhc51y3YtSth+6cmw3Mjm46Hfhn59wnReTXwHn4kS4zgQd6FOK4jJrid/3OJQlBP+7LPb+HYVQT0z7nX4segns+CVvXJLvW+IEfgg9cmdz9jJLoSSPgVcDdIvIdYB7ws2SCZBhGamSaQm45qftr+w6Fq5bt6WmveRl++TFfKOy+togJeEbqxBJ059zTwNPR5zeAIvdYKyMXP+B7+DObL4xtghVzoM8A3ywz7hjfln7AGfDENfDqw/53h30KJp/m12IxDK3EadvesdGPa+83tPP5lfM6i/mEE+GIi5MJn9EjqrWbvnQmTi9+w+UjL/GCPukUOOcnqQXJMKqGATE7K7et3VPQtzZ3Pr74QWsvrxL0CXocJFpXYci+XV9nGFroE3NI4o1RP9x5t8N9n/GjWdrboM8g37kKJuZVRNiCPukUvyGtVReNkDj7Rr/J8QGn+3VHWt7zHaR77e/Pb9/omywbBvn1YsCLOcCu9+D9F8D+p/rJMrneulFRuh22mCSJDls0DCN98q02euUiGDym/GEJmGKHLaa7lqNhGProP7z7a4yKYIJuGEZhPvsnv+DXwR/xk+2mz05lH1MjGcJuQzcMo2vGHgmz3650KIwiMQ/dMAxDCSbohmEYSjBBNwzDUIIJumEYhhJM0A3DMJRggm4YhqEEE3TDMAwlmKAbhmEooaxruYjIWuCtEn8+HFiXYHCqDc32mW21i2b7asm2/ZxzI7q7qKyC3hNEZE4xi9PUKprtM9tqF832abTNmlwMwzCUYIJuGIahhFoS9FsqHYCU0Wyf2Va7aLZPnW0104ZuGIZhdE0teeiGYRhGF9SEoIvIGSLyqoi8LiLfrHR44iIi+4rIUyKySEReFpErovPDRORxEVkSvTdG50VEfhzZu0BEjqisBd0jInUiMk9EHoqOJ4jIc5Ft94hIn+h8Q3T8evT9+EqGuztEZKiI3Ccii6P0O1ZZun0teiYXisivRKRvraadiPxcRJpFZGHWudhpJSIzo+uXiMjMSthSKlUv6CJSB/wE+BAwBfiEiEypbKhi0wp83Tl3MHAM8KXIhm8CTzrnJgNPRsfgbZ0cvWYBN5U/yLG5AliUdfx94PrItg3AZdH5y4ANzrn9geuj66qZHwGPOOcOAqbibVSRbiKyD3A50OScex9QB1xI7abdL4Azcs7FSisRGQZcDRwNTAOuzhQCNYFzrqpfwLHAo1nHs4HZlQ5XD216APgg8CowOjo3Gng1+nwz8Ims63dfV40vYCw+s5wCPAQIfsJGfW4aAo8Cx0af66PrpNI2FLBrMPBmbvgUpds+wNvAsCgtHgJOr+W0A8YDC0tNK+ATwM1Z5ztdV+2vqvfQ6XjoMqyIztUkUTX1cOA5YJRzbhVA9D4yuqzWbL4B+AbQHh3vBWx0zrVGx9nh321b9P2m6PpqZCKwFrg9ak66TUQGoCTdnHPvAD8AlgOr8GkxFx1plyFuWtVUGuZSC4Iuec7V5NAcERkI/Ab4qnNuc1eX5jlXlTaLyFlAs3NubvbpPJe6Ir6rNuqBI4CbnHOHA9voqLLno5ZsI2pK+CgwARgDDMA3ReRSi2nXHYVsqWkba0HQVwD7Zh2PBVZWKCwlIyK98WJ+l3Put9HpNSIyOvp+NNAcna8lm48HzhaRZcDd+GaXG4ChIpLZhDw7/Ltti74fAqwvZ4BjsAJY4Zx7Ljq+Dy/wGtIN4DTgTefcWufcLuC3wHHoSLsMcdOq1tKwE7Ug6C8Ak6Oe9z74TpsHKxymWIiIAD8DFjnnrsv66kEg04s+E9+2njl/cdQTfwywKVNtrDacc7Odc2Odc+PxafMn59wngaeA86LLcm3L2HxedH1VekDOudXA2yJyYHTqVOAVFKRbxHLgGBHpHz2jGftqPu2yiJtWjwIzRKQxqsHMiM7VBpVuxC+yo+PDwGvAUuD/VDo8JYT/BHy1bQEwP3p9GN/++CSwJHofFl0v+JE9S4GX8KMQKm5HEXZOBx6KPk8EngdeB34NNETn+0bHr0ffT6x0uLux6TBgTpR2vwMaNaUb8G/AYmAh8EugoVbTDvgVvi9gF97TvqyUtAIujWx8HfhMpe2K87KZooZhGEqohSYXwzAMowhM0A3DMJRggm4YhqEEE3TDMAwlmKAbhmEowQTdMAxDCSbohmEYSjBBNwzDUML/BxR7MW9/D/1eAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118102240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Q1)\n",
    "plt.plot(T1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "u = numpy.fft.rfft(Q1 - Qbar)\n",
    "y = numpy.fft.rfft(T1 - numpy.mean(T1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "omegafft = numpy.fft.rfftfreq(len(Q1), 1)\n",
    "Gfft = y/u"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.5707963267948966, 0)"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1179795f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ga, pa = bode(omega, G)\n",
    "bode(omegafft, Gfft, ga, pa, numpy.pi/4)\n",
    "plt.xlim(1e-3, 1e-2)\n",
    "ga.set_ylim(0.01, 1)\n",
    "pa.set_ylim(-numpy.pi/2, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}