{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0a3990b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "try {\n",
       "require(['notebook/js/codecell'], function(codecell) {\n",
       "  codecell.CodeCell.options_default.highlight_modes[\n",
       "      'magic_text/x-csrc'] = {'reg':[/^%%microblaze/]};\n",
       "  Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n",
       "      Jupyter.notebook.get_cells().map(function(cell){\n",
       "          if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n",
       "  });\n",
       "});\n",
       "} catch (e) {};\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "try {\n",
       "require(['notebook/js/codecell'], function(codecell) {\n",
       "  codecell.CodeCell.options_default.highlight_modes[\n",
       "      'magic_text/x-csrc'] = {'reg':[/^%%pybind11/]};\n",
       "  Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n",
       "      Jupyter.notebook.get_cells().map(function(cell){\n",
       "          if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n",
       "  });\n",
       "});\n",
       "} catch (e) {};\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "# Import the QICK drivers and auxiliary libraries\n",
    "from qick import *\n",
    "from tqdm.notebook import tqdm\n",
    "from scipy.signal import welch\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9f31dee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "resetting clocks: 204.8\n",
      "\n",
      "QICK configuration:\n",
      "\n",
      "\tBoard: ZCU111\n",
      "\n",
      "\tGlobal clocks (MHz): tProcessor 384.000, RF reference 204.800\n",
      "\n",
      "\t7 signal generator channels:\n",
      "\t0:\taxis_signal_gen_v6 - tProc output 1, switch ch 0, maxlen 65536\n",
      "\t\tDAC tile 0, ch 0, 32-bit DDS, fabric=384.000 MHz, fs=6144.000 MHz\n",
      "\t1:\taxis_signal_gen_v6 - tProc output 2, switch ch 1, maxlen 65536\n",
      "\t\tDAC tile 0, ch 1, 32-bit DDS, fabric=384.000 MHz, fs=6144.000 MHz\n",
      "\t2:\taxis_signal_gen_v6 - tProc output 3, switch ch 2, maxlen 65536\n",
      "\t\tDAC tile 0, ch 2, 32-bit DDS, fabric=384.000 MHz, fs=6144.000 MHz\n",
      "\t3:\taxis_signal_gen_v6 - tProc output 4, switch ch 3, maxlen 65536\n",
      "\t\tDAC tile 1, ch 0, 32-bit DDS, fabric=384.000 MHz, fs=6144.000 MHz\n",
      "\t4:\taxis_signal_gen_v6 - tProc output 5, switch ch 4, maxlen 65536\n",
      "\t\tDAC tile 1, ch 1, 32-bit DDS, fabric=384.000 MHz, fs=6144.000 MHz\n",
      "\t5:\taxis_signal_gen_v6 - tProc output 6, switch ch 5, maxlen 65536\n",
      "\t\tDAC tile 1, ch 2, 32-bit DDS, fabric=384.000 MHz, fs=6144.000 MHz\n",
      "\t6:\taxis_signal_gen_v6 - tProc output 7, switch ch 6, maxlen 65536\n",
      "\t\tDAC tile 1, ch 3, 32-bit DDS, fabric=384.000 MHz, fs=6144.000 MHz\n",
      "\n",
      "\t2 readout channels:\n",
      "\t0:\tADC tile 0, ch 0, 32-bit DDS, fabric=384.000 MHz, fs=3072.000 MHz\n",
      "\t\tmaxlen 16384 (avg) 1024 (decimated), trigger bit 14, tProc input 0\n",
      "\t1:\tADC tile 0, ch 1, 32-bit DDS, fabric=384.000 MHz, fs=3072.000 MHz\n",
      "\t\tmaxlen 16384 (avg) 1024 (decimated), trigger bit 15, tProc input 1\n",
      "\n",
      "\t7 DACs:\n",
      "\t\tDAC tile 0, ch 0 is DAC228_T0_CH0 or RF board output 0\n",
      "\t\tDAC tile 0, ch 1 is DAC228_T0_CH1 or RF board output 1\n",
      "\t\tDAC tile 0, ch 2 is DAC228_T0_CH2 or RF board output 2\n",
      "\t\tDAC tile 1, ch 0 is DAC229_T1_CH0 or RF board output 4\n",
      "\t\tDAC tile 1, ch 1 is DAC229_T1_CH1 or RF board output 5\n",
      "\t\tDAC tile 1, ch 2 is DAC229_T1_CH2 or RF board output 6\n",
      "\t\tDAC tile 1, ch 3 is DAC229_T1_CH3 or RF board output 7\n",
      "\n",
      "\t2 ADCs:\n",
      "\t\tADC tile 0, ch 0 is ADC224_T0_CH0 or RF board AC input 0\n",
      "\t\tADC tile 0, ch 1 is ADC224_T0_CH1 or RF board AC input 1\n",
      "\n",
      "\t8 digital output pins (tProc output 0):\n",
      "\t0:\tPMOD0_0_LS\n",
      "\t1:\tPMOD0_1_LS\n",
      "\t2:\tPMOD0_2_LS\n",
      "\t3:\tPMOD0_3_LS\n",
      "\t4:\tPMOD0_4_LS\n",
      "\t5:\tPMOD0_5_LS\n",
      "\t6:\tPMOD0_6_LS\n",
      "\t7:\tPMOD0_7_LS\n",
      "\n",
      "\ttProc: 8192 words program memory, 4096 words data memory\n",
      "\t\texternal start pin: PMOD1_0_LS\n"
     ]
    }
   ],
   "source": [
    "from qick.rfboard import RFQickSocV2\n",
    "soc = RFQickSocV2('qick_111_rfbrd_v2.bit')\n",
    "# Load bitstream with custom overlay\n",
    "# soc = QickSoc()\n",
    "# Since we're running locally on the QICK, we don't need a separate QickConfig object.\n",
    "# If running remotely, you could generate a QickConfig from the QickSoc:\n",
    "#     soccfg = QickConfig(soc.get_cfg())\n",
    "# or save the config to file, and load it later:\n",
    "#     with open(\"qick_config.json\", \"w\") as f:\n",
    "#         f.write(soc.dump_cfg())\n",
    "#     soccfg = QickConfig(\"qick_config.json\")\n",
    "\n",
    "soccfg = soc\n",
    "\n",
    "print(soccfg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "055745c5",
   "metadata": {},
   "source": [
    "### SET UP CONFIGURATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3e5a0e7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cavity frequencies from CW values, in order of qubits 1-4\n",
    "brightfreq = [6.1874350, 5.8278950, 6.0745800, 5.9588200] # Power was -20 on VNA, 10 on attenuator\n",
    "darkfreq = [6.1881200, 5.8286500, 6.0750950, 5.959500] # Power was -60 on VNA, 10 on attenuator\n",
    "darkfreq800 = [6.18807, 5.8285, 6.075045, 5.95940] # Best for 800 gain\n",
    "\n",
    "qubitresCW = [4.798, 4.651, 4.491, 4.646] # From coarse CW scans\n",
    "qubitresPulse = [4.800, 4.6571, 4.4928, 4.6518]\n",
    "\n",
    "#pipulsegain = 15000 # Good enough for all four qubits\n",
    "#pipulsegain = [25000, 20000, 25000, 25000] # from single shot\n",
    "#pipulseshort = [0.16, 0.115, 0.15, 0.125] # Based on 1D, first peak, in us\n",
    "#pipulselong = [0.48, 0.325, 0.45, 0.375] # Based on 1D, second peak, in us\n",
    "\n",
    "pipulsegain = [29000, 19500, 28500, 28500] #re-optimized from RabiScans\n",
    "pipulseshort = [0.06, 0.07, 0.08, 0.06] #re-optimized from RabiScans\n",
    "\n",
    "#pipulsegain = [0, 0, 0, 0]\n",
    "\n",
    "hw_cfg={\"res_ch\":5,\n",
    "        \"qubit_ch\":2,\n",
    "        \"ro_ch\":1,\n",
    "        \"LB_in\":0,\n",
    "        \"LB_out\":1\n",
    "       }\n",
    "\n",
    "# expt_cfg={\"length\":200, # [Clock ticks]\n",
    "#           \"reps\":1,\n",
    "#           \"gain\":3000,\n",
    "#           \"relax_delay\":1.0, # --Fixed\n",
    "#           \"reps\":1, \n",
    "#           \"soft_avgs\":1,\n",
    "#           \"adc_trig_offset\":175\n",
    "#          }\n",
    "\n",
    "config={**hw_cfg}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2c28e04a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# soc.reset_gens()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7cbc5e4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RO sidebands: [7574.49  7934.06  7687.515 7803.16 ]\n",
      "  i |   f_vco  | DIV | DLY_SEL |   n  | osc_2x |   R  | mult | R_pre |  f_pfd  |   f_out  | Delta f |   Metric   \n",
      "----|----------|-----|---------|------|--------|------|------|-------|---------|----------|---------|------------\n",
      "  0 | 13762.56 |   2 |       2 |  112 |  False |    1 |    1 |     1 |  122.88 |  6881.28 |    0.00 | 2.2400e+02\n",
      "\n",
      "Choosing solution 0 with minimal metric 224.\n",
      "LO locked on attempt 1 after 0.01 sec\n",
      "  i |   f_vco  | DIV | DLY_SEL |   n  | osc_2x |   R  | mult | R_pre |  f_pfd  |   f_out  | Delta f |   Metric   \n",
      "----|----------|-----|---------|------|--------|------|------|-------|---------|----------|---------|------------\n",
      "  0 | 13762.56 |   4 |       2 |  112 |  False |    1 |    1 |     1 |  122.88 |  3440.64 |    0.00 | 4.4800e+02\n",
      "\n",
      "Choosing solution 0 with minimal metric 448.\n",
      "lock attempt 1 failed\n",
      "LO locked on attempt 2 after 0.01 sec\n",
      "  i |   f_vco  | DIV | DLY_SEL |   n  | osc_2x |   R  | mult | R_pre |  f_pfd  |   f_out  | Delta f |   Metric   \n",
      "----|----------|-----|---------|------|--------|------|------|-------|---------|----------|---------|------------\n",
      "  0 | 13762.56 |   2 |       2 |  112 |  False |    1 |    1 |     1 |  122.88 |  6881.28 |    0.00 | 2.2400e+02\n",
      "\n",
      "Choosing solution 0 with minimal metric 224.\n",
      "LO locked on attempt 1 after 0.01 sec\n"
     ]
    }
   ],
   "source": [
    "# high LO for readout, low LO for qubit\n",
    "f_lo_ro = 122.88*56\n",
    "f_lo_qubit = 122.88*28\n",
    "\n",
    "print(\"RO sidebands:\", 2*f_lo_ro - np.array(darkfreq800)*1e3)\n",
    "\n",
    "\n",
    "# soc.rfb_set_lo(f_lo, verbose=True)\n",
    "soc.rfb_set_lo(f_lo_ro, ch=0, verbose=True) # ADCs\n",
    "soc.rfb_set_lo(f_lo_qubit, ch=1, verbose=True) # DACs 0-3\n",
    "soc.rfb_set_lo(f_lo_ro, ch=2, verbose=True) # DACs 4-7\n",
    "\n",
    "# as long as you're not saturating at any point in the chain (check with gain sweep), better to attenuate later\n",
    "soc.rfb_set_gen_rf(gen_ch=hw_cfg['res_ch'], att1=0, att2=14.5)\n",
    "soc.rfb_set_gen_rf(gen_ch=hw_cfg['qubit_ch'], att1=0, att2=16)\n",
    "\n",
    "soc.rfb_set_ro_rf(ro_ch=hw_cfg['ro_ch'], att=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaaa0a32",
   "metadata": {},
   "source": [
    "### SET UP SWEEP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "315b8b23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "qubit IF=1216.460000, readout IF=1052.780000\n"
     ]
    }
   ],
   "source": [
    "# Can choose a phase from calibration\n",
    "#phase = 32.757131 # From calibration?\n",
    "phase = 0\n",
    "\n",
    "# Set up info for labels and saving\n",
    "TWPA = \"on\" # For plot labels\n",
    "date = \"2022-07-26\"\n",
    "shield = \"closed\"\n",
    "source = \"nosource\"\n",
    "\n",
    "# Choose qubit\n",
    "qubit = 2\n",
    "\n",
    "# Set up readout parameters\n",
    "readout_gain = 800\n",
    "ro_pulselength = 3.0 # us?\n",
    "\n",
    "rpos='dark'\n",
    "\n",
    "if(rpos == 'mid'):\n",
    "    resf = (brightfreq[qubit-1]+darkfreq[qubit-1])/2\n",
    "elif(rpos == 'dark'):\n",
    "    resf = darkfreq800[qubit-1]\n",
    "elif(rpos == 'bright'):\n",
    "    resf = brightfreq[qubit-1]\n",
    "\n",
    "adc_offset = 80 # Check on loopback tests\n",
    "\n",
    "# Set up qubit parameters\n",
    "pulsestyle = \"const\" # \"arb\" = Gaussian, \"const\" = square, \"flat_top\" is Gaussian + square\n",
    "qu_pulselength = pipulseshort[qubit-1] # Square pulse or flat top length\n",
    "qsig = qu_pulselength/4 # Sigma for gaussian, where length = 4*sigma\n",
    "qubit_gain = pipulsegain[qubit-1] # Uses full value for Gaussian and flat top, half for square (see loop backs)\n",
    "\n",
    "averages = 5000\n",
    "relax = 10\n",
    "\n",
    "expt_cfg={\"relax_delay\":relax,\n",
    "          \"reps\":averages,\n",
    "          \"expts\":1,\n",
    "          \"start\":qubit_gain,\n",
    "         }\n",
    "\n",
    "readout_cfg={\n",
    "    \"readout_ch\":hw_cfg['ro_ch'],\n",
    "    \"readout_length\":soccfg.us2cycles(ro_pulselength, gen_ch=hw_cfg[\"res_ch\"]), # [Clock ticks]\n",
    "    \"f_res\": abs(soc.rfb_get_lo(gen_ch=hw_cfg['res_ch']) - resf*1000), # MHz, start value\n",
    "    #\"f_res\": brightfreq[qubit-1]*1000, # MHz, start value\n",
    "    \"res_phase\": soccfg.deg2reg(phase, gen_ch=config[\"res_ch\"]),\n",
    "    \"res_gain\": readout_gain, # for dark peak\n",
    "    \"adc_trig_offset\": adc_offset # [Clock ticks]\n",
    "    }\n",
    "\n",
    "qubit_cfg={\n",
    "    \"sigma\":soccfg.us2cycles(qsig, gen_ch=config[\"qubit_ch\"]),\n",
    "    \"pulse_length\":soccfg.us2cycles(qu_pulselength, gen_ch=config[\"qubit_ch\"]),\n",
    "    \"pulse_style\":pulsestyle,\n",
    "    \"qubit_gain\":qubit_gain,\n",
    "    \"pi_gain\": qubit_gain, # not used in this script\n",
    "    \"pi2_gain\":qubit_gain//2,  # not used in this script\n",
    "    \"f_ge\": abs(qubitresPulse[qubit-1]*1000 - soc.rfb_get_lo(gen_ch=hw_cfg['qubit_ch'])),\n",
    "    \"relax_delay\":relax\n",
    "}\n",
    "\n",
    "\n",
    "config={**hw_cfg,**readout_cfg,**qubit_cfg,**expt_cfg} #combine configs\n",
    "\n",
    "print(\"qubit IF=%f, readout IF=%f\"%(qubit_cfg['f_ge'], readout_cfg['f_res']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5acd7d9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Figure params\n",
    "rcParams['figure.figsize'] = 16, 8\n",
    "rcParams.update({'font.size': 22})\n",
    "\n",
    "#helper functions\n",
    "def hist(data=None, plot=True, ran=1.0):\n",
    "    \n",
    "    ig = data[0]\n",
    "    qg = data[1]\n",
    "    ie = data[2]\n",
    "    qe = data[3]\n",
    "    \n",
    "    numbins = 200\n",
    "    \n",
    "    xg, yg = np.median(ig), np.median(qg)\n",
    "    xe, ye = np.median(ie), np.median(qe)\n",
    "\n",
    "    if plot==True:\n",
    "        fig, axs = plt.subplots(nrows=1, ncols=3, figsize=(16, 4))\n",
    "        fig.tight_layout()\n",
    "\n",
    "        axs[0].scatter(ig, qg, label='g', color='b', marker='*')\n",
    "        axs[0].scatter(ie, qe, label='e', color='r', marker='*')\n",
    "        axs[0].scatter(xg, yg, color='k', marker='o')\n",
    "        axs[0].scatter(xe, ye, color='k', marker='o')\n",
    "        axs[0].set_xlabel('I (a.u.)')\n",
    "        axs[0].set_ylabel('Q (a.u.)')\n",
    "        axs[0].legend(loc='upper right')\n",
    "        axs[0].set_title('Unrotated')\n",
    "        axs[0].axis('equal')\n",
    "    \"\"\"Compute the rotation angle\"\"\"\n",
    "    theta = -np.arctan2((ye-yg),(xe-xg))\n",
    "    \"\"\"Rotate the IQ data\"\"\"\n",
    "    ig_new = ig*np.cos(theta) - qg*np.sin(theta)\n",
    "    qg_new = ig*np.sin(theta) + qg*np.cos(theta) \n",
    "    ie_new = ie*np.cos(theta) - qe*np.sin(theta)\n",
    "    qe_new = ie*np.sin(theta) + qe*np.cos(theta)\n",
    "    \n",
    "    \"\"\"New means of each blob\"\"\"\n",
    "    xg, yg = np.median(ig_new), np.median(qg_new)\n",
    "    xe, ye = np.median(ie_new), np.median(qe_new)\n",
    "    \n",
    "    #print(xg, xe)\n",
    "    \n",
    "    xlims = [xg-ran, xg+ran]\n",
    "    ylims = [yg-ran, yg+ran]\n",
    "\n",
    "    if plot==True:\n",
    "        axs[1].scatter(ig_new, qg_new, label='g', color='b', marker='*')\n",
    "        axs[1].scatter(ie_new, qe_new, label='e', color='r', marker='*')\n",
    "        axs[1].scatter(xg, yg, color='k', marker='o')\n",
    "        axs[1].scatter(xe, ye, color='k', marker='o')    \n",
    "        axs[1].set_xlabel('I (a.u.)')\n",
    "        axs[1].legend(loc='lower right')\n",
    "        axs[1].set_title('Rotated')\n",
    "        axs[1].axis('equal')\n",
    "\n",
    "        \"\"\"X and Y ranges for histogram\"\"\"\n",
    "        \n",
    "        ng, binsg, pg = axs[2].hist(ig_new, bins=numbins, range = xlims, color='b', label='g', alpha=0.5)\n",
    "        ne, binse, pe = axs[2].hist(ie_new, bins=numbins, range = xlims, color='r', label='e', alpha=0.5)\n",
    "        axs[2].set_xlabel('I(a.u.)')       \n",
    "        \n",
    "    else:        \n",
    "        ng, binsg = np.histogram(ig_new, bins=numbins, range = xlims)\n",
    "        ne, binse = np.histogram(ie_new, bins=numbins, range = xlims)\n",
    "\n",
    "    \"\"\"Compute the fidelity using overlap of the histograms\"\"\"\n",
    "    contrast = np.abs(((np.cumsum(ng) - np.cumsum(ne)) / (0.5*ng.sum() + 0.5*ne.sum())))\n",
    "    tind=contrast.argmax()\n",
    "    threshold=binsg[tind]\n",
    "    fid = contrast[tind]\n",
    "    axs[2].set_title(f\"Fidelity = {fid*100:.2f}%\")\n",
    "\n",
    "    return fid, threshold, theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "19bbedc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "class NoPulse(AveragerProgram):\n",
    "    def initialize(self):\n",
    "        cfg=self.cfg   \n",
    "        self.declare_gen(ch=cfg[\"res_ch\"], nqz=1) #Readout\n",
    "        self.declare_readout(ch=cfg[\"readout_ch\"], length=cfg[\"readout_length\"],\n",
    "                             freq=cfg[\"f_res\"], gen_ch=cfg[\"res_ch\"])\n",
    "            \n",
    "        f_res=self.freq2reg(cfg[\"f_res\"], gen_ch=cfg[\"res_ch\"], ro_ch=0) # conver f_res to dac register value\n",
    "        \n",
    "        self.set_pulse_registers(ch=cfg[\"res_ch\"], style=\"const\", freq=f_res, phase=cfg[\"res_phase\"], gain=cfg[\"res_gain\"], \n",
    "                                 length=cfg[\"readout_length\"])\n",
    "        \n",
    "        self.sync_all(self.us2cycles(500))\n",
    "    \n",
    "    def body(self):\n",
    "        self.measure(pulse_ch=self.cfg[\"res_ch\"], \n",
    "             adcs=[self.cfg[\"readout_ch\"]],\n",
    "             adc_trig_offset=self.cfg[\"adc_trig_offset\"],\n",
    "             wait=True,\n",
    "             syncdelay=self.us2cycles(self.cfg[\"relax_delay\"]))\n",
    "        \n",
    "    def getAllPulses(self):\n",
    "        allI = self.di_buf[0].reshape((self.cfg[\"expts\"],self.cfg[\"reps\"]))/self.cfg['readout_length']\n",
    "        allQ = self.dq_buf[0].reshape((self.cfg[\"expts\"],self.cfg[\"reps\"]))/self.cfg['readout_length']\n",
    "        return allI,allQ\n",
    "\n",
    "class PiPulse(AveragerProgram):\n",
    "    def initialize(self):\n",
    "        cfg=self.cfg\n",
    "        \n",
    "        self.declare_gen(ch=cfg[\"res_ch\"], nqz=1) #Readout\n",
    "        self.declare_gen(ch=cfg[\"qubit_ch\"], nqz=1) #Qubit\n",
    "        self.declare_readout(ch=cfg[\"readout_ch\"], length=cfg[\"readout_length\"],\n",
    "                             freq=cfg[\"f_res\"], gen_ch=cfg[\"res_ch\"])\n",
    "\n",
    "        self.q_rp=self.ch_page(self.cfg[\"qubit_ch\"])     # get register page for qubit_ch\n",
    "        self.r_freq=self.sreg(cfg[\"qubit_ch\"], \"freq\")   # get frequency register for qubit_ch    \n",
    "        \n",
    "        f_res=self.freq2reg(cfg[\"f_res\"], gen_ch=cfg[\"res_ch\"], ro_ch=0) # conver f_res to dac register value\n",
    "\n",
    "        self.f_ge =self.freq2reg(cfg[\"f_ge\"], gen_ch=cfg[\"qubit_ch\"])  # get start/step frequencies\n",
    "\n",
    "        # add qubit and readout pulses to respective channels\n",
    "        self.set_pulse_registers(ch=cfg[\"qubit_ch\"], style=\"const\", freq=self.f_ge, phase=0, gain=cfg[\"pi_gain\"], \n",
    "                                 length=cfg[\"pulse_length\"])\n",
    "        self.set_pulse_registers(ch=cfg[\"res_ch\"], style=\"const\", freq=f_res, phase=cfg[\"res_phase\"], gain=cfg[\"res_gain\"], \n",
    "                                 length=cfg[\"readout_length\"])\n",
    "        \n",
    "        self.sync_all(self.us2cycles(500))\n",
    "    \n",
    "    def body(self):\n",
    "        self.pulse(ch=self.cfg[\"qubit_ch\"])  #play probe pulse\n",
    "        self.sync_all(self.us2cycles(0.05)) # align channels and wait 50ns\n",
    "\n",
    "        #trigger measurement, play measurement pulse, wait for qubit to relax\n",
    "        self.measure(pulse_ch=self.cfg[\"res_ch\"], \n",
    "             adcs=[self.cfg[\"readout_ch\"]],\n",
    "             adc_trig_offset=self.cfg[\"adc_trig_offset\"],\n",
    "             wait=True,\n",
    "             syncdelay=self.us2cycles(self.cfg[\"relax_delay\"]))\n",
    "        \n",
    "    def getAllPulses(self):\n",
    "        allI = self.di_buf[0].reshape((self.cfg[\"expts\"],self.cfg[\"reps\"]))/self.cfg['readout_length']\n",
    "        allQ = self.dq_buf[0].reshape((self.cfg[\"expts\"],self.cfg[\"reps\"]))/self.cfg['readout_length']\n",
    "        return allI,allQ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "296d21c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b34464ad399e4338bde5914190de20b7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "56cb842d7c29416e97b19d423d564797",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean signal is (-645.9821506944444-393.7910887152778j) (magnitude 756.547659)\n",
      "amplitude noise: 3.391337 ADU, 0.004483 as a fraction of the signal\n",
      "phase noise: 4.745688 ADU, 0.006273 radians\n"
     ]
    },
    {
     "data": {
      "image/png": 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rjrAz7gTcx+83mMCupUHgltyMBEczKwq8b+x3acrvZowx4G5+RFUFV9ftQaK1yvbAg77FYVCwvm2svgkGUFpUT+MugnfGnc9uxbXMGYFLgBlPPnqTX7c1D+paUk8HL67j9bTV0cb0LK39t9/SgHXyyIPtzrd2iXd1OiVpIJLT/PRlVW3sIW+orpLcWUT64YI+2+DOPcep6rzW7ldVP8SNzvZLXBerdbgWPh/g8gjtQmLE4yiu+1VcBYn7s4GNHCq+vNK3vm0S30OhDDeIzjX+swHAybjWRxNV9X/+gdDZuO7e36JhsuiMZi1+TKaq8NO+addKaOpQg+0pfqNQlHat+uu0dKjgc3FBqk9xTRwbBKV888bWCp7sd/Rdslq6fWO/S1N+N2OMqUfdkOtviEh8+PRjgQOBU4D7AqsG69vm1EfNrqd9V4J4F6xzVfX3KdZL18q0qeL17F9U9eRWbG91tDE9S2v/7Qev8c5W1btbX6R29QfgUlxS60OBv/luZwf65fek2rAJJpPIo9YSUVp53+671D2DG3AlCkxQ1XRpLprFn2uv86+w48e7EL8fvC9R1ToR+QTXOnWbsG0DtvbThWnXaugO3IPnn6lqPNXFbrhg1AequqmFmS/P88CpuO7fzzbzWF2WtfgxmSrel7SgiX2B4/00WxSpbyOL/bSvb2YZSkSGkAhULU61XiPiXQeeTNMFYacW7jso+Hvu0oLtK0g099w+3Yq44d6NMaZFfFLjs3FNuQGmihvePW51YFljffvjyY+/bEk3L+p373oozXptWU+3pI6GxHmof3AI3hBWRxvTvSz205Zen31D4hqvpfVPh/F5jOKDlJwWmAru3BA2IEBG8MO2P0VilLUzVbWpKRra4vibAQf42bBg03/8dIwf3j1sH3kkzp3/bcaxf4hrufOMqj4eWBQ/n4V1Yf4yaZ1uwQI/JlMFmyUenW5FEfk2iVw5oaNOdZBXAu+PS7Pe8Sm2gUQXN0Qki9Ti3anSrXNKmmXBY6Xbx3skmmue3sj+GvA3Yv/ys0emqexLsPw+xphW8iN//NrPjiBQD/p+/K/62aPS1Ef5wJF+NrmOhqbVncEur6mOszWJ5Pdhgsmn0x3r7366nYjsmWa9VILf8Zg06x3bgn0bY7qu+L/9HX1XmQZEJJdEfViPv8Z73s8eJyIt7RbbUk2tI4PiSZ6P8S3jT/XzD7cwyA90bnJn/4DjURKBlwtVtTWtl1riPFz+nhgQ1sL1KT8tAo5IsY+jSJw7ZzfloL6V0824bmcXJC2OdxUL6142MGmdbsECPyZTPQvEh0O/XEQGh63kK7v4Rb4SXtl0CFX9D/CWn70yrMwiMpREU9D/+qHkg74OvA/9zl78Ce/hPkKefJyDaDxQEz9WyuP4k/otfvYgEbkw1br+uHkhLbTu9dOhwC9CtskKHMMYY1rrVhLdfi9LCqLHL/qDdXGya0g8BfxdyPJG607qt5Y8Knmhv5n6Pemv05p6PrgvsO49Pq9BSiJSbwQxVX2dRHLXyWGtfkTkYBp5CGOMyTj3k8i9cnNS3pu4K0mfl+VmP+0P/C5VQB3cSK/JI2q10jeB9+nqyKBHca3R84Hfkuha1NGBkjbh/2b3Ad/zH13tR7/qyDLsB1zlZ2emSCT9BC6vDsA1Sa1x4w9cpvjZxaQfiTloCjAEuF5VP0laFk9PMdqPchY/Vi6J3D7NTWHRpVngx2Qkn4TtPD87CPi3iJwrIqNFpK+IbOXzOcwjkUdhhqp29pDgF+Ci3cXAv0TkZBHZUkQGicjJuNYvA3B9b88P2f5NEkMgThWR4T5hXHbSzcvDfjoaeFpE9haR/uKGd5+MG3qzsf6x8WaX+4jICSLSzx8nO+nkfyuJFlg3i8hfReQI/736isjWInKkiNwGLAFOSDrOY4Htp4sbfn4Hf7y9cCMtHErLu70ZY8wmqrqWRDB5W+DEwOLHgH/491NE5Lci8h1fH+0sIn8ALvHLZ6fIjxCvO48VkUNEZLNA3RlPKP0GiVFr7hCR80VkGxEZKCLjca1TDyL96GL/I5FD43IR2dYH1+udD3zehdNxDz+2B94WkQslMdxusYiME5HzRORF4PWQY8WD+lsB80TkKBEZICLDROTnuIv2xWnKaozJMKq6jMTD0yOB2SKyp68PS0TkN7gb+k/T7OO1wD5OBF4XkVIRGeGvEYeKyAH+2rQcmNGG5V+HqycBfioi3xaRgnh9nGKbShLdb+Pnhg98ADwT3Qmc5N/fjbtOT5dEukFswJ/H1L+uDDuIiMwWkVtE5GB/LttCRMaKyK+B53CBtP8Al4dtr6rVuPxK4Lpz/c3/v9ZfRPb2+4h3wb5EVWvD9pNUph2Bn+HSg9wQcsz3gXdxXfn+IiK7+ADQH3H3lpXAk40dJ6Ooqr3slbEvYBKuGZ428roFyArZ/rT4Oo0c516/3kvNWZZiXyfjmhymKmsVcEKa7R9Msd3iwDoRXDPIVMdYikuiFp8/LeQ4g3BPS8K2n5K0bh/chX9jfwcFLgg5VjHuhJ9qm2ub+zvby1726lmvYH0OHNDIun0C9dt7gASWbY4LRqerx54DeqfY93eAmhTbTQysdxDpz1/X4YaVVeDjFMeakWLbupB1f4B7kt1YHb0yxbEuxj14CNvmM+DgwPw+nf3/g73s1VNezan7QrY9JLDt0JDl2biAeLq68JxU9Y7fRwTXUjLahPrn4ZDtl/llV4Ysa6yOPDfNsRp8X7/N2KT1yjr7b9zC/y+ym/B7J78a1N1J/480+Bv4dV5vZL9/AzZvQpknpznPRIFfNOP7/9Nvd0SadfbCBXjCjndmZ/8N2/plLX5MRlPV+3AZ4K/BVTpf45qlrsU1z/stsLOqXqhNG6693anqQ7hEeL/Btbqp9K+FwO3A9po+4drpuKa1b+Ke9mrIMWK4i/wLcd3Lqv265bibiZ1VNW2LH3VDGu4J/Bl3UV+TZt21qnoM7sL/T7jo+gZc/+ovcS2Zpvnj/iZk+5W4E+0U3NCP1bi/5QvAD1S1NSMhGGNMPepa/cS7IJTg6sv4stXA/ribqedxdVgtsArXzXgCcJi6p8lh+37bb/84Lgda6JNJVX0B2B14JHCMFbhcB99V1dAno0kuA36Ka0G0lpDzQeB4j+O6LVyBC2x9hTtfbsA9FX8I92R4ZIrtf43LEfE0rn6uBj7G/Y5jSLRgMsZ0E+pa2B8PnAG8hruWXIe7Br0Il48lbesLVY2p6lW4Fhu3Ae/ggtBRP30L1zLlYKC0jcs/E5fL7WVcF99Y+i1AVRf4MoKrI+9Ls7pxpuNaynyAS+hdg3vI/ChwtKoe7s+taanqNNx55lHgc7+f5bjz5L6q2qQWYSIyCdgX+KuqphyVS1Vf9evNxZ1Dq3Hn0x+o6v815ViZRHy0yxhjjDHGGGOM6dFE5HVcYH62qlr+MtMtWIsfY4wxXZ7PAaBNfA1L2naoiPxGRBaKSJWIVIvIRyJyl4hs08hxS0VknohUiMh6EVngc6HY+dMYYzqZiFwgIo+IyAci8rWI1IrIlyLyvIhMDOTVCtvW6nfTgIjsgAv6QIYmdTYmjLX4McYY0+WJyPa4bi2p7IbrQvkJsK36k5uI7ILrMtgXlycgnnh3LG6kh/XA4b65b/Ix78TlB6jGJfytxTVF743rxnNCV+lCaowxPZGILMONKvUerkvIBmA47sZdcMlZj/Vd4IPbWf1uQonIXcBZuK5Gw313N2MyngV+jDHGZDwReR+XP+AKVf1V4PNXcbmqfgecp34kCHFDhd6Fy1vwjqp+J2l/x+H6mH8B7KeqH/nPi4EXcUGmn2sHD4tqjDEmQUT2Ad5U1Q1Jn5fgAjrFwBmq+sfAMqvfTT1+lK8C4Djc9UIWbvSoX6fd0JgMYoEfY4wxGU1E9gRexSWKHK6qy/3n+bhRkwC29AnLg9sNxj0hBihUN4xrfNkCXMLaU30S+eB2+wMv4W4ahiQ/STbGGNP5ROQq3MASD6lqaeBzq9/NJiIyCvgo6eP3gTGqurETimRMu8ju7AKYhP79++uIESM6uxjGmG7kP//5z1eqOqCzy9HOzvDTZ+NBHy+KG5EjG9fkP1n8yccGEgEiRGQo7qagBmgwwp6qviwiy3FdxfbABZ3SsvrdGNPWekj93hrxLjrV8Q+sfjfJSkpKeP/99wHIycmhT58+DBkypCQnJ6d67NixnVw605O1dR1vgZ8uZMSIESxYsKCzi2GM6UZEpFsPsSwiBcCJfvYPwWWqWisi/wAOB6aKSHJXr+nx7bR+89dd/PR9Va0i3HzcjcEuNOHGwOp3Y0xb6+71e2uIyNbA2X72qcAiq9+NMRmhret4C/wYY4zJZCfgknGuAp4OWX4u8CzwE2C8b+IPMA7YHLgNKEvaZms/TXfCXZK0rjHGmE4iIqcD+wM5wFBgL9zoxdep6uOBVa1+N8b0SBb4McYYk8ni3bzui7fmCVLVRSKyF3AfMB53QxC3APhnyHZFfrqB1Nb7ae9UK4jImcCZAMOGDUu1mjHGmNbbGzg1MF8HXAXcnLSe1e/GmB4p0tkFMMYYY1rCJ2Tcz8/ek2KdvXDD/I4Cjgb6AwOAY3Atfh4TkcnJm/lpq0Y/UNX/U9Wxqjp2wABLw2GMMe1FVX+sqoIbmakEuBWYArzuE/nHWf1ujOmRLPBjjDEmU8Vb+7ymqh8kLxSRvsATuKe2R6jqbFX9WlW/UtUngSNwSZ2vEpFtA5uu89MiUosvW5dmHWOMMR1IVatUtVxVy4DLge8AdwRWsfrdGNMjWeDHGGNMxhGRLGCSn/1DitW+h2vd87qqLkpeqKofA2/guj0fEFi02E+HpynCVknrGmOM6Vr+6Kff9wn9wep3Y0wPZYEf02NVVEBJiZsaYzLO4bhRVzYAD6dYJ554Id2/8jV+2i/w2Zt+WiIivVJsNy5pXWN6PDuvmi5mDS7XTzaJOt7qd2NMj2SBH9NjzZkD5eUwd25nl8QY0wI/8tOHVXV9inU+99Mxgae9m/jPxvjZT+Ofq+pS4L9ALm7UsOTt9sclif4CeK1FpTemG7Lzquli9sMFfdYAX4HV78aYnssCP6bHKS2FoiI41Y/9MGmSmy8t7dxyGWOaRkT6A0f62VTdvACeASpxLX9uEZG8wD7ygNtxTfpXA39L2vY6P73BJ5GObzcQmOlnr1fVWEu/hzHdhZ1XTWcQkX1FZEKwbg8s25vE+eEPqhoNLLb63RjT41jgx/Q406bBsGGQ45//5+TA8OFwzTWdWy5jTJOdgnta+6GqvppqJVVdBZwLRIHzgEUiMltEnsK18Dkb2AicoaoVSds+CvwWGAS8KyJPichfgY+Ab+GSRgcThhrTY9l51XSSkcD9wBci8g8RecDX8e8DrwDbAHNww7pvYvV7DzZlinsZ0wNld3YBjOloo0a5i9STT4bCQti4EaZOhZEjO7tkmUtVWbduHWvXrqWyspJoNNr4RqZZsrOz2WyzzejXrx/Z2T2+6j7dT0OHcA9S1T+JyLvAz4F9gcP8ouW4p8E3q2p5im3PFZFXcEGj/YEs4EN/3N/a02BjHDuvmk7yMnANrm4fDeyFG679C+Ax4H5VfSJsQ6vfjTE9TY+/ezA90yOPuIvTq65yTyRnzYLjj+/sUmUmVWXVqlVs2LCBfv36MWjQILKyshCRzi5at6Gq1NTU8PXXX7N06VKGDx9OJNJzG2yq6rebuf5/SYwA1txjPQg82JJtjelJ7LxqOpqqfgpMbsX2Vr8bY3oMC/yYHqmsDH7zGyguhokTYenSzi5R5lq3bh0bNmxg+PDhZGVldXZxuiURIS8vjy233JJly5axevVqtthii84uljHGbGLnVWOMMabrssCP6ZHGjUu8Ly52L9Mya9eupV+/fhb06QAiQt++fS3wY4zpcuy8aowxxnRdPbevgDGmTVRWVlJUVNTZxegxCgoKqKqq6uxiGGOMMcYYYzKEBX6MMa0SjUattU8HikQixGKWc9IYY4wxxhjTNBb4MaYHqKiAkhI3bQ+WyLnj2G9tTOdq7/rUGGOMMaatWeDHmB5gzhwoL4e5czu7JMYYk9msPjXGGGNMprHAjzHdWGkpFBXBqae6+UmT3HxpaeeWyxhjMo3Vp09UM1gAACAASURBVMYYY4zJVBb4MaYbmzYNhg2DnBw3n5MDw4fDNdd0brmMMSbTWH1qjDHGmExlgR9j2khXzPswapS7WamthcJCN506FUaO7OySGWNMZrH61BhjjDGZygI/xrSRrpr34ZFH3E3K1KluOmtWZ5fIGGMyU1erT7viAwdjjDHGdD0W+DGmlbp63oeyMli4EC6+2E3Lyjq7RMYYk5m6Wn3alg8cLIhkjDHGdF8W+DGmlbp63odx46C42L0vLoaxYzu3PMYYk6m6Sn3aHg8cumqrVWOMMca0ngV+jGmlRvM+2GNUA7z11lscffTR9OvXj8LCQsaMGcM999wDgIggIp1cQmNMpmjLBw5dvdWqMcYYY1rPAj/GtIGUeR/iQR97jNqjvfDCC+y5557Mnj2b4uJijjrqKPr06cOZZ55JWWf3FTHGZJy2TDTd1VutGmOMMab1ulXgR0T2EpHfisgbIvK5iGwUkfUi8o6IXC8iA9JsmyUi54jIv0RkjYjUisgqEXlGRI5p5Ljbicj9gWN+5suxZdt/S9MVheZ9KC11fQGWL3crteAxqjUWSi1TfpvKykomTpxIdXU1kydPpry8nIceeogXX3yRf/7zn9x1112dXURjTAZqq0TTNlqZMcYY0/11q8AP8F3gbGAgUA78FfgXMBS4FHhPRHZI3khEsoFngZnAGGAB8BiwGDgCeFxEbgk7oIjsD7wJTABWAI8Dlb4cb4vI6Lb7eqZLqqhg3GklFOe7CERxMYy9uRSeeAI2bkysV1cHWVnNeoxqORdSy5Tf5tFHH2XFihWMHj2aq6++ul6Xrr322otzzz23E0tnTM+SKQHjpmjLRNNdbbQyY4wxxrSt7hb4uR8Yrqpbq+ohqnqyqh4ObAU8jAsIhT1ePwM4BFgCjPbbnqSqu+ECP3XAz0Vk1+BGIlII/AXoBVygqmP8djsAvwYGAA+JJe/o3sIiENOmwYgRkJtbf91rr23SY1TLuZBapv02L7/8MgAnnngikUjDKre0qxbcmFboqgGWTAkYN0VbJpruaqOVGWOMMaZtdavAj6p+qKpLQj7fAMQvY/YVkbykVQ70098mb6+qfwNe9LN7JG13OjAIeElV70hadinwCbArML5ZX8RkhnQRiGDbeYBIBAoKYN68Ju26vXMudNWbsqbItHwUy31Xv+HDh4cuT/W5MZmsqwVYMi1g3NG6ymhlxhhjjGkf3Srw04i6wDSatGwjTfNV0nw898/9ySuqahTXGii4nulO0kUgKirgjDNcsOfqq6F3bzjkkCY/Rh0wANatg5qa9sm50NVuypojU/NRpGr4F9YKyJhM1RkBlqYEsjMtYGyMMcYY05Z6xB2HiOQC8cu7Z1S1LmmVZ/30HBEZlrTt4bgWQZ8DybfJu/jp/BSHnp+0nukk7dLCJV0EYs4cF7m56SaYMsW1nb/qqpSPUZPL9/dZFTy7rIT+ORVtmnOhuzz1zqR8FIMHDwbgs88+C12+ePHiDiyNMe2rMwIsTQlkZ2rA2BhjjDGmLXTLwI+IbCsi9/rX08BS4Ee4QMw5IZs8DPweGAb8T0SeE5G/iMgbuKDQv4GDVHV94Bh9gH5+NvyOzuUMAti61V/KtEq7tXBJjkBcfHH9yMoFF6CFRcwZfSEV26ZuOx8v3/GHVvBBpITnzpxFCeUcUjOXq6+GAw9sm5wL3eWpdyblo9hvv/0AeOSRR4jFYg2WP/TQQx1dJGPaTUcGWJobyM6kgLExxhhjTFvqloEfoBg41b++h0vq/A/gJFX9PHlldX4CXIT7TQ4BTgR2A1b7bZO3Kwq835CiHPFAUe9UBRWRM0VkgYgs+PLLLxv7XqaZ2r2FS3IE4tZb60VWqqI5rNlsOD9de01o0OlHx7tAzwWTXFOfqfPHs4OWM1PPAuCPsUmsqizivrrSpuVcaKRpU3d56p1J+ShOOOEEiouL+fDDD7n22mtR1U3L3njjDe68885OLJ0xba+jAizNDWRnUsDYGGOMMaYtdZnAj4jMEJEPW/AakrwvVX1FVQXIwrXiOQXYBjec+/Ehx+7jWwZdD0wHRgKFwM7A34GrgHkiEgzgtMlIXar6f6o6VlXHDhgwoC12aQJa3cKlsT5igQhERX4xu19+ENGKdVBbS2WkkGyt5ayVU1nEyNCg07V7zWEHLefp2HjWU8ievAZAFvGWITFqhwyn961NLHATmjbZU++OVVhYyJ///Gfy8vKYPHkyJSUllJaWctBBB7H33nvz4x//GICc+P+kxmS4jgqwNDeQnUkBY2OMMcaYttRlAj/AYGC7FrxS3i2pakxVl6rq/cDBQC3wRxEZnLTqr3Etg65W1WmqukhVK1X1bVU9CRf8+Q5wSWCbdYH3hSmKEG8VtC7FctPOWt3CJSmQki4ONGcOjFw4h6zPl1FZl8PVOpUNFHJczEVWVANBJ98UadClrinSWP03vaiqF02M+bm6r9dC//7py9mMpk321LvjHXroobz66qt8//vfZ8WKFTzxxBOsXr2aO++8kwsvvBCA/o39jY3JEB0ZYLFAtjHGGGNM47pM4EdVJ6qqtOC1uIn7/xT4Jy4Yc3j8cxHJwrUIAnggxeYP+ukhgf2tBb7xs6nGY97KT5tURtM+WnRjkCKQsnp8aYMGNaWl8Eh2KUdPKOQBJgCQQw1X69W8wIHcSBkiIBIIOiU1RcomiuC6AMU7AkVGDCcrP48tqpY1npyoGU2b7Kl359h1112ZPXs2q1evprKykjfffJOzzjqLl156CYAxY8Z0bgGNyUAWyDbGmI4zZYp7dVfd+bt1GfYjd5ouE/jpIPEkOgMDnw0E8vz7VGM+rfHTfkmfv+mn41Jst1vSeqYTtOjGoKwMamvRbBdI2ag5fFA5nMP/7QIp55/icvOcf/QS/ji/hL9sVcYa+m5qsVNLLksYzmWRm/gPYznllKSgk2+KVFddywYKgEALH7JQoG7xUnRjrVs/2IInrNlRd0ne002tWrUqdFSv119/nTL/P+Rpp53WwaUyJvNZINsYY4wxpnE9JvAjItnAfn72o8Cir4GN/v0eKTbf008/Tfr8ST+dEHK8LOAkP/t4swpr2lSLbgw++ABqatCNG1mHy9Vz9+CpLM11gZTvR1xunmv7zCDv43JmLduTYr4AXIudXlSxJmtzxp8/kt69oboa/je/gnvnl8CSJS5w88ADSGEhdw6cRhUFCMp6CgDh6qxr+SJnGJIX0oInJI9PRQU8f/oDaCwGl11mfR66mHfeeYcRI0aw0047cdRRR3H88cez6667sueee7Jy5UpOOeUUjjvuuM4upjGmAzSWOs4YY4wxpq11q8CPiFwjIluFfD4QuAeXtHkpboh2AFS1BnjKz94uIiOTtj0M+Lmf/UvSrv8IfAEcKCLnJS273h/vTeCZFn0h0/F8F6+6ia6Ll2iMXGqJxoR9Vszi91WlrKeIe2onArDZ/W5Epkjdxk0JmWvJRokw5oA+3HYbfPSRa0A0cP4cen1aDjNmuMDNmDFkfbyQ2dtezHMczAYKuZppbKCQHaNvc9c2M4jUBVrwbL45fOc7oXl85syBl9bvisRirpVPY02b0tx52E1J29t+++0555xziMVivPLKKzz55JN89tlnHHTQQdx333386U9/6uwiGmM6SBNy8BtjjDHGtKnszi5AG7sSuFxE3gE+AaLAUGBXoBewEjhWVSuTtrsQGItLFv2+iLzh1x0F7OLXeQi4P7iRqq4XkZNwgZ07ROR0XGui7wA7AF8BJ2tw/GbTtU2bBm+9ReTTxVBdRxX5fMo2nJN7D5KdxRr6sl/2W8jaRRRsaigGRLKQWBRycshR5bNL72bEMTtTUQELRpfy3brZUOX/t/PDd8eumkzNlOs5N3oU07mKM/kdqyjmfiayFUu5ffW1aCzG7QWX8dOsm5A+fVwen08+8ceMsESG89ZD6zjqoSJO8OWpLZ1ENCuP/B8eBQ8+SKjgncfJJzd1kWmhoUOHMnPmzM4uhjGmE5WWwuzZsNGfOiZNgp/8BI5KU1UbY4wxxrSFbtXiBzgfeAw3ytahwHHA9sB/gcuB7VV1QfJGqroMN3T7FOA9XLDnWFxy5ueAUlUtVdVYyLYv+/UfxAWZjsUlkL4b+LaqLmzbr2ja1ahRbLhsGtHqWmrzCsmhjuvypvJabHfWjBrLx4zi4vXTyCFKFXkormuXxKJu+2gUqatjxJ+vgbFjmTMHfrZ2Gmv7DoO8vKSDKctkKFdxDf9jNC9wEENZwgscxEoGMmrVv5BYjOO+vItZv5gP0Sj6ySK0psZtXlPDoEoXBFrGELJwZaghhw39U4xZn2b0r2YMDGaMMaaZmpGD3xhjjDGmTXWrwI+q3qmqJ6rqdqraV1VzVLW/qu6jqter6po021ao6lRVHauqfVQ1W1UHqOphqvpQI8ddqKoTVHWQquap6jBVPVtVV7T9tzTt7auZj7CBQn5bPJVKCrlq+1kUFsKQIe4i/Ye45R/JdgBobiKgo3l5fJo7mhsG37YpiPIJo3j3835odfWmEbsU94+vrk5ZxEi+xxxKKOcXzKCEcj5hJAN9LvKhLOf4K0fz9+eEDXW5iWMBVbEcduBDXuRAIihV5JFLLRd/eSkfbHsUPzre99eK998qK0t552E3JcYY0zJN6SJrOfiNMcYY01m6VeDHmNaIt3g5aUEZ27GQi5ZfzM69FnJfcRkLF7oL9l9dWsGusQXs32s+Z0V+zwvXvEqk72aQlYXm51NXVUtZzbVEv38M4wYtYX1dHkNZwvpIb2Ih/9y243/EkE3DwJ+P6waWQ1299SLA/voiH8W2oY4IUYQ6IqymH9vyMWdzNwC51JJNHTfFLmIHLedX+/gkEvH+Wx9+mPLOw25KjDGmZZqat+eRR1z9OnWq5eA3xvRMnTKad4qDttfw9N12xPL2+sFMh7DAjzFevMXL27njWEUxublQsHUxZ8wcS36+a72z6Ddz2JpP+dWxC/igaBzvP/0prFrFRnKZUnsFEWKU8gA3XlnBY5/sTB41TM2azujYQuaX3kLMD9Uep8BGcqhtQrqtXGrYiXcByELJJsYwlgBsGkZeiKEIfVkNQPFFE0AEJrpk1EyaBCed5D4LufOwmxJjjGm65naRLStzufcvvrjxHPzGGGOMMW3FAj/GeKNGuRY9C6pK2LKggl41Fby2toSR/StYPb6UN8qL+Nmb7ur+ew9OYHWFcMEbLqASidZweXQ6WcQ4kBf5RvvSzwdfTo/+jm34lF0fvIR1FPFAZBIAUYQIkE2sQQsfoF63sPg04jIKgf+vJK0vwEbJpwbf/Sw3173i+YVyclx0a9680DsPuykxxphwYd25mttFdtw4KC5274uLYezY1pehI7c3xhhjTGaywI/pedJc+X722zl8i3L+l7UD70e3o8+ycs4d+ChH/HsaSxhGdcxd3ddIHpKbi+S5nDvZRMnzo2r1JfyKOodactnIybH7gETQJp6UORUhEdSpJo/sFOvH95eXo/TKqiHaqxCiUTj/fKirS/TfmjEDdt/drZx059HamxJjjOmuwrpzdXQX2dYOBW9DyRtjjDE9kwV+TM8TduXr2+tfsMC16Clat4JBrATgNzVn8p/oznxNP3KpZYMUkhOpcwGVaJTa3F4A1JEF1G+FEyRAPtVk+ffx7l3x7TRkm2BrHwGi/p9s8rrB+UhNNajy6z5T0YJCeOQRtKCQGUV+Pt5/yx79GmNMoxrrztURXWRbO+piVx210U5DxhhjTMewwI/pOdJd+U6bBllZRKINu1xFiFHABkbxMRGiZPUuQAoK+Oq3j1BRV8i7NW50r+SWOPFgTDAQFHyfS22D7ZKDRsn7KKRy03xyV7DgehqDspWX8PmIPWD6dJ66ej7fW3kPT0+Zn+i/ZY9+jTGmnpZ052ptF9mmBD9aO+piVx210U5DxhhjTMewwI/pOdJd+Y4aBdOnp9x0JYN4iJPJQslf+yVTC25g/WXTOXLbhUzKm8UGChpsk5x/J/mz5HWTly1hKJr0TzRs+7qQf8ZCDAFefaeQ6tPO5LmLn6GEch6+ZAGP7HEz1dld8NGvMcZ0spZ052ptF9mmBD9GjYLLLoOqqpZ1KetqozZ21RZIxhhjTHdlgR/TczR25TtvHhQUuBGvvHjAZiBfcCG3bvp88hfnMGzqj/jH0tEs3ThgU7etsO5a0DAIFNZKJ9kwlhEhtmkdpX5Ln/j7SNJRg3PH81fyqeF2fgbAH2OT+H7sCXLyIy7/D0BODtHBQ1n86Hwq3lti7e6NMT1OZ3Tnam7wY+ZMNz3mmJaVoSuN2thVWyAZY4wxDXSTYewt8GN6lnRXvmVlcPDBkJW1KfgTI0IdWVSRjxIMCAmrYv3JrV7Ld5lLpN7S8GBOcqufVAGf+LKwVkDg/tFq4L0EQj3Jgafk7bOpo2bI1mQd9wNQdaN91dbyyVYHMqL2Y7786fRmtbuvqIDPP3e5o40xJlO1pjtXS/PUNDX4EQ8QLVjg5h9+2D23WLOmecfrSqM2drUWSMYYY0x3Z4Ef07OEXPluumgfPQ4uvBAGD3YBoXff5Yt75nLqFnOYxhRigRCLoPTnSwAepJQ+rPOfJ6Rq/ZO8XrJ026XaV7z1jyKJPD8p1u+zrBzucyOLxTZuROvq2PaFuwAY+eLv3OelE9I+eo7/ZrNmuQt2ayBkjMlkrenO1dI8NU0NfsQDRLluEElycmDEiEQLoKaoqIDTToP8/PDv0Bm6UgskY4wxpruzwI/pWcaNc1e+JSVuOnZs/Yv2FStgyRKoqYEdd2TI6YezePThnMudZPluV3FZgW5YbSlVgud068Q/k2aGjSQSXgVIRNK2ux8/3v1mZ53l5hcvhv/+FxYtatbhjTGmy2huIKIt8tQ05Zgtah2T1AypKyZR7kotkIwxxpjuzgI/pufxV8C/GT+XoiK4YFIFq9mMo0sL0AkT3DqBK/iCAvhl/q18mb8VNbg2+WEjapHmszBNWa81+w8KtgKKv6+J5CGRCDVF/ZL2LyARLlw7lYr+9e8s4jc68+e7+ZiPham6p9GDB7egcC1QVwfvvZfoYpY8b4wxzdXcQERb5Klp6jGb3Tom6TzXFZMotzYptjHGGGOazgI/pudIejx7/vxJrKos4unYePqylmryEy1pAlfw110Hv9//TwyoXrppCPbk/DthgZXkVjnBxMxhy1NpSk6goGAZUu0LICfmunnlrv+mwfbRGExZdgbPPVq/D1fyjU7Q4MGJbgTtraICqqsTXcyS57uiDRs2MGPGDMaNG0efPn3o1asXJSUlTJkyhfXr13d28Yzp8ZobiGiLPDVNPWaTg1IpznP3x1ykp0smUW5pkiRjjDHGNJkFfkzGa/I1Y1LUQlTpRSW762sAbM7qxLpVVbD55lT0H8lpp0G0V+9mdelqyuhe6dZLJyygFN9XWELodGWpLujHRupHcRS4QcvYjHU8cebcek+Hgzc6WVnus8JC9371atrdokWuS9nixW7+009dwtNPP3XzXbXL2bJly9htt9249NJL+eyzz9hzzz057LDDWL16NVOnTmXvvfdmdUf8gMaYNtVReWqaHJRKPs/l5FA7ZDiT5Zqum0S5K/ZDM8aYthAcDaorjQqVyaNUpSt7W3+nTP2NUrDAj8l4jV0zbgoMDUh6PCvCN3mD2UhSM5WsLDQS4V9vFlDUL5f3yoXCJx5os/K2ZVew5hxTk+YBcirXkEstAlSRhwJZKJdyA+CGf19VWcTd6xL9AuI3OuefD717u25eO+4Igwa1YYFTGDzYdSkTgaz1FZT8sISsDRXxQdgQ6dguZ02hqvzwhz+kvLyc888/n8WLF/O3v/2NJ598kk8++YSJEyfyzjvvcOGFF3Z2UY0xzdTheWrCnnQEPwtphvT7raaysmgk111WwbuxEp5+IPVTkg5tfNMWSZKMMcYY0yQW+DEZq6nXjPUCQ0mPZ3O3HUZ+dp0b1hyI5riownUj7mZu5X5kxWqblYunuYmemzKke0v2lS4HUXDdLGKbKoF8Ntb73IlRO2Q4vW9N9AuI3+jceit89BH06eMeLhcWNqOwLZSf74I6qtD3X3Po9Wk5w951Eb9IxH3ekV3OmuLZZ5/ltddeY4899uC2226joKBg07JevXpx1113MXDgQB544AFr9WNMhunoPDWVs9wJrfLRwJOO5KcfSee50wtnsXAhXLDNHLaPlTN5bOqWNfFddUjwpy2SJBljjDGmSSzwYzJWY9eMYYGh/Z4q4+wDE49new8sQPyNuABLC3egpk74xaKzmcJUIPGPJCyY0tYjerW1avKoJI9ytmsQSGosoBX1W1xWXb9fwOjRcNBB7qaguHhTzKzD5Jxays77FjFiivvD9vv5JHbet4gdriklEumYLmfNMdffjB133HFEQkZRKywsZOzYsdTV1TE/njXbGGOC/Akt/2xX7+WfNQmys90r+elHRUW9Zkj9ImsoHpk4GW4zpeFTkuTz5fLlrn5v18Y3bZEkyRhjjDFNYoEfk7Eau2YMCwx9vc04ym4KPJ697jq4/nrYuBFuuIHqO37Pwdn/pJp8skkMERUMmET9P5vGWtU01tqmJV25Gtsmefk6Csmllm+xsElliRKhFpe856HIRKojRVwxun7SigZd62KxDh1SK+vaaUSGD0Pif9jcHCIjhtPrpms6rMtZcyzyCYfKysoQkdBXPDj05ZdfdmZRjTHtrKVdqS5cN40PK4dRFXX1XlU0h09iW7Oi14jESS4ahaFDYebM+s2Q7ryz0ZY106a5XG3BanzjRnjiiXYO/nRUkiRjjDGmh7PAj8ksSVfN6a4ZG32YWFoKBxwA557r5q+4glFn7M/TdYexHtdvKTmQ4uZjpBLs8tUerYHS7TN5RDEBtmA12cSaXJYIMWZyPuvoTZ5u5JVb5jN0xXyoqEjZtW7dqqoOHVKr4NujkGsSf1iprUWmuT9sR3U5a45oNArA/vvvz6mnnpr2NXz48E4urTGmNRoL7LQ0j/EF0wbQN2sdudRsCujfvuV1VE++ntqqWjQvz/V1PeKIhi1mQk6GGy6dSslRIzeVc9QomD4d+lDBe5TQhwpyc2Hrrdu551VSkqT155TZAF/GGGNMO7DAj8ksSVfNjSXWTPswcdo06Nt302xlXQ5f1GzOZqxjEKuo8aNdxYM5dWQRAbJJPbJWc3REN7EstFnDxs/iOH7OrZTkfMSvI2V89OB8N2TW3LkNWlDdHytlVWURvTd+5T7oyCG1Mugp8VZbbQXACSecwL333pv2tc8++3RyaY0xrZEqsNOiPMaBKNI2H8xhUN0yaslletZUNlDIaYWzGHjDRa51ak2t2+b228N3/MgjaEEhM4qmogWFfDVzVoNyzpsHx+bOoYRyvsvcjul5lZQkafbnY22AL2OMMaYdtCrwIyJFbVUQY9JKcdU87pbStIk1UwaGSkth553hiy82rduLKoawfNN8LrX19pVNtNFiCm07GldzNBbgSdclLb7tD3mM9RQyr3Z3/pVzABcsSPzeo3Yu4tl+pZseGl8Rm0bNlsPolCG1OnwonZYbP348ALO6cHDKGNM6jQV2WpTHOB5F6tsXJkxw21HLVdGreYED+clHZUz8+lY+Ylsq1SVbq5Hc8B2XlfH49Qu5dOXFDK9eyIkLyuqV818jSnnoqULuqXHHuT8yiXVaxKCLO2aELRvgy7SEiOSIyMEi8msReV1EVohIjYgsF5FHReSARrYvFZF5IlIhIutFZIGInCciGfFgvL1Gmu5mI1iHas53bO7v0ezfr4U/eHv/ndpzxPc2328mD0/fgZpcsYnIKBG5SESe9hVqDVARqGCf8stHtWN5TU/VwtE/Uo64Ek9oEEvdbautdFYgqDFhgSIBCqjkY7Zhfb9hSG793/uWnF/wdl0JEw9YwpMczW35l7nuBU0cUquuro3SAXX0UDqtcMwxxzBmzBhefvllzj77bL755psG6yxatIg777yzE0pnjEmlOfl4GjtFJfe26lVTwWtrSxjZP2TnyVGQgDrJYakMp4yb+A9jeVKO5Zf8ilxq2SCF5ESiDZrplJZC0YHjOPGnrs5cWlPMG9Gx9co59A/TyOrXNzGyY14OeaOHs9ltHTPClg3wZVpof+B54CJgOPAf4HHgG+A44EURmRa2oYjcCTwAjAXmAc8Bo4E7gEdFJKvdS2+MMR2o0cCPiHxXRP4GfAjcCHwX2JJEj5dsP/89v/xDEXlWRMa3W6lNt9fggrutR/+IJzQIiPqkxupfUSLUkdXswE1bdANrS8lDzSd/n7Dy7cXrvPt5P2qraqnKTvzeeZ+U8y3K2WnODEoo5/BFM4khfJ0/mFRDagWDPRUVHZoOqEuIRCI88cQT7LTTTtx9992MGDGCfffdl5NPPplDDz2U7bbbjpEjR3KN3eEY0yZamkA5WXPy8TTlFBXsofqD3Dn0WZbYeb0yh2Va9vK1iq91c1YWjmTzSAXvaQmTIg9QSSEfnzIV8V1fg/uLB1Wys90+4sGVXr1cOZ/ZvJThRydawCqgVVXo5psz7MCRiXIl/7Bt9UM38fczJkQMeAzYT1W3VNUjVfVEVd0JOAmIAleJyIHBjUTkOOBc4Avg2367HwDbAh8APwDO78gvYowx7S1l4EdEdhKRfwBPAYcCbwIzgGNwEfH+QK6fboerJG8E3gYOA54WkedFZMd2/QamWwq94G7rvC7z5qG9CogSQYEsokTJopp8qnGtVnJ8966u2monTLqypuvuFV8WQ/iUrVkvfaiOFHLrZlOpjQp1J5zEr5ZOBOBcXOuU3XgDgL45laQaUise7Hn7bZcGCDo2HVBXMHToUP79739zxx13sMsuu/D+++/z2GOP8d5779G7d28uueQS/vrXv3Z2MY3pFlqaQDmupd2OGjtFlZXBigNLufjqIn5Xe2riYIWFrB5fmijzqFFwxRWhx1CEjVkFWEq7ywAAIABJREFUfFZUwvE6i29RTnmvMYwpWsivqhNdX+O/waOPwtFHw9QfL2F1dR5DWUJtLQzqVcHSPiVsWVDBTX18ZMiLZWUTI8Kq6j71f8vgTDzo04YJeTIodZvpIlT1BVU9XlXnhSx7GLjXz05MWny5n16qqh8FtlkJnONnL8uULl/GGNMU6Sq0N4HdgFuAHVV1nKperqqzVfVjVf1GVev89CNVfVJVL1PVMcCOwG3A7n4/xjRJ2gvuts7rUlbG8h0OZh29mcLVVJLPv9iLZziCWOCfRqoWOxKyXJPed0bAqCUtjoKtgQTlurypTI1M5YYzFvLLry/m0Qv+yRc5w9hIbr3tqsmDrGyythpM8pBaixa54E482KPqXtCx6YC6ivz8fM477zxefvllvvnmG2pqalixYgULFizgxhtvZK+99ursIhqT0doqT0yqbke/+EX6Bi6NnaLGjYO6ydP4uHYY1X5YdoAVlb2pfW0+fajYVOb/Xe8CwcnnkJrNB7JVZDlbrCznbjkbgIs3TOWDmpHMrCil9MJiig4Yy6mnuhG69vxxCcvKK1h58Q3kU8MvuBGAI6tnscXKct6d8iiTprnmNnUxYT0FxKJwJndz9NvTmDABHqCU75cWUTvB/7ATJri8Q8tdTry60kloYesT8mRQ6jaTOeL3IEPjH4jIUGAMUAM0CC+q6svAcmAQsEcHlNEYYzpEusDPncBIVb1EVcubs1NVLVfVi4BRfj/GNEnafv5tmNclnvPghLevYjsWMo0pbMNiLuZmfsGNfMZwomQ3up/GAjvJQZfObjnUWHniiZ7v3Hg65dHR/OoP7veeNHMPLqqbQU4g4XUVeeQQZbX2dXl9khL4DB7sgjvB3M/Q5HRAxhjTLK3OE+NbsYwaUBHa7ai8PH0Dl/gpqqICDjoItt22YW+opz4YxYqafuRr1ab6t5hVbMvHzGX8ptESR61ZEHqM/NUr2ab2AzcTcy1SBeWTmqGs+ft8qldWUFvr0td9jzl8i3LW0JfzmAnA+dxBDOH/9CcAbFZ2FmMPKIKLLiKaX8TVTKOSQg7jOf4rYxGByUxjqQyjTvwPq/XPHFnUUatZrU7Ik0Gp20zm2NZPVwQ+28VP31fVqhTbzU9a1xhjMl7KwI+q/kxVV7Vm56q6UlV/3pp9mJ6lo/r5x28Q3soZxyqKycqCVRTzH8YyjclswyJyqWnVMVIlT+5sqYI/gqsQBNiM9ZRuNpdc38Dn/lgpD+pJZJFIhu1+H2GznA3ug6QEPvn5LrgTzP0cibjPUqQDCtVmCaGNMd1aq88fga5MDzzggieXXQabUcG3TijhgkmubmusJVF8NyUlrrtSeTmMH+9y7EyYANvg+rgmWo26WnhPXuPI2BPUapYLpJCon4OhlmDmuSiCIjyfdTgj9WN+PHguf6wppSJWxJ9omBw6eFwAjUbRDRt4bekQtqldyM1czGgWMpNzeFdL2IwKFskorpZp5EVqoaAAgDrJrleuSzZeS9F3Rjar0U8bpggypgERGQSc5mcfCyza2k8/S7P5kqR1jTEm41nfVdPldEQ//wEDYN062LjRzUf9SO1HHumebtYkdWmKC0uM3NmteNrLzIpSqjYKszieq2Ua6/tuRV22a6JTRR6fMYJ/XjuPbInV79MVSOCzerUL8vTt6xYV5NUx6Kv32HGHurB0QKF6YkJoY0zLtOj8EdJH7OE5Rfw5VsrIkfDala7lzJER19Qn2JIoGLxI3s3y5fAT17CG+fNdIOkBSunH16HFqCaPKFn0Ye2mBw9hXYqBTSH4zxhONnWcG/0NAIfeP4mT+AtRItSQQyqS9P7l3EN5PnYQfahgFcVsJZ9TQjlH6Fx6awW/i51BlRS4yFpBAVlahwIxIlRSwH7Ma/YoXK3NxWRMKiKSDdwPbAb8Q1WfCiwu8tMNaXax3k97pznGmX749wVffvllq8rbo7RiyO20mzZhOO/kxY0Wxa9wwEsNV2zJ13jppeZv09QDtnok87YYCr0dhlRvsDsbsr1VLPBjupyO6Oc/fjwsW1a/xXpWFuTlQeGIAdRk9aKOCOtwTzdryErbWid55KzuQoB95RU2FAzgnh1uRKJ1rKeQvEiUKQUz+L+3d4d+/Rr26fIJfKJR9xvHW/fkVrkozjefVgTTAYVKzhH0/+ydeZwUxdnHvzXn7swspzCAyHIsiK6JF6tGTQKi8YgaTdTIoqgxmnhHyapREXYBRfEClUTjFd9gksUYLzCJbxLURKPg9WLAlRs8ACUw7D1XvX9U92xP78zszO7sBfX9fObT091V1dVzdFc/9Ty/Z18ThNZoNLnTrvuHLUasIepmXaSYALWcUR5g5CxlyXksMo06AjzeWM6ePbDffsnGi3TJuPoQYlV0PJ84xjOfCsK0jnGVQCHNNJYc0sooYy1jEsdJBBfrKCEq3InJijgOHEj+zHfw0UgTrqzaqwjP4SC5mnWUUEuAJ6U656eZxnaC9KWWn0fvovzd6TB5MqKggNXnzqSOIl7lRO6WFVRWqs+kLS+efGkxaTQZ+BUwGdhKa2HndElOc0JK+aiUcoKUcsKgQYM60pRGo9F0Cdrwo+lxdGacvzng/PjtEB9RSh/U6NThUOP+446D0k1L2S+2A+H28hWDEIDHyO4FLaLN2Rp7erpHUCqvJet6UG5nY0OQqzbcAD4/cmYlziI/j5y4RD1Uud2tY7qGDQOXi5Lmjyj0RBklN3A47zGSTQAMbtjUphUnlUbQviYIrdFocqNd9w9LjFjc58dNhDmeSm7g/iRtmwhuGoPFzPHO5tNPVftW48Vhh6mm7JzOUsZTQ0mshuP5Jw3GhII9GYAABq/7d9I2ezkTQZzPGMZKcTRuGcFDGAkJT6Hz+CMC8NJihcrUnsPYOpCd+GjAYdzzXETxolxjF3INj/4uwPK/NlPi2sShz81iHDXMYQYr5ATOP19NqrTlxdNhLSaNJgNCiAXApahU7ZOllNtsRWqNZYD0mPtqM5TRaDSaXkVeDT9CiNVCiJgQQqtxaHokVVVw8P4hVsVLKWU1p7EMIZSt4o7N5Vx2Q4sugjPSyMg0IeD26aJMxp/e5AVkfSCw9tsZa0Zu38F7/SZRNGs6rFhB4aoVTBhrTOuaMV1WAZ9QCEdzEwf0CfE5wwjjQRqtihRWHLuWTyqNIC0IrdFo2ktGTRkjRsxRVYn0+Tk7soRtfqVt45IRogV+PCLCz3ZX8klMiQY1N7dcr2IxGD5cGYN8PnXNWkw5EVz8lqmJwyzkWoayjTgQT6j7tD1BYO4P4yKOwIlkFFv4hZwLKJ0g03iUDWbZ1iFkaosjRY/CuNlCMaum3sULdSdQ3C/EngKljedwqFC2d95RZadNg2H+EJ/2bf2Bd5WWn2bfQwhxL3At8CXK6LM2RbFNxrI4Q1MH2MpqNBpNryffHj+mNmxvetbV7EOU3F7OWxuC7I9KQ/s002hwBvi9s5x5hSpziamLIA3RTNh7tXzs+hGZUtfvdu1H8Ol7CIXgxkkrYOPGlmndIUPgkEPUsqgIdu9OxGj5dmziIFbj9LgQSOIitRUnlZZPKnuSRqPR5IJp8DGFllN6o1hixK6aXMPDPhW69EOqqcfPqh9UIvw+fhn+EQNd6iJlit97PFAkQyz/spQ5FSEmT1aXwY/OraKRwpR9cqCMK2E8hl5Py9XXPqFg3n8E4CaaZJRJFcaVyoMzRnrM8nGjT8LWggCiCDxEqHRU8s5TqyllNcf8dxlNTUq02uFQRi9TL8/thgv6L2X4ntUprW1WLaahvhDHXKpVnjUdQwhxN3ADsBM4KUNGYjPFe6kQIvUfFMpsZTUajabXk2/Dz4XAJOCEPLer0XSc8nJ4/nkc0ebEJhdRYjg54Z+zmTKjhHnyZnw0UoffMPvIlILOqd73BsNQR/oYjH7G8Mnj8Pdzctdnxgz2tGmweTNy27YWv/3hw5VYkhGjJRFItwdPoRPhcBAdlGzFyaTlY7Unmct9HSl7wy9No+k5mOFHP/2pWk+pKWOJEbt8RpD+J01g5ky4R1RwIDUc9Yfp3NA8jyJZy4nhZfh8UBBWIcO+SIjvspQh/13NgpOX0dwMkybBwmUlVDIDaH3tbcZNFCdewsRxJYw56YzwmYzzdq+huGVoZ5Z3kv7eZbKFEUgEzSmSGwgELqL8Ln4eT8SVV+xTchq1BFgxthy/v0Uz7xlRzo7GAPO2WVSu+/eHc85JtGfVYlo1zzAQaZVnTTsRQswDKoBdKKPPh+nKSim3Au8BHuDcFG19GxiOChV7q1M6rNFoNN1AXg0/UsoVUsrXpJSv5bNdjSYvVFXByJFId/KgdueVt7Hf0WP497/hChYhgBfFWUgcaQffqTUXej7Z9jGVh5Oa9XXwFfsl2mmIuonvaSRsFTYsKGC7axjxuCSGA4Fkc2QYH+8ZxpY+h+AZkWzFyaTl4/cn60C0JQidb3piKvlIJILT6ezubnQ5QoiJQgiZ5WtEivqFQogbhRArhBC7hRANQoiNQoglQojjMhy3XAjxhhAiJISoM7K4XCWE0Bp5PRxT080MPzK9UaTMrClTVgbz5ysdmg89ZdzP9eyOBrgnci0AT8ansbM5wDJOpZTV7JT9EyHCj0Wm8cobAR5vKicSgXN4Lum+IVCeNV4iuC06OlZSXaczGXxiiISrtQQciZxfLXXTeQaZffofynmaC3ESZyf9ktrHaPNzx3DiwkkYlco9jJvI0GKuDc0mFILt21XZGbKKGE5EzHJeUsKLLyasbWVlELxefUF9r9Uqz5r2I4SYDdwE7EYZfbLx0rnTWN4lhEgocwkhBgOLjNV5Usp4q5oajUbTS9EDV82+gyEs4IhG1LpQg+UR7zzHC/5y/uf5ABNYCcAUuRhhGTxn8vqx01v8MezCoplS1ZtLD1GC7EiUKaSRfu++y849e5CrViUsJPs5diFx8IUYRhwH/dlF1OsnOLy1Facna/n0xFTye/bsoagobYbZvZltwG8yvNYY5dajMrkkEEKMAv4PuAsYAbwGvIzSgfgeylO1FUKIh4HFwATgDeBVYBzwEPCsEGLfs8D1IioqlH6Mx2LrF0K92tKUserQVDmr2MyIhCeNgxjuWANH8Y6xLhPGmzAuwo0R3vvbLnaGW+4pVtJNKLQHCTgt3kLWV1tYy1zIM8xA6QUNZYe6/geKkjyNPoyX4pJRvIQJe/x4iBDfvYe58938x5IsYT0l3Om4tfUBIxF4/vkWw05bKs8ZRZk0GhBCnAncZqyuA64RQjyV4nWztZ6U8lngl8AQYJUQ4iUhxHPAWuBg4HnUdX7vwpoKuxNSb9sPkddGs224k9N959J8yrLZNNCZ59Detrs4jXqnfN32338WLF+eQ/u9AFd3d0Cj6VKqq5V1weFQg1Ag+u8VnMSHhHHTjBsXMWPWNDWZBDRzEdfsbuyz0Oloy5A1cNGDrB05jtjBxfT76CN8Bx2EY1iQUP8RbN/iZpdjIK54mP2HRClY9zGMH69EISyYWj5Dh8IXX6j1AQM6cHIdZMMGJVNkhi5s2gSbN0O/fjB6dNf3R0pJJBJhz5497Nq1ixEjWjm07PVIKT8GLk63XwjxH+PtE9ISDyeE8KMMNmOA2cBsKWXEsn8gMDBFez8ArkQZnL5lioQKIYLAP4CzgauBBR06sS4mFIJjj4U334S+fbu7N53LmjUQDqtri9OpPH4uvBBeeEHp/Vgij1J+LtXVSn/m/aYjIdaUyJhlhmU5bMo5KiV7Ew7g5ch3GMZmitmEi0YgvddOR+8Z+b7nJIw9dbVJ/TuVvwDgJI6MRBDEGdj4KVuuvptvGskSfs8U+hCiSsxMmjwQoCxwo0a1GHYGDSIWqkU2RnD6/Yjm5mSL3NKlLaJMU6bk+Sw1ewnWkcIE45WK14B51g1SyiuFEP8ErgK+jYqI/Bh4Avil9vbRaDR7G1kbfoQQ03JpWEr5dO7d0Wg6mYoKuOEG+NGPlDhxUxPNeNgiRvGQvIIHuQbIzdW+Ld2Eno5dpyibczDLxQBP3R4Ovv4n7J58Aru+/W0+37yZeP/+fMkgGhuVsWT3btizp54+4a+UC40tZqu5WU327tqllrW16qGtu4hEVF+iUWX8EULZqlyu7uuX0+mkqKiIESNG4PV6u6cTPRQhxDdQs7QxlPePldtQRp+npZS32+tKKXeixEDt/MJY3mTNDCOl3C6EuAJYDtwshHiwNz0g7AvP0uXlKqqo2ZBzi8db/r9NTUpbZuvW5Dqm8POzz8Kll6ptN18R4tA3SnHu2UPYWZCkkNxEAQU0JV0zre/ncyNRnBTQnDY0uKOTCHavzFzuP7m2byWOA4eMJESgy3c9DMBipvI0F+JA4oyl+EvY03ctXYrz809poJCasyo5/OXZ6ot47rnkL3DaNLjsMjjzTHjmmRzOUrO3I6V8CniqA/WfAfSPSqPR7BPk4vHzFNlFsZhjBW340fQ8ylSihgcGVHHVmik048dLM1/J/izgZzmnwm1PWFdP9grKlNXLinneDiDuLsTZ3MjAZcsYaIpzFhYy+JtnEX36GYLXlxN/4UVEczMiFkW6XDTGvLi+fyaeZ3vueOvZZ9WDsdernj9+9zuYPLm7e6VJw4+M5Z+llJ+ZG4UQHuAyY3Veq1ppEEIMB44EwsAS+34p5WtCiM+A/YFjgDfb2e8uw24M2Zufpauq4IMPlKdeNKr+w1IqD57rr1dhpBMMv4BzzlF2BpPLL4frroNX+pdz1GfP4ZTqA3PG1DKCiu77hLGUsJ4CmnAQRwK76M9Adhnl3HhpogEf9fgYxFcZ+5yLAaityQZ7VrBMx0t1bbcbpazthXEhgG0EGchX+GhJlqDSzMdStrnIdQ2X8gQFS5bA739P/I/PEcOJG3ATZuzimbzgOIl3dlcw9+F+yV+gPQRMo9FoNBpNzuSi8fN0mtdvUdoH9Ua559FGH00P59Kiapocfua4KqnHzx76sIUDaKRFWMaqe2NfmvRUA46dtgxUuRiwYjhoxms8BHhZ5z4wuQ2Ph9iIUUzdMFvp9FRV4SgegfAoHYeYw81GWcxfju/Zg3hrumG/X01Ea3oeQggf8ENj9XHb7iNRYVxbpZRrhBDHCiHuEEI8IoSoNDyFUnG4sfyPlLIxTZkVtrI9mrbkVPYmrPo8fr9ahsPw6qvK0dOaPGrDBmUUKpIqQ1cgHuLR+nKO/PR5PLLFqOEwMjzOp4IGAux0BHmNbwHwMQcigP6G0UeFfDXiRDKHW9hDUeuwpzbIlMUrU3nzfa73pmyN/m6iuIgylC8opDnhBNWIF4ngM/ZPZAUzz/dLBvHkyNls++d65XV77LHG56mGoGE8bBXF/HL0Pfxo0YTUX2BbokwajUaj0WgykrXhR0p5sZTykhSvi6SUE4ESlI5CCRjxMhpND6WoqoLlj9RwL9P5uqeGmVRxz6D5uIkmZnSjOI0MLCIxaO+Jegz5OGY2M8MtGWQcCSFTJ1FKGv6PGE7ihvJFPBxhbvQm7l13Jq8+G0oM4qNNEerwI8MRZlLJ9yvG9OgELtZ0wzU1al3TIzkXKAJ2oASbrXzNWK4VQjwF/AsVwnU5cDvwphDiWSFEoa3eKGO5OcNxt9jK9mj2tWfp6moVohmJqFAvgIdVRBJTp7aEfq1apbZ9l6WUGjo1t1NFGHerNiO4GclmxlFDZfxWJvAuTuKsYEJaw8sd3MYYNnbWaeYV+zmkmxAQgIs4AnAmtkka8TGAnXiIJE2WBPmSa0uWMXLB9TBxItx0E6A8fUwj2SxRyY/vHNPye+yg5V3rQms0Go1Gk0zesnpJKXcA5SjX91n5alej6RTKyrh5VgGrouP5a/jbrGUsE7+sph4/61EjT6eR1cvUMTANQPsS9vON4aCJQsLGjG4YN3GcvMFx1BHgS/ajAR/fWb+IUlbz/OXLCATg3zdUI/x+Fg2upAE/57IEtxuGD4cVK3rm4LysDIJB9T4YbAkN0fQ4zDCvp62izQam8Oe3gGnAPajJif6obF6fAT8AHrbVCxjLetJTZyzTplgTQlxupH9f+eWXX2Y8ia5gX/Jiq6iA119Xhi1rVi9QoV/FxUr0+TfRcmoJJNKxP800PuAw1qIyPFuNH00UMoPZ3M/1/IVTGIz6Ti9kMZCd14xMsz3V/q4gl+Olm/gQQAFhvqI/fpoI42ELwxG0DDKn/HkaDb97noZmZ4vbmVE37vLyQ8eS5N9jBy3vVi0rjWavwchu1RUJlro4iVNK0vbBmuWrM7Izpcgi1lZisXT7li/PcOwcvsvly7Psg1moPV9gunq5tmWcV6Kapf6sWZbPI9t+WspZP8vu+o32hP9Ge8lrOncp5X9R7u8/yGe7Gk0+sM8APnnuUsZTw3hqOI1lzKeC8dRwOktZy1iixlxmHIfh+eNIqBd0hwFoMTAS9acdaaznk2weACSSDYzCQ4Ra/HgJ4yHCG3yTenwM5ivcRBIpjJ+MT2NHQ4CvFYdwrqth9MPTOdhZw0MFFUQicPLJsG6dHpxr2ocQogSMeBuVicWOeY9zAY9LKSuklOullLullC8CZ6F++hcJIaz52uyObu1CSvmolHKClHLCoEGDOtJUXtiXvNjKyuCYY+Dmm1WYl6mH7vUq2ZjTT4fCSIgyVvAp+yc8fMK42cpwxlNDxNCzMX8E93IDs5nBWTyP16Jtk0s4Vjq9NGuZfBh/0oUnp+pTuvVc086PMpzgvDQzgk+T9rmIEhNu6m+ZqxS2k/bFONv1IotChvtnKAQXX6zEmCAny3t5OQQCcJGy4zFtGj3as1Sj0Wg0mq4ir4YfgzAwtBPa1Wg6hDkDuOvUcnC5OOqBqYl9z1DOm3yD+7meO5y3cwBbcCfCmZTnj5sYzi6fj1UsRsWmbEYN5Dcb6/k0/mQSFwUI4ySOkx0MRiLwGlsAZjCXYWwDwEMYp6H84CRGZOhw/E8tgmCQ6mqoDwSpGz+BaBQefFC1ne3gXLvva2yY3j5vSSlT5Vurtbz/tX2nlHIl8C7qXjgxRb2AvY4Fc19thjI9it7qxdaR//3ChWpp2t0KC5XhZ+FCFd41lnW8wqkJY7aHCE5iFFGfCGkFdX28hge5nSq2cECr40jb+2z1fKxhxNZtHcWuEZSrZ09Hjpmuvb8dexuD1ryu3M0GD24p73YjRo9m4Lwb1Rdtplhrx4zAvqRlpdFoNBpNLuTV8COEGAIcB3S/T7tGY2CfATzlnSrWx0caCgUmgk2MYgazKYjVJs3mZiLdANk+kO8otwINtm0NxvbOwvrgIgEPMbxEeZuj+ZT9+Yr9Mj6ghHHjQLLSc6xKHRQKJTwOliyBsWNbQjCyHZy3131fG4z2PoQQTlT4FrQWdTbZZHmfTmjF3D4kRb3iDF0wn/43ZSijyQPt+d+b1/3331frn3+uHEiam+G5wuTwrp+xABdRIrhxEaWEdUBr40l/dvMhhzKOT1p55uTqHQPJ19ieEEaci/i0tbz9vXWbeqkw6WH/quZbL1Xw0xPXKcEll0tZ4kzBqZ/8RH3RP/mJaqAd7jr7mpaVRqPRaDTZkrXhRwjxrQyv04QQN6CEM/sBL3RajzWaHDFnAB3Gr32js4SH9p+HcDhaBJsdgrpb7uTWJ8bw2PCqxEAV2ha5TEc+B/Jb0mzPpDzbETKJWN/GXEaziSF8kShrrWdizpafsOlJNZg/9dSEx0FJCdxxB8Ri2Q3OO+q+r/Ue9kpORmnK1QN/SFPmPcv7gWnK7Gcs6yzbDHMBpSmEn03KbGU1eaYj//vaWmhoaBF2jsdVhFFjI7x4ZBVbGJEI7xLAFwzlPQ5vde2zXtOcxPDRkBg4ddSbpj0GlvbS1rEynXeq8LRU6d7t7+PGJ2Xq5B3Be7zSNIn50etbBKdmz1Yq3OefD++8Y1Q0vjQpiQ0v5pQVs3My2u9LWlYajUaj0WRLLh4/y4F/pHm9BMxHZTf5ALgtr73UaDrA7berlL3hsFoPh+H4z6uJSYEoKEAUFIDDweHrlnDJq+X8ads3EiFdqQa02QzC8yIQYmFEhuPkW+sn3XHs71vOURhLNdA3PamE/ezfeivpqS2XwXl73fe13sNezaXG8g9SyrpUBaSUnwFvG6uT7fuFEP2BI4zVlZZ6W1FGIw8qa5i93reB4cA24K129l/TBh0J27n/fhg2TL3vg0rV3gdlPXjqnyXsZAA+GhNXqcFs51j+TSPJdr5sDCZ2Ml3/23Nv6ApvoHQeS5nOIR0S2Fh4UCLdvYmTOD4aKKIWKirYs6KG0iemEzl+opoFsBOP8+bJlfxl3ZicjPb7kpaVRqPRaDTZkovh5/UMr/8FfgNcCBwjpdyd535qNO2mqqp1VpcHCyrY8fhLsGkTe/5vE5cNe5nIVyF4/nkc0dRhXulmQ1Otp6rTEeamaUvSueFeqbB/DgKJBJ5kGrUU8TrfJJZI8mspb6bSMZ7achmct9d9X+s97J0IIfYDTjdW04V5mcw1lrcLIQ6ztFEA/BLoi9L5sRtw7jSWdxki0ma9wcAiY3WelDKe+xlosqEjYTslJXCAEYz3PadK1f5dWqwHtRQRx5Fk0HcQ4xPGJtax7MuWbMt2RkavzlCgyyZTmXXdDFsLOv/LwaxpnRlSONUFuKyMP71SQPXqUsLvrmrVvgTiErY9qGYEcjHa91YtK41Go9FoOpOsDT9SyolSyklpXidLKX8kpVycIp2uRtOtlJTALbeA06keHpxOOPX2MoZdcgoEg7y8IshjW07mz6c/BCNHIl3KSpRL2t18ZWJJx9QM7acLA+sImYxcViK4acaNA3iVUziQGv6Xk3AZ4s4RXKqgw6FmdC1PbbkOztvjvq/1HvZaLkR543wspXwzU0Ep5UuoNO6DgbeFEK8LIf4ErAd+iErpPkVKKW31nkUZhoYAq4QQLwkhngPWAgcDzwMP5fe0NHY6ErYzd2M5dQR4UiqXv98wjToCLKacmVTxOD/CATTb7o3jAAAgAElEQVTiQSApIIyLGHX4+ZLWWdhSecFY9XnsWj25Gv47OlHQ1TpB9pAvK7667Qxhe1JZAbhklHsuXUM/EWLydaWUspoFey4hbvcRFYKfDVvCAo+aEdBGe40mexLZr7NJF24psHziLJZPbKtCq2p5Lpzn4+aSMjxLWn1GtroTl7duK13zibKZ+tmZ+cNzSN+eSzcmLp/FxOXZp6tPOohZyV45h8baKmpNC5+2jqUfy5enrtPb6IysXhpNj+O999RsYWWlWr73XuswoLMrSjhnTRVEI62EjbGsp6OzB93pVGbThYHlk3T6D05LprPFTGUbQ5jNjEQ5BzEk8LlvTIfFFtrrvq/1HvZKLjGWqVK4t0JKWQF8H6VD9zXgNJQ++n3A4VLKtWnqXYmyu74HfBulK7QOuBr4gZQyRXyKJp90JGxnv0VVeMeOwOltSdW+iWIC1PIPJvFjw1msgHDiOnYQH1NIEwP5qlVYq/19Jron/2Pnkeq8M6Wmz/SZTX+vnF30YzifAVDBfMNv1GJMOuAAvrXgB7wdm5BstN9PK/VrNBqNRtMeXN3dAY2mK6ioUKnDg0G44ALYuhX69YMPPoBNm1R633gczqM6bRum8cNuDMokhJxLmbaYi0rhbs3u5aMljiVfpOprKqOPBJoooJBGAKK48BAhjAsvURooYIcYyrzhDzFj4WD4xgHqg28nZWUt74PBFm+htkj13Wt6N1LKr7ejzp+AP7Wj3jPAM7nW0+SH9v7vAb7+/RLq66qQF01BFPgpjDTz8IBKlocO49nwmYxiA4VGBkcl6K+ubk60PS9X0mngZXPvcxFN1HuJ0zmDl6GpKWG0nzFDeW5eeimcdu9SfKZS/5QpeT0HjUaj0Wj2ZrTHj2afIFVYUcmgEP+uLcXTqGYOpYT5VHAe1TTjydBaa7pidncq8CjK80cYy0eN7fkk27TEzXhYxikJlQw3KsrTYwziC2nmRnk3J913GsPPmpD44EMhGD9evbpi0lbrPWg0+y5fLaqmHj+rflCJ8Ps5tW4Ja+Ml3E4VLmI04jWM+YJa/IiEXH122CcBck2J3tPo6L2stQZcy3Zr21Fj3tHctpipHC/eZAwbOF68yY93zycUavH2WjmunM/2BCj4qeGiW16urEJaqV+j0Wg0mqzIq+FHCLFaCBETQkTz2a6m+wjtzV7VS5fS59PVnO1dxuDBatNKyvgj5/AQVwNtizinGiSnmvnM10PAVGATEDeW6Yw+7Rm85ypK6ibMOTyXcn+IPtTj53zXEpYsSf4dLV2qBvM1NTq9ukaj6RzMUN7zV1ZwIDUc9YfpFDfVUNlYgdsNv/LdgIsoHlS6RwdxAtTjILVWdzZhTL3V2GMl3TlYjVrZlLfXtWN6+cRx0IAPnzPCWNdGSllNiWsTb5VMY9EiuP569T2e8X4VWxhBY8zd0kj//lr0R6PRaDSaLMm3x4+D1vqGml7M0qVgelXvNdjEfZ6ITeOzUIBnRDkO4x8xzatCvhpsqX2hZRYzhiMxU2ylJ/z429OHXOoIsOTtak1f9uAXjXz3uN1ccYUy+qxercbpUy3WqvJycLn0pK1Go8kd06C89aMQ67ylhLa0zFCYGf0+9JSxgyDRKGwNB3mXCYTDcE3sAbZ4xyYE6CNGoFcmj510xg8r6YT+e7vmTzYDu2y9RWOo++YCrqHRWUTBkH6cKV7k1xF1T34sMo0P1wcYM6M88T1u8ZSwkwH4aGz5LLdtg0MP1TcQjUaj0WiyIN+GnwuBScAJeW5X08XYhY9zSaXaUzEfEmorknN8C7ebL7zF3OWfzdVXQ1ERzPPP4TDHKi7iaaI4aEohh+U0ZoatuUg6I+1vV5NLv1JluTHZ7hvB7nc3cN7JIT77zChnKygEjBqlJ201mu6iN3t1mhMTf75mKSXh1bw3p2WGwprRz+dT2zyeluUF3mcZGtmC1whRdSMTM1dW2uO5mY0QckfpqfcPSG80E0AzLsDJY2WPcoNYyLSja3DeczfE40RQ9+QIbj73FHPKitkMGtTyPTY4+hDHgXRavH50qi+NRqPRaLIir4YfKeUKKeVrUsrX8tmupuupSraN9JpUqpkeYsyHhJc/bp3ju/kXlfxl3RgWLIC1a+Gbj13ERv8hnEc19RRxK/NoxEsTnowPBlZynR3tKaQz5thnu1OVM8vEcVBdfwZD6tYxOZzZXezOO3V6dY2mu+iNXp3mxARTy6klwMXL1QzF8b+eRr3w0+DuC6FQQhy4qgoKCyEcVkagSASWjKrAGQ+3ajud0SId3WWA6an3Dzt2cWc3UZzEmLphNlLCkacFQQjc8TCFjmbw+yl0RXjnNHVPXrZMZWb0+eD+fpVcV/AI8bhsseZVVuobiGavxJ7dO1Oq6c5K951Vavc20oHPmqXSYOeSKt7ahL35pFTpWaY/74403PaU7ub68uWk/M7MdOHZfpWJchm+f/tPJPH5t5EmPdFH63pb/WgHrVKkp2jM/jm2m0y/lTz8f5KaMFY66W/ZIbS4syYl1tnSpFSqPXx8leohJpX30nPnV9MgWnJ8l7y/JEkA+Oyz4ZZb4H5nBUf4ajiSd5E48aDkq+wGkN4yCO8srEagOA4cxLmWhQA8zTRqCbAY5S4mRItBUQidXl2j6Q4626uzMz2JzImJWULpvoRpSde+i/74ontg2TIqKuCTFSGmP1FK2TjVkbPPVve0is1X4Uwj5JxJy21vu9Z3puHKLu5sLjcwkp0741RzDtfdFiAyVf0IRTxOpCFCJCqQ1erGcPWFISqfLeWw0SH+8t8yrj/4r+AzrHmBQJffQHqzh5xGo9Fo9m204UeTFnO21LCN9OgH9EwPMXbvpYGuEEc5VvLlKytUupCaGpXz28QY2a15O8SaQBlXVQWpdFTxKQekFf7c27DP0lp1L6yeP9ZyEnVBWc2BCf0jUA9jmylmppiNw6GMPWbIlxDw4ou9O4RQo+mNdLZXZ2d6EpkTExudJcykCg8RIrjw0cgQvlCFpk2jbFKAXceeCqtX88yHB1HDWBYt7sNXIScH736rVbuZvDf3NoOPSabzak/Yb1t1BTCaTYzgU/7FcWwVLYY7CgqIjynhwuI3WOCpoA8hPoyVcpBczQH/p35IF3xYwThZQ/m7Ke7dXUBv9JDTaDQajQbaafgRQhwkhPieEOJCIcS0VK98d1TT9VRUtKRS7YbxVU5keoixey+dFF7K8MhGinesVIXtOb6Nkd2NpcsIBuHHP4ah3yzhr46TEUATHqUzQOswJ+v7nqzB0BaZRDrThYKZ2w9hDS7DQCYBH43soj+bnGMoKIDhw1u+J48HRo+2PWzqKVWNptPpLK/OrtKHMycmLipU6drv4efELQZn4nFoaGDcrhUA7M8XjGMdfailUfhTGrbt77OhtxmEOmLMyYVMxjJz+31M50D5MYU0EvH6IRrFe2cl595zNFeF72M7QYajBOKejCvP0Z/J+ykcGVT3DPu9uxPZG3UPNRqNRrNvkZPhRwhxrBDiI+Aj4DngKeBJ28vcpunllJWRFP6Ur/FVZzzXt/UQU10Nz4hy/hsJ8EQ8zcjNNrI7aN403v0kgHPMSP7+ToCr4g8C4CWMgzgSQYi+KfuzN84Q240/6R6cUhGiD9EoNDSoUAtTcLWxEW680fawqadUNZouoTO8OrtKH86cmHjlEBWOu/26O7nW+4gK3vL7lTthQQEiFm1VNyBrgdbei6anotW4vbddy/NxLu2Z1EidCl7iQNKEl1vClYSift65fjGnTunLWfJ5vDQnyrqIEsPJ7WJ2t4Sd91bdQ41Go9FoTLI2/AghxgN/BQ4G3gI2Grt+D6xEZegEeB54Oo991OxldNZzfaaHmIoKmPR6FZ4xI3AVpBm5VVQooWep9jfGVIjS2bse55OmEUSESgkjgK8YyI94ghrGAb3bu6ct2poNT3fu5vZfMJeZVCW2L1wI0SgMGqTWf/UrY4eeUtVoupTO8OrsTH0466SBOTFx8cNlvLUhyAMPwL3f+Sv4AzTeXEmtDBAdeyCQ3fXZQUumxnRk0v7Zl8g2rCsT1jBhNzFmypm8UzSJMeceiT+6B8/gfq28S1ecNZftgTHdEnbeW3UPNRqNRqMxycXj52bAB/xESnk88AaAlHKqlPJo4FDgXWAccG2+O6rp/XT2c32mh5iyMtjvGDVyE9E0I7c1ayAcxiObqRd+PESYSSV/k5Op9w7AI5sTA9D+7OIJLmECKxFAPM086t7wYGDq+mTab8ecSW/Ax+F8yLu0uIuZ+j5mivcVK9Tv4Pra7KZUdSSYRpMfOsurs7P04VJNGpSVQUGBuibsvqyCE/avYfGQ6ZTEa/gqNhB8PqQQSZ49VpSnT89nMTASNWjbz3g5jG2LO/nYudzH0k0UZPIQdRHFQzPH1f6Fvg9UAuD8anuibBwH8QIfJ3re6Naw896ke6jRaDQajZ1cDD8TgbVSyl+n2imlXAOcDowAZnS8a/lBCHGIEKJZCCGNMLVMZQ8UQvxWCPG5UWezEOKXQoihnVFvX6OzXaWzeohJNXKzWaREPE6BI4JEcL5jCVJCYbAIiYMILgAcxHECTmMI67QMZVMJH6eis41CndF+NmECAqjHRx0BXuVEft239Sg9GEzW+SkuhqsfyG5KtSdGgmljlEbTQr49idqaNDCvCd+8oYw3Pgnyk5/ADoKc/fGdLG2aTBwnQqS+einDdstQKNvrN1nszxeLgcuBzah+7TRe0th2OZ1r/Mn1PNvyAk217Q5uYYsYiVMYW+KmTpzAQZzmgr5QUdGVsj6t6E26h5reRyKrdBfkgLYewkynna9s1+nSmGfqQ2fRKl24jXR9M+u1VTeRJn15x9KO55xu3pZqva3jT1w+K29p0dv6XNJh/x1Z+2P/PbanfWv9Vge1HiDDD9r8TtuNtW1juXziLJZPTH/MriYXw88QlLaPSQxACOE1N0gpdwCvAWfnpXcdRAjhQmkOubMo+23gfWAq8AXwJ6AB+CnwoRBiXD7r7Yv0CFfpVCM3u0WqoID1ooTjeYO74mpkd/HmKq50P4KLWMYHAqseRC6D5XwZaXpK2uECmghQT4A6Drl4AuYzl8OhtH1Gj07zO0hhmDONKuec03MjwXqiMUqj6S7y7UmUbtKgtlZdAy64QG3fsS7ER5QSiCsL7DuyjKeLZ7Dj8ZcST+n262IzXkAQsRl/8hHOlC9uRQ0q0tFglOlssv0M2prwSLX/Ep7i4/IqhEOAxUMrjAsB/Gr3+QQmTki+3nexxb2zPOQ0Go1Go+kKcjH81JF8v95jLO1eLY3A/h3pVB65BTgSWJSpkBDCj9IqKgSukVIeKaU8X0p5EHAvMAj4nbBNGba33r5Mt7tKpxq5pbBIDS7YQw3j+dA9gT6EeEpczDnepURcha2azFb3Jl0WrHzSGW2mCo1oK2uZ08h79jJnsHChCu8qLYWiIjjxRGX8Sfk7SGGYM40qxx3X88Q1tSyRRtP5pJs0uP9+OHBIiFWylD6E+C5LKWU1p7EMoewHzB92P0MvOx15991JbZrXr7WMpp4Ay5mYMNz3tBv2ljyV6ShtZelKV0Za9qVrYwvFBF8zBggXXpio5yUCwDU8yI6GAI/UWi6u2uKu0Wg0Gk3W5GL4+RQ4wLL+sbGcZG4QQriBo4EvO961jiGE+DpwGyr72LNtFL8E5dG0XEr5kG3fTcB64Ajg1DzV22fpsa7ShkXqt2MraYh56Ff3KaexjEgEvstSDparGfLdI/nX/j9MaN7kmrI93YC3ozPJneEt1FYZq+5PzDizuK0MwHwqqJUBXiwq54UX1Hc+YwbceWea34HFMFd+fZDAxAkJo8qNN8KGDdDU1HPENXWmF42ma0g1aVBSAteVKGPPdoL8BnWxeJpp1MoAvxPl3NOnirqBI1W2LwMBNOJFAMP5DD91nMjfUx63K6+v6RiRpzKdSUfOr7FoMPKJJ+k7xxggNDYifD6a/f0TZcK4iexfTNEDs7XFXaPRaDSadpCL4edfQKkQoo+xvhQV7nW/EOIKIcQZwB+B4cA/89vN3DAMUL9BeSldmUWVs4zlb+07pJQxlFePtVxH6+2z9FhX6YoKmDSJ8pqZeIwZxsVMJY7gt6g4gtI/zGDS5ieB9DOcbWXASkVHZ5jzNTudaaY2XRklbO2giQIcKcpHUJnRXHfMZsyYlu88m9/BnIoQH0RKGehSbvxut9ID6knimj0ifFGj2QewTxr89PVy6kWAKX9RD/9emnGhUrdHcLOJYhaPn82v/1HCj7+aB8gkY32hkSq8H3twZSHv3NHrbEfqz0Vl1kiHzyjTE8kUNhfBCcAHtWM47ZoxzPmLcWOoqIANG6gc+ihRXDQ7C/EQ4fEDjIurtrhrNBqNRpMzuRh+ngM+Q4k8I6X8DLgT6AM8hErjfjoQomvCzTNxG3AY8DMp5fYsyh9uLFek2b/CVq6j9TQ9jbIymD8fx8gR4FGDyWY8NONBuhMyVlkP3rMp11NTA1sfjjKFPlhT8XpoIoaDWcykER8SQQOFeB0R5vkqeeqN3C0ho9cspSS8mhPDyxJGlVtugXXrepbHWLeHL2o0+wB2Y/Ho31bxhWcEYUPCL4wHgHoKcRsZGV/4aAxNTXBOvJo4jpTXsaihIWPHvA62FcbUFUwFHgWKjT4MNF7C2PaoUaY7SWXcSbXNen+J4qKOAF/nQ0qHhxJ2m/L7ywiMCXLEumrq8XObnE0DPn7yzqVKz0db3DUajUajyZmsDT9Syr9JKcdKKV+0bJsJnAtUA/8LPAgcKaXclO+OZosQ4nCUts+fpZRPZ1G+DzDAWN2cppgZPj+qo/U0PYOUmpCWwWQdfjyOGL/2XI2IqJlh+wNA3OVJpHHPZLRJl0a4OzN+ZTpOWyFpqdK7O4AIHg4Sn7Bq8GRiXh+xmbNxBPxcIBbzxNs5CHDa3PifjE/jv5EAz4hy3n+/53mM9djwRY1mL6Z4cgmvT67Cg7peu4kQdft4ePBsGvBzLsoCu5hyTuclrFc26zXZ9BKybjcRabZ3B1OBTaiQ2q+MV9zY1t1GHzuZNO6iOIjh5CPxNQpp5k/OcwnQwMJTliXsNqZDzwJPBQdSw0Oe6SwYMg9/bE+Lnk8Gi7vOsqjpjWSTecma8cie+ciWTKjVvkxJhWbNyiIrVapG8pmpKIv0YZn6aM8Gla6pXDN9LZ+YuU9J5JoCLcu27P01s3hllUHN1p9U527NYpbu99VWu5nabwuz323Vte/PJVNZRzKFWcnqv5LH4+WbXDx+UiKl/KOUcoqU8mQp5XVSyo356Fh7EEJ4UCFejagMp9kQsLyvT1OmzlgW5aFeEkKIy4UQK4UQK7/8stulkfZqrIPBtJqQ1dXg8yNnVuIs8nPFwGqkzw+DB7eUcai/TYPwJYwgSspY0Va4VzYPEd0tLprt8Vsyr3jYwGhulbOZvnsG15y6nqJZ0/nTvBpeqz8S/+YcBDhtbvyuAjeekmImvTG7RxpVemz4okazt2B7kjdtw4FXlEfITCqpx8c/nCfy4tjpjKOG+aiLxe1UsZFRCY8ggCa8SctcsXqt9ASjUE/D7iVqev+EcVNbMBgnMUrlKgAujKnw6bIFUxM6PeYczFvRMu7ner5sCnDbl9eqxkw9n1AorcVdaz5rNBqNRtOaDht+8oUQ4m4hxMfteFkziN0OfA24UUq5NdtDt7fL7ayXhJTyUSnlBCnlhEGDBuWjSU0azMFgMJhBE7KiAtf6GopmqcGk6845ODesg4cfViliPB5l+Ln3XsTYsUTcPmYxk5jLm9C4STb8ZP8z6S5jTzbeSqm2xxFIVNpdM7TiM+8YdpWU8Vk0SLWrnO9cOYZZVAIQKZ9GkysLAU6bG7+IKjf+/Y4eo40qGs2+iO1JPuER4lYeIfcxnQMd66m/YQbvvw87CPIu6mKxnhJupwo3ERooAGAeN/M1VjGLyoQ4PaT38EmlbaZpG/vn6CZKoOkrw0PUnbzT403o9JSXw/nnQzyuDHdbGEFjzKbnc9ddcMIJygBkWNy15rNGo9FoNOlJa/gxBJI7TA7tDAMObMfLbRznSFQmreXAIzl0sdby3p+mjOndYy3b3nqaLsY+GGxuhqjh3d9KE9LuvnHRRWpZXQ19+sAdd4Dfz1sL3ua0DQ+zLHISFdyDiIaB1jOdAplWNtQ6Y9yeWeN8zDTnIixtfSgSwG76sYc+zGImDfiY4lxCLNaSZvnXB6gBu6nBERVuHKOyFODUwjkajSbNk3zJ7eUMGABvRsrYgbpefxEP8v07JtDQ0LqZ81CeQc/xfQRwCn/mFu5gDrfiTHMlTRXSat+/r5HuXtXWvShuDDUFErcRWuchkriXSJcbRzya0OmpqoIDDoCCAmW4m+NWIX1xn0XPZ/XqVm49WvNZo9FoNJr0ZPL4+UQIMU0I0a7xjVBcDHySTXkp5QVSStGO1yajiTMAFxAE/iGEWG6+gAeMMqMs20uM4+4B/mvsL07TPTONvXmsdtfTdD32waDH8PgvLMxBE9Im5DL8gQq+GlXGDO98w7ihGrUaUppxsZ3BCMvfzCp42daDRSq6QhA62zZjODiQGuY4ZjHOsZ5H+1bg87WkWT74TDXTbmpweB0RPHdmKcCphXM0Gk2GJ/k+fTJXtdKHEG4i/JBqAMpYyVk8TxNewriSyrbHA3Jvxy72n8vn8A4TcBBPeFtFHa3nAkU0omZjpk8H1P1j/ny1ye+Hs6Mq5NpRVak8b88/v8UYWF6uCllCxLTms0aj0Wg0rclk+KkDngTWCiFuFUKMyKZBIUSxEGIGsBZ4AtjT8W7mxEHAt22vQ419Pss2q0bP+8ayLE2bR9nKdbSepgtJNRj0+dQsYNbOJDZPoAPOnkBVFdTESqgVffHR2Gow7CVKkB04iOf7lIC2RZgzkc1MdqYyOxjEdSzg75zAbdeGmFYR5K//ncBddynP+0AAHnxQzbQ3GBocdfEcPHe0cI5Go8nwJF9ZCXPbyGEuBFxzDVzNw2ymOOF96CBOBBceInhSiDunu7Zmym64N5LuHmA1/mTyABLAUaxEAB84jkAAzniEBkNbKWIa3bxeGDcOFixItGF1+lzkq+DqE42JgNdfTzYGAvTvn3Dr0c6iGo1Go9GkJpPh51DgOqAfMBvYKISoEUI8JYS4SQjxYyHEecbyJiHEb4QQnwAbgEqgL3A1XZTKXEo5K51XEDDJKPYfy/YPLNVfMJatkmMIIZzA+cbqn2y721tP08VYB4M+H5x4YsedScw2RwYb0w7+47QMbu1ilwBRnK22tZe2Hlrag32Ab75fRwkCQSmr2frAs1x4Vyl9CHHNNWpc7nQqz6r5VHCIW2lw3HOZ9tzRaDS5EV5cTW3cT+PNyU/yZWXwwQcqHCgdbje8+CJs9bR4HzbixYHkOb5PDGebBvT2CvT3RtrrgZppssDc9434m4l1J5IoTlzEiBT4IRZTVrxJkxIi3lanz9+vL+PyGcZEwDHHwLBh0NjYcpAvvoBDD4Xycu0sqtFoNBpNGlzpdkgp48BDQogngYuBK1HeNGPJfH//P2AR8FspZYpo+x7Jk6gU8JOEEFdJKR+27JsHjEF57bySp3qaLqaiQnmgBINwwQWw1ZD+DgZbHEty5ZehcvpHXsRR15TYZs/oJSChaZCqjCutAlByG/Z67cVsT5DcdraEceMmytG8zVGsAOBRLsdFnGWcyknuNxk1Cq64Aq69FlZSBhFV984ngty3OMiZZ8Izz2Q4SCgExx4Lb74JffvmfI4ajWbv4e9HVHDR8w/ywJggU2ouoG7NVo4uVZeHigpoaoK//Y2U2j5eLzz+uJIGuvfzG3ARTXhgXsxvEuXMa6vyAlLX6/ZeI3szqUSsM30GqcK+rAazVPXCuFhHCV8whKN5m8qmSipds/EvWQLhcEK3p2zKlESdVvfpoiKVZMHhaBHsM0IAyyxhXR25v2s0XUU+M4Dni4nLZyXSmCdSV080UlRPTC6rttnKQuZ03xPTl8nmszBTZU802rGnXE/VT3v/ckkF3luZuHxWys8BOp5ufPny1l/jrCzqpfvcZ81K7qq17231ddas1seeuHwWZjXzd5KKxG8p8yHSHNR+1J5Pm1m9pJT1UsqHpZSlwDhUmvRfAn8E/hd4FmXouRQYLaU8zMhU1VuMPkgp61DeOY0oY9dKIcTvhBCrgZ8DXwFTpJQyH/U0mbFl7s0L+Y4cCoXggg1VyANGtIgGWYjjIJberppEEwUJ809HxERzTcGear0toWkncd6mjC2MAOMBymksj2YFOxoDvNynnBV/C/EfSjl0wBY+QnkDZS20qXPxajT7PKau8xlVSsB52jQIjAnynVsmJC4PZWUwYwZMnqycgY4/XtU1bQKjR8P3vgfbtsHPeIC1jE2kcLdmXIwb700jfbY37H3hxp6r4ct+P7Fv8xBlNOuJ4qKE9dzLdFb6jlcutNmm46qqgkeMHB4+n4rp02I+Go1Go9FkJKd07lLKdVLKx6SUV0spz5NSniyl/KGU8hop5ZMWoeVeh5TyNVRY2jPAcOD7KB2gR4CvSylr8llPk57ueu7PxeC0dCn8eV0Jb51i6E8UFBDDwV3OW/g6qziVZXyXl/mtEQVoHfiG6EMMJw0UEsXFn/hewkTU0dnljoYl2L2B7KnptzKcC3iGG5kPiMQDE0DM6WYzxfy8cTa3H7GUg+Rqni27m1JWc5Z7WdtCmzoXr0ajMbDrOsdiUF8P77yj1s3Lw/33K+PP+vUwdKhyBrnmGrUcNky1AfA832eG4w48RKjFTxxBHAd1+Ijj5AuGJF3PoPX1uLuyL/Y02pqgEEAUR1qPoTAermJRIiPb83tOACASU0PSxpib2PAMswRlZfDXvyprX1WV+iFoMR+NRqPRaDKSk+GntyKlXG7o+hzSRrkaKeVUKeUQKaVXSjlCSvlTKeUXnVFPk0x3P/dnY3Cy939oxl0AACAASURBVPGLhdWEon5+O34O+Is4bvA6atyHsNx9Mq9yMgWEiRp/sxZdHxf1BPiYA3ERZQp/SDpGJm2JTK712dZPty2TkGcccCB5nrPYwBgjPXKAZxwXACpEwhGLEKKI6g/HMup2ZfAa8xcV/fhUZCq7owGGTM/wZepcvBqNxsCu62ziMEYtLpfad+ONLR6dFRWwdi088IDSd6mqUi8hlGPIOVKJzc+i0vDKdDKTKurxs5FRSBzEbNdr63WxPYb5zgoV6ykGpXT3D5ctqYF575LAHdzKBsawmHJqCXA3N6k6MowEvLKJt05pw4NHi/loNBqNRpMT+4ThR9M76K7n/lwMTvY+LvRUcMa4Gr7x7HRc62sYcGcFr7+uHloKCpS48VscSy1FzBYzibh9bHSM4ai+NVzre4Iwbpppnd42FenEm/OlAZTJKGQuf8YCYjgYwE4OpAZPvIlaivg1l+EgToB6Q7gzOcxNFHjxjium7wLjy0zlXqVz8Wo0GgvV1cpoE4mAGTAdMTTDmpqUJMyaNS3lU4X0Vler6/ms60McKVdy1gEruI/pnMFLnM5LPMaP2UaQMB7qCCQMP23RUwwvPQHrvUm24TUlgCtZBMDtVLGFEYlsa6AyRtbh54uFSzJP/OjMjxqNRqPR5IQ2/Gh6DN313J+Lwcnex3/Hyrh2blD1MRik9KIJHHOMKtPUpMSNp3MfY1nLTDmLAyIbmDlwEX3GBiluWIOXCC5iNCbS26osXzEyzxSnMgJZjTR2gU2JfXCe3I693VT7QHkrOZC8xBnsIMh8KhjLWg7jA5zEKWU10KKVIUAprEajuOZW8rWzjC8znXuVzsWr0WgMKipUlsAxY9Jn72rLM9R0DKn7w1JGs5GhW1cC8FdO4W2O4T+UMp5P+F9O5EBquI4HkrItpvKyzDYErL3GoWzqdcSTKFP7HT22QKa9r8QQfMI4rkOlbV9PS7a1ZtwIoNpZzljWscBToR0+NRqNRqPJI9rwo+lRdMdzf64Gp2z6aJZxOpXxx9Qy2O0J8sQh9/HaewGeQrkYOYlTQLOhjaCGyPnQljC1FjqK1UjkMgw691BBLQGWcC7rGUMZSnjDYXXv9ypjFuPHt3xQbblXdZH7fmcIiGs0mvxSVkbCkB6NQmGhuqbuv3+LIcg01N94Y+r/dPjccgJDAty2Tl1zfsM0agmwkZFsJ8hwPgNgFpWsZwzf5F80UAi0X9OnLUNQW+12diax9iQCyDRZYGr6pKpn3eZA8qvhcznnoUl82reUAc4Q96OyrbmFSnFwVWwhGxjDVeH7tMOnRqPRaDR5RBt+ND2K7grbz8XglK6PVmNCRYUSG73uuuS6o0bBSW9VsSme7N7eMhCXxFFZwVq2kPS+LY2f5PZSY92fakAfMUrIpP0ttcIoIedLeZzPnSMI05LZLI5AOpwqzeGqVfDYYy0fVFvuVV3kvq8Th2k0vQfz+jx7trITjxihDEFWQ/3q1an/08Mfr2KbZwQRI/w0jIsoToJsx0tzopyLKFGczBSzE9czu9gztK35k43RvjMMO/kKPcuUuj0TLuIJI9C/OJaYMY1h9msJ59CAnxsOWMIF/Zeyf2g1Z3mW8e60B1jvGJsID47ipDFYzF3+2drhU7P3k8iDPSvlZittpR+3pypPW95ofPlySyrrfKU2t3Tc2n42WPti9qetfnU0JXlHyfg5Z0E2/bf+FtpzvHx9Rpnaaatfy5d3Xt9TtpltmnVLuQ5lZu9Fad2zyzet0XQRZWUt74PBFhtAZ1NRAQ8+qI53wQWwdWv6sun6aDUmTJmitlVXq+Xpp8PLLyujkGNACbd/XkU157UarDuRNFCIhwgRBC5ivMAZfJdlOIm1+4HBGu6Vqg17GJgLSRNe5nEzP+ce1jOaw1hFFEEzXjxEmEklR908mZ3hKkbdN4UITlzE2PKtCxn54Qvw/vtw883JHxYo48+UKeqprbm5S3V8ysvhxRfVYUE5HF12GZx5JjzzTJd0QaPR5Ij9+jx1qrp8/PznKqPXD3/YIvps/08XTy7hraurKL7vhwB4CXMTd3Mlv2QM6/ESTlz/Kt1z2STH8OS4+/hi9U7u5iYiCLxEkq6d2RpZ7GFinenJ0562090P7G1KoHbCBPacfDL1RxxBvLBQiS+lYT9gLcnnPgYfNVxNIU2sQcIrr/Bzo0x4ykLWJ/VL8KTzc8SgcJKG096My+Wib9++DBgwAJdLD801Go1Gk3/03UWjoWMGp0zGhDlz4Mgj4ZBD4NRT4e9/V2mJz6OaOvw04CPIDqBlkOwgTj0+nuJiLuFJminkdF5mCs9wMf9DBCduYhmNOCLFe/v+OFCPn0IakzKwmOV3sh8H8gkOJIcY2j1OpDGzKziXJUy56xyOkNUcjJ91x1/E2H8+yecbmhhZU0Pdmq0cXQpvvgl9+1o6YE7fz5ihpvCXLIFzzmnZHwrBscemqNhxqqrggw9g0yblMaATh2k0PR/79fnOO5XXz9/+prYNGKBSvaf8T5eXc9TvqxNhqE7iPMD1/JtjGM/HSJSHZRMFHBt9gwVczU2rL+IPnEc9PoqoTanzky1tabV1dlhXpuOmuzdYiQM7LrmEhilTGNC/P0HSDxyt9504Dj5hHDFcjHd+Qv2gUfj6uXFuXIcjEoZ4HBwO4i43MhzBYfhXmXWj4w7G2yeNsNNehpSScDjMzp072bp1K8XFxTgc2iFfo9FoNPlF31k0+wydpeuSKXrpoouU0QfUjLXp2DKfCsaxjqt5OMkTJ4KTQpq5mbuYSRXbGMpgtrGM05jG/6j2ibXqQyZdCRNzoG+WdQCN+Lmbm1I+fAzkS87gJb5wDKfZCH1ooIB1lHA8bzCfCjweWDysgnVLazjyjQU0r1pLv7kVEAzy4ucTUodTtRXP14lxWDpxmEbT+7n/fvWfNaXCdu2Cxkal/9PqP11VRTS4P6ZZookCtjKCPfShUfiY65pJLUX83XEiS0ZWJLKA9SFEAU04szD5tBU629NJDulN7nvdhAnUT5lCcf/+9EUZfVobjEQrb6hPGU49AYb2rccVC9N35wbcBS4c+w9TKdocDpASx/D9aR4wFAnEcCCB5gFDcfoK+OgjZczb2xFC4PV6GTp0KC6Xi127dnV3lzQajUazF5Kz4UcoBgohgkKI7PJQazQ9gM6yJ2RrTDDLxWJK8Hk7Qe7lhqRBtGnUWcSVbCfIgXxiZMyyJ8ltHUZgkVVuNXhPFW4ggSA7uIm7W51TMx52Mogi6lgqT8VDhFr8eIgyk0pWcDQfeScQi8EFC8o48jTlIjXokCBz/jwho35zWh2ftoSf84ROHKbR9G7sxnZQdoSKihT/6ZISvOedlRDO9xBh1YV3M99fyXlHrKeSWRzhq2GumMHnwyZw8cXwSF053+INvISTjhsnOXtiZ9LRzFuZyEXcOXTyyQzo39+S56ylDfOuJCw9MtseyjYO5z36hzapDZEIfPghfPqp+rKGDVPLXbuI1zUQx0lD32HEcRKvayAUUpkx9yUBfiEE/fr1o76+vru7otFoNJq9kKwMP0KIAUKIGUKIFUAzsAP4HGgQQqwSQswRQgztzI5qNO2lK+wJ2RoTzNnkwYPV+s94gLWMpYlkl3aBTAiP9mdPYns6Yeft7MfVLEqEcFkfUOxYPX8AnDYPIonSwRjG5wBcIxfiIspaxlKPnx8FWk7O52t9rqk8oIYPhxUr2hjEtyX8nCe6S0Bco9HkB7uxHeCRR1QIWNJ/urwcXC5YuDBR10GMM//nXJaddD/1gSDxOPzoF0E+DkygTx91CbqdKjYxMiHybNVAsy7TCe9nyqiYL4NOPjKOWfvbjJPHuDiRVt3c3nDEEQTSHFukmJAwcRPGgUwyCiGl+tKKimDIEOUOO2QIzmFDkKWHUDR2CFv7HMKnkSFs2qSqbNoE770HGza044R7IT6fj8bGxu7uhkaj0Wj2Qto0/AghzgbWAbOAI2nx9BWAEygFfgF8IoS4xFZXCCEOz3OfNZqc6Ap7QrbGhCuuUBOd8+fDAGeIO8QM5opb8TqiRF0q/XmTJUNWKuzeOxGc9KOWhVwLqD91qj+2fdDvsO1Ld6RmPHzCOM7lWcZTw5xmdXLNzXDXXa3PNZUH1Mknw7p1bXhbdVEcVhclDtNoNJ2I1dgeCMCrr6rtSf/pqioYOTKpngAYPpzCe2YzebKSmhkzRl23q6rUa6OjhJlU4TZEneNkTzbeNNkYbXL1KEo3KZCtuLSXGD/mKbxEkurFCwtbeftkc/xmvHzBkOSNQoDXq2YCQN2M/X4K9/PjLlQ36KEj3ES9/oR29P+3d99xctX1/sdfn9neAgLJRkglCQGCtGT5gXglAkoREQREIgTUCxoIXDEsRQ0kixAuvUbwoldQQDao1Ag2AnhFOtIDJKQAIZQUUrd+f398z+xOZmdmd6fstPfz8TiPszOnfc93Z77nzOd8ixmUl/vrZjEIhUJ0dvbnEydmNt7M/svMfmtmb5hZp5k5Mzu2D9tOMbMnzGytma03s2fN7AwzU1cYIlJwEhZsZnYcMA/YGngFaAQmA7sAuwZ/nwu8CtQAt5rZqcG2ZcDdwNcyk3SRvklXPCFRH0F9DSasWAFvvglz58LR5Q+xi3uN6fZzNpfUsLhsPA54r3aXrhpA0TfvnUB7xFDrAFcyA3C0BcPDRzcBS+bJ8Cq2ppMS1lFDKR38hEtYzBhWUs+Tbd0nN306TJ7cs/ZUc7OvDdTR4ftouOEG/36vta3UDktE+qBPwfaxY+Gyy3znPwEXCjH1k2uo2X0MTU3+valT/fXg6qt9EVRXB+eMaGYD1cziIlqo6lHLp699+fSl/I0XtEkk0eiMva0XT2dE/Zyu8zUjBD3ejyXy+BW0sB0f4yJ/PzvnIziV8Tttrqz0q0R0A9TbJgXFEoyWJnFNA64Fvg2Mp48fezO7CbgDmAQ8AfwF2Am4EbjHzPoS80zKrFl9HwF6weRZSY8WHT1Een+HWE9WX48RnZ7Iv/s69Hf0sZI9v75u19uw5cmcQ7r19n9O22cg6oMZ3m8y++9PXiV7fok+K+HjT14wq89f0P58j3NF3MCPmQ0Gfhm8/C/n3B7Ouaucc4875xY6594I/r7SObc7cDb+un+dme0M3AccQ371cSgFKh3xhFT6CIpsbnYHU/jrU7X8fJNve7a3exba2nhn01B252W+velW/h76Mq3mawCFa+pspoIX2ZNSXPAU2thIFT/keipppYpNPYZlB5jP4bSUVPMMk+gMOs+M96XsLCkLjhliPbXMYjYbqOE4YmdYR4ev+RNde6qx0f/e2rzZ1+gvDyox9VrbSu2wRKQP4gbboyP0zc2+2khlJVRW0kmIr26ax9Zbd9cC3bZ0LU9vmMD509Z2FUF/Gj2NToxzuYLyoNltLLHK0kQ3PbFG0grP4y1LdOx4NXqSCR+E4vQlF2t/vQW+DFjFNnzq6minhFVV3X369Gb16h7dAIkk8gpwBXA8MBZ4rLcNzOwY4HTgA2B359wRzrmjgXHA68DRwPSMpVhEJAsS1fg5E6gFfuycu6G3HTnnrgN+AlQCzwKH4puI/SoN6RRJSSrxhHT0ERTZ3OxCmlhuI2gP+kZvpZz3y0fxo4q5vMpuvFQ+idtHzqT9S19hc0kN15fPYCM1PMIhlNOGAffwDToopZLNVLM55jFXsQ3rqWHnSbXMv2Exa9mKDkJ0xPhJYAAlpVy53RzaKutYxBh2ZiFXM4PxLOQK4meYmR+ZPTK/vvQlOMu3POPDD31wqLS0D7Wt1A5LRFIRHaFvbIQHHuAHhy5hx9ASjuBBrqCRDz7wI4GVlsLBLQ+xK6+xvnl+VxF00kEr2Ir1VLGJkgSNvfpSA8axZZCmPx1E91Z7M92dTfd3Px1WFjQ5Dj806DaEDxnEp2wM1VE9Znv43Of8k4BeRHT/0zUXicc5d6tz7lznXLNzblEfN7sgmJ/nnHsrYl8r8TWIAM5Xky8RKSSJCrTDgE+Aq/qxv6uAVUA1vvnXF51z7yWfPJH0SCWekI4+giKbmy22scyyJipCbbRV1FDq2nji4Nm82TGmqynacZc3UHPZTPbZZhE/bL2SP/NlvsKf2YU3ADiGP1JGW1B/Z0ubKKeDEr7PLYxlEcc/28ixZ9TzY+bwNR7gbxwcM40f7Polzls5g6N2fYvzauZSPqiSV5jAZip5ITSJ6moYPrzndlVV3Xmxdq3vxHnYsJ4j7pxzjlpviUiGxIvQX3MNHHoo51xRT+XIeh6rOITn8BeAO5jC6vZa/tf5bRpunEq7ldJhpYxp8u9ZVBnbW585yXa6nMq68TqZjlW7M1bH1NHL4m0buV1HSTkWClHifJ9AZV19A1nEukYLFbD99lS+/Yp/ShDujTuBmpotr7d92ESkz8xsGL7P0lboWZ3ZOfcY8B4wFNh3YFMnIpI5iQI/OwJPOuc6EqyzBedcO/BP/L3BAUHkXCRvxOrHJ119BDU3+5tY5+DYzmbWddTw4zbflKp6/jza22HcuIjgSEMD195VT3k5nMsVLGE0LUHHz5spp4XyHk9ZN1HJT7mU9dRyHPNYW1HPv0snUVbmh5B/tOxQzmCuvxmPsJkKJr/2cwD+/G/fj88XPn2ICbzGlV+aT10dHHwwrIzxjd60qbvGz0MP+U6cDz3U55OZn2KOuCMiki69ROijy3EzWHN2EyvLR3SVo22U8V7ZaDpGjOraT2cQyGihFOhuRhsdJAHo6BEm8svaKWEVW/V4H6CVMt5jWFf/OpHbR/bnFj5mf4ZiD6c3+r3I9/vaVK2rn7lwtyedneBcd580ZrRQyXs2HIfPixCOD0Lbs2n1puIbm11yWXjQmVedc/GGUHsmal0RkbyXKPBTA6xLYp/rgHbn3KrkkiSSPfH68Um1j6ApU+CBB6C11b++KtTITkFTqkk8wz48w95j1nLPPVsGRw46yHegvIixXEgT5bSxjhrK6OBGplNKe9DfT4jNVPAXvsLVzKCBZ9jHnqGmfS1nneU7WAb/o2dF9VjWDR3nXwfNzZaXj2F5uY9k3d4xhU9aa7kN/8T7lEdPYtXaEP+z5tiYVe7b2uCxx7Z82H799f6Yzvn+fWKOuCMiBS9Rp/hp3VkfIvTRI4E99t5YPpzuR+9aTw1ltPH+mXOYO/wy2jb59wzHRqr4CZd1BYGibaaCTVThsJhrlNFBa+0QoGftnDLaWc5wOiihI+qWrDRYO16wJ7oj/3Tore+e9a6W120C60s/4wv4rl6YYUVoewZXrqOTElZX7oAZjOpczJANS/zGxTY2u+Sq0cF8aYJ1lkWtKyKS9xIFfj4GRiWxz5HAR0mlRiRLeuvHJ9U+h5uaYPTo7k6OX6pooHxYPWbwHxXPMJp3mHvEfMaM6RkcaW728+9v3cwGapjNbDZSwzfpHnVmHXX8mUO4GF/1poFnGO3e4XDmc/XV/uFsWEsLfODqcbV1fHrIcRgwuD5EW5tvtnUhTSxnBK2En3iHCOG4/P++wHsxGm4OGQK3377lw3YX8auhtRXuv79/fSKJSGFIpVP8fu+slwh9zHK8uZmN1PDsEb5cdc3z+F5dM5tDNfys1NfIfDT0Za4tmcH3uJVlDKctGOA8XMydw5X8iKsoC0JDsTpZHrr+LWLpIMQGajie37GUEaynf+2aYjXr+rhsKIsY02ttnlgjQHYSopVyQviaSpH7XcFnMRzj3ets1f5xxIZ+jZGDVlM5aihuwm5UjR4K48djFRVYqEjHZpdcVRvMNyRYZ30wr4u3gpmdFgz//uxHH/XvZ094lK1Ey5OxYHLP7WLtqy/7TyYN/RkZK3L/mRhprK/pj0xLrPwbaF1pWeCn/v4f0pmXvX1O+5K+gRjZrLdjpJon8baPzJ/IUfMGYuS8ZCUK/DwH7GNmI/q6MzMbCfy/YFuRvNFbPz6p9jkc62H0LzdNYW1HLbe2+WjTxOtj9xr9s5/Byy/D3yc2snf1Qt771gx2CS3kp/yMcSyiiVmMZyGXhmbyI65mHd21dW5jKuutlt+F/D4rK/157jiuhFBnB9v+1UeVape/zpr2Wn5fMYVFjGUmTVSxGQeUB303zOloZE1HLXewZfrGjPE1k8LnV1W15bmXl8OOO/avTyQRyW/p6BS/3zvrJUIfqxzf6meNtL68kMkPzKD15YVsfUkjdU2NLLhlIVcxg92rFjGrcyYjR8Kh9lfq+ZAyfAv4cFDnRs7k55y+xbGigy6+aW5pV3PdNkowYDYXcQFzqKKFMSzhQY6gnVI2Env88nh99rRRxmYq6KCEf7ftxvlcRmdE4CZeP0CRwZ9lDCdEJ+X4qqmlQSCro7yKTkJUsZlljICy8i2aeFFRge08ntBnh0JNDWVVZb5fntpa2GGH4h2bXXJVopaOfeac+4VzbpJzbtLgwYPTkCwRkcxKFPi5GygBfmVm5b3tKFjnV8E+705P8kQGRrr68Ukk+mH0HeObqBg3gpIKH22y8ti9Rp98sh/Z5BtzGnhycT133QUnnVPPbziZD62eqVNhTXk9T3dO4kKaWMYIXKnfZ6i8jPYdRnKhXUxNjW9+dfnlUPO/N8HIkbgg0mUV5VTsNJJd7rmY2lr4Js2sp4aVDOlKRytlLGUkF+LTN3Uq1NV1d7wZPr+LL+6u2VRRkZm8FJH0SWtzrEA6OsXv986SiNDvenIDg3fz2wzerZ5dp06ChgZ+8+d6ampg0lfreY5JvPMO/NQ1EaJnt4eJfj06YB01lNDJ9ZzVFVQJB4+auIhnaOC3nAjA8TRTSjuGo53SLQIzsfoPArg5NI3hLGd8+VKOLn+IWRVz+CbNOKyreZrvQ6i7H6HoWkktlLN95WqWhMb4ght/XaC0lM72ThaGdqVzyFDaSir5qHz77mAO+GBObW3sXpiLZGz2F198ka9//etss8021NTUMHHiRH71Kz+orZl1B8okF4S7sahNsE54WTJdXoiI5KREgZ+7gOeBLwGPmdne8VY0s4nA48Bk4MVgW5G8Eq+VQLp+FEU/jD7zurGUXtodbSrpSBwhaWiAs8/299dXX+3fcw5++9vuvoPCfQFZu+8LqH1zG7cOn83K2jFbntfYsWw4v4n2Ta10YFhbK6WXzGbUQWP4yU/g6lAje1a/zVl2U/D0uYpy2ri0fDZfPWsMdXW+r863n1vL/Yt85jQ2+mHcL7qoOz1tbT7YNGNGanknIpmT1uZYgbQG0wciMh9l7Vp/mD/+0b92zpev1/Jf/nWwXhtlvM8wOoIxFsNTJyEMaKWCWczGYfyIq3v019OJ+dpAIR8tb6WcTVRSRQultG+5rpXSEqrsOn4nITZSzbadn/Ah9bzfUc8j7hD+2TqJ/62Yxvtsz+1HzGPP0Mt8o+JPPM9etFKOC279IgNJpbRTtnk9n+1cjmvr8Pnc0QFVVZR3trDTsA1sO6KG3XaD7UL9COYUwdjsf//739lvv/24//77qa+v58gjj2TQoEGcdtppNGo0g1y0JJiPTLBOeAzTJQnWERHJK3EDP845BxyF7+Ds/wHPmNlLZnarmV0STLea2SvA08A+wHLg68G2InklXiuBdP0oivkwupc+KaKDTk1NUFLS3VkzbNl/DxD0/VPDjdvOxmpq+E7NvB7nNWUKPPydZloppwTH5s5yfv+teUyZ4vvefKOugTOa6jkO3//F00dczEZq+MF287juOnjrLb+fIc88RNU7PnMaGuCKK/yD+eCBMRUVsNNOcN11qeWdiKRfWptjxZBqp/hJ7ywN0frycj9iYUdUBZ9RLGMj1XQGgZ5S2ikdPZySkhBY9y1VCF8wl9DObC7iUSbzPp9lU9CEy4BOwBHiRqYT6vQd9VfQ0lUrKMyAj9iOn5ZeygvbfhlXWYWddx4hg6crD+D6isauNFdW+uyZfswKRrKM5/7Zyuslu/GwO4RvcTdLGN2VtsgbtZLgvUpasc52Vm8ow7W3wzpf4aF0+RJ4/nnKli+mdFgQxNluO1/7asgQ4kpybPZM1ELLhI0bN3LiiSeyefNmLrzwQl577TXuuusuHn30UR5//HFuvvnmbCdRenohmE8ws6o46zRErSsikvcS1fjBOfcusDe+6ZYDdgO+C5wfTN8Bdg2WNQMTg21E8k50YObqqzP7owjotU+KcNDpnnv8TfDgwb7Pn0jl5TBsWHet+ytoZBdbyLibZ1Dy9kK2mdPY47y+fvcUDut8oKv/njLaOLzjfm5ZN2WLJG07p5EDd1jIXr/1/V9sO8enr/7sKUya3DNzxl44haYm/2Mp/GD+kkvgqKPSmGcikhbpao4V70d6qp3iJ72zNETrr7kmdh/Ey77ZyN/tINZTR5NdxAaqeeWdGo4ufYDLvvgQJ2/3EMsYwWbCNXhKqWYDTczkHXakis1d+3IYpbRzOnMpCRpluajuocPBmW1Yxcz22ewxvoXQkndg990x18ngo/bn9fZxvGYTqGpdy5M7TmFtZy1fbfZl8/VrprKppJbfV07hvcqxPMwhGLCJcpyVxByCHqCKTXTW1Pk+fGDLjpnDwZy1a33Vz9bWGHtITSZqoWXCPffcw4oVK9hpp5246KKLtmjS9fnPf57TTz89wdaSDc655fgWDeXAcdHLzewAYBjwAfDkwKZORCRzEgZ+AJxzq51zJwA7AT8EfgM8Ekx3AGcD451z33LOfZLJxIoMpLT2URFPnD4pop/Ef//7/ib4sMPgiSe6+9ABH1wZPtwHfior4ZXKBj4uqfcPxGP0c9HUBJdVN7GE0V0jd7VSxjvsyEwu3iJJHwxv4Pn36pk/P6L/i14yJ61P+WPIlyfBIrkuXS2o4v1IT7VT/H7vLI1VmAYPhlWr/N/h8nbSJDjn7gZG3TqTL49YyKVlsxjLImaWzuGtMYfyzV8eylG3HM6VQ6+ilE7WUUMlrYSAb9HMOuro7Kpv44M8n1JLGW1B/SGwkFFCR9c6HREjiLXtMJLqo3VbdAAAIABJREFUrcv9Pyg4x/G/m8V7HfXs4l7j6+XzuWX7JkIju8vmUEUZJTuOZO9x6/hocy1ncYM/J9oJOd9ArTM4RqRQVQUll10au2PmxYt91dAlS/zKaRymPdO10NLtscceA+D4448nFOp5Sz0lVxMuc4L5f5vZ2PCbZjYEmBu8vMw519ljSxGRPNVr4CfMObfYOXe9c+5k59zhwTTVOXedc25RJhMpkg29/SjKZAAiHFcJN5oMNzd48kkfSAk/YDXz6yxeDA884O+/lyyBBx+M/0B87Fj47qW+L6ByfF9A5bRxWdVszrzWn1yvN98JMietT/ljyJcnwSL5IJVAbc79SE9jtP6hh3xTr8pKuPRSf16vv+7L+zl/beCllfW0tsKH1POv9kksWgQzZ8I3vgHnjmomRAe1bOgK6EznRg7jYTZSRQhf4yZEJ5W0UhrRYbQFbXfvKj2ZzVQRopONVo1h/HL4bF8VKeIcS2mnghYA/qdlKtc8uidss02PsnnOkGt410bQGtRE2kwFyxjBldteBjXVXccP1/75zc6XwOOP+4tMdF8+22/vo2GxagOlaEAeuKTRe++9B8DIkbG7i4n3vqSPme1tZv8KT/iWCgCXRr3fxTl3D/BzYCjwspk9YGZ/AN7Ct2S4F7hxAE8jof4MiZ2p4bOjh6gOD1+daQMxHHhf9Da0uWRXJoZRT7S/yGWJhnvPNX0O/IgUo0Q/ijIZgAjHVcy6763B/wgpL+++Ka6s9H3o3HwzHHqofxBeXw+HHJL46foTT8AJId9/zyxms4EaLhg7ryuo1aeb7ziZk9an/BEy+SNTtYikWKUSqM25H+lpqMIUXc60t/sO63feGTZs8OV9UxOUlm65XXl593nPrWnklKq76QyV9ujMuYYNfn3ag0ZdnV3BmHDQ5d3TL+Xgd3/N5i8cjKuq5pOzmgjV1XJKzTwYO5Zrt2mibVMb6/Hdk4SPsbmzjDdbRvLc24NY11nDpvO7y+aps8cy9BdNlJd0dAX7zw1dxY43n0fJT38M5pt8WSiEK6/g6G2f8B+GHXbo2TFzZaUP8vRhmPb2dnjllS37pUskC/14p0W8Ubti1QKStBuE74s0PNUF74+Len8LzrnTgW/jm30dABwCvA1MB45xzvUcwk9EJI/piiSSQKwfRQP1lDscVznpJP863Knz9On+PjuVPnQaG+H/9m9kUt1C6i6awd7VC7m9vvsXX59uvjNdtSdKJn9kqhaRFKtUArU5+SM9xbam0eWMc7BxI7wQdPE6dSrsuWd3jKO01JfNP/lJ93mfU3INt9l3KAl+N3aNAGbltOB7vt9MBW+xE7dtfTbl1uZHA7MQVFcz/OMXqa+Hz1w9k5J3FjH82hmE3vL9tQF8r64ZsK4h4cPHqGYTH7vPcM1WsxnbuZB7x3SXzQ0NsPWfm9lkNfx35Ww6K2s4PjTPZ8/zz2PV1dhFF0FdHaFDD/HHamjwJwc9O2bu4zDt4W6A+hNUz3Rz4XTaPqjltHTp0pjLl4Sbw0nGOOcWOOestynOtnc65/Z3zg1yztU45yY6525SEy8RKUQK/IgkEOtH0UA95Q7HVTZtgro6H/CpqUnPTXFDA5xwVQP/eKueWbPgycX1HDNny198vR4nU1V74ujrj8z+1N7JuaYqInkm536k9xaQ7qWAiC5nwi2dwv38tLf7mj+ffNL9uqPDbxO27U1BPztBW10XjPZVvlU15aHuGjczQ5dw6M5LuoIuoUF1cPDB3WmOLmPHjYMJE6g7dxqPX/I4ixlDR3Ab10opnYT4lEHcvbiBD6n35dmYeqZcPakrb958YCFnLplBxZKFHPBgoz9UYyMsWgSzZvk8mzkzbnkersHTMTjxMO2pdAM0wM8UUvLFL34RgObmZjqjh9gE7rrrroFOkoiISEwK/Ij000A95Q7f8zc2+uHTr73W3wT/7GfpuSnuLW6TizffffmR2Z/aOznXVEUkz+RcOdFbwdaHAuKBO9bycucE5py/lpoaX0aEy/uSkp7NvMrK4MorI94IXyRCIaiqIlQSwq66CsaNY3Noyxo3P6+NCrqcfbaPRMcKTIXT/sEH3PLivsypbgKM9VQTAk7jFuZUNMUvzxoa2OvQep899fVse8gknz39COKHa/CsaUs8THsq3QA1NMC22/oA07bbZvyZQkqOO+446uvreeONN7jkkktw4Y75gKeeeoqbbropi6kTERHppsCPSBIG8il39D35yScPTEWbAa7Q0yeJfmQmU3snJ5uqiOSRXCwnYupHAXHh3g+xc+drnDlmPgsXQnV1d3lfWws77rjl+meeCdOmRe2kudlX1bz4Yr/xU0/BTTfx5gMLOeul/6RiRD2T757GN+b4DFy7FiYcWM/Gt9/vGZiKkfa7HqjlV1v/iM0ltVxS3kR7WTXXMIPOMeMyUp71twZPP7oBiimZJmLZUFNTw29+8xsqKiq48MILmTBhAlOmTOHAAw9k//335z//8z8BKAsHyURERLJEgR/JW9nskDdTT7nVyXBiiX5kJlt7J+eaqohI+kUVEK6sjLfbRrLu3IgCIgiwjGnqDrDUj6nl7pIpW5T3a9b4xUcc4efNzTGOF3WRWD+tkQmnNLDjfvUMeeYhePNNtmlZ0VWGrT5sCk+9VkvlD2IEpmIUbiU7jqT0xut468GF/HDZDKquu4xBfMpXbX7C8izZa0wyNXj62A3QFjI4UnzGfPnLX+af//wnX/va11ixYgX33nsvq1ev5qabbuLss88GYLvttstyKkVEpNgp8CN5K5sd8mbqKXcmz6nQg0rJ1t7JuaYqIpJ+UQWEa23j/NbZPPh6RAERJ3q87dyLtyjvL78cXn4ZHnjAzy+5JMbxoi4S978/iZ+8NoWa+i1r7mwuraW5dAqHPt3EMkawqcMfu8VFRK7jFW5HH81et59N/ZhaOOssAC54Yyqr2mqZ8dyUmOVZsteYZGrwDE3cDVBMGRwpPqP23ntv7r//flavXs3GjRt54YUX+P73v8+CYJzfiRMnZjeBktNiDQedaPjw8Pu5OFy0pC7dw5KnSyF83jIx7Hs+UeBH8k4hdsg7EOdUDCNXJVN7J2+aqohIapqb2Wg1XNA2m3UdNRzHvC3L2j5Gj08+2QcywM+nTu15qHCg/dhju8v2C2nirZYRbGzvDizZ6JH8ctTFLCsfy4U0UU4bG6yGcos6drzCLSpYFSovIzTKB4wiy7MpU/xm3/62f53MNaa/NXhqEncDFFOqTcSy4cMPP4w5qte//vUvGoPI2ymnnDLAqRIREdmSAj+SdwqxQ95MnlMhBsriUe0dEYmrsZGVjy/kvjEz2KNyIVfQ2LOsTVPbz3Cgff/9u8v2RYzl4vImymijs9oHlirmzObUy8bQ1gZTSprZSA1vnzQbqmv403fnddfQjFe49TFY1dQEW2/d/TqZa0wyNXiSkUwTsWx66aWXGDVqFJ/73Oc48sgjOfbYY9l7773Zb7/9WLlyJSeddBLHHHNMtpMpIiJFToEfyTuF2CFvJs+pEANl8aj2jkjm5W2z0YYGRu9bT1MTvNtWz/M2idbWqLI2xehxdKD93HN9/zSbN/uy/ei2Zlx1DaGm7sBSONa08YxGJtUt5NLNM/jjZQuZua6xq4bm2p0amHCg7wS6R+HWS7BqyhTYc0/44IPu9zZtgs98pn/XmGRq8CRjoAJM6bLzzjszbdo0Ojs7+cc//sF9993H0qVLOfDAA7n99tu57bbbsp1EERERBX4kPxVih7yZOqdCDJSJSPbke7PR5mbfb4xzfr5FWZti9DhWoL2iortsn1vdyPSDtwwshWNNJ17XwD5frGTWvAmcemYlzzGpq4bmYYclyPNeglXhNIWVlvqaNIMG9evUBsxABZjSZdiwYcydO5dXX32VVatW0dbWxieffMLf/vY3TjrpJCzcYZGIiEgWKfAjeakQm/Rk8pwKMVAmIgOrEJqNTpniO2Vua/Ov29rg/vvTdw6xAu0XXABvv+3L9t8tauC0mVsGliJjTVcd+BC7uNc4IuQjPM7Bxo3w9NN+ecw87yVYFU6TmR+aHuCWW/x7IiIiUhwU+JG8VIhNejJ5ToUYKJPiYmaTzcz1cRrRy74ujVj3nF7WnWJmT5jZWjNbb2bPmtkZZlZ0189CaDba1ASjR295DjvumN5ziA60v/BCH8r2IKo29DwfVbu1bSrrqeU3nVO6RroKpzeZPG9u9gGjpiafpr/8pTCumyIiItI3pdlOgIhkXkND99/19d0/QkTyyAdAos4y9gF2ARYBy+OtZGYNwLmAAxK2wTCzm4DTgc3A34A24CDgRuAgMzvOOdfRj3PIa+GaIyec4IMHLS3512x0IM6hsRFuuMGXsyeeCMvjfhojNDXBiy/CkiXQ3k4bZWyqH8l/b7iYESNgxQq/Wo8+iTKZJhHJqAULgMlp2McAysaQ3oU2/HahnU+hycT/Z8GClL/qaVF0TyxFRCT/OOfecM6dEm/CB3IAfuWcc7H2YWYVwK+BlcB9iY5nZsfggz4fALs7545wzh0NjANeB44Gpqfj3PJJITQbzfQ59KX2Zo8OsiPaiHVU1VBV2sZ2N85m9MFjeOop6Oz0q3V0wLe+1f+maYVYS1ZERET6ToEfKWp5OzqNiHQxs/2AXYEOEtcKagrW+wHQ27f+gmB+nnPurfCbzrmVwLTg5fnF1uSrEJqN5sI5xOwgO4hIlVw8GwsiUldc4ZvXVVb6VSor/et8al4nIiIi2VdUN6wi0fJ9dJpsUcBMcsx3g/nDzrn3Yq1gZv8PmAHc6Zx7INHOzGwYMBFoBXrUB3HOPQa8BwwF9k0h3XknkzVHBqpcyWbtl4QdZMeISI0dC1dcAe3tvnZSeztcfnl+Na8TERGR7FPgR4pSIYxOk035FjBToKpwmVk1cHzw8pdx1qnE1wRaBfxXH3a7VzB/1Tm3Kc46z0StKynKt3IlGQk7yI4TkSqE5nUiIiKSXQr8SFEqhNFpsiFfA2bF8IOyiB0H1AEfAg/GWecSYDxwpnPu4z7sc3QwX5pgnWVR60qS8rVcSSagHGu49946a86FpmkiIiKS3xT4kaKUzM235F/ALF9/UEq/hJt53e6ca4teaGafB34I3Oucu7uP+6wN5hsSrLM+mNfFW8HMTguGf3/2o48+6uOhi0++lSthyQaU+1uDRx0zixSu6FGyNOKTSO7Kxqh26aTAjxQtVZ/vv3wLmOXrD0rpGzMbC3wxePmrGMurgP8FPsWP0NXnXQfzmKOD9ZVz7hfOuUnOuUmDBw9OZVcFLd/KlVQDyqrBIyIiIgNNgR8pWrr5Tk4+Bczy7Qel9Fu4ts+TzrnXYyy/FNgJ+JFzbkU/9rsumNcmWCe8bF2CdaSP8qlcSTWgrBo8IiIiMtAU+JGipZvv5ORbwCyfflBK35lZCTA1eBmzU2fgaKATONnMFkROwKHBOtOC926N2G5JMB+ZIAnDo9aVFORTuZKugLI6nRcREZGBosCPSAK6Me8p3wJm+fSDUvrlEGAHfD88ifruCQEHxJiCTzE7Bq8jP8kvBPMJQXOxWBqi1pUU5Fu5ko6AsjqdFxERkYGiwI9IAroxz3/59oNS+ux7wfxu59z6WCs450Y55yzWhB/eHaAxeG/PiO2WA88D5fhRw7ZgZgcAw4APgCfTeE6SJ1IJKKvTeRERERloCvyIxKAbc5HcZWbbAUcEL+M180rVnGD+30En0uFjDwHmBi8vc851Zuj4ksNSCSir0/nCs2HDBi6//HIaGhoYNGgQVVVVTJgwgVmzZrF+fcy4tIiIyIBS4EckBt2Yi+S0k/C1cd5wzv0zEwdwzt0D/BwYCrxsZg+Y2R+At4BdgXuBGzNxbCls6nS+sLz77rvss88+nHfeeSxdupT99tuPr3zlK6xevZrZs2ez//77s3r16mwnU0REipwCPyIx6MZcJKd9J5j3GMI9nZxzpwPfxjf7OgDfr9DbwHTgGOdcRyaPL4VLnc73Ik862HPO8c1vfpPXXnuN6dOns2TJEh555BHuu+8+Fi1axIknnshLL73E2Wefne2kiohIkVPgRyQO3ZiL5Cbn3O5BvzxXpLCPU4J9XNnLenc65/Z3zg1yztU45yY6525SEy9JhTqd70WedLD38MMP8+STT7Lvvvty3XXXUV1d3bWsqqqKm2++mSFDhnDHHXeo1o+IiGSVAj8icejGXESkeGWy0ok6nY8jzzrYmx8Epo455hhCoZ631DU1NUyaNIn29naeeeaZgU6eiIhIFwV+ROLQjbmISPHKk0onhSXPOthbvHgxAI2NjZhZzCkcHProo4+ymVQRESlypdlOgIiIiEiumDIF7r8fWlr866lT4dRT4cgj4c47s5u2ghfuYO+EE3wb65aWnO5gr6PDd/N1wAEHMGrUqITrjhw5cgBSJCIiEpsCPyIiIiKBpiZ48UVYsgTa23O+0knhCXewN3Omz/R58+DYY7OdqpiGDx8OwHHHHccZZ5yR5dSIiIjEp6ZeIiIiIgGN6phledTB3mGHHQbAPI3+ICIiOU6BHxEREZEIuTiqY56McJ66POpg76ijjmLixIk89thj/OAHP2DVqlU91lm8eDE33XRTFlInIiLSTU29RERERCI0NsINN/i4w4knwvLl2U7Rlp1Nn3BCtlMjAKFQiHvvvZfDDz+cW265hTvvvJM99tiDYcOG8fHHH7Ns2TLefPNN6uvr1RRMRESySjV+RCStiuaptIgUrFyqdJJnI5wXnWHDhvH0009z4403stdee/Hqq6/y+9//nldeeYW6ujrOOecc/vCHP2Q7mSIiUuQKvsaPme0GPAeUA68653aLsc544HDgK8A4YBjQArwG3A3Mdc61JjjGeGAmcCCwLfABMB9ocs6tSOsJieQ4PZUWEUkfdTad+yorKznjjDNUq0dERHJWQdf4MbNS4NdAWS+r/g24GpgMrAD+iA8W7QlcA/zLzLaJc4wDgBeAb0dsuxH4AfBvM9sp1fMQyQd6Ki0ikl5r18LXvw7nn6/OpkVERCR5BR34AX4MTATm9rLeQuB7wGDn3H84505wzh0I7AK8CuyFDwBtwcxqgN8BVcCZzrmJzrlvOed2Aa4CBgN3mZml7YxEclRTE4wY4Z9Gg55Ki4ikKlyDcu7c3OtsWkRERPJHwQZ+zGx34KfAH4B7Eq3rnDvIOfcr59z6qPeX4GvuAHzTzMqjNv0OMBRY4Jy7MWrZecAiYG/gsKROQiSPaAhkEZH0iK5B+eyzvkx97rmcH+FcREREclBBBn7MrAy4DVgPnJ7i7l4I5pX4/nsiHRXMfxu9kXOuA18bKHI9kYKWi0Mgi4jkm+galOXlMGqUr0GZ7c6mRUREJP8UZOAHX9NnT+CHzrmVKe5rXDBvBVZFLdsrmD8TZ9tnotYTKWiNjf5p9IwZeiotIpIs1aAUERGRdCq4wI+Z7YXv2+dh59ztadjl+cH8QedcS8RxBgHhDp+Xxtl2WTAfnYZ0iOS8XBoCWUQkn6kGpYiIiKRLQQV+gj54bgM2AaelYX+nAMfjR+n6cdTi2oi/N8TZRbjPoLoExzjNzJ41s2c/+uijZJMqIiJS0NauhQkT/LwY5EoNSudcdg5chJTXIiKSKaXZTkCYmV0OHJnEpgc5594L/r4Q+BwwzTm3PMX0HATcAjjg+865hdGrpLL/MOfcL4BfAEyaNElXfBERkRjCI1zNnw8nnJDt1GReQ0P33/X13bUpB1JJSQkdHR2UlubM7WJB6+zsJBQqqGeyIiKSI3LpSr49MD6J7coAzGwifiStBfiATdLM7AvAfUA5cJZzrkfnzcC6iL9rgFjPIGtjrCsiIiJ9NGUK3H8/tASNradOhVNPhSOPhDvvzG7aCl11dTXr169n6623znZSisLGjRupqqrKdjJERKQA5cxjBefcic45S2JaEuzia/hAVj3wqJktCE/AtcE6oyPeHxsrHWb2eWA+PphznnPuhjjp/ZTuzp5Hxjmt4cF8SZzlUiAKsQlCIZ6TiOSf6BGuyspg5Eg/wtVAKOaycNCgQaxatYqOjo5sJ6XgOedYs2YNNTU12U6KiIgUoJwJ/KTRLsABUdMewbLqiPdqozc0s32BP+H75Pmpc+7yXo4VHuq9Ic7yfaLWkwIV2QShUBTiOYlI/sn2CFfFXBbW1dVRU1PD0qVLWbNmDe3t7eqHJs2cc7S0tLBixQra29v5zGc+k+0kiYhIAcqlpl4pcc7NAmbFWmZmk4FHgVedc7vFWWcf4BFgEDDLOXdJHw57H3AQ8G3gl1H7KwG+Fbz8Yx/2JXmoEJsgFOI5iUh+C49wNXOmr+kzbx4ce2xmj6myEMyMIUOGsG7dOj799FM+/PBD1f7JgNLSUrbaaiuGDBmiPn5ERCQjCibwk4qgf6A/44M+FzvnZvdx0//Fj/b1JTM7wzl3U8Syy4Ax+No+f0pneiV3NDXBiy/CkiXQ3j7wTRAyoRDPSUTyW2Mj3HCD7+D4xBNheUrDN/SNykLPzBg0aBCDBg3KdlJEREQkSXqs4P0F2ApYA4wws1/HmbaL3Mg5tx5fq2cTcGMwLPtdZvYacA7wMXCCU73ogpXtJgiZUIjnJCL5raGhe1Sr+nqYNCnzx1RZKCIiIoVCgR8v3KB6a+DkBFOPfoGcc48BewF3AsOAbwTr3QLsHmMYeCkw4SYIs2f7+bx52U5R6grxnERE+ktloYiIiBSComjq5ZxbAFiC5XGX9XH/C/H9/EgRykYThEwrxHMSEekvlYUihc3MpgDTgN2BEuANfFcOP3fOdWYzbSIi6VQUgR+RTGqIGNOtvr67OUI+K8RzEhHpL5WFIoXLzG4CTgc2A38D2vCDttwIHGRmxznn1Ju5iBQENfUSEREREZGiYWbH4IM+H+C7ZjjCOXc0MA54HTgamJ7FJIqIpJUCPyIiIiIiUkwuCObnOefeCr/pnFuJb/oFcL6Z6beSiBQEFWYiIiIiIlIUzGwYMBFoBXp02R4M3PIeMBTYd2BTJyKSGQr8iIiIiIhIsdgrmL/qnNsUZ51notYVEclrCvyIiIiIiEixGB3MlyZYZ1nUuiIieU2jeuWQ55577mMzi74IbQd8nI305DnlW3KUb8nJ5Xwbme0ESNzyvVDl8vchnygfU1foeajyPTm1wXxDgnXWB/O6WAvN7DTgtPC6ZrYwTWkLK/TPbiYp75KnvEte4ryz2cnsM61lvAI/OcQ5Nzj6PTN71jk3KRvpyWfKt+Qo35KjfJPexCrfC5W+D+mhfEyd8lDisGDukt2Bc+4XwC/Sk5ye9NlNnvIuecq75OVD3qmpl4iIiIiIFIt1wbw2wTrhZesSrCMikjcU+BERERERkWKxJJgnakYxPGpdEZG8psBP7stYNdICp3xLjvItOco3kW76PqSH8jF1ykOJ5YVgPsHMquKs0xC17kDTZzd5yrvkKe+Sl/N5Z84l3bxVREREREQkr5jZc8DewMnOudujlh0ALAA+AHZwznUOfApFRNJLNX5ERERERKSYzAnm/21mY8NvmtkQYG7w8jIFfUSkUKjGj4iIiIiIFBUzmwtMAzYDfwXagIOAQcC9wLHOuY7spVBEJH1U4yfLzOwQM/ujma0ws1YzW2lmT5jZOXHWLzGzaWb2f2a2xszazOxDM/uTmR3Vy7HGm9lvzex9M2sxs6Vm9nMz+2xmzi5z+ptvqWynfNti+92CPHBm9kov6xZlvgXnfXbwnXzbzDab2Voze9LMfmhm5b0cq2DyTYqLrk/pU+zXqnQq1uuW9M45dzrwbeB54ADgEOBtYDpwTDqDPgN5HxHn+JOD70Ciad/0nG365UqZmC9lRLL/bzNb0oftnJldmOm0ZEsKeZex80zX5041frLEzELAzcCp+CcMTwLvA/XAbsCnzrmxUduUAn8CDgZagH8AHwM70t0J3bXOubNjHO+AYNsq/AXuLWAPYGfgI+ALzrk303uW6ZdMvqW4XVHnW9Q+SoF/4dvEG/Cqc263OOsWbb6Z2bvADvgniM8C7wbr7wdU4juKPNg5tyrG8Qoi36T46PqUHsV+rUq3YrxuSW4Z6PuIBOmYDDwKrAQejrPaxc65RX3d50DIpTIxn8qIZP/fZnYlsF2c9bcBvhb8/UXn3BOZTEu2pJB3SW3Xh/Sk73PnnNOUhQm4DHD4G5IRUctKgIYY25wWbLM0xjaH4AtEB+wdtawGWBEsmx617Mrg/ecIAoG5PCWTbynkd9HnW9R6Fwb7uDGYvxJnvaLON+BvwHeB2qj3RwGvBPu7rZDzTVPxTbo+pS0fi/palYH8LLrrlqbcmgbyPqKXdEwOtluQ7TwZgPxL+/c538qITPy/gXODfS7MdlpyMe8ylOdp/dxlPXOLcQJ2BdrxT0O368d2dwX/4PPjLP9zsPz0qPenB+8/GmObEny1Vgccnu28yVC+JbtdUedb1D52B1qB30cUbPFuoJVv8ff5heDcNwHlhZhvmopzKvbrU5rysKivVRnIz6K7bmnKrWmg7yN62S78HViQ7XzJdP5l4vucb2VEJv7fwBvBPs/LdlpyMe8ylOdp/dypj5/smIb/Z/3SOfdxP7Zr6eN60fsM963w2+gVnW+//Luo9XJVsvmW7HbFnm8AmFkZcBuwHji9D5so3+J7IZhXAttGLSuUfJPiVOzXp3Qo9mtV2hTxdUtyy0DfRxSaXCoTi7qMMLP9gfH4QNztWU5OMUnr5640TYmS/vlKMH/EzIYCJwA74aP3zwJ/cM5tjrHdw8DJwDQzu9M5tyy8wMwOAb6Eb/c6P2q7vYL5M3HS80zUerkq2XxLdrtiz7ewnwJ7Aic751aa2S69HE/5Ft+4YN4KRLfNL5R8k+JU7NendCj2a1U6Fet1S3LLQN9H9EW9mV2E70NoA/AycJ9z7pMk9pVpuVQm5msZka7/93eD+Xzn3Iosp2WgJJvedJ5nej932a4Fqe4YAAAL5UlEQVROVWwTUAF04qtlnQF8GvwdOS0DJsbY1oD/CdbZDPwFH+l7Knjv/4DxUdsMitjvVnHStFew/ONs50+68y2F7Yo636LOsQ34U8R7k4lTZV751ut+fxds+/tCzDdNxTsV8/UpTflX1NeqNOdlUV63NOXWNND3EX3YbnKM44enDcCZ2c6zdORfJr7P+VhGpPP/je9nZl2w7ZHZTEsu5126zzMTnzs19Rp4n8HfIANci28v+XmgDvgccD8wHJhvZlv0qu68U4EfASH86CnHA/sAq/Gdwb0fdbzaiL83xEnT+mBel8T5DJRk8y3Z7Yo93zA/XOht+Ccrp/XxeEWfb/GY2Sn47+tG4MdRiwsl36RIFfn1KR2K/VqVFkV+3ZLcMtD3Eb1ZC1wD/AcwNEjH3sCt+GZj15vZqf3cZyblUpmYj2VEOv/f38TnwQf0rLU70GkZCMmmN93nmfbPnZp69ZOZXQ4cmcSmBznn3oMtgm1rgS8759YGr18xs6Px7Xd3x0e4Z0ccexBwJ/Bl4Gf49n4f4Kt9XgDMBI40s/9wzq0Lb5ZEWtMui/mW7HbFnm/gR0P5HDDNObe8r0lOIq1pl+V8i5Weg4Bb8FH57zvnFkavkkRaRdIiDd+XvL4+pUMWyxzlYcTnkDy+bkluycP7iISccy/Q3T9Q2AvAqWb2EnA9cJmZ3e6c62ufbYnSW0hl4oCWEekoC9P8//5eML/dOdfe30QN5Gcvm3mXgfNM/+cu29Wp8m3C38zGq8aVaBoVbF8X8d7cOMc4I1j+eNT74Wr08UZNeSRYPjvivZyonpitfEthu2LPt4n4qvKPEjVEIHlQZT5b+RZnvS/gI/KO+NVDcyLfNBXnlOr3JdhH3l6fciEPUyirlYfdeZjX1y1NuTVl6zsdZ71e7yNSPNcQ8FGw/y/mc/5l4vs80GVEqnmXzv83vk+l8P7Hp3pumf7s5VLepbpdJj53aurVT865E51zlsS0JNh+HRDu3OmdOIcJvz80/IaZlQAnBS/viLPdncH84Ij0fkp3528j42w3PJgvibM8ZdnKtxS2K+p8A76GrxFYDzxqZgvCE77KLcDoiPfHBscr9nzbgpl9Hl8ttgY//OUNcdKbE/kmxSnV70u+X5/SIYvXOOVhkIfk+XVLcku+3UekwjnXCbwVvNwhTfssmDJxoMuINJSFve2/P//vcKfO/3D9rGWWgbT0ZX+5lHcpbZeJz50CP9nxfDCPNwxjuK3q+oj3huA7OgNf5TGWNcF8m6j3w9XOGuJst0/UerkqmXxLZbtizzeAXYADoqY9gmXVEe9FtkNVvgFmti/wJ/xTq5865y7v5XiFkm9SfHR9So9iv1alSzFftyS3DPR9RCrCaYyZlizJpTKx0MqIXv/fwUOdqcHLX2YzLTkm2fQms11aP3cK/GTHH4L5QXGWh5+IPhvx3idAuD3gvnG22y+YR0fG7wvm347eIPhSfyt4+cc4+80VyeRbKtsVbb4552bFi4rjh2UGeDXi/Rcj9le0+RZmZvvgm7YMAmY55y7pw/EKJd+k+Oj6lB7Ffq1Kia5bkoMG+j4iKWa2B91NenqkJYtyqUwsmDKiH//vw4HP4kf0mpfltOSEZNObwnmm93Pn0txWT1Of2uxVAcuDf/4FUcuOBTqAdmDPqGXzgm3eAMZELfsKfghdB0yNWlYLrAiWnRG17Irg/eeJahOfa1MK+ZbsdkWdbwn2NznYV4++EpRvDnw/E2uC7Zr6cbyCyDdNxTkV+/UpTXlY1NeqDOdtUVy3NOXWlIX7iH2CMviNGMvOAraN8f5++CYoDrgr23mWpvxL6vvcS/7lVRmRjv83PpjggF8U02cv2fSmsN2Afe4s2FAGWFB98y/4f+hC4BVgFL6wd8BZzrkbo7YZBjwRrNcCPAWsBMbiO3cCuAs40fm2hJHbHoCvLloFPIf/AO6BrxL9MfAFl4G2m+mWTL6luF1R51ucfU3Gd575qnNutzjrFG2+mdkq/DCka+iO1MdyjnPu46htCyLfpPjo+pQexX6typRium5JbhnI+4iIzznO13SL3OcafD9Br+ODKRvxoy7ujh896P+Aw53vVyRnDGSZmCj/kt1ntqT6/zazIcC7QBmwr3PuqV6ON5kC+ewlm94UtpvMQH3ush1VK+YJGI1vM/ku0Irv7fte4D8SbLMVcBG+mtin+Ej3R8CfgRN6Od54fMebH+BvzJcBNwOfzXZeZDrfUtyuqPMtxn4mk+DJabHnGymOIFAo+aap+CZdn9KWj0V9rcpQnhbVdUtTbk0DdR8R8Tl3MfbZCNwPLML3xdaGD84/ApwClGQ7n9KVfxHb9ev7nCj/kt1nFvMspf83MCPIi1f7eLyC+ewlm94Uthuwz51q/IiIiIiIiIiIFCh17iwiIiIiIiIiUqAU+BERERERERERKVAK/IiIiIiIiIiIFCgFfkRERERERERECpQCPyIiIiIiIiIiBUqBHxERERERERGRAqXAj4iIiIiIiIhIgVLgR6QfzGyJmTkzm5zEtiVm9rKZLTWzigwkL6PM7Hoz6zCzPbKdFhGRdFP5rvJdRIpDovJe5bkUKgV+RAbONGA3YJZzriXbiUnCJcAm4NpsJ0REJMeofBcRKQwqz6UgKfAjMgDMrBaYDbwD3J7l5CTFObcSuAWYbGZHZDs9IiK5QOW7iEhhUHkuhUyBH5GBcTKwDfBr51xHthOTgl8F8//KaipERHKHyncRkcKg8lwKlgI/IgPj9GAe8+mBmR1sZjeZ2b/N7BMzawnaFt9mZrv092BmtiBRXxVm9utg+Sn92a9z7lXgOeAgM9upv+kSESlAKt9FRAqDynMpWAr8iGSYmY0DdgXeds4tibPazcD3gHbgCWA+0ApMBZ41sy8MQFL76q+AAUdmOyEiItmk8l1EpDCoPJdCp8CPSOZNDuZPJljnHGCoc26ic+4o59zRwE7AD4Bq4BdmZplNZp+Fz+PArKZCRCT7Jgdzle8iIvltcjBXeS4FSYEfkczbM5i/Hm8F59y9zrk1Ue8559wtwD+BXfBPIXLBa8F8r6ymQkQk+1S+i4gUBpXnUtBKs50AkSIwJJh/kmglMxsGfBXYGRgElASLhgbznYBXM5HAfloVzAebmTnnXFZTIyKSPSrfRUQKg8pzKWgK/Ihk3lbB/NN4K5jZbODHJP5ODkpnolIQPo8SoI4E5yUiUuBUvouIFAaV51LQ1NRLJPPCVUJjXgjM7BjgQmATcCowBqh2zplzzoC7wqumMU2pfPfD59EBrEtDWkRE8pXKdxGRwqDyXAqaavyIZN6HwXzbOMuPC+Y/ds7dGmP52CSO2RrMa+MsH5nEPsPC5/GRqo2KSJFT+S4iUhhUnktBU40fkcx7PpjH6+xtm2C+PHqBme1Ccp2yvRfMd46xz3pg7yT2GRY+j+cTriUiUvhUvouIFAaV51LQFPgRybxHg/l+cZa/EcxPNbPy8JtmNgS4jTg188xsHzN7w8zeiLH4b8H8DDP7bMQ22wT7jPlkoZd9hoXP49EE64iIFAOV7yIihUHluRQ0BX5EMsw59w7wEjDGzEbHWOVaYC1+hIC3zWyemT0ILMIX+PfG2XU1MD6YojUDLwCjgFfN7AEzewR4GxiW5D7DDgYccH+CdURECp7KdxGRwqDyXAqdAj8iA2NuMJ8avcA5txhflfN3+A7hvgbsAvwCH61f29+DOeda8QX+z/Gd0B2Cr0Z6G/D5ZPYJYGYTgrT+zTn3ZjL7EBEpMCrfRUQKg8pzKVimvp5EMs/MaoCl+KEUxznnOrKcpKSY2dXA2cDXnHMPZjs9IiLZpvJdRKQwqDyXQqYaPyIDwDm3AbgIGE2Mpwj5IOhk7jRggS4iIiKeyncRkcKg8lwKmWr8iAwQMyvBt+PdCtjJOdeS5ST1i5ldB0wH9nbO/Tvb6RERyRUq30VECoPKcylUCvyIiIiIiIiIiBQoNfUSERERERERESlQCvyIiIiIiIiIiBQoBX5ERERERERERAqUAj8iIiIiIiIiIgVKgR8RERERERERkQKlwI+IiIiIiIiISIH6/yNwkH5ueye4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "config['reps'] = 5000\n",
    "\n",
    "config['relax_delay'] = 1\n",
    "\n",
    "ground=NoPulse(soccfg, config)\n",
    "avgGi, avgGq = ground.acquire(soc, load_pulses=True,progress=True, debug=False)\n",
    "gi,gq = ground.getAllPulses()\n",
    "\n",
    "excited=PiPulse(soccfg, config)\n",
    "avgEi, avgEq = excited.acquire(soc, load_pulses=True,progress=True, debug=False)\n",
    "ei,eq = excited.getAllPulses()\n",
    "\n",
    "fid, threshold, angle = hist(data=[gi[0], gq[0], ei[0], eq[0]],  plot=True, ran=4)\n",
    "\n",
    "e_complex = ei+1j*eq\n",
    "e_mean = np.mean(e_complex)\n",
    "e_mag = np.abs(e_mean)\n",
    "print(\"mean signal is %s (magnitude %f)\" % (str(e_mean), e_mag))\n",
    "e_rotated = e_complex*np.exp(-1j*np.angle(e_mean))\n",
    "e_rmsmag = np.std(np.real(e_rotated)) # noise in the radial direction\n",
    "e_rmsrot = np.std(np.imag(e_rotated)) # noise in the azimuth direction\n",
    "print(\"amplitude noise: %f ADU, %f as a fraction of the signal\" % (e_rmsmag, e_rmsmag/e_mag))\n",
    "print(\"phase noise: %f ADU, %f radians\" % (e_rmsrot, e_rmsrot/e_mag))\n",
    "\n",
    "# plt.savefig(f\"images/Plots_{date}/SS_separated_shield{shield}_{source}_qubit{qubit}_TWPA{TWPA}_shots{averages}_ro{ro_pulselength}_gain{pipulsegain}_L{qu_pulselength}_{pulsestyle}.pdf\", dpi=350)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a2efb9c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3beb2a95048541fcb64172eaa9152508",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xffff5858a7c0>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# there is some phase drift, which we have seen before (it's in the RFSoC, not the RF board)\n",
    "# we suspect some temperature dependence somewhere, probably the ADC\n",
    "# we don't think tens of milliradians is significant compared to real qubit phase shifts, but we can revisit this\n",
    "\n",
    "phases = []\n",
    "for i in tqdm(range(1000)):\n",
    "    avgEi, avgEq = excited.acquire(soc, load_pulses=True,progress=False, debug=False)\n",
    "    avgE = avgEi + 1j*avgEq\n",
    "    phases.append(np.angle(avgE[0]))\n",
    "#     print(avgE[0], np.angle(avgE[0]))\n",
    "plt.plot(phases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b79246c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# ground=NoPulse(soccfg, config)\n",
    "# avgGi, avgGq = ground.acquire(soc, load_pulses=True,progress=True, debug=False)\n",
    "# gi,gq = ground.getAllPulses()\n",
    "\n",
    "config['reps'] = 1000000\n",
    "\n",
    "config['relax_delay'] = 1\n",
    "\n",
    "excited=PiPulse(soccfg, config)\n",
    "# ground=NoPulse(soccfg, config)\n",
    "while True:\n",
    "    avgEi, avgEq = excited.acquire(soc, load_pulses=True,progress=True, debug=False)\n",
    "# ei,eq = excited.getAllPulses()\n",
    "\n",
    "# np.savez(f\"data/{date}/SS_separated_shield{shield}_{source}_qubit{qubit}_TWPA{TWPA}_shots{averages}_ro{ro_pulselength}_gain{pipulsegain}_L{qu_pulselength}_{pulsestyle}\",Iground=gi,Qground=gq,Iexcited=ei,Qexcited=eq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6ac0279e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d338e5a1fdd14d3693f40aa23fe81e64",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class PeriodicPulse(AveragerProgram):\n",
    "    def initialize(self):\n",
    "        cfg=self.cfg\n",
    "        \n",
    "        self.declare_gen(ch=cfg[\"res_ch\"], nqz=1) #Readout\n",
    "        self.declare_gen(ch=cfg[\"qubit_ch\"], nqz=1) #Qubit\n",
    "        self.declare_readout(ch=cfg[\"readout_ch\"], length=cfg[\"readout_length\"],\n",
    "                             freq=cfg[\"f_res\"], gen_ch=cfg[\"res_ch\"])\n",
    "\n",
    "        self.q_rp=self.ch_page(self.cfg[\"qubit_ch\"])     # get register page for qubit_ch\n",
    "        self.r_freq=self.sreg(cfg[\"qubit_ch\"], \"freq\")   # get frequency register for qubit_ch    \n",
    "        \n",
    "        f_res=self.freq2reg(cfg[\"f_res\"], gen_ch=cfg[\"res_ch\"], ro_ch=0) # conver f_res to dac register value\n",
    "\n",
    "        self.f_ge =self.freq2reg(cfg[\"f_ge\"], gen_ch=cfg[\"qubit_ch\"])  # get start/step frequencies\n",
    "\n",
    "        # add qubit and readout pulses to respective channels\n",
    "        self.set_pulse_registers(ch=cfg[\"qubit_ch\"], style=\"const\", freq=self.f_ge, phase=0, gain=cfg[\"pi_gain\"], \n",
    "                                 length=cfg[\"pulse_length\"], mode='periodic')\n",
    "        self.set_pulse_registers(ch=cfg[\"res_ch\"], style=\"const\", freq=f_res, phase=cfg[\"res_phase\"], gain=cfg[\"res_gain\"], \n",
    "                                 length=cfg[\"readout_length\"], mode='periodic')\n",
    "        \n",
    "        self.sync_all(self.us2cycles(500))\n",
    "    \n",
    "    def body(self):\n",
    "        self.pulse(ch=self.cfg[\"qubit_ch\"])  #play probe pulse\n",
    "        self.sync_all(self.us2cycles(0.05)) # align channels and wait 50ns\n",
    "\n",
    "        #trigger measurement, play measurement pulse, wait for qubit to relax\n",
    "        self.measure(pulse_ch=self.cfg[\"res_ch\"], \n",
    "             adcs=[self.cfg[\"readout_ch\"]],\n",
    "             adc_trig_offset=self.cfg[\"adc_trig_offset\"],\n",
    "             wait=True,\n",
    "             syncdelay=self.us2cycles(self.cfg[\"relax_delay\"]))\n",
    "        \n",
    "    def getAllPulses(self):\n",
    "        allI = self.di_buf[0].reshape((self.cfg[\"expts\"],self.cfg[\"reps\"]))/self.cfg['readout_length']\n",
    "        allQ = self.dq_buf[0].reshape((self.cfg[\"expts\"],self.cfg[\"reps\"]))/self.cfg['readout_length']\n",
    "        return allI,allQ\n",
    "    \n",
    "    \n",
    "config['reps'] = 100\n",
    "\n",
    "config['relax_delay'] = 1\n",
    "\n",
    "excited=PeriodicPulse(soccfg, config)\n",
    "# ground=NoPulse(soccfg, config)\n",
    "avgEi, avgEq = excited.acquire(soc, load_pulses=True,progress=True, debug=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28b166a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# turn off periodic outputs\n",
    "soc.reset_gens()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b6e074d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5408ff9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc8904a4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca732c12",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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