qiskit/advanced/ignis/1b_calibrating_a_two_qubit_gate.ipynb

{
 "cells": [
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![qiskit_header.png](attachment:qiskit_header.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Calibrating a two qubit gate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.optimize import curve_fit\n",
    "\n",
    "from qiskit import IBMQ\n",
    "import qiskit.pulse as pulse\n",
    "import qiskit.pulse.pulse_lib as pulse_lib\n",
    "from qiskit.compiler import assemble\n",
    "from qiskit.qobj.utils import MeasLevel, MeasReturnType"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous [tutorial](1a_calibrating_a_qubit.ipynb) we demonstrated how to control a single-qubit device with pulse and calibrate a single qubit gate. In this tutorial we will calibrate a two-qubit gate with a cross-resonance pulse using `ibmq_almaden`, a 20 qubit client-only system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "IBMQ.load_account()\n",
    "ibmq_provider = IBMQ.get_provider(\n",
    "    hub='provide_your_hub_name_here',\n",
    "    group='provide_your_group_name_here',\n",
    "    project='provide_your_project_name_here'\n",
    ")\n",
    "backend = ibmq_provider.get_backend('ibmq_almaden')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = backend.configuration()\n",
    "defaults = backend.defaults()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Measuring CR Rabi oscillation\n",
    "\n",
    "In this section, we measure cross resonance Rabi oscillation on qubit0 and qubit1. The basic idea of a cross resonance gate is to apply a microwave pulse to the control qubit at the frequency of the target qubit resonance frequency. For more information, see this [paper](https://arxiv.org/abs/1603.04821). As the frequency of the drive channel used in previous experiments is adjusted to be at the resonance frequency of corresponding qubit, a cross resonance pulse must be applied from another channel so that a different frequency may be used for the pulse. This is why we use a control channel (`U channel`) in this experiment.  \n",
    "It should be noted that the role of control channels is not only applying CR pulses, but any possible Hamiltonian term as described in `config.hamiltonian`, and consequently the effect of `ControlChannel` stimulus strongly depends on the design of the specific quantum computing backend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# qubit to use for exeperiment\n",
    "control_qubit = 1\n",
    "target_qubit = 0\n",
    "control_channel_index = 1\n",
    "\n",
    "# exp configuration\n",
    "exps = 30\n",
    "shots = 512\n",
    "\n",
    "# Rabi pulse\n",
    "cr_amps = np.linspace(0, 0.2, exps)\n",
    "cr_samples = 200\n",
    "cr_sigma = 4\n",
    "cr_rise_fall = 16\n",
    "\n",
    "# scaling factor for data returned by backend\n",
    "# note: You may have to adjust this for the backend you use\n",
    "scale_factor= 1e-14"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can check if your control qubit and target qubit are connected by bus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[control_qubit, target_qubit] in config.coupling_map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usually we use a qubit with higher frequency as a control."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "defaults.qubit_freq_est[control_qubit] > defaults.qubit_freq_est[target_qubit]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the above qubit configuration, control channel 0 is available to realize a CR pulse. For other configurations, you can refer to the `InstructionScheduleMap` which may be found in `defaults.circuit_instruction_map` to find the proper control channel index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[UchannelLO(q=0, scale=(1+0j))]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config.u_channel_lo[control_channel_index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this experiment, we use two-pulse echoed CR sequence to eliminate unwanted interactions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create schedule\n",
    "schedules = []\n",
    "for ii, cr_amp in enumerate(cr_amps):\n",
    "    # drive pulse\n",
    "    cr_rabi_pulse_p = pulse_lib.gaussian_square(duration=cr_samples,\n",
    "                                                amp=cr_amp,\n",
    "                                                sigma=cr_sigma,\n",
    "                                                risefall=cr_rise_fall,\n",
    "                                                name='cr_rabi_pulse_p%d' % ii)\n",
    "    cr_rabi_pulse_m = pulse_lib.gaussian_square(duration=cr_samples,\n",
    "                                                amp=-cr_amp,\n",
    "                                                sigma=cr_sigma,\n",
    "                                                risefall=cr_rise_fall,\n",
    "                                                name='cr_rabi_pulse_m%d' % ii)\n",
    "    \n",
    "    control_channel = pulse.ControlChannel(control_channel_index)\n",
    "    \n",
    "    # We get the state preparation and measurement pulses we need from the\n",
    "    # defaults `circuit_instruction_map`\n",
    "    pi_pulse_q0 = defaults.circuit_instruction_map.get('u3', (control_qubit,), np.pi, 0, np.pi)\n",
    "    measure = defaults.circuit_instruction_map.get('measure', config.meas_map[0])\n",
    "    \n",
    "    # add commands to schedule\n",
    "    schedule = pulse.Schedule(name='CR Rabi Experiment at drive amp = %s' % cr_amp)\n",
    "    \n",
    "    schedule |= cr_rabi_pulse_p(control_channel)\n",
    "    schedule |= pi_pulse_q0 << schedule.duration\n",
    "    schedule |= cr_rabi_pulse_m(control_channel) << schedule.duration\n",
    "    schedule |= measure << schedule.duration\n",
    " \n",
    "    schedules.append(schedule)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x864 with 2 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedules[1].draw(channels_to_plot=[pulse.DriveChannel(control_qubit),\n",
    "                                    pulse.DriveChannel(target_qubit),\n",
    "                                    control_channel,\n",
    "                                    pulse.MeasureChannel(control_qubit),\n",
    "                                    pulse.MeasureChannel(target_qubit)],\n",
    "                   scaling=10.0, label=False, plot_range=(0, 700))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "cr_rabi_qobj = assemble(schedules, backend, meas_level=MeasLevel.KERNELED, \n",
    "                        meas_return=MeasReturnType.AVERAGE, shots=shots)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "job = backend.run(cr_rabi_qobj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5df01b0e03329400119bf8c8'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job.job_id()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<JobStatus.VALIDATING: 'job is being validated'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job.status()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "cr_rabi_result = job.result(timeout=3600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_qubit_rabi_data = np.ones(exps, dtype=np.complex_)\n",
    "control_qubit_rabi_data = np.ones(exps, dtype=np.complex_)\n",
    "\n",
    "for i in range(exps):\n",
    "    target_qubit_rabi_data[i] = cr_rabi_result.get_memory(i)[target_qubit]\n",
    "    control_qubit_rabi_data[i] = cr_rabi_result.get_memory(i)[control_qubit]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected below we can observe the oscillation of the measured signal of only the target qubit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# auto-phase the output measurement signal\n",
    "\n",
    "def get_amplitude(vec):\n",
    "    i_signal = np.imag(vec)\n",
    "    r_signal = np.real(vec)\n",
    "\n",
    "    mvec = [np.mean(r_signal), np.mean(i_signal)]\n",
    "\n",
    "    src_mat = np.vstack((r_signal - mvec[0], i_signal - mvec[1])).T\n",
    "    (_, _, v_mat) = np.linalg.svd(src_mat)\n",
    "\n",
    "    dvec = v_mat[0, 0:2]\n",
    "\n",
    "    if dvec.dot(mvec) < 0:\n",
    "        dvec = -dvec\n",
    "\n",
    "    return src_mat.dot(dvec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.027191350692821387\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "target_rabi_amp_data = get_amplitude(target_qubit_rabi_data)*scale_factor\n",
    "control_rabi_amp_data = get_amplitude(control_qubit_rabi_data)*scale_factor\n",
    "\n",
    "fit_func = lambda x,A,B,T,phi: (A*np.cos(2*np.pi*x/T+phi)+B)\n",
    "\n",
    "#Fit the data\n",
    "fitparams, conv = curve_fit(fit_func, cr_amps, target_rabi_amp_data, [3.0,0.0,0.1,0])\n",
    "\n",
    "#get the pi amplitude\n",
    "cr_pi_2_amp = (np.pi-fitparams[3])*fitparams[2]/4/np.pi\n",
    "\n",
    "plt.plot(cr_amps, fit_func(cr_amps, *fitparams), color='red')\n",
    "plt.axvline(cr_pi_2_amp, color='black', linestyle='dashed')\n",
    "plt.scatter(cr_amps, target_rabi_amp_data, label='target qubit')\n",
    "plt.scatter(cr_amps, control_rabi_amp_data, label='control qubit')\n",
    "plt.xlim(0, 0.2)\n",
    "plt.ylim(-5, 5)\n",
    "plt.legend()\n",
    "plt.xlabel('CR pulse amplitude, a.u.', fontsize=20)\n",
    "plt.ylabel('Signal, a.u.', fontsize=20)\n",
    "plt.title('CR Rabi oscillation', fontsize=20)\n",
    "\n",
    "print(cr_pi_2_amp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the fit we obtain the CR Rabi oscillation frequency. We may use this to extract the amplitude of a CR π/2 pulse, which is the foundation of the `cx`(CNOT) gate in our systems. To see the pulse schedule for a `cx` gate look in the `defaults.circuit_instruction_map`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4/fXXw/wq2t7cuXPZvXs306dPZ9GiRRQUFDBnzhwmT57MjTfeyJ49e8jNzWXs2LGMHTuWDz/8EID169dzySWXcM011zB06FDuvfdeXnrpJc4//3xGjRrFrl27ADh8+DBXXXUV48aNY9y4cWzatAmAiooKZs+ezahRoxg9enRI6LjvvvvIzs5m/PjxFBcXA/DGG29wwQUXMGbMGPLy8oLlBQUF3HzzzUycOJHMzEyefPLJ4H4efPBBhg0bRn5+PjNnzgx+69+1axdTpkwhJyeH3NxcduzY0arfWVpaWvDfVVVVGNPo1CiMGzeOHj16NLrsrrvuoqCgoMltO5q2bFsg8PcBgZDWp0+fYPlbb73FKaecwumnn+74OdrKd21bIPB7O/fcc8nLy+OLL74Ilv/gBz/gjDPOCHvdw0ltS+vblkGDBnHOOedgWc7jw/Lly7niiisc7ycaxES6AvXdfffd7Ny5k8LCwkaXT58+PdiInWzBggUNviEVFxfTv39/AGJiYkhNTeX48eNNHjwi6V//+hcPP/wwa9asISMjg2PHjgWXHTx4kDVr1rBjxw6mT5/O5Zdf3uL9Hjp0KNiN2q9fPw4fPtzmdQ+3RYsW8c4771BYWEhGRgYFBQVs3bqVNWvWkJiYSFVVFatWrSIhIYGioiJmzpzJhg0bAPjss8/YsmULPXr0IDMzk+uvv57333+f3//+9/zhD3/gt7/9LXfeeSdz584lJyeHvXv3MmXKFLZu3cpDDz1Eeno6H3/8MUDwFGllZSWjR4/m/vvvZ8GCBSxdupT58+eTk5PDunXrMMawdOlSHnvssWBv6M6dO3nzzTcpLy9n2LBh3HTTTWzbto3XXnuNTZs24fV6ycnJYfjw4QDceuut/O53v2PIkCF89NFH3H777RQWFrJq1Sq2bNnCL3/5y2Z/b4sXL2bRokXU1tY2+XlqyqpVq+jfvz9Dhw5t1XbRrC3bljvuuINrr72W5557jqqqKpYtWwYEguvixYt54YUXouYUn5O2ZcuWLbzyyiuN/o12Bmpbvlvb0lK33HILlmUxdepU5s+fH/LF7KuvvuLLL7/sND24URemmrNixYoWr9tYL1S0fstet24dU6dOJSMjAyAk8E2ePBnLsjjrrLOC5527ury8PBITEwHweDz8/Oc/Z9u2bViWRVFRUXC9rKysYJg89dRTyc3NBeDss89m3bp1ALz77rshY4rKysooLy9n7dq1/OlPfwqWd+/eHYC4uDgmTZoEwPDhw1mzZg0QGCdz3XXXcfDgQWpraxk0aFBw20svvZT4+Hji4+Pp1asXxcXFbNq0ifz8/ODrOLHPiooKPvzww2CPIkBtbS0A+fn55Ofnt+h3dMstt3DLLbfw8ssvs3DhQp566qkWbVdVVcXDDz/cIXsxnWhN2/K3v/2NH//4x9x00018+umn3HHHHbz99ts89thjzJo1i+Tk5DDWtHWctC0bN25k8uTJJCUlAYHPXWentqVtPPPMMwwYMIDy8nKuvvpqXnzxxZDnXbFiBZdffjkuV6N3Z+lwOlyYas23x759+7J//3769euH1+ulvLycbt26tVdVW8W27SaDXnx8fMh69blcLvx+f/D/J9+nqnfv3hw4cIB+/fpx4MCBDjHAsSVOPlgtWrSI3r17s3nzZvx+f7BhgkDjdIJlWcH/W5YVHBPh9/t57733gg3PCU29J7GxscFyl8uFz+cDYN68ecydO5f8/HzWr1/Pr3/96+A2J7+HJ7Zp6pSz3+8nPT2dzZs3t+yXQeDUwJtvvgnQYLvp06dz2223tXhfu3btYs+ePWRnZwOBhjwnJ4f169fTt2/fFu+no2lN2/Lyyy/z3HPPAYGDqtvt5tixY/z9739n9erVPPjgg5SVlWFZFvHx8cycObNdXkNjnLQtEL1fQMNFbUuob2tbvs2AAQMASE1N5YorruCTTz5pEKZOnHrsDKJuzFRKSgqVlZVNLl+xYgWFhYUNHo0Ngrz44ov5y1/+AsDq1avJycmJ2oZhwoQJrFy5kqNHjwKEdMU3p0+fPhw+fJijR4/idrtDTmPk5eUFT0EsW7asTb95RIuysjL69u2LZVm8+OKLwQaopS666CIWL14c/P8//vEPAHJzc0PKm7sSsrS0NHha+YUXXmj2eceOHcvq1aupqamhoqIi2GClpaUxePBgVq5cCQQa3m3btn3rvu6///7goE4g5Bt0YWEhp512WrP1OeGHP/whe/bs4fPPP+fzzz9nwIABbNy4scMHqbZsW/r37x883VNUVITb7aZnz56sWLGCDRs2sGHDBm644QbmzJkT0SAFztqW8847j9dff53q6mrKy8tZvXp1uKoZldS2NGxbWsLr9QavAPZ4PBQWFpKZmRlcvnPnTkpKSoJf2DqDqAtT3bt3Jysri0suucTxINErrriCkpISxo8fz9NPP93kFU3RIDMzk7vuuouJEyeSnZ3N/PnzW7xtbGwsv/jFLxg/fjzTpk3jzDPPDC6bN28ea9eu5ZxzzmHt2rXMmzcvHNWPqNmzZ7Ns2TLGjx9PUVFRq0+xPPLII2zZsoXRo0czYsQI/vjHPwKBMTYlJSWMHDmS7OzsYNd9UxYsWMA111xDbm5u8JTKtxk5ciR5eXlkZ2dz1VVXMWLEiODA8aVLl/Lss8+SnZ1NVlYWq1atAgJjmR544IFm97148WKysrLIzs5m0aJFwVN8+/fvZ+rUqcH1Zs6cyYQJE9i5cydDhgzh2WefbXbfHVVbti333HMPL730Epdeeilz587lkUceidovak7aluHDhzNt2jTGjBnD1VdfTU5OTnDZX//6V4YMGcLmzZuZNm1ap7kq62RqWxr65JNPGDJkCCtXrmTu3LlkZWUFl50IR263mylTpjB69GjGjBlD//79ueGGG4LrLV++nOnTp0ftZ+a7MJG6us0YY+vO7l2H7uzeuIqKClJSUqiqquLiiy/miSee6FQDfCMhKSkJtS1dh9qWxqltaXtJSUnYtt1oAuxwY6ZEOpNbb72V7du343a7mTFjhho7EWkTalval8KUSAR15tNqIhI5alvaV9SNmRIRERHpSBSmRERERBxQmBIRERFxQGFKRERExIGITY2QkJDgc7vdXS7MJSQkhMxQ3lV01dct7S8+Ph632x3parS7rvoZ66qvW9pfQkKCv7q6utH732ieqXamuWBEwkvzTHUtalukvXzbPFNdrmdIREREpC0pTImIiIg4oDAlIiIi4oDClIiIiIgDClMiIiIiDkRdmCotLeX5559vk3253W7mzJnD+PHjueyyy9i7d2+b7Lc9zZ49m1dffbVV2xQUFPD4448DsHLlSrKyskhOTubTTz8NRxXbxZNPPsnw4cP56U9/Gumq0KtXr0hXoVlPPfUUo0aNIjs7m4suuojt27c3ut7NN9/MoEGDGDlyZKPLH3/8cZKSkjhy5Eg4qysi0qFFXZgqKytrszC1fPly0tPTWbduHbNmzeKhhx5qk/12JJmZmfz5z39m3Lhxka6KI0uWLOHVV19l6dKlIeVerzdCNYpuV155JR9//DGbN2/mjjvu4O677250vWuvvZbXXnut0WVff/01a9euZeDAgeGsqohIhxd1YWrhwoXs2bOHH/3oR/zmN79xtK+3336badOmATBp0iQ2btxIpObVaolly5YxevRosrOzmTVrVrD8gw8+4MILLyQzM7PJXqqFCxdy7rnnkpeXxxdffBEs/8EPfsAZZ5wR9rqH09y5c9m9ezfTp09n0aJFFBQUMGfOHCZPnsyNN97Inj17yM3NZezYsYwdO5YPP/wQgPXr13PJJZdwzTXXMHToUO69915eeuklzj//fEaNGsWuXbsAOHz4MFdddRXjxo1j3LhxbNq0CYCKigpmz57NqFGjGD16dEjouO+++8jOzmb8+PEUFxcD8MYbb3DBBRcwZswY8vLyguUFBQXcfPPNTJw4kczMTJ588sngfh588EGGDRtGfn4+M2fODPYo7tq1iylTppCTk0Nubi47duxo1e8sLS0t+O+qqiqMaXRqFMaNG0ePHj0aXXbXXXdRUFDQ5LYiIhIQE+kK1Hf33Xezc+dOCgsLG10+ffp0KioqGpQvWLCgQe9LcXEx/fv3ByAmJobU1FSOHz/e5MEjkv71r3/x8MMPs2bNGjIyMjh27Fhw2cGDB1mzZg07duxg+vTpXH755SHbbtmyhVdeeYVNmzbh9XrJyclh+PDh7f0SwmbRokW88847FBYWkpGRQUFBAVu3bmXNmjUkJiZSVVXFqlWrSEhIoKioiJkzZ7JhwwYAPvvsM7Zs2UKPHj3IzMzk+uuv5/333+f3v/89f/jDH/jtb3/LnXfeydy5c8nJyWHv3r1MmTKFrVu38tBDD5Gens7HH38MwPHjxwGorKxk9OjR3H///SxYsIClS5cyf/58cnJyWLduHcYYli5dymOPPRbsDd25cydvvvkm5eXlDBs2jJtuuolt27bx2muvNfq+3Xrrrfzud79jyJAhfPTRR9x+++0UFhayatUqtmzZwi9/+ctmf2+LFy9m0aJF1NbWNvl5asqqVavo378/Q4cObdV2IiJdUdSFqeasWLGixes21gsVrd+y161bx9SpU8nIyAAICXyTJ0/GsizOOussDh061GDbjRs3MnnyZJKSkgDIy8trn0pHUF5eHomJiQB4PB5+/vOfs23bNizLoqioKLheVlYW/fr1A+DUU08lNzcXgLPPPpt169YB8O6774aMKSorK6O8vJy1a9fypz/9KVjevXt3AOLi4pg0aRIAw4cPZ82aNQDs27eP6667joMHD1JbW8ugQYOC21566aXEx8cTHx9Pr169KC4uZtOmTeTn5wdfx4l9VlRU8OGHHzJjxozg9rW1tQDk5+eTn5/fot/RLbfcwi233MLLL7/MwoULeeqpp1q0XVVVFQ8//DCvv/56i9YXEenqOlyYak3PVN++fdm/fz/9+vXD6/VSXl5Ot27d2quqrWLbdpNBLz4+PmS9xkRrSAyX5OTk4L8XLVpE79692bx5M36/Pxh6IBB8TrAsK/h/y7KC4638fj/vvfdeMNSc0NR7EhsbGyx3uVz4fD4A5s2bx9y5c8nPz2f9+vX8+te/Dm5z8nt4Ypum3ku/3096ejqbN29u2S+DwGnHN998E6DBdtOnT+e2225r8b527drFnj17yM7OBgIhMScnh/Xr19O3b98W70dEpKuIujFTKSkpVFZWNrl8xYoVFBYWNng0NsD64osv5i9/+QsAq1evJicnJ2pDx4QJE1i5ciVHjx4FCDnN15zzzjuP119/nerqasrLy1m9enW4qhmVysrK6Nu3L5Zl8eKLLwbDTUtddNFFLF68OPj/f/zjHwDk5uaGlJ84zdeU0tLS4GnlF154odnnHTt2LKtXr6ampoaKiopgGEpLS2Pw4MGsXLkSCIS6bdu2feu+7r//fjZv3hwMUif3zhUWFnLaaac1W58TfvjDH7Jnzx4+//xzPv/8cwYMGMDGjRsVpEREmhB1Yap79+5kZWVxySWXOB6AfsUVV1BSUsL48eN5+umnm7yiKRpkZmZy1113MXHiRLKzs5k/f36Ltx0+fDjTpk1jzJgxXH311eTk5ASX/fWvf2XIkCFs3ryZadOmMWXKlHBUP6Jmz57NsmXLGD9+PEVFRSG9Vi3xyCOPsGXLFkaPHs2IESP44x//CATG75WUlDBy5Eiys7ODpwWbsmDBAq655hpyc3ODp2u/zciRI8nLyyM7O5urrrqKESNGBAeOL126lGeffZbs7GyysrJYtWoVEBjL9MADDzS778WLF5OVlUV2djaLFi0KnuLbv38/U6dODa43c+ZMJkyYwM6dOxkyZAjPPvtss/sWEZFQJlJXtxljbN3ZvevQnd0bV1FRQUpKClVVVVx88cU88cQTnerigUhISkpCbUvXobZF2ktSUhK2bTd6eqvDjZkS6UxuvfVWtm/fjtvtZsaMGQpSIiIdkMKUSATptJqISMcXdWOmRERERDoShSkRERERBxSmRERERBxQmBIRERFxoF2nRjDGzAZmAyQnJ2d19BvwioiISNewdetW27btRjuhIjbP1IgRI+x169+PyHOLiIiItFnD4VIAACAASURBVEZaasqntm2PbGyZTvOJiIiIOKAwJSIiIuKAwpSIiIiIAwpTIiIiIg4oTImIiIg4oDAlIiIi4oDClIiIiIgDClMiIiIiDihMiYiIiDigMCUiIiLigMKUiIiIiAMKUyIiIiIOKEyJiIiIOKAwJSIiIuKAwpSIiIiIAwpTIiIiIg4oTImIiIg4oDAlIiIi4oDClIiIiIgDClMiIiIiDihMiYiIiDigMCUiIiLigMKUiIiIiAMKUyIiIiIOKEyJiIiIOKAwJSIiIuKAwpSIiIiIAwpTIiIiIg4oTImIiIg4oDAlIiIi4oDClIiIiIgDClMiIiIiDihMiYiIiDigMCUiIiLigMKUiIiIiAMKUyIiIiIOKEyJiIiIOKAwJSIiIuKAwpSIiIiIAwpTIiIiIg4oTImIiIg4oDAlIiIi4oDClIiIiIgDClMiIiIiDihMiYiIiDigMCUiIiLigMKUiIiIiAMKUyIiIiIOKEyJiIiIOKAwJSIiIuKAwpSIiIiIAwpTIiIiIg4oTImIiIg4oDAlIiIi4oDClIiIiIgDClMiIiIiDihMiYiIiDigMCUiIiLigMKUiIiIiAMKUyIiIiIOKEyJiIiIOKAwJSIiIuKAwpSIiIiIAwpTIiIiIg4oTImIiIg4oDAlIiIi4oDClIiIiIgDClMiIiIiDihMiYiIiDigMCUiIiLigMKUiIiIiAMKUyIiIiIOKEyJiIiIOKAwJSIiIuKAwpSIiIiIAwpTIiIiIg4oTImIiIg4oDAlIiIi4oDClIiIiIgDClMiIiIiDihMiYiIiDigMCUiIiLigMKUiIiIiAMKUyIiIiIOKEyJiIiIOKAwJSIiIuKAwpSIiIiIAwpTIiIiIg4oTImIiIg4oDAlIiIi4oDClIiIiIgDClMiIiIiDrRrmDLGzDbGfGKM+eTIkSPt+dQiIiIiYdGuYcq27SW2bY+0bXtkRkZGez61iIiISFjoNJ+IiIiIAwpTIiIiIg4oTImIiIg4oDAlIiIi4oDClIiIiIgDClMiIiIiDihMiYiIiDigMCUiIiLigMKUiIiIiAMKUyIiIiIOKEyJiIiIOKAwJSIiIuKAwpSIiIiIAwpTIiIiIg4oTImIiIg4oDAlIiIi4oDClIiIiIgDClMiIiIiDihMiYiIiDigMCUiIiLigMKUiIiIiAMKUyIiIiIOKEyJiIiIOKAwJSIiIuKAwpSIiIiIAwpTIiIiIg4oTImIiIg4oDAlIiIi4oDClIiIiIgDClMiIiIiDihMiYiIiDigMCUiIiLigMKUiIiIiAMKUyIiIiIOKEyJiIiIOKAwJSIiIuKAwpSIiIiIAwpTIiIiIg4oTImIiIg4oDAlIiIi4oDClIiIiIgDClMiIiIiDihMiYiIiDigMCUiIiLigMKUiIiIiAMKUyIiIiIOKEyJiIiIOKAwJSIiIuKAwpSIiIiIAwpTIiIiIg4oTImIiIg4oDAlIiIi4oDClIiIiIgDClMiIiIiDihMiYiIiDigMCUiIiLigMKUiIiIiAMKUyIiIiIOKEyJiIiIOKAwJSIiIuKAwpSIiIiIAwpTIiIiIg4oTImIiIg4oDAlIiIi4oDClIiIiIgDClMiIiIiDihMiYiIiDigMCUiIiLigMKUiIiIiAMKUyIiIiIOKEyJiIiIOKAwJSIiIuKAwpSIiIiIAwpTIiIiIg4oTImIiIg4oDAlIiIi4oDClIiIiIgDClMiIiIiDihMiYiIiDigMCUiIiLigMKUiIiIiAMKUyIiIiIOxES6AiIiIiL1+fzg8bZ8fWMgPjZ89fk2jsKUMeZL4Evbtie0SW1ERESky7Nt2LnPorSy5SfQYlw2Q/r76ZZsh7FmTTx3OHZqjMkGfg1kAzawEZhv2/bfw/F8IiIi0jFVueHgcYuTI5DHC8cqLHw+SIhtWTg6Wm4RfxiOloeunxhn079HeANWm4cpY8wY4D1gH/DLuuJbgfeNMTm2bX/W1s8pIiIiHdPxCot/H7BwWWBOKrcs6JlqY0yTm4awjJ8jpQZKv9mgxmPISLPpleYlNowDm8Kx698BtcAFtm3vAzDGLAe2A48Cl5xY8Y7bb+Ppp58O2XjixEtZ8corYaiWiIiIREp1LZRWNkxGR8oMsS7ole6s9ygxHhLjQ/dR67GpcBv2HLJISQxd5rKcP+cJLQpTxpiBBILQRALBcR1weyPrDQFGAc+cCFIAtm3vM8asBK4zxvS1bfsgwI4dOwBwuVzccsvPSE1LZciQ0x2+JBEREYk2h0oCPVCNxZfUhPCchouNAU8l7DkcOvbKtiE1CVISvSTGOX+eZsOUMaYbsB4YCCwG/gWMB94FEuutPqru56Z6+zgVmEwgiGUBb5xY1rNnT0pLSykqKlKPlIiISCdQUQ1++5teKNuGY+WBHqgeqe03QNwY6NOt4fN5fXC80lBcYtEjxa63jU1q/XTTjJb0TN0FDAZusG17aV3Zk8aYx4Hb6q3bv+7nvnrlDwInLlgccPKCs846i8umTuXO//gP3nrrLSZOnNjiyouIiEh0qayB7XtjqPGElnt84euBaq0YF/j8hr2HLfYeDl0WHwtnDfS2KlC15JrDqUAx8Kd65QsbWTep7qf7RIExJpZAr1RhvXWCbrzxJnr27Mnrf/tbC6ojIiIikebxwv5jhn1HrZDHV4ddlFVDcrxNSsI3jx4pNknxka71N3qn+UPql5JgkxJvU1YJe4+42HfU4nBpy0a/t6Rn6lTgY9u2fScX2rZ9wBhTUm/dqrqfJ/+6MgicDjxYbx1m3XgjHq/N+/+ookfv77P1f7/k9Q/KG6+FCR3lDzR63rVlC9tX8ZFKvD5/pKsh0u4yuieSnOgKKTMmcJVOfaZuWX223fjH2eM1+Btb0MRn3+dvfJHdRHlT9Wms7n6/TfGRisafWKQTK600NHZ48/tNg8HgJ5h6HyzbjqIDNlDtNlhWoE5nnZrKZeenYDWTqVp6NV9Tr7T+7vfX/Tz5VF4p4AcG1f3/TmPMbICfXn99gx3O+FG/FlZJREREJPJaEqZ2AWcYY1wn904ZY/oB6fXW/bju51jgjwC2bVcZYzYAFxIIZefbtn1wxIgR9rr171NSXssTT63k4ftu5PqfPcD3z57I4O+lEhtT7+tfa4NrIz1ZGJpNl058ua+CtJRYYlyhde+RHseE0f0aviaRTuyTfx7lzQ/207tHQki5y2XRLa3xvn7bbvgBNcZu+Fkm0EMUY9Hgg25ovPeoLXh9fo6WuKn/RfrgkWp+OKQb+RMGNvjWLdKZNdWr5PM1LLe/Zf3GGGMaPWYbY7DCeTBvQlpqSpPLWhKm/grMB64Dlp5Ufnf9FW3bLjLGfAJMN8bca9v2iZ6q/wFeAY4AIacLdxf9k2d//5+cccYZ3PeLm1n9wQG2/7uEpIRvTg3YQN+MRLqltsH1i23gyPEajpa4G5T7/DY5w3tz7pk9IlArkejSLyORMwen4PWGNp6HjtWQ0S2GuFhXI1tFV3d/fcdK3Ph8NkkJoU3n9wekMHhAqoKUdDlN/c3HxHStz4JpLiUaY7oDfydw6m4x8E9gAoHep0Tgnyffm88Yk0Ng2oSvgUV1xXPrto8BSoCVffr0uWnYsOG8887bnHLKKbz62l857bTT2P7vEiqqQ+9seOBwFXv2VzDklDSnr7dNfL67lDMHp9GrR+hQf2PgjEHppKVE6E6LIlHGV28wxbHSWv723lckJ8SQHiVfjlpjz/4K+mYkkT/+ew3GU7lc6nkW6czSUlM+tW17ZGPLmu2Zsm37uDHmfOC/CfROGQK3i7kQWNPI+huNMROAgrrHiXvzTSdw9eDdwOWHDh2iqKiIn8+bx2233U56euCM4VmndWtQh//94jhf7CnjqwOVDRqwbqlxpCaHJ7z4/TYHj1TjO2mUq89vY1mG0welc/qg6Ah3ItGqfsBIT40lMT6Gyhpvo5/bSHTdN8W27Qan86rdPnr3SCBGp+xF5CQtGoBu2/ZXwI8bWTS4ifU3ARc1sbvpACfGTLVEv16JnPn9dLze0G+5Xp/N1wcrOfP79YdutY2S8lpsG3qkxxPj+qaRj49z0adnK2f0EhHiYl10T43j8PEaiqpCr9z1ePycNjCVhPjGTv+1v893l+FyhYa7hDiXep5FpIEw3vav7fTslkD++IENyr/YU8a+4ioOH6tp0OglxrtITGjZy/P7bUoraht8C91/uIrTT0nnsv9Hg0pF2srooRmceWroF6CyCg9///wY1W5vVIQpr9dPXIzFOWd0Z2C/5JBl/TL0RUpEQnWIMNWUjO7x9O+dSHWNr8GVA1/ur+DMwektOm1QfLQar9du0Ij36ZHIKf2SFaRE2lDPbgn07BZ6hZ/H6+df/y7BXetrYqv25fb4iYu1+MH30+mj8CQizejQYap7Wjz54weGjGkCOHy8hvUfF/P57lJcLQhTNjDirJ5knd2zwbL6V+2ISNuLjbFIToyhvNLT/MrtoNbjIy6u5b3bItK1dfiWorHGLjkxBn8W1LbiW+6APsmkJGkshEikdEuN47OdxxrMQ5OWEkv3Jualcsq2bQ4crsbjrX/VoZvvD0glMSHypxxFJPp1+DDVGGMMpw1MjXQ1RKQV+vdOYtgPeoZMwlnr8bNnf0XYwlSN20dNrY9+vZKIi/3mCr3v9Ukmo3u8JtoVkRbplGFKRDqe0welNZhuZMfuUr7aX4Ft22EZu+j2+EmMj+HC0X3DFthEpPPT1y4RiVqJCS5iYy083vDMjF5b6yM+ztLYSBFxRC2IiEStpIQY4mNdlJTXkpIU2lzFxlitOg1X4/bhrzceq6TCw/f6JBMfp7FRIvLdKUyJSNRKSowhLSWW42W11LhDLyipqPJw1qnpLTr9V1Hl4eviqgY9UAlxLnp20+k9EXFGYUpEolZSQgy5Y/vj8YRebff1oSo+2nYYd62/RZN8VlR56dszkXFZfYir15uVEqbbUYlI16EwJUGm9jD4ayJdDWkBO7YXuBKaX7ET6JHesOcoMSGG/915nK8OVtIjrfkbJh88Uk1WZk8G9k1udl0RkdZSmJIA24d17D2s2uJI10SaY2LxdTsPf+oPI12TiElNjqF3z4QGt5FqysC+yfTtpZnMRSQ8FKYkwF+L8ddgWwnYsRmRro18C6vqC7Ddka5GRBljuCSnf6uu8ouP08XLIhIeClMSYNeC7cW2ksBq/rSJRJCJxfi6dpgCcLksXLoIT0SigL6qCQDGX4uxPWCUr6OdbWLAVxHpaoiISB2FKQnwe8DvVZjqCEwM+KsjXQsREamjMCUBthtQmOoQTAzGVw12eGYFFxGR1tGRsyvylmL8tSFFlvsAxu8Fo0EoUc+KAduLcR8AK3SOJDsmDSxNQiki0p4UproaXxWuI29heUsbLLKxIQw3k5W2ZZs4LM9hYo680WCZL3UY/vRREaiViEjXpTDV1fjdGG85tg12THrosthekamTtE5MOn4rtsFpPqu6CKNJV0VE2p3CVBdjbC8GP/6YdIhJiXR15LswBlwNZ/K2XWng15QJIiLtTQPQuxrbF3gYvfWdjrGg3lg4EREJPx1RuxrbB7ZfA807I+MKTL4qIiLtSmGqq7G9gB+99Z2ReqZERCJBR9SuxvbWneZTz1SnY1yYE6dxRUSk3ShMdTHG9tad5tNb3+kY65sxcSIi0m50RO1qdKDtxFyAv+5UroiItBeFqa5GYarTsoM9UwpTIiLtSWGqq9GBtvMyrsApXL3HIiLtSpN2dmKm+kusmv2hhd7jgG6Q2zlZGNuLq2QTxHQPltrGwp86FFxJEaybiEjnpTDViVk1+3AdWxOYGfskdmzPCNVIwsqVCLXFWJVffFPmd0NMKnbCKdgKUyIiYaEw1Zn5Pdgx3fAnDYl0TaQ9GBf+pNNCy/y1WO59gMbKiYiEi8ZMdWZ2Lbbe4i7OInCFn8KUiEi46EjbmdlezSfV1RmrblC6wpSISLjoSNuZ2R6FqS7PKEyJiISZjrSdmd+D3uIuzpjAD/wRroiISOelI21nZduBW8eoZ0qw1TMlIhJGOtJ2Wn4C80npLRYCp/pERCQsdKTtrGyfbmgsdYx6pkREwkhH2s7K9hHondJbLKB5pkREwkdH2s5KPVMSpDFTIiLhpCNtpxXomdKknQIWxq+bH4uIhIuOtJ2VeqbkBGMF5hwTEZGw0L35OgvbDvmvsX0Y9UwJBP4G7NoGfyNAcB4qERH57hSmOgGrZCNW9VcNF/hrIKZ7+1dIoouxMO5iYg6+HFJsmxj83c/Dju8XoYqJiHQOClOdgHEfwqraiT++f0i57UoFKzFCtZJoYcf1xXiPNSi3vOX4veWgMCUi4ojCVGdge/HH9cWuF6ZEAHAlYLsa/m0Y7+d1p4JFRMQJDajpFHzorZTW02SeIiJtQUfgjs72YXTVnnxXus2MiIhjOgJ3dLa/7oCoq7LkO1DPlIiIYwpTHZ3tA2z1TMl3pJ4pERGndATu8HQPPnFAPVMiIo7pCNzR1c10bqtnSr4LhSkREcd0BO7obH/dAVFjpqS1DEZhSkTEMYWpDk9jpuQ7Mhb4dc8+ERGndATu4E7cg09vpbRW8J59IiLiiI7AHZ1tB071qWdKWstYYHsjXQsRkQ5PR+COrm4Aut5KaT2FKRGRtqAjcIenMVPyHRkLbI2ZEhFxSjc67kj8bvCHjnExnlI0z5R8N3U9U94yGlwN6kpWQBcRaSGFqY7C9uM68hbGc6zhMn+NDnzSesaF5TmGKV5Zr9zCnzYSf0pmZOolItLB6AjcUdhejLcM4zmObWJDHv74QQpT0mp2XC/8rvQGf09Wzb4GPaAiItI09Ux1FHX34LNje0Js90jXRjoDY0FstwbFtucQgbF4IiLSEurO6ChOXLWnHigJNxvdZkZEpBV0ZO4wAjc0tvWWSdgZjF9TJoiItJSOzB2FeqakvWjKBBGRVtGRuYPQbWOkvQRuM6MwJSLSUjoydxS2Xz1T0j6M0Q2QRURaQUfmjkK3jZF2o9vMiIi0ho7MHYY/8DCm2TVFHNENkEVEWkVhqqOwA1fz6S2T8NOYKRGR1tCRuaOwfWDrhsbSDoyFCZ5WFhGR5ujI3GH46h56yyTcrEBw18SdIiItoiNzB2FOHNg0ZkrCzViAX2FKRKSFdG++aGT7wFseUmQ8xwK9BSJhFwhTxnMM25/0TbGJgZiUiNVKRCRaKUxFIatyO1bpJ430DChMSfjZxoXlPYLrSGFoeUwqvoxJClQiIvUoTEUjfy2Wez++xO+HFNux3SNUIelSYtLxG4uTw7vxlmG8ZWC7AYUpEZGTKUxFI9uLbSVATHq7Pq3XXYPPq/mFOoLYhEQslys8OzcGYtJCimxcWN7jGNun/lERkXoUpqKQ8Xtp72sD/D4v+/65FXdlefMrS0QZY8gYfDrdvze4HZ/UhQali4g0TmEqGvlrwzqflNddg98XelCsLjtOTXkpxrKIiU8I23OLc5XHDlN2+ADJPXo1WBYTH4/lCsPH2mi6BBGRpihMRSO7FjtMPVNedw1ff/YpnpqqBss87mrS+34Po+kXopo/vQdVJcfYs2Vjg2XdBgyi1/fPCMOzWoBPt5kREWmEwlQ0sr1h65mqra6ipqIMK8ZFTGx8yLKUjD4KUh1AfHJKYLxUvakyKo8fobqsJDxPalyBGdHVMyUi0oDCVDSyPWELU56aanxeD8k9MhScOihjDHGJSQ3KfV4PnuoqfB4PrtjYNn7SE7eYUZgSEalPYSoa+d2A8yu1Sg58TVnxvpBZ0/1eL9h+BalOyBUbh6e6kr3bPsaKqfto2zZWTAy9Tzur0QDWGjZgbK+u5hMRqUdhKtrY/kAPQBv0TFUeP8KxvbtJSA2dYiExTfNVdUaxCYl4aqqpKS8Nlvn9XrAN3foOdBymwOjmxyIijVCYija2r+6A5SxM2baNp6qSxPTuJHfv2TZ1k6hmjGn0vS45+DVej7ttnkQD0EVEGtCNjqON7QX8dfP6fHc+jwevpxZXTBuPnZEOx2Dw1rZBmDIAGjMlIlKfeqYiyVMSuIHxSYyvMtAz1YowVXboALVVFaG7rq7G7/VozijBWBZVJUc50uBG2Ya0Pv1bfvrPBuMuxlTtqv8E2PHfA0vNiYh0Te3a+hljZgOzAQYOHNieTx2VXBX/i1X2acMF/lr8iYNbvJ9je3dTenAvMXH1gpMxDcZLSdcTn5xC2aGDlB86eFKpDRhiExJaHKZsKx6r6gusqi9Cy2PS8PWciJ3Qv+0qLSLSgbRrmLJtewmwBGDEiBG6KMhfDRj8iac2XNbCAeg+jwefp5akbj1JTOvWtvWTTiE+OZX45NQG5SUH9+GtrW3xfuzEwdj1B6DbHiz3vsB0HiIiXZT65SPJX4ttYhxduefz1OL3enDFxrVhxaQrMMbgdde0cqP6f6uxgXF+ClMi0oUpTEWSvwZMy98Cn6cWnzf0aqrqshJ8Xi+xji97l67GFRNDTXkptdUNby0UG5+AsVoQ8o0F2Jp/SkS6NIWpSLH9GH/LZzr3+/3s++dW3JXlDZZ53TVYmv5AWskVE0t1WUmDe/wZY5ExeAjd+p/S8p1pygQR6cIUpiIleGuOll2153XX4K6qwOuuIS45JWRZcs9eWC3pRRA5SUJqOp6aaux6fUrlh4tJ69OKweQ24FeYEpGuS2EqUmwP4GvxFAg+Ty1+j4eEtG7EaroDaQPGsohLSm5QXptc2eipv2+lnikR6cLUnREpduAeeXYLw5S31o3P69EknBJ2rpgYPNVV2P5W3DpGA9BFpAtTz1Sk2N7Aw2rYy1Rx5BDH930ZcvLF7/Xi9/mwXM5vgCzybayYWDw11ezZ+uE3N0wGXLHx9D39rIZXjhoXxtdGt6sREemAFKbagXEfwHjLQstqD2FsX6M9U1Vlxzm6dzcJKWkh5QmpaQ3WFWlrcYnJVJceD7lhsrfWTWx8ArXfG0RivTBlY4G3BKtyR4N9+RNOAVdi2OssIhJJYQlTxphs4NdANoHhqRuB+bZt/z0czxftrJKPcFUXYZ905Z6x/di2DXH9GqxfW1FOfHIKKT17tWc1RQCwXC6Se2SElPn9fiqOHGx8XqqYFFxVX2DX7P6mzLbBSsTueRF28hlhrrGISGS1eZgyxowB3gP2Ab+sK74VeN8Yk2Pb9mdt/ZxRzfZj/FX4Y7phx4cGJ5+nFk9l6EBfv99HbU0VrliNjZLoYVkWtt+m8vgRYhNC5zQzJpG45EyMMaHbVO7A+Gs1/5SIdHrh6Jn6HVALXGDb9j4AY8xyYDvwKHDJiRXvuP02nn766ZCNJ068lBWvvBKGakWIvzYwBUIjk3Me/rKI0gN7G5T7PG7dU0+ijjEWpQf3UXpwX0h5TFw8/X4wlKRuPRpuZLf8djUi0jF1iWN5M1oUpowx/wXcB3zftu0v6y37EvjStu0JxpghwCjgmRNBCsC27X3GmJXAdcaYvrZtHwTYsSMwxsLlcnHLLT8jNS2VIUNOd/6qooldC7YX24T2NNl+PzWlx/G6axochExSCjHx8e1ZS5FmpWT0xudpGI4qjx+htrqyYZgyBvwamC7SmdTU1JCQEHrhVJc4ljejrXumRtX93HRyoTHmVGAyYIAs4I0Ty3r27ElpaSlFRUWdMsUafy015cep9cRCzDcHIk9NNR53DQmpacQmaICuRD/L5cJqZDC5MS7KDx1scJWfVVNKQnxZg/VFpGPavXs3+XmTeP6FZYwYMSJkWWc/ljenrcPUiWmT99UrfxA40TUz4OQFZ511FpdNncqd//EfvPXWW0ycOLGNqxRZfl8NxUU7qCjzghXaO+X3eEjt03AAukhHEpsQT9nhA5QfORhS7rLL6WN6k9o3QhUTkTZ1/3/dR0VFJQMGDGiwrLMfy5vT1mHqxMjUYN++MSaWQK/U68AVJ60DgG37+T9Z8TyYnsyKpxeS1X1XG1cpsjwVh3FXVmB7fcQmhd4GxsRYeMoPoekOpSOzgNjYhhN8Vh85TFnxbtwfPUmgU7pzi0sfSEyS7pEpnZPH46Vw9RvM/MlkUmt3UbV3F7bfQ83hz/GU7wdg6lBfpzuWx/c4jdQhlzS/om3bzT6A/yIwxcHgRpZ9CbxX9+95dev96KTl/erK/qfu5+y6clsPPfTQQw899NCjgzw+aSontbRnyv6WZSfvY3/dz5P7AEsBPzCo7v/7AIYPH8669e8D4K06wrlZOYzMGsZTTz7awip1HJYrHmPFNb+iSCfj91Ri04rb0nRQlV+8waHPCnGlDAyZIsIYi7ikJLpCz5x0Xrbtp6ykjPNufp6bpw5j9mXDAKg6+jU9Bp5K77H/L3bdMW549kWd6lgek9ATKy5wQi0tNaXp9Vq4v2N1P3sQ6IkCwBiTQKDnqaiu6OO6n2OBPwLYtl1ljNkAXEgglH1af+fvb/6MvV/v5xf/eS9xaQNbWCURiXauxG6RrkK7sL73Qyq/3oyH0KucPO5qLK+P+KSmG2GRaFdVcpT4uFiGndGXNzbt4oYpI4mLdZGakUFanwHEpp8KxvDuu+922WN5S290vLPuZ2698jtO3odt20XAJ8B0Y0z/k9b7HyANOAr4Tt7B1q1buenGWZxxxhlcceWVram7iEhUiEvuwSlnZzJ45HnBx6ARY0lITsPr1vQQ0rF53W5Sevbmgf/8KV8fKuPe5z4m5dRzGDz0HNIHnAbGdPljeUt7pv5/4HPgAWNMT2A3MA4YAxypt+5twLsEZjxfVFc2l8Cg9O7AdmPMyj59+jD9xz/mnXfe5pRTTuHl5SuIi9OpMBHpeGwrjpjYWExsLJx026iE9G6UFte/uLluWWo6rhjdHlWiS3V5KX6vN6TM5/WQmN6dC88ezH//1yzm3f8Mo/PmM+Wis+jZeyCf/fv5Ln8sN3WDwZtf0ZgzCMxufgGBGc7fJtAztYG6STtPWncsUEDovfl+QaAX2T/OWAAAH6RJREFU625ggjEm49RTT+Py/3M5t912O+npmvFbRDomU/0VMYf/hj/+e3DS+Miy4v0c+/pLTh52ats2Xrcb2/aRlN7IrPEiEeLzeqg8dpjE9B6hY/9cMfT6/hkkpXcHYMu2f/Pf/99fef/DbZRVehg4cFCXOJanpaZ8atv2yMaWtThMtbURI0bYJwagi4h0ZMZ9kJhDr2GbOOyQiU0tcKUEZoM/ydeffUL5kWJSM/q0b0VFvkVtdRVedw2Dho8lPiX1mwW2H3wV1L8WzVW9G2/GRPxpWe1b0Qj5tjClPmYREYdsVyL+2O4YbwXG983Ny42vAn9cH4hJC1k/LjkV/8HGT/+JRIrPU4srNq7B7cxM7WGMvxrbCi33x/cHS3fwAIUpERHnYtLxZUwC+5uxJsZXievomv/b3r0GSXbW9x3//U9f5747lx3Nzu5qJSEBKi7SsgZsMMZyDAIUbOIkJDaOE9tQTqUqghg7YLsqcaVcKewEk6qYuLgWsZ3YWNiAL3GFYBmUmGCEBDFYki3rutrb7GV2ZufW3ef886J7V9NzenZ75un7fD9vduc5PX2e6dOnz6+f5znPI/NKam6ZXGFIFllq0Wh3V3FsQsXNrQJAiyVxRcsLp7V1yo4krmhidj61NJS8rCQ/o3hq6z1oSn1R2KsIUwDQCtn6AOTRkGQZyePUQ0enZjT3wpelypcXzmjl4gJhCm1V2dhQfmhE4zfMK1+sW5RE+UbTeHhcbYHK7e9QDfsPYQoA2sGykiLJS6lNueKQ9h86miqvlEupNQ6BVosrZUXZnKZvvFWZXO76v+Bx3Y0VSGt2nikAwE5EWXnUuGVqO9l84foPAgIllYqy+UJzQUqSKZai4vUfuIfRMgUA7RIVJV1s+uHZfEEWRVpaOCXbNF9VlMloeN9U3e3qwPUkcUWrixe09a79jcvLGp89uM1vNXwmeYagfy2EKQBoF8tXbytvUmFkTBNzR+Rx+ep0CnG5pLVLi6qUNpQr0DqA5pVWV5TJFVQYHVOUyVwtH5ue1fD+6Z09mREXroVXBwDaJSo2vJtvO/nhEc3f/vK6stLaqp5+6CtKKmWJMIUdiCsVFYZHdfhl3xHWqukiTF0HY6YAoF2iwo5aphrJ5vOKstnUEh/A9SSVsnJDQ63pHiZMXROvDgC0iUc5WXxZVr5QXy6TshN16/htJ8pklc0XtLKypPyWQGVmdd032LvcXUlcf7NDpVxSfqjBVAfXUlmWebmuyOSEqevg1QGAdomGlAzdmLqjL0pW5V6W5w809TSF4VGtLJ7TymL9uvJJuayR/dPKFZmFeq+7dPo5Rbn6S3q+OKTsThYd9liZ9WeV5GfqipPCXHXeNGyLMAUAbZKM3q5k6OjWUmUX/kjatOzM9UweuVmjM/Xr+CXlshae/OvqwHTC1J6WxLGiTEaT8zdpdLo+oA+N7WDhYY+rSyNNvFJJ8fDz5WZSZqRFtR1MhCkAaBeLpGy6m8UtI9vBWKr80LDyQ8Op8osnntL6xnpQFdH/kkpFmWxW47MHNTwRMEu5J5IieXai4fsW22MAOgB0muUlNT+Z53byI6OKK+XrPxAD7cqM5uGTvsbVLwDGOLydomUKADot2tn8U9vJFYeVVCp1Cya7JLlreN9kw9Ys9C9319LZk3KvX6I4jisanZzZ2fiohjtI5ISpXSFMAUCnWbYlYWpselZRNv0xvnTmOa1fXiJMDZhKaUPZXEHjN8yrMFK/GHauUFSUCb2k17r5CFM7RpgCgE6zvExJ05N5bqcwOqbC6FiqvLS2opUL5wOfHb0mLpeUyeU1ffTWtsyGbx5XW6WYBmHHeMUAoNNa1M23nfzQiJK4rMvnz9bvNpvV8MRk2/aL1imtraq0ermubP3ykvbNHW7fgtieMGZqlwhTANBhHmVV7VJpj6Hxfdo/f1S+KbDFlYpWF8+rUi4pmwscW4O2W7t0USOTM8rkclfLRiZn2rzgdSIpR8vULvCKAUCnWVb1Q4hba2h8nw699BV1ZZXShp5+6CtaPnNK+WHmDOplSRLLItPkoaManz3YuR17IkW0TO0GYQoAOs0y1YkQOyibL2hsZk5ZFkvuC5lsTkMhc0btBnfz7RphCgA6LtPWMVPbOXDLCzu+T/STWLLc9R+GFMIUAHSaZWTusrWnU5s8PyNlmNIAbeSJbONkes3I8oLiQge7FQcIYQoAOsxzk4pHX5xqnbJ4WVY6Jx860qWaYU+IV2UeK8nPVu8svVJcPCQvznexYv2LMAUAHeaFWcUzb06VZxb+h6x8sQs1wt6SyKOc4qnvkXJT3a7MQGBtPgDoFVGuK2OpsMfUFjSWGGjeKoQpAOgVlpO1cf4pQFL1PcZdey1FmAKAXmG0TKEDmOm85QhTANAjqjOjh67YB1xPrZuPMNUyhCkA6BVc3NAJjJlqOcIUAPQMPpLRCV6dgd94v7UKryQA9ArLiG4+tJ0ncssQplqIVxIAeoVFHV+zD3tRwrIxLUaYAoCekaFhCu3nhKlWI0wBQI9wBqCjI5LqBLFoGcIUAPSKK+NYmGsKbWS0TLUcYQoAeoVlqmOmCFNoK1qmWo2FjgGgV1hGUqRo7Qn55oudZeWFQwxOx84kZdnGc9o6EM8qlyTj8t9KvJoA0CM8O6Fk5HYpKT0fnOI1RRsn5cmalBnubgXRV6yyJEVFeX5KiopXy2O/RUl+pos1GzyEKQDoFdlxxVN31RXZxhlFZz8rbvPDziVSlFc89UYpU7z+w7FrjJkCgF5mGTmD0rEbzhp8nUKYAoAe5hap+lFNmMJOJbVlYwhT7UaYAoBednW6BLr5sEPu1TDOsjFtxysMAD2teoef0TKFHUu4a69DCFMA0MuYyBO75YSpTiFMAUAvY8wUdo2ZzjuFMAUAPS0jiVnRsQu0THUMYQoAetmVbj5aprBDpkSyfLersScQpgCgl5nJLUvLFHbOWYOvUwhTANDrLCtmQMeOmeTMMdURhCkA6HW0TGE33Jmws0MIUwDQ66KcGDOFXSFMdQRhCgB6nWVltExhV7jMdwL3TAJAr7OcrHJJ0cqjW8ojJcWjUsQdW3udrT8ji1fryzymZapDCFMA0OOS4ZvkmeG6MouXZWtPS/EKYQqyeFXJyIvk+QN15V6Y61KN9hbCFAD0OC8elhcP1xfGK8quPyvzhPv8IMmUDL9APnJbtyuyJ9GZCgB9qboAMgPTUZ3p3OjS6yLCFAD0I6uFKQamQ0l1lnzCVNcQpgCgH1mm2hpByxQ8kUSY6ibCFAD0I4uqs1vTMgXVwpQIU91CmAKAfsUyM5BqY6ZomeomwhQA9CvL0TKFTWGKS3q38MoDQL+yrBgzBSmRK2JR4y4iTAFAv4pomYIYgN4DCFMA0K8sJ6Nlas+zK1MjcEnvGl55AOhXjJmCxAD0HkCYAoA+5RF380G6OjUCYaprWJsPAPqVZSR549Yp7uwaTO5KBWiPJcuLeaa6hzAFAP3KslKUV7T2t/XFSVlxYU7K7e9SxdAWnihaeUSK8qlNSXaclqkuIkwBQJ9Khm6RZ0a3lLoyl74mi1fpABw0XpEyw0pGX6KkeKh+W2aktrwQuqGjYcrM3iXpXZJ0+PDhTu4aAAZPdlSe3RqmJF/6hszjLlQIbeWxZJGSkdvkhblu1wabdLRT3d0/4u7H3f349PR0J3cNAHtHlOcuv0HkcW2Ba7rzeg0jFAFg0EQFSbRMDZ5ErozcGKHTawhTADBoLE833yC6Op8UYarXEKYAYNDQzTeQjG6+nkWYAoAB41FOTOY5iGJVJ+ekZarXEKYAYNBwsR1MntRapji+vYYwBQCDxjLMOTSIPJHTzdeTCFMAMGiuLDODARNXF7dGz6GtEAAGjWVlnsjWT9SXRwV5fqY7dcLOxCuy8sW6oqh0RvHWmc/REwhTADBgPDuheOjm2hibK4WJovUT8tx+xtz0gWjjlDx/oHYzQVWcm5LnZ7tYK2yHMwoABowX5hTPvq2uzNZPKDr7+er6boSp3ucVJcM3KZl4VbdrgiYwZgoA9gLLVcdSJZVu1wRNMcny3a4EmkSYAoA9wKN8dRkSJ0z1B5ciBpv3C8IUAOwFUV6KcjLCVO9zl2RyWqb6BmEKAPYCy0uiZaoveEWKstUAjL7AKEQA2AuinDzKKlo/U10sF70rWZeUZcxUHyFMAcBekTsgL5a6XQtcT2ZUSVSUZ0e6XRM0iTAFAHtEPPk6ycvdrgaaEjEAvY8QpgBgr7BIskK3awEMHDrOAQAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAhCmAAAAAnQ0TJnZu8zsQTN78Ny5c53cNQAAQFt0NEy5+0fc/bi7H5+enu7krgEAANqCbj4AAIAAhCkAAIAAhCkAAIAAhCkAAIAAhCkAAIAAhCkAAIAA2W5XoF2ePnlZpXLS9OPnZoY0OpxrY40AAECzzp5f06XL5aYfbybdeHBUuWzn24mCwpSZPSXpKXd/fUtqswsraxXFsdeVnb+0rge+fkYXFjeaeg6LTHe8cFJ3vHgytW2kmFW2CwcGAIC9wN11ebUi33QpL1difenBM3ruzErTzzNUzOqVL53WzYfH6sqL+YwK+UyrqttQy1umzOz7Jf2QpFdIeqmkgqTvdfc/a/W+Llza0P/6ykmtrscNtq3rhUcnFEV23ec5fW5NjzyxqCeeW05te+kL9un4S2ZaUl8A6KaVdWmjfP3PRHRfFEnjQ65oD3yXf+rkZX3lGwup3qTFpQ0dmRvRULG5qPK3zy7roUfO6xuPXagrn50s6k3ffUhm7Xvvt6Ob70ck/bCkb0l6RNIdrXjSUjlWsqXX7sSZFZ08u6qpfQVlMvUv0k3zY00FKUk6MFnU8kq5LhVL0smFVT353IpedPM+ZTY9VxRZ21MuAFxP4tJ6KV2+vGY6sxgp2fyZ5tJ6ybTRfK8JuigTSSNDrq3X//Gia3Z/kirPZaRcjw/ccXdtlNLDb06cXtVTJ5d19OBoXfn87HDTQUqSbjw4ossrlbqyxeWSzlxY16mFNe0fz9dty2ajlnUJmm9NEDv55QbdfGY2L+mcu2+Y2Xsl/YoatEwdO3bMjx07po9//ON1z/nGN96t373vvrqyUwur+vKDZ1SupA/C+cUN3XZ0fNd/w7VcWi7p/KUNjY/Wj6Uq5jO661UHNbWv0Jb9AsBmlTgdmlzS6YuRzi1FqS+CnkhrJalYf+1QJpKGC7v/zEfnlCrpVsQ4qQboQoPhvcMF140HEuUy9cd3uCg12a6wa+95971NXcsffeKSHnrkvJKkvo6lcqI4TnTohpGW161SSfT4M8uankxfrydG87r7tfPK55prHBkfG/26ux9vtK2pyGdmhyX9R0lvlGSSviTp3Y0e6+7PbfMcRXdf31z22GOPSZIymYx+6qf+ucbGx/SCF9yqrQHv9Lk1PXXysuamh1LPe2Su9S/+FeOjOcWJ1x34xKsp+sz5dMqV1HRrGAA0srKuunDkLj1zLqPF5fRnSzmRMiYNNQhIEyNKtV6gf2QzjYNvqVINVZsliXRuybSytiUUmDS7L9EN+9MNEUOFarjeqfX1dRWLxbqyZq/lz56+rKdPXtb8geG68lzWNLM/fX1vhWw20vzssDZK9cOBVtcrWl2LtXBhQ3Mz9fs20467BK8bpsxsn6QvSzos6dcl/ZWk75F0v6Rm//o5SY+Z2Q+5+4ObN0xNTenSpUt6/PHH9Tuf/l396V+c0qf/5Mm6X67ErrHhrGYm6w9gu5mZJifSaXZxaUPffPSC/vKv6/tloyjSa+48oINb3igAsFmSSJfX0x/Wl1ZMz52PUhfLjbI0lJcKuS2tDible7xrB6213fEu5l2VLcOHV9ar76czi+nUtH800fxUuhuxkPOGLV+S9OSTT+qet7xZv/Gbv6Vjx47Vbdt8Lf/Ub/62vvh/09fyy6sVHZwZ6vi1fGwkp7GR+j/K3fXoE0v63w+dTj2+WMjqrlfNpX7nWpo5DX9W0lFJP+7un6yVfdjMPiTp3ib3805Jo5JObN3wohe/WK/73rfo3/+79+vXPvYZafTlWl6paGTo+aq5S6OjI1pYSn/4ZKPGTZj5nGso3XDUEodvGNHicnqgwunz6xofyaW6I0eHs5ra19k3D4DuWytJy6vpD6jFFdPCYqRUe4FXu/QmRupD00ixOiYG2E4mSrc0FXKuUkWpbuCNsun0YqTz6XuuNDbkumF/Un9dNWnfiOsX/+2/0eXLK5qfn5ckJYnr2TNrWtuIdfTm2/R9b7hHv/xLP68Pf/Qzqgy/TLlspOymSrlMExPFhtfyXJRuSc1kpNFie7qlzUzzs0NaWasfYxUnrlMLa/rGREFH5kY0XGyuIaeZMPWDks5I+q9byj+g5sPUd0n6z+6eioAbG4lKE3erOPwB/cZ/+6xe8YYXaXxsWEtbZjWIL1Ykpe/ay0SuRo1xhZyU2dJ3bJKKOVd+S9g0Sbbj5s70L2yUTQ89eknf/JuluvL94wVNjBWaHki33Tg29/RJcXWbtv+dBg/GoNqmZXq7Fmvb5heqzdzbPVdzzd9xLK1uXGPnA8QTU7miVDhKEmllI/33u0uJW6qlyVTt3tnKbLsj9fz2pg3+4RgcO/isbnTd8G3Kper52ej9Wo5NmajBtTNb0h/84R/rtd//Dv3RX0SSFrS2Udaps6s6uVBtXFgsvEHF4V/Rpz/zB3r5XbdrajI9DOe5C5VUmeTKROm3ZjZTbRjZKpI0VHRtHTt+rc+t7aWv5WcvruvPv3lBX/l/F3TwwLB++E1HrjuE57oD0M1sXdLX3P27G2y7KOmb280ztWkAuiS9y90/umkbl3QAANAvwgaga/t83GwGTCTdsLngzjvv1Je+/IAkaaOU6M47XqLjx79Dv/6RTzT5lDsXJ9LSqqX+mI2S6dySKWnwV5ZjS03JYFZtftwqUnXAYHZLi1hkUjaTHgB48bJptcG31jiW1suR8tn65ynmXEMNbyDceesC9o5rtWg2OrXdq+fJ1vNhvVztAtgqY9LkWNKgxdc1VnTl9sjCAtt9cd3Rt0b37Vufr9FiHbxf9I1rt1A2Ktv++rCTC8T62qpuv+2w3v2v3qd73/MzV58jn6tv2Tl+7KVtv5ZvlBu3+K6smS6sWOrNHyeWGk8mVefyajSPV8Zco8XqeLKxoUg3zlafcHxsNP3gmmbC1BOSbjOzjLtfrY6ZzUmaaOL3peqcUz9qZr/s7qlpyf/8/3xJJ559Rj/3/vdruNjegQFj24wNv2muQaFL62VPXVRM1fk8th5KsytN9I3eoOm/K0mqd2ZsdX450tlFq7+jR9UL3GopffBzmcYXOeBaKnF1PqKt75z1kml8xFO3WBfyrkNTnuqaiiIp36hvCsDgGJ/Qq1/9an3u9z+t97/vp1UopL/Z33///R25lg8Xpf1jjbc1ahQplV0NZlZSxrbrVpeyV+eubO7a2kyY+pyk90n6J5I+uan8Xze1h6pPqjq1wn1m9uPuvnBlw8MPP6x3/uRP6LbbbtM/fPvbd/CUrdXwW6VJw22cSiqK0vPASNL8VKL5qfqyOJGeOB1pbUsaj2t3BZ1dNGW3HM2MSePD6bs1sLeUKtW7erZ+JKxtmKbGk9TA5olh18GpRNPjBHQAz/v5X/gF/d177tGPvuMd+vCHP6zpmedXB+nla3mj62yrNTNmar+kb0iaV3VqhG9Ler2k71R1aoRvb5m082WS3lr78TWS7pb0CUkzkt4iaVHSZ2ZnZ995xx136gtf+J86cuSIfv+zn9Mtt9zSwj9tb3CXnj4baWnLHUPu0mrJtLKmVPdLMeca4ebCgZMk0qUt3XPu1Sbx6YnGgzWPzCSpO8cAYDsf+9hH9TPvfa8mJib01re+VVNT0/rWt761J67l15q0s6kZ0M3siKQPSnqDqn1Yf6bqpJ1fVHoG9H+q+hasrf5Y0ivNbPrmm2/R2/7e23Tvve/WxESzPYZo1pmLpnNbJvqLY9PFy5aax0aSZNLQHhnf0s+SRFqvpDuT40TaNyINF+sPbjaqhqbG4+0AYGceeughfehXP6gHHnhAy8vLOnz4yJ64lgeHqXY4duyYXxmAjs5JEmlhyVSJ0y1ZFy6bNkr0CfY6k2vfWON51MaGXOPDtDQBQKsFLyeDwRFF0uw+V6NBdQenrnXXF3rJbpaBAAC0B2EKV0UmJvQDAGCH+H4LAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQgDAFAAAQoKNhyszeZWYPmtmD586d6+SuAQAA2qKjYcrdP+Lux939+PT0dCd3DQAA0BZ08wEAAAQgTAEAAAQgTAEAAAQgTAEAAAQgTAEAAAQgTAEAAAQgTAEAAAQgTAEAAAQgTAEAAAQgTAEAAAQgTAEAAAQgTAEAAAQgTAEAAAQgTAEAAAQgTAEAAAQgTAEAAAQgTAEAAAQgTAEAAAQgTAEAAAQgTAEAAAQgTAEAAAQwd+/Ojs2WJT3WlZ2j3aYlnet2JdByHNfBxbEdXBzb1rnR3Wcabch2uiabPObux7u4f7SJmT3IsR08HNfBxbEdXBzbzqCbDwAAIABhCgAAIEA3w9RHurhvtBfHdjBxXAcXx3ZwcWw7oGsD0AEAAAYB3XwAAAABCFMAAAABOh6mzOxuM3vMzB43s/d1ev9oHTM7bGb3m9kjZvZtM7u3Vj5pZl8ws7+p/bu/23XF7phZxsweNrM/rP18k5l9tXZsf8fM8t2uI3bOzPaZ2X1m9mjt/P1Oztv+Z2bvqX0Wf8vM/ruZFTlnO6OjYcrMMpJ+TdKbJN0u6R+b2e2drANaqiLpp939xZJeLelf1I7n+yR90d1vlfTF2s/oT/dKemTTzx+Q9Ku1Y3tR0k90pVYI9Z8k/Ym7v0jSy1U9xpy3fczM5iX9S0nH3f0lkjKS/pE4Zzui0y1Tr5T0uLs/4e4lSb8t6Qc6XAe0iLufcveHav9fVvUDeV7VY/qp2sM+JekHu1NDhDCzQ5LeIuljtZ9N0l2S7qs9hGPbh8xsXNLrJH1ckty95O6L4rwdBFlJQ2aWlTQs6ZQ4Zzui02FqXtKzm34+UStDnzOzo5LulPRVSbPufkqqBi5JB7pXMwT4kKSflZTUfp6StOjuldrPnL/96WZJC5I+WevC/ZiZjYjztq+5+3OS/oOkZ1QNUZckfV2csx3R6TBlDcqYm6HPmdmopM9Iere7L3W7PghnZvdIOuvuX99c3OChnL/9JyvpmKT/4u53SloRXXp9rzbG7Qck3STpoKQRVYfUbMU52wadDlMnJB3e9PMhSSc7XAe0kJnlVA1Sv+Xuv1crPmNmc7Xtc5LOdqt+2LXXSHqrmT2lanf8Xaq2VO2rdSFInL/96oSkE+7+1drP96karjhv+9vfkfSkuy+4e1nS70n6LnHOdkSnw9TXJN1au7sgr+rguM93uA5okdoYmo9LesTdP7hp0+cl/Vjt/z8m6XOdrhvCuPv73f2Qux9V9Tz9U3f/EUn3S/r7tYdxbPuQu5+W9KyZvbBW9H2S/kqct/3uGUmvNrPh2mfzlePKOdsBHZ8B3czerOo33IykT7j7L3W0AmgZM3utpAck/aWeH1fzc6qOm/q0pCOqnuD/wN0vdKWSCGZmr5f0Xne/x8xuVrWlalLSw5Le4e4b3awfds7M7lD1xoK8pCck/TNVv1xz3vYxM/tFSW9X9U7rhyX9pKpjpDhn24zlZAAAAAIwAzoAAEAAwhQAAEAAwhQAAEAAwhQAAEAAwhQAAEAAwhQAAEAAwhQAAECA/w/hug2TmCUOvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x864 with 2 Axes>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "defaults.circuit_instruction_map.get('cx', (control_qubit, target_qubit)).draw(show_framechange_channels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-22T01:29:36.602627Z",
     "start_time": "2019-08-22T01:29:36.594016Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Dec 11 12:03:36 2019 EST</td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2019.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import qiskit.tools.jupyter\n",
    "%qiskit_version_table\n",
    "%qiskit_copyright"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}