{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "header",
   "metadata": {},
   "source": [
    "# The Van der Pol Oscillator\n",
    "\n",
    "Modeling relaxation oscillations and nonlinear damping in neural systems."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "learning-objectives",
   "metadata": {},
   "source": [
    "## Learning Objectives\n",
    "\n",
    "By the end of this tutorial, you will be able to:\n",
    "\n",
    "- Understand the Van der Pol equations and the role of parameter $\\mu$\n",
    "- Distinguish between harmonic oscillations ($\\mu \\approx 0$) and relaxation oscillations ($\\mu >> 1$)\n",
    "- Simulate Van der Pol oscillators across different parameter regimes\n",
    "- Explain why relaxation oscillations are relevant for neural dynamics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "background",
   "metadata": {},
   "source": [
    "## Background / Theory\n",
    "\n",
    "### The Van der Pol Oscillator\n",
    "\n",
    "The **Van der Pol oscillator** was originally developed by Balthasar van der Pol in 1926 to model electrical circuits with vacuum tubes. It has since become a paradigmatic model for **relaxation oscillations** - oscillations characterized by slow buildup and rapid discharge phases.\n",
    "\n",
    "### Original Form (Second-Order)\n",
    "\n",
    "$$\n",
    "\\frac{d^2x}{dt^2} - \\mu(1 - x^2)\\frac{dx}{dt} + x = 0\n",
    "$$\n",
    "\n",
    "### Two-Dimensional Form (Lienard Transformation)\n",
    "\n",
    "BrainMass uses the Lienard form:\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\dot{x} &= \\mu\\left(x - \\frac{x^3}{3} - y\\right) + I_x(t) \\\\\n",
    "\\dot{y} &= \\frac{x}{\\mu} + I_y(t)\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "### The Key Parameter: $\\mu$\n",
    "\n",
    "| $\\mu$ Value | Oscillation Type | Characteristics |\n",
    "|-------------|------------------|------------------|\n",
    "| $\\mu \\to 0$ | Nearly harmonic | Sinusoidal waveform |\n",
    "| $\\mu \\approx 1$ | Transition | Mixed character |\n",
    "| $\\mu >> 1$ | Relaxation | Sharp transitions, flat plateaus |\n",
    "\n",
    "### Neuroscience Relevance\n",
    "\n",
    "The Van der Pol oscillator captures features of neural dynamics:\n",
    "- **Slow-fast dynamics**: Rapid action potentials followed by slower recovery\n",
    "- **Threshold behavior**: Excitation must exceed threshold for transition\n",
    "- **Refractory period**: Post-spike period of reduced excitability\n",
    "\n",
    "It is closely related to the FitzHugh-Nagumo model (Tutorial 014)."
   ]
  },
  {
   "cell_type": "code",
   "id": "imports",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-21T13:55:01.875406900Z",
     "start_time": "2026-03-21T13:54:59.101486700Z"
    }
   },
   "source": [
    "import brainmass\n",
    "import brainstate\n",
    "import brainunit as u\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Set simulation time step\n",
    "brainstate.environ.set(dt=0.1 * u.ms)"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "id": "single-node-header",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-21T13:55:02.126549800Z",
     "start_time": "2026-03-21T13:55:01.876862300Z"
    }
   },
   "source": [
    "# Create Van der Pol oscillator\n",
    "node = brainmass.VanDerPolStep(\n",
    "    1,  # single node\n",
    "    mu=1.0,  # moderate nonlinearity\n",
    ")\n",
    "\n",
    "# Initialize states\n",
    "node.init_all_states()\n",
    "\n",
    "# Set initial condition away from origin\n",
    "node.x.value = np.array([0.5])\n",
    "node.y.value = np.array([0.0])\n",
    "\n",
    "print(f\"mu = {node.mu.value()}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mu = 1.0\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "id": "single-node",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-21T13:55:02.210166500Z",
     "start_time": "2026-03-21T13:55:02.127540200Z"
    }
   },
   "source": [
    "# Define simulation step\n",
    "def step_run(i):\n",
    "    node.update()\n",
    "    return node.x.value, node.y.value\n",
    "\n",
    "\n",
    "# Run simulation (2000 ms for slower dynamics)\n",
    "indices = np.arange(20000)\n",
    "x_trace, y_trace = brainstate.transform.for_loop(step_run, indices)"
   ],
   "outputs": [],
   "execution_count": 3
  },
  {
   "cell_type": "code",
   "id": "simulate",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-21T13:55:02.281527900Z",
     "start_time": "2026-03-21T13:55:02.213367100Z"
    }
   },
   "source": [
    "# Define simulation step\n",
    "def step_run(i):\n",
    "    node.update()\n",
    "    return node.x.value, node.y.value\n",
    "\n",
    "\n",
    "# Run simulation (2000 ms for slower dynamics)\n",
    "indices = np.arange(20000)\n",
    "x_trace, y_trace = brainstate.transform.for_loop(step_run, indices)"
   ],
   "outputs": [],
   "execution_count": 4
  },
  {
   "cell_type": "code",
   "id": "viz-header",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-21T13:55:02.684714200Z",
     "start_time": "2026-03-21T13:55:02.282702400Z"
    }
   },
   "source": [
    "t_ms = indices * brainstate.environ.get_dt()\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
    "\n",
    "# Time series\n",
    "axes[0].plot(t_ms, x_trace[:, 0], 'b-', label='x(t)', linewidth=1)\n",
    "axes[0].plot(t_ms, y_trace[:, 0], 'r-', label='y(t)', linewidth=1, alpha=0.7)\n",
    "axes[0].set_xlabel('Time (ms)')\n",
    "axes[0].set_ylabel('Activity')\n",
    "axes[0].set_title(f'Van der Pol Oscillator (mu={node.mu.value()})')\n",
    "axes[0].legend()\n",
    "axes[0].set_xlim([0, 1000])\n",
    "\n",
    "# Phase portrait\n",
    "axes[1].plot(x_trace[:, 0], y_trace[:, 0], 'k-', linewidth=0.5, alpha=0.7)\n",
    "# Mark nullclines\n",
    "x_null = np.linspace(-3, 3, 100)\n",
    "y_null = x_null - x_null ** 3 / 3  # x-nullcline: y = x - x^3/3\n",
    "axes[1].plot(x_null, y_null, 'g--', label='x-nullcline', linewidth=2)\n",
    "axes[1].axvline(0, color='orange', linestyle='--', label='y-nullcline', linewidth=2)\n",
    "axes[1].set_xlabel('x')\n",
    "axes[1].set_ylabel('y')\n",
    "axes[1].set_title('Phase Portrait with Nullclines')\n",
    "axes[1].legend()\n",
    "axes[1].set_xlim([-3, 3])\n",
    "axes[1].set_ylim([-3, 3])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "id": "visualization",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-21T13:55:03.056669100Z",
     "start_time": "2026-03-21T13:55:02.694240100Z"
    }
   },
   "source": [
    "t_ms = indices * brainstate.environ.get_dt()\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
    "\n",
    "# Time series\n",
    "axes[0].plot(t_ms, x_trace[:, 0], 'b-', label='x(t)', linewidth=1)\n",
    "axes[0].plot(t_ms, y_trace[:, 0], 'r-', label='y(t)', linewidth=1, alpha=0.7)\n",
    "axes[0].set_xlabel('Time (ms)')\n",
    "axes[0].set_ylabel('Activity')\n",
    "axes[0].set_title(f'Van der Pol Oscillator (mu={node.mu.value()})')\n",
    "axes[0].legend()\n",
    "axes[0].set_xlim([0, 1000])\n",
    "\n",
    "# Phase portrait\n",
    "axes[1].plot(x_trace[:, 0], y_trace[:, 0], 'k-', linewidth=0.5, alpha=0.7)\n",
    "# Mark nullclines\n",
    "x_null = np.linspace(-3, 3, 100)\n",
    "y_null = x_null - x_null ** 3 / 3  # x-nullcline: y = x - x^3/3\n",
    "axes[1].plot(x_null, y_null, 'g--', label='x-nullcline', linewidth=2)\n",
    "axes[1].axvline(0, color='orange', linestyle='--', label='y-nullcline', linewidth=2)\n",
    "axes[1].set_xlabel('x')\n",
    "axes[1].set_ylabel('y')\n",
    "axes[1].set_title('Phase Portrait with Nullclines')\n",
    "axes[1].legend()\n",
    "axes[1].set_xlim([-3, 3])\n",
    "axes[1].set_ylim([-3, 3])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-21T13:55:03.073297800Z",
     "start_time": "2026-03-21T13:55:03.059131500Z"
    }
   },
   "cell_type": "code",
   "source": [
    "@brainstate.transform.jit(static_argnums=1, static_argnames='n_step')\n",
    "def simulate(mu_val, n_step: int = 30000):\n",
    "    model = brainmass.VanDerPolStep(1, mu=mu_val)\n",
    "    model.init_all_states()\n",
    "    model.x.value = np.array([0.5])\n",
    "    model.y.value = np.array([0.0])\n",
    "\n",
    "    # Simulate\n",
    "    def step(i):\n",
    "        model.update()\n",
    "        return model.x.value, model.y.value\n",
    "\n",
    "    return brainstate.transform.for_loop(step, np.arange(n_step))\n"
   ],
   "id": "9110543b520541af",
   "outputs": [],
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "id": "mu-effect",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-21T13:55:04.222330300Z",
     "start_time": "2026-03-21T13:55:03.077292900Z"
    }
   },
   "source": [
    "# Different mu values\n",
    "mu_values = [0.1, 0.5, 1.0, 3.0, 5.0]\n",
    "\n",
    "fig, axes = plt.subplots(2, len(mu_values), figsize=(15, 6))\n",
    "\n",
    "for idx, mu_val in enumerate(mu_values):\n",
    "    # Create oscillator\n",
    "    x, y = simulate(mu_val, 30000)\n",
    "\n",
    "    # Time series (top row)\n",
    "    t = np.arange(30000) * brainstate.environ.get_dt()\n",
    "    axes[0, idx].plot(t[-10000:], x[-10000:, 0], 'b-', linewidth=0.8)\n",
    "    axes[0, idx].set_title(f'$\\mu$ = {mu_val}')\n",
    "    axes[0, idx].set_xlabel('Time (ms)')\n",
    "    if idx == 0:\n",
    "        axes[0, idx].set_ylabel('x(t)')\n",
    "\n",
    "    # Phase portrait (bottom row)\n",
    "    axes[1, idx].plot(x[-10000:, 0], y[-10000:, 0], 'k-', linewidth=0.3, alpha=0.7)\n",
    "    # Nullcline\n",
    "    x_null = np.linspace(-3, 3, 100)\n",
    "    axes[1, idx].plot(x_null, x_null - x_null ** 3 / 3, 'g--', linewidth=1.5, alpha=0.7)\n",
    "    axes[1, idx].set_xlabel('x')\n",
    "    if idx == 0:\n",
    "        axes[1, idx].set_ylabel('y')\n",
    "    axes[1, idx].set_xlim([-3, 3])\n",
    "    axes[1, idx].set_ylim([-3, 3])\n",
    "\n",
    "plt.suptitle('Transition from Harmonic to Relaxation Oscillations', y=1.02, fontsize=14)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:13: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:13: SyntaxWarning: invalid escape sequence '\\m'\n",
      "C:\\Users\\adadu\\AppData\\Local\\Temp\\ipykernel_32900\\1202397000.py:13: SyntaxWarning: invalid escape sequence '\\m'\n",
      "  axes[0, idx].set_title(f'$\\mu$ = {mu_val}')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x600 with 10 Axes>"
      ],
      "image/png": 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ZMW7Lysq8N954o6589+7d3qhRozwA3ttvv11Xfuqpp3oAvAULFtSzZ8OGDVo7CSGEEFK4MIULIYQQ0sDZY4896v1sX9C3b996Za1atcKkSZNQWVmJ9957r97+Pn36ZER3AsAFF1wAANrjmzdvXq+sffv2TraH5cEHH8Tu3btx5ZVXZuT5LS8vr0vpoEuZEdanqFx99dX18vG++eabWLZsGcaPH4+xY8dm7LvuuuvQvn17PPjgg6iurq7XnhrFL3JT7969G7/97W8zIu3FPte0NIK//vWvmD59uvbviy++qHd8eXm59mGw++67L0aPHo158+Zh165d9fafdNJJGDNmjNGOX/7yl+jUqVPd60MOOQR9+/bFpk2bcOONN6Jly5Z1+0477TQ0adIkw9dVq1bh9ddfx6BBg+pFjV988cUYOHAgXnvtNaxevbreuWfMmIE2bdrUvd57771xxBFHYMmSJdiyZYvRZgB4+umnsWbNGpx77rkYN25cvf3du3cPrO/C/fffDwD43//937pflwB+7uef/exn2L17N/7xj384t7dhwwZcf/31uP7663HttddiwoQJ+P73v49WrVph5syZGcf+6U9/QsuWLXHXXXfVRXEDQNOmTesiqP/5z39azxl2Lho6dChuvvlmLFu2DJdccgn+53/+Bxs3bsS9995b7zkEIl2PinhQ6r/+9S+rfUFUV1fjn//8Jzp06IBf/epXGfuOP/54HHvssVi6dCnefPPNenV//etfZzz/oWPHjjjppJOwZcsWLFmyxHrutWvXAoBTXnNxzNdff51RrpujW7VqhfLycgD+fHLPPfegoqICt99+e71fsFRUVKBVq1bW87tSUVGhfY8YPXo09t1339jXC4h+z5xzzjk44ogj6l6XlpbivPPOA+D+/tehQ4fY9hNCCCEktzCFCyGEENLAGTp0qFY8AoB169bhpptuwgsvvIAvvvgCO3bsyNgvHowps//++6OkJHMNXoiAmzZtqiubMGECpk6dismTJ+PVV1/FuHHjMHLkSK1QlhQix7YuP/Hhhx+OZs2aYcGCBfX2ufoUl0MOOaReWZDNrVq1wkEHHYSXX34ZS5YswX777Ve3r127dvXEeCHE7bXXXmjRooV2n+6aBvH222/jsMMO0+4bNWpUXb5omQULFuB///d/8cYbb2Dt2rX1BPMNGzbUe2isrm9kdCk0unbtiuXLl9fbV1pais6dO2f4Kq77yJEj66XwKSkpwVFHHYXFixdjwYIF9cTIAw88sN655fHRunVro93vvvsuAOC4444zHhOXDz/8EC1atND2oXiWgG7cm/j222/r8koLWrVqhVdeeSVjLGzfvh0LFy5Et27dMnKOC8R1X7x4sfWcUeaiyy+/HC+99BIeeOABAMAll1yCiRMn1jvO8zzcd999mDVrFhYtWoTKysqMHONh7wmVxYsXY+fOnRg9enS9+w7wr8Err7yCBQsW4Mgjj8zYZxtb2eSoo45C165dcdNNN+Gjjz7CiSeeiJEjR2KfffbJuEcWL16MLVu2YMyYMYmlabExZ84c/PGPf8Q777yDDRs2ZDy/wvR+Foao94zr9TrrrLPwxBNP4LDDDsM555yDY445BkceeWRBPfiaEEIIIe5QQCeEEEIaOKb83Rs3bsTBBx+MVatW4YgjjsCYMWPQtm1blJaWYsGCBXj66adRVVVVr54ciSsQUdA1NTV1Zb1798b8+fNx/fXX4/nnn8cjjzwCABg4cCBuuOEGnHHGGUm4l4HIm63zOZVKoUuXLlizZk29fa4+xUVnV5DNQFr4VnOCB9kctE8X/Z0kb731Fo4++mgAvmi81157oVWrVkilUnjqqafw0UcfaceVLc98FH9lX6P2s+3ctvEhHsSoi8pPis2bNxsjkIP8MrH33nvXid6bNm3CU089hUsuuQSnnHIK3n///TpfvvvuO3iehzVr1tQT3GW2bdsWeL6oc1EqlcLJJ59c98BY3TMbAF9o/9Of/oQePXpg4sSJ6Nq1a1109fTp07VthyFfYwvwf2EEQPvLCRVxjLCnoqIC8+fPx3XXXYdnnnkGzz//PAA/Uv3aa6/FT37yEwC5GcMyjz76KM4880y0atUKY8eORe/evdGiRYu6h3jqfvkSlqj3jOv1OuOMM/DUU0/htttuw5///GfcddddSKVSGD16NG699VZrTn1CCCGEFBYU0AkhhJAGjhptK/jrX/+KVatW4Te/+U29tAM33XQTnn766djnHjx4MB577DHs2rULH3zwAV544QXccccdOPPMM9GtW7eMn8IngRA3vvnmm7qHzAk8z8M333yjFUByhe5ayDbrECka8ml3GG688UZUVVXh3//+d72HJs6fP9+YQsY0TpMiX/0sHmaoW7hJijZt2mDdunXafXH9atu2LSZNmoSamhr86Ec/wuTJk/HUU09ltHnggQfi/fffj9Q+EH0uWrFiBa6++mq0b98e3333HX70ox9h3rx5GSlG1q1bh7vuugtDhgzB22+/nREhvnbt2kDh35V83sPDhw/HrFmz8Oqrr+JHP/qR8bhNmzbhP//5D5o2bZoRRd2zZ0/MmjULtbW1+O9//4uXX34Zd9xxByZPnox27drh7LPPzskYlrn++uvRrFkzfPDBB9hrr70y9j300EOJnCOb94zgpJNOqkvH8+abb9Y9zHbcuHFYvHix9UGnhBBCCCkcmAOdEEIIaaQsW7YMgP8lX+Xf//53oudq0qQJDjvsMEyfPh133HEHPM/Ds88+G1hHiGBhIsCHDRsGwP/5v8o777yDnTt3FlzkX5DN27Ztw/vvv4/mzZtj7733zrFl0Vi2bBnat29fTzzfvn07/vOf/+TJqnQKmHnz5sHzvIx9nudh3rx5GcclhUgR8fLLL8dqp6SkxHgvDBs2DNu3b69LFyMjxlVcv374wx/igAMOwNNPP4233noLANC6dWvss88++PTTT2OlG4kyF+3evRvf//73sWXLFjz88MOYMmUK3nrrrXqC+PLly+F5HsaMGVMvvYqp7bBzz8CBA9GsWTO899572L59e739SV0DHaeffjpatWqFJ554IjBVzq233oqdO3fizDPP1KaZKSkpwf77749rrrmmLmf97NmzAfi/SGjTpg3ee+89fPfdd4n7oLJs2TLss88+9cTzr7/+GsuXL693fNT3imzfM4LWrVtj3LhxuOeeezBp0iR88803eOeddxJpmxBCCCG5gQI6IYQQ0kgREdpvvPFGRvmDDz5Y91P+OHzwwQfan8CLKM1mzZoF1hcPkXNJTSA455xzUFZWhttuuy0jr3F1dTV+/vOfAwAmTZrk3F4uOOKII9CvXz+88MIL9R6O99vf/hbffvstzj777ETy/uaCXr164bvvvsPHH39cV1ZTU4OrrroK69evz5tdPXv2xOjRo/Hxxx/j3nvvzdh3zz334NNPP8XRRx/t9DDGMEycOBHdu3fHAw88gJdeeqnefteo3vbt2+PLL7/U7hMPMZw6dWpG2prVq1fjtttuQ1lZGb7//e9HsD5NKpXCtGnTAPgPvhRcfvnl2L59Oy688EJtqpYVK1Zg5cqVgW1HmYumT5+Ot99+G1deeSXGjBmD3/3udzjggAPwu9/9LkMYF22/9dZbGXnPv/zyS0ydOlXbdti5p2nTpjj77LOxYcMGzJgxI2Pfiy++iJdeegn9+/dP/Bc3gP8shBtvvBHV1dWYMGECPvvss3rH/PWvf8WMGTPQoUOHuge7AsDHH3+sjZpX5+iysjJcdNFFqKysxE9/+tN6QnVlZSW2bt2amE+9evXC0qVLM2zbuXMnLrnkEm0KqijvFdm+Z+bNm6cV9EXUu+39jxBCCCGFBVO4EEIIIY2U//mf/8HNN9+Myy67DK+//jp69eqFjz76CK+++ipOPfVUPPHEE7Ha//vf/467774bRx11FPr164c2bdrgk08+wfPPP4/27dvj/PPPD6w/cOBAdOvWDQ899BDKy8vRvXt3pFIpXHbZZaioqNDW6devH26++WZceeWVGDJkCL73ve+hZcuWeOaZZ7BkyRKcdNJJOPfcc2P5lTQlJSWYNWsWxo4di+OPPx5nnHEGevXqhbfffhtz5sxBv379cNNNN+XbTGcuu+wyvPzyyxgxYgS+973voVmzZpgzZw7WrFmDUaNGaSPtc8XMmTMxYsQIXHjhhXjmmWcwaNAgfPzxx5g9ezY6deqEmTNnJn7O8vJyPPLIIxg3bhzGjx+PcePGYejQodi8eTMWLFiA7du31z1INoijjz4ajzzyCE4++WQMGzYMpaWlmDhxIoYMGYL/+Z//wRNPPIGnn34aQ4YMwYknnoht27bh4YcfxsaNG3Hrrbcm8vDeiRMn4sADD8Rrr72GuXPnYuTIkbjoooswf/583H///XjzzTcxZswYdOvWDd988w0WL16Md955Bw8++CB69+5tbDfsXDRv3rw6wVwIwk2bNsWDDz6IAw88EOeeey4++ugjtG3bFl27dsVpp52Gxx9/HAcddBCOOeYYfPPNN3j22WdxzDHH1EW/q3392GOP4bTTTsP48ePRrFkzDB06FBMmTDD6cPPNN2Pu3Ln47W9/i7feeguHHnooVq5ciUcffRQtWrTAfffdV+9BxUlx+eWXY8OGDfjNb36D/fbbD+PGjcM+++yDnTt3Ys6cOfjoo4/QpUsXzJ49O2OB6JVXXsHVV1+NI444AgMGDECHDh2wfPlyzJ49G82aNcPkyZPrjr3hhhswf/58/P3vf8f8+fMxfvx4lJeXY/ny5XjxxRfxxhtvJBaxfdlll+Gyyy7DsGHDcPrpp2P37t145ZVX4Hkehg4dWi8N1OjRo5FKpfCLX/wCH3/8MSoqKtC2bVtceumlxnNk+565/PLL8dVXX2HEiBHo3bs3UqkU3njjDbz77rs47LDD6v1ChxBCCCGFDQV0QgghpJHSvXt3zJ07F9dccw3+9a9/Yffu3TjggAPw8ssvY/Xq1bEF9LPPPhs7d+7Em2++iXfffRdVVVXo3r07LrnkElx99dXo2bNnYP3S0lI88cQT+PnPf45//vOf2LJlCwDg3HPPNQroADBlyhT0798ft912Gx544AFUV1djwIABuPXWW3H55ZdnPdd2FEaMGIH58+fjhhtuwMsvv4zKykp069YNP/3pT/GrX/0KHTt2zLeJzpx44ol47LHH8Lvf/Q4PPPAAWrRogaOPPhpPPvkkbrjhhrzatvfee+P999/H9OnT8eKLL+K5555Dp06dcP7552PatGn18uYnxeGHH47//Oc/mDFjBl566SX861//Qrt27TBo0CBcfPHFTm3cfvvtAIDXXnsNzzzzDGpra9G9e3cMGTIEqVQKjz32GG6//Xbcf//9uPPOO9G0aVMccMABmDJlCiZOnJiYL9dffz0mTJiAX//615g3b17dgx2PP/54/OUvf8Gzzz6LrVu3onPnzthrr71wyy23YMyYMYFthpmLvvvuO5x77rlo3rw5/vnPf2b8MmPvvffGH//4R1x44YW48MIL8eijjwIAZs2ahd69e+Pxxx/HnXfeiZ49e2LKlCn4+c9/jscee6yePRdeeCFWrlyJhx56CDfffDN2796N8847L1BA79SpE9555x385je/wdNPP41///vfqKiowMknn4xp06Zh8ODBYbs6FDfccANOOukk3HHHHZg7dy5eeuklNG3aFP3798f111+Pn/70p/Vybo8dOxYrV67EvHnz8MQTT2Dr1q3Yc889ceaZZ+Kaa67BoEGD6o5t1qwZXnnlFfzpT3/CAw88gL/85S8oLS1Fz549cfHFFwcukIRl8uTJaNKkCe6880785S9/Qdu2bXHCCSdgxowZ2odPDxo0CPfddx9uvfVW3HnnnaiqqkKvXr0CBfRs3zNTp07FE088gQ8++AAvvfQSmjRpgt69e+Pmm2/GT37yk4w8/YQQQggpfFKemgSSEEIIIYQQQgghhBBCCCHMgU4IIYQQQgghhBBCCCGE6KCATgghhBBCCCGEEEIIIYRooIBOCCGEEEIIIYQQQgghhGiggE4IIYQQQgghhBBCCCGEaKCATgghhBBCCCGEEEIIIYRooIBOCCGEEEIIIYQQQgghhGiggE4IIYQQQgghhBBCCCGEaKCATgghhBBCCCGEEEIIIYRooIBOCCGEEEIIIYQQQgghhGiggE4IIYQQQgghhBBCCCGEaKCATgghhBBCCCGEEEIIIYRooIBOCCGEEEIIIYQQQgghhGiggE4IIYQQQgghhBBCCCGEaKCATgghhBBCCCGEEEIIIYRooIBOCCGEEEIIIYQQQgghhGiggE4IIYQQQgghhBBCCCGEaKCATgghhBBCCCGEEEIIIYRooIBOCCGEEEIIIYQQQgghhGiggE4IIYQQQgghhBBCCCGEaKCATgghhBBCCCGEEEIIIYRooIBOCCGEEEIIIYQQQgghhGiggE4IIYQQQgghhBBCCCGEaKCATgghhBBCCCGEEEIIIYRooIBOCCGEEEIIIYQQQgghhGiggE4IIYQQQgghhBBCCCGEaKCATgghhBBCCCGEEEIIIYRooIBOCCGEEEIIIYQQQgghhGiggE4IIYQQQgghhBBCCCGEaKCATgghhBBCCCGEEEIIIYRooIBOCCGEEEIIIYQQQgghhGiggE4IIYQQQgghhBBCCCGEaKCATgghhBBCCCGEEEIIIYRooIBOCCGEEEIIIYQQQgghhGiggE4IIYQQQgghhBBCCCGEaKCATgghhBBCCCGEEEIIIYRooIBOCCGEEEIIIYQQQgghhGiggE4IIYQQQgghhBBCCCGEaKCATgghhBBCCCGEEEIIIYRooIBOCCGEEEIIIYQQQgghhGiggE4IIYQQQgghhBBCCCGEaKCATgghhBBCCCGEEEIIIYRooIBOCCGEEEIIIYQQQgghhGiggE4IIYQQQgghhBBCCCGEaKCATgghhBBCCCGEEEIIIYRooIBOCCGEEEIIIYQQQgghhGiggE4IIYQQQgghhBBCCCGEaKCATgghhBBCCCGEEEIIIYRooIBOGgVVVVX4+c9/jm7duqF58+Y49NBD8corrzjV3bp1K6ZNm4Zx48ahffv2SKVSmDVrVnYNJoQ0aOLMSXPmzEEqldL+zZ8/P8uWE0IaGkl8zokzpxFCiMzHH3+MM844A3379kWLFi3QsWNHHHXUUXjmmWec2+CcRAhJkiS+f3FeKn7K8m0AIblg0qRJeOyxx3DFFVdgr732wqxZs3D88cfj9ddfx4gRIwLrbtiwATfccAN69uyJoUOHYs6cObkxmhDSYIkzJwkuv/xyHHzwwRll/fv3z4a5hJAGTBKfc5KY0wghBAC++OILbNmyBeeddx66deuG7du34/HHH8fEiRNx991348c//rG1Dc5JhJBsEOf7F+el4ifleZ6XbyMIySbvvvsuDj30UPz+97/HVVddBQDYuXMnBg8ejM6dO+Ott94KrF9VVYXvvvsOe+yxB95//30cfPDBuO+++zBp0qQcWE8IaWjEnZPmzJmD0aNH49FHH8Xpp5+eC5MJIQ2YuJ9z4s5phBBio6amBgceeCB27tyJxYsXBx7LOYkQkjRxv39xXmoYMIULyRp9+/bFueeeW6989OjRGDlyZM7seOyxx1BaWpoRrdCsWTNccMEFePvtt7F69erA+uXl5dhjjz2ybSYhJMs0lDlJZsuWLdi9e3c2zCSEZJlCmZPifs5Jck4jhOSPQpmTdJSWlqJHjx7YtGmT9VjOSYQ0HApxXory/YvzUsOAKVxIVti6dStWrlyJSy65pN6+//73vzjnnHMC6+/atQuVlZVO52rfvj1KSsxrQR9++CEGDBiANm3aZJQfcsghAIAFCxagR48eTucihBQnDXFOOv/887F161aUlpbiyCOPxO9//3scdNBBTjYSQvJLIc1JceHnLEKKn0Kck7Zt24YdO3agsrISs2fPxgsvvIAzzzzTWo9zEiENg0Kcl6J+/+K81DCggE6ywqJFi+B5HoYOHZpR/uWXX2Ljxo0YMmRIYP0333wTo0ePdjrXihUr0Lt3b+P+r7/+Gl27dq1XLsq++uorp/MQQoqXhjQnNW3aFKeddhqOP/54dOzYEZ988gluueUWHHnkkXjrrbcwbNgwJzsJIfmjkOakuPBzFiHFTyHOSVdeeSXuvvtuAEBJSQlOPfVU/OlPf7LW45xESMOgkOaluN+/OC81DCigk6ywaNEiAKg32X300UcAYJ3shg4d6vxEYtvPjnfs2IHy8vJ65c2aNavbTwhp2DSkOWn48OEYPnx43euJEyfi9NNPx5AhQzB16lS8+OKLTnYSQvJHIc1JceHnLEKKn0Kck6644gqcfvrp+Oqrr/DII4+gpqYG1dXV1nqckwhpGBTSvBT3+xfnpYYBBXSSFRYuXIguXbqgS5cuGeX//e9/UVJSgsGDBwfWb9euHcaMGZOILc2bN0dVVVW98p07d9btJ4Q0bBr6nNS/f3+cdNJJeOKJJ1BTU4PS0tLYdhJCskchzUlx4ecsQoqfQpyTBg4ciIEDBwIAfvCDH+C4447DhAkT8M477yCVShnrcU4ipGFQiPOSTJjvX5yXGgYU0ElWWLRoUb2VQsDP7dS3b1+0bNkysH51dTU2btzodK5OnToFTlZdu3bFmjVr6pV//fXXAIBu3bo5nYcQUrw0hjmpR48eqK6uxrZt2+rl1yOEFBaFNCfFhZ+zCCl+imFOOv3003HRRRfhs88+w9577208jnMSIQ2DYpiXXL9/cV5qGFBAJ1lh4cKF9R7yUltbi9deew1HHXWUtf5bb72VWL6q/fffH6+//jo2b96cMam98847dfsJIQ2bxjAnLV++HM2aNUOrVq0i1SeE5I5CmpPiws9ZhBQ/xTAniRQHtocCck4ipGFQDPOS6/cvzksNAwroJHHWrVuH9evX162mCe644w5s2LAB++23n7WNJPNVnX766bjllltwzz334KqrrgIAVFVV4b777sOhhx5a97Tj7du3Y9WqVejYsSM6duzodG5CSOHT0Oak9evXo1OnThltfvTRR5g9ezbGjx/v9AR5Qkj+KLQ5yRXTnOQ6pxFCCpNCm5PWrVuHzp07Z5Tt2rULf/vb39C8eXMMGjQIAOckQhoyhTYvuX7/4rzUsKGAThJn4cKFAICXX34ZP/nJTzBw4EDMnz8fL730EgDggw8+wDvvvINDDz3U2EaS+aoOPfRQnHHGGZg6dSrWrVuH/v374/7778fKlSvx17/+te64d999F6NHj8a0adNw/fXXZ7Txpz/9CZs2bap7OvIzzzyDL7/8EgBw2WWXoaKiIhFbCSHJ09DmpDPPPBPNmzfH8OHD0blzZ3zyySe455570KJFC9x0002J2EgIyR6FNicBbp9zTHOS65xGCClMCm1Ouuiii7B582YcddRR2HPPPbF27Vr84x//wOLFi3HrrbfWRXpyTiKk4VJo85Lr9y/OSw0cj5CE+cMf/uCVlpZ6zz33nNevXz+vWbNm3rHHHustXLjQ69evn9e9e3fvgw8+yKlNO3bs8K666ipvjz328MrLy72DDz7Ye/HFFzOOef311z0A3rRp0+rV79WrlwdA+7dixYrcOEEIiURDm5Nuv/1275BDDvHat2/vlZWVeV27dvXOPfdc7/PPP8+hB4SQqBTinOTyOSfoc5LLnEYIKUwKbU765z//6Y0ZM8br0qWLV1ZW5rVr184bM2aM9/TTT2ccxzmJkIZLoc1Lrt+/OC81bFKe53k5V+1Jg+ZHP/oR5s2bh88++yzfphBCCOckQkhBwTmJEFJIcE4ihBQanJdIIcJEqSRxFi5cWJebjhBC8g3nJEJIIcE5iRBSSHBOIoQUGpyXSCFCAZ0kiud5+OSTTzjZEUIKAs5JhJBCgnMSIaSQ4JxECCk0OC+RQoUCOkmUFStWYOvWrZzsCCEFAeckQkghwTmJEFJIcE4ihBQanJdIocIc6IQQQgghhBBCCCGEEEKIBkagE0IIIYQQQgghhBBCCCEaKKATQgghhBBCCCGEEEIIIRrK8m1ALqmtrcVXX32F1q1bI5VK5dscQogjnudhy5Yt6NatG0pKGs66H+ckQooTzkmEkEKCcxIhpNBoiPMS5yRCipck5qSiEdBnzpyJmTNnYuXKlQCAfffdF9dddx3Gjx/v3MZXX32FHj16ZMlCQki2Wb16Nbp3755vMxKDcxIhxQ3nJEJIIcE5iRBSaDSkeYlzEiHFT5w5qWgE9O7du+Omm27CXnvtBc/zcP/99+Okk07Chx9+iH333depjdatWwPwO6xNmzbZNJcQkiCbN29Gjx496u7hhgLnJEKKE85JhJBCgnMSIaTQaIjzEuckQoqXJOakohHQJ0yYkPH6xhtvxMyZMzF//nxnAV38zKZNmzac8AgpQhraT+U4JxFS3HBOIoQUEpyTCCGFRkOalzgnEVL8xJmTikZAl6mpqcGjjz6Kbdu24fDDDzceV1VVhaqqqrrXmzdvzoV5hBBCCCGEEEIIIYQQQhoARfU0h4ULF6JVq1YoLy/HxRdfjCeffBKDBg0yHj9jxgxUVFTU/TFfFSGEEEIIIYQQQgghhBBXikpA33vvvbFgwQK88847uOSSS3Deeefhk08+MR4/depUVFZW1v2tXr06h9YSQgghhBBCCCGEEEIIKWaKKoVL06ZN0b9/fwDAgQceiPfeew+333477r77bu3x5eXlKC8vz6WJhBBCCCGEEEIIIYQQQhoIRRWBrlJbW5uR45wQQgghhBBCCCGEEJkZM2bg4IMPRuvWrdG5c2ecfPLJWLJkSb7NIoQUCUUjoE+dOhXz5s3DypUrsXDhQkydOhVz5szB97///XybRgghhBBCCCFEgmIVIaSQmDt3LiZPnoz58+fjlVdewa5du3Dcccdh27Zt+TaNEFIEFI2Avm7dOvzgBz/A3nvvjWOOOQbvvfceXnrpJRx77LFZOd9BBwGHHAL07AmkUv62e3f//1QKGDTI/0ulgIMPBo44AujWzX/do0e6XioFDBgA7LWX///w4cDIkUDv3v7r7t3T/6dSQP/+QL9+/v8/+AEwdiyw997+6169gD590sf26QP07ev/P3AgMGYMsN9+6X2iHfFanKdPH+DYY30fUyn/OGGf8FXY37UrcNZZwPnn+6/32ittj9ov7dsDEyb4/gmb9tknfWyPHuljW7cGDj00bcPgwWnbRb/suaf/f7NmwLBhwP77+6+HDvVf62woLfXb2Xdfv78OPtj/k20Q7aZS/nEDB/r/H3oocPjhQJMm6f6Wr/nee6f79MgjgREjgBYt/Ne9e/vHy9dc9PfFFwMTJwLt2qX7Xx4f/fqlX596KnDcccAee6T3yde8b9+0/UOH+tdcnLd///R4EDaJMbnPPn674trts0/9ay7O2b8/cOaZwLhx6bEu+kj0izi2d29/LIlrM2SI36dyf4tju3f3r/eAAf7rAw4AfvKTrNy+DZILLkj3Xb9+6X5u0sTvSzFODj44fV+lUv6YFnPVoYcCRx+d3jd2LDBqVHpsjRjh32+plD//HXBA+toNGwa0bJm+b8RYGzAgbUvLln4dMSaHDUu3kUr57QsfjjjCHzti39FH+/OjuNeOOCLThwMPTI+5YcOA8vL0+BT3Zf/+aV8rKvxzi/vlgAPStrRu7d/D4h4dPTo93oUtog+HDUvblUr55cKWPn38+1DMGUOHpn0fMCBtS6dO/rk7dkzbMmSI//+eewJHHeXPteKajB7t/9+0qT+fyu81si0HH+yfU/i+//5ASYlvz+DBQJcu6esl5uJu3fzjxLU8+OD09Rs40D93mzbpfjjqqPT8ftRR/j0t3suELc2b+2NL2DlwYLqv27Tx2xdtynNur15+P5SUpMensPPgg4Fjjkn7eswx6fPttZc/Pioq0vVGjPD/79jRH7tifhsyJN1HnTv7/SLG+ODB6fG4117p44g7xxyTvo/33dd/nxHXYezY9PUbPz49Hk84Id3XEyem65x8MnDKKen/zzor/f8556TH5Gmnpa/fhAnp8x1/vD8WUym/TXEvHnts+nPA6NHpMX344elzH3SQX1/MhaLddu2AE0/0/y8p8ctbt/ZfH398eg4ZNy79Pjh6tN+2GMfiHHvvnW6rfXvf96ZN0z526JD+X8xpJ52Unm9OOCFt+5gx6bl75Mj0vD5oUPp8HTr4don76/jj058JJk5Mf4465RT/+qRSwOmnA9/7nv//97/vvxblp56aPt9JJ6U/S5xwQrqvxo9P+zR+fPoz1NixaT9Gj07bfuSRaXuPOCLdP/vu69so3n/E/23a+PaK+X/ChLRP8jU48sj0e8ghh6TnksGD07526ZI+X1mZfw4xfiZMSM/JJ56YntuOPx447LD0WBTzzogR6Xl7333T90SnTr5dok9OPDE9h558sm9bKgWccUbalnPO8felUv61+NGP8nNvJ0Wuxapf/MIf25Mn++Pg3HP963biiX5/Hnmk/3lq6FDgZz/z54lf/9ofv7fc4vf7Pff424ce8rfPP+9v//1vf/vRR/528WJ/+/nn/vazz/ztkiWZx735pr996SV/++ij/va++4C2bYE77/Tt+N3vfLt++Uv/Prv8cn8s/uhH/nfCc84BLr3Uv/9+8Qv/3rn5Zn/8zZzpz2MPPuh/NnnxRb/tuXP9c733nr/95BN/u2yZv12+PPO18GXRIn/71luZtj/yiL/9f//P/6xx661+302b5n+XuOIK/z47/3z/Pfu00/x74Nhj/f4/5BDfn332Aa680v8MNH26f2/ffnu6X1Ip4PHH/e2//uVv33kn0wdhq7Bd9eG//8304ZVX/Hv8qaf87yYPPOB/HrnnHv++/sMf/Llq2jR//rviCt/W888HLrvMn3uuuca/16dP9+eb3//efx/485/9/v773/1zPflkpu3z52f2qxgr6laMqf/8x9/OmeNvn3nG3/7jH/72//7Pn7duvtl///vFL/z3jcsu88f/pEm+naed5o+X444DzjvPf2+67DL/u/Kvf+2Pld//3v98dM89/mfDhx/OHPdi/IrxvHRpVm7dnPHiiy9i0qRJ2HfffTF06FDMmjULq1atwgcffJC1c37ve/41qa31+3DNGn++GTPG359KAfPm+fdvixZ+WefOwN/+lh47gH8tp00D1q/3y6qr/TF6wgnpdpYvB377W/89DPA/C/zrX8ATT6Tb6dPHP/+nn6bLRo7055mqKr9s0ybgkkv8z9ii7Q8+AP7yF/9zkij7+999G0v+f1Vxr738sfXll/5+z/Pn4R/+0P8/lQJWrfLPf9RR6XbmzAH++U9/XAP+e+Xf/ga8+27axgkT/LG+YYNftnMncOGF/vgW7SxbBsyY4fcf4H9XePFFYPbsdDu9ewM33ZSeKwD/fvn+94Fdu/yyb7/15+BDDkm3/e67wKxZvo+i7P7703MT4M9tv/wl8NVXflltrf+ec8EF6TorV/r+y337+uvpew/wv5/87W/A+++ny048Ebj6amDjRr9s+3bf/9GjM/2/+Wb/ugNAq1bAs8/6f7L/N96Ynv8BfyyedRawe7dftn49MGVKpv9vv+3PQfvsky67997MazR0qH+Nvv7aL6up8fWeH/84c4zecos/V4my115Lv7+Idv7+d+DDD9NlJ5zgz8ubNvll27b57Y4YgdziNSIqKys9AF5lZaX1WP8W5x//+Jftv6Tv3WIijF9lZfm/VvzjX2P4S/rezRW/+93vvIMOOshr1aqV16lTJ++kk07yFi9eHKqNsH7l+1rxj3+N4S8b926+WLdunQfAmzt3rtPxYf3q2dPvs+bN7f1aUeFv+/b1t4ce6m9POMHfnn++v73mGn/7+9/721mz/O0TT/jbp5/2t089lfn6wQf97Z13+tvrrvO3l1zib08/3d+OGuVv99nH33bqZLe9pMTf7rGHvx082N8efbS/nTTJ3151lb+96SZ/e++9/vbxxzNtVX14+GF/e9dd/nb6dH87ebK/PfNMf3vMMf52yBB/27Wrvy0ttfvQsaO/HTjQ3x51lL899VR/e9FF/vbXv/a3t9/ub//5T31/z56d6dv992det5//3N9ecIG/nTDB3x5+uL/t18/fNm1qt719e387YIC/HTHC3558sr/90Y/87S9+4W//8Ad/+/e/B9suyh94ILPeL3+Z2SennOJvjzzS3+69t7/t0MFue5Mm/nbPPf3t0KGZ1/Kss/ztpZf629/+1t/++c/+9rHH3O/3YpiXPv/8cw+At3DhQqfjo/gk+n73bn87Z47n9e/v/y/2//a36TlHlI0dm75nRVmTJp73+ef+/998k25b7H/8cc877LDMssmTPe/qqzPLhgxJj7fa2nQ7W7b4248+Ss+Ros6f/pSeH0XZmWd63m23ZZbtuafnvfee//+OHem2a2r87b/+lb7vRZ3rr/e8Cy/MLBszxvP+9rfMslTK85Yv9///6qv6/j/8cPq+EGUXXeR5U6dmlg0a5HnPPlvf/+3b/e3776fvJ1Hnj3/0vIkTM8tOO83z7rgjs6xLF8/78EP//61b69v40kvpOVuU/epX6fcGUTZqlOf94x+ZZYDnffGFv121Kj3Xiv0PPph+TxFlF1zgty+XDRjgeS++mDkuAc/budPfzp/v+yHXueWW9Pwsyk4+2fP+7/8yyzp08Lz//tf/f/Pm+v4/95znDRuWWTZ1qudddllm2YgR6fci2X9x3Zcv97xmzdL7XUhiTiqaCHRCCGnMiBV5QgjRwZ8lE0IKncrKSgBA+/bttfurqqqwefPmjL8wlJf722bN7Mfu3Blvu2OHv62t1bcvouaETWG3TZuabRfnrK7Wb8W0L2wU2+3bM1/X1LjZLvqzefPM17ZtEHH7X30MmugTYbv43GzrX7EV5cLHINR+FFvTGFH3C9s9L3MrENG8an+q18O0P2js7Nqlt1XYJPaLrShXx1hDoLa2FldccQWOOOIIDB48WHtM3DlJx44d6UjzsGW7dqWv944d/i9BbHW2batftn17uky+pmLs6eroymx26z6ChvFf3I/yvSL7r96vurJt2+qXyf7Lc4mYP+T9ujry+Vq2NPsi5gqXOkHtyHOE7H9QnSC7o/rvcj4X/8OOI/l9Vsxvujq5gJIMIYQUAeINjRBCdLz44osZr2fNmoXOnTvjgw8+wFHid7KEEJInXMSqGTNmYPr06ZHPoYqkZWX+T9J1CNFA7DdtVUFR3S9EaPE5Tf28Vlrqb4XQJbZCBBBCr07QtYmVqrhpem3bqgixRohpNhFa9U0V9XTY+t3U/+p1MInPotwkmJu24niRfkKH2n+qCG3biuOFL6aFDNPYEVsxdsRWLreNHdN4NgnotjFTjEyePBmLFi3CG2+8YTwm7pwkI8ZTVVV6nAlsZfI8JsaF2C+uFeCPLbWd6mp920KIla+psLG6uv5CmK5Mdz65TNe2zldbO7KP4h4X+8UClakdU98KX8T9qNqo+qork88n6u7cmZ5PdP6bfG3b1ty2TuS29VmQ3VH9Dzqfzf8gG3fu9FN5mY5zbScXMAKdEEIikOsHY1FAJ4SEwRbpCWQnsooQQnQIseqhhx4yHjN16lRUVlbW/a1evTrUOVRR2uXXezaB3FXQNUUTC1SbVNHTJEaHsd0m8outbRFARQh2Jh9UX8Q26LOryXbX66EeZ0KIbaYFDHXrMnbENbb1q8lmtVz0kzqG1Gh6W39HWcBwvQ6u/V0sXHrppXj22Wfx+uuvo3v37sbj4s5JOkxCbFCZSUB0EXltYqgsoOr228pE2+I+2LkzPWeY2g5rt07ktfWZre2k/FcXOaqrM0V+0RdB7djEcJP/rgJ6UFlS118s2snR4q7tmK5lWP9zAQV0QgiJQK7TJVBAJ4S44hLpCfgLgRUVFXV/PXr0yKGVhJDGgqtYVV5ejjZt2mT8hSGKgO4aCW0TcNUoYlUM1UUJu2yDPv8lZbsqipoWA4IinoO2OlxF57ACumqz2v9ht0Fka+yYbLdFoOfCdtMvOooFz/Nw6aWX4sknn8Rrr72GPn36BB4fd04C6guoUYTvOEK0fD45wljMl7q2o4jTQuS3Cahx2jbtdy0Ttu3cmb5PXPvWZndS/agri3v9VZHftshhEuddRW5bnWz5nwuYwoUQQiKQ63QJJVzuJIQ44vKzZMCPrJoyZUrd682bN1NEJ4Qkhud5uOyyy/Dkk09izpw5VrEqLkkIiXEjolVMQnqYraltIQaFjXw2+WLK5y5sd41Ed7FdEDeFi7q15RUvJAE9rCgd1vaSEvM1bawC+uTJk/Hggw/i6aefRuvWrbF27VoAQEVFBZq7JMCPQLNmfh5uW+SwTmhMQuSWy0SU8M6dmalABGGju10FVFkAt6UZ0aUCScp/ORVRFP8rKurbHZRmRPglx9fZrr9sg1gsc/0FgutChJziSYjp8kKeSYhX/XddQPj/fxQbaLfoE/mXDEGLHLp2cgEFdEIISQCXdAlxYAQ6IcQFEek5b968wEhPwI+sKs/H7x8JIY2CXItVpkjdIJKO4hZf/k2pXEx5rbMhQtuE87DpaEwitM2HKLbHjaIXhF3ASCKFTtj0M6axY7PdNIZccqG7Lr40NAF95syZAIBRo0ZllN93332YNGlSVs7pKqDqooTlVCCCOCk8dA/hFedxiRwPEpCDhOjKSne75QfdBoncrqlQdDbKYrEQ6uV7xZauRGeD7RoF5RfX5ftWr1FQ2za7bYsc5eX+Az9dF3lkX3Qiv20hIshG+VkRcju6tvPxFYYCOiGExMQ1XUJVVRWqpHeXMPmGKaATQoLIdaQnIYTYyLVYZRJF1eg6Gdfc52FF6ai25kKEtvlgEv+jRtFnw3aTWG1L6RI2Ej2bUdwm30z9b8uJrtvaHiYaNdK/WPFMnZtFXB/qaBO5VcLkl3YRedWHcQZFMsdJBeLqv9xeGP9d06yoIq8alR1ko+y/TuR3bUdndxj/4/xKQPV/+/bgtmW75QdV2/yP+lBTl0UeRqATQkgR4pouIc6T3CmgE0KCyMfPkgkhJIhci1VBQqJJQBeEfailLZrbFE3sYqtuG4RrihBXId0m5JpSuZgeZplKmdsKG8VtKzfZbuv/oPQ0caO4XW229bvJdtMvAbL5CwzijhqBbIvutUXg6uroynRRwkHtyAK6LXe5bKO8P6yNujKTgKrOIyZxum1b//8wqUDC5ml3FbnDpFmxLaA0aZK5QGgSp4X/YRY5wqaCibPI4Sryq76qIn++Urgwqy4hhMTA9cFYQLwnuTMHOiEkiJkzZ6KyshKjRo1C165d6/4efvjhfJtGCCE5wSbsBpFUFLdaLlBF0bDRxEk+TDRuKpeoD0bVETWViMlmIXa7ptLJZRR92MUBWyodlxQuNlzHMQX06Lg+6FHN9y0LklVV6evpmgpFzncu71fRpRlxFVCrqjKFeoEuxUlQmS7fd3V1+HQ1plQoQQsRNhvltnUCsi3NiC63uWg76KGmtbXh07XohOikrn9S/psi+XUif9jrnwsYgU4IIRGIki4hTr5hRqATQoLIx8+SCSGkkMilCCpeC5HKFglt+hxnEp915ab0MEIsjivkmqLoBerbTBgROm4e96i/BFBtV6+HmpM+ybETNvJcXQQwLVyETaUTlMKosaVwyQdBwmdQvnNdtPXWrZnthI3kds1T7ZpLO4w4q7PbNRWIqKMKtlEi2XU2BvkflO/c1Ldy2zr/27Tx/5f9d7Fx61Z7LnXXRQ6bjTr/1V8yuNita1vk0nf9JYNp0SUfAjpjGgkhJAKTJ0/GAw88gAcffLAuXcLatWuxQ/79W4JQQCeEEEIIMWOLiA4iW1HcUR/MmWQqF1fBPG46FFtEdBjb44r/YmvKX67abvMhiLAPD436KwYVdexEWQQQ19K1/22pkEh9dBHISYm8SaTZsAnRQSJ/1LZ1D4wM244tT7etbVchXn6oqdqOmjddLMgF2WhLBWMSuaPmO4/iv2x3UD/K80GYnOxq23HHfy5jiCigE0JIBHKdLoECOiGEEEKIGZdIXBOu4qdNlBaCik1At4nQUdJxZEv8FyK0LZVLLvO4h41IVxG22xYw5DzurrZH7X/T2DH1vyAfaWiIO64ipy0VRlAKC/k4NRVImIeR6qKbgwRUU5oR1xQmSfkflArERZwOsjuKEG3rk7D+29K1CP/lSP4oixNx/Y+a71z23zVKXrfIkQuYwoUQQiKQ63QJFNAJIYQQQszYBMQmTaI/TDSqsB41HYopPUoQSQm5tnQoKqZ87qr4H/Qw0WyJ/7ZfAAjipKGx5XF3FftNx6tR9K6pdLKx+MIULuEJygEdJZJbJ1gGiaW2HOC2FB6u6UpEHVWw1/kflO/dZmM2/NdFN6tpRlzTlWzbpm9bvAfI+eyj+K9rO6gdNUq+pCRzTjFdf/Ew0ijpWnRCvJjPqqrcf8ngmmZo5870mMo2jEAnhJAigAI6IYQQQoiZXETihk0hokYXq5gi0ePY7irMhhWhTelQbA8VdRFyk4o8N0Vxq8KvTYTORRqasP2v2q7mc09y7DAHenK45DuPmkvbRUBVy8R4saUwcY2SlusEtSM/1FTNdx43hYdrmpGdO9P3su1Bl6rIH0WIDyPyq2WmtqMsxLimqwn6JUOYRR65zMXGKAsouvGfCyigE0JIEUABnRBCCCHETDYFdNfI9LCR6CquD+YMY7urCG0Sn5PO5x5E1P4Om4teoL7OxsNEXW019b8oF2NH/U7guoBREqD8MAI9ewiRTzwmSyfExk3hkZTIK+f7DhL5w0QJ60R+nf9yHV3brv7rbJTLoor8NhvD5DtX05XEjcAPs8jhIkSbUsHY0uyE9T9udLv6UNNcQAGdEEKKAArohBBCCCFmbFHE2UyDEnZryoleSCK0qR2TCG0TcrPxCwBXEVpdwDD5YFvACMLW33EXX2wLGGokepj+F225ppsh7gQ9MDTKQxTFGLZFCYcRol0eahpV5NUtIOjadhX+bfnO4+RSD1qICBOBHRQl7uJ/0K8EZP+jRPJH9d8ll35QmiHd9Rf/79qlF8aDUsHYrke2YA50QggpAiigE0IIIYSYEWKSTYQuLTVHgodNgxJ1a0uHkotI6Khb0Y4pn7it/5O0PWqaGkHYBYwk87jbFgFsDxU1EbT4YkolJLClbqGA7s7zz/v96ZLvWy1Tx5ksIMoRwa1b29uWiRsl7BrdLMaoTuTXCbHyuBICqnyvmtKVtGxZv0yuo7PR1X/VxjDitLzIEfRQ0yAhWgjOqo1BQnSUXwmYItnVdsKkGRLXM0y+c7msWbPM66/zP18COiPQCSGkCAj62SUhhBBCSGNHCDWuKS2CiJrKJawILQgr5MaxPWpKEVcR1RQJnY1UInF/AaCijp0w/W96mKirKB12UcD0KwbbLzGCYAqX5Jg3D3j55fRrIfLt2FFfiFbFciG6i/IdO9zyfcvCpizAyscFpQIJihJXBV2BLhWIqK+rI/uvE2J37ND7HxTJrbPb5L+wURb5XRY5dNfNZGOQ//JChM5GuR25rnqNZBvkMtdIft0vGYLE6TD+B11r2UbbGNWVBY3RXEBJhhBCigBGoBNCCCGEmFEFQltKiyCiPtTSFj2sHmdKxyFIMpVIXNHfFgltE3JdfBCiTrai512FdGG7Kv5nI49+1Ih009ixLQK42G4b98SOGkUtrp/ch7o0K9XV9Rf5dJHsctoLeWyo6XjktuV2xL22a1d6rlTHmtyObLf6Sw7hi7Bb94sPXTs6/3ftCvY/yFeT/3I7om11rKttB9ltazvIf10ZkGm3uhhsalv1NZs2yvOMbGPYtk026vzXta27/rZf1iQJBXRCCCGEEEIIIUWNSSCMI0K7ip9xRVIVmwgqBF4dUdOgJG27uoCRjYe5qvttCxdqO9kUocNGopsEc1s7ar5kgforBiFABY0dWVQNspXYUQV0nWBrEjl1AqK4blHESbnMVUA15cV3aVsnRAeJ06Z25DLRJ0G+hhGnw/ofRkBOyn/ZBvX6h/FfZ3dY/03t6OzWtR3Ff90ij24u0v2SIVtQQCeEkCLAFCFDCCGEEELqf6G3pUMJIm6aDVUMtYnQSUaiJ2W7q7BuezBnlF8A2Gwy2aZGsNt8UXPh21LpuKT/CWtz3K3rQ0XDjH9TOhoK6O6oAro8HsS10vXn7t3pcSZfBzEWddciqExtW1x/XR2dOCv/r4uI1onTQYsFsjirs9EkoLrYGGYBwdX/MCK3a5R4lCh5lwUNlwWEOFHiOhvlMnH9bb8ScG3bxdfx44HDD6/fTrYoGgF9xowZOPjgg9G6dWt07twZJ598MpYsWZJvswghhBBCCCGE5BmbMBhFhLaJoK6R0TYR1JQOJYrtqg/ZTiViEnJtkejZsF0V3MKK0CZ0DxONa7tr9Lyt34XPaiR6nCh6VUDXCWskGCGgq2kmXFJYuAixrmUysshpE3l1NgpkIV5XFiQW2/yX2wnqM5s4bRLng/wPc43UNCvyIkcUcTpslLypb13FeV3btihx3RgVc8WuXe7+xxmjqo19+gAtWtRvJ1sUjYA+d+5cTJ48GfPnz8crr7yCXbt24bjjjsO2bdvybRohhGQd5kAnhBBCCDGjpqawidBBqSwErjm5bWKpayS6SpIitMlWm82ux9tQc6KHsd01LU1UAT1qPncdtuhtU/9FrWdawFARPriMe0agx8cUge4SgeuaZkMQRfgMqhPGRp2AGjeXuGsku6vdQeK8fJwu4tsmxOvSjAT5qjufjGkhQm1HF4Gvtq3qB7qxFUWId10ssV1/268bbP7n4xf6Dm9fhcGLL76Y8XrWrFno3LkzPvjgAxx11FF5sooQQgghDYXx4/NtASGEkLiYUokIxBf/EodQMiEOiTbEa9NWiCnq1na8eh4TYQR0ky2uon/YiHVVhBao6VDkramOa3+Z+j2sgK76IIQndSsvvtgWDkyLL6b+N10v17FkGztRBHTTmCB2ggR01xQmrmJ5mBQmOnG6vDyzjklAjRIlr9oT1f8oKUxKSzPTNJnaFg8odRX55TQ7rjZGyQEedpFDtbtJE78N1yj5oAUL1cagRQ5dJL+r/66LHGKb6yDDoolAV6msrAQAtG/fPs+WEEIIIYQQQggpBEx5rQWqkBuETRhXRU1XEdS0FaKAKR2KsFmIoEHigU3sD7sYYBOz1X5XEbZG6X/XhQrTIoFJMFf3q2K0KR1NFNvVXPGm/ou6SGNaODItYLjYbloEIHaipnCRxVldLmlbXmzdgyeDHkapaydMDmzXaGtb23Gi5EWb8oKcLUo8SPhNUuRWr1s2c6AD+n6U21Gj5MNEietstAnxSaWZCfI/lxRNBLpMbW0trrjiChxxxBEYPHiw8biqqipUSY9k3bx5cy7MI4QQQkgRwlRJhBBSvKjCpElQjCPgukZAuwrspnITqoBbWmoWNW0CrmuUd9hFAFMqkTj9n/QvAEwiti0NSpLif1KLLq5Cujp2Uimzn7Z+I3aCHiKqlsnoontt4rQQcVUBuUkT/1qGFWLDRCDbhFhT7mpT27r0ILa2TQJyWRlQVWUXYsPmadeJ01EWEEwpTHT+u+Sul3Gto1vkCSPOh42Sd2lbFyUvFn50i0q5pCgj0CdPnoxFixbhoYceCjxuxowZqKioqPvr0aNHjiwkhJBkobBHSPbhfUYIIcWPLTItThRxXCHdVWh3jSaOEoEeVuyPKv6bxOgkFjCy/YsAkw9JCOhJjxnVZ/VhoiouPthsJnbCpnCxRYkLkkqFYctTnnSUsK5tk/8uD6MMEyUeth+jitNq22FSmKi/nrHVCbMQE7Yfozzo05YDXdcnrgtDQTngmcLFwqWXXopnn30Wr7/+Orp37x547NSpU1FZWVn3t3r1aufzdOwY11JCCCGEFBMU0AkhpHhRo5RthMkFHTfyOW4UsYqLCOoatR03f7vpeJM9uRSho4rVJlQf4tiedOS5beyE6X9b5D+xowrothQuujQjumjbOJHMtgdPholADhLnowjfYQXdJCPZXcpkokR3m3xxffBolOsftIAQtk/Utl0WeXQ21ta62+j6y4lcUjQCuud5uPTSS/Hkk0/itddeQ58+fax1ysvL0aZNm4w/V37wgzjWEkIIIaTYoIBOCCHFjymViCkSOgjX9BvZEkltudDDRBFnK1o7bCoXQZwc9En1s+k62iimFC4m24M+86jCedj+Ib7AGkb4DRvJ7RIl7Sqg6vJ0C2xR8rYUJrb0KEE5ucPmF1dtDIqSj5JfXIx/0yKHzka1zJSnPaygbbv+pgWEOClcbIs8YW2MEsmuOy6XFE0O9MmTJ+PBBx/E008/jdatW2Pt2rUAgIqKCjRv3jzP1hFCCCGk2KGATgghxY8tDYrAJRd0rkVQ15zTSaShSUqcNh2nvqfGWcBw7W/1YYJRFwtsOdFdbFfrRh0zUceYaocgibFD7LimcBHzj00sFZJXkNAsY4uSjpIeRJdmxFX4FmXyeBQ27toVHG0fJpJdbdvml2vEu1xfV2ZL16K7RuKBsSb/w4rzMlHE+aBFBbWObiHC5QGlqo1NmgA7dkT/JQNTuBiYOXMmKisrMWrUKHTt2rXu7+GHH87K+fglmhBCCGlc8L2fEEKKF1O6CdeHKupIWjgP256KKRI9CNc0KEkvBrimEolje7bE5yTS0Nh+nZB09Lyt/9XXLimMTD4QOyYBXcYWJe4qKoZ5GKVax3S+IAE1SiS7XEc+zvVhnC75teW25Sj5sH1risAPGxFvuv5R0qy4RsnrosTDLkSYrptrHdsCgu76265R0C8ZcknRRKB7OY7P55doQgghpHHB935CCCl+oqQSsQnWucpfnYs0KLnKyW2zPUoO+qQFcltf2HwI8xDXpH614FpfkMTYoYAeHlcBvazMFwqDRG5TxK9O5JZ/jSHGZ5CgGyXNjFxHFjldHv6p899Ux/VBp2FE96B+tKUw0bUj12na1GyjyX9V5HeNktf5YvPBdSFCtaGkxL/3bdfI5cGyMrZFDtc+ySVFE4Gea/glmhBCCGlc8L2fEEKKl7APPIwSCZ10mhPXNCpxbDfls44rMscVcl3ec7MVPR/WdpU4UfRJCem5WHwxpZ8hdoSArhMQdRG4SaUwiRPJbIsc14mzthQmQf7LAqprtLHN/yQeaqoK3+JeCVqIcLUB0Led6yhx20KMfJxrLnW17aipgMI+AyCXUEAnhBBCCAEFdEIIaQi4pkERJCGCZiua2yZYurxvRRX9kxZyTbik0IkrhEdd8DCRRB7xXEWkC1RBPcoCRj4Eq2JFjUCXU1jID6MUhE2FYWrbFiUd1I5JQHaJEg4j8uui5MNGW9v8t4nuNpFbLovzoM8oNuquWzYfRqtrxzUVUNjodls7rgsx4jjmQC8Q+CWaEFJIcE4ihBBCCDFjirROMpVIVOE2rhiq+hBGyHQV/aOmS7HVE5h8CJOD3lUIT/oXAlFsF7gutmRrMSZK+hkBU7hEp6ws8x6IIzqbxMkoKTwEQSlMZEwCqovIq7MrTJ2wfWLyK6iOLU+7vIBgSnESdD7Vrih1wkTgx1lAMC3yBEXJ6/rEtjhh68ewZbmEAroBilWEEEJI44Lv/YQQUvzYROg4beY6DYfJ9jDRxK6CbbZst4muYUTobNtu6iNBEgJ6tvvZ1u9hFmFc2yT1EXmjBbKYrouiFvtNkdoCsV9uW64jyl3ryGVyHfkeU6PEdXVsvsSpoxNnTXa79pnYX1urX4iQx7yaS15Xx3Td1DRILnVcfdHZbbv+qmDvecE2utbRXTd5gUB3L8j7dWU6u3Q25BIK6Ab4JZoQQghpXPC9nxBCih9bKgtBklHcSYufNgFXJcko7qRTjthE6ChpaEznEqKPa/+6Xo8kFl/ijp2o9QQm4TzM4gsFdHeEgK4TEHX9KZepdeRrpruv5Guta0euE5TXPoyNUX0JU0dnt66uyX91v2uZ6dyu7QT5F6WOzf8odYKEbZONtmup9pM8bl19tY11tS5TuBQI/BJNCCGENC743k8IIcWLSSywiX4u4mhS0cCu4qepvikSPUhAN4k/YW2Ju1hgIk4qEdd+C7twoW5tvwCIYntSiy9hFzDU/g6Tg54CujslJXphFwgvaNfU6Oe3IBE0jmDt0k5Ykdvmv6s4H0b4D9t2NhYQorQdtU/k/5Psx6A6hbCAkEsooBNCCCGEgAI6IYQ0BMKKoC7kSgS1tRfF9qhR2Elvk4zitvlkK3dd4FBFmig+RB0zpmj6qNczSgS6yQdip7TULHLmU5yN0o6LyO8qtIapE1ZUdWnHdSEiiqDrEvHvUifIl6DFB5PdURY5kliIyObYUstzBQV0A/wSTQghhDQu+N5PCCHFi+khoqYobjHnh4lAT0o4j9qO+j6VxCKAySZXEdrVB9ODOJOI4hZbm+gcVXw2CTVR8oi7ivZJL2BEsV31gQK6O0EpXKJEd7vUdWknTiqUJITvMHXiCr9Rzxfm3PmKwAaiCf+ubUdZQAg6LkyUvFpHV1f8zxQuBQK/RBNCCCGNC773E0JI8WMTIAVhBETTF/pciZ9JRHHHFcDjbtUFjCi2Z2shI6wIHecXANnub9N5oixcCCigh0d9iGhSQqR8H0URJ12Oc7HHtSxb4ixgj+4OKovifxTh27XtIP9NUde6/fJ84NKPQeK0WidI0LYJ367tmK5l0DjKJRTQDfBLNCGEENK44Hs/IYQUPzZxVCWOkJiUQOsqxJtE6DiR0EmLzbY+Um2OEoGebZttIrTqgwthhfSkBXeT2BSl/4uVefPmYcKECejWrRtSqRSeeuqprJ1LFdBtIm9Ycda2P67IrRNGbSK3S+R8mDpB/WQ7Txy7bec22e36gFbXOq7thFlAiXKNgtq2Cd86u5MYt+r/uYICOiGEEEIIKKATQkiS5FKsAsxReyZRtJAioeNGE7tgE4dt/RZWlDZto0RCF/qDUF3I1hgJ63uUsWNaQCg2tm3bhqFDh+Kuu+7K+rlcHyKq7ncVLE11bGJpnBQuYUVeFwHZ5Xw1NeEj2cP0k3q+MHVsNoSNknddIIjil+tCg22/bRzJ/2crB7z6f66ggG6AX6IJIYUE5yRCsk+x32e5FqsIISSIXIpVMmHF0CgidLbFzrBCbj7yuBeyCG07LqzvSSy+JN3vUduJ8+uFfER8Jsn48ePx29/+FqecckrWzyUeIppEChObgKwTJ+OI4S42BrWTlIDuGoHsKjqbxHmbjVFzoAPRcuDryuI8MDTsgkWUOrrzyf+Hadtl4Uf8zxzoBUKxf4kmhGQfilWEkEIiX2IVIYToyKVYBdgj0NXXUb7vqQJCWEE3KQE4ShSxIKoQa+rHqL4J4ixgxBXKo4rQcWy3bbMtqMfROYpdQM8lQTnQ44icSQvaSUYgq754nl3Q1+XNdk0zEiSCu9qYzb617Xe9HrZFjqQXPqLana12bG3nkrLcn7I4oIBOCLEhxKof/vCHOPXUU/NtDiEkJsX+3j9+/HiMHz8+32YQQkgkqqqqUFVVVfd68+bNkdpxFRLjpKOIK3qGraeeL8kHQkYV+eMKvXHEf5Otrg97jSu4RyGs+J+tsRRn8aWxCehx5qSgHOhRont17bgKsWHPF8WefIj8uW47rDgv7zeV6a5RSYnZl7jjyGUxxOZXNq5/UDu6vlPLcwUFdEIIiQjFKkIaFsUuoIclKbGKEEKSYMaMGZg+fXrk+qYv+dkQErMlKmdTfBa4CuXZEtR3747ug82mpEVpm1AfxgdTVGncfhTbqIswYWhsAnqcOUmIoFHEWZcy+f8womoxRGC7nC+pdlzE6SQe9BnGHjF2dEK7a9u6OnEXUKKMI3UeUuu4/krAtDjBFC4FQmP7Ek0IIYQ0dhrbe/+MGTNQUVFR99ejR498m0QIacRMnToVlZWVdX+rV6+O1I4qKtlE0CiYhImooqhJBLW1p9rjQthzuIrIpq1NuE1yAcOUxsfma9jrlkQUt2v/qT659rPqkzrGBFF+vdBYiDMnBUURB4mFpqjkIAHVVUCXr7Wt7aAIZVOZ2rbNnjDnU49Lqp2oArJaxxQl7Sro53pxIpvXLUo/hi1T/88VjEA30Ni+RBNCsg+jPQkpbBrbe//UqVMxZcqUutebN2+miE4IyRvl5eUoLy+P3U7YSNwomETgsOKm7bhsCLlRRWZbdLZNKFfPHwdbP8cVzE3R3TbfXFDbcB0bgrDCu02Ij2J7YyHOnFRa6m/DityuoqqpTlICchQbXR90aovuFn0XVFf+31YWJPLaFidcfbUJyDZR2TXi3VW8dxW5PS9eCpekRP6wvqjH5goK6AYa25doQkj2ifvTZEJIdmls7/1JiVWEEFIImERUU3R3EhHotijrbG8FqVR4f1xFepOYnJTtcUiqH23XzdRXUUiq/6IuZCTxC4BiZevWrVi6dGnd6xUrVmDBggVo3749evbsmei5dGk4BDZRUXwetYncpnZSqXDCL+DXCSPyqnXCCKg2IVZefBC+2EReV//VukJALikJHzlt8j9I0Ffr2BY5VP/jiOVB1yjM9Re/knC5/nH8t/WtfK/kCqZwMdDYvkQTQrJPUj9NJoRkB773E0JIcmzduhULFizAggULAKTFqlWrVmX1vCaB0jXC14VsC7dhy5P0wVVMjivwJrGAYWvbtR/VdrO5CJCUQF4ICxjFxvvvv49hw4Zh2LBhAIApU6Zg2LBhuO666xI/lyygy4Ku/HBRVRiU94k6paWZaV2CxGIhAos68nHyVtQRC2+e59exicXq+eQ6Ort0NurqiP06/9WHsQb5b6qjE/7FcUBaQDcJ40IsVoVm4YvOBpv/qsjv4ouujiz8y9c/6LoJH4SNujry4oVoW7VH9UUW1eUURmod01gXZfL1kMe/zv9cwwh0QgjJEXGiPSnsEZJ9iv0+y2VkFSGE2Hj//fcxevToutciZdR5552HWbNmJX4+3U/TXbZxzpUrId0kDMfB1h9J+5jkdQjbn0mLz0mK0bnq3yQXLoqVUaNGwcuRE6qAKIuusoAoi4XyVieGqsepbcuio6ltcZxc1yTEBtmoipxymdy2yUa5HVl0tdlt8z+M3Wo7QWKxSztBfWsTotWFD7kd8esfVVTWjQ+bL/JiSWmp37bu+pvsDns+k+ju2t+ygK5rO9dQQDdQ7F+iCSHZh2IVIQ2LYn/vz7VYRQghQeRSrJKJKpLGIWnBPJdRxNkWbG3idTZtdu3PsP2epBgddTGgEMR/YkYW0GXRURZLXUXFXbv0dXWCZpA4q55P7FPbkfcHtaMTUF3aNomupvPt3l1fnA/y39a3sg1Apr02e0xiuSlyWieM796tX+SQy2T/d+3KFLllcd5kt0l01vmvs1G3WKD6GnQt5b4N6rOgMrUd9VoyB3oBUexfogkh2YdiFSENi2J/78+lWHXNNTk5DSGEhMYWga4KunGIKxonJewmYbvrOU0+mnx2PW8Uko7KjupTFFzPldQCBgX03CI/CDNMtK1J+FXFUF3buvQgtshp0Y4tXUeUMp2Ncmoal3bk5zuoorqL/0HCvy7Xuq6O5wFlZeZfBJgWH0Tbah35Gqj+q+lhRD/KkeNyehi1jhCi1T4zCdZyO3KdINHdxRfVfzkHulxHt+gg2yh8lUV8dXzkkpLcni4e8+bNw4QJE9CtWzekUik89dRTWTtXsX+JJoRkHyFWqX8UzwkhDZ2FC/NtASGEZBI2WjgJAdEmsMaNLlbbMR0fBZvQGte3bC5gxI3SDnt8NsTnpAXysIs1cWwmduT80rLwZxIQ1QdmqgIiUF+cDhJGPc8cgWxqR1xfm+guyoQvpqh0oL7IqxNdXSKZ5YhntW917cj+66LERXvCRlmo10VE63yVr6VJ2FV91dVRhXG5HYE6JnQCslxH9UW1W5xX+O9aR2eXHPGuXiOT/7JYrvqvWyww9XOuKSoBfdu2bRg6dCjuuuuufJtCCCE5hYt6hGQf3meEEFL8RBUks3kum9gZVwBO0vawYrKrrUlGQpsWEJK2PRvivyCsqB9V5HddECHJIn+mNImuushpgS4CWydOq/tN7ZhEXFM7JgFZV6YK/64Csq6OEL5FlLncjslXuUyuI18H0zUA0oKvaEcnFqvCt2yP2t9BkdPqudU66jVQ/Q9TR+eLbLfqv1xHvUY64VvUMY3bIP/D+iLXEQsITOFiYfz48Rg/fnxOzsUv0YSQQoIfcgnJPnzvd4d9RQgpNFwFySQFxKSEWVN72RCfbbaHtcXWXj4XLsL2Yy59SLofowrvYeD3kfDIAjNgFhBNYrjajlpH7DOVqZHT8vWXBXS1DmAX/nW+mCKHdb7YhH95vKmCta7P5Po6/00irmqPLkpcJ3zL7Qjh3yRiq22L8+rq6MR5XZ/phH/VniBfovhvstEm4puupa2Oi/+5pKgE9LBUVVWhqqqq7vXmzZud6/KLISGEENK44Hu/O+wrQkihUshCblQR1FYeh1z5kE3bXSP7kxLUs/kLgKg+5dJ2YscmoKplQWKoqR019YquDlBfxBd1TNHNap0gQddkV5A4q6ITg039KP9vEv5tIq9cx9S2LJab5izTYoiMSZw2+WU6LqgfbWNLFcPl9uT/TRH6Ol9SqXQddbHEJpYHjb0o/ueSokrhEpYZM2agoqKi7q9Hjx7OdfnFkBBCCGlc8L2fEEKKn7CCbhLnSkqQdY2az6b47+qDKS1N1Oj7bPrgGj3v6lMS2M6pHhd3LJl8jmIzccck3pqEaFEWdJx8bFB0r65tuY4tAjmqyKseb6pjEr6D+sl1sUA9TtemrW0Z1X/1GgVdX3nr6kuYPnGpE+R/UB15HLmMCVcb5Tq6BYugcas7Z65o0AL61KlTUVlZWfe3evVq57r8Ek0IIYQ0Lvje7w77ihBSaJgEQlt5NkRoV5Ezav0kFgHinjuuzUksYITNFR+2Hwtp7NgWBaIK7yQ7iM9JamqSMJHD6vGijkmctYnlOuFXPk6XmsUkjKp1XARruY5JvDYJtmq6Fl1dWz/ahH/Zf1sdwBxNbSoz1dEdp/adem5bHbkf5X2qX6Z+VNEtIJgWMeTjdfONbkyE8T+V0i9y5IIGncKlvLwc5eXl+TaDEEJiQ7GKkOzD+4wQQho+uRBBk4rOdhVTs4npHK4idTai51Ubwgrm+ehHFVeBPOkFC6ZwyQ0uEdjysbayILFYV8dFVFZtC/LBJMTrjpO3roJ20PlUW4WAqmsnSJyW29aJ+LrzqD6Y+tHki+kaudSxXX+b/65lYceWrszFF1GuPjBWxmSDad7K9Xe3Bh2BHgd+iSaEEEIaF3zvd4d9RQgpNMIKgkkIua5pMpIScAtJ9DT5qr7OhnBuO4drvXxiW5hQX9t8tQnvpvbCUAj9VmwkKSCGjcBW9wcJzbpc6rrzmCKwdcfbbLT1k64d1zo6X+Q+EfuC2gnqM52vQf6ruArROl+jiuqmaySXBdmju/+DFhp053FZLDGNN50PuaaoItC3bt2KpUuX1r1esWIFFixYgPbt26Nnz56JnotfDAkhhBBCCCGEZBsKkyTXcMxlFxeRUz5WFRDluqbj5DJB3AjksOKsKR2LqzhrqxNViI/SZ0Fty//r7HaxS+e/qUzUUfOCh2nHdF11aWSCHkbr4mvQ4ksU/20LCCZBP9sUlYD+/vvvY/To0XWvp0yZAgA477zzMGvWrETPRQGdEEIIaVzwvd8d9hUhpNCgIEhIduE9Fh7X6GZ1X5CoLtdRy1wiuWW7VFt1dVRfVIIWCILOoyMoStzFbp09at+5tCPXcV3QkNt3Ff51bbssaAT5YLLRRZxW99uuaxgbxP/qcwFcfHGtk22KSkAfNWoUPM7ahJBGCMUqQrIP7zN32FeEEEIIIXpMn5PCiI7yNqiOq3gdFCVsiugVZXKEclKR47aIb1cBOYkIbJs4XVJSP42OaTEkjI2iTO1vl0juoDphhPawCwxhxpHNV3WxxGURR20nlxSVgJ5L+MWQEEIIaVzwvZ8QQoqTiy8G7r4731YQ0rBhLGN4TKKjTuQ1HSfKwwiMNnFarSMf5yJoy2Vx7XYR1aPUkX11WZwwLUTY6oQR/sP2rc5/m91q2hcZm93queUxqkaBi/0lJfZ2XH0NWvgwLSrkEj5E1AC/RBNCCgl+YCWEFBL8nEQIKSR27Mi3BYQQksZFnDUJtjaxNIzwqx7nIqDq2lbbk+u4iJyuUeK6RYWgtm3nsx0Xp21X4VdXJ6gd1QadPbY6ruez1RGo4zbIV/l43blNkfyu/psWeXIBBXQD/GJICCGENC743k8IIYQQQpLCJQLZ5bgwYqmrWB5GnA2qE8buMFHJ8jZIQLUtRIQ5n4uvru24tq0eZ1tAUdsOI5brbHRd5LEtuujqBJXpsI2jfEMBnRBCioBCeMMgpKHD+8wd9hUhpJDgnEQIKSSCBEs5AtckzgLmaFzRpqvwrbbnKhCr/gRFDpv8d42clvfpfA4jIAeJyqbIad25TX3met1skdVxFixs7ejquPofZrFE3obxNeh6uLStGwu5gAK6AX4II4QQQgghhBBCCMmPYFXsBAnaAp3IKdcV/8tb2/l0bYexMYzdog1XId507qh26+oERTybRF6xQGCqY2onqEy1R32AadR2bHVcj4vStlpHLRPHBy0wBNltE+LVeyKXUEA3QAGdEEIIaVzwvd8d9hUhhBDSuKCA7o6LgBpHsIwTba2KuDpx3iaq60R+2W9TO6a25X2qEOtSR8bkl+k4k91qHV191/1B1yioPZNdprKgRQeTrUHnMdktl5n6W1dHN/6DbJAfiBp07lxBAd0AvxgSQgghjQu+97vDviKEFBKckwghhUiQOB10fJCorraj1ikp0deRjxPR1qJcFjZdxFIgWAwNslE+t81Xl4hnk8gfph3Xvtf5Kou8rsKwi6+6OrprrS4q6CK5bf1t81+uq6tj8992PeR9QcfpFhByCQV0A/wQRggpJDgnEUIIIYQQQvJFvsWrYsIkfAelq3AVkOX2bQKyXDfIRnGcqY6rEGuLkhdis01oVW2IIpa7lqkLCLbzqftNZTYbw/giLzrYbBT7otitux5qHVeR2zYeXUR1Ux3Zx1xCAZ0QQgghBFyoCgP7ihBCCCEkGFXkc4luluuEFT7l9nTCZ1AUtUsEsjg+yJ6gfggSU20it7zVnc+2OBFkg2qjvFX/N7Wj7je1bbNbrmv6NUGQWO7yCwTTAoLqq85GUSdonLguRJj8Dxpb6uJELqGAboBfDAkhhJDGBd/7CSGkOOH8TQgpRETksByBna2oXJM4qRJGDI0a3e1SxxSpHDW6W9dPrhHYpgUEF9E5SCxW+0S9/qYoapcFBPkcurpBfaLDZTzKx7lEoKtt69ox4eJ/rqGAboAfwgghhBBC9PBzEiGEEEKIGZ2AGiRuhonADmo7TJlqg2s7QWK7TVS1RS3Lx6n5xXV1bL6q9rmK6qY6ru241Anq27htq3Vc2naxUdSxLSDoyoLq6BYx5Ouv259rKKAb4BdDQgghpHHB935CCCGEED35jv4sRsIKsa7RvXKb6vmi1tEdFyRYhhFibUK8LsLY1Zeg8+l8drXbpY7Yp5apPrjaLZ9P105Q20ELCDYxPMk+cRX5XcaEi425hAI6IYQQQggooIeBfUUIKSQ4JxFCCo0oEchAMhHIpkjeoPOJdDOmiGC5rsAmcsvHufqvI8ivMBHYLm0HlQX5YhKY1TryVq6jO58age/atzrhO+i62XKgqzbY+lR3HhndYoC6P+yCTi6ggG6AH8IIIYSQxgXf+wkhhUY+viASQogOzkfhMQmNshCpHi/v1wnNprZNQrSL8OsqaOpE13xHIOtEZ1dRPYrdMmLRwWSPXNfkv9qPNruD7IkSte2ygCCEfJ2NJpHftICgO0493uRLvqGAbqAQLg4hhBBCSCHCz0mEEEJI44ICejh0wmCYCGyTOGkS1VXB0lbH1o6rYK/u153HFN1sisC2LQbI5abo7iAbXIR4m8hfoqipLkK8TixX60ZdQJAF7TDjTR4zrgsfah2T/7prJD9YV9eOyzXSLVTkAgroBvjFkBBCCGlc8L3fHfYVIbmBgpUbnJMIIYWILgJXFRBdRF65jm6/fD6TgKqeT67jEoFs2q8TYoP8D4pAFgRFN6vHqXXE8a4LCEE2qucJI/LqyoKuh+uiirq/ttZeR7VBFzmvqxNUZhoTQefWRbLLiwqqqO6SeiiXlEWpVFVVhXfeeQdffPEFtm/fjk6dOmHYsGHo06dP0vblDX4II4QQQhoXfO8nhBQaFNAJIaQ4MQmatuOC5n1bVK7cnskGW3SzSwRyEu3o6pgEWXUBQee/XGYSlU3XIygdjdqnOrttdVyvh8n/oPOp7Yh9tkUHm91hfYnSjmms22zMF6EE9DfffBO33347nnnmGezatQsVFRVo3rw5Nm7ciKqqKvTt2xc//vGPcfHFF6N169bZspkQQgghhOQRLjYQQgghhARjE5VVcVON+A7KLy3QicWiTFdXLnPZH6bMZX9YUV1X19QnrhHYQb7GEZDlNlwFZJc+CfoFQtCigmm8qXVKSoLzubtcI7V/XK+RKAuTuz5fYrpzCpeJEyfizDPPRO/evfHyyy9jy5Yt+Pbbb/Hll19i+/bt+Pzzz/GrX/0Kr776KgYMGIBXXnklm3ZnHX4xJIQQQhoXfO8nhBQa+Y62KhY4fxNCCg2XyGL1OLFfPS5MxLOtjmu0tU2ITaqdKNHNaj/JdYRdJiEe8MVi0+KEiaAFDUGUhQjdftNxpjLbfpeFCN1+2wKCvA0SuV2uf1A/BYnzucQ5Av2EE07A448/jiZNmmj39+3bF3379sV5552HTz75BF9//XViRuYDfggjhBBCGhd873eHfUUIIYQQEkwUUVktk9txaVvsE2UqLoKl2l4YITZqBLLrYoHrAoKub8O2E+UaBdXR5fuWhWjdcaa25T6W6+sWEFRcr5taJ2gBwVbHdJw6bl2uv8uCRzZwFtAvuugi50YHDRqEQYMGRTKoUOAXQ0IIIYQQQkg+yccXxGKE390IIYWGKqCahD9bJK+M2o5OsA16iKjajvhfVyfIF1ubJhvFcWJrEpB1Aqq8VfvJ5Kvat7a2o4rzap247cj7kmrbdt1cxla2FhDCtq32U65wTuEi07dvX3z77bf1yjdt2oS+ffvGNiqIu+66C71790azZs1w6KGH4t13383q+QghJAjOSYQ0HBqCAJOrOakh9BUhxUCxC+j8nEQIKTRyOS8FReW6CL9qO6ZoYrUduTyM8Bt0vrDtuAijtnaCIpDluqbz2ewS+12uR5Dwa+sf3eKESUA2tS1vXfvb9brp2pa3Ua9RNq+/7t7KNpEE9JUrV6KmpqZeeVVVFdasWRPbKBMPP/wwpkyZgmnTpuE///kPhg4dirFjx2LdunWJn4tfDAkhNnI5JxFCsk+xv/fzcxIhpJDg5yRCGhbFvqAH5P6zkkn4A9zESZH+QuTsVtsJEn7l/S7Cb5DwGbYdga0sSEAWPrm2HfbcLm3rrqVa5no+WzthROWgRZegvlXtto0jnT22cSvjuoCg2q1LYSNv80UoAX327NmYPXs2AOCll16qez179mw8+eST+M1vfoPevXtnw04AwG233YYLL7wQ559/PgYNGoQ///nPaNGiBe69997Ez8UvhoQQG7mckwghxAbnJEIaHvn+shgHfncjhBQauf6s5Coqy/uAaOK0Kjrr2onTdlSRV96XlIAcZKOcA1wVYuXjdL66RE6r4rQpL7iuHfW4oAWEXFyjqG2HrWOzUd662J2v93znHOgAcPLJJwMAUqkUzjvvvIx9TZo0Qe/evXHrrbcmZpxMdXU1PvjgA0ydOrWurKSkBGPGjMHbb7+d+Pn4IYwQEkSu5yRCSPYp5vd+fk4ihBQS/JxECCk08vFZKRvirC7KV25H9xDJoAjrIBvV8wSVmRYEXERl+XhXcVpuT/XfdA3UMvm42lo3u+U6NTX6COygay3XUX1R7ZZ/geA6jkzXX21bPbewy2WMyn3m8mBRtY7Y2hYQbG3nmlACeu3/P6L69OmD9957Dx07dsyKUTo2bNiAmpoadOnSJaO8S5cuWLx4sbZOVVUVqqqq6l5v3rzZ+Xz8YkgICSLXcxIhJPsU83s/5yRCGib5+IKYBJyTCGl4FOt8JAg7LyUxJ8WJynWJtk4qArm0FNi9291uIW7u3u1uozhOCKg6G02+qAKysEGcV9SR67osOsh2i3PYFhDUKGiT/3KZ7KvJRnG8i/Ct88Xl+uvKVOFf50uUsRV1jIZtO5dEyoG+YsWKnIrnUZkxYwYqKirq/nr06OFct5i/RBNCCpM4cxIhJPs0tvd+fk4ipPApdsEqDJyTCCGFRNzvbmpUs0AWg8VxusjhoDqAWZwU+2xCrE2cFPt0dUQZEBwlHyVy3DVyOqw4q+sTtY6rgCz3T5hFDrWOqy9B+8UYCxofpgUE12st77f5r9bR2aWLkhf7TMK4nKddtSdXOAvoDz30kHOjq1evxptvvhnJIBMdO3ZEaWkpvvnmm4zyb775BnvssYe2ztSpU1FZWVn3t3r1aufz8UMYISSIXM9JhBASBOckQkghwTmJkIZHsS/ohZ2X4s5JugjsIAE5TFRuUgIykHldXdoW7Yi6QSKvGjluOs4ktAtMdusefhmmjuy/qzgfJPwH9Ynskywgu9SR7bb57yrEu0R3R23btU5cu3ONs4A+c+ZM7LPPPvjf//1ffPrpp/X2V1ZW4vnnn8c555yDAw44AN9++22ihjZt2hQHHnggXn311bqy2tpavPrqqzj88MO1dcrLy9GmTZuMP0IISQLOSYQ0PIp58TzXc1Ix9xUhxUSxClb8nEQIKTTCzktJzElBEcimsiCxUK4j8k+boptt0b9hbHQR+YPEWbVPTMKvrR1XG5OyO46N6gKC7hrJbbv2ies10tkoxowatW5qWxwn6ujaVhcQ5OPUX2AELegE/QLB9KuNXH8fcc6BPnfuXMyePRt33nknpk6dipYtW6JLly5o1qwZvvvuO6xduxYdO3bEpEmTsGjRonp5pZJgypQpOO+883DQQQfhkEMOwR//+Eds27YN559/fuLn4hdDQoiNXM5JhJDsU+zv/fycREjDo1gFdIBzEiENjWKejwS5npfCCK2mh1+qdaJG/NrER10EsqvQKv8fVMdUZhNITVHSJjFY1z9hbQTqC8im66u2rfajbLfOV5M4rdqtu0ZhFwvEONMtzshty/vEsapdajtq39rqyP4H9a2unXzMR6EeIjpx4kRMnDgRGzZswL///W+sWrUKO3bsQMeOHTFs2DAMGzYMJSWR0qo7ceaZZ2L9+vW47rrrsHbtWuy///548cUXsyLW80MYIcRGLuckQgixwTmJEFJIcE4ihBQauZ6XwoqcalSuKk7q6ugES/XcJpFTZ6OuLCgC2SZyhxH+TW3Lx6k22vbrjlP7ST7eJE7rzmHrb1sdl/1B/gXVMfW9vLUtptgWS8Iu8qhlat+7tm2yO9uEEtAFCxcuxCmnnKLdd/fdd+Oiiy6KZVQQl156KS699NKstS+ggE4IcSFXcxIhJPs0hPd+fk4ipGFR7BGf/JxESMOh2OcjQS4/K7mIqmHEYpMY7FJHJwbLx4kyVwHZRbDX2WXy37RfbVvtM9cy0Y68NQnjNgHZ1LZJ5HbxRc6Lrst3rhOQg3Kgm9pxaVteLAnTt7qyoNQ0Udo2XY9cEClcfNy4cbj66quxa9euurINGzZgwoQJuPbaaxMzLp/wiyEhhBDSuOB7PyGEFCecvwkhhUoYMVjd7xK1HVdAtkUJ64gSbWzzRRaQg4RYlz5xObdLdHPYvo3btiBs30Ztx3T9w6bUcWlbd5xpv7wN6sdcE0lAf/311/Hkk0/i4IMPxieffILnnnsOgwcPxubNm7FgwYKETcwP/BBGCCGEEKKHn5MIyQ35+IJICCEkGcIKn2EEy7giZ7YF5KjtBLWt2y+3o+Iq8tquW5DI63I9dNdfzcMu71fbDhKQTb6Yzi33je58oly10dRfQWPLVkftJ1NZUD/mkkgC+vDhw7FgwQIMHjwYBxxwAE455RT87Gc/w5w5c9CrV6+kbcwL/GJICCGENC743k8IKTQooBNCCgXOR+HQReCGFaJ17enaVvcL1KhtUzvivAKbOC8II3K6ithB51HzsOv6x2XRwWRP0DWS+1MuiyoGm/rR5otLnaCxFZT2xTZOXIRxF7tNdTwveJFHHG9a+Mg2kZ/4+dlnn+H9999H9+7dUVZWhiVLlmD79u1J2pZX+CWaEFJIlJbm2wJCGj5873eHfUUIKSQ4JxFCChGTWCoeygkER+iqAqKrgKwKkTp7gsRJ1S5XkVPncxiB3LaooOtH26KCzldblHiQ/7p21LIw54sjINt8lX1xtdHF7qDxGCTOq3V0fav2vYxtHGWbSAL6TTfdhMMPPxzHHnssFi1ahHfffRcffvghhgwZgrfffjtpGwkhpNHz/vv5toAQQgghuSYfEVaEEKKD81F4bGIokN6vywGutqO2bXtAo3wO1R65PKyAbCvT+a/WcRGnTeK8aztq39kEZFcxOKi/XaK7df0cVUBWzxfUts4vtb2gtl3HVpA4r9qqE+Jdr1FRCOi33347nnrqKdx5551o1qwZBg8ejHfffRennnoqRo0albCJ+SHXF4IQQoLgnERI9uF95g77ipDcQMHKDc5JhJBCI0h0BcKJ07pIXpMYLlCjlnXtyHaEEZBN4m6QjVEEZJs4r7PRZI9aptaJIiBHEXldFxDEvrC+yNfIpW2X3O1h2g57DQRBiwEuiwW5oCxKpYULF6Jjx44ZZU2aNMHvf/97nHjiiYkYlm/4IYwQQghpXPC93x32FSGEENK44IJeNExisExU0VGuG1aIV/fbBEvV7jBRwi5iaBgh1kXkjWpjNtoW6XpczifXMQnxJnt06VHkbdB1DVqwCLvIka1rpPMll0SKQFfFc5mRI0dGNqaQ4BdDQkghwTmJkOzD+4wQUmhQsCKEkOIlrIDsGpVsEy9dooRNZdmMQI4rqgb1bRjR1bZgIbenazvofDpfddfN1naQryb/VV/VfrCJ02HaNtmjiviqT7Jdsv+6Y4Psycfno8gPEW3o8Es0IdmnQ4d8W1A8cE4ihBQSnJMIIYUE5yRCSCGSrShpoH7qDbnMVMcmKmc7AtnWJ0H+q/bbRGedzzpBO6iO/KBXdb+81fml9onNf91+mzivs0H+X86RH2SjvC/IP1udoPFm6pOwCx9Bwn+2oYBugB/CCMk+8+bl24LigXMSIdmH9xkhpNDIR4QVIYSQ+KgCsipO6o6Ty2yYooRNttjq2IRWU6RyUB2dDeJ/l2jiILtt4ryLeK+WBYnzpmvkIvzrjlPbDnogrMluuY74P+7ijG2RR+1bm8jvshCjSz0TZtEpV1BAN8Av0YRkH95nhJBCgnOSO+wrQnIDBXRCCGlYuIic8nFqHdfobheRM6hM3gadp6TEzW6b/0IADity28RZeevajnxetW1T34rjgn4F4CIGB0VgizJTO2odtR9s9riI2Or+oIWfKNdNHXsq4jg5r3yuoIBugF8MCSGFBOckQgghhBA9/JxESG7gol44gsRQm6gaVEctc42cDiNoB4nctraDRF5TtHEYcTaMEO2yMBCmn4L6ViZInFbR3Ve2Mp2AHGSDqGMS2nX+qylsdD7o6uiEdPX/MOK87f7IFRTQCSF5g1923GFfEZJ9eJ+5w74iJDdQrCKEkOLEJoy7CL8CV3E6yAbRjrxPPU9QBLK8DYoOton8OlwWBlQbXaOf5X06AVlng9gvBGQ1l3iQ3boIdJ1dtuh2na/y9Zfryseq7QRFluvKbHVMbYcpi5KuxrZgkQsooBvgF0NCsg/vM3fYV4RkH95n7rCvCCGEkMYHF/XCYRIDTccJTKKiqU6Q+Cjv1z14VD6f2rYcgWyLbnYVOV0i3lW7VdHV5KstKj3IV1vEs07kDtO2ydco7YT1NYrIHaVtGVdf5eN19pjq2BZlsgEFdAP8YkhI9uF9RgghhBBihmKVG/xMSQgpROSo3TDibFAdF7HcJbo3qoDqGjms1lHF0Dg2mkT3bInFct+q/axb5BA2hu0n03V1WSxxXbyQtzZfw9jo0icuNrgsuqht5woK6Ab4IYyQ7MP7zB32FSHZh/eZO+wrQrLPnnsCTZvm2wpCCEnDRT13bGJxlOjeoHZsx6lt2+qYfFHr2MpcoqnFOYLqyFuZILt1ddS+d7HBxX/bcbrz6OrIdge1E+X6e146kt9lMcAmtOvakXOzu4rzprQ3tgWUXEMB3QC/GBKSfXifucO+IiT78D4jhBQSDz8MVFTk2wpCCCFxUMXAoP8Be/SzaZ9JLNe1LQuRQTaGEeJ1xwVFIOvqqMK2rZ0gMTioHVtUuqhjS2FjalsQNXJc54/sa5g+s/kq/tf5alqwMbUj2+y6gKT2p2sdRqAXEPwSTQgpJDgnEUIKCc5JhGQf3mfusK8IIYWISwS2KpDKZWEjuW1CY9R2gvaXlATXcSlTI5DlraltXT53W2R1UB1dJLeuHdeodJuvYaLSbaKzq/+qf2qbtkWeoLKghQiXBQIXu3XnyyUU0A3wQxgh2Yf3GSGkkOCcRAgpJDgnEUIKjXykTShWVBHQJOKGEQtN4myU6GbXtuVjXSKe5TK1jq4sbB1TPwWJ3Lo+NfWjzX/hq+pzkMitu29cFid02K612r5aR43ad12wiLp4o2vb5n9Ye3IFBXQD/MBKSPbhfeYO+4qQ7MP7zB32FSGEEEJIMC5CtDjOVEfebxMV1eNt7cj7Xc+n+merI3AR+T3PLPLb/A+yyyZy29oOipLW1VHttvlfW5sZyW+qI5cHRbKLMtGmafFGrWPyT3c+uUzXTpjFCdM1Closkv3KFRTQDfCLISHZh/eZO+wrQrIP7zNCSCHBOckd9hUhuYER6OGwicpAOFFR7LOVBUVOm/KGy1vZhiB7wgifunZsZXKfmeoECeRB/eNaptoji/xR2zGJ2FHqmMaJrh9t55Ovm9inq+Nit2uZrc+CynINBXRCSN7glx1CCClOOH8Tkn14nxFCSPGiCn4uAqnYH6ZOtsRJk4Cs+ucifLvYYKrjKpaH6VtbHZNdJZKCqvNfrmOz29RnJruDxonufKp/Lv1j6hNd2pcwCwjiXEH+m/br2kml6ke/54KiEdBvvPFGDB8+HC1atEDbtm2zfj5+YCUk+/A+I4QUEpyT3GFfEZJ9eJ+5w74iJDfkI+qz2LEJ3y5CtO64sKKqzi65jhAkTfbI7clCdJgI7KA+0YnTpjq687n2mSwGu4rzYYThJET+MHWCFghcxox6DhcbTXXkMt31ELiI/Grfylu5nVxSNAJ6dXU1zjjjDFxyySU5OR8/hBFCCCGNC773E0IKCc5JxYn6xV+9jqbyhojJxyT6wNbPxYxpzKivXX1vyH1V6EQRVeMIsWLrKs4GjY24Ir+63yTyy3ap+01luoUIk91qOy7ivMl/2Rbb9QiyW9eOsNGUV1zXJ0n4L8pqa4P7JKkxGnYBSV5gCVosygVluT9lNKZPnw4AmDVrVk7OxzcVQrIP7zNCCClOOH8TQgqZoMg3eX9JSX3RIOq5RFsmYce031bf1Zd8oNpu8yGsby7njro1tZdL1H5Tx2NYn9T+V18n4Wu+xloxYhpzJrEwSDQNelijTsTUiaFyO67Cp2gnqE5YQVf3MEpbZLG4P0z9aBNf5TrqOYLKTEK0zgadWB7kX9wxEXQNXM6jq2NanDD1s66dsONEtz+sL7miaCLQo1BVVYXNmzdn/LnCL4aEZB/eZ4SQQoJzEiGkkOCc5I7oK5ECwCR2qMcn0cdqW+rWZJOp3LRfvFa3SY4TU7+YXiflcxxbTf3hakvYfsxmf7v6FHWbjTFDgtGJk+J/m4Ao9sn/hxUVw4i3QdG9LqKqSYh2EdWD6qh9FtS2Tvh2iW421Ykq8tpstNWRt2o7NiHaVDeoTtCYUNs2+WXqW9M1MvmvW/TTLU7kYzGvQQvoM2bMQEVFRd1fjx49nOvyTYWQ7MP7jBBSSHBOcod9RUj24X0WHlfxOUnh3HSupERP120cH1yFV1s/FqLtUcVl1+sYh6T6z/U6JWk7ccNF5NUdF6aOOF6+rkHCr1xmq2OL+A0jFruIuDobXfpMFlWDbNC1bbNBtz+ojhz9H8d/sU+0GSRO6+xy7VvTcfJW17dBdUzjxHVsqf67jsdsk1cB/dprr0UqlQr8W7x4ceT2p06disrKyrq/1atXO9flmwoh2Yf3GSGEEEKIHn5OCk9UwTGb5zQJs3FtTmIxwBYRHlV8ttmahJAbV3yOGuWt1ouDbYzY+jeqzXHGTj6iPosVWfALG4EcJFS6CNau0d1qmWqXEINN6VNMArIsfMr+B53HJN6qfeYiqiblv/y/bFeQ/6qNOv/VMpuNMjZfwrbteo1sYrlpjAaNHVPbLtcoHwJ6XnOgX3nllZg0aVLgMX379o3cfnl5OcrLyyPV5QdWQrIP7zNCSCHBOckd9hUh2Yf3mTtBwmNNjV1gjHpOnWAQV9TMRfoT2QfRtsgxXFOjz2muqxfX9yQE9LBis2v/qudJcuy4th11zIQ5DwXx7CME4qZN/dc2kVeuU1pqrqMTFWtq/Do6gVRuRz6frY7ORmGbqY7qS00NIKQ5XaS2qU5pabBYrtZVfXERYk351eWtTrw1PdRSdz71Wgblc5friHvbJkQLwV69Ljb/5fO4RqAH+R90PtkX3VjW9ZlrnVyRVwG9U6dO6NSpUz5NIIQQQggBQLEqDOwrQkghElWITOJc2Urlks1UIlFskRckbIKu63mSsD1qFLdp0cW0TUo4lwWlpHOeu7YXRYCi4B4OsQAlxpB8DWpq/P+F4KkKkaKOQBaV5ePkayKLjkF1ZPFarhMkqstpRHSiq05UF3V0YqiwQfU/qI6wa9eu+qKyKgbL/gcJsbLdNiFaFd3lPvM887WsrQWaNNHbUFqqj8A39ZlY2Ay6/jr/dOdW66i+qA+RtdWRy+Q6NTW+//L4F/t0/SifQz5WnjflOrkirwJ6GFatWoWNGzdi1apVqKmpwYIFCwAA/fv3R6tWrRI/H78YEpJ9eJ8RQgghhOjh56TwRBUgoxBX5MyncJ4vH3Lx4NOo/RjVt6g2u/x6IRf59CmIZw9ZvJTFUCFKqulRVCHaVEcnKpeV1RdYVRvkOqLMJDqbxHC5TCd8m4RonRgaJKCr/utEVWGrLJAH2S37Lwuxpjpqn8gCsa6OsEMVkOVf9Kiisrj+4hzCRlMd3fVVfVEj1XWLKq7+6+qY/A8S1XXXUvZfJ97rFj7UslxTNAL6ddddh/vvv7/u9bBhwwAAr7/+OkaNGpX4+fiBlZDsU8z32Y033ojnnnsOCxYsQNOmTbFp06Z8m0QIiUkxz0m5hn1FSPbhfeaOq/CYzTQo6jmiphZxjaSOI+CabHcVdJMSdpO0XRC2H03tZdOHbC9chBXiw0DBPRyy4KkKlqqAbKrjIkSLtkWEttx2kBAt15Ej1U2iuizeiv9FHVlADuN/2Do6X+TobtlGUUcW0HX+q77YBHJxHpt4r/6KRWejKiDL10NXRydEy9dAXeTQic5ymeyXGFu6a6n6KtfRnVvtW7WuaQFFV0f1X47AzyUJvO3mhlmzZsHzvHp/2RDPAX5gJSQXFPN9Vl1djTPOOAOXXHJJvk0hhCREMc9JhJCGB+ek8BSDCOqatiOsAB+FbEc4u/oaBVdR31VIj+pTHNtdBe+kFy6SWHwhbqhiYBgh2iYgm8p0bat1dGKoTVSWxWBT27o6pvOp7ejOk0plLiCowq+uTG7bpY5sj+64VCrTRpM4L9uoltkWOXSLF6ZFDrmsrEw/toL6Vl2I0fkv+6L6Kqde0dktpyuyjVvbONL1mWp3LuHUaYAfWAnJPsV8n02fPh0/+9nPsN9+++XbFEJIQhTznHTjjTdi+PDhaNGiBdq2bZtvcwghCVDMc1K+MAm2pmjjOLgKqnGjhLMhnLsK3Sbh1tROLgXcpKOww4rTSZD0wkTU6xUGRqC74yL8AnYhUtQJEnmDBFRdhLJJ0FeFT52oKgufLgJqkBhqakcnOptsNJWJtk0id1A7rpHTrgsf8vlsvxLQXY+ga62rq15/l4UY3Xirrc1cxFAXJ2zCt25M6BZighYQTCK/+oyAXEAB3QA/sBKSfXifEUJIMvBXMYQ0PIr5c1KuF/XiCrhR+jqpqOu44mmSQnrSAq7reeKQlA+uviWJbayE7cewY7Exka9AAzlvtE5oFOKkTkCWU2oEiapy2+rDH01CtC4HdlBUdipV/6GdLkKs6r/qC1BfdLX1mWuZrm059UhQxLdr20GR/LKvOl9cIvmDysKI/God2y8QgsrUCHTd9XftW2GjbQElaCEml1BAN9AY31QIIdmlqqoKmzdvzvgjhBQOxfzen+tfxTACjRASRL4W9VTBURUOy/7/J4AlIeTGjYC2CbfZiB62teUqjAvRIm7UdxyiLjBEFdxNPkXBNg7D9mPcbWMgn4EGukUYcQ+pAmFQHbVMFmV1x5nqqOcOOp98vGsdOSpb9lUeb/KYVhcQ1LZFPdVuuY7Jf10/u/qvirhye2HquFwj1X8XG02+BLVj87+0NHPekRcDdP7L40P8Ly+W6GzQjSl5LOt81bXNFC4FRGN6MyEkXxTafXbttdcilUoF/i1evDhy+zNmzEBFRUXdX48ePRK0nhASl0Kbk7JNnEU9CuiEZJ9inpPylepOFRzFl3T1C3ySfZstUTOseBoGtV+EoKuWm7YmYT2XAm7SEec239Q+iINtMSVX2yjv5cX6/p/P9Jvy2DKJinKZXB4kBpvKTMKoS50obZvKTPee7XxRbVTPF6WOzRfbNQp73cQCQphzm+wOaifs4oSpTLc/TJ8E9aOrjaYFnVxQltvTEUJImkL7YnjllVdi0qRJgcf07ds3cvtTp07FlClT6l5v3ryZIjohJG/MmDED06dPj1S3WL9AE1JMFNrnpGxTVVWFqqqqutdhFvVMAqFJAM6FgGsSYm3ivslmIXKLbRzhoLQU2LUrvGhs68dcCLhyVKSLjbb+dO1/db/si6sfqu25EsptCx0kO4QVkG0ir01U1omTYeuIaGCTjTbhM8r5XIVvV3E+jDirs9u0uGaqYzqfi8jtcv3D+hJnYSTMGA1bR1dmWkAIa2OuoIBugG8mhGSfQrvPOnXqhE6dOmWt/fLycpSXl2etfUJIPAptTrr22mtx8803Bx7z6aefYuDAgZHaj7OoRwGdkOxTaHNStomzqCdQBUKTKBpHwBWCqUnYDhvNrdrkKuTGwVV8tgnqAlfbTT6HQU2REHYhIuxxSdpuE9CTWhSwjcU4NKb3/ziLejJhxeAodZKKEi4tBXbvzqwT1caoZer+OAsRubJREEecTupXArp2crXIESe6P+71yAU5Pl3x0Ng+sBKSD4r5Plu1ahUWLFiAVatWoaamBgsWLMCCBQuwdevWfJtGCIlIoc1JV155JT799NPAvzi/iikvL0ebNm0y/gghhUOhzUnZTnU3depUVFZW1v2tXr3aua4qRNpEbFMOaheSEmB10YRRjg9juypARPVB1Iu7WBBVQNfZErXfw+6Ps/hii4LP1VYQZewUEsWSfjOsqGjb7yrYAtGi0l3b1s1JOl/Dni+MPTYbw9ZxFYNNdaKURUkPEyRy69oxte2SwkV3fW022sZEmOvvmq4mFzAC3UChfWAlpCFSzPfZddddh/vvv7/u9bBhwwAAr7/+OkaNGpUnqwghDYls/yomDo0pAo0Q4pPtVHdJ/FLPJH6ahOMoiAjNpCObw9aLEwFt65+kfUsyAjppW8MuJujSpIRJ4RLmXHHHkOk8URBjp5De/ws9/aaLWBhXQA46Lmp+bZe2bT64nq+01D2Fh02IdRWQbSJ/TU39ttW6qg8uvrr2t0s7Qf4lvRBjW5wII/wHnS/OAkquoIBuoJiFPUJI9pk1axZmzZqVbzMIIQlSzO/9q1atwsaNGzN+FQMA/fv3R6tWrRI/XyF9gSakoVJoc1IhL+oJTAKiWm7KFR3mHEkLuK45uZMQ/8P64OqLmp/dVC+KCG0S/5MW9wtp7CQtuIt24theSBRL+s0oEchhxeIwkbquIretnSDxNhsCaljh12UBQedLba297Sj2RBGn44jhtjqm44Jy4IfxP04KlyiCfi6ggG6g0D6wEtIQ4X1GCCkkinlOyvWvYiigE5J9inlOyvWinumLui2KWK1vO4fnuYv0SQm8OrEjrO0CV2E2aTE6KfFfzh8e1sa4vse1vaYme/3q6kMc8V+0Uazv/7mek2SCBN0w4mQSZWHqBEUY29opLU0uhUvYSP6kxOkgMVj+37Tw4dq2WmbzwdWXKHVKSvQLCNlKBRR1jDKFS4FRzB9YCSkWeJ8RQkgy5PpXMcX6BZqQYqKYPyflK9Wdq8AYtW05dYurGGoS3JOKHnYZJ0IUsYn/rosApuNd60dNPxPG1rA2ZXPslJT4AnrUfk9qG4dcC1VJk485ySW/dBgB1bUdmzgdVtDMtsif1AMqw+YFN9VRj9P1XZQ6OrvlhQZb/7j6b7M7qG2xUKkeF+S/rU/k41zuCRdfVV9yRZFPg9mjmD+wElIs8D4jhBQSnJMIIYVEMc9Js2bNgud59f6y/ZwYVzFVkI30G1GjhF1TuEQhrmicbQHX5Tq4LkhEXbCwHRflVwuCpAXzqPWTsL1YydecBEQTkMOKpa5icJQ6LkK0zm4XX002JiHyR2077K8EoojzuV5AiVNHZ5dpf9gFHZfx79J2rijyaTB7FPMHVkKKBd5nhJBCgnOSO4xAJ4QUEmL+NonQti/ZrlHcQW1nS3RWz5Nk7m2T7SbBN66vgjA+mKIqw9oS13aTrXHE/6THiOvCkXgPD2O72PL9PzxRRN4kxGKTOBm2ju44OR2NTUAOKwbHbScpAdkm8iaRu9vkfxQbXepGtTFKLnmXcaTz2VRHnot0Yy8XUEAnhOQNilWEkEKCcxIhpJDgnBQeVxE0ShqUbAvntnZVETRsCpckbXcVbE22qwSJsq42xxXYbYsHgjjpZ5KyLar4n8TiC3HHNfWGwCYgxhGQkxJnowj/+Won6gJC1LIo9rjY6PoLhKC6tsUJ3RgNs8gRVBZ3/DOFSxHAD6yEEEIIIYSQfMHvI+GJGkXsElUbVnANK3Kq0fOutrtgsjnsw1fjCu6mNChB/R91oSHugoW6VW12sd3Uv9lehHFduHBBtZUR6OFxFRWjiJxhhWaXtqPaEKYdm4Acp50wwm/UB52GEb6TWkCIIs4n1bar3dlc5LCN61xAAd0AP7ASkn14nxFCCgnOSe7wCzQh2Ydzkju2FC62/NXioWlBhBU/wwq5YQXcMIQVbE1CbFQROk7+cFMKl6REZtdyk81BY8ckoOVKULeJ/y4kKcY3VrIpRLqK6ro68jgIqiOPoyCx01THVQx1PU8cX1zqBC0WBIn9Jr/C5vg2+RBUR/bFJjSH7TOb/1HqhFnkcSnLFZwGDfADKyHZh/cZIaSQ4JzkDgV0QrIP56TwRBVwg0RQm8iZtOhpak+13SUC2tWHbG9V28Ng6u+kFjBs9Wz9m0T6mag+uLYbJfJf9cG1P0ga0c9NmvhbWVQUi31hBNSgOmKfvL+0VB/dLv4XdtnqyOcTdeQxpTu37Kuujk1AFW3qcq3r6ph80bUdto7N/zh1TP7b6qiitOyL3F9Bv25wHTPZ8N8mhuuuv25sUUAvEPiBlZDsw/uMEEIIIUQPIz7dMUWgmwREtZ6LgB5VvIwrAJsioF1sF9jadvUxqiBsysEdR/yP2v9ht6Z+j5N7PlcLGTZbo/zygoRHCIiyYBlUJlNWBuzeba8j19WVySJn2Dqm41SBXLbNtY4s2OpE4CZNgJoa//+mTc11bOdz7fu4/st1dDYG+S/8A/TCuFxHIOqUldX31XT9RR1d/7j6L58vSh35Wqp2qXXU/TpxPldwGjRAYY+Q7MP7jBBSSHBOcocRaIRkHwpW7qgRc7rIuaiEFZmT2rra7pJCJG70fFzhPIrtgqT6P6yYrS5cJJE6J2o/Rr1uNvE/Sv/z/T88QSKvTohUy4SAHFTHJNgKZHFSiJKmdtQ6rsepNrr4b2pHPp+6gBDFRlcRXNe26brp2g5bx2SjQLeA4tqPJhtt41F3XFA/6URuWx3bQkTQ+Lf1WTbhxzID/BJNSPbhF0NCSCHB935CSCHBz0nhMQmUKnFSWOQ6DYot/UyYFCKuonFSviUpoEeNoo+6cKGOnTjic1whPOnFFzFmovx6gbgTVuS2ibNJCJ9AZnSzTpx0FT51NkYRUG2+iHEath2TOC9HSYt7Ic7iRBR7bO24LiDE9V/dbysL+oWB6dxBdWy/wDD5r/tlQS7gxzID/BJNSPbhfUYIKSQoVrnDCDRCsg8/J7ljSuFiSh0iSOLhoUmlElHzG5sEXJUo6WfiCuau0eCq7UlEQGc7at5V/I+SfsZVSE9KWBeYFl2i5P4n7qjibFxRNal25LJdu6K3HZQeJUzEt2t0c1ILEXLbLtHNYSKno/hq80W3gKD71YKrjfJ1C9t20AJJlDpJjdFcURTT4MqVK3HBBRegT58+aN68Ofr164dp06ahuro636YRQmLAL4aEkEKCcxIhpJCgYBUekwgaJ3942AjopKOKVZsFcUTQpG00tZdEBHpSAnlYnwWmMSSEtyBsEf5JR82b8uWrvsTJPc8FdHdcxVk1L7SLgB5WQJYjkAW2/OpBwqdJnJaj21Vx1lVoN+XXDlvHJPLKdYPE2aDrYbpGcrS1ywJKmLz4SY0juU+C/Jd9cb3+YceoS9su/ZgrchzwHo3FixejtrYWd999N/r3749FixbhwgsvxLZt23DLLbdk5Zz8Ek1I9uF9RggpJChWEUIKCc5J7pgi0G1iX5wIaNfo7rjpT2zif5ICumvKF1ffVZvD2K7WDetDUqlbTISx3ZaLPumtKeI/ysKRajtxJ2rqDd0DIcPUCToulUo+PYhJnI7jv65tWztVVW42ugrIYftWbjtK7nLXBQTTmAibK1z23zWFi84G+X9deiA5577Obt35wi6g5IqiENDHjRuHcePG1b3u27cvlixZgpkzZ1JAJ6SI4X1GCCkkOCe5wwg0QrIPBSt3TA8RNYnPYXJA5zrnuc32JKLnkxL5bZHntrQzNvG/tjZ76U5s6VVMiLETJX9+0v0f9lcLgjjjnu//7qjirE2IDRIagcy6QXnBw0SOi+upq+MqfOpETFcBOUzbYgzaBPQgMVi+J0wCuss1CrOAEBTJ7Rqp7dKO7mGzrjaGvR4mkVvXdtgULlGi23NF0X4sq6ysRPv27bPWPr9EE5J9eJ8RQgoJzkmEkEKCAnp4VGHSJORGEdCjpuGIWl+QhAiqRhGHjepOKgJatT3KA1DDpnAJK7ibxo66cOGSwsU1wj9Xiy9xFo44H4UnSEDWiYq21BtymU6cVOvozmeKHA4rxJtETFt0e1A7OhvDtK3WcV2wUOuoZaZ+DHqQZVLXyOS/Tix3uUby3FBWFn4BwSbyR/l1Q9Aijym6XU3rkiuKIgJdZenSpbjzzjut0edVVVWoEstQADZv3ux8Dn6JJoQQQhoXfO8nhBQSnJPcUYUMm9gXJoVIriPPXSOgc/kAzqj5w00kkTrHNQ1N2H5PIno+lfKvU9z0M4WYOocR6O4kncLDlnrENXJc17a4vmVl0R9+KmOy0aUdk/9BbYeJ7te1HRRZHiZKPGx6mDAR2HJZ2LFlS4+i8z+bD/+0LSq4Rrc3qgj0a6+9FqlUKvBv8eLFGXXWrFmDcePG4YwzzsCFF14Y2P6MGTNQUVFR99ejRw9n2/iBlRBCCGlc8L3fHX6BJiT7MOIzPLbo7STzh2f7gZymCOhiSiGiEifyP2wqlqhb17z5UaLnk0o/4xr5b7I9TgoX4o4QEHWpN4IE2zACqk4sj/LwS13bcQX0sO24pkeJkl88zIM1g9LjxEnhEiU9ilwnCXHeZLfrw19taYbk43TjX60TJoWPLr+8Lvo/m+Q1Av3KK6/EpEmTAo/p27dv3f9fffUVRo8ejeHDh+Oee+6xtj916lRMmTKl7vXmzZudRXR+iSaEEEIaF3zvJ4QUEhTQ3VFzoLumsAiTgzvbkehCCLC9F4mf7gsBIYikU7bY6rk+vDKbEeiuiwSmdkxjR+AqoNfUuAvn2Uqdo5JE/3MB3R1xr4rrI4uTSUX3ho3kNaXH0KXekNt2yeddWqpvR/Y/yMYwKWxc/VfLXPzXtV1dXb8sbJS86Vrb8tTr2g6bHiaMOK17GKlLrny5HZONugWkKP6rfZsr8iqgd+rUCZ06dXI6ds2aNRg9ejQOPPBA3HfffShx+ERZXl6O8vLySLbxSzQhhBBCiB5+gSYk+1BAd0cViE2EyV8dNgI9KaFdYPo+KkSJMAJ63HQoYQVcE2EEXFcb1P1CUBGijSowmfYL0cYUaZ1E9HZSY8TUro0wPqj9lWuhqiHiGpVty4Ftyl2t1omaHkPX9o4d/v9BebrVMuGrLnLY5r+rOBtWnLb5r+tbU5lcB/DvQ52vrtHdcQRkuc90edptfRvUtul8NhvVBRTX9EBRUtjkiqLIgb5mzRqMGjUKvXr1wi233IL169fX7dtjjz3yaBkhhBBCGgpcPCeEFBKck8Ij+kwI5GpEbjbE56iip2vaE4Fqe5APQrxRf26v/pTetjWJz+p+Vx+SiIC29bMqwpheq1tb9LzobyEkBdGkCVBVFb6fowrpJgFdvR/UrY7SUn+/aeyQ6OgE5LIyfcoQ1wdU2qK71bIwqTeC6tjOZ7M7roDsksLGVuYqzsoLCGH6RJ3nXBdQXBY5dL8ICLInjP+6CHw1HYuL/+pc2aRJeu7RRc7Hie7PFUUxDb7yyitYunQpli5diu7du2fs87IUAsUPrIQQQgghhJB8wQj08Iivhup3OVEeRkBv0gTYuTO8COoa8axuBTYBV2yDRGiTgB5VKLdt1fQzav+r4rOLCB1W9I/qg832KGPH1fawCxo28V/FNHaCBHSRwsHkA3+BFh5d5LTcr1VV6f/lfXKZjNyOwCTOq+2YRG5bdLdaRyfyuzz8VNd2UM5xm4Aqt61LPaK2EzWFi1pHJ/KGsVsnlofJ0y7akRdQ4jz81fX6B4n86nhT5wqT/1FsVP3PFUXxsWzSpEnwPE/7ly0ooBNCCCGNC773u8Mv0IRkHwro7qgP1rSJz7kUQVVRxHS8SRBXc56Hsd1mc9ICril/eJj+F+PeZGNY223H2e4zVfyPMnaS7m91TNlyzscZ94xATw6TOBtHiHVNaxKU1sPUti66Oe4DSl0E1DBR0kmkdXGx0UXkDtO3+fI/qo0uY9TUt1FsDOt/ruDHMgP8Ek0IIYQQQgjJFxTQ3TE92FG8ViOfo0RAJyV+mgTeJMX/sGJzVN9UUVXtf5OAa4uez4XtOmFGR5j+Vx8I67oIENUHdY7IxsIRI9CTwyQgyvsBvcgZVfhV244qzoeN7o5ro+prKqUXYl3bjvKAUvl8LlHyprbDRndH/QVCUOqdoGh6UztB0e26SH5b27LdQalnXPK0u8xl2YAfywxQQCeEEEIaF3zvJ4QUEhTQ3RHijipCm6K3gwRcVQQNK0aHFXgF6vU2ic9JRBEntShgijhPQsA19WNSCxmiv03R82LsqJHnQQJy1P52zTVvizwXiLGT5MIRI9CjY0u9IciFOG1KYSIfpxOng4RPm4CahIBs898mIIsyOeWHSUAOSo+jS1cTRUDWLRYEpYcxtWNrW94PZPpvEvnVBQSR1kmuo1vkiXKNklpAyBX8WGaAX6IJIYQQQvQwAo2Q7MPvI+6YHo6YDfE5rKBrynmu5m51FZ9tDw91sT0pcdpkuylveBIR0K4LE662m1BtTjJve9zFADF2TKlbTAtHQf2vy2ets5Hv/9EpL0/nO5dTglRXp/fLW1OZ+twEAGjWLLgdUUcua948/b+wp7w83Y7OblGnvDxdp1kzfysLv6LM1I5YwGrSJD1eRR25rs5unfDbvHl9/+V2RJ1mzTLrqO00a1ZfQG7WrL7/sg2u/jdpkm5Hvq+E/3LfBtmt81++/qLPbP7rbJT9l8uE3aKOPAZ1/ss+CJo2rZ/vX/ZFd91MqYcooBcY/MBKCCGEEKKHX6AJIYWE+DItRJk40ds2oTZbecNND97MhoCeVOS56TuzSXx2EaHDisxhfXEVn9XI8yQj/22LLabXppQ56tZke5TUOYxAT45mzfwHEwPB4qwsIMqisk74lMtE2+JaNWuWvuZy22o78mKeLMTq2hZ1ZIFUJ3zbBHRB8+bubeuEb7ltnf/yedQyncgr+6orC/LV5L9st9p28+bpPtEJyDq7df7L4yioHV3fmtoR6BZQdL7Kdtn8l8tUcT6K/7mCAroBCuiEEEJI44Lv/e5QQCeEFBJCIFSjt7MhgtoEXFO6Dd1P6GXiRA/bbE96McCUusUWeR4mf7gtajupvOEqpjGTj18v2MZOEpHnAls/U0CPj06cNInTqshtEz514rQucjiK8C23I8ZJeXn6fncVkMOI02H9l23ULU4EtaNGU6u/ZJL7VidO6/yPI85Huf5yO2KOk6PJg9qR723dryTktnX+B/WJyUa5THeNwvqfKyigG+CXaEIIIYQQQggpfFSBMIqAK3AVbKOK0VEf/BhFwHW11TXK2yT+C0wR59nMH+66tQnAtl8tBPW/uKY2oTxsfnf1ddjI/zjpZ5jCJTnktBZBAqKcrkT0cxRxMihy3CR82tJ66ERVEamuE75NAnKQyK9LPRNF5Nf5n1R0vy7Vi60dnY26sjjR7TpxWh5vQb9ACGO3eA+QFxqChG9TO7qyoCh5CuiEEEIIIQUEF88JIaQ4EUKGTQRVIwtlooqcUQVcNWpbffBjNh8eGjUC3UYS4n9U8VnOlas73rRw4Wp7NsR/068XTMeZPqfEEf9VH2zjnYTDFt1sihzWidNyHYEu2lo+Lk50b5DwL5cFtSPfdzaRWxZnxZhNSkAO8t8lcjzolwNBwr+pju66yalndHnhbfnlg6LbgyLHw9itO1/cXwnoUu/oft0Q5H+uoIBugF+iCSGEkMYF3/vdYQQaIaSQsEU+ZzN/tauwLoQk03uNLf1GkiKoax5xtb4pdYstb3gSEdBxI9JVm1WSyNueVMR/Ur9aUK+Hiw+2/ub7fziipjVxEb5NdeQyVZwuL68vTuseWmpqxzW6PUrkuK4sqehmMX7Ly/WR0zYbVf/lVCdBvjZtmml30AKKQNd2lNzltl83hLlGQcJ/UDuplHsKH92vG4IerJsPKKAb4JdoQoiJlStX4oILLkCfPn3QvHlz9OvXD9OmTUO1+shyQkhRwfd+QggpTmxR20mmbgmb9kQVcFUB0pQ3PMwigJo/POkc3CbhXGDr9yj9H1ZsNh1nynnumjInycUXV9/USHTVZlPOedMCUtAvL1yFc0agRyOK8OsqoMr3oUnkjZpz3GajKXJatTGq/2Gjm+U6ttQrOuE3jv/ifjX5H2R3nOuvthPkf9CvG0zXyPXXDUGpgMLk7teNW1eRP1fwURAG+CWaEGJi8eLFqK2txd13343+/ftj0aJFuPDCC7Ft2zbccsst+TaPEEKyjvi5OCGEFAKFLILaHvyYlO01NeHFUNetKfe2KXWIScjVUVLitxNW9Hf1SbXdJv7n4tcLrtfBlnYmiYWjsjK/HVcfGIEejihpLWQBUYwFXVoLWfiUI4vltqNGDqt1gqLkg6Kyo0Z3b9iQWV+XeqSsTJ+TXNe2zu4w/gdF98t2Bz20M0x0/7ffZtaJeo1cf92ga1tXR9e2mKfkXzfEfbDotm2ZdSigFzgU0AkhJsaNG4dx48bVve7bty+WLFmCmTNnUkAnpIjhe787/AJNCCkk4ojQqZQ/p0VNc2LbL59H3iZhu6CszBc4oqadMR2n2qrO/apQHkV8btIk2Pao4r+ac15FRGXHSZ1jE/9dU7mYygVqxLnY2vo9G+I/CUdUATEorUXc9ChxcncLwkS369oOm9ZDl3omiv+yyF8M/supd3R50eU6ugWUoOuva8dV5I/76wab/2IBQU69oy4g2eb4bMAULgb4JZoQEobKykq0b98+32YQQhoh+UgrxQh0QkghEUdIdI10DiuoC+EjrnCezej5sJHnptQhSeZtT2rhwmR72Mj/JB4eGvY6qGNHENb2bIwdLqCHw5Yew/SAzmwKv6IdOa2HKk7L4qQtSl53PlOUfFgbbW3L+wW2FC66h1aKz7WmNCNBkfw6G+VrFCeFj+44W3S77hrpyoJS76i+BkWyy2VJPFhUl9bF9suBXMEIdAMU0AkhrixduhR33nmnNfq8qqoKVdI7zebNm7NtGiEkBMX63p+PtFIU0AkhhYQpdYjrwyvDpLCImjfcZnMuRWjba1vecFvUdtj+j7M1+Wrqf1Pamai2V1dHz89u8sV17MTxQf3lheuWhEMnTqv7dWJh0tG9rvmsdW2Xl9f/3Gdrx1WcdvXflBc7SJw2ifwiPYzOBlM+bzUmxSYqy3nR1chpUzvZyF2va2fr1szz6a6/aQHF1rauH4X/pgWUoBz4uvPlU0BnBLqBYv0STQiJzrXXXotUKhX4t3jx4ow6a9aswbhx43DGGWfgwgsvDGx/xowZqKioqPvr0aNHNt0hhDQSxo0bh/vuuw/HHXcc+vbti4kTJ+Kqq67CE088kbVzUkAnhBQScVJYuKbXCCuCCsLmDQ/jg+vDQ8NG0ccVcJN8eKvrwoXYb8obrkZvJ5H+JCnbXdP+uKabEdug92pbP1JATwadqKjud8ld7ZoX3VQnqCxKWhfXtnV50U3n1tUJsjGKOOu6OBE3uj8o9U5cG+PmF3dZnFD9Vx9EbPqVhO6hpbYFJNfc7RTQCSGkgLjyyivx6aefBv717du37vivvvoKo0ePxvDhw3HPPfdY2586dSoqKyvr/lavXp1NdwghIWlIi+fZTivFn3ATQkzkI61UIT08VBD3oZXZTCFiSx1i8sEW6RwmB3pY8d/1OpgIm3YmG2MnbM5z17Q/YR7aGtf2Yn3/z8e8BOjFySZNMgVWV3EyKNVFnOheuSzMQyuDItB1bYt2UqngdsL47xrd79q2q/82kV/XtqmdsCl85Id2qnnR5bbDpIcJm7vdthCjO59rehib/2Iu0o35bMN1RAMN6Us0IcSNTp06oVOnTk7HrlmzBqNHj8aBBx6I++67DyWm37pKlJeXozwfMz0hpFGRi7RSjEAnhJjIR1qpbKY/cU2Hooqg6gMfxbzpGvmcxMNDbYKtujUJtwJX0Vn4ZhP/a2qiR2mb0s6YbFejt9XXYcR/14eHhv31goroP2Gr64JFNh4eWlZW3BpJPuYlIFzucps46ZJ6RW7HlB4lqB05clgnzrraE2SjizjvGpXsKs66+i+nnknKf90Cirxf3K9h/Q8T3W8T/nX+R4nuFw//lG1U/ZdlkygpfOSyXEMB3UAxvzkQQrLLmjVrMGrUKPTq1Qu33HIL1q9fX7dvjz32yKNlhJA4FNp7/7XXXoubb7458JhPP/0UAwcOrHsdNq3U9OnTI9lGAZ0QYmLcuHEYN25c3eu+fftiyZIlmDlzZtaEqqgPD929O7k0HKqAG/WhlbmIgLblDVe3trzhUdOfVFWFTyHimnM+7kNbbeJ/dXVyEeimhYtcjh1T/4tfCoj9xRqBno95CcgUCEVfuqS1cBEndQKyPIZsorOtTBU+Tftdy8KI00G5u7Npo1wWJfWMS+qd8nL3vOhBbSflv+lho0n4r0s9oy4gqXOKa3R7PlK4UEA3UGhfogkhhcMrr7yCpUuXYunSpejevXvGPq9YP1USQgruvf/KK6/EpEmTAo+Jm1ZqypQpda83b97s/GwGCuiEkDC4pJWK86sYUxRxEEJAjyuCqqktVDHUljokySjisNHbtrzWwnZT5LMtIjqM7VG3th+BquJ/UrnPgwT0sNdBxXXs5PLhoaWlhfc5KS7ZTncH2EVVnQgYJr+4Kk6r4qyLOJkrG+WyIHHWVEd9+Kd8nHhoZ5Mmmf6r90I+/XcVp03tqAsIutQ78j0a9xpl039xXcK0vW1b/bJcQwHdQEN7cyCEJMekSZOsohYhpPhQc7/mm0JOK0UBnRDiimtaqTi/ilHFUJe0xk2a+F/S40Zv26ZbV/FT2Cy2QSKoEP+FaCS2UQVdFVvamSTSz4j3XHUbtv8Fah5pgdqvav+G6XdB2AUK268WBHGj5l2j53ftMkeaq2NKjhqtrTX3c7HhMi/FWdQTxE294tqO7aGVcdoOk8IjbM5x1/ziYfKii3tYtjtO267txElr49J2UJS8nBc9yq8bcjFGdb6EaVv3C4xcw4eIGmAQKSGEENK4KDQB3RWRVqpnz551aaXWrl2LtWvXZu2c/JxESOPj2muvRSqVCvxbvHhxRp0waaXiPGxdFaHVL98y8pd0oL5gGCQgylux35b2RBVohW1iK8QetTwI1Qaxbd5cvxW+quUmAVfM8apN6tYkTgcJ6LZ+dd2K92xT/5v61ebDjh1m2+UUHEFbW/+r0aLqdRBjR7XRZLv6OghTf7reD598Yj9HLsnmvDRjxgxUVFTU/bn+Sk+mWbP6Y6q8PF0mi7NyzumgOq5lOuE7Sjvywo4YY67t6PyXy0zivK6OTkAOOs4kILvaKNqRvx/I4nTYdsLYGCf1TLZS7wCZqXcKwcZcwwh0Ay6r5oQQQghpOIgvh8VGPtJK8XnIhDQ+sp1WKs6vYlTxr1mz+j/3FnTo4KcCaNcOWL/e3wJA27b+tqLC37Zp429bt/a3rVr525YtM89leqilihBJ5VQLsu2i/TZtgM2b9YJB+/bAxo1A9+7AkiXAnnsCX3wBdOsGfPCBvwUA8Uiejh3T9WTfWrTItEEV5E2ogq8aPS58Ly2tH6ksytq3B9au9fv9q6/S/S62ot9N/S+2toUN0c/q4oK4fqJ9cf07dgS+/NLfqqkixDXp0QNYuRLo2tX3oUsXYNEiQPxYTPSz6ouwWfiiRqKr0fdCQFffwk39r/qqo2VL/57o0MFPBdGhA7Bihb+V+8F2P4htoZDNeSlOqjvBHnsA27dnlnXpkr4/xLVs0iR93Ssq6gd1tGtXP+WPrqxz5/TikbjPZVq3To9DQdu29aN7u3at/2uMnj3TizWtWvlzlOybLhtOly71f7XYuXPaf3khLMj/tm3rz63t2tU/rkuXtMgv7nWZ1q3rl7drVz89yp571vd/jz3Sc3KrVv6cIy9aiXtJpnNnvf9Cb5R9Evd7RUX969q2bf37u337+r9a2WOPtE0tWqRTNglat07PhXI7asxNjx71/e/YMe1/69b1r4fuR7OdOtWfxzp1Sl9/3ZzVpk39ttu21dudayigG+jQAXjySaBXL/8G27jRH/hduvgDYNky/4bp3BnYsgX4+mt/QmnVyp98amr8DzCeByxf7g+MTp38G3PNGn9Atm7tH1tb69etrfWPLStLH/vll/4HJHHs7t1A377pdktL/YG8bRuwerV/o4tjq6uB/v39m2blSt+vTp38Y1et8ifFNm38Y6uqgN69/fa+/tpvv2NHf0IUHxLEsTt2+DY0aeLfaDU1mcd26eIfu3Gj70O/fn6769albdi+3X/D7tTJnyC+/dbvx379fP/XrUv7tmOH72vHjuljN2/2bSgv9z/4lpb612zHDr/d9u39YzduBL77LvPYZs38/Tt3+u22bZs+duPG9LHr1vmTTvv2fv+sWOH71batf9yGDeljv/nGt1tMtMuX+2OhbVv//OvX+/3brJnfZ02b+te8utpvt0WLdLvr1mUeW1bmn2fXLv/Y5s3T7a5d64/R5s3961ZS4r+uqfE/kJaXp48VY7R58/QE2aePv/3qK/96tm0LbNrkj9GePdM21Nb6NqRSvn1lZX6fiWO7d0/bUFPjX8dUyu+jsrJ0u19+6feROJaE49//9vt60yZ/LOyzjz+Wli3z+7l37/T90ru3f/03bvTH+h57+PfN9u3+mBb3esuW/uv16/1r17mzf66lS/3x0bKlf403bQIGDfLnBvFFoWtX/xqvWQPsu6/f5pIlfp3u3dP3ed++/r4NG/yx1LmzPyZ37EjPqevW+fXatvXHXGmpf47du33/+vXzx83q1X67e+/t27tokT+mOnXy661bBwwe7I+/Tz7xPxB16+bPL6tW+e14nu9vWZk/b8h9tHu3P8e0auXf71995Z9XzC8rV/rzarNmvm81Nf7r6mr/fH37+vfG6tV+f++7r+/zxx/7vnbu7J9v7Vq/3u7d/vzRvLlvq5iPu3b1bdq82e+XVq38+0fMV9u2+a/FHLRsmd9nffr4di5ZAgwY4Ndbvtxva999/fOJPhPXXcx7VVV+/1VU+L5v2OCXdevmn2/HDn+uatnS/7Iu5tnKSt+ffv38eeSzz/zjevb0+33ZMn/sNG/u2wX4Y3fHDr/Pevf237fWrvXfM/r08a/xunX+vN+ypW/Lrl3pcVxb67dXXu5fE/n9QfhTVgYsXuzb2bWrX75qFbDffr6dn3zi1+/Xz/fvs8+Agw7K+W2dCPlIK/XnPwM/+5nfh3vskX5f6dDBv+5irqmt9a9LmzbpcdyypX/PrlzpX4sePfz7qbbWvx6rV/vXZOBAf5767jt/zHz7rX+/DBjgt79mjT9+AL9O585+26tW+efq1Mk/xvP88fjNN75tAwb4bX75JXDAAf7/n3/uX/+tW/379aCD/Pt6wQJg2DB/TlmwwLejeXPgv//122zb1h9LHTr49/iyZb5PPXv67e/YAey1l3+vrVvn19+61fd90CB/DvnkE///pk39cwwY4N8TH37o3w/t2/vl3bv7Y10+39Klvj1duvjzVW2tfz0qK31fxZf0b77x+6NZM//+bdfOb3fZMr/P9tzTb6tJE/9zxMqV/nzRv7/vx7Ztvl3r1vn91a+f78f69b5dtbX+OTp0SL/Hi7nzm298G3r29O/lTZv8OXz9en/cDBniX9uVK/3rUVnp348HHJCeJ4YMSb//7L2378eiRf7YadvWv9fbtfN9F5+Pe/Xyr7+wfcMG/3yDB6fnpqFD/bll0SL/HGVlwH/+48+XLVsC773nj8OKCl+cFHP8xx/716JjR/985eX+PPP11/64Ee/Vmzb5NlZX+9dnzz39Pl6+3L82FRW+r+J6yu+hy5bl8o52I9tppeLw5JP+9e3Qwe/3Vq388SNEiN270zlk27b1x1/Xrv7c0aePfx0GDfLv7QMOAM46Cxg+HBg1Cjj6aP/eHTfOv29OPNE/37hx6e1TT/nbp58Gxo8HnnkGGDvWn5dGj/bfe4YPB447Dth/f+AHP/DbvPpqfx5bs8Yfv99+69u3dat/D+3e7X++Lynxx2rz5v6Yrqjw78VOnfx7bM89059zLrjAH8PHHw8ceCBw8MHA4Yf79+BRRwH/+pfv08svA2PGAC++6Nv1wgvAscf622OO8W0fOdLvl+HDfT8OPBA4+2z/PrriCv98X3zhn/+bb/z+qaz0x3F1ddr26mr/s8Hmzf4x69b5ny+EzUuW+DZfcok//552GnDEEf7fqFH+PXzssf59Pm4c8Nxz/vb5533bhQ8vv+z79uqrvq99+wKHHea3s//+wMkn+/1+ySX+vmnTfDvWr/fHTmWl38e7dvnzfirl2y4eYCi+D3funP5Ov3y5b9/ll/vzyPe+Bxx6qH/+o47y56xjj/X9Hj8+PUbUMfTkk8AJJ/j7x43zz3H00f48JcbOAQcA553nX5PPPvN9WL3aH8tiMUiMHeFDWZn/ea5lS9+/jh0zx0zfvv574KBBwEUXpfvp0EOBESP8vhswwPdlzhx/W0hkc16Ks6gH+O8h++3n9/9xx/ll777rzznt2gGvvOKXffSRf33atAHeeMO/J+bP99+rAOCll/yxcMIJwNSpftm//uXfKy1a+PclALzzjv/+0bWrfx+XlvrXtrTUvw+ffNK/H2bPTgvmzz3n3+87d/r3tmhn773T7+OAP95at/bfq556yrd/4cK0oC7mlSFD/DlBtDNkiD8Wjzkm7X+vXv44fO21tP/Nm/t9MHeuf+433/TnC8C/v48+Gjj1VOCaa9LnO/BAv85++6Xb7tbN/3v2WX/sL1vm+55KAU884d9LTz3l3y+A30+jR/v+n366X/b++/5nt2bN0gtGS5b47ytdu/r3aMeO/mczEXEu7o1hw/zPtADw9tv+/VtT48+lok969fLn7rlz/bL//tc/V7Nmfp8MGgTMm+e/lwkbjznGn1uuusove/VV/1ytWvn9JezeYw/f/9mz/fe8FSvSIvsTT/g2PvaYPweIsXXUUf51POkkv2zBAn+MNm/utwP4n7PE58VnnvHHwQcfpBdQ3njD79sDD0x/fnnrLd9/z/PnEuF/jx6+nW++6du2cKH/GbhpU/+6Dh3q94Pw//nn02P0pz/1y157zZ+rck3KK5In3k2cOBELFizAunXr0K5dO4wZMwY333wzuolldgc2b96MiooKVFZWok2hLZ0SQow01Hu3ofpFSEOnod67DdUvQho6hXbvirRSvXr1wv33349SKURwDxEa7UCh+UUIcafQ7t8k5qVC84kQ4k4S92/RRKCPHj0av/jFL9C1a1esWbMGV111FU4//XS89dZb+TaNEEIIIYQQQgjyk1aKEEKC4LxECIlL0TxE9Gc/+xkOO+ww9OrVC8OHD8e1116L+fPnY5fLY7IJIYQQQgghhGSdSZMmwfM87R8hhOQDzkuEkLgUTQS6zMaNG/GPf/wDw4cPRxPTU1oAVFVVoUp6jPlmkUSJEEIIIYQQQgghhBBCCLFQNBHoAPDzn/8cLVu2RIcOHbBq1So8/fTTgcfPmDEDFRUVdX9hn5hMCCGEEEIIIYQQQgghpPGSVwH92muvRSqVCvxbvHhx3fFXX301PvzwQ7z88ssoLS3FD37wg8Cf3EydOhWVlZV1f6tXr86FW4QQQgghhBBCCCGEEEIaAHlN4XLllVdi0qRJgcf07du37v+OHTuiY8eOGDBgAPbZZx/06NED8+fPx+GHH66tW15ejvLy8iRNJoQQQgghhBBCCCGEENJIyKuA3qlTJ3Tq1ClS3draWgDIyHFuQ0SrMxc6IcWFuGcb2kNeOCcRUpxwTiKEFBKckwghhUZDnJc4JxFSvCQxJxXFQ0TfeecdvPfeexgxYgTatWuHZcuW4de//jX69etnjD7XsWXLFgBgLnRCipQtW7agoqIi32YkBuckQoobzkmEkEKCcxIhpNBoSPMS5yRCip84c1LKK4IlwYULF+KnP/0pPvroI2zbtg1du3bFuHHj8Ktf/Qp77rmnczu1tbX46quv0Lp1a6RSqcBjN2/ejB49emD16tVo06ZNXBcKhobqF9BwfaNf/irhli1b0K1bN5SUFNWzjwPhnNRw/QIarm/0i3MSwHFQjDRU3xqqX4C7b5yTGu44aKh+AQ3Xt4bqF8DPSpyTGq5fQMP1raH6BeR+TiqKCPT99tsPr732Wux2SkpK0L1791B12rRp0+AGGdBw/QIarm+N3a+GErkgwzkpTUP1C2i4vjV2vzgn+TT2cVCMNFTfGqpfgJtvnJN8Guo4aKh+AQ3Xt4bqF9B4PytxTkrTUP0CGq5vDdUvIHdzUsNYCiSEEEIIIYQQQgghhBBCEoYCOiGEEEIIIYQQQgghhBCigQK6gfLyckybNg3l5eX5NiVRGqpfQMP1jX4RoOH2V0P1C2i4vtEvAjTc/mqofgEN17eG6hfQsH1LmobaVw3VL6Dh+tZQ/QIatm9J01D7qqH6BTRc3xqqX0DufSuKh4gSQgghhBBCCCGEEEIIIbmGEeiEEEIIIYQQQgghhBBCiAYK6IQQQgghhBBCCCGEEEKIBgrohBBCCCGEEEIIIYQQQogGCuiEEEIIIYQQQgghhBBCiIYGK6DPmDEDBx98MFq3bo3OnTvj5JNPxpIlSzKO2blzJyZPnowOHTqgVatWOO200/DNN99kHLNq1SqccMIJaNGiBTp37oyrr74au3fvzjhmzpw5OOCAA1BeXo7+/ftj1qxZefVt48aNuOyyy7D33nujefPm6NmzJy6//HJUVlZmtJNKper9PfTQQ3nzzeWajRo1qp7NF198ccYxhXbNbH6tXLlSey1SqRQeffTRuuMK7XoBwMyZMzFkyBC0adMGbdq0weGHH44XXnihbn+x3mPZgHMS56RiumbFOi9xTnKHcxLnpGK6ZpyTCut6ZQPOSZyTiumacU4qrOuVLRrqvMQ5iXNSoVyzopuTvAbK2LFjvfvuu89btGiRt2DBAu/444/3evbs6W3durXumIsvvtjr0aOH9+qrr3rvv/++d9hhh3nDhw+v2797925v8ODB3pgxY7wPP/zQe/75572OHTt6U6dOrTtm+fLlXosWLbwpU6Z4n3zyiXfnnXd6paWl3osvvpg33xYuXOideuqp3uzZs72lS5d6r776qrfXXnt5p512WkY7ALz77rvP+/rrr+v+duzYkTffXK7ZyJEjvQsvvDDD5srKyrr9hXjNbH7t3r07w5+vv/7amz59uteqVStvy5Ytde0U2vXyPM+bPXu299xzz3mfffaZt2TJEu8Xv/iF16RJE2/RokWe5xXvPZYNOCdxTiqma1as8xLnJHc4J3FOKqZrxjmpsK5XNuCcxDmpmK4Z56TCul7ZoqHOS5yTOCcVyjUrtjmpwQroKuvWrfMAeHPnzvU8z/M2bdrkNWnSxHv00Ufrjvn00089AN7bb7/teZ7nPf/8815JSYm3du3aumNmzpzptWnTxquqqvI8z/OuueYab999980415lnnumNHTs22y7Vofqm45FHHvGaNm3q7dq1q64MgPfkk08a6+TbN51fI0eO9H76058a6xTDNXO5Xvvvv7/3wx/+MKOs0K+XoF27dt7/+3//r0HdY9mAcxLnpGK7ZsU6L3FOcoNzEuekYrtmnJMK53plA85JnJOK7ZpxTiqc65UtGuq8xDkpTTFcL8/jnJSv+6vBpnBRET83ad++PQDggw8+wK5duzBmzJi6YwYOHIiePXvi7bffBgC8/fbb2G+//dClS5e6Y8aOHYvNmzfj448/rjtGbkMcI9rIBapvpmPatGmDsrKyjPLJkyejY8eOOOSQQ3DvvffC87y6ffn2zeTXP/7xD3Ts2BGDBw/G1KlTsX379rp9xXDNbNfrgw8+wIIFC3DBBRfU21fI16umpgYPPfQQtm3bhsMPP7xB3WPZgHMS56RiumbFOC9xTgoH5yTOScV0zTgnFdb1ygackzgnFdM145xUWNcrWzTUeYlzEuckAeekYMrshxQ/tbW1uOKKK3DEEUdg8ODBAIC1a9eiadOmaNu2bcaxXbp0wdq1a+uOkS+E2C/2BR2zefNm7NixA82bN8+GS3XofFPZsGEDfvOb3+DHP/5xRvkNN9yAo48+Gi1atMDLL7+Mn/zkJ9i6dSsuv/xyAPn1zeTXOeecg169eqFbt27473//i5///OdYsmQJnnjiiUCbxb5C9Uvmr3/9K/bZZx8MHz48o7xQr9fChQtx+OGHY+fOnWjVqhWefPJJDBo0CAsWLGgQ91g24JzEOUnsy7dfQMOblzgnhYdzEucksS/ffgGck4rtHvv/2Lvv8KbK/g3g90nTdO9FaUsLFChlFRCQKUOWvkUQRV+VoYgLRcQFKoKgoqLgwvE6UURRFGkFGcpW9iotmxZaSnfpoCtNcn5/9JdIpUBHkuckuT/X1Uvbpjl3SXs3+Z5znmMJ7CR2kvFzor8vgJ1ka79jlmKvvcROYieJfsxsqZMcYoA+depUJCcnY8eOHaKjmN31vreSkhLceuutiI2Nxdy5c2t9bvbs2ab/79q1K8rKyrBw4ULTL49IV/u+Li/tTp06ITQ0FEOGDMGZM2fQunVra8dssOs9XhUVFVi+fHmtx8ZIqY9Xu3btcOjQIRQXF2PlypWYOHEitm7dKjST0rGT2ElKYm+9xE5qOHYSO0lJ2EnETmInKQk7iQD77SV2EjtJ9GNmS51k90u4PP744/jtt9+wefNmhIeHmz7erFkzaLVaFBUV1bp9Tk4OmjVrZrrNv6/wanz/erfx9va2+J6nq31vRqWlpRgxYgS8vLywatUqODs7X/P+evXqhfPnz6OqqgqAuO/tet/XvzMDwOnTpwEo+zGrz/e1cuVKlJeXY8KECde9P6U8XhqNBtHR0ejevTsWLFiALl264L333rOL3zFLYCexk2zpMQNsr5fYSQ3DTmIn2dJjBrCTlPR4WQI7iZ1kS48ZwE5S0uNlKfbaS+wkdpISHjNb6iS7HaDLsozHH38cq1atwqZNm9CyZctan+/evTucnZ3x559/mj524sQJpKeno3fv3gCA3r1748iRI8jNzTXdZuPGjfD29kZsbKzpNpffh/E2xvuwhOt9b0DNnsJhw4ZBo9EgISEBrq6u173fQ4cOwc/PDy4uLgCs/73V5/uqKzMAhIaGAlDmY9aQ7+uLL77AqFGjEBQUdN37Ff14XY3BYEBVVZVN/45ZAjuJnXR5Zlt4zIxsvZfYSXVjJ7GTLs9sC4+ZETtJ/ONlCewkdtLlmW3hMTNiJ4l/vCzFXnuJnVQ7M8BOYifVU4MvO2ojHn30UdnHx0fesmWLnJWVZXorLy833eaRRx6RW7RoIW/atEnet2+f3Lt3b7l3796mz+t0Orljx47ysGHD5EOHDsnr1q2Tg4KC5FmzZpluk5qaKru7u8vPPvusfOzYMXnJkiWyk5OTvG7dOmHfW3FxsdyrVy+5U6dO8unTp2vdRqfTybIsywkJCfJnn30mHzlyRD516pT80Ucfye7u7vLLL78s7Hu73vd1+vRped68efK+ffvktLQ0efXq1XKrVq3kAQMGmO5DiY9ZfX4WZVmWT506JUuSJP/+++9X3IcSHy9ZluWZM2fKW7duldPS0uSkpCR55syZsiRJ8oYNG2RZtt3fMUtgJ7GTbOkxM7K1XmIn1R87iZ1kS4+ZETtJGY+XJbCT2Em29JgZsZOU8XhZir32EjuJnaSUx8zWOsluB+gA6nz76quvTLepqKiQH3vsMdnPz092d3eXx4wZI2dlZdW6n7Nnz8ojR46U3dzc5MDAQPnpp5+Wq6ura91m8+bNclxcnKzRaORWrVrV2oaI723z5s1XvU1aWposy7L8+++/y3FxcbKnp6fs4eEhd+nSRf7kk09kvV4v7Hu73veVnp4uDxgwQPb395ddXFzk6Oho+dlnn5WLi4tr3Y/SHrP6/CzKsizPmjVLjoiIuOIxkGVlPl6yLMsPPPCAHBkZKWs0GjkoKEgeMmSIqexk2XZ/xyyBncROsqXHzMjWeomdVH/sJHaSLT1mRuwkZTxelsBOYifZ0mNmxE5SxuNlKfbaS+wkdpJSHjNb6yRJlmUZRERERERERERERERUi92ugU5ERERERERERERE1BQcoBMRERERERERERER1YEDdCIiIiIiIiIiIiKiOnCATkRERERERERERERUBw7QiYiIiIiIiIiIiIjqwAE6EREREREREREREVEdOEAnIiIiIiIiIiIiIqoDB+hERERERERERERERHXgAJ2sYtKkSRg9erSw7Y8fPx6vv/66xe7/6NGjCA8PR1lZmcW2QUTmw04iIiVhJxGRkrCTiEhJ2EmkBJIsy7LoEGTbJEm65ufnzJmDp556CrIsw9fX1zqhLnP48GEMHjwY586dg6enp8W2c8cdd6BLly6YPXu2xbZBRNfHTqrBTiJSBnZSDXYSkTKwk2qwk4iUgZ1Ug52kfBygU5NlZ2eb/n/FihV4+eWXceLECdPHPD09LVo01/Pggw9CrVbjk08+seh21qxZgylTpiA9PR1qtdqi2yKiq2Mn1WAnESkDO6kGO4lIGdhJNdhJRMrATqrBTlI+LuFCTdasWTPTm4+PDyRJqvUxT0/PK065GThwIJ544glMnz4dfn5+CAkJwWeffYaysjLcf//98PLyQnR0NH7//fda20pOTsbIkSPh6emJkJAQjB8/Hvn5+VfNptfrsXLlSsTHx9f6eFRUFF599VVMmDABnp6eiIyMREJCAvLy8nDbbbfB09MTnTt3xr59+0xfc+7cOcTHx8PPzw8eHh7o0KED1q5da/r80KFDUVhYiK1btzbxX5SImoKdVIOdRKQM7KQa7CQiZWAn1WAnESkDO6kGO0n5OEAnYZYuXYrAwEDs2bMHTzzxBB599FHceeed6NOnDw4cOIBhw4Zh/PjxKC8vBwAUFRVh8ODB6Nq1K/bt24d169YhJycH48aNu+o2kpKSUFxcjBtuuOGKzy1evBh9+/bFwYMHceutt2L8+PGYMGEC7rvvPhw4cACtW7fGhAkTYDxJY+rUqaiqqsK2bdtw5MgRvPnmm7X2hGo0GsTFxWH79u1m/pciImtgJxGRkrCTiEhJ2ElEpCTsJLI6mciMvvrqK9nHx+eKj0+cOFG+7bbbTO/fdNNNcr9+/Uzv63Q62cPDQx4/frzpY1lZWTIAeefOnbIsy/L8+fPlYcOG1brfjIwMGYB84sSJOvOsWrVKdnJykg0GQ62PR0ZGyvfdd98V25o9e7bpYzt37pQByFlZWbIsy3KnTp3kuXPnXvP7HzNmjDxp0qRr3oaIrIedxE4iUhJ2EjuJSEnYSewkIiVhJ7GTlIxHoJMwnTt3Nv2/k5MTAgIC0KlTJ9PHQkJCAAC5ubkAai7esHnzZtMaWJ6enoiJiQEAnDlzps5tVFRUwMXFpc4LU1y+feO2rrX9adOm4dVXX0Xfvn0xZ84cJCUlXXGfbm5upj2cRGRb2ElEpCTsJCJSEnYSESkJO4msjQN0EsbZ2bnW+5Ik1fqYsaQMBgMA4NKlS4iPj8ehQ4dqvZ06dQoDBgyocxuBgYEoLy+HVqu95vaN27rW9h988EGkpqZi/PjxOHLkCG644QZ88MEHte6zsLAQQUFB9fsHICJFYScRkZKwk4hISdhJRKQk7CSyNg7QyWZ069YNKSkpiIqKQnR0dK03Dw+POr8mLi4OAHD06FGzZIiIiMAjjzyCX375BU8//TQ+++yzWp9PTk5G165dzbItIlI2dhIRKQk7iYiUhJ1ERErCTqKm4gCdbMbUqVNRWFiI//73v9i7dy/OnDmD9evX4/7774der6/za4KCgtCtWzfs2LGjydufPn061q9fj7S0NBw4cACbN29G+/btTZ8/e/YsMjMzcfPNNzd5W0SkfOwkIlISdhIRKQk7iYiUhJ1ETcUBOtmM5s2b46+//oJer8ewYcPQqVMnTJ8+Hb6+vlCprv6j/OCDD+K7775r8vb1ej2mTp2K9u3bY8SIEWjbti0++ugj0+e///57DBs2DJGRkU3eFhEpHzuJiJSEnURESsJOIiIlYSdRU0myLMuiQxBZUkVFBdq1a4cVK1agd+/eFtmGVqtFmzZtsHz5cvTt29ci2yAi+8BOIiIlYScRkZKwk4hISdhJZMQj0Mnuubm54ZtvvkF+fr7FtpGeno4XXniBZUdE18VOIiIlYScRkZKwk4hISdhJZMQj0ImIiIiIiIiIiIiI6sAj0ImIiIiIiIiIiIiI6sABOhERERERERERERFRHThAJyIiIiIiIiIiIiKqAwfoRERERERERERERER14ACdiIiIiIiIiIiIiKgOHKATEREREREREREREdWBA3QiIiIiIiIiIiIiojpwgE5EREREREREREREVAcO0ImIiIiIiIiIiIiI6sABOhERERERERERERFRHThAJyIiIiIiIiIiIiKqAwfoRERERERERERERER14ACdiIiIiIiIiIiIiKgOHKATEREREREREREREdWBA3QiIiIiIiIiIiIiojrYzAD9448/RufOneHt7Q1vb2/07t0bv//+u+hYROSg2ElEpDTsJSJSEnYSESkJO4mImkKSZVkWHaI+EhMT4eTkhDZt2kCWZSxduhQLFy7EwYMH0aFDB9HxiMjBsJOISGnYS0SkJOwkIlISdhIRNYXNDNDr4u/vj4ULF2Ly5MmioxARsZOISHHYS0SkJOwkIlISdhIR1ZdadIDG0Ov1+Omnn1BWVobevXuLjkNEDo6dRERKw14iIiVhJxGRkrCTiKihbGqAfuTIEfTu3RuVlZXw9PTEqlWrEBsbe9XbV1VVoaqqyvS+wWBAYWEhAgICIEmSNSITkRnIsozS0lI0b94cKpVyLt3ATiJyTErtJKBhvcROIrIP7CQiUhql9hI7icgxmaWTZBtSVVUlnzp1St63b588c+ZMOTAwUE5JSbnq7efMmSMD4Bvf+GYnbxkZGVZsnOtjJ/GNb479prROkuWG9RI7iW98s683dhLf+MY3pb0prZfYSXzjm2O/NaWTbHoN9JtvvhmtW7fGp59+Wufn/73HsLi4GC1atEBGRga8vb1r3XbcuHH48ccfr7vN7NJsTFs3DWqVGktHL4Wzk3PTvgkiBzdu3DgAuObvX0lJCSIiIlBUVAQfHx9rRWswc3ZSfZVpy/DA6gcgQ8Zn8Z/Bx9Uy/z71eZzIMcmyjLvuugsrVqxwmKNxbKWTgGv3kiU6ySAbMOnXSajUVeLtoW+jhW+LRmcnovphJ13b1DVTkVeeh9kDZqNTSKdG3w8R1Z+t9JKITnrhjxdw+uJpTOs5Df0i+zX6foio/szRSTa1hMu/GQyGWoX2by4uLnBxcbni497e3lcUnrOzc71K0MvLC/6+/ijVlqLAUIC2fm0bHpyITJyda3ZC1ef3T+nDOXN2Un15wxstm7VERkkGsquzEREc0aj7uZb4+Hg4OzsjMTHR7PdN9sHZ2Rn33Xefw/2MKL2TgGv3kiU6CQBiw2NxJPcIsqqz0NG7Y6Pvh4gahp1Ut04tOmF7+nZkajPR17tvo++HiBpO6b0kopM6R3bGuapzOK8936T7IaKGa0on2cwAfdasWRg5ciRatGiB0tJSLF++HFu2bMH69eutmkOSJLQNaIuTBSdxseKiVbdNRMqhlE4CgLYBbVFYUYjiqmKrb5sIADp27Ijk5GTRMRyeUnqpXUA7nCo8hUvaS1bdLhEpi5I6aXfmblRUV1h1u0SkLErppLYBbaFWqVGpq7TqdomoaWxmgJ6bm4sJEyYgKysLPj4+6Ny5M9avX4+hQ4daPcuzfZ6Fu7O74vemEpHlKKmTHur+EJ7s9aRFOunChQsA4HBHFlPDLFiwAPHx8YiPj+fPikBK6aW7Ot6F8V3GQyUp56JhRGR9Sumk4dHDcWvbW6FW2cxLXyKyAKV0Up+IPugT0QcaJ41Vt0tETWMzzyK++OIL0RFMPDQeoiMQkWBK6iR3Z3eL3ffDDz9ssfsmIvNSSi+5ql1FRyAiBWAnEZGSKKWTODgnsk08NKiJbPgarERkh9hJJIrxyPP4+HjBSUhJ2ElEpCTsJCJSEnYSke3gAL2Rvjz4Je5ffT/2XdgnOgoREX5M+REPJjyIP1L/MNt9GgehXJKDiBpq7am1eOS3R7Dy6ErRUYiIsO3cNkxdMxVfHFTGEahE5Nj2X9iPJ39/Eot3LRYdhYjqiQP0RiqtKkV+eT6O5R8THYWICBXVFcgpy2EnkVDGnS06nU5wEhKtWl+NzNJMdhIRKYIsy0gvScfRvKOioxARQa1SI7UoFcm5yaKjEFE9cYDeSO2D2gMAjuXxhSERiWepTnJ3t9z66mS/xowZIzoCCWbspOP5x3l6MhEJZ+yk1IupqNJVCU5DRI6ubUBbSJCQV56H/PJ80XGIqB44QG+k9oE1T8JOFp6EzsAj7YhIrJjAGADA+dLzKK0qbfL9GZdvWbFiRZPvixxL165dRUcgBWjl1woaJw1KtaXILM0UHYeIHFyQexD83fyhl/U4VXhKdBwicnBuzm5o6dsSQM3BBkSkfBygN1K4dzg8NZ7Q6rVIu5gmOg4ROThvF2+EeYUB4JMwEmvevHkAeDFRR6dWqdHWvy0Anq1HROJJkmQ6AIqdRERKwFUNiGwLB+iNVOtJGNf3JCIFiA2KBcBOIiJlML0wZCcRkQLwtRsRKQlfuxHZFg7Qm8C4ZAL3GBI13r333is6gt0w15FVL774IoB/LghJ1FDGn53q6mrBSUgkYyfxon1EpAS8NgMRKYlxnsRrMxDZBg7Qm6BDUAe08W+DSN9I0VGIbNZdd90lOoLdiA2KRUvfloj2j27S/SQlJZkpETm622+/XXQEEigmMAYR3hHoGNwRBtkgOg4RObhWfq0Q5ROFnmE9UamrFB2HiBxckHsQYgJi0K9FP5RVl4mOQ0TXoRYdwJZ1CO6ARcMXiY5BZNMkSRIdwW6EeYfh/ZHvi45BRAQA8HLxwke3fiQ6BhERgJprM3xwyweiYxARAah5Hbxw2ELRMYionngEOhERXYHLt1BTGX+GeDFRIiIiIiIismUcoJtBla4KF0oviI5BRAQA0Oq1OF9yvlFfy2EnEZmbzqBDenG66BhERAAAg2zA2aKzomMQEQEAZFnGuaJzvDYDkcJxgN5ESTlJuGvlXViwfYHoKEREOFt0FnevvBuz/pzFJ2EkHM9koILyAty98m5MXzcdWr1WdBwicnBVuirc8/M9eOL3J1BUWSQ6DhE5OFmWMTlhMh7//XEebECkcBygN1GkTyT0sh7nis+htKpUdBwicnBhXmEAgKLKIp4ZQ4rBMxscl7+bP9yc3VBtqMapglOi4xCRg3NRuyDIPQgAkJKbIjgNETk6SZJMr99S8thJRErGAXoT+bj6INwrHDJkHMs/JjoOETk4ZydntAtoB6DxT8J41DARmYskSegY1BEAXxgSkTJ0DGYnEZFyGDspOTdZcBIiuhYO0M2AhUdEStLYTuJRwmQJ3CFDHYI7AODzJCJSBnYSESmJsZNS8lK4BCeRgqlFB7AHHYI7YN2ZdTwNkIgUoUNwByCFpyaTssTHx3OY7qCMO/WO5R+D3qCHk8pJcCLbtH//fsydO9fs97tgwQJ07NjR7PdLpFQdgmqGVWeLzuKS9hI8NZ6CExGRI2sb0BZqlRqFFYXIvpSNUK9Q0ZGIqA4coJuB8YXh6YunUamrhKvaVXAiInJkMYExcJKckFuei9yyXAR7BIuOREQOLNInEh7OHiirLkPqxVS0CWgjOpLi5Ofn4/777zfLfT344IPo27cvqqur8dtvvyEhIeGat581a9Y1P88dX2Rv/Nz8EOYVhszSTBzLO4YeYT1ERyIiB6Zx0qBdQDuk5KUgOTeZA3QiheIA3QwC3QMR7B6M3PJcHM8/jrhmcaIjEZEDc1W7orVfa5wsPImU3BQEt6z/AJ2DErKExMRExMfHQ6/Xw8mJRx87GkmS0CGoA/Zc2IPk3GSHHqCvXbsWH3/8cb1ua44+njJlCqZMmdKgrxkzZgx0Op3p/X8v7/Xoo4/illtuaXI2IpFig2KRWZqJ5NxkDtCJSLgOQR2QkpeClLwUDG09VHQcIqoDB+hmMjpmNAAg3DtcbBAiIgC3tLkF/ar6ISYwpl63P3LkiIUTEQGjR4/mThoHNaTVEMQExqB78+6io1jNihUrsGzZsmveRom/D6tWrarz48ZB+scff1xrJ4ASvwei6xkQOQBB7kG4ofkNoqMQEaFXeC8AcKjnSUS2hgN0M4lvx4vvEZFyDGk1pEG3f+GFFyyUhIgI6BPRR3QEi7vWhZiXLVsGHx8fK6Yxv38Pyo3f7+XfN4fpZCvimsXxrGEiUoy2AW3RNqCt6BhEdA0coBMREZHFGZdx0el0UKv59INs37UG5o4wSL78e+QwnYiIiIjsGV/BmlFuWS4OZx9G24C2iPSNFB2HiBxcYUUhDmcfRnOv5mgX2E50HCIANesrc7DmmIori5GUkwRvF290adZFdJxGudrQ3NF/pq81THf0fxtSrjJtGZJykuCkckLPsJ6i4xCRg6vUVeJIzhFU6irRP7K/6DhE9C8q0QHqa8GCBejRowe8vLwQHByM0aNH48SJE6Jj1bIsaRne3/M+tqdvFx2FiCzMFjpp1bFVWLRrETambqzX7TnkILJdttBJf6T+gbf+fguJJ22ra+Lj401vRqtXr0ZiYqLpjf7x73+Tf//bkeNQei/tOr8Lr+94HT+m/Cg6ChFZgdI7KSU3BfO2zcPSw0tFRyGiOtjMAH3r1q2YOnUqdu3ahY0bN6K6uhrDhg1DWVmZ6GgmnUM6AwCScpIEJyEiS2MnETUcB42WY0uddCT3CPQGveA011bX0PzygblKZTNPoYUx/ls98cQTADhId0RK7yVjJ50qPIXy6nLBaYjI0pTeSR2CO8BJckJOWQ5yLuWIjkNE/2IzS7isW7eu1vtff/01goODsX//fgwYMEBQqtqMT8JOFpxERXUF3JzdBCciIkuxhU7qENwBKkmFrEtZyC3LRbBHcJ2340CDrC0+Pp7DdDOzhU5q7d8aHs4eKKsuQ+rFVLQJaCM6Ui2HDx/GSy+9VOtj/DltumHDhmHYsGFYsmQJ1q1bx6VdHIjSeynIIwihnqHIupSFlNwU9AjrIToSEVmQ0jvJVe2KtgFtcSz/GJJykjDUc6joSER0GZs9fKa4uBgA4O/vLzjJP4I9ghHiEQK9rMfRvKOi4xCRFSmxk9yd3dHGv2ZAdSTniOA0RGRNSuwklaRCx+COAIDDOYcFp/mH8cjoy4fnXJrF/KZOncqlXRycEnupS0jN9Rh4th6R42EnEVFD2OQA3WAwYPr06ejbty86dux41dtVVVWhpKSk1pulGY9CV9ILQyKyLCV3kvFJGDuJlIJDScuzhU5SwgvDay3RQpZT1xrpZP/q00siX7spoZOIyHoU30m5SZBl2eLbI6L6s8kB+tSpU5GcnIwffvjhmrdbsGABfHx8TG8REREWz6akF4ZEtsSWnyAouZMuf2Foy//GZH84NLMcW+iklLwU6Aw6i2+vLlcbnJN1JSYm4s033wRQ85iMHTtWcCKypPr0kohO6hTSCQCQWpSK0qpSi2+PiJRBqZ3ULrAdNE4aFFYUIrM00+LbI6L6s7kB+uOPP47ffvsNmzdvRnh4+DVvO2vWLBQXF5veMjIyLJ7P+CQsrSiNF6MhagDjKXS2Rumd1D6oPdQqNQoqCpBblnvV23F4RGQflN5JLXxawMfFB1q9FmcKz1h8e5fj4Fx5YmNjTY+BVqvljjU7Vd9eEtFJvq6+aOHdAgBwLP+YxbdHROIpuZM0ThrEBMQAAJcFJlIYm7mIqCzLeOKJJ7Bq1Sps2bIFLVu2vO7XuLi4wMXFxQrp/uHv5o+5N81F24C2cHd2t+q2iWzZ5s2bMWbMGNEx6s1WOknjpMHcm+Yi0jcSvq6+Vt020dUkJiZyUGZmttJJkiRhZr+ZaObZDIHugVbZ5p133onKykrT+xyaK8/lncCLDNuPhvaSiE4CgCd6PQE/Vz+EeIZYfdtEZD220kkPdH0ArmpXNPdqbvVtE9HV2cwAferUqVi+fDlWr14NLy8vZGdnAwB8fHzg5uYmOF1t3Zt3Fx2ByOZ8+eWXNjVAt6VO6tKsi+gIRHXioMx8bKmTjBcStYZ/H3FOymV8fIxnCqxatQpqtc28VKE62EovxQTGiI5ARFZgK53U2r+16AhEVAebWcLl448/RnFxMQYOHIjQ0FDT24oVK0RHIyIHZC+dxKOAieyDvXSSudS1XAvZBuNjNWbMGP6NsnHsJSJSEnYSETWFzRzWYWsXv1t5dCX2ZO7BjN4z0Myzmeg4RGRmttZJa0+txV/pf2FClwloF9hOdBwidO7cGUlJvOC2udhaJ21O24yt57bi1ja3okdYD7PeNwfnto9LutgHW+qlXed34Y/UP9A3oi8GtRwkOg4RWYAtddKh7ENYd3od2ge2x20xt4mOQ0SwoSPQbc3+C/txLP8YDmYdFB2FiAhHco4gKTcJB7IOiI5CBAB47bXXREcggU4WnMT+rP3Yd2Gf2e5TlmUOz+3I5Y8fj0QnSztbdBa7M3dj1/ldoqMQESH7Ujb+yvgLf2f8LToKEf0/DtAtpGtoVwDgsIqIFIGdRErFwZhjMncnbd68GaNGjQIAJCQkcHhuJzhEJ2vpFtoNAHA45zD0Br3gNETk6IyddLzgOMq0ZYLTEBHAAbrFdG1W88IwKTcJOoNOcBoicnTGTjpRcIJPwohIuE7BneAkOSG7LBtZpVlNuq/4+HgsWrQIQM3AVZIkc0QkheAQnawh2j8anhpPlFWX4VThKdFxiMjBBXsEI8wrDAbZgCO5R0THISJwgG4xrf1bw0vjhfLqcpwq4JMwIhIryCMI4V7hkCHjcM7hWp+bPXu2oFTk6HiUsONyc3ZD+8D2AICD2Y1f7o5LtjgGDtHJ0lSSCnEhcQDAJTiJSBGMB0Cxk4iUgQN0C1FJKsQ1iwPAJROISBmMSyb8+0lYz549RcQhMuFAzDFdrZPq6/Kdfxye2z8O0cnSTJ3UhJ16RETmwk4iUhYO0C3ItMeQhUdECmDspANZB2zqKvREZJ+MnXQ453CDl7srLS3FoUOHAHB47kguf6xnzpwpMAnZIy53R0RKYlzuLutSVpOXuyOipuMA3YK6hXaDu7M7gtyDYJANouMQkYPrFNIJnhpPRPpGolJXKToOETm4aP9oBLoHIiYwBiVVJQ362nvuuQcAh+eOyPiYp6SkCE5C9ibIIwhRPlGIDYxFUWWR6DhE5ODcnN3QMbgj2gW0wyXtJdFxiByeWnQAexbgHoDlty+Hk8pJdBQiIriqXbFszDJ2EilKYmIil2NwUJIk4fP4zxvcScafFw7PHZexN+Lj4/lzQGb17oh3+TyJiBTjlYGvsJOIFIJHoFsYy46IlISdRErFIbpjauzwnMg4OOfPBJkTnycRkZKwk4iUgwN0K5BlGZklmQ1e35OIyFJyy3JxIIkXOCYiZSisKGzQ6ck86pgADtHJcoori1FcWSw6BhERAKBMW4aC8gLRMYgcGgfoVvDcxufwyJpHcDz/uOgoRER4ddurmJwwGU8seEJ0FCIAwLhx40RHIIGW7FmCib9OxJazW655Oy7dQtfCITqZy7eHv8X4VeOReJJdQ0TirT6+Gvf8cg++T/5edBQih8YBuhWEeoUCAPZm7hWchIgIaO7VHABQ5FEkNgjR/xs/frzoCCSQ8XnSvgv7rnobvV5vrThkY7hDhcytuVdzyJD52o2IFCHcOxwG2YB9F/ZBlmXRcYgcFgfoVnBD8xsAAHsv8EkYEYln7KRi92LI4JMwUg4eQeqYejTvAQBIyklCpa6yztuMHj0aAIelVDcu5ULmdEPzGyBBQmpRKgorCkXHISIH1ymkE1ycXFBQUYC0ojTRcYgcFgfoVtAttBtUkgoZJRnILcsVHYeIHFxsUCzc1G6odqpGmUuZ6DhE5ODCvcMR7B6MakM1knKSRMchG8UhOpmLj6sP2vi3AXDtM2OIiKxB46RBl5AuANhJRCJxgG4FnhpPxAbGAgB2n98tOA0ROTq1So1uod0AcBkXUo5p06aJjkCCSJKEHmE1R6HX9TyJa59TQ/EUd2qqa3USEZG1sZOIxOMA3UpuDL8RALDr/C7BSYiI/umki54XBSchqjF06FDREUggYyftztwNg2wQnIZslXEny6hRowQnIVtn7KSD2QevurQUEZG19ArrBQkSThaeREF5geg4RA6JA3QrMT4JS85LRmlVqeA0ROToejTvAUmWUKGpQGZJpug4RCZcfsExdQzuCA9nDxRXFeN4/nHTxysqKgDw6HOqPy7lQuYQ6ROJUM9QVBuqcSDrgOg4ROTg/Nz8EBMYA6DmYAMisj616ACOIsQzBHd3uBttA9rCzdlNdBwicnAeGg+EFYbBVeuKQPdA0XGIyMGpVWqM7zweXi5eiPKNMn183Lhx4kIRkcOSJAl3dbgLMmR0Cu4kOg4REUbHjMagykGmgzOJyLo4QLeiezvfKzoCEZFJ84vNAQAuahfBSYiIgFvb3io6AtmJxMRExMfHIz4+nmcvUKMNaTVEdAQiIpM+EX1ERyByaFzChYiIiBSBgy6qC38uqDG4lAsRERERmQsH6FaWejEVSw8txeHsw6KjEBGhwrkCy48sx98Zf4uOQmTCgZfjyrmUg59SfsKfqX+iqKhIdBwicnCFFYX49fivSDiRIDoKERFKqkqw5uQarEheIToKkcPhAN3KNqVtwspjK/Fn2p+ioxAR4aLnRXyf/D3Wn14vOgoREY7kHsE3Sd8g4UQCxo8fLzoO2TgehU5NdabwDL44+AV+PvYzZFkWHYeIHFzOpRx8sv8T/HT0J2j1WtFxiBwKB+hW1ju8N4CaKydX66sFpyFShvnz54uO4LD8LvkBAA7nHEZpVangNETk6HqF9YJKUiG1KBWVzpWi4xCRg4trFgd3Z3cUVhTieP5x0XGIyMFF+0cjyD0IVfoq7LuwT3QcIodiUwP0bdu2IT4+Hs2bN4ckSfj111/Nev/WOKogNigW/m7+KK8ux4GsAxbfHpEtiIuLEx2hUSzdSdbgVu2GVr6toJf12Hl+p+g4RFzvugnsoZO8XLwQFxIHACj0LBQbhuwCj0IXxx46ydnJGTeG3QgA2J6+XXAaImoKe+gkSZLQr0U/AMD2c+wkImtSiw7QEGVlZejSpQseeOAB3H777Wa///PnzyMiIsLs93s5SZLQL6IfEk4mYEf6DvQK72XR7RGR5Vi6k6ylX4t+SC1KxfZz2zGs9TDRcYiokeypkw5kH0ChZyG+f+l70XHIDiQmJiI+Ph4LFy7Es88+KzqOw7CnTtp0dhP+yvgLD3Z7ECpJGcegybKMgwcPYs6cOaKjKAZ3wtO12Esn9W/RH6uOr8LeC3tRqauEq9pVdCQih2BTA/SRI0di5MiRFrv/devWYcqUKRa7f6P+kf2RcDIBuzJ3QavXQuOksfg2icj8LN1J1tKvRT98k/QNknKTUFxZDB9XH9GRiBAfH88Xwg1kL510Y/iNWLJ3CcpdyuHezF10HLIj27Zt4wDdiuylk7qGdoWHswcKKwpxNO8oOgZ3NNt9V1RUYNy4cWa7v2uJiIjAkiVLIEmSVbZnKdXV1Vcdfl7vTBM+r3Bs9tJJ0f7RCPEIQU5ZDvZd2Gc6Ip2ILMumBuiWlpCQYJUBeruAdgh0D0R+eT4OZB3AjeE3WnybRERXE+oVimi/aJy+eBo7z+/EiOgRoiMRkQPzcvFCXLM47MRO7Ejfgbs73i06EtkB41Ho3DlHDaVWqXFj+I34M+1P7EjfUe8BuizLGDVqVKO3279/fzz33HON/np75ezsXO/f4dOnT+Opp54yvV/XgJ19QLZGkiT0b9EfK4+txI70HRygE1mJXQ/Qq6qqUFVVZXq/pKREYJp/GAtvU9omXrSPyIEotZOAmjNjsi5loaK6QnQUIrISRXdSi/74n+F/0Oq1oqMQkZUovZO2ndsGvUF/1dvUd519DmytJzo6us5/7/3792Pu3LkArnzc+PiQkaI7KbJmGRdZliHLss2fWUJkC+x6gL5gwQK88soromPU6e6Od2Nil4lwUjmJjkJEVqLkTrqlzS2IbxsPZydn0VGITEeKkmUpuZP6R/ZHXFocJnSZIDoK2REeha5sSu6kuGZxWHb7Mrg7115W6mp/q3744Qd4eHhYIxo1Qvfu3a/oAONjefljyp5wbErupJa+LfHtmG/h5eIlOgqRw1DGFVAsZNasWSguLja9ZWRkiI5k4u7szuE5kYNRcie5ql05PCfF0Wp59LElKbmTNE4aOMl8nkTkSJTcSU4qp1rD85KSklqD1ltvvRWJiYmmNw7Pbc/lj5+RcYebkn4WyXqU3EmSJHF4TmRldn0EuouLC1xcXETHuCZZlnG26Cxa+rUUHYWILMxWOimtKA2t/FqJjkKEsWPH8ugvC7KVTjpbdBZRvlE8PZnMgkehK5ctdBIAnC08i6mTpkIFFVatWgW12q5fUjskYzcY17F/7LHHrvgc2T9b6aScSznw1HjCQyN+x50syygrK8P58+fx559/Yt26daIjWdx3330Hb29v0THICmzqr/2lS5dw+vRp0/tpaWk4dOgQ/P390aJFC4HJGqdMW4Ynfn8C+eX5WDp6Kfzc/ERHIqIGsLdO0hv0mPb7NKSXpGPJLUvQwsf2vgciR2ZvnSRDxqw/ZyElLwWvDX4NnUM6i45ERA1gb50EAAu2L8C7v7yL1h6t8ff3f4uOQxYmSZJpYH75Ei/Dhw/H448/LjIaNYI9dtLHez/G2tNrMaXbFIxq989Fi2VZxrFjx/D8888LTOcY7r333ibfB3fM2QabGqDv27cPgwYNMr0/Y8YMAMDEiRPx9ddfC0rVeB4aD/i7+SOvPA/bzm3DbTG3iY5ERA1gb53kpHJCqFco0kvSsTltMybGTRQdiRzY4MGDsWnTJtExbIq9dZIECeHe4UjJS8GmtE0coJPZ8Ch067C3TgKAcO9wAIBre1fBScjaLh+kr1+/HuvXr2d/2Bh77qRNaZvw2TOfNeo+5s+fj7i4ODOmomup69oZV7ueRufOnfHaa69ZOhLVk00N0AcOHAhZlkXHMKvBLQfjRMEJbErbxAE6kY2x107anbkbW85twYQuE7hkAgnz1FNPcYDeQPbYSYOiBmH9mfX4O+NvPHrDo3BRK/9UaiKqYY+dNLjlYACAV1svFFUWwdfVV2wgsrrLB+nGoRcH6bbBHjtpQOQAfHHwC3y//nt0cu4Et2o3fPHFFwgODhYdja7ien2h1WoxduxYAEBSUtIVw3X2jTh2fRFRW9C/RX+oVWqkFqUivThddBwicnA9mveAh7MH8svzcST3iOg4RHb3QocaJjYoFsHuwajQVWB35m7RcciOGF+ATpo0SWwQsilh3mHwrPSEQTZg27ltouOQQP++2CiRCD6uPji26RgAYMKcCUhMTOTw3MZpNJpaFzVOTEzE22+/bfq8cecde8f6OEAXzMvFC91DuwMA/kj9Q3AaInJ0zk7O6N+iPwB2EinDqFGjrn8jsluSJJmO+Pwz9U/BacgeFRQUiI5ANiagNAD5+fnsJDINt4CaodbcuXPFBiKHNO/+eQCAzWc3wyAbBKchS2jXrl2tgbqRcZDOA46sgwN0BRjaaiiAmnWrdAad4DRE5OhubnUzAOCvjL9Qpi0TnIaIHJ1xgH4w+yByy3IFpyF7wtOgqTECSgOQdiYNqUWpOF14+vpfQHbP2CX79+/nUaFkdWN6joFar0Z+eT4OZh0UHYeswDhI//DDDwHUHHAUHx+P0tJSwcnsGwfoCtC9eXf4ufqhuKoYh7IPiY5DZnT5nsAybRlOFZxCZkkmKnWVAlMRXVvbgLZo4d0CWr0WO8/vFB2HzOjyTqrUVeJkwUmcLzmP8upygamIri4+Ph6hXqHoHNwZMmQumWBnLu8knUGHE/knkFGcgUvaS1bNwYEXNYTaoIb/JX8AwJazW8SGIbO6vJNkWcaJ/BNIL05HSVXJdb+WS7qQKBonDQJKA7Bnzx5sPrtZdBwyo38fWX668DTOFZ3DxYqLkGUZkZGRtY5Kv+eee9g/FmRTFxG1V2qVGo/c8Aj83fzRLqCd6DjUSJe0l3Ak5whS8lJwPP84MkszMePGGegR1gMAcDTvKOZtm2e6fZB7EKJ8o9AlpAu6hXZDuHc4L9hIVvXjjz9i3LhxV3xckiQ80PUBqFVqdA7pLCAZmUNFdQWSc5NNnXS+5DwmdpmIoa1rznrKKM7A0xueNt0+wC0AkT6R6BTSCd1Cu6Glb0vhnZSYmMgngQQAuKfTPRhdPRrdQruJjkKNVKWrQkpeCo7mHcWxvGPIKMnAqHajcEfsHQCAwopCPLPxGdPtfV190cK7hamTov2joZLMf+zP3LlzuewCNUhiYiIG3zEYM/vORK/wXqLjUCPpDDoczTtqeksvTke/Fv3wYLcHAQDVhupaneSl8UKkTyQ6BHdA12Zd0S6wHdSq2uOUy5+3xMfH8ywXspqQ4hB4VHlgWq9poqNQI+kNepwsOInk3GRTJ7UPao9n+vzTQy9uetF04JOHswcivCNMnfTLr79ArVKbjkYHeKaduXGArhB9IvqIjkCNUKYtw/b07fgr/S8cyT0Cvayv9fkLpRdqvR/oHojy6nKUV5cjrzwPeeV52HthL3AQmNJtCka141q/ZD1ubm5X/Vz35t2tmITMpUpXhR3pO/BXxl84mH3wimXBLu8kg2xAkHsQyqvLUVZdhoKKAhRUFOBA9gEsPbwUd8beiQldJlj7WyCqU4fgDqIjUCNU66ux6/wu7Ejfgf1Z+1Glr6r1+cySTNP/6w16BLsHo1xXjkvaSyiqLEJRZRGScpPw3ZHvMLTVUIsMBrp3r/l7x2EXNYRHlQfemPoGf2ZsjN6gx74L+7A9fTv2Xth7xRl4l3eSVq81XcS6VFuKUm0pkvOSkZyXjBUpK9CjeQ+8fNPLV2wjMTER+fn5uP/++9krZDUbf9mI+Ph4fLLkE0ybxiG6rZBlGYeyD2F7+nbsOr8LpdraS7B4l3jXuq2Piw/UKjVKq0pRVl2G4wXHcbzgOH4+9jPa+LfBouGLuCPPgjhAVyBZloUf9Uf1k1aUhiV7l5jej/COQMfgjogNikWUbxRCPUNNn+sR1gNfhX0FACitKkVGSQZO5J/AgawDOJp/FDeG32i6bX55PtzUbvDQeFjvmyG6CnaS7cgty8W7u981vR/qGYqOwR3RIagDWvq1RHOv5qbPtQtshy9v+xJAzc7A8yXncbLgJA5mH0RSTlKtHbtFlUWQIMHH1cdq38vl+OSPLsdOsh1l1WV4Z+c7pgMMAt0D0Tm4M2KDYtHKr1WtTgr1CsUXt30BoOYMmvMl53G68DQOZh/E4ZzD6BvR95/71ZahQleBQPdA635DRHVgJ9kOnUGHxbsWo6y65ho/vq6+6BzcGR2CO6C1X+taneSp8TR1UpWuCpmlmThTeAaHsg/hYPbBWp2k1WtRUF6AUK+a136BgYH4+eefMXbsWD6HIavauHEjnnjiCQBgL9kAGTLe3/M+8svzAdSc6dIpuBM6BHdAtH80wrzCTLeVJAn/i/8fgJrOuVB6AWkX03Ao+xAOZB+o9dotISEB50vO47H7HuPR6GbEAbqClFaV4tukb5Gcm4wPRn4AJ5WT6Ej0L8m5ycgry8OgloMAAB2COqBXWC/EBMagT0SfWk+6rsXLxQuxQbGIDYrFmPZjUKWrgovaxfT5JXuW4ETBCUzoMgHDWw/nHz8SokpXhW+TvsWu87vwwcgP4OZ89SPWSYxTBadw5uIZjIgeAQCI8InAoKhBaO7VHH0i+iDCO6Je/eGh8UC7wHZoF9gO8e3ir+ikpYeWYkfGDtzT8R7cFnObRZZRIPq3N954AzNnzjS9b5AN+C7pO2w+uxlvD3sb/m7+AtNRXdKL03Ew6yBui7kNQM1w6pY2t8BV7Yo+EX3Q2q91vTrJzdkNbQLaoE1AG4xsMxJavbbWUgkrj67E6hOrcXv723FXh7vg7OTcpNzGo7U46KL6Mv7MrDy6Er+f+h2zb5qNKN8o0bHoX7IvZWP7ue24I/YOSJIEF7UL4tvGo0JXgb4RfRETGFOvTnJRu6CVXyu08muFoa2HXnGG3++nfseXh77ErW1uxYQuE+CqdoVGo8HXX3+NSZMmsVvIKhITE9Hr3l54bM1jeOSGR9ClWRfRkehfCisKseHMBtwZeyecVE5QSSqMbjcaF0ovoG+LvugQ1KFec0CNkwZRvlGI8o3CoJaDYJANtXpp27lteGfnOxjy4hCsX7AeaoOaPWQGHKAriKvaFX9n/I3iqmLsOr8LfVv0vf4XkVWkXkzF14e+xsHsg/Bw9kCv8F5wd3aHJEl4acBLTb7/ywdVZdoyZF/KRqm2FEv2LsGO9B2Y1msagj2Cm7wdoobQOGmw78I+5JTlYFPaJtza9lbRkej/ZZZk4pvD3+Dv839DrVKjZ1hP0zBxRu8ZTb7/yztJZ9AhvTgdlbpKfHnoS+xI34HpN05HhE9Ek7dDdC0dOtRetkUlqWp2ZJfn4fdTv+PezvcKSkb/lluWi+VHlmNT2ibIkNE1tCta+LQAADzU/aEm37/GSWP6f1mWkVaUhmpDNVakrMDOjJ2YfuN0tAlo0+TtEDXU7MWzET04GmtOrsHUnlNFx6H/V1RZhB+Sf8C60+ugl/WICYxBp5BOAGCWvx3/Xvs8rSgNBtmAxJOJ2JO5B9N6TUPnkM4ICAjAs88+i4ULF3J4RVZR7lKOX/78BeHe4RygK8gl7SX8fPRnJJxMgFavRZhXGPpH9gcA00EHTaGSVLWeK525eAYyZPyZ9ieCJgUhdUUq/Mr82ENNxEPIFMTZydl0FOFvJ38TnIaAmidf7+56F0+uexIHsw9CrVJjQOSAK446MCcPjQc+vOVDTO46GRonDQ7nHMa036dh34V9FtsmUV0kScJ/2v4HQE0n/fsq4GR9Zdoy/G////DY2sfw9/m/IUFC/xb9YZANFtumWqXG28PexrSe0+Dh7IGThScxff10bD271WLbvNwnn3xile2QbYhvV3Ma6u+nf0e1vlpwGqrSVWFZ0jI88tsj+DPtT8iQ0Tu8N5wky51FKUkS5tw0BzP7zoSPiw/SS9Lx7MZnkXgisUl/p/iCkhqqefPmCC6qOcBl09lNuKS9JDgR6Qw6/Hz0Z0xJnII1p9ZAL+vRtVlXiy+LOf3G6Xhl4CsIcg9CTlkOXtz0Ir4/8j1kWcaAAQNMt+OF0cnSQopCAAC7M3cjtyxXcBoyyAasPbUWDyU+hJXHVkKr16J9YHuLL0H3QNcHsHDoQoR7heNi5UX43eaHDhM6wAADe6gJOEBXmJHRI+EkOSE5LxlpF9NEx3FYBtmAxBOJpheEAHBT5E34+NaP8ViPx+Dt4n2de2gaJ5UTRseMxgcjP0BMQAzKqsswb+s8rEhewSEmWdWQlkPgpnbD+dLzOJR9SHQchyXLMjalbcLDvz2MxJOJMMgG9ArrhQ9v+RAzes+w+JMwSZIwtPVQLLllCbo16watXou3d76Nzw98Dr1Bf/07aIKwsLDr34js2n333Wf6/xvDb0SAWwCKq4qxI32HwFS06/wuPLrmUaxIWYFqQzU6B3fGO8PewQv9X0CYt2V/byVJQt8WffHRrR+hX0Q/6GU9/nfgf3hv93vQ6rVNum++sKT6+vTTT+FV6YXk7cnQ6rXYeGaj6EgOLSknCY+vfRxfH/4albpKtPFvg9cGv4Z5g+ahlV8ri2+/W2g3LLllCUa0rjkgbnnycry2/TWUactq7aBLTU21eBZyXH/88gd8yn2QkpKCNSfXiI7j0E4WnMRT657Cx/s+Rqm2FC28W2D2gNl48+Y30T6ovcW3HxMYg/dGvoex7ccCAFJ0Kej0ZCdUO1XzuU4jcYCuMAHuAabF/xNP8kgYUdKL0/HZgc9QVl2GaL9ovD30bTzT5xk082xm1RzNvZpjwc0LcEv0LZAhY92ZdSipKrFqBrJ/19op4+bshptb3QyAnSRSQUUBluxdguKqYoR7hWP+oPl4acBLpiUSrCXAPQBzBs7BuNhxAIBNaZuQV55n1QzkeIqLi03/r1apcUubWwDUdBJ3KotRpi3D+7vfR155HoLdg/Fi/xfx6uBX0TagrVVzeLt447m+z2Fy18mQIOGvjL9wvuS8VTOQY5MgIaS45ojP307+ZtEzwujqdAYd3tv1HjJLM+Hr6ounbnwK7wx7B51DOls1h5uzG6b2nIrpvabDWeWM/Vn7kXqxZmBuHKI/+eSTVs1Ejie4OBiXyi5hQ+oGVOmqRMdxSLIs46O9HyG1KBUezh54uPvDeH/k++gZ1tOq17fTOGkwKW4SXuz/ItzUbkjJS8Hs92cD4AEDjcE10BUovm08tqdvx9ZzWzEpbpLFj3amK0X5RuG/Hf8LH1cfjIgeIfSCeWqVGo/2eBRtA9oiJjAGPq4+wrKQfRo1atQ1T12/tc2tSDyZiH0X9iGrNAuhXqFWTEcAEOgeiEldJqFKX4XRMaOvWHvTmlSSCuO7jEdr/9YI9gi22o5FrtlHRsNbD8cPyT/gVOEpnCw4iXaB7URHcjgeGg881P0hpBen464Od9W6boK1SZKE0TGj0dK3JQA06UhT44Uhx40bhx9//NFcEcmOJSYm4tZRt8JL44Xc8lzszdyLXuG9RMdyOGqVGlN7TsXu87sxocsEiy/Zcj1DWg1BC58WyC/PN629DvCCxWQd277fhqgnonBJewlbzm7B8OjhoiM5HEmS8HjPx5FwIgGTu04WPsO5MfxGvDPsHZwqPIW+Lfpi6dKlmDhxIruogXgEugLFBMagtV9raPVarD21VnQch5BblouXN79c66il/3b6L25pc4vQ4fnlhrQaUuuU6NSLqRZfOoEIAMK8w9A9tDtkyEg4kSA6jkO4WHERr257Fcfzj5s+Ft8uHnfE3iF0eH65PhF9EO0fbXr/XNG5Ji+dQPRvn3322RUf83H1wYDImjVlfz3+q5UTOaYybRne/vtt7MncY/rYwKiBmNBlgtDh+eW6NOtS64JpWaVZjV6TuqKiwlyxyAGoZBWOJh4FwE6ylipdFT7e+3GtZXO6hXbDoz0eFT48N2oT0Aa9I3qb3t3v2noAAFyNSURBVC8oL0BhRaFpWMWjP8lSVJIKIcUh2LNnD349/ivP1rMCnUGHbw9/i5VHV5o+Fu0fjRm9ZwgfnhtF+ERgcMvBAAB/f3+EtQxDpXMlu6gBlDEZpFokScK4DuNwR/s7MKz1MNFx7N6O9B144vcncDD7ID7a+5HoOPXyd8bfeHrD01iydwn/IJJVjG0/FvFt4zGm/RjRUezegawDeOL3J7A7czc+2P2BTfyOJ+Uk4dmNz+LNHW9yxx6ZVbNmdZ/hMDpmNIa2Gop7O99r5USO53j+cUz7fRq2ntuKJXuX2MSOstOFp/HMxmcwf+v8BuddvXq1hVKRverSpQtCikIwoMUATOk+RXQcu3e26CyeWv8U1p5ei88OfIbSqlLRka4rsyQTz258FnM2z0GZtsz08YMHDwpMRfbsqVFPwf+SP6b1mmbVJUMcUfalbDy38Tn8ePRHLEtaZhMXby0oL4BquAonmp+A1kmLjz6yjTmYaBygK1SfiD6YGDcR/m7+oqPYLa1eiyV7luDNv95EeXU5YgJi8GQv21iTzklygt6gx8bUjfjm8Dei45AD6BTSCQ91fwjBHsGio9gtvUGPpYeWYs6WOSiuKkaUTxSe6/ucTTzpVUkq6Aw67LmwxyI79jp27GjW+yPbs2ZN7QthRflGYVqvaQj3DheUyP7JsoyVR1fi+T+eR255Lpp5NMML/V6AxkkjOtp1qVVq6A16HM0/irf+eqtBO/ZUqpqXRzwii+rr1VdfhUavwbY3tlnlYpWOSpZlrDu9Dk9veBoZJRnwd/PHC/1fgJeLl+ho16VWqaEz6HC2+CzmbZ2Hn3/9GQDw8ssvC05G9uqeO+9BdHY0nnvgOdFR7NqO9B14ct2TOFV4Cp4aTzzb51mbeL1sfJ7UpW8XnAg7gcT1XMalPjhAtxG2cASiLckqzcKzG57FujPrIEHCuNhxeOPmNxDiGSI6Wr30Cu+FJ3o+AQBYeWxlrdMXiayBnWRehRWFeHHTi1h5rOa0v1uib8E7w99BpG+k4GT10zG4I57v+zwkSNiYuhE/H/vZrPe/YMECs94f2Z5PPvnkmp9nJ5nXJe0lzN82H0sPL4VBNmBAiwF4b+R7NrPefJRvFF4a8BKcVc7YnbkbXx36SnQkcjDsJPOq1FVi8a7FprNguod2x/sj3kdcszjR0eolxDME8wbNg4ezB47mH8X7u99HQkLNsojcWUfWwE4yL51Bh//t/5/pYMz2ge3x/oj30bdFX9HR6sXH1QfzBs2Dn6sfOvXrhDPNzuA/8f8RHUvxOEBXuJTcFMzeNBtbz20VHcVupF5MxVPrn0JqUSq8Xbwxb9A8jO8yHk4qJ9HRGmRo66G4p+M9AICP9n2EUwWnBCdqOr2eSz9YW9++Dfsjf6bwDF7b9hoST3IvtbnkXMrBk+ueREpeCtzUbpjVbxYe7fGoTRzleble4b3w6A2PAgC+OfwNDmQdEJyIHEFGcQbe/vttLEtaJjqK3SipKsGTvz+JvRf2wlnljCd6PoFn+jwDd2d30dEapGNwRzzT5xkAwOoTq7Hl7JZ6fy0vqEUNZfyZGTZmGD7c86HNLAtpC7R6LZ7Z8Aw2n90MlaTCpC6TMOemOYpZV7i+onyj8GL/F+EkOWHrua1IOJGArl27AgA2bdokOB3Zo8TERGidtOh8f2e89ddbouPYDYNswEubXjK9Hr6j/R1YMGQBgjyCBCdrmBDPELwy8BW4OLmg3cB2OB9wnjv0roMDdIVLzk3GoZxD+DHlRxhkg+g4dqGFTwu08GmB9oHt8d6I92zmyIW63N3xbvQK6wWdQYfXd7yO4spi0ZGapKCgQHQEhzNz5swG3f504WnsytyFn4/9bBPr4NqCYI9gtPVviyifKLw74l30iegjOlKjjWwzEsNbD4cMGQv/XojsS9lmvf/Kykqz3h/ZhmsNMy+UXsDWc1uReDLRJtbBtQVeGi90adYFzTya4e1hb2NY62E2sZRUXfpE9MFdHe4CAHyw5wOkXkxt0NfzhSQ1VLVTNdafWY+NqRttYh1cW6Bx0qBnWE/4ufrhtcGvYWzsWJvtpE4hnfBgtwcBAF8e+hJ3TL0DALB48WKRsciOGVQG5PrmYkfGDpwpPCM6jl1QSSr0iegDD2cPzB4wGxPjJtrcwZhGLf1ampYxzgvMQ6FnIZ5//nnBqZSLA3SF+0/b/8DD2QMZJRnYkb5DdBybVVFdYdoBoVapMXvAbLw+5HUEugcKTtY0kiRhRu8ZCPMKQ355Pn49/qvoSE2SkpIiOgJdx+CWgxHoHojCikKsO71OdBybpdVrTTsgJEnC032extvD3kZzr+aCkzXdw90fRruAdrikvYQVySvMet933nmnWe+PbF/PsJ5o6dsSFboKrDq+SnQcm6U36FGpq9lBJUkSHrnhESwesdgu1nO+p9M9uCH0Bmj1Wnx7+FvRcciOJSYmwrPKE52DO0Mv6/FD8g+iI9ksWZZrXWzzvs734YORH6BjsO1fE+XWNrdiSMshMMgGfH3oay7lQha18ZeN8C/1x549e/Ddke9Ex7FZ/+6k+Lbx+OQ/n6BnWE+Bqcyjf2R/3B5zO7p174aMgAwkH00WHUmxOEBXOA+NB0bHjAYALD+yvEEXQaIaWaVZeHrD0/j60Nemj3m5eEGtUosLZUbuzu54sf+LeCDuAUzoMkF0nCbZuJFruSuds5Oz6Wi+n47+ZBq4UP0VVhRi1h+z8OGeD03rEbo7u8NF7SI4mXk4OzljVr9ZuKfjPZjac6roOGRH6houSJKEezvdCwBIPJlo82diiVBaVYq5W+bijR1vmA420Dhp4KnxFJzMPFSSCk/3eRrjYsdhZr/6n3XFZVyosf7++G8AwJ9pfyKrNEtwGttTqavEm3+9iTlb5pgONlBJKptbsuVqJEnCYz0ewx3t78D8QfNt9mh6sh3NC5tDgoS9F/biRP4J0XFsjs6gw0d7P8LTG542DdElSYKvq6/YYGY0ocsEjIsdh6S3k6CCijv0roIDdBswqt0oeGm8kFma2aD1Gwk4nH0YMzbMQEZJBrae22q3p3dH+ERgTPsxNv8E7MiRI6IjUD3c3OpmhHiEoKiyCGtOrhEdx6acKjiFp9Y/hZOFJ7H3wl7kleeJjmQRAe4B+G+n/9rNjkpStp5hPdHGvw0qdZVYeXSl6Dg25XzJeTy94WkcyjmE5NxknCs6JzqSRXhqPDG+y/hG7ajki0hqiISEBHhWeeKG0BtgkA34Pvl70ZFsSl5ZHp7f+Dz+yvgLZy6esdthn8ZJg4lxE+Gh8QDwzw479g1Zwh+//IGAkgDs2bOH14xpoOLKYry06SWsO7MOF0ov4FD2IdGRLMJJ5YTxXcbD19UX//vf/wAAzz33nOBUysMBug1wd3bH7e1vBwB8n/w9dAad4ES2Ye2ptXh5y8u4pL2EdgHtsHj4Yni5eImOZXFVuir8mPIjf06oQTZv3lzv26pVavy3438BAD8f+xnl1eWWimVXtp/bjpl/zkRhRSEivCOwaNgiBHsEi45lcTqDDiuPrkRFdUWT7ufll182UyKyRdc6Gvjyo9DXnl6LwopCa8WyaQezDuKZDc8g61IWgt2DsXDoQrT0ayk6lsXJsozVx1fjYsVF0VHIDhkPZtn64VYAwJazW5BRnCEyks04kX8CMzbMQGpRKnxcfPDa4NfQKaST6FgWJ8syNpzZgE+XfwoAGDt2rOBEZI/CCsMgyZJphzld37mic5ixfgZS8lLg7uyO2QNmo2+LvqJjWVxoaCgKPQuxN3Wv6CiKY3MD9CVLliAqKgqurq7o1asX9uzZIzqSVfyn7X/g4+KDnLIcbD27VXQcRdMb9Ph036f4eN/HMMgGDIoahNeHvA5/N3/R0SxOlmXM3TIX3yZ9i1XHuBasNdhLJy1atKhBtx8YNRBhXmEo1Zbi91O/WyiVfZBlGcuPLMdbf78FrV6LHs174O1hbyPUK1R0NKt45+93sPTw0iYf8dKjRw8zJbJv9tJJV2Mw1H1B9W6h3dA+sD20ei3//tXDbyd/w5wtc1BWXYbYwFgsGr7IIYbnAPDp/k/x+cHP8fmBz697Wy7j0nT23kl1effdd+FR5YHe4b0hQ8ZPR38SHUnxtp7dill/zkJRZRGifKKwaPgixAbFio5lFStSVuCDPR9gyZ4lkCFDq9WKjmT3HLGXNqzagKCSIOzZs8fs1yiyR/su7MOzG59FbnluzUXVh76NHmGO8Vpk3el18B/lj7PBZ3Fr/K2i4yhKgwfoEydOxLZt2yyR5bpWrFiBGTNmYM6cOThw4AC6dOmC4cOHIzfX/q9w7qp2xYPdHsTjPR7HoJaDRMdRtDf/ehO/nfoNADCh8wQ8deNT0DhpBKeyDkmSMKz1MADADyk/cN1FC3PkTnJSOeHBbg9ictfJuC3mNtFxFO3jfR+bTuEeEzMGLw14Ce7O7oJTWc/w6OEAatanPlVwSnAa++YInXTbbXX3jSRJuD/uftzb6V7c2/leK6eyLcuPLMen+z+FDBlDWg7Bq4NftZu1hetjaKuhkCBhW/o27L+wv15fw2UVGscROqkurVu3BgBsWrwJY2LG4OHuDwtOpGxrT63F2zvfRrWhGr3CemHhsIUOcYae0cCogdA4aZCUm4Sn3nsKADvHkhy1l4CatdBDikLwTJ9nREdRtB3pOzBv6zxU6CrQMagjFg1fhAifCNGxrKZ3eG94abxQ7lKOHN8c0XEUpcED9OLiYtx8881o06YNXn/9dWRmZloiV50WLVqEKVOm4P7770dsbCw++eQTuLu748svv7RaBpEGRg3E8OjhUEk2d+KAVQ2IHABXtStm9ZuFOzvcafPrgjfUwKiBiAuJg1avxUd7PzJdpJDMz9E76YbmN2B0zGiuc30dfSP6QuOkwbSe0/BA1wccrsPjmsVhYORAyJDx4Z4PeTFsC3L0Tmof1B53d7wbrmpX0VEUrVdYL7iqXTGpyyQ82etJODs5i45kVa39W2NUu1EAgI/2fsSLYVuQI3fSyy+/DLdqNzzQ9QHTOtdUt67NusJL44Wx7cfixf4vOlyHN/NsZloa8YuDX6BaVS04kX1z5F5a/+t6ROZH4r477xMdRdE6BHVAgHsAhrYaivmD5zvEMsCX83H1weSuk9GzZ09kBmRi2JhhoiMpRoNfxf/666/IzMzEo48+ihUrViAqKgojR47EypUrUV1tubLXarXYv38/br75ZtPHVCoVbr75ZuzcubPOr6mqqkJJSUmtN3uh1WtRVFkkOoZiVOv/+dnr16IfPo//HH0i+ghMJI7xyu4aJw0O5RzCtnNizhixd+yk2nQGHQrKC0THUIzLO6lLsy74ctSXGNp6qMBEYj3Y7UF4ajyRWpSK307+1qT74lFZdXOETmrIchqyLCO/PN+CaWzL5Z3U2r81Po//HGNjxzrcQQZG93W+D0HuQcgtz73uqexcxqVxHKGTrsW47Jjxb5Ysy8gts/8jXOvr8k4K9QrFx7d+jElxkxy2k0bHjEaUTxRKtaUYMWsEAD7fsYSG9pI9dVJd2En/uLyT/Nz88O7wd/FEzycc9kCxwS0Ho1NwJ9zQ6wacCzqHBQsWiI6kCI06DC4oKAgzZszA4cOHsXv3bkRHR2P8+PFo3rw5nnrqKZw6Zf5TtPPz86HX6xESElLr4yEhIcjOzq7zaxYsWAAfHx/TW0SEfZx2cSTnCB757RG8u+tdHl2MmovzPLrm0VovlB3pVOS6hHqFYlzsOADA14e/RpWuSnAi+2NPndTU4cCZwjN4fO3jeG37azDIda9P7Ej2XdiHh357qNZFwxy9k3xcfTCpyyQANRfDLqmyrxcgSmBPnXQ91xsqXCi9gOnrpuOFP1+AVs+1ZI/lHcNDvz2EE/knTB9z9E5yVbualtX49cSvyL5U9+/I5e644w5Lx7IrjtRJV/PYY48BAAorCjHrz1mYsX4GSqtKBacS72zRWTy65tFaSyg5eiepVWpM7TkVAPBn2p8ocykTnMg+NbSX7K2TgJrXfTqVDm0fboupa6dyiA4g+1I2nlz3JDanbTZ9zMfVx2F36AE1B2VO7TEVapUaRR5F+P0Qr3kGNPEiollZWdi4cSM2btwIJycn3HLLLThy5AhiY2OxePFic2VstFmzZqG4uNj0lpFhH1dA93fzx8XKi9iftR/b07eLjiOMLMv4Luk7vLPzHeSU5WDNyTWiIynKmPZjEOQehPzyfKw6zguqKYE9d1JRZRFOFZ5y6N9DWZaRcCIB87bOQ355Pn4+9rPoSIoytPVQtPJthbLqMnx/5HvRcQj220m+rr4oripG1qUsh79Q1ua0zXhh0wvIL883XYuBavQM64m4kDjoDDosPbT0urevquLBCJZmb500cuRIAMD4ceNRUlWC4qpifHXoK8GpxNqbuRfPbnwWOWU5+O7IdzwY7DIxgTG4KfImAEDPR3pChsyj0AWzt04ycjI4wSAZUKmrxCf7PnHo38OjeUcxY/0MZJRkYPmR5bWORHd0Yd5huLXNrejZsyfSA9Pxn/j/iI4kXIMH6NXV1fj555/xn//8B5GRkfjpp58wffp0XLhwAUuXLsUff/yBH3/8EfPmzTNr0MDAQDg5OSEnp/Yi9jk5OWjWrFmdX+Pi4gJvb+9ab/YgzDvMdHTxZwc+wyXtJcGJrE+r12Lh3wvxQ8oPAIDbY27HhC4TBKdSFo2TBpPiJqFvRF8MjBooOo7dYSf9w8/NDxO7TAQAfJP0jUMum6Az6PDxvo/x2YHPIEPG0FZD8XjPx0XHUhSVpMLkbpPRrVk3jGwzUnQcu+MonZSQkHDd27g7u5uOLv752M9IL063dCzFkWUZ3x7+Fot2LYLOoMONYTdiZr+ZomMpiiRJmNxtMjoGdcSY9mNEx7E7jtJJ15OQkAAVVHi4a00nbUzdiOTcZMGprE+WZaw+vhrzt81Hpa4SnYM745WBrzj0EZ51mRQ3CTEBMbin0z2QwH8bc2toL9ljJwHAb4m/ISo3Cnt378XeC3ux83zdy2rZu01pm/DiphdRqi1FtF803hz6psNdF+Z67u54N2ICYhBeEC46iiI0eIAeGhqKKVOmIDIyEnv27MG+ffvwyCOP1CqTQYMGwdfX15w5odFo0L17d/z555+mjxkMBvz555/o3bu3WbdlC+6IvQPhXuEoqizC14e+Fh3HqgorCjHzj5nYnr4dapUaT/Z6Evd3vZ9PwOowIHIAZvabiWaedb9Qocazx05qytEHI6JHICYgxiGPZCitKsXcLXPx++nfIUHCA3EPOPSaedfSOaQzXhn0Clr4tGjU17/22mtmTmQ/7LGT6mL8W//kk09e83a9I3qjV1gv6GU9PtzzoUN1UqWuEm/seAM/Hv0RAHBH+zvwQv8XHO7CfPUR5RuFBTcvQNuAtte8HddBbzhH6aTrMXbWSw+/hBGta9a2XrJniUMd5agz6PDhng/x+cHPIUPGsFbD8MqgVxzuwnz1EegeiIXDFqJzSGdT7/AodPNhL/1j488bEXoxFKdOncKn+z9FmdZxlg2SZRlLDy3F4l2LoTPo0Ce8D964+Q34u/mLjqY4nhpPLBy2EDt+2AEJksP3UYMH6IsXL8aFCxewZMkSxMXF1XkbX19fpKWlNTXbFWbMmIHPPvsMS5cuxbFjx/Doo4+irKwM999/v9m3pXTOTs6mddLWn1mPA1kHBCeyjvMl5/HU+qdwqvAUvDRemD9oPm5udfP1v5AANG1ASleyt04aNWpUo79WkiQ83vNxOElO2J25G1vObjFfMAXLL8/HMxueweGcw3BVu+LF/i9iTPsx3KFXTw3tpM6dO1soiX2wt066ltTU1Ove5pEbHoGr2hXH8o9h9YnVVkgl3iXtJTy/8Xn8ff5vqFVqTO81HRPjJrKT6ul6neToLxwbypE66VqMg9DfXvsNvq6+OF96Ht8d+U5wKuvQ6rWYvWk2NqRugAQJk7tOxuM9H+dBBvUkg6/dzI29VEOSJDS/2BwVuRUorCjEZwc+Ex3JKmRZxuvbX8fKYysBAONix2Fmv5lwUbsITmYbHL2TGjxAHz9+PFxdxRzBctddd+Htt9/Gyy+/jLi4OBw6dAjr1q274iIQjqJjcEf8p03NOkTv7nrXIS7KFugeCG+NNyK8I7Bo+CJ0DO4oOpJNyC3LxaKdi/DOzndER7Er7KTaIn0jcXfHuwEAn+z/xCEuSuPr6gs/Nz8EuQdh4dCF6BXeS3Qkm1BUWYSP9n6EOVvmiI5iVxylkwICAup1u0D3QEzuOhkAsPTwUpwtOmvBVMrg4eyBEM8Q+Lj44PXBr2NIqyGiI9mEMm0Zvjz4JZ7e8DQvhm1GjtJJ9aU2qDG1R80BUL8c+wVHco4ITmR5zipnhHuHw03thpdvehmjY0Zzh149VOmqsPzIcrR4pAUMkoE778yIvfSPNQlr0DKnJfbu2Ys/0/7EjvQdoiNZnCRJiPSNhLPKGU/3fhrju4xnJ9WDzqDDA289gCORRzDyNsdditPmdv0+/vjjePxx868r26tXL+zevdvs92tpk+Im4XDOYbTwaWG366QZZAMkSJAkCa5qV8wZOAduajd4aDxER7MZ5dXl2Hy25qrSd8TegSjfKLGB7IilOslW3Rl7J/Zf2A9XtavdHl0kyzJkyFBJKqhVaszqNwsyZPi6+oqOZjN0Bh02pm6EzqBDUk4SOofwyHJzcYRO+vrrrxEfH48TJ06gXbt217zt8NbDsTdzL4qriuHiZL9HF+kNejipnCBJEmb0noGSqhIEewSLjmVTNpzZgLLqMuxI34EBkQOu+PxPP/2EO++8U0Ay2+YInVQfiYmJiI+Px2uPvoZhLw3DiYITdr2EyeWd9PAND2N0zGiEeYeJjmUzVJIKG1M3Ir88H7k+uWhWxOU4zYm99A+vSi+EFoYi2D0YAW71O0DBFhk7CQDu7XQvboq8CRE+EYJT2Q4nyQmb0jah0rkS2X7ZouMI0+Aj0O3VwIEDRUdoFBe1C968+U083/d5u3wSdkl7CfO2zsPKoytNHwt0D+TwvIGifKPQL6IfAGD5keWC05ASmWtpDCeVE+YMnIN5g+bZ5TpylbpKLPx7Ib48+KXpYz6uPhyeN1Cge6BpLdhlScu4vBQ1yjPPPHPd2xgHym/e/CZCvUKtkMq6dAYdPt77MRbtXGT6PXJVu3J43kAeGg+Miam5kOjyI8uhN+ivuI2oM3DJ/kzpPgWLhi+yywNaDLIBy5KW4ZWtr5h+j9QqNYfnDeTs5Iy7OtwFAGg3ph30kp5HoZNFJCYmonlhc2R9mYX2Qe1FxzE74wWMn//jeWj1WgA1zw05PG8YSZJwX+f70LNnT+T45GDE6BGiIwnBAfr/i4mJER2h0bxcvEynnciyjPzyfMGJzCPtYhqeWvcU9mftx49Hf0RxZbHoSDbNeDX3ned3IvXi9deOJcdizoszemo8a50KZy+dlFWahWc3PIvt6dvx28nfcKH0guhINu3ODndC46TBsfxjOJh9sEFfyxeR1BAeGg/TUUeA/XRSYUUhXvjzBaw9vRbb07fjZMFJ0ZFs2qh2o+Cl8UJmaSa2ntt61duxf6ixjGuh3zmm5u+fkb10UmlVKeZtnYcVKStwMPsg9mftFx3Jpt3c6maEeISguKoYuT72vywiibMmcQ2cDE6mv2/20kmVukq8s/MdfH7wc5woOOEw1+iylJ5hPdHGvw30Kj2yfLNExxGCA/T/5+9v+0dKVuoq8eZfb+LpDU+jsKJQdJwm+SP1Dzyz8Rlkl2Uj2D0Yb938FnxcfUTHsmkRPhGmU5J/SvlJcBpyBNX6aizZswSPr33c5ofNu87vwvT103G2+Cx8XHzw2uDX0NyruehYNs3fzR8jo2vW0GMnUUMZB1HvvFP/a3sYZAOWHlqKh397GKcKTlkqmlUk5SRh2u/TcCz/GDycPTB7wGy0C7z2cjZ0bW7Obri9/e0AgJVHV/LMGLIIY3fFx8dDlmX8cuwXTEmcgkPZh8QGa6JTBacwfd107M/aD42TBk/3fho9w3qKjmXT1Cq16dpCsbfHwiAZkJycLDgV2bs7nr8DUxKnYNu5baKjNMn5kvOYsX4Gtp7bCpWkwkPdHsLQVkNFx7JpkiThnk73oGfPnsjzyXPItdA5QP9/KpXt/1PIsoz04nQUVhTi9e2vo0pXJTpSg1XqKvH+7vfx3u73oNVr0T20O94d8S5a+rUUHc0u3BF7BwDgr4y/kFXqmHsNyXokScK54nMoqy7Dq9teRWlVqehIDaYz6PDlwS/x2vbXUF5djvaB7fHeiPfQIbiD6Gh2YXTMaKhVaiTnJeNE/gnRccgGbdmypd63lSAhvTgdWr0Wr+943SaPsDLIBvyY8iNe2vQSiquKEeUThUXDF6FHWA/R0ezCyOiRcHd2R0ZJBvZk7rni80888YSAVGTPzpech86gw1t/vYXzJedFx2kwWZax9tRaPPfHc8gtz0WoZygWDl2IgVEDRUezCwOjBiLQPRAXKy8i3ysfs2bNEh2J7JRx596ZnDPQGXR4b/d7NvvcfNu5bXhq/VPIKMmAv5s/Xh/8OuLbxfNioWbQPbQ7onyioFfpHfLMGNufGpOJm7MbXhrwEjycPXCi4ATe/OtN6Aw60bHqzSAbMPOPmdiYuhESJNzX6T7MuWmOXa7tLkqUbxRuCL0BMmSsPrFadBxSIHOemq5WqTGz30wEuAUgoyQD87fNt6kde7IsY/7W+Vh1fBUAYHS70Xh9yOsIcLffC+xYW6B7IAZGDgQA078zUX3Nnz+/Qbc3roce5hWG/PJ8vLz5ZZvbsffurnfxbdK3kCFjSMsheHvY2zwbxow8NB64JfoWAMCvx3+94vPDhg2zciKyR8ZB1ahRo/DIDY+gXUA7lGpL8fLml21ux95Xh77Cx/s+hs6gQ6+wXlg8fDFa+bUSHctuqFVqjG43GgDQ9b9dIYNnxpDlJCYmIqwgDCe2nIBWr8UrW1+xuR17q46twsK/F6JSV4lOwZ3w7vB3eeCTGUmShDti76hZC903B7+v/110JKviAN3ONPdqjjk3zYHGSYO9F/big90f2MwpqCpJheGth8PfzR+vDn4Vd3W8i3sJLeCujndhQucJGN95vOgo5AD83fwxb9A8eGo8cSz/mE3t2JMkCcOjh8NT44mX+r+Eyd0mQ61Si45ld8bGjsW42HF49IZHRUchGxMXFwegYTv+PDQeposcZ5RkYN7WeajUVVooofkNaz0M7s7umN5rOp7s9SRc1C6iI9mdUe1GYXS70Xi6z9NXvc2dd95pxURkj4xD9LGjx2L2gNkI8wpDXnmeze3YG9xyMNyd3fFg1wfxYv8X4aHxEB3J7gyPHo74tvF4+aaXIUHidRjIon5L/A3RWdE4uu0oSrWlmL15tk3t2OvXoh+8NF64u8PdmD9oPvzc/ERHsjv9WvRDfNt4tMtsh48+/Eh0HKviAN0OtQ9qj5l9Z0IlqbDp7Ca8t/s901XQlSa/PB9nCs+Y3h8RPQIf3fIROod0FpjKvsUExuDODnfyCS5ZTQufFnh5wMumHXtv7HjDdBV0pSmuLMaxvGOm9/tE9MHn8Z+jV3gvgansW7h3OMZ3GV/v61y8+uqrFk5E9i7YIxjzBs6Dh7MHjhccx9wtc1FeXS46Vp3Kq8uRlJNker9jcEd8OepLDGk1hAcZWIifmx8md5uMQPfAq96mstJ2drqQ8nm7eGPeoHmmM/Ze3PQiiiqLRMeqk1avxcGsfy78HeUbhS9HfYnbYm5jJ1mIq9oVD3V/iGcbkdU8N+M5tLnQBkk7kpBfno9Zf8xCzqUc0bHqpDPosO/CPtP7QR5B+Cz+M9zb+d5aF5An83FSOeGh7g/hz5//FB3F6jhAt1M9wnpgxo0zoJJU2JG+A5mlmaIj1WJcL++xNY/h9e2vm164SpLEwa6V2coZCmR5P/1kuQs5tg9qjxf6vQCNkwb7LuzD6cLTFttWY8iyjC1nt+DRNY9i/rb5uFhx0fQ5dpJ1Xa+TunTpYqUkZAuMR3E2VKRvJOYOnAt3Z3ek5KXgSM4RMydrur2ZezF17VTM2zoPmSX/PI9jJ1kXnyeRpVy+lEuwRzDmDZoHX1dfpBWl1bkGv2jJucl4Yu0TmLt1bq0LMbOTrCcxMREyZB6FThY1cOBAOBuc0e5COxz+6zCyy7Kx9dxW0bGucLrwNGasn4FXtr6C/Rf2mz7OTrKu/8T/R3QEq+G56HbspqiboHHSwM3ZDS18WoiOY3K26Cw+3vsxjuYfBQBE+kSiTFsGd2d3wckcy74L+7AieQWGth6KYa25picBrq6uFr3/7s27Y+5Nc1FcVYzYoFiLbqshskqz8On+T7E/q+aJV5RPFC5pL/GUPytLzk3GD8k/oHNIZ4zrME50HLIx8fHxDR6mxwTGYMGQBThdeFpRZ5nkl+fjiwNfYEfGDgBAqGeoYo+Qt2dnCs9g+ZHlaObZDFO6TzF9PDExkcMrMhvjz5Oxw968+U3szdyrqOfmJVUlWHpoKTakbgBQszwfO8n6zpecx/Ijy5EakorWOa1FxyE7Z+ym9ufbI7wgHHfGKmfpsvLqcnx/5HsknEyAQTbAS+OFKr3tXGfLXuSV5SF0TCi27N4iOorVcIBu53pH9K71/tG8o3BxckFrf+v/0S2tKsX3yd/jt5O/QYYMV7UrJnSegFvb3gqVxJMhrC2jOAPHC46jUleJoa2G8rRLsopOIZ1qvZ92MQ2XtJeu+Lg1VOoq8VPKT/jl+C/QGXRQq9S4u8PdGBs7lmudC5BXlofDOYeRUZKB29vfzseArKKVX6taF7zLuZSDs0VnhQzUtXotEk8k4oeUH1Cpq4QECaNjRuPeTvdyrXMBSrWl2HNhD9zUbri387080IMs5ueff8bYsWNNQ/TbYm4zfa64shiHcw5jQOQAq+fSG/RYf2Y9vk36Fpe0lwAAw1sPx/1x9/MITwGq9dXYnr4dbW5ug8oVXEaKLM84RD+//jwO9zuMuLg4VOoqsf3cdtzc6marzw+MZwx/degrXKysOVu4f4v+eLj7w/VeCpLMR5Ik5LjnoNi9GEPGDnGIJV04tXQgeWV5eH3763hm4zP4MeVHq66LXlRZhCmJU5B4sua0s74RffHRLR8hvl08h+eCDG09FC5OLjhbfBYpeSmi45CCWOvIutKqUry2/TW8uOlFLD201KrrolfpqvDwbw/jx6M/QmfQIS4kDh+O/BB3dbyLg1tB+rXoB19XXxRWFGLX+V2i45ANMR55vmLFiibdT5WuCq9tfw2vbn8VH+39CBXVFeaIVy8G2YDp66bj68Nfo1JXiZiAGCwevhgPdH2Aw3NBuoR0QYR3BCp0Ffgz9coXhVzahcxFo9GY/v/y52A6gw5v7HgDC/9eiIV/LURJVYnVMsmyjBc3vYiP932MS9pLiPKJwhtD3sDjPR/n8FyQln4t0TGoIwyyAbneuTwThqzC+Bxr9uzZ+OSTT7B452K8v+d9zNs6z+oXF33zrzexaNciXKy8iFDPUMy5aQ6e6/sch+eCBLoHok94HwBAjo8y18g3N04uHYi7sztiAmOgM+jwbdK3eHrD0zief9xi26vWV5v+39fVFx2DOyLKJwrzB83HzH4zEeQRZLFt0/V5ajwxKGoQACDxROPWkCVqChe1CzoGd4QMGSuPrcS036fhUPYhi23v8k5yUbugR/MeCPUMxax+szBv0DyEeYdZbNt0fc5OzhjRegQAIOFEguA0ZIuWLVvWpK93UjmZLmL+++nfMXXtVOzM2GmxQanOoDPdt0pSoXd4bwS4BWB6r+l4a+hbQs4WpH9IkoT/tK1Z13PNqTVX/ByMGjVKRCyyU5cvQVVUVAQAcJKc0KVZF0iQsC19Gx5b8xg2pW2CQTZYJIPeoDfdtyRJ6BvRF94u3ni4+8N4d8S76BDcwSLbpfqLb1czNC/0L4RBsszPAdG/GfvptzW/YdWnq6BWqbEvax+mrp2KNSfXQGfQWWS7sizXuu9+LfrBTe2G8Z3HY8ktS3BD8xsssl2qv/+0/Q969uyJAu8CHD1zVHQci+MA3YF4aDzwYv8XMePGGfDUeOLMxTN4duOzmL91Ps4UnjHbdlIvpuLjvR9j4q8Ta+2VnH7jdLw38j3ENYsz27aoaYwvDHee32n1PchEGicNpt84HS/1fwn+bv7ILM3E7M2z8eKfL+Jo3lGzDa3Ol5zHFwe+wMRfJ+Js0VnTx++Pux8f3foR+kT04RJGCjGyzUg4SU44ln8MqRdTRcchG5KQ0PSdLmqVGg92exCvDX4NzTyaIa88D6/veB3PbHgGB7IOmK2Tcsty8e3hb/HA6gdwOOew6eN3drgT/4v/H4a0GsJOUojBLQfD3dkdmaWZOJh9UHQcsnPGIdX48eMB1Ayx7+54N94Z9g4ifSJRXFWMxbsWY9rv0/B3xt9mG6QXVhTix5Qf8VDiQ9h2bpvp4yPbjMRn8Z/hP23/AyeVk1m2RU3TK6wXAt0D0blnZxR6FvIodLKaxMRESJAQUhyC0hWlaBfQDuXV5fhk/yd49LdHsTlts9kG6aVVpfj1+K94dM2j+O3kb6aP943oiy9GfYFxHcbB2cnZLNuipokNikVL35YwSAZMmDtBdByL4wDdwUiShEEtB+GjWz6qWfcaEvZc2IMZG2aguLK4UfdpkA1IvZiK5UeW44m1T+DJdU9i7em1KNWWYlPaJtPtPDWeXK5FYSJ9I9ExqOYI4I1nNoqOQwrw448/Wn2bvcJ7YcktSxDfNh5qlRpJuUl4/o/ncaH0QqPuT5ZlpBen46eUnzBj/Qw8uuZR/HriV5RqS7HhzAbT7Tw0HlyuRWH83fzRO7zm2h3rT6+/5m0XLFhgjUhkI4wDZ3MMEzqHdMYHt3yAcbHj4OLkgpOFJzFny5xGL3cmyzIulF7A6uOr8fzG5/FgwoP48eiPuFh5sVYnuapdoXHSXOOeyNpc1a4Y0nIIgOt3EpE5GIfol3dZm4A2WDx8MSZ2mQgPZw+cKz6HBTsW4O+Mvxu1DVmWkVuWi7Wn1uKlTS/h/tX349ukb5Fbnlurk9QqNdf+VxgnlRNGRo8EAOR55wlOQ47G2E9u1W44/sFxPNL9Efi4+CC7LBuLdi1q0t/JwopCbDyzEa9seQUTfp2ALw5+gczSTGw4s8F0AIMkSfBy8TLL90LmIUkSbm1zK3r06IE87zy7X96OkwMH5efmh2m9pmFs+7H4Pvl7GGRDrbWj3tzxJtyd3dHCpwWaeTaDm7Mb3NRu0Oq18HLxQgufFgCAc0Xn8Nwfz9W6ErtapUbv8N4YET0CnYKtf2FAapjh0cORnJeMDakbcFfHu7iTw8G5ubkJ2a6nxhMPdX8Io2NGY0XyCuSW5dZaUuXDPR9Cb9AjwicCoZ6h8NB4wFXtCp1BB1e1q+kigIUVhXh87eMo1ZaavlaChJ5hPTG89XB0b97d6t8bNczw6OHYkbEDW85tueb6z3//3bjBAVF9uKpdMb7LeMS3i8dPKT8hJS8FHYL+Wb7gq4M1F7CK8I5AmHcYPJw94ObsBr1BD5WkQrvAdgBqLgw6JXEKCisKa91/XEgchkcPx43hN1r1+6KGG9Z6GBJPJmJ35m4UVRbB19UXK1euxB133CE6Gtkp44X7jBcVBWqWObsj9g6MiB6BVcdW4a+Mv9Ar7J+LHf+U8hPOFp1FC58WCPMOg6fGE25qNxhkA2TIiA2KBVAzPH9szWM4X3q+1jZjA2MxInoE+rboa71vlBrl5lY347sj36H9Te1R8ZP1rtVBBNT0kyzLGDVqFD55+hPMfmU2zrmcw/oz62td7Pj3U7/jcM5htPBpgXDvcHi7eMNN7QYZMrR6rWnJPACY9ccsJOcl19pOa7/WGBE9AgMiB/CsPIUbEDkAnx/8HJWaSgwcNxBbf9oqOpLFcIDu4MK8w/BMn2dq7SkqrCjEjowdV/2aQVGDMKP3DABAqFcotHotXNWu6BLSBX0i+qBH8x7cM2hD+kT0wY70Hbgp8qaanwP+fSKBgj2C8USvJ2p1UpWuCpvSNqHaUF3n18SFxGH+4PkAAD9XP6gkFTROGnQM6ojeEb1xY/iN8HX1tUZ8MoMuIV3QL6Ifbmh+A3foUYPUNXRqKl9XX0zpPgWyLJtewMmyjD/S/rjqBf2ifKLwwS0fAKhZqspL44WSqhK0D2yP3uG90TuiNwLdA82SjywvyjcKQ1sNRbR/NFzVrgAAFxde2JUs6/I+W716NVSqmr+HnhpPjO8yHvd1vq/WUGnz2c3IKMmo8778XP3wzZhvANQcLejv5o+sS1loG9AWvcNrnieFeoVa/psis/B380d823gEugfi0x8+NevfPKL6kCTJ1FHz59S8BktISKjVSTvSdyApNwl/Zfx15ddDwuq7V5tu7+/mDwkSWvu1Ru+I3ugd3hsRPhHW+Waoydyc3TAmZgxSE1PhVi3mYDxr4QCdAKBW2bk7u+P5vs/jXNE5pBenI788HxW6ClToKqBRaeDj8s+R6honDT4c+SGaeTbj2ng2SuOkwUsDXhIdgxRG9JPxyztJJanwXN/nkHYxDRklGcgty0V5dTkqdBVwVjkjwD2g1te9NfQtBHsEc3kWGyVJEp7v97zoGES1XN5JMmRM7zUdaUVpyCjOQPalbJRXl6NcVw5nlfMVF0l/aUDNdR64PIvtmtZrmugI5ICMA6rbbrsNd999N+69917T52p1kizjoe4P4XThaWQUZyDrUlZNJ1WXw0lyuuIggidvfBLeLt6mHUJkex7s9iAA4AvDF4KTkCNLTEzEK6+8gn379pkurG18/Xhv53txQ/4NyCjJwIXSCyjTlplWLXB3dke1odr0vOj+rvdjas+pXDLKht3T6R7c8809dn9dBk4X6Aqualf0a9EP/Vr0q9ftL19mgYjI3JydnHFj+I31XuqguVdzCyciIqUyDpy0Wi00GssMrFWSCj3CeqBHWI963b6ZZzOL5CAi+2fstB9++AE//PBDnQc3SJKEuGZxiGsWV6/7DPYINnNKEsX480Ekypw5cwD8c90G438TExNNS0ddD8/Ksy+iD8SzJJ4bTUQAgIsVF/FTyk/4I/UP0VGIiFBaVYrVx1cj4USC6Chkg8aOHSs6AtmZiuoKrDu9Dj8k/2D6mL1fLIuU4fJBBIelZKTVa7EpbRPSA9Jx8eJF0XHIwSUmJl7RVewrx6Iz6FDoWYizQWft9vkRB+hEBADYe2Evvkn6Br8c+8VuC4/qx173GJNtOZZ/DJ8f/Bw/Hf0JeoNedByyIatXrxYdgexQenE6luxdgp+O/oSK6poL9xlPWSeytMTERIwYMQIAh+hU42LFRSzetRg5fjm4+4G7RcchAnD1QTp7y/5V6ioRPDoYuT65SCtKEx3HIjhAJyIAQN+IvnBWOSOjJAOpF1NFxyEiB9cttBu8XbxRVFmEQ9mHRMchG2K82B5frJE5tQ1oizCvMGj1Wvyd8bfoOOSApk6dahpMcSBFIZ4h6BDUATJkFHgWiI5DVItxkH61YTr7y/54ajxx/I/jAIDNaZsFp7EMDtCJCADgofFAr7BeAIDNZ+2z8Khh+MSGRFKr1Lgp8iYA7CRqOB8fn+vfiKgBJEnCoKhBANhJJNa/B1IrVqwQmIZEMnZSvnc+zyAmxaprmA5cOVCPj4/Hhg0bBKWkhqqurr7i8QsoDQAAbD231S7PIOZFRInIZHDLwdiRsQNbz23F/XH3w0nlJDoSETmwQVGDkHgyETvP70RFdQXcnN1ERyIbsWzZMtOTeS5LReYyqOUgLDuyDEk5SdCqtdDoLHOhWqLrufxI9GXLlmHZsmXsOgfUr0U/qGQVKjQVSCtKQyu/VqIjEV1TXT11+UFbH3zwAT744IN635+Hhwd++OGH69/QweXm5uLZZ59FYWGhRbezZfkWTFg1ARcrL+JwzmF0C+1m0e1Zm80M0F977TWsWbMGhw4dgkajQVFRkehIRHana2hX+Lj4mJZM6N68u+hIisVOIrK8aP9ohHmFIbM0E39n/I0hrYYAAHx9ffk79y/sJCLLC/YIRoegDkjJS0G+Vz6aX2wuOpJisZOso64lXZYtW8azcBzE3WPvhm8zX0QPjsbmtM0coF8DO0m5rrfzLz8/H/fff3+dnysrK+NZ01bQp08fjB8/HuHh4de97YDIAVhzag02p23mAF0UrVaLO++8E71798YXX3whOg6RXVKr1BgQOQCJJxOxKW0TB+jXYO+dlJiYyCcjJJwkSRjccjC+TfoWm9I2mQboS5cuxW233SY4nbLYeyc1hrHHeBQ6mdOgqEFIyUtBgVcBQi+Gio6jWOwk67p8kH7fffdd8XGyL8eOHcNzzz0HALWWTJgUN4lnEF8FO8l2BQYGsstsyKCoQVhzag12nt+JSl0lXNWuoiOZjc0M0F955RUAwNdffy02CJGdGxQ1CBvObIDGSQNZliFJkuhIisROIrKOgVEDsSJlBTw0HtAb9HBSOZkuEkn/YCcRWUe/Fv3w+cHPodFpYFAZRMdRLHaSGJcP0i//7+WfI9v174NbFi1ahJatW2LSr5MQ7h2OosoiBLgHCEqnbOwkIuswXnTdS+OF/PJ8hHtf/6h1W2EzA/TGqKqqQlVVlen9kpISgWmIbEO0fzS+u/07uKhdREexO7bYSTxyk0QL9gjG8tuXs5MswBY7qaF4FDqZm4fGA8vGLMMd39whOordcYROshZj3xkMBtMZWxym25bRo0dDr6/7Inz/+9//EBr6zxkwX4z6gs+TLICdRNRwkiThvRHv2WUn2fUAfcGCBaY9jURUP5IkCSm7gIAAFBQUWH271sROImoce3wCpgTsJKLGYSdZBjvJ/FQqVa1heV1HphtxqG5dmzdvxqJFi+p9+2s9Puwky2AnETWOvXaS0AH6zJkz8eabb17zNseOHUNMTEyj7n/WrFmYMWOG6f2SkhJEREQ06r6IHFFGcQaCPIKssm7VhAkTsHjxYotv51rYSUTKllWaBU+NJ7xcvERHsQp2knnwKHSyFK1ai8KKQvi7+YuOYhXsJNv37w68fJB+rWvfsDuvTpZljBo1yiz35ezsjJ9//rnRS2gWVRZBq9ci2CPYLHmUjp1EpGylVaUo1ZaiuZd9XHRd6AD96aefxqRJk655m1atGn8laRcXF7i42OeeDyJLe3Xbq9iduRtP934aA6MGWnx7vXv3Fj5AZyfVxguJkpIs2bME686sw+SukzE6ZrToOFbBTjKf5557Dm+99ZboGGRHzvufh19/PyScSMCkuEmi41gFO8n+XG0w/u/nf019PvjJJ58gLCysSfdRX3q9HnPnzsWhQ4essr36WLFiBdzd3S26jcQTifjswGcY3HIwpt843aLbUgp2EpFybT27FYt3LUZcszjMHThXdByzEDpADwoKQlBQkMgIRHQVLX1bYnfmbmw/t90qA3Q3NzeLb+N62ElEyhXlGwUA2JG+w2EG6Owk8+nfvz/eeustHoVOZuOudYeHhwe2n9uOiV0mOsRF19lJjqO+PTl79ux6DaofeeSRJiayro8++sjmjjRu5dcKMmTsOr8L1fpqODs5i45kcewkIuVq7d8aelmPQ9mHUFpVahdnENvMGujp6ekoLCxEeno69Hq96Q91dHQ0PD09zbadlJQUdOjQwWz3R2Sr+rXohx9SfsCB7AMo05bBQ+MhOpKiWKuTlIADJ1KCPhF98On+T3Gi4ARyy3JFx1EcR+qkxuJSLmROPmU+cFW7Irc8F6cKT6FtQFvRkRSFneQY5s+fLzoC/b/YoFj4u/mjsKIQB7MPomdYT9GRFIWdRGRd4d7hiPKJwtnis9h1fheGth4qOlKTqUQHqK+XX34ZXbt2xZw5c3Dp0iV07doVXbt2xb59+8y6nZkzZ5r1/ohsVaRvJCK8I6Az6LA7c7foOIpjrU4iohp+bn7oFNwJQM1R6FQbO4nIejIyMuAkO6Fn85oBFTvpSuwkIuuSJAl9I/oCYCfVhZ1EZH39I/sDALanbxecxDxsZoD+9ddfQ5blK94GDhwoOhqR3TI+Cdt1fpfgJMrjKJ30+eefi45AZNK3BTvpahylk5rKeOQ5r+9ATfHYY48BqN1JsiyLjKQ47CQi6zO+dtuTuQc6g05wGmVhJxFZn7GTknKSUKYtE5ym6WxmgE5E1tc7ojcAYH/WflTpqgSnIRFCQkJERyAy6RXWCwBwPP84tE5awWmIyNF1C+0GZ5Uzsi5l4VzxOdFxiMjBtQ9qDx8XH5RVlyE5N1l0HCJycGHeYYjwjoBe1mPfBds/24MDdCK6qpa+LRHsHgytXouD2QdFxyGBeLQmKUGAewDa+reFDBlFHkWi45CN4lHoZA4qlQquald0bdYVAM+MISLxVJLKtPb5zoydgtMQEQG9w2sOytx53vY7yWYuIkpE1idJEibGTYSb2g1dmnURHYeICHd1vAs6gw6vfv+q6ChkwxISEjBq1CheUJQazLhUy+rVqwEAt8XchgGRA3BD8xtExiIiAgCMjB6J2KBY9GjeQ3QUIiIMjBqIQPdA9ArvJTpKk3GATkTXNCBygOgIREQmPcN6ori4GE6yk+goZMMkSRIdgWzUqFGjar3fOaSzoCRERFdqE9AGbQLaiI5BRAQAiPCJQIRPhOgYZsElXIiI6Jp4dCYpzX333Sc6AtkBLuVCRERERET1wQE6EV3XhdIL+ObwN/jl2C+io5BAHDKRUmjVWmT6Z+Lbw9+KjkI2bvbs2QDYb1Q/f//9N4ArdywXVxZj5dGV+HTfpyJiERHVUqYtw+rjq7F452LRUYiIoNVrsfbUWryx4w0YZIPoOI3GAToRXVdGcQZ+OvoT1pxcY1r7k4hIFK1TzQA98WQidAad6Dhkw3r27Ck6AtmQBQsW1PnxSl0llh5eirWn16K0qtTKqYiIrvTVoa+w6ewmZJVmiY5CRA5OJanwzeFv8FfGXziRf0J0nEbjAJ2IrqtLsy5wVjkjtzwXGSUZouMQkYPzqPKAs94ZFboKHM07KjoO2Tgu5UIN4evre8XHQjxDEOEdAYNswMHsg9YPRUR0GQ+NBzoEdQAA7L2wV3AaInJ0apUa3UO7A7DtTuIAnYiuy1XtarpI1t5M2y08ajyug05KIkGCT7kPAHYSmUdCQgIADtHp6ow/G99+W/fSUT2a9wDATiIiZbih+Q0A2ElEpAz20EkcoBNRvRgLb9+FfYKTkEgcLpFS+Jb5ArDtoxhIOSRJMv1/bm6uwCSkRFlZ118CoUdYzQB9f9Z+m17fk4jsg7GTkvOSUamrFJyGiBxd9+bdIUHC2eKzyC/PFx2nUThAJ6J6MR5ZdTT/KC5pLwlOQ0SOzrvcGypJhczSTK7vSWZhPNNm8uTJgpOQ0jz00EMArn02VkxgDDycPVCqLbXp9T2JyD6EeYWhmUcz6Aw6HMo+JDoOETk4bxdvtAtoB8B2D8rkAJ2I6qXW+p5ZXN+TiMRSG9Sm9T1t9UkYKQ/XQ6d/M/4sLF++/Jq3U6vU6BbaDQA7iYjEkyTJdBS6LS+ZQET2w9Y7iQN0Iqq3Hs17wNvFG2XVZaKjkABcB52UpkfzHvBw9kCVvkp0FLIjHKKTUWZmpun/vby8rnv7Hs17wFXtimpDtSVjERHVS4/mPaBx0nBZKSJShB7Ne0CtUkOSJMiyLDpOg6lFByAi2/HfTv/FxLiJUEnc9+bI4uPjOUwnRbilzS0Y1W4UnFROoqOQnWLfObZHHnkEQP13IPdt0Rf9WvSDs5OzJWMREdVLp5BOWH77crioXURHISJClG8Uvrv9O7g7u4uO0iicghFRvbmqXTk8JyJFSEhIgIvahcNzsojLB6Y8Et0xGR/3huxA0ThpODwnIsVQq9QcnhORYkiSZLPDc4ADdCJqBFmWUVpVKjoGCeDkxGElKYMkSab/ZyeRJVw+ON2/f7/AJGRtxuH522+/3ej7YCcRkZKwk4hISWyxkzhAJ6IGOZJzBJNWT8L8bfNFRyEBfv31V9ERiGpJu5iGBxMexNMbnhYdheyQcYg+d+5cGAxcQ9YRXH7GQbt27Rr89XlleXg48WFMSZzCdYeJSLjy6nI8vvZxjF81HuXV5aLjEJGDM8gGPL3+adzzyz3ILcsVHadBOEAnogYJ8QxBYUUhThScQJmWFxN1VFzSgJQixDMEBRUFyLqUhexL2aLjkB0yDtFvu+02wUnI0i7/29bYte/93fxRXFWMsuoynCo4Za5oRESN4u7sDq1eC72sx5GcI6LjEJGDU0kq0xKcB7MOCk7TMBygE1GDBHsEI8wrDAbZgKScJNFxiMjBuTu7IyYwBoDtPQkj22EcpsbHx0OWZcFpyBKMw/M2bdo06cKxTionxDWLAwAczGYnEZF43UK7AQAOZB0QnISIyHY7iQN0ImowY+HxhaFjaspggaip6jr7oWuzrgDYSWRZxu4bNWoUCgsLBachc8nIyDD1yieffIJFixY1+T6NnWRrLwyJyD7xeRIRKYmxkw7nHIbeoBecpv44QCeiBjM9CePRng7tkUceER2BCADQNdQ2n4SR7TEO0SdOnMilrOxAfHw8HnvsMQA1j21YWJhZ7tfYSVzujoiUoFNIJzhJTlzujogUoU1AG3g4e6CsugynC0+LjlNvHKATUYN1CukEtUqN7LJsZJVmiY5DgmRmZoqOQAQAiPaPhpfGC+XV5ThZcFJ0HLJzl5+FwyG67TLHeudXc/lyd0dyueYwEYl1+XJ3h7IPiQ1DRA5PJalscrk7mxignz17FpMnT0bLli3h5uaG1q1bY86cOdBqtaKjETkkV7Ur2ge2B2BbhWcu7CQiZVFJKnQJ6QKAncROsg4O0W1XfHy86TF7+OGHLbYsmWm5Owc8W4+dRKQ8jtxJAHuJSGlscR10tegA9XH8+HEYDAZ8+umniI6ORnJyMqZMmYKysjK8/fbbZtvOuHHj8OOPP5rt/ojs2eCWgxHtH412Ae1ER7E6a3WSkiUmJnJoRIoyIHIA/Nz8TEtMORJ2khiX92B8fDyvD6Fw+/btwyuvvGJ639KP143hN6JaX43eEb0tuh0lYicRKc8NzW/AhdILuDH8RtFRhGAvESlL12ZdcVPkTege2l10lHqTZFmWRYdojIULF+Ljjz9Gampqvb+mpKQEPj4+KC4uhre39xWfz83NxeTJk/kCiEiQqw0grve7qwSW6CSlMw6O2JlkTfHx8UhISIAkScIy2MLvriN2kiiyLGPUqFEAgFmzZqFPnz6CE9G/Xb7D95dffoGzs7PANOZnC7+77CQix2ILv78N7SVb+J6IqG7m+P21iSVc6lJcXAx/f3+z3mdQUJBZ74+IHIclOomI6iZyeG4r2EnWI0mSaUfiggULeHaOgly+XEvLli2RmJhod8NzW8FOIiKlYS8RUUPYxBIu/3b69Gl88MEH1z3VpqqqClVVVab3S0pKrnl7viAnaphKXSWO5R2Ds5MzOgZ3FB1HGEt1ktJxGRdSGq1eixP5J1BtqDatq+eIHLWTRPv3ki6iz5RwVFqtFmPHjq31MVFnSukMOpwqOIWSqhL0Cu8lJIMSsJOIlMEgG3Cm8AyyL2Wjf2R/0XGEqk8vsZOILEuWZZwtOou0ojQMbjlYdJzrEnoE+syZMyFJ0jXfjh8/XutrMjMzMWLECNx5552YMmXKNe9/wYIF8PHxMb1FRERY8tshcjjrT6/Hy1texsqjK0VHMQt2UuNwiE7Wcr2ftZ0ZO/HCphfwzeFvrJTIsthJticxMdE0rB01ahT70YqMR5sbh+f+/v61Hg8RUnJT8Nwfz+HjfR/DRlfNrIWdRGTbMoozMGPDDLy7+13oDDrRcczCkr3ETiKyrOKqYkxbNw2Ldy1GaVWp6DjXJXQN9Ly8PBQUFFzzNq1atYJGowEAXLhwAQMHDsSNN96Ir7/+GirVtef/de0xjIiIuOaaN7wIFFH9pV1Mw7R10+CqdsX3Y7+HWtW0k1pEr4GuxE5SOq6DTtZ0vZ+3wopCTPx1IiRI+O727+Dl4mWRHOwkqo9/D8/Zk5bx73/nFStWwN3dXVCa2rR6Le5aeRd0Bh0+ufUThHmHWWQ77CQiqg9ZljF+1XgUVxXjjSFvoENwB4ttyx56iZ1EZHlT10xFekk6ZvWbhT4RlruOkDk6SegSLkFBQfVedzwzMxODBg1C9+7d8dVXX133CRgAuLi4wMXFpakxiegqonyj4KXxQqm2FKcKTqF9UHvRkZqEndRwCQkJpovnEYnm7+aPCO8IZJRkIDk3Gb0jeouO1CTsJNtmHJhfvqzL5R+nxqvryH4l/rtqnDRoH9geR3KPICknyWIDdGthJxHZNkmS0DmkM7anb0dSTpJFB+jWYsleYicRWV6XZl2QXpKOw9mHLTpANwebuIhoZmYmBg4ciBYtWuDtt99GXl4esrOzkZ2dLToakUMzPgkDgKScJMFprIed9A/j+r5cpoCsxcvr2keVs5Mcu5OU6N/LiFx+YUuqP+O/2+X/dr/++qvwZVquh53ETiJSEkfsJIC9RKRUttRJNnER0Y0bN+L06dM4ffo0wsPDa33OHtYTJLJlnUM646+Mv5CUk4S7Ot4lOo5VsJOIxFm+fPk1P985pDPWnFqDwzmHrZRIPHaSbeAR6Q1X146Gr7/+GgEBAQLSNE7nkM747sh3SMpNgizLDnFhWXYSkXIZh1XHC46jSlcFF7VjHGHNXiJSpo7BHSFBwvnS8yisKIS/m7/oSFdlE0egT5o0CbIs1/lGRGJ1CekCADiWfwxavVZwGutgJ9W2bNky0RHIAdT396tTcCdIkJBRkoGLFRctnEoZ2Em25WpHpOfm5gpMpQyXH2V++fA8ISHB9O9mS8NzAGgb0BYuTi4oqSrBueJzouNYBTuJSLlCPUMR6B4InUGHY/nHRMexGvYSkTJ5ajzR2q81AOUfhW4TA3QiUq7mXs3h7+aPakM1ThacFB2HBPDx8QHAZVzIsuq71r6Xixda+rYEAKTkpVgyElGTGAfCLVvW/LxOnjzZNDjW6XSC01nH1QbmwD//PomJiTZ91LZapUZsUCwAIDk3WXAaInJ0kiShU3AnAOwkIlKGTiE1nZSSq+zXbjaxhAsRKZckSZjWcxoC3AMQ6RMpOg4RESZ3mwx3Z3fTIJ1Iyd5//33T/xuHyGPGjKl1G3tY5uV6O1nt4Xu8mns63YN7O92L1v6tRUchIsLomNEY1noY2ga0FR2FiAhDWw1F99DuaBfYTnSUa+IAnYiarHvz7qIjkGBfffUV7r//ftExiAD8s74nka25fIg8f/587NmzB8CVw+eEhATFHpVdn7ORlJzfEmICY0RHICIyaeXXSnQEIiKTCJ8IRPhEiI5xXRygExFRkwUGBgKoGZzY81GEJBZ/tsiRzJ49u9b7L774IpKSataGvNaSRpb+PWnocl38vSUiIiIiW8cBOhGZxdazW7E/az9Gx4zmUQ1EZFanT59u8NfsPr8buzN3Y2DUQB6RTnbhtddeu+JjVVVVuOOOO2p9TMT1KBztiPLGOJx9GDvSd6BraFf0iegjOg4RObjj+cex7dw2tPRtiaGth4qOQ0QOLu1iGjalbUKgeyBui7lNdJw6cYBORGaxPX07dmfuRkvflhygO6jVq1fjttuU+ceObNtTTz3V4K/Ze2EvNqZuhKfGkwN0slsuLi71OsJbp9PhhRdewLFjx657W3d3d3z99ddwc3MzR0T6f0dyj2DdmXWo0ldxgE5Ewp0uPI3Ek4no2qwrB+hEJFxmaSZ+PfErWvm24gCdiOxbx+CO2J25G8m5yRjTfsz1v4DsjkqlAsBlXEgZOgR1wPoz6xV/NXcia1Cr1XjrrbdEx3BoHYM7YkXKCnYSESlCx+COAIBj+cegN+jhpHISnIiIHFmHoA4AgLSiNJRpy+Ch8RCc6Eoq0QGIyD4YCy8lLwWyLAtOQ0T25tlnn23Q7Y0vDE9fPI1KXaUlIhER1VtMYAxUkgq55bnILcsVHYeIHFykTyQ8nD1QqatE6sVU0XGIyMH5ufkhzCsMMmQczTsqOk6dOEAnIrNo5dcKrmpXlFWX4VzxOdFxSBAeeU7mZjAYAAADBgxo0NcFeQQh2D0YBtmA4/nHLRGNiKjeXNWuiPaLBgAehU5EwkmSZDoAKjk3WXAaIqLaB2UqEQfoRGQWTiontA9sDwA4knNEcBoSTcRF7Mg+NWVd/Q7BNU/C2ElEpATGTuKwioiUgJ1EREqi9E7iAJ2IzMa4x5BHexKRErCTiEhJjJ10LP/6F3MlIrK0yzuJS3ASkWjGTjpz8Qy0eq3gNFfiAL0O/ONB1Djtg9pDgoSy6jLRUUgg4zIu6enpgpOQvfj0008b9XWxQbEAgApdBf+2E5Fw7YNqztTTG/SKfGFIRI6ltX9raJw0cFW7oqiySHQcInJwwR7B8Hfzh7eLN3Iu5YiOcwW16ABKVFlZCTc3N9ExiGxObFAsvh/7vSKvmEzWN3XqVK6JTk1iXAqoefPmjfr6cO9wLL99ObxcvMwZi4ioUbxdvPHd7d/B28VbdBQiIqhVanx121fsJCJSBEmS8MHID+Cl8YIkSaLjXIFHoNdh2bJloiMQ2SS1Ss3hOQEA7r33XtERiCBJEofnRKQoHFQRkZKwk4hISbxdvBU5PAc4QK9TQkKC6AhENo/LJTi2u+++GwAvJkrKwU4iIiVhJxGRkrCTiEhJZFlWXC9xgE5EZnW26Cye2/gcnv/jedFRiMiGFRcXA0CTlwHKLcvFC3++gEfXPGqOWERETVJaVYqXN7+MSasnQWfQiY5DRA5OZ9Bh3tZ5uPeXe1FaVSo6DhERFv61EONXjceF0guio9TCAToRmZWnxhPH8o/hRMEJVOoqRcchgYyDT6XtOSbbcN9995nlfrxdvJGSl4LM0kzkl+eb5T6JiBrLU+OJU4WnUFhRiLSLaaLjEJGDU6vUuFB6AaXaUhzPPy46DhERCioKUFxVjGP5x0RHqYUDdCIyq0D3QAS6B8IgG3Cq4JToOKQAo0aNEh2BHNj/tXf/sVGUeRzHP1tqf2C7RaAFlFIoZypJQUxLUeDUnpwYTbTxR9SooYYY/ygYxBgaotbEBBM12AQM6h9XE+LPiJ6Xai5qPapJRQkYwB8UKUFre+LS5ooWjm7YuT9Me6JTmN2d7fNM5/1KJoFlu/3myfAOPDudzcvOU/mkcknSNzG7/hEGIHwikYgumXKJJLFZBcAK86bOk0STANhhuEm2/d+NDXQAvhsJnmXvGGLsVVdXmx4BAdTX1ycp/du3DJtXTJMA2GO4SV/HvjY8CQDQJAB2sfX/bmygA/Cdre8YYuw1NTVJ4sNEkZz6+npfX48mAbDJyNWefVztCcC84SYd7D/IZzMAMO6Sqb/+pF738W6rPpuBDXQAvvvtO4bc/xqAacNNOvyfw3w2AwDjLp5ysbIiWTp24phigzHT4wAIuZnRmSrIKdD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     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "id": "sweep-mu",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-21T13:55:06.389583500Z",
     "start_time": "2026-03-21T13:55:04.324049200Z"
    }
   },
   "source": [
    "# Different mu values\n",
    "mu_values = [0.1, 0.5, 1.0, 3.0, 5.0]\n",
    "\n",
    "fig, axes = plt.subplots(2, len(mu_values), figsize=(15, 6))\n",
    "\n",
    "for idx, mu_val in enumerate(mu_values):\n",
    "    # Create oscillator\n",
    "    model = brainmass.VanDerPolStep(1, mu=mu_val)\n",
    "    brainstate.nn.init_all_states(model)\n",
    "    model.x.value = np.array([0.5])\n",
    "    model.y.value = np.array([0.0])\n",
    "\n",
    "\n",
    "    # Simulate\n",
    "    def step(i):\n",
    "        with brainstate.environ.context(i=i, t=i * brainstate.environ.get_dt()):\n",
    "            model.update()\n",
    "            return model.x.value, model.y.value\n",
    "\n",
    "\n",
    "    x, y = brainstate.transform.for_loop(step, np.arange(30000))\n",
    "    t = np.arange(30000) * brainstate.environ.get_dt()\n",
    "\n",
    "    # Time series (top row)\n",
    "    axes[0, idx].plot(t[-10000:], x[-10000:, 0], 'b-', linewidth=0.8)\n",
    "    axes[0, idx].set_title(f'$\\mu$ = {mu_val}')\n",
    "    axes[0, idx].set_xlabel('Time (ms)')\n",
    "    if idx == 0:\n",
    "        axes[0, idx].set_ylabel('x(t)')\n",
    "\n",
    "    # Phase portrait (bottom row)\n",
    "    axes[1, idx].plot(x[-10000:, 0], y[-10000:, 0], 'k-', linewidth=0.3, alpha=0.7)\n",
    "    # Nullcline\n",
    "    x_null = np.linspace(-3, 3, 100)\n",
    "    axes[1, idx].plot(x_null, x_null - x_null ** 3 / 3, 'g--', linewidth=1.5, alpha=0.7)\n",
    "    axes[1, idx].set_xlabel('x')\n",
    "    if idx == 0:\n",
    "        axes[1, idx].set_ylabel('y')\n",
    "    axes[1, idx].set_xlim([-3, 3])\n",
    "    axes[1, idx].set_ylim([-3, 3])\n",
    "\n",
    "plt.suptitle('Transition from Harmonic to Relaxation Oscillations', y=1.02, fontsize=14)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:26: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:26: SyntaxWarning: invalid escape sequence '\\m'\n",
      "C:\\Users\\adadu\\AppData\\Local\\Temp\\ipykernel_32900\\4055819095.py:26: SyntaxWarning: invalid escape sequence '\\m'\n",
      "  axes[0, idx].set_title(f'$\\mu$ = {mu_val}')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x600 with 10 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "id": "relaxation-analysis",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-21T13:55:10.769479300Z",
     "start_time": "2026-03-21T13:55:06.409562200Z"
    }
   },
   "source": [
    "x, y = simulate(5.0, 50000)\n",
    "t = np.arange(50000) * brainstate.environ.get_dt()\n",
    "\n",
    "# Focus on steady state\n",
    "x_ss, y_ss, t_ss = x[-20000:, 0], y[-20000:, 0], t[-20000:]\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
    "\n",
    "# Time series with annotations\n",
    "axes[0].plot(t_ss, x_ss, 'b-', linewidth=1)\n",
    "axes[0].set_xlabel('Time (ms)')\n",
    "axes[0].set_ylabel('x(t)')\n",
    "axes[0].set_title('Relaxation Oscillation ($\\mu=5$)')\n",
    "\n",
    "# Annotate phases\n",
    "axes[0].annotate('Fast jump', xy=(t_ss[2000], 1.5), fontsize=10, color='red')\n",
    "axes[0].annotate('Slow phase', xy=(t_ss[8000], 1.8), fontsize=10, color='green')\n",
    "\n",
    "# Phase portrait showing slow-fast structure\n",
    "axes[1].plot(x_ss, y_ss, 'b-', linewidth=1, alpha=0.7)\n",
    "\n",
    "# Nullcline (cubic)\n",
    "x_null = np.linspace(-2.5, 2.5, 200)\n",
    "y_null = x_null - x_null ** 3 / 3\n",
    "axes[1].plot(x_null, y_null, 'r-', linewidth=3, label='Slow manifold (x-nullcline)')\n",
    "\n",
    "# Mark regions\n",
    "axes[1].fill_between(x_null[x_null > 1], y_null[x_null > 1], 5, alpha=0.2, color='green', label='Slow (right branch)')\n",
    "axes[1].fill_between(x_null[x_null < -1], y_null[x_null < -1], -5, alpha=0.2, color='green')\n",
    "\n",
    "axes[1].set_xlabel('x')\n",
    "axes[1].set_ylabel('y')\n",
    "axes[1].set_title('Slow-Fast Dynamics in Phase Space')\n",
    "axes[1].legend(loc='upper right')\n",
    "axes[1].set_xlim([-3, 3])\n",
    "axes[1].set_ylim([-2, 2])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:13: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:13: SyntaxWarning: invalid escape sequence '\\m'\n",
      "C:\\Users\\adadu\\AppData\\Local\\Temp\\ipykernel_32900\\2480255506.py:13: SyntaxWarning: invalid escape sequence '\\m'\n",
      "  axes[0].set_title('Relaxation Oscillation ($\\mu=5$)')\n",
      "C:\\Users\\adadu\\AppData\\Local\\Temp\\ipykernel_32900\\2480255506.py:13: SyntaxWarning: invalid escape sequence '\\m'\n",
      "  axes[0].set_title('Relaxation Oscillation ($\\mu=5$)')\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "iteration over a 0-d Quantity is not allowed",
     "output_type": "error",
     "traceback": [
      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
      "\u001B[31mTypeError\u001B[39m                                 Traceback (most recent call last)",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\artist.py:1402\u001B[39m, in \u001B[36m_get_tightbbox_for_layout_only\u001B[39m\u001B[34m(obj, *args, **kwargs)\u001B[39m\n\u001B[32m   1401\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m1402\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m obj.get_tightbbox(*args, **{**kwargs, \u001B[33m\"\u001B[39m\u001B[33mfor_layout_only\u001B[39m\u001B[33m\"\u001B[39m: \u001B[38;5;28;01mTrue\u001B[39;00m})\n\u001B[32m   1403\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m:\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\axes\\_base.py:4587\u001B[39m, in \u001B[36m_AxesBase.get_tightbbox\u001B[39m\u001B[34m(self, renderer, call_axes_locator, bbox_extra_artists, for_layout_only)\u001B[39m\n\u001B[32m   4586\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m a \u001B[38;5;129;01min\u001B[39;00m bbox_artists:\n\u001B[32m-> \u001B[39m\u001B[32m4587\u001B[39m     bbox = a.get_tightbbox(renderer)\n\u001B[32m   4588\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m (bbox \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[32m   4589\u001B[39m             \u001B[38;5;129;01mand\u001B[39;00m \u001B[32m0\u001B[39m < bbox.width < np.inf\n\u001B[32m   4590\u001B[39m             \u001B[38;5;129;01mand\u001B[39;00m \u001B[32m0\u001B[39m < bbox.height < np.inf):\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\text.py:2030\u001B[39m, in \u001B[36mAnnotation.get_tightbbox\u001B[39m\u001B[34m(self, renderer)\u001B[39m\n\u001B[32m   2028\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mget_tightbbox\u001B[39m(\u001B[38;5;28mself\u001B[39m, renderer=\u001B[38;5;28;01mNone\u001B[39;00m):\n\u001B[32m   2029\u001B[39m     \u001B[38;5;66;03m# docstring inherited\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m2030\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m._check_xy(renderer):\n\u001B[32m   2031\u001B[39m         \u001B[38;5;28;01mreturn\u001B[39;00m Bbox.null()\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\text.py:1590\u001B[39m, in \u001B[36m_AnnotationBase._check_xy\u001B[39m\u001B[34m(self, renderer)\u001B[39m\n\u001B[32m   1588\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m b \u001B[38;5;129;01mor\u001B[39;00m (b \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mself\u001B[39m.xycoords == \u001B[33m\"\u001B[39m\u001B[33mdata\u001B[39m\u001B[33m\"\u001B[39m):\n\u001B[32m   1589\u001B[39m     \u001B[38;5;66;03m# check if self.xy is inside the Axes.\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m1590\u001B[39m     xy_pixel = \u001B[38;5;28mself\u001B[39m._get_position_xy(renderer)\n\u001B[32m   1591\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m.axes.contains_point(xy_pixel)\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\text.py:1581\u001B[39m, in \u001B[36m_AnnotationBase._get_position_xy\u001B[39m\u001B[34m(self, renderer)\u001B[39m\n\u001B[32m   1580\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"Return the pixel position of the annotated point.\"\"\"\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m1581\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._get_xy(renderer, \u001B[38;5;28mself\u001B[39m.xy, \u001B[38;5;28mself\u001B[39m.xycoords)\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\text.py:1475\u001B[39m, in \u001B[36m_AnnotationBase._get_xy\u001B[39m\u001B[34m(self, renderer, xy, coords)\u001B[39m\n\u001B[32m   1474\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m xcoord == \u001B[33m'\u001B[39m\u001B[33mdata\u001B[39m\u001B[33m'\u001B[39m:\n\u001B[32m-> \u001B[39m\u001B[32m1475\u001B[39m     x = \u001B[38;5;28mfloat\u001B[39m(\u001B[38;5;28mself\u001B[39m.convert_xunits(x))\n\u001B[32m   1476\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m ycoord == \u001B[33m'\u001B[39m\u001B[33mdata\u001B[39m\u001B[33m'\u001B[39m:\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\artist.py:278\u001B[39m, in \u001B[36mArtist.convert_xunits\u001B[39m\u001B[34m(self, x)\u001B[39m\n\u001B[32m    277\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m x\n\u001B[32m--> \u001B[39m\u001B[32m278\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m ax.xaxis.convert_units(x)\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\axis.py:1802\u001B[39m, in \u001B[36mAxis.convert_units\u001B[39m\u001B[34m(self, x)\u001B[39m\n\u001B[32m   1800\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mconvert_units\u001B[39m(\u001B[38;5;28mself\u001B[39m, x):\n\u001B[32m   1801\u001B[39m     \u001B[38;5;66;03m# If x is natively supported by Matplotlib, doesn't need converting\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m1802\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m munits._is_natively_supported(x):\n\u001B[32m   1803\u001B[39m         \u001B[38;5;28;01mreturn\u001B[39;00m x\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\units.py:64\u001B[39m, in \u001B[36m_is_natively_supported\u001B[39m\u001B[34m(x)\u001B[39m\n\u001B[32m     62\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m np.iterable(x):\n\u001B[32m     63\u001B[39m     \u001B[38;5;66;03m# Assume lists are homogeneous as other functions in unit system.\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m64\u001B[39m     \u001B[38;5;28;01mfor\u001B[39;00m thisx \u001B[38;5;129;01min\u001B[39;00m x:\n\u001B[32m     65\u001B[39m         \u001B[38;5;28;01mif\u001B[39;00m thisx \u001B[38;5;129;01mis\u001B[39;00m ma.masked:\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\saiunit\\_base_quantity.py:1195\u001B[39m, in \u001B[36mQuantity.__iter__\u001B[39m\u001B[34m(self)\u001B[39m\n\u001B[32m   1194\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m.ndim == \u001B[32m0\u001B[39m:\n\u001B[32m-> \u001B[39m\u001B[32m1195\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m(\u001B[33m\"\u001B[39m\u001B[33miteration over a 0-d Quantity is not allowed\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m   1196\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(\u001B[38;5;28mself\u001B[39m.shape[\u001B[32m0\u001B[39m]):\n",
      "\u001B[31mTypeError\u001B[39m: iteration over a 0-d Quantity is not allowed",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001B[31mTypeError\u001B[39m                                 Traceback (most recent call last)",
      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[10]\u001B[39m\u001B[32m, line 38\u001B[39m\n\u001B[32m     35\u001B[39m axes[\u001B[32m1\u001B[39m].set_xlim([-\u001B[32m3\u001B[39m, \u001B[32m3\u001B[39m])\n\u001B[32m     36\u001B[39m axes[\u001B[32m1\u001B[39m].set_ylim([-\u001B[32m2\u001B[39m, \u001B[32m2\u001B[39m])\n\u001B[32m---> \u001B[39m\u001B[32m38\u001B[39m plt.tight_layout()\n\u001B[32m     39\u001B[39m plt.show()\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\pyplot.py:2843\u001B[39m, in \u001B[36mtight_layout\u001B[39m\u001B[34m(pad, h_pad, w_pad, rect)\u001B[39m\n\u001B[32m   2835\u001B[39m \u001B[38;5;129m@_copy_docstring_and_deprecators\u001B[39m(Figure.tight_layout)\n\u001B[32m   2836\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mtight_layout\u001B[39m(\n\u001B[32m   2837\u001B[39m     *,\n\u001B[32m   (...)\u001B[39m\u001B[32m   2841\u001B[39m     rect: \u001B[38;5;28mtuple\u001B[39m[\u001B[38;5;28mfloat\u001B[39m, \u001B[38;5;28mfloat\u001B[39m, \u001B[38;5;28mfloat\u001B[39m, \u001B[38;5;28mfloat\u001B[39m] | \u001B[38;5;28;01mNone\u001B[39;00m = \u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[32m   2842\u001B[39m ) -> \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m2843\u001B[39m     gcf().tight_layout(pad=pad, h_pad=h_pad, w_pad=w_pad, rect=rect)\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\figure.py:3640\u001B[39m, in \u001B[36mFigure.tight_layout\u001B[39m\u001B[34m(self, pad, h_pad, w_pad, rect)\u001B[39m\n\u001B[32m   3638\u001B[39m previous_engine = \u001B[38;5;28mself\u001B[39m.get_layout_engine()\n\u001B[32m   3639\u001B[39m \u001B[38;5;28mself\u001B[39m.set_layout_engine(engine)\n\u001B[32m-> \u001B[39m\u001B[32m3640\u001B[39m engine.execute(\u001B[38;5;28mself\u001B[39m)\n\u001B[32m   3641\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m previous_engine \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(\n\u001B[32m   3642\u001B[39m     previous_engine, (TightLayoutEngine, PlaceHolderLayoutEngine)\n\u001B[32m   3643\u001B[39m ):\n\u001B[32m   3644\u001B[39m     _api.warn_external(\u001B[33m'\u001B[39m\u001B[33mThe figure layout has changed to tight\u001B[39m\u001B[33m'\u001B[39m)\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\layout_engine.py:188\u001B[39m, in \u001B[36mTightLayoutEngine.execute\u001B[39m\u001B[34m(self, fig)\u001B[39m\n\u001B[32m    186\u001B[39m renderer = fig._get_renderer()\n\u001B[32m    187\u001B[39m \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28mgetattr\u001B[39m(renderer, \u001B[33m\"\u001B[39m\u001B[33m_draw_disabled\u001B[39m\u001B[33m\"\u001B[39m, nullcontext)():\n\u001B[32m--> \u001B[39m\u001B[32m188\u001B[39m     kwargs = get_tight_layout_figure(\n\u001B[32m    189\u001B[39m         fig, fig.axes, get_subplotspec_list(fig.axes), renderer,\n\u001B[32m    190\u001B[39m         pad=info[\u001B[33m'\u001B[39m\u001B[33mpad\u001B[39m\u001B[33m'\u001B[39m], h_pad=info[\u001B[33m'\u001B[39m\u001B[33mh_pad\u001B[39m\u001B[33m'\u001B[39m], w_pad=info[\u001B[33m'\u001B[39m\u001B[33mw_pad\u001B[39m\u001B[33m'\u001B[39m],\n\u001B[32m    191\u001B[39m         rect=info[\u001B[33m'\u001B[39m\u001B[33mrect\u001B[39m\u001B[33m'\u001B[39m])\n\u001B[32m    192\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m kwargs:\n\u001B[32m    193\u001B[39m     fig.subplots_adjust(**kwargs)\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\_tight_layout.py:266\u001B[39m, in \u001B[36mget_tight_layout_figure\u001B[39m\u001B[34m(fig, axes_list, subplotspec_list, renderer, pad, h_pad, w_pad, rect)\u001B[39m\n\u001B[32m    261\u001B[39m         \u001B[38;5;28;01mreturn\u001B[39;00m {}\n\u001B[32m    262\u001B[39m     span_pairs.append((\n\u001B[32m    263\u001B[39m         \u001B[38;5;28mslice\u001B[39m(ss.rowspan.start * div_row, ss.rowspan.stop * div_row),\n\u001B[32m    264\u001B[39m         \u001B[38;5;28mslice\u001B[39m(ss.colspan.start * div_col, ss.colspan.stop * div_col)))\n\u001B[32m--> \u001B[39m\u001B[32m266\u001B[39m kwargs = _auto_adjust_subplotpars(fig, renderer,\n\u001B[32m    267\u001B[39m                                   shape=(max_nrows, max_ncols),\n\u001B[32m    268\u001B[39m                                   span_pairs=span_pairs,\n\u001B[32m    269\u001B[39m                                   subplot_list=subplot_list,\n\u001B[32m    270\u001B[39m                                   ax_bbox_list=ax_bbox_list,\n\u001B[32m    271\u001B[39m                                   pad=pad, h_pad=h_pad, w_pad=w_pad)\n\u001B[32m    273\u001B[39m \u001B[38;5;66;03m# kwargs can be none if tight_layout fails...\u001B[39;00m\n\u001B[32m    274\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m rect \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m kwargs \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m    275\u001B[39m     \u001B[38;5;66;03m# if rect is given, the whole subplots area (including\u001B[39;00m\n\u001B[32m    276\u001B[39m     \u001B[38;5;66;03m# labels) will fit into the rect instead of the\u001B[39;00m\n\u001B[32m   (...)\u001B[39m\u001B[32m    280\u001B[39m     \u001B[38;5;66;03m# auto_adjust_subplotpars twice, where the second run\u001B[39;00m\n\u001B[32m    281\u001B[39m     \u001B[38;5;66;03m# with adjusted rect parameters.\u001B[39;00m\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\_tight_layout.py:82\u001B[39m, in \u001B[36m_auto_adjust_subplotpars\u001B[39m\u001B[34m(fig, renderer, shape, span_pairs, subplot_list, ax_bbox_list, pad, h_pad, w_pad, rect)\u001B[39m\n\u001B[32m     80\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m ax \u001B[38;5;129;01min\u001B[39;00m subplots:\n\u001B[32m     81\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m ax.get_visible():\n\u001B[32m---> \u001B[39m\u001B[32m82\u001B[39m         bb += [martist._get_tightbbox_for_layout_only(ax, renderer)]\n\u001B[32m     84\u001B[39m tight_bbox_raw = Bbox.union(bb)\n\u001B[32m     85\u001B[39m tight_bbox = fig.transFigure.inverted().transform_bbox(tight_bbox_raw)\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\artist.py:1404\u001B[39m, in \u001B[36m_get_tightbbox_for_layout_only\u001B[39m\u001B[34m(obj, *args, **kwargs)\u001B[39m\n\u001B[32m   1402\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m obj.get_tightbbox(*args, **{**kwargs, \u001B[33m\"\u001B[39m\u001B[33mfor_layout_only\u001B[39m\u001B[33m\"\u001B[39m: \u001B[38;5;28;01mTrue\u001B[39;00m})\n\u001B[32m   1403\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m1404\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m obj.get_tightbbox(*args, **kwargs)\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\axes\\_base.py:4587\u001B[39m, in \u001B[36m_AxesBase.get_tightbbox\u001B[39m\u001B[34m(self, renderer, call_axes_locator, bbox_extra_artists, for_layout_only)\u001B[39m\n\u001B[32m   4584\u001B[39m     bbox_artists = \u001B[38;5;28mself\u001B[39m.get_default_bbox_extra_artists()\n\u001B[32m   4586\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m a \u001B[38;5;129;01min\u001B[39;00m bbox_artists:\n\u001B[32m-> \u001B[39m\u001B[32m4587\u001B[39m     bbox = a.get_tightbbox(renderer)\n\u001B[32m   4588\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m (bbox \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[32m   4589\u001B[39m             \u001B[38;5;129;01mand\u001B[39;00m \u001B[32m0\u001B[39m < bbox.width < np.inf\n\u001B[32m   4590\u001B[39m             \u001B[38;5;129;01mand\u001B[39;00m \u001B[32m0\u001B[39m < bbox.height < np.inf):\n\u001B[32m   4591\u001B[39m         bb.append(bbox)\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\text.py:2030\u001B[39m, in \u001B[36mAnnotation.get_tightbbox\u001B[39m\u001B[34m(self, renderer)\u001B[39m\n\u001B[32m   2028\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mget_tightbbox\u001B[39m(\u001B[38;5;28mself\u001B[39m, renderer=\u001B[38;5;28;01mNone\u001B[39;00m):\n\u001B[32m   2029\u001B[39m     \u001B[38;5;66;03m# docstring inherited\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m2030\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m._check_xy(renderer):\n\u001B[32m   2031\u001B[39m         \u001B[38;5;28;01mreturn\u001B[39;00m Bbox.null()\n\u001B[32m   2032\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28msuper\u001B[39m().get_tightbbox(renderer)\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\text.py:1590\u001B[39m, in \u001B[36m_AnnotationBase._check_xy\u001B[39m\u001B[34m(self, renderer)\u001B[39m\n\u001B[32m   1587\u001B[39m b = \u001B[38;5;28mself\u001B[39m.get_annotation_clip()\n\u001B[32m   1588\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m b \u001B[38;5;129;01mor\u001B[39;00m (b \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mself\u001B[39m.xycoords == \u001B[33m\"\u001B[39m\u001B[33mdata\u001B[39m\u001B[33m\"\u001B[39m):\n\u001B[32m   1589\u001B[39m     \u001B[38;5;66;03m# check if self.xy is inside the Axes.\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m1590\u001B[39m     xy_pixel = \u001B[38;5;28mself\u001B[39m._get_position_xy(renderer)\n\u001B[32m   1591\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m.axes.contains_point(xy_pixel)\n\u001B[32m   1592\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;01mTrue\u001B[39;00m\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\text.py:1581\u001B[39m, in \u001B[36m_AnnotationBase._get_position_xy\u001B[39m\u001B[34m(self, renderer)\u001B[39m\n\u001B[32m   1579\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34m_get_position_xy\u001B[39m(\u001B[38;5;28mself\u001B[39m, renderer):\n\u001B[32m   1580\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"Return the pixel position of the annotated point.\"\"\"\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m1581\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._get_xy(renderer, \u001B[38;5;28mself\u001B[39m.xy, \u001B[38;5;28mself\u001B[39m.xycoords)\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\text.py:1475\u001B[39m, in \u001B[36m_AnnotationBase._get_xy\u001B[39m\u001B[34m(self, renderer, xy, coords)\u001B[39m\n\u001B[32m   1473\u001B[39m xcoord, ycoord = coords \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(coords, \u001B[38;5;28mtuple\u001B[39m) \u001B[38;5;28;01melse\u001B[39;00m (coords, coords)\n\u001B[32m   1474\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m xcoord == \u001B[33m'\u001B[39m\u001B[33mdata\u001B[39m\u001B[33m'\u001B[39m:\n\u001B[32m-> \u001B[39m\u001B[32m1475\u001B[39m     x = \u001B[38;5;28mfloat\u001B[39m(\u001B[38;5;28mself\u001B[39m.convert_xunits(x))\n\u001B[32m   1476\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m ycoord == \u001B[33m'\u001B[39m\u001B[33mdata\u001B[39m\u001B[33m'\u001B[39m:\n\u001B[32m   1477\u001B[39m     y = \u001B[38;5;28mfloat\u001B[39m(\u001B[38;5;28mself\u001B[39m.convert_yunits(y))\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\artist.py:278\u001B[39m, in \u001B[36mArtist.convert_xunits\u001B[39m\u001B[34m(self, x)\u001B[39m\n\u001B[32m    276\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m ax \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mor\u001B[39;00m ax.xaxis \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m    277\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m x\n\u001B[32m--> \u001B[39m\u001B[32m278\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m ax.xaxis.convert_units(x)\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\axis.py:1802\u001B[39m, in \u001B[36mAxis.convert_units\u001B[39m\u001B[34m(self, x)\u001B[39m\n\u001B[32m   1800\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mconvert_units\u001B[39m(\u001B[38;5;28mself\u001B[39m, x):\n\u001B[32m   1801\u001B[39m     \u001B[38;5;66;03m# If x is natively supported by Matplotlib, doesn't need converting\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m1802\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m munits._is_natively_supported(x):\n\u001B[32m   1803\u001B[39m         \u001B[38;5;28;01mreturn\u001B[39;00m x\n\u001B[32m   1805\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m._converter \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\units.py:64\u001B[39m, in \u001B[36m_is_natively_supported\u001B[39m\u001B[34m(x)\u001B[39m\n\u001B[32m     61\u001B[39m \u001B[38;5;66;03m# Matplotlib natively supports all number types except Decimal.\u001B[39;00m\n\u001B[32m     62\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m np.iterable(x):\n\u001B[32m     63\u001B[39m     \u001B[38;5;66;03m# Assume lists are homogeneous as other functions in unit system.\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m64\u001B[39m     \u001B[38;5;28;01mfor\u001B[39;00m thisx \u001B[38;5;129;01min\u001B[39;00m x:\n\u001B[32m     65\u001B[39m         \u001B[38;5;28;01mif\u001B[39;00m thisx \u001B[38;5;129;01mis\u001B[39;00m ma.masked:\n\u001B[32m     66\u001B[39m             \u001B[38;5;28;01mcontinue\u001B[39;00m\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\saiunit\\_base_quantity.py:1195\u001B[39m, in \u001B[36mQuantity.__iter__\u001B[39m\u001B[34m(self)\u001B[39m\n\u001B[32m   1186\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"Solve the issue of DeviceArray.__iter__.\u001B[39;00m\n\u001B[32m   1187\u001B[39m \n\u001B[32m   1188\u001B[39m \u001B[33;03mDetails please see JAX issues:\u001B[39;00m\n\u001B[32m   (...)\u001B[39m\u001B[32m   1191\u001B[39m \u001B[33;03m- https://github.com/google/jax/pull/3821\u001B[39;00m\n\u001B[32m   1192\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m   1194\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m.ndim == \u001B[32m0\u001B[39m:\n\u001B[32m-> \u001B[39m\u001B[32m1195\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m(\u001B[33m\"\u001B[39m\u001B[33miteration over a 0-d Quantity is not allowed\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m   1196\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(\u001B[38;5;28mself\u001B[39m.shape[\u001B[32m0\u001B[39m]):\n\u001B[32m   1197\u001B[39m     \u001B[38;5;28;01myield\u001B[39;00m Quantity(\u001B[38;5;28mself\u001B[39m.mantissa[i], unit=\u001B[38;5;28mself\u001B[39m.unit)\n",
      "\u001B[31mTypeError\u001B[39m: iteration over a 0-d Quantity is not allowed"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error in callback <function _draw_all_if_interactive at 0x00000205F9EF1B20> (for post_execute), with arguments args (),kwargs {}:\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "iteration over a 0-d Quantity is not allowed",
     "output_type": "error",
     "traceback": [
      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
      "\u001B[31mTypeError\u001B[39m                                 Traceback (most recent call last)",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\pyplot.py:278\u001B[39m, in \u001B[36m_draw_all_if_interactive\u001B[39m\u001B[34m()\u001B[39m\n\u001B[32m    276\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34m_draw_all_if_interactive\u001B[39m() -> \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m    277\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m matplotlib.is_interactive():\n\u001B[32m--> \u001B[39m\u001B[32m278\u001B[39m         draw_all()\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\_pylab_helpers.py:131\u001B[39m, in \u001B[36mGcf.draw_all\u001B[39m\u001B[34m(cls, force)\u001B[39m\n\u001B[32m    129\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m manager \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mcls\u001B[39m.get_all_fig_managers():\n\u001B[32m    130\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m force \u001B[38;5;129;01mor\u001B[39;00m manager.canvas.figure.stale:\n\u001B[32m--> \u001B[39m\u001B[32m131\u001B[39m         manager.canvas.draw_idle()\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\backend_bases.py:1893\u001B[39m, in \u001B[36mFigureCanvasBase.draw_idle\u001B[39m\u001B[34m(self, *args, **kwargs)\u001B[39m\n\u001B[32m   1891\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m._is_idle_drawing:\n\u001B[32m   1892\u001B[39m     \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28mself\u001B[39m._idle_draw_cntx():\n\u001B[32m-> \u001B[39m\u001B[32m1893\u001B[39m         \u001B[38;5;28mself\u001B[39m.draw(*args, **kwargs)\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\backends\\backend_agg.py:382\u001B[39m, in \u001B[36mFigureCanvasAgg.draw\u001B[39m\u001B[34m(self)\u001B[39m\n\u001B[32m    379\u001B[39m \u001B[38;5;66;03m# Acquire a lock on the shared font cache.\u001B[39;00m\n\u001B[32m    380\u001B[39m \u001B[38;5;28;01mwith\u001B[39;00m (\u001B[38;5;28mself\u001B[39m.toolbar._wait_cursor_for_draw_cm() \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m.toolbar\n\u001B[32m    381\u001B[39m       \u001B[38;5;28;01melse\u001B[39;00m nullcontext()):\n\u001B[32m--> \u001B[39m\u001B[32m382\u001B[39m     \u001B[38;5;28mself\u001B[39m.figure.draw(\u001B[38;5;28mself\u001B[39m.renderer)\n\u001B[32m    383\u001B[39m     \u001B[38;5;66;03m# A GUI class may be need to update a window using this draw, so\u001B[39;00m\n\u001B[32m    384\u001B[39m     \u001B[38;5;66;03m# don't forget to call the superclass.\u001B[39;00m\n\u001B[32m    385\u001B[39m     \u001B[38;5;28msuper\u001B[39m().draw()\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\artist.py:94\u001B[39m, in \u001B[36m_finalize_rasterization.<locals>.draw_wrapper\u001B[39m\u001B[34m(artist, renderer, *args, **kwargs)\u001B[39m\n\u001B[32m     92\u001B[39m \u001B[38;5;129m@wraps\u001B[39m(draw)\n\u001B[32m     93\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mdraw_wrapper\u001B[39m(artist, renderer, *args, **kwargs):\n\u001B[32m---> \u001B[39m\u001B[32m94\u001B[39m     result = draw(artist, renderer, *args, **kwargs)\n\u001B[32m     95\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m renderer._rasterizing:\n\u001B[32m     96\u001B[39m         renderer.stop_rasterizing()\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\artist.py:71\u001B[39m, in \u001B[36mallow_rasterization.<locals>.draw_wrapper\u001B[39m\u001B[34m(artist, renderer)\u001B[39m\n\u001B[32m     68\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m artist.get_agg_filter() \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m     69\u001B[39m         renderer.start_filter()\n\u001B[32m---> \u001B[39m\u001B[32m71\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m draw(artist, renderer)\n\u001B[32m     72\u001B[39m \u001B[38;5;28;01mfinally\u001B[39;00m:\n\u001B[32m     73\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m artist.get_agg_filter() \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\figure.py:3257\u001B[39m, in \u001B[36mFigure.draw\u001B[39m\u001B[34m(self, renderer)\u001B[39m\n\u001B[32m   3254\u001B[39m             \u001B[38;5;66;03m# ValueError can occur when resizing a window.\u001B[39;00m\n\u001B[32m   3256\u001B[39m     \u001B[38;5;28mself\u001B[39m.patch.draw(renderer)\n\u001B[32m-> \u001B[39m\u001B[32m3257\u001B[39m     mimage._draw_list_compositing_images(\n\u001B[32m   3258\u001B[39m         renderer, \u001B[38;5;28mself\u001B[39m, artists, \u001B[38;5;28mself\u001B[39m.suppressComposite)\n\u001B[32m   3260\u001B[39m     renderer.close_group(\u001B[33m'\u001B[39m\u001B[33mfigure\u001B[39m\u001B[33m'\u001B[39m)\n\u001B[32m   3261\u001B[39m \u001B[38;5;28;01mfinally\u001B[39;00m:\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\image.py:134\u001B[39m, in \u001B[36m_draw_list_compositing_images\u001B[39m\u001B[34m(renderer, parent, artists, suppress_composite)\u001B[39m\n\u001B[32m    132\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m not_composite \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m has_images:\n\u001B[32m    133\u001B[39m     \u001B[38;5;28;01mfor\u001B[39;00m a \u001B[38;5;129;01min\u001B[39;00m artists:\n\u001B[32m--> \u001B[39m\u001B[32m134\u001B[39m         a.draw(renderer)\n\u001B[32m    135\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m    136\u001B[39m     \u001B[38;5;66;03m# Composite any adjacent images together\u001B[39;00m\n\u001B[32m    137\u001B[39m     image_group = []\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\artist.py:71\u001B[39m, in \u001B[36mallow_rasterization.<locals>.draw_wrapper\u001B[39m\u001B[34m(artist, renderer)\u001B[39m\n\u001B[32m     68\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m artist.get_agg_filter() \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m     69\u001B[39m         renderer.start_filter()\n\u001B[32m---> \u001B[39m\u001B[32m71\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m draw(artist, renderer)\n\u001B[32m     72\u001B[39m \u001B[38;5;28;01mfinally\u001B[39;00m:\n\u001B[32m     73\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m artist.get_agg_filter() \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\axes\\_base.py:3226\u001B[39m, in \u001B[36m_AxesBase.draw\u001B[39m\u001B[34m(self, renderer)\u001B[39m\n\u001B[32m   3223\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m artists_rasterized:\n\u001B[32m   3224\u001B[39m     _draw_rasterized(\u001B[38;5;28mself\u001B[39m.get_figure(root=\u001B[38;5;28;01mTrue\u001B[39;00m), artists_rasterized, renderer)\n\u001B[32m-> \u001B[39m\u001B[32m3226\u001B[39m mimage._draw_list_compositing_images(\n\u001B[32m   3227\u001B[39m     renderer, \u001B[38;5;28mself\u001B[39m, artists, \u001B[38;5;28mself\u001B[39m.get_figure(root=\u001B[38;5;28;01mTrue\u001B[39;00m).suppressComposite)\n\u001B[32m   3229\u001B[39m renderer.close_group(\u001B[33m'\u001B[39m\u001B[33maxes\u001B[39m\u001B[33m'\u001B[39m)\n\u001B[32m   3230\u001B[39m \u001B[38;5;28mself\u001B[39m.stale = \u001B[38;5;28;01mFalse\u001B[39;00m\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\image.py:134\u001B[39m, in \u001B[36m_draw_list_compositing_images\u001B[39m\u001B[34m(renderer, parent, artists, suppress_composite)\u001B[39m\n\u001B[32m    132\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m not_composite \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m has_images:\n\u001B[32m    133\u001B[39m     \u001B[38;5;28;01mfor\u001B[39;00m a \u001B[38;5;129;01min\u001B[39;00m artists:\n\u001B[32m--> \u001B[39m\u001B[32m134\u001B[39m         a.draw(renderer)\n\u001B[32m    135\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m    136\u001B[39m     \u001B[38;5;66;03m# Composite any adjacent images together\u001B[39;00m\n\u001B[32m    137\u001B[39m     image_group = []\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\artist.py:71\u001B[39m, in \u001B[36mallow_rasterization.<locals>.draw_wrapper\u001B[39m\u001B[34m(artist, renderer)\u001B[39m\n\u001B[32m     68\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m artist.get_agg_filter() \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m     69\u001B[39m         renderer.start_filter()\n\u001B[32m---> \u001B[39m\u001B[32m71\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m draw(artist, renderer)\n\u001B[32m     72\u001B[39m \u001B[38;5;28;01mfinally\u001B[39;00m:\n\u001B[32m     73\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m artist.get_agg_filter() \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\text.py:1990\u001B[39m, in \u001B[36mAnnotation.draw\u001B[39m\u001B[34m(self, renderer)\u001B[39m\n\u001B[32m   1988\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m renderer \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m   1989\u001B[39m     \u001B[38;5;28mself\u001B[39m._renderer = renderer\n\u001B[32m-> \u001B[39m\u001B[32m1990\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m.get_visible() \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m._check_xy(renderer):\n\u001B[32m   1991\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m\n\u001B[32m   1992\u001B[39m \u001B[38;5;66;03m# Update text positions before `Text.draw` would, so that the\u001B[39;00m\n\u001B[32m   1993\u001B[39m \u001B[38;5;66;03m# FancyArrowPatch is correctly positioned.\u001B[39;00m\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\text.py:1590\u001B[39m, in \u001B[36m_AnnotationBase._check_xy\u001B[39m\u001B[34m(self, renderer)\u001B[39m\n\u001B[32m   1587\u001B[39m b = \u001B[38;5;28mself\u001B[39m.get_annotation_clip()\n\u001B[32m   1588\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m b \u001B[38;5;129;01mor\u001B[39;00m (b \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mself\u001B[39m.xycoords == \u001B[33m\"\u001B[39m\u001B[33mdata\u001B[39m\u001B[33m\"\u001B[39m):\n\u001B[32m   1589\u001B[39m     \u001B[38;5;66;03m# check if self.xy is inside the Axes.\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m1590\u001B[39m     xy_pixel = \u001B[38;5;28mself\u001B[39m._get_position_xy(renderer)\n\u001B[32m   1591\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m.axes.contains_point(xy_pixel)\n\u001B[32m   1592\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;01mTrue\u001B[39;00m\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\text.py:1581\u001B[39m, in \u001B[36m_AnnotationBase._get_position_xy\u001B[39m\u001B[34m(self, renderer)\u001B[39m\n\u001B[32m   1579\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34m_get_position_xy\u001B[39m(\u001B[38;5;28mself\u001B[39m, renderer):\n\u001B[32m   1580\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"Return the pixel position of the annotated point.\"\"\"\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m1581\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._get_xy(renderer, \u001B[38;5;28mself\u001B[39m.xy, \u001B[38;5;28mself\u001B[39m.xycoords)\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\text.py:1475\u001B[39m, in \u001B[36m_AnnotationBase._get_xy\u001B[39m\u001B[34m(self, renderer, xy, coords)\u001B[39m\n\u001B[32m   1473\u001B[39m xcoord, ycoord = coords \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(coords, \u001B[38;5;28mtuple\u001B[39m) \u001B[38;5;28;01melse\u001B[39;00m (coords, coords)\n\u001B[32m   1474\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m xcoord == \u001B[33m'\u001B[39m\u001B[33mdata\u001B[39m\u001B[33m'\u001B[39m:\n\u001B[32m-> \u001B[39m\u001B[32m1475\u001B[39m     x = \u001B[38;5;28mfloat\u001B[39m(\u001B[38;5;28mself\u001B[39m.convert_xunits(x))\n\u001B[32m   1476\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m ycoord == \u001B[33m'\u001B[39m\u001B[33mdata\u001B[39m\u001B[33m'\u001B[39m:\n\u001B[32m   1477\u001B[39m     y = \u001B[38;5;28mfloat\u001B[39m(\u001B[38;5;28mself\u001B[39m.convert_yunits(y))\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\artist.py:278\u001B[39m, in \u001B[36mArtist.convert_xunits\u001B[39m\u001B[34m(self, x)\u001B[39m\n\u001B[32m    276\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m ax \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mor\u001B[39;00m ax.xaxis \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m    277\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m x\n\u001B[32m--> \u001B[39m\u001B[32m278\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m ax.xaxis.convert_units(x)\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\axis.py:1802\u001B[39m, in \u001B[36mAxis.convert_units\u001B[39m\u001B[34m(self, x)\u001B[39m\n\u001B[32m   1800\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mconvert_units\u001B[39m(\u001B[38;5;28mself\u001B[39m, x):\n\u001B[32m   1801\u001B[39m     \u001B[38;5;66;03m# If x is natively supported by Matplotlib, doesn't need converting\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m1802\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m munits._is_natively_supported(x):\n\u001B[32m   1803\u001B[39m         \u001B[38;5;28;01mreturn\u001B[39;00m x\n\u001B[32m   1805\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m._converter \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\units.py:64\u001B[39m, in \u001B[36m_is_natively_supported\u001B[39m\u001B[34m(x)\u001B[39m\n\u001B[32m     61\u001B[39m \u001B[38;5;66;03m# Matplotlib natively supports all number types except Decimal.\u001B[39;00m\n\u001B[32m     62\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m np.iterable(x):\n\u001B[32m     63\u001B[39m     \u001B[38;5;66;03m# Assume lists are homogeneous as other functions in unit system.\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m64\u001B[39m     \u001B[38;5;28;01mfor\u001B[39;00m thisx \u001B[38;5;129;01min\u001B[39;00m x:\n\u001B[32m     65\u001B[39m         \u001B[38;5;28;01mif\u001B[39;00m thisx \u001B[38;5;129;01mis\u001B[39;00m ma.masked:\n\u001B[32m     66\u001B[39m             \u001B[38;5;28;01mcontinue\u001B[39;00m\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\saiunit\\_base_quantity.py:1195\u001B[39m, in \u001B[36mQuantity.__iter__\u001B[39m\u001B[34m(self)\u001B[39m\n\u001B[32m   1186\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"Solve the issue of DeviceArray.__iter__.\u001B[39;00m\n\u001B[32m   1187\u001B[39m \n\u001B[32m   1188\u001B[39m \u001B[33;03mDetails please see JAX issues:\u001B[39;00m\n\u001B[32m   (...)\u001B[39m\u001B[32m   1191\u001B[39m \u001B[33;03m- https://github.com/google/jax/pull/3821\u001B[39;00m\n\u001B[32m   1192\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m   1194\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m.ndim == \u001B[32m0\u001B[39m:\n\u001B[32m-> \u001B[39m\u001B[32m1195\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m(\u001B[33m\"\u001B[39m\u001B[33miteration over a 0-d Quantity is not allowed\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m   1196\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(\u001B[38;5;28mself\u001B[39m.shape[\u001B[32m0\u001B[39m]):\n\u001B[32m   1197\u001B[39m     \u001B[38;5;28;01myield\u001B[39;00m Quantity(\u001B[38;5;28mself\u001B[39m.mantissa[i], unit=\u001B[38;5;28mself\u001B[39m.unit)\n",
      "\u001B[31mTypeError\u001B[39m: iteration over a 0-d Quantity is not allowed"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "iteration over a 0-d Quantity is not allowed",
     "output_type": "error",
     "traceback": [
      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
      "\u001B[31mTypeError\u001B[39m                                 Traceback (most recent call last)",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\IPython\\core\\formatters.py:402\u001B[39m, in \u001B[36mBaseFormatter.__call__\u001B[39m\u001B[34m(self, obj)\u001B[39m\n\u001B[32m    400\u001B[39m     \u001B[38;5;28;01mpass\u001B[39;00m\n\u001B[32m    401\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m402\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m printer(obj)\n\u001B[32m    403\u001B[39m \u001B[38;5;66;03m# Finally look for special method names\u001B[39;00m\n\u001B[32m    404\u001B[39m method = get_real_method(obj, \u001B[38;5;28mself\u001B[39m.print_method)\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170\u001B[39m, in \u001B[36mprint_figure\u001B[39m\u001B[34m(fig, fmt, bbox_inches, base64, **kwargs)\u001B[39m\n\u001B[32m    167\u001B[39m     \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mmatplotlib\u001B[39;00m\u001B[34;01m.\u001B[39;00m\u001B[34;01mbackend_bases\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mimport\u001B[39;00m FigureCanvasBase\n\u001B[32m    168\u001B[39m     FigureCanvasBase(fig)\n\u001B[32m--> \u001B[39m\u001B[32m170\u001B[39m fig.canvas.print_figure(bytes_io, **kw)\n\u001B[32m    171\u001B[39m data = bytes_io.getvalue()\n\u001B[32m    172\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m fmt == \u001B[33m'\u001B[39m\u001B[33msvg\u001B[39m\u001B[33m'\u001B[39m:\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\backend_bases.py:2157\u001B[39m, in \u001B[36mFigureCanvasBase.print_figure\u001B[39m\u001B[34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001B[39m\n\u001B[32m   2154\u001B[39m     \u001B[38;5;66;03m# we do this instead of `self.figure.draw_without_rendering`\u001B[39;00m\n\u001B[32m   2155\u001B[39m     \u001B[38;5;66;03m# so that we can inject the orientation\u001B[39;00m\n\u001B[32m   2156\u001B[39m     \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28mgetattr\u001B[39m(renderer, \u001B[33m\"\u001B[39m\u001B[33m_draw_disabled\u001B[39m\u001B[33m\"\u001B[39m, nullcontext)():\n\u001B[32m-> \u001B[39m\u001B[32m2157\u001B[39m         \u001B[38;5;28mself\u001B[39m.figure.draw(renderer)\n\u001B[32m   2158\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m bbox_inches:\n\u001B[32m   2159\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m bbox_inches == \u001B[33m\"\u001B[39m\u001B[33mtight\u001B[39m\u001B[33m\"\u001B[39m:\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\artist.py:94\u001B[39m, in \u001B[36m_finalize_rasterization.<locals>.draw_wrapper\u001B[39m\u001B[34m(artist, renderer, *args, **kwargs)\u001B[39m\n\u001B[32m     92\u001B[39m \u001B[38;5;129m@wraps\u001B[39m(draw)\n\u001B[32m     93\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mdraw_wrapper\u001B[39m(artist, renderer, *args, **kwargs):\n\u001B[32m---> \u001B[39m\u001B[32m94\u001B[39m     result = draw(artist, renderer, *args, **kwargs)\n\u001B[32m     95\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m renderer._rasterizing:\n\u001B[32m     96\u001B[39m         renderer.stop_rasterizing()\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\artist.py:71\u001B[39m, in \u001B[36mallow_rasterization.<locals>.draw_wrapper\u001B[39m\u001B[34m(artist, renderer)\u001B[39m\n\u001B[32m     68\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m artist.get_agg_filter() \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m     69\u001B[39m         renderer.start_filter()\n\u001B[32m---> \u001B[39m\u001B[32m71\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m draw(artist, renderer)\n\u001B[32m     72\u001B[39m \u001B[38;5;28;01mfinally\u001B[39;00m:\n\u001B[32m     73\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m artist.get_agg_filter() \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\figure.py:3257\u001B[39m, in \u001B[36mFigure.draw\u001B[39m\u001B[34m(self, renderer)\u001B[39m\n\u001B[32m   3254\u001B[39m             \u001B[38;5;66;03m# ValueError can occur when resizing a window.\u001B[39;00m\n\u001B[32m   3256\u001B[39m     \u001B[38;5;28mself\u001B[39m.patch.draw(renderer)\n\u001B[32m-> \u001B[39m\u001B[32m3257\u001B[39m     mimage._draw_list_compositing_images(\n\u001B[32m   3258\u001B[39m         renderer, \u001B[38;5;28mself\u001B[39m, artists, \u001B[38;5;28mself\u001B[39m.suppressComposite)\n\u001B[32m   3260\u001B[39m     renderer.close_group(\u001B[33m'\u001B[39m\u001B[33mfigure\u001B[39m\u001B[33m'\u001B[39m)\n\u001B[32m   3261\u001B[39m \u001B[38;5;28;01mfinally\u001B[39;00m:\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\image.py:134\u001B[39m, in \u001B[36m_draw_list_compositing_images\u001B[39m\u001B[34m(renderer, parent, artists, suppress_composite)\u001B[39m\n\u001B[32m    132\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m not_composite \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m has_images:\n\u001B[32m    133\u001B[39m     \u001B[38;5;28;01mfor\u001B[39;00m a \u001B[38;5;129;01min\u001B[39;00m artists:\n\u001B[32m--> \u001B[39m\u001B[32m134\u001B[39m         a.draw(renderer)\n\u001B[32m    135\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m    136\u001B[39m     \u001B[38;5;66;03m# Composite any adjacent images together\u001B[39;00m\n\u001B[32m    137\u001B[39m     image_group = []\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\artist.py:71\u001B[39m, in \u001B[36mallow_rasterization.<locals>.draw_wrapper\u001B[39m\u001B[34m(artist, renderer)\u001B[39m\n\u001B[32m     68\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m artist.get_agg_filter() \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m     69\u001B[39m         renderer.start_filter()\n\u001B[32m---> \u001B[39m\u001B[32m71\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m draw(artist, renderer)\n\u001B[32m     72\u001B[39m \u001B[38;5;28;01mfinally\u001B[39;00m:\n\u001B[32m     73\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m artist.get_agg_filter() \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\axes\\_base.py:3226\u001B[39m, in \u001B[36m_AxesBase.draw\u001B[39m\u001B[34m(self, renderer)\u001B[39m\n\u001B[32m   3223\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m artists_rasterized:\n\u001B[32m   3224\u001B[39m     _draw_rasterized(\u001B[38;5;28mself\u001B[39m.get_figure(root=\u001B[38;5;28;01mTrue\u001B[39;00m), artists_rasterized, renderer)\n\u001B[32m-> \u001B[39m\u001B[32m3226\u001B[39m mimage._draw_list_compositing_images(\n\u001B[32m   3227\u001B[39m     renderer, \u001B[38;5;28mself\u001B[39m, artists, \u001B[38;5;28mself\u001B[39m.get_figure(root=\u001B[38;5;28;01mTrue\u001B[39;00m).suppressComposite)\n\u001B[32m   3229\u001B[39m renderer.close_group(\u001B[33m'\u001B[39m\u001B[33maxes\u001B[39m\u001B[33m'\u001B[39m)\n\u001B[32m   3230\u001B[39m \u001B[38;5;28mself\u001B[39m.stale = \u001B[38;5;28;01mFalse\u001B[39;00m\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\image.py:134\u001B[39m, in \u001B[36m_draw_list_compositing_images\u001B[39m\u001B[34m(renderer, parent, artists, suppress_composite)\u001B[39m\n\u001B[32m    132\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m not_composite \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m has_images:\n\u001B[32m    133\u001B[39m     \u001B[38;5;28;01mfor\u001B[39;00m a \u001B[38;5;129;01min\u001B[39;00m artists:\n\u001B[32m--> \u001B[39m\u001B[32m134\u001B[39m         a.draw(renderer)\n\u001B[32m    135\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m    136\u001B[39m     \u001B[38;5;66;03m# Composite any adjacent images together\u001B[39;00m\n\u001B[32m    137\u001B[39m     image_group = []\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\artist.py:71\u001B[39m, in \u001B[36mallow_rasterization.<locals>.draw_wrapper\u001B[39m\u001B[34m(artist, renderer)\u001B[39m\n\u001B[32m     68\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m artist.get_agg_filter() \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m     69\u001B[39m         renderer.start_filter()\n\u001B[32m---> \u001B[39m\u001B[32m71\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m draw(artist, renderer)\n\u001B[32m     72\u001B[39m \u001B[38;5;28;01mfinally\u001B[39;00m:\n\u001B[32m     73\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m artist.get_agg_filter() \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\text.py:1990\u001B[39m, in \u001B[36mAnnotation.draw\u001B[39m\u001B[34m(self, renderer)\u001B[39m\n\u001B[32m   1988\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m renderer \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m   1989\u001B[39m     \u001B[38;5;28mself\u001B[39m._renderer = renderer\n\u001B[32m-> \u001B[39m\u001B[32m1990\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m.get_visible() \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m._check_xy(renderer):\n\u001B[32m   1991\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m\n\u001B[32m   1992\u001B[39m \u001B[38;5;66;03m# Update text positions before `Text.draw` would, so that the\u001B[39;00m\n\u001B[32m   1993\u001B[39m \u001B[38;5;66;03m# FancyArrowPatch is correctly positioned.\u001B[39;00m\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\text.py:1590\u001B[39m, in \u001B[36m_AnnotationBase._check_xy\u001B[39m\u001B[34m(self, renderer)\u001B[39m\n\u001B[32m   1587\u001B[39m b = \u001B[38;5;28mself\u001B[39m.get_annotation_clip()\n\u001B[32m   1588\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m b \u001B[38;5;129;01mor\u001B[39;00m (b \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mself\u001B[39m.xycoords == \u001B[33m\"\u001B[39m\u001B[33mdata\u001B[39m\u001B[33m\"\u001B[39m):\n\u001B[32m   1589\u001B[39m     \u001B[38;5;66;03m# check if self.xy is inside the Axes.\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m1590\u001B[39m     xy_pixel = \u001B[38;5;28mself\u001B[39m._get_position_xy(renderer)\n\u001B[32m   1591\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m.axes.contains_point(xy_pixel)\n\u001B[32m   1592\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;01mTrue\u001B[39;00m\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\text.py:1581\u001B[39m, in \u001B[36m_AnnotationBase._get_position_xy\u001B[39m\u001B[34m(self, renderer)\u001B[39m\n\u001B[32m   1579\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34m_get_position_xy\u001B[39m(\u001B[38;5;28mself\u001B[39m, renderer):\n\u001B[32m   1580\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"Return the pixel position of the annotated point.\"\"\"\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m1581\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._get_xy(renderer, \u001B[38;5;28mself\u001B[39m.xy, \u001B[38;5;28mself\u001B[39m.xycoords)\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\text.py:1475\u001B[39m, in \u001B[36m_AnnotationBase._get_xy\u001B[39m\u001B[34m(self, renderer, xy, coords)\u001B[39m\n\u001B[32m   1473\u001B[39m xcoord, ycoord = coords \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(coords, \u001B[38;5;28mtuple\u001B[39m) \u001B[38;5;28;01melse\u001B[39;00m (coords, coords)\n\u001B[32m   1474\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m xcoord == \u001B[33m'\u001B[39m\u001B[33mdata\u001B[39m\u001B[33m'\u001B[39m:\n\u001B[32m-> \u001B[39m\u001B[32m1475\u001B[39m     x = \u001B[38;5;28mfloat\u001B[39m(\u001B[38;5;28mself\u001B[39m.convert_xunits(x))\n\u001B[32m   1476\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m ycoord == \u001B[33m'\u001B[39m\u001B[33mdata\u001B[39m\u001B[33m'\u001B[39m:\n\u001B[32m   1477\u001B[39m     y = \u001B[38;5;28mfloat\u001B[39m(\u001B[38;5;28mself\u001B[39m.convert_yunits(y))\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\artist.py:278\u001B[39m, in \u001B[36mArtist.convert_xunits\u001B[39m\u001B[34m(self, x)\u001B[39m\n\u001B[32m    276\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m ax \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mor\u001B[39;00m ax.xaxis \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m    277\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m x\n\u001B[32m--> \u001B[39m\u001B[32m278\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m ax.xaxis.convert_units(x)\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\axis.py:1802\u001B[39m, in \u001B[36mAxis.convert_units\u001B[39m\u001B[34m(self, x)\u001B[39m\n\u001B[32m   1800\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mconvert_units\u001B[39m(\u001B[38;5;28mself\u001B[39m, x):\n\u001B[32m   1801\u001B[39m     \u001B[38;5;66;03m# If x is natively supported by Matplotlib, doesn't need converting\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m1802\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m munits._is_natively_supported(x):\n\u001B[32m   1803\u001B[39m         \u001B[38;5;28;01mreturn\u001B[39;00m x\n\u001B[32m   1805\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m._converter \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\matplotlib\\units.py:64\u001B[39m, in \u001B[36m_is_natively_supported\u001B[39m\u001B[34m(x)\u001B[39m\n\u001B[32m     61\u001B[39m \u001B[38;5;66;03m# Matplotlib natively supports all number types except Decimal.\u001B[39;00m\n\u001B[32m     62\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m np.iterable(x):\n\u001B[32m     63\u001B[39m     \u001B[38;5;66;03m# Assume lists are homogeneous as other functions in unit system.\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m64\u001B[39m     \u001B[38;5;28;01mfor\u001B[39;00m thisx \u001B[38;5;129;01min\u001B[39;00m x:\n\u001B[32m     65\u001B[39m         \u001B[38;5;28;01mif\u001B[39;00m thisx \u001B[38;5;129;01mis\u001B[39;00m ma.masked:\n\u001B[32m     66\u001B[39m             \u001B[38;5;28;01mcontinue\u001B[39;00m\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~\\miniconda3\\envs\\brainx\\Lib\\site-packages\\saiunit\\_base_quantity.py:1195\u001B[39m, in \u001B[36mQuantity.__iter__\u001B[39m\u001B[34m(self)\u001B[39m\n\u001B[32m   1186\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"Solve the issue of DeviceArray.__iter__.\u001B[39;00m\n\u001B[32m   1187\u001B[39m \n\u001B[32m   1188\u001B[39m \u001B[33;03mDetails please see JAX issues:\u001B[39;00m\n\u001B[32m   (...)\u001B[39m\u001B[32m   1191\u001B[39m \u001B[33;03m- https://github.com/google/jax/pull/3821\u001B[39;00m\n\u001B[32m   1192\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m   1194\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m.ndim == \u001B[32m0\u001B[39m:\n\u001B[32m-> \u001B[39m\u001B[32m1195\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m(\u001B[33m\"\u001B[39m\u001B[33miteration over a 0-d Quantity is not allowed\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m   1196\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(\u001B[38;5;28mself\u001B[39m.shape[\u001B[32m0\u001B[39m]):\n\u001B[32m   1197\u001B[39m     \u001B[38;5;28;01myield\u001B[39;00m Quantity(\u001B[38;5;28mself\u001B[39m.mantissa[i], unit=\u001B[38;5;28mself\u001B[39m.unit)\n",
      "\u001B[31mTypeError\u001B[39m: iteration over a 0-d Quantity is not allowed"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 10
  },
  {
   "cell_type": "code",
   "id": "relaxation-detail",
   "metadata": {},
   "source": [
    "# High mu relaxation oscillator\n",
    "model = brainmass.VanDerPolStep(1, mu=5.0)\n",
    "brainstate.nn.init_all_states(model)\n",
    "model.x.value = np.array([0.5])\n",
    "model.y.value = np.array([0.0])\n",
    "\n",
    "\n",
    "def step(i):\n",
    "    with brainstate.environ.context(i=i, t=i * brainstate.environ.get_dt()):\n",
    "        model.update()\n",
    "        return model.x.value, model.y.value\n",
    "\n",
    "\n",
    "x, y = brainstate.transform.for_loop(step, np.arange(50000))\n",
    "t = np.arange(50000) * brainstate.environ.get_dt()\n",
    "\n",
    "# Focus on steady state\n",
    "x_ss, y_ss, t_ss = x[-20000:, 0], y[-20000:, 0], t[-20000:]\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
    "\n",
    "# Time series with annotations\n",
    "axes[0].plot(t_ss, x_ss, 'b-', linewidth=1)\n",
    "axes[0].set_xlabel('Time (ms)')\n",
    "axes[0].set_ylabel('x(t)')\n",
    "axes[0].set_title('Relaxation Oscillation ($\\mu=5$)')\n",
    "\n",
    "# Annotate phases\n",
    "axes[0].annotate('Fast jump', xy=(t_ss[2000], 1.5), fontsize=10, color='red')\n",
    "axes[0].annotate('Slow phase', xy=(t_ss[8000], 1.8), fontsize=10, color='green')\n",
    "\n",
    "# Phase portrait showing slow-fast structure\n",
    "axes[1].plot(x_ss, y_ss, 'b-', linewidth=1, alpha=0.7)\n",
    "\n",
    "# Nullcline (cubic)\n",
    "x_null = np.linspace(-2.5, 2.5, 200)\n",
    "y_null = x_null - x_null ** 3 / 3\n",
    "axes[1].plot(x_null, y_null, 'r-', linewidth=3, label='Slow manifold (x-nullcline)')\n",
    "\n",
    "# Mark regions\n",
    "axes[1].fill_between(x_null[x_null > 1], y_null[x_null > 1], 5, alpha=0.2, color='green', label='Slow (right branch)')\n",
    "axes[1].fill_between(x_null[x_null < -1], y_null[x_null < -1], -5, alpha=0.2, color='green')\n",
    "\n",
    "axes[1].set_xlabel('x')\n",
    "axes[1].set_ylabel('y')\n",
    "axes[1].set_title('Slow-Fast Dynamics in Phase Space')\n",
    "axes[1].legend(loc='upper right')\n",
    "axes[1].set_xlim([-3, 3])\n",
    "axes[1].set_ylim([-2, 2])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "id": "period-analysis",
   "metadata": {},
   "source": [
    "# Measure periods for different mu\n",
    "mu_range = np.array([0.5, 1.0, 2.0, 3.0, 4.0, 5.0])\n",
    "periods = []\n",
    "dt = brainstate.environ.get_dt() / u.ms\n",
    "\n",
    "for mu_val in mu_range:\n",
    "    x, y = simulate(mu_val, 100000)\n",
    "\n",
    "    # Find period from zero crossings\n",
    "    x_ss = x[-50000:, 0]\n",
    "    crossings = np.where(np.diff(np.sign(x_ss)) > 0)[0]\n",
    "    if len(crossings) > 2:\n",
    "        period = np.mean(np.diff(crossings)) * dt\n",
    "        periods.append(period)\n",
    "    else:\n",
    "        periods.append(np.nan)\n",
    "\n",
    "periods = np.array(periods)\n",
    "\n",
    "# Plot period vs mu\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.plot(mu_range, periods, 'bo-', markersize=8, linewidth=2, label='Measured')\n",
    "\n",
    "# Theoretical approximation for large mu: T ~ (3 - 2*log(2)) * mu ~ 1.614 * mu\n",
    "mu_theory = np.linspace(0.5, 5, 50)\n",
    "T_theory = (3 - 2 * np.log(2)) * mu_theory\n",
    "plt.plot(mu_theory, T_theory, 'r--', linewidth=2, label=r'Theory: $T \\approx 1.614 \\mu$ (large $\\mu$)')\n",
    "\n",
    "plt.xlabel('$\\mu$', fontsize=12)\n",
    "plt.ylabel('Period (ms)', fontsize=12)\n",
    "plt.title('Oscillation Period vs Nonlinearity Parameter')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "id": "period-sweep",
   "metadata": {},
   "source": [
    "# Measure periods for different mu\n",
    "mu_range = np.array([0.5, 1.0, 2.0, 3.0, 4.0, 5.0])\n",
    "periods = []\n",
    "\n",
    "for mu_val in mu_range:\n",
    "    model = brainmass.VanDerPolStep(1, mu=mu_val)\n",
    "    brainstate.nn.init_all_states(model)\n",
    "    model.x.value = np.array([0.5])\n",
    "\n",
    "\n",
    "    def step(i):\n",
    "        with brainstate.environ.context(i=i, t=i * brainstate.environ.get_dt()):\n",
    "            model.update()\n",
    "            return model.x.value\n",
    "\n",
    "\n",
    "    x = brainstate.transform.for_loop(step, np.arange(100000))\n",
    "\n",
    "    # Find period from zero crossings\n",
    "    x_ss = x[-50000:, 0]\n",
    "    crossings = np.where(np.diff(np.sign(x_ss)) > 0)[0]\n",
    "    if len(crossings) > 2:\n",
    "        period = np.mean(np.diff(crossings)) * float(brainstate.environ.get_dt() / u.ms)\n",
    "        periods.append(period)\n",
    "    else:\n",
    "        periods.append(np.nan)\n",
    "\n",
    "periods = np.array(periods)\n",
    "\n",
    "# Plot period vs mu\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.plot(mu_range, periods, 'bo-', markersize=8, linewidth=2, label='Measured')\n",
    "\n",
    "# Theoretical approximation for large mu: T ~ (3 - 2*log(2)) * mu ~ 1.614 * mu\n",
    "mu_theory = np.linspace(0.5, 5, 50)\n",
    "T_theory = (3 - 2 * np.log(2)) * mu_theory\n",
    "plt.plot(mu_theory, T_theory, 'r--', linewidth=2, label=r'Theory: $T \\approx 1.614 \\mu$ (large $\\mu$)')\n",
    "\n",
    "plt.xlabel('$\\mu$', fontsize=12)\n",
    "plt.ylabel('Period (ms)', fontsize=12)\n",
    "plt.title('Oscillation Period vs Nonlinearity Parameter')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "id": "exercises",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "In this tutorial, you learned:\n",
    "\n",
    "1. **Van der Pol equations**: The Lienard form with nonlinear damping\n",
    "2. **Parameter $\\mu$**: Controls transition from harmonic to relaxation oscillations\n",
    "3. **Relaxation oscillations**: Characterized by slow phases and fast jumps\n",
    "4. **Slow-fast dynamics**: The trajectory follows the nullcline during slow phases\n",
    "\n",
    "### Key Insights\n",
    "\n",
    "- Van der Pol captures **threshold dynamics** relevant to neural excitability\n",
    "- Large $\\mu$ creates **spike-like** waveforms similar to action potentials\n",
    "- The model exhibits **limit cycle** oscillations for all $\\mu > 0$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "references",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "1. van der Pol, B. (1926). On relaxation-oscillations. *The London, Edinburgh, and Dublin Philosophical Magazine*, 2(11), 978-992.\n",
    "\n",
    "2. FitzHugh, R. (1961). Impulses and physiological states in theoretical models of nerve membrane. *Biophysical Journal*, 1(6), 445-466.\n",
    "\n",
    "3. Strogatz, S. H. (2015). *Nonlinear Dynamics and Chaos*. Westview Press. (Chapter 7)"
   ]
  }
 ],
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