{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5-Minute Tutorial: Getting Started\n",
    "\n",
    "Welcome to ``brainpy.state``! This quick tutorial will get you up and running with your first neural simulation in just a few minutes.\n",
    "\n",
    "## What You'll Learn\n",
    "\n",
    "- How to create neurons\n",
    "- How to build simple networks\n",
    "- How to run simulations\n",
    "- How to visualize results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Import Libraries\n",
    "\n",
    "First, let's import the necessary libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T07:17:12.262478Z",
     "start_time": "2025-10-10T07:17:12.257706Z"
    },
    "execution": {
     "iopub.execute_input": "2026-05-11T06:20:02.192545Z",
     "iopub.status.busy": "2026-05-11T06:20:02.192289Z",
     "iopub.status.idle": "2026-05-11T06:20:04.401727Z",
     "shell.execute_reply": "2026-05-11T06:20:04.400506Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n"
     ]
    }
   ],
   "source": [
    "import jax\n",
    "\n",
    "import brainpy.state\n",
    "import brainstate\n",
    "import saiunit as u\n",
    "import braintools\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Create Your First Neuron\n",
    "\n",
    "Let's create a simple Leaky Integrate-and-Fire (LIF) neuron:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T07:17:12.278071Z",
     "start_time": "2025-10-10T07:17:12.273488Z"
    },
    "execution": {
     "iopub.execute_input": "2026-05-11T06:20:04.404633Z",
     "iopub.status.busy": "2026-05-11T06:20:04.404237Z",
     "iopub.status.idle": "2026-05-11T06:20:04.408462Z",
     "shell.execute_reply": "2026-05-11T06:20:04.407703Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created a LIF neuron!\n"
     ]
    }
   ],
   "source": [
    "# Set simulation time step\n",
    "brainstate.environ.set(dt=0.1 * u.ms)\n",
    "\n",
    "# Create a single LIF neuron\n",
    "neuron = brainpy.state.LIF(\n",
    "    1,\n",
    "    V_rest=-65. * u.mV,      # Resting potential\n",
    "    V_th=-50. * u.mV,        # Spike threshold\n",
    "    V_reset=-65. * u.mV,     # Reset potential\n",
    "    tau=10. * u.ms,          # Membrane time constant\n",
    ")\n",
    "\n",
    "print(\"Created a LIF neuron!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Simulate the Neuron\n",
    "\n",
    "Now let's inject a constant current and see how the neuron responds:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T07:18:40.403662Z",
     "start_time": "2025-10-10T07:18:40.322794Z"
    },
    "execution": {
     "iopub.execute_input": "2026-05-11T06:20:04.411767Z",
     "iopub.status.busy": "2026-05-11T06:20:04.411492Z",
     "iopub.status.idle": "2026-05-11T06:20:04.593567Z",
     "shell.execute_reply": "2026-05-11T06:20:04.592675Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulation complete! Recorded 2000 time steps.\n"
     ]
    }
   ],
   "source": [
    "# Initialize neuron state\n",
    "brainstate.nn.init_all_states(neuron)\n",
    "\n",
    "# Define simulation parameters\n",
    "duration = 200. * u.ms\n",
    "dt = brainstate.environ.get_dt()\n",
    "times = u.math.arange(0. * u.ms, duration, dt)\n",
    "\n",
    "# Input current (constant)\n",
    "I_input = 20.0 * u.mA\n",
    "\n",
    "# Run simulation and record membrane potential\n",
    "def step_run(t):\n",
    "    with brainstate.environ.context(t=t):\n",
    "        neuron(I_input)\n",
    "        return neuron.V.value, neuron.get_spike()\n",
    "\n",
    "voltages, spikes = brainstate.transform.for_loop(step_run, times)\n",
    "\n",
    "print(f\"Simulation complete! Recorded {len(times)} time steps.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Visualize the Results\n",
    "\n",
    "Let's plot the membrane potential over time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T07:18:43.855438Z",
     "start_time": "2025-10-10T07:18:43.755841Z"
    },
    "execution": {
     "iopub.execute_input": "2026-05-11T06:20:04.595968Z",
     "iopub.status.busy": "2026-05-11T06:20:04.595677Z",
     "iopub.status.idle": "2026-05-11T06:20:04.876054Z",
     "shell.execute_reply": "2026-05-11T06:20:04.875301Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA90AAAGGCAYAAABmGOKbAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAs8ZJREFUeJzs3Xd4U9UbB/Bv0j3oogto6WDvUabIRlBBREEEZCMgKig4AFGGKAgIyk8URGQjQ9ziAAQRkL333lAKdNLd5v7+SHNzk6Zt0mbcG7+f5+F5SJom5/bc3HvGe96jEgRBABERERERERFZndrRBSAiIiIiIiJyVux0ExEREREREdkIO91ERERERERENsJONxEREREREZGNsNNNREREREREZCPsdBMRERERERHZCDvdRERERERERDbCTjcRERERERGRjbDTTURERERERGQj7HQTERERERER2Qg73URECrB8+XKoVCocPHiwyNdcvXoVKpUKH3/8sfjc33//DZVKZfJfnz59iv3MqVOnQqVSISwsDBkZGYV+Hh0djW7dupX+oBQiOjra4O/m4+ODZs2aYeXKlY4ummJ98803+PTTT232/j/88AOeeOIJBAcHw93dHRUrVkTv3r2xbds2m32muWbMmIEff/zRLp/1xRdfYPny5Wa/XqVS4dVXX7VdgSxk6/OEiMheXB1dACIisr0xY8agadOmBs9FR0eb9bsJCQlYuHAh3njjDRuUTBkaNmwoHv+dO3ewZMkSDBo0CNnZ2Rg+fLiDS6c833zzDU6ePInXX3/dqu8rCAKGDh2K5cuXo1GjRhg3bhzCw8Nx584d/PDDD+jYsSN2796NRx55xKqfa4kZM2agV69e6NGjh80/64svvkBwcDAGDx5s88+yBVudJ0RE9sZONxHRf0Dr1q3Rq1evUv1uw4YNMWfOHLz88svw8vKycsksl56eDh8fH7t+ZqVKldC/f3/x8eDBgxEbG4tPPvmEnW4ZmTt3LpYvX47XX38d8+bNg0qlEn82adIkrFq1Cq6ubPoQEZF9MbyciIiKNXnyZNy9excLFy4s8bUajQaffvop6tSpA09PT4SFhWHkyJFISkoyeJ1KpcLUqVML/X50dLTBrJwurH7Hjh14+eWXERoaioiICPHnX3zxBerUqQMPDw9UrFgRr7zyCpKTkw3es127dqhbty5Onz6N9u3bw9vbG5UqVcLs2bMt+jtIhYSEoGbNmrh06VKpjv/gwYPo0qULgoOD4eXlhZiYGAwdOlT8uXSpwCeffIKoqCh4eXmhbdu2OHnyZKHybNu2Da1bt4aPjw8CAgLw9NNP48yZMwav0S0XuHjxIgYPHoyAgAD4+/tjyJAhhZYPbNmyBY8++igCAgLg6+uLGjVq4J133jF4TXZ2NqZMmYKqVavCw8MDkZGRePvtt5GdnV3s365du3bYtGkTrl27JobsS6MuEhISMGzYMISFhcHT0xMNGjTAihUrin1PAMjMzMTMmTNRs2ZNfPzxxwYdbp0BAwagWbNm4uPLly/jueeeQ1BQELy9vdGiRQts2rTJ4Hd0SzQ2bNiADz/8EBEREfD09ETHjh1x8eJFg9deuHABPXv2RHh4ODw9PREREYE+ffogJSUFgPa8T09Px4oVK8Rj153v165dw8svv4waNWrAy8sL5cuXx3PPPYerV68afIbuO7F7926MGzcOISEh8PHxwTPPPIN79+6Jr4uOjsapU6ewY8cO8bPatWtX4t+xtMeu+54dOnQIjzzyiHheL1q0yGT5jY9L91l///23+H7FnSdERErC4V4iov+AtLQ03L9/3+C5oKAgqNUlj722bt0aHTp0wOzZszFq1KhiZ7tHjhyJ5cuXY8iQIRgzZgyuXLmCBQsW4MiRI9i9ezfc3NxKVf6XX34ZISEhmDx5MtLT0wFoO5HTpk1Dp06dMGrUKJw7dw4LFy7EgQMHCn1WUlISHn/8cTz77LPo3bs3Nm7ciPHjx6NevXp44oknLC5PXl4ebt68icDAQIuPPyEhAZ07d0ZISAgmTJiAgIAAXL16Fd9//32hz1m5ciXS0tLwyiuvICsrC/Pnz0eHDh1w4sQJhIWFAQC2bt2KJ554ArGxsZg6dSoyMzPx2WefoVWrVjh8+HChjkrv3r0RExODmTNn4vDhw1iyZAlCQ0Mxa9YsAMCpU6fQrVs31K9fH++//z48PDxw8eJF7N69W3wPjUaD7t27Y9euXRgxYgRq1aqFEydO4JNPPsH58+eLXbM8adIkpKSk4ObNm/jkk08AAL6+vgC0Hed27drh4sWLePXVVxETE4Nvv/0WgwcPRnJyMl577bUi33fXrl1ITEzE66+/DhcXl6Irr8Ddu3fxyCOPICMjA2PGjEH58uWxYsUKdO/eHRs3bsQzzzxj8PqPPvoIarUab775JlJSUjB79my88MIL2LdvHwAgJycHXbp0QXZ2NkaPHo3w8HDcunULv/76K5KTk+Hv749Vq1bhxRdfRLNmzTBixAgAQJUqVQAABw4cwL///os+ffogIiICV69excKFC9GuXTucPn0a3t7eBuUZPXo0AgMDMWXKFFy9ehWffvopXn31Vaxfvx4A8Omnn2L06NHw9fXFpEmTAEA8ZyxV0rHrJCUl4cknn0Tv3r3Rt29fbNiwAaNGjYK7u7vBoJI5ijtPiIgURyAiItlbtmyZAEA4cOBAka+5cuWKAECYM2eO+Nz27dsFACb/XblypdjPnDJligBAuHfvnrBjxw4BgDBv3jzx51FRUULXrl3Fxzt37hQACGvWrDF4nz/++KPQ8wCEKVOmFPrMqKgoYdCgQYWO+9FHHxXy8vLE5xMSEgR3d3ehc+fOQn5+vvj8ggULBADC0qVLxefatm0rABBWrlwpPpednS2Eh4cLPXv2LPZvoCtT586dhXv37gn37t0TTpw4IQwYMEAAILzyyisWH/8PP/xgdl16eXkJN2/eFJ/ft2+fAEAYO3as+FzDhg2F0NBQ4cGDB+Jzx44dE9RqtTBw4EDxOV19Dh061OCznnnmGaF8+fLi408++USs96KsWrVKUKvVws6dOw2eX7RokQBA2L17d5G/KwiC0LVrVyEqKqrQ859++qkAQFi9erX4XE5OjtCyZUvB19dXSE1NLfI958+fLwAQfvjhh2I/W+f1118XABgcQ1pamhATEyNER0eL55XuO1SrVi0hOzu70OedOHFCEARBOHLkiABA+Pbbb4v9XB8fH4NzXCcjI6PQc3v27Cl07uq+E506dRI0Go34/NixYwUXFxchOTlZfK5OnTpC27Zti/9DSBif0+YeuyDov2dz584Vn8vOzhbPz5ycHIPyG19/dJ+1fft28bmizhMiIqVheDkR0X/A5MmTsWXLFoN/4eHhZv9+mzZt0L59e8yePRuZmZkmX/Ptt9/C398fjz32GO7fvy/+i4uLg6+vL7Zv317q8g8fPtxg9nLr1q3IycnB66+/bjBbP3z4cPj5+RUKEfb19TVYk+3u7o5mzZrh8uXLZn3+5s2bERISgpCQENSrVw+rVq3CkCFDMGfOHPE15h5/QEAAAODXX39Fbm5usZ/bo0cPVKpUSXzcrFkzNG/eHL/99hsAbVK3o0ePYvDgwQgKChJfV79+fTz22GPi66Reeuklg8etW7fGgwcPkJqaalC+n376CRqNxmS5vv32W9SqVQs1a9Y0ONYOHToAQKnr+rfffkN4eDj69u0rPufm5oYxY8bg4cOH2LFjR5G/qyt/uXLlzP6sZs2a4dFHHxWf8/X1xYgRI3D16lWcPn3a4PVDhgyBu7u7+Lh169YAIJ5D/v7+AIA///zTZLb/kkgjSHJzc/HgwQNUrVoVAQEBOHz4cKHXjxgxwiCEvnXr1sjPz8e1a9cs/uySlHTsOq6urhg5cqT42N3dHSNHjkRCQgIOHTpk9XIRESkFO91ERP8B9erVQ6dOnQz+eXp6WvQeU6dORXx8fKE1mjoXLlxASkoKQkNDxQ6q7t/Dhw+RkJBQ6vLHxMQYPNZ1LGrUqGHwvLu7O2JjYwt1PCIiIgqt8Q0MDCy01roozZs3x5YtW/DHH3/g448/RkBAAJKSkgw6IuYef9u2bdGzZ09MmzYNwcHBePrpp7Fs2TKTa6GrVatW6Lnq1auL62GL+jsAQK1atXD//n0xHF+ncuXKhf4OAMS/xfPPP49WrVrhxRdfRFhYGPr06YMNGzYYdMAvXLiAU6dOFTrO6tWrA0Cp6/ratWuoVq1aoWUPtWrVMjheU/z8/ABol1KY+1lF/d1MfVZJf7eYmBiMGzcOS5YsQXBwMLp06YLPP/9cXM9dkszMTEyePBmRkZHw8PBAcHAwQkJCkJycbPI9SiqPNZn7WRUrViyU5FB3Thiv4SYi+i/hmm4iIjJLmzZt0K5dO8yePbvQbCmgXecbGhqKNWvWmPz9kJCQEj8jPz/f5PNlzZpe1BpfQRDM+v3g4GB06tQJANClSxfUrFkT3bp1w/z58zFu3DgA5h+/SqXCxo0bsXfvXvzyyy/4888/MXToUMydOxd79+61+brVkv4WXl5e+Oeff7B9+3Zs2rQJf/zxB9avX48OHTpg8+bNcHFxgUajQb169TBv3jyT7xUZGWmz8helZs2aAIATJ07YZDsuc86huXPnYvDgwfjpp5+wefNmjBkzBjNnzsTevXsNEgCaMnr0aCxbtgyvv/46WrZsCX9/f6hUKvTp08dkxEFZz2lLWPOzTCW4A4r+7hMROQN2uomIyGxTp05Fu3bt8OWXXxb6WZUqVbB161a0atWqxE5yYGBgoSzjOTk5uHPnjlnliIqKAgCcO3cOsbGxBu9x5coVsYNsK127dkXbtm0xY8YMjBw5Ej4+PhYdPwC0aNECLVq0wIcffohvvvkGL7zwAtatW4cXX3xRfM2FCxcK/d758+fF5GjSv4Oxs2fPIjg4uFTbq6nVanTs2BEdO3bEvHnzMGPGDEyaNAnbt29Hp06dUKVKFRw7dgwdO3YsshNVnKJ+JyoqCsePH4dGozGY7T579qz486I8+uijCAwMxNq1a/HOO++UmEwtKiqqyL9bSZ9VnHr16qFevXp499138e+//6JVq1ZYtGgRPvjgAwBFH/vGjRsxaNAgzJ07V3wuKyur0PfEEqWpm7K4fft2oS39zp8/DwDiOaubJTc+LlNRDPYuPxGRrTC8nIiIzNa2bVu0a9cOs2bNQlZWlsHPevfujfz8fEyfPr3Q7+Xl5Rk0sqtUqYJ//vnH4DWLFy82e7arU6dOcHd3x//+9z+D2bavv/4aKSkp6Nq1qwVHVTrjx4/HgwcP8NVXXwEw//iTkpIKzRA2bNgQAAqFmP/444+4deuW+Hj//v3Yt2+fmHG9QoUKaNiwIVasWGHw9z158iQ2b96MJ5980uLjSkxMLPSccfl69+6NW7duicculZmZWSik3ZiPj4/JkOknn3wS8fHxYgZuQPu3++yzz+Dr64u2bdsW+Z7e3t4YP348zpw5g/Hjx5uchV29ejX2798vftb+/fuxZ88e8efp6elYvHgxoqOjUbt27WKPwVhqairy8vIMnqtXrx7UarVBvfr4+JjsSLu4uBQq82effVamGeCiPstW8vLyDAbkcnJy8OWXXyIkJARxcXEA9Nnapd///Px8LF68uND7FXWeEBEpDWe6iYgUZOnSpfjjjz8KPV/cVkrWNmXKFLRv377Q823btsXIkSMxc+ZMHD16FJ07d4abmxsuXLiAb7/9FvPnz0evXr0AAC+++CJeeukl9OzZE4899hiOHTuGP//8E8HBwWaVISQkBBMnTsS0adPw+OOPo3v37jh37hy++OILNG3a1CBpmq088cQTqFu3LubNm4dXXnnF7ONfsWIFvvjiCzzzzDOoUqUK0tLS8NVXX8HPz69QJ7lq1ap49NFHMWrUKGRnZ+PTTz9F+fLl8fbbb4uvmTNnDp544gm0bNkSw4YNE7cM8/f3N7kXeknef/99/PPPP+jatSuioqKQkJCAL774AhEREWLSsQEDBmDDhg146aWXsH37drRq1Qr5+fk4e/YsNmzYgD///BNNmjQp8jPi4uKwfv16jBs3Dk2bNoWvry+eeuopjBgxAl9++SUGDx6MQ4cOITo6Ghs3bsTu3bvx6aeflpgk7a233sKpU6cwd+5cbN++Hb169UJ4eDji4+Px448/Yv/+/fj3338BABMmTMDatWvxxBNPYMyYMQgKCsKKFStw5coVfPfdd2Ztpye1bds2vPrqq3juuedQvXp15OXlYdWqVXBxcUHPnj0Njn3r1q2YN28eKlasiJiYGDRv3hzdunXDqlWr4O/vj9q1a2PPnj3YunUrypcvb1E5pOLi4rBw4UJ88MEHqFq1KkJDQ8Vkd7ZQsWJFzJo1C1evXkX16tWxfv16HD16FIsXLxa38KtTpw5atGiBiRMnIjExEUFBQVi3bl2hAQtd+U2dJ0REiuOwvOlERGQ23TY7Rf27ceNGsVuGlbSNkSnSLcOM6bYHkm4ZprN48WIhLi5O8PLyEsqVKyfUq1dPePvtt4Xbt2+Lr8nPzxfGjx8vBAcHC97e3kKXLl2EixcvFrllWFHbay1YsECoWbOm4ObmJoSFhQmjRo0SkpKSCpW1Tp06hX530KBBZm1HZLw1mtTy5csFAMKyZcvMPv7Dhw8Lffv2FSpXrix4eHgIoaGhQrdu3YSDBw+K7yGty7lz5wqRkZGCh4eH0Lp1a+HYsWOFyrF161ahVatWgpeXl+Dn5yc89dRTwunTpw1eU1R9Gm/h9NdffwlPP/20ULFiRcHd3V2oWLGi0LdvX+H8+fMGv5eTkyPMmjVLqFOnjuDh4SEEBgYKcXFxwrRp04SUlJRi/6YPHz4U+vXrJwQEBAgADOrh7t27wpAhQ4Tg4GDB3d1dqFevnsHf1xwbN24UOnfuLAQFBQmurq5ChQoVhOeff174+++/DV536dIloVevXkJAQIDg6ekpNGvWTPj1118NXlPUd0hXR7qyXb58WRg6dKhQpUoVwdPTUwgKChLat28vbN261eD3zp49K7Rp00bw8vISAIjne1JSknjcvr6+QpcuXYSzZ8+a/Z0wteVWfHy80LVrV6FcuXICgBK3D0MRW4aVdOyCoP+eHTx4UGjZsqXg6ekpREVFCQsWLCj0OZcuXRI6deokeHh4CGFhYcI777wjbNmypVD5iztPiIiURCUINsi4QURERKV29epVxMTEYM6cOXjzzTcdXRyiErVr1w7379/HyZMnHV0UIiLZ4ZpuIiIiIiIiIhthp5uIiIiIiIjIRtjpJiIiIiIiIrIRrukmIiIiIiIishHOdBMRERERERHZCDvdRERERERERDbi6ugCyI1Go8Ht27dRrlw5qFQqRxeHiIiIiIiIZEgQBKSlpaFixYpQq4uez2an28jt27cRGRnp6GIQERERERGRAty4cQMRERFF/pydbiPlypUDoP3D+fn5Obg0pmk0Gty7dw8hISHFjqiQY7B+5It1I2+sH/li3cgb60feWD/yxbqRNyXUT2pqKiIjI8U+ZFHY6TaiCyn38/OTdac7KysLfn5+sj0B/8tYP/LFupE31o98sW7kjfUjb6wf+WLdyJuS6qekZcnyLj0RERERERGRgrHTTURERERERGQj7HQTERERERER2Qg73UREREREREQ2wk43ERERERERkY2w001ERERERERkI+x0ExEREREREdkIO91ERERERERENsJONxEREREREZGNsNOtMAlpWThyPQnHbz9EYnqOo4tDRERERERExWCnW2F+OXYHPRftxYgN57D74n1HF4eIiIiIiIiKwU63wqgk/xccVgoiIiIiIiIyBzvdCqOW9LoF9rqJiIiIiIhkjZ1uhVGp9L1ugb1uIiIiIiIiWWOnW2FU0pluxxWDiIiIiIiIzMBOt8IYrOlmr5uIiIiIiEjW2OlWGIPwcs51ExERERERyRo73QojDS/XsM9NREREREQka+x0K4wKTKRGRERERESkFOx0KwwTqRERERERESkHO90KozbIpOawYhAREREREZEZ2OlWGGl4uYbh5URERERERLLGTrfSSMPL2ecmIiIiIiKSNXa6FYbR5URERERERMrBTrfCqFUMLyciIiIiIlIKdroVRsXwciIiIiIiIsVgp1thpJ1uIiIiIiIikjd2uhVGGl4ucKqbiIiIiIhI1tjpVjAN+9xERERERESyxk63wqikM93MX05ERERERCRr7HQrjMGWYexzExERERERyRo73QpjsKbbgeUgIiIiIiKikrHTrTCGW4ax201ERERERCRniuh0X716FcOGDUNMTAy8vLxQpUoVTJkyBTk5OQavO378OFq3bg1PT09ERkZi9uzZDiqx7TC8nIiIiIiISDlcHV0Ac5w9exYajQZffvklqlatipMnT2L48OFIT0/Hxx9/DABITU1F586d0alTJyxatAgnTpzA0KFDERAQgBEjRjj4CKzHYKbbccUgIiIiIiIiMyii0/3444/j8ccfFx/Hxsbi3LlzWLhwodjpXrNmDXJycrB06VK4u7ujTp06OHr0KObNm+dknW7u001ERERERKQUiuh0m5KSkoKgoCDx8Z49e9CmTRu4u7uLz3Xp0gWzZs1CUlISAgMDTb5PdnY2srOzxcepqakAAI1GA41GY6PSl4Gko52vEeRZxv84jUYDQWDdyBHrRt5YP/LFupE31o+8sX7ki3Ujb0qoH3PLpshO98WLF/HZZ5+Js9wAEB8fj5iYGIPXhYWFiT8rqtM9c+ZMTJs2rdDz9+7dQ1ZWlhVLbR0pKSni/x8+fIiEhAQHloZM0Wg0SElJgSAIUKsVkTbhP4N1I2+sH/li3cgb60feWD/yxbqRNyXUT1pamlmvc2ine8KECZg1a1axrzlz5gxq1qwpPr516xYef/xxPPfccxg+fHiZyzBx4kSMGzdOfJyamorIyEiEhITAz8+vzO9vbYEP9DPdPj4+CA0NdWBpyBSNRgOVSoWQkBDZXiD+q1g38sb6kS/WjbyxfuSN9SNfrBt5U0L9eHp6mvU6h3a633jjDQwePLjY18TGxor/v337Ntq3b49HHnkEixcvNnhdeHg47t69a/Cc7nF4eHiR7+/h4QEPD49Cz6vVallWrouLvkwCVLIsI2nX3sv1HPqvY93IG+tHvlg38sb6kTfWj3yxbuRN7vVjbrkc2ukOCQlBSEiIWa+9desW2rdvj7i4OCxbtqzQAbZs2RKTJk1Cbm4u3NzcAABbtmxBjRo1igwtVyIVmEiNiIiIiIhIKeQ5ZGDk1q1baNeuHSpXroyPP/4Y9+7dQ3x8POLj48XX9OvXD+7u7hg2bBhOnTqF9evXY/78+Qah406BW4YREREREREphiISqW3ZsgUXL17ExYsXERERYfAz3Wyvv78/Nm/ejFdeeQVxcXEIDg7G5MmTnWq7MABQG2wZ5sCCEBERERERUYkU0ekePHhwiWu/AaB+/frYuXOn7QvkQJKJboaXExERERERyZwiwstJT8XwciIiIiIiIsVgp1thDBOpObAgREREREREVCJ2uhVGzZluIiIiIiIixWCnW2kknW4Np7qJiIiIiIhkjZ1uhWF4ORERERERkXKw060whonU2OsmIiIiIiKSM3a6FUbN9OVERERERESKwU63wqgM1nQ7rhxERERERERUMna6FUbS52Z4ORERERERkcyx060wBtHl7HMTERERERHJGjvdCqOS9Lq5ZRgREREREZG8sdOtMNLwckaXExERERERyRs73Qojnelmn5uIiIiIiEje2OlWGINEaux1ExERERERyRo73Qqj5ppuIiIiIiIixWCnW2EMspc7rhhERERERERkBna6FUzgTDcREREREZGssdOtMGomUiMiIiIiIlIMdroVxiC8nL1uIiIiIiIiWXO15MVnzpzBunXrsHPnTly7dg0ZGRkICQlBo0aN0KVLF/Ts2RMeHh62KivBuNPNXjcREREREZGcmTXTffjwYXTq1AmNGjXCrl270Lx5c7z++uuYPn06+vfvD0EQMGnSJFSsWBGzZs1Cdna2rcv9n6UCw8uJiIiIiIiUwqyZ7p49e+LNN9/Exo0bERAQUOTr9uzZg/nz52Pu3Ll45513rFVGklAzvJyIiIiIiEgxzOp0nz9/Hm5ubiW+rmXLlmjZsiVyc3PLXDAyTRpezn26iYiIiIiI5M2s8HI3NzcsWLAAycnJZr2pOR10Ki1VyS8hIiIiIiIiWTA7e7luzXa/fv2wbds2W5aJisHs5URERERERMphdqc7Pj4eixYtwp07d/DYY48hJiYG06dPx40bN2xZPjIi3aeb4eVERERERETyZnan28vLCwMHDsT27dtx4cIFDBgwAF9//TViYmLw+OOP49tvv+VabjuQBpezy01ERERERCRvZne6pWJjY/H+++/jypUr+P3331G+fHkMHjwYlSpVsnb5yAj36SYiIiIiIlKOUnW6dVQqFVxdXaFSqSAIAme67cBgn272uYmIiIiIiGStVJ3uGzdu4P3330dsbCwee+wx3L59G1999RXu3Llj7fKRESZSIyIiIiIiUg6z9ukGgJycHHz//fdYunQptm3bhgoVKmDQoEEYOnQoYmNjbVlGkjDodHNVNxERERERkayZ3ekODw9HRkYGunXrhl9++QVdunSBWl2m6HQqBZWK4eVERERERERKYXan+91338WAAQMQEhJiy/JQCdQGM91EREREREQkZ2Z3useNG1fouYcPH0Kj0Rg85+fnV/ZSUZGkidS4TzcREREREZG8WRwffuXKFXTt2hU+Pj7w9/dHYGAgAgMDERAQgMDAQFuUkSSYSI2IiIiIiEg5zJ7p1unfvz8EQcDSpUsRFhZmsMaYbI9/bSIiIiIiIuWwuNN97NgxHDp0CDVq1LBFeagEhonUONVNREREREQkZxaHlzdt2hQ3btywRVnIDNLAAg373ERERERERLJm8Uz3kiVL8NJLL+HWrVuoW7cu3NzcDH5ev359qxWOCpOGl3OfbiIiIiIiInmzuNN97949XLp0CUOGDBGfU6lUEAQBKpUK+fn5Vi0gGeI+3URERERERMphcad76NChaNSoEdauXctEag7AfbqJiIiIiIiUw+JO97Vr1/Dzzz+jatWqtigPlUC6TzcTqREREREREcmbxYnUOnTogGPHjtmiLGQO7tNNRERERESkGBbPdD/11FMYO3YsTpw4gXr16hVKpNa9e3erFY4KUzG8nIiIiIiISDEs7nS/9NJLAID333+/0M+YSM321JJet4ZT3URERERERLJmcXi5RqMp8p8tO9zdu3dH5cqV4enpiQoVKmDAgAG4ffu2wWuOHz+O1q1bw9PTE5GRkZg9e7bNyuMoBluGsc9NREREREQkaxZ3uh2lffv22LBhA86dO4fvvvsOly5dQq9evcSfp6amonPnzoiKisKhQ4cwZ84cTJ06FYsXL3Zgqa1PxTXdREREREREimFWePm6devQp08fs97wxo0buH79Olq1alWmghkbO3as+P+oqChMmDABPXr0QG5uLtzc3LBmzRrk5ORg6dKlcHd3R506dXD06FHMmzcPI0aMsGpZHEltsEUbe91ERERERERyZlane+HChZg2bRqGDBmCp556CrVq1TL4eUpKCnbv3o3Vq1djy5Yt+Prrr21SWJ3ExESsWbMGjzzyiJjIbc+ePWjTpg3c3d3F13Xp0gWzZs1CUlISAgMDTb5XdnY2srOzxcepqakA9GH0ciNIypSvEWRZxv86jUYDQWDdyBHrRt5YP/LFupE31o+8sX7ki3Ujb0qoH3PLZlane8eOHfj555/x2WefYeLEifDx8UFYWBg8PT2RlJSE+Ph4BAcHY/DgwTh58iTCwsLKVPiijB8/HgsWLEBGRgZatGiBX3/9VfxZfHw8YmJiDF6vK0d8fHyRne6ZM2di2rRphZ6/d+8esrKyrFh668jJ01dsTk4uEhISHFgaMkWj0SAlJQWCIECtVswKjv8E1o28sX7ki3Ujb6wfeWP9yBfrRt6UUD9paWlmvc7s7OXdu3dH9+7dcf/+fezatQvXrl1DZmYmgoOD0ahRIzRq1MjiP8aECRMwa9asYl9z5swZ1KxZEwDw1ltvYdiwYbh27RqmTZuGgQMH4tdff4XKIOTaMhMnTsS4cePEx6mpqYiMjERISAj8/PxK/b62Iu10u7m5ITQ01IGlIVM0Gg1UKhVCQkJke4H4r2LdyBvrR75YN/LG+pE31o98sW7kTQn14+npadbrLN4yLDg4GD169LD010x64403MHjw4GJfExsba/DZwcHBqF69OmrVqoXIyEjs3bsXLVu2RHh4OO7evWvwu7rH4eHhRb6/h4cHPDw8Cj2vVqtlWbmuLvr/C4Asy0ja7fPkeg7917Fu5I31I1+sG3lj/cgb60e+WDfyJvf6MbdcFne6rSkkJAQhISGl+l1d/LxuPXbLli0xadIkMbEaAGzZsgU1atQoMrRciaSz+gLTlxMREREREcmaPIcMjOzbtw8LFizA0aNHce3aNWzbtg19+/ZFlSpV0LJlSwBAv3794O7ujmHDhuHUqVNYv3495s+fbxA67gyYu5yIiIiIiEg5FNHp9vb2xvfff4+OHTuiRo0aGDZsGOrXr48dO3aIoeH+/v7YvHkzrly5gri4OLzxxhuYPHmyU20XBnCfbiIiIiIiIiVxaHi5uerVq4dt27aV+Lr69etj586ddiiR4xiEl3Oum4iIiIiISNYUMdNNhnT9bg373ERERERERLJm1ky3Jeui582bV+rCkHlU0K7nZng5ERERERGRvJnV6T5y5IhZb1aW/bLJfCqVChAEhpcTERERERHJnFmd7u3bt9u6HGQBtQrIB2e6iYiIiIiI5I5ruhVIF0/AfbqJiIiIiIjkrVTZyw8ePIgNGzbg+vXryMnJMfjZ999/b5WCUTFU2lXd7HITERERERHJm8Uz3evWrcMjjzyCM2fO4IcffkBubi5OnTqFbdu2wd/f3xZlJCP6mW6HFoOIiIiIiIhKYHGne8aMGfjkk0/wyy+/wN3dHfPnz8fZs2fRu3dvVK5c2RZlJCPqgoR1Gva6iYiIiIiIZM3iTvelS5fQtWtXAIC7uzvS09OhUqkwduxYLF682OoFpMLEJPHscxMREREREcmaxZ3uwMBApKWlAQAqVaqEkydPAgCSk5ORkZFh3dKRSexzExERERERKYPFidTatGmDLVu2oF69enjuuefw2muvYdu2bdiyZQs6duxoizKSEd1+6MxeTkREREREJG8Wd7oXLFiArKwsAMCkSZPg5uaGf//9Fz179sS7775r9QJSYbrwcg373ERERERERLJmcac7KChI/L9arcaECROsWiAqGcPLiYiIiIiIlMGsTndqair8/PzE/xdH9zqyHYaXExERERERKYNZne7AwEDcuXMHoaGhCAgIEDt9UoIgQKVSIT8/3+qFJEPqgj8/u9xERERERETyZlane9u2bWJY+fbt221aICqZGF7OmW4iIiIiIiJZM6vT3bZtW/H/MTExiIyMLDTbLQgCbty4Yd3SkUn68HIHF4SIiIiIiIiKZfE+3TExMbh3716h5xMTExETE2OVQpF52OcmIiIiIiKSN4s73bq128YePnwIT09PqxSKiqcuWNSt4VQ3ERERERGRrJm9Zdi4ceMAaEOb33vvPXh7e4s/y8/Px759+9CwYUOrF5AK06/pdmgxiIiIiIiIqARmd7qPHDkCQDvTfeLECbi7u4s/c3d3R4MGDfDmm29av4RUiIlAAyIiIiIiIpIhszvduqzlQ4YMwfz587kftwMxezkREREREZEymN3p1lm2bJktykEW0K2p17DPTUREREREJGsWd7rT09Px0Ucf4a+//kJCQgI0Go3Bzy9fvmy1wpFpuvBy9rmJiIiIiIjkzeJO94svvogdO3ZgwIABqFChgslM5mRbKuj26Wa3m4iIiIiISM4s7nT//vvv2LRpE1q1amWL8pAZ1LqZbva5iYiIiIiIZM3ifboDAwMRFBRki7KQmcTwcva6iYiIiIiIZM3iTvf06dMxefJkZGRk2KI8ZAYxvNzB5SAiIiIiIqLiWRxePnfuXFy6dAlhYWGIjo6Gm5ubwc8PHz5stcJRERheTkREREREpAgWd7p79Ohhg2KQJdQqznQTEREREREpgcWd7ilTptiiHGQBXb54Dae6iYiIiIiIZM3iNd0AkJycjCVLlmDixIlITEwEoA0rv3XrllULR6apGF5ORERERESkCBbPdB8/fhydOnWCv78/rl69iuHDhyMoKAjff/89rl+/jpUrV9qinCSh3xmdvW4iIiIiIiI5s3ime9y4cRg8eDAuXLgAT09P8fknn3wS//zzj1ULR6bp1nRr2OcmIiIiIiKSNYs73QcOHMDIkSMLPV+pUiXEx8dbpVBUAoaXExERERERKYLFnW4PDw+kpqYWev78+fMICQmxSqGoeCoxezl73URERERERHJmcae7e/fueP/995GbmwtA2wG8fv06xo8fj549e1q9gFSYbk03Z7qJiIiIiIjkzeJO99y5c/Hw4UOEhoYiMzMTbdu2RdWqVVGuXDl8+OGHtigjGVGL4eXsdRMREREREcmZxdnL/f39sWXLFuzevRvHjh3Dw4cP0bhxY3Tq1MkW5SMT9OHlREREREREJGcWd7pXrlyJ559/Hq1atUKrVq3E53NycrBu3ToMHDjQqgWkwhheTkREREREpAwWh5cPGTIEKSkphZ5PS0vDkCFDrFIoKh5nuomIiIiIiJTB4k63IAhip0/q5s2b8Pf3t0qhqHi6P7+GU91ERERERESyZnZ4eaNGjaBSqaBSqdCxY0e4uup/NT8/H1euXMHjjz9uk0KSIYaXExERERERKYPZne4ePXoAAI4ePYouXbrA19dX/Jm7uzuio6O5ZZidmAg0ICIiIiIiIhkyu9M9ZcoUAEB0dDSef/55eHp62qxQVDy1pNddVLg/EREREREROZ7Fa7oHDRoET09PHDp0CKtXr8bq1atx5MgRW5TNpOzsbDRs2BAqlQpHjx41+Nnx48fRunVreHp6IjIyErNnz7ZbuexJ2sXWMMSciIiIiIhItizeMiwhIQF9+vTB33//jYCAAABAcnIy2rdvj3Xr1iEkJMTaZTTw9ttvo2LFijh27JjB86mpqejcuTM6deqERYsW4cSJExg6dCgCAgIwYsQIm5bJ7oxmug274URERERERCQXFs90jx49GmlpaTh16hQSExORmJiIkydPIjU1FWPGjLFFGUW///47Nm/ejI8//rjQz9asWYOcnBwsXboUderUQZ8+fTBmzBjMmzfPpmVyBGkXmxPdRERERERE8mXxTPcff/yBrVu3olatWuJztWvXxueff47OnTtbtXBSd+/exfDhw/Hjjz/C29u70M/37NmDNm3awN3dXXyuS5cumDVrFpKSkhAYGGjyfbOzs5GdnS0+Tk1NBQBoNBpoNBorH4V1SJdw5+Xnw4UT3bKi0WggCIJsz5//MtaNvLF+5It1I2+sH3lj/cgX60belFA/5pbN4k63RqOBm5tboefd3Nxs9gcRBAGDBw/GSy+9hCZNmuDq1auFXhMfH4+YmBiD58LCwsSfFdXpnjlzJqZNm1bo+Xv37iErK6vshbeBvNxc8f8JCffg4WpxwALZkEajQUpKCgRBgFrNupET1o28sX7ki3Ujb6wfeWP9yBfrRt6UUD9paWlmvc7iTneHDh3w2muvYe3atahYsSIA4NatWxg7diw6duxo0XtNmDABs2bNKvY1Z86cwebNm5GWloaJEydaWtwSTZw4EePGjRMfp6amIjIyEiEhIfDz87P651mDu/tl8f8hISHwdHNxYGnImEajgUqlQkhIiGwvEP9VrBt5Y/3IF+tG3lg/8sb6kS/WjbwpoX7M3dHL4k73ggUL0L17d0RHRyMyMhIAcOPGDdStWxerV6+26L3eeOMNDB48uNjXxMbGYtu2bdizZw88PDwMftakSRO88MILWLFiBcLDw3H37l2Dn+seh4eHF/n+Hh4ehd4XANRqtWwrV7plmEol33L+l6lUKlmfQ/9lrBt5Y/3IF+tG3lg/8sb6kS/WjbzJvX7MLZfFne7IyEgcPnwYf/31F86cOQMAqFWrFjp16mTpWyEkJMSsbOf/+9//8MEHH4iPb9++jS5dumD9+vVo3rw5AKBly5aYNGkScnNzxfD3LVu2oEaNGkWGliuVdF9ujcBUakRERERERHJlUad7/fr1+Pnnn5GTk4OOHTti9OjRtiqXgcqVKxs89vX1BQBUqVIFERERAIB+/fph2rRpGDZsGMaPH4+TJ09i/vz5+OSTT+xSRnuSJlJjl5uIiIiIiEi+zO50L1y4EK+88gqqVasGLy8vfP/997h06RLmzJljy/KZzd/fH5s3b8Yrr7yCuLg4BAcHY/Lkyc63RzeMtgzjTDcREREREZFsmd3pXrBgAaZMmYIpU6YAAFavXo2RI0c6pNMdHR1tsrNZv3597Ny50+7lsTdpeDm73ERERERERPJl9or0y5cvY9CgQeLjfv36IS8vD3fu3LFJwahoaml4uXy3rSMiIiIiIvrPM7vTnZ2dDR8fH/0vqtVwd3dHZmamTQpGxZHOdHOum4iIiIiISK4sSqT23nvvwdvbW3yck5ODDz/8EP7+/uJz8+bNs17pyCSDRGrscxMREREREcmW2Z3uNm3a4Ny5cwbPPfLII7h8+bL4WLrWmGxHzezlREREREREimB2p/vvv/+2YTHIEipwn24iIiIiIiIlMHtNN8kHw8uJiIiIiIiUgZ1uBTLYp5sB5kRERERERLLFTrcCGezTzT43ERERERGRbLHTrUAMLyciIiIiIlIGdroViOHlREREREREylCqTvfOnTvRv39/tGzZErdu3QIArFq1Crt27bJq4cg0hpcTEREREREpg8Wd7u+++w5dunSBl5cXjhw5guzsbABASkoKZsyYYfUCUmHSfbq5ZRgREREREZF8Wdzp/uCDD7Bo0SJ89dVXcHNzE59v1aoVDh8+bNXCkWnSfbrZ5yYiIiIiIpIvizvd586dQ5s2bQo97+/vj+TkZGuUiUqiKvklRERERERE5HgWd7rDw8Nx8eLFQs/v2rULsbGxVikUFc8gkRpnuomIiIiIiGTL4k738OHD8dprr2Hfvn1QqVS4ffs21qxZgzfffBOjRo2yRRnJiFqyqJtruomIiIiIiOTL1dJfmDBhAjQaDTp27IiMjAy0adMGHh4eePPNNzF69GhblJGMGG4ZRkRERERERHJlcadbpVJh0qRJeOutt3Dx4kU8fPgQtWvXhq+vry3KRyZIdgyDwJluIiIiIiIi2bK4063j7u6O2rVrW7MsZCa1dJ9uB5aDiIiIiIiIimdxpzs9PR0fffQR/vrrLyQkJECj0Rj8/PLly1YrHJWMM91ERERERETyZXGn+8UXX8SOHTswYMAAVKhQASoV96+yN8PwcseVg4iIiIiIiIpncaf7999/x6ZNm9CqVStblIfMoALDy4mIiIiIiJTA4i3DAgMDERQUZIuykJkkO4ZxyzAiIiIiIiIZs7jTPX36dEyePBkZGRm2KA+ZQRrSzz43ERERERGRfFkcXj537lxcunQJYWFhiI6Ohpubm8HPDx8+bLXCkWkG+3Sz001ERERERCRbFne6e/ToYYNikEWkidS4qpuIiIiIiEi2LO50T5kyxRblIAuoGV5ORERERESkCBav6SbHY3g5ERERERGRMlg8052fn49PPvkEGzZswPXr15GTk2Pw88TERKsVjkxTMbyciIiIiIhIESye6Z42bRrmzZuH559/HikpKRg3bhyeffZZqNVqTJ061QZFJGMG+3Szz01ERERERCRbFne616xZg6+++gpvvPEGXF1d0bdvXyxZsgSTJ0/G3r17bVFGMsJ9uomIiIiIiJTB4k53fHw86tWrBwDw9fVFSkoKAKBbt27YtGmTdUtHphmElxMREREREZFcWdzpjoiIwJ07dwAAVapUwebNmwEABw4cgIeHh3VLRyapmL2ciIiIiIhIESzudD/zzDP466+/AACjR4/Ge++9h2rVqmHgwIEYOnSo1QtIhakMHrHXTUREREREJFcWZy//6KOPxP8///zziIqKwr///otq1arhqaeesmrhyDTpPt0a9rmJiIiIiIhky6JOd25uLkaOHIn33nsPMTExAIAWLVqgRYsWNikcmWawZRg73URERERERLJlUXi5m5sbvvvuO1uVhcwkDS8X2OsmIiIiIiKSLYvXdPfo0QM//vijDYpC5lIxvJyIiIiIiEgRLF7TXa1aNbz//vvYvXs34uLi4OPjY/DzMWPGWK1wZJpBeDkTqREREREREcmWxZ3ur7/+GgEBATh06BAOHTpk8DOVSsVOtx0YZC9nn5uIiIiIiEi2LO50X7lyxRblIAsY7NPtwHIQERERERFR8Sxe0y0lCAITeTmAWjLVreHfn4iIiIiISLZK1en++uuvUbduXXh6esLT0xN169bFkiVLrF02KoJh9nKHFYOIiIiIiIhKYHF4+eTJkzFv3jyMHj0aLVu2BADs2bMHY8eOxfXr1/H+++9bvZBkhOHlREREREREimBxp3vhwoX46quv0LdvX/G57t27o379+hg9ejQ73XbAfbqJiIiIiIiUweLw8tzcXDRp0qTQ83FxccjLy7NKoUyJjo6GSqUy+PfRRx8ZvOb48eNo3bo1PD09ERkZidmzZ9usPI4kXdPNPjcREREREZF8WdzpHjBgABYuXFjo+cWLF+OFF16wSqGK8v777+POnTviv9GjR4s/S01NRefOnREVFYVDhw5hzpw5mDp1KhYvXmzTMjmCYfZy9rqJiIiIiIjkyqzw8nHjxon/V6lUWLJkCTZv3owWLVoAAPbt24fr169j4MCBtillgXLlyiE8PNzkz9asWYOcnBwsXboU7u7uqFOnDo4ePYp58+ZhxIgRNi2XvSk5kZpGI+DivYeILu8Dd9cyJc+XteTMPNy7nYJ6EYGOLorNCIKA03dSUd7HA+H+no4ujs2kZ+fh1O1UNKocADcX5z1nbyRmID0nDzXD/RxdFJvJy9fg2M0UVA3xhb+3m6OLYzNJ6Tm4fP8hGlcONBikdTYX7qbB3VWNqPI+ji6KzWTl5uP4zRTUj/CHp5uLo4tjM/EpWbiXlo16Ef6OLorNaAQBx24mIyLQByHlPBxdHJtJy8rFmTtpiIsKhIvaea8/V+6nI1+jQdXQco4uis3k5mtw7EYyalbwg6+HxSuSyYhZf8EjR44YPI6LiwMAXLp0CQAQHByM4OBgnDp1ysrFM/TRRx9h+vTpqFy5Mvr164exY8fC1VV7CHv27EGbNm3g7u4uvr5Lly6YNWsWkpKSEBhouvOTnZ2N7Oxs8XFqaioAQKPRQKPR2PBoykLf05Z3OQ1dT8zAuA3HcPh6MprHBGHt8OaOLpLVaTQCVu29hll/nENWngYf9qiDvs0qO7pYVncvLRuTfjyJrWcS4OXmgt3j2yHA273kX3QwjUYDQRDM/s78c/4eJnx/AvGp2Xi6YUV80ruBjUtof9l5+fhk6wV8tfMKBAFYObQpHq0a7JCyWFo/lrh87yHe3HgcR2+kIDLQC9veaOt0DUJBEPDd4Vt4/9czeJidh9c6VsVrHatZ5b1tWTeWSs3MxdRfTuPHo7fhqlZh67g2qBzk7ehiWd2ha0l4c+NxXHuQgVZVy2PV0GZFvlZO9WOJvHwNvtx5Bf/76wJy8wV8+nwDdG9Q0dHFsrqbiekY9/0FHLyRBn8vN/w7vj283J1vEOXPU/F498dTeJCeg/7NK+P9p+s4ukglsvS7k5mTj4/+OItVe69DpQK+f6klGkQG2LaQDnDmTire+PY4zsanoWZ4OWwa3cohg7hKuLaZWzazOt3bt28vU2GsYcyYMWjcuDGCgoLw77//YuLEibhz5w7mzZsHAIiPj0dMTIzB74SFhYk/K6rTPXPmTEybNq3Q8/fu3UNWVpaVj8I6MtLTxf8nJacgIUHeDUdBEPDH2UTM2X4dGTnaE3PflUTcvnMXri7yLrslEjNyMX3zVey5mio+t/PsHXSMdq5Z4H+vpGD65qtIytTmcMjMzceeMzcQFyn/0V6NRoOUlBQIggC1uuhZ65w8DT7fdQvrjyaIz+2/fB8JCQlF/o4SXU3Mwnu/XcaF+5nic7vO3EJ1P8fc3MytH0sIgoCfTz3AvL+vIztPO2B5IykTZ67eQqiv/AeKzJWWlYeP/rqOvy4kic/tvZCAvvWsM3Noi7opjWO3H2Ly75dxNy0XAJCnEbDz1HU8ViPIYWWytnyNgGX772DpvjvQFIyxH7qaVOz1Ry71Y4m7aTmY8scVHL31UHxu19nbaFHBuWbUtl9Mwowt15CWnQ8ASMnMxcHzN1Aj1HkGirJyNZi34wZ+PnlffG7/5XuKuGda8t25cC8D7/52BdeStP0DQQB2nrmJCh459iiqXQiCgA1HE7Bg1y3k5msvQGfj03D9djy8HBBto4RrW1pamlmvc+iVbcKECZg1a1axrzlz5gxq1qxpEOJev359uLu7Y+TIkZg5cyY8PEofpjNx4kSD905NTUVkZCRCQkLg5yfPMEtfX32nzs/fH6GhoQ4sTfFSM3Px7k+n8OvxO4V+FhwS4jQh5v9eeoBxG04iIS3b4HkPT09Z148lcvI0mLvlPL7aeaXQz/wDAhAaWt4BpbKMRqOBSqVCSEhIkRfvK/fT8dr3R3HydqrB89l5gtPUpW5GdMrPZ5GZm2/wM29vb4cdpzn1Y4nUrFy8+8Mp/Hqi8PWnfPnyCPX3KvNnyMHh60l4ff1p3EzKNHjexc3NanVp7bqxVL5GwKIdl/DpXxeRrzFcV+Xn5+c03807KZkYt+E49l1JNHg+K09T7DE6un4s9eepeEz4/ixSMnMNnvfy8nKauszKzccHm87gm/03Cv0sMDAQoaHOEUp/Nj4Nr317FBcSHho8r4FaEXVpzndHEASs2nsdM34/h5w8w0FpX19fRRynORLTczD+uxP462zhwZLg4BD4OCDEXAnXNk9P8ybXLP7rZWVl4bPPPsP27duRkJBQaEr98OHDZr/XG2+8gcGDBxf7mtjYWJPPN2/eHHl5ebh69Spq1KiB8PBw3L171+A1usdFrQMHAA8PD5OddrVaLdvKVRuERKpkW84TN1Pw0upDuJWcafLnKrV8y26uvHwN5v91AQu2Xyxyfb3SjxEArj/IwOh1R3DsRrLJn6tUyqlLXVlNlfeHIzfx7g8nkZ6j7Yi6u6rFG6yXu6tijrE4D7Pz8N6PJ/HDkVumX+Dguiyufixx7EYyRq89guuJGUV8jnyv8ebSaAQs3HEJ87acFzuiLmqV+H+1levSWnVjqYTULLy+/ij+vfSgqIIpvi4B4K8zd/Hmt8eQlKHtiErr0sO15L+7o+rHEtqO6Gms3nu9iFc4R12ei0/D6LWHcf7uQ5M/d4brjyAI+Gb/dbz/y2lk6+6Tbi7iQK6ri3KOsbjvTlJ6Dt7aeBxbz9w18ZuAs5yz+y4/wGvrjiI+1XSUr8qB1xa5X9vMLZfFne5hw4Zh8+bN6NWrF5o1a1am+P6QkBCEhISU6nePHj0KtVo/itayZUtMmjQJubm5cHPTJsjZsmULatSoUWRouVKppdnLZZpJbcOBG3j3p5Nih6WcpytmPFMPq/deE0fwZVp0sz14mI2X1xw2mJFoXS0Yr3Wsil6L9gJQ/jECwLazd/Ha2qNIy9aGk7u5qDDhiVpISs/Bgu0XASj/OHPyNJjy8yms3a9vCMaG+OCzvo0wdPkB3E3Nlu13zRIXE9IwYuUhXL6vX6LSp2kk2tUIxUurDwFQfl0KgoDV+67j/V9OiaFx5TxdMatnfWw6cQebCqJuFH6YSM3Kxdh1Rw1mJJpEBWLms/Xw2Cf/AHCO3S32XX6AV745jPsPteGbahUwpmM1lPN0w/RfTzu4dNaRrxHw8eZzWPj3JfG5iv6emN+3Eab8dAqn76Q6QU0Ct5Mz8dLqQzh+M0V87om64RjQIgr9luwD4Bzn7K/Hb+Otb4+LnU9PNzWmdKuNQ5fvYuOxewCUf5yZOfmY8P1x/HT0tvhcrQp+WNCvEbr9b1ehCCqlOn07FSNWHTSIIhrSKhp1K/rjjW+PAVD+vUQQBCz+5zJm/XFWXM4S5OOOub0bYOmuK9h54b74Oiobizvdv/76K3777Te0atXKFuUxac+ePdi3bx/at2+PcuXKYc+ePRg7diz69+8vdqj79euHadOmYdiwYRg/fjxOnjyJ+fPn45NPPrFbOe1FOs4ht69Adl4+pv582qDz0qhyAD7r2wgRgd74Zl9Ro9vKcvJWCkau0s/iu6hVeKNzdbzUpgpuJuk7NBoFX6QEQcAXf1/Cx5vPiR2x6PLe+KxvY9SL8Me8zefE1yr5OBPSsjBq9WEcuqZfC9srLgLTuteBj4crVAX7BWiUe4gAgK2n7+L19UfxsGDwxNfDFTOerYfuDSrin/P3xNcp+caqvf6cwlpJOGejygH4X59GiAzyxm+SMHONgiv0wt00jFylHzxRq4BXO1TDmA5VxYEGAJBx3pkSCYKA1XuvYdovp5FXUFfhfp74tE9DtIgtjxX/XhVfq+TrT0pmLl5bdwR/n9N/BzvXDsPsXvUR4O0u3u+V/L0EgL2XH+CVNYfxIF07eOLppsaUp+qgT9NInL6jX8qj4K8l8jUC5vx5Dot26AdPaoaXw4J+jRAb7IPDV/QzpUo+zptJGRi56hBOSZZgDWoZhYlP1oKnm4t4zir5ewkAPx29hfHfHUdWrvZCGuTjjjm96qNjrTD8elw/2KDk72ZmTj7Gf3ccPx/TH88jVcrjk+cbIszPE8t2XxWfV/I5KxcWd7orVaqEcuXsmzDJw8MD69atw9SpU5GdnY2YmBiMHTvWYC22v78/Nm/ejFdeeQVxcXEIDg7G5MmTnW67MABiJwCQ16zU/YfZGL7yII5cTxafG9AiCu91qy2u3ZYOGCj1gmx8IQ4t54GF/RsjLkqbyMegfhxSwrJLz87DWxuP4bcT8eJzT9QNx5znGui3jVAp/ziP3UjGyFWHxHAqd1c1Zj5TDz3jIsTXiI1ehR6lRiNgwfaLmLflvPhczfBy+HJAnLjVkpwH8syVkJqFUWsMB09efDQG45+oKW715gzbZ/15Kh7j1h8Vl0AEeLvhs76N0LqaNmosT6OfYVLqOZudl48pP53CugP6wZPW1YIxv08jBPlok98ZnLPKPExcuJuGEasO4UrB4ImLWoVJT9bCkFbR4rmq73Q7qpRlIwgCVvx7FdM3nRFD5SODvLB4QBPUqqDNmyPXNo0lkjNyMHrtEXFWEACebVwJM56pB083F+26VMg/SrEkey8/wMtrDiOxYPDEx90Fc3s3wON1K4iv0R2lQg8R+RoBH/1+xiB/TYMIfywaEIcKBXlAnOGcvZmUgRErDxkMer3WsRrGdKwm7uxhcMdU6HHKicWd7rlz52L8+PFYtGgRoqKibFGmQho3boy9e/eW+Lr69etj586ddiiRgxk0kOXxLbhwNw1Dlh8QQ3A8XNWYYdR5AZTdUBIEAfO2nMdn2y6KzzWqHIBF/eMQ5idJomCwkbr9ymct8SlZGLL8AM4UXIhVKuCNx6rjlfZVDTothvvFK+9ANx2/g7EbjopLICr4e+LLAXGoHxFg8DolNyCycvPx5rfHDBIZdq1XAXOeqw9vd/3lX+kNiDN3UjFk2QFx8MTDVY1ZPeujR6NKBq8zPGftWEArEAQBX++6gg9/OyOWvVYFPyweEIfIIrbLUtoxAtrOy/CVB3Hgqn7wZESbWLzdpQZcXfTr5pRcl4B2O8KX1xwWI08Cvd3w+QuN8UgVw+36dN9NBR4i8jUC3v/lFFbsuSY+17paMD7r28hgi0mVwlv2NxIzMGjZfly+px88ebdrLQx+JLroe6ady2gNGw7cwDs/nBAjT6LLe2PxwCaoHmY4Eac7ZiUeY0ZOHsasPYKtZ/TLdp6Li8D0HnXhKcncrZJhO9wSR64n4cUVB8XIEx93F3zyfEN0rmOYA0vpxyk3Fne6mzRpgqysLMTGxsLb21tcP62TmJhYxG+StUjzqMkh3GP3xft4afUhpGVpGw/hfp5YMqgJ6lYqnJlTrdDZ0dx8DSZ8dwLfHb4pPte7ifZC7OFquIWCWsEXqfN30zB46X7cTtF2Xnw9XDG/T0N0rBVW6LVKrUsABZ2Xs+LjJlGBWNg/DiHlCidVVGoDIiUjF8NXHcT+gpwDKhXwZucaeLldlUIzvko+Z/+9eB8jVx0Scw5U8PfE4gFNUC/C1PVH/38lHaepzkv3BhUxq2f9Qnv9Kvl7adx5KWrwBDCMWlDacW48dBMTvjsudl6KGzzRnbNKG9jUdl6OGiSfeqltFbzVpYY4i6ZjmKfGbkW0imM3kjFsxQEx50B5H3d8/kJjtIgtvJuHUicdBEHAp1svYP5fF8TnWlcLxoK+jeHv7Vbo9UpdEnEvLRvDV+lzDriqVZjyVG30bxFV/D1TWYeJLafvYvTaw2K0ZlGDJ4Cyv5tyZHGnu2/fvrh16xZmzJiBsLAwpwjXUxo5hSh9f/gm3t6obzzUqeiHrwc1Rbh/yenzlRJenpaVi5fXHBbDxlQq4N2utTG0VbTJ819aP0paU7n38gOMWHkQqQWDJxGBXlg+pCmqhppeTmLYgFBGXWo0Aj7dcQPrjuhHsXvFRWDGM/WK3L5OiQ2IW8mZGLR0Py4WbOHi5eaCz/o2QqfahQdPABhMwchhIM9cPx65hbc2HhPXMTeIDMCSgU1MDp4Ahh01pRxnZk4+xqw7gi2n9Z2XsZ2qY0zHqqavPwr8XgLaPBlDlh/AvYJtF4N9PbB0cJNCkSc6SlyqJAgCPt9+ER9v1i/16Fw7DJ/2aWgQeWJApbycEvfSsvHiigM4Jum8zOpZv1Dkm44S6xLQ5skYvfaImDSsSogPlg9pVmTkiRK/m7n5Gkz64QQ2HNRPOAxpFY13u9YuNHiio8TosKuJWXjzF/22i+U8XLFoQBxaVQ0u4jeUdy8BgNV7r2HyTyfFMreMLY9F/eNMDp4AhtEZSvpuypXFne5///0Xe/bsQYMGDWxRHjKDXMY5Vvx7FVN+PiU+7lAzFJ/1bVTsPn4qhY2a3UvLxqCl+8U1L+6uasx/viGeqFehyN9RYjjObyfu4PV1R5GTrx0lqFvJD0sHN0VouaIHT5QW3pmTp8Hr647it5P6DvdrHavh9U7Vih08VNqaynPxaRi4dB/upmo7L+V93LF0cFM0iAwo8neUGF6++J9LmCGJVuhUKxSf9W1caOZXSmlLIlIyczFk2X4cLsiT4apW4aOe9dGriM4LoLzvJQDsunAfI1cdFNepx4b4YEUxnRfA8JxVwmU2XyNg8k8nsUaSTHRQyyhMfqpOkZ0XwGhNpQLcTMrAC0v24doD7VZ9JXdelHnOfnvwBsZ/d1zsvDSLCcLiAXEGYfPGlJbvJTMnH6PWHDJI8vdu11p4sbXprXx1lBYddvxmCkZsOIvULO31p4K/J5YNaYqa4X5F/o7S2nmmlkd2b1ARc56rXyhaU8oZ8r3IicWd7po1ayIz0/S+y2Qfjr5B6bJaz/lTn726f4vKmNa9brGNB0BZSRniU7LQb8leMdTR38sNXw9qgibRQcX+nqPrx1LfH76JN789JjYe2lYPwRcvNC528ARQVqhcVm4+Xl5zGNsKtldyUasw45m6eL5p5RJ/V62gBsTJWyno//U+JBfs8xsT7IPlQ5qKCdOKoqSwa1Ohji80r4xp3esYrPk1RUkhyYnpORi4dB9O3tIO+Pl6uGJh/8ZiwrSiKC28/K8zdzFq9WFxwC8uKhBLBjZBoE/RnRdAWedsXr4Gb208jh+O3BKfm/BETYxsE1titKBhGKsg6+jCK/fT8cJXe8XlSeF+nlg+tPjOC6Cs7yUArNpzFe/9pJ9weKpBRXxcQucFUNY982F2HoYtPyBuieruosa85xugW/2KJf6ukpZEHLiaiCHL9uNhtrbDXauCH5YNLjlaU0lh14Ig4P1fTxtkIh/ZNhbju9SEuqQ2u4KOUwks7nR/9NFHeOONN/Dhhx+iXr16hdZ0+/kVf3GlsjMMkbTvt0AQBHz0x1l8ueOy+Nyr7avijc7VzWoMKKWhdDMpA/2+2ofridrR+or+nlg5rDmqhvqW+LtKCmFdt/86Jv5wQryY9oqLwMxn64nZnovjyPPQEpk5+Rix6qC4PMDDVYWFL8Shg4l16qbojlLOxwhoE6MMXLpfzK3QIDIAywY3FbM9F0cpN1ZBEDDrD8Mted7sXDjJX1GUEt6ZkJqF/l/vw/m72uUB5X3csXJYM9SpWHidujElher+fuIORq89Ii5P6lw7DP/r28ggYVFRDI/TViUsu9x8bYTNpoLt6lzUKsx9roHJdeqmGN9PXGTa5z5/Nw0vLNknLg+IDfHBmhebi9mei6Okc/bLHZcw83d9hM3gR6IxuVvtEjsvgHJCdVOzcjF4qT7CxtfDFUsGNTG5Tt0UlUKWROy6cB/DVx4Ulwc0iw7E14Obopyn6VBrKaVETWk0At796aTBdr1TnqqNIa1izPp9pRynUljc6X788ccBAB07djR4XjcCm5+fb+rXyIocNVoqCAKm/3oGS3frt1GY8ERNvNS2itnvoYQO6bUH6ej31T5xD+7KQd74ZnhzRAQWHeoopZRMrCv3XMVkyWj9gBZRmNa9jlmNB0AZYUcPs/MwdPkBMZmYj7sL5nSvgnY1ip8tlBJD5eR6kAD2X9GO1uvCc5tGB2KpmY0HQBmdUUEQMO2X01gu2Z95crfaGPqoeY0HQBlRKLeTM9Hvq724WhCeG+bngTUvNi8yt4IxpQyg/HT0FsZtOCZuI/VUg4qY17uBWQN+gDKWRGTn5eOVNUfEZGJuLios6NcYXYwyBBencKNXfr3uk7dSMODrfUgqiLCpGV4Oq4Y1LzK3gjGF3DLx6dbz+HSrPsLmlfZV8GbnGmZHHyhhpjspPQcDl+7HiVva9fj+Xm5YObRZscuTjIlrumVcmdvO3sVLqw+Lu5c0r6xdUudTqnumLUpYdvkaAeO/O46Nh7Tr8VUqYNaz9dG7aaTZ76GEdp6SWNzp3r59uy3KQRZwxLYTuhkmXYdbpQKmP10X/VtYtm2c3EfNbiRm4Pkv94pbD8UG++Cb4S3MSgyno4SGvfF6/OGtY/DOk7UsCl2Ue6M3IycPg5fux8GCfZvLebhi2ZAmiPDMteh99ElhZHiQ0IbHDVq6Xxytf6RKeSwZ1KToxEwmyP2cNdXh/vCZunihuYXXH5k3IBJSsww63JUCvPDN8OYlLg8oihyPEdB2uF9ff9QgwmZWz/olLk8yIPOoqZw8DUat1i9p8XBVY9GAOLSvEWrR+8j9nD0Xn2awpKV+hD9WDm1W7NpmY0oIL/98+0WDDvebnavj1Q7VLHoPwzXd8jvSlMxcvLBkn5jDJsjHHauHNUftipZFsMo9D8rOC/fw0ir9kpbHaoXivY6Vis0HYkzu30uNRsBbG4/h+8PaJS0uahXm9W6ApxuaF2GjI/d2ntJY3Olu27atLcpBFjCcybDPt+DTrRcMQjotHS3TkfPN9W5qFl5Ysk/scFcP88XqF5sXm0zMFOlMsdyOEdBuVyPtcFuyPEBKLePpiey8fIxcdUjscPt7uWHVsGaoW9EPCQkJJfy2IbEBYe1CWsHJWykYuuyA2OFuWz0EXw6IMys8V0rO30sAmLv5vNjhVqmA2T3r47kmll9/5LwOLyk9B/2/3id2uKPLe2PN8BaoFFByeK4xtaogkkhuBwlg86l4jNtwTCxav+aV8cHTdc2OsNGRc13mawSM3XBU7HB7ublgyaAmxSYTK4qcIxeu3E836HDHRQVi2ZCm8DNztlDHeN263CzbfcUgh405ycRMUTlixsRM6dl5GLJMnzQ2pJwHvnmxOaqZ2EaqJHKODtt/JRHDVx4UO9zd6lfA3OfqI+nBfYveR87fS0EQMOXnU2KH21Wtwmd9GxWbALgoakngkRwHipTGvDguIzt37kT//v3xyCOP4NYtbaWuWrUKu3btsmrhyDR77w/4+faLBkmLpveoW6oONyDftVuJ6Tnov0S/hrtqqC/WDm9hcYcbkPe6rd9P3MHbG4+Jj19pXwVvdjE/PE5Krmsq8/I1GP3NEXENdzlPV6x5sXmRWw+VRK4NiAt30zDg633i/tStqwVj8UDLO9yAfL+XALDw70tYsF2fcXVOrwal6nAD8j3O1KxcDFy6X1zDXSnAq9QdbkC+ayp3XbiPV785IoaU921WGR/2sLzDDcg3akqjETDx++PYdFy7htvDVY2vB5euww3I935yMykDL3y1V1zD3SDCH8tL0eEGjLbZlM8hAgDWH7iOab+cFh9PeKJmqTrcgHzvmVm52rwnujXc5X3csXZ4i1J1uAH5Rocdv5mMocsPiPtTd6kThk+fb2j2khYpuX4vAWD2n+ewau81ANoZ7gX9StfhBuT93VQii8+07777Dl26dIGXlxcOHz6M7GztBTclJQUzZsywegGpMMNZKdt+C77Zd91ghHdyt9oYYGFIuZQcJ0dTs3IxaOl+XCjY0zgyyAurhzVHeV/z1qMZk+sI6N/nEjBm3RHxwjn4kWi82blGqd9PjmFH2pCq49hcsKexl5sLlg9pirqVSk5AVRQ5NiCuP9Buy6NbQ9ksOgiLBzQpMXtuUeQaXr5qz1XM+kOftGha9zrFbpdVMvmds5k5+Ri67IC4hjK0nHYNd2k73IA811QeumY4w/R0w4r4oEfdUmfjlmN4pyAImL7ptLinsatahYX9G+ORKqXrcAPy2SJUKiFNGxWmy1JeM7wcVgxtZnYOCWNyrEsA+PnYbUz4/oT4eEzHahblsDFmONEtjyPNzddg9Noj2H3xAQDtIPXKYc3MShpbFDlGh2m30tyPhwWD1G2rh+B/fRuVuONFUeQaHfb59otY+Lc+KnVOr/p4vG7pOtwADJfxyOWmqWAWn20ffPABFi1ahK+++sogc3mrVq1w+PBhqxaOTLNXA/mvM3fx7o/6G874x2talLTIFLndXLPz8jFi5UGxwRvm54E1wyxbw21MjhFkR64n4aXVh5Cbry1Rr7gITO5Wu0zbz8hxn8rpm06L2/K4u6jx1cAmiIsqfou3ksitAXH/YTb6f70PCQUzTPUq+WPJ4CYWrUczJsdtiH49fttgW563utTAoEeiy/Secjtn8zUCxqw7Ii6DCPR2w5oXmyM6uHRruHXktqbywt00DJEsg+hUKwwfP9fAsjXcRuSYyOiLvy+J2/KoVcCnfRqiQ03zdkkoitwGN9MLklPq9uGODfbBqmHNLVrDXRy5NOz/vXQfb2zQ5x148dEYjO1k2RpuY3IbkBcEARO/P4EtBYPU3u4uWD7EvF0SiqM7Z+VwjIB2+9fBy/aLyyCaxwRhUf+4Ug9SA8YTSPI40HX7DSfJpj9dB882LssgtXwH5JXK4k73uXPn0KZNm0LP+/v7Izk52RplohLYI0TpyPUkvPLNYfH9h7eOwah2pR/h1ZHTOjxBEDDhuxPYe1mb2TrQ2w2rhzVH5fLmZSkvitwyQV9/kIEXVxwUQ6qerBeOj56tV6qQTim5ZaJfuuuK2ODVhVQ9Wq30M0w6ajFU1/EHmZmTjxdXHBSXQVQL9cWKoc1KFdIppTa4pjj+OA9eS8K4DfplEKPaVcEr7auW+X3tvTSnONrdIE6LDV5fD1esGla6NZTG5BRenpCWhcHLDiC1YCu7VlXLY0G/RqUK6ZRSG1x/HH+gPx29ZdDg/ejZ+mbtaVwS6ZpKRx9nXr4Gr35zWNw7XrsMwvws5UUxyIPi+KrE+btpGLlKP0jdt1kkJnW1LNGoKXILSf7fXxfFzNb6QerAMr+vrjrlcIxpWbkYsvwA7hREZTSI8MfXg5uWaZAaML7+lOmtrGLH+XuY9ONJ8fH4x2tiQMvoMr+vnNrszsDiu154eDguXrxY6Pldu3YhNrZ061zIMrYOL79yPx3DJJ20pxpUxMQnalnlveW0pvKTLefFWVFPNzWWDWlm1QYv4PiLVHJGDgYv348H6TkAgBaxQfjk+YalDqmSktOays2n4jF9k37d3cxn6qGzBdvymMPRdZmvEfD6+iM4eiMZABDu54mVw5qZtQ93SeQ0m3Y9KQsjVh4St3J5Li4Cb3cp/TIIKTkd59e7rojJ4XRhyGVZBiEllyURGTl5eHHFQXH7xToV/fDlgCalyjtQHEfX5d7LD/DWt8fFx291qVHqvCfGDDNeO44uOdP2c/cAaMOQlw1patY+3CWRU9h1QmoWhiw7gLSCQaIONUMx/enSL4OQklOk33eHbuKTrefFx58837DUeQeMiXlQrPJupZebr8Er3xzBmYLkcJFBXvh6cFP4elicQ7oQOUVNnb6dilfWHBZzZQxtZZ1JMkBex+kMLG55Dx8+HK+99hr27dsHlUqF27dvY82aNXjzzTcxatQoW5SRjNgy3CMlIxdDlu1HoqST9vFz9cs8K6ojlwbEhoM38L9t2sEjlQqY36cRGlqwD2Vx5NKAyM7Lx4hVh3D5XjoAoEqID77sX/p1v8bkEpF87EYyxqw7In4XRneoarUGLyCfBsTM387gz1PaWVEfdxcsHWydBi8gnxvrg4fZGPfjRSRnasMAH60ajBnP1rNa+LtcjvP3E3fw4W9nxMcznq2H1tXM3zu+JHL4buZrBIxZexTHb2qX7lT098RSKzV4AfmsqbyY8BAjVx0S16r3bRaJl63U4AXkEzn15T+XsWbfdQDa/ca/HBCH6lYYpAbks1QgPTsPQ1ccEAeJ6lbyw2dlWPdrTC45bf69eB8TvtcPEr3zZE10rV+Gdb9FcGRdCoKAd384iX/OaweJArzdsHxIMwSXMlePMbmEXd9JycTQ5QfEtepd6oRhUlfrTJIB8jlOZ2Hx3W/ChAnQaDTo2LEjMjIy0KZNG3h4eODNN9/E6NGjbVFGMmKr0dK8fA1eXXtY3LKmRlg5fFmG5EymyKEBsf9KIt6RJEd5t2ttdLHirKgcwnF0N5z9V7Sh88G+7lg+pBn8vcsWhiwlh+O8m5qFF1fqozKeblgR4x6rbtXPEMebHHjDWX/gOpbsugJAGzr/+QuNLd47tThyaPTqZiVupmjXqtcIK4cv+jcucxiylBzO2ZO3Ugz2qB7TsRp6lzIbe1F0x+nIRtLsP89i6xntIFE5D1csHdIUYX6lz5VhTA7bTKVk5OLFFQeQUjBI1LZ6iNVmRXXkMLiw+VQ8Pvpdn9Bwdq/6ZUoOZ0wO30uNRsC4DUcNQueXDmoKHysNEgH2TYJblGsP0jFqzWExdH5AiygML2U29qLol0Q47gK0ZOcVrD94A4A2dH7xgCaoElL65HDG5PC9zMrNx/CVB8VtbhtGBuDT5xuVKVeGMbUMjtOZWNyaUalUmDRpEhITE3Hy5Ens3bsX9+7dw/Tp021RPjLB8AZlva/BzN/Pitsslfdxx9eDm8Dfy3qdNMDxodd3UjLx8ppDyCsIwxnUMgpDW0Vb9TPkEEK/au81fFuwVsvTTY0lg5oiMqhsa9WNOfo4s/Py8dLqQ+KWNc1igjC7V32rJwXTvZ2j6vLw9SS896M+odj0p+uiXY1Qq36GHLYF+eDX09h/VZtQLKScB5aWcvshczmiPh88zMbIVYeQXRA6/2zjSmVOzmSKrjYddc7+fOw2vtxxGYB2kGhh/zjUDLfeIBHg+KUCuiR4ukHqmuHl8PkLja02K6pjMNOksepbm+ViQhrGrj8qPn7jsep4plHZkjMZk8Na5wXbL4qRRLrQ+VArDhIBRvdMB9RlenYehq88KA4SdagZiilPlS2hqim676aj7iU7L9zDzN/1kUQf926AZjFlS6hqzNHtH0EQ8M73J8RBosggLywZVLaEqiY5+DhvJGagz+K9uJ6UZffPtoVSD+G5u7ujdu3a1iwLmckW4R4bD93E1wUzaa5qFb54oTEiAq3bSQMcG6qSlZuPl1Ydwv2H2tD5VlXL470yZvA2xdHhOPsuP8D7kn1FZ/dqYLXQeSlHHqcgCJjy0ykcKdhXtFKAFxa+0NiqURk6YiZWq79zyRJSszBqtT50dVDLKPRrXtnqn2P4FbD/kX578AZW7NHuK+rmosKiFxqVacusojhyTaU2CdURMXS1YWQAZloxdF7KkUsiTt9Oxdsb9Unw3utayyoJDY05eqnA3M3nsKMgdDXQ2w1fDWxitdB5KUceZ2pWLkasPIT0HG3W+W71K+DVDmVPaFiIg9c6/3Xmrri+WaUC/te3kdVC56UcubOJIAh4a+MxnL+r3Rq1SogP5vexTn4XY/rdExzTSRu9Vr816ugOVdG9QdkTGhpz9FKBZbuv4vuCnETe7i74amATq4XOSzlycDMzJx8jVx3C6TupGHI7BSuGlkOT6PL2LYSVmX2HGDp0qFmvW7p0aakLQ2aycljdiZspeOcHfbj11O510DzWNie2oxoQgiDgvR9P4ljB+sKIQC8s6Gv9WQkADm1A3E7OxCvfHBZn8ke0ibXJDQeAQWXa+zjX7LuOdQe0oWMermp8OSCu1Puql8RRDYicPA1GrTmMu6n6mfx3u9lmoNOR4eXHbiQbZF19s31lNKpc9gy6pjiyATHjt7PYc1m7F25IOY8yb1lTHEclUktKz8GIVfrlHr3iIsq8zVtRHDno99uJO/iiYC9cF7UKn/drbPVIIh1HHadGI2DsuqO4fF+bE6RmeDmbRBIBjv1eXrr3EK+v0y/3eLNzDbS3ciSRjiOTj37x9yX8diIegHa5x+KBTUq9r3pJHLUiKyNHO5Ov2xqsY81QjO1k3eVmOo4ML//30n2DnCAfP9fA6pFEOo4akBcEAZN+PIHTBUnwyvu4lWnveLkwu9O9fPlyREVFoVGjRg7PiPpfZ81kZCmZuXj5G32m4BeaV0b/FlFlfNeiOWrt1tr9NwzCrRcPaIJAK2R9NsVW4f8lycnT4OU1h8WZ/EerBlst67Mpjtpm6sj1JEz9WR9uPatnfatlfTbFUdsvfbjpNA4V7N9cwd8TX7xg3fXNUo7afikpPQcvrTa8/jxd1/qzojqOWgf887HbWLpbG0nk5qLCov6NEe5v3dBVKUfs063RCHht/VHcTNLO5DeI8McHPay7vlnKUVsWXkx4iDe/1c/kv/NkLTxipazPpjjqu7lg+0X8dTYBgDYJ1VcDm8Db3foz+YDjvpcZOXl4adUhpBUkoXqyXrhVk+AZc9Q5u+vCfXy8+VxBGbT7x1tzfbMxcZtNOx6kIAiY9MNJnI1PA6DdP/6TPg2tlgTYmEH7x47HeTc1C6O/OSJmKn+5XRU8Wc/6SfB0DNt5NvuYQtbuv4HvD+tn8j/qVsWmy83sxewr6KhRo7B27VpcuXIFQ4YMQf/+/REUZN01EmQea30JBEHA+I3HcSOxoJEUGYApT9UpY+mK54i1W2fupGLaL4adNGsmoTLmqJmJjzefE7eTigj0smrWVVNUDpjST8nMxei1R8SZ/GGPxqBHo0o2/UxHJIL+4+QdMdzavWAm3xahYzqOOGcFQcCb3x4T909tEhWI97rWQnLifZt9piPCy6/eTzdI3Di1ex3ERdn23umI8PIv/7ksZgoO9nXHwv5xVt8aTMoRUVNZufl49ZvDyCgIt36mUSWr5wQx5ohzdu/lB/i0INxarQI+69vIZjP5gONmDaf+fAoXErTh1tXDfDGnVwObDRIBjpk1vJeWbZC4cWyn6uhYK8y2H6ob9LPtpxj49tBNcQtYH3cXLB4YZ9NOmiO+l/kaAa+tOyJuAdu2egje6Gy7iRXAMVEoZ+MN2+wfPVsPseVtdy+xJ7Nb5J9//jnu3LmDt99+G7/88gsiIyPRu3dv/Pnnn5z5tjOVlWZSl/97FX+c0oYb+Xu54fN+jeDuartOGgC7h15n5OTh1W8Oi4mLBraMwtMNbdxJc0ADYvvZBCz+R5u4yM1FuybfVjP5OvZu9AqCgAnfHRdn0hpXDsCEJ2ra/HPtnXH/RmIG3t6o385lcrfaqB8RYNPPdEQDYunuq+JMWpCPOz5/obHNrz/2TuSYnZeP0WuPiNu5PNOoEvo1s/6afGP2XhJx6FqiwUza/D6NUNEGa/KlHDFQ9MGm0+JMWrVQX8x4xjZr8g3Z95x98DAbr63Tr4l9vVN1q25nZ4ojwq5/PHILGw5qo9+83V2wsH+cVTOVm2Lvc1ajETB2/VHcf6hdotSmeghebW+DNflGxOO00/fyYkIapvwk6aT1rI+qodZfk2/I/p3Rz7ZdwN7L2h1pKvh74tPnG1o1U7kp9m7nZeTk4ZU1+jb7oJZR6GaD7ewcxaIWjoeHB/r27YstW7bg9OnTqFOnDl5++WVER0fj4cOHtiojGbHGV+zYjWTMkKwJmftcA5skTjNm71GzyT+dwqWCfaprV/DDO09ab//Cotg7VC4+JQvjNhwVH098opbNO2mA8XHa/OOwet91/H5SO0jk5+mK//VtZLNwayl7LonIzddgzLojSM3SdtK61quAF2yQOM2YvTujx28m4yNJdtm5vRtYdTupoth7AOWj38/ixC1tHomYYB9Mt2G4tZTajjPdyRk5GLP2qBju+Eq7qmhlw3BrHbUd/o5Sm47fweq92n2qPVzVWNCvsfUzBZugtmOjV6PRRp/o8kg8UqU8XrFDJ83e2xJduZ+OSZI8NtOfrmvTcGsdew/IL9xxCbsuaiOHQst5YF7vBjYLt5ay5/UnKzcfr6w5gsxcbfRJ32aReMpWeWwk7Pm9BIA9lx7gf39dAKDNI/G/vo1sPrEC2L9tYNxmn2iHNrs9lbrFqlaroVKpIAgC8vPzrVkmKkFZkx5l5OTh9fVHxX0aR7SJRafaNg43KmDPDukPR25i4yH9SPaCfo1sGu6oY891W7pwo6SCxCGdaoVhiI3DHXXsuc3UmTupmP6rPiP7HDsNEgH2XRIxd/N5MSN7ZJAXZva0x0yafWea0rJy8eo3R8Trz8g2sTZLXGTMnufs1tN3sWz3VQDaJQIL+jWySXZrU+y1ZZggCBj/3XExI3vT6EC8boMt0Ewx3H7Jtsd5IzEDE77TR59M614HNcJtPZOmZc8kh0t3X8H2c9olAuV93O0ykwbAaFsi235Udp52iYAuI3vPxhHoGWfdLdCKYs9tpg5eTcS8LfqM7J/2aWjTJUpS9txmc/qvp3Hurjb6pEZYOUzuZtslkjr27Iw+eJiN19fro0/GPVYdTaPts7zXnueso9rs9mRRpzs7Oxtr167FY489hurVq+PEiRNYsGABrl+/Dl9f5WeVUwrDTp3lX4KZv53FlYKMpA0i/PGWDZNtGbNXGOut5ExMluxt/OEzdRFrh5FsY7a+5Xy18zL2XdGHG82xUXZZk+w00pudl4/X1x0Vk20NfiQaXeqE2+zzjNnrnN13+QG+/EebEdnNRYUFfRvbLXGIPWdgpv96GtcTtXsbN4wMwJsOu/7Y7kjvP8zGeEkn7d2utVCnou2S/RmzVyK1bw/dFPc2DvB2w/9snEdCyl7fS41GwBsbjonJtp5qUBHPN4204Scasld02Nn4VMz+45z4eN7zDa2+T3VR7BmB8unWCzh1W5sROTbEB+8/bZ9OGmC/8PL07DyM3aCPPhndoRoeqWL76BMdcZtNG19/tp29izX7tNEnnm7agU17RJ8A9huo1iWI00WfPFo1GKPa2i7ZnzF7nbPGbfYZz9RzSJvd1swedn/55Zexbt06REZGYujQoVi7di2Cg+33JSa9suz1uP1cAlbt1SZo8nRT45PnG9olRFfHHg0IjUbAW9/qG0nPNq6EZxrZZyRbR4WCurHhVepcfBrmbdaPZM/vY59wIx17XYw/3XpBHMmuGV4OE5+0/TpuKXucsw+z8/DmxmMG29Y0sMHe6kWxVwNi6+m74jpKH3cXfGanJQI69shjpG0knRCT3XSqFYYBNtwRwjTbN3pvJmXg/V/00Seze9ZHBX/bruOWsldndOnuK9h/VTuwGRHohRnP2GeJgI49Bopy8jQYt/4YcvK1A5sj2sSibXXbruOWstdf89C1RHy5Qz+w+VnfRjZfxy1VlrabJT787YyYILdJVCDG2GJv9WKIg342PMqk9ByM/06/RGDKU3VQzQZ7qxfFXoN+Px69JeZeCvJxx7zn7bNEQMcelzpTbXZbJ8h1FLOvNosWLULlypURGxuLHTt2YMeOHSZf9/3331utcGRaacPNktJzDBI0Tepa2+4jSWpJ+9pWF+RVe6/h30va/XAr+ntianf7jWTrqFVAvmC7ULmcPA3GbTgqNpKGt45Fsxj77iZgj63RjBtJnzzf0GZ7GxfFHuFVH27SN5KaRQfhxdaxNvmcoqjtsCQiMT0HEyRZvCc/VdumGZFNscdx/nDkljj7G+Tjjo/stERAStcms9X3UttIOi4miOsVF4HOdow+AezzvbxwNw2z/9QniPv4uQY229u4KPY4Zz/bdkHcD7d6mC/GPWabvY2LYo9t0TJy8vDGhmMGCeLsGX0CWC8JbnH+PpeAbwpmf73dXTC3dwO7RZ/o2GObzfd+Ool7adrZ3w41Q9HHjtEngH3O2TspmZgsSRD3YY+6CC1nn+gTHXscpxza7PZidqd74MCBdm84kGmlDS9/V3KRals9BP3tkKCpMEnZNdZ/98v3HmKmJEHT7F4NHLO3X8FUt60GFhZs04fIOaKRBNh+pNdUI6lWBdtt9VYUW1/2tp9LwNr9+kbSnOfq22cdpYSt61IQBLz340kxi26HmqHo3cS+jSTA9rOGt5MzMeVnaYhcXbuto5TSzzTZxoo9V7Hnsr6RNPmp2jb6pKLZeklEbr4G4zYcE5e1DG0Vgxax5W3wSSWwcej10RvJ+OJv7cCmq1qFeb0b2n0dpT3WrX/0+1lcfaBd1tKocgBGtrHvwCZg++NMycg1WNbyzpO1EFXex/ofVAJbZy//5dht/Hr8DgDtzjsfPWv/gU0pW9SlNl/GCaQVJFXt0bAinrDhftxFsXVEo2za7HZidqd7+fLlNiwGWaI07fE/T8VjU8FFKsDbzb5rfyVs2ejNL8i8mpWr3x7s0WqOWQKhKuh12+IidfxmMj6XNJLmPmf/RhJg+wbELBk0kgDbhrGmZOQaJGhyVCNJyhYN+1+P38GmE/rrj6MaSbZsQOiSikkbSY/XdcxWJ7ZcU3nlfjpm/XFWfDznOcc0kgyXClj/QBf+fUnMPF8lxMeuuU+kbHnOZuXmY5zR2t+6lew7+wvYfqnA7ov3sXKPflnd3OfsP/sLGIeXW/9Ap/5ySlz727pasF12vjDFluHlCWlZeO+nk+Lj6T3q2i33gJStB6q/2X8d/5zXJjUM8/PAtO51bfApJbPl4Ga+RsAbMmmz24v9rzpUZpauv0zNysVkyUVqWvc6DrlIAbZtQKzacxWHCzI/R5f3tssezkXRh3da931z8zUY/90JsZH0aoeqqBdh/0YSYNvw8kPXkrByr+MbSYBtl0R89MdZsZHUpnqIwxpJ0jVi1r6xJmfkYKpk9nf6045pJAG2bUD8cOQWdl7Qbs8T7ufpsEYSILn+WPkodUl9dI2kQS2j7LI9mCm23GbqYkIaFmy7CEC7PY8jZn91bLk12ufbL+JywfY89SP88XJ7+yVokrLlYHxmTj4mSpa1THi8psMSNNlyoPrvcwn44cgtAEA5T1fMdtDECiDZMswGvdFpv5xGcsFuLV3rV0B3O2wPZoottxKNT8nCR7/pBzZn9awPf2/HzP7aMsnhqj1Xxd1aHN1mtxd2upXIwi/7R7/rG/bta4Q47CIF2O5CdTs5E3P+1Gdend2rAbzd7ZcgxZittuxZuusKzhSsvatVwc8ue6iaw5prt3LyNHjn+xMGScUcmcXSVttM7b+SKIaV+7i7ODREzpaJ1Gb8dkZMKvZ4nXC77KFaFFutA05MzzHY0m7Gs3Ud1kgCbLemctPpB9h7RZ9UbLwDG0m2qkuNRsA73580SCpmz6SGxmx1nOfvpmHh3/p8GR8/18CuSQ2lDI/Ruu/9v20XxN0SmkUHYWDLaOt+gAVsdS/JyMnDuz/qJ1be61bbrkkNjdlqy7BtZ++KEZuB3m6Y/rTjBjZt2Rmd+vMpMalY7yYRaGenLTVNsdU5K7c2u72w061AagtuUPsuPxCTavi4u+CDZxy79sVWI9pTfj4l7rvZt1lluycVK8QGf+IbiRn4ZKs+W/nMZ+s5rJGkLYNtZpq+2nlZzFZet5IfBj8SbcV3t5wtbq7Zefl45wf97MtbXWqgYoDjG0mAdQfD9lx6IGYrL+fhiml23J7HFJXBAlnrve+Hm84gSTL70qFmmPXevAysWZf3H2bjfztvio8/6FFXFgObgHWPc8PBG2K28qjy3nito332HS+KLY5ToxEw8fsTyCtoQLzUtgqq2zHzszFbfS/P3EnFV/9cBgC4u6gx49m6ds38bMxWHbX5Wy/gZpI2EWeL2CA8Z6d9x4uiO0xrtgvSs/PwnmRLqfe61UaQHXdrMWarJRGbT8WL2crL+7jjnSdrWe/NS8Gwu2CdAxUEAZN/klmb3U7Y6VYggy97MV+CrFzDsKq3H6+JSg5s2AO2aUD8cTIeW05rswUH+3pgwuOOD1Gx9ppKQRAw6UdpWGc0Gjpw9gWwzezolfvpmP/XBQDawaWPnq3vsLByU6x1b13092VcTHgIAGgQGYABDpx9AWzTgMjKzcckycDC20/URJiDwsp1bDHot/vifXx3uGBgwdMVUxyQVMyY/jit1xr88LezSM3SNpK6N6jo0NkXALBFPy0hLQszftMn9fmwRz2HhZXr2GJw85v913HoWhIAICbYx+ERU7b4XuYbDSyMalcFVUMdN7AA2GbLsFO3U7Bk1xUAgLurGjMcPLECQKxQa3ZG5205j1vJ2oGFVlXL4xkHbylli3P2YXaeQSLOyU/VRoC34wYWANu02f88FY+tZ+TVZrcX+bRmyWzmzkot2XkZl+9r12s1rhyA/nbfK7Ywazcg0rJyDdaLTnmqtkPDOnV0g+nWCq/6+dhtMalGBX9PvOmgpD5S1l4qoNvfWJot2BFJfYwZHKcVMu5fuvcQn2/Xrxf96Nl6ds9WbswwesY65+wXf18yuP680Mwx69WlDI7TCnVpPLAw4Ymadt/SxRS1lcPLd164h5+O3gYA+Hm64r1ujh9YsMVWNtN/PYPUgkR4zzSqJIukPtYOL09IzTJIhPdhj7oOH1iwxbZoa/Zdw9EbyQCA2BAfh61Xl7L2THe+RsA730tyvLSv6tClWDpqKx/niZspWLZbO7Dg4arGhz0cP7BgScSpuT7+8xzupGQB0CbCc+RSUB1pZIg1jjMtK9dgYEEubXZ7YadbgcwZLb2VnIkFkob9DBk07AHrNyDmb72A+FTtRap9jRB0q++YbMGFWHHLnofZefhgk3725f2n68LXw/FrX6w90vvL8TviXo2VArww1gHboJlizeMUBAFTfz5lsL+6I7ZBK8TKs4ZX76djkSTD/sxn6zs0rFPH2oN+X+64LGbYbxIViL5NHT+wAEiyB1vhGpuTpzHYK3bikzURUs7+26AZs3bU478X7+OXY9qBhQBvN7zb1bFhnTrWjkKZ8dsZMcN+r7gIPOKgRHhS1o6auv8wG3P+0K8XnfFMPXi4OnZgATDep7vs77d2/3Ucu6nNsF8t1BcvtXX8wAJg3dlRjUbAuz+dFDt8YzpWQ3SwY3f40LJuXZ6+nYqVe64C0CaPlcPAAmD97+anWy8Y5JiSTZvdTtjpViDDC7fpL8GHm04bpOGvGS6Dhj2s24C4mPAQy/+9CkA7+vn+03VlcZECJGuarHCR+mzbBXF/9c61w/BYbXmsF7XmjTUzJx8zf5MOLNSBjwwGFgDrHueW03fFDNeVArwcvl5Ux9oN+w82nREHFl5sHYsa4Y4N6zSlrN/NW8mZWLjDcGBTDgMLgHXXVC7bfQVXCiIW6lf0QW8HrxfVseYASl6+BtN+0SfCm/hETZR3wP7qplhzTeWha4n4sSBiwd/LzeHrRXWsvf3Sx3+eExNR9YqLcMz+6iZYM7w8JSMXczfrBxY+6FEX7q7yaNJb87v5/ZFbOFYQsVA11BcjHLR1qDGrD8b/ckocWBjdoRoql/cu03tajRW/mxcT0rBCpm12e5HHN5QsUlJ4+e6L9/HbCX0ihtc7yWPGELBeA0IQBEz/9bRBIpjIIJlcpCBZ013G97lyPx1LJeu13u3q+LBOHWs2lBbuuCSGVbWrEYKOteQxsABYrwGRlZtvELEwqWsteLk7fvYFsG7D/p/z98T1WqHlPPBqB3lk2Aese87O/O2MwcCmIxNRGdOds2UdQElIy8JnBVtnqVTAG+0qy6aRZM1Q3TX7rovJG+tH+OO5uMgyvZ81WWvQT6MRMPVn/cDCm52rOzQRlZQ1Z4BP3EzB+oM3AGiTN46X0XpRayas/GTreTF5Y/cGFdFcJgMLgPVmRx9m5xkshZjyVG2HJo+VsmakzaYTd7D/ij5544utY8r2hlZkrQF5QRDw/q9nZNtmtxd5nL1kEcPRUsNvQW6+xmC9xPgnasLfSz7rJay1Dmbb2QTsKFjjXNHfUzZhVTrW2qf7g19PIzdf+ybDW8fIZ/QThg2lsiwVuJmUgS936EOR5bBeVMpa652/3nVF3LqmRWwQnqgbXtaiWY211lTm5mvwvmTrrAlP1JTFUggda+0tv+/yA/xasHVNkMwGNgHrbdkz+49zeFgwY9inSSRqhMrn+mOte0lSeg7mbTkvPp7avY5sIhYA6303vz10AyduaUORa4aXQ18Z5FiQssaSCEEQMO2XU+J9d0zHarJYCqEjPavK8t08F5+GVXuvAQC83Fww8Un5DCwA1jtnjaP8WlcLKWvRrMZaOSUyc/IxQzIY/17X2rJYCqFjrfX5f51JEPMSybHNbi/sdCtQcaPCK/dcE7MiN4wMQK/G8ggF1LHGiHZ2Xr7BnrgTn5TPjKHICg2Iv88l4K+zCQCAMD8PvNxOPjOGgPVmYGb8dgbZBcnThrSKRhUZJIIxVPZzNj4lS0yeplYBU56qI5sZQ8B6MxPS60+jygHo0dCxGWaNWeOczdcImPqLdMawhqwGNgHJcZbhe3n0RjI2HtJnZR/3mDyWQuhZZwZm7pZzSMnUzhg+26gSGlcOLGvBrMoaYaypWbkGe+JOeaqOrHaFAKyzJOLnY7dxsCAre2yIDwY5eLtJY9aImtLOGJ4Sk6e93K6KQ/fkNskK56yco/wA60VNLdpxCbcLovzaVA9Bx1oO3hXCiDWOMzsvH9M36e+Z78goys/e5HXVJbMUNcKfkpmLz7ZdEB9Pk9mIPWCdxv3y3VfF5EXNYoJkmYihrA0I4xnDiU/Uks0aZx1rdBr3XHogLoUI9nXHaJmscZayRqN39h9nkVGwJ+ULzaPkkTxNwho31gcPs/HpVsmM4VMyvP5Y4TjXH7iBM3dSAQB1Kvrh+abyCUXWEcPLS/n7uhlDnbGdqstmjbOONb6XZ+NT8c2+6wAAb3cXjH9CXjOGgHVCkj/76wLuP8wBADxZLxwtq8gnFFmnrEsiMnLyMPM3fSjye91qy2aNs45hSHLpDnTz6bvYfVGbcDQi0AvDZbLGWcoag5sfbpJvlB9gnbDrW8mZWCSJ8pvcrbasBuMB6xznst1Xca2gzd48Jghd68mvzW4v8roikVmKCi9f+PclJBes8XmmUSU0cPA+zqaUdaQ3OSNHzMqunTGU30UKkG7ZU7qr1PoDN3D5njZ5UVxUIJ5u6PitI4wZbr9k+XEKgoCZv+vDqt7qUgN+nvKaMQSMw6ss//2Tt1Lw/ZFbALTJi8bJJCu7lMoK4YALtl8UsyI/Fxchy+tPWUMCM3Ly8IlkYGHKU3VksSuEsbJuWfj7yXgcuZ4MQJsVeUBLx283acwaWxZ+9PtZ8Xx/tUNVh+8jb0pZl/HcTMrAin+1ocgermrZJE8zVtZzdtnuq+JOJh1qhqK9o/eRN8Fw9xbLfz8vX2OwxvndrrUcvt2bKWX9bu67/ABbz8g3yg+wzk488zafF6P8Bj0SjaqhcovyK/vyuuSMHFlH+dkbO90KpDIxXXMrORNLC/YxdHdR443O8mvYA2W/UH3x9yWD7U7qVPS3VtGsqwxrutOz8/DpVn3Ewrtda8nyIlXWWcPfTsTj+E39GsNeMkpeJFXWkd7ZkrDO0R2qIlAmyYukypqU6kZiBlYXrDH0dFPLYh/5kpSmLpfuuiKuMXy8TjiaxQRZuVTWISZyLMUx5uZr8LHknJ34ZE3ZJC+SKmvU1J5LD/D3Oe0aw0oBXhjaSj7Ji6TKOms4b8t5cSeBoY/GICJQXjOGOmU5Z5PSc8QtCtUqyHZgoazX2W8P3RQH45tFB6FLHfnkBZEqSxSKIAj4SDKw8EbnGrKL8gPK3v45G5+K749ol+/4e7lhTAf5RfkBMDjQ0hyncZu9dkV5RfnZm/zupGQRXcd13ubzyCkYMRvcKlrGN1YJC7/Bt5IzDbYIk1vyIin9lmGW/+7SXVdw/6G2Yf9E3XA0ktkaQ52ydEZz8zWY86f+xjr+iZqynDEEytaA+PfifTF5SKUAL1nOGAJG38tSmLflvBgKOOzRGFnOGAJli7RJTM/Boh2XAWi3CHvrcfkOLIhJqUrRTNpw8AYuF2wR1iwmSJYzhkDZGr3GDfuxj1WX5YwhYLyzgGXO3EnFD5IoG1knLxLPWct9vv2iuEVY7yaRspwxBIzumRb+bmZOPj6RJPwb/0RNWQ7GA2VbEvHnqbtilE31MF/0lFleIh1Tk1+WmPPHOfFv83K7KvD3ll+UH1C2wU3jNvtYGUb52Rs73QokXScpCNobq3TE7BUZhuLolOU69ekWycDCI9GoGCCz5CESpb0VJqbn4Mt/9A17Wc8YlqEzuv7ADXFdfvOYILSrLp+spMZK24AQBMEgFHDcY9VllZVUqiwJDk/fTsWPR7UN+wBvN4yUccO+LA2Iz7dfFDN5924SKcOEf4VZWpcZOXmYL4mymSDnhn0ZBv3+PBUv7v1bI6wcnmkkr4R/UmU5zjl/6hv2r7avKruEf1L6gWrLDvJmUgZW7tGHzythMB6wvC6X/XsFCZJM3nFR8hyMB0o/uJBnNBj/dhcZD8ZL/m9p+2f/lUQxSW4Ff0/ZJfyTslqbvVW0/BL+OQA73Qpk+GUHZv1x1vDGKtMRM6D0ayrP303Dd4e1Awt+nq4Y1U6+DXug9Gu6F2xTTsO+tNuCZOTkYf5fymjYA6VfU/n7yXgck4TP95Bxw74s67Zm/2l4/ZHjunyd0q7Pv5mUgVV79OHzr3eSaShgAd130+KG/e6rYsO+S50w2WXylirtUqW8fI3Bko+3H68h24Y9UPrv5r7LD7CtoGFf0d9TtlE2OqU9Zz/ZckEMnx/SKgbh/vKMsgFKf84mZ+RgoSR8/m0ZR9kApT/OjYdu4lJB+HyTqEDZZfKWMmj/aMz/PUEQ8JEkl83YTvKNsgFKv82mcZv95bbynQy0J/ktlKASSS9oh64l4WjBiL2cw1d1SjvSO/uPc2LHblS7qgjwlt+6WAOlWNNtvC5W7g370mZila6LlXP4vE5pzllt+Ly+YT/+cfmO2AOln03be9lwXWz/FjK//hiEl5t/oAbrYlvJN3xepzTh5cbrYt+Sc5QNSj8DI10X2zQ6EB1qyrdhD5RuSYRx+PzrMg6f1ynNOXsuPk2M8vPzdMUoGUfZAKVfKmC8LrZqaDkrlsr6ShM5lZWbb5DLRv6D8fr/W3LObjl9F4cLwuerhvri2cbyHYwHrNdml/NkoD0paqZ706ZNaN68Oby8vBAYGIgePXoY/Pz69evo2rUrvL29ERoairfeegt5eXmOKawNSUeedB1uABjTsarsb6ylScpw9EYytp65CwAI9/PEkFbR1i+XlYmhchZcjBdsu6jIhj1gfl2mZOYqJ3y+QGlu/D8cuYUrknWx7WrIN3weKF0DQhAEzNusX2M4TkENe8D8BsSlew/x4xFlhM/rqEox6PfVzstG62Jl3rAvxUBRdl4+PlNQlI0xc2ea/j53TxHrYqVKkwfl063nxde/IvMoP6B052xCWhZWKCSXjU5pcves2XddzD7fqVYYmkTLM0mlTmk6oxqNgHmSdflvd6kBVxkmqZQqzT3zmALb7PaimJnu7777DsOHD8eMGTPQoUMH5OXl4eTJk+LP8/Pz0bVrV4SHh+Pff//FnTt3MHDgQLi5uWHGjBkOLLn1mWoiVA7yxrMKurEC5jcg/idpJI1WwsACpOHl5r3+RmKGGIpTztMVI9vIv2FfmqUCy3dfFUfsezauJOvweR1Lwzvz8jXiFhmA9sYq94Z9abay2XP5AfZfTQSgHbGXc/i8Tmm2Rluw7aL42pFtqsh6XayOpctbktJzxIa9u4saYzrKO8oGANSStqq595KNh27idop+W6m4KHk37AHLl/EIgoBPJffMcY9Vl3WUjY6l5+zZ+FT8fjIeABBSzkPW62J1ShN2vXjHZXFbqf4tomSdy0bH0ntmVm6+uF81ALzZRQEDC6W4l2w+fRdn49MAAA0iA/BY7TBbFM2qSrO8Ttpmf7WDMtrs9qKITndeXh5ee+01zJkzB8OGDROfr127tvj/zZs34/Tp09i6dSvCwsLQsGFDTJ8+HePHj8fUqVPh7i7zcGRLmLh/vtqhqiy3dTFm6f6Nx28mG6xLe06m20oVxdyRwS/+voi8giv3kFYxsh+xBywf6U3LysXXu/Sz3K+2l3/DHrD8OH88ehvXCpLEtapaXvYj9oDhDIy5MxPShFujO1RVRMPe0kG/y/ce4qeCJHGB3m4YKPPlOzr6SBvzfL3rCtJz8gEAvZtGKKJhb+msYU6eBl9s1zfs5b58R0dl4bThPxfui0niaoaXQ+fa8txWqhALs5d/9pd+YPOltlUU0bC3NDrsXlo2Vu/TJ4kb2TbWNgWzMkuXRKzdf91gyVnNcPlvK2Xp91IQBIPO6Osdq8l+MB6w/Jw9cTNFTBJX0d8TvZsoq81ua4rodB8+fBi3bt2CWq1Go0aNEB8fj4YNG2LOnDmoW7cuAGDPnj2oV68ewsL0I0ddunTBqFGjcOrUKTRq1Mjke2dnZyM7O1t8nJqaCgDQaDTQWJIdwZ6MWhhRQd7o0aCCfMtrQF/2fDP+xtKL1EttY+GqhuyPU6PRSMI7hRLLeyspExsPaWe5fT1cMLhlZdkfI2DYadGYcZzLd19BasEsd4+GFRER6Gn349RoNGbVSVFKOmfz8jX4bJukM9q+qkLqUl9Gc+py3+UH2HdFO8sdG+yDJ+uGW+U4y1o/JbLwnJXOcg97NAZebmpF1KeOIJR8vUzOyMHyf68AANxcVBjZJtbk79i8bixk6Tm78dAN3ErOBAC0qxGCuhX9ZHMsxZPeM4s+Tl2bZf5fl8XnRneoCkCAxpJMlw4iDS8vqV7O303DbyfvAACCfd3Rt2mE7OtSo9EY9FrMaWMu/ucSsnK1r+nXvDKCfdxlf5xaknM2P7/YMmcbzXK/2r6KItoFgsay68+W03dx+o62f1Gvkj/aVCuvjLqU3jPNOGfn/6UPn7dWm11u9x5TzC2bIjrdly9rbyJTp07FvHnzEB0djblz56Jdu3Y4f/48goKCEB8fb9DhBiA+jo+PL/K9Z86ciWnTphV6/t69e8jKyrLiUVhPctJDg8cDm4Qi8cF9B5XGMunp6eL/k5OTkZBQ9GvPJWRg6xntC0J83dCusgcSivsFmdBoNOIFWaPRlFjmeX9dE/c47tUgBDkPk5HwsNhfkYXk5DTx/+np6cUeZ3pOPr7aqf0eq1VAn/oBDqlLjUaDlJQUCIIAtdq8yJDsbP114MGDB/DRpBf52t/PPBBnueMiyiHaJ08R52yepFGek5tTYpk//kN/Yx0QF4IH9+9ZpRylqR9LPHyoP2dTU1OLPc4byVniLLefpwueqOqtiLoEYJDL5O7du8XOqCzecxsPs7Wz3N1ql4dbThoSEtIKvc7WdWOppMRM8f+ZmZnF1k1evoDPJI3B/g3LK6YuMzP0x5mYmIQEr1yTr9NoNNh2+g6O3tDumFClvCcahagUc5y6xn1eXsnXzI9/vyz2Bfo1DkVq0gOk2rp8ZaTRaJCRob93PHxY/D0zMSNX3DHB3UWFnrX9FFOXOTk54v/v3b+P/IyiI/e+PZqAu6naia+2VQJQ3iULCQn2bXuX5tqWnKm/xmZnF3/PFAQBc//UZywfFBeMe/esc8+0NYM2e0oKEhKK/vvYqs0ut3uPKWlphe+Zpji00z1hwgTMmjWr2NecOXNGHEGYNGkSevbsCQBYtmwZIiIi8O2332LkyJGlLsPEiRMxbtw48XFqaioiIyMREhICPz95hrg8yPMQ/x9d3hsDWss/GYNOuXL6E9PP3x+hoUVnjp28+bD4/1faV0NERWWEyWk0Gri4uADIA1SqYo/xdnImfj39AADg4+6C0Z3rIFDumdkLlE/XXz68vLyLPc4v/r6E1Cxtw/7phhURV72yzctnijYKQYWQkBCzL97eXvpBu8DAIISGml6Hnq8RsOqQPmPwm4/XQmho+bIV2E6kM2Gurm7F1uX+K4k4dFP7PY4u740XHrXe9ac09WMJfz99VJOvb7lij3POP8dRMBaG4a1jERNRwerlsRUPd/3MUUhIKNRFhP6nZOZiw9FjAABXtQpjH6+D0EBvk6+1dd1YKg36kUkPT89i63L9wRu4k6rtCLSpFowODWJsXj5r8fVJFP8fEBCA0FDTy1Xy8/Ox/pR+YOH1x2oiPEz+a0Z1XFzUAPKhVrsUW5cX7qbhrwtJAIDyPu4Y0aEWvN3lP3+k0WhQzjdFfOzl41PscS794yyyCtZy921WGbVj5J8zQ8fL44b4//LlgxFSzsPk67Lz8rHmsD4305tP1EZoqL/Ny2esNNc29wz9wIKrm3uxdfnXmbs4f087eFa3oh+eaa6M0HIA8Cunv86WK+fnkDa73O49pnh6mpf42KFXqjfeeAODBw8u9jWxsbG4c0cbRiRdw+3h4YHY2Fhcv34dABAeHo79+/cb/O7du3fFnxXFw8MDHh6FLwhqtVq2lVs93A81Q71x+UEm3n+6Ltzd5H/D0VEbXGhURf6NT99OxebT2voL8/NAn2aVZVsfpqgk69OKK/finVfEWe5Bj0SjvK+8M5ZLGRyXqui6zMjJw9e7tOGrahUwukM1h9alqqCs5pZBenNUqYs+zl+O38LlgozlzWOC0LKqvDOWS6lU+k53SefsAsm62NEdqln9+mNp/VjCoPNZzDl7/UEGfjx6G4B2K6LBrWIUdv2RnrPqIjvdK/Zcw8Ns/VZElcsXn9jQlnVjKbVkoKe4czYvX4OFf+tDrl/rVF0W5TeXysxzdvfF+zhxR3v9qRbqi671KxZZ73KkO2dLuv58sUM/yz2iTSx8PZUxSA0YfS9R9HEmpedg9V5t29bdRY1R7aoq65yVHmcx5+zGwzcQXzDL3alWGOpFOG77UEuvbS5qwxwCRf2eIAj4THLPHNOxWsGkjDKoVfrjUqmK/vucuWPbNruc7j2mmFsuh/bWQkJCEBJScsM0Li4OHh4eOHfuHB599FEAQG5uLq5evYqoKG1im5YtW+LDDz9EQkKCOBKzZcsW+Pn5GXTWnYGLWoWv+9RESEiIojrcgPlJGb78R3+RGtlGGUlSpMzZ/uT+w2ysP6AdEfZ2d8GLrZWRJEXHcCuJog90/YEbSMrQhkQ+1aAiYhWQsVzKnERqgiBg0Q5Jw14B2Z+lzN1X9cTNFOy6qF3KElXeG083rGjrolmVucm3luy6jPyC2f9hj8ainKf8ExtKFU4YV7jzlZGTJ2Ysd1Gr8Er7qnYpm7WYm8fot5PxuJ6oXfLRulow4qIc17AvDXPPWd1WjAAwumM1RXW4AfO22byRmIFfj2snYYJ83NG/hTISG+qYm5Rz5Z5ryChIbNinWSTC/ZUzGA+Y187Ly9fgq3+Ue880N/fonksPcPymNsKhdgU/RWQslzJ3O9EvJevylZLY0BEU0WPz8/PDSy+9hClTpiAyMhJRUVGYM2cOAOC5554DAHTu3Bm1a9fGgAEDMHv2bMTHx+Pdd9/FK6+8YnImW+lc1CrFhJRLmdOAuJmkv7EGeruhbzPHhCKXha69U9wWCyv3XBO3AunTtDKCfJQzYg8Ybwti+jV5+RpxlhsARrWT/1ZoxszZsmfnhfs4U5AkpUFkAFpWUUZYuZRKpf1OFjeAIh0MG9EmVnHXIHO27ElMz8GGg/rBsMEK2IrImDnn7LcHb4qDYd0bVERkkOmwcrkyZ8tCQRAMGoPKvP7o/1/Ud/PkrRTsuqhdphQV5I2u9ZSzFEJHN/BXXD6ir3ddEQfDBj8SDR8PRTRhReZ8LzNz8rFiz1UA2nbeiDbKGowHzPtu/nHKcDCsXoT9w8rLwpzvJQAskgwsjGpXRTFh5TrmbI12MykDv0gGw/o0VV6b3V4Uc8WaM2cOXF1dMWDAAGRmZqJ58+bYtm0bAgO1o9YuLi749ddfMWrUKLRs2RI+Pj4YNGgQ3n//fQeXnKTMuVAt3XVVvLEObBkNL3cljpgVv/9JZk4+VklurEMfjbZLqayr5AGU307G42aSPmOwErYCMWbOSO9iyY11ZJtYxd1YAW1tCih61P5GYgZ+O6G9sZb3cUfPxhH2KprVmLOVzco9V8WMwc83jVTE9n2FlHDO5uVrsGSX/pwdrrAoG8C82bR/Lz3Aqdv6jMEtY5U5GKZT1HHqklQCwNBHoxWxfZ+xki6ZSek5YmSYl5sLBihslttYUfeSjYdvIjFdu164a70KiCgix4KcGUbBFf65IAhG90zlDYaZEx125k4q/jmvTZgWEeiFJ+oqIy+RlDnbbErb7ANaRCm0zW4fiul0u7m54eOPP8bHH39c5GuioqLw22+/2bFUZKmSGhApGblYd0C7lsnDVa2YfXGNqYrvc+PbQ/qQ6271neDGauJIjWeZlDhiD5TcgNDOMulDrrvUUd6NFShoRAhCkQ2Ir3ddEUe6Bz0SrcjwMcOQ5MIHmpmTj5UFGYNd1CoMe1Q5CbekSgpj/eNUPG4UZP9uXS0YtSsqcDDMjEE/acj1CMUOhhV/nNLIsAAvV/RS4GAYIF2SZboyV++9hszcgr3km0QgUGGRYUDJ95J8jYAlOw3PWSUyOGdN/Hzv5USDkOtWVRU4GCb5f1EDKNLw+RcfjVFcZBhQ8mCYs7TZ7UV5ZwApWkkNiNX79GuZnmsSgfK+ylwaUFwDQntj1YdcK/fGqmeqLp1hlkmr+HNWOsv0YutYRc4yAcWvqXSWWaaSBv2ks0xKHQwDim8oaQfD9OfsS22VN8sElFyX0lmmyCBlzjIBJQ9uSmeZejUIUewsU3ED1Vm5+pBrtQqKy3+iY9hRK2zzqXhxy8lHqwajbiVlhVyLSohoXCzN2dNWoYNhJQyg3E7OxM/HtMk4A7zd0LtppJ1KZl0ltfOkbfbeTSIV22a3F3a6ya6KW1OZnZeP5QWJfVQq4MVHlXljBaRrugv/7A+jxD51Kirzxqo2CK8qfKDOMMsEGK9dNzxO6SxTkI87notT5iwToK9PU2sqV0lmmZ5vGqnIWSbAaK2h0ZfTWWaZgOLXVO65/AAnbmlnmepU9MMjCsw/AJS8Pn+xwSyT8vIP6BS3plI6y+TppkavBkVv5yN34vXHRF1+d/gm7j/UDoY9Wa+C4vIP6KiKuWcKgmCw/tdZrj/G1XkuPg3bz2kHwyoFeCky/wBQ8rr1pbuuIE+3TLJFlCK2tTNFmpDR+DizcvOxbPdV7etUwIutlRkZZk/KvAuRYhW3pvLHI7dwL027fcTjdcIRHexjx5JZmbj9SeEb6+J/lB9yDRQ/0+Qss0xA8bOG0sQ+A1tGKTLkWlTETFNWbr6Y5VqtgmJDroHiz9k/jWaZlDoYBhQ/C7PYSQbDVMVU5u3kTPwimWV6rolyB8OKW1MpnWXq1TgCAV7KbNgDRe/4oTGKDFPi+l+d4r6X+68k4tiNZABArQp+aF0t2H4Fs7LiZkel159hCg25NmZ8jCmZuVi7XxJyrcBknDrF1eWPR27h/sOCNnvdcESVV3Cb3U6Uf7aTohTVgBAEAUt3XRUfK7kzChTdgDh8PQnHJGuZHq2q5Btr0aPZy3brG0lKnmUCij7OtKxcbCgIufZ0U2Ngy2g7l8y6iloS8fOx23iQrvxZJmPFnbPKv/6YHty8dO8h/naCWSag+DWVq/Zec4pZJqDogaLcfA1WSkKuhykyGaeedJ9uqb/PJ+DKfe3+4y1jyysuy7VUceesbsYQAEa0iVHsYBhQ9JKIe2nZ4mCYv5cbnldoyDVQ/ADutwdvIL1gMKxnXASClRxyXcREmSAIRuescgfD7Em5LWFSpKLuI/uuJOLc3TQAQOPKAWhUWVl7qRorqtO94t9r4v+HPuo8N1Zp2FFSeg5+Oqq9sZbzdEUvBYdcA0WHl39/+JZ4Y32mUYTitnwzpguXk56zgiCIs9yAsme5gaJDAk/dTsGBq0kAgOphvoqeZQKK/m6u2qO//gx+JFrRg2GGSwX0z2fl5mNdwSyTm4sK/RWe2KeoZTx/norH3VTtLFOnWmGKn2US13Qb3TSXS+6ZznX90T9/KzkTm0/HAwBCy3mgW/2K9i6aVRV1nOv2X0dOfsE2qc0iFbflm1RR30uNRsCqvZJ2Xiuln7P6/0vvJXsv69vscVGBaBgZYOeSKZNy77ikSEU1enUj9oA2M7LS6ZPC6I8xITVL3HIpyMcd3eord5YJKHqkd8PBG+L+48/FKfvGCpheEiEIgpjYBwAGPaLshj1g+pw9fD1JTIbXIMJf+YNhRYxxrZQ07Ae2jFb0YBhgejubh9l52HjoJgBtZEbvJsqdZQKKnk375dhtcWeIrvUqILScp72LZlVFhXdKz1kl7iVvTN/p1j93+d5Dg2VK7Wsqd826Melxrtl7TeycvtA8Cm4KHgwDTEc05uZrsGafdjBMrQL6N1f2PbOo7+WO8/fEZUqtqwWjaqivfQtmZSqYbug5W5vdXpT9zSbFMbWm6XZyJv48dRcAEFLOA0/UVXZnFJCEykkuUt/svy6GPPZpGqns9b8wHXadbzTKO0Dhs0zGdA2I3Rcf4PI9bchj85ggRe4/bsxUdMYKo86o0pk6Z5MzcvDj0VsAgHIernimUSVHFM2qDIYMCo7zh8M38TA7DwDwTKNKytx/XMJUo9d4MEzJayl1TN0zT99Oxf6riQCAqqG+aKnQZHhSuu+mdADX4F7SIkqxO0PomBooysrNx7qCZUpuLir0ba7swTAAMNVP23L6LuJTswAAHWuFKX6ZUlH5iQyuP85wzzRxzt5OzsTm0/o2++MK3SbVEdjpJrsy1VD6Zt91MRlVv2aV4e6q/NNSv/2SVk6e4SjvCwrdcklKZaJlv+1sAm4maff/bVcjBDFKToZXwNSM/gonHOU1XlNpHJnRVeGRGYDpBoQ0MqNXkwjFR2YAhY9T2xmVdmCi7V8oazPxvTxyIxknb+m3KWzkBCGPphr3q/ZeFZ8b1DJK8ZEZQOHw8vTsPGw8qI3M8HBVfmQGYHowbNPxO+I2hU/UVX5kBmB6cFO6TGmQM3RGJf/XnbNX76cb5Mzo4ASRGf+VNru98C9FdqUyCi/PzssXszy6qlXo17yyo4pmVfotw7QXpj9PxYuZ2R+rHYZKAV6OKprVmFpTaRBy5AQ3VqDw2q0biRn464x2lDfczxOP1Q5zVNGsSmV0zq7df8OpIjMA4/VpJiIznGAwDCi8pnLPpQe4mPAQANAsOgi1Kyo/MsPUmsqVkob9QCfrjALa72ZKRi5+OKKNzPD1cMUzjZWdM0NHv2WY9vEPR24hrSAyo0fDSgjwVnbODKBw+wcwDtN1luuP/v+CIOBsfCr2XdFGZsSG+KBVVSeIzDC6lwAoFOWn9MgMoPC9xFnb7PbCTjfZlfGs4W8n7oiZkR+vG44wP+WP8krpRgadcf2L8WzaxYSH2HnhPgCgcpA32lYPcVDJrMt4pHf1Pun6u8qKX3+nIx6noIvM0DYgnCUyQ8twBmb72QTcSNRGZrSpHoLYEGWvv9MxnoVZLu2MOknD3vh7mZCWhU0FkRmB3m54qoGyk1HpGM8abjh4A1m5BZEZcRHwdYLIDMBw9wRBEAzumU5zzhotFTgi2c2kTkU/NFZ4zgwd43aedJnSICfImQEUjkBJz87DhoPaZQIermo87wSRGQCMIooEbDru3G12W3OO1iIphnFSBoOLsZN0RgHDC7I0M3K1UF+0jFX+KC9gojMqGeUd2DIKaicY5QUMGxCZuflYX7D+zt1Fjb5ONMorDS//81Q8EgoiMzrXDneKyAyg8ECRdJnAYCdp2AOGx3krORNbCyIzwvw80MVJ1t8ZN3rX7b+B3PyCyIxmlZ0iMgMoPNPttDkzxESOwJ7LD3D+rjYyo2l0IOpUVO42YVIG90wAK/cYtn+coTMKGLbzUjJz8aMkMqOnwnczkVLpR4rw49FbSMvSRmY83bAiAhW+m4mOcTtvhdEOGGQZ5xgiJcWQ3lNO30nF0RvJAICa4eXQJMo5RnkBwwvVNwVruQHnCXkEDOsyIzcff5zSbnni6abGc3FOMsoLw8b9byfikVyQGfnJeuHK3n/TiDS8XBc+BmjPWWch/eZdf5AhRmZEBnmhbXXlr7/T0x/p2v03xMiMfs2UnxlZR1qXeRpBHAxTq7QRKM5Cepw7L9zH9UR9ZuQqThKZARgmcly7/4b4/AAnWaYEGNZlUnqOGJkR4O2G7k4SmQEYtg2+P3wLmbnarTWfbVzJaSIzAG19CtCGXRveM6MdVSSrM5xASsWxgjZ7rQp+iHOiNru9OM/ZT4qgNrgY3xT//0IL5+mMAoY3Hd2e1V5uLujhBJmRdaT1tePcPTEzcrf6FRWfGVlKWpe/HLst/r+fwrc8MaZbu3UvLVvc8iS6vLdTZEbWka5Pk9Zln6aVnWL9nY70UH4uyMyuVgHPN3WewTBpXR66moj0HG3Dvm31EEQEKjszspT0OHV1CWgTGDkT3XFm5+Xjz5PaAdwgH3d0qeMcOTMAw3vmn6fikVOQwPHZRhFOE5kBGB6n9Jzt64TnrEYQcPneQ/H6Uz/CH3UrOUdkBmD6XgIA/ZpXdqo2u72w0012JQ07Si0IxfF0UzvVKC9g2FHTd0YroJynE3VGJf/XHSOgTbrlTKTnrO44Y4N90DTauUZ5dUeZUdB4AIDnmzrXjVV6KLpGkotahV5OFPIImD7O9jVCEe7vROvvTBwjoD1nnYmpuizv446OtZynMwroj1O7REAbmvFso0rwcHWmzqj+/4bnrJPdM00cZ4PIANSqoPwEjlK64/yv1aUzttntxTnizEg5TLTfn6xXAf5eztMZBYzWrhdwvotx4WOsEuLjdCFHpvqczzeNdKrOKFD4OF3UKvSMc57IDMB0XbavEep0yWD+G9efws8F+7qjYy1nWiZgWs+4CKfbpuc/cc6aeK5R5QDUCC9n97LYkqnjdLbBeKDwOevl5uJ0nVFT30tnbLPbi3NdtUn21CZaSk6T5VHCOFLVGTujpqJxnbEzanycrmoVnnWSbXqkjOutY81Qp9gzVsrUuemMjUG10Z09pJwH2jvBnrFSpu4lPeMinGbNuo6p43SGPauNGR9mXFQgqoU5V2fU1D3TKa8/RpXp7e7iNLsJSBmfs12dLJoRMD242cfJoonsybnuTiR7xt/fmGAfNIsJckhZ7KmPk4XpAoVHQJ22M2p0nJ1qhSGknPMkUNMxPjv7NHO+xqDxMYaW80C7Gs6xtZ2U8Tnbywk7o6aups44gGt822gaHYiqoc6TQE3H+P7obLPcAApVpo+7C7rVd/7OaLf6FZwqgZqO8XE64wCK8ffSGZfW2ZNz3YVJ9owvUs44MwoYHqebiwrPNHauMF2gcF0+VjvMqbJ565g6Z52R9DjD/DzQppoTdkZVhTujrk7WGQVQqEf6X5gZbRYd5DT7rEsZ3x2dsS4Bw+P0cXdB13oVHFYWWzGuy271K8LHGTujRo+d9p4pOdJYJ4xmBExcf5y0zW4vTtjaIDmTfle1M6PO1xkFDC/GztoZNea8N1a9cD9PtKnufJ1RwPCcfS4u0ik7o//FDkzzmCDEBPs4rCy2Yjyb77TXH8lN09fDFV3rO19nFDBsG3Rv+B/pjDphNBFgeM5WDfVF48rO1xkFDM/ZPk7aGf2vtNntxflaVSRr0rU+HWs535pRHemaSmfLpqsj3WKpor8nWjvhzCgAqCXH2btJhFNtLSXlYnCcztkYlB5jy9jyiHbCzihgeJzOuEwAMLzGlvNwxZNOODMKGF5/ujesCG935+uMAobnrLPeM6V1WT3MF40iAxxXGBuStvOctTMKAC4Fx+WsS+sA/THi/+3de1BU99kH8O8ul2VlYRdYbhvkKoqJQFATXsIb4yhFqDWmsSa1FKL1Ei0xmtgMY6ZKm8mojTNxpmlqOpkY7SS1Nh2TtLk5GlFjJN6QekupUoRWuVR5uchFYPd5/7C7wwoCiZ7dw+73M7MzeM7Z/T27j885v2fP7h549pzdVdh0k0tljA2BzlcLX60Gy6YlujscxTwUf/M6jRmxJvzvOLObo1FGtDEA9/73EiArZyZ7bDP6cLIZ/j5amA06FPyPZ12buz/7rz7PmxyD2DDPuc5xf2kxRpgNOvhoNVg5Y5y7w1HMjJQIaDQ3J/b5kzyzGdX5+uDh5Jv71uXTk6D395xLS/WXlRiGMf4+CNL5YvH/Jrg7HMXk/PcSaNMnhCM9xnOuc9xffEgA4v67b101c7zHNqOPTAiHr1aDe0x6j7scY3/2Y2ZhVpzHfppxSnwIjHo/+PtqsWK65x4zXUUjIuLuINSkra0NRqMRra2tCA5W5zUFbTYbmpqaEBERAe2tP1M7CjS0dqPPZkNMiGdO7O356fEzINKo96jrjN7qRp8VzR09iDbq3R3KiHzb2rl6/Qb0fj4e+ZFHOxHB5ZYuRBv1bnsDxRX7tus3+tDTZ0NooL8ij68WjW3dMOr9EOB3d/Y/ajzu9FltaGy/AYsxwGMbGAD4v44e+PhoEDzELyOrMT/f1OWWLkQG6Tzyqy32/ASZwtDRa/X4M4ZN7d0I0vmNijfDvm3t2GyCK61dsBj1Tp9i8DStXb0QEZjGuOeYORr2bSPtHT13BkmqFWX07IONXUzIGNXuIO4Wna/PqGm474Snvovdn0aj8dg3wvoz6HwBz0+nx117fDC+PlrcY/L8/U+Ih79BZOcNudT7+yDQwy4rNRhPf1MBuPl1AW84ZvKa3HePZ3cERERERERERG7EppuIiIiIiIhIIWy6iYiIiIiIiBTCppuIiIiIiIhIIWy6iYiIiIiIiBTCppuIiIiIiIhIIWy6iYiIiIiIiBTCppuIiIiIiIhIIWy6iYiIiIiIiBTCppuIiIiIiIhIIb7uDkBtRAQA0NbW5uZIbs9ms6G9vR0BAQHQavm+idowP+rF3Kgb86NezI26MT/qxvyoF3OjbqMhP/ae0d5D3g6b7lu0t7cDAMaOHevmSIiIiIiIiEjt2tvbYTQab7teI8O15V7GZrPhypUrCAoKgkajcXc4g2pra8PYsWPxr3/9C8HBwe4Oh27B/KgXc6NuzI96MTfqxvyoG/OjXsyNuo2G/IgI2tvbYbFYhjwbzzPdt9BqtYiJiXF3GCMSHBys2v+AxPyoGXOjbsyPejE36sb8qBvzo17MjbqpPT9DneG2U+eH44mIiIiIiIg8AJtuIiIiIiIiIoWw6R6FdDodSktLodPp3B0KDYL5US/mRt2YH/VibtSN+VE35ke9mBt186T88IfUiIiIiIiIiBTCM91ERERERERECmHTTURERERERKQQNt1ERERERERECmHTPcq8/vrriI+PR0BAADIzM3Hs2DF3h+SVNm7ciAceeABBQUGIiIjAY489hqqqKqdtpk+fDo1G43Rbvny5myL2Lr/4xS8GvPYpKSmO9d3d3SguLkZYWBgMBgPmzZuHxsZGN0bsPeLj4wfkRqPRoLi4GADrxtUOHTqEOXPmwGKxQKPR4IMPPnBaLyJYv349oqOjodfrkZOTgwsXLjht09zcjIKCAgQHB8NkMmHx4sW4fv26C5+FZxoqN729vSgpKUFqaioCAwNhsVhQVFSEK1euOD3GYPW2adMmFz8TzzRc7SxcuHDAa5+Xl+e0DWtHOcPlZ7DjkEajwebNmx3bsH6UMZI59EjmaXV1dZg9ezbGjBmDiIgIvPDCC+jr63PlU/lG2HSPIrt27cLzzz+P0tJSVFRUID09HbNmzUJTU5O7Q/M6Bw8eRHFxMb766ivs3bsXvb29yM3NRUdHh9N2S5cuRX19veP2yiuvuCli73Pfffc5vfaHDx92rHvuuefw17/+Fe+99x4OHjyIK1eu4PHHH3djtN7j+PHjTnnZu3cvAGD+/PmObVg3rtPR0YH09HS8/vrrg65/5ZVX8Otf/xpvvPEGjh49isDAQMyaNQvd3d2ObQoKCnDu3Dns3bsXH330EQ4dOoRly5a56il4rKFy09nZiYqKCqxbtw4VFRXYvXs3qqqq8Oijjw7Y9qWXXnKqp5UrV7oifI83XO0AQF5entNrv3PnTqf1rB3lDJef/nmpr6/Htm3boNFoMG/ePKftWD9330jm0MPN06xWK2bPno2enh4cOXIEO3bswPbt27F+/Xp3PKWRERo1HnzwQSkuLnb822q1isVikY0bN7oxKhIRaWpqEgBy8OBBx7JHHnlEVq1a5b6gvFhpaamkp6cPuq6lpUX8/Pzkvffecyz7+uuvBYCUl5e7KEKyW7VqlSQlJYnNZhMR1o07AZD333/f8W+bzSZRUVGyefNmx7KWlhbR6XSyc+dOERE5f/68AJDjx487tvn0009Fo9HI5cuXXRa7p7s1N4M5duyYAJDa2lrHsri4ONmyZYuywdGg+Xnqqadk7ty5t70Pa8d1RlI/c+fOlRkzZjgtY/24xq1z6JHM0z755BPRarXS0NDg2Gbr1q0SHBwsN27ccO0TGCGe6R4lenp6cPLkSeTk5DiWabVa5OTkoLy83I2REQC0trYCAEJDQ52Wv/vuuzCbzZg0aRLWrl2Lzs5Od4TnlS5cuACLxYLExEQUFBSgrq4OAHDy5En09vY61VJKSgpiY2NZSy7W09ODd955Bz/5yU+g0Wgcy1k36lBTU4OGhganWjEajcjMzHTUSnl5OUwmE6ZOnerYJicnB1qtFkePHnV5zN6stbUVGo0GJpPJafmmTZsQFhaGjIwMbN68WdUfv/Q0Bw4cQEREBCZMmIAVK1bg2rVrjnWsHfVobGzExx9/jMWLFw9Yx/pR3q1z6JHM08rLy5GamorIyEjHNrNmzUJbWxvOnTvnwuhHztfdAdDIXL16FVar1ek/FwBERkbi73//u5uiIgCw2WxYvXo1srOzMWnSJMfyH/3oR4iLi4PFYsHp06dRUlKCqqoq7N69243ReofMzExs374dEyZMQH19PX75y1/i4YcfxtmzZ9HQ0AB/f/8BE9PIyEg0NDS4J2Av9cEHH6ClpQULFy50LGPdqIe9HgY77tjXNTQ0ICIiwmm9r68vQkNDWU8u1N3djZKSEixYsADBwcGO5c8++ywmT56M0NBQHDlyBGvXrkV9fT1effVVN0brHfLy8vD4448jISEB1dXVePHFF5Gfn4/y8nL4+PiwdlRkx44dCAoKGvA1M9aP8gabQ49kntbQ0DDoscm+To3YdBPdoeLiYpw9e9bpO8MAnL6XlZqaiujoaMycORPV1dVISkpydZheJT8/3/F3WloaMjMzERcXhz/96U/Q6/VujIz6e+utt5Cfnw+LxeJYxroh+mZ6e3vxxBNPQESwdetWp3XPP/+84++0tDT4+/vj6aefxsaNG6HT6Vwdqlf54Q9/6Pg7NTUVaWlpSEpKwoEDBzBz5kw3Rka32rZtGwoKChAQEOC0nPWjvNvNoT0RP14+SpjNZvj4+Az45b7GxkZERUW5KSp65pln8NFHH6GsrAwxMTFDbpuZmQkAuHjxoitCo35MJhPGjx+PixcvIioqCj09PWhpaXHahrXkWrW1tdi3bx+WLFky5HasG/ex18NQx52oqKgBP+bZ19eH5uZm1pML2Bvu2tpa7N271+ks92AyMzPR19eHS5cuuSZAckhMTITZbHbsy1g76vDFF1+gqqpq2GMRwPq52243hx7JPC0qKmrQY5N9nRqx6R4l/P39MWXKFHz++eeOZTabDZ9//jmysrLcGJl3EhE888wzeP/997F//34kJCQMe5/KykoAQHR0tMLR0a2uX7+O6upqREdHY8qUKfDz83OqpaqqKtTV1bGWXOjtt99GREQEZs+ePeR2rBv3SUhIQFRUlFOttLW14ejRo45aycrKQktLC06ePOnYZv/+/bDZbI43TEgZ9ob7woUL2LdvH8LCwoa9T2VlJbRa7YCPNZPy/v3vf+PatWuOfRlrRx3eeustTJkyBenp6cNuy/q5O4abQ49knpaVlYUzZ844vXFlf+Px3nvvdc0T+abc/ENu9A388Y9/FJ1OJ9u3b5fz58/LsmXLxGQyOf1yH7nGihUrxGg0yoEDB6S+vt5x6+zsFBGRixcvyksvvSQnTpyQmpoa+fDDDyUxMVGmTZvm5si9w5o1a+TAgQNSU1MjX375peTk5IjZbJampiYREVm+fLnExsbK/v375cSJE5KVlSVZWVlujtp7WK1WiY2NlZKSEqflrBvXa29vl1OnTsmpU6cEgLz66qty6tQpxy9gb9q0SUwmk3z44Ydy+vRpmTt3riQkJEhXV5fjMfLy8iQjI0OOHj0qhw8fluTkZFmwYIG7npLHGCo3PT098uijj0pMTIxUVlY6HYfsv9x75MgR2bJli1RWVkp1dbW88847Eh4eLkVFRW5+Zp5hqPy0t7fLz372MykvL5eamhrZt2+fTJ48WZKTk6W7u9vxGKwd5Qy3bxMRaW1tlTFjxsjWrVsH3J/1o5zh5tAiw8/T+vr6ZNKkSZKbmyuVlZXy2WefSXh4uKxdu9YdT2lE2HSPMq+99prExsaKv7+/PPjgg/LVV1+5OySvBGDQ29tvvy0iInV1dTJt2jQJDQ0VnU4n48aNkxdeeEFaW1vdG7iXePLJJyU6Olr8/f3lnnvukSeffFIuXrzoWN/V1SU//elPJSQkRMaMGSPf//73pb6+3o0Re5c9e/YIAKmqqnJazrpxvbKyskH3ZU899ZSI3Lxs2Lp16yQyMlJ0Op3MnDlzQN6uXbsmCxYsEIPBIMHBwbJo0SJpb293w7PxLEPlpqam5rbHobKyMhEROXnypGRmZorRaJSAgACZOHGibNiwwanpo29vqPx0dnZKbm6uhIeHi5+fn8TFxcnSpUsHnCRh7ShnuH2biMjvfvc70ev10tLSMuD+rB/lDDeHFhnZPO3SpUuSn58ver1ezGazrFmzRnp7e138bEZOIyKi0El0IiIiIiIiIq/G73QTERERERERKYRNNxEREREREZFC2HQTERERERERKYRNNxEREREREZFC2HQTERERERERKYRNNxEREREREZFC2HQTERERERERKYRNNxEREREREZFC2HQTERGNYgsXLsRjjz3mtvELCwuxYcMGxR7//PnziImJQUdHh2JjEBERKUkjIuLuIIiIiGggjUYz5PrS0lI899xzEBGYTCbXBNXP3/72N8yYMQO1tbUwGAyKjfODH/wA6enpWLdunWJjEBERKYVNNxERkUo1NDQ4/t61axfWr1+PqqoqxzKDwaBoszucJUuWwNfXF2+88Yai43z88cdYunQp6urq4Ovrq+hYREREdxs/Xk5ERKRSUVFRjpvRaIRGo3FaZjAYBny8fPr06Vi5ciVWr16NkJAQREZG4s0330RHRwcWLVqEoKAgjBs3Dp9++qnTWGfPnkV+fj4MBgMiIyNRWFiIq1ev3jY2q9WKP//5z5gzZ47T8vj4eLz88ssoKiqCwWBAXFwc/vKXv+A///kP5s6dC4PBgLS0NJw4ccJxn9raWsyZMwchISEIDAzEfffdh08++cSx/jvf+Q6am5tx8ODBO3xFiYiIXI9NNxERkYfZsWMHzGYzjh07hpUrV2LFihWYP38+HnroIVRUVCA3NxeFhYXo7OwEALS0tGDGjBnIyMjAiRMn8Nlnn6GxsRFPPPHEbcc4ffo0WltbMXXq1AHrtmzZguzsbJw6dQqzZ89GYWEhioqK8OMf/xgVFRVISkpCUVER7B+2Ky4uxo0bN3Do0CGcOXMGv/rVr5zO4Pv7++P+++/HF198cZdfKSIiIuWx6SYiIvIw6enp+PnPf47k5GSsXbsWAQEBMJvNWLp0KZKTk7F+/Xpcu3YNp0+fBgD85je/QUZGBjZs2ICUlBRkZGRg27ZtKCsrwz/+8Y9Bx6itrYWPjw8iIiIGrPvud7+Lp59+2jFWW1sbHnjgAcyfPx/jx49HSUkJvv76azQ2NgIA6urqkJ2djdTUVCQmJuJ73/sepk2b5vSYFosFtbW1d/mVIiIiUh6bbiIiIg+Tlpbm+NvHxwdhYWFITU11LIuMjAQANDU1Abj5g2hlZWWO74gbDAakpKQAAKqrqwcdo6urCzqdbtAfe+s/vn2socZ/9tln8fLLLyM7OxulpaWONwP60+v1jjPzREREowmbbiIiIg/j5+fn9G+NRuO0zN4o22w2AMD169cxZ84cVFZWOt0uXLgw4IyzndlsRmdnJ3p6eoYc3z7WUOMvWbIE//znP1FYWIgzZ85g6tSpeO2115wes7m5GeHh4SN7AYiIiFSETTcREZGXmzx5Ms6dO4f4+HiMGzfO6RYYGDjofe6//34AN6+jfTeMHTsWy5cvx+7du7FmzRq8+eabTuvPnj2LjIyMuzIWERGRK7HpJiIi8nLFxcVobm7GggULcPz4cVRXV2PPnj1YtGgRrFbroPcJDw/H5MmTcfjw4Tsef/Xq1dizZw9qampQUVGBsrIyTJw40bH+0qVLuHz5MnJycu54LCIiIldj001EROTlLBYLvvzyS1itVuTm5iI1NRWrV6+GyWSCVnv7qcKSJUvw7rvv3vH4VqsVxcXFmDhxIvLy8jB+/Hj89re/dazfuXMncnNzERcXd8djERERuZpG7NfrICIiIvoGurq6MGHCBOzatQtZWVmKjNHT04Pk5GT84Q9/QHZ2tiJjEBERKYlnuomIiOhb0ev1+P3vf4+rV68qNkZdXR1efPFFNtxERDRq8Uw3ERERERERkUJ4ppuIiIiIiIhIIWy6iYiIiIiIiBTCppuIiIiIiIhIIWy6iYiIiIiIiBTCppuIiIiIiIhIIWy6iYiIiIiIiBTCppuIiIiIiIhIIWy6iYiIiIiIiBTCppuIiIiIiIhIIWy6iYiIiIiIiBTy/71Vmq/VuxAhAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of spikes: 17\n",
      "Average firing rate: 85.00 Hz\n"
     ]
    }
   ],
   "source": [
    "# Convert to appropriate units for plotting\n",
    "times_plot = times.to_decimal(u.ms)\n",
    "voltages_plot = voltages.to_decimal(u.mV)\n",
    "\n",
    "# Create plot\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.plot(times_plot, voltages_plot, linewidth=2)\n",
    "plt.xlabel('Time (ms)')\n",
    "plt.ylabel('Membrane Potential (mV)')\n",
    "plt.title('LIF Neuron Response to Constant Input')\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Count spikes\n",
    "n_spikes = int(u.math.sum(spikes != 0))\n",
    "firing_rate = n_spikes / (duration.to_decimal(u.second))\n",
    "print(f\"Number of spikes: {n_spikes}\")\n",
    "print(f\"Average firing rate: {firing_rate:.2f} Hz\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5: Create a Network of Neurons\n",
    "\n",
    "Now let's create a small network with excitatory and inhibitory populations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T07:19:06.974038Z",
     "start_time": "2025-10-10T07:19:06.958649Z"
    },
    "execution": {
     "iopub.execute_input": "2026-05-11T06:20:04.878439Z",
     "iopub.status.busy": "2026-05-11T06:20:04.878126Z",
     "iopub.status.idle": "2026-05-11T06:20:04.937232Z",
     "shell.execute_reply": "2026-05-11T06:20:04.936406Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created network with 100 neurons\n"
     ]
    }
   ],
   "source": [
    "class SimpleEINet(brainstate.nn.Module):\n",
    "    def __init__(self, n_exc=80, n_inh=20):\n",
    "        super().__init__()\n",
    "        self.n_exc = n_exc\n",
    "        self.n_inh = n_inh\n",
    "        self.num = n_exc + n_inh\n",
    "        \n",
    "        # Create neurons\n",
    "        self.neurons = brainpy.state.LIF(\n",
    "            self.num,\n",
    "            V_rest=-65. * u.mV,\n",
    "            V_th=-50. * u.mV,\n",
    "            V_reset=-65. * u.mV,\n",
    "            tau=10. * u.ms,\n",
    "            V_initializer=braintools.init.Normal(-65., 5., unit=u.mV)\n",
    "        )\n",
    "        \n",
    "        # Excitatory to all projection\n",
    "        self.E2all = brainpy.state.AlignPostProj(\n",
    "            comm=brainstate.nn.EventFixedProb(n_exc, self.num, 0.1, 0.6*u.mS),\n",
    "            syn=brainpy.state.Expon.desc(self.num, tau=2. * u.ms),\n",
    "            out=brainpy.state.CUBA.desc(),\n",
    "            post=self.neurons,\n",
    "        )\n",
    "        \n",
    "        # Inhibitory to all projection\n",
    "        self.I2all = brainpy.state.AlignPostProj(\n",
    "            comm=brainstate.nn.EventFixedProb(n_inh, self.num, 0.1, -5.0*u.mS),\n",
    "            syn=brainpy.state.Expon.desc(self.num, tau=2. * u.ms),\n",
    "            out=brainpy.state.CUBA.desc(),\n",
    "            post=self.neurons,\n",
    "        )\n",
    "    \n",
    "    def update(self, input_current):\n",
    "        # Get spikes from previous time step\n",
    "        spikes = self.neurons.get_spike()\n",
    "        \n",
    "        # Update projections\n",
    "        self.E2all(spikes[:self.n_exc])  # Excitatory spikes\n",
    "        self.I2all(spikes[self.n_exc:])  # Inhibitory spikes\n",
    "        \n",
    "        # Update neurons\n",
    "        self.neurons(input_current)\n",
    "        \n",
    "        return self.neurons.get_spike()\n",
    "\n",
    "# Create network\n",
    "net = SimpleEINet(n_exc=80, n_inh=20)\n",
    "print(f\"Created network with {net.num} neurons\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 6: Simulate the Network\n",
    "\n",
    "Let's run the network and visualize its activity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T07:19:11.531447Z",
     "start_time": "2025-10-10T07:19:10.189416Z"
    },
    "execution": {
     "iopub.execute_input": "2026-05-11T06:20:04.940026Z",
     "iopub.status.busy": "2026-05-11T06:20:04.939729Z",
     "iopub.status.idle": "2026-05-11T06:20:06.388847Z",
     "shell.execute_reply": "2026-05-11T06:20:06.388012Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "100ad20e1f3c434ba4ee2535c24c1362",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Network simulation complete!\n"
     ]
    }
   ],
   "source": [
    "# Initialize network states\n",
    "brainstate.nn.init_all_states(net)\n",
    "\n",
    "# Simulation parameters\n",
    "duration = 500. * u.ms\n",
    "times = u.math.arange(0. * u.ms, duration, dt)\n",
    "I_ext = 16 * u.mA  # External input current\n",
    "\n",
    "# Run simulation\n",
    "spike_history = brainstate.transform.for_loop(\n",
    "    lambda t: net.update(I_ext),\n",
    "    times,\n",
    "    pbar=brainstate.transform.ProgressBar(10)\n",
    ")\n",
    "\n",
    "print(\"Network simulation complete!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 7: Visualize Network Activity (Raster Plot)\n",
    "\n",
    "Create a raster plot showing when each neuron fired:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T07:19:55.083063Z",
     "start_time": "2025-10-10T07:19:49.506767Z"
    },
    "execution": {
     "iopub.execute_input": "2026-05-11T06:20:06.391730Z",
     "iopub.status.busy": "2026-05-11T06:20:06.391490Z",
     "iopub.status.idle": "2026-05-11T06:20:08.003976Z",
     "shell.execute_reply": "2026-05-11T06:20:08.003160Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total spikes: 1587\n",
      "Average firing rate: 31.74 Hz\n"
     ]
    }
   ],
   "source": [
    "import jax\n",
    "spike_history = jax.block_until_ready(spike_history)\n",
    "\n",
    "# Find spike times and neuron indices\n",
    "t_indices, n_indices = u.math.where(spike_history != 0)\n",
    "\n",
    "# Convert to plottable format\n",
    "spike_times = times[t_indices].to_decimal(u.ms)\n",
    "\n",
    "# Create raster plot\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.scatter(spike_times, n_indices, s=1, c='black', alpha=0.5)\n",
    "\n",
    "# Mark excitatory and inhibitory populations\n",
    "plt.axhline(y=net.n_exc, color='red', linestyle='--', alpha=0.5, label='E/I boundary')\n",
    "\n",
    "plt.xlabel('Time (ms)', fontsize=12)\n",
    "plt.ylabel('Neuron Index', fontsize=12)\n",
    "plt.title('Network Activity (Raster Plot)', fontsize=14)\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "# Add text annotations\n",
    "plt.text(10, net.n_exc/2, 'Excitatory', fontsize=10, bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))\n",
    "plt.text(10, net.n_exc + net.n_inh/2, 'Inhibitory', fontsize=10, bbox=dict(boxstyle='round', facecolor='lightblue', alpha=0.5))\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# Calculate statistics\n",
    "total_spikes = len(t_indices)\n",
    "avg_rate = total_spikes / (net.num * duration.to_decimal(u.second))\n",
    "print(f\"Total spikes: {total_spikes}\")\n",
    "print(f\"Average firing rate: {avg_rate:.2f} Hz\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "Congratulations! 🎉 You've just:\n",
    "\n",
    "1. ✅ Created individual neurons with physical units\n",
    "2. ✅ Simulated neuron dynamics with input currents\n",
    "3. ✅ Built a network with excitatory and inhibitory populations\n",
    "4. ✅ Connected neurons with synaptic projections\n",
    "5. ✅ Visualized network activity\n",
    "\n",
    "## Next Steps\n",
    "\n",
    "Now that you've completed your first simulation, you can:\n",
    "\n",
    "- **Learn more concepts**: Read the [BrainPy-style Modeling Guide](../brainpy-guide/architecture.ipynb)\n",
    "- **Follow tutorials**: Try the the examples in the repository for deeper understanding\n",
    "- **Explore examples**: Check out the [Examples Gallery](../examples/gallery.rst) for real-world models\n",
    "- **Experiment**: Modify the network parameters and see what happens!\n",
    "\n",
    "### Try These Experiments\n",
    "\n",
    "1. Change the connection probability in the network\n",
    "2. Adjust the excitatory/inhibitory balance\n",
    "3. Add more neuron populations\n",
    "4. Try different input currents or patterns\n",
    "\n",
    "Happy modeling! 🧠"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Ecosystem-py",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.11"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "100ad20e1f3c434ba4ee2535c24c1362": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HBoxModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HBoxModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HBoxView",
       "box_style": "",
       "children": [
        "IPY_MODEL_2cd5227d1e9143b09c775be57843b0ec",
        "IPY_MODEL_cea6d2c55bab4e02ad4c6aab83526023",
        "IPY_MODEL_4dc6167e4a884a93a8126bde270f8d77"
       ],
       "layout": "IPY_MODEL_d143d2950426464588fad4b917647e74",
       "tabbable": null,
       "tooltip": null
      }
     },
     "199c700590ce44cab4c8c70137ec85cf": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "background": null,
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "2cd5227d1e9143b09c775be57843b0ec": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_dcedc31b81c24ee5927d7adbee3b46c6",
       "placeholder": "​",
       "style": "IPY_MODEL_199c700590ce44cab4c8c70137ec85cf",
       "tabbable": null,
       "tooltip": null,
       "value": "Running for 5,000 iterations: 100%"
      }
     },
     "4dc6167e4a884a93a8126bde270f8d77": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_8fd8f76a6aac4cf6a2af848d11e3d881",
       "placeholder": "​",
       "style": "IPY_MODEL_ea3e91230fc548e5b84dd59bd4e17d3a",
       "tabbable": null,
       "tooltip": null,
       "value": " 5000/5000 [00:00&lt;00:00, 11909.01it/s]"
      }
     },
     "5b787fc66877483db5dcd533246f17d0": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "ProgressStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "ProgressStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "bar_color": null,
       "description_width": ""
      }
     },
     "7a6d872c107b44deb83cab52aa476133": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "8fd8f76a6aac4cf6a2af848d11e3d881": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "cea6d2c55bab4e02ad4c6aab83526023": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "FloatProgressModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "FloatProgressModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "ProgressView",
       "bar_style": "success",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_7a6d872c107b44deb83cab52aa476133",
       "max": 5000.0,
       "min": 0.0,
       "orientation": "horizontal",
       "style": "IPY_MODEL_5b787fc66877483db5dcd533246f17d0",
       "tabbable": null,
       "tooltip": null,
       "value": 5000.0
      }
     },
     "d143d2950426464588fad4b917647e74": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "dcedc31b81c24ee5927d7adbee3b46c6": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "ea3e91230fc548e5b84dd59bd4e17d3a": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "background": null,
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}