{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Projections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the package allows to use most of the MoleculeKit projections (as long as the obtained matrix has more than one rows of features), we report here the ID values computed on different metrics, tested both on [villin](#villin) and [NTL9](#ntl9).\n",
    "\n",
    "To highlight the structural features of the two proteins we selected the atomic distances (Cα, Cβ) and torsion angles (φ/ψ, χ) as test projections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-10T13:24:53.643203Z",
     "iopub.status.busy": "2025-11-10T13:24:53.642248Z",
     "iopub.status.idle": "2025-11-10T13:24:55.488634Z",
     "shell.execute_reply": "2025-11-10T13:24:55.487887Z"
    },
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Settings loaded.\n"
     ]
    }
   ],
   "source": [
    "%run maintainer/sett.py #import packages and plot setup\n",
    "Path(\"../extra\").mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Villin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-10T13:24:55.535252Z",
     "iopub.status.busy": "2025-11-10T13:24:55.534721Z",
     "iopub.status.idle": "2025-11-10T13:36:42.439668Z",
     "shell.execute_reply": "2025-11-10T13:36:42.435957Z"
    },
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "topology='examples/villin/2f4k.pdb'\n",
    "trajectory='examples/villin/2f4k'\n",
    "protein = 'villin'\n",
    "\n",
    "data = []\n",
    "states = ['u0','u1', 'u2', 'f0', 'f1', 'f2'] \n",
    "\n",
    "for state in states:\n",
    "    mol = Molecule(topology)\n",
    "    mol.read(trajectory+f'_{state}.xtc')\n",
    "    mol.set('resname', 'LEU', 'resname NLE')\n",
    "    mol.set('resname', 'HIS', 'resname HIP')\n",
    "    mean_all, mean_last= intrinsic_dimension(mol=mol, projection_method='Distances', id_method='global', verbose=False)\n",
    "    data.append({\n",
    "            'trajectory': state,\n",
    "            'projection': 'Cα Dist.',\n",
    "            'mean_all': mean_all,\n",
    "            'mean_last': mean_last,}) \n",
    "    mean_all, mean_last= intrinsic_dimension(mol=mol, projection_method='Distances', projection_kwargs={'step': 3}, id_method='global', verbose=False)\n",
    "    data.append({\n",
    "            'trajectory': state,\n",
    "            'projection': 'Cα Dist. 3',\n",
    "            'mean_all': mean_all,\n",
    "            'mean_last': mean_last,}) \n",
    "    mean_all, mean_last= intrinsic_dimension(mol=mol, projection_method='Distances', projection_kwargs={'sele' : 'name CB'}, id_method='global', verbose=False)\n",
    "    data.append({\n",
    "            'trajectory': state,\n",
    "            'projection': 'Cβ Dist.',\n",
    "            'mean_all': mean_all,\n",
    "            'mean_last': mean_last,}) \n",
    "    mean_all, mean_last= intrinsic_dimension(mol=mol, projection_method='Distances', projection_kwargs={'sele' : 'name CB','step': 3}, id_method='global', verbose=False)\n",
    "    data.append({\n",
    "            'trajectory': state,\n",
    "            'projection': 'Cβ Dist. 3',\n",
    "            'mean_all': mean_all,\n",
    "            'mean_last': mean_last,})     \n",
    "    mean_all, mean_last = intrinsic_dimension(mol=mol, projection_method='Dihedrals', id_method='global', verbose=False)\n",
    "    data.append({\n",
    "            'trajectory': state,\n",
    "            'projection': \"φ/ψ\",\n",
    "            'mean_all': mean_all,\n",
    "            'mean_last': mean_last,}) \n",
    "    mean_all, mean_last = intrinsic_dimension(mol=mol, projection_method='Dihedrals', projection_kwargs = {'sincos':True},id_method='global', verbose=False)\n",
    "    data.append({\n",
    "            'trajectory': state,\n",
    "            'projection': \"Sin/Cos φ/ψ\",\n",
    "            'mean_all': mean_all,\n",
    "            'mean_last': mean_last,}) \n",
    "    mean_all, mean_last = intrinsic_dimension(mol=mol, projection_method='Dihedrals', projection_kwargs = {'dihedrals': ('chi1', 'chi3', 'chi4', 'chi5')}, id_method='global', verbose=False)\n",
    "    data.append({\n",
    "            'trajectory': state,\n",
    "            'projection': \"χ\",\n",
    "            'mean_all': mean_all,\n",
    "            'mean_last': mean_last,}) \n",
    "    mean_all, mean_last = intrinsic_dimension(mol=mol, projection_method='Dihedrals', projection_kwargs = {'dihedrals': ('chi1', 'chi3', 'chi4', 'chi5'),'sincos':True},id_method='global', verbose=False)\n",
    "    data.append({\n",
    "            'trajectory': state,\n",
    "            'projection': \"Sin/Cos χ\",\n",
    "            'mean_all': mean_all,\n",
    "            'mean_last': mean_last,}) \n",
    "    \n",
    "    \n",
    "data = pd.DataFrame(data)\n",
    "data[\"folded\"] = data[\"trajectory\"].str.startswith(\"f\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-10T13:36:42.441919Z",
     "iopub.status.busy": "2025-11-10T13:36:42.441613Z",
     "iopub.status.idle": "2025-11-10T13:36:42.910015Z",
     "shell.execute_reply": "2025-11-10T13:36:42.909147Z"
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "states = data[\"trajectory\"].unique()\n",
    "fig, ax = plt.subplots(figsize=(7,7))\n",
    "\n",
    "projections = data['projection'].unique()\n",
    "width = 0.3  \n",
    "\n",
    "for i, proj in enumerate(projections):\n",
    "    # Get all data for this projection first\n",
    "    proj_data = data[data['projection'] == proj]\n",
    "    total_points = len(proj_data)\n",
    "\n",
    "    color_indices = np.arange(total_points) % colors.N\n",
    "    proj_colors = [colors(ci) for ci in color_indices]\n",
    "\n",
    "    folded_mask = proj_data['folded'].values\n",
    "    proj_data = proj_data.copy()\n",
    "    proj_data['color'] = proj_colors\n",
    "\n",
    "    for folded, group in proj_data.groupby('folded'):\n",
    "        y_vals = group['mean_all'].values\n",
    "        group_colors = group['color'].values\n",
    "        x_center = i\n",
    "        x_shift = -width/2 if folded else width/2\n",
    "        n_points = len(y_vals)\n",
    "        jitter = np.linspace(-0.05, 0.05, n_points)\n",
    "        x_vals = np.full_like(y_vals, x_center + x_shift) + jitter\n",
    "\n",
    "        ax.scatter(x_vals, y_vals,color=group_colors,edgecolor='black', marker='o', s=64,label=f\"{proj} - {'Folded' if folded else 'Unfolded'}\")\n",
    "\n",
    "x_ticks = []\n",
    "x_tick_labels = []\n",
    "for i in range(len(projections)):\n",
    "    x_ticks += [i - width/2, i + width/2]\n",
    "    x_tick_labels += ['F', 'U']\n",
    "ax.set_xticks(x_ticks)\n",
    "ax.set_xticklabels(x_tick_labels)\n",
    "ax.set_box_aspect(1)\n",
    "\n",
    "sec = ax.secondary_xaxis(location=-0.05)\n",
    "sec.tick_params('x', length=0)\n",
    "sec.spines['bottom'].set_linewidth(0)\n",
    "sec.set_xticks(range(len(projections)))\n",
    "sec.set_xticklabels(projections)\n",
    "\n",
    "color_map = {s: colors(i % colors.N) for i, s in enumerate(states)}\n",
    "handles = [\n",
    "    plt.Line2D(\n",
    "        [0], [0], marker='o', color='w',\n",
    "        markerfacecolor=color_map[s], markeredgecolor='black',\n",
    "        markersize=8, label=s\n",
    "    )\n",
    "    for s in states\n",
    "]\n",
    "ax.legend(handles=handles, title=\"Trajectory\", loc='best')\n",
    "ax.set_ylabel(\"ID\")\n",
    "plt.tight_layout()\n",
    "plt.savefig('../extra/villin_projections.pdf', dpi=300)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NTL9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-10T13:36:42.911647Z",
     "iopub.status.busy": "2025-11-10T13:36:42.911502Z",
     "iopub.status.idle": "2025-11-10T13:50:23.564920Z",
     "shell.execute_reply": "2025-11-10T13:50:23.564138Z"
    },
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "topology='examples/NTL9/ntl9.pdb'\n",
    "trajectory='examples/NTL9/ntl9'\n",
    "protein = 'NTL9'\n",
    "\n",
    "\n",
    "data = []\n",
    "states = ['u0','u1', 'u2', 'f0', 'f1', 'f2'] \n",
    "\n",
    "for state in states:\n",
    "    mol = Molecule(topology)\n",
    "    mol.read(trajectory+f'_{state}.xtc')\n",
    "    mol.set('resname', 'LEU', 'resname NLE')\n",
    "    mol.set('resname', 'HIS', 'resname HIP')\n",
    "    mean_all, mean_last= intrinsic_dimension(mol=mol, projection_method='Distances', id_method='global', verbose=False)\n",
    "    data.append({\n",
    "            'trajectory': state,\n",
    "            'projection': 'Cα Dist.',\n",
    "            'mean_all': mean_all,\n",
    "            'mean_last': mean_last,}) \n",
    "    mean_all, mean_last= intrinsic_dimension(mol=mol, projection_method='Distances', projection_kwargs={'step': 3}, id_method='global', verbose=False)\n",
    "    data.append({\n",
    "            'trajectory': state,\n",
    "            'projection': 'Cα Dist. 3',\n",
    "            'mean_all': mean_all,\n",
    "            'mean_last': mean_last,}) \n",
    "    mean_all, mean_last= intrinsic_dimension(mol=mol, projection_method='Distances', projection_kwargs={'sele' : 'name CB'}, id_method='global', verbose=False)\n",
    "    data.append({\n",
    "            'trajectory': state,\n",
    "            'projection': 'Cβ Dist.',\n",
    "            'mean_all': mean_all,\n",
    "            'mean_last': mean_last,}) \n",
    "    mean_all, mean_last= intrinsic_dimension(mol=mol, projection_method='Distances', projection_kwargs={'sele' : 'name CB','step': 3}, id_method='global', verbose=False)\n",
    "    data.append({\n",
    "            'trajectory': state,\n",
    "            'projection': 'Cβ Dist. 3',\n",
    "            'mean_all': mean_all,\n",
    "            'mean_last': mean_last,})     \n",
    "    mean_all, mean_last = intrinsic_dimension(mol=mol, projection_method='Dihedrals', id_method='global', verbose=False)\n",
    "    data.append({\n",
    "            'trajectory': state,\n",
    "            'projection': \"φ/ψ\",\n",
    "            'mean_all': mean_all,\n",
    "            'mean_last': mean_last,}) \n",
    "    mean_all, mean_last = intrinsic_dimension(mol=mol, projection_method='Dihedrals', projection_kwargs = {'sincos':True},id_method='global', verbose=False)\n",
    "    data.append({\n",
    "            'trajectory': state,\n",
    "            'projection': \"Sin/Cos φ/ψ\",\n",
    "            'mean_all': mean_all,\n",
    "            'mean_last': mean_last,}) \n",
    "    mean_all, mean_last = intrinsic_dimension(mol=mol, projection_method='Dihedrals', projection_kwargs = {'dihedrals': ('chi1', 'chi3', 'chi4', 'chi5')}, id_method='global', verbose=False)\n",
    "    data.append({\n",
    "            'trajectory': state,\n",
    "            'projection': \"χ\",\n",
    "            'mean_all': mean_all,\n",
    "            'mean_last': mean_last,}) \n",
    "    mean_all, mean_last = intrinsic_dimension(mol=mol, projection_method='Dihedrals', projection_kwargs = {'dihedrals': ('chi1', 'chi3', 'chi4', 'chi5'),'sincos':True},id_method='global', verbose=False)\n",
    "    data.append({\n",
    "            'trajectory': state,\n",
    "            'projection': \"Sin/Cos χ\",\n",
    "            'mean_all': mean_all,\n",
    "            'mean_last': mean_last,}) \n",
    "    \n",
    "data = pd.DataFrame(data)\n",
    "data[\"folded\"] = data[\"trajectory\"].str.startswith(\"f\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-10T13:50:23.570463Z",
     "iopub.status.busy": "2025-11-10T13:50:23.570256Z",
     "iopub.status.idle": "2025-11-10T13:50:23.936323Z",
     "shell.execute_reply": "2025-11-10T13:50:23.935777Z"
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xqT9NnTp1okaNGlSuXBk3NzcOHjzIxIkTqVy5cuqNQqNGjaJx48a88MILdO3ala1bt7Jo0SKWLl1q3fAiIiJ/U7VqVaZMmWLtGCK5nk2V2Vq1arF06VLGjx+PyWQiKCiI/v378+abb+Ls7AxAgwYNWLlyJSNHjkw9CnXWrFl06dLFyulFRERExNIM5uzscJ8LxcTE4O3tne6G1fHx8Zw9e5aSJUvi6upqpYTyT/q6iIiI5B2ZdbW/s/k1syIiIiIiGVGZtVHx8fEsXLiQzp0706xZUzp37szChQuJj4+3drQ0Zs+eTbly5XB1daVKlSqsXbvW2pFEREQkD1GZtUGrV6+mWLFAevbsyeUbh/DMf5vLNw7Rs2dPihULZM2aNdaOCMCSJUvo378/3bp1Y8OGDdStW5eQkBD27Nlj7WgiIiKSR2jNrI2tmV29ejUhISG0DK7MOxM6UbpcwdRrp09c572hK9m05g/CwsJo3769RbP9U/ny5alevXrqccMA9erVw8fHh/Xr1z/099OaWRERkbxDa2btUHx8PP369aVlcGXmfPt8miILULpcQeZ8+zwtgyvTr1/fHF1y0LhxY9q1a5fmsQMHDmAwGNi2bRtnzpzhxIkTdO3aNc2Yp556ih9//JGEhIQcyyYiIiJyj8qsDVm+fDnh4bd4Z0InHBzS/9I4OBgZ9VEI4eG37js8wpIyOnWtYsWKJCYmcvbsWWvEEhERkTxGZdaGrFq1itoNyt43I/tPZcoXolb9MoSFhVko2f0iIyMB8PHxSfO4r68vABEREZaOJCIiInmQyqwNiYqKpFBR72yNLVTUm6ioyBxOJCIiImLbVGZtiI+PL9cuR2dr7LXL0fj4+OZwoozdm4GNjk6b996MrZ+fn8UziYiISN6jMmtDOnbsyN5dJzl94nqm404dv8YvP50iJCQkx7K4urqSmJiY5rF7RRX+t1b23trZe44dO4azszOlSpXKsWwiIiIi96jM2pAuXboQEODPe0NXkpJiSndMSoqJ998KIyDAn9DQ0BzLEhgYyPHjx/n7zm2bNm1K/e9SpUpRrlw5li9fnuZ5S5cupVmzZjg7O+dYNhEREZF7HK0dQP7H1dWVOXPm0rFjR/p2nnnfPrOnjl/j/bfC2LTmD1atWpWje62GhoYye/ZsBg8eTMeOHfn555/v2z1hzJgxdO/endKlS9OkSROWLl3K3r172bFjR47lEhEREfk7lVkbExwcTFhYGP369aVe+XeoVb8MhYp6c+1yNL/8dIqAAH9WrVpFcHBwjuZ48sknmTBhAlOmTGHevHm0adOGGTNm0Lx589QxTz/9NHFxcYwfP57x48dTvnx5wsLCqFu3bo5mExEREblHJ4DZ2Algf8+wYsUKwsLCiIqKxMfHl5CQEEJDQ/Ps6Ve28HURERERy8juCWCambVRrq6u9OjRgx49elg7ioiIiIjN0g1gIiIiImK3VGZFRERExG6pzIqIiIiI3VKZFRERERG7pTIrIiIiInZLZVZERERE7JbKrIiIiIjYLZVZEREREbFbKrMiIiIiYrd0ApiNio+PZ/ny5axatYqoqAh8fPzo2LEjXbp0sZmjXJcuXcqyZcvYu3cvly9fZuLEibz55pvWjiUiYpNu3bpFWFgY165dw93dnWbNmlGlShVrxxKxe5qZtUGrV6+mWLEi9OzZk7M3vscx/2+cvfE9PXv2pFixIqxZs8baEQFYsWIFZ86coV27dtaOIiJis6Kjo+nXrx9FiwTy/ICBjHv3E4YNeZuqVatSt049du/ebe2IInZNM7M2ZvXq1YSEhPBEcD5emVCKEuVcUq+dP5HA50PD6dixI2FhYbRv396KSf+amTUa//p+aObMmVbNIiJii6KiomhQ/wlOHT9LqZRuFKcFLsnemEjhOr9w4tcVNG7UhLXr1tCiRQtrxxWxS5qZtSHx8fH069ebJ4LzMfHbImmKLECJci5M/LYITwTno1+/3sTHx+dYlsaNG98343rgwAEMBgPbtm0DSC2yIiKSvhcGvcCp4+eomzKesoTigjcARhwoTF3qmsbjl/IYnUI6ExUVZd2wInZKbcSGLF++nPDwSF6ZEICDgyHdMQ4OBl7+KIDw8EhWrFhh4YQiIpJdV65cYdny5ZRNeQovSqQ7xgEnKpteJi4ujvnz51s4oUjuoDJrQ1atWkW1Bh73zcj+U1B5F6rW9yAsLMxCyURE5EEtWbIEg9mBYjTLdJwrvhSiLnNmz7NMMJFcRmXWhkRFRZC/qEO2xuYvaiQqKiKHE4mIyL916dIl8jkUxAmPLMd6mYO4fOmyBVKJ5D4qszbEx8ePm5dTsjX25mUTPj5+OZxIRET+LVdXV5LMcZgxZzk2mbu4uGT+UzkRSZ/KrA3p2LEjv++K5fyJhEzHnTuewIGfYgkJCcmxLK6uriQmJqZ5LDIyMsfeT0Qkt2nYsCGxyeFEcjzTcWZM3HDcTaMmT1gomUjuojJrQ7p06UJAgC+fDw0nJSX97+RTUsxMfiucgABfQkNDcyxLYGAgx48fx2z+X45Nmzbl2PuJiOQ2rVq1onixIE6xItPZ2Sv8REzyFV566UULphPJPVRmbYirqytz5sxnx5o7DOl85b4Z2nPHExjS+Qo71txhzpz5OXoSWGhoKBcuXGDw4MFs3ryZ9957777dE44cOcKKFStSHz906BArVqxgw4YNOZZLRMReGI1GPvn0Y66xl0NMI5m02ymaMXOV3Rw0fk5IxxDq1q1rpaQi9s1g/vvUWx4SExODt7c30dHReHl5pbkWHx/P2bNnKVmypFWOjl29ejX9+vUmPDySqvU9yF/UyM3LJg78FEtAgC9z5swnODg4x3NMnDiRKVOmEBERQZs2bXj++edp3rw5W7dupXHjxowZM4Z33333vueVKFGCc+fOPfQ81v66iIj8G3PmzGHAgOdxwo0iKU3woCjJxHLNcSeRyWcJDm7P0qVLcHNzs3ZUEZuSWVf7O5VZGyyz9zKsWLGCsLAwoqIi8PHxIyQkhNDQ0Dxb5Gzh6yIi8m+cOXOGmTNnsmD+19y8eR1XVzeaNmvK4MEv0axZMx1CI5IOldks2HqZlfvp6yIiIpJ3ZLfM6ltBEREREbFbKrMiIiIiYrdUZkVERETEbqnMioiIiIjdUpkVEREREbulMisiIiIidktlVkRERETslsqsiIiIiNgtR2sHkPTFx8ezfPlyVq1aRWRUBL4+fnTs2JEuXbrowAARERGR/6eZWRu0evVqAosVoWfPnhw5s5G7Dr9y5MxGevbsSWCxIqxZs8baEYmJiWHMmDHUqlULHx8fChYsSHBwMIcOHbJ2NBEREclDNDNrY1avXk1ISAiPN3Rj+MuBFC7hlHrt6vkklkyOpGPHjoSFhdG+fXur5bxw4QIzZ86kX79+fPDBB8THx/Pxxx9Tp04d9u3bR8WKFa2WTURERPIOg9lsNls7hDVkdt5vfHw8Z8+epWTJkhb9kX58fDyBxYpQ8pFEXpmQH6OD4b4xphQznw+9ydk/nbl08YrVlhzExsZiMBhwd3dPfezOnTuUKFGCZ555hilTpjz097TW10VEREQsL7Ou9ndaZmBDli9fzq3wSJ562TfdIgtgdDDQbbAPt8IjWbFiRY5lady4Me3atUvz2IEDBzAYDGzbtg0PD480RRYgX758lClThitXruRYLhEREZG/U5m1IatWraJCVfc0SwvSUyTImfJV3QkLC7NQsuyJiori8OHDWmIgIiIiFqMya0MioyLwyZ/+jOw/+eQ3EBkVkcOJHszQoUMxGAwMHDjQ2lFERETyHLPZTGxsLElJSdaOYlEqszbE18ePqJvZW8IcddOMr49fDifKvrlz5/LVV18xdepUAgMDrR1HREQkzzh16hRvvPEGfr7+5MuXD2dnZx6vVoO5c+cSHx9v7Xg5TmXWhnTs2JFjB+K4ej7z76iunEvk+IE4QkJCLJQscxs2bGDAgAGMGjWKXr16WTuOiIhInrFkyRIqVXyEaZ/Pwj+6EY/zBlV4iat/GOjXtx+1atbh2rVr1o6Zo1RmbUiXLl3wD/BlyeRITCnpz9CaUswsnRKFf4AvoaGhOZbF1dWVxMTENI9FRkbeN27Pnj2EhobSq1cv3nvvvRzLIyIiImlt3ryZHt17UCi5Ps1S5vAI/QikMSVoRS3TaJ7gM84cu0Srlq1JSEiwdtwcozJrQ1xdXZk7Zz6/7bzL50Nv3jdDe+VcIp8PvclvO+8yd878HN2eKjAwkOPHj/P3nds2bdqUZsyRI0do27YtTZs2ZcaMGTmWRURERO43fNjb+FKBqryCAy73XfemFNWTR/LHoQMsW7bMCgktQ4cm2Jjg4GDCwsLo2683b3a+RPmq7vjkNxB108zxA3H4B/iyatVigoODczRHaGgos2fPZvDgwXTs2JGff/45zVZgN27coFWrVri5ufHaa6+xb9++1GteXl5UqlQpR/OJiIjkZb/99hv79v9CLUZiwCHDcT6UoaDxcSZP/oJnn33WggktR2XWBrVv355LF6+wYsUKwsLCiIyKoGgpP0a+EUJoaKhFDgx48sknmTBhAlOmTGHevHm0adOGGTNm0Lx5c+CvWdlLly4B0KxZszTPbdSoEdu2bcvxjCIiInnV7t27MRocKGCukeXYgqY67N8/HZPJhNGY+34orzJro1xdXenRowc9evSwWoYhQ4YwZMiQNI/9fdlBHj08TkRExOqSkpJwMDhhNGc8K3uPESfMZjPJyck4OztbIJ1l5b56LiIiIpLLFS9enCRTPLe5mOXYaE6TP6BgriyyoDIrIiIiYnfatm2Ln48/59iQ6bhk7nLZYSvP9e9roWSWpzIrIiIiYmdcXFwY/MpLnDes5zr70h1jIpmDhskYHFJ4/vnnLZzQcrRmVkRERMQOjRw5kv37f2P9ug8obn6SkrQhH8Uwk8I19nDGuIpow2lWLF1OiRIlrB03x6jMioiIiNghR0dHwsJWMnbsWKZMnsrWW+twMDhhMidjxkyDug35cOyXPPHEE9aOmqNUZkVERETslKOjI++88w7Dhg1j48aNXLx4EWdnZ+rWrcujjz5q7XgWoTIrIiIiYmfMZjM7duzg6NGjADzyyCMEBwdjMBisnMzyVGZFRERE7MiCBQv44L2xnDx9HMP/38tvxkT5shV5Z8xInnnmGSsntCztZiAiIiJiJ0aPHk2vXr1IPBNEa5bQi1P04hRPsoi7p4rSvXt3PvjgA2vHtCiVWRsVHx/PwoUL6dy5M02bNqNz584sXLiQ+Ph4i2X49NNPKV68OA4ODnTs2JHExESGDBlCoUKF8PDwoEWLFhw/ftxieURERPKyNWvW8N5771GdoTQ1z6QQdTBgxICRwtSjqfkrqvEao0aNYuPGjdaOazEqszZo9erVBAYWo2fPnpw6dBVu+3Lq0FV69uxJYGAx1qxZk+MZTp48yRtvvEH37t3ZuXMnEyZM4OWXX+arr75i7NixrFy5koSEBJo1a0Z0dHSO5xEREcnrJn38KYUcavAYg9K9bsBAFV6mgEMVPpn0qYXTWY/WzNqY1atXExISQr3KrXn+lfcpVrBs6rWL108yc+UoOnbsSFhYGO3bt8+xHMePH8dsNtO/f39KlSrFpUuXmDVrFtOmTaNv379OEalZsybFixdn5syZDB06NMeyiIiI5HXnzp1j+46tPMHnGMj4Ji8DBsqldOeHzUO5fPkyRYsWtWBK69DMrA2Jj4+nb99+1Kvcmnef/yZNkQUoVrAs7z7/DfUqt6Zv3345tuSgd+/eBAcHA1C6dGkMBgObNm3CZDLRpUuX1HF+fn60bNmS9evX50gOERER+cuFCxcACCDr7bb8eQyAixcv5mgmW6Eya0OWL1/OrVvhPN/pfRyMDumOcTA6MCDkPW7dCmfFihU5kmPUqFF89NFHAKxcuZLdu3ezc+dOChQogK+vb5qxFStW5NixYzmSQ0RERCApKYldu3YBkEzWE1kpJADg7Oyco7lshcqsDVm1ahWVy9a9b0b2n4oXKsdjZeoQFhaWIzlKly5NuXLlAKhWrRp16tTB0dERHx+f+8b6+voSERGRIzlERETyuvDwcBo2fIK3334bo8GRC2zK8jnn+Z58Hl6UL1/eAgmtT2XWhkRGRuHvXSRbYwO8ixAZGZWzgURERMRqEhISaNOmLSeOnmLKkE20a9iLE8bFJJDxjdfxRHLaYSl9+/XGw8PDgmmtR2XWhvj6+nAr+kq2xoZHX8HX1ydnA/2Nr69vursWREZG4ufnZ7EcIiIiecWyZcv49ddfGPvCch4rU5enW72K0SWJzYbe3CX8vvFx3OBHh964eTnwxhtvWCGxdajM2pCOHTvyx8ndXLx+MtNxF66d4NCpPYSEhFgoGVSoUIHr168TGRmZ5vFjx45RoUIFi+UQERHJK6ZOnUaNSk2pWLIGAIUDgvj41TDi3S6ywtCAXQzhNGGcYiU7eJ1vDQ0w+1xj0+aNFC9e3MrpLUdl1oZ06dIFf/8AZq4cRYopJd0xKaYUvgx7B3//AEJDQy2WrWXLlhiNRr799tvUxyIjI9m0aRNt2rSxWA4REZG8ICkpib1799CoWoc0j1cIqs6C936hT8dhRPvsZQevsZPXOcN3dOgUzNHjf/L4449bKbV1aJ9ZG+Lq6srcuXPo2LEjo2d25/lOafeZvXDtBF+GvcPPf2xg1apVuLq6WixbYGAgzz33HEOGDMHBwYGiRYsyduxYvL29ef755y2WQ0REJC9ISPhrRwJ3V8/7rvl45qf7k2/Q/ck3SEi8i9lspt3rgTRp0gR/f39LR7U6lVkbExwcTFhYGH379uPZdx7nsTJ1CPAuQnj0FQ6d2oO/fwCrVq1K3QfWkj7//HPy5cvHsGHDuH37NvXr12fz5s14e3tbPIuIiEhu5uHhgbeXN2cu/0kzumQ4zsXZjQvXTpCcnJQnDkhIj8FsNputHcIaYmJi8Pb2Jjo6Gi8vrzTX4uPjOXv2LCVLlrTo7Oc/M6xYsYKwsDAiI6Pw9fUhJCSE0NBQq2WyNlv4uoiIiFjK4MGDWbRwKUs+PIqzk0uG46YsfYttB5dx6dLFXPXvY2Zd7e80M2ujXF1d6dGjBz169LB2FBEREbGCF154genTpzNl6RBee+YzjMb7b3X67dh2Vu+cxVtvDc1VRfZBqMyKiIiI2KCKFSvy5Zdf8txzz3E94iLdWrzC4xUaYTAYuHbrAmt2zGHZ5ik0btKYUaNGWTuu1ajMioiIiNiovn37EhAQwPBhI3jjs2DyuXvh5OhM1O1beObz5JVXX+aDDz7IM0fXpkdlVkRERMSGtW/fnuDgYHbu3MmePXtISkqiePHidOrUKc+c8pUZlVkRERERG2cwGHjiiSd44oknrB3F5ujQBBERERGxWyqzIiIiImK3VGZFRERExG6pzIqIiIiI3VKZFRERERG7pTJro+Lj41m4cCGdO3emSeNmdO7cmYULFxIfH2+xDJ9++inFixfHwcGBjh078v7779OiRQt8fHwwGAzs27fPYllERERE0qMya4NWr15N0SLF6NmzJz+vusj57e78vOoiPXv2pGiRYqxZsybHM5w8eZI33niD7t27s3PnTiZMmMDMmTNJTEykefPmOf7+IiIiItmhfWZtzOrVqwnpGEIxmtGY4XibSv11wQTRnGF/1Dg6duhI2Kow2rdvn2M5jh8/jtlspn///pQq9VeGCxcuYDQa2bZtG99++22OvbeIiIhIdmlm1obEx8fTp3e/v4qseQbelEpz3ZtSNDbPoBjN6NO7X44tOejduzfBwcEAlC5dGoPBwLx58zAa9dtFREREbIvaiQ1Zvnw5EZHhVDcPx4hDumOMOFDdPIyIyHBWrFiRIzlGjRrFRx99BMDKlSvZvXs3bdu2zZH3EhEREfkvVGZtyKpVqyhkrHnfjOw/eVOaQsYahIWF5UiO0qVLU65cOQCqVatGnTp1yJ8/f468l4iIiMh/oTJrQyJuReFmKpitsa6mgkREROVsIBEREREbpzJrQ/z8fbhrvJ6tsfHG6/j5+eRsILFLKSkpREZGWnQbNxEREWtRmbUhHTt25JrpV6I5k+m4aE5zzbSPkJAQCyUTe3DgwAH69euHZz4v/Pz8cHNzo1bNOixYsIDExERrxxMREckRKrM2pEuXLvj5BrDfMA4TKemOMZHCfsN4/HwDCA0NtXBCsVWTJ0/m8ccfZ8WCjVSIf54mTKM+H3H1Nxd69erFEw0bExERYe2YIiIiD532mbUhrq6uzJs/h44dOrLNMJDq5uFpbgaL5jT7DeO5yI+smr8KV1dXi+bbvn07N2/e5M8//wRgy5YtnDt3jqCgIGrUqGHRLPI/ixYt4pVXXuFRBlA9eSjGv/2xLmfqxnX2s2VfP4LbdWDHzm04OKS/U4aIiIg9Upm1McHBwYStCqNP736sjGxKIWMNXE0FiTde55ppH34+Aayavyp1H1hLGj16NNu3b0/99VtvvQVAr169mDdvnsXzyF/rY4e99TZBtKYGwzFgSL12nf0cYwHn2USK6S4/7/6JJk2a8uGHH9CgQQMMBkMmrywiImIfDGaz2WztENYQExODt7c30dHReHl5pbkWHx/P2bNnKVmypMVnP/+eYcWKFYSFhREREYWfnw8hISGEhoZaLZO12cLXxdZs2LCBNm3a0I5V5KcqAGZM/ML7HGEuHhShGE1xJYB4bnHR8AOx5msMHDiQqVOn6iAMERGxWZl1tb+zqZnZ5cuX8/XXX7N//34iIyMpW7YsL7/8Mn369EmdRWrcuHGa2cF7jh49SoUKFSwdOce4urrSo0cPevToYe0oYsP27t1LPscCBCRXSX3sdz7lCPN4jIEE0RrD35bGlzWHcp7vmTljOl5eXqmHY4iIiNgrmyqzn3zyCUFBQUyaNIn8+fPzww8/0L9/fy5evMjo0aNTx9WvX5+PP/44zXODgoIsnFbE+pKSknAwuKQuL7hLOIeYQTm6UZL7T20zYCSI1iQSw6RJn/Dqq69SuHBhS8cWEbGo6OhowsLCuHz5Mq6urjRs2JCaNWtquVUuYVNlds2aNQQEBKT+umnTpty6dYtPPvmEUaNGpf5I1MfHhzp16lgrpojNCAoK4nbyVeK4gTsFOMkywEApMl9TXZK2nDavYNasWYwaNcoyYUVELCwuLo633nqLuXNnc/duPD7+zsTHpRAXm0y1x6swccIkmjVrZu2Y8h/Z1IK5vxfZe6pVq0ZMTAyxsbFWSCRi27p06YKLizMnWALADfYTwKM4k/HaIgAn8uFvqsKuXbssEVNExOLi4uJo3qIps+fMoPubHqy/UJrNN0qxPboMn68NBI9TtGrVkm+//dbaUeU/sqkym55du3ZRtGhRPD09Ux/bvn07Hh4euLq60qhRI3bs2JHl6yQkJBATE5PmQ8Te+fj40O+5vhwyTuMae0khEQdcsvVcB1xIiNdhCiKSO7311lscOLCfGVsCGTgmPwWKOgHg4GCgYVtPZm4pRrNQT3r06M6lS5esnFb+C5sus7t27WLJkiW8+eabqY81atSIzz//nI0bNzJ//vy/vvNq3pzdu3dn+lrjxo3D29s79aNYsWI5HV/EIiZOnEiDhvX4wdiTOK4RzTnMmDJ9jhkzdxzPUiKouIVSiohYTnR0NHPnzubZIT48Vtst3TGOjgZGflkQB8cUvvrqKwsnlIfJZrfmunTpErVr16ZixYps2rQpwy2EYmNjeeSRR6hUqRLr16/P8PUSEhJISEhI/XVMTAzFihWz2a255H76umQsISGBDz74gM8/m8LtO9HU4V0K8HiG48P5g595my1bttCkSRMLJhURyXnz5s2jX7++bLhYmvxFnDId++HAq/y60YPz5zQ7a2uyuzWXTc7MRkVF0bp1a/z9/fn2228z3QvTw8ODtm3bsn///kxf08XFBS8vrzQfIrmFi4sL77//PjduXqNUyTIcMk4jnvSPr40nksOO03n0kco0btzYskFFRCzg0qVL+Pg7Z1lkAcpWduHK5WsWSCU5xebK7N27d2nXrh3R0dFs2LABb29va0cSsRuurq78uOUHvAo48pPjG5xhNUncASCJWM6whp8c38TVN5lV363UtjQikiu5urqScDcFkynrHz7H3THj4uJsgVSSU2xqa67k5GS6du3K0aNH2blzJ0WLFs3yObGxsaxdu5aaNWtaIKHlxMfHs3z5clatWkXErUj8/H3p2LEjXbp00Y/YJVNBQUH88use3njjTb79di5/pszCycGNpJS7GB0cCOnYkUmTJlG8uNbLikjuExkZycWLF4m9k8zu72Op3zpfpuN/XBFLg4YNLZROcoJNldkXXniBtWvXMmnSJGJiYtizZ0/qtWrVqvHLL78wceJEQkJCCAoK4sqVK0yaNIlr166xfPlyKyZ/uFavXk2f3n2JiLxFgPERnE1+JBqPs3JlT1595TXmzZ9LcHDm+4g+DJ9++imffvoply9fpmXLlpQuXZotW7Zw7tw5ChYsyJNPPsn777+f7pZqYl2BgYEsXbqEa9eusW7dOiIjI/Hx8aFNmzYUKVLE2vFERHLEr7/+Srt2bYmMjMQvwJ05425Ru4UHjo7p/xRqzw93+PPXWMavGWzhpPIw2dQNYEFBQZw/fz7da2fPniU5OZmXXnqJgwcPcuvWLTw8PKhXrx6jR4+mVq1aD/RemS0qtuaNRqtXryakYwgFqUVFc2/y8b/Z6Ttc5qhhHtf5hbBVYbRv3z7Hcpw8eZLy5cvz1ltvERwczL59+5g9ezZ9+/alSpUqnD9/nnfeeQd3d3cOHDiAi0v2toP6L3QDmIiIZOT06dPUrFmDUuX9mbvyeY79eYWnn/yc5l08GfllITw8HdKM37s5lqGhV6lZvT6bNm3GwcEhg1cWa8nuDWA2VWYtyRbLbHx8PEWLBOISVZYa5mEYuP8PlpkU9hnGk+BzkstXLuVYvrVr1xIcHMzp06cpVaoUt27dws/PL80ay59//pn69euzYsUKOnfunCM5/k5lVkREMtK3X182fv8d2w+PwtvHHYA1K/bzYo/ZODpD6+6elK3sStwdEz8svc2R/Xdp2rQxK1eu0v05NsqudzPIq5YvX05E5C0qmnunW2QBDDhQ0dyLiMhbrFixIkdy9O7dO3UZQ+nSpTEYDKxZs+a+m4WqVasGwJUrV3Ikh4iISHZERkayZPFier/QMLXIAgSHVmfPqQ95/tUn2b0eJgy+zrSR4Zz5M4WVK1eyadNmFdlcQGXWhqxatYoA4yNplhakJx+BBBgrERYWliM5Ro0axUcffQTAypUr2b17N23btr1v3L2jUCtWrJgjOURERLLj999/5+7deIJDq993rUigL2+9157fzn/EleQZLN7wCvHxiTzyyCNaWpBL2NQNYHldxK1InE1+2RrrbPIjIiIyR3KULl2acuXKAX/NvgYFBd03Jj4+njfffJNq1arRrFmzHMkhIiKSHfcORXJzz3qLLXcPlzTPEfunmVkb4ufvS6Ix/Y3u/ynRGIGfn28OJ8rYwIEDOXv2LAsWLNBepSIiYlWBgYEA/Hkw61O8/jx4EYPBQOHChXM6lliIyqwN6dixI+GmP7nD5UzH3eES4aYjhISEWChZWiNHjuSbb75h+fLlPProo1bJICIics+jjz5KtWpVmTd9R6bjzGYz86fvpF27ttpWMhdRmbUhXbp0wc/Xn6OGeZhJSXeMmRSOGubj5+tPaGiohRPClClTGDt2LLNnz6ZVq1YWf38REZF/MhgMvPrqa2xe9weL5vyU4bhPP1jP4QMXeOWVVy0XTnKcyqwNcXV1Zd78uVznF/YZxt83Q3uHS+wzjOc6vzBv/lyLb0+1ePFiXnnlFcaNG0fPnj0t+t4iIiKZefbZZ3l+4PO81m8Br/ZbwOEDF4G/ZmN/+ekUz3WZyUfvrOb999/XvR65jG4AszHBwcGErQqjT+++bIkcSICx0v+fABZBuOkIfj7+rJq/yiIngP3d9u3b6dWrF02bNqVRo0ZpTmcLDAxMXa8kIiJiDQaDgenTplOhfAU+/ngii+f8hKeXO2aTmTt37lKmTGnmz5+vyZhcSGXWBrVv357LVy6xYsUKwsLCiIiIxM+vAiEhwwkNDbXKgQFbt24lKSmJH3/8kR9//DHNtdGjRzNmzBiLZxIREfm7v5YbvMpLL73Ehg0bOH78OEajkccee4xmzZphNOoH0rmRTgCzoRPAJHP6uoiIiOQd2T0BTDOzIiKSoejoaM6cOQNAyZIl8fHxsW4gEZF/UJkVsWMHDx5k9uzZnDp5CgdHB6pVq8Zzzz1H8eLFrR1N7Nzhw4eZNGkSSxYvIT4hHgAXFxe6du3KG2+8QZUqVaycUETkL1o8ImKHbt26RcuWrahatSqLv17O7WsO3DqfzCcff0bJkiUZMGAAiYmJ1o4pdmrt2rXUrFGTjWu38GybYcwYvp0Zw7fTp90otny/i1q1avHtt99aO6aICKCZWatKTk7m1q1bREdHk5KSgqOjI35+fvj6+mqRumQoJiaGJk2acun8FUb3n0/DasE4OjgBEBd/m3W7FvDlvHcID7/F8uXLdPa4PJBDhw7RJbQLNSo2Z9Rzc3Fx+t/69ApBj9O56SDGzh3AM888w88//0z16tWtmFZERDOzmcrJe+Nu3LjBwYN/cPHiJe7GpJAUayQ2OpGzZ8/yxx9/EBMTk2Pvba/y6L2K9/nwww85feoMn7y2niY1OqUWWQB3V0+6NH+RMf0XEBa2kuXLl1sxqdijSZMm4eNZ4L4ie4+TozMj+nxJIb8STJww0QoJRUTSUplNh5PT/89yxcXlyOvfuHGDCxcu4GT2xIsgPCiMOwXJR1E8KY4h2ZmTJ09y+/btHHl/e3Xv63Hv65MXxcfHM2vWbNrW60XJIhUzHFe/SluqVWjI1KnTLJhO7F1UVBRLliyhfcN+6RbZe5wcnenQqD/frvyWGzduWDChiMj9tMwgHQ4ODvj4+KT+Je3u7o7BYHgor52UlMTFixdxJh8ueGMmhZR/HF3rgj8m8w3OnjlL2XJlH9p72yuz2UxcXBw3btzAx8cnT//YfNeuXURE3KJ1/aw3/W5d91nGzh1AeHi4ziCXbDl9+jQJCQlUr9gky7E1KjYhOTmZEydOUKBAAQukExFJn8psBgoVKgTw0GcdoqOjiYqKxg0zkPHMr4lEEogiOSUZNze3h5rBXvn4+KR+XfKqyMhIAPL7FslybH7foqnPUZmV7Li3lCdb30D//xgt/xERa1OZzYDBYKBw4cIUKFCApKSkh/a6oZ27cvNPFx6hb6bjzMCvjmPo0K05o0aNemjvb6+cnJzy9IzsPd7e3gDcirqGp7tPpmPDo64CaF9QybZSpUrh5OTEwRO7KFe8aqZjDxzfhdFopEyZMpYJJyKSAZXZLDg4ODzUEnXi+AlSzpfn1j+WFqTnhkMsFy5c0GlXkqpBgwb4+PiycffXDOz8QaZjv9/zDXXq1CV//vwWSif2zs/Pj9DOoXy3dRYhTZ5Pc3Ph36WYUvhux5e0b9+BwoULWziliEhaugHMwvz8/YgnPMtxZszcNYTj5+dngVRiL9zd3enXry9rd83lwrUTGY7be/gH9h3ZyksvvWjBdJIbvPHmG1yPuMBH818gOeX+n0qlmFKY9PXLnL96nCFD3rRCQhGRtFRmLaxL185cN/5KAlGZjgvnD+4kX6dTp06WCSZ24+233yaweFFe+7QNPx1cR4rpf7P8CYl3+W77bN6Z+Qzt2gXz1FNPWTGp2KPq1auzYMECtu5fwXMf1CVs65dcvH6SS9dPsXrHHAaMrc/3e75h9uzZ1KtXz9pxRUQwmPPo6v2YmBi8vb2Jjo7Gy8vLYu8bERFB0SKBFEioRxVewcD9N1okE88eh7cpUMaRI0cP5/ndDOR+N27coEuXruzYsZ1CAcWpGFSTFFMyB07s4HZsFD2f7cnML2fi4uJi7ahip/bu3cuECRP57rtVpKT89Q2T0WikXbtghg4dQv369a2cUERyu+x2NZVZC5dZgIULF9KzZ0+K8gTl6U4+/roz3YyZSI5z1GE2cc4X2bFjGzVq1LBoNrEvv/76K1999RWnTp3GwcGBatWq8vzzz1O6dGlrR5Nc4tq1axw/fhyz2Uy5cuUoUiTrnTRERB4GldksWLPMAixevJgXX3iJyKgI/B0q4pTiSbzjdaKSz1MyqDRLly2mZs2aFs8lIiIiYgtUZrNg7TILf53mtGzZMjZs2MCdO3cICAigW7dutGzZEqNRy5lFREQk71KZzYItlFkRERERSV92u5qm/0RERETEbqnMioiIiIjdUpkVEREREbulMisiIiIidktlVkRERETslsqsiIiIiNgtlVkLOnbsGC+//DIFAgrh6OCIt5cvTz/9DLt27SKP7pAmIiIi8p+ozFrIpEmTqFSpErOnL6LgrQ7UNI2h9O0+bFyxm4YNG9K3b1+Sk5OtHVNERETErjhaO0Be8OWXX/Lmm2/yGIOolvwqDrikXquSPJhTrGDB/BG4uLgwY8YMKyYVERERsS86ASyHTwC7e/cuRQoHkj+6GQ2YmOG4I8xnL6M5fvw45cqVy7E8IiIiIvZAJ4DZiOXLlxMVHcFjvJDpuHJ0w93Rj+nTp1somYiIiIj9U5nNYd9//z3+xop4UzLTcY64UjS5Odu27LRQMhERERH7pzKbQ2JiYni2x7MsXrwYo8ktW89xxJ34+Ls5nExEREQk99ANYDng9u3bNGnSlBPHTlGtfCOOnjhEiikRB5wzfV608TiVihe1UEoRERER+6eZ2RwwYsQIjh89waevr2dwtwncNd3iPBszfU40p7li2k2fPr0tE1JEREQkF1CZfchiYmKYN3ceoU1fpGyxypQsUpHHyzVhv3Esd7iU7nOSiOVnh6EUzF+Yzp07WzixiIiIiP1SmX3I1q5dy53YO7Rt2Dv1sRH9ZuLh7cI6YyeOMJcEogFIIYHTfMcGh07cdjnB6rWrcHFxyeCVRUREROSfVGYfsuvXr+Pm6kEB3/+tfQ3wKcy04T9S+/GG7DN+yBJDDZYaa7KIyuzgFR6pV5ifd++iVq1aVkwuIiIiYn90A9hD5u7uTkLiXRIS7+Li/L9dDPy9CzK6/zxuRV9j5+9riImN4MT5A/x8aD3btm/BYDBYMbWIiIiIfdLM7EPWtGlTTCYT2/aHpXvd37sQHRv3p2fbt7h1+xrNmjVXkRURERH5l1RmH7KyZcvSokVLlvzwGXcTYjMct/fwJo6e2cdLL71owXQiIiJiMpnYv38/P/zwA3v37iUpKcnakeQ/UJnNARMmfMSNyAsMnxrKjYi0OxiYTCa2//YdY77qyZNPtqZt27ZWSikiIpK3JCQk8PHHH1O6dElq1KhBy5YtqVOnDsWLBzJmzBhu375t7YjyLxjMZrPZ2iGsISYmBm9vb6Kjo/Hy8nror//TTz/RoUNHIiMjqFe5NWUCq5CQGMeuP9Zy8dop2rULZsmSxXh4eDz09xYRsRSz2UxCQgLOzs4YjZofEdsVGxtL27at+ennn6jTwp3GHfLhX8iR6IgUdq69w0/r71KubAV+/HErAQEB1o4rZL+r6W+eHFK/fn3Onj3DtGnTSHQK5/t98/j52CoaNqnFrl27WL36OxVZEbFbv//+O8899xz5PDxxc3PD2cmZ1q3bsG7dOkwmk7Xjidyn/4Dn2PvrbkZML8ig9/JTsbobBYo6UfYxV/oOD2DM3IJcuHSC0NBO5NF5PrulmdkcmpkVEcmtxo0bx4gRI8jnWIAiyc3woBCJ3Oaqw3YiUk4SHNyeZcuW4urqau2oIgCcPXuW0qVL0/stP5qHZvxv/m8745j02nV2795NnTp1LJhQ0pPdrqatuUREJNtmzJjBiBEjKMdTlEt+CiMOqddKp3TgKnvYsO5jevfuzZIlS6yYVOR/5syZg3s+Rxq0zZfpuKr13ShY1JWZM2eqzNoRLTMQEZFsiY+P5+3hIylOCyrQPU2RvacwdXjM9AJLly7l999/t0JKkfsdPXqUkpWccHXLvPYYjQbKP+7IsWN/WiiZPAwqsyIiki0rVqwgIuoWZeiU6biiNMLDMT9Tp061UDKRzBmNRsjmokqzGdD+73ZFZVZERLJl9+7d+DoFkY/ATMcZcSB/ci127fzZQslEMvfoo49y+nAicXcyvzkxJdnM0X1JPPZoFQslk4dBZVZERLIlISEBo9k5W2MdcCExITGHE4lkT9++fUlMMLF9deb7yO7bFkf4tQQGDhxooWTyMKjMiohIthQvXpw75sskE5/l2NvGM5QIKm6BVCJZCwwMpE+fviz9IpqDP8elO+b04QTmjI2kdetWPP744xZOKP+FdjMQEZFsefbZZxkzZgyX2U4JWmU47g6XuWE6wKTnFlownUjmvvjiC65evcLEV9ZTrYEHjTp4EPD/hybsWBPLr1vjqFWzJosXL7V2VHlA2mdW+8yKiGRbSEgnNq7ZQt2UsXhy/8xrErH84jAGJ/9ozp4/o71mxaYkJyczb948vvhiMgcPHkp9vGzZUrz44ssMGDAANzc3KyaUv8tuV1OZVZkVEcm2iIgIGjZoxOkT5whKaU8JWuKKP8nEc5ntnHVYhdkthq3btlC9enVrxxVJl9ls5syZM0RERODl5UXZsmV1HLMNUpnNgsqsiMi/ExUVxbBhw5g/fyHx8XE4GV1JNiWAAVo/2ZqJH0+kUqVK1o4pInZOZTYLKrMiIv9NdHQ0a9eu5ebNm3h4eNCiRQuCgoKsHUtEcgkdZysiIjnK29ub7t27WzuGiORxWiAiIiIiInZLZVZERERE7JbKrIiIiIjYLZVZEREREbFbKrMiIiIiYrdUZkVERETEbqnMioiIiIjd0j6zIiKSLfHx8axbt47z58/j7OxM7dq1qVmzprVjiUgepzIrIiKZSk5O5oMPPmDK51OJiArH2cGdFFMyKeZEHq9ag7HjP6BVq1bWjim5yNWrV5k1axZzZ8/n6vWruDi70LjxE7z40os0a9YMo1E/WJb/0XG2Os5WRCRDycnJdOoUyrq166hgfpYK9MCb0phI4TLb+dPwJdf5hfkL5tOjRw9rx5VcYNWqVTz91DOkJJkpbGqIJyVI5i7XHX8iKvkcbVq3YdnyZXh4eFg7quSw7HY1lVmVWRGRDH3wwQeMfuddmpq/pBhN77tuIoWfeYuzjt9x+PAhypcvb4WUklts2bKFli1bUchUh8rmF3EiX+o1M2au8wsHHD6hWcvGrF27RjO0uVx2u5p+F4iISLoSExOZ/NkXlDM/nW6RBTDiQF0+xBlPpk+fbuGEkpuYzWZefeV1fM0VqGZ+M02RBTBgoBC1qZryBhs2rGfz5s1WSiq2RmVWRETStWnTJm7euk4FMl8+4IALpZO7MnfOfPLoD/vkIdi7dy+HDh+ktKkzRhwyHFeQmvg6luSLL6ZaMJ3YMpVZERFJ18WLFzEaHPChXJZj/ahEzO0o7ty5Y4Fkkhtt374dFwcPCvB4puMMGCiY3IDtW7dbKJnYOpVZERFJl7OzMyZzCiYSsxybTHzqc0T+jcuXL2M0O2PIRjVxxI34hHgLpBJ7oDIrIiLpqlevHgDn+T7LsReMG6hWpTouLi45HUtymZiYGEJCOjNlyhTiTdEkEJXlc25zgUIFC+d8OLELKrMiIpKuihUr8kTDxhxxmEVKJrOzERzhkmkbLw4eZMF0khvExcXRvFlLNq7ZTG3exYgTF/gh0+ckEcdVh+307tvTQinF1qnMiohIhsaO+4AowzG2GwaTxP3rYcM5zI+OfXj0kco888wzVkgo9uzjjz/mwG8HaZHyNZXoRSk6cpJvieZMuuPNpHDIMA2MJvr372/htGKrtM+s9pkVEcnUmjVr6NqlG+ZkB0qmhODPY6SQwCXjJi6ZdlL5saps/H49hQvrx76SfUlJSRQPDML7RmPqMfavx7jDBp4imrOUJZTitMAFb8yYuMkBThu+JcLwJ4uXLKZLly5W/gwkp2W3q+k4WxERyVRwcDAnTh7nyy+/5Ksv53DsxkKMRiM1qtdi7OAFdOnSBVdXV2vHFDuSmJjIhx9+yLUbV6hBt9THnchHa5awl/c5zmKO8TWu+JFMHEnEUr5sBRZOXqfjkyUNzcxqZlZE5IEkJSXh4OCg05fkX7l69SqtW7fh4MEDAHTjF9wpcN+4eCI5xzriuMFdwjnBIv744w8ee+wxCycWa9EJYCIikiOcnJxUZOVfiY2NpWXLVly5eIM3ekwG4C430x3rii8V6MHjvE5RngDAx8fHUlHFjuhvIxEREbGIuXPncvTIESYMXkXLOk/j5e7PKb7N8nmnDSt47JEqBAYGWiCl2BuVWREREclxZrOZadOm06BqMCWLVMTFyZW2DXtyyriMaE5n+Lxr7OUiW3jp5RcwGAwWTCz2QmVWREREclxkZCRHjx6h0eMdUx975snXKZQ/kI3Gp7nIFsyYUq+lkMgpVvKjQ18aPdGY3r17Wz602AXtZiAiIiI5Lj7+r+Nn3V3zpT7m6e7DZ2+u453pPdh8pi/exhIEmKpjxsRldpBABB2DO/H11wt0VLJkSDOzIiIikuP8/f1xcXHh9OXDaR738yrAlKHfM3XoZurWboBrySsYip4hgQhmz55NWNi3eHh4WCm12ANtzaWtuURERCyiV69ebN6wg4XvHcDB6JDhuPHzBvLnxZ2cPXcGB4eMx0nupq25RERExKYMHjyYq+Hn+XLlO2Q0l/bzwfVs2ruYwS+/pCJrAUlJSaxYsYIWLZpTpEghihQpRPPmzVi+fDlJSUnWjpctKrMiIiJiETVq1OCzzz5j6Q+TGTn9KQ6e2JVaai9dP8XUZcMYNeMZOnToyOuvv27ltLnf+fPnqVq1Cl26dOF2/AW6D6hJ9wE1iU28RNeuXalSpTLnzp2zdswsaZmBlhmIiIhY1NKlSxk18h1OnjpBPncvHB2diYoJx9fXj5deepF33nkHR0fdo56Tbt26Ra1aNTERy1fL+1P58eJprh/6/QLPdZmF0ezG3r2/EhAQYPGM2e1qKrMqsyIiIhZnNpvZsmUL+/fvJzk5mVKlStGhQwfc3NysHS1PGDlyJJ9P/oStf4ykeFD6RfXi+Vs0qfwBg196lQ8//NDCCVVms6QyKyIiInlRYmIixYoFEtztUcZOfirTsW+/spTvFv/BxYuXcHFxsVDCv+gGMBERERG5z+HDh7lx4yYhT9XMcmzI0zW5eTOcw4cPZznWWlRmRURERPKQuLg4ALx8sl7S4eXtluY5tkhlVkRERCQPKVSoEACnjl3Lcuyp49cBKFiwYI5m+i9UZkVERETykDJlylCrVg0Wfrkry7ELZ+6kZs3qlCtXzgLJ/h2VWREREZE8ZvDgV9j6/Z+s+GZvhmNWLv6FLRsP89JLL1sw2YPTJm4iIiIieUz37t3ZvPkHXnp2Lod/v0jflxqnbtF14Vw4c6duY8YnP9Kz57M8++yzVk6bOZVZERERkTzGYDAwZ85cSpYsxaeffsKMTzYTVLogBgOcPXUdLy9PRo0axTvvvIPBYLB23Expn1ntMysiIiJ5WGxsLMuWLePIkSMAVKpUia5du+Lh4WHVXNntapqZFREREcnDPDw86NOnT4bX7969y+bNm7l58yb58uWjSZMm5M+f34IJM2dTN4AtX76cDh06EBgYiIeHB1WrVmXOnDn8c/J49uzZlCtXDldXV6pUqcLatWutlFhEREQkd7pz5w5DhgyhSNFCtG/fnn79+tGtWzcCA4vSo0d3zp07Z+2IgI2V2U8++QR3d3cmTZrEmjVraN26Nf379+e9995LHbNkyRL69+9Pt27d2LBhA3Xr1iUkJIQ9e/ZYMbmIiIhI7hETE0Pjxk8wddpn1G9nYNLKQBbuDWLa98UJfcGTjZu/pXbtmhw9etTaUW1rzWx4eDgBAQFpHhswYABLly4lMjISo9FI+fLlqV69OosWLUodU69ePXx8fFi/fn2230trZkVERETS161bV9ZtWMWIGfkJKu9y3/WYyBTGDbqJo6kgx46dwMnJ6aFnyG5Xs6mZ2X8WWYBq1aoRExNDbGwsZ86c4cSJE3Tt2jXNmKeeeooff/yRhIQES0UVERERyZXOnz/PihXf0u0lr3SLLICXrwPPv+vLmTPnWLNmjYUTpmVTZTY9u3btomjRonh6enLs2DEAKlSokGZMxYoVSUxM5OzZsxm+TkJCAjExMWk+RERERCSt+fPn4+pmpH7rfJmOCyrvQvkq7sya9ZWFkqXPpsvsrl27WLJkCW+++SYAkZGRAPj4+KQZ5+vrC0BERESGrzVu3Di8vb1TP4oVK5YzoUVERETs2NmzZwks7YKre9Y1sWQlR06fOWmBVBmz2TJ76dIlunXrRpMmTXj55f9+jNrw4cOJjo5O/bh48eJDSCkiIiKSuzg5OZGclL1bqpKTzDmyXvZB2GSZjYqKonXr1vj7+/Ptt99iNP4V894MbHR0dJrx92Zs/fz8MnxNFxcXvLy80nyIiIiISFo1a9bk3PG73LyalOk4U4qZA7sSqVWzroWSpc/myuzdu3dp164d0dHRbNiwAW9v79Rr99bK3ls7e8+xY8dwdnamVKlSFs0qIiIikts8/fTTeORzZ+OizO8v2rs5lvBrCbz44osWSpY+myqzycnJdO3alaNHj7Jx40aKFi2a5nqpUqUoV64cy5cvT/P40qVLadasGc7OzpaMKyIiIpLr5MuXj7dHjGLj4hg2Lo6+7/AqgMO/3GXWh5F07NiB6tWrWyHl/9jUcbYvvPACa9euZdKkScTExKQ5CKFatWq4uLgwZswYunfvTunSpWnSpAlLly5l79697Nixw4rJRURERHKPoUOHEh4ezscff8y2sLs06uBGgUBHbkeZ+GnDXY7si6VFi+Z8/fU31o5qW4cmBAUFcf78+XSvnT17lqCgIOCv42zHjx/PhQsXKF++PGPHjqVdu3YP9F46NEFEREQkc9u2beOLqV/w3apVJCenANCgQT1eeOElunTpgqNjzs2LZrer2VSZtSSVWREREZHsSUhIICoqinz58uHh4WGR98xuV7OpZQYiIiIiYntcXFwoWLCgtWOky6ZuABMREREReRAqsyIiIiJit1RmRURERMRuqcyKiIiIiN1SmRURERERu6UyKyIiIiJ2S2VWREREROyWyqyIiIiI2C2VWRERERGxWyqzIiIiImK3VGZFRERExG6pzIqIiIiI3VKZFRERERG7pTIrIiIiInZLZVZERERE7JbKrIiIiIjYLZVZEREREbFbKrMiIiIiYrdUZkVERETEbqnMioiIiIjdUpkVEREREbulMisiIiIidktlVkRERETslsqsiIiIiNgtlVkRERERsVuO1g4gIiIiIrbjl19+Yd68eVy4cB5nZxfq1KlD3759CQgIsHa0dGlmVkRERES4dOkS9RvUpXbt2qxaN5c7Dju4eucHRr0zgsDAIowaNQqTyWTtmPfRzKyIiIhIHnf9+nUaPlGfhJQbfLo6kAZt8uHgYAAgMjyZJZMj+fCDD4iKjmLK5ClWTpvWv56ZjY6OZuzYsdSvX5/8+fPj4uJC/vz5adCgAePHjycmJuZh5hQRERGRHDJ06BBux11n1s5AGgV7phZZAN8ARwa9l5+3vijIF1O+YOfOnVZMej+D2Ww2P+iT/vjjD9q0acPVq1cxm814enri5eVFTEwMt2/fBiAwMJCNGzdSqVKlhx76YYiJicHb25vo6Gi8vLysHUdERETEKsLDwwkMLMLA933pNcQ/w3Emk5nQiuep83h7Fi9ekuO5stvVHnhmNj4+ns6dO3Pz5k1GjBjB2bNniY6O5uLFi0RHR3P27FlGjBjB9evX6dSpEwkJCf/pExERERGRnLNhwwYSEpJo38c703FGo4HgPvlYuXIl/2IuNMc8cJldsmQJp0+fZtGiRbz//vuUKFEizfUSJUrwwQcf8PXXX3PixAmWLMn55i4iIiIi/05kZCQurg74BmR9K1XBYk4kJiYRFxdngWTZ88BldvXq1dSqVYvOnTtnOq5Lly7UqlWL77777l+HExEREZGc5e3tTUJ8CjGRKVmODb+ajKOjA25ubhZIlj0PXGYPHjxIy5YtszW2ZcuWHDx48IFDiYiIiIhltGrVCicnR9YuiM50nNlsZs28O7RvH4zRaDu7uz5wkps3b1K8ePFsjS1evDg3b9584FAiIiIiYhmFChWic+fOfP1xNOHXkjMct3ZBNKf/jGPQoBctmC5rD1xmY2NjcXd3z9ZYNzc3YmNjHziUiIiIiFjOhAkTMZi86f/EJfZvj01zg1fs7RTmT7zFe/2u0btPb5o1a2bFpPd74EMTbOnuNRERERH574oVK8bOHT8R0qkD/Rv/SelK7pSr5kjCXTN7f7jL3VgTL770Ep9M+gSDwZD1C1rQA+8zazQaqVatGkWLFs1y7OXLlzlw4AApKVkvKLY07TMrIiIikpbZbGbLli3MnTuX8xfO4uLsQt269RkwYADFihWzaJbsdrV/VWYfhMFgUJkVERERkQeS3a72wMsMTCbTfwomIiIiIvKw2M6+CiIiIiIiD+iBZ2Y/+eSTB36T119//YGfIyIiIiKSFa2Z1ZpZm3bp0iWWL1/O9evX8fDwoEWLFtSuXdvm7qQUERGRhyvH1sxu3br1PwUTyY4bN27wwqAXWbUqDANO5HMoQIL5Nu+88w5VHqvGlKmf07BhQ2vHFBERESt74JnZ3EIzs7br+vXr1K1dnxuXb1M5eTClCcGJfJgxcZnt/GH8ggjjIdasXU2rVq2sHVdERERyQI7NzIrktH79+nPz8h1aJ3+LJ//b086AkUCaUNhUn20MIrRzVy5dvoC3t7cV04qIiIg1aTcDsSmnT59m/fq1VE1+I02R/TsHnKlrGktcXBwLFiywcEIRERGxJSqzYlMWL16MszEfJQnOdJw7BSlOc+bNVZkVERHJy1RmxaZcvXoVL2MxHHHLcqy3uSxXr1yzQCoRERGxVSqzYlPc3NxI5Ha2xiZyGzc31xxOJCIiIrZMZVZsSuPGjYlOushNDmY6zkQSFx030KxFEwslExEREVukMis2pWnTpgT4FeAgUzBjynDcSVZwJ/k6L774ogXTiYiIiK1RmRWbkJyczHvvvUfx4iUIj7jBRTazi7fuW3JgIoWTLGev4R369OlDlSpVrJRYREREbIH2mRWrS0lJoVu3p/hu1So6Nh5Ah0bPcezcfj6a/yLnzesoae6AFyVJJIrzjmuJTr5Az2d7MXPmTGtHFzuUkpJCZGQkBoMBX1/fBz6iW0REbIv+FhermzRpEt+tWsV7A79hcLcJFC9UjpZ1nmbJ2MM80+Zlbvvt5YBxEoeYiU9RA/kDCrJq5SrKli7P0KFDOXPmjLU/BbEDFy9eZPjw4RQqVJj8+fMTEBBAYGAx3n33Xa5fv27teCIi8i/pOFsdZ2tVycnJBJUoSdVSzRjy7BfpjjGbzXwZNprF33+Ki8GboubGuODDXcK56rCdJHMcH3/8Ma+99pqF04u92Lp1Kx3ad8BsMtKqTneqlquPyWxm39EtbP5lKR753Fm/fh01a9a0dlQREfl/2e1qKrMqs1b1ww8/0LJlS758eyflildNd8zcNWOZv3YcFelFaTpgxCn1WjLxHGcxp1nJtGnTGDRokIWSi704cuQItWrWomJQbd4dsBAPt7R/3qNu3+Tt6d24GnmG33//jeLFi1spqYiI/F12u5qWGYhVXbx4EYDSgY+le/1W9DW+Xj+RcnSjLKFpiiyAI648Qh9K0IqhQ94iNjY2xzOLfRk/fjxeHgG8P3DRfUUWwMczP+NeXEFKEnz22WeWDygiIv+JyqxYlYuLCwAJiXHpXl+3az4GsyOl6Zjp65SlK7FxsXzzzTcPO6LYsVu3brFs6TI6PNEfNxePDMd5efjRpu6zzJkzl7t371owoYiI/Fcqs2JVdevWxWAwsP2379K9/tvR7eQ3V8OJfJm+jjsF8DdWZOvWrTkRU+zUoUOHSEhMoF7l1lmOrVulNdHRUZw+fdoCyURE5GFRmRWrKlWqFK1aPcmKH78gISn+vuvxiXdxJOMZtb9zSHHTrJqkkZycDICTo3OWY50d//opQVJSUo5mEhGRh0tlVqzu/fff4/LN07wzozu3YyPTXPP3KUSs4WKWr2HGRJzjZQoXLpxTMcUOBQUFAXD07P4sxx49ux8HBweKFSuWw6lERORh0qEJYnU1atTgu9Xf0blTZ7oMr0Dzml2pULI6SUmJXL11jgjzcaI5izclM3yNmxzkdvJVunfvbsHkYuvKlClDw4ZP8N2OL2lSoxMGgyHdcSaTidU7v6J9+w4EBARYOKWIiPwXmpkVm9CyZUtOnDzBiLeH8cf5rXy8cDBTV7yFf2F3fH38OewwjWTSX0KQSAxHHWfx2KNVqF+/voWTi6178803OHjiZ77e8DHp7URoNpuZvmIE564c5/XXtVexiIi90T6z2mfWJqWkpGA0GjEYDPz66680btQEl8SClE7pSiHqYMSBFBK5wk+ccliCs1ciP+/5iXLlylk7utigd999lzFjxlDnsZZ0ajKIquUaYjab+PXIj3y7dTq/H9vBlClTeOmll6wdVURE/p8OTciCyqx9OXDgAIMGvsCevbtxcciHq9Gbu6ZIElPiaNqkGTO/nEGZMmWsHVNs2JIlSxg3djx/HDqY5vFatWozatRI2rVrZ6VkIiKSHpXZLKjM2qcDBw6wdu1aoqOj8fPzo1OnTpQvX97ascROmM1mfv31V44dO0ZycjLXr19ny5atXLpwGRcXF55o3IBBgwZRsWJFa0cVEcnzVGazoDIrknft27eP9u06cvX6ZYoY6+BtqkAyd7nsuJm45Fv069eP6dOn4+TklPWLiYhIjshuV9NuBiKSpxw+fJgmjZvhEV+aEObjY/rf8pSU5AROsox5c94jPj6BhQsXZLgDgoiI2AbtZiAiecqrr76GU3wBmqcswIe066wdcKECz1LfPIFvvvmabdu2WSekiIhkm8qsiOQZJ0+e5McfN/NoyiCc8cxwXCk64udYhmnTplswnYiI/BsqsyKSZ3z//fc4GJwJom2m4wwYCEoOYf269RZKJiIi/5bWzIpInnH79m2cHTxwTHZN93oiMVxmBwlEcpvzxN2NxWw2a92siIgNU5kVkTwjICCAhJQYEojCBZ/UxxOIZj8TOM3K/z9pzgiYAAPPPNOdjz4aT/Hixa2UWkREMqOtubQ1l0iecePGDYoWDaRa8ls8ynMAxHOL9XQljhuUIpjitMAVf5K4zUW2cM5xNe4+Rnb9tEMnzImIWFB2u5rWzIpInlGgQAG6du3Knw7Tuc0FAHbwOvHcoiETKM/TuBGAAQPOeFGajtRPnkRilAvt2rYnJSXFyp+BiIj8k8qsiOQpn332KYWK+bLRsQt/MJPLbOcRniMfgemOd8WXx5IHc/LUcTZu3GjhtCIikhWVWRHJU/Lnz89Pu3dSt3EV9jMOJzwpQr1Mn+NLeXwdSjNnzhwLpRQRkexSmRWRPKdQoUJs+uF7WrVqhY+hFEYyP7bWgAGvlDKcPnXOMgFFRCTbVGZFJM8KCAjAbEzO1tgUknBxcc7hRCIi8qBUZkUkz6pduzYRpmPEE5npuBSSuOWwn7r1alsomYiIZJfKrIjkWc8++yzOLi6c4btMx11kM3dTohk0aJCFkomISHapzIpInuXj48OIEcM4xbecYQ1m7t92+wo/86fxK3r16k358uWtkFJERDKjE8BEJE8bOXIkkZGRfPrpp1x03EDR5Oa4U4AEorjisJVbKSfo3LEzM2fOsHZUERFJh2ZmRSRPMxgMfPLJJ2zfvp2WIXU44bCQfXzEYcOXVG9WktWrV7Ns+TJcXFysHVVERNKh42x1nK2I/E1ycjJ37tzBw8MDJ6fMt+wSEZGck92upmUGIiJ/4+joiI+Pj7VjiIhINmmZgYiIiIjYLZVZEREREbFbKrMiIiIiYrdUZkVERETEbqnMioiIiIjdUpkVEREREbulMisiIiIidktlVkRERETslsqsiIiIiNgtlVkRERERsVsqsyIiIiJit1RmRURERMRuqcyKiIiIiN1SmRURERERu6UyKyIiIiJ2S2VWREREROyWyqyIiIiI2C2VWRERERGxWzZVZk+dOsXAgQOpWrUqjo6OPProo/eNady4MQaD4b6PY8eOWSGxiIiIiFiTo7UD/N2ff/7JunXrqF27NiaTCZPJlO64+vXr8/HHH6d5LCgoyAIJRURERMSW2FSZDQ4OpkOHDgD07t2bffv2pTvOx8eHOnXqWDKaiIiIiNggm1pmYDTaVBwRERERsXF22R63b9+Oh4cHrq6uNGrUiB07dmT5nISEBGJiYtJ8iIiIiIh9s7sy26hRIz7//HM2btzI/PnziYuLo3nz5uzevTvT540bNw5vb+/Uj2LFilkosYiIiIjkFIPZbDZbO0R67q2ZPXz4cKbjYmNjeeSRR6hUqRLr16/PcFxCQgIJCQmpv46JiaFYsWJER0fj5eX10HKLiIiIyH8XExODt7d3ll3Npm4A+zc8PDxo27YtK1asyHSci4sLLi4uFkolIiJiGeHh4cyZM4ewsG+Jio7Ex9uXTp1C6dOnDwEBAdaOJ5Lj7G6ZgYiIiPxl1qxZBAYWZeSo4Zjz/UlQ1euYPP7k7beHERhYlNmzZ1s7okiOs/uZ2djYWNauXUvNmjWtHUVERMRi5s2bR//+/Wka4knXFwvh6eOQei0mMoVl0yJ57rnncHR0pFevXlZMKpKzbKrMxsXFpa57PX/+PDExManLBxo1asSxY8eYOHEiISEhBAUFceXKFSZNmsS1a9dYvny5NaOLiIhYzO3btxn88ks8EZyPviP8MRgMaa57+TrQb4Q/yYlmBr/8EqGhoXh4eFgprUjOsqkye+PGDbp06ZLmsXu/3rp1K4GBgSQmJjJixAhu3bqFh4cH9erVY8aMGdSqVcsakUVERCzum2++IS42jtDnA+8rsvcYDAY6D/Rh1/pLLFq0iP79+1s4pYhl2FSZDQoKIqvNFTZu3GihNCIiIrZpw4b1VKrhhn+hzP8Zz1/YiYqPu7F+/XqVWcm1dAOYiIiInbl95zb5vNOfkf0nT18jt2/roCDJvVRmRURE7EyAf37Cr5qyNfbmZRMBAflzOJGI9ajMioiI2JmuXbty6vBdLpxMzHTcuWMJnDl6l27dulkomYjlqcyKiIjYmQ4dOlCkSCEWTookKTH9e00SE0x8/UkURYsWJjg42MIJRSxHZVZERMTOODk5sWTJMk4fSmbcCzc4+tvd1BuozWYzR/ffZdwLNzl7JJklS5bh6GhT93uLPFT63S0iImKHGjZsyI8/bqFPn558MOAMhYq54htgJPKmiWuX4ilXrjQ//riAevXqWTuqSI5SmRUREbFT9evX59ixk2zZsoVVq1YRFRWFj48PISEhNG3aNMM9aEVyE4M5q41dc6mYmBi8vb2Jjo7Gy8vL2nFERERE5G+y29W0ZlZERERE7JbKrIiIiIjYLZVZEREREbFbKrMiIiIiYrdUZkVERETEbqnMioiIiIjdUpkVEREREbulMisiIiIidktlVkRERETslsqsiIiIiNgtlVkRERERsVsqsyIiIiJit1RmRURERMRuqcyKiIiIiN1SmRURERERu6UyKyIiIiJ2S2VWREREROyWyqyIiIiI2C2VWRERERGxWyqzIiIiImK3VGZFRERExG6pzIqIiIiI3VKZFRERERG7pTIrIiIiInZLZVZERERE7JbKrIiIiIjYLZVZEREREbFbKrMiIiIiYrdUZkVERETEbqnMioiIiIjdUpkVEREREbulMisiIiIidktlVkRERETslsqsiIiIiNgtlVkRERERsVsqsyIiIiJit1RmRURERMRuqcyKiIiIiN1SmRURERERu6UyKyIiIiJ2S2VWREREROyWyqyIiIiI2C2VWRERERGxWyqzIiIiImK3VGZFRERExG6pzIqIiIiI3VKZFRERERG7pTIrIiIiInZLZVZERERE7JbKrIiIiIjYLZVZEREREbFbKrMiIiIiYrdUZkVERETEbqnMioiIiIjdUpkVEREREbulMisiIiIidktlVkRERETslsqsiIiIiNgtlVkRERERsVsqsyIiIiJit1RmRURERMRuOVo7gIiIiGQtMjKSCxcu4OjoSKlSpXBzc7N2JBGboJlZERERG7Z3716eeqobBQoUoGrVqjz66KMULlyIV155hXPnzlk7nojVGcxms9naIawhJiYGb29voqOj8fLysnYcERGR+8yePZsBAwZQskwBeg5sSM16pUlOSuGHdYdYNOtnTClG1q1bT926da0dVeShy25XU5lVmRURERu0efNmWrZsSc/nGzLui6dxcEj7w9ToqDieDZ7GySPhHDz4B4GBgVZKKpIzstvVtMxARETEBn344Qc8XrsU46feX2QBvH3cWbB6EEnJCUybNs0KCUVsg8qsiIiIjTl+/Djbtm2n/ytNMBoz/qfax9eDbr3rMGvWVyQlJVkwoYjt0G4GIiJicy5dusTGjRuJiYnBz8+Pdu3aERAQYO1YFnP48GEAGreslOXYJq0qMWvyFm7cuEHRokVzOpqIzVGZFRERm3H69GneePN11qxei9lswsXNkfi4ZFxcnOjW7SkmTvyYAgUKWDtmjjOZTAAYjYYMx5w9dYMb12I4d/pmmueI5DUqsyIiYhOOHDnCE40a4OAcR6+hvtRvnQ83DyO3o1LYvvo2q79Zyq5dO9i1azeFCxe2dtwcVa5cOQB27zjJk+2rpD5uNpv5dtEvzJr8I7//cj71cUdHA1OmTGHIkCHkz5/f4nlFrEm7GWg3AxERq0tJSaFChXIkGq4yYnp+PH0c7htz43ISH/S/yWOVarN163YrpLSs2rVr4eJ5h+U/vILBYMBkMvHGgK9ZNPsn6rbMR6fnvSlZ0YU70Sa+XxLNmrl38PcrxJYft1GqVClrxxf5z7SbgYiI2I3169dz6tQZnhvpm26RBShQ1Inur3uzbdsODh48aOGEljds2HB2/niUCaPXYDab+XzcRhbP+Yn3FhRh6vfFaNbJi1IVXahcx40hnxVi6aESmB3Dad2mFYmJidaOL2IxKrMiImJ1c+fNpVRFN0o/4pLpuOqN3fHL78L8+fMtlMx6QkJCGD9+PJ+8v4529SYwZfwGnnrZl3bPeqc7vnBxJyauLMSJ46dYuXKlhdOKWI/KrIiIWN3582cpXj79Gdm/c3Q0EFjagYsXL1oglfW99dZbrF27lthoZ2LvJPLUYL9Mx5d9zJWaTTyZPmOqhRKKWJ/KrIiIWJ2riyuJ8dm7hSMxHpydnXM4ke1o27YtHTp0pEgJN4qVzvrzrtnMlSNH/rRAMhHboDIrIiJWV69eA/74OZHE+My3l4q8mczJQ3epW7euhZLZDkPGu3SJ5GkqsyIiYnUDBw7kTkwSW8JuZzpu3dfRuLi68uyzz1oomW2oVKkSl8/d5eLprG/s+vXHeCpVesQCqURsg8qsiIhYXenSpRk4cCCLPo9i1/o7/HPXSFOKmXULo9nwTQzvjBqNt3f6N0HlVqGhofj6erFkSkSm404eiufXrbcZNPBFCyUTsT4dmiAiIjZh8uTJ3Im9w/R3vmbt/Ds0aOuKt58D4deS2bkmnuuX4xk2bBhDhw61dlSLc3Nz4403hjJq1EgqPu5Ku54+9425eiGJIZ2uUa58GTp16mT5kCJWokMTdGiCiIjNMJvNbNmyhWnTp7J2zVoSE5Nwc3ela5euvPDCi9SqVcvaEa3GZDLRf0B/5syeQ50WnnR+3uuvQxNiTHy/OJo183RoguQu2e1qKrMqsyIiNslsNpOYmIizszMG3f0E/PX/ZPHixXw++VN+2bsv9fGA/H70f+55XnvtNR1nK7mGymwWVGZFRMSenTp1imvXruHm5sajjz6Ki0vmB06I2JvsdjWtmRUREbFDZcqUoUyZMtaOIWJ12s1AREREROyWyqyIiIiI2C2VWRERERGxWyqzIiIiImK3VGZFRERExG6pzIqIiIiI3VKZFRERERG7pTIrIiIiInZLZVZERERE7JbKrIiIiIjYLZVZEREREbFbKrMiIiIiYrdUZkVERETEbqnMioiIiIjdsqkye+rUKQYOHEjVqlVxdHTk0UcfTXfc7NmzKVeuHK6urlSpUoW1a9daOKmIiIiI2AKbKrN//vkn69ato0yZMlSqVCndMUuWLKF///5069aNDRs2ULduXUJCQtizZ4+F04qIiIiItRnMZrPZ2iHuMZlMGI1/9evevXuzb98+Dh8+nGZM+fLlqV69OosWLUp9rF69evj4+LB+/fpsv1dMTAze3t5ER0fj5eX1cD4BEREREXkostvVbGpm9l6RzciZM2c4ceIEXbt2TfP4U089xY8//khCQkJOxhMRERERG2NTZTYrx44dA6BChQppHq9YsSKJiYmcPXs2w+cmJCQQExOT5kNERERE7JtdldnIyEgAfHx80jzu6+sLQERERIbPHTduHN7e3qkfxYoVy7GcIiIiImIZdlVm/4vhw4cTHR2d+nHx4kVrRxIRERGR/8jR2gEexL0Z2OjoaAoVKpT6+L0ZWz8/vwyf6+LigouLS84GFBERERGLsquZ2XtrZe+tnb3n2LFjODs7U6pUKWvEEhERERErsasyW6pUKcqVK8fy5cvTPL506VKaNWuGs7OzlZKJiIiIiDXY1DKDuLi41L1iz58/T0xMDCtWrACgUaNG5M+fnzFjxtC9e3dKly5NkyZNWLp0KXv37mXHjh3WjC4iIiIiVmBThyacO3eOkiVLpntt69atNG7cGPjrONvx48dz4cIFypcvz9ixY2nXrt0DvZcOTRARERGxXdntajZVZi1JZVZERETEdtnlCWAiIiIiIg9CZVZERERE7JbKrIiIiIjYLZVZEREREbFbKrMiIiIiYrdUZkVERETEbqnMioiIiIjdUpkVEREREbulMisiIiIidktlVkRERETslsqsiIiIiNgtlVkRERERsVsqsyIiIiJit1RmRURERMRuqcyKiIiIiN1SmRURERERu6UyKyIiIiJ2S2VWREREROyWyqyIiIiI2C2VWRERERGxWyqzIiIiImK3VGZFRERExG6pzIqIiIiI3VKZFRERERG75WjtACJinyIjI9m9ezdxcXEULFiQevXq4eDgYO1YIiKSx6jMisgDOX/+PO+++y6Llywi/m5C6uPFixflxRdf5rXXXsPJycmKCUVEJC9RmRWRbPvzzz9p0rQRKdyhfR936rbKj4enkSvnk9i6MpoRbw9n27atrFr1Hc7OztaOKyIieYDBbDabrR3CGmJiYvD29iY6OhovLy9rxxGxefHx8ZQvXxaD6y2GTc2Pl+/9Swr+2BPHJ6/dZNCgl/j888+tkFJERHKL7HY13QAmItmybNkyLly4xEtj/dItsgCV67jTvq8XX836kqioKMsGFBGRPEllVkSy5auvZvJYHQ+KBGW+fKBpiCeJiYksWrTIQslERCQvU5kVkWw5eeokZStnfWOXT4AjhQJdOHXqlAVSiYhIXqcyKyLZ4uDggCkle2NNKeDoqPtLRUQk56nM5nIpKSnEx8dbO4bkAo9Xq87BnxLI6p7RK+cSuXrxLlWrVrVMMBERydNUZnOhhIQEFi5cSJ06tXBycsLNzY2A/H4MGTKEM2fOWDue2KlBg17g7LG7HN2f+TdHGxbF4B/gS+fOnS2UTERE8jKV2Vzmxo0b1KtXh549exJnOELvt/wYOCaAWi1TmPHl51SsWIGlS5daO6bYoVatWlG7dk2mvh3B+RMJ9103m81sXBzNlpW3Gfn2O7i4uFghpYiI5DXaZzYX7TObkJBA/fp1OX3uT974NIBSldKWiYR4E7M/vMWeTXFs3Pg9zZs3t1JSsVc3b96kZcvmHDp0iOqNPajXyh13TyNXzyexNSyOc8fvMmTIED766CMMBoO144qIiB3LbldTmc1FZfabb76hR48evDe/CKUfSX9WLCXZzIcDr+PlVIlfftln4YSSG8TGxjJnzhymTp3C8eMnATAYDLRt24bBg1+mZcuWVk4oIiK5gcpsFnJjma1fvy4xyYcYPq1ApuP2bYvl0zdvsG/fPqpXr26hdJLbmM1mrl69SlxcHAEBAfj4+Fg7koiI5CI6ASyPMZlM7NnzCzWauGY5tloDdxydjOzevdsCySS3MhgMFClShDJlyqjIioiI1ajM5hImkwmTyYSzS9brFB0cDTg5GUlMTLRAMhEREZGcozKbSzg6OlKoUH7OHc+6oF49n8TduGSKFStmgWQiIhnLoyvdROQhUpnNRfr0eY5d6+KIjzNlOu7Hb2Pw9fMmODjYQslERP7n+vXrfPjhh5QpU+r/98J2pVGjJ1iyZIl+YiQiD0xlNhd5/vnnMSUb+er9WyQnpz/bcWjPXX5YfocXBr2Eq2vW62tFRB6mH374gbJly/Dhh+9Tq1ERxk7pxohx7Ukx3uTpp5+mXr26XL9+3doxRcSOaDeDXLSbAcDKlSvp1q0rJSs606aHJ483csfR0cCVc4n8+O1tNq+4Q4vmLfnuu9U4OTlZO66I5CH79u2jYcMG1G9Slqlf98XXzyPN9d9+OUuvDjMILFKSXbt+ws3NzUpJRcQWaGuuLOTWMguwfft2RowYxs8/78Ho8NfNXgnxKfgH+PLCoJcYNWqUiqz8K4mJidy+fZt8+fLphC95YE8+2YoLV46w8ZdhuLre/3dQbGwCe3ac5Jk2U5g1axb9+vWzQkoRsRXZ7WqOFswkFtKoUSN++mk3Bw8e5OeffyYxMZFixYrRpk0bLS3IgMlkYvPmzWzfvp2EhAQKFy7MU089RdGiRa0dzerMZjM//PADU6d9wdo16zCZTBgMBlo92ZIXX3iJtm3b6rQvydKpU6f4/vtNTJ7X+74iu+PHo8z5Yivfr/4Dk+mv+ZVXXxuMwWCge/fu+sZJRDKlmdlcODMrD2b16tW8/vornD59Dv8CLri6OxB+LYHkJDOhXbowbeo0/Pz8rB3TKlJSUhg4aCCzvppFucrutO/rScFAR25eTWbN3Dsc/S2WZ555mnnz5mu2PxPJycmsXr2ajRs3cufOHfLnz0+3bt2oW7dunvlGYMGCBfTq1Yszdybj4fFXOTWbzbw39FumffwDZR9zI2SAF8XKOBMTkcLaBdHs2RRLvfp1Wbd2A97e3lb+DETE0jQzK5INX3/9NT179qRyXTfeGV6YclVcMBgMxN0xsWvdHcK+WknDhgfZtetnfH19rR3X4kaMGMGc2bMZPacw7Xt7pyle3V70ZdOy24zqsRQvb2+mT5tuxaS267vvvuOFFwZy5co1ipdxI5+3kZvbUpg8eTJVq1bmm28WU6lSJWvHzHHx8fEYDAbc3P73Tc+0jzcx7eMfePOzgjz9sm+a31+tn/Hm4M9xvNJ2H6FdOrPp+x/yTPEXkQejmVnNzOZZFy5coEyZ0tR70o3nRvljNN7/D+WVc4m81+8GHdt3Y8GChVZIaT3Xr1+nWLFAnhvlQ/9R+TMc981nEXzy+g3OnDlDUFCQ5QLagWXLlvHUU0/xeEN3Oj3vTVD5v2YkTSYzh/bcZcnkGGJuOfHTrt25vtCuX7+etm3bsvWPd6j0WFHi4hKpWnQIT/Zw460phTJ83vbVt3mtwyW2b9/OE088YcHEImJtOs5WJAtffvklTs7w7Jt+6RZZgCJBzgT38WTp0qXcuHHDwgmta86cOTg4mun2UuZLLDr19yGflyNffvmlhZLZh8jISHr36UXdlh68+nH+1CILYDQaqFLPnZFf5cfTL5FevZ61YlLLaN68OYUKFWT+9O0ArF62j5joeLq/lvnvr4bt8hFUzo3p06dZIqaI2CGVWcmzvv5mAXWfdMPNI/M/Bo2C82Eyp7BixQoLJbMNv/zyC9WecMPL1yHTcW4eRmq3cOWXX/daKJl9mDdvHklJifR4PeNvljw8Hej6ojf79v3Gr7/+auGEluXs7MwLL7zI11/tYuv3f3Jw/3lKVXIjsJRzps8zGg3Ub+vG/t9+sVBSEbE3KrOSZ12/foMiQVnftJTP2wFvP+c8t5F7UlIiztm8idzJxUBiYkLOBrIzy5Yt4fGGbnj7Z/7NQNX6bvjld2HZsmUWSmY9w4YN48knn+TZ4Gns3XkKx2zeM+jkbCA5OTlnw4mI3VKZlTzLzdWV2NuZH/0LYEoxczc2Oc9t4B4UVJLjvyeTkpL5snqz2czRfUmUDCptoWT24Wb4DfIXzfoeW6ODgYBCjoSHh1sglXU5OTmxcmUYQ4YM5cyJcM4cjScmMiXL5x3anUCpUmUskFBE7JHKrORZTZs1Z+8P8WR1D+SBn+8SdyeZZs2aWSiZbejTpw/XLsazY82dTMf9siWOc8fv0rdvXwslsw+enl7ZKmpms5mYqBQ8PT0tkMr6nJyc+PDDDzl8+E/MKQZWz4vKdPypw/Hs33GH/s89b5mAImJ3VGYlz3rxhRe5fDaeX36My3BMcpKZNfNiqF69GjVr1rRgOuurXr06DZ+oz0cv3uTi6cR0x1y/lMSH/W9Q7fEqutP8H9q2CWb/1nji4zKf/T/5RwLXLsbTpk0bCyWzDaVKleLZnr2YPjKCgz+n/2cw4kYyI56+TlDJ4oSEhFg4oYjYC5VZybMaN25Mp84hzBh9i5823kk9eeie6IgUJg8L59zRZD755DPrhLSyrxcuIp9bIXrVvsiX793k+qUkzGYzN68mMWdcOD1qXMSQ4k/Yyu+0B+g/DBgwgPi7JtbMj85wTEqymW9nRlOyZHFatmxpwXS24YspX1C9ei0GNrvEuBevceJgPHfjTFy7mMTc8eE8U+0CMTfdWbd2A87Omd8oJiJ5l/aZ1T6zeVp8fDw9ez3L8mUrKFTMlZpNXXB1N3L5TBK/bonD2cWVZUuX57lZs99++40pX0xh6ZIl3L0bD4CDo4GUZDMGA5jN4OrqzFNPP8PYD8dSuHBhKye2TR9++CEjR46kfR9vgnv54J7vf/MH4deSWTAxkoM/3WX9+g20aNHCikmtJz4+nnHjxjFj5jRuXP/fumEXF2eeeupp3n33XUqUKGHFhCJiLdntaiqzuajMnjt3jk2bNnH79m0CAgJo164d/v7+1o5l88xmM3v27GHq1C/Yum0LCfHxFClShF69+tKnT588d5Tt559/zmuvvUbRYv50f64eFR4twu2YeFZ+8wvbfjhCiRIlePvtt+nUqZN+f2XBbDYzbtw43nlnFE4uRqo1dMHTx4Ebl1L4Y3cc+TzzseibxbRt29baUa0uMTGRbdu2cfPmTTw8PGjYsKF+f4nkcSqzWchNZfb48eO88ebrrF+3AYMB3NwdiL2TjIuLM08//QwTJ04kICDA2jHFDixevJhnnnmGF95swcjxnXBwSLsS6eD+83RvM5UypSuyY8dOHB11InZ2XLlyha+++ooNG9Zx+04MBfIX5Omnu/PMM8+QL18+a8cTEbFJKrNZyC1l9uDBgzRp8gSe/kn0HOpN62e8cfMwEnEjme/mRLHw42jy+xdj546fKFiwoLXjig0zmUyULVuaClV8mfPt8xmugd2z8yQdnviYVatW0aFDBwuntB9ms5nTp09z69YtPD09qVChAkajblMQEckuHWebByQlJdEuuA0Fg5JZ+GsxOvX3TT3Nyq+AI32GBTBvTzEiYy7Rq3fuPy5T/ptNmzZx5sw5XhjSItObueo0LEv12qWYNm2qBdPZj5SUFGbNmkW1x6tQtmxZ6tSpwyOPPELpMiWZMGECsbGx1o4oIpKrqMzaIbPZzHfffUf16o9z6eIVxswriKdP+qcMFS/jzGuT/Pl+4w8cPXrUwknFnvz6668E5PeiRp1SWY5t1aEyv/yi40X/KTExkc6hnRgwYADegef49LtAlhwsyfTNxXm0YTSjRg3niUYNiIiIsHZUEZFcQ2XWzphMJgY8P4COHTty8fIZKtd1p1xl10yf06yzJ74BzsyfP99CKcUeJSUl4eTsmK0ttlxcHElKSrJAKvvy2uuvsWH9Wj5bU5TP1gTSqL0n5Sq7UruZB+/NL8L8vSU4e/4IoV06ZXlYh4iIZI/KrJ159913mT1rNp/N6Unhol5UeNwly+c4uxgp9Ygzly5dskBCsVclS5bk+tUoLl/Metbw91/PU7JkUM6HsiPXr1/nqy+/ZOB7/jRsm/5pXuWruvLOnAJs3bKdPXv2WDihiEjupDJrR6Kjo5k06WNeeqsVT/epj7OLU5anC92TEIc2Hf+H+Ph4tm3bxnfffceOHTvy/ExjaGgo7u5uLJi5I9NxN65Fs+7b3+nXr7+FktmHuXPn4uBoptMA30zHNWybj8BSbsyYMd1CyUREcjeVWTvyzTffEB8fz3ODmwBQs15Zdq2NIzEh80J75Vwif+67Q926dS0R0+ZFRUUxbNgwihUrQpMmTejYsSONGjWiWPGijB49Os/eoOPp6cnzzw9k2sQf2PFj+uurY2MTGPj0bDw9PenVq5eFE9q2w4cPU7G6G16+6a9fv8doNFCruQuHDh+0UDIRkdxNZdaO/P777zxWrQSFivgA0GvgE0TcTGL13IyPywSYPzECL698PPPMMxZIadtu3LhB/QZ1+WLaJ7TsYWDJgZL8cK0sX+8L4olOyUyYOJZGjRsSFRVl7ahWMXbsWJo0acozrb9g2IuLOHLoMikpJmKi77Lgyx20qjGOg/susWrVd/j6Zj4DmSdl80Tfv05R05pZEZGHQTue2xGTyYSD4/++/yhXsTBP963HhJd34+ljpGU3rzQ37yQnm5n30S2WT4vk008/xcPDwxqxbYbZbKZL187cuHWWBb8Uo2SF/6039i/oSKXqboQ858PApofp1ftZvlu1xopprcPZ2ZnVq9f8dbzojOnMnbY99ZrRaKRdu7asWPoBlStXtmJK27Nt2zb279/P2XOx3I5OwdM749lZs9nM/q0J1KvxqAUTiojkXiqzdqRMmTIsXbqImOi7eHm7ATBhenfi7yYy/Ol9zBkbSZtnPfEr4MDls0mEfRXNzStJjB49mldeecXK6a3vl19+Ycf2XXy6OjBNkf27CtVceXNyft7puZZjx45RoUIFC6e0PmdnZ0aPHs3w4cP58ccfuXLlCq6urjRo0IASJUpYO57NGT9+PMOHD6dshSIkJcLqudF0fzXjI5B3b4rl3Im7zP9qoAVTiojkXjoBzI5OALty5QrFixdnzKTODHilWerjZrOZHZuPMm/6NjavO0xiYgpGIzRr1pzx4z/i8ccft2Jq2/Hcc8/x/ZZFhJ0sgYNDxj8PTkww0abYWfr0HMzHH39swYRib7755ht69OjBG++0ZciYYN4YsJAVX+/ms7WB1G52/09CzhxNYGDTy5QrXY1dO3/O1jZoIiJ5lY6zzYI9llmAPn16s2zZEpZuepla9cvcd/3yxQhCGn9CPrcADh48hIND5jej5CVPNGpAvmKH+PDrolmOfbndRfwcmvLdd99ZIJnYI5PJRIUK5Sj7qFfq8b/x8Un07jiVHZuP0SzUk5DnfChS0omIG8msXxjNugV3KFmyLFt+3EaBAgWs/SmIiNi07HY1LTOwM1OnTuPs2bOENvuMLj1r06N/Q4JK5yci/A7LF+5hwYxdeLh7s2bNOhXZfzAajZhSsjfWlAJGJ90fKRnbtm0bJ0+e5uPZb6bOsLq6OrFwzWDmTd/OnKlbGNTiQup4F1dH3ho6gtdffx1vb29rxRYRyXVUZu2Mu7s733+/iYkTJzJjxnS+/mpX6jVPz3w8+2xPRo0aRaFChayY0raYzWZWrlzJqZNnuPPHHRITTDi7ZFxU78SkcHBXPG++UcWCKcXe/PHHH7i7u1C7QdqfkDg5OdD/5aY8N7gJRw5dJiL8Dts3HeGLCZsYPXo0RqO+SRIReZhUZu2Qi4sLI0eOZNiw/2vvzsOiqts+gH8P2zADMgoKCqiIYJmIG5qKguGu5Z6auUOvpYlmUS659bpUZpJW7vlk+phl4oqYCQYJpaZm+aSolz5qilkii7iw3O8fvjM5sh5EhoHv57r4gznn/M49Xw7DzZlzfjMViYmJuH79OhwdHdG+fXtUq1bwJw9VVXl5eXhl/CtYtXIVmgXUxx9H8hCzKR19RlcvdJtta2/izu08hIWFlV+hZHHy8vJgZWVV6HWviqKgib8nAODCuesAOB0XEdHjwGbWgtnY2CAoKMjcZVRoCxcuxOpVqxH52Ui8MCYQLw1Zifde/QXuXrYI6JT/Bp34XRlYNvUvhIaFwdPT0wwVk6Vo1KgRMjNv47fjl+DXvG6R6/6UcBY+Pt689IeI6DHgDWAWdgMYldytW7fg4eGOIWNa43+XDAYAZGXdM96gE9irGvqMdkLNOja4dikbUWvScWh/Jvr174vNX37Fj/+lIuXk5MDLqz6CujdA5NqRha7357V0tKo3DfPmLUBEREQ5VkhEZNl4AxhVeV9//TXS0zMQ+v8f/wsAOp0dNu4Ox1frk/DZJ3F48/nLxmXOLnp88cUXeOGFF3gGjYplY2ODN96IwGuvvYY2gQ0xbGxgvnXS024jdOBKODnpMXbsWDNUSURU+bGZpUrr119/hbdvbXh51zJ53NbWGi+GdsCwsYG4+sdNpKfdxobVCYjZehrDhw83U7VkiSZNmoRTp0/htdCViI46jtGvBMGveV1k3bqLXd8cw78+jcetjBzs2RMDFxcXc5dLRFQpsZmlSuv+DTqFT0qvKArcPWvA3bMGnPRa5OXllWN1VBkoioLlny5Hu7btEBn5IV7s/bFxmb29BkOHvoBp06ahUaNGZqySiKhyYzNLldaTTz6JZctScPWPVNTxqFHkuokHzuDJJxuXU2VUmSiKglGjRmHkyJE4ceIELl++DI1GgxYtWvBsLBFROeCEh1RpDR06FPb29vh8RXyR6508cRlJ8cn4n/8ZV06VUWWkKAqaNWuG3r17o0uXLmxkiYjKCZtZqrT0ej0mTHgVy97di5gdvxS4zpXLqXjp+dVo1MgX/fr1K98CiYiI6JHxMgOq1ObPn49z585iTP8V6NW/OUaO6wjvRm5Iv5mFrf8+hI1rElHNsQZi9kdzKi4iIiILxHlmOc9spZebm4tVq1bh44+X4j//OWV8vHp1PcaMGYs333yTH/9LRERUwZS0V2Mzy2a2yhAR/PLLL7h27Rp0Oh1atmwJB4f8nwJGRERE5scPTSB6iKIoaN68ubnLICIiojLEG8CIiIiIyGKxmSUiIiIii8VmloiIiIgsFptZIiIiIrJYbGaJiIiIyGKxmSUiIiIii8VmloiIiIgsFptZIiIiIrJYbGaJiIiIyGKxmSUiIiIii8VmloiIiIgsFptZIiIiIrJYbGaJiIiIyGKxmSUiIiIii8VmloiIiIgsFptZIiIiIrJYbGaJiIiIyGKxmSUiIiIii8VmloiIiIgslo25CzAXEQEApKenm7kSIiIiInqYoUcz9GyFqbLNbEZGBgCgbt26Zq6EiIiIiAqTkZEBvV5f6HJFimt3K6m8vDxcuXIF1apVg6Io5b7/9PR01K1bF5cuXYKTk1OFH9fcmJc6j/N5VcbMmJc6zEsd5qUO81KnMuclIsjIyIC7uzusrAq/MrbKnpm1srKCp6enucuAk5PTYzlAHte45sa81Hmcz6syZsa81GFe6jAvdZiXOpU1r6LOyBrwBjAiIiIislhsZomIiIjIYrGZNRONRoPZs2dDo9FYxLjmxrzUeZzPqzJmxrzUYV7qMC91mJc6zKsK3wBGRERERJaPZ2aJiIiIyGKxmSUiIiIii8VmloiIiIgsFptZIiIiIrJYbGbL2Zw5c6AoSr4vPz+/Uo85evToQrefPHkyvLy8Sj22uTEvdZiXOsxLHealDvNS53HkBVTezJjXP6rsJ4CZk1arRWxsrMljOp3OTNVUfMxLHealDvNSh3mpw7zUYV7qMK/72MyagZWVFdq2bWvuMiwG81KHeanDvNRhXuowL3WYlzrM6z5eZkBEREREFotnZs0kJyfH5Htra2soimKmaio+5qUO81KHeanDvNRhXuowL3WYF8/MmsWtW7dga2tr8rVx40Zzl1VhMS91mJc6zEsd5qUO81KHeanDvO7jmVkz0Gq1iI+PN3nM29vbTNVUfMxLHealDvNSh3mpw7zUYV7qMK/72MyagZWVFQICAspsPBsbG+Tm5ha4LDc3F7a2tmW2L3NgXuowL3WYlzrMSx3mpU5Z5wVU7syY1328zKASqFWrFlJSUgpcduXKFbi6upZzRRUb81KHeanDvNRhXuowL/WYmTqWmBeb2UogODgYN2/ezPdWQ3p6OuLi4hAUFGSmyiom5qUO81KHeanDvNRhXuoxM3UsMS9eZlAJdOvWDR07dsSAAQMwa9Ys+Pn54cqVK3j//fdhbW2N8PBwc5dYoTAvdZiXOsxLHealDvNSj5mpY4l5sZmtBKysrLB7927MmjULixcvxpUrV6DX6xESEoJvvvkGderUMXeJFQrzUod5qcO81GFe6jAv9ZiZOpaYlyIiYu4iiIiIiIhKg9fMEhEREZHFYjNLRERERBaLzSwRERERWSw2s0RERERksdjMEhEREZHFYjNLRERERBaLzSwRERERWSw2s0RERERksdjMEhEREZHFYjNLRERERBaLzSwRERERWSw2s0RERERksdjMEhEREZHFYjP7CHbs2IFu3brB2dkZdnZ2aNCgAcaNG4fk5OQyGd/LywuKokBRFNja2sLV1RXPPPMMIiMjcevWLZN1Dxw4AEVRcOTIkRKPP2fOHCQmJpZJrUDVziMtLQ0DBw6El5cXtFotatWqhZ49e+Lw4cOlGg8oWZ4ZGRl4+eWX4eHhAUdHR7Ru3Rp79uwpdmxDPoYvBwcHeHt7Y+jQodi3b1++9UePHg0/P78S1378+HHMmTMHWVlZJd6mJKpyJtHR0QgODkatWrWg0Wjg7e2NKVOmIC0trVTjlafk5GQoioKLFy+auxRs3LgRbdq0gV6vh5OTExo3boywsDD8+eefxnW8vLzw6quvlnofw4YNQ2hoqPH7e/fuITIyEgEBAXB0dIRWq4W/vz/mzJmDmzdvPsrTeSymT5+Ozp07m7uMSonH32MiVCpvvfWWAJBBgwbJli1b5Pvvv5fPPvtMOnToIM2bNy+TfdSvX18GDRokSUlJ8sMPP8jWrVtl4sSJotPpxNfXVy5dumRcNy0tTZKSkiQzM7PE4wOQRYsWlUmtVT2Pa9euybBhw2TNmjWyf/9+2bp1qwQFBYmjo6OcPn1a9XglzTM8PFw0Go1ERkbK/v37ZdSoUaLVauXy5ctFjh8XFycAZN26dZKUlCRxcXGydu1a6dKliwCQ8ePHm6x/9uxZ+eWXX0pc/7p16wSAXL9+Xd0TL0JVz+SLL76QiIgI2bJli8TFxcmyZcvExcVFunbtWqrxytMHH3wg/v7+5i5D3nvvPVEURaZMmSJ79uyR6Ohoef/996VZs2Zy7Ngx43pHjx6V8+fPl2of2dnZUqNGDdm6dauIiNy+fVuCg4NFo9EY9xsbGyuLFy8WLy8vmTx5chk8s7Ll5+cnH374obnLqHR4/D0+bGZLYffu3QJAZs6cWeDynTt3lsl+6tevLxMmTMj3+NGjR0Wn00mXLl0eafyyamaZR8EyMjLEzs5O5s+fr2o7NXnWq1dPxo4da/w+PT1dABhfyApjaNwOHz6cb9m0adMEgGzYsEFV3Q8q62aWmRRs1apVAkD++OOPMhvzcejUqZNMnz7d3GWIh4eHjBkzpsBlubm5ZbKPAwcOiEajkYyMDBERiYiIECsrK9m3b1++dW/fvi3fffddmey3rFy4cEEASHJysrlLqXR4/D0+bGZLISQkRNzc3OTevXvFrpuWliZjx44VJycncXFxkfHjx0t2drb0799fXn/99SK3Lax5E7l/gAKQU6dOiUjBf4jXrl0rTz31lNjb24uzs7MEBgbKoUOHROR+4/bwV1xcXAkTMMU8CpaXlydOTk4ye/ZsVdupydPKykreffdd4/fJyckCQHbs2FHkdkU1btnZ2VKnTh1p166d8bFRo0ZJkyZNjN+npqZKWFiYuLu7i0ajEU9PTxkyZIiI/NO0PfhVv379Yp9LUZhJwb755hsBUOqzOI9q9erVEhQUJBcvXpRmzZpJtWrVZNKkSZKdnW1cJzU1VWxsbCQpKUlETH+PDbkY6PV61b8vauh0uhI11Q+/1hh+1nFxcdK8eXPR6XTSunVrOXLkSL5t33jjDenevbuIiGRlZYmjo6MMGDCgRPVduHBBBg4cKE5OTqLT6aRbt25y4sQJk3W2b98urVq1EgcHB9Hr9dKqVSvZvXt3kePevHlTxowZIwsXLpT169dLjRo1xNvbW3bt2pVv3Y8//lieeOIJEcn/OxEcHCyjRo0SEZGoqCizHXuzZ88WR0dHGTJkiMm7b0uWLBErKytV75iUp6p4/DVo0EA8PT1l5cqVxsfy8vKkRYsWEhAQUKK6SoLXzKqUk5ODgwcPonPnzrC1tS12/b59+2L37t1YtWoVFi9ejM8//xwLFy5ETEwM+vbtW+o6unXrBgD48ccfC1weHx+P0NBQ9OrVC9HR0Vi/fj06d+5svD4mKSkJADBx4kQkJSUhKSkJLVu2VF0H8zCVl5eHnJwcXL16Fa+//jqsrKwwcuTIEm+vNs+8vDxYW1vj7t27OHbsGMLCwuDs7IygoCDVtRvY2NggJCQER44cQXZ2doHrTJkyBbt27cKCBQuwd+9eLFq0CBqNBgDQu3dvvP322wCAmJgYJCUlISoqqtT1MBNTubm5uHPnDo4ePYp33nkHffr0gZeXV6nGehRr1qzBSy+9hKCgIKxevRp//fUXIiMjsXz5ckybNs24XkxMDJydndGmTZtyr/FhrVq1wooVK7BmzRqkpKSo2jYlJQXh4eGIiIjAV199hTt37qB///75joedO3fi2WefBQD8/PPPyMzMRI8ePYodPyMjA506dcKxY8ewYsUKbNiwAX///TeCgoJw6dIlAMC5c+cwaNAgNGnSBFFRUdi8eTMGDx6M1NTUIsd+9tlnsWPHDjzzzDOYMWMG+vfvj/bt26Nfv344fvx4ofVXVGFhYVi8eDG2b9+Ojz76CMD9/BYsWIAXXngB/v7+Zq6wYFXx+Nu4cSM6d+6MCRMmGK+Z37x5M44dO4aFCxeqyqBIZdYWVxEpKSkCQKZOnVrsuomJiQJA1q9fb3wsIiJCNBqN1KxZU3JycorcvqgzkadOnRIAxjNQD/8HvWjRInF2di5yfJTB2+rMw9SMGTOMZ95cXV0lMTFR1fZq8hT5p+bQ0FDjfufNm1fsdkWdhRQRmTp1qgCQlJQUEcl/FrJJkyYyZcqUQscvy7fUmYkpDw8P4/Pq0aOHquvCy5Kvr6906NBBRETGjBkjwcHBIiIyfvx40el0cufOHRERefHFF41n80TMe2b2119/FR8fH2N+DRo0kPDw8HxnFws6M6Yoivz222/GxwzHS0JCgvGxs2fPmpyt/PLLLwWAxMTEFFvbRx99JIqiyH/+8x/jY3///bc4ODgYj6uvv/5aAEh6enqJn/PBgwcFgKxZs0ZERBRFkXXr1klWVpbUrFlTQkNDjetmZmaKRqMxvitVUc/MGsyYMUN8fX1F5P7ZWltbWzl37pzZ6ilOVTz+RERycnKkfv36Mm/ePMnOzhYfHx8JCQlRNUZxeGa2lBRFKXYdw53svXr1Mj7WqVMn3L17F88++yysra1LvX8RKbKOli1b4saNGxg9ejT27dtX5neVP4x53Dd+/HgcPnwYO3bsQNu2bdGrVy8cPXpU9TglyfNBU6dOxfbt2zFx4kTMnTsXkydPVr3PB5Ukz3/961/44IMP8Ntvvz3SvkqKmdwXHR2NxMRErF69Gr///juee+455ObmPrb9FSQtLQ1nzpzBgAEDANw/G27Qs2dPZGVl4fTp08jNzcWePXvw3HPPlWt9hfHz88PJkyexe/duTJo0CXq9HkuXLoW/v3++M5QPc3d3R5MmTYzfP/XUUwCAy5cvGx/buXMn/Pz88p0pL8mxm5CQAD8/PzRu3Nj4mLOzM7p27YoffvgBAODv7w9ra2sMGzYMO3fuLNFMFoYZXQw/K8NxrNVqERwcbPL6tG/fPmi1WnTo0KHYcSuC0aNH48yZM9ixYwc+/PBDhIWFwdvb29xlFaoqHn8AYG1tjREjRmD9+vVYs2YNzp49iwULFpRo25JiM6uSi4sL7O3tSzTFTGZmJmxtbeHi4mJ8zDCVz6O8pQ78cwDXrl27wOUhISH44osvcPLkSXTv3h01a9bEyJEjcePGjUfa78OYhyl3d3cEBATgueeeQ1RUFLy9vTFr1qwSb68mzwf5+PigT58+WLp0Kd59910sXbrU+NZQaVy+fBl2dnZwdnYucPmyZcswYsQILF68GE2bNkW9evWwfPnyUu+vKMzElL+/P9q1a4ewsDBs374dcXFxj3QZR2lkZGQAAFxdXfMtc3NzM66TmJiIzMxM42VAFYGdnR169eqFyMhIHDt2DDExMcjKysI777xT5HbVq1fPNw4A3Llzx/jYw2/Re3h4AECJjt3U1FRjdg9yc3Mzvk41atQIu3btQlpaGvr3749atWqhT58+RY6fnp4OW1tb1KhRo8CxDT9LQ/09evSAjY1NsfVWBD4+PggMDMSLL76InJwczJw509wlFauqHX8GI0eORHJyMiIiItCvXz88/fTTxW6jBptZlWxsbBAYGIj9+/cjJyenyHXd3NyQnZ1tcrD997//BQDo9fpHqmPv3r0AgHbt2hW6zvDhw3H48GH8+eefWLZsGbZt24aIiIhH2u/DmEfhrKys0KJFC5w9e7bE26jJszBt27aFiKi+JssgJycHsbGxaN26daF/1PR6PSIjI3H16lWcOHEC3bp1w/jx45GQkFCqfRaFmRTO398ftra2qo6xslCjRg0oioJr167lW2a4fs7FxQW7du1CUFAQqlWrZlzu7Oxc4NyW2dnZyMzMhFarfWx1F6R79+5o1qwZfv/990caJz09HQkJCSbNRKtWreDo6Gh8fSqKs7OzyVyjBteuXTP5B6pHjx6Ij4/HjRs3sGHDBvz8888YM2ZMoeO6uLggOzu7wH/cU1NTjScXRATR0dEm9Rv2W9DPyzBeef+8HjZixAhkZmYiPDwcderUMWstpVHZjz8DX19ftG3bFllZWZg3b14Jn1XJsZkthSlTpiAlJQXz588vcHl0dDQAoFmzZgBgMnH+V199BQA4efJkqfd//PhxfPrpp+jevTt8fX2LXb9mzZoIDQ1F165dTX5hbG1tTRrL0mIeBcvJycFPP/2k+m2vkuZpYPiHwMCQZYMGDVTt12DWrFm4evVqiSftbtq0KZYsWQIAxjwLOmvwKJhJwX766SdkZ2eX+1urDg4OaNmyJbZv355v2a5du+Dm5mY8i/PwJQZ+fn5ISEjIl8O3336L3NxcVR9EoVZBzfft27dx6dKlQt/VKam9e/dCr9eb/EOt1WrxyiuvYOvWrYiLi8u3zZ07dxAbGwsA6NChA3799VecPn3auDw1NRXfffddgW/7Ozk5YfDgwRg6dGiRjVDHjh0BIN/PKisrC7GxscblR44cwfXr19GzZ0/jOr6+vtBoNPj222/zjbtnzx64uroWeHa+PB06dAjAP39fKrKqePwZpKWlITk5GY6OjvDx8SnNUyxamV6BW4W8+eabAkAGDx4sW7dulfj4ePn8888lODjYZAL3wMBAadOmjZw/f14OHTokDg4O0rp1a3n66afl7t27Re7jwQ8JOHjwoGzbtk3Cw8NFp9PJE088YTK35MMX6s+aNUsmTJggX3/9tXz//ffy8ccfi06nk7ffftu4jb+/vwQEBMiBAwfk8OHDxou6Q0JCpGHDhsxDRR4rV66U0NBQ2bRpkxw4cEC+/PJLeeaZZ8TW1lbi4+NVZakmTwCiKIq89tprEhsbK2vXrhVXV1fp27dvkeM//AEBBw4ckM8++8z4AQETJ040Wf/hm53at28vixYtkj179si3334rw4cPFzs7O+PNA0ePHjXetPXjjz8ap3e5cOGCWFtby9y5c5mJykz69+8v8+fPl507d8p3330nixcvltq1a4u/v3+xvzuPw44dO0RRFAkPD5du3bpJs2bNZMaMGaIoiixdulTOnTsnAPLdkJOcnCwODg7SqlUrGTRokACQ6dOni16vl8DAwGJvBH0UtWrVkrFjx8rmzZslPj5eNm3aJIGBgaIoikRFRRnXK2xqpAelpqYajxcRkREjRsiIESPy7dMwab29vb28/vrrEhMTI7GxsbJkyRJp2LChcdL69PR08fLykoYNG8qmTZskKipKAgICpHr16nLx4kUREVmxYoWMGjXK+Dqzbt06cXV1leHDhxf5vAcOHCjOzs6yfv16ASDh4eESFBQker3e+GEzM2fOlI4dO+bbdsGCBWJlZSWjR48WHx8fad26tQwcOFAURZHly5cXH/pjdOrUKbG2thYnJyfp2bOnWWspiap6/ImIvP3222JnZyf29vayefNmtdEVi83sI9i2bZt06dJFqlevLra2tuLl5SXjxo2TM2fOGNe5dOmS9OnTRxwcHMTBwUGmTZsmKSkpxgO4KPXr1zfe9WhjYyM1a9aU4OBgiYyMzHcH88PN286dO6Vz585Sq1Yt0Wg00rBhQ5k9e7bJ/I8JCQnSsmVL0Wq1JvOqBgcHl2oOzKqcxw8//CDdu3cXV1dXsbOzk3r16snAgQPl+PHjKhI0VZI8AUjv3r2ladOmotVqpV69ejJu3Di5ceNGkWMb8jF8abVa8fLykiFDhhQ4ufbDL6YRERHStGlTcXR0FCcnJwkMDJS9e/eabDNnzhzx9PQUKysrY37nz58XAKW+Y70qZ7Jw4UJp3ry5VKtWTRwcHKRJkyYyc+ZMSUtLKya1x+ff//63+Pj4iKIoAkA8PDzkgw8+EJH7d0c3bty4wO3Onz8vr776qvF3OiQkRD755BOT38fH4ZNPPpEePXqIh4eH2NnZibu7u/To0UNiY2NN1lPbTOTm5krNmjUL/SN99+5dWbJkibRs2VJ0Op3Y29tL06ZNZe7cuXLz5k3jehcuXJABAwZItWrVRKfTSdeuXU3m+UxMTJTevXtLnTp1jK8zkyZNKvbu8lu3bsm4ceNEr9cbXz/btm1rMnNHixYt5L333itw+23btkm/fv1Eq9VK9erVZfjw4XLw4MEi91kenn/+eXFzc5MtW7aItbW1XL161dwlFamqHn/Xrl0TR0dHmTBhggwZMkR69epV4sxKis0skQVDGUwnVtkwk/I3atQo49RcBl27dpWIiIgit3t4ai5LdfDgQbG1tTVpDCqqB8/mGfzxxx8CQE6ePFnktg9OzWVuR48eFUVR5KOPPpKcnBypU6eOyYelVCUV/fibNGmS6HQ6uXr1quzevVusra2L/XhxtSzjlkUiIrIoBV1nWVm1b98e9+7dM3cZpebu7m6csstSTJ8+HXXr1sXLL79snPpp3rx5qF69Onr37g1PT09zl1huKvLxd/HiRaxYsQKTJ09G7dq10b17d7i5uWHo0KF444030Lt37zKZPYM3gBEREZHFSEhIQExMDGbPnm28sXLq1Kno3LkzpkyZgi1btpi5QjKYO3cu7O3t8dZbbwG4P+fs+vXr8ddff2HgwIHIzMwsk/0oYmn/jhERERER/T+emSUiIiIii8VmloiIiIgsFptZIiIiIrJYbGaJiIiIyGKxmSUiIiIii8VmloiIiIgsFptZIiIiIrJYbGaJiIiIyGL9H+AEB3Q1EuJMAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "states = data[\"trajectory\"].unique()\n",
    "fig, ax = plt.subplots(figsize=(7,7))\n",
    "\n",
    "projections = data['projection'].unique()\n",
    "width = 0.3  \n",
    "\n",
    "for i, proj in enumerate(projections):\n",
    "    # Get all data for this projection first\n",
    "    proj_data = data[data['projection'] == proj]\n",
    "    total_points = len(proj_data)\n",
    "\n",
    "    color_indices = np.arange(total_points) % colors.N\n",
    "    proj_colors = [colors(ci) for ci in color_indices]\n",
    "\n",
    "    folded_mask = proj_data['folded'].values\n",
    "    proj_data = proj_data.copy()\n",
    "    proj_data['color'] = proj_colors\n",
    "\n",
    "    for folded, group in proj_data.groupby('folded'):\n",
    "        y_vals = group['mean_all'].values\n",
    "        group_colors = group['color'].values\n",
    "        x_center = i\n",
    "        x_shift = -width/2 if folded else width/2\n",
    "        n_points = len(y_vals)\n",
    "        jitter = np.linspace(-0.05, 0.05, n_points)\n",
    "        x_vals = np.full_like(y_vals, x_center + x_shift) + jitter\n",
    "\n",
    "        ax.scatter(x_vals, y_vals,color=group_colors,edgecolor='black', marker='o', s=64,label=f\"{proj} - {'Folded' if folded else 'Unfolded'}\")\n",
    "\n",
    "x_ticks = []\n",
    "x_tick_labels = []\n",
    "for i in range(len(projections)):\n",
    "    x_ticks += [i - width/2, i + width/2]\n",
    "    x_tick_labels += ['F', 'U']\n",
    "ax.set_xticks(x_ticks)\n",
    "ax.set_xticklabels(x_tick_labels)\n",
    "ax.set_box_aspect(1)\n",
    "\n",
    "sec = ax.secondary_xaxis(location=-0.05)\n",
    "sec.tick_params('x', length=0)\n",
    "sec.spines['bottom'].set_linewidth(0)\n",
    "sec.set_xticks(range(len(projections)))\n",
    "sec.set_xticklabels(projections)\n",
    "\n",
    "color_map = {s: colors(i % colors.N) for i, s in enumerate(states)}\n",
    "handles = [\n",
    "    plt.Line2D(\n",
    "        [0], [0], marker='o', color='w',\n",
    "        markerfacecolor=color_map[s], markeredgecolor='black',\n",
    "        markersize=8, label=s\n",
    "    )\n",
    "    for s in states\n",
    "]\n",
    "ax.legend(handles=handles, title=\"Trajectory\", loc='best')\n",
    "ax.set_ylabel(\"ID\")\n",
    "plt.tight_layout()\n",
    "plt.savefig('../extra/NTL9_projections.pdf', dpi = 300)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mdintrinsicdimension",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}