diff --git a/cookbook/assets/mst_network_tyk2.json b/cookbook/assets/mst_network_tyk2.json
new file mode 100644
index 0000000..a7254f5
--- /dev/null
+++ b/cookbook/assets/mst_network_tyk2.json
@@ -0,0 +1 @@
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diff --git a/cookbook/repairing_broken_networks.ipynb b/cookbook/repairing_broken_networks.ipynb
new file mode 100644
index 0000000..60c374a
--- /dev/null
+++ b/cookbook/repairing_broken_networks.ipynb
@@ -0,0 +1,661 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bc2dc4e7",
+   "metadata": {},
+   "source": [
+    "# Repairing a broken ligand network\n",
+    "\n",
+    "In a relative binding free energy (RBFE) campaign some transformations may fail. If\n",
+    "enough edges drop out, the completed network can split into disconnected pieces,\n",
+    "and a disconnected network can no longer rank all of its ligands.\n",
+    "\n",
+    "This notebook takes the TYK2 system from the JACS benchmark\n",
+    "set and repairs the network:\n",
+    "\n",
+    "1. load the stored, connected **planned** network,\n",
+    "2. parse a directory of **result JSONs** to find which edges actually completed,\n",
+    "3. reconstruct the **completed** network and diff it against the plan,\n",
+    "4. **repair** the breaks in two different ways:\n",
+    "   - by ligand name\n",
+    "   - by lomap score\n",
+    "6. **rebuild** alchemical transformations for the new edges and write them out."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef334d57",
+   "metadata": {},
+   "source": [
+    "## 1. Fetch the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b5d4a134",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fetching /Users/hannahbaumann/.local/share/mamba/envs/openfe/lib/python3.13/site-packages/openfecli/tests/data/rbfe_results.tar.gz\n"
+     ]
+    }
+   ],
+   "source": [
+    "import locale\n",
+    "locale.getpreferredencoding = lambda: \"UTF-8\"  # colab hack; not needed locally\n",
+    "\n",
+    "# Fetch and extract the CLI tutorial results from Zenodo\n",
+    "!openfe fetch rbfe-tutorial-results\n",
+    "!tar -xf rbfe_results.tar.gz"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09d26ef5",
+   "metadata": {},
+   "source": [
+    "## 2. Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8d681408",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import pathlib\n",
+    "import warnings\n",
+    "\n",
+    "import networkx as nx\n",
+    "from rdkit import Chem\n",
+    "\n",
+    "from openfe import (\n",
+    "    AlchemicalNetwork, ChemicalSystem, LigandAtomMapping, LigandNetwork,\n",
+    "    ProteinComponent, SmallMoleculeComponent, SolventComponent, Transformation,\n",
+    ")\n",
+    "from openfe.protocols.openmm_rfe import RelativeHybridTopologyProtocol\n",
+    "from openfe.setup import KartografAtomMapper, lomap_scorers\n",
+    "from openfe.setup.ligand_network_planning import generate_network_from_names\n",
+    "from gufe.tokenization import JSON_HANDLER\n",
+    "\n",
+    "from konnektor.network_tools import merge_two_networks\n",
+    "from konnektor.network_planners import MstConcatenator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89e33757",
+   "metadata": {},
+   "source": [
+    "## 3. Load the planned network\n",
+    "\n",
+    "This is the connected network the campaign set out to run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "8a702d3c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "planned connected: True  edges: 9\n"
+     ]
+    }
+   ],
+   "source": [
+    "planned = LigandNetwork.from_json(\"assets/mst_network_tyk2.json\")\n",
+    "\n",
+    "print(\"planned connected:\", planned.is_connected(), \" edges:\", len(planned.edges))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bdf56bda",
+   "metadata": {},
+   "source": [
+    "## 4. Reconstruct the completed network from the results\n",
+    "\n",
+    "In a real campaign a failed transformation simply has no results. We simulate two\n",
+    "failures by dropping their result files before parsing:\n",
+    "\n",
+    "- `ejm_31 -- ejm_46` disconnects the network into two fragments (5 and 4 ligands);\n",
+    "- `ejm_31 -- ejm_48` was `ejm_48`'s *only* transformation, so `ejm_48` disappears\n",
+    "  from the results entirely.\n",
+    "\n",
+    "We then read every remaining result JSON, keep the transformations that succeeded\n",
+    "in both legs, and rebuild the network from their ligand mappings. We also recover a reference complex system\n",
+    "(for the protein, solvent and cofactors) and the run's protocol (for its\n",
+    "settings), both used when we rebuild transformations later."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d0f1f442",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def _load_result_json(path):\n",
+    "    \"\"\"Load a result JSON, or None if it isn't a usable result.\"\"\"\n",
+    "    ru = json.load(open(path, \"rb\"), cls=JSON_HANDLER.decoder)\n",
+    "    if not isinstance(ru, dict):\n",
+    "        return None  # e.g. the ligand network file\n",
+    "    if ru.get(\"__qualname__\") in (\"AlchemicalNetwork\", \"Transformation\"):\n",
+    "        return None  # an input/alchemical network file\n",
+    "    if \"unit_results\" not in ru:\n",
+    "        return None\n",
+    "    if all(\"exception\" in u for u in ru[\"unit_results\"].values()):\n",
+    "        return None  # every repeat failed\n",
+    "    return ru\n",
+    "\n",
+    "def _units(ru):\n",
+    "    data = ru[\"protocol_result\"][\"data\"]\n",
+    "    return data[next(iter(data))]\n",
+    "\n",
+    "\n",
+    "def _extract_edge(ru):\n",
+    "    \"\"\"Return (mapping, phase, stateA) for a result, or None if inputs are stripped.\"\"\"\n",
+    "    units = _units(ru)\n",
+    "    if not units or \"stateA\" not in units[0][\"inputs\"]:\n",
+    "        return None\n",
+    "    inputs = units[0][\"inputs\"]\n",
+    "    stateA = ChemicalSystem.from_dict(inputs[\"stateA\"])\n",
+    "    mapping = LigandAtomMapping.from_dict(inputs[\"ligandmapping\"])\n",
+    "    is_complex = any(isinstance(c, ProteinComponent) for c in stateA.components.values())\n",
+    "    return mapping, (\"complex\" if is_complex else \"solvent\"), stateA\n",
+    "\n",
+    "def parse_results(result_files, require_both_legs=True):\n",
+    "    \"\"\"Reconstruct the completed network directly from the results.\"\"\"\n",
+    "    phases = {}\n",
+    "    mapping_by_pair = {}\n",
+    "    reference_complex_system = None\n",
+    "    reference_protocol = None\n",
+    "    for path in result_files:\n",
+    "        ru = _load_result_json(path)\n",
+    "        if ru is None:\n",
+    "            continue\n",
+    "        if reference_protocol is None:\n",
+    "            settings = _units(ru)[0][\"inputs\"].get(\"settings\")\n",
+    "            reference_protocol = RelativeHybridTopologyProtocol(settings=settings)\n",
+    "        parsed = _extract_edge(ru)\n",
+    "        if parsed is None:\n",
+    "            continue\n",
+    "        mapping, phase, stateA = parsed\n",
+    "        pair = frozenset({mapping.componentA, mapping.componentB})\n",
+    "        phases.setdefault(pair, set()).add(phase)\n",
+    "        mapping_by_pair[pair] = mapping\n",
+    "        if phase == \"complex\" and reference_complex_system is None:\n",
+    "            reference_complex_system = stateA\n",
+    "\n",
+    "    edges = [mapping_by_pair[pair] for pair, seen in phases.items()\n",
+    "             if not require_both_legs or seen == {\"complex\", \"solvent\"}]\n",
+    "    return LigandNetwork(edges=edges), reference_complex_system, reference_protocol"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "be375fe0-99b4-49c9-830a-6bdcd77a6b75",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "found 54 result files\n",
+      "kept 42 after simulating failures\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/hannahbaumann/.local/share/mamba/envs/openfe/lib/python3.13/site-packages/gufe/components/smallmoleculecomponent.py:286: UserWarning: The atom hybridization data was not found and has been set to unspecified. This can be fixed by recreating the SmallMoleculeComponent from the rdkit molecule after running sanitization.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "completed edges: 7  ligands: 9\n",
+      "connected: False (broken, as intended)\n",
+      "recovered protocol: True\n"
+     ]
+    }
+   ],
+   "source": [
+    "RESULTS_DIR = pathlib.Path(\"results\")\n",
+    "\n",
+    "result_files = sorted(\n",
+    "    p for p in RESULTS_DIR.rglob(\"*.json\")\n",
+    "    if not any(part.startswith(\"shared_\") for part in p.parts)\n",
+    ")\n",
+    "print(f\"found {len(result_files)} result files\")\n",
+    "\n",
+    "# Simulate two failed transformations: drop their result files (both legs) before\n",
+    "# parsing. Matching on ligand name in the path is robust to the filename format.\n",
+    "DROP_EDGES = [(\"ejm_31\", \"ejm_46\"), (\"ejm_31\", \"ejm_48\")]\n",
+    "result_files = [\n",
+    "    p for p in result_files\n",
+    "    if not any(a in p.name and b in p.name for a, b in DROP_EDGES)\n",
+    "]\n",
+    "print(f\"kept {len(result_files)} after simulating failures\")\n",
+    "\n",
+    "completed, reference_complex_system, reference_protocol = parse_results(result_files)\n",
+    "print(\"completed edges:\", len(completed.edges), \" ligands:\", len(completed.nodes))\n",
+    "print(\"connected:\", completed.is_connected(), \"(broken, as intended)\")\n",
+    "print(\"recovered protocol:\", reference_protocol is not None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ad134e2-8617-46d0-8be2-e9457279cc75",
+   "metadata": {},
+   "source": [
+    "## 5. Inspect the damage\n",
+    "\n",
+    "The two failures show up in different ways. A missing **edge** between ligands\n",
+    "that survive elsewhere leaves the reconstructed network *disconnected*.\n",
+    "`decompose` finds it with no reference needed. A missing **ligand**, whose every\n",
+    "transformation failed, leaves no trace in the results at all; the only way to\n",
+    "know it should be there is to compare against the planed network.\n",
+    "\n",
+    "Here `ejm_48` is the missing ligand. We recover it from the planned network and\n",
+    "fold it in as its own fragment, so the repair reconnects it too."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "72cdee1f-c103-4467-a73a-297d96a076e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "missing ligands: ['lig_ejm_48']\n",
+      "fragment 0: ['lig_ejm_46', 'lig_jmc_23', 'lig_jmc_27', 'lig_jmc_28']\n",
+      "fragment 1: ['lig_ejm_31', 'lig_ejm_42', 'lig_ejm_43', 'lig_ejm_47', 'lig_ejm_50']\n",
+      "fragment 2: ['lig_ejm_48']\n"
+     ]
+    }
+   ],
+   "source": [
+    "def decompose_network(network):\n",
+    "    g = network.graph.to_undirected()\n",
+    "    pieces = []\n",
+    "    for comp in nx.connected_components(g):\n",
+    "        edges = [e for e in network.edges\n",
+    "                 if e.componentA in comp and e.componentB in comp]\n",
+    "        pieces.append(LigandNetwork(nodes=comp, edges=edges))\n",
+    "    return pieces\n",
+    "\n",
+    "\n",
+    "def missing_ligands(planned, completed):\n",
+    "    \"\"\"Ligands in the plan with no results at all.\"\"\"\n",
+    "    have = {n.name for n in completed.nodes}\n",
+    "    return [n for n in planned.nodes if n.name not in have]\n",
+    "\n",
+    "\n",
+    "fragments = decompose_network(completed)\n",
+    "missing = missing_ligands(planned, completed)\n",
+    "print(\"missing ligands:\", [n.name for n in missing])\n",
+    "\n",
+    "# fold each missing ligand in as a singleton fragment so the repair reconnects it\n",
+    "fragments += [LigandNetwork(nodes=[lig], edges=[]) for lig in missing]\n",
+    "for i, frag in enumerate(fragments):\n",
+    "    print(f\"fragment {i}: {sorted(n.name for n in frag.nodes)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e9eea99a-4bb2-44ef-955c-8225b3608177",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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hhaA1zN1444167bXXzjVo2VgshKfJkyfr4MGDKlKkiKtlBgAgnBC0RoBu3bqpZ8+e7vqdd97p5ncivNhmEsOHD3fX+/Tpo+TJkwd6SQAAJCoasSLE6dOn1ahRIzd03hp11qxZo+zZswd6WUgklkG3aQGZM2fWjh07lD59eo4tACCskGmNEFbXOn36dBUrVkzbt29Xq1atXMMOwsPQoUPd1+7duxOwAgDCEpnWCPPTTz+pWrVq+uuvv3TvvfdqzJgxuuqqqwK9LCTA6tWr3e80ZcqU2rZtm/LkycPxBACEHTKtEaZkyZJ69913XaA6btw4NyIJ4bFla/v27QlYAQBhi0xrBJ9O7tevn2vY+eyzz1SvXr1ALwnxsHXrVlfycfbsWX333XcqV64cxxEAEJbItEYom+N59913u67z22+/XT///HOgl4R4ePXVV13AWr9+fQJWAEBYI9MawU6cOKG6detq1apVKlGihPt6zTXXBHpZiKVDhw4pf/78blOBzz//XA0aNODYAQDCFpnWCJYmTRrNmjVL+fLl08aNG3XHHXe4zCtCw9ixY13AWrZsWZdpBQAgnBG0RrhcuXJpzpw5Sps2rattfeyxxwK9JMSCjSsbOXKku/7www8zAQIAEPYIWqEKFSpo0qRJ5zrRbTtQBLf33ntPe/bscR862rVrF+jlAACQ5Aha4bRp00ZPPfXUuW1fV6xYwZEJUlFRUefGXPXq1UupU6cO9JIAAEhyNGLhHOtCb926tatzzZkzp9vq1Rp9EFxsK14bUZYuXTrt3LlTWbJkCfSSAABIcmRacf4PQ7JkmjJlihudtH//fjVv3tw1+iC4+LKsnTt3JmAFAEQMMq34l+3bt6ty5cr6/fff3QxXq59kq9fg8OOPP6pMmTLu92GzdYsWLRroJQEA4BdkWvEvBQsW1MyZM91e9u+//76ef/55jlKQGDZsmPt62223EbACACIKmVbE6M0331TXrl3d9RkzZqhVq1YcrQDat2+f+0Bh466WL1+uGjVq8PsAAEQMMq2IUZcuXdS7d2933bZ8XbduHUcrgF577TUXsFarVo2AFQAQcci04rJOnz6txo0b64svvlCBAgXcRIEcOXJw1Pzs+PHj7vgfOHDAlWzYlAcAACIJmVZcVooUKVwj1rXXXqsdO3aoZcuWOnnyJEfNz2zDBwtYCxcu7OpZAQCINAStuKLMmTPrww8/1DXXXONqKXv06OEG3MM/zpw5o+HDh7vrDz30kJInT86hBwBEHIJWxMp1113nMq42y3XChAl69dVXOXJ+MnfuXDfeKlOmTLrnnns47gCAiETQilhr2LChhg4d6q737dtXn3/+OUfPj5sJdO/eXenTp+eYAwAiEo1YiBMrC7CpAhMnTnTlAqtXr3ZZWCSNr776SlWrVnUzc7dt26Y8efJwqAEAEYlMK+LEdmJ6/fXX3cilw4cPq1mzZjp06BBHMYmzrO3atSNgBQBENDKtiJf9+/e7rV537typBg0a6OOPP3aTBpB4LLNq27SePXvWzcgtX748hxcAELHItCJecubM6SYKpEuXTvPmzVO/fv04konMmt0sYK1fvz4BKwAg4pFpRYJ88MEH5wbd27avnTt35ogmgj///FP58+fX0aNH9dlnn7kmOAAAIhmZViRIq1atNHDgwHPd7TbHFQk3duxYF7CWKVPGlV8AABDpyLQiwewUdtu2bTVjxgy3xatt9WpbjiJ+/vnnHxUpUkS7d+92M3GZzQoAAJlWJALbcGDSpEm6/vrr9dtvv7mJAseOHePYxtP06dNdwJorVy61b9+e4wgAAEErEsvVV1+tOXPmuEzrd999p44dO7oMLOI+B9c35qpnz55KnTo1hxAAAIJWJCYrCZg1a5ZSpUrlGrSee+45DnAcLVq0yI23sqkMViMMAAA8NGIhUdmmA2PGjHHXn332Wb3//vsc4TjwbZNrdaxZs2bl2AEA8P9oxEKS6Nu3r4YNG6a0adO6iQI33HADR/oKNmzYoNKlS7tdxzZv3qxixYpxzAAA+H9kWpEkhgwZokaNGunvv/9W8+bN3Q5auDwL8k2LFi0IWAEAuAiZViTpgPxq1app06ZNql69uqvXpLHo0iyot5pgG3e1bNky1axZkz+ZAABcgEwrkkymTJk0d+5c93XlypWusci64/Fvr732mgtYq1at6uqCAQBAdAStSFLFixd3c0eTJ0/uZrkOHz6cI36R48eP63//+9+5WmCraQUAANERtCLJ1a9f/1y9Zr9+/fTpp59y1C8wefJkHThwQIULF9Ztt93GsQEA4BIIWuEXvXr1UteuXd2GA3fccYc2btzIkf//LXB92ec+ffooRYoUHBcAAC6BRiz4jdVs1qtXT0uXLnVlA6tXr1bmzJkj+jdgu4jZtACr+925c6fSp08f6CUBABCUyLTCb3w7ZRUsWFA///yz2rRpo9OnT0f0b8C3Zet9991HwAoAwGWQaYXfrV+/3nXIHzt2TA8++KBeffXViPwtrFmzRlWqVHElAdu2bVPevHkDvSQAAIIWmVb4Xbly5TR16lR3feTIkRo/fnxEZ1nbtWtHwAoAwBWQaUXAvPDCC3rqqaeUMmVKLViwQLVr146Y34ZlVosWLeoasdatW6fy5csHekkAAAQ1Mq0ImP79+6tt27Y6deqUWrZs6QK5SGElERawWmMaASsAAFdGphUBH6z/n//8R2vXrnVlA8uXLw/7hiTb3jZ//vw6evSom1nbqFGjQC8JAICgR6YVAZUuXTrNnj1buXLlcg1ad911l8tAhrNx48a5gLV06dJq2LBhoJcDAEBIIGhFwOXLl0+zZs1S6tSpXQD7zDPPKFxZKYQ1n5mHH36YLVsBAIglglYEhWrVqrkMpK9B67333lM4mj59unbt2qWcOXOqQ4cOgV4OAAAhg6AVQcNKA/r16+eud+rUydW5hpOoqCgNHTr03La2llkGAACxQyMWgsqZM2fUvHlzffzxx252qQ3gz507t8LBwoULdfPNNytt2rRuy9asWbMGekkAAIQMMq0IKsmTJ9c777yjkiVLavfu3brtttt04sQJhdNmAvfccw8BKwAAcUSmFUHpl19+cVucHjp0yJUNTJ48OaSbln766SeVKlXKvYfNmzerWLFigV4SAAAhhUwrgpIFdTNmzHCZV9vy1VcLGqqGDRvmvlrpAwErAABxR6YVQe21115Tz549XYZy7ty5atKkiULN/v37VbBgQZ08eVJLly5VrVq1Ar0kAABCDplWBLUePXrovvvuc5337dq104YNGxSKgbcFrFbuULNmzUAvBwCAkESmFSExkL9+/fr68ssvVbRoUa1evTpkGplsm9oCBQrowIEDbkbr7bffHuglAQAQksi0IuilTJnS1bcWLlxYW7ZsUZs2bVwgGwqmTJniAtZChQq5SQgAACB+CFoRErJly6YPP/xQ6dOnd/NOH3roIQW7s2fPavjw4e56nz59lCJFikAvCQCAkEXQipBRpkwZvf32264py+pEx4wZo2D20UcfufFW11xzjTp37hzo5QAAENIIWhFSmjVrpkGDBrnrNlVg8eLFCvbNBKyRLEOGDIFeDgAAIY1GLIQcmyRw5513up2zrCHrq6++UpEiRRRMbPtZmxZgJQFbt25Vvnz5Ar0kAABCGplWhBwrDxg/frwqV67smpxsYP+RI0cUjFlWG9NFwAoAQMKRaUXI2r17twtc9+7d68oGZs2apWTJAv85bPv27W4015kzZ7Ru3TqVL18+0EsCACDkBf5/eCCe8ubNq9mzZyt16tRussCAAQOC4li++uqrLmC9+eabCVgBAEgkZFoR8qy2tUOHDu66TRdo3759wNZy+PBh5c+f35UrfPLJJ7rlllsCthYAAMIJmVaEPAtSH3/8cXe9S5curgkqUMaNG+cC1lKlSqlRo0YBWwcAAOGGTCvCgg3yb9GihebOnavcuXPr66+/Vp48efy6Btuly6YY7Nq1yzWKWQANAAASB5lWhAVrwLLSgNKlS7vGLAtg//77b7+uYfr06S5gzZEjx7lyBQAAkDgIWhE2bIC/NWTZ7FYrEejataub6eoP9jq+MVe9evVSmjRp/PK6AABECoJWhBU7PT9jxgw31N8atF5++WW/vK7tzPXtt98qbdq0uv/++/3ymgAARBKCVoSdOnXqaNSoUe76k08+6epck5ovy9qpUyeX6QUAAImLRiyErQceeED/+9//lD59eq1cuVJlypRJktf56aef3LQA26lr06ZNKl68eJK8DgAAkYxMK8LWiBEjVLduXR09etTtmPXHH38kyesMHz7cfbXXIGAFACBpkGlFWDtw4ICqVq2qLVu2uLKBefPmKWXKlIn2/Pv371fBggV18uRJLVmyRLVr10605wYAAOeRaUVYs/pSmyhgkwWsWerBBx9M1Oe38gMLWKtUqaJatWol6nMDAIDzCFoR9qze1CYJWM3pG2+84QLNxHDixAktW7ZMN9xwg55//nn3/AAAIGlQHoCIYeOvbLvX5MmTuzKBm266KdBLAgAAsUTQiohhGwDcfffdeuutt5QlSxZ99dVXKlq0aKCXBQAAYoGgFRHFTunfeOONLmAtWbKkVq1apYwZMwZ6WQAA4AqoaUVEse1VZ8+erTx58rj5qu3bt9eZM2cCvSwAAHAFBK2IOLlz59acOXNcAPvxxx+7XbMAAEBwI2hFRKpUqZImTJjgrg8ZMkRTp04N9JIAAMBlELQiYrVr1+5clvXee+/V999/f9n716kj9enjXS9UyHbckt8MHChdf73/Xg8AgGBD0IqIZvNVmzdv7jYI6Nu3b6wft2aN1K2b/OaRR6QFC/zzWidPegGyjZ1dt+7fP580SSpXzuqDpVy5pJ49/bMuAEBkSxHoBQCBlCxZMlcaUKNGDf3xxx+xflz27PKr9Om9iz88+qiUJ4/03Xf//tmwYdIrr0j//a9UtapNY5B+/dU/6wIARDYyrYh4tsWrbfWaOXPmWB+Li8sDNm6UbBdXyz6WKiXNn+9lKmfPjt3z7d4ttW0r2RKyZpWaN5e2bYu5PKBTJ6lFC2nwYClnTilTJunZZ6XTp6V+/aQsWaR8+aT/L9uNtU8/lebNk4YO/ffPDh2SBgyQpkyR2reXbMRt6dLSrbfG7TUAAIgPglZAUuHChV1DVnycPesFkOnSSatXS2PHSv37x/7xx49Ldet6mdQlS6Rly7zrjRpJ//wT8+MWLpT27PEeYxlQC2ybNvUCX1tH9+7eZefO2K1j/36r7ZWsJ83ey8W++MJ7rxZglyzpBcVt2sT++QEASAiCVuD/VaxYMV7HwjKTW7Z4Gcjy5b2M66BBsX/8tGlWpiCNHy+VLesFhBMnSjt2SIsXx/w4y6aOHCldd53UubP31QJg6y0rXlx64gkpVSpp+fIrryEqysveWpBbqdKl72NlABa0WnbXsswzZkgHD0r1618+uAYAIDFQ0wok0KZNUv78XlOST5UqsX/82rXSL79IGTJEv93qRS0Yjomdmrdg18fKBMqUOf998uReqcFvv115DaNGSX/95QW6MbGA9dQpL1Bu0MC77d13vfe9aJHUsOGVXwcAgPgiaAUSyLKUVr8aXxYMWpL37bfj1vCVMmX0720Nl7rNnv9KrNRg1Sopderot1vWtUMHafJk25TBu81qdi9cX7ZsXlYYAICkRNAKJFCJEl7QZjWhlu30jcSKrQoVpPfek3LkkDJmDMyvw7KnL7xw/nurlbXMqa3LpgSYmjXPZ5atntVYeYANXShYMACLBgBEFGpagQSymk7rpO/YUVq/3qsh9TVixSYDa5lMy1baxIClS6WtW6Uvv5R695Z27fLPr6dAAa+0wHe59lrvdntfvgDVbrM12rpWrJB++MF7zxa0WyMZAABJiaAVEe/EiRNaunSp3nzzzXgdC6sdtdFWR49KlStLXbt6o6GMjcC6EuvUtwkAFji2bOk1Yllj1d9/By7zGhNrNrPMa5Mm0o03euUIn33277IEAAAS21VRUVaRB0SOo0ePauXKlVqyZIm7rF692u2IdcMNN+ibb75JlNewbKtNEbAGK8tWJpQ1SFkW1sZhAQAQiahpRdg7ePCgli9ffi5IXbt2rc6cORPtPjlz5lQDX0t8PMya5c1WtVFTFqjaKXSrAU1owGofKW3UlG3hesMNCXsuAABCGUErws7evXvd6X5fkPr999//6z6FChXSf/7zn3OXYsWK6aoEjAA4csTb/tQG7Vt9ar163nanxuaa2uVSatf2dqGKyeHDXre+lR3Y/NX4SsgaAAAIBpQHIKRZdcv27dvPBah2+fnnn/91vxIlSpwLUGvXrq0CVkDqJ9Zhb5dLSZtWyps3MtYAAEBCELQi5ILUTZs2RQtSd160j6hlTMuXLx8tSM1h86QAAEDIojwAQc1qT+30/oVB6u+//x7tPilSpFDlypXPBag1a9ZUpkyZArZmAACQ+AhaEVT++ecf1yjlC1CtgeqwFXZeIE2aNKpevfq5TGrVqlV19dVXB2zNAAAg6RG0IqCOHz/uRk75glQbRfW3DSi9QIYMGVSrVq1zQWrFihWV+uL9RgEAQFgjaIVfWdZ0xYoV54LUNWvW6NSpU9HukzVr1mid/Vafmtwm+AMAgIhF0IokZfWny5YtOxekrlu3TmfPno12n7x580YLUkuWLJmg8VP+ZAF4r169XINY//791apVq0AvCQCAsMT0ACSqXbt2nQtQbVbqhg0b/nUfm4l6YZBqM1NDJUi9lBdffFFPPvmkUqZMqcWLF6tGjRqBXhIAAGGHoBXxZtnFLVu2ROvs37p167/uV6ZMmWjjp/LkyRN2x6FNmzaaMWOGcuXK5RrJwu09AgAQaAStiDU7rW+Z0wuDVNt96kLJkiVThQoVzgWp1kBlNarh7ujRo26iwQ8//OC+Llq0iGYxAAASEUErYnT69GlXg3rh6f6DF22rlCpVKlWpUuVckGqnxq3bPxJZ1rlSpUr6888/1a1bN40ZMybQSwIAIGwQtOKcEydOuG5+C059M1Itg3ghm4dqgakvSLWA1eamwvPZZ5+pcePGrmTAglYLXgEAQMIRtEYwC0htLqovk2rzUk+ePBntPrazlNWh+oLUG264wTUcIWY0ZgEAkPgIWiPIoUOHoo2fsoYh2yb1Qjlz5ozW2W9NVFanitijMQsAgMRH0BrG9u3bd+5Uv12+//57F1BdqGDBgtGC1OLFi4f0+KlgQWMWAACJi6A1jGzfvj1aZ//mzZv/dZ8SJUpEGz9VoECBgKw1EtCYBQBA4iFoDVGWMd20aVO0IHXnzp3R7mMZU9sC9cLxU3b6H/5DYxbgX3XqSNdfL40YIRUqJPXp4138YeBAafZsad06/7weEGkIWkOE1Z7a6f0Lg1TbIvVCKVKkcCOXfEFqzZo1XSMVAovGLCAwQav9E3n11VK6dP55bRu2Yr2s/hhNba9Ttar03XfSt99679lnzRrp8celtWsteSFVriwNGRL9PkAoImgNUqdOndJPP/2kb7/9Vt98842bl3rkyJFo90mdOrXKli3rhvnbxa6nTZs2YGvGpdGYBQQmaA1nvXtLP/8sffpp9KDV/psoWFBq3twLXE+flp55Rlq61LbZlhj+gpAWBSDJHTlyJKpMmTLWBRdVvXr1qBMnTnDUgSRw441RUb17e9cLFoyKGj78/M9++ikqqmbNqKjUqaOiSpaMivriC+tMjYqaNSt2z71rV1RUmzZRUZkyRUVlyRIV1axZVNTWred//swzUVHly5//vmPHqKjmzaOiBg2KisqRIyrqmmuiogYOjIo6dSoq6pFHoqIyZ46Kyps3KurNN+P2Hj/5JCqqRImoqB9/9Nb/7bfnf7ZmjXfbjh3nb1u/3rvtl1/i9jpAsGGWEeAH6dOn1+zZs125hs3GffDBBznugB+dPSu1aOGVCqxeLY0dK/XvH/vHHz8u1a1rf5elJUukZcu8640aSf/8E/PjFi6U9uzxHjNsmFf32rSplDmzt47u3b3LRS0JMdq/X7r3Xmnq1EuXPVx3nZQtm/Tmm966/v7bu166tJeBBUIZQSvgJ0WLFtW7777rGuTGjh3rLgD8Y948m+ghTZkilS8v1aolDRoU+8dPmybZyOrx46WyZaWSJaWJE6UdO6TFi2N+XJYs0siRXjDZubP31QLgJ5+UiheXnnjCtsOWli+/8hosX9qpkxfkVqp06fvYLtq2nrfekqxazALrzz+XPvnE+h5i/36BYETQCvhRo0aNNOj//6fs2bOnVqxYwfEH/GDTJil/filXrvO3VakS+8dbU9Mvv3hBoQWCdrGA9MQJLxiOiWU4L9yfxQa4WNDrkzy517j1229XXsOoUdJff3mBbkwss2rBcc2a0qpVXjBsa2jc2PsZEMr43AX42eOPP+6a62bMmKFWrVq5ncny5MnD7wFIQpalTMi+KVZeULGi9Pbb//5Z9uwxP+7ixidbw6Vus+e/Eis1sEA0derot1vWtUMHafJk6Z13pG3bpJUrzwfLdpuVI8yZI91xx5VfBwhWBK2An1l5wMSJE7Vx40b98MMPat26tRYtWuSmQQBIGiVKeKfyrSbUN67aRkPFVoUK0nvvSTlySBkzBua3ZGUGL7xw/nurlW3Y0FuXjb8yVnpgweqFAbrv+9gExkAwSxbso0t8Q6FtSHRijjCxv8A2BBoIBBqzAP+qX9/qyqWOHaX1673T5r5GrNhkYC2TaQ1ONkrKxkdt3Sp9+aU3espGSfmDbWBYpsz5y7XXerfb+8qX7/z7PHRIeuAB6aefpB9/lO65x6tntUYyIJQFddB6IftE3K1b4j3f3r3SLbcoIA4elHr18gryrfvT/iGyZvLDh6Pfr1kz72dp0ki5c0t33eV9skZ4oDEL8B+rHbVEhW0AYMP2u3aVBgzwfmb/xl6J/VttEwDs3+SWLb1GLKsdtTrRQGVeY8ooz53rBebVq0u1a3v/b3z2mff/CBDKgnpzgXAdEv3DD96wZ+sCLVVK2r7d6wYtV06aMeP8/YYP9/7RsX9odu+WHnnEu53enfDCjllAYFi21aYIWIOVZSsTyhqkLAtr47AARHCm9eLygI0bvX9s7BOyBX7z58ftlP+F97Widft++nTvU6mNCbFP4ps3exleK3L3zeO7aOdUTZjgdWZaOaIFlz17Xvm17bTOBx9It97q/UN5003e6BX7dGy7l/g89JBUrZo3W69GDW93EyvCP3Uqdu8RodOYZXWttguaNWbtIZ0OJIlZs6QvvvD+zbf/M+zsnXXZJzRgtdSPTRBYsMD7/wBAhAetiTkkOiaW/bTTRd9849X/tGsnPfqo9Oqr3qdn+0fp6afP3//11726IfuH7/vvpQ8/lIoVi99rW2mAnWKKaY6elRRY16oFr2zDF56NWWXKlNG+fftcAHvSNhYHkKhsi9MePbxT6Hamy5IT1lFvBg8+P8rq4suVSsns329Lnti8VZu/Gl8JWQMQCVKE8pBoG6Dsm7lnmUorQE8IO/1unZjGiustaLVPzvZJ3HTpIk2adP7+1sXZt693Xx/7RzCuDhyQnn9euu++f//sscek0aO9jlDLun70UdyfH6HTmFWpUqVzO2aNGTMm0MsCgo5N3LAPePFx993e5VKsRKtNm0v/zM6+XU6mTFJifM5MyBqASJAsEodEx8RqSn18I1EuHAJtt/kGQNtXO4t7880Je00bFN2kifcp3TK9F+vXT/r2Wy9Qt0YC+wc3eKuQkRA0ZgGXZ7ON77U9TJOAbRRgZ8oudcmb1z+/mWBYAxDMkkXikOiYXHja3ff8F9/mm3OXGJ967VSV1cnaqR+rtbrUaX8bsWJjTSyLbNsI2lZ8VteK8MSOWcC/Wb/w4MGDdfvtt1M6A0SwZKE+JNonLkOiE4Nt5WfNYVY+EN8Ma4MGXg2U1cLGZuSKL8NKuWN4ozELOM/qu++55x71///Ghfbt23N4gAiVItSHRA8Z4mUs4zIkOrEMHOjVINkOKVYkb+uwESo2g/Vy7H4WsFqd6ltveQGsXXzbAVoZwFdfeRebkGDb7/36q9cEZu/bxmAhfLFjFuD5448/1LJlSy1dulTJkyfXqFGjdP/993N4gAiVLBKHRCcWC5ptDNf//ueNOWnaVPr55ys/bu1ab+qBTRywWiUbleW77Nx5vvxg5kyvZtY2IbAh1tZ7YDuwsNtn+GPHLEQ62+a4WrVqLmDNmDGjPv74YwJWIMIF9eYCSTUk2k6vW3Br8/rq1fPXCoG4++yzz9S4cWNX02fTBLol5rZwQJBasGCBG/32559/qlChQvroo49UmgGoQMQLyUxrQoZE22n4d9+VkiXzamOBYEZjFiLNuHHj3J97C1hr1Kihr776ioAVQGgHrfEdEt28uTf79OWXpXz5kmZttglATK9PsgBxRWMWIsGZM2f0yCOPuLMJp0+fdg1XlnHNboX+ABBO5QEX7x5ll0uxWtGknndnAfWFkw0uZGOtbFtWIC6OHj2q6tWru8Hq9nXRokVKTXEzwujPd4cOHfShjVKR9Oyzz+qpp55yTYkAENZBKxCOtmzZ4nbMstOmlo1ixyyEg127dunWW2/VunXr3AexSZMm6Y477gj0sgAEoZAtDwAiDTtmIdysXbtWVapUcQFrjhw5tHjxYgJWADEiaAVCCI1ZCBczZ85U7dq1tXfvXtdotXr1ajfiCgBiQnkAEGKsoqdNmzZuH/ZcuXK5bFWePHkCvSwg1n9+hwwZ4hoMfR/E3nvvPTeLFQAuh6AVCEE0ZiEU/fPPP+revbsmTpzovu/Zs6eGDx+uFClCcnNGAH5GeQAQgtgxC6HmwIEDatCggQtYkyVL5rZktQsBK4DYItMKhDB2zEIo2Lx5s5o2baqff/5ZGTJkcOUAt9xyS6CXBSDEkGkFQhiNWQh2NlPYGqwsYC1YsKBWrFhBwAogXsi0AiGOxiwEqwkTJui+++5zO1xVrVpVc+bMUc6cOQO9LAAhikwrEOJs1yCrEyxTpoz27dun1q1b6+TJk4FeFiLY2bNn9dhjj6lLly4uYLXNAizjSsAKICEIWoEwQGMWgsWxY8fcBycba2WefvppvfPOO0pre2gDQAJQHgCEERqzEEh79uxxW7J+8803SpUqlSsP6NChA78UAImCoBUIMy+++KKefPJJpUyZ0m2LWaNGjUAvCRHAAtVmzZpp9+7dypYtm2bPnq2aNWsGelkAwghBKxBmaMyCv1mDVfv27XX8+HGVKlVKc+fOVZEiRfhFAEhU1LQCYYbGLPjzA9LQoUN12223uYDVNg+wkVYErACSAkErEIZozEJSO3XqlLp166Z+/fq54PX+++/Xxx9/rGuuuYaDDyBJELQCYapo0aJ69913XeZ17Nix7gIkhkOHDrmNLcaPH++2ZB0xYoRee+01tmQFkKSoaQXCHI1ZSEy//PKLmjRp4rZmtYz+tGnT3PcAkNQIWoEwR2MWEsuSJUtc/erBgweVP39+ffTRRypXrhwHGIBfUB4AhDkas5AYJk+erHr16rmAtUqVKvrqq68IWAH4FUErEAFozEJCtmS1ub+dOnVyzVe33367m/+bK1cuDioAvyJoBSIEjVmIKxtj1aZNG1cXbQYMGOBqWNmSFUAgUNMKRBgasxAbe/fudTtcff31125LVpsUcNddd3HwAAQMQSsQYWjMwpV89913atq0qXbt2qWsWbNq1qxZql27NgcOQEARtAIR6OjRo6pevbp++OEH93XRokVKnTp1oJeFIGBbsLZr107Hjh1TiRIl3IQAKy0BgECjphWIQDRm4VIZ+OHDh6t58+YuYL355pvdlqwErACCBUErEKFozIKPTQXo3r27Hn74YRe82vasn376qTJnzsxBAhA0KA8AItxLL72kJ554QilTpnSjjGrUqBHoJcGP/vzzTzfGav78+W6m79ChQ/XQQw+56wAQTAhagQhHY1bk2rJli2u42rhxo66++mq98847bmIAAAQjglYANGZFoGXLlqlFixY6cOCA8uXL5xqwrr/++kAvCwBiRE0rABqzIszUqVNdo5UFrJUqVXJbshKwAgh2BK0AHBqzImNLVtvV6u6779Y///yjli1b6ssvv1Tu3LkDvTQAuCKCVgDnNGrUSIMHD3bXe/bs6UYeITz8/fffbv7qoEGD3PePP/643n//faVLly7QSwOAWKGmFUA0NGaFn3379rn5q1YGYFMixowZo3vuuSfQywKAOCFoBfAv7JgVPtavX69bb71VO3bsUJYsWTRz5kzdeOONgV4WAMQZ5QEA/oUds8LDJ598opo1a7qA9dprr9WqVasIWAGELIJWAJdEY1Zol3iMHDnSZVgta163bl2tXLlSxYsXD/TSACDeCFoBxIjGrNBz+vRp10TXu3dvNy2gS5cu+uyzz1xpAACEMmpaAVwWjVmh4/Dhw2rTpo3mzZvntmEdMmSI+vbty5asAMICQSuAK6IxK/ht3brVbcm6YcMGN8bq7bffdjteAUC4oDwAwBXRmBXcbJ5u1apVXcCaJ08eLV26lIAVQNghaAUQKzRmBad33nlHN910k37//XfdcMMNbhZrhQoVAr0sAEh0BK0AYo3GrOCqNR44cKA6dOigkydPusyqZVjz5s0b6KUBQJKgphVAnNCYFXgnTpxwO1pNmzbNff/oo4/qxRdfVLJk5CEAhC+CVgBxRmNW4Ozfv99lVW2jgBQpUuiNN95wY60AINzxsRxAnNGYFRg//PCDa7iygDVz5sxutBUBK4BIQdAKIF5ozPIv2yCgRo0a2r59u4oVK+YCV9vpCgAiBUErgHijMcs/Ro8erSZNmujIkSO68cYbXcB67bXX+unVASA4UNMKIEFozEraLVkfeughF7SaTp06acyYMUqVKlUSvioABCeCVgAJRmNW4vvrr790xx136NNPP3Xf23SAxx57jC1ZAUQsglYAiWLLli2qVKmS/vzzT3Xr1s1lBBE/VrdqW7Ja41XatGn11ltvqWXLlhxOABGNmlYAiYLGrMRh9apVqlRxAWvu3Lm1ZMkSAlYAIGgFkJhozEoY2yygTp06+u2333T99de7LVktew0AoDwAQCKjMSt+x+z555/XM888476/9dZb9c4777h5uAAADzWtABIdjVlx25K1a9euevvtt933ffv21csvv6zkyZPzJxMALkBNK4BEx45ZsfP777+rXr16LmC1LVnHjh2roUOHErACwCWQaQWQpLs4NW7c2J3+tmkCNlUAng0bNrgJAVu3btU111yjDz74QDfffDOHBwBiQKYVQJKhMevSvvjiC1WvXt0FrEWKFHETAwhYAeDyyLQCSFI0ZkX3xhtvqGfPnjpz5oxq1aqlWbNmKVu2bPwpBIArINMKIEldddVVmjhxosqUKaN9+/apdevWOnnyZMQddQtSbUvW+++/312/++67NX/+fAJWAIglglYASS7SG7OOHDmi5s2ba8SIEe77QYMGadKkSUqdOnWglwYAIYPyAAB+E4mNWTt27HBzV9evX680adJoypQpuv322wO9LAAIOQStAPzqpZde0hNPPKGUKVNq8eLFqlGjRtj+BmxHq2bNmmn//v3KmTOnPvzwQ7dFKwAg7ghaAfhVpDRm2fu766673OYB5cqV09y5c1WgQIFALwsAQhY1rQD8KtwbsywoHzx4sCsBsIC1SZMmWrZsGQErACQQQSsAvwvXxiwLvjt16qT+/fu77/v06aM5c+YoQ4YMgV4aAIQ8ygMABEw4NWb98ccfatmypZYuXeq2YR09erS6d+8e6GUBQNggaAUQUOHQmLVx40a3JeuWLVuUMWNGvf/++2rQoEGglwUAYYWgFUBAhXpj1oIFC1xd7p9//qnChQvro48+UqlSpQK9LAAIO9S0AgioUG7MGjt2rBo2bOgC1po1a2r16tUErACQRAhaAQRcqDVm2Tasffv21X333eeud+jQwW3Jmj179kAvDQDCFkErgKBQtGhRvfvuuy7zahlMuwSjo0ePuoarYcOGue+fe+45TZ061e12BQBIOtS0AggqwdyYtWvXLrcl67p165Q6dWpNnjxZbdu2DfSyACAiELQCCCrB2pj19ddfuy1Z9+7dqxw5crj5q9WqVQv0sgAgYhC0AgjKU/DVq1fXDz/84L4uWrTIZTYDZebMmbrzzjv1999/u4Yx25K1UKFCAVsPAEQiglYAQclmnlaqVMl15tumA7b5QCCyvpMmTdKoUaPc9zYh4MUXX3SNYwAA/yJoBRC0wmnHLABAwjA9AEDQatSokQYPHuyu9+zZUytWrAj0kgAAAUKmFUBQC9bGLACAfxG0Agh6wdaYBQDwP8oDAAS9UNsxCwCQ+AhaAYSEUNkxCwCQNAhaAYRsY9Z333132fvXqSP16eNdt7GqI0bIbwYOlK6/3n+vBwDhjqAVQEh57LHH1Lp1a506dUr9+vWL9ePWrJH8OTHrkUekBQuS9jWaNZMKFJDSpJFy55buukvasyf6fXr3lipWlKwEmCAaQCgjaAUQUqw8YOLEiW5nqj/++CPWj8ueXUqXTn5j+w9kzZq0r1G3rjR9urRpk/TBB7Yhg9S6dfT7REVJnTtLbdsm7VoAIKkRtAII2casDBkyxPoxF5cHbNwo1arlZSlLlZLmz7eAWJo9O3bPt3u3FwhmzuwFp82bS9u2xVwe0KmT1KKFZNUNOXNKmTJJzz4rnT4tWcI4SxYpXz5pwoRYvyU99JBUrZpUsKBUo4b0+OPSqlXSqVPn7zNypPTAA1KRIrF/XgAIRgStAEK2Mcu2VI2Ps2e9ANIyr6tXS9bT1b9/7B9//LiX5bRs6pIl0rJl3vVGjaR//on5cQsXeqfv7THDhnmBbdOmXuBr6+je3bvs3Bn393TwoPT2217wmjJl3B8PAMGOoBVAyKphEVo8zJvnnUqfMkUqX97LuA4aFPvHT5smJUsmjR8vlS0rlSwpTZwo7dghLV4c8+Msm2qZz+uu807Z21cLgJ98UipeXHriCSlVKmn58tiv5bHHpKuv9rK99vpz5sT+sQAQSghaAUQcqwHNn1/Klev8bVWqxP7xa9dKv/wiWXWCZVjtYgHpiRNeMByT0qW9YNfHygQs6PVJntwLPn/7LfZrsdKCb7/1AnF7/N13e3WsABBuUgR6AQDgbxbUWf1qfFl5gXXk2+n4SzV8xeTi0/a2hkvdZs8fW9myeZdrr/UyvhaMW11r9eqxfw4ACAUErQAiTokS3qn0/fu9bKdvJFZsVaggvfeelCOHlDGjgoYvw3ryZKBXAgCJj/IAABGnfn1r5JI6dpTWr/dqSH2NWLHJwHbo4GU3bWLA0qXS1q3Sl196M1F37ZJffPWVNHq0tG6dtH27tGiR1L69974uzLJaGYPdZ98+6e+/vet2uVzDGAAEI4JWABHHaj9ttNXRo1LlylLXrtKAAd7PbATWldjUAZsAYIP9W7b0TstbY5UFhf7KvKZNK82cKd188/nGrjJlvODZNhLwsfd2ww3SmDHS5s3edbtcvAkBAAS7q6KiKNkHAMu22hQBy0xatjKhbBKAZWFtHBYAIOGoaQUQkWbN8rr+bdSUBap2ar9mzYQHrJYG+PVXbwtXy2gCABIH5QEAItKRI1KPHl5Tlu1WZWUCvhmntmuVb5TVxZdbbrn88x4+7O2wZfNWbf5qfCVkDQAQjigPABCyPv74YzVp0iTRn9d2l7JLTLWkefMm+ksG5RoAIJgQtAIIOWfPntWAAQP02Wef6Ztvvgn0cgAAfkB5AICQcvToUbVq1UovvvhioJcCAPAjGrEAhIwdO3aoWbNm+u6775QqVSq98MILgV4SAMBPyLQCCAmrVq1SlSpVXMCaI0cOLV68WI0bNw70sgAAfkLQCiDovfPOO6pTp47279+vcuXK6auvvlL1C7d9AgCEPYJWAEHfcNWhQwedPHnSlQYsX75cBQsWjPExn376qSpUqKDu3bsn2jqOHz+uGTNm6Pbbb3fP7bvce++9mj9/vk6dOpVorwUAuDSCVgBB6dixYy5IHDRokPv+scce06xZs5TeBpVexr59+/Ttt99q586dibaWdOnSqXXr1po+fbqGDRumIkWKaP369Ro/frzq16+vwoULu/paywQDAJIGQSuAoLNr1y7Vrl1bM2fOdA1XkyZN0ksvvaRkya78T5ZlZI09LrFdddVVrkzBsq5bt25V//79lT17du3evVtPPfWU8ufPrzvvvNPV37JDNgAkLoJWAEHF6lUrV67ssqUWEC5cuFAdO3aM9eP/+ecf9zV16tRJuEq5ANWyq5bRnTp1qqpWrerKBN5++21Xb2vvwYLtv//+O0nXAQCRgqAVQNCYNm2abrzxRneKv0yZMi6ArVmzZpyew5dpTeqg1cdex5ddtfVagG23rV27Vvfcc48Lbh9//HFt377dL+sBgHBF0AogKBqunnnmGbVr104nTpxQ06ZNtWLFChUqVCjOz+XvoPVCvuyqZV9t84MCBQrowIEDevnll10dbIsWLVzjFqUDABB3BK0AAso68++44w4999xz7vtHHnlEs2fPVoYMGeL1fIEMWn2srMGyq1u2bHHNY/Xq1XOB+Zw5c1zjVqlSpTR69Gj99ddfAVsjAIQaglYAAWMNTP/5z3/0/vvvK2XKlHrzzTf13//+V8mTJ4/3c/pqWpOiESuuUqRI4bKrX3zxhTZs2KCePXu66QcbN25Ur169lDdvXnfbTz/9FOilAkDQI2gFEBBff/212+HKaj+zZs3qTpt37tw5wc8bDJnWSylZsqRGjRrlAnXLspYoUUJHjx7Va6+95jKvlo21DPPp06cDvVQACEoErQD8zuadWoZ1z549LmCzBib7PjEEa9DqkzFjRj3wwAMu82qBumVibZTXggULdNttt6lo0aJuvNfvv/8e6KUCQFAhaAXgN9aAZLWrbdu2daOgGjdurJUrV7ompcQS7EHrhTNfb775Zlfz+uuvv7oaWMs479ixQ0888YSbOmCTCNasWRPopQJAUCBoBeAXFqS2b9/eTQkwDz30kD788EOXeUxMSbm5QFKxbWlt2oBtqmDTBypWrOjex5QpU1wJhc2AtVmwvvcGAJGIoBVAktu7d6+bv2pzWK05ady4cW471IQ0XAV6c4GkkCZNmnPZVZv7avNfLfi28om7777bZV9tF67E3KIWAEIFQSuAJPXNN9+4+aUWiGXJksV10nft2jXJXi9UygOuVDrgy65auYDtvGWTBqzOdfDgwW5+batWrbRo0SJmvgKIGAStAJLMBx98oFq1armOeeuWt4xhnTp1kvSIh0PQeqGcOXO67Oq2bds0Y8YMd/xs5uvMmTN10003uZ3DXn/9dTeJAADCGUErgCRpuBo0aJBat27talkbNmzoTndbZ3xSC8Wa1tiwsgpfdvX7779X9+7ddfXVV7spBD169HCZ2N69e2vTpk2BXioAJAmCVgCJyrZhtVrMAQMGuO8tkProo490zTXX+OVIh3JNa2z5squWwX711VdVvHhxt7vWyJEjXUbbPiTMnTtXZ86cCfRSASDRELQCSDT79u1zp6/feecdlxl84403NGLECHfdX8KtPOBy7IPAgw8+6HbY+vzzz3Xrrbe6eth58+apWbNmKlasmNth7MCBA4FeKgAkGEErgESxbt06N55p9erVypw5swui7rvvPr8f3UgKWn1sc4IGDRq4EWJbtmxRv3793O/A6mAfffRR5cuXT126dHFNcQAQqghaASSYbT9as2ZNN4rpuuuuc4GrNQkFQiQGrRcqXLiwhgwZ4ma+vvnmm7r++utdycaECRPc/Ff7PVkm3FdGAQChgqAVQIIarmzLUdt+9Pjx46pfv75ruLIay0DxBWPh1ogVV+nSpVPnzp1ddnX58uVq166dK9NYsWKFOnTooAIFCujpp592dbEAEAquirL/dQAgjix7161bNzdL1PTs2VPDhw/3a/3qpeTKlUv79+/Xd999p3LlygV0LcG4yYNt7GC1xnbd2AYPLVu2dL+/2rVru5pYAAhGBK0A4syCQsuurly50gU91rVuY5eCgdVy/vnnn645yUoV8G+nTp3SrFmzNHr0aC1duvTc7WXLlnXBq2VibZwWAAQTglYAcbJ+/XrXpW47NWXKlEnvv/++6tWrF1SnxW027NatW93OUbjy7/O1115zGXM7br6pBFZaYB9EbAIBAAQDglYAsWbd6e3bt9exY8dc3arNAg22bKZlfm3HKKvVzJMnT6CXEzIOHTqkSZMmuQDWJhD43HLLLS772qhRIzelAAAChaAVwBVZ6bvN+3z88cfd9ZtvvlnTp09XlixZguro2TB9X03tH3/8oaxZswZ6SSHHAn4bV2alA5988sm52203M8u83nPPPa4EAwD8jaAVwBVHSNm81cmTJ7vv77//frcLU8qUKYPuyNkEA18t5pEjR5Q+ffpALymk/fLLL27nLRuXZXXCJm3atG7HswceeEDly5cP9BIBRBCCVgAx+v33313DlY1MslPDFqzaqeJgPsXty/7a6KtgDKxDkZWD2GzXUaNG6fvvvz93e61atdyfB5s+wLEGkNQIWgFc0g8//OAarmxXJWvMsXIA23Up2LeRzZ0797nT3IxvSlxWGrJs2TJXOvDBBx+4cgxjx9yy8TYCzXf8ASCxEbQC+JePPvrIDaM/evSoq2W070uUKBH0R8omGhQsWNDthmVzZJF0rNFt7NixGjNmjBuBZqyeuHXr1i77WqNGDT40AEhUBK0AomXShg0b5vaut+t16tTRjBkzQqah6eeff9a1116rjBkz6vDhw4FeTkSwMgzLulr21Xbb8rHtYy14tQ8/NoYMABKK+SUAzgUfXbt21SOPPOIC1nvvvVfz5s0LmYDV1zRmLNMK/7Dtci0wtbrntWvXuvmuadKk0bp169yfp3z58rkPQb/++iu/EgAJQqYVgBsP1apVKy1ZssQ1XFm29cEHHwy507sWNFWqVMkFSjt37gz0ciLWgQMH3MSB//3vf64m2tifpSZNmrjsa/369Zn5CiDOyLQCEW7Dhg2qUqWKC1jttPrHH3+s3r17h1zA6ssW+7J/CBzLzlt21UZm2YYU1sBn2XurjbZNCqw+2iZRUMIBIC4IWoEI9umnn6p69epuy9MiRYpo5cqVLqgIVZQHKOh2J7MJFLZZwcaNG1323j4YWe1xnz59lDdvXjf31yZVAMCVELQCEciyXsOHD1fTpk31119/6T//+Y9Wr16tUqVKKZQRtAYv2+7Xsqu7du1yZQOlS5d281/feOMNlS1bVnXr1nUNXadPnw70UgEEKYJWIMLYKXSbqfnwww+7WaZdunTRF198oWzZsinUEbQGvwwZMrjsqm1SsGjRIldLbRnZxYsXu3FZhQsX1qBBg/Tbb78FeqkAggxBKxBhDTJWXzhu3DhXs2oNV3Y9XGpAfUFruLyfcGZ//nwj1aw8pX///sqePbvLxA4YMED58+fXXXfd5c4A2JkBACBoBSLETz/9pKpVq+rLL7902a65c+fqoYceCsmGqys1YjHyKrRYgPrCCy+4iQ9Tp051f07td/nWW2+pWrVqrlFw8uTJbBgBRDiCViACWCOM/ee/ZcsWFSpUyA2Bt/FD4YbygNBmHzbuvPNOrVq1Sl999ZU6duzobvv666/VqVMnN8rsiSee0Pbt2wO9VAABQNAKhDE7rTpq1Cg1btzYNVzVqlXLBQNlypRROCJoDR+VK1fWpEmTXPb1xRdfVIECBVx5y0svveQmXbRo0ULz58+ndACIIAStQJg6deqUa3ixMUPWcGWZKvtP3uoGwxU1reHH/rw+/vjj7izBrFmzdPPNN7s/z3PmzHGbFNjEC9tC1j6UAQhvBK1AGDp48KCbtzpmzBhXszpkyBC3Q1G413pS0xq+UqRIcS67ahtiPPDAA0qfPr2b/9qrVy8389V227LabQDhiaAVCDObNm1yjSwLFy50/6lbRsp2JwqnhquYUB4QGUqWLOmyq7t373ZfbYeto0eP6rXXXnOZ13r16mn27NnMfAXCDEErEEZs3qoFrLZ9ptUALl++3O1IFCkIWiOL7a5lGVfLvFoG1jKxyZIl04IFC3TbbbepaNGirgb2999/D/RSASQCglYgTFiW6ZZbbnH7udeoUUNr1qxRuXLlFEkIWiOTnUWwWleref31119dDWzWrFm1Y8cON23ARmpZTbdNIUgqdepIffp41wsVkkaMkN8MHChdf73/Xg8IFIJWIMTZtpeWbbJ6vjNnzujuu+92pQE5cuRQpPHVtLK5QOQqWLCgmzZgmxTY9IGKFSu6DzM259UmEtjoN5v/6vuAkxTWrJG6dZPfPPKItGCBf17LDpsFyFZttG7d+du/+05q185m7kpp01oJh/Tqq/5ZEyIHQSsQwg4dOuSyq7aXu2Wb7FSo/Ucd7g1XMSHTCp80adK4Oa92xsHmvtr815QpU7odtmynLcu+2i5cNlIrsdmAjnTp/Pe7SJ9eyprVP6/16KNSnjz/vn3tWu99v/WW9OOPUv/+0hNPSKNH+2ddiAwErUCI2rx5s8saWS3f1VdfrZkzZ+qxxx6LiIarmBC04mL298HqvG2nLQtQbectmzRgda6DBw9W4cKF1apVKy1atCjRZr5eXB6wcaNUq5YF0lKpUtL8+V6mcvbs2D3f7t1S27ZS5sxecNq8ubRtW8zlAZ06SS1aSIMHSzlzSpkySc8+a2dlpH79pCxZpHz5pAkT4va+Pv1UmjdPGjr03z/r3FkaOVK68UapSBHpzjule+6RZs6M22sAl0PQCoQgazSx/4gtcLWMkTVcWRNKpCNoxeXkzJnTZVe3bdumGTNmqE6dOq6kxj7w3XTTTW7Tjddff91NIkgsZ896AaRlXlevlsaO9bKQsXX8uFS3rpdNXbJEWrbMu96okZXDxPy4hQulPXu8xwwb5gW2TZt6ga+to3t37xLbRPP+/dK990pTp8Y+i3z4sBcgA4mFoBUIMW+88YYaNmyoP//802VabYer8uXLB3pZQYHNBRDbma++7Or333+v7t27K126dG4KQY8ePVwmtnfv3m58XEJZZnLLFmnKFMn+mlrGddCg2D9+2jQpWTJp/HipbFmvVnTiRGnHDmnx4pgfZ8GiZT6vu87LgtpXC4CffFIqXtw7dZ8qlbR8+ZXXYAloy95akFupUuzWvXKlNH26dN99sX+vwJUQtAIh1HBlu1vZLleWHerQoYP7TzdXrlyBXlrQYHMBxJUvu2ozX0eMGKHixYu73bVGjhzp5r/aB8S5c+e6v3PxYXGvNSdd+Ne0SpXYP95qRX/5RcqQwcuw2sUC0hMnvGA4JqVLe8Guj5UJWNDrkzy5V2rw229XXsOoUZJtOGaBbmxYTauVMDz9tFS/fuweA8QGQSsQAiyr2qRJE42y/z1kmZpBrkbPmk1wHuUBiK9MmTK57KrtsPXZZ5+padOmrh523rx5atasmZpbFBYPlqVMSJm5lRdUrOh16l942bxZat8+5selTBn9e1vDpW6z578SKzVYtUqy/s4UKaRixbzbLevasWP0+27YIN10k1dKMGBArN8mECspYnc3AIFiGwXYBgH2n6mdwrRgtWXLlvxCLoGgFQllmxNYdtUuW7dudVnY8ePHa48ViMZDiRLeqXyrCbVsp28kVmxVqCC9955kE+wyZlRAWJnBCy+c/94ORcOG3rqqVo2eYbWA1QLZuJRAALFFphUIYnb63xquLGDNly+fli1bRsB6GdS0IjHZZIEhQ4a4ma/PPPNMvJ7DTo8XLeoFcuvXezWkvkas2GRgO3SQsmXzTrcvXSpt3Sp9+aXUu7e0a5f8okABK6M4f7n2Wu92e182hcAXsFrDmL3fhx+W9u3zLmxGhsRE0AoEqXHjxqlBgwY6ePCgqlSp4hqubrjhhkAvK6hR04qkYGc44lseYLWjNtrKBhJUrix17Xr+tHlsqnusU98mAFjgaCdYrBHLGqv+/jtwmddLef99L0B9+20pd+7zF3vPQGK5KiqxBtMBSLSGq379+rmmEHPHHXdowoQJSmvbzOCySpcu7TrAbUewupb2AYKQZVttioA1WFm2MqGsQcqysDYOCwhn1LQCQeTw4cNq166dPrUp3pKee+45DRgwIKI3DIgLaloRjGbN8rr+bdSUBap2ar9mzYQHrJZy+vVXbwtXTsIgElAeAASJLVu2qHr16i5gtazq+++/r6eeeoqANQ4IWhGMjhyRevTwmrJs3qmdMp8zx/uZ7VrlG2V18eWWW648vN922LJ5qzZ/Nb4SsgbAnygPAILAkiVLXIPVgQMHlCdPHn344YeqaHNuEOcdj3777TetX79eZS8cSgkEqYMHvculWEVQ3ryRsQYgNigPAALszTffdBsGnDp1SpUqVdKcOXNc4Iq4I9OKUGMbBQR6q9NgWAMQG5QHAAFiO+z07dtXXbt2dQFrmzZt9OWXXxKwJgBBKwCEL4JWIABsm0jbZWfYsGHu+4EDB2ratGlutA7ixwahELQCQPiiPADwM9tlx3a4+vHHH902rJMmTVLbtm35PSTCqDDfBL9U1pkCAAgrBK2AH9mOVrfddpv++OMP5c6d29WvVmb6dqJuLGBS2ybpAICwQnkA4CeWUb3ppptcwFqhQgWtWbOGgDUR+UoDDEErAIQfglbADw1Xjz76qO655x7XcNW6dWs34iovc2SSJGhNliyZUqTgJBJCx5EjRzR69GjVqFHDfaC1y4MPPqiff/450EsDggpzWoEk/s+oQ4cOmjt3rvveNguwpisLrJC4tm3bpsKFC7s64b9tY3YgxOzdu9ftgjdu3Dj3Ydd2wmvfvr27rUiRIoFeHhBw/M8JJJHt27erZs2aLmC109Vvv/22+8+HgDVpa1opDUCosjr3119/XT/99JNrzrTGQvt3o0SJEurVq5f2798f6CUCAUXQCiSBFStWqEqVKvr+++/dLk02f9UyJkg6jLtCuChevLgbgbd27Vo1bNjQlRVZ+UDRokX19NNPu5F5QCQiaAUS2dSpU1W3bl23nej111/vGq6qVq3KcU5iBK0IN1bb+tlnn2nhwoXuQ/CxY8f0/PPPu1KB4cOH68SJE4FeIuBXBK1AIjl79qyeeOIJ3X333e5UtY22shFX+fPn5xj7AUErwpV9CF61apU++OADXXfddTpw4IAefvhhd92mklj9KxAJCFqBRHD06FG1atVKL730kvv+ySef1IwZM3T11VdzfP1c08rGAghH1pTVsmVL/fDDDxo/frzy5cunHTt2uKkk5cqVczOffZtrAOGKoBVIIPuPo1atWpo9e7YLmKw8YNCgQTRc+RmZVkQCG+fWpUsXbd68Wf/973+VOXNmbdiwQS1atHAjs6x+HghXBK1AAqxcudLVmn333XfKkSOHFi9erDvvvJNjGgAErYgkadOm1SOPPKJff/3VndlJly6dKyGoU6eOGjdurHXr1gV6iUCiI2gF4slG0VitmY2hsdNzX331lapXr87xDBCCVkSiTJkyuTM7v/zyi3r06OEysZ9++qluuOEGNyN6y5YtgV4ikGgIWoF4NFz179/fZVQtUGrWrJmWL1+uggULciyDIGilphWROuP1tddeczNe27Vr525755133IzXnj17at++fYFeIpBgBK1AHNjImdtvv12DBw923z/22GOaNWuW0qdPz3EMMDYXAKRixYq5YPWbb75Ro0aNdPr0aRfM2oxX25Hv8OHDHCaELIJWIJZ27typ2rVra+bMmS6bZ6NmbFoAO1wFB8oDgPOsPMDKBBYtWuTmRB8/flwvvPCCm/H6yiuvMOMVIYmgFYgFq1e1hqtvv/1W2bNnd8O+O3bsyLELIgStwL9ZY5Y1jNoZoZIlS+rgwYOugct23ZowYYLLxAKhgqAVuILPP/9cvXv3djVjVhpgXbk1a9bkuAUZalqBmGe82kis9evXu0DVNjzZtWuXG51lTaQW0DLjFaGAoBW4Atv72zIVViM2ffp05cmTh2MWhKhpBS7PJgvYZgQ249VKBLJkyeIat2zTApt8YiP7gGBG0AogLFAeAMROmjRp3DawNuN1wIABbsbr6tWr3Qg/a96yMiggGBG0AggLBK1A3FxzzTV6/vnn3SzXBx54wGVirRyqQoUKbmyWzX4FgglBK4CwQNAKxE+uXLk0evRobdy4Ue3bt3e3TZs2zTVu2YYFe/fu5dAiKBC0AgirmlY2FwDix2a52k5/Vh5wyy23uMkCr7/+upv9ahuqMOMVgUbQCiAskGkFEsf111+vTz75xDVmVatWzc14tQ1VbMbr0KFD9ffff3OoERAErQhrdepIffp41wsVkkaM8N9rDxxo//j77/UiHUErkLhuvPFGrVixQrNnz1apUqXcjNd+/frp2muv1ZtvvsmMV/gdQSsixpo1Urdu/nu9Rx6RFixI2tewQPyqq6JfHn88+n127JBuvVW6+mopWzbpwQftVLrCDkErkDQzXps3b+5mvE6cOPHcjNeuXbuqbNmybodAZrzCXwhaETGyZ5fSpfPf66VPL2XNmvSv89xzkvVJ+C4DBpz/2ZkzUpMm0rFj0rJl1lwhffCB1Levwg6bCwBJJ3ny5OrUqZOb8Tps2DBlzZrVNW61atXKlRDYdrFAUiNoRcS4uDxg40apVi2bWSiVKiXNn+9lKmfPjt3z7d4ttW0rZc7sBafNm0vbtsVcHtCpk9SihTR4sJQzp5Qpk/Tss5Ltotivn5Qli5QvnzRhQtzeV4YM1v17/mLBss+8edKGDdJbb9le5FK9etIrr0jjxkl//aWwwuYCgH9mvD700ENuxutTTz2lq6++2m1zfdNNN7mNWGwTFiCpELQiIp096wWQlnldvVoaO1bq3z/2jz9+XKpb1wsQlyzxsph2vVGjy596X7hQ2rPHe8ywYV5g27SpF/jaOrp39y47d8Z+LS+/7AXNFiAPGhT99VeulMqUkS7cxKthQ8tKSmvXKqxQHgD4T8aMGfXcc8+5Ga89e/ZUypQpNW/ePFWsWFF33HGHfv75Z34dSHQErYhIloHcskWaMkUqX97LuFrAF1t2mj1ZMmn8eKlsWalkSWniRK9+9HI7IVo2deRI6brrpM6dva8WAD/5pFS8uPTEEzaySVq+PHbr6N3bW4udmevZ08sk9+hx/uf79nlZ3QtZgGyvYT8LJwStgP/lzJlTo0aNcqUCHTp0cDWw7733nmvcuv/++5nxikRF0IqItGmTlD+/dzrdp0qV2D/espS2WYydmrcMq10sID1xwguGY1K6tBfs+lhAaUGvT/LkXtb0t99it46HHrIOX6lcOalrV+mNN6Q335QOHDh/Hyt5uFhU1KVvD2XUtAKBY+Ow3nrrLTfjtXHjxm6ywBtvvOFmvz755JP6888/+fUgwQhaEZESGrRZeUHFitK6ddEvmzdL/7+hzCWlTBn9e1vDpW6z54+PatW8r77dFy0ovzijeuiQdOrUvzOwoY6aViDwypcvr48//lhffvmlatSo4Wa6vvjiiy6oHTJkCDNekSAErYhIJUp4p/L3748+Eiu2KlSQrGQrRw6pWLHol2uuUcB8+633NXdu72v16tIPP3hTBS4sjUid2gu6wwnlAUDw+M9//qNly5Zpzpw5Kl26tA4dOqTHHntMxYsX1/jx45nxinghaEVEql/ftiyUOnaU1q/3akh9jVixycB26ODNPLWJAUuXSlu3Sl9+6dWY7tolv7Amq+HDvQyvvf706dJ990nNmkkFCnj3adDAm4xw111eQGtzY21+7L33WiOFwgpBKxBcrL61WbNm+u677zRp0iQVKFBAu3fv1r333qsyZcpoxowZzHhFnBC0IiJZ7aiNtjp6VKpc2asH9c03tRFYV2JTB2wCgAWHLVt6jVjWWGW7G/orGLRs6Xvvebt+WWD69NNeMPruu9Hf58cfe++pZk2pTRtvasLQoQo7BK1A8M547dixo5vxOmLECGXLlk2bNm3S7bffripVqmhBUu/CgrBxVRRbWQCOZVttioDVg1oWNqFsEoBlYW0cFpLeNddco7/++sv9x2inIAEEJ/t7ahsUvPLKKzpqmQN39qu+q321kVlATMi0ImLNmiV98YW3IYBtLGBbvFo2MqEBqzV52QQBSx7YtAD4B5lWIHRmvA4cONDNeH3wwQfdjNcvvvhClSpVUtu2bd0HT+BSCFoRsY4c8WaaWlOW7VZlZQJz5ng/s12rfKOsLr7ccsvln/fwYe90vc1Ctfmr8ZWQNUQaO2FE0AqElhw5cujVV191pQJ33XWXq4GdPn26m/F63333aY/txAJcgPIA4BIOHvQul5I2rZQ3b2SsIZTGXaW2Il830uuQMtkeuQBCyvr169W/f3999NFH7vu0adO6TKxNHchsu6Ig4hG0Agh5VheXwXZ6kHTs2DGls045ACHJRmU9/vjjWv7/WwPah1D7vlevXvzdjnCUBwAIeb7SAOPLuAIITbVq1dLSpUv14YcfutFYtpuWBa3WYDl27Fidst1REJEIWgGETdBqo3XsAiC0WX3rrbfeqnXr1mnKlCkqWLCgq3G1WlcLZN9//31mvEYgglYAIY8mLCA82YdQa9KyZi1r2sqePbubLtCmTRtVrlxZ8230CyIGQSuAsAlaU9nIBgBhx8p+rCnLxmTZuKz06dNr7dq1br6rXb7++utALxF+QNAKICymBxjqWYHwZg2XzzzzjAtee/fu7T6oWrbVsq62w5ZlZBG+CFoBhDzKA4DIm/FqW8JakHr33Xe7GtgZM2aodOnS6tatm3bv3h3oJSIJELQCCHkErUBkKlSokCZPnuxmvFrj1pkzZzRu3DgVK1bMzXc9GNOwa4QkglYAIY+gFYhsNlHARmTZjFcbmXXixAkNGTJERYsW1UsvvaTjx48HeolIBAStAMKmppVGLCCy1axZU0uWLHG7apUtW9bNeH3iiSdc5nXMmDHMeA1xBK0AQh6ZVgA+Vt/apEkTffvtt5o6daorIdi7d6+6d+/ual6nT5+us2fPcsBCEEErgJBH0ArgUjNe77zzTm3cuFEjR450M15//vlntW3b1k0bmDdvHhsUhBiCVgAhj6AVQExsFF6vXr3cmKxnn33Wjc365ptv1LBhQ9WrV09r1qzh4IUIglYAIY+aVgBXYsHq008/7YLXPn36uBr4hQsXqkqVKmrdurXLyCK4EbQCCHlkWgHElpUJDB8+3G0H26lTJyVLlkwffPCBq3e99957tWvXLg5mkCJoBRDyCFoBxFXBggU1ceJEN+O1efPmrjlr/PjxKl68uB599FFmvAYhglYAIY+gFUB8WYZ19uzZWr58uWrXru1mvP73v/9VkSJF9OKLL+rYsWMJPrh16kh9+njXCxWSRozw3+9r4EDp+usVFghaAYRN0MqcVgDxVaNGDX355Zf6+OOPVa5cOR0+fFhPPvmkm/H6+uuvJ9qMV+v76tbNf7+nRx6RFizwz2vZP8UWIF91lbRu3fnbDxyQGjWS8uSxxjgpf36pZ0/pr7/i9vwErQDCphHLuoQBICEzXhs3buxmvL711lsqXLiw9u3bpx49eqhkyZKaNm1agme8Zs8upUvnv99R+vRS1qz+ea1HH/UC04slSyY1by59+KG0ebM0aZI0f77UvXvcnp+gFUDIozwAQGKy5qwOHTq4iQKjRo1Sjhw53NSBdu3aqVKlSvr888/jPeP14vIAG1pQq5aUJo1UqpQXzFmmcvbs2D3f7t1S27ZS5sxecGrB4bZtMZcHdOoktWghDR4s5cwpZcokPfusdPq01K+flCWLlC+fNGFC3N7Xp59K8+ZJQ4f++2e2tvvvlypVslpi6eabpR49pKVL4/YaBK0AQh5BK4CkYCVHPXv2dAHrc88958ZmWRa2UaNGuummm7R69eoEPb8lbS2AtMyrPdXYsVL//rF//PHjUt26XjZ1yRJp2TLvup2K//8TUJe0cKG0Z4/3mGHDvMC2aVMvuLR1WAbULjt3xm4d+/dL994rTZ0auyyyvfbMmdKNNypOCFoBhDyCVgBJKX369Hrqqaf066+/6uGHH3bB7OLFi1WtWjW1bNlSW7dujdfzWmZyyxZpyhSpfHkv4zpoUOwfP22ad+p9/HipbFmpZElp4kRpxw5p8eKYH2fZ1JEjpeuukzp39r5aAPzkk1Lx4tITT1jALi1ffuU1WMLZsrcW5Fom9XLatfOC2rx5pYwZvXXHBUErgJDH5gIA/CFbtmx65ZVX3Haw99xzjysjmDVrlm6//fZ4Pd+mTV5TUq5c52+rUiX2j1+7VvrlF9s4wcuw2sUC0hMnvGA4JqVLe8Guj5UJWNDrkzy5V2rw229XXsOoUV5DlQW6VzJ8uPTNN17pg63v4YcVJynidncACD5kWgH4U4ECBTRhwgT17dtXAwYM0Pbt2+P1PJaltPrVhJQXVKwovf32pRu+YpIyZfTvbQ2Xui02PWdWarBqlTcV4EKWde3QQZo8+fxtFpzbpUQJLyiuXVt66ikpd27FCkErgJBH0AogUDNeLdNqGxTEhwVvdirfakIt2+kbiRVbFSpI770n5cjhnW4PBCszeOGF6PWqDRt666paNebH+frY/n9iYawQtAIIeQStAALJ5rrGR/36UtGiUseO0pAh0pEj5xuxYpOB7dBB+u9/vYkBzz3ndf1bEGxNTjYJwL5PagUKRP/eShSMvS/f63/yiReYV67s/XzDBm88Vs2a3jSF2KKmFUDIo6YVQCiy2lGr7zx61AvounaVBgzwfmYjsK4kXTpvAoAFji1beo1Y1lj199+By7xeStq00rhxXqOZrdF2B7NpBR99FLfnIdMKIOSRaQUQzC7s5L9whqqvRMBGVfn4OvaLFYvdc+fKFb1u9GJ2+t2X/TQ22P9y64tpnbFlmdOLR9jaWK4VK5RgBK0AQh5BK4BQNWuWF1TaqCmbBNC7t3fa3E6vJ0RUlPTrr94WrjfcoLBAeQCAkEfQCiBUWR2r7Q5lGVebd2plAnPmeD+zXat8o6zSX3S55ZbLP+/hw94OWzZv1eavxldC1pDYroqK7z5kABAkSpQooU2bNrlh3zfGdYsVAAhSBw96l5jqRPPmjYw1+FAeACBsGrFSXzwoEABCmG0UYJdIX4MP5QEAQh7lAQAQ/ghaAYQ8glYACH8ErQBCHkErAIQ/aloBhLxSpUrp1KlTuvrqqwO9FABAEmF6AAAAAIIe5QEAAAAIegStAAAACHoErQAAAAh6BK0AAAAIegStAAAACHoErQDCRp06Up8+3vVChaQRI/z32gMHStdf77/XA4BIQ9AKICytWSN16+a/13vkEWnBAv+81smTXoB81VXSunXnb580ybvtUpfffvPP2gAgqbC5AICwlD27f18vfXrv4g+PPirlySN9913029u2lRo1in5bp07SiRNSjhz+WRsAJBUyrQDC0sXlARs3SrVqSWnS2A5a0vz5XgZy9uzYPd/u3V5QmDmzlDWr1Ly5tG1bzOUBFiy2aCENHizlzCllyiQ9+6x0+rTUr5+UJYuUL580YULc3tenn0rz5klDh/77Z2nTSrlynb8kTy4tXCh16RK31wCAYETQCiDsnT3rBZDp0kmrV0tjx0r9+8f+8cePS3XrepnUJUukZcu865bV/OefmB9nAeOePd5jhg3zAtumTb3A19bRvbt32bkzduvYv1+6915p6lTvvVzJlCne/Vq3jv17BYBgRdAKIOxZZnLLFi+IK1/ey7gOGhT7x0+bJiVLJo0fL5UtK5UsKU2cKO3YIS1eHPPjLJs6cqR03XVS587eVwuAn3xSKl5ceuIJKVUqafnyK68hKsrL3lqQW6lS7NZtWdz27b0MLACEOmpaAYS9TZuk/Pm9U+Y+VarE/vFr10q//CJlyBD9dqsVtWA4JqVLe8Guj5UJlClz/ns7fW+lBrFpkho1SvrrLy/QjY2VK6UNG7xAHQDCAUErgLBnWUqrX01IeUHFitLbb8et4Stlyujf2xoudZs9/5VYqcGqVVLq1NFvt6xrhw7S5MnRb7essNXY2roBIBwQtAIIeyVKeKfyrSbUsp2+kVixVaGC9N57Xgd+xowKCCszeOGF899brWzDht66qlaNft+jR6Xp06UXX/T7MgEgyVDTCiDs1a8vFS0qdeworV/v1ZD6GrFik4G1TGa2bN7EgKVLpa1bpS+/lHr3lnbtkl8UKOCVFvgu117r3W7vy6YQXMgCWZtSYOsGgHBB0Aog7FntqI22sgxk5cpS167SgAHez2wE1pVYB75NALDAsWVLrxHLGqv+/jtwmdfLefNNb502pQAAwsVVUVFW7QUAkcWyrTZFwBqsLFuZUNYgZVlYG4cFAEh81LQCiAizZnmzVW3UlAWqdmq/Zs2EB6z2sf/XX70tXG+4IbFWCwC4GOUBACLCkSNSjx5eU5bNO7UygTlzvJ/ZrlW+bVgvvtxyy+Wf9/Bhb4ctm7dq81fjKyFrAIBIQHkAgIh38KDc5VJsMH/evJGxBgAIZgStAAAACHqUBwAAACDoEbQCAAAg6BG0AgAAIOgRtAIAACDoEbQCAAAg6BG0AgAAIOgRtAIAACDoEbQCAAAg6BG0AgAAIOgRtAIAACDoEbQCAAAg6BG0AgAAIOgRtAIAACDoEbQCAAAg6BG0AgAAIOgRtAIAACDoEbQCAAAg6BG0AgAAIOgRtAIAACDoEbQCAAAg6BG0AgAAIOgRtAIAACDoEbQCAAAg6BG0AgAAIOgRtAIAACDoEbQCAAAg6BG0AgAAQMHu/wCrjNRQrXkNVQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from openfe.utils.atommapping_network_plotting import plot_atommapping_network\n",
+    "\n",
+    "plot_atommapping_network(planned)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "751f8ad5-225a-461c-8951-cf35b9cb822e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plot_atommapping_network(LigandNetwork(nodes=planned.nodes, edges=completed.edges))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9b19391",
+   "metadata": {},
+   "source": [
+    "## 6. Repair — by ligand name\n",
+    "\n",
+    "When you know which ligands should bridge the gaps, name the edges explicitly.\n",
+    "Here we chain one representative ligand from each fragment; in practice you'd\n",
+    "choose these from the chemistry."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "73cea531",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "bridging by name: [('lig_ejm_46', 'lig_ejm_31'), ('lig_ejm_31', 'lig_ejm_48')]\n",
+      "connected: True\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/hannahbaumann/.local/share/mamba/envs/openfe/lib/python3.13/site-packages/konnektor/network_planners/generators/explicit_network_generator.py:85: UserWarning: Generated network is not connected as a single network.\n",
+      "  warnings.warn(\"Generated network is not connected as a single network.\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "def repair_by_names(completed, ligands, mapper, names):\n",
+    "    \"\"\"Reconnect a broken network with bridging edges specified by name.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    completed : LigandNetwork\n",
+    "        The disconnected network to repair.\n",
+    "    ligands : list[SmallMoleculeComponent]\n",
+    "        The pool the named edges are drawn from. Must include any missing\n",
+    "        ligands that are absent from ``completed``.\n",
+    "    mapper : AtomMapper\n",
+    "        Builds the atom mapping for each new edge.\n",
+    "    names : list[tuple[str, str]]\n",
+    "        The bridging edges to add, as pairs of ligand names, e.g.\n",
+    "        ``[(\"lig_ejm_31\", \"lig_ejm_46\"), ...]``.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    LigandNetwork\n",
+    "        ``completed`` with the named bridging edges merged in.\n",
+    "    \"\"\"\n",
+    "    patch = generate_network_from_names(ligands=ligands, mapper=mapper, names=names)\n",
+    "    return merge_two_networks(completed, patch)\n",
+    "\n",
+    "\n",
+    "reps = [sorted(frag.nodes, key=lambda n: n.name)[0].name for frag in fragments]\n",
+    "bridge_names = list(zip(reps[:-1], reps[1:]))\n",
+    "print(\"bridging by name:\", bridge_names)\n",
+    "\n",
+    "mapper = KartografAtomMapper()\n",
+    "\n",
+    "# include the missing ligand(s) in the pool, since they aren't in `completed`\n",
+    "ligands = list(completed.nodes) + list(missing)\n",
+    "repaired_network_by_name = repair_by_names(completed, ligands, mapper, bridge_names)\n",
+    "print(\"connected:\", repaired_network_by_name.is_connected())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "287fc9e5",
+   "metadata": {},
+   "source": [
+    "## 7. Repair — by lomap score\n",
+    "\n",
+    "You can also let the scorer choose the bridges: the concatenator proposes candidate edges\n",
+    "between fragments, scores them with lomap, and keeps the best. \n",
+    "`n_connecting_edges` determines how many edges to connect the fragments with."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "d06afced",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "connected: True\n",
+      "edges added: [('lig_ejm_31', 'lig_ejm_48'), ('lig_jmc_28', 'lig_ejm_47'), ('lig_ejm_47', 'lig_ejm_48'), ('lig_ejm_46', 'lig_ejm_48'), ('lig_ejm_46', 'lig_ejm_47'), ('lig_jmc_28', 'lig_ejm_48')]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def repair_by_score(fragments, mapper, scorer, n_connecting_edges=1):\n",
+    "    \"\"\"Reconnect broken fragments with bridging edges chosen by score.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    fragments : list[LigandNetwork]\n",
+    "        The connected pieces to join back together, including single-node\n",
+    "        networks for any missing ligands.\n",
+    "    mapper : AtomMapper\n",
+    "        Builds the atom mapping for each candidate edge.\n",
+    "    scorer : Callable\n",
+    "        Scores a mapping by expected difficulty (e.g. a lomap scorer).\n",
+    "    n_connecting_edges : int, optional\n",
+    "        Number of bridges to add per join, by default 1.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    LigandNetwork\n",
+    "        A single connected network: the fragments' edges plus the chosen\n",
+    "        bridging edges.\n",
+    "    \"\"\"\n",
+    "    concatenator = MstConcatenator(\n",
+    "        mappers=mapper, scorer=scorer, n_connecting_edges=n_connecting_edges\n",
+    "    )\n",
+    "    return concatenator.concatenate_networks(ligand_networks=fragments)\n",
+    "\n",
+    "\n",
+    "def repair_edges(repaired, completed):\n",
+    "    \"\"\"The newly added bridges, i.e. what still needs simulating.\"\"\"\n",
+    "    return list(set(repaired.edges) - set(completed.edges))\n",
+    "\n",
+    "mapper = KartografAtomMapper()\n",
+    "scorer = lomap_scorers.default_lomap_score\n",
+    "\n",
+    "repaired_network_by_score = repair_by_score(fragments, mapper, scorer, n_connecting_edges=2)\n",
+    "print(\"connected:\", repaired_network_by_score.is_connected())\n",
+    "\n",
+    "new_edges = repair_edges(repaired_network_by_score, completed)\n",
+    "print(\"edges added:\", [(e.componentA.name, e.componentB.name) for e in new_edges])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c2fa5f7",
+   "metadata": {},
+   "source": [
+    "## 8. Rebuild the alchemical transformations\n",
+    "\n",
+    "Turn the new repair edges into runnable transformations — a solvent and a\n",
+    "complex leg each — and write them out. Protein, solvent and cofactors come from\n",
+    "the reference complex system, and the **protocol (with all its settings) is the\n",
+    "one recovered from the results**, so the repair edges match the original run.\n",
+    "\n",
+    "The only exception is a charge-changing edge: the completed run may contain no\n",
+    "charge-changing example to copy, so we take the recovered settings and add the\n",
+    "explicit charge correction on top."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "fbdc9724",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import copy\n",
+    "\n",
+    "\n",
+    "def _formal_charge_difference(mapping):\n",
+    "    a = Chem.rdmolops.GetFormalCharge(mapping.componentA.to_rdkit())\n",
+    "    b = Chem.rdmolops.GetFormalCharge(mapping.componentB.to_rdkit())\n",
+    "    return a - b\n",
+    "\n",
+    "\n",
+    "def _protocol_for(mapping, reference_protocol):\n",
+    "    \"\"\"Reuse the campaign's protocol; add charge correction for charge changes.\"\"\"\n",
+    "    if abs(_formal_charge_difference(mapping)) < 1e-3:\n",
+    "        return reference_protocol\n",
+    "    from openff.units import unit\n",
+    "    settings = copy.deepcopy(reference_protocol.settings)\n",
+    "    settings.alchemical_settings.explicit_charge_correction = True\n",
+    "    settings.simulation_settings.production_length = 20 * unit.nanosecond\n",
+    "    settings.simulation_settings.n_replicas = 22\n",
+    "    settings.lambda_settings.lambda_windows = 22\n",
+    "    return RelativeHybridTopologyProtocol(settings=settings)\n",
+    "\n",
+    "\n",
+    "def _components_from_reference(reference_complex_system):\n",
+    "    protein = next(c for c in reference_complex_system.components.values()\n",
+    "                   if isinstance(c, ProteinComponent))\n",
+    "    solvent = next((c for c in reference_complex_system.components.values()\n",
+    "                    if isinstance(c, SolventComponent)), SolventComponent())\n",
+    "    cofactors = {name: comp\n",
+    "                 for name, comp in reference_complex_system.components.items()\n",
+    "                 if name.startswith(\"cofactor\")}\n",
+    "    return protein, solvent, cofactors\n",
+    "\n",
+    "\n",
+    "def build_alchemical_network(repair_network, reference_complex_system, reference_protocol):\n",
+    "    protein, solvent, cofactors = _components_from_reference(reference_complex_system)\n",
+    "    transformations = []\n",
+    "    for mapping in repair_network.edges:\n",
+    "        protocol = _protocol_for(mapping, reference_protocol)\n",
+    "        if protocol is not reference_protocol:\n",
+    "            warnings.warn(\n",
+    "                f\"charge-changing edge {mapping.componentA.name} -> \"\n",
+    "                f\"{mapping.componentB.name}; added explicit charge correction\"\n",
+    "            )\n",
+    "        for leg in (\"solvent\", \"complex\"):\n",
+    "            sysA = {\"ligand\": mapping.componentA, \"solvent\": solvent}\n",
+    "            sysB = {\"ligand\": mapping.componentB, \"solvent\": solvent}\n",
+    "            if leg == \"complex\":\n",
+    "                sysA[\"protein\"] = sysB[\"protein\"] = protein\n",
+    "                sysA.update(cofactors)\n",
+    "                sysB.update(cofactors)\n",
+    "            transformations.append(Transformation(\n",
+    "                stateA=ChemicalSystem(sysA),\n",
+    "                stateB=ChemicalSystem(sysB),\n",
+    "                mapping=mapping,\n",
+    "                protocol=protocol,\n",
+    "                name=f\"{leg}_{mapping.componentA.name}_{mapping.componentB.name}\",\n",
+    "            ))\n",
+    "    return AlchemicalNetwork(transformations)\n",
+    "\n",
+    "\n",
+    "def save_transformations(network, out_dir):\n",
+    "    out_dir = pathlib.Path(out_dir)\n",
+    "    (out_dir / \"transformations\").mkdir(parents=True, exist_ok=True)\n",
+    "    with open(out_dir / \"alchemical_network.json\", \"w\") as f:\n",
+    "        json.dump(network.to_dict(), f, cls=JSON_HANDLER.encoder)\n",
+    "    for transform in network.edges:\n",
+    "        transform.to_json(out_dir / \"transformations\" / f\"{transform.name}.json\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "42ac44ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "wrote 12 transformations\n"
+     ]
+    }
+   ],
+   "source": [
+    "# use the repair produced by the score-based strategy\n",
+    "repair_network = LigandNetwork(edges=new_edges)\n",
+    "\n",
+    "alchemical_network = build_alchemical_network(\n",
+    "    repair_network, reference_complex_system, reference_protocol\n",
+    ")\n",
+    "save_transformations(alchemical_network, \"repair_transformations\")\n",
+    "print(\"wrote\", len(alchemical_network.edges), \"transformations\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}