AssemblyPlanning-DQN.ipynb

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   "source": [
    "## Assembly Tree DQN Approach"
   ]
  },
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   "cell_type": "markdown",
   "id": "e2fd718d",
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   "source": [
    "#### Formulation\n",
    " - State Space $\\mathcal{S}$ = {Every Possible Subassembly}\n",
    " - Action Space $\\mathcal{A}_{\\hat{s}}$ = { Every Connection that can currently be removed in state $\\hat{s}$ }\n",
    " - $\\mathcal{T}(s'|s,a)$ = Feasability Checks\n",
    " - $R(s,a)$ = Cost of removing a certain connection in line with the dynamics \n",
    " - Graph Assumption: Order doesn't matter\n",
    "\n",
    "#### Remarks\n",
    " - Note that the use of \"H\" and \"G\" are switched here compared to the paper"
   ]
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  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "18fe214c",
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   "outputs": [],
   "source": [
    "import sys, random, math, time, json\n",
    "import numpy as np\n",
    "import pprint as pp\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "from pyvis.network import Network\n",
    "from itertools import count\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "import helpers\n",
    "\n",
    "%matplotlib inline"
   ]
  },
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   "source": [
    "## HELPER FUNCTIONS"
   ]
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   "cell_type": "code",
   "execution_count": 4,
   "id": "501d1556",
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   "outputs": [],
   "source": [
    "# Temporary Instantiations\n",
    "Rewards = {}\n",
    "seqConstraint = {}\n",
    "\n",
    "\n",
    "def hierarchy_pos(G, root=None, width=20., vert_gap = 0.4, vert_loc = 0, xcenter = 0.5):  \n",
    "    # From Joel's answer at https://stackoverflow.com/a/29597209/2966723.  \n",
    "    def _hierarchy_pos(G, root, width=1., vert_gap = 0.2, vert_loc = 0, xcenter = 0.5, pos = None, parent = None):   \n",
    "        if pos is None:\n",
    "            pos = {root:(xcenter,vert_loc)}\n",
    "        else:\n",
    "            pos[root] = (xcenter, vert_loc)\n",
    "        children = list(G.neighbors(root))\n",
    "        if not isinstance(G, nx.DiGraph) and parent is not None:\n",
    "            children.remove(parent)  \n",
    "        if len(children)!=0:\n",
    "            dx = width/len(children) \n",
    "            nextx = xcenter - width/2 - dx/2\n",
    "            for child in children:\n",
    "                nextx += dx\n",
    "                pos = _hierarchy_pos(G,child, width = dx, vert_gap = vert_gap, \n",
    "                                    vert_loc = vert_loc-vert_gap, xcenter=nextx,\n",
    "                                    pos=pos, parent = root)\n",
    "        return pos          \n",
    "    return _hierarchy_pos(G, root, width, vert_gap, vert_loc, xcenter)\n",
    "\n",
    "\n",
    "# Generating Subassemblies in a recursive manner\n",
    "def recurGen(p, H, G):\n",
    "    currCon = list(H.edges())\n",
    "    if(len(currCon) > 0):\n",
    "        i = G.number_of_nodes() + 1\n",
    "        for c in currCon:\n",
    "            Hnew = H.copy()\n",
    "            Hnew.remove_edges_from([c])\n",
    "            if(c in seqConstraint.keys() and seqConstraint[c] in Hnew.edges()):\n",
    "                pass # This means a feasability constraint has been failed!\n",
    "            else:\n",
    "                idString = str(Hnew.edges())\n",
    "                nextState = next((x for x, y in nx.get_node_attributes(G,'idS').items() if y == idString), None)\n",
    "                if(nextState is not None):\n",
    "                    Rew, Hnew = R(p, c, H)\n",
    "                    G.add_edge(p, nextState, a=c, r=Rew)\n",
    "                else:\n",
    "                    G.add_node(i, idS=idString)\n",
    "                    Rew, Hnew = R(p,c,H)\n",
    "                    G.add_edge(p, i, a=c, r=Rew)\n",
    "                    G = recurGen(i, Hnew, G)\n",
    "            i = G.number_of_nodes() + 1\n",
    "        return G\n",
    "    else:\n",
    "        return G\n",
    "\n",
    "\n",
    "# Getting next set of possible states and actions\n",
    "def nextGen(H):\n",
    "    currCon = list(H.edges())\n",
    "    G = nx.DiGraph()\n",
    "    G.add_node(1, value=H, idS=str(H.edges()))\n",
    "\n",
    "    if(len(currCon) > 0):\n",
    "        for c in currCon:\n",
    "            i = G.number_of_nodes() + 1\n",
    "            if(c in seqConstraint.keys() and seqConstraint[c] in Hnew.edges()):\n",
    "                pass # This means a feasability constraint has been failed!\n",
    "            else:\n",
    "                Hnew = H.copy()\n",
    "                Hnew.remove_edges_from([c])\n",
    "                G.add_node(i, idS=str(Hnew.edges()))\n",
    "                Rew, Hnew = R(1,c,H)\n",
    "                G.add_edge(1, i, a=c, r=Rew)\n",
    "        return G\n",
    "    else:\n",
    "        return None\n",
    "    \n",
    "    \n",
    "# Checking Feasability\n",
    "def T(sN, s, a):\n",
    "    # Sequential Constraints are already handled via the tree generation!\n",
    "    pass\n",
    "\n",
    "\n",
    "# Cost structure which allows for an intermediary construction zone \n",
    "# between the supply vehicle and construction area\n",
    "def Rcaz(s,a,H): # Reward Function Assuming there is a CAZ\n",
    "    Rewards[(s,a)] = 0\n",
    "    \n",
    "    APieces = list(nx.connected_components(H))\n",
    "    Hnew = H.copy()\n",
    "    Hnew.remove_edges_from([a])\n",
    "    BPieces = list(nx.connected_components(Hnew))\n",
    "    if(Hnew.number_of_edges() == 0): # Check if fully deconstructed\n",
    "        Rewards[(s,a)] = 1\n",
    "    elif(len(BPieces) - len(APieces) > 0):\n",
    "        diffPieces = [list(i) for i in BPieces if i not in APieces and len(i) <= 3]\n",
    "        # Check if removing last connection for a given part\n",
    "        for i in diffPieces:\n",
    "            # Check if creating multiple assemblies and sizes of these new assemblies\n",
    "            if(len(i) == 1):\n",
    "                if(Hnew.nodes[i[0]][\"loc\"] == \"CL\"): # Going from CL to SV\n",
    "                    Hnew.nodes[i[0]][\"loc\"] = \"SV\"\n",
    "                    Rewards[(s,a)] += -(0.0468 + 0.0499) #SV-CL + CL-SV\n",
    "                else: # Going from CAZ to SV\n",
    "                    Hnew.nodes[i[0]][\"loc\"] = \"SV\"\n",
    "                    Rewards[(s,a)] += -(0.0403 + 0.0420) #SV-CAZ + CAZ-SV\n",
    "\n",
    "            elif(len(i) == 2): # Have to fix last bit problem\n",
    "                for p in i:\n",
    "                    if(Hnew.nodes[p][\"loc\"] == \"CL\"): # Going from CL to CAZ\n",
    "                        Hnew.nodes[p][\"loc\"] = \"CAZ\"\n",
    "                Rewards[(s,a)] += -(0.0307 + 0.0415) #SV-CL + CL-CAZ2\n",
    "\n",
    "            elif(len(i) == 3): # Have to fix last problem\n",
    "                for p in i:\n",
    "                    if(Hnew.nodes[p][\"loc\"] == \"CL\"): # Going from CL to CAZ\n",
    "                        Hnew.nodes[p][\"loc\"] = \"CAZ\"\n",
    "                Rewards[(s,a)] += -(0.0307 + 0.0475) #SV-CL + CL-CAZ3\n",
    "    return Rewards[(s,a)], Hnew\n",
    "\n",
    "\n",
    "# Reward Function Assuming there is NO CAZ (i.e, structures are constructed at the Supply Vehicle)\n",
    "def RNOcaz(s,a,H):\n",
    "    s = str(s)\n",
    "    Rewards[(s,a)] = 0\n",
    "    \n",
    "    APieces = list(nx.connected_components(H))\n",
    "    Hnew = H.copy()\n",
    "    Hnew.remove_edges_from([a])\n",
    "    BPieces = list(nx.connected_components(Hnew))\n",
    "    if(Hnew.number_of_edges() == 0): # Check if fully deconstructed\n",
    "        Rewards[(s,a)] = 1\n",
    "    elif(len(BPieces) - len(APieces) > 0):\n",
    "        diffPieces = [list(i) for i in BPieces if i not in APieces and len(i) <= 3]\n",
    "        # Check if removing last connection for a given part\n",
    "        for i in diffPieces:\n",
    "            # Check if creating multiple assemblies and sizes of these new assemblies\n",
    "            if(len(i) == 1):\n",
    "                Rewards[(s,a)] += -(0.0468 + 0.0499) #SV-CL + CL-SV\n",
    "\n",
    "            elif(len(i) == 2):\n",
    "                Rewards[(s,a)] += -(0.0749 + 0.0499) #SV-CL + CL-SV2\n",
    "\n",
    "            elif(len(i) == 3):\n",
    "                Rewards[(s,a)] += -(0.0869 + 0.0499) #SV-CL + CL-SV3\n",
    "    return Rewards[(s,a)], Hnew\n",
    "\n",
    "\n",
    "\n",
    "def Rcustom(s,a,H):\n",
    "    s = str(s)\n",
    "    Rewards[(s,a)] = -0.1\n",
    "    \n",
    "    APieces = list(nx.connected_components(H))\n",
    "    Hnew = H.copy()\n",
    "    Hnew.remove_edges_from([a])\n",
    "    BPieces = list(nx.connected_components(Hnew))\n",
    "    if(Hnew.number_of_edges() == 0): # Check if fully deconstructed\n",
    "        Rewards[(s,a)] = 1\n",
    "    elif(len(BPieces) - len(APieces) > 0):\n",
    "        diffPieces = [list(i) for i in BPieces if i not in APieces and len(i) <= 3]\n",
    "        # Check if removing last connection for a given part\n",
    "        for i in diffPieces:\n",
    "            # Check if creating multiple assemblies and sizes of these new assemblies\n",
    "            if(len(i) == 1):\n",
    "                Rewards[(s,a)] += -1 #SV-CL + CL-SV\n",
    "\n",
    "            elif(len(i) == 2):\n",
    "                Rewards[(s,a)] += -1.5 #SV-CL + CL-SV2\n",
    "\n",
    "            elif(len(i) == 3):\n",
    "                Rewards[(s,a)] += -1.75 #SV-CL + CL-SV3\n",
    "    return Rewards[(s,a)], Hnew\n",
    "\n",
    "\n",
    "def Rsimple(s,a,H):\n",
    "    lab = str(s)\n",
    "    Hnew = H.copy()\n",
    "    Hnew.remove_edges_from([a])\n",
    "    edges = fullE.copy()\n",
    "    if(edges.index(a) < H.number_of_edges()-1):\n",
    "        Rewards[(lab,a)] = H.number_of_edges() - edges.index(a)\n",
    "    else:\n",
    "        Rewards[(lab,a)] = -1\n",
    "    return Rewards[(lab,a)], Hnew\n",
    "\n",
    "\n",
    "\n",
    "# Allows you to pick which reward function to use!\n",
    "def R(s, a, H):\n",
    "    lab = str(s)\n",
    "    if((lab,a) not in Rewards.keys()):\n",
    "        Rewards[(lab,a)], Hnew = RNOcaz(s,a,H)  # You can switch out RNOcaz() with a different reward function,\n",
    "                                                # to run the code with an alternative objective in mind. Some\n",
    "                                                # Examples are provided above.\n",
    "    else:\n",
    "        Hnew = H.copy()\n",
    "        Hnew.remove_edges_from([a])\n",
    "    return Rewards[(lab,a)], Hnew"
   ]
  },
  {
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   "id": "e2b4822d",
   "metadata": {},
   "source": [
    "## Scenario Initialization"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1f7a319f",
   "metadata": {},
   "source": [
    "##### \"Hubble\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bd47c522",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CONFIGURATION: \n",
      "\t20 Parts,\n",
      "\t19 Connections in Assembly,\n",
      "\t4980737 Potential Subassemblies, \n",
      "\t9437185 Potential Edges, \n",
      "\t0 Sequential Constraints\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the inputs (will later read in from file [JSON])\n",
    "\n",
    "Scenario = \"Hubble\"\n",
    "\n",
    "#define topology\n",
    "H = nx.Graph()\n",
    "H.add_edge(1,2)\n",
    "H.add_edge(2,3)\n",
    "H.add_edge(3,5)\n",
    "H.add_edge(4,5)\n",
    "H.add_edge(5,6)\n",
    "H.add_edge(6,7)\n",
    "H.add_edge(7,8)\n",
    "H.add_edge(5,9)\n",
    "H.add_edge(9,10)\n",
    "H.add_edge(5,11)\n",
    "H.add_edge(11,12)\n",
    "H.add_edge(12,13)\n",
    "H.add_edge(5,14)\n",
    "H.add_edge(14,15)\n",
    "H.add_edge(15,16)\n",
    "H.add_edge(16,17)\n",
    "H.add_edge(17,18)\n",
    "H.add_edge(18,19)\n",
    "H.add_edge(18,20)\n",
    "nx.set_node_attributes(H, \"CL\", \"loc\")\n",
    "\n",
    "numParts = H.number_of_nodes()\n",
    "numConnections = H.number_of_edges()\n",
    "numActions = (numConnections-1)*pow(2,numConnections) + 1\n",
    "numStates = numConnections*pow(2,numConnections-1) + 1\n",
    "\n",
    "#SEQUENCING CONSTRAINTS (will later read in from file [JSON])\n",
    "# Of the format {connection touple: connection touple that must be remove first}\n",
    "seqConstraint = {}\n",
    "\n",
    "print(\"CONFIGURATION: \\n\\t%d Parts,\\n\\t%d Connections in Assembly,\\n\\t%d Potential Subassemblies, \\n\\t%d Potential Edges, \\n\\t%d Sequential Constraints\"\\\n",
    "      % (numParts, numConnections, numStates, numActions, len(seqConstraint)))\n",
    "\n",
    "nx.draw(H, with_labels=True, font_color=\"#FFFFFF\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5e8971ed",
   "metadata": {},
   "source": [
    "##### \"International Space Station\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d62bfc05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CONFIGURATION: \n",
      "\t32 Parts,\n",
      "\t31 Connections in Assembly,\n",
      "\t33285996545 Subassemblies, \n",
      "\t64424509441 Potential Edges, \n",
      "\t0 Sequential Constraints\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the inputs (will later read in from file [JSON])\n",
    "\n",
    "Scenario = \"ISS\"\n",
    "\n",
    "#define topology\n",
    "H = nx.Graph()\n",
    "H.add_edges_from([(1,2), (2,3), (3,4), (4,5), (6,3), (7,1), (8,7), \n",
    "                  (9,7), (10,1), (11,10), (12,11), (13,11), (14,13), \n",
    "                  (15,13), (16,2), (17,16), (18,17), (19,17), (20,19), \n",
    "                  (21,19), (22,19), (23,22), (24,22), (25,16), (26,25), \n",
    "                  (27,25), (28,27), (29,27), (30,27), (31,30), (32,30)])\n",
    "nx.set_node_attributes(H, \"CL\", \"loc\")\n",
    "\n",
    "numParts = H.number_of_nodes()\n",
    "numConnections = H.number_of_edges()\n",
    "numActions = (numConnections-1)*pow(2,numConnections) + 1\n",
    "numStates = numConnections*pow(2,numConnections-1) + 1\n",
    "\n",
    "#SEQUENCING CONSTRAINTS (will later read in from file [JSON])\n",
    "# Of the format {connection touple: connection touple that must be remove first}\n",
    "\n",
    "seqConstraint = {}\n",
    "\n",
    "print(\"CONFIGURATION: \\n\\t%d Parts,\\n\\t%d Connections in Assembly,\\n\\t%d Subassemblies, \\n\\t%d Potential Edges, \\n\\t%d Sequential Constraints\"\\\n",
    "      % (numParts, numConnections, numStates, numActions, len(seqConstraint)))\n",
    "\n",
    "nx.draw(H, with_labels=True, font_color=\"#FFFFFF\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "aa775002",
   "metadata": {},
   "source": [
    "##### \"JWST Space Telescope\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2aefeeed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CONFIGURATION: \n",
      "\t180 Parts,\n",
      "\t256 Connections in Assembly,\n",
      "\t14821387422376473014217086081112052205218558037201992197050570753012880593911809 Potential Subassemblies, \n",
      "\t29526982755515629833010601177215416502583846089738343830061683922017848058183681 Potential Edges, \n",
      "\t0 Sequential Constraints\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the inputs (will later read in from file [JSON])\n",
    "\n",
    "Scenario = \"JWST\"\n",
    "\n",
    "#define topology\n",
    "H = nx.Graph()\n",
    "\n",
    "H.add_edges_from([(1, 2), (1, 3), (1, 4), (1, 5), (1, 20), (3, 11), (3, 14), (4, 11), (4, 14), (6, 7), \n",
    "                  (6, 8), (6, 9), (6, 10), (7, 21), (9, 11), (9, 15), (10, 11), (10, 15), (11, 12), \n",
    "                  (11, 13), (12, 14), (12, 15), (12, 20), (12, 21), (13, 14), (13, 15), (13, 16), \n",
    "                  (13, 17), (13, 18), (16, 19), (16, 106), (17, 19), (18, 19), (22, 23), (22, 24), \n",
    "                  (22, 25), (22, 26), (22, 41), (24, 32), (24, 35), (25, 32), (25, 35), (27, 28), \n",
    "                  (27, 29), (27, 30), (27, 31), (28, 42), (30, 32), (30, 36), (31, 32), (31, 36), \n",
    "                  (32, 33), (32, 34), (33, 35), (33, 36), (33, 41), (33, 42), (34, 35), (34, 36), \n",
    "                  (34, 37), (34, 38), (34, 39), (37, 40), (37, 106), (38, 40), (39, 40), (43, 44), \n",
    "                  (43, 45), (43, 46), (43, 47), (43, 62), (45, 53), (45, 56), (46, 53), (46, 56), \n",
    "                  (48, 49), (48, 50), (48, 51), (48, 52), (49, 63), (51, 53), (51, 57), (52, 53), \n",
    "                  (52, 57), (53, 54), (53, 55), (54, 56), (54, 57), (54, 62), (54, 63), (55, 56), \n",
    "                  (55, 57), (55, 58), (55, 59), (55, 60), (58, 61), (58, 106), (59, 61), (60, 61), \n",
    "                  (64, 65), (64, 66), (64, 67), (64, 68), (64, 83), (66, 74), (66, 77), (67, 74), \n",
    "                  (67, 77), (69, 70), (69, 71), (69, 72), (69, 73), (70, 84), (72, 74), (72, 78), \n",
    "                  (73, 74), (73, 78), (74, 75), (74, 76), (75, 77), (75, 78), (75, 83), (75, 84), \n",
    "                  (76, 77), (76, 78), (76, 79), (76, 80), (76, 81), (79, 82), (79, 106), (80, 82), \n",
    "                  (81, 82), (85, 86), (85, 87), (85, 88), (85, 89), (85, 104), (87, 95), (87, 98), \n",
    "                  (88, 95), (88, 98), (90, 91), (90, 92), (90, 93), (90, 94), (91, 105), (93, 95), \n",
    "                  (93, 99), (94, 95), (94, 99), (95, 96), (95, 97), (96, 98), (96, 99), (96, 104), \n",
    "                  (96, 105), (97, 98), (97, 99), (97, 100), (97, 101), (97, 102), (100, 103), \n",
    "                  (100, 106), (101, 103), (102, 103), (106, 161), (107, 108), (107, 109), (107, 112), \n",
    "                  (107, 113), (108, 110), (108, 111), (109, 110), (110, 112), (110, 113), (111, 112), \n",
    "                  (111, 113), (112, 114), (112, 115), (112, 116), (112, 120), (113, 117), (113, 118), \n",
    "                  (113, 119), (113, 121), (114, 122), (115, 122), (116, 122), (116, 157), (120, 122), \n",
    "                  (120, 132), (117, 123), (117, 159), (118, 123), (119, 123), (119, 131), (121, 123), \n",
    "                  (122, 124), (123, 125), (123, 126), (123, 127), (124, 128), (124, 129), (124, 130), \n",
    "                  (125, 133), (126, 133), (128, 133), (129, 134), (129, 137), (130, 135), (131, 136), \n",
    "                  (131, 138), (133, 139), (135, 140), (137, 141), (137, 145), (137, 149), (137, 153), \n",
    "                  (141, 142), (141, 143), (142, 144), (143, 144), (145, 146), (145, 147), (146, 148), \n",
    "                  (147, 148), (149, 150), (149, 151), (150, 152), (151, 152), (153, 154), (153, 155), \n",
    "                  (154, 156), (155, 156), (157, 158), (159, 160), (161, 161), (161, 162), (161, 163), \n",
    "                  (161, 164), (161, 165), (161, 166), (161, 167), (161, 168), (161, 169), (161, 170), \n",
    "                  (161, 171), (161, 172), (161, 173), (161, 174), (161, 175), (161, 176), (161, 177), \n",
    "                  (161, 178), (178, 179), (179, 180)])\n",
    "\n",
    "nx.set_node_attributes(H, \"CL\", \"loc\")\n",
    "numParts = H.number_of_nodes()\n",
    "numConnections = H.number_of_edges()\n",
    "numActions = (numConnections-1)*pow(2,numConnections) + 1\n",
    "numStates = numConnections*pow(2,numConnections-1) + 1\n",
    "\n",
    "#SEQUENCING CONSTRAINTS (will later read in from file [JSON])\n",
    "# Of the format {connection touple: connection touple that must be remove first}\n",
    "seqConstraint = {}\n",
    "\n",
    "print(\"CONFIGURATION: \\n\\t%d Parts,\\n\\t%d Connections in Assembly,\\n\\t%d Potential Subassemblies, \\n\\t%d Potential Edges, \\n\\t%d Sequential Constraints\"\\\n",
    "      % (numParts, numConnections, numStates, numActions, len(seqConstraint)))\n",
    "\n",
    "nx.draw(H, with_labels=True, font_color=\"#FFFFFF\", node_size=175, font_size=7)\n",
    "plt.savefig(\"RESULTS/\"+Scenario+\".eps\", format=\"eps\", dpi=1200)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f3df6514",
   "metadata": {},
   "source": [
    "##### \"Lattice\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c2a3f569",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CONFIGURATION: \n",
      "\t9 Parts,\n",
      "\t12 Connections in Assembly,\n",
      "\t24577 Subassemblies, \n",
      "\t45057 Potential Edges, \n",
      "\t4 Sequential Constraints\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the inputs (will later read in from file [JSON])\n",
    "\n",
    "Scenario = \"Lattice\"\n",
    "\n",
    "#define topology\n",
    "H = nx.Graph()\n",
    "H.add_edge(1,2)\n",
    "H.add_edge(2,3)\n",
    "H.add_edge(1,4)\n",
    "H.add_edge(5,4)\n",
    "H.add_edge(5,2)\n",
    "H.add_edge(6,5)\n",
    "H.add_edge(6,3)\n",
    "H.add_edge(7,4)\n",
    "H.add_edge(8,7)\n",
    "H.add_edge(8,5)\n",
    "H.add_edge(9,8)\n",
    "H.add_edge(9,6)\n",
    "nx.set_node_attributes(H, \"CL\", \"loc\")\n",
    "\n",
    "numParts = H.number_of_nodes()\n",
    "numConnections = H.number_of_edges()\n",
    "numActions = (numConnections-1)*pow(2,numConnections) + 1\n",
    "numStates = numConnections*pow(2,numConnections-1) + 1\n",
    "\n",
    "#SEQUENCING CONSTRAINTS (will later read in from file [JSON])\n",
    "# Of the format {connection touple: connection touple that must be remove first}\n",
    "\n",
    "seqConstraint = {(2,5): (1,4),\n",
    "                 (2,5): (3,6),\n",
    "                 (4,5): (1,2),\n",
    "                 (4,5): (7,8),\n",
    "                 (5,8): (4,7),\n",
    "                 (5,8): (6,9),\n",
    "                 (5,6): (2,3),\n",
    "                 (5,6): (8,9)}\n",
    "\n",
    "print(\"CONFIGURATION: \\n\\t%d Parts,\\n\\t%d Connections in Assembly,\\n\\t%d Subassemblies, \\n\\t%d Potential Edges, \\n\\t%d Sequential Constraints\"\\\n",
    "      % (numParts, numConnections, numStates, numActions, len(seqConstraint)))\n",
    "\n",
    "nx.draw(H, with_labels=True, font_color=\"#FFFFFF\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c12e70e3",
   "metadata": {},
   "source": [
    "##### \"2 x 3\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5ee47dd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CONFIGURATION: \n",
      "\t6 Parts,\n",
      "\t7 Connections in Assembly,\n",
      "\t449 Subassemblies, \n",
      "\t769 Potential Edges, \n",
      "\t4 Sequential Constraints\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the inputs (will later read in from file [JSON])\n",
    "\n",
    "Scenario = \"2x3\"\n",
    "\n",
    "#define topology\n",
    "H = nx.Graph()\n",
    "H.add_edge(1,2)\n",
    "H.add_edge(2,3)\n",
    "H.add_edge(1,4)\n",
    "H.add_edge(4,5)\n",
    "H.add_edge(5,6)\n",
    "H.add_edge(2,5)\n",
    "H.add_edge(3,6)\n",
    "nx.set_node_attributes(H, \"CL\", \"loc\")\n",
    "\n",
    "numParts = H.number_of_nodes()\n",
    "numConnections = H.number_of_edges()\n",
    "numActions = (numConnections-1)*pow(2,numConnections) + 1\n",
    "numStates = numConnections*pow(2,numConnections-1) + 1\n",
    "\n",
    "#SEQUENCING CONSTRAINTS (will later read in from file [JSON])\n",
    "# Of the format {connection touple: connection touple that must be remove first}\n",
    "\n",
    "seqConstraint = {(2,5): (1,4),\n",
    "                 (2,5): (3,6),\n",
    "                 (4,5): (1,2),\n",
    "                 (4,5): (7,8),\n",
    "                 (5,8): (4,7),\n",
    "                 (5,8): (6,9),\n",
    "                 (5,6): (2,3),\n",
    "                 (5,6): (8,9)}\n",
    "\n",
    "print(\"CONFIGURATION: \\n\\t%d Parts,\\n\\t%d Connections in Assembly,\\n\\t%d Subassemblies, \\n\\t%d Potential Edges, \\n\\t%d Sequential Constraints\"\\\n",
    "      % (numParts, numConnections, numStates, numActions, len(seqConstraint)))\n",
    "\n",
    "nx.draw(H, with_labels=True, font_color=\"#FFFFFF\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3e9312f7",
   "metadata": {},
   "source": [
    "##### \"3-Piece\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2514fc4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CONFIGURATION: \n",
      "\t3 Parts,\n",
      "\t3 Connections in Assembly,\n",
      "\t13 Subassemblies, \n",
      "\t17 Potential Edges, \n",
      "\t0 Sequential Constraints\n"
     ]
    },
    {
     "data": {
      "image/png": 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lW8hTn288qs83HbvlNT3dXbWxb127WlOZnJx800L4z18/d+6cLJbrf788PT2zdIv5rylivnz5DL1TAABulJCQoODgYHXp0kWTJk0yHcchOfSE8v3lB5Rxk/WSxQp66NlqpfTbqStaf+iMOlfL2tQxw2LV+8sPaO7L5tZeZGZmKiEhIctrEZOSkm64hq+v73Wl8MEHH7xpUfT29maKCACwWyNGjFBmZqY++ugj01EclsMWymNnErX9+Pmbfjz20jU9PGy9JKlYwXxZLpSZFqu2Hz+v42cTFeKXc0cKXb16NctrEc+fP69/Dpbz589/XRGsUKGC6tWr968TxeLFi3NUAgDAKZw4cUKTJk3SoEGD5OfnZzqOw3LYVjFvd8xtjwa6W26uLvp2V4yGtKpw08/JyMhQQkJCltciJicnX/d6FxcXFS9e/LpCWLFixZseg+Pl5cUUEQCAfxg0aJB8fHzUr18/01EcmsMWyogjZ3OlTEr/mVKu/ukPBZ7eedOimJCQcMMUsWDBgtdNEStVqnTTtYjFixeXm5tbruQHAMAZ/Pzzz5o3b56mTJkiLy8v03EcmkMWyqupGYq5kHz7T8yGcylSr9595Vuk0P9KYUBAgCpXrnzTtYj8YQYAIO8MGDBA5cqV08svv2w6isNzyEJ5MiFJub113cXFRT8fj1WlksVy+SsBAIA7tX79em3YsEHLly9n30AecMiHEaf9yzFBuSGPvgwAALgDFotFAwYMUGhoqJ5++mnTcZyCQ1Z2D/e86cl59XUAAEDWzZ8/X/v27dPOnTvZsJpHHLIRlfb1Um7/8XH579cBAAC2IyUlRYMGDVLr1q0VGhpqOo7TcMgJpZenu0r5FNTJ22zMebJsCRXwcFMhz//8NoT4FVLTivdI+s8u8ZT0m9/TLuVbUF6eDvnbBwCA3Zo8ebJiY2MVHh5uOopTcdhGVK+cn+buPnnLo4M+aV1RJYv9/yMUWzwcqBYPB0qSao3erNhL1/71dW6uLqpXlsNRAQCwJRcvXtTw4cPVvXt3lS9f3nQcp+Kwz/I+diZRDT/flmvX39i3To4+KQcAAGTPgAEDNGnSJB0/flwBAQGm4zgVh1xDKUll/L1VO6S43FxzdjWlm6uLaocUp0wCAGBDYmJiNGHCBPXv358yaYDDFkpJGtGmktxzuFC6u7poRJtKOXpNAACQPR999JEKFy6sd955x3QUp+TQhfJen4Iaeovnbd+NYa0q6F6fgrf/RAAAkCd++eUXzZkzR4MHD5a3N3cQTXDYNZR/NynimMauP5qNK1gluaieb6Jmvt0pp2IBAIAc0KxZMx07dkyHDh1Svnz5TMdxSg49ofzLG/XKaFTbSvJ0d73jNZVuri7ydHdTxeT9mvteF61ZsyaXUgIAgDu1efNmrV27ViNGjKBMGuQUE8q//HkhWe8vP6Dtx8/LzdXllkcK/fXx2iHFNaJNJQUW8VS7du20YcMGRUREqFq1anmYHAAA/JPFYlG1atXk5uamXbt28VQcg5yqUP7l2JlEzdsdo4ijZxWTkKy//wa46D+Hltcr66cuT5S6bjf3tWvX1KBBAx09elSRkZEqU6ZMnmcHAAD/sXDhQnXu3Flbt25VnTp1TMdxak5ZKP8uKTVDJxKSlJZhkYe7q0r7et3yCTgJCQmqVauW0tLSFBkZKX9//zxMCwAAJCktLU0PPvigHnroIa1atcp0HKfn9IXybpw8eVI1atRQYGCgtmzZokKFCpmOBACAU/niiy/Ut29f/fLLL6pQIWdPdMGdo1DepX379qlOnToKDQ3VqlWrWAgMAEAeuXz5soKDg9W6dWt9/fXXpuNATrLLOzdUqVJFy5cv1+bNm9W9e3fRywEAyBtjxoxRcnKyhg4dajoK/otCmQ1PPfWUZs+erTlz5mjQoEGm4wAA4PDi4uL02WefqU+fPgoKCjIdB/91890nyJLOnTsrPj5eb7/9toKCgtSrVy/TkQAAcFiDBw9WwYIFNWDAANNR8DcUyhzQr18/xcbG6o033lBAQIDatGljOhIAAA7n0KFDmjlzpsaPH68iRYqYjoO/YVNODrFYLOrcubNWrFihjRs3qlatWqYjAQDgUFq1aqWDBw/qt99+k6enp+k4+BsKZQ5KTU1VkyZNtH//fu3YsUMPPfSQ6UgAADiEbdu2qW7dupo/f746d+5sOg7+gUKZwy5duqQ6dero0qVLioqKYsEwAADZZLVaVaNGDWVkZGjPnj1ydWVPsa3h30gOK1q0qNauXStJatq0qS5fvmw4EQAA9m3p0qXavXu3xowZQ5m0UUwoc8mhQ4dUs2ZNValSReHh4az1AADgLqSnp6tChQoKDg7+38AGtoean0v+erZoVFSUunbtKovFYjoSAAB2Z/r06Tp+/LhGjx5tOgpugQllLlu2bJnat2+vPn36aPz48abjAABgNxITExUcHKxmzZpp1qxZpuPgFphQ5rK2bdtq4sSJ+uyzzyiUAADcgbFjx+rKlSsaNmyY6Si4DQ42zwOvv/664uLi1L9/fwUEBHDcAQAAt3H69GmNGzdOvXv3VqlSpUzHwW1wyzuPWK1WdevWTQsWLFB4eLjq169vOhIAADarZ8+eWrRokaKjo1WsWDHTcXAbFMo8lJ6erpYtWyoyMlLbt29X5cqVTUcCAMDmHDlyRBUqVNDo0aPVv39/03GQBRTKPJaYmKh69eopPj5eUVFRuu+++0xHAgDAprRt21Y//vijjhw5ovz585uOgyxgU04e8/b21po1a5Q/f341adJECQkJpiMBAGAzIiMjtXz5cn3yySeUSTvChNKQY8eOKTQ0VGXLltXGjRtVoEAB05EAADDKarWqdu3aunr1qn766SeeimNH+DdlSJkyZbRmzRrt27dPzz77rDIzM01HAgDAqBUrVmjnzp08YtEOMaE0bM2aNXr66afVo0cPTZ48WS4uLqYjAQCQ5zIyMlSpUiWVLFlSGzZsMB0Hd4hzKA1r3ry5pk6dqu7duysoKEgffPCB6UgAAOS5b775RocPH9a8efNMR8FdoFDagJdffllxcXEaNGiQgoKC1K1bN9ORAADIM0lJSRo8eLCeffZZPfroo6bj4C5QKG3Ehx9+qLi4OHXv3l3+/v5q2rSp6UgAAOSJ8ePH68KFC/rkk09MR8FdYg2lDcnIyFC7du20ceNGbdmyRY8//rjpSAAA5KqzZ88qODhYr7zyisaPH286Du4ShdLGJCcn66mnnlJ0dLQiIyMVEhJiOhIAALnmzTff1Ny5cxUdHS1fX1/TcXCX2JNvYwoWLKhVq1apWLFiatKkic6ePWs6EgAAueL48eOaMmWK3nvvPcqknWNCaaNOnDihGjVqqGTJkoqIiFChQoVMRwIAIEd16NBBUVFROnr0KA/4sHNMKG1U6dKlFRYWpiNHjqhDhw5KT083HQkAgByzZ88eLV68WMOGDaNMOgAmlDZu48aNatq0qbp06aJvvvmGg88BAHbParWqXr16SkhI0L59++Tm5mY6ErKJY4NsXIMGDTRr1ix16dJFJUuW1Mcff2w6EgAA2RIWFqatW7dq9erVlEkHwYTSTnz66ad699139dVXX+m1114zHQcAgLuSmZmpypUrq0SJEtq8eTN33hwEE0o78fbbbys2Nlavv/667rnnHrVu3dp0JAAA7tjs2bP166+/as+ePZRJB8KE0o5YLBZ16tRJq1at0saNG1WzZk3TkQAAyLLk5GSVLVtWNWvW1KJFi0zHQQ5il7cdcXV11Zw5c1S9enW1bNlSv/32m+lIAABk2RdffKEzZ85o+PDhpqMghzGhtEOXLl1SrVq1lJiYqKioKAUGBpqOBADALZ0/f17BwcHq2rWrvvjiC9NxkMOYUNqhokWLKjw8XBaLRU2bNtXly5dNRwIA4JaGDx8uq9WqDz/80HQU5AIKpZ0qWbKkwsPDFRMTo7Zt2yo1NdV0JAAA/tUff/yhyZMn691331WJEiVMx0Eu4Ja3ndu2bZsaNWqktm3b6ttvv5WrKz8jAABsy3PPPaeIiAgdO3ZMXl5epuMgF3BskJ2rU6eOvv32W3Xo0EGBgYEaO3as6UgAAPzPjz/+qPnz52vatGmUSQfGhNJBTJw4Ub1799b48ePVt29f03EAAJDValXDhg0VFxenAwcOyN2dOZaj4t+sg3jzzTcVFxenfv36KTAwUB07djQdCQDg5NavX69Nmzbp+++/p0w6OCaUDsRisahr16767rvvFB4ernr16pmOBABwUpmZmapataq8vb21bds2norj4PhxwYG4urpqxowZOnPmjFq3bq3t27fr4YcfNh0LAOCE5s2bp/379ysyMpIy6QSYUDqgxMRE1a1bV2fOnFFUVJRKlSplOhIAwImkpKSoXLlyeuyxx7R06VLTcZAHOGPGAXl7eyssLEweHh5q0qSJLly4YDoSAMCJTJo0SXFxcRoxYoTpKMgjTCgd2JEjR1SzZk2VL19eGzZsUIECBUxHAgA4uAsXLig4OFidO3fWl19+aToO8ggTSgdWrlw5rV69Wj/99JOee+45ZWZmmo4EAHBwI0eOVHp6ugYPHmw6CvIQhdLBPfHEE1q0aJFWrFih3r17i4E0ACC3xMTEaOLEiXr77bfl7+9vOg7yEIXSCbRs2VJTpkzRl19+qdGjR5uOAwBwUB9++KGKFCmi/v37m46CPMaxQU7ilVdeUVxcnAYOHKjAwEC98MILpiMBABzI/v37NXfuXE2ePFne3t6m4yCPsSnHiVitVvXo0UOzZs3S6tWr1bhxY9ORAAAOokmTJvrjjz908OBB5cuXz3Qc5DEmlE7ExcVFX331lU6dOqV27dpp69atqlq1qulYAAA7t2nTJq1bt05LliyhTDopJpROKCkpSfXr19eJEycUFRWlBx54wHQkAICdslgsevzxx+Xh4cFTcZwYm3KckJeXl1avXq0iRYqocePGOnfunOlIAAA7tXDhQv30008aM2YMZdKJMaF0Yr///rtCQ0N13333afPmzfLy8jIdCQBgR1JTU1W+fHk9/PDDWrFihek4MIgJpRN74IEHFBYWpkOHDqljx47KyMgwHQkAYEe++uorxcTEaOTIkaajwDAmlND69evVvHlzde3aVdOnT+eWBQDgti5duqSQkBC1adNG06dPNx0HhjGhhBo1aqRvvvlGM2bM0JAhQ0zHAQDYgdGjRys5OVlDhw41HQU2gGODIEl6/vnn/3fweVBQkHr06GE6EgDARsXGxurzzz9X//79FRgYaDoObAC3vPE/VqtVvXv31pdffqnly5erVatWpiMBAGzQyy+/rJUrVyo6OlqFCxc2HQc2gEKJ62RmZqpjx44KCwvTpk2bVKNGDdORAAA25ODBg6pcubI+++wz9e7d23Qc2AgKJW6QkpKiRo0a6dChQ9q5c6fKlStnOhIAwEa0aNFCv/32m3777Td5eHiYjgMbwaYc3CB//vxasWKF/P391aRJE506dcp0JACADdi6davWrFmjESNGUCZxHSaUuKk///xTNWrUUIkSJbR161bWyQCAE7NarXriiSdksVi0e/duuboyk8L/408Dburee+/V2rVr9ccff6hdu3ZKS0szHQkAYMiSJUu0Z88ejRkzhjKJGzChxG1t2bJFjRs31jPPPKM5c+bwjQQAnExaWpoeeughlStXTmvWrDEdBzaIcyhxW08++aTmzp2rTp06KSgoSKNHjzYdCQCQh6ZNm6bff/9dy5cvNx0FNopCiSzp0KGDTp06pT59+igoKIijIgDASVy5ckXDhg1T165dValSJdNxYKMolMiyt956S7GxserTp48CAgL0zDPPmI4EAMhlY8eOVWJiooYNG2Y6CmwYhRJ3ZPTo0YqPj1eXLl3k5+enunXrmo4EAMglp06d0rhx4/TWW2/p3nvvNR0HNoxNObhjaWlpatasmX744Qft2LFDFStWNB0JAJALXn31VS1ZskTR0dEqWrSo6TiwYWzXxR3z8PDQsmXLVLp0aTVp0kR//vmn6UgAgBx2+PBhzZgxQx988AFlErfFhBJ37dSpU6pRo4YKFSqk7du3q1ixYqYjAQBySJs2bbRv3z4dPnxYnp6epuPAxjGhxF0LCAhQeHi4Tp06pdatWyslJcV0JABADti5c6e+//57ffLJJ5RJZAkTSmRbZGSknnrqKbVo0UILFy6Um5ub6UgAgLtktVpVs2ZNpaSk6IcffuBhFsgS/pQg20JDQ7Vw4UItW7ZMffv2FT+jAID9+v777xUVFaXRo0dTJpFlTCiRY6ZOnarXXntNo0eP1rvvvms6DgDgDqWnp6tixYoqXbq01q1bZzoO7AjnUCLHvPrqq4qLi9OAAQMUEBCg559/3nQkAMAdmDFjho4dO6ZFixaZjgI7w4QSOcpqtap79+6aM2eOwsLC1LBhQ9ORAABZcPXqVYWEhKhhw4aaO3eu6TiwMyyOQI5ycXHRlClT1KhRI7Vt21Y//fST6UgAgCwYP368Ll68qI8//th0FNghJpTIFUlJSapXr55iYmIUFRWl+++/33QkAMBNnDlzRiEhIXr11Vc1duxY03FghyiUyDVnz55VzZo15eLiosjISBUvXtx0JADAv3j99dc1f/58RUdHy8fHx3Qc2CFueSPX+Pn5KTw8XJcvX1aLFi2UnJxsOhIA4B+OHTumadOmaeDAgZRJ3DUmlMh1P/zwg5588knVq1dPy5cvl7s7hwsAgK145plntGvXLh09elQFChQwHQd2igklct1jjz2mJUuWKDw8XL169eLgcwCwEbt379aSJUv0ySefUCaRLUwokWdmz56tbt26aciQIRo8eLDpOADg1KxWq+rWratLly7p559/5rG5yBbuPSLPdO3aVXFxcfrggw8UFBSk7t27m44EAE5r9erV2r59u8LCwiiTyDYmlMhTVqtVb7zxhqZOnarvv/9eLVq0MB0JAJxORkaGKleuLH9/f23atEkuLi6mI8HOMaFEnnJxcdEXX3yhU6dOqUOHDoqIiFD16tVNxwIApzJ79mwdOnRIs2fPpkwiRzChhBHXrl1Tw4YNdfjwYUVGRqps2bKmIwGAU0hOTlaZMmVUp04dLViwwHQcOAh2ecOIAgUKaOXKlfLz81OTJk10+vRp05EAwCl8/vnnOnfunIYPH246ChwIE0oYFRMToxo1auiee+7Rli1b5O3tbToSADis8+fPKzg4WN26ddOECRNMx4EDYUIJo0qVKqW1a9fq+PHjat++vdLS0kxHAgCH9cknn0iSBg0aZDgJHA2FEsY9/PDD+v777xUREaHu3btz8DkA5ILff/9dX375pQYMGKASJUqYjgMHwy1v2IyFCxeqc+fOeu+99zRy5EjTcQDAoXTu3Fnbtm3TsWPHVLBgQdNx4GA4Ngg2o1OnTjp16pT69eunoKAgvfHGG6YjAYBD+OGHH7Rw4UJNnz6dMolcwYQSNqd///767LPPtHjxYrVr1850HACwa1arVU899ZTOnDmj/fv3y92dWRJyHn+qYHM+/fRTxcfH67nnnpOfn59q165tOhIA2K3w8HBFRERo5cqVlEnkGiaUsEmpqalq2rSpfv75Z+3YsUMVKlQwHQkA7E5mZqYeeeQRFS1aVFu3buWpOMg17PKGTfL09NTy5ctVqlQpNWnSRLGxsaYjAYDd+fbbb3XgwAGNGTOGMolcxYQSNi0+Pl41atRQ4cKFtX37dhUtWtR0JACwC9euXVPZsmX1xBNPaPHixabjwMExoYRNCwwMVHh4uOLi4tS6dWulpKSYjgQAdmHixIk6ffq0RowYYToKnAATStiFnTt3qkGDBmrZsqUWLlwoV1d+FgKAm7lw4YKCg4P17LPPavLkyabjwAnwtzLsQs2aNbVgwQItXbpU/fr142k6AHALI0aMUEZGhj766CPTUeAkKJSwG61bt9akSZM0YcIEjRs3znQcALBJJ06c0MSJE/XOO+/I39/fdBw4CQ6kgl3p2bOn4uLi9M477yggIEDPPfec6UgAYFM+/PBDFStWTP369TMdBU6EQgm78/HHHysuLk4vvvii/P391aBBA9ORAMAm7Nu3T/PmzdOXX36pQoUKmY4DJ8KmHNil9PR0tWrVSjt37tS2bdtUpUoV05EAwLjGjRvr5MmTOnDggPLly2c6DpwIhRJ26+rVq6pXr55iY2MVFRWl0qVLm44EAMZs2LBBjRo10rJly9SmTRvTceBkKJSwa2fPnlVoaKjc3d21c+dO+fr6mo4EAHnOYrHoscceU4ECBbRjxw6eioM8xy5v2DU/Pz+Fh4frwoULatmypZKTk01HAoA8t2DBAv388888YhHGMKGEQ9i7d6+efPJJNWjQQEuXLpW7O/vNADiH1NRUlStXTo888oiWL19uOg6cFBNKOITHH39cS5Ys0Zo1a/T6669z8DkApzF58mTFxsZq5MiRpqPAiVEo4TCaNm2q6dOna9q0afrkk09MxwGAXHfp0iUNHz5cL7/8ssqXL286DpwY9wXhUF588UXFxcXpww8/VFBQkF566SXTkQAg14waNUopKSkaMmSI6ShwchRKOJwPPvhAcXFx6tGjh/z9/dW8eXPTkQAgx/3555+aMGHC/54cBpjEphw4pMzMTLVr104bNmxQRESEqlWrZjoSAOSoF198UWvWrFF0dLS8vb1Nx4GTo1DCYV27dk0NGjTQ0aNHFRkZqTJlypiOBAA54sCBA6pcubK++OILvfHGG6bjABRKOLaEhATVqlVLaWlpioyMlL+/v+lIAJBtzZs315EjR3To0CF5eHiYjgOwyxuOzdfXV+Hh4bp27ZqaN2+uq1evmo4EANkSERGhsLAwjRw5kjIJm8GEEk5h3759qlOnjkJDQ7Vq1Srly5fPdCQAuGMWi0XVq1eXq6urdu3axVNxYDOYUMIpVKlSRcuXL9fmzZvVvXt3Dj4HYJcWL16sH374gUcswuYwoYRTWbBggZ599lm9//77Gj58uOk4AJBlaWlpevDBB/Xggw9q9erVpuMA1+EcSjiVzp07Kz4+Xm+//baCgoLUq1cv05EAIEumTp2qEydOaMWKFaajADdgQgmnY7Va1a9fP02YMEFLly5VmzZtTEcCgFu6cuWKgoOD1apVK82YMcN0HOAGFEo4JYvFos6dO2vFihXauHGjatWqZToSANzUoEGDNG7cOB07dkwlS5Y0HQe4AYUSTis1NVVNmjTR/v37tWPHDj300EOmIwHADeLj4xUSEqK33npLI0eONB0H+FcUSji1S5cuqU6dOrp06ZKioqIUFBRkOhIAXKdHjx5atmyZoqOjVaRIEdNxgH/FsUFwakWLFtXatWslSU2bNtXly5cNJwKA/3fo0CHNmDFDgwYNokzCpjGhBCT9+uuvqlWrlqpUqaLw8HB5enqajgQAevrpp/XLL7/o8OHDfF+CTWNCCUiqUKGCVq5cqaioKHXt2lUWi8V0JABObseOHVq5cqWGDx9OmYTNY0IJ/M2yZcvUvn179e3bV+PGjTMdB4CTslqtCg0NVVpamvbu3StXV+Y/sG0cbA78Tdu2bfXFF1/ozTffVFBQkPr162c6EgAntGzZMu3atUsbN26kTMIuMKEE/sXAgQM1atQoLViwQJ06dTIdB4ATSU9PV4UKFfTAAw8oPDzcdBwgS5hQAv9ixIgRio+P1wsvvCA/Pz/Vr1/fdCQATuLrr7/W8ePHtXjxYtNRgCxjQgncRHp6ulq0aKFdu3Zp27Ztqly5sulIABxcYmKiQkJC1KRJE82ePdt0HCDLWJgB3ES+fPm0ZMkShYSEqGnTpjp58qTpSAAc3Lhx43T58mV9/PHHpqMAd4QJJXAbp0+fVmhoqDw9PbVz5075+PiYjgTAAZ0+fVohISHq2bOnPv30U9NxgDvChBK4jXvuuUfr1q3T+fPn1bJlS127ds10JAAOaNiwYfLw8ND7779vOgpwxyiUQBaUKVNGq1ev1r59+/Tss88qMzPTdCQADuTIkSOaNm2a3n//fRUrVsx0HOCOccsbuANr1qzR008/rR49emjy5MlycXExHQmAA2jXrp1++OEHHTlyRPnz5zcdB7hjHBsE3IHmzZtrypQpeuWVV1SyZEluTQHItqioKC1btkyzZ8+mTMJuMaEE7sKwYcM0ePBgzZw5U926dTMdB4CdslqtqlOnjhITE/Xjjz/Kzc3NdCTgrjChBO7Chx9+qNjYWHXv3l3+/v5q2rSp6UgA7NDKlSu1Y8cOhYeHUyZh15hQAncpIyNDbdu21ebNmxUREaHHH3/cdCQAdiQjI0OVKlVSUFCQNmzYwJps2DV2eQN3yd3dXQsXLlSlSpXUvHlzRUdHm44EwI7MnDlThw8f1ujRoymTsHtMKIFsOn/+vGrWrKnMzExFRkbKz8/PdCQANi4pKUllypRRvXr1NG/ePNNxgGxjQglkU/HixRUeHq6kpCQ1b95cV69eNR0JgI377LPPlJCQoE8++cR0FCBHUCiBHHD//fcrLCxMhw8fVocOHZSenm46EgAbde7cOY0ZM0a9evXS/fffbzoOkCMolEAOeeSRR7Rs2TJt2LBBr776qlhNAuDffPzxx3JxcdEHH3xgOgqQYyiUQA5q2LChZs6cqZkzZ+qjjz4yHQeAjTl+/Li++uorDRw4UMWLFzcdB8gxnEMJ5LAuXbooPj5eAwYMUFBQkF577TXTkQDYiA8++ED+/v566623TEcBchSFEsgF77zzjuLi4vT666/rnnvuUevWrU1HAmDY3r179d1332nGjBkqUKCA6ThAjuLYICCXZGZmqlOnTlq9erU2bdqk0NBQ05EAGGK1WlW/fn2dO3dO+/fv56k4cDgUSiAXpaSkqHHjxjp48KB27typ8uXLm44EwICwsDA1b95cq1evVvPmzU3HAXIchRLIZRcvXlTt2rWVmJioqKgoBQYGmo4EIA9lZmaqSpUq8vX1VUREBE/FgUNilzeQy4oVK6bw8HBZLBY1a9ZMly9fNh0JQB6aM2eODh48qDFjxlAm4bCYUAJ55ODBg6pdu7YeffRRrV27Vh4eHqYjAchl165dU9myZVWjRg199913puMAuYYJJZBHKlasqBUrVmjHjh3q1q2bLBaL6UgActkXX3yh06dPa8SIEaajALmKQgnkoTp16mjevHlauHChBgwYYDoOgFyUkJCgkSNH6rXXXlNISIjpOECuolACeax9+/aaMGGCxo4dq88//9x0HAC5ZPjw4crMzNSHH35oOgqQ6zjYHDDgzTffVGxsrPr27auAgAB17NjRdCQAOejEiROaPHmyBg0aJD8/P9NxgFzHphzAEIvFoq5du+q7775TeHi46tWrZzoSgBzSpUsXbdq0ScePH5eXl5fpOECuo1ACBqWlpalFixbavXu3duzYoUqVKpmOBCCbfvrpJ1WtWlVTp05Vjx49TMcB8gSFEjAsMTFRdevW1ZkzZxQVFaVSpUqZjgQgGxo2bKjY2FgdOHBA7u6sLINzYFMOYJi3t7fCwsLk4eGhJk2a6MKFC6YjAbhL69ev18aNGzVq1CjKJJwKE0rARhw5ckQ1a9bUgw8+qPXr16tAgQKmIwG4AxaLRY8++qgKFSqk7du381QcOBUmlICNKFeunFavXq0ff/xRXbp0UWZmpulIAO7AvHnztH//fh6xCKfEhBKwMatWrVLr1q3Vs2dPTZw4kb+YADuQkpKicuXKqWrVqlq2bJnpOECeY4EHYGNatmypKVOmqEePHipZsqTee+8905EA3MbkyZMVFxen9evXm44CGEGhBGzQK6+8ori4OA0cOFCBgYF64YUXTEcCcBMXL17U8OHD9corr6hcuXKm4wBGUCgBGzV48GDFxcXp5Zdflr+/vxo3bmw6EoB/MXLkSKWlpWnw4MGmowDGsIYSsGEZGRlq3bq1tmzZoq1bt6pq1aqmIwH4m5iYGJUtW1YDBgzQ0KFDTccBjKFQAjYuKSlJ9evX14kTJxQVFaUHHnjAdCQA/9WtWzetXbtWx48fl7e3t+k4gDEUSsAOnDt3TjVr1pTValVkZKRKlChhOhLg9H755RdVqVJFkyZNUq9evUzHAYyiUAJ24vfff1doaKjuu+8+bd68WV5eXqYjAU6tadOmio6O1q+//qp8+fKZjgMYxcHmgJ144IEHFBYWpkOHDqljx47KyMgwHQlwWps3b1Z4eLhGjBhBmQTEhBKwO+vXr1fz5s3VtWtXTZ8+nYPPgTxmsVhUrVo1ubu7Kyoqiv8GAXFsEGB3GjVqpG+++UYvvPCCgoKC2FkK5LHvvvtOP/74o7Zt20aZBP6LCSVgp0aNGqWBAwdq6tSp6tGjh+k4gFNITU3Vgw8+qIoVK2rlypWm4wA2gwklYKcGDBiguLg49ezZU/fcc49atWplOhLg8KZMmaKTJ09q9erVpqMANoUJJWDHMjMz1bFjR4WFhWnTpk2qUaOG6UiAw7p8+bKCg4PVpk0bTZ8+3XQcwKZQKAE7l5KSokaNGunQoUPauXMnzxIGcskHH3ygzz77TMePH1dgYKDpOIBNoVACDuDixYuqVauWkpOTFRkZqYCAANORAIcSFxenMmXKqG/fvho+fLjpOIDNoVACDuLPP/9UjRo1VKJECW3dulWFCxc2HQlwGN27d9f333+v6OhoFSlSxHQcwOZwsDngIO69916tXbtWf/zxh9q1a6e0tDTTkQCH8Ouvv2rmzJn66KOPKJPATTChBBzMli1b1LhxYz3zzDOaM2eOXF35uRHIjpYtW+rQoUP67bff5OHhYToOYJM4NghwME8++aTmzp2rTp06KSgoSKNHjzYdCbBb27Zt0+rVq7VgwQLKJHALTCgBBzVhwgT16dNHEyZMUO/evU3HAeyO1WpVjRo1lJGRoT179jDtB26BCSXgoN566y3FxsaqT58+CggI0DPPPGM6EmBXli5dqt27d2vz5s2USeA2mFACDsxisej555/XkiVLtH79etWtW9d0JMAupKen66GHHlKZMmUUFhZmOg5g85hQAg7M1dVVM2fO1JkzZ/T0009rx44dqlixoulYgM2bNm2aoqOjtXTpUtNRALvAhBJwAleuXFGdOnV0/vx5RUVF6d577zUdCbBZiYmJCg4OVrNmzTRr1izTcQC7wKIQwAkULlxYa9eulbu7u5o2baqLFy+ajgTYrLFjx+rKlSv6+OOPTUcB7AaFEnASAQEBCg8P16lTp9S6dWulpKSYjgTYnFOnTmns2LF66623mOQDd4BCCTiR8uXLa9WqVdqzZ4+ef/55ZWZmmo4E2JShQ4fK09NT7733nukogF2hUAJOJjQ0VAsXLtSyZcvUt29fsYwa+I8jR47o66+/1qBBg1SsWDHTcQC7wqYcwElNnTpVr732mkaPHq13333XdBzAuLZt2+qnn37SkSNH5OnpaToOYFc4NghwUq+++qri4uI0YMAABQQE6PnnnzcdCTBm586dWr58uebOnUuZBO4CE0rAiVmtVnXv3l1z5sxRWFiYGjZsaDoSkOesVqtq1aql5ORk/fjjjzwVB7gLTCgBJ+bi4qIpU6bo9OnTatu2rbZu3apHH33UdCwgT61YsUKRkZFav349ZRK4S0woASgpKUn16tVTTEyMoqKidP/995uOBOSJjIwMVaxYUaVKldL69etNxwHsFj+KAZCXl5dWr14tb29vNW7cWOfPnzcdCcgTM2bM0JEjRzR69GjTUQC7xoQSwP9ER0crNDRU999/vzZv3qyCBQuajgTkmqSkJIWEhOipp57St99+azoOYNeYUAL4n+DgYK1Zs0YHDx5Ux44dlZGRYToSkGvGjx+vCxcu6JNPPjEdBbB7FEoA13nssce0ZMkShYeHq1evXhx8Dod09uxZjRkzRm+88YZKly5tOg5g9yiUAG7QpEkTff3115o+fbqGDRtmOg6Q44YNGyY3Nze9//77pqMADoFjgwD8q65duyouLk4ffPCBgoKC1L17d9ORgBxx7NgxTZ06VZ988ol8fX1NxwEcAptyANyU1WrVG2+8oalTp+r7779XixYtTEcCsq1Dhw6KiorS0aNHVaBAAdNxAIdAoQRwS5mZmWrfvr3WrVuniIgIVa9e3XQk4K7t3r1bTzzxhGbOnKlu3bqZjgM4DAolgNu6du2aGjZsqMOHDysyMlJly5Y1HQm4Y1arVU8++aQuXryon3/+WW5ubqYjAQ6DTTkAbqtAgQJauXKlSpQooSZNmuj06dOmIwF3bM2aNdq2bZtGjRpFmQRyGBNKAFl28uRJhYaG6p577tGWLVvk7e1tOhKQJZmZmapcubL8/Py0adMmubi4mI4EOBQmlACy7L777tPatWt1/PhxtW/fXmlpaaYjAVkye/Zs/frrrxozZgxlEsgFTCgB3LHNmzerSZMm6tSpk2bPns1f0LBpycnJKlOmjGrXrq2FCxeajgM4JCaUAO5Y/fr1NWfOHM2dO5eDoWHzJkyYoHPnzmn48OGmowAOi4PNAdyVTp06KT4+Xv3791dQUJDeeOMN05GAG5w/f16jRo1Sz549FRwcbDoO4LAolADuWr9+/RQXF6fevXsrICBA7dq1Mx0JuM7w4cNltVo1aNAg01EAh0ahBJAtn376qeLj4/Xcc8/Jz89PtWvXNh0JkCT9/vvvmjx5sgYPHqwSJUqYjgM4NDblAMi21NRUNW3aVD///LN27NihChUqmI4E6Nlnn9WWLVt07NgxeXl5mY4DODQKJYAccfnyZdWpU0cXLlxQVFSUSpYsaToSnNiPP/6oxx57TNOnT1f37t1NxwEcHoUSQI6Jj49XjRo1VLhwYW3fvl1FixY1HQlOyGq1qkGDBjp16pR++eUXubuzugvIbRwbBCDHBAYGKjw8XHFxcWrdurVSU1NNR4ITWrdunTZv3qxRo0ZRJoE8woQSQI7buXOnGjRooFatWmnBggVydeVnV+SNzMxMPfrooypcuLC2bdvGoftAHuFHNwA5rmbNmpo/f77at2+vgIAAffbZZ/zFjjwxb948/fLLL4qKiuLPHJCHmFACyDVfffWVevXqpU8//VRvv/226ThwcCkpKSpbtqyqVaumJUuWmI4DOBUmlAByTc+ePRUbG6t33nlHgYGBevbZZ01HggObOHGi4uPjNWLECNNRAKfDhBJArrJarXrxxRc1f/58rV27Vk899ZTpSHBAFy5cUHBwsDp37qwvv/zSdBzA6VAoAeS69PR0tWrVSjt37tS2bdtUpUoV05HgYN555x199dVXio6Olr+/v+k4gNOhUALIE1evXtWTTz6puLg4RUVFqXTp0qYjwUGcPHlS5cqV08CBAzV48GDTcQCnRKEEkGfOnDmjmjVryt3dXTt37pSvr6/pSHAAL7zwgtavX6/jx4+rUKFCpuMATonD4QDkGX9/f4WHh+vChQtq2bKlkpOTTUeCndu/f7++/fZbDRkyhDIJGMSEEkCe27Nnj+rVq6cGDRpo6dKlPM0Ed61Jkyb6448/dPDgQeXLl890HMBpMaEEkOeqVaumxYsXa82aNXrjjTfEz7W4Gxs3btS6des0cuRIyiRgGBNKAMbMnDlTL730kj7++GMNGjTIdBzYEYvFoscee0z58+fXzp07eSoOYBj3mQAY8+KLLyouLk4ffvihAgMD9dJLL5mOBDuxcOFC/fzzz9q+fTtlErABTCgBGGW1WtWzZ099/fXXWrlypZo1a2Y6Emxcamqqypcvr8qVK+v77783HQeAmFACMMzFxUWTJ0/W6dOn9cwzzygiIkLVqlUzHQs27Msvv1RMTIzCwsJMRwHwX0woAdiE5ORkNWjQQMeOHVNUVJRCQkJMR4INunTpkoKDg9WuXTtNmzbNdBwA/8UubwA2oWDBglq1apV8fX3VuHFjnTlzxnQk2KDRo0crJSVFQ4YMMR0FwN9QKAHYDF9fX4WHhys5OVnNmzfX1atXTUeCDYmNjdXnn3+ufv36KTAw0HQcAH/DLW8ANmffvn2qU6eOQkNDtWrVKs4YhCTppZde0qpVqxQdHa3ChQubjgPgb5hQArA5VapU0fLly7V582a98sorHHwOHTx4ULNnz9ZHH31EmQRsEBNKADZr/vz5eu655/T+++9r+PDhpuPAoBYtWujw4cM6dOiQPDw8TMcB8A8cGwTAZj377LOKj4/XO++8o6CgIPXq1ct0JBiwZcsWrVmzRosWLaJMAjaKCSUAm2a1WtWvXz9NmDBBS5cuVZs2bUxHQh6yWq2qXr26JGn37t08FQewUUwoAdg0FxcXjRs3TvHx8ercubM2btyoWrVqmY6FPLJ48WLt3btXERERlEnAhjGhBGAXUlNT1aRJE+3fv187duzQQw89ZDoScllaWpoeeughlS9fXqtXrzYdB8AtsMsbgF3w9PTU8uXLVbJkSTVp0kRxcXGmIyGXTZs2TX/88YdGjRplOgqA22BCCcCuxMXFqUaNGipWrJi2bdumIkWKmI6EXHDlyhUFBwerZcuW+uabb0zHAXAbTCgB2JWgoCCtXbtWMTExatOmjVJTU01HQi749NNPdfXqVQ0bNsx0FABZQKEEYHcqVKiglStXKjIyUl27dpXFYjEdCTno1KlTGj9+vPr06aOSJUuajgMgCyiUAOxS7dq1NX/+fH333Xd65513TMdBDhoyZIjy58+vAQMGmI4CIIsolADsVtu2bfXFF19o/PjxGj9+vOk4yAG//fabvv76aw0aNEhFixY1HQdAFrEpB4DdGzhwoEaNGqUFCxaoU6dOpuMgG1q3bq39+/fr8OHD8vT0NB0HQBZxsDkAuzdixAjFx8frhRdekJ+fn+rXr286Eu7Czp07tWLFCs2bN48yCdgZJpQAHEJ6erpatGihXbt2adu2bapcubLpSLgDVqtVNWvWVGpqqvbu3StXV1ZkAfaE/2IBOIR8+fJpyZIlCgkJUdOmTXXy5EnTkXAHli9frqioKI0ePZoyCdghJpQAHMrp06cVGhoqT09P7dy5Uz4+PqYj4TbS09NVsWJFlS5dWuvWrTMdB8Bd4MdAAA7lnnvu0bp163T+/Hm1bNlS165dMx0JtzFjxgwdO3ZMo0ePNh0FwF1iQgnAIe3evVv169dXo0aNtGTJErm5uZmOhH9x9epVhYSEqFGjRpozZ47pOADuEhNKAA6pevXq+u6777Rq1Sq9+eab4mdn2zRu3DhdvHhRH3/8sekoALKBQgnAYTVv3lxTpkzRV199pZEjR5qOg384c+aMPv30U/Xu3Vv33Xef6TgAsoFzKAE4tO7duys+Pl4ffPCBAgMD1a1bN9OR8F/Dhg1Tvnz5NHDgQNNRAGQThRKAw/vwww8VGxur7t27y9/fX02bNjUdyekdPXpU06ZN04gRI9iJDzgANuUAcAoZGRlq27atNm/erIiICD3++OOmIzm19u3ba8+ePTp69Kjy589vOg6AbKJQAnAaycnJeuqppxQdHa2oqCgFBwebjuSUdu3apRo1amj27Nl64YUXTMcBkAMolACcyvnz51WzZk1lZmYqMjJSfn5+piM5FavVqrp16+ry5cv66aefOM4JcBDs8gbgVIoXL67w8HAlJSWpefPmunr1qulITmX16tXavn27Ro8eTZkEHAgTSgBO6eeff1adOnVUu3ZtrVixQvny5TMdyeFlZGTo4YcfVkBAgDZu3CgXFxfTkQDkECaUAJzSI488omXLlmnDhg169dVXOfg8D8yaNUu//fabxowZQ5kEHAwTSgBO7dtvv9Xzzz+vQYMG8bSWXJScnKwyZcqobt26mj9/vuk4AHIY51ACcGpdunRRfHy8BgwYoKCgIL322mumIzmkzz//XOfOndPw4cNNRwGQCyiUAJzeO++8o7i4OL3++uu655571Lp1a9ORHMq5c+c0atQo9erVS/fff7/pOAByAbe8AUBSZmamOnXqpNWrV2vTpk0KDQ01HclhvPXWW5o1a5aio6NVvHhx03EA5AI25QCAJDc3N82dO1fVqlVTy5YtdfjwYdORHEJ0dLS++uorvffee5RJwIExoQSAv7l48aJq166txMRERUVFKTAw0HQku9apUyft2LFDR48eVcGCBU3HAZBLmFACwN8UK1ZM4eHhslgsatasmS5fvmw6kt3au3evFi1apKFDh1ImAQfHhBIA/sXBgwdVu3ZtPfroo1q7dq08PDxMR7IrVqtVTz31lM6ePat9+/bJ3Z09oIAjY0IJAP+iYsWKWrFihXbs2KFu3brJYrGYjmRXwsPDFRERoVGjRlEmASfAhBIAbmHJkiXq0KGD+vfvr08//dR0HLuQmZmpKlWqyMfHR1u2bOGpOIAT4MdGALiF9u3ba8KECerdu7eCgoLUp08f05Fs3ty5c3Xw4EHt2rWLMgk4CSaUAJAFAwYM0JgxY7Rw4UJ17NjRdBybde3aNZUtW1Y1atTQd999ZzoOgDzChBIAsmDkyJGKj4/XCy+8ID8/P9WrV890JJs0ceJEnT59WiNGjDAdBUAeYkIJAFmUlpamFi1aaPfu3dqxY4cqVapkOpJNSUhIUHBwsLp06aJJkyaZjgMgD1EoAeAOJCYmqm7dujpz5oyioqJUqlQp05FsRv/+/TVt2jRFR0fLz8/PdBwAeYhjgwDgDnh7eyssLEweHh5q0qSJLly4YDqSTThx4oQmTZqkd999lzIJOCEmlABwF44cOaKaNWvqwQcf1Pr161WgQAHTkYx6/vnntXHjRh0/flxeXl6m4wDIY0woAeAulCtXTqtXr9aPP/6oLl26KDMz03QkY37++Wd9++23GjJkCGUScFJMKAEgG1atWqXWrVurZ8+emjhxolOeu9ioUSPFxMTo4MGDPBUHcFL8lw8A2dCyZUtNmTJFPXr0UMmSJfXee++ZjpSnNmzYoA0bNmj58uWUScCJMaEEgBwwZMgQDR06VLNnz9YLL7xgOk6esFgsqlq1qgoWLKgdO3Y45XQWwH/w4yQA5IDBgwcrNjZWL7/8svz9/dW4cWPTkXLd/PnztW/fPu3cuZMyCTg5JpQAkEMyMjLUunVrbdmyRVu3blXVqlVNR8o1KSkpKl++vB599FEtW7bMdBwAhrHLGwByiLu7uxYtWqQKFSqoWbNm+v33301HyjVffvmlYmNjNXLkSNNRANgAJpQAkMPOnTunmjVrymq1KjIyUiVKlDAdKUddvHhRwcHB6tChg6ZMmWI6DgAbwIQSAHJYiRIlFB4eritXrqhFixZKSkoyHSlHjRo1SqmpqRo8eLDpKABsBIUSAHLBAw88oLCwMB06dEgdO3ZURkaG6Ug54s8//9SECRP09ttvKyAgwHQcADaCW94AkIvWr1+v5s2bq2vXrpo+fbrd74Z+8cUXFRYWpuPHj8vb29t0HAA2ggklAOSiRo0aacaMGZoxY4aGDh1qOk62HDhwQLNnz9ZHH31EmQRwHSaUAJAHRo0apYEDB2rq1Knq0aOH6Th3pVmzZjp27JgOHTqkfPnymY4DwIZwsDkA5IEBAwYoNjZWPXv2VEBAgFq2bGk60h2JiIjQ2rVrtXjxYsokgBswoQSAPJKZmakOHTpo7dq12rx5s5544gnTkbLEYrGoevXqcnNzU1RUlN2vAwWQ81hDCQB5xM3NTd9++62qVq2qFi1a6MiRI6YjZcnixYv1ww8/aMyYMZRJAP+KCSUA5LGLFy+qVq1aSk5OVmRkpE0fv5OWlqYHH3xQDz30kFatWmU6DgAbxYQSAPJYsWLFtHbtWqWnp6tZs2a6cuWK6Ug3NWXKFJ04cUKjRo0yHQWADWNCCQCGHDhwQLVq1VK1atW0Zs0aeXh4mI50nStXrig4OFhPP/20vv76a9NxANgwJpQAYEilSpW0YsUKbdu2TS+99JIsFovpSNcZM2aMkpKS7P78TAC5j0IJAAY9+eSTmjt3rubNm6eBAweajvM/cXFxGj9+vPr06aOgoCDTcQDYOM6hBADDOnTooPj4ePXt21dBQUHq3bu36UgaMmSIChYsqAEDBpiOAsAOUCgBwAb06dNHcXFx6tOnjwICAvTMM88Yy3Lo0CF98803Gj9+vIoUKWIsBwD7waYcALARFotFzz//vJYsWaL169erbt26RnI8/fTTOnDggH777Td5enoayQDAvlAoAcCGpKWlqVmzZvrhhx+0Y8cOVaxYMU+//vbt21WnTh3Nnz9fnTt3ztOvDcB+USgBwMZcuXJFderU0fnz5xUVFaV77703T76u1WpVaGio0tPTtWfPHrm6sm8TQNbw3QIAbEzhwoUVFhYmd3d3NW3aVBcvXsyTr7ts2TLt2rVLY8aMoUwCuCNMKAHARh0+fFg1a9ZUxYoVtW7dOuXPnz/XvlZ6eroqVKig4OBgrV27Nte+DgDHxI+gAGCjypcvr1WrVmnPnj16/vnnlZmZmWtfa/r06Tp+/LhGjx6da18DgONiQgkANm7FihVq27atXn/9dU2YMEEuLi45ev3ExESFhISoadOmmjVrVo5eG4BzYEIJADbu6aef1pdffqmJEyfq008/zfHrjxs3TpcvX9awYcNy/NoAnAMHmwOAHXj11VcVGxurAQMGKDAwUF26dMmR654+fVpjx45V7969VapUqRy5JgDnwy1vALATVqtVL7/8subOnauwsDA1bNgw29fs2bOnFi1apOjoaBUrViwHUgJwRhRKALAj6enpevrpp7V9+3Zt27ZNjzzyyC0/Pyk1QycSkpSWYZGHu6tK+3rJy/M/N6eOHDmiChUqaPTo0erfv39exAfgoCiUAGBnkpKSVK9ePcXExCgqKkr333//dR8/diZR83bHKOLIWcVcSNbfv8m7SCrlU1D1yvlpz4Lx+jVyo44cOZKrRxIBcHwUSgCwQ2fPnlXNmjXl6uqqnTt3qnjx4vrzQrLeX35A24+fl5urizItN//27uoiWaxSsFeaZvVqrHt9CuZhegCOhkIJAHYqOjpaNWrUUHBwsHqOnavh4ceUYbHeskj+k5uri9xdXTS0VQV1epxNOQDuDoUSAOzYDz/8oKZvT5BXjU7ZvtbbjcrqjXplciAVAGfDOZQAYMeOW/1ypExK0tj1R7Vob0yOXAuAc6FQAoCd+vNCsgav/DVHr/nRyl/154XkHL0mAMfHLW8AsFPPz9ityN8T/nXNZI0HfNXmkSBVLVVMAUXz68q1DB2Iu6QJm47pYPyVm17TzdVFoQ/4au7L1XMzOgAHw4QSAOzQsTOJ2n78/E034HR54j6VLFZAMyP/0Iuz9mro6l/lW8hTy3vVVI0HfG963UyLVduPn9fxs4m5FR2AA2JCCQB2aMjKXzV398mbFkpfLw8lJKVd92sFPdy09e0ndeTMVXWZsfum13ZzddHz1e/TkFYVcjQzAMfFhBIA7FDEkbO3PB7on2VSkpLTMnXs7FUFFLn1IeaZFqsijp7NdkYAzoNCCQB25mpqhmLuYuOMt6e7KgYW0bEzV2/7uTEJyUpKzbibeACcEIUSAOzMyYQk3c1apWFPV1QBDzdNijh228+1SjqRkHQXXwWAM6JQAoCdScuw3PFr+jUsqzaPBOnjNYduucs7u18HgHOiUAKAnfFwv7Nv3W89VUa965fRmHWHNSfqZK59HQDOi+8WAGBnSvt6ySWLn/vWU2XUt0FZfbbxqL7cEp3lr+Hy368DAFlBoQQAO+Pl6a5SPgVv+3lv1g9R3wZl9cXmY5qw6fbrJv+ulG9BeXm6321EAE6G7xYAYIfqlfO75TmU3Wvdr/4Ny2nLkbOKOHxWj9xb9LqP//znpZte283VRfXK+uVgWgCOjkIJAHboueqlNCvqxE0/3uBBf0nSk+X89GS5G8th6YFrbvraTItVXZ4ole2MAJwHT8oBADt1q2d53y2e5Q3gbrCGEgDs1Ig2leTumtXtOVnj7uqiEW0q5eg1ATg+CiUA2Kl7fQpqaA4/b3tYqwq6NwsbfgDg7yiUAGDHOj1eSm83Kpsj13qnUTl1fJy1kwDuHGsoAcABLNwbo8Erf1WGxXpHayrdXF3k7uqiYa0qUCYB3DUKJQA4iD8vJOv95Qe0/fh5ubm63LJY/vXx2iHFNaJNJW5zA8gWCiUAOJhjZxI1b3eMIo6eVUxCsv7+Td5F/zm0vF5ZP3V5opRC/LxNxQTgQCiUAODAklIzdCIhSWkZFnm4u6q0rxdPwAGQ4yiUAAAAyBZ2eQMAACBbKJQAAADIFgolAAAAsoVCCQAAgGyhUAIAACBbKJQAAADIFgolAAAAsoVCCQAAgGyhUAIAACBbKJQAAADIFgolAAAAsoVCCQAAgGyhUAIAACBbKJQAAADIFgolAAAAsoVCCQAAgGyhUAIAACBbKJQAAADIFgolAAAAsoVCCQAAgGyhUAIAACBbKJQAAADIFgolAAAAsoVCCQAAgGyhUAIAACBbKJQAAADIFgolAAAAsoVCCQAAgGyhUAIAACBbKJQAAADIFgolAAAAsoVCCQAAgGyhUAIAACBbKJQAAADIFgolAAAAsoVCCQAAgGyhUAIAACBb/g+9UrbC+fe1bAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the inputs (will later read in from file [JSON])\n",
    "\n",
    "Scenario = \"3Piece\"\n",
    "\n",
    "#define topology\n",
    "H = nx.Graph()\n",
    "H.add_edge(1,2)\n",
    "H.add_edge(2,3)\n",
    "H.add_edge(3,1)\n",
    "nx.set_node_attributes(H, \"CL\", \"loc\")\n",
    "\n",
    "numParts = H.number_of_nodes()\n",
    "numConnections = H.number_of_edges()\n",
    "numActions = (numConnections-1)*pow(2,numConnections) + 1\n",
    "numStates = numConnections*pow(2,numConnections-1) + 1\n",
    "\n",
    "#SEQUENCING CONSTRAINTS (will later read in from file [JSON])\n",
    "# Of the format {connection touple: connection touple that must be remove first}\n",
    "seqConstraint = {}\n",
    "\n",
    "print(\"CONFIGURATION: \\n\\t%d Parts,\\n\\t%d Connections in Assembly,\\n\\t%d Subassemblies, \\n\\t%d Potential Edges, \\n\\t%d Sequential Constraints\"\\\n",
    "      % (numParts, numConnections, numStates, numActions, len(seqConstraint)))\n",
    "\n",
    "nx.draw(H, with_labels=True, font_color=\"#FFFFFF\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "61982c2c",
   "metadata": {},
   "source": [
    "##### \"4-Brick\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a27b7c91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CONFIGURATION: \n",
      "\t4 Parts,\n",
      "\t3 Connections in Assembly,\n",
      "\t13 Subassemblies, \n",
      "\t17 Potential Edges, \n",
      "\t0 Sequential Constraints\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the inputs (will later read in from file [JSON])\n",
    "\n",
    "Scenario = \"4Brick\"\n",
    "\n",
    "#define topology\n",
    "H = nx.Graph()\n",
    "H.add_edge(1,2)\n",
    "H.add_edge(2,3)\n",
    "H.add_edge(3,4)\n",
    "nx.set_node_attributes(H, \"CL\", \"loc\")\n",
    "\n",
    "numParts = H.number_of_nodes()\n",
    "numConnections = H.number_of_edges()\n",
    "numActions = (numConnections-1)*pow(2,numConnections) + 1\n",
    "numStates = numConnections*pow(2,numConnections-1) + 1\n",
    "\n",
    "#SEQUENCING CONSTRAINTS (will later read in from file [JSON])\n",
    "# Of the format {connection touple: connection touple that must be remove first}\n",
    "seqConstraint = {}\n",
    "\n",
    "print(\"CONFIGURATION: \\n\\t%d Parts,\\n\\t%d Connections in Assembly,\\n\\t%d Subassemblies, \\n\\t%d Potential Edges, \\n\\t%d Sequential Constraints\"\\\n",
    "      % (numParts, numConnections, numStates, numActions, len(seqConstraint)))\n",
    "\n",
    "nx.draw(H, with_labels=True, font_color=\"#FFFFFF\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "995a8c27",
   "metadata": {},
   "source": [
    "##### \"N Long Chain\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0128b533",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CONFIGURATION: \n",
      "\t50 Parts,\n",
      "\t49 Connections in Assembly,\n",
      "\t13792273858822145 Subassemblies, \n",
      "\t27021597764222977 Potential Edges, \n",
      "\t17 Sequential Constraints\n"
     ]
    },
    {
     "data": {
      "image/png": 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PrRYiovJ5kJiu1/Fi9TyeqeJJUyNRfCawJzynfo+91xMR6G/LTGAtCr6BzOjVCPLQWMQklO+NIOy/e6hfvT6qVatWpteVdqX0w75NEHovEeM2XYCfhyPWvtYKXVYcRZb6xT315tNRGN2+LksKERGVQaoyS7/jcbeoVBhQGoGCmcDaZFg7Yv6eCCw+eJ2ZwFrkfQNpVdcJfh6OWHboRrnHmzztPYy5+jekUimcnZ01/3Nxccn3dcHHHyqtEZnnhiB3pfSdbaHYNqmD5vEBvrXR+fN/AABXYpLwNEWJtvWq42xU9lZ83pVSX5ljuX8OIiJzY2tlod/xmOVdKvxTMrDS10wU5csEfpqiNPqiq5VZyDvvG0h7L2fUd7XDyVndAAC1HKT4Mbg9Zv92BUdvPSnVeCEfvo+6ogmIj49HfHw8nj59qvnv27dva/47NTX/arJNo46o+WqI5mttK6VO1SQQi0R4lqrUPBaTkAZ3J5tC84hNTGdASURUBtreS3Uh0/N4pooBpQHpmgnsamdtlEVXK7OQd3R0NHbs2IGfD54E2r8BAFh37A7WHbujuebkrG6Y8MMF3HqUUupxB3YLKFUgp1AoNMFlfHw8/riViG3R2c8Vt1IqFDguXlSAza0WIqKy8ZU5wsu5GqLiU/Gi0m+2hYOaoVdTN7jaWePnie2RqsxC1xVHtY4jAuDtaovm7g4VPmdTwIDSQPSVCdyzqZtRFV0tzfZ91NNUnbbv7969ix07dmD79u24cOECpFIp+g8YgHvWajzNEBdZ7xEA3upSH2M61kMNWyusGNoCGZlZGPDVSc1qYVnfQKRSKWQyGWSy7MA+wzUO26IvASh+pRQAathaab6vzMlG60FybrUQEZXNlStX8PD4NghNX0HBe/X5eyIwf09EqcYRAIwP8OI59lLip5WBFJUJPL1nI/Recwy3HqVg1+QA2EiyE/EtxGI0rmWPvl8cx42HyQBeZAKP6VCv0uevTWm373MDvrJs39+7dw87duzAjh07cO7cuewgsn9/zJgxAwMHDoSdnR22nInW+kbRafk/mv9ee+wO1uZZvdQ2N13eQPJutRS3UnogPA5jO9TDmiP/wc/DEa721rhwN6HQeNxqISIqHZVKhaVLl2LRokVo3LwFrP0GQZf8HKlEjCAj3AU0VgwoDaC0mcBD1r/o2d2veS2826OhJpjMZSyZwBWxfX///n1NEHn27FlYW1ujX79+2Lp1KwYOHAh7e/t81wf6y7D44HWdW23p8gbiK3OEt4stooqogZZr2aEbWDW8Jf6Z2RWqLDVmbL+cL8ObWy1EZG50OXd/5coVjB8/HleuXMHs2bPx0Ucf4cC1Jzq14l0W5GdUO4DGjn9SBpBbMzFXUZnAeQ1vUwfbL97P95ixZALrc/s+8clD7Ny5E9u3b8eZM2dgZWWFfv364eeff8bAgQPh4FB0gGUvlWBpkK9B30BEIhGCAzxLXCl9mqLE2O/PFzkOt1qIyFzocu5epVJh2bJlWLRoERo1aoSzZ8+iTZs2AIAgfw88TVGWa7EjpL+PUeYoGDMGlAZQ1pqJbg7W6ODljBnbL2t93tCZwKXZvl8+xA+t61VHhioLyRmZ+GTvNVyLe1E8XKHKwkujpuPKrq9hZWWFvn374qeffsIrr7xSbBBZkDG8gRjDSikRUVWgy7n78PBwjB8/HmFhYfjwww8xf/58WFtb53vtpM7ecLGzyvc9tO0e5d66W0vEWBbkx2CyHBhQGkBZayYObVUHR248QkKaSvt4BswELu32/Z/XHmKOPBxZagHdm9TE16/5o/vKY/nGUdRtjx9/bI9Bg16Bo2P5A2RDv4HoY6X0jZZ23GohIpNW3nP3j54rEH/yV3zyySdo2LBhvlVJbYL8PdDTxw27c1ZBI7Wsgnq52CL4JS8E+cv43ltO/FMzgLLWTBzWxgPzC7QQzDeeAX/5S7t9f/j6Y81/h95LgMzJBiIRkNsyWyQSI8O6Olp276RTMJnL0G8guqyU2t/+C/O++BZ1f/wRQ4cO1eu8iIiMgS7n7pcduon4fUfw/vvvY8GCBYVWJbWxl0owpqMnRneol31OM0mB1IxM2FpbQuZkg+buDjxipCMGlAZQ2kxgAOjgVQMSCzFO3H5a5HiGzAQuT8vD4Je8cPTmE00wmZc+t+8N/QZS3pXSvj49MGHCYwwbNgyffvop5s6dyzc6IjIZOp+7FwTUHjwTcz/qA+syLgaIRCL4eTjBj83m9I4BpQGUNhMYAIa3rYOd/97XGnwZQyZwWbfvA1vKMMC3NoZ/c0b7eBWwfW/IN5DyrpT+/PPPaNKkCebNm4fr16/ju+++g1QqrezpExHpnbZz9wBgbSnGVyP80cDNDgplFp6kZCBEfhUxBWv0ikRQZcGoyuYRIBIEbaEKVbTcmom6/uEvGtwcYzrUq9Q2h3kduhqHyT9nF/Ke0qU+xgd4QpWV/UZRy0GKpylKzfb9QN/amNm7MUZ9dxYPkhRax1s/ujX6NqtVYfM1JM3fURlWSn/99VeMHz8erVq1glwuR82aNSt51kRE+iMIAnqsOqZ1QcXaUoyO9Z1x9Gb2ca+xHeuhp4+b1ooYuQsqh6d34Q6OkWBAaSDJChXaLjmscybw0Zld8ee1R5XS5jCXWq3GhQsXsH//fvx+IhRJHSZrvS7v9v0A39p4v3djjN54DrFaOsLk2vt2J/auLuD8+fMYNGgQpFIp9u3bh+bNm2u9zlA3FUREpXUlJhGD/neqVNf6yhzx1Uj/IlsjAvzMMCbc8jYQfWQCD23lga4rj1Zom8NciYmJ+PPPP7F//34cPHgQT548gZOTE/r07YvbVlmIV1oUu9q65v9a4klyBjaMaa157LWN55CYk7luDNv3xqpdu3aaoDIgIADbtm1D//79Nc9XZu90IiJdaGsxW5TxAZ44cv1RsdcYumwevcAVSgMrbdmEgno0qYkjNx6XfGEBIf19SmxzCGSvdl2/fh379+/H/v37cfLkSWRlZaF58+YYMGAABgwYgI4dO8LS0lLv2/ekXUpKCkaNGoV9+/Zh5cqVePfdd7H7cmyZkn50uakgItLVrksxmLmj5IWUt7rWR08fN7z23dlid/JWDmuBIa34nmYMuEJpYLmZwB9sD4VKEEEsEpUYFAxt5YGfzt0r1/crqs0hAKSnp+Po0aOaIDI6Oho2Njbo3r07vvrqK/Tv3x/16hUO+FjIu3LY2dnht99+w5w5czB9+nQcjFLiZrVmJb6uPL3TiYgqQt6yeUWZ1NkbfZvVwuiN50r8XDFk2TzKjyuURiAzMxMNmjRHg16vQd3gZa2ZwN45mcC9fGqi68qjOgdvF0N6wc7aEvfu3cP+/ftx4MABHDlyBOnp6fD09NSsQnbt2hU2NiWXJZKHxui0fb9meEt2JiiD99Zsxe5H5d/m4Z83ERlCSWcoJ3byQmBLGUZ9dxbPFSVX/eAZSuPBgLKUKjLhYdeuXRg6dCj+/fdf+Pv7F5sJrG17uWCbw8+H+KG5zBGCIEClFvDZoRs4fSc+3/dsLdzGrf3f4erVq7CwsECnTp00QaSPj0+5fqbybt+XdhuesukroSv3poKIqLIUl+Vdy0GKs3N64G58qqaEnDJLjcC1pwuNwyxv48NPkxJURsLDF198gc6dO6NVq1YAUGTNxNK2OVy0/5rmzq5pbQf8NLE9Wn36V55x1LiQVA1dW7XCRx99hN69e8PJyalcc8/L0C0PzUXeGm4/TmgHVztrCIKAlIwsfLw3AtfinsPPwxHzBzSFrbUlBEHAov3XcSbyxU1FhkrNGm5EVOnUajW8M+/hjlCjUCD48LkCnnP2l2ocAcD4AC8Gk0aEAWUxdGlaX1qXLl3CiRMnsHPnzhKvLW2bw7zbBA7S7IAiL5FIDJGTOz6Y96XetwoM3fLQ1BW8qXh76yXN33fvpm5YPsQPA78+iW9Gt8aM7WE4ExmP+q622DKhPbqtPIqMzBe/y5tPR2F0+7p8QyaiSnHlyhW88cYbOB96BZ7Tf4FahxCE5+6NDz/Ni1DepvVlTXj44osvUK9ePQwePLjEa8vS5vDDPo3R37c2HG0kmPzTv1rHq6hyC4ZueWjKCt5U5L15sJdaQi0IqF5NAicbK82K5J0nqXiuUKFr45r4I+IhgOzf2ztPUnH1wXOePyKiCpWWloaFCxdi5cqVaNSoEY4f+RNPbD11One/LMiPCxJGhn8bWujStL64LOqCHj58iF9++QVLliyBpWXxfxUpKSmIuPWi53dJbQ4/++MmPvvjJl6q74w5/X0wdP1pqLLyr1RWRJvDvNgzVf+01XBbOawFOno7AwDGbTqPhDQVnqZkoG+zWjgU8RAt6zjBy8UWHtULJ1exhhsRVaQ///wTU6ZMQWxsLBYsWIBZs2bBysoKAPA0RVnuc/c8KmV8GFAWoHPTegCz5VfQs6lbiXdP69evh5WVFSZOnAgg+2zJvXv3cPPmTdy8eRM3btzQ/HdsbCxsGnVEzVdDAADtvZxR39UOJ2d1A5B9mPnH4PaaNoe5Tt2Jh52VJRq72ePqg+f5vj/LLVQ9eXun58qt6TaklQxz+/sgePMFTNpyEbP7NsHb3Rrg5qNkXIxOQGZW4ROtFX1TQUTm6fHjx5gxYwZ+/vlndOvWDQcPHkSjRo3yXcNz96aFEUUBRTWtL5hJnWtIKxlWDmuJCT9cwN85hcZLSnhITk5GeHg4Vq9eDR8fH7z55pu4efMm/vvvP6SnZ69AWVlZoVGjRmjcuDHGjRuHJk2awLKmN+YcTQQArDt2B+uOvVixzG1zeOdJKjydqyE6PjtJp4WHI5ztrHDvWVqhecicSi4HRMaluBpuuy7FYnGgL5yqSXDjYTLGb76gee7w9C7473Fy4fF4U0FEeiQIAjZt2oQPPvgAALBp0yaMGzeuyGNOPHdvOvg3k4e2LGpAeyY1kL0q+Fq7erh0L6HQazafjkInNzVu3LiB4xF3cfPeY9x/+Bhx96PxJPomlA9vAwCio6NhZ2eHjh07Yvz48WjSpAkaN26MevXqwcIif/AgCAI2XNVebiGXhUiEFUNbwF4qgVoQkKbMwpSfL+U7a8c2h1WXe56bADtrS1SzssDj5AwAQJ+mbkhIUyIxTQVXO2s8Scl+fETbOkhTZhYqHQXwpoKI9OfmzZt48803cezYMYwZMwYrV66Eq6tria/juXvTwIAyj4IJD0DRmdQAsPRVXyzafw2z+zbJ93huwkOr4E9QrVFHSJzrALZ1gPqAZX2gNgBR8mO4JkTgyMalpS43JBKJEBzgifl7Igo912n5P5r/HvrNmWLHYbmFqstX5ghvF1tEPU2FvdQS60a1hlQihiAA8alKTPzhIgDgtfZ1MbilO0QQ4fbjFLxZIDGLNxVElJcutZYzMjLw2WefYfHixahTpw7++usv9OzZs8xz4Ln7qo0BZR7aEh6KyqQe3b4ubj1KxuX7iUWO59hxGETQfiZEbeeCx/bd0HbJ4TKVG2KbQ/OW96YiLkmBwLXaO058ceQ/fHHkvyLH4U0FEQG611o+ceIE3njjDdy+fRuzZs3CvHnzStVdjUyP2NATMCYFEx5yM6m3nL2b73GP6jYY0bYuVv11q8Qxi9qaFomy/+hzyw1tOBFZqjnaSyVYGuRbqmuLwnILVVugvwzWEt3+6fKmgojkoTFou+QwFuyJQLSWYBJ4UWu57ZLDkIfGaB5PSEjApEmT8PLLL6N69eq4dOkSFi9ezGDSjLH1Yh6HrsZh8s+XNF9P6VIf4wM8ocrKXg2s5SDF0xQllh66jnn9fTRFol3trPFckYmVf93Etgv3y/39y9JfmW0OzRt7pxORLsr7GTK3XxM4PLiAd999FwqFAsuWLcObb74JsZjrU+aOAWUeJTWtz82kzpvlDQDbJnXAtyciNVne5VXW/soFO/mw3IJ54U0FEZWHrjekT/esQL+mLvjiiy/g7u6ux5lRVcZ9zzzyJjzoI8rWVmrovR4NMailO1SZAhLSlBix4azm+rL2V2a5BfNW2hpugAARRLypICKday0LgoDagTOxaX5ffqZQPlyhLGDLmWjM3xOhc0DZzN0BH/RujAY17TSrmsEBnmjnVQPvbAuFKkuAq701nuSUfAFeZN4ent6lzMkSmgw9llswO8kKVbE3FZbp8VgwogtvKoio2M+4Hye0g6udNQRBQEpGFj7eG4Frcc8LXScCsHBw81IvfpB5YEBZQLJChbZLDuuURW1lIca2NzpoSg3lBpRnZnfHiA1ncTe+cJHxvPa+3Ynt8KjMtN1U/Hv8L3w0dRzi4uLg5uZm6CkSkQEJgoAeq4quZewgtdTULO7d1A3vdG+IgV+fLHSdLosfZLrMermiqLpbSwKbY8aOK+UeV1upITtrS9SwtUKfprXQr3ktAMDGk1HYFx5X6PXsr0zloa2GW7vaA/DxOxaQy+WYPHmy4SZHRAanrdZyXnkbYNhLLaEuYr0pt9by1QfP+VlFGmYZUJam7lbPxi44fPMpIAhAGe7AcksNLTt0I9/jlhYiWFtaZJdrWXca7o5S/DblJdx6nFwoyYf9lUlfatSoge7du2Pnzp0MKInMnLZaywWtHNYCHb2dAQDjNp0v9louflBeZhdQFsyM1ibqaSoin6RAUGdCYilBVs5NWlFZ1Hkfb+/ljPqudjg5qxuA7FJDPwa3x+zfriAlIxPy0FgAwIMkBS7efQY/mVOhgJL9lUmfhg4dismTJ+PJkyelaoNGRKapYK1lbWbuyM7+HtJKhrn9fRC8+ULR43Hxg/Iwq8JRG05EYvr2MChUaggouui4AAAiEUQWlsgUsnske7nYar3Wy8UWrwfU1Xy97tgdtF96BJ2W/4NOy//Bw+cKjN10DkdvPcGesAfo0rgmgOyzKi3rOOH6w8IHntlfmfQpMDAQALB7926DzoOIDMvWyqLU1+66FIuO3s5wqlZ0a2AuflBeZvPbIA+NKUfNvuyt7kPXHmH18Bao72qnSXiwsRQh9r+rOLl3M5Z/8hvsR3wOSQ13QFR0jP75Hzfw+dAWGNM+OzNu7dE7iHjwIqBkf2WqCK6urujatSt27tyJSZMmGXo6RGQg7sUsVthZW6KalQUe51Qe6dPUDQlpSiSmqYp8DRc/KC+zyPLWR+a2VCLGhbk9cS3sErZu3Ypff/0Vjx49QoMGDTBq1CjY+ffH/84+0bnc0CKWYqAKsG7dOkybNg2PHj2Cs7OzoadDRAaQm+WtLTGntqMU60a1hlQihiAA8alKLDlwvciyQczypoLMYoVyd2hsoTOTBYuOb5vUAe5ONkhRZN+N7boUi42nojTXK1RZaDHodUT99SNq1aqFkSNH4rXXXkObNm0gEomQrFDhu391D1rZX5kqQlBQEKZOnYo9e/YgODjY0NMhIgNISUmBbewFCFY+hQLBuCQFAtcW3SkuLwHA+AAvBpOUj8kHlIIgYNPp6HyPNXN3gH8dJ8Qk5K8H+fHeiCLbJwqCAGmLvvhr1hh069YNFhb5z6LYSyVYGuSrUzurZUF+LDxNFaJWrVro3Lkzdu7cyYCSyAzt27cPb731Fp4lp6HW5E3IRPmDQS5+kDYmn5STW3crdyvaykKMRYObY97vV8s0jkgkRpqlA9x82hYKJnMF+XsgpL9PueYZ0t+HLfGoQg0dOhR//fUXTl2/j0NX47DrUgwOXY3DlZhEmMHJFyKz9OjRI4wYMQKvvPIKmjVrhquhF/H5cH+dxuTiB2lj8r8RBetuaSs6nmtuPx/M6tMYtx+n4LNDN3BfyzUl1d0qbX/l3HtD9lemypCsUEFo0Bmu47/CqB8LF+33drFFcIAnAv1lsJcWndVJRFWDIAjYvHkzZs6cCQsLC/z8888YOXIkRCIRPD2BpynKciSqcvGDimbyAWXeultFFR0HgOnbLyMuSQEAGNuxHr4f1xa91hwvPF4p6m4F+Xugp49bsf2VvVxsEfySF/srU4XLW3tVUkP7B0HU01TM3xOBxQevY2mQL4L8PbReR0TG7/bt23jzzTfx999/Y+zYsVi5ciVcXFzyXcPFD9I3k8/yPnQ1DpN/vgQAmNKlPsYHeEKVlf2Pp5aDFE9TlJj92xUcvfUk3+tuLuyL9suOFCqZsH50a/RtVqvU319bf2WZkw2auzvwQDNVuA0nIsu9CjGps3cFzIiIKopKpcKqVavw8ccfo3bt2li/fj169+5d7GuSFapiFz+8ufhBpWTyAeWVmEQM+p/2zLWTs7phwg8XcOdJKqpXk+BpihIA0LdZLcwb4INOy/8p9Jq9b3diqymqEuShMTolia0Z3pKrEURVxMWLF/H6668jPDwc06dPxyeffAJbW+0NObTh4gfpyuRvN3xljvB2sUVUnsScgqwsxPh+XFtYWWbX33qWqsSkHy/mu4ZFx6kqSVaoMEcertMYs+VX0LOpG1cliIxYamoq5s+fjzVr1sDPzw/nz59H69atyzyOSCSCn4cT/HjahcrJ5D8pRCIRggM8MX9PRKHn8q5AFrWKmYt1t6gqKVh79ccJ7eBqZw1BEJCSkYWP90bgWtxz+Moc8cmgZrC2FMPa0gI7/r2Pb45HAgAyVGrIQ2NZaJ/ISP3xxx+YPHkyHj58iKVLl2L69OmQSJhUR4Zh8gElAAT6y7D44HUWHSezoK326ttbL+G5IjuhrHdTNywf4oeBX5/Esld9sfrwLRy+/hiONhIcmdEFR248xu3HKQCAzaejMLp9Xd5IEemRZns5MR2pyizYWlnA3ckGvjLHUv1be/r0KaZPn46ffvoJ3bt3x19//YUGDRpUwsyJimYWASWLjpM5ya29mlduMAkA9lJLqPMcnXbIKRNUzcoCqiw1knIS0QQAd56k4uqD5zw3TKQHyQoV5DkJMFFFJcAUU75LEAT8/PPPeO+996BWq7Fp0yaMGzeON3xkFEw+KScvZrySOchb2SCvlcNaoKN3dh/vcZvO47/HKWha2wHfjmkNkUgEZ9vsEiLy0Nh8rytrZQMiKixv+S6g5BI9Bct3RUVFYcqUKfjjjz8wYsQIrFmzBm5ubhU/caJSMqslN9bdInOQt/ZqXjN3ZK/QD2klw9z+PgjefAFvvOyNpQdvYH94HOpUt8G2Nzoi7H5ivhXO0tReJaKilXYxI/fzSKFSY/r2MDxNUSK4Y118+eWX+Oijj+Ds7Ix9+/ZhwIABFTthonIw+daLBQX5e+DC3J5YOKgZvFy0l1TwcrHFwsHNcTGkF4NJqnJsrbS3Bs2161IsOno7w8XOCn2a1sL+8DgAwP2EdFy+n4jW9arnH49HPYjKTR4aU66dMQBYfOA6/IPewPvvv4/XX38dERERDCbJaJnlJ4W9VIIxHT0xukM91t0ik+PuZJPvaztrS1SzssDj5AwAQJ+mbkhIU+JZqhIZmVlo71UD56KeoXo1CfzrOOGb43fyvV5WYDwiKh1dy3cJghrJTV7B3x9OQtdOHfU4MyL9M8uAMhfrbpEpKlh71V5qiXWjWkMqya6zGp+qxMQfLkItAFO3XsK8AU1hKRbBUizChhORuBKTBIC1V4l0VbB8lzbv9miI6T0bofeaY7j1KCXfcyKRGCJLK9y3dK/IaRLphVkHlESmqGDt1bgkBQLXaq+zeupOPF75+qTW51h7laj8tJXvKqiZuwP86zghJiGt2OtYvouqArM7Q0lkDgL9ZbCW6PbPm7VXicovt3xXcR3aFg1ujnm/Xy12nLzlu4iMGQNKIhOUW3tVF6y9SlR+DxLTi31+Rq9GkIfGIiah+OtyxZYwHpGhMaAkMlFB/h4I6e9TrteG9PdhhQMiHRRVvgsAWtV1gp+HI7acvVv68Vi+i4wcA0oiEzapszdWD28BqUQMEV7UWC0o9zmpRIw1w1uykD+Rjoor39Xeyxn1Xe1wclY3nJzVDbUcpPgxuD26NnItejzuFpCRM6tOOUTmKlmhwu6clm8F2zICOS3fXvJCkL+M29xEenAlJhGD/qc9Ga6gk7O6YcIPFwpleee19+1ObIFKRo2fHERmoGDt1Skz50JSzR6z35/O2qtEFaBg+a7yYvkuqiq45U1kRnJrr1rEXUXdrDj0bVYLvjJHBpNEepZbvksoRTjZafk/Ra5OsnwXVRUMKInMUFpaGqpVq2boaRCZrGfPnmHf/xZArcoAdDhZxvJdVFUwoCQyQwwoiSrO/v370bx5c/y5fw/+zysL0GF1keW7qKpgQElkhhhQEulfUlISJk6ciIEDB6Jly5a4evUqPp86nOW7yCzwtofIDDGgJNKvw4cPY8KECUhMTMR3332HCRMmaM49TursDRc7K8yRh2t6e2vbBM9dx7SWiLEsyI/BJFUpDCiJzFBaWhpsbGwMPQ2iKi8lJQWzZs3CunXr0L17d3z//feoV69eoeuC/D3Q08et2PJdXizfRVUYf2OJzIwgCFyhJNKDEydOYPz48Xj48CG+/vprTJkyBWJx0SfJCpbvepCkQGpGJmytLVm+i6o8BpREZkapVEIQBAaUROWUnp6OkJAQrFmzBgEBAfjjjz/QoEGDUr8+t3yXn0cFTpKokjGgJDIzaWlpAMCAkqgczp07h3HjxiE6Ohqff/453nvvPVhYFN1mkchcMMubyMwwoCQqu4yMDMydOxcBAQFwcHBAaGgoZs6cyWCSKAdXKInMDANKorIJDQ3FuHHjcOPGDSxatAizZs2CpSU/Pony4golkZlhQElUOiqVCgsXLkS7du0gFotx4cIFzJ07l8EkkRYMKInMhCAIuBKTiKO3E2HbvDtCn2Z/LejQFo7IVEVERKBjx45YuHAhZs+ejfPnz6NFixaGnhaR0RIJ/DQhMmnJChXkObXvorTUvvN2sUVwgCcC/WWwl0oMMEMi/RIEIbssT2I6UpVZsLWygLuTDXxljiWW5cnKysLKlSvx0UcfoX79+vjhhx/Qtm3bSpo5UdXFgJLIhMlDY8rUnWNpkC+C/FnLhKomXW+ebt26hfHjx+Ps2bN4//33sXDhQkil0sqYOlGVx4CSyERtOBGJxQeul/l1If19MKmzdwXMiKji6HLzpFar8dVXX2HOnDmQyWTYvHkzXnrppcqZOJGJYEBJZILkoTGYvj2s3K9fM7wl+whTlaHLzVNPDxGCg4Nx7NgxTJs2DUuXLoWtrW0FzJLItDGgJDIxyQoV2i45DEXOSk15SCViXAzpxX7CZPR0vXlK/uMr2D+7ge+//x7du3fX48yIzAs/LYhMzO7QWM22HwD8OKEdXO2sIQgCUjKy8PHeCFyLe45dkwNgI8ku9GAhFqNxLXv0/eI4bjxMRoZKDXloLMZ0qGeoH4OoRMkKFebIw8v9ekFQw7H3FJya2Rm1XWvocWZE5ocBJZEJEQQBm05H53vs7a2X8FyRCQDo3dQNy4f4YeDXJzFk/WnNNf2a18K7PRrixsNkzWObT0dhdPu6JWbFEhlKwZungk7O6oYMlRoZmVkAgLVH72BfeJzmeZFIDAFiHL6TjDEMKIl0woCSyISExyYhskB2a24wCQD2UkuotZxyGd6mDrZfvK/5WgBw50kqrj54Dl+ZY4XNl6i8tN08aTNl67+49Sil2Gt480SkOwaURCbkQWK61sdXDmuBjt7OAIBxm87ne87NwRodvJwxY/vlQq+LTUxnQElGSdvNU3nw5olIPxhQEpmQVGWW1sdn7shOWhjSSoa5/X0QvPmC5rmhrergyI1HSEhTFR4vI7PQY0TGoKibp4K++D9/iETA5fuJWP7HTTxLVWq9jjdPRLph60UiE2JrZVHs87suxaKjtzOcqr0o6jysjUe+7e584zHLm4xUUTdPeQ3/5gz6fXkCA786icQ0FVYOK7p1Im+eiHTDgJLIhLg72eT72s7aEjXtrTVf92nqhoQ0JRJzViM7eNWAxEKME7efah1PVmA8ImNR0s0TADxIUgAAMtUCNp6KQlvPohNvePNEpBv+CyIyIb4yR3i72CLqaSoEZCfhrBvVGlKJGIIAxKcqMfGHi5rrh7etg53/3kfBPB0RAG9XWzR3d6jU+ROVVsGbp4JsJBaQWIg0SWmDWrgj4kFSkdfz5olINwwoiUyISCRCcIAn5u+JAADEJSkQuPZUkdfPKKIgtABgfIAXs17JaBW8eSrIxc4K60e3hlgkgkgE3H+Whplaft9580SkH+yUQ2Ri2CmHzMWWM9GYvydCa0BZFosGN2cRfyId8QwlkYmxl0qwNMhXpzGWBfkxmCSj5+eYASFLCUHQ7eYpiH3riXTGgJLIBAX5eyCkv0+5XhvS3weB/IAlI7d161Z0at8GFv/+CpGo/B9lvHki0g8GlEQmalJnb6we3gJSiRgiZJ8V0yb3OalEjDXDW2JSZ+/KmyRRGaWmpmLChAkYNWoUBg8ejMu7N/DmicgI8AwlkYlLVqiwOzQWm05Ha+0s4u1ii+CXvBDkL+NKDRm1sLAwjBgxAvfu3cPatWsxduxYTeKYPDQGc+Thmt7e2j7Ycm+qrCViLAvyYzBJpEcMKInMhCAImDx3Kf44cQ4rv/wfbK0tIXOyQXN3B2Zzk1ETBAHr1q3DjBkz0KRJE2zbtg1NmjQpdB1vnogMhwElkRmZOHEirl69inPnzhl6KkSlkpCQgIkTJ0Iul+Ptt9/G559/DqlUWuxrBEFAeGwSHiQpkJqRyZsnokrAWzQiM/Lo0SPUrFnT0NMgKpVTp07htddeQ3JyMuRyOQIDA0v1OpFIBD8PJ/h5VOz8iOgFJuUQmZFHjx7Bzc3N0NMgKlZWVhaWLFmCLl26oG7durh8+XKpg0kiMgwGlERm5PHjxwwoyajFxcWhd+/emDdvHubMmYN//vkHdevWNfS0iKgE3PImMhOCIHCFkozaoUOHMHbsWFhaWuLw4cPo3r27oadERKXEFUoiM/H8+XNkZGQwoCSjo1Qq8cEHH6Bfv35o06YNwsLCGEwSVTFcoSQyE48ePQIAJuWQUYmMjMSIESNw+fJlrFy5Eu+99x7EYq51EFU1DCiJzERuQMkVSjIWv/76K9544w24uLjg1KlTaNu2raGnRETlxNtAIjPx+PFjAAwoyfDS0tIwadIkjBgxAv3790doaCiDSaIqjiuURGbi0aNHsLS0RPXq1Q09FTJj4eHhGDFiBKKiorBx40YEBwez2DiRCWBASWQmHj16BFdXV55PI51pOtEkpiNVmQVbKwu4O9nAV+ZYZHAoCAK+/fZbvPfee2jYsCH+/fdf+Pj4VPLMiaiiMKAkMhMsGUS6SlaoIM/plR1VVK/sAE8E+stgL5VoHk9MTMSkSZOwc+dOvPXWW1ixYgVsbGwqc+pEVMHYy5vITAQFBSE9PR2HDh0y9FSoCpKHxmCOPBwZKjUAQNsHR+7apLVEjKVBvgjy98DZs2cxYsQIJCUlYePGjXj11Vcrbc5EVHm490VkJtglh8prw4lITN8eBoVKDQHag0nkPC4AUKjUmL49DCM/+Q6dOnWCu7s7Ll++zGCSyIQxoCQyYYIg4EpMIg5djUOctC5Ubs1wJSYR3Jig0pKHxmDxgevleu0ZRW0Mn/U5jh07hnr16ul5ZkRkTLjlTWSCynvWjSivZIUKbZcchiJnm7s8pBIxLob0gp01j+wTmTIGlEQmprxn3YgK2nImGvP3RGj9HbKyECNkgA9ebugKVZYaEQ+eY/r2y4WuEwFYOLg5xnTgCiWRKeMtI5EJ2XAislTbk7kBQu5Zt6cpSkzq7F2xk6MqRRAEbDodXeTzH/ZtDLUgoNvKowAAV3vrIq/dfDoKo9vXZb1JIhPGM5REJkKXs26LD1zH7tBYPc+IqrLw2CREPk3VujppI7HAsNZ18PkfNzWPPUnO0DqOAODOk1RcffC8YiZKREaBASWRCUhWqDBHHq7TGLPlV5CSkamnGVFV9yAxvcjn6jlXQ0KaEtO6NcCeqS9h+xsdEVDfudjxYosZj4iqPm55E5mA3aGxmjOTAPDjhHZwtbOGIAhIycjCx3sjcC0ue4XovR4NMailO1SZAhLSlBix4SwAIEOlhjw0lmfdCACQqswq8jlLsQj1nG3x3+MUfPbHTfjUssdPE9uj15rjeJaq1D4eb1aITBoDSqIqTttZt7e3XsJzRfYHeO+mblg+xA8Dvz6J4ABPNK5ljz5rjkOVJRQ698azbpTL1sqiyOdiE9ORpRaw+3L2MYnrD5NxPyEdjWra4WzUM+3jMcubyKRxy5uoitN21i03mAQAe6kl1DnFHN542RvLDt2AKiv767zn3njWjfJydyq6NWJCmgqn7jzFy41cAQAyJxvUqW6DO1pKVOWSFTMeEVV9vGUkquKKOuu2clgLdPTOPtc2btN52FlbooatFfo0rYV+zWsBADaejMK+8Lh8r4tNTIevzLFiJ01Gz1fmCG8XW0QVkZgTIg/H50NbYHbfJlCrBcyVh2tNzBEB8Ha1RXN3hwqfMxEZDgNKoiquqLNuM3eEAQCGtJJhbn8fTN9+GdaWFpBKxAhadxrujlL8NuUl3HqcjFuPUl6Mx7NuBEAkEiE4wBPz90Roff5+Qrrm/G1xBADjA7x4jILIxHHLm6iKK+6sGwDsuhSrWalMyciEPKc80IMkBS7efQY/mVP+8XjWjXI0s0uHkKnUqVWnVCJGkL9Mj7MiImPEgJKoiit41s3O2hI18yTb9GnqhoQ0JRLTVNgT9gBdGtcEADhILdGyjhOuP8x/ZpJn3QgADh48iO6dO0JyeadOq4vLgvzYdpHIDPBfOVEVV/Csm73UEutGtYZUIoYgAPGpSkz84SIA4PM/buDzoS0wpn12aaC1R+8gIicJh2fdCMiuGvD5559j9uzZGDBgAH7+eR1+DXtarqL5If19EMjVSSKzwF7eRCaguJ7LZbGIPZfNWlpaGl5//XX88ssvCAkJwcKFCyEWZ29klbVH/LIgPwaTRGaEASWRCUhWqNB2yWEo8hQ3LyupRIyLIb24PWmm7t27h8DAQNy8eRObN2/GsGHDCl2TrFBhd2gsNp2ORqSWEkHeLrYIfskLQf4y/h4RmRkGlEQmQh4ag+nbw8r9+jXDW3JFyUydOHECQ4YMQbVq1fD777+jRYsWxV4vCALCY5PwIEmB1IxM2FpbQuZkg+buDszmJjJTDCiJTMiGE5HlPus2qbN3BcyIjN0333yDt99+G506dcL27dvh6upq6CkRURXELG8iEzKpszdWD28BqUQMEV6caSso9zmpRIw1w1symDRDSqUSU6ZMweTJkzF58mT8+eefDCaJqNy4QklkgnjWjYrz+PFjDB06FGfPnsXatWvx+uuvG3pKRFTFMaAkMmF5z7qNGjcB//fqYEwZN4Jn3czYpUuXEBgYCKVSid9++w0BAQGGnhIRmQBueROZMJFIBD8PJ/RtVgv2T6/BKeUufGWODCbN1LZt29CpUye4ubnh4sWLDCaJSG8YUBKZCWdnZ8THxxt6GmQAWVlZmDNnDkaOHIkhQ4bg+PHj8PDwMPS0iMiE8PAUkZlwcXHB06dPDT0NqmRJSUl47bXXcOjQIaxYsQIzZszgCjUR6R0DSiIz4ezsjAcPHhh6GlSJbt68icGDB+PRo0c4cOAA+vTpY+gpEZGJ4pY3kZnglrd5OXDgANq1awexWIwLFy4wmCSiCsWAkshMMKA0D4IgYNmyZRg4cCC6du2Ks2fPokGDBoaeFhGZOAaURGbCxcUF8fHxYKUw05WWloaRI0dizpw5mDdvHuRyORwcHAw9LSIyAzxDSWQmnJ2doVQqkZKSAnt7e0NPh/Ts3r17CAwMxM2bN7Fjxw4MHTrU0FMiIjPCgJLITDg7OwMA4uPjGVAaOU1B+sR0pCqzYGtlAXcnmyJriB4/fhxDhw6Fra0tTp8+jRYtWhhg1kRkzhhQEpmJvAGlp6enYSdDWiUrVJDntMyMKqplZoAnAv1lsJdKAADr16/HtGnT0LlzZ2zfvh0uLi6VPW0iIrZeJDIXd+/ehaenJ/744w/07t3b0NOhAuShMZgjD0eGSg0A0PbGnLs2aS0RY9ErTXFk41J88803mDZtGlauXAmJRFJp8yUiyosrlERmIneFksXNjc+GE5FYfOB6idflBpkKlRof/HYVSWFP8d1332HixIkVO0EiohIwoCQyE7a2trCysmLpICMjD40pVTCpjWPXYDi3aqnfCRERlQPLBhGZCZFIxFqURiZZocIcebhOY8yWX0FKRqaeZkREVD5coSQyI7m1KMk47A6N1ZyZzMtBaoltkzpovpZaWaBu9WpovfgwktJV+a7NUKkhD43FmA71Kny+RERFYUBJZCYEQYBd3aa4mWaDXZdiSixFQxVLEARsOh2t9bnnikz0/+qk5utJnb3R3qtGoWAy1+bTURjdvi7/HonIYBhQEpm4vKVoHviOAQDM3BGmeV5bKRqqeOGxSYjUUhpIm2GtPbDiz5tanxMA3HmSiqsPnsNX5qjHGRIRlR7PUBKZMHloDNouOYwFeyIQXUTwEvU0FfP3RKDtksOQh8ZU8gzN14PE9FJd16quE6pXk+DIjcfFXhdbyvGIiCoCA0oiE7XhRCSmbw+DQqWGAO11DZHzuIDsUjTTt4dhw4nIypukGUtVZpXqumGt62DXpVhkqYsvGZzKxBwiMiAGlEQmSJdSNIsPXMfu0Fg9z4gKsrWyKPEaG4kFBvrVxo5/75c8njVPMBGR4TCgJDIxLEVTNbg72ZR4zQDf2rjxMBl3npR81lJWivGIiCoKb2mJTEzeUjQ/TmgHVztrCIKAlIwsfLw3AtfinmPbpA5wd7JBiiI7a3jXpVhsPBWlGYOlaCqer8wR3i62iHqaWuRxhOFt6mD7xeJXJ0UAvF1t0dzdQe9zJCIqLfbyJjIhgiCgx6pjmiDFQWqJ54rslcbeTd3wTveGGPj1SWyb1AHfnojE30UkeuQGKYend2Epmgq05Uw05u+JKDKgLK1Fg5sz+Ccig+KWN5EJyS1Fkxug5AaTAGAvtYS6lPePeUvRUMVp7aKGkKmELvf1UokYQf4yPc6KiKjsuOVNZEK0laJZOawFOno7AwDGbTqveXxuPx/M6tMYtx+n4LNDN3A/ofBrYxPTWduwgly+fBn9+vWDtElnoMO4co+zLMgPdkzIISID4wolkQnRVopm5o4wBHz2N1b+dRNz+/sAAKZvv4yeq4+h7xcncD76Gb4f11b7eEzMqRB//fUXXn75ZchkMpzd9hVCcv5eyiqkvw8CuTpJREaAASWRCSmuFM2uS7Ho6O0Mp2oSxCUpNI//eOYu6taoBqdqhbvksBSN/m3ZsgX9+/dHp06dcPToUbi5uWFSZ2+sHt4CUokYImSfYdUm9zmpRIw1w1tiUmfvyps4EVEx+GlBZELylqKxs7ZENSsLPE7OAAD0aeqGhDQlUhSZcLGzwtMUJQCgb7NaeJKSgcS0wn2iWYpGfwRBwLJlyzB37lxMmDAB69evh0TyIogP8vdATx837M5pk6mtLaOXiy2CX/JCkL+M29xEZFT4jkRkQvKWorGXWmLdqNaQSsQQBCA+VYmJP1yExEKM78e1hZVl9uPPUpWY9OPFfOOwFI1+ZWVlYdq0aVi3bh0+/vhjzJ8/X2v2vL1UgjEdPTG6Qz2ExybhQZICqRmZsLW2hMzJBs3dHZh1T0RGiWWDiEwMS9EYl7S0NIwcORL79+/HN998g4kTJxp6SkQ6EwQh+6YnMR2pyizYWlnA3ckGvjJH3vSYKa5QEpmYQH8ZFh+8DkVOcfPyYCka/Xj69CleeeUVXLlyBXv27EH//v0NPSUinSQrVJDnHMuI0nIsw9vFFsEBngj0l8FeWvhcNpkurlASmSB5aAymbw8r9+vXDG/J7GEd3blzB/369UNSUhL279+PNm3aGHpKRDqRh8Zgjjxc04lLW/CQuzZpLRFjaZAvgvw9Km1+ZFjM8iYyQUH+HixFY0AXLlxAQEAAAODMmTMMJqnK23AiEtO3h0GhUkOA9mASOY8LABQqNaZvD8OGE5GVN0kyKAaURCaqtKVoIKhZikaPDhw4gK5du8LLywunTp2Ctzf/PKlqk4fGYPGB6+V67eID17E7NFbPMyJjxC1vIhOXrFAVW4pGFX8fk7o0xPtDu7AUjY42btyIN998EwMGDMAvv/yCatWqGXpKRDpJVqjQdslhnc9kXwzpxfcXE8e/XSITV1wpmtoO1hjaoz1upbSB3agehp5qlSUIAj755BN88sknmDx5Mr766itYWvLtlaq+3aGxmjOT1pZifDXCHw3c7KBQZuFJSgZC5FcRk5iO5UP80LpedWSospCckYlP9l7DtbjnAIAMlRry0NhKqxrBDHTD4AolkZlbvXo1PvzwQ9y/fx9ubm6Gnk6Vo1KpMGXKFGzcuBFLlizB7Nmz+aFFJkEQBPRYdQxRT1MhIDug7FjfGUdvPgEAjO1YDz193DD2+/Po6VMT/9x8giy1gO5NamLeAB90X3kMwIu6toend6nQfxvMQDcsnqEkMnPjx4+HpaUlNm7caOipVDkpKSkYPHgwfvjhB/z444+YM2cOg0kyGeGxSYjMCSYBICNTrQkmASD0XiLq1sg+1nH4+mNkqYWcxxMgc7JB7j8FAcCdJ6m4+uB5hc1VHhqDtksOY8GeCERrCSYBIOppKubviUDbJYchD42psLmYKwaURGauevXqGDlyJNavX4+srCxDT6fKePToEbp27YqTJ0/iwIEDGDNmjKGnRKRXDxLTi31+fIAnjlx/VOjx4Je8cPTmExTc/4wtYbzyYga6cWBASUR46623cP/+fezfv9/QU6kSbt26hY4dO+LBgwc4fvw4evXqZegpEeldqrLoG8y3utaHl4stPv/zZr7HA1vKMMC3NubKwwu9Jj4pRe9zZAa68eAZSiICALRv3x7Vq1fHoUOHDD0Vo3bmzBm88sorcHV1xaFDh1CvHttTkmk6dDUOk3++VOjxSZ298YpfbYzeeA7PFZmaxwf61sbM3o0x6ruzeJCkKPS6x78thlNyNBo1aoSGDRtq/teoUSPUr18fUqm0TPNjBrpx4Z8gEQHIXqUcP348bt++jQYNGhh6Okbp999/x4gRI9CmTRv8/vvvqFGjhqGnRGakMrOXo6Oj8fe+/QA88z0+sZMXBrVwx6jvzuYLJgfkBJOjN57TGkwCwPyZbyP1/nX8999/uHTpEn799VckJycDAEQiEerUqaMJMPMGnF5eXrCysio0Xmkz0N/qWh9DWnnAy9kWr2+5iL9vPNaMUdkZ6KaMK5REBABIT0+Hh4cHgoODsWLFCkNPp8KV9cN53bp1ePvttxEUFISffvqpzKspROVVWdnLt27dwq5du7Bz505cunQJ1tbWqPfWRmRYOwIQoZaDFGfn9MDd+FSkZmQHk8osNQLXnsZ/n/bDk+QMJKYpNeO9tvEcEtNURWZ5C4KAR48e4b///tP879atW/jvv/9w+/ZtpKdnn7m0sLCAp6dnvhXNBg0aYPFlC8QkKUvMQG9ZxwnPUpVYPsQP356IzBdQVlYGujlgQElEGh988AE2btyI2NhY2NjYGHo6FaKsH86CICAkJARLly7FO++8g1WrVsHCwsIAMydzVJH9swVBQEREhCaIvHr1KqpVq4YBAwZgyJAh6N+/P3Zfjcf8PRFFJrqU1qLBzcu0CqhWq/HgwQNNgJk34IyMjARq1EXt8WuKfL2vzBFfjfRH1xVHNY9tm9ShUECZa+/bneArcyzLj0QFMKAkIo07d+6gQYMG2LRpE8aPH2/o6ehdWT+cF73SFHu/+ghbtmzBihUrMGPGDK5iUKXZcCKyXAknIf19imyhKggCQkNDNUHkrVu3YG9vj0GDBmHIkCHo06dPvg5PxnhOMSsrCz8du4oFfxVd+mflsBZITFNi0f4Xf37FBZTrR7dG32a19DI/c8UzlESkUb9+ffTt2xdr1641uYCytB/OuUGmQqXGB79dxfPb6fjll18wYsSIip0gUR66Zi+72lkj0F8GIHu17/z589i1axd27dqFqKgoVK9eHYGBgVi1ahV69uwJa2trrWPZSyVYGuSL6dvDyv2zLAvy02vSi4WFBeycnAFoDyhzM9Bf+65wpnlRcrfwqfwYUBJRPm+99RYGDRqECxcuoG3btoaejl7o8uHs0GU8pI1b6ndCRMVIVqgwR0vZnbKYLb8Cm4TbOPD7b9i1axdiY2NRs2ZNBAUFYciQIejatSskktKdtwzy98DTFGW5V0tzA1t9srXSfuxkUmdv9G1WC6M3nivTqqots7x1xj9BIsqnf//+qFu3LtatW2cSAaW+Ppx7NnVjaRGqFLnZy8VlLn8+xA/NZY4QBAEqtYDPDt3A6TvxmjHSlVkY+v5ncHgUhiFDhmDIkCHo1KlTuc//TursDRc7qzIdGVkW5FchwSQAuDsVPuNdVAZ6aci0jEdlwzOURFTI0qVLsXDhQsTGxlb50jhbzkQXmVTwckMXfNCnCcQiwFIsxrcn7mDXpcKFjkUAFpYxqYCoPPL2z7YqJnPZQWqpCZqa1nbATxPbo9Wnf+UdCO72Fjgxu7dek8iSFSrszklqiywqqe0lLwT5yyr0Bqxgn/HiMtDf6lIfYzrWQw1bK6RmZCEjMwsDvjqJZ6lKZnnrEQNKIirk8ePH8PDwwLJlyzBjxgxDT6fcCn7oFBT6US+M3HAWNx4mw8PJBkdmdEGrT/8q1CGEHzpUWa7EJGLQ/05pfU5b5jIAdPCqgf+91gqtFx8u9JqKyl7WlN1KUiA1IxO21paQOdmgubtDpf0bKe5msSzKmoFO2nH/hogKqVmzJoYNG4Z169bhvffeg1hcNbu0hscmaV1Fycshp26fndQSCWkqKLMKn7sSANx5koqrD56ztAhVqOL6Zxfsnf1hn8bo71sbjjYSTP7pX62viU1Mr5DfWZFIBD8PJ/iVrkJRhQj0l2HxwevlzkAXBDWsxCIEVdC2vLmpmp8SRFTh3nrrLdy+fRuHDx+GIAi4EpOIQ1fjsOtSDA5djcOVmEQY+wZHcR/OADB16yWsH90aJ2d1w443O2LmjstQZRX9M8WWMB6Rrorqn62td/Znf9xElxVHMXXrJczp7wOJReGVQVPOXs7NQC8vkUiMuN9X4pN5c5CVVXTfciodrlASkVYBAQHwbdUWn/xyFEvDrSq0Q0dFKerDGQAsxCK81bUBJm25iH/vJsDPwxHfjm6DPl8cR1K6Svt4JvzhTMZBW/ZySZnLp+7Ew87KEo3d7HH1wfP845l4IpkuGehz+zVBeq1AzJgxA1evXsUvv/wCJycn/U/STHCFkoi02n05Fml9PkKMWwCii9g2jnqaivl7ItB2yWHIQ4suMmwoRZUWAbITGdzsrfHv3QQAwJWYJDxKVqBpbYeixzPxD2cyvILZy7mZy6M3ntMk4ViIRfB0flF8vIWHI5ztrHDvWVqh8cwhe3lSZ2+sHt4CUokYIrzINC8o9zmpRIw1w1vijZfr491338XBgwdx9uxZdOjQAbdu3aq8iZsYvjsSUSEvioCLIRJpLw8C5C8CPn17GJ6mKIvs0GEI2kqL5IpLSkctRym8XWwR+TQV9ZyroV6Naoh8mlLka8zhw5kMy1fmCG8XW0Q9TYWbgxQfDWiKu/Gp2DapA4DszOXh35zFiqEtYC+VQC0ISFNmYcrPl/KVyslNJGvuXvQNkikJ8vdATx+3YjPQvYrIQO/duzfOnz+PQYMGoV27dvj111/Rp0+fypy+SWCWNxHlIw+N0akrxprhLSus9lxZlZTlPaiFO97qWh+574Jrj97G3itxha5jljdVJmYv66a8GehJSUl47bXXcOjQIaxYsQLvvfdekddrvkdiOlKVWbC1soC7kw18ZY5m+x7BgJKINIyxb6+u+OFMVY0p/jusKrKysjB37lwsX74c48ePx/r16/O1pUxWqCDPWQWtiufKKxLPUBKRRm6HDgD4cUI7HHynMw5M64Ttb3QsdLZwSCsZopcOQPcmNfM9nqFSQx5auDi4oQT6y2AtEQM63DtLJWKWFqFKo2v2MqD//tnmwsLCAp999hm2bNmCX375Bd26dcPDhw8BZO/etF1yGAv2RFTZc+UViQElEQHI3sLZdDpa8/XbWy+h35cn0P+rk/juZCSWD/HTPFfLQYrX2tXDpXsJWsfafDrKaEoK2VlboqXqOqDDNhQ/nKmyBfl7IKS/D4DseollUVH9s83J6NGjcfz4cURHR6Nt27ZY8MtxTN8eBoVKDQHFnysX8OJc+YYTkZU3aQNjQElEAF4UAc99o8x7wN9eagl1ngBx6au+WLT/GpSZxRcBNzRBEDBv3jz8umwmOts9KdcY/HAmQ+nrKUHSoS9gKRLKlL1sTIlxVVm7du1w8eJFVPfvjR+uJJdrjMUHrmO3Ee3YVCTechMRAO1FwFcOa4GO3s4AgHGbzgMARrevi1uPknH5fmKx41VUh47SEgQB8+fPx5IlS7By5UrMmDEe8tAYzJGHa7b1ta0y5H5oW0vEWBbkx2CSDCYkJARWD8JwcmZnHP4vqczZy6Q7+xquULYYCqiyyr3LMVt+BT2bupn8341p/3REVGraioDP3JGd7T2klQxz+/vgo9+vYkTbuhiy/nTJ4xm4CPjHH3+MTz/9FJ9//rmmH7kupUWIKtO///6LLVu2YP369ajtUh1jXKpjdId6Bu+fbW52h8YiI1MNiESwthTjqxH+aOBmB4UyC09SMhAiv4qYxHQ421ph1fAWqFvDFspMNUJ2h+NiTo3b3HPlpp7UxyxvIgIAHLoah8k/Xyry+RsL++LD365gXn+f7DdYAK521niuyMTKv25i24X7+a5fP7o1+jarVaFzLsrHH3+MTz75BMuXL8cHH3yg9ZrylhYhqmiCIKBbt2548uQJwsLCYGnJGxtDKFh2zNpSjI71nXH0ZvbxmbEd66GnjxvGfn8ey4f44UFiOtYc+Q9+Ho5Y+1ordFlxFFlqwWzKjvG3lIgA5C8CbmdtiWpWFnicnAEA6NPUDQlpSvx++QF+v/xAc922SR3w7YlI/H3jcaHxDFUE/JNPPsEnn3yCZcuWFRlMAoBIJIKfhxP8PCpxckSlsGfPHhw7dgwHDhxgMGlAuefKc2VkqjXBJACE3kvEhJe8AAADfGuj8+f/AMjuuvU0RYm29arjbNSzfOfKDXkMqKLxN5WIAOTv0GEvtcS6Ua0hlYghCEB8qhITf7hYqnEM2aFj0aJF+Pjjj7F06VJ8+OGHlf79iXSlUqkwa9Ys9OrVC3379jX0dMyatnPleY0P8MSR64/gVE0CsUiEZ6lKzXMxCWmFOnUZ+lx5RWNASUQAslfsggM8MX9PBOKSFAhce6rE14zYcLbQYwKA8QFelb618+mnn2L+/PlYvHgxZs+eXanfm0hf1q9fj//++w87duww6e3RqkDbufJcb3WtDy8XW7z2XTikEgsIBVL8tP3dGfpceUVj2SAi0tAUAdeBIYqAL1myBB999BE+/fRTzJ07t1K/N5G+JCQk4JNPPsGECRPg5+dX8guoQtlaWWh9fFJnb/RtVgvjN52HQqVGYpoKAFDD1kpzjczJptAKp62JJ/kxoCQijarYoWPZsmUICQnBwoULERISUmnfl0jfFi9eDIVCgUWLFhl6KgQU2rIGgImdvDCohTtGbzyXr1bvgfA4jM3J4vbzcISrvTUu3M3f+MFQ58orC7O8iaiQDScisfjA9VJfLwhqiERihPT3qdSiyp999hlmz56Njz/+GAsWLKi070tUXprqAonpSFVmwdbKAu5ONrDNiEfTpk0REhKC+fPnG3qahMJZ3rUcpDg7pwfuxqdqtq+VWWoErj0NFzsrrBreEnWqV4MqS42Pfr+Kc1HPAMBssrwZUBKRVmUpAg61Cqn/fIczv3yB+vXrV8r8Pv/8c8yaNQsLFizAxx9/XCnfk6i8khUqyHPqn0ZpqX8qVSYhNXQfLu5cB7cappu4UdVsORON+Xsiimy1WFqLBjdnHUoiMl/JClWxRcC9c4qAd/O0RbfOHWFtbY0zZ87A3t6+Que1YsUKfPDBB/joo4/wySefmPRdP1V9pbk5y17lF0EqscDSIF8E+bOelTFIVqjQdslhKFRl66eel1QixsWQXibfKIEBJRGVqDRFwK9fv4727dujR48e2LVrF8TiijmivWrVKsycORPz5s3DwoULGUySUSvr8ZFclX18hIomD43B9O1h5X79muEtzaKFKwNKItKbvXv3YvDgwZg/f36FbEOvXr0aM2bMwNy5c/Hpp58ymCSjxkDEdPDGoGQMKIlIr5YsWYKQkBDs2rULr776qt7GXbNmDaZPn445c+Zg8eLFDCbJqHGr1PSU5Vy5tUSMZUF+ZnVDwICSiPRKEAT83//9Hw4cOIAzZ87A11e3MkQA8OWXX+Ldd9/Fhx9+iKVLlzKYJKOXm8xhZSnGVyP80cDNDgplFp6kZCBEfhUxeWoUDmklw8phLTHhhwv52piKACw0g2SOqqS058qD/GVmdyPAgJKI9C41NRUvvfQSnj9/jgsXLsDZ2bncY3399deYNm0aPvjgA3z22WcMJsno5S03Y2UpRsf6zpoe0GM71kNPHzeM/f48gOxSNP97rRVEIuDrf24XCijNodxMVZR7rnzt5l8g33cQP2z8ttC5cnPDwuZEpHe2trbYvXs3kpOTMXz4cGRmlq/l2P/+9z9MmzYN77//PoNJqjLCY5MQmVO7MCNTrQkmASD0XiLq1qim+Xrpq75YtP8alJmFt8YFAHeepOLqg+eVMGsqC5FIBD8PJ3ggHuK7F9C3WS34yhzN+j2KASURVQhPT0/s3LkTx48fx/vvv1/m169duxZvv/02ZsyYgeXLl5v1GzVVLQVb7uU1PsATR64/AgCMbl8Xtx4l4/L9xGLHiy1mPDIspVIJKyurki80A+a1wU9ElapLly744osvMHXqVPj5tUCb3kGFOoRou6tfv349pk6diunTp2PFihUMJqlKSVVmaX38ra714eVii9e+C4dHdRuMaFsXQ9afLnm8jPKt8FPFUyqVkEgkhp6GUWBASUQVanTw69hzPQnzz6lg+d+pQs97u9giOMATgf4y2Esl+OabbzBlyhS8++67WLlyJYNJqnJsrSwKPTapszf6NquF0RvPQaFSo1Xd6nBzsMaRGV0AAK521vjsVT+s/Osmtl24n388M0vuqEq4QvkCf0uJqMJoymzY+cGyiPy/qKepmL8nAosPXkdvp6f4auZkvPPOO1i9ejWDSaqS3J1s8n09sZMXBrVwx6jvzuK5Inu1cU/YA+wJe6C5ZtukDvj2RGS+pJxcsgLjkfFQqVQMKHMwoCSiClGoEHARwWFumKlQZWHPkxroN30F1qycwWCSqixfmSO8XWwR9TQVbg5SfDSgKe7Gp2LbpA4AAGWWGoFrS97qzs3ybu7uUMEzpvLiCuULDCiJSO/koTHl6CqRHUBes26C3y8/MKuCwGRaRCIRggM8MX9PBB4+V8Bzzv4SXzNiw9lCjwkAxgd48ebKiDGgfIFZ3kSkV8kKFebIw3UaY7b8ClKYiEBVWKC/DNYS3T5ipRIxgnhjZdQYUL7AgJKI9Gp3aKymNVlBXRq5Ys/Ul3Dwnc6QTwmATy17rddlqNSQh8ZW5DSJKpS9VIKlQbp1iVoW5Gd23VaqGmZ5v8CAkoj0RhAEbDodrfU5B6klVg9vienbw9DvyxNYdugG1vyff5FjbT4dBTbyoqosyN8DIf19yvXakP4+PPZhxARBwJWYRDy2dkdqzeY4dDUOV2ISzfo9i7c+RKQ3uR1CtKnnbIv4lAzceZICADgX9Qyy6jZo5u6AiAKdQHI7hHz5939o7GZfZL1KImM3qbM3XOyssqsd5Kzcaws5cn+zrSViLAvyYzBppJIVKshzenlHPU0F6vQGAEz++RKAwmXQzAkDSiLSm+I6hEQ/TUUNWyv413FC6P1E9GnqBjtrS3hUr1YooMy1+vB/mv825zdqqtqC/D3Q08cNu3MCEW03XV4utgh+yQtB/jJucxspTRm0Io70APnLoC0N8kWQv0clztCwRII5r88SkV7tuhSDmTvCiny+nWcNzOjVCLbWlrgY/QwvNXDBZ4du4IiW2nsF5V3BMbc3ajIdgiCgS9AYWDm5Yco702FrbQmZkw2auztwBd6IFSqDVkoh/X0wqbN3BczI+PA2iIj0RluHkLzORz/TlEexshDjQkhP3M7ZAi/Ji3qVakzfHoanKUqzeaMm0yESiaB4cAuNXKQY0oo3RVVB+cqgZVt84Dpc7azN4ggDk3KISG8KdggpyNXeWvPf07o3wOk7T3E3Pq1c32vxgevYzUxwqoKSkpLg5ORk6GlQKbAMWulxhZKI9CZvhxBtZ2lm9mqEtvVqwMJChEv3EvDhris6fb/Z8ivomXMWk6iqSEpKgqOjo6GnQaWQtwzagleaopePGzyqV0PvNcdw61H27oqfhyPmD2gKW2tLCIKARfuv40xkvGaM3DJoYzrUM8jPUFn4LkxEepO3Q4g2s38r+k7/xwnt4GpnDUEQkJKRhY/3RuBa3HN8PsQPzWWOEAQBKrWAzw7dwOk72W/W5vJGTaaFAWXVULAM2sHwh/jmWCR2Tu6Y77pvRrfGjO1hOBMZj/quttgyoT26rTyKjMwXyTubT0dhdPu6Jn1OlgElEelVoL8Miw9eh6KYTEht3t56Cc8V2dtCvZu6YfkQPwz8+iQW7b+mebxpbQf8NLE9Wn36l+Z15vBGTaZDqVRCoVAwoKwCCpZBOx/9rNA11atJ4GRjpVmRvPMkFc8VKnRtXBN/RDwE8KIM2tUHz+ErM92/d56hJCK9Km+HkNygMXsMS6hzClDkfdxBapmvcHDeN2qiqiApKQkAGFBWAcWVQcuVkKbC05QM9G1WCwDQso4TvFxs4VG98Hny2FKMV5VxhZKI9C7I3wNPU5TZmZGCAJRy9XDlsBbo6O0MABi36bzm8Q/7NEZ/39pwtJFg8k//FnpdbGK6Sd/5k+lgQFl1pCqzSnXdpC0XMbtvE7zdrQFuPkrGxegEZGYVPkWeauKJOQwoiahCTOrsDVFGMhYeugOxxAoiiLQm6uSVW8NySCsZ5vb3QfDmCwCAz/64ic/+uImX6jtjTn8fDF1/Gqo8b9im/kZNpoMBZdVRUhm0XDceJmN8znsVABye3gX/PU4uPJ6JJw9yy5uIKsyZX75ExrbpmNenIbxcbEv9ul2XYtHR2xlO1fJ3xDl1Jx52VpZo7Gaf73FTf6Mm08GAsuooqQxaLle7F+XQRrStgzRlpiZxMC9ZKcerqvguTEQV4ubNm9i8eTNWr16N17s2xsQujRAem4QHSQrciHuONUdetFW0s7ZENSsLPE7OAAD0aeqGhDQlUhSZ8HSuhuicWpUtPBzhbGeFe8/y16409TdqMh2JiYkAGFBWBQXLoC0c1Ay9mrrB1c4aP09sj1RlFrquOIrX2tfF4JbuEEGE249T8GaBYzkiAN6utmju7mCQn6OysPUiEVWI4cOH49y5c7h16xasra3zPScIAnqsOqZ5o67tKMW6Ua0hlYghCEB8qhJLDlzH7ccp2Pp6e9hLJVALAtKUWVjx501NRmXuG/Xh6V2Y5U1VwqZNmzBhwgQolUpIJOxJb+y2nInG/D0RJR7XKcmiwc1NvrwZVyiJSO8uXbqEHTt24Pvvvy8UTAKF61XGJSkQuPaU1rGGfnOmyO8jABgf4MVgkoyeIAgIj03Cv48yUb1VXxy5+RTuTjbwlTny99eIlbcMWl5SiRhBZtB6kSuURKR3/fr1Q3R0NMLDw2Fpqf2+NVmhQtslh3V4oxYglVjgYkgvdsoho5WsUEEeGotNp6MRlaemYS5vF1sEB3gi0F8GeylXLI3Rb5fuY8aO8nf1WjO8JXt5ExGV1fHjx3Ho0CEsWrSoyGASKH+9yhdEaIfbpc7EJKps8tAYtF1yGAv2RCBaSzAJAFFPUzF/TwTaLjkMeWhMJc+QSuPSb98g4e+N5XptSH8fswgmAQaURKRHgiBg7ty5aNWqFV599dUSrw/y90BIf59yfa8O1g+wZdE7mDNnDrjRQsZmw4lITN8eBoVKDQEo8gxe7nMKlRrTt4dhw4nIypsklWjr1q1YvHgx5g3tiNXDW0AqEUOE7PPb2uQ+J5WIsWZ4S0zq7F15kzUw7hMRkd4cPHgQp06dwqFDhyAWl+5+dVJnb7jYWWGOPBwZOdvf2j58c9/ArSViLAvyQ6D/AHRwSsX06dORnp6ONWvW8CwaGQV5aEx2Uf9yWHzgOlztrM1mVcuYnT9/HhMmTMC4cePw/vvvQyQSoaePG3bnHGGI1LLq7OVii+CXvBDkLzO7ozg8Q0lEeqFWq9GqVSs4Ojri6NGjZQ7ukhWqYt+ovYt4o163bh3eeustvPnmm1i7dm2pA1miiqD72eDs1S2eDTas2NhYtG3bFp6envjnn3+0VqrILYOWmpEJW2tLyJxs0NzdwWxvbPnbSkR6sX37doSFheHkyZPlekO1l0owpqMnRneoV6Y36ilTpkAqlWLixIlQKBTYuHEjLCx4rpIMY3doLDJUalhbivHVCH80cLODQpmFJykZCJFfRUxiOrZN6gB3JxukKFQAsgv5bzwVpRkjQ6WGPDTW5MvMGKu0tDQEBgbC0tIScrm8yEoVfh5O8PMwwASNFFcoiUhnKpUKTZs2RePGjbFv3z6DzOGXX37BmDFjMHToUGzZsoU1/qjS5a2vamUpRsf6zjh68wkAYGzHeujp44ax35/Htkkd8O2JSPx947HWcVhf1XAEQcDIkSOxd+9enDp1Ci1btjT0lKoMrlASkc42b96M27dvY+fOnQabw8iRI2FtbY0RI0YgIyMD27Zt07qyQFRRwmOTNMc1MjLVmmASAELvJWLCS16lGkcAcOdJKq4+eA5fGTvqVKZPP/0Uv/76K3bt2sVgsox42IiIdKJQKPDJJ59gxIgRaNGihUHn8uqrr0Iul+PgwYMICgpCenq6QedD5uVBYtG/b+MDPHHk+iPN13P7+eDQu53x9Uh/1KmuvXVobDHjkf7t2rUL8+fPx6JFi0pVpYLyY0BJRDpZu3YtHj58iIULFxp6KgCAAQMGYN++fTh69CgGDhyI1FTt9f+I9C1VmaX18be61oeXiy0+//MmAGD69svoufoY+n5xAuejn+H7cW21j5eRWWFzpfxCQ0MxduxYjBgxAiEhIYaeTpXEgJKIyu358+dYsmQJJkyYgIYNGxp6Oho9e/bEoUOHcP78efTt2xfPnz839JTIDGgrsj+pszf6NquF8ZvOazK/45IUmud/PHMXdWtUg1O1wmd+bZnlXSkePnyIwYMHo2nTpvj+++95brWcGFASUbmtXr0aKSkpmD9/vqGnUsjLL7+Mv/76C+Hh4ejVqxcSEhIMPSUyce5O+beuJ3bywqAW7hi98RyeK7JXGy3EIrjYWWmu6dusFp6kZCAxTVVoPJmT9q1w0h+FQoGgoCBkZmZi9+7dsLHhn3l5McubiMrl6dOn8Pb2xqRJk7By5UpDT6dIly5dQq9evVC3bl389ddfcHFxMfSUyETlzfJ2c5Di7JweuBufqtm6VmapMXLDOfz6RgdYWYohCMCzVCU+3X8N1x8ma8ZhlrfuNHUiE9ORqsyCrZUF3J1s4Ctz1PyZCoKA8ePHY/v27Th+/DjattV+9IBKhwElEZXL+++/j2+//RaRkZFGH6SFh4ejZ8+ecHV1xeHDh1GrVi1DT4lM1JYz0Zi/J6LIVoultWhwc9ahLIdkhQrynAYJUUU1SAjwRKC/DOu+XI0PP/wQv/zyC0aMGGGA2ZoWBpREVGYxMTFo0KAB5syZgwULFhh6OqVy48YN9OjRA3Z2djhy5Ag8PFiRmPSPnXIMRx4aU+oWrpZiAXG/r8L0oJewaNGiSpujKWNASURl9uabb2LXrl2IjIyEg4ODoadTanfu3EH37t1hYWGBv//+G56enoWuKc1WGVFx5KExmL49rNyvXzO8JXt5l9GGE5Fl6p8uCAJEIhHm9muCN16uX4EzMx8MKIlIq6ICK5v0J/Dx8cHy5csxY8YMQ0+zzO7evYsePXpAqVTiyJEjmuz0smyV2UvZhYeKV9YAJ1dIfx9M6uxdATMyXQzgjQMDSiLKp6TASqpKQuql/bj023q4OtkbYIa6i42NRc+ePZGUlITDhw/jZoZDqbfKrCViLA3yRZA/t8ypeGXZgrWWiLEsyI+BTRnxiIHxYEBJRBql+QAUBDVEIhGkEosqHVg9evQIvXr1wlOXFrBqX/YD+VxJotJIVqiwO+cGLbKole+XvBDkL2NAUw55k6AWvNIUvXzc4FG9GnqvOYZbj1IAAL4yR3wyqBmsLcWwtrTAjn/v45vjkZoxRAAWMglKZwwoiQiAeW7RbTlxAx8duFPu13OrjEpLc4QkSYFjp87ii5XLcXDnz3i5uSfP5pZT3jJNAoB2njVw71kadk7uiAk/XNAElPundcLqw7dw+PpjONpIcGRGF4zYcBa3H2c/zzJN+sHC5kQEeWhMuYJJAFh84Dp2h8bqeUYVL1mhwuK/onQaY7b8ClLYHo9KQSQSwc/DCX2b1cKogAZIv3UG4sQYBjA6CI9NQmROMAkA56Of4eFzhdZrHXLOPVezsoAqS42kPIXkBQB3nqTi6gN21NIF19eJzFyyQoU58nCdxpgtv4KeTd2q1Jbd7tBYzdb+jxPawdXOGoIgICUjCx/vjcC1uOdYPsQPretVR4YqC8kZmfhk7zVci3vxoZOhUkMeGsutMiqTBg0aQCQS4ebNm+jcubOhp1NlPUhML9V1H+y8gm/HtMbM3o3hbGuFOfJwPEnJKHRdbGI6fGWO+p6m2ag67/5EVCHyBlZFnUFytrXCquEtULeGLZSZaoTsDsfFuy9aGVa1wEoQBGw6Ha35+u2tlzSt8Xo3dcPyIX4Y+PVJ/HntIebIw5GlFtC9SU18/Zo/uq88lm+szaejMLp9Xa40UalJpVJ4enri5s2bhp5KlZaqzCrVdW+87I2lB29gf3gc6lS3wbY3OiLsfmKhM62p3G3QCbe8icxYwcDqYPhDDF1/BjEJafmu+7BvE4TeS0S3lUfxwa4wrPm/lrAQ5w+gNp+OQlU5kl1wqyw3mAQAe6kl1Dk/x+Hrj5Glzv7v0HsJkDnZIG/cyK0yKq/GjRvj1q1bhp5GlWZrZVHiNdWrSdCnaS3sD48DANxPSMfl+4loXa964fGq0A6LMWJASWTGSnsGaYBvbfx49i4A4EpMEp6mKNE2zxtyVQustG2VrRzWAqc/7I6ZvRpj5o7CNe2CX/LC0ZtPoC1mji3l1htRrkaNGnGFUkfuTjYlXpOUrkJGZhbae9UAkB1g+tdxws1HyYWulZViPCoaw3EiM1aaM0hO1SQQi0R4lqrUPBaTkKb1zbyqnEHStlWWG0QOaSXD3P4+CN58QfNcYEsZBvjWxvBvzmgfj1tlVEaNGzfG2rVroVKpIJGwUH55+Moc4e1iq8nyXjioGXo1dYOrnTV+ntgeqcosdF1xFFO3XsK8AU1hKRbBUizChhORuBKTpBknN8u7uXvV6fpljBhQEpmx0p5BEgpUpCzqvGBVCayK2yrbdSkWiwN94VRNgsQ0FQb61sa7PRpi1HdnEZ8nqM43HrfKqIwaN26MzMxMREVFoVGjRoaeTpUkEokQHOCJj/ZcBSDC/D0RmL8notB1p+7E45WvTxY5jgBgfIAXz0HriFveRGasNGeQEnPKa9SwtdI8JnOy0bq6WVUCq7yrq3bWlqhpb635uk9TNySkKZGYpsIA39qY2bsxRm88hwdJ2suRANwqo7Jr3LgxAPAcpQ6ePXuGvV/Ph1qVAa1nUUpJKhEjiPVkdVY13v2JqEKU5gwSABwIj8PYDvWw5sh/8PNwhKu9NS7kyfLOVVUCq7xbZfZSS6wb1RpSiRiCAMSnKjHxh4sAgDX/1xJPkjOwYUxrzWtf23hOE2Rzq4zKy93dHdWqVcPNmzcxcOBAQ0+nyvnzzz8RHByMtLQ0vP7pK9gRU/7VxWVBflWq5JmxYqccIjNWsNNE3jNICWlKzRkkFzsrrBreEnWqV4MqS42Pfr+Kc1HPNONUxU4TeVu26WIRW7ZROfn7+6Nt27b49ttvDT2VKiM1NRWzZs3C2rVr0atXL3z//ffw8PAwy05fxoYBJZGZM9fAKlmhQtslh6HIqcFZZoIAa0sx/v2oN1c3qFxGjBiBuLg4HDt2rOSLCWfOnMHYsWMRGxuLFStWYMqUKfluYOWhMZgjD9fU1dX2npZ7tbVEjGVBfmydqkc8Q0lk5gL9ZbCW6PZWUBXPINlLJVga5Fv+AUQiPDv0Nf7+44D+JkVmhbUoS0epVCIkJASdOnWCi4sLwsLC8NZbbxXaDQny98CFuT2xcFAzeLnYah3Ly8UWCwc3x8WQXgwm9Yy31URmLjewmr69cO3F0qqqZ5CC/D3wNEVZrq2yGd088c8NawwePBjz5s3Dxx9/DAuLkpOciHI1atQIDx8+xPPnz+HgwHO42oSHh2Ps2LG4evUqFi1ahFmzZsHSsuj3GnupBGM6emJ0h3oIj03CgyQFUjMyYWttCZmTDZq7O1SZYzlVTdX7BCAivdMlsArp71Ol7/QndfaGi51VubbK3u65C5999hlCQkJw8eJF/Pzzz6hRo0alzZ2qtkaNGsGqVgP8+HcYatf1gq2VBdydbOArczT7oCcrKwurVq3CvHnz0LBhQ5w/fx7+/v6lfr1IJIKfhxP8PCpwkpQPz1ASkYY5n0FKVqiwOzQWm05HF+rxCwDeLrYIfskLQf6yQquxf/75J0aOHAlHR0f89ttvaNmyZSXNmqqiZIUK8tBYbDwZibvPCpff8naxRXCAJwL9ZbCXml/R88jISIwbNw6nTp3CzJkzsWjRIkilUkNPi0rAgJKI8tElsDIFgiCUa6ssOjoar776Kq5fv44NGzZg9OjRlThrqirKetO2NMgXQf7mscwmCAI2btyI6dOnw8XFBT/88ANefvllQ0+LSokBJRFpJQgCVny/HQuWf4ENm36Es6MdzyCVID09HVOmTMEPP/yAadOmYcWKFbCysir5hWQWWNqmaA8fPsTrr7+O/fv34/XXX8eqVatgb29v6GlRGZje8gIR6YVIJEINpCD91hmMaO/FhJNSsLGxwaZNm9C+fXu8++67uHTpEnbs2IHatWsbempkYPLQmHIFkwCw+MB1uNpZm8zxkoJ27tyJyZMnw9LSEnv37mWh9yqKZYOIqEjp6emwtrZmMFkGIpEIU6ZMwdGjRxEZGYlWrVrh1KlThp4WGVCyQoU58nCdxpgtv4KUjEw9zUh/BEHAlZhEHLoah12XYnDoahyuxCSiNJufCQkJGD16NIYNG4auXbvi6tWrDCarMK5QElGR0tLSYGNTNdopGpuAgABcunQJw4cPR9euXbF69WpMnTqVxwXM0O7QWM2ZSWtLMb4a4Y8GbnZQKLPwJCUDIfKriEnMTs55r0dDDGrpDlWmgIQ0JUZsOAsAyFCpIQ+NNZrmAbmJRZtORyOqqLPWxSQW/fXXXwgODkZKSgq2bNmCUaNG8d9GFccVSiIqUlpaGqpVq2boaVRZtWrVwpEjR/D2229j2rRpGDt2LNLS0gw9LapEgiBg0+nofI9tvXAP3VceQ/+vTuLIjcdY8mp2gf3gAE80rmWPPmuOo88XxzFtW2i+120+HVWqlb+KJg+NQdslh7FgTwSitQSTABD1NBXz90Sg7ZLDkIfGaB5PS0vDtGnT0Lt3bzRp0gTh4eEYPXo0g0kTwICSiIqUnp7OgFJHEokEq1evxtatW7Fr1y4EBAQgMjLS0NOiShIem4TIp6mabO6MTDWO3nyieT70XiLq1sj+N/bGy95YdugGVFnZVz9JztBcJwC48yQVVx88r6ypa7XhRCSmbw+DQqWGAO1Z6sh5XACgUKkxfXsYNpyIxLlz5+Dv74+NGzfiq6++wp9//ok6depU3uSpQnHLm4iKxC1v/Rk5ciSaNWuGV199FW3atMHPP/+Mfv36FXm9pnxRYjpSlVksel1FPUgsXGcyr/EBnjhy/RHsrC1Rw9YKfZrWQr/mtQAAG09GYV94XL7rYxPT4StzrLD5FkfXxKL4fSvR1MkJe/bsQePGjfU8OzI0BpREVCSuUOqXn58fLly4gNGjR2PAgAFYuHAh5s6dC7H4xWaRrmfTyLikKrOKfO6trvXh5WKL174Lh1RiAWtLC0glYgStOw13Ryl+m/ISbj1Oxq1HKS/GM1Bijq6JRYKgRs2B0/HHvF5wsmWRclPEgJKIisQVSv2rXr069u7di0WLFuGjjz7C+fPnsWXLFjg6OhYqeq1N7tm0xQevm1XR66rK1kp7hYRJnb3Rt1ktjN54DgqVGgqVGikZmZCHxgIAHiQpcPHuM/jJnPIFlLYGaiaQN7FowStN0cvHDR7Vq6H3mmOa+e2aHAAbSfbNkYVYjMa17NH3i+O48TAZIpEYagB7wx8ZTWIR6RfPUBJRkbhCWTHEYjEWLFiAvXv34vjx42jbti0W7jhd7rNpZLzcnQrfkE3s5IVBLdwxeuM5PFe8WHHcE/YAXRrXBAA4SC3Rso4Trj/Mf2ZSpmW8ilYwsehg+EMMXX8GMQn5E8yGrD+N/l+dRP+vTmLNkVu48fA5bjxMzneNsSQWkf5xhZKIipSWlgYHBwdDT8NkDRw4EBcvXsTAaQvx/aWEco1h6kWvqzpfmSO8XWwRlZOYU8tBio8GNMXd+FRsm9QBAKDMUiNw7Wl8/scNfD60Bca0z17BW3v0DiJyknBEALxdbdHcvfL/PeYmFuU6H/2sxNcMb1MH2y/ez/dY3sQiQ50DpYrDgJKIipSWlgY3NzdDT8OkuXnUA9qMBFRZQDmTbWbLr6BnUzeT7K1e1YlEIgQHeGL+nggAwMPnCnjO2a/12oQ0FV7/8aLW5wQA4wO8DJKQVVJiUUFuDtbo4OWMGdsva33ekIlFVHH47kNEReKWd8XbHRqLjEy1Jpj8cUI7uNpZQxAEpGRk4eO9EbgW9xxvda2PIa084OVsi9e3XMTfNx5rxjC2oteUX6C/DIsPXoeimLOxJZFKxAgy0Cp0cYlF2gxtVQdHbjxCQppK+3hG2PGHdMczlERUJBY2r1jail6/vfUS+n15Av2/OonvTkZi+RA/AMDpO/EI3nyhyO1Gnk0zXvZSCZYG+eo0xrIgP4OtQBeVWFSUYW08Cm135xuPK+kmiQElERUpPT2dWd4VqGDRawD5kjTspZZQ5wSJl+8n4t4z7V12jKXoNRUtyN8DIf19yvXakP4+Bj0jqy2xqCgdvGpAYiHGidtPi7zGEIlFVPF4m0BEReIKZcUq6mzaymEt0NHbGQAwbtP5Uo/Hs2nGbVJnb7jYWeUrDaVtTTn3lKS1RIxlQX4GT7gS4u+iWmYyUi1sIRKJsXBQM/Rq6gZXO2v8PLE9UpVZ6LriKABgeNs62PnvfWhbLDdkYhFVPAaURFQk1qGsWEWdTZu5IwwAMKSVDHP7+yB484XSjcezaUYvyN8DPX3csDuneH2kluL1Xi62CH7JC0H+MoNtc6vVahw4cACrV6/G33//jTrdR0HcbgQAYP6eCE2SUUEztocVOaYhE4uo4nHLm4jyEQQBV2IScTA8DiLvDrgPZ1yJSeT5vApQ0tm0XZdi0dHbGU7VStcRh2fTqgZ7qQRjOnriyIwu2DP1Jawf3Rorhvoh/c8vMFh6HUdmdMGYDvUMEkympKTgf//7H5o0aYJXXnkFqamp2LZtGy7v3gCppGxnKQsyZGIRVTy++xARAO0t/2r0fw/yJ4D8f6fY8q8CFDybZmdtiWpWFnicnAEA6NPUDQlpSiQWkS1bEM+mVS0ikQh+Hk7wy2l29LW9Eg+vXTDICt69e/fw9ddfY8OGDUhOTsaQIUPw448/okOHDpprlgb5YnoxK5AlMWRiEVU8kcBlByKzV7DlX0nnutjyTz8EQUCPVcc0Ra9rO0qxblRrSCViCAIQn6rEkgPXs8sGdamPMR3roYatFVIzspCRmYUBX53Es1Sl5mza4elduJ1YhU2bNg1HjhzBtWvXKu17nj17FqtXr8auXbtgZ2eHN954A2+//Tbq1q2r9foNJyKx+MD1Mn+fkP4+mNTZW9fpkhFjQElk5vgBYVhbzkRj/p6IIlstlo6ARYN9WYeyivvmm28wdepUpKamwtrausK+T2ZmJnbt2oXVq1fj3LlzaNiwId59912MGzcOdnZ2Jb6+rDegxpBYRBWPZyiJzJg8NKZcwSSQ3fJvd2isnmdkfgL9ZbCW6PBWLAhQqzJwcN0nePas5JZ4ZLx8fX2RlZWF69fL92+yJAkJCVi+fDm8vb0xYsQI2NraYu/evbhx4wamTp1aqmASyE4sujC3JxYOagYvF1ut13i52GLh4Oa4GNKLwaSZ4AolkZlKVqjQdslhnbt3XAzpxXNROpKHxuh0Nm1I7WRsnD8ZNjY2+OabbzBo0CA9zo4qy/Pnz+Ho6IgtW7Zg9OjRehv31q1b+OKLL7B582ZkZmZi1KhRePfdd9GiRQudxxYEAeGxSXiQpEBqRiZsrS0hc7JBc3cHHr8wM1yhJDJTu0NjNVtWC15pipOzuiF66QA0cnuxSvFW1/o4MqMLIhf3R/cmNQuNkdvyj3Sja9Hrle+MQEREBNq0aYPBgwdj9OjRiI+P1/MsqaI5ODigXr16CA8P13ksQRBw5MgRvPLKK2jcuDF27tyJWbNm4d69e/j+++/1EkwCLxKL+jarhSGtPNC3WS34yhwZTJohBpREZqhgy7+D4Q8xdP0ZxCTk78RSUrs/gC3/9GVSZ2+sHt4CUokYIrw4g1ZQ7nNSiRhrhrfUnGOVyWTYs2cPfvzxR+zfvx/NmjXD7t27K2fyVGa55bkOXY3DrksxOHQ1DldiEtGseXOdAkqFQqEJGHv27KkJIO/evYsFCxbAzc1Njz8F0QvcpyIyQ7kt/3IVFTBevp9Y7Dh5W/6xQ4vudC16LRKJMGbMGPTs2ROTJ09GUFAQRo4ciS+//BIuLi6V9WNQMbSV58rL3nccks7/jmSFqkzluR49eoR169Zh3bp1ePLkCQYOHIg1a9agW7duXC2kSsGAksgMFdXyr7zY8k9/cotej+5Qr9xn02rXro3du3dj69atmDZtGpo1a4Z169bh1VdfraSfgrQpmB2tTTJsIGo7Am0XH8bSV0suzxUWFoY1a9Zg69atsLS0RHBwMN599100bNhQ39MnKha3vInMUFEt/8o9Hlv+6Z2uZ9NEIhFGjRqFa9euISAgAEOGDMGIESPw5MmTCp45abPhRCSmbw+DQqWGAO2ldrKJIBKJoMhUY/r2MGw4EVnoCrVajb1796J79+5o2bIljhw5gk8//RQxMTH4+uuvGUySQTCgJDJDJbX8K/N4zPI2WrVq1cJvv/2GrVu34q+//kKzZs2wc+dOQ0/LrOirPFdKSgq+/vprNG7cGIMGDUJ6ejp+/fVX3LlzBx988AGqV6+uz2kTlQkDSiIzVLDln67Y8s+4iUQijBw5EteuXUPnzp0xbNgwDB8+HI8fPzb01ExeskKFOXLdsrY//C0M730wGx4eHnjvvffQunVrnDlzBmfOnMHw4cMhkbAVKhke61ASmaGCLf8WDmqGXk3d4GpnjYQ0JVKVWei64mix7f4AsOVfFSQIArZv346pU6dCJBLhf//7H4YNG1aqvz9NzcHEdKQqs2BrZQF3JxuWiSlG3k5IC15pil4+bvCoXg291xzDrUcpmuve69EQg1q6Q5UpICFNiREbzmqeEwQBihObMb5TfUydOrXItohEhsSAkshM6aflH7BocHO2/KuCHj9+jKlTp2Lnzp0YMmQI/ve//xVZUqakzGRvF1sEB3gi0F9WpsxkU1fwxq2dZw3ce5aGnZM7YsIPFzQBZXCAJ9p51cA720KhyhLgam+NJ8kZeUeCl3M1/D2TGdtkvLjlTWSmdG75h+xaiEFsq1Yl1axZEzt27MD27dtx7NgxNGvWDNu2bStUU1QeGoO2Sw5jwZ4IRGsJJgEg6mkq5u+JQNslhyEPjamM6VcJueW5cv9Ez0c/w8PnikLXvfGyN5YdugFVVvaV+YNJABAhKj4d4bFJFTthIh0woCQyU/ZSCZYG+eo0xrIgP7ZdrOKGDRuGa9euoUePHhg5ciSGDBmChw8fAih9ZnLucwpV0ZnJ5qg05bnsrC1Rw9YKfZrWgnxKAORTAjDQt7bWaydtuYgtZ6KRrFDpe6pEOmNASWTGytXyL2cFa3bfxgjk6qRJcHV1xa+//oodO3bg5MmTaNasGT743w69ZCabs9KU57K0EMHa0iJ7tX/daUzdegnzBjTN1wI116PnGVwJJqPFgJLIzJW15Z+VpQjx+1Yh7u8tlTdJqhRDhw7FtWvX0L3PAPwaJdbcPJTHbPkVpJh5fdLSlOdKTFMhJSMT8pwA/EGSAhfvPoOfzEnr9VwJJmPFgJKIEOTvgQtze2LhoGbwcrHVeo2Xiy0WDm6OSx/1wazhXfHpp5/ixIkTlTxTqmguLi4YNG0hxBJrICcB5McJ7XDwnc44MK0Ttr/REU1rOwAAnG2t8ENwW/wzsyv+ePdltKn3og5ihkqtCZLMVWnLc+0Je4AujWsCAByklmhZxwnXHz4v8XVcCSZjwixvIspHUxqmmJZ/WVlZ6Nq1K+7evYuwsDAWVDYhBTOTgewg57kie7Wxd1M3vNO9IQZ+fRLLh/jhQWI61hz5D34ejlj7Wit0WXEUWWrBrEtKCYKA48eP49sNG3DM9mVYVK8NkUhcZHmu6tUk+HxoC9SpXg0A8MOZaGw9f69U30sqEeNiSC+eZSaDY0BJROVy9+5dtGjRAn369MG2bdvMLmgwVVdiEjHof6eKfH5IKxnGdfTEoP+dQsTHfdD58380dUl3v/USlh28jrNRzzTX7327k9n0eX/06BF++OEHfPfdd/jvv//QsGFDdBj9AU6ku5dYnsvaUoyvRvijgZsdFMosPEnJQIj8KmIS07FrcgBscioyWIjFaFzLHn2/OI4bD5MhArCQpbvICPCWhojKpV69evj222/xf//3f+jbty+Cg4MNPSXSg6Iyk1cOa4GO3s4AgHGbzsOpmgRikUgTTAJATEJaoW3e2MR0kw4os7Ky8Oeff2LDhg3Yu3cvLCwsMHToUGzYsAEvv/wyUjIy0XbJYShU6hLH2nrhHo7ezO61PrZjPSx51Rdjvz+PIetPa67p17wW3u3REDceJmse23w6CqPb1+VNHRkUz1ASUbkNHz4cwcHBmDZtGm7dumXo6ZAeFJWZPHNHGAI++xsr/7qJuTmVAYQC627aAppUE03MuXfvHj7++GN4eXmhf//+uH37NlatWoW4uDj89NNP6NIle6u/tOW5MjLVmmASAELvJaJujWqFrhvepg62X7yv+VoAcOdJKq4+KPnMJVFFYkBJRDr58ssv4e7ujtdeew1KpbLkF5BRKykzedelWM1KJQDUsLXS/LfMyabQCqetCZ3tU6lU+O2339CvXz94enpi5cqV6Nu3L86dO4ewsDBMmzZN63ni8pTnGh/giSPXH+V7zM3BGh28nLUmO8WWouYlUUViQElEOrGzs8PWrVsRFhaG+fPnG3o6pKOCW9Z21paoaW+t+bpPUzckpCmRmKbCgfA4jM05u+fn4QhXe2tcuJuQ7/WyUmY6G7Nbt25h1qxZ8PDwwJAhQ5CQkIANGzYgLi4O3377Ldq1a1fidnPB8lzFeatrfXi52OLzP2/me3xoqzo4cuMREtIKFzY31ZVgqjpM59aRiAymTZs2WLx4MWbPno1evXqhR48ehp4SlZOvzBHeLraaLG97qSXWjWoNqUQMQQDiU5WY+MNFAMCyQzewanhL/DOzK1RZaszYfhlZ6uxt8Nws7+buDob7YXSQnp6OXbt2YcOGDTh+/DiqV6+OsWPHYuLEifD1LV+HqSB/D/T0ccPu0Fh8/c9tPCrUYjE78OzbrBZGbzxX6NzlsDYemP/7Va1jm9JKMFVNzPImIr1Qq9Xo1asXbty4gbCwMLi4uBh6SlROW85EY/6eiBIzk4snYOErzTA2wEtPs6ocYWFh+O677/DTTz8hMTER3bp1w6RJkxAUFASpVKq/73M/AYPXns732MROXghsKcOo785qyjTl6uBVAyuHt0Sn5X9rrTdvTtn0ZJwYUBKR3sTGxqJFixbo1KkT5HI5s06rqGSFqtSZyVoJAtSZGXD6exm+XPU5unbtqtN8NLVRE9ORqsyCrZUF3J1s4Ctz1MvvWHJyMn755Rd89913uHDhAtzc3BAcHIyJEyeiQYMGOo+vTcF6n7UcpDg7pwfuxqdqtq+VWWoE5gSdq4a3wP1naVh9+L9845hzvU8yLgwoiUivfv/9dwQGBmLdunWYPHmyoadD5SQPjcH07WHlfv07bRywY8UsnD17FkOHDsXnn38OT0/PMo2RrFBBHhqLTaejEfU0tdDz3i62CA7wRKC/DPZSSZnGFgQB586dw4YNG/Drr78iPT0d/fr1w+uvv44BAwZAIinbeOWhn5VgYBHrUJIRYEBJRHo3ZcoUbN68Gf/++y+aNm0KoOJXmUj/NpyIxOID18v8upD+PpjU2RtqtRpbt27Fhx9+iGfPnuGDDz7Ahx9+CFtb7e0985KHxmCOPBwZOauk2j6ocn9rrCViLA3yRZC/R4njxsfH46effsKGDRsQERGBevXqYeLEiRg/fjzq1KlThp9SdzqvBIOdcsh4MKAkIr1LS0tDmzZtIJFIcOT4KRy49rRCVpmo4pU1sFsW5IdAf1m+51NSUrB06VKsXLkSrq6uWL58OUaMGFHkjYSugWxBarUaR48exXfffYddu3ZBEAQMHjwYkyZNQs+ePSEWG67gia4rwWuGtyz0501kCAwoiahChIWFocu49+HcdxrUouzahvpaZaLKlaxQYXfO1nNkUTcFL3khyF9W7EpZZGQk3n//fcjlcrz00kv44osv0Lp163zX6DPAiouLw+bNm7Fx40bcuXMHjRs3xuuvv46xY8eiZs2a5f4e+pYbQAuCGiJR6YPbogJoIkNgQElEFYIfkqZHc2whSYHUjEzYWltC5mSD5u4OZTq2cOTIEbz33nuIiIjAhAkTsGTJEtSsWVNvW8CftsrCT5u/w759+yCRSDB8+HC8/vrr6NSpk1Eer0hJSUGzAcEQdxgFiLNX6cuzEkxkSAwoiUjvuI1HJcnMzMQ333yDjz76CFlZWZg/fz5qtA/Ewv03IABY8EpT9PJxg0f1aui95hhuPUoBAGyb1AHuTjZIUWQX9951KRYbT0VpxhUEAc/+XIsGQhwmTZqE1157DU5OTgb4CUtvxowZWL9+Pc5dCsPlBInOK8FEhsCAkoj0iokGVBbx8fFYsGAB1q1bh7pTNkCwdwMAtPOsgXvP0rBzckdM+OFCvoDy2xOR+PvGY+0DCgJkDpY4OaePUa5GFvTvv/+iXbt2WLp0KWbNmgVAfyvBRJWJ79ZEpFe7Q2M1CRxFrTItH+KH1vWqI0OVheSMTHyy9xquxT3XjJGhUkMeGstSKGbA2dkZX3/9NboPHY8Zf7zoXX0++ln5BhSJEJucBXloLIL8ZUYdgGVmZuKNN95A8+bNMX36dM3jIpEIfh5O8ONxYqpC2MubiPRGEARsOh2t+fpg+EMMXX8GMQlp+a7789pD9F5zHP2/Oolvjkfi69f8C421+XQUuIFiPqq5lP6Iw9x+Pjj0bmd8PdIfdapr7xU+Y0cYeqw6hi1nopGsKNz72hh8+eWXCA0NxYYNGyql7iVRRWJASUR6Ex6bhMiczh9A9irTw+eKQtcdvv5Y0/M59F4CZE42yLuQJAC48yQVVx88L/RaMk2pyqxSXTd9+2X0XH0Mfb84gfPRz/D9uLZFXhv1NBXz90Sg7ZLDkIfG6GuqenH37l189NFHmDp1Ktq1a2fo6RDpjAElEenNg8T0Mr8m+CUvHL35RGt/4thyjEdVk62VRamui0t6cYPy45m7qFujGpyqaV/dE3L+p1CpMX17GDaciNTDTHUnCAKmTp2K6tWrY/HixYaeDpFe8AwlEelNaVeZcgW2lGGAb20M/+aM9vFyehqTaRIEATdu3MC+ffvw29GLgN/YYq+3EItQvZoET1OUAIC+zWrhSUoGEtNKt6W9+MB1uNpZV3gFgZK6Qu3cuRP79++HXC6Hg4NDhc6FqLIwoCQivSntKhMADPStjXd7NMSo784iPlWpfTxmeZschUKBY8eOYf/+/di3bx+ioqJgY2OD7j16IMNShYRMCQQACwc1Q6+mbnC1s8bPE9sjVZmFfl+cwPfj2sLKUgxBAJ6lKjHpx4tl+v6z5VfQs6lbhVQQKE3v8f/zd8PHM2chMDAQgYGBep8DkaHw3ZqI9MbdSXuCREEDfGtjZu/GGL3xHB4kFT5jqRnPUaqvqZEBPXjwAAcOHMC+fftw+PBhpKamom7duhg4cCAGDBiAbt26wcbGBlvORGP+nggAwPw9EZr/zmvQ/04BAKwtxfhqhD/+N6oVFMosPEnJQIj8KmIS0+Hn4Yj5A5rC1toSgiBg0f7rOBMZD6DiKggUbFGpTdTTVCz98w6shq/EwH4N9fr9iQyNdSiJSG8EQUCPVccQlZOYk3eVKSFNiVRlFrquOIr/Pu2HJ8kZSEx7sTL52sZzmq1LQVBDFR8Lu+OrETx+PMaOHQuZjIXOqwq1Wo2LFy9i37592L9/Py5dugSxWIyOHTtqgsjmzZsXKulTlhqm1pZidKzvjKM3nwAAxnash54+bhj7/Xmcmd0dM7aH4UxkPOq72mLLhPbotvIoMjLVEAHwdrXF4eld9FZSqOy9xwUAInaFIpPCgJKI9Cp3lUnXN5YxTSSIPrwFu3btQkZGBnr16oXg4GAMHjwYUilXLotT0hm+ivD8+XP8+eef2L9/Pw4cOIDHjx/DyckJ/fr1w4ABA9C3b184OzuXOE55uyz5yhzx1Uh/BK09hdMf9oDPgkOa5w692xmrD/+HPyIeah7b+3Yn+Mocy/x99DXfXOwKRaaCW95EpFeB/jIsPnhd5045H47oDrtxvfH1119jx44d2LRpE0aMGAEnJyeMHDkS48ePR9u2bfUaIBkiENOn0pzhCw7wRKC/DPZS3ese3rp1S3MW8sSJE1CpVGjWrBnGjx+PgQMHomPHjrC0LNvHTJC/B56mKMu44geMD/DEkeuPkPD/7d1/UNR1Hsfx1/LDXQT54S8QkF9dY6KA2BRaOtVYyAGVpHZdyA3U0TR33fRrpnK6uin/MOfOqemaG/s1oHMVd93lDRdjYV4/KDUzyTBKS04RFLwIkJ8uP/b+QFYXVlz5Liw/no+Z/WN3P9/v7jIMvPbzfX/en/Yu/dh6VmkLwvTeN3VaNDdYsTP9FTmgX2VtU4fhQNnS2aX12ysMnWMkazqB0cQMJQC3G6lZmyNHjqiwsFDbtm1TbW2t4uPjlZubq5ycHIWFhQ379UY7iI2EgTV8zv6w90dis6+XNmYlKCv58rZisVqtKisrs4fI77//XmazWTfddJP9UnZMTIyhz9HPlc/T7zc3XqGb54fq7tf2qrOrV1eFTdMTaVdpZoBZh+tbFBZoUWllvbbuOWY/Zs7xnUqc1qGoqChFR0fbb+Hh4S6H4Atn4/trOn8WGjCopjMhIkjP3LZAZh8vmX289faXJ/TyJ30tjEySnr19IbtCYdwjUAIYEZdfV9bHlbqynp4e7dq1SwUFBdq+fbu6u7uVlpamvLw8ZWZmymw2u/x6oxHERtpI/qzr6+u1Y8cOvfvuuyotLVVLS4vCw8PtAXLFihXy9/cf7lsfUktnl/51LuhXOQn6kpS/PE63Js7Rutc/15lO522mPnj4Bj1dfEi7jzbYH4ut2anmbz5RdXW1GhrOP+7t7a3IyEh7wBwYOKOiouTn5zeoXnioms6S3y3T8x8c0QffnlaQn692PXKD7np1r3443ToiNZ2AJzDHDmBE5C+P08yAKZcV1p7LSnSpnszb21upqalKTU1VU1OTioqKVFhYqDVr1mj69OnKzs5Wbm6ukpOTh/wn7WoQ63/f/Q2yf2y1jpnFFNvLa4YVJiXnfRltNpvKy8vtC2r27dsnk8mklJQUPfbYY8rMzFRSUtKohJ9pFl/lLI3RuiXReqe8Ro++/bXD8/cui9VtSeHKfm2vQ5icFWDW/1rPSpLuumau2q3dDmFSkl587hn7Je/W1lZVV1fr+PHjDrejR4/qww8/VG1trcM2oLNnz1b4wiVqvPY++2Nnu3vtYVKSyqubdM/1sfb7gedmtqdO8VZXT6+a+xeg6fyuUO6o6QQ8hRlKACPqUrNMcTP9lXd9rLKSIwzXkVVWVmrr1q3atm2b6urqlJiYqNzcXGVnZ2v27NkOYyfCYorLWRV9MRZfL3384FLtKftIJSUlKikp0alTpxQYGKiVK1cqMzNTaWlpg35+o23gjGBYoEV716/Q8YY2ewN8a0+vVv1ltx5ccaVuXxQuk0z64XSrni4+ZN9hZzgzgl1dXaqpqXEIm1/UdakiaMlFj9m8NklN7VZtKPlW8XMC9UrO1TKZTJrh3/cla3t5rcP4LeuuVtqC4ZdtAJ5GoAQwKuwLXpo71Xa2W/5mH0UE+2lheKDbZ7u6u7tVWlqqgoICFRcXq7e3VxkZGcrLy1N6ero6e+SWILb/yVs8upjiwhq+P9war1vmhyoyZKpSX/hYR+pbHcauXhyhzWsX6Z6tX+g/350+/4TNpuZdr6hp/781b948ZWRkKDMzU8uWLZOv79iqF3VXB4ENbqhZ/OeBGj36tvMvJANrOl/4xSLtrKxXScUpzQ3xU9F9S5Xz+ucOX7A2r03S6sVjq5QCuBxc8gYwKkwmkxIjg5U4Cv8zfXx8lJ6ervT0dDU0NOitt95SYWGhVq1apVmzZmnprx5Xp+9Vki4exP64OlELI4Jks9nU1WvTpve+c7hsOlINsl1ls9lUsPuY/f6Oijq9/HGV/nH/0kFjwwItuvvaaB2obnR6rpiVefr7m5t15ZVju9m2uzoIZLlhZvliu0LlL49T2oIwrXv9c3V29Spkqq9Wxofpob99JUk60dihr0406eroEIdAya5QGO+8PP0GAGAkzZgxQw888ID279+vgwcPal1OjsrbgmSz9YWSHRV1WrNlj2oa2x2O21BSqZ+/WKb0P3+q9e9U6KVfLh507sLd/5WnLvJU1Dar6tzlX0nad+wn1Z1xvuvQxjsStKGkUtZuJ0HMZFJjzxR1TvXsJW1XTLP4amNWgqFzPJeV6JZZZWe7QvXXdF64QKi5o0tnu3uUEjtdkhQy1VfJc4N1uL7F4dgIF3eZAsYqvhIBmDQSExOV+/BTeufc9n1SXxBz5sJFHoEWn0HB0dXFFL29vero6HD7rdE/WkpYe8nPvC4lSkfqW/TViaYhx7mjL+NoGG6fSqlvVbu76l4TIoIUN9PfoabzqYx4HW9oU1F+X21lf03nb988oN9nxMvHyyQfL5NeLavS1zXNks7XdC4MD3TL+wI8hUAJYFI52dTh8tjHV85TesIcBfn56v6/ful0zH0PPyGfU4cuGvysVqvT4y7GYrHIz89vyFtwcLD8QmLVdIlzRYb46a5rorR6y+5Lvm7/wpbxYCQ7CLjKZDIp77oY+37jdWc6FbO+xOnYz4426NaXPnX6nE1S7nWxtAzCuEegBDCptFl7XB676f3D2vT+YV1/xQytT5+vNVt2q6vHMbpYAoI0Nzb2kiHQlZvFYnE5WLx36JTuf+PAkGMWR4UoNNCsXY/cIKmvnc6mOxK1eedhFX1xwmHseKvhy0qO1M3zQ4fsIBDrxg4Czoylmk7A08bXXxAAMOhiiymG8tnRBgVM8dG80Gk6dPKMw3OPP/qQR9q9OKvhG6j44EkVHzxpv1+Uv0SvlFU5rvI+ZzzW8F3Yp3K0OggMfP2NWQmG2k+5q6YT8DR+iwFMKq4EMW8vk+aG+OlYQ99CnaTIIM0ImKLqn9oHjfVUEBtYw/fsbQt0S3yoZgWY9ca9KWqz9ujGP310yfNMhBq+0ewgMNBYqekEPI0+lAAmlYENsi8MYo3tVrVZe5T6/Cd689cpmmbxVa/NpnZrj/5Uelh7qs63DRoLW+aNpb6Mk93lbuHp7ppOwNMIlAAmnYkSxNy1U46nG7RPFKO5KxQw1hAoAUw6EymITYQtJCea0dwVChgrCJQAJqWJFMReLasadg1f/vK4EXhHACYbdsoBMCllJUfqyfT5wzp2rC2myF8ep+fvTJLF10smna/VG6j/OYuvl164cxFhEoDbMEMJYFKbSIspqOED4CkESgCT3kQLYtTwARhtBEoAOIcgBgDDQ6AEAACAISzKAQAAgCEESgAAABhCoAQAAIAhBEoAAAAYQqAEAACAIQRKAAAAGEKgBAAAgCEESgAAABhCoAQAAIAhBEoAAAAYQqAEAACAIQRKAAAAGEKgBAAAgCEESgAAABhCoAQAAIAhBEoAAAAYQqAEAACAIQRKAAAAGEKgBAAAgCEESgAAABhCoAQAAIAhBEoAAAAYQqAEAACAIQRKAAAAGEKgBAAAgCEESgAAABhCoAQAAIAhBEoAAAAYQqAEAACAIQRKAAAAGEKgBAAAgCEESgAAABhCoAQAAIAhBEoAAAAYQqAEAACAIQRKAAAAGEKgBAAAgCEESgAAABhCoAQAAIAhBEoAAAAY8n/tImWAjYJ6WQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the inputs (will later read in from file [JSON])\n",
    "N = 50 # Number of Parts\n",
    "\n",
    "Scenario = str(N)+\"LongChain\"\n",
    "\n",
    "#define topology\n",
    "H = nx.Graph()\n",
    "for i in range(1,N):\n",
    "    H.add_edge(i,i+1)\n",
    "nx.set_node_attributes(H, \"CL\", \"loc\")\n",
    "\n",
    "numParts = H.number_of_nodes()\n",
    "numConnections = H.number_of_edges()\n",
    "numActions = (numConnections-1)*pow(2,numConnections) + 1\n",
    "numStates = numConnections*pow(2,numConnections-1) + 1\n",
    "\n",
    "#SEQUENCING CONSTRAINTS (will later read in from file [JSON])\n",
    "# Of the format {connection touple: connection touple that must be remove first}\n",
    "e = list(H.edges())\n",
    "seqConstraint = {e[i] for i in range(0,H.number_of_edges(),3)}\n",
    "\n",
    "print(\"CONFIGURATION: \\n\\t%d Parts,\\n\\t%d Connections in Assembly,\\n\\t%d Subassemblies, \\n\\t%d Potential Edges, \\n\\t%d Sequential Constraints\"\\\n",
    "      % (numParts, numConnections, numStates, numActions, len(seqConstraint)))\n",
    "\n",
    "nx.draw(H, with_labels=True, font_color=\"#FFFFFF\", node_size=175, font_size=8)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f8176626",
   "metadata": {},
   "source": [
    "##### \"Random Tree\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "201cadfa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CONFIGURATION: \n",
      "\t100 Parts,\n",
      "\t99 Connections in Assembly,\n",
      "\t31374352355648677687043404333057 Subassemblies, \n",
      "\t62114879411183240673338457063425 Potential Edges, \n",
      "\t0 Sequential Constraints\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the inputs (will later read in from file [JSON])\n",
    "N = 100 # Number of Parts\n",
    "\n",
    "Scenario = str(N)+\"RandomTree\"\n",
    "\n",
    "#define topology\n",
    "H = nx.random_tree(n=N)\n",
    "nx.set_node_attributes(H, \"CL\", \"loc\")\n",
    "\n",
    "numParts = H.number_of_nodes()\n",
    "numConnections = H.number_of_edges()\n",
    "numActions = (numConnections-1)*pow(2,numConnections) + 1\n",
    "numStates = numConnections*pow(2,numConnections-1) + 1\n",
    "\n",
    "#SEQUENCING CONSTRAINTS (will later read in from file [JSON])\n",
    "# Of the format {connection touple: connection touple that must be remove first}\n",
    "seqConstraint = {}\n",
    "\n",
    "print(\"CONFIGURATION: \\n\\t%d Parts,\\n\\t%d Connections in Assembly,\\n\\t%d Subassemblies, \\n\\t%d Potential Edges, \\n\\t%d Sequential Constraints\"\\\n",
    "      % (numParts, numConnections, numStates, numActions, len(seqConstraint)))\n",
    "\n",
    "nx.draw(H, with_labels=False, node_size=50)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "85b66397",
   "metadata": {},
   "source": [
    "## DQN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad6f0272",
   "metadata": {},
   "source": [
    "Here we provide the code for our DQN approach. To effectively run this section, you must run the import statements, the helper function block, and any one of the scenario initialization blocks. Then the code block below will train our DQN to find an effective dissassembly strategy while adhering to the provided sequential constraints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "df41bbf8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lattice cuda\n",
      "0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Kartik\\anaconda3\\envs\\graph\\lib\\site-packages\\torch\\nn\\modules\\loss.py:928: UserWarning: Using a target size (torch.Size([36, 1])) that is different to the input size (torch.Size([36, 2])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
      "  return F.smooth_l1_loss(input, target, reduction=self.reduction, beta=self.beta)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200\n",
      "400\n",
      "600\n",
      "800\n",
      "Complete\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(Scenario, device)\n",
    "\n",
    "# ~~~ MAIN ~~~\n",
    "BATCH_SIZE = H.number_of_edges()*3  # BATCH_SIZE is the number of transitions sampled from the replay buffer\n",
    "GAMMA = 1                           # GAMMA is the discount factor as mentioned in the previous section\n",
    "EPS_START = 0.85                    # EPS_START is the starting value of epsilon\n",
    "EPS_END = 0.05                      # EPS_END is the final value of epsilon\n",
    "EPS_DECAY = 1000                    # EPS_DECAY controls the rate of exponential decay of epsilon, higher means a slower decay\n",
    "TAU = 0.01                          # TAU is the update rate of the target network\n",
    "LR = 1e-3                           # LR is the learning rate of the AdamW optimizer\n",
    "\n",
    "\n",
    "HQ = H.copy() # Copy of the fully connected structure to use for the Q-Learning segment\n",
    "\n",
    "fullE = list(HQ.edges())\n",
    "state = [1 if e in HQ.edges() else 0 for e in fullE]\n",
    "n_actions = HQ.number_of_edges()\n",
    "\n",
    "n_observations = HQ.number_of_edges()\n",
    "policy_net = helpers.DQN(n_observations, n_actions).to(device)\n",
    "target_net = helpers.DQN(n_observations, n_actions).to(device)\n",
    "target_net.load_state_dict(policy_net.state_dict())\n",
    "optimizer = optim.AdamW(policy_net.parameters(), lr=LR, amsgrad=True)\n",
    "memory = helpers.ReplayMemory(100000)\n",
    "\n",
    "if(True):\n",
    "    episode_durations = []\n",
    "\n",
    "\n",
    "def to_graph(state):\n",
    "    Hs = nx.Graph()\n",
    "    for i in range(len(state)):\n",
    "        if(state[i] == 1):\n",
    "            Hs.add_edge(fullE[i][0], fullE[i][1])\n",
    "    nx.set_node_attributes(Hs, \"CL\", \"loc\")\n",
    "    return Hs\n",
    "\n",
    "\n",
    "\n",
    "steps_done = 0\n",
    "def select_action(state, test=False):\n",
    "    global steps_done\n",
    "    sample = random.random()\n",
    "    eps_threshold = EPS_END + (EPS_START - EPS_END) * math.exp(-1. * steps_done / EPS_DECAY)\n",
    "    steps_done += 1\n",
    "    if sample > eps_threshold or test:\n",
    "        with torch.no_grad():\n",
    "            # t.max(1) will return the largest column value of each row.\n",
    "            # second column on max result is index of where max element was\n",
    "            # found, so we pick action with the larger expected reward.\n",
    "            qvals = policy_net(state)\n",
    "            choice = qvals.max(0)[1].view(1, 1)\n",
    "            if(state[choice] == 1):\n",
    "                return torch.tensor(fullE[choice], device=device, dtype=torch.long)\n",
    "            else:\n",
    "                for c in range(len(qvals)):\n",
    "                    qvals[choice] = np.NINF\n",
    "                    choice = qvals.max(0)[1].view(1, 1)\n",
    "                    if(state[choice] == 1):\n",
    "                        return torch.tensor(fullE[choice], device=device, dtype=torch.long)\n",
    "    else:\n",
    "        actionSpace = [fullE[i] for i in range(len(state)) if state[i] == 1]\n",
    "        return torch.tensor(random.choice(actionSpace), device=device, dtype=torch.long)\n",
    "    \n",
    "\n",
    "fig = plt.figure(1)\n",
    "def plot_durations(show_result=False):\n",
    "    durations_t = torch.tensor(episode_durations, dtype=torch.float)\n",
    "    if show_result:\n",
    "        plt.title('Result')\n",
    "    else:\n",
    "        plt.clf()\n",
    "        plt.title('Training...')\n",
    "    plt.xlabel('Episode')\n",
    "    plt.ylabel('Total Reward Earned')\n",
    "    plt.plot(durations_t.numpy())\n",
    "    \n",
    "    # Take 100 episode averages and plot them too\n",
    "    if len(durations_t) >= 100:\n",
    "        means = durations_t.unfold(0, 100, 1).mean(1).view(-1)\n",
    "        plt.plot(means.numpy())\n",
    "\n",
    "    plt.pause(0.001)  # pause a bit so that plots are updated\n",
    "\n",
    "\n",
    "\n",
    "def optimize_model():\n",
    "    if len(memory) < BATCH_SIZE:\n",
    "        return\n",
    "    transitions = memory.sample(BATCH_SIZE)\n",
    "    # Transpose the batch (see https://stackoverflow.com/a/19343/3343043 for\n",
    "    # detailed explanation). This converts batch-array of Transitions\n",
    "    # to Transition of batch-arrays.\n",
    "    batch = helpers.Transition(*zip(*transitions))\n",
    "\n",
    "    # Compute a mask of non-final states and concatenate the batch elements\n",
    "    # (a final state would've been the one after which simulation ended)\n",
    "    non_final_mask = torch.tensor(tuple(map(lambda s: s is not None,\n",
    "                                          batch.next_state)), device=device, dtype=torch.bool)\n",
    "    non_final_next_states = torch.stack([s for s in batch.next_state\n",
    "                                                if s is not None])\n",
    "    state_batch = torch.stack(batch.state)\n",
    "    action_batch = torch.stack(batch.action)\n",
    "    reward_batch = torch.cat(batch.reward)\n",
    "\n",
    "    # Compute Q(s_t, a) - the model computes Q(s_t), then we select the\n",
    "    # columns of actions taken. These are the actions which would've been taken\n",
    "    # for each batch state according to policy_net\n",
    "    state_action_values = policy_net(state_batch).gather(0, action_batch)\n",
    "\n",
    "    # Compute V(s_{t+1}) for all next states.\n",
    "    # Expected values of actions for non_final_next_states are computed based\n",
    "    # on the \"older\" target_net; selecting their best reward with max(1)[0].\n",
    "    # This is merged based on the mask, such that we'll have either the expected\n",
    "    # state value or 0 in case the state was final.\n",
    "    next_state_values = torch.zeros(BATCH_SIZE, device=device)\n",
    "    with torch.no_grad():\n",
    "        next_state_values[non_final_mask] = target_net(non_final_next_states).max(1)[0]\n",
    "    # Compute the expected Q values\n",
    "    expected_state_action_values = (next_state_values * GAMMA) + reward_batch\n",
    "\n",
    "    # Compute Huber loss\n",
    "    criterion = nn.SmoothL1Loss()\n",
    "    loss = criterion(state_action_values, expected_state_action_values.unsqueeze(1))\n",
    "\n",
    "    # Optimize the model\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    # In-place gradient clipping\n",
    "    torch.nn.utils.clip_grad_value_(policy_net.parameters(), 100)\n",
    "    optimizer.step()\n",
    "\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "    num_episodes = 1000\n",
    "else:\n",
    "    num_episodes = 1000\n",
    "\n",
    "for i_episode in range(num_episodes):\n",
    "    # Initialize the environment and get it's state\n",
    "    state = np.ones(len(fullE))\n",
    "    if(i_episode % 200 == 0):\n",
    "        print(i_episode)\n",
    "        # Maybe randomize the initial state?\n",
    "\n",
    "    Hi = HQ.copy() # Copy of H for this episode\n",
    "    # state, info = env.reset()\n",
    "    state = torch.tensor(state, dtype=torch.float32, device=device)\n",
    "    totR = 0\n",
    "    for t in count():\n",
    "        action = select_action(state)\n",
    "        reward, Hi = R(state, tuple(action.tolist()), to_graph(state))\n",
    "        totR += reward\n",
    "        observation = [1 if e in Hi.edges() else 0 for e in fullE]\n",
    "        terminated = (Hi.number_of_edges() == 0)\n",
    "        reward = torch.tensor([1+reward], device=device)\n",
    "        done = terminated\n",
    "        #print(state, action, reward)\n",
    "\n",
    "        if terminated:\n",
    "            next_state = None\n",
    "        else:\n",
    "            next_state = torch.tensor(observation, dtype=torch.float32, device=device)\n",
    "\n",
    "        # Store the transition in memory\n",
    "        memory.push(state, action, next_state, reward)\n",
    "\n",
    "        # Move to the next state\n",
    "        state = next_state\n",
    "\n",
    "        # Perform one step of the optimization (on the policy network)\n",
    "        optimize_model()\n",
    "\n",
    "        # Soft update of the target network's weights\n",
    "        # θ′ ← τ θ + (1 −τ )θ′\n",
    "        target_net_state_dict = target_net.state_dict()\n",
    "        policy_net_state_dict = policy_net.state_dict()\n",
    "        for key in policy_net_state_dict:\n",
    "            target_net_state_dict[key] = policy_net_state_dict[key]*TAU + target_net_state_dict[key]*(1-TAU)\n",
    "        target_net.load_state_dict(target_net_state_dict)\n",
    "\n",
    "        if done:\n",
    "            episode_durations.append(totR)\n",
    "            break\n",
    "\n",
    "print('Complete')\n",
    "\n",
    "plot_durations(show_result=True)\n",
    "plt.ioff()\n",
    "plt.savefig(\"RESULTS/DQN/\"+Scenario+\" Training.eps\", format=\"eps\", dpi=1200)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d79d397c",
   "metadata": {},
   "source": [
    "## Baselines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d0342648",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.06149999999999989\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fullE = list(HQ.edges())\n",
    "state = torch.tensor([1 if e in HQ.edges() else 0 for e in fullE], dtype=torch.float32, device=device)\n",
    "n_actions = HQ.number_of_edges()\n",
    "numBaselines = 5\n",
    "\n",
    "Hi = HQ.copy()\n",
    "\n",
    "Qr = 0\n",
    "Qstrat = []\n",
    "baseR = [0]*numBaselines #np.zeros(numBaselines) # [0]*numBaselines\n",
    "baseStrat = [[] for i in range(numBaselines)]\n",
    "baseState = [[1]*len(state) for i in range(numBaselines)] #np.zeros([len(state), numBaselines])\n",
    "for t in range(n_actions):\n",
    "    qvals = target_net(state)\n",
    "    choice = qvals.max(0)[1].view(1, 1)\n",
    "    if(state[choice] == 1):\n",
    "        Qstrat.append(fullE[choice])\n",
    "        reward, Hi = R(state, fullE[choice], to_graph(state))\n",
    "        Qr += reward\n",
    "        state[choice] = 0\n",
    "    else:\n",
    "        for c in range(len(qvals)):\n",
    "            qvals[choice] = np.NINF\n",
    "            choice = qvals.max(0)[1].view(1, 1)\n",
    "            if(state[choice] == 1):\n",
    "                Qstrat.append(fullE[choice])\n",
    "                reward, Hi = R(state, fullE[choice], to_graph(state))\n",
    "                Qr += reward\n",
    "                state[choice] = 0\n",
    "                break\n",
    "    for b in range(numBaselines):\n",
    "        potentialChoices = [i for i in range(len(baseState[b])) if baseState[b][i] == 1]\n",
    "        choice = random.choice(potentialChoices)\n",
    "        baseStrat[b].append(fullE[choice])\n",
    "        reward, Hi = R(baseState[b], fullE[choice], to_graph(baseState[b]))\n",
    "        baseR[b] += reward\n",
    "        baseState[b][choice] = 0\n",
    "        \n",
    "\n",
    "print(Qr)\n",
    "\n",
    "data = {}\n",
    "data[\"DQN\"] = {\"Strat\": Qstrat, \"R\": Qr, \n",
    "               \"Hyperparameters\": {\"Batch Size\": BATCH_SIZE, \n",
    "                                   \"Gamma\": GAMMA,\n",
    "                                   \"EpsilonStart\": EPS_START,\n",
    "                                   \"EpsilonEnd\": EPS_END,\n",
    "                                   \"EpsilonDecay\": EPS_DECAY,\n",
    "                                   \"Tau\": TAU,\n",
    "                                   \"LearningRate\": LR,\n",
    "                                   \"NumEpisodes\": num_episodes}}\n",
    "for b in range(numBaselines):\n",
    "    data[\"Base-\" + str(b)] = {\"Strat\": baseStrat[b], \"R\": baseR[b]}\n",
    "\n",
    "json.dump(data, open(\"RESULTS/DQN/\"+Scenario+\".json\", \"w\"))\n",
    "\n",
    "plt.bar([\"Base-\"+str(b) for b in range(len(baseR))], baseR, color=\"b\", label=Qr)\n",
    "plt.bar([\"DQN\"], Qr, color=\"r\", label=Qr)\n",
    "plt.plot([-0.5,5.5], [Qr,Qr], ls='--', c='r')\n",
    "plt.ylabel(\"Rewards\")\n",
    "if(Scenario[-1].isdigit()):\n",
    "    plt.title(Scenario[:-1]+\" Reward Distribution\")\n",
    "else:\n",
    "    plt.title(Scenario+\" Reward Distribution\")\n",
    "plt.savefig(\"RESULTS/DQN/\"+Scenario+\" Reward Distribution.eps\", format=\"eps\", dpi=1200)\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "30a5a35d",
   "metadata": {},
   "source": [
    "## Assembly Generation and Tabular Dynamic Programming Methods"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0de997e0",
   "metadata": {},
   "source": [
    "### Running the Assembly Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dc273fe4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Assembly Generation Time: 2.002001\n",
      "[(1, 2), (1, 4), (2, 3), (2, 5), (3, 6), (4, 5), (4, 7), (5, 6), (5, 8), (6, 9), (7, 8), (8, 9)]\n",
      "DONE: 1296 Nodes and 6048 Edges\n"
     ]
    }
   ],
   "source": [
    "# Simple initial initialization (will later read in from file [JSON])\n",
    "Rewards = {}\n",
    "fullE = list(H.edges())\n",
    "\n",
    "# Generate the assembly tree\n",
    "start = time.time()\n",
    "G = nx.DiGraph()\n",
    "G.add_node(1, idS=str(H.edges()))\n",
    "G = recurGen(1, H, G)\n",
    "end = time.time()\n",
    "print(\"Assembly Generation Time: %f\" % (end-start))\n",
    "\n",
    "print(fullE)\n",
    "print(\"DONE: %d Nodes and %d Edges\" % (G.number_of_nodes(), G.number_of_edges()))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ba04eb42",
   "metadata": {},
   "source": [
    "### Value Iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fcc5b5e0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converged!\n",
      "Value Iteration Time: 0.051000\n"
     ]
    }
   ],
   "source": [
    "def ValueIteration(G, H, maxIter, eps):\n",
    "    S = range(1, G.number_of_nodes()+1)\n",
    "    A = list(H.edges())\n",
    "\n",
    "    V = {s: 0.0 for s in S}\n",
    "    Vnew = V.copy()\n",
    "    π = {s: (0, (0,0)) for s in S}\n",
    "    delta = max([abs(V[s] - Vnew[s]) for s in S])\n",
    "\n",
    "    c = 0\n",
    "    while(c <= maxIter):\n",
    "        delta = 0\n",
    "        for s in S:\n",
    "            nextStates = G[s]\n",
    "            for sN in nextStates.items():\n",
    "                newV = sN[1]['r'] + V[sN[0]]\n",
    "                if(newV > Vnew[s] or Vnew[s] == 0.0):\n",
    "                    Vnew[s] = newV\n",
    "                    π[s] = (sN[0], sN[1]['a'])\n",
    "        delta = max([abs(V[s] - Vnew[s]) for s in S])\n",
    "        if(delta < eps):\n",
    "            print(\"Converged!\")\n",
    "            return Vnew, π\n",
    "        V = Vnew.copy()\n",
    "        c += 1\n",
    "    print(\"Done\")\n",
    "\n",
    "maxIter = 100000\n",
    "eps = 1e-6\n",
    "start = time.time()\n",
    "V, π = ValueIteration(G, H, maxIter, eps)\n",
    "end = time.time()\n",
    "print(\"Value Iteration Time: %f\" % (end-start))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5ecf6dd8",
   "metadata": {},
   "source": [
    "### Tabular Q-Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c44ea41",
   "metadata": {},
   "outputs": [],
   "source": [
    "maxIter = 10000\n",
    "c = 0\n",
    "G = nx.DiGraph()\n",
    "Q = {}\n",
    "ϵ = 0.3\n",
    "gamma = 1\n",
    "policy = {}\n",
    "\n",
    "while c < maxIter:\n",
    "    # Get random state\n",
    "    currPos = H.copy()\n",
    "    numCuts = random.randint(0, currPos.number_of_edges()-1)\n",
    "    edges = list(currPos.edges())\n",
    "    for i in range(numCuts):\n",
    "        rc = edges.pop(random.randint(0, len(edges)-1))\n",
    "        currPos.remove_edges_from([rc])\n",
    "    # print(currPos)\n",
    "    #nx.draw(currPos, with_labels=True, font_color=\"#FFFFFF\")\n",
    "    \n",
    "    # Get random action\n",
    "    nextPos = currPos.copy()\n",
    "    edgeList = list(currPos.edges())\n",
    "    if(len(edgeList) > 0):\n",
    "        A = edgeList[random.randint(0, len(edgeList)-1)]\n",
    "        nextPos.remove_edges_from([A])\n",
    "    else:\n",
    "        continue\n",
    "    \n",
    "    # Find \"sN\"s\n",
    "    G.add_node(1, idS=str(nextPos.edges()))\n",
    "    nearest = nextGen(nextPos)\n",
    "    # print(nearest)\n",
    "    if(nearest is None):\n",
    "        continue\n",
    "    \n",
    "    # Pick best and go\n",
    "    actions = nx.get_edge_attributes(nearest,'r')\n",
    "    maxA = random.choice([k for k in actions.keys() if actions[k] == max(actions.values())])\n",
    "    s = currPos.copy()\n",
    "    sN = currPos.remove_edges_from([maxA])\n",
    "    \n",
    "    label = str(np.array(nx.adjacency_matrix(s)))\n",
    "    if((label,A) in Q.keys() and sN is not None):\n",
    "        Qp = [0]\n",
    "        for e in list(sN.edges()):\n",
    "            if((sN,e) in Q.keys()):\n",
    "                Qp.append(Q[sN,e])\n",
    "        Q[(label,maxA)] = Q[(label,maxA)] + ϵ*(actions[maxA] + gamma*max(Qp) - Q[(label,maxA)])\n",
    "    else:\n",
    "        Q[(label,maxA)] = actions[maxA]\n",
    "        \n",
    "    c += 1\n",
    "\n",
    "pp.pprint(Q)\n",
    "print(len(Q.keys()))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b145cf9a",
   "metadata": {},
   "source": [
    "## Graphing Suite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "421f5ac5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(1, 2), (2, 3), (3, 4), (4, 5), (5, 1140), (1140, 1141), (1141, 386), (386, 387), (387, 388), (388, 389), (389, 404), (404, 405), (405, 406), (406, 407), (407, 408), (408, 409), (409, 32), (32, 33), (33, 20)]\n",
      "[(1, 2), (2, 3), (3, 5), (5, 4), (7, 8), (6, 7), (5, 6), (9, 10), (5, 9), (12, 13), (18, 19), (18, 20), (17, 18), (16, 17), (15, 16), (14, 15), (5, 11), (5, 14), (11, 12)]\n",
      "Reward: -0.8935000000000002\n",
      "DONE: 12096 Nodes and 67824 Edges\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    pos = hierarchy_pos(G,1)\n",
    "except:\n",
    "    pos = nx.planar_layout(G)\n",
    "actions = nx.get_edge_attributes(G,'r')\n",
    "nx.draw(G, pos, with_labels=True, font_color=\"#FFFFFF\")\n",
    "nx.draw_networkx_edge_labels(G, pos, edge_labels = actions)\n",
    "\n",
    "try:\n",
    "    Rew = 0\n",
    "    S = range(1, G.number_of_nodes()+1)\n",
    "    path = [1, π[1][0]]\n",
    "    strat = [π[1][1]]\n",
    "    path_edges = [(1, path[-1])]\n",
    "    while(True):\n",
    "        currState = path[-1]\n",
    "        nextState = π[currState][0]\n",
    "        if(nextState in S):\n",
    "            Rew += actions[(currState, nextState)]\n",
    "            path.append(nextState)\n",
    "            strat.append(π[currState][1])\n",
    "            path_edges.append((currState, nextState))\n",
    "        else:\n",
    "            break\n",
    "    print(path_edges)\n",
    "    print(strat)\n",
    "    nx.draw_networkx_nodes(G,pos,nodelist=path,node_color='r')\n",
    "    nx.draw_networkx_edges(G,pos,edgelist=path_edges,edge_color='r',width=1)\n",
    "except:\n",
    "    print(\"No Policy Found\")\n",
    "\n",
    "\n",
    "# strat = fullE.copy()\n",
    "# strat.reverse()\n",
    "# Re = 0\n",
    "# s = np.ones(len(fullE))\n",
    "# for t in range(len(s)):\n",
    "#     Re += R(s, strat[t], to_graph(s))[0]\n",
    "#     s[fullE.index(strat[t])] = 0\n",
    "\n",
    "print(\"Reward: \"+str(Rew))\n",
    "print(\"DONE: %d Nodes and %d Edges\" % (G.number_of_nodes(), G.number_of_edges()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32c2f27a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "nx.draw(H, with_labels=True, font_color=\"#FFFFFF\")"
   ]
  }
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