Step2_Analysis_comparison.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generalized Network Analysis Tutorial - Step 2\n",
    "\n",
    "In the second part of our generalized network analysis tutorial, the user is presented with another jupyter notebook where all the correlation maps calculated in the first part can be translated into molecular visualizations or plots. Here we provide an opportunity for users more comfortable with python programming to tailor the analysis to their specific scientific questions.\n",
    "\n",
    "The Network Analysis Tutorial is part of the work entitled **Generalized correlation-based dynamical network analysis: a new high-performance approach for identifying allosteric communications in molecular dynamics trajectories**, by Marcelo C. R. Melo, Rafael C. Bernardi, Cesar de la Fuente-Nunez, and Zaida Luthey-Schulten. For more information see http://faculty.scs.illinois.edu/schulten/. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:09:43.829281Z",
     "start_time": "2020-06-04T23:09:34.192482Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "000c65448ddd46ccbaf7fe3daf89f166",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the python package\n",
    "import os\n",
    "from dynetan.toolkit import *\n",
    "from dynetan.viz import *\n",
    "from dynetan.proctraj import *\n",
    "from dynetan.gencor import *\n",
    "from dynetan.contact import *\n",
    "from dynetan.datastorage import *\n",
    "\n",
    "#from numpy.linalg import norm\n",
    "from itertools import islice\n",
    "from itertools import combinations\n",
    "from scipy import stats\n",
    "#from scipy import signal\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "\n",
    "# For visualization\n",
    "import nglview    as nv\n",
    "import ipywidgets as widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "# Allows better plotting inside jupyter notebooks\n",
    "%matplotlib notebook\n",
    "# importing matplotlib module \n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:09:46.385284Z",
     "start_time": "2020-06-04T23:09:46.083635Z"
    }
   },
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%R install.packages(\"librarian\", repos = \"https://cran.csiro.au/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:09:48.851893Z",
     "start_time": "2020-06-04T23:09:46.501424Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <span>BoolVector with 7 elements.</span>\n",
       "        <table>\n",
       "        <tbody>\n",
       "          <tr>\n",
       "          \n",
       "            <td>\n",
       "                   1\n",
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       "                   1\n",
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       "          \n",
       "            <td>\n",
       "                   1\n",
       "            </td>\n",
       "          \n",
       "            <td>\n",
       "            ...\n",
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       "          \n",
       "            <td>\n",
       "                   1\n",
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       "          \n",
       "            <td>\n",
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      "text/plain": [
       "<rpy2.robjects.vectors.BoolVector object at 0x7fa845f1b780> [RTYPES.LGLSXP]\n",
       "R classes: ('logical',)\n",
       "[       1,        1,        1,        1,        1,        1,        1]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make sure all the following packages are installed in your R distribution\n",
    "%R librarian::shelf(data.table, ggplot2, ggrepel, gdata, RColorBrewer, colorRamps, rPref, cran_repo = \"https://cran.csiro.au/\")\n",
    "#%R require(data.table)\n",
    "#%R require(ggplot2)\n",
    "#%R require(ggrepel)\n",
    "#%R require(gdata)\n",
    "#%R require(RColorBrewer)\n",
    "#%R require(colorRamps)\n",
    "#%R require(rPref)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function: Open an NGL View window displaying the molecular system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_widget():\n",
    "    # http://proteinformatics.charite.de/ngl/doc/#User_manual/Usage/Selection_language\n",
    "    w = nv.show_mdanalysis(workUviz.select_atoms(\"all\"))\n",
    "    w._remote_call(\"setSize\", target=\"Widget\", args=[\"800px\", \"600px\"])\n",
    "    w.parameters = dict(theme='light')\n",
    "    return w"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function: Set basic visual representations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_widget_representations(widget):\n",
    "    widget.clear_representations()\n",
    "    widget.add_cartoon(\"backbone\")\n",
    "    widget.add_hyperball(\"charged and (not backbone or .CA or (PRO and .N))\")\n",
    "    w.add_representation(\"hyperball\",\n",
    "        selection='(not protein) and not (.1MC1 or .2MC1 or .1MC2 or .2MC2  or .1MC3 or .2MC3)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Files and system definitions (same as in \"ProcessTrajectory\" notebook) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:10:22.898264Z",
     "start_time": "2020-06-04T23:10:22.892936Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define mutant file IO locations. wt, P67L, E56K, R75Q\n",
    "alleleList=[\"wt\",\"dF508\",\"S945L\"]\n",
    "temperature=\"310K\"\n",
    "#Path where results will be written (you may want plots and data files in a new location)\n",
    "workDir = \"./results/\"\n",
    "fileNameRoot = \"1to3\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the Graph Data and PDB data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:09:45.368030Z",
     "start_time": "2020-06-04T23:09:45.364339Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "atomToNode int64 (12623,) 12623\n",
      "contactMat int64 (1332, 1332) 1774224\n",
      "contactNodesInter int64 (159,) 159\n",
      "corrMatAll float64 (3, 1332, 1332) 5322672\n",
      "distsAll float64 (3, 1332, 1332) 5322672\n",
      "interNodePairs int64 (203, 2) 406\n",
      "maxDirectDist float64 () 1\n",
      "maxDist float64 () 1\n",
      "nodeDists float64 (4, 886446) 3545784\n",
      "nodesIxArray int64 (1332,) 1332\n",
      "numNodes int32 () 1\n",
      "atomToNode int64 (12612,) 12612\n",
      "contactMat int64 (1331, 1331) 1771561\n",
      "contactNodesInter int64 (132,) 132\n",
      "corrMatAll float64 (3, 1331, 1331) 5314683\n",
      "distsAll float64 (3, 1331, 1331) 5314683\n",
      "interNodePairs int64 (155, 2) 310\n",
      "maxDirectDist float64 () 1\n",
      "maxDist float64 () 1\n",
      "nodeDists float64 (4, 885115) 3540460\n",
      "nodesIxArray int64 (1331,) 1331\n",
      "numNodes int32 () 1\n",
      "atomToNode int64 (12629,) 12629\n",
      "contactMat int64 (1332, 1332) 1774224\n",
      "contactNodesInter int64 (140,) 140\n",
      "corrMatAll float64 (3, 1332, 1332) 5322672\n",
      "distsAll float64 (3, 1332, 1332) 5322672\n",
      "interNodePairs int64 (186, 2) 372\n",
      "maxDirectDist float64 () 1\n",
      "maxDist float64 () 1\n",
      "nodeDists float64 (4, 886446) 3545784\n",
      "nodesIxArray int64 (1332,) 1332\n",
      "numNodes int32 () 1\n"
     ]
    }
   ],
   "source": [
    "dnad={}\n",
    "mdU={}\n",
    "for allele in alleleList:\n",
    "    d = DNAdata()\n",
    "    dataDir = \"./results/%s/%s/\" % (allele, temperature)\n",
    "    fullPathRoot = os.path.join(dataDir, fileNameRoot)\n",
    "    d.loadFromFile(fullPathRoot)\n",
    "    dnad[allele] = d\n",
    "    \n",
    "    dcdVizFile = fullPathRoot + \"_reducedTraj.dcd\"\n",
    "    pdbVizFile = fullPathRoot + \"_reducedTraj.pdb\"\n",
    "    u = mda.Universe(pdbVizFile,dcdVizFile)\n",
    "    mdU[allele] = u\n",
    "    \n",
    "    dnad[allele].nodesAtmSel = mdU[allele].atoms[ dnad[allele].nodesIxArray ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Encode Clustering Coefficient, Node and Edge Betweenness Centrality into the nxGraph data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Note: btws is the same as the results dervied from nx.edge_betweenness_centrality()\n",
    "for k in dnad.keys():\n",
    "    for w in range(dnad[k].numWinds):\n",
    "        for u,v in dnad[k].btws[w]:\n",
    "            dnad[k].nxGraphs[w].edges[u,v]['btws']=dnad[k].btws[w][u,v]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Note: edge-betweeness weighted clusting coefficient\n",
    "for k in dnad.keys():\n",
    "    for w in range(dnad[k].numWinds):\n",
    "        c = nx.clustering(dnad[k].nxGraphs[w],weight='btws')\n",
    "        for x in range(dnad[k].numNodes):\n",
    "            dnad[k].nxGraphs[w].nodes[x]['bwcc']=c[x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    ....finished computing for key wt, window 0\n",
      "    ....finished computing for key wt, window 1\n",
      "    ....finished computing for key wt, window 2\n",
      "    ....finished computing for key dF508, window 0\n",
      "    ....finished computing for key dF508, window 1\n",
      "    ....finished computing for key dF508, window 2\n",
      "    ....finished computing for key S945L, window 0\n",
      "    ....finished computing for key S945L, window 1\n",
      "    ....finished computing for key S945L, window 2\n"
     ]
    }
   ],
   "source": [
    "# Note: this is the node betweennes centrality, which might be useful in a different context.\n",
    "for k in dnad.keys():\n",
    "    for w in range(dnad[k].numWinds):\n",
    "        c = nx.betweenness_centrality(dnad[k].nxGraphs[w])\n",
    "        print(\"    ....finished computing for key %s, window %i\" % (k,w))\n",
    "        for x in range(dnad[k].numNodes):\n",
    "            dnad[k].nxGraphs[w].nodes[x]['btws']=c[x]\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get some basic information about the graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['contactMat', 'atomToNode', 'nodesIxArray', 'numNodes', 'nodeDists', 'corrMatAll', 'distsAll', 'preds', 'maxDist', 'maxDirectDist', 'btws', 'interNodePairs', 'contactNodesInter', 'nxGraphs', 'nodesComm', 'nodesAtmSel', 'numWinds'])\n",
      "dict_keys(['graph_attr_dict_factory', 'node_dict_factory', 'node_attr_dict_factory', 'adjlist_outer_dict_factory', 'adjlist_inner_dict_factory', 'edge_attr_dict_factory', 'graph', '_node', '_adj', 'nodes'])\n",
      "{'degree': 5, 'eigenvector': 0.00011401732080243934, 'modularity': 0, 'bwcc': 0.0034735658203200654, 'btws': 8.865132704438603e-05, 'name': 'Q2', 'segid': 'LAS'}\n",
      "{'weight': 0.9538186230654225, 'dist': 0.04728174818237034, 'btws': 2.8033568503604236e-06}\n"
     ]
    }
   ],
   "source": [
    "k='wt' ; w=0\n",
    "print( dnad[k].__dict__.keys() )\n",
    "print( dnad[k].nxGraphs[w].__dict__.keys() )\n",
    "print( dnad[k].nxGraphs[w].nodes[1] )\n",
    "print( dnad[k].nxGraphs[w].edges[1,2] )\n",
    "#for k in dnad.keys():\n",
    "#    for w in range(dnad[k].numWinds):\n",
    "#        print( nx.info(dnad[k].nxGraphs[w]))\n",
    "# Notes: edge['weight'] = corrMatAll = 10^(-1*distsAll)\n",
    "# eigenvector is a centrality measure, distinct from betweenness centrality.\n",
    "# nodesComm is the community ID via Louvain heuristices\n",
    "# print( [ len( dnad[k].nodesComm[w]['commNodes'][x] ) for x in dnad[k].nodesComm[w]['commNodes'].keys() ] )\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a map of all equivalent nodes between different analyses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Does not current account for exchanged solvent residues between different dnad maps,\n",
    "# since atom selection runs by name X and resname Y and resid Z\n",
    "def map_node(dnadDict, keyFrom, keyTo, nodeFrom):\n",
    "    if nodeFrom >= dnadDict[keyFrom].numNodes:\n",
    "        return np.empty(0, dtype=int)\n",
    "    \n",
    "    selectionString=\"resid %s and resname %s and name %s\" % \\\n",
    "        (dnadDict[keyFrom].nodesAtmSel[nodeFrom].resid,\n",
    "         dnadDict[keyFrom].nodesAtmSel[nodeFrom].resname,\n",
    "         dnadDict[keyFrom].nodesAtmSel[nodeFrom].name )\n",
    "    return getNodeFromSel(selectionString, dnadDict[keyTo].nodesAtmSel, dnadDict[keyTo].atomToNode)\n",
    "\n",
    "def create_nodemap(dnadDict):\n",
    "    dictMap={}\n",
    "    for kFrom in dnadDict.keys():\n",
    "        nNodes=dnadDict[kFrom].numNodes\n",
    "        if kFrom not in dictMap:\n",
    "            dictMap[kFrom]={}\n",
    "        for kTo in dnadDict.keys():\n",
    "            if kTo == kFrom:\n",
    "                dictMap[kFrom][kTo]=np.arange(nNodes,dtype=int)\n",
    "                continue\n",
    "            dictMap[kFrom][kTo]=np.zeros(nNodes, dtype=int)\n",
    "            for i in range(nNodes):\n",
    "                #getSelFromNode is ambiguous when there are multiple nodes per residue group.\n",
    "                #selText=getSelFromNode(i,dnadDict[kFrom].nodesAtmSel)\n",
    "                jArr = map_node(dnadDict, kFrom, kTo, i)\n",
    "                if len(jArr)==1:\n",
    "                    dictMap[kFrom][kTo][i]=jArr[0]\n",
    "                elif len(jArr)==0:\n",
    "                    dictMap[kFrom][kTo][i]=-1\n",
    "                else:\n",
    "                    print(\"ERROR in create_nodemap: A node selection has resulted in multiple outcomes!\")\n",
    "                    dictMap[kFrom][kTo][i]=-1\n",
    "                    \n",
    "            numMissing = np.sum(dictMap[kFrom][kTo]<0)\n",
    "            print(\"= = Note: mapping from %s to %s containing %i missing nodes.\" % (kFrom, kTo, numMissing) )\n",
    "            \n",
    "    return dictMap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "= = Note: mapping from wt to dF508 containing 17 missing nodes.\n",
      "= = Note: mapping from wt to S945L containing 9 missing nodes.\n",
      "= = Note: mapping from dF508 to wt containing 16 missing nodes.\n",
      "= = Note: mapping from dF508 to S945L containing 17 missing nodes.\n",
      "= = Note: mapping from S945L to wt containing 9 missing nodes.\n",
      "= = Note: mapping from S945L to dF508 containing 18 missing nodes.\n"
     ]
    }
   ],
   "source": [
    "nodeMap = create_nodemap(dnad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define shorthand for getting a label string from a node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapResidueNames={'ALA':'A','CYS':'C','ASP':'D','GLU':'E','PHE':'F',\n",
    "                 'GLY':'G','HIS':'H','HSD':'H','HSE':'H','ILE':'I','LYS':'K','LEU':'L',\n",
    "                 'MET':'M','ASN':'N','PRO':'P','GLN':'Q','ARG':'R',\n",
    "                 'SER':'S','THR':'T','VAL':'V','TRP':'W','TYR':'Y',\n",
    "                 'MG':'Mg','ATP':'Atp','POPC':'Popc','SOL':'h2o'}\n",
    "\n",
    "def name_node(dnad, node):\n",
    "    selectionString=getSelFromNode(node, dnad.nodesAtmSel).split()\n",
    "    return \"%s%s\" % (mapResidueNames[selectionString[1]], selectionString[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_peak_locations(arr, numReturn, width=1, sortByValue=True):\n",
    "    xx=np.flip(np.argsort(arr))[:numReturn]\n",
    "    if sortByValue:\n",
    "        return xx\n",
    "    else:\n",
    "        return np.sort(xx)\n",
    "    # Not a signal property so should not use scipy signal\n",
    "    #x=signal.find_peaks_cwt(arr, width)    \n",
    "    #xx=np.flip(np.argsort(arr[x]))[:numReturn]\n",
    "    #if sortByValue:\n",
    "    #    return x[xx]\n",
    "    #else:\n",
    "    #    return x[np.sort(xx)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot some standard network properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[992 991 990 266 493 882 989 879 263 897] [0.06213317 0.035194   0.03431819 0.03189849 0.02917863 0.0257425\n",
      " 0.02545443 0.02522342 0.02501719 0.02348106]\n"
     ]
    }
   ],
   "source": [
    "# Note: Graphs edges made by DNAD all have weights, which seems to be the coreelation matrix.\n",
    "# Nodes pairs without edges have weight zero in the corrMatAll.\n",
    "# print( dnad['wt'].nxGraphs[0].edges[1,2]['weight'], dnad['wt'].corrMatAll[0,1,2] )\n",
    "\n",
    "#for u, v, weight in dnad['wt'].nxGraphs[0].edges.data(\"weight\"):\n",
    "#    if weight is None:\n",
    "#        print( u,v )  \n",
    "k='wt' ; w=0\n",
    "c=np.array([ dnad[k].nxGraphs[w].nodes[x]['bwcc'] for x in range(dnad[k].numNodes) ])\n",
    "d=np.flip(np.argsort(c))\n",
    "print( d[:10], c[d[:10]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "k='wt' ; w=0\n",
    "#help(nx.clustering)\n",
    "#(dnad[k].nxGraphs[w], 1000)\n",
    "# deg=[dnad[k].nxGraphs[w].degree[x] for x in range(dnad[k].numNodes)]\n",
    "# plt.hist( deg, bins=xMaxLoc+1, histtype='step', align='left', label='%s %i' % (k,w))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# betweenness sum, averaged between windows\n",
    "wStr=None\n",
    "plt.figure(figsize=(16, 4))\n",
    "for k in dnad.keys():\n",
    "    nodeWeightSum = np.zeros(dnad[k].numNodes, dtype=float)\n",
    "    for w in range(dnad[k].numWinds):\n",
    "        deg=dnad[k].nxGraphs[w].degree(weight=wStr)\n",
    "        nodeWeightSum+=[ deg[x] for x in range(dnad[k].numNodes) ]\n",
    "    plt.plot(np.arange(dnad[k].numNodes), nodeWeightSum/dnad[k].numWinds, label='%s' % (k))\n",
    "        \n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Degrees')\n",
    "plt.xlabel('Node ID'); plt.ylabel('Sum')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# (Edge) Betweenness-weighted mean of Clustering Coefficients\n",
    "fig = plt.figure(figsize=(16, 4))\n",
    "for k in dnad.keys():\n",
    "    nodeWeightSum = np.zeros(dnad[k].numNodes, dtype=float)\n",
    "    for w in range(dnad[k].numWinds):\n",
    "        nodeWeightSum+=[ dnad[k].nxGraphs[w].nodes[x]['bwcc'] for x in range(dnad[k].numNodes) ]\n",
    "    plt.plot(np.arange(dnad[k].numNodes), nodeWeightSum/dnad[k].numWinds, linewidth=0.8, label='%s' % (k))\n",
    "    if k=='wt':\n",
    "        nPeaks=20\n",
    "        d=get_peak_locations(nodeWeightSum, nPeaks, sortByValue=False)\n",
    "        dHeight=np.repeat(0.058, len(d))\n",
    "        dDelta=-0.003 ; dShiftCriterion=50\n",
    "        for i in range(nPeaks):\n",
    "            if i>0 and d[i]-d[i-1] < dShiftCriterion:\n",
    "                dHeight[i]=dHeight[i-1]+dDelta\n",
    "            plt.axvline(d[i], linestyle='dotted', linewidth=0.4, color='grey') # vertical lines\n",
    "            plt.text(d[i], dHeight[i], name_node(dnad[k],d[i]), ha='center', va='center')\n",
    "\n",
    "plt.axvline(507, linestyle='dotted', linewidth=0.4, color='grey') # vertical lines\n",
    "plt.text(507, 0.055, name_node(dnad['wt'],507), ha='center', va='center')\n",
    "\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Betweenness-weighted sum of Clustering Coefficients')\n",
    "plt.xticks(np.arange(0,1300,100))\n",
    "fig.get_axes()[0].set_xlim((0,1350))\n",
    "fig.get_axes()[0].set_ylim((0,0.06))\n",
    "plt.xlabel('Node ID'); plt.ylabel('Sum')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Metamean (Edge) Betweenness-weighted mean of Clustering Coefficients\n",
    "wStr='bwcc'\n",
    "fig = plt.figure(figsize=(16, 4))\n",
    "nRepl=0 ; nPeaks=30\n",
    "nodeWeightSum = np.zeros(dnad['wt'].numNodes, dtype=float)\n",
    "for k in dnad.keys():\n",
    "    nRepl+=dnad[k].numWinds\n",
    "    for w in range(dnad[k].numWinds):\n",
    "        for i in range(dnad[k].numNodes):\n",
    "            j=nodeMap[k]['wt'][i]\n",
    "            if j>=0:\n",
    "                nodeWeightSum[j]+=dnad[k].nxGraphs[w].nodes[i][wStr]\n",
    "\n",
    "nodeWeightSum/=nRepl\n",
    "plt.plot(np.arange(dnad['wt'].numNodes), nodeWeightSum, label='%s' % ('aggregate mean:\\n%4f' % np.mean(nodeWeightSum)))\n",
    "consensusBWCC = nodeWeightSum\n",
    "\n",
    "d=get_peak_locations(nodeWeightSum, nPeaks, sortByValue=False)\n",
    "dHeight=np.repeat(0.033, len(d))\n",
    "dDelta=-0.002 ; dShiftCriterion=50\n",
    "for i in range(nPeaks):\n",
    "    if i>0 and d[i]-d[i-1] < dShiftCriterion:\n",
    "        dHeight[i]=dHeight[i-1]+dDelta\n",
    "    plt.axvline(d[i], linestyle='dotted', linewidth=0.4, color='grey') # vertical lines\n",
    "    plt.text(d[i], dHeight[i], name_node(dnad[k],d[i]), ha='center', va='center')\n",
    "\n",
    "plt.axvline(507, linestyle='dotted', linewidth=0.4, color='grey') # vertical lines\n",
    "plt.text(507, 0.023, name_node(dnad['wt'],507), ha='center', va='center')\n",
    "    \n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Consensus Edge-betweenness-weighted Clustering Coefficients')\n",
    "plt.xticks(np.arange(0,1350,50))\n",
    "fig.get_axes()[0].set_xlim((0,1350))\n",
    "fig.get_axes()[0].set_ylim((0,0.035))\n",
    "plt.xlabel('Node ID'); plt.ylabel('Sum')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mean Node Betweenness\n",
    "wStr='btws'\n",
    "fig = plt.figure(figsize=(16, 4))\n",
    "for k in dnad.keys():\n",
    "    nodeWeightSum = np.zeros(dnad[k].numNodes, dtype=float)\n",
    "    for w in range(dnad[k].numWinds):\n",
    "        nodeWeightSum+=[ dnad[k].nxGraphs[w].nodes[x]['btws'] for x in range(dnad[k].numNodes) ]\n",
    "    plt.plot(np.arange(dnad[k].numNodes), nodeWeightSum/dnad[k].numWinds, label='%s' % (k))\n",
    "    if k=='wt':\n",
    "        nPeaks=20\n",
    "        d=get_peak_locations(nodeWeightSum, nPeaks, sortByValue=False)\n",
    "        dHeight=np.repeat(0.075, len(d))\n",
    "        dDelta=-0.005 ; dShiftCriterion=50\n",
    "        for i in range(nPeaks):\n",
    "            if i>0 and d[i]-d[i-1] < dShiftCriterion:\n",
    "                dHeight[i]=dHeight[i-1]+dDelta\n",
    "            plt.axvline(d[i], linestyle='dotted', linewidth=0.4, color='grey') # vertical lines\n",
    "            plt.text(d[i], dHeight[i], name_node(dnad[k],d[i]), ha='center', va='center')\n",
    "\n",
    "#plt.axvline(507, linestyle='dotted', linewidth=0.4, color='grey') # vertical lines\n",
    "#plt.text(507, 0.075, name_node(dnad['wt'],507), ha='center', va='center')\n",
    "    \n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Mean Node Betweenness')\n",
    "plt.xticks(np.arange(0,1300,100))\n",
    "fig.get_axes()[0].set_xlim((0,1300))\n",
    "fig.get_axes()[0].set_ylim((0,0.085))\n",
    "plt.xlabel('Node ID'); plt.ylabel('Sum')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# MetaMean Node Betweenness\n",
    "wStr='btws'\n",
    "fig = plt.figure(figsize=(16, 4))\n",
    "nRepl=0 ; nPeaks=30\n",
    "nodeWeightSum = np.zeros(dnad['wt'].numNodes, dtype=float)\n",
    "for k in dnad.keys():\n",
    "    nRepl+=dnad[k].numWinds\n",
    "    for w in range(dnad[k].numWinds):\n",
    "        for i in range(dnad[k].numNodes):\n",
    "            j=nodeMap[k]['wt'][i]\n",
    "            if j>=0:\n",
    "                nodeWeightSum[j]+=dnad[k].nxGraphs[w].nodes[i][wStr]\n",
    "\n",
    "nodeWeightSum/=nRepl\n",
    "plt.plot(np.arange(dnad['wt'].numNodes), nodeWeightSum, label='%s' % ('aggregate mean:\\n%4f' % np.mean(nodeWeightSum)))\n",
    "\n",
    "\n",
    "d=get_peak_locations(nodeWeightSum, nPeaks, sortByValue=False)\n",
    "dHeight=np.repeat(0.075, len(d))\n",
    "dDelta=-0.005 ; dShiftCriterion=50\n",
    "for i in range(nPeaks):\n",
    "    if i>0 and d[i]-d[i-1] < dShiftCriterion:\n",
    "        dHeight[i]=dHeight[i-1]+dDelta\n",
    "    plt.axvline(d[i], linestyle='dotted', linewidth=0.4, color='grey') # vertical lines\n",
    "    plt.text(d[i], dHeight[i], name_node(dnad[k],d[i]), ha='center', va='center')\n",
    "\n",
    "plt.axvline(507, linestyle='dotted', linewidth=0.4, color='grey') # vertical lines\n",
    "plt.text(507, 0.04, name_node(dnad['wt'],507), ha='center', va='center')\n",
    "    \n",
    "plt.legend(loc='center right')\n",
    "plt.title('Mean Node Betweenness')\n",
    "plt.xticks(np.arange(0,1350,50))\n",
    "fig.get_axes()[0].set_xlim((0,1350))\n",
    "fig.get_axes()[0].set_ylim((0,0.08))\n",
    "plt.xlabel('Node ID'); plt.ylabel('Sum')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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w66t+kyylBPDee+8xduxYpk6dysKFC9ur+YIgCILQ5TnggAOYN28eNTU1FBcXM3HiRObOnctbb73FoYce2tHNy0rBLLxa67hS6mLgJSAI3K21XqiU+omz/1bgd0Bv4L9Oeum41np8tnOdS18DPKKUOgdYCXy3UM8gCIIgdCzGPWr58uUccMABHHXUUcl9K1euZODAgSxbtozDDz+cZ599FoBB3zqNUHkPIgnYtOwJbrjhBv7yl7+w//77s2LFCioqKpg5cyYnnngiS5Ys6ahHEwRBEIQWk8sSWyjC4TBDhw7lnnvu4eCDD2bMmDG8/vrrfPXVV506e3RB6/BqrWdqrYdrrYdpra92tt3qiF201udqrXtqrcc5P+Nznetsr9JaH6G13sv53FLIZxAEQRA6DhOnu2LFCqLRKDfffHNy38CBAwHYY489mDx5cjI5VbiiJ0opAqEwRx43jXnz5gHQrVs3KioqADj22GOJxWJs3ry5nZ9IEARBELoukyZN4rrrrmPSpEkceuih3HrrrYwbNw6lFOFwmFgs1tFNzKCgglcQBEEQ2oLu3btz4403ct111xGLxdi6dWvSVXnz5s2888477LPPPgDEau1qd1prXnnpJUaOHAnA+vXrk/G4H374IZZl0bt37w54GkEQBEHomhx66KGsW7eOiRMn0r9/f0pKSpLuzOeddx5jxozhe9/7Xge3Mp1CJq0SBEEQhDZjv/32Y+zYscyYMYNhw4Zx/vnnEwgEsCyLK6+80hG8y/n62ZuI1deggW7jxnD7v/8OwGOPPcYtt9xCKBSitLSUGTNm4ITTCIIgCIKQB0cccUSaFffLL1NpmK699lquvfbajmhWTkTwCoIgCJ2W2tratHUTpwvw6aefpu2r3lYDwPDTfwtAlBCnT9oj6cZ88cUXc/HFFxeyuYIgCIIgdDLEpVkQBEHYIYhrK2PblrpoB7REEARBEITOggheQRAEYYcglkivl1taFKAuEu+g1giCIAiC0BkQl2ZBEAShS1BRUZF0cQ4Gg4wePRqAIUOG8Mwzz5BIaFY9fxP1qz4nWFxGQCn2ufi3wH4AvPHGG1x66aXEYjH69OnD7NmzO+pRBEEQBEFoJ0TwCoIgCK2mvZM/mXJFbmKWbeEdPPl79NznICoryugzxC5RVF1dzYUXXsiLL77IkCFD2LhxY973ksRWgiAIgtB1EcErCIIgtJrOIAoTlgYUFpBAEQoFiWu7bQ8++CAnnXQSQ4YMAaBfv355X7czPJsgCIIgCC1DYngFQRCELkdjYyPjx49nwoQJPPXUUwDEHQvvurce5ou7f8VXM+8k6tTq/fLLL9m6dSuTJ0/mgAMO4L777uuopguCIAjCDsEf/vAHrrvuOn74wx+y++67M27cOMaNG8eNN94IwOTJk9l7772T2413VSQS4dRTT2XPPffkoIMOYvny5clr/upXv2LfffdlxIgRXHLJJWit/W7dLMTCKwiCIHQ5Vq5cycCBA1m2bBmHH344o0ePJh6qpN+3vkdpeQU6EWfL7OnMff5B+ME3icfjzJs3j1mzZtHQ0MDEiROZMGECw4cP7+hHEQRBEIQuzz/+8Q9OOeWUjO0PPPAA48ePT9t211130bNnT5YuXcqMGTO44oorePjhh3n33Xd55513+OSTTwA45JBDmD17NpMnT25V28TCKwiCIHQ5Bg4cCMAee+zB5MmT+fjjj4knIFxhx+wGQmF2P/BoNny9CIDBgwczZcoUysvL6dOnD5MmTWLBggUd+QiCIAiC0OW4+uqr2XvvvTnyyCNZvHhxi67x9NNPc9ZZZwFwyimnMGvWLLTWKKVobGwkGo0SiUSIxWL079+/1W0WC68gCILQpdi6dStlZWUUFxezefNm3nnnHX71q19Rry1itVsJV1SitWb9Z+/RY+BQAE444QQuvvhi4vE40WiUDz74gMsuu6xjH0QQBEEQWsoLV8L6T9v2mruMhqnXZN09b948ZsyYYU8yx+Psv//+HHDAAQBcfvnl/OUvfwHg/vvvT1ZSOPvsswkGg5x88sn85je/QSnFmjVr2HXXXQEIhUJ0796dqqoqJk6cyGGHHcaAAQPQWnPxxRczYsSIVj+WCF5BEAShS7Fo0SLOP/98AoEAlmVx5ZVXMnLkSN77ch1rnv03ifptgKb/0L35xjRb1I4YMYIpU6YwZswYAoEA5557LqNGjerYBxEEQRCELsRbb73FtGnTKCsrA+D4449P7vNzaX7ggQcYNGgQNTU1nHzyydx///2ceeaZvnG5SimWLl3KokWLWL16NQBHHXUUb775JpMmTWpVu0XwCoIgCK2mLZJKNIWpwXvwwQfz6aeZs9rxeII9Tv89RcQBGLFrb1Cptl1++eVcfvnlzb5vezybIAiCIDSLHJbYQtKcygWDBg0CoLKykjPOOIMPP/yQM888k8GDB7Nq1SoGDx5MPB5n27Zt9OrVi7vvvpsJEyZQUVEBwNSpU3n//fdbLXglhlcQBEFoNVrrDheGJkuzIaAUltX6dpln6wzPKAiCIAgdxaRJk3jyySdpaGigpqaGZ599Nuux8XiczZs3AxCLxXjuueeSnlXHH38806dPB+Cxxx7j8MMPRynFkCFDmD17NvF4nFgsxuzZs8WlWRAEQRAMsYSVth5QKkMEC4IgCILQMvbff39OPfVUxo0bx2677cahhx6a9dhIJMIxxxxDLBYjkUhw5JFH8uMf/xiAc845hx/84Afsueee9OrVixkzZgB2AqvXXnuN0aNHo5RiypQpHHfcca1ut9oZZqvHjx+v586d29HNEARB2GGpq6sDoLy8vGD3uPrqq3nwwQcJBoMEAgFuu+02brnlFmbPnk337t2paYgSOPhsuvfflZqVn/P1k9dT1msXhvQu55RTTuF3v/sdixcv5tRTT01ec9myZfzpT3/i0ksvbfLZDIV8RkEQBEHIxqJFi9rE4rkj4PddKKXmaa3He48tqIVXKTUF+DcQBO7UWl/j2b8PcA+wP3CV1vo6Z/vewMOuQ/cAfqe1/pdS6g/Aj4FNzr5fa61nFvI5BEEQhI7lvffe47nnnuOjjz5KZmeORqNAKlHGs/OWcdWjH4ETwztgrzEccNbvuf8nhyZF6t577838+fMBSCQSDBo0iGnTpnXEIwmCIAiC0A4UTPAqpYLAzcBRwGpgjlLqGa31567DtgCXACe6z9VaLwbGua6zBnjSdcg/jTgWBEEQdnzWrVtHnz59KC4uBqBPnz4Zx3jdl5WCRA4vplmzZjFs2DB22223tm2sIAiCIAidhkImrToQWKq1Xqa1jgIzgBPcB2itN2qt5wCxHNc5AvhKa72icE0VBEEQWsN97y3ny/U1Bbv+0UcfzapVqxg+fDgXXnghs2fPTu676qqrGDNmDHfd8BeseKo7Wf/V57x+w4VMmzaNhQsXZlxzxowZnH766QVrsyAIgiAIHU8hBe8gYJVrfbWzrbmcBjzk2XaxUuoTpdTdSqmeficppc5TSs1VSs3dtGmT3yGCIAhCG3HjrCW8/Pn6gl2/oqKCefPmcfvtt9O3b19OPfVU7r33Xv72t7/xxRdfMGfOHGq2VbPpg6cAKOs/lHOuf5hv/uw//OQnP+HEE09Mu140GuWZZ57hu9/9bsHaLAiCIAhCx1NIwetXpKlZGbKUUkXA8cCjrs23AMOwXZ7XAdf7nau1vl1rPV5rPb5v377Nua0gCILQDCxLY+lmvuBbQDAYZPLkyfzxj3/kpptu4vHHH2fAgAEopSguLmbS1JNoWLfUPra4jNLSchKWTmaJNOURAF544QX2339/+vfvX+BWC4IgCILQkRRS8K4GdnWtDwbWNvMaU4GPtNYbzAat9QatdUJrbQF3YLtOC4IgCB2EiZO1ClgCaPHixSxZsiS5Pn/+fHbbbTfWrVsH2LVy57z5KiV97G4nVluNUpqEBXPnzsWyLHr37p08/6GHHhJ3ZkEQBEHYCShkluY5wF5Kqd2xk06dBpzRzGucjsedWSk1QGu9zlmdBnzW2oYKgiAILSfhCN1Clrytra3lpz/9KdXV1YRCIfbcc09uv/12/u///o9NmzahtabbwD3oe7Dtorx18Qfc+9S1NMTh8qf7MmPGDJSyHY/q6+t55ZVXuO222wrXYEEQBEHYAfErEVhXV8cvf/lLotEoBxxwAHfddRehUEpmzpkzhwkTJvDwww9zyimnADB06FAqKysJBoOEQiFMCdkf/vCHfOc730ke1xYUTPBqreNKqYuBl7DLEt2ttV6olPqJs/9WpdQuwFygG2AppS4FRmqttyulyrAzPJ/vufTflVLjsL3nlvvsFwRBENoRW/AqrALe44ADDuDdd9/N2P7aa68ll++Z/QU3vLAQTZy+B0zh2HPO4/XFm3j58iMpKytLHldWVkZVVVXe9zZCWRAEQRB2ZrKVCDzooIOYNWsWw4cP53e/+x3Tp0/nnHPOAewSgFdccQXHHHNMxvVef/1136oLbU1B6/A69XFnerbd6lpej+3q7HduPdDbZ/sP2riZgiAIQiuIWxoN5KgA1C4knDhiI7yDgQBxrVstWEXwCoIgCIJ/icBNmzZRXFzM8OHDATjqqKP429/+lhS8//nPfzj55JOZM2dOh7W7oIJXEARB2PExsbsdLXi9McSBgCpoXLEgCIIgdBTXfngtX2z5ok2vuU+vfbjiwCuy7j/66KP505/+xPDhwznyyCM59dRTmTRpErFYjLlz5zJ+/Hgee+wxVq2yC/WsWbOGJ598ktdeey1D8CqlOProo1FKcf7553Peeee16bO4KWTSKkEQBGEnIJ6M4S2suKyoqMjYdu+999K3b1/GjRvHn388ja0LXk3u+8OpE/nstss46KCDOP7445PbZ82axf7778+4ceM45JBDWLp0aUHbLQiCIAg7An4lAqdPn86MGTO47LLLOPDAA6msrEzG71566aVce+21BIPBjGu98847fPTRR7zwwgvcfPPNvPnmmwVrt1h4BUEQhFaRaCfBm41TTz2Vm266if+8vJDbXvsCiAMQLipmj7Ov47YfTWTi8IHJ4y+44AKefvppRowYwX//+1/+8pe/cO+993ZI2wVBEAShJeSyxBYSUyJw8uTJjB49munTp/Pss8/y1ltvAfDyyy/z5ZdfAnaVhNNOOw2AzZs3M3PmTEKhECeeeCIDB9r9cr9+/Zg2bRoffvghkyZNKkibxcIrCIIgtIpkWaJO5tJsOOvuTDeq7du3A7Bt27ZkpysIgiAIQnaylQjcuHEjAJFIhGuvvZaf/OQnAHz99dcsX76c5cuXc8opp/Df//6XE088kbq6OmpqagCoq6vj5ZdfZtSoUQVrt1h4BUEQhFaRSJgY3o5RvI8//jhvvvkmqlt/YmNOIdytBwDxaJSvpv8KVIgnDrQ46aRpANx5550ce+yxlJaW0q1bN95///0OabcgCIIgdCWylQi89tpree6557AsiwsuuIDDDz8853U2bNjAtGl2nxyPxznjjDOYMmVKcv/555/PpZdeCsCuu+7Ke++916p2q44aoLQn48eP16a2kyAIgtC2LNtUy9TrX+XEcYO49rRvFOw+FRUV1NbWpm2rqqqioqKC4uJiTr7oN7z03FPsc/pvADh+RA8eW1RLTfVmrBev4bVZsxg2bBgnnXQSV1xxBQcddBD/+Mc/WLx4MXfeeWfW+9bV1aWtl5eXt/3DCYIgCEITLFq0iBEjRnR0MzoFft+FUmqe1nq891hxaRYEQRBahaU7Loa3d+/eyfII44+aRsP6Zal9ffsBUNRjFw45dBIff/wxmzZtYsGCBRx00EGAHf/rV99XEARBEIQdAxG8giAIQquId2BZonXr1iWXP/vgDYp726Xd4421JOIxABL123nvvXcZOXIkPXv2ZNu2bcmEGq+88orMlguCIAjCDozE8AqCIAitIt5OMbz19fUMHjw4uf7zn/+cTZs28cwzzxAKhdhuFTH42xcB0Lh5Ldf+7Fq21MWwtOavv7uSkSNHAnDHHXdw8sknEwgE6NmzJ3fffXdB2y0IgiAIbYXWGqVURzejQ2nueEMEryAIgtAq2sul2bIs3+1/+9vfAPjD4x/xxJxlQJyKwcO5/I7H+PerS4gQ4pQzjkoeP23atGSyDEEQBEHoKpSUlFBVVUXv3r13WtGrtaaqqoqSkpK8zxHBKwiCILSKuNVJyhKhCQUVJOz1oGswEI37i2VBEARB6CoMHjyY1atXs2nTpo5uSodSUlKS5vHVFCJ4BUEQhFaRsDouaZWbuKUpCirijuANuLJURBMieAVBEISuTTgcZvfdd+/oZnQ5JGmVIAiC0CqSMbztcK8nn3wSpRRffPFFclswGGTcuHHcdfkZfPnwNcntd177W7649UJW3HMZJx11CPPnzwdg69atTJs2jTFjxnDggQfy2WeftUPLBUEQBEHoCETwCoIgCK2iPcsSPfTQQxxyyCHMmDEjua20tJT58+dz5t/+x9jv/ya5XSkYMPkH7Hb2P7nv2TcYN24cAH/9618ZN24cn3zyCffddx8/+9nPCt5uQRAEQRA6BhG8giAIQqtIxvAWOIi3traWd955h7vuuitN8Bosy4nhdVCklmOJVNs+//xzjjjiCAD22Wcfli9fzoYNGwrYckEQBEEQOgoRvIIgCEKrSDjZkwudtOqpp55iypQpDB8+nF69evHRRx8B0NjYyPjx43ng9+dQ9cX7yeOVgvVvPcTyuy/lX3+5ikgkAsDYsWN54oknAPjwww9ZsWIFq1evLmzjBUEQBEHoEETwCoIgCK0iYYFGUWiP5oceeojTTjsNgNNOO42HHnoIgJUrVzJ37lyOuuCPfPHcndRv3UACxZkX/YI9z72RXc+8jurqrVx77bUAXHnllWzdupVx48bxn//8h/32249QKHsOx+VV9UQTmkAgQCAg3aYgCIIgdCUKmqVZKTUF+DcQBO7UWl/j2b8PcA+wP3CV1vo6177lQA12gYm41nq8s70X8DAwFFgO/J/Wemshn0MQBEHITsrCWzjFW1VVxWuvvcZnn32GUopEIoFSir///e8MHDgQgMo+A+k9bAwNG5ZT3HMXuvfqi1IbCYRCTD7uu7z/1HQAunXrxj333APY9fx23333rFkv6yJxpv33XY4e2Z9/fHdswZ5PEARBEITCULCpaqVUELgZmAqMBE5XSo30HLYFuAS4Dn8O01qPM2LX4UpgltZ6L2CWsy4IgiB0EO1Rh/exxx7jzDPPZMWKFSxfvpxVq1ax++678/bbbyddlWu3bWXr8s8p6TOYolCAjU5crtaa9157iVGjRgFQXV1NNBoF4M4772TSpEl069bN976mfu/7X1cV7uEEQRAEQSgYhbTwHggs1VovA1BKzQBOAD43B2itNwIblVLfbsZ1TwAmO8vTgTeAK9qgvYIgCEILSNbhLWBhooceeogrr0yf3zz55JP53e9+x+bNmwkEAqyqqmX44f/H/zvzSPboU8GZ3z+Dr1atRwP9D9if39z+LwAWLVrEmWeeSTAYZOTIkdx1111Z76ucvFeWhmWbajh3+jye/tlh9OtWUqAnFQRBEAShLSmk4B0ErHKtrwYOasb5GnhZKaWB27TWtzvb+2ut1wFordcppfr5nayUOg84D2DIkCHNbbsgCIKQJ0bwFjKG94033sjYdskll3DJJZck18++421q6+r55p59Abjyhru49sXFRAlx7nFjqKioAGDixIksWbKkWffXGlZsaaCqLsrq6gYRvIIgCILQRShk9g3ls605w6Fvaq33x3aJvkgpNak5N9da3661Hq+1Ht+3b9/mnCoIgiA0g5RLc+Hr8ObCQhMMpLqeqKsUkbssUbOu6ZymtU6WXYq38FqCIAiCILQ/hRS8q4FdXeuDgbX5nqy1Xut8bgSexHaRBtiglBoA4HxubJPWCoIgCC3CsjQKWxR2JPGEJhgAy7KwLIsxg7qjsGdfowmrRddMOM9maYtEwl6OWy27liAIgiAI7U8hBe8cYC+l1O5KqSLgNOCZfE5USpUrpSrNMnA08Jmz+xngLGf5LODpNm21IAiC0CxsC68ueB1egGAwyLhx4xg1ahTHHXcc1dXVyX0PXXgYL1zzE4455hjOPvtshvYuZdbPD0VriyfvuJ7hw4czYsQIbrzxRsAW6Jdccgl77rknY8aMSdb1dWOLeI3WENcWoMXCKwiCIAhdiILF8Gqt40qpi4GXsMsS3a21XqiU+omz/1al1C7AXKAbYCmlLsXO6NwHeFLZ2UJCwINa6xedS18DPKKUOgdYCXy3UM8gCIIgNE0qhrfwQrC0tJT58+cDcNZZZ3HzzTdz1VVXARAIF3H8Vbfxp28PTx6vlKJu4etsaVzLF198QSAQYONG2zHohRdeYMmSJSxZsoQPPviACy64gA8++CDtfkbEW5ZGOyuJ9lD2giAIgiC0CQWtw6u1ngnM9Gy71bW8HtvV2ct2wLfgoda6CjiiDZspCIIgtIJEC8sSba6NcOEDH3HzGfvTt7K42fedOHEin3zySdq2gMpMH1H98Yt89/f/JhCwnZr69bNzHT799NOceeaZKKWYMGEC1dXVrFu3jgEDBiTPTehUfLJ5zrgIXkEQBEHoMhTSpVkQBEHYCUi0MGnV/e+t4MOvt/C/91c0/56JBLNmzeL4449PbrPiUZ7924WceOKJvPTSS8ntka3rWTD7BcaPH8/UqVOTGZrXrFnDrrumUk0MHjyYNWvWpN3Hcol5I37jLYwHFgRBEASh/SmohVcQBEHY8Ym3sCyRcYH2s8pmo6GhgXHjxrF8+XIOOOAAjjrqqOS+I3/7IHsM6sv3RxRx+umnM3bsWHbffXd0IoYKhZk7dy5PPPEEP/rRj3jrrbd8XbCVpy1uEW9yVYmFVxAEQRC6DmLhFQRBEFqFEYXNjeE1urEZejcZw7tixQqi0Sg333xzcl9Rt94EAoohQ4YwYcIEPvvMznVY3L0Pu4+3I2GmTZuWdIMePHgwq1alysWvXr2agQMH+rYR7LJHIFmaBUEQBKErIYJXEARBaBUma3FzXZo1xsLb/Ht2796dG2+8keuuu45YLMbWrVuJRSMEAootW7Ywd+5chg+3k1f1H3UwX39qJ6OaPXt2cvvxxx/Pfffdh9aa999/n+7du6fF73qfSerwCoIgCELXQ1yaBUEQhFaRcCyezTV8piy8LVC8wH777cfYsWOZMWMGw4YN491/X8xH4SCvloW44IIL2HvvvQEYdthpLHn8ekaPfoKKigruvPNOAI499lhmzpzJnnvuSVlZGffcc09mG10m3oRkaRYEQRCELocIXkEQBKFVuDMZNwdzfHP0bm1tbdr6s88+m1w+8NJbGbtLKZceNjTtmJLySo75+b+4/czxaduVUmku0f5tTC0bw25MBK8gCIIgdBnEpVkQBEFoFS1NWuV4NDcraVUuEpYm6HOtgFLNFuMGP5fmhGRpFgRBEIQug1h4BUEQhFaRcEyfiRZaeFsSw+vbDgtUQCVdpM1nMKBa7IbsPi9ZlkgsvIIgCILQZRALryAIgtAqElqjUTTX7pmM4SV/xTt58uS0GrsA//rXv7jwwgt5+7ffZvpvfsSUKVM455xzCAQCBAIBUIq5j9/C8OHDGTFiBDfeeCMADzzwAGPGjGHMmDEcfPDBLFiwION+WoNG2c/nLIvgFQRBEISug1h4BUEQhFaRaKFLc0tieE8//XRmzJjBMccck9x2930PUDHph6hQET++ZjoXTtot7ZzVH75IXdUGln7xBYFAgI0bNwKw++67M3v2bHr27MkLL7zAeeedxwcffJD+bDozaVVcXJoFQRAEocsgFl5BEAShVRiLZ1Nxsh8sq2L0H15iW30MSAnkYDN8mk855RSee+45IpEIAMuXL+frlatZU2yL3FAgs1tb9vYzjPz22ba1F+jXrx8ABx98MD179gRgwoQJrF69OuNc37JEYuEVBEEQhC6DCF5BEAShVSTyrMN70+tLqWmMM391ddrxzQnh7d27NwceeCAvvvgiADNmzODgo76DUgodj3L7//sh3/nOd5L7AWo3r2XFnFcZP348U6dOZcmSJRnXveuuu5g6dWrGdu0WvFrKEgmCIAhCV0MEryAIgtAqkmWJmvD0DQftLse4BBstGWhm1irj1gy24D3oyOMB2P2CO7ng79O5+eab+f3vf8/XX39ttyseIxAuYu7cufz4xz/mRz/6Udr1Xn/9de666y6uvfbazGdzPZN5zlhCBK8gCIIgdBVE8AqCIAitImnxbMLCG3KEbcxRkakY3uYJ3hNPPJFZs2bx0Ucf0dDQwOC9RtrXr+xFIAC77bYbEydO5NNPPwWgrEdfdhn7LQCmTZvGJ598krzWJ598wrnnnsvTTz9N7969M+7ltlonkhZeieEVBEEQhK6CCF5BEAShVcQtO49xU56+xsIbS7pA29ubW5WooqKCyZMn86Mf/YjTTz+duKVJNNai41Gqa6Ns2rSJOXPmsNdee2FZFkP2O5SNi+cBMHv2bIYPHw7AypUrOemkk7j//vuT27xY2uRo1lgJ+1MsvIIgCILQdZAszYIgCEKrsJJZmnMLwXDQlrbxpIW0+VmaDaeffjonnXQSM2bM4JU1mtjmVWx86T/cWRTk2coiLrjgAoYPH47WmrFTz+CV2/7M6NGjqaio4M477wTgT3/6E1VVVVx44YUAhEIh5s6d63m21LJxb5YYXkEQBEHoOojgFQRBEFqFEbBN6cCQsfDGNT++by6vfL6hxfecNm1aUmC/sOpLSgaPYOg5/2bXyiB3/2Bs2rFlFd044Ly/M/Nnh6Ztv/POO5PiNxtul2Zzv7i4NAuCIAhCl6GgLs1KqSlKqcVKqaVKqSt99u+jlHpPKRVRSv3StX1XpdTrSqlFSqmFSqmfufb9QSm1Rik13/k5tpDPIAiCIOQm0UwLb8yy0sSu1UqLacLSKAXfGT2Aq749ImN/QKkmM0hnvbZPDG9cXJoFQRAEoctQMAuvUioI3AwcBawG5iilntFaf+46bAtwCXCi5/Q48Aut9UdKqUpgnlLqFde5/9RaX1eotguCIAj5k8izDm8qS3P6ca31EI5bmnAwwJ9OHEV9fT0NDQ1p+wMB1WI3ZLeIN/V3xaVZEARBELoOhXRpPhBYqrVeBqCUmgGcACQFr9Z6I7BRKfVt94la63XAOme5Rim1CBjkPlcQBEHoHBgh2JSjbyhgklalH9lS66shYVnJDNB+BJRKs9S6qaiooLa2FoBgMMjo0aMBGDJkCM888wyWBfXLP2HTG9O5e0aQ7fEgm/pfA4wD4I033uDSSy8lFovRp08fZs+e3apnEQRBEAShbSmk4B0ErHKtrwYOau5FlFJDgf2AD1ybL1ZKnQnMxbYEb/U57zzgPLAHLoIgCEJhyN/Ca8oSpR/XWotpLKEJ5hC8QaWaqpgEQGlpKfPnz09vm9ZsePlWBp70//juEQdx3z13MefJO+GyE6iurubCCy/kxRdfZMiQIWzcuLFVzyEIgiAIQttTyBhev9FHs0Y1SqkK4HHgUq31dmfzLcAw7On1dcD1fudqrW/XWo/XWo/v27dvc24rCIIgNINUDG/u41IuzekW3lYaeElYugkLb8tFtdYalMKKNmBZGitST0n3PgA8+OCDnHTSSclJ1X79+rXoHoIgCIIgFI5CCt7VwK6u9cHA2nxPVkqFscXuA1rrJ8x2rfUGrXVCa20Bd2C7TguCIAgdRL5Jq4wVtq1dmuOWTmaA9iPfGN7GxkbGjx/PhAkTeOqpp5y2Qf8pF7Hm0T9z20+PY/vCNxh17JkAfPnll2zdupXJkydzwAEHcN9997XqOQRBEARBaHsK6dI8B9hLKbU7sAY4DTgjnxOVUgq4C1iktb7Bs2+AE+MLMA34rO2aLAiCIDSXeJ4uzWZv1OvS3A4xvPmI6pUrVzJw4ECWLVvG4YcfzujRo0lYpVTPfYZB3/0t3z78mzw2/Xbef/CfcMFhxONx5s2bx6xZs2hoaGDixIlMmDCB4cOHt+p5BEEQBEFoOwomeLXWcaXUxcBLQBC4W2u9UCn1E2f/rUqpXbDjcLsBllLqUmAkMAb4AfCpUmq+c8lfa61nAn9XSo3DHjstB84v1DMIgiAITWPEZFNGVGMBjsbb1qU5bjURw5unhXfgwIEA7LHHHkyePJmPP/6Y+l7DiWxcTunA4UTjFpUjDmHTU38FYPDgwfTp04fy8nLKy8uZNGkSCxYsEMErCIIgCJ2IQlp4cQTqTM+2W13L67Fdnb28jX8MMFrrH7RlGwVBEITWYZcZUk0KXiM6I/FE2va2qMMbCiiUUgQCAQJONujUZ9MW3q1bt1JWVkZxcTGbN2/mnXfe4Ve/+hWf15WRiNQT3bKWSKIX9V8voHKXoQCccMIJXHzxxcTjcaLRKB988AGXXXZZq55FEARBEIS2paCCVxAEQdjxSVgaTdOJoYzrcsRj4W2tS7Ox8CqVEr0AdnQMBFWgybYtWrSI888/n0AggGVZXHnllYwcOZJF81bTf8pFrH3yWp6bGSYaKOHQc38HwIgRI5gyZQpjxowhEAhw7rnnMmrUqFY9iyAIgiAIbYsIXkEQBKFVJPJ2abY/vYK3lQZe4gkrWePXj1wuzaYG78EHH8ynn36asd/SmorhE6gYPoF9B3Zj4drtlPTqndx/+eWXc/nll7fuAQRBEARBKBiFzNIsCIIg7AS4xWSuTM1Jl+ZYuktzU9md87l/rhhepZp2t86G2xXaxB576wgLgiAIgtB5EcErCIIgtIq4lbLY5hKWqRhey3d7y++vCQdzJa3KnUF6/fr1nHbaaQwbNoyRI0dy7LHH8uWXXxIMBrnie1NZcc+lrH386mTs8cbF89h///0ZNWoUZ511FvF4HLCF+yWXXMKee+7JmDFj+Oijj1r1XIIgCIIgtB4RvIIgCEKrcOndnMJS6yxJq1ppMG3KwhtQ6aL63a828/rijck2TZs2jcmTJ/PVV1/x+eef89e//pUNGzZQWlrK1ffPZLez/8XAk68iErPQ2uKzB//KjBkz+Oyzz9htt92YPn06AC+88AJLlixhyZIl3H777VxwwQWtezBBEARBEFqNCF5BEAShVaRbeHO4NGdJWpVPjdyc90/onDG83izNZ9zxAWffMweA119/nXA4zE9+8pPk/nHjxnHooYfabXMJ5Ug8QaKhBhUKJ0sPHXXUUTz++OMAPP3005x55pkopZgwYQLV1dWsW7cOQRAEQRA6jrySVimlgsC3gaHuc7TWNxSmWYIgCEJXIWFpFLYwzKVdE47ObYx5BG8blCUKBhSWZZFIJJIuxomEbUkOkt1t+rPPPuOAAw7w3dfY2Mjvzj6e9dsj9JpwEmX7HkyotBIrEWfu3LmMHz+exx57jFWrVgGwZs0adt111+T5gwcPZs2aNQwYMKBVzycIgiAIQsvJN0vzs0Aj8ClgNXGsIAiCsBPhFpP5uDTHE22cpdmyKA5n785UwE5apbVOlirKh5UrV/Lisgh//t9LrH7odxT3G0K4xwCGnXoVl112GZFIhKOPPppQyL63X/Kt5txPEARBEIS2J1/BO1hrPaagLREEQRC6JPE0wZu5f/nmOh6euyp5nNfa2lqX5qZieIOO6LQ0eHNb7bvvvjz22GO+5w0cOBDrq2UU9diFsiGjaFz/NeEeAygbPIK3bn2LSx76mLq1n7DXXnsBtkXXWHsBVq9ezcCBA1v1bIIgCIIgtI58Y3hfUEodXdCWCIIgCF2ShKUxhkw/8frj++Zyyxtf8fXmOgBiVtvG8MYSmlBOwZtqp5vp7y7nsMMOIxKJcMcddyS3z5kzh9mzZxOJRNAaEvXbaVjzBUV9bHflhm1bAXh63gpu/c8Nyfjf448/nvvuuw+tNe+//z7du3cXd2ZBEARB6GDytfC+DzyplAoAMUABWmvdrWAtEwRBELoEcUtTHAgQTVhon6CXmOPCbARnItG+Ft5AwFh40+/z+2cWslvvMp588kkuvfRSrrnmGkpKShg6dCi/+tWvGD9+PFsb4mzaVk+vg06i2BG8G955hBEjfs/ajTVUjjuWww8/HIBjjz2WmTNnsueee1JWVsY999zTqucSBEEQBKH15Ct4rwcmAp9qvyAlQciHz56APsNhl1Ed3RJBENoQy9KEQ4powl+8mi2m+4h5LK2JFmaGWFlVz+CepcQti1Awu8OScWn2S1zVGLMYOHAgjzzySMa+Tz/9lP++sZR/v/hZ2vbeh5/Dor9MZeiVz6dtV0px8803t+RRBEEQBEEoEPm6NC8BPhOx2wWYezdsX9vRrfDnsbPh1m92dCsEQWhDtNbELU3YEZy5rLVGb3qTVrWka1m6sZZJ/3id/76xlISV26U5m4UX7Bq9ufDLIJ0t47MgCIIgCJ2PfC2864A3lFIvABGzUcoSdTIat8Fzl8GR2+GQSzu6NYIg7OD86rEF1EbsEkChpODNfrwRim1Rh3fdtgYA3ltWRbwJl2aTKdnysSQ3lUXZ73nilk66aQuCIAiC0LnJV/B+7fwUOT9CZyTuzEVE6zq2HYIg7BQ8Mnd1cjnsZIbys9aaTXFHcTbGEmn7W6Idgy4R25SFN5m0yq9sUBP3ySbG66MJ3+2CIAiCIHQu8hK8Wus/FrohQhtgBG+svmPbIQjCTocRvLksvDEnWZX3mBZFy7iyQtsW3hwxvIHsMbxN4efSDFDnWLYFQRAEQejc5CV4lVKvk8o7kkRrfXibt0hoOYmo/dkZLbwS/i0IOzShQNMxvNG4vym3JS7NAWUsynZMcItjeJvIZJFNI4vgFQRBEISuQb5Jq34JXO78/BaYD8xt6iSl1BSl1GKl1FKl1JU++/dRSr2nlIoopX6Zz7lKqV5KqVeUUkucz555PsOOT9LC29Cx7fAjEevoFgiCUECCwSAalSVLs70t6vFdLnLifhMtmA8LukSsieENBAIEg0FCoRChUIhgMGj/KOc+PupVNeHUbGmNRqX9AMnYZUEQBEEQOjd5CV6t9TzXzzta658DB+U6RykVBG4GpgIjgdOVUiM9h20BLgGua8a5VwKztNZ7AbOcdQEgYQRvJ7TwGuuzIAg7JEXB7ImhDN5ET388YV+G9S1voYXX/kxoTcLSSZdq32NzuTQ3EcSb0No3k3NdRGJ4BUEQBKErkJfgdayq5qePUmoKsEsTpx0ILNVaL9NaR4EZwAnuA7TWG7XWcwCv+S/XuScA053l6cCJ+TzDTkHcuDR3whheSyy8grAjE8qjLJHXpTkUUASUalEMbzLzsqbJGN6Ayu7S3BRaQ1EodW1jWRYLryAIQnbqY/Vsqt/U0c0QBCD/LM3zSMXwxoHlwDlNnDMIWOVaX00TVuE8z+2vtV4HoLVep5Tq53cBpdR5wHkAQ4YMyfO2XZxEJ05aJS7NgrBDEwpmF5Vmk9fCGwragrclyaTMNS1LN52l2dGrfvcJNFGWKGFpgkpRFAwQTVgUhwI0RCWGVxAEIRc/fPGHLNqyiE/P+rSjmyIIuS28SqlvKKV20VrvrrXeA/gj8IXz83kT1/YbReQ7qmnNufbBWt+utR6vtR7ft2/f5pzadYl34qRVIngFYYemOVmaDaFAgEBA5TwnG8YqnHAEb646vKmkVdmvkw1La5RSyeczn3VREbyCIAjZWLRlUUc3QRCSNOXSfBsQBVBKTQL+hu1GvA24vYlzVwO7utYHA2vzbFeuczcopQY4bRoAbMzzmjs+ndnCKy7NgrBDU+S4FK/amv/7x3ZpbllZImOtNbV9c2ZpdpUw8tKUm7PWdiZnY8EudtybxaVZEAShaVpUdk4Q2pimBG9Qa73FWT4VuF1r/bjW+rfAnk2cOwfYSym1u1KqCDgNeCbPduU69xngLGf5LODpPK+542OyNHfGGF6x8ArCDk0oGEABZ98zJ2NftvFOKBhosUuzlXSTtheCQTsWWGuNZVlYlpVcD5A9aVUiR5Itc04ACDvPZ+J5jUtzrmRZgiAIOzsRY4wRqGqo4rTnTmNd7bqObspOR5OCVyll4nyPAF5z7csZ/6u1jgMXAy8Bi4BHtNYLlVI/UUr9BEAptYtSajXwc+A3SqnVSqlu2c51Ln0NcJRSaglwlLMuQCoTcme08IrgFYQdGlv4NU+4hgKqxS7NxjJrEmGFAipN7Lp/AjlieJuy8Frajg8OB+znKw4awWtnaQ4H863uJwiCsPMhgjfFM189w8KqhTz4xYMd3ZSdjqaSVj0EzFZKbQYagLcAlFJ7Yrs150RrPROY6dl2q2t5Pba7cl7nOtursMW34CXeiV2apSyRIOzQhHO4FGfDTloF2xtjnHHH+1xz0hiG9C7L61wjVCOO4M2VpTmYI0uz1YTatrSdETruHNejrAiIJl2aRfAKgiBkpyHeQPfi7h3djE5BzAnvCwfCHdySnY+cPbXW+mrgF8C9wCE65YgfAH5a2KYJzcaIykQUEp0svszqZO0RBKFN2VLf/EmtoFOW6OOV1bz7VRXXv7I473ONtTYaN5bWXDG8OVyam7LwWnYdXuPC3KeiCBCXZkEQhHxojDd2dBM6DUbwPrHkCd5c/WYHt2bnosmpaa31+1rrJ7XWda5tX2qtPyps04RmE3e5jcQ6WaZmsfAKwg5NS9ySQ4FA0voK0BhLNON+9g0bnHNKwsGsx6ayNPvF8Dbt0hxQKnmfPhUlANRF7fVQDsuyIAjCzk5jQgSvIeaE91U1VnHRrIs6uDU7F9JT70i4RWVnS1wlMbyCsEOwaku9b9bN076xK+VFQSqKc0fKuMsHhYIKdxncxlgTGaRcOMmZk0mrSnMI3mDSwpu5r6kEogmt09rYtzLdwttEGV9BEISdGrHwpoiLt2OHIYJ3R8IteDtbHK8IXkHo8ny2ZhuH/v117n13eca+knCQE/cb7CuG3duKXDGvIcel2dDQDAuv1xU5l+DNlbTKvW17Y4wvN9Sk7TdliQzGpXl7QyzrNQVBEASbhnhDRzeh0xCTEp0dhgjeHQm3S3O0k7k0u//JrfwHtYIgdB5WVNkTaXOWb8lI9hQMKIKBpl2bTVkfs+y2+EaaIXi9wrq0yBa8X9d8zfwt89Pb5ohqU6bIjVs4n3rb+xz9z/S4KkvrNLdr49K8YXtjcr8gCIKQIuYycoiFN4UI3o5DBO+ORKe28Eb9l7WGhup2b44gCK3Da2ENBGz35KaSQLkFb1k41GKXZq97sonhfWDpA9z85c1p+5QjqhNaZwhyt3BftG67z310mhW6e6ntsr29MZ7cLwiCIKSoj6fGoFKWKIX3u/DziBIKgwjeHYH1n8LmpZ6kVZ1N8LpmtRqqU21d+CTcMBIaMweagiB0TrTOFHrBgG1JbaoDL3YJ3pKiQJqYbIy33qW5IdFAxEofVBgjcsLSGe1uSq9qTVobg56szCJ4BUEQ0nG7MYtLc4raaG36eqw2y5FCWyOCd0fg1kPgpgPAPXPUmZNW3bAP3DbJXt602M4oXb+5Y9olCEKLiHuEXkjZ8bh+AtC9Jc3CWxRKc2luTpZmr7Auc1yao4lohtuYuw6v1wXZzyJtrL5frN9OJG6hFPx40h4ctndfQgERvIIgCLlwW3glS3OKOk8FlepIdcc0ZCckdzpNoWsR78Quzd64hU1f2J91G+3PiMxyCUJXIpHwujQHUIFA0zG8rqRVpeEgbv3YEG2GhdfKjOFVyiJiRYhZMZRSKEfoBp2sUwkr8zxvLDLYInjNlgam/OstAEb1K+bCyXsCsLKqLuNYQRAEIYXbqisxvCm8gnd7ZDtUdlBjdjLEwrsjkYhAUYW93NmSVmWrw1vrCN7O1l5BEHISs9KDaEPBQFK8+olIg9ulORhIiVKAxngzyhJ5blESDqKUImpFiVpRgsEggUCAQCCVGCth6QyBmi1z87aG1CSdUoGkgA65BHtlSQgr/yYLgiDsFNS7jC4ieFN4XZjFwtt+iODdkYhHobSnvVy1FB45EyI1uc8pNNvXwYaFsG6B//66TfZnVCy8gtCV8ArFUCDdddiNe9Xt0gykZUCONkfwei28TgxvY6KRhE5g6dS1AoFUu7TnFn5Zlr3u2u6yRG7B279biVh4BUEQPLiTMzUkJIbX4LXwbmnc0kEt2fkQwbsjkYhAaQ97eeFT8PnTsOHzjmwRPP9zuOVg+Oi+zH2JeMrC29HCXBCEZuEVheFgICksc4lAr+ANtLAXct8jGFCEnWRSUcebxB3HG3QnrdLepFX+Ft6IK4FWWtKqtBJFRRLDKwiC4CHq8uoTC6+NpS22RrambatqqOqg1ux8iODdkYjW2y7NgRBsX21vi3Rw9uNcJYfqq1wWXnFpFoTOjruEkDuGd1CPUgb3LEsKQ6+G1K60VcWhoOea6Umg8sUtVEsdd2ZIWRaiVmrA5bbwegWqt7yRvU3TEHVZiF1tdCet6l1ebF9XRK8gtB0f3gE3HwSN2zq6JUILcb9/a6O1aS7OOyvbI9uJW/G0bZsbJGFreyGCd0eiZh0UlUO4PLWtozuMcAn0HOq/b+vylCtzR7k0f3C7bQ0XBKFJ3CIz7gSv/uKo4bxx+WSUUmnlf9LPSy27k1ZBusUU8heP7uNMDV4gWZIoZsVYsm0JVY1VSZGasDKzNGez8Da4Mka7M0m7yxL1Ki8CMq3dgiC0goVP2okt37yuo1sitJCYU5mjNFTKs8ue5aAHD0oLM9kZqWrMtOZuatjUAS3ZORHBuyOxfa0teIvKUts62sIbj0C3Qf77Ni5MLXdUluYXLodHz+qYewtCF8MtZM3y7n3LCTsiNhjwj+F1nxf2ujR7DLz5ikf3YaVFqWsaC29DvIFz3zyXX7z/i2S74n51eLMkrXIL3u4lqYIGIZcPthG8fqJZEIRmsuQVmHMnVO5ir1ev6Nj2CC3GuDT3Ke2T3Lazu+96n7+yqFIsvO2ICN4dCZOlOewSvI0dLXgbIVzqv2/dJ6llSVolCJ2eeMJt4bWXjfXUXRfXm7k47vIbLs4QvOmKN55n2mO3cDUJq2KJGAltC9WffvhTANbXr0/eI+EjeP3ijRNa0+gqkdS9NIzWGq01bgO1qf0rcbyC0AY8cAo8/wuIOTGf9VvscKd4JPd5QqfDuDS7Be+a2jUd1ZxOgVfcDq4YLIK3HSmo4FVKTVFKLVZKLVVKXemzXymlbnT2f6KU2t/ZvrdSar7rZ7tS6lJn3x+UUmtc+44t5DN0eryDtaIKj4W3g5NBxRohVOK/b+1HqWURvILQ6XGLUSN+TY1brXUyOVQuC29m0iqv4M3XwusSvEW2BdadHKUubucF2Lfnvkm36bifS7OfhTeRbuHtUeYSvK7mBvNI0iUIOxSr5kDD1qaPaw2mhmvDVvjrQLjrqMLeT2hz/Cy8O7vg9bo0D64cLC7N7UjBBK9SKgjcDEwFRgKnK6VGeg6bCuzl/JwH3AKgtV6stR6ntR4HHADUA0+6zvun2a+1nlmoZ+gSWIn0dW8Mb4e7NDdCqNh/nylVFCxqX5fmxm1Q9VX73U8QdhDcYtSIX3cSp2xZmt3neWN4M1yaE80XvCWOiG5MZGYDDalQSpgmrIz6vX5iNW5ZGYLX4E6ylXThFguvsCPSuC09oaRlwV1Hwn0nFva+MUfw1jsCIVtZQ6HTYrLki+BNUdVQRSiQCo8ZXDGYmmhNWkZroXAU0sJ7ILBUa71Max0FZgAneI45AbhP27wP9FBKDfAccwTwldZagjn8cJXeAFIxvIEwVA7sBC7NEQiVwpjTsh/TY7f2tfDePRX+s3/m9qqv7FrGgiD44hfDG3KZPANZ6vC6z2vSpdkvbbJvW1LL/brZXiTu2o8A/Uv7E7WiBJ2cVr4xvD5a1dKaBpdLc4/SIt82uGODTdt/cNcHzFkutRWFHYCHvw8zL7f7xVsOgcXP29vXzS/sfY1nWs26wt5HKBgmaZUI3hSbGzbTu6R3cr1fWT8Atkc7eJy+k1BIwTsIWOVaX+1sa+4xpwEPebZd7LhA362U6ul3c6XUeUqpuUqpuZs27cAuAwmP4C2uhIpdYJdRUNar81h4T7oNfuty5+g22P4s7Wn/tKfgNcmy3IPy2k22CH4xw/NeEAQHvxhedwZjY/10h+FqrdMtvE24NMda4NI8tLcdxuEVvD2KehCzYrmzNFua+micjTUp63Dc0jS6LbzlYfxICnxLs70xxv3vr+CtJZv55aNikRJ2ALYut0Xn9tWw4VN48iftc986iWvs6kStKCEVoltRt+S26sbqjmtQJ2B9/Xr6l/dPrvcsseVLTbSDQw93EgopeP2KK3pHMjmPUUoVAccDj7r23wIMA8YB64Dr/W6utb5daz1eaz2+b9++zWh2F8MreIvKYeo1cMajUNyt42N4464Y3mDKlYM+e9mf4TIo7ZFyXWpP3MXQ650Odvlb7d8OQegiuGN4kxZeV9Zivxher341x5/2jV2BTJfmRL4uza4L79rTX/B2L+puW3hdMbyZdXg1J9z0DgdePSttW1qW5iYsvAmtuerJz/jjs58D0K3EXyALQqcnHrGTRYHtIZaIQcKpHeqpIVow6kXwFpKtjVvZULehoPeIJqKEg2GUa5jvF3KyM7GhbgP9y/pz7aHXMmHAhORkgFh424dCCt7VwK6u9cHA2mYeMxX4SGud/M/UWm/QWie01hZwB7br9M6Ln0tzSXeo6Gtbezu6Dm+2GN7Rp9ifsXrouzds+hLWfATrP22/trkTbyQnBvzmYARBAG8Mb7qFty5Wh8YWiQmfWF9DMABf/HkKV08bDWS6NMfyzNLs1q2DetqZ4L0DquJAMdFENJlYK1uW5iUb0z1MEla6S3Opxyo9qEcpF04elhTSCUuzYXvq3t1LRfAKXZRHfwh/391204hstwWwmRz2TrAXCisOZSlXWFxxj0LrmfTwJI587MiCXb8mWsPa2rUUBYuwSL3P3UkFdza01myo38Au5btw7B7HcsfRd1BZVAmIhbe9KKTgnQPspZTa3bHUngY84znmGeBMJ1vzBGCb1todtHE6HndmT4zvNOCztm96F8Ib7O78AwFQ0q1jBW8ibndcflmahx4Kpz8Mpz0E/UfbJZXuOAwePLX92lfvirMzFmaloGY9PH1xqjSCIAhAyvqqNSRcSau01hzx9BHM3Pjf5P7kOR6BqZSiJBxMCuXMGN78LLzuZFNDevlbeIuCRUStaNKK7Jel2S/BstfCu0v39HfYO1cezq+m7ONKWgW9y1NWYBG8QpdlsZMHtG4jaMvum2P19jbtSpKZKLC1t9tA14ry/0cVOiXHPXkcr658laJAEQf0PwCA4mDxTm3h3R7dTkO8gf5lKZdmY+EVwds+FEzwaq3jwMXAS8Ai4BGt9UKl1E+UUiYQZCawDFiKba290JyvlCoDjgKe8Fz670qpT5VSnwCHAZcV6hm6BH4uzYYeu8G21amMh+2NGXyGfQRvuBT2ngK7TbTjjQ3tma25wSV4azfanyoAL10FH9+f6vgFQQBS8bUaTSyRsvA2OGVEPq15A0gXo94yQ0GPD3MooyxRnhZe57r/Pm0cu2YTvAFb8CqlCAUUCcvydWn2knBieEcO6MbCPx5Dz/KmklZZ9K5IHdOtVCxSQhcl6HhkbV5if8aj/jk26jYWth1DD4XDfwsHXWB7skkt3najPlbPvA3zWny+Kb9TFCxieM/hfHrWp0waPGmntvBuqLcdVd0xvN2KRfC2JwXtlZ2SQTM92251LWvgoizn1gO9fbb/oI2b2bXxxtS4Be+AMfaM7MbPYdAB7dsuSHVQfhbecGlquc9wGHwgVK9on05NBe3vxW3hTXbeKmU1VwUtUy0IXQ5j1U1Y6Vmaa2P2gDis7MGy24rqjckNeiy6xeH0/7NsFt4VVXX0Ki+i0omPtbQmFFCcMC6V5zCb4AVbnNoWXs8z+ZYlsi28ZUVByotD1GV5L5mEW5bWyXaZewlCl6S0J9Suh81f2uuJCETrM4+r2+SxwrYxJd1h0i/hwzvs9UiN/+S50OZc+daVvL7qdd489c1kYqWWEA6k3omlodKdWvBuqreT57otvMalWWJ42wcZ0Xd1MlyaXYJ3lzH257pP2q89YGc8fv/WlGXZL4Y35BK8wTCc+wqMPa19rNFBxxKTZuF1MnmrgO3GZZYFQUiSLL9jWcnlUCBArWMBCgccwesT62uoKEmfZy0JBT338Lfwfusfb3Dqbe8n1xNaZ2R49nVpdt6RoYAikch0aXZnYzZYTgxvSTiYsc9NKoY33VIci4v7pdBFKXUETpqFty7zOL9tbYkRt0UV9ufLv4EPbivsPQUAPq+yk+9536fNJeAaQ5UES3Zql+a6mP3/Uh5OjdGLg8UUBYpE8LYTMqLvyqyeB1VL07cFXa53PYfaMb0bP2/XZvHMT+HFK2DVB/a628Jr3KWCPs4F4XJ7NtnKHIC2KeY78rPwKpWymiux0giCG2N9tZM/pWJ4TYddZARvjhjegT1K09a9onLO8q2cf/9cX1fjz9elBgZaZ1qLjTXXUBwsTro0Gwuv97qbajIHdXFLE4lblDjWZ6VU2o8hGEg9Y8xVGDiaZy1hQeh0lPWyP6scwZvI4tLsZ/VtS8ykeLGTl+STGfDCrwp7TwGwQ1bagrjLA7EkVLJTW3hN2E9ZqCxte2VRpbg0txMSaNSVufPw1PKJt9iZFHu4kl4rZa9va+di36ZzrHWSa7sF7wXvwpossSFFzosgWmcn3CoUoSKIkJ6l2R3DawS3xAwJQhpGLMYTOil+gwFFbSTdwptm7fSIv0E90t0SSzwuzde88AUA67c3Msgjjr1t8XoO+1l4EzpBQicIBQN2Hd48BG9C2wI2HEwJXj+SdXh1SvAWBQMieIWui7GobnK7NPtZeAucb8NYeIsrcx8nFIx4K8tQxVxVREzSKq111vfpjkx93J4gKg2l92kieNsPsfDuKHQbBON/5LN9ICx+Hp77efu1JewIV5P52C14++wJY7NkYjZxvbECzxz7WXi3O5MCSqVcmjsq2ZcgdFKMu3HclfwpFFRJl+aUhTe74B3QPb3DLw75uw27xaz2ibO1fFyavS5zRQH7fz1mxVIWXs+1Ntd6wkKw446jcYuiUO4uMhRM1feNxTX9uxWzR99yYnERvEIXxWRi3rbS/oxH/fvktu6nvVmfvRZeof1wXpExb9nLZuI+vzRUiqWtVl+zq5K08IbTLbz9yvqxfNvyDmjRzocI3h2FYJYyGN2chC5z74L7ToStywvXhu3rYP5DqTjiN/9hf/rF8PphYhsKHRtkvit3DO92p/yzCqQ6fBG8gpBG0sJr6WTG5mBAUROzZ6jDvoI3XWCWF6c7FnmTVhmUqya2n3uzZemMkkaRRISgCnLc4OP4y35/ociZ3IokIlmzNNdHM60YCe0I3mDuLjLgqsMbsyxCgQBFIbHwCl0Yb16QrBbeNu6nvfGiZgK8vG/b3kdIksgSPmZq57ZEnFraHdqR+lsqcQwfRvjtbNTH6lEoSoLpHk6HDjqUxVsXs7pmdQe1bOdBBO+OQqAJwQuw7HV445rCteHB/4OnfpLZYfplafbDuDQXWmia78pt4XW32XQChbY0C0IXI+aK4W1whGJpOJh0ySoK2P/rbk0ZbcLamS0xlDuOzJv4CmxR6s2GHElEKAoUcfGIi/lGn28kLbzRRNSVpTn9Wg0+SasSliaa0ISbsPAGA26XZk1RKEA4GMiwagtCl8Fb6lBb0FidedznT7ftBLo3hMi4NLvDtEDq8bYh2QSt8ahpieCtd42b3OcbwbuzxvE2xBsoDZVmuHNP3nUyAO+te68DWrVzIYJ3RyGbhbeke/q6yp11tFUYK6m3w2yuhbe1QjNSC7dNgjUf+e83cSluC68h7kqaJRZeQUjDWEdjCc22BttNuKI4lHRpDjnvF7cV1Vg7fzBhN279/v4Z18wmeN3lifwEr6XxtfAWB1Pvm6TgtaKOhVfjTQLtN36OW5poPNGkhTfosvDGExbhoKIoGJAszULXxTthDVDvyndhxhDL34IbM/+f2+y+7qRYp9yTWt5JBVMh8Cb5M5jJxph3LJcHJhux93xj2Wxt5ueuSn28PiN+F2BItyGEAiGx8LYDIni7Kl5XlGyC1xv/Eijgr9ykoK/f7NmR5+DPnbSqNayeA+sWwCu/899vXsImw7U7piLeCOaFvZO63ghCNozwTFgW1fUxupeGUUolLbzGFU77xPBOHb0LU0YNyLhmcRYraq5avmBcmtO3ZQhex6W5Id6QEcP7+AUTGd6/wv/eVspim4tkHV4nS3MoECAcChARC6/QVfETOe4Ej+7+UrdRRYU5d8E7N9rLR18NEy6CvY5K7R91Enz7env5o/vEyttGNCVoW5K0qi6eGr/Fdep8I/Z2VpfmhnhDRvwu2KWbBpYPZG3t2g5o1c6FCN6uitf9x12OyM3Y09KTWRWytmzAmfmt25TadsxfYWCes8BtlbTKzBRnsyx7Z5Ird0ktxyO2hRjEwisIHuKOkIs7Ft4epfZEm4nhtZwBTpqFN57KXuxHNgtvWqZnn9q8lseleVP9JpZvX05xoJh4PE48HifkFCL43svfI2jq8DrXLSsKEcwyARi3NNFEKobXsqy0H0Mo4Epa5bhAFwWVJK0Sui6+gtflDRX2WKkaW1lDdOsKeP7n8P7N9npFf5jy18z7FDuVG174FXz9ZuvuKQBNuyy31qXZjZmI7KhavC8tf4k/vfenDrk32N+Ln4UXYGDFQNbUtnM1lZ0QEbxdFa9bSCBLhalAECZenFqPFKCUgJltNa5OdS4L78SL8q9na1ya18yDxS+0vD3GQhzMInitGIw9I7Ve6bI6xRtT5RZi9XZJp5Xvt7wtgrADEXclrdrWEKObI3gbnMkhM6Pv9kBOluvJYi31liUyuAWvX9KqhJXu0nz4o4ezcMvCpFUXUi7NACpYk2bhDQYU2TyWYwk7uVW4qaRVjuBNljEKKIpCEsMrdGH8XJqrV6WWw56cHBs/b939vpqVvh7KMnnv9lb79JHW3VMACh/D66ajY3h/OfuXPPrlox1yb3AsvKFMCy/AoIpBInjbARG8XZUMC28Wl2aA0p6pZb+41dawcRH8sQcsfTXlLh2rhz7D4ReLm3ct49L81vXw0Gktb5Mph+T+TmINttuUZdkz2GW9UvvcFt5YY0owxxrgpm/A3ce0vC2CsAORSLo024K3uyN4zcDISgreTJfmbOKxJEtZooTWnHLLu3zj6lezxPBq3wgNt0uzO/GVDlelZWkOKJURA2xoiNqumk25NJsYXuPSHA5K0iqhi+Nn4XXFZeK1Upn+tqV4Q5iyTVQbCy/ACknw0xZkE7StydLsjuHtWZwaexrrZleJ4bW0xcNfPNxm7c0Wwwu24N3SuGWndfduL7KYBYVOT74uzQAlPVLL7lictmDtx/bnJ4+mu0t33zVdSOaDN74hEcst5LPhJ3jfugHe/LudxCsRTd/ntvDG6l1liRrTO3pB2MmJJ5NW2TG8u/exvTJM8pOE87/jFryReG7Bm60sUTyhmbtiq7Ps79LsJ1iLA6kBc6/i1MRWIrg5LUtzMJBD8DqZm8PB3N4pxqU64bg0lxbZgrepzNSC0GlJs/AqMnJweBNhNlS37D6bl8Cnj2Ym0szHwlu9wq7bG5QhbGvIFsPbGguvEW2/HP9LjtotFYdtklbVxzu2+kXCShAMNJ289eXlL/OXD/7Curp1XHrApa2+b0O8gb6l/iW2ejhj9JpoTVZRLLQesfB2VbxuR9lcmiE9UVV9G1t4zaxr47Z0weuNv8kHr+Ct8ya/aoJtq+H6EbD8bXvdHYNrZs6qV9pC2l3GadTJMPRQu3ySOwmH2zUn0fzkDYKwo2GEZ4aF1xk4Wfi5NNsr2ZJTZbPwukWzb1kiSyctrG7c9XuHdRvGjMNmoFBYgc0kLI3RzkGlMsoaGRodwZutzQYjmC2PS3PUJ8mWIHQJEtFUeJG7pGCFM4Fd1jv9+MZtLbvP/06G2dfa4tVNNguvuy1WPPM8odk0lZSqJVmaTYzuMUOPYWDFwOT2/uX9USi+3vZ1s6/ZWrKVSsqFyUtRHaluszZkE7Pu8nlC4RDB21XxxkE0ZQkdezqgWj4bmw1jWW7clj5T2xLB600y5U5+lQ8fPwA1a+1yCQCRmtQ+49ZdtxnQ6RbxXUbDD5+Dyf8v/XpuwSw1eQvLgofhuZ/b8eCfPmZn2RY6HUZ4RhMW2xtTSavMICJp4bWa4dKcrSxREzG8WqdiaN0DhUYr/d3Yr7QffUv7EgtUpVl4lcJXMEP+Ls3GAvyvV5ewcO12wsGAXZZIXJqFrkoilgovcvfJvXa3P8u9gre6ZfcxYxivYM6WbLLnbjDkYDjyD/b6lmUtu6+QpKmyRC3J0mwsvMaia6gsqmRYj2Es2NT+fbs7PjbbMxumL5xekDbmiuE1eSdE8BYWEbxdlbjnHyOXSzPAtFvh8N9AZFu6EGwt5h80sj09OVWoxP/4XCgFZ78Ipz5gr9dtbN75VUvS193PaSzRtevtz2AYhh1uLxvruLvN4bJ0kSsZmwvLk+fB3Ltg3r3w+DnZS0oJ7crKqnqO/udsNtfaIRQJS6NCW6lpjKE1yaRVpqP2i+GNJl2a/cVl1rJELpH7/CfrMvYnXGWJ3LPwfjFX3Yq6oVWjU4c35dKcLZ9eyqU5dxdpBPEX6+13TSioCAeVuDQLXZdENOUB5e4TezqC123hLe7e8kl04xHmDbPKNpYJFcOPXoBx37PXq75q2X2FJGe+cCZbGn28/pxX7/PLnmf09NFsbcw/FM68f4t9Ji7G9h3LJ5s+wdLt+36sakzFmTclKq+bex3fn/n9Nm9DQ7yhaQtvE2JcaB0ieLsq3kFdU4IXYMBY+3Pt/LZvR+P2dDdrn3pjebHbROg3wl5urkvzpi/S192C17St1hHRwSI47UH42YKUUHdnn+y+qy3iDRLL2z6s/cj+9NaZFjqEO95axpcbapn5qS046/QKKva6lnDPdwGoKLYni0xHbSwCCR8LbzZraSCLW7HbwvvvWUsy9idcMbzuAZlf2YuiQBGomE+W5twxvE1beNP3F0nSKqEro7XdVxY79anLXTGHxrJb0h3Omw3/dz+Udm+5S7MRvN6kV0Xluc8r72sLcjN5LTQLr9h8d+27mcc4Sas+WP8BAMu3L8/7+iYLc7GPa/rQbkOpida0e3Imt8jN5abtrh+v27DWc8yK0RBvoKLIv+67WHjbBxG8XRVv0qp8Sv+YerjTv5NeZqBV7TAW3m3pVlBv6YLmYDrZ2mZaeL3HR10lmMz3VbvB/gyGbbfrnkNTx3QfnFruPQy2Lk+ti4W3fdjmuB61NvOn0CYY0Rly8gBEsP/HgmV2HJYRfGYQkUpalbpG1OXSvKl+EzfMu4FElgkNtwBtSjRql+BtysJbHCxGEyNhWWl1gbMK3mh+Fl7v/lDQjuGNuyzJgtBlsBKAhn2nwbeusOvhGqKOx1NxNxg4DkYebyfEbKlLsxG8dZvTJ+yNJTnrecq2Mjd3QlwAUu9oQzY3WzdBb2KxHDTGGykJlhBQme/OcDDdI6i9cIvcbDG8M5fNZFVNG42LPdREbeNLt6JuvvuN4G1pRui1tWv5dNOnLWvcTkRBBa9SaopSarFSaqlS6kqf/UopdaOz/xOl1P6ufcuVUp8qpeYrpea6tvdSSr2ilFrifPb0XnenwC14+wzP7xx37E1r6ty6cVt40wRvCy28YGdjDBY336U56omzdVt4zfdV4xK8Xvruk1r2fqfeawuFYZvT4Yjg7RQkLFschgKp5ExuQo6bcipLc3aX5qJggKs/uJp7PruH99f517YOuQTo9obc8WMJS2MFq7C0lWbh9Rs0FAWL0CpGPKFpjNntKQkHm8zS3JSF17vflCWClNAXhC6DESLhUjjs1+kWXtOfurMll7TGwuv879VvTq+U4FdrzEt5H+kjWog3LtfPzdZr3WyW4E00+rozQ8dZMt2uwn73jlkxrnjrCk597tSMfW+tfov5G+e36v7bHW/BbsW5BW9LkoQBHPP4MZwx84yWNW4nomCCVykVBG4GpgIjgdOVUiM9h00F9nJ+zgNu8ew/TGs9Tms93rXtSmCW1novYJazvvPhHtTtPTX/8378uv3Z0llZL8lYYp0e89qSGF6DmcGtb0YJJa3T3Y577GZbeI0lyXxfEadz9nMB7zbItTwwfZ8krWofqlfan/VV9u9U6FDiiZT7L2RmS/6w6mnu/PTOlCtzljq8oYAiEFDJgZQpTfFV9VeMnj6aysrNnLTfoDSLaXVD7kFRvV7Hmsrfcvdnd7M9mgo/OGSXQzKOLQoUORZenZaBOYuBN5W0qqkY3mCm4C1KWr1F8ApdDGP98vaP3YekPKbcbpmlPZqM4f1szTZe/XxD5g63BdD0vQPG5dfOst7NT2opAGR412hv2aks27Lx4tcvprkoGwuvHx0Rq1ofq08TuX73Nm7YtbGUV6Bx/d7YsJEfvPCDVrXB9E9NWXglhrewFNLCeyCwVGu9TGsdBWYAJ3iOOQG4T9u8D/RQSg3wXsjnnOnO8nTgxDZsc9fBCM3v/BOO+H3+5w3a33ZJaqvyRO7ZMvfMYVNxOE1R2qN5ojzeCO7YFGOt/fJFuPPIzE454GPhdVt7vKUXRPC2DyZzpxVvu0kZocUkXZqDqXqzNvbnM6tv4d8f/Ts5oDDucukxvJpwKMHCzQspcSbCzADjpeUvAXDBt+u44dRxaS7G2xpyz3ZHsC087697Pzngenzq4/xizC8IhUKEQiGCwSDBYJCSUAkWcVvwxhMUhQIEXDG8IY/ybXRZeLXW3PTpTSzbvoxAIMDWaGoizpuIK+SUJTLPLQhdioRH8Fb0tz8P/w0M3M9e7r1n6vg8XJq/85+3Ofe+uZk73IK3tCec9Ryc+XR+7SzvIy7NLcTr0uyXidnr9pvNDXjBpgVc/ublXPPhNcltjfHG5HveS2stmc3l1RWvctCDB6VlXfaz8DZ6q56AfzKvFtKkS7OUJWoXCil4BwFuh/jVzrZ8j9HAy0qpeUqp81zH9NdarwNwPvv53VwpdZ5Saq5Sau6mTTvgTKD5B93zSMijiHYaZb3azh3I6z542G/g+P/A6FNad92SHs3L/uh1Oe67t/054wxYPQeqlqbvz1bG6ZS74Tv/SnflAhG8HUGduKy1Fq0197zzNRu3Z3bo+RB3XJqNMMxmtUwJ3rhzX9e+uEWo/2Oc9vxp1DleGEagmll0UzvXLSCbEryWNm7WVvJ6A8oHEPKpSV4UtC28cUsTiVmUOKLUuDSXFqXeoUqlZ2neFtnG/xb/j4tmX8Qrq15h6jNT+XjjxwCEgulW4ljCSrk0S6Zm4fOnu1aJNTPgNv1jWS/4wzYYeyoc+gu4aA70c4X+lHS3w5laglvwFpXB7ofaE935UNYHtn4N73udAoWm8IpXr+C1tJWxLZvgNfVt19SuQWvN1roojYkcFt52tmS+t/Y9AOasn5Pc5vcsfoJ3Y33zQurW1q7l1RWv+u7L18Lb0hheIT8KKXj9nMW8U965jvmm1np/bLfni5RSk5pzc6317Vrr8Vrr8X379m36hK6G+cdoietwWe+2E7ze8khlvWD/M1N1b1tKcy287gRVkBmD640zypbVetTJMP7sTMHbkTG8dZthc2aW2h2GhGeGuayP/VkvM/itZXlVPX989nMufOCjFp1vXJqNxTabiEuvw6vTLLzRhIUqXuna7yN4lbG0prqk7U0IXuPQYQRvKBDyFbtgz6BbRJMuzab2rxHyZS7BWxIKUu9yaTYZSyOJCPM3zQfgs82fJY8PudyaI3ErKdpF8O7kRGrgkTPh7maEHHU0ScHr0z8GgtDX068WlduTwVZL/tZdw7+wf7mWrJQ7fcSLO2dEW2vwujQ3JYCzbQOSiaksbXHL7K/Y78+vsK2xPruFN9C+ws4ISbersp91uSGRmZS0qqF5Y+TTnz+dy964zHdfkzG8zvdifheN8UYe+uKhrBMN2Wjvck9djUIK3tXArq71wcDafI/RWpvPjcCT2C7SABuM27Pz2czMRjsI8RwdU1O0peD1vrhak6zKTXMtvMYCe+Qf4cRboHKX9P3eazX1vWVYeDswS/N/9oebxjd9XFcl6qkL3WOI/dkal7UZ34NbM2M5dzaMRXZLfctm1I1wNe65sSyZhzXaVYYikR7DG7eSY1tzTE20hme/epY7Pr0DSA2cQi4Lb3W9f2e/vTHG9+/8gI019n6tdc4ah+a+FjHilpUmeE1JpPKilFAOBVWaS7OxXseteFJQuweAQVcoRCRuUexcO5qQ0lo7NUtesT/zqaDQWfC6NDdFUTkZ+TvyxW3hDTczBKqltX+FJl2a/dxqr597PQurFmZsdwvelxfacdo1kfomLbxbG7e2izgz/U2tyyCSK4bXzbZo85KxGRdov+cyFt7KosqMfZCZzOvZZc/y1w/+yvSF032Pz4a4ROemkIJ3DrCXUmp3pVQRcBrwjOeYZ4AznWzNE4BtWut1SqlypVQlgFKqHDga+Mx1zlnO8llAnkEfOxhJC69/NryclPVufQyv1rDyA1t4uztH7wxwS2m2hddJWNVvJIw7Iz2TJPgUt/e3BCUp65W+3pF1eFuaBbOrEPFY57s7UQ2tee4vnoP1kqbfWGhbMuRevL6Gt5dudq5jYVk660w/uLJ9Kis9S3PCSrosmxq526Pb+fXbv04eY/anZWlu9Be8L366nreXbmZzrT3ItrRFY7wxp+AtChZhJZNWWZSE7a6vb4X9/nS7NIcCKi1plZllj1kxQipT8LoTvETiCYodd2mTDVro5NRVwZqWeUDkZKWTidyvikLjtlQfXLcZHv6B3UdVfQXzH2r7tuSLGTBn8ZTIwCSwiragf1StsPDuf2ZquUXW5Z0Xr4U3Q/D6CMLFWxdz5swzM7YbLG0lQzuiVqTJGN6fvvZT7vr0ruY0u0UYweuuzZ5vDO+2SMvGH+7vc/m25SzZuoSaaA3FwWLf2sSQ6dJsLL6zVsxq1r3FJTo3BRO8Wus4cDHwErAIeERrvVAp9ROl1E+cw2YCy4ClwB3Ahc72/sDbSqkFwIfA81rrF5191wBHKaWWAEc56zsP9VvsTtGU2cnyD5ST0jaI4f3kYbj7aPhkRnrWxgH7te66htKetptyvskNTIdb5FiYvYI3WpPu/t2Uy3UgCIdclkqi0RnKErVTood2J+Kx8HZrA8ErAKlYVNUCK9Mx/3qTiOOWG7O0vayyWy1TgjeRVoc3lrBQyt5gZtq9g4mkS7PLPbi20V9cFzti1bTFuDTnqidpW3jjxKwEjfGUhXdgD/ud4HbBDgZUWlki4wKn0SkLr/ZvWyRmJa8diYuFt0vwzj9h+nFtnxXeTNg67ozUVdl1xrevhacvtt2dAVa8C4uegbXz4ZZvwlM/6TgRl8ul2Y+k4K3NfZwfbpFR1EzPsH77wNFXO/euyX2skEZM505Ilc1KaEI70s511V83+RCiiRxJqwKpv6tXVrySXP5448fNdiHOhyKfv2PfGN5EpuB113ZvDm7Be9xTx3HSMyexPbo9a/yuu51ul2aAz6o+y3qOHzErRnVjtQjfLOQ5jdcytNYzsUWte9utrmUNXORz3jJgbJZrVgFHtG1LC8wdR0CfvWDarU0f2xT/nQi16+GQn9uZhvOpWeelrJdtsYw1NH9m1VDtqpdasQvseRRU9m9Ze/wo6WF/Nm5LxevkwrhUhbMIXrDdnLcut5f7jmj6mkf+IXXNjkpa5a63HKnJtDx3dRq3pX4nBuOO3haCd/U82LYS9p3W+mt1QYylsrXE4rYrsMoleIP2u0SpRHoMb1wnLbgmaZXXXczsd5f5qY1kEbyOBVUFTAkkK5kZVGuN1hrLEQymDJIZaFlW3HZpDhnBa7d5q8vlOxhQScEeDiqi0dQ+Pwuvm0jcSrYvIhbezk3jNjvpUvUqW7A1bG3b96tJ5lT1FTx3Gcy9O7Wv2+CUl5GpNx+pAVPeJd7Q+koHLaFFLs3kZeFNWDotC3ta39aSUKiS7van+T0KedGkhTeL4A342MeMNVhrnRS8ESuS1ZIZdiULdedbOPOFMxlSOYTnT3o+jyfIH7+JXr/nc5dVMpi42+biJ6irGqroYca0PoSdqiGmbXXx1P9TzIol9zfF3PVzufzNyzl9n9P59UG/bvqEnYxCujQLhjVzYcFDEGu04wqXv9Pya9Wutz/jjS2vdWtK7rTGrdktKENF8P3H4ISbW349LyZbY76xOkkLb0X6p5sKV1xvqBmxzx0peLetTi3viBbPmw+CGafby5VO7ePibvZPWzzvuzfCS79p/XW6KPXR7C7IzSFuWTTGE3laeC201vz0oY/57xtLiSZSMbymPEONx6pvYsF6lqc69rpIU2LdsfBiJWN4jdj1/hjBG9dRGmNW0ko8yBG87nhht6HPbeGFVPxbtrIaJeFg0sLbKBbezsviF+CaIbBqDtSss7fVtnE6kOSAWaeLXYDtq1OCuNapIuG2krbERbgtSE4c5zm2aIbgzcjw7rbwVjZVjdIHt+AV8qI+Vp9M4PSng/8EZAo0d4InN37i0Z2s0OyOWZGs4SVuIWwEr4l5XVmzMt/HyBs/cZtvluZsXjxN4Xf9FTUr2K1yt6znBFSAcCCctMzWuULommP5fnLpkwAsq16W9zm5qI3Wsrlhx0keKoK3PalaascVzry89deKbPe3YuaDEbwNrRC87g6xJW7VTWFmwz68Lb/jm3JpBih2RHC/kc1sS/eOS5JRvSK13MIZx06NGWwC9NojtVzS3X8gozVsacbLvHZDpsv0TkTSpbmV14kltBOT6hZx6QPYlOC1690+u2Atf39xMbF4KobXDKZqYum/EzPo6VWeepfUZbHwJmNjXRbeppJWGZexhI6mJa0ygjfiyqi8sSZleSoOBdNi2syAxD0oNAJ5xIBuXHPyaLHwdgVWOJPOy99yCd71bXsPd7me8n52KJGbyHb7j8dYeN1hRi1xEW4LjPgu9632mEkzYnjj3oR3MZfIGPyN/O7nRgRvszlkxiGc+YLtSm/cjr0W3ros+UoCysfCm/Cx8OYqS+Ryaf5448f8+q1fFzTRkvvapn/I18LrRecZ8uDn/bOqZhVDug3JeV5RsCjZtnqXgWVTff5lVU2oULmTBO7l5S+nXau5HPfUcRz2yGEtPr+zIYK3PTHZ8doia2Pd5tYL3tbE8brLEbUkcVZT9B5mf354ux3b1BRJwevMOPvV2Q0Ww08/gh+91Ly2lPexS+RYHWCxcSfb2tE79v772p+RmuyCd969cON+tmUmH2o3pAaWOyFt5dIcT+hMl2aV3rGnYmgt/vDs58ntdgyvveytw2sworJHaer/tjaLdToVl+yK4U00ZB1kQWqglSBKJJ6Ks+3jJK0at2uPjHOe++khaVmawV/wGn56+J70qSh2xfCK4O20mGyp0VqocYRuzQa46xh4/FzbtTfeyjg4d5x6ed/Mft+K22FFxrK83hWv11EW3lo70y4V+QpeY+FtelIx5v5/0Drdwtt9cJ4NdGEE7+fP7LTv9+bitj6a96VXoJk8C9lKvPldL2Xh1cSsCMVZxoRhz7js2WXP+ibJiltx/jXvX2xtTE82+qf3/sR3n/1uzjZ9uO7DZP1d97NVhCvS2uzGa+H1c8nOlbCxqePiVpyh3YbmPK8oUJRsm3vSYWND/p4nJht0xIqwsGohv5j9C/724d/yPt/N3Z/dvUNZd0EEb/uy3VRlagPBW7O+YwWve9aoJaWRmqL3MPjFl7Zr69w8svmZF4RfeQMzMxkqtq9bkj15gC9lfeDrN+HqXdIHJe2Be+BTt7l1pXo6OwPG2J/lfbIL3hXv2p/ZrLxr59txu4bajYDuOItJB1PfVjG8CcsWmkbkKp0heM0MujfONxK3Mt54XsFrXIS7uwRvtjFs0vLrFryxBkpz5CMwAxhNnIZoghLHChsIKF7/5WTuPCtV9uv5Sw7h7SsOY9Qge0DtHiD5uZwZwk78cSpLs7g0d1qMJ1D1qlSiptoNsOp9+PRRuG0SXOdkV57xPVj8ov91ctG4PRWmUdrT34oZ2Q51jgVng6vsS0cK3mBx/jGxxflbeNNcmhMxMNnNDzi7ZUYA08YPb4Nlrzf//J0cI0qzuTT3Lumdtl35jFvNZKCdpVmBSqDRWb1t/JJI+Vlc31z9Jnd9dhfXzrk2bfujXz7KF1u+AOC/8//Lu2vepbqxOs2Kec7L53DeK+fZ13aJaWP19LXweurw9izJTGrql9jKj2y1c5uy8IaD4VQMb6wumeRqY33zBW8sEWObM37aULch7/MNjfFG/jnvn80+r7Mjgrc92fxl212rdkOqs2kubRHD654RK4SFF+wkWL32SM3A5yJaZyfx8ovNNQkxWtrOcuf7SkThretbdo2W4i7Z89jZ8I9hHWNpLgReRTPue3DGozD2DHsw41cWwMyeBoKZ+wBe+BU8+7PUuhG6O6lbc8oa2rrrxJz6takYXp1MGmUwSau8cb5fbqhJy75styt9gGFmxbuVplsVjHgEKB08nVC3j1Oxvar5Ls0E4qzf3khJOEhttJbpC6ezW+/SNKG978DuDO6ZSqLjjtdNWnh9JlCKQumCVyy8nRjzO61aktrmDq/Y+LmdZTlaZ5c4+3p2866vtf3OMX1OWU+YdhtM8OTobNyesvBuTHlFdJxL80ao6J//C6MZLs1Rt+A1E15HXw3H/at5bTS4RXld22f43dEJB8IEVTCrS/M+vfZJ2+7n0mzEnaUtu6a5sgVbPi7NBj8BaoRqNnfnhngDtyy4hfNfPZ9DHz40q9XX/e4uDZWiUHnV4e1ZnCl48818nM0S3K8st9dEcbA4NaEar2Nw5WCCKtgsl2aTaCuSiCRjkPOx1HvJ5tbe1RHBW2jcg/rNS7If11xaY+E1JXnazMKbXwa5FlHaM7OGrpfG7fDlS9BjV//9xvLTUsFb5soSXd/OFla/gYR7YNSVcYvQcJk9yBp+tJ3pu6Q7VC3LjJ02nYlP5wvY/xfbVuW+106ESVoVjVsMvfJ5bpv9Vd7nuse88YS2Y1KVy51Ypc9kdy+yB6Dh7vPTtkfiFqXh9N+Xd9Bh1t3C07seqlxE6aCHk8/kdmluzBE3BlAccP73HZFcEg5w7ZxruW7udby39r20+r9e/GJ4/QYEJsN0MmmVM9mwYXsjx/zzTdZWNx0nJrQT5r26aXFq2wYf751VH9ifTfVBftfXCRh+jL1+8CV2IsZxZ9jr5X3tT7eFVyfSz+8Iajfk784MKZfmD2613bNdrNvWwIoqV7bZhGssZNzFWzNZXuzy1KrdYI8D2nKMtQOxLbKN0dNHp20LBUKEA+GsFt59e++btt03aVXCJXgVqIC9ns2l2c/C6yckTbysn8gGWF+XbgTJlvDK/e4uChZRFCzyTTjYGG+kMpwaT1cWZY6tWyt43df3w+3SXB+rpzJcSZ/SPs2y8Jqa8NFENHmtoMs48MmmT5JW4FzUxztBGc4CIIK30LhnqMxMbht4NINOf+E3h2DIFhStErztNHjLR/D+d6ItAg+6IPPckh4pC29Lk2u5yyI1Nv2yaFOiNZnizgzCujomaVrvvTLjqgMh2039gVMgEYcHT4Ov30pZt7PF19VtStW/dLOTCt6GqG1VWeOIrX+9mv+AsCyc6ijjCU9ZIpVAeVyaJ+4ykT277Umo28cZ1yoO5+5q/FyaAXqUmfXUYDlZrqgFFt7d+9jXm193L08tfQqwBwe56hS7B4RmsJCWtMrcI2Rfw2vhfWTOKhZvqOGhD9s+C6nQQsyErfmsHAhrF2Qe9/Wb9mdzBa9JMNh3b/jDNtj1QHu9+yD73bb7JHu9Zr2/NbeQgjdSC9fvA8veyNxnLLz5YgTMlmXw1g1puyb+7TW+9Y/UPdJcmo1FraWVJsAexxz6S3u5Zh3872S4aXzniOeN1MLGLzq6FUlWbF+RsS2kQoQCIV8Lb0iFGNZjWNp236RVVsqlWUFyEjTb5GOuxFduTBJDv1JIAF9v+9p3e65rFwWLCAfC/hbeRGNa/1HmUyYrkmdMfzbBW95EmTF30qq6WB1l4TL6lfVjU0NuC6/5rtxEE9FkO0xJo1gixvdmfo+fvfazjOO9tCbRVWdGBG+hcf+TtLV1sKUWXrDdmttK8CZjkwtAaY/cGZK1tks8VOwCB/wwfd8vFsMvv2y9S3Opy72lvRNHRetSGavBzvS5JlNQdEnM399Rf0rF7xqMG85qp2zIly/A/05KWUH8XG6iddnLR+2IGa7zoCFmd3rGupLLkunFnVU1mtDpZYlUAgLpM+UloRLG9Bmb4dJcVhRMZvDMhhGVe/ZLD9NICmDXNWs9Mbx1sTosbfkOUgxG8E7a2/5f/qLhheS+XGIX0gdN1dFqwH9AUBS0JwhCwQChgEpaeAPOd57wZqkVOg6vyBwwxj+Eomqp/dnc8B8zMeqdlC7tCee/ZVt8AbZ4PC567+W0r4CCt2qJ/U59+bfp262E05c2w8Lr/t9xeR5tqskUB1G3i78J1WltreEjfmuHPW1fC6s/tLe1ZR+96DnYvLT55/3vZPjvQWB1jrAGvzquwUCQUCCUaeGN1lJRVMEu5bukbfeL4XUnrQoolbTw5pp8zHYNN0bEed/NJjHism35VWrw5l/IZuFtiDcks1a77+Mmkoiwvm59k8mrssXwNlVLNxxML0tUHi6nb2nfNAvvlsYtjJ4+mme+eia5za89UStl4TUuzVsa7XfYp5s/zdkOyLTw5puhurMjgrfQuGev6nLP1DQbv1qz+VLWu3XtcQverZmzh22GsfBm+4cz7Zjwk8z43VBx6sestwT3C7K9hVOk1o7V/vYNcM6rtoWgvd2qC0W9YzUp65257/Dfwr7T7OU1ThKqRDTl0uw3IMz197yTWni9SauCwfwEr9aaSNziksP3ZED3EsfCm+7S7LXwGhc5b9Kq074xJG2wlGvgtGe/St745eTk9m4lYfYd2C0tQVZdxM4IevxYe2C+NWL/HeWTpbmyLPM9olBMXzidkkEP+J6bJngj1YC/y1c4lHqu4lAgaeE1Yj+xgwwadgiirt9fuAx6ZEkoY+qgN8fC++jZttgB/+RP/Uem3nlGUJvBcN+9U/drTvm15mDulYjaQv7Nf9jCbPlbtlg01ufm4vI8emdpZh+VVpbI9GFu76mWUjkwPf66LespP/w9uLkFJZNWvW9/+nkbdQAWmcI7qIKEA+HMLM2xWsrD5ezTax8mDJiQ3J7LOhtJROy5D8fC65flOBu+Ls34uzSbZFL51pl1i9vtke3ZLbzxxjTBW+6T/LSqsYrvPPkdXvg6NVkas2I8svgREq68Kub79LO85qI4WJxWlqg8XE7fsr5pFt6V220voYcXP5zc5hsDnYgmLdJGaBvB6+dW7sUbspPQiSxHdi1E8BYadzB8MsNum/g0t87C23sv2Lio5e4/8Ubo4RTSzjKj1SaU9rStetkESyTLTLqbdY6rWr8RLWvD7ofan7t90x4QtOfANVprl9D4xjmw6zdsa29H1QTORqQG/j0Olr/TvPOMQPUTvOW9U+5qS19NbTceE1EfS26uDNYieAEI5pmMxoi14nCQcDBA3PKUJVLxjBje0mApRYFwmjj94cFDuerbI5IDGIDuxZkiwN1pD+1TjjFElxQFef6SQ9PEdV0kzsDupfTpZs9cm4GF1yLhxgzABnT3n2W/bu51hLv5z3y7Z+yNuI4kIhkDxSJXYq6ScDBp4TWbLbHwdh7cE2ax+uxuvNvW2J/NEbwLn0gtl/XyP8b03VWOhdf8je11tB3C8vrVdvm11iSWzIYRGPEIPHcpvPYXuy7xezfb/ejeU5t3vQvfhwPPt9/nzvu5qi5zEJ7m0my8e/ze/c2l2wDYvia1XrcRPnscvny5ddc1dYK1BR//D1a8l995z16aWi7E768F+ImicCDsb+GN1VIRrqAoWMQdR9+R87rm3Eg8QsLSSQtvSTNc1b0Jo8Dl0uwRvMbSmC2u9cXlqWzqCSuRJm63R7dTGir19c6pj9VTFirjqROe4vajbqdHcY+MY1bXrCaSiKSV6pm+cDp/fv/PPLn0yeQ20y9ks/Rmo1tRt7QJVePSvC2yLXNSwNWV+N0nmogmKyFkCF6fxGFevN9RzIphaYsnljzhayHvKojgdXPX0fDkBU0f1xzcLs1t7RffGsE7cD+7g7r/xPSauvkSq08l3hg+peXtaArjTpxtwGGETM54Zuft0NJ29trDjsPa66hU7cT2Ilqb7vZV2qPTzBonWfEubP0aZl/b9LFuNn9puy5ns66YWszuOswmsUzjNpj/YLr1XSy8GXhL4wTydGlOCt5QgFBQEfNYeG2XZrtjv/PoO/nn5H8yqGIQ4WAYFUhg/ucO26cfwYBKE4d+gtfbaYcCdtdUauKI3YI3Gqc4HMjoeHet3BWllO+PGYAVF1ksvTp9QO8345+rbYbqSDXLty1PToCFXYLXz8IrercTEatLF1vZBG+dM7DO5WWUi+5ZEikWd7PDU1bPtdd7DrU/9z0x3XNrdZ71xpuDEXKJWCq78fK3YcnL8K0rUkke86XfiNS72nFVTpYOc9/W7dJsJifL2sDC23tPu7yUoXYDPPYjeDB3vdYmcfezT18E9+QxftiwEObdk1pvTdhYG+JnRTUuzX4xvH4WzlwW3sZEox02YwRvDm8bL341zbMJXvMcVY2Z3+viLYu5fPblacfGrFgy4/L26HYqiyp971cXq6O8qJxhPYYxceBEepdmTsQYke0uT7TNCYNwJ4Iy2ZGzZZjOxuCKwaypXUPMihFJRCgNldK31B5j58rU7Ct4rWiynUFl96FG8OZjffdaeONWnOeXPc/v3/09d32WR5nQTooIXjerPoAFD6bWP7ofXrqqdWVg/P7o28pC2NKkVQCD9rc/l72RcqtqDrEGu2P89Vo41d8VsE0w8avZRJ6JlcpVW/f7T8C021sfL2S+7/aM443WpZef6owW3q3L7c+euzXvvE1f2IMVv1JSYP99lfWBDS7Lm5n4+OwxeOoC2xJiyHBlc4m7nVTwegeekTxrw5rjSsJBwoGAI3gTvi7NPUt6cuRuRxIIBFzuUvaAxQhWd+efj+B19K6/4I3EKQ4FM4SqEbyBQIBgMEgwGCQQCBAIBJKdfCQRySiR5E6A8utvD89oW7aBy00f38RxTx1HsLdtSXKXUCoJB5OCNygxvJ2PaB30H2Uvh8v941bdrn+5vIxykU3QBQLwjXNT1tYfzoTz37RdoN33Xfl+8+/ZFKYkUCKSsiybWOJ9jm3ZNY1Idzyuan0Eb1pZImP5zGYBbw79R5Fm8qppft1RX7JNstdvyT6G+/Qx+9PEsNZX2ZbhlR2baNLvHVYSLPHP0hyt9RW8fqEobmvmNusryna91752Myy8fiXezHW99zTP4bayGry13RsTjUQT0aTnT9yKUxGu8L1fXbyO8lDqmfuUZv7fbqjfkNYGN+4YVzMR21zBu2vlrkQSEVbX2GEUZaGyZCkjI7azJajyEklEklZaEwfdHJdmb8hOzIolz9/ml+ugiyCCNxev/xXeuwk+mt7ya/hldos32C/N1/7SfDHtrqnVGgvvLq4U9XX+7iE5MYK3qNzOllgomrTw5uHSvOcRMPbU1rfFxGO1ZxxvxMfCW7u+c5VfMG0pbebgZePnTbuZdx/sv91Yc+c/mLnN4BbgO2nSqur6GH0qUjO62xvj6a6FWfBaeOMJ26U5EHDOdZUlcs/mJxNzOMI4KXhd4tSUL3KT1cJbZJ/vrvlbF0lQEg5knJNP0iq/TJtfbEllUz39oEy3aO99uhXZ75pFWxbZbSuyB0JuC29RKJC0rpshmyUxvJ2HaL09SXr6w7bQNO+ufq5SLMbqamhupmZIzdz4ceB5dpbikh52boYBY+3t7r/RVR82/55NYSy88UjKQ8bk4WjuO9xgJmUdMeEneNPKEtVvdsR9G5Q0dI9lwPYcagv8ft8bF8Hfd4d59/qfs/VrexL3IkfgfvWabRm+++gOzR7tZ+HtW9bX18IbSUR8Batfcj+32FoTTlm2myV4fSyupr3ZLLzG9deN99jGuC143eK1oqiCmljmxFVdNN2q7Sd4TSkktwu2bz4K3TLBO7jSHuss2GSH4JUES+jhGHyMyPTzRsrsO+3fqRGt5vdrBOv6uvUsrFqYsy0ZLs2JWEYSrK6ICF6D38vI1HXdkl8KdF/8/uhjDfD8z+1kEctez96ed2/KtFq5Z3h679nydoWK4SLHXaq2BcmrjOAtNE3VDE4K3laI/3wxgre9LbzumnDG4n3T+Ja5oheCTY5gaI6rd+1Ge5DVt4WCN3kd12y+N4bX7RrY3uWk2gmtNUs3+pQ1cdhSH2XqqF2YOiol5Krrm47BicRtsWZieGNODG8w4CpL5IhQ94xxSvCm6t1CeufvFx/ldU82Y6sSYzXNsPAG0gYzgyoG5Xwet4XXi9ta4LUSmLYbtzB3+1fV2G6UJm6tyGXhLXZZeM1Av8taeF/7Cyx4uOnjuhLROvv9sPcU6LOnnR/h+4/Dsf9IHePtX/MVvD7WMV8q+sLBP4U9JqdvP/V+OPV/dpm9NfPSwzbagrhL8JrB8tbloIL+SbbywfS/OVyaH/hgRep/oG5z27gzQ2ZIzNo2qmLg9/v+xPk/8CvpBHaZqYpdUu7yc1wxsOvmt027WoCf+AqogK+FN5qI+loB/cSdW4AlSPVDzXFprolmClDTXrfI1lrnDD/xE7wxK0ZxsJhfH/RrHvr2Q3Qr6pbdwtuE4F1Xty6tbdmIW3GWbl3KkuqUUcLvu/NiBO9v37Gzp5eGS+kWtidXjUjPlqDKjZmQ3e6Mjc3v1wjexkQjpz13Ws62+Fl4vWWOuiIieA3uGY1Zf7atuyaxRUvdIb+YmUqY5L2XEbLZ/ng2LISXr4Inz09tS8TTBW+fVghesDtcyN/Ca1nwj71g7t22lboZqedbjIkTrssmeJ3fTS6X5raiQ1yaazItvIbaNnLdai3mb7k5pTTeu9lWNaNPyX2cEby5kpsYoV23iTQ3ZrfFbwdzaf545VaOumE2N85aypE3zOaT1dUZxyQszbaGGD3Li7jl+wdw4+n7AbC1vumJksaY/Z4pCQUIB1UyS3MgYKyWCVTQ/n1XhFMTC2b21yS3KgkH7YFKHi7Ns1fN5pezf4nWOpngqcRYeF2CtzYapyQcJGbF6F3Sm5dOfol7p9yb83lCgRBBFfQVvFUNqXdLNsHrHgwNKrfFtRmoFYXttoVcGbBLXBZe48rZVfUub/4Dnjyvo1vRtsTq0t8PAHsemf5+9cbf5it43SEoTXH4b+D/PB5kww6DEcfBkIPsfnb9J/lfLx/MWCcRsccUYI8BynqllxlqDmZS1nnP1jZmCt43Fm/i0blOrG19VdtkaAa7zd/4cWq9rYSl+/dtrFoLneRE2Vyxa9ZD5S7+4VOmpnMHkE2k+Vl4o4mob2Ijv9I07olKK+ASvDksvG+e+iZ79kiNXdNqmjv3MMLWXYe3qSRQ3v0NiQZiVoxwIMzp+5zOqD6jqAhXZAhsrXUyK7Ihl4U3rQ9x/l3cSRnjVpxpz0zjolkXJbflYxUdWDEwbb0kWEKFM2lv2uz9Pc5cNpN7F96bts0IXmMFN9/Lmto15ItfDK+5t1h4dwTcFqC3rrMT8JiZIL/C8Pkw43SY6WSadVvpYo2ua2YZBZl/Xrdl0/yjjTwBftkGLq0lPex4oXzT+Edr7Y7xucvaz8Jb1svOWrl4Jqz/LHN/4w5s4d34hf18xT4WXmi+4L3rGLj3O23StDTMwMCvNm42lr9tZ702yU6y0XN3+zOROYDKuH/dJqgcYC+X9Un/+9zBBO8Nr3zJko21/PNV231vc22mkNvWEENr6FVmT6pVltgdVY3PYNSL28IbMjG8cbdLc5xA8XoGlA9IdsrgygDpErxxHU8bEAzrkfqd79fPFuExK8bFr13MS8tfImpFk9ZR4xL9i2P2SJ6jNejgZmKJGEXBIgZWDMyZoTnZtmCR78DPnQDlow0fcf/n96ftN4Mmw4ie6V4JQ/uG+fdp4ygOpazAbguv+Ux0kpqcOz1aO6EiPi7wZlJzj8mZ1s58Ba+xoP7gqZa20GawUw5nzUetu46XWMolM63CQkvdmSHVRzkD89pInJ5lmZP5yXdP7ca2ydBsmHotnPGIPWnhpjVeUO7f96kPQHH3VL4Kv+zLWqcEr9/EgcnI3QF4J/qMx4qv4LX8LbzZkiP5kUvw9izpSaVrPOwWoN6ET+5yOH6TlWlt8bzbjUtz2OU2X1FUYSfYcj1LQ7wBjU4TvH51eJMZqV3tyFViz00+cbPhQJg7j74zuV4SyhS87ntrrbnirSt4btlzadfp5rzDtkXtcWrcihO34ny22Wf8nAWv4I1ZsaTV1520q6shgtfgJ2Jaa+F1Y1xzwZ61Ndf0K68C6QP8mg12kXhjyRoysXnF4bOhlG1Bzbcer1v4R+vbR/AGgnbHuOx1uPWbmfvzytLcRjQVT9yWaG1bVcp6wdjTU9vdz+muPZgPq963ay22JVqnvo9sf8t+1Kxv2l0ZYL/vw5hT4ZuXpLb18SQWMoOPus12HNyok+F7j6S7NO9ggrdnWXoHWhQMZhyzxSkN0rPcqUFbbAteP3dDL24Lr52l2SlL5HJpDhSvZ3jP9N9FcnDhWGRLi4IZ7sr79k7FSd439T6O2u2otMFKfaw+WbOzW4l9vVGDU4ORQMlKPtJX8M7ad5rlXlUcLCaSiGQk/nB37r9793f8fc7f0/bHrFiaxWNEr3TBG9eNnDAu3aW6JBRIJv4yMdPmO+3UvPNveCplmUgL9fnwDlgwo/3b1NbUbbInj7v5vH967ArfnW4LnJYK3mg9HHKZbaltDZUDIVgM1Sth3Sf++UBagtuLwf1MrUkgZazakVQM7y7dM8cHoaCyE3FtXpxKGtYWBIIw/JjUhKehJe99reGB78Kcu2w3799stF3f+49MHVOzPvO8v/Szv9tKZ/Jt6KHp+wtVVzkPvGLx23t8G6BZLs3e41ZsX8Hba95mQPmAjGPnfr2dx+etztoet7XY7WJs+gHz6RbjLRG8MSuW9ixGaLvvad7/bsGrlEoLk3H3M021o8ET2jWs+zDuPubunOcY3N5SpaFSwoEwpaFSPlz/IWtr1yafUaOzClg/l+al1UtpiDewR/fUxHGuOsFeT6eYFUtez6+sU1ehoIJXKTVFKbVYKbVUKXWlz36llLrR2f+JUmp/Z/uuSqnXlVKLlFILlVI/c53zB6XUGqXUfOenhWkFPfgltXFe3snPbNRV2e6+brzrZT3T143lNtsfj1tcfvEsvHsjrHey1eYxW5Q35X3ztxSm1S6sg24Dsx/bluSaCY5st2OmApkD/jbHLXgLnYBixbu2O/zhv4G+blHhzkbp0+kWmoat6TPV0bqUlSBfl2bLshNvVTZtlaO4Ak66HSb9MrWtnzPwMOn1kxbejVDZH065GwYdkEpU1nefHU7wGmutoT6aKWKN67IRxxXOOX4JZbx4Y3jjlu3SbFyVVSBBoHhTmuDVWhNSHpdmJ9bWzbAew9ilfBd+fdCvAXswYTJgAtw8/2YqR1wJKkJlSQitdVom5UA49a4Oe5Le2O7QFolEgkQigWVZWJaF1tq28FrRvOojuo+JJqJpblx7dNsj7Vjv4EBrnWbhjcaN4G1Ftv/24pXfwfz/pdbd/9Mzf2mH2HTl5FvLZsOdR9jL2UKC9j3Rfu8YwTtkov2Zj+CNR+33Yb5xvLkIBOxkVu/eCLcdak96twVuC6/bg6w1Ftdklmb7PWvXys608vWuXQJ3H2OHZg2Z0PL7ZcNbXmr+/1KZk/OlcZtdomnr13afH3L6mWP+Cof8HPY9ye6/3CTiqXwt5Y4x4ofPpdzme+/VulwwrcT9Dr7tqNv4w8F/AHJYeH1cmmtjtWn1b3/+xs8B/5jO0+94n1886hPO52CRGh+7JxxnfGFPqBlR6X0PQ/Z42KwWXlf7jKD0E7zepIcvnvwi+/ezq5m4LdJ+gtfdThPra7hqwlWM7D3Se4ov7jaUOiGDlUWVzFk/hymPT0m795db05OzDakcktY+k+gqZsWYv3E+ALt1SyXy9Kt/bNgW2cbQbkMZ1n1Y8hrGYuwX9tNVKJjgVUoFgZuBqcBI4HSllPe3PhXYy/k5D7jF2R4HfqG1HgFMAC7ynPtPrfU452dmmzTYz8JrfrG5Mrw2VMM/9oDX/ux/rsHrLmTul00kmO1aw/a19rJJymNewG1BRb/8XZq9oqH3Xm3XjlzkymTdUN0+8btgl88pqrRL4dx1dGHvtcopR7HvSenbhx0Bk22h0CaC9+P/wYbP8z/+fyfDf/ZP/U7c5aLydWlu2GLXM/bOxufLsMPtT/Pyf/sG2LzUttyYmG+wXf9/t8WuOb2DCd76aPr/RIOPmDIW3l6Ohbe8yBG8+bg0x1JZmsOuLM1GyAIoZTGkWyphjFvwGgtvKBjIcHsrChbxyimvcPo+tudCOBBOG/Q8vNhODKOCDXQrDfP4kse5bu51qQuo1ODCO9jSWmf9KQ4W0xhvbHKGHtIHA8bCu28v2zJdEUrNwvcv6582AKiL1THmvjGs1y+nYngdwev3O+q0GFHrF87T1jGlhSYetQVJPAL3HW9bTKHp/mvEd+DY6+AHT9rCJR/Ba/6O/dylW4LbC2bevdlzWeRDIg6z/gTbs8Tylfb0354PSZfmlIW3X7fMcYqqc02uG5fttsQreF/5HTx+TvOu4fZ42+3g1PKg/eHI39uTEDXrU/8jm5fCza5ncccmmwm5IRNg++rmJXZsQ8w7b2zfsXyj/zeS781QIJQm1ixtEbfiWWu1HvHoEcll4xa9smZlmiB0s3xzljGBe97elTX5Xx/9C0gJSD8Lr0ka6M0F8cAX6eUxTQyvW7ybdtbEathYv5FHFj+S7Hvc1lXDzUfczOPHP55m/Y0kIsk+xfDf+f9NLq+uTbds5+PObPBaeCHlXq3Rye9AoTLcjqftNQ2AvXrY77WtEft9FbfiLNi0gD6lfTh0cMrrIJdr8sb6jQzvOZwrD7oyeQ2x8ObmQGCp1nqZ1joKzABO8BxzAnCftnkf6KGUGqC1Xqe1/ghAa10DLAJyp+FsLbniMr2dfqwhlcbfdIIf3u45x/NHYWIRvG482V6ASSGsYZvTQZkXcVtaeCv6tcylGaBPOwneXDHUGz5rv3ZAylK/+sO2czHzY9Ni2+XOK+aDIZh8hS0WWyt4tYanL4JbJmY/pnFberzSmnlO+76AOw6Hx8+111UwfwuvccX2Dk6a4kcv23Fa486AXQ+Cw35jb//qNbjpAHvZm/kzELQHYztYWaJtDZ4EHdFMMbXV69LcDAtvtXP9ypKQLVoTFo1xC63SxWvP4vRBshlIXXrUHkze2558MB3z+P7jk1ZdN9kHBJr5W2fxx/f+mLbVJMty3y8fioPFRBPRrDUp3UQSEaobqxk9fTSzVs4iHAxz6+RbeePEN5IDEYBRfUalieMV2+1+YU3i9QwLb6QruDQbTH/o5920ek77tqW1XDvUFiTePt6blMpLaU848Md26E5pT/t9++5/0i2kXky/39qa7wZ3THEiYvd3LWXJS/DW9dnLLLbGwhsI2pMCJmlVJE5lSZjffifdxqHrqwF4f+8rCzNRXdHXf3tzvBKMx9tex8CJt2Tu7znUjtN+4xq7z3vjryl35fE/Sk3IApx0hx1ms+tB9vrVu8C8VpS5bCHRRJTiYDH/O/Z/aV4x4UA4TVSad6PXc8aPod2GJpf9kjwBTL7uDZ6enznB4s7p4Jc12bTDCN/fvvNbLnnNDmsyQtftngswZ336e8kvhjcpeKM1XD77cv78/p/56Ws/BfCtPVxRVMHwnsPTYnrrY/WMuW8M//n4P75uwaaOrsHPWp4NdxtMHLRbYLr7rrp4+njreyO+x1unvsXBAw9O2x5LxFiwaQFj+47llL1O4ayRZwHpltq317zNp5tsD1KtNRvqN9CvrF9yAjtmxdgedQRvXASvH4OAVa711WSK1iaPUUoNBfYD3JW7L3ZcoO9WSvlOSyqlzlNKzVVKzd20KQ9Bl0vweq1Dj5wF/x5jv0SzJbbyWruqnBfi6O/mPs7gziS3vYCCt9wRvPkkVPEOfnrs5n9cW5NNSMUjdjbrgfu3TzsgPWnUyvftvwGTyKItWDDD/lvcuAj67p39uMpdMt2qcuEnzvOZqbtub7vuoMH8zle+b4vfle/Z690H5R/DW+MMKJpr4R1ykB2nFQzDOS/DxAvT93cbBEN94ryLK+3/4a7siunimQVree2LdK8Mr8UXYP32RpSC3sbCW5y/4F21pZ5gQLFLtxLCAdvCW5fYQFRtZmzfscnjvLPsRoB+c6+e3Hv2gUDKteqc0eckrbp+53hRgRg3f/bnzO0h+z3Uq6QX39kj/yRsJobXT/D2LUsfKDfEG9Jm6kuCJYQDYUpCJWnt3bvn3kStKAnH48EMdkoD3ZMxvCZLc5ey8NZXwX0n2HXovbTGytgRxOpsQeLux3vvmbtGrpfSnvDZY/Dyb+Dj+7MfZ96pbeHSDKkJ9X2cv/OtrXCLbWqCdvdJLb822G7NkZpkRvfyohDnHLI7C/94TOoY511wyYJdiedRD7zZmLGRdzLVW7IuF0bwHvVH/4zbY8+w80jMvsauNuB2sd372PSEVcOPses8d3P1dc+68lG0E9kSUXldmo03jlukmZhQL0kxOuG37FqZffLovvdWZGxLi+H11OFNWImkJdO07amlT7F8+3Ig1ef0LO6ZdDn2492175LQiTQ3XrdLs7Esb2qwx9W56ri7hahJcvi/Rf/z7UvW1q5NW2+Ohdc9mWomYd3fj/leLG1lWFpLQ6X0KOmRcb9tkW2sqlnFqD6jUEoxqo9tdHPHGl/w6gWcMfOM5P0a4g30L+ufnCyIJWLJskbi0uyPn6O9d8SZ8xilVAXwOHCp1tqYaG4BhgHjgHXA9X4311rfrrUer7Ue37dvllk/N9msnJUDMoXekpfsz1h9dldJ7+B/v+/Zn94yLNlEglvkbVud3sa2dmm24umuqdkwor7fSFv4BNspPblJWFTk6Xw2LLTjpQbu1z7tANL+hNd/Yidy+ffYVHx1a1j/mR0j9/TFsHmJHXuajeZaeN1/wyYhWkN10+d5X24mrm3prPTt3Qbbfx9fvW7HTOUSl8bCW9lMC68Xdwf1m03w889hl9GZxxVX2h4W7ZFsrB245CG7zuTUUbvw/Qm2S7FXTF3wv3n869Ul7N67nBIn03E4GKAkHMhP8G5tYGCPEkLBAAGlWLmlnhUNcwGYtue05HE9S/wtvG43OVMeoXuRf43PbtkSzin/dvaobKBXSS9mnzqbo4fmH1pgLLx+Ls19S9P7iMZ4Y1q5IhPLBOm1Ic0AxQwCjIW3NNSdxrjFxprGpOCtqo3w4/vmsqa6CwwYqlfYtUbn3ZO5r74Z4qEj2b4W/uD6mzP916n/g4vnNu9abnffXDkvzD3aysJ7wNn254jj7BKGz/4M/nNAyxIgub3J3NbtPs7E6h6tTLJVXAnR2jTvEICyolR+jYBjWNhGeV7voWYz+Bt2bocj/5C+3bix54MJ8crmgVRUBue/ZffBX75I2pjAPSHuxsT1dhCRRMTXTbk4WJxmsTMCzi2aXjnlFc4fkyqPaSb36uP1jOkzhv/b+/84YZjXeTPFwrXbMkoauS283jJBjYnGZKJDb3wxpFyaK4squf3o27l43MW+931lxSuUh8s5erdUH5HMehyrycjEXJqjzGapK0GrEX5BFfTNB+G1gGZYeKtXpleFceHXt/j9fqJWlLpYne/v1P27KwoUJQV675Leadc1Ls3e73hTva0z+pb1Tfbn1ZHqZD8uLs3+rAbc0z6DgbX5HqOUCmOL3Qe01k+YA7TWG7TWCa21BdyB7TrdOmo2wNv/8t9XuYs96H/gu/C1J8NtpCZd8LqXvX8Uh/wcfluVmZk2a9IqR/BaCVcMbyEsvM5AL584XvN8338CfpY9IUGbM+ly+ObPUkkhDNXOzGHvVtYjbg5ud7bajbD0VXvZPQDZ8jV89gTNxny/a+bZf3O9ds9+bEX/Zgpe1wv21d/Dw99vepLDL3banLPinfTt3Qfb++4/0Y6Z2pyjbJaxTFfkkbQqF+6Z9FCO/wkTX/b3HN9nF6S0KMifTxhFQGW6NL/wmf0d771LemxVRXEor7JEq7bUs2tPe0AwuJf9GSjaQohSxvQdkzzODD4MJrmTe+bbWHi9xxr6lPi7w6mQv2fHmKGZrtT5UBQsytvC2xhvTEuktU+P9MmnGybfwFMnPJUmeJdsXcKNH99otx2LhKU58OpZ/7+9sw6Po1r/+OesJRt3aaRt6q5UaalQo7gUKO7cCxfXi3NxLvyK3uIuBYqUUlpK3d29TZM0jbuvzu+Ps7OS7KapIpnP8+TJ7uzs7szZ3Tnnte/L2oNygZRX2cD8nYWs3P8XMBhz1gR+7GiiZX8k+Y1qjd2K/uFH32/WW+ClaFfg/dwpzSeohrf3JfBkpVyHqIvr0v1SQfho8XZUeBvkN/4O9x84uoi3P8ISYPtMgt4/gwFiD1M2XgmWGp9FvMFaiUUxYMFIVf1JMHjDEuCxIuhxge/2iqyWv0ZNoXQuBDJeAYzB0hlxeKNvsCRQHXTjrhonsxzKD2pKc2M6RXWipL7EbeSohqb3viHGEB/Hpurcq7PVuQ3Bseljacg/nx6OF6je7VuC0mBzUljle77eBq/NafN5v3p7vV/RKhV1jgk3hROkDyI5LHCmWPfY7j6R23CjR6XZ2+DtGduz2Si1uq9O6NwGol6nD9jf2Buf9HCHHab1gq8ugx0/wqG1AZ/nr7WTGoG3OqTBmxCSwKT2k3hr7FvufbzHskNUB7ehqkap1ddVP0e1v7CKOu8lhCS4DV5VNNKoM2opzQFYB3QSQrQXQpiAy4BZjfaZBVztUmseAlQqipIv5BXyA2CXoiivej9BCOH97b4AOI6iFhdFO6WB0fuypo+paZf7foOtM3wfa2zwehuNjdNw9aamEdGwxMDpumpEriLHI85zsiK8IBVuj4TqvT6WBcPxYgqTBq93uyZ17Bu3jziZeC98akuk2qR6W+X1fvDddUefQqum1asRhOZUjMOT5QJG7TNorXU/f1fpLnp90stXxc/7e7rqTdj1s+/CzdYgL77ex1zl5Z9Sv6f1rmNsbCyrx+oSsmj2+1RdIBcGxsC9+lrMNT8fOVpzKlpW/QEUV1sQQmA26v2mNIOM6noTFmRoUVui3HKPwXvn2E60jwsFnQWDMPssfhqLlTQb4Q3wOw1U/6Uz+Y+kFdYWEtXcYjQAzaU0NzbGGxwNPoqkHSN9nWrj2o6jQ1QH94Kv3l7Pktwl7sfteBYFxdW+i72i6lO72G0x3mUtOSsD71eeBZ+cAweXnvRDOi5Eo+WNxWv+Olq6nAWR6ZA2pPlsHjWL5ESlNPuj7fBjc6h6G2bei+ngCF+hpWPFVYITXrGLCYYNhJVtl8Y5EGl2iSTZqqkiFBBUNRxZLf2Yadwy8WgjvKHxR3YApAwAFNlRQSWQwdu4PvoUtyiyOCx+U2v7JMjylC3FW9z7QdMaXu/Wcm6D117nNgQdToGtYghd49qB0nR9ur/IN0uyccR3aJuhPHv6swB8u/dbVuXLUim7086P+3/02Ve9fqvR2uZShhNDfKP0oS5HT7Wt2scQfnPsm83qQajn6e2c1Qt9wD7E3sQEewnW5roM3OwV8O018MG4gM/Tua5fb4x5w73Nux9vra2WUGMoL418iZGpnnKEpNAkwoxhxJvjfYx4dbwaZyUdrvHUWHuX8iSEJLidC5mV8vvaNaarFuH1h6IoduB2YB5SdOobRVF2CCFuFULc6tptDpAJ7EdGa9WivOHAVcAYP+2HXhJCbBNCbAVGA3cf98GqkbLYDk0f875wuiNqLmPPUu0bOfO+7f2lMIX5Xjyvnyeb0hvNzYhW+akNdkd4T6DBq6batCTCa60FhG8q6alCfU/vmmdvj/2pQr1QhyZIo051Rqhp54qCO8WppSJOKqoHXk0xaS4CqqYDq8bxO2fACzK9dc5BKVy+NNdrQeov9d67Fu2jifLiu/NHcNikKJt3bXJtsYz4WvzUuuuMntqy01yKmN5CV42pLjj+6K5K+5FHFi3zPvdT7Fk/mehcTiezyeCT0uzd/uaOsb5jExZsOGIqYYPNQUmNlZRoM/X2ej7e8SEJEQaEsGLUBfnU7eoaGRXq4sPb4K20VKIXerd3vTGxZv9iObog/wZvUV3RMUV43Qavn0WKmu6lUm+vd9d23dDzhib9hlW8Fw9V1ioMwsDwNsOxKYEXBY0N4D8N3nNN9ir/+0SkQt5Gaexu/vLUHNexUJHTVKhOvQ4EUJRtlhH3wF1bod3pUHnIf/aL0wlzH5KRQX9riePl6llw7htSFKk6T0aTW6K9oeJde200wz9Xw60rAu9/tMR7+lN3N1fIG9UF5Nfk8/XtaYzuEk+wvYpKRRodJ9XgBd9+7dX+ryV+qcprWcs89TP2TgkN5HxvLALV3Px4EgiU0twtphsGncHd09VfDS/4KimrEb46W53baFR7jSdFBnP5oHQ+us5XgXt/ke/6wzvCazaYefC0B93XUm/F4z3le3hshW87Lr2rBaU6B/gThVKNzMYGr9rXtrFQVuPSnMao0dGEEE+kXi/0flOuG+NjkDcuBQMpUNoMo9JG8ehgKdBZUi/XiKrB2zgtG6QDeeXlK5l/8Xyfz1ytX24c4fWuOR7+1XCeXvU0IEuQVCfAgQrZirJzdGdKG0rZWLix2WP+s3JS+/AqijJHUZTOiqJ0UBTlWde26YqiTHfdVhRFuc31eC9FUda7ti9XFEUoitK7cfshRVGucu3bW1GUcxVFyQ98BC1ErSmM9pPy6K36qu6nLvIaR3hz13tSmlRj55+r4e5GQej0IbIpvTG0ZX14VVQv5fHWPnoTdhQGr6VGpkIdb+rTsaCmiHnXPKt1EKfS4L38Sxh4A7TpK8eswqW5tncu/F8v2POr1/E1I4TmTeEOqcTt3RMRmqZBeaNmHqjtEUr3yc52tga3aqzPRODP4M3b1PT2nl9li63XesMnXoJAK9/0RHzDG/Vfjmgjx8QUDv2uktvqj2DwtmRBcaLoOtlz+y/enkhRFHQCzEY9L1wk65VDTHrqvfrwqv13n7ugFx0TfOvew4IMR2xLpKY8R4cYeXfru0zbOA1r8HqEzopJZ27WE656hG0OG1aH7HlbYakgwhThk9rojXeE19uA1pn8iyPV2etICj36749q8KpRjAdPe9D9WJ/4PnSM6sio1FGAJ6W5e2x37hpwl7sFR2N8DF5LFdHB0UQGRWJxBnZ2FVY1o/J7Itn9y9H1IPWecwL1Ko71UkYNMCannJL9cu5V2fqtTBvc8YPvfmophT8hopYghMzKUpz+DZb6cmkMn/EghMQ0ffx4yTgD+l8tVYIBfrnHo07fEhpHeBO6QVLPwPsfLV4lOH3DXXNfdR7jZ45nyuwphAUbCXZUU4Wcy1tSWnFcXPEtnPEQxGQcncBjeZZnjJsjKl06e71pqa5JS9cGJwirw39vXZPeRFRQlDsLR43kNo6aehuIaoTPO8KrGrwmvY7nL+zFqM6yRKR3aiQRwQb2NorwXt/zegBSw1J5aNBDpIan+q2h9SeQdFf/u7i6+9Vc1Pkiv8cKHsGnxFCvtbIrYBVuDKfaWu3z2o0dt41RDXvv12supdnbMPahcIfXQUbJ/zmrm31v8GRSqQavmtLsT1kaZC2wXqf3idSrBq+7hte1Tsyr9Ri83o7qUGOoe67Prc4lMiiSM1LPAHCX7vzV+AMslz8h1QUQFOlpORMUAcOkVDkj74drZku5eXXBH8jgnXMffH+TvK0asqawwGkuppAj9+FViXDV/gZHHrmdwtFgjpbnXtxMXZLDBl9fAZmLmgpHnSrUH7atThq6Gz6WHnxTmGyJcKpI7gNnvyqN0eoCT2S3aCdU5shUZpWWtsL53zD4v+5Na+Oaa9ujGowfnAk/3U6m0cCZaW34auMbHoVDxS5Vn//bGQr9pOH5m3T3zYf1XkI1fa+U/9e9B++OkrcTGolpxXaUDpx/53q83keK8J5Kgzc8Cc6fLm//xdsTNdicOBX419iOJEfKiatxSrOn/25TwzQs6MgR3mpX5CUs2OAWbjLonaCzYtI1n4auTpB2p50Bnw/gurnXUWmpbKLm7I23wRtq8EzgOmNFwOd49xNsKSa9SRrhrkVd34S+7sd6x/fmh/N+4IHTHgBkSnNxXXHghYsLb4NXPc9wUzhWZ+AI7ylLaV7xOqyY1vL9/bUgAny0Jb2N3Op8WVKx9Zs/TgHdWiuNvvfHSoGquQ/DnHvlY7tn++6rzt/HM4epbW/8lWy0pBTlRBDl6n295SuX+nQzbftUGip9a3gbd4s4TpxOharYvu77YXWuNMkqTzwiLMiA2VHjifDWn+QIb3Q7GP2wS+CxhRFep0M6LVpi8Or0gR1DR6IlIqEnEKvDv0ozyHRdNWrrjvA22te7nlTdt95e7zZ41XrsMJdQmRCC3+85gy9uHEyftCg25VT4vN6k9pPYds02fr3oVy7sdCHguZYeqdVcRmQG9592vzt66W9/1YBNMLuu33mb4JUukL+FMFMYNbaao0rNVc/TO2KsF4EN3pQw2WymSVTdO5U9bZC0JdQ1ZDM0NngtDgt19rqABq+K99io+zZOaS5vaCrmadKZMOqNboPZrtgJM4YxOn00V3a7kh0lO/zWV//Z0QxekOlB4UmgepiMITD+GXjokJzg2o+QUa36MlnrGMjgBalSCy3rx2cMObJolYrqWU/seWLrZ4WQ53dgUeBFS0WOXDwU7Tx27/jx4o7w1sIPt0q1ypxVpza6601ovFz0OG3SYaDiXeN7JC/ul5fBkpc8970jvMGRzde4xmR4Fj6bP+fN6CiKDQZe3/u1+wJW0VAhDdiaQlj4zJHPaejt8jtuqZJtMK76Ec643+v4XAsmNRPi9HvgtJvgPK/WJcYQmXIfKMLrdMrjOZUGL3i+J3/xCK9qrIYFeSIJZpPeJ6W5ok5ORFEhTRc4kWYTpbXNG1zqe4QHGd3OkyB9EEJnJUgvr5E9Ynv41A2pqBOsqmS5pXgLlZbKgIJV4LuY8lbDNAQF/v2clnhawMcC0TjCa9KbmNp1Kmnhae76JvVYGuyyhte9YDrCsdfb66m0VhJhiiDcFE6DowZv9dYgg2eqLao+RRHe6vyWGUMgrxM7f5S3G0c7giPg6p+kkRThyu6ITJeOqwVPSSfvgYUn7LCPisbpgKvflka4P4NFbe93PAavuwTIjwGlbjva/uJHS+N2gEfS3/j6ClnukrcJ+l4BD2Z7ukacAJxOhfPfXsHAVzfyn3SXmJZ6/a/2RI8Swo2EOmuoxFVHebIjvCphiS2P8FYdlinKLTF4AXpfKp3x9+6Be5oJGoAU+zzXVY/Zkg4JJ5BAolUgDSDV+HFfGxtFg2/tcyvndjgXkBFeh9MhDV5X5LPQdU1LjPBcyzsmhBEebGRg2xh25VeRWdz8tUg1xFRDSjUaG9M4U6i5tOIgVe9GFbArzybMFOaO8KaHp/PDuY0yQdR1sNMh13CFOxmdNppre1zrk1lk0BkC1vCqhrHPvOd0yNKvSNe6LWWAdMY0MngXTVnEnAvn+GxTDV51XrU5bVRbq49o8KoZV9C0hleN8Fb6KVNTX9f7+epn3Se+Dw2OBl+NmL8ImsFbXy4FfCKSPUIOqnHl3Rhd7aNWne9ZEFhr5ALa+0unfkHUWtPm6l1Nof7bEqm9Xb37lKpqyok9WnRaR0WHMdKruW++/8e91YDV4zjVuGt461ytAJAXij/M4PVaCF/0Hox+pKnoWQDpeUAafnt/hUXPerZ5L6KO1MYgKBzu2Azdz4PQeHINrh6rTiuLcxcDUG4pl+14msM7jei0G+Q4B0XCxR/KqK2/z7v/1XDJJ/KcJ//XswgG6UAJiYG6AC2Aagqkk+BE1fC2FLfB20ID4A/GYnf4jcTW+jF4ZUqz53NWU5qj/Ri8GfGhFFZZOOu1ZZTW+Dd81YVoWLDB7cHW6e2gs2IUcgHx9dlf+yhDglyIqB7haRunubdvKNzgm1rmh3M7nMtjQx7zmcCdisPHo67WZV3U6aImoirex6DT6Zr8CSGatCUy6Uw8PPhhn8WFasBWWauosFS4I7xCCJ8/lQijnCN2le1ib/led4TXiROExwOeEuX5nRVVWZqItpxwFEVet/2VxjQmZw18cbHnWqSq3qvXhtAEyBgFF70PE56VbX06jZNzYaZLqOuPEjLxV//W9SzoMrnp9qq8pnoaR4tqzNb4aWN4pHY2J4qwBPnZqNF2f8ei4rD7RrqTeoE56oQezpbcCrbmVmJ1ONlS5KkprhOCKq/FfBfdPlJFMbmKnFNOeg2vSnhSyyO8qm5FSw3e8/8HDx6U7+E9D/qj41jpcIBTGuFVFIXDNYd9xZO8CDGGuHuyqtd7m0PHIq9e72aDmWt6XANI555qIKvX6yKXCnNCeFOjelB7+b5jXlmC1R645tw7pXlM2hgGJDZN1/dXs+ov7fmhQQ8xKm0UAxMHyg2qxkhDJeHGcGqsNdTZ6+gZ15OO0R1lV4ncDbDoOXi1m8xqfDpGOor+N5SOEe24d+C9Ps5ZRVECRjkzImWA6u4BXhJDVXlSePX0u+DcN2XAIDJVrr29iDPHNVGM9ifsWNZQ1mzvYPA4oAXCPXZqurc6bmo6uzfq63pHiNXnd42RGX5qXa+iKEzfMp3MilMrxHYsaAbvMlcb35gMz0ToLyob7sfgtVTJP+8LnVrDYa2V+zWnqBwaLy+w3vVHIO9XHoIxj8neeH0uh3H/gUkvw7CT0LS89xTZ83Xew/4fV2uXz3gIpnzmf5+TjfqZNFR6jLja4j9OgddbKCm2I5zxgDQ+vVnyokyzszVIA9dbYKRxvS74fg/SWtBtS6eXwhy1xRQa9Iyp9V10VjRU+C6GvLx17iiBd51wZDrcuRXu3eX53ppC4aof4D5Xm6GUgTKtu8f5geuVzDHSw9/YmVN6QKZYg6xHO5X8xSK8U95ZTc8n5jXZrhrBod4R3kYpzeWuCG90iBG7086TK59kf7lUS+3kqundmV/F8gDtcVSDNzzY4O7Vh64BobOgI3DWgWpUNsau2Lm5980Bnwfw7OnPMqXLlCb9cNtGtCUlLIVnT3+WBZcsYNroaTw65NFmj8Hb6PU2UoMMvirN/o5VXQwcqpaLkCMZvKpa9PQt090pzWqtlNB7FmJtvAxei91JVYOdj1Yc5Jv1voudE0Z9uRTUa4mDJ3u57331mqrWZHo7vczRsidseLL8jRftlNsD9bE/2RTvlnWU9+2DsY/Lbd3O9e3NPtKVpZK7zqOqf6y0JKW5Oe2FE4EQMO5pGOdq/xJo7HfO8qjCqpwEh3l2qec6v7tC/jayDQYGt0vjdnu2+7F+K6/BIJysd0oxqZPSlsgfIbEyAPF8mqeNoD8cNpj3iJwnXYrTR0SnP7quGTq9dCifwghvdlU2pQ2l9E/s7/dx7wivWu7x+A+7ue7jdZTXeiKYqsFTZ69zpzWrRqqateLP4B2SEcPlg6QBtzW3IuBxehu8YaawJqnK3WK68cmkT5o8r0u072fVP6E/w1KG8caYNzwGqppK3FDhTmmut9d73vPrqfD+GLlmq86HjZ/6vokrQ8C7K0GDvcFHvRo8/drTI9LZds02Jmd4Od5KXM652I7Q/yrZSjEyFbKWQVbz4nEpYSl+HRZHcpyqYxhmDHPPW3qdHpPORL1Dfub+IrzqPOgvJVotT1IVo6usVby1+S1u+u2mZo/lz4Bm8KpCUOOf8UyGasqBN94Gr5pCoaY0ewtUqF5Xa52M/DaXftz9XLBWy/qjQ+s82w+6vOZdJ8PVP8IF0yEyBQbfDFEnsH5XJSgcOk+QY+HvB6RGeIf8wzPhn2pUT1ZjOf8/KsLrvaBSa6ob98097DJgS/bKz/gNr+f4S7FqqJBpxdf9Kj2AXiiKwrbibU0vcKHx2IAyvZ6uVt/0moq89b6LoSiv73W8qw43uTdcORNGPiAN2LD4pg6fDmPkIu6e3XD93COn1IfEwJ458FwyFO2W2wq2wxv9ZQpk+lApmHIq+YsZvFsOVTTZpiiKJ/raXEqza5ESFWJiT9keZu6b6Va69BaxMun9X/7VGt53dj7D8sPSGFKwIIQNoTTfA7zxIiUmOIZPJn4SUOW4MW0jfNM1Y4JjmHvRXM7tcC4GnYGx6WN90qyOhiB9EAoKBXXyt+fPO27QGdALPdlVcqF+pBreMGMYBuE5nnBTuHthIvQeYzMp0tdR8OnKLJ76eSefrso6pnM5Iuo1217v28rNH40jhOoco2pP+GtZozp5Vedjc1HGk0nRTul8DEuQEZNblkpnXHIfzz49L/bcPt5IdFCEnOO3f99UqbmmUEZeT8WcNPhm6CFrH32Mb0u1dApba+Gbq+CjSb7PSzh+g3dvYTXj/28J23IrufnT9azwcpzVEoyCYGmINCQ26T1jVO0KKGx0SmdxdYON7Ycr2VNwkq/JqnPaUtVUyMybgq3yb+ILJ7fkxhx5SiO8Gwo3APiNmIL/Gt4DRTJi6603oF4v62x17vpXd0pzlQWjXvjNKhJC8MAEud5YczCwtoe3wRthimhynX96+NPu6KI3aRFpfD52sfv+J5M+aVrXW+aK8NZXEG6SolXeKtNNsvH2+KYUUynLIdwRY6DeUe9Tw/v8iOeZee5MZp47kwntJjQ9wf0LZXvSFK/PweyyHT4+q1nxKiGEe145r8N5XNBR9plODg3cgxg8KcmN57pwU7jb0K20VNIm1Dc7wW0cCz3CpeGgOjxUh26tK4tVfZ0q659fH0UzeKsLod0Iuchv0x8mPOdbk6iipjRX5npa0VhqpCfdW4pe/ZGW7JFGanNkjPakj+V5yXzXl0sD7wSnHjVLeLJMt6j3k4panS+P51T2u22MaoT9WQxe70WgwXWRD5QGVbhDfr7lWbDpC1jwn8ApVl0nQ9th7myDOlsdH2//mFX5q5g6ZyqLDy3G5rR5eqeFxlFskE6WBIeTCXb5/Tuzto6KhnKUylxs0S4jIiTO0w9QNVoTe0HHM2HMI0c+54jkpu0V/O7n9b1XIwxqlgD4XvBPFW6D989/UfbG6fQ4OCZOW8bl78lJ0TvCG2k2UuklAFNaayU8yIDJoCOrKsu9vaS+hOQoz+cXqIZOjSIvOjzXs1HXADoLxiOIVjVWM3562NMBIwv+aDyBB0rDOxZOS5J1v+9ufZdgfTARpqbZIUIIjDqje5F4JINXCEGEV5ZJpaXSExU2er5roSbfcXllvqx/UlMBTzjev7cjpTU3jhCePU063rrJmj2/IondvBTc9UEt6+N+onE6ZdRWvZ4I4TF01XkVAotGHgtCyIh33kbY95vvYzWF0vA+VT3q1ci7t7PhjQEyDbNxqveE52HAtRDqvwXY0fDxyiz2FtZwzUdr+W1nId9uyCXCJVakoMOmN1OsbyokWa3TMc1+IVWEERcWxP7iGq75cC0PfLcFRVF8rnUnlK5ny/KfdiOguJm6QzXDqsukwPucCIKjTqlKc051DkadkXYR7dzbNmSXs3C3XIP4q+FVFPl5epe9eEd4v98n+0Cr19Ci6gbiw4LQ6fx/96NDTXRNCmd1pn/lffU4VPxFeJvrtzv13XUBH0NRPAavK6VZreF1v2fj0q+Dy3zvu+r/vWt46+31PinNE9pOQK/T0zm6c1OnrK0edv8M7c/w1cEZcI1HGDS7+Sjv1d2vJsIUwb0D7+Xp4U8z76J5XN7t8mafo45hYwXshJAEd5/5CksFKeG+tooaWBFCNDGajXojQfogt3J3uUXaDI4jlc+dIjYXbQ74mGbw1hR6am6EgKG3+W8pEBwlvbeuRuqA9KbWFPvWWypOGd3NWgEdxjb/3jo93L5eGiIFW71et+rUp+qqY+C9UFKpzpcez1M1kQP1Vgd2h1f6mfqZHG7U/yv4FI+TNwNvkOnmKqrgTq9LpCdPxfuz/emfsOy/ULrP97UmvwrJfSHVV4zn273f8sqGV3hgqVSP/T3nd25fcDsTZ06Uk1NoPIWuxUViWArPl1axusddxDkclOr1vBpmpH+UgiMsibzTbyf/6h/gzKdkSlxyH7kIOtGc+YRHBbTEtcDwXnR7L0ZPFUcZ4f1m/SE2+4mynmq8jdI9hZ5jDwvyLChjQ02U11lZnbeGd7e+S0mNhThXatn+Cnm9anA0MPqb0by+aRoz/zEMCFxD588QbhOjIHQOhrZvvk6tsaBIc+rM/mjcbuhEGrx94vvQI7YHTsVJYmhiwDZJ7jRujmzwgm9amdlgdj9HZ/AYvOf0acPT5/Xg6fM8EbY+qZEUVVvYmBOg3v148NZdOKLBWySvOwOugxsXSkfrhGdlrf7gf8CoB5s+JzhSphHfsRmi23rqV0+VWrOiwKzbpYM2fWjTx3U6WQZ06eeyjd9VP8LUb2Q2y/Ey1VWWoWaHqXivJU4FBpNcl3g7LNS0au/2JwADr4NzXjshb6vqBZR5pbtWeV0zTI46SvwYvP9q04ak8x/k+38O45w+yWzKqaC01sr2vCpu/3IT46ctRVEUDhTXuLNMTgg6vcy+iussHQGBvqOH1kpx0sjUE/fe/jBHndKU5vKGcqKDon2udxf9byXXfywNfLPB7I7YuiOWLoO32MvgDdIHoRM6MisymbFnBkOShzCszTBsDifF1RYSIpp3hg7JiGV9Vrm7hVFjvA3ccGN4E4O3cbmLNw2BaoNL9ssgiSv9loYKOkZ3xOq0oqBII95hb9ohw9HIEVl1WHap+Ggy08+cTreYbtiddp/64UC6EoAMclQcgkGN0n6TesH5b0mnnKoiH4ALOl3A8suWu3sGtwlrc0RFa7VncePynYSQBJYfXs7k7ydTZ69zZ2B1jJJrs8Z9kr3/g4zyqr2ZK1zZCiddl6KFePcVbkzrNngVpeWKsULICFeJl8FbnS8nG+9oX0OFjGo5LDIVtCWvm9QLCrzaxjRUnvpoqndf18ZUHGrae/UkoigK3R6fy51fb0ZRFBbvKcJmipQRVDVNWK2l+KNqeEG2J7pguu+2x8vgwvd8m9Hv+LHpc7fOkP97Xyr/n3YD3LKkSQS1sFYuYNS0kVkHZrEybyUAOVU5LKg+yDOx0ihIiM7AWFdC6Ox7SLXZqdbr+DhKjs/hm+czYc2jTPrtGimaEN9Fpv+dyJ7OKhFtpMBNUi8ocilXek8o3vXPpwo1pef3J5pmCfjhge+2cv5bzXtcTxYWu8dTGsgo9Y7wxoYFoShw0/wbeWPTG5TUWIgNlQ4XtXZXNXxnHZhFv7QohPBdpDqcChUusasai51go86nXqnMIj+/yKDmVSEbE3GUv0/vlDGA+JATW0LRLVam0rfEkL2j3x0tMtjtihzHCe0mcFf/u4gzx6FDhzDK3+x71/RhdfmXXHJaIhf083jSR3SS53bh2yvJKgnct/eILH4R9rii8U4H1Jb6Oi6PVMdbUywNtXOmQapX9oUpBCa9EHguCkuQhkRogpwHf7gV3jn6dlHHRMk+2PyFvN12mP99ht8h641BCvB1niCzWY6XpD4yk0sd4/wtkLdZGv0n43raHOHJTURvANmDWUUf5HHGHgdWu5N1WWXszPM4cs7tI9cFQzNiuXlkBlMGSmOxyKAnxej726/GgcW8hv7p0QzvINdMcWFBOJwKv2zLZ39RDfN2FDD2lSXc+EkjXZMTQXxXsFT6V9gGmS2QOtD/YycSczSUHXCnyZ5syhvK3UZSY9o99AtOp8lTw+uKWCpOOb+U1HicGkII4s3x/Jz5M3X2Osakj6GoykanR35l2b4Sko5g8A5uH0O9zRHQkextkIebwt2RxUFJg9h2zTaf+agxPsaWWldrt8qWZW94ZRjVVzC5/WS3gWc2mF3ZKV7PzxjV9A0qD0uV8+zlDE8Z7q7Nrar3r4PRhOwV0H6kvAb5IyLliAYvNHUoA7J8YdVbTUsswB3Vv7bntT7b1fkvpzrHvd/qqau5/zSpd+Bt8MaaZVaIt6BkmCmMWlcnGTXC6+Q49RFOEDsKAmcbtS6DN3cDvDfGswCwVMuanpaKTIS3gRxpbKAPguyVLsVZr+fbGzzGa0sFIhJ7SM+jKmrUUHnqI5eq0e8TGaiFj8+WBnxKy9MSjwZFUdy9PnfmVbFiXzGFrjS/X7blM29HIdd+tI6PV2TJlHOQYmBqLdbJ9sYeLTq9dGLEuPrRJvb0ac3gJm+TFK84fzo8FjjNZ/Xh7YEfy1/NXZv+y94gE70bLGQkeSbrtlG+UdQ5B2VNikNx8P2+7/lg2wdHcVLHSEJ3j8Hr3Zc39sgGb0l9yYn1GHpPFP4cEF54G5x/BMVedVO/bs/nld/2+KSWmY16dPoGvt37LbW2WnbXzQGvyaaktpKoUDt1tjq2lmz1fmmX6BKEmQw+UZRnf9lF36fn02BzUN1gIzRIUG2t5ra+t9E7vjer82Uq9ZFUIRsTaTo6x11yWDJbr/Ycc6DWFMeKqp7ZnGf8iaFP8NbYt7ipd8tEOJwu7Ycru11JmCkMg85AdHAsOoM0eItYwjtb3+GTHZ/41F73TPFc41Vl7aNGUWDxc/CVy3H222PwcoYUiFMJ1Otdpbbo+NT3w+Jli7gtX8m570jvdyJQM2Qmv9pUO+Fko9NJB4FalvLTbTDnvlMf4QXpVMzbLGtT9y/wbN/7q+e22grnOHn4+21cMn0VewqruX10R368bThPn9eDlQ+N4Z2rB/Dvs7rx0sV9IDSeEr3Ob72l2gpleMc4zunThnev9i1v+WhFFtB8recxE+/SESje3fSxmiKoyG6ZWOTxMugWGeFd/TaHaw7T/7P+7CnzozZ+gii3BDZ4ASprZVaLw+loEuHNKa3lp80ew/y98e+5b7ePbO/jqEuJbt6pMqxDHBHBBl6euyfg3K6KJQUbgsmrleumlpTE+GTD//gP14k1cgRFpEJDJXqdngdPk1krseZYz5pXnRP8lVx5f2ecTne004LCCEMMb499u5mDc8pMt4TugfeJSGlRP16/LH4B5v0bdv7U5KHRaaPZds02zs4422d7Y0eyUWck1Bjqrs/1/nzUOdxbITvMGEZhXSGjvxnN7ANSBd6pOKmz1fH+tvfJrspm8BeDT+r3OhC5lYHX063L4J31Lzi8wZNi6m4j0EKBAu/eom2HeZqOhybAP1ZBv6vk/cMbpGBVS4UP4jpJQ1n9gTZU/QERXtXg9YoM5G2WCnIA7U4/KW87beM0Rn0zikU5i7j454u5cf6NrMvyjPNvO+XFqLTW6vG+Kk7PRan/1SfluI6bK76BW5bB2f/X9DG1zqzH+XLxFEDt2OqwklO9H1vFAOw1nUh1TvV5/LWNryEQvNj2fN67ZC4GNbVv3H9oO3maz74fbf/IffuJlU8wbeM0nIqTz3d+zo6SRulvJ4qEbjIVqL7C08N3zGNHdDAtylnE6G9GsyR3yYk9HvU7U9W8Z337YU991dbcCrJLT8EC3otCr7rO5+bs5o2F+xnwjFQXfXRyN5Y/OJr/bX2Np1c9zUWzLmL24f+hD/UYOKUN5axRbmPwl4MpayjjjFSpiJ0RmUGlpZK82jwizEYKKhvc6WXfb5KTbUmNhaoGO2Eh0uMfZ45rkrIbCKfTidPpZPVUj/jG0UZ4wdeLfbQGr9PpxOFwYLfbsdvtOBwOHA4HTpczsX2kNI5UwQ1/XNz54iY9htVzU/+8cbg867HBnvrIxJBEhCulWe0TWVpf6nNu8eGeiMgxG7w1jbzZq12torz74qrpfI2pyoPNX0pn1PEoC6tRVJWKk6Q87U2Jy+DtedHJfy9/hCdJ4UFrLRTulA6G+vJTb/Cm9JfH8e21MPtu38eG3g6PFEKfS4/7baobbMzc6FmQj+4aT9+0KKJCTLSJMuOk1v2b2nPFlxwwmYgPT2VYnW/LmEPVh6i0VLKvcgdvXN6P/unRPj2qT4qhqxLnUvP1V8eb66oBbVRSdFJoN1yWAVTmsjBnITanjZn7TkCqfQDUlGaVJsamItNdGxwN7hreMFMQSRHBfLIqmzu/3szuAnktU6+fIKOC1V6t87yV6P0RGWLk9jEdWZtVRo8n5vmt531/wvukhqXSLaabO732ks6XHMXZAtu+lb9FtW5XJbmPWyxsUPIgfrngF8amj/VE/C+YLnV1ht/peU5InBT7zFzk2VZb5DMP9nXAiNRmMlsqc2SAJqGpA8hNRJsWRXj9v75qN1SC3TcVO1DZjmq8Tmo/ialdpzI6fbTPdpU7vtrEtkPyN+zt7A4zhbGxaCMl9SWsKVjj3v76ptd5beNr3L/kfursdXyz55tjO6fjoKQucH186zJ4VWVcVTBATY89Uv80leF3yXSHiz+EYbd7tofFQ2J3TwrzoXUQm9Hymlc14qV6rf+IlGajWb6nd7pPidfEkD7kuN9iR+kOius89UaHaw7z4fYPAbhj0R3og/MxhGZyx3ceT/X3G6VxUl5r9aT/AkydAXfvDChaddIEMFpKTIZUQE49Dc76L9wwX6o/Alz8EVzwDkx6ye9TrQ4rF826iNHfjMaiVGOr6k39oRvYtac35rLr+eqsr0gMScTisNAhqgNnjfoPITEZciJ9pBCG30FqnCe7INwU7lZh9GZT0SZeXPci18277qQMgdujWbxbtmGKyYCR9zX7u3AqTl5c9yKAWyH4hHHjAkgd5Ik6++HXbflc9L9V7vvnvrmCM15e7G67cCrIr2zaV1AlOdJMbFiQuwG9Kl7mrQhcZfNNs7pn4D2su2IdTw2TbUz2le8jPNjAr9sL+MfnUpxJ5/pMymqtVNXbMAfL70ucOc6nwby/noeNCTWGMip1FNB8JLUlNK7pbRGKQsTm6YTt/Ap9+QGfh9qGSwG37rHNeNuPkrQIKeqkpn4BJIYmuFOaVYNXrQ1+c2o/Pr1+EPFhnrqqslpPtP3nLXnc+IlvS5CAVHjavvi0Pasp8AjUBUpp/vGfrmiIcnyqtD0vkv1Iz3nddUxeta2WmpPT+7p0n4xKn0phRxd3LryTd8w66VRY+YYUvFGd4QEcB+8tzXRHyvYUVDfbj/SocAt26Xy/CwBth4Ox+TTTI1FntXP3jM28t9RTBiKMJby/9xF2lO5g7sG5rMxbyYgZIxj33TgW5izk4vk3ABAVEs9LHaZwZzuPQyS7Kpt7l9zLFXOucKu6/uf8ngBcPMA3W2vWljwf5+NxE54ks6p2fC/T/r05vEGqb3ure59MwpKgptAjDsTJ00dpnNLs3cIOwGKVDvd6ez3FdcUYCCUhPIS4cI8OSX5Fg3tNpdbSJoYkUuKVeZQS1ei7piiQv1UaclnLwdZA//Ro9zG88lvT6F+f+D78etGvtItsx3U9rmP5Zct9y082fgYLn/V7nmdl9+KnXJfRuPBZT+9dtSNFWLxP7XR6RDqGte97HEXtTpddUYIjpQaJ0MEDB5q2myzP9ukmcE5NgDmxoVJGXQtdrdvim+lMEZEigwJ+NEYURWk+201t/7jrZ3gmAQ4sCryv+nYusbH+Cf15ePDDbq0MnUssVedqvTprSx4Nru+HtzEcbvS/7t5YKDV21LlOLfc5lZQ3Vtz24tj6O/zVsNXLH5/a+7Q8W/YDXeAS7vEneuGPPpd6vKXek7gqWqX2Nq3KPbpaELWmsWS/rDE6RtEqh9PBi+te5IKOF7hr1Y6KsCTfCG/JXinUdf++41ZDtjqsXDb7MiJMEVzQ8QK2lmwlvyYfoRipyz8PU+wSgq19sYXPx5z6GaFBOmpKBlBfMgyEhYMltTjNseimfCYNJj/CYu8tzeSjVXs4f5CeT/e+zm197uQfQ8a5Hy+oLTjqBbTVYaXOVufut3nUCOERKkgbBP2vkbVxsR2a7Fpnq+P+pfdzesrp7C3fS5/YIazfkc4ZaSNY6GoCX1TYmarKJHrGDKCwbo6P8iLgXuAY9UZ6xfWic3Rn6u31zDk4hwhDAlV2T0To/W3vA3Ki21y0mfiQ+CNG1LIqs8ipzmkSAfOL2nqoaKf87YX4aW/S6Pw3FG5wG3Ez9szA6rAihKBtRFuu73n9kd+zOYSQx7Tr54C7BIowDHp2ATufnkCI6eRfMjOLA0cfo0OkAVlQ61trrzN5jltvzvJ5rH1Ee4QQZETJdN4DFQewOeT37/dd8vugimuW1FjILa8nIbGew04ZtQzSy/61feP7cvrOeZC7B0b/u1nHxaujXvXrZDlajqoFkdOJcflLhJdlEbznR7lt9XNY0s/AMXkahGaQFpHGl2d9SeeYlrVJAqA8G1FbhRKdgSjaKY2sUE9a/vQzp7OpaJOPBzwpJBGdYSVXD21LWYNc8KhOirN7Swer3eFkRKc4lu0roazWs3D8Zv0hlu0r4d5vt3DlkHRGdorH0KiFlNXuxGTQyblMpaRR5Cq2k/zdWWvg3dFSuEjNiHE6fQUAe1zQ8vHwR9+pnghFpZfB+2p3mcXyYNbxvX5jivdKEaITxMaccmJDTbSN9dSpZRbXEB8eRHiwx2nTYG9g4aGFLARuAVj8vO8L+YnwFldbeHaOdLL1S4tmwrSlXD20LU+f19O9T73VQXmd1R0lUxQlYHTGh5SBUozKWgfzHpbbzn1TZrG1REPkCMxYd4gfNsnrcXx4EF/dNIQPd77Bz9nLWZ7n65Cstlbz/Fo5Hmemn8mZbccR2a8LNwLdO57N57s+Z3PxZrfTbH3BemZnzibUGMreZx/j8UXTEcZocBpRHGbu+GoTAAvvPYOM+DCOGyEARabf/3Czr4BZ3mbpoD0Btc4tIjwJDm9w10q26LM+BmwOG9W2ap/1iyo21qNNBDvyqiiskMdQZ6tjf8V+jI5kEiKCSI8JYfthaTxc9/E6bj2jAw9N6so353xDQW0BQghKqj1OuZSoRuUuy16Bhf/x3A9NoHePizEzkHqCWZdVzluL9nPbaP8ilnqd3ldDQRWqAxh0c5MWmUNt5SQGJUD73rJm1hAktTtuWSadUmvfleUblYc9HVTmugT5ItN8nX4XvCuDEtBUZHPz5/TuMpkXDWmUF24huaoG1n8kr6/ezHsENn0mS/GMoTIA4qLB5iDY6CXs1m44IODXB+F8mR59uKKezTkVvDRvN0a9jvl3j/T/PSlzOXUPuAJFa96RmgWrp0t9oV4XN3nKuR3OxWw0M77teJ/t7SLacWmXS5na1Sub0NWKUBXAAt96Xm92lcnr3MFK6Wyo+gO6YlQHymiitRi8VYdhgyelkwVPy0bkAKMeCphS2ixBYWAKk4sJtf7Ju5boaCbj0HjpVSrcJn/URxnhXZSziGBDMDqh46vdXzH7wGxWTl2J3Wk/ugVjeJJvu5ziPdIYPw5jt9pazcHKg27ltCprFZ/s/ASBQNfQjbqSsTwzbirVDVcxtlsCM7OSmbn3B0JNZjD8zsiuZlaWzmBDznXc800w0y471+/71FrsvLr5P+gTtvBZjg0RDO/veoEs6wK2F+/k0q4X8/L6l/n8rM+xOWxsKtrUbI3e7MzZ1Fpr2Vy8mdmZs1k8ZbFPBOeYMTWtgXxk+SOkh6fTNqItS3OXsjR3KQArVo9BOCK45KxUrhvejoTwYC54ewVT31+DLqgjoRlwRtoZAd/qy8lfArCleAtzDs6hOK8/QQmeVjPeEdSrfr2KlLAUvjnnGw5UHCAjMoMnVz5JnDmOyRmT6ZvQF4Cpv0yl2lbNg6c9yK9Zv/LeuPd4ad1LGHQGHh3yqO8BRKZJx83eedKj3ky99YzdM3h+7fM4FAcxwTFMzpjMZzs/44f9nr6Jk9pNIjms+b5zKvX2epyKs+mFObajjMbUV/iNDjmayQzo/vg8Ztw8hMEZJ+B70AwHimuICjFSUddUsCoqxMSXu75kV9kuxrcdT1JoEp/u/BRdkOd3qw+V0RiTzsQ7495xT5IRpggSzAlkVmaSV+Hr2FAjvJtzKsgpqyMu/QAGi4H0iHQ+nfQp2VXZjEkbDU9FySds/BTu3BIwgmTUG4nUH3uWSrw53l3v1yLqy2Heo5g2f46iM2KN7kxNl4uJWf0cQTlLsG6fAYnSIOgV2QGyV0NG4N+ODx9OJKQ6j7rrFmH+dAJCcciMDZ0ecteTMPiWJn0XE0MTEfoGHpzUnv+skY7WTYWbmDhzIi+PfJle8b0w6HV8ev0gOj/6qzvCqyiKuy3Uwt1FLNxdxDVD23Jm90QMOh1DO8Qyd3sBt36+gWVXhJP2/Y2eN1XLT4IipNM0MgUOIbN28jbCrI0egzd7hRTwGXGfNFa9Wvdkl9aSEB6MuVErpSMSliTr4PbNl/23O4yR73GisTVA/mYYfMtxvczMDbn8trOAf47qyIVvS22O1y/vh14IeqdGcuarSzDqdcy6/XS+XX+If43tRE6NR13/59AQCg0Gzq+uIU6NrntFeGssdu6ZsZlVmaUYI9ehOEIY+bJ87NNV2aw8UMpVQ9oyZWAaE6YtJaesjjl3jOC+b7cwonMcD09qgdNap5NK+96qzG36Qf+rjmtsPluVRVKkmQ+We9JCrx6WwJaKeWwrX4lO6Ny16yp6oaegtoAJ7Sbw3zP+6/PYsJRhVFgqWHbY0+7l14O/Mj97PiBVZH85PIORQ4azt3Ir5cWdaci/BGGo5NsNuTw4sZl00KOh+3nSCNn/uxT50enlmitvk2wJeKoIT4LqAmpci/OTpXBbYakAICZIBggOldW5RaPuGdeZx3/aweacOsypst3QgcoD2Bu6kxxrZnLvNnyz3pPG/vHKgzw0qStx5jjizNJ57R3hTfaO8DZUwpIXpUMGRba8jGqLae3b3G+YyNN2eR16ed6egAZvE4p2em5+chVrlW5Yh97NhX2kodpF5FIakkFoZBpkr5LXuNgOnraRnSfC/MdlfXufy+EzLydf4/panZeDMUY6ijGFyfPY+Cls/JSzItOgyhX4mn0XHFzq6uHscnqpQp15G6H7+SiGYJbuLeb9ZZms2F/C+X1TuGJIWwa0jZYlkkNvg9Vvw4h7Yc59fFXYjzdLPPXEJTVW4k1WmYlgCpGZNFtm+HaOATi0Rn63VWO+kcHbYHPQYHMwsd3EJkOsEzr3Oi6nVDqsFaccP+/srsYCYpMzJvNL5i80Jqc6h0NVh1h6eClXdLuiyeMngzp74Iyi1mHwWmtgzgPQcZy8sNV5pfwdT23qP1bArtkeT5N3vz8vkaeC2gJMehMxwTHsKdtDRlSGb6qfENBpvFRWnPCc/FG1QLTqh30/kByWzB2L7gBwL7qqbdXcvuB21hWs4/kRzzOszTCqrFVHViYNT5ZCXCAngYJtx+0lfmzFYyzIWdBke/W+B1Ds0Vw8IJVLT0tzL8rvj7+f+0+7n+yqbK6acxUrS6WasSF8Oz9u7sS0y/oBUr02wuV5L6mt4LIf7sEQuR6DCMLumjusuiLmHJQ/wpfXy5XGb1m/MWPPDCwOC3MOzmF4m+E4FAdTu01lxu4ZnJVxFjtLd/LUqqd8jnf6lunc0f8OjDojwYbm08ScihOH09G8TD3w8faPSQ1PZdaBWQB0iurieQ1bJDcO7cONIzJI9FI/fPKcHjwwcytOSxumJHzIeR1kJsHrC/bx85Y8Hju7OyM7ezyftRY7QY72vDbsO67/YC/2ms7ojOXoQw9gilmJ0xqNziRV9vJq8pg0cxJV1ip3c3aAr/fINhzdY7u7pejVtONpG6e564/O63AeveJ7eU5QCHnx/v0Jeb/D6CZjYHFYcDgdvLftPXcftxt73cglnS/hvA7nkR6RzuHqw0yZPYXr513PmPQxJIQkMLXbVL/pspuLNqMXep5f+zyZlZn8cv5vVFnqaB/t8t5GuXqKVh7ya/AeKm8+KvnKb3v55tYWZoQcIweKa4hN+x1L/R7qc272eSw61Minqz4F4Lqe19Ezric7SnewxXkQNXnIELofo87IqqmrmvQu7BDVga3FW7GZQ8DWG7WqRXUcv75wP+DkkHUFI1NGEhkUSWRQJB2iOvgKj9UUyEiJn8+0WcqzpeND6JpVyJ930TwfpchmcdjgiymQuxZ7u1GUT3qH+gYLCIEuqQcRc25Fv38ejHlInuj8x2Hde7INz+l3Be6fDbImyiU6F/KR17nOuNJT/tHzYhlN+OISOPNJSB/svtaO+maUO2ui2lZNta2a7/Z95/6dCCGICTVRVmuhusHGxGnLOFxR7+PwWLSnmFlb8kiONDPnzhH8uj0fEzbC5j/ge6wHFgECLnwXvrpMCuZtn9m0RQ3A+g9kS5uR9/lEtSx2B2e8vJgRneL47IbBgIwI3fDJOp49vxfd2zQzL+l0Moqx1+VU2+ZVv9VQdeKEGPM3yzmypZlZAXh1/l4OV9Qzb4fHWaRGFVUsdieXvruKijob+ZUNjBzgWVz+O0Eu+ncGB1OgE/yjopIRUe3Ir6znpbl7mLu9AJsoY3RPWF0vr5HVex8Dh3TC7S+q4YlZO3hiluvzEVbOen0JoGNnfhUPTeza8shffDf5V7yr5SVaAWiwOXjsJ8935r+X9CHEpGdR2au8u0p+tk8OfZLsqmwGJQ9iQ+EGusV0Y3bmbBYdWsSQZP/lT0Pb+H5ec7M8ztcZe+Q8v7FEKuMbozbQKzWS3TULeX/z+RRWXsTDZ3UjPty3vcpRM/kVmdU35z4Z1U0dIA2H+jLpKDhVhCeBvZ5yVy9UNb37RKNmAqnO+hEvLSJD5PGm8Tvigz7icEU9epei/72L76XSUomlJo6MDMHwDrFcP7w9H66QTo9QP9lNpbUWYkJNPHN+T+LUEo2s5fCxy3kw4Vnfcrif7+LajZ/ypWMM+xXpAK+3Ouj/n/ncPqaj2/ity1rP07/ncev5Y2kX53Ja75HCm2VKGAnFqzibVcz+cQf8vAYTn5Ah8tgWfAbpEW2koy1vk69gW1xnmd68/DWZbXbIVXvaYQyc+3rgQWzTX/bPHfe0VKIvdpVEVbq6l6iipDu+l7XZZz4p75s8zvZdCZNYsjSTF37dTUJ4EKFBBr7fdJjvNx1m9cNjSYoMljX3a6bDd9dB/hbuYyE/if+jzNiGWquD/UU1xH/VXa7T/7kK3hwEqhHaYawnwltfBrtmeY7f6QSdDqdT4e5vNvPTZnm8WS8EdvAcrqhn5MsyNdpplc4SkzDz/rJMLh+UzoEKGVVuF9GOrKosru5+tV+DN6syi7N+OAuQ9onqKAFpv/RN6OtTF368KIpCgyNwhlzrMHgVRS6yLnoPPj3P1+CNPo7Bjm7nW8vrPTl5Kb2d9/1l1DlL+e+Iady37C6u7HYlDw5q1Nuw1xRZbL9ApoAoQRF8vfsrxrcd7xNZ3FW6i+zqbIa1GcbjKx/3eYl5WfMwNwwjJCLHLfhz9+K70QkdBmFg4ZSFzUq7E54oF7KKIusfaosgffDRjQkyjWZz8WaSQ5NZlLOI9NCuhNj6sXG/GaclAV1QEeO7dOXFi3oTFeK/mXjbiLbMvnA2+8r38fLa/2MHUlSitGY8P2/J48mfd7LsgdEkRQZz0VcvUGaUj88+fw6zt5TQYK/j/Zymtalf7Z6BzSm9kvsr9rvbtXy+63MAPtn5CQCpYank1kjvZv+E/ny952u+3vM1Z6SewZtj3wx47iX1JVz969VYHBYeOO0BPt/5Oe+Me4d3tr5Dvb2eW/vcSq21FpPexCsbXvF57r6KPTjqU0A4cVQM4V9XdnIb9SpTTkvjnD5tuPfbzXywpIDlu5fRKTGMlQdKKau18tDMrfx+7xnu1NvXF+zjvWWZnN4pHtDhtLTBaWmDoz4dFB2WkjMxxS4lLc5GpCGFGuMyrulxDTP2zKBXXC+eHPokU3+ZisVpYWfpThrz1e6v3Ld/3P+jr8EL8iKuGrx+BGYeWvoQv+dIQaYXRrxASlgKveN7oxM6usRIB0DH6I68OPJFXln/Cp/ulMbegYoDdInpwpi0MazMW8n4duOptFRyy3wZ9VHTacd8OwonNhZevJT40GgpQAFSWCfJ91gzi2tYvEfWmJvif8NRn4qjpjvDOsSy8oCM0sVHHOeC6wg4nQqZxbXoI37FEAoIGyie74De0MDhmsPc2f9OesbJlMiOUR3ZULjB53UmtZ/UxNgFmRHwwtoXMKdkUecIwVHbGYvdQYPNE60RxjJqHeVNswfKs+T/C9+TyrT7fz86g7e6EF7rDb0vk9fOJS9A9/NlenR8F59d/TqLHDZZE9X9fJmVU7Rberq3fSuV5C94B0uHs6G+3n0ttqUMpu60fxG24jlY+jKc8QAUupTPN3wkr3NXu9Qta0thzr0w4XnZgg5kyxlAMYUhrDU42gzENvQugmde6TmurGUydS5nJXw4HgZeT+IA2Z+7wdHAgcoDBOuD3XVNv2f/zqODH+XjHR9TbatGH1NAae3FvLFwP4cr5ALm2mHtaB8Xyo68Kt511U/WWOxY7U6CDXruNMwkumqX7DMbmQbvngF7fpHf7y6TZH/3qLZybHY3WoiU7JPjOOxfTVI4d+VLh9ayfSXYHU52F1SzM7+KTTkVPDBzC6O7JNAmysxlXk5KHy7/GjZ8Ihddq72US0v3nxilf1uDVKIWekg7+rkJZF3q2oNlHK6oplPX5ZSWpnH9kJ58vetH8nK7EZX+I5V54zm3yyDWZheSV1lGSLsvWGIpZcVqHYojgjOTL2VBsVSsXRxixobCvcltmEs4l727guzSOsIjDxOS+h6r6z2ZCuGd/4NO6OkbM5JDdTuwWEIozD2NyOQlWEURurqeJIakk3k4nDGvhDK8YyxPndsTve4Ihq9OJx3wVYf9lvscDZtyKty3z+kbRbVpPgZztPs6DXBOh3Pc15fTU2TQYGz6WLaXbqdnbE/8ER0czbkdzmXWgVnc2OtG3t/2Ph2jOrrn4MHJg1mTv4a2EW2ptlazu2YhZn0ouqQ5/JJbxd7v2vLyxBvonBhx5PEIhCFI/j7m3Ccjb6kDpGEEJ8zgVRSFX7cXYNLrSI8NYd72Aq4e2o46m52kiGD5u3EJpVbsmQ16TyT2RLOuUK6Lesb25jlXWv19hm84S78W629X0Dv4KrY1SAeJ2qImscHIv1aPhJQPePyci90Gr7e4GEBueR3btm9hYFoiZ/VyXS+dTvjZS/SpsQjYmMfQ7fieZ4Nnc2n5rQDM31VIvc3hE+0N+XgsLwB3zF/M6xd3g0OrUTZ9wRZnR/7PfhGfmKTD/Wy9NFrP068gSNj5NieMrn2TcCfA95rieW8h4Ly34P0z4XsvR/LEF5rXMAgKg2tcBmTj39bIe+GXez33vTMqvfRwJs8NxcluJvdO5v+m9GV/UQ2frc7ih02HuebDtbwxtR+dE5Oh0wR5HXfxS8SLlNywjjGvLmPHoRKG2hvknJWzymPsAiWT3yf028swdxkrVfu/8yr/qsyByDSyF36IfWsOIB0QBZUN0tD2w/TFHu0LW9npXDu4F+u2t+frdbuwORSmdpvKnvI9fH7W51RZqkiLSOPfg//NnrI95FTnsK5gHSNTR7Is15PRkVWZ5TZ4KxoqeHzl48SZ41g05cg1xy2lvM6GogusM9I6DN6wRNl03hwtFwf5W2SqxZXftVxYqqXEdoLSfSih8Zz94f+hF3rqdHKxfN+yuwD4Ye+vDE8ZzivrX+G98e/JL0GncdDjQhl5ADY7a3huzesszlnKrX1v5rOdn/HksCeZMlv+gO8beJ/P2zYUnkVIaCVFuRNB2LjjLCPd4jrxyf7/sKN0B3bsDPtqGFd0u4J7B97rX0wmPFl6zuvLYc27clvakcWqHE4Hep2eOlsdu8p2sb5gPW9udhmFimDXtnE4LSkkRgRx/vAUDpXV8cbl/Y84aUWYIhiQOIBre17J/Uu3YIpex4BnfgNhQx92kJkbOvHTgZ8oDZpHvDGDewbcR0pEAreMkNGVbTP+jdWhsK7ySwwh2dRl30RI2/dc4zWJaRdMZs2hfaSEJfHmLpnumByaTLAhmP+e/g77qzbTMboD1dZqrp17LQBLcpewtXgr7SPbE2YM40DFAT7Y/gFTukzhw20fEhEUwaFqqZp33xL5Gb264VW3B/vH/T9Sb6/nmu7X+Jzr+UlPMTdzEcU5Z4JiYEKPxCbGrorZpOfBiV1Ztq+EPYXV7CmUi9Srh7bls9XZDHp2Ae3jQgkLMlBY3YBTgaV7i+mTGsmWXJlieG6vLvy0OZyMuFAyi8dzwKUl1jVpBNaYZDrU9+H+UV146Ju9pCjP8dF1A/nXgn8xtM1QYs2xPLzsYSa2m8jcrLmEm8IZkjyEb/Z+w6biTTw+5HF3CjR6A1z2pfTONhIEOVR1yL2Iurzr5YxvNx6jzsjiPUU8+uN2Xrmkjzt9eFzbcYxrOw6b08aZ357pTnV+Ya0UAntr81s0OCzU2epBeCKDTmSU7JFFr/Hu2U+yIC+IsYBSkdNEJuTfP8h2YvExpTTESZXbDMMFXDKoHysPOsBp9mkPdDI4UFxDvVLknrDTOv9MqTUXW/kgzuyazovrngGgW0w3d0retT2uZebemTgUB4nBGQidjVt6u9I9LTWyzrDnhRDfjYs7X8xXu78iuyqbM/vWM2+FnfyKBuqCFxHebjbVu/+DPljW8XeJ6SLVZ+0NsnWaavAmdJcpWPsXSA9+S3DYYN9v8vbWrz3bd/4odQMu/lCm4gaKAq7/SKaNgYxYjrgH3vYyeLqfB30ug9qm3t36PtdhPrQE/faZ0uD1VjauyoO9v8kxiuss27tEt5NG9fc3Q8ke0BlouPRbDDtnYh1ypxSDuuoHqd3w4QRp+Hund67/kMSh//A5hnFtx/Fzpqwdr7JW0f9zL+PPDDtKu7ByvyfNPDbUxHl9UzDoPBF4m0Nhf1ENVQ02huh2kRnSm4xu50gHZeogafSrwiiqLkSn8bBimue9rHXwwy1Sm2Hov5qM1dbcCvftjo/I1jYxrgjQ9sNV7po+vU5g1AvO65OCzvs6HpYAZ9wva2y9Dd4V02QkvLv/kpRmqSvzLDSzl8vznPyqrE9rISv2l9A/PZqDJbVc/v4yqh15hKctp0BshDh4Zz9ghND2i7ABIWkfs7jhc+zxdp9SQZstgvrcq/hxXyrhrqG2uTIR6p0Obvrma0oivuDMnt1YU7SA+JAUesb15EDFAe4deC8vrH2B7KpsNpYuIjEkkTpyMbfJIim8LV1ixjE/ez75bMecAtkHYzm4OhWHU+He8V2ICDbKuu1A6PQeh94xUlln44PlmZhil/DQhN78mPkOizdKg6dLdBdeGvkSZoPZrzNNr9PTJ7550adnT3+WBwc9SLgxnJ5xPRmUNIjS+lJ+z/mdCztdyNe7v+ayrpexu3Q36wvXc3nXy3lg6QOsFwvJBi78YQmTUq/n6YljCTMdY11vRAqYY9zOLPI3y1T8lraRDECDzcGsLTJ69uBPi1FsUagZNK/Ml/X1E3okMmVgGn1COxIHVFirwRxM5clI/QfW5K+hQ2QHsor0vLs0EyN2glxzoqlgI1+3CWZCxf1U5/8LR7KMht5o2wJ6YMVrsOtn+ol+bFI6kVfZwJxt+W7j9sVfd7Ms6G7sxSagWF6HctdK59b4Z6VjwavuE4DQWOhyFoO3fMWeoFUMtbzB12s9Nf+KoiC8rs+J9sM4Zk1Hv+1rBPCz40rK24ykU24PfjQ9Rg+d1DC4VS+vrTvtKdzwfR4zVL+0MZjCqgYabA5u/GQ9Z3SO59GeF8H27+Tj/a5qUqNrczjRC0FRtYXrP15HUbWFST2TpLja2dOkQa+2J+17hRS4ekH+7krys3FflarzORjWnyklN+BEx7AOsfz34j6YDDq6t4ng+Qt7M7lXG/711UYmv76MZ87vyaVDbkU5vJ7/Oq+ksqaWZyzTCc+fw+WmLXw7L4cbXedV+vs0YnUGlNvWgRCcPX0TBVV38t2EoZhMv9Lb6slUqf76RsItRbSvyOZVo5G1lq4UE8XGnHLPZzl3N2nRIVzYP4WiKgsz1h/i4gGpjOwczx1fbWLWimQKq2QA6PuNuXzUZxBLLpVBtcigSIqqGpjS+TL0OsHcrLkcLNBhrriaUdGD+T3va/TmQxysOkj32O68tO4ld1ukkpb2MW4heRX1CF3gUihxsmoH/kwMHDhQWb/epcicuwHeHyPbo4y8r9nn1Vnt2J1KQMNDZe3BMrq3iZA9FusrwFbP4qJa/rXCUyPgtMaiMzWVYb+9z71c3v1CqZpWesDdJPu+YbcxL1/+iI06IzanjbMzzmZ25mzPa9oicNSnYa/pjr1SRpSvH96eeTsK3JGCgW2juWRgKkvL36TEms3O0p10ju7MY0Me8xglKjt+kO0NknrJdOakXnDzUt96Bi/sTjt7y/dyw7wbuKr7VawrWM96l0cRwFI0AXttRy7oPpQbR7QnPSaE0KBj87F8u/tHnl7zGLUHbyco4RcMoQex12ZgCM0kydSDr89/K2CN7XWfLGHx3nwyYpLIrMhB6GtxNqSBl8lz0aBQftyYz6hOGRh0MG9HCR0TwpjQI5HCKgs3jY4mNTKWiTMnUmGpQC/03Nz7ZuZnz3d7qFXig1MpbjhyT7U4Qze66m5n3q79OK2y7uPli3uTEBHM0IzY5hc3yHrTOqudqe+tIcig4+PrB/HL1jxmbjzMWi/xpSsGp7Mzv4pHJ3cjJjQIvRCYTXreX5bJP0Z1YMX+UtZnlzGyczz//Hwj9bamfWhHdIrjhtPbs2J/CWsOlhEXXYXVEk5M2lwaiodTr1RSFfoNFZYyGuwNfHX2V+6ep41xKk6eXvU0M/fNxGwwM+v8WSj2SN5Zksnewmp3NBUgPMjAwHbRjOmawFm9kokJNTEvczmH6/YyJHkIT616il1luzDrQ6XBm3c+hpBMHPXtQDgITvoJxRaNMJYT6RxI7p4L2RV0PQ19ryX6gpfd71NSY2HQs78zbmARK2te9XvcHepfoKTSzPIHj18MJhAz1uXw2JL/Iyj+94D7CASLe9xBzIJn4eIPwBhCZkQCEUERPilDWOvgyyme2k6QHuRLPmLYzHFUW6uxVfXikdOe58Vd5yF0DqyH/sElI+r4Ofsz1qZNIWiJS0n8ul9lCtjvT8LDubDhY/jtUbh7h8ye2fUzjhVvYO81laDB1/oecOEO+GC8LC0xmD2e6at+gO9vkZkkIBVU79kpPeqNebKZeuB+V8FZL4PRTG1tLXV1ddTXy/cwm2UEM3Lr+5iWvQD/2iivsV0myzqzbD9K4HqTrNeyW6BNXxjyD2rTfT/z0FBXutrMG2UUFeTCqeOZsGY69Zd+waC10ok2InEQt/W7jQJLOXHmOK6cI6PDI1NHuuv1GwrOwVY+nAcmduHl+et5+pJ4ruwzhtxymVp2zdB2fLwyi+gQI+V1VrYG3cQs5zBOv/NTmfKXv1UeR9+pHrE4kCUqH03y3O92rhRtu/Rz6ObpzfjT5sO8v+wg2w6XEZ7+FfUVPbBXuSJewkab9I1UFPUmIzaaw+U2ymvlNeL+CV0YkhGDEMKtwOomd4Ns3ff9zR4F4fsPtNxQLTsov2O7Z8O1v8jyowVPw/Jp8FCO/+8J0gG7qWgTK/NWkhE6kOkr1rOnJJdwuhKk11Md8THCJBdaE9pNoMZWQ1RQFCadiR/2/8C5Hc5lxeEVtItsh9Vh5WDlQZ4Y+gRlpWnY7UbaxkRzzYdr0ZsPEpQ4G735ME5bODqjRywlwZxAmaWM6WdOZ3DyYLcIlSrC53A6CNIHsb5wPdtKtnFRp4sINYYyL2seVdYq3tj4hrt8xFYxAHtdBiER2fyr3x30a5NGn7QojPrm54eWYnc4cSgKKw+Ucv+3W6hwZGJu58liurn3zZTWl3L3gLt9RYROIVXWKv6z+CN+zf0Coa8HZzCdgs7lvxNvJiPmGFpBfXqeXKvdsgQ+PlvWvN+y9JiPz2J38MTPG5i552fXvPMz4Uo36qsyaNDvw1p2Ov3bhrFudww6YxlxQWms7LOQyw7/xJ4gE6lhqfx60a9HfqPG1JXJQI6fwI3VYWX4V8O5sNOFDN9RTZfsLwgTFkLxioCFJ/N2/595ad5OwrvJ2s1NmbkYhG999tLoi7g6X2Zo7XhqAiVF+WS+dxWjdS7D6pKPpTJyXals1fXAgcDaL2vehV/vB+Ah2420FYUki1Lust3Oae2i+WRgFiGzfR2G1YqZ2213sMTZm4+vG8TjP+1gUqqdXrv+S2dTCZ0dcg3WreFDEkQFS4LuwSEM/HrBNm7/cpN07Lt6Bs+7vgNdvhwsI/o3LwakkfvsL7vomRLJqy716P5to5m91SPiuuvpiZTUWEiJDEI34wqph9BVpuuq89MKRw+6PbSY3PI6enzcnU8aRvC0/Wr+fVZXbh7ZVKgUZMnIXTM2s2xfMW9N7c+nq7JYnVnG6+e15dx5w/2PIVDTZhgTy+8nt9w3oqnHQSd9PpePGcg1y+S8ZTFF81LtZB4xfokIieblmomU972V58bGsnhPMfd+v4se+hxygztzsNZEKPUsOc9C7NJHGVHxBLlKPB3iQ7l4QBovzpU9id+5agBdk8Ixm/QMenYBUwenc+Pp7bnqg7Vu+0PiJLLbk0ztNoUqa5W7hE/lhOnjAPN2FHDv2snsuH7jBkVRmigHn1SDVwgxEXgN6S96X1GUFxo9LlyPnwXUAdcqirKxuecKIWKAGUA7IAuYoihKeXPH4WPwAtQUy/o9r7Q5p+Lkgy1fkxQSj80aTkJoLM/Mn09+XQ7/HnE9KREx9EuPIjTIwJxtOXyzeQdvXHImv+3K5t+LXyY5qCenJQ3kwj4diAiBq356CJt5HUEiCrvNzA1dHubdzV/Q0TyW3ZavEToLOlMpQi+9Ef/qeyc39b4B8fOdzNDbeKbEayHmDMFhC0MfVITiCMZRn47OVIRSdBWTOg0mxKTnkcndyC2vo2NCOBV1VnbkVbGvsJr3lh10f/mEgAE9d3NY+RmLUs2lXaZwWtJpjE4bLdNsSg/A20NklFfoZIsbg68nt7S+FLPBzJJDy3h0xSPYFTtOxWMgKU4DiiOE4MopXN3nLDokhDK5V/JxqxAW1RUxceZEbGrvYxfhumSWXjGnWXGuijorDTYnEWYD1320jvSYEHqmRDJrSx57CqrRCahq8Minm/Q6zuyewLwdhW4Ro86JYUzqmcwhx1wWFX3kI7ceLKIJ0kVSlTcWi2k79pou6AxVhIZWU1VwOv267yOvNAiLLhujqYG6sj5Exe0i9+DpKI4wIs1Gpl85AL1OMKj98aWjqTicChf+byU6Ae9fPZBYrxYozZFXUU9BVQMmvY5NOeWEBhl4df5eCiobsB+h1ZNJr2Nin2DW2x8jIiiM0WmjmdptKmnhaSiKws6ynaSFp/G/zf/j812f0z1qAMaqs9m8PxyL3YHNIV//ov6p9E2L5KOVWfRsE8nag2UUVMnfiVEvsDkU+qVHsSu/CqNeR7X7s1O45YwOFFdbGNQuhgFto/lm0x6UoCxm5MiabMUewlWVtVwnoki4a5l7ofDID9v4Yk0OZ46ay5rCxX7PL0yfSMGeq9jyyNQjOsGOlbtmrGNBzV0MiU1idWXTfpH397yJwftX0mVXo8XReW9DP5coRH2FNHQPrQEEjH9GRmmrDsN62QasV3tPJKh613OEdX0MIRyMSh1Dnb2Gkoosftq1HrqcJaPzXc+WBmLlISlWVbRLXisAEnpAkaz5UxCImxd50gOtdfDRRE805ZzXpJaCtRbiO8s6une9Uqf7TIWB18vnq2KCGz6Bn6VOAf2uhE2y/ABDMNy/32dhFcjgDSvdgvlrr5T6GxfI9MbpjTQczNEyw0U91gHXul/XG7fBm71Knl9kuiyZSeoFr/UFWz0zRt3Gaemjyfhsioy8DbsddAaWGBRCKg8zILID79bs462dHxGr78Lo1PHsrZMZJABRQVHU2moZkTyeHvEZzN/WwLrtHUikjDXBt/OE/Trmh57DfRO6sGh3Ebec0YEebSL4xw+fYjaYMIhgciuK+Tz3WfTeKZMj74cxHoG5khoLZ7zyMxbDAfRBRZjiZSQ+yphMra2S+po2GML2Y9KZUFAw6cz0DDufbXnFFB3uizBUYjDYeOzM84gOMTKiUzyRZq/fR8UhWPqSFHsZeIOso2zJfPDRWVJ4BmSm0RXfeoRmbvLoQjicDmbs+oE5mfMxikj2Va2n0lbs5wU9PDr4Uapt1VzZ7Uq3JoPD6aC4vthHyV8VZlLbdKgs31fClR+sQW8+iCluIV10t7BX91/0wQW8OuJtxmWMOHrRSC+qrdXMzZrLxsKN/JI5BwV5HIojGMVpwqBEMCrxUk5L6cVlffs1UfEOhN3hpLLexqa8bN5a8xNj0sYye89aDlXlYXPaiYjbjtOYi0Gn5599/0mXmC4MazPsmM7hZLC3NJPnl3/Ivso9VCpy4R3u7EmHsIGc3/007A0JdIyLY0DbIzhVfn8KVr4uHXkfjJfBD6/fBMgSgpJqC9sOV7KjeB9VDTUcKC2nylaOU19KnaOSOH0Pcut2U+8sQzHv9nF6NIfDEs/khEdYXvkANUil4y/P+pIOUR181N4DUnoAFj0nI5Xj/gPD72iyy7rDK7n+91t4zdCeMfuW+D547ptynffLPeQnjWZo1o08emkDQQv+x9k1e4m6dQ5i3r+lEJOLTGcSd9pu56roHYypnUOccEWldQZwerWf6XelTB0OxKG18MG4JpvXOrvwvv0s3jX9X5PHzrE8Q0r3YVw9rC3DOsjPVlEUduRV0V2fi+7zC1ieciM/6CdQU1vDS7lX8Hj95fzk9Fzf+6dHsT2vigHp0Tw0JJie7ZIoUiKptdh5f9lBvl7Xsv7hN41oT+fEcIZ1jEMvBNN+30vnGB2JC++mlzjI/SmfUZK1jQVB9/O87XL6Xf4EE3okNbsGrrHYGfvKYgqrPBlku/8zkeBVr8Ki56lrfyYhmfOwRGawIO12PttYyhZnB+qQ166+aVEMah/D+8syudrlHAV43vAelxsWMdbyMokZvflktBXj8pcgaxmVSiiRwndeq1BCmW4/h9uNswhDloW9b7qC3bVh/DN+O+37jWZzmylc8ME21ICRUK9N6GgfF8pBl2Ph7N7JrM8qZ0DbaJbUPIoIPuTaX6CgkBCSQFFdEc+d/hzndGjUy/0YeXvpZv538Cq2X7v91Bq8Qgg9sBcYB+QC64DLFUXZ6bXPWcC/kAbvYOA1RVEGN/dcIcRLQJmiKC8IIR4CohVFaVQQ60ubzh2Vm996maLacurs5VzQ/QyqLTUEm2BTwU62FG8j29J8z0/FacRgzaB7bB82ly1GZypG1PXGqS9GF+xpGO1oSERnaEAYKhmZeCFvTXyqSYuBlftL2F9UzfzM9awr/wG9OQedsZLkoK6khKWxsWQpNks0UZV3UGevIizIhNmkJ6t2M0nGPpzevjPF1RYemNiFzonNKyhb7U53VO5gSQ2L9hRjU6oISvoJY/hOEA7CyKB9WG96x/dgRGovhmT9hC15IFUpIymvr2JH2VZW5m6g3FLB2tI5gOL6kxOwoepMTI52GHQGJnYcSkq0mSkDU33aOZwINhdt5stdX9IrvhfnZFzAtHX/48Iuk+id4L9mqKUoikKDzYnZpCe3vI5go564sCD2F1VTZ3VQVmvlH+7Ip0L7eBNFVQ3o23yCzaHgyLsBndAxNCOWzJJaeqVEUmd1sGJ/CcM7xrF0b7HbWDToBKO6xLMpp4IhHWL591ndMBv17rTBPysNNgcLdhURF2aiV2okP27Ko3/bKJ6ZvYszuyVgNOh44dfdWGxOHKYDxLb7hQby0AsDw5LORCcEi/M8tSn62sFU5JyPEIKL+6cSHWriqiFtMZv0HvELF4qisHhvMZnFtRwqq2N3QRXltTYGtItmV34VvVIiGdstkfBgA/3SovxOLIsyt/PoynupcuQDCk+UlDK4/dWY+k5l7gErLy04yNn9i/m94V3CHA7OqK2hyGjiDCtcUF3D+/FJvGOyElofQ1T1ZdwyfBi9UqIJCTLgdCgkRZox6IXreEFBwWapZvb61xgYnEjb2K5SRMgYIsUsrLWyTULfKyCuIxabjaWZmdw793mUqA18kF+IA1gXlchCvZVig4E2Njtf5hVgBNkP/PR7ZCbG4hdk24WR90khoo2fSAMzbTCcfrdMLVPZ+BkcWMiLBYv5PCJwSuBNlbXcEZwuF4S/3AebXUbm6EdkWrCiyIVi7lrXMwT1U2YQ/NNNiMTuUqzswCIpYFSWKVOWI1Nla67GbPpC9sHUG6VoB8hFU/tRUplzuSviftMiWQeqKNIQikpvksIZyOANCTYRuvw5z+s/Xi4zV0r2g7VaGrmH1kmjtHi3jPDGdXYbZgENXpCRyOh2HiOuLBNeb3k94BOdB/K9TUa5zeipx8EoEUaxcFILZDnrXCMsGBnZB93BfZxnyyLy9Nf4x+oSqnQbMYRvh6ohtImMpED/g8/rd68P4+b6alYawrggrjuh459GEVBlbWDpvkO8v2EujvCl6AxS3dJsMKMTOmptvuds1BlJDU+l1lZLUV0RjXHUpyH0tRjsbckI60FMSAQx5jCyqw/QPbY7ZxbPJ+3A9+iS+mPucRGhXScRFNEGnd0i692qC2TZg9MOh9ejfH4R+cPvItNZR/z6j0h22Al3KjjGPkVBr6v5dMtclh9eQW7DFhR9JU5rFMJQh9Mag652IImmLuQbv6RNUC/GdxzA1vLl7K7YwD/7/Ivre1/Z5PiPlq25FRwsqWXl/lIePqsrW/IOUVpfzYW9+h73a3uzu2w35Q3lmPUhvLruXRx2A7srtmEV0qjX25PpEDqYTtHt6JXUljX5a8EZQlpEG4rqCjARzZ7SgzTYoKD+IHUcQhhq3Z+3N52iujA4+TQu6XJJwAydPwvfbl/Kp1t/JMu6CITH4FIUgd4Ri9MRRKQhjRCjmZjgaDpFZ5AYkkJ6dBQdbOW0/eESghQFvTGEw9cuZUdNPXtKcjlYcZgthbsosu4CQxVCX4fQNd9PVGCgY1g/xmUMJ8Ycxrh249hRsgOn4iSvNo8aaw1dYrrwzOpnSAhJYEvxFvdzL6+sZmZ4FFadAx06XhrxCoOSTic0SOeTOu50KjJ4UbCV+F+uJ7j2sPsxe9uROGM7ggL2uI4UB4fy4tY3WCEsLM/OxeQ0kNnvfrr1GiQzVzpPkD1fZ94I++Zxv+1mLCKYl/Vvc7DdpXS9zlWOULBdZmgU+QrfLVX60GXUFSS2aSszW1a/JTuL1BTBxOeb7zJit0oHZpv+MjU4LAnW/M/39dNuJcasp3DXSraf/hbDuiRzWrtmggGK4uNEyyqp5cW5u9ldUM0F/VIwGXSM757IygOlPDFrBw6n4sqW8QRQLh2YRpekcEwGqYb//JxdDGwXQ++USN5dlunW91BpGxtCm0gzqzJlRtot+p952PgV65yd6aU7SJ0SzPSO03nwisktqjnfmVfF6wv20Tc9ioFtoxmonq+1Vq4ZyrMhLBGrMPH07B0s2FXEI5O70TkxnNRoMyEmA3VWOyEmA8v2FbNkTzHpkXrmL11OtjGDn24bTnSoSb7Oa54WScvMoxnKdhx2GwZHLXqnjbyIvrRJy5BCXH7IMmawoaENTkVHd1028foaZjKWMFsZMSEGhhj2EpvcHkd0O2rKi7ntQDKF6Qep1GVxieUCQg25WHLsfJmxk6TwWN4Z9z+Sw/wHxmwOJ1a7s0XZofd8/SzzLV//IQbvUOBJRVEmuO4/DKAoyvNe+7wDLFYU5SvX/T3AKGT01u9z1X0URckXQiS7nu+reNIIc3uz0vHJwNLnij0Ug7UjTkcwDlMOPaOGYFPqCDLCVb0n89m2WUQGhbK6cAkOXQUogiB9CDolBEVx8sjQh/hx768U1RVR2lCEXbHy5OCXOKdr8yqSDqfCvqJqMotreH7lW5QqG13tRRTOTnic5yad7xonqRa5I6+SHm0ifft3HSWlNRaqGuws2FXI7oJy9tbP55B1GTbDYYSQkVpFEbi9N67UFrlNh7CkE6p0xCCCOaf9BbSJSGBU53jSYlrglfwL02BzYLE5+XlrHp+vzqZDQhhmo56EcBO3nNHRHdFwOBX3xc3pVNDpBLUWu9sYstqdJ9wR8Geiss7G+8szeW9ZJlbKMcXNwxC+E6FvwGmLxF7VE6O1K8PaDOPcvqn0T49295482dicNlDg7JkXkVfvabehUxQE4BCCNjY7X+QVeLUZSZR1+dZq5lRn8lBcFIrromxUFHSKglMIFMCJyxXk56JtVBT0ikd3WHH9vhTXrg7A6XrehdU1PJUwUtbM5m/2vIjOCL2nSKPgtJs8pQY1RVKV97BLuMoUBuf/r9l6SWXjZ9QveYnPqWCvyUiCw8H42jrei4pke5CJGTUGkq7/XdZk2uplGmzOKhj9qKzDApkps3++TOO11lIblIBh4wcELXxC1rTqTVK87/S75QLrSDgdkLkIlrwMh1Z7trfpB5NehrTTAj/XRUCDNyREGqmrp8uIsBoNbyHNGrz+WPW2jIyHxkvnRHwXyFktdRKylsmejYc3wuq3sZZlsijETKrdQXubIN5DuwAADYFJREFUjd/j05lIKCa7hbqqXKZFhDC4oYFvw8PYEhxETYASE/ex6RIJN0Zh1kfQM7Y3P2d/AkdYsCcEp/DwkPuotdVyesrp2J12iuqKCDYEsyZ/DRd3vhi90GPQGbA6rZQ3lFNvr2fJoSWEmcLYWLCZvWUHMRsi2VKyGkU0bakVCJNTQY+CA4FTyN+QI0A0RK8ovo85QogQXRmWOI5JGeOICDZSY7EzJCOWYKOekhqLjwOtxf1t/+TYHDbmHljK6kN7+D1nPrXiAEIceS2nU4IJ16di0Om5qc+V7C3fT2JoHIPa9MShODgt6bQm0ew/O1aHlV1FeXy3fRl6UzV7Sw+RW5ONQScotWXhxIYiLO61TItQDCSYOhEblESkOYQ24bEMTulJvb2e7rHd0Ov0JIcms7N0J+nh6SSFNh/Bc7+s6/u3oXADTy38gkNlpXxSkUmEspM7EuM5aPJdG5ic7pkCz6pM3rNiREFgxIZTyBwAp9cx6BWFfsXt2F56GVcPbcft5w5veoy2BnjrNKlUDVQEtSH8pl/QxzVyduz7HQQ4czdSljyS2rhePj2rj5uKQ7DrZxpWvsPW9Cvpff49BBv17jXUiaS81soHyw+yZG8xbWND6J0aSdekCEZ0igv4GVrsDnJK60iLCeG9pZks31/C5kMVWOxOIoIN9G8bzbguMYyv+o6ILR9i6Dga3Zh/I6LbntBjPxZqLHYURfFdd+6cJeeikBjZuslhk1md+3+nqqqcsH5T0Ol1sOdXlO+upzBlPIlXvovYOQvyN2Nf/wk6Ww06FOzGMHQRyehK9+EUenSKQ2Y61ZW72r8KKWKIvLZ7j/BLMVF8Fil1O/SKgqHRJUz43Fb8bvfGKgThTifLbth1yg3ei4GJiqLc6Lp/FTBYUZTbvfaZDbygKMpy1/0FwINIg9fvc4UQFYqiRHm9RrmiKI2Kh0AIcTOgSrH1BLaf+LP8SxMHnNiK8b822ng0RRuTpmhj4os2Hk3RxqQp2pj4oo1HU7QxaYo2Jr5o49EUbUya0lZRlPjGG0+mSrM/I7yxdR1on5Y8t1kURXkXeBdACLHen7XfmtHGxBdtPJqijUlTtDHxRRuPpmhj0hRtTHzRxqMp2pg0RRsTX7TxaIo2Ji3nZOau5AJpXvdTgbwW7tPccwtdqcy4/jctJtLQ0NDQ0NDQ0NDQ0NBo9ZxMg3cd0EkI0V4IYQIuA2Y12mcWcLWQDAEqFUXJP8JzZwFqE9NrgJ9O4jloaGhoaGhoaGhoaGho/EU5aSnNiqLYhRC3A/OQrYU+VBRlhxDiVtfj04E5SIXm/ci2RNc191zXS78AfCOEuAHIAS5pweG8e+LO7G+DNia+aOPRFG1MmqKNiS/aeDRFG5OmaGPiizYeTdHGpCnamPiijUdTtDFpISe1D6+GhoaGhoaGhoaGhoaGxh/FX0t/XkNDQ0NDQ0NDQ0NDQ0OjhWgGr4aGhoaGhoaGhoaGhsbfkr+1wSuEmCiE2COE2C+EeOiPPp5ThRDiQyFEkRBiu9e2GCHEfCHEPtf/aK/HHnaN0R4hxIQ/5qhPHkKINCHEIiHELiHEDiHEna7trXlMgoUQa4UQW1xj8pRre6sdExUhhF4IscnVJ7zVj4kQIksIsU0IsVkIsd61rdWOiRAiSgjxnRBit+uaMrSVj0cX13dD/asSQtzVysfkbtd1dbsQ4ivX9bbVjgeAEOJO13jsEELc5drWqsbkRK3NhBADXNfk/UKI14UQ/lp5/iUIMCaXuL4nTiHEwEb7/63HJMB4vOyab7YKIX4QQkR5Pfa3Ho8TiqIof8s/pNjVASADMAFbgO5/9HGdonMfCfQHtnttewl4yHX7IeBF1+3urrEJAtq7xkz/R5/DCR6PZKC/63Y4sNd13q15TAQQ5rptBNYAQ1rzmHiNzT3Al8Bs1/1WPSZAFhDXaFurHRPgE+BG120TENWax6PR2OiBAqBtax0TIAU4CJhd978Brm2t4+E6x57AdiAEKZb6O9CptY0JJ2htBqwFhiLn8V+BSX/0uZ3gMekGdAEWAwO9tv/txyTAeIwHDK7bL7a278iJ+vs7R3gHAfsVRclUFMUKfA2c9wcf0ylBUZSlQFmjzechF2q4/p/vtf1rRVEsiqIcRCpmDzoVx3mqUBQlX1GUja7b1cAu5KKkNY+JoihKjeuu0fWn0IrHBEAIkQpMBt732tyqxyQArXJMhBARyAXJBwCKolgVRamglY6HH8YCBxRFyaZ1j4kBMAshDEgjL4/WPR7dgNWKotQpimIHlgAX0MrG5ESszYQQyUCEoiirFGnZfOr1nL8c/sZEUZRdiqLs8bP7335MAozHb67fDcBqINV1+28/HieSv7PBmwIc8rqf69rWWklUZI9jXP8TXNtb1TgJIdoB/ZARzVY9JkKm7m4GioD5iqK0+jEBpgEPAE6vba19TBTgNyHEBiHEza5trXVMMoBi4CMh097fF0KE0nrHozGXAV+5brfKMVEU5TDwX2TbxHygUlGU32il4+FiOzBSCBErhAhBtqNMo3WPicrRjkGK63bj7a0BbUzgemTEFrTxOCr+zgavv3x1rQdTU1rNOAkhwoCZwF2KolQ1t6ufbX+7MVEUxaEoSl+kt3CQEKJnM7v/7cdECHE2UKQoyoaWPsXPtr/VmLgYrihKf2AScJsQYmQz+/7dx8SATDf7n6Io/YBaZBpiIP7u4+FGCGECzgW+PdKufrb9bcbEVYN5HjLFsA0QKoS4srmn+Nn2txkPkBE7ZCrmfGAuMg3T3sxT/vZj0gICjUFrHptWPSZCiEeQv5sv1E1+dms143G0/J0N3lykB1ElFZlW1FopdKU54Ppf5NreKsZJCGFEGrtfKIryvWtzqx4TFVdK5mJgIq17TIYD5wohspAlEGOEEJ/TuscERVHyXP+LgB+QqYWtdUxygVxXNgTAd0gDuLWOhzeTgI2KohS67rfWMTkTOKgoSrGiKDbge2AYrXc8AFAU5QNFUforijISmbK5j1Y+Ji6Odgxy8aS0em9vDbTaMRFCXAOcDVzhSlOGVjwex8Lf2eBdB3QSQrR3eZ4vA2b9wcf0RzILuMZ1+xrgJ6/tlwkhgoQQ7ZFCEmv/gOM7abjU6T4AdimK8qrXQ615TOJVpT8hhBm5SNtNKx4TRVEeVhQlVVGUdsjrxUJFUa6kFY+JECJUCBGu3kaKZ2ynlY6JoigFwCEhRBfXprHATlrpeDTicjzpzNB6xyQHGCKECHHNPWORuhGtdTwAEEIkuP6nAxcivyutekxcHNUYuNKeq4UQQ1zfr6u9nvN3p1WOiRBiIvAgcK6iKHVeD7XK8ThmTpT61Z/xD1knshepXPbIH308p/C8v0LWDtmQnp4bgFhgAdKrugCI8dr/EdcY7eFvqOQGnI5M59gKbHb9ndXKx6Q3sMk1JtuBx13bW+2YNBqfUXhUmlvtmCBrVre4/nao19FWPiZ9gfWu386PQHRrHg/XOYYApUCk17ZWOybAU0gH4nbgM6SKaqsdD9c5LkM6h7YAY1vjd4QTtDYDBrq+WweANwHxR5/bCR6TC1y3LUAhMK+1jEmA8diPrNXd7Pqb3lrG40T+CdfAaGhoaGhoaGhoaGhoaGj8rfg7pzRraGhoaGhoaGhoaGhotGI0g1dDQ0NDQ0NDQ0NDQ0Pjb4lm8GpoaGhoaGhoaGhoaGj8LdEMXg0NDQ0NDQ0NDQ0NDY2/JZrBq6GhoaGhoaGhoaGhofG3RDN4NTQ0NDQ0/gQIIRQhxCte9+8TQjx5lK9Rc5T7Zwkh4ly3HUKIzUKIHUKILUKIe4QQ2jpBQ0NDQ+MvjTaRaWhoaGho/DmwABeqBugfQL2iKH0VRekBjEP2K3/iDzoWDQ0NDQ2NE4Jm8GpoaGhoaPw5sAPvAnc3fkAI0VYIsUAIsdX1P921vb0QYpUQYp0Q4j+NnnO/a/tWIcRTR3MgiqIUATcDtwshxLGfkoaGhoaGxh+LZvBqaGhoaGj8eXgLuEIIEdlo+5vAp4qi9Aa+AF53bX8N+J+iKKcBBerOQojxQCdgENAXGCCEGHk0B6IoSiZynZBwDOehoaGhoaHxp0AzeDU0NDQ0NP4kKIpSBXwK3NHooaHAl67bnwGnu24PB77y2q4y3vW3CdgIdEUawEeLFt3V0NDQ0PhLY/ijD0BDQ0NDQ0PDh2lII/WjZvZRAtxWEcDziqK8c6wHIYTIABxA0bG+hoaGhoaGxh+NFuHV0NDQ0ND4E6EoShnwDXCD1+aVwGWu21cAy123VzTarjIPuF4IEQYghEgRQrQ4NVkIEQ9MB95UFMWfQa2hoaGhofGXQDN4NTQ0NDQ0/ny8AnirNd8BXCeE2ApcBdzp2n4ncJsQYh3grvtVFOU3ZAr0KiHENuA7IPwI72lW2xIBvwO/AUcldqWhoaGhofFnQ2iOWw0NDQ0NDQ0NDQ0NDY2/I1qEV0NDQ0NDQ0NDQ0NDQ+NviWbwamhoaGhoaGhoaGhoaPwt0QxeDQ0NDQ0NDQ0NDQ0Njb8lmsGroaGhoaGhoaGhoaGh8bdEM3g1NDQ0NDQ0NDQ0NDQ0/pZoBq+GhoaGhoaGhoaGhobG3xLN4NXQ0NDQ0NDQ0NDQ0ND4W/L/Tla4QUjdBCwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mean Eigenvector Centrality\n",
    "wStr='eigenvector'\n",
    "fig = plt.figure(figsize=(16, 4))\n",
    "yMax=0 ; nPeaks=10\n",
    "for k in dnad.keys():\n",
    "    nodeWeightSum = np.zeros(dnad[k].numNodes, dtype=float)\n",
    "    for w in range(dnad[k].numWinds):\n",
    "        nodeWeightSum+=[ dnad[k].nxGraphs[w].nodes[x][wStr] for x in range(dnad[k].numNodes) ]\n",
    "    nodeWeightSum/=dnad[k].numWinds\n",
    "    plt.plot(np.arange(dnad[k].numNodes), nodeWeightSum, label='%s' % (k))\n",
    "    yMax=np.max( (yMax,np.max(nodeWeightSum)) )\n",
    "    if k=='wt':\n",
    "        d=get_peak_locations(nodeWeightSum, nPeaks, sortByValue=False)\n",
    "        dHeight=np.repeat(yMax*0.98, len(d))\n",
    "        dDelta=yMax*-0.05 ; dShiftCriterion=50\n",
    "        for i in range(nPeaks):\n",
    "            if i>0 and d[i]-d[i-1] < dShiftCriterion:\n",
    "                dHeight[i]=dHeight[i-1]+dDelta\n",
    "            plt.axvline(d[i], linestyle='dotted', linewidth=0.4, color='grey') # vertical lines\n",
    "            plt.text(d[i], dHeight[i], name_node(dnad[k],d[i]), ha='center', va='center')\n",
    "\n",
    "#plt.axvline(507, linestyle='dotted', linewidth=0.4, color='grey') # vertical lines\n",
    "#plt.text(507, 0.075, name_node(dnad['wt'],507), ha='center', va='center')\n",
    "    \n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Mean Eigenvector Centrality')\n",
    "plt.xticks(np.arange(0,1300,100))\n",
    "fig.get_axes()[0].set_xlim((0,1300))\n",
    "fig.get_axes()[0].set_ylim((0,yMax))\n",
    "plt.xlabel('Node ID'); plt.ylabel('Sum')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#help(nx.draw_networkx)\n",
    "#dnad[k].nxGraphs[w].edges[0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'embed' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-6d07ff1dfaeb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;31m#    else:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;31m#        nodeLabels[x]=''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m     \u001b[0mnodePositions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0membed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membedding_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m \u001b[0medgeColors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m \u001b[0mdnad\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnxGraphs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0medges\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'btws'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdnad\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnxGraphs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0medges\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0medgeCmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mListedColormap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m91\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'embed' is not defined"
     ]
    }
   ],
   "source": [
    "k='wt' ; w=0\n",
    "\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "from sklearn.manifold import MDS\n",
    "from matplotlib.colors import ListedColormap\n",
    "from matplotlib.cm import ScalarMappable\n",
    "\n",
    "figSize=16\n",
    "fontSize=12\n",
    "labelFontSize=8\n",
    "# Computed 2D embedding of atom positions based on the computed distance matrix in DNAD.\n",
    "if False:\n",
    "    embed = MDS(n_components=2, dissimilarity='precomputed')\n",
    "    embed.fit_transform(dnad[k].distsAll[w])\n",
    "    print(embed.embedding_.shape)\n",
    "\n",
    "#Eliminate nodes that have zero neighbours.\n",
    "nodeIDs   = [ x for x in range(dnad[k].numNodes) if dnad[k].nxGraphs[w].nodes[x]['degree'] > 0]\n",
    "#nodeColors  = [ dnad[k].nxGraphs[w].nodes[x]['btws'] for x in nodeIDs]\n",
    "nodeColors  = [ consensusBWCC[x] for x in nodeIDs]\n",
    "nodeLabelBound=0.015\n",
    "nodeLabels = {} ; nodePositions={}\n",
    "for x in nodeIDs:\n",
    "#    if dnad[k].nxGraphs[w].nodes[x]['btws']>0.03:\n",
    "#        nodeLabels[x]=name_node(dnad[k],x)\n",
    "#    else:\n",
    "#        nodeLabels[x]=''\n",
    "    nodePositions[x]=embed.embedding_[x]\n",
    "edgeColors = [ dnad[k].nxGraphs[w].edges[x]['btws'] for x in dnad[k].nxGraphs[w].edges() ]\n",
    "edgeCmap=ListedColormap([ [0.0,0.0,0.0,x] for x in np.linspace(0.1,1,91)])\n",
    "\n",
    "fig = plt.figure(figsize=(figSize, figSize))\n",
    "nx.draw_networkx(dnad[k].nxGraphs[w], label='Map data using %s window %i, with some nodes labelled' % (k,w),\n",
    "                 with_labels=False, font_size=fontSize, pos=nodePositions,\n",
    "                 nodelist=nodeIDs, linewidths=0, labels=nodeLabels, node_size=20, node_color=nodeColors, cmap='viridis_r',\n",
    "                 width=1.0, edge_color=edgeColors, edge_cmap=edgeCmap)\n",
    "ax=fig.get_axes()[0]\n",
    "\n",
    "for x in nodeIDs:\n",
    "    #if consensusBWCC[x]>nodeLabelBound:\n",
    "    if dnad[k].nxGraphs[w].nodes[x]['btws']>nodeLabelBound:\n",
    "        plt.text(nodePositions[x][0], nodePositions[x][1], name_node(dnad[k],x),\n",
    "                 fontsize=labelFontSize, bbox={'facecolor': 'white', 'alpha': 0.7, 'pad': 1})\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "axInset1 = inset_axes(ax, width=\"30%\", height=\"2%\", loc='upper center')\n",
    "fig.colorbar(ScalarMappable(cmap='viridis_r'), cax=axInset1, orientation=\"horizontal\",\n",
    "             label='consensus edge-betweenness-weighted clustering coefficient', ticks=[])\n",
    "#axInset1.xaxis.set_ticks_position(\"bottom\")\n",
    "\n",
    "if False:\n",
    "    plt.show()\n",
    "else:\n",
    "    dataDir = \"./results/%s/%s/\" % (k, temperature)\n",
    "    outputFilename = dataDir + \"betweennessGraphLabelled.pdf\"\n",
    "    plt.savefig(outputFilename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Export standard network data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import vmdio\n",
    "#from importlib import reload  \n",
    "reload(vmdio)\n",
    "#from vmdio import export_vmdDataTcl\n",
    "\n",
    "for k in dnad.keys():\n",
    "    #outputX=range( mdU[k].atoms.n_atoms )\n",
    "    outputX=range( dnad[k].numNodes )\n",
    "    nodeWeightSum = np.zeros(dnad[k].numNodes, dtype=float)\n",
    "    for w in range(dnad[k].numWinds):\n",
    "        nodeWeightSum+=[ dnad[k].nxGraphs[w].nodes[x]['bwcc'] for x in range(dnad[k].numNodes) ]\n",
    "    nodeWeightSum/=dnad[k].numWinds\n",
    "    dataDir = \"./results/%s/%s/\" % (k, temperature)\n",
    "    outputFilename = dataDir + \"load_betweennessWeightedClusterCoefficient.tcl\"\n",
    "    selectionList=[ getSelFromNode(node, dnad[k].nodesAtmSel) for node in outputX ]\n",
    "    vmdio.export_vmdDataTcl(outputFilename, selectionList, nodeWeightSum )\n",
    "\n",
    "for k in dnad.keys():\n",
    "    #outputX=range( mdU[k].atoms.n_atoms )\n",
    "    outputX=range( dnad[k].numNodes )\n",
    "    nodeWeightSum = np.zeros(dnad[k].numNodes, dtype=float)\n",
    "    for w in range(dnad[k].numWinds):\n",
    "        nodeWeightSum+=[ dnad[k].nxGraphs[w].nodes[x]['btws'] for x in range(dnad[k].numNodes) ]\n",
    "    nodeWeightSum/=dnad[k].numWinds\n",
    "    dataDir = \"./results/%s/%s/\" % (k, temperature)\n",
    "    outputFilename = dataDir + \"load_nodeBetweenness.tcl\"\n",
    "    selectionList=[ getSelFromNode(node, dnad[k].nodesAtmSel) for node in outputX ]\n",
    "    vmdio.export_vmdDataTcl(outputFilename, selectionList, nodeWeightSum, storageString='occupancy')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "k='wt'\n",
    "#dnad[k].nodesAtmSel\n",
    "#mdU[k].atoms[0].index1\n",
    "np.repeat(0.02,5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Manual analysis of betweenness. Take mean betweenness of the above consensus edges from all windows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remap_set(setInput,nodeMap,keyFrom,keyTo):\n",
    "    countMissing=0\n",
    "    setOutput=set()\n",
    "    for s in setInput:\n",
    "        if s[0]>=len(nodeMap[keyFrom][keyTo]) or s[1]>=len(nodeMap[keyFrom][keyTo]):\n",
    "            countMissing+=1\n",
    "            continue\n",
    "        a=nodeMap[keyFrom][keyTo][ s[0] ]\n",
    "        b=nodeMap[keyFrom][keyTo][ s[1] ]\n",
    "        if a>=0 and b>=0:\n",
    "            setOutput.add( (a,b) )\n",
    "        else:\n",
    "            countMissing+=1\n",
    "    if countMissing>0:\n",
    "        print(\"    ...Note: remap_set between %s and %s has removed %i missing edges\" % (keyFrom,keyTo,countMissing) )\n",
    "    return setOutput"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "topFraction=0.05\n",
    "setsBetweenness=[]\n",
    "listKeyID=[]\n",
    "for key in dnad.keys():\n",
    "    #setsBetweenness[key]=[]\n",
    "    for w in range(dnad[key].numWinds):\n",
    "        numTop = int(topFraction*dnad[key].nxGraphs[w].number_of_edges())\n",
    "        s=set()\n",
    "        for k,v in islice(dnad[key].btws[w].items(),numTop):\n",
    "            s.add(k)\n",
    "        setsBetweenness.append(s)\n",
    "        listKeyID.append(key)\n",
    "\n",
    "nSets=len(setsBetweenness)\n",
    "#overlapsBetweenness=[]\n",
    "matOverlap=np.ones((nSets,nSets))\n",
    "for i, j in combinations(range(nSets),2):\n",
    "    iSet=setsBetweenness[i]\n",
    "    # construct nodemap\n",
    "    jSet=setsBetweenness[j]\n",
    "    #nodeMap[listKeyID[i]][listKeyID[j]][i]\n",
    "    jSetMapped = remap_set(jSet, nodeMap, listKeyID[i], listKeyID[j])\n",
    "    \n",
    "    matOverlap[i,j] = len(iSet.intersection(jSetMapped))*2/(len(iSet)+len(jSetMapped))\n",
    "    matOverlap[j,i] = len(jSetMapped.intersection(iSet))*2/(len(iSet)+len(jSetMapped))\n",
    "    #print( \"= = = overlap between windows %i and %i: %2.2f%% overlap\" % (i,j,100*overlap) )\n",
    "\n",
    "print( \"= = Mean overlaps of pairs between all windows: %2.2f%%\" % (100*np.mean(matOverlap)) )\n",
    "print( matOverlap )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Retrieve the top betweenness from all windows, then identify a consensus subset by intersection.\n",
    "# print(\"= = Dimensions of the correlation matrix (windows, node, node): %i %i %i \" % dnad.corrMatAll.shape )\n",
    "def calc_concensus_betweenness(dictDnad, key):\n",
    "    dnad = dictDnad[key]\n",
    "    meanNumEdges=np.mean([dnad.nxGraphs[x].number_of_edges() for x in range(dnad.numWinds) ])\n",
    "    numTop=int(meanNumEdges/20)\n",
    "    print(\"= = Retrieving top ~5%% (%i of ~%i edges) of node betweenness from all %i windows of %s ...\" \\\n",
    "        % (numTop,meanNumEdges, dnad.numWinds, key) )\n",
    "    setsBetweenness=[]\n",
    "    for window in range(dnad.numWinds):\n",
    "        s=set()\n",
    "        for k,v in islice(dnad.btws[window].items(),numTop):\n",
    "            s.add(k)\n",
    "        setsBetweenness.append(s)\n",
    "\n",
    "    overlapsBetweenness=[]\n",
    "    for i, j in combinations(setsBetweenness,2):\n",
    "        overlap = len(i.intersection(j))/numTop\n",
    "        print( \"= = = overlap between windows i and j: %2.2f%% overlap\" % (100*overlap) )\n",
    "        overlapsBetweenness.append( overlap )\n",
    "    print( \"= = Mean overlaps of pairs between windows: %2.2f%%\" % (100*np.mean(overlapsBetweenness)) )\n",
    "\n",
    "    setOverlapAll=setsBetweenness[0]\n",
    "    for i in range(1,dnad.numWinds):\n",
    "        setOverlapAll=setOverlapAll.intersection(setsBetweenness[i])\n",
    "    numOverlapAll=len(setOverlapAll)\n",
    "    print( \"= = %i edges found in top 5%% of all windows (or %2.2f%% of %i edges).\" % \\\n",
    "          (numOverlapAll, 100*numOverlapAll/numTop, numTop) )\n",
    "    \n",
    "    # Make a new ordered-dict based on the mean value of all windows.\n",
    "    tempDict={}\n",
    "    tempDictStd={}\n",
    "    for k in setOverlapAll:\n",
    "        meanValue=np.mean([ dnad.btws[x][k] for x in range(dnad.numWinds)] )\n",
    "        sigmaValue=np.std([ dnad.btws[x][k] for x in range(dnad.numWinds)] )\n",
    "        tempDict[k]=meanValue\n",
    "        tempDictStd[k]=sigmaValue\n",
    "\n",
    "    concensusBtws=OrderedDict()\n",
    "    concensusBtwsStd=OrderedDict()\n",
    "    for k in sorted(tempDict, key=tempDict.__getitem__, reverse=True):\n",
    "        concensusBtws[k]=tempDict[k]\n",
    "        concensusBtwsStd[k]=tempDictStd[k]\n",
    "        #print( \"...nodes %i ~ %i : %.4f +- %.4f\" % (k[0], k[1], tempDict[k], tempDictStd[k]) )\n",
    "    \n",
    "    return numOverlapAll, concensusBtws, concensusBtwsStd\n",
    "\n",
    "# n, cv, cs = calc_concensus_betweenness(dnad, 'E56K')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n, cv, cs = calc_concensus_betweenness(dnad, 'wt')\n",
    "\n",
    "for k,v in islice(cv.items(),10):\n",
    "    print(k,v,cs[k])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define some shorthand nodes for later visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:10:29.211185Z",
     "start_time": "2020-06-04T23:10:29.202288Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define the segID of the Ligand being studied.\n",
    "ligandSegID = \"ATP\"\n",
    "\n",
    "nodesATP1 = getNodeFromSel(\"segid \" + ligandSegID + \" and resid 1503\", dnad.nodesAtmSel, dnad.atomToNode)\n",
    "headNodeATP1 = getNodeFromSel(\"segid \" + ligandSegID + \" and name PB and resid 1503\", dnad.nodesAtmSel, dnad.atomToNode)\n",
    "print(\"Nodes for first ATP molecule: %s of %s\" % (headNodeATP1, nodesATP1))\n",
    "\n",
    "nodesATP2 = getNodeFromSel(\"segid \" + ligandSegID + \" and resid 1504\", dnad.nodesAtmSel, dnad.atomToNode)\n",
    "headNodeATP2 = getNodeFromSel(\"segid \" + ligandSegID + \" and name PB and resid 1504\", dnad.nodesAtmSel, dnad.atomToNode)\n",
    "print(\"Nodes for second ATP molecule: %s of %s\" % (headNodeATP2, nodesATP2))\n",
    "\n",
    "nodeAll = getNodeFromSel(\"all\", dnad.nodesAtmSel, dnad.atomToNode)\n",
    "nodeLabels=nodeAll[::10]\n",
    "nodeF508 = getNodeFromSel(\"protein and resid 508\", dnad.nodesAtmSel, dnad.atomToNode)\n",
    "nodeR352 = getNodeFromSel(\"protein and resid 352\", dnad.nodesAtmSel, dnad.atomToNode)\n",
    "#print(nodeLabels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-29T04:01:31.633150Z",
     "start_time": "2020-05-29T04:01:30.563342Z"
    }
   },
   "outputs": [],
   "source": [
    "w = create_widget()\n",
    "set_widget_representations(w)\n",
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-29T04:01:31.754040Z",
     "start_time": "2020-05-29T04:01:31.636810Z"
    }
   },
   "outputs": [],
   "source": [
    "# Plot highest betweennesses (edges most used by optimal paths)\n",
    "w.clear_representations()\n",
    "w.add_representation(\"cartoon\", selection='backbone and not 1451-1480')\n",
    "w.add_representation(\"hyperball\",\n",
    "        selection='508 and (not backbone or .CA or (PRO and .N))')\n",
    "w.add_representation(\"hyperball\",\n",
    "        selection='(not protein) and (not POPC)')\n",
    "\n",
    "distOnes=np.ones_like(dnad.distsAll)\n",
    "window=0\n",
    "# Retrieve information from one window. k denotes an edge between two nodes. v denotes betweenness.\n",
    "# Retrieve top 5% of such edges.\n",
    "# The dists argument controls the thickness of cylinders, so put the average correlation coefficient matrix here.\n",
    "# The win arguments seems just to be fed into the distsa argument, so put 0.\n",
    "meanCorMat = np.zeros( (1,dnad.numNodes,dnad.numNodes), dtype=np.float32)\n",
    "meanCorMat[0] = np.mean(dnad.corrMatAll,axis=0)\n",
    "maxMeanCorMat = np.max(meanCorMat)\n",
    "for k,v in islice(concensusBtws.items(),numTop):\n",
    "    #print(k,v, dnad.corrMatAll[0, k[0], k[1]])\n",
    "    viewPath(w, path=getPath(k[0], k[1], dnad.nodesAtmSel, dnad.preds), \n",
    "             dists=meanCorMat, maxDirectDist=maxMeanCorMat, nodesAtmSel=dnad.nodesAtmSel,\n",
    "             win=0, opacity=0.75, color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-29T04:01:31.759732Z",
     "start_time": "2020-05-29T04:01:31.756567Z"
    }
   },
   "outputs": [],
   "source": [
    "# Save the current visualization in a figue.\n",
    "w.download_image(filename='nglview_highestBetweenness.png', factor=4, trim=True, antialias=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-29T04:01:31.854567Z",
     "start_time": "2020-05-29T04:01:31.763392Z"
    }
   },
   "outputs": [],
   "source": [
    " w.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze Communities \n",
    "We keep the communities that have more than 1% of nodes in all windows. Then we group communities across replicas by largest intersection. This is needed because we have no guarantee that the same community will be assigned the same ID in different windows of the same simulation.\n",
    "\n",
    "We fin ally rank the communities by modularity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:10:35.825349Z",
     "start_time": "2020-06-04T23:10:35.722709Z"
    }
   },
   "outputs": [],
   "source": [
    "import networkx.algorithms.community.quality as nxquality\n",
    "\n",
    "# Creates a list of windows and order them according to graph modularity.\n",
    "windModul = []\n",
    "for window in range(dnad.numWinds):\n",
    "    modul = nxquality.modularity(dnad.nxGraphs[window], \n",
    "                         [ set(nodesList) for nodesList in dnad.nodesComm[window][\"commNodes\"].values()])\n",
    "    windModul.append((window, modul))\n",
    "    \n",
    "windModul.sort(key=lambda x:x[1], reverse=True)\n",
    "\n",
    "# Keep the window with the highest modularity as a reference for community matching\n",
    "refWindow = windModul[0][0]\n",
    "\n",
    "for wind, mod in windModul[:5]:\n",
    "    print( \"Window {} has modularity {:1.4f}.\".format(wind, mod) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:10:36.127259Z",
     "start_time": "2020-06-04T23:10:36.094895Z"
    }
   },
   "outputs": [],
   "source": [
    "cutoff = max(10, np.ceil(0.01*dnad.numNodes))\n",
    "\n",
    "def matchComm(mCommID, mWindow, refWindow, dnad, cutoff=1):\n",
    "    \"\"\"\n",
    "    Returns the community ID for the reference window that has the largest\n",
    "    intersection with the matching community at the matching window.\n",
    "    Communities at the reference window with less than *cutoff* percent of nodes\n",
    "    are ignored.\n",
    "    \"\"\"\n",
    "    \n",
    "    trgtComm = -1\n",
    "    intersectSize = 0\n",
    "    for commID in dnad.nodesComm[refWindow][\"commOrderSize\"]:\n",
    "        # Skip community if it has less than one percent of the nodes.\n",
    "        commSize = len(dnad.nodesComm[refWindow][\"commNodes\"][commID])\n",
    "        if commSize < cutoff:\n",
    "            continue\n",
    "        \n",
    "        tmpSize = len( set(dnad.nodesComm[refWindow][\"commNodes\"][commID]).intersection( \n",
    "            set(dnad.nodesComm[mWindow][\"commNodes\"][mCommID]) ) )\n",
    "        \n",
    "        # Selects the largets intersection\n",
    "        if intersectSize < tmpSize:\n",
    "            intersectSize = tmpSize\n",
    "            trgtComm = commID\n",
    "    \n",
    "    return trgtComm, intersectSize\n",
    "\n",
    "print(\"Using reference window {0} with highest modularity {1:<1.4}\".format(*windModul[0]))\n",
    "\n",
    "communities = defaultdict(list)\n",
    "for window in range(dnad.numWinds):\n",
    "    for commID in dnad.nodesComm[window][\"commOrderSize\"]:\n",
    "        \n",
    "        # Skip community if it has less than one percent of the nodes.\n",
    "        commSize = len(dnad.nodesComm[window][\"commNodes\"][commID])\n",
    "        if commSize < cutoff:\n",
    "            continue\n",
    "        \n",
    "        matchID, interSize = matchComm(commID, window, refWindow, dnad, cutoff)\n",
    "        \n",
    "        communities[matchID].append( (commID, interSize, window) )\n",
    "        \n",
    "communities = {key:val for (key,val) in communities.items() }\n",
    "communities.keys()\n",
    "\n",
    "# Creates a list of communities ID from the dictionary keys\n",
    "# Orders the keys according to mean intersection size over all windows.\n",
    "tmpList = []\n",
    "for key,val in communities.items():\n",
    "    tmpList.append((key, np.mean([pair[1] for pair in val]), len(val)))\n",
    "tmpList.sort(key=lambda x:x[1], reverse=True)\n",
    "tmpList\n",
    "\n",
    "# Creates a pandas data frame for plotting and analysis\n",
    "commList = []\n",
    "genCommID = 0\n",
    "for key in [x[0] for x in tmpList]:\n",
    "    val = communities[key]\n",
    "    for valList in val:\n",
    "        commList.append( [genCommID, *valList ] )\n",
    "    genCommID += 1\n",
    "\n",
    "commDF = pd.DataFrame(data=commList, columns=[\"genCommID\",\"commID\",\"interSize\",\"Window\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:10:36.816115Z",
     "start_time": "2020-06-04T23:10:36.794165Z"
    }
   },
   "outputs": [],
   "source": [
    "# Changes \"genCommID\" for communities that are matched to the same community in the reference window.\n",
    "c = commDF.groupby([\"genCommID\",\"Window\"]).cumcount()\n",
    "c *= 0.1\n",
    "commDF[ \"genCommID\" ] += c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:10:37.185313Z",
     "start_time": "2020-06-04T23:10:37.122868Z"
    }
   },
   "outputs": [],
   "source": [
    "# Creates a NumPy 2D array to organize data and transform it in a pandas DF.\n",
    "# Not pretty but its pynthon...\n",
    "nodeCommNP = np.empty([dnad.numNodes, dnad.numWinds])\n",
    "nodeCommNP.fill(-1)\n",
    "\n",
    "#Group by general community ID\n",
    "grpBy = commDF.groupby(\"genCommID\")\n",
    "for genCommID, group in grpBy:\n",
    "    for winIndx,commID in group[[\"Window\",\"commID\"]].values:\n",
    "        for node in range(dnad.numNodes):\n",
    "            if dnad.nxGraphs[winIndx].nodes[node][\"modularity\"] == commID:\n",
    "                nodeCommNP[node, winIndx] = genCommID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:10:37.328246Z",
     "start_time": "2020-06-04T23:10:37.314017Z"
    }
   },
   "outputs": [],
   "source": [
    "# Removes nodes that were not classified in a \"big-nough\" (bigger than 1%) cluster in *any* window.\n",
    "nodeCommDF = pd.DataFrame(data=nodeCommNP,columns=[\"Window\"+str(i) for i in range(dnad.numWinds)])\n",
    "nodeCommDF[\"Node\"] = [i for i in range(dnad.numNodes)]\n",
    "nodeCommDF = nodeCommDF[ nodeCommDF.min(1) >= 0]\n",
    "# So we don't get \"blank\"/empty areas in the plot\n",
    "nodeCommDF[\"NodePlot\"] = [i for i in range(len(np.unique(nodeCommDF[\"Node\"])))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:10:37.789366Z",
     "start_time": "2020-06-04T23:10:37.770084Z"
    }
   },
   "outputs": [],
   "source": [
    "# Checks that target nodes are classified in ALL windows\n",
    "nodeCommDF.loc[ nodeCommDF[\"Node\"].isin(nodesATP1) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:10:38.201629Z",
     "start_time": "2020-06-04T23:10:38.170785Z"
    }
   },
   "outputs": [],
   "source": [
    "# Melts for plotting.\n",
    "nodeCommDFmelt = nodeCommDF.melt(id_vars=[\"Node\",\"NodePlot\"], value_name=\"Cluster\", var_name=\"Window\")\n",
    "# Makes it easier to plot\n",
    "nodeCommDFmelt[\"Cluster\"] = nodeCommDFmelt[\"Cluster\"].astype('category')\n",
    "# Makes it easier to plot\n",
    "for i in range(dnad.numWinds):\n",
    "    nodeCommDFmelt.replace(\"Window\"+str(i),i, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:10:38.792384Z",
     "start_time": "2020-06-04T23:10:38.762821Z"
    }
   },
   "outputs": [],
   "source": [
    "nodeCommDFmelt.loc[nodeCommDFmelt[\"Node\"].isin(nodesATP1)].groupby(\"Node\")[\"Cluster\"].apply(np.unique)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:10:39.108358Z",
     "start_time": "2020-06-04T23:10:39.098285Z"
    }
   },
   "outputs": [],
   "source": [
    "trgtClusters = np.unique( nodeCommDFmelt.loc[nodeCommDFmelt[\"Node\"].isin(nodesATP1), \"Cluster\"].values )\n",
    "trgtClusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:10:39.988933Z",
     "start_time": "2020-06-04T23:10:39.535287Z"
    }
   },
   "outputs": [],
   "source": [
    "# Add readable info to nodes\n",
    "def getTagStr(i):\n",
    "    # Store atom names for residues with multiple nodes\n",
    "    if len(getNodeFromSel( getSelFromNode(i, dnad.nodesAtmSel), dnad.nodesAtmSel, dnad.atomToNode)) > 1:\n",
    "        atmStr = \":\" + dnad.nodesAtmSel.atoms[i].name\n",
    "    else:\n",
    "        atmStr = \"\"\n",
    "        \n",
    "    retStr = dnad.nodesAtmSel.atoms[i].resname.capitalize() + \\\n",
    "            \":\" + str(dnad.nodesAtmSel.atoms[i].resid) + \\\n",
    "            atmStr + \\\n",
    "            \"_\" + dnad.nodesAtmSel.atoms[i].segid\n",
    "            \n",
    "    return retStr\n",
    "\n",
    "nodeCommDFmelt['resid']     = np.vectorize(getTagStr)(nodeCommDFmelt[\"Node\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:10:40.109538Z",
     "start_time": "2020-06-04T23:10:40.058119Z"
    }
   },
   "outputs": [],
   "source": [
    "# Write data for Ploting (plots from ggplot in R are much better!)\n",
    "nodeCommDFmelt.to_csv(os.path.join(workDir, \"cluster.csv\"),index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:10:40.336573Z",
     "start_time": "2020-06-04T23:10:40.273435Z"
    }
   },
   "outputs": [],
   "source": [
    "# Get all nodes that make contact with target nodes in any window\n",
    "contactNodes = np.unique( np.where( dnad.corrMatAll[:,nodesATP1,:] > 0 )[2] )\n",
    "contactNodesTrgts = list(nodesATP1)\n",
    "for node in contactNodes:\n",
    "    if len( set(trgtClusters).intersection( \n",
    "            set(np.unique(nodeCommDFmelt.loc[ nodeCommDFmelt[\"Node\"] == node].Cluster)) ) ) :\n",
    "        contactNodesTrgts.append(node)\n",
    "\n",
    "# Save data to file\n",
    "pd.DataFrame(contactNodesTrgts, columns=[\"contactNodesTrgts\"]).to_csv(\n",
    "    os.path.join(workDir, \"contactNodesTrgts.csv\"),index=False)\n",
    "\n",
    "pd.DataFrame(nodesATP1, columns=[\"nodesATP1\"]).to_csv(\n",
    "    os.path.join(workDir, \"nodesATP1.csv\"),index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare pandas data frame with community data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:10:42.968288Z",
     "start_time": "2020-06-04T23:10:42.962222Z"
    }
   },
   "outputs": [],
   "source": [
    "# Prepares variable names for multi-system comparisons. In this tutorial, we only have one system.\n",
    "system1 = \"all\"\n",
    "cDF = nodeCommDFmelt\n",
    "cDF[\"system\"] = system1\n",
    "refWindow1 = refWindow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:11:39.531285Z",
     "start_time": "2020-06-04T23:11:39.498328Z"
    }
   },
   "outputs": [],
   "source": [
    "# Loads VMD-compatible color scales to match community colors in R plots, NGLView, and VMD figures.\n",
    "comColorScale = getCommunityColors()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:11:40.648977Z",
     "start_time": "2020-06-04T23:11:40.577426Z"
    }
   },
   "outputs": [],
   "source": [
    "%%R -i workDir,refWindow,comColorScale -o colorValues,clusterIDs -w 800 -h 450\n",
    "\n",
    "dataPath = file.path(workDir, \"cluster.csv\")\n",
    "\n",
    "dt <- fread(dataPath)\n",
    "clusterIDs = dt[, unique(Cluster)]\n",
    "\n",
    "colourCount = length(unique(dt$Cluster))\n",
    "\n",
    "# We only have 50 availabl colors\n",
    "colourCount <- min(colourCount,50)\n",
    "\n",
    "rgbCodes <- data.table(comColorScale)\n",
    "\n",
    "colorValues <- sapply(seq(colourCount), function(x) rgb(rgbCodes[x, .(R,G,B) ],  maxColorValue = 255) )\n",
    "\n",
    "setorder(dt, Cluster)                      \n",
    "colorValues = setNames(colorValues, dt[, unique(Cluster)])\n",
    "colorValues           \n",
    "\n",
    "print(paste(\"Creating palette for\",colourCount,\"clusters\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:11:43.424320Z",
     "start_time": "2020-06-04T23:11:43.417006Z"
    }
   },
   "outputs": [],
   "source": [
    "# Builds a color dictionary from the cluster color scale built in R\n",
    "colorValDict = {}\n",
    "colorValDictRGB = {}\n",
    "for key,val in zip(clusterIDs, list(colorValues)):\n",
    "    colorValDict[key] = val\n",
    "\n",
    "for key,val in colorValDict.items():\n",
    "    colorValDictRGB[key] = tuple(int(val.lstrip('#')[i:i+2], 16) for i in (0, 2 ,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### View community in the structure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Connections to the ligand: \n",
    "First, we show all direct network connections between the ligand and surounding nodes from the active site. The next **two** jupyter notebook cell should be executed to show a sliding selection widget that will allow you to compare the network architectures of the acitve site in different simulation windows.\n",
    "\n",
    "Solid lines indicate connections between nodes in the same community, while dashed lines indicate connections between nodes of different communities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-29T04:43:25.251621Z",
     "start_time": "2020-05-29T04:43:24.528123Z"
    }
   },
   "outputs": [],
   "source": [
    "w = nv.show_mdanalysis(workUviz.select_atoms(\"all\"))\n",
    "\n",
    "w._remote_call(\"setSize\", target=\"Widget\", args=[\"800px\", \"600px\"])\n",
    "w.parameters = dict(theme='light')\n",
    "\n",
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-29T04:43:27.314671Z",
     "start_time": "2020-05-29T04:43:27.291089Z"
    }
   },
   "outputs": [],
   "source": [
    "windowSlider = widgets.IntSlider(\n",
    "    value=refWindow, min=0, max=dnad.numWinds-1, step=1, description='Window:', readout_format='d',\n",
    "    disabled=False, continuous_update=False, orientation='horizontal', readout=True )\n",
    "\n",
    "def updateCommByTarget(value):\n",
    "    w.clear_representations()\n",
    "    w.add_cartoon(\"backbone\")\n",
    "    #w.add_hyperball()\n",
    "    showCommunityByTarget(w, nodeCommDF, nodesATP1, windowSlider.value, dnad.nodesAtmSel, dnad, colorValDict)\n",
    "    \n",
    "windowSlider.observe(updateCommByTarget,  names='value')\n",
    "\n",
    "windowSlider"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-29T04:43:40.943133Z",
     "start_time": "2020-05-29T04:43:40.935999Z"
    }
   },
   "outputs": [],
   "source": [
    "w.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Entire communities:\n",
    "\n",
    "Second, we show all nodes from a chosen community. The following jupyter notebook cell different options to choose which community is shown. The first will select a community ID and show all nodes. The second allows you to select a set of nodes and show them in the structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-29T04:01:35.748681Z",
     "start_time": "2020-05-29T04:01:34.811521Z"
    }
   },
   "outputs": [],
   "source": [
    "w = nv.show_mdanalysis(workUviz.select_atoms(\"all\"))\n",
    "\n",
    "w._remote_call(\"setSize\", target=\"Widget\", args=[\"800px\", \"600px\"])\n",
    "w.parameters = dict(theme='light')\n",
    "\n",
    "opac = 1\n",
    "\n",
    "w.clear_representations()\n",
    "w.add_cartoon(\"protein\", color='lightgray', opacity=opac)\n",
    "#w.add_hyperball(ligandSegID)\n",
    "\n",
    "shapeCounter = [0]\n",
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-29T04:01:36.095702Z",
     "start_time": "2020-05-29T04:01:35.751685Z"
    }
   },
   "outputs": [],
   "source": [
    "# Dysplays all nodes of a cluster and colors them by cluster ID.\n",
    "\n",
    "clusterID = 0\n",
    "showCommunityByID(w, cDF, clusterID, system1, refWindow, \n",
    "                  shapeCounter, dnad.nodesAtmSel, colorValDictRGB, system1, refWindow1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-29T04:01:36.755540Z",
     "start_time": "2020-05-29T04:01:36.098710Z"
    }
   },
   "outputs": [],
   "source": [
    "# Dysplays a given list of nodes and colors them by cluster ID.\n",
    "\n",
    "clusterID = 0\n",
    "nodeList = cDF.loc[ (cDF.system == system1) & (cDF.Window == refWindow1) & (cDF.Cluster == clusterID) ].Node.values\n",
    "\n",
    "showCommunityByNodes(w, cDF, nodeList, system1, refWindow1, shapeCounter, dnad.nodesAtmSel, colorValDictRGB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-29T04:01:44.454763Z",
     "start_time": "2020-05-29T04:01:36.758085Z"
    }
   },
   "outputs": [],
   "source": [
    "# Color ALL nodes by community\n",
    "\n",
    "nodeList = cDF.loc[ (cDF.system == system1) & (cDF.Window == refWindow1) ].Node.values\n",
    "showCommunityByNodes(w, cDF, nodeList, system1, refWindow1, shapeCounter, dnad.nodesAtmSel, colorValDictRGB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-29T04:01:44.462432Z",
     "start_time": "2020-05-29T04:01:44.457746Z"
    }
   },
   "outputs": [],
   "source": [
    "w.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make more plots: Clustering of nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:11:51.340335Z",
     "start_time": "2020-06-04T23:11:51.335796Z"
    }
   },
   "outputs": [],
   "source": [
    "plotFilePrefix = \"test_\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:11:53.451259Z",
     "start_time": "2020-06-04T23:11:52.080711Z"
    }
   },
   "outputs": [],
   "source": [
    "%%R -i workDir,plotFilePrefix -w 1200 -h 500\n",
    "\n",
    "dataPath = file.path(workDir, \"cluster.csv\")\n",
    "plotPath = file.path(workDir, paste0(\"Plots/\",plotFilePrefix,\"Clusters_Node_vs_Window.png\"))\n",
    "\n",
    "dt <- fread(dataPath)\n",
    "dt <- dt[,.(NodePlot,Window,Cluster)]\n",
    "dt <- dt[, Cluster := as.factor(Cluster) ]\n",
    "\n",
    "p <- ggplot(dt) + \n",
    "    geom_raster(aes(x=NodePlot, y=Window, fill=Cluster)) + \n",
    "    scale_fill_manual(values = colorValues) +\n",
    "    labs(x=\"Node\", y=\"Window\") +\n",
    "    theme_bw(base_size=20)\n",
    "\n",
    "ggsave(plotPath, p, device=\"png\")\n",
    "\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:11:57.969450Z",
     "start_time": "2020-06-04T23:11:56.766480Z"
    }
   },
   "outputs": [],
   "source": [
    "%%R -i workDir,refWindow,nodeLabels,plotFilePrefix -w 2000 -h 500\n",
    "\n",
    "dataPath = file.path(workDir, \"cluster.csv\")\n",
    "plotPath = file.path(workDir, paste0(\"Plots/\",plotFilePrefix,\"Clusters_Node_vs_Window_Grouped.png\"))\n",
    "\n",
    "dt <- fread(dataPath)\n",
    "dt <- dt[, Cluster := as.factor(Cluster) ]\n",
    "\n",
    "setorder(dt, Cluster, Window)\n",
    "dt <- dt[, NodePlot := as.factor(NodePlot) ]\n",
    "\n",
    "# Get the actual indices of the nodes in the re-ordered x-axis (grouped by cluster)\n",
    "trgtIndices = data.table(trgt = c(which(dt[Window == refWindow]$Node %in% nodeLabels))  )\n",
    "\n",
    "breaksNodePlot = dt[Window == refWindow,][ Node %in% nodeLabels, NodePlot ]\n",
    "\n",
    "labelsNode = dt[Window == refWindow,][ Node %in% nodeLabels, Node ]\n",
    "\n",
    "# Build base plot\n",
    "p <- ggplot(dt) + \n",
    "    geom_raster(aes(x=NodePlot, y=Window, fill=Cluster)) \n",
    "\n",
    "# Add intercept lines for target nodes\n",
    "p <- p + geom_vline(data=trgtIndices, aes(xintercept=trgt), alpha=0.8, linetype = \"dashed\")\n",
    "p <- p + geom_hline(aes(yintercept=refWindow), alpha=0.9, linetype = \"dashed\")\n",
    "\n",
    "# Finish building plot\n",
    "p <- p + scale_fill_manual(values = colorValues) +\n",
    "    scale_x_discrete(limits=dt[Window == refWindow]$NodePlot, breaks=breaksNodePlot, labels=labelsNode) +\n",
    "    labs(x=\"Nodes\", y=\"Window\") +\n",
    "    theme_bw(base_size=20) +\n",
    "    theme(axis.text.x = element_text(angle = 90))\n",
    "\n",
    "#scale_x_discrete(limits=dt[Window == refWindow]$NodePlot, breaks=breaksNodePlot, labels=labelsNode) +\n",
    "\n",
    "ggsave(plotPath, p, device=\"png\")\n",
    "\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:12:02.374531Z",
     "start_time": "2020-06-04T23:12:01.374346Z"
    }
   },
   "outputs": [],
   "source": [
    "%%R -i workDir,refWindow,nodesATP1,contactNodesTrgts,plotFilePrefix -w 2000 -h 500\n",
    "\n",
    "dataPath = file.path(workDir, \"cluster.csv\")\n",
    "plotPath = file.path(workDir, paste0(\"Plots/\",plotFilePrefix,\"Clusters_Node_vs_Window_Grouped_ActiveSite.png\"))\n",
    "\n",
    "dt <- fread(dataPath)\n",
    "dt <- dt[,.(Node,Window,Cluster,resid)]\n",
    "dt <- dt[, Cluster := as.factor(Cluster) ]\n",
    "dt <- dt[, Node := as.factor(Node) ]\n",
    "setorder(dt, Cluster, Window)\n",
    "\n",
    "dt <- dt[ Node %in% contactNodesTrgts, ]\n",
    "\n",
    "dt <- dt[, resid := sapply(strsplit(resid, \"_\"), '[', 1) ]\n",
    "\n",
    "# Build base plot\n",
    "p <- ggplot(dt) + \n",
    "    geom_raster(aes(x=Node, y=Window, fill=Cluster)) \n",
    "\n",
    "# Get the actual indices of the nodes in the re-ordered x-axis (grouped by cluster)\n",
    "trgtIndices = data.table(trgt = c(which(dt[Window == refWindow]$Node %in% nodesATP1))  )\n",
    "\n",
    "# Add intercept lines for target nodes\n",
    "p <- p + geom_vline(data=trgtIndices, aes(xintercept=trgt), alpha=0.9, linetype = \"dashed\")\n",
    "p <- p + geom_hline(aes(yintercept=refWindow), alpha=0.9, linetype = \"dashed\")\n",
    "\n",
    "# Finish building plot\n",
    "p <- p + scale_fill_manual(values = colorValues) +\n",
    "    scale_x_discrete(limits=dt[Window == refWindow]$Node, label=dt[Window == refWindow]$resid) +\n",
    "    labs(x=\"Nodes\", y=\"Window\") +\n",
    "    theme_bw(base_size=20) + \n",
    "    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))\n",
    "\n",
    "ggsave(plotPath, p, device=\"png\")\n",
    "\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:12:12.966612Z",
     "start_time": "2020-06-04T23:12:12.254606Z"
    }
   },
   "outputs": [],
   "source": [
    "%%R -i workDir,refWindow,headNodeATP1,nodesATP1,plotFilePrefix,ligandSegID -w 600 -h 450\n",
    "\n",
    "headNodeATP1 = as.integer(headNodeATP1)\n",
    "\n",
    "dataPath = file.path(workDir, \"cluster.csv\")\n",
    "plotPath = file.path(workDir, paste0(\"Plots/\",plotFilePrefix,\"Clusters_Node_vs_Window_Grouped_\",ligandSegID,\".png\"))\n",
    "\n",
    "dt <- fread(dataPath)\n",
    "dt <- dt[,.(Node,Window,Cluster,resid)]\n",
    "dt <- dt[ Node %in% nodesATP1, ]\n",
    "dt <- dt[, Cluster := as.factor(Cluster) ]\n",
    "dt <- dt[, Node := as.factor(Node) ]\n",
    "dt <- dt[, Window := as.factor(Window) ]\n",
    "dt <- dt[, resid := sapply(strsplit(resid, \"_\"), '[', 1) ]\n",
    "dt <- dt[, resid := as.factor(resid) ]\n",
    "\n",
    "setorder(dt, Cluster, Node)\n",
    "\n",
    "# Build base plot\n",
    "p <- ggplot(dt) + \n",
    "    geom_raster(aes(x=Node, y=Window, fill=Cluster))\n",
    "\n",
    "# Finish building plot\n",
    "p <- p + scale_fill_manual(values = colorValues) +\n",
    "    scale_x_discrete(limits=as.factor(nodesATP1), label=dt[ Node %in% as.factor(nodesATP1), unique(resid)]) +\n",
    "    scale_y_discrete(limits=dt[Node == headNodeATP1]$Window, labels=NULL) +\n",
    "    labs(x=\"Nodes\", y=\"Window\") +\n",
    "    theme_bw(base_size=20) + \n",
    "    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))\n",
    "\n",
    "ggsave(plotPath, p, device=\"png\")\n",
    "\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Combine data for Interface and Active Site analysis\n",
    "\n",
    "Combines interface edges with label information for plots.\n",
    "\n",
    "Creates structure wirh optimal paths and labels for Active Site analysis, shaping information for interaction between ligand and active site residues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:12:22.083358Z",
     "start_time": "2020-06-04T23:12:21.799500Z"
    }
   },
   "outputs": [],
   "source": [
    "window = 0\n",
    "\n",
    "# Gets all pairs of nodes with non-zero correlations\n",
    "# nonzeroPairs = [(i,j) for i,j in np.asarray(np.where(dnad.corrMatAll[window, :, :] > 0)).T if i < j]\n",
    "nonzeroPairs = [(i,j) for i,j in np.asarray(np.where(dnad.contactMat > 0)).T if i < j]\n",
    "\n",
    "# Combines cartesian distance and correlation\n",
    "# We use the mean cartesian distance (0:mean, 1:SEM, 2:Min, 3:Max)\n",
    "distType = 0\n",
    "# carCorMat = [ [i, j, \n",
    "#                getCartDist(i,j,dnad.numNodes,dnad.nodeDists, distType), \n",
    "#                np.mean(dnad.corrMatAll[window,i,j]) ] \n",
    "#              for i,j in nonzeroPairs ]\n",
    "carCorMat = [ [i, j, \n",
    "               getCartDist(i,j,dnad.numNodes,dnad.nodeDists, 0), \n",
    "               getCartDist(i,j,dnad.numNodes,dnad.nodeDists, 1), \n",
    "               np.mean(dnad.corrMatAll[:,i,j]),\n",
    "               stats.sem(dnad.corrMatAll[:,i,j])] \n",
    "             for i,j in nonzeroPairs if np.mean(dnad.corrMatAll[:,i,j]) > 0 ]\n",
    "\n",
    "carCorMat = pd.DataFrame(carCorMat, columns=[\"i\",\"j\",\"Cart\",\"CartSEM\",\"Corr\",\"CorrSEM\"])\n",
    "\n",
    "def interNode(i,j,dnad):\n",
    "    # Checks if the pair of nodes exists in the \"interNodePairs\" 2D array.\n",
    "    return len( ( dnad.interNodePairs == [i, j] ).all(axis=1).nonzero()[0] )\n",
    "\n",
    "# Adds interface information (true/false)\n",
    "carCorMat[\"EdgeType\"] = carCorMat.apply( lambda x: \"Interface\" if interNode(x[\"i\"], x[\"j\"], dnad) \n",
    "                                        else \"Internal\" , axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:12:25.321625Z",
     "start_time": "2020-06-04T23:12:24.303655Z"
    }
   },
   "outputs": [],
   "source": [
    "# Initialized data frame with pairs of source node (all nodes in the system) and target node (defined above).\n",
    "\n",
    "dataTmp = []\n",
    "\n",
    "window = 0\n",
    "for window in range(dnad.numWinds):\n",
    "    \n",
    "    df = pd.DataFrame(np.asarray([ (i,trgt) for trgt in nodesATP1  for i in range(dnad.numNodes)]),\n",
    "                 columns=[\"node\", \"targets\"])\n",
    "    \n",
    "    df[\"distances\"]  = df.apply( lambda row: dnad.distsAll[window, row[\"node\"], row[\"targets\"]], axis=1)\n",
    "    # Selects the mean distance (0:mean, 1:SEM, 2:Min, 3:Max)\n",
    "    distType = 0 \n",
    "    df[\"cdistances\"] = df.apply( lambda row: getCartDist(row[\"node\"], row[\"targets\"], dnad.numNodes, \n",
    "                                                         dnad.nodeDists, distType), axis=1)\n",
    "    # Selects the standard error of the mean distance (0:mean, 1:SEM, 2:Min, 3:Max)\n",
    "    distType = 1\n",
    "    df[\"cdistSEM\"] = df.apply( lambda row: getCartDist(row[\"node\"], row[\"targets\"], dnad.numNodes, \n",
    "                                                         dnad.nodeDists, distType), axis=1)\n",
    "    df[\"path\"]       = df.apply( lambda row: list(getPath(row[\"node\"], row[\"targets\"], \n",
    "                                                          dnad.nodesAtmSel, dnad.preds, win= window)), axis=1)\n",
    "    df['path_lens']  = df.apply( lambda row: len(row[\"path\"]), axis=1)\n",
    "#     df[\"mincdist\"]   = df.groupby(\"node\")[[\"cdistances\"]].transform(lambda x: np.min(x) )\n",
    "\n",
    "    def getTagStr(i):\n",
    "        return dnad.nodesAtmSel.atoms[i].resname.capitalize() + \":\" + str(dnad.nodesAtmSel.atoms[i].resid) + \\\n",
    "                \"_\" + dnad.nodesAtmSel.atoms[i].segid\n",
    "\n",
    "    df['resid']     = np.vectorize(getTagStr)(df[\"node\"])\n",
    "\n",
    "    ptnIXs = dnad.nodesAtmSel.select_atoms(\"protein\").ix_array\n",
    "    nclIXs = dnad.nodesAtmSel.select_atoms(\"nucleic\").ix_array\n",
    "    def getType(nodeIndx):\n",
    "\n",
    "        if dnad.nodesAtmSel.atoms[nodeIndx].ix in ptnIXs:\n",
    "            return \"Aminoacid\"\n",
    "\n",
    "        elif dnad.nodesAtmSel.atoms[nodeIndx].ix in nclIXs:\n",
    "            return \"Nucleotide\"\n",
    "\n",
    "        else:\n",
    "            return dnad.nodesAtmSel.atoms[nodeIndx].resname\n",
    "\n",
    "    df[\"type\"] = np.vectorize(getType)(df[\"node\"])\n",
    "\n",
    "    df[\"Interface\"] = df[\"node\"].apply( lambda x: x in dnad.contactNodesInter)\n",
    "\n",
    "    # \"Cleans\" all -1 distances betweeen nodes not connected by any path.\n",
    "    df.loc[ df[\"path_lens\"] == 0, \"distances\" ] = 0\n",
    "    \n",
    "    df[\"window\"] = window\n",
    "    \n",
    "    dataTmp.append(df.copy())\n",
    "    del df\n",
    "    \n",
    "df = pd.concat(dataTmp)\n",
    "del dataTmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:12:26.253576Z",
     "start_time": "2020-06-04T23:12:26.230770Z"
    }
   },
   "outputs": [],
   "source": [
    "# df.head()\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:12:26.817921Z",
     "start_time": "2020-06-04T23:12:26.791442Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df[ (np.isin(df[\"node\"],getNodeFromSel(\"segid \" + ligandSegID, dnad.nodesAtmSel, dnad.atomToNode))) & (df[\"window\"] == 0) ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add labels to selected residues\n",
    "\n",
    "Using references form the literature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:12:31.608634Z",
     "start_time": "2020-06-04T23:12:31.590262Z"
    }
   },
   "outputs": [],
   "source": [
    "df[\"Label\"] = 0\n",
    "\n",
    "tags = dict()\n",
    "#\"resname H2U and element N\"\n",
    "tags[\"OMPn\"] = getNodeFromSel(\"resname OMP and name N1\", dnad.nodesAtmSel, dnad.atomToNode)[0]\n",
    "#\"resname H2U and element P\"\n",
    "tags[\"OMPp\"] = getNodeFromSel(\"resname OMP and name P\", dnad.nodesAtmSel, dnad.atomToNode)[0]\n",
    "\n",
    "for label, node in tags.items():\n",
    "    if not node in df.node:\n",
    "        print(\"Skiping node not found:\",node)\n",
    "        continue\n",
    "    df.loc[ df.node == node, 'Label'] = label\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:12:32.687560Z",
     "start_time": "2020-06-04T23:12:32.663679Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df.loc[ (df[\"resid\"] == \"Omp:301_OMP\") & (df[\"window\"] == 0)  ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adds label and type information for all pairs of connected residues in the system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:12:44.755637Z",
     "start_time": "2020-06-04T23:12:36.374275Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "carCorMat[\"iRes\"] = carCorMat.apply(lambda row: df[ (df[\"node\"] == row[\"i\"]) & \n",
    "                               (df[\"targets\"] == headNodeATP1[0])][\"resid\"].iloc[0] , axis=1)\n",
    "\n",
    "carCorMat[\"jRes\"] = carCorMat.apply(lambda row: df[ (df[\"node\"] == row[\"j\"]) & \n",
    "                               (df[\"targets\"] == headNodeATP1[0])][\"resid\"].iloc[0] , axis=1)\n",
    "\n",
    "carCorMat[\"iType\"] = carCorMat.apply(lambda row: df[ (df[\"node\"] == row[\"i\"]) & \n",
    "                               (df[\"targets\"] == headNodeATP1[0])][\"type\"].iloc[0] , axis=1)\n",
    "\n",
    "carCorMat[\"jType\"] = carCorMat.apply(lambda row: df[ (df[\"node\"] == row[\"j\"]) & \n",
    "                               (df[\"targets\"] == headNodeATP1[0])][\"type\"].iloc[0] , axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:12:44.776217Z",
     "start_time": "2020-06-04T23:12:44.758762Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "carCorMat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:12:44.867790Z",
     "start_time": "2020-06-04T23:12:44.779617Z"
    }
   },
   "outputs": [],
   "source": [
    "carCorMat.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:12:44.968226Z",
     "start_time": "2020-06-04T23:12:44.870894Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create a copy only with interface connections, and check that i < j\n",
    "carCorMatInterface = carCorMat[ carCorMat[\"EdgeType\"] == \"Interface\" ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare Intra and Inter segment correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:12:51.480310Z",
     "start_time": "2020-06-04T23:12:48.669368Z"
    }
   },
   "outputs": [],
   "source": [
    "%%R -i carCorMat,workDir -w 800 -h 500\n",
    "\n",
    "plotPath = file.path(workDir, \"Plots/Interf_Intern_Cart_vs_Corr.png\")\n",
    "\n",
    "cartCutoff = 4.0\n",
    "\n",
    "dt = data.table(carCorMat)\n",
    "dt  = dt[Cart < cartCutoff]\n",
    "\n",
    "p <- ggplot(dt) + \n",
    "    geom_point( aes(x=Cart, y=Corr, color=EdgeType), alpha=0.7, size=3 ) +\n",
    "    geom_smooth( aes(x=Cart, y=Corr, color=EdgeType) ) +\n",
    "    labs(x=\"Mean Cartesian Distance (A)\", y=\"Mean Correlation\", color=\"Edge Type\") +\n",
    "    scale_color_brewer(type='qual', palette=6) +\n",
    "    theme_linedraw(base_size=20) + xlim(c(2.5, cartCutoff))\n",
    "\n",
    "ggsave(plotPath, p, device=\"png\")\n",
    "\n",
    "p\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare network connectivity, network distance and cartesian distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:13:02.246460Z",
     "start_time": "2020-06-04T23:13:01.883625Z"
    }
   },
   "outputs": [],
   "source": [
    "# Select all nodes involved in at least one \"Interface\" connection.\n",
    "tmpDF = carCorMat[ carCorMat[\"EdgeType\"] == \"Interface\" ]\n",
    "\n",
    "tmpNodeSet = set(tmpDF[\"i\"])\n",
    "tmpNodeSet.update( set(tmpDF[\"j\"]) )\n",
    "\n",
    "nodeContacs = []\n",
    "for node in tmpNodeSet:\n",
    "\n",
    "    tmp = tmpDF.loc[  (tmpDF[\"i\"] == node) | (tmpDF[\"j\"] == node) ][\"Corr\"]\n",
    "    tmp2 = tmpDF.loc[  (tmpDF[\"i\"] == node) | (tmpDF[\"j\"] == node) ][\"Cart\"]\n",
    "    \n",
    "    label = df.loc[ df[\"node\"] == node ][\"resid\"].unique()[0]\n",
    "    \n",
    "    nodeContacs.append( [node, int(tmp.size), \n",
    "                         tmp.mean(), sp.stats.sem( tmp ),\n",
    "                         tmp2.mean(), sp.stats.sem( tmp2 ),\n",
    "                         label] )\n",
    "\n",
    "nodeContacs = pd.DataFrame(nodeContacs, \n",
    "                           columns=[\"Node\", \"NumContacts\", \"MeanCorr\", \"SEMCorr\", \n",
    "                                    \"MeanCart\", \"SEMCart\", \"label\"])\n",
    "nodeContacs = nodeContacs.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:13:11.069484Z",
     "start_time": "2020-06-04T23:13:10.443508Z"
    }
   },
   "outputs": [],
   "source": [
    "%%R -i nodeContacs -w 800 -h 600\n",
    "\n",
    "dt <- data.table(nodeContacs)\n",
    "sapply(dt, class)\n",
    "\n",
    "ggplot(dt) + \n",
    "#     geom_point(aes(x=NumContacts, y=MeanCorr, color=MeanCart), size=3) +\n",
    "    geom_point(aes(x=NumContacts, y=MeanCart, color=as.double(MeanCorr)), size=3) +\n",
    "    geom_label_repel(data=dt[NumContacts >= 10 | MeanCart <= 3 | MeanCorr > 0.8 ], \n",
    "                    aes(x=NumContacts, y=MeanCart, label=label)) + \n",
    "    scale_y_log10() +\n",
    "    labs(x=\"Number of Contacts\", \n",
    "         y=\"Mean Cartesian Distance\",\n",
    "         color=\"Mean Correlation\") +\n",
    "    scale_color_gradient(low=\"blue\",high=\"red\") + \n",
    "    theme_linedraw(base_size=20)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Active site connections\n",
    "Here we visualize the residues that make direct connections to the ligand, and display their generalized correlation coefficients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-04T23:17:07.975264Z",
     "start_time": "2020-06-04T23:17:06.982510Z"
    }
   },
   "outputs": [],
   "source": [
    "%%R -i carCorMatInterface,workDir,ligandSegID -w 2000 -h 500\n",
    "\n",
    "plotPath = file.path(workDir, \"Plots/Interface_AAres_vs_Corr_NoLabel.png\")\n",
    "\n",
    "corrCutoff = 0.0\n",
    "\n",
    "dt <- data.table(carCorMatInterface)\n",
    "\n",
    "iResList <- dt[iType == \"Aminoacid\" & jType != \"SOL\"][Corr > corrCutoff][,iRes]\n",
    "dt <- dt[ iRes %in% iResList, ]\n",
    "dt <- dt[jType != \"SOL\", .SD[which.max(Corr)] , by=.(iRes,jRes)]\n",
    "\n",
    "dt <- dt[, iRes := sapply(strsplit(iRes, \"_\"), '[', 1) ]\n",
    "\n",
    "colours <- c(\"Aminoacid\" = \"red\", \n",
    "          \"SOL\" = \"blue\", \n",
    "          \"Nucleotide\" = \"darkorange\")\n",
    "colours <- c(colours, setNames(\"purple\",ligandSegID))\n",
    "\n",
    "shapes <- c(\"Aminoacid\" = 19,\n",
    "          \"SOL\" = 15, \n",
    "          \"Nucleotide\" = 17)\n",
    "shapes <- c(shapes, setNames(19,ligandSegID))\n",
    "\n",
    "p <- ggplot(dt) + \n",
    "    geom_linerange( aes(x=iRes, y=Corr, ymin=Corr-CorrSEM, ymax=Corr+CorrSEM), size=1 )+\n",
    "    geom_point( aes(x = iRes, y = Corr, color = jType, size=Cart) )+\n",
    "    labs(x=\"Aminoacid Residue\", y=\"Mean Correlation\", color=\"Residue Type\", size=\"Mean Distance\") + \n",
    "    scale_colour_manual(name = \"Residue Type\",\n",
    "                     values = colours) +\n",
    "    guides(colour = guide_legend(override.aes = list(size=5))) +\n",
    "    theme_classic(base_size = 20) +\n",
    "    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),\n",
    "          panel.grid.major.x = element_line(color=\"black\", size=0.5, linetype=\"dotted\"))\n",
    "\n",
    "ggsave(plotPath, p, device=\"png\")\n",
    "\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-29T04:02:08.127424Z",
     "start_time": "2020-05-29T04:02:07.299339Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "w = nv.show_mdanalysis(workUviz.select_atoms(\"all\"))\n",
    "w._remote_call(\"setSize\", target=\"Widget\", args=[\"800px\", \"600px\"])\n",
    "w.parameters = dict(theme='light')\n",
    "w\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-29T04:02:08.882463Z",
     "start_time": "2020-05-29T04:02:08.129756Z"
    }
   },
   "outputs": [],
   "source": [
    "# Paint all connections to the ligand\n",
    "window = 1\n",
    "\n",
    "set_widget_representations(w)\n",
    "\n",
    "tmpDF = carCorMat[(carCorMat[\"i\"].isin(nodesATP1)) | (carCorMat[\"j\"].isin(nodesATP1))]\n",
    "tmpDF = tmpDF.loc[carCorMat[\"EdgeType\"] == \"Interface\"]\n",
    "\n",
    "for pair in tmpDF[[\"i\",\"j\"]].values:\n",
    "    viewPath(w, getPath(pair[0], pair[1], dnad.nodesAtmSel, dnad.preds), \n",
    "             dnad.distsAll, dnad.maxDirectDist, dnad.nodesAtmSel, window)\n",
    "    w.add_ball_and_stick(getNGLSelFromNode(pair[0], dnad.nodesAtmSel, atom=True))\n",
    "    w.add_ball_and_stick(getNGLSelFromNode(pair[1], dnad.nodesAtmSel, atom=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-29T04:02:08.889747Z",
     "start_time": "2020-05-29T04:02:08.885856Z"
    }
   },
   "outputs": [],
   "source": [
    "w.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pretty Figures - Preparing files for VMD\n",
    "\n",
    "The next few steps will define the files that will be created for loading into VMD, a popular molecular visualization software.\n",
    "\n",
    "**Important:**\n",
    "\n",
    "Make sure you have all the files created for VMD. For instance, if you are not interested in allosteric communications and suboptimal paths, just select two arbitrary nodes so that the path files are created correctly. Alternatively, you may want to adapt the tcl scripts provided at the end of the notebook to your particular case. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-29T04:02:12.717797Z",
     "start_time": "2020-05-29T04:02:08.892077Z"
    }
   },
   "outputs": [],
   "source": [
    "import copy\n",
    "\n",
    "# This will be used to look for the maximum and  minimum betweenness value in the graph.\n",
    "# The maximum value will be used ot normalize all betweenness values for better vizualization.\n",
    "# The minimum value will be used in case a betweenness value could not be assigned for a given edge,\n",
    "#    also helping visualization.\n",
    "\n",
    "# Initialize variable with high value.\n",
    "minimumBetweeness = 100\n",
    "# Initialize variable with low value.\n",
    "maximumBetweeness = -1\n",
    "\n",
    "for pair,btw in dnad.btws[winIndx].items():\n",
    "    if btw < minimumBetweeness:\n",
    "            minimumBetweeness = btw\n",
    "    if btw > maximumBetweeness:\n",
    "            maximumBetweeness = btw\n",
    "\n",
    "# Normalize the value.\n",
    "minimumBetweeness /= maximumBetweeness\n",
    "            \n",
    "for winIndx in range(dnad.numWinds):\n",
    "\n",
    "    normCorMat = copy.deepcopy( dnad.corrMatAll[winIndx,:,:] )\n",
    "    normCorMat /= normCorMat.max()\n",
    "    \n",
    "    ##########################################################################################\n",
    "    ### Create PDB file with the system in the first step of each window, for VMD vizualization.\n",
    "    \n",
    "    pdbVizFile = os.path.join(workDir, \n",
    "                            \"networkData_Structure_window_{}.pdb\".format(winIndx))\n",
    "    \n",
    "    # Calculate number of frames per window.\n",
    "    winLen = int(np.floor(workUviz.trajectory.n_frames/dnad.numWinds))\n",
    "    \n",
    "    # Positions the trajectory at the middle of each window.\n",
    "    workUviz.trajectory[(winIndx+1)*round(winLen/2)]\n",
    "    \n",
    "    with mda.Writer(pdbVizFile, multiframe=False, bonds=\"conect\", n_atoms=workUviz.atoms.n_atoms) as PDB:\n",
    "        PDB.write(workUviz.atoms)\n",
    "    \n",
    "    ##########################################################################################\n",
    "    ### Create network data file with ALL edges and their normalized weights.\n",
    "    \n",
    "    fileName = os.path.join(workDir, \n",
    "                            \"networkData_AllEdges_window_{}.dat\".format(winIndx))\n",
    "    with open(fileName, \"w\") as outfile:\n",
    "\n",
    "        for pair in np.asarray( np.where( np.triu(normCorMat[:,:]) ) ).T:\n",
    "\n",
    "            node1 = pair[0]\n",
    "            node2 = pair[1]\n",
    "\n",
    "            # Get VMD indices for the atoms\n",
    "            pdbIndx1 = dnad.nodesAtmSel.atoms[node1].id -1\n",
    "            pdbIndx2 = dnad.nodesAtmSel.atoms[node2].id -1\n",
    "\n",
    "            string = \"{} {} {}\".format(pdbIndx1, pdbIndx2, normCorMat[ node1, node2])\n",
    "            \n",
    "            outfile.write( string + \"\\n\" )\n",
    "    \n",
    "    \n",
    "    ##########################################################################################\n",
    "    ### Create network data file with ALL NODES, the maximum normalized weight of edges it belongs to,\n",
    "    ### and the community it belongs to.\n",
    "    \n",
    "    fileName = os.path.join(workDir, \n",
    "                            \"networkData_AllNodes_window_{}.dat\".format(winIndx))\n",
    "    with open(fileName, \"w\") as outfile:\n",
    "        \n",
    "        for node1 in range(dnad.numNodes):\n",
    "            \n",
    "            # Get the VMD index for the atom\n",
    "            pdbIndx1 = dnad.nodesAtmSel.atoms[node1].id -1\n",
    "            \n",
    "            # Get the community the node belongs to\n",
    "            community1 = int(nodeCommNP[node1, winIndx])\n",
    "            \n",
    "            # Find the node to which \"node1\" is connected with highest correlation.\n",
    "            node2 = np.where( normCorMat[node1,:] == normCorMat[node1,:].max() )[0][0]\n",
    "            \n",
    "            # Skip nodes not assigned to any community\n",
    "            if community1 < 0:\n",
    "                continue\n",
    "            \n",
    "            string = \"{} {} {}\".format(pdbIndx1, normCorMat[ node1, node2], community1)\n",
    "            \n",
    "            outfile.write( string + \"\\n\" )\n",
    "    \n",
    "    \n",
    "    ##########################################################################################\n",
    "    ### Create network data file with INTRA-COMMUNITY edges and their normalized weights.\n",
    "    \n",
    "    fileName = os.path.join(workDir, \n",
    "                            \"networkData_IntraCommunities_window_{}.dat\".format(winIndx))\n",
    "    with open(fileName, \"w\") as outfile:\n",
    "        \n",
    "        for pair in np.asarray( np.where( np.triu(normCorMat[:,:]) ) ).T:\n",
    "            \n",
    "            node1 = pair[0]\n",
    "            node2 = pair[1]\n",
    "\n",
    "            # Checks if both nodes belong to the same community. \n",
    "            # If they don't, skip this edge. We only write intra-community edges in this file!\n",
    "            if nodeCommNP[node1, winIndx] != nodeCommNP[node2, winIndx] :\n",
    "                continue\n",
    "            \n",
    "            # If both nodes do not belong to any community (assigned to community -1), also skip the edge.\n",
    "            if nodeCommNP[node1, winIndx] < 0:\n",
    "                continue\n",
    "            \n",
    "            community1 = int(nodeCommNP[node1, winIndx])\n",
    "            \n",
    "            # Get VMD indices for the atoms\n",
    "            pdbIndx1 = dnad.nodesAtmSel.atoms[node1].id -1\n",
    "            pdbIndx2 = dnad.nodesAtmSel.atoms[node2].id -1\n",
    "\n",
    "            string = \"{} {} {} {}\".format(pdbIndx1, pdbIndx2, normCorMat[ node1, node2], community1)\n",
    "            \n",
    "            outfile.write( string + \"\\n\" )\n",
    "    \n",
    "    \n",
    "    ##########################################################################################\n",
    "    ### Create network data file with INTER-COMMUNITY edges and their normalized weights.\n",
    "    \n",
    "    fileName = os.path.join(workDir, \n",
    "                                \"networkData_InterCommunities_window_{}.dat\".format(winIndx))\n",
    "    with open(fileName, \"w\") as outfile:\n",
    "        \n",
    "        for pair in np.asarray( np.where( np.triu(normCorMat[:,:]) ) ).T:\n",
    "            \n",
    "            node1 = pair[0]\n",
    "            node2 = pair[1]\n",
    "\n",
    "            # Checks if both nodes belong to the same community. \n",
    "            # If they don't, skip this edge. We only write intra-community edges in this file!\n",
    "            if nodeCommNP[node1, winIndx] == nodeCommNP[node2, winIndx] :\n",
    "                continue\n",
    "            \n",
    "            # If either node does not belong to any community (assigned to community -1), also skip the edge.\n",
    "            if (nodeCommNP[node1, winIndx] < 0) or (nodeCommNP[node2, winIndx] < 0):\n",
    "                continue\n",
    "            \n",
    "            community1 = int(nodeCommNP[node1, winIndx])\n",
    "            community2 = int(nodeCommNP[node2, winIndx])\n",
    "            \n",
    "            # Get VMD indices for the atoms\n",
    "            # VMD uses a 0-based index, so we subtract 1 from the PDB index\n",
    "            pdbIndx1 = dnad.nodesAtmSel.atoms[node1].id -1\n",
    "            pdbIndx2 = dnad.nodesAtmSel.atoms[node2].id -1\n",
    "\n",
    "            string = \"{} {} {} {} {}\".format(pdbIndx1, pdbIndx2, \n",
    "                                             normCorMat[ node1, node2], community1, community2)\n",
    "            \n",
    "            outfile.write( string + \"\\n\" )\n",
    "    \n",
    "    \n",
    "    ##########################################################################################\n",
    "    ### Create file with edges listed by betweeness value (highest to lowest).\n",
    "    \n",
    "    fileName = os.path.join(workDir, \n",
    "                            \"networkData_Betweenness_window_{}.dat\".format(winIndx))\n",
    "    with open(fileName, \"w\") as outfile:\n",
    "        \n",
    "        for pair,btw in dnad.btws[winIndx].items():\n",
    "            \n",
    "            node1 = pair[0]\n",
    "            node2 = pair[1]\n",
    "\n",
    "            # If either node does not belong to any community (assigned to community -1), also skip the edge.\n",
    "            if (nodeCommNP[node1, winIndx] < 0) or (nodeCommNP[node2, winIndx] < 0):\n",
    "                continue\n",
    "            \n",
    "            community1 = int(nodeCommNP[node1, winIndx])\n",
    "            community2 = int(nodeCommNP[node2, winIndx])\n",
    "            \n",
    "            # Get VMD indices for the atoms\n",
    "            # VMD uses a 0-based index, so we subtract 1 from the PDB index\n",
    "            pdbIndx1 = dnad.nodesAtmSel.atoms[node1].id -1\n",
    "            pdbIndx2 = dnad.nodesAtmSel.atoms[node2].id -1\n",
    "\n",
    "            string = \"{} {} {} {} {} {}\".format(pdbIndx1, pdbIndx2, \n",
    "                                                normCorMat[ node1, node2], btw/maximumBetweeness, \n",
    "                                                community1, community2)\n",
    "            \n",
    "            outfile.write( string + \"\\n\" )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Write Optimal and Sub-Optimal Paths\n",
    "\n",
    "Using the convenience functions \"getNodeFromSel\" and \"getSelFromNode\", one can easily probe the system and determine the relationship between node in the network graph and the atoms and residues they represent in the actual system.\n",
    "\n",
    "- getSelFromNode\n",
    "- getNodeFromSel\n",
    "\n",
    "See examples below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-29T04:02:12.730219Z",
     "start_time": "2020-05-29T04:02:12.720388Z"
    }
   },
   "outputs": [],
   "source": [
    "srcNode = getNodeFromSel(\"resname PHE and resid 508\",dnad.nodesAtmSel, dnad.atomToNode)[0]\n",
    "print(\"Source node:\", srcNode)\n",
    "\n",
    "trgNode = getNodeFromSel(\"resname ARG and resid 352\",dnad.nodesAtmSel, dnad.atomToNode)[0]\n",
    "print(\"Target node:\", trgNode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-29T04:02:12.848687Z",
     "start_time": "2020-05-29T04:02:12.732788Z"
    }
   },
   "outputs": [],
   "source": [
    "getSelFromNode(nodeF508,dnad.nodesAtmSel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create a list of important paths:**\n",
    "\n",
    "Once you have chosen the nodes that define each path of interest, create a list in the cell below with the indices of the source and target nodes.\n",
    "\n",
    "To make sure that the VMD scripts will run without the need to be adapted, the user must select at least one pair of nodes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-29T04:02:12.959829Z",
     "start_time": "2020-05-29T04:02:12.852749Z"
    }
   },
   "outputs": [],
   "source": [
    "# For example, to write the paths between node 0 (Valine 11) and nodes 58 and 60, the following\n",
    "#   list must be created:  \n",
    "# nodesForPaths = [ [0,58], [0,60] ]\n",
    "#\n",
    "\n",
    "nodesForPaths = [ [*nodeF508,*nodeR352], [*nodeF508,*headNodeATP1] ]\n",
    "nodesForPaths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-29T04:02:13.365287Z",
     "start_time": "2020-05-29T04:02:12.962583Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "# Determine how many extra sub-optimal paths will be written.\n",
    "numSuboptimalPaths = 5\n",
    "\n",
    "pathListFile = open(os.path.join(workDir, \"paths.list\"), \"w\")\n",
    "\n",
    "for srcNode, trgNode in nodesForPaths:\n",
    "    \n",
    "    tmpList = getSelFromNode(srcNode,dnad.nodesAtmSel, atom=True).split()\n",
    "    srcNodeSel = \"\".join([tmpList[1],tmpList[4],tmpList[10]])\n",
    "\n",
    "    tmpList = getSelFromNode(trgNode,dnad.nodesAtmSel, atom=True).split()\n",
    "    trgNodeSel = \"\".join([tmpList[1],tmpList[4],tmpList[10]])\n",
    "    \n",
    "    # Adds the path suffix to the file\n",
    "    pathListFile.write(\"_{}_{}\\n\".format(srcNodeSel, trgNodeSel))\n",
    "    \n",
    "    for winIndx in range(dnad.numWinds):\n",
    "\n",
    "        normCorMat = copy.deepcopy( dnad.corrMatAll[winIndx,:,:] )\n",
    "        normCorMat /= normCorMat.max()\n",
    "\n",
    "        ##########################################################################################\n",
    "        ### Create file with edges listed by betweeness value (highest to lowest).\n",
    "        \n",
    "        # File name is created based on selections, not node index, for readability.\n",
    "        \n",
    "        \n",
    "        \n",
    "        fileName = os.path.join(workDir, \n",
    "                                    \"networkData_Paths_window_{}_{}_{}.dat\".format(winIndx, \n",
    "                                                                                srcNodeSel, trgNodeSel))\n",
    "        with open(fileName, \"w\") as outfile:\n",
    "\n",
    "            allPaths = []\n",
    "\n",
    "            # Reconstructs the optimal path from Floyd-Warshall algorithm\n",
    "            pathFW = nx.reconstruct_path(srcNode, trgNode, dnad.preds[winIndx])\n",
    "\n",
    "            allPaths.append(pathFW)\n",
    "\n",
    "            # Behind the scenes, use Dijkstra algorithm to find sub-optimal paths\n",
    "            for pathSO in islice(nx.shortest_simple_paths(dnad.nxGraphs[0], \n",
    "                                                srcNode, trgNode, weight=\"dist\"), 1, numSuboptimalPaths + 1):\n",
    "                allPaths.append(pathSO)\n",
    "\n",
    "            # Create a counter of number of paths that go though each edge, among all (sub-)optimal path(s).\n",
    "            pathCounter = defaultdict(int)\n",
    "            for pathIndx, pathIter in enumerate(allPaths):\n",
    "                # Iterate over edges in the path\n",
    "                for i in range(len(pathIter)-1):\n",
    "\n",
    "                    node1 = pathIter[i]\n",
    "                    node2 = pathIter[i+1]\n",
    "\n",
    "                    pathCounter[(node1, node2)] += 1\n",
    "\n",
    "            # Normalize the count\n",
    "            maxCount = np.max(list(pathCounter.values()))\n",
    "            for pair, count in pathCounter.items():\n",
    "                pathCounter[pair] = count/maxCount\n",
    "\n",
    "            for pathIndx, pathIter in enumerate(allPaths):\n",
    "                # Iterate over edges in the path\n",
    "                for i in range(len(pathIter)-1):\n",
    "\n",
    "                    node1 = pathIter[i]\n",
    "                    node2 = pathIter[i+1]\n",
    "\n",
    "                    # Get the community each node belongs to\n",
    "                    community1 = int(nodeCommNP[node1, winIndx])\n",
    "                    community2 = int(nodeCommNP[node2, winIndx])\n",
    "\n",
    "                    # If either node does not belong to any community (assigned to community -1), \n",
    "                    #     also skip the edge.\n",
    "                    if (community1 < 0) or (community2 < 0):\n",
    "                        continue\n",
    "\n",
    "                    # Get the betweeness value\n",
    "                    try:\n",
    "                        btw = dnad.btws[winIndx][( node1, node2)]\n",
    "                    except:\n",
    "                        # If one could not be calculated (very few paths going though this edge)\n",
    "                        # set an arbitrarily low value.\n",
    "                        btw = minimumBetweeness\n",
    "\n",
    "                    # Get VMD indices for the atoms\n",
    "                    # VMD uses a 0-based index, so we subtract 1 from the PDB index\n",
    "                    pdbIndx1 = dnad.nodesAtmSel.atoms[node1].id -1\n",
    "                    pdbIndx2 = dnad.nodesAtmSel.atoms[node2].id -1\n",
    "\n",
    "                    string = \"{} {} {} {} {} {}\".format(pdbIndx1, pdbIndx2, \n",
    "                                                     normCorMat[ node1, node2], \n",
    "                                                     btw/maximumBetweeness, pathCounter[(node1, node2)], \n",
    "                                                     pathIndx)\n",
    "\n",
    "                    outfile.write( string + \"\\n\" )\n",
    "\n",
    "pathListFile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write TCL files to load all data we just wrote down and visualize in VMD.\n",
    "prepTclViz(\"networkData\", str(dnad.numWinds), ligandSegID, workDir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write PDB files for the last frame of each window rather than the first.\n",
    "selection = workUviz.select_atoms(\"all\")\n",
    "print(\"Loaded trajectory is %i frames divided into %i windows.\" % (workUviz.trajectory.n_frames, dnad.numWinds))\n",
    "frameList=np.arange(workUviz.trajectory.n_frames/dnad.numWinds-1,\\\n",
    "          workUviz.trajectory.n_frames,\\\n",
    "          workUviz.trajectory.n_frames/dnad.numWinds,\\\n",
    "          dtype=int)\n",
    "for i,frame in enumerate(frameList):\n",
    "    fileName=\"%s/networkData_Structure_window_%i_last.pdb\" % (workDir,i)\n",
    "    selection.write( fileName, frames=workUviz.trajectory[[frame]] )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rendering high-quality images with VMD - Step 3\n",
    "\n",
    "In the third step, the user can load files produced by the Jupyter notebooks into VMD. An easy to load script will handle all the work, creating a simple graphical  user  interface  (GUI)  that can be used to  easily  render publication-quality  images.   These  renderings  can  represent many aspects of the biomolecular system.\n",
    "\n",
    "There are *two options* to load the system into VMD and open the GUI:\n",
    "\n",
    "**1. From inside VMD.**\n",
    "\n",
    "    After opening VMD, in the VMD Main window, go to: Extensions > Tk Console. \n",
    "    \n",
    "    In the Tk Console, type the following command to navigate to the folder where the Analysis Results were saved (same as in the beginning of this tutorial Step 2): \n",
    "    \n",
    "`cd << path to folder >>`\n",
    "    \n",
    "    In the results folder, type the following command to load the results and the GUI: \n",
    "    \n",
    "`source network_view_2.tcl`\n",
    "\n",
    "**2. When loading VMD.**\n",
    "\n",
    "    If you choose to load VMD from a terminal (command line) window, navigate to the folder where the Analysis Results were saved (same as in the beginning of this tutorial Step 2): \n",
    "    \n",
    "`cd << path to folder >>`\n",
    "    \n",
    "    Load VMD with the following command: \n",
    "    \n",
    "`vmd -e network_view_2.tcl`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to use the Network View 2 GUI\n",
    "\n",
    "The Network View 2.0 GUI was created in a way that allows for easy interaction without deep background knowledge on VMD. If you are an expert VMD user, you can still change anything in the representation, but the GUI might erase your changes when loading some of the specialized features.\n",
    "\n",
    "The GUI allows user to visualize and render all the properties of the network presented in the Step 2. For instance:\n",
    "\n",
    "**1.** To visualize the communities just click on \"All Communities\". You can navigate diferent windows by clicking in the window \"step\" at the top-left corner. \n",
    "\n",
    "**2.** To load the betweenness just click on \"Betweenness\". You can, at the same time, visualize the communities of the protein nodes, by clicking on \"Show/Hide Colors\" in the \"Color Protein by Communities\" tab (only works for proteins).\n",
    "\n",
    "**3.** The \"Representations\" tab allows the user to show or hide parts of the structure.\n",
    "\n",
    "**4.** If you want to render the network in a higher or lower resolution, the \"Network Drawing Resultion\" tab provides options. Note that after selecting a new resolution, you must load the network representation again.\n",
    "\n",
    "**5.** Three options are available for quickly start rendering. The first two are GPU-only, and therefore depend on your computer having compatible GPU hardware. The third option uses CPU and should run in most computers. If you want to use a different rendering option, in the VMD Main window, go to: File > Render. Then, select the desired rendering option. **Attention** the quick menu for rendering will always save files with the same name. If you want to render multiple figures, and keep all of them, rename the just-rendered images to avoid overwriting the files. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-16T13:58:37.462224Z",
     "start_time": "2020-04-16T13:58:37.453579Z"
    }
   },
   "source": [
    "# ---- The End ----"
   ]
  }
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