{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Folding experiments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Folding experiments with RNAsubopt, tree decomp/scanning BP2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-23T00:10:41.752062Z",
     "start_time": "2021-04-23T00:10:41.146693Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/yixiongsun/rnabayespairing2/bayespairing/test\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import os\n",
    "print(os.getcwd())\n",
    "os.chdir(\"../src\")\n",
    "from Bio import SeqIO\n",
    "from folding import Fold\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scanning.src.classes import SSETree, SSE\n",
    "import pickle\n",
    "from scanning.scanning import exact_matching, yield_matching\n",
    "import json\n",
    "import RNA\n",
    "md = RNA.md()\n",
    "md.uniq_ML = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-23T00:10:41.775585Z",
     "start_time": "2021-04-23T00:10:41.754278Z"
    }
   },
   "outputs": [],
   "source": [
    "def sample_structures(seq, n=20000, constraint=\"\"):\n",
    "    # Sample 20k structures\n",
    "    fc = Fold(seq)\n",
    "    if len(constraint)<len(seq):\n",
    "        ss_mfe, mfe, fee = fc.constraint_folding()\n",
    "    else:\n",
    "        ss_mfe, mfe, fee = fc.constraint_folding(c=constraint)\n",
    "    \n",
    "    ss = fc.non_redundant_sampling(n)\n",
    "    return list(ss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-23T00:10:42.343831Z",
     "start_time": "2021-04-23T00:10:42.321532Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_rotations(strands):\n",
    "    if len(strands)<2:\n",
    "        permutations = [strands]\n",
    "        return permutations\n",
    "    strands = list(strands)\n",
    "    current_permutations = []\n",
    "    is_first = strands[0]\n",
    "    current_p = strands.copy()\n",
    "    current_permutations.append(tuple(current_p.copy()))\n",
    "    for i in range(len(strands)-1):\n",
    "        moved = current_p[0]\n",
    "        current_p.append(moved)\n",
    "        current_p.pop(0)\n",
    "        current_permutations.append(tuple(current_p.copy()))\n",
    "    return current_permutations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-23T00:10:42.850873Z",
     "start_time": "2021-04-23T00:10:42.829492Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_db(model):\n",
    "    BNs = pickle.load(open(\"../models/\" + model + \"_models.pickle\", \"rb\"))\n",
    "    db = {}\n",
    "    for i in BNs:\n",
    "\n",
    "        sse_mod = SSE(BNs[i].canonical, root=False)\n",
    "\n",
    "        strands= sse_mod.strands_len\n",
    "        rotations = get_rotations(strands)\n",
    "        for rotated, rotation_form in enumerate(rotations):\n",
    "            db.setdefault(rotation_form, []).append((BNs[i],rotated))\n",
    "    return db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-23T00:10:43.361813Z",
     "start_time": "2021-04-23T00:10:43.339039Z"
    }
   },
   "outputs": [],
   "source": [
    "def module_search(sampled_ss, db, sampled_seqs, output):\n",
    "    all_matches = []\n",
    "    for i in range(0, 100):\n",
    "        print(i)\n",
    "        ss = sampled_ss[i]\n",
    "        #fc = Fold(sampled_seqs[i])\n",
    "        # matched is list of list of matches ie 10000 sampled ss with n modules found for each\n",
    "        matched = []\n",
    "        for j in range(0, len(ss)):\n",
    "            tree = SSETree.from_bracket(ss[j][0],seq=sampled_seqs[i])\n",
    "            exact_matching(tree,db)\n",
    "\n",
    "            module_matches = []\n",
    "\n",
    "            for r in yield_matching(tree):\n",
    "                #print(r.module.ID)\n",
    "                #r.eval(fold_compound=fc)\n",
    "                #print(r.score)\n",
    "                #print(r.node.positions)\n",
    "                # Save as dict\n",
    "\n",
    "                module_matches.append((r.module.ID, r.node.positions))\n",
    "\n",
    "            matched.append(module_matches)\n",
    "        all_matches.append(matched)\n",
    "    pickle.dump(all_matches, open(\"../test/\" + output + \".pickle\", \"wb\"))\n",
    "    return all_matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-23T00:10:43.935552Z",
     "start_time": "2021-04-23T00:10:43.913592Z"
    }
   },
   "outputs": [],
   "source": [
    "# process modules with a map func + removing duplicates\n",
    "def process_modules(modules, map_func):\n",
    "    all_modules = []\n",
    "    count = 0\n",
    "    for sequence in modules:\n",
    "        print(count)\n",
    "        count += 1\n",
    "        sample_array = []\n",
    "        for sample in sequence:\n",
    "            module_dict = {}\n",
    "            for module in sample:\n",
    "                # check if module exists in dict\n",
    "                mapped = map_func(module[0])\n",
    "                if mapped in module_dict:\n",
    "                    # key exists, check if list contains another\n",
    "                    equal = False\n",
    "                    for m in module_dict[mapped]:\n",
    "                        if check_equal(m, module[1]):\n",
    "                            equal = True\n",
    "                            break\n",
    "                    if not equal:\n",
    "                        module_dict[mapped].append(module[1])\n",
    "                else:\n",
    "                    module_dict[mapped] = [module[1]]\n",
    "            sample_array.append(module_dict)\n",
    "        all_modules.append(sample_array)\n",
    "    return all_modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-23T00:10:44.630419Z",
     "start_time": "2021-04-23T00:10:44.607476Z"
    }
   },
   "outputs": [],
   "source": [
    "def cob_map_func(i):\n",
    "    if i in [0]:\n",
    "        return 0\n",
    "    if i in [1,2,3,4,5]:\n",
    "        return 1\n",
    "    if i in [6,7,8,9]:\n",
    "        return 2\n",
    "    if i in [10,11,12]:\n",
    "        return 3\n",
    "def tpp_map_func(i):\n",
    "    if i in [0,1,2,3]:\n",
    "        return 0\n",
    "    if i in [4,5]:\n",
    "        return 1\n",
    "    if i in [6,7,8]:\n",
    "        return 2\n",
    "def sam_map_func(i):\n",
    "    if i in [0,1,2]:\n",
    "        return 0\n",
    "    if i in [3,4,5,6,7]:\n",
    "        return 1\n",
    "    if i in [8]:\n",
    "        return 2\n",
    "    if i in [9,10,11,12,13,14]:\n",
    "        return 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-23T00:10:45.298316Z",
     "start_time": "2021-04-23T00:10:45.278059Z"
    }
   },
   "outputs": [],
   "source": [
    "def check_equal(strands1, strands2):\n",
    "    flatten = lambda t: [item for sublist in t for item in sublist]\n",
    "    s1 = flatten(strands1)\n",
    "    s2 = flatten(strands2)\n",
    "    s1.sort()\n",
    "    s2.sort()\n",
    "    return s1 == s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-23T00:10:45.761333Z",
     "start_time": "2021-04-23T00:10:45.739683Z"
    }
   },
   "outputs": [],
   "source": [
    "def boltzmann(e):\n",
    "    \"\"\"Return Boltzmann factor of given energy\"\"\"\n",
    "    kT = (RNA.K0+md.temperature)*RNA.GASCONST*1e-3\n",
    "\n",
    "    return np.exp(-e/kT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-23T00:10:46.336683Z",
     "start_time": "2021-04-23T00:10:46.311798Z"
    }
   },
   "outputs": [],
   "source": [
    "# analyze distribution of shapes found\n",
    "# lets look specifically at module 0\n",
    "def get_counts(module_number, processed):\n",
    "    \n",
    "    all_counts = []\n",
    "    for seq in processed:\n",
    "        count = 0\n",
    "        for sample in seq:\n",
    "            if 0 in sample:\n",
    "                count += 1\n",
    "        all_counts.append(count)\n",
    "    return all_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-23T00:10:46.997754Z",
     "start_time": "2021-04-23T00:10:46.976274Z"
    }
   },
   "outputs": [],
   "source": [
    "# redo analysis with boltzman probability\n",
    "# here, as long as the module is found, we add the boltzmann probability\n",
    "# different from bp2 that looks at specific module positions\n",
    "def get_boltzmann_probs(module_number, ss, processed):\n",
    "    \n",
    "    all_probs = []\n",
    "    for i in range(0, len(ss)):\n",
    "        # 100 sequences\n",
    "        boltzmann_sum = 0\n",
    "        total_prob = 0\n",
    "        for j in range(0, len(ss[i])):\n",
    "            # get the prob\n",
    "            energy = ss[i][j][1]\n",
    "            prob = boltzmann(energy)\n",
    "        \n",
    "            boltzmann_sum += prob\n",
    "        \n",
    "            # check if module 0 exists\n",
    "            if 0 in processed[i][j]:\n",
    "                total_prob += prob\n",
    "        all_probs.append(total_prob/boltzmann_sum)\n",
    "    return all_probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-23T00:10:47.606288Z",
     "start_time": "2021-04-23T00:10:47.584093Z"
    }
   },
   "outputs": [],
   "source": [
    "# redo analysis with boltzman probability\n",
    "# here, as long as the module is found, we add the boltzmann probability\n",
    "# different from bp2 that looks at specific module positions\n",
    "def get_co_dynamics(modules, ss, processed):\n",
    "    \n",
    "    all_probs = []\n",
    "    for i in range(0, len(ss)):\n",
    "        # 100 sequences\n",
    "        boltzmann_sum = 0\n",
    "        total_prob = 0\n",
    "        for j in range(0, len(ss[i])):\n",
    "            # get the prob\n",
    "            energy = ss[i][j][1]\n",
    "            prob = boltzmann(energy)\n",
    "        \n",
    "            boltzmann_sum += prob\n",
    "        \n",
    "            # check if module 0 exists\n",
    "            contains = True\n",
    "            for m in modules:\n",
    "                if m not in processed[i][j]:\n",
    "                    contains = False\n",
    "                    break\n",
    "            if contains:\n",
    "                total_prob += prob\n",
    "        all_probs.append(total_prob/boltzmann_sum)\n",
    "    return all_probs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Cobalamin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## No constraints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-03T20:09:08.469611Z",
     "start_time": "2021-04-03T20:08:10.059246Z"
    },
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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   ],
   "source": [
    "cob_seqs = []\n",
    "for record in SeqIO.parse(\"../test/cob_sample.fasta\",\"fasta\"):\n",
    "    cob_seqs.append(str(record.seq))\n",
    "\n",
    "cob_ss = []\n",
    "for i in range(0, 100):\n",
    "    print(i)\n",
    "    ss = sample_structures(cob_seqs[i], 10000)\n",
    "    cob_ss.append(ss)\n",
    "\n",
    "pickle.dump(cob_ss, open(\"../test/cob_ss.pickle\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-03T20:44:32.051651Z",
     "start_time": "2021-04-03T20:09:08.471995Z"
    },
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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   ],
   "source": [
    "cob_modules = module_search(cob_ss, get_db(\"cob\"), cob_seqs,\"cob_modules\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-26T18:43:51.409436Z",
     "start_time": "2021-03-26T18:43:23.582687Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "cob_processed = process_modules(cob_modules, cob_map_func)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Repeat for constraints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-03T20:07:58.948410Z",
     "start_time": "2021-04-03T20:07:58.897558Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'cob_seqs' 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-17-96d8835c39fc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\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      7\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msample_structures\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcob_seqs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcob_ss\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\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[0m\u001b[1;32m      9\u001b[0m     \u001b[0mconstrained_cob_ss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'cob_seqs' is not defined"
     ]
    }
   ],
   "source": [
    "cob_ss = []\n",
    "for record in SeqIO.parse(\"../test/cob_sample_ss.txt\",\"fasta\"):\n",
    "    cob_ss.append(str(record.seq))\n",
    "    \n",
    "constrained_cob_ss = []\n",
    "for i in range(0, 100):\n",
    "    print(i)\n",
    "    ss = sample_structures(cob_seqs[i], 10000, cob_ss[i])\n",
    "    constrained_cob_ss.append(ss)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-26T02:24:56.000908Z",
     "start_time": "2021-03-26T02:24:53.678701Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pickle.dump(constrained_cob_ss, open(\"../test/cob_constraint_ss.pickle\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-26T03:21:02.009417Z",
     "start_time": "2021-03-26T02:47:03.707767Z"
    },
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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   "source": [
    "cob_constraint_modules = module_search(constrained_cob_ss, get_db(\"cob\"), cob_seqs,\"cob_constraint_modules\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-26T18:44:29.984744Z",
     "start_time": "2021-03-26T18:43:55.269339Z"
    },
    "collapsed": true,
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   },
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    {
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   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-19T01:06:41.866731Z",
     "start_time": "2021-04-19T01:05:46.374827Z"
    },
    "hidden": true,
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    {
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   ],
   "source": [
    "# load \n",
    "cob_modules = pickle.load(open(\"../test/cob_modules.pickle\", \"rb\"))\n",
    "cob_processed = process_modules(cob_modules, cob_map_func)\n",
    "cob_ss = pickle.load(open(\"../test/cob_ss.pickle\", \"rb\"))\n",
    "cob_constrained_modules = pickle.load(open(\"../test/cob_constraint_modules.pickle\", \"rb\"))\n",
    "cob_constrained_processed = process_modules(cob_constrained_modules, cob_map_func)\n",
    "cob_constrained_ss = pickle.load(open(\"../test/cob_constraint_ss.pickle\", \"rb\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-19T01:06:47.646243Z",
     "start_time": "2021-04-19T01:06:44.584318Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "cob_boltz = get_boltzmann_probs(0, cob_ss, cob_processed)\n",
    "cob_constrained_boltz = get_boltzmann_probs(0, cob_constrained_ss, cob_constrained_processed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-05T23:54:51.004053Z",
     "start_time": "2021-04-05T23:54:50.835755Z"
    },
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# model these probabilities?\n",
    "# use a box plot to compare boltzmann probability distribution -> show higher here\n",
    "# do we need a control? lets use the shuffled sequences -> show no boltzmann prob with or without constraints\n",
    "cob_means = [np.mean(cob_boltz), np.mean(cob_constrained_boltz)]\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.bar([0,1], cob_means, align='center', alpha=0.5)\n",
    "#plt.ylim([0, 1])\n",
    "plt.xticks([0,1], labels=[\"Unconstrained\", \"Constrained\"])\n",
    "plt.ylabel(\"Boltzmann probability\")\n",
    "plt.xlabel(\"Cobalamin Junction\")\n",
    "plt.savefig(\"../test/cob_bolt_avg.png\", facecolor='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-05T23:42:15.434301Z",
     "start_time": "2021-04-05T23:42:15.412161Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "68\n"
     ]
    }
   ],
   "source": [
    "# look at direct comparisons of boltz prob instead of just average\n",
    "total = 0\n",
    "for i in range(0, 100):\n",
    "    if cob_constrained_boltz[i] > cob_boltz[i]:\n",
    "        total += 1\n",
    "print(total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-05T23:55:43.024873Z",
     "start_time": "2021-04-05T23:55:31.979710Z"
    },
    "hidden": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# looking at co occurences of these modules with 0\n",
    "# modules (0,1), (0, 2), (0, 3), (0,1,2), (0, 1, 3,), (0,2,3), (0, 1,2,3)\n",
    "\n",
    "# modify function to return a count + boltzmann prob\n",
    "module_tuples = [(0,1), (0,2), (0,3), (0,1,2), (0,1,3), (0,2,3), (0,1,2,3)]\n",
    "\n",
    "all_combos = []\n",
    "for module_combo in module_tuples:\n",
    "    all_combos.append(get_co_dynamics(module_combo, cob_ss, cob_processed))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-05T23:58:34.915519Z",
     "start_time": "2021-04-05T23:58:34.701479Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "means = np.mean(all_combos, axis=1)\n",
    "x_pos = np.arange(len(means))\n",
    "plt.bar(x_pos, means, align='center', alpha=0.5)\n",
    "plt.ylim([0,0.1])\n",
    "plt.xticks(x_pos, labels=module_tuples)\n",
    "plt.ylabel(\"Boltzmann probability\")\n",
    "plt.xlabel(\"Cobalamin module combinations\")\n",
    "plt.savefig(\"../test/cob_bolt_avg_combo.png\", facecolor='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-05T23:59:18.925869Z",
     "start_time": "2021-04-05T23:59:08.588078Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# looking at co occurences of these modules with 0\n",
    "# modules (0,1), (0, 2), (0, 3), (0,1,2), (0, 1, 3,), (0,2,3), (0, 1,2,3)\n",
    "\n",
    "# modify function to return a count + boltzmann prob\n",
    "module_tuples = [(0,1), (0,2), (0,3), (0,1,2), (0,1,3), (0,2,3), (0,1,2,3)]\n",
    "\n",
    "all_c_combos = []\n",
    "for module_combo in module_tuples:\n",
    "    all_c_combos.append(get_co_dynamics(module_combo, cob_constrained_ss, cob_constrained_processed))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-06T00:00:04.516305Z",
     "start_time": "2021-04-06T00:00:04.297492Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "means = np.mean(all_c_combos, axis=1)\n",
    "std = np.std(all_c_combos, axis=1)\n",
    "x_pos = np.arange(len(means))\n",
    "plt.bar(x_pos, means,  align='center', alpha=0.5)\n",
    "#plt.ylim([0,0.1])\n",
    "plt.ylabel(\"Boltzmann probability\")\n",
    "plt.xlabel(\"Cobalamin module combinations\")\n",
    "plt.xticks(x_pos, labels=module_tuples)\n",
    "plt.savefig(\"../test/cob_bolt_avg_combo_constrained.png\", facecolor='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-19T01:37:06.343661Z",
     "start_time": "2021-04-19T01:37:01.730881Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "\n",
    "energies = []\n",
    "e2 = []\n",
    "\n",
    "for j in range(0, 100):\n",
    "    for ss in cob_ss[j]:\n",
    "        energies.append(ss[1])\n",
    "\n",
    "    for i in range(0, len(cob_ss[j])):\n",
    "        m = cob_processed[j][i]\n",
    "        if 0 in m:\n",
    "            e2.append(cob_ss[j][i][1])\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "sns.distplot(np.array(energies))\n",
    "sns.distplot(np.array(e2))\n",
    "plt.xlabel(\"Energy (kcal/mol)\")\n",
    "\n",
    "plt.savefig(\"../test/cob_dist.png\", facecolor='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-19T01:34:32.960175Z",
     "start_time": "2021-04-19T01:34:28.111437Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 5, 6, 7, 8, 10, 11, 13, 14, 15, 17, 18, 22, 24, 25]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(4, 4, sharex=True, figsize=(16,8))\n",
    "\n",
    "e1 = []\n",
    "e2 = []\n",
    "\n",
    "indices = []\n",
    "for i in range(0, 100):\n",
    "    found = False\n",
    "    for j in range(0, len(cob_ss[i])):\n",
    "        m = cob_processed[i][j]\n",
    "        if 0 in m:\n",
    "            found = True\n",
    "    if found:\n",
    "        indices.append(i)\n",
    "        \n",
    "    if len(indices) > 15:\n",
    "        break\n",
    "\n",
    "print(indices)\n",
    "for j in indices:\n",
    "    e = []\n",
    "    for ss in cob_ss[j]:\n",
    "        e.append(ss[1])\n",
    "    e1.append(e)\n",
    "\n",
    "    e = []\n",
    "    for i in range(0, len(cob_ss[j])):\n",
    "        m = cob_processed[j][i]\n",
    "        if 0 in m:\n",
    "            e.append(cob_ss[j][i][1])\n",
    "    e2.append(e)\n",
    "    \n",
    "counter = 0\n",
    "for axis in fig.axes:\n",
    "    sns.distplot(e1[counter], ax=axis)\n",
    "    sns.distplot(e2[counter], ax=axis)\n",
    "    axis.set_xlabel(\"Energy (kcal/mol)\")\n",
    "    counter += 1\n",
    "\n",
    "fig.savefig(\"../test/cob_sample_dist.png\", facecolor='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-19T01:36:03.191617Z",
     "start_time": "2021-04-19T01:35:57.661280Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 3, 5, 7, 8, 9, 10, 12, 13, 14, 15, 17, 19, 22, 24]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(4, 4, sharex=True, figsize=(16,8))\n",
    "\n",
    "e1 = []\n",
    "e2 = []\n",
    "\n",
    "indices = []\n",
    "for i in range(0, 100):\n",
    "    found = False\n",
    "    for j in range(0, len(cob_constrained_ss[i])):\n",
    "        m = cob_constrained_processed[i][j]\n",
    "        if 0 in m:\n",
    "            found = True\n",
    "    if found:\n",
    "        indices.append(i)\n",
    "        \n",
    "    if len(indices) > 15:\n",
    "        break\n",
    "\n",
    "print(indices)\n",
    "for j in indices:\n",
    "    e = []\n",
    "    for ss in cob_constrained_ss[j]:\n",
    "        e.append(ss[1])\n",
    "    e1.append(e)\n",
    "\n",
    "    e = []\n",
    "    for i in range(0, len(cob_constrained_ss[j])):\n",
    "        m = cob_constrained_processed[j][i]\n",
    "        if 0 in m:\n",
    "            e.append(cob_constrained_ss[j][i][1])\n",
    "    e2.append(e)\n",
    "    \n",
    "counter = 0\n",
    "for axis in fig.axes:\n",
    "    sns.distplot(e1[counter], ax=axis)\n",
    "    sns.distplot(e2[counter], ax=axis)\n",
    "    axis.set_xlabel(\"Energy (kcal/mol)\")\n",
    "    counter += 1\n",
    "\n",
    "fig.savefig(\"../test/cob_cons_sample_dist.png\", facecolor='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-19T01:37:26.796363Z",
     "start_time": "2021-04-19T01:37:22.298440Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "\n",
    "energies = []\n",
    "e2 = []\n",
    "\n",
    "for j in range(0, 100):\n",
    "    for ss in cob_constrained_ss[j]:\n",
    "        energies.append(ss[1])\n",
    "\n",
    "    for i in range(0, len(cob_constrained_ss[j])):\n",
    "        m = cob_constrained_processed[j][i]\n",
    "        if 0 in m:\n",
    "            e2.append(cob_constrained_ss[j][i][1])\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "sns.distplot(np.array(energies))\n",
    "sns.distplot(np.array(e2))\n",
    "plt.xlabel(\"Energy (kcal/mol)\")\n",
    "\n",
    "plt.savefig(\"../test/cob_cons_dist.png\", facecolor='w')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TPP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T22:25:25.019419Z",
     "start_time": "2021-03-25T22:24:59.028198Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "tpp_seqs = []\n",
    "for record in SeqIO.parse(\"../test/tpp_sample.fasta\",\"fasta\"):\n",
    "    tpp_seqs.append(str(record.seq))\n",
    "\n",
    "tpp_ss = []\n",
    "for i in range(0, 100):\n",
    "    print(i)\n",
    "    ss = sample_structures(tpp_seqs[i], 10000)\n",
    "    tpp_ss.append(ss)\n",
    "\n",
    "pickle.dump(tpp_ss, open(\"../test/tpp_ss.pickle\", \"wb\"))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T22:41:23.531009Z",
     "start_time": "2021-03-25T22:25:25.022391Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "tpp_modules = module_search(tpp_ss, get_db(\"tpp\"),tpp_seqs, \"tpp_modules\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-26T18:46:22.401605Z",
     "start_time": "2021-03-26T18:44:29.991131Z"
    },
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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   "source": [
    "tpp_processed = process_modules(tpp_modules, tpp_map_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-26T03:23:37.839312Z",
     "start_time": "2021-03-26T03:23:21.366561Z"
    },
    "collapsed": true,
    "hidden": true
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   "outputs": [
    {
     "name": "stdout",
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   ],
   "source": [
    "tpp_ss = []\n",
    "for record in SeqIO.parse(\"../test/tpp_sample_ss.txt\",\"fasta\"):\n",
    "    tpp_ss.append(str(record.seq))\n",
    "    \n",
    "constrained_tpp_ss = []\n",
    "for i in range(0, 100):\n",
    "    print(i)\n",
    "    ss = sample_structures(tpp_seqs[i], 10000, tpp_ss[i])\n",
    "    constrained_tpp_ss.append(ss)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-26T03:23:38.832413Z",
     "start_time": "2021-03-26T03:23:37.842072Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pickle.dump(constrained_tpp_ss, open(\"../test/tpp_constraint_ss.pickle\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-26T03:36:24.137759Z",
     "start_time": "2021-03-26T03:23:38.835495Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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   "source": [
    "tpp_constraint_modules = module_search(constrained_tpp_ss, get_db(\"tpp\"), tpp_seqs,\"tpp_constraint_modules\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-26T18:46:29.089828Z",
     "start_time": "2021-03-26T18:46:22.410780Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "tpp_constraint_processed = process_modules(tpp_constraint_modules, tpp_map_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-26T19:57:28.937063Z",
     "start_time": "2021-03-26T19:57:28.734260Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[456, 44, 196, 2671, 4361, 140, 4181, 4367, 419, 117, 126, 243, 139, 34, 204, 494, 17, 466, 1136, 1783, 866, 2252, 102, 2, 1088, 1652, 1190, 4175, 471, 133, 2952, 280, 1655, 1538, 290, 2, 775, 96, 49, 239, 2330, 355, 26, 277, 12, 33, 108, 3748, 1937, 14, 504, 203, 3350, 245, 2078, 4366, 88, 1547, 252, 1224, 16, 69, 371, 2862, 525, 205, 2114, 614, 2707, 1799, 10, 5, 241, 29, 158, 25, 1077, 2429, 796, 114, 336, 1510, 523, 15, 115, 824, 77, 22, 519, 147, 397, 119, 22, 388, 369, 94, 144, 20, 3682, 24]\n",
      "89611\n"
     ]
    }
   ],
   "source": [
    "# analyze distribution of shapes found\n",
    "# lets look specifically at module 0\n",
    "all_counts = []\n",
    "for seq in tpp_processed:\n",
    "    count = 0\n",
    "    for sample in seq:\n",
    "        if 0 in sample:\n",
    "            count += 1\n",
    "    all_counts.append(count)\n",
    "print(all_counts)\n",
    "print(sum(all_counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-26T19:57:37.276671Z",
     "start_time": "2021-03-26T19:57:37.128300Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 24, 934, 2572, 2, 2182, 19, 22, 871, 283, 57, 533, 842, 70, 2254, 1121, 154, 484, 0, 1712, 5182, 0, 24, 0, 26, 0, 1477, 240, 476, 312, 2940, 3335, 10, 2404, 417, 0, 347, 292, 34, 1, 0, 170, 0, 27, 10, 1, 242, 0, 3339, 464, 792, 1000, 3571, 1200, 1696, 135, 108, 127, 2666, 0, 772, 1600, 3948, 2898, 1001, 132, 2141, 2074, 2768, 1611, 1, 3, 0, 93, 0, 0, 2658, 52, 0, 683, 3, 2830, 5116, 1, 125, 1730, 120, 24, 4043, 332, 357, 38, 352, 363, 3187, 100, 4113, 0, 3696, 317]\n",
      "96483\n"
     ]
    }
   ],
   "source": [
    "# analyze distribution of shapes found\n",
    "# lets look specifically at module 0\n",
    "all_counts = []\n",
    "for seq in tpp_constraint_processed:\n",
    "    count = 0\n",
    "    for sample in seq:\n",
    "        if 0 in sample:\n",
    "            count += 1\n",
    "    all_counts.append(count)\n",
    "print(all_counts)\n",
    "print(sum(all_counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-26T20:05:22.851688Z",
     "start_time": "2021-03-26T20:05:09.174515Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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   ],
   "source": [
    "tpp_ss0 = []\n",
    "for record in SeqIO.parse(\"../test/tpp_sample_ss0.txt\",\"fasta\"):\n",
    "    tpp_ss0.append(str(record.seq))\n",
    "    \n",
    "constrained_tpp_ss0 = []\n",
    "for i in range(0, 100):\n",
    "    print(i)\n",
    "    ss = sample_structures(tpp_seqs[i], 10000, tpp_ss0[i])\n",
    "    constrained_tpp_ss0.append(ss)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-26T03:23:38.832413Z",
     "start_time": "2021-03-26T03:23:37.842072Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pickle.dump(constrained_tpp_ss0, open(\"../test/tpp_constraint_ss0.pickle\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-26T20:24:15.182720Z",
     "start_time": "2021-03-26T20:06:14.840912Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "tpp_constraint_modules0 = module_search(constrained_tpp_ss0, get_db(\"tpp\"), tpp_seqs,\"tpp_constraint_modules0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-26T20:45:08.913107Z",
     "start_time": "2021-03-26T20:45:01.779409Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "tpp_constraint_processed0 = process_modules(tpp_constraint_modules0, tpp_map_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-26T20:45:21.039525Z",
     "start_time": "2021-03-26T20:45:20.843869Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 10000, 265, 9999, 1, 9999, 10000, 3821, 9999, 10000, 4780, 6214, 8459, 95, 10000, 1190, 153, 9999, 0, 10000, 9999, 10000, 1, 0, 2542, 9999, 1455, 953, 596, 3356, 10000, 10000, 10000, 10000, 10000, 1, 3096, 271, 168, 0, 10000, 338, 67, 6699, 7, 0, 3151, 1, 10000, 488, 10000, 10000, 9999, 10000, 10000, 1, 10000, 9999, 4402, 0, 765, 10000, 10000, 10000, 9999, 527, 10000, 648, 2746, 10000, 10000, 0, 12, 10000, 0, 0, 10000, 351, 373, 10000, 8, 10000, 10000, 0, 9369, 10000, 1187, 15, 10000, 4777, 10000, 63, 844, 10000, 10000, 6990, 10000, 0, 6228, 397]\n",
      "527862\n"
     ]
    }
   ],
   "source": [
    "# analyze distribution of shapes found\n",
    "# lets look specifically at module 0\n",
    "all_counts = []\n",
    "for seq in tpp_constraint_processed0:\n",
    "    count = 0\n",
    "    for sample in seq:\n",
    "        if 0 in sample:\n",
    "            count += 1\n",
    "    all_counts.append(count)\n",
    "print(all_counts)\n",
    "print(sum(all_counts))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-23T00:11:19.228075Z",
     "start_time": "2021-04-23T00:10:53.846521Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "# load \n",
    "tpp_modules = pickle.load(open(\"../test/tpp_modules.pickle\", \"rb\"))\n",
    "tpp_processed = process_modules(tpp_modules, tpp_map_func)\n",
    "tpp_ss = pickle.load(open(\"../test/tpp_ss.pickle\", \"rb\"))\n",
    "tpp_boltz = get_boltzmann_probs(0, tpp_ss, tpp_processed)\n",
    "tpp_constrained_modules = pickle.load(open(\"../test/tpp_constraint_modules.pickle\", \"rb\"))\n",
    "tpp_constrained_processed = process_modules(tpp_constrained_modules, tpp_map_func)\n",
    "tpp_constrained_ss = pickle.load(open(\"../test/tpp_constraint_ss.pickle\", \"rb\"))\n",
    "tpp_constrained_boltz = get_boltzmann_probs(0, tpp_constrained_ss, tpp_constrained_processed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-06T00:05:31.389789Z",
     "start_time": "2021-04-06T00:05:31.174617Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "means = [np.mean(tpp_boltz), np.mean(tpp_constrained_boltz)]\n",
    "\n",
    "plt.bar([0,1], means, align='center', alpha=0.5)\n",
    "plt.ylabel(\"Boltzmann probability\")\n",
    "plt.xlabel(\"TPP Junction\")\n",
    "plt.ylim([0, 0.25])\n",
    "plt.savefig(\"../test/tpp_bolt_avg.png\", facecolor='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-06T00:02:46.933706Z",
     "start_time": "2021-04-06T00:02:46.912486Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "62\n"
     ]
    }
   ],
   "source": [
    "# look at direct comparisons of boltz prob instead of just average\n",
    "total = 0\n",
    "for i in range(0, 100):\n",
    "    if tpp_constrained_boltz[i] > tpp_boltz[i]:\n",
    "        total += 1\n",
    "print(total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-06T00:03:38.301735Z",
     "start_time": "2021-04-06T00:03:29.864412Z"
    }
   },
   "outputs": [],
   "source": [
    "# modify function to return a count + boltzmann prob\n",
    "module_tuples = [(0,1), (0,2), (0,1,2)]\n",
    "\n",
    "all_combos = []\n",
    "for module_combo in module_tuples:\n",
    "    all_combos.append(get_co_dynamics(module_combo, tpp_ss, tpp_processed))\n",
    "\n",
    "all_c_combos = []\n",
    "for module_combo in module_tuples:\n",
    "    all_c_combos.append(get_co_dynamics(module_combo, tpp_constrained_ss, tpp_constrained_processed))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-06T00:08:05.778900Z",
     "start_time": "2021-04-06T00:08:05.581913Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "means = np.mean(all_combos, axis=1)\n",
    "x_pos = np.arange(len(means))\n",
    "plt.bar(x_pos, means, align='center', alpha=0.5)\n",
    "plt.ylim([0, 0.25])\n",
    "plt.xticks(x_pos, labels=module_tuples)\n",
    "plt.ylabel(\"Boltzmann probability\")\n",
    "plt.xlabel(\"TPP module combinations\")\n",
    "plt.savefig(\"../test/tpp_bolt_avg_combo.png\", facecolor='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-06T00:08:18.010923Z",
     "start_time": "2021-04-06T00:08:17.838844Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "means = np.mean(all_c_combos, axis=1)\n",
    "std = np.std(all_c_combos, axis=1)\n",
    "x_pos = np.arange(len(means))\n",
    "plt.ylim([0, 0.25])\n",
    "plt.bar(x_pos, means, align='center', alpha=0.5)\n",
    "plt.xticks(x_pos, labels=module_tuples)\n",
    "plt.ylabel(\"Boltzmann probability\")\n",
    "plt.xlabel(\"TPP module combinations\")\n",
    "plt.savefig(\"../test/tpp_bolt_avg_combo_constrained.png\", facecolor='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-23T00:14:05.824086Z",
     "start_time": "2021-04-23T00:13:59.791012Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "\n",
    "energies = []\n",
    "e2 = []\n",
    "\n",
    "for j in range(0, 100):\n",
    "    for ss in tpp_ss[j]:\n",
    "        energies.append(ss[1])\n",
    "\n",
    "    for i in range(0, len(tpp_ss[j])):\n",
    "        m = tpp_processed[j][i]\n",
    "        if 0 in m:\n",
    "            e2.append(tpp_ss[j][i][1])\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "sns.distplot(np.array(energies))\n",
    "sns.distplot(np.array(e2))\n",
    "\n",
    "plt.xlabel(\"Energy (kcal/mol)\")\n",
    "\n",
    "plt.savefig(\"../test/tpp_dist.png\", facecolor='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-23T00:14:15.582903Z",
     "start_time": "2021-04-23T00:14:11.769676Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "\n",
    "energies = []\n",
    "e2 = []\n",
    "\n",
    "for j in range(0, 100):\n",
    "    for ss in tpp_constrained_ss[j]:\n",
    "        energies.append(ss[1])\n",
    "\n",
    "    for i in range(0, len(tpp_constrained_ss[j])):\n",
    "        m = tpp_constrained_processed[j][i]\n",
    "        if 0 in m:\n",
    "            e2.append(tpp_constrained_ss[j][i][1])\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "sns.distplot(np.array(energies))\n",
    "sns.distplot(np.array(e2))\n",
    "\n",
    "plt.xlabel(\"Energy (kcal/mol)\")\n",
    "\n",
    "plt.savefig(\"../test/tpp_cons_dist.png\", facecolor='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-19T01:52:46.716796Z",
     "start_time": "2021-04-19T01:52:41.675777Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x576 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(4, 4, sharex=True, figsize=(16,8))\n",
    "\n",
    "e1 = []\n",
    "e2 = []\n",
    "\n",
    "indices = []\n",
    "for i in range(0, 100):\n",
    "    found = False\n",
    "    for j in range(0, len(tpp_ss[i])):\n",
    "        m = tpp_processed[i][j]\n",
    "        if 0 in m:\n",
    "            found = True\n",
    "    if found:\n",
    "        indices.append(i)\n",
    "        \n",
    "    if len(indices) > 15:\n",
    "        break\n",
    "\n",
    "print(indices)\n",
    "for j in indices:\n",
    "    e = []\n",
    "    for ss in tpp_ss[j]:\n",
    "        e.append(ss[1])\n",
    "    e1.append(e)\n",
    "\n",
    "    e = []\n",
    "    for i in range(0, len(tpp_ss[j])):\n",
    "        m = tpp_processed[j][i]\n",
    "        if 0 in m:\n",
    "            e.append(tpp_ss[j][i][1])\n",
    "    e2.append(e)\n",
    "    \n",
    "counter = 0\n",
    "for axis in fig.axes:\n",
    "    sns.distplot(e1[counter], ax=axis)\n",
    "    sns.distplot(e2[counter], ax=axis)\n",
    "    axis.set_xlabel(\"Energy (kcal/mol)\")\n",
    "    counter += 1\n",
    "\n",
    "fig.savefig(\"../test/tpp_sample_dist.png\", facecolor='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-19T01:52:51.418390Z",
     "start_time": "2021-04-19T01:52:46.720367Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(4, 4, sharex=True, figsize=(16,8))\n",
    "\n",
    "e1 = []\n",
    "e2 = []\n",
    "\n",
    "indices = []\n",
    "for i in range(0, 100):\n",
    "    found = False\n",
    "    for j in range(0, len(tpp_constrained_ss[i])):\n",
    "        m = tpp_constrained_processed[i][j]\n",
    "        if 0 in m:\n",
    "            found = True\n",
    "    if found:\n",
    "        indices.append(i)\n",
    "        \n",
    "    if len(indices) > 15:\n",
    "        break\n",
    "\n",
    "print(indices)\n",
    "for j in indices:\n",
    "    e = []\n",
    "    for ss in tpp_constrained_ss[j]:\n",
    "        e.append(ss[1])\n",
    "    e1.append(e)\n",
    "\n",
    "    e = []\n",
    "    for i in range(0, len(tpp_constrained_ss[j])):\n",
    "        m = tpp_constrained_processed[j][i]\n",
    "        if 0 in m:\n",
    "            e.append(tpp_constrained_ss[j][i][1])\n",
    "    e2.append(e)\n",
    "    \n",
    "counter = 0\n",
    "for axis in fig.axes:\n",
    "    sns.distplot(e1[counter], ax=axis)\n",
    "    sns.distplot(e2[counter], ax=axis)\n",
    "    axis.set_xlabel(\"Energy (kcal/mol)\")\n",
    "    counter += 1\n",
    "\n",
    "fig.savefig(\"../test/tpp_cons_sample_dist.png\", facecolor='w')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SAM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-23T00:14:53.029099Z",
     "start_time": "2021-04-23T00:14:26.631958Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
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      "99\n"
     ]
    }
   ],
   "source": [
    "sam_seqs = []\n",
    "for record in SeqIO.parse(\"../test/sam_sample.fasta\",\"fasta\"):\n",
    "    sam_seqs.append(str(record.seq))\n",
    "\n",
    "sam_ss = []\n",
    "for i in range(0, 100):\n",
    "    print(i)\n",
    "    ss = sample_structures(sam_seqs[i], 10000)\n",
    "    sam_ss.append(ss)\n",
    "\n",
    "pickle.dump(sam_ss, open(\"../test/sam_ss.pickle\", \"wb\"))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-26T19:47:13.835139Z",
     "start_time": "2021-03-26T19:17:41.139293Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
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    }
   ],
   "source": [
    "sam_modules = module_search(sam_ss, get_db(\"sam\"), sam_seqs, \"sam_modules\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-26T19:47:37.905673Z",
     "start_time": "2021-03-26T19:47:13.866125Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
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   ],
   "source": [
    "sam_processed = process_modules(sam_modules, sam_map_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-26T03:36:40.065264Z",
     "start_time": "2021-03-26T03:36:24.147656Z"
    },
    "collapsed": true
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   "outputs": [
    {
     "name": "stdout",
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   ],
   "source": [
    "sam_ss = []\n",
    "for record in SeqIO.parse(\"../test/sam_sample_ss.txt\",\"fasta\"):\n",
    "    sam_ss.append(str(record.seq))\n",
    "    \n",
    "constrained_sam_ss = []\n",
    "for i in range(0, 100):\n",
    "    print(i)\n",
    "    ss = sample_structures(sam_seqs[i], 10000, sam_ss[i])\n",
    "    constrained_sam_ss.append(ss)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-26T03:36:41.166957Z",
     "start_time": "2021-03-26T03:36:40.067342Z"
    }
   },
   "outputs": [],
   "source": [
    "pickle.dump(constrained_sam_ss, open(\"../test/sam_constraint_ss.pickle\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-26T03:50:20.324654Z",
     "start_time": "2021-03-26T03:36:41.169402Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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   ],
   "source": [
    "sam_constraint_modules = module_search(constrained_sam_ss, get_db(\"sam\"), sam_seqs,\"sam_constraint_modules\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-26T19:48:14.587905Z",
     "start_time": "2021-03-26T19:47:37.913071Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "sam_constraint_processed = process_modules(sam_constraint_modules, sam_map_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-26T19:58:07.614429Z",
     "start_time": "2021-03-26T19:58:04.569727Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1596, 82, 26, 41, 181, 44, 4, 3551, 21, 83, 0, 11, 168, 66, 33, 2, 128, 702, 2780, 10, 306, 1, 5528, 25, 5, 201, 0, 703, 46, 4138, 10, 85, 77, 1240, 16, 187, 33, 363, 234, 91, 48, 17, 145, 3233, 53, 241, 1066, 13, 115, 74, 54, 9, 360, 292, 92, 1284, 10, 20, 64, 32, 313, 33, 477, 112, 200, 186, 63, 6, 1091, 404, 599, 20, 85, 918, 37, 143, 5, 2, 0, 29, 50, 92, 1, 12, 3, 42, 26, 18, 13, 14, 20, 827, 368, 1799, 132, 518, 0, 667, 732, 73]\n",
      "40170\n"
     ]
    }
   ],
   "source": [
    "# analyze distribution of shapes found\n",
    "# lets look specifically at module 0\n",
    "all_counts = []\n",
    "for seq in sam_processed:\n",
    "    count = 0\n",
    "    for sample in seq:\n",
    "        if 0 in sample:\n",
    "            count += 1\n",
    "    all_counts.append(count)\n",
    "print(all_counts)\n",
    "print(sum(all_counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-26T19:58:10.614515Z",
     "start_time": "2021-03-26T19:58:09.424187Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1044, 3, 9, 0, 81, 135, 3, 1527, 1, 158, 4, 17, 201, 76, 0, 0, 7, 52, 239, 9, 1, 0, 4421, 4, 3, 176, 3, 0, 0, 3788, 26, 18, 181, 1065, 22, 170, 114, 115, 75, 91, 0, 15, 34, 2323, 0, 241, 0, 1, 50, 74, 8, 0, 285, 122, 169, 914, 20, 6, 7, 0, 723, 46, 1, 55, 215, 337, 0, 0, 457, 97, 0, 11, 185, 1098, 46, 159, 0, 55, 0, 2, 0, 0, 0, 17, 0, 0, 0, 0, 38, 6, 0, 310, 432, 346, 26, 0, 7, 0, 833, 29]\n",
      "23639\n"
     ]
    }
   ],
   "source": [
    "# analyze distribution of shapes found\n",
    "# lets look specifically at module 0\n",
    "all_counts = []\n",
    "for seq in sam_constraint_processed:\n",
    "    count = 0\n",
    "    for sample in seq:\n",
    "        if 0 in sample:\n",
    "            count += 1\n",
    "    all_counts.append(count)\n",
    "print(all_counts)\n",
    "print(sum(all_counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-26T20:48:41.827507Z",
     "start_time": "2021-03-26T20:48:32.400769Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "sam_ss0 = []\n",
    "for record in SeqIO.parse(\"../test/sam_sample_ss0.txt\",\"fasta\"):\n",
    "    sam_ss0.append(str(record.seq))\n",
    "    \n",
    "constrained_sam_ss0 = []\n",
    "for i in range(0, 100):\n",
    "    print(i)\n",
    "    ss = sample_structures(sam_seqs[i], 10000, sam_ss0[i])\n",
    "    constrained_sam_ss0.append(ss)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-26T03:23:38.832413Z",
     "start_time": "2021-03-26T03:23:37.842072Z"
    }
   },
   "outputs": [],
   "source": [
    "pickle.dump(constrained_sam_ss0, open(\"../test/sam_constraint_ss0.pickle\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-26T21:11:36.602435Z",
     "start_time": "2021-03-26T20:48:48.880436Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "sam_constraint_modules0 = module_search(constrained_sam_ss0, get_db(\"sam\"), sam_seqs,\"sam_constraint_modules0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-26T21:11:56.952891Z",
     "start_time": "2021-03-26T21:11:36.622976Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    }
   ],
   "source": [
    "sam_constraint_processed0 = process_modules(sam_constraint_modules0, sam_map_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-26T21:11:57.094978Z",
     "start_time": "2021-03-26T21:11:56.960612Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9383, 1, 10000, 2992, 75, 3382, 10000, 1451, 2725, 1068, 1, 10000, 2726, 1937, 10000, 0, 20, 3744, 302, 47, 10000, 0, 7117, 10000, 6, 10000, 2, 0, 0, 10000, 0, 2197, 5380, 4572, 10000, 9394, 7006, 5067, 1606, 10000, 0, 10000, 1056, 5510, 0, 1654, 0, 218, 2501, 10000, 10000, 0, 10000, 3314, 7513, 1143, 10000, 10000, 3653, 0, 7168, 1375, 10000, 2710, 619, 7507, 2198, 0, 6101, 2043, 0, 1275, 10000, 10000, 4701, 3860, 6686, 0, 0, 10000, 0, 3, 0, 9462, 748, 589, 0, 0, 234, 0, 0, 846, 10000, 2474, 13, 0, 8, 3, 7201, 4949]\n",
      "381536\n"
     ]
    }
   ],
   "source": [
    "# analyze distribution of shapes found\n",
    "# lets look specifically at module 0\n",
    "all_counts = []\n",
    "for seq in sam_constraint_processed0:\n",
    "    count = 0\n",
    "    for sample in seq:\n",
    "        if 0 in sample:\n",
    "            count += 1\n",
    "    all_counts.append(count)\n",
    "print(all_counts)\n",
    "print(sum(all_counts))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-23T00:16:32.452482Z",
     "start_time": "2021-04-23T00:15:10.045050Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    }
   ],
   "source": [
    "# load \n",
    "sam_modules = pickle.load(open(\"../test/sam_modules.pickle\", \"rb\"))\n",
    "sam_processed = process_modules(sam_modules, sam_map_func)\n",
    "sam_ss = pickle.load(open(\"../test/sam_ss.pickle\", \"rb\"))\n",
    "sam_boltz = get_boltzmann_probs(0, sam_ss, sam_processed)\n",
    "sam_constrained_modules = pickle.load(open(\"../test/sam_constraint_modules.pickle\", \"rb\"))\n",
    "sam_constrained_processed = process_modules(sam_constrained_modules, sam_map_func)\n",
    "sam_constrained_ss = pickle.load(open(\"../test/sam_constraint_ss.pickle\", \"rb\"))\n",
    "sam_constrained_boltz = get_boltzmann_probs(0, sam_constrained_ss, sam_constrained_processed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-06T00:14:11.278998Z",
     "start_time": "2021-04-06T00:14:11.113167Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ALJKkWWS6IabHJLmR5sqlLdpl2s+bDx6ZJGmsppuLad5MBiJJml28I1qS1MkEIUnqZIKQJHUyQUiSOpkgJEmdBk0QSQ5IcnmSVUmO66hPkhPa+ouS7NmW75Dk60kuS7IyyauGjFOStK7BEkSSecCJwCJgd+CwJLtPWm0RsEv7Wgyc1JavAf5bVT0c2Ad4eUdbSdKAhuxB7AWsqqorqupW4HTg4EnrHAycVo3zgG2SbFdVV1fVdwGq6ibgMmDhgLFKkiYZMkEsBK4c+byadb/k17tOkp2AxwLf6dpJksVJlidZft11193dmCVJrSETRDrKJs/hNO06Se4HfBY4pqpu7FiXqjqlqiaqamLBggV3OVhJ0p0NmSBWAzuMfN4euKrvOknuTZMcPtE+sEiSNIOGTBDLgF2S7JxkM+BQYMmkdZYAh7dXM+0D3FBVVycJ8GHgsqp694AxSpKm0OuRo3dFVa1JcjRwFjAPOLWqViY5qq0/GVgKHAisAm4BjmybPwl4MXBxkgvastdX1dKh4pUk3dlgCQKg/UJfOqns5JHlAl7e0e6bdJ+fkCTNEO+kliR1MkFIkjqZICRJnUwQkqROJghJUicThCSpkwlCktTJBCFJ6mSCkCR1MkFIkjqZICRJnUwQkqROJghJUicThCSpkwlCktTJBCFJ6mSCkCR1MkFIkjqZICRJnUwQkqROJghJUicThCSpkwlCktTJBCFJ6mSCkCR1MkFIkjqZICRJnUwQkqROJghJUicThCSpkwlCktTJBCFJ6mSCkCR1MkFIkjqZICRJnQZNEEkOSHJ5klVJjuuoT5IT2vqLkuzZt60kaViDJYgk84ATgUXA7sBhSXaftNoiYJf2tRg4aQPaSpIGNGQPYi9gVVVdUVW3AqcDB09a52DgtGqcB2yTZLuebSVJA5o/4LYXAleOfF4N7N1jnYU92wKQZDFN7wPg5iSX342Y1XgAcP24g5gtXj3uADQVj9PW3TxGHzxVxZAJIh1l1XOdPm2bwqpTgFM2LDRNJ8nyqpoYdxzSdDxOhzdkglgN7DDyeXvgqp7rbNajrSRpQEOeg1gG7JJk5ySbAYcCSyatswQ4vL2aaR/ghqq6umdbSdKAButBVNWaJEcDZwHzgFOramWSo9r6k4GlwIHAKuAW4Mjp2g4Vq9bhkJ3uCTxOB5aqzqF9SdImzjupJUmdTBCSpE4miFkiyU5JLplU9qYkrxljPH9zF9t+ayPGcMn619S4JXlQktOT/CjJpUmWJtl1I237OXdlJoUkB22saXrG+X9xnEwQmspOQGeCSDLtxQ1V9cQhAtLslCTA54BzquohVbU78HrggRtpF8+hmXKna99THotVtaSq3raRYtgkmSDuAZKck+TtSc5P8oMkT27L5yU5PsnF7WSHr2jLn5bke235qUn+pC3/SZI3J/luW7dbW75fkgva1/eSbAm8DXhyW3ZskiOSfCbJF4Czk9wvyVdHtnXwSLw3t+/7t7GfkeT7ST7RfpmQ5HFJzk2yIslZ7RQra8svTPJt4OUz96+su+GpwB/aKxMBqKoLgG8meWeSS9pj5IWw3uPibW0P5KL22H4icBDwzvZYfEjb9i1JzgVeleTZSb7THrtfSfLAdltHJHl/u/zRNBODfivJFUkOWRtrktcmWdbu880j5W9IM2HoV4CHDf6vOBtVla9Z8KL5xX7JpLI3Aa8BzgHe1ZYdCHylXf6vwGeB+e3nbYHNaaYp2bUtOw04pl3+CfCKdvnvgQ+1y18AntQu34/m8uf9gS+OxHIEzY2N27af5wNbtcsPoLlUee1VcTe37/sDN9Dc6Hgv4NvAvsC9gW8BC9r1XkhzKTPARcB+7fI7J/+b+Jp9L+CVwHs6yp8HfJnmUvUHAj8DtpvmuNgWuHzkONqmff8ocMjIds8BPjDy+f4jbV428n/lCOD9I9v4TLu/3WnmegN4Js3lsmnrvgg8BXgccDFwH2Cr9vh+zbj/rWf6NeSd1NowU11vvLb8zPZ9BU0yAXg6cHJVrQGoql8leQzw46r6QbvOx2h+ib+3YzvPbZf/A3h3kk8AZ1bV6vYH3WRfrqpftcsB3pLkKcDtNPNnPRD4xaQ251fVaoAkF7Sx/wZ4JPDldj/zgKuTbE3zpXBu2/bjNDP66p5pX+CTVXUbcE37i//xwI10HxfnAb8DPpTkX2m+rKfyqZHl7YFPtb3QzYAfT9Hm81V1O3Dp2l4GTYJ4JvC99vP9aGaX3hL4XFXd0sa4Sd6o6xDT7PFLml9Co7bljsnIft++38YdNziGfvNbjVpnO9WM074M2AI4b+3QU4ffjiz/LbAAeFxV7QFcQ9N7mWp/o/sMsLKq9mhfj6qqZ07x92j2W0nzi3uy6Y7FdY6L9ofOXjS94ucAX5qm/eix+D6ansKjgP9C93E4eZ8ZeX/ryLH40Kr6cFu3yR+LJohZoqpupvkV/TSAJNsCBwDfnKbZ2cBRa0/UtW2+D+yU5KHtOi8Gzp2iPW27h1TVxVX1dmA5sBtwE82vqKlsDVxbVX9I8lSmmRGyw+XAgiRPaPd/7ySPqKrfADck2bdd7283YJsan68Bf5Lk79YWJHk88Gvghe25sgU0QzfnT7WRJPcDtq6qpcAxwB5tVZ9j8eft8ks2MPazgJe2+ybJwiR/Bvw78NdJtmjPyT17A7c7JzjENLscDpyY5F3t5zdX1Y+mGO4B+BCwK3BRkj8AH6yq9yc5EvhMmziWASdPtYHWMe2X/G3ApcC/0QwbrUlyIc347a8ntfkE8IUky4ELaBJTL1V1a3uS8IR2WGk+zRDYSprpVk5NcgvNf17NclVVSf4aeG+ay0p/R3O+6xiaIZsLaX6N/0NV/WKaHuqWwL8k2Zzml/2xbfnpwAeTvBI4pKPdm2iO95/TDFPtvAGxn53k4cC32/9nNwMvqqrvJvkUzbH9U+Abfbc5lzjVhiSpk0NMkqROJghJUicThCSpkwlCktTJBCFJ6mSC0JzVzqWzsp1j54Ike4/UzU9yfZK3TmpzTpKfZeTa4iSfTzu/VMc+Omf5nKr8bv49+7dzE639fFSSwzfmPqRR3gehOam9Ce9ZwJ5V9fskD6CZhmGtZ9LcsPeCJK+vO1/v/RvgSTSTzW1DM3/QbLA/zXX634I/PrZXGow9CM1V2wHXV9XvAarq+qq6aqT+MOCfaSaQ22dS29OBQ9vl53LH/FV3SdsrmWiXH5DkJ+3yEUnOTPKlJD9M8o6RNgekmSn3wjSz5u4EHAUc2/aGnjzaS0myR5Lz2t7S55Lcf2Tf68wELPVhgtBcdTawQ/ul+IEk+62tSLIF8DSayeA+SZMsRn0VeEqSeTSJ4lMMZw+a2WwfRTMtxQ7ttBQfBJ5XVY8Bnl9VP6G5I/497ZxBk+/sPQ3471X1aJpZSN84Uje/qvaiubP5jUg9mSA0J7VzWz0OWAxcRzPb5xFt9bOAr7czdX6WZs6deSPNb6OZA+uFwBbtl/NQvlpVN1TV72imOXkwTY/m36vqx+3f8qvpNtAxC+7HaOY9WqtrJmBpvUwQmrOq6raqOqeq3ggcTfN8Amh6DE9vh3pWAH9K89CbUafTzBL66dHCJP+rHeK5YANCWcMd/9cmzzQ61Wy3G3MOnK6ZgKX1MkFoTkrysCS7jBTtAfw0yVY0zynYsap2qqqdaJ6XMXmY6RvAW2mGoP6oqt6wdmroDQjnJ9wxHXbXZHOTfRvYL8nO7d+ybVveOatpVd0A/Hrk/MJ6Z/CV+vDXhOaq+wHva69CWkPzRLDFNCedv7b25HXrX4B3pH00KzQzlALH3439z+eOX+7HA59O8mKaqbGnVVXXJVkMnJnkXsC1wDNonvx3RprHu75iUrOXACcnuQ9wBc2suNLd4myu0gCSfI5m+vWl445FuqscYpI2siQX0zxP4+xxxyLdHfYgJEmd7EFIkjqZICRJnUwQkqROJghJUicThCSp0/8HU5Xop3kEu6QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "means = [np.mean(sam_boltz), np.mean(sam_constrained_boltz)]\n",
    "\n",
    "plt.bar([0,1], means, align='center', alpha=0.5)\n",
    "plt.xticks([0,1], labels=[\"Unconstrained\", \"Constrained\"])\n",
    "plt.ylabel(\"Boltzmann probability\")\n",
    "plt.xlabel(\"SAM-I Junction\")\n",
    "plt.ylim([0, 0.1])\n",
    "plt.savefig(\"../test/sam_bolt_avg.png\", facecolor='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-06T00:11:49.656796Z",
     "start_time": "2021-04-06T00:11:49.635371Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44\n"
     ]
    }
   ],
   "source": [
    "# look at direct comparisons of boltz prob instead of just average\n",
    "total = 0\n",
    "for i in range(0, 100):\n",
    "    if sam_constrained_boltz[i] > sam_boltz[i]:\n",
    "        total += 1\n",
    "print(total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-06T00:11:44.941069Z",
     "start_time": "2021-04-06T00:11:24.385331Z"
    }
   },
   "outputs": [],
   "source": [
    "# modify function to return a count + boltzmann prob\n",
    "module_tuples = [(0,1), (0,2), (0,3), (0,1,2), (0,1,3), (0,2,3), (0,1,2,3)]\n",
    "\n",
    "all_combos = []\n",
    "for module_combo in module_tuples:\n",
    "    all_combos.append(get_co_dynamics(module_combo, sam_ss, sam_processed))\n",
    "\n",
    "all_c_combos = []\n",
    "for module_combo in module_tuples:\n",
    "    all_c_combos.append(get_co_dynamics(module_combo, sam_constrained_ss, sam_constrained_processed))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-06T00:13:48.259595Z",
     "start_time": "2021-04-06T00:13:47.902370Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "means = np.mean(all_combos, axis=1)\n",
    "std = np.std(all_combos, axis=1)\n",
    "x_pos = np.arange(len(means))\n",
    "plt.bar(x_pos, means, align='center', alpha=0.5)\n",
    "plt.ylim([0,0.1])\n",
    "plt.xticks(x_pos, labels=module_tuples)\n",
    "plt.ylabel(\"Boltzmann probability\")\n",
    "plt.xlabel(\"SAM-I module combinations\")\n",
    "plt.savefig(\"../test/sam_bolt_avg_combo.png\", facecolor='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-06T00:14:40.318843Z",
     "start_time": "2021-04-06T00:14:40.104033Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "means = np.mean(all_c_combos, axis=1)\n",
    "std = np.std(all_c_combos, axis=1)\n",
    "x_pos = np.arange(len(means))\n",
    "plt.bar(x_pos, means, align='center', alpha=0.5)\n",
    "plt.ylim([0,0.1])\n",
    "plt.xticks(x_pos, labels=module_tuples)\n",
    "plt.ylabel(\"Boltzmann probability\")\n",
    "plt.xlabel(\"SAM-I module combinations\")\n",
    "plt.savefig(\"../test/sam_bolt_avg_combo_constrained.png\", facecolor='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-06T00:17:40.874178Z",
     "start_time": "2021-04-06T00:17:40.710594Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# look at individual increase\n",
    "\n",
    "increases = [68, 62, 44]\n",
    "\n",
    "plt.bar([0,1, 2], increases, align='center', alpha=0.5)\n",
    "plt.xticks([0,1, 2], labels=[\"Cob\", \"TPP\", \"SAM-I\"])\n",
    "plt.ylabel(\"% sequences\")\n",
    "plt.ylim([0, 100])\n",
    "plt.savefig(\"../test/increases.png\", facecolor='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-23T00:17:01.409475Z",
     "start_time": "2021-04-23T00:16:56.634760Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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LSU5OBmD16tXxCEkQYicUkPc06GsNAeRmowRMCIcdfoxaFQZd3/5EZFv07K62Iw2S5b6HurgkhO3btzNz5kwApk6dyu7du6PHysvLyc3NxWKRx2/PmDGDwsJChg0bhtfr5YYbbiAUCnH77bczderUbp/j9/uRJIni4uJ4fBl95vP5RGy9kOixle34lHGAExOuurp2x5NTvTh6UKYdM5Y0oH7PpzSHc2MWWyy+bwG1icomB6l6JbV1tdHyienadq97Um5Q+PEGw5RVVpOkjNDU2ISzrqLPMcZaov/M9VdscUkILpcLk+nI7EaVSkUoFEKtVuNyuaLNQgBGoxGXy0VSUhKLFy/mqquu4uDBg/zkJz9h/fr1qNVdh6jTydXZSZMmxePL6LPi4mIRWy8kemzjDPJS0OacPMxBb/sTDHqM2dnHLxs+BowZZClbyIrR1xqr79uBRhfN3gjn5KWQk50TLdcbDO1e96R83PBMPjlwAIXeijriJC09jREpI/scY6wl+s9crGPrKsHEpVPZZDLhdrujryORSPQP+7HH3G43ZrOZMWPG8IMf/ACFQsGYMWOwWq00NIht+IQEZG+dg9CXUUYA6RPkjuUEU9HkIRyRTmgPhK6kt/ZBNLn9fb6XEH9xSQjTp09n8+bNABQVFZGfnx89lpeXR0VFBTabjUAgQGFhIdOmTeONN95gxYoVANTX1+NyucjIyIhHeILQN44q+WNf+hAAMvLluQgJ1r5e2rqGUY6lF3MsjmFOUqNRKWh0ioQwGMSlyWjOnDls3bqVhQsXIkkSy5YtY926dXg8HhYsWMDSpUtZvHgxkiRRUFBAVlYW8+bN47e//S1XX301CoWCZcuWddtcJAgDxl4lr2GkPfFVQNtJnwA+G7gbjuyklgBK651oVArS+zDktI1SoSDNqKPJHQD6fj8hvuLyF1epVPLggw+2K8vLy4t+Pnv2bGbPnt3uuFar5fHHH49HOIIQW/ZqSB7R9/ukj5c/Nuw7sYTgaYHAMbN/tebOz+2F0sMuspOTUCp6vktad9JMWuodPkRCSHziLbggnChHlbz0RF9lTJQ/Hi6GMTN7fl3ACWUftS8b972+xwNIkkRpvYvJOckxuR/I/QjFtQ4iCdY0JnQkZioLwomyV4MlBjWE5GGgT4W6b/p+rxg52OTB5Q+RY+17/0GbdJOWiAQOXyRm9xTiQyQEQTgBipAPvM2QHIMagkIBOVMSKiFsKWsE6NW2mV1pG2lk8yXmMh3CEaLJSBBOgMZTL38SixoCQPYU+PIZCAdBpenz7eyeQKfrBZl1aiwG7XGv31raSFayjjTj8c/tqTSREAYNkRAE4QSo2xJCLGoIADmnQjgADXsh+5Q+365tcxuQ+wPKG9zsqbFzw3mjmXtSx8ljRwtHJD4rb2Tm+AwUMepQBjBqVSRplNi8IiEkOpEQBOEEaDyH5U9iWUMAqP3mxBNCJAw1O+RkkjYOSG93+K0d1RRWtKAAvjrQzM9m5XHnJRO7vN2uajsOX4jTR/diF7huKFqHntp9YqXTRCcSgiCcAI23rYYwDFz1vb9ROCSvdKrSgFoPFVthwmVg6OEfYykC256TkwEKeG0hhnMehWx5VeFau5fCihbOHJPKRZOy+Lqyhac/LidJo+LK6UdqN0c3JW0plVcGmDEqhV3Vjt5/bZ2wGjRUN4u9EBKdSAiCcALUnnowZoK6j2Pqgx44sE3+3JwFlZ/Lw0l7mhAqtsrJYPIP5VrG1y+S/fVjNEy5AoCNew+jUyu5eHI2eq2KWy4cx/4GN3/aUILdG4x2Gp+fn47FoCUYjvD6tkPMGJVCSgz7D9qkGrTsFUNPE54YZSQIJ0DjqY/NHISjpYyWawuhHr6DdjdA8Tp5HsOYWWBIg7NuQec4SFL5eg47fOypcXBOXjp6rQoAlVLBgtNHkmbS8eqXlTS52i8l8Z+iGqpavPzsgryOz4uBFKOWsATNLlFLSGQ9SghHL18tCEOZnBBi1H/QJmUsREJw+Nuenb/jZXlU0skF8tBVgLzZhE3DSNn2BM1NctPP1ada2l2WpFFx3Vlyk9I/PjsYTQq+YJinPy5jYraZ702KzxIaKQZ5BFWt2CwnofUoIfz9739n/vz5vPzyyzgcsW1bFIRBQ5JQew7HZtmKo7VtllO9/fjn+hzwzVp5dJLxqMUfwwHc1omoDu+mqnQHYww+huk6vhtPM+n40Tmj8QTCPPNJOSs/LueKp7ZS3uDm13PyYzq66Ggprf0UtXbvcc4UBlKPEsITTzzBc889h0Kh4NZbb2XJkiV8+eWX8Y5NEBKLz44q5Il9k5HOJPdL1Ow4/rlfvwgBF+R1XKrCZ5XXRhrt28vUZFeXt8hNNXDzBXlkmHWs2XaIw04/L1x/OnNP6uPqrd1o65eoETWEhNbjTuXGxkZqampoaWkhLy+P9evX8/bbb4t9j4Whw9667HWs5iAcLXWsnBAiEVB28T4tEoFtf4Ph08HacZOZcFIKDm0Ws8JF7E+e3O3jMsw6bjo/jzPHpDAy1YhWHd/uRI1KiUGjEPsrJ7geJYSrrrqKpKQk5s+fz6233opWK2f7xYsXxzU4QUgojtaNcWLdhwByQjj0BdTvlpez6Ez5Rmg5CGfd0uVttqumcK5yExqDvUeP1WlUcU8GbZJ1KtFklOB69JNw7733snr1ai6//HK0Wi1fffUVIPctCMKQ0VZDiEdCyJwEKGDvO12fs+05uWmpm5VN3/LNQKsIkeY+EPsY+0hOCKKGkMi6rSEUFhZSVlbGP/7xD66//noAwuEwr776Ku+8080PriB8FzmqkRQqFH3dOrMzOrPcFFS8Di68u+PxpnIo+QDO/02Xax7Zgyre904gmKTG5KmkOfZR9ok5SUl5rY9wREKljE/ntdA33dYQkpOTaWxsJBAI0NDQQENDAy0tLdxxxx39FZ8gJA57NSF9BihV8bn/uDny0NPGso7HPn9KTgSn/6TLy7916Qmiplk7DJO3Kj4x9kGyTkU4IolmowTWbQ0hPz+f/Px85s+fT2Zm4mzxJwgDwl5F0JBJ39ck7cK478EnK2DPv+CCO4+Uu5ug6FWYskCe1Wyr7PTyb50GFEgEjMPJsG1DEQnGK9JeSdbJ7z+rW7yMSDEMcDRCZ7pNCL/61a/485//zJVXXtnh2JYtW+IWlCAkJEcVQXN+/O5vzpaHk37xNJz5U3nfZoBND0PYD2f/otvLv3UaGGsK4TMOR9nyOTpbGWjTur0mFI5Q1eJpV+YPxmdVUrNOrllV20QNIVF1mxD+/Oc/Ayf+xz8SifDAAw+wb98+tFotDz/8MKNGjYoe37hxI0899RRqtZqCggLmz58fPdbU1MSVV17J888/324fZkEYUJEIOGoIZp0X3+dcdD+sOh+2/En+vOJzKHxeTgaZXa9UKkkSe5wGZmX7cRnkYbGG5j2QfWa3j/MGI+wob9/bMC3X2tevolNm7ZEagpCYejTKaNu2bWzevJlPPvmEiy66iHXr1nV7/oYNGwgEAqxZs4YlS5awYsWK6LFgMMjy5ct5/vnnWb16NWvWrKGhoSF67L777iMpKXbb9wlCTHgaIRwgZIhDh/LRck6FU+bDlifgtWvg5QKw5nbe0XyUQ44Q9pCak60BAupkAmozhqY98Y31BKlVClIMGlFDSGA9SgiPPfYYo0eP5qWXXuK1117j9ddf7/b87du3M3OmvGn41KlT262FVF5eTm5uLhaLBa1Wy4wZMygsLATg0UcfZeHChaK/Qkg8tkMABOOdEAC+/0c462dQ+l8YMxN+9A5ojd1eUlQnL1NxsiUACgUu/XD0zcXxj/UEZVuSREJIYD2amKbT6UhLS0OtVpORkUEg0P2KhS6XC5PpyJ6sKpWKUCiEWq3G5XJhNpujx4xGIy6Xi3/961+kpqYyc+ZMnn322R4F7/f7kSSJ4uLE+8EH8Pl8IrZeSMTYzJVbGQG4NOnR2DI0PoJ1de3OS0714uhlmcbaREOtW34x6jr5H0CdB+qOfD86e+422yh0ygjZChulDjvNCispzn00VO8npNIzMV1LbV1th6+rs/ITOfdEykPBICk6JQfq7Qn3/5uIP3Nt+jO2HiUEk8nE9ddfzzXXXMMrr7xCTk73W/GZTCbcbnf0dSQSQa1Wd3rM7XZjNptZvXo1CoWCzz//nOLiYu666y5WrlxJRkZGh/u30enkNeknTZrUky+j3xUXF4vYeiEhY2t8HwBFymgmtsVmq4TsY9b/Megx9rYsPY10a277Mk+LvE/C0YKqDs/9tjzCBJMXa7IRi1ciwigUji+YaHDSlDIWvcFATnbH39vOyk/k3BMpr62rJTdDz9fVTiZOnBi3hfR6IyF/5lrFI7auEkyPEsKTTz5JZWUl48aNo6SkhKuuuqrb86dPn86mTZu47LLLKCoqIj//yMiMvLw8KioqsNlsGAwGCgsLWbx4MZdcckn0nEWLFvHAAw90mwwEoV/ZKsCQhqTp5+GSASeUfdS+bMTp7V4GI7D7cIArs46MFnInyU1bKc59NKWcGvcweyorOQlfMEKTO0C6qY+bDAkx16OE0NTUxKZNm1i/fn207Be/6HoI3Jw5c9i6dSsLFy5EkiSWLVvGunXr8Hg8LFiwgKVLl7J48WIkSaKgoICsrH5olxWEvmipAOuo4583APbZ1fjDEpPNHkAe2hnQWAhrTFidJQMb3DGyLXISqG7xioSQgHqUEG699VbOPvvs4zYVtVEqlTz44IPtyo4eQjp79mxmz57d5fWrV6/u0XMEod/YKuQRQAloR7M8VU5OCK39cwoFPkseKY59AxdYJ7KT5RGE1TYvp460DmwwQgc9SghGo5Ff//rX8Y5FEBJTJCyPMpp0eXyfEw51nIUcPP5icDub1aTpleTo2s9M9lnHYT3wHkiRWEbZJ1kWOSHUiJFGCalHCWH8+PG8++67TJo0KdoRNGbMmLgGJggJw1kLkWD8m4yCHjiwrX3ZMf0FndnRrOHUbB3H9tH6rONJC3sxeaqA1NjFeRzjk0PoIx4ywn6mmORtOr1KA7V1YNapMenUVInJaQmpRwmhuLi4Xa+0QqHgpZdeiltQgpBQ6loneGkMZGh8R97F9+Dde7w1+RWUO9UUTOnYHu+zyM20FlcZ0MUeC3Ggj3hwf/sBwVQD7ma5o9s4eS4g/+0YZhVzERJVjxLC6tWrcTqdVFdXM3LkSIzG7ifJCMJ3SlOp/NFWSbDq4JEhnz149x5v2xrl/oMzhuugpv0xv1mu0Vhc5f0dVreGW/Vi+YoE1aOE8MEHH7By5UrC4TCXXHIJCoWCn//85/GOTRASg6MaUIA+FVyNAx0NAJ5AGH8ozKe1erRKifw0NfY6HcHwkf4Ca7KJgCGL0b5iQuF6ppj8eJUGSh093jk3Loan6Pm60jagMQid69HSFS+88AJr167FarXy85//nA0bNsQ7LkFIHPZD8sqjqoH9Q3o0fyhMSb2Lzw+rGWfw4guEKa61cVQ+QB324VGa0BzeRXDfBtzffoA+4un6pv1kuNWA3RvE5Q8NdCjCMXqUEBQKBVqtFoVCgUKhQK/XxzsuQUgcLRVgTLxJkt6wkgOeJCaauv4j79VloPc3JtRIo+Ep8t8PMdIo8fQoIZx++uksWbKE+vp67rvvPk455ZR4xyUIicN+CIzpAx1FB/tceiQUTDR3nRA8ukyUUgiVL3E21BxubZ2LIPoREs5x68B79+5FqVSyZ88efvCDH5CcnMyiRYv6IzZBGHheG3hbErKGUGgzoVNGmGjq+g+rVycnMrWnHhSJMVR8uFVe/qNK1BASTrc1hPfff5+7776b4cOHc8cdd5CcnMzatWtFH4IwdDS3jtBJsBpCKAJf2sxMt7jQKaUuz/Pq5ESmdh/ur9COK9OsQ6NSiBpCAuq2hvDSSy/x8ssvYzAcWdDrf//3f/nZz37GRRddFPfgBGHANR+QPxoSq4awvVmHI6Tm7BRHt+dFVDr8mmQ0nnowdXtqv1EqFeRY9KIPIQF1W0NQq9XtkgHIy1erVKq4BiUICaOprYbQ/d7E/e3DWj06ZYRpFvdxz/XqMuQmowQiJqclpm4TQlfrlUciiTNiQRDiqrkczNmg0g50JFEN7jAf1Bo4zepE201zURuvLgO1tyEhRhqFwhGqWjykGLRUNnmoapH/2T3db7ol9I9um4zKyspYsmRJuzJJkigvT6yZj4IQN837wZJ7/PP60YotLfjCCubl9GySnFeXgSISQhewxTewoygiYZR+G9A+kXqDEXaUN+MPRWh0+dm49zBqpZLz89OxGBIn6Q5V3SaEP/3pT52WL1y4MB6xCELiaSqHvK6Xau9v71XpeLPYzfVjnQxLCh7/AsDT2rFs8DfQH+/DjfWF5JWvJCnQgj9zNrXp53Q4x6rXIAEOb4hUo0gEiaLbhHDGGWf0VxyCkHi8LeBthpSB3xinzKHi76UGXjugZ0qWlhvHOalq6tm1vtaEoO+HhGD0HGLUljvwq034UyeSe/gjIkoNLi5pd561tTZg8wREQkggPZqYJghDUvN++aNl5ICFUO8K8csvk7nov2m8cTCJG8Z5+OdV2RjUx+87aBNW6QjrLOj9DXGMFJAkTt/zCJJSxd7R19Ey+TqchpFkN30BUrjdqVaDvCifzdOzWo7QPxJncRZBSDRNrQkhZZS8fEU/K3WomP9OLe6Ajl9OdPOjcR7SkyRQKzjRFYmChiz03vgmhJzGLQxr3Ertqb8iEEoGhYK61DMZX/UG5trPgOnRcy16OSG0eEVnciIRNQRB6Eq0hjCi3x99yK3k2s1WNCoF713UzJKT3XIy6KWQIbN1TaPw8U/upUkHXsSjy6RpXEG0rDl5In5NMmmlb7Q7V6NSYtapRQ0hwYiEIAhdaS6H5BGgTurXx0YkuP2rZHxhBS9fmcm45L7/EQ8ZslBKIbTu2hhE2JHVsY/spi8pGXU1KI9qeFAoabKcgrFhB5pw+3kHVoMGmxhumlDikhAikQj33XcfCxYsYNGiRVRUtK9ub9y4kYKCAhYsWMDatWsBCIfD/Pa3v2XhwoVce+21VFZWdnZrQeg/TeWQNrbfHheMSLR4Aqws1rKtSctvJtnItajwBGKTEAB0jgN9vldn8iteJaTSUzbyqg7H7MYxKKQwI1y72pVbDVpRQ0gwcUkIGzZsIBAIsGbNGpYsWcKKFSuix4LBIMuXL+f5559n9erVrFmzhoaGBjZt2gTA66+/zq9+9SuWL18ej9AEoXueFnmLTFslNJWBMbPftsoMRyJ8WeXlz3uTmZrsIl99GIc3hCcQosUTiP7zhcLtNsLpiZAhEwCdPfYJQRF0M6r2fSpyLiGgtXQ47jSMJKLUkuvc0a7cqtdg9waJSL1vChNiKy6dytu3b2fmzJkATJ06ld27d0ePlZeXk5ubi8Ui/+DMmDGDwsJCLr30UmbNmgVATU0N6enHX0zM7/cjSVK7/Z4Tic/nE7H1wkDGlqHxEdy7HkXIR7bPhsPWhMppx1FXB8hvaGpbP09O9UbL2/SlzJwe4uWDFkIRmJdShsPpJxAMEvb62VdzZIzp5HQvYV8Au8MeLQsEg0TCYQKBI+WBYDD6ud+UhldlQtVcRq3mSLPRxHQttXXtm5E6K+uuPMm+HU3Yyzb9TGrrahmbI+Fw2AmY1NHnO1MmMdL5NdUeT/QeipCXUERi/6EaTklT4qzr/477NuL3QRaXhOByuTCZjqykpVKpCIVCqNVqXC4XZrM5esxoNOJyueRg1GruuusuPvzwQ/785z8f9zk6nbyx+KRJk2L8FcRGcXGxiK0XBjQ2W6W8Z3LrqKLk7LFg0GNs3Ue5tq6OnLY9lY8qj+pD2Td2ia2ODC7NbGF8ehKQhFajIazVYkk+8s67qzKlSoX2qHKtRnPkc60Wvz4Lk+cQOdk50ev0BkO7112VdVeeUfRvHIZRRPIuIkehwGR0oEi2tIvFZzmTrN3PYld4o/ewSQ446EZnSiEtPY0RKQM3vHeo/T50lWDi0mRkMplwu48suhWJRFCr1Z0ec7vd7RLEo48+ygcffMC9996LxzPw2/0JQ5SndVmIflz2+g+f2TGoIlzZwyUpTpRXl4HOcRBFDEcanaw8iLH2C7xj5zLF7GSKyUGqrmNzlitDHnKqry+MlrXNRWgRHcsJIy4JYfr06WzevBmAoqIi8vPzo8fy8vKoqKjAZrMRCAQoLCxk2rRpvP3226xatQoAvV6PQqEQq6oKA8fdACjA0D8JYUu9hs0VPv43uwmzOj6L0Hl1GSgjAYyempjdM3v/m0goqAnocX/7Ae5vP0AtdewoNuTkI6Ek7fAXTDE5mGJyMCNL/vNj94qO5UQRlyajOXPmsHXrVhYuXIgkSSxbtox169bh8XhYsGABS5cuZfHixUiSREFBAVlZWVx88cX89re/5dprryUUCnH33XdHm4QEod+5G0BvBZUm7o8KS/DINyaGm1XMzWyJ23PaNsuxuMpwGWPQPCNFsFa8T8A6joCmY2fy0VQKcGlS0Bz8FHdgGADpk+eiUytpESONEkZcEoJSqeTBBx9sV5aXlxf9fPbs2cye3X7BMIPBwJNPPhmPcAThxLkb+6128FZFEsV2DY9fbEHbFL8RN22L3FmdpVRnXdjn+2U1fYXWU0/LyJ7dy6HJJNtV1a4sxaAVcxESiJiYJgidcTf0yz7K3hA8vsfIqSlBLhuvj+uzIiodfuNwrM59Mbnf2Op/E9aY8KWd1KPzndpMVEEnmqAzWiZPThM1hEQhEoIgHCvghqCnXxLCC2UGar0q7p7i6nJDqljyWceT4uh7QtAEnYys24Bt5EU9blZzaOW5EEbfkT4Mq0GDTaxnlDBEQhCEY7njO8LIEwjjC4U5ZA+yap+emRle8o1uIv0wP8tnHYfZU4k6dPytN7uTW/cB6ogP2+jLenyNU5OBhAKj98hcBqteiy8YweUP9SkeITZEQhCEY7lbVwWNUw3BHwrj8Ib46x4t9qCKS9PqKal30R/zdX3W8SiQsDpL+3SfsVX/xm4cizd1co+vCSs1hPQZGHxH9nduG3pa7+if2eBC90RCEIRjeRqRh5ymxe0R3mCEd+pTmWFxkmfsvz+GPut4QF6MrrfMrgNk2IrYP+KHcILNXCFjFoaj9mVo2yinzi4SQiIQCUEQjtUPQ04/PODDHVbxg+zmuD2jM0F9Jn6NhRTn3hO+NkXhZorJwVkN/0RSqNCPn9XpJLTuhAxZ6ALNKCNyR3JbDaFO1BASgkgIgnCsfhhh9NZeLyOS/Ewweo9/ciwpFLQkTyTVceJr46hDLtx73sdS9jY241jsB7Z3OgmtOyFjFgogqbWWYNKpUSkV1IsaQkIQCUEQjuVujOuSFfscGvY0BPleuu1EW1xioslyElbHPpRh/wlfa3HtRxty0mg9tVfPDhrk9Zvamo2UCgVWvYZ6x4nHIsSeSAiCcDSfXR5yaohfDeHtQwa0Kjg/zX78k+OgyToFlRTqVS0hw7aToEpPizn/+Cd3IqxPJaJQofcdjpZZDBrRZJQgREIQhKO17Z0cpyajiAQb6gycM0KHKU5rFh1Po2UKAGm2b07oOoXfQYpzL02WU5CUvVzkQKHCq0tv17GcoteKhJAgREIQhKPZW3fqi1OT0fYmDY1+Fd8b07/bch7Nl5SBSz+MdNvOE7pOV/4BSilMQy+bi9p4dZno24000tDkCuAPxW+/Z6FnREIQhKO1VBLPIafvVunQKiXOGzmwCzc2Wk8l/URqCJJE0r5/49Zl4UnKPv753fDoMtAF7SiD8uS4tqGntTZRSxhoIiEIwtHslXEbchqRYH21jnMyfBi1A/ur12SdgtFXh9rdcQe0zqTa96Bu2svh1OknPPfgWN4kuTmubX/ntqGnVS39POJK6EAkBEE4Wktl3PoPdjSpqfOqmJM98Bs/HU6ZBoCh+vMenT++cg2SWk9Ta/9DX3h18ppGSfb9AKQZ5RrCwaa+Lach9J1ICIJwNFtF3PoP3q1KQquUOD9z4JtGWpIn4dOmYjr08XHP1QTtjKpdj2/cJYRVfW/q8mushBUadA45ISTrNWjVSipEQhhwIiEIQhtPM/gdcakhRCR4v1rH+VkBTJr+WLWoc6l6hbxjmdmFL/t0zFWbGG/ufrXRMdXvoI748E0siE0QCgXepAyS7HKTkVKhYLhVz4HGga85DXUiIQhCm6Zy+WMcEkJRs5par4rLRgzsBCx12Bfd6rIxbEDps5HStKPrCySJ8ZVrabScQjh9Yszi8Ogyo30IACNS9KKGkABEQhCENk2tK4AaM2N+6/dam4suGpY4M3LtprFIKDDXfdHlOZnNhVjc+ynLvSqmz/bqMtD4mtAGbEBrQmj2EOmPNcCFLomEIAhtGktBqY75kFNJkni/WsfMrADJA9hcdKyQ2kgweRTWig9A6jyuSQf+gV9joSLnkpg+u22kkcVVBsCIFAOBUIRaMUFtQMUlIUQiEe677z4WLFjAokWLqKioaHd848aNFBQUsGDBAtauXQtAMBjkjjvu4JprrmHevHl89NFH8QhNELrWVAqWEaBUxfS2O+sDVHsGvrmoM57s09G5DpHZvK3DsVTbboY3bKZ4zI8Jq2K7vaendaSR1SknhJEp8v0PNopmo4EUl4SwYcMGAoEAa9asYcmSJaxYsSJ6LBgMsnz5cp5//nlWr17NmjVraGho4D//+Q9Wq5VXX32V5557joceeigeoQlC1xrLIGV0zG/7XqkHjULiopzESwje9CmENGbGH/pn+wOSxJTSv+LTWCkZdXXMnxtUmwlrTNEawvDWhHBAJIQBFZeEsH37dmbOnAnA1KlT2b17d/RYeXk5ubm5WCwWtFotM2bMoLCwkEsuuYRbb701ep5KFdt3aYLQrUgYmvfHPCFIErxX6ua8rAAWbeI0F0WpNNhGX8rIug2k2PdEi8dWvc2wxq3sybuJkNoY++cqFPiSx2JprSFkmHXoxNDTAdfLFaq653K5MJlM0dcqlYpQKIRarcblcmE2m6PHjEYjLpcLo9EYvfZXv/oVt91223Gf4/f7kSSJ4uITX7WxP/h8PhFbLwxEbBpXNePCfpzqdFx1de2OJad6cbSWBYNBals/P7q8s3MBdjkMVDky+XHWYWrrWgAIKXUogkHsjvarnUbCkQ5lgWCQcCDQrryrskg4TOCo8sBRzwiY1Ngd9nZlbeXVo65Cd3AjZ25fwprxf0QdcHNa8XLsaacSHHMhYxU1AKjDph7HZ3fYo8/sKj6XYQTJNZ9SW1tDS6qCHJOKXQfrKS7u/zXBxe+DLC4JwWQy4XYfyfSRSAS1Wt3pMbfbHU0QtbW13HLLLVxzzTVcfvnlx32OTidPkpk0aVIsw4+Z4uJiEVsvDEhspdUAmHNPxuy1tT9m0GPMltfvqa2rI6f186PLOzsX4MlqMwaNgoWTkzBp5PIWT4CgRoMl2dLuUqVK2aFMq9EQ1mrblXdVplSp0B5Vrj3qGW3l2mOeq9VqSUoZxpfT/o/ZX93IT3dfjXJ3BL9pJPvTLkRReWQmsybnoh7HZ0m2dIjl2PjCmnz0h95jTIqGtPQ0Jg5Po6TeOSA/l0Pt96GrBBOXJqPp06ezefNmAIqKisjPP7J2el5eHhUVFdhsNgKBAIWFhUybNo3GxkZuuOEG7rjjDubNmxePsASha21DTmPYZNToibDukI65eXqCQT8tnoCcDMIDs+x1dw6nnc77573JnnE3UX/m7yi7+EUCmuS4PtNvGQscGWmUn2XiYJMbX1CsejpQ4lJDmDNnDlu3bmXhwoVIksSyZctYt24dHo+HBQsWsHTpUhYvXowkSRQUFJCVlcXDDz+Mw+Hg6aef5umnnwbgueeeIylp4JYJFoaQxlJIsoA+NWa3fK9KizukZO7YJErqXdHy3FQDidhDZjePY5d5HNNyrUjNlXF/ni9ZTghtI43GZ5mJSLC/wc3kYfFNRkLn4pIQlEolDz74YLuyvLy86OezZ89m9uzZ7Y7fc8893HPPPfEIRxCOr6kU0sb3eSXPo/2z0ki2LsC0bA0H62N22++McFIKPm0qFpc8Qzw/S246Lj3sFAlhgIiJaYIA8pDT9PExu93XTWp22XRcktmCYiA2Th4kbKZxWF1yc93odAMqpYKSeucARzV0iYQgCH4XOGsgbVzMbvn3UgMmdYQL02wxu+d3kd2ch8VZDpKETq1idJqhXfOa0L9EQhCEJrkNO1Y1hEqXkvXVOgpy3SSpEnDuQQKxm8ahCbtROeVRXvlZZkpFDWHAiIQgCG0JIS02CeGxPSY0SrhmtPjDdjw2s/w9VzYUU9XiITs5iYomD+WHnVS1eLB7ul+aW4ituHQqC8Kg0lgKKCB1LLgP9+lW39T7WXcoiV9MdJOZFMFmP/41Ay1Vr2AKjujrjLCfgC5Cf8wZtptaB5s07GVzeBreYBgJeGtHDcOses7PT8fSuueyEH8iIQhCUylYR4Kmb0OcQxG4f1MzqdoIP53gIRSMUXxxJu+R8HH0dTDVgDr3jH55dlCTLO+N0FICqZBtkf8Pau1ehllju6CecHyiyUgQGkv73FzkCYR5fJeOHXUBfjO5hVDQn5AT0BKRzTweXfM+ANJNOrRqJVUt3gGOamgSCUEY2iIReae0PnYob61Xsao0mbl5SeSpGiipdyHyQc/YTXnoWkpQSOHodprVNpEQBoJICMLQZj8EQTdk9n6tmANOFb/5Oo3spAB3nSMmVJ0ou3kcyrAfo0ceaTTCqqfW7iMUERm1v4mEIAxth1sX+cqc3KvLG3wKfrzFggJYOq4Kk1b8Sp2oFrO81lmKQ/6/GJ6iJxyRqLcn3v4R33Xip1cY2g5/K3/MOPEN5F2BCNdvsVLvU/Gn05rI0g2SXuQEYzeNJ6LUkta6H8OIFAMAVTbPQIY1JImEIAxth78Fy0hIOrGmnkAEbn6ngWK7mqfPsnNqihgv31sRlRZ/2kRSHXJyTjFo0GtUVIuO5X4nhp0KQ5enBWp3ykte21pX9wwef5N3Xxhu+yqZLdU+/u80J7NzArSIN7MnrN38h/R8MkrfZ7w5QKlTy4gUPYfEN7XfiRqCMHR5m6G5HFQaKPtI/hfu/p3+oYCR+ZssrK9O4u6ZFuZkOhJ2j4NEJ89/+AD3tx/gD0moQm4sjhIAxqQbqXfIe0gI/UckBGHosh+S91I25xz31J3Nau4sNHP1trHsc2i4fWw1V+TrKal3iSGmMRA0jwBA3yx3LI9Nl7fU3VFpG6iQhiTRZCQMXY3yu1HM2R0OeQJh/KEwjqCCP3zTwLqSVJJUEX6YY2duegNWjdjVK5ZChkzCCjX6lmJIg+EpBrRqJV9XtnDd2aMHOrwhQyQEYeg6XAwKFZg61hD8oTDf1Lj53d7R1Po9FOQ08v2sZsZalNS5RDKIOYUKt34YhqY9MA5USgWj0wzsqLANdGRDimgyEoauw9/KzUWqzt8X/eNQFtU+LX+am8L8YY0YVKJdKJ6chlz0LftQheTO5LwMExXNHuodx+/oF2JDJARhaJIkqP8WLCM6Pfzp4SQ2NVm5IruJM4fr+jm4oclpGIlCCpNu+waAsRkmAD4tbRzIsIYUkRCEoclWAX6HvMrpMSQJniszk6ENMG+Y+GPUX1yGkUgoyGz5GoAcSxKZZh3/3VM3wJENHXFJCJFIhPvuu48FCxawaNEiKioq2h3fuHEjBQUFLFiwgLVr17Y7tnPnThYtWhSPsAThiJod8kdLx4TwZaOGXTYdl2c1oxbbIfebsCoJn3UcGa0JQalQcH5+Op+UNOAJhAY4uqEhLglhw4YNBAIB1qxZw5IlS1ixYkX0WDAYZPny5Tz//POsXr2aNWvW0NDQAMBzzz3HPffcg98v1jAR4qymCJRqMA/rcOiZfQZStWEuTB8Eu9t8x3jSTyXd9g2KiLwMyPnjM/CHImwuaRjgyIaGuIwy2r59OzNnzgRg6tSp7N69O3qsvLyc3NxcLBYLADNmzKCwsJBLL72U3Nxc/vKXv3DnnXf26Dl+vx9JkiguLo79FxEDPp9PxNYL/RFbbtkWdJYxNDS0bxKyq118XKfjhlGNeF02vEAgGMTukJNDljE1+vnR5QGTGrvDftyytnLVMWUAkXCkQ1kgGCQcCLS/vouySDhM4Kjy3sYSi/jsDnv0mScSX2PGZNLCb6DYv5Ea0xQunJhDsk7Jms9KGKWyES9D/fehTVwSgsvlwmQyRV+rVCpCoRBqtRqXy4XZbI4eMxqNuFwuAObOnUtVVVWPn6PTyZ19kyb1funieCouLhax9ULcYwsH4V/FMPlycrLbz0F4uTyIEon5Y4PYHfKbFq1GgyVZ/lyhUEQ/P7pcq9ViSbYct6ytXHVMGYBSpexQptVoCLfe53hlSpUq+syexNdVLLGIz5Js6RBLT+IL555LZIeKU8J7iWTPJTMzncumDGfdzhpyx47HqIvPSPmh9vvQVYKJS5ORyWTC7T6yI2skEkGtVnd6zO12t0sQghB3NUXyHgjDT2tXHIzA2j1uLswJkKUXcw0GQkRjotF6KjmNW6Nl82aMwB0I896u2gGMbGiIS0KYPn06mzdvBqCoqIj8/Pzosby8PCoqKrDZbAQCAQoLC5k2bVo8whCGKEmS2F7RTHGtg1Bna0pUbJE/Dp/RrvijWi0NnjALx4hVNgdSbfq5pDqKSfLLzXkzRqUwNsPI2sJDAxzZd19c6l9z5sxh69atLFy4EEmSWLZsGevWrcPj8bBgwQKWLl3K4sWLkSSJgoICsrKy4hGGMATtqrKz9F/fsKdGXkUzzaTl/u9PZvqoFMw6NRaDFg5uhfR8MKa3u/bFMgPDzSouzA7gFHOhBkxNxrmcWvoXshs/ByaiUCiYf9pIVry/l/IGF3kZpuPeQ+iduCQEpVLJgw8+2K4sLy8v+vns2bOZPXt2p9eOGDGiw1BUQeiJ4loH1/7tC3RqFf87dThqlYKP9zVw6+tF/HDqcH4zNx+LTgmVX8Ap89pfa1PxeYOWpeeZUStF08RAakmehEeXwcj6jwiFf0RVi4ezx6aiVip45uNybr1o/JHkLsSUWMtI+E6os/u47vmv0GtV/PWaaZTWy/1Uk4cl89pXlfy7qJppuRZ+NLIRAk4YfV67618sN5Ckklh4sgnEXLSBpVByKPsi8g79izKPk69r5SGoJw+38J+dNUzKSebik7JEQogDMVNZGPQCoQg/f2U7bn+Il244kxyLPnpMp1Zx7ZmjGJVm4KF3iqn8fK08/2Dc96LnlDpUvHkwiStH+bAmqQbiSxCOUZl9MeqIH1PFR9Gyc/PS8YcibK9oGcDIvttEQhAGNUmSuP8/e/i60sb/zZvChOyOI9Y0KiWLzhrN6HQjoT3vYM86E/QpAEQk+N3XZowaiSUnufo7fKELjSnT8GrTSN7/brRseIqeUWkGPitvJBQRCw3Gg0gIwqBl9wRY9l4xr31Vyf87K5epI61UtXjwBzsOGdVrVfx1joGxihqeqJrA6i8qqLYHuKvQxFeNWn41wYYy7McXCosd0AZQql7BFJODU8xuPLkXYq7cwERdU/T4+eMzaPEE2fDt4QGM8rtLJARhUAqGIzz0bjHPfXqAqSOtTMxOZnNJI5tLGgmEpU6vyar+LwDVWbO49+3dnLtyH29U6Lk8q4lJmsOU1LtweENiB7QBdPS2mjVSKopwgOyDb0ePT8g2k52cxMtfVBCJdP7/LPSe6FSOB08LBJxkaHxHNm/XmsGQMrBxDRLNnhAvfnaQz8ubcPqD6NQqclMNjEw1YNapqbJ5eWtHFYeavZybl8YlJ+egVHS/Cp1CCmPc8zqHU6ZzwWlTyR3tBq+Ns1XFmMO2/vnChBPiScomYBpB6v7/QPYNoFCgVCi4YEIGa7YdYv2eOi475fjbnwo9JxJCPAScsO8DFOXbIUkCvRWmLRIJ4TicviB/+GAfr351iGBYYpg1iTSjjkDIz1cHmnH5j6x4edbYVG65cBw9bUrOadiK1llJydRfolQoyMswMcUUIa0uQGVznL4goc+82adjKXuLdNtOGlOmAnDKcAtf7m/ijx+WMPekbFRKsSRtrIiEEA9734GPHyXNZztStv1FmPEjuOAuMKQOWGiJ6sv9Tdy+dic1Ni8TMnRccmoumeYkAM7JS0WlVGDzBvEFw1iSNBh0avzBMF8e6NmIk/GVrxMyZFKV9b3jnywkDG/mVIwVG5h04AU+TXkSkJfFvnHmGO55ew9v7ahm3ozONzkSTpxICLEUicCH98LnfwVLLs3DZ5OaOxm8zWCvgm1/gz1vwQ+fhvEXDXS0CUGSJJ7dvJ9H1+8lN9XA09dO59v9ldFkAOANRthRaetw7bRca4+ekWbbxfCGT2mYcTsRpSZGkQv9QVLpaB4/j5HfvkCyaz8O01gALsjP4JThFp74sIT/OSUHvVYMF44F0akcK5IE65fKyWDqNXDebfhT8jkoZfFw9XSurL+enyc9Sl1Qj/TKPPjoQXAP7baKQCjCkrU7Wf7+Xi49OYd3fjWTU0ZYjn/hiZAkpu39A15tKs1TfhLbewv9omncPEIqPSeXrYqWhSMSN50/hmqbl/9bv5eqFg92T2AAo/xuEAkhVj7/K3y1Cs66BWb9Fl9EydMHs5m9PpV/lOlREcamTOES5728Fz4DPn2c4L9/CaGhuRnQoWY3C5/9nH/tqObG88aw9NIJ2DyBToeM9sXomnfJbPma+ik/J03lZorJEf2XqhNDiQaDsM7KvlHXMrr2PdJsuwC51uj0hZkywsLqLyp46+tqnH6xq1pfiYTQV54W+GYtfHgfjJsDZ95Eg83Fgo9TWF2VyVWjfbx3YS1PXZrKraMO8sSUQ/zTfB1/DM5DU/IOkZeuAO/QmnlZ1eJh0d+/YuchO1fNGMHYDBOfljZ1O2S0N8zuCk7f8xAN1qm4Rs0huG9DdEij+9sPUEvBmD1LiK89eTfi1aYxvfhRkI4k8stOzkGlVPDm19WExTDUPhMJoa+aSuGd28CYgSf3QnZ/uYErXqlkn13FI5OrWTq5CavmyDsXgyrCz8bU4xl+Dr8M/IJI5VeE/z4XbENjad+iQzaueOozGl0BfnzuaKblxmfklTZgZ+bXtxFRatg69TF5uQph0AqpjRRNuI0M204mHHwlWp6s13D5lGEcbHLz2leVAxjhd4NICH0R9ME7v4ZICE67gUqvjus/S8Ppj3BvfiUZwaouJznNybAxa24BPw7ehbfxEJFnL4TSD+Uax3fU+t21LHz2c5I0Sp75f9Pjtoyx0m/jwsKbMXsq2TLtcTz67ONfJCS8A8N/SFXmLKaW/Ald05Edv6blWjl5WDLPfXqALaViZcK+EAmhL96/E+p3w9RrOazO4adfpeMKqfjzJamMMx5/Qf3/GatiwbQsrgneS5MnRPj1/0eg+P1+CLzvdlfbeXZzOf+3fi9/+3Q/H+87jMPXeROMNxDmkXe/5WevfM2knGTevuVcRqcb4xKX0VNF3r+vIMW5j6qzHyRr1CTRXzDItS1nMcXsxHnmb4hozYxafx2TNTWAvK3pldNHMCrVwM9e2c6+OucARzx4iXp0b21/Eb5+EU7/Cc3Wk1n0iZV6n5Lfjj/E5IzhHKg+/i3CQKbUyJV5euaX3c9zmj+Qt+7n4KqCmb8BZeLl67LDTn6zdidFVfLm6CqFgrAkt90qFXDSMAunjU4hL8OERqWgtN7Fv3fW0OD0c82Zudz3/ckkaVRUtXhiHtuIug2ctes+lErYm3s1TpsHbB8AkDl1FmJTzMFJXs7i4+jrvcOu5KSDLzLxk5+zf/pz+JIySNKo+L95U/j5K19z9XNf8NINZ3Dy8BiPWBsCRELojZL/wru3Q95s7NNu5rqXdnPApeIvpzViDZ/49ouTzF5+OiHCtaX3c6/6Jb6/6RF5E5f/fQZMmXH4Anrnje1V3PP2LpLUKr4/JYdTR1gxaFV4A2FSTRp2VTnYccjGq19W4g/J78hVSgXn5KVx/+WTmTrSSqNLHlUVy9FEBm8t0/b+gVF1/6XJchKKuY/g3PN1zO4vJBaPPoeWyYuwFr/KxV8s4tNpT9BimUS2JYm1Pz2ba//2JQuf/YL/mzdFLG1xgkRCOFEl/4W1iyDrJJove47rX/6GfXY1z55jZ6rFT0l97247xuBn6YQ6llX+ks88+Ty4fzWqv5yG4nv3wvQfgXrgNgMJhSM89t99rPpkP2ePTeOuSyfwbc2RarlBp2ZSjgVfUGJ8lpmIJOHwys1HZ+elUVzrxOENsbnkSPtuTyeVdUmKkFT/NWfsepEx1f8BhZKd439J8djruSg5AoiE8F0WSBnPgVl/YdiWu7n482vYk3cT4dzbUScZ+cvVU7nn7T38/JWvueyUbO64eAJjxLabPSISQk9FIvDlSvjvPZB1EmUXv8RPXthNjc3HyrPtXJgToK+tIMOSgvz7/43ij5/NY+43E1kW+QdnvvcbAh//Ac30a1Ccdj1Yc2Pz9fRQVYuH29fs5KuDzfy/s3K5//KTqHf4gK7baZUKBdbW3aySND2cQSpJqMMeNPYWhrn2kN1QjjrkQR32kuKQmNTQhCbsQR3yYPZUkmbfTVKgmbAqCfuY/6Fh0nWoDNmcjBed6BobEgzDJ1Mx9x8M+/oPTCl7mnDNWmrGXc3BtP9l4Rkj+aj4MOt31/HJvgZ+fO5orjt7NFnJSce/8RAWl4QQiUR44IEH2LdvH1qtlocffphRo0ZFj2/cuJGnnnoKtVpNQUEB8+fPP+41A+rgVtj4EFR+TnDcJTydehdP/a0Yo07Fc/87klPch2jxEJM19M1KP3cO30WJWcOzpbfxTEMZPwmv45wtf4Qtf6Ql/TSSxs9CP+5cGDZdXjgvDsobXLyw5QBrC6tQKRXc+/1JzD0pm3qHr0/NPQopjMlThengl0w+sItk9wEyvQfQOw+gCsob1Izr5LpsQEJBSKXHrR9Gbfq5aCd8D7tlMs7SLXBwJ7ATAOVZYlmQoUAd9uEo/wKH5TzM6pHk2bYy8pu/MIK/0midwilZs7nstJPYYMvm6Y/LWflxOeeOS2fm+HTOGpvGScMsYmG8Y8QlIWzYsIFAIMCaNWsoKipixYoVrFy5EoBgMMjy5ct544030Ov1XH311Vx44YXs2LGjy2viLhIBKQzhAJK7gbCjnoitEkVNEYrS/6JuLsGnTeXtrCUsKz0Nh7+Ws0fqueXMNMZaJEr2y3/IclMNfQ4lDJTUy/e7aUQLzmw9G1tu5KWAkYmNG7jo8DYmNTwOn/8BAKfKii1pJF5DDmF9OpgyUJky0ZrT0OiTUSWZUCWZULd+RKEhrFBidzo43NSMNww2bwS7L0K13cfeWgdf7G9mX70DjSLCtBHJfG9iBiZlkM++rQQpwqkjzGgDdhREUEhhlJEQGnsLFmcjqkgAVdiPOuwlKdBEarOb6fWH0PsOk+w+QLL7IKqIvMTASMCjyyRiGUmjaSJ+rZVkSwrlzQHGTL+IqvLdRJRaclItVDrCDD/t+zQfNXjLbNFh9Xm7qasIQ4XTOIrmkZNQpeYilX9MctUnTNv3BNOA+QoV3qw8DkrZ7K61sm+/mdUY8SnNGC1pWFJS0RBm58EazAYDRoOeZKMBvT4JrTYJrVaHTqNGp9Gg06pQKRUokEc3yR/lz78L4pIQtm/fzsyZMwGYOnUqu3fvjh4rLy8nNzcXi0UeATBjxgwKCwspKirq8pqY2/In+HgFkUgIIiGUHJnhqODINyUgqdgWmcA7kcX8y3ceSQED38tyUzDCjsbfAgdAPXVW/OIEzOoIczJsnHrWabSU2dljP5dNTRFUtgos4RbMvmqyXTVkOHeSrXCQrOhZp/ZZR31+dD0sLClQKSRoq1k3tP47xoRO7tnZO3uADFUSQX06fnMurtwzcRpGYsrJp0Y5jIjGSKouwqEdHwKgSTXQ5K1lZMZJeKvlB0e0BiJKD+qIv91ok2CqAXXuGT36eoUhwpTNQSkbhi9Ak+nE5K2Rt06t28dY9yEmSl+h1Bz1rsLV+g+gh3NDI5ICCbnGGkaBdNS/CAr+FJ7H38P/0z5hoJD/uEC7MsWR4q6fF4mgVFa0K8tMTuL9W2f2vEm2hxSSJMV8vvfvfvc7Lr74Yi644AIAZs2axYYNG1Cr1RQWFvLyyy/zpz/9CYAnn3ySYcOGUVRU1OU1XSkqKkKn08U6fEEQhO80v9/P1KlTO5THpYZgMplwu93R15FIJPqH/dhjbrcbs9nc7TVd6ewLEgRBEHonLsMxpk+fzubNmwH5XXx+fn70WF5eHhUVFdhsNgKBAIWFhUybNq3bawRBEIT4i0uTUduIoZKSEiRJYtmyZXz77bd4PB4WLFgQHWUkSRIFBQVce+21nV6Tl5cX69AEQRCELsQlIQiCIAiDj5jBIwiCIAAiIQiCIAitREIQBEEQgEG6lpHT6eTXv/41Xq8XjUbDY489RkZGBkVFRTzyyCOoVCrOO+88fvGLX/R7bM8++yyffvopAA6Hg8bGRrZu3ZoQsYXDYZYvX87u3bsJBAL88pe/5MILL0yI2CRJ4vzzz2f06NGAPKR4yZIlCRFbm/LycubPn89nn32GTqdLiNg8Hg9LlizBbrej1+t57LHHSE1NTYjYnE4nd9xxBy6Xi2AwyNKlS5k2bVpCxHa0Dz/8kPXr1/P4448DJER8A7aUjzQI/eMf/5AeffRRSZIkac2aNdLy5cslSZKkH/zgB1JFRYUUiUSkG2+8Udq9e/dAhinddNNN0ubNmxMmtjfffFO6//77JUmSpLq6OumFF15ImNgOHjwo/fSnP+1QngixSZIkOZ1O6Sc/+Yl01llnST6fL2Fie+GFF6S//OUvkiTJ/78PPfRQwsT25JNPRn/GysvLpSuuuCJhYmvz0EMPSXPnzpVuu+22aFkixPfBBx9Id911lyRJkrRjxw7p5ptv7pfnDsomo/z8/OgkNpfLhVqtxuVyEQgEyM3NRaFQcN555/H5558PWIz//e9/SU5OZubMmQkT25YtW8jOzuamm27innvuYfbs2QkT2549e6ivr2fRokX85Cc/Yf/+/QkTmyRJ3Hvvvdx+++3o9XqAhIntxz/+MT/72c8AqKmpIT09PaFiW7hwISDXTnU6XcLE1mb69Ok88MAD0deJEl93y//EU8I3Gf3zn//kxRdfbFd23333sXXrVi677DLsdjuvvPIKLpcLk+nImudGo5FDh+K7cX1nsS1btowpU6awatUq/vjHPwIkTGwpKSnodDpWrVrFtm3b+O1vf8vjjz+eELHdd9993HTTTVx66aUUFhZyxx138NRTTyVEbMOGDeOyyy5j4sSJ0bJE+T9t+3m77rrrKCkp4YUXXki42BoaGrjjjju4++67ByS27uK77LLL+PLLL6NlAxXfsY6NQ6VSEQqFjrt6Q18lfEK46qqruOqqq9qV/eIXv+DGG29k4cKF7N27l1/+8pe89tprHZbESE5O7vfYAMrKykhOTo62+XW2XMdAxPbrX/+aWbNmoVAoOOOMMzh48GDCxOb1elGp5IW6TjvtNOrr6zEajQkR25w5c3jzzTd58803aWho4IYbbmDVqlUJEVubl156ifLycn7605/y9ttvJ0xs+/bt4/bbb+fOO+/kjDPOwOVy9Xts3cV3rIH4fehJHD1ZyicWBmWTUXJyMmazGYC0tDTcbjcmkwmNRkNlZSWSJLFlyxZOO+20AYnvs88+4/zzz4++TpTYZsyYwSeffALA3r17ycnJSZjY/vrXv0bfwe3du5dhw4ZhNpsTIrYPP/yQ1atXs3r1ajIyMnj++ecT5vu2atUq3n77bQAMBgMqlSphYisrK+PWW2/l8ccfjy5amSixdSVR4huopXwSvobQmVtvvZV77rmHV199lVAoxEMPPQTA73//e37zm98QDoc577zzOPXUUwckvgMHDnDuuee2K0uE2ObPn8/999/P/PnzkSSJ3//+9wkT20033cQdd9zBJ598gkqlYvny5QkTW1cSIbaCggLuuusu3nzzTcLhMMuWLUuY2B5//HECgQCPPPIIIP+xXblyZULE1p1EiG/OnDls3bqVhQsXRpfy6Q9i6QpBEAQBGKRNRoIgCELsiYQgCIIgACIhCIIgCK1EQhAEQRAAkRAEQRCEVoNy2KkgAHz55ZfcdtttjBs3LlqWkpLCn//85wGJ55lnnuG8885j06ZNpKenc/XVV/fqPrNnz+b9999Hp9PxwQcf4HK5KCgo6NO9vvjiCxoaGpg3b16v7iMMDSIhCIPaWWedxRNPPDHQYVBbW0tJSQk333wzmzZtitl9P/nkE26//fY+3+eCCy7gxhtvZO7cudFJnYJwLJEQhO+kRYsWMXHiREpLS3G5XDz55JMMHz6c1atX884776BQKLjsssu47rrrWLp0KTabDZvNxqpVq3jiiSfYvXs36enpVFdX8/TTT3PDDTfwz3/+E6vVyquvvorH4+HGG2+MPu+1115j7ty57WKoqKjg9ttv55FHHiEjI4OlS5fidDqRJIlHH32UpKQkHnjgAfx+PzabjVtuuYWLLrooer0kSbS0tJCens7ll1/OaaedRklJCWPGjCEtLY3CwkK0Wi3PPvssXq83utR0OBzm1ltv5eyzz24XzwUXXMBbb73FddddF99vvjBoiT4EYVD74osvWLRoUfTf3/72t+ixKVOm8I9//INzzz2Xd999l7KyMt577z1effVVXn31VTZs2MD+/fsBuabx+uuvs23bNmw2G2+88QbLli2jtrYWhULB5ZdfzrvvvgvAf/7zH6644op2cXz11VdMmDAh+vrAgQMsWbKExx9/nIkTJ7Jy5Upmz57N66+/zm233cY333zD/v37uf7663nhhRe49957eeWVV9rd85tvvuHkk08G5DV1vv/97/PKK69QWFjI9OnTeeWVVwgGg5SVlbFy5UrOOeccXnnlFZ588kl+97vfEYlE2t1vwoQJfPXVVzH73gvfPaKGIAxq3TUZTZ48GYDs7GwaGxspKSmhpqaGH//4xwDY7XYqKysBGDNmDAD79+9n6tSpAKSmpjJ27FgA5s2bx69//WtOP/100tPTSU9Pb/estnfybTZv3oxarY4u2HfgwIFo+33bO/fS0lJWrlzJG2+8gUKhIBQKtbvnpk2buPjii6OvTzrpJEBeyysvLy/6ud/vp7y8nMsvvxyArKwsTCYTzc3N7e6XkZGBzWbr8nspCKKGIAwZY8eOZdy4cbz00kusXr2aK6+8MrpomEKhAGD8+PEUFRUBcsI4ePAgQHSxvWeeeabTjtnU1FQcDkf09Y9+9CPuvvtu7rzzTsLhMHl5eezatQuAbdu28dhjj/Hkk0/ywx/+kMcee4wzzzyTY1eR2bt3bzSpHR1jZ/Ly8igsLASgvr4eh8OB1Wptd47D4SA1NbUH3ylhqBI1BGFQa2syOtpzzz3X6bkTJ07k7LPP5uqrryYQCDBlyhSysrLanTNr1iw2b97MwoULSU9PJykpCY1GA8iLAz788MM89thjHe59xhlnsHPnToYNGxYtO+ecc1i/fj3PPfccN998M3fffTf/+c9/AHkt/p07d/LII4+watUqcnJyaGlpiV5rs9nIzMzs8ffhpz/9KXfffTcffPABPp+PBx98sMNyyTt37uzQryAIRxOL2wnCUcrLy9m7dy//8z//Q0tLC9///vfZtGkTWq2W9957j9LSUm699dYO11VXV/Poo48O2JDXnli8eDFPPvlku41XBOFoooYgCEfJycnhD3/4Ay+++CLhcJjf/OY3aLVa/vjHP1JYWMjTTz/d6XXDhw9nwoQJ7Nq1i1NOOaWfoz6+jz/+mLlz54pkIHRL1BAEQRAEQHQqC4IgCK1EQhAEQRAAkRAEQRCEViIhCIIgCIBICIIgCEKr/w/zAF08DT50fQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "\n",
    "energies = []\n",
    "e2 = []\n",
    "\n",
    "for j in range(0, 100):\n",
    "    for ss in sam_ss[j]:\n",
    "        energies.append(ss[1])\n",
    "\n",
    "    for i in range(0, len(sam_ss[j])):\n",
    "        m = sam_processed[j][i]\n",
    "        if 0 in m:\n",
    "            e2.append(sam_ss[j][i][1])\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "sns.distplot(np.array(energies))\n",
    "sns.distplot(np.array(e2))\n",
    "\n",
    "plt.xlabel(\"Energy (kcal/mol)\")\n",
    "\n",
    "plt.savefig(\"../test/sam_dist.png\", facecolor='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-23T00:17:07.396180Z",
     "start_time": "2021-04-23T00:17:04.159474Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "\n",
    "energies = []\n",
    "e2 = []\n",
    "\n",
    "for j in range(0, 100):\n",
    "    for ss in sam_constrained_ss[j]:\n",
    "        energies.append(ss[1])\n",
    "\n",
    "    for i in range(0, len(sam_constrained_ss[j])):\n",
    "        m = sam_constrained_processed[j][i]\n",
    "        if 0 in m:\n",
    "            e2.append(sam_constrained_ss[j][i][1])\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "sns.distplot(np.array(energies))\n",
    "sns.distplot(np.array(e2))\n",
    "\n",
    "plt.xlabel(\"Energy (kcal/mol)\")\n",
    "\n",
    "plt.savefig(\"../test/sam_cons_dist.png\", facecolor='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-19T01:52:46.716796Z",
     "start_time": "2021-04-19T01:52:41.675777Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(4, 4, sharex=True, figsize=(16,8))\n",
    "\n",
    "e1 = []\n",
    "e2 = []\n",
    "\n",
    "indices = []\n",
    "for i in range(0, 100):\n",
    "    found = False\n",
    "    for j in range(0, len(sam_ss[i])):\n",
    "        m = sam_processed[i][j]\n",
    "        if 0 in m:\n",
    "            found = True\n",
    "    if found:\n",
    "        indices.append(i)\n",
    "        \n",
    "    if len(indices) > 15:\n",
    "        break\n",
    "\n",
    "print(indices)\n",
    "for j in indices:\n",
    "    e = []\n",
    "    for ss in sam_ss[j]:\n",
    "        e.append(ss[1])\n",
    "    e1.append(e)\n",
    "\n",
    "    e = []\n",
    "    for i in range(0, len(sam_ss[j])):\n",
    "        m = sam_processed[j][i]\n",
    "        if 0 in m:\n",
    "            e.append(sam_ss[j][i][1])\n",
    "    e2.append(e)\n",
    "    \n",
    "counter = 0\n",
    "for axis in fig.axes:\n",
    "    sns.distplot(e1[counter], ax=axis)\n",
    "    sns.distplot(e2[counter], ax=axis)\n",
    "    axis.set_xlabel(\"Energy (kcal/mol)\")\n",
    "    counter += 1\n",
    "\n",
    "fig.savefig(\"../test/sam_sample_dist.png\", facecolor='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-19T01:52:51.418390Z",
     "start_time": "2021-04-19T01:52:46.720367Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17, 18]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yixiongsun/anaconda3/envs/rnabayespairing2/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
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sczuvSU/ruDVs0bpjxw5qa2v57W9/y+233w5AJBLhd7/7HZs2bUpKgCI9RHpa0AOKLRdVA4tew5FTBEC3qym1wYkhSQ4LkVqjycHTXRCfID2sQoydnCcnCE8LAK1aDgCFNj0dx3dpRjsRg50KQyu/l6J13Bq2aHU6nbS3txMMBmlri35coSgK99xzT1KCE+mju72RXEBvz0MDlEgvufn5APR2Nqc0NjE0yWEhUktyUIj0Jjk6QXiiPa1HQ9FZn/NPKVoBItY8poRbaezyo6oaOt3pJ8cS6WXYorWyspLKykqWLFlCUVFRsmISacjT0UwuYM06OTy4oKAYgEh3S4qiEqcjOSxEakkOCpHeJEcniJ5oB8rhUC42fQSzoX9RGrHkUdzVTDCs0uYJUOy0pCJKMQbDFq3f/OY3eeSRRwYdgvT6668nLCiRfk70pmblnCxazWYzbi0Lna8tVWGJ05AcFiK1JAeFSG+SoxPE8Z7Ww6FscowD12ENW/Jxtu3CQJh6l0+K1nFo2KL1kUceASRpBYS7WwlrOnKys/tt71ByMPulaE1XksNCpJbkoBDpTXJ0gvC0gDWX1qCZ3EGK1og1Dx0qk5V2DrV5uHh63iAHEekspsWj3nnnHbZt28Zrr73G1VdfzcaNG4d9vqqqLF++nKqqKpYtW0Z9fX2//Zs2beKmm25i6dKlLF++HFVVR/8TiKTQvG104KTAbuy33aPPJivkSlFUIlYjzWEhRHxJDgqR3iRHxzlPC9jyaQ/oh+xpBZhpaOPDVk+yoxNxEFPR+tBDDzFt2jTWrFnD73//e9atWzfs87ds2UIwGGT9+vXcfffdrFq1qm+f3+/n4YcfZs2aNaxbtw6Px8PWrVvH9lOIhNP72nHjxPKRewQCxmzyVReqqqUoMhGLkeawECK+JAeFSG+So+OcpwXNVkC7X0eOITJgd8QaLVrn2jukaB2nYipazWYz+fn5GAwGCgsLCQaDwz5/586dLFiwAIC5c+eye/fuvn0mk4l169ZhtVoBCIfDmM3m0cYvksQUcOHR5wzYHjFlka9009bZnfygRMxGmsNCiPiSHBQivUmOjnOeFkKWfPyqbtDhwaoxC1Vv4Syziz0NXWiadLaMN8Pe03qCw+Hg9ttv5+abb+bpp5+mtLR02Od7PB4cDkffY71eTzgcxmAwoNPpKCgoAGDt2rX4fD7mz58/7PECgQA1NTX9tvn9/gHbki3VMSSz/exAB17jTMLhMNopn3XozFkAvPfOdsqnTk9KLB+VSX+H0RppDgsh4ktyUIj0Jjk6jmkaeFrxTorepzrY8GAUhaB9EjMMbbi8QepdPqYV2JMcqBiLmIrWn/70pxw5coSZM2dy4MABbrrppmGf73A48Hq9fY9VVcVgMPR7/NBDD1FXV8ejjz6Kogy/VpLZbGb27Nn9ttXU1AzYlmypjiGZ7Xu1LiKW6CeQkVM+nDJbox9OZJnUlP0uMuXvMJbCeKQ5LISIL8lBIdKb5Og4FuiGsJ9OXS7AoD2tAEHHZIq8DQDsqHdL0TrOxFS0ulwutm7dyubNm/u2feMb3xjy+fPmzWPr1q0sWrSI6upqKisr++1fvnw5JpOJxx9/HJ0uphHKIpVCvdjpRbMOnGkty2EDoLf5YLKjEiMw0hwWQsSX5KAQ6U1ydBzraQHARQ4wRE8rEHSUkdfyDk6Ljp31bm68oCxZEYo4iKloveuuu7j00ktjHiqxcOFCtm/fztKlS9E0jQceeICNGzfi8/mYM2cOGzZs4MILL+S2224D4NZbb2XhwoWj/ylEQvncLdgAnT1/wD6T1QlAuLMhyVGJkRhpDguRUGE/HL+1IFNIDgqR3iRHxzFPtGhtUaPXpEP1tBpyJ6OL+LmyuJeddbJc43gTU9Fqt9v59re/HfNBdTodK1eu7LetoqKi7/t9+/bFfCyReh1tDdgAc9bAolXVW/BjQvG2JD8wEbOR5rAQCeOuh59dBJ/9CVz45VRHkzSSg0KkN8nRcex40XosnI1Zp2HTD7GUpr0QgAX63TzfdgFdvhDZNuPgzxVpJ6axuWeccQZ/+ctfOHToEHV1ddTV1SU6LpFGWpuOAWDPKRy4U1Ho1Odj9rcnOSoxEiPN4VAoxD333MPNN9/MjTfeyCuvvJKkSMWE17In+nXvn1IbR5LJeTTx/KEIW/e10uMPpToUMQ5Jjo5jnlYA6oNOCswRhpoqJ+KYBMAsXSMA7x5xJyU8ER8x9bTW1NT0mwRGURTWrFmTsKBEeulxNQGQkzdI0Qp4TAVk98owi3Q20hz+85//TE5ODg899BBut5svfOELXHXVVckIVUx0huNLnAV9qY0jyeQ8mnj//uz7bPqgiXMnZ/P8l6bE9qm8EMdJjo5jnhbQGTnsM1NgGfrcErGXoCk6snqPogAfHOviijOLkhenGJOYita1a9fS09NDQ0MDU6ZMwW6X2bYySeT4J1hmRx7qIOtahSwFFPp24Q2EsZtj+i8lkmykOXzttdfyqU99qu+xXq9PdIgiU6jH7zVSM6s3TM6jidXY2ctfdjWRZzfxQUMX2w7lcHmqgxLjiuToOOZpBUcxrd4IU02RoZ+n0xMx5+IIdVCWbeRAS0/yYhRjFlOF8dJLL7F69WoikQjXXnstiqJw5513Jjo2kSb03lZ8mpk2vw7HYG8GjiKKO9wcdns5oyQ7+QGK0xppDp84WXs8Hr75zW/yrW99a9jjy1rK6dl+OsTw0fazjh2iDIiEAhxIUlyp/h2AnEcT7bUDbWga/L8bz+XrT7/Lyx92c/nMVEclxhPJ0XHM0wKOIlobQlxQMsT9rMdFLHmY/W5m5pvZ3dBFly9Its2UpEDFWMRUtP7mN7/hmWee4Stf+Qp33nknixcvlkTOIBZ/K+26gZMwnWB0FmFUIrQ1H5OiNU2NJoebmpr4+te/zs0338x111037HNlLeX0bD8dYhjQfmQvAHoiSYsrHdZSlvNoYr1b7ybPbmJWsYMzS7J4t8EHUrSKEZAcHcc8rUScZXQHVArMw/S0AmFrPpbuo8zIM/FaXQft3oAUreNETLd8KIqCyWRCURQURcFqtSY6LpFGHIFWugwFQ+635UTvB+huPZKskMQIjTSH29vb+fKXv8w999zDjTfemKQoRUaIHB8WHBl8SYKJSs6jibW7sYsZBXaCYZU5k7M50B6gJzTEbCxCDEJydBzztNBrjnaunK5ojVjyMah+znIGUDU40tGbjAhFHMRUtF500UXcfffdtLS0sHz5cs4555xExyXSSE7Ehdc0+CRMAM68EgD8Lila09VIc/jnP/853d3dPP744yxbtoxly5bh9/uTFK2Y0E4UrWpmFa1yHk2ccETlYKsXi1FPMKJx9iQnqgbvumQpCxE7ydFxKhIGbxvdhjwACszDDw8OW6PF7Zmm6DI5h9o8iY1PxM1phwfv27cPnU7Hnj17uP7663E6nSxbtiwZsYk0EImoFGhuaq1FOIZ4jpIV7WlVO48lLzARs9Hk8Pe+9z2+973vJSlCkVEiwejXDJqISc6jiXWo3UswolKabQGgsjgLBajuMHBZSTC2g/jcEDw+KYspC2y5iQlWpCXJ0XHM1w5ouInmbKElAsN0noZt0WvWsvBR9MpZ1LV7kxCkiIdhe1pffPFF/vM//5PJkydzzz334HQ6eeaZZ9iyZUuy4hMp5mpvxqyECDtKhn6SJYcQBvSepuQFJmIiOSzSToYND5YcTLyapm4ASrOjwzntZgMV+Wbe6xhBT2uwB2pfif4LyoyimURydJzzRHtMW7XonCqnHx6ch6roMXbVkWc3caQjs5ZfG8+G7Wlds2YNTz31FDabrW/bF77wBf71X/+Vq6++OuHBidRzNx+hCLA6h76nFUWh01CApbdZZmFLM5LDIu309bRmRtEqOZh4e5u6MeoVCrPMfduuyznEkmOriLxmRV98NuRMTWGEIp2NJkdVVWXFihXs378fk8nE/fffT3l5eb/n9Pb2cvvtt/PDH/6QioqKhP4MGa0nWrQ2RZzoFcg1qbiGe76io9dcgNF9iIIsM0dcUrSOF8P2tBoMhn5JDOBwOGTNxgzSdXxypdzsHPKGuU/AZyokJ9xGyNuRrNBEDCSHRdo5MSw4Q4YHjyYHVVVl+fLlVFVVsWzZMurr6/vt37RpEzfddBNLly5l+fLlqOrw93BNdAdbvUzJtaHXRSdeUsK9fLn9x+iJgLcVXv3vFEco0tlocnTLli0Eg0HWr1/P3XffzapVq/rt37VrF7fccgtHjx5NSMziFN0NANQFsymwG9DFMP9ar7kQfechCh0mGjp7iahagoMU8TBs0aoog//lM/0EmUm8rugbbkF+HgZt6IvMoLWQEjrocHcmKTIRC8lhkXb6hgdnRtE6mhwc7oLY7/fz8MMPs2bNGtatW4fH42Hr1q1xj3s8qWv3UJZ3cqZXZ+2fyAo08+3Qv/Jezqegbhs0705hhCKdjSZHd+7cyYIFCwCYO3cuu3f3//8VDAZ57LHHmDFjRvwCFYPrbgBFx36vnTybnlDk9Nc3veZC9J5mJlsjhCIax9zS2zoeDDs8uLa2lrvvvrvfNk3TOHjwYEKDEukj4o5+gmXLLsA73Gi+rGJK2rfxVneAyuSEJmIgOSzSzonhwZEYJ8gZ50aTg8NdEJtMJtatW9e3HEc4HMZsNg96nEwQUTWOdPj42IyTa4nn7FtPOGc6e12zeCpSzIW6F+GD9VAyJ4WRinQ1mhz1eDw4HCenp9Tr9YTDYQyG6GX1BRdckJhgxUBdDZBVSlNPmByjjhhqVnrN0cmYZhkaASuH2ryU59sTG6cYs2GL1ocffnjQ7UuXLk1ELCINGT3HcCm5WAxmCA9dtVqzSzArYdyutiRGJ05HcliknRM9rJoanYxJf9pJ7Me10eTgcBfEOp2OgoLoHANr167F5/Mxf/7808YRCASoqanpt83v9w/YlmxjjaGpJ0QoopFnVmhqbmKOvQtry048c/+ZWd4u/taRR6BsHsr7z3Kw7IvwkV61E+0XGv2EmpsBMOa4aGtK3oyiqf47pLr9VMcwmhx1OBx4vSf/j6iq2lewjlY65miq248lhqlN+1GMebS6eplSYiAYDNLV3YUaUenq7iLoMKAPhYgEg31fW0IWKoH8rj3Ahfxj90FKtKHvhE317yHV7adLDMNm2MUXX5ysOESayvI30WksZpi5gwFwFJQC0Nt+OOExidhJDou0c+qw4LAf9EMtpjUxjCYHT3dBrKoqDz30EHV1dTz66KNDDm88ldlsZvbs2f221dTUDNiWbGONoWV/K3CUqUXZhCJOCto2o6ARnn4585obefOwgZbJC5na9ENm54ah9NzB2+88AiXHz3QF+RQkceKmVP8dUt1+MmMY7KJ7NDk6b948tm7dyqJFi6iurqaycuxjzNIxR1Pdfkwx/NVNpOQ8uhoilGY7MJlMZDuz0el1ZDuzMZlM6I1GIqd8NeVNQXXZqDQ0k2M10o192DZS/XtIdfvJjGG4wnjYe1pFZouoGgXhZnzW05WsYMqPnuBDrvrTPFMIkdFOHRacIUOER2revHls27YNYNAL4uXLlxMIBHj88cf7hglnqsPH11icnBP9PTiObCXkmEQk7wzmZEXvU3sldE70yXWvpSRGMfEsXLgQk8nE0qVLefDBB7n33nvZuHEj69evT3VomUXToKsBj7kYgCJ7jL3dio5I/pnkd+3mjGIHuxu7EhikiJeEjMuSqcAnhuZOL6W46HCUnva5mr2YEAaMPVK0CiGGcWqhGg6kLo40tnDhQrZv387SpUvRNI0HHniAjRs34vP5mDNnDhs2bODCCy/ktttuA+DWW29l4cKFKY46NeravdhNenJsRmjXsDW9hWfqFaAo5JvCTLWFeLUlh9vzz4BDr8HH/y3VIYsJQKfTsXLlyn7bBruWXbt2bbJCykzedogEcOkLASiwxb4yQrhgNjl7N3DmVCvrdjYRCEcwG2RlhXSWkKL11JkPq6urWbVqFatXr+7bv2vXLr7//e/T0tKSiOZFnBw7cpDJSgRTbtnpn6zT02kuJc/XIIkvhBjaqeuzhv2piyONne6CeN++fckOKW0davcyJc+Goig4vXUY/C58pZdwYrXwC/P8vNjgI3LxJ9F/sA7CQTDIWuJCTAjdxwBo5njRajdAjBMBhwrPwqoGuNjewtqIxoctHuZMzk5UpCIOEjI8WKYCnxhcx2oByCqMoWgFgo4pzFSOUdvqSWRYQojxTIYHizg67PJSlhsdGlzYsRMAX+klffsvyvfTG9Koy7oAQl5ofDclcQohEqArusLFkXAuMNKe1rMAOFuLzhK9q0GGCKe7hPS0xnsq8HScUS0dYkh0+2110Q8bTNklqBGVYCiE1dj/cw5N01CPzy+uKzyDGa5/8PN33kVXefr7YONlov8dhJhQ+g0Plp5WMXqBcIQGdy9Xn3n8fjb3TkK2IkLOaaC2AjAv149OgU3dFXwLJTpEeOrHUhi1ECJuOg4BsD+YT47Vi9kQe1+c6iwjaMiiqGcPTstMPjjWxRdl7sq0lpCiNd5TgafjjGrpEEOi29/zlxZUFPQ5Zej0EUxGIzp9/zcERVH6tjnLzoJ9YOttZPbsKxIW10dN9L/Dqe0IMe71mz1YelrF6NW1e1E1KM+3gaZR1LED3+RL+i1r4zRqnFlo5tUjYb5Vem50MqbL/yOFUQsh4sZVC7Z86rxGipwjXK9aUWjPOZeC5neZM/lWtte2JyZGETcJGR58upkPxfjg9NbhMpaAIcY3gqJZ0a+N7ycuKCHE+PbRJW+EGKUTt6KU59sw9hzF7m+h95ShwSdcXGZjT2M3wakL4OjbEIzxpjchRHrrOAQ55TR1dDPZDnlmNeaXWnQa+uKzMLr2cV52L0c6fBztkPeGdJaQolWmAh//unwhpkSO0mUfwX3H1jzchkKKut5H9XYkLjghxPgVCYJy/L6jiMweLEbvYKsXRYGpeTZsTW8B4CsZOL7voik2IqpGjeV8UENw5I1khyqESATXQbTsyTS4eymKNGPQQqd/zXFKpBeXN4CCxlXm6Ei2bR+2JSpSEQcJGR4sU4GPf7UtncxRmjiUd/WIXufKO58LW97kUGMbM8/IS1B0QohxKxICkw0CPTI8WIxJbZuHslwrZqMeW9NbBIxOAnmzBjzvvFIrJr2Ozd3TOU9njA4RnnlVCiIWQsRN0Ac9jXhtn8cT1jHVHj79az7Ca52MpuiZ6ttDUVYlfz/Qzi2XlJ/+hSIlEtLTKsa/proazEoYXdHAC4DhmKbMo0Dp5lCtLMkghBhEJAgme/R7GR4sxqC21cO0PDuBUARr89u05c4DZeBljcWgY85kJy/X9hCedEF0MiYhxPjm+hCAJv0kAKbZY+9lPUHVGQk5JmN3fcCF03J5vbYdl0fOS+lKilYxKO/hdwAwTDpvRK/LnR59frD+7bjHJISYANQQGI8XrbLkjRiliKpxqM3DpFwrkZ4WzF11aKXnUxhpoTDSgpmTF7AWncalkwwcbPPSXXgBNL0PPrmFRYhx7Uj0loD9kRNF68h7WgGCzmlYO2qYV2rFEwjzVp28N6QrKVrFoOwtO+hVrATzRzYrrs45iTZdATkuWQtPCDGISOhkT2uoN7WxiHHrSIePQFhler4NW1P0Q9JgwVmE9m8htH8LulPubVMivZyvRpdwe0+bBWhQty0VYQsh4qX5fdAZ2enJw6rXKLZGRnWYYHY5OjXEx21H0CnwxkFXnAMV8SJFqxggEI4wo3cPR21ngy72hZoBUBRanOdwZnA3exs66fJJT4oQ4hSRIJizTn4vxCjsaugCoLIkC1vTW6gGK705Q69UcKYziF0f4U/ucrDlQ82fkxWqECIR2g6As5TdbRFmZgVR1dhnDj5V0DkNgIKO9yjPt/MPKVrTlhStYoDa2g85UzmCv/BcJuk6+g2zioW1/AIKlG5e2voqPYHRDdcQQkxQ0tMq4mB3Qxcmg47pBXZszW/jK74QdEPPLalX4GO53bx80Eew8rOwf7MsfSPEeKVp0LYPzTmZmvYgJUYfkdHVrGhGO/6saVib3+HMkiwOtnk55pb3hnQkRasYwL3jWXSKhjkrD0Owq98wq1iUzvoYANmNf09EeEKI8SwSBJMj+n1YlrwRo7PrWBezS7IwBzsxu2roLR241M1HXVHQhT+ssUX/SQh54YN1SYhUCBF3roMQ6KbVXI4nqFFhG9vkSb6Cc7E1v8Ps4ugHqlv3tcYjShFnUrSK/lSV8sMb+FA3HWdOwagOoWUV0WScyizPO/hDo7vHQAgxQUWCYDCDzghh6WkVI6dpGrsbupheYEdf9zcUNDxll532dTNtfqbnmlhdVwSTL4DtP0WRIepCjD/H11p+R42ucHGmY4w9o2UXog92c5WlhrJcK1tqWsHnhs4j0HmEXMtYAxbxIEWr6Cf4/gamhA+zq+SGMR0nMOljXKjs4419R+MUmRBiQggHQW+MFq7S0ypGoa7dS08gjKIomOu3Ejbn4C8897SvUxT43FlOdjX2UHfOt8F9mNJ3HoDWfbD3z/DBM7Dzt7D7OVDlA1ch0tbRN8GSw4udUym06Sgxj3y5m1NFis9BQ6Gg6e98srKQ7bXttHZ0QO0rUPsKhogsg5MOpGgV/Xi3PcIxrYDcs8e28HrOmfMxKyEa3n81TpEJISaESDDay2owyzqtYlTePBRdkmJanhXH0dfwln0y5kkDP392NnazngcPlMLl/0l2/WZ4/BJ4+b+g6T3orIctK+D3S6MfsAgh0oumweHtRErn8rcWM1dMt6IoYzykOZtQVhlZLW/x6bOLCasav6+WpW/SzdCzFojMU7eNXPcu/k/5J+6cYqfnw9EfKlx6AUGMFLRsx+X5Z/Id5vjFKYQYnzQtuk7riZ7WkBStYuS2H2yn0GFmVuQAht42vFOviPm1DpOez54ziWd2HKVu0TfQTLOZYfOCNQe6GwEFfO2w9Yfwwr/D9Y8k7OcQQoyCqxbcdRwsW4w3rOPK6RZoGvthAzln4Dj6N4qMvUwvsLP+AzdfLwKDdO+lDflTiChNI7zlflq0XDwVn0U31o+tDBY68udxje4d/vDm/vjEKIQY3yLHh3DpjaCXnlYxcsGwyvbadi6Ylsu05s2oOhM95deM6BifmzsJo0HHz179kEDuGTD3i1B8Nii66Bji85bCJ74N7z4J+15I0E8ihBiVA5sB+IPvXKx6jY+VxeeG00D+bBRUnIf+wvyKAhq7Q/zpqNzMmk6kaBVR+1/A0PAWj4Y/zw3n5KCN8XAWnYZ51kKm6tr48N2taNpYjyiEGPdOTHqjM4LRIkWrGLHXa9vo9IW4sjKPqU0v4Z16BarZOaJj5NpNXHNWMX9+vxGXb4hl2S7/Tyg+BzZ9C3rdYw9cCBEfB14iUjibtR+a+PRkPxbDGDtZjgs5yvBnV5BT8ztmlziYVWDm0Robfrm9PW1I0SqgqwHtT9/gAOUcmfwZKvNNYz6kEuklNOXj+HU2PtXzR16V6cOFECeKVulpFaO0Yecxsq1GLtWqsQXa6Jr5+VEdZ/G8MlQVntnVOfgTDCb43M/A2w5/XT7qeIUQcdTdCPXb2ZO1AG9Q5fNlPajx6hNRFFwVN2Bt38XMhj+w/Ow2bvavp/q1P6HraYhTI2IspGjNdJEwPPdVwsFe7gj8G7deWDzmXtY+Bgv+KZ/kKv17vPaHX3CswxuvIwshxoEOb5Afv97KWcs3c86Kl1j5hx3RHUar3NMqRuxoh4/Nu5v5zLml5O56Ap+5iJ5pnxrVsUqyLXz2vFL+sr+bnfVDTLgyaS5c+nV4dw3UbRt94EKI+Hh/HWgqDzTOZU6xBZO/I37XrIB7+mfxFV/IJbt/wPy37uDLhs2c730d66Z/ia4NK1JKitZM99qP4Mg/+EHky5SWV3LJpPjOzdVb9gm6cs/h+8H/5a3/+yaNR+qia18JISa0Iy4fn/vZ67x22MvCs4q4vLKQN/cfAeCPBwKEdSbpaRUj8r9/PYBBr+PWyU04jv2dA+VfjPbaj9LtHy+nKMvEl371Npv3dw3+pMvvhdzpsPEuCMm6wkKkjKpC9dO0ZM/lzc4cvnFp4ZhnDR5AZ+DIZ57mzTk/oPuK+9kx627+O3sFvWFo+dVNaPIekFJStGayQ6+hbXuI7bar+X1gPt+52IZOG9taVwPoDBg++/84Unoti3s3oD6xkBe2vBTfNoQQaeXDlh5u+sU/6OoNsfIzldxx2UzuvGImP/5cBQCbakNsq/fj7ukhHFFTHK0YD/66t4U/vtfAP108mWnv/DchWzH7y28e0zHNRgOfPsPBtAIbd/zxCI/XWAYONTTZ4LqfQscheOW/x9SeEGIM9m0EVy0PuT/JxdNyuXiKLSHNaEYbh6bcQLDiWhSDhTvO1fNk3l0U9x5k+y/uIhiWc1aqJKRoVVWV5cuXU1VVxbJly6ivr++3/9VXX2Xx4sVUVVXxzDPPJCIEcTrttYSf+SeO6ibz1Y6b+a9P5nJ20djvZR2UyU7RZ+7lyJWPY9HDFTv/jeefeoyQXKymrdPlsBBDee1AG0t+8QYRVeN/l5zHrGJH3z6bEr2n9VuXTwWzE3+3i/k/epX/eWk/Rzt8qQo57cg5tL/dDV1855lqKovs3BF4AkvbB7TM/wERw9gvWu0mHatuOJcrKxz8vz1Orn8ll38c9vR/0ozL4KKvwpuPwTu/GnObYvyTHE2yUC+hv67kmG4Sf9V9nMtnFaEmaYJPvQIf+9gC3sy/gU+0r+f/e+gRvv+n3Tyz4yidPlnLOZkSsk7rli1bCAaDrF+/nurqalatWsXq1asBCIVCPPjgg2zYsAGr1coXv/hFrrjiCgoLCxMRiviIUDhM29sbcL76XfyhCN823MNDiyazsMJKomvI/OlziBT9kqY/3svna/+Tt1c+xbGiK5j3qS8xbeZZiW1cjMhwOSzER0VUjbcOuVj7Zj0v7m5mZpGD7392NgVZFnp9J4tRfa8LgLLiQmadewaGHX/jjAIbj/+tlp9treUTMwu4dk4Jn5hZQHm+DSXuY7/GBzmHRjV19fKb7Yf5zfY6pln9/NS6hrw9m3Cd81V6ZnwGjnTGpR2TQceqa0t5cdub/Lw2m5vX1TH/nW6WXTqNkmwLZblWCq5dBZ1H4C93R+9tu/xesIxs1mIxcUiOJo8nEGTPr77G2e5afqB9j5VfOBdvIAIEkhaDoijkfG4VrU/v5L7QI3xxRw5PvlHIvTqFi6blcs7kbGYUOjir1MnZk5wY9DKQNRESUrTu3LmTBQsWADB37lx2797dt+/gwYNMnTqV7OxsAC644AJ27NjBpz/96USEklEC4Qivf9hObyhCIKQSCKv0BgLkNL1Or7sJvbuOi/zbmak0sE+dwuYZ/8VjnzyXLGOYZI120NvzcXzxCfa8voby+r9wcdtj8NRjHGIybboijDoNq1GPZs2lxz6VXkc5WLLxF5yN6pyCTgGdokT/6eDi6fnJCTzDDJfDYuI62uFjf3MPEU1DVTUimkZE1VA1jYgKqqoRVjW6/SHc3iAd3iBH3T72NHTTEwhjN+v58vxyFs8rQ6cbeNI2dUd7I1R7Eaq9CJ0W4ftX5OFeOIuX9jTzwq5mvvd89P+ayaCjxGmh2Gkmz24iz24mz27EbjagVxT0OgVFUdArnPxep6BXFHQ6Bd3x7TpFoanRQ32kCd3x55xblkNhljmpv9uRmOjnUE3T2FHvpsMb5PARD/v9DYQiKqGIhtsX5Ji7lw8PH6HS9QpFips/5DRzlv9dlJYgbfO+RfuF34l7TDpF4ZoSLzMM7bxjnc9v3u3kjqd29u2fXmBneu63uNVp5fI3Hyfw9m85mncpXc5ZuPPn0lkyH7NBh1GvANEPW0585nLio5cTH8KcfBz9euyYjyalpa8tg07HpRX5GOXCN21N9BxNhXqXl72N3fSGIngDYXQN72Bvfosz2v7K2cph/uS8mX+6/p8ocVp4qy75c6NoRis7Lvwfrtnxz/xF+U+Ozfwcf+2axMau83myvpPg8Z6fLLOBc6dkU5xloTDLTK7dhF45+Z5gNemxmfTYTAYMOoUTHcYn+o1PLBGpQd++Y8e81EeaBjx3MKd+1Dvwc19l0H39X9P/RSceHT3mpZHo+1Se3cT5U3OHiSIxFC0BC2j+13/9F9dccw2XXXYZAJdffjlbtmzBYDCwY8cOnnrqKR5++GEAfvrTnzJp0iRuuummIY9XXV2N2Zy+FxhCJEMgEGDu3LlJaWu4HB6M5KgQ8cvReJ9DQXJUCJAcFSLdDZejCelpdTgceL0nlzdRVbXvYvej+7xeL1lZWcMeL1kX6kKIqOFyeDCSo0LET7zPoSA5KkQ8SY4KkXwJGXsyb948tm2LrmlWXV1NZWVl376Kigrq6+vp7OwkGAyyY8cOzj///ESEIYQYpeFyWAiRWHIOFSK9SY4KkXwJGR6sqiorVqzgwIEDaJrGAw88wN69e/H5fFRVVfHqq6/y2GOPoWkaixcv5pZbbol3CEKIMRgshysqKlIdlhAZQc6hQqQ3yVEhki8hRasQQgghhBBCCBEPMjWdEEIIIYQQQoi0JUWrEEIIIYQQQoi0JUWrEEIIIYQQQoi0JUWrEEIIIYQQQoi0JUWrEEIIIYQQQoi0JUWrEEIIIYQQQoi0JUWrEEIIIYQQQoi0JUWrEEIIIYQQQoi0JUWrEEIIIYQQQoi0JUWrEEIIIYQQQoi0JUWrEEIIIYQQQoi0JUWrEEIIIYQQQoi0JUWrEEIIIYQQQoi0JUWrEEIIIYQQQoi0ZUh1ALGorq7GbDb32xYIBAZsS7ZUx5Dq9iWG5LYfCASYO3duwtsZDcnR9Gw/HWJIdfvJjEFydORSHUOq20+HGFLdfjJjkBwdmVS3LzGkR/vJjGG4HB0XRavZbGb27Nn9ttXU1AzYlmypjiHV7UsMyW2/pqYm4W2MluRoerafDjGkuv1kxiA5OnKpjiHV7adDDKluP5kxSI6OTKrblxjSo/1kxjBcjsrwYCGEEEIIIUbo/fffZ9myZQO2v/rqqyxevJiqqiqeeeaZFEQmxMQzLnpahRBCCCGESBf/93//x5///GesVmu/7aFQiAcffJANGzZgtVr54he/yBVXXEFhYWGKIhViYpCeViGEEEIIIUZg6tSpPProowO2Hzx4kKlTp5KdnY3JZOKCCy5gx44dKYhQiIlFelrTVJcvSG8ojNVoINtmSnU4Qog01eULAsj7hJi4fO7oV1tuauMQ4hSf+tSnOHbs2IDtHo+HrKysvsd2ux2Px3Pa4wUCgQH38/n9/pTeh5vq9sdrDLkWMET8AIT1Ftz+5LafCOkQgxStaaonEKbHHyZskYtRIcTQegJhQN4nxAQW7Il+laJVjAMOhwOv19v32Ov19itihyITMU2gGDqPQO2b0e9nXkVJztTktp8AE3YiJlVVWb58OVVVVSxbtoz6+vp++1966SUWL17MjTfeyLPPPpuIEIQQQ5D8FEIIIRKjoqKC+vp6Ojs7CQaD7Nixg/PPPz/VYQkx7iWkp3XLli0Eg0HWr19PdXU1q1atYvXq1QBEIhF+/OMf89xzz2Gz2Vi0aBFXXXUVeXl5iQhFCPERkp9CCCFEfG3cuBGfz0dVVRXf/e53+cpXvoKmaSxevJji4uJUhyfEuJeQonXnzp0sWLAAgLlz57J79+6+fXq9nhdeeAGDwYDL5QKi4/2FEMkh+SmEEEKMXVlZWd+SNtddd13f9iuvvJIrr7wyVWEJMSElpGj1eDw4HI6+x3q9nnA4jMEQbc5gMPDyyy+zcuVKLrvssr7tQ0nHm9MTHUPQ4CCiMxL0hOhprh/0ORP9dzBeYkh1+yMV7/yEzMzRdGk/aIj+LdP1fSLV7adLDEIIIYQYvYQUrR+9CV1V1QEXvtdccw1XX3013/3ud3n++edZvHjxkMdLx5vTEx3DMbePHn+YLIuBstwpSW8/VhJDetycPhLxzk/IzBxNl/aPuX0Aafs+ker2kxmDFMZCCCFEYiRkIqZ58+axbds2AKqrq6msrOzb5/F4+NKXvkQwGESn02G1WtHpZLnYUwXCEe79wy4e/1ttqkMRE5DkpxBCCCGEGE8S0tO6cOFCtm/fztKlS9E0jQceeKDfDerXXXcdt9xyCwaDgVmzZnH99dcnIoxx69WaVv7+YTsAt1wylbJcW4ojEhOJ5KcQQgghhBhPElK06nQ6Vq5c2W9bRUVF3/dVVVVUVVUloukJYUe9u+/7nfWdfGxGQQqjERON5KcQQgghhBhPZNxfGqpp6mZWSRZmg47D7d7Tv0AIIYQQQgghJqiE9LSK0dM0jZqmbubPLEBVNepdUrQKIYQQQgghMpf0tKYZlzeI2xdiRoGdKXk2Drt8qQ5JCJFmIqrGe0fcqKqW6lCEEEIIIRJOitY009TpB6A420JZrpXWngC+YDjFUQkh0sljW2v5wuP/4GevfkggFEl1OCLOVFVl+fLlVFVVsWzZMurr+6/B+9JLL7F48WJuvPFGnn322RRFKYQQQiSPFK1pprk7WrQWOswUOy3RbV3+VIYkhEgzG99vBOBP7zcSjEhv60SzZcsWgsEg69ev5+6772bVqlV9+yKRCD/+8Y/57W9/y/r16/nVr35FR0dHCqMVQgghEk+K1jRzomgtyjJT4DBFt0nRKoQ4rrnLz4etHqwmPXXtXvzS0zrh7Ny5kwULFgAwd+5cdu/e3bdPr9fzwgsvkJWVRWdnJwB2uz0VYQohhBBJI0Vrmmnp8qPXKeTaTeTbzQA0SdEqhDhuX3M3ADecPxlVgw9bPSmOSMSbx+PB4XD0Pdbr9YTDJ28TMRgMvPzyy3zuc5/jwgsvxGCY2HMqHmjz4+6V22SEECKTTewz3TjU3O2n0GFGr1PIP9HT2i1FqxAi6sOWaJF6zdnFPP3WEerbvXy8Ij/FUYl4cjgceL0nZ45XVXVAYXrNNddw9dVX893vfpfnn3+exYsXD3vMQCBATU1Nv21+v3/AtmQ7XQzNPSFu/8NRJjn0/PIL0Q91k9l+MqQ6hlS3ny4xCCHSmxStaaal209xdvReVotRj9NioKmrN8VRCSHSxYGWHgocZmYU2LEYdRzrlPeHiWbevHls3bqVRYsWUV1dTWVlZd8+j8fDHXfcwa9//WtMJhNWqxWd7vSDpsxmM7Nnz+63raamZsC2ZDtdDG9trwOg0ROhx1zEpTMLktp+MqQ6hlS3n8wYpDAWYvySojXNuDxBSo4XrQCFWWaauwIpjEgIkU6Oun2U5VoJhlUm51hpcEvROtEsXLiQ7du3s3TpUjRN44EHHmDjxo34fD6qqqq47rrruOWWWzAYDMyaNYvrr78+1SEnRJcvyD8Ouvoe7zzijnvRKoQQYnyQojXNdPqCnFma1fe4KMtMc7dclAohopq6/MwschCMaJTl2tjf0nNyp88NwZ5+zy80+qHzyMkNpiyw5SYpWjEaOp2OlStX9ttWUVHR931VVRVVVVXJDivpegJhdjV0cW2lk78d6uFIh6xbLoQQmUqK1jTj9oXItZn6HhdkmWWiFSEEAJqm0dTlZ/7xe1gn51j5+4dtBMNq9AnBHqh9pd9rQs3NUFJycsPMq6RoFeNCKKLi6XZzVekxDmfPlqJVCCEymMwenEb8oQi9oQi5NmPftqIsM+2eIIGwLGshRKZzeYMEwypFx9dwnpxrRdWgQe5rFRNQc5ef5fonuenw97lD/7wUrUIIkcGkaE0jnb4QADmn9LQWOqLL3rT1yH2tQmS6ps6T6zgDTMqJFq9StIqJqLndxWf0bwEw3/93WroCRFQtxVEJIYRIBSla04jbFwQgz27CrvZQGGlhijm67EGLLHsjRMY7UZwWH+9pnZRtjW6XyZjEBKQe24FNCeArvYTC4FEKtXb5AFcIITKUFK1p5ETRmmMzog96CO3fQoFrJwB7G7vpOr5fCJGZTix/VeSM9rRmWQzYTXrpaRUTkqVtFwDaebcAcJ7uoPxfF0KIDCVFaxo5MTz41ImYCi3Re1nfquugJxBOSVxCiPTQ2hPAoFOwGqJv3YqiMCnHyjHpaRUTUG73PlqUAnSTzkVD4UzlKLWtPad/oRBCiAlHitY00nj8E2S9cnJbjlHFqFfo7g2lKCohRLpwe4NkW42E1JPbSrMtfe8dQkwkRYEjNBunoCk6wlmTqdQdpd4lkzEJIUQmiqlo3b17d6LjEEBrT/S+VQ0IH59sQlGg0G6g2y+9rGJokqOZwe0L4rQa+20rzbHS1OUnHFGHeJVIB5KjI6RpFEeacJsmoQFq7gwqdY1950kh4k1yVIj0FlPR+sQTT7BkyRKeeuopuru7Ex1TxurqDWPUK6gaqJoGmorO52KBuZawryv6JJ8bOo9EvwpxnORoZnD7QmR/tGjNthBRNfY3y989nUmOjpCvgyx8eKyTAVCdkylT2mjpkqJVJIbkqBDpzRDLk37yk5/Q1dXFpk2buOuuu8jLy2PJkiVccsklgz5fVVVWrFjB/v37MZlM3H///ZSXl/ft37RpE08++SR6vZ7KykpWrFiBTicjlbt7Q9hM0T+JZc868t55HF3Yx4+ADpyE2v4IRUVQ+wrMvCq1wYq0MpIclfwcv9zeIJNyrP22nXi8v8XD2TNSEZWIxUjPo5nO31qLBQhmlQEQcUzGQYBAV0tqAxMTluSoEOkt5ivR9vZ2Ghsbcbvd5ObmsnnzZu69995Bn7tlyxaCwSDr16/n7rvvZtWqVX37/H4/Dz/8MGvWrGHdunV4PB62bt069p9kAvD29pJvgUmH/4jjjf8h5JhE5xmLeWrSfxLRFHK2/gdoskadGFysOSr5OX65fSGc1v6fNZZmR5e/kcmY0t9IzqOZztt8AADVebxodUZ7XM2eoymLSUx8kqNCpK+YelpvuukmLBYLS5Ys4a677sJkis5u+5WvfGXQ5+/cuZMFCxYAMHfu3H73CZhMJtatW4fVGu0dCIfDmM3mMf0QE0WXL4gt3InlzYdRs0rpOPufQGegxTuPR+sbWNn0JLg+THWYIg2NJEclP8cnTdPo9AXJ+cjw4Dy7CbNBd3wpEPlbpauRnkczXajtEKqmYMspBcDojH7NDTThD0WwGPWpDE9MQJKjQqS3mIrW++67j3PPPbfv8dtvv83FF1/ME088MejzPR4PDoej77FeryccDmMwGNDpdBQUFACwdu1afD4f8+fPH7b9QCBATU1Nv21+v3/AtmSLdwxuX4hZagvmrkMEzv8ywbAKBCm1KzwduZQfmNYS3PVnOoylGHNc+P1MuN/BeIwh1e3DyHI03vkpksMTCBNWtQETMekUhdJsCw3uXiAnJbGJ0xvpeTTjdRyiiTxy7dHLFM2eB0CZ0kpjZy8zCh3DvVqIEZMcFSK9DVu07tixg9raWn77299y++23AxCJRPjd737Hpk2bhnydw+HA6/X2PVZVFYPB0O/xQw89RF1dHY8++iiKogx2mD5ms5nZs2f321ZTUzNgW7LFO4bu9fVcbDkIEVCnXoqpwwVAiU2jAyfdlslkt35A6QXnQ0E+bSHvhPsdjMcYktX+YIXxaHI03vkJmfPBUirbb+qJLnulDwfo9fVf9qPIYeRIew/t7SZCzc399oVCIZpO2WbMcdHW5CVZUv03SHUMoz2PZjpDVx11ajF5tuM9qgYLAVMeU8JtNHX5pWgVcSM5KsT4MGzR6nQ6aW9vJxgM0tbWBkQXs7/nnnuGPei8efPYunUrixYtorq6msrKyn77ly9fjslk4vHHH5cJXo6LqBpd/ghnmQ6g6s2oeTPheNFaZI+etBstFWS3/gNUWf5GRI0mRxORn5nywVIq2w8e7QSOMqkoF6vN1m9fWb6D6oYe8vLz0JWU9NvX1NxM6anbCvIpyJkal5hikeq/QTJjGKwwHu15NKNpGo6eOmq1i7nWevI9KGIrpLjXTW1rD/NnFqQwQDGRSI4KMT4MW7RWVlZSWVnJkiVLKCoqivmgCxcuZPv27SxduhRN03jggQfYuHEjPp+POXPmsGHDBi688EJuu+02AG699VYWLlw4tp9knOvuDaEBZWojYXsp6E7er3OiaK3TT2d25G/gaU1NkCLtjCZHJT/HJ7cvCDDgnlaITsYUDKu0esKUDNgrUmm059GM5mnFEu6mXpmMzXiyaNVlFVHScYTtHb5hXizEyEiOCjE+DFu0fvOb3+SRRx7hhhtuGLDv9ddfH/J1Op2OlStX9ttWUVHR9/2+fftGGueEF70g1SiONBOyndtvn92kw6qLcECbwiKAnqZUhCjS0GhyVPJzfDpRtH70nlaASdnRibMOtPmlaE0zoz2PZrS26HtQi7m832bNXkiprpqW7kAqohITlOSoEOPDsEXrI488AkjSJoPbF6KQTixaL122ogF/mFxTmL2hSWiKHqWnedBjiMwjOZo53N7oPa3Zg/W05kSXvanr8PPJnGRGJU5nNDma8Wspt0eXu+my9h/GrtqLyKWHbrcLfG6w5aYiOjHByHlUiPEhprPcO++8w7Zt23jttde4+uqr2bhxY6LjyjidviCVumMAhG3FA/bnGcM0Bsxo2WXS0yoGkByd+Dp9QRTAqBs4MVZRlgW9TuFIZyj5gYmYjCRHM34t5bZ9eLChWPsXpXp7IQBB12H83i66jo8+ECIe5DwqRHqLqWh96KGHmDZtGmvWrOH3v/8969atS3RcGcftCzFTaQQgbB94T0WuMUx7QI+WOx2kp1V8hOToxNfhC+K0GoloA/fpdQrFWWaOdslFfLoaSY5m/FrKzbupo4x8+0fWYrU6AVAC3QRCYXoCMimhiB85jwqR3mJap9VsNpOfn4/BYKCwsJBgUC6M4s3tDTJFaSWiGFCNWQP25x0vWtXsqegPb4OI9KiIkyRHJz63L4TTOvRb9qQcK0e7ZIKadDWSHE3EWsrjaVmqWW37qYmcR65ZQY2oJ3dY8gHI1TppcnsJeF30NNfHvf1kS3UMqW4/XWKQ86gQ6S2motXhcHD77bdz88038/TTT1NaWprouDKOyxtkrs5F0JgNg6yLmWsKE9YUPJZScjX1eG9rXvIDFWlJcnTi6/QFybYMvJ/1hNJsC1ubutC0Qd9CRIqNJEcTsZbyuFmWytcBfjcH1MkU2A3o9KcMCHNEhweXKG46gnqmTsqnLHdKfNtPgVTHkOr2kxnDcIXxSHL0dPed/+Y3v2HDhg3k5UWv037wgx8wY8aM+P0gQmSgmIrWn/70pxw5coSZM2dy4MABbrrppkTHlXE6vAGm6lwEjNmD7s81RodBtRuKyQXoOgomKVpFlOToxOf2hsi1D1O05ljxBFXcQYU88yBjiEVKjSRHM3qt87b9ANRqk6m0feRnMztRdUaKlQ4ae0Ikb7VhkQlGkqOn3ndeXV3NqlWrWL16dd/+PXv28KMf/Yg5c+YkI3QhMkJMRavL5WLr1q1s3ry5b9s3vvGNhAWViTq8QUqVdoKmikH35xmjw4EbKOIMgK5jUHhe8gIUaU1ydOJz+4JMy7cNuX9SdnQG4cMePXlmudcv3YwkRzN6LeX2aNH6oTqZXMtH7mlVFFRbISUhN4e6QnwsBeGJiWskOTrcfecQLVp/+ctf0tbWxuWXX86//Mu/JC5wITJETEXrXXfdxaWXXipDDhOos6ubXK2Lo0bnoH+UEz2tR8O5oNMfL1qTG6NIX5KjE5/7+ERMQynNiU7Mc8SrZ16+FK3pZiQ5mtFrKbftJ6y30Eg++R/taQUUewGTejp4vVvuNxTxNZIcHe6+c4DPfOYz3HzzzTgcDr7xjW+wdetWrrjiimGPmY73nae6/fEaQ6HRT6g5OmmqMcdFW5P3NK+Ib/uJkA4xxFS02u12vv3tbyc6loxm9ERnDg4ac4YtWlt9Gljzo0WrEMdJjk5svcEI/pA66BqtJ5Q4LSjAh10TbLjoBCE5GqO2fXRZy9G8OvKt+gG7NVsBk5Q9NHbLBzMivkaSo8Pdd65pGrfddhtZWdFJNS+77DL27t172qI1He87T3X74zaGziNQUhL9viCfgpyx3cwwLn8HY2hnKDFd3Zxxxhn85S9/4dChQ9TV1VFXVxe34ESU3R9de3Woe1oNOsg1RmjxRsAmRavoT3J0YnMfX49yuKLVZNBR5DBw2CNFazqSHI1R2wHazNELvFzrwP/Lmr2AAtw0SU+riLOR5Oi8efPYtm0bwID7zj0eD5/97Gfxer1omsZbb70l97YKEQcx9bTW1NT0q3wVRWHNmjUJCyrTBMMq+ZFW0A1dtAIUmCO0niham6pBk8lWRJTk6MQWS9EKMCXbyFFvTG/rIskkR2Pg74buYxwrvAqnWYdJrwxYl1i1FWImiKenE03OgSKORpKjw913XlVVxbe//W1uvfVWTCYTl156KZdddlmyfgwhJqyYrm7Wrl1LT08PDQ0NTJkyBbvdnui4MorbF2SS0o6KjtAga7SeUGg5XrTm5UPQgy7YncQoRTqTHJ3YOn3RidiGW6cVoCzbyLY2KVrTkeRoDNo/BOCgNon8QXpZIdrTCpAT6aCzN8TYFrwR4qSR5Ojp7jv//Oc/z+c///lEhSpERorp6uall15i9erVRCIRrr32WhRF4c4770x0bBnD5QlSprTTay5AUwbew3NCgTnCAffxnlbA5G1MVogizUmOTmwd3hM9raZhn1eWbaIjqKc7pOA0Si9UOpEcjcHxmYNrIpMosA1+LtRsJ9Zq7aC5y885k5MWnZjgJEeFSG8x3fz0m9/8hmeeeYacnBzuvPNOtmzZkui4MkqHN8hkpZ2grXjY5xWZw7h8KkFz9JNmo0fuaxVRkqMTW2ff8ODhP2esyIsWtfu7pLc13UiOxqD9AOgM7O4toMg+RNF6vKe1WHHT2OlPZnRigpMcFSK9xVS0KoqCyWRCURQURcFqtSY6rozi8gaYRDuqvWTY55VYI2hAk64IAJOnIQnRifFAcnRi6/CeGB48/D2tswrNAOzplKI13UiOxqDjEGSX0eKDwqGKVlu0aC2hg4ZOXzKjExOc5KgQ6S2movWiiy7i7rvvpqWlheXLl3POOeckOq6M0tblo0TpQJ89/NpgpZbja7X6rWArwCQ9reI4ydGJrcMbwGkxYNQP/5ZdaDeQa4rwvmvo2wxEakiOxqCjjkhWGT1BjcIhhgejN6EabJSbujja0Zvc+MSEJjkqRHo77cfx+/btQ6fTsWfPHq6//nqcTifLli1LRmwZw+NqwKRECGRPgq6hn1dqjRatx3x6yJmKUXpaBZKjmaDdG6TAYT7t8xRFoTIryJ5OA75gBJtJitd0IDkaA00D92F8Mz4NQJ5NhzrEbdkRs5PyYCfH3NLTKuJDclSI9Dfsx/Yvvvgi//mf/8nkyZO55557cDqdPPPMMzLOP87CHUcBULOGHx5caI6gV+CYVw/ZZdLTKiRHM0SHJ0ieffhJmE74lGkXX+xdh1bzZ3APss5gJBxd+Hy4fz53nH+CzCU5GiOfCwLduE2TACiw6hhqKrGIOYdJSjtH3dLTKsZOclSI8WHYntY1a9bw1FNPYbPZ+rZ94Qtf4F//9V+5+uqrEx5cplB6osVn9J7WjiGfZ9BBiUMf7WktnYqx988Q6gWj3HeRqSRHM0OHN0h5vm34J6kRHNv+my+7/4RPb8ZyLAxHXyM7/2wovA30x4vekA/q3hn+WDOvAltufILPcJKjMeqIfsDSqo9+eFtoH/oz9bAln6LOHXT6gnT1hk67frEQw5EcFWJ8GLan1WAw9EtiAIfDgV4vQ87iyXJ86RrVMXxPK8Bkp4GjXh1kH1+dzn04gZGJdCc5mhlc3gD5pxkeXLL9PiwH/kRj8ZWcH/gFv5vx/+CMa7C59sBbP4ewzLSaCpKjMTo+KuCYEp1Ff6glbwAi1gKMaoBi3Bxu9yYlPDFxSY4KMT4MW7QqijLodlVVhz2oqqosX76cqqoqli1bRn19/YDn9Pb2snTpUg4ePDiCcCceVdVwBprp1TvQTI7TPr/MqeeoN3pPKxCdbVFkrNHkqOTn+KKqGm5fiPxhhgdn1b1I7t61+M5ZhnbGNej0BnZ7HDBrEe4Zn4t+uLXj16CGkxe4AEZ/Hs04HYcAhUPhQvQK5FmH6Wm1RmcQnqa0UCdFqxgjyVEhxodhhwfX1tZy991399umadppL2S3bNlCMBhk/fr1VFdXs2rVKlavXt23f9euXXz/+9+npaVlDKFPDM3dfkpox2sZfubgE6ZmG2j16/HaJmGHviFVIjONJkclP8eXrt4QEVXDZtITCEUG7FfCvRT/YwX+/LPxXfR1dB/+jZmOEPu7TUAv/rzZkGWH938H1b+DKZck/4fIYKM9j2acjjpwTuKoR6HYoUenKESGmojJmg/AdF0zB9s8SQxSTESSo0KMD8MWrQ8//PCg25cuXTrsQXfu3MmCBQsAmDt3Lrt37+63PxgM8thjj/H//X//3whCnZjqXT7KlHZCjunEclfOzNzos/Z0mrjA5EQ/2EQrImOMJkclP8cXlzcAgNWkJzjIVXzeB7/C6Gmk8YqHseuib+mVziAvNtpPzr465WIIdMO+TfD2L2DyhckKP+ON9jyacdx1kDudo11BypzDL2wQMeeg6YycZ21ja3NPkgIUE5XkqBDjw7BnhosvvnhUB/V4PDgcJ4e66vV6wuEwBkO0uQsuuGBUx52I6ts9nK2005vzyZiePyMv+jvc19rLOY4yrO0fJjI8keZGk6OSn+OLyxMEIMc28GMtJdxL3ge/pKf8anyTLsUeifaOV2YFeS6SRb1Hj+XEkyuugt4OeO8pCAegfH6SfoLMNtrzaMbpqIMZV3C0IcAnpliGf66iQ82dxnmewzze1J2c+MSEJTkqxPhw2nVaR8PhcOD1nrzPRFXVvgvi0QgEAtTU1PTb5vf7B2xLtnjEsH9fHUsVH122SRhCYbq6uwg6DKgRlWAwerGqRtS+7ZPsOkw6lX2tPnz2qRib3ubDFP4eJsrfYTy3P1Lxzk+Y2Dma6varD0eHP+qCPnp9/f9OeQf/iCHQSVPlrfT6fIR0YYLBIDMsAPlsO+zlipwQTc3N0RcUfILCcAT9rg24eyGQUzFom8YcF21N8blXMNV/g3SJYSRUVWXFihXs378fk8nE/fffT3l5eb/n9Pb2cvvtt/PDH/6QiorB/47jRqAHvK2E7cW0eCJMyT79+5FaOJsZ7ldo8HnxBcPYTAm5nBFCCJEmEvIuP2/ePLZu3cqiRYuorq6msrJyTMczm83Mnj2737aampoB25ItHjHYXn8bAOuUc8FoINuZjclkQqfXYTJFJ17R6XV9241GPeX2MPVdEcKllRiObmb21EKwF4z55xmNifJ3GA/tx+uiO975CRM7R1Pd/vb2Q0ArZ0wpHjBhSHHtOgI5ZxCZfjlWRcEY6QGTiTNzwa5XqQnkc42xk9KSU2Ymn3s/bLidvLo/waX/BjlTBjZakE/BicnexijVf4NkxhCvHM24+86Pz4Lfboz+P52cZTg5tH0IauFZWPb9melKEx/WN3LeGfH5/yqEECI9DTt78GgtXLgQk8nE0qVLefDBB7n33nvZuHEj69evT0Rz45urFoBQ7oyYXzLDHqLWFSCQffzT9da9iYhMTFCSn+NLS7cfs0FHlqX/Z4yWtvextlbjPvtW+Egxa9DBuTkB3m4fZMZhow0u+hqY7NGlcLqOJjJ8MQqx3nc+Y0bs5420drxoPRQpAmBajp7T1KxECqMfQsxVDrK/cej1zYUQQkwMCelp1el0rFy5st+2wYYvrV27NhHNjxuBcASH5zARo56IcwrG3tg+NT8jK8hLzWHC9uMzDh/bAdNjuydWCMnP8aW5O0BJtmVAL2vunrWoBhtdZ9ww6Ovm5gZYXWulI6hnwNzkFid87E5483F44zG45F8gd3pifgAxYom47zydh/C3HH6LYuDdTjugUe6M3hYzlGAwSCRrCqrRzgVqLe8d6+acUf4c6fI7GO+3MUyEGIQQ6U1uAkmhQ21epilN9FinEIgonGbqiT6zndF7XasPHGKGyQGN7yUuSCFESrV0+Sl29n930AU6cdY+T1flTahm56Cvuzi/l9W1ObzVncvZgz3BXhgdHvzm4/Dmarjoq1Aw9qHiYuwScd95Og/hLzb6wOzkQ6+VyVkBnFbTkMvdANFbaAwGwoVnc3HzQf7UGRn1z5Euv4PxfhvDeIlBCmMhxq+EDA8WsTnQ0sMs5Sj+nJmDLmUxlFnOIAqwp8cGudOg6f2ExSiESK3m7oFFa87+Z9FFArjPXjbk62ZlhSg0h3mtPWvog9vy4OP/Fv369i+hZU+8whZjMG/ePLZt2wYQt/vO05q7DnKm8GFHiIo842nvZz0hXHQO09V6jrZ1JTY+IYQQKSdFa6r43Bw+dIAZumYsk84a0UvtBo3puQb29ViiQ/o668HbnqBAhRCpomkazd1+cq1GAqHI8Y0qOXvX4iu5iED+0O8digILCnt5223H3RsZuhFLNlz6DcgqhR1PQOO7cf4pxEhl3H3nHXWozjIOdoSZkWs47f2sJ4SKzkGPypRALe2eQEJDFEIIkVoyPDhVgj0EDr0BgJI/c8Qvn1Nk4vU6C1rudBSAo2/BmZ+Jb4xCiJTq9IUIhlWybca+0Rj2htcxd9XRcMG3T/v6TxT28odjWbzeYuS6aUPfI4jJAR/7OrzzS3h3LRSdBR/713j9GGKEMuq+czUMXUfpKbucQERjZt7A9YiHEi6aA8BcXS37DzdSMEfuyxZCiIlKelpTRNM0TD31AIQLRn4fx5wiEx1BA83mcjBaofaVeIcohEixY+5eAIqd5r5tuXueJGzJp2fGotO+/oI8P1a9ymutMdwxb7TAJXdAfgW8/D04uHXUcQsRK6OvFdQwDUoxABW5sX+WrllyCNmKOUtXz74md6JCFEIIkQakaE2Rpp4QZ6oH6Tbko1lzR/z6OUXRT6M/6LLClI/Bhy+DFvt9sUKI9Ffnik7GU5ZrA8DQ04CjfgudZy5F05uHeykAJh1cnOvjtRZrbPcJ6k1w4ZchbzqsXwYtspyWSCyj5xgAtaFCACpG0NNq0Wko+TOZoz/C/jZ/QuITQgiRHqRoTZFdDT18XLcHb/boJtg4s8CEXtH4wG2EGZdF11pslVnxhJhI6ttPFK1WAHJrngZNw33Wl2I+xvwCL20BPTvaYny7N9rgc4+DyQbP3AqBnhHHLUSsTN5GAN715lPi0OM0x35ZokR6UfNmMI1G9jXKZExCCDGRSdGaIm2HqnEqPoyFM7DoNAojLZgJxfx6s0Fhhj3ABx1GmBZdhJ79f0lQtEKIVKhzeSlxWrAY9RAJkrPv93jKryKcVRbzMT6W50WvaLzcaIq94awSuPHX0HEQ/vxvMopDJIyp5yjoTfyjzcqcohH8Hz1OzZ+JHhXNVUtXb+znUCGEEOOLFK0pYm54kwgK4ZwKlEgvof1b0GkjO+Ge5QzwXocBrzE/OkR49x8SFK0QIhUOt3spy7USCEVwHvoLht523GfdOqJjZBtVzssJ8GqzdWSNT/sEXHkf7PkjvDcBJvwRacncXUckZxofuiOjK1rzohMZnqk7wo7DHfEOTwgh+nT5ghxz+/r96/IFUx1WxpCiNQX8oQiVnndoNE5DM9pGfZy52b14wjp2NfbAzKugdS/UvxHHSIUQqXTY5aM020IwrJK36wkC2TPwTrlsxMe5otjHIY+RD7v1I3vh/G9FR3Jsvhfc9SNuV4jTMXXX0WmbhgbMLjTFvEbrCVrWJDSDhTm6I2zd15qQGIUQAqAnEGbbgfZ+/3oC4VSHlTGkaE2BXTX7OU93EE/uyNZn/ah5uT4UNN443AUGKyg62PVsnKIUQqRSa7efDm+Q6QV2rC3vYm17H/c5X47m+QhdXhR9r/hT/Qh7snQ6+PzjgALP3wnqMMvmCDFSQS8mbxN1TAZgdkHsa7T20elRc2dwkeUor+xrRZOh7EIIMSFJ0ZoC7uo/A5BbduaYjpNjVJlu8/P6ET+YHVB4Jux/US4shZgAdh+fWGZWSRa5u58gYnLSWXnjqI5VYI4OEd7cEMPSNx+VMxU+vQrqX4e3Vo+qfSEG1X4AgDe9JUzPMVBgG+FIgOPUvAoq1HqaunrZ3dAdzwiFEKKfxs5e/vx+A7/YdpD/+/shfvZqLfubZcLCZJCiNQVyjm6hRVeMObtozMea6/TyXnOQNr8Ck+ZBTyMcfSsOUQohUumDY10oCpxlceE89EJ0mRujfdTHu7LYR63HyK7WwMhfPPcWmLUItvwAWveNOgYh+mneBcAL7YVcUmYeeS/rcWreTMwRD1N0HTz15mG5x0wIEXehiMpP/nqAn22tZWe9GwWFiKrx3LvH+NTD2/j6796lrWcU51cRMylak+xoczvnBatpLZoPijLm4308rxtVgxePWaDkHDBYZIiwEBPAm4dczCrOIn/nT9F0BjrO/dqYjndNiQ+jorFhj2fkL1YUuO6nYM6C574KYTkxizg4toOgIYu9wWI+Vnb6dYeHouZHJ2NaPKmDv+xqps0j/z+FEPHjD6nc9uu3ee7dBi6tyOfeT8/ma5+cwR2XVfDcHZfyTx8v5697Wrj6f19jw46jqQ53wpKiNclqtv8RsxKi+Lxr4nK8KdYgZ+QZeP6IGQxmmHFFdLZPuagUYtzyBsK8W9/JwuJusg48h/vsWwnbi8d0zGyTyoIiH3/a76OzNzLyAziK4HOPQcuuaI+rEGN1bAcHTbMw6nVcWjaKoevHqflnoOnNLHIcwBMIs2VvSxyDFEJksnBE5cFtrbx5yMV/fWY21507KboM3XFGg57KYid3Xl5BlsXAv2/4gO+91IB/FKdZMTwpWpNI0zTUmhfoUexYppxPKBKfe08/fYaNdztMHPHo4OzPQ28H7NsUl2MLIZLvpT3NBCMqN/f8Fk1vxjX3zrgc97pJXjr9Ki82jHxpEQBmXQsXfw3efAxqX4lLTCJD+bvRWvfy997pXDLZjMM8hssRg4Xg5EuY3vYKM/PN/P7to0RGOg2xEEJ8RKc3wL9veJ+3j/n41tWVXDmrcMjnFjkt/MsnK7j54ik89V4HN2zNpa5ndPfpi8FJ0ZpEb9a2cVHoHRoKPsGRrjBxqln53Jk2TDqNH++2wdSPRSdO2fnb+BxcCJF0f3yvgRudeylt/Cvt5/8bEWtBXI57Ub6f6TkGfvGhk/Bo338WroSis+APXwP34bjEJTLQob+hoPFqYBbXTB/90OAT1Nmfw+ht5vtlO6nv8LHpg8Y4BCmEyGS//Hsdz7/XyLxJVgocZoKR4T8M0+sUvvbJGfzs+jIavTo+syWXddUuuc8+TqRoTaKtf/0T+UoP5rM/G9fjljgMLJ3azZ+O2Xj6PTfMuw3qtoHrYFzbEUIkXm1rDx98eJj7+BXB3DNwnfcvcTu2XoHvXJrNYa+Rn+8f5RrRRissWQtqGH5XBf6uuMUnMkjNn/HosjhkOosrp49+aPAJ6uQLCZZewPyDP+GyPDf/b/N+uv2hOAQqhMhEG99v5PG/HeScydl8fGrs58vekMq0bD0PnFnHFIuf725u5Ht/2o0vKOu5jpUUrUny2oE2pjX+haDOSmBGfO5nPdU/V3RxcV4vy//ayFbrNaA3w+s/iXs7QojE+sXfavlf8y/ICrtouPzHoB/lUN4hXDHdwqdKvfxkr50tx0Y5dKlgJixZA65aWHcLBL1xjVFMcD3NqHueZ0PwUm48Lw+TfuyTEqIohK5cCQYzj+n+B19XO//+zPsyTFgIMWJ//7CN7zxTzXll2dx4QRnKKCZOzTeF+f6sI9w0x8nG95u4/KG/se7tIwRHPcxJSNGaBF29IX7w3A6uM7yNZ/q1aMZR9nAMw6CDB85rZ06xha/98Rh1ZddD9e+gZU/c2xJCJEaj28fcXfdzlbIT18fvw190ftzbUBSFf5/VQbk9xB1v5fGjXXZ6QqMoGmZcBp9fDfXbYe0N4OuIe6xiAlJV2tZ/g3BE4xXn51kyxxm/Y1uy8F+9CrvvKBuLfs6rextY/qfdaJoUrkKI2Lx7xM2/rN1JRaGDVYvPwagffamkV+C283NZfcs8ynKtfPcPu5j/o1f535f3s7+5R96bRsiQ6gAmOn8owteffpfLvC+SZfBy+OwvJawtu0Hj/z6bxz9vqGPx/qvYZn8Zy3Nfw/DVv4Ip/oWyECKOgj5a19zGLfotHDv7a/Sc9U8Ja8ph1Fh9YQs/PZDH6v121tVZ+LczPVTNCGI3jOAkeu6SaE/wc1+FX3wSFv8qel+9EB915C04sJnOD16gsHsfv7LexgM3XkCWPkwwjrd7acVnEZz/75T9fRV/LP41S966FYtRz72fPhPDGC4+hRAT36v7WvjG796jwGFmzZcvJhinyWdml2bx06Vzeauugz+828Cjr9byyKu1TM6xMq88hzmTsrl4Wi5zynJOWyT3+EM0dflp7Ozt+9rYGf3qC4YJqxo6BQocZoqdFuZMdnJpRQHT8+3odHEY1ZJCCSlaVVVlxYoV7N+/H5PJxP333095eXnf/ldffZXHHnsMg8HA4sWLWbJkSSLCSKljbh//qHXxq9cP0dLSzC+cf6E3/2J6Sy9OaLt2s45HLmjh/vez+FbHV/lly/9S/7PPMvnWX2MomBbbQXxuCHRHL0adpQmNVySf5GeaiYRh3yYCm+/j3O6jvFD0VabP/15c1nEejtOocd/ZLm4s6+GRAzms/CCbB3drfKwgxGe8HXxh/mTMhhiGD5/9eciZAs/eDr/+FMy5ET7xbSiZk9D4J7IJkaORMESC0NOE9rdVKLueIYyew2o527Pv4rOfv5k8YwA1XjMSniJcuQjF52LOzl/xetZ+/uMfN3P57vl86dJp3HhBGQWOsU/8JDLbhMhR0afHH+J//3qA3/7jMGdPcvLEbRdR5LRwzO2Ly/F7QyrvHekE4LPnTuLrV8zk2R3HqGnq5q97W9j4fhMQnchpUo6FQocZu9mASa8jEFbp6O4h+GIrzV1+PIH+98aeKFCLsqKv0TSNdm+QpoYu/v5hO+veiX4Q7bQYOH9qLvOm5nJBeS7nTckmy2KMy8+XLAkpWrds2UIwGGT9+vVUV1ezatUqVq9eDUAoFOLBBx9kw4YNWK1WvvjFL3LFFVdQWDj0NNLpStU0unwhApHoYky9wQh/29/GH99roPpoJ6BxSVYHT5X+Glunm8OXLk9KXCYdfLW8Bds1i3jyxXa+1PUU7Y9dzd9L/oldjvnonUXMKs6isiSLmUUO7CYDOjQUdx0cfQv2vxidyMnfCZac6DDAmQth+gLIKU/4xbRIrEzJz7Tl7wJXLZG2Wtz7tmGtexl7oJVj2iR+YlrBP193W1JzbHZ2kMcvbOU9t5k3XFZeb7Py3Rcb+N/X2/nU2SWcMzmbM0uzKMqyYDXq0esVDLroP71Oid7rM/kC+Nft8PrD8MZjsHsDFJ8DMy4jixLI9kffO6y58v4Rg3GZo10NcGAzkd1/hJbd6P3uvl0hDPw8/HnW6z7Dknml3HpBEQYlAgkcGReau4xw6QXkvP4jfhX6Me9G/sYPN3+W37w8iYsqpzCjJJ+pBQ60nl5yJ/kpyjKP+14IkTzjMkdFn3BEpbHTz/6WHl470Mrz7zXiDYb50iXl3LvoTGymxA5EzbObuHh6HhdPz0PVNNp7AtgtBrp7Q9S7fLh9QTp9IfyhCGaDDoPeSGGWlQum5pDvMFOYZabIaSbXauRgmw/9Ke9d50/N6SuQVU1jUraFA60e9jR2saehm20H2tCInopnFWdxzuRsphXYmZpnY0qejVybEafFSJbFkHajUxLyV9m5cycLFiwAYO7cuezevbtv38GDB5k6dSrZ2dkAXHDBBezYsYNPf/rTiQglruravfzrUzvp8AbpDUbwBMJo1A14XkWhnd9W/oNPNP4GQ8iL2mOh4apH8Reel9R4Z+QaKbhwNq/U/RsVjX/mpqaHuImH6NGs9GBFRUenphBUAmThw6xEP73p0LJ4R5vNQd00Jgfa+fjebRTu/RMAQQx0KTkolQsp+OLPk/rziPiYqPk5Lvg6CP7P2ZhUH3rAoln4h3oWm43/hLf8Km6dX4HJmPx13RQF5uUFmJcX4M6Znbxnv5Qn3/fx3LvHWPtm/bCvM+l1mA06TAY9ZsN8zsy/mJ+fdxDj/r/A2/9HWSQAbxx/wSfvgSu/l5wfahwbNznaeYTI00vxtR8hS+sBoF4t4U31fFq0XAIY8WPmWMF8rpwzhWf1H5Bzxiw0/AktWE/Qis/C/4UnMOzbyPk7/4/nzDujO+pAPaTgw0wvZvRbI/gJYSSMHxM+LDQqRdyl+y+CBgcmg46LpuXx4yXJPYeL9DVuclQA8PWn3+XdI278oQj+kIo/HOHE7aQWo45PnV3CVz8xg3PKspMem05RKHJa+HhFXr8iMRCK8FZd9MO/puYmSktK+hWkXb4wMwoc6F29wx67NMdKc3eAS2cUcOmMAs4tc3KgxcOuhi72NHTxyr5WOryD36Nx4sNpg05BQcNsPIZep6BTFBQFFKLzZCgKxx/333717CL+6zNnxetXlZii1ePx4HA4+h7r9XrC4TAGgwGPx0NWVlbfPrvdjsfjGfZ4gUCAmpqaAdsH25ZoP/lUrJ+UfYkP+cj9q50Nfd/qdXo6J10e3ewBJl1OpweccxbRecq2E/s/ur3TA0y7Bidw5CPPZdLlOCed3F4+CcIsZajflhdo+8i2qcf/AbQf//dRbcd//6n4O3xUqmNIRvuBQCAux4l3fp6ILV1yNN1iGND+Ta/2e1gGfPXEg1A7dI6+rTboey841akDnE59bxnKDOAHl5mB3FHFUctU+NgVg+9M0d9DcjRBOXrlEwM2DTV9WBeX0TVEqK7T/wgnnTgHxsQEU6ui/0bAAvziI9sS+X8o7d6nJmgMGZmjY5Tq9uMRwzfmWWGedfgn9TRSUzNwfefznXC+0wl4odPL+R+dO+6j2zq96HV6nHMWnbKtYcBzPnqc3raBs/CfeM6Q7ccYT7/H3V5mW2H2TB3MzGW05/lYjfRvN1yOJqRodTgceL0nf/mqqmIwGAbd5/V6+yX3YObOnZuIMIXISPHOT5AcFSKeJEeFSG+So0IkX0IGK8+bN49t27YBUF1dTWVlZd++iooK6uvr6ezsJBgMsmPHDs4/P/7LOgghBif5KUR6kxwVIr1JjgqRfIqWgEWCTsyqduDAATRN44EHHmDv3r34fD6qqqr6ZlXTNI3Fixdzyy23xDsEIcQQJD+FSG+So0KkN8lRIZIvIUWrEEIIIYQQQggRD+k1l7EQQgghhBBCCHEKKVqFEEIIIYQQQqQtKVqFEEIIIYQQQqQtKVqFEEIIIYQQQqQtKVqFEEIIIYQQQqQtKVqFEEIIIYQQQqQtKVqFEEIIIYQQQqQtKVqFEEIIIYQQQqQtKVqFEEIIIYQQQqQtKVqFEEIIIYQQQqQtKVqFEEIIIYQQQqQtKVqFEEIIIYQQQqQtKVqFEEIIIYQQQqQtKVqFEEIIIYQQQqQtQ6oDiEV1dTVms7nftkAgMGBbsqU6hlS3LzEkt/1AIMDcuXMT3s5oSI6mZ/vpEEOq209mDJKjI5fqGFLdfjrEkOr2kxmD5OjIpLp9iSE92k9mDMPl6LgoWs1mM7Nnz+63raamZsC2ZEt1DKluX2JIbvs1NTUJb2O0JEfTs/10iCHV7SczBsnRkUt1DKluPx1iSHX7yYxBcnRkUt2+xJAe7SczhuFyVIYHCyGEEEIIIYRIW1K0CiGEEEIIIYRIW+NieHBa87kh2AOmLLDlpjoaIYQYmRPvYaeS9zMhxoeP5q/krkiBLl+QnkCYLLOBbJsp1eGICUqK1rEK9kDtKzDzKjlRCCHGnxPvYaeS9zMhxoeP5q/krkiBnkCYbQfa+WRlgRStImFkeLAQQgghhBBCiLQlRasQQgghhBAxUlWV5cuXU1VVxbJly6ivr++3/6WXXmLx4sXceOONPPvssymKMvG6fEG6fMFUhyEyhAwPFkIIIdKIqqqsWLGC/fv3YzKZuP/++ykvL+/b/9JLL/HLX/4SRVGoqqripptuSmG0QmSeLVu2EAwGWb9+PdXV1axatYrVq1cDEIlE+PGPf8xzzz2HzWZj0aJFXHXVVeTl5aU46vjrCYRTHYLIIAkpWk93wj3hvvvuIzs7m3//939PRBhCCCHEuCMXxOJ0Tkx8UxBWsaQ6mAy0c+dOFixYAMDcuXPZvXt33z69Xs8LL7yAwWDA5XIBYLfbUxKnEBNJQorW4U64J6xbt44DBw5w0UUXJSIEIYQQYlySC2JxOicmvvns1IgUrSng8XhwOBx9j/V6PeFwGIMhelltMBh4+eWXWblyJZdddlnf9uEEAgFqamr6bfP7/QO2JdPp2g8aTv4OmpqbcOUp9DTXD/n8RMSQDKmOIdXtp0sMCSlahzvhArz33nu8//77VFVVcejQoUSEIIQQQoxLibggFkLEj8PhwOv19j1WVXVAHl5zzTVcffXVfPe73+X5559n8eLFwx7TbDYze/bsfttqamoGbEumYdv3uenu6mDtLi/13RrnTC4hvyCfstwpyYshSVIdQ6rbT2YMwxXGCTnTDXfCbW1t5Wc/+xk/+9nPePHFF2M6Xjp++nQihvZ2P6HmZow5LtqavKd/UZzbT4ffQabHkOr2R0qG7wuR3hJxQZzO59FMf/8eTQxBg4Om5iZ6C+14m5v7to/mWmS8/g5Sad68eWzdupVFixZRXV1NZWVl3z6Px8Mdd9zBr3/9a0wmE1arFZ1uAs57Guyh8YOtPLS1FACjXscVZxamOCgxkSWkaB3uhLt582bcbjdf+9rXaGtrw+/3M2PGDG644YYhj5eOnz6diKGgwA4lJVCQT0HO1KS3nw6/g0yPIR0+fRoJGb4vRHpLxAVxOp9HM+H9O94xHHP7KC3RsFpDOEtKTu4YxbXIeP0djLadeFi4cCHbt29n6dKlaJrGAw88wMaNG/H5fFRVVXHddddxyy23YDAYmDVrFtdff31c2k03b7Sb+76vbfWkMBKRCRJStA53wr311lu59dZbAfjDH/7AoUOHhi1YhRDxJcP3hUhvckEsRHrT6XSsXLmy37aKioq+76uqqqiqqkp2WEm3r9uEzaijssRJY2dvqsMRE1xCitbTnXCFEKkT7+H7IEMP07X9WGIoNEZvcThVPG93GA+/g3QjF8RCiPGgzmOgIt/ElFyr9LSKhEtI0Xq6E+4J0sMqRPLFe/g+yNDDdG0/phg6j0RvcThVHG93GBe/gzi2I4QQmaKp18DZ+UYm5VjxBML0BiOpDklMYDLloBAZRobvCyGEEGIsNE2jxa/nqqxo0QrQ1NXLGcVZKY5MTFRStAqRYWT4vhBCCCHGoqM3QlBVKHEYmJwTXS24Qe5rFQkkRasQGUaG7wshhBBiLJq6QwBMytIz09wFQEdnVypDEhPcBFw4SgghhBBCCJEojceL1lJrhKxjf0OPhrvHl+KoxEQmRasQQgghhBAiZi2eaNFa4tCjUyDbGKbNG6bLF0xxZGKikqI1DnzBCP6QmuowhBBCCDHBhSIq4Yhcc4jUcvnCAORao6VEjjFMmzdETyCcyrDEBCZFaxyEXQcJuepSHYYQQgghJjBfMMy1D2/jxp+/QVdvKNXhiAzm9kVwGlUMYR86TzM5xggunyx5IxJHitax6m7CufNx7M/fDqokqxBCCCESY9MHTRxs89LuCbK9tj3V4YgM1tEbJt8QwPTnO8h++3/5LH/H5ZVeVpE4UrSO1cFXAdD53dC6N8XBCCGEEGKi+kdtO3l2ExeW57KvuTvV4YgM5vKGucpQja6zHoDFwT/T3RsgomopjkxMVFK0jlXrnpPfH9uRujiEEEIIMaHtb/Ewq9jBeVNyaPcEafXIEGGRGh29YS5iL5rBguecW8nWurhSeVeGrYuEkaJ1rFwHCeWegWawQtv+VEcjhBBCiAlIVTXq2j1MzbNxXlk2AMc++Bu88wR0HUttcCLjuHsjnK3uRy2aQ6jgLHw6B5/Vv4nLGwSfGzqPRL8KESdStI6FpoG7joi9GNU5Gdo/lCQVQgghRNw1dfvxh1Sm5NuYUejAqQtw8d77oWUXVD8dvSYRIgk0TcPTG6A00ohWOBsUHUdtZ3Ol7j26Ot0Q7IHaV6JfhYgTKVrHQBfqgVAvqiUH1VkGnYclSYUQQggRd4faPACU59nQ6xSus+/BFumBSRdATxN0S2+rSA5fMEKR2ooeFTVnCgDtzrOwKQHsR7emODoxUUnROgZGXwtAtGh1lEJ3Y4ojEkIIIcREVNfuBWBKng2AK/Xv040dZl8XfULrvlSFJjJMhzfIDCV6zatlR4vWcFYZHZqDkubXUhmamMCkaB0Dg68NgKDRiWYvhJAPwoEURyWEEEKIiaatJ4BOgTy7CYCzIgd4O1KJW5cLWaXQUZviCEWmcPuCTFeaAdCcUwhFVCwGhbe0OUztfFuGqouEiKlo3b17d6LjGJdO9LSGjNlotoLoxoBMQS+ST3JUiPQmOSrGqt0TJNdmIhRWUUI+SkJH2K1N50C3AXKmQucxKRbGQHI0dtGe1iaCejsRs5OIGt3+gWku2eF26DiU2gDFhBRT0frEE0+wZMkSnnrqKbq7pSg7wdjbiqboqA/n4NFHZ/IjIPeziuSTHBUivUmOirFyeQLk2IwEIxoW114UNHar0znYo4fsKRDyRu9tFaMiORo7ty/IVKWFoKWg3/Yj9nOi3zS+l4KoxERniOVJP/nJT+jq6mLTpk3cdddd5OXlsWTJEi655JJEx5fWDL3thMx5fPGNMhY4Gvkt4OnuwBBSsaQ6OJFRJEeFSG+So2KsXN4g+VYdhZEWLG1vAnBQN52p3QYoL4s+qXUvTP1YCqMcvyRHY9fhDXGu0oFmLkA5ZXvQUUZPl42s1r1QPj9l8YmJKeZ7Wtvb22lsbMTtdpObm8vmzZu59957Exlb2tMHOuggm4im8EFPFhD9PQXDkRRHJjKR5KgQ6U1yVIyFyxMg36IQ2r8F5fDrRHRGrDlF1Pboo/e0ggzLHKNYc1RVVZYvX05VVRXLli2jvr6+3/5NmzZx0003sXTpUpYvX46qqsn6EZLC7Q1SpHSiWJz9tufbjezVpkc/PBEizmLqab3pppuwWCwsWbKEu+66C5MpOgnAV77ylYQGl+4M/k5aI1nMsAfpCDuJoMMY9qQ6LJGBJEczW5cvCEC2zZTiSMRQRpKjqqqyYsUK9u/fj8lk4v7776e8vLxv/6ZNm3jyySfR6/VUVlayYsUKdDqZV3Gic3mC5E0xA6DvbcdvymNGnpH3jhnAYAZLDrgPpzTG8WwkObplyxaCwSDr16+nurqaVatWsXr1agD8fj8PP/wwGzduxGq18p3vfIetW7dy1VVXJfXnSSRPTydZSi/dJif6U7bn2wzsikzlYtdWFG1iFeoi9WIqWu+77z7OPffcvsdvv/02F198MU888UTCAhsP9IFOmsLTqcwJgT2bjmPZGMPeVIclMpDkaGbrCYQBKVrT2UhyNNMviMVA/lCEnkCYPGu0RDD4XXhNhVTkGtl0QI8vDDZHEXTUpTjS8WskObpz504WLFgAwNy5c/tN4mQymVi3bh1WqxWAcDiM2WxOcPRJdnyJx4i5f9FaaNfzoVaKEvaDvys1sYkJa9iidceOHdTW1vLb3/6W22+/HYBIJMLvfvc7Nm3alJQA05k+4KYhdA5TbCFKJplxHXGQHfKnOiyRQSRHhUhvo8nRjL8gFgN0eKOjKfJsBtAi6P0d+O2zmJYbvYw71GNgjr0Imt6LziCsKMMdTpxiNDnq8XhwOBx9j/V6PeFwGIPBgE6no6AgOkHR2rVr8fl8zJ9/+vs7A4EANTU1/bb5/f4B25JpqPaDruhw6IBixRBRCQaDdHV3kVuicEiLDlV3Hd2PVuqirWlsnTmp/h2kQwypbj9dYhi2aHU6ndF7NINB2tqia5IqisI999yTlODSWsiPPuzDpTmZYgtzTqkJt5aFI9ib6shEBpEcFSK9jSZHM+WCOB1iSHX7scbwoSu6BrxDH0HtakXRVNwRK9Oc0X6uHUc9TMFKdtDLh++9TthaMNzhRtx+oqUyhtHkqMPhwOs9WYypqorBYOj3+KGHHqKuro5HH30UJYYPEcxmM7Nnz+63raamZsC2ZBqq/ew/rQdAZ89Hp9dhMpnIdmaT7TRTp5YAkG8KQ0E+BTlTExJDMqU6hlS3n8wYhnsfGLZorayspLKykiVLllBUVBT3wMY1XzsALpxcaAtRkWdkOw6mR1wpDkxkEslRIdLbaHI0Uy6I0yGGVLcfawwt+1uBBkpybFhd0eVYdNmTmZZnQq9ouHT5ZBfPhCN/5YxcDabH/jONl99BvNr5qNHk6Lx589i6dSuLFi2iurqaysrKfvuXL1+OyWTi8ccfn5D3m9uCrQCo5v4TMRU5DDSTR1gxYvDJ9bCIr2GL1m9+85s88sgj3HDDDQP2vf766wkLalzwRovWDi2LUksEk14hbHZiDftSHJjIJJKjQqS30eRopl8Qi4FcnuPDg616dIFOAALGHEx6hTJbODqDcMXxgqv9AEz/ZIoiHX9Gk6MLFy5k+/btLF26FE3TeOCBB9i4cSM+n485c+awYcMGLrzwQm677TYAbr31VhYuXJjQnyNZVFXDGWrHb7Sh6fvfmuAw6TAb9HQbC8nzd6YmQDFhDVu0PvLII4Bc/A7qeE9rl+LEaYzOkGa0ZmPv9uLTtFRGJjKI5Kho6wmw+m8HuXZOCWW5tlSHIz5iNDmayRfEYnAub3R4cJ5Njz7QhYZC0Bhdaq/cHuZAlwGfzoHNYAWXLHszEqPJUZ1Ox8qVK/ttq6io6Pt+37598QkuDfX4wxTRgc80cAi6oigUOMy0U0Beb2fygxMTWkyzB7/zzjv09vaiaRr//d//zV133cV1112X6NjSmzc67EGx5qAo0ZOJ2Z6DoUcl5OtOZWQiA0mOZq7/3rSHP7/fxPbadp6/8+Pk2GVSnnQ0khzN5AtiMTiXJ4jZoMNm1KEPdKKaskCJ9rKX24Jsb7PgDWnYcqaAqzbF0Y5Pch6NTYcvSIniJmApHHR/gcNEU08+lb1HkhyZmOhiGlf00EMPMW3aNNasWcPvf/971q1bl+i40t/xnlaDLbdvk82ZA0Crqy0VEYkMJjmamTRN4/Xa6Adoh10+3jrckeKIxFAkR8VYNHX1kmMzEtFAH+giYs7p21duDxPRFBp8Bsgtl6J1lCRHY9PhDVKkuInYBp/sq8Bhpj6SF13yRg0nOToxkcVUtJrNZvLz8zEYDBQWFhIMBhMdV/rzthFGj91x8ib0nOxoAdvmkpvPRXJJjmam2lYPHd4gX/vkDEx6HVv2tqY6JDEEyVExFq09AQw6HaqmoQ92ETFn9+2bZg8BUOc1QM40cB+GSCg1gY5jkqOxcXv8FNEJjsEnrSpwmDgYzAW0vvlfhIiHmIpWh8PB7bffzqc//WmefvppSktLEx1X+vO249ayKHIcH2GtQV5etGjtckvRKpJLcjQzvXe0E4CLpuVyblk2bxwaxXuPpkE4EN/AxACSo2Is3L4QDrMBNA19oLNf0VpuO160eozRnlYtAp0yNHOkJEdj43M3Y1QiGLKKB+yz6DTKrAHqw3nRDT3NSY5OTGQx3dP605/+lCNHjjBz5kwOHDjATTfdlOi40l7Y00a75qTIHq37I4DdGS1avd3uFEYmMpHkaGY62ObBqFcozbZy8fQ8Hv/bQeravUwvsMd2AFWFP90Jh7fD2TfA9AWJDTiDSY6KsXB7g5Tl2lACXShquN/wYIdRI9cYos5jgJzy6EZXLeRXDH4wMSjJ0diEOhsAMGcX4w9F+u1TIr3k9+yjUcuPbvC0JDs8MYHFVLS6XC62bt3K5s2b+7Z94xvfSFhQ40G4pw2XlkWxQ9+3TTv+yWfQI0WrSC7J0cx0qM1LWa4NvU7homl5wEFe3dfKVz4xPbYD7H0eDr8OJgfUPA+l54Il+3SvEqMgOSpGS9M0On0hzizRo/NEe65Uc/88nWwJcthrgNxp0Q2uWuBTyQ10nJMcjY3a1QiAzlEI7oE9qYXmCC3a8flePHLLioifmIYH33XXXXg8HgoKCvr+ZTrN204HTorsJ4tWTHYi6NB6pWgVySU5mpkOtnmYmhdd5qYk28K0fBtb943gIuGDZyCrFObfFe11PfJGgiIVkqNitDyBMMGIit1sQO+N9lyd2tMK0aK1zmNEM9rB7ITGavB95FrE544OG+48MnCfkByN0YkPTiL2we9pLbSodGEnrBjBKxOTiviJqafVbrfz7W9/O+aDqqrKihUr2L9/PyaTifvvv5/y8vK+/Zs2beLJJ59Er9dTWVnJihUrxt0C6YZeFy7tTGba9eA9vlHR0avYMAS7CYQjmA36YY8hRLyMNEfF+BdRNY64fHy8Ir9v26UV+WzYeQxPIBy9/204AQ8cfBXOvQnshZA3A5reh8prExx5ZpIcFaPl8kQnBHKYDej6itZsOGWupUmWAJ6wjtbOHoqtudBUDcEeOGWFA4I9UPtK9PuZV/XfJyRHY2TqbSGCDtWaN+j+PGMYUPDos8mR4cEijmKqFM844wz+8pe/cOjQIerq6qirqxv2+Vu2bCEYDLJ+/XruvvtuVq1a1bfP7/fz8MMPs2bNGtatW4fH42Hr1q1j+ymSLRzAGPbQoWX172kFAjobOUoPRzt8KQpOZKKR5KiqqixfvpyqqiqWLVtGfX19v/2bNm3ipptuYunSpSxfvhxVVRMdvhiF1h4/YVWjNNvat+2S6XmEIhrb9rfS5TvNzJcNOyASgPL50ccl50BPk8z2mCAjPY8KcYLLG50ozW42oPO0oCl6VKOj33MmW6L5ftAdin4IJcMyR0xyNDY2fwudulzQDf7BqFFRcZgU3EqO9LSKuIqpp7Wmpoaampq+x4qisGbNmiGfv3PnThYsiE7oMXfuXHbv3t23z2QysW7dOqzW6IVWOBzGbDaPKviU8UVn6OzWZWM3KnSesitisJIX9HC43cfMoqyUhCcyz0hy9NQPlaqrq1m1ahWrV68GTn6otHHjRqxWK9/5znfYunUrV111VVJ+DhG7xs5eAIqdJ98/z56UjUGn8EZdB+dOySHbZhr6AMd2RL+WnBP9vnhO9B7X1j0JjDpzjfQ8KsQJ7af2tHa1EDE5Qenf53CiaK3tCPNxR1H0Q6lQb9JjHc8kR2PjDLbSZSzEOcxziux62sK5TJcPT0QcxVS0rl27lp6eHhoaGpgyZQp2+/AzU3o8HhyOk58C6vV6wuEwBoMBnU7Xd5/A2rVr8fl8zJ8/f9jjBQKBfm8kEL24/ui2ZDG7DzADCJuziUQiBINB1Ei0NyqsGMhRenh17yEmKx0JjSOVvwOJIX3ah5Hl6IT/UClDNHT6ASjKsvRts5r0nD05m13HOmM4wE7IP+PkxEv2ArDlQ3ttAqIVIz2PCnHCieHBJ+5pjZgHTpaWawxj16sccAXBURjd2HkECmclM9RxTXI0NrkRF17HjNMWrY3uXPDuji6rpihJi09MXDEVrS+99BKrV68mEolw7bXXoigKd95555DPdzgceL3evseqqmIwGPo9fuihh6irq+PRRx9FOc1/ZrPZzOzZs/ttq6mpGbAtaWqjM6dhycFgMGAymdDpo596GixO8nsP49M7Eh5fSn8HEkPS2x+uMB5Jjsb7QyWRGk2n9LSq2sntF5XnsuaNeoLh0wzrbtgJFVf235Z/BjS/D2pk8NeIURvpeVSIEzpODA826dF5WghbB06AoyhQbg9xsCMExSeK1voBzxNDkxw9vd5ghCJcHLBeOuzziux6jrTnw//f3n3Hx1Gdi///zPaquuq2XGTLDRtjejGEYgi+gZvQ7ISYhFSSe3NJ4ksK+cbhEjDkxyWBJEAghVCDKTckdOJgcDBgbGO5N8m2epdW2t5mfn+sLCwsyyq72pX0vF8vvyTNzs55vPajmWfOmXNiAQh1y6z0IiEGVbQ++uijPPvss3z1q1/l29/+NldfffWAibxo0SLWrVvH0qVLqaiooLy8vM/rq1atwmQy8eCDD465CZiA3uHB+n4mMYgarOQpHg63eUc7KjGBDSVHE31TCdJvNES6xJDM9nceasNh0hH0dBHTGQEIeyMUGvyEYyqb9tURatP6jUEfclPubaZZyUff1k6kKT4bpMWQR3YkQOeBD2nKCCYkzlT/G6RLDEM9jwpxRJs3jMNswKADnb+FWNbMfvebYo+w5cgzrQCdNaMY5dgnOXpiHZ3tlCgBovaiAffLs+s5GMkBI+BpkqJVJMSgilZFUTCZTCiKgqIovUMHj2fJkiVs2LCB5cuXo2kaq1ev5qWXXsLv93PSSSfx/PPPc9ppp/GlL30JgBtuuIElS5aM/G8zSjRfKwpgdhxbtEb0doxEaWuXyUzE6BlKjibjplLajYZIkxiS2X7gw81MytHIdeXiCUYBcFoMXFGQxx1vN1MfUFg2Z3b/MVS/B0DBvPPBlQuFhfHtWVY49BLZ3n1kn7ksIXGm+t9gNGMYqDAe6nlUiCPafWGybEYsoXYUNXrMcjdHTLVHea1RpVuzkGHOBPfhUY1zrJMcPTFPS/xGiJZRPOB++TYdO9Se2YW7G2SYukiIQRWtp59+OitXrqS5uZlVq1Yxf/78AffX6XTcfvvtfbaVlZX1fr93795hhJo+Ql0tGDQd9ozsPsPyAKKG+JqJ4a4WWfZGjJqh5Oh4v6k0UTS4AxRn9b2oivY8W59rN7G7ofv4Mwi39vwOzut7wwJLJjjyofbDRIc74Q31PCrEEe3eENk2E7bgUcvd9GOqPb4GTlW3nlMcedApw4OHQnL0xPxt8aLVkFUy4H55dj3N9HTseJqSHZaYIE5YtO7duxedTseuXbu48sorycjIYMWKFaMRW9oKdTXjwUmuzcgnalYi+njRmoWH2o4AM/Idxx5AiAQaao6O95tKE0VjV4BFU7L6bAtEVLZWdVBe6GRPowdPKNr/m1v3g9EOGZOgu67va7nl8eddYxHQG5MT/AQj51ExEu3eMAWZZmzB+MX/8YrWaT1F685OHafY86FZJsEZLMnRwYl21gNgzpk04H4FDj3N2pGitSHZYYkJYsCxf6+99hq33norJSUl3HLLLWRkZPDss8+ydu3a0YovLUU8rbRrGeQ5jr2gO9LTmqN0U93uO+Z1IRJJcnRi8oejdPojfdZoPdqsAif17gDdgUj/B2jdC66Z0N/wb9dMiPihblMCI564JEfFSLX74j2t9t6iNavf/YptUawGhT1uPWROik+AI5MxnZDk6ODF3PGbnM68yQPul2/TE8RMSO8g1FPoCjFSA/a0Pv744zz55JPYbLbebZ/73Of41re+xSWXXJL04NKWry1etNoN8Im+1og+PkV6juLhcLs/BcGJiURydGJq6FnupiSr/6K1vCA+wmNvk4c5/e3Sth+mntf/wV3loOihci1MOScR4U5okqNiJGKqRocvTLbNiK2jEU1vRjPY+t1Xr8CMHAMHPEaY0VNUNGyF7KmjF/AYJDk6eMbuwzSTg9FiB5/7uPu57PFH49z6HJxdjaMUnRjvBuxpNRgMfZIY4jOP6vUT+zlNQ6CdDpw9RWtf0Z7hwYUGL/ubPaMdmphgJEcnpsau+HI3n3ym9YjyAicAexq7j30x2A3d9cefGMNohaKTofKfCYl1opMcFSPR6Q+javQ809pEzFEw4HDfWS4T+7uNdOhz0XTGeNEqBiQ5OnhOfy0thoGfZwUw6RWyjDHcuhxMnhrwd45CdGK8G7BoPd5SF6p6gvX/xjlzuBOfIRuj/tjPR9UZ0fRmJpsDHGqT4cEiuSRHJ6YG95Gi1dLv63azgUnZVvY09nPjrO1A/KtrgNkcp54LjRXgbR1hpEJyVIxEqye+RmuOPT48WLUXDrh/ea6RroiejQ1R1NyZUP/RaIQ5pkmODp4r0kC3beChwb37mmO0kIWuqw7C0okjRm7A4cGVlZWsXLmyzzZN06iqqkpqUGktEsCqeglZcvt/XVFQLVkU4aOu00+XP0ymzTS6MYoJQ3J0Yqp3B1GIzxYc++QU5j1mFTjZWutG05x9X2g/UrSWH/umI6acC+/9Bg68Cadcn5igJ6jh5Kiqqtx2223s27cPk8nEHXfcwZQpU/rsEwgEuPHGG7nzzjv7TKQmxpcjRWuuw4Qt0ISad+aA+89yxefaqA6YiRaegn738xD2g6n/IcVCcnSwtJCHXK2ToKN0UPsXWaPUBLI5L9YNmtwAECM3YNF633339bt9+fLlyYhlbPDEx+aHrAXH3UWzZJEX9tDUEaRTilaRRJKjE1ODO4DLYea9qg5OKc3qd5/yQif/3NtCmz/W94X2yvgzqwM955Y7E5zFUPEUTFsc32Zygu3YtanFwIaTo2vXriUcDrNmzRoqKiq4++67eeihh3pf37FjBz/72c9obm5OdLgizbQcKVqteiyhNoKO4197QLynFaAmYCY6/yzMO56CmvdghjybeTySo4PjaThABkDO9EHtX2KNUuXOQtGr4O+QZ6vFiA1YtJ5xxhmjFcfY0d3zQHlG0fH3sWaTE+5C1aCxK8hUlyx7I5JDcnRiauwKUJBhHnCfWT3Pte5vC3H+0S+0V0L2FDAMcDMtGoSCuVD1Fux6EcxOmHGxFK3DMJwc3bJlC4sXx28WLFy4kJ07d/Z5PRwO88ADD/CDH/wgITGK9HWkpzUr1oYOlZh94KI1w6yj0BKl2m8mVrQADFbY87IUrQOQHB2c9sPbyQDsxbMHtX+xNcrOWBboAV9LMkMTE8QJ12kVfQU767EA+swBHkQ3O3DE4gsw13UGRicwIcSEUdsRYLrLPuA+01x2DDqFfW3Bvi+0V0LujBM3UnxqfAbh+s0w/cIRRCuGyuv14nB8fLNTr9cTjUYxGOKn7FNPPTVVoYlR1uIJYjPpMfvjy92o9gLwD7wywQxHmMNeS3xStXmfhZ0vwGV3jkK0E0cycjQUCrFnz54+24LB4DHbRtPR7Qf2bqBYM+DFQXtbO/mGKOFwGDWmEg6H6eru6v1ejakUGIP8s2et1q6GKhpiw/t7pPozSIcYUt1+usQgResQeVpqsACWnOMXrZolC3PYDUC9W4pWIUTiaJpGU3eQ06YO3OtpMuiYke9gX1vo6DdDexVMXXzihjKK4sPADv0Lpl0wwqjFUDgcDny+jyfyU1W192J4uNLxgjgdYkh1+yeKoaq+lSyLHq39MABhs4uwuxKgT5HQ1d1F2GFAjalMswV5ry0bt8dPh+sipob+QstLt6M7eTmRpnjxa8xqp7XRd8L2R0s6xDAUychRs9nMnDlz+mzbs2fPMdtG09HtV792kP3aJM6fP5WWqBWjLwgmEzq9DpPJRGZGZu/3Or2O0gxo7ilaM3UBMof590j1Z5AOMaS6/dGMYaDfA1K0DpG/vRa/ZibPlQ+4+91Hs7nQR/3kGsPS0yqESKhWb4hwVKUgo/+Zg4+2YFImL26tp9EdoCjLGn8mP+KH3EFOCjLtAtjyKDTtgJlLRhi5GKxFixaxbt06li5dSkVFBeXlA0yaNUjpeEGcDjGkuv0TxRBa7yYvw4At0g6ALqsYU2d8JNfRRUJmRmZvsTArK4Zao1DvVzh78XVQ/zfy9z4Op10JhT2zD7tycWWVnrD90ZIOF8RDkYwcTWtqjDzPHrbpTmd+zAf0v9za0YosUVopQENB8clM9GLkBlzyRhwr6q6nSctmUu7xZ+JT7XkAzHf6qO8ceBiPEEIMRW1H/EZY4SCK1nNnuAjHNNbt63meqD3eQzOo4cEAhfPBlgsH3x5GpGK4lixZgslkYvny5dx11138+Mc/5qWXXmLNmjWpDk2MsubuILkOM0ZfA2GDA8104jkyTsoMA1DR2HPT/PJfgM4Ab66SWVwTZMLlaEMFNs1HtaUcYlEygo2YiQz4FrMecm1GupUM8MozrWLkpKd1iHTeZtp0uRRZjBDsfx/NFi9aZ9s9vCLDg4UQCVTXcyOsIMNMh2/gi4aTJ2ViN+l590AbXzhzytCLVkUX723d9X/QUAFZg1vqQIyMTqfj9ttv77OtvyUznnjiidEKSaSAqmo0uIOcU5aLsbsBv2XgNVqPKLDEcJkiVDT0XH9kToo/0/r370DWlI9nBBfDNtFyVNv/Gpqm0OWYCRE/sQMbME0764Tvm5xpoMWdTaZMxCQSQHpah8gRasJvyR9wH80ef73M3EWDO0gwEhtwfyGEGKzajiNF64l7Wg16HadNdrKhqp1oTI0/z2qwxpezGazJZ4LRFh8mLIQYNW3eEOGYSkGmBYO3Ab9l4JmDjzbL4eejhgCa1rOO8ykr4usv730Jwt4kRSzGpVgEdcsTvKMuwJUxtPV+Z+YYqI1lE/NI0SpGTorWIdAiQXJjrYSdUwfez5YLwHRTFzFVY1dD9yhEJ4SYCGo7AuTYTViM+kHtf+aUDLoCEbZUd/bMHFwGuiH86jeY4xe7VeugrXKYUQshhqrO/fGjAEZPLT7rAKsWfMLJGT5afVF21HfFNygKnP/fEAtDzQfJCFeMV/teRe9r4snYJZQ5wgSjMSKxwQ0zL88x0KDmoMnwYJEAUrQOQWvdAXRoWF2TyQg2YtUdpwfVYEa1ZFOi7wRgW6179IIUQoxrtZ1+ijJP3MsKEI2pLCrNwahXeG5LHbHWA4OfhOlo084HvRE+eGDo7xVCDEt9z0SOJdYwhpAbj23yoN+7KNOLXoE3djV9vDF3BuSUQe3G+EziQgzGpj/iMReyTj2FKbYw3YEog6xZKc810qxlYwi5IRo64f5CDESK1iFoOLgLgNzCycQOrEWvHf95MtWejz3UQr7TzKbDHXT5w6MVphBiHKvrDFA4yKI1EFExGfWcMS2Hf+yoRddVPfjnWY9mdkL5p2HH8xCR5/SFGA1HlsybpMULT69t0qDf6zSoLCqx8dqOpr4vlCwCXyu0H0hYnGL8Mnlq4NA7vO38DAVOE07j0G52TM820Kn0LM/maRp4ZyFOQIrWIWit3gtAblHpCYdGxOz5GL31zC3KoKLWjScUHY0QhRDjWCSm0uAOkO80D+l9l8wpIDfSiKJGh1e0Asy5AkLdsO/V4b1fCDEktR1+Mq1GnIF6ALxD6GkFuGCanYNtPipbjnqGtXABoMCBNxMYqRivMg++BDoDD3vOZW7+4G6WHs2gU7DZe2a87q5PcHRiopGidQjUlr1040BnzTnh0IhY5hRMXYc4eZKDxq4gTV3HmWpYCCEGqbrdT1TVKM468Rp5RzttSjYLTI0AeDJmDq/xSadDRglU/GV47xdCDMnBVh9Tc23QcRAYWk8rwEVl8WLh9Z2NH280OyF7Khx+N1FhivHI3wmdh8mueYNA8dns7DJzZsnQbpYekZOdCYC3YV8iIxQTkBStgxSKxigIHKDOND0+ocEJxLKmoYuFOCvbB8Dm6s5khyiEGOcONHsAmJIztBkcDXodF2a3omoKO0KDn4G0D50e5l8LVW+Br314xxBCDFpVq5eSbCs6dzVRSy5Rg31I7893GJlXnMFL2xv7vpA3C5p3g78jgdGKcSXsgQ9/j97fwhbrOQCcXjy8VTLLXHZCmoGWQzsTGaGYgKRoHaQPq1opp5aujFmD2j+WPR2Akmg12TZjfOZOIYQYgf3NXhRg8hCLVoD5xgZqKGDNthEUnCddDVoM9vxt+McQQpxQdzBCiyfElFwbpu4awhlThnWcc8py2dfkobrd9/FG1yxAg0PrExOsGJ+ad6Epeu5tPImZzjAzc4ZXtM7OjFFDAaFm6WkVIyNF6yBt274FmxIiVjB/UPvHsqYBYOnYx8mTsviouvPj9dKEEGIY9rd4KMqyDHq5m6Pl+qtwO8p4bWcTHb5hTgxXOB9yZ8LO/xve+4UQg1LV8xzqlBw7pu7DRIZRtFp0GlfEL0V4bdNeiPQ8ppRVCiY7HHw7QdGKcaltP52WUra26bm21IcyiFGG/THowG2ZjK37kFwHixGRonWQgpUbAOjMPWVQ+2smB8GcWdga32dhaRbtvjAHWmRBbyHE8FU2e5maO7QhggBKNIjTX4Nj8gLCUZW/fFgzvAAUJd7bevhdmQlSiCTa1xR/FGB6porRW08oe+gTqCmxAHmN7zA3M8xr22rja7RCfKj/pNPh4LpEhizGE38HdNezPnYSDpPC0hL/iA5nyJ1GsdpIVZOMOhTDJ0XrIBxq8zHNvw2/MQePbfB3O30l52Fr/JBFRfEZ1zZUtiUrRCHEOBeOqhxs8zLNNfSi1dy5D50WwzJpAaeUZvHc5trh3/E+6SpAg10vDu/9QogT2lLdicNsoChUDUAoZ3CPJvXnogI/2zqN1HQdtUxf6dnQebh3kich+qj9EIAnvKdx1RwHdsPIekhzp8zFqMTYtuW9REQnJigpWgfh7b1NnKfbSaD4rEFNwnSEd8oSdLEQM1pepyTLyrsHpGgVQgzPnsZuIjGN2YXOIb/X2lIBQCB/IZefVMjhdj/v7GsdXiB5s6DgJNj5wvDeL4Q4oZ0NXeQ7zRja4kvthbKHX7RekBfvJXtu11GjvaacG/9a9dawjyvGsdoPCOmsbFXLWH7S0G+UfpJjcvzRurpdG2SIsBg2KVoH4dD2DRQobqIzLh3S+/zF5xDIO5mC92/n1uy1VO7bxUfVHXT5h/k8mRBiwtpaEx9WNbc4Y8jvtbRUEDDlEHWUcN4MFxajjhe21g0/mJOugroPwT3MYcZCiOOKxFSqWnyUZFkxd+xDNViJZJQO+3iTbFFOyfDy9A4v3kjPjfes0vifKhkiLI6l1XzARnUu8zODTM8yEDnROo8noDpL8BsyKfDs5t0Dw7xhKiY8KVpPoCsQoaDxLWLoYfKZLHB0YyZy4jcCKAp1Sx4mlDObf2t8gHfMN5Px9Gcw1r0fXwNLCCEG6aMaN/lOM/nOoa+VZ22poCNzPigKiqJw3gwXb+1pIRCODS+YeVfFv+766/DeL4Q4ru11XYRjKpNybJg79hLKngnKyC7XripqoyOgcsf2+NqtKAqUXQwH3wE1moCoxbjRcQilu561kZP4XJGbGDDCmhWLHpT8uZyu38+TG+VmpxgeKVpP4M2dDXxG2YA7/wz0BiO+3W+g0wZZtAJRZwnV//5/eJa/yMs5XyI3WI35hRXQtC2JUQshxpOYqvFuZRvnznANeQZHQ6ANs7uS1uyFvdsum1eILxzjiQ8ODy+gnGlQvEiGCAuRBBsPxZelmp5rwdK6jWDeghEfs9wR5GuLnDxzyMqLNT03vsougrAHa7usnymOcugdAHbp53BB/sgmYDpCiQUIW3IpUxo4sHcHrZ5QQo4rJhYpWk9g/wevMUXXgm3+Zwbfw0p8qvm8WDPFug7yYs3oMwqZu+RLXBu5DV9EgZe+C2HfCY8jhBDb6tx0+MJ8albekN/rbIxPfNHoOrt320klmZw1PYdf/eMAlW3B4QV10tXQuA3aq4b3fiFEvzYe7GBqro2S8GH0ES/+gtMTctybz8zg1JwQP9ripKLeS1fROaDocTR9kJDji/HBt28dzVoWU7Kt6Ia3yk2/QrlzAVjKBp74oDpxBxYThhStAzjc5mNRy/MEFSvqpNOH1MOqxAJE9q3FEO4ism8tWixMgcPAnDwz3wp+G63zMLz+4+QFL4QYN/62tR6TXsecQgehyNCG9GY0biBqyaYzY06f7f9x4QzMRh03/bUGX3QYVybzPhf/Kmu2CpEw3lCU9w+2c/rUHFydFQAECk9NyLEVncLP5rVh0WmsfKWejpgFppyDs3YdyOQ4AkDT0A6+w/vqPC7O70rooWOWHMKTzuEb5jd54/2PCA7xXCaEFK0DeP6V17hcv4muonPBYEnIMT9b2M4O43xesl0FHz0Gu/+WkOMKIcYnbyjKX7fWc8GsPGKaQjg2hIvLWJjMunX4Jn3qmGfiXA4zt10xl6r2ED/5yDn0a9bMEig9B3Y+Lxe8QiTIur0thKMqn5qVR0nrvwg7JhFxDn8Spk/Ks8T44dwOqjrC/HnDYTjpasyeamjanrA2xNjlOfwRjmgn3fmn4TIl/lln35k3Y1PCPBW7hQ+f/Fl8pI7M8SIGSYrWfkQ87Tzyt7Usrbodvz4TrfSchB3bYVC56bQMVnb8O+3OWfD370BXfcKOL4QYX/74r0N0B6Ncd9qkIb/XUfMWhnAXXTM/1+/rCydn8R9nu3ixxsIDe4ZxY27BddC6Fw7KDKRCJMJzW+rId5qZn2+koP0DvFMvHdJSe4PxqfwAl5TZeeKDajbZz0fTGWDrkwltQ4xNO/7xODFNYfYZS5Jy/Fh2GdWffZFm42TOr/4N/seuge4RzGQvJhQpWo8WCdL49u/Zdt9VfOmjZczU1RO95OdoRltCm1lxsoPTJ9m5rv0bRMIhtOe+DOHEPOwuhBg/Kls8PLCukk+V5zHdNcS18jSN3O2PELYV4Zu0uN9dAhGVS8tsXJjv5393Z3Dju5lsbDUOfh29hV+IL5ux9jZQZaiXECOxq6GL9ftbWX76ZCx7/4pBDdE97fKktPWN03IoyrTyhSf3sd50PpHNj/Hhrn1ERzpNrBizNh1qp7j+dfbbTmF6SWFS2rDoNDKzc4l+5rf8RPs2hq4aPH/5GgSkt1WcWFKKVlVVWbVqFcuWLWPFihVUV/d94Pqtt97i6quvZtmyZTz77LPJCGHItKYddP36XIre/m/yY00czjmPwFVPEC09N+Ft6RSFX12eS3FuFjcHv4FWtwnvY9eCvyPhbQnxSWMxPyeiVk+Ibz35EVaTnrPLcoc2LBhwHnoVW9Mmmk76OugMx91PpyjcPr+N75S72dJuZNk72Vz+VCNPH7TgP9HoMIMZLv5ZfEKm9fcMKT5xfJKjE084qvKTv+4kx27i03NzyKx4mE7nLAJFZyalPZtJx73Xncy/zS/iwcgV6GNhKv/yQ069Yy3fW1PB3yrqqWn3D/4G1gQz3nL01R2N/OFPjzBVaSLjrBVJa+fIfC9Fdo3rzpzO/xj+E5O7ksbffJqQpy1p7Yrx4fhXMiOwdu1awuEwa9asoaKigrvvvpuHHnoIgEgkwl133cXzzz+P1Wrl85//PBdeeCF5eUOfFXOkgpEYu+o7YeMjLNjzS0KanV9k/j8+f8VlZNRsQOeagVENk4yJuR0mHb84uZX3TZ/j9nVRbq17GM8vT8Nw0Y+xnnFD/GJQiCQYK/k5UUViKu/sa+X/vbgTtz/MnZ87iVB0aBeO9rr1FL29kkDeAtpmLsN6gv0NOvjClG6umuRhXaudZxsLuPWjDO7a7uDs/AgzM6IsopvT5kXItBr7vvmkq+HAP+Dtu+LPti5eCQbT0P7Sog/J0Yml1Rfll09/REWtm19cNY+SjXdgdlfywaJfk5/gocFHy7Qa+e4lM+k61UX79q/zhR2PYMqZze17PsVft8YfW8qwGJjqsjM5x8a0XDvT8+yUFziZke/AYtQnLbZ0Nx5y1B+O8uauZl74qI6PDtTymu0pwrYSguX/jjmW/E6UfHOUr51ZxDPVP2F53Z10/fJMdi38KYWnf47ywgz0iZy6WIwLSSlat2zZwuLF8eFoCxcuZOfOj9cAq6qqorS0lMzMTABOPfVUNm/ezOWXD38ITG2Hn3f2txJTNaKqhnrkq6YRjWnEtPg2gKiq4Wr/EFPjR2jdjZynbKNM18h65TSqFv2Ir2YexGbR6CJ+RyiZKaMo8Olpek4JTeWh6v+P82p+x6lv/jddb97GZsMiWsxTUM2Z7HUtweDMI8NixGyMd47rFAV3exdthlYWz0yvX4QivY12for+haIx/lbRQLs3TIcvxKE2PwfbvNS0+4mqGlNzbdy/fCGluXa21rgHdUxdsJOLP7iRgs4thLJmUHfZH0AZ/K95i17jyhIfyxbn8/w/3+MDj4t93SbearQR3VuN8kI101x2XA4zGRYDVpMBq1GHQ38TV+W6Oemdu/G/9wi1ueexaebNBE05tLR08X77IRQFFEBRlN7vUZSebXDR7HyKMk9UXk8MkqPjU6cvzCs7GvGHo3QFIlS1+Njf4uFgqw+DDh6bvZGzPvgfzB37aJ//VeoLLiQ/yTEFIiprdzew9MwfYvFUc83hh7jY8Q+6Z5zD+9lXstHror4zwLYaN6/vaCLW0/OqU6A0x8Y0lx2HxYjdpMdq0mMz6THodGjEf8eFIiqKAgadgk6nYNAp6BUFvU6HXkfv19YWN0Xth9ApoNfFX790XgEuR3rewE/XHG33hnh1RyPdwSi+UPyPPxwjElOJxLSeryrtvjD7mz0URuv5snUD92dtIjvUQO0FjxPGeOKGEsSi17jm0xez4Z/dlB1ew4Vbb6Zyyy9YoyzAZy2izjKDbcpc8jb5sPX8/7KZDL1frT3XxEdu6yqATqegUxQWlWYztzhj1P4uIvkULQljP37yk59w6aWXcsEFFwDwqU99irVr12IwGNi8eTNPPvkk9913HwD3338/xcXFXHvttcc9XkVFBWZzev7iEmK0hEIhFi5cOOLjJDo/QXJUCJAcFSLdSY4Kkd4GytGk9LQ6HA58Pl/vz6qqYjAY+n3N5/PhdDoHPF4ifsEIIeISnZ8gOSpEIkmOCpHeJEeFGH1JmYhp0aJFrF+/HojfOSovL+99raysjOrqatxuN+FwmM2bN3PKKackIwwhRD8kP4VIb5KjQqQ3yVEhRl9Shgerqsptt93G/v370TSN1atXs3v3bvx+P8uWLeOtt97igQceQNM0rr76aq6//vpEhyCEOA7JTyHSm+SoEOlNclSI0ZeUolUIIYQQQgghhEiEpAwPFkIIIYQQQgghEkGKViGEEEIIIYQQaUuKViGEEEIIIYQQaUuKViGEEEIIIYQQaUuKViGEEEIIIYQQaUuKViGEEEIIIYQQaUuKViGEEEIIIYQQaUuKViGEEEIIIYQQaUuKViGEEEIIIYQQaUuKViGEEEIIIYQQaUuKViGEEEIIIYQQaUuKViGEEEIIIYQQaUuKViGEEEIIIYQQaUuKViGEEEIIIYQQacuQ6gAGo6KiArPZ3GdbKBQ6ZttoS3UMqW5fYhjd9kOhEAsXLkx6O8MhOZqe7adDDKlufzRjkBwdulTHkOr20yGGVLc/mjEkKkdVVeW2225j3759mEwm7rjjDqZMmdL7+htvvMEjjzyCoigsW7aMa6+99oTHTMccTXX7EkN6tD+aMQyYo9oYsHv37kFtG22pjiHV7UsMo9t+qv+eA5EcTc/20yGGVLc/mjGkw9/1eCRH07P9dIgh1e2PZgyJaueNN97QfvjDH2qapmlbt27Vbrrppt7XotGotmTJEq27u1uLRqPapZdeqrW3tw8rtlT/26S6fYkhPdofzRgGamdM9LQKIYQQQgiRDrZs2cLixYsBWLhwITt37ux9Ta/X8+qrr2IwGGhvbwfAbrenJE4hxhMpWoUQQgghhBgkr9eLw+Ho/Vmv1xONRjEY4pfVBoOBN998k9tvv50LLrigd/tAQqEQe/bs6bMtGAwes200pbp9iSE92k+XGKRoFUIIIYQQYpAcDgc+n6/3Z1VVjylML730Ui655BJ+9KMf8eKLL3L11VcPeEyz2cycOXP6bNuzZ88x20ZTqtuXGNKj/dGMYaDCWGYPHgO6/GG6/OFUhyHGCVVVWbVqFcuWLWPFihVUV1f3ef2NN97g6quv5pprruG5555LUZTjnL8T3DV9//g7Ux2VECLdHPldIb8f0sqiRYtYv349EJ9Aqby8vPc1r9fLF7/4RcLhMDqdDqvVik4nl9sTlb+rjWDrYehqkFweIelpHQM8oSgAmTZTiiMR48HatWsJh8OsWbOGiooK7r77bh566CEAYrEY9957Ly+88AI2m42lS5dy8cUXk5OTk+Kox5mwByr/2XfbjIvBlp2aeIQQ6enI7wr5/ZBWlixZwoYNG1i+fDmaprF69Wpeeukl/H4/y5Yt44orruD666/HYDAwa9YsrrzyylSHLFJBVdH2v0HA24FlzoVQt0lyeQSkaBVigpEJJIQQQojh0+l03H777X22lZWV9X6/bNkyli1bNtphiXSz8XfY3/gxhqLTYc6FqY5mzJOiVYgJZqJMIJEOMRyv/TxjkEhTU59txqx2Wht9x+ybrBhGS6rbT5cYhBBCTDCb/wiAsXUHaGqKgxn7klK0JmPRZSFEYkyUCSTSIYbjtu+ugcLCvttcubiySkcvhlGS6vZHMwYpjIUQQgDga4P2SmJZ09C7D0Fn9YnfIwaUlCfDj35mbuXKldx99929rx15Zu7Pf/4za9as4Q9/+AMdHR3JCEMI0Q+ZQEIIIYQQIgmOTJ5W+RYA4flfiG9v25/CoMaHpPS0yjNzQqQvmUAiffjDMULRGGaDHluqgxFCCCHEyByZPK15F+gMRMqWYP3XneBpAJtMajkSSSlaE/3M3ER/Xi5siH+Wnqa+Qwsm0meQzjGkuv2hkgkk0kcoGmN/s5fyAocUrUIIIcR40XEQMidhNFtQTRnouhug4KRURzWmJaVoTfQzcxP9ebm6Tj+eYISyvBJMho+Hak6kzyCdY5Dn5YQQQgghRC93NRjtKLEwMWtuvGgVI5KUh9XkmbnE6vSFWfbwB3zxDxvRNC3V4QghhBBCCCH6o6nQVQuOvPiP5gzwt6c4qLEvKT2t8sxcYv3rQBvdwSgfHu6gssXLzAJnqkMSQgghhBBCfFKwG6IhsMeLVtXkhM4DKQ5q7EtK0SrPzCXW7sbu3u+31rilaBVCCCGEECId+VriX48uWiP+eCErhk3G5Y4BVa1eFpVm4bQY2F7vTnU4QgghhBBCiP74WuNf7UeGB/d0NoU8KQpofEhKT6tIrMauIItnujAb9Oyo76bLH051SEIIIYQQQohPCrhB0YMlE+jpaYX4cjhi2KSnNc35QlHc/gjFmVbK8u0cbvPhCUXxhKKpDk0IIYQQQghxtFBXfE1WJV5maaaeZUCDUrSOhBStaa7eHQCgMNPClBw7XYEI3cFIiqMSQgiRLKqqsmrVKpYtW8aKFSuoru67Rvcbb7zB1VdfzTXXXMNzzz2XoiiFEEL0K9gNNlfvj709raHu47xBDIYUrWmutsMPQL7TTIZVD0BDZyCVIQkhEiymanQHIjL0XwCwdu1awuEwa9asYeXKldx99929r8ViMe69917+/Oc/s2bNGv7whz/Q0dGRwmhFsnT5QgQjaqrDEEIMVbAL7PGi9bEKD3+sKYhvD3tTGNTYJ0VrmmvsCgKQYTXiNJuAj3tfhRDjQySmUdMRkGH/AoAtW7awePFiABYuXMjOnTt7X9Pr9bz66qs4nU7cbjcAdrs9FWGKZNrxPM77p6PufSXVkQghhqqnp3VTm5G73u3iNwdyiehtEPanOrIxTSZiSnNt3vj02FlWI1ZjvKe1uVumzBZCiPHK6/XicDh6f9br9USjUQyG+CnbYDDw5ptvcvvtt3PBBRf0bh9IKBRiz549fbYFg8Fjto22VMeQ6vb7jUGNMePVH2MMezFteojG8usxZrXT2ugbnfZTIB1iECIhomGI+MDu4sUaMxaDgpEYXThwRaRoHQkpWtNcmzdEhsWAQa9Dr1OYbOhk/qE/oZ/7jVSHJoQQIgkcDgc+38cFiqqqxxSml156KZdccgk/+tGPePHFF7n66qsHPKbZbGbOnDl9tu3Zs+eYbaMt1TGkuv1+Y2iogEAL4fwFmFq2U5RlBVcurqzS0Wk/BUYrBimMRdL52+JfbblsbDVxZokZZ6SNlnY7uWE/SmqjG9NkeHCaa/OEybHHhwUrwCPGX3J588PkvCpFqxBCjEeLFi1i/fr1AFRUVFBeXt77mtfr5Ytf/CLhcBidTofVakWnk1P5uHIo/m/vOe078Z/d1QPsLIRIK94WAAKmXCo9BhYWmpiXGaJdtRMOSU/rSEhPa5pr94XI7ilabY0fMEWr4qB+GtObtmDqOgSk9u6oEEKIxFqyZAkbNmxg+fLlaJrG6tWreemll/D7/SxbtowrrriC66+/HoPBwKxZs7jyyitTHbJIpNqNkFNGcOpF8Z89TamNRwgxeL5WAGqiWQDMyDFi0yJ04SAaasGcwtDGOila01ybN8x0l51sxUfmoReJYOQO43/xp9j3KOj4EPxngy071WEKIYRIEJ1Ox+23395nW1lZWe/3y5YtY9myZaMdlhgtLbuhcD6a0YZqy0Pna0t1REKIwfLFe1r3BzKAGGU5BqyhCBtwoMgzrSMiY4rSXJsnRLbdiCHqxVC1loPGmWwOFKCaHOgr34SwLFQshBBCjAthP3Qcgrz4KCo1Y9LHz8gJIdKftxUUPTu7rRgUjRKnAYteQzFnYFF9oMkyVsMlRWsaC0ZieEJRsm0mlEAnxkAL9dZZdIcg7JiEIdCS6hCFEEIIkQj+Tji8HtAg/6ii1dee2riEEIPnawVLBpWdUQrMYQz6+NRLJlsGOjSCPulsGi4pWtPYkeVucuwmDJ1VAARsRQB4zQUYgp1yx0YIIYQYD8Ie2P33+Pc9RavmKIRQN8FQhC5/OIXBCSEGxdcC5gyqu6IUmT/OWbMjE4CQpyNVkY15UrSmsTZv/D97tt2E3n0wvtGeD0C7oRBFi0J3Y6rCE0IIIUQi+VpB0UHOdABUmwvQiHha8YSiqY1N9FJVlVWrVrFs2TJWrFhBdXXfGZ5ffvllrr32WpYvX86qVatQVelgmDB8bWDJpMkbw2WK9G52OLMA6HBL0Tpcgypad+7cmew4RD/aj/S02kzoOw+h6s3Y7fEF55t0BfGdZCp8geTomKWpZHn2ow/KSWy8kxwVg+LvAEcB6I0A7PDEz/mqtzWVUU0IQ8nRtWvXEg6HWbNmDStXruTuu+/ufS0YDHLffffx+OOP88wzz+D1elm3bl0yQhbpyNtC2JiJN6yRY/r4RlNWVryntb1DzvfDNaii9Y9//CPXXXcdTz75JN3d3cmOSfT45PDgqK2AbLMGQCOu+E4yFb5AcnSssla9xqyaZ3B9dD9Gn+TyeCY5KgYl0AEZxQB8VNPJHR/Ge+iamupTGdWEMJQc3bJlC4sXLwZg4cKFfQpek8nEM888g9VqBSAajWI2y0InE0IkAKFuPPp4gZpj/LhodWVnAdAtPa3DNqglb371q1/R1dXFyy+/zM0330xOTg7XXXcdZ555ZrLjm9B6hwfbjOjdhwhmzSDbFAOgNpaDBihStAqGlqOqqnLbbbexb98+TCYTd9xxB1OmTOl9/eWXX+axxx5Dr9dTXl7Obbfdhk4nTxIkXLALc+27dNtKcQYacO1/msDMc1IdlUgSOY+KQfF3QMFJALy6o4lmLb6k3a6DdZwh/1WSaig56vV6cTgcvT/r9Xqi0SgGgwGdTofLFe9YeOKJJ/D7/Zx77rknbD8UCrFnz54+24LB4DHbRlOq2x9rMRi99cwAavwmAMwRN2pMJRwOk10UL2Q9Xe1D/vuMpc8gmQa9TmtbWxsNDQ10dnZSVlbG66+/zosvvshdd92VzPgmtFZPCIfZgDXSiS7YSdSWj1EHGWaFtogJ1ehA75WiVcQNNkePHtZUUVHB3XffzUMPPQR8PKzppZdewmq18v3vf59169Zx8cUXp+KvNC51+cMogQiGw5uwaTEOFl/BLPe/yKpdi9FXA4otvqPJKWswjzNyHhUDikUg2NXb07r5cAenzZiEWqfg6ZBz/WgYbI46HA58Pl/vz6qqYjAY+vx8zz33cOjQIX7zm9+gKMoJ2zabzcyZM6fPtj179hyzbTSluv0xF0O1GwCvbRIApVlmdHodJpMJvS1etEYDHoqnlJFpMyW+/SQarRgGKowHVbRee+21WCwWrrvuOm6++WZMpvgH/dWvfrXf/aUnJzHavCFcDhPGjv0ARGzx51hzrXo6wzpiJid66WkVDC1HZVhTanlCUdwdAUobthOxFxEy5xLMW4C1bTt89BgUl8d3nHGxFK3jyFDPo2IC8jYBGmQU0+4N0eYNM7sgE3+9HX2wg05/mEnZtlRHOW4NJUcXLVrEunXrWLp0KRUVFZSXl/d5fdWqVZhMJh588EG5vp1IPPHJUeui8XP30c+0ojcRxEQs0IUnFB1S0SriBlW0/vSnP2XBggW9P3/44YecccYZ/PGPf+x3f+nJSYx40WrG0F4BQLSnaM2x6mgP6YmZnOBtTmGEIl0MJUcnyrCmdIihv/bDBgeh7jYc/jo8xefS1d2FN38SWShozbtp1GUAYMxqp7XR199hRxzDaEp1++kSw1DPo2ICOrIagLOI/c1eAGbkmogarOREPWyrdTO/JCt18Y1zQ8nRJUuWsGHDBpYvX46maaxevZqXXnoJv9/PSSedxPPPP89pp53Gl770JQBuuOEGlixZMmp/F5EiPR1Jh2LZZJp1mHRan5cDig19uBtV0/p7tziBAYvWzZs3U1lZyZ///GduvPFGAGKxGE8//TQvv/zycd8nPTmJ0e4NU5bnwNixD9VoRzXFL2ZzbHoqPTpURwZ07gZNg0EMPRHjz3BydKIMa0qHGPprv67TT6RuHTpUYtllZJKJwWYjml2GzV9HZmFhfEdXLq6s0qTEMJpS3f5oxtBfYTzc86iYgHw9MwQ78tnXGJ8IqCzHjGK0khvy8G6DTOCVDMPJUZ1Ox+23395nW1lZWe/3e/fuTV7AIn15m0Bv4mAggwKH/piXwzorzqiXdm+Y0hx7CgIc2wYcs5CRkUFbWxvhcJjW1lZaW1vp7OzklltuGfCgx+vJAYbdkzMRtXlD5DpMGNv3Ecue3luY5lp1dIZ0xIwOiPgh7E1xpCJVhpOjixYtYv369QDHHdYUCoV48MEHe28uicSyte8AIOz8uCiN5s5G76mP34QS48Zwz6NiAvK3xb/aXOxr9uK0GHDZ9cT0NgoMHqpaRz7yQhxLclQkjKcJ7Hk0h/QU2I8tsWIGC5mKj3p3IAXBjX0D9rSWl5dTXl7OddddR35+/qAPmuienIk49DCqanT6IxDsRte2j0jpuYTD8dmEcyw63BE9QcVCBlC57X0izslJieNExvu/Q7q3P5wclWFNqWdr247fnIdmtAF+ACI55VgrXwZ/O9hdqQ1QJMxwz6NiAvK1g84AZidVLQ1MzbWjKAoRg50cmqhslRvUySA5KhLG0wj2PBobdMwu6qfEMljIxEtFpxStwzFg0fpf//Vf/PrXv+aqq6465rV33333uO9L9APqE3HoYXN3EDjEySU2jPs6COXM6J0UIJf4kAOfMf6g94zCDChNzWcx3v8d0qn9/grj4eSoDGtKMU3D2rGbDltZn83R3J7fk121UrSOI8M9j4oJyN8GZieoMeo7PCwssmImQrfeikPtpsMfpN0bItchj1QlkuSoSBhPE2rWFNpDOvLtOlD7vqw3mslSmqSndZgGLFp//etfA0NPWunJGblWTwiAgsAhgPjw4J7e61xrvGjt0DKYBuBtSUWIIg0MN0dF6uh8TRjC3fhzCjn60jOaOQ1N0aN01ULxKSmLTySW5KgYNF8bmDOIhXw0e8Lku9rRaRlEDHZ0qGTgZ1+Th3NmSNGaSJKjImE8TfjzFqGhUOgwwCceQ4/preTI8OBhG9TswZs2bSIQCKBpGj//+c+5+eabueKKK467v/TkjFyrN160uoJHF63x5+ByrPHe6RYtK76zT4rWiW6oOSpSx9S2GwC/Jb9P0YreiGrLk2WsxinJUXFCvlYwZ9DmjxHVFAqtMQCi+vgyN7lKNwdavJwzQ0ZiJIPkqBiRkBdC3bj1OQAUOHT9Fq0WwrR1ukc/vnFgUItH3XPPPUydOpXHH3+cv/zlLzzzzDPJjmvCO9LT6vIfJGZyoto+fs4i1xb/Z2tWMwAFvK2pCFGkEcnRscPYFh/m7bcUHPNazF4gy1iNU0PJUVVVWbVqFcuWLWPFihVUV1f3ef3ll1/m2muvZfny5axatQpVVY9zJDGm+NvB4qTBEy9WCyw9RashXrSWmPwcaPGkLLzxTs6jYkR6zt1tSk/Raj+2XzCqt8R3dbeNXlzjyKCKVrPZTG5uLgaDgby8vN4JgUTyHClanZ4qQlkz+yxp0zs8OGoGa5b0tArJ0THE0LaHsLWAWM/J62gxe0H8wjUaSkFkIpmGkqNHr3W+cuVK7r777t7Xjqx1/vjjj/PMM8/g9XpZt27daPwVRDLFIhDoBHMGjd74agtHelojPT2t5c4QlS0yGVOyyHlUjIgnvs5yQywLoN8lb6L6+IoMulA3XYHIqIU2XgyqaHU4HNx4441cfvnlPPXUUxQVFSU7rgmv1RPCaTZg7txPOKfvRFYOk4JRp9ERNoAtV55pFZKjY4ixbQ/BrLJ+X4vY4r2vwc7G0QxJjIKh5KisdT4BHVmj1Xz8ntbptqAUrUkk51ExIt3x83ZNNAuLXiPTfOzKKEeK1iy81LT7RzW88WBQz7Tef//91NTUMGPGDPbv38+1116b7LgmvFZviBl2H3p/O4bMQvREOHJPRlEUckwxOiMGyMiBrjpw14DJCbbslMYtUkNydIyIhjF2HqCr/PO9m8Kq0rs0a9CSRwYQ65aidbwZSo4eb61zg8Ew7LXOJ+LScWOh/SMxHNqxl2lAhz9GTTCIWaeiD3lQYyrtvnjPa5HeQ5s3zKbdB3EoiRuNkS6fQapjkPOoGBFPAwCVoUwKrYF+l/M8Mjw4S/FS3eFj/qTMUQ1xrBtU0dre3s66det4/fXXe7f953/+Z9KCEvGe1gXGeAKEfN1YtL7DCHJMarxotWRCw1ao/CfMuFiK1glKcnSMaD+AokYJZpYR6db4n525vNFoo8QaZbU9wkxrLqqiR++T51rHm6HkaKLXOoeJuXTcWGj/SAzT7PHe1JzCKbTVKJTYVJxOOzq9DmdWLlqzlQJTvGemJWbh9PnTE9p+OnwGqVo67gg5j4oR6W4Ek4Nqn5FCq6/fXWI9Pa2Zio9q6WkdskEND7755pvxer24XK7ePyK52jwh5ujrAIjajl3sOtccozVsBGs2hGRiholOcnSMaN4FQDCzjD/VFvB6o50LXV1ENIWb3+ikI2wkaMpF55MZhMeboeTookWLWL9+PcBx1zoPhUI8+OCDvcOExRh3ZAI2SwYNnihFtr6Ta6mWbHKV+Ln+cJtc7CaDnEfFiHgawFlEkydCobX/yfGODA8uMgVlePAwDKqn1W63873vfS/ZsYijtHpClOXUouktqKZjhw8UWmPs6LSALQdiIYjJhAETmeToGNG8E01vYluwgLfa9Fw/pZsrXU1ETZnc+GERD1Zm8T/mPLJ9DamOVCTYUHJU1jqfgI4UrSYnjZ4Yi12xPi+rlizssS6MeoXq9v57ccTIyHlUjEh3I1pGEc1NUVx5UVTt2F1iOjOaomOSJcimDsnjoRpU0Tpz5kxeeeUV5syZ0zsMadq0aUkNbCILhGN4QlFKIoeI2Av7zBx8RKElRlfUQNiUhQmkt3WCkxwdI5p3E8meyV92enHqo3y1rIvWLpjuiHDNHBvP7NT4z+JCXN27ICJ3YceToeSorHU+AXmawZxBRDHQ4otRXNq3p0azZGEItJPnNHO4TS52k0HOo2JEPE2ES84kHNPIMUXop2YFRUEzOSkwBqSndRgGVbTu2bOnz3MAiqLw+OOPJy2oie5gqxcFFVfgEJH8k/vdJ98Sn5ihnSyKQIrWCU5ydIxo3kVX3hls2R3k2qJOrPqPT2tfOMnOMzt9vBecxhyA9oOQNztloYrEkhwVA/I2g91Fc0CHBhRZP9HTas3G4K4m32nhsFzsJoXkqBg2VQVPI93GPODjmb/7o5kzydX7aewK0twVoCBTHvEYrEEVrU888QQej4f6+nomT56M3W5PdlwTWm2nnxKlDVPMj99e2O8+LlO8aG2KZUjRKiRHxwJ/B3ga2OIsRqfARa4uiI+TACDfrucsV5C/d5XzVT3QfiBloYrEkxwVA/K2gM1FYyC+tqNTHyISU3t/Q6iWHEyBNvLzTFTUuvEEIzgtxtTFOw5Jjoph87eDGqFDnwtA/gBFq2p2khX1ogG7mzxStA7BoIrWN954g4ceeohYLManP/1pFEXh29/+drJjm7DavWHmKDUARO39rxNW2NPTWhtxcgpI0TrBSY6OAS27AXizzcXJhRZyTFGOLloBPlPs5f+1FRMzGNG3V6YgSJEskqNiQN5myJ9Ngz8+P2bQ5yGW9fElmmbNQRcLMdkevxiuavWxcHJWKiIdtyRHxbD1LHfTpOUAAxetmjkTZzR+zV7d5oNZyQ9vvBjU7MGPPvoozz77LFlZWXz7299m7dq1yY5rQmvuDrJAdxBN0RM9Tk9rnjmGgsbBoDO+QYrWCU1ydAzomTn43e4Czi219bvLeXkB7HqVOl0RtFeNZnQiySRHxYA+0dOa2zOa6gjVGl/OrtQcHxpc2eId3fgmAMlRMWw9a6vXRbPQK5Br7n/2YADVnIE12o0CHJZJ1YZkUEWroiiYTCYURUFRFJliP8mau4OcpttPNGcmmt7U7z4GXXyIcK1XAaNNitYJTnJ0DGjeid+QRStZnDm5/6LVqINFWV4qIqVobTI8eDyRHBXHo0T8EPGB3UWjX4fDpGDTf2LJG2t82GGBvhujXuFAi5zzE01yVAxbz8ioQ34LeQ4D+gGWztaZnRjDbnLsJnk+fYgGVbSefvrprFy5kubmZlatWsX8+fOTHdeE1tLlZ6GuimhB/5MwHVFkiVDdFUU1OSAsJ7CJTHJ0DGjexT5KWTApi2yr/ri7nZ7lZXdsMoqvBQLu0YtPJJXkqDgeQ6gj/o3NRa1PR6Hj2N8PqiXe02qLdDI520aV9LQmnOSoGLauGkBhn8dMcUb/nU29TDaUUDcFDgPVUrQOyQmfad27dy86nY5du3Zx5ZVXkpGRwYoVK0Yjtgkru7MCKyG6C0+ByPH3m2SNsMkdIZbhQCc9rROW5OgYoMZQm3fzUfBTLJ7pAqLH3XW+08eTTIr/0LoXSs8anRhF0kiOioEYAu3xb3p6Wguzji1aNWv8WTlLuJ0pufM4IEVrQkmOihHxtoDZSZ1HZW6BecBdNUsWChrT7BHeOBwlGlMx6AfVhzjhDfgpvfbaa9x6662UlJRwyy23kJGRwbPPPivj/JPsNO87hDESmXT2gPtNtkVp9atEjE4ZHjxBSY6OER2H0EUD7NUmc8a03AF3teg1HNnxafO15t2jEZ1IIslRcSKGYE/RasulKain0HFsf8KRnlZLqJ2pLju1HX6CkeNP9iIGT3JUjJinEc2aTYMnQknGwLN6az3Pp0+z+IiqGjUd0ts6WAP2tD7++OM8+eST2GwfP3/1uc99jm9961tccsklSQ9uIvKHwlykfcAu+xlMNg083XqRNQxAl5KBLbRvNMITaUZydIxo3glAi3UG01w2uhoH3n1egQ2Px0qsehtZp49CfCJpJEfFiRiC8eHBQXMOneGm+PDgT9ajeiMxcyaWcAel2VZUDd4/2M6iyVlk2k4wHFEMSHJUjFh3A2FzLqGoRvEJi9b4qIlJJg+QS2WLl+l5jlEIcuwbsKfVYDD0SWIAh8OBXn/857HEyHTs3UCh0smenItOuO8ka3zscJuWCdEgREPJDk+kGcnRsUFr2kkMHYUzFqIoA8zQ0OMsV5ADWgne2u2jEJ1IJslRcSKGYDsoOpoi8RvV/T3TChC15mEJt/eu6/j6jiY8oeM/aiAGZzg5qqoqq1atYtmyZaxYsYLq6upj9gkEAixfvpyqKpkJflxTVfA00mVwAZzwmVbNFh9tVaTvBqCyVYb6D9aARevxLq5U9fhTOYuR0Xb9lZBmpLHgUyfct6SnaG2IZcY3+DuSGJlIR5KjY4OnZhsH1SLOmlUyqP1zTVGqDdPJ7t4bPyGKMUtyVJyIPtgB9jwavPH/E0XHLVpzMYc6KMmyolOgxSM3qhNhODm6du1awuEwa9asYeXKldx99919Xt+xYwfXX389tbW1CY1VpCFvM8QitOrixWimWSESO/7/nSM9rc5oB/lOM/ub5PG+wRpweHBlZSUrV67ss03TNLlrlCyqSs7hV3lHXcBZxUbMRAaahwmbXiPPpqM6Eh8fj799VMIU6WM4OaqqKrfddhv79u3DZDJxxx13MGXKlD77BAIBbrzxRu68807KysqSEvuE0ryLvdpkzi1zER7gZHa0YO5c7K1v4m/cg61kXpIDFMki51FxIoZgOzjyqe+Kn/GLnAb6u4yNWV1YundjMugoyrTS4gmObqDj1HBydMuWLSxevBiAhQsXsnPnzj6vh8NhHnjgAX7wgx8kPmCRXtw1ANSr8bkoMvRhBjzNG21oejPWcBvT8+zsqO8ahSDHhwGL1vvuu6/f7cuXLx/woHJRPEx1m7CHW/kn1/C5qrXo8k/8LMWULAOVPilaJ6rh5OjRd4grKiq4++67eeihh3pf37FjBz/72c9obm5OdLgTU7CbjGA9bbaLyM+wUNc5uEkX7JPnQysc2raeeVK0jlnDPY+KicMQ7ICcYhq64/NUFDr0/RatUasLezh+ni/NsVElwwoTYjg56vV6cTg+fg5Rr9cTjUYxGOKX1aeeeuqQ4wiFQuzZs6fPtmAweMy20ZTq9sdCDBnV71MC7PRlYjUoOJQg4XAYNabS1d2FGlP7/qxqxKy5aO5aJhWa+PBQBxU7dmE2HH/wa7p/BqNlwKL1jDPOGNZB5aJ4mPa9ShQ9B63zUZSmQb1lSqaBXW058YHeUrROOMPJUblDPLpCDTswA9bCGeCuISMYQWdW8Z3gfQtml+PdYsVz4F1w/1t8o8kJtuxkhywSaLjnUTFxxHtaT6G+O4LLHMNsOHa4qkWnoVgdmCPd5ERbKc2xsfFQO+GoDDMfqeHkqMPhwOf7+Le4qqq9Betwmc1m5syZ02fbnj17jtk2mlLd/piIoe1VACq1IgodeswmEzGTCZ1eR2ZGJjq9DtMnftZsLnJiPmYXZ6NubYKsEuZMzhpe+6NktGIYqDAeWYYdh1wUD9P+N9iqzMXlGPxMgJMzDbwczgYLUrSKQZkod4jTIYZgMMiOD1/mNCBD89P47lNEdWaUqefQ1d1F2GHo/aqPRIiFw70/G6J+qvWlZHXsoOFfT6EoYJz9aVojg7uhdXQMqf4MJvr/AyGOS9N6n2mtbwxTZO1/YiUlFiDsaccOmNyVzMifiaohM4+myKJFi1i3bh1Lly6loqKC8vLyVIckUsVdA9YcaoJ2ijIGN8Geas3F0l5LWU/u7mroYuEARauIS0rRmuiL4vF+QZxtAVPXIXJa9/BaZAWFBn+fIQVAv99rmsbUTD0hTER1ZiLt9Rwe5c9kPP07jNX2h2qi3CFOhxj27NlDrLuKJi2HT80twmaATn+YiNFIZkYmJpOp96veaCT2iZ8DmWUs6niNw6ZspueawZWLK6t0yDGk+jOYKP8PxtLvAZEmgm50agQcBdR3Ryi3Hn/t1Zg5CwCdt5HZRYsA2NXQzaXzCkcjUnGUJUuWsGHDBpYvX46maaxevZqXXnoJv9/PsmXLUh2eGE3uGsgopr5Rz6ySwV1LqdYcrKGPKMgw4zAb2NXQneQgx4ekFK2Jvige9xfE7hrYHh9e8J46jxuyFTINHw8pAPr9PhwOM9MV3xYwZOLUfKP+mYyrf4c0bz9RF8Ryh3h0ZXbuotY8k9OH8Sswp3g6uk6Ng4cPMj03tXkmRo/MCzGBeOKPSan2fBq6I1yQPUDRaok/GqD3NuKaaibTamRXg0zikgo6nY7bb7+9z7b+8vCJJ54YrZBEqnQcIpIzk86wnimZgyxa7flYI13oogFm5NvZVutObozjxIBL3gzXokWLWL9+PYBcFA9WVx0xxcgBrYRi60BzBvc1OcOASafRSaYMDxaDsmTJEkwmE8uXL+euu+7ixz/+MS+99BJr1qxJdWjjTne3m8lqHbG8ucN6f3ZeCX4s0HYgwZGJdCbLaUwg3XUAdBnzCMc0Ci3HX3dVNTlRFR06TyMAk7Ot0kMjRCpFguCupsMSHwE1JWtwRWssI778ndFTy8x8J/uaPATCx79hJeKS0tMqwyaGoauONlMJsYCeYkuEzkHWrXqdwjRHhCY1k1IpWsUgyB3i0dNQtQOAwhkLAW3oB9DpqbeUUx7YiT9yBbaERifSlcwLMYF01QNQr+UCXooGGB6MoiNsyETv7Slac2zsbOimzRvC5TCPQrBCiD46DoKmUqeLF6Glg+1pdcb3N3lqOalkEc9tqaOi1s3ZZblJC3U8SErRKhfFQ6Rp0F3HIeMZ5Fh1OI0anUN4+wxHhJqOLM7w7UtaiEKIoQs37gKgtPxkaKwY3kEKT6L08HY2VrdypowQnhBksrSJ0/7kmm3YFR07a+Nn/UzN27s0RthhOGbZDJ/OgbOrjoDfj00LAPDSezs4a7J92DGk+jNIlxiEGLK2/QDsU4sAmJyhRx3EWuwxRzEQ72mdM+MCFGDT4Q4pWk8gKUWrGKLueogE2K6bxtRBDi042gxnhKpmF+CBkBfMMpOgEKmmqhqZ3XvpMBaQY8/FH44RisaIDOKEdkRM1cgsmkHskEKgfjvw+eQFLNKGTJY2cdpnRxsxg52utgbAQVmusXdpjE8uk2EymQjbizG6K7BazCwom8SLe7pp0xzMmTN72CGk/DMYxRikMBYJ1fPozjZ/PgUWL1ajDt8gzvFmezaq3oLTU4XTYmR6np1NhzuSHe2Yl5RnWsUQtcR/iW4ITRv0Q9xHm58VokHLif/QXZ/IyIQQw7SroZuTqMSfdzIAoWiM/c1ehlCzEolpHOqGvYZZTPNsQVNlTcaJQOaFmEA8TcRMTg579WRb4iOtBuK3FKDEQpi6D2PU6ygvcPB+VTtd/vAoBSyE6NW2HzJLOdCto9R+/OfRP0lRgwQNTgxdhwGYX5LJ5upO2r3BJAU6PkjRmg5a9qApOj4ITmFa9tCL1pmOEK1Kz5CCrroEByeEGI4tO3cxSWnDMv0cugORIfWwflJ79kKm0Ehd1c4T7yzGPJksbQLxNhMzZXDYqx/USCu/pQAAc1v80YOFk7LYXtdFiyeU1DCFEP1o3gl5szjcEaLUNviiFSBkykbvaQBgXnEmgXCM9w9Kb+tAZHhwOmjZjd9aRChgYmaOEYZ4w9SkA4cjA0JIT6sQaaJz778AiE06k5qOALkj6CTNmzqPUKuBro/+yuTTP5OgCEW6knkhJghNg+5GYq6TOezWc/ZU4wnfErDkoxrt2Os3wLTzmVeSyV821bK9zs3MAucoBC2EACDkgZY9BGf8Gx07Y0weQk8rQNCUTZa7AtQYp5RmoVcU3qts4zMLipMT7zggPa2ppmnQsocGY3wNvnLXiU9a/ZnqsqFqCq11lTJMSIgU6/JHyGnfQlgxE8mbN+LjFWaY+UB3CqXN/4DY4JfEEkKkMX8HxEKEDBk0BgbX06opeiKTzsJR80/QVOYVZ2DQKWypcSc/XiHExxq3ARrV1viz2GWOoRWtfnN8qL/RU4PdbGBanp0NlbIKyECkaE01TyMEOtitTSXTGKPArh/WYc7Ki9BCFtWHDuAJDS1xhBCJtXZPM4uUfXRlzQP98G5EfVJj7llkqF34d7+WkOMJIVKs8zAATYoLiC+XMZjHCLTpF2H0N3NJ4DUm69qYX2Rns0ziIsToqvkAgAp1GgAznUO7oRyw5AFg6dgLwOxCJ9Udfg63+QZ624QmRWuqNW4D4N3gNGY4IyiKMqzDzM0I0UwuRl9jIqMTQgzDuh0HmaurQSs6OWHHnFU2g1Ytg44NjyXsmEKIFOqoAuCgWghAaaZ+UBO1xUrPJmIrwPX+Hejf/RXnTDKzv9lLXac/mdEKIY524E0oWkhFu5FMi558ywBrLPcjYM5DQ8HcEV+ucm5RBgDPb6lNeKjjhRStqda4DQ2FN73TKXcOf1ivpmmEbIVkhRvRtIFnHxRCJI8vFMVf9R56VIJ5CxN23IUujXWG8ylsejs+rFAIMba1VwIKFeES9IpGWfYgR2XoDHTO+xIxRzHZe5/iRs+DgMYr2+WmtRCjwtsKdZug/DL2NXUzO8/CUPucVJ0JNaOkt2jNspmYX5LJ37Y1yHX8cUjRmmqN2wg4p9ClWjk1e2RTXRtzplCitXC42Z2Y2IQQQ7ZuXwtnattRdSb8rsT1tCoKBGdegYEons3PJOy4QogUaa+CjGL2+Z1Md8YwGwZ/1Ruz5NB52QN4J52P6+Df+E7OZtZsrkVV5WJXiKTb+gRoKtE5n2VPo4dyl3lYKwREs2dibt/V+/PFs/Op7QhQUetOYLDjhxStqda4jcOGMvSKxsLskU1ZnzdpOgZFZd9eWRZDiFR5bnMdFxl3opSeiWawJPTY55y+iN3qFAKbZNZYIca8jirIKqXSZ2FO5jDmotDp8Uy9nEjePG6KPU11azdv7GpKfJxCiI+pMdj0B5h8Jnt9TgKRGPMKLENag/2IaP48zF2H0Ac7AThvpguzQcdfPqxJcNDjgxStqeRthe56NoZKmZMRwW4Y2R1SR0F8SYTW6l0n2FMIkQwN7gC7DxxgplaNUnZRwo8/I9fCpqxPk+/ZTbRpd8KPL4QYJZoG7VWEnKU0hUzMHk7RCqAoBBbeiD3UzOez9vC/b+4b0ZrQQogT2P96fHnJ/LlsrW4D4OSi4d2gjuTPB8DSshUAu9nAp08q5MWKBtq9svbyJ0nRmkpN8UmY/uEu5ty8kQ0NBohlTI5/016JT2YQFmLUvbCljnOVnpEOZRcm7LgxVaPTHyYYUZl8/peIaHpq3vpjwo4vhBhl7moIdXNIH1/ubmHO8Jey0k05F9WWx80Zb1PV6uNP7x5KVJRCiKP5O+GDB8GeDwUn8VGdlzy7gSnO4XU6GfLnoCk6clo29m675tRJhKMqT22U3tZPkqI1leq3oqGwQ53G2a6Rz/qnWTLxGnKYSS3/OtCagACFEIMVjqr85cMalmfuAHseFCbuedZITGN/s5dwNMb5p8xho34RmZV/RYvJzSkhxqSmHQBsDEzGoKicMoKiVdEihF3zcLV+wMXFER54u5I9jV2yZrsQida2Hw5vgNlLURU97x72cWa2F506vPxVdBC1FWBq+LB32zSXnQtn5fHHdw/RFZB12Y8mRWsq1W+hyVSK2eZkpiMxJxd31jwW6g/xzIcyZbYQo+mvW+to7+rm9MgWmP0Z0CXn16tBryO2YDm5ajvb3vm/pLQhhEiyph2g6HitPZ85jgBWw8gOFypchKKp/HTyNjyBKHe+slfWbBci0Xa/GP866zPsdhto9ce4qGhk1++h7JkYmyswROPrs0ZjKjecPYWuQIRfr90/woDHFylaU0XT0Oq38GFoKounOdANb3nWY3RmncR06tlyoI4Wz8iHHAshTiwaU3nw7SpuyKtEH/XTWvrppPZynLP0i3QoWQTe+z0xmS1UiLGncTux7DI2NcZYlOkd8eEitgKijiImNb7JkrkFvF/VTn1nIAGBCiEAUFXY9VdwlUNGEWsbTQCcXzCyZ09D2bNR1CiFbe8DEIioNHaFuHBWHk98UENli2fEoY8XUrSmSlctiq+FTdHpXDDNkZBDWnQazsLp6FA5SankzxsOJ+S4QoiBPflBNdXtfr6WsZGYNZe1/plJ7eUwmix0zPoCZ0Y28dTr65PWjhAiCTQNajdSY51DTIOLCvx0+sOM5P5TJKZRb5+HoWUn35inodcpPPB2ZeJiFmKiq94AnkaYfAaqpvH8YSvnTLbgsozsxnE4YwqqycGklnV9tn9t8XQsJh3fevIjfGGZXA2kaE2dwxsA2GuYwxmTbQk5pBIL0NbWiqbouD57D4++d5jKlpHfwRVCHF+DO8A9b+zjymmQ3/AWvrmfR9MZk95u2eX/iaYohN5/ZFTWdOvyh6nr9MtzckKMVOs+CHTwum8GpVkmpjti7G/2MtIxE+2ZJ6GhMKXhFS6Ylcf6/W28va8lISELMeFtfwZMdiiczzuHg9T59Vw7zz7iG07o9ISmL6G08Q30gY7ezWWOCKuXFHCozceP3migsUtGTkjRmiK+3a/TqmUyZe7pmPQJGhsMxPQWwpnTWcJGbHqNG/64ke21nQk7vhDiY+GoyvefrSCmafy8ZCOKFsM3/4uj0raSWUJs9r9zvX4tt/x5LVWtyb1B5QlFWb+/TZ6TE2KkDr4NwNPNk7lqXiaJ6kOJGDOIFJ9O5oH/Y3FZLqU5Nn729114JWeFGBElGoTdf4cZl6DqTNzzXicltigXTrUk5IZTcN7n0ath8jeujo/EAAxRL6cG3ue+z5RQ3x1h6f3/4m9b60f+lxnDpGhNgRa3h+j+f/IeC1hx9rSEH99feCZmXz2PLarEF46x7JGN3Ld2vyyDI0QCxVSNH//fDj442MG9/1ZC5vY/wtx/J5Y5ZdRiMF3y/7AqEb6uPsvyRz5g0+GOE79pnJCeXzFWaXv+Rp1xKm5TCdfOz0rosUMzLsfUXU2BZycrl8ykriPAzX/ZSigaS2g7Qkwkzrp1EOqG2Z/hd/ts7G6N8J/lXRgT1OlkzJ1G6+wVZO1bw5S/XcX8Aw9gqn4bXdjD+dMc3PdvJbgcZm5eU8HNz2yl0X3iXtfxeI4c4Xx1/VNVldtuu419+/ZhMpm44447mDLl4wu5t956iwceeACDwcDVV1/Nddddl4ww0lKLJ8hvf/cAt+Oh8OzlOC0GSPD/p6BrHtGiU5n30SrennQRvw8t4b61Uf5e0cDDKxYxsyAjsQ2KMUXyc+S6gxG+v6aCtXta+N7FM/m32tUQDcKF/693n2hMJWxwEIok52Kxyx/Goy8m75Qvc+1Hf+ID/TksfyTMDWdP4T8vnEGuw5yYNnpudiXr7zFcR3p+zy93kWkz9XntSNxOs+GY18YCydFxrOMgVL/P89HP8d1Pl+M0qyRyLFR46oWoG+5mRu3zhE++gP+6eAa/WnuALzzyAfctP4XJOYl5HGmikxydQDSN3H1/AVc5f+su556ddVw+w8pF+b6ENaHEAhzSTcF2zg8w7H2JeZWPoKtUcSp6gpqHwhlf59fLp/LHDYd5YUsdHx7q4CvnTuOU0izsZgO+YIRARAUFOnxh6joD7G3q5mCrj7On57JkbgGnlGZjMoztvsqkFK1r164lHA6zZs0aKioquPvuu3nooYcAiEQi3HXXXTz//PNYrVY+//nPc+GFF5KXl5eMUNLKtlo3//X0Jn4bWIPfVkTG/KWEY0mY+VPREbrkDrR/3kFm43v8ILqWLxefyjdbr+PGP6s8d9PZFGVaE9+uGBMkP4fPE4zw1631/PqfB+j0R/j5lXNZEXw6PqPgRT+FvHLojK+5HIiorN3dwNIzMpMTS0/RdsFZt1JS8y/+13c/0+f+gl++d5inPqhhybwCLpyVT74WZbamoSiDuyOsaRqtnhDVHX4qajp5r6qddl8YBej0R3h6YzW5DjNZNiPZNhNFmRYKMy0UZlgoyrSSn2HGYtQn7O8ZisboDkTxBCP4QjF84Si+UJTaDj/b67roDoRxOS3odWAx6MmymYjEYuxu9HDZvIIxWbRKjo5fTS/fQY6mZ2fB5/h/c/KIBpsSenzN5KBz7heZvuNPHG6+kUvnLaLBHeRvFfVcdO/bXDgrnwWTMsnPsFCQYaEgw9zvJC/RmIo7EMFpMWA2JC6fxwvJ0Qmk4iks7v28OXMV33ulntNyQ/z43ELUhgS3oygE515H6/zvsP1gA5dZdmF+75dYP/w15v3b4KpH+Op508i0Gtl4sJ07X90z4OGybEb0isIf3z3EH949hNWo55TSLE4tzebsslxOnhwveMeSpES7ZcsWFi9eDMDChQvZuXNn72tVVVWUlpaSmRm/kDv11FPZvHkzl19+eTJCGTWaphGKqrR6QjR1B2nsCtLUFaCxK0hLd4iadg+djQf5geVvzFcO0nzur0GXxP8sJjuesivwTP00TjzkfPQH/k//Y571X8S3f3WQ4rL52CwWsu0mJmVbmZJrJ9duwmzQYTboMRt1vd+bDDr0iVqTR6TcRMzPE9E0jZiqEVXjX4ORGO5ABLc/Qr07wMFWLxW1bjYe7CAQiXHZpCg/Oc9N8e5fQ937+OYsI3rqf5Kc8vRjqgZ1PUXxkZ7PiM5C4789Rv7/XcN/HPwW1yxYzovBU3n8QJhXtjcC4HipgdIcG1NdNvKdFjIsBsxGPeGoSjim4vZHaOwK0OAOUNcZwB/+uFdVIX7yK86yYtLrsBj1dPrDHGz10uYLEwgf2wObYzdRmBEvZg3RALl7ouiU+IVwRNVQeo7sD0fxh2OoPc/whKIqoUgMTzBKVyCCJxglHBv+E393v7aXDIuBLIuekn91kec0k+c043KYe7/Pc5jJthsx6XWYDD1/9LpBF/nJIDk69qmqRjim0h2I0OgO0NRYQ3TTn/m3thd41nwV377yPN6r7ODSksTfuG479ftYDrzK5Ne/QtN5P+e0woV85kun8deP6vngYDtv7m4+5j3Zf6unMNNKTFXpCkRo9YR6J5cpybIyvySTecUZZNn6n2ROAzzBKK2eEJ3+ME6LgUnZNqbm2pmUbcXccx1h1Me/GvQKBt2RbQrhmIaqaujGyLWG5Og4EYui+tqIVm+Eyn+gq34XLRLEmzWL9tzTCUYizNr7IDuYzTd3lHPlHAffL67FYphKMsYeWXQaebFmzizWozfOwT3netRQA7M2/ZbQ3z5H4+K7KM8p5sZzT6WxK8DhNj8Os4E2bwiLUc+UXDsGHZgNOnQ6HVtr3CwszWRfo4d3K9vYdDh+I/o36yrRKVBe4GTRlGzmFmUwq9DJpGwrRr0Og04hpmqoGqiahqppdAbi52VzzzkyFbmalKrJ6/XicHy8jIterycajWIwGPB6vTidzt7X7HY7Xu/YneFWVTUuvW89B1u9/c4eZjPpcTlMPBT5KfPMO0GDtlO+Q0fZ50YnQL2R4KzrqC9fgWvLfVy78zGWaWuhCv6ou4ZfhK4Z1EWhTgGDXofVqOcPXzqN06fmjELwIhkmUn4ezxPvH+bOV/cQ6ylSTzTzn6LAdJed606bxHXlOuY+dz7K2xGijiI+nPdTKidfy/lhlUx7cuOOqirr97cBcEppFhDv0X2vxcFpV72G/p8/Y9qBZ7lJfZIvF5/FoSue4/82VrG9OUirN8TWGje+UBRPKHpkrgdMeh1Oi4GiLAtTcu2cNyOPKbk2puTaMBt0HGjxYtDpOKU0i601bs4vdzEp20Zdp5/1+9soL3Dw7oE2puTaiKgazV1BGruDNHXF/zS7/eiaw2iaRjQWv7lnNemxGvUoSnwyqyybEbvZgEmvI9NmYlKODb2i4PaHmV3kZHK2jUhM5VCbn9OnZjMl1443FGFbbRdnTM+hwGkhqmoEwjHc/jAHWrxsqe4kw2rgQLMXnRYjElPZVuemzRPC10+h/UlGvYKCgqLE//1Lsqy8+b0LRuUGnuTo2PUfT33EP3Y39zmvrjQ8y3cMLwKwO/siFl5zD7Ek3rRWzRm8dfrDLNn530xa+22uBTrm3oDr/Dv5r4tnENM03L4wtZ0BPjjYgUWn0hXWaO4OYTLomFXgJMduwuUw0eYNU9PhZ2dDF6/vOnGvsN2sJ9NqxBuM0h0c6jwah1AUMOjiBe2N507lB5+ePazPINkkR8e2YCTGU//ff3Bj+Bl0ioYJ8GhW3lfn4sPC6d6dzKh/G4D3tZN4JO9WfnfGZBYXaXTtT97a6EosQGT/e9hKz0BnMICiEDj5S7zeXsBn6+5h2ov/zjQgvGkmoc+vI2+qGYNeRygS67kZFP/+6FGcNqOeBZMyCURUvrZ4OpFojKpWL9vrutnd2MXfKxp4emPNICP8eD+jXmFqrp03v3f+qN3kVTRNS/inf9ddd3HyySezdOlSAM4//3zWr4+vJbh3717uvfdefv/73wOwevVqFi1axKc//enjHq+iogKzeeTPZwkxloVCIRYuXDji4yQ6P0FyVAiQHBUi3UmOCpHeBsrRpNzqW7RoEevWrWPp0qVUVFRQXl7e+1pZWRnV1dW43W5sNhubN2/mq1/96oDHS8QvGCFEXKLzEyRHhUgkyVEh0pvkqBCjLyk9rUdmVdu/fz+aprF69Wp2796N3+9n2bJlvbOqaZrG1VdfzfXXX5/oEIQQxyH5KUR6kxwVIr1Jjgox+pJStAohhBBCCCGEEIkwthfsEUIIIYQQQggxrknRKoQQQgghhBAibUnRKoQQQgghhBAibY2polXTNBYvXsyKFStYsWIF9957LxCfJvzaa69l+fLl/Pa3vx2VWKqqqjj11FMJhUKjGoPf7+db3/oWX/jCF/jqV79KR0fHqLYP4PF4uOmmm/jiF7/IsmXL2Lp166jHcMQ//vEPVq5c2fvzaMagqiqrVq1i2bJlrFixgurq6qS2NxZIjkqOHi2V+QmSo/2RHJUcPZrkaPpJlxxNVX6C5OjR5Dr3KNoYcvjwYe2b3/zmMduvvPJKrbq6WlNVVfva176m7dy5M6lxeDwe7etf/7p21llnacFgcFRjePTRR7Xf/OY3mqZp2gsvvKD9/Oc/H9X2NU3T7r//fu3RRx/VNE3TqqqqtM9+9rOjHoOmadrPf/5z7bLLLtO++93v9m4bzRjeeOMN7Yc//KGmaZq2detW7aabbkpaW2OF5Kjk6BGpzk9Nkxztj+So5OgRkqPpKR1yNJX5qWmSo0ekOkfTLT/HVE/rrl27aG5uZsWKFXz961/n4MGDeL1ewuEwpaWlKIrCeeedx/vvv5+0GDRN46c//Snf//73sVqtAKMaw5e//GW+9a1vAdDQ0IDL5Rr1z+DLX/4yy5cvByAWi2E2m0c9Boivk3bbbbf1/jzaMWzZsoXFixcD8fXVdu7cmbS2xgrJUcnRI1KdnyA52h/JUcnRIyRH01OqczTV+QmSo0ekOkfTLT8NKW19AM899xyPPfZYn22rVq3iG9/4BpdffjmbN2/mlltu4YEHHsDhcPTuY7fbqa2tTVoMxcXFLF26lNmzZ/du83q9SYmhv/ZXr17NggULuOGGG9i/fz+PPvpo0to/UQytra3ccsst3HrrrSmJYenSpWzcuLF3WzJj6M8n29Pr9USjUQyGtE2rhJIclRwdqP1U52d/bUqOSo6C5OiR9iVHUy/VOZrq/DxeDJKj6ZGj6Zafaftb4dprr+Xaa6/tsy0QCKDX6wE47bTTaG5uxm634/P5evfx+XxkZGQkLYYlS5bwwgsv8MILL9Da2spXvvIVHn744aTE0F/7Rzz++ONUVVXxzW9+kxdffHFUPwOAffv28f3vf58f/OAHnHHGGXi93lGP4ZMcDkfSYhhMe6qqTpgTLUiOHq/9IyZKjqZrfvbXpuSo5OjRJEf7khwdfanO0VTn5/FiOEJytK+Jfp07poYH//a3v+29E7F3716Ki4txOp0YjUZqamrQNI13332X0047LWkx/OMf/+CJJ57giSeeIC8vjz/96U84HI5Ri+Hhhx/mxRdfBMBms6HX60e1fYDKykpuvvlm7r33Xi644AKAUY+hP6Mdw6JFi1i/fj0QfzC+vLw8aW2NFZKjkqPHk4r2JUePJTkqOXo8kqPpIdU5mur8BMnR45no17lj6nbWN77xDW655Rbeeecd9Ho9d911FwD/8z//w3//938Ti8U477zzOPnkk0c9ttGK4eqrr+aHP/whL7zwArFYjNWrV49q+wD33nsv4XCYO++8E4gn0UMPPTSh/h0gfjdyw4YNLF++HE3Tev8tJjLJUcnRgYx2+5Kjx5IclRwdiORo6qVrjo5m+5KjxzeRr3MVTdO0lEYghBBCCCGEEEIcx5gaHiyEEEIIIYQQYmKRolUIIYQQQgghRNqSolUIIYQQQgghRNqSolUIIYQQQgghRNqSolUIIYQQQgghRNoaU0vejHcbN27ku9/9LjNmzOjdlp2dza9//euUxPO73/2O8847j3Xr1uFyufj85z8/rONcdNFFvPbaa5jNZt544w28Xi9XX331iI71wQcf0NrayjXXXDOs4wgxHJKjgz+W5KhIBcnRwR9LclSkguTo4I8lOdqXFK1p5qyzzuJXv/pVqsOgsbGR/fv3c9NNN7Fu3bqEHfedd97h+9///oiPc8EFF/C1r32Nyy67DKfTmYDIhBgcydHBkRwVqSI5OjiSoyJVJEcHR3K0Lylax4gVK1Ywe/ZsDhw4gNfr5f7776ekpIQnnniCl19+GUVRWLp0KTfccAM/+tGPcLvduN1uHn74YX71q1+xc+dOXC4X9fX1PPjgg3zlK1/hueeeIysri6effhq/38/Xvva13vb+8pe/cNlll/WJobq6mu9///vceeed5OXl8aMf/QiPx4OmafziF7/AYrFw2223EQqFcLvd/Md//AeXXHJJ7/s1TaOzsxOXy8UVV1zBaaedxv79+5k2bRq5ubls3rwZk8nEI488QiAQ4JZbbsHr9RKLxbj55ps5++yz+8RzwQUX8Ne//pUbbrghuR++EIMgOSo5KtKb5KjkqEhvkqOSowORZ1rTzAcffMCKFSt6//zhD3/ofW3BggX8+c9/5txzz+WVV16hsrKSV199laeffpqnn36atWvXcvDgQSB+F+uZZ55h06ZNuN1unn/+eVavXk1jYyOKonDFFVfwyiuvAPD3v/+dz372s33i+PDDD5k1a1bvz4cOHWLlypXce++9zJ49m4ceeoiLLrqIZ555hu9+97ts376dgwcPcuONN/Loo4/y05/+lKeeeqrPMbdv385JJ50EgM/n4zOf+QxPPfUUmzdvZtGiRTz11FNEIhEqKyt56KGHOOecc3jqqae4//77+clPfoKqqn2ON2vWLD788MOEffZCDIbkqOSoSG+So5KjIr1JjkqODof0tKaZgYZMzJ07F4DCwkLa2trYv38/DQ0NfPnLXwagq6uLmpoaAKZNmwbAwYMHWbhwIQA5OTlMnz4dgGuuuYbvfe97nH766bhcLlwuV5+2jtwlOmL9+vUYDAb0ej0QT+wjY+yP3BU6cOAADz30EM8//zyKohCNRvscc926dVx66aW9P8+bNw+AjIwMysrKer8PhUJUVVVxxRVXAFBQUIDD4aCjo6PP8fLy8nC73cf9LIVIBslRyVGR3iRHJUdFepMclRwdDulpHcOmT5/OjBkzePzxx3niiSe46qqrKC8vB0BRFABmzpxJRUUFEE/0w4cPA1BcXIzT6eR3v/tdvw945+Tk0N3d3fvzl770JW699VZ+8IMfEIvFKCsrY8eOHQBs2rSJe+65h/vvv59///d/55577uHMM89E07Q+x9y7d2/vL6OjY+xPWVkZmzdvBqC5uZnu7m6ysrL67NPd3U1OTs4gPikhUkNyVHJUpDfJUclRkd4kRyVHj5Ce1jRzZMjE0X7/+9/3u+/s2bM5++yz+fznP084HGbBggUUFBT02edTn/oU69evZ/ny5bhcLiwWC0ajEYDrrruOO+64g3vuueeYY59xxhls27aN4uLi3m3nnHMOr7/+Or///e+56aabuPXWW/n73/8OwOrVq9m2bRt33nknDz/8MEVFRXR2dva+1+12k5+fP+jP4Zvf/Ca33norb7zxBsFgkNtvvx2Doe9/123bth0z9l+IZJMcjZMcFelKcjROclSkK8nROMnRIdLEuFZZWam9/PLLmqZpWkdHh3bOOedooVBI0zRNe+WVV7T77ruv3/fV1dVp3/nOd0YtzuH4yle+onk8nlSHIcSISI4Kkd4kR4VIb5KjE4P0tI5zRUVF/O///i+PPfYYsViM//7v/8ZkMvHLX/6SzZs38+CDD/b7vpKSEmbNmsWOHTuYP3/+KEd9Ym+//TaXXXYZDocj1aEIMSKSo0KkN8lRIdKb5OjEoGjaJwZjCyGEEEIIIYQQaUImYhJCCCGEEEIIkbakaBVCCCGEEEIIkbakaBVCCCGEEEIIkbakaBVCCCGEEEIIkbakaBVCCCGEEEIIkbakaBVCCCGEEEIIkbb+f3fL5x04+KvAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x576 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(4, 4, sharex=True, figsize=(16,8))\n",
    "\n",
    "e1 = []\n",
    "e2 = []\n",
    "\n",
    "indices = []\n",
    "for i in range(0, 100):\n",
    "    found = False\n",
    "    for j in range(0, len(sam_constrained_ss[i])):\n",
    "        m = sam_constrained_processed[i][j]\n",
    "        if 0 in m:\n",
    "            found = True\n",
    "    if found:\n",
    "        indices.append(i)\n",
    "        \n",
    "    if len(indices) > 15:\n",
    "        break\n",
    "\n",
    "print(indices)\n",
    "for j in indices:\n",
    "    e = []\n",
    "    for ss in sam_constrained_ss[j]:\n",
    "        e.append(ss[1])\n",
    "    e1.append(e)\n",
    "\n",
    "    e = []\n",
    "    for i in range(0, len(sam_constrained_ss[j])):\n",
    "        m = sam_constrained_processed[j][i]\n",
    "        if 0 in m:\n",
    "            e.append(sam_constrained_ss[j][i][1])\n",
    "    e2.append(e)\n",
    "    \n",
    "counter = 0\n",
    "for axis in fig.axes:\n",
    "    sns.distplot(e1[counter], ax=axis)\n",
    "    sns.distplot(e2[counter], ax=axis)\n",
    "    axis.set_xlabel(\"Energy (kcal/mol)\")\n",
    "    counter += 1\n",
    "\n",
    "fig.savefig(\"../test/sam_cons_sample_dist.png\", facecolor='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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