diff --git a/02_Services.ipynb b/02_Services.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# P2PSimpy Services \n",
+    "\n",
+    "Service is a key component to the simulation that helps to define a running scenario for your experiment.\n",
+    "Each service has access to peer class and Simpy environment. \n",
+    "\n",
+    "At the moment simulation has two abstract service classes: \n",
+    "1. Runner  \n",
+    "2. Handler\n",
+    "\n",
+    "\n",
+    "**Runner**\n",
+    "\n",
+    "Typically runner works as a producer of events, or when you require an action initiative from a peer. For example, a message producer is a runner.\n",
+    "\n",
+    "Function to implement: *run()*.\n",
+    "\n",
+    "\n",
+    "**Handler**\n",
+    "\n",
+    "Handler works as a reactive service that handles upcoming messages. \n",
+    "\n",
+    "Functions to implement: \n",
+    " - handle_message(msg) - react upon recieving a message.\n",
+    " - @property messages - list of messages that this service can handle.  \n",
+    "\n",
+    "\n",
+    "\n",
+    "For example, connection managers is both a runner and a handler as it produces and reacts on messages. \n",
+    "\n",
+    "# Creating new services\n",
+    "\n",
+    "\n",
+    "\n",
+    "The simulation at this point is not useful yet. \n",
+    "Let's create own service on top of what we have now: let's build a gossip network.\n",
+    "\n",
+    "This is an important building block which in different forms in almost any p2p system ([wiki](https://en.wikipedia.org/wiki/Gossip_protocol), [hyperledger gossip](https://hyperledger-fabric.readthedocs.io/en/release-1.4/gossip.html), [course slides](http://www.cs.cornell.edu/courses/cs6410/2016fa/slides/19-p2p-gossip.pdf)). \n",
+    "\n",
+    "\n",
+    "## Gossip Message Handler\n",
+    "\n",
+    "There are multiple ways to implement Gossip: [ttl, anti-entropy etc](http://www.cs.cornell.edu/Projects/Quicksilver/public_pdfs/2007PromiseAndLimitations.pdf).\n",
+    "\n",
+    "We will implement one of the simplest versions with ttl (Time to live), where each message has a predefined $ttl$ parameter. Ttl shows a stoping condition for the peer, i.e. if ttl=0 peer should stop, otherwise ttl must be decremented and the message gossiped further.\n",
+    "\n",
+    "\n",
+    "Let's first implement a handler: a service that reacts on incoming gossip messages: \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-03-18T19:22:51.428453Z",
+     "start_time": "2020-03-18T19:22:51.413633Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Message gossip\n",
+    "from p2psimpy.services.base import BaseHandler\n",
+    "from p2psimpy.messages import BaseMessage\n",
+    "from p2psimpy.storage import Storage\n",
+    "\n",
+    "from re import split \n",
+    "from copy import copy\n",
+    "\n",
+    "# Define a special message GossipMessage: Message with ttl\n",
+    "\n",
+    "\n",
+    "class GossipMessage(BaseMessage):\n",
+    "\n",
+    "    __slots__ = ('sender', 'data', 'ttl')\n",
+    "    size = 1024\n",
+    "    \n",
+    "    def __init__(self, sender, data, ttl):\n",
+    "        super().__init__(sender, data)\n",
+    "        self.ttl = ttl\n",
+    "        \n",
+    "class GossipService(BaseHandler):\n",
+    "    \"\"\"\n",
+    "    Simple gossip service to handle gossip messages and rely them to neighbors. \n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, peer, fanout=3, exclude_peers: set=None, exclude_types: set=None):\n",
+    "        super().__init__(peer)\n",
+    "        \n",
+    "        self.fanout = fanout\n",
+    "        if exclude_peers is None:\n",
+    "            self.exclude_peers = set() \n",
+    "        else:\n",
+    "            self.exclude_peers = exclude_peers\n",
+    "        self.exclude_types = exclude_types\n",
+    "        \n",
+    "        self.strg_name = 'msg_time'\n",
+    "        self.peer.add_storage(self.strg_name, Storage())\n",
+    "\n",
+    "    def handle_message(self, msg):\n",
+    "        # Store message localy \n",
+    "        msg_id = msg.data\n",
+    "        self.peer.store(self.strg_name, msg_id, self.peer.env.now)\n",
+    "        if msg.ttl > 0:\n",
+    "            # Rely message further, modify the message\n",
+    "            exclude_peers = {msg.sender} | self.exclude_peers\n",
+    "            # Use peer gossip - it will sample self.config.fanout and exclude sender\n",
+    "            # If you need to exclude some peers: add it to the set\n",
+    "            self.peer.gossip( GossipMessage(self.peer, msg.data, msg.ttl-1), \n",
+    "                             self.fanout, except_peers=exclude_peers, except_type=self.exclude_types)\n",
+    "\n",
+    "    @property\n",
+    "    def messages(self):\n",
+    "        return GossipMessage,\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Peer object has a gossip function that will send message to random sample of `fanout` connected peers.\n",
+    "Peers are excluded `except_peers` and `except_type` from the sampling. \n",
+    "\n",
+    "This is a basic service to relay messages to the random peers.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Message producer \n",
+    "\n",
+    "GossipService ensures that message will spread in the network. \n",
+    "\n",
+    "But to first start the gossip message we need a message producer. \n",
+    "We will inherit it from a `BaseRunner` class.\n",
+    "\n",
+    "MessageProducer will have msg rate, ttl and fanout and a timeout for start.\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-03-18T19:22:53.606858Z",
+     "start_time": "2020-03-18T19:22:53.596493Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from p2psimpy.services.base import BaseRunner\n",
+    "\n",
+    "class MessageProducer(BaseRunner):\n",
+    "\n",
+    "    def __init__(self, peer, init_timeout=1000, msg_rate=5, init_ttl=3, init_fanout=10):\n",
+    "        super().__init__(peer)\n",
+    "        # calculate tx_interval\n",
+    "        self.init_timeout = init_timeout\n",
+    "        self.init_ttl = init_ttl\n",
+    "        self.init_fanout = init_fanout\n",
+    "        \n",
+    "        self.tx_interval = 1000 / msg_rate\n",
+    "        self.counter = 1 \n",
+    "        \n",
+    "        # Let's add a storage layer to store messages\n",
+    "        self.strg_name = 'msg_time'\n",
+    "        self.peer.add_storage(self.strg_name, Storage())\n",
+    "\n",
+    "\n",
+    "    def produce_transaction(self):\n",
+    "        \n",
+    "        self.peer.gossip(GossipMessage(self.peer,\n",
+    "                                       '_'.join((str(self.counter), str(self.peer.peer_id))), \n",
+    "                                       self.init_ttl), \n",
+    "                         self.init_fanout)\n",
+    "        self.peer.store(self.strg_name, str(self.counter), self.peer.env.now)\n",
+    "        self.counter+=1\n",
+    "        \n",
+    "\n",
+    "    def run(self):\n",
+    "        yield self.env.timeout(self.init_timeout)\n",
+    "        while True:\n",
+    "            self.produce_transaction()\n",
+    "            yield self.env.timeout(self.tx_interval)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-03-18T19:22:56.159650Z",
+     "start_time": "2020-03-18T19:22:55.654713Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Define locations \n",
+    "from p2psimpy.config import *\n",
+    "from p2psimpy.consts import *\n",
+    "from p2psimpy.services.connection_manager import BaseConnectionManager\n",
+    "import networkx as nx\n",
+    "from random import choice\n",
+    "        \n",
+    "class Locations(Config):\n",
+    "    locations = ['Ohio', 'Ireland', 'Tokyo']\n",
+    "    latencies = {\n",
+    "        'Ohio': {'Ohio': Dist('invgamma', (5.54090, 0.333305, 0.987249)),\n",
+    "                 'Ireland': Dist('norm', (73.6995, 1.19583092197097127)),\n",
+    "                 'Tokyo': Dist('norm', (156.00904977375566, 0.09469886668079797))\n",
+    "                },\n",
+    "        'Ireland':{'Ireland': Dist('invgamma', (6.4360455224301525, 0.8312748033308526, 1.086191852963273)),\n",
+    "                   'Tokyo': Dist('norm', (131.0275, 0.25834811785650774))\n",
+    "                  },\n",
+    "        'Tokyo': {'Tokyo':  Dist('invgamma', (11.104508341331055, 0.3371934865734555, 2.0258998705983737))}\n",
+    "    }\n",
+    "    \n",
+    "# Define peer     \n",
+    "class PeerConfig(Config):\n",
+    "    location = Dist('sample', Locations.locations)\n",
+    "    bandwidth_ul = Dist( 'norm', (50*MBit, 10*MBit))\n",
+    "    bandwidth_dl = Dist( 'norm', (50*MBit, 10*MBit))\n",
+    "    \n",
+    "class GossipConfig(Config):\n",
+    "    exclude_types={'client',}\n",
+    "    \n",
+    "peer_types = {\n",
+    "                'peer': PeerType(PeerConfig,   {BaseConnectionManager: None,\n",
+    "                                                GossipService: GossipConfig }),\n",
+    "                'client': PeerType(PeerConfig, (BaseConnectionManager, MessageProducer))\n",
+    "             }\n",
+    " \n",
+    "# Create network topology\n",
+    "num_peers = 25\n",
+    "num_clients = 1\n",
+    "G = nx.erdos_renyi_graph(num_peers, 0.3)   \n",
+    "nx.relabel_nodes(G, {k: k+1 for k in G.nodes()} ,copy=False)\n",
+    "\n",
+    "client_edges = [(i, choice(list(G.nodes()))) for i in range(num_peers+1, num_clients+num_peers+1)]\n",
+    "G.add_edges_from(client_edges)\n",
+    "\n",
+    "types_map = {k: 'peer' if k < num_peers+1 else 'client' for k in G.nodes()}\n",
+    "# Assign a peer type to the peers \n",
+    "nx.set_node_attributes(G, types_map , 'type')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-03-18T19:22:58.044427Z",
+     "start_time": "2020-03-18T19:22:57.311042Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def visualize_peer_client_network(G):\n",
+    "    plt.figure(figsize=(10,10))\n",
+    "\n",
+    "    # Draw client/ peer network \n",
+    "\n",
+    "    master_nodes = [n for (n,ty) in \\\n",
+    "        nx.get_node_attributes(G,'type').items() if ty == 'peer']\n",
+    "    client_nodes = [n for (n,ty) in \\\n",
+    "        nx.get_node_attributes(G,'type').items() if ty == 'client']\n",
+    "\n",
+    "    pos = nx.kamada_kawai_layout(G)\n",
+    "\n",
+    "    nx.draw_networkx_nodes(G, pos, nodelist=master_nodes, \\\n",
+    "        node_color='blue', node_shape='o', node_size=500)\n",
+    "    nx.draw_networkx_nodes(G, pos, nodelist=client_nodes,  \\\n",
+    "        node_color='green', node_shape='^', node_size=100, label=1)\n",
+    "\n",
+    "    nx.draw_networkx_labels(G, pos, labels={k:k for k in master_nodes}, font_color='w')\n",
+    "\n",
+    "    nx.draw_networkx_edges(G, pos, edgelist=G.subgraph(master_nodes).edges(), width=1.5)\n",
+    "    nx.draw_networkx_edges(G, pos, edgelist=G.edges(nbunch=client_nodes),  style='dotted')\n",
+    "\n",
+    "    \n",
+    "visualize_peer_client_network(G)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-03-18T19:23:10.851606Z",
+     "start_time": "2020-03-18T19:23:10.379755Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from p2psimpy.simulation import BaseSimulation\n",
+    "\n",
+    "net_sim = BaseSimulation(Locations, G, peer_types, logger_dir='logs')\n",
+    "net_sim.run(5_000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's see if this gossip protocol converges and what is the time for convergence. \n",
+    "\n",
+    "\n",
+    "You can inspect time when peer has recorded the message by looking at `peer.storage`.\n",
+    "\n",
+    "For this experiment we used simple dictionary *first-seen* storage. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-03-18T19:23:12.723078Z",
+     "start_time": "2020-03-18T19:23:12.716276Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time messages produced: \n",
+      " {'1': 1000, '2': 1200.0, '3': 1400.0, '4': 1600.0, '5': 1800.0, '6': 2000.0, '7': 2200.0, '8': 2400.0, '9': 2600.0, '10': 2800.0, '11': 3000.0, '12': 3200.0, '13': 3400.0, '14': 3600.0, '15': 3800.0, '16': 4000.0, '17': 4200.0, '18': 4400.0, '19': 4600.0, '20': 4800.0} \n",
+      "{'1_26': 1179.665291546718, '2_26': 1273.2494674163, '3_26': 1474.350205566521, '4_26': 1674.3979181531047, '5_26': 2006.8604795719993, '6_26': 2147.376544686587, '7_26': 2275.414185598644, '8_26': 2475.4147589206964, '9_26': 2672.2824250221324, '10_26': 2873.895523402151, '11_26': 3181.0017587159564, '12_26': 3275.0819338024226, '13_26': 3474.0101736168963, '14_26': 3674.400675357518, '15_26': 4005.0173525485993, '16_26': 4073.4226303297746, '17_26': 4273.373120851877, '18_26': 4474.127560972722, '19_26': 4673.925328800376, '20_26': 4873.402150567647}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pprint import pprint\n",
+    "\n",
+    "client_id = 26\n",
+    "print(\"Time messages produced: \\n %s \" % net_sim.peers[client_id].storage['msg_time'].txs)\n",
+    "\n",
+    "# Put any peer id you want to inspect\n",
+    "peer_id = 1 \n",
+    "\n",
+    "print(net_sim.peers[peer_id].storage['msg_time'].txs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-03-18T19:23:14.341589Z",
+     "start_time": "2020-03-18T19:23:14.331124Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1: 179.66529154671798,\n",
+       " 2: 73.24946741629992,\n",
+       " 3: 74.35020556652103,\n",
+       " 4: 74.39791815310468,\n",
+       " 5: 206.86047957199935,\n",
+       " 6: 147.37654468658684,\n",
+       " 7: 75.41418559864405,\n",
+       " 8: 75.41475892069639,\n",
+       " 9: 72.28242502213243,\n",
+       " 10: 73.89552340215096,\n",
+       " 11: 181.00175871595638,\n",
+       " 12: 75.08193380242255,\n",
+       " 13: 74.01017361689628,\n",
+       " 14: 74.40067535751814,\n",
+       " 15: 205.01735254859932,\n",
+       " 16: 73.42263032977462,\n",
+       " 17: 73.37312085187659,\n",
+       " 18: 74.12756097272177,\n",
+       " 19: 73.92532880037561,\n",
+       " 20: 73.40215056764737}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Calculate the delay \n",
+    "\n",
+    "def total_delay(sim, peer_id, storage_name):\n",
+    "    store = sim.peers[peer_id].storage[storage_name].txs\n",
+    "    for k, peer_time in store.items():\n",
+    "        msg_num, client_id = k.split('_')\n",
+    "        client_time = sim.peers[int(client_id)].storage[storage_name].txs[msg_num]\n",
+    "        yield (int(msg_num), peer_time - client_time)\n",
+    "        \n",
+    "dict(total_delay(net_sim, peer_id, 'msg_time'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-03-18T19:23:16.146107Z",
+     "start_time": "2020-03-18T19:23:16.139528Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1: 36.51373803165848,\n",
+       " 3: 37.500677650845546,\n",
+       " 4: 193.4232116591097,\n",
+       " 5: 112.26424970148469,\n",
+       " 6: 111.54071581687867,\n",
+       " 8: 110.40673976503649,\n",
+       " 9: 36.94355037871992,\n",
+       " 12: 38.253949787664624,\n",
+       " 13: 109.74961675605164,\n",
+       " 15: 110.90154207892965,\n",
+       " 16: 37.596091230562706,\n",
+       " 18: 110.43926990219006}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dict(total_delay(net_sim, 25, 'msg_time'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Try to play around with different peer_id values. Try peer that directly connected to the client, then try peer on the edge of the network (further from the client).  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "____________________________"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Analyze and visualize  gossip\n",
+    "\n",
+    "\n",
+    "When we implement gossip it is important to analyze following: \n",
+    " - What is the average time for the peer to receive a message? \n",
+    " - What is the convergence speed for my gossip protocol? \n",
+    " - Can we guarantee that all peers will see the message? At what time message is finalized?   \n",
+    "\n",
+    "\n",
+    "Let's go one by one and answer these questions for this gossip protocol implementation. \n",
+    "\n",
+    "\n",
+    "## Convergence"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-03-18T19:23:30.608837Z",
+     "start_time": "2020-03-18T19:23:30.335710Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>10</th>\n",
+       "      <th>...</th>\n",
+       "      <th>16</th>\n",
+       "      <th>17</th>\n",
+       "      <th>18</th>\n",
+       "      <th>19</th>\n",
+       "      <th>20</th>\n",
+       "      <th>21</th>\n",
+       "      <th>22</th>\n",
+       "      <th>23</th>\n",
+       "      <th>24</th>\n",
+       "      <th>25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>179.665292</td>\n",
+       "      <td>114.184894</td>\n",
+       "      <td>72.253601</td>\n",
+       "      <td>35.905259</td>\n",
+       "      <td>145.001729</td>\n",
+       "      <td>109.711269</td>\n",
+       "      <td>114.314092</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>73.830195</td>\n",
+       "      <td>36.427956</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>72.342489</td>\n",
+       "      <td>71.338971</td>\n",
+       "      <td>72.768738</td>\n",
+       "      <td>36.122949</td>\n",
+       "      <td>36.513738</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>73.249467</td>\n",
+       "      <td>115.390923</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>36.843989</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>112.234365</td>\n",
+       "      <td>139.253883</td>\n",
+       "      <td>111.120712</td>\n",
+       "      <td>139.430398</td>\n",
+       "      <td>140.110092</td>\n",
+       "      <td>...</td>\n",
+       "      <td>139.722474</td>\n",
+       "      <td>217.464972</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>37.326867</td>\n",
+       "      <td>74.354448</td>\n",
+       "      <td>73.848687</td>\n",
+       "      <td>145.920326</td>\n",
+       "      <td>73.762645</td>\n",
+       "      <td>37.081515</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>74.350206</td>\n",
+       "      <td>139.217366</td>\n",
+       "      <td>148.029594</td>\n",
+       "      <td>37.008895</td>\n",
+       "      <td>73.681544</td>\n",
+       "      <td>110.250500</td>\n",
+       "      <td>138.934178</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>110.447249</td>\n",
+       "      <td>74.070342</td>\n",
+       "      <td>216.903094</td>\n",
+       "      <td>147.559731</td>\n",
+       "      <td>73.363970</td>\n",
+       "      <td>73.565759</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>217.876937</td>\n",
+       "      <td>37.500678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>74.397918</td>\n",
+       "      <td>115.324692</td>\n",
+       "      <td>74.944565</td>\n",
+       "      <td>37.052158</td>\n",
+       "      <td>73.874330</td>\n",
+       "      <td>109.877186</td>\n",
+       "      <td>115.140775</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>246.020264</td>\n",
+       "      <td>138.967931</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>193.296660</td>\n",
+       "      <td>74.398283</td>\n",
+       "      <td>193.225677</td>\n",
+       "      <td>73.529042</td>\n",
+       "      <td>180.506593</td>\n",
+       "      <td>180.698008</td>\n",
+       "      <td>74.022545</td>\n",
+       "      <td>37.332384</td>\n",
+       "      <td>193.423212</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>206.860480</td>\n",
+       "      <td>141.187265</td>\n",
+       "      <td>76.227465</td>\n",
+       "      <td>38.486717</td>\n",
+       "      <td>75.179787</td>\n",
+       "      <td>39.188741</td>\n",
+       "      <td>142.071736</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>141.159387</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>113.014683</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>39.177362</td>\n",
+       "      <td>75.833756</td>\n",
+       "      <td>75.149034</td>\n",
+       "      <td>75.699807</td>\n",
+       "      <td>76.322884</td>\n",
+       "      <td>38.708123</td>\n",
+       "      <td>112.264250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>147.376545</td>\n",
+       "      <td>140.117725</td>\n",
+       "      <td>146.701454</td>\n",
+       "      <td>36.886736</td>\n",
+       "      <td>74.410228</td>\n",
+       "      <td>111.632517</td>\n",
+       "      <td>115.456001</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>139.493807</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>74.087958</td>\n",
+       "      <td>37.147595</td>\n",
+       "      <td>149.001629</td>\n",
+       "      <td>147.914407</td>\n",
+       "      <td>74.585282</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>193.162513</td>\n",
+       "      <td>111.540716</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>75.414186</td>\n",
+       "      <td>115.681956</td>\n",
+       "      <td>74.591892</td>\n",
+       "      <td>37.162354</td>\n",
+       "      <td>74.091192</td>\n",
+       "      <td>37.945999</td>\n",
+       "      <td>115.167802</td>\n",
+       "      <td>218.532414</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>37.426890</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>74.672286</td>\n",
+       "      <td>75.787368</td>\n",
+       "      <td>180.511111</td>\n",
+       "      <td>37.394621</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>75.414759</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>74.914959</td>\n",
+       "      <td>36.877999</td>\n",
+       "      <td>180.647896</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>114.916438</td>\n",
+       "      <td>192.963110</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>270.922244</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>111.529933</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>37.116799</td>\n",
+       "      <td>74.205166</td>\n",
+       "      <td>74.420780</td>\n",
+       "      <td>148.138857</td>\n",
+       "      <td>75.456271</td>\n",
+       "      <td>37.381455</td>\n",
+       "      <td>110.406740</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>72.282425</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>180.467410</td>\n",
+       "      <td>36.372994</td>\n",
+       "      <td>72.567913</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>114.398710</td>\n",
+       "      <td>110.381665</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>73.746922</td>\n",
+       "      <td>36.591334</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>73.048782</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>73.031790</td>\n",
+       "      <td>37.227722</td>\n",
+       "      <td>36.943550</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>73.895523</td>\n",
+       "      <td>189.944984</td>\n",
+       "      <td>74.411882</td>\n",
+       "      <td>36.773403</td>\n",
+       "      <td>74.054536</td>\n",
+       "      <td>37.565312</td>\n",
+       "      <td>189.908273</td>\n",
+       "      <td>111.819086</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>139.571154</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>111.913055</td>\n",
+       "      <td>73.818111</td>\n",
+       "      <td>37.582455</td>\n",
+       "      <td>73.996942</td>\n",
+       "      <td>74.152116</td>\n",
+       "      <td>148.907444</td>\n",
+       "      <td>74.452915</td>\n",
+       "      <td>37.091281</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>181.001759</td>\n",
+       "      <td>140.092746</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>37.278782</td>\n",
+       "      <td>75.416347</td>\n",
+       "      <td>111.484933</td>\n",
+       "      <td>115.253850</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>271.237572</td>\n",
+       "      <td>139.969384</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>193.237394</td>\n",
+       "      <td>74.646420</td>\n",
+       "      <td>38.242472</td>\n",
+       "      <td>74.596064</td>\n",
+       "      <td>228.883273</td>\n",
+       "      <td>74.425178</td>\n",
+       "      <td>75.256557</td>\n",
+       "      <td>37.603313</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>75.081934</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>74.528737</td>\n",
+       "      <td>37.746708</td>\n",
+       "      <td>74.333093</td>\n",
+       "      <td>110.468176</td>\n",
+       "      <td>140.054228</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>139.902994</td>\n",
+       "      <td>...</td>\n",
+       "      <td>140.654888</td>\n",
+       "      <td>112.409624</td>\n",
+       "      <td>74.803932</td>\n",
+       "      <td>110.976987</td>\n",
+       "      <td>206.006366</td>\n",
+       "      <td>74.019314</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>75.896837</td>\n",
+       "      <td>111.223171</td>\n",
+       "      <td>38.253950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>74.010174</td>\n",
+       "      <td>139.050444</td>\n",
+       "      <td>73.794083</td>\n",
+       "      <td>36.880066</td>\n",
+       "      <td>180.191191</td>\n",
+       "      <td>37.360185</td>\n",
+       "      <td>114.820083</td>\n",
+       "      <td>192.818537</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>270.748719</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>109.806005</td>\n",
+       "      <td>180.661990</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>149.966867</td>\n",
+       "      <td>73.281597</td>\n",
+       "      <td>73.248970</td>\n",
+       "      <td>74.518831</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>109.749617</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>74.400675</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>74.371001</td>\n",
+       "      <td>36.599187</td>\n",
+       "      <td>75.267893</td>\n",
+       "      <td>37.677354</td>\n",
+       "      <td>139.329739</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>139.474187</td>\n",
+       "      <td>139.575067</td>\n",
+       "      <td>...</td>\n",
+       "      <td>139.741899</td>\n",
+       "      <td>217.432077</td>\n",
+       "      <td>74.280688</td>\n",
+       "      <td>37.004554</td>\n",
+       "      <td>74.128595</td>\n",
+       "      <td>73.896230</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>74.659292</td>\n",
+       "      <td>36.870899</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>205.017353</td>\n",
+       "      <td>139.357968</td>\n",
+       "      <td>230.458998</td>\n",
+       "      <td>37.043194</td>\n",
+       "      <td>230.520340</td>\n",
+       "      <td>110.967144</td>\n",
+       "      <td>115.042650</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>138.760705</td>\n",
+       "      <td>139.642557</td>\n",
+       "      <td>...</td>\n",
+       "      <td>139.037781</td>\n",
+       "      <td>193.093289</td>\n",
+       "      <td>74.256957</td>\n",
+       "      <td>111.870832</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>73.335178</td>\n",
+       "      <td>73.761478</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>193.262063</td>\n",
+       "      <td>110.901542</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>73.422630</td>\n",
+       "      <td>115.268173</td>\n",
+       "      <td>73.381570</td>\n",
+       "      <td>36.996531</td>\n",
+       "      <td>73.207544</td>\n",
+       "      <td>37.564325</td>\n",
+       "      <td>188.466979</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>188.439714</td>\n",
+       "      <td>115.543233</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>110.395401</td>\n",
+       "      <td>75.742444</td>\n",
+       "      <td>37.274660</td>\n",
+       "      <td>146.790381</td>\n",
+       "      <td>73.649584</td>\n",
+       "      <td>75.040228</td>\n",
+       "      <td>72.892035</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>37.596091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>73.373121</td>\n",
+       "      <td>140.299861</td>\n",
+       "      <td>74.111220</td>\n",
+       "      <td>36.931292</td>\n",
+       "      <td>73.654541</td>\n",
+       "      <td>37.683451</td>\n",
+       "      <td>188.345028</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>110.267767</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>37.748226</td>\n",
+       "      <td>74.631148</td>\n",
+       "      <td>74.645653</td>\n",
+       "      <td>74.586516</td>\n",
+       "      <td>74.156427</td>\n",
+       "      <td>37.210826</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>74.127561</td>\n",
+       "      <td>188.392097</td>\n",
+       "      <td>75.153095</td>\n",
+       "      <td>37.262877</td>\n",
+       "      <td>74.285727</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>140.184934</td>\n",
+       "      <td>111.155986</td>\n",
+       "      <td>188.329118</td>\n",
+       "      <td>189.196717</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>110.367665</td>\n",
+       "      <td>147.700649</td>\n",
+       "      <td>110.263433</td>\n",
+       "      <td>75.282329</td>\n",
+       "      <td>74.637885</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>74.655581</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>110.439270</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>73.925329</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>74.417209</td>\n",
+       "      <td>36.537590</td>\n",
+       "      <td>73.195101</td>\n",
+       "      <td>110.552637</td>\n",
+       "      <td>115.086776</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>138.869353</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>110.921041</td>\n",
+       "      <td>73.520880</td>\n",
+       "      <td>36.809914</td>\n",
+       "      <td>73.975992</td>\n",
+       "      <td>73.701962</td>\n",
+       "      <td>73.829479</td>\n",
+       "      <td>204.416265</td>\n",
+       "      <td>37.059862</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>73.402151</td>\n",
+       "      <td>138.866272</td>\n",
+       "      <td>180.440072</td>\n",
+       "      <td>36.877448</td>\n",
+       "      <td>73.897342</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>114.883316</td>\n",
+       "      <td>111.365500</td>\n",
+       "      <td>139.962258</td>\n",
+       "      <td>139.439670</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>148.655567</td>\n",
+       "      <td>37.631615</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>74.427782</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>180.608353</td>\n",
+       "      <td>111.229309</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>20 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            1           2           3          4           5           6   \\\n",
+       "1   179.665292  114.184894   72.253601  35.905259  145.001729  109.711269   \n",
+       "2    73.249467  115.390923         NaN  36.843989         NaN  112.234365   \n",
+       "3    74.350206  139.217366  148.029594  37.008895   73.681544  110.250500   \n",
+       "4    74.397918  115.324692   74.944565  37.052158   73.874330  109.877186   \n",
+       "5   206.860480  141.187265   76.227465  38.486717   75.179787   39.188741   \n",
+       "6   147.376545  140.117725  146.701454  36.886736   74.410228  111.632517   \n",
+       "7    75.414186  115.681956   74.591892  37.162354   74.091192   37.945999   \n",
+       "8    75.414759         NaN   74.914959  36.877999  180.647896         NaN   \n",
+       "9    72.282425         NaN  180.467410  36.372994   72.567913         NaN   \n",
+       "10   73.895523  189.944984   74.411882  36.773403   74.054536   37.565312   \n",
+       "11  181.001759  140.092746         NaN  37.278782   75.416347  111.484933   \n",
+       "12   75.081934         NaN   74.528737  37.746708   74.333093  110.468176   \n",
+       "13   74.010174  139.050444   73.794083  36.880066  180.191191   37.360185   \n",
+       "14   74.400675         NaN   74.371001  36.599187   75.267893   37.677354   \n",
+       "15  205.017353  139.357968  230.458998  37.043194  230.520340  110.967144   \n",
+       "16   73.422630  115.268173   73.381570  36.996531   73.207544   37.564325   \n",
+       "17   73.373121  140.299861   74.111220  36.931292   73.654541   37.683451   \n",
+       "18   74.127561  188.392097   75.153095  37.262877   74.285727         NaN   \n",
+       "19   73.925329         NaN   74.417209  36.537590   73.195101  110.552637   \n",
+       "20   73.402151  138.866272  180.440072  36.877448   73.897342         NaN   \n",
+       "\n",
+       "            7           8           9           10  ...          16  \\\n",
+       "1   114.314092         NaN         NaN         NaN  ...         NaN   \n",
+       "2   139.253883  111.120712  139.430398  140.110092  ...  139.722474   \n",
+       "3   138.934178         NaN         NaN         NaN  ...         NaN   \n",
+       "4   115.140775         NaN  246.020264  138.967931  ...         NaN   \n",
+       "5   142.071736         NaN         NaN  141.159387  ...         NaN   \n",
+       "6   115.456001         NaN         NaN  139.493807  ...         NaN   \n",
+       "7   115.167802  218.532414         NaN         NaN  ...         NaN   \n",
+       "8   114.916438  192.963110         NaN  270.922244  ...         NaN   \n",
+       "9   114.398710  110.381665         NaN         NaN  ...         NaN   \n",
+       "10  189.908273  111.819086         NaN  139.571154  ...         NaN   \n",
+       "11  115.253850         NaN  271.237572  139.969384  ...         NaN   \n",
+       "12  140.054228         NaN         NaN  139.902994  ...  140.654888   \n",
+       "13  114.820083  192.818537         NaN  270.748719  ...         NaN   \n",
+       "14  139.329739         NaN  139.474187  139.575067  ...  139.741899   \n",
+       "15  115.042650         NaN  138.760705  139.642557  ...  139.037781   \n",
+       "16  188.466979         NaN  188.439714  115.543233  ...         NaN   \n",
+       "17  188.345028         NaN         NaN         NaN  ...         NaN   \n",
+       "18  140.184934  111.155986  188.329118  189.196717  ...         NaN   \n",
+       "19  115.086776         NaN         NaN  138.869353  ...         NaN   \n",
+       "20  114.883316  111.365500  139.962258  139.439670  ...         NaN   \n",
+       "\n",
+       "            17          18          19          20          21          22  \\\n",
+       "1          NaN   73.830195   36.427956         NaN   72.342489   71.338971   \n",
+       "2   217.464972         NaN   37.326867   74.354448   73.848687  145.920326   \n",
+       "3   110.447249   74.070342  216.903094  147.559731   73.363970   73.565759   \n",
+       "4   193.296660   74.398283  193.225677   73.529042  180.506593  180.698008   \n",
+       "5   113.014683         NaN   39.177362   75.833756   75.149034   75.699807   \n",
+       "6          NaN   74.087958   37.147595  149.001629  147.914407   74.585282   \n",
+       "7          NaN         NaN   37.426890         NaN   74.672286   75.787368   \n",
+       "8   111.529933         NaN   37.116799   74.205166   74.420780  148.138857   \n",
+       "9          NaN   73.746922   36.591334         NaN   73.048782         NaN   \n",
+       "10  111.913055   73.818111   37.582455   73.996942   74.152116  148.907444   \n",
+       "11  193.237394   74.646420   38.242472   74.596064  228.883273   74.425178   \n",
+       "12  112.409624   74.803932  110.976987  206.006366   74.019314         NaN   \n",
+       "13  109.806005  180.661990         NaN  149.966867   73.281597   73.248970   \n",
+       "14  217.432077   74.280688   37.004554   74.128595   73.896230         NaN   \n",
+       "15  193.093289   74.256957  111.870832         NaN   73.335178   73.761478   \n",
+       "16  110.395401   75.742444   37.274660  146.790381   73.649584   75.040228   \n",
+       "17  110.267767         NaN   37.748226   74.631148   74.645653   74.586516   \n",
+       "18  110.367665  147.700649  110.263433   75.282329   74.637885         NaN   \n",
+       "19  110.921041   73.520880   36.809914   73.975992   73.701962   73.829479   \n",
+       "20         NaN  148.655567   37.631615         NaN   74.427782         NaN   \n",
+       "\n",
+       "            23          24          25  \n",
+       "1    72.768738   36.122949   36.513738  \n",
+       "2    73.762645   37.081515         NaN  \n",
+       "3          NaN  217.876937   37.500678  \n",
+       "4    74.022545   37.332384  193.423212  \n",
+       "5    76.322884   38.708123  112.264250  \n",
+       "6          NaN  193.162513  111.540716  \n",
+       "7   180.511111   37.394621         NaN  \n",
+       "8    75.456271   37.381455  110.406740  \n",
+       "9    73.031790   37.227722   36.943550  \n",
+       "10   74.452915   37.091281         NaN  \n",
+       "11   75.256557   37.603313         NaN  \n",
+       "12   75.896837  111.223171   38.253950  \n",
+       "13   74.518831         NaN  109.749617  \n",
+       "14   74.659292   36.870899         NaN  \n",
+       "15         NaN  193.262063  110.901542  \n",
+       "16   72.892035         NaN   37.596091  \n",
+       "17   74.156427   37.210826         NaN  \n",
+       "18   74.655581         NaN  110.439270  \n",
+       "19  204.416265   37.059862         NaN  \n",
+       "20  180.608353  111.229309         NaN  \n",
+       "\n",
+       "[20 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.DataFrame({k: dict(total_delay(net_sim, k, 'msg_time')) for k in range(1, num_peers+1)})\n",
+    "df.sort_index(inplace=True)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Code above should produce a table with message number on the rows and peer id on the columns. \n",
+    "\n",
+    "You can also probably see `NaN` values in the table. These indicate that gossip protocol didn't converge fully, and some peers never saw some messages. To guarantee convergence we either need to implement fancier gossip based on [set reconciliation](https://www.cse.ust.hk/~yike/sigmod14.pdf)  or tweak the parameters: `fanout` and `ttl`. We need to fine-tune the parameter to achieve required effect. \n",
+    "\n",
+    "Usually gossip is fast to converge, but some slow and peer on the edge of the network might break it.\n",
+    "\n",
+    "\n",
+    "We can also represent our table as a heatmap. White color corresponds to `Nan`. You can see what peers are slower, or what message is least/most seen:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-03-18T19:23:42.864587Z",
+     "start_time": "2020-03-18T19:23:42.085378Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "\n",
+    "def show_heat_map(df):\n",
+    "    plt.figure(figsize=(12, 7))\n",
+    "    ax = plt.axes()\n",
+    "\n",
+    "    sns.heatmap(df, ax=ax)\n",
+    "\n",
+    "    plt.title('Heatmap of arrival time', fontsize = 20) # title with fontsize 20\n",
+    "    plt.xlabel('Peer number', fontsize = 12) # x-axis label with fontsize 15\n",
+    "    plt.ylabel('Message number', fontsize = 12) # y-axis label with fontsize 15\n",
+    "\n",
+    "    plt.show()\n",
+    "\n",
+    "show_heat_map(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Average time "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-03-18T19:23:47.011627Z",
+     "start_time": "2020-03-18T19:23:46.179700Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def show_avg_time(df):\n",
+    "    plt.figure(figsize=(10, 5))\n",
+    "    ax = sns.boxplot(data=df, )\n",
+    "    ax.set_title('Average time to arrival', fontsize= 20 )\n",
+    "    ax.set_ylabel('Time (ms)', fontsize=12)\n",
+    "    ax.set_xlabel('Peer id', fontsize=12)\n",
+    "    plt.show()\n",
+    "show_avg_time(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Convergence speed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-03-18T19:24:50.630183Z",
+     "start_time": "2020-03-18T19:24:49.862692Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1a21cc3d10>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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KySxwduAyq1XqmSz1Sh1lmlfhr+gVUok06WSGXCbH6v5eHDWDciqHqikEWj04fD6cHs+ruh/LMjGrVfRsAT0epxxNUUnIdK/uwR30I8nT/zZq1g1i+49TGMlQSmTJj6TRYxkeueu/msdITgeesN271bZ2WXNK0RsO4vIv7AVQZ5JRNyinc5RSOcqpPHq+RM+GXnxdkTkd14yFMWuSX7zxPxy6rvOxj32Mr371q2zcuJEvf/nLfOQjH+ELX/jClO8jHPad9blYzIEsL4657fn4OBwOB21t/rkexrwknpeFRbxeU2caJqVUjsJImkIsTSGW4ZXnDvPzx17k1it78aj2avTOdBL9+RTVXJ5qtoBRGWsCb3VKhC/wnFuWRTadI5lIk4ynGRmMMXI6xsjgCPHhBKlkhkwmR65QolqrTfjea5f30d/WBYDX52LHa5bjW9ZD243XnHUf1VKZfCJLMZ2jmM7b/7IFStkSpVyJUr5MuaBTKuiUS1Uq5Yn3BXDNjau5/tdvwN/be0nP6bkMvniMPd98hFw0RV6vEiuUKRZ1elf3sO1tN+HvaCXQ0Yon7McxT9cem63fLcuyqBTL5GMZ8vEMhUSOUipPMVOgnCtRzpfRizqVcoWqXqNaqWOMqyCOWnfkFG/4xHtmZcznMmNhrKOjg927dzc/jsVitLe3Nz8+dOgQqqqyceNGAH7jN36Df/7nf76o+0gmC5jmxNBnmib1+tlP9kIjy455+ThM0xRnykxCnEG0sIjXa3K1kk45maWczFFOZBvXs+jp/MRpMMVJIVcAwN/ioi3sw+WWKZ84SdXrwen14Orpwunz4tRUygdfYTCe5cAjz5FOpIkNxUkMx0mOJEgn0mTSOXL5IoVyGdM8+++e7HCiKQqa4sKnuOiMdOD3uAn6PAQDXoJBPx3tYSTZCQ4HkuLkYKJG5fRh9Af2oed1ykWdcrGCXqyc82+rwyHh9rjQvCpun5vIkgiaX8Pt09ACPtwBD1prEM2jEsiepFQx0afp56gYz3Dwx08TP3ASXXYQr9aIDyfxtfq59d23s/m2bSiN0FsGysni+W9wjrya3y2jbqBn8pSSOcrpPOVMgXK2gJ4vUymUqRR1KuUqVb1KrVKnVqufc9tTSQJZduJyyShuBX+rD9WjovrcuP0e3AEvWosXT2uA8OolM/73wOGQJi0gjZqxMLZz504++9nPkkql0DSN+++/n09+8pPNry9fvpxoNMqxY8fo7e3lwQcfZMOGDTM1nDlVLBb4nd95L3/7t/9EV1c3L730Iv/yL/9AqVRi1ao+/vRP/wJFmb19zgRBuHyZhoGezttBK5FrBq5yIku9XGkeJ0kSiltGURwEW1RcLieK6sSlyrgCXuqnUhgvHcfZ10PO7yFdKJFM5kgcGiI1kiSTypDJ5skXipTHVcjGc8t2wNIUhXbNx6pQBJ/Hjd+j4fN68HrtsCU5nZiWhWGYVKt1KuVqI1jppKNlUsNlBg7Ezrp9xeVE86i4fSoev5twV6sdrPweO1gFvGhBH+6gHy3oQ/VObXrPzGUwchK4tUt/IRrqepWjDz3HiSf2kSlXSOpVMskcLe0t3PHbv8SGm65CVhZeY71lWdTKVUqpDKVk3g5X2QJ6rkQlX0Iv6FRKlUawqlGr1s9bgHA6JRRFRnHJuDUXgbAf1evG7dPscBX0orX48IT8eMJB1IAXh3PhrOQ/o5WxD33oQ7z73e+mVqvx1re+lY0bN/L+97+fD37wg2zYsIG//uu/5s4778SyLMLhMHfddddMDWfOvPzyS/zt3/4Vp06dBOxg9rGP/RF///efo69vNZ/4xEf50Y9+wFve8tY5HqkgCIvF6KKd44PW6KV+xtl1TsWJ4nLgUR0oAQ+y7KRi1dGdDvKGQaZUJZUukc7myWYKZLJ5cvkCuUIRwzT5q7/74oT7dkhSI2DZISvUEsarqng1Fa9Hw+PW0Nxu3KqCZUnUDYNatY5erqKXGhWPPKTzJdKUOE0CsCsdbs2F2+tC86iE2ny4V7ah+dy4/V40vx2u3EEvWtCPu8WPrM7MoqNWpWyPSXVf+m2YFkN7D3HwvmeJRlMkyhWKhTLh7ghv/P03c8V1G+bVchKGYaBnipSS2YlVq1ypWbWqlu1gVanUqFWNSduVwD7PQ1acKC4nLlXB3+pF9bhxedxoAQ2334u7xYen1YcWCuIJ+5Hdi3sBWck617O1AEw2TRmNnqCzc/kcjehsn/70J3n969/IJz/5cT772c9z8OAr3H//fdx112cAyGQyGEadcHhi8+B8naacb8/vfCGmvRaWxfJ6mfW6PaWYzDXDVimeRk/lMKr15nGSJCHJFrpRRzdqFKsV8nqFQq1OrlwhWyiRK5bJl0oU9cqkb6KK04lHceGW7ZAlSQ6Keo2VnRGCXg+aS8WlKDglB3XDpFqxK1hn/o0eJcsOO1hpo9OCKprXjdtvV660xlRSW08Yw6GgBnxIioLkmPtqh3FqADOTQL5y2yUFhMzJEV7+wRMMvHKSeKGEXqnRsaKTnW95Df1Xr52VXrCaXrE3605mKaUL6JkCer5kh6uiTqVkh6vqVKpWDglZsYOVqrlQ3C57StA/vmrlx9PqxxMO4G7xLaiq1XSYs2nK+WDPQ3vY/eDuCx94Cbbduo2tt2y94HF//Md/NuHj06dPoWkaf/InH2Zo6DQbN27mAx+4c0bGKAjCwmdZFtV8aVwvV4ZSLE05maWSK1Gp1ylUKhR0nVKtRqlWoVCpUqxWKOiNf5UKldrZTegS9lShW3HhlhXaPQG0oAu3y/6nygouWUaWZEzDpFYd2wanWK2SdpTQLDdWWcLAAhlcmpOAR2sELA23R8Wpl/B1teHrasMd9KEFfSiaBrIMThlJOnf4mI/B2aqUkdSLP2NRzxV55UdPse+xfcRyRWp1g57VS9j5azew6qq+S678mIZJJVegmMxRTuUoZQro2SJ6roReKFMd18Req9So1YxzhmQARXHYU4Kqgr/Fi0uze61Un4YW9NrTuyE/ntYAWnjitkfz8fVaCBZ1GJuPDMPg2Wef5vOf/zIdHZ18+tOf5Gtf+wrve9//nuuhCYIwh4xqrVnhKoykiB0fIjEUIxlPkyuVGoGrQrFapVRtXFaqGNbZFQun5MCtKHbQkhVaAl7cioJLVlBlBdkhI+NAlpyTBgCHU0LzuOxmdo8LzaPg9tqVK82n4fZrnDgZ5+c/2s1b//BNdPd243C5wCmDrIBj7HbNik5519O4+vpROrtn/HmcDZZeRgpMfRV+s25w+ME9PPPDJ4mm8himybL1K7j+125g2RUrznoN6pUqxUSusQRDo9cqW0TPj1atKnbVSq9esGrlcEgoihPFJeNyu/C1eBu9VuMa2Vt9aK1+vOEg7qAfh3J5Va3mg0UdxrbesnVK1avZFA6HueKKK+nu7gHglltu4557xPpqgnA5KJfKjBwfInpskJETQ4ycHCaVSJPNFSiUdYqVCqValfIkVSwAl1NGbQSsoOql3RvE5VRwOWVkyYnaCFtOyTHhDd6lyrg9SiNYjYUst1dF82uNgOVpVj0Uj4YkK+BUmtUrzrjN3EN7gN14urpxtoTP/aCbBZjF0e9jGXWo16bUL2ZZFid2HeCRrz1ANJbBtCw6eiKsWrMUTXFy4qG9HLr3CTtYlatUq3Vq1fr5q1ayA9kl41IVfEGvPR3odTenBLUWX6ORPYAnHECZ4kkJwtxa1GFsPtq+/Rq++MUvMDISpaOjkyeeeJw1a9bN9bAEQbhEpmmSzxZIJ9Ok4mligyPEBmPETkdJxVLkMvlmL1bNNM76fgkJVbZDlupUCHvcuJwyLqf9sSo3rssyDsmB5JBwuxU0j4LmdTWXYtA8in3dpzXPGNQCXtwBD05VtQOVU2lUry48PThlF3yfP+/GlQtOLVegkClTqSbRDycop4uUcwUq+TKVYplK0a5aFYtlEpkixUoVC9AUGb9bRS5WObH3KHBm1UrB29IIV43qo738gg+t1Q5WnlY/jgV4ZqVwYeJVnWUdHZ380R99lI985A+pViusXt0vesYEYR6qVWtkUllSiTSJaIL4UJxENE5yJEk6mSGTzpIrFClVJm94lx0OVGdjatDpIuD34pLlRsBSxqpcioKm2cFK86r2pU8dq2J57YCl+e1gpXo1JEUZC1aj/86YHpxp1kVvczT/0phlWVRyJbuJfbTXqtHIXsmXqZRGl1+oUa3UqFfrGOepWsmyA0uCTKFMrrFMSLjVR/8VK4gsieAONM4QbA3giQRw+TyiaiUAIozNmu98597m9Z07r2fnzuvncDSCcHmyLItSsUwxl+Ho4dPEB2PEhmLEh+Iko0nS6Sz5QpFiuTxpwzvQqFrZQSqoeGjTAhMClkdx2cs4aCoej4I34G7+0/xuPF4Vzac2l2OQ3aodpMYHq9Eqlnz29OC8MdXz8GfxfP16rW43sCdzlNI59LQdrMqjyy+UKlRHG9mrdeo145zDkyQm9Fp5Axouj4qqSKiyhbZsCZ4WP1qrH08oQL5Y5pG7f87AKyeQJFjR18Nt738jbSs6Z+8JEBYsEcYEQVjwDMMkl8mRTtpb6AwfHyZ2OkpiJEkmlSWfL1AolSlXKxiTrPDukKTGtKAdsto8Qfu6U7FXfne58LpUvKqK2yXjdst4vApevxt/q0Zrm49AewAtFEDW3GdNB06oYsnTND04xyymWhkbPe4ib9+yqBTKjKQzDA+MUEoVKOfGrWtVaJwh2FjbqlarYxjnqVo5HSguJ4qq4PFrqB4Vl0e1m9j9HrQWL+6gH2/YjycUxBWYvGpVHziEpZdQ1l0FQPTYMD/6jx8z8NIADkli2dJ2bnnfG+haJ5YAEqZOhDFBEOYtXa8QH4wxcnqEoeNDxAZjJGN2wMrlChTLZcoVHf0cVSzZ4WwGLJ/sJqL57cVIXS68LhdeVcXXWMJBVZwosgPF6cTlcuDxuuwpw4CG1uJBCwfwtLUiB3043NpZlax5Wb2aSaOtYFN83EbdRB9JUWxs0FzOFNBz9hmCeqFMtVShUq5S0+1G9nrNOO9WN82qlarg8ftRPe4ztrrxNatWWsiPrLqm52E3lrU4deAkj3/rFxx/+ThOh8SSjhDX/49bWHHN+svvZ0F41UQYEwRh3vmHj/8LTz78DDVjsoZ37DMIZRm3U6HNG0RTFLxuN37NTdCnEW7x0Rb20xL24vO7yUbzFOOlCW/ukgSKKqN6XLgDGu6QH09bK57udtT2EA7XIljx27KgXkGqVezLegVMEzO0FC5iYdF6rU5qJEViMEF8ME5iKMGJAycA+Nn3HsTf4uMNb3vdGXdtceqFozz/7YfJpwsYxnPnvH2n04Gi2FstaV43wTYVtVG1CrYFkVRXY/mFAN5IEJffMyMLo1qGAfUaVr0GtcZl83oVq1bl0N4Bnn7mBLFoBtnpoCcSZPsbdrD6tduQXWJbO+HSiDAmCMK8s/vp53DLLjauWg7FKt2rltDqd9PqsOhes5LW7jC+zpC9KruqIslOpOgA1KoQCDV7sDKnUxy9fy+VXAl/UCWwsgetvZXudcuoal6ci+XN07LANCYGr9HL0UOQQHZhubRJe+ktyyKXyk0IXIkh+3p6JD1hA29f0EekJ8L1b7qehx9+kmWrlky4naGXj/P8dx8lOZjC7ZaJhDz4u9twB73jNmj2o4UCeEIBu2/uHF7tIqKTBqxadexz9RpWrRG6zpjCtiyLYrZCJlHm2OEYBw4NkytVkJ0OVi5pY92O9fS/ditaq/+SxycIIMKYIAjzTK1ao1gssyrUyetvv4GhvYd5013/i/IrB6gODRO89eYJFSvLNGDwKNQq0NWL5PFjVGsc//luorsP4A4HWLF1JaoCkdtvBCC0kFcJt0yoV88OXeOWzbAcMigqlurFUlQsWQXZBZKEXtJJHBlqBq7xwauqj23orbgUIj0Runu72fSaTUS6I7T1tBHuDuPxeQB7WY/vfPNetl63GYDowVM8952HiZ9M4PEqbLu5H5/HQ71Qpvcdb5i+p+CsgFWFRqA6q6o1SY8gYE8vKwqSrCB57K2WLKdMKauTHkqTPpkkfTJGKpljOJ0nr1dRVYVtt2zmmjdfj78jNG2PRxBEGG3WbJkAACAASURBVBMEYV5JJdMAqLJCrajjbjRSm6USTs/EpmrLMmF4ACol6FyJ5PGTGRjmyL2PU8kU6L7mCpbdvIXUAw8jB1rn6iFdGssCsw61xvTi6GW9OrHapahYqm8sdCkqdcMiPZKeNHDl02MhVHJItLa3EumOsPKKlc3AFemOEAgHLjgVmElmqdfquGWZn/3N1xk5NoJbk9l8Qx9rXnctSjjM4H1PIDkqF364owGrGarsgJWJQ71QuuSAhWx/bH/eBYrSPInCsiyKsQypY8Okj50gNTBMragDUJWdDGfyJGIZPAEPt77tJjbftg1FXSTVVGFeEWFshn3pS1/goYd+DsDOndfxe7/3/zW/ds893+IXv/g5n/vcF+ZqeIIw76TijTDmVKgWyrgDXgDMYglnMNg8zrIsiJ6AcgHal2G6PBz/6VNEdx3AHfKz4T1vILCsA7NWxyyVkVcum5PHMyWWeXboqlWQxm11ZDkVkFUstx9TVrFkF/mcTnwwQWIoOiFwpaKpCdOK3qCXSHeENVvXTAhc4a4w8qtYRPToC4cAGHh4Hz2hVjZdt4q1r78GV6TNPsA0wTDAMjEzqUuqYOmNipUkK0henx2sZAVJcU0IWlM5S9WyLIrxDOljw6SODZEeiFItlAFQg14i/UuouxRefu4wpw6cxBP0cuu7bmfza0UIE2aWCGMzaNeuZ9i162m+/OX/QpIk/s//+QMeeeQX3HjjzQwMHONrX/sKPT1LLnxDgnAZSSXGKmOVQpmWngiWaWKWyyjdXUAjiMVOQTELkR5yqSqHf/h9KpkCXTvWs/yWrTgbIaOesytBcjAwNw9oPMsCo9acXhwNXRi1sWqXJNmhS/NjySrlGiRiORLD6bN6uc6cVgx3h+la2cWG6zY0A1ekJ9KcVnz1w7fANEmfGOb57z/Os7teAWDjluVce/tmXJoLKR2jHh9sBiwzn8WqGhjHD43dkCw3g9RYwHKdVclCVmhvD1zylLJlWZTiWVIDQ40ANjwWvgJewn09hFZ109rbRTKW4fF7HuH4i8fwBL3c8s7XsuX27SKECbNChLEZFA5H+P3f/xCKYv8yL1++gpGRKNVqlc985i5++7d/h5/+9EdzPEpBmF+S8RQAbsVFJV/GHfBglu03UKdHswNBcgjyKQxfmJNPH2X42Vdwt/q58n++nuDyiYts1rM5AOTALDdZm+bkDfVnVrsUlbriJZkuEx/JkYimSQwl7RX/B8+YVpQa04o9EZavW94MXG09bVOaVpzMaMCiXh03RVgbq2CN+1wulmXfsyc4eTSFrDjwhV0QhWuv78VFFWqWHaa8/mYFi4E8DoeB3L+hGbBm6ixVy7IoJbKNacdhUgNDVPMTw1drbxeh3m60kB9Jkjh96BQ//PcfMvDiUTwBD7e887Vsfu02XO7pWQpDEKZiUYex+3/0IPf98IEZue073vRabn/jrec9prd3VfP6qVMneeihB/j3f/8Sn//85/ilX3oTXV3dMzI2QVjIUok0TqcT1eWirldR/R7MUgkAh8cD6Rhk4uSKTo789Bn0dJ6uq9fZ1bBJzo6s5/JITidO7/RUh84yWu1qVLqaocsYW/vMkhxYsouc7iCeKBEfyRGPZkkMJ0gMTjKtGPAS6YnQv6Wftu42Ij124Ap1hlCmcAao1Ty7clyYGj1j8IyARa1mT5NORpZBdlEo1HjpicMcf2UYh8PB2qt6WH/bFv7rWw/RGk3i2X79uQOW8xCSLCF5vBf1tE7FaPhKDww3A1glb/+sqH4Pod5uQqu6Ca3sQgsHJoxx8NBpHvvOwwy8cBTN7+Hm37yNLbdvFyFMmBOLOozNF8eOHeX//t87+f3fv5NodIiRkSh/8Ad/yN69u+d6aIIw76QSaXweD7LsBMAd8GKW7OqGVNcxksOc3J9i+KVTdjXs3a8neJ4tZ+rZHM6Af3qqMaYxSUN9BamxgJkF6DWIJ8vEY3nisRyJkQyJwSTxM85WlF0yke4IXSu62LBzQzNwRbojePxnB8fRgGVVymcFrAmVrNGlG861YqqsjJsidI9rbh/txVKax5TSeV6452GO7j2GBPRt6GHDL1+Hd2k3kiSRSHyXtq628z+3lgWO6amEWZZFKZkb1/M1TCU3Fr7sqlcXrb3deM4IX6MGD5/m8e88wrHnj9gh7B23seV1IoQJc2tRh7Hb33jrBatXM+3FF5/nT//0I3zwg3/Ibbe9jrvu+gsGBo7xnve8g3K5RCqV5OMf/xP+8i//ek7HKQjzRTKewuvRkJz2lJs76MEolsDpIH/4CEefHUTPlencvpYVt2674Fph9Vwetesi9we0LDAay0eMD11GHbC3X0omi3aVK14gHssRH06TGE6dNa3Y0tZCW08by9etsMNWz9i0oiRJjYA3bs0rPYdRSI4LWGPThxcMWIqCpJ4/YE0llJYyefZ9734O7zqCZUHvui42vmknvuVLJnx/fDjBmo2rL/BUWpe8/ZNlWeRH0pzec6TZ81XJFQFw+TVCK7ubAcwTCZ73sQ0dGeSxbz/cCGEaN73jVra+7moRwoR5YVGHsbk2MhLlox/9MH/xF3/N1q3bAfjoRz/R/Prevbv50pe+IIKYIIyTSqTxaBqORjXF7fdQPznMyMkMicE8atDHFe+6g5aVXRe8LUOvYFWqyMHz9ItNtnxErQqWST5XJhbNEk8UiccKJGI54tE0qVgG0xib2vP4PbT1tNG/uZ9IT4RIV4i29hZaw34Uh3V2BSt9CiN2MQFLe9UBayr0XJF9P3iUQ08fwjRMlq/pZOObdhLsXXbWfdTrdZLxFG2dbee/UdO0pzunwLIsyqm8XfU6NkxqYJhKthG+fFqj6mX3fF0ofI0aOjLI4995hKPPHbZD2P+4la13iBAmzC8ijM2gb3zja1QqVT772X9sfu7Nb/5V3vzmt87hqARh/rIsi1QiRdvyFc032loqzfHHD1PV63Rs6Wfl7VdPeeX8Cc37lmWv0VWvUBrO4sjloVahUiqTGMkSj+WIjeRINCpdiWiaSvmMacXOEB1L2rhyWz/hSIBIm59wyItHdZ5RwSpDsQzFYSZs6HS+gDUuaM1kk/tkKsUyL//gMQ48eYB6zWDZ6nY2vmknratXnHMcqVgay7Ro6wyf97Yty8JxjtuwLItyOj827XhsGH1c+Gpd2cWyjStxtYfwtE0tfI0aPjrE4995hCN7D+H2adz49lvYesfVqNq5V/sXhLkiwtgMuvPOD3PnnR8+59e3bNnGli3bZnFEgjC/lUtl9HIFze1ubkR98PuP43LJ9N24no4bd0zthow61CsY8REAnJUUyRcGSIzkiDeCVzyWJx7Nkm+8+YO9X2VLyE+4LcjmbauIhL2EWz1EWjUCfnezWjfujsAoY1XnX8CaimpJZ/+PHueVx/dTqxj09EbY9Cs7Ca/pveBYY8NxANq6LlQZs+wntqGczpM6OmQ33A8Mo2cKACheN6HeLlas7CbU24W3vQVJki56O6ThY40QtkeEMGHhEGFMEIR5wbIsDu0/CoBiShi1Kg6fh45VbYRaFHyrJlmTb7TaVdPt8FUqET0ZZehEnFg0w/CxOIlEnux/PYlpjk0HejwuwmEffSvDhMPLiUS8RMJ+QiEviuKcUMGacDl+JXd5dKHR+RWwpsKyLL73z3dTPZahWjHoWh5i05t20nZF3wUfT6lQIhlNsfdxe+Pvtq7Iee/HrBsUE3le+vbDpI6dEb5WdrHiho2N8NX6qp7LkeNRHvv2wxzefRC3180Nv3Ez2+7YgeoRIUyY/0QYEwRhziRiSZ5/5gX2PLaXl188QDbfeKMu1UBxogVUVty2neJzL+Jwq2PBq6ZjlItEjw8zeCLB6ZMJTp9IEh1MYzR6uWTFSYtfoy3sZcOmpYTbg0Q6WmjrChHqCFExaAQs1xkVrIUZsC7GUw88zr9+7Vu86/qbueP33kjHxv6xaeFqnVQsRSKaJBlNkhxJkhi2L5PRFKWCffaiXrencFsjLZPeR+HUCMfue5rEyTj1uoXiUWnt7WbFazbS2tuFr70VaRrOskxFUzz6zV/wypMv2SHsbTez7fUihAkLiwhjgiDMmkKuwAu7X2LPo7vZ99x+Ygl7gVePy8WyUIibNl7BVTs2cWzfSfKxNC63glQtYRgmw/teYiSemzR4ub1ulqzq4fptV9KzqofuVd2EOkKkfvIATsVBy+23TQhYrQt5o/BXyTRNBo8NAmCFvezZd4zkA7ua4SubytnLaDTIspNQR5hIV5gVa5YT7gwT6Qyz95kX+MWPHkU+ozm/OBRn4KdPETsep1438bT6uOKXrqF93YppCV+jCuk8j9/zKC88tBen7GTnW17Djl/eidvrnrb7EITZsijDmH0q9eL+n+1csCwTEM+rMHWVSpVXXniFXQ/v4sU9L3NqMIplWShOJ0tbW9m8fTObtl7Jmm3raOluQXEYGOUiLz+zn0yxxP7jVfZ9+tuMDGUwGtOMZwavnlU9hDpDk/7Om7U68hmbi18OSvkSiWjSDlhnVLZSI0lSxSwATzz6PO6nDhAMBwl3hlmzeQ3hzhCRjnAzdAVCk6/sf+TgsQmfL0UTDNz3NLGjI9TqJlrQw/rX76Bj46ppff71os7TP3yCXT95GtMwueq2rVz3qzfga/FN230IwmxbdGFMll0Uizm83skX/BMunmVZGEadfD6NyyX+1ymcm1E3OHLgGLsffpbnd+3j2MAp6oaBQ5Lobmnhxg3r2XDVOjZcvY7wklYcVo3o8WEG9r3E6R83Kl5DaYx6Y6pRdrKsr4dtG/2suPYqlm1ac87gdSbLNLEME4dr0f2Zo1atkRxJkRhOkBxJkRwNXtEkyZEU5WJ5wvFev4dwZ5ield1svPZKXnluH0NPj/De338r299wE8olPEemaSE5JMrxFMfve5ro4WFqNRO3X2PNHVfTddXqaa2E1ao19vz0WZ76wePoRZ31123ghrfdRGtnaNruQxDmyqL7K9Xa2kY6HadQyMz1UF4Vh8MxYXuUueZwONE0Hz5fcK6HIswjlmVxauA0ux/dzXNPPc/BQ8eoVO1tgNr9frb19XLlhtVs3rGeSHeQVCzF0PERnnzwmbOCl9vrpqe3m+u2XsGp544ilXXWr1/GjnfeRnH3Hnw71iG3tk55bGalAoDDtfDWkzINk0wyQzKaIjGSJDmctC/HTSWOp7gUQh0hIp1heq/oJdy4Plrd0rzahONjnzgFwJJVXZcUxABqxTKSBc987ntUqyaq1836X95Oz9Z+pEvYI/NcDMPg+Qf38Ni3H6GQzrNq82pufPstdJxn1wVBWGgWXRhzOmUikQsvBjnfXezp3IIwW+IjCfY8upu9T+xl//4j5It2Q3eLprG+p4cr1vWyactqVI9MfCTN6ZNJvv+NXzA8Pnh5VHpW9XDd1itY0rfkrKnGbx77MuVqHafipLnGxUUyG1sPOdT5F8Ysy6KYL5EcTpA4q7KVJBVLY9THVigbXck/0hFm3da1RDojhDtDhBu9XP4W/0VtEl4p6wBnhbQpfW86x4n7n2Fwz0H7bFZZZt0d2+i5eh0O5/SFMMu0OPDMfp74ziPET8fp6V/Cr3zw11i2fvm03YcgzBeLLowJgjC98rkCex/fw97H9vDSvoMk0na/kcflYmV7G2u3b2T5ii4UVWYklmXwZIqv/v8PTBq8elb1sKRvyQWnGkdXt3c4neOy2MVNeTUrY+rcnFVX1av2FOKEnq2x0KWXKxOO9wa8RDrDLO1bylXXbWpWtcKdYUJtrcjK9P251kt2GFNcU39uqtkCJ37+LEP7jlOpGJiAU5G54U/eiaOxj+h0GXjxKA9/40Gix4bpXNHBW//o7fRt7RetJ8KiJcKYIAgTVPQKLz7zIrsf2cVLLxzgdDQGgOJ0siwcYuNVV9Le3orTqRCLZTl4IMb+l6LAuOC1Zb3dXN/XQ7gzfNFvoqNT9K/mTd7Q7cDhcM9MGDMMg0yiMZUYHRe0Go3yufQZU4mqYleyOkP0begjMq6yFWoPo83iWYCVRtVQmULVsJYvcvLnuxh8cQBdryOrMqtft43hFzwM/PzpaQ1iQ0cGefgbD3LipQGCbUHe+Htv5qa37CSZKl74mwVhARNhTBAuc0bd4MALB9j1i2fYt3c/A6eGMEwThyTREQiwuXcFLf4AWA7isRzDpwsMny6MC15XvKrgNRnTMMGykORx05QXebNWyW5id2gXPxUH9lRiIVs8Z2UrFU9P2J9Scki0RlqJdIW5Yvu6CZWtcEeYQKt/3lR2qhU7jLnOUzWsFUqc+sUeTj93BL1cx+mSWXXrFlbcsAmnS+bRF/ZPsiPBpUkOJnjkvx/i4LOv4Al4uO09d7D5tq3IijytU5+CMF+JMCYIlxnLsjh+6DjP/vwpXty9j8MDp6nU7Kb7Vo+HFW3teFwaRsXCMkHPQaZeoWdVD/1Xb6Snb3qD12RMw+6Xck5xg+lJb0PXQQLpPFNxFb3SaIpPkYgmxhrmG6GrqlcnHO8L+gh3hlm+Zjlbbtw8YQmI1rZWnNM8XTdTKnoVSZJwTjL1WS+VOf3wXk7tOUy5VMMpO1l54yZW3nwV8rhKmmmaF9WnNplcIsvj33mEFx9+HkVVuP6tN3L1G68VWxcJlx0RxgThMjByephn7n+SF559kQNHTlBoNHB7FBctmg9FU9BkFdnhxK2qdPd2s6RvaTN4hTpCr/qN92JYhollNaYpL7FnzNArWBIkUwVSxycuAZFLZoiejpFvbM0zyqW6iHTZlaz+TasnVLbCnWHciyQkVKs1ZIcDxr2m9XKFoUef4+SuA5SKNRyygxXXX8nKW7eiuM+ezjTNS1/PsZQv8dT3H2fPz54FC7a9/mp2vuU1eALeS35MgrCQiTAmCIuNZZGJJ9nz8yfY/dSL7D94nEzR7rlRHE48ipsuXxivouH3exvBawk9fUvmJHhNxjRMJEAaX7mZ5H3fsizymTzJ6MRG+UQ0Sez4ILlcGdP6efN4h8NBa3srXUvb2XDNhrOWgPAFffNmKnGmmKZJrVpDdjqRHA4MvcrwEy9w4un9FAtVHE4Hy65dz6rbtqJ4zt3HZlnmRU8hVvUqu378NM/c+yRVvcqVN2ziNb9+I8G2ybdUEoTLhQhjgrCQWRYYdcqZDLsffpYnf7Gbg0dPkWzs8eiQJDyymw5viBafj97+FSxbs5yeVUvoWT0/gtdkTNPEaVk4ZCd6WScaz1HefYB0rjxhCYhkNNXsfxrlb/UT6QiztLOV0Ooueq7ZQqSnnXBnmNa2FpxO52W9dEy1VMGwTGSnk8HHXuDk069QyFeQHBJLtq+h7/btuHwX7rMzjalPUxp1e62wJ+55lGK2yOpta7jx7bfQtrT91T4cQVgURBgThIXKNHDEj/PjHzzOF/7zPkzLQgI0WaXTH2LZkm6u3HYFK9b3zuvgNco0DDKnExza9QrRRAq9VmX/V+6lpE9cAkLVVMIdIdq621i7ec0ZjfIhVLeKZVkk7r0PWXXS8tqd07oI6XxnmSb1kk6tqFMvlqnmC8SOnmbfnv0cOTHEYCrFqUwCl+xk/wPPI0kSPVtW03fH1ah+z6S3WcwXGT4VZehklOFT9r8DLxxCO0/lrDkey+Jrf/4Vhg6fZum65fzqh3+DJf1Lp/thzxqzVqcSS6KPxKmMJHBqbtpuumauhyUscCKMCcJCZdRIxJJ86esP0KJ58at+3vG7b6Vvs71l0HwOXgDVok5iYJgjew5y6PlDDA7GSJdK1BrN+6os07+ijZ6lbUSWdNC15UoiXRG8Ae95pxKtep368UOY1TrOSOuiCWJGtUa9qFMrlqkXy9Qa/+pFnVqhRDVfpJIrkU7lGIglOZVKEc1lieWzZPVis/XOLSu0+YJsWbmSpdvXsPLWLbiDPgzDIDo4wvBo4DoZZejUMMOnRiYs0+F0OunoaWfNhj6237D1guM2DZOhw6fZ9vqrue1/3rHgpoHNag09lqAykkAfSVBNZuyKtCThCrfg7hLVPeHVE2FMEBYoyzT57L/+AMu02LFpE4l4lmt+6bq5HtakLMuiEMuQODbEkb2HOPryUUbiadLlcjN8ed0u+pa1sWb9cl5+ZoBt1/Sy83/9Gg5l6ivoW3qZ+sBBzMbejHJ7x4w8nulimSb1csUOVYWyXc0qlakVxgcuu8Jl1uqA3ThvmBaGYZIt6pxMpjidTjGczRAv5sjppebtexSVdn+QK5Yvo79vBZtvupqeK1aRSKQZOhnlxVNRfvb3X2Xo5DAjg7EJq/77gz66lnay+dpNdC3tpHtZJ11LO2nriiBfxFmuo7fpb10Y+wUblWozeFVGElTTGfskEoeEGg4RuLIfd0cEtS2MYxoX4hUub+InSRAWqKcf28PefUe59corCHZGyOXLF/6mWVKv1kifjJE4OsSx5w8xcOgkyVxhQvjyaS7WrOpi7cZe1m1bT1vfchweu1fp4K6/QXErFxXEzGwa48QRcEjQtQyOJ3Boc7OxvVGtN8NUfXwF68zPlSp2leUMkuJEkmVM7MVl6zWDdLbIQDTOYDrFSC5LvJgjXxl7zb0uN53BFrb0r2bNlf30XbUW3CrR03a1a+DUME9+9uuTVrm6lnaw5dpNdC3romupHbr8Qd/0PBeNMGZvbTX/GGUdPZZsBrBaY4cJHA7UthDBDWtROyKobSEcr2KpFUE4H/GTJQgLUKlY4vP/eDftfj+/+q5f4qFHX8I1yfIDszaeTIHUwDDxw4Mc33eEU6dGSBdLE8KX36Oyfu1S1l7VR/+2K+joW4Y0yZvb6Jv3VFd2tywLMzaEOXwKNC/yyn4qiTQAzktc8HXS+2lUsSYNVeOnDwtjVawJHBKKR0P2ulF8GlokCEC9UqVa1qkWdSrFCnqxQnwoxVAmw3A2zUg+S7yQo1DVmzflVzU6W1rZ1rWWzmXdRJZ0otcNoqdHGDo5zI9/+gTGvY+OHX9GlWu00nWxVa5LYTSei8nWNJsL9VJ5QuWrlrVP5JBkJ2pbGM9V6+3KV6QVyTk/A6Sw+MyP3w5BEC7K177wTTKZHO+95TV0b11N9f69uGZpQ2zTMMgOJUkeHSZx+BQnDpwkmkiTKZ0Rvrxurty4krVX9dO//UraVnRPaZqqWUmZQhizDAPj1DGsTBKpJYxzWS+Sw4nZWEdtKpUxs1YfC1fnmyos6ZNWsRwuBcWnIXvcaO0hAr0aciN0yaqMJFnUCkX0dI5SPEcpnScdTVEuVKlW6uT1MkPZDMO5NLF8jlg+S6k2dtJCwO2hPdjCulCIlnALbr+PfL7E8OkRDhwd4sDRIfv5moUq16Wo1+zXU56jBXHrxZIdvKJ2AKs3zjSWZBm1PYy3dxnuzgiuUCuSWO1fmCMijAnCAnNo/xF+cs/9bF62jJ2v3YjDKVPVqzNWGasWdVLHoySODJI4cprTx4ZJ5u0px2y5TLURvgJ+Dxu3rqb/qjWsvfpKIj2X1thsTrEyZlUr1AcOQrmEo2sZjvauZtirF0sYpkUlV6IeTZ3Vf3W8WqWcKVAr6pjV2tk3LkkoXjeyV0PxedA6Qihezf648U/2ulG8GpIDzHKZSipNYThJMZYhe3KQUqZEOV9BL9WwLLuCl9PLRPMZooUs0UyakVyWcs1emkMCfKpGyBdgmc+LW9MwTYlUMkNZNzg9lOT0UHJOq1yXwqjPXmXMsizqhVKj8mWf7Vgv2D10kqLg7gjj61+BuyOCK9SyaE7uEBa++febKwjCORl1g3/9m//A51a5/ZottK9qw5QkqnoVX8urr36MNdoPkzx8iuSxYUaiKdKlEplymYxeptoIS8Ggl0071tG/eS3929YR6Yy86vuH81fGRqtY1USS6sAxapU6phagPnKCevHghKlCgNjdP5nw/Q6XguJ1o7X40NpD+L0aSiNU2cGrUdXSXBPeqC3LxNJ16oUipWiCzMnTFBNZSukC5ZxOuVilXhvbpxIJStQYKeQ4nUwwmEgSzWao1GuNL0t4XCo+1UPY34plSdRr9n6gRh0K+Soef5AlyzrZcfP2eVPluhRGbeZ6xizLop4vNoOXHk1gjO5JqrpQOyL41/Xhbo+gtAaRpmkvTUGYbiKMCcIC8sNv/YTjR07w5quuov/mzUgOAIlq5dIqY/VqjczJOIkjp0keOU3yZJxMrki6VCJb0cmUSlQa4ail1cemzVeyZsta+jevIdwRnpbHZFkWRuOMwlqxTHYoCUBuMMPxex8b15t1ripWHNnjboYqra2VejKJ0+kguOXKZgVL9mg4XfafvMkWfbUsC2o1jFKRYnSYUjRJMZ6hmCpQzpYoF6ropYn379IUVL9GzQlDqSTHh6Kcio4wnE5TNeyKkISEW3HhllU0WcPpkFEcMpIkNatcXUs7m4FrPle5LkV9tGdsGh6PZVnUsvkJPV/G6JS0W8XdEbH7vToiKC0L4+xNQQARxgRhwRgZjvGNL36H/u5Otmy+gnBfJ2Sj0KiMTSWMlTMFu+p16BTJgWGy0QyFSoV0qUS+ViFVLFFpvHm2hgJsun4T/ZvX0L9p9UWHL7uKde6lGpqN76UymGO9WOWKff+lRJ6yN4Xs1ewqlkfFWddxGjpKSxC1txfF70PW1LOmmxI/ewg5GKClf9lZ47IMg2o2SyU6QnE4QSmWsatcmSLlfIVysYZRH6tyOZwSWtCLvydCOOAhWchzcmiII0dOcPTUELFshrppHy8hoThlZIeCJntQnDIu2UVHTzs9y7vOCl4Lrcp1KUYrnfIlVMYsy6IUT5F75SSVkTh6LInZWATYqblRO/4fe28eHeld3Xl/nqf2fa/Svkvdrd6N9w2CTQx5MQRsMxAzBN6QN4FxkuMcyEKABDNzICEGZkhCQoYZBzIhBmMCThhjwBjbGHC3u93qfdO+S6Xa12d7/3iqS5K7bavbrZZa/fucoyOV9DxVt6q6qr597/ferBbn6gAAIABJREFUG8XZEMWZiGH1b/xVVoKNixBjAsFlgGEY/MOD/xt0nTf29NF+29XLVjWeS4zpmk5mMsn8yXGSJ8dYGJujlC1RqFZJl0vklQrJfIFytSa+IgF23bKL3l199O3oOaf4WprFeqWuQqVQQq+c24tldTvqZUFXLLTMf2X1uMjnS7zwmW/QftNm+u64xbxdpYo2fBKjoCPHO5AbW1/2g9cwDPRiGTkaQVmYpzKfJj+dNAXXQp5StkQpX6VSWt7x6PA4cIe8BLoCeOIhLC4Hk1PTHDtygoP7fsno1AypQh69ZuKXkLBarNgtDjx2Gz6Xm+aWJlq7W2jtaduQWa4LYbHs/OqPgaEbVFPpJZmvJHrV9NRZPC5cTQlTgCWiWH2vPPxXILicuHLfIQSCy4ifPfkLXvj5fm7fvpW2rd0EOxuhmAbMeZRKRcFikZk6PEzy+CjJwSnSUwuoikahWiVXrZBTyszlCpRrIikUDbLz1t307uilp78Lv9e1KKqmkkydHK9ntV4ui3UG2WatmduduKJBfO2Ndf+VzePE6q2Z3s+RxXop5dEZYNFjZBTzqEMnQFWxtPcghxa9aYaioJeKKNkshekFCrMpCvMZctNZqkfmKJf2oGuL8cpWGXfQQ7S7CXvIj6chgicWQKlU2f/zfTy/7xCnfjDKzMIC+XKpPrX+TMbLZXXhd3loSMTp3tRJV38XHVs6aWprvCKyXBfC4miLszNjhq5TXUhTnq5NuJ9NYijmv0+r14OrtZFYbwuK24vV67mkcQsElxIhxgSCdU4+V+B//vd/orW5gV2NzbT9ylU1o32G5JFxpsaPYxgGp39ygOILgxSVKkW9SqZSZjadp1QTX4Ggj01bOmltjNIUDeKxyKbIOjbCxIGTTJzjtq1uZ11UuWKhegZraWeh1ePEYrddtPt7pkQo26zoqXnUkVMYsgUp0kB+ap7CiycozGdN83yuTCmvUC0vz3LZ7DKesI/wpja8iQjuhjDeeJCqqnBq/zH27TvEocePMz41Syqfp6IuLhuXkHBYbYTdASKBIF0drWzZtYXenX107ujGbl+7eW6XI0sbMgxNp5JMLXY7zi5g1LotrX4vno5mnIkYjkQEq8fckxm9gpe6C64chBgTCNY5//wP/0omleHtN21D8vsY+M4zLIzOodS8VZqho2gqM8UcQ6n5etnR67TTEvbTFPTTGPLhczkAkHUda7GE4XHhjAaxtbvOKhXaPE6sbuclbf03DAOtUGD+yEkASqNTHDs5RilfNc3zhQPoS7JyFpsFd8hDqDOKJxHC2xjFEwti0arkfrEX19W7efonexnc8yIHDx9jLp2iVK2g6osrf2RJxmNzEvX7aYxG2dzfx85rt9O+tYtgc3RFs84EZ6PrOvlUnux8hpGB0wCknz9A9bkKRm0Uii3ox9PdVjfdW9ZoW4JAsB4QYkwgWOc8+X+f5vrrduO3OpgdnsXlthFNeAg3Bok2+fjWw7+grClIFehoitPeGqezu4VIY3SZuLJ5zZ8vZhbrtaKXK8wePc3YoUEmT04yPZlBUTQkSSIznSM3X8Lpd+GJBIlsCeFtCONJhPFEg9h9LiRJMjvssgUq82lKg6PkT42QSuX41r9+nuG5JJlylqqmYLfaCDg9hFxe2hsb2Lazn+5tvUQ7Gwi3xrE51s/jst6pFCtk5zNkkhmy8y/5SmbIJrMYS4WzRcJuAW9nR73b0eJ0rN0dEAjWGUKMCQTrGE3TqZQrRCNBtKz54XbNja34dm/HGXRz4pf7OTg8icNq4789/N9wrPMPOF1RmD8xzNjAaSZPTjA9nqJSy/AFwh56ruqiZWsnTd4Kg3vGURSJW//k3vr5hqZRSWUpTc6Qnk9Rnk9TmU8vG3kxm8vz8LMvMptNkavmsTvt3L7pamKSm+btnVx99630bG8Xpa+XQVM18qmcKbaWCqz5bO17hkqxsuwc2SLjj/jxRQI0dzfT2RHDVi7hcdmIbuqg+YaduCPBNbpHAsH6R4gxgWAdU62YXia71YqmVbG77FhtFmSni/mZBb7ydz9AliSuv377uhRiuqaRGhxj7MBJJk5MMDWapFwyhZM34KJ9axut27to2dWHL+wHwFAV9FMD6JrpHVsYOEFlPk15PkVlIQtnxkhYLTgjQfy9bdh8bqojoxw+PcY//3gv6XKWXDFHe6KRa+JdNDU1cfU9r6d5a8eaPA7rBcMwKBfKSwRWpia6svXf5Rdy5sy1Jbh8LvyRAMF4iLb+DgLRAP5oAH/E/O4JetCLJTIHj5M/NQKSDW9PP4Ftm7B63Wt0bwWCywchxgSCdUylYmYgbDYbqlbBEzI/2FRZ5iuf+ybVqorbaueaW3euZZh1DMMgMzbF2IsnmDg6yuTIPMWCKSjdHgctvU20bO2gdXcfgdroDMMwUItl8sOTlOfTlOeSVKZmySeLaIrO7LP7sbgcOKMhPDsbcEZDOKJB7AEvkiyjZnMs/PQ5fvLCUf7t6f3klDyqpvK65h52d/Wx+2030nfrduQrYOmzpmpkk9lzlg3PXK6Wq8vOsVgt+CJ+AtEAHds6l4msQDSAL+J/xRl2Sq5A6hf7yZ8eBUnC29dJYFtf3YAvEAheHSHGBIJ1TKU24NJmtaLpBi6/C8Mw+MY/fJeJ0Rn8bjdNfh/hpouziuhCyE3PM7b/OONHR5gcmiWfNSeiO102mjrjNG9pp+2qTQSb42AYVDN5KvMpZgfHKc+lqCTTaKXFspfN78HudWBz2ZDcNnp+821Y3M5zzpRSUmlmf/Iz/vWJPTw9cJR8tYDP6eZXe3Zy01tvZedbb8Dh2RjGcMMwKOVKdYGVOYdXK5/Ow0smj7gDHvyRAJGmKJ07ukyxVRNcgWgAt99zQWuClFyezMBxCoOmCPP1deIXIkwguCCEGBMI1jGVWhbDapFNMea28eyBEV54+jCvu2ErJ14YpLcpjNV96QRHIZlhbP9RJo6MMHF6mmza3AVod1hpbI+y81faadnVS7g1QXUha5YYT48x8suDVJJpjNqoA0mWsUcCeNubcESDOGMhHJEgsqGinTzIzEwBJDtWj+uccVTnFxj9wU/58nd+yuHRERRdoTvSyK//6pu48TduI9h4cdY1XSrUqrqsdHgmm7XUJK9Wl4/wsNqsdXHVtaunLrDO/M4X8WO7yA0bSjZP5uAxCoNjpgjb1GWKMPe5nyeBQPDqCDEmEKxjzmTGqC2hnltI84Onj7D75l3kZ1JEoz7iAQ/WVRwLUMoWmNh/3Ox4PD1FKlkAwGaz0NAWof/GLTRt7sAb9KLUxFfulwdJ/uA5qHmPZLsNRzRIsL8bZzSIIxrCEfIhnaN0aJSrYJinStZzj9aozMxx+Ls/5PPffILpdBKLLHPHruv4jT94Ly3bO1ftsbhQDN2gmC2Y2axlHYjZ+u+KmcJZ53lDXvyRAPG2BD1X9dVLh2fKiK5aR+mlQMnmyQwcozA0hiRL+DZ349/aK0SYQHAREGJMIFjHnDHwG1WVYrXKz545QCIR5PVvvZmvfvKr3P6GfiRNuqiZsUqxzMTAScYHTjNxapLkTBYAi0Um0RKia1s7sZY4Pp8LJZWlPJ8m+7P9ZGvnWz0unLEQvu6Wur/Ldj6rawwDQzcwDLNL76WUJ6d5/B+/xVcff5qiUiLq8fN79/0WN77zDWvmC1MqyksM8cu7D7PJDJqiLTvH5rDVM1gNnQ11gVXPaoV9WG1r/xatZHNmOXJoFEm24NvcTWBbn5gLJhBcRNb+lS4QCF6WM5kxpVTl4OQkSPCB3/o1nv3RCzhcdno746SHU1jsF/5SrparTB0eZGzgFBPHx5mfTptCSJaIJXxsu6qDSCyAx25FTefQyyXUUyOkJAl70Ie7MWaWGWsZL6vrNXZ1GmZazIxhuRjLD47yhU/+Hc8cP4pmaNxy9dV89LMfwRNcvVVEhm6QT+eXdSBmXuLVKuVKy86RJAlv2Ic/EqChq4lN125e5tXyRwM4Pef2wa0XlEzOzIQNjyHJFvxbevFv7RUiTCBYBYQYEwjWMWc8YweOnCZfrfKbb78Gp9/HwecOcv1tO5E047yzYpqiMnlshPEXTzBxfJzZiQV03UCSIBz20NvXQMjnwiPDGV+3lC8g18ZInMl2OSIB5FVYgG2o5ugLwwBpSWbsyH88w2c//7+YyMzhsNr45F98hJvffMtrvr1KqUIumT2nIf6Md+vMmI0zOFyOurhq6m1e1n3ojwbwhnyX7fR+JZ0lPXCM4vA4ktWCv78Xf78QYQLBarKqYuyxxx7jy1/+Moqi8P73v59777132d8HBwf58z//czKZDLFYjM9//vMEAoHVDEkguKw4M9piNplhW2Mjve0x9uw5iaZq3HD7LrJ7jmN7FTGmqRqzpyYYffE4E8dGmRlNotXERcDvpL05SMBtJ+hzYrXI9TESS7NdZ8ZIXBJqYkyvlSnTk0ke++/f4OGnn6KklOlobeULD/0l/oD/Va9K1/TFAaZLyojlbIH5yQWyyQzlQnnZOZIs4Y/48UcCNPe1smVJ6TAQCeCL+nFewoaJS0U1nSWzVIRt7TMzYetwfp1AsNFYNTE2MzPDF77wBR599FHsdjvvfve7ue666+jp6QHMNu0PfehD/Nmf/Rm33norf/3Xf81XvvIVPvrRj65WSALBZcfQsWEAEn4/PfEoum6w55kB+q7qI9YYZqGiYA14lp2j6zpzQ+asr7FDQ8yMzqPWlm973XaaYl7CfidBnxN3OFAXXM5oEEcsZO6kXMPymaGYYkxVNSaHpvnWf/kMe8aPYxhw17vexoc++tv1+MrF8lnreDJLvFq5heVreQCcHifhhjD+WICWLW0EXuLV8oa8Z5VHNzLVVMYUYSMTSFYr/m19ZiZMiDCB4JKxamLsueee4/rrrycYNFdg3HHHHTz++OPcd999ABw+fBi3282tt94KwO/+7u+SzWZf9voEgiuN2ck5nn38OQA2xeJYrRZODM2RS+W5674bAQOlrOJocDA7OMXwLw8xcXSE2ckUSs0s7nbaaAh7CAVcJFqj+FsSOGMhM/MVCWBxvPwwz7VCr1Q4cXCKUycneer0ISay8zisdv7TO99OIhThm5/9F3K1sQ+V0jnW8tR8WW39Hfij/mXdh/5oALvTTizmu+LXIZ0lwrZvwr+lR4gwgWANWDUxNjs7SywWq1+Ox+MMDAzUL4+OjhKNRvnjP/5jjhw5Ql9fH5/4xCdWKxyBYF1TLlWYGpliamSK8dMTjJ8aY3xoEqO2+sdhtWK1Sew7PEk4EaZvdx/awiTVUpWf/mAf2YefAcDlsBIPuYk3BGnsaSLc04ozEcER8p9zjMR6IZ/MMDlwkqM/P8iJo6PMpDMcnRuhrFYIOHzsbOxm8oVhUr5ZAtEAoYYw7ds6lomsQDSAJ+C9oAGmGwlD09BKZbRSBa1UQiuW0coV83upjFYu13+WbEKECQTrgVUTYy/dbQYsK32oqsrzzz/PP//zP7N9+3a++MUv8tnPfpbPfvazK76NSGT1OqjWA7GYb61DEJwHK3m+NFVjamyG0VPjjBwfY+jIEGODkyTn0/VjLLJMwOWk2efFY4dUIYssSfhbwowdOMU7/r87ifgsHPz3wyQzJbLpAl0tQXbduoXu267F0xBb1116AIV0nkNP7eP5H+3h5PFR5jM5MuUiak18WiSZilqlPRrnM1/6c+KtCYKxwCuu5TlfLqfXl2EYaBUFpVBEKZTO8VVEyZdQiiW0l6w7OoPV7cTmceP0e7A1RHGGA8S297727tdLwOX0XAnE83UhrJoYSyQS7N27t355dnaWeDxevxyLxWhvb2f79u0AvPWtb+X3f//3z+s2ksk8un626NsIiDLK5cVLny/DMMgkM0wMTzExOM7Y8VEmh6eYm1k0z0uAz+Ug6HbR3NZILB6muaOJhq4mPIkQ7miQhx56hMH0ApIkMZHMANB71SYmfrmf+ZNzpDRweRx0NAWJbG6jZHVRms+vxUPwiuTTWQ48uZeDPx9geHCCZC5HqeYNAwi4XPS1t7BpZx/9mxoZ+NkRvvJ/v89NV28m0dsOQCZXgVzl5W7ivFgvry9DN8xMVamMXqqglmrZq1IZvfZdrf1svKSjE8xuU4vLaX55PbhjEeTaZavLiexymH9zOs7ZgJHKVyF/bvG2Xlgvz5VgZYjn69zIsvSKCaRVE2M33ngjX/rSl1hYWMDlcvHEE0/w6U9/uv733bt3s7CwwLFjx9i8eTNPPvkkW7duXa1wBIJVo1QocWximsP7TjJ6bJjJ4SlmJucoL8lQuGw2gm4nfYkosXiIpvYGmnta8TdGccWCuCL+cw4sLRVLuFwODMPg+IkJWlrChGxljj13CsNhY3Zigs27O5FlY91kwwzDYGJ4khd/soejLxxlbHSKVKFYz5Y7rTYiXg+tTQn6r9nC9lteR7SzoS4W1NHTPPfDfQAEgpdnd7WuanVRteyruLxMqFcqZ+2SBHNjwRmR5YxFFgWXy4nFbYori9uJZLOtm+ddIBBcOKuaGbv//vt53/veh6Io3H333ezYsYPf/u3f5vd///fZvn07f/u3f8vHP/5xSqUSDQ0N/NVf/dVqhSMQvGZURWVmfJaJwXFGjw4zPjjBzMQc2eziGhurRSbodtES8BHrDtHYlqC5p5VIewJ3NIgrEkA+j/lThXwJp8NBsaqwsJDnumt2MrPnOJVcmWI8hmEYdPQ0oY5Nwhp9KBdyBU4eOsXAcwc4MXCCyck5qqq5Q9EiyQRcLnpjcdpaEuy4YQed1/QTaImdc7o+ALpGrmiOmwhGgpfqbrwqhmGgV6rL/Vcv82Uo6tlXIEl1EWX1uHBEQqawcjmxuBxYXK76z+vZ3ycQCC4+qzpn7M477+TOO+9c9rt//Md/rP+8c+dOHnnkkdUMQSA4bwzDYGE2xcTgOCNHhpk4Pc7U2AwLyQx6LbsjSeB3Ogl5XPRujtHa0USivZGG7mY88dB5i66Xo5Av4LDbSRaKyLJEd2uI6SPThPo7GXhygJb+dtxuB7lLZFpXFZXRwXFOHT7Nkb2HOX10iFR6sQvaY3cQ9/iI+nx0tjXQf10/8a3dhNoTK388dJ1c0ZxoH4yEVuNuLMPQ9SWG91cSWRXQz1EqtFrqWSt7KIClKbEkk+Wo/yw7HFd8c4FAIDg3YgK/4IqmkCswfnqc0cNDjJ0aY2p0hrm5FMqSzIbbbiPocbGtq4VEc4yWrmaaN7Xjb4jgivqRrdZV80kU80V8difJdJGutgi5iSxWlx0a42TnMlx3160YhZyZFLvImTHDMJifSXL6yCCnjgxyfOAEY0PjqKo5NsNusRJwuuiLNRDzeWlvTdCxs4v41i6CHY1Y7bYLu2FNo1gyM2PhWOSC49cV9WXLhClVpZTJo5Ur6OVz+9Bkh70upGx+39mlwprQkm0XeD8FAoGghhBjgisCpaowOTzFyOFBxk6MMjUyxezMAoXi4vR1u8VC0O2kpylGoilGc2cTrZvaCbc11EXXpaZQKOKR7eiaTmdjiEq+Svtbb+b5nwzg8Djpft0mZp78JZIsIfHaxFixUGLw2BCnj5ri69Th0+SyZjOALEn4HS6afUFCLjcxn49EIkRzfxuJ7d2EupqxXaxOR12nWDafl1AiuuxP9VLhSwXWObJYhnqOUqEsYXE6cfo9WL0eHPHIWRks0/DuXLaKSSAQCFYTIcYEGwpd15mbmGPk8CCjx0eYHJ5idmqedDbPmWkrsiQRcDlpCPlJbOmkqaOJ1s3tJLqbcUeDWGzr52VRzJcoyQ48kpOgbMOTCOJsjjP4wnH6X78Tq92KoWmmifs8tJimaYwPTXDqyCCnjw5y+ugQk6NTdZO9x+EwxVe8kZDLQ8TrIRDy0NDXTOPOHsLdLdg9F28lkDkbyywTVpJZipUKsiShjE4yOzyxvFR4rrE5NutiqTASWi6slpUK7UiSJDq+BALBuuK8PnWy2Sx+/6vvgxMILgXZhQxDB08zenSYyaFJZqbmSS5kUJeMAPA67ISDPrrbG2lsa6C1r52WLR14E6F1JbrOhabplIolipKTtkQYq9VCw+5uTvziCJqisfX1OwHQNa3mRXp5NZacW+D0EVN0nT46yNCJ4foScofdRtDpoiscI+B0EXS58TodeHxOGnoaadjRTbSvHYfffV7xG4aBca5S4RJhdSazpVeXj1coV6u47Q4qswuLpcJgYNHovqSj0OJ0Iq/z51IgEAheiRW9gw0ODvJ7v/d7ZLNZHnnkEd7//vfzN3/zN3R3d692fAIBlWKZ4UODjB4ZrHcwziXTlCuLc6ocVivhgIf+vnYaWxO09rXSvrWLQHN83Yuul6NUM7EDxL0eQgkPNo+LIz99jnhnA9G2BACGqtc8Y+ax5VKZwePD9YzX6aODpGpDZS0WC9GQnxZ/EHfQTsDpwm2347BZcXscJLoSNO3sIdzXhjt87v94GbqBXqmc5cNaXi40/25o2tlXIMt1EWX1e3EkosuyV+roKaqagtvpoOWeXxOjGwQCwYZnRZ9S//W//lc+9rGP8bnPfY5EIsF73/tePvnJT/J//s//We34BFcQqqIwcWyE4cNDjJ8aM0XXXIpsYVGUWGSJkM9DR0sDja1xWrpbaN/aRby7GcuFGsbXKcWFecA0ykcCPnxhF/MzWZJjc7zhN+8AQNd0pmeTjE1M8aORaYZGZhgbnqgvx45EQ8T8ftq8ARyaBb/DiSzL2K0WXG478bYoTTu7iGzpwh3yopUq6OUyWi5HbnauPnB0mcgqn7tUuHQ2lj0WxnqmNFgTWuYQUiey/ZVnY+XGTlPVVKJ+rxBiAoHgimBFYiydTnPTTTfxuc99DoB7772Xb37zm6samGDjomsas0OTDB8aZPzkGFNjM8zNLpDKFhZHRwB+j4tYJMj2Hb00dzfT1t9Fy+Z2bJfB+pbXjKaSGjoNQMjtxhX1I0kS+587TKZaYuD4Cb737z9i8Pgw5VoTgtvtpKWjmWt3b8NeVrGWDWwW8yVus8o4HTbCCT/x9hj+qB+b3YJerqClkqR+PEGyqpwdhwQW5xnvlQt7OHiOrkLz62LMxjJ0naqiUNUUvO7zK4sKBALB5cqK6zeVSqX+v9S5uTn0c8zbEQhejv3fe4Y9T+9jdmaBZDpHVV0sX7kcNqLhAJ09rTR3NtG2pYP27T24fFfmh3G5UOLwT5/h3x/5KQCSVeIn+wd49OksqVwRgBPfnqKtu4Wb3nQDxtA0dt2CXgVUIGdgtdhweq34vHbCIRderx2H02K+hqsFtNky1LoG7UE/lsb42R2Fl3g2lqFr6LkMlUIVRVPx+zyX5HYFAoFgrVmRGPuN3/gNfuu3fotkMsmDDz7If/zHf/DBD35wtWMTbBCqhRI/ePiHjKeyxCMBNm/ppKmjkdZN7XTu6CUQWz9T1i8luq4zeHSIgecPcuroIGPDEySTCxTLZYzajhxZkhicmcHtcNDRFMVvcfPm9/8/XPfm67E77IzuPc5TR7+PzWHB4pDx+exE4n7CTSF8iTBWj+ucIkuyWde8BGhUKxiFPOpCkursPEomj1JSmJ5KoRk6wfCV+e9CIBBceaxIjN199920t7fz1FNPoaoqn/70p7nppptWOzbBBsHQdAzDIJYI88n//cm1DmdNmJua48AvD3Ls4AlGTo0yOzNHNl9ANxYzzHaLlaDXQ1djlI7GKFG7m8bGKF/69o+4/qrtXHXL6/jZw09x9Ruvxu4wZ3oNfO9n2Kwym/rizE7keP0f3o3jEkytP18MQ4dSEb2QQ0unqc4nUXMllFIVXaktTrdZsUfD1Oa9Eu/uWLuABQKB4BKyIjGWz+fZt28fH/3oR5mYmOChhx5i9+7duIWnQyBYRiFf4PALRzmy/yiDJ4aZHJ8inc6iaIsDSC2SjN/loqcxTktDhL6eVrZftZlYayOyz4dVLyKrJV58eC+VWnbIH/KzMJnEHfDUy7dTB06xMJOhb3cHqGUkCaxu11rc7bMwVBWjkMMo5tCyWZRkCqVQQS1V0apmiVqyWLBFwtgb4jgSMSx+H5IkkXv4cQAisfUnKgUCgWA1WJEY+9M//VNaWloA8PtNI/EnPvEJHnzwwVUNTrBBMMyi20ZqjFMUlaHjQwzsOczJI6eYHJ1kdjZJubq4WkdCwuNw0BgK0hQL0t3RxLYdvXRs6cQW8CN7vcgu11nlQnk2Q1WTMVSNM/sB/H4vyfF5Ii2x+nEH/u1ZZFli+9tu4Ng3n0S2SMiOizQF/zwwDAMqZYxCDr2QQ8/lUDNZlJKCUqyiVWpCVJawhUO4GuLY41GswQCSfPaU+4WkOYYjEo+e9TeBQCDYiKxIjA0PD/OlL30JAJ/Px8c+9jHe9ra3rWpggo2DUR+DcPmpMV3XmRqb5vC+I/US48z0HPlCgaXDHZxWO0GPm0RzAx0tMbb0d7Fl5ybcsRAWrw/Z6zmn8Dj7BjUkrUqlZB5brK308fq9DL0wyNY3mINeZw+eZnYyRdfOTpx+D5qiYZHlld3Ga8TQNYxiwcx8FXLo+RxasYxSrKKUVdSSYo6+kMAaCuLpjGGPR7FFQivquEwlUwDEG4QYEwgEVwYrEmOqqpLP5/F6vQAUCoUlH7ACwatQ+6eynqWYYRgszKc4cegkR148xuCxISbHp0lns8s6h22yFZ/LRV9TI60NUXp7W9m2q4+OrZ2UZbuZ7Xoti6Or5ky1csEUYfmagcpms6JWFSItMQzDYOC7zyJJsPOeN4Cuo6oGsnV1hNgZo31dfBULaFUVtVRFKWuoxSpG7TGyBvy4m1tM8RWNXNBk/HQqA0CsMfYqRwoEAsHGYEXvlL/+67/OPffcw5vf/GYkSeKHP/wh73znO1c7NsEGoS7c10mdMp/NM3xqlMP7j3Ly8CnGRyaYn19AUZf6uiy47XZawxGzxNjZRP/2Xto2teMMB7F4fUhOR70igM2QAAAgAElEQVTEGLpIuw4lpYwBVHNlkCRy+RJWWUatmD6rcEuM+YMnmZ5I0bylDV8siJZJo2s6losgxpYa7esCrFpBV3WUkoJa1VEKFQzFfKwsXg/Ojlbs8Sj2WATZ8dpnwGUyWWyyFZdnffjfBAKBYLVZkRj7nd/5HXp6evj5z3+O1WrlIx/5CK9//etXOzbBRsEwWIs8aqVSZWxonBOHTnJs4AQjp0aYmZmnVC7Xj5ElCYfVTtTjIxH209ESZ1N/F739XfibYlh9fmSP+5KU/wCkagmsDiqZOew+N5nRCZxWG4VMAYBgIsQv/seP0HWDXe+8xTzJMNA0A4fj/GNcarQ3CnmMYh50HV3VUKoGakVHyZfQK+buSNnpxNHYYIqveATLKjTx5HJ5HDb7mo/eEAgEgkvFimsIt9xyC1dffXU9y5FOpwkGxRwgwatjGICxeokxTdWYHJ9m6OQwR188xuBxs4sxm8vXj5EAu8WO1+GgrSlMayJCX28bvdu6SXQ14wgFsHi9SNY13GNpGKCUMFwBqukc9qCPzIsZHDYbhXQeX9RP7vgQUxMpEl0NhFvjtdN0MzP2KiXBlxrtjUIeKmZZVNd0VM3MwCm5ElptL6Zks2GPR7DHTd+XxetZdZGUy+dxXYQMm0AgEFwurOiT55/+6Z948MEHURRzXYphGEiSxNGjR1c1OMFG4eKUKQ3DYG5mnuFTo5w8fIqTR04xNjzBwkIKXV/MvdktVpxWB+2ROM2xIF0dTfT1d9LS14a/MWaWGB3rMPOiVpAMA8PuopLJE+xtJZPO4bRayS5kCTdGOP7DvSiqzo63L875MzQdXTew2peb419qtDcKeaiN2DAkGdWwoJYllGwRNVsTrhYL9mgYV3cn9njE7Hi8xI9ToVgk6PJd0tsUCASCtWRFYuzrX/863/jGN9i6detqxyPYiFyAgT+TyjIyOMrgiWFOHDzJ8KkRZmfml/m6rLIFh8VOwhsiEfbT3hynb3M7bX1txDpbsAf9yG73+hNdL4NUNcunqi6jlSrYgz6y6RxNbi/5hRxNLREmRzOEmiI0bGqtn2dUFTTNFGN6KrkovkpFzjz4hs2JJttRKzJKtoCSytQ6HiVskRCe/j7TdB8OXbKS7MtRLJVojiTWNAaBQCC4lKxIjMViMSHEBBfMKxn4i4USY0PjjAyOcuLwKbOLcWKaUmnR12WRZBxWO367m2jMR0siQl9vK+29bTT2tuBNRM0S40VYVL2mKCUM2UIlZ84qkz0uyqUyNl8QXdcpz6UoV1Su+bXrAAOjWEAv5KhMjAFgkXS0kZMgy+DyoHsCKEXFXDOUnMTQzCYAayiAu7fL9H1Fw2tbmn0JpWIJVdPwesReSoFAcOWwonfhm266iX/5l3/htttuw7HEyyE8Y4IVYRhohk6lXOKpHzzL0MlhTh45zfjwOJnMYgeihITDasNhsRMPx2mKmiXGzr5WmrtbiHQ2m8NS7Zd+sOmlQKqWwOaiOmeWDMu1rJZNNkVmIV/BE3DRHFBQD+6F2jiJas3cb3HYqdqDKOkc1aExjJqtwOLzLu94XMeP30JtxpjXK8SYQCC4cliRGPvKV75CtVrlgQceqP9OeMYEKyWdyrB/fBDdMDj0wEkAHBZTdDV4w2aJsSVOd08rTd3NNPa04o5HzjmdfsOgqaBWMEpFyvMp8yuZI5uukBxfIJcpcvqJPQCominKymWVq69pQ8ZACseRPD4kj5fyT35pXmW+RP7ISWS3C0dzreMxFsXicq7Z3VwJ1UqVwwePsf/5A+z5+QsABAL+NY5KIBAILh0rEmMDAwOrHYdgAzM9MYNuGDTHE7zl19/Es99+hjtv66dz9yZiO7Zg822AEuPLYRigVqBSppJKUZlLU0pmKCTzLExnSCULFEsKhYpCsaxSri564vIj0wA4bVYawh5KJY2ed/wqVu/iOIlqcp7hvYPIskSwMUjktpuxeNa3T07TNE4ePcW+PQfYv+cAhw4cpVqpIltkNvX30pdop7O99dWvSCAQCDYIKxJj1WqVn/70pxQKZjlE0zRGR0e5//77VzU4wcYglzZLkZFgELtsw2K10N/bgKejGXswsMbRXRwMwwBNxVDKaNkcpdkk5fk0pWSO1EyW5GyOQrFKsaxQqKiUKgqLVjqJQDxI86Y4kZZY/WtkdIJDf/xFAi4nhg6xrgYcS4SYXiww9vjPKOQVmtr8uCJBrOuwvGcYBiODo6b4ev4AB/YdopA330s6ezp46zvfzO5rdrLjqm14vR4+95//G27vxZ9fJhAIBOuVFYmx+++/n7GxMebm5ujv7+fAgQNce+21qx2bYIOQy5geKJvNTno+jT/kQ5aldTOR/7wxdFCr6IUC5bkk5bk0kwtZUjMZFqazZHNlimWVYlmhWFHQlozd8Aa9xHobibSdEV5Rgg0RrPazX4pDQzVjvkWmVFbp39WzGIJSJf/ifkaOz+N0WYm3Bl/bGqaLzPTkDPueNzNf+/ceIFVb/t3Y3MAb3nQzu67ewa6rdxCOhJadp+s6alXFtgYLzwUCgWCtWJEYO3r0KE888QR/8Rd/wQc+8AEMw+BTn/rUascm2CDkajOs7DYrmWSGQKTmB1rvYswwQNcwykUqyQVKswuUk1nysxnmpzJk0kUKS0SXoi7usHS4HURa43S2Jwg3x4i0xog0R7G7Vj7MVFW1ZZdbdnSZYek65aOHGT4wiarobL6hA1QVybp2pd7UQtoUXnsOsH/PAFMTZok1FAly1TU72XX1Tq66dicNTa88skKpmE0HNsf6EZYCgUCw2qxIjMXjcaxWKx0dHZw4cYK3vOUtlEql1Y5NsEHI1ybhW202MvMZmjoaANaXr8nQMZQKykKa8twC5fk0+dk0yckM6YU8hZJCoaxQLCtUlEWRZLVZCTdF6NrUgicarJcYXf7X7tvSamJM0wx8Xjv+RAjDMKieOk5ufJbpsQzBsJOGm3Yz9+OfX9IRFYV8kQP7DvLingH27TnA0KlhADxeDztft513vudtXHXNTtq72s7rcTgjxuxOkRkTCARXDit693a73Tz22GNs3ryZb37zm3R1dZFOp1c7NsEGIZcrYJFlLFYLyeQCW67qM/+wFmLMMEBXUbM5yrPzlOdSFOczLEymWJjNkS9UKVZM4VWuqPWdmrIsEUiEaOtPmFmumujyRQJIskTsIi0KX4paG3CrajrR5hCSJFEdG0GZmWbwWApZkui5tgM8AdB0pFUsU1YrVQ4PHDVLj3sPcPzISXRNx+6ws21nP2/8L+/jqmt20ru5B8tryNApZXMHpihTCgSCK4kVibFPfOITfOtb3+KjH/0ojzzyCO9973uFeV+wYgr5IlbJgmEYqFWVQNgHqCCvshgzdPRSkcpsktLcAqW5FOmpFMnpLNlsmWLFzHQVyyq6sejr8oV9JDY3EW1LEG6JEmmJEWwIvyaRcSGcyYzJkkRDdxPq/BzKyBDJnER2LkdDs4/g7q2gmeXRi1mm1FSNE8dOse/5A7y4d3nH4+atfbznN+9h97U72bp9M/aLKJyqZ8SYU5QpBQLBlcOKxFhnZyd/9Ed/BMAXv/jFVQ1IsPEoFIpYZBmtJhoCIR8oqYuXGTMMDLVKZX6Bcq2LMTu9wPxkmsxC0exgLCsUKyqqtujrcnmdRNob6G5P1DJdcUJNkXVTIlNrYy5kWSLWFqVy4ii608PgT47idFroeF0bki+EXttWIL3KovBXwjAMhk+PsG/PAV7cM8CBFw5SKBQB6Ort4M673mJ2PO7ehmcVOx3rZUqRGRMIBFcQK3r3fuMb37jM9yFJEi6Xi97eXv7kT/6EeDy+agEKLn8KxRIWi1zP9PjDPphJXZCnytA1lHSGyuw8pbkUhZkU8+MLpObzFErVuuiqLvF12Rw2Qo0RWjsSRFriRFpjhJujuHzre3xCuWj6Mj1OO3J6FingYmKiglKs0NIbxrt1C5IkYSg10XaenrGpiWn27xk4q+OxqaWRN/zqrey+xux4DIUv3aYNpXImMybEmEAguHJY0bv37bffTqFQ4N5770WWZR555BEKhQKbNm3ik5/8JH//93+/2nEKLmOKxRJW2YJaEw2BkBdmeOXMmGGglYqUZuapzC5QmE2xMJFkYSZDLl+pZbuWD0mVLTLBeJCO7Q1EW+OEa74ub9i3vpoFVkhmegEAv8eObJFQo62MfudxAkEHif4mJJ8pkvSat+zVDPwLyRQv7h2odz1OTcwAEI6EuOraXey+egdXXbuLROPa/edKGPgFAsGVyIrE2N69e3n00Ufrlz/+8Y9z991385nPfIZvf/vbqxacYGNQLFXw2JyoiorFasHjc1MEkGQA9KpCZW6e8myS4lyK9ESS+ck0uezy0RFL9437wj6auluI1EuMMQLxELJFXrP7ebFZGJ8DIOhz4t7az4vfeR6LRSLR7MPbv7kuMI0zYuwlZcp8vsDAC4fYv9cctjp0egQwOx53Xb2du97z6+y+diftna3rRqzWPWNitIVAILiCWJEYKxQK5PN5vF4vAPl8nnK5vKqBCTYGmqZTqlTwO9yoioov6CU/OEp2tsDM939GciZDeqGw6OsqLx+S6va5iHQ20tfRQKQlRrglRrgpgtW+8T+ss7NmZswbcDE/niU9PE2iwUO4p6GeFYNFMaboOof3HKgPWz1+1Ox4dDgcbNvVz21veQO7r9lJ7+ZuLOt0/ZTophQIBFciKxJjd911F+9617t485vfjGEYPPHEE9xzzz18/etfp6ura7VjFFzGzNeyMRYkKtkCLqfBf/zvn5w1JNXutBFujNDe2UC0tdbF2BzD4VnfS64vJoZhYChVjEKB/MQ05aIpTOxeN8f//TlcXjvhuHtZVgzgxz/6GT/43hOcePB/oSgKskVmy9ZN/MYH3sXuq3fQv2ML9stAvObTeaaHpgBRphQIBFcWKxJjH/jAB9iyZQtPP/00VquVT3ziE1x//fUcOnSId7zjHasdo+AyZvy5F2s/SaiagaaD7HbQGvOQ2LWJWE8b4ZYonqB33ZTKLgWGpqGXiuiFPHqhgJbPoWZzaIUyalVjZiqLYRhIkkQllUMpVUm0+Qh1L8+KjQyN8fkvPURTJMjb734Lu6/bzY7dW3F71ndzAkClWGH06DDDB4cYOTTE3NgsAKGGsBBjAoHgimJFYuyee+7h3/7t37jllluW/X7btm2rEpRg46DkzY5AzTAwDAO7xcI77ruT6rGj+G65Gotn/S22vpgYhoFRKaMXCuhFU3Rp2RxKJo+maKhVDa2qoSkaxpLybFUBA5DAXP49V6JxVxue9sZlovXh//kNbFYLn/3UH9J8/VWX/g6eB6qiMnlynOFDQwwfHGTy1ASGbmC1WWnZ0sa2W3bQvr2TREcD0mrPoBMIBIJ1xIrEmNPpZHp6moaGhtWOR7DBqBYrAKiahmSR8HhcWCRTdFzK9T2XAkNV0YuFerZLzWZQ0jnUctUUXFUNVdExlsw6k+02bKEgrlAAW9CPLejHHvSjvnACfrzPPMZp7rOUJQPsrvq583NJfvyjn/ErV/XTdPWOS3tnV4ChG8yMTDNyaIihg4OMHxtFqShIkkRjdxM3vP1mOrZ10tzXes5F6QKBQHClsKJ3wFKpxG233UZDQwNu92L547HHHlu1wASXP4am1sWYYRjIskQwGljS/bf+fUznwjAMjFKxJrwKaLksQ5kslWzRFFy1TJe+xBMnWS3YAn48oQC20KLosrjO7YlTysqSG6xdhyyBY/H4b/3jv6DpOvf85t3rQtgahkF6JlXPfI0cHqKUMzOjkeYoO35lNx3bOmnr78B5BXkBBQKB4NVY0Tv4n/3Zn612HIINiJrNs7hlSMLhdOAN+jAUBWQZSV7/YygMpWqWGAt5tFweJZ1GyeRRK0ot06WhK4uiC1nC5vfhbAhgr4uuABaP67w8cWpFOet3kizVM2OFbI7vf/8nXLetj67r1q48WcgUGKmJr+FDQ2TmzMGxvrCPnqv6aN/WSce2Lnxh35rFKBAIBOudFYmxa6+9loGBAY4cOcI73/lODh8+zO7du1c7NsFljpLOYCzZ+eiwWnEHvRiquu6yYoaum5muYgE9n0dNpamms6jFyqK3S9HqWSoAq9eNozGKPRgg0p6gLNux+jwXRWQqFaXuGTNqNypZLGAxX7Lf/eq/UixXeff/+58uaeNDpVRh7OgIw4eGGDk4xOyoOTjW6XHS1t/BdXfeSMf2TsKNkSuqIUMgEAheCysSY48++ihf/epXqVQqvOlNb+LDH/4w999/P+9617tWOz7BZUw1m10mxiwGuAMeDEVds7KaYRgY1cpiB2Mmi5JKo+aKNTO9ilpdLrosLge2SARPOFgvL9qCPlMc1QjHfMzN5S5anGqlWtujLtWzi7LTzK5V8gX+7bEfs7Wnne23XHvRbvNcaKp2lule13QsNgutm9p4w3tuo31bJw1djciXQaZTIBAI1iMr+kT8+te/zsMPP8x73/teIpEIjz76KB/84AeFGBO8Imq2UNc0EmCTZDxBL4aqvKal1ivF0NR6F6OayVJNplCyebRytd7FuLSDUbZZsQUDOCNB7MFamTHgR16DGV1KRTFXDSxJLkk1v9gPvvYIyWyeP/iTD1/02zV0g9nRmbrpfuzoSN1039DVaGa+tnXSvKkV22Uwu0wgEAguB1b0iSjLcn36PkBjY+O6neAtWD9Uc4Vl+ydtFguegAdDySPZL94cKcMwMMolM9uVzVJdSKGks6i1mV1aVUNf0sEoWWRsAR+O5iD2cOBVzfRrgVpRzmyLwtDN2CWnCyWd5TuP/ZjWpgQ33H7zRbmt9GyK4YOLpvtitghAuCnC9jfsqpvuXV7Xq1yTQCAQCC6EFYmxYDDI0aNH6x6Q733vewQCgVUNTHCZY+hU82UsrjPZEwmLJJuesWQa+QKHkhqKYprp8zmUZIpqKoOSK6BV1LPN9JKEzefGGQ9gD4ewhS7MTL8WqOUqkiSZibEzYszh5tlvPcbo3AIf+dh9F1wWLGaLjBxeNN2nZ1IAeENeunb20L7dNN37I/6LdG8EAoFA8EqsSIx97GMf4w/+4A8YHR3l5ptvxuFw8Hd/93erHZvgckatohSrlJd4r2RJMsuU08qresYMXccoFdHyeZSFBZSFDEo2h1qsoipmtmspVo8TRyyELRLCHg5iD/ovmpl+LThTGgTAMMVYNVvgO48/TTgU4PY7b1vxdVXLVcaOjZqZr0NDzAxPA+BwO2jr7+Cat1xHx/YuIs3RdS9SBQKBYCOyIjHW1dXFd7/7XYaHh9E0jc7OTmzrrBtOsL6QlArVokK6ZM4Zs1osSJKEK+ChsqSb0jTUV9EKObODMZlCTedQCqVzm+kddqzBAO4zoisUwBZYbqbfCKgVhUUtZoqxIz9+lsMjE/z2fb/5iq8/TdWYOj1Zz3xNnBgzTfdWCy2bWnn9u99I+7ZOGruakC2Xp1gVCASCjcSKxNjrX/967r77bu666y6am5tXOybBBsCoVlBKVVJZc+inzWrBYrVgVUqUNZ3y9AyliSmUbKE+s+ssM73fizMYwB4Nm96uNTLTrwXqGQM/i2Lse0/9ApfLyVvv/rVlxxqGwfzYHEO1zNfokWGq5SpI0NDZyLVvvYGObZ20bGrD5rgyHj+BQCC4nFiRGHvooYd49NFHec973kNvby/33HMPt99+O9Z1MPVbsD5R8zkwYCFtjnuQZQmXy0px3z6y8wWYLwCmmd7qdeNO+LFHamXGdWamvxRoVZXs9AKZyXkyk0nyyQxKRUVCAl2nWK3w/IlB7nrP2/F6l+/zfPKff8jz//5zAEKNYbbesoOO7Z20b+0UpnuBQCC4DFhxmfIjH/kIf/iHf8gzzzzD3/7t3/LAAw/w3HPPrXZ8gssUQzFXHlVqa31kJGw2C3gDQBZfbxv+7f2XhZn+YmIYBqVUnsxksi68spNJcrNpzgwUs9it5gJtuwXZYs4Zy5SL6LrBzpfsoEzPptn7f3/Jlhu38Sv33k4gKhprBAKB4HJjxamtZDLJ9773Pb7zne9gGAYf+tCHVjMuweWObhrstdoeSiQJq1UGmznSwhEJYvVeWEfl5YJaUcxs18Si6MpMJlFqPjoAd8RPoClC865uAk1RAk0RPFE/3/qjr+AoWZElCU3TKSrmOdFYZNltPPfo00iyxG3/+U34wqL7USAQCC5HViTGfvd3f5d9+/Zxxx138OlPf5qdO3eudlyCy52az0k9syzbMLBa5bovTLZdvDlja41hGGRn00weGlkUXRPz5Ocz9eYDq8OGvzFCy+4eAs2m6PI3hrG5HC97nQZmB6qq6BSUMgCRWLh+TGp6gYGfvsjr7rhGCDGBQCC4jFmRGHvjG9/Igw8+iMfjefWDBQLDAMPMjKm1zJiEhNVmwagNX5Xtl6ffUClXyU7VvF0TZ7Jd88sWe3uiAQJNEVpf14e/KUqwOYI77DcXfa8QQzfAMJAk0FSdQrWC1WIhGFosQ/7s0WewWCzc8PaLM/xVIBAIBGvDij4R77zzTh5//HEymeWLnz/wgQ+sWmCCyxhDNxdcGwZaTXwZmJmxM5Pw19ui8Jdi6AaFhexLSozzFOaz9WOsTjuBpght12yieVMLFr8Hf2MYq+MiZP0MA8MwkJBQFY1cuUQo6K8Pel2YSnLo6QNc82vX4Q35XvvtCQQCgWDNWJEYu//++5mdnaWvr++KMlsLLhBdxwA03cCo1enqmTFVA8nsolwvKOXqWaIrM5lEq57xu4E3FiTYEqP92i34myIEmiK4w7766yF2kReFG7opxpBArWrkymXCocVS5LPffhqr3cr1b7vpot2mQCAQCNaGFYmxwcFBvv/974tRFoKVUStRavqS1UQG2Ow2dEU11/xIl16MGbpOfj5LdnKe9ESSbE10FRcWRZTN5SDQHKHj+i34mxa9XdZLPN9sqWdMU3Wy5RI97Q0AzI/PceTZg1z71hvwBL2vfEUCgUAgWPesSF01NDSsdhyCjYRuTs1Xdb1e1pYwTexGTYxxHv6pC6FaLJOdXJzblZmcJzu1sCTbJeGLBwm3J+i8cWs92+UKetdF9teolSnNRw6yxRKRcBBYmhW7cQ0jFAgEAsHFYkVirK+vj/e9733ccsstOJ2LwziFZ0xwTmoZMU0zlmwykrA57GZmTAYuUmZM13Tyc+n62Igz5cZSOl8/xu52EGiO0nlDTXQ1R/AnwljWcROBWaY0h/BXVIWyohAOBZgbm+Xozw9xw9tvxu0XDTUCgUCwEVjRp1GhUKC9vZ3R0dHzuvLHHnuML3/5yyiKwvvf/37uvffecx731FNP8cADD/Dkk0+e1/UL1ifSsjLlksyY046u1sqUF5AZqxTKZmlxYsnA1OkFdMW8PUmW8SWCRLsb6yXGQHMUp9+9LrJd54NRN/BDvlIbaxEJ8uwjP8XutHPtW29Y2wAFAoFAcNFYkRj7zGc+A8DExASqqtLe3v6q58zMzPCFL3yBRx99FLvdzrvf/W6uu+46enp6lh03Pz/PX/7lX15A6IJ1i64BRs3Av4jN6cBQq8iS9IqZMV3TyM+ma6JrUXiVM4X6MQ6vi0BThO6bt9dLjL6GMBbrBlkYvqRMeUaMWbBw7BeHuOmuW3H7NvbAXIFAILiSWJEYGxkZ4cMf/jCzs7Pouk4oFOIf/uEf6O7uftlznnvuOa6//nqCQdPncscdd/D4449z3333LTvu4x//OPfddx8PPvjga7gbgnWFrmMYEtoSz5gsS9hcdvRsCYtt0TNWyZfqpcUzZcbc9MLiCAyLjD8RIt7XUhddgSYz27WRMQwzOyZJi5mx0SOTONwOrvm169c4OoFAIBBcTFYkxh544AE++MEP8o53vAOAb3/723zqU5/ia1/72sueMzs7SywWq1+Ox+MMDAwsO+ZrX/sa/f39YqL/RkPXQJZRtcVuSgkJq9OJkUoyNVfi9P96gsxMikq2WD/G6Xfjb4oS39RKoNkUXb54EHmjZLteBbWqkBqZYv74kHlZ1cGAXNkUYzNDc7zhXW8Uy78FAoFgg7EiMZZMJutCDOCuu+7ioYceesVzlg6HPcNS386JEyd44okneOihh5ienl5huMuJRDZ2W38sdnkO88wX56gioddLbaYY8/qcVMcNhgfT+GIG7bu6iLTFCbfFibTFcV3m2a6VPl+GYZCamGNy4CQTh4aZHZkjmypSLqvLjlMV3TTxe+1YZBmrbOGO996G5zJ/nNYLl+vr60pEPFeXF+L5On9WJMY0TSOdTtdLjgsLC696TiKRYO/evfXLs7OzxOPx+uX/v707D47srO9//z7n9K7Wrm6t3dpHs3vGxvZgg13OL2MH7GGdWxAIgzFxkoLYdZ3cBDBQcUERKIcKASfXN/BLKj8SSOFLiHOdS4yzcokXvNsz41kkjfattUvd6u2c89w/utWSPDOSxpampdb3VQUz6j49Oq2j7v74+zzP93nyyScZHx/nwx/+MOl0mkgkwsc+9jF++MMfrvvkJyej2PbFoa8QbHQT0atJT6ZIJtKgyDV9dRg60dkFVDITODp+7XrC7+jIPSaatIhu0+cLl79eyWicqa4+xs/2Md4zxuxElIWF1IrfW0PXcBg6fq8LX5GL0mAJVS11DP5rElc8SXlTJcbJUzjdThaSFgvb+Oe0VWzn19dOI9dqe5HrdWm6rq1aQFpXGPuN3/gNPvKRj/Ce97wHgH/5l3/hk5/85KqPuemmm3jkkUeYmprC6/Xy1FNP8dWvfjV3//3338/9998PwODgICdOnLiiICa2MAVW0kSxVCF1aAYqlSaZzKx8LKosXeUf2H7MlMlk9wCT53uJdA4zPTJDdD5JOrvSEzJtKhyGjsfpwON1Ulzmo7IxSEVHmLKWhot6nBm/eBHDtEinUmiaRlGxDE8KIUQhWlcYu/XWW/nrv/5r0uk0A8abJcUAACAASURBVAMDjI2NcfTo0VUfU11dzQMPPMCJEydIp9McP36cgwcPcu+993L//fdz4MCBDXkCYitSmNkht8X6j6Hr2Kk0qdRiGCu5zGO3NqUU0cgUU519jJ8fYGJgMjPEGE+vOM5h6DgMHa/fg7/YTVltOZXtISp3hfHXVK5rHpxlmhiGTjqdRtc06SsmhBAFal1h7POf/zwf//jHOXHiBMlkkr//+7/nwQcf5Hvf+96qjzt27BjHjh1bcdulHtPQ0CA9xgqMmTKXdZEHh2Fgp1IkUxa6oePeBlWeZGyB6e5+Js8PEOkeZWYiSiyaXDHEqOsazmVDjCVVxVS21lKxq5HSxlpcXvdb/v62ZWM4DMx0Gg0Nn8wVE0KIgrSuMDY9Pc2JEycAcLvd3H333Tz++OObemJiG1MKK5lGMwwUNhoahqZhp0xSKQtfsWdLNWG1TIu5/mEmO/uIdA4zNTJDdC6Rq+JBtmmtQ8ftNPB6nPjLiqgIB6jqCFPe0kCoo46Jiejlv8lbOS/Lxu10kUovgKbJPpRCCFGg1j2Bf2xsjOrqaiDTqPVSqyWFWGQmTTSng8UiUmaYMkUyaeGvyk+osG2b+OQ0U939RM4OMtE/wdx0jEQ8zfJfZ0PXcDoMSorcFJV4KKsup3JXA5XtYYrrA+jGxQ1rNyNc2lamipheMNEUFJXKMKUQQhSidYWxu+++mw984AO8+93vRtM0nnnmGf7wD/9ws89NbFsKM2miuxzZxqUahqZjJVMkEybBss0fbkstxJnp6mOie5BI1ygz4/PEoimsZb3PdC2zitHnceLzuSmpLKaqpZby9jDlrXU438YQ40YwLQvDMEgmk5kJ/GWyXFwIIQrRusLY8ePH2b9/P8899xyGYfDpT3+aXbt2bfa5ie1KgZlMozscKDL7Kxq6jpVIY9uKooqNCxWWaRIdGGaye4Cx88NMDc8QnY2TfPMQo6Hjcuh4itz4y3xUhAJU7gpT0daAt7JkSw2bLlqcM5ZKZCbwF5VLGBNCiEK0rjAGsHv3bnbv3r2Z5yIKhsJKmGhOY2k7JE3LtXnwvYW2Fkop4hNTTHf1M941xHjfBHNTMRYWUhcNMToMPTPE6HdTWlNGVXuIivYQxQ3BbbV3pWVZ6LpOMplC03SKSmXOmBBCFKJ1hzEhrsRSZSwzn0rTNNLpzBChP1C26mPTCwvMXuhn4sIQkc5RpiPzxKLJzPZAWZoGTsPA53bi9bkoqfRT2VxLRXuIitZ6nEVbf7XmWmzLxjAMUqk0OhpFZTJnTAghCpGEMbHhlGVjpUw0nw+FQs8OAS6GqaJgOZAZYowNDjN1YZCx8yNMDk8zPxMnmVy5LdBizy5/iYviUh/lDVVUtoWo2BXCFyjbkkOMG8EyLQxDx0ybmTljUhkTQoiCJGFMbDgrlWmAqmeHKRfDWGwhhaVs/vv/epyZqRjxhfQltwUqLnLhK3JTVl1OVVs9FW0NlDTWYDh31q+rtbia0jTRNQ23L78LCoQQQmyOnfXpJq4KM5ntRp+dwK9rmVYQkzMJHLrO8MB0ZlsglwOv10lJRTGVzTVUttVT3h7C5d+ZzU2TiST9vYP0Xuint7uP3pEBen/aTyy2QF1pVcFWAIUQYqeTMCY2llLYiVTmr5kv0fVMiHA7DXxOJ/WtQfYevw1fTQW6fnHPrkKXTqcZ7BtaCl0X+unp7mNkcBTbzgzlOhwOdHRq62vYW+ul3L09t48SQgixNgljYmPZJraZmfNlK5WpjGXDmG5o6IaGr7wYf11VPs/yqrBMi+HBkVzY6sv+Odg3hGVlVpbquk59qI6WtiZ+5Y5baW5tpLElTEO4js9+6AFuuvUIrsFp5qKpPD8bIYQQm0XCmNhYZgo7O1Hfzu5NuThMqSsNDe2SHey3M9u2Gewf5qUX3shVunq7++jvHSSdnT+naRo1ddU0tzZy06030tzaSFNLmFBjAy636zL/rkLX9cxE/m3UkkMIIcSVkTAmNpRmprCyYcyybBQKIxvG0DL/07bp0KRSivGxiRXDi73ZilcikcwdF6wO0NQa5robD9OUDV3h5hBer+eKvp9t2ei6jmlaGM5LBzYhhBDbn4QxsbGsdC6MpdPmijljSmXzmLG1J6IrpZienKH3Qt+K4cW+7sxk+kUVleU0tTby3g/ewYFrOqgMBmlsCeP3b0w/MNu20TSwLIXTK5UxIYQoVBLGxIbSzBS2nQlbqbQJKAwylTAt28ViK03an52Zy4WtXMWru5+52bncMSWlJTS3hvkf770tN7zY2BKmtGxpUn0gUMz4+PyGnptt22iAZdt4dlhbDyGE2EnkHV5sLDNFdkEgqZS5YgI/WmbuVD7CWDQao+9Nw4u93f1MTU7njikq8tHU2si7f+WdueHFptZGyivy01jWtm10wLbUjuuxJoQQO4m8w4uNo1R2mBLQNNLJzL6Ri3PGNE3LDlNuXhiLxxP09wxcNLwYGRvPHePxuGlsCXP9TdfR1BqmqaWR5tZGqoKVW6aXl1IKZSs0pbBsG4dLXqpCCFGo5B1ebBwrjUamkqO7HKRmYiuavmrZ/9uIOWOpZIqBvsGl4cWuzJ+jw2O5zcmdLieNTSEOXLsv1zKiubWR6trglhoqvRSV3ZlAUwrbVhhOZ57PSAghxGaRMCY2jpnphWVZNobTSSrb/FVfVm3S0K5oNaVpmgz2Dy8NL2ZD19DAcK5BqmEYhBrr6djbzh3H/gdNLY00tTZSV1+zbVtCLD43DYVlK6mMCSFEAZN3eLFhNCvTU8s2FbrTIJnMhLHcMCUaaKAbFwcky7IYGRrNzeVaHGYc6BvCzDaR1XWduoZamlrD3Hr0XTRlK1314TqcBVY5yoUxW2XmjF2mF5kQQojtT8KY2DhmCqXp2KaJ7nKSyoax3JCgBijF5Owsw794nt7upQn1/b0DueMBauuraWwJc+Td19PYku3V1dSA27MzNsvOhbHFOWPuwgqbQgghlkgYE2+PsjMhLL5AYjhCbGaB0d5RphNJpsYzKxU1NF4b6ebpvgWm41HS/2nlHl4VrKS5tZHD1x/MrV5sbA7h9Xnz9YzyKjYfo797gK7XzgFgx2KZYUpZTSmEEAVL3uHF+igFtolKJUhPz5AYn2a8b4S+8yMMD08zPjXPdCzOzEICK1vVMe1M6ErbJhemRggUlXCgNsyhmw9yzdGbaWoN4y/25/NZ5Y1pmoz0j9J35gJ953oYuDDI4OAYM7Ox3DFFHhc1PiedtrS2EEKIQibv8OJiyoZ0CjsWJR6ZZG5knL6zgwz2jzM2PsfU/AIzCwkSaTP3ELfDoKzIy+6GANWVfmprK/npL04zGp1AqUw4u7l5N+3VNRy581co29uar2d3VSmlmJmape/sBfrOXGCge4DB/lFGxqawrOwCBF0jWF5Ma32AhiP7CTU30Li7icpwPZrXwy+e+YZUxoQQooDJO/xOtljtSsZJTUwTHZ1g8PwgAxdGGR2bYXI2xnQswfyyfRcNXaPU5yVUVUawwk9ddRkNrbUEmuvxVJVj+P0YxX40h4P/578fBMhVynxON4ah4SgqzCHIVDLFQFcfvae7GejqZ6B3mKGhcaILidwxJUUe6gJl3HZkP6HmOsK7mmhob8JdWY5+icCVyD5WwpgQQhQueYffKWwbzCTm3BwLoxNELgzTe26I4eEpJpYNMdrZHl0a4Pe4qSj20REKUBMsIdRUTX17CE+wEkdxcSZ0uVyXbZS6uHm2lR2u9LoyYczY5pPwlVKMD43Rd7o7M8TYM8TQUISxidmlHmcOg5rKEvZ3hAg11hFuC9G4p4Wy+hp09/qfv5XKVB9lmFIIIQqXvMMXGqXAMrETMZKRSWYGxug53cfgwASR7BDj9EKclLk0id7rdFDu97E/UEp1VQkN4QCh9hAlDUEcxcXoxX50r/eKutPbtp1bHZm2LTQ03IYDh0PH8G6fMLYwH6P3jS76zvQw0N3PYP8Yw6OTJFLp3DGVJUU01FVy+Jr2zBDjnmbq2ppw+H1vu6O/mb1O0mdMCCEKl7zDb2e2jUonSE/NMD8cof+NXga6RxmNzDI5G2U6liC2rF2EQ9cpK/LSXF1BdWUx9XWVhHfVU9lYh7OsBKPYj15UdEVNWS8nlUhhZ+eKmZaJ2+FE1zUMQ9+SlTHLtBju6qPvjW76u/oY7BtlaHicyZlo7hiPy0lddTk3XNtBQ3M9jbsaCe9twx+oIBgs2fCNwgHMxcrYNm1eK4QQYm0SxraD7J6PZixKYnSc4bP99J4dZGRkKreKcS6eXBpi1KDE6yFY5idY7qe2poxwax01bSE8lWUr5nVtlmQ8mQtjKTsTxgAMh37JuVFXi1KK2dEJek930X++l4HeIQYHI4xEpjGzE+p1TSNYUUJjqJp3vesQofZGmva2EmyqR9/En9mlWNmGt1IZE0KIwiXv8FuNbaGScSbPjjF4qpueU30M9Y8TmVhcxRgnnQ0NAD6Xkwq/j6baCmoDJYSaawh1NOKrrlwKXavM69osyXgSKxsOU6aJJxvGnJ6rdy6p6AIDZ7vpO9tDf/cggwOjDI9OMhdbmlDv97mpr6nk1ncfJtzaQOPuFhr2tuLZIn3Olipj8lIVQohCJe/w+aIUykxhzs4xNzBK72td9PeMMjY2w+RcjJlYgoVl85KchkG530t7fVVmiDEcoGl3I2UN1W95XtdmWqyMOQyDpJnE7/agaRpO38YPUVrpNJO9Q/S+0U1/Vz+DfSMMjUwwNjWHnd1w2zB0agPl7N3TTKilgfCuJpr2tVFeU7Xh57ORrGz7EFlNKYQQhUve4a8G28JeiLEwMs7AyW56zw0yOjrFxHSUqegC8/EkKnuormmU+jzUVZYQrPBTX19JuL2B6rYGnCUbO69rMy2GMZfTQTQRxePI7K3oKvK85X9TWRax8Sn6z3TTd76Pwd5hBocyQ4wLyaXgWl5SRH19gEPX7yPUFqZpXxt1rSEc27C6ZKZlAr8QQhQ6eYffSEqhzCSpiSlGz/TSc7qH4f4JIpNzTGcbpZr20hCj3+2iothHe0MgN6+rYU8YT3k5laEg86a+qfO6NlMyO4fN6TAwbQu3w4UGeIp9az5WKUV6bp7R7n76zvYycGGQocExhkYnmZyN5YKr2+WgrqaS667fS6g1TOOeFhr3tuIvLtrU53Y1mWmZwC+EEIVue37SbwW2hTk/z3R3Pxde7WawZ4SxyAyTcwvMxOJv6k7voNzvZXc4SE2ghHBzNY17m/FXV2EU+zH8/ot6T3kDxUQ3YXXe1ZKKp7BtG5eRqeB5nE40TcNdshSUlFLY8QRzw2PZeV39mVWMIxOMTMzm2m9oQFVlKaHmem5uqSe8q5mmfW0E6gJLm5AXqNwwpVTGhBCiYMk7/HqYKRKjES68cIaeM/2MjkwxMRNlOhq/qDt9mc9LOFBGdWUxDeEATXuaqGyswVFSjO7fWvO6NtPiMKWuZyo6HocTNBgfGePMX/3fDPQMMzQcYXh8lploPPc4n9dNQ32Ad+9vJ9zeSNO+VupbQni2UW+yjWTKnDEhhCh48g6/ltQCsdOneeYnL/P4i28AmUpNsddNZYmPPY1B6uoraNwVom5XCFdp6baZ17URlFKMDYzx2vMnOffaefov9DM+Psn8QhxLWRRZmV8xt+FkMhblse/+BABd16iprqRjXyvh9jCNu1sI72qivKpsR4TV9VqsjEkHfiGEKFzyDr8GzTIxE+ncsOOH7r6Lm47eQPL5X+JubcHT3LRt53Vdqdh8jJPPn+bMK29w4WwPIyMRZuejpKylyfO6puH3eGgMVtIcqiE2G6VnYgyP00UinTnuf//qZzl040Gp9qzD4mrQQh+OFUKInUw+DdeSbVyabZlF/a4wbq+LJKB7PQUZxEzTpOtkNydfPEXnqU6GBkaYmp4lnkrkJs8D+FxuAqV+6oKVtLaE2H94N8372/BUlGEUZbYC+uNPZTYL9zhcKDJzwOrCdRLE1kllF3xoulQLhRCiUMkn4lqUQilyG0AX2hDa6MAorz53knOvnqW/p5/x8Snm4/Fc93wAl+Gg1OejtS5IY7iWPQfa2XvdXvzVVThL/KsOx87H4mhouAwHieyG4Q5ZGbhuS5Wxwvq9E0IIsUTC2FqUDailPmArgsf2+YCMzsc4+fwpTr94mp5zPYyOjjMbjZK2llZ9GppOscdDa02QhrpqOvY0cfCGg1Q11eEsLX5LWwFF4wncDge6puUCnrRpWL/cfwTIMKUQQhQsCWNrUfbKytgWr1CYpsX5k+c5+ctTdJ7uZHhghMmZWRLppQ3DNTSK3G5qy0qpra6irS3Egev20bS/DVd5KYbbtSHnopQilkziMjJtLRYDrVTG1i83TFlgFVkhhBBLJIytRSlAu0xlLH+UUowMjPLqs69x9tWzDPQMMj4xRTQeX1bHy7SUKPX56AjV0dRUz96D7ey9fj++YCWG17OpH/J2Os1CMonL4UTTyG1kLpWx9VP29viPACGEEG+dhLG12IsT+Jc+FJVa7QEbb252ntefP8kbL5ym53wvo2PjzEVjmNk5WAAO3aDE66W9vpZwfTUde1s5eOQglY11OPxFefkwT85HSZppit0+NLRcSJQwtn5KVlMKIUTBkzC2FmWj0FgsNumbWElKp9KcPXmeU8+9Ts+5Hvr7hpianSOZXtk6osjlob6ygvqaAG3tYQ5cv5/G/W24SovRjK0TdJKzmTBW6ctUxhYzrFGAK1A3iy3DlEIIUfDkU3ENmlKgadjZKKEZOvD2SmO2bTPUO8xrz2WHGHuHGJ+cIpZIrDjO63RRUeSnuqqc5uYG9h7qyA4xVqA7nW/rHK6GhZl5klYat8OBhpYbpnRsocC41ckwpRBCFD4JY2tZ7DN2ieGitYoVSimmJ2c49cIpTr9wit6ufkYjE8xFo7lgApnWEcUeL6FQFeGGGjr2t3Hz0RtxVZRjeD0b/5yukqmxSYDsBP5MhNV0Dd2QIbf1Whwel2FKIYQoXBLG1qJsMi0sVu8zthCL03m6k9effY3uMxcYGhplenb+otYRRW4P4UCQhtoAbbuaOHDDfsL723AWF634twOBYsa38UbhAJPZMOZ2ZFZT2iipil2hxWHKbdRFRQghxBWSMLYWpVBoZAtj2LbNhc4+Xn7mND3//EsGRyeYmJwhnlraMFxDw+t0ESguoSZYQXNziH3X7qbjHfsoClbsmJ5RUxPTQLYyRmZhquyxeGWUrdA0TeaMCSFEAZNPxrUoG8tWvNjXw2h0ivtO/B8rZoy5DSfFXi/NNdWEQ7XsPtDOnhv2EWgJ7cjgkUom6D11ltPPv8a//+J5IDMMm6uMyUrKK6KUkvliQghR4HZeWrhSSjE7F6MzEsGpO6gpraC1qZ7mymIOvOswTTdfh9vvy/dZXnW2bTPafYHTv3yVMyc76ekdZnh8kqn5+dx8OA2Ncq+fmspyDF3DUjauDWoou1PYliVz7IQQosBJGFuTIpnMtJbwu3wcvfkdfOD+/435p5/Fd007rgIPYkopZiIRzvzyFd545SzdFwYYHB1nfGaWtLXU58zndFPuL+ba1nYaG+vYtX8X6Uic2PAEHdc00flSL4m0SXFZcR6fzfZjpi3ZVF0IIQqcvMuvQyqR2UpI0zQ8XteydgOFVbGIR6N0vvQ6p148Rdf5XvqHxhibmmFh2Xw4p+Gg3Otnd0OYUH017btb2HvjNYT2tOL2rVz5+Yv/+f+yMDKJmcosYkik0wRLg1f1OW13VtrckcPdQgixk8i7/FqUIpnIhBEN8Hg9ua78aNszjJlmmv7T5zn53Cucf6Ob3oFRRiemmF2ILW37pOmUenw0VAZoqA3Q2tbInnfsp/XavfjLStb3jZRC01gKY6k0xaX+zXlSBSpTGZN5dkIIUcgkjK1DKjtMqWkaHp87t3kzW7wyZts2kYEBTj39MmdfP09P3xDDkUkm5+exFp8D4Hd5qSopYW9TI80tIXZfs5vdNx6mvLbyba3iU3amYa6ZMtGAeDKFX8LYFZHKmBBCFD55l1+TIpXKhjE03EXeXGVsKw1Tzk1Nc/rZlzjz8ht0dy/N60qaS1speRwuKvzFHGppJRyupWNfO/vfeS3VraHNmSSuFBpgpS1sZZNKpymROWNXxEybOGT7KCGEKGjyLr8WtbIy5va6c135yUPLgUQiTudLJzn9y9foOt+Xndc1TTS5tJWSQzco8/lpr6sjVF9DW0cz+48covFAB0731dtGSSkFWiZQpLKT/YtLJYxdCTNtYrjkZSqEEIVM3uXXtLwyBi7v0jDlZlbGLMui5/Q5Tj79EufPdNPXP8LoxBTTsRhq2W4AJR4fteUVNNQGaWkNs+8d+2i/4RC+kqJNO7f1UraNhoaZtkhmdyKQOWNXxkpb0ptNCCEKnISxdVgKYzoun4dcO/4NCmORoRFO/n8vcO7UOS70ZOd1zc1h2staR7jcVJWU0tEYoqmpgd0Hd7Pv5mspq6na2t3ZtUygSJuLYUwqY1fCTJu4PNKbTQghCpmEsdVk5zylsqsBF4cp1VtcTRmdi3Lq6Rc48/Jpurv6GRodJzIzSyKdyh3jMhxU+Is52NJMY6iWXXvb2HfTYWrbmrZf8087O2fMtKUy9hZZaRNHcWH3shNCiJ1uU8PYE088waOPPko6nebuu+/m4x//+Ir7/+3f/o1HHnkEpRQNDQ18/etfp7S0dDNP6S1JZytjuqZlK2OrD1OmU2nOv3SSU798la5zPfQPjRGZnmEuvpA7xtB0ynxFtNTUZPp1dTSz/4aDNF6zB5fHXRAbhStlZypjpk3SXJwzJmHsSphpE0NaWwghREHbtDA2NjbGt771LX7yk5/gcrn46Ec/yo033khbWxsA0WiUhx56iH/4h3+gurqab3/72zzyyCN86Utf2qxTegsyw5G5YUoNPEVeSMUz92rQd7aL1//7JTrf6KKvf4SRiSmmY0tbAgGUeHwEy0o53NFGa1uYvdfupePGa/EVejBRCtCwLYtUtjImrS2ujGVKB34hhCh0m/Yu/8wzz3DkyBHKysoAuOOOO3jyySf53d/9XQDS6TQPPfQQ1dXVAHR0dPDEE09s1um8Ndk8lUqbKKVIpJP8zbf/hr6+IUbGp5j4o/+TdDZkAHidLqpKSmkP1dHcVE/HwV3sf9c7KKsJbu15XZtAKYVtZSqIlqVImmm8Xg+GIVWe9bJtGzOVlsqYEEIUuE0LY5FIhEAgkPs6GAzy+uuv574uLy/nV3/1VwFIJBJ897vf5ROf+MRmnc5blElj6bSFaZv0zgzR++9DOHSDCn8x+xrDNIZr6djTyv6bDlPb0YKu77wPzmQsznTPECMnuxk9N8T8dIxk0kSpzNCuz+0kZVr4irz5PtUtSSnF/OQc4wOR7P/GmRiMMDE4jpkyL9pmSgghRGHZtDCmlg3TLbpUdWh+fp7PfOYz7N69mw9+8INX9D0qKzd3yMs2TWbGMhWKxefz8B/8JtceuYbZM520/caHNrXiFQhsrZWHZspkomeY/pfO0vvKBaZGZ0nEU1j2ymutaxoOXcdh6Ph8LsqDpZSreVQ0tuWe00Zaz3Obn55ntGeU0d4xRnpHs38fJRFb6hNXUllCbXMNHde1U9NUw/6b91NUIpP4N1oh/y4WGrlW24tcryu3aWGsurqaF198Mfd1JBIhGFy5SXQkEuHTn/40R44c4cEHH7zi7zE5GcW2Lw59G8a2MABLZfplAYTaG0ikMpPRJyaim/at8zmBXylFNDLFxLk+Bl/tYnJoioVoEtO0Vxynaxq6puF26rhdDvylXipCAWoOthI80IrDvdSS4cWHHsWai277RQmX8+brlVhIMDEwnqt2TWT/XJhbWsTh8XsJhILsvXk/gVCQQChIVSiI17+ygriQtFgo0J9bvhTCApmdQq7V9iLX69J0XVu1gLRpYeymm27ikUceYWpqCq/Xy1NPPcVXv/rV3P2WZfE7v/M7vOc97+Ezn/nMZp3G25QJempZ4NMNZ6bPWIHMAUvMLzDZ2c/QK+eIXIgQm4uTSlksj7gamV8kl8PA5dTxFXkor6ugen8T1Yd34S1ZT4VyE0NzHqWTaSaGxul9eY4Lb/RnhhgHIsxNzuWOcbqdBEJB2q/roCobugLhIEWlRTtuLqEQQoiLbWpl7IEHHuDEiROk02mOHz/OwYMHuffee7n//vsZHR3ljTfewLIsfvaznwGwf/9+vva1r23WKV25bH6wstv6AGiGI7MdUh62Qno7zFSa6d5hhl8+x+j5IeamYiQT5opVn5AdYjR0nA4dr89FabCUYEeYuhv2UFRV9pbDQ+ZHuL1+ZstZpsXU8CTjg4uVrkzVa3psKvd7YjgNKuuqCO1pzFW5AqEgpVWlaNvs90UIIcTVs6lr5o8dO8axY8dW3Pa9730PgAMHDnD27NnN/PYbRqml4TnNMLLJYms2YLVtm9mhcSInOxk+2ct0ZI5EPI1pXXqI0eU0cLsdlFT4CbTWU3fDHsobqzd+qyeltkUx0bZtZiIzmWHF/khumHFqZDK3OlTTNSpqKqlurGHfuw4QCAfpONiMcrm3X2NeIYQQeScNjNbBznaSB9AMHWVaW2J4aWFmnrGTnQy/2s3k4BSxWJJ02lpxjKZlm9U6DNwuA3+pj8qmauoO7yKwtwnjavaw2gI/s0VrrWBcVBYsoyoUpP0dHVQ1BAiEglTWVeF40+bdMk9CCCHEWyVhbA1KqRWrBTXdAGVe1WCRiieZPNfL0MvniPSMEZ1NkExlWkfkzovMvC6noeNyGvj8bsrrq6i9po3aQ2248txWIp8zxhbmYiuqXIvzupLxZO4Yf7mfqlCQw0ffsTSZviEg+0IKIYTYdBLG1sGybXKTxgw9O0y58WHMMi1meoYYfuUckfPDTE9GSSbSl2wdYeg6TkPH43NRFiylen8ztdfuojhQvuHntRGUUpteGtlp2wAAEmBJREFUTVzXCsYiD4FwNfvefWDVFYxCCCHE1SJhbFUKFCvmW+mG422HMaUU82OTjLx8hpHT/cyMzRGPp0i/qXWEpmX2sHQ7ddweJ8UVfqp3hah7Rwdl4Wr0jZ7XtZmU2rDp+4srGJcC16VXMFaFgrRdtysXugKhIEVl/i0xxCyEEEIskjC2hsww5bKQpOuZKs86V8fF52JEXj3L8GsXmBieIjafJG1alxxidDkMXC6DohIfdR0NVB1oJdARwuFybuyT2iYs02JqZHJZpesSKxgdBpX1soJRCCHE9iVhbB0s216awK8bl+wzlkokmXyjm5FXuxjrHSc6u0AyZV3UlDbXnd5p4CtyU9FQSe2hdmoOtOIpXuqyXmgTwjN7hl86HK1rBaOmUVG7bAVjNnSV11TICkYhhBDbmoSx1SjgTRP4bWBudJrIhVEmf/43zE3FSCTMy7eOcBl4vE7KgmVUH2ih7nA7/sBb79e1XSkyrS3mJmYzVa7BZXO73rSCsTRQlm2SuitX6brUCkYhhBCiEMin2xqUAnPZMOVPHvo7DG1pM/AVrSPcjsy8rj2N1B1upyxcjeHYeRuHv9nguQEGz/YTjS7wF5/9s9ztsoJRCCGEkDC2BrU0ZyxbyHIbDgxdp6TUQ2BXiNrDuwjuCuH0SoC4nLPPnSYejVNU7OP2e96bndsVwFcsm18LIYQQEsZWo2yUpbAsOzdh3O9143A7eNf7rqH4yA35Pb9tYmE+DoZObXMd191xfb5PRwghhNhSZObzapTCtmxM20ah0DU986dD3/jtggrYwlwMhcKb58azQgghxFYkiWI1ys6GscwWQ7qmgQKHoW+7jcLzKT6/gGVbEsaEEEKIS5Awthployw712dM1zQUoDt0kMrYusXnFzAtC6/Pk+9TEUIIIbYcSRSr0JTCtlR2mBJ0Tc9VxmSYcv1iszFM08InWw4JIYQQF5FEsZoVlbHFOWNgODSpjK1TOpUmmUgB4PVJGBNCCCHeTBLFapTCTFtYykapzD6RAIZUxtYtPreApTLDvD6ZMyaEEEJcRBLFapRNMpVe/CK3MbeuS2VsvRbmF7CzYUwm8AshhBAXk0SxGqVIZIfYFGBoWqa1hQ6aJj+69YjPL+T255QwJoQQQlxMEsVqlE0ykamMKaUwdAMUGFIZW7eFZcOUMmdMCCGEuJgkitUom0RiaZjSyAawTBiTPmPrsXyYUlZTCiGEEBeTMLYapUgklypjjsU5Y4YmE/jXKT6/gK2yw5TSZ0wIIYS4iCSK1ShFKmlm/kp2mJJMGJNhyvVZmFtAd2V+bh4JY0IIIcRFJFGsQkNhq8xWSLZSOJa1tkCTYcr1SETjGM5MGHO6nHk+GyGEEGLrkTC2GgXKzgxRAjiMTKgwDA2JYuuTiCXQHQaapmFkf35CCCGEWCJhbA2LQQyWmr7qUhlbt3g0juEwcGQDmRBCCCFWkjC2BmVn5osBOHOVMfmxrVciFkczDBwyRCmEEEJckqSKtShFNovh0B1AZphSxinXJxGNoxkaDocMUQohhBCXImFsVQqUylXGHMu3Q5I0tiZlKxILCTRdw+GUypgQQghxKRLG1mAvmzPmWD5MKfOf1pRYSGSqirqGwymVMSGEEOJSJIytxVa5SfzOZaspxdoSsUTmLxo4pTImhBBCXJKEsTUsbuUD4Mg1fdVllHIdErF47u8yZ0wIIYS4NAlja7Ct5XPGHGi6JnPG1ikRzYQxBbKaUgghhLgMCWNrsKylOWNOQ8dwZlZUypSxtS0OUyqlpDImhBBCXIaEsVUplG0vzRnTDQyXI3ufpLG1xLOVMVspHE7HGkcLIYQQO5N8Qq5GZSpjS8OURq4yJlnsYkop5iZmGekZpvP1Lk4/f5qx6AyJpE1LR1O+T08IIYTYkiSMrSGdSgOgoWW29cmFsZ1dVIzNxhjtHaHz9S56z/Uy3D/C5OQ08WSSlGXmjtM0jbpALUduuz6PZyuEEEJsXRLGVqNs4okUSik0TcOl6xjZflnaDtkSKbmQZKxvlM7Xu+g528NQ3zCT41PE4glSVhq17NiSEj/hUIhwa4i2A200dzRS21AjQ5RCCCHEKuRTcjVKkUikANA1DafTmQtj6IU1IT2dSjM+EKH7VDcXTl9gsHeY8cgk0ViMpGnmhmoBiop81DXVEmpuoHVfC637WqgL1+Jyu/L4DIQQQojtScLYapQikUqjUBiajkPXMRzbuzJmWzZTI5N0n+qm61Q3AxcGGR+dYG4+StJKr9hxwOv1EKwPUt9UR+veFtr3t1HfXIfH68njMxBCCCEKi4SxVdkkUmlQZPqLadq2qYwpWzEzPkPvGz10nuyiv3uAsZFxZmfmSJipFaHL7XZRVVNBfbiO5j1N7LpmF6Hmenx+Xx6fgRBCCLEzSBhbjVIkl1XGdF3DcGQqYlulMqaUYmE2Ru+ZXs6/3knf+X5Gh8aYmZklnkphLdtBwOV0UlFZRkeohubdmdDV2BamuNSfx2cghBBC7GwSxlajbJLpzMpAXdNzKyozN1z9ylgilmDgXD/nXj1H7/k+RgbHmJqaIZ5MYNrLtm1yGJSXl9HS0EJje5hd17TTsruZkvISNOlWK4QQQmwpEsZWoxSpZWFM2QrD2PzKWDqVpuu1bp77z1foWWwbMTFNLB7HtK3ccYauU1ZeSqitgXBrJnS17muhoqpcQpcQQgixTUgYuxylwFakTROlFLqmZf506IBCM95+ZcwyLcb6xzj3yjm637jAUN8wE5EporEY6WWhS9c0SkqLaQk1EW5toG1/G7uuaaequhJd3xrDpUIIIYR4aySMXY6ysW2blJUJRXq2yWumMmbBFYQgZSsmRyc4+/I5uk93M9gzzPhYZgXjmxukFvuLCLeEaNvdSENbIx2HdlHTUI2+ReaoCSGEEGJjSRi7HKVQpk3asjIT+LNzxAxDA01Du0QYU0oxNzXHuZfP0nky0zYiMjrO7Ow8STO94lh/kY+6cC31jXW07m2m41AH9c11OByZSxIIFDM+Pr/5z1MIIYQQeSVh7HKUwrZs0mamcuXQl1XGdEVsNsb51zrpPNlJf9cAY8MRZmZmSaRSK7rS+7wegrUB6sKZFYwdh3fT1B7G6XLm4UkJIYQQYquRMHZZNralcsOItlK8PNDPM/+rh7nYAvHU36/oSu9xu6moKqe2oZqmXU10HNpF674W3B53vp6AEEIIIbYBCWOXo+loDh1ndqJ+0jTpnZ7A5XBQ5vfRvq8t0zbi0C52HWzHV+TN8wkLIYQQYjuSMHY5hhNH0x6+/t2v8sp/PkdRSQnt1+0j0BBAWTa6DDMKIYQQYgNIGFuN4SDQWMvtd39wxc0b0dZCCCGEEAJA+iUIIYQQQuSRhDEhhBBCiDySMCaEEEIIkUcSxoQQQggh8kjCmBBCCCFEHkkYE0IIIYTIIwljQgghhBB5tKlh7IknnuC9730vR48e5Qc/+MFF9585c4YPf/jD3HHHHXzxi1/EzO4DKYQQQgixU2xaGBsbG+Nb3/oWP/zhD/mnf/onfvSjH9HV1bXimD/4gz/gy1/+Mj/72c9QSvHYY49t1ukIIYQQQmxJmxbGnnnmGY4cOUJZWRk+n4877riDJ598Mnf/0NAQiUSCQ4cOAfChD31oxf1CCCGEEDvBpoWxSCRCIBDIfR0MBhkbG7vs/YFAYMX9QgghhBA7wabtTamUuug2TdPWff96VFb6r/zEtpFAoDjfpyCugFyv7UWu1/Yh12p7ket15TatMlZdXc3ExETu60gkQjAYvOz94+PjK+4XQgghhNgJNi2M3XTTTTz77LNMTU0Rj8d56qmnuOWWW3L319fX43a7eemllwB4/PHHV9wvhBBCCLETaOpS44Ub5IknnuAv//IvSafTHD9+nHvvvZd7772X+++/nwMHDnD27Fm+9KUvEYvF2Lt3L1//+tdxuVybdTpCCCGEEFvOpoYxIYQQQgixOunAL4QQQgiRRxLGhBBCCCHySMKYEEIIIUQeSRgTQgghhMgjCWNCCCGEEHkkYUwIIYQQIo8kjG0BJ06c4M477+T9738/73//+3nttdd44okneO9738vRo0f5wQ9+kO9TFEA0GuWuu+5icHAQgGeeeYZjx45x++23861vfSt33JkzZ/jwhz/MHXfcwRe/+EVM08zXKe9ob75eX/jCF7j99ttzr7N//dd/BS5/HcXV8ed//ufceeed3HnnnTz88MOAvLa2sktdL3ltbQAl8sq2bXXzzTerdDqdu210dFTddtttanp6WsViMXXs2DHV2dmZx7MUr776qrrrrrvUvn371MDAgIrH4+rWW29V/f39Kp1Oq3vuuUf913/9l1JKqTvvvFO98sorSimlvvCFL6gf/OAH+Tz1HenN10sppe666y41Nja24rjVrqPYfE8//bT6yEc+opLJpEqlUurEiRPqiSeekNfWFnWp6/XUU0/Ja2sDSGUszy5cuICmadx77728733v4+/+7u945plnOHLkCGVlZfh8Pu644w6efPLJfJ/qjvbYY4/xR3/0R7n9U19//XUaGxsJhUI4HA6OHTvGk08+ydDQEIlEgkOHDgHwoQ99SK5dHrz5ei0sLDA8PMyXv/xljh07xne+8x1s277sdRRXRyAQ4POf/zwulwun00lrayu9vb3y2tqiLnW9hoeH5bW1ARz5PoGdbm5ujne+85089NBDJBIJTpw4wXve8x4CgUDumGAwyOuvv57HsxRf+9rXVnwdiUQuukZjY2MX3R4IBBgbG7tq5yky3ny9JicnOXLkCF/5ylfw+Xz89m//Nj/+8Y/x+XyXvI7i6mhvb8/9vbe3l5/+9Kd84hOfkNfWFnWp6/XDH/6Q559/Xl5bb5NUxvLs8OHDPPzww/h8PioqKjh+/Djf+c53LjpO07Q8nJ24HHWJXcQ0Tbvs7SK/QqEQf/EXf0FlZSVer5dPfOIT/PznP5frtUV0dnZyzz338LnPfY5wOHzR/fLa2lqWX6+WlhZ5bW0ACWN59uKLL/Lss8/mvlZKUV9fz8TERO62SCSSG24RW0N1dfUlr9Gbbx8fH5drtwWcO3eOn/3sZ7mvlVI4HI7LXkdx9bz00kvcfffd/P7v/z4f/OAH5bW1xb35eslra2NIGMuz+fl5Hn74YZLJJNFolH/8x3/kT/7kT3j22WeZmpoiHo/z1FNPccstt+T7VMUy11xzDT09PfT19WFZFv/8z//MLbfcQn19PW63m5deegmAxx9/XK7dFqCU4o//+I+ZnZ0lnU7zox/9iKNHj172OoqrY2RkhM9+9rN885vf5M477wTktbWVXep6yWtrY8icsTy77bbbeO211/jABz6Abdt87GMf47rrruOBBx7gxIkTpNNpjh8/zsGDB/N9qmIZt9vNN77xDe677z6SySS33norv/ZrvwbAN7/5Tb70pS8Ri8XYu3cvJ06cyPPZit27d/Nbv/Vb/Pqv/zqmaXL77bdz1113AVz2OorN91d/9Vckk0m+8Y1v5G776Ec/Kq+tLepy10teW2+fpi41sCuEEEIIIa4KGaYUQgghhMgjCWNCCCGEEHkkYUwIIYQQIo8kjAkhhBBC5JGEMSGEEEKIPJIwJoTYEe655x6mpqa499576erqyvfpCCFEjrS2EELsCB0dHTz77LNUVFTk+1SEEGIFafoqhCh4X/jCFwD45Cc/SVdXF4899hgLCwv86Z/+KcFgkM7OTrxeL/fddx9/+7d/S09PD7fffjsPPvggAP/xH//Bo48+SjqdxuPx8LnPfY7Dhw/n8ykJIQqIVMaEEDvCYmXs+PHjfPvb32ZhYYFPfepT/PjHP2bv3r385m/+JtFolO9///tEo1FuueUW/v3f/514PM59993H97//fcrLy+ns7ORTn/oUTz31FD6fL99PSwhRAKQyJoTYsRoaGti7dy8A4XCY4uJiXC4XFRUVFBUVMTs7ywsvvEAkEuHuu+/OPU7TNPr7+9m9e3eezlwIUUgkjAkhdiyXy7Xia4fj4rdE27Z55zvfyZ/92Z/lbhsZGSEYDG76+QkhdgZZTSmE2BEMw8A0zSt+3JEjR3j66afp7u4G4Oc//znve9/7SCaTG32KQogdSipjQogd4ejRo3zsYx8jFotd0ePa29v5yle+wu/93u+hlMLhcPDoo4/KfDEhxIaRCfxCCCGEEHkkw5RCCCGEEHkkYUwIIYQQIo8kjAkhhBBC5JGEMSGEEEKIPJIwJoQQQgiRRxLGhBBCCCHySMKYEEIIIUQeSRgTQgghhMij/x/ACBG0H3cVOgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def conv(df):\n",
+    "    a = df.values\n",
+    "    a.sort(axis=1)\n",
+    "    df2 = pd.DataFrame(a)\n",
+    "\n",
+    "    df2.columns = ((i+1)/25 for i in df2.columns)\n",
+    "    df2.index = pd.RangeIndex(start=1, stop=21, step=1)\n",
+    "\n",
+    "    return df2.stack().reset_index().rename(columns={'level_0':'msg_num', 'level_1':'convergance', 0: 'time'})\n",
+    "\n",
+    "plt.figure(figsize=(10,6))\n",
+    "sns.lineplot(x='time', y='convergance', data=conv(df), ci='sd', estimator=\"median\", hue='msg_num')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "hide_input": false,
+  "kernelspec": {
+   "display_name": "Python Conda",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Intro.ipynb b/Intro.ipynb
index a1b0e07..c61d92b 100644
--- a/Intro.ipynb
+++ b/Intro.ipynb
@@ -1,5260 +1,901 @@
 {
  "cells": [
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# - Running an application on top of the network "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
-    "# 1. Form a network topology \n",
-    "'''\n",
-    "Options:\n",
-    " a. The topology is formed with this rules: \n",
-    "  - Any other rules? \n",
-    " Change the network topology \n",
-    " b. How fanout affect it? \n",
-    "'''\n",
-    "# 2. Decide on the number of peers and how they are connected and finding each other.\n",
-    "\"\"\"\n",
-    "a. Byzantine nodes \n",
-    "b. Crashing nodes \n",
-    "c. Slowdowns \n",
-    "\"\"\"\n",
-    "# Add dissemination layer \n",
+    "#  Introduction\n",
     "\n",
-    "# 3. Adding storage on top of the network layer \n",
     "\n",
-    "# 4. Computation tasks for the peers \n",
+    "Each blockchain works in a peer-to-peer environment. P2P environment is a network where peers are connected to each other and exchange information and run applications on top of it.\n",
     "\n",
-    "# Incentives experimetns \n",
+    "The goal of this notebook is to explain the building blocks for any blockchain system. \n",
+    "- We will build a system block by block.\n",
+    "- Distributed systems are hard, there are many unpredictable errors: race conditions, nasty bugs, deadlocks.\n",
+    "- We will simplify the world to get to juiciest parts without leaving out the essentials.  \n",
     "\n",
-    "# Sybil nodes added\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 1. P2P environment \n",
     "\n",
+    "We will simulate the p2p enviorment locally to get the idea of how such system work and get the design decision that devloper make when designing such system.\n",
     "\n",
-    "## 1.1. Introduction\n",
+    "To show different tradeoffs and why developers and researcher choose certain designs we need a way to simulate such environment. Here we will simulate network and message exchange with a [SimPy](simpy.readthedocs.io/en/latest/).\n",
     "\n",
+    "For the simplicity of use we implement a P2P network simulation: `p2psimpy`. \n",
     "\n",
-    "Each blockchain works in a peer-to-peer environment. P2P environment is a network where peers are connected to each other and exchange information and run applications on top of it.\n",
+    "# BaseSimulation\n",
     "\n",
+    "The simulation starts with a `BaseSimulation` class. This class represents the network of peers with their physical representations in the world, such as `location`, `bandwidth`. \n",
     "\n",
+    "`BaseSimulation` requires 3 main parameters in the constructor: \n",
+    " - *Locations* - generator class for the locations. \n",
+    " - *Topology* - networkx graph with attribute `type`, or a dictionary `peer_id -> {'type': peer_type }` \n",
+    " - *Type map with services* - map `type -> PeerType object ` \n",
     "\n",
+    "Let's go one by one. \n",
     "\n",
+    "## Locations \n",
     "\n",
+    "Simulator works in a space with different locations to simulate network latency. You have to define your own locations and latency between them. \n",
     "\n",
-    "\n"
+    "Locations are defined with as a `Config` class. \n",
+    "This class can be used as generators of parameters, saved and loaded as yaml file. Parameters can be constant, or probabilistic(with a probability distribution).\n"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T21:45:52.392001Z",
-     "start_time": "2020-03-08T21:45:52.387811Z"
-    }
-   },
-   "outputs": [],
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "from dataclasses import dataclass, field"
+    "Location configuration should contain at least two fields: `locations` and `latencies`. \n",
+    "- `locations` is an array or a tuple with names of locations.\n",
+    "- `latencies` is a dictionary (matrix) with latencies of pairwise connections.\n",
+    "\n",
+    "Depending on the experiments latencies can be constant, but a more realistic model is a probabilistic distribution.\n",
+    "Such distribution is represented with `Dist` class. \n",
+    "\n",
+    "`Dist` class is a wrap around [scipy.stats](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.norm.html#scipy.stats.norm), which has rich collection of distributions.  \n",
+    "\n",
+    "Finally,  `Config.get()` samples all the parameters from distributions and returns them as a dictionary.  \n",
+    "\n",
+    "Let's try out:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-03-08T21:07:48.000733Z",
-     "start_time": "2020-03-08T21:07:47.993956Z"
+     "end_time": "2020-03-16T17:02:53.830508Z",
+     "start_time": "2020-03-16T17:02:53.271376Z"
     }
    },
    "outputs": [],
    "source": [
-    "@dataclass\n",
-    "class ConnectionConfig:\n",
-    "    ping_interval: int \n",
-    "    max_silence: int\n",
-    "    min_keep_time: int\n",
-    "    min_peers: int\n",
-    "    max_peers: int\n",
-    "    peer_list_number: int\n",
-    "    peer_batch_request_number: int\n"
+    "from p2psimpy.config import *\n",
+    "\n",
+    "class ConstLocations(Config):\n",
+    "    locations = ['LocA', 'LocB']\n",
+    "    latencies = {\n",
+    "        'LocA': {'LocB': 10, 'LocA': 2},\n",
+    "        'LocB': {'LocB': 1}\n",
+    "    }    \n",
+    "\n",
+    "class DistLocations(Config):\n",
+    "    locations = ['LocA', 'LocB']\n",
+    "    latencies = {\n",
+    "        'LocB': {'LocB': Dist('gamma', (1, 1, 1))},\n",
+    "        'LocA': {'LocB': Dist('norm', (12, 2)), 'LocA': Dist('norm', (2, 0.5))},\n",
+    "    }    \n",
+    "    "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-03-08T21:07:49.410468Z",
-     "start_time": "2020-03-08T21:07:49.391630Z"
+     "end_time": "2020-03-16T17:02:55.284705Z",
+     "start_time": "2020-03-16T17:02:55.266118Z"
     }
    },
    "outputs": [
     {
-     "ename": "TypeError",
-     "evalue": "__init__() missing 7 required positional arguments: 'ping_interval', 'max_silence', 'min_keep_time', 'min_peers', 'max_peers', 'peer_list_number', and 'peer_batch_request_number'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-32-0067223aa2d8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mc\u001b[0m  \u001b[0;34m=\u001b[0m \u001b[0mConnectionConfig\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[0;31mTypeError\u001b[0m: __init__() missing 7 required positional arguments: 'ping_interval', 'max_silence', 'min_keep_time', 'min_peers', 'max_peers', 'peer_list_number', and 'peer_batch_request_number'"
-     ]
+     "data": {
+      "text/plain": [
+       "{'latencies': {'LocA': {'LocB': 10, 'LocA': 2}, 'LocB': {'LocB': 1}},\n",
+       " 'locations': ['LocA', 'LocB']}"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "c  = ConnectionConfig()"
+    "ConstLocations.get()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-03-08T23:06:41.871325Z",
-     "start_time": "2020-03-08T23:06:41.865053Z"
+     "end_time": "2020-03-16T17:02:55.950456Z",
+     "start_time": "2020-03-16T17:02:55.941368Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'latencies': {'LocB': {'LocB': 1.0359081925315285},\n",
+       "  'LocA': {'LocB': 13.121250721732467, 'LocA': 1.535848054229478}},\n",
+       " 'locations': ['LocA', 'LocB']}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "import typing\n",
-    "from dataclasses import dataclass, field, fields"
+    "DistLocations.get()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T22:44:51.330970Z",
-     "start_time": "2020-03-08T22:44:50.587283Z"
-    }
-   },
-   "outputs": [],
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "from p2psimpy.config import Dist"
+    "Every time you call `Config.get()` it will return new sample"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-03-08T22:44:51.570141Z",
-     "start_time": "2020-03-08T22:44:51.008Z"
+     "end_time": "2020-03-16T17:02:58.162455Z",
+     "start_time": "2020-03-16T17:02:58.151777Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'latencies': {'LocB': {'LocB': 1.3541387728779295},\n",
+       "  'LocA': {'LocB': 11.085785333651884, 'LocA': 2.5023088242924105}},\n",
+       " 'locations': ['LocA', 'LocB']}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "DistLocations.get()"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "d = Dist()"
+    "For the simplicity we can represent latencies sample as pandas dataframe. It provides readable table. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-03-08T22:44:51.879043Z",
-     "start_time": "2020-03-08T22:44:51.856214Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'd' 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-3-db1f707df758>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\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[0;31mNameError\u001b[0m: name 'd' is not defined"
-     ]
+     "end_time": "2020-03-16T17:03:01.013549Z",
+     "start_time": "2020-03-16T17:03:00.637706Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>LocB</th>\n",
+       "      <th>LocA</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>LocB</th>\n",
+       "      <td>2.199402</td>\n",
+       "      <td>10.974388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LocA</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.868041</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          LocB       LocA\n",
+       "LocB  2.199402  10.974388\n",
+       "LocA       NaN   1.868041"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "d.generate()"
+    "import pandas as pd \n",
+    "\n",
+    "lat_sample = pd.DataFrame(DistLocations.get()['latencies'])\n",
+    "lat_sample"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 135,
+   "cell_type": "markdown",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-03-08T23:07:07.683457Z",
-     "start_time": "2020-03-08T23:07:07.675275Z"
+     "end_time": "2020-03-16T16:34:34.176798Z",
+     "start_time": "2020-03-16T16:34:34.165723Z"
     }
    },
-   "outputs": [],
    "source": [
-    "@dataclass\n",
-    "class A:\n",
-    "    p: typing.Any \n",
-    "    v: int = 1\n",
-    "        \n",
-    "    def __post_init__(self):\n",
-    "        if type(self.p) == Dist:\n",
-    "            self.p = self.p.generate()\n",
-    "    \n",
-    "    def __repr__(self):\n",
-    "        for k in fields(self):\n",
-    "            print(k)\n",
-    "        return 'fe'\n",
-    "    \n",
-    "    "
+    "*Notice that table has **NaN** values. They will be automatically filled with symmetric value if not specified.*\n",
+    "\n",
+    "We can transform the table to fill them up symmetrically. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-03-08T23:09:12.327603Z",
-     "start_time": "2020-03-08T23:09:12.319656Z"
+     "end_time": "2020-03-16T17:03:03.301164Z",
+     "start_time": "2020-03-16T17:03:03.270037Z"
     }
    },
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>LocB</th>\n",
+       "      <th>LocA</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>LocB</th>\n",
+       "      <td>2.199402</td>\n",
+       "      <td>10.974388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LocA</th>\n",
+       "      <td>10.974388</td>\n",
+       "      <td>1.868041</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "<slot wrapper '__getattribute__' of 'object' objects>"
+       "           LocB       LocA\n",
+       "LocB   2.199402  10.974388\n",
+       "LocA  10.974388   1.868041"
       ]
      },
-     "execution_count": 147,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "B.__getattribute__"
+    "lat_sample[pd.isnull(lat_sample)] = lat_sample.T[pd.isnull(lat_sample)]\n",
+    "lat_sample"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 149,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T23:11:11.441262Z",
-     "start_time": "2020-03-08T23:11:11.434347Z"
-    }
-   },
-   "outputs": [],
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "from enum import Enum"
+    "---------------------\n",
+    "Let's visualize the data."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 270,
+   "execution_count": 43,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-03-08T23:43:59.785959Z",
-     "start_time": "2020-03-08T23:43:59.780616Z"
+     "end_time": "2020-03-17T08:45:43.087216Z",
+     "start_time": "2020-03-17T08:45:40.205001Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# -- Prepare data: \n",
+    "#  - Trasfer to dataframe\n",
+    "#  - Fill null values with symmetic \n",
+    "#  - Traform into list of tuples: 'from', 'to', 'value' \n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set(style='darkgrid')\n",
+    "\n",
+    "def prepare(x):\n",
+    "    x = pd.DataFrame(x)\n",
+    "    x[pd.isnull(x)] = x.T[pd.isnull(x)]\n",
+    "    return x.stack().reset_index()\\\n",
+    "                    .rename(columns={'level_0':'from', 'level_1':'to', 0: 'value'})\n",
+    "                    \n",
+    "# 2. Get 100 samples from the configuration with latencies\n",
+    "df = pd.concat(prepare(DistLocations.get()['latencies']) for _ in range(100))\n",
+    "\n",
+    "# Visualize in a grid of histograms\n",
+    "g = sns.FacetGrid(d1, col='to', row='from', height=4, aspect=1)\n",
+    "g = g.map(sns.distplot, 'value')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "w = Wrap(Dist())"
+    "## Peers and Topologies \n",
+    "\n",
+    "Simulation can work in two modes: \n",
+    "\n",
+    "1. **NetworkX**. When you pass a network topology: peers with all connections and peer types\n",
+    "\n",
+    "2. **Emergent**. Peer communicate with each other through bootstrap peers and network topology emerges itself, as peers exchange data with each other. Usually peer-to-peer network follow into this category. You can read more [here](https://en.wikipedia.org/wiki/Bootstrapping_node), or  read how [Bitcoin network](https://en.bitcoin.it/wiki/Network) is formed.  \n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "### Networkx topology\n",
+    "\n",
+    "Let's first see how we can form a network topology.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 274,
+   "execution_count": 47,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-03-08T23:44:16.797181Z",
-     "start_time": "2020-03-08T23:44:16.788760Z"
+     "end_time": "2020-03-17T08:46:35.686945Z",
+     "start_time": "2020-03-17T08:46:35.321005Z"
     }
    },
    "outputs": [
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "'Dist'"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "execution_count": 274,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "w.rep()"
+    "import networkx as nx\n",
+    "\n",
+    "peer_num = 10\n",
+    "# Generate a network topology \n",
+    "G = nx.erdos_renyi_graph(10, 0.5)\n",
+    "\n",
+    "# Assign a peer type to the peers \n",
+    "nx.set_node_attributes(G, {k: 'basic' for k in G.nodes()}, 'type')\n",
+    "\n",
+    "sns.set(style='dark')\n",
+    "nx.draw_networkx(G)\n"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "NetworkX is a library that has rich semantics and collection of graph generators. You can read more [here](https://networkx.github.io/documentation/stable/reference/generators.html).  \n",
+    "\n",
+    "You can choose any graph, generate them and put `type` as node attributes. `type` is a name for the peer type. We explain later how it will be used in a simulation.  \n",
+    "\n",
+    "\n",
+    "\n",
+    "### Emergent topology\n",
+    "\n",
+    "What if topology is not known?\n",
+    "\n",
+    "In this case we can model the peer discovery mechanism through *bootstrapping* peers. General bootstrap process is following: \n",
+    "1. Bootstrap nodes are create in the system first. The goal of bootstrap nodes is to maintain list of peers and connect them with each other upon request.\n",
+    "2. New peer joins the network. Peer sends a `Hello` message to one of bootstrap nodes. Bootstrap nodes are usually known in advance. Bootstrap node responds to the peer with a random sample of known online peers. \n",
+    "3. The peer connect with other peers. \n",
+    "\n",
+    "\n",
+    "This is all abstracted in simulation, but you can specify your own bootstrap logic later. \n",
+    "\n",
+    "\n",
+    "To run default bootstrap you should pass as a parameter `topology` a dictionary with type `peer_id` -> {`type`: type_name}\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Peer types and services\n",
+    "\n",
+    "Simulation also requires `peer_types` that is a map that describes peers in the system and the running scenario. \n",
+    "\n",
+    "Below we give an example of a simple map with one type: `basic`. Peer is described with a `PeerType(PeerConfig, services)`. `PeerConfig` describes with a configuration physical representation of a peer in the system. \n",
+    "Each peer at minimum must have bandwidth and a location.  \n",
+    "\n",
+    "Finally, peers must have services to run in the system. They work as a running scenario. \n",
+    "There some implemented standard services that you can user in your simulation. But you will also implement your own later!\n",
+    "\n",
+    "Each peer should have at least a connection service to build a network of peers and react on introduction messages. \n",
+    "By default there two connection services: `BaseConnectionManager` and `P2PConnectionManager`. Both support basic connection primitives: connect, disconnect unresponsive peers. The difference is that later one will actively pull new peers to connect at least to `min_peers`, but when the number of connections is higher than `max_peers` it will start to refuse new connections and disconnect slowest peers. \n",
+    "\n",
+    "Let's see how it looks in code:\n"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 646,
+   "execution_count": 48,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-03-09T01:03:34.222004Z",
-     "start_time": "2020-03-09T01:03:34.150616Z"
+     "end_time": "2020-03-17T12:14:21.135774Z",
+     "start_time": "2020-03-17T12:14:21.056057Z"
     }
    },
    "outputs": [],
    "source": [
-    "from yamlable import yaml_info, YamlAble"
+    "from p2psimpy.config import *\n",
+    "from p2psimpy.consts import *\n",
+    "    \n",
+    "class PeerConfig(Config):\n",
+    "    location = Dist('sample', DistLocations.locations)\n",
+    "    bandwidth_ul = Dist( 'norm', (50*MBit, 10*MBit))\n",
+    "    bandwidth_dl = Dist( 'norm', (50*MBit, 10*MBit))\n",
+    "\n",
+    "# Let's add ConnectionManager - that will periodically ping neighbours and check if they are online \n",
+    "from p2psimpy.services.connection_manager import BaseConnectionManager\n",
+    "# For each service you can define own configuration, or use default values.   \n",
+    "# Lets use base connection manager - that will periodically ping peer and disconnect unresponsive peers.\n",
+    "\n",
+    "services = (BaseConnectionManager,)\n",
+    "peer_types = {'basic': PeerType(PeerConfig, services)}"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "How to use these configuration. Configs are used to get value. \n",
+    "They work as a generator and attribute descriptors.  "
+   ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 681,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-09T01:17:27.158084Z",
-     "start_time": "2020-03-09T01:17:27.151838Z"
-    }
-   },
-   "outputs": [],
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "from random import choices"
+    "# Putting all together\n",
+    "\n",
+    "\n",
+    "Let's combine all you learned so far and run a simple simulation. \n",
+    "\n",
+    "1. Define locations and simulation world parameters.\n",
+    "2. Define topology and number of peers with their types.  \n",
+    "3. Define what each peer type is with a `PeerConfig` and what it does with services. \n",
+    "\n",
+    "\n",
+    "After we defined all this we can create a simulation object and run it with `.run(time)`.\n",
+    "\n",
+    "Simulation has it's internal clock and scheduler for all events. The parameter `time` is a time until which the simulation is run. \n",
+    "\n",
+    "***All the time is milliseconds**!\n",
+    "\n",
+    "\n",
+    "\n"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 723,
+   "cell_type": "markdown",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-03-09T01:25:11.508496Z",
-     "start_time": "2020-03-09T01:25:11.504400Z"
+     "end_time": "2020-03-11T09:50:33.374122Z",
+     "start_time": "2020-03-11T09:50:33.347834Z"
     }
    },
-   "outputs": [],
    "source": [
-    "v = {'name':'sample', 'params': \"('Other', 'Loc', 'Loc2')\"}\n",
-    "d = Dist.from_repr(v)"
+    "## Random given topology \n",
+    "\n",
+    "1. Given network with peer types and ids \n",
+    "2. No network given - emergent P2P topology through bootstrap peers. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 724,
+   "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-03-09T01:25:12.959554Z",
-     "start_time": "2020-03-09T01:25:12.953601Z"
+     "end_time": "2020-03-17T12:36:59.402649Z",
+     "start_time": "2020-03-17T12:36:59.359782Z"
     }
    },
    "outputs": [
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "'Loc2'"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "execution_count": 724,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "d.generate()"
+    "from p2psimpy.config import *\n",
+    "from p2psimpy.consts import *\n",
+    "import networkx as nx\n",
+    "\n",
+    "class Locations(Config):\n",
+    "    locations = ['LocA', 'LocB']\n",
+    "    latencies = {\n",
+    "        'LocB': {'LocB': Dist('gamma', (1, 1, 1))},\n",
+    "        'LocA': {'LocB': Dist('norm', (12, 2)), 'LocA': Dist('norm', (2, 0.5))},\n",
+    "    } \n",
+    "\n",
+    "# Generate network topology \n",
+    "G = nx.erdos_renyi_graph(10, 0.5)\n",
+    "# Assign a peer type to the peers \n",
+    "nx.set_node_attributes(G, {k: 'basic' for k in G.nodes()}, 'type')\n",
+    "\n",
+    "class PeerConfig(Config):\n",
+    "    location = Dist('sample', Locations.locations)\n",
+    "    bandwidth_ul = Dist( 'norm', (50*MBit, 10*MBit))\n",
+    "    bandwidth_dl = Dist( 'norm', (50*MBit, 10*MBit))\n",
+    "\n",
+    "# Let's add ConnectionManager - that will periodically ping neighbours and check if they are online \n",
+    "from p2psimpy.services.connection_manager import BaseConnectionManager\n",
+    "# For each service you can define own configuration, or use default values.   \n",
+    "# Lets use base connection manager - that will periodically ping peer and disconnect unresponsive peers.\n",
+    "\n",
+    "services = (BaseConnectionManager,)\n",
+    "peer_types = {'basic': PeerType(PeerConfig, services)}\n",
+    "\n",
+    "# Display the topology \n",
+    "nx.draw_networkx(G)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 719,
+   "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-03-09T01:24:18.310739Z",
-     "start_time": "2020-03-09T01:24:18.288326Z"
+     "end_time": "2020-03-17T12:41:42.680842Z",
+     "start_time": "2020-03-17T12:41:42.523014Z"
     }
    },
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "dictionary update sequence element #0 has length 7; 2 is required",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-719-fc408b1b194b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mPeerConfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\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[0;32m<ipython-input-670-d6a2552a5eca>\u001b[0m in \u001b[0;36mget\u001b[0;34m(cls)\u001b[0m\n\u001b[1;32m     25\u001b[0m                     \u001b[0mfull_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\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     26\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m                     \u001b[0mfull_dict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfull_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: dictionary update sequence element #0 has length 7; 2 is required"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "PeerConfig.get()"
+    "from p2psimpy.simulation import BaseSimulation\n",
+    "\n",
+    "sim = BaseSimulation(Locations, G, peer_types)\n",
+    "\n",
+    "# Let's run the simulation for 5 seconds\n",
+    "sim.run(5_000)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 731,
+   "cell_type": "markdown",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-03-09T01:27:02.710142Z",
-     "start_time": "2020-03-09T01:27:02.700999Z"
+     "end_time": "2020-03-17T12:40:15.632268Z",
+     "start_time": "2020-03-17T12:40:15.613568Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<__main__.Dist at 0x1090ec2d0>"
-      ]
-     },
-     "execution_count": 731,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "PeerConfig.location._wrap"
+    "----------------------------\n",
+    "\n",
+    "What's next? There is no output.\n",
+    "\n",
+    "The output by default is written to the logs in `\\logs` directory. Let's see what is the output. \n",
+    "Each peer writes own log with events happening like received message, connecting to a peer etc. \n",
+    "\n",
+    "For example:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "from p2psimpy.config import Config"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Ireland:\n",
-    "  Ireland:\n",
-    "    name: invgamma\n",
-    "    parameters: (6.4360455224301525, 0.8312748033308526, 1.086191852963273)\n",
-    "  Ohio: &id001\n",
-    "    name: norm\n",
-    "    parameters: (73.6995, 0.19583092197097127)\n",
-    "  Other: &id002\n",
-    "    name: gamma\n",
-    "    parameters: (0.9275, 5.25834811785650774, 50)\n",
-    "  Tokyo: &id003\n",
-    "    name: norm\n",
-    "    parameters: (131.0275, 0.25834811785650774)\n",
-    "Ohio:\n",
-    "  Ireland: *id001\n",
-    "  Ohio:\n",
-    "    name: invgamma\n",
-    "    parameters: (5.540906482531864, 0.33330564880631053, 0.9872493535175394)\n",
-    "  Other: *id002\n",
-    "  Tokyo: &id004\n",
-    "    name: norm\n",
-    "    parameters: (156.00904977375566, 0.09469886668079797)\n",
-    "Other:\n",
-    "  Ireland: *id002\n",
-    "  Ohio: *id002\n",
-    "  Other: *id002\n",
-    "  Tokyo: *id002\n",
-    "Tokyo:\n",
-    "  Ireland: *id003\n",
-    "  Ohio: *id004\n",
-    "  Other: *id002\n",
-    "  Tokyo:\n",
-    "    name: invgamma\n",
-    "    parameters: (11.104508341331055, 0.3371934865734555, 2.0258998705983737)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 725,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-09T01:25:21.808425Z",
-     "start_time": "2020-03-09T01:25:21.801765Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "class PeerConfig(Config):\n",
-    "    location = Wrap( Dist('norm', (5, 1)))\n",
-    "    latency = Wrap( Dist('norm', (5, 1)))\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 728,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-09T01:25:35.879270Z",
-     "start_time": "2020-03-09T01:25:35.874441Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "class FullConfig(Config):\n",
-    "    peer_config = ConfigWrap( PeerConfig)\n",
-    "    peer2_config = ConfigWrap( PeerConfig)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 732,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-09T07:51:53.573064Z",
-     "start_time": "2020-03-09T07:51:53.514729Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "mappingproxy({'__module__': '__main__',\n",
-       "              'location': <__main__.Wrap at 0x1090ecb10>,\n",
-       "              'latency': <__main__.Wrap at 0x1090ec0d0>,\n",
-       "              '__doc__': None})"
-      ]
-     },
-     "execution_count": 732,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "PeerConfig.__dict__"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Locations(Config):\n",
-    "    latencies = {\n",
-    "        'Ohio': {'Ohio': Wrap( Dist('invgamma', (5.54090, 0.333305, 0.987249))),\n",
-    "                 'Ireland': Wrap( Dist('norm', (73.6995, 0.19583092197097127))),\n",
-    "                 'Other': Wrap( Dist('gamma', (0.9275, 5.25834811785650774, 50))),\n",
-    "                 'Tokyo': Wrap( Dist('norm', (156.00904977375566, 0.09469886668079797)))\n",
-    "                },\n",
-    "        'Ireland':{'Ireland': Wrap( Dist('invgamma', (6.4360455224301525, 0.8312748033308526, 1.086191852963273))),\n",
-    "                   'Other': Wrap( Dist('gamma', (0.9275, 5.25834811785650774, 50))),\n",
-    "                   'Tokyo': Wrap( Dist('norm', (131.0275, 0.25834811785650774)))\n",
-    "                  },\n",
-    "        'Tokyo': {'Other': Wrap( Dist('gamma', (0.9275, 5.25834811785650774, 50))),\n",
-    "                  'Tokyo': Wrap( Dist('invgamma', (11.104508341331055, 0.3371934865734555, 2.0258998705983737)))\n",
-    "                 },\n",
-    "        'Other': {'Other': Wrap( Dist('gamma', (0.9275, 5.25834811785650774, 50)))}\n",
-    "    }\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cache = [Locations.get()['latencies'] for i in range(2)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "IndexError",
-     "evalue": "pop from empty list",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-65-adbcfb3a3c83>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\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[0;31mIndexError\u001b[0m: pop from empty list"
-     ]
-    }
-   ],
-   "source": [
-    "cache.pop()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import yaml"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from p2psimpy.config import Config, Wrap, Dist"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class PeerConfig(Config):\n",
-    "    pass"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('locations.yml') as s:\n",
-    "    pr = yaml.load(s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "PeerConfig.from_repr('Locations', pr['Locations'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Ireland': <p2psimpy.config.Wrap at 0x7fa352c2d3c8>,\n",
-       " 'Other': <p2psimpy.config.Wrap at 0x7fa352c2d518>,\n",
-       " 'Tokyo': <p2psimpy.config.Wrap at 0x7fa352c2d278>}"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "\n",
-    "gen('Ireland', 'Ireland')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def gen(*args):\n",
-    "    val = PeerConfig.latencies\n",
-    "    get_v = val\n",
-    "    for k in args:\n",
-    "        get_v = get_v.get(k)\n",
-    "    return get_v\n",
-    "            \n",
-    "        \n",
-    "    \n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def gen(sub):\n",
-    "    return "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.041943756846178"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "PeerConfig.latencies['Ireland']['Ireland'].get()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cache = PeerConfig.get()['latencies']\n",
-    "for k, v in cache.items():\n",
-    "    for k2, v2 in v.items():\n",
-    "        cache[k][k2] = list()\n",
-    "\n",
-    "for _ in range(20):\n",
-    "    gen = PeerConfig.get()['latencies']\n",
-    "    for k, v in gen.items():\n",
-    "        for k2, v2 in v.items():\n",
-    "            cache[k][k2].append(gen[k][k2])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Ireland': {'Ireland': [1.1947438518175009,\n",
-       "   0.9430266049154912,\n",
-       "   0.9317216464713983,\n",
-       "   1.1699613313691324,\n",
-       "   1.065539057139247,\n",
-       "   0.9688221945938943,\n",
-       "   1.0495363168099905,\n",
-       "   0.9423230435777031,\n",
-       "   1.0367610449084754,\n",
-       "   0.9455554144765743,\n",
-       "   0.9367480497661058,\n",
-       "   1.0300312560088676,\n",
-       "   1.1707020243612267,\n",
-       "   0.9236039369560832,\n",
-       "   0.9494367478412231,\n",
-       "   1.2400627963417135,\n",
-       "   0.988020276833575,\n",
-       "   0.9525788791962337,\n",
-       "   0.9600741107416513,\n",
-       "   1.2172790877115731],\n",
-       "  'Other': [60.389456722195995,\n",
-       "   24.20227964254246,\n",
-       "   35.5714270736436,\n",
-       "   85.57281236487664,\n",
-       "   26.59242488507358,\n",
-       "   246.72943874393945,\n",
-       "   7.031323157428869,\n",
-       "   31.49999191757533,\n",
-       "   135.96898972034617,\n",
-       "   254.6171312314924,\n",
-       "   25.536981337097117,\n",
-       "   25.764735599453545,\n",
-       "   52.11263792466242,\n",
-       "   10.112348993887885,\n",
-       "   24.153287310124956,\n",
-       "   59.84387186253137,\n",
-       "   19.906715131942757,\n",
-       "   54.11904898663425,\n",
-       "   44.30796025387606,\n",
-       "   20.90313364694374],\n",
-       "  'Tokyo': [131.1410249638176,\n",
-       "   130.7821200418501,\n",
-       "   131.16285551031848,\n",
-       "   131.12912816329478,\n",
-       "   131.21419587361157,\n",
-       "   131.34144075682312,\n",
-       "   131.45703141520337,\n",
-       "   131.40266806312562,\n",
-       "   130.82491216329518,\n",
-       "   130.9896997520125,\n",
-       "   131.28139091453693,\n",
-       "   131.2806437764367,\n",
-       "   130.42305472801638,\n",
-       "   131.0280995962908,\n",
-       "   130.6424110292527,\n",
-       "   131.60800297873692,\n",
-       "   131.2323113818258,\n",
-       "   131.02660512841166,\n",
-       "   131.47844628855216,\n",
-       "   131.62406248456992]},\n",
-       " 'Ohio': {'Ireland': [73.55655397213805,\n",
-       "   73.35359490370855,\n",
-       "   73.71784406167114,\n",
-       "   74.0366234480463,\n",
-       "   73.73585569782036,\n",
-       "   73.49481584900643,\n",
-       "   73.54116931065802,\n",
-       "   73.70062308416539,\n",
-       "   73.65293622168404,\n",
-       "   73.58797122718188,\n",
-       "   73.52744513071256,\n",
-       "   73.69506197203464,\n",
-       "   73.52323087622932,\n",
-       "   73.95394593847885,\n",
-       "   73.64833974110745,\n",
-       "   73.40791798830504,\n",
-       "   73.67415222084928,\n",
-       "   73.57342937362726,\n",
-       "   73.60439749561547,\n",
-       "   73.66531194115872],\n",
-       "  'Ohio': [0.441105624786832,\n",
-       "   0.4837288889636888,\n",
-       "   0.606606691929416,\n",
-       "   0.5506379534244623,\n",
-       "   0.41976345641430557,\n",
-       "   0.43610743003938546,\n",
-       "   0.598082934365678,\n",
-       "   0.5280569390923262,\n",
-       "   0.5185996746299146,\n",
-       "   0.5760005051757685,\n",
-       "   0.5229044741295839,\n",
-       "   0.47717017684368546,\n",
-       "   0.5164743438923636,\n",
-       "   0.5803033808968249,\n",
-       "   0.4626958233534235,\n",
-       "   0.4649525886330117,\n",
-       "   0.6937267056369817,\n",
-       "   0.5842783647360561,\n",
-       "   0.5155164418160245,\n",
-       "   0.42700603152973166],\n",
-       "  'Other': [12.272002598234208,\n",
-       "   47.640132522940185,\n",
-       "   26.06968254874594,\n",
-       "   39.0776712766965,\n",
-       "   57.57469921112835,\n",
-       "   72.38038907952138,\n",
-       "   35.125476556777244,\n",
-       "   61.367226453214506,\n",
-       "   54.606730293929026,\n",
-       "   147.07454181959656,\n",
-       "   83.40981237195886,\n",
-       "   23.67924658664827,\n",
-       "   74.09250608983177,\n",
-       "   33.80110112433965,\n",
-       "   21.10907036606121,\n",
-       "   69.22932464732224,\n",
-       "   43.14175041996964,\n",
-       "   13.565391338590569,\n",
-       "   50.3057876272695,\n",
-       "   5.870436751035368],\n",
-       "  'Tokyo': [156.0303153921205,\n",
-       "   156.0618623487435,\n",
-       "   155.96900267771517,\n",
-       "   156.0583500104474,\n",
-       "   156.0552768336205,\n",
-       "   155.97460418558018,\n",
-       "   156.11523210883308,\n",
-       "   155.75804619564727,\n",
-       "   155.980623449017,\n",
-       "   156.03279089760613,\n",
-       "   156.0536383046618,\n",
-       "   155.99080235154648,\n",
-       "   156.13258437569664,\n",
-       "   156.01762579853374,\n",
-       "   155.9915150213973,\n",
-       "   155.83341168641732,\n",
-       "   156.02804755190664,\n",
-       "   156.1098066147508,\n",
-       "   156.13883938896493,\n",
-       "   156.02210566653596]},\n",
-       " 'Other': {'Other': [14.225147263663,\n",
-       "   7.95255096314955,\n",
-       "   44.186371312375535,\n",
-       "   18.121849430846176,\n",
-       "   31.094223575356036,\n",
-       "   52.488074652248734,\n",
-       "   21.5390073298642,\n",
-       "   189.198774037322,\n",
-       "   43.52860234966615,\n",
-       "   15.123732742210937,\n",
-       "   61.56854202725149,\n",
-       "   19.09044962135921,\n",
-       "   22.711256465447406,\n",
-       "   75.83699703646658,\n",
-       "   21.47658800803536,\n",
-       "   11.021280298463115,\n",
-       "   66.08874863303129,\n",
-       "   45.763072803586795,\n",
-       "   44.284675643796085,\n",
-       "   46.37493085657646]},\n",
-       " 'Tokyo': {'Other': [24.708261332532405,\n",
-       "   88.72021214475318,\n",
-       "   81.3389353544416,\n",
-       "   18.89161397743217,\n",
-       "   39.793121198867404,\n",
-       "   49.03938369995205,\n",
-       "   40.644002022409445,\n",
-       "   48.81231536520301,\n",
-       "   36.6212480899372,\n",
-       "   20.317588046345346,\n",
-       "   70.19412730595295,\n",
-       "   41.061830910777275,\n",
-       "   50.909699252426456,\n",
-       "   18.268821413023304,\n",
-       "   109.8171899119465,\n",
-       "   92.584738694556,\n",
-       "   82.64372959358593,\n",
-       "   66.66908903368616,\n",
-       "   41.870800955905175,\n",
-       "   15.735303187358696],\n",
-       "  'Tokyo': [0.6035588418753832,\n",
-       "   0.5049820766359195,\n",
-       "   0.5783031833525835,\n",
-       "   0.588874904192174,\n",
-       "   0.6507356339492867,\n",
-       "   0.4603605149269171,\n",
-       "   0.47421861712895974,\n",
-       "   0.5180186122391903,\n",
-       "   0.6642518821461358,\n",
-       "   0.4908767714870957,\n",
-       "   0.5105970342426416,\n",
-       "   0.48833346794257565,\n",
-       "   0.6341484880411246,\n",
-       "   0.5462995809797644,\n",
-       "   0.7320943886906683,\n",
-       "   0.484337854374426,\n",
-       "   0.4978528925048604,\n",
-       "   0.5051861388286633,\n",
-       "   0.5460397415583335,\n",
-       "   0.6019821164736341]}}"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cache"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "val = [for _ in range(20)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'Wrap' object has no attribute 'generate'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-7-309dac0407d6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPeerConfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlatencies\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Ireland'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Ireland'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m: 'Wrap' object has no attribute 'generate'"
-     ]
-    }
-   ],
-   "source": [
-    "val = PeerConfig.latencies['Ireland']['Ireland']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "val = PeerConfig.latencies['Ireland']['Ireland'].generate(300)\n",
-    "try:\n",
-    "    last, val = val[-1], val[:-1]\n",
-    "except IndexError:\n",
-    "    val = PeerConfig.latencies['Ireland']['Ireland'].generate(300)\n",
-    "    last, val = val[-1], val[:-1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from itertools import product"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-12-4c9ecc37f3b3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# init the dictionary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mgen\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPeerConfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'latencies'\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      5\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mk2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv2\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/project/BlockchainEngineering/p2psimpy/config.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(cls, n)\u001b[0m\n\u001b[1;32m    132\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minspect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetmembers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\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    133\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'_'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 134\u001b[0;31m                 \u001b[0mfull_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\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    135\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfull_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    136\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/project/BlockchainEngineering/p2psimpy/config.py\u001b[0m in \u001b[0;36m_get\u001b[0;34m(cls, val)\u001b[0m\n\u001b[1;32m    119\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\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    120\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    122\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\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    123\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/project/BlockchainEngineering/p2psimpy/config.py\u001b[0m in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    119\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\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    120\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    122\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\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    123\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/project/BlockchainEngineering/p2psimpy/config.py\u001b[0m in \u001b[0;36m_get\u001b[0;34m(cls, val)\u001b[0m\n\u001b[1;32m    119\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\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    120\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    122\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\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    123\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/project/BlockchainEngineering/p2psimpy/config.py\u001b[0m in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    119\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\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    120\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    122\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\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    123\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/project/BlockchainEngineering/p2psimpy/config.py\u001b[0m in \u001b[0;36m_get\u001b[0;34m(cls, val)\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\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    124\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mWrap\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mConfigWrap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/project/BlockchainEngineering/p2psimpy/config.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\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    124\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mWrap\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mConfigWrap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/project/BlockchainEngineering/p2psimpy/config.py\u001b[0m in \u001b[0;36m_get\u001b[0;34m(cls, val)\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\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    124\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mWrap\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mConfigWrap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/project/BlockchainEngineering/p2psimpy/config.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\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    124\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mWrap\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mConfigWrap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/project/BlockchainEngineering/p2psimpy/config.py\u001b[0m in \u001b[0;36m_get\u001b[0;34m(cls, val)\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\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    124\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mWrap\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mConfigWrap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/project/BlockchainEngineering/p2psimpy/config.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\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    124\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mWrap\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mConfigWrap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/project/BlockchainEngineering/p2psimpy/config.py\u001b[0m in \u001b[0;36m_get\u001b[0;34m(cls, val)\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\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    124\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mWrap\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mConfigWrap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/project/BlockchainEngineering/p2psimpy/config.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\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    124\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mWrap\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mConfigWrap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/project/BlockchainEngineering/p2psimpy/config.py\u001b[0m in \u001b[0;36m_get\u001b[0;34m(cls, val)\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\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    124\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mWrap\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mConfigWrap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/project/BlockchainEngineering/p2psimpy/config.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\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    124\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mWrap\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mConfigWrap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/project/BlockchainEngineering/p2psimpy/config.py\u001b[0m in \u001b[0;36m_get\u001b[0;34m(cls, val)\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\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    124\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mWrap\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mConfigWrap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/project/BlockchainEngineering/p2psimpy/config.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\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    124\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mWrap\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mConfigWrap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/project/BlockchainEngineering/p2psimpy/config.py\u001b[0m in \u001b[0;36m_get\u001b[0;34m(cls, val)\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\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    124\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mWrap\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mConfigWrap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/project/BlockchainEngineering/p2psimpy/config.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\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    124\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mWrap\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mConfigWrap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/project/BlockchainEngineering/p2psimpy/config.py\u001b[0m in \u001b[0;36m_get\u001b[0;34m(cls, val)\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\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    124\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mWrap\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mConfigWrap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/project/BlockchainEngineering/p2psimpy/config.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\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    124\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mWrap\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mConfigWrap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/project/BlockchainEngineering/p2psimpy/config.py\u001b[0m in \u001b[0;36m_get\u001b[0;34m(cls, val)\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\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    124\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mWrap\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mConfigWrap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/project/BlockchainEngineering/p2psimpy/config.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\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    124\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mWrap\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mConfigWrap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/project/BlockchainEngineering/p2psimpy/config.py\u001b[0m in \u001b[0;36m_get\u001b[0;34m(cls, val)\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\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    124\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mWrap\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mConfigWrap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/project/BlockchainEngineering/p2psimpy/config.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\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    124\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mWrap\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mConfigWrap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/project/BlockchainEngineering/p2psimpy/config.py\u001b[0m in \u001b[0;36m_get\u001b[0;34m(cls, val)\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\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    124\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mWrap\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mConfigWrap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/project/BlockchainEngineering/p2psimpy/config.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\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    124\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mWrap\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mConfigWrap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/project/BlockchainEngineering/p2psimpy/config.py\u001b[0m in \u001b[0;36m_get\u001b[0;34m(cls, val)\u001b[0m\n\u001b[1;32m    118\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mdict\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    121\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "cache = PeerConfig.latencies\n",
-    "  \n",
-    "# init the dictionary \n",
-    "for _ in range(20):\n",
-    "    gen = PeerConfig.get()['latencies']\n",
-    "    for k, v in gen.items():\n",
-    "        for k2, v2 in v.items():\n",
-    "            if type(cache[k][k2]) != list:\n",
-    "                cache[k][k2] = []\n",
-    "            cache[k][k2].append(v2)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Ireland': {'Ireland': [0.9961821812540133, [0.9961821812540133]],\n",
-       "  'Other': [26.021231227700422, [26.021231227700422]],\n",
-       "  'Tokyo': [130.96881149353032, [130.96881149353032]]},\n",
-       " 'Ohio': {'Ireland': [73.38753981716, [73.38753981716]],\n",
-       "  'Ohio': [0.6590986840343376, [0.6590986840343376]],\n",
-       "  'Other': [29.806930733135466, [29.806930733135466]],\n",
-       "  'Tokyo': [156.07531307305734, [156.07531307305734]]},\n",
-       " 'Other': {'Other': [41.725549016287445, [41.725549016287445]]},\n",
-       " 'Tokyo': {'Other': [44.665930176638824, [44.665930176638824]],\n",
-       "  'Tokyo': [0.4797200409247394, [0.4797200409247394]]}}"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cache"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-112-538966951854>, line 1)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-112-538966951854>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    for k, v in v1[0].items()\u001b[0m\n\u001b[0m                             ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
-     ]
-    }
-   ],
-   "source": [
-    "for k, v in v1[0].items():\n",
-    "    {k:}\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v2 = {k: [dic[k] for dic in v1] for k, v in v1[0].items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Ireland': [{'Ireland': 1.0001005677044914,\n",
-       "   'Other': 94.25689813860379,\n",
-       "   'Tokyo': 131.01423405684355},\n",
-       "  {'Ireland': 1.0387181628392206,\n",
-       "   'Other': 33.86131018483214,\n",
-       "   'Tokyo': 131.23716417193003},\n",
-       "  {'Ireland': 1.1031983667203433,\n",
-       "   'Other': 24.433441760863214,\n",
-       "   'Tokyo': 131.13275888065397},\n",
-       "  {'Ireland': 1.0009305965396955,\n",
-       "   'Other': 88.41421418242084,\n",
-       "   'Tokyo': 131.07084628789124},\n",
-       "  {'Ireland': 1.037817552546067,\n",
-       "   'Other': 10.9265137960917,\n",
-       "   'Tokyo': 131.28867262723185},\n",
-       "  {'Ireland': 0.9673015262305551,\n",
-       "   'Other': 36.89545183362512,\n",
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-       "  {'Other': 23.999881357961353, 'Tokyo': 0.5530832475746572},\n",
-       "  {'Other': 14.102327723057822, 'Tokyo': 0.5685823482797329},\n",
-       "  {'Other': 95.28991006894101, 'Tokyo': 0.4565677798805413},\n",
-       "  {'Other': 51.62464687325255, 'Tokyo': 0.5255161377983724},\n",
-       "  {'Other': 33.33591754873297, 'Tokyo': 0.5912696044416704},\n",
-       "  {'Other': 23.886560594158436, 'Tokyo': 0.5318691192751738},\n",
-       "  {'Other': 9.54363596445724, 'Tokyo': 0.4277483333738516},\n",
-       "  {'Other': 18.562247183739565, 'Tokyo': 0.5162388318711524},\n",
-       "  {'Other': 87.7981544074819, 'Tokyo': 0.5577708262560469},\n",
-       "  {'Other': 60.476858711878016, 'Tokyo': 0.6043769122120305},\n",
-       "  {'Other': 61.23125359644178, 'Tokyo': 0.6576082639390535},\n",
-       "  {'Other': 40.618223398277294, 'Tokyo': 0.5279526152595426},\n",
-       "  {'Other': 61.26938418765544, 'Tokyo': 0.46886186926138274},\n",
-       "  {'Other': 5.383272745099878, 'Tokyo': 0.45695138865833823},\n",
-       "  {'Other': 39.22650062218937, 'Tokyo': 0.642992782777638},\n",
-       "  {'Other': 23.573659976899336, 'Tokyo': 0.4813834415483017},\n",
-       "  {'Other': 81.67567133377315, 'Tokyo': 0.5188590776183352},\n",
-       "  {'Other': 69.88725576374213, 'Tokyo': 0.5450274863523397},\n",
-       "  {'Other': 88.25601788937148, 'Tokyo': 0.4894401865337903},\n",
-       "  {'Other': 23.564078526991462, 'Tokyo': 0.4693119082887397}]}"
-      ]
-     },
-     "execution_count": 111,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "v2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
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-       "  'Other': {'Other': 23.32242661758214},\n",
-       "  'Tokyo': {'Other': 69.88725576374213, 'Tokyo': 0.5450274863523397}},\n",
-       " {'Ireland': {'Ireland': 1.0612291733649915,\n",
-       "   'Other': 52.96554639294507,\n",
-       "   'Tokyo': 130.78810570556976},\n",
-       "  'Ohio': {'Ireland': 73.77704731545384,\n",
-       "   'Ohio': 0.46447384854290175,\n",
-       "   'Other': 194.80387687734375,\n",
-       "   'Tokyo': 156.11481748885961},\n",
-       "  'Other': {'Other': 103.8787015371164},\n",
-       "  'Tokyo': {'Other': 88.25601788937148, 'Tokyo': 0.4894401865337903}},\n",
-       " {'Ireland': {'Ireland': 1.00423995524907,\n",
-       "   'Other': 108.03045043550418,\n",
-       "   'Tokyo': 130.9435476604372},\n",
-       "  'Ohio': {'Ireland': 73.52416028841252,\n",
-       "   'Ohio': 0.44821235047278785,\n",
-       "   'Other': 28.7787175393875,\n",
-       "   'Tokyo': 156.13312354626441},\n",
-       "  'Other': {'Other': 16.058278735820643},\n",
-       "  'Tokyo': {'Other': 23.564078526991462, 'Tokyo': 0.4693119082887397}}]"
-      ]
-     },
-     "execution_count": 108,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "v1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Ireland': {'Ireland': 1.0466536899195447,\n",
-       "  'Other': 53.197031849254124,\n",
-       "  'Tokyo': 130.8797079850948,\n",
-       "  'Ohio': 73.778319102741},\n",
-       " 'Ohio': {'Ireland': 73.778319102741,\n",
-       "  'Ohio': 0.5050895825327728,\n",
-       "  'Other': 18.499717942888946,\n",
-       "  'Tokyo': 156.0575503197866},\n",
-       " 'Other': {'Other': 42.64608590977521,\n",
-       "  'Ireland': 53.197031849254124,\n",
-       "  'Ohio': 18.499717942888946,\n",
-       "  'Tokyo': 75.85432491063835},\n",
-       " 'Tokyo': {'Other': 75.85432491063835,\n",
-       "  'Tokyo': 0.49571145849721937,\n",
-       "  'Ireland': 130.8797079850948,\n",
-       "  'Ohio': 156.0575503197866}}"
-      ]
-     },
-     "execution_count": 103,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "make_symmetric(v1)\n",
-    "v1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<itertools.product at 0x7f55ba717ca8>"
-      ]
-     },
-     "execution_count": 83,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "comb"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Ireland': {'Ireland': 0.9637758199499432,\n",
-       "  'Other': 6.539256396050316,\n",
-       "  'Tokyo': 130.79748944147212},\n",
-       " 'Ohio': {'Ireland': 73.77539860771886,\n",
-       "  'Ohio': 0.5756994577203448,\n",
-       "  'Other': 30.95315796031479,\n",
-       "  'Tokyo': 156.00620033318185},\n",
-       " 'Other': {'Other': 186.8656336283209},\n",
-       " 'Tokyo': {'Other': 113.60710402729549, 'Tokyo': 0.5397017830045977}}"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "v1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def create_config(name):\n",
-    "    class NewConfig(Config): pass\n",
-    "    NewConfig.__qualname__ = name\n",
-    "    NewConfig.__name__ = name\n",
-    "    return NewConfig\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t = create_config('fef')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'fef': {}}"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t.repr()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "PeerConfig.from_repr()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('t.yaml') as s:\n",
-    "    pr = yaml.load(s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'PeerConfig': {'location': {'Dist': {'name': 'ded', 'params': '123'}}}}"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pr"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v = Dist(**pr['PeerConfig']['location']['Dist'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{('location', <p2psimpy.config.Wrap at 0x7f5e2ce47c88>)}"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'mappingproxy' object has no attribute 'update'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-7-a0f3adafef4b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mPeerConfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'PeerConfig'\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[0;32m~/project/BlockchainEngineering/p2psimpy/config.py\u001b[0m in \u001b[0;36mfrom_repr\u001b[0;34m(cls, yaml_dict)\u001b[0m\n\u001b[1;32m    119\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfrom_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myaml_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dict__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0myaml_dict\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    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAttributeError\u001b[0m: 'mappingproxy' object has no attribute 'update'"
-     ]
-    }
-   ],
-   "source": [
-    "PeerConfig.from_repr(pr['PeerConfig'])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "    \n",
-    "\n",
-    "class PeerConfig(Config):\n",
-    "    location = Wrap( Dist('ded', 123))\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<generator object Config.get_attr_repr at 0x7fb457937f68>\n"
-     ]
-    }
-   ],
-   "source": [
-    "with open('t.yaml', 'w') as s:\n",
-    "    yaml.dump(PeerConfig.repr(), s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 745,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-09T08:22:37.245858Z",
-     "start_time": "2020-03-09T08:22:37.171040Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "class K:\n",
-    "    @dataclass\n",
-    "    class L:\n",
-    "        k:int = 2\n",
-    "        \n",
-    "        \n",
-    "    def __init__(self, conf):\n",
-    "        self.__dict__.update(conf)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 750,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-09T08:23:22.470782Z",
-     "start_time": "2020-03-09T08:23:22.463345Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "class M(K):\n",
-    "    pass\n",
-    "    class L:\n",
-    "        m: int = 3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 753,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-09T08:23:36.316869Z",
-     "start_time": "2020-03-09T08:23:36.306991Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3"
-      ]
-     },
-     "execution_count": 753,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "M.L.m"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 656,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-09T01:08:40.007894Z",
-     "start_time": "2020-03-09T01:08:39.999847Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'name': 'norm', 'params': '(5, 1)'}"
-      ]
-     },
-     "execution_count": 656,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "d.to_repr()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 638,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-09T01:01:28.251834Z",
-     "start_time": "2020-03-09T01:01:28.247008Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "val = {'fefe': {'fef1': 'f3f3', 'fefe': 'fefef'}}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 694,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-09T01:19:14.558321Z",
-     "start_time": "2020-03-09T01:19:14.540346Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "with open('test.yoml') as s:\n",
-    "    k = yaml.safe_load( s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 701,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-09T01:20:26.493258Z",
-     "start_time": "2020-03-09T01:20:26.487883Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "d = Dist.from_repr(k['FullConfig']['peer2_config']['PeerConfig']['latency']['Dist'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 705,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-09T01:20:55.489772Z",
-     "start_time": "2020-03-09T01:20:55.483587Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import scipy.stats\n",
-    "from ast import literal_eval as make_tuple"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 706,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-09T01:20:56.432539Z",
-     "start_time": "2020-03-09T01:20:56.424856Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4.456666639733772"
-      ]
-     },
-     "execution_count": 706,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "d.generate()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 570,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-09T00:45:26.304686Z",
-     "start_time": "2020-03-09T00:45:26.296656Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "v = {'FullConfig': {'peer2_config': {'PeerConfig': {'latency': {'Dist': {'name': 'norm', 'params': '(5, 1)'}}, 'location': {'Dist': {'name': 'sample', 'params': \"('Other', 'Loc', 'Loc2')\"}}}},\n",
-    "  'peer_config': {'PeerConfig': {'latency': {'Dist': {'name': 'norm', 'params': '(5, 1)'}}, 'location': {'Dist': {'name': 'sample', 'params': \"('Other', 'Loc', 'Loc2')\"}}}}}}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "C"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 417,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-09T00:11:23.240139Z",
-     "start_time": "2020-03-09T00:11:23.231659Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'latency': 5.447048390257572, 'location': 'Loc'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(dict(PeerConfig.get()))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 338,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T23:59:18.505871Z",
-     "start_time": "2020-03-08T23:59:18.498974Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Config': {'fl': Dist(name='norm', params=(5, 1))}}"
-      ]
-     },
-     "execution_count": 338,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Config.repr()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 300,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T23:53:25.275435Z",
-     "start_time": "2020-03-08T23:53:25.268450Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"Dist(name='norm', params=(5, 1))\""
-      ]
-     },
-     "execution_count": 300,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "repr(Config.fl)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 301,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T23:53:34.861787Z",
-     "start_time": "2020-03-08T23:53:34.855235Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'fl': Dist(name='norm', params=(5, 1))}"
-      ]
-     },
-     "execution_count": 301,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Config.get_attr()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 282,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T23:49:00.129732Z",
-     "start_time": "2020-03-08T23:49:00.120137Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5.115875198929158"
-      ]
-     },
-     "execution_count": 282,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Config.fl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 268,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T23:43:33.307906Z",
-     "start_time": "2020-03-08T23:43:33.294617Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('__class__', type),\n",
-       " ('__delattr__', <slot wrapper '__delattr__' of 'object' objects>),\n",
-       " ('__dict__',\n",
-       "  mappingproxy({'__module__': '__main__',\n",
-       "                'fl': <__main__.Wrap at 0x1a1dc5ff10>,\n",
-       "                'get_attr': <classmethod at 0x1a1dc5ff90>,\n",
-       "                'repr': <classmethod at 0x1a1dc5f6d0>,\n",
-       "                '__dict__': <attribute '__dict__' of 'Config' objects>,\n",
-       "                '__weakref__': <attribute '__weakref__' of 'Config' objects>,\n",
-       "                '__doc__': None})),\n",
-       " ('__dir__', <method '__dir__' of 'object' objects>),\n",
-       " ('__doc__', None),\n",
-       " ('__eq__', <slot wrapper '__eq__' of 'object' objects>),\n",
-       " ('__format__', <method '__format__' of 'object' objects>),\n",
-       " ('__ge__', <slot wrapper '__ge__' of 'object' objects>),\n",
-       " ('__getattribute__', <slot wrapper '__getattribute__' of 'object' objects>),\n",
-       " ('__gt__', <slot wrapper '__gt__' of 'object' objects>),\n",
-       " ('__hash__', <slot wrapper '__hash__' of 'object' objects>),\n",
-       " ('__init__', <slot wrapper '__init__' of 'object' objects>),\n",
-       " ('__init_subclass__', <function Config.__init_subclass__>),\n",
-       " ('__le__', <slot wrapper '__le__' of 'object' objects>),\n",
-       " ('__lt__', <slot wrapper '__lt__' of 'object' objects>),\n",
-       " ('__module__', '__main__'),\n",
-       " ('__ne__', <slot wrapper '__ne__' of 'object' objects>),\n",
-       " ('__new__', <function object.__new__(*args, **kwargs)>),\n",
-       " ('__reduce__', <method '__reduce__' of 'object' objects>),\n",
-       " ('__reduce_ex__', <method '__reduce_ex__' of 'object' objects>),\n",
-       " ('__repr__', <slot wrapper '__repr__' of 'object' objects>),\n",
-       " ('__setattr__', <slot wrapper '__setattr__' of 'object' objects>),\n",
-       " ('__sizeof__', <method '__sizeof__' of 'object' objects>),\n",
-       " ('__str__', <slot wrapper '__str__' of 'object' objects>),\n",
-       " ('__subclasshook__', <function Config.__subclasshook__>),\n",
-       " ('__weakref__', <attribute '__weakref__' of 'Config' objects>),\n",
-       " ('fl', 5.351717111397251),\n",
-       " ('get_attr', <bound method Config.get_attr of <class '__main__.Config'>>),\n",
-       " ('repr', <bound method Config.repr of <class '__main__.Config'>>)]"
-      ]
-     },
-     "execution_count": 268,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inspect.getmembers(Config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 261,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T23:40:57.059979Z",
-     "start_time": "2020-03-08T23:40:57.053576Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.976644971623131"
-      ]
-     },
-     "execution_count": 261,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Config.fl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 254,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T23:39:35.217375Z",
-     "start_time": "2020-03-08T23:39:35.209564Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Config': {'fl': Dist(name='norm', params=(5, 1))}}"
-      ]
-     },
-     "execution_count": 254,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Config.repr()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 170,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T23:16:30.355750Z",
-     "start_time": "2020-03-08T23:16:30.342195Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('__class__', type),\n",
-       " ('__dict__',\n",
-       "  mappingproxy({'__module__': '__main__',\n",
-       "                'fl': Dist(name='norm', params=(5, 1)),\n",
-       "                '__dict__': <attribute '__dict__' of 'Config' objects>,\n",
-       "                '__weakref__': <attribute '__weakref__' of 'Config' objects>,\n",
-       "                '__doc__': None})),\n",
-       " ('__doc__', None),\n",
-       " ('__module__', '__main__'),\n",
-       " ('__weakref__', <attribute '__weakref__' of 'Config' objects>),\n",
-       " ('fl', 3.600098720576689)]"
-      ]
-     },
-     "execution_count": 170,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inspect.getmembers(Config, lambda a:not(inspect.isroutine(a)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 175,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T23:17:17.177501Z",
-     "start_time": "2020-03-08T23:17:17.172174Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "variables = [i for i in dir(Config) if not callable(i) and not i.startswith('_')]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 183,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T23:19:41.606069Z",
-     "start_time": "2020-03-08T23:19:41.597106Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "('one', 'first')\n",
-      "('three', 'third')\n",
-      "('two', 'second')\n"
-     ]
-    }
-   ],
-   "source": [
-    "import inspect \n",
-    "  \n",
-    "class Number : \n",
-    "      \n",
-    "    # Class Attributes \n",
-    "    one = 'first'\n",
-    "    two = 'second'\n",
-    "    three = 'third'\n",
-    "      \n",
-    "    def __init__(self, attr): \n",
-    "        self.attr = attr \n",
-    "          \n",
-    "    def show(self):  \n",
-    "        print(self.one, self.two, self.three, self.attr) \n",
-    "          \n",
-    "for i in inspect.getmembers(Number): \n",
-    "      \n",
-    "    # to remove private and protected \n",
-    "    # functions \n",
-    "    if not : \n",
-    "          \n",
-    "        # To remove other methods that \n",
-    "        # doesnot start with a underscore \n",
-    "        if :  \n",
-    "            print(i) \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 176,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T23:17:17.902709Z",
-     "start_time": "2020-03-08T23:17:17.893341Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['fl']"
-      ]
-     },
-     "execution_count": 176,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "variables"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T23:08:09.217887Z",
-     "start_time": "2020-03-08T23:08:09.212266Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "class B(object):\n",
-    "    fl = Dist('norm', (5, 1))\n",
-    "    \n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T23:07:08.731155Z",
-     "start_time": "2020-03-08T23:07:08.724853Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "b = B(Dist('sample', ('fdedeef', 'fedededfe')))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 138,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T23:07:09.244152Z",
-     "start_time": "2020-03-08T23:07:09.234472Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Field(name='p',type=typing.Any,default=<dataclasses._MISSING_TYPE object at 0x10ab6fe50>,default_factory=<dataclasses._MISSING_TYPE object at 0x10ab6fe50>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),_field_type=_FIELD)\n",
-      "Field(name='v',type=<class 'int'>,default=1,default_factory=<dataclasses._MISSING_TYPE object at 0x10ab6fe50>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),_field_type=_FIELD)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'fe'"
-      ]
-     },
-     "execution_count": 138,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "repr(b)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T23:05:35.286957Z",
-     "start_time": "2020-03-08T23:05:35.278556Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5.976751414416254"
-      ]
-     },
-     "execution_count": 128,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b.fl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T23:03:27.637198Z",
-     "start_time": "2020-03-08T23:03:27.620895Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "__init__() missing 1 required positional argument: 'p'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-108-9cbae60fcdba>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mA\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      2\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: __init__() missing 1 required positional argument: 'p'"
-     ]
-    }
-   ],
-   "source": [
-    "b = A()\n",
-    "b.p"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T22:57:06.684924Z",
-     "start_time": "2020-03-08T22:57:06.680236Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "d = Dist('fef', (2, 3))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T22:53:39.263728Z",
-     "start_time": "2020-03-08T22:53:39.257953Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import yaml"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T22:57:10.566901Z",
-     "start_time": "2020-03-08T22:57:10.553299Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "with open(\"test.yml\", 'w') as s:\n",
-    "    yaml.safe_dump(repr(d), s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T22:55:39.389821Z",
-     "start_time": "2020-03-08T22:55:39.382898Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "with open(\"test.yml\") as s:\n",
-    "    val = yaml.safe_load(s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T22:55:40.829151Z",
-     "start_time": "2020-03-08T22:55:40.822108Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'A(_p=99.67633238744052, v=5)'"
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "val"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T22:53:46.622502Z",
-     "start_time": "2020-03-08T22:53:46.617049Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "a= A()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T22:52:40.661337Z",
-     "start_time": "2020-03-08T22:52:40.656807Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "a = A(Dist('norm', (5, 1)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T22:52:55.024449Z",
-     "start_time": "2020-03-08T22:52:55.016995Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dist(name='norm', params=(5, 1))"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a._p"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T22:52:41.183778Z",
-     "start_time": "2020-03-08T22:52:41.175225Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.8259615222292496"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.p()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T22:47:26.309272Z",
-     "start_time": "2020-03-08T22:47:26.301842Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5.2345374255440715"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.p"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T22:24:10.312428Z",
-     "start_time": "2020-03-08T22:24:10.231351Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "__init__() missing 1 required positional argument: 'l'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-6-5369d9a7735b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mB\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      2\u001b[0m \u001b[0ml\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: __init__() missing 1 required positional argument: 'l'"
-     ]
-    }
-   ],
-   "source": [
-    "b = B()\n",
-    "l = b.l"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T22:19:46.053940Z",
-     "start_time": "2020-03-08T22:19:46.047338Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dist()"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "l"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T22:18:40.010272Z",
-     "start_time": "2020-03-08T22:18:39.997544Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'K' 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-12-b56f456fb51e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mk1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mK\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mB\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m: name 'K' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "k1 = K(B())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T22:02:58.007318Z",
-     "start_time": "2020-03-08T22:02:57.992607Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "__get__() missing 2 required positional arguments: 'inst' and 'obj'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-101-92e9377811fe>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mk1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get__\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[0;31mTypeError\u001b[0m: __get__() missing 2 required positional arguments: 'inst' and 'obj'"
-     ]
-    }
-   ],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T21:58:41.402505Z",
-     "start_time": "2020-03-08T21:58:41.396329Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "class K:\n",
-    "    \n",
-    "    def __init__(self, config):\n",
-    "        self.config = config\n",
-    "        \n",
-    "class M:\n",
-    "    \n",
-    "    def __init__(self, config):\n",
-    "        self.__dict__.update(config.__dict__)\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T21:58:42.525955Z",
-     "start_time": "2020-03-08T21:58:42.521793Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "k1 = K(B())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T21:58:48.995063Z",
-     "start_time": "2020-03-08T21:58:48.987715Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "k2 = M(B())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T21:59:17.662607Z",
-     "start_time": "2020-03-08T21:59:09.123639Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "103 ns ± 4.11 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit\n",
-    "k1.config.l"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T21:59:23.168803Z",
-     "start_time": "2020-03-08T21:59:17.744313Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "66.3 ns ± 2.08 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit\n",
-    "k2.l"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T21:48:11.370602Z",
-     "start_time": "2020-03-08T21:48:11.366295Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "d = Dist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T21:33:28.295215Z",
-     "start_time": "2020-03-08T21:33:28.291231Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "b = B(1, 2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T21:35:07.474883Z",
-     "start_time": "2020-03-08T21:35:07.470566Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "c = C(1, 4, 'fefe')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T21:41:54.517022Z",
-     "start_time": "2020-03-08T21:41:54.499717Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "__post_init__() missing 1 required positional argument: 'kl'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-63-4dd0dde81123>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mb\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mB\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<string>\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, l, p)\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: __post_init__() missing 1 required positional argument: 'kl'"
-     ]
-    }
-   ],
-   "source": [
-    "b= B(1, 2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T21:06:17.933308Z",
-     "start_time": "2020-03-08T21:06:17.924945Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{}"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "c.__dict__"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T21:04:49.729506Z",
-     "start_time": "2020-03-08T21:04:49.722835Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import attr\n",
-    "\n",
-    "@attr.s\n",
-    "class ConnectionConfig:\n",
-    "    ping_interval = attr.ib(default=1)\n",
-    "    max_silence = attr.ib(default=1)\n",
-    "    min_keep_time = attr.ib(default=1)\n",
-    "    min_peers = attr.ib(default=1)\n",
-    "    max_peers = attr.ib(default=1)\n",
-    "    peer_list_number = attr.ib(default=2)\n",
-    "    peer_batch_request_number = attr.ib(default=4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T21:04:49.947704Z",
-     "start_time": "2020-03-08T21:04:49.939761Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "c = ConnectionConfig()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-08T21:04:50.360614Z",
-     "start_time": "2020-03-08T21:04:50.354680Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'ping_interval': 1,\n",
-       " 'max_silence': 1,\n",
-       " 'min_keep_time': 1,\n",
-       " 'min_peers': 1,\n",
-       " 'max_peers': 1,\n",
-       " 'peer_list_number': 2,\n",
-       " 'peer_batch_request_number': 4}"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "c.__dict__"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Simulation of a peer-to-peer environment\n",
-    "from p2psimpy.simulation import BaseSimulation\n",
-    "\n",
-    "# Define the simulation - logger dictionary  \n",
-    "\n",
-    "sim = BaseSimulation()\n",
-    "# add services to the simulation\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "## TODO: add service to peer type \n",
-    "sim.add_peer_service('basic', TxGossipService, gossip_config)\n",
-    "sim.add_peer_service('basic', TransactionProducer, tx_producer_config)\n",
-    "sim.add_peer_service('bootstrap', MockHandler, MockConfig(messages=[GossipMessage, TxMessage]))\n",
-    "#sim.add_peer_service('basic', MockHandler, MockConfig(messages=[GossipMessage, TxMessage]))\n",
-    "\n",
-    "sim.init_bootstrap_servers()\n",
-    "\n",
-    "sim.add_peers(peer_num)\n",
-    "sim.start_all_peers()\n",
-    "\n",
-    "sim.run(3600) # in ms "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Message gossip\n",
-    "from p2psimpy.services.base import BaseHandler, BaseRunner, MockHandler\n",
-    "from p2psimpy.peer import Peer\n",
-    "from p2psimpy.storage import Storage\n",
-    "from p2psimpy.messages import BaseMessage\n",
-    "\n",
-    "from attr import dataclass\n",
-    "from random import choice\n",
-    "\n",
-    "\n",
-    "class GossipMessage(BaseMessage):\n",
-    "\n",
-    "    def __init__(self, sender, data, ttl):\n",
-    "        super().__init__(sender, data)\n",
-    "        self.ttl = ttl\n",
-    "\n",
-    "class TxMessage(GossipMessage): pass\n",
-    "class BlockMessage(GossipMessage): pass\n",
-    "        \n",
-    "@dataclass\n",
-    "class GossipConfig:\n",
-    "    fanout: int\n",
-    "\n",
-    "\n",
-    "class GossipService(BaseHandler):\n",
-    "    \"\"\"\n",
-    "    Simple gossip service to handle gossip messages and rely them to neighbors. \n",
-    "    \"\"\"\n",
-    "\n",
-    "    def __init__(self, peer, config):\n",
-    "        super().__init__(peer, config)\n",
-    "\n",
-    "    def handle_message(self, msg):\n",
-    "        if isinstance(msg, GossipMessage):\n",
-    "            if msg.ttl > 0:\n",
-    "                # Rely message further, modify the message\n",
-    "                sender = msg.sender\n",
-    "                msg.sender = self.peer\n",
-    "                msg.ttl -= 1\n",
-    "                self.peer.gossip(msg, self.config.fanout, {sender})\n",
-    "\n",
-    "    @property\n",
-    "    def messages(self):\n",
-    "        return GossipMessage,\n",
-    "\n",
-    "\n",
-    "class TxGossipService(GossipService):\n",
-    "\n",
-    "    def __init__(self, peer, config):\n",
-    "        super().__init__(peer, config)\n",
-    "        #self.peer.add_storage('tx_store', Storage())\n",
-    "    \n",
-    "    def handle_message(self, msg):\n",
-    "        #self.peer.store('tx_store', msg.data.tx_id, msg)\n",
-    "        super().handle_message(msg)\n",
-    "    \n",
-    "    @property\n",
-    "    def messages(self):\n",
-    "        return TxMessage,\n",
-    "\n",
-    "\n",
-    "@dataclass\n",
-    "class MonetaryTransaction:\n",
-    "    sender: str\n",
-    "    receiver: str\n",
-    "    amount: int\n",
-    "    tx_id: str\n",
-    "\n",
-    "\n",
-    "@dataclass\n",
-    "class TransactionProducerConfig:\n",
-    "    receivers: list\n",
-    "    tx_rate: int\n",
-    "    init_ttl: int\n",
-    "    init_fanout: int\n",
-    "    init_timeout: int\n",
-    "\n",
-    "\n",
-    "class TransactionProducer(BaseRunner):\n",
-    "\n",
-    "    def __init__(self, peer, config: TransactionProducerConfig):\n",
-    "        super().__init__(peer, config)\n",
-    "        # calculate tx_interval:\n",
-    "        self.tx_interval = 1000 / self.config.tx_rate\n",
-    "        self.counter = 1 \n",
-    "\n",
-    "    def produce_transaction(self):\n",
-    "        # select receiver\n",
-    "        receiver = choice(self.config.receivers)\n",
-    "        tx = MonetaryTransaction(self.peer.name, receiver, 1, self.peer.name +\"_\"+ str(self.counter))\n",
-    "        # gossip the trasaction to the network\n",
-    "        self.counter += 1\n",
-    "        msg = TxMessage(self.peer, tx, self.config.init_ttl)\n",
-    "        self.peer.gossip(msg, self.config.init_fanout)\n",
-    "        \n",
-    "\n",
-    "    def run(self):\n",
-    "        yield self.env.timeout(self.config.init_timeout)\n",
-    "        while True:\n",
-    "            self.produce_transaction()\n",
-    "            yield self.env.timeout(self.tx_interval)\n",
-    "\n",
-    "\n",
-    "@dataclass\n",
-    "class MockConfig:\n",
-    "    messages: list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-05T11:33:21.335986Z",
-     "start_time": "2020-03-05T11:33:21.324442Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from p2psimpy.logger import init_log, reset_log\n",
-    "reset_log()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-06T15:17:34.102977Z",
-     "start_time": "2020-03-06T15:17:33.787184Z"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-06T17:18:09.290307Z",
-     "start_time": "2020-03-06T17:18:09.117986Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import yaml"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-06T17:18:37.961587Z",
-     "start_time": "2020-03-06T17:18:37.940905Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "class Test:\n",
-    "    \n",
-    "    def __init__(self):\n",
-    "        self.t1 = 2\n",
-    "        self.t2 = 3\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-06T17:18:47.128164Z",
-     "start_time": "2020-03-06T17:18:47.115093Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "t1 = Test()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-06T17:19:32.874357Z",
-     "start_time": "2020-03-06T17:19:32.832495Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "with open(\"test.yaml\", 'w') as s:\n",
-    "    yaml.dump(t1, s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-06T17:21:33.734836Z",
-     "start_time": "2020-03-06T17:21:33.640122Z"
-    }
-   },
    "outputs": [
     {
-     "ename": "ConstructorError",
-     "evalue": "could not determine a constructor for the tag 'tag:yaml.org,2002:python/object:__main__.Test'\n  in \"test.yaml\", line 1, column 1",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mConstructorError\u001b[0m                          Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-10-a10faf81d642>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'test.yaml'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mt2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0myaml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msafe_load\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/miniconda3/site-packages/yaml/__init__.py\u001b[0m in \u001b[0;36msafe_load\u001b[0;34m(stream)\u001b[0m\n\u001b[1;32m    160\u001b[0m     \u001b[0mto\u001b[0m \u001b[0mbe\u001b[0m \u001b[0msafe\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0muntrusted\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    161\u001b[0m     \"\"\"\n\u001b[0;32m--> 162\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSafeLoader\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    163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msafe_load_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/site-packages/yaml/__init__.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(stream, Loader)\u001b[0m\n\u001b[1;32m    112\u001b[0m     \u001b[0mloader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLoader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mloader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_single_data\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    115\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m         \u001b[0mloader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/site-packages/yaml/constructor.py\u001b[0m in \u001b[0;36mget_single_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     41\u001b[0m         \u001b[0mnode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_single_node\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     42\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnode\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconstruct_document\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\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     44\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/site-packages/yaml/constructor.py\u001b[0m in \u001b[0;36mconstruct_document\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mconstruct_document\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconstruct_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\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     48\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate_generators\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m             \u001b[0mstate_generators\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate_generators\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/site-packages/yaml/constructor.py\u001b[0m in \u001b[0;36mconstruct_object\u001b[0;34m(self, node, deep)\u001b[0m\n\u001b[1;32m     90\u001b[0m                     \u001b[0mconstructor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconstruct_mapping\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtag_suffix\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconstructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode\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     93\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconstructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtag_suffix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/site-packages/yaml/constructor.py\u001b[0m in \u001b[0;36mconstruct_undefined\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m    419\u001b[0m         raise ConstructorError(None, None,\n\u001b[1;32m    420\u001b[0m                 \u001b[0;34m\"could not determine a constructor for the tag %r\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtag\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 421\u001b[0;31m                 node.start_mark)\n\u001b[0m\u001b[1;32m    422\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    423\u001b[0m SafeConstructor.add_constructor(\n",
-      "\u001b[0;31mConstructorError\u001b[0m: could not determine a constructor for the tag 'tag:yaml.org,2002:python/object:__main__.Test'\n  in \"test.yaml\", line 1, column 1"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Peer_1:basic INFO 0.5642461520769093: Received message <Hello:> from Peer_2:basic\n",
+      "Peer_1:basic INFO Connecting to Peer_2:basic\n",
+      "Peer_1:basic INFO Connecting to Peer_7:basic\n",
+      "Peer_1:basic INFO 1.6675048549334301: Received message <Hello:> from Peer_7:basic\n",
+      "Peer_1:basic INFO 2.8302113666605466: Received message <Hello:> from Peer_7:basic\n",
+      "Peer_1:basic INFO Connecting to Peer_2:basic\n",
+      "Peer_1:basic INFO Connecting to Peer_4:basic\n",
+      "Peer_1:basic INFO 5.194451802113656: Received message <Hello:> from Peer_4:basic\n",
+      "Peer_1:basic INFO 5.244345235466082: Received message <Hello:> from Peer_2:basic\n",
+      "Peer_1:basic INFO 5.408725622485689: Received message <Hello:> from Peer_9:basic\n",
+      "Peer_1:basic INFO Connecting to Peer_9:basic\n",
+      "Peer_1:basic INFO 10.675733948012986: Received message <Hello:> from Peer_4:basic\n",
+      "Peer_1:basic INFO 12.770379593216953: Received message <Hello:> from Peer_2:basic\n",
+      "Peer_1:basic INFO 1000.576017793764: Received message <Ping:> from Peer_7:basic\n",
+      "Peer_1:basic INFO 1001.2741734062128: Received message <Ping:> from Peer_2:basic\n",
+      "Peer_1:basic INFO 1001.3247919476762: Received message <Pong:> from Peer_7:basic\n",
+      "Peer_1:basic INFO 1001.4524176163745: Received message <Pong:> from Peer_2:basic\n",
+      "Peer_1:basic INFO 1004.4572762463415: Received message <Ping:> from Peer_9:basic\n",
+      "Peer_1:basic INFO 1005.9754455465892: Received message <Ping:> from Peer_2:basic\n",
+      "Peer_1:basic INFO 1006.5732491717029: Received message <Ping:> from Peer_4:basic\n",
+      "Peer_1:basic INFO 1009.8972649367447: Received message <Pong:> from Peer_9:basic\n",
+      "Peer_1:basic INFO 1012.3742718508353: Received message <Pong:> from Peer_4:basic\n",
+      "Peer_1:basic INFO 1014.4650270099263: Received message <Pong:> from Peer_2:basic\n",
+      "Peer_1:basic INFO 2000.6924919144851: Received message <Ping:> from Peer_7:basic\n",
+      "Peer_1:basic INFO 2000.7760026862484: Received message <Ping:> from Peer_2:basic\n",
+      "Peer_1:basic INFO 2001.6706582491836: Received message <Pong:> from Peer_7:basic\n",
+      "Peer_1:basic INFO 2002.0488778608737: Received message <Pong:> from Peer_2:basic\n",
+      "Peer_1:basic INFO 2004.6915759932695: Received message <Ping:> from Peer_9:basic\n",
+      "Peer_1:basic INFO 2005.3571466609155: Received message <Ping:> from Peer_4:basic\n",
+      "Peer_1:basic INFO 2005.8234206898787: Received message <Ping:> from Peer_2:basic\n",
+      "Peer_1:basic INFO 2010.1890304590825: Received message <Pong:> from Peer_4:basic\n",
+      "Peer_1:basic INFO 2011.5717372731303: Received message <Pong:> from Peer_2:basic\n",
+      "Peer_1:basic INFO 2012.3958743954213: Received message <Pong:> from Peer_9:basic\n",
+      "Peer_1:basic INFO 3000.6030753914897: Received message <Ping:> from Peer_7:basic\n",
+      "Peer_1:basic INFO 3000.966867901417: Received message <Ping:> from Peer_2:basic\n",
+      "Peer_1:basic INFO 3002.0429457290247: Received message <Pong:> from Peer_2:basic\n",
+      "Peer_1:basic INFO 3003.4324234723376: Received message <Pong:> from Peer_7:basic\n",
+      "Peer_1:basic INFO 3003.8827829463444: Received message <Ping:> from Peer_4:basic\n",
+      "Peer_1:basic INFO 3004.903043439791: Received message <Ping:> from Peer_2:basic\n",
+      "Peer_1:basic INFO 3005.4990373243068: Received message <Ping:> from Peer_9:basic\n",
+      "Peer_1:basic INFO 3010.1088652657395: Received message <Pong:> from Peer_4:basic\n",
+      "Peer_1:basic INFO 3010.4242582206302: Received message <Pong:> from Peer_9:basic\n",
+      "Peer_1:basic INFO 3011.5025828997527: Received message <Pong:> from Peer_2:basic\n",
+      "Peer_1:basic INFO 4000.533206228579: Received message <Ping:> from Peer_7:basic\n",
+      "Peer_1:basic INFO 4000.9369788298627: Received message <Ping:> from Peer_2:basic\n",
+      "Peer_1:basic INFO 4001.1691031635987: Received message <Pong:> from Peer_2:basic\n",
+      "Peer_1:basic INFO 4002.842041843051: Received message <Pong:> from Peer_7:basic\n",
+      "Peer_1:basic INFO 4003.72833096014: Received message <Ping:> from Peer_2:basic\n",
+      "Peer_1:basic INFO 4004.926275937606: Received message <Ping:> from Peer_9:basic\n",
+      "Peer_1:basic INFO 4006.169212955045: Received message <Ping:> from Peer_4:basic\n",
+      "Peer_1:basic INFO 4010.502484820322: Received message <Pong:> from Peer_9:basic\n",
+      "Peer_1:basic INFO 4010.843028487586: Received message <Pong:> from Peer_4:basic\n",
+      "Peer_1:basic INFO 4012.915421524362: Received message <Pong:> from Peer_2:basic\n",
+      "\n"
      ]
     }
    ],
    "source": [
-    "with open('test.yaml') as s:\n",
-    "    t2 = yaml.safe_load(s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-06T17:21:25.853753Z",
-     "start_time": "2020-03-06T17:21:25.832689Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t2.t1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class fefService(BaseSer)\n",
-    " config_calss = ferfConfig"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class ByznatinePeer(Peer):\n",
-    "    services = {fefef, fefe, fefe}\n",
-    "    config_class = ByzantinePeerConfig\n",
-    "    "
+    "with open('logs/Peer_1:basic.log') as s:\n",
+    "    print(s.read())"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "f=  FefeConfig(fefef, fefef)\n",
+    "## Emergent topology\n",
     "\n",
-    "sim = 20 peers Byznaine with Congigs \n"
+    "Now let's try emergent topology, we will use the same configuration, but use bootstrap peer for discovery and building a network.  \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-03-06T15:17:38.157509Z",
-     "start_time": "2020-03-06T15:17:35.539192Z"
+     "end_time": "2020-03-12T15:16:27.055983Z",
+     "start_time": "2020-03-12T15:16:26.986708Z"
     }
    },
    "outputs": [],
    "source": [
+    "from p2psimpy.services.connection_manager import P2PConnectionManager\n",
     "\n",
+    "num_peers = 10\n",
     "\n",
-    "peer_num = 30\n",
-    "\n",
-    "\n",
-    "gossip_config = GossipConfig(fanout=5)\n",
-    "receivers = ['basic_'+str(i+1) for i in range(peer_num)]\n",
-    "tx_producer_config = TransactionProducerConfig(receivers=receivers,\n",
-    "                                               tx_rate=3, \n",
-    "                                               init_ttl=3,\n",
-    "                                               init_fanout=5, \n",
-    "                                               init_timeout=2000) \n",
+    "topology_specs = {i:{'type': 'basic'} for i in range(1, num_peers+1)}\n",
     "\n",
-    "    \n",
-    "    \n",
+    "class ConnectionConfig(Config):\n",
+    "    min_peers = 4\n",
+    "    max_peers = 8\n",
     "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Investigate the peer storage \n",
-    "sim.peers['basic'][0].storage['tx_store'].txs\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-06T09:50:03.782737Z",
-     "start_time": "2020-03-06T09:50:03.408441Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "run_sim()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-06T09:50:14.436879Z",
-     "start_time": "2020-03-06T09:50:14.109455Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "run_sim()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-06T09:50:56.290294Z",
-     "start_time": "2020-03-06T09:50:56.271106Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "'tx_store'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-11-b3b2a572fda2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Investigate the peer storage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpeers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'basic'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstorage\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tx_store'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtxs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Visualize the transactions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'tx_store'"
-     ]
-    }
-   ],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-04T16:10:26.920324Z",
-     "start_time": "2020-03-04T16:10:25.048571Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "sim.run(4600)"
+    "peer_types = {'basic': PeerType(PeerConfig, {P2PConnectionManager: ConnectionConfig})}\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-03-04T16:22:23.494313Z",
-     "start_time": "2020-03-04T16:22:23.015061Z"
+     "end_time": "2020-03-12T15:16:27.068788Z",
+     "start_time": "2020-03-12T15:16:27.059355Z"
     }
    },
    "outputs": [],
    "source": [
-    "%matplotlib inline"
+    "sim = BaseSimulation(Locations, topology_specs, peer_types, logger_dir='logs2')\n",
+    "\n",
+    "# Let's run the simulation for 5 seconds\n",
+    "sim.run(5_000)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-03-04T16:23:03.195084Z",
-     "start_time": "2020-03-04T16:23:03.182310Z"
+     "end_time": "2020-03-12T15:10:02.460425Z",
+     "start_time": "2020-03-12T15:10:02.450412Z"
     }
    },
    "outputs": [
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "DegreeView({'basic_1': 25, 'basic_31': 19, 'basic_44': 22, 'basic_5': 14, 'basic_16': 15, 'basic_6': 18, 'basic_27': 15, 'basic_38': 15, 'basic_35': 17, 'basic_33': 15, 'basic_34': 20, 'basic_10': 17, 'basic_17': 18, 'basic_4': 15, 'basic_3': 21, 'basic_29': 17, 'basic_56': 15, 'basic_39': 18, 'basic_37': 14, 'basic_23': 17, 'basic_2': 15, 'basic_40': 23, 'basic_32': 27, 'basic_49': 20, 'basic_25': 15, 'basic_15': 15, 'basic_57': 21, 'basic_20': 17, 'basic_28': 17, 'basic_53': 17, 'basic_47': 17, 'basic_54': 16, 'basic_41': 20, 'basic_51': 20, 'basic_48': 21, 'basic_22': 17, 'basic_60': 19, 'basic_21': 19, 'basic_52': 17, 'basic_12': 23, 'basic_45': 18, 'basic_8': 17, 'basic_14': 15, 'basic_30': 14, 'basic_24': 16, 'basic_26': 19, 'basic_36': 14, 'basic_46': 14, 'basic_9': 20, 'basic_55': 14, 'basic_18': 19, 'basic_11': 20, 'basic_7': 14, 'basic_43': 18, 'basic_42': 14, 'basic_19': 23, 'basic_59': 20, 'basic_58': 17, 'basic_13': 21, 'basic_50': 18})"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "execution_count": 9,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "G.degree()"
+    "G1 = sim.get_graph()\n",
+    "nx.draw_networkx(G1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-04T16:22:26.793054Z",
-     "start_time": "2020-03-04T16:22:24.926987Z"
-    }
-   },
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x1080 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Peer_1:basic INFO Connecting to Peer_11:bootstrap\n",
+      "Peer_1:basic INFO 13.068476026463767: Received message <Hello:> from Peer_11:bootstrap\n",
+      "Peer_1:basic INFO 14.639619829680399: Received message <RequestPeers:> from Peer_11:bootstrap\n",
+      "Peer_1:basic INFO 510.6906712034973: Received message <PeerList> from Peer_11:bootstrap\n",
+      "Peer_1:basic INFO 1001.0155446481016: Received message <Hello:> from Peer_9:basic\n",
+      "Peer_1:basic INFO Connecting to Peer_9:basic\n",
+      "Peer_1:basic INFO 1002.9837811526241: Received message <PeerList> from Peer_9:basic\n",
+      "Peer_1:basic INFO 1005.1131840040766: Received message <RequestPeers:> from Peer_11:bootstrap\n",
+      "Peer_1:basic INFO Connecting to Peer_2:basic\n",
+      "Peer_1:basic INFO 1011.0534445456536: Received message <PeerList> from Peer_11:bootstrap\n",
+      "Peer_1:basic INFO 1011.2118146573121: Received message <Hello:> from Peer_2:basic\n",
+      "Peer_1:basic INFO 1012.4833997047816: Received message <RequestPeers:> from Peer_2:basic\n",
+      "Peer_1:basic INFO 1500.9060844021499: Received message <RequestPeers:> from Peer_9:basic\n",
+      "Peer_1:basic INFO 1502.4161183817168: Received message <PeerList> from Peer_9:basic\n",
+      "Peer_1:basic INFO 1506.9878302720017: Received message <Hello:> from Peer_8:basic\n",
+      "Peer_1:basic INFO Connecting to Peer_8:basic\n",
+      "Peer_1:basic INFO 1511.5859825152977: Received message <PeerList> from Peer_2:basic\n",
+      "Peer_1:basic INFO 1514.608497462654: Received message <PeerList> from Peer_11:bootstrap\n",
+      "Peer_1:basic INFO 1517.692164325575: Received message <PeerList> from Peer_8:basic\n",
+      "Peer_1:basic INFO 2501.1834571289965: Received message <Ping:> from Peer_9:basic\n",
+      "Peer_1:basic INFO 2502.1531974669642: Received message <Pong:> from Peer_9:basic\n",
+      "Peer_1:basic INFO 2504.1327241814306: Received message <Ping:> from Peer_2:basic\n",
+      "Peer_1:basic INFO 2504.3353198626232: Received message <Ping:> from Peer_8:basic\n",
+      "Peer_1:basic INFO 2505.305488769264: Received message <Hello:> from Peer_3:basic\n",
+      "Peer_1:basic INFO Connecting to Peer_3:basic\n",
+      "Peer_1:basic INFO 2506.220255789725: Received message <RequestPeers:> from Peer_11:bootstrap\n",
+      "Peer_1:basic INFO 2506.4143949452136: Received message <Ping:> from Peer_11:bootstrap\n",
+      "Peer_1:basic INFO 2511.3773168553275: Received message <Pong:> from Peer_8:basic\n",
+      "Peer_1:basic INFO 2512.155079766583: Received message <Pong:> from Peer_11:bootstrap\n",
+      "Peer_1:basic INFO 2514.3050815796746: Received message <Pong:> from Peer_2:basic\n",
+      "Peer_1:basic INFO 2515.9068774198468: Received message <PeerList> from Peer_3:basic\n",
+      "Peer_1:basic INFO 3501.3565221634494: Received message <Ping:> from Peer_9:basic\n",
+      "Peer_1:basic INFO 3501.769197870563: Received message <Pong:> from Peer_9:basic\n",
+      "Peer_1:basic INFO 3505.901054723864: Received message <Ping:> from Peer_3:basic\n",
+      "Peer_1:basic INFO 3506.2883197614633: Received message <Ping:> from Peer_11:bootstrap\n",
+      "Peer_1:basic INFO 3507.0251258372205: Received message <Ping:> from Peer_2:basic\n",
+      "Peer_1:basic INFO 3507.087276927674: Received message <Ping:> from Peer_8:basic\n",
+      "Peer_1:basic INFO 3510.560637745563: Received message <Pong:> from Peer_3:basic\n",
+      "Peer_1:basic INFO 3511.725115964656: Received message <Pong:> from Peer_8:basic\n",
+      "Peer_1:basic INFO 3512.1896455527485: Received message <Pong:> from Peer_2:basic\n",
+      "Peer_1:basic INFO 3515.03676554631: Received message <Pong:> from Peer_11:bootstrap\n",
+      "Peer_1:basic INFO 4006.728868250879: Received message <RequestPeers:> from Peer_11:bootstrap\n",
+      "Peer_1:basic INFO 4500.991845438711: Received message <Ping:> from Peer_9:basic\n",
+      "Peer_1:basic INFO 4501.799030752164: Received message <Pong:> from Peer_9:basic\n",
+      "Peer_1:basic INFO 4505.318941739256: Received message <Ping:> from Peer_2:basic\n",
+      "Peer_1:basic INFO 4505.8499837163145: Received message <Ping:> from Peer_8:basic\n",
+      "Peer_1:basic INFO 4508.328019525814: Received message <Ping:> from Peer_3:basic\n",
+      "Peer_1:basic INFO 4510.773079722933: Received message <Pong:> from Peer_3:basic\n",
+      "Peer_1:basic INFO 4511.044514015669: Received message <Pong:> from Peer_2:basic\n",
+      "Peer_1:basic INFO 4513.153102060254: Received message <Pong:> from Peer_8:basic\n",
+      "\n"
+     ]
     }
    ],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import networkx as nx\n",
-    "from matplotlib.animation import FuncAnimation\n",
-    "from networkx.drawing.nx_agraph import graphviz_layout\n",
-    "\n",
-    "from time import time, sleep\n",
-    "\n",
-    "\n",
-    "G = sim.get_graph()\n",
-    "fig = plt.figure(figsize=(15, 15))\n",
-    "plt.axis('off')\n",
-    "#pos = nx.shell_layout(G)\n",
-    "pos = nx.kamada_kawai_layout(G)\n",
-    "\n",
-    "nx.draw_networkx(G, pos)"
+    "with open('logs2/Peer_1:basic.log') as s:\n",
+    "    print(s.read())"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "from simpy import Environment\n",
-    "evn = Environment\n",
-    "School()"
+    "------------------\n",
+    "\n",
+    "Now you get how to work a simulation, in the next notebook we will take a look into services and implement our own.  \n"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {
   "hide_input": false,
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python Conda",
    "language": "python",
    "name": "python3"
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.9"
-  },
   "pycharm": {
    "stem_cell": {
     "cell_type": "raw",
@@ -5273,7 +914,12 @@
    "title_cell": "Table of Contents",
    "title_sidebar": "Contents",
    "toc_cell": false,
-   "toc_position": {},
+   "toc_position": {
+    "height": "524px",
+    "left": "441px",
+    "top": "355px",
+    "width": "256px"
+   },
    "toc_section_display": true,
    "toc_window_display": false
   },
@@ -5308,5 +954,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/p2psimpy/input/config.yml b/input/config.yml
similarity index 100%
rename from p2psimpy/input/config.yml
rename to input/config.yml
diff --git a/p2psimpy/input/locations.yml b/input/locations.yml
similarity index 100%
rename from p2psimpy/input/locations.yml
rename to input/locations.yml
diff --git a/p2psimpy/input/logger.yml b/input/logger.yml
similarity index 100%
rename from p2psimpy/input/logger.yml
rename to input/logger.yml
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new file mode 100644
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diff --git a/p2psimpy/__pycache__/visual_simulation.cpython-37.pyc b/p2psimpy/__pycache__/visual_simulation.cpython-37.pyc
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diff --git a/p2psimpy/config.py b/p2psimpy/config.py
index dcc256e..3e290ff 100644
--- a/p2psimpy/config.py
+++ b/p2psimpy/config.py
@@ -1,10 +1,13 @@
 import inspect
-import yaml
-
-import scipy.stats
 from ast import literal_eval as make_tuple
+from collections import namedtuple
 from random import choices
 
+import scipy.stats
+import yaml
+
+PeerType = namedtuple('PeerType', ('config', 'service_map'), defaults=(None, {}))
+
 
 class Dist:
 
@@ -13,15 +16,12 @@ def __init__(self, name: str, params):
         self.params = params
 
     def to_repr(self):
-        return {'name': self.name, 'params': str(self.params)}
+        return {self.__class__.__name__: {'name': self.name, 'params': str(self.params)}}
 
     @classmethod
     def from_repr(cls, yaml_dict):
         return cls(**yaml_dict)
 
-    # def __str__(self):
-    #    return str({'Dist': {'name': self.name, 'params': str(self.params)}})
-
     def generate(self, n=1):
         """
         Generate 'n' random values with given distribution
@@ -40,6 +40,12 @@ def generate(self, n=1):
         res = dist.rvs(*param[:-2], loc=param[-2], scale=param[-1], size=n)
         return res if n != 1 else res[0]
 
+    def get(self):
+        return self.generate(1)
+
+
+class DistAttr(Dist):
+
     def __get__(self, inst, obj):
         return self.generate(1)
 
@@ -65,7 +71,7 @@ def generate(self, n=1):
         return self._wrap.generate(n)
 
 
-class ConfigWrap:
+class ConfigAttr:
     def __init__(self, cls):
         self.cls = cls
 
@@ -80,6 +86,11 @@ def get(self):
 
 
 class Config:
+    @classmethod
+    def save_to_yaml(cls, yaml_file):
+        with open(yaml_file, 'w') as s:
+            yaml.dump(cls.repr(), s)
+
     @classmethod
     def _serialize(cls, val):
         if isinstance(val, (tuple, list, dict)):
@@ -88,7 +99,7 @@ def _serialize(cls, val):
             else:
                 return list(cls._serialize(k) for k in val)
         else:
-            if type(val) == Wrap or type(val) == ConfigWrap:
+            if type(val) == Dist or type(val) == ConfigAttr:
                 return val.to_repr()
             else:
                 return val
@@ -97,7 +108,7 @@ def _serialize(cls, val):
     def _deserialize(cls, val):
         if type(val) == dict:
             if 'Dist' in val:
-                return Wrap(Dist(**val['Dist']))
+                return Dist(**val['Dist'])
             else:
                 return {k: cls._deserialize(v) for k, v in val.items()}
         else:
@@ -124,13 +135,13 @@ def _get(cls, val):
             return {k: cls._get(v) for k, v in val.items()}
         elif isinstance(val, list):
             return [cls._get(v) for v in val]
-        if type(val) == Wrap or type(val) == ConfigWrap:
+        if type(val) == Dist or type(val) == ConfigAttr:
             return val.get()
         else:
             return val
 
     @classmethod
-    def get(cls, n=1):
+    def get(cls):
         full_dict = dict()
         for i in inspect.getmembers(cls):
             if not i[0].startswith('_') and not callable(i[1]):
@@ -145,43 +156,11 @@ def from_repr(cls, cls_name, yaml_dict):
             setattr(cls, k, v)
 
 
-class PeerNameGenerator:
-
-    def __init__(self):
-        self.peer_indexes = dict()  # type -> number map
-
-    def generate_name(self, peer_type: str):
-        if peer_type not in self.peer_indexes:
-            # New peer type => Init
-            self.peer_indexes[peer_type] = 0
-        self.peer_indexes[peer_type] += 1
-        return peer_type + "_" + str(self.peer_indexes[peer_type])
-
-
-class ConfigLoader:
-
-    @staticmethod
-    def load_services():
-        with open('p2psimpy/input/config.yml') as s:
-            return yaml.safe_load(s)
-
-    @staticmethod
-    def load_latencies():
-        with open('p2psimpy/input/locations.yml') as s:
-            return list(yaml.safe_load_all(s))[1]
-
-
 def load_config_from_yaml(yaml_file):
     class NewConfig(Config): pass
 
     with open(yaml_file) as s:
-        raw = yaml.load(s)
+        raw = yaml.safe_load(s)
     cls_name = list(raw.keys())[0]
     NewConfig.from_repr(cls_name, raw[cls_name])
     return NewConfig
-
-
-def from_config(cls, name_gen: PeerNameGenerator, peer_type: str, config: dict):
-    params = [name_gen.generate_name(peer_type)]
-    params.extend([load_config_parameter(config[field]) for field in ('location', 'bandwidth_ul', 'bandwidth_dl')])
-    return cls(*params)
diff --git a/p2psimpy/defaults.py b/p2psimpy/defaults.py
new file mode 100644
index 0000000..b38f32f
--- /dev/null
+++ b/p2psimpy/defaults.py
@@ -0,0 +1,19 @@
+from p2psimpy.config import *
+from p2psimpy.consts import MBit
+
+from p2psimpy.services.connection_manager import P2PConnectionManager, BaseConnectionManager
+
+
+class BootstrapPeerConfig(Config):
+    bandwidth_ul = 100 * MBit
+    bandwidth_dl = 100 * MBit
+
+
+class ConnectionConfig(Config):
+    max_peers = 100000
+
+
+def get_default_bootstrap_type(locations, active_p2p=False):
+    BootstrapPeerConfig.location = Dist('sample', locations)
+    services = {P2PConnectionManager: ConnectionConfig} if active_p2p else (BaseConnectionManager,)
+    return PeerType(BootstrapPeerConfig, services)
diff --git a/p2psimpy/logger.py b/p2psimpy/logger.py
index f2458f1..340a2fe 100644
--- a/p2psimpy/logger.py
+++ b/p2psimpy/logger.py
@@ -3,10 +3,10 @@
 formatter = logging.Formatter('%(name)s %(levelname)s %(message)s')
 
 
-def setup_logger(name, log_file, level=logging.INFO):
+def setup_logger(name, log_file, level=logging.INFO, mode='w'):
     """To setup as many loggers as you want"""
 
-    handler = logging.FileHandler(log_file)
+    handler = logging.FileHandler(log_file, mode=mode)
     handler.setFormatter(formatter)
 
     logger = logging.getLogger(name)
diff --git a/p2psimpy/messages.py b/p2psimpy/messages.py
index 21f8bdc..e545a2a 100644
--- a/p2psimpy/messages.py
+++ b/p2psimpy/messages.py
@@ -1,4 +1,5 @@
 class BaseMessage(object):
+    __slots__ = ('sender', 'data')
     base_size = 20
 
     def __init__(self, sender, data=None):
@@ -10,7 +11,9 @@ def size(self):
         return self.base_size + len(repr(self.data))
 
     def __repr__(self):
-        return '<%s>' % self.__class__.__name__
+        msg_type = '%s:' % self.__class__.__name__
+        data = self.data if self.data else ""
+        return msg_type + data
 
 
 ########## Messages ###############
@@ -36,6 +39,9 @@ class PeerList(BaseMessage):
     def __init__(self, sender, peers):
         super().__init__(sender, set(peers))
 
+    def __repr__(self):
+        return 'PeerList'
+
 
 class Hello(BaseMessage):
     """Offer a peer to connect"""
diff --git a/p2psimpy/network.py b/p2psimpy/network.py
index 3e4814b..943e364 100644
--- a/p2psimpy/network.py
+++ b/p2psimpy/network.py
@@ -5,17 +5,18 @@ def __init__(self, sender, receiver):
         :param locations: Map that contains the latencies between locations
         """
         self.env = sender.env
-        self.get_latency = self.sender.sim.get_latency_delay
-
         self.sender = sender
         self.receiver = receiver
 
+        self.get_latency = self.sender.sim.get_latency_delay
+        self.start_time = self.env.now
+
     def __repr__(self):
         return '<Connection %r -> %r>' % (self.sender, self.receiver)
 
     @property
     def latency(self):
-        return self.get_latency(self.sender.location, self.receiver.location)
+        return max(self.get_latency(self.sender.location, self.receiver.location), 0)
 
     @property
     def bandwidth(self):
@@ -32,11 +33,12 @@ def send(self, msg, connect=False):
 
         def _transfer():
             bytes = msg.size
+            sender = msg.sender
             delay = bytes / self.sender.bandwidth_ul
             delay += bytes / self.receiver.bandwidth_dl
             delay += self.latency / 2
             yield self.env.timeout(delay)
-            if self.receiver.is_connected(msg.sender) or connect:
+            if self.receiver.is_connected(sender) or connect:
                 self.receiver.msg_queue.put(msg)
 
         self.env.process(_transfer())
diff --git a/p2psimpy/peer.py b/p2psimpy/peer.py
index c0d57ba..6a31fbb 100644
--- a/p2psimpy/peer.py
+++ b/p2psimpy/peer.py
@@ -1,4 +1,3 @@
-
 import os
 import random
 
@@ -12,7 +11,8 @@
 
 class Peer:
 
-    def __init__(self, sim, name: str, location: str, bandwidth_ul: float, bandwidth_dl: float):
+    def __init__(self, sim, peer_id: int, peer_type: str,
+                 location: str, bandwidth_ul: float, bandwidth_dl: float, **kwargs):
         """
         Physical representation of a Peer
         :param sim: Simulation environment
@@ -21,14 +21,17 @@ def __init__(self, sim, name: str, location: str, bandwidth_ul: float, bandwidth
         """
         self.sim = sim
         self.env = sim.env
-        self.name = name
+        self.peer_type = peer_type
+        self.peer_id = peer_id
+        self.name = str(peer_id) + ":" + str(peer_type)
         self.location = location
         self.bandwidth_ul = bandwidth_ul
         self.bandwidth_dl = bandwidth_dl
+        self.__dict__.update(kwargs)
 
         peer_repr = repr(self)
-        self.log_name = os.path.join(sim.sim_dir, peer_repr+".log")
-        self.logger = setup_logger(peer_repr, self.log_name )
+        self.log_name = os.path.join(sim.sim_dir, peer_repr + ".log")
+        self.logger = setup_logger(peer_repr, self.log_name)
 
         # Message queue for the received messages
         self.msg_queue = Store(self.env)
@@ -52,7 +55,7 @@ def __init__(self, sim, name: str, location: str, bandwidth_ul: float, bandwidth
         self.env.process(self.run())
 
     def __repr__(self):
-        return '%s %s' % (self.__class__.__name__, self.name)
+        return '%s_%s' % (self.__class__.__name__, self.name)
 
     def __lt__(self, other):
         return self.name < other.name
@@ -81,7 +84,7 @@ def connect(self, other):
         :param other: peer object
         """
         if not self.is_connected(other):
-            self.logger.info("Connecting to %s", repr(other))
+            self.logger.info("%s: Connecting to %s", self.env.now, repr(other))
             self.connections[other] = Connection(self, other)
             # We create bilateral connection
             if not other.is_connected(self):
@@ -106,12 +109,14 @@ def receive(self, msg):
         :param msg:
         :return:
         """
-        assert isinstance(msg, BaseMessage)  # Make sure the message is known
-        self.logger.info("%s: Received message %s", self.env.now, type(msg))
-        if type(msg) not in self.mh_map:
-            self.logger.error("No handler for the message %s", type(msg))
-            raise Exception("No handler for the message %s", type(msg))
-        for service_id in self.mh_map[type(msg)]:
+        msg_type =  type(msg)
+        msg_sender =  msg.sender
+        self.logger.info("%s: Received message <%s> from %s", self.env.now, repr(msg), msg_sender)
+
+        if msg_type not in self.mh_map:
+            self.logger.error("No handler for the message %s", msg_type)
+            raise Exception("No handler for the message %s at peer %s", msg_type, repr(self))
+        for service_id in self.mh_map[msg_type]:
             self.handlers[service_id].handle_message(msg)
 
     def send(self, receiver, msg):
@@ -120,29 +125,40 @@ def send(self, receiver, msg):
         If receiver is not connected will raise and exception
         """
         # fire and forget
-        assert msg.sender == self, "Sending peer should be same %s %s" % (msg.sender, self)
         if receiver not in self.connections:
             self.logger.error("%s: Sending message to a not connected peer %s",
                               self.env.now, repr(receiver))
             raise Exception("Not connected")
+        self.logger.info("%s: Sending message <%s> to %s", self.env.now, repr(msg), receiver)
         self.connections[receiver].send(msg)
 
-    def gossip(self, msg, f, except_set: set = None):
+    def _get_connections(self, exclude_bootstrap=True, except_set: set = None, except_type: set = None):
+        if except_set is None:
+            except_set = set()
+        if except_type is None:
+            except_type = set()
+        if exclude_bootstrap:
+            except_type.add('bootstrap')
+        conn_set = set(self.connections.keys()) - except_set
+        return (p for p in conn_set if p.peer_type not in except_type)
+
+    def gossip(self, msg, f, exclude_bootstrap=True, except_peers: set = None, except_type: set = None):
         """
         Send to f neighbours selected randomly
         :param msg: Message
         :param f: the fanout parameter (number of peers to gossip to)
-        :type except_set: connected peers to exclude from gossip
+        :param exclude_bootstrap: Exclude bootstrap from gossip
+        :param except_peers: connected peers to exclude from gossip
+        :param except_type: exclude from gossip type of peers
         """
-        if except_set is None:
-            except_set = set()
-        gossip_set = set(self.connections.keys()) - except_set
-        for other in random.sample(list(gossip_set), min(f, len(gossip_set))):
+        gossip_set = list(self._get_connections(exclude_bootstrap, except_peers, except_type))
+
+        for other in random.sample(gossip_set, min(f, len(gossip_set))):
             self.send(other, msg)
 
-    def broadcast(self, msg):
-        """Send to all connected peers """
-        for other in self.connections:
+    def broadcast(self, msg, exclude_bootstrap=True, except_set: set = None, except_type: set = None):
+        """Send to all connected peers except given """
+        for other in self._get_connections(exclude_bootstrap, except_set, except_type):
             self.send(other, msg)
 
     def add_service(self, service):
diff --git a/p2psimpy/peer_factory.py b/p2psimpy/peer_factory.py
index e169b27..0c40b9c 100644
--- a/p2psimpy/peer_factory.py
+++ b/p2psimpy/peer_factory.py
@@ -1,34 +1,38 @@
-import logging
+from itertools import count
 
-
-
-from p2psimpy.config import load_from_config, PeerNameGenerator, ConnectionConfig, \
-    SlowdownConfig, DisruptionConfig
+from p2psimpy.config import PeerType
+from p2psimpy.peer import Peer
 
 
 class PeerFactory:
-    CLASS_MAP = {
-        'ConnectionManager': BaseConnectionManager,
-        'Downtime': Downtime,
-        'Slowdown': Slowdown
-    }
-
-    CONFIG_CLASS_MAP = {
-        'ConnectionManager': ConnectionConfig,
-        'Downtime': DisruptionConfig,
-        'Slowdown': SlowdownConfig
-    }
 
     def __init__(self):
-        # Services of a peer type
-        self.services = {}
-        # Configuration data classes for each service
-        self.configs_classes = {}
-        # Exact configurations
-        self.raw_configs = {}
-        self.full_configs = {}
-        self.name_generator = PeerNameGenerator()
-        self.logger = logging.getLogger(repr(self))
+        self._last = 1
+
+    def create_peer(self, sim, type_name: str, peer_type: PeerType, peer_id: int = None):
+        # Create a peer from PeerConfig
+        if not peer_id:
+            peer_id = self._last + 1
+            self._last = peer_id
+        else:
+            if int(peer_id) > self._last:
+                self._last = int(peer_id)
+
+        peer_config_gen = peer_type.config
+        services = peer_type.service_map.keys() if type(peer_type.service_map) == dict else peer_type.service_map
+        service_configs = peer_type.service_map if type(peer_type.service_map) == dict else None
+
+        p = Peer(sim, peer_id, type_name, **peer_config_gen.get())
+
+        for service in services:
+            serv = None
+            if service_configs and service_configs[service]:
+                serv = service(p, **service_configs[service].get())
+            else:
+                serv = service(p)
+            p.add_service(serv)
+        p.start_all_runners()
+        return p
 
     def load_from_conf(self, config):
         # Parse the full configuration and add services
@@ -41,56 +45,3 @@ def load_from_conf(self, config):
             # Add peer configuration
             # Exception not known service
             self.raw_configs[p_type]['Peer'] = config[p_type]['Peer']
-
-    def add_service_with_conf(self, peer_type, service_class, service_config_class, raw_config):
-        """
-        Add service for the peer type
-        :param peer_type: type of the peer
-        :param service_class: Class type of the service
-        :param service_config_class:
-        :param raw_config:
-        :return:
-        """
-        if peer_type not in self.services:
-            self.services[peer_type] = list()
-            self.configs_classes[peer_type] = dict()
-            self.raw_configs[peer_type] = dict()
-
-        # Add new service for the peer type
-        self.services[peer_type].append(service_class)
-        self.configs_classes[peer_type][service_class.__name__] = service_config_class
-        self.raw_configs[peer_type][service_class.__name__] = raw_config
-
-    def add_service(self, peer_type, service_class, service_config):
-        if peer_type not in self.services:
-            self.services[peer_type] = list()
-        if peer_type not in self.full_configs:
-            self.full_configs[peer_type] = dict()
-
-        # Add new service for the peer type
-        self.services[peer_type].append(service_class)
-        self.full_configs[peer_type][service_class.__name__] = service_config
-
-    def update_peer_type(self, key: str, full_config: dict):
-        self.raw_configs[key] = full_config
-
-    def create_peer(self, env, peer_type: str):
-        if peer_type not in self.raw_configs:
-            self.logger.error("Cannot create peer. %s not known", peer_type)
-            raise Exception("Peer type not known %s", peer_type)
-        if 'Peer' not in self.raw_configs[peer_type]:
-            self.logger.error("Cannot create peer. 'Peer' config for known for %s", peer_type)
-            raise Exception("Peer config not found %s", peer_type)
-
-        # Create a peer from PeerConfig
-        p = Peer(env, PeerConfig.from_config(self.name_generator, peer_type,
-                                             self.raw_configs[peer_type]["Peer"]))
-        for service in self.services[peer_type]:
-            if peer_type in self.full_configs and service.__name__ in self.full_configs[peer_type]:
-                p.add_service(service(p, self.full_configs[peer_type][service.__name__]))
-            else:
-                config_class = load_from_config(self.configs_classes[peer_type][service.__name__],
-                                                self.raw_configs[peer_type][service.__name__])
-                # Initialise the service and add to the peer
-                p.add_service(service(p, config_class))
-        return p
diff --git a/p2psimpy/services/__pycache__/base.cpython-37.pyc b/p2psimpy/services/__pycache__/base.cpython-37.pyc
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diff --git a/p2psimpy/services/__pycache__/connection_manager.cpython-37.pyc b/p2psimpy/services/__pycache__/connection_manager.cpython-37.pyc
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diff --git a/p2psimpy/services/__pycache__/connection_manager.pypy36.pyc b/p2psimpy/services/__pycache__/connection_manager.pypy36.pyc
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index 0000000..67f0e18
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diff --git a/p2psimpy/services/__pycache__/disruption.cpython-37.pyc b/p2psimpy/services/__pycache__/disruption.cpython-37.pyc
new file mode 100644
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diff --git a/p2psimpy/services/__pycache__/disruption.pypy36.pyc b/p2psimpy/services/__pycache__/disruption.pypy36.pyc
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diff --git a/p2psimpy/services/base.py b/p2psimpy/services/base.py
index 3673080..41fdd50 100644
--- a/p2psimpy/services/base.py
+++ b/p2psimpy/services/base.py
@@ -1,6 +1,12 @@
+from p2psimpy.logger import setup_logger
+
+
 class BaseService(object):
     def __init__(self, peer,  **kwargs):
         self.peer = peer
+        self.__dict__.update(kwargs)
+
+        self.logger = setup_logger(repr(self), peer.log_name, mode='a')
 
     @property
     def env(self):
diff --git a/p2psimpy/services/connection_manager.py b/p2psimpy/services/connection_manager.py
index 492cbd2..2e4ae5c 100644
--- a/p2psimpy/services/connection_manager.py
+++ b/p2psimpy/services/connection_manager.py
@@ -1,6 +1,5 @@
 from random import sample
 
-from p2psimpy.logger import setup_logger
 from p2psimpy.messages import Hello, PeerList, Ping, RequestPeers, Pong
 from p2psimpy.peer import Peer
 from p2psimpy.services.base import BaseHandler, BaseRunner
@@ -28,7 +27,6 @@ def __init__(self, peer: Peer, **kwargs):
         self.last_seen = dict()  # a map: peer -> timestamp
         self.known_peers = set()  # All known peers
         self.disconnected_peers = set()  # Connected in past, now disconnected
-        self.logger = setup_logger(repr(self), peer.log_name)
 
         def disconnect_cb(p, other):
             assert p == self.peer
@@ -66,7 +64,6 @@ def recv_hello(self, msg):
         if other not in self.peer.connections:
             self.peer.connect(other)
             self.peer.send(other, Hello(self.peer))
-            self.peer.send(other, RequestPeers(self.peer))
 
     def disconnect_unresponsive_peers(self):
         now = self.env.now
@@ -81,14 +78,11 @@ def disconnect_unresponsive_peers(self):
     def connected_peers(self):
         return self.peer.connections.keys()
 
-    def run_script(self):
-        self.ping_peers()
-        self.disconnect_unresponsive_peers()
-        yield self.env.timeout(self.ping_interval)
-
     def run(self):
         while True:
-            self.run_script()
+            self.ping_peers()
+            self.disconnect_unresponsive_peers()
+            yield self.env.timeout(self.ping_interval)
 
 
 class P2PConnectionManager(BaseConnectionManager):
@@ -107,21 +101,21 @@ def __init__(self, peer: Peer, **kwargs):
           min_keep_time: int - Minimum time to keep connection between peers. Default: 3000 ms
 
           peer_list_number - Number of peers in a PeerList request upon requesting peers, Default: 1
-          min_peers - minimum number of peers to have in connections. Default: 10
-          max_peers - maximum number of peers to have in connections. Default: 20
+          min_peers - minimum number of peers to have in connections. Default: 15
+          max_peers - maximum number of peers to have in connections. Default: 25
           peer_batch_request - number of peers to request peers at the same time. Default: 5
         """
         BaseConnectionManager.__init__(self, peer, **kwargs)
 
         # Connection Manager Attributes
         self.peer_list_number = kwargs.pop('peer_list_number', 1)
-        self.min_peers = kwargs.pop('min_peers', 10)
-        self.max_peers = kwargs.pop('min_peers', 20)
+        self.min_peers = kwargs.pop('min_peers', 15)
+        self.max_peers = kwargs.pop('max_peers', 25)
         self.peer_batch_request = kwargs.pop('peer_batch_request', 5)
 
     @property
     def messages(self):
-        return super().messages, RequestPeers, PeerList
+        return super().messages + (RequestPeers, PeerList,)
 
     def handle_message(self, msg):
         """
@@ -150,6 +144,16 @@ def peer_candidates(self):
         candidates = self.known_peers.difference(set(self.connected_peers))
         return candidates.difference(self.disconnected_peers)
 
+    def recv_hello(self, msg):
+        """
+        Receive introduction message
+        """
+        other = msg.sender
+        if other not in self.peer.connections:
+            self.peer.connect(other)
+            self.peer.send(other, Hello(self.peer))
+            self.peer.send(other, RequestPeers(self.peer))
+
     def disconnect_slowest_peer(self):
         """
         Try to disconnect the slowest peer
@@ -176,7 +180,7 @@ def monitor_connections(self):
                                 self.env.now, len(self.connected_peers), self.min_peers)
             candidates = self.peer_candidates
             if len(candidates) < needed:
-                self.peer.gossip(RequestPeers(self.peer), self.peer_batch_request)
+                self.peer.gossip(RequestPeers(self.peer), self.peer_batch_request, exclude_bootstrap=False)
             for other in list(candidates)[:needed]:
                 self.peer.bootstrap_connect(other)
 
@@ -188,6 +192,9 @@ def monitor_connections(self):
             for i in range(num):
                 self.disconnect_slowest_peer()
 
-    def run_script(self):
-        self.monitor_connections()
-        super().run_script()
+    def run(self):
+        while True:
+            self.monitor_connections()
+            self.ping_peers()
+            self.disconnect_unresponsive_peers()
+            yield self.env.timeout(self.ping_interval)
\ No newline at end of file
diff --git a/p2psimpy/simulation.py b/p2psimpy/simulation.py
index 6dee488..83979a4 100644
--- a/p2psimpy/simulation.py
+++ b/p2psimpy/simulation.py
@@ -1,33 +1,35 @@
+import os
 import random
+from itertools import groupby
 
 import networkx as nx
 from simpy import Environment
 
+from p2psimpy.config import load_config_from_yaml
+from p2psimpy.defaults import get_default_bootstrap_type
 from p2psimpy.logger import setup_logger
 from p2psimpy.peer_factory import PeerFactory
-
-from p2psimpy.peer import Peer
 from p2psimpy.services.connection_manager import BaseConnectionManager, P2PConnectionManager
 from p2psimpy.services.disruption import Downtime, Slowdown
-
-from p2psimpy.config import load_config_from_yaml
-from p2psimpy.utils import make_symmetric
+from p2psimpy.utils import Cache
 
 
 class BaseSimulation(object):
-    """ Class to represent different topologies and p2p network simulation
+    """
+    Main class to run simulation.
     """
     known_services = [BaseConnectionManager, P2PConnectionManager, Downtime, Slowdown]
-    cash_val = 40
-
+    cash_val = 50
 
-    def __init__(self, locations: dict = None, services: dict = None, logger_dir='logs', **kwargs):
-        # Initialize the logger
-        # Set the random seed for replaying simulations
-        self.services = {service.__name__: service for service in self.known_services} # Create a mapping to read from configs
-        # Update service if they are not None
-        if services:
-            self.services.update(services)
+    def __init__(self, locations, topology, peer_types_map, logger_dir='logs', **kwargs):
+        """
+            Initialize simulation with known locations, topology and services.
+            :param locations: Known locations. Either a yaml file_name, or a Config class.
+            :param topology: subscribable object map: peer_id -> type, optionally also connections to other peers
+            :param peer_types_map: A map with 'type' -> PeerType objects
+            :param logger_dir: directory to store peer logs. default: './logs/'
+            :param random_seed: for reproducibility for your experiments.
+        """
 
         if 'random_seed' in kwargs:
             self.random_seed = kwargs.get('random_seed', 42)
@@ -36,127 +38,107 @@ def __init__(self, locations: dict = None, services: dict = None, logger_dir='lo
 
         # Setup logging dir
         self.sim_dir = logger_dir
-        self.logger = setup_logger(__name__, self.sim_dir+'sim.log')
+        if not os.path.exists(self.sim_dir):
+            os.mkdir(self.sim_dir)
+        self.sim_log_name = os.path.join(self.sim_dir, "sim.log")
+        self.logger = setup_logger(__name__,  self.sim_log_name)
 
         # Init the environment
         self.env = Environment()
-        # Init map with peers
-        if full_config:
-            # Init from the config file, Load config and create peers.
-            # Read peer types
-            #self.peer_map = full_config peer_types
-            # peer type and service
-            # peer type - Peer
-
-
-
-            pass
-        else:
-            # Load defaults
-            pass
-
-
-        self.peers = dict()
-        self.peer_factory = PeerFactory()
-
-        # Load Locations
 
-        if not locations:
-            # Load defaults
-            pass
+        # Init locations and latencies
         if type(locations) == str:
             # yaml file - try to load
             self._location_generator = load_config_from_yaml(locations).latencies
+            self.all_locations = load_config_from_yaml(locations).locations
         else:
+            # Load location generator
             self._location_generator = locations.latencies
+            self.all_locations = locations.locations
 
         # Generate location to store in cache
-        self.locations_cache = {} # self._load_cache(self._location_generator, self.cash_val)
-        # Create peer factory from config file
-        # ConfigLoader.load_services()
-        #
-        # self.locations = ConfigLoader.load_latencies()
-        # self.env.locations = self.locations
-
-        # self.logger.info("Start simulation")
-
-
-    def _load_cache(self, generator, cache_num, subfield = None):
-
-        if subfield:
-            return [generator.get()[subfield] for i in range(cache_num)]
-        else:
-            return [generator.get() for i in range(cache_num)]
-
+        self.locations = Cache(self._location_generator, self.cash_val)
+
+        # Parse topology
+        self.peers_types = {}
+        self.topology = {}
+        self.types_peers = {}
+        if type(topology) == dict:
+            # The map with form peer_id -> {'type': ..., other_peer ids}
+            self.peers = {p: None for p in topology.keys()}
+            for k in topology.keys():
+                self.peers_types[k] = topology.get(k).get('type')
+                self.topology[k] = topology.get(k).get('neighbors')
+        elif type(topology) == nx.Graph:
+            # The graph with topology of peer_ids with peer attributes
+            self.peers_types = nx.get_node_attributes(topology, 'type')
+            self.topology = topology
+            self.peers = {p: None for p in topology.nodes()}
+
+        # map type -> set of peers
+        self.types_peers = {k: {j for j, _ in list(v)}
+                            for k, v in groupby(self.peers_types.items(), lambda x: x[1])}
+
+        # Process given peer_types_map
+        self.peer_types_configs = peer_types_map
 
-
-
-    def add_peer_type(self):
-        pass
-
-    def get_latency_delay(self, origin: str, destination: str, n=1):
+        self.peer_factory = PeerFactory()
+        # Create peers for this simulation
+        for p in list(self.peers.keys()):
+            peer_type_name = self.peers_types[p]
+            peer_type = self.peer_types_configs[peer_type_name]
+            self.peers[p] = self.peer_factory.create_peer(self, peer_type_name, peer_type, p)
+
+        # Bootstrap connect peers
+        # If there are connections =>
+        for p in list(self.peers.keys()):
+            if self.topology[p]:
+                for c in self.topology[p]:
+                    self.peers[p].bootstrap_connect(self.peers[c])
+            else:
+                # Connect to bootstrap server
+                if 'bootstrap' not in self.types_peers:
+                    # No bootstrap configuration is known - use default bootstrap
+                    use_p2p = kwargs.get('bootstrap_p2p', True)
+                    num_bootstrap = kwargs.get('num_bootstrap', 1)
+                    self._init_default_bootstrap_servers(self.all_locations, num=num_bootstrap,
+                                                         active_p2p=use_p2p)
+                b_s = random.choice(list(self.types_peers['bootstrap']))
+                boot_peer = self.peers[b_s]
+                self.peers[p].bootstrap_connect(boot_peer)
+
+    def get_latency_delay(self, origin: str, destination: str):
         """
         Get latency delay according to the latency distribution
-        :param locations: map that contains distance between locations (in ms)
         :param origin: from location
         :param destination: to location
-        :param n: the size of the latency vector
-        :return: list of latencies
         """
-        try:
-            locations = self.locations_cache.pop()
-        except IndexError:
-            self.locations_cache = self._load_cache(self._location_generator, self.cash_val)
-            self.locations_cache)
-            locations = self.locations_cache.pop()
-
-
+        return self.locations.fetch(origin, destination)
 
+    def _add_peer(self, p):
+        self.peers[p.peer_id] = p
+        self.peers_types[p.peer_id] = p.peer_type
+        if p.peer_type not in self.types_peers:
+            self.types_peers[p.peer_type] = set()
+        self.types_peers[p.peer_type].add(p.peer_id)
 
-        if origin not in locations or destination not in self.locations[origin]:
-            raise Exception("Location connection not known")
-
-        distribution = self.locations[origin][destination]
-        if type(distribution) == float or type(distribution) == int:
-            return distribution if n == 1 else [distribution] * n
-        return distribution.generate(n)
-
-    def init_bootstrap_servers(self, num=1):
+    def _init_default_bootstrap_servers(self, locations, num=1, active_p2p=False):
         """
         Initialize bootstrap servers: create bootstrap peers and start them immediately.
         :param num: number of servers
         """
-        self.logger.info("Init bootstrap servers")
+        self.logger.warning("Init default bootstrap servers")
+        bpt = get_default_bootstrap_type(locations, active_p2p=active_p2p)
+
         for i in range(num):
-            p = self.peer_factory.create_peer(self.env, 'bootstrap')
-            self.bootstrap_peers.append(p)
-            # Bootstrap servers start immediately
-            p.start_all_runners()
+            p = self.peer_factory.create_peer(self, 'bootstrap', bpt)
+            self._add_peer(p)
 
     def get_peers_names(self, peer_type):
         if peer_type not in self.peers:
             return None
         return (p.name for p in self.peers[peer_type])
 
-    def add_peer_service_with_conf(self, peer_type: str, service_class, service_config_class, config):
-        """
-        Add peer service for the type of peer. Load configuration that might contain distribution samples.
-        :param peer_type: type of the peer
-        :param service_class: Class with a service implementation
-        :param service_config_class: configuration dataclass for the service class
-        :param config: the actual configuration for peer as a dictionary
-        """
-        self.peer_factory.add_service_with_conf(peer_type, service_class, service_config_class, config)
-
-    def add_peer_service(self, peer_type, service_class, service_config):
-        """
-        Add peer service for the type of peer
-        :param peer_type: type of the peer
-        :param service_class: Class with a service implementation
-        :param service_config: configuration object for this service
-        """
-        self.peer_factory.add_service(peer_type, service_class, service_config)
-
     def start_all_peers(self):
         """
         Start all peers' runners.
@@ -165,56 +147,36 @@ def start_all_peers(self):
             for p in self.peers[t]:
                 p.start_all_runners()
 
-    def add_peers(self, peer_num: int, peer_type: str = 'basic'):
-        """
-        Create and add peers to the simulation environment.
-        Peers will connect to a random bootstrap server and start all services.
-        :param peer_num: number of peers to create
-        :param peer_type: Type of peers to create
-        """
-        self.logger.info("Creating %s peers of type %s", peer_num, peer_type)
-        for i in range(peer_num):
-            p = self.peer_factory.create_peer(self.env, peer_type)
-            # Select random bootstrap server
-            bootstrap_server = random.choice(self.bootstrap_peers)
-            p.bootstrap_connect(bootstrap_server)
-            if peer_type not in self.peers:
-                self.peers[peer_type] = list()
-            self.peers[peer_type].append(p)
-
     def get_graph(self, include_bootstrap_peers=False):
         G = nx.Graph()
-        current_peers = [p for peer_type in self.peers.values() for p in peer_type]
-        if include_bootstrap_peers:
-            current_peers.extend(self.bootstrap_peers)
-        for peer in current_peers:
-            G.add_node(peer.name)
+        for p in self.peers_types.keys():
+            if not include_bootstrap_peers and self.peers_types[p] == 'bootstrap':
+                continue
+            G.add_node(int(p))
+            peer = self.peers[p]
             for other, cnx in peer.connections.items():
-                if include_bootstrap_peers or not str.startswith(other.name, 'bootstrap'):
-                    G.add_edge(peer.name, other.name, weight=cnx.bandwidth)
+                if include_bootstrap_peers or other.peer_type != 'bootstrap':
+                    G.add_edge(int(p), int(other.peer_id), weight=cnx.bandwidth)
+        nx.set_node_attributes(G, self.peers_types, 'type')
         return G
 
     def avg_bandwidth(self):
         bws = []
-        for peer in self.peers:
+        for peer in self.peers.items():
             for c in peer.connections.values():
                 bws.append(c.bandwidth)
         return sum(bws) / len(bws)
 
     def median_bandwidth(self):
         bws = []
-        for peer in self.peers:
+        for peer in self.peers.items():
             for c in peer.connections.values():
                 bws.append(c.bandwidth)
         bws.sort()
         return bws[int(len(bws) / 2)]
 
-    def run(self, until=None):
+    def run(self, until):
         self.env.run(until)
 
     def stop(self):
         self.env.exit(0)
-
-class P2PSimulation(Simulation):
-
-
diff --git a/p2psimpy/storage.py b/p2psimpy/storage.py
index 750f61f..a06aa59 100644
--- a/p2psimpy/storage.py
+++ b/p2psimpy/storage.py
@@ -2,7 +2,8 @@
 
 class Storage:
 
-    txs = dict()
+    def __init__(self):
+        self.txs = dict()
 
     def add(self, tx_id, tx):
         if tx_id not in self.txs:
diff --git a/p2psimpy/utils.py b/p2psimpy/utils.py
index 0c51601..3a1656f 100644
--- a/p2psimpy/utils.py
+++ b/p2psimpy/utils.py
@@ -1,4 +1,5 @@
 from itertools import product
+from copy import deepcopy
 
 
 def make_symmetric(matrix):
@@ -9,17 +10,60 @@ def make_symmetric(matrix):
         if c[0] not in matrix or c[1] not in matrix[c[0]]:
             matrix[c[0]][c[1]] = matrix[c[1]][c[0]]
 
-class Cached:
 
-    def __init__(self, generator):
-        self._gen = generator
-        self._cache = {}
+class Cache:
 
-    def get(self, *args):
-        for k in args:
+    def __init__(self, generator, cache_num=20, symmetric=True):
+        self.gen = generator
+        self.cache = deepcopy(generator)
+        self.num = cache_num
+        self.symmetric = symmetric
 
+    def fetch(self, *args):
+        try:
+            val = self._pop(*args)
+        except (IndexError, AttributeError, TypeError):
+            generator = self._get(self.gen, *args)
 
-        if len(args) > 0:
-            # there is more arguments
-            if key not in self._cache or :
-                # Trigger create
+            if hasattr(generator, "params"):
+                self._set(generator.generate(self.num), *args)
+            else:
+                self._set( [generator] * self.num, *args)
+            val = self._pop(*args)
+        return val
+
+    def _set(self, value, *args):
+        if self.symmetric and len(args) == 2:
+            if args[0] not in self.cache or args[1] not in self.cache.get(args[0]):
+                self.cache[args[1]][args[0]] = value
+            else:
+                self.cache[args[0]][args[1]] = value
+        elif len(args) == 0:
+            self.cache = value
+        else:
+            self.cache[args[0]] = value
+
+    def _get(self, val, *args):
+        if self.symmetric and len(args) == 2:
+            if args[0] not in val or args[1] not in val.get(args[0]):
+                return val.get(args[1]).get(args[0])
+            else:
+                return val.get(args[0]).get(args[1])
+        for attr in args:
+            val = val.get(attr)
+        return val
+
+    def _pop(self, *args):
+        last = None
+        if len(args) == 0:
+            last, self.cache = self.cache[-1], self.cache[:-1]
+        elif len(args) == 1:
+            last, self.cache[args[0]] = self.cache[args[0]][-1], self.cache[args[0]][:-1]
+        elif self.symmetric and len(args) == 2:
+            if args[0] not in self.cache or args[1] not in self.cache.get(args[0]):
+                last, self.cache[args[1]][args[0]] = self.cache[args[1]][args[0]][-1], \
+                                                     self.cache[args[1]][args[0]][:-1]
+            else:
+                last, self.cache[args[0]][args[1]] = self.cache[args[0]][args[1]][-1], \
+                                                     self.cache[args[0]][args[1]][:-1]
+        return last