{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CIC Current System Network Graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Graph overview \n",
    "\n",
    "Modeling as a weighted directed graph with agents as nodes. A network is a set of items (nodes or vertices) connected by edges or links. \n",
    "We represent a network by a graph (N, g), which consists of a set of nodes N = {1, . . . , n}.\n",
    "\n",
    "#### Node types\n",
    "* Agent\n",
    "\n",
    "An agent is a user of the CIC system.\n",
    "* Chama\n",
    "\n",
    "A chama is a savings group consisting of multiple agents. Redemptions of CICs for fiat occur through chamas.\n",
    "* Trader\n",
    "\n",
    "A trader is an agent interacting with the bonding curve for investment/arbitrage opportunities.\n",
    "* Cloud\n",
    "\n",
    "The cloud is a representation of the open boundary to the world external to the model.\n",
    "* Contract\n",
    "\n",
    "The contract is the smart contract of the bonding curve.\n",
    "\n",
    "### Edges between agents\n",
    "The edge weight gij > 0 takes on non-binary values, representing the intensity of the interaction, so we refer to (N, g) as a weighted graph.\n",
    "E is the set of “directed” edges, i.e., (i, j) ∈ E\n",
    "\n",
    "#### Edge types\n",
    "* Demand\n",
    "* Fraction of demand in CIC\n",
    "* Utility - stack ranking. Food/Water is first, shopping, etc farther down\n",
    "* Spend\n",
    "* Fraction of actual in CIC\n",
    "\n",
    "![](images/dualoperator.png)\n",
    "\n",
    "\n",
    "![](images/v3differentialspec.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assumptions\n",
    "(Defining data structures, not just initialization. Baking in degrees of freedom for future experimentation)\n",
    "\n",
    "* agents = a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p\n",
    "* Agent starting native currency is picked from a uniform distribution with a range of 20 to 500. Starting tokens is 400.\n",
    "* system = external,cic\n",
    "* chama = chama_1,chama_2,chama_3,chama_4\n",
    "\n",
    "Chamas are currently set to zero, it can be configured for more detailed analysis later on.\n",
    "* traders = ta,tb,tc\n",
    "\n",
    "Traders are currently set to zero, it can be configured for more detailed analysis later on.\n",
    "* Utility Types Ordered:\n",
    " * Food/Water\n",
    " * Fuel/Energy\n",
    " * Health\n",
    " * Education\n",
    " * Savings Group\n",
    " * Shop\n",
    "* Utility Types Probability \n",
    "  * 0.6\n",
    "  * 0.10\n",
    "  * 0.03\n",
    "  * 0.015\n",
    "  * 0.065\n",
    "  * 0.19\n",
    "* R0 = 500\n",
    "* S0 = 200000\n",
    "* P = 1\n",
    "* priceLevel = 100\n",
    "* fractionOfDemandInCIC = 0.5\n",
    "* fractionOfActualSpendInCIC = 0.5 # if an agent is interacting with the external environment, then the actual spend is 100% shilling.\n",
    "* kappa = 4\n",
    "\n",
    "\n",
    "## Initial State Values\n",
    "\n",
    "# Equations\n",
    "\n",
    "## Generators\n",
    "* Agent generation for each time step: Random choice of all agents minus 2 for both paying and receiving.  \n",
    "\n",
    "* Agent demand each time: Uniform distribution with a low value of 1 and a high of 500. \n",
    " \n",
    "### Red Cross Drip\n",
    "Every 30 days, the Red Cross drips 4000 shilling to the grassroots operator fiat balance.  \n",
    "\n",
    "### Spend Allocation \n",
    "\n",
    "#### Parameters:\n",
    "* Agent to pay: $i$\n",
    "* Agent to receive: $j$\n",
    "* Rank Order Demand: $\\frac{v_{i,j}}{d_{i,j}}$\n",
    "* Amount of currency agent $i$ has to spend, $\\gamma$\n",
    "* Amount of cic agent $i$ has to spend, $\\gamma_\\textrm{cic}$\n",
    "* Percentage of transaction in cic, $\\phi$\n",
    "* Spend, $\\zeta$\n",
    "\n",
    "\n",
    "if $\\frac{v_{i,j}}{d_{i,j}} * 1-\\phi > \\gamma_{i} \\textrm{and} \\frac{v_{i,j}}{d_{i,j}} * \\phi > \\gamma_\\textrm{cic} \\Rightarrow \\zeta = \\frac{v_{i,j}}{d_{i,j}}$ \n",
    "\n",
    "else $ \\Rightarrow \\zeta = \\gamma$\n",
    "\n",
    "Allocate utility type by stack ranking in. Allocate remaining fiat and cic until all demand is met or i runs out.\n",
    "\n",
    "\n",
    "### Withdraw calculation\n",
    "\n",
    "The user is able to withdraw up to 50% of the their CIC balance if they have spent 50% of their balance within the last 30 days at a conversion ratio of 1:1, meaning that for every one token withdraw, they receive 1 in native currency. We are assuming that agents want what to withdraw as much as they can.\n",
    "This is one of the most important control points for Grassroots economics. The more people withdraw CIC from the system, the more difficult it is on the system. The more people can withdraw, the better the adoption however. The inverse also holds true: the less individuals can withdraw, the lower the adoption.\n",
    "\n",
    "## Distribution to agents\n",
    "#### Parameters\n",
    "FrequencyOfAllocation = 45 # frequency of allocation of drip to agents\n",
    "* idealFiat = 5000\n",
    "* idealCIC = 200000\n",
    "* varianceCIC = 50000\n",
    "* varianceFiat = 1000\n",
    "* unadjustedPerAgent = 50\n",
    "\n",
    "```\n",
    "# agent:[centrality,allocationValue]\n",
    "agentAllocation = {'a':[1,1],'b':[1,1],'c':[1,1], \n",
    "                   'd':[1,1],'e':[1,1],'f':[1,1],\n",
    "                   'g':[1,1],'h':[1,1],'i':[1,1],\n",
    "                   'j':[1,1],'k':[1,1],'l':[1,1],\n",
    "                   'm':[1,1],'o':[1,1],'p':[1,1]}\n",
    "```\n",
    "\n",
    "Every 15 days, a total of unadjustedPerAgent * agents will be distributed among the agents. Allocation will occur based off of the the agent allocation dictionary allocation value. We can optimize the allocation overtime and make a state variable for adjustment overtime as a result of centrality. We are currently assuming that all agents have the same centrality and allocation.\n",
    "\n",
    "Internal velocity is better than external velocity of the system. Point of leverage to make more internal cycles. Canbe used for tuning system effiency.\n",
    "![](images/agentDistribution.png)\n",
    "\n",
    "### Inventory Controller\n",
    "Heuristic Monetary policy hysteresis conservation allocation between fiat and cic reserves. We've created an inventory control function to test if the current balance is in an acceptable tolarance. For the calculation, we use the following 2 variables, current CIC balance and current fiat balance, along with 2 parameters, desired cic and variance.\n",
    "\n",
    "Below is \n",
    "```\n",
    "if idealCIC - variance <= actual <= ideal + (2*variance):\n",
    "    decision = 'none'\n",
    "    amount = 0\n",
    "else:\n",
    "    \n",
    "    if (ideal + variance) > actual :\n",
    "        decision = 'mint'\n",
    "        amount = (ideal + variance) - actual\n",
    "    else:\n",
    "        pass\n",
    "    if actual > (ideal + variance):\n",
    "        decision = 'burn'\n",
    "        amount = actual - (ideal + variance) \n",
    "    else:\n",
    "        pass\n",
    "\n",
    "if decision == 'mint':\n",
    "    if fiat < (ideal - variance):\n",
    "        if amount > fiat:\n",
    "            decision = 'none'\n",
    "            amount = 0\n",
    "        else:\n",
    "            pass\n",
    "if decision == 'none':\n",
    "    if fiat < (ideal - variance):\n",
    "        decision = 'mint'\n",
    "        amount = (ideal-variance)\n",
    "    else:\n",
    "        pass\n",
    "    \n",
    "\n",
    "```\n",
    "\n",
    "If the controller wants to mint, the amount decided from the inventory controller, $\\Delta R$ is inserted into the following minting equation:\n",
    "\n",
    "- Conservation equation, V0: $V(R+ \\Delta R', S+\\Delta S) = \\frac{(S+\\Delta S)^\\kappa}{R+\\Delta R'} =\\frac{S^\\kappa}{R}$\n",
    "- Derived Mint equation: $\\Delta S = mint\\big(\\Delta R ; (R,S)\\big)= S\\big(\\sqrt[\\kappa]{(1+\\frac{\\Delta R}{R})}-1\\big)$\n",
    " \n",
    "\n",
    "\n",
    "If the controller wants to burn, the amount decided from the inventory controller, $\\Delta S$ is inserted into the following minting equation:\n",
    " - Derived Withdraw equation: $\\Delta R = withdraw\\big(\\Delta S ; (R,S)\\big)= R\\big(1-(1-\\frac{\\Delta S}{S})^\\kappa \\big)$\n",
    " \n",
    "\n",
    "There is a built in process lag of 7 days before the newly minted or burned CIC is added to the respective operator accounts.\n",
    "\n",
    "### Velocity of Money \n",
    "\n",
    "Indirect measurement of velocity of money per timestep:\n",
    "\n",
    "$V_t = \\frac{PT}{M}$\n",
    "\n",
    "Where\n",
    "\n",
    "* $V_t$ is the velocity of money for all agent transaction in the time period examined\n",
    "* $P$ is the price level\n",
    "* $T$ is the aggregated real value of all agent transactions in the time period examined\n",
    "* $M$ is the average money supply in the economy in the time period examined.\n",
    "\n",
    "\n",
    "\n",
    "## Simulation run\n",
    "* 5 monte carlo runs with 100 timesteps. Each timestep is equal to 1 day.\n",
    "\n",
    "\n",
    "## Proposed Experiments\n",
    "![](images/experiments.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define cadCAD Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: cadCAD in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (0.3.1)\n",
      "Requirement already satisfied: pandas in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from cadCAD) (1.0.3)\n",
      "Requirement already satisfied: funcy in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from cadCAD) (1.14)\n",
      "Requirement already satisfied: wheel in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from cadCAD) (0.33.6)\n",
      "Requirement already satisfied: pathos in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from cadCAD) (0.2.5)\n",
      "Requirement already satisfied: fn in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from cadCAD) (0.4.3)\n",
      "Requirement already satisfied: tabulate in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from cadCAD) (0.8.2)\n",
      "Requirement already satisfied: python-dateutil>=2.6.1 in /home/aclarkdata/.local/lib/python3.7/site-packages (from pandas->cadCAD) (2.8.0)\n",
      "Requirement already satisfied: numpy>=1.13.3 in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from pandas->cadCAD) (1.18.2)\n",
      "Requirement already satisfied: pytz>=2017.2 in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from pandas->cadCAD) (2018.7)\n",
      "Requirement already satisfied: ppft>=1.6.6.1 in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from pathos->cadCAD) (1.6.6.1)\n",
      "Requirement already satisfied: pox>=0.2.7 in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from pathos->cadCAD) (0.2.7)\n",
      "Requirement already satisfied: multiprocess>=0.70.9 in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from pathos->cadCAD) (0.70.9)\n",
      "Requirement already satisfied: dill>=0.3.1 in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from pathos->cadCAD) (0.3.1.1)\n",
      "Requirement already satisfied: six>=1.5 in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from python-dateutil>=2.6.1->pandas->cadCAD) (1.14.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install cadCAD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import math\n",
    "from decimal import Decimal\n",
    "from datetime import timedelta\n",
    "import numpy as np\n",
    "from typing import Dict, List\n",
    "\n",
    "from cadCAD.configuration import append_configs\n",
    "from cadCAD.configuration.utils import bound_norm_random, ep_time_step, config_sim, access_block\n",
    "\n",
    "\n",
    "# The following imports NEED to be in the exact order\n",
    "from cadCAD.engine import ExecutionMode, ExecutionContext, Executor\n",
    "from cadCAD import configs\n",
    "\n",
    "\n",
    "import pandas as pd\n",
    "from tabulate import tabulate\n",
    "import matplotlib.pyplot as plt\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import clear_output\n",
    "import networkx as nx\n",
    "from collections import OrderedDict\n",
    "pd.options.display.float_format = '{:.2f}'.format\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Supporting functions\n",
    "\n",
    "default_kappa= 4\n",
    "default_exit_tax = .02\n",
    "\n",
    "#value function for a given state (R,S)\n",
    "def invariant(R,S,kappa=default_kappa):\n",
    "    \n",
    "    return (S**kappa)/R\n",
    "\n",
    "#given a value function (parameterized by kappa)\n",
    "#and an invariant coeficient V0\n",
    "#return Supply S as a function of reserve R\n",
    "def reserve(S, V0, kappa=default_kappa):\n",
    "    return (S**kappa)/V0\n",
    "\n",
    "#given a value function (parameterized by kappa)\n",
    "#and an invariant coeficient V0\n",
    "#return Supply S as a function of reserve R\n",
    "def supply(R, V0, kappa=default_kappa):\n",
    "    return (V0*R)**(1/kappa)\n",
    "\n",
    "#given a value function (parameterized by kappa)\n",
    "#and an invariant coeficient V0\n",
    "#return a spot price P as a function of reserve R\n",
    "def spot_price(R, V0, kappa=default_kappa):\n",
    "    return kappa*R**((kappa-1)/kappa)/V0**(1/kappa)\n",
    "\n",
    "#for a given state (R,S)\n",
    "#given a value function (parameterized by kappa)\n",
    "#and an invariant coeficient V0\n",
    "#deposit deltaR to Mint deltaS\n",
    "#with realized price deltaR/deltaS\n",
    "def mint(deltaR, R,S, V0, kappa=default_kappa):\n",
    "    deltaS = (V0*(R+deltaR))**(1/kappa)-S\n",
    "    if deltaS ==0:\n",
    "        realized_price = spot_price(R+deltaR, V0, kappa)\n",
    "    else:\n",
    "        realized_price = deltaR/deltaS\n",
    "    deltaS = round(deltaS,2)\n",
    "    return deltaS, realized_price\n",
    "\n",
    "#for a given state (R,S)\n",
    "#given a value function (parameterized by kappa)\n",
    "#and an invariant coeficient V0\n",
    "#burn deltaS to Withdraw deltaR\n",
    "#with realized price deltaR/deltaS\n",
    "def withdraw(deltaS, R,S, V0, kappa=default_kappa):\n",
    "    deltaR = R-((S-deltaS)**kappa)/V0\n",
    "    if deltaS ==0:\n",
    "        realized_price = spot_price(R+deltaR, V0, kappa)\n",
    "    else:\n",
    "        realized_price = deltaR/deltaS\n",
    "    deltaR = round(deltaR,2)\n",
    "    return deltaR, realized_price\n",
    "\n",
    "\n",
    "\n",
    "def iterateEdges(network,edgeToIterate):\n",
    "    '''\n",
    "    Description:\n",
    "    Iterate through a network on a weighted edge and return\n",
    "    two dictionaries: the inflow and outflow for the given agents\n",
    "    in the format:\n",
    "    \n",
    "    {'Agent':amount}\n",
    "    '''\n",
    "    outflows = {}\n",
    "    inflows = {}\n",
    "    for i,j in network.edges:\n",
    "        try:\n",
    "            amount = network[i][j][edgeToIterate]\n",
    "            if i in outflows:\n",
    "                outflows[i] = outflows[i] + amount\n",
    "            else:\n",
    "                outflows[i] =  amount\n",
    "            if j in inflows:\n",
    "                inflows[j] = inflows[j] + amount\n",
    "            else:\n",
    "                inflows[j] = amount\n",
    "        except:\n",
    "            pass\n",
    "    return outflows,inflows\n",
    "\n",
    "\n",
    "def inflowAndOutflowDictionaryMerge(inflow,outflow):\n",
    "    '''\n",
    "    Description:\n",
    "    Merge two dictionaries and return one dictionary with zero floor'''\n",
    "    \n",
    "    merged = {}\n",
    "\n",
    "    inflowsKeys = [k for k,v in inflow.items() if k not in outflow]\n",
    "    for i in inflowsKeys:\n",
    "        merged[i] = inflow[i]\n",
    "    outflowsKeys = [k for k,v in outflow.items() if k not in inflow]\n",
    "    for i in outflowsKeys:\n",
    "        merged[i] = outflow[i]\n",
    "    overlapKeys = [k for k,v in inflow.items() if k in outflow]\n",
    "    for i in overlapKeys:\n",
    "        amt = outflow[i] - inflow[i] \n",
    "        if amt < 0:\n",
    "            merged[i] = 0\n",
    "        else:\n",
    "            merged[i] = amt\n",
    "            pass\n",
    "        \n",
    "    return merged\n",
    "\n",
    "        \n",
    "def spendCalculation(agentToPay,agentToReceive,rankOrderDemand,maxSpendCurrency,maxSpendTokens,cicPercentage):\n",
    "    '''\n",
    "    Function to calculate if an agent can pay for demand given token and currency contraints\n",
    "    '''\n",
    "    if (rankOrderDemand[agentToReceive] * (1-cicPercentage)) > maxSpendCurrency[agentToPay]:\n",
    "        verdict_currency = 'No'\n",
    "    else:\n",
    "        verdict_currency = 'Enough'\n",
    "        \n",
    "    if (rankOrderDemand[agentToReceive] * cicPercentage) > maxSpendTokens[agentToPay]:\n",
    "        verdict_cic = 'No'\n",
    "    else:\n",
    "        verdict_cic = 'Enough'\n",
    "    \n",
    "    if verdict_currency == 'Enough'and verdict_cic == 'Enough':\n",
    "        spend = rankOrderDemand[agentToReceive]\n",
    "    \n",
    "    elif maxSpendCurrency[agentToPay] > 0:\n",
    "        spend = maxSpendCurrency[agentToPay]\n",
    "    else:\n",
    "        spend = 0\n",
    "        \n",
    "    return spend\n",
    "\n",
    "\n",
    "def spendCalculationExternal(agentToPay,agentToReceive,rankOrderDemand,maxSpendCurrency):\n",
    "    '''\n",
    "    '''\n",
    "    if rankOrderDemand[agentToReceive] > maxSpendCurrency[agentToPay]:\n",
    "        verdict_currency = 'No'\n",
    "    else:\n",
    "        verdict_currency = 'Enough'\n",
    "    \n",
    "    if verdict_currency == 'Enough':\n",
    "        spend = rankOrderDemand[agentToReceive]\n",
    "        \n",
    "    elif maxSpendCurrency[agentToPay] > 0:\n",
    "        spend = maxSpendCurrency[agentToPay]\n",
    "    else:\n",
    "        spend = 0\n",
    "        \n",
    "    return spend\n",
    "\n",
    "\n",
    "def DictionaryMergeAddition(inflow,outflow):\n",
    "    '''\n",
    "    Description:\n",
    "    Merge two dictionaries and return one dictionary'''\n",
    "    \n",
    "    merged = {}\n",
    "\n",
    "    inflowsKeys = [k for k,v in inflow.items() if k not in outflow]\n",
    "    for i in inflowsKeys:\n",
    "        merged[i] = inflow[i]\n",
    "    outflowsKeys = [k for k,v in outflow.items() if k not in inflow]\n",
    "    for i in outflowsKeys:\n",
    "        merged[i] = outflow[i]\n",
    "    overlapKeys = [k for k,v in inflow.items() if k in outflow]\n",
    "    for i in overlapKeys:\n",
    "        merged[i] = outflow[i] + inflow[i] \n",
    "        \n",
    "    return merged\n",
    "\n",
    "def mint_burn_logic_control(ideal,actual,variance,fiat,fiat_variance,ideal_fiat):\n",
    "    '''\n",
    "    Inventory control function to test if the current balance is in an acceptable range. Tolerance range \n",
    "    '''\n",
    "    if ideal - variance <= actual <= ideal + (2*variance):\n",
    "        decision = 'none'\n",
    "        amount = 0\n",
    "    else:\n",
    "        if (ideal + variance) > actual:\n",
    "            decision = 'mint'\n",
    "            amount = (ideal + variance) - actual\n",
    "        else:\n",
    "            pass\n",
    "        if actual > (ideal + variance):\n",
    "            decision = 'burn'\n",
    "            amount = actual - (ideal + variance) \n",
    "        else:\n",
    "            pass\n",
    "\n",
    "    if decision == 'mint':\n",
    "        if fiat < (ideal_fiat - fiat_variance):\n",
    "            if amount > fiat:\n",
    "                decision = 'none'\n",
    "                amount = 0\n",
    "            else:\n",
    "                pass\n",
    "    if decision == 'none':\n",
    "        if fiat < (ideal_fiat - fiat_variance):\n",
    "            decision = 'mint'\n",
    "            amount = (ideal_fiat-fiat_variance)\n",
    "        else:\n",
    "            pass\n",
    "    \n",
    "    amount = round(amount,2)\n",
    "    return decision, amount\n",
    "    \n",
    "#NetworkX functions\n",
    "def get_nodes_by_type(g, node_type_selection):\n",
    "    return [node for node in g.nodes if g.nodes[node]['type']== node_type_selection]\n",
    "\n",
    "def get_edges_by_type(g, edge_type_selection):\n",
    "    return [edge for edge in g.edges if g.edges[edge]['type']== edge_type_selection]\n",
    "\n",
    "def get_edges(g):\n",
    "    return [edge for edge in g.edges if g.edges[edge]]\n",
    "\n",
    "def get_nodes(g):\n",
    "    '''\n",
    "    df.network.apply(lambda g: np.array([g.nodes[j]['balls'] for j in get_nodes(g)]))\n",
    "    '''\n",
    "    return [node for node in g.nodes if g.nodes[node]]\n",
    "\n",
    "def aggregate_runs(df,aggregate_dimension):\n",
    "    '''\n",
    "    Function to aggregate the monte carlo runs along a single dimension.\n",
    "    Parameters:\n",
    "    df: dataframe name\n",
    "    aggregate_dimension: the dimension you would like to aggregate on, the standard one is timestep.\n",
    "    Example run:\n",
    "    mean_df,median_df,std_df,min_df = aggregate_runs(df,'timestep')\n",
    "    '''\n",
    "    df = df[df['substep'] == df.substep.max()]\n",
    "    mean_df = df.groupby(aggregate_dimension).mean().reset_index()\n",
    "    median_df = df.groupby(aggregate_dimension).median().reset_index()\n",
    "    std_df = df.groupby(aggregate_dimension).std().reset_index()\n",
    "    min_df = df.groupby(aggregate_dimension).min().reset_index()\n",
    "\n",
    "    return mean_df,median_df,std_df,min_df\n",
    "\n",
    "\n",
    "def plot_median_with_quantiles(df,aggregate_dimension,x, y):\n",
    "    '''\n",
    "    Function to plot the median and 1st and 3rd quartiles of the monte carlo runs along a single variable.\n",
    "    Parameters:\n",
    "    df: dataframe name\n",
    "    aggregate_dimension: the dimension you would like to aggregate on, the standard one is timestep.\n",
    "    x = x axis variable for plotting\n",
    "    y = y axis variable for plotting\n",
    "\n",
    "    Example run:\n",
    "    plot_median_with_quantiles(df,'timestep','timestep','AggregatedAgentSpend')\n",
    "    '''\n",
    "    \n",
    "    df = df[df['substep'] == df.substep.max()]\n",
    "    firstQuantile = df.groupby(aggregate_dimension).quantile(0.25).reset_index()\n",
    "    thirdQuantile = df.groupby(aggregate_dimension).quantile(0.75).reset_index()\n",
    "    median_df = df.groupby(aggregate_dimension).median().reset_index()\n",
    "    \n",
    "    fig, ax = plt.subplots(1,figsize=(10,6))\n",
    "    ax.plot(median_df[x].values, median_df[y].values, lw=2, label='Median', color='blue')\n",
    "    ax.fill_between(firstQuantile[x].values, firstQuantile[y].values, thirdQuantile[y].values, facecolor='black', alpha=0.2)\n",
    "    ax.set_title(y + ' Median')\n",
    "    ax.legend(loc='upper left')\n",
    "    ax.set_xlabel('Timestep')\n",
    "    ax.set_ylabel('Amount')\n",
    "    ax.grid()\n",
    "    \n",
    "def plot_median_with_quantiles_annotation(df,aggregate_dimension,x, y):\n",
    "    '''\n",
    "    Function to plot the median and 1st and 3rd quartiles of the monte carlo runs along a single variable.\n",
    "    Parameters:\n",
    "    df: dataframe name\n",
    "    aggregate_dimension: the dimension you would like to aggregate on, the standard one is timestep.\n",
    "    x = x axis variable for plotting\n",
    "    y = y axis variable for plotting\n",
    "\n",
    "    Example run:\n",
    "    plot_median_with_quantiles(df,'timestep','timestep','AggregatedAgentSpend')\n",
    "    '''\n",
    "    \n",
    "    df = df[df['substep'] == df.substep.max()]\n",
    "    firstQuantile = df.groupby(aggregate_dimension).quantile(0.25).reset_index()\n",
    "    thirdQuantile = df.groupby(aggregate_dimension).quantile(0.75).reset_index()\n",
    "    median_df = df.groupby(aggregate_dimension).median().reset_index()\n",
    "    \n",
    "    fig, ax = plt.subplots(1,figsize=(10,6))\n",
    "    ax.axvline(x=30,linewidth=2, color='r')\n",
    "    ax.annotate('Agents can withdraw and Red Cross Drip occurs', xy=(30,2), xytext=(35, 1),\n",
    "            arrowprops=dict(facecolor='black', shrink=0.05))\n",
    "    \n",
    "    ax.axvline(x=60,linewidth=2, color='r')\n",
    "    ax.axvline(x=90,linewidth=2, color='r')\n",
    "    ax.plot(median_df[x].values, median_df[y].values, lw=2, label='Median', color='blue')\n",
    "    ax.fill_between(firstQuantile[x].values, firstQuantile[y].values, thirdQuantile[y].values, facecolor='black', alpha=0.2)\n",
    "    ax.set_title(y + ' Median')\n",
    "    ax.legend(loc='upper left')\n",
    "    ax.set_xlabel('Timestep')\n",
    "    ax.set_ylabel('Amount')\n",
    "    ax.grid()\n",
    "\n",
    "\n",
    "def first_five_plot(df,aggregate_dimension,x,y,run_count):\n",
    "    '''\n",
    "    A function that generates timeseries plot of at most the first five Monte Carlo runs.\n",
    "    Parameters:\n",
    "    df: dataframe name\n",
    "    aggregate_dimension: the dimension you would like to aggregate on, the standard one is timestep.\n",
    "    x = x axis variable for plotting\n",
    "    y = y axis variable for plotting\n",
    "    run_count = the number of monte carlo simulations\n",
    "    Note: Run aggregate_runs before using this function\n",
    "    Example run:\n",
    "    first_five_plot(df,'timestep','timestep','revenue',run_count=100)\n",
    "    '''\n",
    "    mean_df,median_df,std_df,min_df = aggregate_runs(df,aggregate_dimension)\n",
    "    plt.figure(figsize=(10,6))\n",
    "    if run_count < 5:\n",
    "        runs = run_count\n",
    "    else:\n",
    "        runs = 5\n",
    "    for r in range(1,runs+1):\n",
    "        legend_name = 'Run ' + str(r)\n",
    "        plt.plot(df[df.run==r].timestep, df[df.run==r][y], label = legend_name )\n",
    "    plt.plot(mean_df[x], mean_df[y], label = 'Mean', color = 'black')\n",
    "    plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "    plt.xlabel(x)\n",
    "    plt.ylabel(y)\n",
    "    title_text = 'Performance of ' + y + ' over the First ' + str(runs) + ' Monte Carlo Runs'\n",
    "    plt.title(title_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initilization \n",
    "\n",
    "# Assumptions:\n",
    "# Amount received in shilling when withdraw occurs\n",
    "leverage = 1 \n",
    "\n",
    "# process time\n",
    "process_lag = 7 # timesteps\n",
    "\n",
    "# red cross drip amount\n",
    "drip = 4000\n",
    "\n",
    "# system initialization\n",
    "agents = ['a','b','c','d','e','f','g','h','i','j','k','l','m','o','p']\n",
    "\n",
    "# system actors\n",
    "system = ['external','cic']\n",
    "\n",
    "# chamas\n",
    "chama = ['chama_1','chama_2','chama_3','chama_4']\n",
    "\n",
    "# traders\n",
    "traders = ['ta','tb','tc'] #only trading on the cic. Link to external and cic not to other agents\n",
    "\n",
    "allAgents = agents + system\n",
    "\n",
    "mixingAgents = ['a','b','c','d','e','f','g','h','i','j','k','l','m','o','p','external']\n",
    "\n",
    "UtilityTypesOrdered ={'Food/Water':1,\n",
    "                    'Fuel/Energy':2,\n",
    "                    'Health':3,\n",
    "                    'Education':4,\n",
    "                    'Savings Group':5,\n",
    "                    'Shop':6}\n",
    "\n",
    "utilityTypesProbability = {'Food/Water':0.6,\n",
    "                    'Fuel/Energy':0.10,\n",
    "                    'Health':0.03,\n",
    "                    'Education':0.015,\n",
    "                    'Savings Group':0.065,\n",
    "                    'Shop':0.19}\n",
    "\n",
    "\n",
    "R0 =  500 #thousand xDAI\n",
    "kappa = 4 #leverage\n",
    "P0 = 1/100 #initial price\n",
    "S0 = kappa*R0/P0\n",
    "V0 = invariant(R0,S0,kappa)\n",
    "P = spot_price(R0, V0, kappa)\n",
    "\n",
    "# Price level\n",
    "priceLevel = 100\n",
    "\n",
    "fractionOfDemandInCIC = 0.5\n",
    "fractionOfActualSpendInCIC = 0.5\n",
    "\n",
    "def create_network():\n",
    "    # Create network graph\n",
    "    network = nx.DiGraph()\n",
    "\n",
    "    # Add nodes for n participants plus the external economy and the cic network\n",
    "    for i in agents:\n",
    "        network.add_node(i,type='Agent',tokens=400, native_currency = int(np.random.uniform(low=20, high=500, size=1)[0]))\n",
    "        \n",
    "        \n",
    "    network.add_node('external',type='Contract',native_currency = 100000000,tokens = 0,delta_native_currency = 0, pos=(1,50))\n",
    "    network.add_node('cic',type='Contract',tokens= S0, native_currency = R0,pos=(50,1))\n",
    "\n",
    "    for i in chama:\n",
    "        network.add_node(i,type='Chama')\n",
    "        \n",
    "    for i in traders:\n",
    "        network.add_node(i,type='Trader',tokens=20, native_currency = 20, \n",
    "                        price_belief = 1, trust_level = 1)\n",
    "        \n",
    "    # Create bi-directional edges between all participants\n",
    "    for i in allAgents:\n",
    "        for j in allAgents:\n",
    "            if i!=j:\n",
    "                network.add_edge(i,j)\n",
    "\n",
    "    # Create bi-directional edges between each trader and the external economy and the cic environment            \n",
    "    for i in traders:\n",
    "        for j in system:\n",
    "            if i!=j:\n",
    "                network.add_edge(i,j)\n",
    "                \n",
    "    # Create bi-directional edges between some agent and a chama node representing membershio      \n",
    "    for i in chama:\n",
    "        for j in agents:\n",
    "            if np.random.choice(['Member','Non_Member'],1,p=[.50,.50])[0] == 'Member':\n",
    "                network.add_edge(i,j)\n",
    "\n",
    "    # Type colors \n",
    "    colors = ['Red','Blue','Green','Orange']\n",
    "    color_map = []\n",
    "    for i in network.nodes:\n",
    "        if network.nodes[i]['type'] == 'Agent':\n",
    "            color_map.append('Red')\n",
    "        elif network.nodes[i]['type'] == 'Cloud':\n",
    "            color_map.append('Blue')\n",
    "        elif network.nodes[i]['type'] == 'Contract':\n",
    "            color_map.append('Green')\n",
    "        elif network.nodes[i]['type'] == 'Trader':\n",
    "            color_map.append('Yellow')\n",
    "        elif network.nodes[i]['type'] == 'Chama':\n",
    "            color_map.append('Orange')\n",
    "            \n",
    "    pos = nx.spring_layout(network,pos=nx.get_node_attributes(network,'pos'),fixed=nx.get_node_attributes(network,'pos'),seed=10)\n",
    "    nx.draw(network,node_color = color_map,pos=pos,with_labels=True,alpha=0.7)\n",
    "    plt.savefig('images/graph.png')\n",
    "    plt.show()\n",
    "    return network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/aclarkdata/anaconda3/lib/python3.7/site-packages/networkx/drawing/nx_pylab.py:563: MatplotlibDeprecationWarning: \n",
      "The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
      "  if not cb.iterable(width):\n",
      "/home/aclarkdata/anaconda3/lib/python3.7/site-packages/networkx/drawing/nx_pylab.py:660: MatplotlibDeprecationWarning: \n",
      "The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
      "  if cb.iterable(node_size):  # many node sizes\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "genesis_states = { \n",
    "    # initial states of the economy\n",
    "    'network': create_network(),# networkx market\n",
    "    'KPIDemand': {},\n",
    "    'KPISpend': {},\n",
    "    'KPISpendOverDemand': {},\n",
    "    'VelocityOfMoney':0,\n",
    "    'startingBalance': {},\n",
    "    '30_day_spend': {},\n",
    "    'withdraw':{},\n",
    "    'outboundAgents':[],\n",
    "    'inboundAgents':[],\n",
    "    'operatorFiatBalance': R0,\n",
    "    'operatorCICBalance': S0,\n",
    "    'fundsInProcess': {'timestep':[],'decision':[],'cic':[],'shilling':[]},\n",
    "    'totalDistributedToAgents':0,\n",
    "    'totalMinted':0,\n",
    "    'totalBurned':0\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exogenous \n",
    "def startingBalance(params, step, sL, s, _input):\n",
    "    '''\n",
    "    Calculate agent starting balance every 30 days\n",
    "    '''\n",
    "    y = 'startingBalance'\n",
    "    network = s['network']\n",
    "\n",
    "    startingBalance = {}\n",
    "\n",
    "    timestep = s['timestep']\n",
    "\n",
    "    division =  timestep % 31  == 0\n",
    "\n",
    "    if timestep == 1:\n",
    "        for i in agents:\n",
    "            startingBalance[i] = network.nodes[i]['tokens']\n",
    "    elif division == True:\n",
    "        for i in agents:\n",
    "            startingBalance[i] = network.nodes[i]['tokens']\n",
    "    else:\n",
    "        startingBalance = s['startingBalance']\n",
    "    x = startingBalance\n",
    "\n",
    "    return (y, x)\n",
    "\n",
    "def update_30_day_spend(params, step, sL, s,_input):\n",
    "    '''\n",
    "    Aggregate agent spend. Refresh every 30 days.\n",
    "    '''\n",
    "    y = '30_day_spend'\n",
    "    network = s['network']\n",
    "\n",
    "    timestep = s['timestep']\n",
    "\n",
    "    division =  timestep % 31  == 0\n",
    "\n",
    "    if division == True:\n",
    "        outflowSpend, inflowSpend = iterateEdges(network,'spend')\n",
    "        spend =  outflowSpend \n",
    "    else:\n",
    "        spendOld = s['30_day_spend']\n",
    "        outflowSpend, inflowSpend = iterateEdges(network,'spend')\n",
    "        spend = DictionaryMergeAddition(spendOld,outflowSpend) \n",
    "\n",
    "    x = spend\n",
    "    return (y, x)\n",
    "\n",
    "def redCrossDrop(params, step, sL, s, _input):\n",
    "    '''\n",
    "    Every 30 days, the red cross drips to the grassroots operator node\n",
    "    '''\n",
    "    y = 'operatorFiatBalance'\n",
    "    fiatBalance = s['operatorFiatBalance']\n",
    "    \n",
    "    timestep = s['timestep']\n",
    "\n",
    "    division =  timestep % 30  == 0\n",
    "\n",
    "    if division == True:\n",
    "        fiatBalance = fiatBalance + drip\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "    x = fiatBalance\n",
    "    return (y, x)\n",
    "\n",
    "\n",
    "def clear_agent_activity(params,step,sL,s,_input):\n",
    "    '''\n",
    "    Clear agent activity from the previous timestep\n",
    "    '''\n",
    "    y = 'network'\n",
    "    network = s['network']\n",
    "\n",
    "    if s['timestep'] > 0:\n",
    "        outboundAgents = s['outboundAgents']\n",
    "        inboundAgents = s['inboundAgents']\n",
    "        \n",
    "        try:\n",
    "            for i,j in zip(outboundAgents,inboundAgents):\n",
    "                network[i][j]['demand'] = 0\n",
    "        except:\n",
    "            pass\n",
    "\n",
    "        # Clear cic % demand edge weights\n",
    "        try:\n",
    "            for i,j in zip(outboundAgents,inboundAgents):\n",
    "                network[i][j]['fractionOfDemandInCIC'] = 0\n",
    "        except:\n",
    "            pass\n",
    "\n",
    "\n",
    "        # Clear utility edge types\n",
    "        try: \n",
    "            for i,j in zip(outboundAgents,inboundAgents):\n",
    "                network[i][j]['utility'] = 0\n",
    "        except:\n",
    "            pass\n",
    "        \n",
    "        # Clear cic % spend edge weights\n",
    "        try:\n",
    "            for i,j in zip(outboundAgents,inboundAgents):\n",
    "                network[i][j]['fractionOfActualSpendInCIC'] = 0\n",
    "        except:\n",
    "            pass\n",
    "        # Clear spend edge types\n",
    "        try: \n",
    "            for i,j in zip(outboundAgents,inboundAgents):\n",
    "                network[i][j]['spend'] = 0\n",
    "        except:\n",
    "            pass\n",
    "    else:\n",
    "        pass\n",
    "    x = network\n",
    "    return (y,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# System\n",
    "\n",
    "# Parameters\n",
    "agentsMinus = 2\n",
    "# percentage of balance a user can redeem\n",
    "redeemPercentage = 0.5\n",
    "\n",
    "# Behaviors\n",
    "def choose_agents(params, step, sL, s):\n",
    "    '''\n",
    "    Choose agents to interact during the given timestep and create their demand from a uniform distribution. \n",
    "    Based on probability, choose utility. \n",
    "    '''\n",
    "    outboundAgents = np.random.choice(mixingAgents,size=len(mixingAgents)-agentsMinus).tolist()\n",
    "    inboundAgents = np.random.choice(mixingAgents,size=len(mixingAgents)-agentsMinus).tolist()\n",
    "    stepDemands = np.random.uniform(low=1, high=500, size=len(mixingAgents)-agentsMinus).astype(int)\n",
    "    \n",
    "\n",
    "    stepUtilities = np.random.choice(list(UtilityTypesOrdered.keys()),size=len(mixingAgents)-agentsMinus,p=list(utilityTypesProbability.values())).tolist()\n",
    "\n",
    "    return {'outboundAgents':outboundAgents,'inboundAgents':inboundAgents,'stepDemands':stepDemands,'stepUtilities':stepUtilities}\n",
    "\n",
    "\n",
    "def spend_allocation(params, step, sL, s):\n",
    "    '''\n",
    "    Take mixing agents, demand, and utilities and allocate agent shillings and tokens based on utility and scarcity. \n",
    "    '''\n",
    "    # instantiate network state\n",
    "    network = s['network']\n",
    "\n",
    "    spendI = []\n",
    "    spendJ = []\n",
    "    spendAmount = []\n",
    "\n",
    "    # calculate max about of spend available to each agent\n",
    "    maxSpendShilling = {}\n",
    "    for i in mixingAgents:\n",
    "        maxSpendShilling[i] = network.nodes[i]['native_currency']\n",
    "        \n",
    "    maxSpendCIC = {}\n",
    "    for i in mixingAgents:\n",
    "        maxSpendCIC[i] = network.nodes[i]['tokens']\n",
    "\n",
    "\n",
    "    for i in mixingAgents: \n",
    "        rankOrder = {}\n",
    "        rankOrderDemand = {}\n",
    "        for j in network.adj[i]:\n",
    "            try:\n",
    "                rankOrder[j] = UtilityTypesOrdered[network.adj[i][j]['utility']]\n",
    "                rankOrderDemand[j] = network.adj[i][j]['demand']\n",
    "                rankOrder = dict(OrderedDict(sorted(rankOrder.items(), key=lambda v: v, reverse=False)))\n",
    "                for k in rankOrder:\n",
    "                    # if i or j is external, we transact 100% in shilling\n",
    "                    if i == 'external':\n",
    "                        amt = spendCalculationExternal(i,j,rankOrderDemand,maxSpendShilling)\n",
    "                        spendI.append(i)\n",
    "                        spendJ.append(j)\n",
    "                        spendAmount.append(amt)\n",
    "                        maxSpendShilling[i] = maxSpendShilling[i] - amt \n",
    "                    elif j == 'external':\n",
    "                        amt = spendCalculationExternal(i,j,rankOrderDemand,maxSpendShilling)\n",
    "                        spendI.append(i)\n",
    "                        spendJ.append(j)\n",
    "                        spendAmount.append(amt)\n",
    "                        maxSpendShilling[i] = maxSpendShilling[i] - amt \n",
    "                    else:\n",
    "                        amt = spendCalculation(i,j,rankOrderDemand,maxSpendShilling,maxSpendCIC,fractionOfDemandInCIC)\n",
    "                        spendI.append(i)\n",
    "                        spendJ.append(j)\n",
    "                        spendAmount.append(amt)\n",
    "                        maxSpendShilling[i] = maxSpendShilling[i] - amt * (1- fractionOfDemandInCIC)\n",
    "                        maxSpendCIC[i] = maxSpendCIC[i] - (amt * fractionOfDemandInCIC)\n",
    "            except:\n",
    "                pass\n",
    "    return {'spendI':spendI,'spendJ':spendJ,'spendAmount':spendAmount}\n",
    "\n",
    "\n",
    "def withdraw_calculation(params, step, sL, s):\n",
    "    ''''''\n",
    "    # instantiate network state\n",
    "    network = s['network']\n",
    "\n",
    "    # Assumptions:\n",
    "    # * user is only able to withdraw up to 50% of balance, assuming they have spent 50% of balance\n",
    "    # * Agents will withdraw as much as they can.\n",
    "    withdraw = {}\n",
    "\n",
    "    fiftyThreshold = {}\n",
    "\n",
    "    startingBalance = s['startingBalance']\n",
    "\n",
    "    spend = s['30_day_spend']\n",
    "    timestep = s['timestep']\n",
    "\n",
    "    division =  timestep % 30  == 0\n",
    "\n",
    "    if division == True:\n",
    "        for i,j in startingBalance.items():\n",
    "            fiftyThreshold[i] = j * 0.5\n",
    "        if s['timestep'] > 7:\n",
    "            for i,j in fiftyThreshold.items():\n",
    "                if spend[i] > 0 and fiftyThreshold[i] > 0:\n",
    "                    if spend[i] * fractionOfActualSpendInCIC >= fiftyThreshold[i]:\n",
    "                        spent = spend[i]\n",
    "                        amount = spent * redeemPercentage\n",
    "                        if network.nodes[i]['tokens'] > amount:\n",
    "                            withdraw[i] = amount\n",
    "                        elif  network.nodes[i]['tokens'] < amount:\n",
    "                            withdraw[i] = network.nodes[i]['tokens']\n",
    "                    else:\n",
    "                        pass\n",
    "                else:\n",
    "                    pass\n",
    "        else:\n",
    "            pass\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "\n",
    "    return {'withdraw':withdraw}\n",
    "\n",
    "# Mechanisms \n",
    "def update_agent_activity(params,step,sL,s,_input):\n",
    "    '''\n",
    "    Update the network for interacting agent, their demand, and utility.\n",
    "    '''\n",
    "    y = 'network'\n",
    "    network = s['network']\n",
    "\n",
    "    outboundAgents = _input['outboundAgents']\n",
    "    inboundAgents = _input['inboundAgents']\n",
    "    stepDemands = _input['stepDemands']\n",
    "    stepUtilities = _input['stepUtilities']\n",
    "    \n",
    "    # create demand edge weights\n",
    "    try:\n",
    "        for i,j,l in zip(outboundAgents,inboundAgents,stepDemands):\n",
    "            network[i][j]['demand'] = l\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "    # Create cic % edge weights\n",
    "    try:\n",
    "        for i,j in zip(outboundAgents,inboundAgents):\n",
    "            # if one of the agents is external, we will transact in 100% shilling\n",
    "            if i == 'external':\n",
    "                network[i][j]['fractionOfDemandInCIC'] = 1\n",
    "            elif j == 'external':\n",
    "                network[i][j]['fractionOfDemandInCIC'] = 1\n",
    "            else:\n",
    "                network[i][j]['fractionOfDemandInCIC'] = fractionOfDemandInCIC\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "    # Create utility edge types\n",
    "    try: \n",
    "        for i,j,l in zip(outboundAgents,inboundAgents,stepUtilities):\n",
    "            network[i][j]['utility'] = l\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "    x = network\n",
    "    return (y,x)\n",
    "\n",
    "\n",
    "def update_outboundAgents(params,step,sL,s,_input):\n",
    "    '''\n",
    "    Update outBoundAgents state variable\n",
    "    '''\n",
    "    y = 'outboundAgents'\n",
    "\n",
    "    x = _input['outboundAgents']\n",
    "\n",
    "    return (y,x)\n",
    "\n",
    "def update_inboundAgents(params,step,sL,s,_input):\n",
    "    '''\n",
    "    Update inBoundAgents state variable\n",
    "    '''\n",
    "    y = 'inboundAgents'\n",
    "\n",
    "    x = _input['inboundAgents']\n",
    "    return (y,x)\n",
    "\n",
    "\n",
    "def update_node_spend(params, step, sL, s,_input):\n",
    "    '''\n",
    "    Update network with actual spend of agents.\n",
    "    '''\n",
    "    y = 'network'\n",
    "    network = s['network']\n",
    "    \n",
    "    spendI = _input['spendI']\n",
    "    spendJ = _input['spendJ']\n",
    "    spendAmount = _input['spendAmount']\n",
    "\n",
    "    for i,j,l in zip(spendI,spendJ,spendAmount):   \n",
    "        network[i][j]['spend'] = l\n",
    "        if i == 'external':\n",
    "            network[i][j]['fractionOfActualSpendInCIC'] = 1\n",
    "        elif j == 'external':\n",
    "            network[i][j]['fractionOfActualSpendInCIC'] = 1\n",
    "        else:\n",
    "            network[i][j]['fractionOfActualSpendInCIC'] = fractionOfActualSpendInCIC\n",
    "\n",
    "    outflowSpend, inflowSpend = iterateEdges(network,'spend')\n",
    "\n",
    "    for i, j in inflowSpend.items():\n",
    "        if i == 'external':\n",
    "            network.nodes[i]['native_currency'] = network.nodes[i]['native_currency'] +  inflowSpend[i]\n",
    "        elif j == 'external':\n",
    "            network.nodes[i]['native_currency'] = network.nodes[i]['native_currency'] +  inflowSpend[i]\n",
    "        else:\n",
    "            network.nodes[i]['native_currency'] = network.nodes[i]['native_currency'] +  inflowSpend[i] * (1- fractionOfDemandInCIC)\n",
    "            network.nodes[i]['tokens'] = network.nodes[i]['tokens'] + (inflowSpend[i] * fractionOfDemandInCIC)\n",
    "        \n",
    "    for i, j in outflowSpend.items():\n",
    "        if i == 'external':\n",
    "            network.nodes[i]['native_currency'] = network.nodes[i]['native_currency'] - outflowSpend[i]\n",
    "        elif j == 'external':\n",
    "            network.nodes[i]['native_currency'] = network.nodes[i]['native_currency'] - outflowSpend[i]\n",
    "        else:\n",
    "            network.nodes[i]['native_currency'] = network.nodes[i]['native_currency'] - outflowSpend[i]* (1- fractionOfDemandInCIC)\n",
    "            network.nodes[i]['tokens'] = network.nodes[i]['tokens'] - (outflowSpend[i] * fractionOfDemandInCIC)\n",
    "\n",
    "    # Store the net of the inflow and outflow per step\n",
    "    network.nodes['external']['delta_native_currency'] = sum(inflowSpend.values()) - sum(outflowSpend.values())\n",
    "\n",
    "    x = network\n",
    "    return (y,x)\n",
    "\n",
    "\n",
    "def update_withdraw(params, step, sL, s,_input):\n",
    "    '''\n",
    "    Update flow sstate variable with the aggregated amount of shillings withdrawn\n",
    "    '''\n",
    "    y = 'withdraw'\n",
    "    x = s['withdraw']\n",
    "    if _input['withdraw']:\n",
    "        x = _input['withdraw']\n",
    "    else:\n",
    "        x = 0\n",
    "\n",
    "    return (y,x)\n",
    "\n",
    "def update_network_withraw(params, step, sL, s,_input):\n",
    "    '''\n",
    "    Update network for agents withdrawing \n",
    "    '''\n",
    "    y = 'network'\n",
    "    network = s['network']\n",
    "    withdraw = _input['withdraw']\n",
    "\n",
    "    if withdraw:\n",
    "        for i,j in withdraw.items():\n",
    "            # update agent nodes\n",
    "            network.nodes[i]['tokens'] = network.nodes[i]['tokens'] - j\n",
    "            network.nodes[i]['native_currency'] = network.nodes[i]['native_currency'] + (j * leverage)\n",
    "\n",
    "        withdrawnCICSum = []\n",
    "        for i,j in withdraw.items():\n",
    "            withdrawnCICSum.append(j)\n",
    "        \n",
    "        # update cic node\n",
    "        network.nodes['cic']['native_currency'] = network.nodes[i]['native_currency'] - (sum(withdrawnCICSum) * leverage)\n",
    "        network.nodes['cic']['tokens'] = network.nodes[i]['tokens'] + (sum(withdrawnCICSum) * leverage)\n",
    "\n",
    "    else:\n",
    "        pass\n",
    "    x = network\n",
    "    return (y,x)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Operating Entity\n",
    "\n",
    "# Parameters\n",
    "FrequencyOfAllocation = 45 # every two weeks\n",
    "idealFiat = 5000\n",
    "idealCIC = 200000\n",
    "varianceCIC = 50000\n",
    "varianceFiat = 1000\n",
    "unadjustedPerAgent = 50\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "agentAllocation = {'a':[1,1],'b':[1,1],'c':[1,1], # agent:[centrality,allocationValue]\n",
    "                   'd':[1,1],'e':[1,1],'f':[1,1],\n",
    "                   'g':[1,1],'h':[1,1],'i':[1,1],\n",
    "                   'j':[1,1],'k':[1,1],'l':[1,1],\n",
    "                   'm':[1,1],'o':[1,1],'p':[1,1]}\n",
    "\n",
    "# Behaviors\n",
    "def disbursement_to_agents(params, step, sL, s):\n",
    "    '''\n",
    "    Distribute every FrequencyOfAllocation days to agents based off of centrality allocation metric\n",
    "    '''\n",
    "    fiatBalance = s['operatorFiatBalance']\n",
    "    cicBalance = s['operatorCICBalance']\n",
    "    timestep = s['timestep']\n",
    "\n",
    "    division =  timestep % FrequencyOfAllocation  == 0\n",
    "\n",
    "    if division == True:\n",
    "        agentDistribution ={} # agent: amount distributed\n",
    "        for i,j in agentAllocation.items():\n",
    "            agentDistribution[i] = unadjustedPerAgent * agentAllocation[i][1]\n",
    "        distribute = 'Yes'\n",
    "        \n",
    "    else:\n",
    "        agentDistribution = 0\n",
    "        distribute = 'No'\n",
    "\n",
    "\n",
    "    return {'distribute':distribute,'amount':agentDistribution}\n",
    "\n",
    "\n",
    "def inventory_controller(params, step, sL, s):\n",
    "    '''\n",
    "    Monetary policy hysteresis conservation allocation between fiat and cic reserves.\n",
    "    \n",
    "    '''\n",
    "    fiatBalance = s['operatorFiatBalance']\n",
    "    cicBalance = s['operatorCICBalance']\n",
    "    timestep = s['timestep']\n",
    "    fundsInProcess = s['fundsInProcess']\n",
    "\n",
    "\n",
    "    updatedCIC = cicBalance\n",
    "    updatedFiat = fiatBalance\n",
    "\n",
    "    #decision,amt = mint_burn_logic_control(idealCIC,updatedCIC,variance,updatedFiat)\n",
    "    decision,amt = mint_burn_logic_control(idealCIC,updatedCIC,varianceCIC,updatedFiat,varianceFiat,idealFiat)\n",
    "\n",
    "    if decision == 'burn':\n",
    "        try:\n",
    "            deltaR, realized_price = withdraw(amt,updatedFiat,updatedCIC, V0, kappa)\n",
    "            # update state\n",
    "            # fiatBalance = fiatBalance - deltaR\n",
    "            # cicBalance = cicBalance - amt\n",
    "            fiatChange = abs(deltaR)\n",
    "            cicChange = amt\n",
    "\n",
    "        except:\n",
    "            print('Not enough to burn')\n",
    "\n",
    "            fiatChange = 0\n",
    "            cicChange = 0\n",
    "        \n",
    "    elif decision == 'mint':\n",
    "        try:\n",
    "            deltaS, realized_price = mint(amt,updatedFiat,updatedCIC, V0, kappa)\n",
    "            # update state\n",
    "            # fiatBalance = fiatBalance + amt\n",
    "            # cicBalance = cicBalance + deltaS\n",
    "            fiatChange = amt\n",
    "            cicChange = abs(deltaS)\n",
    "\n",
    "        except:\n",
    "            print('Not enough to mint')\n",
    "            fiatChange = 0\n",
    "            cicChange = 0\n",
    "\n",
    "    else:\n",
    "        fiatChange = 0\n",
    "        cicChange = 0\n",
    "        decision = 'none'\n",
    "        pass\n",
    "\n",
    "    if decision == 'mint':\n",
    "        fundsInProcess['timestep'].append(timestep + process_lag)\n",
    "        fundsInProcess['decision'].append(decision)\n",
    "        fundsInProcess['cic'].append(fiatChange)\n",
    "        fundsInProcess['shilling'].append(cicChange)\n",
    "    elif decision == 'burn':\n",
    "        fundsInProcess['timestep'].append(timestep +process_lag)\n",
    "        fundsInProcess['decision'].append(decision)\n",
    "        fundsInProcess['cic'].append(fiatChange)\n",
    "        fundsInProcess['shilling'].append(cicChange)\n",
    "    else:\n",
    "        pass\n",
    "    \n",
    "    return {'decision':decision,'fiatChange':fiatChange,'cicChange':cicChange,'fundsInProcess':fundsInProcess}\n",
    "\n",
    "\n",
    "\n",
    "# Mechanisms \n",
    "def update_agent_tokens(params,step,sL,s,_input):\n",
    "    '''\n",
    "    '''\n",
    "    y = 'network'\n",
    "    network = s['network']\n",
    "\n",
    "    distribute = _input['distribute']\n",
    "    amount = _input['amount']\n",
    "\n",
    "    if distribute == 'Yes':\n",
    "        for i in agents:\n",
    "            network.nodes[i]['tokens'] = network.nodes[i]['tokens'] + amount[i]\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "    return (y,network)\n",
    "\n",
    "def update_operator_FromDisbursements(params,step,sL,s,_input):\n",
    "    '''\n",
    "    '''\n",
    "    y = 'operatorCICBalance'\n",
    "    x = s['operatorCICBalance']\n",
    "    timestep = s['timestep']\n",
    "    \n",
    "    distribute = _input['distribute']\n",
    "    amount = _input['amount'] \n",
    "\n",
    "    if distribute == 'Yes':\n",
    "        totalDistribution = []\n",
    "        for i,j in amount.items():\n",
    "            totalDistribution.append(j)\n",
    "        \n",
    "        totalDistribution = sum(totalDistribution)\n",
    "        x = x - totalDistribution\n",
    "\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "    return (y,x)\n",
    "\n",
    "def update_totalDistributedToAgents(params,step,sL,s,_input):\n",
    "    '''\n",
    "    '''\n",
    "    y = 'totalDistributedToAgents'\n",
    "    x = s['totalDistributedToAgents']\n",
    "    timestep = s['timestep']\n",
    "    \n",
    "    distribute = _input['distribute']\n",
    "    amount = _input['amount'] \n",
    "\n",
    "    if distribute == 'Yes':\n",
    "        totalDistribution = []\n",
    "        for i,j in amount.items():\n",
    "            totalDistribution.append(j)\n",
    "        \n",
    "        totalDistribution = sum(totalDistribution)\n",
    "        x = x + totalDistribution\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "    return (y,x)\n",
    "\n",
    "def update_operator_fiatBalance(params,step,sL,s,_input):\n",
    "    '''\n",
    "    '''\n",
    "    y = 'operatorFiatBalance'\n",
    "    x = s['operatorFiatBalance']\n",
    "    fundsInProcess = s['fundsInProcess']\n",
    "    timestep = s['timestep']\n",
    "    if _input['fiatChange']:\n",
    "        try:\n",
    "            if fundsInProcess['timestep'][0] == timestep + 1:\n",
    "                if fundsInProcess['decision'][0] == 'mint':\n",
    "                    x = x - abs(fundsInProcess['shilling'][0])\n",
    "                elif fundsInProcess['decision'][0] == 'burn':\n",
    "                    x = x + abs(fundsInProcess['shilling'][0])\n",
    "            else:\n",
    "                pass\n",
    "        except:\n",
    "            pass\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "\n",
    "    return (y,x)\n",
    "\n",
    "def update_operator_cicBalance(params,step,sL,s,_input):\n",
    "    '''\n",
    "    '''\n",
    "    y = 'operatorCICBalance'\n",
    "    x = s['operatorCICBalance']\n",
    "    fundsInProcess = s['fundsInProcess']\n",
    "    timestep = s['timestep']\n",
    "\n",
    "    if _input['cicChange']:\n",
    "        try:\n",
    "            if fundsInProcess['timestep'][0] == timestep + 1:\n",
    "                if fundsInProcess['decision'][0] == 'mint':\n",
    "                    x = x + abs(fundsInProcess['cic'][0])\n",
    "                elif fundsInProcess['decision'][0] == 'burn':\n",
    "                    x = x - abs(fundsInProcess['cic'][0])\n",
    "            else:\n",
    "                pass\n",
    "        except:\n",
    "            pass\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "    return (y,x)\n",
    "\n",
    "def update_totalMinted(params,step,sL,s,_input):\n",
    "    '''\n",
    "    '''\n",
    "    y = 'totalMinted'\n",
    "    x = s['totalMinted']\n",
    "    timestep = s['timestep']\n",
    "    try:\n",
    "        if _input['fundsInProcess']['decision'][0] == 'mint':\n",
    "            x = x + abs(_input['fundsInProcess']['cic'][0])\n",
    "        elif _input['fundsInProcess']['decision'][0] == 'burn':\n",
    "            pass\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "\n",
    "    return (y,x)\n",
    "\n",
    "def update_totalBurned(params,step,sL,s,_input):\n",
    "    '''\n",
    "    '''\n",
    "    y = 'totalBurned'\n",
    "    x = s['totalBurned']\n",
    "    timestep = s['timestep']\n",
    "    try:\n",
    "        if _input['fundsInProcess']['decision'][0] == 'burn':\n",
    "            x = x + abs(_input['fundsInProcess']['cic'][0])\n",
    "        elif _input['fundsInProcess']['decision'][0] == 'mint':\n",
    "            pass\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "    return (y,x)\n",
    "\n",
    "def update_fundsInProcess(params,step,sL,s,_input):\n",
    "    '''\n",
    "    '''\n",
    "    y = 'fundsInProcess'\n",
    "    x = _input['fundsInProcess']\n",
    "    timestep = s['timestep']\n",
    "\n",
    "    if _input['fundsInProcess']:\n",
    "        try:\n",
    "            if x['timestep'][0] == timestep:\n",
    "                del x['timestep'][0]\n",
    "                del x['decision'][0]\n",
    "                del x['cic'][0]\n",
    "                del x['shilling'][0]\n",
    "            else:\n",
    "                pass\n",
    "        except:\n",
    "            pass\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "    return (y,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# KPI\n",
    "\n",
    "# Behaviors\n",
    "def kpis(params, step, sL, s):\n",
    "    ''''''\n",
    "    # instantiate network state\n",
    "    network = s['network']\n",
    "\n",
    "    KPIDemand = {}\n",
    "    KPISpend = {}\n",
    "    KPISpendOverDemand = {}\n",
    "    for i in mixingAgents:\n",
    "        demand = []\n",
    "        for j in network.adj[i]:\n",
    "            try:\n",
    "                demand.append(network.adj[i][j]['demand'])\n",
    "            except:\n",
    "                pass\n",
    "\n",
    "        spend = []\n",
    "        for j in network.adj[i]:\n",
    "            try:\n",
    "                spend.append(network.adj[i][j]['spend'])\n",
    "            except:\n",
    "                pass\n",
    "\n",
    "        sumDemand = sum(demand)\n",
    "        sumSpend = sum(spend)\n",
    "        try:\n",
    "            spendOverDemand = sumSpend/sumDemand\n",
    "        except:\n",
    "            spendOverDemand = 0\n",
    "\n",
    "        KPIDemand[i] = sumDemand\n",
    "        KPISpend[i] = sumSpend\n",
    "        KPISpendOverDemand[i] = spendOverDemand\n",
    "\n",
    "    #print(nx.katz_centrality_numpy(G=network,weight='spend'))\n",
    "    return {'KPIDemand':KPIDemand,'KPISpend':KPISpend,'KPISpendOverDemand':KPISpendOverDemand}\n",
    "\n",
    "def velocity_of_money(params, step, sL, s):\n",
    "    ''''''\n",
    "    # instantiate network state\n",
    "    network = s['network']\n",
    "\n",
    "    KPISpend = s['KPISpend']\n",
    "\n",
    "    # TODO: Moving average for state variable\n",
    "    T = []\n",
    "    for i,j in KPISpend.items():\n",
    "        T.append(j)\n",
    "        \n",
    "    T = sum(T)\n",
    "    \n",
    "    # TODO Moving average for state variable \n",
    "    M = []\n",
    "    for i in agents:\n",
    "        M.append(network.nodes[i]['tokens'] + network.nodes[i]['native_currency'])\n",
    "        \n",
    "    M = sum(M)\n",
    "    \n",
    "    V_t = (priceLevel *T)/M\n",
    "\n",
    "    return {'V_t':V_t,'T':T,'M':M}\n",
    "\n",
    "\n",
    "# Mechanisms\n",
    "def update_KPIDemand(params, step, sL, s,_input):\n",
    "    y = 'KPIDemand'\n",
    "    x = _input['KPIDemand']\n",
    "    return (y,x)\n",
    "\n",
    "def update_KPISpend(params, step, sL, s,_input):\n",
    "    y = 'KPISpend'\n",
    "    x = _input['KPISpend']\n",
    "    return (y,x)\n",
    "\n",
    "def update_KPISpendOverDemand(params, step, sL, s,_input):\n",
    "    y = 'KPISpendOverDemand'\n",
    "    x = _input['KPISpendOverDemand']\n",
    "    return (y,x)\n",
    "\n",
    "\n",
    "def update_velocity_of_money(params, step, sL, s,_input):\n",
    "    y = 'VelocityOfMoney'\n",
    "    x = _input['V_t']\n",
    "    return (y,x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# partial state update block\n",
    "partial_state_update_block = {\n",
    "    # Exogenous\n",
    "    'Exogenous': {\n",
    "        'policies': {\n",
    "        },\n",
    "        'variables': {\n",
    "            'startingBalance': startingBalance,\n",
    "            'operatorFiatBalance': redCrossDrop,\n",
    "            '30_day_spend': update_30_day_spend,\n",
    "            'network':clear_agent_activity\n",
    "        }\n",
    "    },\n",
    "    # Users\n",
    "    'Behaviors': {\n",
    "        'policies': {\n",
    "            'action': choose_agents\n",
    "        },\n",
    "        'variables': {\n",
    "        'network': update_agent_activity,\n",
    "        'outboundAgents': update_outboundAgents,\n",
    "        'inboundAgents':update_inboundAgents\n",
    "        }\n",
    "    },\n",
    "    'Spend allocation': {\n",
    "        'policies': {\n",
    "            'action': spend_allocation\n",
    "        },\n",
    "        'variables': {\n",
    "        'network': update_node_spend\n",
    "        }\n",
    "    },\n",
    "    'Withdraw behavior': {\n",
    "        'policies': {\n",
    "            'action': withdraw_calculation\n",
    "        },\n",
    "        'variables': {\n",
    "        'withdraw': update_withdraw,\n",
    "        'network':update_network_withraw\n",
    "        }\n",
    "    },\n",
    "    # Operator\n",
    "    'Operator Disburse to Agents': {\n",
    "        'policies': {\n",
    "            'action': disbursement_to_agents\n",
    "        },\n",
    "        'variables': {\n",
    "        'network':update_agent_tokens,\n",
    "        'operatorCICBalance':update_operator_FromDisbursements,\n",
    "        'totalDistributedToAgents':update_totalDistributedToAgents\n",
    "        }\n",
    "    },\n",
    "    'Operator Inventory Control': {\n",
    "        'policies': {\n",
    "            'action': inventory_controller\n",
    "        },\n",
    "        'variables': {\n",
    "        'operatorFiatBalance':update_operator_fiatBalance,\n",
    "        'operatorCICBalance':update_operator_cicBalance, \n",
    "        'totalMinted': update_totalMinted,\n",
    "        'totalBurned':update_totalBurned,\n",
    "        'fundsInProcess':update_fundsInProcess\n",
    "        }\n",
    "    },\n",
    "    # KPIs\n",
    "    'KPIs': {\n",
    "        'policies': {\n",
    "            'action':kpis\n",
    "        },\n",
    "        'variables':{\n",
    "            'KPIDemand': update_KPIDemand,\n",
    "            'KPISpend': update_KPISpend,\n",
    "            'KPISpendOverDemand': update_KPISpendOverDemand        \n",
    "        }\n",
    "    },\n",
    "    'Velocity': {\n",
    "        'policies': {\n",
    "            'action':velocity_of_money\n",
    "        },\n",
    "        'variables':{\n",
    "\n",
    "            'VelocityOfMoney': update_velocity_of_money\n",
    "        }\n",
    "    }\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'N': 5, 'T': range(0, 100), 'M': [{}]}]\n"
     ]
    }
   ],
   "source": [
    "# config\n",
    "params: Dict[str, List[int]] = {\n",
    "    'month': [0,12,36,50,100]\n",
    "}\n",
    "\n",
    "\n",
    "sim_config = config_sim({\n",
    "    'N': 5,\n",
    "    'T': range(100), #day \n",
    "#     'M': #params,\n",
    "})\n",
    "\n",
    "seeds = {\n",
    "    'p': np.random.RandomState(26042019),\n",
    "}\n",
    "env_processes = {}\n",
    "\n",
    "\n",
    "append_configs(\n",
    "    sim_configs=sim_config,\n",
    "    initial_state=genesis_states,\n",
    "    seeds=seeds,\n",
    "    env_processes=env_processes,\n",
    "    partial_state_update_blocks=partial_state_update_block\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run cadCAD model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "exec_mode = ExecutionMode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                            __________   ____ \n",
      "          ________ __ _____/ ____/   |  / __ \\\n",
      "         / ___/ __` / __  / /   / /| | / / / /\n",
      "        / /__/ /_/ / /_/ / /___/ ___ |/ /_/ / \n",
      "        \\___/\\__,_/\\__,_/\\____/_/  |_/_____/  \n",
      "        by BlockScience\n",
      "        \n",
      "Execution Mode: single_proc: [<cadCAD.configuration.Configuration object at 0x7f8787b0a690>]\n",
      "Configurations: [<cadCAD.configuration.Configuration object at 0x7f8787b0a690>]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/aclarkdata/anaconda3/lib/python3.7/site-packages/cadCAD/utils/__init__.py:113: FutureWarning: The use of a dictionary to describe Partial State Update Blocks will be deprecated. Use a list instead.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "single_proc_ctx = ExecutionContext(context=exec_mode.single_proc)\n",
    "run1 = Executor(exec_context=single_proc_ctx, configs=[configs[0]])\n",
    "run1_raw_result, raw_tensor_field = run1.execute()\n",
    "df = pd.DataFrame(run1_raw_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set subsetep to last\n",
    "df = df[df['substep'] == df['substep'].max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "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>network</th>\n",
       "      <th>KPIDemand</th>\n",
       "      <th>KPISpend</th>\n",
       "      <th>KPISpendOverDemand</th>\n",
       "      <th>VelocityOfMoney</th>\n",
       "      <th>startingBalance</th>\n",
       "      <th>30_day_spend</th>\n",
       "      <th>withdraw</th>\n",
       "      <th>outboundAgents</th>\n",
       "      <th>inboundAgents</th>\n",
       "      <th>operatorFiatBalance</th>\n",
       "      <th>operatorCICBalance</th>\n",
       "      <th>fundsInProcess</th>\n",
       "      <th>totalDistributedToAgents</th>\n",
       "      <th>totalMinted</th>\n",
       "      <th>totalBurned</th>\n",
       "      <th>run</th>\n",
       "      <th>substep</th>\n",
       "      <th>timestep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...</td>\n",
       "      <td>{'a': 0, 'b': 0, 'c': 338, 'd': 398, 'e': 166,...</td>\n",
       "      <td>{'a': 0, 'b': 0, 'c': 57, 'd': 398, 'e': 166, ...</td>\n",
       "      <td>{'a': 0, 'b': 0, 'c': 0.16863905325443787, 'd'...</td>\n",
       "      <td>21.78</td>\n",
       "      <td>{'a': 400, 'b': 400, 'c': 400, 'd': 400, 'e': ...</td>\n",
       "      <td>{}</td>\n",
       "      <td>0</td>\n",
       "      <td>[e, l, g, h, d, f, c, l, f, o, g, l, external, f]</td>\n",
       "      <td>[a, h, m, l, m, l, d, f, a, b, h, d, i, d]</td>\n",
       "      <td>4500</td>\n",
       "      <td>200000.00</td>\n",
       "      <td>{'timestep': [], 'decision': [], 'cic': [], 's...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...</td>\n",
       "      <td>{'a': 83, 'b': 0, 'c': 0, 'd': 664, 'e': 0, 'f...</td>\n",
       "      <td>{'a': 83, 'b': 0, 'c': 0, 'd': 289.125, 'e': 0...</td>\n",
       "      <td>{'a': 1.0, 'b': 0, 'c': 0, 'd': 0.435429216867...</td>\n",
       "      <td>11.87</td>\n",
       "      <td>{'a': 583.0, 'b': 578.0, 'c': 371.5, 'd': 401....</td>\n",
       "      <td>{'c': 57, 'd': 398, 'e': 166, 'f': 254.5625, '...</td>\n",
       "      <td>0</td>\n",
       "      <td>[h, f, a, d, d, i, l, h, m, d, l, m, h, i]</td>\n",
       "      <td>[e, external, o, a, l, external, o, k, m, m, d...</td>\n",
       "      <td>4500</td>\n",
       "      <td>200000.00</td>\n",
       "      <td>{'timestep': [], 'decision': [], 'cic': [], 's...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>(a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...</td>\n",
       "      <td>{'a': 0, 'b': 195, 'c': 0, 'd': 258, 'e': 317,...</td>\n",
       "      <td>{'a': 0, 'b': 195, 'c': 0, 'd': 60.1875, 'e': ...</td>\n",
       "      <td>{'a': 0, 'b': 1.0, 'c': 0, 'd': 0.233284883720...</td>\n",
       "      <td>21.00</td>\n",
       "      <td>{'a': 583.0, 'b': 578.0, 'c': 371.5, 'd': 401....</td>\n",
       "      <td>{'a': 83, 'i': 214, 'c': 57, 'd': 687.125, 'e'...</td>\n",
       "      <td>0</td>\n",
       "      <td>[j, g, j, k, external, k, k, k, d, b, m, k, e,...</td>\n",
       "      <td>[m, c, p, d, l, external, o, o, a, m, p, b, c, e]</td>\n",
       "      <td>4500</td>\n",
       "      <td>200000.00</td>\n",
       "      <td>{'timestep': [], 'decision': [], 'cic': [], 's...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>(a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...</td>\n",
       "      <td>{'a': 8, 'b': 0, 'c': 140, 'd': 0, 'e': 623, '...</td>\n",
       "      <td>{'a': 8, 'b': 0, 'c': 140, 'd': 0, 'e': 227.87...</td>\n",
       "      <td>{'a': 1.0, 'b': 0, 'c': 1.0, 'd': 0, 'e': 0.36...</td>\n",
       "      <td>16.09</td>\n",
       "      <td>{'a': 583.0, 'b': 578.0, 'c': 371.5, 'd': 401....</td>\n",
       "      <td>{'b': 195, 'j': 328, 'k': 198.0625, 'm': 415, ...</td>\n",
       "      <td>0</td>\n",
       "      <td>[c, a, k, e, e, f, m, p, j, i, i, k, m, e]</td>\n",
       "      <td>[p, l, j, i, g, p, f, b, k, a, f, a, p, e]</td>\n",
       "      <td>4500</td>\n",
       "      <td>200000.00</td>\n",
       "      <td>{'timestep': [], 'decision': [], 'cic': [], 's...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>(a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...</td>\n",
       "      <td>{'a': 134, 'b': 0, 'c': 202, 'd': 0, 'e': 0, '...</td>\n",
       "      <td>{'a': 134, 'b': 0, 'c': 38.9375, 'd': 0, 'e': ...</td>\n",
       "      <td>{'a': 1.0, 'b': 0, 'c': 0.19275990099009901, '...</td>\n",
       "      <td>8.44</td>\n",
       "      <td>{'a': 583.0, 'b': 578.0, 'c': 371.5, 'd': 401....</td>\n",
       "      <td>{'p': 92, 'b': 195, 'j': 669, 'k': 231.0625, '...</td>\n",
       "      <td>0</td>\n",
       "      <td>[m, k, j, a, p, c, external, p, o, o, l, m, c,...</td>\n",
       "      <td>[c, i, e, h, b, a, external, f, h, f, b, l, a, m]</td>\n",
       "      <td>4500</td>\n",
       "      <td>200000.00</td>\n",
       "      <td>{'timestep': [], 'decision': [], 'cic': [], 's...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              network  \\\n",
       "8   (a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...   \n",
       "16  (a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...   \n",
       "24  (a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...   \n",
       "32  (a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...   \n",
       "40  (a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...   \n",
       "\n",
       "                                            KPIDemand  \\\n",
       "8   {'a': 0, 'b': 0, 'c': 338, 'd': 398, 'e': 166,...   \n",
       "16  {'a': 83, 'b': 0, 'c': 0, 'd': 664, 'e': 0, 'f...   \n",
       "24  {'a': 0, 'b': 195, 'c': 0, 'd': 258, 'e': 317,...   \n",
       "32  {'a': 8, 'b': 0, 'c': 140, 'd': 0, 'e': 623, '...   \n",
       "40  {'a': 134, 'b': 0, 'c': 202, 'd': 0, 'e': 0, '...   \n",
       "\n",
       "                                             KPISpend  \\\n",
       "8   {'a': 0, 'b': 0, 'c': 57, 'd': 398, 'e': 166, ...   \n",
       "16  {'a': 83, 'b': 0, 'c': 0, 'd': 289.125, 'e': 0...   \n",
       "24  {'a': 0, 'b': 195, 'c': 0, 'd': 60.1875, 'e': ...   \n",
       "32  {'a': 8, 'b': 0, 'c': 140, 'd': 0, 'e': 227.87...   \n",
       "40  {'a': 134, 'b': 0, 'c': 38.9375, 'd': 0, 'e': ...   \n",
       "\n",
       "                                   KPISpendOverDemand  VelocityOfMoney  \\\n",
       "8   {'a': 0, 'b': 0, 'c': 0.16863905325443787, 'd'...            21.78   \n",
       "16  {'a': 1.0, 'b': 0, 'c': 0, 'd': 0.435429216867...            11.87   \n",
       "24  {'a': 0, 'b': 1.0, 'c': 0, 'd': 0.233284883720...            21.00   \n",
       "32  {'a': 1.0, 'b': 0, 'c': 1.0, 'd': 0, 'e': 0.36...            16.09   \n",
       "40  {'a': 1.0, 'b': 0, 'c': 0.19275990099009901, '...             8.44   \n",
       "\n",
       "                                      startingBalance  \\\n",
       "8   {'a': 400, 'b': 400, 'c': 400, 'd': 400, 'e': ...   \n",
       "16  {'a': 583.0, 'b': 578.0, 'c': 371.5, 'd': 401....   \n",
       "24  {'a': 583.0, 'b': 578.0, 'c': 371.5, 'd': 401....   \n",
       "32  {'a': 583.0, 'b': 578.0, 'c': 371.5, 'd': 401....   \n",
       "40  {'a': 583.0, 'b': 578.0, 'c': 371.5, 'd': 401....   \n",
       "\n",
       "                                         30_day_spend withdraw  \\\n",
       "8                                                  {}        0   \n",
       "16  {'c': 57, 'd': 398, 'e': 166, 'f': 254.5625, '...        0   \n",
       "24  {'a': 83, 'i': 214, 'c': 57, 'd': 687.125, 'e'...        0   \n",
       "32  {'b': 195, 'j': 328, 'k': 198.0625, 'm': 415, ...        0   \n",
       "40  {'p': 92, 'b': 195, 'j': 669, 'k': 231.0625, '...        0   \n",
       "\n",
       "                                       outboundAgents  \\\n",
       "8   [e, l, g, h, d, f, c, l, f, o, g, l, external, f]   \n",
       "16         [h, f, a, d, d, i, l, h, m, d, l, m, h, i]   \n",
       "24  [j, g, j, k, external, k, k, k, d, b, m, k, e,...   \n",
       "32         [c, a, k, e, e, f, m, p, j, i, i, k, m, e]   \n",
       "40  [m, k, j, a, p, c, external, p, o, o, l, m, c,...   \n",
       "\n",
       "                                        inboundAgents  operatorFiatBalance  \\\n",
       "8          [a, h, m, l, m, l, d, f, a, b, h, d, i, d]                 4500   \n",
       "16  [e, external, o, a, l, external, o, k, m, m, d...                 4500   \n",
       "24  [m, c, p, d, l, external, o, o, a, m, p, b, c, e]                 4500   \n",
       "32         [p, l, j, i, g, p, f, b, k, a, f, a, p, e]                 4500   \n",
       "40  [c, i, e, h, b, a, external, f, h, f, b, l, a, m]                 4500   \n",
       "\n",
       "    operatorCICBalance                                     fundsInProcess  \\\n",
       "8            200000.00  {'timestep': [], 'decision': [], 'cic': [], 's...   \n",
       "16           200000.00  {'timestep': [], 'decision': [], 'cic': [], 's...   \n",
       "24           200000.00  {'timestep': [], 'decision': [], 'cic': [], 's...   \n",
       "32           200000.00  {'timestep': [], 'decision': [], 'cic': [], 's...   \n",
       "40           200000.00  {'timestep': [], 'decision': [], 'cic': [], 's...   \n",
       "\n",
       "    totalDistributedToAgents  totalMinted  totalBurned  run  substep  timestep  \n",
       "8                          0            0            0    1        8         1  \n",
       "16                         0            0            0    1        8         2  \n",
       "24                         0            0            0    1        8         3  \n",
       "32                         0            0            0    1        8         4  \n",
       "40                         0            0            0    1        8         5  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.reset_index(inplace=True)\n",
    "del df['index']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['agents'] = df.network.apply(lambda g: np.array([get_nodes_by_type(g,'Agent')][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['agent_tokens'] = df.network.apply(lambda g: np.array([g.nodes[j]['tokens'] for j in get_nodes_by_type(g,'Agent')]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['agent_native_currency'] = df.network.apply(lambda g: np.array([g.nodes[j]['native_currency'] for j in get_nodes_by_type(g,'Agent')]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "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>network</th>\n",
       "      <th>KPIDemand</th>\n",
       "      <th>KPISpend</th>\n",
       "      <th>KPISpendOverDemand</th>\n",
       "      <th>VelocityOfMoney</th>\n",
       "      <th>startingBalance</th>\n",
       "      <th>30_day_spend</th>\n",
       "      <th>withdraw</th>\n",
       "      <th>outboundAgents</th>\n",
       "      <th>inboundAgents</th>\n",
       "      <th>...</th>\n",
       "      <th>fundsInProcess</th>\n",
       "      <th>totalDistributedToAgents</th>\n",
       "      <th>totalMinted</th>\n",
       "      <th>totalBurned</th>\n",
       "      <th>run</th>\n",
       "      <th>substep</th>\n",
       "      <th>timestep</th>\n",
       "      <th>agents</th>\n",
       "      <th>agent_tokens</th>\n",
       "      <th>agent_native_currency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>(a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...</td>\n",
       "      <td>{'a': 362, 'b': 353, 'c': 0, 'd': 0, 'e': 0, '...</td>\n",
       "      <td>{'a': 362, 'b': 353, 'c': 0, 'd': 0, 'e': 0, '...</td>\n",
       "      <td>{'a': 1.0, 'b': 1.0, 'c': 0, 'd': 0, 'e': 0, '...</td>\n",
       "      <td>14.32</td>\n",
       "      <td>{'a': 3549.1844038994227, 'b': -759.6566540305...</td>\n",
       "      <td>{'a': 407, 'b': 517.5246942932349, 'c': 359, '...</td>\n",
       "      <td>0</td>\n",
       "      <td>[external, external, o, a, a, b, h, j, m, o, m...</td>\n",
       "      <td>[l, e, d, j, p, m, external, m, g, h, k, f, i,...</td>\n",
       "      <td>...</td>\n",
       "      <td>{'timestep': [], 'decision': [], 'cic': [], 's...</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>96</td>\n",
       "      <td>[a, b, c, d, e, f, g, h, i, j, k, l, m, o, p]</td>\n",
       "      <td>[3843.699663730596, 1181.2082080925675, 858.53...</td>\n",
       "      <td>[4405.7172278645585, 2458.3895564163895, 1173....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>(a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...</td>\n",
       "      <td>{'a': 303, 'b': 0, 'c': 0, 'd': 164, 'e': 0, '...</td>\n",
       "      <td>{'a': 303, 'b': 0, 'c': 0, 'd': 164, 'e': 0, '...</td>\n",
       "      <td>{'a': 1.0, 'b': 0, 'c': 0, 'd': 1.0, 'e': 0, '...</td>\n",
       "      <td>18.23</td>\n",
       "      <td>{'a': 3549.1844038994227, 'b': -759.6566540305...</td>\n",
       "      <td>{'a': 769, 'b': 870.5246942932349, 'c': 359, '...</td>\n",
       "      <td>0</td>\n",
       "      <td>[o, i, j, d, a, d, o, o, g, g, p, j, j, o]</td>\n",
       "      <td>[m, b, o, i, d, a, g, external, k, k, l, h, k, l]</td>\n",
       "      <td>...</td>\n",
       "      <td>{'timestep': [], 'decision': [], 'cic': [], 's...</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>97</td>\n",
       "      <td>[a, b, c, d, e, f, g, h, i, j, k, l, m, o, p]</td>\n",
       "      <td>[3736.199663730596, 1375.573975395193, 858.534...</td>\n",
       "      <td>[4298.2172278645585, 2652.755323719015, 1173.5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>(a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...</td>\n",
       "      <td>{'a': 521, 'b': 537, 'c': 0, 'd': 0, 'e': 0, '...</td>\n",
       "      <td>{'a': 521, 'b': 537, 'c': 0, 'd': 0, 'e': 0, '...</td>\n",
       "      <td>{'a': 1.0, 'b': 1.0, 'c': 0, 'd': 0, 'e': 0, '...</td>\n",
       "      <td>15.31</td>\n",
       "      <td>{'a': 3549.1844038994227, 'b': -759.6566540305...</td>\n",
       "      <td>{'a': 1072, 'b': 870.5246942932349, 'c': 359, ...</td>\n",
       "      <td>0</td>\n",
       "      <td>[p, m, a, o, g, h, m, l, l, l, a, b, l, b]</td>\n",
       "      <td>[f, a, i, g, a, l, o, h, e, c, o, p, g, k]</td>\n",
       "      <td>...</td>\n",
       "      <td>{'timestep': [], 'decision': [], 'cic': [], 's...</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>98</td>\n",
       "      <td>[a, b, c, d, e, f, g, h, i, j, k, l, m, o, p]</td>\n",
       "      <td>[4018.122226032855, 1107.073975395193, 970.822...</td>\n",
       "      <td>[4580.139790166818, 2384.255323719015, 1285.80...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>(a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...</td>\n",
       "      <td>{'a': 177, 'b': 322, 'c': 0, 'd': 109, 'e': 0,...</td>\n",
       "      <td>{'a': 177, 'b': 322, 'c': 0, 'd': 109, 'e': 0,...</td>\n",
       "      <td>{'a': 1.0, 'b': 1.0, 'c': 0, 'd': 1.0, 'e': 0,...</td>\n",
       "      <td>28.10</td>\n",
       "      <td>{'a': 3549.1844038994227, 'b': -759.6566540305...</td>\n",
       "      <td>{'a': 1593, 'b': 1407.524694293235, 'c': 359, ...</td>\n",
       "      <td>0</td>\n",
       "      <td>[l, i, d, p, d, j, o, a, f, b, m, f, g, p]</td>\n",
       "      <td>[k, p, a, b, o, external, l, i, o, e, b, c, c, m]</td>\n",
       "      <td>...</td>\n",
       "      <td>{'timestep': [], 'decision': [], 'cic': [], 's...</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>99</td>\n",
       "      <td>[a, b, c, d, e, f, g, h, i, j, k, l, m, o, p]</td>\n",
       "      <td>[3971.622226032855, 1230.0205571398026, 2492.6...</td>\n",
       "      <td>[4533.639790166818, 2507.201905463625, 2807.62...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>(a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...</td>\n",
       "      <td>{'a': 0, 'b': 118, 'c': 0, 'd': 503, 'e': 0, '...</td>\n",
       "      <td>{'a': 0, 'b': 118, 'c': 0, 'd': 503, 'e': 0, '...</td>\n",
       "      <td>{'a': 0, 'b': 1.0, 'c': 0, 'd': 1.0, 'e': 0, '...</td>\n",
       "      <td>16.48</td>\n",
       "      <td>{'a': 3549.1844038994227, 'b': -759.6566540305...</td>\n",
       "      <td>{'a': 1770, 'b': 1729.524694293235, 'c': 359, ...</td>\n",
       "      <td>0</td>\n",
       "      <td>[f, h, l, p, d, k, f, b, d, h, m, g, k, f]</td>\n",
       "      <td>[o, m, e, external, a, p, k, k, m, g, o, f, g, k]</td>\n",
       "      <td>...</td>\n",
       "      <td>{'timestep': [], 'decision': [], 'cic': [], 's...</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>100</td>\n",
       "      <td>[a, b, c, d, e, f, g, h, i, j, k, l, m, o, p]</td>\n",
       "      <td>[4030.622226032855, 1171.0205571398026, 2492.6...</td>\n",
       "      <td>[4592.639790166818, 2448.201905463625, 2807.62...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               network  \\\n",
       "495  (a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...   \n",
       "496  (a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...   \n",
       "497  (a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...   \n",
       "498  (a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...   \n",
       "499  (a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...   \n",
       "\n",
       "                                             KPIDemand  \\\n",
       "495  {'a': 362, 'b': 353, 'c': 0, 'd': 0, 'e': 0, '...   \n",
       "496  {'a': 303, 'b': 0, 'c': 0, 'd': 164, 'e': 0, '...   \n",
       "497  {'a': 521, 'b': 537, 'c': 0, 'd': 0, 'e': 0, '...   \n",
       "498  {'a': 177, 'b': 322, 'c': 0, 'd': 109, 'e': 0,...   \n",
       "499  {'a': 0, 'b': 118, 'c': 0, 'd': 503, 'e': 0, '...   \n",
       "\n",
       "                                              KPISpend  \\\n",
       "495  {'a': 362, 'b': 353, 'c': 0, 'd': 0, 'e': 0, '...   \n",
       "496  {'a': 303, 'b': 0, 'c': 0, 'd': 164, 'e': 0, '...   \n",
       "497  {'a': 521, 'b': 537, 'c': 0, 'd': 0, 'e': 0, '...   \n",
       "498  {'a': 177, 'b': 322, 'c': 0, 'd': 109, 'e': 0,...   \n",
       "499  {'a': 0, 'b': 118, 'c': 0, 'd': 503, 'e': 0, '...   \n",
       "\n",
       "                                    KPISpendOverDemand  VelocityOfMoney  \\\n",
       "495  {'a': 1.0, 'b': 1.0, 'c': 0, 'd': 0, 'e': 0, '...            14.32   \n",
       "496  {'a': 1.0, 'b': 0, 'c': 0, 'd': 1.0, 'e': 0, '...            18.23   \n",
       "497  {'a': 1.0, 'b': 1.0, 'c': 0, 'd': 0, 'e': 0, '...            15.31   \n",
       "498  {'a': 1.0, 'b': 1.0, 'c': 0, 'd': 1.0, 'e': 0,...            28.10   \n",
       "499  {'a': 0, 'b': 1.0, 'c': 0, 'd': 1.0, 'e': 0, '...            16.48   \n",
       "\n",
       "                                       startingBalance  \\\n",
       "495  {'a': 3549.1844038994227, 'b': -759.6566540305...   \n",
       "496  {'a': 3549.1844038994227, 'b': -759.6566540305...   \n",
       "497  {'a': 3549.1844038994227, 'b': -759.6566540305...   \n",
       "498  {'a': 3549.1844038994227, 'b': -759.6566540305...   \n",
       "499  {'a': 3549.1844038994227, 'b': -759.6566540305...   \n",
       "\n",
       "                                          30_day_spend withdraw  \\\n",
       "495  {'a': 407, 'b': 517.5246942932349, 'c': 359, '...        0   \n",
       "496  {'a': 769, 'b': 870.5246942932349, 'c': 359, '...        0   \n",
       "497  {'a': 1072, 'b': 870.5246942932349, 'c': 359, ...        0   \n",
       "498  {'a': 1593, 'b': 1407.524694293235, 'c': 359, ...        0   \n",
       "499  {'a': 1770, 'b': 1729.524694293235, 'c': 359, ...        0   \n",
       "\n",
       "                                        outboundAgents  \\\n",
       "495  [external, external, o, a, a, b, h, j, m, o, m...   \n",
       "496         [o, i, j, d, a, d, o, o, g, g, p, j, j, o]   \n",
       "497         [p, m, a, o, g, h, m, l, l, l, a, b, l, b]   \n",
       "498         [l, i, d, p, d, j, o, a, f, b, m, f, g, p]   \n",
       "499         [f, h, l, p, d, k, f, b, d, h, m, g, k, f]   \n",
       "\n",
       "                                         inboundAgents  ...  \\\n",
       "495  [l, e, d, j, p, m, external, m, g, h, k, f, i,...  ...   \n",
       "496  [m, b, o, i, d, a, g, external, k, k, l, h, k, l]  ...   \n",
       "497         [f, a, i, g, a, l, o, h, e, c, o, p, g, k]  ...   \n",
       "498  [k, p, a, b, o, external, l, i, o, e, b, c, c, m]  ...   \n",
       "499  [o, m, e, external, a, p, k, k, m, g, o, f, g, k]  ...   \n",
       "\n",
       "                                        fundsInProcess  \\\n",
       "495  {'timestep': [], 'decision': [], 'cic': [], 's...   \n",
       "496  {'timestep': [], 'decision': [], 'cic': [], 's...   \n",
       "497  {'timestep': [], 'decision': [], 'cic': [], 's...   \n",
       "498  {'timestep': [], 'decision': [], 'cic': [], 's...   \n",
       "499  {'timestep': [], 'decision': [], 'cic': [], 's...   \n",
       "\n",
       "     totalDistributedToAgents totalMinted  totalBurned  run  substep  \\\n",
       "495                      1500           0            0    5        8   \n",
       "496                      1500           0            0    5        8   \n",
       "497                      1500           0            0    5        8   \n",
       "498                      1500           0            0    5        8   \n",
       "499                      1500           0            0    5        8   \n",
       "\n",
       "     timestep                                         agents  \\\n",
       "495        96  [a, b, c, d, e, f, g, h, i, j, k, l, m, o, p]   \n",
       "496        97  [a, b, c, d, e, f, g, h, i, j, k, l, m, o, p]   \n",
       "497        98  [a, b, c, d, e, f, g, h, i, j, k, l, m, o, p]   \n",
       "498        99  [a, b, c, d, e, f, g, h, i, j, k, l, m, o, p]   \n",
       "499       100  [a, b, c, d, e, f, g, h, i, j, k, l, m, o, p]   \n",
       "\n",
       "                                          agent_tokens  \\\n",
       "495  [3843.699663730596, 1181.2082080925675, 858.53...   \n",
       "496  [3736.199663730596, 1375.573975395193, 858.534...   \n",
       "497  [4018.122226032855, 1107.073975395193, 970.822...   \n",
       "498  [3971.622226032855, 1230.0205571398026, 2492.6...   \n",
       "499  [4030.622226032855, 1171.0205571398026, 2492.6...   \n",
       "\n",
       "                                 agent_native_currency  \n",
       "495  [4405.7172278645585, 2458.3895564163895, 1173....  \n",
       "496  [4298.2172278645585, 2652.755323719015, 1173.5...  \n",
       "497  [4580.139790166818, 2384.255323719015, 1285.80...  \n",
       "498  [4533.639790166818, 2507.201905463625, 2807.62...  \n",
       "499  [4592.639790166818, 2448.201905463625, 2807.62...  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create dataframe variables   \n",
    "tokens = []\n",
    "for i in df.index:\n",
    "     tokens.append(sum(df['agent_tokens'][i]))\n",
    "        \n",
    "df['AggregatedAgentCICHolding'] = tokens  \n",
    "\n",
    "currency = []\n",
    "for i in df.index:\n",
    "     currency.append(sum(df['agent_native_currency'][i]))\n",
    "        \n",
    "df['AggregatedAgentCurrencyHolding'] = currency  \n",
    "\n",
    "AggregatedSpend = []\n",
    "for i in df.index:\n",
    "     AggregatedSpend.append(sum(df['KPISpend'][i].values()))\n",
    "\n",
    "df['AggregatedAgentSpend'] = AggregatedSpend   \n",
    "\n",
    "AggregatedDemand = []\n",
    "for i in df.index:\n",
    "     AggregatedDemand.append(sum(df['KPIDemand'][i].values()))\n",
    "        \n",
    "df['AggregatedAgentDemand'] = AggregatedDemand  \n",
    "\n",
    "\n",
    "AggregatedKPISpendOverDemand = []\n",
    "for i in df.index:\n",
    "     AggregatedKPISpendOverDemand.append(sum(df['KPISpendOverDemand'][i].values()))\n",
    "        \n",
    "df['AggregatedKPISpendOverDemand'] = AggregatedKPISpendOverDemand  \n",
    "\n",
    "\n",
    "AggregatedGapOfDemandMinusSpend = []\n",
    "for i in df.index:\n",
    "     AggregatedGapOfDemandMinusSpend.append(sum(df['KPIDemand'][i].values())- sum(df['KPISpend'][i].values()))\n",
    "        \n",
    "df['AggregatedGapOfDemandMinusSpend'] = AggregatedGapOfDemandMinusSpend  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['KPISpend'][1]['external']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "subset = df[['timestep', 'VelocityOfMoney', 'operatorFiatBalance',\n",
    "       'operatorCICBalance', 'totalDistributedToAgents', 'totalMinted',\n",
    "       'totalBurned', 'run', 'substep', 'AggregatedAgentCICHolding',\n",
    "       'AggregatedAgentCurrencyHolding', 'AggregatedAgentSpend',\n",
    "       'AggregatedAgentDemand', 'AggregatedKPISpendOverDemand',\n",
    "       'AggregatedGapOfDemandMinusSpend']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_df,median_df,std_df,min_df = aggregate_runs(subset,'timestep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "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>timestep</th>\n",
       "      <th>VelocityOfMoney</th>\n",
       "      <th>operatorFiatBalance</th>\n",
       "      <th>operatorCICBalance</th>\n",
       "      <th>totalDistributedToAgents</th>\n",
       "      <th>totalMinted</th>\n",
       "      <th>totalBurned</th>\n",
       "      <th>run</th>\n",
       "      <th>substep</th>\n",
       "      <th>AggregatedAgentCICHolding</th>\n",
       "      <th>AggregatedAgentCurrencyHolding</th>\n",
       "      <th>AggregatedAgentSpend</th>\n",
       "      <th>AggregatedAgentDemand</th>\n",
       "      <th>AggregatedKPISpendOverDemand</th>\n",
       "      <th>AggregatedGapOfDemandMinusSpend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>96</td>\n",
       "      <td>15.73</td>\n",
       "      <td>16500.00</td>\n",
       "      <td>198500.00</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>-1137.20</td>\n",
       "      <td>19352.37</td>\n",
       "      <td>2937.30</td>\n",
       "      <td>2482.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>-455.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>97</td>\n",
       "      <td>17.29</td>\n",
       "      <td>16500.00</td>\n",
       "      <td>198500.00</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>-1190.60</td>\n",
       "      <td>19298.97</td>\n",
       "      <td>3159.83</td>\n",
       "      <td>2265.00</td>\n",
       "      <td>66.37</td>\n",
       "      <td>-894.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>98</td>\n",
       "      <td>19.40</td>\n",
       "      <td>16500.00</td>\n",
       "      <td>198500.00</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>-1203.81</td>\n",
       "      <td>19285.75</td>\n",
       "      <td>3279.11</td>\n",
       "      <td>2934.00</td>\n",
       "      <td>10.01</td>\n",
       "      <td>-345.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>99</td>\n",
       "      <td>14.27</td>\n",
       "      <td>16500.00</td>\n",
       "      <td>198500.00</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>-1243.41</td>\n",
       "      <td>19246.15</td>\n",
       "      <td>2684.56</td>\n",
       "      <td>1840.00</td>\n",
       "      <td>19.34</td>\n",
       "      <td>-844.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>100</td>\n",
       "      <td>14.77</td>\n",
       "      <td>16500.00</td>\n",
       "      <td>198500.00</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>-1265.61</td>\n",
       "      <td>19223.96</td>\n",
       "      <td>2701.91</td>\n",
       "      <td>2093.00</td>\n",
       "      <td>8.65</td>\n",
       "      <td>-608.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    timestep  VelocityOfMoney  operatorFiatBalance  operatorCICBalance  \\\n",
       "95        96            15.73             16500.00           198500.00   \n",
       "96        97            17.29             16500.00           198500.00   \n",
       "97        98            19.40             16500.00           198500.00   \n",
       "98        99            14.27             16500.00           198500.00   \n",
       "99       100            14.77             16500.00           198500.00   \n",
       "\n",
       "    totalDistributedToAgents  totalMinted  totalBurned  run  substep  \\\n",
       "95                   1500.00         0.00         0.00 3.00     8.00   \n",
       "96                   1500.00         0.00         0.00 3.00     8.00   \n",
       "97                   1500.00         0.00         0.00 3.00     8.00   \n",
       "98                   1500.00         0.00         0.00 3.00     8.00   \n",
       "99                   1500.00         0.00         0.00 3.00     8.00   \n",
       "\n",
       "    AggregatedAgentCICHolding  AggregatedAgentCurrencyHolding  \\\n",
       "95                   -1137.20                        19352.37   \n",
       "96                   -1190.60                        19298.97   \n",
       "97                   -1203.81                        19285.75   \n",
       "98                   -1243.41                        19246.15   \n",
       "99                   -1265.61                        19223.96   \n",
       "\n",
       "    AggregatedAgentSpend  AggregatedAgentDemand  AggregatedKPISpendOverDemand  \\\n",
       "95               2937.30                2482.00                         13.00   \n",
       "96               3159.83                2265.00                         66.37   \n",
       "97               3279.11                2934.00                         10.01   \n",
       "98               2684.56                1840.00                         19.34   \n",
       "99               2701.91                2093.00                          8.65   \n",
       "\n",
       "    AggregatedGapOfDemandMinusSpend  \n",
       "95                          -455.30  \n",
       "96                          -894.83  \n",
       "97                          -345.11  \n",
       "98                          -844.56  \n",
       "99                          -608.91  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot of agent activity per timestep\n",
    "plot_median_with_quantiles_annotation(subset,'timestep','timestep','AggregatedAgentSpend')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_median_with_quantiles_annotation(subset,'timestep','timestep','VelocityOfMoney')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_median_with_quantiles(subset,'timestep','timestep','operatorCICBalance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_median_with_quantiles(subset,'timestep','timestep','operatorFiatBalance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_median_with_quantiles(subset,'timestep','timestep','totalMinted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_median_with_quantiles(subset,'timestep','timestep','totalBurned')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_median_with_quantiles(subset,'timestep','timestep','VelocityOfMoney')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "first_five_plot(subset,'timestep','timestep','AggregatedAgentCICHolding',5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_median_with_quantiles_annotation(subset,'timestep','timestep','AggregatedAgentCICHolding')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_median_with_quantiles(subset,'timestep','timestep','AggregatedAgentCurrencyHolding')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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enp5GN0MikUjKSEdNIpFIVOQ0ahKJJGxIoSaRSCQqxWJRDraRSCShQgo1iUQiUSmVSlKoSSSSULGshBr1UpdjmSD3jUTChFqhUGh0MyQSiaTMshFq8Xgc09PTUpAYQCnF9PQ04vF4o5sikTQUGfqUSCRhY9mM+ly/fj1GRkYwNTXl6ffZbHZJC5l4PI7169c3uhkSSUORoU+JRBI2lo1Qa2lpwebNmz3/fnh4GLt27fKxRRKJJGzI0KdEEgyKoiASWTZBPF+Re00ikUjARBoA6ahJJAEgi0l7Rwo1iUQigSbUpKMmkfiPFGrekUJNIpFIoDlp0lGThBlFURrdBE8Ui8Xyw5DEHVKoSSQSCWToU9IcNOv5KYWad6RQk0gkEsjQp6Q5kEJt+SGFmkQikUATaqVSSdZblISWZhU7skahd6RQk0gkElQ6FfKGIgkrzXpuSkfNO1KoSSQSCSqdChn+lISVZhU7Uqh5Rwo1iSTEyBBc/RBvIs3qWkiWPoqiNOXITxn69I4UahJJiMnn841uwrJBOmoSpyiKglQq1ZB1U0qb0pmSjpp3pFCTSEKMLBJZP2SOmsQpiqJgZmamYetuNkeNi0sp1LwhhZpEEmKkUKsf0lGTOKVUKmF2drYhqQnN6KjJGoW1IYWaRBJiZOizfsgcNYlTFEVBsVjEwsJC3ddNKW06R41fT80mMMNC4EKNEBIlhOwnhPwf9e/NhJBfE0KOEEK+QwhpVd9vU/8+on6+SVjG36rvv0QIuTLoNkskYUDmdNQXGfqUOIULpUaEP5vRUZNCrTbq4aj93wAOCn9/AcAdlNKzAcwCeJ/6/vsAzKrv36F+D4SQ8wFcB2AbgKsAfJkQEq1DuyWShlIoFJruyTlIgg4zydCnxCn8upybm6v7NdrMjpp8APJGoEKNELIewB8A+Bf1bwLgCgDfU7/yDQDXqq+vUf+G+vnr1O9fA+B+SmmOUnoMwBEAlwTZbokkDBQKBVmeQ4VSGmgnr7/5yRuKxAp+riiKgrm5ubqvu9mcKemo1UYs4OXfCeB/AuhW/+4HMEcp5b3gCIAh9fUQgFMAQCktEkLm1e8PAXhSWKb4mzKEkA8A+AAArF69GsPDw75uSCqV8n2ZktpZyselVCqhWCzixIkTjW6KJ/w+NpRSsGe3YFhcXCy/JoRgeno6sHU1kqV8zdSLUqlUHuiTSCTQ1tZW8zKdHpd8Po9IJIJYLOjbt38Ui0Xk83kQQho2WrYWGn3NBHakCSF/CGCSUrqPELInqPVwKKX3ArgXAF71qlfRPXv8XeXw8DD8XqakdpbycRkfH0cymcSWLVsa3RRP+Hls8vk8stksVqxY4cvy9ORyORw4cKDivZ07dyIaXXpZFkv5mqkX09PTOH78OAAm6i+88MKazxWnx+XYsWOIx+NYu3ZtTeurJyMjI5iYmAAA7Nq1C5FIc41jbPQ1E+Te+l0AbyKEHAdwP1jI8y4AvYQQLhDXAxhVX48C2AAA6uc9AKbF9w1+I5EsWWSOmgZ3F4PCaNky/CkxQ7wuKaWYnZ2t27qbMUdNDHnK8Kd7AhNqlNK/pZSup5RuAhsM8AtK6TsBPALgrerX3gPgR+rrB9W/oX7+C8oSdB4EcJ06KnQzgC0Angqq3RJJWJBCTYOXQwgKo5uHHFAgMUN/XdYznNfMOWqAFGpeaESQ+2MA7ieEfBbAfgBfU9//GoBvEUKOAJgBE3eglP6WEPIAgBcBFAH8JaVUHmnJkkcKNY2gHTWjm4d01CRm6K/LZDKJQqGAlpaWwNfdjI6aLH1TG3URapTSYQDD6utXYDBqk1KaBfA2k9/fBuC24FookYQP6ehoKIoS6P6QQk3iBiOhNDMzg9WrVwe+7mauowZIR80LzZXRJ5EsI6SjphF06NNo2VIoS8wwE2r1oNkdNSnU3COFmkQSQkqlUlNOvhwUMvQpCRNG1+Xi4mJdzplmzFGT07PVhhRqEkkI4W6OFGqMRoQ+paPmDLH+3HLB7Lqsh4BqNketWCxWFO5uNpEZBqRQk0hCCBcJcmYChnTUwksikSgXf10umAmlegioZstR07e1mdoeFqRQk0hCiOjmNNPTc1DwcE9QwlXmqHnj4EHggx9ciSeeWJqzOJhhdk3W48GqGR01q78l9kihJpGEECnUKuFP4UF18tJR88Z99wE//WkX/u3fostqf5m5QvUSas3kSunPi2Zqe1iQQk0iCSFSqFXC90FQLpeZUJOhZ2sWFtj/J060YWpqqrGNqSONDH0qigJKadOcm9JRqx0p1CSSECKFWiV8H9TTUQtyfUuFdJr9f/JkGyYnJ5fNudro0KdVG8KGdNRqRwq1JkTePJY+UqhVEnTo02y5zXKtNcpd4UJtdLQNuVwR09PLI1et0YMJgOYRPFKo1Y4Uak3IchthtRwRhVqzhDiCJMjQJw8lGdEsQq1RfUIqxfZboRDB+HgrJiYmGtKOeiMdNefI0GftSKHWhEihtvSRjlolQTpqVk/4zTLys1F9QjKpCZNTp9qQy+UwOzvbkLbUE+moOUd/zVo9GEmMkUKtCZFCbWmjrzwuhVqwOWpWN7xmefpv1HRj6bR2wz1xIg4AS95Vs0rkD1qAiMe4WfoFo2uoWURmWJBCrQmRQm1pY/QEutzhHXsQDtdScNSCnrnBDJ6jBjBHjb2XRiqVqntb6oXV9Rj0tdqMFf6N2tksbQ8LUqg1IVKoLW30N9xGCbUwhSeCdNSsltksjlqpVGq4UDt5Ml5+nUgk6t6WemF1PQZ9zYjLb5YHOKNrqFmuq7AghVoTks/nQ3UTlfhLWIRaWNwkMdQkQ5/GNMpRS6VI+TV31ICl/TApHTV3yNBn7Uih1oQ0qlOW1Af9sW2UKA/LzVbs1GXo05hG9QnifOynT7eB35Pz+Xzd21IvGumoNVuOmtm0b1KouUMKtSakUWEOSX0Ii6MWlput/ubk9/5YKqHPerdVUYBMht1CVq/Oo1QiGB1lrlqhUFiyrr901JzT7PUJw4IUak0GDwPJE33p4kWo+X0+hOkc09+Q/G6XdNS8kcmw/+PxEjZuzALQwp+U0qbZd26ROWrOMbtWm0Fkhgkp1JqMoOc8lDQeL0LN7/OhEQ6NGfrtr6dQC8LBC4JGCDU+sLO9XcEZZzChxkt0AOEJnfuNdNScIx01f5BCrckIskyBJBx4EWp+hykVRQlNZ6rf/iBEqRVh2Q9WNCIdgo/4bG9XsGEDE2XigIKwhM79RjpqzjG7tppBZIYJKdSajKAnp5Y0Hi+DCfy+SYdJqAUd+rRbXjM8FDXCUeNCLR5XcMYZTKiJJTqWo1ALWjyJy28GsSNDn/4ghVqTIR21pY1Rbk8jHDUZ+gxufUHQCGHNhVpHR6kc+pSOmnTURGTo0x+kUGsyZI6af4Qxh8aoA3Oao+bnTSLMjpqXcz/DM98dLF9PM1xrfP7Eeh6zhQV2Xra3KxgayiEapRgfb0Uux2qrhfH68oOwCLVmcKWko+YPUqg1GUFOTr3cCOONxEgUOBFq+vlBayVMQq1WR41Sajn/5FJw1BrhtC8ssHXG4wpiMWDt2hwoJRgZYa7acnTU6jmYoJkdNSnU3CGFWpMhHTX/yGazjW5CFWESamHpTGvNUbObe3IpCLVG9AupFBMN7e1s3TxPjYc/l6pQszpf6lnwNizXpxUy9OkPUqg1GfxCLZVKTfFEFWaWkqPmd04ZrygehptBraM+k8kkcrmc6eTQdjfXsD8UiVNs1bOtySQ7Lh0dbL/yPDU+oGCp1lKTjppzpKPmD1KoNRniCS6fSmqjWRw1J0/pQThqQDjOsVodtYWFBQDAojjfkcmyjah1HwTtsgQ9xZYZYo4agHKJjpMnl/aAgrDkqDWzUAOkWHODFGpNhnhxLsWn1XpBKV1SjtpSFmq15KgpioK0OjzRq1Cr9ToLWqyI+6eex4uHPuPxytCnWKIjjNdYrYTFUaOUhl6sWV1fYehbmgUp1DySTCYbIpSko+YPYSo/IVJL6NNPoRamQSv67XczW0AqlSrf3LwKtVqEVrFYDNw5aNTDmz5HbcOG5VGiIyyTstu1pdHYXafSUXOOFGoeKZVKOHXqVN3XKx01f+A30LBNHF2Lo+anqAqTo2bUoTs995PJZPm1kVBzsn215IPm8/nAb6aNCn3qhdratXnEYgqmplqxuMhuLctNqNXTUQPCLXbsrq0wtz1sSKFWA7Ozs5ibm6vrOqVQ84cwOUYiMvRZjdH2O20Xz08DWE6ifh/Z7bNSCfiXf1mDRx7xdq35Xd/OiEb1CdoUUmwfRqPA+vVLf+RnWHLU7NrSaOyu0aD6loWFhSUnAqVQq5GTJ0/W9aSQoU9/4PvO6z4MKvcmLIMJwiRkjbbLqROmd9H0hW/t9tmjj/biq18dwi23RB20tJp6OGqNF2ra+vVzfsocNX+Rjpr9Oo8ePYoDBw5gcnIydBETr0ihViOFQgEjIyN1W99ycNTq0bnXKtSs6nJ5xSoU6yTXYzk5ak7OfTHsyeEDCzh2++xXv1oBAJicJLbrMyKfz9d11Gc9w/npNNsnolDTDyiQjpq/LCVHzezao5RWOOFumJycLKeBnDp1CgcOHMDMzIynZYUJKdR8IJFIGN4UgqBR+Sj1ZHx8PPB11OIYlUrAxz7Wgoce8rdNVsfTyVN8EDlqYXhi9+qoGV2TeofNajmUAk8+yYTa7Kzt6gwpFAp1ddT4OusB35WVQo3XUmsrty0MYt9P7I5nkGJNv+4wXJ9meA19ZjIZnDx50vV+LJVKmJycrHgvn8/j2LFjeOmll0Itau2QQs0DlAK3396GRCJWfu/EiRN1OREaNRS/XhQKBUxPTwf+ZMr3nZeObv9+4GtfW4Fbb/W3jbUKteXkqDlpl9FTuV6oWe2zEyfaMDbGBMf8fAReTslcLo9Cob4jAesl1NJpdvvgOWqAcYmOpeaq2fXzQd4HloOjlkqlkMvlqkSXHVNTU5bLPH78uKvlhQkp1Dzw938P3HZbO2688VyMj7cAYOG6sbGxwNe91EOfXKQFvW21hD551NNlP2JLmIRaWHLUxKr7InbtKhQKhgWNs9ms42l4uJvGvkfgxTT/8IdX4eKLe2ExJ3zN+DFpvVsopchkqkOfy6FER1COmpPfNVOOWvXAHVQ87Ji1nacnjI2NOT6XFUWxFXazs7MYHR11tLywIYWaB264Adixo4hTp+K48cZzMTLSCoAp+qDR56OE+YnKC9PT0wCCFwi1CLXFRXYMvIbDzLDqlKw68aWco2a2TXYduFUqguiqWe2zvXt7Kv72kuryxBNdGB2NIshKPo1w1EqlEjIZ7qhp61+1qoC2NgWzsy1IJtkAjKU0oMDswUHEa5/sRaiFuf8X+45UKoI3vWk7Pv3pjYafi3ChViqVcPr0aUfrSiQSjs778fHx8j2mmZBCzQMDA8CDD6axfXsKY2NtuPHGc3H8eJvhKDO/qaVKe9hJpVJlFyTom00tjlEymVf/J/CzmWHMUWv0+WW23XbtskpGFq9Rs+Xk8wT79nUBAAYH2XFxK9SSySIWFlh6RJBapRF9QrFYRCbDhJgo1CIRzVXjeWpLyVFzIoxkjhpDPA8PHerAxEQrnn66u/yeWe5pLpcrO2+JRML2nkopxcTEhON2nThxom455X4hhZpHenoovvSll3HRRUlMTbXixhvPxcsvtwcyGpBjNGXIUgp/JhKJ8utwhz61tvnpqtUq1PycUoZ3oo2emN1s3XbHrVZH7dlnu5DNRrFlyyLOOosdl+lpdzfgkye1NgYp1BoR+mRCrTpHDagu0bHchJp01BjiNXrqFMtZTKW0MjdG1146ncbNN2/G2952fjkH3K6w/MzMjKtzjFKKo0ePhnKuZzOkUKuBzk4Fd9/9MnbvnsfsbAs++MFzsHdvcJ2S1zIFzYCiKJgVVE+YhVo6rf3GLxedUmop8p1OxeKHqNKHdxrpqpltt9X5kcvlLDtuJ0Jt716Wn7Z79wJ6e9m+mJx0tx/qJdQa8fCWyxWRz0dACEVbW6V4WMolOhrtqDVTjprYb3B3NZ2Ogu9Co34llUrh8cd7cPx4Oz7xic1QFPaeVYkNL1UC9u2L4/WvL+G733X904YghVqNxOMU//APRw8+7f8AACAASURBVHH55XNYWIjhk5/sC2xdtVRoDzszMzN1HShRS+gzndba5leJnomJCcsbmtMim3503GEKr5ttt5XTZxfWEAcUmC2DDyS47LIFrFzJbo5TU+727ciI1valJtQWFti+aG9XQHQl5njoc2Rk6RW9lY6acyodNXYuUErK04sZXXsLC2mk08x1+/WvV+Ab31gDABgZGTHc1tnZWVfOWCYTwRe/uAE33ngunniiE1/5ivPtaSQx+69I7GhtpbjpppP45S97cfJkK7LZLOLxuP0PXVLLnIdhR5/gGeR2iTd5b0JNOw5+CLVisWj7VGjViftdsiVMeTBW6y4Wi4hGq2cMsCuWyUYsZkyv0UQihpdf7kA8XsLOnSm8+CJ/391NcXRUO2ZLLfQpCjU9PKcvkWAj4kulEkqlkuGxajaCdNSczkAiElZHTT8hu1iuJZWKoqtLKadqRCKaXzQ1xS6USIRCUQi++tV1uOiiJC68MI39+/cjEolU/HNzrj/9dDc++9mNGB3VRiTXYfyfL0hHzSf6+gqIRChmZlowO+stT83uySCsoc9arf5sNlsV9gtyu2qp5K4oSnnUJ+CPUDt9+jS+8Y1+/NEfXYCpqRbT9Zrhd+izGRw1wLhdxWIR8/PztstdXFw03S7upl18cQqtrRQrVzLLyG2OmlgJIMjoXxDHK5/PV023JbKwwNapz08DgIGBSqHGl7cUCNJRc0KzOGpiP6QoqBBHYp6aeK5mMhnwS3dwsIB3vWscpRLBzTdvxvx8VF0WK6Ccz+cN5+41olAg+NznzsCHPnQORkfbcM45i7jjjiMAgKmp5phiSgo1n4jFgJUrWQd17Jj7kZ+KotiGCLxWaA+aWgdQGA2XDlKo6feZm32Yy+VQKGixnlpz1DKZDBKJBIaHezE21oYDBzoNv1fP0GepVMLjj6/Av/7rGlDa2HPManuMzpGpqSlHN6/FxUWL/DRWluOyy9hdY+VK9v7MjLtOfXxcO0/qGfqsdd7XdDqNQ4cOWQreZJILtep9zYXa9PTSE2pO9quso1bZZ0xOtiCX06SG2YCCdDpdLunS3V3EX/zFaWzblsb4eBs+85mNngpOA8APfziAH/xgELGYgj//81F885sHcemlzHVPJICQat0KpFDzEd5BHT/uvlPK5XK2N8QwOmp2SfBOfm8k1IIUB7UItWw2i3xeu2xqddRGRkZAKXNiAZQ7Kj31zlG7444NuOeeIYyOtjaNo0YpdVzL0EyoKQrw61+zEgK7d7POXBNq7ub7HB/XjmU9Q5+A935hbm4Ohw8fRqFQsHTUUil21zQSan19RUSjFPPzMeTzbJ8tFaEmHTVnVA4kqEwxsBJq/LOurhJaWig+97lX0NlZwvBwHx54YNBTWx5/nD143XTTSbz//eOIxVi6UmdnCaUSgQMDvuFIoeYjPDdjbMx9x5TL5WxvskEJtVpCl7lcrqZk4WQyabgNtboCVuiX61aoiY5aLUJtYWGhnE81OxtT33Mv1MTt8StHbW6OtSedjobWUdO3a2ZmxvH1kMlkDL976FAH5uZasHZtDhs3svO6v58d79lZd0JtcrI+Qs2vfmFiYgJHjx4tL89KqHFHLR6vXnckokUXuKu2VAYUNDpHrVmEmnjd8hGfHLPQZyqVEhw19vuhoTxuueU4AODOO9eXZwJyCquHyB68XvOaSkW2ciVbRzPkqUmh5iPcUZuaanXtMuXzeU9zo/lxEzWbbscJzGHy/rTMq1AbEZRbWKujJtr4ToTa6dOnq25UlFKMjIwAYDkUySQTRmInJlJPR61YLJXnccznI6F11PTnh5uil2ZOsFiWg49m5EJtbs55d1ksljA5qd1Uwi7UTp06VT4fOdls1lQ8JJPs/Y4O4/NNn6cmHTV7ltJgArFdvIYax8hRK5VKaq6y5qhxXv/6Obz61fMoFCJ45pluuOG557qQy0VwzjmLGBio7MekUFumiKOd3FY+9hr69GMaKUVRPIcvmcPkXVBZVZ2ul1Bz09npHbVEwrpzLRaLGBsbw4EDB3D48GFMT09DURQkEomyY8HdNADlSvZ6nI769KPjXlxUUCyyriGXI6EVamK7ksmkpQNkhNHoULEsB2dggO0LNjG7M7ckkcgjmw3eUTOb0sjttSMWmxaXbbZP+fOVUegTWN5CrZ511JrJURsaYheBkVDjD+xGQg0ALr6Y3U/NcnjN4A9ePCdNpL+f7Tsp1JYZmqPW4slRs7vJeq3SboeiKJ6n1KjVUbO6uQYl1GoNfVbmqFl3yuI2JJNJHD9+HM8//3zF5MAzMzHhO4131ObmtG0qFBrrqDkNfbpx0zh6lzOViuD557sQjVJcckm1UEsmY47PSbHYLVuX6+Y5wo8+oVgsmp5fdkLNKPQJSKHmBS+hTyCcrlqlUGOO2vnnV4oxQDtP7YTa9u3s8xde6HLVDv7gxfNNRfr72b40eEYJHVKo+YjoqGWzWdejCb04akDtgqYWRy2Xy3nOJ7Mb6Rq20Gc+n4eiKOUEacA+9Gl0g+J1pTizs1qIzItQ8ztHTRRqYXbU+PmRy+UcleSw4+mnV6BUIrjggjS6urT1dnVF0dqqIJeLYH7emeAQi92yNtbcPEP86BOsvmvkeLMJ2dk1wAreVufu6YVaoVAIrfvjhkaHPo2+E8b9qtWpBEZHWwEA557LziUvjtrWrYuIRimOHGkvT11mx/R0DIcPd6CtTcHOndX3Ny7UpKO2zBgcZJ04r4XlxqVyMpjA7HM/hFo+n/f01Mtz27z81i5UFTahpk0Yr92Y7BLMnWxDpaNmHPqsp6MmRgTz+YhvT+xeEsqdOGpe3DQjHnmkF0B10nE0GkFvL9vHTqeRaiahZnXtGl2jxWIRi4vshtrRUcLatWuxevXqiu/09zeullqQDxZBOWr8N3a/NUt/CRu8TePjrSgWIxgczGPVKnZOWAk13v/xwQSc9nYFZ52VQalEcPBgh6M2PPUUc9MuuihZNc0ZAAwMSKG2LNE/RTp1qXjowauj5kfoE3AnLPl6+bqbSah5DX1qolS7bBYWCKya6WQbxBy1MIQ+RXMqnydVVca9UCqVcPr0ade/s8tRK5VKhuVd3FIoEDz6KBvGf8UV2pyzvJo+F2pOp5Hike1olN0M6h36dCvUzE4bI0dNnJA9HlcQi8Wwfv16nH322YjF2LncyKK3Qa4nKEeNCzS73zaLo8bbxAcSbNiQK7tk+tCnGE0yc9QAMfzpLE9NHBhkhBRqy5S+viIiEYrZ2RYUCsSxUONOg5fyHIA/jhrgvnCtOFLUSxuWgqMGAMJc8lU4c9TsQ59WT9p+z0wgCjU+wrXWh4Hx8XHfHTVKKcbHx325UT3zTDdSqRjOPDNTLssBoDy9jVuhNjbGzpE1a5hoCEo7+NEnfOADnfjd392FP/uzc3HXXUP45S97MDfHzsNSqVQlfIrFIrJZtl/a25WymO3p6cHWrVvR1dUlFL3VHkL0y1lcXMTLL7/suJ1OCbK2ZNCOmtXyzZYbZkeNDyQ444ysoVArlUoV9x19eQ6R7dvZ95wMKKCUzRUKmAu1lSv5NR3+2QnkXJ8+Eo0yy39qqhXT0zG0tCw6muOOd2CNDH0C7h01UagF4agFFcKoVaiJjhrA8tRWrTL+jVtHzWzUZ+McNU2otba2elpeoVDA5OSkp9/b3RgnJyc9tUnPL37Bwp6ve12l6tY7ak7n++SzEgwN5TA62lb30GexWASl1DB/TM+jj7ahWIzghRe68MILXfjWt9j727al8aUvvYxMJlNx7ERHTRRqANDa2opzzjkH09PMUjRy1CilGBsbs53f1ivN6Kjx31iJPLPPwuio8X6IT8Zu5aiJJZqsHLULLmDfe/75LlAKWJ3aR460Y3q6BYODeZx5pnHpqUG1fi4Tau5qJNYb6aj5DB9QMDXFOjYnLhV3GsTJwo0IOvTJpkdyLvqCFmphDX1yR40Q1nFaDShw66jlcpGKwQocs2Ovf9/uHHLCwoI49RF7Xcs5NjY2VtOAk1o+d0KpBPzyl0yo/f7vz1V8xkVIXx871k7n+5yYYL9bt45d2/UOfQLOjpmiaPXh7rzzZbzvfWO46KIkWloU/Pa3nXjhhc6q8KeYo6YXagBACMGWLaze1cxMSzmsmsvlkMlkcOjQIYyNjZVLi/jtCElHrfFojhoLfZ5xhibUxDzcUqlkKNS6u6vP3TPOyGHFiiKmp1swMWFd+FYc7Wkm6GTocxnjJU9NFDlWF13QjhrgzlWrJfRZKBRsbyTcFfAToxuDk4nZS6VSeRu5kFq5krXfKkXKraMGGIc/nQo13tZaSCa1nq1QqC30mcvlyjW6vLSrHjeh557rwsxMC4aGctiypfLhQQt92otyTqlUKg8oWreOXdu5XDDhFTcFgY1IJgFFIejsLOE1r1nAhz50Gvfee7gsWOfnY1UPVKw4KXfUSuV9JNLREUNPTxGKQoRZNxZw8OBBQ+HnJ40WarU4ak6d81rXFzR6R80s9MncWe38snLUIhHNVbMLf3KhZlQ/jcOFWiIRbjcNkELNd8RaaoAz4SPm7lh1WkHlqH3xi53ledTc5KmJ7XbrqFkVuhXxu9M12792N4tK95BdNnxkW+2OGruRtbTwELRzoWYkZPx01Lgo9XozPX36NCilyOUIvvnNARw54lywmBVz9Rs+2vOKK2arnr65W7RyJWuHk2mkstm8INRy6nv27fDbcXR27rH/V6yoPL49Pezv+flolVCzCn1yWlpaqh5azR68/BZqjQ59BjXqs9kctWIROH26DYRQrF+fQ2cna+fiYqQ8Ebr+GrcSaoAm1KzqqWWzBPv3d4EQapqfBgArVkTQ0kKRyRA4vB01DCnUfEYLfbLOaXFx0fbCFQVPvUOf09PAP/xDN/7+7zdgdjbmWKixm6/WbreCymkVeb+FmtcCoZXuIbtZ8xuRmVBz4ghSqtVR27ChunI3x83TdK03PtFRq2UwgaIomFF3zmOP9eDOOzfgs591fhPz6wb08MN9+OM/3oYDB6qH9VMK/OIXfQCAK66Yq/pcC32yv+fno7bn5MREEcViBCtWFMs3HCeOmheBYbWPnFw7ExPsuK5YUbmc3l4u1GLIZrMV55lXoeZlG0Sc9gVL1VFrphw1RVFw+nQbSiWC1avzaGujiEZZORdKCRYXq6VHLkeQz0cQiymG5TQAZyM/9+/vRj4fwXnnLaK31/zcikQI+vubYxqpwIQaISROCHmKEPIcIeS3hJBPqe9vJoT8mhByhBDyHUJIq/p+m/r3EfXzTcKy/lZ9/yVCyJVBtdkPeC013jlRSm3dI7GDtrohmnVotUwjxXVZqUTwy1/2IJPJOLop53K5io5DX8TVjkYJNT8dNS7KzYSak7ZnMhHkchHE46Xy8owGFFh10sUi8JOfrCw/HNQe+tS6Be6oeVmmuP18kne7mRxE/LoBff/7AzhxIo5bbtmMbLbSEXvxxQ5MTLRi1ao8tm2rnneWh/W4UFtYiNkKKj4rweBgAa2tzstzeDnXa3XU+ChW7qBx+N/8uInXq12OGsDy1AYH2TLMhFqhQHDjjefg5pvtJ9qmlDp+WCgUCoE5sdJRs0dRFFBKhRGf2slvFP7kiG6aWV4Zv0YPHeqoGn3PsZqNQCQSiTTNfJ9BOmo5AFdQSi8EsBPAVYSQ3QC+AOAOSunZAGYBvE/9/vsAzKrv36F+D4SQ8wFcB2AbgKsAfJkQYj2MsoEYPUVaTTyez+erBI8ZTuc9dIOol37+c3Y3cuKqGU3i7sYRCJtQs+vsKoVapaNmlqPmZH/wsOfKlcVy+Mlt6HPv3h7ceutmfPnL68rv1YIo1Lw6aqlUqqIdPKfJTQUYP4RasaiFSU6diuOee4YqPudhzz175mCQaiWEPtnfCwv2jhovdjs4mEdrK3vtJPTpt1Bzcsy4UNOHPrmjZibU+PHs6DCemQAAVq9myzYTaocPt2P//m5897v2o4GtprnS46QmpVfEivtmq1juOWpafppWQ41jJdSsSnNwVqwoYeNGNo3f4cPtht9xkp8GsIeJZpnvMzChRhm8W25R/1EAVwD4nvr+NwBcq76+Rv0b6uevI6wHuAbA/ZTSHKX0GIAjAC4Jqt21og99AvZCTcSrkPAqaES99NRTKzA/H3WUV2ck1Jy2gVJq+Hsj/O5w/Qh9ckdNE2recwd52LOvryiMinJe9FZRlHKtqtOn2RNsrUItlRIdNW9CTT/hOXdg3OSC+OEUHDrUgVwugt7eAqJRivvvX4X9+5lwqwx7GhfD444aF2rz8/aOGi92OzhYQEuLc0etEaFPTaiZhz6BypxSMfTZ1WWes7d6NTtfp6eNhdqJE3F1Hfa3ITcDi4IYSSoum1LgT//0fNxww3mm3/GyXGBphD71NdQ2bND6TqeOmhW8nppR+HNysgVHj7ajvb2EHTvM77sAE2o89zTs830GmqNGCIkSQp4FMAngYQBHAcxRSnmvPwKAP+IOATgFAOrn8wD6xfcNfhM6tMEE2lOiVehTXwTUrIOxuxj9EGqlEsGjj/YG7qhls1nHnVkYQp/V+XjOHDU3AwlWriyUb5ZuppFSFKXsbvBl1Z6jpnWiXgcT6NvKb+wWzyy2y/DCs88yUXb55fO44YZxUErw6U9vRDZLcPRoHCdPxtHbWzCcCxAwdtTsznM+AcPAgDuh1ojQJy83Uh36ZOeikaPG5vq0F2pr1rD/zRw1LtSyWWLrODqdK5QnpwfhqPFlZzIRvPJKOw4d6jSc0aHeddTCFvqsHvHpLvRp5agB1iM/+WwEF1+cLKcdmBGJRJqmREegBW8ppSUAOwkhvQB+CMD4EcQHCCEfAPABAFi9ejWGh4d9XX4qlapYZqlUMqy0rihAJLId8/MxjIxMlUfyTZmcCYVCoaJDnZ6eNqzWzToI83Dh7OxsefoWN+zb1wfgwvLfDz3UjksuedF2/kSjuUlnZmbQ0mKfb2K27wBgcTGGu+++ABdfPIU3vGEUiUQCr7zyiumy9MfFDv3+Ftt+6NAhw9+I+55SzWWKRCYAnIVTp1IYHv5N1e/y+bztDePECba/4vEkgDSANRgfzxru/8cee6wqzFQsFjE9vR4AkEhEMTExgZmZGRw+fNhyvVYsLOwuv04mC5iYmAAhxNWcmnzb+W+mpwcAALOzOQwPP+VoGVbniVN+/Wv2TLd58yj27BnDz37WhRMnuvHFL/ahq4udB5dcMo7paeNtm5ubQzQaVUfCXo75+ShefPFFHDlyxHSdR46cCaAf8fgMFhbYo/rCQgbDw09btjWXy1me62a/MbtRT01NYWxsrOp98Zo5enQTgBUgZK7i+BaLcQBbMTvLjvvk5GR5WfPzGRSLuxCJKJiYOIXhYbO+rRPAaoyNGc/Hevjw2vLrn/zkV1i50lwA8220Kx4OsAdjftz8ZnFxETMz2kP4yEgC8Xjl/o9EIqb9vRlslGQR+/fvN+3H2UNZtaJNJBI4deqUwS8aA2/nsWNbAQAdHaOYmGDiKhZbC6AHo6PpqnNiZISo31m07GvWrVsEsBHPPhuv+N7CQgu+/OXzAQA7d56y7a8WFhZQKp0B4Czs23cCw8PHTL/r9j7jN3WZmYBSOkcIeQTAZQB6CSEx1TVbD0ANFGAUwAYAI4SQGIAeANPC+xzxN+I67gVwLwC86lWvonv27PF1G4aHhyEuc25uDkePHjX87uBgERMTrYhGh7B6Net8tmzZghUrVlR99/jx4xVzFfb19eHMM8+s+t7i4iIOHjxo2r5169Zh7dq1pp+bwaOc55+fxsGDHdi/fxCdnWuxc+eZ6OnpMf3dc889VyVCBgYGsHHjRtt1jo6OmlYlv+uuITz22BqMjvbh+uuL6OzsxHnnmet7/XGx4+TJk4adaH9/PzZt2mT4G/FYczctGqU46yxW1DOb7TJsw9GjRzE3Vz2SUKRY7AcADA3FsHYty7lQlBVVk1wDwAUXXIC2traK9yYmJhCLcSeuFf39q7F6tfm22MESxTWjnZA4Vq9ejUgkgl27djlezrFjx3Dw4EFhO5izVSq1OT5eMzMzOHbMvPO0g1Lg0CG2f/fsiWH9+kF89rMjuOGG8/Dgg5vKdfCuvjpruL8B4Mwzz0RfXx/S6UVEoxSZTAzr1m3EBReca7reT3yCTe1w1lltWLuWWXGRSLvtdh88eBBbt251tY0vvfSSqQNOCMGuXbuqxL14zdx9N2vr0FB7xT7o7mbnwMJCW/n9Cy64AIQQPPHEiwBYftpZZ52Jc845x3D9ra1s1oj5+Q7D/Tsx0Vt+vXXrq2G16ZOTk4jFYljJrU0TisUinnvuOWzcuBEDAwOW33VLPp/HCy+8gHxeuwZ7elZXjSxsbW3F9u3bXS07kUjgqaeewvbt203PxYWFBcOHeLs+st7Mzc3h0KFXMDXVjkiE4sILu9HSwq7/gQH2YBqN9pVzGDmxGDu2/f0x033APgfi8RLGxzvR2jqEvr4iKAVuv/1MTE+3Y8eOFK6/Po9YzHwZADuf9+1j9+fW1vXYs8f83uX2PuM3QY76HFSdNBBC2gG8AcBBAI8AeKv6tfcA+JH6+kH1b6if/4Iyr/dBANepo0I3A9gCwNkjeYPQ11IDzPPU9I6BmQNjZ6c7rUumh5t0a9fmsWtXCoVCBI89Zh3+FCdjF3Ea+jRr68hIK+67j83FxPNawhD6FNvLQ4GtrUo5VGk216ebYrd9fcWy5e9mGikx9MmW11JTKCSbLZUHEADaYAK3E7ObhT4zGefFJWsN6Zw40YbZ2Rb09xdw3nnsfNq6dRHveQ8LgU5Pt6Czs4RLLjHPyeSuTDQaKVdLN8tJ5G2emtImJq/HqM/Z2RiuvHIH/vEfKzNCnOSC8rpw+tBne7uClhYFuVykPFKWjwgXS3MYFbvlDA2xzxKJFuijdoqi5TABgM3zjOMcNX7eBRH65MsWH2TEa0//PTc4GfXZTIMJRkdboSgEa9fmy+F/wG4wAbtu7EKfsRhw/vmsT+bhzx/9qB+PPNKHzs4SPvOZY3ASXCKEgGv5sM/3GWSO2loAjxBCngfwNICHKaX/B8DHAPwNIeQIWA7a19Tvfw1Av/r+3wC4CQAopb8F8ACAFwH8FMBfqiHV0GIk1MzEiV7cWJXgsGJ+ft5T58SFWlubUp7n8Oc/77UcUGDW+Tu90ZiFcO+6az2KRXZKzs/HUCyGQ6iJIps7aq2tVB1GTpFMRmDUTDfTR9mN+gScCbXp6VhNN6m5ucp1iNNZuRFO+u/ywQRuhFqtNyCen3bhhSmsXNlXdpbe//4xnHUWOwd/7/fmLHNZuBCJRCJlYW4132c+n8fkJAuNscEEfHo2+/a6GdnIURQFL73E5jX83/97APqf242u5tNH8Zw0DiHVAwqMhJpVeLG3lwnhQiGChYXK701OtiCb1d4ze9jhON03XOgEkbfF18+3H0DFQ42+DV6WvRSmkCqVSuWpo8SBBIA/gwmAynpqx4+34fbbWdDtpptOYmjImWHASsiw18tWqFFKn6eU7qKU7qCUXkAp/bT6/iuU0ksopWdTSt9GKc2p72fVv89WP39FWNZtlNKzKKXnUkofCqrNfsFrqdk5apRSx6M+7TopSmlFCNUpolDjBT/37u1BIpE17QDMhJoTR61YLBoKmH37uvDII32Ix0vlCtazsy2+JwZ7GfVZ6aixS6a1VUEkoo2WM3IE3DlqBU+jPsXpfAA+t6L3jnturrLDEiegd3MczBy1fJ6YljWwW4ZbnnuOCbWdO1NoaWlBZyd7+m5tpfjCF47iyitn8P73V+dwiXAhEolEKqr1m+2LTKaAmZkYCKHo79ccNaP5W0W8znspHv/5+RiOHKksWWAl1IrFIubn2fbpy3MA1bXUFhcXVaHGfhOPWwu1WCxWnr1DP/KTDyTg+CXUanXUcjlUuX/6ZfOHDvZ9fx01J0JNfy6F0VHTRnxWPqHUWp6Dw0d+/uY3Xfi7v9uMbDaKN75xGm98o4M53lQikUhZqIV9Gik5M0EAaKMBtc6pUChUCRkjYeN11Cdbn3ehFo8rGBwsYMeOFHK5CB5/fIVhIjJgLtScFN41unEoCnDHHSwh/t3vnsDatezi5mUn/HTV3DpquVyu4jMt9Fk5Wk6/652GasQ6aryDqsVRm5mJ+SLUeIK0eCNyc/OrdtQiwmtvy3ALd9R27UohEomgt1fLidq0KYfbbjuGTZusrS5RqHFRblX0dnS0CEoJVq4sIhZDuY6anaPGj63bbdYf/6ef7q743Cr0WSgUym6ZPvQJGDtq4ojPjo6SpVCzmp2AOy6c2Vnra6Ueoc8TJ9hUYR/9qPWyxXPZL0fNacHb06dbccUVF+Kuu7Qwd5gdNXHEJwDDidk5bhw1PvLz2We7cehQJ4aGcvjYx066aichBKtWsf58ejrcUijcrWtSjGqpAdWumtGINrMJwp1cjJlMxrJmmxH8phmPs05IDH9OTk4a3pCsOn87V81IqP34x/04dKgTq1bl8a53TZSTvHlY0E9HzapOndF+1+9PPkk5F2rcidDPTuBUXPI6ak7Kcxi1r1qo1eaozc8ranvYdolP77U5atoN3ekpWotTkEjEcOpUHO3tJWzZsghCSIVQc0pl6FNz1MyOr1jsFgCiUYAQilKJGJZy4HgVGPrj/8wzlULNylHL5fJlEWbkYuhLdORyOeRyOcehT9FR0wu1EyeY49LWxmutWR9rt6FPL33Gk08Ci4sEe/caf27sqBk7MV5C2Ha/UxQFR4+2I5uN4qmntIFp9ZoT1ymlUqlcmsNN6FNz1OyP3cBAEWvWsPtnNErxmc8cQ1eXu33OCt5GQAjF/Lxx+kpYkEItAIxqqQHVeWpmosboRuv0wk+4rNy3uMgucN5hcqH2+OM9yGTYCE09ViUT3Aq1xcUI7rmHVdT/q78aRXu7Ikx27q+jRil1Xcm9+phVN/PLAgAAIABJREFUOmpcXOmFmpMwsKJooc/e3mK5g0omo4bhF7PQp/hUzx01rx03D+GuXMn2ud+hT6A+jhoPe27fnkYsxoRWW1sb4vG4zS8rEYUIFy5WjtqpU1yosf1HCBwNKPDiqPEbtCjU9u/vqhCEuVzO9JyfnS2gVCKIx0uGcytqoV7twSGZTJaPZTxuPZggEolg1SrjaaS447J1K1PtMzP+CDWvziRrE/vfLD3X6WACwL2r5jT0yYXhxETl/gyTq6Yoiq2jZiTU0mnnjhoA/M7vsAN1442nbYvb6iGEgBCClpaIaVQkTEihFgC1OGqA8Q3R6YU4Ozvr6mlOE2rs/zVrCti2LY1sNopf/aoHMzMzFe3WF3/VYyeq9ELtm99cjUSiFeefn8ZVVzG1o38K90uo2QkNo8+rHTXWUfIqGfwi9+KoLSxEoSgEK1awMFksBrS3l6AoBOm0s9yX6sEEtc33Oc+qNdTsqFmFPuvhqPGwJy9kywcSuHHVeGfO4WUYrIreisVuOU4GFHhxgvg+Fo9/KhXDSy9VTj5v5oBr83wanyta6FO7qS4uLpbdUTtHDQBWrWLbXu2osRs5D2H5PZjAi6OmCTXrGQBEd9go9Cl+1ylOC97y9c3NtVTMWxumPLV0WlHLU1GsW+dcqLkJfQLAhz88gnvuOYz3vc+41JMV/LqORCLo62PnSpiL3kqhFgBcqOkTaPXujJngqcVRK5VKmLXr9QQymUpHDdCm0+Fzf46MjJQ/00/GrseNozY52YJvfYuVL/+bvzlVnmtRH/qsl1DT73dKqcExY43UCzX905ibEZ+8owC0EJRRR+YsR602ocZz1LhY9uKo6UMxioKKEX71dNS4UOPOjxuhphchPT1s/8/Px0yP79gYuwHwPgCAo9kJvDhB/Dd6V0efp2YW/jSb55OjH0ygLc9ZjhoArFnDp+jR+sJ8nuD0aXYjP+88djJY5ahxhzjoHDUu1BZMpoh0mqMGBOmoaevjo4uBcDlqx46xNq5bl6sqk+Ek9OlUqK1YUcKllyZNJ3C3Qgo1CXp6iojFFMzPxypyGFgirtZpmomaWhw1wF34k980RaFWGf6MIJVKlQu32tVlshJq+jDM/v1dyOUiuPTSBezcqdksPOzmd+jTbh/q93s2m63qOLXyHOxiNwt9up0+imNVS83ZqM9Y+X0vsAr8rBOMRlluFd8tTm9++nbqhYQXR+3OO4fwhS9ssPi2xuJiBC+91IFolJYdG94xd3Z2Opo9A6gWar29rD1Woc+JCbatolDTRn6ar8uLwNALtXPOYRfzvn3OhFoiYTzPJ8co9MmW5yxHDQB4DW5RqJ061QZKCdaty6G/n63D6tmS7xM3oU8vI2i5UDMrIem0PAdfvxuc5qiJ65uYaK34LCycPs3OiXXrqk94Px21WhBzT/l5PjkZnn2oRwq1AIhEUO6A9Ja/6NAE4agBbLoLp1PvGDlq69fnsX17Cul0FD/9KasWPTo66qiAppVA0btTPCdhzZrKC5rvu3o7avrPjQZmcIepra0ywVyfDO12QnaOmKemx+lgAqNtcQp3E7q6SuURi24nZrcKewJAOu3sJsaXk88TfPvba/Dd764yTd4WOXCgE6USwbnnLqKjg22DmEtlNeOGiD7/qq+PtZuHPhcWFjAxMYHjx4/j0KFD2L9/PyYnmajhgwkAZ6FPL46aPvT5mtewuPX+/V0VJVDMrlnuAps5avpRn9rytBw1L0KNhz3POCNXPt/n582PqxuhJl4j7oWaVv7CSFQHOZjA6ahPcX3j4+F01Pj13dFR3SZeemlxMVJR809R6ivUjBy1iYnw7EM9UqgFhFEtNUC7+SuK4qpUhN2Fn0pFKhLQnbpq+lGfnD/5E+YDf+c7g6CUdfZTU1M1OWp64cNv4PoLmjtMfs9O4FaoGRUp5jlbbW1UfRrjRVArO9haHTUjoeZsZoIYFKUWR439z4RaZd0mr46amNMDAMmks5sYX454k+eOphVioVtOZa6Zs/CnmaPGnPIcXn75ZYyMjGB6ehrpdBqKolQUu+W4GUxQi6O2fn0OmzZlkMlE8dvfahNWmzlqZhOyc7hQ04c+uVCxm5kAANat4+UPtGPIa2xt3Jgtn+9zc/ZCzU3oU/ydE1IpYGZGa4PRgAIjR82vwQROC95WOmraPg2ToybW5tQTjbL+nlKiK9kTAaUE7e0lR7MK1Ip43q5cyc7B8fHw7EM9UqgFBO+o9Y4aFytWjpfRTdbqxvuzn/Viz55deOc7t+KnP+1DschqqjnpLMQ6aiKvf/0s+vsLOHKkA7/5DbvxjY2N2U5VZSXU9LlzvMPnrgcnqFGfbkOfRo6aVp6D3chryVETp4/iaELNWehTFGrxOBuIMD/vvZYaD32Kjhq/OThdpp2jlkq5E2riw45ZqElEP5AAqBRq3d3dtgIDsHbUzODXeyNy1OJxBa96FVMYYpmOfD5ftVyx2K39YALvOWqDgzG0tipIp6Pl33FHTRRq8/Pmx8NL6FP8nRN42JNjJdR4JADwbzCBU0dNFIZi6DNMjprZPYVjlIfLXzspdusHYn/AhVqYZyeQQi0gzAo9ZjIZKIpiKWjcOGqUAvfey8pbHD7cgb/7uzPx5jdfgG99qxdjYyZZsRXtYf/rh+e3tFC8+c3MVXvggVXldtlNSWNW9DaVSlVts5mj1ttbBCEUc3NsGqlayk2IuHHUKKWG26o5apWhz5kZL46aNn0Ux2oaKf1+Za4sRT7PagHxEPL0dC1CjR2TSkfNXejTqjQHAKRS7kKflYno1l1WscimlQEqHTVRdLHjtqLqt3qqHTUu1Iwf+XM5JpJjMaUscgBnRW+9OGr60Kco1PR5anonvFAolAWn+WCCyjpqHLE8h51Qa2mprqUmCjVxGjazU9Zr6DMoodaoHDU2k024c9TEB0ejki+AFv40Emr1CHsClUKtv58/ENZl1Z6QQi0gzGqpcQHgl6O2d+8KvPJKOwYH8/j4x0/gjDOyGBtrw+23n4Ft27rw4IPW7bSyqf/4jxOIRimGh3sxPu4sARswdtVm9Nn20J5M+YXLicWYWKOUlG8SfrhqboTa4uKiYWcrlucQQ598cmu+HCcdtTh9FMdN6FNMLu7oqBwt6zVHLZXiSfel8jnBxalTwWwX+nSSoyaO8hMdNbupmF5+uQOZTBQbNmQxMKDtA6IbGuYk/Fkt1Nj/yWQURvdFLkQGBgoQzbh6OmoXX8zE6XPPdVXsK/1DRz6ft5yVAGA3zWiUIp2OVoSc+fo6OqitM2k0O4Em1HKIRLSbMy8Noycsjho/NnZTSOnb4QQnoz7Z9W6coxYWocbqOrI2mjlqRgMK6i3UKkOfPMWiLqv2hBRqAWFWSw1gITUroebGUfv2t1cDAN7+9km85S0JfO97v8Xttx/B+eenMTcXxde/bt1OK6E2OFjAFVfMolQi+P73B60XJKAXVZRSw5IhZqFPQBMdXvLUzMqTuAl9moV4+RNtPM5u5NyNEIWa81kJtOmjOG6FGr9ptrfTipCxP45a5WACwNnNz4/BBOIy3DhqRmFPIzHR09NTJd706H/X2hpBVxd7gDAatcavdTHsyX4XjKNmJNT6+oo4++xF5HKRsrMIWAs1s1GfhKBiflOOdt3aH0e9UJubi2J+PoaOjlL5fX7Om4389Jqj5uYa8O6oGZ9DXh01p3XUgHCGPsVR6Eb3FMBYqPFUj0Y6aolEeOVQeFvW5JjlqAFMqFmFPp2O+nzppXY89dQKtLeX8Ja3sMeBSATYs2cef/EXbEYBs6HmnEzG+unn7W+fBAD88IcDjkbcAdWOWjKZNKn6bxz6BMQ8NXdCrVgsYsrEw3bjqJlNxcVdinic3ci7u1noZmEhUh5p52RWAkAMfTorz6HvxMVOsaOjcrSs1447mawOfYrH3YmQ8CP0KS5DdKXtzsHnn68OexoJslgsVp6k3Qy9oyY6qEZ5arydYrFbIDhHzSj0CcAwT81L6BMwrqXGj2dXl30bY7FYhVDTKtZny/WvtAEFxstoROhzbs68D3ZSRy0IR02fo5ZOR5FKRTytLyjEmVLcCLVGhj4HBti+n50NrxwKb8uaHD7q00youXXUjDrwf/935qZdc02i6qm4vZ0nvlq308pRA4ALL0zjnHMWMTfXgocf7rNemIpeqBiFPQE7R42P/HQX+szlckilUob70A+hph9MEIlojgR3BNw6ak7Lc1g5akyo8Zpu3h013vF3dYmhz1odNbYthFD1b3dCrXLUp3WXxR3Y9eu168ssPGeXp6b/HaubZ5xgD5g7am6Emt00Z0a/MRNqYp6adejT/FwxqqXG19fVZf/gFolEMDjIlj89HasIe3I0V9p4Gfx6qkfoc9WqvNoW86iGGMpv5KhPQAt/htFRcxP61Ob5bFzoc3bWeOo+NwXkg0IKtYDQctSqhRqf3NgM/UVndPFOTrbgv/5rJSIRine8Y7Lqc36R2Ak1Pg2J2UVFiOaqPfDAKsMTWY8oVCil5WK5eqwcNa+zE/CZE4wuLrvOjOdgsc7GuAyJ5qiR8sWun5jdSVsLBYJkMoZolFZ0Tt5Dn5XhYi85aoqC8tRVHR2lssAQc52c3BDMHDWeYO+k4K24nspRn9bigN/IxPPZLMRpV/jWyFHjotzI8fQj9Am4H7ShvzFedFEKhFC88EJn+fouFAoVy2V14OwdNaORn/x42hiSZVav1sqs8NIcZ5yhXV/8xj09bXxu1aM8B6+htnkza9f8vLGjRmmwjpr+tX6ZfH2xGFs+D3+GxVGrHExgLdTEke2NdNTa2yPo7CyhWCSGru74uPspqvxGCrWA6OkpoaVFQTIZq5iTjWN1Yek7GKPvfuc7q1AqEVxxxSyGhqpDbdxRs5uuh7fNbIQOAFx55Qx6eop48cVOHDhg3zuLjtr8/LzpzV0bTGDuqLkt0cEFlpFQc9JxF4tFyxIkPKmahz6B6onZ3ZXmqEw858tyUp5D76gNDPB2eAt9plKoqGXEO1rxZlSLUOPOoZMppMwcNbscNS4qedgWMHfUvAm16pwtfTvFYrdsPc5nJgDcl0HRxDpbxooVJZx77iIKhUh5Ki32PU0c5fOFstg0G0zAPqse+ckdUudCTZtG6vhxbcQnh5/z+qLRnKBDn6USwGfJO/NM5jzOzxsXl87lCCjV+nO/p5Di6zH7Dn9Q2bCBqX4u1MLkqBk9LIlYO2reBkG5RT8KnD+QTExUtnl2dta2dmg9kEItIAgxL9Fhhz78YZSc/YMfDAAArr9+wnAZTkKfxSJQLBJEIhSxmHnHEo9TXHMNy4F74AH7QQWiULGyjbWpaKo7Gb7v3A4m4E5lMpmscvacdPSlUslSqIlzffIbub6Wmrtit5Udk5vQpxhm6OwUhZq30Ccfdcc7Un15Dr5OO8xCnzzvMJ22D5lpIoRUuFd2oz758eEuFuDdUdMLPHG6GSNHzajYLWuLfejTi8Awc9QA4zw1Mfy5sFBAPh9Ba6ti+ZBm5Kjx9XV3O7t9iPN98hw1o9CnvsQNUDkVlJP5Pr0I3okJ9gDW11coP0zMzxvXLBRrqAH+zUwgft9sG8XyHHqhFhZHzWvos5GOWjQaLY+8Hx+vvPbGxsbq0h47pFALEKsBBXaInbX+InzwwX4kkzFceGEKF1xgLCq4+LEaYSfmp9lNbPu2t00hEqF4+OE+zM1Z107ijpqiKKZhT0B8Mrcf9en05iWGlEWR6PT3xWLRND8NEMtzaKFPfvN256hVTx9FCBHCAvZTSOlDn4ODitoOb44an5BdE2qV5TkAfxw1m1J8FcsQK9qztjhz1ETxEUzoM5hRn4BzgcHq6AHFYgTRaOXDll2e2tycFrY3u/YJIVWDCSgVBxM4G1zEZyeYmmrFqVPmoU8joaa/bt0INafXPM9PW7MmX07DWFgwdtT0A2Pq7ajx6507knx2glodNb+EXuVgAuN9EDahJk4jNT6urX9ubs62bmi9kEItQMxqqTlBvPAqXwP33ccGEZi5aUBljppZn2E2fZQRa9fmcd55iygWI+WEYDOKxSIURcH8/LxloV6e62HkqHkNfQYt1LTyHKSm0KfR9FFsJKKCSIQik4lC32Tr0CfROWqK6853dpZ9n3eWRoMJanHU+La6cdT0OZ52jhpvq+iomYU+YzZz1Rg5amaDCSj1ZzAB4G5OVdG9EAUXn4z+6NH28vXPQziU0rIjaFSao6enB1u3bkVPT09VeY58nkBRCFpaFMTj1g9snLVro4hEKObnY8jnIxgczFc8nGmDceyFmt05rZ/r04lgEoUab5dZeQ5+Lmsuae05anqn0Oy3Yo7aGWdUOmpOazeaYTfjjFOasY6aWegzLG4aIIVaoFgNKLDDzFEbHu7F6Ggb1q/P4rWvNXerYjGWcKooxhMMA/YjPvXw7eEJ/lYUCgXT0Z4AC59QStDWphjO7aafmD2fz9t2fsVisWK/pVKpsmhy+sSZzWYty2toOWqk7Ljwm3cioajf8TZ9VEtLCwgxn0bKKvTZ0cFctc7OEgqFCFKpqOun7Lk5tnxegNioPIc/jpq9UDOa5xOwd9SMSgOYOWqEEEuxZl2eo/J3R4/GkclEMTCQr7rZBOmomYWZenpK6OwsIZ2Olt0/7g6whyj2njiDAhdoZ599Njo6OhCLxapCn1q6gv2sBJy2tljFec5FBoeH+42yJNwKNaNrxA4jR00v1PSlOXhJBz8cNf13rUKf/FrkjiQf9ZnP53H69GnH69Rj9XDqBq911BrtqPHzfHKStXl+fr4sXn/1q25H97wgkUItQKyK3tph5qg9/TQLZVx7bQJ2/aRdnprZ9FFm8BwjXjLDikwmg3mzUuOoHF1oBM8ZmJuLlaeWsbOhjUbSclfNqUth1WbA2FHjrkMiQR0/2RrVUGttZZ0uv3Hpw2tWjlpnJ1EtfO0YuRdqlaFPr46amVDj548boaZ3o+0cNS6knQwmAKzDn1aDCfTHZu/eHgDA7t0LVaFE7qhls+bnhddRn2ZCjRBg7Vp2PZw+3VZebqFQ0DlqbF1btmwpCzROLBarCn3y0hROJmTniEVvgcqBBLwWIWA8M0EtjprR742odNS4iKg8iPrSHFyomZXncOOoGV3XRojlOfg+nJxsLTum4+PjlqkmVuv3K8TntY5aI8tziKFPXoKTu2mZTAQf+chmvO1tr8ZkdXGFuiGFmo/oO32rWmp2mDlq/MmWz+tohXOh5qxT0eeNWTE5OWkpWKxqqAHMEezpKUJRtGmk7Ox5I6HGXT2nNz+7J0v9FFKANjJuepq6rqEmOhr8/HFaokM/6pMQUlHWxK1QMx9M4M5RMwt9ao6afbejn+czErF2MAAWfuSikosjwNxRA6zDn1aDCfShz717WU223bur59d1MpjA66hPK/di3TrWR5w+rYndbDarCjXuBrN1iQKN09LSYuGo2U/ILi5HFGpiflp3d3e5DXNz1ceplhw1o98bUemo8T4zYtgHVztqtc9MYJR7aoSiaEKtt7eI7u4icrlIxYjc48ePux6lWCwWLctFucHq4YFjNSl7o3PUEgk2EI3fB371qxXIZqM499wkVq2qS9MMkULNR/SVzr2O+gQqO2vxwjU7oY1uOHa11LhQc5KjBoh5Y/bbkzRK8hDgHZ5+nk8R/ewEXhw1PguE05ufXQfLRUt7e6Qq9DkzQz3MSqDdDPgxNAt96tunD31GIpGKaaTc1lLTO2otLf6W5+Db6s5RY/uJP5iIc07q0QYSVOZr+euoVQ8myGYJnn22C4RQQ6HG96NTR82P0CcArFtX6agB7BpitQ0rS3MY7SO/Qp/i7AQAsGlTpVDjDjIf4CBSa+jTmVBjx0UMfS4uRiquZf30UQNs0D1yuYhhDrAboaZvs9lvCwVAUYg6cARYvZq1jw8oANi588orr7hy9PwUat4dtcq+L2jMQp+JRKQiN+3nP2dF3i+/vLEztkuh5iPxeLxCMPmVoyZ23GZJwF6EmpvBBAAqRECtaI6avVDjoVY7oWb2JDk7O+t5knI9VqHPmRniYZ5P9v1oNFq+8TXOUWM3CJ5QzUPitQ4m0HLU2LZms1EoivWNTO+oDQ2xG4mVo2Y0kACwdtTMhJqRcKkMfWrXwL593cjnI9i6dRG9vdX7J6jBBPZCrdpR40JN348YbW9LS4swRVoUpZI3oVYd+hRLc6wQpk2zd9SCCH2eOMH+FwcTLC5GK65lvl5enqO3F4jFKCglhg8PbgcTOPltJsO+x4/16tW8pESr7nsZHD9+3PH6C4UCCoWCLyM/ndRRE8WworAyUbkcG7nMo0BBI57vYnmORIKUTYZsluDxx1lKgxRqS4jW1tZynhFQXQvMDWaOGr9566uJG3WadkVvgwx92qHNSmC+bv36vIQ+AX+FGu+U29tJVehzZsb56FQudrnlzqaj0komAMYlIOyFmveJ2RdUM8jv8hxclIsTvafT1svRO2pcdFgNJuBhKDE/DfDmqBldT2wKKZ5PFSs7KU8+aR72FNvjp6PGRwq6ddSqQ59Fy1Gx0SgTc5QSJJPRco5WPO5cqLFppIrqMpVy7lw0GkV7eztWrGBtX1ionsKn1tCn3b5MpdgDVmsrm9Cei4h02tpR6+pig3cA44eHWhw1M8HEi5Pza0hz1KqrCszOzmJiwrwqgAjfx364ak7qqEWjLJJCKcHiYqQiSmRXJsovzBw1sfTUk0+uwOJiFOefn8aaNY0teiuFmo/ohZoYi3f7sGLmqHGh5iT0qdVSM16He6Hmn6PGn0ytHDV9qJUNTzfvTMw+S6fTvg0/Fx01fehzbi7iSKhRqtVR42JUFGpGoQGO/oZuFvqcnq49R83vwQTt7Ur54WFhwXo5ekeNiw6rwQS8nfrz2UuOmpEIiUQiaG2laG8voVQi5e3i+WmXXWYs1HjoM5/3bzCB2YTsIlzcjo1VO2raPJ/mQo2LWHFAAd/mjo6S48EEALBmDWvf+vW58ihvnioSj7PR38UiqeqrgnbUTp1i/69enUckoqViLC5Gkc9XO2r8oaO7WxRq/jpqZiJPm0XGXqgBwOjoqCPByPeR07QNK/5/9t48yLLsLg/8zr33LblVZa1Ze1cv1d1St7okBEgICQktYI82B8PIhOwZwUQYIsCAHWCBFWELD54IGzOEDaNhQiMWgYVHQbAIDMY0cldLCLSMJapptbq7ulu1dC2ZWbkvb79n/jj5e+fcc88599z77st8mZ2/iIzMfMt9593lnO9+3+/7/ZpNcW2QPGsLdY4j2XO78tMAe44aKR2AlD3f+c78Bo2yYx+olRiVSiUB1MJQTGh055AnTIwa53Z3jBmole36TJbMGCSyzATi89LA0CZ/9no956RcFlAzmQnEnSDH6mqARiMbqG1uBmi1AtTrvf4xCoIgVZctq42UiVGjCWdxMX+/z5UV1v8+gNlMkLUA6UCu2xUAKgg4qlXeBxTr69m5Rs2m6IcaRTGOH+9sjcUlfSYZB4oijJpN+gSQyFO7c6eCq1fHMDHRw2tes27cVpaZQF9M84BhP0ZNOgOptpgqfboYNb3obRHpEwBe97ouDh7s4q1vldZOAmqqpKybFvMANdNzWdeAaiQAhImpWhVljVZX00CNvv/UFOsDNZPzcxhmAr3dH41Zlz7V7fq2zQPKYdR8856luzbs35C68pXLDt31OT4eo1KJ0WyGaDYZ2m2Gz31uGgDw9rfvA7U9FTqjBrjZEVeYHEfNZoBuN0Ctlm77MoiZwJdRm5wU/Us3N0Nj/9I84WMmMEmtNsC1Xf3YZMK6AEaMMYShBM6Li9n7UuanyWOczFGzS596YUyXmSC/9KkDtbSZAHADiTSbJss5MCbPybW1bKAme2d2jDKsHrJ9VPLaKJKjZmPUACScn1SW49u+bdXKIGTVUSuSAO8D1CYnYxw40EWzGSaYAgBe0icg9oNqKCCgkkf6BIDTp4E///PL+PEfv6mMT/QhVUt06N0JthuoAZLlX1oyuT5J0XBLn4OU5zCBPLWGmi+jBvilYtBrBgVqosxHcoy2SDJq21uaA0gzaoxBYdUq+NKXDmBjI8SDD27i3LnBmcZBYx+olRRBECCKotKAmqmOGk2upsa1pkmTJm8bmbS5SUnjfpMKY+XlqdGE50oeNUmtNkatLNdSVkh5TfyvL97z89n7khhJtQioKn2a7OsUwzQTkFGXwLM0EySBTj6glqyX58uo9Xq9fg21o0c7RhlWD5v06QIiNunTxajJ5PfIWZaDIstMYFrUs46dj/QJiI4iQNJQAEgzxMGDbgmzUqlojJq8bvMAtUqlkqr7qDJqtE/v3tUZWf8cNdNzRYAaGQrUfp96eQ7BqInrYtAcNR9GzWQcoTGrrk89fIBaWYyaj+OTQrbKi7a9NAeQZtQAKHlqET77WcGmveMd9l7V2xn7QK2kIIA2TEZNGgnSJ3QR6ZP6gPq6PoF0yYyiIc0E9ovT1Alh54GaZNQAKHlq0lCQFab2USagZmr87cpRG9xMkMyRk7lV/oxauoaalMrEb5qg/Rm1o0c7RhlWD5uZIItRMz1vAyGqTLe4GOHLXxYFqG35aep48gC1LIDhw6gBZkMBIFtCuXLUACp6Kw0UBLxN/Xldoc9PY2Nj/X2c3KdJcOSbaE/PvfRSHR/72Kl+Qe2sa4CAGrFTgJyTlpfTN8sm6bNsRs30XrXYLYEgSgeYn6/C9jW3G6hlnY8UZkatHMOXT+iMGiBvnOfmKnjyyX2gtiejbKBmYtRcSZdFgBoxbba7H9MClqc7gSvITOCa8E3sXavVMk6+2wXUOh03o7aykrykLl+ewBe/OJV4zNSQ3eT6zFueIwgCTEzEqNViNBoh7t5t4MaNG86eq2qsrSWlT2LUypA+dUaNbhJsIRi1tPTpU54jD6MGmK8dF1CjY/3Xf30A6+sRzp1r4vRpuzwi69GZny/CqPllMXo6AAAgAElEQVQDtTSj1m4HaDZDRFGc2WFAL3orGVLn8IzbUUOtOakyagsLblNFFlD77d+ewW/8xkl8/vMHrdtQ49q1ZL4XIPNm19dZ/zikzQTMaSYou4WUCahVqxyHD3fQ6zGrwpFH+hzUTFCEUVNz1HbSTABIRu3P//ww1tYiPPDAJs6f3551JSsGt+/tBwAJ0OgOnS42NWkyb3S7XURRZGDU0pNPsTpqdkatXq+j1+ulLnRVWhskdEnMFMQOLS2JNlK0djYajX5+C8V25KiJyvdJRk0HV2rF+itXxvAjP/Igut0Af//vz+Gf/tMbiKJ0DTXA7Pp0ATUqz0ATIzFqIteigzt3apibY6hW5zA3NwfGGKampnDw4EEcN5TYFkaV5Ofb8sIGY9RoEXRLWJxzDaiJ1/sUvM1TRw0Q145+ntvAi1r09oknhCvMJXuK8eyc9Gli1NbXyc0pyiFkM2pSEmKM5jXn8FKhAzX1+hXnZjpHLS9Q45z351n6nS19cgBMkz6TRW/HxsZS0qeao2YyEwyDUZNzj9xHMzNtLC5WMDtb7TNsauRh1OI4RqfTcRaBdkUSqLmBqgrU6HttZ46afs6rtdSk7LnzJgKKTEaNMfZZn8de6aGe3OrfRRk1QE7G6Ry19AltrqNGE455+1RA0XRRTU1NGcFfWSU6ZHkO+4SmtpFSAZBJ/twORq3XAzgX1nPa3bTfVXkIEG7Hn/u58+h2xSX26U8fx0/+5AWsrobGhuyqmUC6Pu1ATWdUiFEDzGBaOP1WcePGDSMIaDSAbpehUon7wMKWF1YkR40Amk+Omt6QXUif2YyazUyQxaiZFiYf6ZPOYZfsKcaTn1ErT/pMl+hYXRXfl76HP6Mm66jlBWr6XKIzajSWpSU3UHMxVaJ8j/gutF8459bzNY6Bl18WIMFkJhAlOtr9bdNjgF6eY3tz1NQbEZJsbc7PLKCmy8uDzKM+7aMoTIzadro+9Zs3tZYazdmjInsCDqDGGKszxg4DOMoYO8QYO7z1cx7A6e0a4G4JVfJU/x4EqKl3OoC7zYZa3oHCtzOBiaY+cOCAcbFS63QNEj45aoAZGOrOz6zSHGWFrHyfbvitF6n9zd88geeeG8fJky388i9fwaFDHXzpSwfwgz/4MJ55RixSuuszzajJoqoUNKnrjAoxakB2BwkT+6gXu1W/52BmApv0ad1E/1hK12fbK0etSB01wAzUbOBFlKuQ3z+KYrz+9Wv950yfFUXusQ9X+hQL782baUbN1ZVAjj0ylufQCO3MUPdxFEWo1+v9/1Xpc0lZG4tIn7Q/VJbLti9nZwVDOz3dQb0uLzS6edzclHUR8xa8HY7rM32sswwFWUBN38eDADXBqPm5PlXD1E6bCeh/9cb53nsbuO++nS1yq4brVvNHAPx3AA9v/aafzwD4P4c/tN0VwwBqen6ES/o0ATXfHDXTJG9n1Mp1fWbdRZk+T2fUtttIYAJqasmGK1fG8IlPnAQA/Mt/eQ1vetMqPvnJZ/HAA5u4fr2Oy5fFKkdUO22HFvpqlaNWixNFVSlURo1zM1DLkqdNjKRe7FZ8z/LNBD45anQ8SfpUGTXKETS/L7+ZACjOqAHAxYsb/YU9DEPjNTMMM0Fe1+edO9V+0e21NTE/+TJq6rlNnzeI9Kn3RC4LqKlgRgVPtn1pcnyK8YmxbGzYGbUyOxP4MGqmHDVAtpGylejIOo/KBmqDmQlGg1EDRotNAxxAjXP+Hzjn9wL4ac75fZzze7d+LnLO94GaFsNi1MSCLC5kW1cCoBijZqujNj4+bl10ypI+fVpIJT/PDtSK5Kc988w4/vk/v9dpbdeDQIIKBNKuz0pf8vz+75/Dt32bYFtOnWrj13/9Obz1rTLvQWfUADtDR6ECtW6XoddjiCKOSkWVPt3HyBeojY/Tojd4eQ49R83FqEmgJq6jY8c6irEhm1HLU/AWKG4mAIDv+A5ZwFW0XEq/T4LM7WfUxsdjTE930G4H/ZudtbVkx4EsRk01E8gcrXx1FNX5yQzU6DPk4yY2KEv6NDFqeYGaZNTCBKPGORTXq7szQdktpJJALZmjBhSXPvXnBzEUqEBtN5kJgDSjNkr5aYCHmYBz/iuMsTcBOK++nnP+W0Mc166LYTFq5j6f6ROaMeZg1ETCrB42oHbggKgNZZY+yzET+DRlB9R+qfJUFRNysy+fFLkL/IM/OIrHHz+M17xmAx/84JzXe0zJvDqjdunSNHo9hlOnWviJn7iZeP/4eIx/9+9exG/91gxu3KjjgQckYFKBWq/Xw9RUD/PzdN6kK6Srk+LYmDi+uvRpYz3zArXBGDWz9OlqFNFqtbC5GWBjI0S1GuPAgV5/fD6dCYYpfQpGTS5mqpHABu6K1FErK0cNEDcJy8sV3LpVxbFjnT5Q85E+BaMm8y8JtOUFarStVquVMgKpjNrystxuEemTQNNgQC1pJlC3Hcei3VUUBU4zAb3Hp82Wv+szfX5nFb2N4xi9Xs96bpYvfeZn1Gj/7aSZIAgCnDnTRBhy3HtvIzE3j0JkAjXG2G8DuB/A3wCgPckB7AO1rYiiKHHgy2bUKKiulm+Omm8LKf2impoS5SRcjNqg0qePmUB8nhkYNhqNgYAaJfTrFdtdobaPotCBWq8nXvMv/sU143cLAuAHfzDdLFln1Gy11FRGLQnU0vWA8kiflKOmStFjY2JM7XYAztFvmFyGmcBSDg+AOJ6qkYAxe6mQ5Pu2x0xw/HgbURTjyJEuHnxQfpEoioyLbFZ+3TBdn4DIU3vmmQnculXDxYsbSo6aH6NWqXBMTPSwsRH2r/upqfxALYoitNttI6Mmm927AZav9OmTo5ZX+ky2vQuc0ieNxyd8GDXVKJEHqAGCNdsuoNZsis8pAtR2mlE7fryDT37yGzh8uLttzeF9w2eV+lYAr+Z5uNxXWOgTfZmuT1NDdr0wIOU2pYEaTThmRo0UQ5UhYoz173hNQO3AgR6iKMbGRohWi3n3CVVDlRB8zQQ6MGw0Gjh06NDW98gvfZI7Mw9QM5kJdOkTQELy9A3ajt5GSnd+0mWYBGriOV8zQbfbTdnwdUaNMYZKJUSlEqPTCdBuy2NdrDNBUvrc3LTPhM1mMwHUAFmLrNNhCdCohk36LJtRm57u4WMfu4JDh7pQX6aW0lGDzATdLkMcA/qmi1TUz8uoAbKWGuWoEVPmAmp0Azg93cXGRti/bqamspkiPSqVCsbGxoxshmybZgZqTz01gTt3qvi+77NLeXmlz2vXBOCyS59JM4GerpEF1OLYr3vDIDlqR492EAQcCwsVtNssdZMCCKCmmjf05/T/fZlAPQSjVkmN0RQ7zajpcwIdp4cfHi0mjcLnaDwN4MSwB7KbQy9yOyxGzSZ90kmXP0ctTaVPTk72t2OaZNQ2UkXlz6SE4H6tDXSozs8id4ESqPl/B2kmkI/Rvjp+vI2DB7s4d66Zkjx9wsao6UDNJH2OjycZNZ/jo4NbHaiRucFUv6yI9Ek3DVL6dOWatRM11ABRqiUMOeKYWauwm6Rp+i6uyJujBgCvf/16yhWmllhRgzF3iY4yctRcrdj0Wmp5ctSApKGAoqj0qbNp9Pl0vlPHBCAJsD7ykXvxkY/ch/l5+1jzmgmuXhW/bdLnxkbYv1kWfSzJACXOr2ExatkFb+XzUSSvkbm5/M7PbreLtbUQzz8/1n+sKKuWZP3866htd46ayZ1dBJhuZ/iM7iiAZxhj/5Ux9kf0M+yB7abQgZoqhZbLqJmlT/qsomYC9W6cZE/A3gdRslzFDAUke9Li7Qqby1SV74qU5hiEUTPlqNXrHH/4h0/jP/7Hb2TKuXqobGgaqPlLn9L1mW340OXPpSWxXRWoBUHQB/HqYjSYmaDXf9wEUNrtNuI4VoCaXESzaqlJ6TMfoxZFkfUOW48smbBIntowXZ9AupaazFHzA2qqoYBicjL/whZFUSo/jT5/fDxGGHI0mwEon532wepqiDt3BMhUc9j06Hbj/jXqw6jduCF+p6XPpMOTWCZi1Oh5Amp5JG1T+DNq5hzMLPkzC6h99KPn8Q/+wavwzW8K1q2ooSCP6zMJhsVNO10jww7TfDDqQM1nlfq5YQ9it4cO1OixZrM5FEZNlz5tQI0WR2LO9Gg20xc+GQkAF1AbjFGTpTmyJzIb6Gi32ymzhW9wLu/cl5fzALV0jpq6MBel7k0Ngn1cn2ppDkBOQAcO9BCGHGtrkVUO0YHaygpVnJelJgSjli7RUcxMkMxRazaFaUI/Z+luXpc+ASE5Nxp296TNTOAzCevdCVzSp2sbtqhWKV0g/dwwXZ9Aupaa2pkAyM+o1WoxqtX885mLUWNMzGvLyxUsLPRw8mTY3wcEIAC3CYUKeAPZQG1jA1hcDFCpxAn3NZA0EwDy5kHK+ElGzWYm8GXUfAve2tozDQLUOp0Orl6tg3OGy5cncO+9zcKMWp46amGIft4jsLP5acAeAGqc8ye3YyC7OVxATU1MNeWnuEJl1LpdsegFAU8xNlnS5+YmjHk9OlALwxDjShM/GzuQlQOVFb7FbgEJCpeWKqn9t7m5mcsGT9FoBP3q03mAmt7nEyjnAlf3cxYTa3Z9Jt8bBALgzs9XsbgY4cSJ9ESdBmpIfC4xarIGmJ/0aWfUktInATU9P8wN1FRGLT0Gm5kgi1EDBIigBc2U70nhOt5hGFrPx7yMWq/XA+fcOnZZR0+cH66FkRijO3dE8+4ijJoK1MbG7C5CV4yNjaFWq6UeV1nk5eUK7t7t4dgx3t+XKlCz3XQCSRCXZSZ46aUOgApmZtqpOVmdswEJ1OQNpp/06XsTmV/6TL5eFr0txqjRHPjcc+MAFgYCar6MGiDmmp0AaqbzfdSBWuboGGNrjLHVrZ8mY6zHGHP3THmFhQ2oASKHYGysB85ZH6D4hsqo0YI9NdVLTSy2nLIwFIsb58zosiOgRhfV5ORkYmHIYtSKOj91lsUV1SrHgQNd9HosxS41Go1CjJrajmp1NYKvcurKURsk1OOW1UbKh1ED8js/iVGzSZ8qo5ZVIiH5OckEbB2o6WGqoUYh3ZPmfV60jhqQNBRkJdfbokzpE8hmLlX2wvUV63WOI0dE8+75+YpiJvDPUVOlz7ExvyR5PUyyp/r5st9nnGDBXnpJ5k+5GDXqXQxk56g99ZSok6XLnkDSTACoQM0sfQ6ao+YrfdpAkGTUzNe6S0ZvNLp9Z/nzz4uJZDBGzQwmTaE6zHWVaJixGxm1zNFxzqc45wc45wcAjAH4HwH8X0Mf2S4KE1Arw/mpSnuyNIe5K4H6Ww1biY5OR5SSCEPeT+hXZU/anumkNhWhzRMbG/6Mmvp5xLJQNBqNQoyamrAM+LNqpvIcRRYsPUyMmq08h8n1SUBNPf5ZrKeQUuSEbDMTEMBQ83A4597gQpoJkq7PRsMM1MjkoJsJALX3aJb0WYxRo3Ad0yxGzfbevGYCIJu5zMNenD4tPvj69ToajQhhyPuAw49RU8u2FHMF2kJK9nQDmGwJd/WqL6PmJ332ej288IK4UTE1Mld7fQISuOh9Tl0Fb4FhMmrJ57O6E9gYtTiOsbQk99OVK2OI47LMBH6MmunvYceeZNTU4CL+EMD3Dmk8uzJM9v4ynJ+i+nwyV8mUB2WTPgF7gVFT+yjVSEBhYtUGlz79GTXAnhNXVPrUgZkvUCPGRnW6D0v6JEBukz6zGDWfPEKVVVtZEe+lc5V6j5rMBIAdROSVPvVwSZ8SNJZrJgCS57kLqLm25cOomfK0bQu6iwnJC9SoldSzz4qTZWpK1orKm6NWrxdj1Gyh35wsLHCNUVOBmn07pBCIv5PSpzpPzM3NYX1d/G0CCGpngjiWNw90g1m269NU8NbEstlyMIkVfOqpSTzxxHSqP7ANqKmyJyC+782btUJmAuqgk+ecVNeynSzNAZRzwz3M8JE+v0/5+X7G2L8BMDrdSnc4KpWK8cCrQE1tQJs36KJRpU893Iwa5VskH9e7EkRRhLGxMehhBmqDSp/5GDUbMGw2m4WkT52l8i3RsR3SZ5pRy85Rozv8JFDL5/xc2yr7RnKEnqPm253At46ajVFrtVrY2AiwuRmiVosT57sEjf5mAt/jU5b0aUsXKJNRo4Xct10PIA0F3/iGAGpqiZ+8rs/x8WI5arbQz/mlJQnUNjaCvuMTEADMBoDU/rF6gr/aN3lubq7PjpnKmgRB0p2cZtSGm6Nmekxl1PQbkQsXNvHYY+tYWYnwz/7Z/fjRH72AF16Q4Lbb7Rr3WafTSc19zz03hjiOM1tP6ZHHhUyxU4zanpQ+AbxX+fleAGsA3j/MQe2mMMme+uODOD8JqGV1JVB/q2GTPnWgpsueFKYJedDuBEUZNf3z1D6oeULNUQP8S3SQmUBl1IYlfcocNXt5DgIs4+NpRjWrjRTgZtRkHTWz3GgCEfrx4DwpfYZh6GTUqAyC3pWAIqsxu6kgsQ+bBmyH9Jk/R83GqBVZFKlEh0gYR4Ihy8uojY3ZTQ5FQnc6q0BNlT0B8Z1t+0tl23o9lsg9pe3dvXsX3W63fwPh6pEKiPOX9jfdYE5NDZdRMz2mAjV9zFEEfPzjz+HDH76OAwe6+MpXDuCDH3w1/u2/PYvlZVliRI9ut5ua+4rmqdE+8q2jBuxLn3nCJ0fth5Sff8Q5/985537NEV8B4QPUiKUYBKjJYrfpydtH+swCaibZExiW9FksR23Q/qIUeo6aL1CTjI1cpMq4wNVtZHUmMEmfasUDWUstX9HbtTW39OnDqOkLaLcbbDWNFzWSqB1RGHJ0uwGazeQ2pJEgXUMNgNGBmnx/mnHYLkaNrpPtMBPkKc1BQYza9esC+AzGqOVnsV2hO52XliSwUh2fgBuoqdInvZaCWKXZWdG+TZfk9ZB1vuQ21Ibs4r3pz1GjaMFb02NJBjW93SgCPvCBefz+7z+ND3xgDowBv/u7x/ETP3EBQDZQozmWCt8WBWr7jNpwwkf6PMMY+wPG2NzWz+8xxs5sx+B2Q9iAWhAE/cl7EEaNLjBZQ60Yo6bnqEmgxre2awZqpoVHr9OlB+fo38mZgizZquvHFYMCQz2IUaNJOq+ZQGXUTFWu84aJURsfjxEEHJubYcqVyjnXOhPIz89T9LbZbIJzjk5HJGkHAe+fL3pnAh0c+QA1kopoYReFZeUkvr6efL2en6YaCQDAVNNNDZOZwPfY+Oao2SZ0vQWYHkWkTxujVgyoJUEvMWQ+568AavJ4KxV8SgmdRV5eZv3vTo5PtTSLDQDp+Ws6UFtaWurf+GbtO5VRo7CX5yi34K3pMVfBW0Duw+npHj784Rv41KeeQaUS45lnJtBoBEag1ul0+nPft3+7KOQwOKPmV0cN2GfU8oTP6H4DwB8BOLX188dbj+0H7EBNfW4QoEYXbFHp04dRi6LIWN8IMDNqQQAcOmQHAp/+9DG8852vxZe/bAZ/et5SVgyaE6cHAbV77hGMUn4zgV8Ve98wAbUgcNdSSzJqaYaPkvBd+0zcpTf7js+JiV4iwTwvo6Y/RjW+CPzRuSTPyeRi5DISANnNzU3lOYowakWAWhbQczFqNuBhY9SKsBcnTrTBmPwc39IcFJOTYf9cIKBSVug5asvLaUbtwgUxYbkYNf1mVAdqd+7c6f+vd8zQg24i1ZJK9B66px1WCynTY72e21Gpr0MPPNDs3+gsLESZjNqjj25gfLyHubkqlpai3IaCIjcPKjjbaTPBXgBqxzjnv8E57279/CaAY0Me164JF1CjyZ9OSGKSioStKwFQLEdNdX26FqYi3Qn+238TzdIvXzbXTaL9kFf6LBuonT8vgJp/jhoxauX2iTMBNcDdRsrk+gTkJEQgh2REWzQaDaxuVUVUJ04CaqbyHIAfo9ZsRlvjE6+l64Em8bU1/fVUmqOa+A4UNtBIYbqb92XUSOoFikmfplp4auw0o1ap8EQpCmKvfM9dVf4sG6jROGhuW1kJUkDtVa8SE1hR6XNxcTGRk+kyEwDJFkcUklET/9dqAGMcnU5g7D87CKOmv7fV4uBcpBGYpmvTjTYpEXfvVhxATVyThw93+2D4+efHSshRG11Gba8CtQXG2D9kjIVbP/8QwMKwB7ZbwlSag6IMRo3CJX0OmqPmAmp5uxP0etJZZpPd8poJypc+xeffc4+YjHxdn3ISSj4+PKBmbyOlSp9q30V6/8GDPVQqMdbWotQCpkaj0UjVUKMxMcZylefQH9MXQ19GzVRDDYAVNFKYzAR5jk1Wnplre4MwatuRowbIPDXAvysBhWooMHSBGjiCIOiDRwJqzSbDzZs1hCFPMGq+0qd6zq6RrXkrsprZm6VPYtTE+SdkfPGc6eZhEEZNfy+lk9qS9KvVagqAqIYiE+hXGbVDh7p48EFx9/788+OFgFqvJ4w+jHFj2zo9dopRM53zu748B4D/FcAHANwBcBvA9wP4oWEOajfFsKVPCmJV1CRgiiLlOag4ZL3uLl6ZtzvBN79Z7y/Q9qr40kxw8OBBXLhwAefOncPMzAymp6dRrycTiI8e7SAI+Nad4eBuM5KRy2LUsi7yLFbHxsZIRi0N1NTkYhOjxpgEOnqhYDVsQK1IeQ4bo6YDNds5mZWj5mLUOJePq82d8+QP0k3XIGYCwA3Ums3kPnKxLoNKn/p3p1pqQH7pU2XUys5Ro3HIkjQCqF27JnpQnj3b7D/ndn3aGbX0a91mAtlGSm6DQJvaYIGmKlOemg+jZgNz6WvJnftlquGnpoxk5agdOtTBQw8JoPbcc2N9B7Zv6F0JfC67fUbNP3x6fV4D8L5tGMuuC1G5fbsZtWLSZ7rgLQfAMhk1O1AzOzGfflrebtsYMGkmiFGt1lOlQTjneOqpp/p3gVEEHD/exp07Ndy5U8XZs8UqZ1Po0uegOWo+JRtcOR95pU+adF1ADRAA99atGubnqzhzxvz5LqBWpDxHcttJiVuXPlVGrdvt9rdZJEdNraGmzsN5JmAa3yBmAtv7aT82tQqUrsXQJn2ub1VrzQJqtVot4exNMmr5pE+VUbN0ghooGGMJBrnX6/Vlz3vvbSbKuhSRPtOvzW8mIHCnfv+xMY6lJba1veQ14cOo+QI1urG2AbVKpZI6lknpM917S2XUpqe7ePBBkj6locBUW9MUSaDmxyQmGbWdbSFFppoi5Z62IzJXKMbYvQB+HMB59fWc81c8eHOxaerzZQC1QTsTrK8LYEYhL3xeqvT59a9LoGbLKSMJwdbcmTGGw4cPY25OVoE5dUoAtVu3BgNqvZ7cl2fPikVsZSVKNXw3BYGBPECNiqAOAtR06ZMWcBNQU99PjJQrT63dbmN5OQYQJFy4uvRZppnAlDdJbBrnrvIcZhlWHV+RrgQUPtKnbXv+0md2X0cK0z7udDq4e/cugGywUa1WNaAm92de6TOKIrznPQtYWKjgu787XzFUnwiCIDFPxrF0fN53X7N/HgqgZmZf8gC1LDOBbCOlMmrkTpWf4zIU+DBSttfogEEvp6SHiVFTDUUmRq3V6vZvWqenu5icFG7+q1fraDYZ2u12TqCW7B2dFaPk+qTHXW3bdjJ8rtI/BHAVwK8A+D+Un1d8bCdQKyp9JoGaDGIzypY+fRg1mZRrZ/OOHUv6VYgNuHXL7E71jfX1EJyLu/d6nWNiomds+G6KItKnqB3mzoFT97/6NzEYeicFF1BTgQQBnSxDwd27YhJ3S5+DmwkI/JnamhFQ29gI0GiEqNd7/V6UFDQWk/xNi4SeG1OEUct6j+n5LKAmy5z4AzUTo3bnzp3+Ip4F1HSWRWXUqHdnHqD2nd+5io9//HmcO+f1llwhyhkJyZFzho2NUGHUGglGzcZ66PKjzY0JILPgrZQ+xbHkPN3rE3ADtTIZNSl9ml9vlj7trs84jrG8HCCOGQ4c6CKKgHqd4557mohjhhdfHMMquYw8Qs2Z9TESAHK+YYyXWpvPVsGAwnazNcryp4/m0+Sc//LQR7ILIwuo6a5PPdfINzhXW0gVdX0mL3CVSh9M+lR7xQV48cUxhKHY9vp6hFaLpSYXteCtbfv1eh0TExPY2KJdiA24dcu9z7OC7iAJ8B461MHGRojl5WStKFMQa5PHTFCpVKzfkcLGqBFQ0wv0+jJqdEftylEDgIWFLoBaCqgBdhariJmAwJ8EagxxLG4UiPkh4H/kSDeV5+LDqOmLRJEctaycwyBIy29Z0melkl/6pFxE+g7dbrfPpoltZQO1MAz7nzEIo+ZbvqRoyBIdXWxshFhdDRPSJ4VN+lRd0GHI0euxDEYty/WZlD7bbYZeT6QC1Gpyu8Ni1NJpBOK3i1GzSZ8LCxVwLro90Fwk2kdJNo3ioYc28dJLY3j++XFcvLiIM2fOeF1DeXvPAmJd/K7vWsbERC9TzcgTk5OTTjOEi1Eb1fAZ2X9gjH2UMfYdjLFvoZ+hj2wXRBZTQjlsgzJqm5uiwvvYWA+mNV+9kPSTzd5Cyg+oUa6SHpSoquaoPfvsOOKY4YEHGn0gpyfqq22Fxsfdn3306NH+32UxagTUCAQdOiR+++SpEZOTB6hlMWqMsdT7pXPTPDZ/Rs2vRMfiojg/TYwagexBGDUbUFPbSNHESucLLTJq2IwN6vh06XM3M2pAklWbnZ1NvD5rYdQX7+PH25iY6GFsrJNoFeYT6vcbxoKmF71dWopw40YdjAmWh75jq5UN1AiE2jsG+Lg+k9KnKpWq15ir6O0gjJpN+nQda5f0CSS7E+iOTwqZpzaGbrebcsvaQmXUfIEaY8Av/dKL+Pmfv+r1ep8IggDjGW6X3cio+YzsNQD+EYB/Ayl7/mLWmxhjZxljTzDGnmGMfZ0x9pNbjx9mjD3OGLuy9fvQ1uOMMfbLjLEXGGNPqWCQMfahrddfYYx9qMgXHUZkMWr0GpVGL9BD3FmaA7BLZ4DqsNNZLfG7VnNLn4CZVTPVNiPZ814MwZgAACAASURBVJFHNqzSaKsl7kwrlXirnZAdqB0+fLj/fHmMmtge3UXSb58SHTZGzfUdKpWKE6iZ3kuP0dj03qT5gZp7n4kcNSSkRqorRkyQD6OWZSagbdLiaAJqdL6oiweFzdigjk+XPsvOUQOy7f1lmQkAuZ97vR7m5+cTz/kANXUsUQR87GPP41//6y/3a3GNGqNGAPLppyfQ6zGcPt1Cvc4zpU+1Fybd4NiAWqcj5qAw5AmHsBp0LZD0qdZ+NAE102eVyahlVfw3ATV1Do5jF1CTj1OJDuoJu7i4mPkdgLTrc6dibGyssAt/twO1/wnAfZzzt3LOv3vr5+0e7+sC+CnO+asBvBHAjzHGXg3gZwF8lnN+AcBnt/4HgL8L4MLWzw8D+FVAADsAHwXwBgDfDuCjBO52OnyBWhQJwMQ5SySn+gblp/kANf0ktddRkzlqWSe26fnp6S6CgGNlJeq3OCIjwaOPbihmgyRI0WuoZbF5hw6JQ102o0Z33QQIfEp0DINRM31/2Q7GDtQEK0DyjXksvozayoo4F0yuT8mo5Zc+Wy239Oli1OhGQA1XeY7tlj71UG9mTDc2NjNBFutCoHxubs5g1vCTPtV49NFNPPzwSv//IozaMIEaXZdULJtkzyzXZxzHClBLFl/VQ7Jp9lQHF6Om7rOxMWb9rOHkqKW/u2jNlmbmq1WOgwe76PUYVlaSeWrJ0hxpRu3KlTHEMbC8vOwFOH2AWlbuWBnhA9R8nNujFj5X6dMApvNumHN+m3P+1a2/1wB8A8BpAO8H8Mmtl30SwN/b+vv9AH6Li/gigGnG2EkA3wvgcc75Iud8CcDjAP5O3vEMI3yBGjCYoYAS3U0N2QE3o5Zd8NbNagHmhScM5QVOYIwYtUcf3VCeS76XJj5iGbM+m+TPY8c6iKIYCwsVZwHXrJDSJ+Wo+QM1WQIi+fggOWouoEZjNEmf6h22+vE2M4FrzVhdTbIZas4jMUF6Aj/nPDWBpxk1s/RJ/zca4VahzF4fkNC5RGyAGq6CtzttJshm1MwFb30YtTiOEw5oirzSpyl8908QBJn9TAcJmaMmzsGnnhJA7b77/ICaWlcwi1HLcnwCagupMPFbZ9SIzR6265OYWFMhWRcTrBoKVBld7Uqg5qgdOtTF8eNtNBohXn65hl6vh5WVFWSFj/RZr9cH7oucFePj45nn9G5k1HzMBNMAnmWMfQUATTOcc/5+3w9hjJ0H8DoAXwIwwzm/vfXUHQAzW3+fBnBDedvLW4/ZHtc/44chmDjMzMzg0qVLvsPzivX19cQ2ez1zaQk9ut0u2u026vUHAVRx7doyGFvP9dk3tr59pbKJ2dnZ1PMrKyv9k6zZbCYu/s3NSQCvwuJiE5cu/X/9x2/dehWAGbRai/jqV190fpdWq2VkUA4caGBhoYIrV1axsNDC7GwVY2Md1OvXtorWHsH1663EmG/cEI3yqlXxuE8OBH2nY8cauH17Ak8/vYazZwXy7Ha7xn1ii1u3RM22KFrG7OwsomgcwAncutXO3E6j8RAA4PLlL2FpSZZBF3eT5uTVlZUVMMYS7WvUCMMw9bn0fdvtKoBHsLwcJF7DGOsDzmq1i0uX/qr/XLvd7k/InAO12mvQaES4enUB4+NmoL+0dO/We+cxO7sExlj/XN/YEKh0fT29nz/3uc8l/tfPk83Nk1vbXcTc3BwuXbqEVquFdvs0gDNYWmriK195Goyxvpng5k0BzKNoMfV5m5sxgPuxttZJPTc7S5/bSDy3uLiIK1euGL+3KRqNBp588knna/TvyRhLvEfkSyU1Tlrob99ewqVL3+g/7jp3AMFocM6N5V3W1u7b2vZdzM4up55fXV1Fp9NJuUfVa2ZpaQnPP/+89fPVoPNydXW19AW30+mg0+kgDA8DOIrZWXGDe+TILGZnZ8G5cAe22wH+9m+fSR3TOI6xvv56AECttgZgGktLTeM1/fLL4oayUkmfRxSbm+MAXo21NY7Z2VncukXnZRN//dd/2//+y8sPATiJ+fn11Lbm5+dx+/ZtuEI9V9Tjop+3t28fA3AGcbyR+pwwDDE/P99fa9SYmroHwBheeGEDhw/f6t+MdDod3Lx5Yes7LSW2ec89ZzE3dxxf/nIbtdos7t69m8mGbW5uYm5O7BPOzevUwsICer3eUGuVEajUrz81lpeXjeudOneqwRhDr9crHVPkCR+g9lHlbwbgLQB+wPcDGGOTAH4PwD/hnK+qFzjnnDO1U/AAwTn/OICPA8C3fuu38re97W1lbLYfly5dgrrNtbU1TE2Zm46rsby8jBdffBHT0ww3bgC12nHMzOTrwRKGRwAAx45FmJmZST3/yCOP9Kv5P//88wnw0+uJCS+OxxLjr9XE4nD8+AG84Q0PYtJRxfLq1atYWEh3DTt+HPjmNwFgBrOz4rg++mgDp07N4OxZ8bnt9nRizLdvi+9+4ECAmZkZfMu3fEvmpD83N4cbN27g7Nkebt8GWq1TmJkR1vHZ2VnjPrFFt3sQAHDqVB0zMzM4e1bst1brQOZ24lhMct/1XW/A+fPy8aWlJbz00kvG9zzyyCOoVqv42te+Znz+4MGDeOCBBxKP0TE8Ig471tYqOHp0pp9XxBhDHItLd2IiSBzXGzduJNiX48e7uHEjAmOnMDNjBgStltBOz507iJmZKmq1Gh599FEAwM2bzwEAOK+l9s+jjz6amMCfffbZvktXbFfsr5Mnp3D6dITHHnsML7zwAmZmwq3vMYmLFy8iCAJ8U5xIaDQEkL7nnrHU583MTG29r556bmJCHNepqUriudOnT+PEiRPG722K5557Dg899JDzNS+++CKWlyUwUvcXIBaJr3/964n3HD06tTXOQ4njNT8/j+vXr1s/6/Tp05ibmzPWwer1aluvmcbMTHohfd3rXoeXX345ldumXjNnz57F8ePHrZ+vBh3fxx57LNNIlTdu3bqF27dv48SJpEpx8WK1P9Z6PUajEeKeex7Gq151NvG69fV1xLE4r06cEGNjbNJ4TS8tifN9cpJZr/kwFNdXqyU+v1YTotL0dIg3v/nN/e//e78nXl+rHcTMTBIkjY2N4dWvfrXze9P6ACSPy7Fjx3BOqYPyJ39yCwBw6FD6Opyensb9999vPJdOnmS4fBnodo/hgQeO4d57xU3Z1atX0WqJa+3cueS19prX9PCVrwCzsycxMyNcxxcvXrTezMdxjK997Wuo12kfpccIAMePH8fa2pr1ptUVYRji4MGDmTlzr33ta9Fut/HMM89YX3Pffff1U2rUuHbtWsJVTREEAVZWVlA2psgTmVwf5/xJAKsA3gPgNwG8HcD/7bNxxlgFAqR9inP++1sPz25Jmtj6TavKTQDq1Xdm6zHb4zsaPrKn+roypE9b9WY/6TMJhnx7fQJ+3QnU/DTxnNlMoCbl2hylehw5cgRBEPTz1G7fLm4osLk+BzETZOWoqbKRHi4zQRQBk5NdcM4SpV1UmadedyfP+xgKZKeItBNQtshJf0edZU0nQMt8RFVOpXOy0QhSjJIrR81VnqMMMwHgl0ejH2/9GJYpfc7PzxtBGuCWPklmLkv6BPxz+IqEbiagoO4hgL1HLGA2E5icmEB2aQ4gbSaQRbrNrk+TzJonR63dZrh8+XA/xSBPjprruNiK3urto9RQe37SGJeWlqzfQW9p5jI8ZJUqssWhQ4dSHWz0qNVqfcOSK3aj9GkdGWPswa2yHM9CFLu9DoBtmQl+JWvDTOyNXwPwDc75LylP/REAcm5+CMBnlMf/ly335xsBrGxJpP8VwPcwxg5tmQi+Z+uxHQ3fO0q9lloRoCZrqJmTX33Kc2xu6kBNzXHK7/oEkvkPBNQeeYSAmrlzgezzmQ0QKcIwxPT0dL9X4c2bxZNS9Rw16fosnqNm+x6Msf6+s+1DV46aOj7dUCATot05WT6GAjq/TCUbZNPp9OSmAzV7HbWeEaiRmUAFaq4cNVfBW5uZIO/k6wPU9IleP7auOmp5gZqro4ULqA1ijLCFLWm9jNDLcwDAiROthBOZjq3eDg9IluegazsrR81VRkIUAhfgr9tNzltJM4H4XdRMQMf/d3/3GD7ykTfiM585knicwlXw1p2jZu73aSvPAQAPPSRLdFC4mKw8QK0oE3v48OFMBYu6KBQ953clUAPwLAR79h7O+Zu3wFme/grfCeB/BvB2xtjfbP38DxBlPt7FGLsC4J1b/wPAnwJ4CcALAP4fAD8KAJzzRQA/D+ArWz//29ZjOxp53VKDMWr2rgT6WPRx0UXTarFEaRCS8Iu6PoEka6YzaqY6a0CSvclzZ3706FGcPj2489PGqOlAyBR5GTV1AbdNUFlAjRYdG1AbH89i1NzdCTY3A2xshKhUYqPBg76ryWmZxaipddT8gVpRRs1sJsjLqFEKgSv0460DNRNTLGvAJbeVp/G1Hi6gNkipEVuYXKRlBe0v1TBFRgIKF6OWdH36mgnsyxljSJhebGaCQQveEpijm0+a23SQ5yrPQcfadCzVordpoGYuhXP6dAvj4z3Mz1f71+Pa2pr1pkE6t7PNLUUYtWq1iqmpKVSrVaeSRfXT9iKj5tpr3weRi/YEY+zPAPy/UJtFZgTn/C8dr3+H4fUcwI9ZtvXrAH7d97NHKegOdKekzyAA6vUems0Qm5uyoTAxaj5ALUv6/OpXp7CxEWJmpo2jR7uJ53Tpkya8sbE410U7OTmJkyeF4j2I9Kk7aFXXp0hYtr83b3mOcoDaYIxaVneC69fFlzl7tmWsrZVH+rQxaip76gJqnQ7D2lqEMOTGmxJ3U3aSPofPqGVJn/SYmpgsQWbydbsJqPm4SIuGSfpUOxIAUCRzc3HZMl2fgLiZ3NgIsbERZEqfg5bnoNQG+p23PAeQ7fokB3EQBGi3pfSpuj4BsWZcuNDA5cuTeP75MbzxjSLneWlpyZh7NmxGTc0nm5qaMuZLA5JRKwrUdmV5Ds75H3LOfwDAwwCeAPBPABxnjP0qY+x7tmuAeyHCMEz1jssTg0ifgLk7AV349Xo262BvzC4ucCqOSLInIC5+xpJ11oBkeY48FwZjDOfOie9fhvRJk9PYWIxaLUa7HThr3HW7QBwzBAFPdYewfQ91UhocqCVfJ4Fa8r32HDXz51+7JpDYPffIRdFUJypL+uScG0oKEKOWlD4lUxGg3W737/TVljamuVYWvE0/KWXp4nXUxPvzAzVz+Zrk8bKVFikK1LpdoNsNEIYcUWSXw8rOURvWYmaSPu+9N5l0buoRS9Fqxf0itjTXZtVRy6qgL2upSUZtYiK5r12dCfKU56A5nn6r71Xz7/LmqBFQoxu1TqeDXq+H1VXR7WZiomcs+aHnqQF2+dOXUQvDsBCjdoRcVYBT/lQ7EmTV58zz+CiEj5lgg3P+O5zz90Ik8n8NwM8MfWR7KKIoKkX6NAE1W/shNUy11AiojY9nL2RZjBoFyZ6AqLNmqvrv2z7KFCdOiF57KysRNjbyX1SdDsPmZrg1mcvJhJJpXXlqnY45WR3wY9Ty5Kipj9E+1Gup+UufbjPB9esCqJ07J6kec46am1HTF6VkQV679LmprLou2ROQ+Tl5OhPknXx9zklfRk2NomYCW6hgw4RFh5WjNnygJu/qbIyaCaipvYvVzhemyGofRSH7fQaJHDU1ymbUaH1Q35sEavlAuamNlK0rgRoPPURATd4Jbm5uGste6IyaS/rMy6iNjY31mTIA1uoEYRgmZFHXeb3XpM9UbBWc7ZfB2A+/CMNwIKBGF7Gp4K0PUHMxajojY4osMwGFCtQAkcO2tFTB4mLUBwxqQ/a8k36tVsHJk21cu1bH7dtVPPCAvVaOKYiVOngw2fD70KEu7typYXk5wpkz5jwMm5EAGEz6NL03n/Tp3h7t9yzp89w5uS/V41KpCBax12PodpFgE1Wglu5KwMA523IVw8qoqe9zGQnEWLI7E+jS5zAKbBZj1Mztr8oAaqbYbTlqeaRPU8Fr1cWu3giYwsf1CSChgkhGLfkeupGxuT45585zkI4/dZ8xSZ8CqBWTPg8c6CGKYqytRWi1GLrdLhhjViMBhez5meybefXqVURR1Ad83W63P1YXmKRx5mXUDh8+nPi/VquhWq2m8uX0/p6u83pPMmr7MXiUBdRMjJo+CbgYNboTFWyHP6Nmm5xJ3hSfy/Hww8lbXVMbKVmeIz+jVqlU+oaCIvKn3j6KwqdEB+WnmRg1mxOuDOkzm1FLj0UNuqO2dSfIkj7DUO1OkPyOLkZN5iImDQo6o6aGL6NmYi9kU/bBGDWfGET6HAajZophSJ/bwajV6xwf+MAcfuAHZlN5ir6MmjRQFTcTACqjFiopGzbp0/xZWawaPU/rAgG2PNKneqzT60HS2EWFhW35aRT3399AGHJcvVpPAOONjQ2srKxgc3MT7XY7MU4Ck6ZzkubIQYEaYJY/x7Q7Vtd5uhsZtdEd2R6KQYHaoNKnzqh1OiLfKgw5arXs8YRhaDy5o0gyPvff30jJAtJQIC9OKSH0cl+0lUqlX6KjiKFAL81B4VOiQzI25udN+30nXJ+m8iyTk12020HfSEHBuQrUzNKn2u9Tz8NxA7WkVGSWPsOEE9nmQqOIIg7GBLunN8qQ0uf2M2p5pE+dUStapT0rcXu3Sp8A8OEP38BP//TLqde4zATkBK3XfRi1fDlqGxtS+swL1LKAuGTU7NKn6mjVj7c+N7ucn3fvVhTp032t1Woc5883EccML7zgIbvAfU7SXJhH+iSnpx4m+TMPo7YP1PbDGIMAtXabodUSScMmqr5IjhrdkYpaQX6nQFaJDtVIoD9nYtTymgkAcZFT0dtBGDUClxQ+/T6l9GleWLMYtaJ11GzSJ03cOiNqmoRshoLFxQgbGyGmprqJO2t1TEEQWCVHl/Sp5wGpQC0I1AbrcrxZjBpj9lpqtuMzOoza7pc+geE11vYZhwRq6efU3sUylzFIAXrAP0dN7fdZxEwA+DFq3a5koLPMBPrx9qnhpzo/CajRtWYDaoDZUOAKX9bP95wzsWnA4IzavvS5H8YIw7DPhuUFalL27BqThn2kT6L4CajRpOZTmoPCBjRIWtPz0wBz0VuVaSkG1AZh1GSOmhqUUKvLi2qQ7Gdbp0zfpczyHEXNBIA9T001Eqhv08u92JL480ifKlAD1EVXfv+sHDXAXktt1Bg1/XqxAUzabxsbAb74xSkjsDDFTkif4vOy68wViXxALf1aNUeNMVGSCDAzXf6MmjQTDEv6FD1K5fnTagVbNS9tOWrJ7enzistQoDJqtq4EapgMBa7wKRdjGrMpGGPGNk+AuFlQt8EYSwG1IozarizPsR/lxSCuT2kkyC52C5hPNrpzXF9P3pH6tI+isAG1D35wFm9/+xLe+c50ixG1KjaFWjhyEEatSNFbG6NmywNTw2UmALIZNbVLgRq+rk9beQ6dUXNN1Lrz89o18WXU/DR9G4wxKzhyMWq69KnmqAEwylOu9lEUdkbNfDc/OmYCOW4V09KC/PGPn8I//scP4oknpr3G4AvUXJ0EijAIO8moEUgxSZ/qzaf6WjNQ8zMTSOkz7L+nbOmTc55aE9bXQ6vrU78RyceoyRw1tRSOLWyGAlu4GDV1XD4pL9PT0871QWXV6vW6F2GR9dw+o/YKD72OWp60FEoutdVQyyN9rq2lgdqg0ueb37yKX/iFlzA5mb446W5NlT7LYtRu3qzl2o9AOTlqeYCaPiHp/zPGjEDCJn2q35cW6omJ5HvzSJ+Un6Y6PvXPD4LACo5cjJq+GOqMGjFtKlAjQO+SY2ygcTvNBHpOkGmf6+c2Y6r8KR+n/XbrlgDRvouiT59P21jU1+WNUZA+m83AwOCK3wQSXHlq/gVv02YCPT2qDEaN5niKtbUwUZfQlaPmB9SSbaR8ctQAKX1euTLmxfS66qj5lCpSwyZ7UqhATWfTAHdbP1vsA7VXeIhCf2JximPmLKyqR56uBKb/AXnhrK/THal4vAxGzRXSbaRKn5JRK2ImmJ7uYmxMVAxXG5X7RHaOmp2Sz8pR0/ejKRdDp/x9FtB6naNWi9HpBAnJJw+jZmsjRdKnaiTQxyXMBNmMmt1MkCV9phk1veyLGjIpfzSkzzy5h6ZaanrB0xs3/ICQT59P03h9HnfFMPYnbTdr2yr40gGQzqi5gJrMUctyfVKOWpCZo2YzLvgwavo8phsK8uSoZZkJut1uglFzAbXp6R5mZtpoNEK8/HL2eVmm9JnVgF01FOhGAsB+bu8Dtf2wBk3aReRPV2kOwDdHzQbU+FCBmsxRExcm56qZID+jFgQBwjDos2rEQviG3j6KgiYrd45aPunTNBkVAWqAuTG7ZNSyzQS2NlJUQ80lfQpGzWwmiOO4v5ik20clWYss6ZNzCehdcozJhKD+v91mgjxAzdSYXXf9+SyIgP+iaBsLMHoLUx6glmZwxW8fRs23MwGpIMvLEXo9hkolRq2WHOOgZgLBqJmBGn1HtT1WVo6a6VirRW8F6GspOWr2aw2QrBp1n3GFbYxAPkbNp1VZvV7vf/eyGDWfm4WditG6Uvdo6EAtDxNUhvRJiyXVGqJctTxmgiKJlpQUvrwcIY7FYtrrMURRjErFHySqMUiemt4+isLP9UmTkJ+12zQZFQVqJkOBbx01QEqfc3MS2Ha7kr05ezbJqNmkT9NiRAAtv/SZZNTW10N0uwEmJnqo1+2Lmw007lTBW9sx9GHUVHlLZdR8JP29CNSyF+dsoCZz1AgUm/LZ/KRPep5ucMbH06kiqvRpOm5FGDW9lloc2+uo5XV9AkCvF2d2JqDwNRRw7uf6BLIZNVfjdTWIVcvDqJVtrtmuGM1R7bEog1EzdSUAfIEafa74Xy0O6XtiFmHUKhWOAwe66PUYVlYipRZRbM3tyd5mpTCjRkBHz1GbnBTVuzc3Q+udcZaZIJ08np6MfBdQ/XGTocDGqJmlzzSjdudOFd1ugJmZdmrBskmfrjZSdjOBW/qk70GLiMtIAMAKGk2LxLDukIszakmgpi7itDivr0cp44gp8gC13ZI8nQeopaXPJKNaTo6aOHfpuhkb66XOpygS9f04Z6kcTiC/6xOQcz69t9kUvyuVGPouyiN9CkZNOIw7nQD1uvumCPA3FND1WK2mxwjkMxP45kFOTU2hUqkYt1dE+nS9b6djNEe1x4JOpJ2SPtN11CRQG6b0CSSL3squBPkdnxSi6G0xRs0mfTKWbSig8hy26gRlMmr69lyMWl7pk7CBqSOB6bNFHTV7j007o5ZcDLNy1HySmwFYuySYzATDkjHKkj6ltJWcEwbNByrK3O50+AI1vXwFIDut+JkJfF2f4nlK3TAxauKz7A5TP0YteQ7p0mejIedrPXxu/up10ai+0xFFr31lT8Bf+nSxafo4y2LUpqamjGwaUPycH7VrgmI0R7XHYpg5avqJZWIRJFATFzwBtWFLn0Cy6K3akL0o8BNtpPIzapzbzQRAdp5a3vIcg+So6dszFb3Nw6hVqxwHDwpmk4CorKGWDdRcLXmkPGOWPolR04vo6gtpVrFb9buIsZjLc6jS57AmXfUaG1T6BMQ+6PXk97lxI7tW2b70WSxHLY6zQQUFnbt0bMbGYiP4d+Wp+RS8laqJODH0fp/yxjq9Ld85RWXVfG+KAODUqTYmJnpYWKjg7l37nO1bLkb/2xS+QK1er1tNB/uM2n7kDqplVASoUfsoX+nT9JjMUWNbv7dH+gTUfp8RNjakHLbdjFqjIeh+0bQ5PeFlATVpJvArllguo5ZuI0UT4+RkGqibQnd+Ug21c+eS+Wk60Bd11MzlOYBs6ZMmbh380TkpgVp2sVvAnqNmKgg6zMRg+j6268JUv8wmfeo5Sj7Oz1e69Glj1PQcNR2oqW2Osr6+3oB9fDwtfYrPlIBbD58WUnT8Z2YE2tRdnzoIVcP3WKuGAp8aanJ7apkOO6u2E4waABw5csT4eBEzget9Ox2jdaXu4SjaRoouYnqvHll1uAAVqIn/1b54w2bUCAAtLFQUliW/45NCZ9R8a6m52DT1cVuJDgIG9bqfmcAnR823KGMeRs0O1JJ5ajZGzZTzaKtdBmRLn+PjcQr8lcGopV2faUZtO4Ca6zxO9/tM1lEbFlDb69KnO0fNzaj5NmQHJKNGMTZmkz7Fb9P14cOo0Xpw/LhAZDqjZgNqph7M2Yxa5FVYWg3KU3vuObuhQAK17PJFZeWo6dtVY99MsB+FQi966xt5OxOYHpPSp7ioZc7DcMtzAMkSHWUxalNTPUxNddFshlhZ8bv7srWPoshyfuaVPk37y1eS0rdn6pxgA2qmsQDp7gSUo3b+fBKopQu1FjUTSOnTBP6k61O8zqeuEwDjWDhXgZpcKIY56dIC6boubN0JKDlcB2phKB4vs2aVaRwUo7YolcmoZQM1N9MFiHOJjgkgzAVm6bN4jprKqEmgJo4fgTxVAVHDt9MJkMxT9b3WKMj56cpTc52PJkDpum7yMGq22Jc+96NQFGfUhiF9isfzdiYowlCoRW9l0cjBgBoAnDwpaInZWb8+dFmMWhZQk2aC4tInYyzBdviyMZJRE491u2I8jHEjcMzqTtBsMszOVhFFMU6caCdeZ2bUBjMTmLaZZtTEfnEVuwVgNDZII0Gc6Fm6k9InYGfUdKBG88G994qFOg+jZgIce1X6VOVMu5kg6frUwZNvQ3ZAmIzo5pre485RK8ao0RyvS59pRi25rTxuRzVHjW74fKRPwK85u0w9yD4fAXfv4zLkx30zwX4UiqL9PsuRPqnCNjFq4vF6nec6MYvVUpOMGuUtjY0NZiYAgNOnRaLP7Kxfyx1b+yiKrMbsBAbGxopLn0By0ipqJlCrlJuwiKtEx/x8pS97njnTgn4YhiF9mro20DlJryPpswijZquhNsxJt4j0aWPUJFBrolaLsbxcyay1lT2AWwAAIABJREFU+Epn1HQApO+PrBy1rGK3FKr8aWKHAbeZwIdRo+NPQM1X+szDqNENs2DU/M0EAHDffU2EIcf167VEJxE1shg1PWzzf1ktyvYZtf0oFEUYtTjOBmp5pE+66yTpM6uGjh7F2khJM0EZjFoURWCM9Rm1O3fyMWo2ZjKrPEeeXp+2BuwAvBk1k/RJ30FOiubjZ2bUxP66e7eidCRopV5nkj59GLV0ZwIqgWCWPm2MWraZID0Wk5GAxj6s8GHU9Odo7GnpUxa1PntWSNFZ8qdtYQyCdOuyvQLUKhWOIODodgO0WvJ7i16YvjlqfqU5KKhEB73HxaiZzAQuRk0vdnz8+Gbif+kKFq/XgZrpZjBL+lTNBL5ArVrluO++BjhneOEF83yrmjT0yMOolSF7AsXNBKN2TVCM5qj2YBQBaqKBO8PERC/FfFDkAWo6o2bovOGMQboTqIzaIGYCINmdYG7Ol1ETn2ej+7P6feYpeOtavIsANd31mSXfuGqpzc9XrM3Y9c+l/4swaip76gPUfCuly1pku49Ry5I+BVAT53WW/GkDankW71FblLIWUcbUuUwCILUXZplmAiApfdrqqLmkTxejFscxul0xzzPGcfSouB5tjFpWn08gn/SZda2pkWUo8G3I7noMKA+oAX5rY97ndypGc1R7MIoANVlDzX7n43My1mocjHF0OgG6XQnYJia8htGPwfp9llPwFkh2J9i+HDV/M0HZQG1ysocw5NjYCNHpsEz5xi19VpVm7NlATZgJzI3QAQHUTIuRWjPPJH2qC2m7zbC2FiEMudU0Q0FjUUuFmIrd0tiHFcRcuSb2vGaCyckezpwZDKjlWbxHbVHyGY9eExIgRi25P2w5annMBIDOqNnMBMz4WUA2o0Zz4uRkD+PjXQQBR6MRottNAzX9/M4nfUozgU9PXT2y8tTySPG2x4DhA7X98hz74YwiQE1W0rcvXj45aozJiWljQ71D8xpGP4oAtXqdY3xcVMWenRUXYZmMGgG1OBbS7vKyuQ2UlD5tOWrpEhhq5JE+feUwXzaGsaShQDJq/tLnkSMdMMaxuBjhpZeIUUtLny5GzSZ96rJnrycWLWF2MDNqah01lU3LWqtN7J6thtOwGbWs68EG1Gx11Kamun2gVlT6zLN472agRoYoQOxHXXrLYtSK5aiVayZQHZ9TUz0EQbIoepb0macrxvS0AIErK1E/zcBX+gSye3769vmksEmfZeWoAeb9sFsZtWIZ3fuRO4oANXqdLT8NMJ9YphN0bEz0stzclBf++Hg+xqF4d4IONjfD/uIzPt4rbCYACKitAgBu3pzEd37n6xKT5MGDXXz601/H0aNyIiLQa2PUDhzogjExkXW7SEnNWYyaum9cBR3V53zrqNG4FxeFbJEF1EzbjSIhQy8sVPp3xSZGzcR+ZZXnSDvwaNIWi49L+mw0AqWuU/bCIXPUsqXPYTNqWddDXulzcrLX3wdlMmp7E6iZpc+s8hx5zQRJ6dNsJqAuRnnNBGoNNZrjp6Z6WF2NsL4eKoya2VFpm2eCIEjdPIWhmIfv3q2i3RY3XypbWKlU0OnYpVCSPq9cGUevJ7anhquOWh4zwU4zaqN2TVCM5qj2YKiuT5F7lv2erK4EgL8Or/b7pAs/L1ArXktNjP/mTbH4TEwMzqiNj8d47DHRZV69m6tWY6ysRPjP/zlZsTpL+gxDc2FZiixGDfBLMC8ifQLJsRUxEwDSUNDribxHEzAySZ9ZZoK0kYDGl2zIrn6GupAuLJCRIDtnxsTuyYbQyf0xuoyaHahRjpqLUeMcaDbFe3wW770ofdoZNXdT9kHMBHZGjRk/C8iWPlUjCSCB4dpa1D8/6MbaB5QD2c5PQLBp6lepZ8grBw70cPJkC61W0E+dUCOv9DlsMwFg3g/7QG0/nBGGIaJILF5xzPrJ1q4gFsjW5xPwkz4BeQGtrcV99+d2ATXKjyBAVUaOGgB84hPP4VOf+gt8/vNfw5e//N/xhS98Db/wCy8CAP7oj44mwDCBL1dehsv5mcWoAXK/+zBqQRA4J400UJOGgiLSJyDz1ADBpvmW9nBVXjcxapSfNjYm9qe5PEeaUfORYlzlOXTQMqqMGnUmoEVclb+OH2+jUolx927VWgpBdTjqh8tWv69IYvV2xyBAjfaJLM9hLkKbp44akJdRy28m0POQaa5XpU8bo5YfqMnrX58Hs4Aa4DYU5K2jZmN+B1Fa9Ng3E+xH7ijSmF11g9nC92Qkl9PaWtzPUTNVtXfFoI3ZKcrIUQOAIACmp9tbzkLx3BvfuIqjR9u4fr2Oy5elWyIrRw1w9/vMcn0Ccv/45KhlfX/9GKrdCYpIn4B0fgJm2dM2Lmkm8GPUZJ9PP0bNtzQHkFXwdncwasSQmMpzhKGsEWhj1fKyFzRen8d2MooDNZ5iGMtyffqU5xgfF9ssYiZQQbr4La6BtbUwxaj5FLwFsp2fQNrx6QPU7r9fLBzkGlejDEatTDYN2Jc+96NAFAFqZUqfdAe5vi4ZtYmJfId/0DZSFJOTfCC2w8VYRRHw7ncvAgD++I+PAhBGA2nM8GHU0tvPI326xyfqwOVlY0zS5/h4cUbNZCQAzOcOfWdTjhqAVG6LnLS7xm0mGbUwV+9BWohNZoLtzlHLC9QISOpmAv2GLKtER54+n7ax0HcYpfAZDx1/Fag1GuKxSkXesI2KmcCXUVNz1AAdqKXZKpfjOKuWGpBkr4VhKBskHT8uqOD5+fQ55jIT2M49ffxlA7V96XM/ckcWUPubv5nAr/7qKfzpnx7Gs8+OodlkmcVugfzS5/o6VxiZfN9hUOmT4sCBwRZQFxACgPe+9y4A4PHHD2FzM8DamqhHNznZtdajA9wlOvJIn1n7qVKp5GbUzK5Pv/dSJIGamVEzg3x7+QEgDdSk9Gln1KJI9FHs9VjfDZzHTJAsz2FeJIY56RYB27IGnD1HDUBmiY68xUVNYwFGb1EqyqiZKvdnmQmKSJ+m4s3icfHbZCbIYtR0kE7ngJiziIGVLdIoXHNgXukzDMPMORVIlvjRo8g5mS4IPXxGLescG9XyHPuuz20KOilNQK3bBX7mZ+7vJ1UDQBDwvlTikuvymgnW1mQ+R15GrSzpswygxhizToLnz7dw8eI6Ll+exGc/ewgXLwrTga19FIULqPkwarR/sia9KIpy50qojBpN6vkZNdnX0yZ9mhm1NDhSIw3UktKnKUeNipdubIS4dUtM0D4FOM3lOXaujpor7IyaGFccx2i3GVqtAGHI+9dolqGgiPS514Aa5W0BwMZGUu4EVOY1eQ4MUkdtOIxa0kygrg8uM4HrZtAn9UFl1MIw9GTUZNFsPWznpOuGplKpoN2Wc1KZpTmAYoxa2WCxrBitK3UPB2MMjDEjUPvCFw5iYaGCmZk23vGOJZw/3wBj8sLPs6jaHpfSJ+/foU1O5gNeZUifYkEa/LTLGguxan/8x0cyS3NQ0DhHkVEz56iZ32ubjNSJ2iZ9mku7uBk1dbIFVGedXfoE5KR++7bYqaorzRYmB+pOMGq+ZgL1WEiQKf5X+zxOTUkX3iDS5yslRy0J1MQ5oeZwyV6fSYd9ftcn3WyIG+eyW0j5mAn0hvOAe47xdX2qr/eZ2+lGzyV9+jpTTc+NCqM2imBtn1HbxrA1Zv/MZ0Qu1Qc+MIcPfWgWgLgTvHatjjgGHn64YdyebUF2AbWlJYBzhiiKUankZ9RcTJYtVEZNtMManF7Oqvvzrnct4Rd/8Sy++tUpPP20MBVkATW1erceciKyv9+nWj2NPWsf2oDaykqkuGf93ktx6lQbURTj1Kl2ginIeq/LTACkGTWf8hwASaOVPpOch1FLuj53hlHzYZjVmlZRlM5RM6U3UL/PGzfMJ1uePp8Uph6uw9w/RaJM6TMMxbnSbgdotVi/lE3+FlLx1u8eGLPNreJ3uTlqkTNHrRhQM5sJaF7PmlMPHRJFc5eWKuh0WF/xAex11FzXiK48jIKZAADGx8dTN587HaN1S7XHw1T09u7dCF/4wkGEIce7373Qf22txvHggw0rSAPyMmq9rc8T/9frxZyXxfp9yot/0NIcFFny4sREjHe9awkA8J/+0wyAbKCmNi7Ww4dR88318GHU0mYCcfxURs0G1GyT0YEDPXziE8/h3//7F6yf68pR8zUTSOnTXp5DPJ9cxHzKc5ibso+mmQBIfneTmUB3/QHAiRNthCHH7Gy1v0irkafPp2kcNP5RC5/jRWBFZdSo+K2+P0x5ankL3hKjRje6ZXYmMOWomV2faca4iPTpylEDsufUMASOHhXjXFhIfn6eAsy250ZB+gSAsbzJ29sQo3e17uEwAbU/+ZMj6PUY3vzmlUQlfZ/IA9ToAlrYwoK1Gi8EmIrInxMTcX+SGR+PS6mV4wOI3vc+8WWp0G5WH0kCaiZGzac8h+/iHUXRAGaC4uU5AODRRzetsidgntzqdSl9mtYdHajR/qYm03ZGTU7qExM9Y1VzPcyM2mhKn0DyetHrqCWlz57yHvRbpN26lT7hiiyKtmMwSpGHUVMBLLFr+v4wOYTzArUzZ1q4774G3vKWZesYXWYC/zpq6Rw1AnkE7NXzu4iZYGIi7t+w69Jn1jYpTp4UY9ANBbY6ar7Fv33nzjxRVO4ft90B72CM3tW6h0MHapxL2fP977+be3t5pE8J1OREVWSyLgLuGJOs2nYxagDw2teu92UkwEf6FM8vLFRSgKTTyTYTBEFQGqOmHxsqK7K2FvblG1sdvEGYJNM5EYZCKgfshgI1qHL56dMbxm2aGDXdGWwLkwy7U9JnXkZNdiaQZgICaqq7EHDnqRUBaruBUSuao2aSPtXXqoxaXjNBrcbx6U8/g5/92RsAymfU/MwE6eNdRPoEgLe9bRkXLmzi1Kl26vU+c9fp0+L33FzytYMyasPIC9tn1PajUOhA7fLlCVy/XseRIx286U0rubdXxEywsCDvfLaLUQNkntqgxW4pfCYVxoD3vlfKyVlAbWwsxsRED+120L/TBUST8V6PgTHuLO/hm5TrA9T0HKIoAiYnu4hj1k/mzSt9+oTtLjQrT02Na9cEuDhzxh+o+TaIphIXKqNmkz6HCUbCMPTavkn6VBk1faGmcJXo2KtAzSdvzsSoNRpkJigfqIlxJceoxyBN2VUzifidrqNmcjUXkT4B4Od//ip+53e+kcgv85c+Q5w+LcaiGwpsddR8c9SGAdSKMmq1Wm3kynSM3tW6h0M3ExCb9p73LDgBgC2KALXFxZ0CaoIxmZjYPkYNAN797gUEgZiUsspzAJLZUfPU1Pw01zriy7L4ADXanhqUV0J1x/KaCXzCNrmZJEdTrK8HuHu3imo1xrFjgurwyVHzKXYLJJuy0xooZentY9R8F5Yko0b7UIzLVJmegoCaqURHkRy13SB9AtnjkoyafJ1N+jQDtXyuT5+xZQE1G1jrdDg2NkIwxvvmHhWoSddnvhy17JvA5P+0raxzulqt9oGanh5iA2q+jFrZ+WlAcTMBMHqs2mherXs0VEZtbq6Kv/iLQwCA970vv+wJFMtRW1oK+/8Xmax3E6MGADMzHbz1rSK/5N577cYMClOeGoETXVrTw1f69KmjRttTg4Amja1s6dN2XLIas6tBsufZs61+lXgfRs2n2K14rwQ8BKB3glHz3cc2Ro0WcJ1RoXDVUturjBrgD9SazaDPOMlemMnrU89R63QYej3WL7WRN2zH3FWeA7Czaisr4vHJyV7/WiEJfGMjRLebZNQGzVHLen3W3FWr1XD6tBiomqPG+d6SPoF9oPaKDhWoPffcOBqNEK973Rruucee3O0K20lnkhDoDnJlhYBa/okKGJxRGx/vbZuZgOJf/aur+NSnnnE6aClMJTp8jASAv/TpW6vH1kaKczGeshk1F/A3JWab4vp1sZOo9p/trpYxlmA1fBk1IO383Imm7L6RBGpy4ZV9Ps3dR1wlOvaBmg7UxHNZ0mfe0hx6ZAE1k5kAsBsKVlbE61U2NQwFWOOcYWMjeSMyaMFbW/gCtWq1irNnCajJ13a7DHEsALA+rJ0EaoPUDhw1Q8FoXq17NFSgRlHEREDhOun05/TJabuB2mOPbYAxjocf3txWRg0QLN5DD2WDNEAt0SG/JxkJsuYSX0YN8KP6bc5PirIZNdv5JBg1P+mTGja7gBo9XiRHDUh3J9iJpuy+YTITtNvM2j6K4tSpNhjjuH27mjJw7EufYh8QU5UfqOWXPV1jEykRHN1ugJ4BA9oZNSo8nnyTbCMVodeLFcaY98fh2k/DYtSq1SpOnRJ/q0CNAKrJSesaC2Osv54MQ/rcZ9T2o1DoQG1iood3vGO58PbyALV0/kaxzywK1N7ylhU88cTf4P3vXygFqKkXeZnhZtSypU/fMRWRPtXaRwAwPr49QE3kqCUdi7a4etUfqA3KqGVJn6PHqEnp09aQXX3tiRNtxDHD7dvJO4R9Ri3JqJGxwJajRufHoEDNrmDIuSFP0VsToyb+Vx3e4r3Vqmw4nzXHDMKoua4ZG1Ar0imDgsDhdjFqeYDaKMwfFKN5te7R0IHa93zPYuFJA3CfdPrErF9EthpcWTEIOJqcFGMoy1GTh1XzDVPRW59it4B/wVvfKMqoFV2Ebcclj5lAMmqtzG0WyVED7IyaDqRHAYzYzARZ0idgL9Fhayz+SgJqrZYEamQm0Bk1Oh9of+WtoaaHa74lhSKP81MCteS5rzZmX18Xf/saCWiceY6vem64tl2tVnH4sLj+1tejPvAt0pBdfZ66IpQdphQg3/0i0j3KZ/mKxmherXs0oihCFMmE0UFkTyCv9LmzjJoauwGomcwEZeWo+UYWozY5aZcqy/g8dXs+ZgLO/XLU6HFVji+WoxYkfo86oyalz0ABaubyHIC9RMdeZtT8y3P4M2plATXXPqP5NE/RWzujJisDUMN5Vdb3mffyzLFJ1tfObFWrVTAGzMyI8RGrNgijFkXRUHtrmlqn+cYo5amN5tW6R4NOmp/6qRv4sR+7iUce2cx4hzsGAWpFz8FBgYjeqHqQGAZQU4veUhA4yQK3ZY/H1kZKPMdRrZbLqA1qJpibq6DZDHHoUKffBcI3Ry0Po0ZjoeNiKw0wCmDEBNS6XdZ39KnSp34H/8ADIvnqa1+bSjxuWhiz8pb2co6aqWm5/lpgsNIcgB+jlqcx++qqeK09R00CNfVY+wCbPMdXfa1tDlPzb3Wg5rr+sub6SqUyVOZqkPN+lPLURvNq3aNBk/b73reAH/qhO86aXD7hugiygFrRc3BQNqzMQoKjxqiVzeC4pE9XeZVhludwdSbQjQSubapALQy5kVGyhS7D7kRnAt9Qv38QoN/hodnUgVo3tTC85S2iCPZf/dWBBFNjAmpZ14Iuh+0FoNbr0d/J5/TX0r4b1PXpGhulkphuZGyNzvMwaioIKuIYt4UOpmznkfqZJ06I75oF1Hzb6Q2TUVOPWd75YJ9Re4VG2dWO89xBVyocYSgXsqJAbdAk/lEHagcPdhGGHKurkVL+ge7Yt3fhzwJqrvIsZXye+rieF2YKAmpqL1EfRu3w4U6umxbd2GCTPkcBjOjXCo292YzR7SJR8FQHaidPtvHQQ5toNEJ85SuSVcvbUohiLwC1KBLAvtdjaLWSxW8JKNA26P+yXJ9uRk38Nl0fzWYz9Rjgw6hFRqDmw0D5zrP663yAGvX7pJvZIlK8+nnbJX3mnRf3GbVXaPhQwXm3l+c59UIa5GZhLwO1IEh3J6DyHEXz+oqPxZ6j5mLUhil9uswEen5a1jZpwcxTmgOQgIyOiywIKm9ERoFNA0wLIQE1UZUeEDmrQWBeGN72NuEKf/LJ6f5jRRg1fSy7FagBancCXfokmbCeeJ0O1IZhJnAxanag5mbU1tZCbG6m22OVKX3qc7kPUJPOT/GYZNSSjLYvo7Zd0mfec75SqQxljSkSo3m17uEoE6jkBWoq5T82VnwhG+Q7jDpQA1SgJiYa34K3ZceoMGqq9Okqz2GSPl1A7VWv2sTFi+u5TTU+jNqoADVdcpTMZLo0hwmoUVeNJ5+c7tfoKsqo7TWgtr6eBGr0OC38NjNB2XXUxGeJ36bro9Ew13BcXTV3paD/19dDbG7Sd5IgqEzpswijRm2kqDG7qXOC7xhGmVEDRodVK78Q1X44I4oidLv5GARb5MlRA5ITlK20g08MwqiV6YocFlDT89QoL6te397FP11ihaNWEwUwh8GoufLJfBg1Amrnz/vlqI2Px/i1X3su9zjVHDXOzS2+RgmIhKFssE1jbDTiVGmOarWKIAgSLsELFxo4daqFW7dqePrpCVy8uFGYUdsL0icgv/fGhh+jJuuoDc9M4GojVVz6VIFa3B9Dma5P/XU20GQCamVIn1SeY1gxSI4aIPLU1tfXyxxSoRjNq3UPx84yaqr0uTNAbTcxajQRSTPBzuaoAVL+dMk3w8hRU6vqm6LdZrh1q4ow5Dh9uu21zaKhlgqRRoI4kec2KowaYK6l1mjECUaNmDd9oWRMsmqXLgn585UufRJoofpp+v4gRs2eo7a9ZoI4jtFut1OPr62J17rMBLr06cs++R7fIoza2bPiPXp5jiJmgmEzaoPenIwKozaaV+sejp0Easkctd0P1MrO+aPQi96OivQJSPnTxQq49kleFpbek2UmuHGjBs4ZTp1qJRpeDwOoqezeKBsJKEzdCTY3e4n2UfQa06Kl5ql1u0C3G2z1VSxeW2uU9o8aeRi1zU1R+kLNkRK1KqPE67aj4C2lktiuDxOrRoyqDtRURk1vj+Wbz1WUUbMxXOrnSqBWLdyQnWLY5+Gg0ueoOD9H82rdw1EmUMm76KoX0sRE8UM/KkANGA5zQrXUpPTpV56j7HAxaq7OEq7Jr1arWY+BW/p0l+cw5ae5xjLIBK2yeyYjATC6jBqBq2aTJ4rduoDaxYvrOHiwi+vX63j2WbFwiBxF+ZpXIlDb2OCI4zjRa7JSqfS/5/aaCcTvMoCamVETv33Zp6JADUifS7rcOj0dYmKih1YrwNpaaGwan2cMw4xBpc9arTYS18rOj+AVFvvS5+gDNT1HbRQZNSqwaQvbfomiqJ/D4/N5tK0sRk1vHUXhAn9FY7cxaur1QuPUc9RcjbGjCHjLWwSr9md/dhhAvobsFHstR21zU8iKqvRmYtTonB2mmUACNfN1pwO1Xk+WZqFONRTS9RmlGLVhS59A+lwy9QA9dkzMkfPzlYHqqA07Br05YYxZ58vtjNG8WvdwjIrrc6cYtbIv3u2QPn17fZYdpnNFSp/FgZot72KQ8hxUmuPcueEzauYctd3BqKl11NRit3Rd2BZikj8ff7w4UNtrjFpa+hRAjb7ndtZRy2LUdOfn6qr4TaVZ1CDpc3097JcgyQvUymTUTJ95/LiYh+bnKwNJn8OOQRk1YDTkz9G8WvdwlHnyDiJ92vpE+sQoMWrDCL2Omm9ngrLDzKhRKQc3ULMteEWBWlavz+2VPiVTMsrtoyhMbaSE9OmXowYAb3zjKmq1uH9O6k26fRahvQjUOp0YnU4AxjiqVZ4hfQ7P9UkKhcn1KR5PXhdLS+I8MHXkqFSEu7vXY1hcTJ7fowLUZmZk0dtB6qgNO8pgkUfBUDCaV+seDtcFlBe5u0480+cky3PsDaA2nBw1KX3G8c4xaqbj+8Y3ruLYsTbe9CZ3n9gijJq7hZSf9KmW5gCGZSaQ+XKj3JCdwiR9Npvc20wACKn7DW9YVf7PV0MN2B3Sp89xU4GaWr6CMbEvyGRky1EbjutT/LZdH91uN1GWaXFRjM3WOo1qqc3PJ0Gnr5lgEOlTPwdN5+TJk2Lcc3NVq+tzFG7KBzUTAPuM2isybCdvFEW4//77c53cg7g+JyeLX0R7HajVahxTU130egyrq2qybOkf5QzTMXzd69bxX/7L3+Ktb3UDNRejViRHjdYHE6O2vBxiZSXCxESvb8SgGEaOmgoaKSdot0ifJkYty0xAQfInkL80hz6OUQVqeRk1qqVG4J3mJnGey/PE5U70DR9GzdViTWXVlrcOpV5DjYIA3Py8+D61GveuoQYMn1FT+32qZg41Ro1RKzon7GlGjTH264yxOcbY08pjhxljjzPGrmz9PrT1OGOM/TJj7AXG2FOMsW9R3vOhrddfYYx9aFjj3a6wXUDj4+OoVqs4ffq097YGMRMMcu6NSmcCYHgLsmooGCVGzec5wM2oVSoV4yTqdouS9Jl+jezx2Uz17BxujlqgyNK7Q/qUxXpZoo4avSYIAusC95a3rCAIxHcfBKiNEojVI18LKdZ3RdJjtC/CMEz0Be122cA5aoOYCYAkUFtaymLUxONkaKpWY2NSvy3KBGomFu/kSd4fnyn9YBRAGlDOzckoXC/DnM1+E8Df0R77WQCf5ZxfAPDZrf8B4O8CuLD188MAfhUQwA7ARwG8AcC3A/gogbvdGrYLaGJiAgBw7NgxTE5Oem1rkBy1QYDaII3ZRx2o0fhU+XOUctR8ngPcQA0w3yW6jg2RcCZGzZaf5hpnGYzabjETqNeKWlqEynOo0qd4jRl4HTrUxcWLokp6EaA2SuDVFnmAWrOJVEFY2temPLVhmgmo04svo7a0JH7r7aMoiGkjoFarxbkKw5bp+jR9ruz3aTYTjApQK4NRG4UY2pXLOf8cgEXt4fcD+OTW358E8PeUx3+Li/gigGnG2EkA3wvgcc75Iud8CcDjSIO/XRVZQA0Azp8/73WhFWXUqtU45TTKG0UuxDAMS79Yyt4egWSVUdup8hyAO2/MFS7pE0BK/sw63yRQszNqemkOYPjlOWhhHOXyHCbXZ6cTGBk18Zps+VPt+1q2HLaTka8zAeuXryCQoEqf6uOtFtvRgrdA0vm5vGw3EwASqG1uSsCZB6gNW/o8c0Z83/n5qpFRG5VzbTfkZfrEdsPeGc48FtEGAAAgAElEQVT57a2/7wCY2fr7NIAbyute3nrM9viuDRvAUYFarVbDyZMncfPmTet2iizUlESbVYPLJ4oCtbKjbKA2NTWFlZWVfq7VwsLOSZ+AOI69XnoyL5tRy9oeSZ+mhejaNbFjTIyabRxlFby1SZ+jdPdsaiGVlD673kDtAx+YB+fAd3+3zFd7pUqfzSZTctTS0qf62s3NEM1m8rEyx0bmLJvrUzyn5qiJY5CVo0ZRq3FvIwGAfksytW+sKUxzMhky4jju/63H6dPisbt3Kxgfp3UlP8s77CjDTDAKsWP8JOecM8YGRwxbwRj7YQjZFDMzM7h06VJZmwYArK+vl7bNzc1kIngQBPjLv/zL1Ouazab1QmOMZY5H/5xmkwO4D1HUwaVLn881Zj1arZYRQLgiCAIsLCwM9Ll6rK+vo9FogPNyTqWVlRW0Wi3UauMAZnD9egfr6wK0PfvsZUxNLZXyOb5hOwcWFhZw9epV6/tsx2dtbQ0AFQqVCwdjDEtL9u+2tBQAuIBGI8bs7GziuZdeehAAMDl5C7Oz0pnY7Xad56h+fvrGxkYHwAWsr3dx966QAnu9zcS4FhcX8dJLLxXa/jCCvmu7fRAAsLi42TcTbGzcxle/uthfELvdrrE3JMW73nUHAEBfd2VlxRv4bm5uZh6XnY6s86LdDgHcg7t3G3jqqdsAXg/GmpidncXnP//5rde00e12///2zj1Iruuu89/fvf2cnrdnpsfzkDWSbFm2IhMjtDbxghJckOBkvQkpEvNKBYhJFWwIbAon2T/CLkXVsqGWXXZZtlzg3SQLMVQgxIBxeMTaDUVIGVvGcSTbUelpSdbDY0keaTSj6Tn7x+nTffv2fT/6ntv9+1SpNN19+86Ze8/je3+vg0LhNgBlHDsm+2W53MCFC+dcz+3FpUuXXB82Dx8eBXA3VlYaXePDipr/XnppO4BFAJdax29sbLR+NoxxANOt7127dhGHDp3AK6+8Eri9fvOi1xqi5h3DMBznhc3NTYyO3ocrV0o4c0aKspWVCzh3Tt675eVlnDp1qut7WaD60/LyMo4cORLpHEmu/1HotVA7R0Q3CyHONl2b55vvn4bstYqF5nunAey3vX/A6cRCiEcBPAoAe/fuFfv373c6LDIHDhxAUud87rnnOgbQ5OQklpaWuo5bXV3F4cOHHQdbsVjEnj17PH/Ps88+2/F6dnYEAFCrmbH/luPHj4cWXcPDw9i5c2es32vnwIEDmJ6edtyiJQq7d+/G0aNHsXWrdOetro6BSE5E+/bdhe///kR+TWAOHTrUVSwTAGZmZrC4uOjwDcmRI0dw+fLljveICHffLfN0Go0Gnn/++dZnlUoFd955p+v5Nje/0/xeEfV6vfV+owGcPSutwW99a63DUnf+/HnXfra5uYmDBw+6/j4vZmeV9bmMclkKn/Hxcke7ZmdnQyXmpI0aixMTMtV/c3MMm5uESqWB+fkZ7Nq1q1UGYHl5GceOHQt87jvvvDNQ9XQhBJ577jlcvHgxsbksDQ4ePOhpCZqZkfOYYdQwP78DADA6WsDCwkJrTjx9+jRee+21lqXLMGYAyPAPaz8Jw2233YaRkRHHz8bH5f+bmyXP86v7/OijMrRibm6odfy5c+daP9frndaz2dlx7Nt3J0ZHRwO398UXX8TaWnc4gqJUKuEtb3mL42cvv/wyVlZWMD4+ju3bt3d9fv36dczMbODKlRLW1qSMmJ+fxPS0vD6Li4uYmZkJ3NY0Uevt3Nwcbr755kjnSHL9j0KvnbZPAFCZmx8C8BXL+z/VzP68B8Dlpov0qwB+kIgmmkkEP9h8L9fYn8qsbk8r1WoV42oGsBElhk3FqPkVSw1CFNdnWgGmSZrZVfmKzhi1bJIJAO+SGV44fW69/qZpdlw3P7d0paJicDrPe/ZsCTduGKjX17uCtKNsAB8Ea3kOt2QC3eJR2ltEybarwrXKxRXU9elE0P6v3GG64x8v2c76tJbnsF4He4zaG28ol380tycQb2cChXqgVOU5/JIJFGGTCQD/Me31ubqWbr/TNM3WNlLWNrZ/zmCydEH1J3Z9OkBEX4S0hk0R0auQ2Zv/EcAfE9HPADgB4Eebhz8J4IcBHAFwDcCHAUAIsUxEvwbgmeZx/0EIYU9QyB2maXYUP3QTaoD7QAnS6ewxClu3Xsfo6AbuvnsdQLhBb0eXGDUgOaFmGAZM00SlUsFNN0lz+euvF1sTkE7JBH6LmdPn9ntWrVZx48aNQOdTwdL2ZILjx90zPv0gokgua+suCW7JBLpNyoVCAY1Go9X25eX2huxAdKGm+myY43UnuFAzcP16u+Ct/UHEeqxuQu3y5fAxamGFmt91DCLU3ARXoVDoEmrWGDXdhFqj0dBuTghDakJNCPGQy0c/4HCsAPDzLud5DMBjCTYtc+wBjl6Vj90GZxSL2thYA0899QIWF+sAgpUAcUMnoZaUpU5NTnaL2vS0jBfSyaIWJZnASahdaW46GDWZ4OTJdg21IG2w4pYo4YcSZTdu5KOOGtDu+6rty8uyr6mFOkh5DifC9v00Mq+Txq991mQCFc7mJtRUv7h0qdPCFoU4OxMoVBiDikrwq6OmqNWM0H06TYsaEbX2+5SvRSvJx+t7WWCaJm7cuKHdnBCG/LY8x1gnlKGhIc+JKUmhJs8nUCjEF0xRxFFamUBJnddaumJ0tIFicRNXr5qtelc6CbUoWb9OQk3hN6mXy3I/xUaDYNVWzzwjY1LsW0cFIerEqaxSa2v5qKMGWF2fsp3KwjM83GhteaQIU4E+bN/XpWyCF0EtarI2Wvs9L9enEmppW9S8sj7l550WNb/yHIqRkeQfjOMINQCYnW1fy0pls1XsulgsaiWK+sH1qc/VHCCsA8TL7QnEd32GeT8MUYRaWubwpISa1aJmGNS1OXueXJ9BLGrWAHR/VypZaoDJc3/jG6P4+tfHUa028I53XPL6uss5owq1fNVRA6wWNXkNlXCw11BTBLVIhO37dlGoI2GEmopRq1S8XZ9toRZtn08guOvTy5u/trYGIYSvULO/Pzoafo6L4/pU/S+oUNM1Pg1oXwfd5oQw5LflOSYJoRanIG4ST9VRhFpa5vCkhZrchLzUtW9lFvOP26SXtEUtSMybmoxVEP9nPyuzTj/ykbOYmbnR9Z2oRXn9aG9nRZZixHpb1NS1V8kEQqiFesNxLKUl1PrVolYubzomx9hj1NJyfZpmZ30/N4QQuHZtDW++KS3UtZr3puzt1+HnuLgWNa/tzADg5put4kxPtyeQj/qBfrBQy4AwQs2t4GAehVpaT1pJx6gBnXFq7fcS+TWhcNsQOAmLmhRf5UDnMwyjw5L1hS/UcfJkBUtLq3joofOe3/U6ZxTaRWPza1FT2LePUrBQc8f6wLC62s76tPbv7qxPeZ3Scn3a2+XFhQuyZEat1nDdIcbq+iyXN1Euhxc/cYWaXx9U20gB+iYSAOz6ZCKiBkihUAjUqZ0GTN5cn35PZ3FI2qIGoJn5aU8/T+TXhMJNqMUtz6FQ7k+/SZ2IWk/Nx49X8Nhjsh7RI4+c7AgiDtPGqP1Qbba9uUmtLXZ03pkA6I5RU/RaqPWb61MlE7i5PnuVTCDPLVrt8uL8eZmc5Ob2BFS5kXZGaxTxE8f1aZqm69yjqNcJhtG5M4T8WS+hpv5O3R7ewpDflucY1XH8rGkKp0k7a4saEE6spWkOT0oAWs/jZFHLYleUYrHo+PclUZ4DaAvBIOdTC8dnP7uItTUDP/RDy9i7d8Xze2HbGBRlQVPbMOleR83u+lS4xahxMoE7hQJQKGxic5Nw+XI769d+LWSpHXm9r16Vf3eaFrWZGSm8XnvNe64LItSI2la1Uil8DTUgnkUN8F+fKpUCJidvNH/WV6ixRY2JhJq00xZqbgMxC6GW5uAlokTEmnWir1arHRa1clkgq3Hu9GSbhOvTeu5grk8pho4eraJWa+DjH381dBvs54yKaovahkn3Omo6uT51uzZ2wuz3eelSW6jZ+7dVqCnSFGrbtsn54uRJ77nu4kUZf+ZWQ02hhFyUGmpAPIsaECwsR8WmWq+zbjFqnEzARCIJi1rWrk9AH4sakIz70ytGLcuHRKc6e0kkEwBtoRbM9dmejB9++ExXwcuwJGlR0z2ZwF5HTRE36zNKHTXdCSfUlKWs+3tylxG7UIuW9RmkTdu2SQF24oR3MOvrr8s2eFnUgLaQq1Q2I923uBY1r/qegLy+ao5Uc4NKxNIJTiZgIqE6jt9AUOjq+gxznrTN4UkLNdM0Ua9bnxJjnz4yaVrUKpVKoK2FrBa17dtX8YEP+CcQ9MKi1nZ96p1M0HZ9dgpKL9dnkIUlSoya7oQRapcvK+9E97VysqhFjVELci+2b5dCTe3W4caVK/Lvc9s+SqGEXNQkprhCzd8F3d6dQF1X3dyeALs+mYiYpolyuRz4aVhXodZPFjUnl9DCQvvvy3L+cRJqYZMJDMO5srm0lJUDCb/bbruGcnkTn/rUCSQRFhhHNKgn+LwlE9hdnyMjG67j0W/MFAqF0H9n/7k+lVDr/k6Srs8g10xZ1NRuHW4od31Qi1rA5/ku4ro+/ZD7fapdW+R11c2aBvRHMkFqW0gx7pimGdjtCSQr1JLsrLrEqAHxEwqchN78vB5CTVm9rPtihk0m8JqUq9VqoPP98i+fwM/93BkMDwdb7NK0qNktU7onE9g3ZVe4xajJY4tYW1tzPWeUh5N+c32urqowEmehVi53iqGoQi1Im3bskOc+daqMRkPWVnNCWYH9Y9Sk8PNJvnQlrkXNj0KhgNtvl2m3Cwuyn7JFLR1YqGVAEkItaoxakhN1P1nUnL4/OlrB2NgGLl8uZCrUVL0ztf0MEN6i5nWvqtVqIDeIzESLHoztdM6o2C1ouicTqOsfNJlAHus9ZqL0+TyU5wjSPrulbGTEOSazUunc2ixN1+fYGGFqah0XL5Zw7lwJc3PrjscFtaipz4eGot2vXgi1t73tCr785RcxP89CLU30euwcEEzTxPBw8E3Rnfb+i2pRy0KomaaZWg01RRpCzZpQkEWxWyt292dYi5rX9ZdbZoWPeYtLEskECnsygW4WNRUHaG13sbiJcln0VKj1m0VNUat1/129dn0SEbZskYLFK6FAxdX5WdTars9ofdmvTlpcDMOAaRpYXFxrFe7VUaj1g+szvy3POUETCRT2STtIpxsZGcGOHTuwfft2LC0tYevWrZizlpOOSZwYu6SJK9TcAu1ViY6s55+wQi2sRS1KXTY/epFM0H6tt0UNkAuG1WU7PNwAkfuiyULNHbswHx4OKtTSy/o0DAO33CIteCdOuE8Yx45JEbe42OnWtv+OuBY1rwzMtOKUdYxR6weLGrs+c0KpVMLVq1dbr4NmhI2NjaXWJp2EWhoxaqVSCdPTsqBruZztILcL+yRdn0GegtN4Gk3SoqZ7jBog70Gx2C5pohZidn12EsWi5iTU5M4vvcv6JCKLUHO2qK2vE44cqYJI4LbbrnW0tVKpYH5+HqdPnwYA3HvvFezevYIPfjBikBrkw+b6ercLNq3Mf7aopUN+Wz5gRLGopU1QcdSLwZuG6xMA6nW1RUqs08fGalEjotDWKq97FeR8URb3XlnUSqXNjmLEugoR0zRhGHL7K6Dt2nK7N0GyPqO0QXeiCLWRke5rkaTrM7hFzdv1efRoBRsbBrZsWUOt1m5LvV4HAMzOzmJhYQGAtLh9/vPfwQMPRL9nbttApWFRKxQKWvavPDyc+JH9as8EIs9CrVcWtTiD0U2o3Sy3tMw8Rq1UKoUq3BjGohYEnS1q9gxQXSdle9Fb6fp0r2GXhkVN12tjJaxQM02BoaHua5FFjJqfRe3wYZlEprIlAVWPbLr1ul6vY8uWLQDiz50Vl4krDaGmo9sTYKHG9BD7INCh4+lkUQPiWdXcvrttm7zOExORT50YQbd7cjomrlAL29/i1PkLgtWiZndv6fAQ44Q983NkpOHZVmmBc/88iSLPOhJWqNk3ZFdIV7NobRwOpCvUDMPA3NwaTFPg3LkSrl/v/s7hwzKEYdeudhjLzMxMl3Canp7GLbfcEnvu7KVFTUe3J+A/jvJAvls/QOhoUQOCCYBePWnFESNu3333uwmf/vQZfOYzkU+dGGrSzYNFLW2hZhVneUgkALprqY2MbPjeF6+xw0JN4rTPJ6CK+7b7immKLutrkm2Sew63a4o5Fb596SUp1JRFzTRNzMzMOJ5vamqqZVmLSi8taroKNSAfLn8v9FjtGV/yLNR0t6g5lT9RjIxU8GM/9gaWluK0LBnCWNT6XahZF9w8JBIA3bsTuG0fZcVNqMnSCPlefNyIYlFzEufq+qhjoyYSAOHqVrq5Pzc2gCNH5BjeuXMVgHRzet3HuA+5MtPY2S2cBHlwfQLx57+s0XNGY7ooFAodE5gui5HfADBNs2cLSlSh5ldjTJcFMYxFrd9dn1aLmu7bRyns+33WatGFWr9a04AoQs3ZSqZik9SxUUtzBG2T6nduJTqOHq1ifd3AwsL1lkh3s6YliZP7c9AsaizUmJ5hnbR1WYz8BkAvB28aQk1O9BlnEjTJk0UtyEKQXNZn3ixqyvXpL9Tc+h4LNatQcz/OWqIjanwakIxFrR2fJt2efta0pHDqQ2mU52Chlh56zmiMI1ahpstiFNUikAZRB6Pfohe2OHFamKaJUqkUWqi5bcgeljAPB+m7PvNnUWvHqAV3fU5OTjr+Pf0s1IgogIBt3/OhIfe4M2vmp12oTU1NBe6DYeJCVYkOe4yaik/bufNaz6xpQG8sal7FdXWAhRrTM3QUanGCoZMm6uLl9z23zKksGBoaCixEVB9JapIK0+fSd3121lFL6rxpYnd9eu3zqSgWi5hwSDnO+8Ljh58V2yrUvIanVah1F8kd7iiL4UWY/mx1fQqLhlRCbdeua46ZnmmRpkWt3aeL2j4gAZxMwPQQJXp0GhD94PrMi0UNCLbdk0L1k6QW9aQtamGOs2MVZ3bXp07jw4paLG6//RpKpU3s2LEaaAFxsrz0s0UN8BdqViuql1CTFf+dLWrlchmzs7ORQgm8jpmY2MDw8AZWVgpYXpZjb2MDeOWVtkXNSXynRS8sajq7PYH8P9iwUMsRSqjpZDEYBIuaTk9j1Wo1Fxa1oNcsulDLXx01dU0+8YlT+Nu//WfMza0Huk61Wg21Wq3jvX4Xan5W7E7Xp/t4ME2zZX21JxOUy2UUCoVALsgwMWpE3e7P48crWFuTddamp42eWumdMj+TjFEjIhZqKaPnjMY4kkeh1ssBHHUw5mkQZ2lRS9r1GfacVvJoUWvH8wBDQ6q2V7AF0y4m+l2ohXF9+gk1J4uaYRita1iv1337YVirmz3z0xqfNjIy4nuupLFfzyQfPlXsrM7kaY53Qp8Vn/Elj67PXg7gqBWo87TolcvlwJNsVq7PYrHYZQFyI6pQ8yp4q9ODjBWnrWyC3suJiYmOfpqnPhuFNIRaZ5Hc9gNkEKta2JI49sxPa3za6Oio77mSxm7BS1KoycxavS1qOnlFopBvmTlg5M2ilsUmvcViEWtra6G/kxeIKHDMXC9dnyrofWJiAsPDw4mc0wtrwdu8ZH0CcsHY2NjoeB0EIsL09DTOnDkDIF99NgqlUglEBCGcMzqtoqtWc+9D1hg1N6EGSKvahQsX0Gg411qLblHrFmojI8ESGJIkTYtaHoQaW9SYnqEq6OdFqGVhDo8yIPO26AW1VvXK9bmwsIA9e/ZgcXExlEjzOqcfnRa1fNRRA7oXyDAL5vT0dOL3VFf86hcGFWoyRq3b9WkXFn5WtbBxoSpG7cSJChoN4OWXpVC7665GJvOi9Vom/fDMQi199J3RGEfUk6YueA2ALAZvWNEVpGaTbgQtwJu0Rc2t38WJuUkjmUCn8WHHfi/C9L1CoYDJyUntSyEkhVc/t97z4WFvobZnzwoqlQb27FmxfL97bvIqQBv0eqvjFhelRe3VV8s4dqyC1VUT9fo6brklmwxyq+sz6fmuVCppL4R0fngLQr5bP4AELXjaS9wGaRZPjmGFWt6saUD4RSNti1qcnRvSSCbQbXxYsS+SYe/NzMxMLvtsFLz6lWm2+4BfeY79+y/jwIHncd99V1rvOwk10zRRr9cdzxM2OaZSEZidXUOjQfja12QpjttvzyY+DZDXQfW1pIVaUAs/Ex19ZzTGERZq3gyCUAtKL5IJ4rrik7Co2ZMJdLY2xXF9ArKm3/j4eJJN0pagCQV+BW8BwD4E3Kz9U1NTju8H7VNW67Jyfz711CQAFZ/W+4xPhbqeSQs1nepM9it6rfiML7q5PgF3IZCF6zOsKOlnodaLZIK4+6AmYVHLq+vTKQs0CL3aeihrkhRqVry2O3LLWA7aT2dnZ1s/q4QCVUvtrrv8d6JIE+X+TLoNuuyF3M+wUMsZOlrU3EzfebCo6R5bEYdeuD6zE2r525Qd6Fwkoy6YeYupjErQ3Qm8DDpOfd/vYdfJYhlUUFer1ZZ7c8uW6x2f3XNPtg+FaVnUmPTRd0ZjHNFRqM3PzzvGXuQhmaDfLWpRrTZOOJ0nboX1ZGLU8mNRS0KoDQqGYXg+7EW1qPkJwDhCDWhb1bZubQu1qal1bN+ebSxXWhY1Jn30WvEZX3QUakSEbdu2dUyAhUIhk3ayUGtDRIlaDHWyqFk3Zbf+HOecvcB6P3jB9CdIiQ6/ZwX7dfZ7gKxUKl2/N0yfGhkZwdDQUCtGDQB27VoNXbomadiill/0ndEYR7IoIhsE0zRx6623Zr5JL8eotelnoVYo5D+ZQMdxrBte/es973kd9957A/v2eZ8jrFADgLGxsY7XYfvU7Ows6vX1lnt2z56NzPtlsVjUdv1gvGGhlkN0Dd4slUrYsWOHr8siTQzDCDUR9bNQMwwjUaHmtP1R3OsXVagRtWOU8rIpO8BCLSxec9373ncRTz55FX6GKvsYCCLU7O7PsCJrfHwc1Wq5VU/te75Hj3tdqVS43+UQfWc0xhVdhRogEwu2bt2aaaXqMOKh35MJ0rSoJXGP44gqZUnLy6bsALs+w+IXAxlkLoxiURseHu6YR8L2UyLCzMwMPvrRM3jvey/gwQf12LSchVo+6d9Vqo/RfbuOiYmJTOsFDQ0N4fr16/4Hgi1qYbALoCQeGOIJNdH8v9OiprP4ZotaOLxj1LpjyZyIItQA6f68ePEigGjif2pqCvfffxb3338V4+N3hf5+GlSrVe53OYQtajkkDwMty8UyaNCuaZpau8nikrZFLXuhplyfbYtamE3rs4CFWjisFfXtTE5OBjqH9TqHqUNpdX9G6aeGYWB6ejrTh1Y7bFHLJ/27SjEDS1ChprPlJQn6Xajdd99lbNu2irm5dnZdrVbT2vXJQi08bv1sYmIi0PetYyCMN2J0dLTVP6P2qZmZGa12kmChlk/6e6ViBhJl3m80Gp7H9bPbE8iH67NarYKIIITwP9jGI4+cghAysUCRdQkEP4io1Td5wQxGpVLByspKx3vVajVw/7Ne5zBCjYgwNjaGN954I7JQKxQKgS1/vaBUKvnOi4x+sEWN6UuCbBTc70ItTYsaESUi1AqFQqxNne3rp+5CDWgLBxZqwXDqZ0GtaUB0oQZI92e/hUdwv8sf/dUDGaZJkAW734Vamha1JPecTdI1xEKt/3ASamGsVNYxEPbhYmxsrO+EGpM/uAcyfQkLtXQtakmWiElKqOUlo03dk36PkUwKe18bGhoKZRmLY1EzTdNxezyG6SUs1Ji+JEhQuc716JIgaYtaWkKtXC4ncj6dsuu8YItaOMrlcsdYDuP2BOIJtSi/j2GShoUa05cYhuFZLNM0za5tYvqNQqGQaAak9VxJi9wkrGp5cHsCLNSiYO1vYYPz1cNKsViM5Mbs93mC0R8Wakzf4rVwT0xMaF3GIQmSdu2mZVEDBkuoKeHAQi04qr/VarXQ29Op6xy1UHi/zxOM/rBQY/oWP6HW7+RJqNVqtVjtLZfLuYk5NE2TRVpIVH+LMm7jCjWGyRoWakzf4lb2oVgs5iaeKQ5JWwLU+byqxcchjospL9Y0gIVaFOIINcMwQEQs1JjcwkKN6VtKpZKjm2QQ3J5poCxqaSVhxHF/5kmoFQoFFmohqVQqGB4eDu32VBQKBRZqTG5hocb0NU4L+CC4PdNAidu0hNrIyEjkmlV5EmpsUQtPpVKJVeHfNE0WakxuYaHG9DV292epVMrVoq4bhmGkJtQMw4hUs6pQKOSq1AoLtfAYhoGbbrop8vdZqDF5hoUa09fYRZlO++7lkaS2jnIjivszb8KbXZ/RiLNDQLlc5gLDTG5hocb0NdVqtWOCZ7dnPNK0qAEyoSBs/GDehBpb1HrP0NBQ1k1gmMiwUGP6GiJquT8rlQpP2DFJ24UUZZP2vGXwslDrPWH7FMPoBAs1pu9RFhd2e8bHa7eHpAjj/vTbgUJHDMPITc23foEf0Jg8kxuhRkTvJKKXiegIEX0y6/Yw+UEJNXZ7xqcXC97ExETgMgzDw8O5LLUStcwEE4048W0MkzW56L1EZAL4HQDvAnAHgIeI6I5sW8XkhVqthlqtlqvMQF3phVArlUrYvXs3lpaWfH9f3uLTFJyByDBMUPKSBrMPwBEhxFEAIKLHATwI4FCmrWJygWmamJuby7oZfUGvXEhEhMnJSUxOTuLKlSs4d+4c3nzzza7j8irU2KLGMExQSAiRdRt8IaL3A3inEOJnm69/EsC/EEL8guWYhwE8DAD1ev27H3/88UTbsLKykttFoZ/h+6IvfG/0hO+LnvB90Zde3Ju3v/3tzwoh9jp9lheLmi9CiEcBPAoAe/fuFfv370/0/AcOHEDS52Tiw/dFX/je6AnfFz3h+6IvWd+bXMSoATgNYNHyeqH5HsMwDMMwTN+SF6H2DIBbiWiJiEoAPgjgiabsvRkAAAehSURBVIzbxDAMwzAMkyq5cH0KITaI6BcAfBWACeAxIcS3M24WwzAMwzBMquRCqAGAEOJJAE9m3Q6GYRiGYZhekRfXJ8MwDMMwzMDBQo1hGIZhGEZTWKgxDMMwDMNoCgs1hmEYhmEYTWGhxjAMwzAMoyks1BiGYRiGYTSFhRrDMAzDMIymsFBjGIZhGIbRFBZqDMMwDMMwmkJCiKzbkDhEdAHAiYRPOwXgYsLnZOLD90Vf+N7oCd8XPeH7oi+9uDe3CCGmnT7oS6GWBkT0T0KIvVm3g+mE74u+8L3RE74vesL3RV+yvjfs+mQYhmEYhtEUFmoMwzAMwzCawkItOI9m3QDGEb4v+sL3Rk/4vugJ3xd9yfTecIwawzAMwzCMprBFjWEYhmEYRlNYqPlARO8kopeJ6AgRfTLr9gwqRLRIRE8T0SEi+jYR/WLz/Uki+hsi+k7z/4ms2zqoEJFJRAeJ6C+ar5eI6JvNsfNHRFTKuo2DBhGNE9GXiOglIjpMRPfymNEDIvql5lz2IhF9kYgqPGaygYgeI6LzRPSi5T3HcUKS327eoxeI6O6028dCzQMiMgH8DoB3AbgDwENEdEe2rRpYNgD8WyHEHQDuAfDzzXvxSQB/J4S4FcDfNV8z2fCLAA5bXv8GgN8SQuwA8AaAn8mkVYPNfwXwlBDidgB3Qd4fHjMZQ0TzAD4GYK8QYjcAE8AHwWMmK/43gHfa3nMbJ+8CcGvz38MAfjftxrFQ82YfgCNCiKNCiHUAjwN4MOM2DSRCiLNCiOeaP78JueDMQ96PzzUP+xyAf51NCwcbIloA8ACA32u+JgDvAPCl5iF8b3oMEY0B+D4Avw8AQoh1IcQl8JjRhQKAKhEVAAwBOAseM5kghPh/AJZtb7uNkwcBfF5I/hHAOBHdnGb7WKh5Mw/glOX1q833mAwhoq0A3grgmwDqQoizzY9eA1DPqFmDzn8B8CsANpuvbwJwSQix0XzNY6f3LAG4AOB/NV3Sv0dENfCYyRwhxGkAvwngJKRAuwzgWfCY0Qm3cdJzXcBCjckVRDQM4E8AfFwIccX6mZApzJzG3GOI6N0Azgshns26LUwHBQB3A/hdIcRbAVyFzc3JYyYbmvFOD0KK6TkANXS73hhNyHqcsFDz5jSARcvrheZ7TAYQURFSpP2BEOJPm2+fU2bn5v/ns2rfAPM2AP+KiI5Dhge8AzI2arzp1gF47GTBqwBeFUJ8s/n6S5DCjcdM9twP4JgQ4oIQ4gaAP4UcRzxm9MFtnPRcF7BQ8+YZALc2M3FKkMGeT2TcpoGkGfP0+wAOCyH+s+WjJwB8qPnzhwB8pddtG3SEEJ8SQiwIIbZCjpGvCSF+HMDTAN7fPIzvTY8RQrwG4BQR7Wy+9QMADoHHjA6cBHAPEQ015zZ1b3jM6IPbOHkCwE81sz/vAXDZ4iJNBS546wMR/TBk/I0J4DEhxK9n3KSBhIjuA/B1AN9COw7q05Bxan8MYAuAEwB+VAhhDwplegQR7QfwCSHEu4loG6SFbRLAQQA/IYRYy7J9gwYRfRdkgkcJwFEAH4Z8QOcxkzFE9O8BfAAyo/0ggJ+FjHXiMdNjiOiLAPYDmAJwDsBnAPwZHMZJU1j/d0hX9TUAHxZC/FOq7WOhxjAMwzAMoyfs+mQYhmEYhtEUFmoMwzAMwzCawkKNYRiGYRhGU1ioMQzDMAzDaAoLNYZhGIZhGE1hocYwTF9BRDcR0fPNf68R0enmzytE9D9S/L37ieh70zo/wzCDScH/EIZhmPwghHgdwHcBABH9KoAVIcRv9uBX7wewAuAfevC7GIYZENiixjDMQNC0eP1F8+dfJaLPEdHXiegEEb2PiP4TEX2LiJ5qblcGIvpuIvq/RPQsEX3VsqXMx4joEBG9QESPE9FWAB8F8EtN692/JKJpIvoTInqm+e9tlt/9BSL6BhF9h4g+ks0VYRgmD7BFjWGYQWU7gLcDuAPANwD8iBDiV4joywAeIKK/BPDfADwohLhARB8A8OsAfhpyc/MlIcQaEY0LIS4R0f+ExXpHRH8I4LeEEH9PRFsAfBXArubv3gPgHsjNuA8S0V8KIc707C9nGCY3sFBjGGZQ+SshxA0i+hbkFnFPNd//FoCtAHYC2A3gb+SuMTABqD39XgDwB0T0Z5BbzThxP4A7mt8FgFEiGm7+/BUhxCqAVSJ6GsA+j/MwDDPAsFBjGGZQWQMAIcQmEd0Q7f30NiHnRgLwbSHEvQ7ffQDA9wF4D4B/R0RvcTjGAHCPEOK69c2mcLPv3cd7+TEM4wjHqDEMwzjzMoBpIroXAIioSER3EpEBYFEI8TSARwCMARgG8CaAEcv3/xrAv1EvmhukKx4kogoR3QSZhPBMqn8JwzC5hYUawzCMA0KIdQDvB/AbRPTPAJ4H8L2QLtD/03SZHgTw20KISwD+HMB7VTIBgI8B2NtMODgEmWygeAHA0wD+EcCvcXwawzBuUNvazzAMw6RNj0uGMAyTc9iixjAMwzAMoylsUWMYhmEYhtEUtqgxDMMwDMNoCgs1hmEYhmEYTWGhxjAMwzAMoyks1BiGYRiGYTSFhRrDMAzDMIymsFBjGIZhGIbRlP8PCs37pxUtfwgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_median_with_quantiles(subset,'timestep','timestep','AggregatedAgentDemand')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}