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+{
+ "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)\r\n",
+      "Requirement already satisfied: pathos in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from cadCAD) (0.2.5)\r\n",
+      "Requirement already satisfied: pandas in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from cadCAD) (1.0.3)\r\n",
+      "Requirement already satisfied: fn in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from cadCAD) (0.4.3)\r\n",
+      "Requirement already satisfied: funcy in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from cadCAD) (1.14)\r\n",
+      "Requirement already satisfied: wheel in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from cadCAD) (0.33.6)\r\n",
+      "Requirement already satisfied: tabulate in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from cadCAD) (0.8.2)\r\n",
+      "Requirement already satisfied: pox>=0.2.7 in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from pathos->cadCAD) (0.2.7)\r\n",
+      "Requirement already satisfied: dill>=0.3.1 in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from pathos->cadCAD) (0.3.1.1)\r\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)\r\n",
+      "Requirement already satisfied: multiprocess>=0.70.9 in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from pathos->cadCAD) (0.70.9)\r\n",
+      "Requirement already satisfied: pytz>=2017.2 in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from pandas->cadCAD) (2018.7)\r\n",
+      "Requirement already satisfied: python-dateutil>=2.6.1 in /home/aclarkdata/.local/lib/python3.7/site-packages (from pandas->cadCAD) (2.8.0)\r\n",
+      "Requirement already satisfied: numpy>=1.13.3 in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from pandas->cadCAD) (1.18.2)\r\n",
+      "Requirement already satisfied: six>=1.7.3 in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from ppft>=1.6.6.1->pathos->cadCAD) (1.14.0)\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install cadCAD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/aclarkdata/anaconda3/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
+      "  import pandas.util.testing as tm\n"
+     ]
+    }
+   ],
+   "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",
+    "import seaborn as sns\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": [
+    "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",
+    "\n",
+    "def plot_averaged_runs(df,aggregate_dimension,x, y,run_count,lx=False,ly=False, suppMin=False):\n",
+    "    '''\n",
+    "    Function to plot the mean, median, etc 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",
+    "    run_count = the number of monte carlo simulations\n",
+    "    lx = True/False for if the x axis should be logged\n",
+    "    ly = True/False for if the x axis should be logged\n",
+    "    suppMin: True/False for if the miniumum value should be plotted\n",
+    "    Note: Run aggregate_runs before using this function\n",
+    "    Example run:\n",
+    "    '''\n",
+    "    mean_df,median_df,std_df,min_df = aggregate_runs(df,aggregate_dimension)\n",
+    "\n",
+    "    plt.figure(figsize=(10,6))\n",
+    "    if not(suppMin):\n",
+    "        plt.plot(mean_df[x].values, mean_df[y].values,\n",
+    "             mean_df[x].values,median_df[y].values,\n",
+    "             mean_df[x].values,mean_df[y].values+std_df[y].values,\n",
+    "             mean_df[x].values,min_df[y].values)\n",
+    "        plt.legend(['mean', 'median', 'mean+ 1*std', 'min'],bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
+    "\n",
+    "    else:\n",
+    "        plt.plot(mean_df[x].values, mean_df[y].values,\n",
+    "             mean_df[x].values,median_df[y].values,\n",
+    "             mean_df[x].values,mean_df[y].values+std_df[y].values,\n",
+    "             mean_df[x].values,mean_df[y].values-std_df[y].values)\n",
+    "        plt.legend(['mean', 'median', 'mean+ 1*std', 'mean - 1*std'],bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
+    "\n",
+    "    plt.xlabel(x)\n",
+    "    plt.ylabel(y)\n",
+    "    title_text = 'Performance of ' + y + ' over all of ' + str(run_count) + ' Monte Carlo runs'\n",
+    "    plt.title(title_text)\n",
+    "    if lx:\n",
+    "        plt.xscale('log')\n",
+    "\n",
+    "    if ly:\n",
+    "        plt.yscale('log')\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)\n",
+    "    #plt.savefig(y +'_FirstFiveRuns.jpeg')\n",
+    "    \n",
+    "    \n",
+    "def aggregate_runs_param_mc(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",
+    "    #min_df = df.groupby(aggregate_dimension).min().reset_index()\n",
+    "    #max_df = df.groupby(aggregate_dimension).max().reset_index()\n",
+    "    return mean_df,median_df\n",
+    "\n",
+    "def param_dfs(results,params,swept):\n",
+    "    mean_df,median_df = aggregate_runs_param_mc(results[0]['result'],'timestep')\n",
+    "    mean_df[swept] = params[0]\n",
+    "    median_df[swept] = params[0]\n",
+    "    #max_df[swept] = params[0]\n",
+    "    #min_df[swept] = params[0]\n",
+    "    for i in range(1,len(params)):\n",
+    "        mean_df_intermediate,median_df_intermediate = aggregate_runs_param_mc(results[i]['result'],'timestep')\n",
+    "        mean_df_intermediate[swept] = params[i]\n",
+    "        median_df_intermediate[swept] = params[i]\n",
+    "        #max_df_intermediate[swept] = params[i]\n",
+    "        #min_df_intermediate[swept] = params[i]\n",
+    "        mean_df= pd.concat([mean_df, mean_df_intermediate])\n",
+    "        median_df= pd.concat([median_df, median_df_intermediate])\n",
+    "        #max_df= pd.concat([max_df, max_df_intermediate])\n",
+    "        #min_df= pd.concat([min_df, min_df_intermediate])\n",
+    "    return mean_df,median_df\n",
+    "\n",
+    "\n",
+    "def param_plot(results,state_var_x, state_var_y, parameter, save_plot = False,**kwargs):\n",
+    "    '''\n",
+    "    Results (df) is the dataframe (concatenated list of results dictionaries)\n",
+    "    length = intreger, number of parameter values\n",
+    "    Enter state variable name as a string for x and y. Enter the swept parameter name as a string.\n",
+    "    y_label kwarg for custom y-label and title reference\n",
+    "    x_label kwarg for custom x-axis label\n",
+    "    '''\n",
+    "    sns.scatterplot(x=state_var_x, y = state_var_y, hue = parameter, style= parameter, palette  = 'coolwarm',alpha=1, data = results, legend=\"full\")\n",
+    "    title_text = 'Effect of ' + parameter + ' Parameter Sweep on ' + state_var_y\n",
+    "    for key, value in kwargs.items():\n",
+    "        if key == 'y_label':\n",
+    "            plt.ylabel(value)\n",
+    "            title_text = 'Effect of ' + parameter + ' Parameter Sweep on ' + value\n",
+    "        if key == 'x_label':\n",
+    "            plt.xlabel(value)\n",
+    "    plt.title(title_text)\n",
+    "    if save_plot == True:\n",
+    "        filename = state_var_y + state_var_x + parameter + 'plot.png'\n",
+    "# #         plt.savefig('static/images/' + filename)\n",
+    "#         plt.savefig(filename)\n",
+    "        lgd = plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
+    "        #title_text = 'Market Volatility versus Normalized Liquid Token Supply for All Runs'\n",
+    "        plt.title(title_text)\n",
+    "        plt.savefig('static/images/' + filename, bbox_extra_artists=(lgd,), bbox_inches='tight')"
+   ]
+  },
+  {
+   "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": [
+    {
+     "data": {
+      "image/png": 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8889hbGz8t8aoUCiQlJTENTrxeDxuBmtra/uHjVJyuRwZGRlcyCYmJkJDQwNt2rSBsrIy6urqUFRUBDMzMy5k7ezs0LZt2781XoZhmLcZC9V/KD8/H3369EF1dTW0tLSgpqYGfX19PHz4EEpKSujfvz/09PRw8eJF8Pl86OnpITs7G3w+H8rKylBXV0fPnj1x48YNVFZWYuDAgQgMDERQUBBu374NdXV1SKVSDB48GKtXr4ZQKPzbYyUiZGdnc41OpaWlcHNzg4eHB5ycnP7UnlaFQoGsrCwkJiZyQauqqgoDAwOoqqpCIpFwe2efl4zt7OxgZmb2hwHOMAzztmOh2goSEhIwbNgwyOVy6OnpgcfjQSgU4vr16wAAW1tb+Pj4IDg4GDKZDEKhEOnp6ZBIJFBTU4OKigqMjY3h5OSE8+fPg4jw2WefYeLEiQgMDOQO5a+rq0Pv3r2xevVq2NnZ/eNxFxcXczPYjIwMdOnSBR4eHnBxcfnTa7rPO5Lj4+O5oAWATp06QUNDA1KpFPn5+aiqqoKNjQ0XsqxkzDDMu4iFaiu5fv06Jk2aBABo3749pFIp3N3d8d1330GhUKBDhw4ICgrC+vXrUVBQAD09PaipqeHx48fQ0NAA0LS3tXfv3qivr8eVK1ego6ODnTt3wtHREQEBAbh37x50dXVRVVUFV1dXrFq1Ci4uLq0y/qqqKkRHRyMyMhJxcXGwsbHhjkxs3779n/4cIkJBQQESExMRHx+PhIQENDY2wtLSEtra2lAoFCguLkZWVhY6derUrMu4Xbt2rfJdGIZhXhcWqq3o+PHj8Pf3B4/Hg7GxMWpqajBixAjs3bsXMpkMWlpaCAoKwuXLl3HlyhXweDx06dIFv/zyC/fnzxuVJk+ejB9//BHx8fEQCAQ4ffo0lJSUEBgYiPv376Nt27YoLy+Hvb09Pv/8c/Tq1avVvkdDQwNiYmIQFRWF6OhoGBkZwd3dHR4eHn/rfOHi4mKuVJyQkICamhpYW1tzIVpeXo6UlBRoaGg0C1mBQMBKxgzDvFVYqLayzZs3Y9euXVBSUoJAIEBZWRkmTZqE4OBgNDY2Qk1NDX5+fmjfvj3Wr18PIoJIJEJVVRXS09Oho6MDhUIBNTU1mJmZYdKkSdi4cSMKCwvRvXt3XLhwAVlZWQgMDERMTAw6dOiA0tJSiEQiLFu2DEOGDGnV7yOTyZo1OqmpqXGNTjY2Nn9ra01ZWVmzkC0rK4ONjQ2MjY3B5/NRUVGBtLQ0lJeXw8bGhgtZGxsbVjJmGOaNxkL1JViwYAF+/PFHSKVSWFtbIz8/HwsXLsQXX3yB+vp6qKqqYtCgQfjss88wc+ZMVFRUgM/nw9PTE+fPn4dCoYC+vj6e/18zbNgwWFhY4Msvv4REIsH48eOxf/9+xMbGIjAwEHFxcTA2NkZxcTGMjIywYMECeHt7t/osj4jw5MkT7kziqqoqdO/eHe7u7ujcuTP4fP7f+tzKyspmjU8FBQUQi8UwNzeHuro6amtrkZ6ejoyMDJiamjbbM/tXStMMwzAvGwvVl0ChUMDHxwfJycmorq6Gg4MDMjMzsXbtWixatAg1NTXg8/mwtrbG4cOH4e/vj8jISABA79698ejRI2RkZKBNmzZQKBTQ0NCAiooKVqxYgcjISBw9ehSqqqpYu3YtFi5ciOjoaAQEBCAxMRECgQCFhYXQ0dHB3LlzMWXKlJdWQi0oKOBmsDk5OejWrRs8PDzg7OwMTU3Nv/25NTU13NGKCQkJePr0KSwtLSEWi6Grq4uGhgY8efIEycnJUFNTa7aVRygUspIxwzCvDQvVl6SxsRGDBw9GTU0NSkpK0LVrV6SkpGDHjh2YN28eysrKoKysjLZt2+L48eO4cuUKduzYAT6fj/bt26Nbt244fvw4AMDAwAASiQQ8Hg/W1tZYt24dli9fjsjISBgYGODIkSPo27cv7t69i8DAQCQlJcHKygoFBQXg8XiYOXMm5syZ86fOF/67KioquGfDJiYmws7ODu7u7nBzc/vHe1br6+uRlJTEzWYzMzMhFAphb28PQ0NDyGQyZGZmIjk5GaWlpc26jG1sbLhGMIZhmJeNhepLVFFRgX79+kFXVxfZ2dno3r07YmNjceDAASxYsAC5ubncw8937NgBAwMDzJw5EzKZDBKJBBMnTsSZM2eQnZ0NAwMDSKVS6OjooK6uDmPGjMHHH3+MiRMnIjs7Gw4ODvj+++9hZmaGO3fuYN26dUhJSYGdnR0KCgpQX1+PyZMnY+HChS99XbK+vp57Nuz9+/fRqVMnbh3WxMTkH3++RCJBamoqt40nPT0dpqamcHBwgLm5OZSUlJCTk4OkpCQ8efIEJiYmzRqgOnTo0ArfkmEYpiUWqi9ZTk4OBg0aBHNzc6Snp6NHjx64d+8ejh49iqVLlyIlJQUAwOPxMH/+fPj6+mLChAlITEwEEcHFxQWGhob49ttvwePxYGZmhvLycmhra0NFRQXr169HTU0NFixYgOrqagwbNgwnTpyAuro6bt++jXXr1iE9PR1dunRBQUEBnj17Bh8fHyxfvvwvny/8d8hkMsTHx3PrsFpaWtyRiZaWlq1yhrBUKkVaWhq3jSc1NRWGhoZwcHCAWCyGpqYmcnNzkZSUhOTkZPD5/BYl4/99hB7DMMzfwUL1FYiJiYG3tzecnJyQlJSEnj174vbt2zh58iQCAgJw9+5dyOVy8Pl8DBgwAPv27UNwcDD27dsHPT09AICfnx82b96MgoICmJiYoLGxETo6OqiurkbXrl2xfft2/Otf/8Lu3buhpKSEhQsXYt26deDxeLh58ybWr1+PJ0+ewM3NDYWFhcjOzsaoUaPw+eefv7JmHyJCWloaF7D19fXcDNbBwaHVytMymQxPnjzhjlVMSkpCmzZt4ODgAHt7e7Rv3x5FRUVcyD579gzW1tbNSsb/ZE2YYZj3FwvVVyQiIgJz586Fs7MzUlJS0KtXL1y7dg1nzpzBjh078OOPP0Iul0NTUxPm5uYICwtDbGwsZs+eDS0tLW5rTnFxMY4fPw4VFRVYWVkhPz8fRkZGKC0txYwZM+Dn54dx48bh8uXL0NPTw+7duzFmzBgAwL///W+sX78emZmZ6NmzJwoLC5GSkoKhQ4di1apVrVKa/Styc3O5gM3Ly4OLiws8PDzQrVu3Vi1RKxQKZGZmNju/WFtbG/b29lzJ+NmzZ0hOTkZycjKePHmCjh07tigZsyfzMAzzR1iovkIHDx7Ehg0b0KVLFy7YfvrpJ5w9exbHjx9HaGgoiAi6urrg8/k4efIkOnTogAkTJiAnJwcAYGpqilmzZsHf3x+lpaUQCoWQyWTg8/lcKG/ZsgUmJibw8vJCSkoKF9JOTk4AgGvXrmHDhg3Izs5G3759UVxcjJiYGPTt2xerV6+GhYXFK783ZWVl3LNhU1JS4ODgwD0b9vlsvbUQEXJycprtlVVRUYGjoyMcHBxgY2OD+vp6LmSTkpKgoqLSLGTNzc1ZyZhhmBZYqL5i69atw7Fjx2BjY4OioiL07NkT33//Pc6dO4dr164hKCgIPB4POjo6qK+vx65duzBkyBCsXr0ax44dg6mpKYqLi7F27VpcunQJZ8+eBZ/PR+fOnZGeng6RSIScnBz07t0b27ZtQ1RUFKZNm4bS0lL06tULYWFhXDfulStXsHHjRjx9+hSDBg1CaWkp7ty5Aw8PD6xevRoODg6v5R7V1tbiwYMHiIyMRExMDIRCIVcmNjIyavXrERHy8/ObhaxcLoe9vT0cHR1hb28PPp/fLGRLSkpgbW3d7GAKLS2tVh8bwzBvFxaqr8Hs2bMRHR0NQ0ND1NXVwcPDA2fOnMHZs2eRmJiIZcuWQVVVFaqqqqitrcXChQuxaNEihIeHY+HChTAyMsLTp08xZMgQDBgwAAsWLEBFRQXX+NPQ0IC2bduisLAQixcvxsyZM7F582Z8+eWXkMlkmDx5Mv71r39xa5iXLl3Cxo0bkZeXh48++ggVFRW4evUqnJycsGrVKri5ub22e9XY2Ii4uDiuTPz8IfAeHh5cp29rI6IWRyvW1dVx5WIHBwe0a9cOaWlpXNA+fvwYRkZGzRqgDAwMWMmYYd4zLFRfA4VCgVGjRqGqqgo8Hg+qqqpwcXHBqVOncPr0aRQVFWHWrFnQ0NCAXC5HY2MjBg0ahD179iA/Px8TJkxAeXk5+Hw+eDwedu7ciW3btiEiIgJqamrw8PDAo0ePYG1tjby8PBgYGGD79u0Qi8WYPn06Tp8+DQ0NDXz55ZeYO3cuN66IiAhs2rQJBQUFGD16NGpra/HDDz/A2toaK1asQJ8+fV7jXWu6b6mpqdyj6+RyOTeDtbOze6nl2GfPnjU79am8vBx2dnZcyJqZmSE7O7vZbJbH4zULWXNz85e6V5hhmNePhepr0tDQgAEDBsDQ0BCFhYXcFpATJ05wT7bx8fGBuro6qquroaGhgU6dOuHUqVPQ1dXF0qVLce7cOdjb2yMxMRErVqyAvr4+Fi9ejJqaGtja2kJDQwP5+fmwtrZGfHw8PvroI2zatAllZWXw9vbGgwcP0LFjRxw7dgw9e/bkxvbDDz8gKCgIRUVF8PHxQUNDA86ePQtTU1MsWbIEI0aMeI13rsnzddHnM9ji4mK4urrCw8MDXbp0gZqa2ku9fkVFBdf0lJCQgKKiIojFYi5kLS0tUVpa2ixki4uLYWVlxYWsWCxmJWOGecewUH2Nnj17hv79+6NHjx6Ijo6GnZ0dRCIRjh49irCwMOjp6cHLywtKSkooKyuDkZERpFIpTp06BWtra5w5cwb+/v6ws7NDWloanJycsHHjRixatAg3b96Empoa+vXrh7t370IoFKKxsRGlpaUICAjAmDFjcOvWLUyYMAH5+fno2rUrTp8+DTMzM258Fy5cQHBwMEpKSjB+/HjI5XKcOnUKbdq0wfz58zF27Ng35kjAkpISLmAfP36Mzp07w8PDA66urtDR0Xnp16+urkZSUhJ3IEVubi6srKy4kLWxsYFUKkVqaiq3lSc9PR2GhobNGqAMDQ1ZyZhh3mIsVF+zJ0+eYMiQIRg/fjzOnj0LT09PGBsbIzQ0FKdOnYKpqSm8vLxQW1uLiooKiEQiPH36FF9//TUGDBiAzMxMTJgwgXt0XHFxMfbu3YvU1FSsXr0aDQ0NsLe3R7t27ZCQkAB3d3dER0fD2toau3btgrm5Ob7++musXLkSdXV18PLywqFDh5rt0zx37hy++uorbluPiooKDh8+DDU1NcyePRvTp09/Y8IVaAq4+/fvIzIyEo8ePYKlpSVXJn5VpynV1tYiJSUFCQkJiI+PR3Z2NszNzbnGJ1tbW/D5fGRmZnIhm5ycDCJqFrIikYiVjBnmLcJC9Q1w9+5d+Pj4YMmSJdizZw+8vLygo6ODgwcP4sSJExCLxfD29kZeXh5qampgZWWF1NRULF++HHPnzoVMJoOfnx8uX74MT09P3LhxA1OnTsXUqVMxffp0PHjwAKqqqhg5ciRu3bqFdu3awcjICNHR0Zg8eTK++OILAMD8+fNx6NAhKCsrw9/fH6tXr24WlmfOnMFXX32FiooKTJs2DVpaWggJCYFUKsX06dMxd+5cqKqqvq7b+EISiQSxsbGIiorCvXv30KFDBy5gBQLBK5sVNjQ0cCGbmJiIx48fo1OnTnBwcICjoyPs7OygqamJ4uLiZiFbWFgIS0tLLmTFYvErOQmLYZi/h4XqG+L8+fNYtGgRAgMDsX79ekybNg0AEBISgpMnT6JLly6YOHEiYmNjoaSkBAMDAzx9+hTDhw/Htm3bwOPxcPToUfzf//0fPDw8kJCQAAMDAxw+fBhnzpzBhg0b0NjYiM6dO0MgEODGjRvo27cvkpKS0NjYiKCgIAwYMABlZWUYM2YMbt68iTZt2mDv3r0YNWoUN06FQoGzZ89i8+bNqK6uxowZM9ChQwfs2bMH5eXlmDBhAhYvXvxGnkgkl8uRnJzMPVlHWVmZC1ixWPxKZ9uNjY1IS0vjGp9SU1NhbGzMnfrk4OAAXV1dbsb7PGTT0tJgYGDQ7PF3RkZGrGTMMG8IFqpvkD179mDr1q3YtGkTli9fjiVLlqC2thb79u3DiRNKOFR8AAAgAElEQVQn4OzsjIULFyIiIgK6urpQUVFBQ0MDTE1NcerUKWhrayM1NRUTJ06Euro6jIyMEBMTg+DgYHTu3BlTpkxBUlIS1NTUMHbsWNy+fRsKhQI9evTAjz/+CHd3d2zfvh0GBgaIi4vDmDFj8OTJE1hbW+P06dOwtbXlxqpQKBAWFoYtW7agtrYWs2fPhomJCXbu3Inc3FyMGTMG/v7+0NfXf4139LcRETIzM7l12LKyMu7ZsE5OTq98xi2TyfD48WMuZJOTk9GuXTvuQAp7e3u0bdu22RN5njdAKRSKZiFrYWHBSsYM85qwUH3DrFy5EhcuXMD69eu5mWtJSQn27NmD48ePo3v37li/fj0OHjwIU1NTlJeXo127dqipqUFYWBjMzc3R0NCAOXPm4JdffsGIESNw/vx5DBo0CFu3bsX27duxbds2KBQKdO7cGZ07d8a5c+fQq1cv1NbWIjY2Fn5+fpg/fz54PB5OnTqFefPmobKyEn379sWJEyfQpk0bbrwKhQKnTp3Cli1b0NDQgNmzZ0MsFmPz5s1IS0uDl5cXPv/8cxgYGLzGu/rHCgsLcffuXURFRSEzMxNdunSBh4cHXFxcXkuHrlwub3G0oq6uLncghYODAzp06AAiQklJCVcyTkpK4krGz0NWLBa/kmYthmFYqL6RJk+ejNTUVPj7+2PJkiXYunUrMjMzsXv3bi5YQ0JCsGHDhmadv3FxcThw4AB69+4NANxrhgwZgri4OEilUoSGhkKhUGDq1KnIzMyEqqoqpkyZgsjISBQVFeGTTz7B+fPnoaWlhR07dsDFxQUKhQJffPEFduzYAYVCgRkzZmDr1q3NZkMKhQLHjx/H9u3bIZFIMGfOHHTt2hWbNm3Co0ePMGjQIKxatapZd/GbqrKyEtHR0YiMjER8fDxsbGzg4eGB7t27o127dq9lTESE7OzsZgdSqKmpcd3FDg4OXBm4trYWqampXMimpaWhQ4cO3GzWzs6OlYwZ5iVhofoGUigUGDZsGABg6tSpWL58Ofbv34/ExETs2rUL3377LT744AOcO3cOixcvRrdu3fDw4UP06dMH165dwxdffIHp06cDAOLi4jBlyhS0b98elpaWiIiIwIoVKzBz5kysWbMG+/fvBxHB0dERffr0waFDh9CtWzeYmJjg/PnzGDJkCDZv3gxtbW1UV1dj0qRJCA8Ph5aWFjZt2oRZs2a1GPvRo0exY8cOSKVSfPbZZ3B3d8fGjRsRGRmJ3r17Y9WqVbCxsXnl9/XvaGhowMOHD7lnw3bs2JFbh+3UqdNrGxcRIS8vj+suTkhIAIBmIWtqagolJSXI5XJkZWU1a4CSSqXNuowtLS1ZyZhhWgEL1TdUTU0NBgwYAGtra/Tq1QsbNmzAsWPHcP/+fWzfvh2HDx+Gp6cnbt++jSlTpqBr1664f/8++vfvj+vXr8PLywvBwcHg8XioqanBzJkzERsbi6lTp+LgwYPo3LkzQkJCkJaWhlmzZiE/Px8qKirw9fVFbGwsHj16hGnTpuHWrVvIyMjA6tWrMXHiRABAeno6xowZg8TERJiYmODIkSPw9PRsNn6FQoEjR45g+/btkMvlmDdvHvr06YMvv/wS165dg5ubG1atWoUuXbq8jtv7t8hkMiQkJHBl4uenV7m7u8Pa2vq1zvyICIWFhdxhFPHx8ZBIJM2OVhQKhdwYS0pKuJlscnIy8vPzIRKJmq3NspIxw/x1LFTfYIWFhejfvz8++ugjGBoaYteuXTh79ixu3ryJbdu2ITQ0FD179kRiYiI++eQTWFpaIjk5Gd27d0d8fDxEIhGOHTvGdeLu2rULW7Zswbhx4xAbG4unT59i7969cHV1xYoVK3Ds2DEoKSnBwcEB3t7e2LFjB8zMzDBw4ECEhITAzMwMO3fu5GaZERERmDFjBkpKSuDq6oqTJ0+2mL0pFAocOnQIu3btAtD0XNiBAwfiyy+/REREBBwdHfH555+jR48er/bm/kNEhMePH3ONTjU1NVyjU+fOnd+IWV9JSUmzNdnKykrY29tzQSsSibijHevq6pCWlsaFbGpqKtq1a9dsNmtsbMxKxgzzB1iovuGSk5MxYsQILF68GGVlZThy5Ah++OEHXLlyBV999RUOHTqE3r17Izc3F15eXmjXrh2Ki4thbGyMhoYGroHp+VpmdHQ0pk+fDoFAAFdXVxw8eBBTp07FF198gTt37mDOnDkoKysDj8eDn58fHj9+jCtXrmDixIkoKSlBREQEPv30UwQGBkJVVRUKhQJbtmzB+vXrIZFIMGbMGOzdu7fFlhqFQoGDBw9i165d4PF4mD9/PoYNG4agoCCcO3cOFhYW8Pf3x4ABA17Hbf7H8vPzuYDNycmBs7Mz92zYN2V7UVlZGXfqU0JCAp49ewZbW1uuu9jKyor7YUAulyM7O7tZA5RUKoVYLG5WMubz+a0wMgmAOwDyAdQA0AHQCYA7gNb4fIZ5dViovgVu3ryJyZMnY+vWrbhz5w4iIiIQHh6O8PBwBAcH4+DBg/jwww9RUVGBkSNHgoigpKQEmUwGCwsL3L17F4cOHeJmg1VVVZg8eTLS09OxbNkybNu2DR06dMDhw4fRtm1bLFy4EOfPnwePx4OdnR1mzZqFoKAgqKqqYs6cOThw4AAqKyuxceNGbu23oaEBvr6++O6778Dn87Fy5Ur4+/u32PupUChw4MAB7N69G3w+H/Pnz8fIkSOxZcsWnDx5EkZGRli8eDFGjhz5Rp3S9FeUl5fj3r17iIyMRFJSEuzs7ODu7o7u3bs365x+3aqqqpo9JCA/Px82NjZcudja2rrZ1qJnz541C9n8/HyYm5s3Kxnr6ur+hREUADgP4CSAegAyADwABEAZgDaAcQA+AvBmd48zzHMsVN8Sp06dwooVK3DixAmEhobi7t27uHTpEs6dO4egoCCEhISgb9++aGhowNixY5GXlwdTU1NkZGRg6NChOHXqFNauXcutiwJAcHAw9uzZg9mzZyM5ORl37txBcHAwRo0ahYiICCxatAh1dXUAgIULF6KkpAQnTpzAyJEjIRAIsHv3bnTt2hU7d+6EsbExACA3Nxc+Pj64d+8e2rVrh/3797/wAH6FQoF9+/Zhz549UFNTw8KFCzF69Gjs3LkThw8fho6ODvz8/DB+/Pi3NlyBprLqw4cPERkZiQcPHsDMzIxrdHp+z94UtbW1SEpK4kI2JycHFhYWXMiKxWKoq6tzr6+vr2/RZdymTZtmXca/XTK+BWAFgEYAbQC86AEIDQAqAGgC2ALApdW/M8O0Nhaqb5GtW7di7969+PHHHxEQEMCVZk+dOoUvv/wSISEh6NevHxQKBaZPn4579+6hR48euH79OqZPn46QkBD4+Phg3bp1XFDdvn0bs2bNgoODA/r374+goCAMHjwY27Ztg0Qiwbx583D58mUoKytDLBZj6dKl3JNuVq1ahYsXL+LOnTuYPXs2li5dyn3uL7/8gkmTJuHp06ewtbXF8ePHYW9v3+I7KRQK7N27F3v27IG6ujoWLVoEb29v7N+/HwcOHACPx4Ovry9mzZr1Uh/t9ipIpVLEx8cjKioKd+/ehba2Nhewz5+F+yZpaGhAcnIyF7IZGRkQCARcyNra2jbbw6tQKJCVldWsAUoikTR7/J2FhQVUVSMBLEVTmffP7AGuAVAH4F8AXF/GV2WYVsNC9S2zZMkSXLlyBT///DNmzZqFiooK/PTTTzh27BjWr1+P/fv3c+uSy5cvx7lz5+Dl5YWwsDD4+vri+PHjEIvF+Pbbb7lZR1lZGfe0mrVr1yI4OBgymQyHDh2Cra0tzpw5g+XLl0OhUEAul2PhwoUAmhqfevbsidGjR2Pt2rVQUVHB1q1b8cEHH3DjPXDgAJYvX47a2loMGjQIoaGhaNu2bYvvJZPJ8PXXX3PrsUuXLsXHH3+Mw4cP4+uvv0Z9fT2mTp2KefPmNZstva2ICGlpadyRiRKJhAtYe3v7N6LR6X81NjYiNTWVC9m0tDSYmJhwIWtvb9+iY/jZs2fNTn9qbEyFv38c+Hw9aGq2hba29p9cl61BU3n4FIA3a4bPML/GQvUto1AoMH78eDx9+hTh4eHw9vYGj8fDhQsXcPToUaxbtw579+7FoEGDADTNbnft2oWJEyfi6NGj+PTTT7l/xMPCwrgSpEKhwLp16xAaGoply5bhyZMnOHv2LFasWAFfX1+UlZXB19cXd+7cgbKyMqytrREYGIjg4GAkJydj5cqVyM7ORmhoKPr27YstW7ZwRxQ2NjZi6dKl+OabbwAAs2fPRlBQ0AuDQyaTYffu3di3bx+0tbWxdOlSjB49GmfPnsWOHTu4x9AtWbLknTpYPjc3l3v4ekFBAVxcXODu7o5u3bq9sT9EyGQypKencyGbkpICAwODZtt4/veYSqk0EI2NYaioUENNTTX8/XMxfrweXFzaQUdHG9raOlBXV8eLJ+2FACYB8HsF304OIA1AOQAFmtZ3rfDnZtbM+4yF6ltIJpNh8ODB0NLSwrFjxzBkyBC0b98eZ86cwZEjRxAQEIA9e/ZgyJAhAICjR49i9erVmDp1Kk6ePAlPT09IpVLcvXsXR44cgYvLf9eqrly5gnnz5sHNzQ3e3t5Yvnw5nJycEBISAh0dHRw5cgRr1qyBsrIyGhsbsWjRInTo0AGBgYGwsbHBihUruKBdvnw5dwgFABQXF2PSpEm4fv06dHR0sGXLFkyePPk3v+POnTtx4MAB6OvrczPX8PBwfPXVV8jOzoa3tzc+//zzN/Z84b+rtLQUd+/eRWRkJFJTU+Ho6Ah3d3e4ublBT0/vdQ/vN8nlcmRkZHD7ZJOSkqCvr8897s7RUYh27T4FoA+g6QeqadNiMXNmR4hESqipqUZ1dQ0UCjm0tZsCVkdHG1paWv9ZVpCgaZ31MoCX9YNGBYAIAN8CKMN/G6eU/vPLC4A3ANFLuj7ztmOh+paqqqpC37594ezsjKCgIAwcOBA2NjY4fPgwF6y7d+/G0KFDATTtKf3ss8/g4+ODy5cvw8jICG5ubjh06BA2bdqEsWPHcp9dWFiICRMmoKKiAlu3bkVQUBBycnKwd+9efPDBB8jPz8esWbMQFxcHHo8HS0tLbNmyBdu3b8e///1v+Pn5wdTUFIGBgTAyMsLOnTubrafGxMRgwoQJSE9Ph0AgwLfffgsPD48Xfs/Gxkbs2rULISEhaNOmDZYtW4ZRo0bhxo0b2LRpE1JSUjBixAisXLkSRkZGL/emvwa1tbXcs2FjYmIgEom4MrGhoeHrHt7ver7G+rzDWF09Al5eySAygI6OLg4dKsPt25Xg85XA4ynBx8cY6em1iI+vRF1dI4yMePj4Y3Xo60uhqakJbW0dtG0rgZLSRmhpeb2EEf8IYD2ayswvWu+VoiloCcAwACsBvFmPOmTeAMS8tXJyckgsFlNgYCAVFRWRk5MTzZkzh4iIjhw5QkKhkC5evMi9Pjo6mqysrMjPz48GDBhA7u7uFBISQubm5hQYGNjss+VyOfn7+5NQKKSDBw9ScHAwCQQCCggIILlcTkREe/bsIYFAQGKxmAQCAW3bto1++ukncnJyIk9PT7p37x4tXLiQBAIBLV68mOrr65td4+TJk2RoaEiqqqrUq1cvys7O/s3vKpFIKCgoiKytralHjx70/fffc9/Jy8uLBAIBTZ8+nbKyslrl3r6JJBIJ3bt3j3bu3Enjx48nPz8/OnbsGD158oQUCsXrHt4fUijWUUNDFyosdKP0dFt6+NCERo7UpAsXzKmoyI3q6vrTpUvdqa5uCDU2DqX9+x3Jz09IMtlQqqzsRbm53Sg3tyOFhHSjmTNncn/fcnJyWuH7nyCibkTUn4iG/8GvoUTkTETziEjyD6/LvGtYqL7lYmNjSSQSUUhICGVnZ5OtrS0tX76ciIiOHj3aIljT0tLI0dGRxo8fTxMnTiQ7OzsKCwsjOzs7Gj9+PEkkzf+RuHjxIllaWtKMGTPozp075OTkRP3796e8vDwiIsrKyqJ+/fqRUCgkc3Nz6tevHyUlJdHixYtJIBDQihUr6MGDB/Thhx+Svb09nT17ttnny+VyWr16NWlra5O6ujpNmTKFampqfvP71tfX08aNG8nKyoo8PT2575aQkECffvopmZmZ0bhx4ygpKalV7u+bSi6XU2JiIn3zzTc0Y8YMmjZtGh04cIDi4+NJJpO97uH9Bn8i6kHPw0mhGE6TJxvT1asO9PixHcXEdKJbt/RJIhlERMOppmYwDR9uQDU1g+m/gdadFIoNlJmZSeHh4bRlyxaaPn06ffrppxQYGEjfffcdxcfHt/h7/PtuUFNIDqRfh2dR0QAaPtyAZLJh1DJYh1FTCK9tnVvDvDNYqL4DLl++TEKhkMLDwyklJYWsrKxow4YNRER0/PhxEgqFdP78ee71BQUF1L17dxo6dCitWLGCRCIRfffdd9SzZ0/q2bMnFRUVNfv87Oxs6tmzJ7m7u1NcXBxNmjSJLC0tuYCUy+X01VdfkZmZGTk6OnKz1gcPHlCPHj2oa9eudPXqVdq3bx9ZWlrSyJEjW8xKy8vLydvbm9TV1UlXV5eCgoK4GfGL1NfX0/r168nKyop69uxJ4eHhRESUkZFB06ZNI4FAQKNGjaL79++3yj1+kykUCsrMzKQTJ07QggULaNy4cbRt2zaKior6i+HysgUQkTv9OpymTetEMTE9iWg4yeXDaMuWTvTJJ7r0yScdaexYYxo+3IDy8389e3Qhoh0tPrm0tJRu375NBw4coEWLFtHHH39MS5YsoZCQEPrll1+ovLz8N8akIKKPiKh3i/H8fqg+n7G6ENFvV1iY9w8L1XdEaGgomZub0/379+nhw4ckEolo586dRNRUZhUKhc1midXV1dS/f3/y9PSk7du3k0AgoL1799K4cePIzs6OYmJimn2+XC6n+fPnk0gkopMnT9Lhw4dJJBLR3LlzuX+4U1JSyNPTkywsLMjCwoL69etHjx8/po0bN5JQKKQZM2bQ06dPacqUKSQUCmndunUtgjM1NZVcXFxIVVWVTExMms2yX6S+vp7WrVtHlpaW1Lt3b/rpp5+IiCgvL4/mzZtHAoGAhgwZQrdu3frH9/htUVRURBcuXKCVK1fSmDFjaMOGDXT16lWqqqp6zSMLpaYQenGoXrvmTr6+ZvTvf5tRfr4LN1NtHqrORHTmD6/U0NBAcXFxdOrUKVqzZg2NHTuWZs6cSVu3bqWIiAjKzs7+T8n4wX/GNKzFeP44VJ+H/LZWvUvM242F6jvkyy+/JBsbG8rKyqJbt26RUCik0NBQIiI6depUi2CVSCTk7e1NXbt2pW+//ZZEIhGtWbOGAgICyNzcvEWplogoLCyMRCIRzZs3j1JSUuiDDz6g7t27c+VWuVxOAQEB1KlTJ+rSpQsJhULatm0bPX78mAYPHkxisZjCwsLo1q1b5OrqSs7OznT9+vUW1wkPD6dOnTqRqqoqdevWjRITE3/3u9fW1lJAQABZWlrShx9+SJcvXyaiphmMv78/iUQi6tOnDzejfV9UVVXR1atXacOGDTRmzBhauXIlXbhwgYqLi1/DaPKJyJWaZnhNobRkiYgiItyIaDj9+KMr+fkJqbS0D92505G2b7f5n1AdTETdiajsL19ZoVBQVlYWRURE0JYtW2jGjBnk4+NDt2/3oGfPLKmysjdt3iymESMMafRoI/L27kinTzvT8OEGFBHhRpMmmdLEiSZ09qwzNQ/VgUTkSUR1rXGDmHcAC9V3zJw5c6hbt25UXl5OERERJBQK6cyZpp/sw8LCSCgUUlhYGPd6uVxOc+bM4cLO1taWpkyZwpWNN23a1OIajx8/Jnd3d/L09KTMzExavHgxCYVC2rt3L/eamJgYcnV1JbFYTJaWltSvXz/KyMiggwcPkqWlJY0ePZpyc3O5WezEiROppKSk2XXkcjlt3ryZ9PT0SE1Njby8vFq85n/V1tbSmjVryMLCgvr06UM///wzETXNzAMCAsjKyop69OhBYWFhv1tefhc1NDRQVFQUbdu2jT799FNauHAhnTx5krKysl5ho9NC+nUJOCrqA5oyxZTGjjWmkye70bp1NuTt3ZE+/bQDHThgSMOG/TpUXYno/1ptJGVlZVRR0YVycuwpMVFE0dFGNGqUDoWHW1Fp6Qf09OmHNHy4AQUH21J9/RDKzOxL48YZczPZX6/zEr3ba/jMn8dC9R0jl8tp1KhR9OGHH5JEIqHTp0+TUCikS5cuERFx//3rYCUiCggIIJFIRGFhYdS9e3caPHgwXb9+ncRiMU2ZMoWkUmmz10skEpo5cyZZWlrS999/T+Hh4WRtbU1jx46l6upqIiKSSqXk7+9PnTp1IhcXF27WWlRUROPGjSORSES7d++m7OxsGj16NFlYWNDu3btbhF11dTVNnz6dNDQ0SFtbm5YtW/aHa4XV1dW0evVqEolE1K9fP242XF9fT8HBwWRra0vOzs506NCh9y5ciYhkMhnFxcXR/v37adq0aTRz5kz65ptvKCkp6SXfj+fl1qZmpN/6pVAMp7Q0G8rOdqL/zghdqXXDS0FN5eSm8q5cPpQmTDCiS5fElJpqTZcvG1LPnmp086YFN66DB51oxw77/xmvOxHda8VxMW8zFqrvoPr6eurVqxd5e3uTXC6nQ4cOkbm5Od2+fZuIiM6ePUtCoZBOnjzZ7H179uwhoVBIR44coaFDh5Krqyvdv3+fPDw8qE+fPlRaWtriWocPHyahUEjLli2jgoICGjp0KDk4OHDXIiK6c+cOdenShRwdHcnKyoqbtV68eJHs7e2pb9++lJKSQhcvXiRHR0fq2bNnizVdoqaGqd69e5Oamhq1b9+eDh069If3orq6mlauXEkikYj69+9PN27cIKKmwN+9ezd17tyZHB0dadeuXS1+cHhfKBQKevLkCR09epTmzZtH48ePp507d1J0dPRLanT6hpo6Z38/WKXSwRQTY0qVlR7/ef2plzAWNyIawl3z12uqhYUDaPDgtnT7tiFJJE2dwT/84Epr1lhRy1Bt+feVeT+xUH1HlZWVUZcuXcjPz4+IiHbu3EkWFhZcWH3//fcvDNbnJeLdu3fTjBkzSCwWU1RUFHl7e5OjoyMlJCS0uFZSUhI5OztT3759KTc394V7Wuvr6+mzzz4jMzMz8vDw4Gat1dXVXEPRmjVrqLa2lvz9/UkgENC8efO4We+v3bhxg6ysrEhVVZWsra3pl19++cP7UV1dTZ9//jmJRCIaOHAg17gkl8spNDSUXFxcyNbWljZt2tRiP+37pqCggM6dO0fLly+nsWPH0qZNm+jf//737251+msURHSAmmaJPejXa6z/211bXd2ZMjLaUV3dN/95X2sbSkT96EWh+rxR6e5dW8rIcCCi4XTo0P/OVIdR0ww68yWMjXkbsVB9h2VkZJC1tTUFBQUREdG6devI2tqaUlJSiIjowoULJBQK6ejRo83ed/36dRKJRBQQEEDr1q0jc3NzunjxIhdKP/zwQ4tr1dfX06RJk8jKyoouX75M0dHR5OTkRAMGDOD2tBIR/fzzz2Rvb0/dunUja2trbtYaFRVFrq6u5OrqSlFRUZSSkkIDBgwgsVjcIviJmsJw//791L59e1JTU6O+ffv+7uERz1VWVpK/vz+Zm5vToEGDuBm1XC6n06dPk6enJ1lZWdEXX3zxwkB/31RUVNDly5cpMDCQPvnkE/riiy/oxx9/pGfPnrXCp0cR0SxqKge7ENEHRNTrP//7/Pc+o7NnV9PGjRtf0rrvAfp1R/KvG6eeh2pQkA1FRnak1FRPGj/ehB4+/PWaai8i8qaXE/jM24iF6jsuOjqazM3NueD09/cnOzs7LoAuXrzIlXx/7dGjRyQWi2nOnDl06NAhbstNaGgoCYVC2rp16wuvt3fvXhIKhfR///d/VF1dze1pPXfuHPea6upqmjp1KgmFQurVqxc3a5VKpRQQEEACgYDmzp1LtbW1FBoaSlZWVjRkyBB6/Phxi+vV19fTwoULSUtLizQ0NGjGjBl/KgzLy8tp2bJlJBQKafDgwRQVFcX9WUREBPXr149EIhEtWbLkD5uj3hf19fX0yy+/0JYtW8jHx4eWLFlCYWFh9PTp03/4yVlE9C8iWkBE06mpmWkPEeUSUdP6/bx587ims9ZVRE0zzaYS8K8bp86e/W/3r7d3Wxo5Uo9On/7f7l9XImr5Qybz/mKh+h54HpxXr14lIiJfX19ycnLiDnkIDw8noVBIhw8fbva+rKws6tKlC33yySf0008/kYWFBa1YsYJu3bpFVlZW5Ovr+8KmlpiYGHJycqLBgwdTSUkJt4f2s88+a7ZG9/3335NYLCYPDw8Si8XcrDUlJYX69u1L9vb2dPHiRaqsrCRfX18SCAS0evXqF659Pl/PVVdXJ319fdq8efOfargpLy/nupeHDh3aLFxv3bpFQ4cOJYFAQHPmzGk2437fSaVSiomJoa+//pomT55Mvr6+FBoaSikpKS9lRpmZmUnjxo2j/Pz8Vv9soiXUFI6/vb4rkw2hBw9MqLa2369+vw8R9SSi2pcwJuZtxUL1PbFv3z4SiUQUHx9Pcrmcxo8fT66urtxJM8+D9fm+1udKS0vJ09OT+vfvT/fv3ycHBwcaP348ZWRkkKurKw0cOPCFp9VU/z975x0V1dnt4WdmaEPvvQqIWFGxYRQbIqCAEqJGgxijsSQmRsUae4mJRjR2YxJji8aoiUHFci1o7L0jCoiCNOlthpn3/oFOQiT59MYvN2WetWaxOOU9Zc6affa79/7tkhIRHR0tfH19xbFjx0RKSoqmpvXmzZua7QoKCkS/fv00Wbq/9FqXL18u6tWrJ/r16ydyc3PFqVOnRLt27USzZs00Ig+/5vz586JJkyZCT09PuLi4iISEhOe6P/n5+d/UQPoAACAASURBVGLMmDHC3d1dhIWFibNnz2rWXbhwQURFRQlXV1cRGxtbp8f8b0atVovk5GTx9ddfixEjRoiYmBixfPlyceHChZea/LVr1y4xduzY/4IMY46oqYFtJ37PsGZm+ovkZJ8n/3cTNdPGx+scUcu/F61R/Rfx4YcfCl9fX/Hw4UOhUqlEZGSk6NChgygrq3nT3rdvn0ZA/5dUVFSI0NBQ0aZNG3Ht2jUREBAgunbtKjIzM0VERITw8/MTt2/frvOYixcvFm5ubmL+/PlCqVRqvMLVq1fX2m7z5s0a4QZfX1+N15qVlSWioqKEl5eX+Pzzz4VKpRKLFi0S9erVE3379hVZWVl1Hnfr1q3CwcFB6Ovri1atWtWZYFUXubm5miYA4eHh4vz585p1t27dEgMGDBCurq6iX79+zz3mv40HDx6I7du3i3Hjxom+ffuKjz/+WCQlJYny8j8mkKBWq8XUqVPFpk2bXtKZ/pJUUWNYf54K/vVHpQoVFy86ibKyFqIma7juFzst/260RvVfxptvvilatWolSkpKRFVVlejevbvo3r27Zlr2qY7w559/Xmu/p97t0wzg8PBw0aJFC434g6enp0bF6NecOnVKNGrUSERERIiCgoI6a1qFqJHXi4iIEPXr1xfBwcEar/VpElGDBg1Ejx49NMb2qYe7aNGiOqd6lUqlmDZtmjA1NRVyuVy8+uqrzx0fzc7OFu+++65wc3MTERERtUp80tPTxdChQzXrzpzR1ij+Fvn5+WLv3r1i+vTpIjo6WsyYMUPs27fvd7R4//N4AwcOrDXb8fLIFUJMEjUG018I0UnUlP30EDWeaStRUOAtLl1qLIS48F84vpZ/Alqj+i9DpVKJ0NBQERQUJJRKpSgrKxMdOnQQkZGRGsN04MCBOr1JlUolPvjgA+Ht7S3OnDkjRowYIerXry/OnDkj1q5dqynFqYuCggIRHh4uGjduLE6fPi2ys7PrrGkVQoi1a9dqSl8aNmyo8VpLSkrE0KFDhbu7u5g/f75QqVRi3759ws/PT7Rr165WPPSX5Ofni379+gm5XC5MTEzE5MmTn7v+Mjs7W1PyExkZKS5fvlxr3ejRo4W7u7sIDg6uU25Ry8+UlZWJY8eOiY8//lj07dtXjB8/XuzYseOF46Q//fSTeOutt/6w5/vb5Akh1gsheosaof32osawzhFK5XUxZMib4urVq/+lY2v5u6M1qv9CysrKREBAgBgwYIBQqVSioKBAtGrVSvO/EEIcOnToGenBpyxYsEB4eHiIffv2iQULFgh3d3exc+dOcfjwYeHl5SXefffdOj1HlUol5s2bJ9zc3MSSJUuESqUSH3+8QAQ0cxJrPnpbqLJPCPH4shBVhSI9PV10795d+Pr6irCwsFpe69GjR0WLFi1EQECAOH/+vFAqlWLq1KnCzc1NDB06VBQVFdV53Tdu3BBt27YV+vr6ws7OTnz99dfPfc+ysrLEiBEjhJubm+jTp0+tH9WCggJNuVFgYOB/bAKgRQiFQiHOnTsnli1bJt544w0xcuRIsWHDBnHnzp3nSnRasmSJWLz4/0fI/tChQyIuLu5v0cNWy5+P1qj+S8nOzhZNmjQR48eP1/zfrFkzMXz4cM02hw8fFh4eHmLFihXP7P/FF18INzc3sWHDBk0XnKVLl4qUlBTRsmVLERoa+pulLUePHhVNG9UXc9/pKBRHokXJ9obi0seG4vpiM1G5u4UQ+9oIcWWmUD2+JuLjFwt3d3cRHh5ey2tVKBRi0qRJtRqgp6SkiNDQUFG/fv1nEq5+SUJCgnBzcxP6+vqiYcOGzyUe8ZSHDx9qMpGjoqJqxVXLyso0tcBt27YV33zzzb9SAvFFUavV4ubNm+LLL78Uw4YNE7GxsWLVqlXi8uXLv5noVFFRIYYOHfrMLMefwVO97H9DW0EtL47WqP6LuXXrlvDy8hJLltT0p0xPTxcNGzYUcXFxmm2OHDkiPDw86pzWfVqqs2jRIpGUlCS8vLzEBx98IIqKikRoaKho2bKluHv37rMHfnxJVO3tIG7Fm4pz841E4a4OovpgiLiw2FkcmmYoHm1uLsTeVkLs9Rfi7GiRcuuyCAwMFE2bNhURERG1vNarV6+Kjh07iqZNm2piut98841o0KCBCAoK0ghd/JqnCU8WFhbCwMBABAUFibS0tOe+dw8fPtTEVaOjo2s1Ra+qqhKLFi3SiFw8TbDS8p9Rq9Xi/v37YuvWrWLMmDGif//+YtGiReLEiRPPKF09TRz7/6gjPnHihHjvvfe03qqWZ9Aa1X85T1vEPRXYf9rkfPbs2bW2qVevnvjss8+e2f/UqVPCy8tLxMXFieTkZNG0aVMRHR0tKioqxDvvvCO8vLzEkSNHft4h96QQ+1oLsb+jUB8OEzdXeYuDH8pF2vomQhzuKe5vaCYOfigXVz5zF6pDITWG9Xh/oaosFHPnztUYsUaNGmm81qcG0t3dXcTExIiCggJRUlKiSTSKi4v7TenBkpIS8dZbbwkjIyNhZGQkhg8f/kJKShkZGeKtt94Srq6uom/fvrWMuEqlEqtWrRJ+fn6icePGIj4+/i/WNPyvT25urkhISBBTp04V0dHRYvbs2eLAgQOaKf4tW7aIKVOm/OnGTa1Wi/fee++FZjm0/DvQGlUtGr3fp3q4Fy9eFJ6enpom50L8bFh/uewpt27dEo0aNRKxsbEiOztbdOzYUQQGBorHjx+LZcuW/ZxNXHxHiMR2QhzoLMThnppP9rY24tA0I3FhsbOoPhQqShM6i+NzLUTSHHNR8mOnGsN6ergQqmpx9epV0bZtW9GiRQsRFRVVy2tNT08X4eHhwtvbW6MgdfHiRdGhQwfRpEmT34113rt3T3Tp0kXI5XJhaWn5mxnFv0V6eroYPHiwptzmlyVGKpVKbNy4UbRp00Y0aNBAzJkzR1PGpOX5KSkpEYcPHxbz588Xffv2FRMnThQ7duwQI0eOrLP373+bc+fOiREjRmhnIbTUQmtUtQghagT3vby8NJ7W8ePHhYeHR63Y5PHjx0W9evVEfHy8EKJGx/dpTPbhw4fC399fhIeHi4KCAhEVFSWaNWsmUlJSxP79+4Wnp6c4/GlLod7bqpZBffqp2NdN/DTfShybbSZKdncS6sNh4tryek+82MZC7G0pRG5Ndq9KpdLEU994441aXqsQQmzcuFF4e3uL8PBwkZGRIVQqlVi+fLnw9PQUffr0+V1ZvSNHjggfHx9hYGAg3N3d69Q5/j3S09NFbGyscHV1FQMGDHhGKGLXrl2iY8eOwsvLS0yePPk3k6q0/D5VVVXizJkzYsmSJSIqKkrUr19fLF68WNy7d+9P81rVarWIi4vTKJVp0SKEEBIhhECLFmDChAns3buXgwcPYmtry4EDBxg2bBiffPIJr776KgAnT55k4MCBjBo1imbNmpGYmMjHH38MQHFxMZGRkajVanbs2MHs2bNJSEjgq6++wtYUKhO7USlMaN68JTKZ7JnjCwQ3b94kKysLHx8fnJ2cycnN4dq16zjbGODeMhq9dis12589e5bhw4ejp6eHm5sbp0+f5r333mP06NEUFxczYsQIEhMT8fT0xMPDAzs7O+7fv8+VK1d4++23iYuLQyqVPnMearWaL774gilTplBcXIyfnx9r166lcePGz30vU1NTmTFjBkeOHCEwMJDp06fj6empWX/gwAEWLlxIcnIykZGRTJo0CVtb2+ceX8vPqNVqvvrqKzZs2ICHhwcSiYR27drRtm1bGjZsWOd3/McoAQ4AeygqSuXu3RT8/AKRSrsDoYDlSz6elr8TWqOqRYNarSYmJoZ79+5x8OBBDA0N+e677xg3bhwrVqwgJCQEgNOnT/P6668TFBSEqampxqgCVFZW0r9/fzIyMti1axfbt29nyZIlbJvfneYmZzl3LQOlshr/li2Ry+V1nsejR4+4cfMG1tbWNG7cGKVSyeVLl5BLS6ho9Q2tAyM02yoUCuLi4vj+++/p1q0bJ0+exN7enrVr1yKXy1m1ahXbtm3D2tqaAQMGcPToUWJjY5k8eTJSqZRPP/2UV155pc7zqKysZMqUKaxduxaVSkV4eDhLlix5IeN39+5dZs6cydGjR+nUqROzZs3Czc1Ns/706dPMmzePy5cv06NHD6ZOnYqzs/Nzj6+lBiEEn3zyCWZmZgQHB3Pq1ClOnjxJXl4erVu3pm3btjRv3hw9Pb0/cJR8YA2wG6gGDABd7t69h7m5MVZWBoAU6A4MB5z+6GVp+RuiNapaalFdXU1YWBg6Ojrs3r0bqVTK+vXrmTZtGsHBwZSVlSGEwNXVla+//pr69esTExPD/v37MTIyYuTIkTRv3pzhw4eTmJhI27ZtuX//Poal54gNsmJguD9Xr17lzPVcDiWb8GqgCzuPP0IqgZGR7ujIJKz98T4FJVU0diilS2M9mjf340G+msWbzrP3ohILhwYMGzaMYcOGoaOjA8CRI0cYPXo0lpaWODk58dNPP2m8VoVCweTJk9mxYwdyuZydO3dSv3595s2bx7p16+jUqROLFy/G3Ny8znuSmZnJiBEjOHjwILq6uowaNYrp06e/0A/03bt3mT59OklJSXTp0oWZM2fi6uqqWX/lyhXmzJnD6dOn6dSpE1OnTsXb2/uPfZn/MkpLSxk9ejSjRo2iZcuWAOTk5HD69GlOnjzJ3bt38fPzo23btvj7+2NiYvICo6cDI4AcwBrQ/cVxy0hJuUPTpk2RStVAHmAMLAMavaSr0/K34f9v5lnLX5WSkhLRqlUrMXjwYCFETQyzY8eOwtraWpw6dUpUVVWJ69evi+XLlwtDQ0Px5ptvCpVKJRISEkRMTIwmphUbGyvc3d3FsWPHxPH4liKwoVTsme4k1IfDRMLMeqJdfalYNspNKA+Gim8m+4hAX8ToMBNxa20jcWWtv4h8xVYcnusiDn5oKI5/2kjcWttY3Dq4QDRs2FA4Ozs/o1FcVlYmhg4dKjw8PMTo0aNF48aNa8Vad+7cKSwtLUWzZs3E0aNHhRA1MdCIiAjh5eUlVq1a9btJJ2fOnBHNmzcXcrlcODg4iPXr179w/C45OVn0799fuLq6isGDBz/TAzY5OVnExMQIV1dXER0dXUseUct/5sqVKyImJkYUFhY+s66oqEgcPHhQzJkzR7z22mtiypQpYvfu3c9RkpMthOgufk9wPznZR2Rm+v9iWYcnnzpKyrT8o5HNmDFjxv+3Ydfy10JPT4+wsDDmzZvHo0ePcHJy4tKlS4SFhTFjxgxCQkLw9fWluLiY/Px8zp49S0VFBa+//jrr168nJCQEuVxOZGQkMpmMuLg4Poi0IL9EkJefj4HIx9zGnZM3iglrXISqWomfrwtf7EklqKECibKQsqI8ziWXYqJXhb2FLqWFjzDTK0Vh0Z7X357BiRMn2LRpEz4+PjRo0AAAXV1devXqRf369Vm5ciUuLi7Y2dkxe/ZsFAoFFy5cYOTIkTg7OzN16lRu3LhBZGQkMTExuLi48NFHH7F9+3ZatmyJnZ3dM/fFycmJt99+m3r16nHw4EG2bdvG1q1badq0aS2v8/ewsrIiKiqK0NBQDh48yKxZs7hx4wYtWrTA1NQUKysrevfuTXR0NOfOnWPOnDkcOnQIDw8PXFxcXur3/E/Ezs6OoqIiDh48SIcOHZBIJJp1+vr61KtXj44dOxIeHo6pqSlXrlzh888/5/jx4xQXF2NiYoKpqWmt/WA8kArY/OZxDQ0NSU1Nw9bW9kkM1wAoB04CrwGS39xXyz+Llx3B1/IPwdHRkW3btrF582bWrVuHra0tH374Ib1796Z3797cv38fAG9vb7Zu3cq6detYuHAhUBOLBDh//jzp6ek4OjoS9eEZztx8jIOLF9XV1Vy5cgULMzlt27QmOzub5Ns3sLaywspUF7VajaGhIUaG+siNzbG3twd9S+J3lxH8+oe0atWKS5cuIYRg4MCBtG/fnu+++47z58+TlZVF9+7dOXXqFNbW1pw8eZKoqCg++eQTDh48SOvWrZk4cSKHDh0iIyODVq1a8d1339G7d2/OnTtHixYt6NWrF2PGjKGioqLOe9O/f3/u3bvHhAkTePDgAcHBwYSGhpKWlvbc99fHx4ctW7awb98+ioqKeOWVVxg2bBiZmZkAODs7s3LlSs6cOYO3tzcDBgyge/fuHDp06A98q/8OBg4cSG5uLomJib+5jb6+Pm3btuX9999nw4YNDB48mMLCQmbMmMHbb7/NF198wc2bNxEiFThLzZRvDUOGXObSpaJa48nlcszMzHj0KPsXSy2A+8CFl3l5Wv7iaI2qlt+kcePGrF27ls2bN3Pp0iVUKhULFiygU6dOhIeHU1BQAECLFi349ttv+eqrr7hz5w4ASqWSefPm0bt3b44ePcryxbPxtKokNzeXNm3aoK+vT25uzpNMzQAUCgVFRcU0bNgQPT19ysvLEUIgkUgoKytl91kFTXycCI3oi5WVFWFhYTRt2pSQkBCuXr3KoEGDGDp0KNHR0QQGBjJ8+HC8vb3p2rUrq1evxszMjObNm9O5c2fi4+Nxd3dn7969jBs3jokTJ/Lqq69SWFjIokWLSEhI4MqVK/j7+7N9+/Y6742enh4zZszgzp07REZGcuzYMZo2bcqoUaMoLS197nvs6+vLtm3bSEhIoKCggICAAIYPH64xrra2tnz66adcunSJNm3aMHz4cAIDA9m5c+cf/Hb/uejo6DBu3Di+/vprHj58+B+3l8lkNG3alGHDhrFu3TomTpyIvr4+K1asYNOmaLKzcygsLEatVv/uOE5OjmRnP6K6uvrJEgkgA7b84WvS8vdBO/2r5Xfx8PDA1taWdevWUVFRQUBAAMHBwSQmJrJ9+3bc3NwICQnB3t6ejh07Eh8fT3V1NYGBgWzdupWwsDDs7OzIKtRh9/ffYqZbgp1xJVb2nhy/ko+zwQPMzS3w9PRm4/50vC0L8HR3RKGo4tTtKpytpDRwt+TUrVJKhTUVeu64urry/fffY25uzr59+4iLiyMrK4tz587RsWNH2rZty+PHj7l48SKXLl3CxMSEsrIyrl69iqWlJfv27ePzzz9HpVLh6+tLdHQ0J0+eZO7cuejp6dGjRw9iY2MxMDBg1qxZ7N+/n/bt29eZyGRoaEhUVBTh4eGcOXOGvXv3smLFCvT09GjduvWvphF/G1tbW1577TW6detGQkICc+bMITk5mZYtW2JsbIy+vj5dunRh4MCBFBUV8emnn7JhwwbkcjmNGzf++TglJfDjj7BsGWzaBDt2wLFjIJGAszM8Sez6p2NmZoZcLmfjxo1069btuctqJBIJFhYWNG3alNDQEHx81lFZqU9OTj4ZGQ9YtiyD5ORKfvqpkO3bHyGT1dz3jz++y9dfZ3H0aDlClNOkydOpYgPgJtAP0P9vXKqWvxja7F8tz8X06dNZvnw5AQEBGBsb07FjRzZu3EhaWhrJyckYGhoCEBgYSEZGBjExMbRq1YotW7ZQXV1N69atqc46hrXyHJ6WRTwqkbPnqj4z+5tz9+5dvLx9eGdlJlOjTSl5/BBTUxM+SyilpYegeytr0jOLWXu5FeVKGUqlkry8PO7du0fjxo3ZtWsXdnZ27N27l/fff58WLVqwdu1aUlJSGDt2LEqlkoqKCjIzM8nOzsbCwgITExNyc3Np1KgRPj4+lJaWkpeXx/Xr17G0tGTUqFH4+/tjbGxMfHw8R44cITY2lilTpmgyjuvihx9+YMyYMWRlZeHk5MTixYvp2bPnC9/vK1euMGPGDC5cuEBYWBjTp09HrVYzYMAAFi5cSKNGjVi5ciWff/45Ojo6jOnfn4EKBdKEBKiuBgODnw2oQgFKZc2y6GgYNAhMTf9Pz8HfCSEEs2fPxt3dnZiYmP/DCOVAR8ABqJl9KSwsZMSIm/Tpo0fLlhYolUZMn57Fe+/VIyDAgqKiSk6cuEaPHs3R03uaIZwLfAtoY+L/BrRGVctzM3r0aJKSkjh06BCWlpYoFAp69eqFEIIff/xRU2Jy7do1+vTpQ79+/Zg1a9bPA1SXwem3UBTc5szlNHR0dGjl709efh7Xrl3D1dUNby8vsh494vr1a+jq6mJlYYasOp9bhHDgjh1OTk6MHDkSHR0dEhISmDp1Kvn5+UyaNIn33nuPoqIiYmNjycjIYPXq1QQEBNS6hszMTGJjY0lJSaF9+/YcPHgQHR0dGjZsiIODA4aGhpw+fZqUlBSaNm1K48aNyc7OpqqqikuXLiGTyXjrrbcICQnB0dEROzu7Z4ysWq1m8eLFzJ8/n4qKClq0aMHy5ctp2rTpC9/zS5cuMXPmTC5evIijoyN5eXm4u7vzzTffYGlpWSO0MX8+LgsWYKRUYujmhpunJ7K6PLOqKsjPBzc3WL4cHBxe+Hz+bhQWFjJ69Gji4uJeSLyjhiIgCKidtDZkyGVGjnTFw0Pw5Zd3uHevimXLOvJ0suD+/fsIIX5Rj5wHbAC8/tC1aPl7oDWqWp4btVpN3759ycnJITExEQMDA8rLywkJCcHCwoIdO3ZoptmuX79Onz59iI6OZs6cOT8PUpkH50ahKrrD+ev3USjVtGrlT0VFBRcvXsTKyoomTZpQXFzC9cunMTEQ3CWQj3eW0KNHCC4uLhw6dIghQ4bQqVMnACZOnMjq1atxcXFh9OjRhIaGsmnTJlasWMGQIUOYMmXKM9N/K1as4JNPPqFLly5IJBIOHjzIoEGD6Ny5M+np6Rw7dozvv/8etVpNSEgITZs2RS6Xc+TIEY4ePYqTkxNNmjShvLwcAwMDDA0NadeuHU5OTjg5OeHo6Iiuri5xcXFs2bIFI5WKNzt0YNyIEVhYW4OFBTRoAM85LZmYmEi/fv2orKzE1NSUXr16sWbNGqQZGTBoEKK6msyKClJTU1EoFDg7OVGvXr26veq8PLC2hq+/rjmPfzhnz55l1apVLF26FCMjoxfYUwm0pcZT/Xkaf8iQy7z7rjt+fmasWJFGbm4W773nqwkPKJVKrl69SqNGjdDX16emtnUXTz1eLf9stEZVywuhUCjo0aMHZmZmfPfdd0ilUoqLiwkKCsLLy4sNGzY8Y1ijoqKYN2/ez4MoS+F2POoHP3Lv7h0eF1fRuFlLJMi4ePE8FsZS6nt7Ui0zZ9TSm+y/KmPw4MEkJSXh5+dHTEwM69atw8bGhq5duxIfH09BQQFnzpxBIpFgYmKCmZkZcXFxfPrpp9jb2/Pll1/i6OhY61pSU1MZMmQI+fn5DBo0iC+//BI7OzvWrl2Lh4cHarWaKVOmsH79elq0aEHnzp158OABycnJ3Lx5k8LCQkJCQkhNTeXx48fExcVRVlZGZmYmDx8+pLysDD+5HP/UVBwuXqSishKpRIKdvT0uTk5Ira0hJgZCQsDM7Hfv+/z58/nqq6+QyWRkZmZSVlZG78hINldXo5OdDVZWmm2zs7O5e+8eHxUVMcTentBGjdDV1a09YHY2tG8Pixf/sQfib8LKlSspKytj3LhxL7jna0A28PN0+S+N6rffZnL5cj59+1Irtv3gwQMUCiX16tkDamA/8O+IZ//b0RpVLS9MYWEhXbt2pU2bNqxYsQKoUa4JDg6mTZs2rFq1SrPtzZs3NWU48+fPrz2Qogj1w71c+XEq1aWZ+PrUQ9/Iih+OpZJw3Yy5KxMxM7fA39+fwsJC+vfvz61bt7Czs2PGjBkkJSWxZ88eYmNj6dq1K2VlZcTGxnL9+nXefvtt7t+/T35+PmlpaTx48IBPP/2UiIiIWqegVqtZuHAhK1eupGfPnlRWVnLw4EGNGpNUKuX27duMGDGCnJwcPv74Y4KDg8nKymLz5s189NFHlJaWYmhoiK2tLeHh4Xh4eODp5ITfrl2YnjtHtUpFqb4+eQUFZGZlUa1UIpVKcTQ3x87QEB1DQx4NH45RRASOjo4YGBjUOkchBJ06dcLV1ZXw8HAMDAxITk6mLCmJAUlJyN3dsbO3f2bKNy8/n7spKZSWlmJnZ4e3t/cTzwlQqyE3F3btgl+9bPwTqaqq4v3336dfv34EBga+wJ4JwEx+OQU8btwNunWzpkcPW3Jzqxg58hoREVK6dnXE2NiK3NwqXF0NuHLlMo0a2aCv/y4w5CVfkZa/KlqjquX/xP379wkODmbgwIFMmTJFsywkJISwsLBaesC3b98mMjKS8PBwFixYUOd4c+bM4csvv2TNmjUEBgYyZMgQzp8/z9atW3FwcKBLly7k5OQQGhpKUVERurq6TJ8+HV1dXZYsWYKFhQXvvPMO1tbWLFq0iM8++4xRo0YRGRlJQkICW7ZsISUlhZCQEL788stnPLcbN24wdOhQqqqqiI2NZfXq1c94ratWrWLRokW0adOGpUuXolKp6NWrF9nZ2eTk5GBkZMS8efNwsrLCfeFCTO/dI1sqRVdXF7mhIYaGhhjK5eTn53MvNZXq6moMDAxws7XFSKHgxyZN2GdkhLGxca1pZDMzM95//3309PRwcHBg/vz5NfHBuDiqDh4ko7KS4uJiHOztsbWze8a4FhYWcufOHYqKi7Gxtsa7fn0M5XJ49AjefBNGjHjJT8dfk6dSkZ9++ukL6DeXU6PlawTU5AycPl3AqlXpVFSo6dvXgfr1jVm9+h63buXh6GjJG2+40LWrNZmZD4A8HB3PA9pmCf8WtCU1Wv5PmJmZERAQwKRJkzA3N8fPzw8zMzO6d+/O9OnTKSoqomPHjgBYW1sTFBTEzJkzSU9Pp3v37s+M17FjR4yMjBg/fjxOTk5MnTqVrKwsJk+eTMuWLXnjjTc4deoU586dw9bWFrlczqFDh2jSpAmDBw8mNzeXJUuWYGxszMCBA2nbti2zZs3i4sWLzJw5k0GDBmFlZcUXX3xBfHw8lpaWNGrUSNMtx8bGhsGDB5OZmcnKlSvp3bs3crmc6dOnSXEGaAAAIABJREFUA9CmTRtat25NdHQ0u3fvZt68eSgUCiorK9HT08PAwIDHjx9z8MAB5kml1Hv0CDMfHxwcHTEzN0dPVxelUklRcTHl5eXo6+sjlUopLy8nv6gIpVRKN2DARx/xSr9+uLq6oqOjQ3Z2NocPH+bs2bOUlpby4MEDvv76a5SPH9PhyBF0bG2xtLLCzNycx/n5ZNy/DxIJhoaGDL1yBTe5HHdzc5ycnLCxsSE3L487yckUFhVhamWF3q1bEBv7Yl9+eTkcOQLHj9d8kpOhsLAm8amO7kN/FSwtLRFC8N1332li6f8ZXWrK+Y9QY1glODvLiYy059VXHfD1NcHWVp+QEHsCAgTdu5vh52cPCAwNS9m3zxBDw95Y/Ati11pq0BpVLf9nHBwcqF+/PuPHj6dRo0Z4enpiZWVFhw4dmDx5MhKJhDZt2gA1hjU4OJiZM2eSmppKcHDwM+O1aNECNzc3xo8fj0wmY+LEiRpD26BBAwICAmo61ly+TEVFBY6Ojpw4cQIDAwP69OlDy5Yt2bRpEydOnKBHjx4MGzaMb7/9lsWLF/PKK68QERHB+++/z+XLl1m+fDk//PADMpkMe3t7TExMkEgkdO7cmY4dO/LZZ5/x+PFj3n33XVatWsXOnTvp0KEDrq6u9O3bF0dHRz777DOMjIxYt24dEyZMYOTIkXRycMB49WrydXQwNjFBR0cHXR0d5HK5RobQ3t4eOzs7rK2sMDMzo6ysjMKSEvJzcri5bRvbAZVKhb29Pa1bt6asrIykpCScnJwwMTGhbdu2hPr54XnxIhgbAzUSjZaWlpiZmZH/xLgmFhbSzNAQ9yfxWn19fRwdHLCzs6OgoIDku3cReXlkdO2KvVNNR5UDBw5QUVFRtyd3/z6sWweTJ8OBA3DyJFy5UvP34EHYtq2mdMfZGV4oIejPo0GDBhw+fJiCggIaNXpesftmQBY1ykhyfkszx8jIiLS0VKysLJDJcpFK25OWFkti4sEXnHLW8ndGa1S1/CG8vb0xMTFhwoQJBAYGYm9vj729Pa1atWL8+PGYmprSvHlzoEb3NiQkhFmzZpGSkkKPHj2eGc/X1xd/f38mTZpEbm4u48aNw8fHhwkTJuDo6Ii5uTkdOnTgxo0bJCcn4+bmRkpKCjk5OXTu3JmgoCAKCgqIj4/HwsKCqVOnUlBQwMSJE9HR0aFdu3ZERUXRrFkztm/fzs2bNzl16hTXr1/HyMgIBwcHHB0defPNN7l9+zarV69m4MCByGQyjdfaunVrGjVqRGxsLCdPnmTatGkolUq6dOlCvf37MXv0iAqZjNTUVCQSCUZGRs94RVKpFH19fczMzHB3d8faxoacwkLMS0tZcekSj2UySkpKWL58OatWraKwsBCJREJAQABRUVF0qF8f2e7dlAMlJSUUFhaSl5fH44ICyp7U3B6uqMAsOxsjhQIhBLq6uujo6KCnp4e9vT2Ojo6U5+YStWMHew4exMbGhlmzZnHgwAHCwsI0tccAJCbCO+/A1as1Na5mZjV/jY3BxKTmr0oFJ07A999Ds2Z/yZIdiURCs2bNWLJkCU2aNMHqFwlev7MXNfWqVcApoIwaIYfaXrmODshkhVRU5GJsHA3MxcPDhw0bNtCgQQOsra1/PbCWfyDamKqWl8KsWbPYsmULiYmJGnH5upqcQ01sq1evXgQFBbFkyZI6x7t+/TrR0dG0b9+e1atXc+3aNfr160fz5s3R1dXlrbfeIj4+nlOnTtGhQwccHR2xtrZm7Nix6OnpkZGRQXx8PAYGBrz77rvcuHFD05Zu3bp1GBoakpOTQ2xsLPfv3+ftt98mNTWVoqIiQkJC6N69O6amphw/fpx33nkHS0tLBg0axMKFC2vFWqGm7dwHH3yAlZ4eu8rLMXJyAh0dKiorSU9Lo7q6mo8rK+nl6Mjh/HweK5W0NTdnpLs7er9OLrpxg425uUyurkZHRwcjIyOkUim5ubkYGRlRXl5OdXU19fT02CYEZXI5crkcQ0NDZDIZxSUlPM7PR6FUskQIwiUSvHV0cHZyQqFUoqujg7m5OWbm5pgYGSF59IjHe/cy/5NPWL16NSqVCgcHBwIDA1m1alVN7DkhAaZNqym/+Y0euLUoKYHKSli5Ep68UP3VSEpKYtOmTZpn5Pm5A3wH/ACogKc/nzUvTQpFJ6ZNu87w4Z/h7l7zfBw4cIAjR44wd+7cl3cBWv6yaD1VLS+FwMBArly5Qnx8PP369UNfXx9PT0/c3Nw03ubT/qCWlpaEhoYyZ84cbt26RWho6DPj2draEhERQXx8PImJiQwfPpxXX32VlStXUlxczK1bt1i6dCkVFRVs374duVyOo6Mje/bsoU2bNtja2tKtWzeKi4tZvHgxPj4+TJ06lc2bN7Ns2TLat2+Ph4cH/fv3p7S0lKVLl9K+fXuGDRvGhQsXWLlyJRkZGfj5+TF27FguXLjA559/zrBhwwCYNm0aUOO1enh4MHjwYIpPnsToyBEeK5VYWVmhp6uLlbU1OjIZ396/T1ppKXMbNiTK0ZGE7GweVVZSX1eX0tJSioqKyM/Pp0KpxF1PjxXFxVRUVFBaWkpZWRkqlQpra2tMTU2JiIjgq2++od7Jk5iZm6NQqcjLyyMvL4+SkhKQSBBqNWckEvzNzLAASoqLcXdzw8HRkaqqKrKzs3mclkaegQELnjQCOHHiBAqFgoKCAm7dukVqaioRPj5I3n//GYM65PJl3ORy7OsySPr6NdnF+/ZBWNhfcirYzc2N5ORkrly5QuvWrV9gTyvgFWpKbZoA7ajxYsOAschk4VRUmLBnzx46d+4MgLu7O99++y2urq41zSG0/KPRGlUtL42wsDD27NnDxo0bef3115FKpfj6+mJpacn48ePx9/fXeLEWFhb07NmTuXPncv36dUJDQ5+ZIjU1NeW1117jiy++YPPmzcTExBAbG8sPP/zAtWvXuH//PgsXLtTUoRYVFdG5c2fWr1+Pv78/pqam+Pr60qZNG7Zt28aZM2eYO3cuhYWFTJ48GRMTE1q2bMkrr7xC+/btWbBgAUlJSUydOpVXX32V/Px81q9fz/Hjx4mIiKBHjx6arOYxY8awdOlSTazV2tqattbWmJ4+TVpODmnp6RgZGWH0JOs3saiITvr6FF66RHpKCjpVVXyfnU2DkhJKS0tRVlcje+KZ2hoY0DcxEV1dXa5evYpCoQBq4qzvvPMOUVFRpKSmkn//PnoXLlAuk6FUKJDp6GBtbU1FZSVIJJwWAj+5XDP9m52dTVl5OTY2Ntja2mKto0NKWBjTN27k8OHDGsF4iUSCra0t169fp+XRo9iUl6NnY1OredkP2dm0MTev26hCjWEtKKiZGm7R4uU+aC+JZs2aaWqTnZ2dX3BvfcANqA/4AO7UxFvB09OTrVu34uLigr29PVKpFDMzM7Zt20b37t2fWw9ay98TbZcaLS8NqVTK1q1bqaqq4o033tD8SMfExDBu3DgGDRrEhQs/t8Fyc3MjISGBY8eOMWrUqDq7gJibm5OYmIiJiQldu3aluLiYH3/8kY4dO/Ldd98xa9YsYmNj2blzJykpKSxdupTu3bszYcIEbt26BdS0UVuwYAFt27Zl/PjxtG7dmmXLlrFgwQIGDx5MZWUlrVq14sSJE9jZ2REYGMjhw4fp06cPa9euZcCAAfz0009s2rSJUaNGYWJiwocffsjIkSPx8vKiS5cuxMfHo5ZKMTIyIiAgAGcnJ65cvsylS5dQKpWoVSrURTXtwgSgW15OsRAYGhpiYmKiyQTOyckhPTOT9u3bs3z5coQQyJ94iGVlZUyfPp2OHTsSFRXF3OvXqVYqKXz8GD09PVxdXCguLkZPVxcXZ2ckEgmeXl44Ojjg6uqKhYUFhYWF3Lhxg3s3b3L3/n12FRUxevRozTGgpi42JyeHqMBAmubmkl5czPVr13j8+DEvFC0yN4fNm2u0h/+CGBoaMnbsWJYvX67puPQy0NHRITY2lnXr1mme6Q4dOqBQKDhz5sxLO46WvyZaT1XLS0VHR4fw8HAWLVpESkqKJsu3devWlJeXM3XqVIKCgjRJG+bm5vTs2ZOPPvqIK1eu1Omx6ujo0LdvX06dOsWCBQvo1q0bb775JkVFRSxduhQ9PT369etHdHQ069atY8+ePUyYMIEVK1Zgb2+Pi4sLEomEBg0a0K5dO7777jvS0tKYPn06W7duZc2aNXTs2BEHBwd69+6NqakpkyZN4u7du3Tv3h1nZ2cCAwNp3749aWlppKenY29vzzfffIOJiQljx44lPj6es0eO0KOiAl1TUywtLXFwcCArK4vbycn8T1kZlkoljhIJ+np6PFSrSReCZgoFpaWlGJuY4OHujpO5OTa+vsTu2UN1dTX5+fmYmJhoYqm/5F5uLg5qNU3VavIrK8nKzkahVFKlUKBQKGihUCCvrKSyspLikhIADORyqsrLMamsRDFoEC6vvoq1tTXbtm3TjC+TyTA1NeU9Fxcc0tMpFgKpVEpeXh65ubnIdHQ4UFyMrb4+K9LTWZ+Rwb3yclqbmyP75Xeno1Pjrfr6grv7f+mJ+2PY2NhQVlbG3r17CQwMfGlepLOzM0eOHEEmk1GvXj0kEglWVlZs3LiRkJAQrbf6D0ZrVLW8dAwNDTX1qkqlknbt2gE1b+uZmZnMmjWLnj17arRSzczMCAsLY8GCBVy+fJmwsLBnfnQkEgmRkZGkp6czbdo0WrduTUxMDFKplJkzZ6Krq0toaChDhw7V9HadOHEimzZt0hhUQOPxVlVVsWbNGmKf1Gh++OGH2Nra0qRJE/z8/OjZsycrVqzgiy++0EzvGhsb07x5c3r16oWNjQ3V1dWcOHGC3bt3ExcXR55ajfHhw+hWVWFqbY1EKqVKoUCpUPA/5eVkqlQ00tXFzsGB78rLqS+VUk8iwdrGhuLiYlLv3UOVl0dqly5UeXtjZWXF9evXuXr1KlKpFLVa/YynmGpnx5vNmmH46BHFKhW6urqYm5vjWa8eefn5uLu7o6+vT3FxMXK5nLLiYuwkEn5UKnnt1Cm++eYbvv/+e8rLyzX3XCKRIJPJGGFhga1CgZGVFVKZDCEEpaWlZGdnc6CoiKyKCmY3akQfR0e+zcpCTyrF+9fx0+Ji8PEBP7+X/Zi9NBo2bEhCQgIKhQIfH5+XMqZEItE0mg8JCUFHRwcnJycOHTqEXC7/hdi+ln8aWqOq5b+CpaUlrVu3Ji4uDgcHB02HkG7dunHjxg0WLFhA7969MX5SZ2lmZkavXr1YsGABFy9epGfPnnW+zQcFBaFUKomLi8PLy4s333wTIQTz58+nsLCQ0NBQ3nrrLZKSkliyZAnDhw8nKSmJhw8f0rx5cyQSCRKJBB8fH9q3b8+uXbsQQhAREcHs2bO5ffs2wcHBWFtbM2jQIG7dusXUqVMxMjKiZcuWABrvIywsjNDQUDIyMli6dClVVVV0CgpC76efuPPoEfl5eUglEkpLSzkjkdBUCPZWV3NYqcRNJiPa0hIpUFRYiKWFBV4eHihLSuh05AiLli9n8+bNpKWlIYTA29ubqqoqKisrgZqpdldXV7Jyc1l97x5eQGOpFJlCgUIiobyiArUQNPfzw8rKCgOZDDOlkvqOjiQYGrLa0pLc/HxUKhXV1dWo1WpkMhmurq64urrStWtXQhUK9AsLqZJIUKlUSCQSpFIpFRUVHKmsxK+8HOW9e3i4uFAkBDkKBa1/3XO2vBzq14e2bf9rz9ofRSqV0qRJExYvXkyrVq0w+w86zM+LjY0Nt27dIicnh0aNGiGRSLC3t+eLL74gNDT0uXu8avl7oTWqWv5rODs7a8QcmjdvjvuTKcCQkBBOnTrF0qVLee211zQlDU8zWz/55BPOnTtHr1696jSs7dq1w9ramnHjxmnkCXNzc9m8eTMXL14kIiKCgQMHkp6ezscff0yPHj3Iy8vj9OnTtGnTRtO5xdjYmC5duqBSqdi9ezf9+vVj//79fPXVV3Tt2hVLS0uCg4Px8fFh2rRp/PTTT/To0UPT4g5qam/79OnDa6+9xo4dO9iclER/oKqykuLKSqqrqykvLydJoaC9vj7dgUAdHbxVKozkcuzs7BBqNY8fP4acHA7IZOyFWlO9QgiKi4tp2LAh2dnZSKVSLCwsMDY2Ji8vD6UQJKrVnFersZJKaayjg5VcjkFlJdYGBlBejlwqZa9KhfPKlazNzeV/jh6tNf7Tv0874PTr14+GubmQlkaRQkFhYSG5ubkUFhZSWVXFaaAxYAmUlJaSa2BAqRAEWFrW/rKKi+GVV/6ypTVPMTExwcTEhC+//JKgoCCN0tYfxdPTk6VLl9K1a1cMDAywt7fn1KlTCCHw9PR8KcfQ8tdC+6qk5b9K7969GT9+PEOGDOHmzZtAjWfw1Vdf4eLiQq9evSgtLdVs/7Qs5vz58wwZMqTO5CWAgQMHsnLlSmbOnMknn3zCnDlz6Nq1K5cuXSIkJITi4mJWrVrF+PHj+eSTT3j06BEGBgZMmTKFoicJQ1AzTRcaGsrixYvJzMykdevW1KtXj27durFz506g5iUgKSmJoqIiAgICOHnypGb/3NxcfvrpJ06dOlUj/K9SEVdVhaVMhpFMRlFxMSqVCqhJUFKr1fj7+2NhYUFOdjZpaWno6OpiUlXFvcpKphcWUl5ejpeXV622bUqlkvPnz9OwYUNkMhmVlZXcvXsX8STeaWJmxlngfSHoa2DAEktLlpuakhIdDXPnorN/P3P19Xll+HDeiIlBLpfXmXSUkZHBp59+SmBgIHErVpB+5w6ZWVkUFRWhFgI9PT1NFrCEGmOkp6dH1qNHNVnHv0Yq/dsI9gcFBWFvb8+GDRte2pgODg507tyZLVu2aJa98cYbbN68GaVS+dKOo+UvhNCi5U9g0qRJolGjRiIrK0uzTKlUih49eohu3bqJqqqqWts/fPhQ+Pn5iZiYGKFSqX5z3LNnzwpvb2/xwQcfiJSUFNG3b18RFBQkWrZsKdLT04UQQqxcuVJYW1uLLl26iM8//1wMHTpUPHjw4Jmx1Gq12Ldvn3j99dfFO++8I9zd3cWYMWM0x8/LyxOzZ88Wbm5uYvbs2UKlUokBAwaIxo0bCw8PD6GnpydsbGzE+fPnxRgvL3FOKhU3rKzEbhDdQKzU0xMH5HKhDAkRhR07iv0GBmIPiLMgbjVoIBqamwtqbG+tj4GBgejUqZOIbtFCfKinJ3ZIpWIviO9BrAbRQyoV+k+2HT58uHBwcNDs6+zsLJRKpRBCiIiICCGTyYSTk5OQSCR1HuuXH387O3Hd0FAkmZmJi87O4n+MjMRhExPxo0QigkCs1NUV+w0MxCkbG7HS01O8b2oq8gMChOjZs+bTvbsQ7doJUVr6h5+fP4uioiIRExMjLl269NLGLC4uFv379xcZGRmaZTNnzhQ//PDDSzuGlr8OWkUlLX8agwYNIjk5mQMHDmhiqZWVlZr+rDt37qwVZ3r06BGhoaE0btyYr7766jdjUHfu3CEqKgo/Pz9ef/11fvjhB1QqFUePHmXjxo34+/uzdetWxo0bh6WlJWPHjuXAgQNMnjwZX1/fZ8bLzc1l2bJlpKamcvnyZSwsLFiwYAGjR48mOjqawMBA3nrrLezt7fnggw944403NO3VRowYwf30dD5ft44ZPXtSPyEB58pKDAwMyFMqUahUGBoaolNdjaW+PgZGRhw2MGB8RgaP60hEAtj0wQe8np8P169TUVnJhTt3KKuqQkJNtaQBUAHskMmI3L2bY2fO8MuojpWVFS4uLiQnJ1NeXv7M+E/lCysqKoAapVtPwNbAgGmenlRnZHCvuhorW1uys7OprKxkKTDAzIxXnrSq25adTYVcTo/KSpycnLC1sanp2RodDePHP98D8hfh4sWLLF26lKVLl2JiYvJSxtyxYwc3btxg6tSpANy7d48ZM2awZs2aF1R00vJXR2tUtfxpqNVqwsLCAEhISNAYyeLiYrp3746HhwebNm2qZTxzcnIICQnB19eXr7/++jcN66NHj4iMjMTS0pLAwED09fWprKxkzZo1LF68mIiICH744QcmTZpERUUFkyZN4ujRowwfPpxXXnnlmfGEEBw6dIh169bx4MEDzpw5gxACX19fDh06hIGBAQMGDGD//v1YmZkRIJXSs6iIRoCkogKhq0vzHj0Yc/IkpwsK6COX07yqCn2FApVEgsLEhHR/f6YkJfHBzJlMnjy5zusa7uDAO4WFuHh7Y+riwvUbN8jLz8fI0JCCwkKqKisR1DQlswLSjYx4u7KS/CdTzr+FlZUVnp6epKWlkZOTg7VcTvuKCgYBTtTEhXSkUuRC4CqRIDMwIEOlotTEhKyiIpo0boxCocDIyAgLCwuuXL1Ks6ZNqa6u5vbt29hZWNQIQ3zzDTyRc/w7sXbtWvLz85kwYcJLKX9RKBSMGDGCMWPGaJL2FixYgKenZy0JTy1/f7RGVcufSmlpKUFBQdSvX5/169drlufm5hIcHIy/vz9r1qyptc/TPqo+PjXi5L9lWEtLS+nduzfl5eX4+PhgY2NDfn4+hw4dYuzYsbzzzjvs2bOH2bNn8+DBA0aNGsXt27cJDw8nMjKyzh/P/Px8+vTpw/Hjx1Gr1RgYGDB37lysrKyYOWMGb5ia0jU5GWOlEqVEQglQDTja26MoKkKUlKCWSDhuZsbs0lKKVSp27dpFXFwcDx8+rBVP/jXdgOUmJkhtbEjLzMTQ0JCy8nKsrazIycnR1FgW/iJGbAfcBN6mRv4dQC6Xo1QqNclPT8s91Go1pqamyDIyWFhZibVKRbEQ/PKM9HR1ecXVFZGailQmQ0dXl3wLCxyaN+d2cjJ2traYm5tz9+5djIyMsLe3R1FSQtaNGxwPCiJyzRqM/oIyhf8JhULBBx98QO/evenatetLGfPo0aN8//33LFq0CIlEwsOHD4mLi2PN3/Qeaakbbfavlj8VPT09evbsyYIFC3jw4IHmB8vIyIiwsDDmz5/P3bt3a7WGMzIyIjIykqVLl3LkyBH69OlTpwF8KgJx4MABkpKSOHHiBEqlkqVLl/Lhhx+Snp7OyJEjcXFx4e7duyQmJtKwYUPS0tJ48OABLVq0eGbc8vJyZsyYoSllqa6u5sCBA5w/d44fu3alb24uNs7O3C8uprCqiuon3WAKCgspqaqiFDAwM6O3mxvejx5xSAiOnz9PcHAw58+ff0bQ4SkN7exYAzwqK+NxeTm6uroUFhUhhKCkuJh4ITAsLUVeWYkE0JHJ0NfXp0IqxUOlQgDnnoylq6uLvr6+Ru4QamYHdHR0aG1vz2Z9fSz19akwNib/iUgE1CQiqdRqUgsKMDY2xlpHh6qqKsxVKiS6umSVlWFtbY2eri56eno8yMjAViZDR6FAb/ZsQpYtY+HChaQ9aSpgamr60qZT/9vIZDIaNmzIokWLCAgIeCnn7ebmRmJiIoaGhri5uWFqasqDBw9IT0+nadOmL+GstfwV0BpVLX86xsbGdO7cmalTp6Kjo0OrVq2AmpKa4OBgZsyYQX5+Pp06ddLsY2RkRJ8+ffjss880EoJ1GVaZTIadnR1btmyhvLyc0tJSevXq9b/snXl4VOXZ/z/nzL5nkkx2CCQhgYDsq1o2UUBBRASK4mvFhV+tWGWxtq6vWq0LbmBRq1ItilIXWlkUcWOrIDtCEkJWErJNkslMZp9zzu+PmYwgVIu4YN98risXIZPz5DkzZ+Z7nue+7+/NvHnz+NOf/sSmTZuYf911DKyuZuyBA/Tcvp0BZWVkFBXxr82byR01CvVxLc9qamo4cuQI1dXVKIoSj3nek57OhLo61JmZaEwmZFmmsakJRZaJRCLx3xMEAa1ej1uSMLndDAfWShIfbdoEcMoYqlqt5nd5efRqbaVFUeIOSaIgRA0ggO3AAJMJczhMbm4uQ4YMIS8vj9zcXKzJySQdPco/DAYCkQiRSOQEQe1AI0k84/GQZbPh0+upiNXEdtAC/A34EPBEIiRqNCQYjegVBZqaCIRCJNjtqAIBNH4/wdZWAkOH8lJWFq29ezNixAjWrl1LUVERe/fuZdWqVYwYMYLU1NTTul5+KhISElCr1bz55ptccMEFZ1xXKggCmZmZPP/880ycODFe77xkyRLGjRvXGVv9L6FTVDv5SXA4HPTv359FixaRnZ0dTxhKTExk5MiR3HXXXUQikbgbE0SdmqZNm8bSpUvZuHEj06ZNO0lYJUni17/+NRD9EGtpaWHnzp3ce++9zBw5kvAjj2B69FF6Op0ka7UowSBiIIDG6aRXXR1ty5ah93jQnHMOGI0kJSXxxRdfUFFRgdfrxeFw8O6jjzJ5+3ZKW1uRRJGyI0coKy/HaDSSkZERX9XKihI3m/B4PHhkme6AJxikLj0dSZJOELsO4/Wgz8ftzc34ZZmwoiAdV1ak0WjI79GDD1wuesgyqTodQ4cORRTFaD2rx0N1bS2Ky4U/M5OKmBAcvyJWqVQYjUZmJiYyyu3mQH09TU1NaLVaEmy2eJx2I5AMXANkKAptoRApffrgMRjQq1S0eb0kXnQRQo8eCBdfzOt5efxq9Wr21Nby0ksvkZycjNfrxev10tjYyNVXX83MmTN/VhZ9BQUFbN68mcbGRs4555wzHi81NZX9+/fT1tZGr169MJlMOJ1OioqKGHiWNh7o5PToFNVOfjKys7NJT09n0aJFjBgxIt4pJDU1lWHDhvG73/0Os9l8wodNh7A+++yzbNiwgSuuuOKED2lRFJk0aRL9+/cnMTGRo0ePUlZWRpLTyei//Y3+okiNz8eRpia65OWht1jwBAIoOh21bjcWux3f5s0YPvsM9ahR7Cor49Zbb8XtduNwOHA6nYwtLaVQFGnw+SgvL8fj8ZCVmRlfhWVkZMQblFssFiKRCOGYeIaIZtd+mJyM2+s9oVZRr9cTCAToK8sxcI8iAAAgAElEQVRcAWgdDgKBAFlZWWR16UJTYyMAKampfCGKpLa1kSyKaLVaXC5XXPgBvG1tpAHvShL9+/dHURTMZjPhcBiHw0Gi3c711dVoJIkg0dVxvKG6KBIMBvmCaO+V41uNNzQ0UNfcTMhiwdvcTNIrr6C+8kp2SRJLly9nz5498Tpgl8tFS0sLgiDgcDiorq5m8uTJP5stYIjemPXv358lS5bQu3fv76XReE5OTrzxQ0eLxGeffZZRo0ad2Bi+k58lnaLayU9Knz59kGWZO++8k4svvpjEmCNPVlYWffr0YeHChWRkZNC7d+/4MQaDgenTp/PnP/+Z9evXM3369BOEVa/X0717d8aMGcO8efPI02rp88QTaFUqzN26kZaRQXt7O4dLS0nPyMBmteJ2u0m026k5dgyDw4Gnuho2bOCa11+nNRTioosuor6+niStluurqqhyuzHbbMiyHBckT3s7dceOUVVdTcDvx2az4fV6UalU6LRaIpEIEpCsUrHT46FMkk4wt4hEIjz99NPkBAJ0r6igJRjE4XDQs2dPDhw4QI8ePfD5/RyrreUjj4cCIMNkora2FkmWyS8owO/3U3P0KJFIhNSEBELTpuFwONixYweyLJOamsrw4cNp3b6dX8kyrYJAekYGOq0WURRpi/Vx/RtQBZQBW4FegD12DrKi4PV6Mca2ppMmT+btt99m9erV+P3+r8wuFAWbzRZ3Edq1axePPvoo48ePj7/OPwcMBgPp6en8+c9/Zty4cdHG7WeAzWajoaGB4uJiBg4ciMFgwOPxsGfPntPs7drJ2Uhn9m8nZwXz58/no48+4qOPPjphNfDuu+8yf/58nn322ZOambtcLiZOnEhaWhp///vfT3AgiqMoMGsWTbt3c6C2ll69epGeHl17lZWXU1lRQa/CQnQ6HdVVVSQlJVF65AgmoxGt24160CBsK1cybtw4jh49yihZ5oFIBL/ZjCRJhMNhunbtiqIotLS0EDEaebWhgfKYV25vYKIgoFar46tVh0qFq6CAB+x2/vWvf50grIIg8Nv8fK4qLaVOltGo1fE4p1qjQZZlwqEQTwOTgN4GAwLgDwRQFAWtRhM1vw8ESE1LY6TPh8fjOSFWKooik9Rq7hcEakIhtFotdrsdWZZpampCVhREQeBvgsA5QN/Y/FQqFXqdDm+s1tUKtFgsZHz4IUOHDmXu3Lm8+uqrBINBRFHEYDAwbdo0li9fHm8IsGjRItasWcObb75J/7PYZP9UPPPMMyiKwm9/+9szHsvlcnHTTTexePFi0tPT8Xg8zJ07N/7/Tn6+dNoUdnJW8Pjjj9OrVy+mTp0aj0lC1ObwgQce4De/+Q2bYsk9HSQkJLB+/XoaGhq44oorTp1Je+AAlJfjyM+na3Y2R44coaamBgXIzcmhV2EhRYcO4XK5yM7OpsnpJDHWd9SlUiHv3Mmk3r2prKxEo9FgVRTEWOwyEomQmpaGq62Nuvp6vD4fT9bXY5IkbgEWajQM1GoxGo1IkUjcqkhSq/GUlbF169aTbBgVRWFXSQkRWUYlikQiESKShFqjQRQEhNgYHfj9fvyBAKIoIooi4XCYUDCICqhrbz8haaoDWZaxqtXxN38kEqG+oYGGxkbk2O/KikJElpFkGbVKhV6vR1EUwuEwYmxXIAzg8fDkk08iCAJ9+vTB4XAgCAKpqans3LkTi8nEvz78EAIBREFg8eLFzJ49myuuuIItW7ac3kXyE3PjjTdy8OBBtm7desZjJSQkMGXKFF599VUgavd46aWXnmBn2MnPk05R7eSsQBRFVqxYgU6nY+bMmSeIzezZs1m0aBHXXnstO3fuPOG4hIQE3n//fZqampg2bdrJwvrmmyAIIAjkdO9OQkICx44do7KyEllRot1h8vIoO3KEA7FG3A0NDRiMRnx+P8FwmMnhMAUFBVGT9ePGF2IdaATAaDDQpNXiVhQuADSAHAqREgzS3t5+QrJRIBhEkCR0Ot1Jz4MgCNQaDBg0GlAUFEAlioSCQQLB4CmzeFEUBECjVqNSqZAVBQvwcXv7KR2U9Ho95wweTFJyMimpqQiiiComyqfKcBVEEVmSsFosIAjY7fboawb4iLoFffLJJ4wePZpXXnmFJx97jDl5efRcvJjHNm0i/aqrkEaMgPPOg4cf5u5Zs7j11lu5+uqrWbNmzcnnc5ai1+tZsGABy5Ytw+l0nvF4l112GUVFRRQXFwNw6aWXsnv3bqqrq8947E5+OjpFtZOzBrVazTvvvENtbS033XTTCY/ddNNN3HDDDVx55ZVxY/4OrFYr69evp7m5malTp36V/OPzwcaNkJSEJMv4fD7+2N7OhvZ2FpSUMOb997nniy9o8vl4R6fjTreb5eEwGqsVRZbR6fW0AheHQhw6eJC2tjY8QIdXkSRJeNvbaXW5cLvd1Pt8WBUFNcRN55XY93q9HoGoQGqAFkkiGAzydRRF4Zii8LkokhBbNUqyjCa29dux5tTrdOg0GtLT0lCpVGg0GswWC7379MFkMCAKAhvM5lM2fB8+fDiq9HSEmFiHw+Holq8ootfr0Wg0GGNjCERjinqDAZ/fTyQcxhOrZTUCDVotkiRx+eWXU1NTw9hAgFvWrWNOURG+3bsxZGejysigKhQCqxXefRdmzeLmnTt54re/Zd68ebz22mune6n8ZBQUFDBp0iSeeuqpU5ZDnQ46nY7Zs2fz8ssvoyhKPAlvxYoV39NsO/kp6BTVTs4qrFYr77zzDp999hn333//CY/dcccdXHHFFVx++eVUVVWddNz69etpbW3lkksuYdu2bbz36qvU1tWx/+BB9uzeTWVVFbIkUanX85uEBP7UtSv7/H4erKhgiMfDQkEgIkmsb2mhvb2dQCBAmKi/boffzUGiIikA4djWrKIoRCQJs6LgBkS1GlEU46tHQRAIhUIIgoDBYCC/a1dazzmH5ORkevTogUqliq8QBUEgEAjwaiSClqhVIEAwFIrGS2PxWVmW0ep0ZGVlMWjQIGRZJhQK0dzcjD4Q4CBQHUuosdvt8cbjgiDw2Wefce/q1RysrSXQ2gqAIss4HA4UWaZLVhZJycmIokhiYiJms5mA348syxgMBkKxmxYR2Bnrv+r3+fj4yitpuPHG6E1Edjb1gQCIIllduuD2eGjz+SA1NfpVUsLU1atZvnAhd911F88+++z3eh39kEyfPp1QKMTq1avPeKyxY8fi9/vjnY8uueQSDh8+TGlp6RmP3clPQ2f2bydnHTabjfPPP58//OEPJ5XUXHDBBZSUlPDwww8zefJkmpub2bNnD0uWLOGWW25BURSKi4v54IMPOK9nT/qUlJDcpQuOlBSMRiPrW1qYYLGQGgoRdLsp9ftJVBT6ExWJkCBQQ7RXqEqliq4ggH/odHgkCTcwgGipiSkpiV6FhaAoZGRkILa3U6RW4xZFMiIRCvv04agsM2bQIFytrQQCAQRFIUGvZ/fkyXTJz2f37t3R5KOYUHUkDDWq1Zyv0dBdEAiqVCix+KaiKJiMRgaGwxiCQerr63G1tqJWq/F6vQTcbkzA3UBFIBBvE9dhXKFWq7Hb7YQlCSkcZqQg4I+dZ3t7OxqNBq/Xi9/vp0irJUur5dIhQ+jSpQvH6uriSUomwAXc2dLClClTuK9vX8YdOECZx4MnHKZbdjbX7d9PD5uNLKMRg8FARWUljpSU6A2E0QiBAN2Kihhx110sfOABPB4PI0eO/LEus++MKIr07duXJ598koEDB8a3w78LgiCQnp7Oiy++yMSJE9HE3KnWr1/PmDFjvsdZd/Jj0Smq/5fwN0Dz59C6HzyHwV8PumRQab/92B+ZtLQ0CgsLWbhwIQUFBaSlpVFSUhK/o//iiy947LHHaGpqIhwOo9Pp2Lt3b3xFWFdXx+7PP+eKYJDq5mZcLhftXi8ftbczQKsl02DAHwhQFA5jJSqSoiDgFATqBYEhWi1KzOvXodPxcX4+9bE4mlsQmAi4JYn6ujr8gQAtLS2EQiG6RyLslSQ+UBTWt7YSDocx1dUhxJKOkhWF9wIB7ty2jZ07d+Lz+eJx4I6tV6/Xiy0hgYquXTkvGKQwMZH03FyO1dVFt5EFgeSkJPw+H3LMwQlBQB2JkAQ8AHwaex6Pd4GC6Ja1z+cjHA7TYjRypUZDemoqLq8XR0oKyUlJtHu9+AMB+gO2WPZwKBxGiW2hoyg4gGeI+gxX79vHTQcP4larCUQiBAMBnM3N7AB6azTkJiai1+vx+nx429ux2WzEThja2siKRBj1pz9x9913U1lZyUUXXXTWG0SYzWbsdjsvvvjiGTc1T09Pj18LBQUFdO/enZUrV5KTk0NKSsr3OOtOfgw6RfW/HUWGll1QvBgO/QnqP4amTdC4CRo2QuVrEHCCIR10P33toCzLHDt2jP3791NTU0NbWxuPPPII27Zto6mpKV4DmZeXx+HDh9m7dy/V1dV8+umn1NfXEwqFCAaDGI1GgsBErxejVouiUqHRavksEGCE3U6P5GTS0tL4qLYWh9lM/5glXVUohEur5YKUFMKhECq/n3rgsZihvaIoVEsSA4E8oEvPngwcMICKigoUWaZfr15M7t6dnq2tXNW9O8OSkwmHQvh8PkyyjFqr5T61GucpGlRHIpF45rPP56O6sZFPdTrON5lQVVWhCocJiyJhSaLN7Y43KNcpCjZJQg38AXg/Nl6Hy9K/o12SKAFGt7VhttlwtrUhSxJSJEJ+jx70798fV1sboijSUF8fbVQuy6QDHwDLAI1Wy2WSxHBFoTkcxmw2Y7PZ8Pv9fOLzkSfL9MnMRBAErBYLlZWV8cbmABgMcPgwaXPmMHHGDB566CF27tzJ5MmTz3ph7datGwcPHqSkpIRBgwad0Vjdu3dnyZIljB8/Hr1ej9ls5p133mHcuHFn/fPQyYl01qn+NxPxwd4/gHMrCGrQJoLwtTC6HIZQS/T7nGuhx/+LZsv+CAQCASorKykvL6eiooKKigqqqqowm80kJiYiyzJer5dt27ZRVlZGWloaPp8Po9GI1WrFYDBw+PBhAAYNGsTevXtRq9X06dOHa665hszMTLZcfz3Tm5ro8YtfoBJFrtu3j5uzs1EfPYooiiypqcEOzO3Zk3aPh92Kwvs1NdxoMJCcnIzN72deQwP/+NrcLcArWi09dTpqYkJ+zjnnkJ2dTTgcZsuWLaSmphIMhZAiEUJOJ4IksVCnI1BYSG1tLQ0NDfHxOuo4v87NN9+MyWBg85NPciUwNGYg0VFaIwBtRD161wMtgnDaCTR/nj6d8Z9+irO5mWZFQSbanKAjOcpgMCDLMkZJQna7WQ/cT7SkRgWsEwRUioKfqI2iSq3GZDTyR4+HfpEI1QkJhPV6hickMNNiwdnQQO/eveOlOdTXw7XXwk03cezYMS699FKys7N54403ztho4Yemvb2defPmcfPNN5+xsD7zzDPYbDauueYaZFnm5ptv5rrrrjvjcTv5cekU1f9WpADs+DW0fQm61G8XSjkCwUboOgMKb/9ehVVRFJqbm08Qz/LychobG+NlJYFAALfbTXNzc9SAQJYxm82YTKaoMUN1NT6fjxkzZpCTk0N6ejppaWkkJiZy2223kZycTP/+/Rk+fDgXX3xx/O7eV11NVZ8+uLVaBg8bxo0HDjCvWzfCpaW0t7ez0ufDpihcarOR4nCwU5LYJ0lMbW8n5PWiD4eZajRSd1z3lg5MwEPACABRpM/IkUjA3r178bS3oxZFrIAqEqFZlrkVOCyKZGZm4nK58Hq9cSHtmO/XTRoOHDjAhRdeyLFjx9BqtaRIEjmShEUU0ZhMHPV62SvLjL3wQp588klGjx59ynIPs9l8yjZzKpWKpKQkCoBJTiejZBmDTgc6HRIQDoXQSBKyJFEtCPxFknifr2plz9FoeCEcpik2X4fDgc/nw+/z8ZQso1epmKUonFNQwCuBAH2sVob7fJjMZjIzMqKD+P2g1cK6dUDUGGHy5MkYjUbefffds966b//+/SxevDguit+V5uZm5s2bx1NPPUVKSgrbtm1j1apV8TrgTn4edIrqfyt7fw/1H4Iu7T8XSEWCQAMU3gHZ07/Tnw2Hwxw9ejQunmVlZRw8eBC3240Y85Rta2ujra0Nr9eLXq/HYDCg0+m++oAvKKBHjx6kp6fHv1JSUrj++utxuVx88MEHX20fEm1jNn78eLp168Zrr72GJEk0NjaSmZkJQOiPf6TikUdo0WoZOmwYKlGk+uhRjhw5AjFDg7S0NBLsdpxOJ4IgUJCXR9X27TwTDPICJ3eTsVgs0Q86RSHN42EGcAlR1yOVKBIMh1ErCl8Cq3Q6NsYyiSHaccdqtTJt2rS49d3mzZvx+/3f+Nz+7//+Ly+99BJHY6vsju1dayTCWIMBm6KgBVplmUOCwP6Ys1HHatNgMNDS0hIfTxAEBg8ejMfjoampiUgkgjkQ4LHRo+kdCkFbG872dnY3NrIpIYEdPh8NMf/hjuNHqFQsliSaYi5M6ljmsyTLPBEOcy4wiGgpjzslhQ9VKv7cqxdffvklvXr1wmAwRGt/29pg27b42D6fj6lTp+L1elmzZg0JCQmnfS3+mCxfvpza2lruvPPOMxLA1157jYaGBubPn4+iKNx2223MmDGDc88993ucbSc/JJ2i+jPgo48+4oMPPuDRRx/9zw5or4Qt00GXcvJ277ch+aNx2DHvf2sCU1tbW1w8S0pK2L17N0eOHInHBn0+X9zg3WAwoNVqUavVpKWlkZ+fzznnnEPXrl3jq860tLRvXJUEAgHGjx9PSkoKb7755glGBU6nk/Hjx9O3b18sFgtHjhxh7dq10Q+4SITIvHmUv/YaLRoNQ4YNwx8IsH37dlAUrDYbubm5VFVVYTQYqCovJ00U+USrxbVgAfd9rbQHIDk5mTfeeINrr72Wo0ePYjKZMMsyGZKEKhQiCDQDdYKAXq/H7/fHy1okSSIvLw9ZlgkEAtTW1gLRzF+XyxUX8K9vCWu1Ws4991zq6uooPXyYIQYDl/r9jIuZP+g0GqRIBElRUKnVHDObWerxsFkU8Z4ihisIAv369aOyshKXy4VWq+WGG26gqKiIjz76CICPP/6Y8ePH88gjj7B8+XK+/PLLaHzUauWGG24gv7WVoa+/jiYjg9qaGoLBYNyA4ilZZiLQA9BptbSo1TwvSXw4ZgzNLS00NzfTq1cvBEmC1laIJaF1EA6H+eUvf0lVVRX//Oc/yehY2Z6FhMNhFi5cyMSJE5kwYcJ3Hsfv9zN37lzuuece8vLy2LVrFy+99BJLly4949Zznfw4dIrqz4DTFtWixVD1Jui/Y9/KQAMMeARSoyn9HclD5eXl7N69m88//5xDhw7R0tJCOBwmGAzi9XoRBAEx1jWla9eu9OvXj379+lFYWHjCivOUHr3/IS0tLVxwwQWMHDmSp59++oTHSktLGTp0KFqtluzsbFasWEF+fn70wVCI8L33Uv7nPyMpCvkjRrB561asNhs2m40Emw2tSsXRvXsJBgK8IUk8AeTm51NTU3OCM5E65scrCEI8c3fOnDmsXLkyLirHd59Rq9UnOD11rLJzc3M5fPhwPPlKEASWLl1KSkoKN910E06n86QVsiiKdOvaldtEkWHl5UhE+56qNJp4yY2iKJgMBrLtdlpra6kyGpnr9+OMjTVq1Kj4OTU1NcXPQ6fTMXjwYMrLy3nrrbewWCwsWbKEF154gdTU1GjG8HErXYB+ajUvCQLNoogiy9FsZElCrVLxDDBSreb6AQPQqNWsKS7mzbY2bjcaGTR4MJWVlSQlJpJqs0V3UzZsOOn1lmWZ66+/nh07drB69Wry8vL+42vlx+bo0aPccccdPProo/Fdku/C+vXr2bJlCw8++CAQrc8eP348Y8eO/b6m2skPSKeo/gw4LVGN+OHjC0FtAvH0S2UkWcLfVkdls5Z712dRVFREY2MjwWAw7iOr1WoxmUxkZGTQrVs3+vTpQ7du3Rg0aBBZWVk0NjZy99138/zzz/8g3UgqKiqYMGEC1113HbfffjsQzZy99tpr2bFjBzU1NdhsNh566CF+9atffXWgy0X45ZfZ+fDDdPN4sCQkoLfZcIVCoFaTnJTEc0ePsqSujkPHvS0MBsMJW7MdWcDH/8xgMOBwOOIiFQgE6CLL9Fep6O5wUFNfTyvR5uInRzajYpmeno7FYmHz5s1AtNTi67aLep2OhaEQlykKjcDxj1rMZkxmM263G7VKhae9HYNejwOo9Pu5TqWilWim6fbt21m5ciUPPvgg9fX1QDTuKopi9Hi1mvT09Phr/++wG418ptcTAkSjkXAsaautrY2lokhaYiKPDxyIThS599Ah7O3tjBME/IEAvXr2pK6ujnNSUtDMmAG///2//TsLFiz4WRjxr1u3jo0bN/Loo49+55tHSZK4+eabmTNnDkOGDOHLL7/kqaee4rnnnjujG9JOfhw6X6GzDKfTyQsvvMDBgwdRFIWRI0fSo0cPAF5++WU2bNiAyWTipptuimcFbty4kbfffhun04nNKHBFfjMTzosWpB8od7N4VTmTR6Ty7pZ6RAFuuqwbKpXAs+8coamlnRH5AufmRRtmVzdFeH8feALwWckeEhISGDhwIH379mX48OH069ePjIwMTCbTN56DJEk4nc4fRFS7d+/OihUrmDFjBpmZmVx11VWIosiECRNobW1Fo9Fw+PBhnn766aioFhdHPYDXr0cjSQzr0oXy0lICzc10jZ3HVpOJC//2Nxb07w8GA2IgEN969fv9cdejzZs3M3v27Gg89jj8fj+FhYW8/uqrPDhxIr127aIvgCShqq+PGukDMrAvK4uV4TCbjsv+FQSB1tZW2tra+J//+R+KiopO2SBgllrNNXo9rVotSnMzyDJibDu2T58+1NTU0BwK8RbQLSWFwoYGqoDuBgMvWyxMd7k4cuQISUlJdO/eHZfLBURFPRAIYLVaMRqN+Hw+jh49+q2vhc5qpWbwYAq2bSOk12PQ6wkGg5jNZuTaWnJ8Pu4qKsIlSQxLSOCGnj05WlaG6HZz8OBBUlNSaGxsJGPaNL4pErl48WLsdjvTpk3jr3/9K7/4xS++dW4/BRMnTmTnzp2sXLmSq6+++juNoVKpuPbaa1m+fDkDBw6kT58+ZGRk8OGHHzJx4sTvecadfN901qmeRciyzB133EFubi533303U6dOxWKx0N7ezvr16xk7diy33347Wq2Wl19+mcsuuwxBEGhubmby5Mlce+215KaoWLzsDQYXJJJo1dLYGmTd540UdjPzh9l5mA1q7n9xB6VlVUzq006uI8SqbSH6dhFIspvQmZMYPTib+VOT+Z/7PqbV5ebKK6/klltuobCwkKSkpBOShI7n/vvv5+mnn2b9+vX079+fadOm/WBZi5mZmeTl5bFo0SL69u1LTk4Offv2ZdasWYwZMwZZlvn0k0+4vL2d5MWLobQUEhPBakWwWmmJRChqaqIptl1qrKggsHYt7f36oXE4TrJBVBSFt99+m1WrVrF169aTBE8URayyTOoDDzCmpgabouAEvIAH8IsiisGA0WYjx+3mgrY2bBYL20IhHA4HJpOJtrY2QqEQR44ciWcGdzx/WVlZmHQ6/ujz4VcUWtrb0ev1UdN9RSEQDMbreiVZpgQIt7fTjahVojsSoYdKReGcOZS63TidTlwuV/w8cnJy0Gq1NDc3n2TaL8QSkHQ63QnnrdFo8Pv9hBwOZigK1U1NGM1mEux2QqEQ1/fti6qmht6BAHN792Z0ejpalYqk5ORob1lJQnI6KRVFjk2cSPfu3b/xNR85ciRqtZpFixaRl5f31db+WURHnPrZZ58lPz//O5s3ZGRksG3btnj8PSsri2XLlnHxxRefkdFEJz88naJ6FlFSUsIHH3zA/fffj1arRaVS4XA4qKiooLKykttuuw1BEOjatSuvvPIKEydOxGAwkJmZGc9GTbEJlG5/A1GlpaCrmcbWIJv2t/C/vypApRLJTNbz901NXDXGSpdUE2a9QFGNQlqihhSbiIYAEX8zQV8Ll9/+AV9++SVr165l/fr1rFmzhk8++YQdO3ZQXFxMRUUF1dXVNDY24nK5GDp0KFOnTqVv376YzWZ69er1gz5f+fn5GI1Gfve73zFu3DhSUlIQBAGHw8HFF1/MdZKEvHQpYmoqeocDYokebo+HkpKSaAcYlQpnWxtuWUYfCDAJ+LvTSc0pSmjefvttDh8+HDW2j8VUOzJwzYrCkx4PPQSBelkmHLP+A1CrVNgSEpAkCZVGQ0N7O/qEBM6XZc7p3p01TicIAsFgMH7M12tWPR4PU5KSOK+lhabYNnxHO7kOa7uOBuIAhwEtUGgwYLfbkRQFyeOhePduSjIzaW9vP0E8LRYLGo2GSy+9lIqKipNaxsmyzKxZs3A6nXFDfb1eT79+/Ujo0oX6ykoKW1poDYXw+v0U9u6NyWgkNTWV+vp6qqur0Wq12Gw2BEEgISEBnSQh+Xzcq9Xy8po1XHXVVd+4AwIwdOhQHA4HixYtIjk5mb59+57GFfPjoNfrycrKYsmSJYwbN+7f3oR+E4IgkJ2dzdKlS5kwYQKpqakcOnSI1tbWH/x91cmZ0bn9exbhdDpJSUk55Z3o8SUFx9d2AuzatYuVK1dSW1uLEgkQrG2nW+ZXiTIWoxpRjK54tOpoItF5Q/qRYtehoLB6/z6SHDoSE2WqG3xs2BvkaLNARkYmkiRhtVoZPXo09fX1NDQ0UFlZGU9Y0el08TIKiK7oJEmipqaG559/nvz8fGw2GxaLJf6vxWLBarVitVrj31ssFnQ63WmvbG+88UZqamqYPn06Gzdu/CpD9JNPSFm9Gk9hIaWVleRrtZjNZiKRCEVFRVExUqujTbe9XhSgSZJQ1dTwhNnMS//zP7z65psn9HaVZZkbb7wRh8PBHXfcgd1uR9u1o2gAACAASURBVKvVIksSf3S5yIpEiKSkcFH//lRXVfHlwYOIsYxfj9tNRJJAUdDp9YQkCWNuLpe7XGxSqVgRM7Y/nuOzhRVF4fzqaiSVCkdiIsFAAH9si1pWFOolibcUhRaiTk8oCiaiRvwK0ZW9KMtMqKnh0X37+HpDuObmZoKxNnXZ2dmUlJREbwJUKgoLCzlw4ABr1qyJC7FGo0GlUlFcXMzevXtJveEGtq5axYiWlqhHcltbfAU+aNAg9u/fT3FJCW1tbfTu3RvB48Gh11N7331svv12ZFkmLy+Pxx57jFGjRtGzZ89/+5pfddVV2Gw25s2bR2trK/PmzTuta+bHYMiQIezcuZNly5axcOHC7zRGXl4e/fr145133uGqq65i9uzZ/OEPf2D8+PFnfe3u/2U6RfUsIjk5OW7F959u8YTDYR566CHmz5/PsGHDUKvVPPj/hqKET/6QPhUCAlqNFkeyg96FyfxtyyHyMgRGjTqXVTt1tMb8a8eOHUvv3r3jDjeKouDxeHA6nTQ3N5/w1dTUxNq1a2lpaaG4uBiTyRSvRdVoNNEOLjHnn0gkQigUihsTfJPonupnTqeTBQsWUFNTw2WXXcbHH3+M2WSCZcvAZMJis5EjihwuLaVnQQEtra0E/H4kSUIUBFxtbRBbkYVCIRoFgUEWC6Zdu04QVIiKakdpg1qt5u9//zujR49GPHSI4FVXsbu2ltb6ej799FMsFguiKMaTnI6PzyII+BWFLw8eRK8o3KBWs5JozFWn08UTg/R6Pd26daOpqQmr1Uq/mhqCKhXelhaE2HxkRSEkSbwKDAeGAMXABqORLEVB9vtxNjXR3t5OeloaOT168OpVVzH1nnuQJIkRI0awZcuWeOnTpk2bePbZZxk/fjyDBw+mrKyM1mPHGAmkeb3oRJGw1coev5+qcBi9Xs/VV19NUUkJz7W08PvERK7wevGWlZFsMiEYjZjNZvr06UNxcTGhxkZKmpvpPmAAuueeo/+AAczas4fXX38dn8/HzTffTO/evfnss8++0Uhh0qRJJCQkcM011+Byubj77rv/o+v9x2TOnDnceuutfPbZZ4waNeo7jXH11Vdz6623MnHiRLp27crAgQP55z//yS9/+cvvebadfF90iupZRH5+Pna7nVdeeYUrr7wSURQpKyv7xmMikQjhcBibzYZKpWLXrl3sqTGS3eu7NVEOhWWSbCam/HoZA5sC3H777Xi9XlasWEF1dTV9+vRh8ODBDBw4kLS0NKxWK1qtFlEUGTt2bDyZ58svv+Txxx8nMzPzBME9lQi73e5oWUtCAiaTCaPRiFarRavVxsXb7XZTWloa/97tduPxeNixYwd+v58hQ4bgdDoZMGAAd19+ORd/8QXhpCTU4TBqtZqUlBQOFRWR36MHAwcNovTwYYxGI0drahA0GuRQCLVKRSQS4UhNDZPsdt7KyqKmtvaEbdCcnByKioqYNm0aeXl5bNy4EftTT5HS2IhWq40bx/t8PgRBIBwKRT16BQFZUZAVBX3s3IRY+ZGhrY2pGRl8odGQkJDAvn37gKgAFxcXo1arcblcqCIRghpN1DZQkohI0eSyGqIJUEOJ2haeI4qU6nRkJCaSEUuyamxq4tixY0gqFR+99x6SJCEIAlu2bDnh9Xc6nRw5coRhw4bRFZgM/LK9HUQRu8lEOBjE43YjAEdUKv6p03Fw71527NkDwD8yMnCdcw5PjR+PsGIFNDaCKGJVFHonJ/O52cybKSmsa2zkxVCIYcCiRYvYs2cP5eXleL1eamtref/995k5c+Y3Xqvnn38+b7/9NjNnzqSlpYXFixefVbWcOp2OhQsXcu+999KrV6/vFF9NSUnhoosuYsWKFdxyyy3MmjWLBQsWcMkll2CxWH6AWXdypnSK6lmEKIrcc889PP/888yZMweA0aNHk5ub+2+PMRgMzJ07lz/96U9EIhGGDh3KsF9MgLY3IOTidFvmzrnAxNKNGt7+n9+Qk5PD5MmT2bdvH48++igej4c9e/awa9cuXn/99XhbtszMTD788ENqa2tRqVRkZGRw++23x+edmZn5jXV7kUgEl8t1guB2fF9XVxf/mU6nIzk5maSkJHJyckhKSqKyshKv10tpaSn9+/fn0KFDSKtWIYgi4XAYv99PJBKJJ8as3b6d90WR6lAIqyAwWpbppVLFV36CIOAMhcgPBJgydCgfGAzxGCPAoUOHAFi9ejVbt24lNzmZF6qqaI3ZKZpMJkKhUNQ9KhSiJRzmfUHgmFoN4TAjgCHBIIFgMO7IlCyKTGxp4Z1Q6JQJUuFwGFEUiYgiSiRCSBCi3ryx+lKPLGPlq8bokiwjejy0CAJhtZpu3bqRnp5OSUkJ7W1tfLR1K7IgMGrUKDZt2hQ/744a13vuvpsDd9/NY6JIekYGfqOREFBZXR09N7UaURDoEgiwMBjkmN/PTTodJCVRVlYWNcOfPRtmzYKGBmhvB7UaU0ICxpIS6pYtY1JmJjNnzuT3v/89N954I3379o3XO7e3tzN//nyKi4u59957v/F67d+/P+vWreOyyy7juuuu46WXXjqrhDU3N5fLLruMJ554goceeug7zW369OnMnTuXyspKunXrxnnnncc777zDNddc8wPMuJMzpbNO9b8Vdwl8PgcEDWj+wzvaYBPo02DEK6D9Zg9TRVEoLy9n165d7Nq1i/LycgoLCxk0aBCDBg0iIyPje8387VixHi+4TqeTu+66C7Vajc/nw+12Y7fbecHtJi8hgR5fS2KJyDK/+uIL+koSfb1eSoNB3gTmiiKJRBOK9AYDPq+XRFnmAZ2OtV/bAu6g49zONZt5KhCgVRSRYwJoNBhQqVS0eTy8CBQA5xM1vn9dEJisUtE1JtICYDMa0SoKw09hU6jVasnMzGThwoVMef11ju7aRcGgQXz55Ze43e5o+VAoxBqNhuX9+lFcVITP5+NlRSEbGCsICERv2BJsNlIEgbFtbXhtNpqbmyH22PGGFdcCC3Q66iMRsvPzaXO5sFgslFdURBu0CwI5OTk0NDbS2tKCA3CKIguSkmjXarn//vvjN4Wn4rPPPmP58uVMmDCBe+65hzFjxnD11Vfz4YcfcttttzF69GiOHTuGTqdj5MiRLF++/FuTfY434l+5cuV3Sg76oZBlmTvvvJOBAwcyffp3s/9877332LVrF/fddx9Op5NbbrmFpUuX/iAla52cGWfPLV0n3y/WAhjyLKBEHZJk6d//rhQC/zEwZsHQ575VUCH6wZqbm8uMGTN45JFH+Otf/8pFF11EZWUld955JzfccAPLli1jx44dJ8Umvwsd1njdu3dnyJAhTJgwgSlTppCQkEBiYiLDhg3jL3/5C7t27eIX/fvT0NRERUXFCWOUeL0IWi0jZJlwMEg3ohZ6+2UZYvHdYCCAWqNBDai/VlpyPB2rOp0kYbNasdlsyJJEUmIiVqsVv9+PU6vFLwiMJPpGswMDBYF9kUi0d6pajQK4fT5UwSB2ux2LxYLD4Yj/nZEjR/LSSy8xaNAgKkeMQBeJsDMW783q0gVJksgWRbQaDa8WFxMMhylVq6k77oZGrVZH62w9Hj5pbqY6Ejkhu7ijSbrBYGBGair3ORzUSRLmpCRqjh7F6XRSVl6OxWzmFaMRqWvX6LY5YLFaaQKSZJl7WltJstu/NYlm1KhRXHnllWzYsIE33niDAwcOcOeddzJ37lwSEhJYunQpAwYMAGDLli1ceOGFNB7nOXwqMjIy2LhxI42NjUyePDkeIz4bEEWR+fPn849//IPS0tLvNMbEiRM5duwYe/fuJTk5mbFjx/L3v//9e55pJ98HnSvV/3a8VVD6QrR3qiKDyhhtA4cCcgjkIKgM0GUa5M75t6vagwcP8u+qr77+5lYUhaqqKnbv3s2uXbs4fPgw+fn58Vhs165dv5dVbDgc5vXXX2f48OHk5+d/NeYVV+CqqGBXUVHcIhFgc3Mz79bXs7iwkE8+/ZRwKMSHskxQpWKmyUTA74978ura2pjv87HqFFm5EC3FSU1Npa8k8UBLC42ShNlsxmAw0NzcjM1mo0Sl4rnGRgyx8hpZlpGAXkYjd3TtyrG6Otra2lABNkHg/FiZjslkimYkKwpJSUkEg0H8fj/n9evHksOHqfN6SUpLo6mpidycHI7V1VEnCKzyemmSJApUKnQ6HdlmMwPdbmRJwmA0khSJcLPfzxcGwylFJ8FmY43BgNbtpiUcjpcMRWJdaowmE4l2O7l5eQjAv/71L2RFIT8/n+KiIhLDYX5vNnPTK69w+eWXf+vr989//pM1a9Zw//33c8cdd/D555/zl7/8hfPPP58//vGPqFQqXn31Vfx+PyaTiddee+1b3ZR8Ph+XX345Ho+H995776xayW3atInXX3+dp556KlpjfJps27aNlStX8vTTT+PxePj1r38d72jTydlDp6j+XyHYDLVrofEzCLuiwqpLgozJkDYGVKf/Jv9P8fv97N+/P75VLMsyAwcOZNCgQfTr1+9baxNPmwULYNs2joXDFB06xIABA0hMTOSgx8Ofjhzh1f79qa+vp729neebmjAGAowG/IEAFrMZs8lErsXCc4MHc+ebb8ZXdYqioI7FKN1uNw888ADaxkb63HsvbVotVpsNp9OJ0WRCFARKvF7eikT4rUoVLafhqz6oEN1uNpnNKB4PDbLMTKKlKh1JRB2ewCqVCrPZjKIo3OB280ugjq88iBVZRqPRYLVa8Xq9pKSm0qNHD1ytrRw5cgRJltH5fDRHIlwGWOx2Wk9xszAhM5Olfj/1sozH6yUSE9a0tDREUaS+vh5RFBk2fDi1NTXY7XYURaGoqAhBEOjlcLD62DEe79KFdevWfWNZTAerVq3i008/5eGHH+avf/0rTz75JAsWLIgbjixYsID58+dTXl6OIAg89dRTTJ069RvHjEQi/PKXv6SiooL33nvvrDLif+KJJ9DpdPzmN7857WMVReF3v/sd48eP54ILLmDFihU0Nzfz29/+9geYaSfflU7zh/8rqI1g7wdZUyB7JnSdDpmXgLUHiD9svppGoyErK4shQ4Zw6aWXMnjwYHw+H5s3b+bFF19k9+7dtLa2YjAYSEhIOPNVrM0G69ZhSUtDEASKi4txOBykmUy839REWJbpm5zMh4cPsyEYZEwggF6Wow4/kQiacJiA0cj1Bw7g+1qcc/Dgwdjtdurq6ti6dSvbi4oYpVaTBjS53RQWFmK1WhEEgSE9e7K2poaQSsXAtDQi4TBtWi1tioKF6Iek0WgkXafjqUCAElGMi+rx27MdZUxer5ddwGAgF/AqCkpsnIgk4fP5CMeSvirKy2lyOgkFg1gVBVmSmBtzefr6dnxOTg6TJk3i+kAAY309jV4vkiRFS6C0WlwuF6FwGK1Wy+OhEMHaWhxqNQUFBVitVgKBAMFQiGavlwuysthqt3Pnww/j8/kYM2bMN76evXv3pqWlhddee41FixYxfPhw7rnnHg4dOsSMGTNYs2ZNvDtOaWkp69atQ5IkzjvvvH87piiKXHHFFezcuZOHH36YCy+8kKSkpNO6hH4o+vbty8svv0xaWhpZWVmndawgCHTp0oVly5YxceJECgoKeO655xg+fDhWq/UHmnEnp0unqHbyo9IRG+3ZsydjxoxhypQpJCcnU1FRwapVq3jrrbeoqqoiHA6TmJgYN7o4LdLTYfVq8Puxp6TgDwQ4cuQImRkZDEpM5I2KCpYUF3M4EGCiIJCj1aLVaAiHQqjVanLtdlZnZPBJQ8MJ3WYgKmAPPvggmZmZbNy4EUmSSMnLI7+8HL8o0uZ2E4lEUKlUlJWVUSAIlBoMvNHayifhMG6rlUsHDGBA9+643W68ra1E/H7uA1QGA5FIBIfDwcMxUaqtrSUcDsfnIQFtAwaQ2tBAL5WKiCyjqNUnOTCp1WoMGg3WYBBvJMKvFYXDsTZ0X7dYbG1t5dChQ8yLRPB4vYRkOe58FIrVzIbDYcKhENuBgSYTxnCY+oYGtFot7rY2hgweDIJAfUUF4ogRjJo9m8cff5w33niDiy666Bu3Yfv27UtNTQ1vvfUWs2fPZubMmfzlL39h06ZNJCcnA9FOLeFwmM8//5zt27dz8OBBLrnkkn+bTSsIApdeeillZWXcd999nH/++fEwwE+JVqulR48ePPHEE4wZMybaT/Y0SE5Opri4mMbGRgYMGIAsy2zatOkbbzI6+XHpFNVOflLUajUZGRkMGjSIyZMnM3z4cEKhENu2bePFF19kx44dtLS0oNPpSExM/M9WsaIIWi1s3AhmM8kOBy2trdF+qbJMVksLY3U6hqvV2GUZrUaD3W7H7fFgE0UErZYto0cTEgSOHTt2Qp1qR6zuX//6F5IkEQ6HKQsE+G12NtpQCL+iEAwE8LS3Y7FYiHi95ASDnCsIjFKrmdqtG9lWK6Ig4PV6MXo8vKUofEJUuMxmMy6Xi3Xr1uFyuQiHwyf1VA0JAh9rNHi1WoaaTNjDYXSShJqoNWGCSkXPjAwIh3kjGOReoJxov9a77rqL4cOHs2nTphOeMo1Gw0yPB0WtRhFFdDpd1PJQUZBiJhMAnwPD7HYyjUYkSaKivBxbQgKpKSnY7XYSNBrebmpiXVERS5Ys4eOPP+axxx5DkiTOPffcU4qgIAgMHDiQ0tJS1q5dy8UXX8yvfvUrdu7cyQcffEBxcTETJ05k/Pjx5ObmsmHDBkpLS3n//feZMmXKN8Ynx48fj9vt5ve//z39+/enW7du3379/MA4HA58Ph/r169n1KhRp70zk5ubyzPPPMO4ceMoLCzkpZdeol+/ftjt9h9oxp2cDp2i2slZhcViIT8/n1GjRjFlyhTS09OpqqrinXfeYeXKlVRWVhIMBklMTPzmZI9evaCyEvbvRzCbcaSmUnbkCE1OJ1qNhtzc3KjVn8+HSq2mX9++OCsrSTIaMS9fznUPPBAX1A5RU6lUqNVqAoEA4XCY5OTkqBGF18unHg+XiyJhv5+ISoXFYqG5uTkuyEpMnJqdTqqqqigvL8fgdlOh03GPIBCM/Y2v+/+aTKaTtmuNRiNag4HNra2Yrr+e+tRUioqLkbRamiWJaq2WVRoNR+fMYfHu3bgikXjLsJycHGbPns0LL7yAVqvFYrEQCASQJImrVCq0sdWvIIqIgoAYc/ZSq9VYrVZ2AL01GvKSk5FlGX8gQLvHQ31DA8lJSRiBi554ghqViocffphZs2YxYMAAnn76aVavXs3IkSPjq8/jEQSBwYMHs2/fPj766CN+8YtfxAXzjTfeYNu2bVx99dX06tWLCy+8kLVr11JbW8vKlSsZM2bMCRnTX2fkyJFoNJp47XRBQcF/cin+oBQWFrJ27VrC4fBpNwawWCy0tLSwb98+RowYgVqtZsOGDd/ZtamT7xmlk05+JjQ0NCjr169XHnzwQWXGjBnKbbfdpvztb39TDh06pEQikZMPCAYV5c47FWXgQOVwerrykdGorFOrlfc1GqUkP18JT5ig7EhNVT4zmRRlyBClIilJeWvyZEXZsUNZ/8QTSheHQyGaV6RYLBblueeeUwwGQ/xngKJWqxVA0Wg0yiBRVDaB8gUo74ui8rHJpKxVqZR/QvxrrSgqxV27KoeMRmU5KAmgCIKgGAwGRRCE+LgajeaEv9PxZbPZlLy8PMVutyuiKCqTJk1SmpubFb1er2i12vjvNDU1KX6/XxHF/8/eeUdFdW5h/5mhzcDA0DsMbahSFQWUIti7WJForEG82BPFrrHGaGzXgpUoRuyKiMZGbGhQbKigKCi9t2EYmLa/P5D5wrVE7xdv7v2c31qz4sq87zn7HM6affZ+9/tsJgEgIyMjio2NpbVr15KdnR0xmUzFdwBIRUWF9jIYdA2gc2pqdFVHhy6y2XSRxaKHPB4JQkKI+vWj8VZW9LufHz11cKBUDocKfXwor107uqypSedUVSnfzIxkmZlERJSenk7e3t4UHBxMJ0+eJD6fT8bGxrR69WoSiUTv/BtLpVJatWoVrVy5UvE3vXHjBunp6VHXrl0V86qqqigkJITMzc2Jx+NRSkrKnz4/Bw8eJBsbGzpw4MC/+QT+tRQVFdGoUaMoPz//k+fW19fTqFGjqKCggMRiMY0bN46ysrI+g5VKPhVlpKrkfwYtLS04ODggKCgIgwcPhqWlJYqKipCUlISEhAS8fPkSTU1N0NXVbVmrUlEBunYFrK0he/4c7OpqsOVyUHMzJEIhLPX0YMZiwQSAmlwOpqoqzMrKwLl+HfaZmehZXQ1VsRgFRLB0cUFlZSUeP37cxqbWKFYul6OECKlMJgLc3WEjEEClsREMuRyqADQA6ACw0tVFg0SCfwqFWEkEIVqiQCMjI1haWqK+vv6tQqU/0lpAdOnSJaSkpKDi3j00bN2K6c3NmCCXI4IIPSQSXEhJwe7z55GZkwMmkwltbW2UlJQgJycHpaWlaGxsVHTaaW2IYOfqCq+KCjTI5ZBIpbAwN1dU8BYVFaGquhpXGhrgb2AA4zcFVQ1v0tyurq5Ql0jwpLQU3ZOSYGBgADc3N/Ts2RP37t3Dnj17MG3aNHC5XOzduxfnz59X9Ob9I0wmE/7+/khNTcW9e/fg5+cHHo+HkJAQbNmyBUeOHEH37t1hbm6Or776Cs+fP8fTp0+RlJQEDQ0NdOrU6b3Pj7u7OxwdHfHtt99CRUXlg2P/E7TqXMfHx6Nbt26f1NKttfnExYsX0bVrV2hqaiIpKQlhYWGf0WIlH4PSqSr5n4TJZMLExAReXl7o06cPunbtCgC4d+8e9u7di2vXrqG8vByqqqrQ79QJ2mPHQr9/f6iw2ShvbMTTkhKY8XjQqa1FvVyOl3V14NjYoKqpCYbW1mBoaaFZIoFdeTnmWFvDQksLa3/7DXjT6u19yDgc6IWHY1d1NQpEImjI5WiSy1EDIAvAHhYLmX374rWhIXLy8qCiogI7OzsEBgYiNTUVcrkcKioq73Wqrftgn50+jX+UliJGKoWzWNyi5kQEBgAdIrSrqkJgQQFs1dUx/PvvkXD8OIYOHYq9e/eiuLhYUaykpqYGTU1NtGvXDnliMfq+UaUSNjcr9JVtbGxgbW0NNouF06WlMKqthbi0FBaWlrCytERNbS1KSkrA09aG2syZWHvmDE6cOIFdu3bh/PnzaGpqwtKlS/Hjjz+CzWZj1qxZOHHiBI4fPw6BQABPT882qXwmk4mAgACcO3cOWVlZ6NixIywtLaGnp4cnT55g+/btcHBwgJOTEwYMGAAGg4G0tDRcu3YNubm56N2793vXKfl8Pjp06IB58+ahtrb2b0+Z2tnZKXSPWwUvPhZ7e3skJCTA3t4eHTt2xNGjR2FtbQ1TU9PPZK2Sj0HpVJX8f4Gmpibs7e3RpUsXDB48GHZ2digpKUFKSgr279+P5zk5EGpqwmzIELjOnAlTLhdNR49CzcoK9SoqeJGfj6bmZsjlcpi9+VF6kZsL0tKCjYsLdJ89gw+XiySBAO92dy2IxWI8e/YMTl5eKNTVxS9VVbgsFuMWAKmvL+y7d0favXtIT08HEcHDwwPJyclYvnw5ampqFGu4Q4YMQV1dnaJ3aSsSiQQBANYIBDBXUYGQxUKzujqapFLI0FIdLAYgVlODQCxGZ21t6KWnY2piImZ//z3y8/MBQOG4W6PV0tJSWNvZIdjXF34yGcz4fNTX16O2rg4FBQWQiMUwMzfH+ZoadORwYMHhgMlgoLCoCCpMJnSYTJRXV2NRUxPatW+PwsJCCAQC1NTUgMFgICgoCAsWLMC1a9eQmJiI+fPno6qqCklJSUhNTYWFhQVsbW0VzlBFRQUBAQE4efIkXr9+DR8fH7i5ueHu3bsICAjA6tWrIRAIEBgYiM6dO8PV1RXnzp3DkydPcPnyZQwYMOC9lePW1tYICQnB4sWL8fLlS/To0eMvldT8FBgMBry8vLBjxw7Y2tp+kkNUUVGBrq4uEhMT0atXL3C5XBw9ehTdu3f/265HiVL8QckXQF1dnULd6d69e/BpbMTYBw8g0dFBSVUVdHR0kJeXh5+kUgxgMDAmMBBMJhM3b96En78/XuXlQSAQQL+5Gac1NTH3TyTzAMBCQwPjTUww3cAAeY8eQSqTwcTEBLY8HuTBwQjevh15Ojqw5vGQnZ39lhgDk8lURKutYvcA4MNkYp+GBgRyORrkctg7OKC8vBwyqRT1AsFbES6TwYCxigpIRwejiSDR1YWhoSFyc3NRVVUFBoMBS0tL9OvXDzo6Ohjavz867NsHPHwImJigpLQUT7OyIBaLIVZTww41NXzLZiPIywtqamqQy+UQlJWhsbwcG+ztsSMjA0KhENra2i1z3mxTsrCwgLm5OQYMGAA1NTX885//ROfOndGuXTts2rQJxsbG6N27N2JiYtpsfREKhZg/fz46dOiA0aNH49GjR9iwYQMmTpyIqKgoODk54eeffwaHw0FWVhaGDx+OiooKmJqa4vjx4x9sRvHy5UsMHjwYPj4+2Lt3798qxH///n1s3rwZmzdv/qTuM0SE2bNnY9CgQQgMDMS0adPw1Vdf/e2p7S8ZpVNV8kUhl8nQ2KMHhMXFqBKLUVtbC6FQCAaDgXViMfoB8Dc1hYaGBkQiETgcDirKyyGXy8EEYMxgYKadHS4+fqxQPGqFwWDAwcYG/xCLMYHLRWVFBfLr61EvFgNEUFVTg6OdHZpLS9EoEKBKWxuLWSzcrax857H09PTAYrEgk8nAYrGgKpdjX1kZtFkskLa2QuiB5HJosFhobGxsOY+qKiRvxBpUVFXBZrGgL5Gg2sUFE+rrkZ2dDRUVFbDZbDQ2NsLOzg6JiYlYtWoVEhISwJJIgG+/Be7eBfT0INPQwIXMTKwpLIQ3gMHa2ujQoQO0NDSAqqqWLUw//IDmTp1w+PBhzJw5E3V1dSCilgYHO3di7ty5kMlk0NPTD1HscAAAIABJREFUg5qaGjw8PPDkyRPU1tZizpw52L59O2pqasDn8zFu3DgMGjRIscZYX1+P2NhYhISEYPjw4di8eTPU1NQQERGBkSNHoqKiAr/88gucnJxQW1uLoUOH4vHjx2CxWNi7dy9CQ0Pf+zyUlJRgwIABsLKyQmJi4t8qxL9r1y5UVVVh7ty5nxRpPn78GBs2bMD27dtx//59JCQkYPPmzcpo9W9Cmf5V8kXBePAA6sePg2NlBSNjY5iZmaGsvByNjY24JZfDVVUVDoaGKC4qgo6ODkrLyiCVSsFkMqHBZoPLZKKmuRlX6uvfOnaPkBAsFgjQVSTCi9paSNlsNIhEkLxZv5TL5aitq0O9VAoNfX24m5piEIOB8zU1qEBLm7/OnTuj4k1TcZFIBIFAACaTiWnTpuFoTAxM7t5FQWMjqqur0fxmK4xcLkfzm76tXC4XYDCgq6eHDm/SsA0NDZCoqcG4qgrPHR3h36MH+vXrB3t7e9TX16Ndu3a4cuUKnj17hsLCQrwsKEB1hw7QMjaGZm4uVGpqwOdyMUBfH8Y1NWA2NaH+9WtIBQJo9e4N5ooVQPv2UFVVhaenJxoaGlBZWalou/f06VOEh4dDLpfj5cuX8PLyQkFBgeJl5uTJk+jevTucnZ2RmpqKgoICZGRkgM/nKwRA/P39sXPnTkVqfOfOnfD09MT06dPx4sUL7PwpFnb6QjiZyvBVeAhEDdW4mfEMJ0+egvabl4B3oa2tjeHDh2PPnj04fPgwwsPD/zbH6u7ujqNHj4LFYsHOzu6j5xkbG+PRo0eoq6tDWFgYLl++DDabDR6P9xmtVfI+lJGqki+L774Drl0D/iBCXlNTg5KSEswvLcVAFRU4a2jgRU0NDshkCAPg9UZxSUVFBZoqKtAA0FddHSKZDLW1tZDJZCAirFRVRR8ilL2RG+RwOBA1NUEsFqO5qQkaLBbEzc0gAPZ2dlBRVUXly5fQ1tWFU3o6MkpLMXToUFRWVkIsFoPBYCjk9aRSKeIBmDc1oUYuB5PJhFgiadEP1tKCuoYGKisrFWINxsbGUHkjhi9ubgaTyQRHIMAdT090PXz4rbTotm3boKenB3d3d+Tm5iI3NxenT59GXk4OYjp0QGBNDRpzc2FlYgK5lhb2PnmCI01NUDU3x08//YR+/fopjlVZWYnBgwdjzJgxsLS0xOzZs8FisdClSxc8evQI2dnZsLCwwLRp03Dx4kVkZGSguLgY+vr6mD59OuLi4qCnpwcLCwv06tULkZGRYLFYqKioQGxsLIYPHw5tbW0cPLAPG+cPgVphIqryruN1fiGMDA1gaWkFBggvS5rxfcJrXH2mhvDho7Fu3br3pngbGxsxZMgQ1NfX/61C/K1dntatW/dJClAFBQWIjY3Fjh07kJubi23btmHbtm2fVFGs5K9BGakq+bJYsQLQ0WnZbvMGNpsNQ0NDHCssBK+5GTVCIRLkcvQG4KaiAmZrGo3BAFRUIK+rw/nmZhQ0NoLFYoHBYKAdgEVsNuTGxjA1M4OKigoIgIe7O4wMDVtk5by8WiJPBgP19fUoLSuDCpsNd0tL5GdmImjJEohEIjDfNFgHWrbbGBgYwILJxKSmJtCb9nDqGhowMTaGWCyGQCDAJZEIJ2UyXCbCEyYTDIEAnOZmsDQ0YGZmBk9PT3ANDaGVn4+eCQnIyspCp06doKWlBalUip07d2L06NHg8/mwt7fHzZs3cerUKdjz+VgQF4dUVVXElZejIjgYJ2UyCPh8yFgs5OTk4OjRo0hNTUVQUBB0dXWhqamJsLAwBAUFwcnJCePGjUNWVhbOnj2L/v37Y9KkSbh69Sr2798PS0tLTJs2DVZWVrhz5w6SkpLg7OwMPT09ZGZmQkVFBSkpKbCysoKDgwM6duyIjRs3or2LMULZ+8EsPgNNVQk0uVbQ0jVBVk4+yquFMDa3haGOGkLdCIG8chw8+wQnz6dh4MCBCi3lP6KmpoaRI0fiwoUL2Lx5M/r37/9Ja5t/Fbq6ulBVVcXhw4cRFhb20eu8XC4XZWVlyM7ORp8+fXD79m0Q0QfXlJV8HpSRqpIvB7kc8PVt0QZ+x3rT+AcPoF9UhPtEGATAQUUFLBarRcFIXR0MBqNl3bK6GtMYDNx7M09VVRWLJRL0V1dHs64uGhoawGQyIZPJwGQyocPlorS0FHq6uqirr4eGujoaGxvB4XAglUohbWqCoYoKlnTsiIzcXFRUVCgKjphMJgwNDbElOhq9jh2D9E16VyaVQtDQAEF9Paqrq5EuFMIKAAdAroYGThNhtYUF6ouLoaqqCnd3dxgZGAClpcg+cADzFyzA77//Djs7OzQ3N8PS0hLHjx9XrF/evHkT5eXl6NChA06dOoUpU6YgOjoa3t7eICJUVlYiNzcXaWlp2Lp1K/Lz88FgMNC+fXtERESAz+fD1tYWNjY2iircGzduYPr06WCxWNi6dStyc3Mxc+ZMMBgMeHh4ICAgADk5OUhMTATQIrZfVFQEPz8/aGtrw93dHRMnTkRD2RPU/RoOM2M95BUL4OzsBE12Sw9XiVSK+/fvQSRqgre3N3S0tdFYX4acZ08w+5A6apn2OHHiBKytrd/ziMgRFRWFW7du4fjx43+L+hIRYfHixXB1dUVERMRHz6utrcWUKVOwfv161NXVYe3atYiLi3vnS4SSz4cyUlXyZbF7N6Cl9U6nmlRWhgK5HOZSKbq9caStFa4kl7cIzIvFUGluRjKTidI376NcuRyLATRpaUHnjfCERCJpcZhSKQplMsQ3N+NMUxNKATwQi1GlqgpLiQRq6upQ09AASyLB/YIC/P5GlKEVImqRKSwuhn9VFcrq61FZVYWqqirU19VBJBJBTgQzVVUwpVIwGQwE8Pm4IxTCn88HX08PoqYmFBQUoKysDLoqKkg2NsadjAw0NDQgNzcXhYWFMDY2xqhRo/DLL78gMTERjY2N4HK50NTUhL6+Purr6zFq1CgAUPR8tbS0hL+/P6ZNmwYXFxekpaUhNzcX9+61vG7cv38f+/btw2+//YasrCyoqalh1KhRqKmpwdKlS6Grq4uDBw/i1atXuH79OnR1dcHlcmFubg6pVIrs7GwwGAy8evUK5eXl4PP5OJ4Yj166R2BsoImc/Bro6emhoqIcRkZGYIABFSYT5hbmaGpqQnZ2NjQ0WDAwNIOBgR58zStw9EYN4vYegq+v7zsda6sQf15eHpYsWYLOnTv/x4X4W7fZbN68GW5ubu+UdXwXrc0Srl69ikGDBuHhw4cQCoX/FbKMXxJKp6rky4HBAI4dA2QyQPXtdndJZWWY7uiIV6qqYJqYINTaGgwAciLo6+nB0NAQTU1NUBGJcFhDA1VvCpACmEyEEUFVTw8mJiYw0NeHtbU19PT1UVNfj60NDWgPYBgRmES4paoKPpsN/hslIzMzMxjq68PPyQmJIlFLNPwHmEwmtAGMYrOhaWAAfX19mJiYwMLSEjY8Huzt7fFaRwcpqqq4qaGB5OpqlAiFcFBVhS2bDRaLBQ93d4gaGlBeWIiYR49QVFQEBoMBDocDoVCIsrIy/PjjjygpKQGfz0dWVhakUikaGxvx+PFj9OnTBzKZTKFDrKam1iY16erqiujoaAiFQty+fRsPHz6EgYEBNm7ciM6dO0NTUxOlpaW4ffs2CgsLweVycenSJWzatAlqamr47rvvkJiYCKFQiIULF8Lb2xtCoRAvXryAmpoaBAIBUlNT0d9bDlf9QlQ1MGFubo7S0lIABBDA4XBa/sxgwNDQEGw2G1lZ2WhqEsHEzBLGeuqwNDfGkd9KcOzYMRgbG8PT0/Odj0qPHj3Q0NCAefPmwdPT8z8uxM9ms2Fqaort27ejW7duHx1t8vl87Nu3Dy4uLvD29sa2bdvQu3dvhfazks+P0qkq+bKorwdu3wbe/AD/kaSyMnTW08MQMzMklpSgnsFAmJ0dDA0MIJFIUF5eDnldHUqYTNzx8EBwSAjEYjE8mUz4NzdD19ISRIS6+nqUl5ejqroaRQwGMpqaMEguBwOAmaoq8phM6DEYaMfhgN5U7mqy2VBTUUFh584QCASoq6sD0BK1DB06FFbOzugvEMDY2BgcLhdsNrtly4yKCirEYix69gxzHRww2cYGkTY2uFhQAHsWCyoVFZBIJDA2NoaJmhr0Q0NR4uOD7OxsMJlMsNlsiMVi5OXlwdbWFleuXMHz58/h4+ODmJgYlJSUQF9fH5qamsjIyMDly5dx4sQJHDhwAMnJybh8+TJu3LiBjIwMZGdnw8bGBsHBwXj16hVu376NXbt2gcViYcyYMWjfvj3CwsIQHh6Onj17olu3bnj06BHS0tLw6NEjODg4oLi4GNu3bweLxcLChQsxevRopKWlobKyEkZGBpjkmwuhsBEyYqCurhb6+npoaBCirq4WBoaGbZyHNkcbxsZGePnyJcrKymBiZg0XUykM20/BxSvX8Ouvv6K2tva9PV8DAwOhrq6OOXPmwM7ODhKJBBMmTIBMJnuvM/4rsbKyQl5eHjIyMuDn5/dRc1RVVcHhcHDs2DEMGzYMOTk5qKyshKur62e2VkkryjVVJV8WRUXAoEEt1b//UgQy4eFDTLWxgReXC4FUivnZ2eikq4uv3jSTJgA1WVnYa2WFBWlpigKnThUVmCkQwKNHD6j/IaKQE+FKaSlOlpSgT0kJ2Gw2dHR0cJLBgIOBAcbZ2EAulyP72TNw1dVh4eQEnDyJiooK9OrVCxYWFqipqQGXy8Xdu3cRLZfjW2NjaNnatrG7QCTC9CdPsMXNDWYsFq5UVmLlo0eY6egIXzU1RVRqpa4O1vbt0OnVC5WVlVi2bBkSEhIAtKx3urm5QSwWIygoCBUVFTA0NISWlhaOHj36VpNvuVwOgUCA2tpa1NXVKf7b+qmtrUV6ejquXLmCpqYmsNls+Pv7w8nJCVwuF1wuF7q6usjPz8fdu3dRWVmJqqoqfPPNN2hsbMTOnTuhpqYGV1dX2NjYoKamBlXPf8WGSDFKamQQi8VgMlXAYmmAzdaEVCqBrq4u2rVzx7+6R6lMigcPHqChQQjfdlbQ8v0ev+UaYMyYMWhsbERISEjL/tz3dD06fPgw5s6dC0dHR3h4eMDT0xOjR4/+Nx6+T6epqQnTpk3D2LFjERAQ8FFz5HI5pk+fjoiICPB4PMyZMwc7d+6ElpbWZ7ZWCaB0qkq+RKZOBdLT22yr+SgaGwGJBOPMzPA4NxdZWVktsoEyGTapq4MfEPBW0+nH9fX4MTcX8Z6eAIMBQX09Zj9+DCu5HGMsLWFiYgI1NTXkPngAVrt2sL54EUBL0Ymurq7iOM+fP8eUgQPx44sXkOjqwsbODsZ/sP9AYSFSysvBABBqaIg7BQXoYWaGEC4XNbW1sDY0RHlVFfyqqiCWy2FpaQkWi4WiN/txBQIB/P390bdvX2RnZ2PhwoUIDw/H3bt34ePjgxUrVrx3r+eHaGpqwuLFixEXFwepVApLS0tYvnlJ4fP50NHRQUZGBjgcDm7fvo2amhro6+ujffv2ePnyJfLz8xUNEow1BRjjWwWeEQPq6up4VtiEk3cIHe2BWy9a1KOmDrGHpbkpdiXno75RisFdTDG8qzkIhAs3niDh13xUSExg7NgNjo6OOHr0KAoLC+Hg4ICTJ0++JfDfyoIFCxAXF6do5vCfcqoA8OzZMyxfvhybNm166+Xmfdy/fx/bt2/Htm3bsHXrVhgYGOCrr776zJYqAYC/T5dLiZK/i/nzAW1t4F+kAT9IUxNQVwesWAErJyfk5OQours8ZDBADAZu37jRsmZYVKRo6u3M4YAJILm8HHIiZMlkqFRTg5WlJVTV1JCdnY0XL17AiMPBqTfN2YGWrRUSiUShtOTo6IgtJ07ghYsLzFVU8PjxY1y/cQP5+fmQE2G0pSUO+fjgFx8fTLS2xjwLC3ThcFrSmlIp1IVCWK5dix27d0Mmk+Hp06fIeyO/uGHDBly/fh0sFgtTpkzB/fv38fvvv8PAwACPHj2Cm5sbhg4diqFDh+LZs2efdKtZLBbWrl2L9PR06Ovr4/Xr13j48CF69OiBmJgY9OzZE9ra2pgyZQpKSkqwZ88eqKurIzs7GzNmzMDs2bOhpqYGbW1tfDPEG2czWWhi6EIiEUNdXQPCZgakcmBmbwaCnAkbj7zApYwKbIxxww/fuOBwajHKqpvBAAP2trb4R7gdRvk2QF1dHYWFhZg3bx78/f2Rk5ODwMBA3L17961rKC8vR2VlJY4cOYJr167h5MmTn3QP/l9xcnJCv379sGHDhg82c/gj3t7eMDMzw7lz5xAREYGUlBTFkoKSz4tyTVXJl4e2NuDvD/z6a4tjZbPfWQ0MACBqWYdtaACWLgW6d0fnzp1x7949iEQi1NfXQ43Dwarp02FTXY1mVVUUFBQgNy8PQqEQHC0teOvr41BxMeILC9Esl8OcxYIxi4UACwuYmJpCRS6HoLoa3woE+OfOnTh+/Dji4+Oxfft2SKVShY6rkZERTpWWwpUIXiwWoKWF1/n5yMvLg1giAZfLhcqblHZjYyOkEglYTCYYlZXQnjULGDUKjo6OMDY2xvPnz1FXV4f6+no0NDTA398fAQEBKC8vB4vFwrJly8DlctG/f38MGzYMI0eOxK1bt7B06VJkZmbC19f3k/ZxVr5pzh4bG4urV6/i/PnzuHLlCqytrSEUChVba5ydnXHt2jWMHz8e69evBwAkJCQgPz8fDa8uw4QLWNs4ILijA56/qsCjfBliR1hBLG5CO74pLj6S4LsIZ5gasMDlqOHWkxpYGrFgacSGvo46LA1UwHMLxLqDmXj16hV0dHSwe/duVFRU4Pbt2zh8+DCsrKzQrl07he0bNmzAgAEDEBISgsrKSpw9exaFhYX/USF+FxcXXLhwAY2NjYp2fH+Gra0ttmzZgvDwcNTX1yMrKws+Pj6f2VIlykhVyZcJnw8cOAAEBAAVFUBpKSAStVQGy+WAWAyUlbV8TE2BbduAN6pB6urqWLlyJXR0dODk5ITx48dj0aNHaGxsBN/eHkFBQfDw8ECzWIzbv/+OisxMzNbWRqK3N2IdHFAlkcDwjRQek8GAvlwO3j/+gRmLFgEAHjx4gOfPn6OhoeGtApWvxo/HMi4X0h49YMNmI9DJCa6Ojqiursa1q1eRmZmJRpEILABqVVVQFQpxMyAAiIpSHOPrr79GcHAwOBwOvvnmGxgaGqJfv34YMWIEAgICMGvWLPTu3Rt6enoIDAzE5MmTAbRo016+fBkNDQ0ICAjAzJkzUVtb+1G3u7KyEsbGxhgxYgSePXuG6OhovH79GnPmzMGNGzcUDQVa97T2798f6enpaGpqQocOHZCRkYEH5aa487wB9x61aBd7e3tCX4eN8vJymJiYQthQB6lUCg01qeK86qoMiJpbov2iChG+P5CHqRsfwsrKCtXV1di1axfu3LmD9evXY+3atZDJZJgyZQq+//57AEB6ejpEIhECAwMBAObm5pg9ezauXLmCcePGvbdF31+NiooKZs2ahaNHjyIvL++j5tjY2KBjx444duwYhg8fjitXrrSobin5rCgjVSVfLtraQK9eLc5SUxN48aIlcm1oaNlyExoKLFwITJkCWFi0mdq6d9DJyQlLliyBbfv2uPvbb9B69AhMbW3o6enB3MwMVtbWyGpoQEVJCV69fInLFRW43dSEaFtbsFVUgOpqkL4+aPlytA8MhLu7O27duqVo+3b9+nVUV1fDw8MDLBYLurq6eF1YiDweDx5Tp4Ihk4GTmwsrPT0Ya2mhqaoKxS9eoE4oxA17e2j98AMuNTQg9A/NqxkMBgICAvDo0SN8/fXXiImJQXBwMA4fPoyrV6/i+PHjmDBhAlauXInu3bvj7NmzWL58OXJzc9GzZ098/fXXCAwMxNGjR7Fq1SrU1tbCz8/vg9s2Ghsbcf78eQwcOBDq6uro0aMHhgwZgtTUVDx79gy7d+8Gm81Ghw4dkJiYiP79+0NXVxenT59GREQE7ty5AzMTI/haN0JDDaCGV6gVEl6Uq2Fsf2e8fv0KKqqqSH0sg5VmCTiaauByubh4twJ8Sy3Ymmnih0MvYGPExPwfEhA5YQbs7e2Rm5uLPXv2gM1mY9KkSQgICMCZM2dw9c0LipqaGjIyMpCUlISTJ0/i5cuXKC4uRseOHZGeno7z588jPDz8PyIHyOFwoKenhz179qB79+4fdU4HBwds2bIFPXv2hFwux/3799GxY8fPbusXDSlRouQvQS6RUN6YMZSnr0+5jo7U3KMHUb9+dK5jR4q0sKCBBgY0QkuL/qmhQdd1damYxyNJaCjtW7KErK2tadq0aZScnEwrVqwgU1NT6tu3L+3bt49CQ0OJx+PRmDFjKCMjg8rKyigiIoKqq6tbTlxbS3T7NtGlS0SpqfTjqFE0bOBA4nA45OvrS4MHDyaZTNbGVolEQsOHD6e6ujoiItq2bRslJCTQkSNHyMfHh6ytralfv370+++/ExHR/fv3acCAAWRjY0OzZs2impoaIiK6dOkSBQYGkpOTE23atOmt87Qik8koJiaG9uzZQyKRiJqbm+np06d06dIlGjBgAFlZWZGmpiZ5eXlRly5dqLi4mBobG6l///6UmZlJDQ0NNHToUPK0UaVNE/So7Egn2hOtSX18NEiYEkrSy30oY4MV+fGZ9NtqK7q8WIvurjejWcNt6MpPfkSp/WhmuAUdWuJPcpmMCgoKKCoqir777ju6cOEC8fl8mjRpEkkkEnr9+jV5eXmRrq4u+fn50bNnz6i6upqqq6tpzZo1tGvXLqqvr6eamhoKDAykbt26kUAg+ExPVVvkcjmtWbOG4uLiPnpOQkICrV+/nurr6ykiIoKKi4s/o4VKlOlfJUr+IhiqqrCJj4f5ypXQIkLB3bsof/kSPQwMkODtjVP+/kjs3BnR7drBUUcH6c3N8Hv+HMmPH6O5uRmHDh3CggULcOzYMXTu3BkhISF49eoVfv31V5w9exbq6uoYMmQIhg0bBg0NDRw8eLDlxFwu0KkTEBYGcUAAtty4gYq6OgQHB8PPzw9Xr15F+/btsXnzZoV4w9OnT2Fpaamo/L127RpCQ0ORnJyMQ4cOISMjAw4ODhgxYgT69u0LkUiE06dPIzExUbE2N2/ePHTq1AnXrl3DihUrsH//fnh7eyMhIeHtvq5MJhYvXozi4mKMHz8eY8eOxfXr1wG0RPvPnz/H5MmTkZubi1u3bmHGjBmQSCSIiorCmjVrMGHCBPj7+yO4+xAIG4V4mfMMTk6OUFVVxa1bt/D69St4uLtDW1sbVVVV0NLSgkQiRVFhEWrr6gCSY3w3Hfz2UhfDR4zAli1bFCnd7t2749KlS3jy5AnCwsKgqqqKtLQ0BAcHIysrC3369EFBQQH09PSgoaEBDQ0NaGtrQ1dXFxcuXICqqip69uyJ6urqz/+MMRj4xz/+gVu3biEjI+Oj5oSHh+PBgwcoLy/HgAEDcOjQoc9s5RfO3+3VlSj5/5KGBqr++Wd66upKeQYGVO/iQvL27YkCAoh+/JEoN5eIiB4/fkzR0dHE5XJJQ0ODOBwOtWvXjjIzM0kqldLSpUtp48aNJJfLiYhIIBDQjz/+SJ6ensThcCgqKooKCgoUp71//z45ODgQj8cjDw8Punv3Ls2YMYPi4+PJz8+PHBwcaO7cubR582ZKSEggIqIjR47Qxo0b6dChQ7R27do2l1FVVUWzZ88mGxsb6tWrF12/fp2IiK5fv07dunUjOzs7WrRoEQmFQpLJZBQXF0eurq7k5+dHycnJn3zbsrOzKSgoiDQ1NcnY2Jh27dqluPZWJFnbqGCXCV1exKKn2+2p7EhHSl2qTWmrDUmYEkrNF3rSnXVmdHmxFj3YZEWXF7Eof6cxSTNiieTvjqSJiJqbm2ns2LHk5OREV69eJZlMRrGxsaSrq0smJiZ06tSpd86TSCQ0bNgw8vHxafO3+Jw8fPiQxowZQ7W1tR81/ty5czR//nxqaGigyMhIev369We28MtF6VSVKPnMPEhPp9njxtHS776j169evXPMrVu3yNramjQ1NUldXZ1CQ0MpJSWFhEIhzZw5kw4ePPjWnPnz55ObmxtZWVnRwIED6cKFC7R9+3ZycXEhOzs7MjIyojVr1tDUqVMVcy5cuEB9+vQhbW1tGjRoED1+/JjGjBlDDx48oIiICCotLX2nfTU1NTRnzhyys7Oj7t27U2pqKhG1pH9DQkLIwcGBli9frkjtrl69mhwcHCgsLIxu3rz5yffsyJEjZGlpSZqamtShQwd6+PDh//1SLifK3kzCY86U8YMOXf1eh8qP+tHDzdZ0aRGbXu51JXlqX3q935NufM+iF//k0s8zTKlLQEd6+vTpn55706ZNxOPxaPPmzUREtH//fjIyMiI9Pb23XjpakclkNGnSJHJzc6Ps7OxPvt5/h71799Ly5cvfeul4F1KplKKjoyk9PZ1OnDhBK1eu/A9Y+GWidKpKlPwHkEqllJSURJGRkRQXF/fWGpxMJqMRI0bQ0aNH6cKFC+Ti4kJWVlbk7u5O3377LY0aNYp+/fXXNnNEIhGNGTOGrl27RrNnzyY+n0+6urpkZGRE+vr6ZGZmRn369KHo6Og286qqqqh379701VdfkbGxMfH5fJo2bRrt3r37T6+jrq6O5s+fT3Z2dhQaGkoXL14kIqKzZ89Sly5dyNHRkdauXUvNzc0kEAho7ty5ZGNjo3Dgn4JIJKKZM2eStrY2cTgc+uabb9ret+ILJP9tEBXusaB7q9Upa6sFlR3yorQVWvT4Jy6JTnlQ49lAWjGpHbm3c6MRI0aQjY2Nwlm28vjxYxo6dGibT3BwMHE4HPr666+pubmZbt++TTwej3R0dGjs2LHvXTueM2cO8fl8unPnzidd67+DWCymadOm0blz5z5qfHp6OkVHR1OxYeNcAAAgAElEQVRjYyN9/fXX9Pz5889s4ZeJ0qkqUfIfpK6ujrZu3UqRkZGUkpLS5sdZKpUq/l1YWEjjx4+nGTNmUJ8+fcjMzIwsLCxozZo1beacPXuWFi5cSEQtacixY8eSs7MzsVgs8vb2plWrVtE333zTxoaLFy/SmjVrSC6X08SJEyk8PJw4HA516tSJ4uPj3+sw/ohAIKBFixaRvb09hYSEKH7YT5w4QX5+fuTi4kIbN24kiURCZWVlNHnyZOLxeDR69OhPTj1mZ2dTYGAgsdlsMjU1pZ9//vn/fimXE1U/pMor0XRtpRHd+F6Lig51pEtr21F4kClt3LCOJBIJrV27lng8Hg0bNoycnZ2pf//+VFZW9sHzFhUVUVBQEAUEBNDr16+pqKiI2rdvTzo6OhQUFKQo1vpXVq1aRXZ2dopo/nOSn59PERERVFhY+Kdj5XI5zZs3j86dO0dnz56lxYsXf3b7vkSUTlWJkr+Bly9fUmxsLE2dOpUyMzPfOaa6upqmT59OW7Zsofz8fJo8eTJxuVyys7OjWbNm0cuXL0kikdCkSZPowYMHbeZGRUWRl5cX6enpkZmZGR08eFDhLNesWUMXL16khw8fUnR0NC1btowSExPpp59+Ik9PT3Jzc6NVq1Z9VEVrQ0MDLV26lBwcHCgoKIiSk5NJJpNRYmIitW/fntzc3Gj79u0kk8koLy+PRo0aRTwej2JiYqiqquqT7tnhw4fJwsKC2Gw2+fn5vZXKlclktHXrVrKzs6MxY8bQiRMnqF27dhQWFka5ubn0+PFj8vPzo/bt21PPnj2Jz+fT6dOnP3hOiURCUVFRxOfz6eLFiyQSiSg8PJy0tbXJ0dGRsrKy3jlvx44dZGNj89512L+S5ORkmjlzJkkkkj8dm5OTQ6NHj6b6+nqaMGHCe589Jf8+SqeqRMnfhFwup+vXr9O4ceNozZo174ycGhsbafHixbRs2TISiUSUlpZGYWFh1LdvX7KysqJu3brRvHnzaPr06W3W1g4cOECxsbE0b9488vb2Jg8PD3J2dqa5c+fSoEGDqKqqipYtW0bbt2+nCRMmkFgsJqIWx3T48GEKDg4mW1tbmjZt2kdFQSKRiFasWEF8Pp86d+5Mp06dIplMRj///DN5eXmRp6cn7d69m2QyGT18+JD69+9PNjY2NH/+fBIKhR99z0QiEU2fPp04HA5xOByKiYkhkUjUZkxRURENGDCAHB0dKT4+nqKiosjGxoY2bNhAzc3NNGfOHOLxeDRkyBCytbWlqKiot47xr7Q6ybVr15JMJqOFCxcSl8slExOT96ZfExMTycbGhuLj4z/6+v4d5HI5LV26lPbv3/9R49etW0cJCQl0+fJlmjNnzketySr5eJROVYmSv5mmpiY6ePAgRURE0MGDB6mpqanN9xKJhDZs2EAzZ86k2tpaOnv2LH3zzTeUl5dHq1atIh8fH+JyuW3WLW/evEmTJ0+mmTNnUmRkJMlkMjpz5gwFBQURh8Ohvn37Urdu3Wjq1Kl09erVd9qVlpZG4eHhZG1tTSNHjqR79+796bWIRCJavXo1OTo6kr+/Px07dowkEgnt2rWL3N3dycfHhw4cOEAymYyuXbtGXbt2JT6fTz/++ONHRVqtZGdnU+fOnYnNZpOZmRkdPnz4rTEHDhwgPp9PgwcPpsTExDZR6/Xr18nd3Z0CAgKoU6dO5OPjQxkZGR885+3bt8nFxYVGjhxJIpGIEhMTycDAgHR1dd9ap20lJSWFbG1taePGjR99bf8ONTU1NHr06I9at27d51xRUUHR0dF09+7dz2rbl4bSqSpR8l9CeXk5rVmzhsaNG0fXr19vE0HI5XJKSEigSZMmUXFxMcXHx9Ps2bMVDnjv3r3k4OBA1tbWFBgYSKtXr6bBgwfT2LFjaeTIkYrjJCQk0Nq1ayk4OFixVWTTpk0fjNRyc3MpKiqKeDwedevWjc6cOfOn664ikYjWrl1Lzs7O1KlTJ0pMTCSJREJbt24lV1dX8vX1paNHjxIR0alTp8jX15fatWuniGY/lkOHDpGZmRmx2Wzq0qULvXjxos33VVVVNGrUKLKzs6PNmze3iVoFAgFNnDiRbG1tqV+/fsTj8WjVqlUfPH9ZWRmFhoaSr68vvXjxgu7cuUM8Ho+0tbXpm2++eefcmzdvkr29PS1ZsuSjr+vfIT09ncaPH08NDQ1/Onbfvn20adMmunnz5ltZDiX/byidqhIl/2VkZmbS1KlTKTY2lnLf7Gdt5dy5czR69GjKysqidevW0YoVKxQ/5AsWLKATJ07Qxo0byc/Pj9hsNllYWFBoaKhi/syZM+nmzZs0bNgwioiIoIULF1JAQADZ2dlRdHT0BytCa2pqaMmSJeTk5ETt27enbdu2/Wl02dzcTD/99BO5uLiQr68vJSQkUHNzM61fv56cnZ0pICCAkpKSSCaT0b59+8jd3Z18fX3p5MmTH32/RCIRTZs2TZESnjlz5lvR/pkzZ8jFxYV69OhBP//8c5uo9dSpU8Tn8yk4OJhcXV2pe/fuVFRU9N7zyWQymjp1Ktnb21NycjKVlJRQhw4diMPhUGho6DvXojMzM8nZ2ZmmTZv2SS8Nn8rWrVtp3bp1fzqudb9qbm4uTZ8+/d/a9qTk3SidqhIl/4XIZDJKSUmhyMhI2rp1q0JOkKglIomMjKS0tDRasGABbdu2jeRyOT1//pzGjBmjcCjjx48nc3NzYrPZ1LFjR1q+fDkNHjyYfvnlF5owYQItX75cccy0tDQaOXIkWVtbU8+ePenYsWPv/fGXSCQUFxdHvr6+5OjoSAsXLnxvJewf52zevJnc3Nyoffv2FB8fr0gV8/l8CgwMpPPnz5NEIqH169crnNxvv/320fcsOzubAgICiMVikYWFxVuOWSAQKCLV5cuX06RJkxRRa1lZGYWHhxOfz6fQ0FCyt7enxMTED54vPj6ebGxs6PvvvyeRSETDhw8nLS0tcnJyeitiJmqJ+N3d3WnMmDGfzbE2NTVRVFTUe1P6fyQpKYmWLFlCd+/epejo6M/q7L8klE5ViZL/Yurr6ykuLo4iIyMpKSlJse3m2bNnNGbMGDp58iTFxMQoUqlr1qyhI0eOEIkq6Ld94+jYfGtKXmRKj+O70k+TzKiDoxZZW1tRQEDAO9V/KioqaPHixeTq6kpubm60dOnSD1bpJicnU48ePYjH49GkSZPe6Uz+SGuFrru7O3l7e9Pu3btJKBTSsmXLyMHBgbp27UqpqakkFApp4cKFZGtrS3379qX79+9/9D07dOgQmZqaEovFopCQEHr1L4IbqampCo3huLi4NlHr7t27yc7Ojrp27aqoIv5QFXRGRga5ublReHg4CQQCWrZsGWlra5OJick7t9SUlJSQr68vDRo0iJqbmz/6mj6FnJwcioyM/NMtQ62V4/fu3aM5c+bQ5cuXP4s9XxpKp6pEyf8Ar169ogULFtCUKVMUDqa4uJgmTZpE27Zto7Fjx1JqaiqVPr9Kp5e5kDTZh+oOO9Kj9Tp0b602yX/tQsX7rOj1ThNKmqtJw7rokJeXJy1btuydP74ymYyOHj1KPXr0IGtraxo1ahTdvn37vfbdv3+fRo0aRdbW1jRo0CCFnOH7kMlktGPHDvLw8CBPT0+Ki4ujuro6WrBgAdnZ2VGPHj3o5s2bVFVVRVOnTiUej0cjR458Kx3+PkQiEcXExJCWlhZpa2tTbGxsm1R1c3Mzffvtt8Tj8ei7776jiRMnKqLWvLw8CgsLU6Ss3d3d6caNG+89V01NDfXs2ZN8fHwoOzubjh07RgYGBsTlcmnXrl3vHB8cHExhYWGfTYj/6NGjFBsb+6fR582bN2nq1Kn08OFDmjBhwicViyl5N0qnqkTJ/whyuZxu3bpFEyZMoBUrVlBJSQnV1tbSrFmzaPHixbQ8pis1nHCnkngevd7vTo0pYXR9hR5dX6FHsit9KOMnc7q73owertcjcZIHPUgIpz69upOVlRUNGjTovQVI2dnZNHnyZLK1taWAgADasWPHe6OsoqIimjFjBtnZ2VFgYCAlJiZ+8IddJpPR7t27ycvLi9zd3Wnbtm0KSUQbGxvq06cP3blzh/Lz82nMmDFkbW1NUVFRfxqF/dH2Tp06EYvFIisrK0pJSWnzfUZGhmLv6saNGxVR64sXL2j16tVkbW1NQUFBxOPxaMGCBR/swvPtt9+SnZ0dnThxgu7fv0/W1takpaX1znVUkUhEvXv3poCAAKqoqPioa/kUWnWLWzMY70Mul9N3331Hly5dokWLFr11f5R8OkqnqkTJ/xjNzc10+PBhioiIoP3791NNTQ3t/WEc5W03pIcbTKj2dBBl/GROTb92p2vLdenq91yqPxNMd9aZ0fUVulR40IfoSh+iFB+ih0vp9atXNGfOHHJxcSE3NzeKjY19Z2q4sbGRNm3aRL6+vuTg4EDTp09/K7XailAopDVr1pCbmxu5u7vTunXrPlhhLJPJKD4+nnx8fMjNzY02b95MZWVlNGPGDOLxeDRw4EB68OABPXnyhAYNGkQ2NjY0Z86cj470Dh06RCYmJsRisahbt25tCpFkMhktX76ceDweRUVF0YQJExRR6/3796ljx47k4eFBrq6uFBQU9MEUd+ve1Pnz51NpaSn5+vqSpqYm9ejR4639uJ9biL+8vJwiIyMpJyfng+OysrJo7Nix9PjxY4Uso5J/H6VTVaLkf5TKykpat24dxUwaTjVH3Ch/nyPdWWdKGT+ZU+6+dpS7z43urDOlK0u0KP+AF6WtNqBbawxJerkPUWq/N47Vm6jwDBG1OJdTp04p+pv26tXrvZFmamoqDRkyhKysrKhv377vjXJlMhkdOHCAOnfuTPb29jRr1qz3iva3jk9ISKAOHTqQq6srbdiwgQoKCmjKlCnE4/Fo6NCh9PTpU7p16xZ169aNHBwcaNWqVR/lCEQiEUVHR5OmpiZpa2vTokWL2ticnZ1NXbt2JXd3d1q1apUian3+/DnNmjWLrK2tyd/fn2xsbD6ok5yZmUmenp7Ur18/xZYeLS0tcnV1fUuiUSaTUVRUFLm6un4WIf6rV69SVFTUW9XQ/8rq1avp8OHDtHLlSjpx4sRfbseXhNKpKlHyX8alS5fou+++++jxRdeW06s4Y3qyzY5e7nWjtNUGb1K95vRoC48uLmTR/Y1WdGmRJpUd6djiUFs/F4KJrg5u0dD9A2VlZbR06VLy9PQkR0dHmj59+ju325SUlFBsbCw5OzuTp6cnrVq1qk2l8h9JTU2lAQMGkLW19Z8KFbRKHXbs2JGcnZ3pxx9/pLy8PJo0aZJifTUnJ4dSUlLI39+fXFxcFHKIf0Z2djb5+vqShoYG8Xi8NgU6MpmMNm/eTLa2thQZGUljx45VRK2pqank7u5OXl5eZGtrS8OGDXtv1XNdXR3179+fPD09KTMzk1avXk0cDodMTU0pLS3trfGfU4h/3bp1tHXr1g+OKS4upoiICMrMzKTIyMhPUrlS0hZlk3IlSv6XkUtgLv4N1vbuMDIyQl1dLTgcDhoahJBIxGhqaoZMJkNVVRU0NTVhZGTUdr4qB2gsBGoz2/xvY2NjLFmyBPfu3cO2bdtQXFyMbt26ITQ0FHv27EF8fDxiYmIQFRUFd3d3PHnyBN9++y0uXrwIDw8PjB07Fg8ePGhzzJCQEJw+fRoXLlyAmpoa+vbti969e+PChQtvXRaTycSIESNw69YtLF++HCdOnEDv3r1hb2+PixcvgsViISwsDImJifjll1+wYMEC7NixA76+vjhy5MgHb5mTkxPS09Oxb98+NDY2ok+fPujduzfKy8vBZDIxdepUXLt2DXV1dbh16xYiIyOxZ88erFixAgcPHoSXlxfkcjlycnIQEBCAixcvvnUOHR0dnDp1Cv369UP//v1hY2ODgwcPQiwWo3fv3ti/f3+b8T/88APGjx+PESNG4MqVKx+0/1OZPHkyMjIykJ6e/t4xZmZm6Nq1K65fvw4fHx8kJSX9pTZ8SSidqhIl/8tU3QWkAjBU2DAyNIK7uwe4XF2oq6ujoUGI+vo6SKVSSCRiODrywQCj7XwGo+VTcOKdh2cymQgLC8ORI0fw8OFD9OrVC3FxcZg/fz5KSkpgaWmpGPfVV1/hypUrSE5OhoqKCgYNGoSQkBD8/PPPkEqlimM6OTlhz549yMjIgLe3N2JiYuDn54c9e/ZALpe/df6hQ4ciLS0Nq1atwpkzZ9C3b184ODggJSUFMpkMwcHBSE1NRXJyMsaOHYslS5YgMDAQly5d+uCti4iIQH5+Pr7++mtcvXoVDg4OWLlyJeRyOSwtLXHmzBnMmzcPhw8fhr29PSwsLDBgwAB4enpi/fr1EAqFYLFYmDhxImbNmtXmGlttX7FiBX766ScsWLAA165dw2+//QZdXV1MmTIFc+fObXO9sbGxmDNnDiZMmICTJ0/+6Z/+Y9HS0sKsWbOwZcsW1NTUvHfcyJEjcePGDQQHByMpKQkCgeAvs+FLgkFE9HcboUTJl0plZSV27tyJJ0+egIgQFBQEPp+PX3/9Fc7Ozrhw4QK0tLQwZcoUtG/fHgBw6dIlHD9+HJWVleCq1mKo22v06uIEAMjMrcf6I7no2UEPB3/NhUjUiF4eBG0OC/dLjFHfKMXgLqYY3tUcAPC8oAE7k16gsEYV6uaBCAgIwMSJE6GqqvpBu3///Xds27YNZ86cgZGREWJiYjB+/HhoaWkpxjQ0NGD79u04dOgQhEIhBg4ciBkzZsDc3LzNscRiMeLi4hAfHw+RSISRI0di5syZ0NbWfue5k5OTsXbtWpSWluKrr75C//79sXr1aqSnp6NPnz6IjY1F/P9p776jorracIE/gzCMiDQNFmBmGEC6oIBRPkRUomKsqBQFBJWgWInGJAqWqNGYD7EA9lhIbESxJKBBEnvBhgqKil1EgShIZ8p7//A69/KhKYZIou9vrazlmnPmnH0OK+uZs8/e7964EZs2bUK7du0wb948uLq6/ub1XLt2DUFBQbh8+TLatm2LTZs2oWvXruq/0cSJE3HmzBn0798fBw8eRKtWrfDVV19h/vz5uHjxIrS1tWFgYIB169bB3t7+pccfPnw4jI2NkZiYiMDAQFy6dAk9e/ZEcnIyRCKRet/t27fj008/xdy5czFy5MjfbPefkZSUhJs3b2L27NkQCAQv3WfXrl24cuUKDA0Noaur26Dnf2c0cvczY+8spVJJEyZMoLVr11JVVRXV1NRQTk4OHTx4kAYOHEj79+8npVJJP/74I4WEhKjrs2ZmZtLDhw9JpVLR5QNfk6+7AeV9253ol350ab0nDXBvRVtjOpD8YF/67lNL6mYD+sS3JVXu96G723qSb9fW9GjnB0S/9KMb33an3G/eJ8Whoep1T//McmVffvkljRw5kjw8PEgikVBISEi9d4YqlYrS0tLUA6AGDRqkXtz8f+/Hrl27qEePHiSVSikyMvI3115NTU2lbt26kaWlJcXExNDJkydp0KBBJJFIaNKkSXTz5k36+OOP1euo/pFFub/99ltq2bIliUQi6tevHxUXF6u3paSkkK2tLX3wwQcUGBiofte6atUqkkgk1L59e5JIJK8srl9WVka+vr5kb29PZ86coaCgIGratCk5ODjUK4v4ohB/XFzc77b5j5LL5RQVFUX79u175T41NTU0atQoOnLkCAUGBv7p5fkYv1NlrNFcv34dT548QVhYGEQiEYRCIezs7AA8f6fZu3dvdffrkydPUFJSAgBwc3NDmzZtIBAI4GBnjQ6WOsi58/+66jSbCODn1RaaTTQwuLsVmhsaY/RABzTVbgJxq6YwM26K248qAQCWJs1gbaqNJtr6MDY2Rp8+fZCdnf2Hr0EoFKJXr144evQo9u7di6ZNmyIoKAhubm5YtGgRSkpKIBAI0KdPH+zZswfHjh2DTCZDZGQkXF1dsWTJEpSXlwN43l06ePBgZGRkYMeOHSgqKoKHhweGDRuGM2fO1Du3j48PDh06hMTERBw/fhwjRoyAnZ0dkpKScOvWLfTs2RMaGhpIS0uDnp4evL29MXr0aDx8+PCV1zNixAjcv38fQUFByMjIgEwmw9dffw2VSoVBgwYhMzMTUqkUJ0+ehLe3N9atW4fk5GRs3rwZLVq0QJMmTbBkyRL0798fhYWFdY6tq6uL5ORkBAQEYNiwYfD29sbcuXNx+/ZtuLq64uzZs3Wu7bvvvkNCQgJmzZr1h/8ev0VTUxNTp07Fli1bcP/+/ZfuIxQKERISgpSUFHTv3h3JyckNcu53CYcqY42kuLgYxsbGaNKkSb1tBgYG6n9ra2sDAKqrqwEA586dw7Rp0xAYGIiASQk4d6MSzyrk6v2b62hCQ+N5955Q8/n/4obNtdTbhZoCVNUoAQD5RVX4YnMegr/Mgp+fHzZv3oxnz5691vW0b98eq1atwtWrVxEZGYn09HQ4OTnBz88P6enpUKlUEIvFiI2NxZUrVzBu3DikpKTA0dER4eHhyMnJUR/rxYCjY8eOoVWrVvDz80OPHj2wa9eueu9dP/jgA2RkZGDdunXIzMxEaGgo7O3tsWbNGmRnZ6Nfv34wNjbG7t27UVZWBnd3d3z88cfqHyn/SyQSYe3atbhw4QIsLS0RHR0Na2trnDlzBrq6ulizZg02bNiAM2fOQE9PD0ZGRhg5ciT69++PsLAwKJVK3Lx5Ex4eHvUG/GhoaCA6OhoJCQmYN28e7t27hx07dqC6ulr97vqFLl26ICUlBcnJyZg0aVK9634dJiYmGDlyJL7++mvI5fKX7uPp6QkigomJCQ4fPlzvxwH7bRyqjDWSli1boqioCEql8g9/Ry6X48svv8TgwYORlJSEbTvT4GLXBqSsea02JO65A9OWQqxZ/y127NiBkJAQ0F8cZiEUChEWFoaMjAxkZGTAxMQEEyZMQIcOHTBr1iw8evQImpqaGD16NI4ePYrk5GTU1NTgww8/hLe3N7Zv364OELFYjPj4eFy+fBk9evRAdHQ0XF1dsWLFCtTU1L3m7t27Iz09HRs2bEBWVhYiIiLg4OCAZcuWITMzE8OGDYO9vT2SkpJw9epVuLi44IsvvlD/WPlf1tbWOHfuHNavX4+nT5+ia9eu8PX1RUlJCby8vHDq1Cl06dIFmZmZcHd3x/r163Ho0CGsXLkSurq6ICKMHz8eY8eOrXeOvn374sCBAzh9+jRiY2Pxww8/wNDQEKGhoXWeTB0cHJCamopDhw4hNDS0QYK1V69eMDY2RlJS0ku3CwQCjBo1Cjt37kSvXr2wdevWv3zOdwmHKmONpF27djA0NMSmTZtQXV2N2tpaXL169Te/83wkrxz6+vpo0qQJzp0/jwsP9QHV64VqVVUldFrIIGppjQcPHiAtLe0PfU+hUKC2thYqlQpKpVL97/9laWmJuLg4XL16FTNnzsS5c+fQqVMnDBw4ECkpKVCpVHB1dcXmzZuRlZUFLy8vLFiwAA4ODpg5c6b6KUlXVxfR0dHIzs7G+PHjsWXLFjg6OuLzzz/Hr7/+Wuecnp6e2L9/P5KSkpCTk4OJEyeiffv2WLRoEX755ReMHj0anp6eWLZsGdLT09GhQwesWLHilYEVFBSEBw8eYMSIEUhLS4NUKsWKFSsgFAoRGxuL77//Hjdv3oS2tjaaN2+OCRMmwN/fHx9++CEAID09He7u7jh//nyd41pYWODIkSNo1aoVxowZg3Xr1sHJyQlff/01fH191aOJzc3NcfDgQeTm5sLX1xe1tbV/6G/0KgKBAJMmTcLhw4dx6dKll+7j6OgIqVQKbW1tZGZmIj8//y+d813CocpYI9HQ0MCsWbPw8OFDjBo1CqGhoTh69Ohvfqdp06aIiIjAokWLEBgYiMOHD+N9zw+fzzetffV0iZdS1WLUB4Y4lGcAPz8/rFixQj3i9ffEx8djyJAhOHLkCHbs2IEhQ4bgl19+eeX+Ghoa8PPzw48//ogTJ07A3t4eMTExcHBwwPTp03H37l0YGBggOjoaWVlZWLhwIS5cuABXV1f4+fnh2LFj6uOMHj0aJ0+eREJCAi5evAgXFxeMGjUK165dq3PO//znP0hNTcW2bdtw48YNfPLJJ3BycsKsWbPwww8/4JNPPsGgQYMwa9YsbNy4ER07dsR333330nAViURYv349srKyIJVKMW3aNNjZ2SErKwuurq44ceIEBgwYgAsXLsDZ2RnffPMNsrOzsXjxYmhra+PZs2cYOHAgvvzyyzrHF4lE2LJlC8LCwjBmzBiEhIQgICAAaWlpcHV1RXFxMYDn79gPHjyIkpIS9O3bV/0e+nXp6elh8uTJiIuLe+XUmdDQUPzwww/o3bs3vvvuu790vncJT6lh7G1Qfgs4NQpQKQCh4e/vr6wBan8FbD8BpAF/f/teQqVSYf/+/Vi3bh3Onj0LW1tbBAcHIyAgQD2l5+bNm1i6dClSU1Px3nvvITg4GKNHj64zBSUnJweLFy/GoUOH4OTkhI8//hheXl71znf27FnMnz8fWVlZ6NevHzp06IA1a9agrKwMY8eOhZaWFlasWAEDAwPExMTAx8fnlW3fvHkzoqKi1FOF1q9fD11dXeTm5mLcuHEoKiqCRCLBlStXMHbsWOTk5CAjIwNNmjSBtbU1Nm7cCBMTkzrHTE9Px/jx4+Hl5QUrKyvMmTMH+vr6+Omnn+Dg4ADg+Xv1IUOG4OnTp9i7dy9atmz5l/4Ga9aswdOnTzF9+vSXTrNJTEyEQCDAyZMnMWfOHMhksr90vndCo449Zow1nGc3iDJ6E6W5EqV71S1H+OK/jF5EaW5E+zsR3fvn1HgtKiqiefPmUceOHcnKyoomTJhAV65cUW+vqqqihIQE6ty5M8lkMoqMjKxX2P7x48c0bdo0srCwIHd3d0pKSnpp2cILFy7Q4MGDSSKR0EcffZd9aXUAACAASURBVEQJCQnk4uJCjo6OFB8fTwsWLCALCwvy9vamkydPvrLNlZWVFBoaSiKRiAwMDGjVqlVE9HxqUFxcHJmbm1OvXr3I1taWevbsSfHx8WRhYUFSqZSkUilt3bq13jHv3r1L7u7u5OnpSd999x0ZGhqSnp5enWlOcrmc/P39qUOHDr855eiPqKmpocjISDp48OBLtz99+pQCAwNp48aNNHfu3L90rncFP6ky9jaRPwMepgG3k4DqQoBeDIIiQKAJaGgBpoMA8VBA1/yVh8nJycGcOXNeuu3vnmZx6NAhrFmzBidOnIBUKkVgYCBGjhypfjo9fvw4VqxYgZMnT8LOzg4fffQRBg4cCA2N52+zqqurkZiYiE2bNkGpVCIoKAgTJ06sU5gCAC5fvowvvvgCZ86cgbe3N+zs7LB582YIBAKMHTsWN2/eRHJyMpydnbFgwQL1dKf/de3aNfj5+SE3NxcWFhZITk6Gvb097t27h8jISNy4cQNisRh5eXkYPXo0MjMz1dNnPvjgAyQkJEBXV1d9vNraWkRERODEiROYM2cOPv/8cxQWFmLmzJmYOXMmgOdP+ZGRkThy5AhSUlJgbW392vf7zp07mDlzJmJjY9G6det627dv346bN28iLy8P06dPh42NzWuf653Q2KnOGPsbqJRExWeI7mwnurGW6NZ3RA9/IpKXN3bL/rBnz55RbGwsde7cmczNzWn06NF07tw59fbCwkKKiYkhOzs7cnR0pLlz59YpcP+iKL+npyfJZDKaPHlyvSILRETZ2dnk7+9PYrGYQkND1SvUuLi40KpVqyg8PJzEYjGFhITQvXv3XtnejRs3kqGhIWlra9cpSr9hwwaytLQkT09PsrGxoZ49e9LcuXPJ1NSUTE1NycHB4aWLui9fvpwkEgl99dVX5O7uTtra2uTn51fn6fuzzz5rkEL8KSkpNG3aNFIoFPW2VVdX08iRI2n9+vU0Y8aMv3SedwGHKmPsH+/MmTMUFhZGUqmUOnfuTLGxseq1VJVKJW3fvp28vb1JLBbTiBEjKDMzs873jx49SoMGDSKxWEwBAQF04cKFeufIzc2lwMBA9So6s2fPJltbW3r//fdp5cqVFBAQQBKJhCZOnEhPnjx5aTsrKipo5MiRpK2tTYaGhrRhwwYiev4DwM/Pj8zNzcnT05OkUinNnDmTPDw8qG3bttS6dWuaMWNGve7qI0eOkLW1NQUHB1NoaChpa2tTx44d6/x4WLRoEclksjqr7fxZKpWKoqOjX9olTUSUnp5OU6dOpfDwcMrKynrt87wLOFQZY/8aVVVVtGrVKvL09CSJRELDhw+v85R35coVioiIIKlUSh4eHrR27do6a63m5eXRRx99RBKJhHr16vXSkn3Xr1+noKAgEovFFBgYSJ999hlZW1uTu7s7LV++nD788EMyNzen6OjoVy6RduXKFXJwcCAtLS1ycHCga9euERHRrl27yMbGhtzc3Mja2pp69OhBUVFR1KZNG2rdujV5eHjUe1ecn59Pnp6e5O7uTnPnzqWmTZuSiYlJnfVXV69eTVKp9C+thVpcXEwjRox46bquL0pqJiQk0NSpU9UlM1l9HKqMsX+l7OxsioyMJEtLS+rYsSPNnz9fXau2oqKClixZQq6urmRpaUlTpkypM6jn6dOnFBMTQ+3atSNXV1datWoVyeXyOse/efMmjRw5ksRiMQ0bNoyioqLIysqKunXrRosXL6Zu3bqRlZUVxcbG1vvuC+vXrycDAwPS1tam0NBQqqqqorKyMho9ejSJxWLq3LkzSaVSmjp1Kjk7O1Pr1q3J1NS03iLocrmcIiIiyMrKir766isyMDAgPT09Sk1NVe+zfft2kkgk6qfj13H8+HEKDw+nysrKetvOnz9PY8aMocjISDp16tRrn+Ntx6HKGPtXk8vltHnzZvrggw/IzMyMfH19KTU1Vd2VmpGRQb6+vmRmZkYDBgyos00ul9PKlSvJ1dWVrK2tKSYmpt7C43fu3KGwsDASi8Xk6+tL48ePJwsLC+rZsyfNnz+f3NzcyNHRkTZs2PDS0cYVFRU0fPhw0tbWJiMjI9qyZQsRPV+M3snJiRwdHcnS0pK6d+9OoaGhZGxsTO+99x4NHTq0XlteFO//7LPPSCqVkkgkoq+++kq9ff/+/WRubk5Llix57fu5dOlSWrZs2Uu3zZo1i77++muaMGECP62+AocqY+ytcevWLZo6dSrZ2NiQo6MjzZgxQz04KT8/n6ZPn07W1tbk7OxMCxcuVL+XJSLat28f9erViyQSCUVERNTrhr179y6Fh4eTRCKhgQMH0pgxY9TTZmbMmEEODg7k5uZGe/bseWnbsrOzydbWlrS0tMjZ2Zlu3bpFVVVVNGXKFDIzM6OOHTuSVCqlcePGkZWVFRkbG5OVlRX99NNPdY5z6tQpsrW1paFDh9L7779PQqGQRowYoQ70U6dOkaWlJUVHR7/WPaysrKTw8HA6fvx4vW23b9+mESNG0MSJE+nw4cOvdfy3HYcqY+yto1QqaefOndSvXz8yMzOjvn37UnJyMimVSlIqlbR582by8vIiiURCoaGhdPHiRfV3L1y4QAEBASQWi2nQoEH1wiU/P5/Gjh1LEomE+vXrR8HBwSSVSqlv3740efJksrKyIi8vr5eO6CUiWrNmDenr65NIJKLw8HCqqamhzMxMcnNzo3bt2pFMJiMvLy8aNGgQtWjRglq2bElTpkyp08X8+PFj6tmzJ7m6upKfnx8JhUJyc3NT/0jIzs4mGxsbmjBhwkufnn9Pbm4ujRgxos7Sdy8sXbqUvvjiC/roo49eOlr4Xcehyhh7qz18+JCio6PJ0dGRbGxsKCoqSv0UmpWVRaGhoSSRSMjLy4s2bdqkDqH8/HyaPHkyyWQy8vT0pG3bttUJqIKCAoqMjCSJREJ9+vQhf39/kkgk1L9/fwoPDyepVEr9+/evE9gvVFRUUGBgIAmFQmrRogV9//33pFQqafbs2WRmZkYODg7q9WlNTU2pRYsW5OrqStnZ2epjKJVKmjhxIllYWFBkZCQ1bdqUTE1N6datW0T0vNvaycmpzlPsn7FlyxaaOXNmvW7e4uJiCgwMpMmTJ9d7imYcqoyxd4RSqaT9+/fT0KFDyczMjHr27EkbN26kmpoaevbsGS1atIicnZ3J2tqapk+fru42Lisro4ULF5K9vT21b9+eYmNjqaqqSn3cx48f06RJk0gikZC3t7e6WtPAgQNpxIgR6lHKd+7cqdem7Oxssra2Ji0tLXJxcaF79+5RTk4OdevWjczNzUkqlVLXrl3Jy8uLjIyMyNjYmJYuXVrnGBs3biSpVEphYWGkr69Penp69Msvv6jb1qlTJxowYECdNv8RCoWCpk2bRikpKfW2JSUl0aeffkphYWFUW1v7p477tuNQZYy9c548eUILFy4kFxcXsrCwoLFjx1J2djYplUpKTU2lAQMGqAc9vZj/+aLb2N3dnSwsLGjatGn0+PFj9TGLioooKiqKpFIpde/enfr166fuQh40aBBJJBIaO3Zsne+8sGrVKnWX8Pjx46m2tpZiY2NJLBaTjY0NicViGjJkCBkbG5OhoSH5+PjUOc65c+fIwcGBfHx8SCwWk0gkUg82Ki0tpW7dulGPHj3qvEP+IwoKCmj48OHqp98XKisrKTg4mCZPnkx79+79U8d823GoMsbeaUePHqWgoCCSSCTk4eFBCQkJVFFRQXfv3qUpU6aQpaUlubi4UFxcnHpe6i+//EL9+/dXV1r6/7tlnzx5QtOmTSNzc3Pq2rUr9e7dWx2uvXr1IqlUSp9++mm9gKuoqKBhw4aRUCikli1b0u7du+nu3bvk4+NDZmZmZGZmRu7u7tShQwcyNDQkMzOzOjWBnz59Sn369CFnZ2fq2LEjaWlpUWhoKCmVSqqqqqK+fftS586dqaio6E/dn4MHD1JkZGSd+b5ERKmpqTRu3DgKCgr600/BbzMOVcYYo+fdvMuWLSN3d3eSSqU0cuRIOnXqFNXU1NDatWvJw8ODzM3NKSIiQl0gITc3l8LCwkgikZCPj0+dd4xPnz6l6dOnk0wmI3d3d+rRoweJxWLq378/eXh4kKWlJS1cuLBeWGVnZ5OVlRVpaWlRp06dqKCggNatW0cymYxkMhmZmZlR7969ycjIiAwMDCg8PFwdakqlkqZNm0YymYx69epFQqGQ3n//faqoqCCFQkEBAQHk7Oz8pwrxq1QqWrRoEa1evbrO5wqFgsaOHUvjx4+n5OTk173tbx0OVcYY+x/nz5+n8PBwkslk5ObmRosXL6bS0lLKzMykESNGkFgsJm9vb9q+fTsplUoqKipS1+Ht3LkzrV+/Xj04qLS0lGbMmEEymYw6depEHh4eJJFI6MMPPyQXFxeys7OjVatW1RtMFB8fT3p6eiQSiSgqKooKCgpo2LBh1KZNGzIxMSE3NzeysrIifX19srOzq1P/d9u2bSSVSqlPnz6kra1NZmZmdP/+fVIqlTR27FiytbWtswrQ73n27BmFhobWqb1MRHT69GkKCQmhgIAAKi//99SV/jtxqDLG2CtUVVXRunXrqHv37uq6wRkZGfT06VOaO3cuOTo6kp2dHUVHR1NRURFVV1fT8uXLydnZmWxtbWnevHnqbt6ysjKKiYkhCwsLcnFxoc6dO5NYLKbevXuTvb09ubi41Hviq6ioIF9fX9LS0iJjY2NKS0uj5ORkateuHZmZmZGJiQl5eHiQnp4eGRkZ0fz589XhfPnyZXJyciJ3d3fS09MjPT09OnbsGBERff7552RlZVWvRvJvuXjxIoWEhFBJSYn6M5VKRZ9//jlFRERQUlLSX73dbwUOVcYY+wNyc3Np4sSJZGVlRc7OzjRnzhwqKCigXbt2UZ8+fdShe+LECfU82e7du5NUKqXx48eru1wrKipozpw5ZGlpSc7OzuTq6koSiYR69uxJlpaW1LVr13rrm16+fJksLCxIU1OT3N3dKS8vj8LCwqhVq1bUunVrcnZ2JlNTU2revDl5enrS/fv3iej5U/KAAQPIzs6O2rRpQ9ra2hQfH09ERIsXLyaZTPbKtVRfZv369TRv3rw602xu3LhBfn5+5OfnVydw31Uajb30HGOM/RtYW1tj+fLlyM3NxfTp03Hq1Cl06tQJmzdvRmRkJA4ePAgjIyMEBQXhP//5Dx49eoTU1FRs374djx8/hoeHB/z8/HDlyhXMnj0bly9fxtChQ1FaWgo9PT0UFxdDqXy+/u2YMWPQt29fnDt3DgDg4OCAvLw8xMbG4vLly3BwcMB7772HTZs2oUWLFnj8+DGUSiUsLCxw7tw5uLi4YOvWrdDT00NKSgp8fX0hFAohlUoRFRWF8PBwTJ06FZ9//jnGjBmDlJSUP3QPgoODUVRUhJ9++kn9maWlJTp16oRmzZr97Wvt/is0dqozxti/1d27d2n69OlkZ2dHdnZ2NH36dLp+/TrFx8dT586dSSaT0YQJEygvL4/u3LlDkZGRJJVKqUePHrRr1y71yNyFCxeStbU12dnZkb29vXqJuxfF/P//kokVFRU0cOBA0tLSotatW1NaWhpNmjSJjI2NydjYmBwcHKhly5akq6tL/v7+6u7nXbt2kUwmIycnJ9LS0iJ3d3eqqqqiHTt2kFQqrVfE/1Xu3btHgYGB9ODBA/Vnjx8/piFDhpCvr++fHl38tuFQZYyxv0ipVNKePXto4MCBZGZmRn369KFt27bR4cOHadiwYWRmZkY+Pj60e/duKikpoS+++IJsbGyoY8eOtHz5cqqpqaGamhpavHgx2djYULt27cjGxobMzc3JxcWFzMzMaMyYMVRQUKA+58WLF8nc3Jw0NTWpa9eulJ6eTh07dlSHq6WlJeno6JBUKlWXTMzNzSUXFxeyt7cnoVBIYrGYCgoK6KeffiJzc3OKjY39Q9e7b98+ioqKqlM6ccOGDTR8+HB19/K7SkBE1NhPy4wx9rYoLCzEypUrkZKSgsrKSvj4+MDf3x9paWnYuXMnNDU14efnh8jISKSkpGDt2rV48uQJhg4dimnTpkFXVxeJiYlYs2YNamtrIRAIoFQqoauri2fPnmHIkCGYPXs29PT0AABLly5FTEwMlEoloqKiAADr1q2DUqmEoaEhCgsLQUQYN24cFi5ciMrKSoSGhuL8+fMoLCxEkyZN1N25QUFBCAgIwLx5837zGokIc+fOhYWFBYKDgwEAFRUVGDVqFKqqqrB69Wq0adPmb7zL/2CNm+mMMfb2OnjwIPn7+5NYLKbu3bvT2rVradOmTdSzZ0+SSCQUHBxMZ86cof3791OfPn1IIpHQqFGjKDc3l+RyOS1fvpzs7e3J3NxcXbbQ1taWZDIZzZs3Tz0/taKigvr160daWlrUtm1b2rRpE3Xp0oWMjIyoZcuWZGJiQiKRiJydnSkvL49UKhXNmzePzMzMqEWLFqStrU1r1qxRF+KPjIz83XrBT548oeDgYMrJyVF/tnfvXhoyZMgffuJ9G3GoMsbY3+zp06e0ePFicnNzI5lMRuHh4fT999+rC+97eHjQunXr6MKFCxQcHExisZgGDBhAhw4dIqVSSYmJieTg4EBisZjEYjFJpVKysLAga2trio+PVwfguXPnSCqVkqamJnl5edG8efOoVatWZGBgQGKxmHR0dMjAwEBdyOHHH38kmUxGbdu2JU1NTRo3btyfKsR/+vRpGjVqlHqOqlwup7CwMPLx8flTBSbeJtz9yxhjb9Dp06excuVKHD58GG3btsXgwYNRW1uLXbt24dmzZ+jfvz+GDx+OrVu3Yvfu3WjdujXGjh2LgIAAfPPNN0hISMCTJ09ARNDU1AQAGBgY4NNPP4W/vz80NDTw9ddfY+7cuSAiREREIDs7G2fOnIFAIICGhgbKy8vRvXt3bN26Fb/++isCAwORn5+PwsJCdOnSBdu2bcPgwYPRunVrbN++HSKR6JXXk5CQgJqaGnz88ccAgOPHj+PLL79Ely5dEB0d/Ubu6T8JhypjjDWCyspKbNy4EVu3bsX9+/fh6ekJFxcXHD58GGfPnkWHDh0wZswYXL9+HZs2bQIRITg4GJGRkdi+fTtWrFiBoqIiKJVKCIVCqFQqiMVixMTEwMfHB5WVlRg6dCjS09PRqlUrBAcHY+PGjaisrIRIJEJpaSkMDAyQlJSErl27YtSoUTh06BCKi4vRtm1bZGRkYMyYMRAIBNi7dy90dXVfeh3V1dWYMmUKhg8fDk9PTxARpk6dipycHMTHx8PKyuoN39nGxaHKGGONLDs7GwkJCUhPT4eBgQF69eqF8vJyHDhwALq6uvDz80OrVq3wzTffID8/HwMGDMDUqVORkZGBZcuWoaCgAEqlEtra2pDL5Wjfvj3mzZuH999/H2fPnsWQIUPw8OFDuLu7o3nz5jh69CgAQKVSQaFQICQkBAkJCYiLi8OSJUtQUlICbW1tpKWlYcGCBfj111+xZ88eGBsbv7T9eXl5mD17NuLi4mBsbIyrV69i0qRJ6NSpExYsWPAmb2Wj41BljLF/iNraWmzduhVJSUm4fv06OnXqBHNzc5w+fRr37t1Djx490K1bN+zevRvnz5+Hh4cHpk6diqtXr2LJkiV48OCBOlwVCgU8PDywYMEC2NjYYNGiRepRvQMHDsTRo0dRUlICLS0tVFRUQCwWY/fu3SgoKEB4eDiKioogl8uxevVqHDhwAFevXsXevXshFotf2vbk5GScP38eCxYsgIaGBr788kscPHgQy5cvh4ODw5u8jY2KQ5Uxxv6Bbt68ifj4eKSmpkJbWxudO3dGSUkJMjMzIZPJ8OGHH+Lq1as4ePAgrK2tMXHiRFRUVOC///0v7ty5ow5XlUoFHx8fLFiwAIaGhhg8eDB+/vlntGrVCo6Ojjh+/DhUKhWUSiU0NDQQExOD4cOHIyAgADk5OaisrMS4ceMgl8tx+PBh7Ny5E7a2tvXaq1KpMHPmTLi4uGDo0KEoKChAUFAQHB0dERcXB4FA0Ah38c3jUGWMsX8wlUqFnTt3YuPGjeoShUZGRrh8+TJqa2vRp08fAEBqair09fURFhYGQ0NDxMXFIS8vDwqFAiKRCAKBAP7+/pg7dy5u3LgBX19fPHr0CM7OzigsLERRURE0NDRQW1sLFxcXJCcnIyYmBikpKSgvL4eHhwc6d+6MnTt3YsuWLXBzc6vX1sLCQkRFRWHu3LmwtLTE6tWr8d133yEuLg4uLi5v+tY1Cg5Vxhj7l8jPz0diYiL27NkDpVIJW1tblJSUIC8vD66urhCLxTh27BgqKirg7+8PCwsLJCYm4tq1a1AoFNDW1oZQKMTo0aMxY8YMxMbGYsGCBRAIBGjfvj0uX74MpVIJpVIJHR0drFmzBo8fP8acOXNQWloKMzMzjBw5EklJSVi9ejW8vb3rtfHw4cPYunUrli1bhtraWgwZMgRisRhr1659J55WuaA+Y4z9S5iYmGDBggW4dOkSlixZAoFAgLy8PEilUlRWVmLfvn0QCATo2rUrjh49ipiYGDg5OWHx4sVwcHCAQqFAaWkp4uPjYWNjAx0dHRQUFKBLly44e/Ysmjdvjvfeew8aGhqorq5GUFAQDh8+jB07dsDU1BT5+fmIi4tDUFAQwsPDsXPnznpt7NatGywtLbF+/Xo0b94cERERuHLlCk6ePNkId+zN4ydVxhj7FysuLsaqVauQkpKC0tJSmJqaorS0FM+ePUOHDh1QVVWF7OxsuLq64j//+Q927tyJnJwc1NbWQigUwtjYGDNnzoSlpSX8/f3x6NEjSKVSFBQUQC6XQ6VS4b333sP69esxf/589co5ERER2LdvH6KjozF69Og6baqoqMCkSZMQEREBZ2dnDBs2DCKRCFu3boWGxtv9LNdkzpw5cxq7EYwxxl6Pjo4OPD09ERERAScnJ9y4cQPXr1+HoaEhysrKcPPmTZibm0MgECA1NRVGRkYYOnQoqqqq8OjRI5SUlOCnn37C8ePHERcXBysrK+zfvx8AYGRkhKqqKlRWVmLHjh0YNGgQ7OzscPnyZZw+fRpeXl5ITk6GQqGAu7u7uk1CoRCWlpaIi4uDt7c3zM3NsWPHDlhaWkImkzXWrXojOFQZY+wtIZVKMWTIEIwZMwZyuRxXr15FeXk5NDQ0kJ+fD5FIBH19fZw+fRpEhP79+0NDQwMFBQUoKipCSkoKysrKkJiYiNu3b+PKlSto3rw5iAgKhQKnT59GWVkZoqKicOrUKVy6dAkymQxHjx5FYWEhevbsqW6LsbExysvLsX//foSEhODYsWP4+eef1VWf3lbc/csYY2+xs2fPYuXKlfj5558hFAohEAhQXl4OU1NTVFZWoqqqCq6urrh9+zYuXboEpVIJLS0tuLu7IygoCJ999hkKCwuhq6uL8vJyEBGEQiEmTpyIb7/9Fo8ePUKbNm3QrFkzeHt7Iz4+Xh2aCoUC06dPR8+ePSEWixESEoIvvvgC/fv3b+S78vfhUGWMsXdAdXU1Nm3ahK1bt+LGjRvQ0dFBRUUFjIyMoKGhgZKSEtjb26OoqAg5OTnq8oe9e/dG27Zt1cvJCQQCKBQKAICrqysUCgWysrLQtGlTmJiYwNXVFUlJSepgzc/PxyeffIKvvvoKsbGxuHjxIg4cOAChUNiYt+Nvw6HKGGPvmCtXriAxMRFpaWmora0FPV+xDHp6eigrK4NEIkFZWRlu3LihnoozaNAg5OXl4cyZM9DS0oJcLgcRoXnz5nB3d0d6ejoEAgEkEglsbW2RnJysLsS/f/9+pKWlYfLkyfD19cX06dMxfPjwRr4Lfw8OVcYYe0cpFAps27YNSUlJyMrKgpaWFqqrq6Gnp4fq6mq0aNECtbW16gpNTZs2hY+PDw4fPqwuFvGiEpOrqyuysrIgl8thYmICKysrdSF+IsKCBQtgYmKC/Px87Nu3D+PGjcPWrVuxcePGt2pBcw5VxhhjuHPnDhISErB7926UlJSou38FAgG0tbUhEAiQn58PlUoFXV1dtG/fHqdOnYJCocCLGDE0NIRCoUBZWRmMjIxgaWmJffv2wdjYGKWlpZg0aRIkEom64IRQKMSpU6fQvn37Rr76hsOhyhhjTE2lUmHPnj345ptvcPLkSfVKNjo6OlCpVNDS0kJhYSFUKhX09fWhr6+PO3fuqL+voaEBIyMjFBcXo2nTprCwsMAPP/wAU1NT9OjRA0eOHFHvq6Ojg6NHj6Jjx46NcKV/Dw5VxhhjL/Xo0SMkJiZi27ZtePz4MeRyOUQiEVQqFTQ1NfH06VMAgJ6eHhQKBcrLy9XffTFaWFNTE6ampnj48CFqa2shEolQU1MDIoKWlhYOHTpUZ47rvx2HKmOMsd+kUqmQkZGBxMREHDp0SD24SUNDAxoaGuqpNi+C9AUNDQ2oVKo6xzIyMkJtbS0qKipARNi1axcGDRqE2yW3UVxZjBpFDXS0dCA1kKKFTos3fal/GYcqY4yxP6ykpASrVq3CN998gwcPHkChUKBJkyYgInXYvhgd/DI6Ojrw8fHBwYMHYfieIaKWReFE5QncenoLTQRNQCAIIIAKKvSQ9oC/gz+cWjn9a4rxc6gyxhh7LceOHcPSpUtx4MABVFdXAwCICEql8je/N2HCBHTo0wHfFn2Lcnk5RJoi6Gnr1QlOpUqJJ1VPoCIVnFo54b+9/gt9kf7fej0NgUOVMcbYX1JeXo5169YhPj4e9+7dg1KpVM99fRktcy1YTbRCS8OW0NPW+81jExGKKotg0twE6wesh2FTw7/jEhrM21uAkTHG2Buhq6uLKVOmIC8vD5mZmRg4cOCrKyYZAvLucuRm56LiSUWdTcVXinFtx7U6nwkEAhg3M0b+s3xEHYiCQqX4uy6jQfCTKmOMsQbXq1cvpKen19/QA4AMQAUAAWBrawsLC4vfPR4R4XH5lTEqrQAABDlJREFUY8T2joWnxLOhm9tg+EmVMcZYg2vevDmaNWsGfX19GBkZQSQSQUtfCwILAVD5f3ci4OqVq7iYdRH4ncc7gUAAoaYQ31769m9v+1/BT6qMMcYaXHV1NeRyOXR1ddUDkDZmbcSinxeh+E4xfn34K3ADQOnz/bVNtdHBswMKLxfCbrgdAKCquAr3fr6HiscVEGgIYNzRGE0cmyB5WDKkBtLGubDfodnYDWCMMfb2EYlE6oL6L5y8fxLGhsawbGWJSxsuodK0Er/a/QqogJryGpw6dQrmTc0BAMpaJXJ35KK1W2tY+VqBVISq4ipUoAJXiq5wqDLGGHu3ldaUoolGE1QUVEBZpUTnsM4QCAR48OABsi5mAVXA7Vu3YQ97lNwsgVYzLbRx+3/F9nXb6uJZ2TOU15b/xlkaF4cqY4yxN0KriRaICPIyObT1tSHQeN4tbGpmClMzU+QeysWd4jsAgNqyWogMRPWOIYAAWhpab7LZfwoPVGKMMfZGtGrWCjXKGgibC1H7rBakqjukp2XLlhBLxAAAYXMhqkuq6x2jiaAJDEQGb6S9r4NDlTHG2BsxwHoAlColmrVpBq1mWrh/5D6UtUqoFCqU5ZfV2dfAwgDyCjkenX0ElUIFZa0STx88haaGJjqZdGqkK/h93P3LGGPsjehi2gV62nqoVlbDytcK9zLu4eKaiwCAFrYt0KxVM/W+TYRNYD3MGvd+voeHJx5CoCmAyF6ECPcINBM2e9UpGh1PqWGMMfbGbLiwAfGZ8WjTvM2fKpIvV8rxpOoJtg3dBpmh7G9s4V/D3b+MMcbemEDHQLRv1R6FFYWvrA38vxQqBYoqijDebfw/OlABDlXGGGNvkEhThKV9lsKmpQ0KygtQq6z9zf3La8vxuPwxRnUYhRCnkDfUytfH3b+MMcbeuGpFNRIyE7Dr6i7UKmuhI9RBU82m0BBoQElKlFaXQkUqGDczRqRbJPpa9W3sJv8hHKqMMcYaTUVtBTJuZWBbzjYUlBegRlEDXaEuHI0dEeAQAJe2LtAQ/Hs6VTlUGWOMsQby74l/xhhj7B+OQ5UxxhhrIByqjDHGWAPhUGWMMcYaCIcqY4wx1kA4VBljjLEGwqHKGGOMNRAOVcYYY6yBcKgyxhhjDYRDlTHGGGsgHKqMMcZYA+FQZYwxxhoIhypjjDHWQDhUGWOMsQbCocoYY4w1EA5VxhhjrIFwqDLGGGMNhEOVMcYYayAcqowxxlgD4VBljDHGGgiHKmOMMdZAOFQZY4yxBsKhyhhjjDUQDlXGGGOsgXCoMsYYYw2EQ5UxxhhrIByqjDHGWAPhUGWMMcYaCIcqY4wx1kA4VBljjLEGwqHKGGOMNRAOVcYYY6yBcKgyxhhjDYRDlTHGGGsgHKqMMcZYA+FQZYwxxhoIhypjjDHWQDhUGWOMsQbCocoYY4w1kP8DPa2mUta8grsAAAAASUVORK5CYII=\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 % params['drip_frequency']  == 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': {'drip_frequency': 30}}, {'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 60}}, {'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 90}}]\n",
+      "[{'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 30}}, {'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 60}}, {'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 90}}]\n",
+      "[{'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 30}}, {'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 60}}, {'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 90}}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# config\n",
+    "params: Dict[str, List[int]] = {\n",
+    "    'drip_frequency': [30,60,90] # in days\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": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "exec_mode = ExecutionMode()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "                            __________   ____ \n",
+      "          ________ __ _____/ ____/   |  / __ \\\n",
+      "         / ___/ __` / __  / /   / /| | / / / /\n",
+      "        / /__/ /_/ / /_/ / /___/ ___ |/ /_/ / \n",
+      "        \\___/\\__,_/\\__,_/\\____/_/  |_/_____/  \n",
+      "        by BlockScience\n",
+      "        \n",
+      "Execution Mode: multi_proc: [<cadCAD.configuration.Configuration object at 0x7f54b92c5ad0>, <cadCAD.configuration.Configuration object at 0x7f54b92c5f90>, <cadCAD.configuration.Configuration object at 0x7f54b92c5850>]\n",
+      "Configurations: [<cadCAD.configuration.Configuration object at 0x7f54b92c5ad0>, <cadCAD.configuration.Configuration object at 0x7f54b92c5f90>, <cadCAD.configuration.Configuration object at 0x7f54b92c5850>]\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": [
+    "exec_mode = ExecutionMode()\n",
+    "multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)\n",
+    "run = Executor(exec_context=multi_proc_ctx, configs=configs)\n",
+    "\n",
+    "i = 0\n",
+    "results = {}\n",
+    "for raw_result, tensor_field in run.execute():\n",
+    "    result = pd.DataFrame(raw_result)\n",
+    "    results[i] = {}\n",
+    "    results[i]['result'] = result\n",
+    "    i += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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>4000</th>\n",
+       "      <td>(a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...</td>\n",
+       "      <td>{'a': 246, 'b': 0, 'c': 466, 'd': 620, 'e': 45...</td>\n",
+       "      <td>{'a': 246, 'b': 0, 'c': 5.187631236062657, 'd'...</td>\n",
+       "      <td>{'a': 1.0, 'b': 0, 'c': 0.011132255871379093, ...</td>\n",
+       "      <td>9.77</td>\n",
+       "      <td>{'a': 136.4003802092373, 'b': 848.068007320516...</td>\n",
+       "      <td>{'a': 422.1992395815254, 'b': 3202.55026803101...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[f, h, c, o, f, k, a, p, d, o, h, g, k, a]</td>\n",
+       "      <td>[e, f, f, f, k, h, i, external, b, external, g...</td>\n",
+       "      <td>16500</td>\n",
+       "      <td>198500.00</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>4</td>\n",
+       "      <td>100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4001</th>\n",
+       "      <td>(a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...</td>\n",
+       "      <td>{'a': 246, 'b': 0, 'c': 466, 'd': 620, 'e': 45...</td>\n",
+       "      <td>{'a': 246, 'b': 0, 'c': 5.187631236062657, 'd'...</td>\n",
+       "      <td>{'a': 1.0, 'b': 0, 'c': 0.011132255871379093, ...</td>\n",
+       "      <td>9.77</td>\n",
+       "      <td>{'a': 136.4003802092373, 'b': 848.068007320516...</td>\n",
+       "      <td>{'a': 422.1992395815254, 'b': 3202.55026803101...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[f, h, c, o, f, k, a, p, d, o, h, g, k, a]</td>\n",
+       "      <td>[e, f, f, f, k, h, i, external, b, external, g...</td>\n",
+       "      <td>16500</td>\n",
+       "      <td>198500.00</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>5</td>\n",
+       "      <td>100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4002</th>\n",
+       "      <td>(a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...</td>\n",
+       "      <td>{'a': 246, 'b': 0, 'c': 466, 'd': 620, 'e': 45...</td>\n",
+       "      <td>{'a': 246, 'b': 0, 'c': 5.187631236062657, 'd'...</td>\n",
+       "      <td>{'a': 1.0, 'b': 0, 'c': 0.011132255871379093, ...</td>\n",
+       "      <td>9.77</td>\n",
+       "      <td>{'a': 136.4003802092373, 'b': 848.068007320516...</td>\n",
+       "      <td>{'a': 422.1992395815254, 'b': 3202.55026803101...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[f, h, c, o, f, k, a, p, d, o, h, g, k, a]</td>\n",
+       "      <td>[e, f, f, f, k, h, i, external, b, external, g...</td>\n",
+       "      <td>16500</td>\n",
+       "      <td>198500.00</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>6</td>\n",
+       "      <td>100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4003</th>\n",
+       "      <td>(a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...</td>\n",
+       "      <td>{'a': 346, 'b': 0, 'c': 431, 'd': 245, 'e': 0,...</td>\n",
+       "      <td>{'a': 346, 'b': 0, 'c': 2.5938156180313285, 'd...</td>\n",
+       "      <td>{'a': 1.0, 'b': 0, 'c': 0.0060181336845274444,...</td>\n",
+       "      <td>9.77</td>\n",
+       "      <td>{'a': 136.4003802092373, 'b': 848.068007320516...</td>\n",
+       "      <td>{'a': 422.1992395815254, 'b': 3202.55026803101...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[f, h, c, o, f, k, a, p, d, o, h, g, k, a]</td>\n",
+       "      <td>[e, f, f, f, k, h, i, external, b, external, g...</td>\n",
+       "      <td>16500</td>\n",
+       "      <td>198500.00</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>7</td>\n",
+       "      <td>100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4004</th>\n",
+       "      <td>(a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...</td>\n",
+       "      <td>{'a': 346, 'b': 0, 'c': 431, 'd': 245, 'e': 0,...</td>\n",
+       "      <td>{'a': 346, 'b': 0, 'c': 2.5938156180313285, 'd...</td>\n",
+       "      <td>{'a': 1.0, 'b': 0, 'c': 0.0060181336845274444,...</td>\n",
+       "      <td>20.19</td>\n",
+       "      <td>{'a': 136.4003802092373, 'b': 848.068007320516...</td>\n",
+       "      <td>{'a': 422.1992395815254, 'b': 3202.55026803101...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[f, h, c, o, f, k, a, p, d, o, h, g, k, a]</td>\n",
+       "      <td>[e, f, f, f, k, h, i, external, b, external, g...</td>\n",
+       "      <td>16500</td>\n",
+       "      <td>198500.00</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",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                network  \\\n",
+       "4000  (a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...   \n",
+       "4001  (a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...   \n",
+       "4002  (a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...   \n",
+       "4003  (a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...   \n",
+       "4004  (a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...   \n",
+       "\n",
+       "                                              KPIDemand  \\\n",
+       "4000  {'a': 246, 'b': 0, 'c': 466, 'd': 620, 'e': 45...   \n",
+       "4001  {'a': 246, 'b': 0, 'c': 466, 'd': 620, 'e': 45...   \n",
+       "4002  {'a': 246, 'b': 0, 'c': 466, 'd': 620, 'e': 45...   \n",
+       "4003  {'a': 346, 'b': 0, 'c': 431, 'd': 245, 'e': 0,...   \n",
+       "4004  {'a': 346, 'b': 0, 'c': 431, 'd': 245, 'e': 0,...   \n",
+       "\n",
+       "                                               KPISpend  \\\n",
+       "4000  {'a': 246, 'b': 0, 'c': 5.187631236062657, 'd'...   \n",
+       "4001  {'a': 246, 'b': 0, 'c': 5.187631236062657, 'd'...   \n",
+       "4002  {'a': 246, 'b': 0, 'c': 5.187631236062657, 'd'...   \n",
+       "4003  {'a': 346, 'b': 0, 'c': 2.5938156180313285, 'd...   \n",
+       "4004  {'a': 346, 'b': 0, 'c': 2.5938156180313285, 'd...   \n",
+       "\n",
+       "                                     KPISpendOverDemand  VelocityOfMoney  \\\n",
+       "4000  {'a': 1.0, 'b': 0, 'c': 0.011132255871379093, ...             9.77   \n",
+       "4001  {'a': 1.0, 'b': 0, 'c': 0.011132255871379093, ...             9.77   \n",
+       "4002  {'a': 1.0, 'b': 0, 'c': 0.011132255871379093, ...             9.77   \n",
+       "4003  {'a': 1.0, 'b': 0, 'c': 0.0060181336845274444,...             9.77   \n",
+       "4004  {'a': 1.0, 'b': 0, 'c': 0.0060181336845274444,...            20.19   \n",
+       "\n",
+       "                                        startingBalance  \\\n",
+       "4000  {'a': 136.4003802092373, 'b': 848.068007320516...   \n",
+       "4001  {'a': 136.4003802092373, 'b': 848.068007320516...   \n",
+       "4002  {'a': 136.4003802092373, 'b': 848.068007320516...   \n",
+       "4003  {'a': 136.4003802092373, 'b': 848.068007320516...   \n",
+       "4004  {'a': 136.4003802092373, 'b': 848.068007320516...   \n",
+       "\n",
+       "                                           30_day_spend withdraw  \\\n",
+       "4000  {'a': 422.1992395815254, 'b': 3202.55026803101...        0   \n",
+       "4001  {'a': 422.1992395815254, 'b': 3202.55026803101...        0   \n",
+       "4002  {'a': 422.1992395815254, 'b': 3202.55026803101...        0   \n",
+       "4003  {'a': 422.1992395815254, 'b': 3202.55026803101...        0   \n",
+       "4004  {'a': 422.1992395815254, 'b': 3202.55026803101...        0   \n",
+       "\n",
+       "                                  outboundAgents  \\\n",
+       "4000  [f, h, c, o, f, k, a, p, d, o, h, g, k, a]   \n",
+       "4001  [f, h, c, o, f, k, a, p, d, o, h, g, k, a]   \n",
+       "4002  [f, h, c, o, f, k, a, p, d, o, h, g, k, a]   \n",
+       "4003  [f, h, c, o, f, k, a, p, d, o, h, g, k, a]   \n",
+       "4004  [f, h, c, o, f, k, a, p, d, o, h, g, k, a]   \n",
+       "\n",
+       "                                          inboundAgents  operatorFiatBalance  \\\n",
+       "4000  [e, f, f, f, k, h, i, external, b, external, g...                16500   \n",
+       "4001  [e, f, f, f, k, h, i, external, b, external, g...                16500   \n",
+       "4002  [e, f, f, f, k, h, i, external, b, external, g...                16500   \n",
+       "4003  [e, f, f, f, k, h, i, external, b, external, g...                16500   \n",
+       "4004  [e, f, f, f, k, h, i, external, b, external, g...                16500   \n",
+       "\n",
+       "      operatorCICBalance                                     fundsInProcess  \\\n",
+       "4000           198500.00  {'timestep': [], 'decision': [], 'cic': [], 's...   \n",
+       "4001           198500.00  {'timestep': [], 'decision': [], 'cic': [], 's...   \n",
+       "4002           198500.00  {'timestep': [], 'decision': [], 'cic': [], 's...   \n",
+       "4003           198500.00  {'timestep': [], 'decision': [], 'cic': [], 's...   \n",
+       "4004           198500.00  {'timestep': [], 'decision': [], 'cic': [], 's...   \n",
+       "\n",
+       "      totalDistributedToAgents  totalMinted  totalBurned  run  substep  \\\n",
+       "4000                      1500            0            0    5        4   \n",
+       "4001                      1500            0            0    5        5   \n",
+       "4002                      1500            0            0    5        6   \n",
+       "4003                      1500            0            0    5        7   \n",
+       "4004                      1500            0            0    5        8   \n",
+       "\n",
+       "      timestep  \n",
+       "4000       100  \n",
+       "4001       100  \n",
+       "4002       100  \n",
+       "4003       100  \n",
+       "4004       100  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results[0]['result'].tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(0,len(results)):\n",
+    "    results[i]['result']['agents'] = results[i]['result'].network.apply(lambda g: np.array([get_nodes_by_type(g,'Agent')][0]))\n",
+    "    results[i]['result']['agent_tokens'] = results[i]['result'].network.apply(lambda g: np.array([g.nodes[j]['tokens'] for j in get_nodes_by_type(g,'Agent')]))\n",
+    "    results[i]['result']['agent_native_currency'] = results[i]['result'].network.apply(lambda g: np.array([g.nodes[j]['native_currency'] for j in get_nodes_by_type(g,'Agent')]))\n",
+    "    # Create dataframe variables   \n",
+    "    tokens = []\n",
+    "    for j in results[i]['result'].index:\n",
+    "         tokens.append(sum(results[i]['result']['agent_tokens'][j]))\n",
+    "\n",
+    "    results[i]['result']['AggregatedAgentCICHolding'] = tokens  \n",
+    "\n",
+    "    currency = []\n",
+    "    for j in results[i]['result'].index:\n",
+    "         currency.append(sum(results[i]['result']['agent_native_currency'][j]))\n",
+    "\n",
+    "    results[i]['result']['AggregatedAgentCurrencyHolding'] = currency  \n",
+    "\n",
+    "    AggregatedSpend = []\n",
+    "    for j in results[i]['result'].index:\n",
+    "         AggregatedSpend.append(sum(results[i]['result']['KPISpend'][j].values()))\n",
+    "\n",
+    "    results[i]['result']['AggregatedAgentSpend'] = AggregatedSpend   \n",
+    "\n",
+    "    AggregatedDemand = []\n",
+    "    for j in results[i]['result'].index:\n",
+    "         AggregatedDemand.append(sum(results[i]['result']['KPIDemand'][j].values()))\n",
+    "\n",
+    "    results[i]['result']['AggregatedAgentDemand'] = AggregatedDemand  \n",
+    "\n",
+    "\n",
+    "    AggregatedKPISpendOverDemand = []\n",
+    "    for j in results[i]['result'].index:\n",
+    "         AggregatedKPISpendOverDemand.append(sum(results[i]['result']['KPISpendOverDemand'][j].values()))\n",
+    "\n",
+    "    results[i]['result']['AggregatedKPISpendOverDemand'] = AggregatedKPISpendOverDemand  \n",
+    "\n",
+    "\n",
+    "    AggregatedGapOfDemandMinusSpend = []\n",
+    "    for j in results[i]['result'].index:\n",
+    "         AggregatedGapOfDemandMinusSpend.append(sum(results[i]['result']['KPIDemand'][j].values())- sum(results[i]['result']['KPISpend'][j].values()))\n",
+    "\n",
+    "    results[i]['result']['AggregatedGapOfDemandMinusSpend'] = AggregatedGapOfDemandMinusSpend  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "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",
+       "      <th>Red Cross Drip Frequency</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>14.04</td>\n",
+       "      <td>4500</td>\n",
+       "      <td>200000.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6000.00</td>\n",
+       "      <td>2912.00</td>\n",
+       "      <td>1255.25</td>\n",
+       "      <td>2534</td>\n",
+       "      <td>4.96</td>\n",
+       "      <td>1325.00</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>18.48</td>\n",
+       "      <td>4500</td>\n",
+       "      <td>200000.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6040.00</td>\n",
+       "      <td>2952.00</td>\n",
+       "      <td>1693.62</td>\n",
+       "      <td>3370</td>\n",
+       "      <td>5.59</td>\n",
+       "      <td>1292.75</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>16.27</td>\n",
+       "      <td>4500</td>\n",
+       "      <td>200000.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6049.50</td>\n",
+       "      <td>2961.50</td>\n",
+       "      <td>1466.34</td>\n",
+       "      <td>2441</td>\n",
+       "      <td>5.46</td>\n",
+       "      <td>381.50</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>18.75</td>\n",
+       "      <td>4500</td>\n",
+       "      <td>200000.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6124.94</td>\n",
+       "      <td>3036.94</td>\n",
+       "      <td>1672.00</td>\n",
+       "      <td>2867</td>\n",
+       "      <td>6.48</td>\n",
+       "      <td>1195.00</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>15.17</td>\n",
+       "      <td>4500</td>\n",
+       "      <td>200000.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6385.50</td>\n",
+       "      <td>3297.50</td>\n",
+       "      <td>1568.00</td>\n",
+       "      <td>1914</td>\n",
+       "      <td>5.49</td>\n",
+       "      <td>734.89</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   timestep  VelocityOfMoney  operatorFiatBalance  operatorCICBalance  \\\n",
+       "0         1            14.04                 4500           200000.00   \n",
+       "1         2            18.48                 4500           200000.00   \n",
+       "2         3            16.27                 4500           200000.00   \n",
+       "3         4            18.75                 4500           200000.00   \n",
+       "4         5            15.17                 4500           200000.00   \n",
+       "\n",
+       "   totalDistributedToAgents  totalMinted  totalBurned  run  substep  \\\n",
+       "0                         0            0            0    3        8   \n",
+       "1                         0            0            0    3        8   \n",
+       "2                         0            0            0    3        8   \n",
+       "3                         0            0            0    3        8   \n",
+       "4                         0            0            0    3        8   \n",
+       "\n",
+       "   AggregatedAgentCICHolding  AggregatedAgentCurrencyHolding  \\\n",
+       "0                    6000.00                         2912.00   \n",
+       "1                    6040.00                         2952.00   \n",
+       "2                    6049.50                         2961.50   \n",
+       "3                    6124.94                         3036.94   \n",
+       "4                    6385.50                         3297.50   \n",
+       "\n",
+       "   AggregatedAgentSpend  AggregatedAgentDemand  AggregatedKPISpendOverDemand  \\\n",
+       "0               1255.25                   2534                          4.96   \n",
+       "1               1693.62                   3370                          5.59   \n",
+       "2               1466.34                   2441                          5.46   \n",
+       "3               1672.00                   2867                          6.48   \n",
+       "4               1568.00                   1914                          5.49   \n",
+       "\n",
+       "   AggregatedGapOfDemandMinusSpend  Red Cross Drip Frequency  \n",
+       "0                          1325.00                        30  \n",
+       "1                          1292.75                        30  \n",
+       "2                           381.50                        30  \n",
+       "3                          1195.00                        30  \n",
+       "4                           734.89                        30  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "params = [30,60,90]\n",
+    "swept = 'Red Cross Drip Frequency'\n",
+    "mean_df,median_df = param_dfs(results,params,swept)\n",
+    "median_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot of agent activity per timestep\n",
+    "param_plot(median_df,'timestep', 'AggregatedAgentSpend',swept)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "param_plot(median_df,'timestep', 'VelocityOfMoney',swept)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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TqtzK46U47WxCI4RnKjh9SdLZBr8gXJmuAX5FaLwEbD94nkm4kizaHrdRjt+DmX1JaNDyE0KV/GxCo8AiLxCuEl4ws43lLunOfgpcQmjE9RfKPpH5N/A6oWHbF4TfQnluEZSktPU+idBSd6mklTHtDkJDx2mSviHsm4dXYHkbgYcI++xKwv3ec83scwAz+4Swr78Z+78hHHjfKqouj1W/JxLuA38V51XU8Kc8eVway/oV4eTq+2Y2r4T8XkyorvyKsL3vTuVWnJmtIhwff0L4Dd4OnGZmKxNG+yuhPcBSQmOum+K0FdqH4/q5iXC8XUPYv8YlDJ9HCLSfx2PugZR+DCpuGVsINWbzCPd7vyFcfO0HvFPM+KsJrbq3Ae/EY9FEwsXE/IRR35O0PuZ7EKE18uqyypS0rDmEfWwq4SSjC6FNQXldFvM5j9CO5uY43xmEBll/inmYT7hfXKqiFlvOpUSSEapE55c58u7NR1vCCVhOGrUelZWXzwitgFNpF+EyTFJfQsO78tR4ZIykyYR8PV3VeXGVa294SIFzGSPpXEKV1qSqzotzrnrao55641x1Fq9QOgGXxfuKzjm3C69qds455zLIq5qdc865DPKq5jLst99+1rZt26rOhnPO7VFmzpy50syaV3U+qiMPvGVo27YtM2bMqOpsOOfcHkVSRZ4mt1fxqmbnnHMugzzwOueccxnkgdc555zLIL/H69weZNu2beTl5bF5c4kv+3Euo+rWrUvr1q3Jycmp6qzsMTzwOrcHycvLo1GjRrRt2xYplZcPOVd5zIxVq1aRl5dHu3btqjo7e4yMB974BptRhPfmGuFdso9Kakp4GHxbwsuSLzCzNfENPI8SHky/kfCS6FlxXoMILx6A8FL3kTG9J+HB4vUIbwD5sZlZScuozPJtXbWG/HUbKCgwsuvVpnBjuDKpVb9uyt0mkVWvLoUbN+3cbZDVIIy3UzeQVb/83RnP356U10rMn0nUygp3d7IbNaR2s+LeMla6zZs3e9B11YYkmjVrxooVK6o6K3sWM8voh/CWkx6xuxHhjSqdgN8Dg2P6YOD+2H0K4ZVMAnoD78T0poQ3lDQFmsTuJnHY9Diu4rQnx/Ril1Hap2fPnlYR6z9fZK9kd7BXsjvYysnTKqW7Mue1O7qrSz6qe14TPxsWLKrQflVkzpw5KU3n3O5U3H4JzLAMx5c95ZPxxlVmtsTiFauF1zrNBVoRXnE1Mo42kh0vjj8TGBW35TSgsaQDgJOACRZeD7WG8BqqgXHYPmY2LW78UUnzKm4ZlVm+yp6lq4F8N3Fu71WlrZrjq9y6E97V2NLCC4chvHuyZexuxc7vFs2LaaWl5xWTTinLSM7XdZJmSJpR0SqULVv92fiubHty4M3KyqJbt2507tyZ008/nbVr11Zo+nvuuYcHH3yw2GGjRo2ic+fOdOnShe7du5c4XmVr27YtXbp0oUuXLnTq1Im77rqr1AZsxx57bIXm37dvXw4//HC6detGt27dGDNmTLpZdnuwKgu8khoC/wRutvBi6+3ilepuPTSVtgwzG2pmuWaW27x5xZ54VrdOVmVkz9VwtfbgP/LVq1eP2bNn8+GHH9K0aVMef/zxSpnva6+9xiOPPML48eP54IMPmDZtGvvuu+8u4+Xn757XLb/xxht88MEHTJ8+nc8//5zrr7++xGW//fbbFZ7/s88+y+zZs5k9ezbnnXfeTsPMjMJCP2nfW1TJz19SDiHoPmtmY2PyslhNTPxeHtMXAwclTN46ppWW3rqY9NKW4ZxLQZ8+fVi8ePH2/gceeICjjz6ao446irvvvnt7+m9/+1s6dOjAt7/9bT7++ONi53Xffffx4IMPcuCBBwJQp04drr32WiBcMd58883k5uby6KOPsnDhQk444QSOOuoo+vfvz5dffgnA888/T+fOnenatSvHH388AB999BHHHHMM3bp146ijjuLTTz8ttUwNGzbkySef5MUXX2T16tVMnjyZ4447jjPOOINOnTptHwdg8uTJHH/88Zx66qkcfvjhfP/73y93AF24cCGHH344l19+OZ07d2bRokWMHz+ePn360KNHD84//3zWr18PwOuvv84RRxxBjx49uOmmmzjttNOAXWsPOnfuzMKFCwH429/+tr3c119/PQUFBdvz/vOf/5yuXbvSu3dvli1bBsCyZcs4++yz6dq1K127duXtt9/ml7/8JY888sj2+f/85z/n0UcfLVf5XMky/lrA2Ep5JLDazG5OSH8AWGVmQyQNBpqa2e2STgV+SGhk1Qt4zMyOiS2UZwI94ixmAT3NbLWk6cBNhCrsV4E/mtmrJS2jtPzm5uZaRZ7VvHXVWvLXraewELLq1qZw4yYgvZa4SNSKrW936k5zvpXVXd3zV13yioTSbNU8d+5cOnbsWOHpKlPDhg1Zv349BQUFXHTRRVx99dUMHDiQ8ePHM2bMGJ566inMjDPOOIPbb7+dBg0acMUVV/DOO++Qn59Pjx49+P73v89Pf/rTnebbtGlTFixYUOxVbt++fenUqRN//vOfATj99NM577zzGDRoEMOGDWPcuHG8+OKLdOnShddff51WrVqxdu1aGjduzI9+9CN69+7NpZdeytatWykoKKBevXo7zb/omez77bff9rRu3brx1FNPsWnTJk499VQ+/PDD7X+ZKVoHkydPZuDAgcyZM4c2bdowcOBArr/++l2uaPv27cuSJUu2L3fixImsW7eOQw45hLfffpvevXuzcuVKzjnnHF577TUaNGjA/fffz5YtW7j99ttp3749kyZN4rDDDuPCCy9k48aNvPLKK9xzzz00bNhw+7rs3Lkzr7zyCps2beL2229n7Nix5OTkcMMNN9C7d28uv/xyJDFu3DhOP/10br/9dvbZZx/uuusuLrzwQvr06cPNN99MQUEB69evZ82aNZxzzjnMmjWLwsJC2rdvz/Tp02nWrNlO5Stuv5Q008xyy71j7UWq4n+83wIuAz6QNDum/QwYAjwn6WrgC+CCOOxVQtCdT/g70ZUAMcD+BvhfHO/XZrY6dt/Ajr8TvRY/lLKMSlO7WeOUDqjO7Sk2bdpEt27dWLx4MR07dmTAgAEAjB8/nvHjx9O9e3cA1q9fz6effsq6des4++yzqV+/PgBnnHFGSsu98MILt3dPnTqVsWNDZdlll13G7beH8+dvfetbXHHFFVxwwQWcc845QLgq/+1vf0teXh7nnHMO7du3L9fyEi9KjjnmmBL/p3rMMcdwyCGHAHDxxRfz3//+d5fAC6GqOTd3Rxxat24dbdq0oXfv3gBMmzaNOXPm8K1vfQuArVu30qdPH+bNm0e7du225/t73/seQ4cOLTXvEydOZObMmRx99NFA2GYtWrQAoHbt2tuvmHv27MmECRMAmDRpEqNGjQLCffx9992Xfffdl2bNmvHuu++ybNkyunfvvkvQdRWX8cBrZv8l/M2nOP2LGd+AG0uY1zBgWDHpM4DOxaSvKm4ZzrnyK7rHu3HjRk466SQef/xxbrrpJsyMO++8c5d7o4lVlaU58sgjmTlzJieccEKxwxs0aFDmPJ588kneeecd/vWvf9GzZ09mzpzJJZdcQq9evfjXv/7FKaecwlNPPVXiMoqsW7eOhQsX0qFDB957771Sl538n+qK/Mc6cb5mxoABAxg9evRO48yePTt5su2ys7N3qtouahBmZgwaNIj77rtvl2lycnK25zErK6vMe+bXXHMNI0aMYOnSpVx11VVlF8qVaQ9u4uGcq0r169fnscce46GHHiI/P5+TTjqJYcOGbb8vuXjxYpYvX87xxx/Piy++yKZNm1i3bh0vv/xysfO78847ue2221i6dCkQrviefvrpYsc99thj+fvf/w6EK8njjjsOgM8++4xevXrx61//mubNm7No0SI+//xzDjnkEG666SbOPPNM3n///VLLtX79em644QbOOussmjRpUuZ6mD59OgsWLKCwsJB//OMffPvb3y5zmuL07t2bt956i/nz5wOwYcMGPvnkE4444ggWLlzIZ599BrBTYG7bti2zZs0CYNasWSxYsACA/v37M2bMGJYvD81YVq9ezRdflP6Wvv79+/PEE08AUFBQwNdffw3A2Wefzeuvv87//vc/TjrppJTK5nbmj4x0zqWse/fuHHXUUYwePZrLLruMuXPn0qdPHyDcB/3b3/5Gjx49uPDCC+natSstWrTYXv2Z7JRTTmHZsmV897vfxcyQVOIV1h//+EeuvPJKHnjgAZo3b87w4cMBuO222/j0008xM/r370/Xrl25//77+etf/0pOTg77778/P/vZz4qdZ79+/ba3Lj777LP5xS9+Ua51cPTRR/PDH/6Q+fPn069fP84+++xyTZesefPmjBgxgosvvpgtW7YAcO+999KhQweGDh3KqaeeSv369TnuuONYt24dAOeeey6jRo3iyCOPpFevXnTo0AGATp06ce+993LiiSdSWFhITk4Ojz/+OG3atClx+Y8++ijXXXcdzzzzDFlZWTzxxBP06dOH2rVr069fPxo3bkxWlv9rozJkvHHVnqaijauc252qQ+Mqt8PkyZN58MEHeeWVV2rsMgsLC+nRowfPP/98iffHvXFVxXhVs3POuWLNmTOHww47jP79+5e7UZorm1/xlsGveF114le8rjryK96K8Ste55xzLoM88DrnnHMZ5IHXOeecyyAPvM4551wGeeB1zpXb5s2bOeaYY+jatStHHnnk9hchLFiwgF69em1/lvDWrVurOKfOVV8eeJ1z5VanTh0mTZrEe++9x+zZs3n99deZNm0ad9xxB7fccgvz58+nSZMmPPPMM1WdVeeqLQ+8ztVg4ycv49yrpnHcGf/h3KumMX7ysrTmJ2n7K/G2bdvGtm3bkMSkSZO2vxhg0KBBvPjii2nn3bmaygOvczXU+MnLuP9Pn7BsxRbMYNmKLdz/p0/SDr4FBQV069aNFi1aMGDAAA499FAaN25MdnZ4Am3r1q13ekevc25nHnidq6GeGrWALVt2fin7li2FPDVqQVrzzcrKYvbs2eTl5TF9+nTmzZuX1vyc29t44HWuhlq+ckuF0iuqcePG9OvXj6lTp7J27drtr5fLy8ujVatWlbIM52oiD7zO1VAt9qtTofTyWLFiBWvXrgXCy9UnTJhAx44d6devH2PGjAFg5MiRnHnmmSkvw7mazgOvczXU9Ze3o06dnX/iderU4vrL26U8zyVLltCvXz+OOuoojj76aAYMGMBpp53G/fffz8MPP8xhhx3GqlWruPrqq9PNvnM1VsbfxytpGHAasNzMOse0rsCTQENgIXCpmX0jqTbwFJALFAI/NrPJcZqLgZ8BBnwFfM/MVkpqCvwDaBvndYGZrZEk4FHgFGAjcIWZzcpEmZ2rCif2bQmEe73LV26hxX51uP7ydtvTU3HUUUfx7rvv7pJ+yCGHMH369JTn69zeJOOBFxgB/AkYlZD2NPBTM/uPpKuA24BfANcCmFkXSS2A1yQdTbhSfxToFIPt74EfAvcAg4GJZjZE0uDYfwdwMtA+fnoBT8Rv52qsE/u2TCvQOucqX8arms1sCrA6KbkDMCV2TwDOjd2dgElxuuXAWsLVr+KnQbyS3Ydw1QtwJjAydo8EzkpIH2XBNKCxpAMqsWjOOedcmarLPd6PCIER4HzgoNj9HnCGpGxJ7YCewEFmtg34AfABIeB2AooeldPSzJbE7qVA0el+K2BRwjLzYppzzjmXMdUl8F4F3CBpJtAIKHrQ6zBCgJwBPAK8DRRIyiEE3u7AgcD7wJ3JMzUzI9wDrhBJ10maIWnGihUrUiiOc845V7xqEXjNbJ6ZnWhmPYHRwGcxPd/MbjGzbmZ2JtAY+AToFod/FoPrc8CxcXbLiqqQ4/fymL6YHVfSAK1jWnH5GWpmuWaW27x580otq3POub1btQi8seEUkmoBdxFaOCOpvqQGsXsAkG9mcwgBs5Okoqg4AJgbu8cBg2L3IOClhPTLFfQGvk6oknbOOecyIuOBV9JoYCpwuKQ8SVcDF0v6BJhHuGc7PI7eApglaS6hZfJlAGb2FfArYIqk9wlXwL+L0wwBBkj6FPhu7Ad4FfgcmA/8Bbj1zOrxAAAgAElEQVRhtxbUuRpq7dq1nHfeeRxxxBF07NiRqVOnsnr1agYMGED79u0ZMGAAa9asqepsOldtKdTUupLk5ubajBkzqjobzgEwd+5cOnbsWKV5GDRoEMcddxzXXHMNW7duZePGjfzud7+jadOmDB48mCFDhrBmzRruv//+Ks2ny5zi9ktJM80st4qyVK1Vi6pm51zlMzOWfPUVS776isLCwu3d6Zxsf/3110yZMmX7k6lq165N48aNeemllxg0KNzh8dcCOlc6D7zO1VBLlyxh8+bNbN68mS+/+GJ799IlqTdtWLBgAc2bN+fKK6+ke/fuXHPNNWzYsIFly5ZxwAHhb/H7778/y5al9+pB52oyD7zO1XBmRmFhYVpXukXy8/OZNWsWP/jBD3j33Xdp0KABQ4YM2WkcSYTn2jjniuOB17kaquX+++8SACXRcv/9U55n69atad26Nb16haetnnfeecyaNYuWLVuyJF5JL1myhBYtWqSecedqOA+8ztVQy5Yu3eUq18xYtnRpyvPcf//9Oeigg/j4448BmDhxIp06deKMM85g5MjwpFZ/LaBzpauKlyQ45zKoqOq3sv7B8Mc//pFLL72UrVu3csghhzB8+HAKCwu54IILeOaZZ2jTpg3PPfdcpSzLuZrIA69zNdT+BxywvSFVy/33336lu/8B6b0bpFu3bhT3F7uJEyemNV/n9hYeeJ2roSRxwIEHbu9P7HbOVR2/x+ucc85lkAde55xzLoM88DrnnHMZ5IHXOeecyyAPvM4551wGeeB1zlXIo48+SufOnTnyyCN55JFHAPy1gM5VgAde51y5ffjhh/zlL39h+vTpvPfee7zyyivMnz+fIUOG0L9/fz799FP69++/y/ObnXM7+P94nauhXm/ag4J1G3ZJz2rUgIGrZ6U0z7lz59KrVy/q168PwHe+8x3Gjh3LSy+9xOTJk4HwWsC+ffv6+3idK4Ff8TpXQxUXdEtLL4/OnTvz5ptvsmrVKjZu3Mirr77KokWL/LWAzlWAX/E658qtY8eO3HHHHZx44ok0aNCAbt26kZWVtdM4/lpA50qX8SteScMkLZf0YUJaV0lTJX0g6WVJ+8T02pKGx/T3JPVNmKa2pKGSPpE0T9K5Mb2OpH9Imi/pHUltE6a5M6Z/LOmkjBXauRrk6quvZubMmUyZMoUmTZrQoUMHfy2gcxVQFVXNI4CBSWlPA4PNrAvwAnBbTL8WIKYPAB6SVJTnnwPLzawD0An4T0y/GlhjZocBfwDuB5DUCbgIODIu/8+Sdj5Vd86Vafny5QB8+eWXjB07lksuucRfC+hcBWS8qtnMpiRehUYdgCmxewLwb+AXhIA6KU63XNJaIBeYDlwFHBGHFQIr4/RnAvfE7jHAnxTqvc4E/m5mW4AFkuYDxwBTK7eEztVs5557LqtWrSInJ4fHH3+cxo0bM3jwYH8toHPllHLglVQf+AlwsJldK6k9cLiZvZLC7D4iBMYXgfOBg2L6e8AZkkbHtJ7AQZI+icN/E6ufPwN+aGbLgFbAIgAzy5f0NdAspk9LWGZeTCuubNcB1wEcfPDBKRTHuaqX1ahBia2a0/Hmm2/uktasWTN/LaBz5ZTOFe9wYCbQJ/YvBp4HUgm8VwGPSfoFMA7YGtOHAR2BGcAXwNtAASHfrYG3zexWSbcCDwKXpVaUnZnZUGAoQG5ubuW8Pdy5DEv1L0POud0rncB7qJldKOliADPbqBSbMprZPOBEAEkdgFNjej5wS9F4kt4GPgFWARuBsXHQ84R7uxBOAA4C8iRlA/vG8YvSi7SOac4551zGpNO4aqukeoABSDoU2JLKjCS1iN+1gLuAJ2N/fUkNYvcAIN/M5piZAS8DfeMs+gNzYvc4YFDsPg+YFMcfB1wUWz23A9oT7hU7t0cJu7Nz1YPvjxWXzhXv3cDrhHuuzwLfAq4oa6J4v7YvsJ+kvDifhpJujKOMJVRjA7QA/i2pkHB1mliVfAfwV0mPACuAK2P6MzF9PrCa0JIZM/tI0nOEAJ0P3GhmBSmU27kqU7duXVatWkWzZs38v7KuypkZq1atom7dulWdlT2K0jlbkdQM6A0ImGZmK8uYZI+Tm5trM2bMqOpsOAfAtm3byMvLY/PmzVWdFeeAcDLYunVrcnJydkqXNNPMcqsoW9VaOq2azyZU4/4r9jeWdJaZvVhpuXPO7SQnJ4d27dpVdTacc2lI5x7v3Wb2dVGPma0lVBs755xzrgTpBN7ipvVnPzvnnHOlSCfwzpD0sKRD4+dhwv96nXPOOVeCdALvjwgPuvhH/GwBbix1Cuecc24vl3LVsJltAAZXYl6cc865Gi+dVs0dgJ8CbRPnY2YnpJ8t55xzrmZKpzHU84QnTD1NeH6yc84558qQTuDNN7MnKi0nzjnn3F4gncZVL0u6QdIBkpoWfSotZ84551wNlM4Vb9GLCG5LSDPgkDTm6ZxzztVo6bRq9ufWOeeccxWU1pOmJHUGOgHbX01hZqPSzZRzzjlXU6Xzd6K7Ca/36wS8CpwM/BfwwOucc86VIJ3GVecRXkC/1MyuBLoC+1ZKrpxzzrkaKp3Au8nMCoF8SfsAy4GDKidbzjnnXM2Uzj3eGZIaA38hvBxhPTC1UnLlnHPO1VApX/Ga2Q1mttbMngQGAINilXOpJA2TtFzShwlpXSVNlfSBpJfjFTSSaksaHtPfk9S3mPmNS5pXU0kTJH0av5vEdEl6TNJ8Se9L6pFq2Z1zzrlUVTjwSuqR/AGaAtnlDGYjgIFJaU8Dg82sC/ACO/4bfC1ATB8APCRpe54lnUO40k40GJhoZu2Biex4kcPJQPv4uQ7wp24555zLuFSqmh8qZZgBpb4kwcymSGqblNwBmBK7JwD/Bn5BaDE9KU63XNJaIBeYLqkhcCshiD6XMK8zCa2tAUYCk4E7YvooMzNgmqTGkg4wsyWl5dc555yrTBUOvGbWbzfk4yNCYHwROJ8djbTeA86QNDqm9Yzf04HfEE4CNibNq2VCMF0KtIzdrYBFCePlxbRdAq+k6wgBnYMPPjidcjnnnHM7qS4P0LgKeEzSL4BxwNaYPgzoCMwAvgDeBgokdQMONbNbirl63s7MTJJVNDNmNhQYCpCbm1vh6Z1zzrmSVIsHaJjZPODEON8OwKkxPR+4JWGZbwOfAN8BciUtjGVoIWmymfUFlhVVIUs6gPA3J4DF7Px3p9YxzTnnnMuYavEADUkt4nct4C7Ce36RVF9Sg9g9gPAqwjlm9oSZHWhmbYFvA5/EoAvhirnoBQ6DgJcS0i+PrZt7A1/7/V3nnHOZlk5V8yYzK5RUoQdoxPu1fYH9JOUBdwMNJd0YRxkLDI/dLYB/SyokXJ1eVo58DQGek3Q1oXr6gpj+KnAKMJ9wX7jMvz4555xzlS3jD9Aws4tLGPRoMeMuBA4vY34Lgc4J/asIV+LJ4xlwY3K6c845l0npvBbwhtj5pKTXgX3M7P3KyZZzzjlXM6V0j1dStiTF7oMI/63NqsyMOeecczVRKk+uupZwP/eL2D2R0NDq75LuqOT8OeecczVKKlXNNwOHAo2AuUAbM1spqT7wP+D+Ssyfc845V6OkEni3mtkaYI2k+Wa2EsDMNkraWsa0zjnn3F4tlcBbT1J3QjV17dit+Klb6pTOOefcXi6VwLsUeLiY7qJ+55xzzpUglZck9N0N+XDOOef2Cqm0av6epF2eICXpMkmXVE62nHPOuZoplf/x/ojwsvpkY4GfpJcd55xzrmZLJfDmmNn65EQz2wDkpJ8l55xzruZKtVVzgxhot5PUCKhdOdlyrubZumoN277ZgJlRq05tbNNmAGrVr0vhxtBtiKz6dSncuGnnboOsBmG8cnVDnLbk7uRlV7R7d+e1uudvd+W1EFErK1wTSZDdqCG1mzVOY89z1Y6ZVegD/BR4jfDgjKK0tsC/gNsqOr/q/unZs6c5Vxk2LFhkr2R3sFeyO9jKydOK7S5tWHXrri752FPzV1peEz8bFiyq6l03JcAMqwbH8Or4SaVV84OS1gNTJDUk/H93HTDEzJ6onNMB52qegkKr6iw456qBlN5OZGZPEt5K1Cj2r6vUXDlXA5nHXeccKQReSbcWk7a928weTh7unIPsLJU9knOuxkvlirdRpefCOeec20vIvP6rVLm5uTZjxoyqzoarAbauWkv+uvBPPNWuTeHGTcDOrVupJbLq1qVg46adu0mv1ezu6K7uea3u+Sstr6q145+ee2qrZkkzzSy3qvNRHaVS1fwAMN/MnkpKvx5oZ2aDy5h+GHAasNzMOse0rsCTQENgIXCpmX0jqTbwFJALFAI/NrPJ8RWEzxNeT1gAvFy0XEl1gFFAT2AVcKGZLYzD7gSujtPcZGb/rmj5nUtV7WaN98gDqHOucqXyAI0TgKHFpP+FEFDLMgIYmJT2NDDYzLoQnop1W0y/FiCmDwAeklSU5wfN7AigO/AtSSfH9KuBNWZ2GPAH4vuBJXUCLgKOjMv/s6SscuTXOeecqzSpBN46Vkz9tJkVEv5aVCozmwKsTkruAEyJ3ROAc2N3J2BSnG45sBbINbONZvZGTN8KzAJax2nOBEbG7jFAf4XWX2cCfzezLWa2AJgPHFN2cZ1zzrnKk0rg3SSpfXJiTNuUYj4+IgRGgPOBg2L3e8AZkrIltSNUHx+UOKGkxsDpwMSY1ApYBGBm+cDXQLPE9Cgvpu1C0nWSZkiasWLFihSL5Jxzzu0qlcD7S+A1SVdI6hI/VxKeXPXLFPNxFXCDpJmEVtNbY/owQoCcATwCvE24PwuApGxgNPCYmX2e4rJ3YWZDzSzXzHKbN29eWbN1zjnnUnpy1WuSziLch/1RTP4QONfMPkglE2Y2DzgRQFIH4NSYng/cUjSepLeBTxImHQp8amaPJKQtJlwV58XAvC+hkVVRepHWMc0555zLmJSeXAXMBVaYWc/KyISkFma2PDacuovQwpnYellmtkHSACDfzObEYfcSguo1SbMbBwwCpgLnAZPMzCSNA/6fpIeBA4H2wPTKyL9zzjlXXqk+MrJA0rdSmVbSaKAvsJ+kPOBuoKGkG+MoY4HhsbsF8G9JhYSr08viPFoDPwfmAbPik7P+ZGZPA88Af5U0n9CI66KY548kPQfMAfKBG81se7W1c845lwkpP0BD0hOExknPA9tfEWhmYysna9WDP0DDOecqzh+gUbJUq5oB6hLunZ6QkGaEK1bnnHPOFSPlwGtmV1ZmRpxzzrm9QSp/JwLCfVZJL0haHj//jPdenXPOOVeClAMvoQHUOEIL4QOBl9nRKMo555xzxUgn8DY3s+Fmlh8/IwB/2oRzzjlXinQC7ypJ35OUFT/fIzS2cs4551wJ0gm8VwEXAEuBJYSHVVxRCXlyzjnnaqx0/k7U2szOSEyID9VYVML4zjnn3F4vnSveP5YzzTnnnHNRha94JfUBjgWaS7o1YdA+gL9Y3jnnnCtFKlXNtYGGcdpGCenfEO7zOuecc64EqbwW8D/AfySNMLMvdkOenHPOuRorncZVGyU9ABxJeG4zAGZ2QsmTOOecc3u3dBpXPUt4LV874FfAQuB/lZAn55xzrsZKJ/A2M7NngG1m9h8zu4qd31TknHPOuSTpVDVvi99LJJ0KfAU0TT9LzjnnXM2VTuC9V9K+wE8I/9/dB7ilUnLlnHPO1VApVTVLygLam9nXZvahmfUzs55mNq4c0w6LrxH8MCGtq6Spkj6Q9LKkfWJ6bUnDY/p7kvomTNMzps+X9JgkxfSmkiZI+jR+N4npiuPNl/S+pB6plN0555xLR0qB18wKgItTXOYIYGBS2tPAYDPrArwA3BbTr43L6wIMAB6SVJTnJ+Lw9vFTNM/BwEQzaw9MjP0AJyeMe12c3jnnnMuodBpXvSXpT5KOk9Sj6FPWRGY2BVidlNwBmBK7JwDnxu5OwKQ43XJgLZAr6QBgHzObZmYGjALOitOcCYyM3SOT0kdZMA1oHOfjnHPOZUw693i7xe9fJ6QZqbVs/ogQGF8EzgcOiunvAWdIGh3TesbvQiAvYfo8oFXsbmlmS2L3UqBl7G7Fzi9wKJpmCUkkXUe4Kubggw9OoTjOOedc8VIOvGbWrxLzcRXwmKRfAOOArTF9GNARmAF8AbwNFFQgjybJKpoZMxsKDAXIzc2t8PTOOedcSVIOvJJaAr8DDjSzkyV1AvrE//ZWiJnNA06M8+0AnBrT80loKS3pbeATYA3QOmEWrYHFsXuZpAPMbEmsSl4e0xez40o6eRrnnHMuI9K5xzsC+DdwYOz/BLg5lRlJahG/awF3AU/G/vqSGsTuAUC+mc2JVcnfSOodWzNfDrwUZzcOGBS7ByWlXx5bN/cGvk6oknbOOecyIp17vPuZ2XOS7oRwdSqpzGrgeL+2L7CfpDzgbqChpBvjKGOB4bG7BfBvSYWEq9PLEmZ1AyH41wNeix+AIcBzkq4mVE9fENNfBU4B5gMbgSsrWmDnnHMuXekE3g2SmhEaVFF0FVnWRGZW0t+QHi1m3IXA4SXMZwbQuZj0VUD/YtINuDE53TnnnMukdALvrYTq20MlvQU0x9/H65xzzpUqnVbNsyR9h3BFKuBjM9tWxmTOOefcXi2dVs11CfdZv02obn5T0pNmtrmyMuecc87VNOlUNY8C1hFekABwCfBXwgMwnHPOOVeMdAJvZzPrlND/hqQ56WbIOeecq8nS+R/vrNiSGQBJvQhPmHLOOedcCdK54u0JvC3pS8I93jbAx5I+IPx756jKyKBzzjlXk6QTeAcCTYDjYv8UwtuDnHPOOVeCdKqazyI0ptqP8B/evwJnmNkXZvZFZWTOOeecq2nSueK9GuhtZhsAJN0PTGVHK2fnnHPOJUnnilfs/Iq+gpjmnHPOuRKkc8U7HHhH0gux/yygwq8EdM455/Ym6Twy8mFJkwlPrgK40szerZRcOeecczVUOle8mNksYFYl5cU555yr8dK5x+ucc865CvLA65xzzmWQB17nnHMugzIeeCUNk7Rc0ocJaV0lTZX0gaSXJe0T03MkjYzpcyXdmTDNLZI+kvShpNHxNYVIaifpHUnzJf1DUu2YXif2z4/D22a25M4551zVXPGOIDxuMtHTwGAz6wK8ANwW088H6sT0nsD1ktpKagXcBOSaWWcgC7goTnM/8AczOwxYQ3jQB/F7TUz/QxzPOeecy6iMB14zmwKsTkruQHjWM8AE4Nyi0YEGkrKBesBW4Js4LBuoF4fVB76SJOAEYEwcZyTh/8UAZ8Z+4vD+cXznnHMuY6rLPd6PCIERwlXuQbF7DLABWAJ8CTxoZqvNbDHwYExbAnxtZuOBZsBaM8uP0+cBrWJ3K2ARQBz+dRx/F5KukzRD0owVK1ZUXimdc87t9apL4L0KuEHSTKAR4coW4BjCoygPBNoBP5F0iKQmhEDdLg5rIOl7lZUZMxtqZrlmltu8efPKmq1zzjmX3gM0KouZzQNOBJDUATg1DroEeN3MtgHLJb0F5BKqoBeY2Yo4zVjgWOBZoLGk7HhV2xpYHOe1mHAlnRerp/cFVmWifM4551yRanHFK6lF/K4F3AU8GQd9Sbhni6QGQG9gXkzvLal+vE/bH5hrZga8AZwXpx8EvBS7x8V+4vBJcXznnHMuY6ri70SjCa8PPFxSnqSrgYslfUIIql8RXsAA8DjQUNJHwP+A4Wb2vpm9Q7j/Owv4IJZjaJzmDuBWSfMJ93CLXtzwDNAspt8KDN7NRXXOOed2Ib/oK11ubq7NmDGjqrPhnHN7FEkzzSy3qvNRHVWLqmbnnHNub+GB1znnnMsgD7zOOedcBnngdc455zLIA69zzjmXQR54nXPOuQzywOucc85lkAde55xzLoM88DrnnHMZ5IHXOeecyyAPvM4551wGeeB1zjnnMsgDr3POOZdBHnidc865DPLA65xzzmWQB17nnHMugzzwOueccxnkgdc555zLoIwHXknDJC2X9GFCWldJUyV9IOllSfvE9BxJI2P6XEl3JkzTWNIYSfPisD4xvamkCZI+jd9NYrokPSZpvqT3JfXIdNmdc865qrjiHQEMTEp7GhhsZl2AF4DbYvr5QJ2Y3hO4XlLbOOxR4HUzOwLoCsyN6YOBiWbWHpgY+wFOBtrHz3XAE5VaKuecc64cMh54zWwKsDopuQMwJXZPAM4tGh1oICkbqAdsBb6RtC9wPPBMnOdWM1sbpzkTGBm7RwJnJaSPsmAa0FjSAZVaOOecc64M1eUe70eEwAjhKveg2D0G2AAsAb4EHjSz1UA7YAUwXNK7kp6W1CBO09LMlsTupUDL2N0KWJSwzLyYtgtJ10maIWnGihUr0i+dc845F1WXwHsVcIOkmUAjwpUtwDFAAXAgIdj+RNIhQDbQA3jCzLoTgvPg5JmamRGumivEzIaaWa6Z5TZv3jyV8jjnnHPFqhaB18zmmdmJZtYTGA18FgddQriPu83MlgNvAbmEq9U8M3snjjeGEIgBlhVVIcfv5TF9MTuupAFaxzTnnHMuY6pF4JXUIn7XAu4CnoyDvgROiMMaAL2BeWa2FFgk6fA4Xn9gTuweBwyK3YOAlxLSL4+tm3sDXydUSTvnnHMZkZ3pBUoaDfQF9pOUB9wNNJR0YxxlLDA8dj9OuI/7ESBguJm9H4f9CHhWUm3gc+DKmD4EeE7S1cAXwAUx/VXgFGA+sDFhfOeccy5jFG6DupLk5ubajBkzqjobzjm3R5E008xyqzof1VG1qGp2zjnn9hYeeJ1zzrkM8sDrnHPOZVDGG1c555wrm5mxZWshOdm1kIxt24zsnFoIY1u+kZ0lJMjPh1q1wie/ACTIqgUFBWE+WVlQUAhmkJ0FhYVQWGhkZwszyC8wcrKFIfK3FZKTI8zEtvxC6tSuhaSqXRE1kAde55yrZrasXE3+ug1s3lxI/X3rUbhxE2ZQ2KAuhRs379wNZNXfuXvrxs0A1Erq3lZC96ZiugsRBVkCRM4+DandrHFG10FN5oHXOeeqkW35BWz9ej1TjhgAQO//G8W0716e8e5E/T6Z6IG3Evk9Xuecq0a+WVeAWfWq3i30f51WKg+8zjlXjdRSuE/rai4PvM45V400aphFCu922a1qeaSoVL46nXOuGsnOzqKWR7oazR8ZWQZ/ZKRzLtO2rlpL/rr1ANSqXZuCjZtCd2y9nIluagnFE4DsRhVv1eyPjCyZt2p2zrlqpnazxt6KuAbz+gznnHMugzzwOueccxnkgdc555zLIA+8zjnnXAZ54HXOOecyyP9OVAZJK4AvKjDJfsDK3ZSd6mpvLDPsneXeG8sMe2e50y1zGzNrXlmZqUk88FYySTP2tv+u7Y1lhr2z3HtjmWHvLPfeWOZM8apm55xzLoM88DrnnHMZ5IG38g2t6gxUgb2xzLB3lntvLDPsneXeG8ucEX6P1znnnMsgv+J1zjnnMsgDr3POOZdBHngriaSBkj6WNF/S4KrOz+4i6SBJb0iaI+kjST+O6U0lTZD0afxuUtV5rWySsiS9K+mV2N9O0jtxm/9DUu2qzmNlk9RY0hhJ8yTNldSnpm9rSbfEfftDSaMl1a2J21rSMEnLJX2YkFbstlXwWCz/+5J6VF3O93weeCuBpCzgceBkoBNwsaROVZur3SYf+ImZdQJ6AzfGsg4GJppZe2Bi7K9pfgzMTei/H/iDmR0GrAGurpJc7V6PAq+b2RFAV0L5a+y2ltQKuAnINbPOQBZwETVzW48ABiallbRtTwbax891wBMZymON5IG3chwDzDezz81sK/B34MwqztNuYWZLzGxW7F5HOBC3IpR3ZBxtJHBW1eRw95DUGjgVeDr2CzgBGBNHqYll3hc4HngGwMy2mtlaavi2JrynvJ6kbKA+sIQauK3NbAqwOim5pG17JjDKgmlAY0kHZCanNY8H3srRCliU0J8X02o0SW2B7sA7QEszWxIHLQVaVlG2dpdHgNuBwtjfDFhrZvmxvyZu83bACmB4rGJ/WlIDavC2NrPFwIPAl4SA+zUwk5q/rYuUtG33ymPc7uKB16VEUkPgn8DNZvZN4jAL/1GrMf9Tk3QasNzMZlZ1XjIsG+gBPGFm3YENJFUr18Bt3YRwddcOOBBowK7VsXuFmrZtqxMPvJVjMXBQQn/rmFYjScohBN1nzWxsTF5WVPUUv5dXVf52g28BZ0haSLiNcALh3mfjWB0JNXOb5wF5ZvZO7B9DCMQ1eVt/F1hg/7+9+wmxqgzjOP79FZkWkQhtoiKSiGKokaAkXAj9A2sTWS4szUoQqZ3Qn8WURLUwalG0CIoWQWAlabaIIhcxQVqYTlZi1CKKIlqIIFjkr8XzTt1kRBw9Z+z6+2zmnnPuOee9886d557zvvd57N9s/wlsovp/2Pt60tH69rT6H9e1BN6TYwdweZv5OIuajLFlhtvUiTa2+Srwje3nBzZtAVa2xyuBzX23rSu2H7N9ke1Lqb792PZyYBuwtD1tqF4zgO1fgB8lXdFW3Qh8zRD3NXWLeaGkc9rf+uRrHuq+HnC0vt0CrGizmxcC+wduScdxSuaqk0TSEmoc8EzgNdtPz3CTOiFpEfAJMMG/452PU+O8G4FLqDKKd9s+cuLG/56kxcA627dLuoy6Ap4H7ATusX1oJtt3skkapSaUzQK+B1ZRH9iHtq8lrQeWUTP4dwIPUuOZQ9XXkt4EFlPl/34FngDeZYq+bR9CXqJuux8EVtn+fCbaPQwSeCMiInqUW80RERE9SuCNiIjoUQJvREREjxJ4IyIiepTAGxER0aME3oiOtQo/a9vjCyW9fax9TuBco+2rbRFxikrgjejeXGAtgO2fbS89xvNPxCiQwBtxCsv3eCM6JmmyWtVeYB9wpe0RSfdR1V/OpcqtPUclqrgXOAQsackL5lNlJy+gkhestv2tpLuopAd/Ucn8bwK+A+ZQ6fyeBa9Ihg8AAAGxSURBVLYCLwIjwFnAk7Y3t3PfAZxPJYd4w/b6jn8VEUElQY+Ibj0KjNgebRWdtg5sG6EqPM2mguYjthdIegFYQWVDewVYY3ufpOuBl6l80WPArbZ/kjTX9h+Sxqhasg8BSHqGSnF5v6S5wHZJH7VzX9fOfxDYIen9ZCOK6F4Cb8TM2tbqGh+QtB94r62fAK5uVaBuAN6qrH0AnN1+jgOvS9pIJfOfyi1UgYd1bXk2lQ4Q4EPbvwNI2gQsAhJ4IzqWwBsxswbz/R4eWD5MvT/PoGrBjh65o+017Qr4NuALSddOcXwBd9re+5+Vtd+R40wZd4roQSZXRXTvAHDedHZstY5/aOO5tOow17TH821/ZnuMKlh/8RTn+gB4uCW5R9KCgW03S5onaQ411jw+nTZGxPFJ4I3oWLudOy7pK2DDNA6xHHhA0i5gDzVRC2CDpIl23E+BXVT5uqskfSlpGfAUNalqt6Q9bXnSdqqu8m7gnYzvRvQjs5ojTkNtVvM/k7Aioj+54o2IiOhRrngjIiJ6lCveiIiIHiXwRkRE9CiBNyIiokcJvBERET1K4I2IiOjR37sN9U3E5qF0AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "param_plot(median_df,'timestep', 'operatorCICBalance',swept)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "param_plot(median_df,'timestep', 'operatorFiatBalance',swept)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "param_plot(median_df,'timestep', 'AggregatedAgentCICHolding',swept)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "param_plot(median_df,'timestep', 'AggregatedAgentCurrencyHolding',swept)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "param_plot(median_df,'timestep', 'AggregatedAgentDemand',swept)"
+   ]
+  }
+ ],
+ "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
+}
diff --git a/Colab/CIC_Network_cadCAD_model_params.ipynb b/Colab/CIC_Network_cadCAD_model_params.ipynb
new file mode 100644
index 0000000..0aba421
--- /dev/null
+++ b/Colab/CIC_Network_cadCAD_model_params.ipynb
@@ -0,0 +1,2565 @@
+{
+ "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)\r\n",
+      "Requirement already satisfied: pathos in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from cadCAD) (0.2.5)\r\n",
+      "Requirement already satisfied: pandas in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from cadCAD) (1.0.3)\r\n",
+      "Requirement already satisfied: fn in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from cadCAD) (0.4.3)\r\n",
+      "Requirement already satisfied: funcy in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from cadCAD) (1.14)\r\n",
+      "Requirement already satisfied: wheel in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from cadCAD) (0.33.6)\r\n",
+      "Requirement already satisfied: tabulate in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from cadCAD) (0.8.2)\r\n",
+      "Requirement already satisfied: pox>=0.2.7 in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from pathos->cadCAD) (0.2.7)\r\n",
+      "Requirement already satisfied: dill>=0.3.1 in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from pathos->cadCAD) (0.3.1.1)\r\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)\r\n",
+      "Requirement already satisfied: multiprocess>=0.70.9 in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from pathos->cadCAD) (0.70.9)\r\n",
+      "Requirement already satisfied: pytz>=2017.2 in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from pandas->cadCAD) (2018.7)\r\n",
+      "Requirement already satisfied: python-dateutil>=2.6.1 in /home/aclarkdata/.local/lib/python3.7/site-packages (from pandas->cadCAD) (2.8.0)\r\n",
+      "Requirement already satisfied: numpy>=1.13.3 in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from pandas->cadCAD) (1.18.2)\r\n",
+      "Requirement already satisfied: six>=1.7.3 in /home/aclarkdata/anaconda3/lib/python3.7/site-packages (from ppft>=1.6.6.1->pathos->cadCAD) (1.14.0)\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install cadCAD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/aclarkdata/anaconda3/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
+      "  import pandas.util.testing as tm\n"
+     ]
+    }
+   ],
+   "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",
+    "import seaborn as sns\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": [
+    "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",
+    "\n",
+    "def plot_averaged_runs(df,aggregate_dimension,x, y,run_count,lx=False,ly=False, suppMin=False):\n",
+    "    '''\n",
+    "    Function to plot the mean, median, etc 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",
+    "    run_count = the number of monte carlo simulations\n",
+    "    lx = True/False for if the x axis should be logged\n",
+    "    ly = True/False for if the x axis should be logged\n",
+    "    suppMin: True/False for if the miniumum value should be plotted\n",
+    "    Note: Run aggregate_runs before using this function\n",
+    "    Example run:\n",
+    "    '''\n",
+    "    mean_df,median_df,std_df,min_df = aggregate_runs(df,aggregate_dimension)\n",
+    "\n",
+    "    plt.figure(figsize=(10,6))\n",
+    "    if not(suppMin):\n",
+    "        plt.plot(mean_df[x].values, mean_df[y].values,\n",
+    "             mean_df[x].values,median_df[y].values,\n",
+    "             mean_df[x].values,mean_df[y].values+std_df[y].values,\n",
+    "             mean_df[x].values,min_df[y].values)\n",
+    "        plt.legend(['mean', 'median', 'mean+ 1*std', 'min'],bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
+    "\n",
+    "    else:\n",
+    "        plt.plot(mean_df[x].values, mean_df[y].values,\n",
+    "             mean_df[x].values,median_df[y].values,\n",
+    "             mean_df[x].values,mean_df[y].values+std_df[y].values,\n",
+    "             mean_df[x].values,mean_df[y].values-std_df[y].values)\n",
+    "        plt.legend(['mean', 'median', 'mean+ 1*std', 'mean - 1*std'],bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
+    "\n",
+    "    plt.xlabel(x)\n",
+    "    plt.ylabel(y)\n",
+    "    title_text = 'Performance of ' + y + ' over all of ' + str(run_count) + ' Monte Carlo runs'\n",
+    "    plt.title(title_text)\n",
+    "    if lx:\n",
+    "        plt.xscale('log')\n",
+    "\n",
+    "    if ly:\n",
+    "        plt.yscale('log')\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)\n",
+    "    #plt.savefig(y +'_FirstFiveRuns.jpeg')\n",
+    "    \n",
+    "    \n",
+    "def aggregate_runs_param_mc(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",
+    "    #min_df = df.groupby(aggregate_dimension).min().reset_index()\n",
+    "    #max_df = df.groupby(aggregate_dimension).max().reset_index()\n",
+    "    return mean_df,median_df\n",
+    "\n",
+    "def param_dfs(results,params,swept):\n",
+    "    mean_df,median_df = aggregate_runs_param_mc(results[0]['result'],'timestep')\n",
+    "    mean_df[swept] = params[0]\n",
+    "    median_df[swept] = params[0]\n",
+    "    #max_df[swept] = params[0]\n",
+    "    #min_df[swept] = params[0]\n",
+    "    for i in range(1,len(params)):\n",
+    "        mean_df_intermediate,median_df_intermediate = aggregate_runs_param_mc(results[i]['result'],'timestep')\n",
+    "        mean_df_intermediate[swept] = params[i]\n",
+    "        median_df_intermediate[swept] = params[i]\n",
+    "        #max_df_intermediate[swept] = params[i]\n",
+    "        #min_df_intermediate[swept] = params[i]\n",
+    "        mean_df= pd.concat([mean_df, mean_df_intermediate])\n",
+    "        median_df= pd.concat([median_df, median_df_intermediate])\n",
+    "        #max_df= pd.concat([max_df, max_df_intermediate])\n",
+    "        #min_df= pd.concat([min_df, min_df_intermediate])\n",
+    "    return mean_df,median_df\n",
+    "\n",
+    "\n",
+    "def param_plot(results,state_var_x, state_var_y, parameter, save_plot = False,**kwargs):\n",
+    "    '''\n",
+    "    Results (df) is the dataframe (concatenated list of results dictionaries)\n",
+    "    length = intreger, number of parameter values\n",
+    "    Enter state variable name as a string for x and y. Enter the swept parameter name as a string.\n",
+    "    y_label kwarg for custom y-label and title reference\n",
+    "    x_label kwarg for custom x-axis label\n",
+    "    '''\n",
+    "    sns.scatterplot(x=state_var_x, y = state_var_y, hue = parameter, style= parameter, palette  = 'coolwarm',alpha=1, data = results, legend=\"full\")\n",
+    "    title_text = 'Effect of ' + parameter + ' Parameter Sweep on ' + state_var_y\n",
+    "    for key, value in kwargs.items():\n",
+    "        if key == 'y_label':\n",
+    "            plt.ylabel(value)\n",
+    "            title_text = 'Effect of ' + parameter + ' Parameter Sweep on ' + value\n",
+    "        if key == 'x_label':\n",
+    "            plt.xlabel(value)\n",
+    "    plt.title(title_text)\n",
+    "    if save_plot == True:\n",
+    "        filename = state_var_y + state_var_x + parameter + 'plot.png'\n",
+    "# #         plt.savefig('static/images/' + filename)\n",
+    "#         plt.savefig(filename)\n",
+    "        lgd = plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
+    "        #title_text = 'Market Volatility versus Normalized Liquid Token Supply for All Runs'\n",
+    "        plt.title(title_text)\n",
+    "        plt.savefig('static/images/' + filename, bbox_extra_artists=(lgd,), bbox_inches='tight')"
+   ]
+  },
+  {
+   "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": [
+    {
+     "data": {
+      "image/png": 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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 % params['drip_frequency']  == 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': {'drip_frequency': 30}}, {'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 60}}, {'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 90}}]\n",
+      "[{'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 30}}, {'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 60}}, {'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 90}}]\n",
+      "[{'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 30}}, {'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 60}}, {'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 90}}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# config\n",
+    "params: Dict[str, List[int]] = {\n",
+    "    'drip_frequency': [30,60,90] # in days\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": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "exec_mode = ExecutionMode()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "                            __________   ____ \n",
+      "          ________ __ _____/ ____/   |  / __ \\\n",
+      "         / ___/ __` / __  / /   / /| | / / / /\n",
+      "        / /__/ /_/ / /_/ / /___/ ___ |/ /_/ / \n",
+      "        \\___/\\__,_/\\__,_/\\____/_/  |_/_____/  \n",
+      "        by BlockScience\n",
+      "        \n",
+      "Execution Mode: multi_proc: [<cadCAD.configuration.Configuration object at 0x7f54b92c5ad0>, <cadCAD.configuration.Configuration object at 0x7f54b92c5f90>, <cadCAD.configuration.Configuration object at 0x7f54b92c5850>]\n",
+      "Configurations: [<cadCAD.configuration.Configuration object at 0x7f54b92c5ad0>, <cadCAD.configuration.Configuration object at 0x7f54b92c5f90>, <cadCAD.configuration.Configuration object at 0x7f54b92c5850>]\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": [
+    "exec_mode = ExecutionMode()\n",
+    "multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)\n",
+    "run = Executor(exec_context=multi_proc_ctx, configs=configs)\n",
+    "\n",
+    "i = 0\n",
+    "results = {}\n",
+    "for raw_result, tensor_field in run.execute():\n",
+    "    result = pd.DataFrame(raw_result)\n",
+    "    results[i] = {}\n",
+    "    results[i]['result'] = result\n",
+    "    i += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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>4000</th>\n",
+       "      <td>(a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...</td>\n",
+       "      <td>{'a': 246, 'b': 0, 'c': 466, 'd': 620, 'e': 45...</td>\n",
+       "      <td>{'a': 246, 'b': 0, 'c': 5.187631236062657, 'd'...</td>\n",
+       "      <td>{'a': 1.0, 'b': 0, 'c': 0.011132255871379093, ...</td>\n",
+       "      <td>9.77</td>\n",
+       "      <td>{'a': 136.4003802092373, 'b': 848.068007320516...</td>\n",
+       "      <td>{'a': 422.1992395815254, 'b': 3202.55026803101...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[f, h, c, o, f, k, a, p, d, o, h, g, k, a]</td>\n",
+       "      <td>[e, f, f, f, k, h, i, external, b, external, g...</td>\n",
+       "      <td>16500</td>\n",
+       "      <td>198500.00</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>4</td>\n",
+       "      <td>100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4001</th>\n",
+       "      <td>(a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...</td>\n",
+       "      <td>{'a': 246, 'b': 0, 'c': 466, 'd': 620, 'e': 45...</td>\n",
+       "      <td>{'a': 246, 'b': 0, 'c': 5.187631236062657, 'd'...</td>\n",
+       "      <td>{'a': 1.0, 'b': 0, 'c': 0.011132255871379093, ...</td>\n",
+       "      <td>9.77</td>\n",
+       "      <td>{'a': 136.4003802092373, 'b': 848.068007320516...</td>\n",
+       "      <td>{'a': 422.1992395815254, 'b': 3202.55026803101...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[f, h, c, o, f, k, a, p, d, o, h, g, k, a]</td>\n",
+       "      <td>[e, f, f, f, k, h, i, external, b, external, g...</td>\n",
+       "      <td>16500</td>\n",
+       "      <td>198500.00</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>5</td>\n",
+       "      <td>100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4002</th>\n",
+       "      <td>(a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...</td>\n",
+       "      <td>{'a': 246, 'b': 0, 'c': 466, 'd': 620, 'e': 45...</td>\n",
+       "      <td>{'a': 246, 'b': 0, 'c': 5.187631236062657, 'd'...</td>\n",
+       "      <td>{'a': 1.0, 'b': 0, 'c': 0.011132255871379093, ...</td>\n",
+       "      <td>9.77</td>\n",
+       "      <td>{'a': 136.4003802092373, 'b': 848.068007320516...</td>\n",
+       "      <td>{'a': 422.1992395815254, 'b': 3202.55026803101...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[f, h, c, o, f, k, a, p, d, o, h, g, k, a]</td>\n",
+       "      <td>[e, f, f, f, k, h, i, external, b, external, g...</td>\n",
+       "      <td>16500</td>\n",
+       "      <td>198500.00</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>6</td>\n",
+       "      <td>100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4003</th>\n",
+       "      <td>(a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...</td>\n",
+       "      <td>{'a': 346, 'b': 0, 'c': 431, 'd': 245, 'e': 0,...</td>\n",
+       "      <td>{'a': 346, 'b': 0, 'c': 2.5938156180313285, 'd...</td>\n",
+       "      <td>{'a': 1.0, 'b': 0, 'c': 0.0060181336845274444,...</td>\n",
+       "      <td>9.77</td>\n",
+       "      <td>{'a': 136.4003802092373, 'b': 848.068007320516...</td>\n",
+       "      <td>{'a': 422.1992395815254, 'b': 3202.55026803101...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[f, h, c, o, f, k, a, p, d, o, h, g, k, a]</td>\n",
+       "      <td>[e, f, f, f, k, h, i, external, b, external, g...</td>\n",
+       "      <td>16500</td>\n",
+       "      <td>198500.00</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>7</td>\n",
+       "      <td>100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4004</th>\n",
+       "      <td>(a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...</td>\n",
+       "      <td>{'a': 346, 'b': 0, 'c': 431, 'd': 245, 'e': 0,...</td>\n",
+       "      <td>{'a': 346, 'b': 0, 'c': 2.5938156180313285, 'd...</td>\n",
+       "      <td>{'a': 1.0, 'b': 0, 'c': 0.0060181336845274444,...</td>\n",
+       "      <td>20.19</td>\n",
+       "      <td>{'a': 136.4003802092373, 'b': 848.068007320516...</td>\n",
+       "      <td>{'a': 422.1992395815254, 'b': 3202.55026803101...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[f, h, c, o, f, k, a, p, d, o, h, g, k, a]</td>\n",
+       "      <td>[e, f, f, f, k, h, i, external, b, external, g...</td>\n",
+       "      <td>16500</td>\n",
+       "      <td>198500.00</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",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                network  \\\n",
+       "4000  (a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...   \n",
+       "4001  (a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...   \n",
+       "4002  (a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...   \n",
+       "4003  (a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...   \n",
+       "4004  (a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...   \n",
+       "\n",
+       "                                              KPIDemand  \\\n",
+       "4000  {'a': 246, 'b': 0, 'c': 466, 'd': 620, 'e': 45...   \n",
+       "4001  {'a': 246, 'b': 0, 'c': 466, 'd': 620, 'e': 45...   \n",
+       "4002  {'a': 246, 'b': 0, 'c': 466, 'd': 620, 'e': 45...   \n",
+       "4003  {'a': 346, 'b': 0, 'c': 431, 'd': 245, 'e': 0,...   \n",
+       "4004  {'a': 346, 'b': 0, 'c': 431, 'd': 245, 'e': 0,...   \n",
+       "\n",
+       "                                               KPISpend  \\\n",
+       "4000  {'a': 246, 'b': 0, 'c': 5.187631236062657, 'd'...   \n",
+       "4001  {'a': 246, 'b': 0, 'c': 5.187631236062657, 'd'...   \n",
+       "4002  {'a': 246, 'b': 0, 'c': 5.187631236062657, 'd'...   \n",
+       "4003  {'a': 346, 'b': 0, 'c': 2.5938156180313285, 'd...   \n",
+       "4004  {'a': 346, 'b': 0, 'c': 2.5938156180313285, 'd...   \n",
+       "\n",
+       "                                     KPISpendOverDemand  VelocityOfMoney  \\\n",
+       "4000  {'a': 1.0, 'b': 0, 'c': 0.011132255871379093, ...             9.77   \n",
+       "4001  {'a': 1.0, 'b': 0, 'c': 0.011132255871379093, ...             9.77   \n",
+       "4002  {'a': 1.0, 'b': 0, 'c': 0.011132255871379093, ...             9.77   \n",
+       "4003  {'a': 1.0, 'b': 0, 'c': 0.0060181336845274444,...             9.77   \n",
+       "4004  {'a': 1.0, 'b': 0, 'c': 0.0060181336845274444,...            20.19   \n",
+       "\n",
+       "                                        startingBalance  \\\n",
+       "4000  {'a': 136.4003802092373, 'b': 848.068007320516...   \n",
+       "4001  {'a': 136.4003802092373, 'b': 848.068007320516...   \n",
+       "4002  {'a': 136.4003802092373, 'b': 848.068007320516...   \n",
+       "4003  {'a': 136.4003802092373, 'b': 848.068007320516...   \n",
+       "4004  {'a': 136.4003802092373, 'b': 848.068007320516...   \n",
+       "\n",
+       "                                           30_day_spend withdraw  \\\n",
+       "4000  {'a': 422.1992395815254, 'b': 3202.55026803101...        0   \n",
+       "4001  {'a': 422.1992395815254, 'b': 3202.55026803101...        0   \n",
+       "4002  {'a': 422.1992395815254, 'b': 3202.55026803101...        0   \n",
+       "4003  {'a': 422.1992395815254, 'b': 3202.55026803101...        0   \n",
+       "4004  {'a': 422.1992395815254, 'b': 3202.55026803101...        0   \n",
+       "\n",
+       "                                  outboundAgents  \\\n",
+       "4000  [f, h, c, o, f, k, a, p, d, o, h, g, k, a]   \n",
+       "4001  [f, h, c, o, f, k, a, p, d, o, h, g, k, a]   \n",
+       "4002  [f, h, c, o, f, k, a, p, d, o, h, g, k, a]   \n",
+       "4003  [f, h, c, o, f, k, a, p, d, o, h, g, k, a]   \n",
+       "4004  [f, h, c, o, f, k, a, p, d, o, h, g, k, a]   \n",
+       "\n",
+       "                                          inboundAgents  operatorFiatBalance  \\\n",
+       "4000  [e, f, f, f, k, h, i, external, b, external, g...                16500   \n",
+       "4001  [e, f, f, f, k, h, i, external, b, external, g...                16500   \n",
+       "4002  [e, f, f, f, k, h, i, external, b, external, g...                16500   \n",
+       "4003  [e, f, f, f, k, h, i, external, b, external, g...                16500   \n",
+       "4004  [e, f, f, f, k, h, i, external, b, external, g...                16500   \n",
+       "\n",
+       "      operatorCICBalance                                     fundsInProcess  \\\n",
+       "4000           198500.00  {'timestep': [], 'decision': [], 'cic': [], 's...   \n",
+       "4001           198500.00  {'timestep': [], 'decision': [], 'cic': [], 's...   \n",
+       "4002           198500.00  {'timestep': [], 'decision': [], 'cic': [], 's...   \n",
+       "4003           198500.00  {'timestep': [], 'decision': [], 'cic': [], 's...   \n",
+       "4004           198500.00  {'timestep': [], 'decision': [], 'cic': [], 's...   \n",
+       "\n",
+       "      totalDistributedToAgents  totalMinted  totalBurned  run  substep  \\\n",
+       "4000                      1500            0            0    5        4   \n",
+       "4001                      1500            0            0    5        5   \n",
+       "4002                      1500            0            0    5        6   \n",
+       "4003                      1500            0            0    5        7   \n",
+       "4004                      1500            0            0    5        8   \n",
+       "\n",
+       "      timestep  \n",
+       "4000       100  \n",
+       "4001       100  \n",
+       "4002       100  \n",
+       "4003       100  \n",
+       "4004       100  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results[0]['result'].tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(0,len(results)):\n",
+    "    results[i]['result']['agents'] = results[i]['result'].network.apply(lambda g: np.array([get_nodes_by_type(g,'Agent')][0]))\n",
+    "    results[i]['result']['agent_tokens'] = results[i]['result'].network.apply(lambda g: np.array([g.nodes[j]['tokens'] for j in get_nodes_by_type(g,'Agent')]))\n",
+    "    results[i]['result']['agent_native_currency'] = results[i]['result'].network.apply(lambda g: np.array([g.nodes[j]['native_currency'] for j in get_nodes_by_type(g,'Agent')]))\n",
+    "    # Create dataframe variables   \n",
+    "    tokens = []\n",
+    "    for j in results[i]['result'].index:\n",
+    "         tokens.append(sum(results[i]['result']['agent_tokens'][j]))\n",
+    "\n",
+    "    results[i]['result']['AggregatedAgentCICHolding'] = tokens  \n",
+    "\n",
+    "    currency = []\n",
+    "    for j in results[i]['result'].index:\n",
+    "         currency.append(sum(results[i]['result']['agent_native_currency'][j]))\n",
+    "\n",
+    "    results[i]['result']['AggregatedAgentCurrencyHolding'] = currency  \n",
+    "\n",
+    "    AggregatedSpend = []\n",
+    "    for j in results[i]['result'].index:\n",
+    "         AggregatedSpend.append(sum(results[i]['result']['KPISpend'][j].values()))\n",
+    "\n",
+    "    results[i]['result']['AggregatedAgentSpend'] = AggregatedSpend   \n",
+    "\n",
+    "    AggregatedDemand = []\n",
+    "    for j in results[i]['result'].index:\n",
+    "         AggregatedDemand.append(sum(results[i]['result']['KPIDemand'][j].values()))\n",
+    "\n",
+    "    results[i]['result']['AggregatedAgentDemand'] = AggregatedDemand  \n",
+    "\n",
+    "\n",
+    "    AggregatedKPISpendOverDemand = []\n",
+    "    for j in results[i]['result'].index:\n",
+    "         AggregatedKPISpendOverDemand.append(sum(results[i]['result']['KPISpendOverDemand'][j].values()))\n",
+    "\n",
+    "    results[i]['result']['AggregatedKPISpendOverDemand'] = AggregatedKPISpendOverDemand  \n",
+    "\n",
+    "\n",
+    "    AggregatedGapOfDemandMinusSpend = []\n",
+    "    for j in results[i]['result'].index:\n",
+    "         AggregatedGapOfDemandMinusSpend.append(sum(results[i]['result']['KPIDemand'][j].values())- sum(results[i]['result']['KPISpend'][j].values()))\n",
+    "\n",
+    "    results[i]['result']['AggregatedGapOfDemandMinusSpend'] = AggregatedGapOfDemandMinusSpend  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "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",
+       "      <th>Red Cross Drip Frequency</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>14.04</td>\n",
+       "      <td>4500</td>\n",
+       "      <td>200000.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6000.00</td>\n",
+       "      <td>2912.00</td>\n",
+       "      <td>1255.25</td>\n",
+       "      <td>2534</td>\n",
+       "      <td>4.96</td>\n",
+       "      <td>1325.00</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>18.48</td>\n",
+       "      <td>4500</td>\n",
+       "      <td>200000.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6040.00</td>\n",
+       "      <td>2952.00</td>\n",
+       "      <td>1693.62</td>\n",
+       "      <td>3370</td>\n",
+       "      <td>5.59</td>\n",
+       "      <td>1292.75</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>16.27</td>\n",
+       "      <td>4500</td>\n",
+       "      <td>200000.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6049.50</td>\n",
+       "      <td>2961.50</td>\n",
+       "      <td>1466.34</td>\n",
+       "      <td>2441</td>\n",
+       "      <td>5.46</td>\n",
+       "      <td>381.50</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>18.75</td>\n",
+       "      <td>4500</td>\n",
+       "      <td>200000.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6124.94</td>\n",
+       "      <td>3036.94</td>\n",
+       "      <td>1672.00</td>\n",
+       "      <td>2867</td>\n",
+       "      <td>6.48</td>\n",
+       "      <td>1195.00</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>15.17</td>\n",
+       "      <td>4500</td>\n",
+       "      <td>200000.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6385.50</td>\n",
+       "      <td>3297.50</td>\n",
+       "      <td>1568.00</td>\n",
+       "      <td>1914</td>\n",
+       "      <td>5.49</td>\n",
+       "      <td>734.89</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   timestep  VelocityOfMoney  operatorFiatBalance  operatorCICBalance  \\\n",
+       "0         1            14.04                 4500           200000.00   \n",
+       "1         2            18.48                 4500           200000.00   \n",
+       "2         3            16.27                 4500           200000.00   \n",
+       "3         4            18.75                 4500           200000.00   \n",
+       "4         5            15.17                 4500           200000.00   \n",
+       "\n",
+       "   totalDistributedToAgents  totalMinted  totalBurned  run  substep  \\\n",
+       "0                         0            0            0    3        8   \n",
+       "1                         0            0            0    3        8   \n",
+       "2                         0            0            0    3        8   \n",
+       "3                         0            0            0    3        8   \n",
+       "4                         0            0            0    3        8   \n",
+       "\n",
+       "   AggregatedAgentCICHolding  AggregatedAgentCurrencyHolding  \\\n",
+       "0                    6000.00                         2912.00   \n",
+       "1                    6040.00                         2952.00   \n",
+       "2                    6049.50                         2961.50   \n",
+       "3                    6124.94                         3036.94   \n",
+       "4                    6385.50                         3297.50   \n",
+       "\n",
+       "   AggregatedAgentSpend  AggregatedAgentDemand  AggregatedKPISpendOverDemand  \\\n",
+       "0               1255.25                   2534                          4.96   \n",
+       "1               1693.62                   3370                          5.59   \n",
+       "2               1466.34                   2441                          5.46   \n",
+       "3               1672.00                   2867                          6.48   \n",
+       "4               1568.00                   1914                          5.49   \n",
+       "\n",
+       "   AggregatedGapOfDemandMinusSpend  Red Cross Drip Frequency  \n",
+       "0                          1325.00                        30  \n",
+       "1                          1292.75                        30  \n",
+       "2                           381.50                        30  \n",
+       "3                          1195.00                        30  \n",
+       "4                           734.89                        30  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "params = [30,60,90]\n",
+    "swept = 'Red Cross Drip Frequency'\n",
+    "mean_df,median_df = param_dfs(results,params,swept)\n",
+    "median_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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bI4ApInIw8BfgYKdM9WC6cqIr748AN4nIROc5M0Tk7C7SJvo5mvO4c/+9ju7yOPJfCWxU1WgLyU+d8n0ippsoUg52luZd8ZLzHFlO+fQ/Xd0AsSv2P0vLucevxXKTY2I7GfNSNmNqzo9hRt2CMeu8hClIajAjcZOdc7cDT4lIlYic007wj2NaRP90wvYT40N3IOsnTljXOV4XYMxGazHK7hVM3wiYDPsOZjT1KroowFX1V8APMQM4dmBqcFdjFGlH3In5qD/GDDBc5fgBjMUM5qjFfCC/V9X3MQMz7sGkcylm4NNNncRxvYj4MN0FT2PMal9rZZKMhTece9dgPrhFu3l/R3TnHfwU07KuBH6GGZwGNBfO8zEtjsj7uI4YvgdV/Qoz0OlHmC6LNZgBLxFewzG9tTKh7g4/xpgcfZjn7Kqi9A7wNkZJbMV8C7F0oXREZ+n+HqafsVREdjp+N2AGKi0TkRpM3jxoN+KrB36FybM7MYX0maq6CUBVv8Dk9X85xzWYvsd/R7oTHNP4cZh++GInrMiAvVhkLHWetRhTefuuqn7egbzfwvSNFmPe92172FW5APPtlkb/MIog0jI9D9PlEJmV8iLGooKqfoYp817AtOxqMWNsGjuJcyFmPMdi59tfhhkLFUt4XeXL24kqs1V1BWZg5u8wabsR03+NqgYwlq0LMd/RuTjfsIgMB+YCD7RKm5WYfL7AaTWfjenLLsdYd1ZEpc1rmPf/gvO+PwW+0Um6RNNeHk/BvOsqTN4bhbGWRvMP5xmXYLrZIgt5dZjmMfAzzDe9GaMnY7ICSssuDotl9xARxZjmNvaxHKMxmT+hG1abnpLlS4wZcE8Ke8teRkRmYwakxWKx6VNE5EXgc1W9rZ1zaRjFM1ZVN/dAXD0aXm8iZt2LQsw0xfe7ur6H4x5NnJQ9EfaFRUAslr2GiJyJ6aN7r69lsfR/nO6E/Z0ukRMwlqbXo86f4ph/UzH9vp9gZoTsaXw9Gl5v4nSlZYrpeoz09y/rY7Higv64EpHFEpeIyAcYk+D5Tr+uxdJdhmBM1DmYFumV2nKJ7PkY86xgTNHf1O6ZYXs6vN5kFqaLLdJtdJqqNnR+y76BNcVbLBaLxTKAsKZ4i8VisVgGENYU38MMHjxYR48e3ddiWCwWS79i5cqVO1U1t6/lGAhYxd7DjB49mhUrVvS1GBaLxdKvEJHdWT3S0gnWFG+xWCwWywDCKnaLxWKxWAYQVrFbLBaLxTKAsH3se4GmpiYKCwvx+zvc4Mpi2askJSVRUFBAQkJCX4tisVh6GKvY9wKFhYWkp6czevRoRPZkwy2LpedQVcrLyyksLGS//fbra3EsFksPY03xewG/309OTo5V6nFAOBgk1Bho8wsH42KJ572CiJCTk2MtSBbLAMW22PcSVqnHBxoK4/v0izb+6ZMO3Ke+BpsfLZaBi22xWywWi8UygLCKfR/B7XYzdepUJk2axCmnnEJVVdVu3X/77bdz//33t3vu6aefZtKkSRx88MFMmzatw+t6mtGjR3PwwQdz8MEHM2HCBG655ZZOzctf+9rXdiv82bNnc9BBBzF16lSmTp3KK6+80l2RLRaLpdexin0fITk5mTVr1vDpp5+SnZ3NQw891CPh/u1vf+OBBx5g8eLFfPLJJyxbtoyMjIw21wV7qQ/7/fff55NPPmH58uVs2rSJK664osO4P/zww90O/7nnnmPNmjWsWbOGs846q8U5VSUctpu4WSyW+MIq9n2QWbNmUVRU1Hx83333ceihhzJ58mRuu+22Zv+77rqLAw88kCOPPJL169e3G9bdd9/N/fffz7BhwwBITEzksssuA0yL99prr2XGjBksXLiQLVu2cOyxxzJ58mTmzp3LV199BcDLL7/MpEmTmDJlCkcffTQAn332GYcddhhTp05l8uTJbNiwodNnSktL45FHHuH111+noqKCDz74gKOOOopTTz2VCRMmNF8D8H8r/suJly3gnO9/j0PPOIUf/OKOmBX0li1bOOigg7jggguYNGkS27ZtY/HixcyaNYvp06dz9tlnU1tbC8Dbb7/NuHHjmD59Otdccw0nn3wy0Nb6MWnSJLZs2QLAs88+2/zcV1xxBaFQqFn2m2++mSlTpnD44YdTVlYGQFlZGaeffjpTpkxhypQpfPjhh9x666088MADzeHffPPNLFy4MKbns1gsAwBVtb8e/B1yyCHamrVr17bx29ukpqaqqmowGNSzzjpL//a3v6mq6jvvvKOXXXaZhsNhDYVCetJJJ+k//vEPXbFihU6aNEnr6uq0urpa999/f73vvvvahJuVlaVVVVXtxnnMMcfolVde2Xx88skn65NPPqmqqosWLdL58+erquqkSZO0sLBQVVUrKytVVfXqq6/WZ599VlVVGxsbtb6+vk34o0aN0h07drTwmzJlii5btkzff/99TUlJ0U2bNrVIg1BTk/79ncWamJioX6xdp4119Tr32GP1xRdeaFf+Aw88UKdMmaJTpkzRnTt36ubNm1VEdOnSpaqqumPHDj3qqKO0trZWVVXvuece/dnPfqYNDQ1aUFCgX3zxhYbDYT377LP1pJNOUlXV2267rUVaTpw4UTdv3qxr167Vk08+WQOBgKqqXnnllfrUU0+pqiqgb775pqqqXnfddfrzn/9cVVXPOecc/c1vfqOq5t1WVVXp5s2bddq0aaqqGgqFdMyYMbpz5842zxcP+dJiiQCs0DgowwfCbx8aB7xv09DQwNSpUykqKmL8+PHMmzcPgMWLF7N48WKmTZsGQG1tLRs2bMDn83H66aeTkpICwKmnnrpH8Z577rnN7qVLl/Lqq68CcP7553P99dcDcMQRR3DhhRdyzjnncMYZZwDGqnDXXXdRWFjIGWecwdixY2OKz5QPhsMOO6zNPG2Xx4Pbm8Bhhx3G2PHjAPj2eefx4dKlnBMla4TnnnuOGTNmNB/7fD5GjRrF4YcfDsCyZctYu3YtRxxxBACBQIBZs2bx+eefs99++zXL/Z3vfIdHH320U9mXLFnCypUrOfTQQwHzzvLy8gDwer3NLf5DDjmEd999F4D33nuPp59+GjDjKDIyMsjIyCAnJ4fVq1dTVlbGtGnTyMnJiSX5LBbLAMCa4vcRIn3sW7duRVWb+9hVlZtuuqm5H3njxo1ccsklMYc7ceJEVq5c2eH51NTULsN45JFHuPPOO9m2bRuHHHII5eXlfPvb3+bNN98kOTmZE088kffee6/LcHw+H1u2bOHAAw/sMu7W0712Z/pXdLiqyrx585rTb+3atSxatKjT+z0eTwvTf2TAn6qyYMGC5rDWr1/P7bffDkBCQkKzjG63u8sxC5deeilPPvkkTzzxBBdffHHMz2axWPo/VrHvY6SkpPDb3/6WX/3qVwSDQY4//ngef/zx5n7hoqIitm/fztFHH83rr79OQ0MDPp+PP//5z+2Gd9NNN3HddddRWloKmBbrY4891u61X/va13jhhRcA0xI+6qijAPjyyy+ZOXMmd9xxB7m5uWzbto1NmzYxZswYrrnmGubPn8/HH3/c6XPV1tbyve99j9NOO42srKwu02H58uVs3ryZcDjMiy++yJFHHtnlPe1x+OGH8+9//5uNGzcCUFdXxxdffMG4cePYsmULX375JQDPP/988z2jR49m1apVAKxatYrNmzcDMHfuXF555RW2b98OQEVFBVu3dr6T5dy5c3n44YcBCIVCVFdXA3D66afz9ttv89///pfjjz9+j57NYrH0T6wpfh9k2rRpTJ48meeff57zzz+fdevWMWvWLMAM0nr22WeZPn065557LlOmTCEvL6/ZPNyaE088kbKyMr7+9a+jqohIhy3EBx98kIsuuoj77ruP3NxcnnjiCQCuu+46NmzYgKoyd+5cpkyZwr333sszzzxDQkICQ4YM4Sc/+Um7Yc6ZMwdVMzr99NNP56c//WlMaXDooYdy9dVXs3HjRubMmcPpp58e032tyc3N5cknn+Rb3/oWjY2NANx5550ceOCBPProo5x00kmkpKRw1FFH4fP5ADjzzDN5+umnmThxIjNnzmy2MEyYMIE777yT4447jnA4TEJCAg899BCjRo3qMP6FCxdy+eWXs2jRItxuNw8//DCzZs3C6/UyZ84cMjMzcbvde/RsFoulfyLRfZKW7jNjxgxdsWJFC79169Yxfvz4PpLI0poPPviA+++/n7feemvAxhkOh5k+fTovv/xyh+MTbL60xBMislJVZ3R9paUrrCneYhlgrF27lgMOOIC5c+fGPOjQYrEMHGyLvYexLXZLf8HmS0s8YVvsPYdtsVssFovFMoAYkIpdRDJF5BUR+VxE1onILBHJFpF3RWSD85/lXCsi8lsR2SgiH4vI9KhwFjjXbxCRBX33RBaLxWKxxMaAVOzAQuBtVR0HTAHWATcCS1R1LLDEOQb4BjDW+V0OPAwgItnAbcBM4DDgtkhlwGKxWCyWeGXAKXYRyQCOBhYBqGpAVauA+cBTzmVPAac57vnA086qhsuATBEZChwPvKuqFapaCbwLnLAXH8VisVgslt1mwCl2YD9gB/CEiKwWkcdEJBXIV9US55pSIN9xDwe2Rd1f6Ph15N8GEblcRFaIyIodO3b04KP0HH6/n8MOO4wpU6YwceLE5s1eNm/ezMyZMznggAM499xzCQQCfSypxWKxWLrDQFTsHmA68LCqTgPq2GV2B8DZcKDHpgOo6qOqOkNVZ+Tm5vZUsD1KYmIi7733Hh999BFr1qzh7bffZtmyZdxwww384Ac/YOPGjWRlZXW5HKrFsruEQqHmXeqi3RaLpXcYiIq9EChU1f84x69gFH2ZY2LH+d/unC8CRkTdX+D4deTf6yz+oIwzL17GUaf+gzMvXsbiD8q6HaaING9b2tTURFNTEyLCe++917zP+IIFC3j99de7HZfFEiEUClFXW0t9fT3hcLjZbZW7xdJ7DDjFrqqlwDYROcjxmgusBd4EIiPbFwBvOO43gQuc0fGHA9WOyf4d4DgRyXIGzR3n+PUqiz8o497ffUHZjkZUoWxHI/f+7oseUe6hUIipU6eSl5fHvHnz2H///cnMzMTjMSsLFxQUtNin3WLpCcLhMDu2b6estJSdO3e22ADHYrH0PAN1rfj/AZ4TES+wCbgIU4l5SUQuAbYC5zjX/hU4EdgI1DvXoqoVIvJz4L/OdXeoakVvC/6HpzfT2Niy4GtsDPOHpzdz3Oz8Du6KDbfbzZo1a6iqquL000/n888/71Z4FktXuN1uBmVkUFdXR0NDA0nJyaSnpeGy69dbLL3GgFTsqroGaG8Fo7ntXKvAVR2E8zjweM9K1znbdzbulv+ekJmZyZw5c1i6dClVVVUEg0E8Hg+FhYUMH97u+ECLZY+ImOIbGxtJTEzE39BAXX09KSkpdnMai6WXGHCm+P5O3uDE3fKPlR07dlBVVQVAQ0MD7777LuPHj2fOnDm88sorADz11FPMnz+/W/FYLK0Jh8PkDB7M0GHDyM7OtqZ4i6WXsYo9zrjigv1ITGz5WhITXVxxwX7dCrekpIQ5c+YwefJkDj30UObNm8fJJ5/Mvffey69//WsOOOAAysvLueSSS7oVj8USjdvtJn3QINLS0nC5XM1u21q3WHqPAWmK789E+tH/8PRmtu9sJG9wIldcsF+3+9cnT57M6tWr2/iPGTOG5cuXdytsi6UzopW4VegWS+9jFXscctzs/G4rcovFYrHsm1hTvMVisVgsAwir2C0Wi8ViGUBYxW6xWCwWywDCKnaLxWKxWAYQVrFbLBaLxTKAsIp9H6KqqoqzzjqLcePGMX78eJYuXUpFRQXz5s1j7NixzJs3j8rKyr4W02KxWCzdwCr2fYjvf//7nHDCCXz++ed89NFHjB8/nnvuuYe5c+eyYcMG5s6dyz333NPXYlosFoulG1jFHmeoKiXFxZQUFxMOh5vdZkn7Pae6upp//vOfzSvLeb1eMjMzeeONN1iwwGx6Z7dttVgslv6PVexxRmlJCX6/H7/fz1dbtza7S0tKuhXu5s2byc3N5aKLLmLatGlceuml1NXVUVZWxtChQwEYMmQIZWXd3x7WYrFYLH2HVexxiqoSDoe73VLPaA6yAAAgAElEQVSPEAwGWbVqFVdeeSWrV68mNTW1jdldRBCRHonPYmmPUChEMBhs47ZYLD2HVexxRv6QIW2Uq4iQP2RIt8ItKCigoKCAmTNnAnDWWWexatUq8vPzKXGsASUlJeTl5XUrHoulI0KhEOU7d1JaWkooFGJ7WRnby8oIhUJ9LZrFMqCwij3OKCstbdNKV1XKSku7Fe6QIUMYMWIE69evB2DJkiVMmDCBU089laeeegqw27ZaeheXy0VWdjahYJCtW7bg9/vJGTwYl8sWQxZLT2I3gYlTImbxnjLFAzz44IOcd955BAIBxowZwxNPPEE4HOacc85h0aJFjBo1ipdeeqnH4rNYohERXC4XHo+HUCiE2+3G7Xbb7h+LpYexij3OGDJ0aPNAufwhQ5pb6kOcAW7dYerUqaxYsaKN/5IlS7odtsXSFRFTfCAQIDcvj4rycraXlZE/ZIjdztVi6UGsYo8zRIShw4Y1H0e7LZb+TMQUn5GZidfrJSkpiXA4bE3xFksPYxW7xWLZK4gIHo+nXbdl7xIoryLoq23j70lPw5uT2QcSWXoSq9gtFsteI1qJW4XedwR9tbw/dm4b/zkblljFPgAYkDYwEdkiIp+IyBoRWeH4ZYvIuyKywfnPcvxFRH4rIhtF5GMRmR4VzgLn+g0isqCvnsdisVgslliJyxa7iHwCdDgcXFUnxxDMHFXdGXV8I7BEVe8RkRud4xuAbwBjnd9M4GFgpohkA7cBMxxZVorIm6pqd0mxWCwWS9wSl4odONn5v8r5f8b5P68bYc4HZjvup4APMIp9PvC0mnlly0QkU0SGOte+q6oVACLyLnAC8Hw3ZLBYLBaLpVeJS1O8qm5V1a3APFW9XlU/cX43AsfFEgSwWERWisjljl++qkYWXC8F8h33cGBb1L2Fjl9H/m0QkctFZIWIrNixY0dMz7i3WbhwIZMmTWLixIk88MADAHbLVovFYhmAxKVij0JE5Iiog68Rm8xHqup0jJn9KhE5Ovqk0zrvsZVfVPVRVZ2hqjNyc3N7Ktge49NPP+WPf/wjy5cv56OPPuKtt95i48aNdstWi2UfxZOexpwNS9r8POlpfS2apQeIV1N8hEuAx0UkAxCgEri4q5tUtcj53y4irwGHAWUiMlRVSxxT+3bn8iJgRNTtBY5fEbtM9xH/D7r1NDHwdvZ0Qr66Nv7u9FROqFi1R2GuW7eOmTNnkpKSAsAxxxzDq6++yhtvvMEHH3wAmC1bZ8+ezb333rvHslsslv6BNyezR0a/22lz8UlcK3ZVXQlMcRQ7qlrd1T0ikgq4VNXnuI8D7gDeBBYA9zj/bzi3vAlcLSIvYAbPVTvK/x3gF5HR8044N/Xc07VPe0q9M/9YmDRpEjfffDPl5eUkJyfz17/+lRkzZtgtWy0WS7ew0+bik7hW7CKSCJwJjAY8kXmvqnpHJ7flA68513qAP6nq2yLyX+AlEbkE2Aqc41z/V+BEYCNQD1zkxFEhIj8H/utcd0dkIF1/Y/z48dxwww0cd9xxpKamMnXq1DZLeNotWy0Wi2VgENeKHdOqrgZWAo2x3KCqm4Ap7fiXA22qlk5/+1Wt/Z1zjwOP74a8ccsll1zCJZdcAsBPfvITCgoKmrdsHTp0qN2y1WKxWAYI8a7YC1T1hL4WYiCwfft28vLy+Oqrr3j11VdZtmwZmzdv5qmnnuLGG2+0W7ZaLBbLACHeFfuHInKwqn7S14L0d84880zKy8tJSEjgoYceIjMzkxtvvNFu2WqxWNpFVQmFQs3b7Ubcdie++CfeFfuRwIUishljiheM9TyWlef6Je701A5HxXeHf/3rX238cnJy7JatFoulXcLhMNu++oqsrCySU1IoLioiLy+PlNTU5vE4kWlzrbHT5vqWeFfs3+hrAfY2ezqlzWKxWHqawbm57Ni+HSoqSElJISk5ucUg256aNmfpWeJ6gRpn9bkRwLGOu544l9lisVgGAi6XC6/X23zsTUzsQ2ksu0NcK0kRuQ2znntk/ngC8GzfSbTnmMH3Fkt8YPOjpSvC4TDFRUWkpKSQM3gwVZWV+BsabN7pB8S7Kf50YBqwCkBVi0UkvW9F2n2SkpIoLy8nJyfHzhW39DmqSnl5OUlJSX0tiiXOGTJ0KN6EBMRpvXu9XluG9QPiXbEHVFVFRKF5Vbl+R0FBAYWFhcTrBjGWfY+kpCQKCgr6WgxLHON2u0lKSmpW5NFuS3wT74r9JRH5A5ApIpdh1on/Yx/LtNskJCSw33779bUYFovFsltEK3Kr1PsPca3YVfV+EZkH1AAHAreq6rt9LJbFYrH0O5qCYSqrmlj1cSUZgxI46IB0sjO9Xd9o6XfEtWJ3+ARIxmyzaheqsex1gsEwVTVNNDSESE5yMyg9Aa83rsedWixtKC3zc/G1K2nwhwHYb2QKC++aYpX7ACSuSycRuRRYDpwBnAUsE5Eut221WHqKcFj54stazr9qBd/67n/51neXs/rTKpqawn0tmsUSM/7GEI8/v7VZqQNs/qqeL7709aFUlt4irhU7cB0wTVUvVNUFwCGY6W8Wy16hsrqJ2+5bh682CECDP8ztv1xHta+pjyWzWGInFNLmPBxNja+tn6X/E++KvRyIrlL6HD+LZa8QCiklZf4Wfr66IIF2WuxNwTDllY1W6VvijtQUD986fdcsCJcLjp41mGkH21XjBiLx3se+EfiPiLyB6WOfD3wsIj8EUNVf96VwloGPN0EYPzaddRt21S+H5ieR5G25EUZVdYDX/1rMX5aUkZvj5QdXjGX0iBQSEuK97mzZVzjogHQW3jWZF18v5OJvj2TU8EQSEjyEQiGamprwer24XDa/DgQknlcRclae6xBV/dnekiVWZsyYoStWrOhrMSx7SHVNE8FgmMREN2mppt5bst3Pnb/+nI/XVnPg/mnc+qPxjBy+a83sYDDMi28U8vCTm5vDSUx08cIfDiM3xy7DaYkv/I1BNNxESXExOYMHE2xqorq6mpGjRuHx9F1bT0RWquqMPhNgABHXLfZoxS0iWUCVxnNNxNJvCYeVbUX1/GLhejZuqeOwaVn86LtjGZyTyNC8JH7xk4kEQ4rbBZkZLUcR+2qDvPP+9hZ+jY1hNm+ts4rdEnckJXoIhYTs7GzKd+4EID8/37bWBxBx+SZF5FYRGee4E0XkPeBLoExEvt630lkGIpVVAa655WM+W++jsTHMv5aVc8+DX+CrNf3lGYMSyMnytlHqAF6vi+FD2y7POtgqdUscEwqF2nVb+j9xqdiBc4H1jnsBRs5c4BjgF30llGXgUu8PUV4RaOH3n1UVBJq6NhClpnj43kVjyBi0ywD2jWPzycmy84Mt8Yeq0hQIUF1dTX5+Ptk5OezcuZNw2E7hHCjEqyk+EGVyPx54XlVDwDoRiVeZLf2YpEQ3Xq+LQGBX4TaqIIVYV9Eclp/M0w/OoGxHI+npHgalJ5CRntBL0lose46IkOD1MmLkSLMevCqpqaldmuIb/CFq64IEg2GSEt1k2YVt4pZ4VZKNIjIJKAPmAD+OOpfSNyJZBjLpqR5uuPpA7v7teoJBJS3VzS0/GBfzqlxut5CTnUhOtjW/W+Ift9uN2+1ucdwZtXVB/vJuKY88tYmmoDJ2TBq/vHWSHUMSp8SrYv8+8ArG/P4bVd0MICInAqv7UjDLwCQpyc3RswYzfXIm9fUh0lLdZAyyLW6LBcxskQcXfdl8vGFTLY89t4UfXH4ASUmdVwose5+4VOyq+h9gnIiMUdVNUf5/FZF1sYQhIm5gBVCkqieLyH7AC0AOsBI4X1UDIpIIPI1Z1a4cOFdVtzhh3ARcAoSAa1T1nR57SEufEQ6Hm82O0e7kJDfJSW6TQywWSzPbihva+H32eQ31/pBV7HFIvA6ei/BKjH7t8X0guhJwL6b1fwBQiVHYOP+Vjv9vnOsQkQnAN4GJwAnA753KgqUfEwqFqK2tJdjURDgcbnZ3dU8wGGzjtuwdogd12QFefcPokSm07oKfOT2LtJS4bBvu88SlYheRcSJyJpAhImdE/S4E2s4rant/AXAS8JhzLMCx7KoUPAWc5rjnO8c45+c6188HXlDVRqcrYCNwWI88oKVPqa6qori4mIrycnbu2IHf7+9QYYRCIXbu3ElpSQmhUIiysjK2l5XZ6UF7iWAwSF1tbXOFKuK27F0GpSVw540TyMpMMMvRHp7DeWeOtLscxinxWt06CDgZyAROifL3AZfFcP8DwPVAunOcg1ncJtLUKgSGO+7hwDYAVQ2KSLVz/XBgWVSY0fe0QEQuBy4HGDlyZAziWfoKt9vNsOHD+WrrVmpqasjMzCSlkxHBLpeL7OxsiouK2LplC+JyMXz4cLuYx15AVQkEAuzYsYOMQAC/309TUxMpKR2Pnw2UVxH01bbx96Sn4c2x66LvKSnJbo44LIeJ4zLQsJKUtGtlRkv8EZdvRlXfAN4QkVmqunR37hWRk4HtqrpSRGb3ioCtUNVHgUfBLCm7N+K07BmhUAifz4eqIiLU1tYyaNCgDhW1iOByuXB7zJrabpcLl8vVvJyspfcQEZKSksjOyaGi3Oz9NGLkSFydjOAO+mp5f+zcNv5zNiyJSbFHrAFut7uFuz+hqoTDYdxudwt3d3G7Xe2uzRAOhxERRKSF29J3xKVij2KjiPwEGE2UrKra2Z7sRwCnOiPok4BBwEIgU0Q8Tqu9AChyri8CRgCFzhz5DMwguoh/hOh7LP0YX00Ng3NzSU1NpbioCL/fT4rb3a5yj5jig01N5OXlUV5ezvayMvKHDOl3BX5/JBwOU1e7qwVe6/MxKCOjV9I+FApRX1+PhsOkpafvcqeldVqZiCdUlaamJurr6kgfNMg8k+PujTQLh8P4GxrwJCTg8Xia3QkJCVa59yHxrtjfAP4F/B0zMr1LVPUm4CYAp8X+Y1U9T0ReBs7CjIxf4IQN8KZzvNQ5/56qqoi8CfxJRH4NDAPGAst76LksfUTEFB9piUe72yNiitesLBISEkhMSkJVrSl+LxAxxTcFg4wYOZJGv5+d5eUMGjSo1+IMh8OU79xJXV0dDQ0NZGVn059McJFV5SoqKmhsbKShoQGv10t6L6WZqlJVVUUgEGBQRgZVlZVkZWX1WuXLEhvxrthTVPWGHgrrBuAFEbkTMxd+keO/CHhGRDYCFZiR8KjqZyLyErAWCAJXOavfWfo50QVOjS/E0hUVrN/o47g5+Ywcnkx62q756yKC1tQS9NURPRY+iO237W1EhMTEREaMGIHL5cKVksKIlJReq1S53W7S09KalXpiYiIZGRn9qhLncrlITkkhIyOD6upqXC4XQ4YO7dU0yx8yhMLCQqoqK0lJSbFKPQ6Id8X+loicqKp/3ZObVfUD4APHvYl2RrWrqh84u4P77wLu2pO4LfFPZXWAm+76jE8/rwHgf/9SzM3XHsRxs/Nxu3eZEYO+unb7bWd/EVu/raUlVdUBgiHF5ZIuV/bbndXRukvEFO93lHpjYyO1Pl+/M8UHg0F8Pl/zOIHqqqpeU7bhcBi/308oGEREjDsUsuNQ+ph4V+zfB34iIgEgAAigqtp7tjjLPkNtbZB5xwwmLdXNf1ZVcu3lY1j9SRUzp2eTHcMGLtU1Tfi2+xmS13IGph2Z3THFZQ3cdu861m3wsd/IFH52/QRGj0jB5eoZJeBJT2POhiXt+sdCOBwmOzubQRkZ+Hw+QuFwvzTFe71e8ocMoaGhgZrqagZlZPRafNVVVWRkZJCVnU1JcTF1tbW21d7HiN3evGeZMWOGrlixoq/FsMSAr7YRX40PtyeJwpIAQ3PDJHiT8Hq9ZAzapdjrtxS222If/++/cOOj5Txy37QWFYGOrp+zYQkpowt652H6AVXVAX502yes/3JXpSc3x8tjvzkkbnbCGwij4sOhEIqRO9odK6GQUlVjFm1KT/PgTejcjN9TaSYiK1V1xm7faGlDXLfYnYVizgP2U9Wfi8gIYKiq2kFslm6T6HVTEw5QX1vD0Nxk/A31JCd7SU2JvVAqLvNT3xAiO6sXBR0gNAW1hVIH2FEewN8YP0NX9qbpv7dwud2EQkpldYBEr4uU5NiL+dq6IB/+t5yHn9xEYyDMOacO57QTh5PZyb4JAyHNBhrxPirk98As4NvOcS3wUN+JYxlIeL0ehgzNR1wu/A31pKcPIiU1BY8n9sLJ5YJEu/pWTLjdQsHQ5BZ+6Wkem349THVNgFf+XMSPbvuEex/8guLSBsLh2CyzZTv83PGrz9lRHqDGF+Sx57ay+pOqXpbY0tPEdYsdmKmq00VkNYCqVopIfNjsLP2eUChETU0N4VAIj8dDba2PQRmDUHW3GPgT3W8bCISpqGoClJqglwXnjiJlN1r4+zJZGQncccN4fnz7J1RUNZGe6uHnN06w+9b3IE3BMK/9pZjH/rQVgC++rGX1J1U8sfCQmLYUXrqioo3fux+UMWtGNkmJNp/3F+JdsTc5G68ogIjkAnYXCEuP4ff7ycvLIzklhe3bt9PU1NRmcQ1vTmbzoDd3Y4i07CBbt9UxtCCJcyYnkGo3wogJEWHM6DSeWHgI/sYwiYkuMtITSOiiD9cSOzW+Jt76e2kLv4qqJnaUB2JS7Afu33aQ4YSDBpHgse+oPxHvJdJvgdeAPBG5C7OAzC19K5JloOB2u8nLy2vj7mzOb2Kim7xEN3mDOy4kuzsyeyDjcUtMCsayZ3jcQk6Wl9LtjS3801Jja22PHZPG7K8N5oMPdwJw0AFpnDh3SIvpn5b4J+5HxYvIOGAuZqrbElWNaT/2vsKOirdYLH3Jug01XHXDGgJNpmw/fnYe11x2ABmdDICLptrXRENDiFBYSUl2k5Wxd3o/7aj4niOuFbuIZLfj7VPVzjfQ7kOsYrdYBh79aRpcIBCmqqaJDZtqyc9NZHBOYqej2uMFq9h7jng3xa/CbMRSiWmxZwKlIlIGXKaqK/tSOIvFMvAJh0I01Nfj8/nIy8+nvr6eWscdj8rd63WRNzix0+4iy8Am3kdEvAucqKqDVTUH+AbwFvA9zFQ4i8Vi6VXE5SLB68Xv91NUWMiO7dtJTk7u+sZ+RCiklFcGKK8MEArFrxXXEhvxrtgPV9V3IgequhiYparLAFsdtVgsvY6IkJCQQHp6OsFgELfbPaCWTPXVNvHO+2V897rVXPHjVfxtSSk1vrjt7bTEQLwr9hIRuUFERjm/64HtzhQ4O+3NYtmLVFUHqNgHW3RhZ0/zmpoaUlNTCYfDlJWWNve193e+KmrgFwvXU1Lmp3R7I/c8+AVbttX3tViWbhDvfezfBm4DXneO/43ZVtUNnNNXQlksfU04HEZEEJEW7t6gwR/i8w0+fvf4lzT4Q5x7agGzj8yN24VlVLU5LaLde0rEFJ+Tk0P6oEE0NTXhb2joCVHjgsXvl7Xxe+e9UiZP6J2NYyy9T1wrdlXdCfxP5FhEkoBTVPVlYGOfCWax9CHhcJjGxkbcLheehIRmd4LX2yvKvbwywPdv+YiwYyO77/cbyM7yctThg3s8ru4SCoVo9PtJTDI77kXc3TGbiwherxePx4PL5Wp297UpvqdG6h90QHobv3Fj7Qaa/Zl4N8UjIm4ROVFEngG2AOf2sUgWS5+iqlRVVlJUXEx1dTUlxcVmadxw7/RO/WdlBa2D/vPiEuobgr0SX3cpKytje1kZNTU1lJaW4vf76e60XhFpVprR7r4iFArhq6mhvLyccDhMTU0NFeXle9Q9MGtGNgeP36XIJ45L54iZOT0prmUvE7ctdhE5BmOKPxFYDhwBjFFV2/lj2adxu93k5edTXFREZUUFSUlJZGVn95qyGTG87QjwUSNSutzOsy9wuVwMHz6cwsJCGhoayMrOJikpqde6KfoKESExKYmKigr8DQ0Eg8HmlRPbo8bXhDfBRVJS2zySmZHA7+6ezM6KACgMzvE2r75YXhngw+XlVFQF+PrReQzO9pJo14yPe+JSsYtIIfAV8DDwY1X1ichmq9QtFmOKDwQCNDU1ISI0BgKEgkFcLlevKLCx+6UxfXIGqz6uBiA/N5FzTinAE4frh4fDYRr8/uZjf0MDgwZ136ysqlRUNdHgD5HodZGe6mlXSe4tXC4XiV4vySkpNNTXk+C4W1fuanxNLF9dyWt/LSY3J5HLvjOaIXlJLZaIDQaDlBQXM2ToUACKCgsZOnQo9X7hyutWU1xm0vPx57ey6DfTOWA/uzRyvBOXih14BTgNY3YPicgbOBvBWCz7OqpKVVUVaWlpDM7NpbS0lJqaml5rtWdlernj+glUVjfR2Bgmd3AiOVnxu8liRXk5OYMHk5SURHFREX6/n5SUlG5VegpLGrj2lo8p29GI1+vix1eOZfYRuaQk941yj5jiG+rrSUtLo7a2lorycrJzcprzgKry7+Xl3PXA+ub7/rOygmd+P4PBUev1u1wuPB4PRYWFACQmJiIuF+s31jQrdROnsuhPW7j1h+NI3o093i17n7h8O6p6rYj8AJgNfAv4JZAhIucAf1XV2r6Uz2LpS9xuN3m5uSCCy+UiPy8POuj39dU2EWhSRCA7c8+VcWaGl8y9tGZ4d3C5XIwcNaqNuztKvcbXxC8f/IKyHWZjlUAgzL2/+4JDp2btsWKvqg4QDkPGoIQ92mAlYorPy8sjJSWF9EGDCAVbjnmormnilbeKWvj56oJs2lrXRrFn5+RQXGSujVQOGhrb9tc3NSkxbu1u6UPiUrEDqBnt8j7wvogkACdgprr9Hoi/4biWfZJwONzcHxnt7m3cHk+77mh2VjTyy999wdIVFRQMS+anPxzH2P3SBvQ2qa0HtrVX2VFVmpqacLlcuN3uZreng3QMNIXZuLmuhV8opNTUNpHbwbKtTcEw5RUB3nm/DI9HmHdMPtlZXpqawny+wcdDT2yirj7IufMLOPbIXAbt5tRBl8tFYmIi6vXicrub3dHP6/G4yMxoG27ruILBIKUlJSQmmmcpLSlh+PDhTJ6QQWZGAlXVZrEaEVhw7ki7TXE/oF984arapKp/VtXzMGvHd4qIJInIchH5SEQ+E5GfOf77ich/RGSjiLwoIl7HP9E53uicHx0V1k2O/3oROb6XHtHSDwkGg/j9fsLhcAt3PFBXH2ThH7/kw/9WoArbiowpudquKIaqUlZaSmlJCX6/3wxCrKzs8N0lJ7mZMS2rlZ+r093SdpY38p3v/Zc/PruFh5/czIKrV1BZaRb4ufaWj/h8g49tRQ3c//sNrP60ao+eI1Ixae2OkJbq4aqLxpCYuKuYn3ZwBvm5iW3CSU9PJ3/IEIYMHUp6ejricpE5KIHHHziEC84ZyYlfz+fxBw5h/1GpeySrZe8Sl1UvEfmEjvvUFZjSRRCNwLGqWuu09v9PRP4G/BD4jaq+ICKPAJdgBuhdAlSq6gEi8k3gXuBcEZmAsRJMBIYBfxeRA1V1YCw5ZdljwuFwi37NmpoaBBg+fHhfiwaA3x9m+aqKFn71DSFqfMEWZth9ERFhyNChFBUWUlJcTGJiItlZWR1aW1JTPHz/sv1paAjxn1UVDBuSxE9/OI6M9PaLz3BY+d+3ivE37qoo+OqCrPmsGl9dE6FW9Ye3Fpdy6JQsUnqhJTxiWArPP3IYn35eTW5OIgVDk9tsw+p2u8nKzm5+/mh33uBELj1vNKqK290v2oEW4lSxAyc7/1c5/884/98hhkF0jhk/0g+f4PwUOBYzhQ7gKeB2jGKf77jBDNz7nZhOufnAC6raCGwWkY3AYcDSPXkoy8Ah0soJNDZSUV6Oy+WiYMQIXK1aTaFQiHA4jNvtRlWb3cHKGoK+tkNFPOlpeHMyuy2fJ0HYf3QaH6+tbvZzu4X0tJaffGTes8vlam6x9tbo+nhCVZvntqtql4XK4OxEbvvxOAIBs8pfVmZCp2kUbGfZ3br6JkYMazt1cGRBCgne3lGaCQlmp7djj+x4KhzQolLTuoLjcglmc832qa8PUlcforYuSHq6h8xBCXE5Y2JfIi4Vu6puBRCReao6LerUDSKyCrixqzCc9eRXAgcADwFfAlWqGhlhUghEmlfDgW1O3EERqQZyHP9lUcFG3xMd1+XA5QAjR46M8Skt/R1Vxd9oBlRFVoNzuVzNBaOqEggEKCkuJn/IEJoCASoqKhg5ahRBXy3vj53bJsw5G5b0iGLPSE/gpmsO5JqbP2JHeQBvgvCjK8e26B+NyFdaUsKw4cMJBALs3LGDESNHdtjfvCdU1QTYsq2e1R9XMWNqFiMLUshITyBQXtWrlZuOiJjiExMTGTx4MCUlJVRWVpKTk9PpGIn0tNj6wV0u4ayTh/Pm28UEmoyCT05yMWtGDt4EFzOmZLLiI2N+z89N5JunFZDQTxVhQ0OIv/9rO7/6/QZCYUhLdbPwzintrmZn2XvEpWKPQkTkCFX9t3PwNWIcF+CYy6eKSCbwGjCut4RU1UeBRwFmzJhhx4z2E7qjWCKmeFQZOWoU1VVVVJSXtzDFR5YiHTRoEGWlpQAMzs3dawPshg9N5rFfT6fBHyYx0UVaqofkqLnXEfmSkpObpzplZ2f3aGu9viHIMy9v48XXTfiL/rSVC785iu+cOYJwL1duOiJiineJ4HK7GTZ8uHH34HvJHezlmYcO5ZW3ikjwCGecNJycLC8ej4vbrxtvpg4GwuTlJJIdx1MHu6K2PsivH9nY3L1QWxfiFwvX88Cdk9uY/C17j3hX7JcAj4tIZDeCKuDi3QlAVatE5H1gFpApIh6n1V4AROaCFGEG5RWKiAfIAMqj/CNE32Pp53Sn1RwxxaelpVFbFyZtUCaZmZltTPHQagR7O+cPffNR3ClmbXMNhqjfYpRgd1uuLpeQ00V/uoiQ4ixyApCcktKjCq6uPsQrf275yfzp1W2cdsJQUmIMQ1Wp9jXhcZvKSXeJbMMaIdrdUyR63Qwfmsw1l+7fHGeE/tg+0boAACAASURBVDJ1MBb8/hDBYMu2zNbCesJ2TlyfEteKXVVXAlMiil1Vq7u4BQARyQWaHKWeDMzDDIh7HzgLeAFYALzh3PKmc7zUOf+eqqqIvAn8SUR+jRk8NxazvK1lH6CiymxRmuCRdgvimtoQby0u5a9/L2XEcFOID813N89Ljpi6KysqyM7OoampibLS0ua51RHcKUks+/oFbcLv7ZarqtLY2Ej5zp1kZmXR6IwQ70lTvCptCvlQMBzzalM1viaWrqzg5TeLGJTm4XsXjWHk8GS83r5b9W13LD17av2orArw4YoK1n1Rw3Gz8xk9ImW3p8SBGUNhBr65CYfDze6essqkpHjIzkygomrXbIsjDs0msQ/fjyXOFbuI5AO/AIap6jecUeqzVHVRF7cOBZ5y+tldwEuq+paIrAVeEJE7gdVAJJxFwDPO4LgKzEh4VPUzEXkJWAsEgavsiPh9gy3b6rj13nVs2lrHQfun8bMbJlAwdNfAp0AgzPOvbuNPr5rW9bbiBtau9/HUg4c0t5LNVqoesnOG8Pb75YwsSGbigUOb+137mogpPj8/n6TkZMjIwN/Q0KOm+OQkF3OOyOW9/9vR7HfC3CFmYRdf1/ev/qSKn//q8+bjNZ+u4vk/HEZ+bt8pjj2x9FRUBti4pZZgUDlo/zSyszreia+qOsDNd3/Gx2trAHj9byVcf/VYTvr6kN0amR4Oh6n7f/bOO06Sss7/76eqOueeHHfJu+SwIIISVAwHJyoKGNEznfEMP0REVBAU9EA4Myp3mBOY8RQ40ZMTJagEYYkbJvaEzrHC8/ujunt6Qs90z87s9i79fr32tU/XdHVXdVfX93m+4fPNZpmZmaG/v59MOk06nWZ4w4Y1+47DQQf/8amjuOo/HuOp7VmevSXKe95ywJp4Vtqsnlb/9P8L+E/gkvLjx4AfMGeQl0RK+QBwzBLbn8LOal+4vQC8qs5rXQlc2cxBt9m7sSz40BUPVeU0tz6Z4SNXPsz1VxxJpKzels7o/PbO2Lz9Eimd6XipathNU3LLrePc/0CSu++bRVXg2cd38MqzBjhyyM/pj98B2O73PYWqqnh9vuqNvna8FgT8Dt7/rwdy/NER/nz/LCed0MFJW6L4vBrLNX6wLEk8qfOTW8fmbS/pkr89lORFp7vX7BjXm5l4iXde9FdGx+3rqSPq5OvXHktXx9JhkkzWrBr1Cjf9YAcnn9BBR6TxUkVFUfB5vWQymWoORW9v7yrPYmlUVbBxyMfVlx6Gbkg8brUtYNMCtPo30Cml/KEQ4mKoZqy3V8xt1hUp5TyNbICntmcp6XMFyJom6Oly2R2xagjUrFR0w+KhR1LcfZ9dT/7cEzv48HsP4Z6/xdEinWiRIEII8tv3bNpGrSFvxqg36pKOhJyc9cJeXnR6D86asi4tMDe5mXc8Xi+/un2CdFqnM7o4BFJP7a1VuesvM1WjDjAzW+IXvxnnTa9eu5VzXcr5BIV8HgDNsXyZ3moJBfeNnIF9hVY37FkhRAfl2nUhxIlAQ3H2Nm1Wop5hweOdJ6UJ0NvtQtPmboihoJMPvuMg3nnR36pCJOec1T/PBel2qbzglG7+cPcMmw4K8P637890bIRnHdM7r8xsb6UZl7Tt9p9vUJwd4UXPsyzJL347zme/+DjhoIOrLj2c/7t3llTarlJ92Uv6GOr3kM7oDZef7WmmZoqLtk1OF7AkLCUT7/OpHHlocN6q/YLzhgkvo3S3FBVXfDqVoqu7m0w6zdjo6Jq64tu0Jq1u2D+Andh2gBDiLqCLOi7zNm2aZSnDAmCaFld8+FAuvvJh0hmDcMjBFR8+bFH5zn7DPr7/1RPYOZanI+okHHQsSnA67qgIbzxvAz//zRgzcR2/x83EuO1e9vl8dpZ8nQmGFnjmtcdMpnV++utxwA5tfPHGJ7nqo4eTyRoM9rvZMZLnY1f/g6Bf491vPgBNUxACPB6V0CqSy3YHLzy1m2/+YPs8xblXnDmAqixtXCMhJ1defBh/uneWfzyW5kWndbNhyNu08lvFFe8cGMDpdOL1eNAXNIpps28iKupLrYgQwgWYwCHY0kdbAaWsBNeSbNmyRd577717+jDa7CK6YZFM2W1K3S6VcEhbtaRmqWSSzpo4HAJN0ZkYtw1XX38/brd7r1095baN1F2xu4YGSCR1DNPC4VBW7CxXcetLCYmkTrFkR9xShpN3f/ppvv/V45lN6Lzzor8BcM1lR3DzL0f5v3vsMMdzntXBRe85eLfUTjerf5AvGGzbmeMb39mOYVi88fwNHLS/f7fFoqWU1WusdtxqCCHuk1Ju2dPHsS/Q6iv2P0kpjwUermwoK88du+cOqc0zAYemrKip3ugN3ulU6XCqlEolRkcm8HrtCu6J8XEGBgdxOlvDGNU+R3E6MXN2XBZFIMq17Y3U1gunk/RTO0kndSxLoqkKjrADVzhQd996bv3Nd/2KZx0XJeDXuOFb2wA4fFOQkbF81agD/PHPM7zglAQvOGV56dS1oJ6npx4et8bmg4Jc9qHNSMmKGeNrrcjXTA5FpaFRpSSuMl5Kf6FN69KShl0I0Yst3eoRQhzDnFBxEBrWtWjTZl1ptuxJVRSi0Sj+QACkJJPJVG+Y693+tfZYawVx3AO9VSMiLYs7DzkDgBNv/+aqa+utXJ67Dn3hou2nbm2sLr/2+FwRB5e9wY0yG+NtL4vyP3+cYqDPzRNPLzZ8f3to9xj21dLoCn295YaXw7IsRnbuJByJ4HQ4iMVie71n6ZlISxp24EXAG7GV3q6t2Z4GPrInDqhNG2CeyEezqJpGIBisGu3K2DRNcrkcHo8HIUR1vJZ67fOOo44gzmmP/Lap16mXfCgXti+b+0tDr1vv+E7ZejsdUSePPp7hTa/ewC9+OzHv76ef3NXQ6+8u1nuyth4IIejq7mYqZpdyhkIhnM76NfdtWpOWNOxSypuwBWbOkVLevKePp82+QTZnoBuSUEBb1Y3Ksizy+TyFfJ5wJMJq8lOW6qIlpWRmehqH04nb5SKZTNLb11dVC6t0WzNNs6U6r9VzSWee2llnj107blUR3HjdcTy9PUtPt4u3vX4j3/6x/V6vf9UwB+7fOsmGhmGQiMeJRCJIqI5r5YXrudxlnb7wuwNbVKnGdV9nMlLpCqiq6rxxm9agJQ17BSnlzUKIM7H7obtrtl++546qzd6GrluMjOf58n89RTyhc84/D3DSluiqJDoFkEwmKRQKBMz6kgqV2GQjMp5quRHJyM6dFAsFoh0duN1uLMsimUjg8/vRNK06XmkFtdbJUs1q2dd7v7VYsHZEnHSUm6ac//IhzjyjD4CAX8PpaJ0VcaVJULFYREqJaZqEwmFqTV89l3uznpO1xLIsYpOThMJhnE4nU7EYHo9nniveNE3Gxsbw+/0Eg0HGxsYIBoP4/f62cW8RWtqwCyG+gh1TPx34OraOe1urvU1TJFI6b/3A/dV68yuufZRPXLi56Xisoii4PR678UsmQ4Clb2JSSnbu2EF/fz+5fJ5EPM7whg11b3qVeuMK2WwWv9+PEIJisUgymcTtdpPP53F7PMsaa13XsUwTh9Np94Ivj3eFZrXsVzOPqHXrN6rE53QoVSPfLKWSRSpjd1hzu1SiK/RXbxaHw0FPby/jY3Zp48Dg4LqFVmoxDKtuL/RGkvKEEAwODlY9C5Xxws8mEg4Ti8VIxOMoqorX620b9RaipQ07cJKU8kghxANSysuEENcAv97TB9VmMbOJEo88lmJyqsizt3QQDTtwuVrjh/7wo6mqUa9wy6/GOOGYSFOr9oorPpPJlFcwKqduvW1R7FT1+/DoBUZHbUW5ru7lJxBSSpKJBN3d3ThdLsZGRymVSng8Hnp6e9mxfTv5fJ5QOIzb7a4bqzVNk2QiQTqdpqOzk/jsLJqm0dPbi+r3Vo+1nuFUvZ6qcVWczlWvHOvF3pery69161c8ArtKPUOm+H3c84TJJ699lFzepK/HzbWXHcHQwNrl5Zqmycz0tP15l0MtPT0981zx9aj9HmrRAn6Mch26pmnzxsmUzj8eS/HbO2McvinI6c/pWlRi2EhS3sIM+KWMtaqq1S6AlmXh9Xj2ivyBZxKtbtjL9TbkhBD92K1U+/bg8bRZgniixIWfeJCtT9o3UU17kq9+9hgOOTCwh4/MpmMJWdLuTieOOiub5RBAKBwmEolQyOfJuwqEw+F5N0DTNHEmLHK5HF0eH8p0gqKYL5hYu0pSVZWh4WHA9gpUxpZlEY/HsSwLh8NBKpnEv4wrXlVVItEopVKJ6ampqlFXFIWSx0UsGcfr8uJzapzy6G8X3bQVlxN3/+JJSLOGttlysIWsZmKwFPUM2albb+fjn91GqWRP9sYnC1zxua1c/bHDCK+RNGolBDMwOIiUksmJCSwp6/h4FuxbKuHdOLhou2maxCYnMQyDnt5eJicmcDgcdHZ188vbx/nyfz4NwG2/j3H7H2J8+pLDGmoP22yqSMUVLxSFcChEIh7H6XK1XfEtRKsb9l8KIcLAZ4H7sdNqv75nD6nNQkYn8lWjDmAYki/f9DSfvOhQAv49f4kN9ns45vAQf33INq5+n8pbXrsfHk9zN6GKK95VXjVXxrU3MyklhXyeRCJBR2cnTCe4c9Pi0q/aVZIQovoaUsq5DGopMXSd3t5eXG43yUSiajAq+y3Esix03ZbCNU0TwzBwOp24PR7CkQiJeJy8EAxvrB8a2NM0MjHYlVpvS1I16hW2PpnGNJpPhiwWTabjJX531xTdHS62HBUhGnHicDgYGBysfke149WiqiqdXV2MjY4ysnMnqqbR2dlJOmvy3ZvnEha/cPF+BLUS6vQkufjce9ZLykuldWbH8vR2uXDUyVNY+HlHJYCKJgWunh6cLlfLXk/PRPb8XXcZpJSfLA9vFkL8EnA32pO9ze4jn1/s2s3nzUV9uPcUkZCTyy86lIlYgWTa4MD9fKtWKFsqq70WIQRut5u+/n5cLheFeP3epNmcQS5vIi2J260SDDiQUjI6MoLf7ycQDBIMhapLKq/XW3XlFotFNFVFc8yFEiqueFVVGRwaYnpqynYB9/ZiWRapZBJV0zDLGdvhSGSvvRkv51ZWwyESKR1XnetPEeDzqmRzc9ftMUeE5zWoaZRtO3N88tpH2bYzxxc+sh/57aNk4o55iYL1Jhur8UzUZq0LQCIwDJOr/20YL7YgZ2fI4n8PP5NHFuxbL7SSL5i89b338t0vH09P19Jd85b7vD2R/rYrvsVoacMuhHjFEtuSwINSytgSu7TZA+y/wUck7CCemGua8ppzBgk12bRiPYmEndWWq0uxlmpfqqbhXiYLHsC0JF/75tP89NdjWJbd+e1D7zqYUFCju6eHifFxUqkUHo+H7p4eAKanp7Esi3Akwsz0NMFQaF4YoOKKr2Thd3Z1IaW0XfHFIg6nk97eXgqFAolEouHzWSvX+O5ASvjD3dNcd8MTfO5dS+c2CEVw7WVH8Il/f5TxyQJHbA7y4fcc3HRTmWzOoFg0+PynDuezX3yS4Sj86fAXL3pevSTDZkMWpmlW68v7+/uJxWLMTE9hiiC9fos/H3UmYIsLNUuxaHHnXdOc97LFIYCVaBv11qOlDTvwZuDZwO/Kj08D7gP2E0JcLqX81p46sDZzRCN2f+nv/2Qn47EirzxrgEMObL2b/nKstdrXSm5XvSS55Vdzvcb/9+4ZnnXsNC99Ue/85KXyCl1VVXr7+hgdGWF6agqPx7Motl953lJjt8dDbzlsUClfqrda1w0LAdXs6l2Nme9OTEty6VX/KD9aesUugEMPCfLVzx6Dq5RBZnMo2Wlyc4UJDU3oFAUGehUSs+O8720bcKTia3MSdahM1gSgGwJ/sJtiyeLCTzzM9e/pWXn/mqS8VNogl7eT71KGPeH1+fZO702bxbS6YdeAzVLKSQAhRA/wTeBZwB+AtmFvAYQQ9HS5eeebDsAwJR73M+8GUVuv3og6nW4sjnc++EiSf35hD+NjY3g8HgKBALFYDLfLhc/vx9D1qhiIrut2zLRBV/pKIQSw48XjsQLf/8kIDk1w/suH6O6sH3dtRfSa2HnKcLL5rl/hdCiEw04qzdS0gF1KGI04yW3Lcechq5vQedwahu5Gc7jJpGK45dLfheJ0LpmAuBpvkKMcekmki7zn4gcxLcnYRIF0Rl9hz/lJeanJAu94773VcER3p4tnHxdt6ljatC6tbtiHKka9TKy8bVYIsfKV3Ga34nAoOFrH+75qmhVksSyLYrFIPB6np6eHUqlkj7u757mxkymdfMFEVQRawLfodU44JooQgt6+PhwOu666f2CgejOfnp7G7/fT0dnJ2OgoyVRqyVV7hWbDCxNTRS54z32Ypr3SvfV/JvnOl46nt3vpuGsrotVMQt79aTtL/DWvGOStr+tflwmKy6XicjnQS/m6zzFzee7cvHwC5aJ9alQGl1IcFEKQy5vMxEurOu6uTiff/tLx/OneGdwuleOOiqxaE6BN69Hqhv3OctLcj8qPX1ne5gMaDxK22SVM06waj9rxvkrzgix2VnupWGR0ZATDMOxGL0LMc2P/7Z4ZPvTJhwD44DsO4g3nDvPDn41gmJKzzujlWcdGUBQFl8tVvYnXjnv7+qrv1dffPy+bfimaCS9YluTHPx+pGnWw466/vXOSN5y7Yd5zE8kS07MlUmmd4UEvkZATVd29Mrf14v7C6+UVZ/bzk1vHkBIO3M/HeWcProtRtyd0eTLplO0in1obV7xpmmSzWdxuN5qmVceksxhpO17gAf7rkgHiyVLZlT5n4M1coRpndw/0IjT7GqnNiVBVha4OFy99UX9Dx6T6fTz3kd+UJxgKpmmgKCqW28v0TJFQ0LFXeXb2dVrdsL8LeAXwnPLjm4CbpV3vc/oeO6pnEIZhMD09TWdnJ0B1vDtUtBqh4poWQswrA9udCT1CCByaRjgcZnbWbiXa2dm56BgOPSTAq18+yM2/HOWLNz7BJe/bxHe/cgKKAl6PitejVV+v9rUr1H7m6/H5u5cIoSwMq8STJa649lH+fL9txAJ+ja9feywDfZ41P57lWC7u/7bXBXndOUPVsNBySZO7gqIoeD0e+gcGcDqd6CWT0x67fVF+xWq03zPpNLMzM3h9PjLpNJ1dXWjp7JITtZMfvg1HMErPY3csUv1bbavXhTg7wqjhAEII8gULBcmf7pnl8msfxOVU+NLVR3PwAa2hW9GmxQ27lFIKIe4FklLK24UQXsCP3eWtzW5ASkmpWGRsbGxOyaJG0SKbMzAMuccy4A1dZ3R0lN6+PizTJBaLMTQ83LRhX42kaYWKK352dha3x0OpWGR8fJzeBUpj4aCTf3n1Rs49exAhwO/VljSm642U8MTTGX7663H6ul286Hk9dEZdnHPWAD/773FyeROnQ/D2C/bjec/pqp6joiiMTxaqRh0gnTH42ref5sNv7MOqkcWtsFaGpRn8fg3/OuonZLIGI2N5fnnbOPtv9PH853bjdiu4OiM4O8KLDHuzAj+qqtLT28vIzp1k0mn8gQB+v5/CbGrJ5zudCt7eKLB+MXIhRHUymUqXePuFfyWR1JESCkWLa778BJ/52GGE1kjgp82u0dKGXQjxVuBt2FfsAdg92r8CLJ62tsE0TQSglDsuVca7gsPhoKenpyqPOjA4iOZwUCqZ7BzL85WbniadMTjv7AG2HB1pumRoV9EcDgLBIBPj4wBEIpFVrdYblTStF7dW/V6CoRCRSATTNEmlUkuKpns8atPCOGuNbli86YP3Vednt9w6xg3XHEtXhx13/eOfp3juiR1oSoFAwL6W0uk0gUCA2XgJVYFKZ1ZVgYlYESOd5ferTELbk6xUypfJGhSLFppG1Wj99aEEF1/xcPW5N/9ijM9/+iii4bVpb2qaJplMphr2ymWzGOHW+Qx9Vo7r3zO/lFBRBI5CBoLtBLxWoKUNO7Yr/gTgzwBSyseFEMsKbwshhrAz53uw611ukFJeL4SIAj8ANgLbgHOllHFh/xKvB/4JyAFvlFLeX36tC4CPll/6inI72ZbEsiyymUy1IUN17PHsknE3DINYLFaN5cYmJ+nr7yeZNnjrB+6npNvW4aFHU3zmY4dz0vEda3I+zeB2uaouHFdNF6r1YLm4dWTIbrWqKIrdonPB575cMlve4bObkhQtomEn0XVMZMpkjHkyopNTRZ7cluGEY6J0d7p4xZmD6LrOyM44+XweRVHI53J4PR6O2BzkJzedyHdu3sH//SXOF686kompIoqydzrRlnPpT80UufYrj3PPX+McsNHPR953CAG/xn9+b/u8520fyRGbKi7SZq/QjA7AbKKE36uQzWTo6OzE7/czOTFBoVBomZu1WsjxyMlnLtre9dgdrKfXoE3jtMq1Uo+ilLJUVVoSQqNeceocBvBBKeX9QogAcJ8Q4jbgjcAdUsqrhBAfBj4MXAS8BDio/O9ZwJeBZ5UnAh8HtpTf8z4hxM+llOtbrLpKpJQYpkliehqP10s+l6Ojs3PFD6uR11VVlb6+PiTYAhlS8tAj6apRr3DzL0c56rAQPu/uu6wMXWdqaopwOIxhmkyMj6/KFV9L5UZcabIBAk1T7SYcSxjmChVDXi+prb52+R1c9b0R/vfuGQB6ulx8+TNH0905l43eaIZ7NmeQSOo8vDXFiQd7OHXrHfNU0KSEiYzKK8/q58e/HCMSdvD853Qtjs1qGn39/YyVPTW9vb1oDgc+1WJ8bIzzz+7hdecMkUnF2DDQCVN1P5a9klRG56r/2FoNOzy8NcX7Pvp3vvLZY5bsKr/cXLIRHYBiyeSxJzNc/fnHyGQNbrjmaBxOreqWByjWccXvbhRl6ZNdx/l0myZpdcP+eyHERwCPEOIM4J3AL5bbQUo5DoyXx2khxCPYLvyzsQVuwE7CuxPbsJ8NfLOckHe3ECIshOgrP/c2KeUsQHly8GLge2t5gmuFqqqEw2HyuRz5XK5aB72rSWSV9pMVQ1UZ+5cQs+jqcOHQ1ufXXd8F7qtqqQP4fb5dPmdnRxgR9DE1OoppmmiaRv9Ar91RaxnDvloM06oadbBX0N+5eSfv+pcDqj3Gl/MUVP4OdiZ7MVniQC+MTXiY0l0ce0QEr0etqtD15Uu8ptfDIQf6OPTgID6Pidc7v7OZZVkkk3PqzclksuoN8Xq9JJNTKIqCqqq4XE5WV3S1/pimJJEqIaWdCNjopFMvWfzlr/Pn8FMzJYQQvPm1G/nQ5XZ1wxcu3o+oyyCqJMltm/u8ms0tSKUN/u2Sv1cny694018472WDvP31++F0zmW176oCYG3L3+Xa/7bZu2l1w/5hbPW5B4G3A7fSRBMYIcRG4BhsV35P2egDTGC76sE2+jtrdhspb6u3fan3eRt2LgDD5c5cuxvLssik0xSLRZxOJ/l8nlzZfbqrcfal1MwO3M/PoQcH+Mdjtgs2GNC44LwN1ZvQWrOsVnVNwpLH693lm5VpmkxNTVXLyiYnJpiemlqx/erq32+xX2XbjhylklU17MvtayUzS8a3T3n0dsJ+ByAxDINSsYjL7cYyS2RSMxx7WAhdT5DNGEQi841DpUVt/8AAQgjGx8cxDQOny0UoHCaZTGJZ1qpzGpaiWDLJZAwcDoWAf3EP8GbJ503ueyDOZ7/0OIlEiVNP6uJ9bz+wrsu8FqEIhgY87BiZq093OhWEgCMPDfGf1x/HrXdMMBiR/PnIlyzav9ncgtHx/CIP2P/ePc1rXjFIh9Nlv/8uKgCapkmhULBL56A63tfLV5+JtKxhF0Ko2Cvp1wJfW8X+fuBm4H1SylTtTaKcbb9mHUqklDcANwBs2bJlj3Q+qbjiox0dBINBEokEpmnusiu+HpGwk6svPZzRiTzpjMHB+/sbahG5HtQrD1stqqrSXTbiFZd0Zft64HQo8xLSAF7y/B78vpV/nlMzRVS1nmGV5DIzOFST6UwaTdPo7evD6/USiUaJl0vzlgpdaJpWnaQKIapjwzAYGx3F4XDgcrmYmZnB4XSi+n2c9tjtgEAIyqWHouHVZKmkU8jr3HLrBGed0YOmmjidDjRt9Z95MqPzkU89TKXa7H/+OEVfj5u3vHbjijXX0bCTS9+/ifd/7AEyWRNNE1z07oMJ+DXcLpWD9vfzb/sfuGa947s7XYu27b/Bh9u1NteclJJiocDkxITdcrhQoFAoMLxhvkbBWvZMaLPnaFnDLqU0hRAbhBBOKWVTnj4hhAPbqH9HSnlLefOkEKJPSjledrVXmsiMAkM1uw+Wt40y57qvbL+z+TPZPaiqSigUAuz62sp4PWfjKzVW2ZupVzO+Hg1RFEXwuSuO4vNff5J0RuecMwd49pbGkxBNc+k6acMET8BHMpmoas2rqophGGRrStOy2SyBQGDetVLbRazyGGwDEQwG8Qfsmman04nT6UTzenFEQ9UJQrX1bAPoul1FkE4l+eczoujFWaYKFoODyzckqX2Ppd7v6e1ZFpaQ/989M5z/ssGGrtsD9/fz7S8dTyZr4PNq+H3amhnahUTUPLdctYFM1s7rUBRBR8SJI58B78oG1TQMLCnRNA3LNKvjmvwk3G430Wi0qrUwMDi46DNrRNRob2oK9EylZQ17maeAu4QQPweqdyIp5bX1dihnuX8DeGTB834OXABcVf7/ZzXb3y2E+D528lyybPx/A3xKCBEpP++FwMVrc1rrQ70GIG3Wjl1xhy53Qzx2vzDXXn4ElgWhgFZtwLIrODRBqlioypLmczncHg/FQgHTMBgaHiafyxGPxwkEGhMXUVWVYGjOgPsDgerER9ak2tcz6kutCC1L4nB50BxucpkZhBD4Aj3sGM2z3/DSxsI0DJKpFKFgEKA6rtUNGOxfLJpz6CHBhnsZODSFzqiLzuji1fRaI7M57jn6RYu2N+LStyyLeDxONpult6+PmelpTNNccsclcgAAIABJREFU1ANeAvn8XGihUCigaVrT94q9qSnQM5VWN+xPlv8pQKOyRicDrwceFEL8rbztI9gG/YdCiDcD24Fzy3+7FbvU7Qnscrc3AZT16D8J3FN+3uWVRLo2q3PZmaZZVYWrHT9TWOmGuFyP+NpJgWVJpmdKWFKSMpwEtXoOLYmqaQz29ZFKpUgmk3i8XjxeL4NDQyiKgj8QwOf3N/U9VJ5bkT71er0oikImk8Hr9Va17Zei3orwlEdvh/JuUkqktJYVOzHLyX2FsqEqlkr4/X5qTVQ46ODtF+zHN76zDcOQ7L/Bx1tes3GPiAKtJ4qiEC6710dHRhBCLDbqZVd8oVBgcHCQQqHAzMwMfn97lb0v0tKGXUp52Sr2+SMsWZECSwjblLPh31XntW4Ebmz2GFqZigSrWhaxqYybpdk2pxXRDU3TcLvd1bHH42nIqFQMm2UBgnmdup4J1E4KTNMiq2e45FMPk0jp3HzdZk566DaKJQuXU8HhUFAUu2KgLxK0V9nBIMFgcNF33WhOwlJNSQDSqRSJeByv10s6nUbr6anW8sPiCWA9VT8hpN1rPtpHLpukkJ+ls6O+K97hcNDX11ctx6ttllMh4Hdwzpn9vPj0HnTdwu1WG0qcq9DI5LVZt7RlWRiGgaqqCCGq411FwDy3e+VbXXgOPYoTY3wKl9/H8IYNz6iJ9TOJljbsQohfsLhuPQncC3xVSlnY/Ue1vswmSuwYyZEvmBy0v59o2FmtGy2WTHRdNpRUtRSVzPlUKkX/wACpZJJMJkP/wMC6uO4X3lRUy8K0LAp+LzNZW/+61n27HHmHj9sfyvLTX48RDTt5z1sOYOOQd01c1nsbqqqw6cAA37juOExT8p2f7OSHPx+t/v1lL+njHW/cH1dNadeufL+VfgEdHR0IIarjSmLhju3bSafThMNhPOXVe3XfBRPASnOShSiKQl9/HxNTOl2dnWjqnGdgqbIsyzSJz85Wt8dnZ+nu7p7nigfwerSqBn+pZDE9W8QwJC6nsmKcvZHJa7NuaWlZjOzcSSgcxu12MzkxQW9f37L7rOTpqrjidV2nr7+f2ZkZJiYmGBgcXPYcXJ2RRdvb7Bu0tGHHjrF3MVc7fh62TvzB2Jnyr99Dx7UuzCZKvP/SB3hym51OEA45+MbnjqW708XkVJGbfrCdsYkCL31xH8cfHSEYaE6+VVEUvD4fyWSS7du2AdDd01PXvbGQWkPdiJ56vZvKcx/5DV6fF3+DLmDLkvz+T9N87qtP2Mfc6aKkW/z6fyYJBRwcvim4rkptrYiq2slVUzNFto/kOPG4CHffF2fLUWFiUwVyeXNNhYIMXWdsdNSWKzYMpLRX2KlUqlofn06nCQSDq14FOp0OhgccmKaJXipVe9vrpRJOlwsjnpq7/iT4TIOg6kAN+JjMpDAti3rTl3ze5E/3znD1Fx4jm7MnzVd99DB6unZvS1qhKHT39BCbnCQJBINBXC4XxWX2SadSOJxOPB7PvHHlc6644kOhEFpZd0Ja1qoqRNqJcfsGrW7YT5JSHl/z+BdCiHuklMcLIR6uu9deyoOPpKpGHSCR1Pn+T0d47TlDvPWD9xNP2C3o73sgwUXvOZgzX9BbVwWqHoqi4PZ4yKTT9tjtbrjOvdZQ11t5NYJAkMvlyOfzeDyeeTfsWiouz1TG4Je/LWvBhx1ccN4G/u2Sv5Mv2CnPQwMevvjpo58xxr12guWx4BNvsFde6df2oIZ8gERbQ6GgSpncju3bMU2Tnt5eHA5HVca4q7sbr9fL+NgYpWJxniu+WSzLIpvNMj01RWdXF5l0mlKpxPDw8LKrz6Hh4WUNWTprcNm/P1ItKXz8qQzX3/AEl7x/025VSgRQlqg2qGdQVb8P0zSZnZjA4/GQz+fp6u5e5Onalc5/7RK3fY9WN+x+IcSwlHIHgBBiGLu7G9CyYlerZmp68bw9lzcZGctXjXqFvz6Y4PSTO/H7Gl+1V1zxmXSaSCRCOp1mfGxsWVe8lJLZeIm//yPJ0V3Nt59cCkVV6fX6sCZnKSgCaZjcufmFi55XcXk6HYKeLhf/eCzN857Txc/+e6xq1AF2juZ5aGuKU07sXJPj29PkcgbZvImuW0u2Ha2fgHYb05ksLrcHX2Tt2qgahsHkxASKotDp9mKOT5FTNYSAKAJmkpglk77+fnRdEk8a+H0aLudi417pFV7bJxzmVoSKouDz+SgVi0xP2Tq1g0NDiBUmCitNJKZniiysCnx4a5pCYW09GyshLYuJiQl7pe52MxWL2QmN0VBdI+qwrOpE2Ofz4fN6d1l0qpZm82XatD6tbtg/CPxRCPEkdn7IfsA7hRA+bFnYfYqTT+jgCzc+iWHMzcbPekHvvG5g4ZCDyy7czM7RPDf9YAcvOKWb/l53Q13VKq54zeHA4/FUa5EFttGv6MLXMj1b4s3vu4/ZhM43L50T3qvcoIF5N+lGXHaTUwUiYYv/O+wMYOXVv9ej8fYL9ufevydwu1QmpxZPgDIZY4k99z7SaZ1bfj3Gjd/djmnamdzXfOIIupYQMFmIlBYOp4Ouri4cjrX9aSuKQv/AAMZYjN9vXrosa7Lo5LqvPsGO0TynPruT154zxMKjvuelb6s+37uxfnJcqTQ3b9d1fcVV6Eo17V2dLjRNzPttHX14aLdnyAtFsTsklmvMnTXjpTBNk3Qqha7rOJ1Ostks/kCg4aTTNs9MWtqwSylvFUIcBGwqb9pakzB33R46rHUjGnbwtWuO5YZvPU0ub/Lac4bYOOzFNCXHHRnivgeSvO+tB/L172zjwUfshhB/uHuayy7cTGeHhaIIZmZLPPJYisM3h+jqcC4y+A6Ho2q8dV0nPjtLb18fxUKhOq417rfePsHsAm8BzN2gwb5JG9EgXq+3YTdgPLGyw0VxOqvKXlHgR1cOY5pgeXr545/ntNU9HpUtR0f2CZdiPKXztW9tqz5+anuWr393G+9/+4ENiaPouo5lmUiprpkOuKZpdPf0oCgK9aZPpil550V/q3qWvv/TEUq6xTtf2dl01ng2m6VUKjE4NEQ6lWIqFltRqnl2ZoZwJIIA4vE44Uhk3rXo92lcefFhfPr6rSRSOkceGuTdbz5g2dV6rXtccToxc3ZpnbSs6nXZ7LWlKAou19x0p3ZcD9M06eruxuf1Eo/Hq5PwRmjHzJ+ZtLRhF0K8YsGmA4QQSeBBKWVsqX32ZlxlqcpPXLgZ05TzkuM+ceGh7BjN4fVoVaO+6aAA733LAXzxxqe46I29OPQCas7g8CgwmaOYdeDoDuJekP2qKApSShwOB4ZhMLJzJ6ZpEiyr1dUST84Z4HqGwjItpqem6Ovvr5bxwPybimlKpmbslfbytddzmLn8ki760x67gy9dfTTfvWUnoYDGG87dQDTsoDQyVXUpHv/zG1C9dmKUe6C3avBb3ciPjOUXbXv08TT5vLmiYdc0DY9bIx6P09XVtaaVDiu9lmnKReGi2/8Q44Jzh+nY2Hj2dcUV7/V67cZGkQihcHhFV3y27KpWVJVSsUgwFJqXTa9m0xzTnef7nxzCFrsVKOkpStS/Hmoz3nPbRpYNF60Xlc8A7BBWZdzod9sWk3lm0tKGHbsBzLOB/8F2xZ8G3AfsJ4S4XEr5rT14bOtGZRUxmyjx2JMZZuMlthwd5tCDg4xNzlX4vfk1G/j4Z/7B1EwJvxLlrmMWu0hP3Xo7dEaW7OpUURGrlA0t1UP8ZS/p55ZfjeH1qHQMhDnl0d+iKGpVD9w0TaTHSYcvhMvlmmf8a28qM7NF3vOxe0mmDFRVcMtV8zWqm6HSiOOg/fyoqsBZjuXWThVUr5u7X/CGRfu2etxw/w0+FIV5UqgnHR9tuMQxorkwM1mKO8fnbV/vCc1Sk76+bjeq2rzXoPYaNBPp6qRMcTo57ZHf2n9QRNXYawE//QEvO3fuBF2nt5zcV3tMrRhHtixrSX2Ahb/BtqJkm2ZpdcOuAZullJMAQoge4JvY0q9/APZJww62Uf/Axx7giaftLHmXS+EbnzuWUMDBwQf4eezJDB63ytTMSitfWwTDNE2cTiemaWKaJg6Hg0LZ/e71eikWi4yPj9O3wBXf0+Xma9ccy89+PYbh9ZLRMwhhEYlGKRQKpJJJyGdxWQa+BS1TTdOqNigJBR1cdcnhfPSqf/Ci03vQazpZ1YvXy4VC3wuozT0AuwRqbycYcPCpjxxW7Uh2+nO6Ofelg/OaliznXt0VA1Zx8S6cADYSyy2WTM49e4Af/syup3e7FC5818G73BhoufOpxOhNwyAWi1X17WdmZ+lzuZrODt/tSMnIzp0EgkH8Ph/j4+N0dnUt+h2tN213/b5Hi1/5DFWMeplYedusEGJx4HcvZamb6bYd2apRB7vP9te/vY1L3n8I//7xI/jdXVP4fVo1Iag2KagWISARj5NOp+nq6mJ2dtYWA+nrw+FwVOtfLcsinU4v2t/jVjnkwADvfssBqIog4O1jbGwMp9PJzPQ03T09uJxOxsbG0HUdVVWZTej85s5Jnno6y1kv7OWAjT4CfgeHHhLkxuuORTckk0/G2HzXrwDIAJqqEI04cYT889yfzX6OS1HrlpeGuer46O7A61F59pYObrwuABLcS/QQX869uiv94vVyrXpfXx+WlExOTFSlZyvUC6+MpRSiYSffuO5YCgWT/l4P4WBzOgurRWJn7vcPDKAoChPj44smKS2JEPT09jIxPk4qmcRbDkHs7qS4trt+36PVDfudQohfAj8qPz6nvM0HJPbcYa0dpmlSLBarSTSVcXqJLO9UxsAwJNGIk3POGiCXM3j/2w7k2q88XjcrXAiIRKOUSiVisdhcly9NQ5GSUChUrTuujJfC69EwDYPJySksKZmZnmag3H3LSqbpQkNOzpC1IBsvceKAxaE9Xt598d+59IObOOOUbltUJepiaqbIe696GtMCRYEL33UwPq/KA/eleMFz/ezv1fF66huF2qS6eedax7DvbW55W3xm/RuPLETTNHw+H2NjYwDzmr1UqDUC+YLB+EySqz+/lXhS58Tjorzk+b107GY9AU3TGBgYQJRd2rXjXcHWrJe7bGgrrwN2yKJes5y2m73NWtHqhv1dwCuA55Qf3wv0SCmzwOl77KjWENM0mRgfJxgMIoQgmUwyMDjI0YeF8PtUMtk5hbfzXzaIq5glNz23KnvBJnje5/ava9Sg3KvdsA1/Ja5XSXKr3Exqx4ZhMZso8ad7ZvF6VY49MkJHxIks7z8wMIAQgsmJCbq6uzHS2SXdpZvvupXrrziCp3dkSaR0QgH79b0elRc/v5df3TbBWWf0MTFZ4KYf7uC5J0bp6lBJp1JoagjF7+XUrbcturGahQJ3HnLGovd73vY/8NxHfmvHLFdw47dZjBACr9dLJmNfXz6vd1nj6HFrnHhchP+8fguWlLhdSkNll+tBbV13szXeqbTOQ48m+eOfZ3jhaT3sv8FHwK/NC2Gpfi+nPXb7os+jEXe1YRiMjozQ09uLoiiMj43R19+PQ9MYHxuza9N9PmKxGG63e7e74tvse7S0YZdSSiHEU8CJwKuAp7H7rO8zaJpGT08Pk5N2xKGruxuHw4GmCb5x3XF88wfbmYnrnPvSATYfHMCYnlzSiD5/+//WVa5KxOMIIRjesIHp6WmmYjH6+/sRdW6AsekiF7z3PvJ5e1LR0+XihmuOoSPisvcrr4b6+voQy5RARcMOXN4ig30BAj6Vmelp293o8fCOC/bjuCPDDPV7eO9HHwBgeMCLZRnkswmkVSKfz+P1eumsyfCWUtZ10Rv5EnpHDx+7+lE+/vZ+Tt16m13TvOw30KaCruvEYjGCwSBmWUhloSt+Iaqq0BFtfcW/uqVrpoUyNcERUeg9NYiv24FlmRiGPclOp1JEOzpIZFJoTge9vb1Nr6wVRakq8wF4PB47/i8E/QMD1Tr2Sn17o0Z9XyjvbLM+tKRhF0IcDLy6/G8a+AEgpJT7xCq9FiklxeKc4EqxUCyX+ij0dbv4wDsOwjAkHreCoijkppd+HatUqiv4oRlGta63q7MTydKrGiklhin58S9HOeXEDn7zO7uicHKqyL1/S/Ci03uaWhmZlgRFI5ueoVRMY+g6wWAQhCAccvLC03qYni1Ws96+c/MImw8KsOmAALlsGlVV6erunnejqyRILUUypZMySxx7ZIitk3DM4R1MT07QKZc+TsOQzMSLRMPO1o7FNsGuJEJpmkZ3dzcerxeA/C7Ee9eqRe9aJXY1Urp2yqO3M5ubQppu3O4OIpEIpVKJmelpW1a33LmuWRRFIRgKVT0hwVCo+nk0W9NeSytm+rdpDVp1MfMo8DzgLCnlc6SUnwdW7jqyF2KaJolEAo8visfXQSqVRNdtF2AqlUJVJG6XIJVKVd3pjZDL60zPFLjt95NMzeoUS7bxVDVtyWxh0zDI5/MgJa88q493vGGA97x5v+rfs7nmld2kBT3dUVRVRS+V8Pr8CMUx7wYf8Guc97IhAJ57YpTNB3nI5zI4HHYzkGQyWW0vuxIlXfLej/ydTQcGefaWKLHJiWWbkkzPFvmX993P2MSeaRJYe16NnuNKODvCeDcOLvrXyI1eVVV8fj+qqs4bN4tpmuRyOUql0ryxtYrwyK6cT7NIaaKoKrrlp1Cwqg1owD4nW/yn+XMwDIPxsTE8Hg8+n4/JiQl0fZ/J/W3TgrTkih07rn4+8DshxH8D36d+j/W9mokpna6eQb53yyiFosmbXj1MbEanv0clVXYFulwuMpkMLmdjLk9dNzEMHcPQKRZNBAalkommClyuxV+5lJJCocDk5CTRaBSsPOl0kZc8v5/v3jJKLm9w8gkdTZ+bx2O73y3Lwu32kM2k0TQ3QnhxOu3jcDlVXvXP/Ry+KUg2p+N0SKTHQ1d3N3qpVPUKVLKcK+I6S6EoUChapNI6ouzmdDgcGEqqGh81TMl0jVDOzGyJL9z4JJe8b9Oq2+GuBsMwmJqaorOzEyFEdbywp3gqrZNK68wmdPp73YSDjnVtVVvrudgVL0YulyOXzeLz+chkMnR0dCw6t9WwlHTsWrqkpWURDGr4vCrJZAJVVRkcGmJmepqZ2Vl6e3ubPmZFUYh2dOD3214Gd8UV36bNOtGSV5eU8qfAT8vZ72cD7wO6hRBfBn4ipfztHj3ANeSnt47zx7/MVFeNf/zLLEcdFuIj/3YI/QMD7Ni+HV3X6ejowOV208ja0jAsMukMhXyaLUcGyWWmsNxe3C4X6bROYEG7VyEEbo+HSCTC7OwsAH39A/zoFxOcdHyU171yeNnOaQvdpbbrVUELeHEISU9PL4k0uD1OTEtQKJpVww4QCjo58bhotRUoQZ8t2GFZzM7OEgx3YRhF8tk03T09aMG596ssoGYTJWYLGhsGvTzr2Gg1018IgdoRwRkNI4Tgvgfi/Nsnn5p3/Nt25iiVLPA18OGuIaZh2K1QFWXJlWAqrXPjd7fx419WYrMqX/nM0RywsbXri1VVpbOzk7FikUwmg9fnwx8I7HLWt2EYpFOpqqpcZbxWLml7EmlRzCcI+LuIRCLIcBhVVeno7Fyyl0I9Fk42HEAxnkYL+AlEVt/atk2bRmhJw16hnP3+XeC7QogIdgLdRcA+Y9iPPjzED38+Wn08NlHgX169AcuySCbsir4ujw85naSQyCyrvlUhk7PwoRJAQ2ay+FARBZ3YwzsoOTwkuyIM9LnnK3PFU2jJdDUebY7HOOd4FS3Qjatz+U5htfHLSgghPjuLmjEJRyKUdIXLr/kH//T8HrbtjPP6V22ohhU0TZs3rk2SE0KzY5xTYwRDITq7utF1C0c4iBYOVg33bLzEVDqLFhJ8/lO91UnIUqvP4QEvTqdiG/Iyp53URcC/e38KmqbR09vLzh07ME2T3r6+Rau4TNaoGnWwe4pf8+UnuOqjh82TG14v4skSs/ESlgUdUSfRcGMeI9M0yWaz1eYt+VwOXddXHWeHOZXDRCJBsVi0XeOGQSAQWNXrLYVS9vLA4tKzZiclzUw20hkdw7AlpFej1LdetJPz9l5a2rDXIqWMAzeU/+0zHH+Ayg+vGKZQtGOsLpdKKGBgxFNks1l6ensRU3F+V0eneqmEOb9XxUzm+P2mxftsvutXvPeSHXzt2mOr9cZSSox0hj8s8fzTH78DOhvX+lZVlWAwSDKRwDRNCoUS9z+U46FH0zy8Nc1VHz2cYEAlFoth6Do9vb1MTk6iqSrdNclJiZTO3x9Osml/P6oi0TSNsdERevv6yWYyxONxBoeGUFWVaMTZcC/2UEDj8586in//0uNMThU449Ruzj17YJ6y2+6g0gpVVVWEotiVCuXQQYVkenEcdnKqgK6vfynfbKLEhZc9yNYn7Bv7xiEv1195VMM16vl8nmhHB8FgkKmpKYqFwi654oUQOBwOunt6mJyYAOx2ruoqXNrLJeTtThe5rpvsGM3z+a8/STyp86bzhznmiAihBoV91lsxrp2ct/ey1xj2fRWZy/HXLS9etP30x+9gcNhOKisQr7v/UtnHTqcgT/269th0kVzeqN6kl8s0h6XbYC53PBPj4yAEfr+fTCbFSVu6+eq/H0046CQScsy5akdHGdm50368oIxIU+Gwg9ykU1N4ff5qV7rRkZ0AdHSurve606ly2CFBrr3sCCxL4vNpDXVNWw/U8qq9EmNfSFeHi4BfmydWdMap3YtCKevBPX+NV4062OGK2+6c5PyXD624b+X7BTu+XBnvqivesqxq6aaUkng8Xn3tZlhJaS2ZKmGYoCrssiTuciSSBm/7f3+lWLQ47aQOjtjsx7JMLEvFMIxqEmM92opxberRNuwtTCPGNJfN4nA6cTocc2Ons+6+Xb1+vnnpIOHCDLlts9Xty+myp9Np/E1kSPv9fjzl7lwulwuPx8WhBzsWucaFokB5MlL5WzKts/WJNPFEkZOPj2AYIUYnFQIBgcfrrfbp3lXpzUiDbuVGadZtWSktq3ymtePqMYacfOUzR3Pd155kdDzPC07p5lUvHcC5G7wLT+/ILtr25PYspiVRlZXdxWvduKTiijdMk8GhIUzTZHJycsV+As0yNpHnk9c+yoOPpDj04ACXfnATQ/3eNX2PCo88nqJYtHA6BB9+7yHMTI2iaU6cjjCxyQki0SjBZao62rSpxz5p2IUQNwJnATEp5eHlbVHseviNwDbgXCllXNgW5Xrgn4Ac8EYp5f3lfS4APlp+2SuklDftzvNohFwuR256uuHsY5kv8MjJ/8QjC7ZX4/YLME2T+OwsPl9jmWWqqhIoq+gJIeaNa19zamoKKSV9/f1MxWJMxWJ0dffwm99N8h9fexKAT1y4iU0H+pmIpThwo4NkIkE4HCaXyzE2Olp1xbcCq3FbrmT8VFWwYcjH5R/aTEmXaKoglzd5cCxJX7ebYEDD6Vyf83/BKd18+8c752375xf2NWTU14OKK35oaKiqklgZV1zSFRtfsYPNuqTjiRIfufJhnthmT2r+8ViaD132EF+8+uiG8gssSxJPlHhyW5aDAytPOLo67Lr1ki657LOP8P/euT/Z9CSTE7YwUyAQaBv1NqtinzTswH8BX8DuBFfhw8AdUsqrhBAfLj++CHgJcFD537OALwPPKk8EPg5swe4zcZ8Q4uflWH/LEO3ooFjOPvb5fPbNYBljp9Qk59Q2R6GO675ifJsxoLU3o6VuTLWuWofDQV9/PwCZrMl3bp4zJp/47KNsHPJy3RVH4vOpqGoPHo+HYChEoWDXByy3UnZEQ8STOlLasfX1LBNbK2rPp6KQpgBuIcgVJfmMjmk4Oe/SB7j+iqM4fFNwXY6jr8fN1Zcexte+tQ3DlFxw3jD7Da/PyrVRFgoVVSi5/fz1cYMv3/QUxaLFq18+yBmn9eBtMmRR0q2qUa+wcyxPsdiYV2Bqpsib338/iaTOFy7ejyP+/GuiYSe1P4HayUZfj5tnb4nyp3tncbkUaqNh1r7QqrDNHmOfNOxSyj8IITYu2Hw2dj93gJuAO7EN+9nAN6VdHH23ECIshOgrP/c2KeUsgBDiNuDFwPfW+fAXsTBJxjItLGkhvG6y2awdj9M0WwhE13HWrGIWUuu6rG2OcvzPb5jXOtUq91sXXjfpVIpINLqmq+Nar0JlbFpFtAVZwZVSNE1zzXO/V8bFOivl0x67g/ueNvnSfz5FLm9y7tmDvPj0noYTk/YUtSv/E2//5pLNazbf9SuKRYurP7+V/7jyqDUPKwD4vBonHd/BoQfbE4dQ0IGyxqt1wzCQ0k6MNE2zOm62fn5qpsjFVz5cfXzdDU/S3+vhpOOb017QVEFXh3NeK+Rw0IHDsfLx6LrFd2/ZSSJpJzy++9NPA3D1pYdx8glL5wGEQ04ued8hzCRKDA94GBvZic/nIxQOMz42RjqdXtIVb1mSYsnC7dr1RjfL0W7nuveyTxr2OvRIKcfL4wmgpzweAGp9jiPlbfW2L0II8TbgbQDDw8NNHVQjP56FSTKWZbFj+3a6fR4K6fRc9nEsVs0+rpdYU09n/Z6Xvq06Pu2x25nBboNpGAaZyUnCkcYz41dLOOjkLa/byJWf21rddvzR4Wrb0pU8AbWYluRDlz9U7dH++a8/SW+Xi1NP6lr7A99DjE0Wdnllt1JuwHpMGsC+huPxONlMht6+vqqQ0cDgYNPG6o9/nlm07dbbJ9hyVLipUEU45OTyDx3KhZc/SCZr4vWofOJDmxtqP2sYkolYcdH22PTibQvfMxxyIqWstp1VFKWq0b/wOo8nS/zP/07xl/vjnLglyuknd65bgl87OW/v5Zlk2KuUm8usma9LSlktw9uyZUtTr1v746m9yRrpTHVcm4BlmiYz09N2K8h0lkBeh7xOIZEhAFDIYJqg7sIPUgjB8PAGO36paQxv2LBbtNRVVfCcEzr4xueO5Xd3TbHpwABHHRZa1Sq7WLRYaPPCzKaLAAAUv0lEQVRuvX2SE46J4vG0Rlx+V3neyV143bso+rKHSpoURSESiVAsFqsiPZWugc2y/8bF+R8H7e9HVZsLvaiqYNPBAb7zpePJ5U08HrXhEI7Ho/LKswa46y9zkwxNE5x4XGNeg0oOQYWl8mTSGZ1rvvQ4d/6f3TDirntmePCRJB/414N2q2pim9bnmXQ1TAoh+qSU42VXe6y8fRSoreEZLG8bZc51X9l+53oeYKM3WVVV6evrw5qc4XcHv2DF59dS6yGQRn198spKYXc3R3EVswxpGd5wqhMoQiJGLtG8KIamLT7u/Tf6GnKrrobd6bZUFcErzuznjedvwOPZy3/CNbOv1c60Dz0owPFHh7nnb7ag037DXs56YR+qKjBNsypkVDuuh0NT6Ii6aF5AGTYd5OfKjxzK924ZwetR+dcL9mtYX6ER8gWL3/9pfheo2/8Q4x0X7N827G3m8Uy6Gn4OXABcVf7/ZzXb3y2E+D528lyybPx/A3yqrHgH8ELg4t18zItQVZVwJIIQoiF52YUs7HLVClimicQ+t7VaQTo0hROPi3L3fXZJ39CAh3PO6l929WWr3YlF40bYnW7LjqiTd72pH9ceqr9fCyo16YZpMjAwwPT0NJMTE6tyxUfCTj5+4WbSGQPTtBXcomEnZlmPPxKJoDkczExPEwqFcLpc6zJhDfgdnPrsLo4+LIyqijU3tkLYXgXDmJsCadr8pLs2bWAfNexCiO9hr7Y7hRAj2NntVwE/FEK8GdgOnFt++q3YpW5PYJe7vQlASjkrhPgkcE/5eZdXEun2NLuSxKbrFsm0zshYngM7vZy69Q4Whqt3Z3KMZZrk8nn0UolQOFy3wUs9llspX/qBHlIZHV2XhEOOZUuWpJSUSqWqKEhlvLubdSzsG15PPti5Fxt1sD1C4UiEUDhclddtdjJVSzjoJBxc8P2WyyzHyp3VItFo1RNlGEa1bG6tqPQ6CAUd1br7tXx9n1fl1S8f4ls/2lHd9oZXDuHfzXLIbVqfffKKkFK+us6fFi0Fy9nw76rzOjcCN67hoe1xnt6R5Z0X/Y1CuYTnvLMHeOP5Gwj492y2eDweJ5fPE2iyfelyK2UnNByftyyLqVgMy7KIdnQwFYvh9/uJdnTs1lr5Z1LCUu2kaT0mUKqq0tXdzbann6ajs5PxsTG8Ph/hcJix0VGCwSDBUGhNasWllOi6zvT0NN3d3ViWxfTUlN20aI3OzevROP/lgzznhA7+/o8ExxweZqDPs8eUE9u0LvukYd8XWSpW2CyJVIlrvvx41agD/OBno7zynwebMuylkoUl5bwbSrOxzAqKquIt96nOZrME9tAlqaoqvX19jI6MEJucxOV273ajvifYl0uaTMMgFoshhCCTThONRonFYqSSSRwOB/41FICpiDDppRJjo6NYloWjwTbLzRAKOAhtcnDYOukXtNk3aBv2FqLeTVb1+5idmSEYCqFp2tw44GvoplzJttcsycXnh7As+6aQMpy8+9NPk8kai15jKXTdZCJW5Js/2kGxaPG6Vw4x2O/F5bRX3MFgEFVVq2OHw7Gica+44rPZLC63G/KLG5/sDiqu00r7VLNcY72vkckaOB2iWgbm7Ahj+QJk8wYg9hohn4YQAlXT6B8YqNbKC0VBWhaudYiza5pGZ2cnk5OTAPSs4Wp9NlHCsiQOTWl5LYY2e562YW8h1HAAw+PE5bKlJovFYnVsTE4yNjqK0+mkVCoRCAZxREI4/397dx4dV3necfz7zKbRalmysLUYzFZjh8VgQ1yak+SkkLI1Tk9IICWFJiQ0h5KSpBBo6IGQnCRNIWQBQssBCrRpgAJNCOSQUkJCaoqxDbEBg4NrbPAuy5K8SB5JM0//uFdC8iZszWhGd36fczi6950ZzXt5rXnmXe77NI5+f/n+FqTNWvgE9XXJA67cHX4LXjbr9Hf08cnTE2wfSPHZL7/IvT+cyxGtaTK7d7Nhxw5SFRVkdu+mpqbmoOZMJ4fzrZmOTj6w8qm9elJj7UGONqIwOBSfTqdpOuwwNm7YQOe2bZHptXfv6GfR0m08/tQm2porufgThzO1qYLuHQPc/+BafvrkRqrScS7/zFG8f/6USKyyjsfjNDY2EovFcHc2bdxIIh6nrqGBjq1bSaVSeR+Kb29vJ5VKDSVD2lc63oORzTqr1+7ihptW8Na6Xk6YVccNV81i2mHp0V8sZWvi//VGiLuzedMmqqqrSSWTdHZ20tzSQjqdZuq0aaxds4ZMJsPkhgYqUqmhxVSHqiIV545/nHPADTgO9KUgl4MH/nMdV1/xBzQ3N7NmzRp2h+k6322PaHAo3isricViVDTU4w31eQ2m2WyWrVu3MnnyZOLx+NDx8BGFwaH4wQVVzS0teV9cVSzZrPP0s1u45Z9WAfDi8i4WvtDB/bfN5X+e7+Chx9YDwRTLt76/klm310YisMM7C03dnabDDiOZTBKLxUilUkPH+TA4FF9ZWcmUpiZyuRzbOvbeOOdgdXX38+UbltPZFYxkvfzadr7+3df59nXvUc9d9isiY27REIvFmNbcPJRvvDEMkIO9SYBUKkVXZyd9/f1jHiqun5RkemsV8fihD0lOqk1g5Ni6Nbi/NpFI0NXVRf9B1C82LD3l8ON8cXcGBgbYsH49GzdupGfXrn3WLZFIDL338OOJrntHP488vmFEWUdnH13d/TyzcO90sUuXdY1X1cZNLBYjnU6TSCRGHOdTMplkSlMT8Xh86His79G7OzsU1ActX9FN/0B+s9pJtCiwlxB3J5N5ZwvK3ZnMUAByoLmlheaWFiqrqoKd58YY2M2gq7uPTVt2s7UjQyZzcCvSa6rjnP+RNozg9qHmlhZa29pIJZPkcrmSmaNOJBJMmzaNXC5HXyZD45Qp72r+PyriMaOudu8Ak0rF9plEZuYxE3/h3L7smTa4EPKdrjadju21U+JRR1Tnfd9+iRYF9hLi7nRu28aUKVNoaWmhZ9cu+vr6iMViNDU1kUqlglt4wuOxDiPmcs4133iF8y9dxIV/9QJPPrOZXT3vbiHdpNok/3r7qUxpqCAe3odcUVFBPB5n6rRpealfvmSzWbZs3jy0bee2jo6hBCSF0tubpaMzQ0dnhmy2uL2rSXVJrrj06BG78b33lMlUpuMsOLuF98ysBYIvegvOmsb01spiVVX2UFeT5OtfmUVVGNwbG1LccNVx7yqNrJQvK5VeVVTMmzfPlyxZckivHezl7rn72VgD5L4SfbhDe2+cT13z+xHlD911Gi1T3/lg71mzbr87wVXNaBtTvcbLwMAA7Vu20NDYSCKRYMvmzTQ0NpJKpQrSc+vq7uOuH6/hiac2UVuT4MrLjmH+KQ1UF3HeOpPJ0tndz+9e6aJ5aprDW6uGErx0dffRuztLPGZUVsaLvqeBjNTXl2X7zgEymRzpdJz6uuSYps9KlZktdfd5xa5HFCiw59lYAvt42tbZxxe+uoy163pGlN9y4wmcdkrD0Plo2b8mikO9z/7g38d5+OfruPXu1SPKH/jnU2lrKW4+c5FSpsCeP9FY+ioHrboqztyT6kcEdjM4vG1k8InKTmj5nvvcn109Azz7/N6roV9+bbsCu4iMCwX2MlVREeeSC45g3YZeXnipk7aWNDdePRsz2NqRoaY6QXqMKUHLUWU6zuyZtSx7tXtE+dH7SC0qMpqojJjJ+FJgL2ONk1N87epZZDI5MPjOrSt5fmknFRUxPnfRDM49Yxq1tQc331ruH0TJZIwLF7SxdFkXv/+/ncRi8PE/bWVqkzYUkYOXr2yHUl4U2EvUeAXIutokA5U57ntwLc8v7QQgk8lx2z2rmT+34aADuz6IoLGhgu9+7QR6d2dJJIyqykRkNnyRkWmGhx+LlAp92pSo8QyQPb1Zli7fe1OS11ftYMbh+RlCjqVS+8z/HtWe/OT6FKNv9isTTS6XY1dPD12dnbS0trJr5066u7tpaW1VcJeSocAuVFXGmXfSZJav2D6i/LhjavP2HtmeXn4968N7lZdTT14mvlgsRmU6Tac7b61di7vT2NhY7GqJjKDALiQSMT56Tguvr9rBc4u3cft1R9JS51RmO+lZ0/nO8yLauxY5GLFYjOqaGrq7ujAzampr1VuXkqLALgA01Kf4+y8dx+5MjqrtW/jNzDP3eo5611LuBofiu7u6qKuro6enhw3r1xdsKH5/qZzHmu1Qok2BXYbU1Sapq4WenYe+ccv+Pog8p6QVMvENDsU3NTVRXVNDfX09vb29BXu/qOwjIeNLgb1ETdRv6vv7INrXwjkpb9t39rNufS+/+d+tzJ5Zy4mzJzF5UunvgZ5IJqmOx4nFYsGwfHgsUioU2EtU1L6pT9QvKlIYAwM5fvXbdm7+0RtDZaef2sB1XzxuQuQZHx7IFdSl1Ciwy7iI2hcVGZvuHf3c/e9rRpQ9t3gbvbuzEyKwi5QyBXbZi3rXUnhGNrt3AirlpBIZOwX2UZjZWcAPgDhwl7v/Q5GrVHDqXUuh1dUkuOhj07nj3jeHyk6cXUdlurjD2oXe8bHct1yW8aHAfgBmFgduB84E1gGLzewxd19R3JqJTGzJZIzzzmzm6Bk1/NevN3PirEl84PQp1Bd58Vyhd3zUlssyHhTYD+w0YJW7rwYwsweABYACu8gYTapLMn9uA6fOqSce1wI0kXzRX9OBtQJvDztfF5aNYGaXmdkSM1vS3t4+bpUTiQIFdZH80l9UHrj7ne4+z93nNTU1Fbs6IiJSxhTYD2w9MH3YeVtYJiIiUpI0x35gi4FjzexIgoB+IfDnxa2SiBRKoW/11K2kMh4U2A/A3QfM7ArglwS3u93j7q8WuVoiUiCFvtVTt5LKeFBgH4W7/wL4RbHrISIi8m5ojl1ERCRCFNhFREQiRIFdREQkQhTYRUREIsRc6ZTyyszagbUH8ZIpwNYCVadUleM1Q3ledzleM5TndY/1mo9wd+3wlQcK7EVmZkvcfV6x6zGeyvGaoTyvuxyvGcrzusvxmkuVhuJFREQiRIFdREQkQhTYi+/OYlegCMrxmqE8r7scrxnK87rL8ZpLkubYRUREIkQ9dhERkQhRYBcREYkQBfYiMbOzzGylma0ys2uLXZ9CMbPpZvaMma0ws1fN7MqwvMHMnjKzN8Kfk4td13wzs7iZvWRmj4fnR5rZorDNHzSzVLHrmG9mVm9mD5vZ62b2mpn9YdTb2sy+FP7bfsXMfmJm6Si2tZndY2ZbzOyVYWX7bFsL/DC8/uVmdkrxal5+FNiLwMziwO3A2cBs4JNmNru4tSqYAeBv3X02MB/46/BarwWedvdjgafD86i5Enht2Pl3gO+5+zFAJ3BpUWpVWD8AnnT344CTCK4/sm1tZq3A3wDz3P14gvTOFxLNtr4XOGuPsv217dnAseF/lwF3jFMdBQX2YjkNWOXuq929D3gAWFDkOhWEu2909xfD4x0EH/StBNd7X/i0+4CPFqeGhWFmbcC5wF3huQEfAh4OnxLFa54EvB+4G8Dd+9y9i4i3NUH660ozSwBVwEYi2Nbu/iywbY/i/bXtAuB+DzwP1JtZ8/jUVBTYi6MVeHvY+bqwLNLMbAZwMrAImOruG8OHNgFTi1StQvk+8BUgF543Al3uPhCeR7HNjwTagX8JpyDuMrNqItzW7r4euBl4iyCgdwNLiX5bD9pf25blZ1ypUGCXcWFmNcAjwBfdffvwxzy45zIy912a2XnAFndfWuy6jLMEcApwh7ufDOxij2H3CLb1ZILe6ZFAC1DN3sPVZSFqbTuRKbAXx3pg+rDztrAskswsSRDUf+zuj4bFmweH5sKfW4pVvwL4I+AjZraGYJrlQwRzz/XhcC1Es83XAevcfVF4/jBBoI9yW58BvOnu7e7eDzxK0P5Rb+tB+2vbsvqMKzUK7MWxGDg2XDmbIlhs81iR61QQ4dzy3cBr7n7LsIceAy4Jjy8BfjbedSsUd/87d29z9xkEbfsrd78IeAY4P3xapK4ZwN03AW+b2cyw6I+BFUS4rQmG4OebWVX4b33wmiPd1sPsr20fAy4OV8fPB7qHDdlLgWnnuSIxs3MI5mHjwD3u/s0iV6kgzOx9wG+Bl3lnvvmrBPPsDwGHE6S5/YS777kwZ8Izsw8CV7n7eWZ2FEEPvgF4CfiUu2eKWb98M7M5BAsGU8Bq4NMEHYjItrWZ3QhcQHAHyEvAZwnmkyPV1mb2E+CDBOlZNwM3AD9lH20bfsm5jWBaogf4tLsvKUa9y5ECu4iISIRoKF5ERCRCFNhFREQiRIFdREQkQhTYRUREIkSBXUREJEIU2EVKTJgh7fLwuMXMHh7tNWN4rznhrZciEhEK7CKlpx64HMDdN7j7+aM8fyzmAArsIhGi+9hFSoyZDWb7Wwm8Acxy9+PN7C8JsmdVE6TDvJlgI5i/ADLAOeHmIEcTpAVuItgc5HPu/rqZfZxgU5EsQbKSM4BVQCXBdp/fBh4HbgWOB5LA19z9Z+F7/xkwiWDzlX9z9xsL/L9CRA5BYvSniMg4uxY43t3nhBnxHh/22PEEGfLSBEH5Gnc/2cy+B1xMsJvhncDn3f0NM3sv8COC/eqvB/7E3debWb2795nZ9QS5xK8AMLNvEWyB+xkzqwdeMLP/Dt/7tPD9e4DFZvaEdhMTKT0K7CITyzNhXvsdZtYN/Dwsfxk4McyidzrwH8GungBUhD8XAvea2UMEyUr25cMECWyuCs/TBNuFAjzl7h0AZvYo8D5AgV2kxCiwi0wsw/cbzw07zxH8PccIcoHP2fOF7v75sAd/LrDUzObu4/cb8DF3XzmiMHjdnvN2mscTKUFaPCdSenYAtYfywjDX/ZvhfDphdq2TwuOj3X2Ru18PtBOk1dzzvX4JfCFM4oGZnTzssTPNrMHMKgnm+hceSh1FpLAU2EVKTDjcvdDMXgFuOoRfcRFwqZktA14lWIgHcJOZvRz+3ueAZQTpRWeb2e/M7ALgGwSL5pab2avh+aAXgEeA5cAjml8XKU1aFS8iowpXxQ8tshOR0qUeu4iISISoxy4iIhIh6rGLiIhEiAK7iIhIhCiwi4iIRIgCu4iISIQosIuIiETI/wMbbttYK4fH5AAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot of agent activity per timestep\n",
+    "param_plot(median_df,'timestep', 'AggregatedAgentSpend',swept)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "param_plot(median_df,'timestep', 'VelocityOfMoney',swept)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "param_plot(median_df,'timestep', 'operatorCICBalance',swept)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "param_plot(median_df,'timestep', 'operatorFiatBalance',swept)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "param_plot(median_df,'timestep', 'AggregatedAgentCICHolding',swept)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "param_plot(median_df,'timestep', 'AggregatedAgentCurrencyHolding',swept)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "param_plot(median_df,'timestep', 'AggregatedAgentDemand',swept)"
+   ]
+  }
+ ],
+ "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
+}
diff --git a/Colab/images/graph.png b/Colab/images/graph.png
index 237c46c..76d72e4 100644
Binary files a/Colab/images/graph.png and b/Colab/images/graph.png differ
diff --git a/Simulation_param/.ipynb_checkpoints/CIC_Network_cadCAD_model_params-checkpoint.ipynb b/Simulation_param/.ipynb_checkpoints/CIC_Network_cadCAD_model_params-checkpoint.ipynb
new file mode 100644
index 0000000..5de9b02
--- /dev/null
+++ b/Simulation_param/.ipynb_checkpoints/CIC_Network_cadCAD_model_params-checkpoint.ipynb
@@ -0,0 +1,1007 @@
+{
+ "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": [
+    "# Run cadCAD model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/aclarkdata/anaconda3/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
+      "  import pandas.util.testing as tm\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[{'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 30}}, {'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 60}}, {'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 90}}]\n",
+      "[{'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 30}}, {'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 60}}, {'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 90}}]\n",
+      "[{'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 30}}, {'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 60}}, {'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 90}}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import math\n",
+    "import pandas as pd\n",
+    "from tabulate import tabulate\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "\n",
+    "from model.parts.supportingFunctions import *\n",
+    "\n",
+    "pd.options.display.float_format = '{:.2f}'.format\n",
+    "\n",
+    "%matplotlib inline\n",
+    "from tabulate import tabulate\n",
+    "from typing import Dict, List\n",
+    "\n",
+    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
+    "import ipywidgets as widgets\n",
+    "from IPython.display import clear_output\n",
+    "\n",
+    "# The following imports NEED to be in the exact order\n",
+    "from cadCAD.engine import ExecutionMode, ExecutionContext, Executor\n",
+    "from model import economyconfig\n",
+    "from cadCAD import configs\n",
+    "\n",
+    "exec_mode = ExecutionMode()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "                            __________   ____ \n",
+      "          ________ __ _____/ ____/   |  / __ \\\n",
+      "         / ___/ __` / __  / /   / /| | / / / /\n",
+      "        / /__/ /_/ / /_/ / /___/ ___ |/ /_/ / \n",
+      "        \\___/\\__,_/\\__,_/\\____/_/  |_/_____/  \n",
+      "        by BlockScience\n",
+      "        \n",
+      "Execution Mode: multi_proc: [<cadCAD.configuration.Configuration object at 0x7f4a099f8890>, <cadCAD.configuration.Configuration object at 0x7f4a098ff450>, <cadCAD.configuration.Configuration object at 0x7f4a09937350>]\n",
+      "Configurations: [<cadCAD.configuration.Configuration object at 0x7f4a099f8890>, <cadCAD.configuration.Configuration object at 0x7f4a098ff450>, <cadCAD.configuration.Configuration object at 0x7f4a09937350>]\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": [
+    "exec_mode = ExecutionMode()\n",
+    "multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)\n",
+    "run = Executor(exec_context=multi_proc_ctx, configs=configs)\n",
+    "\n",
+    "i = 0\n",
+    "results = {}\n",
+    "for raw_result, tensor_field in run.execute():\n",
+    "    result = pd.DataFrame(raw_result)\n",
+    "    results[i] = {}\n",
+    "    results[i]['result'] = result\n",
+    "    i += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>4000</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': 0, 'd': 0, 'e': 0, 'f': ...</td>\n",
+       "      <td>{'a': 0, 'b': 0, 'c': 0, 'd': 0, 'e': 0, 'f': ...</td>\n",
+       "      <td>{'a': 0, 'b': 0, 'c': 0, 'd': 0, 'e': 0, 'f': ...</td>\n",
+       "      <td>0.72</td>\n",
+       "      <td>{'a': -2.1724474214163934, 'b': 87.78588308123...</td>\n",
+       "      <td>{'a': 564.306616354587, 'b': 2785.025646787363...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[h, a, a, k, p, c, c, g, o, o, i, f, b, b]</td>\n",
+       "      <td>[k, d, p, p, l, p, d, e, p, e, d, b, external, o]</td>\n",
+       "      <td>16500</td>\n",
+       "      <td>198500.00</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>4</td>\n",
+       "      <td>100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4001</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': 0, 'd': 0, 'e': 0, 'f': ...</td>\n",
+       "      <td>{'a': 0, 'b': 0, 'c': 0, 'd': 0, 'e': 0, 'f': ...</td>\n",
+       "      <td>{'a': 0, 'b': 0, 'c': 0, 'd': 0, 'e': 0, 'f': ...</td>\n",
+       "      <td>0.72</td>\n",
+       "      <td>{'a': -2.1724474214163934, 'b': 87.78588308123...</td>\n",
+       "      <td>{'a': 564.306616354587, 'b': 2785.025646787363...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[h, a, a, k, p, c, c, g, o, o, i, f, b, b]</td>\n",
+       "      <td>[k, d, p, p, l, p, d, e, p, e, d, b, external, o]</td>\n",
+       "      <td>16500</td>\n",
+       "      <td>198500.00</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>5</td>\n",
+       "      <td>100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4002</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': 0, 'd': 0, 'e': 0, 'f': ...</td>\n",
+       "      <td>{'a': 0, 'b': 0, 'c': 0, 'd': 0, 'e': 0, 'f': ...</td>\n",
+       "      <td>{'a': 0, 'b': 0, 'c': 0, 'd': 0, 'e': 0, 'f': ...</td>\n",
+       "      <td>0.72</td>\n",
+       "      <td>{'a': -2.1724474214163934, 'b': 87.78588308123...</td>\n",
+       "      <td>{'a': 564.306616354587, 'b': 2785.025646787363...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[h, a, a, k, p, c, c, g, o, o, i, f, b, b]</td>\n",
+       "      <td>[k, d, p, p, l, p, d, e, p, e, d, b, external, o]</td>\n",
+       "      <td>16500</td>\n",
+       "      <td>198500.00</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>6</td>\n",
+       "      <td>100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4003</th>\n",
+       "      <td>(a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...</td>\n",
+       "      <td>{'a': 330, 'b': 590, 'c': 303, 'd': 0, 'e': 0,...</td>\n",
+       "      <td>{'a': 352.69163522161693, 'b': 850.37760837978...</td>\n",
+       "      <td>{'a': 1.0687625309745967, 'b': 1.4413179803047...</td>\n",
+       "      <td>0.72</td>\n",
+       "      <td>{'a': -2.1724474214163934, 'b': 87.78588308123...</td>\n",
+       "      <td>{'a': 564.306616354587, 'b': 2785.025646787363...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[h, a, a, k, p, c, c, g, o, o, i, f, b, b]</td>\n",
+       "      <td>[k, d, p, p, l, p, d, e, p, e, d, b, external, o]</td>\n",
+       "      <td>16500</td>\n",
+       "      <td>198500.00</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>7</td>\n",
+       "      <td>100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4004</th>\n",
+       "      <td>(a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...</td>\n",
+       "      <td>{'a': 330, 'b': 590, 'c': 303, 'd': 0, 'e': 0,...</td>\n",
+       "      <td>{'a': 352.69163522161693, 'b': 850.37760837978...</td>\n",
+       "      <td>{'a': 1.0687625309745967, 'b': 1.4413179803047...</td>\n",
+       "      <td>20.94</td>\n",
+       "      <td>{'a': -2.1724474214163934, 'b': 87.78588308123...</td>\n",
+       "      <td>{'a': 564.306616354587, 'b': 2785.025646787363...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[h, a, a, k, p, c, c, g, o, o, i, f, b, b]</td>\n",
+       "      <td>[k, d, p, p, l, p, d, e, p, e, d, b, external, o]</td>\n",
+       "      <td>16500</td>\n",
+       "      <td>198500.00</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",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                network  \\\n",
+       "4000  (a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...   \n",
+       "4001  (a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...   \n",
+       "4002  (a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...   \n",
+       "4003  (a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...   \n",
+       "4004  (a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...   \n",
+       "\n",
+       "                                              KPIDemand  \\\n",
+       "4000  {'a': 0, 'b': 0, 'c': 0, 'd': 0, 'e': 0, 'f': ...   \n",
+       "4001  {'a': 0, 'b': 0, 'c': 0, 'd': 0, 'e': 0, 'f': ...   \n",
+       "4002  {'a': 0, 'b': 0, 'c': 0, 'd': 0, 'e': 0, 'f': ...   \n",
+       "4003  {'a': 330, 'b': 590, 'c': 303, 'd': 0, 'e': 0,...   \n",
+       "4004  {'a': 330, 'b': 590, 'c': 303, 'd': 0, 'e': 0,...   \n",
+       "\n",
+       "                                               KPISpend  \\\n",
+       "4000  {'a': 0, 'b': 0, 'c': 0, 'd': 0, 'e': 0, 'f': ...   \n",
+       "4001  {'a': 0, 'b': 0, 'c': 0, 'd': 0, 'e': 0, 'f': ...   \n",
+       "4002  {'a': 0, 'b': 0, 'c': 0, 'd': 0, 'e': 0, 'f': ...   \n",
+       "4003  {'a': 352.69163522161693, 'b': 850.37760837978...   \n",
+       "4004  {'a': 352.69163522161693, 'b': 850.37760837978...   \n",
+       "\n",
+       "                                     KPISpendOverDemand  VelocityOfMoney  \\\n",
+       "4000  {'a': 0, 'b': 0, 'c': 0, 'd': 0, 'e': 0, 'f': ...             0.72   \n",
+       "4001  {'a': 0, 'b': 0, 'c': 0, 'd': 0, 'e': 0, 'f': ...             0.72   \n",
+       "4002  {'a': 0, 'b': 0, 'c': 0, 'd': 0, 'e': 0, 'f': ...             0.72   \n",
+       "4003  {'a': 1.0687625309745967, 'b': 1.4413179803047...             0.72   \n",
+       "4004  {'a': 1.0687625309745967, 'b': 1.4413179803047...            20.94   \n",
+       "\n",
+       "                                        startingBalance  \\\n",
+       "4000  {'a': -2.1724474214163934, 'b': 87.78588308123...   \n",
+       "4001  {'a': -2.1724474214163934, 'b': 87.78588308123...   \n",
+       "4002  {'a': -2.1724474214163934, 'b': 87.78588308123...   \n",
+       "4003  {'a': -2.1724474214163934, 'b': 87.78588308123...   \n",
+       "4004  {'a': -2.1724474214163934, 'b': 87.78588308123...   \n",
+       "\n",
+       "                                           30_day_spend withdraw  \\\n",
+       "4000  {'a': 564.306616354587, 'b': 2785.025646787363...        0   \n",
+       "4001  {'a': 564.306616354587, 'b': 2785.025646787363...        0   \n",
+       "4002  {'a': 564.306616354587, 'b': 2785.025646787363...        0   \n",
+       "4003  {'a': 564.306616354587, 'b': 2785.025646787363...        0   \n",
+       "4004  {'a': 564.306616354587, 'b': 2785.025646787363...        0   \n",
+       "\n",
+       "                                  outboundAgents  \\\n",
+       "4000  [h, a, a, k, p, c, c, g, o, o, i, f, b, b]   \n",
+       "4001  [h, a, a, k, p, c, c, g, o, o, i, f, b, b]   \n",
+       "4002  [h, a, a, k, p, c, c, g, o, o, i, f, b, b]   \n",
+       "4003  [h, a, a, k, p, c, c, g, o, o, i, f, b, b]   \n",
+       "4004  [h, a, a, k, p, c, c, g, o, o, i, f, b, b]   \n",
+       "\n",
+       "                                          inboundAgents  operatorFiatBalance  \\\n",
+       "4000  [k, d, p, p, l, p, d, e, p, e, d, b, external, o]                16500   \n",
+       "4001  [k, d, p, p, l, p, d, e, p, e, d, b, external, o]                16500   \n",
+       "4002  [k, d, p, p, l, p, d, e, p, e, d, b, external, o]                16500   \n",
+       "4003  [k, d, p, p, l, p, d, e, p, e, d, b, external, o]                16500   \n",
+       "4004  [k, d, p, p, l, p, d, e, p, e, d, b, external, o]                16500   \n",
+       "\n",
+       "      operatorCICBalance                                     fundsInProcess  \\\n",
+       "4000           198500.00  {'timestep': [], 'decision': [], 'cic': [], 's...   \n",
+       "4001           198500.00  {'timestep': [], 'decision': [], 'cic': [], 's...   \n",
+       "4002           198500.00  {'timestep': [], 'decision': [], 'cic': [], 's...   \n",
+       "4003           198500.00  {'timestep': [], 'decision': [], 'cic': [], 's...   \n",
+       "4004           198500.00  {'timestep': [], 'decision': [], 'cic': [], 's...   \n",
+       "\n",
+       "      totalDistributedToAgents  totalMinted  totalBurned  run  substep  \\\n",
+       "4000                      1500            0            0    5        4   \n",
+       "4001                      1500            0            0    5        5   \n",
+       "4002                      1500            0            0    5        6   \n",
+       "4003                      1500            0            0    5        7   \n",
+       "4004                      1500            0            0    5        8   \n",
+       "\n",
+       "      timestep  \n",
+       "4000       100  \n",
+       "4001       100  \n",
+       "4002       100  \n",
+       "4003       100  \n",
+       "4004       100  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results[0]['result'].tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(0,len(results)):\n",
+    "    results[i]['result']['agents'] = results[i]['result'].network.apply(lambda g: np.array([get_nodes_by_type(g,'Agent')][0]))\n",
+    "    results[i]['result']['agent_tokens'] = results[i]['result'].network.apply(lambda g: np.array([g.nodes[j]['tokens'] for j in get_nodes_by_type(g,'Agent')]))\n",
+    "    results[i]['result']['agent_native_currency'] = results[i]['result'].network.apply(lambda g: np.array([g.nodes[j]['native_currency'] for j in get_nodes_by_type(g,'Agent')]))\n",
+    "    # Create dataframe variables   \n",
+    "    tokens = []\n",
+    "    for j in results[i]['result'].index:\n",
+    "         tokens.append(sum(results[i]['result']['agent_tokens'][j]))\n",
+    "\n",
+    "    results[i]['result']['AggregatedAgentCICHolding'] = tokens  \n",
+    "\n",
+    "    currency = []\n",
+    "    for j in results[i]['result'].index:\n",
+    "         currency.append(sum(results[i]['result']['agent_native_currency'][j]))\n",
+    "\n",
+    "    results[i]['result']['AggregatedAgentCurrencyHolding'] = currency  \n",
+    "\n",
+    "    AggregatedSpend = []\n",
+    "    for j in results[i]['result'].index:\n",
+    "         AggregatedSpend.append(sum(results[i]['result']['KPISpend'][j].values()))\n",
+    "\n",
+    "    results[i]['result']['AggregatedAgentSpend'] = AggregatedSpend   \n",
+    "\n",
+    "    AggregatedDemand = []\n",
+    "    for j in results[i]['result'].index:\n",
+    "         AggregatedDemand.append(sum(results[i]['result']['KPIDemand'][j].values()))\n",
+    "\n",
+    "    results[i]['result']['AggregatedAgentDemand'] = AggregatedDemand  \n",
+    "\n",
+    "\n",
+    "    AggregatedKPISpendOverDemand = []\n",
+    "    for j in results[i]['result'].index:\n",
+    "         AggregatedKPISpendOverDemand.append(sum(results[i]['result']['KPISpendOverDemand'][j].values()))\n",
+    "\n",
+    "    results[i]['result']['AggregatedKPISpendOverDemand'] = AggregatedKPISpendOverDemand  \n",
+    "\n",
+    "\n",
+    "    AggregatedGapOfDemandMinusSpend = []\n",
+    "    for j in results[i]['result'].index:\n",
+    "         AggregatedGapOfDemandMinusSpend.append(sum(results[i]['result']['KPIDemand'][j].values())- sum(results[i]['result']['KPISpend'][j].values()))\n",
+    "\n",
+    "    results[i]['result']['AggregatedGapOfDemandMinusSpend'] = AggregatedGapOfDemandMinusSpend  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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",
+       "      <th>Red Cross Drip Frequency</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>10.51</td>\n",
+       "      <td>4500</td>\n",
+       "      <td>200000.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6000.00</td>\n",
+       "      <td>4869.00</td>\n",
+       "      <td>1189.00</td>\n",
+       "      <td>1389</td>\n",
+       "      <td>3.20</td>\n",
+       "      <td>138.00</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>9.72</td>\n",
+       "      <td>4500</td>\n",
+       "      <td>200000.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6350.50</td>\n",
+       "      <td>5219.50</td>\n",
+       "      <td>1057.00</td>\n",
+       "      <td>1057</td>\n",
+       "      <td>4.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>19.57</td>\n",
+       "      <td>4500</td>\n",
+       "      <td>200000.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6323.00</td>\n",
+       "      <td>5192.00</td>\n",
+       "      <td>2333.25</td>\n",
+       "      <td>3275</td>\n",
+       "      <td>7.14</td>\n",
+       "      <td>941.75</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>15.67</td>\n",
+       "      <td>4500</td>\n",
+       "      <td>200000.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6435.00</td>\n",
+       "      <td>5304.00</td>\n",
+       "      <td>1734.38</td>\n",
+       "      <td>3737</td>\n",
+       "      <td>6.85</td>\n",
+       "      <td>789.75</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>20.01</td>\n",
+       "      <td>4500</td>\n",
+       "      <td>200000.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6435.00</td>\n",
+       "      <td>5304.00</td>\n",
+       "      <td>2227.06</td>\n",
+       "      <td>3140</td>\n",
+       "      <td>6.99</td>\n",
+       "      <td>498.25</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   timestep  VelocityOfMoney  operatorFiatBalance  operatorCICBalance  \\\n",
+       "0         1            10.51                 4500           200000.00   \n",
+       "1         2             9.72                 4500           200000.00   \n",
+       "2         3            19.57                 4500           200000.00   \n",
+       "3         4            15.67                 4500           200000.00   \n",
+       "4         5            20.01                 4500           200000.00   \n",
+       "\n",
+       "   totalDistributedToAgents  totalMinted  totalBurned  run  substep  \\\n",
+       "0                         0            0            0    3        8   \n",
+       "1                         0            0            0    3        8   \n",
+       "2                         0            0            0    3        8   \n",
+       "3                         0            0            0    3        8   \n",
+       "4                         0            0            0    3        8   \n",
+       "\n",
+       "   AggregatedAgentCICHolding  AggregatedAgentCurrencyHolding  \\\n",
+       "0                    6000.00                         4869.00   \n",
+       "1                    6350.50                         5219.50   \n",
+       "2                    6323.00                         5192.00   \n",
+       "3                    6435.00                         5304.00   \n",
+       "4                    6435.00                         5304.00   \n",
+       "\n",
+       "   AggregatedAgentSpend  AggregatedAgentDemand  AggregatedKPISpendOverDemand  \\\n",
+       "0               1189.00                   1389                          3.20   \n",
+       "1               1057.00                   1057                          4.00   \n",
+       "2               2333.25                   3275                          7.14   \n",
+       "3               1734.38                   3737                          6.85   \n",
+       "4               2227.06                   3140                          6.99   \n",
+       "\n",
+       "   AggregatedGapOfDemandMinusSpend  Red Cross Drip Frequency  \n",
+       "0                           138.00                        30  \n",
+       "1                             0.00                        30  \n",
+       "2                           941.75                        30  \n",
+       "3                           789.75                        30  \n",
+       "4                           498.25                        30  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "params = [30,60,90]\n",
+    "swept = 'Red Cross Drip Frequency'\n",
+    "mean_df,median_df = param_dfs(results,params,swept)\n",
+    "median_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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mEGCCiIwD/h8wzqtTE3BDOZGN9/uBq0VkjHefmSJyRht5E3kf9WXcC3+bp7sSPPkvA9aqaqSF5Dde/T4GN0wUrgdby/O2eM67j2yvfvpRWwGg/Yr9H9Lw2+OX2hPIM7HNxj2UdbiW80O4WbfgzDrP4SqSMtxM3FTv2vXA4yJSIiJnNhP9I7ge0Xte3NW086ZbkPVTL64rPafzcWajVThl9wJubARcgX0DN5t6BW1U4Kr6B+BnuAkcBbgW3OU4RdoSN+Fe6v/hJhiu8NwARuAmc1TgXpA/q+pi3MSMW3H5vAU38enqVtL4pYiU44YLnsCZ1Q5uZJJsD694YVfiXriHdzJ8S+zOM/gNrmddDPwWNzkNqK+c5+B6HOHncSXteB9UdQNuotPPcUMWK3ETXsK8hGd6a2RC3Rl+gTM5luPus62G0hvA6zgl8S3uXWjPEEpLtJbvb+PGGbeIyDbP7SrcRKUlIlKGK5v770R6lcAfcGV2G66SPl1VvwFQ1a9wZf3f3nkZbuzx/fBwgmcaPwY3Dr/Ziys8Ya89Mm7x7nUzrvH2fVX9ogV5z8aNjW7GPe/rdnGocj7u3d0S+cMpgnDP9FzckEP4q5RncRYVVPVzXJ33DK5nV4GbY1PTSpp34+ZzLPTe/SW4uVDtia+tcnk9EXW2qi7DTcy8B5e3a3Hj16hqAGfZugD3Hs3De4dFZCAwE7irUd4sx5Xz+V6v+QzcWHYhzrqzLCJvXsI9/2e85/0ZcHwr+RJJc2U8DfesS3BlbwjOWhrJu949voUbZgsv5NVinreD3+Le6XU4PdkuK6A0HOIwjJ1DRBRnmlsbZTmG4gp/4m5YbTpKlq9xZsBdqeyNPYyIHImbkNYei01UEZFngS9U9bpmrnXDKZ4RqrquA9Lq0Pg6E3HrXuTiPlNc3Jb/Dk57KDFS94TZGxYBMYw9hoicjhujezvashhdH284YR9vSOQ4nKXp5YjrJ3nm33TcuO+nuC9CdjW9Do2vM/GG0rLEDT2Gx/uXRFmsmKArrkRkGDGJiLyDMwme543rGsbu0g9nou6J65Fepg2XyJ6DM88KzhR9lu6eGbaj4+tMpuOG2MLDRqeoalXrQfYOzBRvGIZhGHGEmeINwzAMI44wU3wH06tXLx06dGi0xTAMw+hSLF++fJuq9o62HPGAKfYOZujQoSxbtizaYhiGYXQpRGRnVo80WsFM8YZhGIYRR5hiNwzDMIw4whS7YRiGYcQRNsZuGHFMbW0tubm5VFe3uLmaYexRUlJSyMnJITExMdqixC2m2A0jjsnNzSUjI4OhQ4cisiubvRlGx6GqFBYWkpuby7Bhw6ItTtxiit3YawkUlhAsr2jinpDRjaSeWVGQqOOprq42pW7EDCJCz549KSgoiLYocY0pdmOvJVheweIRM5u4z1jzVtwodsCUuhFTWHnsfGzynGEYhmHEEabYDcPoVPx+PxMnTmTs2LGcdNJJlJSU7FT466+/njvuuKPZa0888QRjx45l3LhxTJo0qUV/Hc3QoUMZN24c48aNY/To0fz6179udYLiwQcfvFPxH3nkkey///5MnDiRiRMn8sILL+yuyMZeRFwqdhH5qYh8LiKficjTIpIiIsNE5L8islZEnhWRJM9vsne+1rs+NCKeqz33L0Xk2Gjdj2F0ZVJTU1m5ciWfffYZPXr04N577+2QeP/1r39x1113sXDhQj799FOWLFlCZmZmE3/BYOdskb148WI+/fRTli5dyjfffMOll17aYtoffPDBTsf/17/+lZUrV7Jy5Urmzp3b4JqqEgrZBoJG88SdYheRgcAVwBRVHQv4gbOA24A7VXVfoBi4yAtyEVDsud/p+UNERnvhxgDHAX8WEf+evBfDiDemT5/Opk2b6s9vv/12DjzwQMaPH891111X737zzTez3377ceihh/Lll182G9ctt9zCHXfcwYABAwBITk7m4osvBlyP9yc/+QlTpkzh7rvvZv369Rx11FGMHz+emTNnsmHDBgCef/55xo4dy4QJEzj88MMB+Pzzz5k6dSoTJ05k/PjxrFmzptV76tatG/fffz8vv/wyRUVFvPPOOxx22GGcfPLJjB49ut4PwDvvvMPhhx/OiSeeyP7778/3v//9divo9evXs//++3P++eczduxYNm7cyMKFC5k+fTqTJ0/mjDPOoKLCTQZ9/fXXGTlyJJMnT+aKK65g9uzZQFPrx9ixY1m/fj0ATz31VP19X3rppdTV1dXLfs011zBhwgQOOugg8vPzAcjPz+fUU09lwoQJTJgwgQ8++IBrr72Wu+66qz7+a665hrvvvrtd92d0IKoaVz9gILAR6IGbHPhP4FhgG5Dg+ZkOvOEdvwFM944TPH8CXA1cHRFvvb/WfgcccIAaXYOabcW6fd3GJr+abcXRFq3DWLVqVbRF0PT0dA2FQhoMBnXu3Ln62muvqarqG2+8oRdffLGGQiGtq6vTE088Ud99911dtmyZjh07Vrdv366lpaW6zz776O23394k3uzsbC0pKWk2zSOOOEIvu+yy+vPZs2frY489pqqqDz/8sM6ZM0dVVceOHau5ubmqqlpc7J775Zdfrk899ZSqqtbU1GhlZWWT+IcMGaIFBQUN3CZMmKBLlizRxYsXa1pamn7zzTcN8kBVdfHixZqcnKxff/21BoNBPfroo/X5559vVv799ttPJ0yYoBMmTNBt27bpunXrVET0ww8/VFXVgoICPeyww7SiokJVVW+99Vb97W9/q1VVVZqTk6NfffWVhkIhPeOMM/TEE09UVdXrrruuQV6OGTNG161bp6tWrdLZs2drIBBQVdXLLrtMH3/8cVVVBfTVV19VVdUrr7xSb7zxRlVVPfPMM/XOO+9UVdVgMKglJSW6bt06nTRpkqqq1tXV6fDhw3Xbtm1N7q+5cgks0xjQIfHwi7tZ8aq6SUTuADYAVcBCYDlQoqphm1wurgEAOxoCqGpQREqBnp77koioI8M0QEQuAS4BGDx4cIfej9F5JPXMiqvZ77GIqlJVVcWkSZPYtGkTI0eOZObMmagqCxcuZOHChUyaNAmAiooK1qxZQ3l5OaeeeippaWkAnHzyybuU9rx58+qPP/zwQ1588UUAzjvvPH75y18CcMghh3DBBRdw5plnctpppwHOqnDzzTeTm5vLaaedxogRI9p9r2GmTp3a4nfaU6dOZfjw4QCcffbZ/Oc//2liagdnip8yZUr9eXl5OUOGDOGggw4CYMmSJaxatYpDDjkEgEAgwPTp0/niiy8YNmxYvdzf+c53ePDBB1uV/a233mL58uUceOCBAFRVVdGnTx8AkpKS6nv8BxxwAIsWLQLg7bff5oknngDcPIrMzEwyMzPp2bMnH3/8Mfn5+UyaNImePXu2mrbR8cSjKT4bmAMMAwYA6ThTeqehqg+q6hRVndK7t+06aBiRpKam8t8lS1izZg2hUIg/e2PsqsrVV19dP468du1aLrroojZi28GYMWNYvnx5i9fT09PbjOP+++/npptuYuPGjRxwwAEUFhZyzjnn8Oqrr5KamsoJJ5zA22+/3WY85eXlrF+/nv3226/NtBt/7rUzn39FxquqzJo1qz7/Vq1axcMPP9xq+ISEhAam//CEP1Vl/vz59XF9+eWXXH/99QAkJibWy+j3+9ucs/C9732Pxx57jEcffZQLL7yw3fdmdBxxp9iBo4F1qlqgqrXAi8AhQJaIhC0UOUB4oG8TMAjAu54JFEa6NxPGMIx2EFYI4vORmpLCnXfeyZ133UVdXR3HHnssjzzySP248KZNm9i6dSuHH344L7/8MlVVVZSXl/OPf/yj2bivvvpqrrzySrZs2QK4HutDDz3UrN+DDz6YZ555BnA94cMOOwyAr7/+mmnTpnHDDTfQu3dvNm7cyDfffMPw4cO54oormDNnDv/73/9avceKigp+8IMfcMopp5Cdnd1mnixdupR169YRCoV49tlnOfTQQ9sM0xwHHXQQ77//PmvXrgVg+/btfPXVV4wcOZL169fz9ddfA/D000/Xhxk6dCgrVqwAYMWKFaxbtw6AmTNn8sILL7B161YAioqK+Pbb1ndRnTlzJvfddx8AdXV1lJaWAnDqqafy+uuv89FHH3HssTbnOBrEo2LfABwkImniapWZwCpgMRC2d80HXvGOX/XO8a6/7Y33vAqc5c2aHwaMAJbuoXswjLggbJ7WUAjx+Zgwfjzjxo3jb3/7G8cccwznnHMO06dPZ9y4ccydO5fy8nImT57MvHnzmDBhAscff3y9ebgxJ5xwApdffjlHH300Y8aMYfLkyZSVlTXr909/+hOPPvoo48eP58knn6yf0HXllVcybtw4xo4dy8EHH8yECRN47rnnGDt2LBMnTuSzzz7j/PPPbzbOGTNmMHbsWKZOncrgwYN54IEH2pUnBx54IJdffjmjRo1i2LBhnHrqqe0K15jevXvz2GOPcfbZZzN+/Ph6M3xKSgoPPvggJ554IpMnT643qQOcfvrpFBUVMWbMGO655556C8Po0aO56aabOOaYYxg/fjyzZs0iLy+v1fTvvvtuFi9ezLhx4zjggANYtWoV4Ez3M2bM4Mwzz8Tvt/nGUSHag/yd8QN+C3wBfAY8CSQDw3GKeS3wPJDs+U3xztd614dHxHMN8DXwJXB8e9K2yXNGLBHtyXOhUEhra2s1WFvb5HhvZPHixfUT2eI1zbq6Op0wYYJ+9dVXLfqxyXOd+4u7yXMAqnodcF0j52+Aqc34rQbOaCGem4GbO1xAw9hLEJH6XlvjYyP+WLVqFbNnz+bUU09t96RDo+MR11AyOoopU6bosmXLoi2GYQCwevVqRo0aFW0xDKMBzZVLEVmuqlNaCGLsBPE4xm4YhmEYey2m2A3DMAwjjjDFbhiGYRhxhCl2wzAMw4gjTLEbhtFpVFdXM3XqVCZMmMCYMWPqN3pZt24d06ZNY99992XevHkEAoEoS2oY8YMpdsMwOo3k5GTefvttPvnkE1auXMnrr7/OkiVLuOqqq/jpT3/K2rVryc7ObnMpVMMw2o8pdsMw6ln4Tj6nX7iEw05+l9MvXMLCd/J3Kz4Rqd+ytLa2ltraWkSEt99+u37jk/nz5/Pyyy/vtuyGYThMsRuGATilfts9X5FfUIMq5BfUcNs9X+22cq+rq2PixIn06dOHWbNmsc8++5CVlUVCglsfKycnp8Ee7YZh7B6m2A3DAOCBJ9ZRUxNq4FZTE+KBJ9btVrx+v5+VK1eSm5vL0qVL+eKLL3YrPsMwWscUu2EYAGzdVrNT7jtLVlYWM2bM4MMPP6SkpKR++8/c3FwGDhzYIWkYhmGK3TAMjz69knfKvT0UFBRQUlICQFVVFYsWLWLUqFHMmDGDF154AYDHH3+cOXPm7HIahmE0xBS7YRgAXHr+MJKTG1YJyck+Lj1/2C7HmZeXx4wZMxg/fjwHHnggs2bNYvbs2dx222388Y9/ZN9996WwsJCLLrpod8U3DMMjLnd3Mwxj5znmyL6AG2vfuq2GPr2SufT8YfXuu8L48eP5+OOPm7gPHz6cpUuX7nK8hmG0jCl2wzDqOebIvrulyA3DiD5mijcMwzCMOCLuFLuI7C8iKyN+ZSLyExHpISKLRGSN95/t+RcR+T8RWSsi/xORyRFxzff8rxGR+dG7K8MwDMNoH3Gn2FX1S1WdqKoTgQOASuAlYAHwlqqOAN7yzgGOB0Z4v0uA+wBEpAdwHTANmApcF24MGIZhGEasEneKvREzga9V9VtgDvC45/44cIp3PAd4Qh1LgCwR6Q8cCyxS1SJVLQYWAcftWfENwzAMY+eId8V+FvC0d9xXVfO84y1AeIbQQGBjRJhcz60ld8MwDMOIWeJWsYtIEnAy8Hzja6qqgHZgWpeIyDIRWVZQUNBR0RpGXFBSUsLcuXMZOXIko0aN4sMPP6SoqIhZs2YxYsQIZs2aRXFxcbTFNIy4IW4VO27sfIWqhnewyPdM7Hj/Wz33TcCgiHA5nltL7k1Q1QdVdYqqTundu3cH3oJhdH1+/OMfc9xxx/HFF1/wySefMGrUKG699VZmzpzJmjVrmDlzJrfeemu0xTSMuCGeFfvZ7DDDA7wKhGe2zwdeiXA/35vmonBmAAAgAElEQVQdfxBQ6pns3wCOEZFsb9LcMZ6bYcQlqkre5s3kbd5MKBSqP3YGrl2jtLSU9957r35luaSkJLKysnjllVeYP9+9jrZtq2F0LHGp2EUkHZgFvBjhfCswS0TWAEd75wCvAd8Aa4G/AD8AUNUi4EbgI+93g+dmGHHJlrw8qqurqa6uZsO339Yfb8nLaztwC6xbt47evXvz3e9+l0mTJvG9732P7du3k5+fT//+/QHo168f+fm7tzWsYRg7iMuV51R1O9CzkVshbpZ8Y78K/LCFeB4BHukMGQ0jVlHV+l66iOxWXMFgkBUrVvCnP/2JadOm8eMf/7iJ2V1EdjsdwzB2EJc9dsMwdp6+/fo1UbAiQt9+/XY5zpycHHJycpg2bRoAc+fOZcWKFfTt25c8zxKQl5dHnz59dl1wwzAaYIrdMAwA8rdsaTKerqrkb9myy3H269ePQYMG8eWXXwLw1ltvMXr0aE4++WQef9wtK2HbthpGxxKXpnjDMHadsGl8dybNRfKnP/2Jc889l0AgwPDhw3n00UcJhUKceeaZPPzwwwwZMoTnnnuuQ9IyDMMUu2EYHv3696+fKNe3X7/6nno/b5LbrjJx4kSWLVvWxP2tt97arXgNw2geU+yGYQCup95/wID688hjwzC6DjbGbhiGYRhxhCl2wzAMw4gjTLEbhmEYRhwRk2PsIvIprWzSoqrj96A4hmEYhtFliEnFDsz2/sMrwj3p/Z8bBVkMwzAMo8sQk6Z4Vf1WVb8FZqnqL1X1U++3ALcZi2EYXYS7776bsWPHMmbMGO666y4A27bVMDqRmFTsEYiIHBJxcjCxL7NhGB6fffYZf/nLX1i6dCmffPIJ//znP1m7dq1t22oYnUismuLDXAQ8IiKZgADFwIXRFckw4pPXe0ymrnx7E3d/RjrHFa3YpThXr17NtGnTSEtLA+CII47gxRdf5JVXXuGdd94B3LatRx55JLfddtsuy24Yxg5iuverqstVdQIwARivqhNVdddqGMMwWqU5pd6ae3sYO3Ys//73vyksLKSyspLXXnuNjRs32rathtGJxHSPXUSSgdOBoUBCeOcpVb0himIZhtFORo0axVVXXcUxxxxDeno6EydOxO/3N/Bj27YaRscS0z124BVgDhAEtkf8DKNDCAZDFBbVkF9QTUlpINrixCUXXXQRy5cv57333iM7O5v99tvPtm01jE4kpnvsQI6qHhdtIYz4pKamjo8/K+WmO7+gpLSWUSMyuOnq0fTtnRJt0eKKrVu30qdPHzZs2MCLL77IkiVLWLduHY8//jgLFiywbVsNo4OJ9R77ByIybmcDiUiWiLwgIl+IyGoRmS4iPURkkYis8f6zPb8iIv8nImtF5H8iMjkinvme/zUiMr8jb8yIPmUVQX5182eUlNYCsHpNOX+8fw0V24NRliy+OP300xk9ejQnnXQS9957L1lZWSxYsIBFixYxYsQI3nzzTRYsWBBtMQ0jboj1HvuhwAUisg6owc2M13asPHc38LqqzhWRJCAN+BXwlqreKiILgAXAVcDxwAjvNw24D5gmIj2A64ApuFXwlovIq6pqH9zGCSWltQRqGy5w+L9VZdTU1NEtPdZfjY7Hn5He4qz43eHf//53E7eePXvatq2G0UnEeu11/M4G8D6NOxy4AEBVA0BAROYAR3reHgfewSn2OcATqqrAEq+339/zu0hVi7x4FwHHAU/v+u0YsURWZiKJCUJtcIdyHzuyO8nJ/lZCxS+7+kmbYRixRUyb4r3V5wYBR3nHlbQt8zCgAHhURD4WkYdEJB3oq6p5np8tQF/veCCwMSJ8rufWknsTROQSEVkmIssKCgraf4NGVMnolsCNC0aT0c21b/cdls4vfjBir+ytG4YRP8R0DSYiYVP4/sCjQCLwFHBIK8ESgMnAj1T1vyJyN87sXo+qqoi0uMnMzqKqDwIPAkyZMqXD4jU6l5RkP9Mm9+DJe6cQDCrJST6ys5KiLVaHo6r2OZkRMzjjqNGZxHSPHTgVOBnvEzdV3QxktBEmF8hV1f965y/gFH2+Z2LH+9/qXd+EswqEyfHcWnI34ojERB+9eiTTr09KXCr1lJQUCgsLrTI1YgJVpbCwkJQU+/KkM4npHjsQiOxdeyb1VlHVLSKyUUT2V9UvgZnAKu83H7jV+3/FC/IqcLmIPIObPFeqqnki8gbwu/DsedzmM1d35M0ZRmeTk5NDbm4uNkRkxAopKSnk5OREW4y4JtYV+3Mi8gCQJSIX49aJ/0s7wv0I+Ks3I/4b4Ls468RzInIR8C1wpuf3NeAEYC1uDP+7AKpaJCI3Ah95/m4IT6QzjK5CYmIiw4YNi7YYhmHsQSTWTXQiMosdW7UuVNVF0ZSnLaZMmaLLli2LthiGYRhdChFZrqpToi1HPBDrPXaAT4FU3Lfkn0ZZFsMwDMOIaWJ68pyIfA9YCpwGzMV9Z27bthqGYRhGC8R6j/1KYJKqFgKISE/gA+CRqEplGIZhGDFKTPfYgUKgPOK83HMzDMMwDKMZYr3Hvhb4r4i8ghtjnwP8T0R+BqCqf4ymcIZhGIYRa8S6Yv/a+4UJf3ve1iI1hmEYBlBUHGB7VZDkJB9pqQm2ZPJeQEw/YVX9bfjYWyimRGP9+zzDMIwYIb+gmiuu+YRNedWIwDmnDeLc0wfRPSMx2qIZnUhMjrGLyLUiMtI7ThaRt3E993wROTq60hmGYcQ+VdV1/OXJdWzKqwZAFf76940Ul9RGWTKjs4lJxQ7MA770jufj5OwNHAH8LlpCGYZhdBWqq+tYu257E/fcvKooSGPsSWJVsQciTO7HAk+rap2qribGhw8MwzBigW7dEjhseq8Gbn6f257YiG9iVUnWiMhYIB+YAfwi4lpadEQyDMPoOiQm+Dj76O6cOG4IVdV1+HyQmZFIYsU2Ar5uJPXMiraIRicRq4r9x7jtVnsDd6rqOgAROQH4OJqCGYZhdBkqK1k++dgmzjPWvGWKPY6JScXu7aU+UkSGq+o3Ee6vicjqKIpmGIZhGDFNrI6xh3mhnW6GYRiGR20wRFFxAPs4eO8kJnvs3qduY4BMETkt4lJ3ICU6UhmGYcQ+JaUBXnptM4ve28rtl/aOtjhGFIhJxQ7sD8wGsoCTItzLgYujIpFhGEaMU1sb4rlXNvHE8xsAqAnURVkiIxrEpGJX1VeAV0Rkuqp+uLPhRWQ9rhFQBwRVdYqI9ACeBYYC64EzVbVYRAS4GzgBqAQuUNUVXjzzgV970d6kqo/v1o0ZhmF0ImUVtbzxTv6O82ASo97/f/TMTiYxUerdEzK6NRs+UFhCsLyiiXtChs2i70rEpGKPYK2I/AqnjOtlVdX27Mk+Q1W3RZwvAN5S1VtFZIF3fhVwPDDC+00D7gOmeQ2B64ApuA1olovIq6pavPu3ZRiG0fEk+H307Z1MfkENAJffsg6AZx+cysD+qW2GD5ZXsHjEzCbubc2iD4WUYFBJSor1aVt7B7H+FF4BMoE3gf8X8dsV5gDhHvfjwCkR7k+oYwmQJSL9cQvjLFLVIk+ZLwKO28W0DcMwOp3M7on89NIRpCTvqNpPOb4/Gd06rw9XVBLguVdzufGPq3n3gwJKy2zJ2mgT6z32NFW9ahfCKbBQRBR4QFUfBPqqap53fQvQ1zseCGyMCJvrubXk3gQRuQS4BGDw4MG7IK5hGEbHMHRwGs88MJUNm6vo1SOJzO6JnbbpS0lpgGt+9zmfri4DYPH727jonCGcO3cwSYmx3m+MX2I95//pLUqzsxyqqpNxZvYfisjhkRe95Wo77EMQVX1QVaeo6pTevW0WqmEY0SMxwUevnslMHpfF4IFpZHbiTm6VVXX1Sj3Ms6/kUl4R7LQ0jbaJdcX+Y5xyrxaRMhEpF5GytgKp6ibvfyvwEjAVtzNcfwDvf6vnfRMwKCJ4jufWkrthGIYB+HzSxC0x0UcH9puMXSCmTfGqmrGzYUQkHfCparl3fAxwA/Aqbqe4W73/V7wgrwKXi8gzuMlzpaqaJyJvAL/z9oHHi+fq3bohwzCMGCYhoxsz1rzVrHtzpKb4OfLgXrzzwY55ypeeN6xTrQRG28S0Yvc+RTsXGKaqN4rIIKC/qi5tJVhf4CUXlATgb6r6uoh8BDwnIhcB3wJnev5fw33qthb3udt3AVS1SERuBD7y/N2gqkUde4eGYRixQ1LPrJ36rC2zeyI//8EIZh/Tn9VflXHI1J7075tCQkKsG4PjG9EYXnNQRO4DQsBRqjrK6z0vVNUDoyxai0yZMkWXLVsWbTEMwzB2iz39TbuILFfVKR0e8V5ITPfYgWmqOllEPgbwFpRJirZQhmEY8c6uftNuRJ9Yt5fUiogfbyaGiPTG9eANwzAMw2iGWFfs/4eb1d5HRG4G/gP8LroiGYZhGEbsEtOmeFX9q4gsB2YCApyiqrYfu2EYhmG0QEwrdm+99q3A0xFuiapqaxYahmEYRjPEtGIHVuAWiSnG9dizgC0ikg9crKrLoymcYRhGvLKz37QbsUOsK/ZFwAuq+gaAiBwDnA48CvwZt6CMYRiG0cHs7DftRuwQ64r9IFW9OHyiqgtF5A5VvVREkqMpmGEYRlfC9lrfe4h1xZ4nIlcBz3jn84Ct3idw9tmbYRhGO7Hv0vceYv1zt3Nwm6+87P0GA2cBfnYsCWsYhmEYhkdM99hVdRvwo/C5iKQAJ6nq87i13Q3DiGHM/GsYe56YVuwAntn9WOBsYBZukZrnoyqUYRjtwsy/0ScUCuHzxbpx1uhIYlaxi8gROFP8CcBS4BBguKpWRlUwwzCMLkIwGKSqqor09PRoi2LsQWJSsYtILrABuA/4hbe3+jpT6oZhGO0jFApRXV1NwdatBDIzSU5O5LDVb5CQ0LDat+/S44+YVOzAC8ApuFnwdSLyCt5GMIYRq5SUBqgJhPD7hYz0BJKT/dEWydiL8fl8pKWlkZWdTUlxMQCDhw5potiN+CMmn7Cq/kREfgociRtb/z2QKSJnAq+patPZOIYRRbYV1fDrW1bx2RdlpCT7+NH39mHmYX3olr7jFQsGQ2wrCvD6W1sQn3DcUX3p1SMJv9/GP43OIRQKsb2iAhFBVamoqCAjIwO/3xqd8UxMKnYAVVVgMbBYRBKB43Cfuv0Z6BVN2Yy9g9raEJVVdaSn+UlIaFn5VtfU8cjfvuWzL8q88xC337uGAydmN1Ds24oCnHf5Mqqq6gD469838uS9U+jbO6VzbySKtHdZUps93/GETfGqyqDBg6msrKSkuJiMjIxoi2Z0MjGr2CPxNn35B/APEUltTxhvNv0yYJOqzhaRYbiFbnoCy4HzVDXgrWD3BHAAUAjMU9X1XhxXAxcBdcAV4aVtjfinqDjAc6/msvKzUqZOyubUEwaQnZXUrN/Kqjr+t6q0ifuGTVUM6LejuL76Rl69Ug+H+39vbuHCs4d2uPyxQnuXJbXZ8x1P2BSfmpqK3++nW7dupKenW299LyAmFbuIfErLY+oKTGhHND8GVgPdvfPbgDtV9RkRuR+nsO/z/otVdV8ROcvzN09ERuMsBGOAAcCbIrKfqtY1TsiIL0rLarn+jtWs+F8JAIri88HpswfW98BFpN5/WqqfyeOzWL9xx9xOERiSk9Yg3lBoR5G+5+phdE8I0C0dKtfn1rtbD9XoSCI/c7NP3vYeYlKxA7O9/x96/096/9+hHZPoRCQHOBG4GfiZuFr4KNzncwCPA9fjFPsc7xjcpL17PP9zgGdUtQZYJyJrganAh7t8V0aXoLqmrl6pjx/dnet+sR+giEBtbS2qSlJSUr1yT0n2M3/eYDZsqmTZyhIy0hP46ff3pXtGw9drznH9ef4fm6ipCdE9IcDqQ05skrb1UI09xfbKIJVVdYhAeloCqSnWk48XYlKxq+q3ACIyS1UnRVy6SkRWAAvaiOIu4JdAeDCpJ1CiqkHvPBcY6B0PBDZ66QZFpNTzPxBYEhFnZJgGiMglwCUAgwcPbvP+jNjG5xOSEoVArXLe3EHU1VZQU72d5MQebNtaTHJyMn369m1g0uyZncwNvxxNdU0Inw8yMxJJTGzYQ+rVI5mn7p3CS//KIz3Nek9djaLiANU1dSQl+sjISCQ5qes+w5LSAH9+9BveWJyP3y+cc9ogzjg5h8zuidEWzegAYr1kiogcEnFyMG3ILCKzga17cq92VX1QVaeo6pTevXvvqWSNTiIjPYGLzh0KwG9uW01ldSpJSUkUFRbi9/ubKPUw3TMS6dMrmV49kpsodYDERB/9+6bygwuGk9EtJtvURgts3lLFDxes5MyLl3L2ZR/xnyXbqKoOth0wRnn/oyJeeyufuhAEapXHnt3Ahk22TEi8EOu1y0XAIyKS6Z2XABe2EeYQ4GQROQFIwY2x3w1kiUiC12vPATZ5/jcBg4BcEUkAMnGT6MLuYSLDGHFMSoqfk47tzyEH9mRrYTU5A5LZklcLuJW8gsEgPp+vwTi7sXvUJacy+oPXcB/DONNwenpCTCyeUlZRy+/v/YqNm6sAqKqq48Y7v+D5h6aRmhLrVWhTAoE6PvyosIn7spXFjBuV2UwIo6sR06XS63VPCCt2VW069bhpmKuBqwFE5EjcynXnisjzwFzczPj5wCtekFe98w+962+rqorIq8DfROSPuMlzI3BL2xp7Ad27JdK9WyKDBqZQVFhIYlIS/fv1Y2tBAUWFhS322o2dp6y8ll/930ZWftbw9X7+oWn07xn9TwEDgRBfrmn4KV4wqJSV19K7Z3KUpNp1EhN9TJvcg3c+2NbAffJ4m9sRL8S0YheRvsDvgAGqerw3U326qj68C9FdBTwjIjcBHwPhOB4GnvQmxxXhZsKjqp+LyHPAKiAI/NBmxO99+P1+evTogQI+v5/evXqhnvvu0N7vu+OR0rIAwaDi8wvZmUnU1obI9XrDkZSU1dK/b/QVe0qyn0njMnlvyY5ebnKyr8uOR4sIhx7Uk2Wf9Obt/xTg9wtnnDSQoYNsPfl4QcKmr1hERP4FPApco6oTPFP5x6o6LsqitciUKVN02bJl0RbD6CTqgkEUSEhIaHDcWahqvck/8rirkpdfzQ1/WM2nq8vYd1g61185in59UnjgiXU8/+qOka70ND9/u+9AevaIjR5xQWENN9yxmo8/K+X+a/chJzNEYqKPyMfR1T5VLK+opao65GbFp/pJS0ugrq6OumAQv1emw8d7wjolIstVdUqnJ7QXEOuK/SNVPVBEPg7PjheRlao6MdqytYQp9vglFApRWFhIVWUl/QcMYFtBAcG6OgYOHNgp3wiHQiFqa2vx+/34fL7646661ndJaYArf/sZq9eU17v17Z3MX/4wGfHBE89+y9v/2cbA/in84gcjGJKTFlPL7ZaW1RKoDZFaWsC7+ze/mE7a0JwoSNZxBINBNnz7LZlZWSQlJlJQUED/AQNISUnp9EalKfaOI9ZriO0i0hPv23UROQhoc5zdMDoDn89HdnY21VVVbNywARFhwMCBnVrhbcnLIyEhgazsbLbm55OVlUVmVtYeXWyko5Z7rQ1qA6UOkF9QQ1VNHQP7pfL9C4bznbmDSUiIPTN3oLCExPIKEgENxe+InM/no0/fvmzNzwcgKzu7wZoNRtcg1hX7z3CT2/YRkfeB3sAZ0RXJ2JsR3Fg7tbWISKfOjhcR+g8YwKbcXPK3bCEtLY3umZkdrtRDoVB9nJHHYXZ3ude6OqWkNEBdnTKwfwqb8qrrr2WkJ9R/D56c5Ce5R2xOSIzMg4PefCLK0nQeqkqkFVdDoShKY+wqsWPnap7PgSOAg4FLccu7fhFViYy9llAoRFFxMYFAgH79+uH3+9myZQuhTqr8VJVQKFRf0dbV1dHRQ2fBYJDt27dTV1fX4LijqKtTvvq6nAt/soLrfr+an182giyvN56W6uf6X44iMyO2euddhbq6uvpnFXm8O6gqBVu3kp2dTZ8+fSgtLSUQCHR4uTM6l1jvsX+oqpNxCh4Ab+W5ydETydhbCZviMzMzSUxMpH9yMqFQqFPNlPlbtpDerRvZ2dls3rSJ8rKyDjPFqyq1tbUUbN1KZmYmNYEANTU1DBo0qO3A7aSkLMCCmz+nsDhAYXGAv/19IzcuGE3P7CTSUv10z0hodjEfoymNh0TCjb6krO5UilIbDNKzZ8/dmujm8/nIGTQIv9+PiDQ4NroOManYRaQfbvnWVBGZhLOAgltsJq3FgIbRySQkJNTPTo887gzCY/g+nw+fz8fAnJz6446KPzkpiV69erFtm/umeWBOTofOgA4ElMKiQP35sk9KWPZJCX9/ZBq9uuA34AB1ldX15viUgf2QBJdfnf2pYktDIoetfoNiqaNP3767nYbP5yMpaccuhpHHRtchJhU7cCxwAW61tz9GuJcDv4qGQIYRJqzIw6ZPv9/f4LgxZeW1VFXX4fMJaal+0tPa99qJCImJO8zUkccdRUiViu3b688rKirIysrqMOWelOSjb+9k8gtq6t0GDUzt0r30j06+pP44VmbCJyYl1W/PahgxqdhV9XHgcRE5XVX/Hm15DKMxoVCIqqoqysrK6Nu3L1WVlZSVl9O30Yp0xSUBbv2/L3n/oyL8Pjht9kDmzxtMVvfo94RUlUCglkBNDRmZ/QgGaykvK6Z790wi9cPuLKaTnZnI768dyzW/W0VuXhWDc1L53a/G0KOFve1jlVheUEjER20gQGFh4W6b4o34ICYVexhV/buInIibNJcS4X5D9KQyDNebTkpKIlBTw6bcXILBINnZ2Q38hELK2/8p4P2PigCoC8Hzr27iyIN7kTUm+ootGFRWfFrBhDED+c1tqxm7fwbzTsnhq6+3M2bkjjXDk3pm7fLCKz6fMHxIOn++bSK1QbeoS1dT6rB7edDZ+P0++vcd0KGTHo2uTUwrdhG5HzemPgN4CLeWu63XbkSd8Bh7t4wMykpL8fl8DSa1BQJ1VGyvY+IQ4YnfNNztN4NSAoVEXVHUBkP84418fnf3V5SVB1m2soQXX8vj0vOGNVDsrVFXV1f/CWDkcSQiQo/srqfMuxLJycmg2iTvjb2TmFbswMGqOl5E/qeqvxWRPwD/irZQhhEKhaisrKSstJT09HSqqqrYkpdH3379KK+o49lXcvnm2+384vTuLD3kxCbh2/sNeGlZLQVFNeRvrWG/4d3I7J5AUlLHVN5pqQnMPWkg70fs9FVVVce0A3q0GKbxzOxg0M0dSOyewdbKcrKys0lPT9+jC+jsLbQ2HLAn8rs2GKK0rBZVSEn2kdHNPlOMVWJdsYd3hqgUkQG47VT7R1EewwA8U3xiIj169qR7RgbBYJDKykpUYfH7BTz5/EYAFszb9W0wS8tq+dNDX/P6YrcKWFKicO9tExk1onuH3APAyBHduOWaMTz90kbS0xK49PxhrZrKW5uZrYlCakoKPp8v7ta4jwWiORxQWRlkyYoi/nDfGsrKg0yf0oMFP9rfLDExSqwr9n+KSBZwO7ACt7TsQ9EVydgbCC864/P5GhyHERESk5LISEjA5/eT6PORkZDA9qoQb/27oN5fSWntLstQXlFbr9QBArXKXQ+s5bZrx3bY5LuMbokcdlAvJozJxO+Xds/Yb470bt3q86s2EMDn9+P3++uPO2NWf1ehrq6uflJb5HFXoawiyHW/X014nZoPPiriyRc2cNn8YR1mQTI6jpi2l6nqjapa4s2MHwKMVNXfRFsuI74JhULU1NQQCASaHEciIvUVdPg4JdnPvsN2zJTuntE+RVlcEmDVV2Us/biIwqIaQiGlYnvTyVCFxW5p1o6me0bibil1n89HaUkJ2ysrUVW2bdvG5k2bqKioYPPmzZSWlLQ6uasuGKxf4SzyOB4IBoNuw6BgsMFxV2L9xkoaP46PPi6motIm7MUiMd1jF5HTmnErBT5V1a1REMno4rRnQxNVpbysjO3bt9M9M5PSkhK6d+9Odo+Wx57DJCX6OPf0Qfx3RRHDBqfXr4PeGsUlAa6++XM++6IMcI2Bh+88gN69ksnOSqS4ZEevf/bR/egeg0uw+nw+evfoTWpKCn6/n779+rFp0ya2FRSQkpJCdo8eLfZSQ6EQZWVllJSU0LdfP0qKi6mtrWXQ4MENTPgdtRlNNKipqWHzpk2ISINlgrsKgwemNnEbN6o7aanWW49FYlqxAxcB04HF3vmRwHJgmIjcoKpPRkswo2vSng1N/H4/PXv1IhAIUFpSQnIbiqkxvXsmc+8tE9leGSQhqZojvlwUYc734/MJ/ox0SkoDvLdkG9mZSXTPSMDvc5/E9e+bwre529lveDfuv30SDzy+jty8Ko49si/HzuhDYkJsGtoiTfHBYJCQ10Ovra0lFAq1mH8+n4/umZlUV1ezJS8PEWFgTk6Tcfnd3YymOfZEYyEhIYF+/fuTu9HNu+g/YECX23q3e0YCP710X+599BsCgRAjR2Rw4TlDSUk2xR6LxHrpSgBGqWo+gIj0BZ4ApgHvAc0qdhFJ8a4ne3G8oKrXicgw4BmgJ66BcJ6qBkQk2Yv3ANwEvXmqut6L62pcA6MOuEJV3+ikezVihFAoRCAQIBAI4PP5CNTUUBuxm1t76JGdRI/sJOrqkqn0Q0lxMQB9+vQiJS2NquogoZoAgwemkJqSwFWXD+OLtf1Y9E4Bs2b05cEn11NYFOD02QP4yaX7ICJ075aI379nJqGFd/mKnABXWVWHLy2tzZnZqkrhtm0kJafSPbMnpcX5lJSU0KO1xpEqochdxfZQj3ZXGwtFxQEqq+tITvSRnuYnrZVhjGAwyNb8/PqdALd5e5xHKvdYt0Z0S0/khKP7cfj0XgSDSkqKj+xMmzgXq8S6Yh8UVuoeWz23IhFpbVZSDXCUqlaISCLwHxH5F24b2DtV9RnvG/mLgPu8/2JV3VdEzgJuA+aJyGjgLNwCOQOAN0VkP1W1gaU4praolPD/W9gAACAASURBVGBpGb1JxC8+6kJ1BDdtRTIzSO6V3XYEHm5ltwAlxcX07t2bQCDA1vx8Bg8Zgt8HJUX59MrqRlqaUFJcwMh9+zJowBDm/2g5tUGn2B58cj1+n3DWqTl7TKmDU0abN22iX79+IMKWvDy6Z/Xh7se3MGViNocf1KvFIYGysjokIZui4lp+/tuV3HH9aMorleSUEN0zmir2sCm+NhBgYE4OxcXFbMnLa2KKjyaRSwZXbA9QXVPL93/xCeXbg1zynaGcfNwAMrq1XJ2Kt9a/iJCfn+/KRoQy12Ad74w6pkm43bFG7AztmdyXmuInNcV66F2BWFfs74jIP4HnvfO5nls6UNJSIHXN/XDzN9H7KXAUcI7n/jhwPU6xz/GOAV4A7hFXo8wBnlHVGv4/e+8dJllVrf9/9kmVY1fnMMwQhwwSJSuKoD8JishVxHBFRfQa8ApeEREUROGLCRQVFRPq9Qp48UpSgggiIHGGNDCpc3flXOec/fvjVNVUT3d1HqYH+n2eeWbX6RN2nTpnr73ftda74GUhxIvAIcCDi/HllrE0YWVz3LfH1IMsczDsNXW67p4eNE3D6/PhDwQQQvDsiznaYy0UcuOUSxAKR3l5U4mxuFk36jXced8IJx3fQeQVVGxTFAW3201/1S+s6y7GEyYPPRrnrvtGiYR0jjgkNuWxuaLFhz/9OKWyRbki+fBnnqBUtvjdjw5teq1gKIQ/EEDTNFpbW7d51by5wLIsstks2UyGjs5OctkMdqXAdVfux7997BGu+9nLHHtEa1PDrmkaHR0d9RV7rV1YIjXeTdNkfGyMlpjze9baO5q7YBlbsNR/uY8DpwFHVj//DPh91XAfN92BQggVh27fBfgesA5ISilr4aibcSrIUf1/E4CU0qwG6LVUtz/UcNrGYxqvdQ5wDkBfX9/cvuEyXtVQqylfjZ8BdlnpI53KANDq8UEyzeqgiuWTdaW6tGlw3uUv09ftxeWa6AIwTbMeid/YXiwoikI4HCaXyyGlRDcC/Oz3/fVI/Vv+PMgB+4TxepwhpHH1GbYl1362Ayll/TusWuFDUZob6sb71NjeelXbDOVyuV5e1DTNSfd9IVAUBY/HQyIeZ9PGjdi2jdsb4Z4Hx9lndZAPntnH6HiJrg5301iCuX6fbYlszkRTBe7q6ltK6QT3DQwgcAoD7WjBfcuYiCVt2KWUUgjxCJCSUt4lhPACfpwqbzMdawH7V/Pg/wDssQ37eT1wPcBBBx20/EYsYSyVYh4uQ1AsJAkEo4hMlnunYAhWP3AbLVGDj569sm5AE8kyPq/C2OgwQlFoa2tjaGgIQ9dpicUWzZiZpsng4CButxuEIJ8b4yNn9bFxc4FnnsvQ1+2dUKGtma969QO3EQnrfOmze8yLcWg878G3Xj9luVTV72Pzpk1EIhF0XWdkZITOri7cbveirPprkyafz0cmk3HSGt1eHntiMxd/dldsM0tLLESlUiGRSBCLxbCSmSl95tK2uWf3NwGv/Co9k62w5vkMN928mVBA54P/toLOdje6rjtZDJs3A9Dd3f2a1hx4NWBJG3YhxIdxVsJRYGec1fL3gckjSBNIKZNCiL/iRNeHhRBaddXeA/RXd+sHeoHNQggNCOEE0dW219B4zDJmCduWVEwb1xIQsthW6l21FCZVVSe0m0FVVXr7+lAUhVJD2dRGxKIubrjmQKJhA8uyWbc+x6VXP0tXh5vPn7eKVGKYDevXo6oqkfb2RZUVVRSFUChEMBTCNKvpaGmLDZvztMVcnHFKz6yi81tbXPz02wcRCi7cUDQrl2pZFq0ujdERJwM2FA5jGMasjPpsJno1Kj6TyRAMhchmMuRzY3zuvF1RhE2+mGdkZAizUkE3DJCy6UTn2LV3zPl7zwa5vEkiWeGJZ5KsWuGjs8M9ScRozfMZPnvxU/XPDzw8xq9/cAjhoMrIyEj9ea1NjJap+B0XS/2X+ziOT/sfAFLKF4QQbTMdJIRoBSpVo+4B3oQTEPdXHD/9TcDZwC3VQ26tfn6w+ve/VNmCW4FfCSGuxgme25XlIjRzQjxR5v/+MsSa5zO8+Zg29t87vCiD/FKCbdsUi0Xi4+N0dnVRjicxU1lUbaJhb4xwVhRlRkOsaYJgxAVAIlXmP774JJmsSSpdoVCkTsNrmlb33y4WrGQGVyZPKZ0HwCvBZeX5zVWrKbv9WJZk4+Y8Pp9GyzSyoqoqpv37YkAIMeG7K3O4D7OZ6NWo+Na2Nnw+H8FgkFKxhKKpgI5Lb2FsbAyAjo6OebEmVr44JRuhBfzk8iaZrMnIWInOdjdBv4arIc3MtiX/eirJhV99Binhrce3c+Zp3ShC4PepSCkpliS/vWXzhGsWijZrnstw+EEhVEVxAiWB0ZGRZSp+B8dSN+ylajoaANXV9GyeuE6ceu4qjrreb6WU/yuEWAPcJIS4DPgX8OPq/j8Gfl4NjovjRMIjpXxGCPFbYA1gAh9fjoifPRKpMp/7ylM896JDSd779zE+8r6VvPuUngk07lLDdKu4ZmlJqt+LZVkOJWzC/atPmLTPQiKc8wWLTNYJD7nm0n3ASiKlpCXWSnx8rB7wpKoqxZJFLm/idqmT1OSyORNDVzBmEM5ptuI85rm7+enNY9zy5yEAujrcfOdr+xGY17eqXsuy0dT5Pw+2bTMyPOys1HWd0dFR3B7PolLxuq7XJ1C1thCCSqXC6HAcXdfraW1t7e2zOm+jMdc72skUJKGgjh7aMgHMFyz+765BvvXDdUgJuib45pf34YB9wvWYhWSqUv/7m49t5YNndmGWc7hdHkqlEpl0mki0hUh48oTa7VLqVHxtQtLYtm27PgFtbC9jaWOpG/Z7hRBfADxCiDcB5wJ/nOkgKeWTwAFTbH8JhwHYensROL3Jub4KfHWO/V50WJZVX5U1tpcy8nmrbtRruOnmTZz0xnZaoq7t1KuZMd0qLr9+c9O852AoRCIeBxbf5eBxq+iaoGJKvn/jej77sZW4vAq6YdDV3V1nAMYTZX78y5f5x2MJdlvl55Mf3oXOdjfpTIV/Pp7gj3cM0dvp5r2n99EWc835GbJsWTfqAANDRX76mw2c91bfnL9TKl3hmefS3HnvCHvvEeS4I1vnVatdCEFPTw9qlTrucblQq4Z3sdDICtTatVx/l9tNOBwDLJLJhJOTb09/PpjoWlj9wG2879J+3vOOXv79PVuK/OTyJt+74aW6nGvFlFz+7ee4/psH1guwSClJZ5zs32LRBgTlcp6R4UHK5TL+QABFEbz/jJ2478Fx8gULw1A4+/RedlnluBwaDXZj2ls6nSZQzeSotZcp+qWPpf4LXYCTY/4U8BHgT7wGi8BYlkU8HicYDKJpWr09Wz/i9sJUUdAuQ12SfZ5JIMSybHJ5C7UJXySlJBGP4/P5UAtzK/zSjCFQPW7y6x361Cvhvy9fQSptkjZ1vvOj9Xz07FXEohpSOgNxLm9x1bXPc99DThnW4dESL2/M84NvHsBfHxjlm9e+AMAjj8N9/xjnhmsOpCUytwnWVDr1L63Pofha5xSUWKnY3Hr7ID+48WUA7rx3hLvvH+FrX9iLcIPwyWx84M0yD7Y1hBBkcpJM3ssHP/Mwe+0e4JMfXkWuIBFNqGzhcbP6gdvQVAWvV60b5LTpfOdHnkhw5qm9hEOOoS2X7Qnpj5omiCfLE8R8/H6NU07s4pe/38R9D41z2OuiHHpAkFw2haIoxGIxFEWhs93FL687mKfXpjhw3xCKqOD1qliWRaFQwOPxTLh3NWnlbCaDYRjkcrm6ZPBSfIeXsQVL1rBXafQbpZTvAX64vfuzXSElZqXCwMAALsOgVCoR8PuXfDlMr0flqMNauP+hLfW+P3r2yiXpY59OgSynern1jkH+8Wici89qlscuiESjBINBihsH5nTtZgxBM3bgqGfv4tMf3Y1w9T7WnoFSyeJvD49P2HfzYIFc3uS//zgx5nM8XmZwuDhnw65roi59W8MbjmzFaImgts2spV9DOmvyq//ZNGHbk2vS5IsW4YZKtwsNdkymyoyMlUkky+y8k49wSEdbJEneQtHi2p+8zB33OEF7Dz2a4KFHH+Xmnx6KL+DjqLW3I0RNjc9JgytaKu+7tB+PR+XKL+3NJy59YsI5D9w3jKeSJb/eCagM2fDLL/diWTZZ20CPhvB5VTwutS6a4zJUzjyth852N6l0maMOC5IYH8bldlMulRgdGSHW2oqqqrS2uDj2iNaqdv0wwVAIq1pyuKe3d4Jh1zSNru5uNm7YQKVSIdbaiuGaO8uzjFceS9awSyktIcQKIYQhpSxv7/5sT6iaRntHBxvWr6dYLBKJRjFcriXv7woFdf7zvN045cQsz72Y4YhDWmhvdb+iCmoLhZRwzfUv1kux5vJTe5OFgFAo9Iq4SDRVEJwiIE0IQVvMxdBIqb5N1wSaKvD7Jr/qtRS6uUBRBFd9ZV/+3/dfJJEq87Y3dXDCce3z+k11bfIxsw1827rS3pb+bXknkqkyl3/7OR54OA6Ax6Pyw6sOYKfeubsNpkK5bDMwVJy0vX+wSDRiIDxeykUnM9fwhEB4cLudYMPxRJnHn07xkfet5Ge/2UCxZHPQ/mHefUovVmpkygndkWvuJO/PIwCPx8vw8DCqotASixEOGrz9hE6KRQtFMbGqRYsqlQrZzMTs4JpwUmtrK6OjznPd1dU1iemwLItEVQq5Vr3P6/Uu+XFnGUvYsFfxEvBANTq9nhMkpbx6+3XplYdlWYxXo251XSdZfcGWOhUPEAkZHHpglEMPnP1qbinBlpK/PjA6845sMSqq38fRz95RjyyuFX7Z1rny4ZDOF/5jdz578VNUTIkQ8PEPriIQ0PnEh3bm3Aser1PpB+8fJjpFMFUN01Hgr9spxHcu3w9pSwJ+bV71uEMBjX9/705c+d0X6tuOPLRl1pKlEyRvgaGhIbq6uzGMLROe0fFy3agDFAoW37vhJS4+f/WUE525IuDXOOn4jnpVPgBDF3S0uwn4VFLpUsN2DZ/fwOvR+PE1B3LXvSOkMhVOOrGLE45rByQuQyUU1Mmnpr6eqipEIhEGBwbYsH49QD2+otGVZANeoJQbRA34pixgJKUkn8/XP+fyeSdVb6t9ioUCnZ2d6IbB4MAAZqWyTMXvAFjqhn1d9Z8CCwq83bEhJZZt11+wkZERZDVXevkF27YQgM+jkck5Eelp02D1A3+iNWagNsQQ1Iy2lBLTrTOWtujs7qJQKDCeSNDXu2Kb+34VRbDX7kF++6NDGRou0hpzEfBreNwqO6/0cdP1h/DYk0m6Otzs1Oud4MveGjNR4PMJcmuEpikce0Qru60KcN+DY+y5e4C99gjO2k2jqiper5eBAcft4ff7J93fRGoi0aco0NbqYrFeGUURHHN4C4XCztz85wEiIYNPfnhnomED265QyCWJRKLY0iaVHCcS8SGEIBZ18e5Te2e+wKTrORN7Xdcpl8u4XK56dP50riRXy0T3kZSSSqVCPp+nq7sbyzQZGRkhGAxOouK7e3qq11bqbSEEyXSZ4ZESA0NF9tw9QCioL1d6W0JY0oZdSnnJ9u7DUoCqabS1tdWrizW2Z4O5iqe8miGlxLKs+v2rtZtBCMHHP7iKK77zPADnXf4yH/q3FZyxT+eUVLYQArfHUxef0XW9HlXcDLUyp1tLos4HLpdKq8vxpU7Ybii0tei89fgOyuMJzMQI+cTEY1/pSmJBv05wV509dp37nF0IgT8QIJt1Vqn+Ke7xqhU+vB6VfMGitcXgy5/bk+fXZbj2hnWcdHwnfT0eAv6FxXuEggbv/P+6eNMxbY7uQLUwjmVpdHd3o1UV3Pw+36JQ2GOjo1QqFadSXqFEfv1mVFWdkzxtjYrvW7HCuWeN7a3Q2OdaO5Wu8K3r13HnvU5sgaYJvvPV/dhnz9Ck45exfbCkDbsQ4o9MzltPAY8AP6imqb0qsbUBahSMmMugb1kWuVyOYqFASyw2ob0UjbttSxLJMoWSjctQCPi0uqb1YkBKSf/mzfgDAYLBIAP9/YQjEdwN9LkQClI6ObtawMcxr/ez714h1j6fYZeVPlpbXNP6p+d6X2t9CgSDeDwehoeGaG9vRw/4FiR/O0GXXFL/To2ypo1YrEpii12CNJkuUyzaqIrA63Vy803TZHhoCJ/P8ZcPDw3R3dODLRXcLue3iYR0fnj1gVz305d4x9u6uOb6F3nhJadft9w+xMXn78Ebj2qbVsN+NlBVpZ56tmXbtonUD4XDhCMRDMMgn9zMPVUp4rnK0041yagxHDOVY83kzLpRBzBNyTXXv8hVl+wzLQu0jFcOS9qw4/jYW4FfVz+fgaMTvxtOpPxZ26lf2xy2bbNp40bC4TBen4+B/v668tVc6HchBIauMz42RqlaVzwSbSGZqlAqlwn4tQWvWhYTueEEmf4Eti0pI1CCGpZLRQ8u3moy1trK8NAQqWQSwzAc4yBUXMEAQ4P9mGYFj9dLpKUVl0vDBQT8On3d3kW5/tYQQtDW3s7w0BDpVIpAMIjb40H1+zFaZl9Nbms0FZlZe/uczzUXoZLpaGE17KzOVVWdUAq1GcYTZS66Yg1PrkmhqoJ/O62Xd5/ag9+r0tLSgs/vx7YlbneOgeESP/rFRs48rZeVvV68Xo0VPV4u+sweWMkUF70nhKMWTfW6OYpjCbxtUUoli0LRwufVtrt40nTxDbrLYWK2VttrxMG3Xo/qdQNOoZlaymSziVU2V+GJZ1L8+FcbkBI+dc7OdHd4UBRBKKhPCowsFiezA4lUBcteVqtbKljqhv31UsqDGz7/UQjxTynlwUKIZ7Zbr14htMRaGRsdIZFI4PF4cLs9c/apK4qC4XLh9fnIZbNomoaNm9P//WHKFcl739nLe97RuySMeypdIT2U5JnDT5r0t8VaTdZoyJrAiKuqTlYoVshmc3UavFgskMuVsCyJz7vt703j79rsF36lRYpsW5JMVfB6BOVSHm91UpnLZvH6fPMSKink85imSTAYrLcDW/l2a6iYNrf8eQC/r1b1TbBufZaR0RKhVf6q8IrC6HiRP94+zq//sJlyRXLvg2P86OoD2X0XZxLh82qkB/KsPeKtk65xzHN3M6b5+OlNG3j62TSHHhjljFN6FhxDMB/UStUaLWG0SHBa4z0dVK+bh45/36Ttzd6hgaEin7/UGU5Pf3s3o+Nlrrl+HcWSxXtO6+Xow2N1FwNAJKzT2mIwOr4lhuHtJ3QQXAJjyDIcLHXD7hdC9EkpNwIIIfpwqrsBvKpT4PIFG9PasnKwbI1iycKvzY3Sq1HxtcG4kM9TyCW4+pK9Oe8LT/GL/97EyW/pmpVhX2yKdWuYpo1pbttZv5SSgf5+dMMgEAgwPjaGYRh4PD7MSgGXO4TPHyCTHqdcKuJxb/sBXkrJ8NAQwRoVPzyMx+OpG1Ko/o7ZLIbL5YiFVNuubZRXnM2ZPPxYnD/8aYBLL9iDZDxOJpvFMAwy6TRdhjGv6GgJxONx8oUCxUKBSKQ5I1EsWhxzeJST3xzjtrsDHLB3iK52nbGEPeFZVPMWb9zd5o0XdBFtDxAfzuDOj5F7OVUPlFNF8+fqgsue4dkXnJSwF1/O0T+Y54JP7IG/SX31ZljI+2FZFplMxhE4UlXS6TS+6uRpNvd4otb87CRta/jT3cOAE+V/9OExPvmFJ+pKd1d853liLS4Oe92WrJZo2OC6Kw/ghl+t5x3HBGhxO/LFlf4BatJMr3S8xjImYqkb9s8CfxNCrMNZyKwEzhVC+HBqs79q4XYJxkaGMVwehOKiVEhSLrkpGyqGrmLbEsuSM9KGNSq+JRbD7fahqAVMs8z6zVtSXTLZCp3t7hn7NB3FuhgvsdutUppBw3wx0NbWhm4YDpuh6+iGQTJtkS34WL8pz1Xf/yffvmxvxhMV9tpDMhsCfiGDuhCC7p6euqGsCYVsPaDnCwXGx8fxer3kcjlira3ouj5n/61oyglswcBQgS9duRaAi77+LF8+fzdSiUFKxeKChEp8Ph85r5d8Po9hGITC4aa0vtej0hIWxMeHeeOREaRdIJ1KsPNOfZQ3D00t3vP07VOuzJtVVbOlrBv1Gu57aJxPnWM1NexSSqRto6jqhPZC349sNksqmcTt8ZDLZtE1bdaTp0Z52rlWkFvZ5zzhq3cN8MjjCbYWzfvjHYMcsHeoXnhGCEFHm5vPfGxXzP5B7t19csnhxRoTljE/LGnDLqX8kxBiV7bUUn+uIWDumu3UrUXHVP7Ljf0FQqFWRsdtHn0yyVuOayOdBd2wyWYt/u8vQzy/LstJx3ewetfABKqsETUqXjcMTBMefSJLKlPhuzc4Up7BwPTVuV5J+LwaMrBtH0lFUXB7trg0au2AT/D02jLfuPZFSiWbz31lDddcuu+0gUS5nMnIeIn7Hhzj/9tf8ODe8wtIUxRlQv61YUy+pqqqtLW1sWnTJnK5HH6/v766a8Rs/Naq1zNjUN5f/ubk7qsKnHFyN8XClklLNpOZt1BJPp8nn8/jcrkolUp1/fGp+quqCm63G483SKEawh9r7aRSWTxWx7ZlXYO/hqBfb5oSJ6sqkNlcjmAwiG1Z9fZCoKoqnZ2dbNywgVw2SygcxjPDPW7mi5ezEapvwFGHxvj9/w4wnijTNcUEf2WfF22KBYTbpZJf1qpZkljShl0IcdpWm3YWQqSAp6SUI1Mds6PBsizy+TxerzNrrrX9Pp1v/2gdDz4Sd8orPp/huCNaCQZdfPpLT7KuKjl59/2jfOaju3DyWzpRGypkTZXW5nKpHHZwC7/6n020t7ro7vTwmY/usqiRrNOtXNVwYMY0vYVGKM8GE/zZ1bbLpXL4QVFu+sEhFIsWXo867X2RUvKvp5NccJnjmzxqZfei9K159TgfRU1gVcu05nK5SUp3lmWRSqUoFYu0d3SAx8XRz96BqqoohoGVLzj7FYuI6m/QjFHYZaVj5FtjLvbczU86OUS0pQ2v18VAf/+0QiVNte/9PiwpiUQiBEMh8vk8ZmV6XX1dVxBsCdYSwsIwXJSa7D/XuUaxaPPed/bxk5s2VM8Pn/rILvV8+lS6QqVio6iCSEhHSkm5UiERj1MulSgWi2iatmDDblkW6VSq/s5mMxmCwen97NNJEc8F0YjBNZfty+h4iaBfY/+9Qjz+jKOS093p5pQTu1AVMWWMxzKWJpa0YccpAHM48BccKv5Y4FFgpRDiK1LKn2/Hvi0KbMtidGQEn9+PEIJsJkNPTw/RsM5Zp68gmXbqbx96YJRDDoiSSJXrRr2GX/5+E8e8vrW+8rZtm0KhgGVZ+Hw+isVivR30q3zkrJ34t1N68HgVdE1ZVInX6ejIeKWIz+vFO8VKs4bZFP3YVqjlgM8GiVSF7//s5UXvw3T3r+DWiMVi+Px+RkdHKZXLaLpeH/gVRcHn85FKJtm4YQO2bdPe0YHb46G4cYB7Vs+eMn3dvmH23TPIk2vSXPT157jyS3shbYmu6/StWAEwZ4MDoDUwCrXJbLNnoaZ8Vijk6epyxH7GRkfr158KzfrUyFLU1PeKJZv+lEDX4buX70cqU2G3VX5CAUdPfmikyMVXruWZ59L0dnn48n+uZpedfHg8HsKRCMlEwqGlOzsXJZ0tn88TbWlFN9wk4yOUSiXsdBYrk5u071QTsppg1XzeoWjYqAcMXnbhniRSFUzTJhwyqFQk+UKZXCZFKBxCVVXK5fKSTJddhoOlbtg1YLWUchhACNEO3AgcCtwH7PCGXdU0Ojo7GRocBKC9vR1N11EUhV1X+fnqhXtimpJgwEk7qVWDaoShK9XCEVv85tK2wbYp+osM5zKEQmGkV5LL5cik03R2dZHJZBjLZOicQid6sWGaJoV8mUgkMu1Mv9EoNK5ezUy23p7Ob12p2KSzJiDx+3Rc28pnLyXlytwoz4WitbUVASiqWm8jBMViESklHo+n7moo5PNouo7b7Z7XyioSNvjqF/YikzWd1MOyJFId+BcSrDeX3O5JYj+GQaC6ip0r7HKZhAaBQACvz8/g4AC6P8g/Honz899t5tSTOjn3A6vwuJ0hMZWukB2K84Uzg1hWAEUI1NQohQ1J9KCPTDZdT9lLJpOEwwvzJ2eyFoa7hd/fNsgtfx7iuiv3I5mBaCk3K7+9aZrYto2u6yghP1rAi94w6ZsOW7NEBtAOCL+Xq37Wz18eGOWH39wPl15moL+f9vZ2MtksAf+2n2wvY35Y6oa9t2bUqxipbosLIeZWG3OJQkrH2NaQy+Vwezz1z1tHq8f0Ijdd2ku5vMWohEMGMpudUnTkqLW3Y7gMDHcAyxJ4vV6SiQQbN2xASklrW9uU/bIsSSpdQShbBCsWupr2+XzTDja2LSdQ8XMNRkpnKvz5L8P85NcbsGzJe97Ryykndm2TanKhoMF3LlhJbtzRCY+Ftv2rNJVRrJXcTCWTtHd0UCwWKeTzhEIh0uk0IyMjtDX5jWdCJGTMKFYyV8w1yHCuzI7qcTd1A/h1hbGxMZR4HMPlwu12s3FzkU+dszPHH91WN+oAlmXT5rW4f/Xk1Mtjn78LTXcm5MVikWQiAeHwgt6PsXiZ93/y0frnM855hD12DXDNeTP/dpZlkUwkyGQytLe3E4/HkVLS3dMzK8Pe7D07cs1d/PmvzvD76S89za+/fzDDQxvrbr7BwUHavAGOWnv7JLfMK8GwLaM5lrphv0cI8b/A76qf31Hd5gOS269biwczkcKVyuFVnQHUyhQo5gfQg4EpBzqZz/PEwSdO2t48ElZQLpfJZrIEgn68bhWvz0cmnUZRFLxe76TBM52p8Ne/jXLTzZtxuRTOff8q9l4dxLuAEppCCLLZbD2Nq/GayXSFNc+l+cv9oxy4X5jDXxclEjYmRefOhA2b8nz7R+vqn3/4i/XsvktgQqrOYkFVBT5R5pFqBPbBt17fkG7Ugaim1PIXoAAAIABJREFUJW7rAU5VVcLhMKVikXQq5ajoud14PB78gQCmaS6pegKLlVkxn3KummWRiMdxuVzVwigaF3xyN1y6RFEnshpej0pFTM3INNLvjr6EU6NcnUWfbNuu56sLIertijn5WqXS7GRiVVUlEo1SLpcZGhqq67ov1AduVmvzqqrgxst3wx4aICZV7KFxApqKR6pIaVMJ+/FHo8s+9yWEpW7YPw6cBhxZ/fwI0C6lzAHHTXWAEKIXh653SibB9VLKbwkhosBvgJ2A9cC7pJQJ4Yx63wJOAvLA+6WUj1XPdTbwxeqpL5NSLnqKnZXNc//qEyZtXzxBFgWXJ4Rtm2iaIJPNkkmnCYVCZLNZBgcGJlHxTz+b5hvXbqm69dkvP8UvrzuYvu75Py6qqtISCU8qN1sqWfzu1s387DcbAfjzX4d5/SFRvvSZ1ZhTDHbT4d4HJ1dhu+u+EQ45ILJNgvIax7HGdKPjXrgb7049i369qWDbNqVqEJeqaSQTCcLV3HDDMBxqdgcccMcTJXJ5C5eh4PVoBOaYU741LMtiaHAQIQStbW0MDgwghCDa0kL/wDABv59oS0v9XrndGpYyTWZB9X3Z+t6OJ8oMDhdwu1RaopMZj5qiZDQaxeVyMTg4SHtHB92dHrra3QwMb1HJfu87+1CU5sSkZUmS6TKViiQa3qLkJ6Wsr6oXMqkzdAVFcQy7Uixw3xQxGkc/ewepQg6vz4drBygl/VrBkjbsUkophHgJOAw4HXgZ+P0Mh5nAZ6WUjwkhAsCjQog7gfcDd0sprxBCXABcAHweOBHYtfrvUOA64NDqROBi4CCcCcKjQohbpZSJSVdcwhgdL/HUuKC700MgiLNCb2/H6/USDIUolSbGFxdLFv9759CEbVLCA/8Yp++0mTO6p6Mj3X4/Yiu1tGzO5Dc3T4zi/fvDcbI5E+8cB6V9Voe46eb+Cdv23yv0ikTaLxbmSudKKSkUCgSDQaItLSTicXK5HEY1T792r7dVUGKxZJHJmpimxO1S6n74hWBopMh5Fz7O0EgJIeDdp/Rw1ul9TVM6Z4tAMIinGnPQ3tHBQH8/Q4ODuNxuIlOsOOdqE5OpMgKby7/1PCv7PJx/7q4k02XCwS33RBGClpYWxqplmH1+Py6XC6+qcu2V+3PLnwfZsCnP29/Sye6r/DA+3OxyvLwxx/lffoqPvX8n9t9Tw7Ztenp7GR8bY2R4eFoqfkIdgSYFZBRF8I2L9+FbP3xxUqW8GlRVpTUaqV/HtqxJ7/gyXnksScMuhNgNOLP6bwxnpS2klFOu0hshpRwEBqvtjBBiLdANnIwTVQ+OuM09OIb9ZOBG6VRZeUgIERZCdFb3vVNKGa/26U7gLWzRrd8hoKkKa17I8fpDWusDo6qqKIqCoij1dg26JljZ5+W+ByeeZ0Xv7HTS50yRCsHV5++EXylVr6+g6wr+3BhiGrWwqbDvXiGOPryF+x4cB+CQA8IccWjLnM6xvdHs/pXHkxPSmGwbyhWbjGXg7wgRCGooilJfrW/tXpkPdT0TcnmTu+8f4Vs/XEepZLPbzn6u+OLetMVcMx/cBPmCyQ9ufJmhEed5kBJ+/YfNvO1NnQsy7Kqq4qtWWKvEk1TSWaKWAFQomJQ2DU7w81uWhWXNnjGqmBYCk1xmjKsv2QuwyKSGibV2TIorUKUkJlWE143ZoFkQi7r4wLtXYJoSoxr0WS5PPSHD6+VLF69hLF7mquvW8Z2v7UM07CNfhLb2dkc0Z5Z6/s0KyAgBhxwQ4buX748Rbz7BoCElL1+t616TbV7G9sGSNOzAs8D9wNuklC8CCCE+PdeTCCF2Ag4A/oFD4Q9W/zSEQ9WDY/Q3NRy2ubqt2faprnMOcA5AX1/fXLu5KFC9Ho5aexfJtJOmYugKoZCO6vfx6XNCEyqkTVWKsX4eVeHUk7q4674R+gcdWvCAfUKsnkdpzdnA79PoDNo8vN9ktbA3bLi/XnENQCgqAjFplWlZluNnDBlc/Nk9SKZNpASPWyEUXBriOwtFM9/06gdu4+Szn+N97+rjfe/qW1BNbMeYWRMC87auUtaITNbkG997oR4LYVmSdeuzOBkJ2rQV8JqhVLInpXMCDAwXZj25bIba9zAzOe7Z7fhJf290f6mqihEKcOzzd9dX7lI6xm4qlsM0JRXT2bGQG0ZKiW64yRUsvM2K8Tx3J4lCDpfLhacqlKQoAsMQ1etJRNCJbdkao+MlNm52dAnyBYvzLnySYEDn21/dz2EI5lIBcoIc7cT4ECEE0bBBPtncSGczGYrFIv5AgEw6TUtLC1pVNW8Z2wdL1bCfBrwb+KsQ4s/ATTSvjTElhBB+HNr+U1LKdOPssUrxL5p8lZTyeuB6gIMOOmibip03o1RtTeMT39rIwFCBQtHG71NZ0ePliovaicyx7Gks6uK6rx/AeKKMrjsvtqIIsjkTv29xHxmXoeBp0j+7VCLrNSjk8wgh6OzqxOWauBIwTZNEPE4k6gTIpVNxWqLReRUomSu2Z8791vjNLZs57a1dCzLslUSKcjKNoqiA46dVVa1pZb2BoWLdqB+0X5j3nt7H93/2Mhs35zny0BbO++DOk8qZznTP/H6NY18f46UNW4y7pgl23sk3ZZ+L1QCzhXzvZnDFIrhis6uu53FrDAwV8HuD5DIOY+QPRCmXnWyPqaAoCp3tXVOubuOJMrffM8yzL2R4yxva2XP3IKEGxkLXBLvv4ue5Fx0moFC08XrlvEocLzQ+pK29nf7Nm8lU9e0DweCyr307Y0kadinlzcDN1ej3k4FPAW1CiOuAP0gppxVDFkLoOEb9l1LK/6luHhZCdEopB6tUe025rh/obTi8p7qtny3UfW37PQv6YlNgurSdqRSktIB/yhdveLTAV/5zdyoVyUVXPssV/7WaiikxTXteOubRiEE0YpDPm6zbkOMnv96Args+d+6u+Lw6Ho86Y/nO2aLZKWoCJe0dHaRSKYaHBunt65swCNq2TS6Xo1wu1yOOpyssspjYFvT29oQ9x0DO7k43qgKWDe9/9wouvOwZMjkTgDvuGUFVBJ/+6C4TVu4z3TNdUzj1rV0k0xVu/+swsaiL88/dtW7UttRmh2TG5Oe/dRTjzj5jBd2dnm1i4GcDKSXdHTqDA6MYhhspLdLJYTq7ukmPN6f03dXqgo1IJMucf8lTPL/OeWfvvn+UT354Z047qQtNc16WcMjg0s/vyUVXrOG5dVl6uz1c+vk9Cc+Q2jnfd3Y6NcF8tUqfqqrk83kqlcoyFb+dsSQNew3V6PdfAb8SQkRwAug+DzQ17NUo9x8Da6WUVzf86VbgbOCK6v+3NGw/TwhxE07wXKpq/G8Hvla9LsCbgQsX7ctVMZ0s5FT03dHP3kVe806SO42EdEbLcVQdrv/GfiQTI/jcOgG/B3Nwairw2OfvZrhocO/fx+jq9LD/3qFJ5SoHR0qc+/nH0XWFb122N7aVQ4gglkW9dvi2Wh0LIejq6kbTNFwuF7Y1OchH13XaOzoYHBgAoKe3F01/7ZWPPOPknkVnU2ZC0K9z2YV7cdX3X6BSkXWjXsPfHh7nI2evnDMlHwkZnPuBVZz9rj6EIurPZCJZ5pJvruX5l7Jc/l9788kvPE7NDX7v38f4xbUH09u9MLp+vhBC1KVl/cEQAsikUyiKYDrp9qmMXzZn1o16Db/8/SbeeGQrLdEt8QtdHR6uumQfKqZEVcWMpWZrmgeeBp2M2aLZOGVZFvHhYaLRKMFQiOGhIYqFwjIVv52xpA17I6rR6HXKexocAZwFPCWEeLy67Qs4Bv23QogPARuAd1X/9iecVLcXcdLdPlC9XlwIcSnwz+p+X6kF0m1PjI6XuOLap/nqF/aitWXLSy4UQSTSwvDwIKMjm1FUlVi4DV1Tm+pqW5bkhZcy/PhX69lz9yB+r2CXVcEJA8Qtfx5ASmiPuejucJNODlEuFeo1y2s56fOdnVuWhd1k5JNSkk6liFZ9dkwxgbAsi/GxMRRFQQJjo6O0tbdvs8lGsWiRylRYtz5Hd6eHSEhfcLT21piqKFAjDr71elSvU6zDCGv88aqV6JqCks2Aa+LgG0+UKZUtdE0hGNDrQVlTQc5ROMDjcfT1f3z1gVRMiaIwwYit6PGia/N7LtwudcLq27Yld903wiNPJHnjUa389YFRGmPbLBtuuX2Q8z6487yutxjQNI1wJFI3aLW22zW3FfJUWRyGrkwZpj/bOg9SSkqlEiPDw4TCYVwug6PW3j7pPZmrG0lVVdra26v9VurtZaO+fbHDGPbZQkr5N5r74yctW6vR8B9vcq4bgBsWr3eLgzXPZ/jhz1/m0x/dte6f1jUVgURTVSq2jaaq6Lo6oTDM1lAU2GNnwbVf38/JgzXz6JozuNcMSm+nh39/zwp+ctNGRuMV2lraSYw7MYj+YIxyRcHlmnnwrlXFEtVI/HK5gmnB3/8Z58g9/Ry19vZqn9SqeIdD83kiwWkHidqkoLunByklI8PDzFnZZpawbclTa9Oc/+Un60blrNN7ec87evH7Fse4W5ZFOp3G7/ejKEq93UiFStOale77wFCB8y95io2bC3g9Kl/41O4cdmC0qR92PpMzTVNoibrI5U0+/oGd+d5P1mHbEApqfP683RYteLFi2vzraacwSbFk4/NM/g4z0dD1Pm/D2Iip1AH1kJ+j1t5Ftspo+Hwamjo5CLQGn0/j0AMi/ONfWzJrzzlrp1l/v6kghMDtdhNtaSE+7sQA9PT1zlp2djrMRSZ4Ga8MXnWG/bWCp55NUyhYdcNuVYvJ2LZNa1sb42NjjI+NEWttnfY8xUKGoLdCIVdG1XQUxakgls1m8QcCvP3EdtKpDAcfsC8tYZ1c1hH8E0JQLmbQNBe2rc2YK27bNiPV/rXEYoyMjOB2uwkGdF4YFbSGDQqFArqm0NU9B+36TI6oJaj0O+k4UQTl/mHsWdRBnyuSqQrf+N7zE1aKv/z9Jk49qRv/1LFd80I2myWdSuF2u8nlnKhpdyRY/z6zqd6Vzla48rvPT4icvuQba/ntjw6dV4DVTPB5Nd725g6OO7KVfMEk4NMWtWqgy1A57ogY9z04xsOPxTn7jD7+eMcg8aQj4BIN65xwXPsMZ3HwSsdGuGMR3LEIRsmqZmtMf//DQZ0vfmYPnn42zfMvZTn6sBid7e4FF2uSUpJvkK/O53IEgtNPnJexY2LZsO+geN2+YbzeiSlsLbEYAtB0HaNUwczkKW0abCpAYUvw+cPkssmqClc7liWpmCbj4+OUSiUqpkmlXGbXlT1UKja5TAlfoA0hFAq5UVTFxrLsaiR1c6iqSnt7O/1VURBN0ylVfCRTWXo7NQp5R2Qlk8kwNjpKrLV12qpfTsS2ipmZXZGMxYAExhIThTpq+eSLBVVV6erqYsP69eRyOSLR6LwKuVTKkrUvZCZuMyWZrDnBhdOIBdcC8Gr4vBow/zz26XDw/lHefUoP//OnAa77yUt894r9efaFDELAgfuEJ0XgLzXMJbAvEjY46rAYRx0Wm7A9lS7z0oY8jz6Z4OD9o+zU651VLYQaFV8ul+np7aVUKjE+NkYgMPc01vkE4y7jlcWyYV+imEC9Sie/N5WpkKroHHxAhA+cudOEgUIIgV4NGhNCYGXz3LObY/Ca6ZgrHjdkMnjQQErswUE0VQGPm3ZhYGUKuAFV1TEHhpFuL9FYF9+89gVGx8t8/aK9GB0rEwrNPGBJKev/qlvQdYUn1qTZf69uvH4dn99PIBicVt+8RumPj48Ta23dVqz7lPC6VU44ro0/3r5Fma+n04N3EVfAtYIe4FRxS6dSdVp+LnC5FPbfO8QDD8cnbAsGmr/ySz3KPxzS+ff37sSZpzlZIUG/Tt92CpbbHsgVTG783aa6UuNPb9rI+07v46x39c3IAgghcLlc9Up5qqrirbbnisXS+1/GtsOyYV+i2HqQNSo2dqaC34ZL9plahauZMZwqT9W2bQob+qdMbzp27R1T+nCPff5unh0SbB4sUqlI/vFYgkMOnF2BlRoVr+s6rW1tDA0OopDhmMNjXP2Dl3nTMa0cfpAf9yz0zSVQLBYZHBggNAd1sIXC41E556yVxKIu7ntwjF1X+vjwWSsXfaVYqKb4uata4pVKZZJC4Ezw+zTO/9huZLJreHJNmtYWg4s+s8eCNde3N7YOqnstIZ+3+O8/TpRMvunmTZz61q4ZDTss+8JfS9ix3/LXEHRdIRZdPIpTmYeesxBw0P4Rrlq5D7Z0fIGz1WFXVZX2jg7AiR7u6OyiVLZ56NFNnPTGDvbbK1SlcacP4qoxE7HWVidQji0DVGO0uDStui96MSnCSMjgfe/q47STHDEYzxRBXAuBqqp0dnVVVciUCe0aZkuZt8ZcXP5fe1Mu2ygqhIPGgv20iwnTNJFSomkalmXV28v5z00gJ4vdWNYrSFktY4fBsmFfxiRMl/ZkWdasgqJyeZNy2SYY0OqR+Y2pNYahYxjwHx/eZc59MysVxsbGHNdDQ1161evmoePfN+mY4164m6LhI1+wKJVsAn5tQatsXZtfsZPZ+iZnWlnNhTLfFrXoFwO2bZNMJMhms3R0djI+NoZt23T39NQrk6mqOqE9V4P/avMFu90Kxx/Vyp33bali+JY3tONd5MnlMnZ8LBv21xhsW1Kp2Oh6c1p3OkGN8fFxotNIttq2ZGC4yLU3rKN/qMgJx7Zz0vHtixohbUtwGS40VxjdU6jqySswhYCN0ye49icv1avWdbW7+e4V+9EWcy9an2aDZd/kFtQK1hRLJQb6+50a4t1OKYZcLkd8fJyu7m4ymQyZdJq+FSvmbNjnc79rpU9VVZ3QXgoI+HU+ec4uvG7/CA8/luDwg6Mc/rponelaxjJqWH4iXqVopGtNS5JIlrEsycujkI0nOHDfucuumqZFIZ9HTiPZmkiW+ej5/yKZdtKQrv3pS5TLFu85vQ9dW7j8rBCCZ57N4nJ7+Px//osvf24PujtaOe/CJ/nmx6ZO7bNsOaEU7cBwkZ/etJH/+PDOuF7F/tpmK9/pUChY5ApOvrXfp83Jn21ZVt1t0NieFg3skMT5fX1eL9lMhs2bnBpMNRfOtoZlWYyNjmJZFh0dHYwMD9frty8V4x4JGbztTZ2ccGz7tJPzbYmlVCNhGVNj2bC/SlGja5OpMp+75Ol66tN3L1xJh5KksjmP5nZx7FpHndeSMJZwjLHt9rD6gduIRgwMXUFK6QzUXhcdLZF69P1UGB4r1Y16DbfdPczb39JJNLIlRmC+K6Nc3uRXf+jnoUedaO/PXvw0p5zYyTe/vA+t5pYI8EZ/uyptbrzIWQ2mTYPzLn+ZlzbkKJbsWRn2HZHStW2bcrlMPp8nFApRqVTq7Wb3OZku8/PfbeIPt/WjKIL3vrOXU9/aPaH4SDNYlkU8Hsfv82G4XPW2q0mqXo2KNy2L7u5uxsbGGB4aorunB4TAMAyKRae64GKIqMwGiqIQiUYZ6O9n/fr1jqRxd/e0k5OFPhvzmgzBdjPqsPSzJ5axbNhf9bBseG7dlnzmoFZmzesnl0g9+tm7OPfqIbI5x8juvUeQK764F4ZPYWhwEBMJpQLaqElHR0dTKn6qqOvWFmOCAp5lWRRH49i5/CTD3mxArA+gEs5/R4D8SY7edc1QH31YjPbQlsG/mb999QO3AfDGo1tnra2+o1LopmmSTCQolUoUCwW83ulTw554OlVPpQLJj365gQP2ibDfXqFZX3NwcBCXy0WpVMLva67aU6PiQ+EwmqbR3tFRj+3I5XKk02lira1kMxkG+vvnRcXPFUII5xnUdcqlEpquzxjMt5BnwzJNBgcHiba0YBgGQ4ODtMRiuFyu5epoy1gQlg37qxy6Jthvz1BdjrMZVFXwi2sP5tkXMoSCOj2dHsIhg0rFWX139zi5w8NDQ9MG1wUDOie+oZ3/+4ujBOdyKXzqnF0mBHEJIaBQ5L49ZpZFrWG6euQH7x9m1QofmiUmyK5OBUUIzjq9lzcd3b6kIsQXG4qi4PV6CQQCZDIZVFWlta2tvjKEiWwJCO75+9ik8/z9n+NNDfvWq1Uf4JIqQtEoBV1NV+s1NE4OG9s+rxe9qwuXy4XP66VcqUx1+KKjRsVXymVaYjES8TijIyPbjooXApfbzdDgYD04UNO0ZaO+jAVj2bAvASRTZXJ5Z4D1elUiixhoFgzo/Nen9+CiK9aw9oUM6jTpabGoiyMPnZhSp2kanV2OxKuUst5uRkF6An7O+9Aqzjytl9HxEqtW+AgHJz5m80m1m67PF5/fVw3OM2aUXY21GHzg3d3TFkPZVnglfZM1Kj6bzaLrOpVKhWQiQTAUolQqMTY2Rnd3N8VCgfF4nJ6eHg4/OMqd945MOM+B+zRfdTabbB397B2kCya+aaj46aBqGu5aFLyibGnPEXO93zUqPhxx3E0ejwcp5TYztKqqEo1GyaTTWJZFS0vLkvHlL2PHxrJh385IJMtc/I01PPaks6Lee48gX/uvvWYswTgXdLS5+cbFe1OpSDzpkSn3UQyjaf33mrGsUZUwPQUZ2ilMKGiwasXUVKxlWViLJCyjaQJvw0RopnrTiiK2i1GHV943aZomXp+PWCxGoVAgl8vVFchURWHTxo1IKYlEHZGhQ/aPcPThjh67EHDS8R3svsvcJx2qqhJcoHh+oyGfj1EvlSxMjx/fLO53TelQVVVkOoeVzdHI9xTYNvEUpmkyODCAqmm4DIPx8XEMl2uZil/GgrFs2LczHn0yWTfqAE8/m+b+h8Y4+S1dszp+tsE3tXSzfHbqQdLKF2ZVMWwx4PRx8Wlwy7LI5/Mz+pJfC6hR8R6Px5EPbWjbto3f7yced4INA4EAqqoSDqlc8Ind+I8P74wQ4PVo867xHolEZh0ItjUWEpBmWTaDwyV+ctN6UmmTM07uZo9dAwT8UwcA1oonKYpCrLWVcio9JxfRQiCEcGSUAwEURSGVSi1T8ctYFCwb9u2Mtc+nJ3zeb68QnW1u4okSoaA+bdlVy7JIJpO43W7cbne97fF4mg4OzehJOV3y+gyYq+LbYlLxjbBtm7HRUbw+H36Pi6PW3j5J2GQ+tPeOmt7T+Aw01ncvFArE43GCoRCFQoH+zZvp6e11VtqBxakvPxtKuZkBl7bNPbu/adL22RjXeLLCBz/1KPmCs+Z+6NE4V39lHw45YGrpY0VRiEajDAwMsGH9emJyblT4Qp4NVVUnZClMl7GwjGXMBcuGfTvjDUe18ZtbHP3n09/ezV67BfneT16iULQ445Qe3nR024wD7fDQUD0S2T1Dzm8zOriZT7oZRd84EZhO8a3xWqWyTSZboVCwaPF65zQgzmYAVVWVjo4OBgcHyQGt7e3EMwKvWcBlFlAUgZnJ1o2JFvCjhQOI6kTDtqx6uxGvpvQeRVFwuVzEWlvx+XyEw2Hy+fx26Uszd04tBXM+eOLpZN2o13DTHzaz527BKdkHIUQ9+r1Sqcx5wrnQZ2M2+u1LWTRnGUsTr0rDLoS4AXgbMCKl3Lu6LQr8BtgJWA+8S0qZEM6b/C3gJCAPvF9K+Vj1mLOBL1ZPe5mU8meL3de+bg8XfGI3brp5M8cd0crHL3i8rtnx/77/Im0xF0cdGpvyWFVViUQi5LJZSqUSPr9/2tX6fNCMop/r4Fsu2zzyeIKLr1xDseRIzV59yb7svot/VoPpbAZQKSW5hnrT6XSW8YRBJZ9i7REnTdr/uBfuJq+Cx+NB13XSmQwejwfDMF7VeuWapk2oGDef6nGwNJmMYEBHVUVdQ11VBaGgjqY1cUFZFiPDw9XgtRhyLPFKdndGWJbF8PAwuq7T0tLiTOLdbsLh8LJxX0ZTvCoNO/BT4LvAjQ3bLgDullJeIYS4oPr588CJwK7Vf4cC1wGHVicCFwMH4YhiPSqEuFVKuahvfsCvc+Ib2zn68Bh/umtoUhnS2+4c4uD9IrinqN5kmiaJRBLTNHG73eSyWbxeHz6fd5v76RSvm9UP/AmQ6KGpH6PG1b5tS7pEmev/s7Oee/7lb6zl2q/vPyvd9sYa7I3tRti2TSaToaOzE8uCsdEh+roC5Dc0N9KFfJ5EIoHX4yGXy9HW1vaKCaJsT0xF088VS5HJ2H2XAP/7i8O44VcbeOypFN/+2r5Ypt1UQa9GxQtFwZYKppKecr/tBSEEkXCYwcFBspkMQghira3LfvhlTItXpWGXUt4nhNhpq80nA8dW2z8D7sEx7CcDN0onOfshIURYCNFZ3fdOKWUcQAhxJ/AW4NeL3V9VVQgGFFb2TY4kXrXCh65PbWQqFUm5IvH6WsgWVDxunWLRRtUsvJ65vfhz9b3n00XOvmwzUlJXddsazVb7NZGYzYMFLHvm6lRSSkzTJJVKEYlEsG273t6ayqwJmTzyRII9d+vhBzeu59TXNb8X7R0dbNy4kVwuRzAYxOvzLQ+aOxgaffU6zoT3Q8e7+cipLYzEB2lta0NKOeVkTQiB4XLV2wW/j8OevIN80cJlKHg9Goqy/VgIRVEwXC40TatP4OdTEGcZry28Kg17E7RLKQer7SGgvdruBjY17Le5uq3Z9kkQQpwDnAPQ19c37w7utrOfg/cP88/HkwD0dHo47a1dkwLobNtGCMFYvEK+YPDEM2m+e8NL/NenduOFl0Y487RevJ65XXuuvndNFdx642F8/2cvo00T4Dcd9t0ziDELacyapG0mnaZSLlMulx2DvpVmfaNB7uv2cdbHH2V0vMypr5t64gGQTqWwLQtN08hkswSCwVc9Fb/UoXo9U04yVb+vXpu+kbVp5qs/au3tKC51Rgam8W+etih6NIxesPB6VLRFqG+wENSoeNu2iUSjJOJxksnkMhW/jGnxWjLsdUgppRBi0QoZSymvB64HOOigg+Z93kjY4OLzV5POmJQrNtGwQTRiTIoetm1n9dHq95LSBMccHuWOe0eS5vVpAAARQ0lEQVS59Orn2X0XP+995/wnF7OFpimMp4f5wLu7CGBy1No7UNWJgWfTRdofsE+YL35691mVFVUUBcMwiLa0MD7mqKPVoribIRI2+MbF+3D9z1+edvJQKBRobWvD6/UyMjxMpVx+TVDxSwHNWCLFZeDuapuwTUpJsVikf+NG2tvbqVQqxONx+lasmPYawWBwzgyMpjkM2lJAjYpXNQ1N03C7XKjLKXHLmAGvJcM+LITolFIOVqn2mlJLP9DbsF9PdVs/W6j72vZ7tnUnwyFjUonTZiuSN2y4D0++gBAKX/tQlEIhRCRi4DbzwOII3DQbfFW/D7ddIZsZpagoqIZKZ1fnBGnQZqv9tpiLr164YtZpVbUVezKRqFOS42NjtMRiTY27y1DYZaWfi89fjZJNT/oOtg14vbRGQwjh0Pht7Q6J81oaNLdngZu5+OhFtTBMKBRieNiRK57J1yyEYHRsDN0wcLvdO+RkTVEUXNW+i6oEba29jGU0w2vJsN8KnA1cUf3/lobt5wkhbsIJnktVjf/twNeEEDW+983Aha9wn6eFlS9w/+oTJm1fTDGN6QbfqGmSzWSwbZuWlpZpB9nGXHdh22jjw+THZ2dAaoZd0zQ6OjuplMuMj4/Pqv8+rwbeKODkMVcqNi9vzHHVdS8yPFrkhDe0c+YpPYRD6janNmulU2uDcq29mBOJuRrqHa3AjdIYUzHDfVNVlWg4tMMzMIsR6LiM1xZelYZdCPFrnNV2TAixGSe6/Qrgt0KIDwEbgHdVd/8TTqrbizjpbh8AkFLGhRCXAv+s7veVWiDdqxVTGZ1mME2Tgf5+dF1HNwxGR0fRDQOXy1U/rnG1L01r3sp2NSq+o7PTCRxyuertuSKVqXDu5x+nWHLcBL/87024dIWz3rU49eKng2ma9G/eTEdnJ1LKeplSw1gYu9JozBdyn5cypJSUy2US8TgtsRhmpcLw8PA2oeKXsYwdHa9Kwy6lPLPJnyYtTarR8B9vcp4bgBv+//buPTiu+jrg+Pfs3Zd2pd2VLMmxZDs2lKGABz9wE5ommQxJmgRooS0ttKRJExImk5KmaTMp6R+0TB+ZTpjQtHlMmDxI0k5KAE8hkCYllBkydHjYoRjMY8wAsb2Sbcl6P1d79/SPe3e9lq2HpV3v1d3zmfH43qtd7e+nn7Rn7+9xfjUsWk0JtbsLcV2XQqFAIpGovInG4/EFg6eI0NrWRiaTQUQYjccrW1yWc2/H1+WIZL116rOH+8/4fZar+s25ODy24u7jQ0emK0G97JHHj3PtlT01zc9/Jo7j0NraSn9fHwBtmUxNegmq77ov/9n3lni0p+AnC0rUJmV/3ZW74nt7e4nF46hqZf39YuvpLaibZhTKwG7OjqoyMz3NsWPHWNfZydTUFDPT04veDTmOQy6Xq7xxlo9LpRL5fJ50KkU2l6Mvnyeby9VoxN+z3O7j6g1hysed604vyaaelmXNzl8tESGVTjM+Pg5AKpVqSBfxyGiBex44wo/++yhf+8zimQqDxHGcM2ZqC+J6emMayQL7GlCP/O7VRIRkSwu59vbKrPPejRuXvNtZaOyvq6uLo/39jI2NEY/HaW1tpTBy+h12Pbmuy/DQEDl/M5LycS4b43ev6mHPw95dcy4T41M3nb/izU7ORrFY5NjRo7S2tYHfFb9x06ZVd8Wfza+B6yo/eewY37/XW8lZrNEue8aY4LDAvgYsdEdSODFS05SeszMzJ793oUA0Gl12V7HrukQiESKRSGWykqo2bE24qjI1NcX09DSO4zA7O0tbJkNbOs7HPriFG67dyPhkkc6OOLlMfbvgyxzHobu7mxZ/97lUKrXqrvjJqSLjE3PLfvz4RJFHHx+onI8V41z0xMPksnGSiZMfzoK+wY0xZmEW2NewWnVBlrviZ2Zm6N24kempKQYHBpacmFTmui5DQ0OkUm0kkzH6+/qIxWJkslkGBwaIJxK0tKXPaV7xaDTKhp4eDh86xNzcHOvXr698yMi0xsgssI1nPTmOQ7r1ZG786uOVmpp2mZ45uemJOzVTGWdP9r4JiXofHMo/52QywnlvTvHSQW844JYvvA7Ad758GRdsCV8wL3/gFJFTjo0JMwvsAVPy+1XL49Xl43oqd8Vv2rwZx3G8TUL8PaKXw3WV2ZlZJicmyGSzdHZ2EovHvdnssVhl9yzWtS/9zRYwVywxMjrHgZfH2NG9dPex67oMDg56a34jEYaGhkgkk6ess2+E6qBSiwAjwLgbr6TpnfCvdnXGiWXbTvvgl0w4fPSPtrBv/whHj88CcPV730R357nptTiXXNdl6MQJbzvUaLRyHLPMgibkLLAHyOzgMLMjo373rOC6RRzHIZZpJb6KoLgcy9k+ciGv/XKKTT3rOXb0ECPDw7R3dPHMc8PsurSd1nRtEoPk+6f5+F/8gumZEl/5/Fa2P/1f5LJxqj97VN/9l/PL9/T24jgO/f39uK4bujzb6VSUJw+6fP/efOXa9df0ctONG4i3nPnPe31Xkm/csYvxiTmScYdUyqnJHuxBNFcsku/rIx6LUSgUaMtkllzKGQSu61ZyHFQfG7McovO3EzOrsnv3bt27d++Knjv1xpEFZ3untmxcbdHqZnRslvGxIeYK3nh2qVSiLbueeDxGWw26vCenivz9nS/z8ydPTUrzjTt2csmFmQWfV86pP78bdi28sZ+N0bE53jg8ydPPDnPZpe2ctyVVt3kD5WRB84NOUPOWl0ol3njdG27oWLeObDYb+LYv9zSkW1tJJBKV42QyGergLiL7VHV3o8sRBnbHblatJekwMVYi1dpFqeRQKg6TiEMqVZs3+2JRGRo+fYLYyOjik8aql7rNzc0Ri0aRSIRCoUAsGsVpcLd8rWQzMbZfkmP7JfVf8lUqlThy+DDZXI50Ok1fPk9nVxepVP23Cj5brusyODCAiBCNRhnxt+ddC13xEolwtL+feDxOoVDwVlIYs0zB+ks0a1Is5tDVvZ79L05y9z2HyHV0EXFiNXujz7RFue63ek65lk45XHj+8iZ7qSr9fX0MDA4yOTFBXz7P9PQ01lu1Mus6OxkeGuLI4cPE/TzsQQvqZarKhp4eenp7SSaTlFQD3+6O49DR0YHjOJXhg0QiEdifsQmecNyymIbysoJFueId3bzzbV0k4rXtlhUR3rqrg9s/dxF7Hu6js8Nbsta+zExxkUiEnp4e8vk8U5OTZLJZWhqUHGati0QiJPz9y4HKpiRB5DgOXd3dlWGD6uMgc12XEydO4LouyWSS8bEx0ul0oD9AmWCxwG5qxnEi1GuoNdMW493v6ObXdrQTi0ZoaVn+C5Un0pUV5+Yg4HdtQVUqlejL50m2tJBOpzkxOEgikQhkVzysblJoIzmOw4aeHuLxOCMjI4H98GSCyQJ7gCyW89p4VjJ7W1U5fvw4uVyOVDpNf18f09PTNVlH3oy6/ZwA5WRE5WNTG+V0zeXehepjY5bDAnuAWM7r+ohEImzavLny5lg+tqB+9hzHoaWlpfKzqz42tbNWexpMMFhgN6FXnhVd1ugkNWtdrZPsGGNqy/p2jDHGmBCxwG6MMcaEiAX2JYjI+0XkFRF5VURubXR5jDHGmMVYYF+EiDjAV4EPABcDfygiFze2VMYYY8zCLLAv7i3Aq6r6mqoWgP8ArmlwmYwxxpgFWWBfXC9wuOr8iH/NGGOMCSRb91MDInIzcLN/OiEir5zF0zuBwdqXKtCasc7QnPVuxjpDc9Z7tXV+c60K0uwssC8uD2yqOt/oXzuFqt4F3LWSFxCRvc22VWEz1hmas97NWGdozno3Y52DyrriF/cMcIGIbBWROHAD8GCDy2SMMcYsyO7YF6GqRRG5Bfgp4ADfVtUDDS6WMcYYsyAL7EtQ1R8DP67jS6yoC3+Na8Y6Q3PWuxnrDM1Z72ascyCJ2vaVxhhjTGjYGLsxxhgTIhbYjTHGmBCxwN4gzZKDXkQ2ichjIvKiiBwQkU/71ztE5BEROej/397ostaaiDgi8qyIPOSfbxWRp/w2v8dfaREqIpITkftE5GUReUlEfj3sbS0in/F/t18QkR+ISDKMbS0i3xaR4yLyQtW1M7ateP7Fr/9+EdnVuJI3HwvsDdBkOeiLwF+q6sXA5cCf+nW9FXhUVS8AHvXPw+bTwEtV5/8E3KmqvwIMAzc1pFT19WXgJ6r6q8B2vPqHtq1FpBf4M2C3qm7DWz1zA+Fs67uB98+7tlDbfgC4wP93M/D1c1RGgwX2RmmaHPSq2q+qv/CPx/He6Hvx6vtd/2HfBa5tTAnrQ0Q2AlcB3/TPBbgCuM9/SBjrnAXeCXwLQFULqjpCyNsab3VRi4hEgRTQTwjbWlUfB4bmXV6oba8BvqeeJ4GciGw4NyU1Ftgboylz0IvIFmAn8BSwXlX7/S8dBdY3qFj18s/A54CSf74OGFHVon8exjbfCgwA3/GHIL4pImlC3NaqmgfuAA7hBfRRYB/hb+uyhdq2Kd/jgsICuzknRKQVuB/4c1Udq/6aemsuQ7PuUkSuBo6r6r5Gl+UciwK7gK+r6k5gknnd7iFs63a8u9OtQA+Q5vTu6qYQtrZdyyywN8ayctCHhYjE8IL6v6vqHv/ysXLXnP//8UaVrw5+A/htEXkDb5jlCryx55zfXQvhbPMjwBFVfco/vw8v0Ie5rd8DvK6qA6o6B+zBa/+wt3XZQm3bVO9xQWOBvTGaJge9P7b8LeAlVf1S1ZceBD7sH38YeOBcl61eVPXzqrpRVbfgte3/qOqNwGPAdf7DQlVnAFU9ChwWkQv9S+8GXiTEbY3XBX+5iKT83/VynUPd1lUWatsHgQ/5s+MvB0aruuxNnVnmuQYRkSvxxmHLOej/ocFFqgsReTvwc+B5To43/zXeOPsPgc3AL4E/UNX5E3PWPBF5F/BZVb1aRM7Du4PvAJ4FPqiqs40sX62JyA68CYNx4DXgI3g3EKFtaxG5HbgebwXIs8DH8MaTQ9XWIvID4F1427MeA/4G+E/O0Lb+h5yv4A1LTAEfUdW9jSh3M7LAbowxxoSIdcUbY4wxIWKB3RhjjAkRC+zGGGNMiFhgN8YYY0LEArsxxhgTIhbYjQkYf4e0T/rHPSJy31LPWcVr7fCXXhpjQsICuzHBkwM+CaCqfap63RKPX40dgAV2Y0LE1rEbEzAiUt7t7xXgIHCRqm4TkT/B2z0rjbcd5h14iWD+GJgFrvSTg5yPty1wF15ykI+r6ssi8vt4SUVcvM1K3gO8CrTgpfv8AvAQ8K/ANiAG/K2qPuC/9u8AWbzkK/+mqrfX+UdhjFmB6NIPMcacY7cC21R1h78j3kNVX9uGt0NeEi8o/5Wq7hSRO4EP4WUzvAv4hKoeFJG3Al/Dy1d/G/A+Vc2LSE5VCyJyG95e4rcAiMg/4qXA/aiI5ICnReRn/mu/xX/9KeAZEXnYsokZEzwW2I1ZWx7z97UfF5FR4Ef+9eeBS/1d9N4G3Otl9QQg4f//BHC3iPwQb7OSM/lNvA1sPuufJ/HShQI8oqonAERkD/B2wAK7MQFjgd2YtaU633ip6ryE9/ccwdsLfMf8J6rqJ/w7+KuAfSJy2Rm+vwC/p6qvnHLRe978cTsbxzMmgGzynDHBMw60reSJ/l73r/vj6fi7a233j89X1adU9TZgAG9bzfmv9VPgU/4mHojIzqqvvVdEOkSkBW+s/4mVlNEYU18W2I0JGL+7+wkReQH44gq+xY3ATSLyHHAAbyIewBdF5Hn/+/4v8Bze9qIXi8j/icj1wN/hTZrbLyIH/POyp4H7gf3A/Ta+bkww2ax4Y8yS/FnxlUl2xpjgsjt2Y4wxJkTsjt0YY4wJEbtjN8YYY0LEArsxxhgTIhbYjTHGmBCxwG6MMcaEiAV2Y4wxJkT+H9q1HKKDEoqUAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot of agent activity per timestep\n",
+    "param_plot(median_df,'timestep', 'AggregatedAgentSpend',swept)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "param_plot(median_df,'timestep', 'VelocityOfMoney',swept)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "param_plot(median_df,'timestep', 'operatorCICBalance',swept)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "param_plot(median_df,'timestep', 'operatorFiatBalance',swept)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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7yJ8idBf8XVa5GYfuCXqup+mN4Fy0TdEotK3FBPS4dTFwplJqK7AauM2Sawrtrf4Xo8vxYdbzuI3ulcdHgTtFZBiAVb+EZo91F1535TK6zn4cuFxEDrHq9yQROd5Swj5Bl9mrrGd1MtqYM8RctBHqARF58xNgvIgcAPwfcIBVpzrQQ2ORHwuPAjeJyP5WOtNE5Ofd5E1kOsJl3OJ64HwRuS7URonIeBFZFH2z1cvyG+B3InJBxHt0uIg81kMZoIdtXEQ5CZXfw9n12SG7UqZ6rEi8Ke3n/r/ak5usLsvZ6EKwGf1l8He0VTjobrKX0BVXI9pSPNG6dhvwjOg5u6d1EPyT6C++lVbYrWijwF1CKfVfK6zrLKfz0N1w69GN62J0VxXoF+RdtLX/WrppMJRS96ML1i3oyr4I+BW64e6MeejC8R+0Qepayw1gH7QFtwf9Qv5VKfUB2tDmbnQ+l6MN5W7qIo7rRcSN7opbiO6mPCyqi6wnvG7duw79gj+xk/d3Rm+ewe/QPQd1wB/QY4ZAuDE4Ef1FFXoe19GD90EptQ1tGHcteghoHdpIOMSrWN3HUV3SO8P/oLtw3eh0dqeYvQu8g26UtqLfhZ4MSXVGV/m+DD0ToVxEqi23G9BGYZ+KSCO6bI7aifiagfvRZbYa3SicopT6HkAp9S26rH9onTeijbY/Cg3PWEMNs9B2FKVWWCEDz57IWG6ltRStLF6ulOqsG/tM9LhzKfp537qLQ79z0e9ueeQP3QiGvrzPRg/hhGZNvYjuMUIp9RW6zluE/pL0oG2k2rqIcz7aHmeJ9e5/irZl60l43ZXL24ios5VSq9GGvA+j83YT2jYApZQX3XN3Pvo9Oh3rHRaRwcAM4MGovFmDLudzlVLV6KGPP1l5MwZdX4by5lX0819kPe8v6WSqZgfsUMatnoyjrN/3IlKLttd5u6MAlFKL2b6kQClaOZmHrit7ys60cWehn2Mt2i5o4U7EEyn3rpQpbehiMOwqIqLQXZ2b+lmO4eiXzdmLXqm+kuU7dLfqrjQuhj2MiExDG6b1pEeqXxGRF4GvlVK3dnAtGW0/so9Sqqux+Z7G1afh7U5ErztTjJ62+0F3/g09o6dl4Mew6I/BsMcQkVPQY6zL+lsWQ+xjDc/sZXWN/xTdk/ZaxPWfiR4uTUJP1fsvesbSrsbXp+HtTqyhyXTRQ7khe41P+1msmGdXyoBRJAyGPkJElqMN0n5pjcsbDL0lDz1dzwP8BbhCtV/y/0S2L1q3D3CG6l03c1+HtzuZgp5BVY22CThJKdXS9S2GHrDTZcAMbUQhIr9GTzlSaE3sAvT48CK0cdca4FyllNfShBeiF2qpQa/mtsUK5ya0EVYAvZrnu3s4KQaDwWAw7HZMj0QElpHPVeglbcei55mfgTba+bNSam+00dBF1i0Xoefv7o1eOe8eK5wx1n37o63L/yq7cUMwg8FgMBj6i1jcxGV34wASRcSHXoegDG2pe5Z1/Rm0dfIj6C6g2yz3xcDDIiKW+yKlVBuwWUQ2sX2d+g7JyspSw4cP7+u0GAwGww+aNWvWVCulsvtbjh8zRpGIQClVIiKhjX1a0NNS16A3bgnNBChGL4yC9V9k3esXkQb08Mdg2hv9RN7TIcOHD2f16tV9lRSDwWD4USAiO7OqrWE3YIY2IhC9sNGJ6J3UCtBLZv90N8Z3qYisFpHVVVVVuysag8FgMBh2G0aRaM/R6CW9q5RSPvQCKT8B0q3V00AvHxpaDrcEa8la63oa2ugy7N7BPWGUUo8ppSYrpSZnZ5ueOYPBYDDEHkaRaM824FBrDq2gV1dbj97MKbTE8ly2r072BttXoDsVWGZNk3kDvTxzvIiMQE+hWbWH0mAwGAwGwx7D2EhEoJT6t4gsRi+57Eev4/8YetnnRSIyz3ILLQH9BPCsZUxZi56pgVLqKxF5Ca2E+NHrCgQwGGIEn89HcXExra2dbtppMOxREhISKCwsxOl09rcohijMOhIDhMmTJytjbGkYKGzevJmUlBQyMzMR2ZVNRA2GvkMpRU1NDW63mxEjRrS7JiJrlFKTO7nVsAcwQxuGHwTBoKKmto11X9azabOH+gZvf4sU07S2tholwjBgEBEyMzNND9kAxQxtGGIWnz9IfYOP77Z4GJyfyBXXr6O+wQfA5PHp3HrdaDLS4trdo5Sirt6HAtJTHdjtRpfuDKNEGAYSpjwOXIwiYYhZikqaufTaz5k9Kx+3pzKsRMTH2xgzKpXm5gCBQBspyQ7i4+w0NftZ+596Hnnme7zeIGefMpQZR2STmmLGXA0Gg2FXMZ9jhpgjENDDGAue/J7WtiDpqU4qq/VQhgjMu3EM1TVtnHXFZ8x7YAPlla383/vlbCtp5qY7v2JbcQvllW3c/8hGNm9r6ufUGDrDbrczYcIExo4dy89+9jPq6+t36v7bbruN++67r8NrCxcuZOzYsRxwwAFMnDixU399zfDhwznggAM44IADGDNmDLfcckuX3fWHHXbYToU/bdo0Ro0axYQJE5gwYQKLFy/urcgGQ7eYHgnDgKfR7aOotIV//buaY2fk8dGqGrIGxVFTq5WHNV/UMf3wbD7/bz2TDkhna1Ezby+tID7exqXnjeTy69Yxdr9UikqSwmHmZsdz09WjqKrxUr2livhgK36fIj4pnmBrKyhQIiCCzSY4U5OJy0zvryz4UZKYmMi6desAmDt3LgsWLODmm2/udbj//Oc/efDBB1myZAkFBQW0tbWxcOHCHfz5/X4cjr6vIj/44AOysrLweDxceumlXHbZZTzzzDMdxv3xxx/vdPjPP/88kyd3bHuolEIphc1mviENfYcpTYYBRSCgaG7xEwzqCq+p2c/7Kyu57H8+p7bOx5ov6nDYYdmHVZx9aiFXXzKSdV81kJhg55cXjuSgiRl8sb4BgEMmDWLFx9W4PX6qatoYnJ8Yjuemq0cx/7FN3HbvBuxtLawcdTQfj51Jy3dbWDF6FivGzGLl6Jms3O9olu87A7/b019ZYgCmTJlCScn2Nd3uvfdeDjroIMaNG8ett94adr/zzjvZd999Ofzww/nmm286DOuuu+7ivvvuo6CgAID4+HguueQSQH/RX3PNNUyePJn58+ezZcsWjjrqKMaNG8eMGTPYtm0bAC+//DJjx45l/PjxTJ06FYCvvvqKgw8+mAkTJjBu3Dg2btzYZZqSk5N59NFHee2116itrWX58uUcccQRnHDCCYwZMybsB2D58uVMnTqV448/nlGjRnH55ZcTDPZsp/otW7YwatQozjvvPMaOHUtRURFLlixhypQpTJo0iZ///Od4PLp8v/POO+y3335MmjSJq666itmzZwM79u6MHTuWLVu2APDcc8+F033ZZZcRCATCst98882MHz+eQw89lIqKCgAqKiqYM2cO48ePZ/z48Xz88cf8/ve/58EHHwyHf/PNNzN//vwepc/Q/xhFwjBgqKv3svjNYub9+Wu2FTfz+jtlbPzew5P/0Evpz5yWzeEHp3P4QS5Onp3PlAPTmX5YEvfeuj8fr6phxhHZ/OyYfCYeoHsO4pxCm1dXtps2N5GTGc/EA9IZVuiiqrqNzduaGZTupCcToHtYZxt2A4FAgKVLl3LCCScAsGTJEjZu3MiqVatYt24da9asYeXKlaxZs4ZFixaxbt063n77bT777LMOw/vyyy858MADO43P6/WyevVqrr32Wq688krmzp3Lf/7zH84++2yuuuoqAG6//XbeffddvvjiC9544w0AHn30Ua6++mrWrVvH6tWrKSws7DZtqampjBgxIqx0rF27lvnz5/Ptt9/u4HfVqlU89NBDrF+/nu+++45XXnmlwzDPPvvs8NBGTU0NABs3buQXv/gFX331FUlJScybN4/333+ftWvXMnnyZB544AFaW1u55JJLePPNN1mzZg3l5eXdyr9hwwZefPFFPvroI9atW4fdbuf5558HoKmpiUMPPZQvvviCqVOn8vjjjwNw1VVXceSRR/LFF1+wdu1a9t9/fy688MJwr1AwGGTRokWcc8453cZvGBiYoQ3DgKDR7ePuv3zDR5/VMvf0oTy3uIh3Pqjg3lvHEgjopj7OYUMpPy3N9QzOcVFd1YjTGcfEsTmMG5OOK1EX55lTc1i9ro5/r63jT78by2v/LMXvV/zh/g1cc+nejB2dyhdf6l6L+Hg7Zi2VgUlLSwsTJkygpKSE0aNHM3PmTEArEkuWLGHixIkAeDweNm7ciNvtZs6cObhcLoCw4rGznH766eHjTz75JNxgn3vuuVx//fUA/OQnP+H888/ntNNO4+STTwZ0r8mdd95JcXExJ598Mvvss0+P4ossfwcffPAO6yREXhs5ciQAZ555Jv/617849dRTd/AXPbThdrsZNmwYhx56KACffvop69ev5yc/+QmgFacpU6bw9ddfM2LEiLDc55xzDo899liXsi9dupQ1a9Zw0EEHAfqZ5eTkABAXFxfu0TjwwAN57733AFi2bFlYabDb7aSlpZGWlkZmZiaff/45FRUVTJw4kczMzC7jNgwcTI+EYUDQ0hbgo89qAZg8IYP3VlYCsPzjai49bzjHzchh/uPfYXfEk5ySSmtLMwCDsnJo8xJWIgAy0uO45df78fRfDiQ/N4Gn/3Igx8/MY+qULEbvk0JmRhyTJ2SQmGinrKIVu737aWVmSHnPE7KR2Lp1K0opFixYAOiG96abbmLdunWsW7eOTZs2cdFFF/U43P333581a9Z0ej0pKanTayEeffRR5s2bR1FREQceeCA1NTWcddZZvPHGGyQmJnLcccexbNmybsNxu91s2bKFfffdt9u4o6c/7sx0yMhwlVLMnDkznH/r16/niSee6OJucDgc7YZSQgaiSinmzp0bDuubb77htttuA8DpdIZltNvt+P3+HcKN5OKLL+bpp5/mqaee4sILL+xx2gz9j6keDbuVYFARCATDx37rWCmFz7/9WJBwgx7wK+LidNHcUtTEzKmZ/PKCIfzmir1ICjST0NhElrKTjYNgWQWOmgraquvw+XwopfDW1OOoqSClqZrktgay26q56rgErj4lnUEtVfi3FZPcVs+rfxzCP+8fhgMzbjGQcblc/OUvf+H+++/H7/dzzDHH8OSTT4bH9UtKSqisrGTq1Km89tprtLS04Ha7efPNNzsM76abbuK6664Ld917vV7+/ve/d+j3sMMOY9GiRYD+0j/iiCMA+O677zjkkEO4/fbbyc7OpqioiO+//56RI0dy1VVXceKJJ/Kf//yny3R5PB5+8YtfcNJJJ5GRkdFtPqxatYrNmzcTDAZ58cUXOfzww7u9pyMOPfRQPvroIzZt2gToIYhvv/2W/fbbjy1btvDdd98B8MILL4TvGT58OGvXrgX08MvmzZsBmDFjBosXL6ayUiv+tbW1bN3a9a7eM2bM4JFHHgH0sFVDg+4dnDNnDu+88w6fffYZxxxzzC6lzdA/mKENQ69pavbT3BIABfHxQptXYbOB3694/Z0ygkHFSccW8M6ycuobfZxx0hA++FcVxWUtnH3KED5ZU0tSop3TThzMC68Us/TDSs46eQhPPL+F2TNzcbs9tDQ3MGxwLv7KOj4cvWMlc+TX71HtrmfosGH43R4+2GcGAIe+v5BPjz6vy+OD3niMQ9/XXa2ufYYzbcMSHagIYi1Y5UhJ3q15aOiaiRMnMm7cOF544QXOPfdcNmzYwJQpUwBt1Pfcc88xadIkTj/9dMaPH09OTk64uz2a4447joqKCo4++mitxIp0+gX80EMPccEFF3DvvfeSnZ3NU089BcB1113Hxo0bUUoxY8YMxo8fzz333MOzzz6L0+kkLy+P3/72tx2GOX36dJRSBINB5syZw+9+97se5cFBBx3Er371KzZt2sT06dOZM2dOj+6LJjs7m6effpozzzyTtrY2AObNm8e+++7LY489xvHHH4/L5eKII47A7XYDcMopp7Bw4UL2339/DjnkkHAPypgxY5g3bx6zZs0iGAzidDpZsGABw4YN6zT++fPnc+mll/LEE09gt9t55JFHmDJlCnFxcUyfPp309HTsdvsupc3QP5i9NgYIsbjXhqfJT5s3wHOLi3jl/0r5zWVgn4OBAAAgAElEQVR7U17VyouvFfPgvPHcctdX1Nb7ePCOcfzp4W8prWjlzpvG8NSirWza3MSNV+7Lv9fW8e+1tbS1BfjD9WMYlOFk/bdujpySSUtrkM/W1XH8jFw87hqam5vJEScrRs3cQZapXy8hcWgBNrud1m2lO6VIRDJ941Jcw7s3kvuhs2HDBkaPHt3fYhgsli9fzn333cdbb731g40zGAwyadIkXn755U7tSzoql2avjf7H9EgYdpqmZj/ffufh3Q8qOHhSBi+/UUJ2ZhxZWfHc+9eNDCt0UVTSTG29j/Q0Jy2tAUorWnE4hNRUJ5s260Wg9tkrmcnjUznvtELE4yE/pQ1oY6/xgj3gIdXXwuwDBFtjPcnNbSRjR9T2YYiD3ngMuysBAEdiAq1bSxDMMroGQ6yxfv16Zs+ezZw5c3pspGoYOBhFwrDTFJW2cOVvv+Cow7NZ9189vjl0sIsN3+pu0EBA4XDoIYFgUIVtH5QCW4SB2PDCRKoqy0hw2hiUoli+74zwta56EkLYXQnd+tkV/H4/drsdESEQCGCz2cw6/4Z+Zdq0aUybNu0HG+eYMWP4/vvv90hchr7HGFsaOkUpRU2dl2+/c1Nc1kKD24ffH+SVt/TCQJu3NbH/fqkAbCtpZvS+KQAUl7WQnRlPYX4ijW4/SsE+I5MJBBTlla1MHJsGwKdraklKzsTvayMYDPRPIjvA4/Hg8/kIBAJ4PB78lhGnwWAwGHbE9EgYOqW8so3Lr/88vBT1WScXct7Ph5KdFQ/A5m3N2O3CScfm8+aScqqq2zjvtCEserWYexd8yx03juGbTW5Kylu453f7s+7LBqqq27j1utFs+KaRqro2XElOGhtsKH/PGupAc2u4xyFhcG63fiKNJ22uhHbHR254FwCx2QgEFaCwJ7to8nior6/H5XLhcbux5+ZidzhMr4TBYDB0gFEkDLjdPjYXNfPeikqOmZaD3SGUlbey8tPqsBJx3mlDGb1PCn9a8C3nnzGct5aUU1PnZd6fv+aSc0bw0mMHY7MLcXHCnOMGI6K36d5ruAubzUYgEGDGEVnYbDb8fj9TDspARCgrLcVms/V4O+/PTrg0fByeXdGFn+kblxLMySDRWqSopaWFxMRERIRgMEjRtm047ELB0MFs27oVu6eRwYWFFBcV4XG7SUtLw+Vymb0JDAaDoROMIvEjx+8PsOyjKu5dsJFJ49LZWtLCXfO/4VcX7UVxmV50ZsRQF/vtk8JNd34FQElZK3f+dgz1DT7i4mzsPTyZQRlxUeH6KS8rJTMrC6fTSUV5OWnp6bhcLspKS3HGxZGZmUlmVpZerKa0crekTymoqqoiLT2dlJQUqquqSHS5SE9PD89fHzRoEHV1dQBkZGTQ2NhIMBjEbrfj9nhITU1FIhbXMRgMBsN2zPTPAcLunP4ZCCjavAES4u0oBa1tAXw+xYaNjSQm2rntTxuorvVy3S/34a33yvH7FXk58UwYk0ZVrRe7XSivasXnC7Lyk5pwuHfeNIYjD8vuJM4AtTU1uN1ucnJyaG1ro7GhgaSkJFJSU6koL0cpRVJyMpmZmQQbPO02xrLFxRFobgGbYE9I0McANkGs3gF7YgKBltb2/qP9pCThT4ijorycpKQkUtPSqKyoIC8/n8qKCrKys3E6nZSXlZExaBDx8fFUlJeTkppKUlIS5WVlYQXox9QrMRCmf7a2tjJ16lTa2trw+/2ceuqp/OEPf2Dz5s2cccYZ1NTUcOCBB/Lss88SFxfXfYCGmMdM/xyYmB6JHzi19V7+b0kZX29yc+l5I/l0dS02O9TWeXn25SLuuHFM2K/dLtx45d6kpzr428KtzDk+j4DfT0mFjySXncQEGDnUxcKXi5g9Mx+x7fiFHprlYLfbGTRoEG63m5qaGgoLC3E3NpKkbKiKGjKDNpyuRALuFtrcJe0af0dK323ZHQgEaLA2LnIlJdHc1EQgEKCyooL8/HwQweFwkG/tBGm328nLz0cpFT4GflRKxEAhPj6eZcuWkZycjM/n4/DDD+fYY4/lgQce4Ne//jVnnHEGl19+OU888QRXXHFFf4trMPxoMYrED4SmZj9NzQFa2wIkuxwkJdlxu/384b4NfP5lA5fPHcHfFm5m5SfVPHjHOP72jF7i9oN/VXHmyUN4+Y1iEuJtpKf4aayv5lcXDae1pZnq6mpysrKwO3Qje/YpQ5g9K5eMNAdKaXuHQCAQXomusrKSrCxtC1FWWorT6STHlUzz1hIylQ1pamH56FlA14tC9aUi0eTxUDB4MHa7neqqKnIt48mioiLyCwqw2+3tVtLr7NjQOUuWV/C3hZuprG4jJyuey84bwaxpHRvD9hQRCW+j7fP58Pl8iAjLli3jH//4BwBz587ltttuM4qEwdCPmM+sHwCeJj+L3yrh1Is+5Ybbv6Siuo0FT35PVa2Xz61dLg8YncamzR4KchNQCkbvm8JBEzL4ZHUNM6dm8ezDk6it95KSkkJCQgLVlaWICKlpabS2tqKUorSkhEDAR1qKjZLiYgIBL8FgkJLiYjweD8FgEBUMUlpSgq+ugQw/DArY8Ne7WbnfLD4cfQytJd1vTdyXOBwOhg4bRlxcHDabjaHDhpGQmEhcXFzY3dg+9I4lyyu45+FvqahqQymoqGrjnoe/Zcnyil6HHQgEmDBhAjk5OcycOZO99tqL9PR0HA79DVRYWEhJSUmv4zEYDLuO6ZH4AeD2+Hj82S0A/OKCkdx+/waKSlqYeWQONhsEg+B0CPPnjQVg3Zdu7r9tf5RSfPt9MyrgobLCw0k/LQiv5yA2Gw6nE7fbTWtrK4kuFympqVRWlJGfkkaOOAmUVWFzJZKrHKjqegKuNtJbAyiEYIObFdaeGL1dIKo3mCGJ3c/fFm6mra39xmdtbUH+tnBzr3sl7HY769ato76+njlz5vD111/3KjyDwdD3GEUihgkEAogI9Q0+3lk0hfdWVDJuCDx+fT5eX5D4pFbevn84SilstnpsgXgCzS1MHWEjUF1LoLmFkU7B5k0g3g++bWXYXAmkNHtJxQZ1jaS2+EjDgc3dTGJzK4nYCTY2saKD4Ym+XF3SEDtUVrftlPuukJ6ezvTp0/nkk0+or6/H7/fjcDgoLi5m8ODBfRaPwWDYecznWowS8PuprKjA6/Wy9wgXnsYaZh2ZgcPbyopRR/PJ2Fm0freFFaNnsXLMMSzfbybNG7ewYvQxLB8VcTx61k4cz2JFPwxPGAY2OdYCZT117ylVVVXU19cDev2P9957j9GjRzN9+nQWL14MwDPPPMOJJ57Yq3gMBkPvMD0SsYpI2KAxLyWNpGYfgaYq7AN806rOVp2MnrVhiB0uO28E9zz8bbvhjfh4G5edN6JX4ZaVlTF37lwCgQDBYJDTTjuN2bNnM2bMGM444wxuueUWJk6cyEUXXdTbJBgMhl5gFIkoRCQd+DswFlDAhcA3wIvAcGALcJpSqk60ld584DigGThfKbXWCmcucIsV7Dyl1DN9KWeg3k1Kq5/EoI1Ag5uVA8AeoTPaL2udhzj0TAh7fDyJBb0bQzf0PyE7iL6etTFu3Dg+//zzHdxHjhzJqlWrehW2wWDoO4wisSPzgXeUUqeKSBzgAn4LLFVK3S0iNwI3AjcAxwL7WL9DgEeAQ0RkEHArMBmtjKwRkTeUUnV9JaSv0RPeLTOWlIe+XCPCMHCYNS2314qDwWCITYwiEYGIpAFTgfMBlFJewCsiJwLTLG/PAMvRisSJwEKllwf9VETSRSTf8vueUqrWCvc94KfAC3sqLbuTnmyKtbsWmDIYDAbDwMIoEu0ZAVQBT4nIeGANcDWQq5Qqs/yUA6FPr8FAUcT9xZZbZ+7tEJFLgUsBhg4dulOCer3BDt27skGwJ1gNfeQxO+6K2d1xuyWozfCEwWAw/KgxikR7HMAk4Eql1L9FZD56GCOMUkqJSJ9sUKKUegx4DPReGzslqKNjo8ronS+T9h7WCwkNBoPBYOgao0i0pxgoVkr92zpfjFYkKkQkXylVZg1dhLaqLAGGRNxfaLmVsH0oJOS+vC8FtUXsc9GVPYLBYDAYDLsTo0hEoJQqF5EiERmllPoGmAGst35zgbut/9etW94AfiUii9DGlg2WsvEu8EcRybD8zQJu6ktZnanJTNu4FBVUKPS+BDYx9ggGg8Fg2LMYRWJHrgSet2ZsfA9cgF646yURuQjYCpxm+X0bPfVzE3r65wUASqlaEbkD+Mzyd3vI8LKviMtMNwqD4QdPfX09F198MV9++SUiwpNPPsmoUaM4/fTT2bJlC8OHD+ell14iIyOj+8AMBsNuQfSEg9hCRH7TgXMDsEYptW5Py9MXTJ48Wa1evbq/xTAYANiwYQOjR4/ubzGYO3cuRxxxBBdffDFer5fm5mb++Mc/MmjQIG688Ubuvvtu6urquOeee/pbVMMeoKNyKSJrlFKT+0kkA7G7RPZk4HK2z5C4DD298nERub4/BTMYfmwopSgrLaWstJRgMBg+7u1HSkNDAytXrgyvXBkXF0d6ejqvv/46c+fOBbSi8dprr/U6DQaDYdeJVUWiEJiklLpWKXUtcCCQQ8QaEAaDYc9QXlZGa2srra2tbNu6NXxcXlbW/c1dsHnzZrKzs7nggguYOHEiF198MU1NTVRUVJCfnw9AXl4eFRW9367cYDDsOrGqSOQAkVsL+tBrPbREuRsMhj2EUopgMNjrnogQfr+ftWvXcsUVV/D555+TlJTE3Xff3c6PiKBXqjcYDP1FrCoSzwP/FpFbReRW4CPgHyKShJ5hYTAY9hC5eXk7NOYiQm5eXq/CLSwspLCwkEMOOQSAU089lbVr15Kbm0uZ1dtRVlZGTk5Or+IxGAy9IyYVCaXUHWi7iHrrd7lS6nalVJNS6uz+lc5g+HFRUV6+Qy+EUoqK8t5tN5+Xl8eQIUP45ptvAFi6dCljxozhhBNO4Jln9B54Zhtxg6H/ieXpn2vRCz85AERkqFJqW/+KZDD8eAkNM/TlTLCHHnqIs88+G6/Xy8iRI3nqqafCW4o/8cQTDBs2jJdeeqnP4jMYDDtPTCoSInIlenfNCiAACHqXzXH9KZfB8GMkLz8/bFiZm5cX7onIswwie8OECRPoaFr00qVLex22wWDoG2JSkUBvpDVKKVXT34IYDD92RIT8goLweeSxwWD44ROTNhLonTUb+lsIg8FgMBh+7MRqj8T3wHIR+T8ipnsqpR7oP5EMBoPBYPjxEauKxDbrF2f9DAaDwWAw9AMxqUgopf7Q3zIYDAaDwWCIMUVCRB5USl0jIm+iZ2m0Qyl1Qj+IZTAYDAbDj5aYUiSAZ63/+/pVCoPBsEeYP38+jz/+OEopLrnkEq655hpqa2vNNuIGwwAipmZtKKXWWP8rOvr1t3wGg6Hv+PLLL3n88cdZtWoVX3zxBW+99RabNm3i7rvvZsaMGWzcuJEZM2bssP+GwWDYs8RUj4SI/JcOhjRCKKXMglQGwx7mnUGTCLibdnC3pyTx09q1uxzuhg0bOOSQQ3C5XAAceeSRvPLKK7z++ussX74c0NuIT5s2jXvuuWeX4zEYDL0jphQJYLb1/0vrPzTUcQ5dKBgGg2H30ZES0ZV7Txk7diw333wzNTU1JCYm8vbbbzN58mSzjbjBMMCIKUVCKbUVQERmKqUmRly6QUTWAjf2j2QGg6GvGT16NDfccAOzZs0iKSmJCRMmYLfb2/kx24gbDP1PTNlIRCAi8pOIk8OI3bQYDIZOuOiii1izZg0rV64kIyODfffd12wjbjAMMGK18b0I+KuIbBGRrcBfgQv7WSaDwdDHVFZWArBt2zZeeeUVzjrrLLONuMEwwIipoY0Q1uyN8SKSZp2bfTcMhh8gp5xyCjU1NTidThYsWEB6ejo33nij2UbcYBhAxJQiISK/6cQdMHttGAz9gT0lqdNZG73lww8/3MEtMzPTbCNuMAwgYkqRAFL6WwCDwdCe3kzxNBgMsU9MKRJmjw2DwWAwGAYWMWlsKSKFIvKqiFRav/8VkcL+lstg+CGhlFmaxTBwMOVx4BKTigTwFPAGUGD93rTcDAZDH5CQkEBNTY2pvA0DAqUUNTU1JCQk9Lcohg6IqaGNCLKVUpGKw9Mick1fBCwidmA1UKKUmi0iI4BFQCawBjhXKeUVkXhgIXAgUAOcrpTaYoVxE3qKagC4Sin1bl/IZjDsKQoLCykuLqaqqqq/RTEYAK3cFhaajueBSKwqEjUicg7wgnV+Jrox7wuuBjYAqdb5PcCflVKLRORRtILwiPVfp5TaW0TOsPydLiJjgDOA/dG9Je+LyL5KqUAfyWcw7HacTicjRozobzEMBkMMEKtDGxcCpwHlQBlwKnBBbwO17CyOB/5unQtwFLDY8vIMcJJ1fKJ1jnV9huX/RGCRUqpNKbUZ2AQc3FvZDAaDwWAYiMRkj4S158YJuyHoB4Hr2T7NNBOoV0r5rfNiYLB1PBgosuTxi0iD5X8w8GlEmJH3tENELgUuBRg6dGjfpcJgMBgMhj1ETCkSIvIQXW8jflUvwp4NVCql1ojItF0NZ2dQSj0GPAYwefJkY9VmMBgMhpgjphQJtBFkiD8At/Zh2D8BThCR44AEtI3EfCBdRBxWr0QhUGL5LwGGAMUi4gDS0HYaIfcQkfcYDAaDwfCDIqYUCaVUyCYBEbkm8rwPwr4JuMkKexrwP0qps0XkZbQNxiJgLvC6dcsb1vkn1vVlSiklIm8A/xCRB9DGlvsAq/pKToPBYDAYBhIxpUhEsaeGAm4AFonIPOBz4AnL/QngWRHZBNSiZ2qglPpKRF4C1gN+4JdmxobBYDAYfqhIrC44IyJrlVKT+luOvmLy5Mlq9erV3Xs0GAwGQxgRWaOUmtzfcvyYiakeCRFxs70nwiUijaFLgFJKpXZ8p8FgMBgAAoEASinsdjvBYDB8HNpF2WDYWWJKkVBKmd0/DQaDYRdQSqGUwuN2U1dXR8HgwTQ2NNDU1MSQoUONImHYZWJqQSoROUhEju3A/VgRObA/ZDIYDIaBjt/vx+v1IiIkJSfjdDopLiqisbGR7Jyc/hbPEOPElCKBXoZ6fQfu64F797AsBoPBMOAJBoM0NjRQWlJCc3MzXq8Xh9MJgIjgcDhMb4ShV8SaIpFirWrZDsstqx/kMRgMhgGNzWYjLT2dhIQE2lpb8ba10eTxkJ2TQ1x8PGWlpWaXV0OviCkbCSCji2uuPSaFwWAwxBBKKQKBAI2NjeTm5ZFfUEB8fDyJiYn4/f7uAzAYuiDWeiTeF5E7JaIfTjS3A8v6US6DwWAYUCilCAaD4aGNQCCgDSwbG2ls1BPeHA4H8fHx2Gyx1hQYBhKx1iNxLXpnzk0iss5yG49eOvvifpPKYDAYBhBKKbxeLz6fD5fLRUpqKikpKYjNRlZWFioYDNtFGPsIQ2+JKUVCKdUEnCkiI4H9LeevlFLf96NYBoPBsNsJBAKICDabLTwc4XA42rmHCAaDtDQ3U1tbS2pqKh6PB1dSEpmZmdjtdrDb+ysZhh8gMaVIiEjkSpahjbDSQ+5KqbV7XiqDwWDYvQQCAWpra0lJTsbhdNLQ0EBCQgIiohUJAIeDYDCIPnSQkppKa2srjY2NOByO7UqEwdDHxJQiAdzfxTUFHLWnBDEYDIY9STAYpKysjJzcXIKBABXl5RQMHkxdbS0iQlZ2NjU1NQT8fnLz8vB6vbS0tGC32/H7/bQ0N5PochllwtDnxJQioZSa3t8yGAwGw57GbreTnZ3NtuZmKsrLGTpsGC0tLdTX1ZGekUF5WRmVFRVkDBpERXk57sZGgkqRkJhIbm4uDfX1uD0eEl1mcpuh74kpRUJEzkFvNPZslPu5QEAp9Y/+kcxgMBh2jdDS1aANH0PHkTYPgUCAmpoagsEgObm51NfV4ff7cTqd2KxFpdra2rCJ4HQ6qa2tJTsnh9TUVGw2G6lpaaSC6Y0w7BZibc7PlcCrHbi/gp7RYTAYDDFFIBBg29attLa24vV62bZ1K16vl0AgEP4PKRf5+fkkJCSggJzcXHJyc6mtrQ1P7axvaMDr9ZKZmUlNdTXVVVUEAgHsdrtRIgy7jZjqkQCcSilPtKNSqklEnP0hkMFgMPQGESElJYXysjIAEhMTcTqdiAilJSXh6Zsul4ugUthsNjIyMsIzNTIGDcJms2G320lPTyc9LQ1nXByJLhfK8m8w7E5irYQlikhStKOIpABx/SCPwWAw9JjQrIrIY5vNRnKK3tjYZreTlZ0d9pObl4fH46GstBSP201CQgI2mw2HwxHe+js+Ph6n04ndbsfpdOKMi8Nms+F0OomLizPrRBh2O7GmSDwBLBaRYSEHERkOLLKuGQwGw4DE7/fTUF9PIBDA7/cTCATCv1DPQ35+PtVVVfj9fq1oROyB0ZlCICLhazabLdwDEeluMOxOYkqRUErdB7wOrBSRGhGpAVYAbymlzO6fBoNhQBLa66K+vp7amhoCgQBVlZXhXolBmZlkZWdjt9tRQFlpKQAVFRW4XC7y8vJobm7G4/G069UwGAYCsWYjgVLqURFZAlRZ524AERmhlNrcr8IZDAZDB4g1myInN5fqqipSUlMJBIOUluh19VJTUwG9kFRubi5bt2yhprqa/IIC7HY7NpuNwYWF4WODYSARqyVysVLKHVIiQm79Jo3BYDB0QqgHIRgMUl9XRzAYpK62lqysrPDQhsvlwuFw4Pf7KS8rw2a34/P79ZRPawnsuLg4M/PCMCCJqR4JEdkPvcdGmoicHHEpFUjoH6kMBoOhY/x+Py0tLSQmJoZtIwYXFgJQXlaGw+lEgPKKCgoKChARHE4nOTk52Ox2qqurjZ2DYcATU4oEMAqYDaQDP4twdwOX9ItEBoPBEEXQ2l3T6/VSVVlJWloaQFiJAIiPjyczKwuAGkthcDgcZGVlYbPZ9LLXEccGw0AlphQJpdTrwOsiMkUp9Ul/y2MwGHaNUEMrIu2O+xOlVFiGYDAYtkWIdO/KX+jY7/PhaWoiJSWFuLg4BmVmUltTA0BKamp4jYiQcSXQ7jhy+MIMZRhigZhSJCLYJCK/BYYTkQal1IX9JpHBYOgRwWAQr9cbXusg8ri/lAmlFD6vF5tlzOj3+8M9BD6fL3zs9/tRSuF0OgkEAgQDARxOJ8FgkEAgoJeq9nqpranB5/WSkppKU1NTOJ4mj4fUtLQdVpo0CoMhlolVReJ14EPgfSDQz7IYDD86QsqA06kXlPX5fDgcDmw2Gz6vF4e1QFJHKKWoq62lra2N9IwM/D4f8QkJ4QWWlFJ7vGFVSlFeXo7NZiMvP5/KykpUMEhefj411dV4fT4KCgqora2lpaWFgoICGhsacHs8DC4owNPUREN9fXhnzvSMDHw+HwG/H5/XS+GQIXjb2qiuribFmqFhMPxQiNVZGy6l1A1KqZeUUv8b+vW3UAbDj4WgNXWxoaEBpRRlpaXU1tTQ1NRESUkJrS0t4f0horHb7eTk5urtrS0lAkCAlpYWWpqbCQT27PeBiJBfUIDf76estJTs7GydxtJSBmVmIkBJcTFpaWk47HZKiotJSk4mLi6OkpISEhISSExMpLamBmdcHB63m5bmZlrb2igcMgSbzUaiy8WQoUNN74PhB0esKhJvichxfR2oiAwRkQ9EZL2IfCUiV1vug0TkPRHZaP1nWO4iIn8RkU0i8h8RmRQR1lzL/0YRmdvXshoMe4qQQqCUIhgMhm0EcvPyqK+ro6amJryUc8iwMCExEREJT28EPYMhtGKjz+cL717pswwSq6qqqCgvx+v19ks6Q2mz2+3hHTntNtv2Y0sBCNlCiEh4amYorfHx8fitoZDCIUNIiI+nvKwMEQkPZ/S3LYjB0NfEqiJxNVqZaBWRRhFxi0hjH4TrB65VSo0BDgV+KSJjgBuBpUqpfYCl1jnAscA+1u9S4BHQigdwK3AIcDBwa0j5MBhiiUAgQGtrK4FAAJ/PR3FRET6vVysDVoNvt9vb9SA4rOGO0NLPoWWhK8rLqaurw1fbgK+kgixlx1nvIb6xmWwcuIJ6JkNaenq/DG1UlJeTkJhIbl4e1dXVxMXFkZefT11tLXa7nfyCAhoaGhARBhcW4nG7CQaDDC4spKWlBZ/PR3p6OkGlyM3LC/dChBaVMhh+qMSkjYRSKmU3hVsGlFnHbhHZAAwGTgSmWd6eAZYDN1juC5X+ZPtURNJFJN/y+55SqhZARN4Dfgq8sDvkNhh2B0opvF4vZaWlZAwapBdNcjqprKwMb1+dmZVFclIS27ZtIy09nZTkZNosRUNESM/IoKqykkAgQFp6OpUVFcQrOx+OPmaH+I7YsIQ28dPS3Eyiy7VHGt9ARI9CvrWOg81mIzc3N2xgmZ2TA+hVJzMzM1FK4XA4SM/IIC09HafTSWpqKikpKXozLYdjwMxE6QhvTT1+9w6bKONISSYuM70fJDLEOjGpSIh+O88GRiil7hCRIUC+UmpVH8YxHJgI/BvItZQMgHIg1zoeDBRF3FZsuXXmHh3HpeieDIYOHdpXohsMnRLZiNji4gg0t+gLNkGsaYyhBkVEiI+LIzMzk5qaGuLafKS16aGKtqJycm3xSJ0bvy9I4ZAh4YaztqwMu91OckoKLpcLm81Gc3Pz9p4Gf8ey2e120lNT8Hq9JLpcuz0vQr0kaenpJCQkUFtTQ0pqKgkJCWEjUtAKxM4cR8/IGGj43R4+2GfGDu7TNy41ioRhl4hJRQL4KxAEjgLuADzAAuCgvghcRJKB/wWuUUo1Rs0hVyLSsRXZTqKUegx4DGDy5Ml9EqbBEE1o2MFut7drRA59fyGfHn0eAAe98Rh2lzZ6TBicF1Y27MlJeIM+4uLioKWN5fsevUP40zcuJT5Lj9x5a+rJVg78bX6cdi/e6nqyEOyuRLzbSskEhI6/0iuqWtm01cHEsWkEg8Luaosj14hA69gAACAASURBVH6Ij4+norwch9WLkDFo0IDsRTAYBjKxqkgcopSaJCKfAyil6kQkri8CFhEnWol4Xin1iuVcISL5Sqkya+ii0nIvAYZE3F5ouZWwfSgk5L68L+QzGHpCaH8HpRRNTU34fT7SMzLoZCIFdldCWKmI5KitK4lvbsFlt0Ng+66TkYqHxDlp2rQ1fG356FlAe0Ul+jiSUFjONAfgwbO5icT/b+/Ow+Qoq8WPf09Xd8909+z7lo0kQCK7YVG8QARZVbyIK4Iogooo3osKooKIu7grIioKei+IKD+iomERLosSSSAQSICEQEIms+9b7+f3R9V0ekKGJJNJenrmfJ5nnqmqru55ayqZPv2+5z1vZRAtLZ70T8ipVIqB/n6KioszQy/9/f0kk0nKysvx+/3TMpDI7onSpM2YN5MrXwOJhIg4gAKISDVuD8Ue8YZMfgWsU9XvZT20DPgg8E3v+11Zxy8RkdtwEyv7vGBjOfD1rATLk4HP72n7jNkV6XSa4eFhRITCwkJEhN7eXoIFBbCbS1Cnh6OZfIbsACA78HitIGFXjBfE7JWudlX6+/vpHxigtrY2s5ZFKBSit6eHwsJCCgsLp90Km9v3RBkzmfI1kPgRcCdQIyJfA84GvjgJr3sscC6wRkRWe8euxA0gbheRC4BNwLu9x+4GTgc2AMPAhwBUtVtErgUe9877ymjipTF7m6oSi0bp6+sjUlREcXExoXCYjvZ2anyBnT4/u7fBi9UnVWo4mnkzK2ioJb0PB/Ucv5+GxkY2b9rk5mIUFlLtlafu6+ubtj0SxuxNeRlIqOr/iMgq4ETcOjbvUNV1k/C6j3ivtyOvyk7yZmt8YpzXugm4aU/bZMzuchyHsvJyotEoAb+fRDzOyPAwkaIiGN55jYbtexsm2+NvvyizffCKv1FasvPgZjypVIp0Oo3jOJk6EJkaD14uxGjJa7/fTyqVoq+3F3AXyqqorMTn8+E4DqWlpXmxQNZrJcw6hYXufvb2drIDucLGOsTvJqP4i4v2zQWYaScvAwmvTkM7WdMpRSSgqonctcqYqSHW1UOyf5DiVApffAinsIBa9cNQDF9hAcevW44g+MKFme1dsX1PwmSoKJv4+hqqSiwWo7WlhYZGd1LU1uZmamtrCQSD7vTUykrS6XQmaBARhoeHqa2ro6CggJatWzOlvafyTIts4yXMbr8/3pBTdiC3dP39hOduW5HUmInIy0ACeAI3ybEHtwehDGgVkTbgQlVdlcvGGZNLqYGhMbMrtn+zGbV0/f0UzGnE7/cT7+rhhBfuZ3AoSWHhjt/Ys9+Ajl97z45/dlawEV4wl+PX3kNalUAkxHHPLsfnk1dNNd1RTYNdISIUFBRQXFLC1uZmqqqrKS0tpbOzk9q6OuLxOG2trVRVV5NIJNja3ExBQQF19fXgVapsbGwEr3aEMWZi8jWQuBe4Q1WXA4jIycA7gV/jTg09OodtMyZvBAIBYrEUN9zZxcZNw1SUBfnYqaHM49mBQbC+lo7eJAVBB8IhFj36VwJ+H4U1Ef7jmeUA+Pw+OnsSpNPKK5ti/OC2Dq689AAqQ0HClVWECnf8qX/p+vtfdWy8rvbRstt+vz9Txnr0+OjwRXdXF7W1tTRv2UJnRwd19fVs3rSJ4eFhimMxwpGIW/ciT3oh9pQNZ5i9KV8DiWNU9cLRHVW9R0SuU9WPikhBLhtmTL4ZHkmxcnUvL20e5iPnzCWR2DY9MLsX4sjVyznv2mYOWFDE204u4Lrrm6mvLeTj5xdx1bc2UVtdwMc/uB9fvu6lMa//7Z+u57qrDxo3iAhWlu3y7IzRGSkd7e00NDaSSqXo7e2ltq4Ox3HY2txMZWUloXCY9rY2/H4/VVVVtLW1ZfIlOjs7aSgoGFN0arqz4QyzN+VrINEiIpcDt3n77wHavSmhezwN1JiZJBx2OOKQMl7aPMzGTUNweAWLHv0rIkJFWZDevjiptBIsLWJ2U4jnNwxy8fkhDphfxPMvDhIM+Dj8oFJe3jJMKPTqYCEScnCc3c+DGF0kzHGczGJffr+fUChEUVERW5ubqamtpaGxMVNZsqGhAX8gkEm6rK2ry1TcbGhowOc47hLh3kJcUz2xcpTVgTBTWb4GEu/HXRTr/3n7jwLvBRy2Tc00xryG0WmXBUGH8949h83NIzz0WCdnv62R2+7t5OEVXRy4oIgvfPpABgYSaNLPD689lPauGH5H+OaXDqKrJ04qqVx12SJ6+xOEQg6L9y9m7QsDAPj9wsc/tB/FRbv36V9VSSQSRKNRIpEIgwMD9PT0uIEB21YkbW9ro7GxMdPbkF2qerSXAqC2tjYzIyN7O19kJ1geuezGbXkoC+dywjovX8WbqXHCunvGbnuPZeelGDOZRMcrdZdHRKQQeJuq/iHXbZmoJUuW6MqVK3PdDDMNjPn0quArCJAcHCGNgM/H8EiKWCxFqiBEUV0Ffkfw+91P7dFYGscBxxEScUV8UBzxU1Cw67kE3b1xXtgwQGtHjKOPqKCiLLBbzwc332F4aIiOjg6KiospKiqir6+PgN9PMBiks7OTuvp6hgYHGRwcZPacOXkz62JXbd8LMVoxNJsNU4CIrFLVJblux0yWrz0SeMMYpwDvA94CPALkbSBhzGQJVpYx7A/z6OPdFEUc7vzrVlY+1csH3z2bV7YO8Y9HOgiFHK67+mBu/d3LPLKik8a6EJ+7ZH/2mxsh4N+zGQwVZUGOWVK5R6/hOA6RoiKisRiDAwMUFxdnqlLW1tbSNGsWjuNklh3Pp96FXWXVKE2+yLs5TyJyvIj8HHgZuAA3iNhPVc/OacOMmSIGBhP84MYNfOOHz1NaHGDlU24BpsMOKuORFZ0AnHFSHX/7Ryt//0cbg0Mpnn9xkEuufIq+/qlRikVVSSaTDA0OUu4V14pGozQ0NDA4OJgpbe04DsFg0KZvGpNDefW/T0S2AN/A7X1YrKrvBEZUdTi3LTNm6hiJprj/4Q4ABoeSVFa469l19cRoqHOndh50YAn/Wjm2avvISIqOrti+bew40uk0sWiUYDBIcUkJkXCYhsZGAsEgVdXVVFdXT8teiFHRaGrcBdaMmWrybWjjDuAduLM0UiJyF3tjMQBj8piI4BNIKdx591Y+ecF8vvr957jz7q1c/KH9+NK31tLeGWN2Y5iu7rEls8tLJ2UR3QlLJpOoqjs7IxwmFA67iZF+PwqZPIjplg/R1RPn6bV9HDpL8CdGGBxMUhzaFihNtA5Eb1+cRFIJOIL4IJ5QHAcSCWXNun7qqgtobAjl/L6b/JZ3yZbeCp0n4OZGnA6U4g5x3K2qEyuRNwVYsqXZFdkJeNn8xUWZWgxDw0luX7aF2+9qZmAwySlLa/jA2bMZGk5SW+0uxtXbF8fv9/HJK5+ity+BCHzknLm8862NFEVy8/kilUrR1dXFyPAwdXV1dHS4vSr1DQ3TLnDI1tUT52OffZL2jijLrtuPRxa7VUlfqyLpayVYxuMphqMpRkZSXPWtdbR2RPnOVQfzk5tepK8/wac+Mp/PfeUZEkn3b//Rr6/gS/91AGV5GkxYsmXu5VuPxOhCWQ8AD4hIADgVd+rn9UBVLttmzK5Ie0t5+3w+0uk06XSaweE0qFIUcTJTGFOpVOYNNJVKuYtPbTcNcHSVzsLGOhL9g4hAoLiIc85q5N1va+CPd7dw6OJSKsqCzJsdybShurKAVEq5+cdL6B9IEAn7CYecnAUR4PYyVFRU0BKL0dzcjM/no7GpaVrlP8TiKQaHUohAIpFm1VM9JJJKS1uUhfsVkUhuK4OT3QsRqKulsy+J3++DcHjc1+/qifO7OzZzwPwili1vZd36AT78/jncfPsmVj/Tx8Uf2o+bb9+cCSIAVqzqpqsnnreBhMm9vAsksnmLdP0Z+LOIhHZ2vjG5MlrW2XEcEokE7W1t1Dc0ANDT3U1ZeQWqQndXF+UVld4bTYJAwF3Uqqe7m9KysdUfs1fp3D6oSPX0IuLjPUtLKKjacdVIxxEqy4NUlk+tN5B8z3xIpZTevjhbW6NUlgcIBB3aOqJUVRSwbHkLf16+lW9fdTCXfvFpFi0s5rCD3fszPJIkO+0juxrlokf/ynnXNrPk0DKuvWIWPb1xnnmun+dfHOS0E2t5pXkYv+Pj1jtfYcUTPfz4G4fy9No+ABbMK+IPy5oBKAo7O0yoHRxK7sXfiJnu8iqQEJE1jJ8TocCh+7A5xuyS7LLOdfX1OI6Dz+ejra2NqqoqhkdGSCY7qKysYCQaJd7aQlV1Nf39/VRUVAAQjUYZam6mMr3jT+fZQUW2pevvh6ryvXp9kyWVStHd3U0qlaKxsZGOjg7aWltzNrQRjaUYiaYoivjRNAyNJImE/QgwOJwkHHJwfMLAUJJQgUMg4GNwKEH/QJKPfvZJNA3fveZgPv/1p6irLuS0E2u55fbNHHdMJQ882snwSIo1z/XzlYubOGF+E6rKa5XbOOb15fzXRxcyEk3z/RvW8/CKLi78wFx+fesm/v6PNn76zcNY8UQPAM1bR9h/fhEvvDjIK80jHLCgmJWre/jXqm5OWVrLDTdvK2NeVhqgqX7PP4el0+lMFdFUKuXm6vh8pFMpZAcFwEbzYQKBwJhtk3/yKpAA3up9/4T3/bfe9w9gSZdmivL5fITDYYqKimhtaaG0tJTS0lI6Ojro6+2lpqaG1pYWurq6qKuro2XrVhgcJjQQZWSgmUA4RHksjQIyjf+Zjw5taHk5fr+fuvr6TKnrfa2jK8avb32ZlrYYl140n7v+tpUXNg5x2ccXcs+DbTz5TB+XfWwB/3y8i0cf7+bTFy3gmef68PuEx5/qpX8gyTtOq+euv7fQ1R1n6bHVrFztvsmLTzKVOePxNIOd/ax94+kAlI5TL6KiLMh7zqzgks+v5torXsfDK7oAOPzgMn71vy8D7rBJccTPwFCS39+1hcs+vpCvfv85/vTXZr5y+WLaOqI8+u8uTjyuhis+uT9/+0cbDXWFfPh9cykv27NeqVQqxcDAAJFwGPH5GBwcJFRYiD8QcIMKr7x5KuWW9xYRurq6iI6MUFdXR3tHBz6RTKBt8kteBRKquglARN6iqodnPXS5iDwBXJGblhmzcwqEQiFC4TCtLS0UFxdTUlpKe1sbgUCAispKOr0Ew0TfAA97lQyzk+6me2Gi7BLX2dv7Uk9vnMuuXsPGTUN85uKFXHf9elY/08dF587ll//zMg/9q5P3nNnEnXdv5c/3tHLqm2t5+LFOfnfHK1x60XzaOtwptOWlQdasc4cXtraOcOl7a/jwiQWICHUNRZx77BwAsuOk8WZnaCjM5Vc8QzyhbJ8g76XccPd9rVx03ly+d8MGXto8zE9u2sg3v+gulhYOO/zk64cSS6QJ+H2UFvs57g1VBIM+Cnez6uh4hgYH6evtpa6+npGREXq6u2lsaqKjvR3x+aipqaGjo4N0KkVtXR1lZWW0e/kwjuNQ19hoQUSeytcsJhGRY7N23kj+XouZptLpNKlUKjO0MTw0RFFxKY7jUFZeTll5Rab7t7qmDp/PQYG6+kZ8vh3/QR19oznmvlsobKzdtxc0DaTT+qrtVCpNLOZ+Uk6lleGRlLt4GTBvdoTVz7jBwMGLSzMFvY48vJz7Hmp3tw8r597/c7efXNPH8W9wc76fWNPL8W90t1es6iYiMdYdewZr33g6wxte5uGDTuHhg04htrUt06bH334Rj510Ho+ddB7idwjPbSI8t4nNfX7iCbe9W7aOcMjiEgDWrR/gTUe7VUTve6iDra1Rfv3D1/Ptqw7ims8uoq6mgMZ6d3pnZUUBDbUhqisLCAYdSooDkxZEOI5DXX096XSarc3NVFdXZ1ZarayqIhaN0tbaSnl5OYlEguYtW8YERNO3n21myKseiSwXADeJSKm33wt8OIftMWaM0YJKvb291NTWUlBQQENjI/2DaRLJNIWFEb71k/UkEsqVl+7PSDTNiie6WHpsHT+9aSMXnbLjzPzsBLw3b3qYpevvdwsXpWxFyPGk00pnd5xlf28hEBBOWVrL8gfaSCbTnH5SPcuWb6VvIMkH3jmL5Q+0cezRlTg+SKXd5xYW+IjG0oyMpCgpCtDbn2BgMEFleQFbWkYYGExSVRGkrSPGoys6+e9zajj94DmMRNNU1/l5y/fmkFbw7cHCxJXlQXw+t/fh+t9s5NrLF7P2+X5e2jTEJRfM501HVbLq6V4W719MVUWQBfP2zsJco8Hx6HBT9nZ/fz+qSlVVFQP9/aRSKfxesODz+Ugmk5lqpBUVFfT19ZFOpzO9Fu1tbTa0kafyMpBQ1VXAoaOBhKr25bhJZgbLLqKUSqVQVRzHwXEcorEYW5ubSSQSRIrKiCUK+PnNm2jtjPL8BrcexP0Pd/DVKxZx5Hw/W1a/zLuO9pNO7jyLvqdjiJGyan596ybOP6Oc/1h3H4PDSZIJpaDARyTsz8uVHkeLKDk+obwssMcVLLt743zo0pX09Sf56TcP4+LLV9PeGeMH1x7CJ69cTWt7jK9f+To+/7VnefHlIWLxNO8+s4lb79zCvf/XzvnvncMNN7/EX+9r5aJz5/Kd69ezbLk7jHDNd9Zljn/h688STyj9bf2sO9bNeZisYamiiJ/Pf+oArvvZenp6E3zn+hf40dcOo6zEj+P4aKoPcdqJtTjO3u2YVVVe2byZsvJySktLad6yhaKiIsrLyxkZHqa6poZQKERHRwdV1dVEIhFaW1oQEeobGujs7MwkZZaWllJWVjYmH8aCiPyUl4GEiNQCXwcaVPU0EVkMvEFVf5XjpplpTFUzb2qj3bLpdJru7m5Ghoepra1ldP6ez+fD5ziUlpTQ29uLiBCJFPOH32+mqMjP84+NLSrl9/sIJqOsO/YMYNfedEIhh4uvWkNnd5yTjq/hqp9tYfOWkczjJ59Qw2Ufr2NqTe58bS1tUa7+9lrWvjBAY30h13xuMQvnRUim3ABjzbp+5s0OURQJ8Ozz/VSUBZnTFH7NZMGVq3vo60/SVB+ipW2E9s4YpSV+orEUre0xHB+UFPt58WV3OON///QKF5+/Hz/5xqH09Sc4cGExx7+hipdfGWbhfkXcduNRbHx5iPlzI9x249FseGmQWY0hbv35UazfOEhVxci4bdmRXalaGQ77WfqmapYcVk4ikaaw0KFiu2ve20EEkFmGva2tjWQySU1tLW2trQwPD1Pf0JBZyr26uhpw/x9UV1cjPh+O41BZWYkA/kCAZCIB3tDedC53PhPkZSAB/Ab4NfAFb/8F4PeABRJmr1BVEokEPhEcv9/9I4j7B7GiooLWWIxYPE4ykWBgYIC6+npSySS9vb2EQiGisRg9PR2cflIt0RHloX91MuDN3d9vToTFB5Tg641mfl72m0t4wVyOe3Y5ivuHub07DgiEwnR6Ja7LS4NjggiA+x9q5xMf2o9IOD/+m/f1x7n2e+tY+8IAAM0tUT5z9Rp+d/0SWtqjXPy51VSUB/nifx/Ixz67mpFomoBfuOjceZx4XDWOI5SVBF71hvr6/Rxu+VIjjiMURWLc8qVG9xOxl2Lilt7e9kaWTsNPbtrIgnkRfvDVQygrcd+wZzVuG25qrNs2XbK+tjCzXVtdyPDLW3brurOHq16ramVhgTNpOQ2jVJV0Oo3jOKiqOxyxg4Jo2cQbylCvZ2H03N6eHiqr3JyQ7ETZQDCYOc/v92emiPq9qZ4WROS//PgL82pVqnq7iHweQFWTImKDxGbSpdPpTAXKjvZ2UqkU9Q0NdHV1EYvFqKmpQXw+VJWe7m5q6+qIxmJeYakKSkpLGRgqoKqqnJGRIQZ705T6otz+tdkkkopP3N4IX287mt42hp795vKmdfcRr6wnGksRi6U5/9pViMAN367OnOPzuW+GqdS2tLWqygLIoz/SyaTy9Nr+zP7C/Yq44P1zGB5J8eNfbiSRVE4+oZbb79rCSDSNCNzxg0Uk+ocYeqmZ6roII10xUHAihaSH3cAsDPzb6+k5ctmNlHqFu0JOEXd/dw4oBIqj/O27c1CFQFGIxOAIPp/gDPYw1O4FeD7BKSwkNTwydhvwhbf9vPGMCQ4XzuWEdfdkXnf0zXlfDkWpKslEgh4vABjtXausrERVM9vZQYGqZqYwl5WXs3nTJiKRCEXFxbS1thIKhYgUFY2ZspsdKIx33OS3fA0khkSkEi/ZV0SOASxPwkyqdDpNLBbD7/eT7h+kLOFWLRzZ1ExpOEQqCemWTnyhAsrjaQQf6ZYOStSdopfqGyBUXMrXfvQcqnDOWU1UVTqUpRI8uL+7nsKRy24klalIueNZGAG/UFpZALif2k86rob7Hmpn9bN9nHmqW6vgkRVdvP+sWfz2D5sBcHzw2U/sT3lp/hT48TnCgrkRNrw8RENdIf/10QVc+73nOO/dszPVGLMrM77+kDLSQ8OsOuIU4NX5CDvKTcgu3PVa5+9oe3eesyPb9zxEFsyZ2C9qEqVVGRoaQoGSkhJGhodpjcfHJFFmExEam5oywUWDN2VztJy53++fViXNza7J10Div4FlwHwReRSoBt6V2yaZ6UZV6fKSwypSPv7vgJMyj433JpJdqjq8cC6plha+el4ZvlAhicEBfPGxuRHbv7HtTGlJkP/66ALOe/dsunvjnHx8Ne96eyPtHTHmzg5z+km1bG2NMndWmJJiPz5f/nzqKy8NcvVnF/GZL6/hrDMa+cVvX6KlLcq/VrrVGG/87UuZyoxPr+3nwIXFxOMTnwmxt0y1nofxiAiBQIDKqio6vYJQ1TU1tLW2AowJGEbzg3w+H8GsoYrsoY+CgoJ9fxFmSsjXQOJZ4HjgANzS/M9jdSTMJBudG9+8ZcsujxDs6ifeHdnVpaJLSwKUlgTYb467CFc1MHfWtgW5ZjWMv6jTVDenKcyN3z2CeDzNb257GYBHVnSy9NgqLr1wPg/+s5PaqgKuvXwRL20ewu+feoHSVOx52BFVJZVM0t3dTWEoRHFJCe1tbW4PXDpNX28vpWVliAiJeJxA0M0VGd22GRZmVL4GEv9S1SNwAwoAvMqWR+SuSWOJyKnADwEH+KWqfjPHTTK7KdbVQ7J/kPIUKHs/BWdXk+6mM5/PXUhsaDjJkkPLefCfnaTTcM11z/GGJeVc/ZkDKWGE9OAIRzYGEKufsUfSqgSDQWpqakh7ZaxramszycXgDvFt3bqV0lK3mFp3d3dmSMMYyLNAQkTqgEYgJCKHs22hwBLcnKopQUQc4KfAW4AtwOMiskxV1+a2ZWZ3pAaGMrkM07009VQTCfu58sP1fOy0MPF4iuq6IiQWQ3rbSAEPZpUP35mxPT1WDXSUiBAMBqmtrcVxHNLpNLV1dTiOQywWo7WlhfLycvyBAFXV1Zny7ZVVVQSD+TSp2OxteRVIAKcA5wNNwPeyjg8AV+aiQeM4CtigqhsBROQ24EzAAok8kj0DYlJf197Yds3IME8deSrgLma1o6Gh8fIRfKFC/uOZ5QCkEbr7EhQWOkSKi9wVUQFfMLjt/HDhTrdHZ2qcsO6esds7OG8q5UK8ltFKkzB2RoXf788EDxWVlZBVzjqZSLxqvQ8zs+VVIKGqNwM3i8g7VfWPuW7Pa2gEXsna3wIcvf1JInIRcBHA7Nmz903LzIRkv2EVNNbhhEPuG4cIvlABJ6y7x1udc+eyhzBO9Mpcb2+qvwFNFePlIzz0r06u/PqmMed+5uMLecchVfu0ffksEY8TDAYJhUI0b9lCVVUVjuPQ1tZGpKjIhjZMRl4FEqNU9Y8icgbwOqAw6/hXcteq3aeqNwI3AixZssRC/Ckme5779m9YoYaaHT4n3tW700+82Z9YfQVBCsd5LTNxBy8q4T+OqeThx7zltg8qzSygZXYumUzS19dHbW0t6VSKplmzMr0Xs+fMsSmeZoy8DCRE5AbcnIilwC+Bs4F/57RRYzUDs7L2m7xjJo9M5G9lsLKMYGXZ5DfG7JbysiBXfPIALr0whSqECn2Uldq4/q7y+/2ZgEG9uig+ny/zZUy2vAwkgDeq6iEi8rSqXiMi3wX+lutGZXkcWCgi83ADiPcC789tk8zu8meNp29/3Owd8a5ekgNurQ1N7nhGxu5OkzW7z4YtzO7I10BidFGBYRFpALqA+hy2ZwyvZPclwHLc6Z83qeqzO3mamWKsd2HfSw4M8sDCEwG3uNeuFHaye2RMbuVrIPEXESkDvgM8gVsq+5e5bdJYqno3cHeu22FMvsqXwk7GzHR5GUio6rXe5h9F5C9AoaraWhvGGGPMPpaXgYSInLWDY33AGlVtz0GTjDHGmBkpLwMJ4ALgDcAD3v4JwCpgnoh8RVV/m6uGGWOMMTNJvgYSfmCRqrYBiEgtcAtu0aeHAAskjMlDNlPGmPyTr4HErNEgwtPuHesWkUSuGmWM2TM2U2Zqy56em81mz8xs+RpIPOglWf7B2z/bOxYBenPXLGOMmb62n57rhN3CwoWNdZkAw4KKmSdfA4lPAGcBb/L2bwb+qO5KMktz1ipjjJkhnHBhZiG1bEvX32+BxAyTl4GEqqqIrAT6VPU+EQkDRbirgBpjjNmHsnsnJBhgaIO3YJoVD5sR8jKQEJELcVfNrADm4662eQNwYi7bZYwx00F2LoQvGCQ1PPKa52f3ThyTteR7NuupmL7yMpDAHdo4ClgBoKrrRcSWUDTGmEmQnQuRHRiMliyfDJa4OX3kayARU9X46DLPIuLHLZNtjDFmLxm7YFrtTs9/rSEP0sqDi05+1XOs5yL/5Gsg8X8iciUQEpG3ABcDf85xm4wxZlrLXv/kxE0PZ2p+jLdS62sNeUxm74bJrXxdWP4KoANYA3wUd3GsL+a0RcYYM4Ok43HCc5sIz23KLOVuZqa865EQEQe4RVXPAX6R6/YYY0y+Gi9PQdPp3Xqd7IqkvmBw25LvZkbIu0BCVVMiMkdEgqoaz3V7jDEmcbNoTgAAEMdJREFUX2UnVWbLHrYYExhsN51z1HgVSYdf3rIXWm2mmrwLJDwbgUdFZBkwNHpQVb+XuyYZY0z+yk6MTA2PZIYrfAVBChsmf1LcaOJmYWPdmKERW1cl/+RrIPGi9+UDinPcFmOMyXt7o1LluEMeVqhqWsnLQEJVr8l1G4wxxrw2W4RtZsjLQEJE/syr60b0ASuBn6tqdN+3yhhjjJl58jKQwM2RqAZu9fbfg7vOxv64MznOzVG7jDEmb2QPPYxXC8KYncnXQOKNqnpk1v6fReRxVT1SRJ7NWauMMSaPZA892AwLM1H5GkgUichsVd0MICKzcVf/BLApocYYs5uyeye2P27Ma8nXQOIy4BEReREQYB5wsYhEgJtz2jJjjMlDlhhpJiovAwlVvVtEFgIHeoeez0qw/EGOmmWMMcbMOHkZSIjIWdsdmi8ifcAaVW3PRZuMMcaYmSgvAwngAuANwD9whzZOAFYB80TkK6r62xy2zRhjjJkx8nX1Tz+wSFXPVtV3Aotx60ocDVw+kRcUke+IyHMi8rSI3CkiZVmPfV5ENojI8yJyStbxU71jG0Tkiqzj80RkhXf89yISnPCVGmOMMVNYvgYSs1S1LWu/3TvWDSQm+Jr3Agep6iHAC8DnAURkMfBe4HXAqcD1IuJ4q5D+FDgNN5B5n3cuwLeA76vqAqAHtwfFGGOMmXbyNZB4UET+IiIfFJEPAnd5xyJA70ReUFXvUdWkt/sY0ORtnwncpqoxVX0J2AAc5X1tUNWN3iqktwFniogAbwbu8J5/M/COibTJGGOMmeryNZD4BPBr4DDvayWgqjqkqksn4fU/DPzN224EXsl6bIt3bLzjlUBvVlAyevxVROQiEVkpIis7OjomodnGGGPMvpWXgYSqKm6Z7CTwn8BSYN3Onici94nIMzv4OjPrnC94r/s/e6n5Gap6o6ouUdUl1dXVe/vHGWOMMZMur2ZtiMj+wPu8r07g94Dsai+Eqp60k9c/H3grcKIXrAA0A7OyTmvyjjHO8S6gTET8Xq9E9vnGGGPMtJJvPRLP4eYfvFVV36SqPwYmZaUZETkV+BzwdlUdznpoGfBeESkQkXnAQuDfwOPAQm+GRhA3IXOZF4A8AJztPX80h8MYY4yZdvItkDgLaAEeEJFfiMiJuHUkJsNPgGLgXhFZLSI3AKjqs8DtwFrg78AnVDXl9TZcAizHHVa53TsX3Cmo/y0iG3BzJn41SW00xhhjphTZ1oOfP7zZGWfiDnG8GbgFuFNV78lpw/bAkiVLdOXKlbluhjHG5BURWaWqS3Ldjpks33okAPBmZ/yvqr4NNwfhSSZYiMoYY4wxE5eXgUQ2Ve3xZj+cmOu2GGOMMTNN3gcSxhhjjMkdCySMMcYYM2EWSBhjjDFmwiyQMMYYY8yEWSBhjDHGmAmzQMIYY4wxE2aBhDHGGGMmzAIJY4wxxkyYBRLGGGOMmTALJIwxxhgzYRZIGGOMMWbCLJAwxhhjzIRZIGGMMcaYCbNAwhhjjDETZoGEMcYYYybMAgljjDHGTJg/1w0wxhgztQ0OJRkeSaGqhAp9JBKKApGQQyhkbyMznf0LMMYYQyKZprsnzj8f76Io4ueIQ8qpLA/S2xfnF797mT/f08JHzplLqNDhpv/dxEgsxYXnzuUtx9Xg+HwUFzkMjaRIp5RIxE80miKVUsJhh3hcSSbTFBY6pNJKPJ6mIOijuCiQ68s2k8ACCWOMMbR3xDj/UysZiaZpqC3k8k8G6e1P0N4R5a6/t1Ba4mfxASV8+otPA3DGW+poqA3x6S89zaIFxZx+Uh0/+uWL1FYXcO67ZvPDX7xIQVC45IL5/PiXLxKNprjiUwdw/W828uxz/ZxwbDUXvH8ufkfwOUJFWTDHvwEzURZIGGPMDBdPpPntHzYzEk3jOMJVn1nEN374PHOaQtTVFgIwd1aEZ5/rByDgF95+cj0XX/4kaYUvXHogn/zCU8TjaT590QI+95VnGBhM8q2rDuKL31hLW0eMqy47kK9+/3k2bhqiqT7Emac2cNW31rL2hQHmNIW55nOLmDc7guNILn8VZgIs2dIYY2a4ZCLN0EgSgKMOL+ffT3azacswL24a4uBFpQBsbR1h/twIAI31Ida/NEgqDWWlAdo7Y8TjbhCSTisDg+5rRUIObR0xAGqqCti4aQiA979zFj+8cQNrXxgAYNOWYS778hp6+xP79LrN5LBAwhhjZqjBoSRPr+3j5799ibNOb0QEKsqDtLZFAWhuiTI0lOScd86ifzBJ/0CCs85ooLc/TkOd21MxNJSkssIdlkillFChg+O9s4gIBQXuTiyeprTE7QSf1Rhi3fqBMW3p6o4Tjab2xWWbSWaBhDHGzFDrXujn4stX88e/bOWRFV1c9+WDCRf6OOm4msw53/npC0RjKX77kyUcdUQFH37/HH79gyXMaQrzn6fXE08oTz3bx/vPakIEHni0g498YB6OD/56bwuXfHg//H7hzru38umLFhAM+ujqijO7KTSmLUURJxN0mPwiqprrNhhgyZIlunLlylw3wxgzQwwMJvjCN9byxNO9mWMNdYVce8Vi6qoLWPVUL7/5/WYcR7jwA3M59HWlRML+V73G8EiKWCxNOOTOyIhG00QiDuk0DI8kKYr40TQMDicpKfKTVjcnY2goyWeveYaunjhFEYdrL38dhx1USiCwe8GEiKxS1SWT8ksxE2LJltsRkcuA64BqVe0UEQF+CJwODAPnq+oT3rkfBL7oPfWrqnqzd/z1wG+AEHA3cKlaxGaMmUJ8PiG0XQ/A1tYog4NJSucXs/RN1Rx+cBkIlJfueEZFcVFgJ1M4CzJb1Vnb4A6D3PSD1xONpSgo8FFa7N/tIMJMDXbXsojILOBkYHPW4dOAhd7XRcDPvHMrgKuBo4GjgKtFpNx7zs+AC7Oed+q+aL8xxuyqSNjPhefOw+/fNkti7qww8+a4CZUiQnlZcNwgYk85jlBZEaSxPkRVRQGBgLNXfo7Z+6xHYqzvA58D7so6diZwi9ej8JiIlIlIPXACcK+qdgOIyL3AqSLyIFCiqo95x28B3gH8bZ9dhTHG7IJZjWFuveEoHn6sk6rKIIe9rszqOZjdZoGER0TOBJpV9Sl3NCOjEXgla3+Ld+y1jm/ZwfEd/cyLcHs5mD179h5egTHG7J6CoI/62kLefWZTrpti8tiMCiRE5D6gbgcPfQG4EndYY59R1RuBG8FNttyXP9sYY4yZDDMqkFDVk3Z0XEQOBuYBo70RTcATInIU0AzMyjq9yTvWjDu8kX38Qe940w7ON8YYY6YdS7YEVHWNqtao6lxVnYs7HHGEqrYCy4DzxHUM0KeqLcBy4GQRKfeSLE8GlnuP9YvIMd6Mj/MYm3NhjDHGTBszqkdigu7Gnfq5AXf654cAVLVbRK4FHvfO+8po4iVwMdumf/4NS7Q0xhgzTVlBqinCClIZY8zus4JUuWdDG8YYY4yZMOuRmCJEpAPYtBtPqQI691JzprKZeN0z8ZphZl73TLxm2LPrnqOq1ZPZGLN7LJDIUyKyciZ2583E656J1wwz87pn4jXDzL3u6cKGNowxxhgzYRZIGGOMMWbCLJDIXzfmugE5MhOveyZeM8zM656J1wwz97qnBcuRMMYYY8yEWY+EMcYYYybMAgljjDHGTJgFEnlIRE4VkedFZIOIXJHr9uwNIjJLRB4QkbUi8qyIXOodrxCRe0Vkvfe9PNdtnWwi4ojIkyLyF29/nois8O7370UkmOs2TjYRKRORO0TkORFZJyJvmO73WkT+y/u3/YyI3CoihdPxXovITSLSLiLPZB3b4b311jT6kXf9T4vIEblrudlVFkjkGRFxgJ8CpwGLgfeJyOLctmqvSAKXqepi4BjgE951XgHcr6oLgfu9/enmUmBd1v63gO+r6gKgB7ggJ63au34I/F1VDwQOxb3+aXuvRaQR+BSwRFUPAhzgvUzPe/0b4NTtjo13b08DFnpfFwE/20dtNHvAAon8cxSwQVU3qmocuA04M8dtmnSq2qKqT3jbA7hvLI2413qzd9rNwDty08K9Q0SagDOAX3r7ArwZuMM7ZTpecylwHPArAFWNq2ov0/xe4y6aGBIRPxAGWpiG91pVHwK6tzs83r09E7hFXY8BZSJSv29aaibKAon80wi8krW/xTs2bYnIXOBwYAVQ6y3VDtAK1OaoWXvLD4DPAWlvvxLoVdWktz8d7/c8oAP4tTek80sRiTCN77WqNgPXAZtxA4g+YBXT/16PGu/ezri/b9OBBRJmShORIuCPwKdVtT/7MXXnLk+b+csi8lagXVVX5bot+5gfOAL4maoeDgyx3TDGNLzX5bifvucBDUCEV3f/zwjT7d7ORBZI5J9mYFbWfpN3bNoRkQBuEPE/qvon73DbaFen9709V+3bC44F3i4iL+MOWb0ZN3egzOv+hul5v7cAW1R1hbd/B25gMZ3v9UnAS6raoaoJ4E+493+63+tR493bGfP3bTqxQCL/PA4s9LK7g7gJWsty3KZJ5+UG/ApYp6rfy3poGfBBb/uDwF37um17i6p+XlWbVHUu7n39h6qeAzwAnO2dNq2uGUBVW4FXROQA79CJwFqm8b3GHdI4RkTC3r/10Wue1vc6y3j3dhlwnjd74xigL2sIxExRVtkyD4nI6bhj6Q5wk6p+LcdNmnQi8ibgYWAN2/IFrsTNk7gdmI277Pq7VXX7RK68JyInAJ9R1beKyH64PRQVwJPAB1Q1lsv2TTYROQw3wTQIbAQ+hPtBZ9reaxG5BngP7gylJ4GP4OYDTKt7LSK3AifgLhXeBlwN/D92cG+9oOonuMM8w8CHVHVlLtptdp0FEsYYY4yZMBvaMMYYY8yEWSBhjDHGmAmzQMIYY4wxE2aBhDHGGGMmzAIJY4wxxkyYBRLGTCPeKpoXe9sNInLHzp6zBz/rMG8qsjFmBrNAwpjppQy4GEBVt6rq2Ts5f08cBlggYcwMZ3UkjJlGRGR0NdjngfXAIlU9SETOx11hMYK7RPN1uMWfzgViwOleQaD5uMvUV+MWBLpQVZ8TkXfhFhJK4S4wdRKwAQjhljD+BvAX4MfAQUAA+LKq3uX97P8ESnELLv1OVa/Zy78KY8w+4t/5KcaYPHIFcJCqHuatmvqXrMcOwl1FtRA3CLhcVQ8Xke8D5+FWS70R+JiqrheRo4Hrcdf8uAo4RVWbRaRMVeMichWwRFUvARCRr+OW9f6wiJQB/xaR+7yffZT384eBx0Xkr1ax0JjpwQIJY2aOB1R1ABgQkT7gz97xNcAh3kqrbwT+4FYqBqDA+/4o8BsRuR13gakdORl30bHPePuFuCWQAe5V1S4AEfkT8CbAAgljpgELJIyZObLXbEhn7adx/xb4gF5VPWz7J6rqx7weijOAVSLy+h28vgDvVNXnxxx0n7f9GKqNqRozTViypTHTywBQPJEnqmo/8JKXD4G3AuOh3vZ8VV2hqlcBHbhLPW//s5YDn/QWXkJEDs967C0iUiEiIdxcjUcn0kZjzNRjgYQx04g3fPCoiDwDfGcCL3EOcIGIPAU8i5u4CfAdEVnjve4/gadwl7xeLCKrReQ9wLW4SZZPi8iz3v6ofwN/BJ4G/mj5EcZMHzZrwxizV3mzNjJJmcaY6cV6JIwxxhgzYdYjYYwxxpgJsx4JY4wxxkyYBRLGGGOMmTALJIwxxhgzYRZIGGOMMWbCLJAwxhhjzIT9fy71xIjQVvC9AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "param_plot(median_df,'timestep', 'AggregatedAgentCICHolding',swept)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "param_plot(median_df,'timestep', 'AggregatedAgentCurrencyHolding',swept)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "param_plot(median_df,'timestep', 'AggregatedAgentDemand',swept)"
+   ]
+  }
+ ],
+ "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
+}
diff --git a/Simulation_param/CIC_Network_cadCAD_model_params.ipynb b/Simulation_param/CIC_Network_cadCAD_model_params.ipynb
new file mode 100644
index 0000000..5de9b02
--- /dev/null
+++ b/Simulation_param/CIC_Network_cadCAD_model_params.ipynb
@@ -0,0 +1,1007 @@
+{
+ "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": [
+    "# Run cadCAD model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/aclarkdata/anaconda3/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
+      "  import pandas.util.testing as tm\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[{'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 30}}, {'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 60}}, {'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 90}}]\n",
+      "[{'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 30}}, {'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 60}}, {'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 90}}]\n",
+      "[{'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 30}}, {'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 60}}, {'N': 5, 'T': range(0, 100), 'M': {'drip_frequency': 90}}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import math\n",
+    "import pandas as pd\n",
+    "from tabulate import tabulate\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "\n",
+    "from model.parts.supportingFunctions import *\n",
+    "\n",
+    "pd.options.display.float_format = '{:.2f}'.format\n",
+    "\n",
+    "%matplotlib inline\n",
+    "from tabulate import tabulate\n",
+    "from typing import Dict, List\n",
+    "\n",
+    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
+    "import ipywidgets as widgets\n",
+    "from IPython.display import clear_output\n",
+    "\n",
+    "# The following imports NEED to be in the exact order\n",
+    "from cadCAD.engine import ExecutionMode, ExecutionContext, Executor\n",
+    "from model import economyconfig\n",
+    "from cadCAD import configs\n",
+    "\n",
+    "exec_mode = ExecutionMode()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "                            __________   ____ \n",
+      "          ________ __ _____/ ____/   |  / __ \\\n",
+      "         / ___/ __` / __  / /   / /| | / / / /\n",
+      "        / /__/ /_/ / /_/ / /___/ ___ |/ /_/ / \n",
+      "        \\___/\\__,_/\\__,_/\\____/_/  |_/_____/  \n",
+      "        by BlockScience\n",
+      "        \n",
+      "Execution Mode: multi_proc: [<cadCAD.configuration.Configuration object at 0x7f4a099f8890>, <cadCAD.configuration.Configuration object at 0x7f4a098ff450>, <cadCAD.configuration.Configuration object at 0x7f4a09937350>]\n",
+      "Configurations: [<cadCAD.configuration.Configuration object at 0x7f4a099f8890>, <cadCAD.configuration.Configuration object at 0x7f4a098ff450>, <cadCAD.configuration.Configuration object at 0x7f4a09937350>]\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": [
+    "exec_mode = ExecutionMode()\n",
+    "multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)\n",
+    "run = Executor(exec_context=multi_proc_ctx, configs=configs)\n",
+    "\n",
+    "i = 0\n",
+    "results = {}\n",
+    "for raw_result, tensor_field in run.execute():\n",
+    "    result = pd.DataFrame(raw_result)\n",
+    "    results[i] = {}\n",
+    "    results[i]['result'] = result\n",
+    "    i += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>4000</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': 0, 'd': 0, 'e': 0, 'f': ...</td>\n",
+       "      <td>{'a': 0, 'b': 0, 'c': 0, 'd': 0, 'e': 0, 'f': ...</td>\n",
+       "      <td>{'a': 0, 'b': 0, 'c': 0, 'd': 0, 'e': 0, 'f': ...</td>\n",
+       "      <td>0.72</td>\n",
+       "      <td>{'a': -2.1724474214163934, 'b': 87.78588308123...</td>\n",
+       "      <td>{'a': 564.306616354587, 'b': 2785.025646787363...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[h, a, a, k, p, c, c, g, o, o, i, f, b, b]</td>\n",
+       "      <td>[k, d, p, p, l, p, d, e, p, e, d, b, external, o]</td>\n",
+       "      <td>16500</td>\n",
+       "      <td>198500.00</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>4</td>\n",
+       "      <td>100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4001</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': 0, 'd': 0, 'e': 0, 'f': ...</td>\n",
+       "      <td>{'a': 0, 'b': 0, 'c': 0, 'd': 0, 'e': 0, 'f': ...</td>\n",
+       "      <td>{'a': 0, 'b': 0, 'c': 0, 'd': 0, 'e': 0, 'f': ...</td>\n",
+       "      <td>0.72</td>\n",
+       "      <td>{'a': -2.1724474214163934, 'b': 87.78588308123...</td>\n",
+       "      <td>{'a': 564.306616354587, 'b': 2785.025646787363...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[h, a, a, k, p, c, c, g, o, o, i, f, b, b]</td>\n",
+       "      <td>[k, d, p, p, l, p, d, e, p, e, d, b, external, o]</td>\n",
+       "      <td>16500</td>\n",
+       "      <td>198500.00</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>5</td>\n",
+       "      <td>100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4002</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': 0, 'd': 0, 'e': 0, 'f': ...</td>\n",
+       "      <td>{'a': 0, 'b': 0, 'c': 0, 'd': 0, 'e': 0, 'f': ...</td>\n",
+       "      <td>{'a': 0, 'b': 0, 'c': 0, 'd': 0, 'e': 0, 'f': ...</td>\n",
+       "      <td>0.72</td>\n",
+       "      <td>{'a': -2.1724474214163934, 'b': 87.78588308123...</td>\n",
+       "      <td>{'a': 564.306616354587, 'b': 2785.025646787363...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[h, a, a, k, p, c, c, g, o, o, i, f, b, b]</td>\n",
+       "      <td>[k, d, p, p, l, p, d, e, p, e, d, b, external, o]</td>\n",
+       "      <td>16500</td>\n",
+       "      <td>198500.00</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>6</td>\n",
+       "      <td>100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4003</th>\n",
+       "      <td>(a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...</td>\n",
+       "      <td>{'a': 330, 'b': 590, 'c': 303, 'd': 0, 'e': 0,...</td>\n",
+       "      <td>{'a': 352.69163522161693, 'b': 850.37760837978...</td>\n",
+       "      <td>{'a': 1.0687625309745967, 'b': 1.4413179803047...</td>\n",
+       "      <td>0.72</td>\n",
+       "      <td>{'a': -2.1724474214163934, 'b': 87.78588308123...</td>\n",
+       "      <td>{'a': 564.306616354587, 'b': 2785.025646787363...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[h, a, a, k, p, c, c, g, o, o, i, f, b, b]</td>\n",
+       "      <td>[k, d, p, p, l, p, d, e, p, e, d, b, external, o]</td>\n",
+       "      <td>16500</td>\n",
+       "      <td>198500.00</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>7</td>\n",
+       "      <td>100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4004</th>\n",
+       "      <td>(a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...</td>\n",
+       "      <td>{'a': 330, 'b': 590, 'c': 303, 'd': 0, 'e': 0,...</td>\n",
+       "      <td>{'a': 352.69163522161693, 'b': 850.37760837978...</td>\n",
+       "      <td>{'a': 1.0687625309745967, 'b': 1.4413179803047...</td>\n",
+       "      <td>20.94</td>\n",
+       "      <td>{'a': -2.1724474214163934, 'b': 87.78588308123...</td>\n",
+       "      <td>{'a': 564.306616354587, 'b': 2785.025646787363...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[h, a, a, k, p, c, c, g, o, o, i, f, b, b]</td>\n",
+       "      <td>[k, d, p, p, l, p, d, e, p, e, d, b, external, o]</td>\n",
+       "      <td>16500</td>\n",
+       "      <td>198500.00</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",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                network  \\\n",
+       "4000  (a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...   \n",
+       "4001  (a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...   \n",
+       "4002  (a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...   \n",
+       "4003  (a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...   \n",
+       "4004  (a, b, c, d, e, f, g, h, i, j, k, l, m, o, p, ...   \n",
+       "\n",
+       "                                              KPIDemand  \\\n",
+       "4000  {'a': 0, 'b': 0, 'c': 0, 'd': 0, 'e': 0, 'f': ...   \n",
+       "4001  {'a': 0, 'b': 0, 'c': 0, 'd': 0, 'e': 0, 'f': ...   \n",
+       "4002  {'a': 0, 'b': 0, 'c': 0, 'd': 0, 'e': 0, 'f': ...   \n",
+       "4003  {'a': 330, 'b': 590, 'c': 303, 'd': 0, 'e': 0,...   \n",
+       "4004  {'a': 330, 'b': 590, 'c': 303, 'd': 0, 'e': 0,...   \n",
+       "\n",
+       "                                               KPISpend  \\\n",
+       "4000  {'a': 0, 'b': 0, 'c': 0, 'd': 0, 'e': 0, 'f': ...   \n",
+       "4001  {'a': 0, 'b': 0, 'c': 0, 'd': 0, 'e': 0, 'f': ...   \n",
+       "4002  {'a': 0, 'b': 0, 'c': 0, 'd': 0, 'e': 0, 'f': ...   \n",
+       "4003  {'a': 352.69163522161693, 'b': 850.37760837978...   \n",
+       "4004  {'a': 352.69163522161693, 'b': 850.37760837978...   \n",
+       "\n",
+       "                                     KPISpendOverDemand  VelocityOfMoney  \\\n",
+       "4000  {'a': 0, 'b': 0, 'c': 0, 'd': 0, 'e': 0, 'f': ...             0.72   \n",
+       "4001  {'a': 0, 'b': 0, 'c': 0, 'd': 0, 'e': 0, 'f': ...             0.72   \n",
+       "4002  {'a': 0, 'b': 0, 'c': 0, 'd': 0, 'e': 0, 'f': ...             0.72   \n",
+       "4003  {'a': 1.0687625309745967, 'b': 1.4413179803047...             0.72   \n",
+       "4004  {'a': 1.0687625309745967, 'b': 1.4413179803047...            20.94   \n",
+       "\n",
+       "                                        startingBalance  \\\n",
+       "4000  {'a': -2.1724474214163934, 'b': 87.78588308123...   \n",
+       "4001  {'a': -2.1724474214163934, 'b': 87.78588308123...   \n",
+       "4002  {'a': -2.1724474214163934, 'b': 87.78588308123...   \n",
+       "4003  {'a': -2.1724474214163934, 'b': 87.78588308123...   \n",
+       "4004  {'a': -2.1724474214163934, 'b': 87.78588308123...   \n",
+       "\n",
+       "                                           30_day_spend withdraw  \\\n",
+       "4000  {'a': 564.306616354587, 'b': 2785.025646787363...        0   \n",
+       "4001  {'a': 564.306616354587, 'b': 2785.025646787363...        0   \n",
+       "4002  {'a': 564.306616354587, 'b': 2785.025646787363...        0   \n",
+       "4003  {'a': 564.306616354587, 'b': 2785.025646787363...        0   \n",
+       "4004  {'a': 564.306616354587, 'b': 2785.025646787363...        0   \n",
+       "\n",
+       "                                  outboundAgents  \\\n",
+       "4000  [h, a, a, k, p, c, c, g, o, o, i, f, b, b]   \n",
+       "4001  [h, a, a, k, p, c, c, g, o, o, i, f, b, b]   \n",
+       "4002  [h, a, a, k, p, c, c, g, o, o, i, f, b, b]   \n",
+       "4003  [h, a, a, k, p, c, c, g, o, o, i, f, b, b]   \n",
+       "4004  [h, a, a, k, p, c, c, g, o, o, i, f, b, b]   \n",
+       "\n",
+       "                                          inboundAgents  operatorFiatBalance  \\\n",
+       "4000  [k, d, p, p, l, p, d, e, p, e, d, b, external, o]                16500   \n",
+       "4001  [k, d, p, p, l, p, d, e, p, e, d, b, external, o]                16500   \n",
+       "4002  [k, d, p, p, l, p, d, e, p, e, d, b, external, o]                16500   \n",
+       "4003  [k, d, p, p, l, p, d, e, p, e, d, b, external, o]                16500   \n",
+       "4004  [k, d, p, p, l, p, d, e, p, e, d, b, external, o]                16500   \n",
+       "\n",
+       "      operatorCICBalance                                     fundsInProcess  \\\n",
+       "4000           198500.00  {'timestep': [], 'decision': [], 'cic': [], 's...   \n",
+       "4001           198500.00  {'timestep': [], 'decision': [], 'cic': [], 's...   \n",
+       "4002           198500.00  {'timestep': [], 'decision': [], 'cic': [], 's...   \n",
+       "4003           198500.00  {'timestep': [], 'decision': [], 'cic': [], 's...   \n",
+       "4004           198500.00  {'timestep': [], 'decision': [], 'cic': [], 's...   \n",
+       "\n",
+       "      totalDistributedToAgents  totalMinted  totalBurned  run  substep  \\\n",
+       "4000                      1500            0            0    5        4   \n",
+       "4001                      1500            0            0    5        5   \n",
+       "4002                      1500            0            0    5        6   \n",
+       "4003                      1500            0            0    5        7   \n",
+       "4004                      1500            0            0    5        8   \n",
+       "\n",
+       "      timestep  \n",
+       "4000       100  \n",
+       "4001       100  \n",
+       "4002       100  \n",
+       "4003       100  \n",
+       "4004       100  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results[0]['result'].tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(0,len(results)):\n",
+    "    results[i]['result']['agents'] = results[i]['result'].network.apply(lambda g: np.array([get_nodes_by_type(g,'Agent')][0]))\n",
+    "    results[i]['result']['agent_tokens'] = results[i]['result'].network.apply(lambda g: np.array([g.nodes[j]['tokens'] for j in get_nodes_by_type(g,'Agent')]))\n",
+    "    results[i]['result']['agent_native_currency'] = results[i]['result'].network.apply(lambda g: np.array([g.nodes[j]['native_currency'] for j in get_nodes_by_type(g,'Agent')]))\n",
+    "    # Create dataframe variables   \n",
+    "    tokens = []\n",
+    "    for j in results[i]['result'].index:\n",
+    "         tokens.append(sum(results[i]['result']['agent_tokens'][j]))\n",
+    "\n",
+    "    results[i]['result']['AggregatedAgentCICHolding'] = tokens  \n",
+    "\n",
+    "    currency = []\n",
+    "    for j in results[i]['result'].index:\n",
+    "         currency.append(sum(results[i]['result']['agent_native_currency'][j]))\n",
+    "\n",
+    "    results[i]['result']['AggregatedAgentCurrencyHolding'] = currency  \n",
+    "\n",
+    "    AggregatedSpend = []\n",
+    "    for j in results[i]['result'].index:\n",
+    "         AggregatedSpend.append(sum(results[i]['result']['KPISpend'][j].values()))\n",
+    "\n",
+    "    results[i]['result']['AggregatedAgentSpend'] = AggregatedSpend   \n",
+    "\n",
+    "    AggregatedDemand = []\n",
+    "    for j in results[i]['result'].index:\n",
+    "         AggregatedDemand.append(sum(results[i]['result']['KPIDemand'][j].values()))\n",
+    "\n",
+    "    results[i]['result']['AggregatedAgentDemand'] = AggregatedDemand  \n",
+    "\n",
+    "\n",
+    "    AggregatedKPISpendOverDemand = []\n",
+    "    for j in results[i]['result'].index:\n",
+    "         AggregatedKPISpendOverDemand.append(sum(results[i]['result']['KPISpendOverDemand'][j].values()))\n",
+    "\n",
+    "    results[i]['result']['AggregatedKPISpendOverDemand'] = AggregatedKPISpendOverDemand  \n",
+    "\n",
+    "\n",
+    "    AggregatedGapOfDemandMinusSpend = []\n",
+    "    for j in results[i]['result'].index:\n",
+    "         AggregatedGapOfDemandMinusSpend.append(sum(results[i]['result']['KPIDemand'][j].values())- sum(results[i]['result']['KPISpend'][j].values()))\n",
+    "\n",
+    "    results[i]['result']['AggregatedGapOfDemandMinusSpend'] = AggregatedGapOfDemandMinusSpend  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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",
+       "      <th>Red Cross Drip Frequency</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>10.51</td>\n",
+       "      <td>4500</td>\n",
+       "      <td>200000.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6000.00</td>\n",
+       "      <td>4869.00</td>\n",
+       "      <td>1189.00</td>\n",
+       "      <td>1389</td>\n",
+       "      <td>3.20</td>\n",
+       "      <td>138.00</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>9.72</td>\n",
+       "      <td>4500</td>\n",
+       "      <td>200000.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6350.50</td>\n",
+       "      <td>5219.50</td>\n",
+       "      <td>1057.00</td>\n",
+       "      <td>1057</td>\n",
+       "      <td>4.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>19.57</td>\n",
+       "      <td>4500</td>\n",
+       "      <td>200000.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6323.00</td>\n",
+       "      <td>5192.00</td>\n",
+       "      <td>2333.25</td>\n",
+       "      <td>3275</td>\n",
+       "      <td>7.14</td>\n",
+       "      <td>941.75</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>15.67</td>\n",
+       "      <td>4500</td>\n",
+       "      <td>200000.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6435.00</td>\n",
+       "      <td>5304.00</td>\n",
+       "      <td>1734.38</td>\n",
+       "      <td>3737</td>\n",
+       "      <td>6.85</td>\n",
+       "      <td>789.75</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>20.01</td>\n",
+       "      <td>4500</td>\n",
+       "      <td>200000.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6435.00</td>\n",
+       "      <td>5304.00</td>\n",
+       "      <td>2227.06</td>\n",
+       "      <td>3140</td>\n",
+       "      <td>6.99</td>\n",
+       "      <td>498.25</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   timestep  VelocityOfMoney  operatorFiatBalance  operatorCICBalance  \\\n",
+       "0         1            10.51                 4500           200000.00   \n",
+       "1         2             9.72                 4500           200000.00   \n",
+       "2         3            19.57                 4500           200000.00   \n",
+       "3         4            15.67                 4500           200000.00   \n",
+       "4         5            20.01                 4500           200000.00   \n",
+       "\n",
+       "   totalDistributedToAgents  totalMinted  totalBurned  run  substep  \\\n",
+       "0                         0            0            0    3        8   \n",
+       "1                         0            0            0    3        8   \n",
+       "2                         0            0            0    3        8   \n",
+       "3                         0            0            0    3        8   \n",
+       "4                         0            0            0    3        8   \n",
+       "\n",
+       "   AggregatedAgentCICHolding  AggregatedAgentCurrencyHolding  \\\n",
+       "0                    6000.00                         4869.00   \n",
+       "1                    6350.50                         5219.50   \n",
+       "2                    6323.00                         5192.00   \n",
+       "3                    6435.00                         5304.00   \n",
+       "4                    6435.00                         5304.00   \n",
+       "\n",
+       "   AggregatedAgentSpend  AggregatedAgentDemand  AggregatedKPISpendOverDemand  \\\n",
+       "0               1189.00                   1389                          3.20   \n",
+       "1               1057.00                   1057                          4.00   \n",
+       "2               2333.25                   3275                          7.14   \n",
+       "3               1734.38                   3737                          6.85   \n",
+       "4               2227.06                   3140                          6.99   \n",
+       "\n",
+       "   AggregatedGapOfDemandMinusSpend  Red Cross Drip Frequency  \n",
+       "0                           138.00                        30  \n",
+       "1                             0.00                        30  \n",
+       "2                           941.75                        30  \n",
+       "3                           789.75                        30  \n",
+       "4                           498.25                        30  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "params = [30,60,90]\n",
+    "swept = 'Red Cross Drip Frequency'\n",
+    "mean_df,median_df = param_dfs(results,params,swept)\n",
+    "median_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot of agent activity per timestep\n",
+    "param_plot(median_df,'timestep', 'AggregatedAgentSpend',swept)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "param_plot(median_df,'timestep', 'VelocityOfMoney',swept)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "param_plot(median_df,'timestep', 'operatorCICBalance',swept)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "param_plot(median_df,'timestep', 'operatorFiatBalance',swept)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "param_plot(median_df,'timestep', 'AggregatedAgentCICHolding',swept)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "param_plot(median_df,'timestep', 'AggregatedAgentCurrencyHolding',swept)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "param_plot(median_df,'timestep', 'AggregatedAgentDemand',swept)"
+   ]
+  }
+ ],
+ "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
+}
diff --git a/Simulation_param/images/agentDistribution.png b/Simulation_param/images/agentDistribution.png
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diff --git a/Simulation_param/model/__pycache__/economyconfig.cpython-36.pyc b/Simulation_param/model/__pycache__/economyconfig.cpython-36.pyc
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diff --git a/Simulation_param/model/economyconfig.py b/Simulation_param/model/economyconfig.py
new file mode 100644
index 0000000..f398518
--- /dev/null
+++ b/Simulation_param/model/economyconfig.py
@@ -0,0 +1,36 @@
+import math
+from decimal import Decimal
+from datetime import timedelta
+import numpy as np
+from typing import Dict, List
+
+from cadCAD.configuration import append_configs
+from cadCAD.configuration.utils import bound_norm_random, ep_time_step, config_sim, access_block
+
+from .genesis_states import genesis_states
+from .partial_state_update_block import partial_state_update_block
+
+params: Dict[str, List[int]] = {
+    'drip_frequency': [30,60,90] # in days
+}
+
+
+sim_config = config_sim({
+    'N': 5,
+    'T': range(100), #day 
+    'M': params,
+})
+
+seeds = {
+    'p': np.random.RandomState(26042019),
+}
+env_processes = {}
+
+
+append_configs(
+    sim_configs=sim_config,
+    initial_state=genesis_states,
+    seeds=seeds,
+    env_processes=env_processes,
+    partial_state_update_blocks=partial_state_update_block
+)
diff --git a/Simulation_param/model/genesis_states.py b/Simulation_param/model/genesis_states.py
new file mode 100644
index 0000000..988b02b
--- /dev/null
+++ b/Simulation_param/model/genesis_states.py
@@ -0,0 +1,23 @@
+from .parts.initialization import * 
+import pandas as pd
+
+genesis_states = { 
+    # initial states of the economy
+    'network': create_network(),# networkx market
+    'KPIDemand': {},
+    'KPISpend': {},
+    'KPISpendOverDemand': {},
+    'VelocityOfMoney':0,
+    'startingBalance': {},
+    '30_day_spend': {},
+    'withdraw':{},
+    'outboundAgents':[],
+    'inboundAgents':[],
+    'operatorFiatBalance': R0,
+    'operatorCICBalance': S0,
+    'fundsInProcess': {'timestep':[],'decision':[],'cic':[],'shilling':[]},
+    'totalDistributedToAgents':0,
+    'totalMinted':0,
+    'totalBurned':0
+}
+
diff --git a/Simulation_param/model/partial_state_update_block.py b/Simulation_param/model/partial_state_update_block.py
new file mode 100644
index 0000000..b51bbaf
--- /dev/null
+++ b/Simulation_param/model/partial_state_update_block.py
@@ -0,0 +1,89 @@
+from .parts.exogenousProcesses import *
+from .parts.kpis import *
+from .parts.system import *
+from .parts.operatorentity import *
+
+partial_state_update_block = {
+    # Exogenous
+    'Exogenous': {
+        'policies': {
+        },
+        'variables': {
+            'startingBalance': startingBalance,
+            'operatorFiatBalance': redCrossDrop,
+            '30_day_spend': update_30_day_spend,
+            'network':clear_agent_activity
+        }
+    },
+    # Users
+    'Behaviors': {
+        'policies': {
+            'action': choose_agents
+        },
+        'variables': {
+        'network': update_agent_activity,
+        'outboundAgents': update_outboundAgents,
+        'inboundAgents':update_inboundAgents
+        }
+    },
+    'Spend allocation': {
+        'policies': {
+            'action': spend_allocation
+        },
+        'variables': {
+        'network': update_node_spend
+        }
+    },
+    'Withdraw behavior': {
+        'policies': {
+            'action': withdraw_calculation
+        },
+        'variables': {
+        'withdraw': update_withdraw,
+        'network':update_network_withraw
+        }
+    },
+    # Operator
+    'Operator Disburse to Agents': {
+        'policies': {
+            'action': disbursement_to_agents
+        },
+        'variables': {
+        'network':update_agent_tokens,
+        'operatorCICBalance':update_operator_FromDisbursements,
+        'totalDistributedToAgents':update_totalDistributedToAgents
+        }
+    },
+    'Operator Inventory Control': {
+        'policies': {
+            'action': inventory_controller
+        },
+        'variables': {
+        'operatorFiatBalance':update_operator_fiatBalance,
+        'operatorCICBalance':update_operator_cicBalance, 
+        'totalMinted': update_totalMinted,
+        'totalBurned':update_totalBurned,
+        'fundsInProcess':update_fundsInProcess
+        }
+    },
+    # KPIs
+    'KPIs': {
+        'policies': {
+            'action':kpis
+        },
+        'variables':{
+            'KPIDemand': update_KPIDemand,
+            'KPISpend': update_KPISpend,
+            'KPISpendOverDemand': update_KPISpendOverDemand        
+        }
+    },
+    'Velocity': {
+        'policies': {
+            'action':velocity_of_money
+        },
+        'variables':{
+
+            'VelocityOfMoney': update_velocity_of_money
+        }
+    }
+}
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diff --git a/Simulation_param/model/parts/exogenousProcesses.py b/Simulation_param/model/parts/exogenousProcesses.py
new file mode 100644
index 0000000..6caa8d8
--- /dev/null
+++ b/Simulation_param/model/parts/exogenousProcesses.py
@@ -0,0 +1,122 @@
+
+import numpy as np
+import pandas as pd
+from .initialization import *
+from .supportingFunctions import *
+    
+
+def startingBalance(params, step, sL, s, _input):
+    '''
+    Calculate agent starting balance every 30 days
+    '''
+    y = 'startingBalance'
+    network = s['network']
+
+    startingBalance = {}
+
+    timestep = s['timestep']
+
+    division =  timestep % 31  == 0
+
+    if timestep == 1:
+        for i in agents:
+            startingBalance[i] = network.nodes[i]['tokens']
+    elif division == True:
+        for i in agents:
+            startingBalance[i] = network.nodes[i]['tokens']
+    else:
+        startingBalance = s['startingBalance']
+    x = startingBalance
+
+    return (y, x)
+
+def update_30_day_spend(params, step, sL, s,_input):
+    '''
+    Aggregate agent spend. Refresh every 30 days.
+    '''
+    y = '30_day_spend'
+    network = s['network']
+
+    timestep = s['timestep']
+
+    division =  timestep % 31  == 0
+
+    if division == True:
+        outflowSpend, inflowSpend = iterateEdges(network,'spend')
+        spend =  outflowSpend 
+    else:
+        spendOld = s['30_day_spend']
+        outflowSpend, inflowSpend = iterateEdges(network,'spend')
+        spend = DictionaryMergeAddition(spendOld,outflowSpend) 
+
+    x = spend
+    return (y, x)
+
+def redCrossDrop(params, step, sL, s, _input):
+    '''
+    Every 30 days, the red cross drips to the grassroots operator node
+    '''
+    y = 'operatorFiatBalance'
+    fiatBalance = s['operatorFiatBalance']
+    
+    timestep = s['timestep']
+    
+    division =  timestep % params['drip_frequency']  == 0
+
+    if division == True:
+        fiatBalance = fiatBalance + drip
+    else:
+        pass
+
+    x = fiatBalance
+    return (y, x)
+
+
+def clear_agent_activity(params,step,sL,s,_input):
+    '''
+    Clear agent activity from the previous timestep
+    '''
+    y = 'network'
+    network = s['network']
+
+    if s['timestep'] > 0:
+        outboundAgents = s['outboundAgents']
+        inboundAgents = s['inboundAgents']
+        
+        try:
+            for i,j in zip(outboundAgents,inboundAgents):
+                network[i][j]['demand'] = 0
+        except:
+            pass
+
+        # Clear cic % demand edge weights
+        try:
+            for i,j in zip(outboundAgents,inboundAgents):
+                network[i][j]['fractionOfDemandInCIC'] = 0
+        except:
+            pass
+
+
+        # Clear utility edge types
+        try: 
+            for i,j in zip(outboundAgents,inboundAgents):
+                network[i][j]['utility'] = 0
+        except:
+            pass
+        
+        # Clear cic % spend edge weights
+        try:
+            for i,j in zip(outboundAgents,inboundAgents):
+                network[i][j]['fractionOfActualSpendInCIC'] = 0
+        except:
+            pass
+        # Clear spend edge types
+        try: 
+            for i,j in zip(outboundAgents,inboundAgents):
+                network[i][j]['spend'] = 0
+        except:
+            pass
+    else:
+        pass
+    x = network
+    return (y,x)
\ No newline at end of file
diff --git a/Simulation_param/model/parts/initialization.py b/Simulation_param/model/parts/initialization.py
new file mode 100644
index 0000000..0e4f1b0
--- /dev/null
+++ b/Simulation_param/model/parts/initialization.py
@@ -0,0 +1,118 @@
+
+# import libraries
+import networkx as nx
+import matplotlib.pyplot as plt
+import numpy as np
+from .supportingFunctions import *
+
+# Assumptions:
+# Amount received in shilling when withdraw occurs
+leverage = 1 
+
+# process time
+process_lag = 7 # timesteps
+
+# red cross drip amount
+drip = 4000
+
+# system initialization
+agents = ['a','b','c','d','e','f','g','h','i','j','k','l','m','o','p']
+
+# system actors
+system = ['external','cic']
+
+# chamas
+chama = ['chama_1','chama_2','chama_3','chama_4']
+
+# traders
+traders = ['ta','tb','tc'] #only trading on the cic. Link to external and cic not to other agents
+
+allAgents = agents + system
+
+mixingAgents = ['a','b','c','d','e','f','g','h','i','j','k','l','m','o','p','external']
+
+UtilityTypesOrdered ={'Food/Water':1,
+                    'Fuel/Energy':2,
+                    'Health':3,
+                    'Education':4,
+                    'Savings Group':5,
+                    'Shop':6}
+
+utilityTypesProbability = {'Food/Water':0.6,
+                    'Fuel/Energy':0.10,
+                    'Health':0.03,
+                    'Education':0.015,
+                    'Savings Group':0.065,
+                    'Shop':0.19}
+
+
+R0 =  500 #thousand xDAI
+kappa = 4 #leverage
+P0 = 1/100 #initial price
+S0 = kappa*R0/P0
+V0 = invariant(R0,S0,kappa)
+P = spot_price(R0, V0, kappa)
+
+# Price level
+priceLevel = 100
+
+fractionOfDemandInCIC = 0.5
+fractionOfActualSpendInCIC = 0.5
+
+def create_network():
+    # Create network graph
+    network = nx.DiGraph()
+
+    # Add nodes for n participants plus the external economy and the cic network
+    for i in agents:
+        network.add_node(i,type='Agent',tokens=400, native_currency = int(np.random.uniform(low=20, high=500, size=1)[0]))
+        
+        
+    network.add_node('external',type='Contract',native_currency = 100000000,tokens = 0,delta_native_currency = 0, pos=(1,50))
+    network.add_node('cic',type='Contract',tokens= S0, native_currency = R0,pos=(50,1))
+
+    for i in chama:
+        network.add_node(i,type='Chama')
+        
+    for i in traders:
+        network.add_node(i,type='Trader',tokens=20, native_currency = 20, 
+                        price_belief = 1, trust_level = 1)
+        
+    # Create bi-directional edges between all participants
+    for i in allAgents:
+        for j in allAgents:
+            if i!=j:
+                network.add_edge(i,j)
+
+    # Create bi-directional edges between each trader and the external economy and the cic environment            
+    for i in traders:
+        for j in system:
+            if i!=j:
+                network.add_edge(i,j)
+                
+    # Create bi-directional edges between some agent and a chama node representing membershio      
+    for i in chama:
+        for j in agents:
+            if np.random.choice(['Member','Non_Member'],1,p=[.50,.50])[0] == 'Member':
+                network.add_edge(i,j)
+
+    # Type colors 
+    colors = ['Red','Blue','Green','Orange']
+    color_map = []
+    for i in network.nodes:
+        if network.nodes[i]['type'] == 'Agent':
+            color_map.append('Red')
+        elif network.nodes[i]['type'] == 'Cloud':
+            color_map.append('Blue')
+        elif network.nodes[i]['type'] == 'Contract':
+            color_map.append('Green')
+        elif network.nodes[i]['type'] == 'Trader':
+            color_map.append('Yellow')
+        elif network.nodes[i]['type'] == 'Chama':
+            color_map.append('Orange')
+            
+    pos = nx.spring_layout(network,pos=nx.get_node_attributes(network,'pos'),fixed=nx.get_node_attributes(network,'pos'),seed=10)
+    nx.draw(network,node_color = color_map,pos=pos,with_labels=True,alpha=0.7)
+    plt.savefig('images/graph.png')
+    plt.show()
+    return network
\ No newline at end of file
diff --git a/Simulation_param/model/parts/kpis.py b/Simulation_param/model/parts/kpis.py
new file mode 100644
index 0000000..81b4929
--- /dev/null
+++ b/Simulation_param/model/parts/kpis.py
@@ -0,0 +1,92 @@
+
+import numpy as np
+from .initialization import *
+from .supportingFunctions import *
+import networkx as nx
+
+
+# Behaviors
+def kpis(params, step, sL, s):
+    ''''''
+    # instantiate network state
+    network = s['network']
+
+    KPIDemand = {}
+    KPISpend = {}
+    KPISpendOverDemand = {}
+    for i in mixingAgents:
+        demand = []
+        for j in network.adj[i]:
+            try:
+                demand.append(network.adj[i][j]['demand'])
+            except:
+                pass
+
+        spend = []
+        for j in network.adj[i]:
+            try:
+                spend.append(network.adj[i][j]['spend'])
+            except:
+                pass
+
+        sumDemand = sum(demand)
+        sumSpend = sum(spend)
+        try:
+            spendOverDemand = sumSpend/sumDemand
+        except:
+            spendOverDemand = 0
+
+        KPIDemand[i] = sumDemand
+        KPISpend[i] = sumSpend
+        KPISpendOverDemand[i] = spendOverDemand
+
+    #print(nx.katz_centrality_numpy(G=network,weight='spend'))
+    return {'KPIDemand':KPIDemand,'KPISpend':KPISpend,'KPISpendOverDemand':KPISpendOverDemand}
+
+def velocity_of_money(params, step, sL, s):
+    ''''''
+    # instantiate network state
+    network = s['network']
+
+    KPISpend = s['KPISpend']
+
+    # TODO: Moving average for state variable
+    T = []
+    for i,j in KPISpend.items():
+        T.append(j)
+        
+    T = sum(T)
+    
+    # TODO Moving average for state variable 
+    M = []
+    for i in agents:
+        M.append(network.nodes[i]['tokens'] + network.nodes[i]['native_currency'])
+        
+    M = sum(M)
+    
+    V_t = (priceLevel *T)/M
+
+    return {'V_t':V_t,'T':T,'M':M}
+
+
+# Mechanisms
+def update_KPIDemand(params, step, sL, s,_input):
+    y = 'KPIDemand'
+    x = _input['KPIDemand']
+    return (y,x)
+
+def update_KPISpend(params, step, sL, s,_input):
+    y = 'KPISpend'
+    x = _input['KPISpend']
+    return (y,x)
+
+def update_KPISpendOverDemand(params, step, sL, s,_input):
+    y = 'KPISpendOverDemand'
+    x = _input['KPISpendOverDemand']
+    return (y,x)
+
+
+def update_velocity_of_money(params, step, sL, s,_input):
+    y = 'VelocityOfMoney'
+    x = _input['V_t']
+    return (y,x)
diff --git a/Simulation_param/model/parts/operatorentity.py b/Simulation_param/model/parts/operatorentity.py
new file mode 100644
index 0000000..433ee3d
--- /dev/null
+++ b/Simulation_param/model/parts/operatorentity.py
@@ -0,0 +1,287 @@
+
+import numpy as np
+import pandas as pd
+from cadCAD.configuration.utils import access_block
+from .initialization import *
+from .supportingFunctions import *
+from collections import OrderedDict
+
+# Parameters
+FrequencyOfAllocation = 45 # every two weeks
+idealFiat = 5000
+idealCIC = 200000
+varianceCIC = 50000
+varianceFiat = 1000
+unadjustedPerAgent = 50
+
+
+
+
+agentAllocation = {'a':[1,1],'b':[1,1],'c':[1,1], # agent:[centrality,allocationValue]
+                   'd':[1,1],'e':[1,1],'f':[1,1],
+                   'g':[1,1],'h':[1,1],'i':[1,1],
+                   'j':[1,1],'k':[1,1],'l':[1,1],
+                   'm':[1,1],'o':[1,1],'p':[1,1]}
+
+# Behaviors
+def disbursement_to_agents(params, step, sL, s):
+    '''
+    Distribute every FrequencyOfAllocation days to agents based off of centrality allocation metric
+    '''
+    fiatBalance = s['operatorFiatBalance']
+    cicBalance = s['operatorCICBalance']
+    timestep = s['timestep']
+
+    division =  timestep % FrequencyOfAllocation  == 0
+
+    if division == True:
+        agentDistribution ={} # agent: amount distributed
+        for i,j in agentAllocation.items():
+            agentDistribution[i] = unadjustedPerAgent * agentAllocation[i][1]
+        distribute = 'Yes'
+        
+    else:
+        agentDistribution = 0
+        distribute = 'No'
+
+
+    return {'distribute':distribute,'amount':agentDistribution}
+
+
+def inventory_controller(params, step, sL, s):
+    '''
+    Monetary policy hysteresis conservation allocation between fiat and cic reserves.
+    
+    # TODO: If scarcity on both sides, add feedback to reduce percentage able to withdraw, frequency you can redeem, or redeem at less than par. 
+    '''
+    fiatBalance = s['operatorFiatBalance']
+    cicBalance = s['operatorCICBalance']
+    timestep = s['timestep']
+    fundsInProcess = s['fundsInProcess']
+
+
+    updatedCIC = cicBalance
+    updatedFiat = fiatBalance
+
+    #decision,amt = mint_burn_logic_control(idealCIC,updatedCIC,variance,updatedFiat)
+    decision,amt = mint_burn_logic_control(idealCIC,updatedCIC,varianceCIC,updatedFiat,varianceFiat,idealFiat)
+
+    if decision == 'burn':
+        try:
+            deltaR, realized_price = withdraw(amt,updatedFiat,updatedCIC, V0, kappa)
+            # update state
+            # fiatBalance = fiatBalance - deltaR
+            # cicBalance = cicBalance - amt
+            fiatChange = abs(deltaR)
+            cicChange = amt
+
+        except:
+            print('Not enough to burn')
+
+            fiatChange = 0
+            cicChange = 0
+        
+    elif decision == 'mint':
+        try:
+            deltaS, realized_price = mint(amt,updatedFiat,updatedCIC, V0, kappa)
+            # update state
+            # fiatBalance = fiatBalance + amt
+            # cicBalance = cicBalance + deltaS
+            fiatChange = amt
+            cicChange = abs(deltaS)
+
+        except:
+            print('Not enough to mint')
+            fiatChange = 0
+            cicChange = 0
+
+    else:
+        fiatChange = 0
+        cicChange = 0
+        decision = 'none'
+        pass
+
+    if decision == 'mint':
+        fundsInProcess['timestep'].append(timestep + process_lag)
+        fundsInProcess['decision'].append(decision)
+        fundsInProcess['cic'].append(fiatChange)
+        fundsInProcess['shilling'].append(cicChange)
+    elif decision == 'burn':
+        fundsInProcess['timestep'].append(timestep +process_lag)
+        fundsInProcess['decision'].append(decision)
+        fundsInProcess['cic'].append(fiatChange)
+        fundsInProcess['shilling'].append(cicChange)
+    else:
+        pass
+    
+    return {'decision':decision,'fiatChange':fiatChange,'cicChange':cicChange,'fundsInProcess':fundsInProcess}
+
+
+
+# Mechanisms 
+def update_agent_tokens(params,step,sL,s,_input):
+    '''
+    '''
+    y = 'network'
+    network = s['network']
+
+    distribute = _input['distribute']
+    amount = _input['amount']
+
+    if distribute == 'Yes':
+        for i in agents:
+            network.nodes[i]['tokens'] = network.nodes[i]['tokens'] + amount[i]
+    else:
+        pass
+
+    return (y,network)
+
+def update_operator_FromDisbursements(params,step,sL,s,_input):
+    '''
+    '''
+    y = 'operatorCICBalance'
+    x = s['operatorCICBalance']
+    timestep = s['timestep']
+    
+    distribute = _input['distribute']
+    amount = _input['amount'] 
+
+    if distribute == 'Yes':
+        totalDistribution = []
+        for i,j in amount.items():
+            totalDistribution.append(j)
+        
+        totalDistribution = sum(totalDistribution)
+        x = x - totalDistribution
+
+    else:
+        pass
+
+    return (y,x)
+
+def update_totalDistributedToAgents(params,step,sL,s,_input):
+    '''
+    '''
+    y = 'totalDistributedToAgents'
+    x = s['totalDistributedToAgents']
+    timestep = s['timestep']
+    
+    distribute = _input['distribute']
+    amount = _input['amount'] 
+
+    if distribute == 'Yes':
+        totalDistribution = []
+        for i,j in amount.items():
+            totalDistribution.append(j)
+        
+        totalDistribution = sum(totalDistribution)
+        x = x + totalDistribution
+    else:
+        pass
+
+    return (y,x)
+
+def update_operator_fiatBalance(params,step,sL,s,_input):
+    '''
+    '''
+    y = 'operatorFiatBalance'
+    x = s['operatorFiatBalance']
+    fundsInProcess = s['fundsInProcess']
+    timestep = s['timestep']
+    if _input['fiatChange']:
+        try:
+            if fundsInProcess['timestep'][0] == timestep + 1:
+                if fundsInProcess['decision'][0] == 'mint':
+                    x = x - abs(fundsInProcess['shilling'][0])
+                elif fundsInProcess['decision'][0] == 'burn':
+                    x = x + abs(fundsInProcess['shilling'][0])
+            else:
+                pass
+        except:
+            pass
+    else:
+        pass
+
+
+    return (y,x)
+
+def update_operator_cicBalance(params,step,sL,s,_input):
+    '''
+    '''
+    y = 'operatorCICBalance'
+    x = s['operatorCICBalance']
+    fundsInProcess = s['fundsInProcess']
+    timestep = s['timestep']
+
+    if _input['cicChange']:
+        try:
+            if fundsInProcess['timestep'][0] == timestep + 1:
+                if fundsInProcess['decision'][0] == 'mint':
+                    x = x + abs(fundsInProcess['cic'][0])
+                elif fundsInProcess['decision'][0] == 'burn':
+                    x = x - abs(fundsInProcess['cic'][0])
+            else:
+                pass
+        except:
+            pass
+    else:
+        pass
+
+    return (y,x)
+
+def update_totalMinted(params,step,sL,s,_input):
+    '''
+    '''
+    y = 'totalMinted'
+    x = s['totalMinted']
+    timestep = s['timestep']
+    try:
+        if _input['fundsInProcess']['decision'][0] == 'mint':
+            x = x + abs(_input['fundsInProcess']['cic'][0])
+        elif _input['fundsInProcess']['decision'][0] == 'burn':
+            pass
+    except:
+        pass
+
+
+    return (y,x)
+
+def update_totalBurned(params,step,sL,s,_input):
+    '''
+    '''
+    y = 'totalBurned'
+    x = s['totalBurned']
+    timestep = s['timestep']
+    try:
+        if _input['fundsInProcess']['decision'][0] == 'burn':
+            x = x + abs(_input['fundsInProcess']['cic'][0])
+        elif _input['fundsInProcess']['decision'][0] == 'mint':
+            pass
+    except:
+        pass
+
+    return (y,x)
+
+def update_fundsInProcess(params,step,sL,s,_input):
+    '''
+    '''
+    y = 'fundsInProcess'
+    x = _input['fundsInProcess']
+    timestep = s['timestep']
+
+    if _input['fundsInProcess']:
+        try:
+            if x['timestep'][0] == timestep:
+                del x['timestep'][0]
+                del x['decision'][0]
+                del x['cic'][0]
+                del x['shilling'][0]
+            else:
+                pass
+        except:
+            pass
+    else:
+        pass
+
+    return (y,x)
+
diff --git a/Simulation_param/model/parts/supportingFunctions.py b/Simulation_param/model/parts/supportingFunctions.py
new file mode 100644
index 0000000..cddab48
--- /dev/null
+++ b/Simulation_param/model/parts/supportingFunctions.py
@@ -0,0 +1,442 @@
+import numpy as np
+from scipy.stats import gamma
+import matplotlib.pyplot as plt
+import seaborn as sns
+import pandas as pd
+
+default_kappa= 4
+default_exit_tax = .02
+
+#value function for a given state (R,S)
+def invariant(R,S,kappa=default_kappa):
+    
+    return (S**kappa)/R
+
+#given a value function (parameterized by kappa)
+#and an invariant coeficient V0
+#return Supply S as a function of reserve R
+def reserve(S, V0, kappa=default_kappa):
+    return (S**kappa)/V0
+
+#given a value function (parameterized by kappa)
+#and an invariant coeficient V0
+#return Supply S as a function of reserve R
+def supply(R, V0, kappa=default_kappa):
+    return (V0*R)**(1/kappa)
+
+#given a value function (parameterized by kappa)
+#and an invariant coeficient V0
+#return a spot price P as a function of reserve R
+def spot_price(R, V0, kappa=default_kappa):
+    return kappa*R**((kappa-1)/kappa)/V0**(1/kappa)
+
+#for a given state (R,S)
+#given a value function (parameterized by kappa)
+#and an invariant coeficient V0
+#deposit deltaR to Mint deltaS
+#with realized price deltaR/deltaS
+def mint(deltaR, R,S, V0, kappa=default_kappa):
+    deltaS = (V0*(R+deltaR))**(1/kappa)-S
+    if deltaS ==0:
+        realized_price = spot_price(R+deltaR, V0, kappa)
+    else:
+        realized_price = deltaR/deltaS
+    deltaS = round(deltaS,2)
+    return deltaS, realized_price
+
+#for a given state (R,S)
+#given a value function (parameterized by kappa)
+#and an invariant coeficient V0
+#burn deltaS to Withdraw deltaR
+#with realized price deltaR/deltaS
+def withdraw(deltaS, R,S, V0, kappa=default_kappa):
+    deltaR = R-((S-deltaS)**kappa)/V0
+    if deltaS ==0:
+        realized_price = spot_price(R+deltaR, V0, kappa)
+    else:
+        realized_price = deltaR/deltaS
+    deltaR = round(deltaR,2)
+    return deltaR, realized_price
+
+
+
+def iterateEdges(network,edgeToIterate):
+    '''
+    Description:
+    Iterate through a network on a weighted edge and return
+    two dictionaries: the inflow and outflow for the given agents
+    in the format:
+    
+    {'Agent':amount}
+    '''
+    outflows = {}
+    inflows = {}
+    for i,j in network.edges:
+        try:
+            amount = network[i][j][edgeToIterate]
+            if i in outflows:
+                outflows[i] = outflows[i] + amount
+            else:
+                outflows[i] =  amount
+            if j in inflows:
+                inflows[j] = inflows[j] + amount
+            else:
+                inflows[j] = amount
+        except:
+            pass
+    return outflows,inflows
+
+
+def inflowAndOutflowDictionaryMerge(inflow,outflow):
+    '''
+    Description:
+    Merge two dictionaries and return one dictionary with zero floor'''
+    
+    merged = {}
+
+    inflowsKeys = [k for k,v in inflow.items() if k not in outflow]
+    for i in inflowsKeys:
+        merged[i] = inflow[i]
+    outflowsKeys = [k for k,v in outflow.items() if k not in inflow]
+    for i in outflowsKeys:
+        merged[i] = outflow[i]
+    overlapKeys = [k for k,v in inflow.items() if k in outflow]
+    for i in overlapKeys:
+        amt = outflow[i] - inflow[i] 
+        if amt < 0:
+            merged[i] = 0
+        else:
+            merged[i] = amt
+            pass
+        
+    return merged
+
+        
+def spendCalculation(agentToPay,agentToReceive,rankOrderDemand,maxSpendCurrency,maxSpendTokens,cicPercentage):
+    '''
+    Function to calculate if an agent can pay for demand given token and currency contraints
+    '''
+    if (rankOrderDemand[agentToReceive] * (1-cicPercentage)) > maxSpendCurrency[agentToPay]:
+        verdict_currency = 'No'
+    else:
+        verdict_currency = 'Enough'
+        
+    if (rankOrderDemand[agentToReceive] * cicPercentage) > maxSpendTokens[agentToPay]:
+        verdict_cic = 'No'
+    else:
+        verdict_cic = 'Enough'
+    
+    if verdict_currency == 'Enough'and verdict_cic == 'Enough':
+        spend = rankOrderDemand[agentToReceive]
+    
+    elif maxSpendCurrency[agentToPay] > 0:
+        spend = maxSpendCurrency[agentToPay]
+    else:
+        spend = 0
+        
+    return spend
+
+
+def spendCalculationExternal(agentToPay,agentToReceive,rankOrderDemand,maxSpendCurrency):
+    '''
+    '''
+    if rankOrderDemand[agentToReceive] > maxSpendCurrency[agentToPay]:
+        verdict_currency = 'No'
+    else:
+        verdict_currency = 'Enough'
+    
+    if verdict_currency == 'Enough':
+        spend = rankOrderDemand[agentToReceive]
+        
+    elif maxSpendCurrency[agentToPay] > 0:
+        spend = maxSpendCurrency[agentToPay]
+    else:
+        spend = 0
+        
+    return spend
+
+
+def DictionaryMergeAddition(inflow,outflow):
+    '''
+    Description:
+    Merge two dictionaries and return one dictionary'''
+    
+    merged = {}
+
+    inflowsKeys = [k for k,v in inflow.items() if k not in outflow]
+    for i in inflowsKeys:
+        merged[i] = inflow[i]
+    outflowsKeys = [k for k,v in outflow.items() if k not in inflow]
+    for i in outflowsKeys:
+        merged[i] = outflow[i]
+    overlapKeys = [k for k,v in inflow.items() if k in outflow]
+    for i in overlapKeys:
+        merged[i] = outflow[i] + inflow[i] 
+        
+    return merged
+
+def mint_burn_logic_control(ideal,actual,variance,fiat,fiat_variance,ideal_fiat):
+    '''
+    Inventory control function to test if the current balance is in an acceptable range. Tolerance range 
+    '''
+    if ideal - variance <= actual <= ideal + (2*variance):
+        decision = 'none'
+        amount = 0
+    else:
+        if (ideal + variance) > actual:
+            decision = 'mint'
+            amount = (ideal + variance) - actual
+        else:
+            pass
+        if actual > (ideal + variance):
+            decision = 'burn'
+            amount = actual - (ideal + variance) 
+        else:
+            pass
+
+    if decision == 'mint':
+        if fiat < (ideal_fiat - fiat_variance):
+            if amount > fiat:
+                decision = 'none'
+                amount = 0
+            else:
+                pass
+    if decision == 'none':
+        if fiat < (ideal_fiat - fiat_variance):
+            decision = 'mint'
+            amount = (ideal_fiat-fiat_variance)
+        else:
+            pass
+    
+    amount = round(amount,2)
+    return decision, amount
+    
+#NetworkX functions
+def get_nodes_by_type(g, node_type_selection):
+    return [node for node in g.nodes if g.nodes[node]['type']== node_type_selection]
+
+def get_edges_by_type(g, edge_type_selection):
+    return [edge for edge in g.edges if g.edges[edge]['type']== edge_type_selection]
+
+def get_edges(g):
+    return [edge for edge in g.edges if g.edges[edge]]
+
+def get_nodes(g):
+    '''
+    df.network.apply(lambda g: np.array([g.nodes[j]['balls'] for j in get_nodes(g)]))
+    '''
+    return [node for node in g.nodes if g.nodes[node]]
+
+def aggregate_runs(df,aggregate_dimension):
+    '''
+    Function to aggregate the monte carlo runs along a single dimension.
+    Parameters:
+    df: dataframe name
+    aggregate_dimension: the dimension you would like to aggregate on, the standard one is timestep.
+    Example run:
+    mean_df,median_df,std_df,min_df = aggregate_runs(df,'timestep')
+    '''
+    df = df[df['substep'] == df.substep.max()]
+    mean_df = df.groupby(aggregate_dimension).mean().reset_index()
+    median_df = df.groupby(aggregate_dimension).median().reset_index()
+    std_df = df.groupby(aggregate_dimension).std().reset_index()
+    min_df = df.groupby(aggregate_dimension).min().reset_index()
+
+    return mean_df,median_df,std_df,min_df
+
+
+
+def plot_averaged_runs(df,aggregate_dimension,x, y,run_count,lx=False,ly=False, suppMin=False):
+    '''
+    Function to plot the mean, median, etc of the monte carlo runs along a single variable.
+    Parameters:
+    df: dataframe name
+    aggregate_dimension: the dimension you would like to aggregate on, the standard one is timestep.
+    x = x axis variable for plotting
+    y = y axis variable for plotting
+    run_count = the number of monte carlo simulations
+    lx = True/False for if the x axis should be logged
+    ly = True/False for if the x axis should be logged
+    suppMin: True/False for if the miniumum value should be plotted
+    Note: Run aggregate_runs before using this function
+    Example run:
+    '''
+    mean_df,median_df,std_df,min_df = aggregate_runs(df,aggregate_dimension)
+
+    plt.figure(figsize=(10,6))
+    if not(suppMin):
+        plt.plot(mean_df[x].values, mean_df[y].values,
+             mean_df[x].values,median_df[y].values,
+             mean_df[x].values,mean_df[y].values+std_df[y].values,
+             mean_df[x].values,min_df[y].values)
+        plt.legend(['mean', 'median', 'mean+ 1*std', 'min'],bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
+
+    else:
+        plt.plot(mean_df[x].values, mean_df[y].values,
+             mean_df[x].values,median_df[y].values,
+             mean_df[x].values,mean_df[y].values+std_df[y].values,
+             mean_df[x].values,mean_df[y].values-std_df[y].values)
+        plt.legend(['mean', 'median', 'mean+ 1*std', 'mean - 1*std'],bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
+
+    plt.xlabel(x)
+    plt.ylabel(y)
+    title_text = 'Performance of ' + y + ' over all of ' + str(run_count) + ' Monte Carlo runs'
+    plt.title(title_text)
+    if lx:
+        plt.xscale('log')
+
+    if ly:
+        plt.yscale('log')
+
+def plot_median_with_quantiles(df,aggregate_dimension,x, y):
+    '''
+    Function to plot the median and 1st and 3rd quartiles of the monte carlo runs along a single variable.
+    Parameters:
+    df: dataframe name
+    aggregate_dimension: the dimension you would like to aggregate on, the standard one is timestep.
+    x = x axis variable for plotting
+    y = y axis variable for plotting
+
+    Example run:
+    plot_median_with_quantiles(df,'timestep','timestep','AggregatedAgentSpend')
+    '''
+    
+    df = df[df['substep'] == df.substep.max()]
+    firstQuantile = df.groupby(aggregate_dimension).quantile(0.25).reset_index()
+    thirdQuantile = df.groupby(aggregate_dimension).quantile(0.75).reset_index()
+    median_df = df.groupby(aggregate_dimension).median().reset_index()
+    
+    fig, ax = plt.subplots(1,figsize=(10,6))
+    ax.plot(median_df[x].values, median_df[y].values, lw=2, label='Median', color='blue')
+    ax.fill_between(firstQuantile[x].values, firstQuantile[y].values, thirdQuantile[y].values, facecolor='black', alpha=0.2)
+    ax.set_title(y + ' Median')
+    ax.legend(loc='upper left')
+    ax.set_xlabel('Timestep')
+    ax.set_ylabel('Amount')
+    ax.grid()
+    
+def plot_median_with_quantiles_annotation(df,aggregate_dimension,x, y):
+    '''
+    Function to plot the median and 1st and 3rd quartiles of the monte carlo runs along a single variable.
+    Parameters:
+    df: dataframe name
+    aggregate_dimension: the dimension you would like to aggregate on, the standard one is timestep.
+    x = x axis variable for plotting
+    y = y axis variable for plotting
+
+    Example run:
+    plot_median_with_quantiles(df,'timestep','timestep','AggregatedAgentSpend')
+    '''
+    
+    df = df[df['substep'] == df.substep.max()]
+    firstQuantile = df.groupby(aggregate_dimension).quantile(0.25).reset_index()
+    thirdQuantile = df.groupby(aggregate_dimension).quantile(0.75).reset_index()
+    median_df = df.groupby(aggregate_dimension).median().reset_index()
+    
+    fig, ax = plt.subplots(1,figsize=(10,6))
+    ax.axvline(x=30,linewidth=2, color='r')
+    ax.annotate('Agents can withdraw and Red Cross Drip occurs', xy=(30,2), xytext=(35, 1),
+            arrowprops=dict(facecolor='black', shrink=0.05))
+    
+    ax.axvline(x=60,linewidth=2, color='r')
+    ax.axvline(x=90,linewidth=2, color='r')
+    ax.plot(median_df[x].values, median_df[y].values, lw=2, label='Median', color='blue')
+    ax.fill_between(firstQuantile[x].values, firstQuantile[y].values, thirdQuantile[y].values, facecolor='black', alpha=0.2)
+    ax.set_title(y + ' Median')
+    ax.legend(loc='upper left')
+    ax.set_xlabel('Timestep')
+    ax.set_ylabel('Amount')
+    ax.grid()
+
+
+def first_five_plot(df,aggregate_dimension,x,y,run_count):
+    '''
+    A function that generates timeseries plot of at most the first five Monte Carlo runs.
+    Parameters:
+    df: dataframe name
+    aggregate_dimension: the dimension you would like to aggregate on, the standard one is timestep.
+    x = x axis variable for plotting
+    y = y axis variable for plotting
+    run_count = the number of monte carlo simulations
+    Note: Run aggregate_runs before using this function
+    Example run:
+    first_five_plot(df,'timestep','timestep','revenue',run_count=100)
+    '''
+    mean_df,median_df,std_df,min_df = aggregate_runs(df,aggregate_dimension)
+    plt.figure(figsize=(10,6))
+    if run_count < 5:
+        runs = run_count
+    else:
+        runs = 5
+    for r in range(1,runs+1):
+        legend_name = 'Run ' + str(r)
+        plt.plot(df[df.run==r].timestep, df[df.run==r][y], label = legend_name )
+    plt.plot(mean_df[x], mean_df[y], label = 'Mean', color = 'black')
+    plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
+    plt.xlabel(x)
+    plt.ylabel(y)
+    title_text = 'Performance of ' + y + ' over the First ' + str(runs) + ' Monte Carlo Runs'
+    plt.title(title_text)
+    #plt.savefig(y +'_FirstFiveRuns.jpeg')
+    
+    
+def aggregate_runs_param_mc(df,aggregate_dimension):
+    '''
+    Function to aggregate the monte carlo runs along a single dimension.
+    Parameters:
+    df: dataframe name
+    aggregate_dimension: the dimension you would like to aggregate on, the standard one is timestep.
+    Example run:
+    mean_df,median_df,std_df,min_df = aggregate_runs(df,'timestep')
+    '''
+    df = df[df['substep'] == df.substep.max()]
+    mean_df = df.groupby(aggregate_dimension).mean().reset_index()
+    median_df = df.groupby(aggregate_dimension).median().reset_index()
+    #min_df = df.groupby(aggregate_dimension).min().reset_index()
+    #max_df = df.groupby(aggregate_dimension).max().reset_index()
+    return mean_df,median_df
+
+def param_dfs(results,params,swept):
+    mean_df,median_df = aggregate_runs_param_mc(results[0]['result'],'timestep')
+    mean_df[swept] = params[0]
+    median_df[swept] = params[0]
+    #max_df[swept] = params[0]
+    #min_df[swept] = params[0]
+    for i in range(1,len(params)):
+        mean_df_intermediate,median_df_intermediate = aggregate_runs_param_mc(results[i]['result'],'timestep')
+        mean_df_intermediate[swept] = params[i]
+        median_df_intermediate[swept] = params[i]
+        #max_df_intermediate[swept] = params[i]
+        #min_df_intermediate[swept] = params[i]
+        mean_df= pd.concat([mean_df, mean_df_intermediate])
+        median_df= pd.concat([median_df, median_df_intermediate])
+        #max_df= pd.concat([max_df, max_df_intermediate])
+        #min_df= pd.concat([min_df, min_df_intermediate])
+    return mean_df,median_df
+
+
+def param_plot(results,state_var_x, state_var_y, parameter, save_plot = False,**kwargs):
+    '''
+    Results (df) is the dataframe (concatenated list of results dictionaries)
+    length = intreger, number of parameter values
+    Enter state variable name as a string for x and y. Enter the swept parameter name as a string.
+    y_label kwarg for custom y-label and title reference
+    x_label kwarg for custom x-axis label
+    '''
+    sns.scatterplot(x=state_var_x, y = state_var_y, hue = parameter, style= parameter, palette  = 'coolwarm',alpha=1, data = results, legend="full")
+    title_text = 'Effect of ' + parameter + ' Parameter Sweep on ' + state_var_y
+    for key, value in kwargs.items():
+        if key == 'y_label':
+            plt.ylabel(value)
+            title_text = 'Effect of ' + parameter + ' Parameter Sweep on ' + value
+        if key == 'x_label':
+            plt.xlabel(value)
+    plt.title(title_text)
+    if save_plot == True:
+        filename = state_var_y + state_var_x + parameter + 'plot.png'
+# #         plt.savefig('static/images/' + filename)
+#         plt.savefig(filename)
+        lgd = plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
+        #title_text = 'Market Volatility versus Normalized Liquid Token Supply for All Runs'
+        plt.title(title_text)
+        plt.savefig('static/images/' + filename, bbox_extra_artists=(lgd,), bbox_inches='tight')
\ No newline at end of file
diff --git a/Simulation_param/model/parts/system.py b/Simulation_param/model/parts/system.py
new file mode 100644
index 0000000..1cbab40
--- /dev/null
+++ b/Simulation_param/model/parts/system.py
@@ -0,0 +1,279 @@
+
+import numpy as np
+import pandas as pd
+from cadCAD.configuration.utils import access_block
+from .initialization import *
+from .supportingFunctions import *
+from collections import OrderedDict
+
+# Parameters
+agentsMinus = 2
+# percentage of balance a user can redeem
+redeemPercentage = 0.5
+
+# Behaviors
+def choose_agents(params, step, sL, s):
+    '''
+    Choose agents to interact during the given timestep and create their demand from a uniform distribution. 
+    Based on probability, choose utility. 
+    '''
+    outboundAgents = np.random.choice(mixingAgents,size=len(mixingAgents)-agentsMinus).tolist()
+    inboundAgents = np.random.choice(mixingAgents,size=len(mixingAgents)-agentsMinus).tolist()
+    stepDemands = np.random.uniform(low=1, high=500, size=len(mixingAgents)-agentsMinus).astype(int)
+    
+
+    stepUtilities = np.random.choice(list(UtilityTypesOrdered.keys()),size=len(mixingAgents)-agentsMinus,p=list(utilityTypesProbability.values())).tolist()
+
+    return {'outboundAgents':outboundAgents,'inboundAgents':inboundAgents,'stepDemands':stepDemands,'stepUtilities':stepUtilities}
+
+
+def spend_allocation(params, step, sL, s):
+    '''
+    Take mixing agents, demand, and utilities and allocate agent shillings and tokens based on utility and scarcity. 
+    '''
+    # instantiate network state
+    network = s['network']
+
+    spendI = []
+    spendJ = []
+    spendAmount = []
+
+    # calculate max about of spend available to each agent
+    maxSpendShilling = {}
+    for i in mixingAgents:
+        maxSpendShilling[i] = network.nodes[i]['native_currency']
+        
+    maxSpendCIC = {}
+    for i in mixingAgents:
+        maxSpendCIC[i] = network.nodes[i]['tokens']
+
+
+    for i in mixingAgents: 
+        rankOrder = {}
+        rankOrderDemand = {}
+        for j in network.adj[i]:
+            try:
+                rankOrder[j] = UtilityTypesOrdered[network.adj[i][j]['utility']]
+                rankOrderDemand[j] = network.adj[i][j]['demand']
+                rankOrder = dict(OrderedDict(sorted(rankOrder.items(), key=lambda v: v, reverse=False)))
+                for k in rankOrder:
+                    # if i or j is external, we transact 100% in shilling
+                    if i == 'external':
+                        amt = spendCalculationExternal(i,j,rankOrderDemand,maxSpendShilling)
+                        spendI.append(i)
+                        spendJ.append(j)
+                        spendAmount.append(amt)
+                        maxSpendShilling[i] = maxSpendShilling[i] - amt 
+                    elif j == 'external':
+                        amt = spendCalculationExternal(i,j,rankOrderDemand,maxSpendShilling)
+                        spendI.append(i)
+                        spendJ.append(j)
+                        spendAmount.append(amt)
+                        maxSpendShilling[i] = maxSpendShilling[i] - amt 
+                    else:
+                        amt = spendCalculation(i,j,rankOrderDemand,maxSpendShilling,maxSpendCIC,fractionOfDemandInCIC)
+                        spendI.append(i)
+                        spendJ.append(j)
+                        spendAmount.append(amt)
+                        maxSpendShilling[i] = maxSpendShilling[i] - amt * (1- fractionOfDemandInCIC)
+                        maxSpendCIC[i] = maxSpendCIC[i] - (amt * fractionOfDemandInCIC)
+            except:
+                pass
+    return {'spendI':spendI,'spendJ':spendJ,'spendAmount':spendAmount}
+
+
+def withdraw_calculation(params, step, sL, s):
+    ''''''
+    # instantiate network state
+    network = s['network']
+
+    # Assumptions:
+    # * user is only able to withdraw up to 50% of balance, assuming they have spent 50% of balance
+    # * Agents will withdraw as much as they can.
+    withdraw = {}
+
+    fiftyThreshold = {}
+
+    startingBalance = s['startingBalance']
+
+    spend = s['30_day_spend']
+    timestep = s['timestep']
+
+    division =  timestep % 30  == 0
+
+    if division == True:
+        for i,j in startingBalance.items():
+            fiftyThreshold[i] = j * 0.5
+        if s['timestep'] > 7:
+            for i,j in fiftyThreshold.items():
+                if spend[i] > 0 and fiftyThreshold[i] > 0:
+                    if spend[i] * fractionOfActualSpendInCIC >= fiftyThreshold[i]:
+                        spent = spend[i]
+                        amount = spent * redeemPercentage
+                        if network.nodes[i]['tokens'] > amount:
+                            withdraw[i] = amount
+                        elif  network.nodes[i]['tokens'] < amount:
+                            withdraw[i] = network.nodes[i]['tokens']
+                    else:
+                        pass
+                else:
+                    pass
+        else:
+            pass
+    else:
+        pass
+
+
+    return {'withdraw':withdraw}
+
+# Mechanisms 
+def update_agent_activity(params,step,sL,s,_input):
+    '''
+    Update the network for interacting agent, their demand, and utility.
+    '''
+    y = 'network'
+    network = s['network']
+
+    outboundAgents = _input['outboundAgents']
+    inboundAgents = _input['inboundAgents']
+    stepDemands = _input['stepDemands']
+    stepUtilities = _input['stepUtilities']
+    
+    # create demand edge weights
+    try:
+        for i,j,l in zip(outboundAgents,inboundAgents,stepDemands):
+            network[i][j]['demand'] = l
+    except:
+        pass
+
+    # Create cic % edge weights
+    try:
+        for i,j in zip(outboundAgents,inboundAgents):
+            # if one of the agents is external, we will transact in 100% shilling
+            if i == 'external':
+                network[i][j]['fractionOfDemandInCIC'] = 1
+            elif j == 'external':
+                network[i][j]['fractionOfDemandInCIC'] = 1
+            else:
+                network[i][j]['fractionOfDemandInCIC'] = fractionOfDemandInCIC
+    except:
+        pass
+
+    # Create utility edge types
+    try: 
+        for i,j,l in zip(outboundAgents,inboundAgents,stepUtilities):
+            network[i][j]['utility'] = l
+    except:
+        pass
+
+    x = network
+    return (y,x)
+
+
+def update_outboundAgents(params,step,sL,s,_input):
+    '''
+    Update outBoundAgents state variable
+    '''
+    y = 'outboundAgents'
+
+    x = _input['outboundAgents']
+
+    return (y,x)
+
+def update_inboundAgents(params,step,sL,s,_input):
+    '''
+    Update inBoundAgents state variable
+    '''
+    y = 'inboundAgents'
+
+    x = _input['inboundAgents']
+    return (y,x)
+
+
+def update_node_spend(params, step, sL, s,_input):
+    '''
+    Update network with actual spend of agents.
+    '''
+    y = 'network'
+    network = s['network']
+    
+    spendI = _input['spendI']
+    spendJ = _input['spendJ']
+    spendAmount = _input['spendAmount']
+
+    for i,j,l in zip(spendI,spendJ,spendAmount):   
+        network[i][j]['spend'] = l
+        if i == 'external':
+            network[i][j]['fractionOfActualSpendInCIC'] = 1
+        elif j == 'external':
+            network[i][j]['fractionOfActualSpendInCIC'] = 1
+        else:
+            network[i][j]['fractionOfActualSpendInCIC'] = fractionOfActualSpendInCIC
+
+    outflowSpend, inflowSpend = iterateEdges(network,'spend')
+
+    for i, j in inflowSpend.items():
+        if i == 'external':
+            network.nodes[i]['native_currency'] = network.nodes[i]['native_currency'] +  inflowSpend[i]
+        elif j == 'external':
+            network.nodes[i]['native_currency'] = network.nodes[i]['native_currency'] +  inflowSpend[i]
+        else:
+            network.nodes[i]['native_currency'] = network.nodes[i]['native_currency'] +  inflowSpend[i] * (1- fractionOfDemandInCIC)
+            network.nodes[i]['tokens'] = network.nodes[i]['tokens'] + (inflowSpend[i] * fractionOfDemandInCIC)
+        
+    for i, j in outflowSpend.items():
+        if i == 'external':
+            network.nodes[i]['native_currency'] = network.nodes[i]['native_currency'] - outflowSpend[i]
+        elif j == 'external':
+            network.nodes[i]['native_currency'] = network.nodes[i]['native_currency'] - outflowSpend[i]
+        else:
+            network.nodes[i]['native_currency'] = network.nodes[i]['native_currency'] - outflowSpend[i]* (1- fractionOfDemandInCIC)
+            network.nodes[i]['tokens'] = network.nodes[i]['tokens'] - (outflowSpend[i] * fractionOfDemandInCIC)
+
+    # Store the net of the inflow and outflow per step
+    network.nodes['external']['delta_native_currency'] = sum(inflowSpend.values()) - sum(outflowSpend.values())
+
+    x = network
+    return (y,x)
+
+
+def update_withdraw(params, step, sL, s,_input):
+    '''
+    Update flow sstate variable with the aggregated amount of shillings withdrawn
+    '''
+    y = 'withdraw'
+    x = s['withdraw']
+    if _input['withdraw']:
+        x = _input['withdraw']
+    else:
+        x = 0
+
+    return (y,x)
+
+def update_network_withraw(params, step, sL, s,_input):
+    '''
+    Update network for agents withdrawing 
+    '''
+    y = 'network'
+    network = s['network']
+    withdraw = _input['withdraw']
+
+    if withdraw:
+        for i,j in withdraw.items():
+            # update agent nodes
+            network.nodes[i]['tokens'] = network.nodes[i]['tokens'] - j
+            network.nodes[i]['native_currency'] = network.nodes[i]['native_currency'] + (j * leverage)
+
+        withdrawnCICSum = []
+        for i,j in withdraw.items():
+            withdrawnCICSum.append(j)
+        
+        # update cic node
+        network.nodes['cic']['native_currency'] = network.nodes[i]['native_currency'] - (sum(withdrawnCICSum) * leverage)
+        network.nodes['cic']['tokens'] = network.nodes[i]['tokens'] + (sum(withdrawnCICSum) * leverage)
+
+    else:
+        pass
+    x = network
+    return (y,x)
+