Community_Inclusion_Currencies/Colab/CIC_Network_cadCAD_model_params.ipynb

{
 "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",
    "* We are going to perform a parameter sweep in this notebook over the frequency of the Red Cross drips into the system. For our simulation, we will run our model with drips every 30,60, and 90 days, and compare/contrast the performance of our system against over these differences.\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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\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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m/ww4Q0QOctr3sIi811GkHkfX2c87z+qD6ADFLKegAyv3zimbQ4A3icjewJ+AvZ021YN2M+Uq/D8BLhKRPZ37LBWR4ycom9z7GKrj+YzTXk7nmvmUoNvvHhGpQLu7CuW3wPtE5FDnHr5BAX19ocrAH2X42PC7CjnIMWe8D/0g30Br6DeggyVAm5zuRDc+fegI5qCz7VLgFyLSIyL/b5TT34T+8vq7c+4EOtBtSiilXnTOdYGT9XG0Setl9AP/Ldq/BrqS/xUdhf4sEzT6SqkrgPPQASXtaA3ybHRlGovL0A3BC+ggy2edPIBd0JHFA+iX6lql1EPoYJHvoMu5BR38ddE41/iiiPSjXRm3oE1+b80zlxbCH5xjn0e/pDdO8vixmM4z+Cr6C74b+Do6QA8YatCPRX/ZZJ/HBRTwPiiltqGDvc5HmwefRwe+ZrkLxxSbZ96dDP+NNof2o+9zIuXqr8Bf0B3LVvS7UIh7ZyzGK/cH0RHyLSLS4eR9CR049YSI9KHr5vpJXC+G9pe2oOvuWcCHlFKvAyil/oOu6484v/vQgciPZU2mjtn+3ei4gibnXNmgxUJkbHHutQmt8J2hlHplDHk/gg6ga0I/70um6EY9Bf3utuT+oTuV7Bfwx9DukOxonl+jLTcopV5Ct3l3oL8GB9AxQ8lxrnkVOj7lXufdfwId21XI+Saql5eS02YrpZ5GB6dejS7bzWh/PEqpFNqCdir6PToB5x0WkXrgHWj/eW7ZPIOu56copTrQboTvOWWzB7q9zJbNXejnf4fzvP8NHDNOueQyWh2HCdrLaV4znyvRfWEH+hn9pdADned4FrrNa0aXfcO4B+EEb7i4TBURUWiz4eZ5lmMNWin0TsM6NFOyvIY2F06lg3CZY0TkSHRQXiGWoXlFRH4NvKKUGvGlKNpC2oN+H9+YgWvN6PlmE9HzkjSgh4Q+NNH+LiNZChO7uLjMGSLyIbTP8cH5lsWl+HFcHTs77pqj0Rat3+dsf79o12MYPRTwRfRImqleb0bPN5s4br4yxy2ajV94Yp7FKlqKcUYqF5cFiYj8DW2uPNnxU7u4TJflaPN5JfrL90w1fHr1Y9HuUkGbyU9U0zP3zvT5ZpOD0abwrEvrOKVUfPxDXMbCdRO4uLi4uLgscVw3gYuLi4uLyxLHdRPMEVVVVWrNmjXzLYaLi4tLUfHMM890KKWq51uOxY6rDMwRa9as4emnn55vMVxcXFyKChGZzOyiLlPEdRO4uLi4uLgscVxlwMXFxcXFZYnjKgMuLi4uLi5LHFcZcHFxcXFxWeK4yoCLi4uLi8sSZ0kqA85Kdc+JyD3O751E5EkR2Swiv86uVuWsTvVrJ/9JZ/777DkucvI3TWaFtqWAbSs6u5I892IPr20ZoKc3Nd8iubi4uLiMw1IdWngOsJEdy7x+F/ihUuoOEfkJeknS65z/3UqpdSJyorPfCSKyB3p1tD2BOuB+Edk1u4LaUqelLcGnz3+W3j69XtBBB1Rw8RfWU1466oqgLi4uLi7zzJKzDIhes/696KWUEREB3o5ephXgF8BxTvpY5zfO9nc4+x8L3KGUSjqreW1m+FrcS5Z4wuKGX24ZUgQAnnymi6aWxDxK5eLi4uIyHktOGUCvE/1FILuQTCXQk7PsbQNQ76TrcdaFd7b3OvsP5Y9yzBAicrqIPC0iT7e3t8/0fSxIUmmblraRHX9bu6sMuLi4uCxUlpQyICLvA9qUUs/MxfWUUtcrpTYopTZUVy+N2TSjEQ/ve3ftsDyfz2DP3UrnSSIXFxcXl4lYajEDhwAfEJH3AAF0zMBVQJmIeJyv/xVAo7N/I7ASaBARD1AKdObkZ8k9ZkkjIhxyYCXnn7kLd/1fE2WlXj73yZ0pL11qVc3FxcWleFhSLbRS6iLgIgARORL4b6XUx0TkN8CHgTuAU4A/OIfc7fx+3Nn+oFJKicjdwO0i8gN0AOEuwFNzeS8LmdKolw8cVcuRb63C4xFKIt75FsnFxcXFZRyWlDIwDl8C7hCRy4DngBud/BuBW0VkM9CFHkGAUuolEbkTeBnIAGe5IwmGY5pCeZk7esDFxcWlGBCl1HzLsCTYsGGDclctdHFxcZkcIvKMUmrDfMux2FlSAYQuLi4uLi4uI3GVARcXF5dFhmVZWJY1Iu3iMhZuzIDLkqerJ8XAQAav1yAUMiktcQMeXRYmqc4eMv0DI/I9JRF8lWWA7vwbGhoojUaJlJTQ2NhIeXk5kUgEw3C//1xGx1UGXBYtSils28Y0zWHpXDo6k3zuK/9ie2McgHceXs25p6+jzJ062WUBkukf4KFd3jEi/22vPjCkDABUVFTQ3tZGV1cXXp+PUCjkKgIu4+LWDpdFiVKKVCrF4MAAtm0PpXPNpem0za9+3zCkCADc//d2Gprio53SxaUoME2TYDCInjmdYWkXl7FwlQGXRYlt2ySTSTo6Omhvb6epsZFYfHgnn0habH59pMn1je2xuRLTxWXGsSyLxsZGPF4v5RUV9PX2Mjg4iG3bEx/ssmRx3QQuixLTNImEwyTicQYGBjBNk5qammGm0kjYw7uPXMYzL/QM5YnAfnuXjXZKF5eioaqqCr/fj2EY+Hy+obSLy1i4tcNlUaKUIp3JMDg4iMfjwbIs+vv7h7kJRIRDDqrgtI+uprzMy8r6IN/72l5UlLkBhC7FS9ZN4PF4MAxjKO3iMh5uDXGZM5RSKKWG/JfZ9Gz4M7NugmAoRHV1NYMDA8TicSKRyLD9yqI+Tv7wKo49qg4xoLzUO+PyJFM2/f1p4gmLYNCkNOrF63H1cJfJ4ymJ8LZXHxg1P5dcK4BrEXApBFcZcJkzMuk0La2tLF++HKUUrU7a6535L/GsmyAcDut0JEI4EhkxmgDA6zWorJid0QPptMWzL/Tw1e+8TCJpEwmb/M8le7Pn+iiGUfxBXYmkRWdXiof/0c6ymgD77VVGRbk7EmO28FWWDRs14OIyU7jKgMucIYaBAI0NDSjA5/PNapSzkdPxG6MoARNhWdaQ8pCbzkcPW1SY5sgvsN6+DN/4/iskkjp4a2DQ4tL/2cj1V+xHZbl/0jItNN7YOsgZFzyH5cSmrV0d5srL9qHCXZfCxaWocJUBlznDNE0qq6pobmoCdJDTWB3sfJPJZOhob6equhpgKJ3re0119pDq7ScWt7AsRSjoweMRvNEdE8Ck0jb9g5lh525tT7IYJoTrH8jw01veGFIEAF7fOkhDU9xVBlxcigxXGXCZMzLpNK0tLfj8flCKluZm6urrZ8VNMBOk0mmaGht3ZOQt6pXqHeDh9e8ccVzuBDABv0nd8gBNLYmh7bvvUoLPW/wuAqUUqfTI4WrJ5CLQdFwmRf+AjokxRAiFTEJBt2spNtzIEpc5QwyDcCTC8uXLWV5bSygcXrCToXg8HpYtW0YmkyGTybBs2TI8eUpLxhp93HbucO7yMi9XXLo3e64vwTT0sMVvXrjHopjhMFri5aQPrxqWV1nuY+2ayBhHuCxGuntTfP/aV/nQaU9y/Kee5NbfbKe3Lz3fYrlMEld9c5kzPB4PlRUVQ/773PRCI5PJ0NbaimmaiAitbW3U1dUNcxMIEysyIsLK+hDf/dreWJaN12MQXURrH+yzeynXfndffvvHRuqWB/jQe+vdoZmjUMiaAsWIUoqH/9HBA4+0A5DOKG79zTYOeXMFpdHSeZbOZTIsKWVARALA3wE/+t5/q5S6RERuBo4Aep1dT1VKPS/6s/Uq4D1AzMl/1jnXKcDFzv6XKaV+MXd3UrxMN6hvLvF4PFRXV4MI7W1tI9wEpjm6MjDaSK6y6OLsICMRD/vsUcpu60owTUYNonQpfE2BYiOZsnny2a4R+c+92Mteu7nKQDGxpJQBIAm8XSk1ICJe4FER+bOz7QKl1G/z9j8G2MX5Owi4DjhIRCqAS4ANgAKeEZG7lVLdc3IXLrOOx+OhZtmyoQDH3HQWd/j2Dnw+tzCWIn6fwVsOqOCRJzqH5e/vzuJZdCwpZUAppYCsrc7r/Kmxj+BY4BbnuCdEpExEaoEjgfuUUl0AInIfcDTwq9mS3WXuye38Rxv1UOgEMC6zQ3Y2SdM0h6Vd5g4R4fCDq/jXS73c//c2PKZw0odXsaIuON+iuUySJaUMAIiICTwDrAOuUUo9KSJnAt8Ska8BDwAXKqWSQD2wPefwBidvrPwly2LziRZyP+4EMPOHZVl0d3djZTJU19TQ1dWFbdsLerjqYqW81Md5Z+zCmaeuRYBw2EMw4D6DYmPJKQNKKQvYV0TKgLtEZC/gIqAF8AHXA18CvjHda4nI6cDpAKtWrZpg7+JmsflEF9v9TIWF/OUtIkQiEZqbmti2dSu2bVNbW7tgR6csdiJhD5Hw8O5ksX0gLHaWnDKQRSnVIyIPAUcrpb7vZCdF5OfAfzu/G4GVOYetcPIa0a6C3Py/jXKN69HKBRs2bBjPHVGUDAxmaOtI8siTHXzgTW4jvJiwLIvBgQFSqRQVlZUMDAyQTqcpLy9fEAqBYRh4vV78fj+JRAKfz4dvAa/MN1mX0mLoSF2FurhYUsqAiFQDaUcRCALvAr4rIrVKqWZn9MBxwL+dQ+4GzhaRO9ABhL3Ofn8Fvi0i5c5+70ZbF5YMtq1obY+TTFrc9pttHLPnDp3pwLuvxwwFAFAZi9iWBqC4GrK5ILtwk2EYw9ILARHB4/XS0dFBPB4nnU5TWVU132INkXUTJBIJSsvK6OvtpaOjY8G6CSbrUnI7Upe5ZkkpA0At8AsnbsAA7lRK3SMiDzqKggDPA2c4+/8feljhZvTQwk8AKKW6ROSbwD+d/b6RDSacLQZjGZpbE9xzXzP1y4O8/dCaWVtcpxBisTQlIRu/J8Zt124g2Nc+tM0MBXjinR8fcYzbkO1AKUUmk8HKZPD5/ViWNZQeTyEYbdXH2cAwDAKBAKFQiFgshtfrpaSkZEEpK5FIhHAohD8QIBwOY9s2g3EL27IpK12cQzlBT/LT05tGKb3KZrk79bPLDLCklAGl1AvAfqPkv32M/RVw1hjbbgJumlEBx+GVV/s55+IXhn7/9p5GrvvufvO2QpylQAyTdCqBbbcTVKPPxucynFzzr23b2LZNxjBQAT99dprltbVjHmtZFol4nEBQR2pn07PxJZx1E8RisSGFoKuzk/KKigXx5W0YBj6fbyidTBnc/3A71/1iC2tXh7nonPWsqg+NORdEsWLbcOE3/81Lm/oB2HlNmB9+Yx93pUiXabOklIFipa8/zY23bxmW19icYHtTbN4agXDQQ09PimAoQmywHwlFOPI/D6AUiF3Y3PS2rfQXDorSEg8ej+nk2wV/gc6Wb3W2hg2OZf49bONfWb7TynFXRkylUrS2tlJWVkYqnSYei7FylgJTs26CqqoqIiUlJBIJMumFNcVsto4MDGb49lWbeOwpbZx7+T/9nPOVf/HzH22gcpF1ksmUNaQIALy2ZZAHHm3j+PevmEepXBYDrjJQxMxnRKJhQGUZtLX1EwgG6UgMEggGqampIbm9ecLjBwYzPPlMFzfdsYUvn7MryrKIRqOYpkFvby/RaHTY1L9jMZ5v1SiNoJSa0kJI8zFsMJVK4R/DTSAi+Hw+Kquq6OzoAKCuvn7WzPZZN4Fy5MmmF4JVIJ9kyuKJp4d76bp60sTimQWvDIylzCp7dEtbJjPyrd/8+gC2rTCMhWUFcefhKC5cZaAIiJZ4Oe2jazg3x01QXxtgVV1o3mQSEbw+L+UVFZSURLGsDLFYrODjm1sTXPI/G6mq8FFd6WNwsJ14bBCv10s8HicUCg2tCzAVlLMqooiwvLZ2QXZiuYgIXZ2d47oJAOLx+LD0bK74mKtoLJRYgdEQEVatCPHGth31z+sRAv6F/cxhbGX2HVsfGbUjzfhGTubznnctX3CKALjzcBQbrjJQJOy+Swk3/+gA7rm3mfpaHUA4GReBZVlDHWJueqpkv1Q9Hg+maWKaxlDaUxIe1pApBbZSqGCIeMIiGDB56DEdcNjRleKiyzZyxdf3pKujkXQ6TXVNDT6fb1rBcZZlkUFRv2LFgu7IspimyfLlyyd0E8RjMerr60ml03S0t1NSUjLHki48Ksp8fPW83TjnKy/QP5jB4xGCCVxrAAAgAElEQVQuOGvXEePeiwk7lSK0ZqTpf2Aww2UX7sHPbtuCbStOOWEVO60Kz4OELouN4n1blhjhkId1O0U49zO7TPpYy7Lo6emhtLQUERlKF2KGHw8RGeq8smmlFJSE6UslqK6pIZ3O0N3VxWAyzOfPfYHjP7CC4z+wgl3WalOh1yN87lM7MdCvl3UwDIOenh6CweC0O/GsMlEsE9GY4zyPrPK1avXqIX9+Nu0Ca1eHue3aDQwMWoRCJpHQ4pwFLxL2cMRbq9hnT70IUGmJd9EFSbrMD64ysARQSjE4OMjgwABen494LEY4HJ6WGX4iEokEzU1NZDIZPB5twUilFTfevpWDN1Sw756l7L9PKdsa4tQtD5CIDbC8tg6v10NzczOZTGZa8pmmiWEo2lpbF5SbYDp+1IVyDwsRj8egssJPZcV8SzL7iAgV7nBClxnGVQaWAB6Ph7q6OrZt3Uomk6GqunpUM7xt24gIIjIsPRlEBK/XS0VlJR3t2hVQVlHNJ855jkRSB0U9/1IvJx4X5Rtf3IOBQQsrA/X19YgIhmGwYsWKoXNNeG+jdK5KgTcapra0ZGgin4Uyo5vrR3VxcVmIFJUyICIvMk4QvVJqnzkUp2iwLIuuTr3EqGGa9HR3EwqFhpnhbdseCkjzeDxDaa/XOymFQClFOp2mq7MTn89HJpOhp6udy7+yO2dc8C8SSZt9HRNnWamPslGWPJ+Me6DQztWd0W1pEYtniMdtTFPXs4WIZdlFHXHf1ZMiFrPweoVg0CQaWbwTPS0FikoZAN7n/M9OBHSr8/9j8yBL0aCUIplKUVtXh9frHdUMr5Sit6eHVCpFSTRKb08P5RUVznC/ic3TlmU5LgEPKEUgEKCquppMxqK7qwsy4PMKJ31oNbXLF8fypkopLMsasmhk06Zp0teXYmtjnI2v9nPwARWUlXopcRvLOaGrO8VPb3mdhx/voL42yIWfW89Oq0J4PAsjkLS3P80rr/bzlwdb2WPXEt5xeM28mv3TaRuvd0fZFGJF6+hMcs7FL7C1QY/geP+7l/OZU9ZSFnXreLFSVMqAUmorgIi8SymVO5PghSLyLHDh/Ei2sPF4PNTX1YHTaWXTuV/8pmmybPlyGhsa6O3pIRQOF6wI2LbNQH8/XV1d1CxbRjwWI1paimEY+P0mlVXVePotfnH1gZSEPQQC5oIx208HpRQN27dTUlJCSTRKU2Mj5RUV+P1hbGUTCgqPPN7BEQeXY9s2lmVjmgujQ1qsJBIWN/xyC3+6vxWATZsHOPui57n9ugOprPDPs3SQydj85cFWfnzDawDc93Ab9/6tje9dshflc2zB6O5N8dhTnTz1bDeHHlTJm/cvpyzqm9CKlkrb/PJ324cUAYA/3tvCscfUucpAEVNUykAOIiKHKKUec368Fb3WgMsYGDmdujFKB2/bNol4nEwmgxgGiXgcy7IwDGNCN4FhGEOz1LW2tABQEo0OxRz4fB6qK4dXtcVitq+pqaGlpYXe3l78gQCRcBhbCf197fhM4dILdmWgrw3sED5fOcFFpgwsNKVuMJbh70905OVZdHanFoQy0Nuf5vb/3T4sb+Or/QwOWpSP4jKbLfoH0vzwJ5t58FEd1/Pgo+0ce3Qtn/3EWsZ7261MhlRS8Z/XRj7zLdsG2W2dO9S1WClWZeCTwE0iUopeXKgbOG1+RSpulFL09vZSVlZGWXk5Lc3NDA4OFmwdALByZk1Ttj2rC+ksBLJD/LL4fD4Qoac3jc9fTmygjd7uZrxeL2krRCJhEwxM7hoLrbPNZyaUumTKwrIUoWBhzVEiaRGLW4RDJn7f8Lrp8RqsXhGip7d3KE8EShfQF6vHM/KdyIbJ2LZN/6CFzyv4fcaszZERT9hDc31kuee+Fk49cTVjzVpg2zZNTU3U1dfzjsOq+ddLw8t4nz3mUJtxmXGKUhlQSj0DvMlRBlBK9U5wiMsEZN0EoL/0s+nJuAmSiQR19fX09fbS0tLCqtWrxzxGzfFcyrMRqKWUoqmxkUAgQEk0SntbGz6fj0goxGBMj8bQa13p2fBCocm/bovFgjIalqVobU/wi19vpbM7zUnHr2Tn1eFxYys6OpPccPsW/r2xjw37lnPy8auGTTlcWuLlgrN24ewL/0VPXxrDgM+cvBPhKZT9ZClEcSuL+vjUSWv41g83DW1/7zuXEY16SKXS9Pf309ZlsqzKRzwWL3ha7skiAoboBceyTDRfgW3bVFRWIyK8/dBqWtoS/OEvzZSWeDnn9HWLeqXIpUBRKgMi4gc+BKwBPDlBcN+YR7GKntyOfzJj2rNuglA4jMfjobKqinJnaOJYzLXBYLaG9C2vrcXr8SCGgbe+fijd0dGCx+MhFC6jr7cDZQ/gNZfAIPhRGKuTJBjitHNfZmDQYp89oqys85NKZVDKMywYM0tPb4ovf/slXv6PXqhny/YY25tiXPrfuw9TIFbWhfjFjw+gbyBDOGQSDnnmRBkoRHEzTeHQN1dy45X78/Bj7Rx5aDWxmMVzL3Szz+5henu6iYZCDPT1OMs0R2dF1lDQ5Lj31PG/9zQN5X30gyspCXuw+8c+Lrt2Rlmpj09+bA0nHLsCEaGs1Lsgp0R2KZyiVAaAPwC9wDNAcp5lcQFnSmJzRHoxowMk/UNKTzatlGLZsmUYhkHGgkhELyg0WqzGUmCsTvLgf9/HwKBFwG/w3a/uSWdHE35/iFTKpLmp0Vn3omTIVJ5I2kOKQJYnn+kmkbTJNfCYpjgTEM1/jMBolES8rI942WWnCA880sbXv/8KZ522lsGYxZv2KCM+2ANAVc0K/v1KP/vvUz7jMoRDHj5x4hoOf0sVz77Yw0H7VbB6ZVAH9+ZY0bLLbBuGAUE/zU1N1NfXY5jaReOvWJp1ejFSrMrACqXU0fMthMvUMSMhDtv4V0QMRHSjo9c1WPjjq3PJtX7kTn/s82nT9SxYeBcN2Q/JRNLmpl9t5YQP1BAb6KAx1o/f7yccDg/zmXtMwe83SCZ3xKZESzxzbmXKRw8xVZN2ffX2pbnlzm0AmIZw4L5lDPa3YRgGtm3T19vLzmtmzw9fVurlgDeVc8CbhisbuVa0TCZDKpUiEAhg2zaVqdTcm/Vc5oRibar+ISJ7K6VenMxBIhIA/g740ff+W6XUJSKyE3AHUIm2NpyslEo57ohbgAOATuAEpdQW51wXoQMZLeDzSqm/zsytLQ085aVkgn4CAR1Rl0gkCAQCS8KisJiYTiyGx2Owbqcwm98YZNPmAUxP3dA27ygzZEbCHs4+bWeuuO5VQPdJ552xy7wGB9q2TTKZIpmCTMKa3MHC0NwHTa0xfN5yYiKEo8ux0inSqV5Cwfl1LXk8Hm3VyvtzWXwUqzJwKHCqiLyBdhMIoAqYgTAJvF0pNSAiXuBREfkzcB7wQ6XUHSLyE3Qnf53zv1sptU5ETgS+C5wgInsAJwJ7AnXA/SKyq1Jqkq3BzLLQI89zMU2TUCg01ODnppc6g7EMLW0J/vJgKx8+soQjNj1Afvs7FQtKV0+Kx//ZyRvbYhz1tmXULvMTCU+vI51OLIZhwA++sQ+bX+9n371KaW5qwB8IEHWCMf1+/zA3QSBg8q4jqjlo/3K2NcZZszJEtMSDdx4nE7JtRUd7GwBlvsnV3/JSH6efvIYLvv5vfvvHZnZaGeaYd9Tx7Au9rKwPUFtXj8czc8rxVNuHYlnK2mV6FKsycMxUDlI6tDv7NnidPwW8Hfiok/8L4FK0MnCskwb4LXC16B7rWOAOpVQSeENENgNvBh6filwzRSEBTAtJYRjNxO4CL73Sx3mXaKPXr+6CnVaFuOpbb5rWLHXdPSnOv+RFXn1dP/s7ft/A5RfvyaFvrpzXsq8o8/Hm/SuxbZtly5cPfYl66+uH0rlEwl4iYS91C2QWy46uNL5AFfHBVggGOXzjvdgIpmEMKXDjKW777F7KLVdv4P6/txEKeRiI2bxlQ+WsyLqYR6a4TJ+iVAZyZiKsASY1cltETLQrYB1wDfAa0KOUyji7NAD1Troe2O5cMyMivWhXQj3wRM5pc4/JvdbpwOkAq1atmoyYs4bbICxsevpS/Oy2LcPy3tgWo7U9MS1loLM7NaQIZLnxti3stT5K+SxPhVuIK2GsYMyZZigYLi89VXw+IZ2yUErRHh/E6/PiD1YRCHiJlExsdQmHPawNezj95J2mJYeLy3QpSmVARD4AXIE20bcBq4GNaLP9uDim/H1FpAy4C9httuRUSl0PXA+wYcOGOR5ZPz0sS3s8TNMclp4tMhmb3r40AKGgh2BwicYOKLBHiUTLmc9paqcdpfZZthp71a8ZpFBXwmxbijKZDLHBQcKRCEqpofR06nU04qG5qRWvL0ikpJy+nlYMYnT3BpCBfoxEbES83UJ027m4FKUyAHwTeAtwv1JqPxF5G3DSZE6glOoRkYeAg4EyEfE41oEVQKOzWyOwEmgQEQ9Qig4kzOZnyT2m6LEsi472dgzTpLKykvb2djweD+Xl5bOiEAwMZvj74x1c8/PX+PbZK1kWsbHCnmF+8qXSgJaV+vjEiau58LKXhvJW1gWprZnk1IV5VFb42GlViDe27ZhP/hMnrqa81DvqYkuLzTeslCKVStHR0UEylSKZSGBZFuHwWPPtFYbHY7JseS3JlE1jc4raZcvZ3hjnnK8+x8++WMvGQ9474hjXCueyEClWZSCtlOoUEUNEDKXUQyJy5UQHiUi1c2yPiASBd6GDAh8CPoweUXAKeh4DgLud34872x9USikRuRu4XUR+gLZO7AI8NcP3OClSKXvGZvUTEaLRKM3NzQwODKCUoq6+ftY6iPbOJN++Ss/IVmKmeGb/4mxAMxmbnr40tq3Nx2XRqZnf992rjJ9dsR93/7WZ1StCvOuIZVSUT8+UX1Hm49rv7cujT3Tywsu9nPThlZRGvaQ6e0j39Q+tI2E7k0V5ohH8lTM/vn2+EBECgQCVlZV0Ost5r1q1CnOMsZ+2rV+mQibS8fm8+HxQUQ6fOOdZevoypFLTNOW4uMwxxaoM9IhIBD1M8Jci0gYMFnBcLfALJ27AAO5USt0jIi8Dd4jIZcBzwI3O/jcCtzoBgl3oEQQopV4SkTuBl4EMcNZ8jSSIxy0aW+Lc/rvtfPyYMg5/5X4MQ4aZJicbeW4YBj6/H6/PR9oZY+zxeKZkui0kYPHZF3smfd6FRiJp8ewLPXz7yk309KXZc32Ub164BzVVk5/4JhL2sPuuUXbbpWTGzOWWZWGoJO86oop3H1lNIh7H7xdS7YM8vP5dI/Z/26sPwAJTBqYb/GrbNv39/UMTQ/X19VFaVjbM2mVZNu2dKX73p0YSCZsPf6CeZVV+/P7RLWK5MgUtxffPrAagL+MDUlO4y9ljNqbkdlk8FKsycCyQAL4AfAxtvp9wKmKl1AvAfqPkv44eDZCfnwCOH+Nc3wK+NSmpZ4Gm1jinnfsMtg33/q2NgN/gl9cdyLLq0c3KhTQIWTdBJp2mtKyM3p4euru7p+QmKCRgsZCVzpJJm8GOJGWlHrzekTJkMjaxuEUoaM7LuvX9Axm+8u2XSGf0F+VLm/r40c9e5aJzdht3KtxUyqLfmYUvf7+Z9pt3dXWhlCJSUkJPdzdV1dVjNgC2rRiMZeZkGt9CmU7wa9ZNYFkWK1auJJlM0tnRQWnp8El9OrvTfPzsp4nFtW5/973N3PLjDaxaEZqUTLs/9qdCb2tGKERR8laUQkkIwzAwTZN0Oo1hGLOy9oFL8VGUtUApNQggIlHgj/MszryRztj8+vcNw4LLEkmbv/2jgxOOXTHqMYUEc2XdBGXl5Xi9XkLBIIZpzpqbYEVtkA++r467/tQ0LP/Au6/HDGmlxsSm+/UGfBU+guUlw+6hqyfFH/7cxNP/6mHDvuUcd3TtrEfI59PZlRpSBLI8/1IfiYQ1Zofa1ZPi1t9s4/F/drFubZizT9uZ5dOMDRgL0zSpratj+7Zt9HR3E41GCYfDJDr7Rt0/Frf40VWb+Pyn103JurHQEBHMeJJqPKQbWzGAajwkG1qGdZgPPdY+pAgAZDKKO37fwHlnrJuUkunzGmNaE2aDQhQl27Zpa21FKUVFZSVtbW1EwmEqKivdyb5cilMZEJHPAF9HWwdsnEmHgLXzKddcIzBq1H1ompH4hmHgDwSG/Mi56dmgNOrl9JN24qQPrSTY285GJ98MBXjinR8fsf8Rm3Y0cH39aS6/ahOPP90FwL9e6uU/m/v58rnrx139bqaprPBhmoKVswzcXrtFx+wQBmMZfnzDa9z3sJ6wpqE5zquvD3Ddd/ebdnzAaFiWRTweH1pWOhaL4QuUoKwdmmSu8uXzwWnv8JPc1kjcLiNYU/yLLFn9gxN2mN5RVu7zeGTEiICJKC/z4SkJLyizvGmaLK+tpbGhgdaWFvx+v6sIuAxRlMoA8N/AXkqpjvkWZD7xeAxOPG4F//dAK3Hna6aqwsdbN0y/4Z7rWcciYQ+RsIeUlOxYJCU9VhiGngu+py9NKmUNKQJZHn2qk3hi+OI1s00kbPL1L+7Od360iYFBi3U7hfnCZ9YRCY/+iiWSFg8+Onw9+cbmBLGExWx1uz3d3VRUVBApKaG5qYl4LI7kzPM/lvJ1+Cv3wyJQBgrh8IOruOlXW+lxhrn6/QYnHLcC05zcOyAC/qpyqFo4cRd6DQUL2zElWpblLLHt4lK8ysBrQGzCvZYANVV+fnntBh57qpOA3+TA/coX7GpthZDrxhh4vWHUfUSEVzb387XvvszF5+2Gz2cMi972+4wR0/fONsGAh0MOrOS2aw8kk1H4fca4rgpBqKny09yaGMozDfB7Z0dw0zSpq9fzYrV1pgmEqvnn893UhQx2f+xPGIbgLxu9OVhKk0NWlPv4+Y8O4MFH20kkLY46chmVs2CpmQ9s26a9rY1AIEBVdTXNTU10d3W51gEXoHiVgYvQixU9Sc4Sxkqpz8+fSPODaRrUVAX4r/eMmABxxrBtG6UUpmkOSxfCdCKYx+rQlVJc8PUX6evP8NAj7Xzsgyv5+R1bAbj6op2oL1MEe9uJ5bjD52KeAq/XoKpARays1MuFn9uV8y99kUxGsX7nCBd+fj2JpEV7Z5JI2EMwMLMN9NAzU2k+eubTxBM7FKhl1X5+cfHodWihTBU9F9HwhiFUV/rHjLmZD5lmiqybQADT46Guvl6nXUXAheJVBn4KPAi8iI4ZcJklbNse8jWHQiES8Ti2UgSDwYIakeksZDMWSkFfv549+q4/N3HmqWu58pv70NKeYG1Nhkd2H32o3FzNU1BIZLdhCHvtFuXOnx1Ea0eCaMTLeV97gdb2JF6P8IUz1vHOw2vw+2RURWw6HXQoaLLf3mX845873Cuf+fhOGEZ61P0XiC4wK3VpuiwUmQpVSnJHDkxnFIFlWWTSabw+H0qpofRim6xqKVGsyoBXKXXefAuxFFBKkUom6e7uJhyJMDgwQGlpKcHg1BaKGRjMEHeWeg0FzVEj7RNJi4HBDMFgaNQV+wiGKAl76B/MoBRc+/PX2XevUi7/yp6YHa1TkmuyWJaNbWtrQD6FDoHz+01q/CaBgMHFl79Ma7s2cqUziiuufZUj31qNbSVob2ujrq6OWCxGT08Pq1avHqGIWZY1lJebHo3SqJeLPr+eja/2s2nzAIe9pVIPRe1omVJZzBexeIa+/gxbG2KsrAsSLfGOGaMB0/+KtywLAQxniu5seiEwl0qJUop4PE5bayvVNTXEYzEGBwdZtXr1nFzfZXYoVmXgz84iQH9kuJuga+xDXKaCaZqUlpYSTyQYHBjA7/dTXl4+pUawpzfF1Te9xr1/a0OAY4+p47SPrKasdIdPtn8gw18fauG6X7xBMmmz396lXHrBHsP8tum0zeUX78nF33mZnt409bUBvnjWrpREvMQKCCmdzuQ1tq1o70zymz820tmV4vj317N6ZWha4/FTKZvXtgyXx7KhrSPJTquCBINBGhv1bNc1NTUjjs9kMnR0dFBVWQkiQ2mPd+zRFOVlPt56YCVvPXDHCnmp5OyYvLMWjaw1I5su5CtyrGdlRsI88UqaS763cWjmzfPP3IVj3r6MwBjulel0mJZl0djYSEVFBYFAgKamJiorKwkGg0vua1hECAaDREtLaW/To2Hq6uoWjDvJZWoUqzLwEef/RTl5S25oYaFkG9TsfAS5S6tO3PnZxBMJEvE4Hq+XZDJJLB4v2E2Qy9P/6uEvD7YN/f7dn5o45MBKDjpgR6R6T2+KK69/bej3cy/2cvvvtvGZj6/F53yFe70Ge+8e5eYfHUA6nQ3WK3wY4XQmr+nqSfHJc58dija/7+E2rr78Tey719S/ykJBkzfvVzE0zBAg4DcoK9X35M3p1McyxaZTKRqbmjCcKYWnEiM+W1+XViZDQ0MDy5YvR0RoaW6mfsUKfL6JA/PGelZHbLqfK67bNmwK7qtveo3DDqocUxmYLiWRCG2trXq6Zo8Hv9+/5BSBXLILmAFY011Jy2XeKUplQCnlrvc5CdJ9A/xt12nM3JZMUlpaSnlFBb09PSQTiUm7CWxb8cTTIw03/3y+a5gy8NqWkbNKP/9iL7FYBl+OBcE0Cw/Wm0le2tQ3pAhkueXObeyyNjJl60Ao6OGs09YSi2d4/Oku6pYF+coX1lMW9RCPx+nt7aWqqopYLEZTY+MIN4HH42HZ8uU0bN+OBdTW1o7rD57utL6TxTBNSkpKaGluBiASiWCIQTJpTXliHgX09mWG5SWTNhlrdobKmaZJNBqlu7t7aBbHpaoIZN0EscFBamtriTkuA9dNUNwUpTIgIiHgPGCVUup0EdkFWK+UumeeRVuQZBddmQqmaRJ1pmw1DGMoPVmrgGEIhxxUyV8eGu7Tf8sBlcN+r9tppEl6w77lBNODxLa0jdiW34HNdnR3wDfyvgN+gwLWsxmXqgo/Xz1vd5IpGxEoL/UOmWNr6+rw+XyEIxFSqZHz3WcyGdpaW/UskSK0t7dTV1c3pptgOpaRqSAihMJh+vr08I5gKMxD/+jgkSe6+OgHV7JqRZBQcHJNkYjwlgMqeOKZHQrmbruUEPDPTgeddRN4vF6CgQDdXV34fL4l7SZYsXIlpmni8/uJRqOum6DIKUplAPg58AzwVud3I/AbwFUGRiGVHt2EZyuwLQsxDL1iXU46l9yOfzrDkPbbu4zjjqnlj/e2YAh8+AP17LJ2eCddVurlS5/blR/f8BqxuMXBGyo44dgVWL1tBXVgE5m6bdsephzlzronPi+Dm/UQRQxBnEY+V+HYZW2ElXVBtjfF9TaP8MmPrSGY05lNVSHREy8NzzNNk4AzAyQwLJ2LYRjUO2b4ttbWKbkJZgsrk6GluZlwJIIgtLe1csDetdz1fy18+vxnueEH+7N+XQm9/Wn6BzL0D6SpqfQPiyXJxxC46Jz13HLnVp5+voe9do/yqY+tGfeY6VJaVkY4HEZE8Pn9S9pNYJrmjLULLguDYlUGdlZKnSAiHwFQSsXEVUvHxDPKFKugpzPu6+8nFArhMc2htNfrnRUtvyzq5cxT13LKCdqcGA6ZI74II2EPRx25jIM3VGDb+qs7WuIl1jszMhiGMWxZ2txZ995y/y2jzsCXq3BUlPu45jv78s/nu+nsTvG2Q6pHTB8807733Gcx2nPJugmyDXJuejaYrJvBME2qa2oIhUK0dyQpifrY0pBg43/6UApu/912zj19Hdff+gZ/vFePaCiNevjJ9/ajcsTZdlBZ7uOzn1jLYMwiFDBnLVYAdGdXkuMaKCliN8HAYIZk0iIc9hCYw/UTXBY2xaoMpEQkiHYdIiI7kzOqYLGTnU7UMIxh6bEYaypVEYgNDtLT3U0wGGRwcBCPxzPl5YoLIRzyTOhb9/lmNx7AjIQ5YtN92LbNVJrzinIfR71t2YzLNR3m8ittsm4G0zSJRCKICC++0s9Lr/Txh780k84oPvjeWkJBk97+NF6vUBr10NuXobcvw9U3vcalZ6wY18ri95n4R3HdzAZzPUX3bNDUGueq6zez+Y1BDt5QwWkfWTMra2G4FB/FqgxcAvwFWCkivwQOAU6dV4lmic7uFPGEhd9nEAl5CAQMkskkba2t1NXXk+npI93bPyJgLPcrzVca4YhND2ArhVJ62lvDEDwlEZaXR9m2dSuDg4NES0sJhUJF29AVgm3bpPwe2lWaiqpKpHOGTA6LnP6BDC1tCR59soPj9p98/cgql/vvU85Pb3mDdEZxwVnreOuGCNGSEH39GU7+0DL+65hazv7yC/T2ZWhsThD3hqlYs3Dm9y9murpTnPuVF2hypsD+/Z+b6etP86XPrR+moNuWhUIrcbnpsZjrgFSX2aEolQGl1H0i8izwFrS1+5zFuGhRU0uccy/WL6/XI3zuUztz9NuW4fN5MU2Thu3bqbQNHtn9qBHH5n6ljWW2tiyL3t5ebNvGNE0G+vuJRqOz5iZYCBiGQTAYZHltLYFAgETPyEZssTPZmAbLUjz2zw4u+8EmAI7YeepTX1eW+/jp9/fnpVd6efN+ZbQ0N5BK9BMIBunt7aGktIrDDqrknvtaedcRNUQjRdlELUhiCWtIEQC9uNZTz3UPW2bbtixi8TjxeJyKigriOemxFIK5Dkh1mR2K7k0TEQ9wDLCbk7UR6Cnw2JXALcAytIvheqXUVSJyKfBpILuM3JeVUv/nHHMR8EnAAj6vlPqrk380cBVgAjcopb4z/bvbwcBghh/+dPPQy5vOKH7408289cBKaqp8RKNROjqmr//EYzFqamoIhkK0trSQSqVm1U0wVWZylIDH45n2lL4LFaXUCDfSYMyiuydDScRDRblv0jENvX1pfnbrlqHf05lgCbRCcPjB1SilqK+vZ/v27SSTSUrLynnpP0n++XwPp56wimOPrsXjWbxWqrnG7zOGltkuL/Ny9bf3BsD0CJlMBgHEMDANg7pVIE0AACAASURBVP6+PpKJBKlUivJy1zKzFCgqZUBE6tFrEjQDz6GtAu8DrhCRtymlmiY4RQY4Xyn1rIiUAM+IyH3Oth8qpb6fd709gBOBPYE64H4R2dXZfA3wLqAB+KeI3K2Uenn6d6lJJi1efX34V6tSkEpbJJNJOjo6tB+2PzHGGSbGNE2WLV8+Ir0Q3QSzFZSXq2QYPh9HbrxX75A3mqBYsCyLhu3bqaqqwuvz0dzURKSkihtv38ZLm/q55rv7srw6MKlzKhRJZ1XI445ZTig4dv2wbbvg+qMVlR2LjyYScd5ywDL22r2UkrB31Kmei4ns8sAiMiw9X4RDJp89dS0/vvE1vn/JXniNfpRSlISX09LcjMfjoaq6Gr/fTyQSYWBgAI/HQ2lZ2ZTbhNz6MJm64TL3FJUyAHwLuE4pdWVupoh8HrgcOGW8g5VSzWhFAqVUv4hsBMazeR4L3KGUSgJviMhm4M3Ots1Kqded69/h7DtjykAo5OGgAyr403075ov3eYVw0IPXa1JZWUmkpIRkvHla11nqw4MWykIzuUzHBysilJaW0uZME+v1BejpUzz1XDcDgxY33PoG539210mtiBgt8fKRD67gup+/wRvb4hiRKg7b+NehKYWVUhiGgRkJM9DfTzgSGbMu5d+bkclQY/jwRsM09/eSTicpLw0VvdVGKUU6rSen8nq9w9JKqSmvAjodQkEP733XMg4/uIpYPE1FZSUtzU1s3bIFwzSHprqOxeMMDAwQDAaJx+N0dnaO6yYYC9tW9Pb2Ei0pARH6+/spiUQwp7FAksvsUWxP5S1KqVPzM5VSPxKRTZM5kYisAfYDnkQHIJ4tIh8HnkZbD7rRisITOYc1sEN52J6Xf9Ao1zgdOB1g1apVkxGPYMDk9JPX0N+f5tGnOllWHeAr564nEvGSziiUBLnhl1v50IFz34HHExaxWAa/zyQyhz7dhbxQzEwyHR+sYRiEIxG6u7v1bzPAU09pRQDgjW0xEklrUsqA12PwvnfWsmZFmP/P3nnHyVXVjfs5907vO9tLKiQhIY2QAtISQLp0AUFBUcEXeBELKihFEAVBJeJPmkaKSEek+VKCEQggEEBKKCGkbe+7M7s75d57fn/cmclsts32nc08n88k55655dzZmXu+51uf/VcdNR028gJedEPH5XRSX1+P1+0j0mmuNN2evjUp/d3blKlTEUJkvSAA5iq4ubmZSFcXwfx8mpuacDgcFBQWmim9OzoI5ucTiURS7f4m245Ojc4u82/YW0hupnjcVjxuK+BA17SUQGJRVZSE6UxVFPKCQXw+H7FolEh06IFaba2tdHZ2YlFVOjs7zSRNk9REl+1kmzDQ1c97nf281w0hhAd4FLhEStkuhLgVuBbTj+Ba4DfAucMZKICU8g7gDoClS5cOOg9Mfp6dyy/Zi2hURwhBXsB07Pt0c5hvf38DugEr9pzB3q89Q0HQ3q2632iptptaYtxx7xZe39DM7Jkevnf+npQW954IZyTRdZ36ujrcbjfuRI54j8eDy+3eLTUafaHrOtVVVTgcDmx2O+1trRxxSBEffxbmxVcaOfSgwiE55fl9Vg5Yns/yffzEY1Fqa1txuVxouk4gL4/WhPDRW0XFTJlMf0dVVSksLKSmuprGhgasNhuFRUUpbUooFCIajRKLxfAnsnr2RWtbjFvv3sL/ra1FUQSnnVjBmSdNwe/LvB7HrkQbW4i1teOXElWxoUd1OrdWYvP7sAf92Ox2VFXFnvge9fe3STe1CZsVvTMCEoSUFAoLelcc4VLxFBdPaufkbCfbhAG/EOLkXvoF4MvkBEIIK6YgcJ+U8jEAKWVd2vt3sjOTYRUwJe3wikQf/fSPKGZWup1/ps4ujbse2IqeSCp40a+2AHDd5fM4ZP/C0RhCinCHxu9u+5R1rzYB8FpzM1t3vMftN+4zJrHKLpeLxsZGREcEd2cMJdpGtLk99X4ulMk0E+QXFOB0OjEkWK12WtsFH20KccaJFRx7eEmfeScywWJRkdJKXl4ePr8fwzCoralJ2cVD7e34/P5JNbEPBSkluq6jaWb9BE3T0DUNq82G3W7H6/USCoVQVZW8YLBfW/p/3m5JmQt1Q3LfIzvYb0mQfRYM/buuhzt4aa8jevSv2rQWe8FOh8FMbPzpprbw5zv499ye5z3oo2dpkzoOux1yfgMTkmwTBv4NfKmP914a6OBElsI/Ax9JKX+b1l+a8CcAOAn4INF+AvibEOK3mA6Es4A3MIWPWUKIGZhCwBnAmYO/naHRm4pBjkH+2WjM4KXXm7r11dRF6IzoBPs4ZqRQVRWvz0drayuys2vAcMrdFVVVcbvdqYe4xetGonHbr/fB67FgG2aCHiEEVqsVn9+PEIJYLIaUkilTpxLp6qK5uTlVv2J3JmkmsNlsFJeUUFdbS3Nzc8pMEAqFzNDWSITGxkby+zATxDWDV99s6tH/5jstwxIGRou+nkOqaiEej6Ppes5MMEHJKmFASvkNACHEDCnllvT3EhPzQBwAfA14XwjxbqLvcuArQojFmPPsVuD8xPU+FEI8hOkYqAEXSin1xPUuAp7FDC1cI6X8cJi3lxEup4VzTp/G+jeaUiWJ8/NsLNgrI8XIsBACSoocVNfujGCwWAR22+hL+kkzgWEYCLF7rzoHYtdMeSOdr18IkZq4HA4H5RUVqKqK0+Wi3OXa7bUCYAplBQUFqc8qGamTNBP4AwECgQCxWIzOzr4tnFaLwn5Lg6x9uaFb/76LJ54gAHRL9Z2OEDAl4TeVEwQmJlklDKTxKLBkl75HgH37O0hK+Qrmqn5XnunnmOswoxh27X+mv+NGk2nlLu65ZSl/f6aaYJ6NYw4vGRM1fZ7fyk8vmcP3rnyfWKK63kXnzhx23HmmOF1uHM4AesOkyy/VjdGuvDiSDDYaJRvuTUqZmrDS24MlPSto+mdjt9ux2WymTT6t3Rf77xvkqFVFPPfveoQQnHpcGXtMd/e5/0QlF1Y4sckqYUAIsRdmzP+uvgM+YHDB01lAfyFm06cG+N53Zo3peIQQzJ3l46E7llPfGCU/aMPjsuByjuxKsLfY5NZ2nXXrQ7z6RjMXHuca9Dl1XcfQdSxWq1m5MNGeiKuU8Qx31HXTY11NRGwk2yPFRAnl7Ou3pXrcGC47NrtZGyMWjQ7oQDdYBlvjIOC3ccn5szjv7JkIAS6XinuI0QSjzQT8OeXIkIn5jeqbOZhJhgJ09x0IYWYQnFRMxDSfNptCQb6dgvzRKSSk6zqhUChV2CbZjnTp/O72zQADCgPxuEG4Q8PlVLHbzdCp9vZ2WltaKC0ro6WlhVg0mgplGwkmQ352XdfpCJv34PF6U+3+8gakk8x+qKpqt/ZgGYvPsq/f1spP11LX1ozP50NVVVpaWigrLx9308eujsTDZbQ0NNmg+cnRO1klDEgp/wH8Qwixv5TytfEeT45RQEra29pob2/HYbcTDoex2Wy40x6E7ZqNueufxmJRCAZsKedki9dDc2uMB/9RyX/eambeHB/f+Mo0CvPt+Hw+ujo7qa6qQghBWXn5iGoFJqLgNhQk0NTYSDgcJhKJkF9QkNlxiSQ7jQ0NFBUXY+g6jY2NFBUX9yiiNRDj+VkKQcrhDyC/oACbbXyr+o2GcDRaGpqJovnJMXiyShhI4zMhxOXAdNLuQUo57NwAOcYX1WKhrLyc7du2EY6bWdIcDge6oXPGieU88HgVF/1qC1aLYPV1i5g6c6fnerhD4+Y/fMqLr5jOVp9t7eDjz0L85ucL8HkmhgfzaKvhh4OqqngTGoFIJILD4cDr9Wakyk4mC4rH41RXVaHrOnbH6OefGA3isVi3thyLUJ1+mCyCZo6JTbYKA/8AXgZewCwglGOSkKykCKY9tb29HY/Hg9dj5ezTpvGlI8uorY8wc6ob3y5JVyJRnXWvdve6/nRzGIsK7e3tRCIRSsvKaG1pobamZkTNBJneW2dnJ4Zh4PV6U21Phmr4sRhfuiAQiUQGTC+cjsViIT8/P5UKuaioaELcVzrRmEG4I47d6P19KUmFAFosFmpravB4vd3uI9OVelwzaGuPE4kYOBwKfp8Va67wUo4JSrYKAy4p5Y/HexA5RgEp6ezooLikBLvdTk11NbFYDEVR8Hmt+LxWplX07jMghJkpr6U1nuqzWATRmCTP78PtdmO1WikqLjbTGY/DqlUaBk2NjXR0dBDp6iIvONoZGgaHxFSNJ5PiZLomTpkJGhux2e3omkZtTQ0lpaWDNhOMFm3tMR78RxVPPFvDH79X0us+QphZFJPakPR2kkxW6rou+fjTED+69gNCYbNi5A1XzGfvOT5UNfu0JZpuoCqTI1V0jt6ZGL/SwfOUEOKYZJnhycru6IyjWiyUl5dDIh47vT0QAZ+VH3xnFlfcsDGV/OSbZ03H5VRRVTW1uktvjyWqqpqOeR0ddHV1Ybfb8fv9EybkSlVVPIm6AoqipNqZfFZJM4HL5SK/oADDMGhpbp4wk4dhSNa+3MA9D20HoDlqYe76pynMt3ebnC1eTzfhZah/m9a2OD+7YSOhsJmBMBTWuOL6jay5eQn5wdFxvu2LtvYYDU0xauoizN7DQ8BnxW7v/jftS9shXC4+rBE8/Xwts2Z6OPbwEqSUWK3KmIUU5xgbsvWv+V3gciFEDIhh5g6QUsrRz7wzhuyuzjjpxYcGU4hIVRWW75PHQ3euYPPWMFMrXOT5rUMu6jIYMhHckmaCpCCQzEQ3UcwEMLwqllarlfyCgpSwlWwPll0/SynBkBLD7qKxOUqe3zbo1XW4Q+OFl+pT28k03lf+YC+OWFk86DEORFwzaGqOdetraokR18bW/6A9FOe2u7fw5HOmQ6SqCm755SIWzuueJTLWFubfc3pqO1a89xyX/GwrigIHLM/nmbW1vPBSPcWFdv7nnJmUlTgBo9cokoki5ObIjKwUBqSU3vEeQ46JictlweWyUFo8tmknMhXcpGGQFwzi9/sJh0LoRh/G6yxlJEpip3+WkajO+v80cd3qT4jFDAJ+Kzdfu5A9ZwxOO+awK+wx3c17G9u79U8tH3zOikyw2RSmT3GxdcfO7IJTyp3YhpCtczgawo5OPSUIgGm++O2tm/jdLxaSl8hMGYnqdHZpfRxv9n9hWT41dRFuv8cUoj75LMzb77Xy4J3LsVl0aqqrKS4uJhaP09Lc3Kt5JcfEJiv/WsLkq0KIKxLbU4QQy8d7XDly9Ieqqrg9Hnw+X6rUcDKefSTp7NJoaIpS1xChrT0+8AEZEGtqpXNrZY9XrKl1RM7fF+GwlhIEwFS/X/vbj2lpjQ1wZHdsNpWvfXka5aU7hcSjDy2mpHh0VPbBgI3rfzafOXuaE/acPTz8+sr5BAODD1O05QdwTa/o8cpE+OyK9PSvbm6NYeg7NRThDo2Ozt79sEUiYeuKJXmsTdOsmMfpbNrcgc1mw+f3U1dXR0tzM4WFhRPGPJQjc7JSMwD8ETCAQzFLDoeB/wcsG89B7W5MhkQ7Y81IrJz7oy0U5+EnKvnrIzvQNMmKJXn89Ht7DWkSSme8wtu6onpKEEiyZXsHxhDC/YoK7Nx6wz6Ewho2m4LbpeLzDr4McKYr9YoyJ7+5egGaLrGoYtA1InRdT31H0tuDwe+zUlRgp74xmuo77osleHe5774+Tk+i3HV7SDNTnm/t6PZ+wG+eR03PqpgrRJSVZKswsEJKuUQI8Q6AlLJFCDG+mUF2QyZ6/LOUkpbWOJGojs2m4PNYh6SmHW+aW2KEOzVsVgWXs/8JrLY+wl0PbE9t/+ftFp5+roYzT5kyrNLF44XLqRLwW2lt26nhWLY4D5t1aPcSzLOl6niEwnHqG6MIAR6XBaczM6c6i9eDa3pFRtcbapEoTdNoaWkhGAya3+NEOxOBIH3cTuDun5UT7tBo02y8u93gqFXF3T4/l1NF6yOluN2mcPct+7Lh3RbOP3sG73/URlfEFM6+sDxISZGdWCxGS0sLBYWFxGIx6mprc2aCLCRbhYG4MEvXSQAhRCGmpiBHjhRVNV18/6r3qa6N4HKq/Ox7c1ixJNjDk3oi0NfEI50uLrjiMyprugD40hElnH/OTAK+3gWCjZ+EevRteL+Nk44tx+POvoez32dj9S8Wcu1vP+bzbR0sW5zHTy6eg9cz+BV9Oi1tMW6+/TNefKUBiyo465QpfPn4Cvxpn+t4CbtSSnRdJxwKEY/F0DQNIUTGyY/6Gvchn6xl1qKKHs6XLqcFUexj/w+eJxLVsdsU7DYVRTEFnz3yPewx3UNcM/jbbcvZ9HmY/KCN4gI7HrcVXTejfixWK263G28ilXiO7CJbhYHfA38HioQQ1wGnAj8b3yFNHiaD+r+tPc4vV3+aKrfc2aVz9Y0f8eCdKyicgMJAXw/wfd9+NiUIADz5XC0nHlPWpzCwcF7PgJr9lwZxOsb+nnsrODVYLKpgj+kefnftQgxDYrMqwxYEDEOy7pXGVFnguCa568Ht7L8sv5swMF4IIbDZbBQWFVFfVweY+Q6Gm69BUegzCsNZFMRZFETXjT41SFaLQmG+ncJd6pLsGqo7USJjcgyOrBQGpJT3CSE2AIdhhhWeKKX8aJyHlVWEOzTicQOf19rjATHR1f+ZoOkGH3/WfZUci0s6u7IrYWVc66nw2l7ZxZw9eg+oKSqwc/G39+DOv27lxu9Npcit4/NAdEdVap+xEOp0Xae9vR2fzxROku2hThR5Q1S390Y0ZvD6huYe/e9+0MrecyZGdLKu6zQ3NaGqZqGthoaGMcnomI2mpBwjQ1YKA0KIIFAP3J/WZ5VSjozr9CRG0wwqq7u4Zc1mGptiHPfFEo5YWTwhVkRDxUiE5ymKkmrbrAr7LPDznw0tqf2cThW3a+KsWtI1MFLrXUhx7qLFUBSYv1ffkbVej5UTjizl0AMKsbfV89Kco3rsMxShLlOnuZa2GLou8RgdqK3tdDaboXyqlERaO7D6xl+7ZLcp7LdvkPVvNnXr32fBxBB0k2YCRVEoKS1F13Ua6uvHvUZCjslNVgoDwNvAFKAFUzMQAGqFEHXAt6WUG3o7SAgxBbgHKMb0N7hDSrk6IVw8iFn4aCtwWsIpUQCrgWOATuDrUsq3E+c6h52miV9IKe8ejRsdaVrb43z7h+/QlVghr75zM0LASceUDzqRy3hmSNQ0M/5ZURQiEdMUkMynLwC3y8GPLpzNz2/6iPc2tlNcaOeqH87F7x37r3xfxYnSNTD7vXBPr8c6HCpnnTKFJ56tIeCzcsn5exLwdV8l92bWcQEy42TCA5NJHoXmlhiXXfcBH34S4pFfTmPDkiN77DMRtEuKIlh1YAHvbmzlxZcbsFoEZ506lfIS57iOK0nSTFBaVpZSwSfbOXKMFtkqDDwPPCKlfBZACHEEcArwF8ywwxV9HKcBP5BSvi2E8AIbhBDPA18H1koprxdC/AT4CfBj4GhgVuK1ArgVWJEQHq4ClmIKFRuEEE9IKVt6XHGYjITdNZ0t2zpSgkCSp56r5bCDisgbZPjZeGVI1HU9VWK2qLiYjnCYUCiE1+cj1N6O3+/HZrdTXOjglz+dTzxuoCimqllRhubYFO7Q6OzScOud0NnJrv5Rfane9UQpX1VRCObn09jQgMViIZCXl9F1FQW+eeY0Tju+AqFAnt/awzmrL7POyo+ey/wGh4mmGTz2dBUffhLC67bg6sM7faIQ8Nu49ILZXPiNPcxoArelh1/FeAq7QojU5J/ezoTdMY15juGTrcLAflLKbyc3pJTPCSFuklKeL4ToM4uIlLIGqEm0Q0KIj4By4ARgZWK3u4F1mMLACcA90tTPvS6ECAghShP7Pi+lbAZICBRHkWa2GAk0TSMajeJ0OjEMw2w7HINK0bsrybCqdAoL7FiHGKo1HiiKQmFREdVVVezYvp3yigpisRih9nbsDgeBvLzUw7MvR7vB0NoeY83923jh33U8cO00Xt7r8B779LXiFULg9XqprakhHA4jpaS0rKzHhK53RlLaAXt5MUK1IERCyLCp5AeHN7kue+IOVJeZcEdqOp1bK4GR8x+IRHU++Ng0CcyY5iYboso8bgsed9+PwGxNB56t484xvmSrMFAjhPgx8EBi+3SgPhFumFGIoRBiOrAP8B+gOCEoANRimhHAFBR2pB1Wmejrq3/Xa5wHnAcwderUTIaVwjAMOjo6aGpsJC8YJNTejhCCsvIelxkU+UEbh+yfz79fM+2lLqfKRefO7PZQnOgrCyEESqIwjtfrJR6PE41GUVWVaCRCLBrF7nCMWJzzth2dPPZUNXa7gjQGp3pXFAW73Y7NZiMWi+FwOLBarT3G9ubx56XaB3/8HM2KTsWUKX3eg2EYSCkzDuFSXQ5eP/zsHv0jpbZ3OS0cvH8Bb/23lfc2ttEe8g98UI4cOSYM2SoMnImppn88sb0eOANQgdMGOlgI4QEeBS6RUranP1CllFIIMSLGVinlHcAdAEuXLh3UOZNV42LRqFn9TVGYMmVKv+pCKSVNLTHefs9MEbtkYYD8PFu3CSPgs3HphbM598wYLa1xpk9xkRfovnqe6CsLXdepq6tDURT8gQBtra14vV7yCwpoamqis7MTm31k0sx2dum8n8hnH40atA4yvW/STBCPx/H7/bS1tdHa0tKvmUBVVYpL+k/pqsXjVFVVUVJa2qdjmepypoS6vhwURwpFERx6YCFbd3Ty1HM1PbIG5siRY2KTlcKAlLIR+N/kthDCAXxJSvkw8Fl/xwohrJiCwH1SyscS3XVCiFIpZU3CDJBMwl2F6aiYpCLRV8VOs0Kyf92Qb6gPpJR0dZkx5tIwiEQiuJzOPs0Ejc0xvnnJBppbzQkr4Lfyl9X79ogLDvhtQ86MNhFImgkURUFVVQJ5eaa2QFEIBoPA8GOdO7s0Nm/t4OEnKjn+yDIAPG61X7+KZHIYVVVT7aT2wu/3Y7PZcLpcKIqCEKJfDYzVZutXGFAtFtxuNzXV1RTI3u/ViMVSmfKSZoHRJOC38T/nzOSc06fijHdQNE7apcmQJyNHjrEmK4UBgIRJ4EjgK8AXgVeAhwc4RgB/Bj6SUv427a0ngHOA6xP//yOt/yIhxAOYDoRtCYHhWeCXQojk8u4I4LIRubEESTMBQjBt+nTa29poaW7G2Y+Z4Ll1dSlBAMyiLs+sreWc06aN5NDGnaS3dRKrdadmY6Q8rusbo1zw43eREubv5edbZ03n0aeq+g3vqq6qwuFwkBcMUl1Vhc/nwx8I4HCYtnpFUbq1h6OBEULgdrsJh8MIl4OVn77QQ3gYD7OO06km0vrageCYXx8mR56MHDnGmqwTBoQQh2CaCY4B3gAOAGZKKTv7PdDkAOBrwPtCiHcTfZdjCgEPCSG+CWxjp6nhmcR1PsMMLfwGgJSyWQhxLfBmYr9rks6EI0XSTJCsde/z+/H5/f1Odm3tPcuQjlTVut2Np56vTRVvueXPmznm8BJuunoBVm+clZ+u7RFNoHrdFLoc1FRXEw6HsdlseBPVCdMZKT8GLR6nrq4Or9dLp5R0GDGmTJmC1Za9Gp8cOXKMH1klDAghKoHtmCF+P0xEBGzJUBBASvkK0JfutcdSIhFFcGEf51oDrMlo4ENksCk+v3RkCQ/+oxI9UZ5UVeDEo8tGbXyTmZLCnaYVKeHp52uZVu5izilT6GvFG4/HUznkLdaeIYAjiWqxUFhUhMvlAsDlcvUbZTLRnUJz5MgxvmSVMAA8ApyIGT2gCyH+ASOYWSXLKcy385ebl3DPw9uREs4+bSpFBaNTr30wjHSuhLFg1QGFPPSPKqrrzIRGxYV2jlhV1Of+hmFQU12N1WolEAhQX19Pu82GPxAYlftVVRVPWkEYzwDFYSa6U2iOHDnGF5FtKS4Tdv+VmL4CxwB+4JvAM1LKnl5DE4SlS5fKt956a0yuFYmanuOOCVCQR9d1Ojs7UyvYZDsbsqk1t8T4fFsHUkpmTveQ30uOhiSGYRCLxbBYLCiKkmoPt7hMfzS1xNhW2YEiBFPLXQTzbOPqPDdRHPc6t1b26TOQaenhHBMHIcQGKeXS8R7HZCfbNANJ1f2/gH8lIgOOwgwr/CNQMJ5jmyiMphCQjG9PFlBJtntDSkksFqOhvh6/308sFiMSiTBlkDkXxotgnq3XJE29kcwnkFydp7dHg8bmKN+59B1q66MATClz8ofrF+McR+e5TBz3DMPAMAyEEGit7eihjh77D1d4yJlEcuQYPFknDKSTKEz0JPCkEGJiJBafxCRXv/F4HLfbnWr3tdIXQmC32ykoKKCxsRGA8oqKrNAKDIX0yX+067k/+2JdShAA2FHdxcuvN3LE3FG97LAxDIPt27aRX1CApT3MutmZZ3PMlJxJJEeOwZNVwoAQ4n369hGQwKIxHM5uh5SSSCRCc1MTXV4vHeEwLpcrZQLo65iOjp2rv45wGEsgMGkFgrFASklVbVeP/uq6CMyd2NEEQggzOVRjY5/5EXLkyDH2ZJUwAByX+D/p4X9v4v+vknMkHHVUVcXn9RKNRAiHQlgsFgoKCwc0E0SiUcorKojH4zQ0NODz51LVDgchBCceXcYTz9am9cHRhxaDMeK1skaUXXNE5MiRY2KQVcKAlHIbgBDii1LKfdLe+rEQ4m3MaoM5Rph0xzApJU5dx4mKsNr7dQhMmgmmTp2KoihYrdZUO8fwKC9xcvMvFrLmb9tQFfjWV6dTXGiHuvEeWf8koy48Hg9KR3TgA3LkyDEmZJUwkIYQQhwgpVyf2PgCkJthRom+HMMO+eR5OhT6NROkT/yjbUffnXC7LSxdlMfsmR6EAK/HzMIYG0fnuUwc94QQlJaWYrPbiXbV9Ni3N5IOh0KIbu1MaW6J0RXRsVkVXC4VtytbH3s5cowe2fqr+CawRgiR1De3AueO43h2SxRF6ddMkGP08XknTpGpTK6tqioOp7NHdodkiQAAIABJREFUbQbFZkPv3FmHI1lLQfW6kS4HaiJMMxaNptqZCAR1DREuueI9dlR1oSpw7lnTOfmYspTwNNmZKOGeOSY+WSkMSCk3AIuSwoCUsm2ch7TbkhMEcgyW5CSeLjx0bq1k3dwjeuy7atNaWro60DSNQCBAU1MTfr+fQF7egN+9zi6N2+7ewo4qU8jQDbjz3q0cdlDRsIWB9PLRgyklPdLn6i+hVzxuEG8Ps252rk5DjoHJSmFACFEM/BIok1IeLYSYB+wvpfzzOA8tRz/kVik5hkJJaSmVlZU0NTXhdDozEgQAIlGDTzaHevTX1HZRUdp7JHIm31HDMIhFo1isVjPBVKI9lARTmqYRi8VwOByp0F2Hw5GRX42u63R0dOB2uwFSbUVRqG+M8rdHd3Dafln5iM8xDmTrN+Uu4C/ATxPbnwIPYlYknJRMhok0V00ux1CIxWLompZqJ1fAA62g3S4LByzLZ3vlzvLNqiqYVtG3j0t/31G8pqOsEIK6ujpUVcXv99PQ0EAwPx9fL4Wp+sMwDDrCYZqamsjPz6c9FEIaBhVTpgx8MKYWobmpiXAohNVmI9Tejs1mIxpXOe+H79DUHOOEJX1XOc2RI51sFQYKpJQPCSEuA5BSakIIfbwHNZqM5EQ6WLXkZMro1tWlY7UKLJacv2m20NzUhMfrJT8/n5rqatrb2jLSDthtCl85eQpNLTHWvtxAQdDGj/93Nl7v4E0EUkoqd+yguKQEp9NJWXk5lTt20NDQgNvjwev1DjpKRlEUPF4vsViMpqYmhBBMmTIl4/OoqpoaRyQSoaCwEJvNxqbPQzQ1xwZ9jzl2b7JVGOgQQuSTyC0ghNgPyPkNZEBSFWm1WBCJHPpWiwW1HxXnZMjo1haK897GNp56rpbpU12celw5hfnjX8Qpx8CUlJYC5uRXWlaWamdCMGDjB/8ziwvPnYlAEPBbUZTB2/d1Xcft8WKxmDkS4vE4ybou8Vgs1R4sUkoi0WiqHY3FUFQ1I4HAMAxC7e2p7c6EmcDtVvH7LL2WNM+Roy+yVRj4PvAEsIcQYj1QCHx5fIc0MdE0AynBat35cKmtqcFms+H1+WiorycvWIAMxVFjXaiqIP1ZmU1miL7QdcmLLzfwm1s3AbD+jSb+9XIDt924T8a1B3KMLv1pnwZbyntX3C7LiIQTxjVJKKwRzFNpqK/H5/PhDwSorqoiHA4P2Uxg6DpTp02jra2NpsbGQZkJQqEQxSUlWBMls3Vdp7jAzoN3LOOfL9TRrknmrn+G/DwbVuvOH3Y2avVyjC7ZKgx8CBwCzAEE8AkZ5BkQQqzBzGJYL6Wcn+i7Gvg20JDY7XIp5TOJ9y7DDGPUgYullM8m+o8CVgMq8Ccp5fUjdmcjhKZpaJrO9qoo9Y0R9l3oQ7VYsFq6qxYdDg8ffRajRLbx0QHH9jjPZLDnt7XHefDxym591XURGpujOWFggjDRtU9CKMSiYVxuF7G4hfKKCoQQKIrSrT0YkmYCt8eDxWIhEAjg9/sHZSaYOm0aYAoGO7ZvJxgM4nS5qKqs4shVhWzeHsdZ6MQRsGKfAFVMc0xcslUYeE1KuQRTKAAgkYFwyQDH3QX8Abhnl/7fSSlvSu9IRCicAewNlAEvCCFmJ97+f8AXgUrgTSHEE1LKjUO8l1FBbw0Ra22jVCiUFkC8qhNDVZFeD5pz5wSoG3GKCnyIxtFPCDRevgdCAZer54PQZsv5DeToTvp3VNMkimJqBFo1G4GiAlraNDxuBYtl5/dpOGWqh6P1SE++pOs6ecFgqiCY3W7H5XKwz/ycBiBHZmSVMCCEKAHKAacQYh9MrQCAD+jbRTiBlPIlIcT0DC93AvCAlDIKbBFCfAYsT7z3mZTy88SYHkjsO6rCwGAn0q6WEOvnHdmjf9WmtTSEWvH7/bg9XmprqvF7dIyO0V81jNfqL89v48JvzOR7V76HYZh9SxcFCPgycySbDJEcOTIj/Tva0hrj9vu28tTztei65AvLgnzv/FnYbGO7wpZS9ur0m+74qygKLpeLpsS2y+1GyWX8zDEIskoYAI4Evg5UAL9N6w8Blw/jvBcJIc4G3gJ+IKVswRQ6Xk/bpzLRB7Bjl/4Vw7h2Rgx2Iu0rQkACFVOmIISgo1MnECzlg49DlInJ7Ww0d7aPv926nNfeamJqhYvZMz0E/JmZCHIhkUMjHjdoD8cBQcBnQVWzSxOTF7DxrbOmc/RhxYQ7dPac4SYYGHuzUjwep662lpLSUqRhYBiGafKzWtF1HVVVMQyDqsrKhEbARUtzMzarFZfbnUsDniMjskoYkFLeDdwthDhFSvnoCJ32VuBazHnyWuA3jFBqYyHEecB5AFOnTh2JU2aMRe39ASDYqdb0eVVa2mJU10bYY8bktie6nCoup5MvH18x3kPZLWhrj/PUCzXc/1glVovg21+bwUEr8sc8DbCU0pw8VbVbe1fimkF7exwE+L3WVOhpXsBG3jgIAOkkcyrU1tRQWFRES0sLBYWFaJpGQ309hYWFiERqcIfDgRACq82GPdHOkSMTskoYSCKlfFQIcSymPd+R1n/NEM6VqvMmhLgTeCqxWQWku/VWJProp3/Xc98B3AGwdOnSUSmx3NfDLtNnQJ7fxmknTCHa1DJpcgnkGH8+/LidW/+yJbX9y5s/Yc3NS0ZNGOjLlKN6XHQq4PP7MQyDcCiEz+/vJhC0h+L888U67n1oO0KBb5wxjcMPLupR92G8UFWVYH4+DfX1gOkcXF1V1W2iV1UVl8uVcj5Mbw+FSFSno0PDblfxuHdOE/2lP86R3WSlMCCEuA3TR2AV8CfgVOCNIZ6rVEqZLJ92EvBBov0E8DchxG8xHQhnJa4hgFlCiBmYQsAZwJlDvJVho2kaDQ0NFBUVIaVMtQeLPT8P8vNGYYQTl2hUR1UnbwKipHAI5uoy2R7tehLxuMGz63rWUn7p9UZm7+EdlWv2ZcpZ+ekLtBgxotEo0USRI5/f322fTZ+HueVPm1Pbv73tM2bN9LBgrn/X040L8Xicuro6bIksg4WFhVRXVwNQVl6OxWoKLekT83Am6ebWGHc9sI31bzSxx3Q3l5y3J6XFDnRdJ9LVhdPlMvMjJNq5+iSTg6wUBoAvSCkXCiHek1L+XAjxG+CfAx0khLgfWAkUCCEqgauAlUKIxZhmgq3A+QBSyg+FEA9hOgZqwIVSSj1xnouAZzFDC9dIKT9kHNHicaqrqjCkxJL4YY61576UkubWOB2dGg67gstp6baimEiEwnE2fd7Bo09XUVrk4LQTKijMt006laphGOzYvp1AIIDb46GqspL8goJU/vrRwmIRzJvjY+3LDd36584aHUGgP4QQ5AWDtDQ3I4SgorS0x+T13L/rexy39uWGCSMMKIqC1+slEAggpaS2piaVFrm+ro6ysrKUQDBcOjo1/vDnzTy3zvxM6hqibN76X+6+ZV8wItTX1xMIBOjo6EBKSXk/5ctzZBcT82k9MF2J/zuFEGVAE1A60EFSyq/00t1nPQMp5XXAdb30PwM8k9lQRxeLxUJBYSG1NaZyo7i83PQJGGPP/Zq6CBdd9l/qG6MIAWefNpUzTqyYkKVi3/2gjcuu2ym/Pbeujr/8fin5feQcmCjpmAcb1SCEIJifT2NDA83NzdjtdpxO56irdoUQfPGQIta+VM/GT81CQV9YFmTeHN+oXrc3pIT2tjZUVUXXdVpbWnqkMl6wl4+nn6/tdtz8vcZ+rH1hsVjIS4xZ0zQsViuFBQUgBA0NDQOfYBBEojr/eqX7OesaorS0xSkrdhEIBGhtbQVg6rRpOa3AJCJbhYGnhBAB4EbgbcxV/Z/Gd0jjQ9KJyGq1YhgGtTU1lJaVDSv2ebAkVxP1jcm0qnD3g9s5+tCSCScMtLXH+esjO7r1NbfG2bKto09hYKIkxBlsVIMQAqdzZ3U+h9M5ZtqPYMDGDVfOp7NTR1EELqeKP8NQzpFFYrFYKCktJRqJ0NLS0mOPA5bns88CP++8b2Y0X75PgH0Xjv/fO53kpGuxWCgqKkptp7dHAiEEJcWOVNlnAFUBp8P0Sero6Ej1d3Z04PZ4cgLBJCErhQEp5bWJ5qNCiKcAh5Ryt61NYLfbKSgsREpJUyLpyFgSjRps2d7Zo7+uMUJFWe+lYscLRQGno+fK2GGffH4D6eFmbreb5uZmbDYbbrc7FauuKAq6rg+YQU/XdaQ0J9b0dn/k+W3kjbOmXQhBScI04HA6KXE4ekxeeQEb1/54Hl0R06fC6VQI+CZuZsrhpmfujzy/lcsunsMlV7xHLGZ+HuedPQOPWyXe3IY/ZmCx2DEMA6OhhWhLKJdvY5KQlcKAEOLkXvragPellD0NgJMYq9VKYdrqoHCEVwqZ4PFYOHj/fO57dGfKX5tVMLV8ePZEwzDQNA1VURCKkmr3V1RpILweK+efM4N3Ln0XXTcDPPac4aasj/r22YwQgsKiIux2O4qimOFmdrvp39HcjMvlwuFwpNp9mRCS6vVwOExZeTmNjY1o8TjlFRUTxps8k9oG/Y014LcRmBguAuOKEII5e3p46I7l1NRFKMi34/VYcNgtdHZ28fLc3hOZ5YSB7CcrhQHMegH7A/9KbK8ENgAzhBDXSCnvHa+BjQejuVLIBJtV4YwTpxAK6zz/Uj0lhXZ+dOHsEQnNqqmuxmaz4Q8EqKutJS8vD98g8rf3xoypbu6/bRkv/6eJkiIHC+b6xiWZzGiTzEqXNA0k27quowhBXW0tdrudaDSKx+3u8zyqquIPBOjs6mLH9u0IISgrL59QDpcTxZQzGbDbVOz5KgX5dtNPpSFMZwNIbVJXid/tyVZhwALMTeYIEEIUY9YbWAG8BOxWwkA6vaUtHQvyAjb+95t7cO6Z01AUMSKTa1LFW11VRVdNDS6XC+8gK8P1hsOuUlbi5PQTJn8CovS/f7Ktqip5wSDhcJhoNIrX68XucPT7uQohUBWFeKKdTIQznvSZW8DrxhLwpTLzSSlzdu0hku6nst8Lu5Z0yTGZyFZhYEp6siCgPtHXLISIj9egxhtd14lGo9jtdoBUe6wehE6nitM5ctdK5mRP1oo3DMP0TtxNGamoBl3XaW5qQtd1HA4HoVAIl9s9oJkgGo1SXFJCS3MztTU1424m6C+3QHMsQlFREV1dXYRCoRF3tMuRY7KRrcLAuoTj4MOJ7VMTfW6gdfyGNb7ouk5tTQ0+vx8BtLW1UTFlSlY/BOtqa3G73fgDAWqqqwklMshNFFv1WDKSqnBFVSktLcVmt9Pa2tpvUZukmcDr9ab8DoyE02GmRKM6oQ4NAXjcllEvpxvp6qKqshJN0wgEcuaDHDkGIluFgQuBk4EDE9t3A49Kcwm5atxGNc5YLBaKiouprzOVJkVFRWMaYjjSJG3TiqKgKAoVU6ak2pkw2aoNxmIGDc1R/u/FOjwuC4ceVEhBcPDJklRVJRAIpNT96e2+sFgsyESim/R2JrSF4vzjn9Xc+8gOpCE548QpfPn48lELNRRC4PX6aG9rM+8vL2+3FB5HGr0zkjIVOMpLEJadCc5yZD9ZOVNIKaUQ4i2gTUr5ghDCBXgwqxfutkgpiUajqe1oNIozizOECSGwpmVWsw4yy9pkqzZYUx/h6//7FnHNNJXc9+h21qzel4KgfdDnGorTaW/+B5nw+dYO7rh3a2r7rge3MX+uj/32DWZ8jsEgpaS9rQ2Xy0VXVxd1tbUUFRdntYZstNE1DYkp9KW303nz+PNS7VWb1uKaPvl9bnYnslIYEEJ8G7MaYBDYA7O08G1Azyf/boSu67S1tlJcXAxAXV0dXp8v9xCcBMTiBn99ZHtKEAAzWdKb77Rw9GEl4ziygXnptZ65L9a+XM+KJXmj5oSYjDrRNI3OtEQ5OXqi6zqNTU3EYzFKSktTmsXikpIJk30zx+iTlcIApplgOfAfACnlJiHE4KvzTDIsFgvTpk1DJFSi6e0cvZNMoKMoSspZcTCmiDFDgqH3dJ7Ue+mbaCxe4OfhJ7sX9ly6aPiCQPpEJQ2JISWqqqC4ndgSTrQ2mw2LxZITiPtBVVWCwSDVVVVs37YNRVFS5jk1F7K525CtwkBUShlLPkyEEBbMlMRZRTwep7KykkgkMt5DmZQYmkbpk/+vR/+WUAvKR6ZFyTCMPkpAT5wY+iQnHmFw4NKdOfMVIcgPtvDRRyPnM+twOKioqBi0SaY/Fs3zc9iBhaxN5Lw/cEU+y5f0XyFT182Y9mRNgWQ7naRDpWEYRCMR6mpqsKk2Yu2t5FmCOBwOMyQyJwgMiKIoqc86KQxPxN9AjtEjW4WBfwshLgecQogvAhcAT47zmAZNZWUlXq+X6dOn5354o4AejRGK9fxcvbNno9rNPAhSSjRNw0hMOBardcI+CA1DEtcMWlrjqKog4LNisYgRG6uUkqamJiorK5kxY8aInBPM7H4/uGAWF5w7EynNPPf9OQ/quk59XR1erxeny2W2fT5cLlevGhtFUbA7HLg9HjrCYSwWC/7dMOKktT1OW1ucji6N4kIHwYA1o++GrutmVklNo7i4mMbGRurr6iguKckJUrsR2SoM/AQzC+H7mCWHnyELCxVFIpGcIDCKCFXBO392r/3dtofoGDfWKIrAblMpLhwdYUUIQX5+fkaV8Nra40SjOooq8HutWK39T7w+r3VQGSkdTif19fWmQ5thELT2PbHpuk5XZycd4TBOp5Ouri6ampoIBoMZT2ZDSdal6wZxTeIY5TDJTGhti/HrP3zKS683ARAMWLn9piWUFjsGPDZpJpB5eVitVsoSKat3N2FqdyfrhAEhhArcI6U8C7hzvMczXCby5JPtKBZLv9/wpFlA1zRUiwUpJfF4HJttYqcmHs3vTCbnbmiKctWvN/LexnZ8Xgs/vmg2y/cJjljCKVVV8fv9tLW2omkaPp8Pa0IY0DQNMP1jku2kijsvGMTn8xGLRokkomoyCS+NNraghcI9hIFdQ1A7OjU6OnUz8ZWAx5+pYeuODo47opT5e/lGJP32UKmpi6QEATCdS9fcv5Uf/M+sjIQVq9Wauu/0do7dh6wTBqSUuhBimhDCJqWMjfd4JhuaboBMpJ9VJ/fDIBlbb7XZuk0EOfqms0vj1rs+572N7QC0hzSuvGEjD/95vxETBpJmAgm43W7a29txOJ04nU7q6+owDIPi4mJqa2ux2mwUFhZidziwJbJtprejGYSXaqEw62Yf3u8+7aE4Dz9ZxT0PbuPXVy3gN7duorrW9PV56fUmfnTRLI49vHTcfjM1dT39jqpquohGjYw1F9miIcsxOmSrHuhzYL0Q4gohxPeTr4EOEkKsEULUCyE+SOsLCiGeF0JsSvyfl+gXQojfCyE+E0K8J4RYknbMOYn9NwkhzhmVOxwjVFVl8eLFzJ8/n2OPPY6NH9WweVsHtfUR4pox4PFXX301N910U6/v3XPPPcyfP58FCxawzz779LnfSDN9+nQWLFjAggULmDdvHj/72c/6dNIUQnDggQcihOj26o+VK1cyZ84cFi9ezOLFi3nkkUdG4zYmJF0Rnf9+2L1auG5Abf3IOsE6nU7KysooKCwkGAymNAOFRUVous6OHTswpKQgPx9VVVPaAaBbOxMymfhaWuP85f5tOBwqUpISBJLc/1glbe3jlwl97718WC3d7+Pow0rwebNuvZdjnMhWYWAz8BTm+L1pr4G4Czhql76fAGullLOAtYltgKOBWYnXecCtYAoPwFWYRZGWA1clBYhsxOl08u677/Luu+9hs/tYs+Z2pAGhsEZdQ3TIoWv//Oc/ufnmm3nuued4//33ef311/H7e9aITap6R5p//etfvP/++7zxxht8/vnnnH/++X1e+9VXX031Zboiuu+++xKf27uceuqp3d5Lmh/AjGjQo7EeL6Of+06vx5DeTmco5x0JnA6V+XN93foUBYoLB5/4qC9UVcXr82Gz2bq1k46dydTJihAwRivYHdWdgBnKabH0vKbTqTKeJvaA38b/u2Ex8/fyUVHq5KJzZ3LwfgW5FX6OjMlKYUBK+fPeXhkc9xLQvEv3CZjpjEn8f2Ja/z3S5HUgIIQoBY4EnpdSNkspW4Dn6SlgZAXJiUOPxhCaxmGH7E9XqI6ppabN/Pc3/4YVK5azcOFCrrrqqtRx1113HbNnz+bAAw/kk08+6fXcv/rVr7jpppsoKysDwG638+1vfxswV9aXXHIJS5cuZfXq1WzdupVDDz2UhQsXcthhh7F9+3YAHn74YebPn8+iRYs4+OCDAfjwww9Zvnw5ixcvZuHChWzatKnfe/R4PNx22208/vjjNDc3s27dOg466CCOP/545s2bl9oHYN26dRx88MEce+yxzJkzh+985zupSX0gtm7dypw5czj77LOZP38+O3bs4LnnnuMLBx7IkkWLOeXY46h5411CH3zK3+9Yw7y992bJkiVcfPHFHHfccYCpZbnxxhtTvgvz589ny5YtxONx7r333tR9n3/++WixOKEPPsWfF+BH/3MRixYsYL+ly6itrgHMhFMnnXQSixYtYtGiRbz66qtceeWV3Hzzzakx//SnP2X16tUZ3V8Sl9PCRd/Yg7mzTNnb7VK54vtz8XpGdgWaHt6ZbCfNBwClpaUYhkFjY2Mq9HCoZGIa2nOGB0WBSNSgtS3Oor13CrZCwAVfn0nAP36+JnabwrzZPm64Yj5/vGHxqKZ7zjE5yUodkhDiSXrmFWgD3gJul1IORmdZLKWsSbRrgeJEuxzYkbZfZaKvr/7exnkeplaBqVOnDmJIPUk+sIQQ3drDOqdugGEQ+uBTdF3nhSef5KsnnIwqJOtfWcuOHZt57bXXUVXB8ccfz0svvYTb7eaBBx7g3XffRdM0lixZwr777tvj3B988EGv/UlisRhvvfUWAF/60pc455xzOOecc1izZg0XX3wxjz/+ONdccw3PPvss5eXltLaasfS33XYb3/3udznrrLOIxWIZTQQ+n48ZM2akBIe3336bDz74oNfwuTfeeIONGzcybdo0jjrqKB577LEeK3+As846C6fTCcDatWbim02bNnH33Xez33770djYyC9+8Quee+afGFuquPmuP/PH++7m4rPP5bvXXc3atWuZM28ep59+eq9jTmoE4vE4H3/8MQ899BDr16/HarVywQUX8Lf77+fExcvp6Opi6YKFXHHhxVy5+rf8ac0arrz6Ki6++GIOOeQQ/v73v6PrOuFwmLKyMk4++WQuueQSDMPggQce4I033hjw89uVwgI7N141n0jUwGIR+DxWbLbRX1coikJhURECUC0WysrLzfYww98y+R35vFZ+9dO9uemPm/j9nZ9x/c/m09IWY0d1Fwcsyyc/ODGcTnMCQI6hkpXCAKbPQCFwf2L7dMy6BLMxIwy+NpSTJmoejJgHmZTyDuAOgKVLlw75vFJKpGGg6TpWq9X0gE+0hysQdEWjHHTmqdTU1zN7xgxWrdgfgFfXv8ir6//FsmXmhB4Oh9m0aROhUIiTTjoJV6LmwfHHHz+k66ZPgq+99hqPPfYYAF/72tf40Y9+BMABBxzA17/+dU477TROPvlkAPbff3+uu+46KisrOfnkk5k1a1ZG10tf/S1fvrzPOPrly5czc+ZMAL7yla/wyiuv9CoM3HfffSxdujS1HQqFmDZtGvvttx8Ar7/+Ohs3buTgVSvRI1Hi8TjLFixi09YtTCsrZ9aesxBC8NWvfpU77rgjdZ6kz0L6BPfvf/+bDRs2sGzZMgC6urooCObD4uXYrFaOOugQABbPncf6TzcC8OKLL3LPPWZRmaR3vt/vJz8/n3feeYe6ujr22Wcf8vPzM/r8dmU8VsFDqVWRSTrdTPZxOVX22zefP//Oh5QSp1NlntPX45gcObKVbBUGviClXJa2/aQQ4k0p5TIhxIeDPFedEKJUSlmTMAPUJ/qrgClp+1Uk+qqAlbv0rxvkNQeFEAKZ0AjEYjGQEmWEkoE47XZe/tsjdEa6OOWi7/Cnhx/ghwt/js9r4bLLfsJ3vvOdbvunq5n7Y++992bDhg0ceuihvb7vdrsHPMdtt93Gf/7zH55++mn23XdfNmzYwJlnnsmKFSt4+umnOeaYY7j99tv7vEaSUCjE1q1bmT17Nv/973/7vfauwtVghK3080op+eIXv8hf77qb0Aefpvrf/+TjPo+3WCyp9Mi6pqWcHjVN4+yzz+b6669P7atHY4Q++BSrxbJTna4oA/pgfOtb3+Kuu+6itraWc889t999J0PVx0zKPmdaGlpVBcG8oQtBbe1x4pqBqgjyAv2fJxLRqWuM8vg/qwn4rBxzeMmQKlTmyJEpWekzAHiEECm9e6KdFOMHG274BJCMCDgH+Eda/9mJqIL9MCsk1gDPAkcIIfISjoNHJPpGldRqMbHCtaRNAiOBy+Hkhh/+hD/89W40XePoo49izZq/0NLajqYbVFVVUV9fz8EHH8zjjz9OV1cXoVCIJ5/sPfHjZZddxqWXXkptbS1gmgX+9Kfe80J94Qtf4IEHHgDMFfdBBx0EwObNm1mxYgXXXHMNhYWF7Nixg88//5yZM2dy8cUXc8IJJ/Dee+/1e1/hcJgLLriAE088kby8gf0833jjDbZs2YJhGDz44IMceOCBAx7TG/vttx/r16/ns82fAdDR1cln27Yya/oMtldXs3nzZgDuv//+1DHTp0/nnXfeAeC/773H1q1bsVqtHHrooTz66KPU15tyanNzM9u2bev3+ocddhi33norkChg1WZGAJx00kn83//9H2+++SZHHnlkv+dIVn3c9dWbgJCjf2rrI1x23YeceM7rXPzT/7J5a7hf59zKmi7OvvBNHn6iijv/upVzL9lAU8vEiaSONbXSubWyxyvWtDM1tpSSpuYoz7xQy2NPV1HfGEHLIEIpx/iQrZqBHwCvCCE2AwKYAVwghHCz0xmwB0LJALNQAAAaVElEQVSI+zFX9QVCiErMqIDrgYeEEN8EtgGnJXZ/BjgG+AzoBL4BIKVsFkJcC7yZ2O8aKeWuTokjStJMoGsaiqJgJGzJI2EmSGfhXnPZe9ZsHnjwQc4462yOPPpdVqzYH0VAIODjvvv+yuKFC/nyKaeyaOFCCguLWLpkXwxNw9A0M8lPgmOOOYa6ujoOP/zwVAKTvlait9xyC9/4xje48cYbKSws5C9/+QsAl156KZs2bUJKyWGHHcaiRYu44YYbuPfee7FarZSUlHD55Zf3es5Vq1alvPpPOukkrrjiiow+g2XLlnHRRRfx2WefsWrVKk466aRBfoomhYWF3HXXXXz17LNTZaWvufpq9tl3AbffeQfHn2yaWg466CBCIbNOwimnnMI999zDggULWL58ObNnz0YIwcKFC/nFL37BEUccgWEYWK1Wblm9mmnLloOyM8ui89MP4MN3AVi9ejXnnXcef/7zn1FVlVtvvZX9998fm83GqlWrCAQCuVSzY0Rbe4xrfvNRKjfDlu2dfP/K91mzel/ye9E0RKI6dz+0DT1t3mxpjfPu+60cfkhxj/3Hg0zKgze3xPjm996msdkUYm69ewv33LIvpcXOMR1rjswQ2ZpkRQhhB/ZKbH4ySKfBMWfp0qUy6TCX5KOPPmLu3LkZHT8aBXUMTTOdCHdFUdhSFUGL7/xuOOwKFWVOhK51U3sn8c7fme8/W1m3bh033XQTTz311KS9pmEYLFmyhIcffrhPf4vk97Jza2WfD/xcLfvMaWyOcuI5r/fof/CO5ZSX9pwYI1GdX978MS++0r30888vncthB0+M4qyZfDeeeLaGX/+h+7Pi1C+V87/fnImqZq6UFkJskFIuHXjPHMMhK80EQoiTgWOBPRKvY4QQh03mMsbJRCrJrHkjUVlPsVhQ7bYeLx2lmyAAZkhVlsqNI0K60JytAvTGjRvZc889OeywwzJ2vMwxfBRFMK3C1a3P5VSx23t//DrsKuecPq1bNsP8PBuL5/fM0zGR6erqGenT2alhZOfPZ9KTrWaCbwL7Ay9imglWAhuAGUKIa6SU947j2EaNsUoXKgQoqsBIs2larWKs8ruMGytXrmTlypU9+pMOfWrCDJJsj8TfoK9rjgbz5s3j888/H5Nr5dhJMGDj6kvn8v2r3qOlNY7TqXL1pXPxefqOhigvdfLXPy7lyedqCfisfPGQoiE5LyarcibLEifbY2EiWnlAAXf+dQuRqKl9VBQ446QKrJasXINOerJVGLAAc6WUdQBCiGLgHsysgC8Bk1IYGCtUVVBe4qCqpgvDSG47UVWBMbz8LlmLbhgYsRgkojqUEcr1MJHJJOQuR2bMmObmrt8vpatLx+FQBszN4LCrTClzccHXZw7rulJKaqqrsVqtBPPzqamuxufzEcjLG/WqhME8G3ffspT7/76DrojOmSdPyaiKYo7xIVuFgSlJQSBBfaKvWQgxfgnCJwmKEDgdKjOmuZGGKdGr6sA5+ycryfj2eCKs05Jw3Jwsn4emG8TjkkhUx+VQMRJ63ExD7nIMjEUVprPgGCcuF0JQXFJCdVUVVZWV2O12fH7/sAWBTARFq0WhvNTJd8/bEynBNkCZ6xzjS7YKA+uEEE8BDye2T0n0uYHWvg/LkSmKIlCUnpOdUHd6r+/aP1mRUpqZDhOTv67rE7LWuxmqJgflnKXrkqbmGC2tO2XoaNQgrhk5de4oMdb5G3Y1L46EEDsYQTH3PcoOslUYuBA4GUgGgb+FmVa4A1g1bqPaDVAsluz91gwDmQjpA9Di8RFLCT0SGIYkGjNobIoiJeQHbTjsakbldA1D0tLWXZkWCmu0t2sTJsXuZCCZXlpRlIzC8kbyurU1NdhsNvKCQepqa2lrbR0TM0GO7CIrvw3SfBJ/DmjASZgCwEfjOqgsJRKJsHz5chYtWsTee++dKki0ZcsWVqxYwZ577snpp59uZj7cTRFCYLXZUpEcyfZEEAQANM1gW2UnHZ06nV06O6q6iMUzS+4iU//sxJAS2aP0x/9v796joyzvBI5/f5nJZSYJBAK5QEDBooI5NihHbGsrlMJS65FaqXXrrqh0aW2t1NOewnrcXs6e7eJpz1YWra2XKp7tjSIVSz1UFqTSLojIRbGAUGNJICQxFyDkOpnf/vG+iUMShGRmMpf39zknJ/M+zLzv8/LMzPvL+zzP7zFD1ZM5tL2tjXA4PKyzUUSEktJSiktKCAQCjC8ri0k3gUk/KfWOEJFLReS7InIQWAUcxcmVMFtVH0lw9eLupa213HL3Dj5+05+45e4dvLS19vwvOo/s7Gy2bNnCvn372Lt3Lxs3bmTHjh0sW7aM+++/nyNHjjCyoIDHfvoEDY0dtLaFCA2UmyDNRV78ow0EQiHnNnyssrGdagn1u6A3NXde0EUnQ5zldyMFAj6CAUtIFCvhcJgzLS2cOHGCpsbGqFdZ7CsUCtN1juBPRMjKyurNWNrz2Ji+UioYAA4CnwRuVNXrVHUV4Inx7S9treWhR96mtt65FVxb38FDj7wddUAgIr1L+HZ1ddHV1YWIsGXLFhYuXEg4rNz8uS/y29+uo76hk6PVbTQ2dX5gKlVzbp1dYapr2vhb5RmqjrfR0dkd9V+KA/XJZl7gYC2/P4PxJTmMGZ1FMOijeGw2+bl+ggG7YMSKz+djZEEBgUDATQsdmztK3d1hamrbWfXU31ix6hCH32mhdYC5/cZciFQLBj4H1AAvi8gTIjKHWH2yktzPnq2ko+Ps6L+jI8zPnq2Met/d3d1UVFRQVFTE3LlzueSSSygoKHAWzgkrgWARtbU1vc9vbO7qHXFuLlwoFOZYTRvt7U47dnSEqT7eFnVglRv0nTVNze8XCkZeeKpqvz+DwtFZjC8JUDAyc8CBo2boeub6t7e3u7fnY/PZaWjq4s77dvHchuP88eU67v7G61Qfb43Jvo33pFT4r6rPA8+7swYWAN8AikTkMeB3qvpSQisYR3XvdQyqfDB8Ph979+6lubmZm2++mYMHz15Zr99Xl8UBQ6JKv4Cuq0ujzsjm92cwcXyAjk4nS2ROdgb+QY7gdhbC6l/e0dnNmdZucrIz7G7BEPV0E+Tk5FBcUkJ7fQOfOPhSv8Q/g83fsOP1Bs60vn8nQBV+8VwVDyy9jOxs6+Yxg5OSn2531sAvgV+6Kwd+HlgGpG0wUDQmm9r6/hf+ojHZMTtGQUEBs2fPZvv27TQ3NzvZysRHy6laiotLe5+Xl+fHxh8NnoiTybErItWzk78h+n37/YMPAM6nsamT1Wv+zs7dTVw+JZ97Fk2iaKwljRmsnm4CcNKKZ48ZDWNGR50F8COXZfLsv40/qyyQ44PTpyB7mBMamJSX8l/pqtqkqo+rav+5Omnky3dM6pfLPDs7gy/fMSmq/dbX19Pc7KRmaGtrY9OmTUydOpXZs2ezdu1afD7h9+t/xWc/u4C8XD9FY7MpGZs9qLnsxuHzCePcTI7gJHMaV5KD/wKmAA63ljMhfvSTt3luw3Gqjrex6U91fPN7b9LYnB6zSnoW/upZ9Cveo/x9Pl/vxT/ycTQC4XYOfOwzZ/3svno+3S1not638Z6UvDPgRfNmOUuX/uzZSure66BoTDZfvmNSb/lQ1dTUsGjRIrq7uwmHw9x6663ceOONTJs2jdtuu40HH3yQ6dOnc+/XlpCVlY1IcsytT0UiQk52BpMmBgmHQTLAl5E8UxQjtXd08+edDWeVVR5tpb099Qeo9Uz1O1FTw7jx4wmFQtTV1lI2YUJKjbS3sR0mllLnnW+YN6s46ot/X1deeSV79uzpVz558mR27twZ02MZJyDw+5P/S1zEWWCnZy16gEy/XPAshWTWk146KyuL6qoqAEaMGJGUQZkxwyX1P9nGxFg4rJ6fLVEwIotlX7+UyN6gr9w5mdxgevz9ICLkjxjRu22JeIzXpccnOwZE5F3gNE7egpCqzhCR0cBvgIuBd4FbVbVJnD8hVgI3AK3Anaq6e7jqGg6F0AES/4gvw0kXbIYkHFY6u8I0NnUiIhSOysTvz/Dk7VifT6goH8maJ2dSdbyNccU55Of50yIZUU83QX1dHXl5eYRCIY4fO5Zy3QTGxJK98882W1Xfi9heDmxW1RUistzdXgZ8Gpji/swEHnN/DwvtDnN6/9v9yvPLL7UWjUJnV5h3q1p7p06eauli8sRcTwYDAIEcP4EcP8VpNoOgp5ugcMyY3oRbLS0tKddNYEtMm1iyS8cHWwDMch+vBrbiBAMLgGfdNRJ2iEiBiJSqas2AezFJL6xKU3PnWTkUNOyk+i0cZQv2pBufz0d+fn5v10Dk41RhS0ybWEqtd398KfCSiLwuIkvcsuKIC/wJoGf03nigKuK11W7ZWURkiYjsEpFd9fX18aq3iQFh4NHZNoMyfUVe/FMtEDAm1uzOwPuuU9VjIlIEbHIXQ+qlqioigxpVpqqPA48DzJgxw9sj0pKciDC6IIuTp7oIu8Mx/H4hL9c+IsaY9GfhsEtVj7m/64DfAdcAtSJSCuD+rnOffgyYEPHyMrcsJTU3N7Nw4UIuv/xypk6dyvbt22lsbGTu3LlMmTKFuXPn0tTUlOhqxp3fL0yamEtJUTalxTlcPCEY86x+xhiTjOybDhCRXBHJ73kMzAP2Ay8Ai9ynLQLWu49fAO4Qx7XAyXiPF1BVao4fp+b4cVSgtXgUrcWjyLtiCvnll5JffikyxHvaS5cuZf78+Rw8eJB9+/YxdepUVqxYwZw5czh8+DBz5sxhxYoVMT6j5OMMLMugYGQWI0dkWiBgjPEMuwfqKAZ+544m9gO/VNWNIvIasEZEFgN/B251n/8izrTCIzhTC++KdwVP1NTQ3t4OQPWxY72pU+sbGygdN27I+z158iSvvPIKzzzzDABZWVlkZWWxfv16tm7dCsCiRYuYNWsWDz30UFTnYIwxJjlZMACo6jvAhwcobwD6rXngziL42jBUrR9V7Q0EYjEVqrKykrFjx3LXXXexb98+rr76alauXEltbS2lpc7iRCUlJdTW1kZ9LGOMMcnJ7oOmiOKSkn4XfxGhuKQkqv2GQiF2797NPffcw549e8jNze3XJSCSnPnzjTHGxIYFAymi9sSJfquqqSq1J05Etd+ysjLKysqYOdPJmbRw4UJ2795NcXExNTXOMIiamhqKioqiOs4HCYdCdHd09vsJh0JxO6Yxxpj3WTCQYkSEjIyMmP2lXlJSwoQJEzh06BAAmzdvZtq0adx0002sXr0agNWrV7NgwYKYHG8gPRkV+/4MlHLZGGNM7NmYgRRRUlrKCfcv9eKSkt47AiVuv340Vq1axe23305nZyeTJ0/m6aef7l3O+KmnnuKiiy5izZo1UR/HGGNMcrJgIEWIyFmzBqKZQdBXRUUFu3bt6le+eXP/vOfGGGPSj3UTGGOMMR5nwYAxxhjjcdZNYBJOfBnO8ssDlBtjjIk/CwZMwmX4/fZONMaYBLKvYGNMUmps7kRVycn2kRtMja+q7u5uAHw+31mPjUl2qfEJM8Z4RldXmCOVLfxg5SGqj7fx8WvHsHTJhygclZXoqn2g7u5uGhoayMrMZMTIkb2P80eMsIDAJD3rlDWsXLmS8vJyrrjiCh5++GEATy5hbJLDydNd3PfgG1QebaUrpGz5cz0/Xf0OrW3Jn5EyEAjQ2NhIdVUVLadPk52TY6m8TUqwYCBFbBx9FX/IvKzfz8bRV0W13/379/PEE0+wc+dO9u3bx4YNGzhy5IgnlzA2yaGxqYu2tu6zyra/1khrn7Jk4/P5CAaDZGZmEgqFCASDZGdnk5FhX7Mm+dm7NEV0nz4zqPILdeDAAWbOnEkwGMTv93P99dezbt061q9fz6JFiwBnCePnn38+quMYc6FGjvDT9/o5aWKQrMzk/rrq6Sbo6uoiLy+PttZWTp082Tt2wJhkltyfLhN35eXlbNu2jYaGBlpbW3nxxRepqqqyJYxNwuTl+rnvS5fg8zm31wtHZ/Htey9lRH5mgmt2fsFAgNJx4xgzdixFRUXWTWBShg0g9LipU6eybNky5s2bR25uLhUVFf0GO9kSxmY45Qb93PCpEq7/6Fja27sJBn2MGpncgwfB6SYIBIO9i4lFPjYm2VkwEAURmQ+sBHzAk6qakh3rixcvZvHixQA88MADlJWV9S5hXFpaGvcljI3pKxjwEwwkz9dTZ0MzodMt/cr9+XlkFRb0bkcG0oOdQXChxzAmHpLn05ZiRMQHPArMBaqB10TkBVX9a2JrNnh1dXUUFRVx9OhR1q1bx44dO6isrGT16tUsX7487ksYG5PsQqdbeHnKnH7lsw9vjtmFejiOYcy5WDAwdNcAR1T1HQAR+TWwAIhLMODLzx1wsKAvPzfqfd9yyy00NDSQmZnJo48+SkFBAcuXL7cljI0xxiMsGBi68UBVxHY1MDPyCSKyBFgCMHHixKgONr9xd1Sv/yDbtm3rV1ZYWGhLGBtjjEfYyJY4UtXHVXWGqs4YO3ZsoqtjjDHGDMiCgaE7BkyI2C5zy4wxxpiUYt0EQ/caMEVEJuEEAbcBXxzsTlTVpu2ZpKGqia5CUvLn5zH7cP9uM39+Xkodw5hzsWBgiFQ1JCL3An/EmVr4c1V9azD7yMnJoaGhgcLCQgsITMKpKg0NDeTk5CS6Kkknq7Ag7iP6h+MYxpyLBQNRUNUXgReH+vqysjKqq6upr6+PYa2MGbqcnBzKysoSXQ1jzDCzYCCBMjMzmTRpUqKrYYwxxuNsAKExxhjjcRYMGGOMMR5nwYAxxhjjcWJTiYaHiNQDfx/ky8YA78WhOsnMi+cM3jxvL54zePO8oznni1TVsrbFmQUDSUxEdqnqjETXYzh58ZzBm+ftxXMGb563F8851Vg3gTHGGONxFgwYY4wxHmfBQHJ7PNEVSAAvnjN487y9eM7gzfP24jmnFBszYIwxxnic3RkwxhhjPM6CAWOMMcbjLBhIQiIyX0QOicgREVme6PrEi4hMEJGXReSvIvKWiCx1y0eLyCYROez+HpXousaaiPhEZI+IbHC3J4nIq26b/0ZEshJdx1gTkQIRWSsiB0XkgIh8JN3bWkTud9/b+0XkVyKSk45tLSI/F5E6EdkfUTZg24rjv93zf0NErkpczU0PCwaSjIj4gEeBTwPTgH8UkWmJrVXchIBvquo04Frga+65Lgc2q+oUYLO7nW6WAgcith8CfqyqHwKagMUJqVV8rQQ2qurlwIdxzj9t21pExgP3ATNUtRxnqfPbSM+2fgaY36fsXG37aWCK+7MEeGyY6mg+gAUDyeca4IiqvqOqncCvgQUJrlNcqGqNqu52H5/GuTiMxznf1e7TVgOfTUwN40NEyoDPAE+62wJ8EljrPiUdz3kk8AngKQBV7VTVZtK8rXFWhg2IiB8IAjWkYVur6itAY5/ic7XtAuBZdewACkSkdHhqas7FgoHkMx6oitiudsvSmohcDEwHXgWKVbXG/acTQHGCqhUvDwPfBsLudiHQrKohdzsd23wSUA887XaPPCkiuaRxW6vqMeBHwFGcIOAk8Drp39Y9ztW2nvyOS3YWDJiEE5E84DngG6p6KvLf1Jn7mjbzX0XkRqBOVV9PdF2GmR+4CnhMVacDZ+jTJZCGbT0K56/gScA4IJf+t9I9Id3aNh1ZMJB8jgETIrbL3LK0JCKZOIHAL1R1nVtc23Pb0P1dl6j6xcHHgJtE5F2cLqBP4vSlF7i3kiE927waqFbVV93ttTjBQTq39aeASlWtV9UuYB1O+6d7W/c4V9t66jsuVVgwkHxeA6a4I46zcAYcvZDgOsWF21f+FHBAVf8r4p9eABa5jxcB64e7bvGiqv+qqmWqejFO225R1duBl4GF7tPS6pwBVPUEUCUil7lFc4C/ksZtjdM9cK2IBN33es85p3VbRzhX274A3OHOKrgWOBnRnWASxDIQJiERuQGnX9kH/FxV/yPBVYoLEbkO2Aa8yfv95w/gjBtYA0zEWfb5VlXtOzgp5YnILOBbqnqjiEzGuVMwGtgD/JOqdiSyfrEmIhU4gyazgHeAu3D+IEnbthaR7wNfwJk5swf4Ek7/eFq1tYj8CpiFs1RxLfBd4HkGaFs3MHoEp8ukFbhLVXclot7mfRYMGGOMMR5n3QTGGGOMx1kwYIwxxnicBQPGGGOMx1kwYIwxxnicBQPGGGOMx1kwYEwKcVf++6r7eJyIrD3fa6I4VoU7zdUYk+YsGDAmtRQAXwVQ1eOquvA8z49GBWDBgDEeYHkGjEkhItKziuUh4DAwVVXLReROnFXhcnGWhv0RTnKffwY6gBvchC+X4CyRPRYn4cu/qOpBEfk8TqKYbpwFdT4FHAECOKli/xPYAKwCyoFM4Huqut499s3ASJyEOv+jqt+P83+FMSaG/Od/ijEmiSwHylW1wl3pcUPEv5XjrPyYg3MhX6aq00Xkx8AdOFktHwe+oqqHRWQm8BOc9RG+A/yDqh4TkQJV7RSR7wAzVPVeABH5AU765LtFpADYKSL/6x77Gvf4rcBrIvIHyypnTOqwYMCY9PGyqp4GTovISeD3bvmbwJXu6pAfBX7rZIQFINv9/RfgGRFZg7OgzkDm4Syy9C13Owcn1SzAJlVtABCRdcB1gAUDxqQICwaMSR+R+e3DEdthnM96BtCsqhV9X6iqX3HvFHwGeF1Erh5g/wLcoqqHzip0Xte3v9H6H41JITaA0JjUchrIH8oLVfUUUOmOD8BdNe7D7uNLVPVVVf0OUI+zxGzfY/0R+Lq70AwiMj3i3+aKyGgRCeCMXfjLUOpojEkMCwaMSSHurfi/iMh+4IdD2MXtwGIR2Qe8hTMYEeCHIvKmu9//A/bhLLU7TUT2isgXgH/HGTj4hoi85W732Ak8B7wBPGfjBYxJLTabwBgTFXc2Qe9AQ2NM6rE7A8YYY4zH2Z0BY4wxxuPszoAxxhjjcRYMGGOMMR5nwYAxxhjjcRYMGGOMMR5nwYAxxhjjcf8PtHsE5ur7a1UAAAAASUVORK5CYII=\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
}