1590 lines
613 KiB
Plaintext
1590 lines
613 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Aragon Conviction Voting Model - Version 3\n",
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"\n",
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"New to this version 3 model are the following elements:\n",
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"\n",
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"* Adding the realism that not all participant tokens are being allocated to proposals at each timestep.\n",
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"* Refactored parameters and system initialization to make more readable and consistent.\n",
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"* Changed file structure and file names to align with emerging cadCAD standards.\n",
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"* Making the distinction between effective and total supply.\n",
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"* Refining alpha calculations to more accurately reflect the 1Hive implementation. Discussion of alpha and its relation to alpha in the contract and how it relates to the timescales\n",
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"* Updated differential specification and write-up to respect new state variables\n",
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"* Moved all unit denominations to Honey, the 1Hive governance token.\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# An Introduction to Conviction Voting\n",
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"\n",
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"Conviction Voting is an approach to organizing a communities preferences into discrete decisions in the management of that communities resources. Strictly speaking conviction voting is less like voting and more like signal processing. Framing the approach and the initial algorithm design was done by Michael Zargham and published in a short research proposal [Social Sensor Fusion](https://github.com/BlockScience/conviction/blob/master/social-sensorfusion.pdf). This work is based on a dynamic resource allocation algorithm presented in Dr. Zargham's PhD Thesis.\n",
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"\n",
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"The work proceeded in collaboration with the Commons Stack, including expanding on the python implementation to makeup part of the Commons Simulator game. An implemention of Conviction Voting as a smart contract within the Aragon Framework was developed by 1Hive.org and is currently being used for community decision making for allocations of their community currency, Honey.\n",
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"\n",
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"\n",
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"## The Word Problem\n",
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"\n",
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"Suppose a group of people want to coordinate to make a collective decision. Social dynamics such as discussions, signaling, and even changing ones mind based on feedback from others input play an important role in these processes. While the actual decision making process involves a lot of informal processes, in order to be fair the ultimate decision making process still requires a set of formal rules that the community collecively agrees to, which serves to functionally channel a plurality of preferences into a discrete outcomes. In our case we are interested in a procedure which supports asynchronous interactions, an provides visibility into likely outcomes prior to their resolution to serve as a driver of good faith, debate and healthy forms of coalition building. Furthermore, participations should be able to show support for multiple initiatives, and to vary the level of support shown. Participants have a quantity of signaling power which may be fixed or variable, homogenous or heterogenous. For the purpose of this document, we'll focus on the case where the discrete decisions to be made are decisions to allocate funds from a shared funding pool towards projects of interest to the community.\n",
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"\n",
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"## Converting to a Math Problem\n",
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"\n",
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"Let's start taking these words and constructing a mathematical representation that supports a design that meets the description above. To start we need to define participants.\n",
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"\n",
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"### Participants\n",
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"Let $\\mathcal{A}$ be the set of participants. Consider a participant $a\\in \\mathcal{A}$. Any participant $a$ has some capacity to participate in the voting process through their token holdings $h[a]$. In a homogenous fixed token quantity system (like you might see in a democratic allocation of equal tokens per each participant), $h[a] = h$ for all $a\\in \\mathcal{A}$ where $h$ is a constant. The access control process managing how one becomes a participant determines the total supply of \"votes\" $S = \\sum_{a\\in \\mathcal{A}} = n\\cdot h$ where the number of participants is $n = |\\mathcal{A}|$. In a smart contract setting, the set $\\mathcal{A}$ is a set of addresses, and $h[a]$ is a quantity of tokens held by each address $a\\in \\mathcal{A}$. \n",
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"\n",
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"### Proposals & Shared Resources\n",
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"Next, we introduce the idea of proposals. Consider a proposal $i\\in \\mathcal{C}$. Any proposal $i$ is associated with a request for resources $r[i]$. Those requested resources would be allocated from a constrained pool of communal resources currently totaling $R$. The pool of resources may become depleted because when a proposal $i$ passes the reserve is decremented by $R^+= R-r[i]$. Therefore it makes sense for us to consider what fraction of the shared resources are being request $\\mu_i = \\frac{r[i]}{R}$, which means that thre resource depletion from passing proposals can be bounded by requiring $\\mu_i < \\mu$ where $\\mu$ is a constant representing the maximum fraction of the shared resources which can be dispersed by any one proposal. In order for the system to be sustainable a source of new resources is required. In the case where $R$ is funding, new funding can come from revenues, donations, or in some DAO use cases minting tokens.\n",
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"\n",
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"### Participants Preferences for Proposals\n",
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"\n",
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"Most of the interesting information in this system is distributed amongst the participants and it manifests as preferences over the proposals. This can be visualized as a matrix $W\\in \\mathbb{R}^{n \\times m}$, with participants holding randomized affinities from -1 to +1 over all proposals.\n",
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"\n",
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"\n",
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"These private hidden signals drive discussions and voting actions. Each participant individually decides how to allocate their votes across the available proposals. Participant $a$ supports proposal $i$ by setting $x[a,i]>0$ but they are limited by their total token holdings $\\sum_{k\\in \\mathcal{C}} x[a,k] \\le h[a]$. Assuming each participant chooses a subset of the proposals to support, a support graph is formed.\n",
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"\n",
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"\n",
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"## Aggregating Information\n",
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"\n",
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"In order to break out of the synchronous ad hoc voting model, a dynamical systems model of this system is introduced, which is explored further below.\n",
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"\n",
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"### Participants Allocate Voting Power\n",
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"\n",
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"\n",
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"In the above diagram, we examine the participant view. Participant $a$ with holdings $h$ at time $t$ supports proposals $i$ and $j$ with $x$ conviction. The sum of all conviction asserted by participant $a$ is between 0 and the total holdings of participant $a$.\n",
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"\n",
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"### System Accounts Proposal Conviction\n",
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"\n",
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"\n",
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"In the above diagram, we examine the proposal view. Proposal $j$ with total conviction $y$ at time $t$ is supported by participants $a$, $b$ and $c$ with $x$ conviction. The total conviction $y$ at time $t+1$ is equal to the total conviction at time $t$ decremented by an exponential decay $\\\\alpha$ plus the sum of all conviction from $k$ agents in time step $t$.\n",
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"\n",
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"### Understanding Alpha\n",
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"Below are some graphs used to demonstrate, play with, and understand the shapes and choices for the $\\\\alpha$ parameter, which regulates the half life decay rate of the agent preference conviction growth and decay.\n",
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"\n",
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"* https://www.desmos.com/calculator/x9uc6w72lm\n",
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"* https://www.desmos.com/calculator/0lmtia9jql\n",
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"\n",
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"See the [Deriving_Alpha](Deriving_Alpha.ipynb) notebook for more details around alpha and how it is derived.\n",
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"\n",
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"\n",
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"## Converting Signals to Discrete Decisions\n",
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"\n",
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"Conviction can be considered like a fluctuating kinetic energy, with the Trigger function acting as a required activation energy for proposals to pass. This is the mechanism by which a continuous community preference turns into a discrete action event: passing a proposal. See [Trigger Function Explanation](Trigger_Function_Explanation.ipynb) for more details around the trigger function and how it works."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Resolving Passed Proposals\n",
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"\n",
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"\n",
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"\n",
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"This diagram shows the trigger function logic, which depends on token supply $S$, total resources available $R$ and total conviction $y$ at time $t$, as well as the proposal's requested resources $r$, the maximum share of funds a proposal can take ($\\beta$) and a tuning parameter for the trigger function ($\\rho$). Essentially, this function controls the maximum amount of funds that can be requested by a proposal ($\\beta$), using an equation resembling electron repulsion to ensure conviction increases massively beyond that point.\n",
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"\n",
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"## Social Systems Modeling\n",
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"\n",
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"In the conviction voting model, multiple graph structures are used to represent participants and proposals to represent a subjective, exploratory modeling of the social system interacting.\n",
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"\n",
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"### Sentiment\n",
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"\n",
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"Global Sentiment denotes the outside world appreciating the output of the community.\n",
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"Local Sentiment denotes the agents within the system feeling good about the community.\n",
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"Sentiment increases when proposals pass and work is completed in the community, and decreases when proposals fail and community progress stalls.\n",
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"\n",
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"### Relationships between Participants\n",
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"\n",
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"Edges from participant to participant denote influence (to represent subjective social influence) and are assigned randomly as mixing processes.\n",
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"\n",
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"### Relationships between Proposals\n",
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"\n",
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"Edges from proposal to proposal represent conflict, either positive or negative.\n",
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"Some proposals are synergistic (passing one makes the other more desirable).\n",
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"Some proposals are (partially) substitutable (passing one makes the other less desirable).\n",
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"\n",
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"\n",
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"### Notion of Honey supply\n",
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"#### Total supply = $S$\n",
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"#### Effective supply = $E$\n",
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"#### Funding Pool = $F$\n",
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"#### Other supply = $L$, effectively slack. Funds could be in cold storage, in liquidity pools or otherwise in any address not actively participating in conviction voting.\n",
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"$$S = F + E + L$$ \n",
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"\n",
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"System has the right to do direct mints:\n",
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"$$F^+ = F + minted tokens$$\n",
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"$$S^+ = S + minted tokens$$\n",
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"\n",
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"The system may also see the arrival of new funds which come from outside supply and are donated to the funding pool:\n",
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"$$L^+ = L - donated tokens$$\n",
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"$$F^+ = F + donated tokens$$\n",
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"\n",
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"When tokens are added to a liquidity pool or cold wallet and removed from staking on proposals:\n",
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"$$L^+ = L + tokens$$ \n",
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"$$E^+ = E - tokens$$ \n",
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"\n",
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"When tokens are removed from a liquidity pool or cold wallet and staked towards proposals:\n",
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"$$L^+ = L - tokens$$ \n",
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"$$E^+ = E + tokens$$\n",
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"\n",
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"Tokens in $L$ or $E$ are defined at the level of the account holding them.\n",
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"\n",
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"Total supply $S$ can be made a param and the state supply should be only $E$, effective supply."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## cadCAD Overview\n",
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"\n",
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"In the cadCAD simulation [methodology](https://community.cadcad.org/t/differential-specification-syntax-key/31), we operate on four layers: **Policies, Mechanisms, States**, and **Metrics**. Information flows do not have explicit feedback loop unless noted. **Policies** determine the inputs into the system dynamics, and can come from user input, observations from the exogenous environment, or algorithms. **Mechanisms** are functions that take the policy decisions and update the States to reflect the policy level changes. **States** are variables that represent the system quantities at the given point in time, and **Metrics** are computed from state variables to assess the health of the system. Metrics can often be thought of as KPIs, or Key Performance Indicators. \n",
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"\n",
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"At a more granular level, to setup a model, there are system conventions and configurations that must be [followed.](https://community.cadcad.org/t/introduction-to-simulation-configurations/34)\n",
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"\n",
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"The way to think of cadCAD modeling is analogous to machine learning pipelines which normally consist of multiple steps when training and running a deployed model. There is preprocessing, which includes segregating features between continuous and categorical, transforming or imputing data, and then instantiating, training, and running a machine learning model with specified hyperparameters. cadCAD modeling can be thought of in the same way as states, roughly translating into features, are fed into pipelines that have built-in logic to direct traffic between different mechanisms, such as scaling and imputation. Accuracy scores, ROC, etc. are analogous to the metrics that can be configured on a cadCAD model, specifying how well a given model is doing in meeting its objectives. The parameter sweeping capability of cadCAD can be thought of as a grid search, or way to find the optimal hyperparameters for a system by running through alternative scenarios. A/B style testing that cadCAD enables is used in the same way machine learning models are A/B tested, except out of the box, in providing a side by side comparison of muliple different models to compare and contrast performance. Utilizing the field of Systems Identification, dynamical systems models can be used to \"online learn\" by providing a feedback loop to generative system mechanisms. \n",
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"\n",
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"\n",
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"## Differential Specification \n",
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"\n",
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"\n",
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"## File structure\n",
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"* ```Aragon_Conviction_Voting_Model.ipynb```\n",
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"* model\n",
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"\n",
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"In the model folder there exist 4 files and folder, the [```config.py```](model/config.py), [```partial_state_update_block.py```](model/partial_state_update_block.py), [```run.py```](model/run.py), and [```state_variables.py```](model/state_variables.py). The [```config.py```](model/config.py) contains the simulation configurations, aggregating the partial states, and the state variables. The [```partial_state_update_block.py```](model/partial_state_update_block.py) contains the partial state update blocks and how they update the state variables. [```state_variables.py```](model/state_variables.py) defines the state variables and [```run.py```](model/run.py) actually runs the simulation.\n",
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"\n",
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"The mechanisms of the model live within the parts subfolder as:\n",
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"* [```system.py```](model/parts/system.py)\n",
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"* [```participants.py```](model/parts/participants.py)\n",
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"* [```proposals.py```](model/parts/proposals.py)\n",
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"\n",
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"The initial parameters and hyperparameters of the system are defined in [```sys_params.py```](model/sys_params.py) and helper functions, plots, trigger function, etc are in the [```utils.py```](model/utils.py).\n",
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"\n",
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"### Note:\n",
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"When running this notebook simulation, be sure to run from \"Kernal\" -> \"Restart & Run All\"\n",
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"\n",
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"## Schema of the states \n",
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"The model consists of a temporal in memory graph database called *network* containing nodes of type **Participant** and type **Proposal**. Participants will have *holdings* and *sentiment* and Proposals will have *funds_required, status*(candidate or active), *conviction* Tthe model as three kinds of edges:\n",
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"* (Participant, participant), we labeled this edge type \"influencer\" and it contains information about how the preferences and sentiment of one participant influence another \n",
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"* (Proposal, Proposal), we labeled this edge type \"conflict\" and it contains information about how synergistic or anti-synergistic two proposals are; basically people are likely to support multiple things that have synergy (meaning once one is passed there is more utility from the other) but they are not likely to pass things that have antisynergy (meaning once one is passed there is less utility from the other).\n",
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"* The edges between Participant and Proposal, which are described below.\n",
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" \n",
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"\n",
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"Edges in the network go from nodes of type Participant to nodes of type Proposal with the edges having the key *type*, of which all will be set to *support*. Edges from participant $i$ to proposal $j$ will have the following additional characteristics:\n",
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"* Each pairing (i,j) will have *affinity*, which determines how much $i$ likes or dislikes proposal $j$.\n",
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"* Each participant $i$, assigns its $tokens$ over the edges (i,j) for all $j$ such that the summation of all $j$ such that ```Sum_j = network.edges[(i,j)]['tokens'] = network.nodes[i]['holdings']```. This value of tokens for participants on proposals must be less than or equal to the total number of tokens held by the participant.\n",
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"* Each pairing (i,j) will have *conviction* local to that edge whose update at each timestep is computed using the value of *tokens* at that edge.\n",
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"* Each proposal *j* will have a *conviction* which is equal to the sum of the conviction on its inbound edges: ```network.nodes[j]['conviction'] = Sum_i network.edges[(i,j)]['conviction']```. \n",
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"\n",
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"\n",
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"The other state variables in the model are *funds*, *sentiment*, *effective_supply*, and *total_supply*.\n",
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"\n",
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"The system consists of 100 time steps without a parameter sweep or monte carlo.\n",
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"\n",
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" \n",
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"## Partial State Update Blocks \n",
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"\n",
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"Each partial state update block is kind of a like a phase in a phased based board game. Everyone decides what to do and it reconciles all decisions. One timestep is a full turn, with each block being a phase of a timestep or turn. We will walk through the individaul Partial State update blocks one by one below."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"```\n",
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"{\n",
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"# system.py: \n",
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"'policies': { \n",
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" 'random': driving_process\n",
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"},\n",
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"'variables': {\n",
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" 'network': update_network,\n",
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" 'effective_supply':increment_supply,\n",
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"}\n",
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"```\n",
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"\n",
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"To simulate the arrival of participants and proposal into the system, we have a driving process to represent the arrival of individual agents. We use a random uniform distribution generator, over [0, 1), to calculate the number of new participants. We then use an exponential distribution to calculate the particpant's tokens by using a loc of 0.0 and a scale of expected holdings, which is calculated by .1*supply/number of existing participants. We calculate the number of new proposals by \n",
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"```\n",
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"proposal_rate = 1/median_affinity * (1+total_funds_requested/funds)\n",
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"rv2 = np.random.rand()\n",
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"new_proposal = bool(rv2<1/proposal_rate)\n",
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"```\n",
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"The network state variable is updated to include the new participants and proposals, while the *effective_supply state variable is updated for the additiona of new particpant's funds. \n",
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"```\n",
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" {\n",
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" 'policies': { \n",
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" 'random': minting_rule\n",
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" },\n",
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" 'variables': {\n",
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" 'total_supply': mint_to_supply,\n",
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" 'funds':mint_to_funds,\n",
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"\n",
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" }\n",
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"},\n",
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"```\n",
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"A behavior called *minting_rule* is included to record the general expansion of system supply every day. The *total_supply* and *funds* state variables are incrased with these minted values.\n",
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"[To see the partial state update's code, click here](model/parts/system.py)"
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]
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},
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{
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"cell_type": "markdown",
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||
"metadata": {},
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"source": [
|
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"```\n",
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"{\n",
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" # participants.py \n",
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" 'policies': {\n",
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" 'completion': check_progress \n",
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" },\n",
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" 'variables': { \n",
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" 'sentiment': update_sentiment_on_completion, #not completing projects decays sentiment, completing bumps it\n",
|
||
" 'network': complete_proposal\n",
|
||
" }\n",
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"},\n",
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||
"```\n",
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"\n",
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||
"In the next phase of the turn, [to see the logic code, click here](model/parts/participants.py), the *check_progress* behavior checks for the completion of previously funded proposals. The code calculates the completion and failure rates as follows:\n",
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"\n",
|
||
"```\n",
|
||
"likelihood = 1.0/(base_completion_rate+np.log(grant_size))\n",
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||
"\n",
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||
"failure_rate = 1.0/(base_failure_rate+np.log(grant_size))\n",
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||
"if np.random.rand() < likelihood:\n",
|
||
" completed.append(j)\n",
|
||
"elif np.random.rand() < failure_rate:\n",
|
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" failed.append(j)\n",
|
||
"```\n",
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"With the base_completion_rate being 100 and the base_failure_rate as 200. \n",
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"\n",
|
||
"The mechanism then updates the respective *network* nodes and updates the sentiment variable on proposal completion. "
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||
]
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||
},
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{
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||
"cell_type": "markdown",
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||
"metadata": {},
|
||
"source": [
|
||
"```\n",
|
||
" # proposals.py\n",
|
||
" 'policies': {\n",
|
||
" 'release': trigger_function \n",
|
||
" },\n",
|
||
" 'variables': { \n",
|
||
" 'funds': decrement_funds, \n",
|
||
" 'sentiment': update_sentiment_on_release, #releasing funds can bump sentiment\n",
|
||
" 'network': update_proposals \n",
|
||
" }\n",
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||
"},\n",
|
||
" ```\n",
|
||
" \n",
|
||
"The [trigger release function](model/parts/proposals.py) checks to see if each proposal passes or not. If a proposal passes, funds are decremented by the amount of the proposal, while the proposal's status is changed in the network object."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"```\n",
|
||
"{ \n",
|
||
" # participants.py\n",
|
||
" 'policies': { \n",
|
||
" 'participants_act': participants_decisions\n",
|
||
" },\n",
|
||
" 'variables': {\n",
|
||
" 'network': update_tokens \n",
|
||
" }\n",
|
||
"}\n",
|
||
"```\n",
|
||
"\n",
|
||
"The Participants decide based on their affinity if which proposals they would like to support,[to see the logic code, click here](model/parts/participants.py). Proposals that participants have high affinity for receive more support and pledged tokens than proposals with lower affinity and sentiment. We then update everyone's holdings and their conviction for each proposal.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Model next steps\n",
|
||
"\n",
|
||
"The the model described above is the third iteration model that covers the core mechanisms of the Aragon Conviction Voting model. Below are next additional dynamics we can attend to enrich the model, and provide workstreams for subsequent iterations of this lab notebook.\n",
|
||
"\n",
|
||
"* Add a uniswap instance\n",
|
||
" * A next step to model the 1Hive ecosystem would be to model the Uniswap interface. With this interface, agents would be able to add or remove liquidity, buy or redeem Honey for more voting power, and ultimately enter or leave the system. \n",
|
||
"* Mixing of token holdings among participants\n",
|
||
" * Introducing heterogeneous token holdings would be another next step in creating a model more representative of the live system.\n",
|
||
"* Proposals which are good or no good together\n",
|
||
" * Introducing conflict \n",
|
||
"* Affects of outcomes on sentiment"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Simulation"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Configuration\n",
|
||
"Let's factor out into its own notebook where we review the config object and its partial state update blocks. We will initialize the network x object and pull out the simulation configuration, state_variables, and partial state update blocks from the [config.py](model/config.py)."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Params (config.py) : {'beta': 0.2, 'rho': 0.0025, 'alpha': 0.7937005259840998, 'gamma': 0.001, 'sensitivity': 0.75, 'tmin': 1, 'min_supp': 1, 'base_completion_rate': 45, 'base_failure_rate': 180, 'base_engagement_rate': 0.3, 'lowest_affinity_to_support': 0.3}\n"
|
||
]
|
||
},
|
||
{
|
||
"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": [
|
||
"from model import config\n",
|
||
"from model.parts.sys_params import initial_values\n",
|
||
"from model.parts.utils import *"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from copy import deepcopy\n",
|
||
"from cadCAD import configs\n",
|
||
"\n",
|
||
"# Initialize network x\n",
|
||
"for c in configs:\n",
|
||
" c.initial_state = deepcopy(c.initial_state)\n",
|
||
" c.initial_state['network'] = initialize_network(initial_values['n'],initial_values['m'],\n",
|
||
" initial_values['initial_funds'],\n",
|
||
" initial_values['supply'],c.sim_config['M'])\n",
|
||
" \n",
|
||
"# pull out configurations to illustrate\n",
|
||
"sim_config,state_variables,partial_state_update_blocks = config.get_configs()\n",
|
||
"state_variables['network'] = c.initial_state['network']"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"[{'policies': {'random': <function model.parts.system.driving_process(params, step, sL, s)>},\n",
|
||
" 'variables': {'network': <function model.parts.system.update_network(params, step, sL, s, _input)>,\n",
|
||
" 'effective_supply': <function model.parts.system.increment_supply(params, step, sL, s, _input)>}},\n",
|
||
" {'policies': {'random': <function model.parts.system.minting_rule(params, step, sL, s)>},\n",
|
||
" 'variables': {'total_supply': <function model.parts.system.mint_to_supply(params, step, sL, s, _input)>,\n",
|
||
" 'funds': <function model.parts.system.mint_to_funds(params, step, sL, s, _input)>}},\n",
|
||
" {'policies': {'completion': <function model.parts.participants.check_progress(params, step, sL, s)>},\n",
|
||
" 'variables': {'sentiment': <function model.parts.participants.update_sentiment_on_completion(params, step, sL, s, _input)>,\n",
|
||
" 'network': <function model.parts.participants.complete_proposal(params, step, sL, s, _input)>}},\n",
|
||
" {'policies': {'release': <function model.parts.proposals.trigger_function(params, step, sL, s)>},\n",
|
||
" 'variables': {'funds': <function model.parts.proposals.decrement_funds(params, step, sL, s, _input)>,\n",
|
||
" 'sentiment': <function model.parts.proposals.update_sentiment_on_release(params, step, sL, s, _input)>,\n",
|
||
" 'network': <function model.parts.proposals.update_proposals(params, step, sL, s, _input)>}},\n",
|
||
" {'policies': {'participants_act': <function model.parts.participants.participants_decisions(params, step, sL, s)>},\n",
|
||
" 'variables': {'network': <function model.parts.participants.update_tokens(params, step, sL, s, _input)>}}]"
|
||
]
|
||
},
|
||
"execution_count": 3,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"partial_state_update_blocks"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Parameters\n",
|
||
"\n",
|
||
"Initial values are the starting values for the simulation."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"{'initial_sentiment': 0.6,\n",
|
||
" 'n': 30,\n",
|
||
" 'm': 7,\n",
|
||
" 'initial_funds': 4867.21,\n",
|
||
" 'supply': 22392.22}"
|
||
]
|
||
},
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"initial_values"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"$n$ is initial participants, whereas $m$ is initial proposals.\n",
|
||
"\n",
|
||
"Sim_config holds the global hyperparameters for the simulations"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"{'beta': 0.2,\n",
|
||
" 'rho': 0.0025,\n",
|
||
" 'alpha': 0.7937005259840998,\n",
|
||
" 'gamma': 0.001,\n",
|
||
" 'sensitivity': 0.75,\n",
|
||
" 'tmin': 1,\n",
|
||
" 'min_supp': 1,\n",
|
||
" 'base_completion_rate': 45,\n",
|
||
" 'base_failure_rate': 180,\n",
|
||
" 'base_engagement_rate': 0.3,\n",
|
||
" 'lowest_affinity_to_support': 0.3}"
|
||
]
|
||
},
|
||
"execution_count": 5,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"sim_config[0]['M']"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Initial state variable values"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"{'network': <networkx.classes.digraph.DiGraph at 0x7fe4fb288c50>,\n",
|
||
" 'funds': 4867.21,\n",
|
||
" 'sentiment': 0.6,\n",
|
||
" 'effective_supply': 14020.008000000002,\n",
|
||
" 'total_supply': 22392.22}"
|
||
]
|
||
},
|
||
"execution_count": 6,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"state_variables"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Hyperparameter explanations:\n",
|
||
"* $\\beta$ = .2 Upper bound on share of funds dispersed in the example Trigger Function\n",
|
||
"* $\\rho$ = 0.002 Scale Parameter for the example Trigger Function\n",
|
||
"* $\\alpha$ : 0.79370 timescale set in days with 3 day halflife\n",
|
||
"* $\\gamma$: 0.001 The expansion of supply per per day\n",
|
||
"* sensitivity of participant decisions to changes in affinity \n",
|
||
"* tmin = 1 unit days; minimum periods passed before a proposal can pass\n",
|
||
"* min_supp = 50 number of tokens that must be stake for a proposal to be a candidate\n",
|
||
"* base_completion_rate': 45, # expected number of days to complete a proposals.\n",
|
||
"* base_failure_rate': [180], # expected number of days until a proposal will fail\n",
|
||
"* base_engagement_rate' :[0.3], # Probability of being active on a certain day if 100% sentiment\n",
|
||
"* lowest_affinity_to_support': [0.3],# lowest affinity to required to support a proposal\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"#### Exploring the State Data Structure\n",
|
||
"\n",
|
||
"A graph is a type of temporal data structure that evolves over time. A graph $\\mathcal{G}(\\mathcal{V},\\mathcal{E})$ consists of vertices or nodes, $\\mathcal{V} = \\{1...\\mathcal{V}\\}$ and is connected by edges $\\mathcal{E} \\subseteq \\mathcal{V} \\times \\mathcal{V}$.\n",
|
||
"\n",
|
||
"See *Schema of the states* above for more details\n",
|
||
"\n",
|
||
"\n",
|
||
"Let's explore!"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# To explore our model prior to the simulation, we extract key components from our networkX object into lists.\n",
|
||
"proposals = get_nodes_by_type(state_variables['network'], 'proposal')\n",
|
||
"participants = get_nodes_by_type(state_variables['network'], 'participant')\n",
|
||
"supporters = get_edges_by_type(state_variables['network'], 'support')\n",
|
||
"influencers = get_edges_by_type(state_variables['network'], 'influence')\n",
|
||
"competitors = get_edges_by_type(state_variables['network'], 'conflict')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"{'type': 'participant',\n",
|
||
" 'holdings': 758.4701233898538,\n",
|
||
" 'sentiment': 0.36153123371230367}"
|
||
]
|
||
},
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"#sample a participant\n",
|
||
"state_variables['network'].nodes[participants[0]]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Text(0, 0.5, 'Count of Participants')"
|
||
]
|
||
},
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"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": [
|
||
"# Let's look at the distribution of participant holdings at the start of the sim\n",
|
||
"plt.hist([ state_variables['network'].nodes[i]['holdings'] for i in participants])\n",
|
||
"plt.title('Histogram of Participants Token Holdings')\n",
|
||
"plt.xlabel('Amount of Honey')\n",
|
||
"plt.ylabel('Count of Participants')\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/home/aclarkdata/anaconda3/lib/python3.7/site-packages/networkx/drawing/nx_pylab.py:563: MatplotlibDeprecationWarning: \n",
|
||
"The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
|
||
" if not cb.iterable(width):\n",
|
||
"/home/aclarkdata/anaconda3/lib/python3.7/site-packages/networkx/drawing/nx_pylab.py:660: MatplotlibDeprecationWarning: \n",
|
||
"The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
|
||
" if cb.iterable(node_size): # many node sizes\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Text(0.5, 1.0, 'Participants Social Network')"
|
||
]
|
||
},
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"nx.draw_spring(state_variables['network'], nodelist = participants, edgelist=influencers)\n",
|
||
"plt.title('Participants Social Network')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"{'type': 'proposal',\n",
|
||
" 'conviction': 0,\n",
|
||
" 'status': 'candidate',\n",
|
||
" 'age': 0,\n",
|
||
" 'funds_requested': 1635.5704024958463,\n",
|
||
" 'trigger': inf}"
|
||
]
|
||
},
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"#lets look at proposals\n",
|
||
"state_variables['network'].nodes[proposals[0]]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Proposals initially start without any conviction, and with the status of a candidate. If the proposal's amount of conviction is greater than it's trigger, then the proposal moves to active and it's funds requested are granted. "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"All initial proposal start with 0 conviction and state 'candidate'we can simply examine the amounts of funds requested"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"funds_array = np.array([ state_variables['network'].nodes[i]['funds_requested'] for i in proposals])\n",
|
||
"conviction_required = np.array([trigger_threshold(r, initial_values['initial_funds'], initial_values['supply'], sim_config[0]['M']['alpha'],sim_config[0]['M']) for r in funds_array])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Text(0, 0.5, 'Amount of Honey requested(as a Fraction of Funds available)')"
|
||
]
|
||
},
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"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": [
|
||
"plt.bar( proposals, funds_array/initial_values['initial_funds'])\n",
|
||
"plt.title('Bar chart of Proposals Funds Requested')\n",
|
||
"plt.xlabel('Proposal identifier')\n",
|
||
"plt.ylabel('Amount of Honey requested(as a Fraction of Funds available)')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Text(0, 0.5, 'Amount of Conviction')"
|
||
]
|
||
},
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"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": [
|
||
"plt.bar( proposals, conviction_required)\n",
|
||
"plt.title('Bar chart of Proposals Conviction Required')\n",
|
||
"plt.xlabel('Proposal identifier')\n",
|
||
"plt.ylabel('Amount of Conviction')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Conviction is a concept that arises in the edges between participants and proposals in the initial conditions there are no votes yet so we can look at that later however, the voting choices are driven by underlying affinities which we can see now."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Text(0.5, 55.73999999999998, 'Participant_id')"
|
||
]
|
||
},
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1440x360 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"m = len(proposals)\n",
|
||
"n = len(participants)\n",
|
||
"\n",
|
||
"affinities = np.empty((n,m))\n",
|
||
"for i_ind in range(n):\n",
|
||
" for j_ind in range(m):\n",
|
||
" i = participants[i_ind]\n",
|
||
" j = proposals[j_ind]\n",
|
||
" affinities[i_ind][j_ind] = state_variables['network'].edges[(i,j)]['affinity']\n",
|
||
"\n",
|
||
"dims = (20, 5)\n",
|
||
"fig, ax = plt.subplots(figsize=dims)\n",
|
||
"\n",
|
||
"sns.heatmap(affinities.T,\n",
|
||
" xticklabels=participants,\n",
|
||
" yticklabels=proposals,\n",
|
||
" square=True,\n",
|
||
" cbar=True,\n",
|
||
" cmap = plt.cm.RdYlGn,\n",
|
||
" ax=ax)\n",
|
||
"\n",
|
||
"plt.title('Affinities between participants and proposals')\n",
|
||
"plt.ylabel('Proposal_id')\n",
|
||
"plt.xlabel('Participant_id')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Run simulation\n",
|
||
"\n",
|
||
"Now we will create the final system configuration, append the genesis states we created, and run our simulation."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from cadCAD.configuration import Experiment\n",
|
||
"\n",
|
||
"# Create configuration\n",
|
||
"exp = Experiment()\n",
|
||
"\n",
|
||
"exp.append_configs(\n",
|
||
" sim_configs=sim_config,\n",
|
||
" initial_state=state_variables,\n",
|
||
" seeds=1,\n",
|
||
" partial_state_update_blocks=partial_state_update_blocks\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
" ___________ ____\n",
|
||
" ________ __ ___/ / ____/ | / __ \\\n",
|
||
" / ___/ __` / __ / / / /| | / / / /\n",
|
||
"/ /__/ /_/ / /_/ / /___/ ___ |/ /_/ /\n",
|
||
"\\___/\\__,_/\\__,_/\\____/_/ |_/_____/\n",
|
||
"by cadCAD\n",
|
||
"\n",
|
||
"Execution Mode: local_proc\n",
|
||
"Configuration Count: 2\n",
|
||
"Dimensions of the first simulation: (Timesteps, Params, Runs, Vars) = (100, 11, 1, 5)\n",
|
||
"Execution Method: local_simulations\n",
|
||
"SimIDs : [0, 1]\n",
|
||
"SubsetIDs: [0, 0]\n",
|
||
"Ns : [0, 0]\n",
|
||
"ExpIDs : [0, 0]\n",
|
||
"Total execution time: 324.40s\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import pandas as pd\n",
|
||
"from model import run\n",
|
||
"pd.options.display.float_format = '{:.2f}'.format\n",
|
||
"\n",
|
||
"%matplotlib inline\n",
|
||
"\n",
|
||
"rdf = run.run()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"After the simulation has run successfully, we perform some postprocessing to extract node and edge values from the network object and add as columns to the pandas dataframe. For the rdf, we take only the values at the last substep of each timestep in the simulation."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df= run.postprocessing(rdf,0)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"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>funds</th>\n",
|
||
" <th>sentiment</th>\n",
|
||
" <th>effective_supply</th>\n",
|
||
" <th>total_supply</th>\n",
|
||
" <th>simulation</th>\n",
|
||
" <th>subset</th>\n",
|
||
" <th>run</th>\n",
|
||
" <th>substep</th>\n",
|
||
" <th>timestep</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>funds_requested</th>\n",
|
||
" <th>share_of_funds_requested</th>\n",
|
||
" <th>share_of_funds_requested_all</th>\n",
|
||
" <th>triggers</th>\n",
|
||
" <th>conviction_share_of_trigger</th>\n",
|
||
" <th>age</th>\n",
|
||
" <th>age_all</th>\n",
|
||
" <th>conviction_all</th>\n",
|
||
" <th>triggers_all</th>\n",
|
||
" <th>conviction_share_of_trigger_all</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
|
||
" <td>4889.60</td>\n",
|
||
" <td>0.60</td>\n",
|
||
" <td>14020.01</td>\n",
|
||
" <td>22414.61</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>[1635.5704024958463, 2177.89207462645, 375.000...</td>\n",
|
||
" <td>[0.33449968502669863, 0.4454129347614804, 0.07...</td>\n",
|
||
" <td>[0.33449968502669863, 0.4454129347614804, 0.07...</td>\n",
|
||
" <td>[inf, inf, 11238.455532714786, 633287.50254494...</td>\n",
|
||
" <td>[0.0, 0.0, 0.003740978192469739, 0.00047483075...</td>\n",
|
||
" <td>[1, 1, 1, 1, 1, 1]</td>\n",
|
||
" <td>[1, 1, 1, 1, 1, 1, 1]</td>\n",
|
||
" <td>[266.503232036339, 52.749078945560456, 42.0428...</td>\n",
|
||
" <td>[inf, inf, 11238.455532714786, 633287.50254494...</td>\n",
|
||
" <td>[0.0, 0.0, 0.003740978192469739, 0.00047483075...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
|
||
" <td>4912.02</td>\n",
|
||
" <td>0.60</td>\n",
|
||
" <td>14020.01</td>\n",
|
||
" <td>22437.03</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>[1635.5704024958463, 2177.89207462645, 375.000...</td>\n",
|
||
" <td>[0.3329732894576922, 0.44338041766077285, 0.07...</td>\n",
|
||
" <td>[0.3329732894576922, 0.44338041766077285, 0.07...</td>\n",
|
||
" <td>[inf, inf, 11174.230590474432, 572947.78483859...</td>\n",
|
||
" <td>[0.0, 0.0, 0.015214500767545958, 0.00094140099...</td>\n",
|
||
" <td>[2, 2, 2, 2, 2, 2, 1, 1]</td>\n",
|
||
" <td>[2, 2, 2, 2, 2, 2, 2, 1, 1]</td>\n",
|
||
" <td>[541.366516506568, 94.6160506498286, 170.01033...</td>\n",
|
||
" <td>[inf, inf, 11174.230590474432, 572947.78483859...</td>\n",
|
||
" <td>[0.0, 0.0, 0.015214500767545958, 0.00094140099...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>15</th>\n",
|
||
" <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
|
||
" <td>4934.45</td>\n",
|
||
" <td>0.60</td>\n",
|
||
" <td>14020.01</td>\n",
|
||
" <td>22459.46</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>[1635.5704024958463, 2177.89207462645, 375.000...</td>\n",
|
||
" <td>[0.3314592555152592, 0.44136436104901916, 0.07...</td>\n",
|
||
" <td>[0.3314592555152592, 0.44136436104901916, 0.07...</td>\n",
|
||
" <td>[inf, inf, 11111.070175797702, 521232.83488613...</td>\n",
|
||
" <td>[0.0, 0.0, 0.02444213075591761, 0.001102536686...</td>\n",
|
||
" <td>[3, 3, 3, 3, 3, 3, 2, 2]</td>\n",
|
||
" <td>[3, 3, 3, 3, 3, 3, 3, 2, 2]</td>\n",
|
||
" <td>[622.9283976334391, 127.84588811286764, 271.57...</td>\n",
|
||
" <td>[inf, inf, 11111.070175797702, 521232.83488613...</td>\n",
|
||
" <td>[0.0, 0.0, 0.02444213075591761, 0.001102536686...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>20</th>\n",
|
||
" <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
|
||
" <td>4956.91</td>\n",
|
||
" <td>0.60</td>\n",
|
||
" <td>14069.75</td>\n",
|
||
" <td>22481.92</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>[1635.5704024958463, 2177.89207462645, 375.000...</td>\n",
|
||
" <td>[0.3299574343848422, 0.43936456676780056, 0.07...</td>\n",
|
||
" <td>[0.3299574343848422, 0.43936456676780056, 0.07...</td>\n",
|
||
" <td>[inf, inf, 11048.948812591681, 476557.06400915...</td>\n",
|
||
" <td>[0.0, 0.0, 0.03187567649261185, 0.001264695738...</td>\n",
|
||
" <td>[4, 4, 4, 4, 4, 4, 3, 3, 1]</td>\n",
|
||
" <td>[4, 4, 4, 4, 4, 4, 4, 3, 3, 1, 1]</td>\n",
|
||
" <td>[687.6641055840894, 154.22042758564788, 352.19...</td>\n",
|
||
" <td>[inf, inf, 11048.948812591681, 476557.06400915...</td>\n",
|
||
" <td>[0.0, 0.0, 0.03187567649261185, 0.001264695738...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>25</th>\n",
|
||
" <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
|
||
" <td>4979.40</td>\n",
|
||
" <td>0.60</td>\n",
|
||
" <td>14069.75</td>\n",
|
||
" <td>22504.41</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>[1635.5704024958463, 2177.89207462645, 375.000...</td>\n",
|
||
" <td>[0.3284676796309342, 0.4373808398265926, 0.075...</td>\n",
|
||
" <td>[0.3284676796309342, 0.4373808398265926, 0.075...</td>\n",
|
||
" <td>[inf, inf, 11026.824827540717, 439237.51613763...</td>\n",
|
||
" <td>[0.0, 0.0, 0.036126338828180254, 0.00142278434...</td>\n",
|
||
" <td>[5, 5, 5, 5, 5, 5, 4, 4, 2, 1, 1]</td>\n",
|
||
" <td>[5, 5, 5, 5, 5, 5, 5, 4, 4, 2, 2, 1, 1]</td>\n",
|
||
" <td>[739.0448710344735, 152.79894968865366, 398.35...</td>\n",
|
||
" <td>[inf, inf, 11026.824827540717, 439237.51613763...</td>\n",
|
||
" <td>[0.0, 0.0, 0.036126338828180254, 0.00142278434...</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>5 rows × 33 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" network funds sentiment \\\n",
|
||
"5 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4889.60 0.60 \n",
|
||
"10 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4912.02 0.60 \n",
|
||
"15 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4934.45 0.60 \n",
|
||
"20 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4956.91 0.60 \n",
|
||
"25 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4979.40 0.60 \n",
|
||
"\n",
|
||
" effective_supply total_supply simulation subset run substep \\\n",
|
||
"5 14020.01 22414.61 0 0 1 5 \n",
|
||
"10 14020.01 22437.03 0 0 1 5 \n",
|
||
"15 14020.01 22459.46 0 0 1 5 \n",
|
||
"20 14069.75 22481.92 0 0 1 5 \n",
|
||
"25 14069.75 22504.41 0 0 1 5 \n",
|
||
"\n",
|
||
" timestep ... funds_requested \\\n",
|
||
"5 1 ... [1635.5704024958463, 2177.89207462645, 375.000... \n",
|
||
"10 2 ... [1635.5704024958463, 2177.89207462645, 375.000... \n",
|
||
"15 3 ... [1635.5704024958463, 2177.89207462645, 375.000... \n",
|
||
"20 4 ... [1635.5704024958463, 2177.89207462645, 375.000... \n",
|
||
"25 5 ... [1635.5704024958463, 2177.89207462645, 375.000... \n",
|
||
"\n",
|
||
" share_of_funds_requested \\\n",
|
||
"5 [0.33449968502669863, 0.4454129347614804, 0.07... \n",
|
||
"10 [0.3329732894576922, 0.44338041766077285, 0.07... \n",
|
||
"15 [0.3314592555152592, 0.44136436104901916, 0.07... \n",
|
||
"20 [0.3299574343848422, 0.43936456676780056, 0.07... \n",
|
||
"25 [0.3284676796309342, 0.4373808398265926, 0.075... \n",
|
||
"\n",
|
||
" share_of_funds_requested_all \\\n",
|
||
"5 [0.33449968502669863, 0.4454129347614804, 0.07... \n",
|
||
"10 [0.3329732894576922, 0.44338041766077285, 0.07... \n",
|
||
"15 [0.3314592555152592, 0.44136436104901916, 0.07... \n",
|
||
"20 [0.3299574343848422, 0.43936456676780056, 0.07... \n",
|
||
"25 [0.3284676796309342, 0.4373808398265926, 0.075... \n",
|
||
"\n",
|
||
" triggers \\\n",
|
||
"5 [inf, inf, 11238.455532714786, 633287.50254494... \n",
|
||
"10 [inf, inf, 11174.230590474432, 572947.78483859... \n",
|
||
"15 [inf, inf, 11111.070175797702, 521232.83488613... \n",
|
||
"20 [inf, inf, 11048.948812591681, 476557.06400915... \n",
|
||
"25 [inf, inf, 11026.824827540717, 439237.51613763... \n",
|
||
"\n",
|
||
" conviction_share_of_trigger \\\n",
|
||
"5 [0.0, 0.0, 0.003740978192469739, 0.00047483075... \n",
|
||
"10 [0.0, 0.0, 0.015214500767545958, 0.00094140099... \n",
|
||
"15 [0.0, 0.0, 0.02444213075591761, 0.001102536686... \n",
|
||
"20 [0.0, 0.0, 0.03187567649261185, 0.001264695738... \n",
|
||
"25 [0.0, 0.0, 0.036126338828180254, 0.00142278434... \n",
|
||
"\n",
|
||
" age \\\n",
|
||
"5 [1, 1, 1, 1, 1, 1] \n",
|
||
"10 [2, 2, 2, 2, 2, 2, 1, 1] \n",
|
||
"15 [3, 3, 3, 3, 3, 3, 2, 2] \n",
|
||
"20 [4, 4, 4, 4, 4, 4, 3, 3, 1] \n",
|
||
"25 [5, 5, 5, 5, 5, 5, 4, 4, 2, 1, 1] \n",
|
||
"\n",
|
||
" age_all \\\n",
|
||
"5 [1, 1, 1, 1, 1, 1, 1] \n",
|
||
"10 [2, 2, 2, 2, 2, 2, 2, 1, 1] \n",
|
||
"15 [3, 3, 3, 3, 3, 3, 3, 2, 2] \n",
|
||
"20 [4, 4, 4, 4, 4, 4, 4, 3, 3, 1, 1] \n",
|
||
"25 [5, 5, 5, 5, 5, 5, 5, 4, 4, 2, 2, 1, 1] \n",
|
||
"\n",
|
||
" conviction_all \\\n",
|
||
"5 [266.503232036339, 52.749078945560456, 42.0428... \n",
|
||
"10 [541.366516506568, 94.6160506498286, 170.01033... \n",
|
||
"15 [622.9283976334391, 127.84588811286764, 271.57... \n",
|
||
"20 [687.6641055840894, 154.22042758564788, 352.19... \n",
|
||
"25 [739.0448710344735, 152.79894968865366, 398.35... \n",
|
||
"\n",
|
||
" triggers_all \\\n",
|
||
"5 [inf, inf, 11238.455532714786, 633287.50254494... \n",
|
||
"10 [inf, inf, 11174.230590474432, 572947.78483859... \n",
|
||
"15 [inf, inf, 11111.070175797702, 521232.83488613... \n",
|
||
"20 [inf, inf, 11048.948812591681, 476557.06400915... \n",
|
||
"25 [inf, inf, 11026.824827540717, 439237.51613763... \n",
|
||
"\n",
|
||
" conviction_share_of_trigger_all \n",
|
||
"5 [0.0, 0.0, 0.003740978192469739, 0.00047483075... \n",
|
||
"10 [0.0, 0.0, 0.015214500767545958, 0.00094140099... \n",
|
||
"15 [0.0, 0.0, 0.02444213075591761, 0.001102536686... \n",
|
||
"20 [0.0, 0.0, 0.03187567649261185, 0.001264695738... \n",
|
||
"25 [0.0, 0.0, 0.036126338828180254, 0.00142278434... \n",
|
||
"\n",
|
||
"[5 rows x 33 columns]"
|
||
]
|
||
},
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df.head(5)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<matplotlib.axes._subplots.AxesSubplot at 0x7fe4f05b7290>"
|
||
]
|
||
},
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"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": [
|
||
"df.plot('timestep','sentiment')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"The above plot demonstrates system sentiment changing over time as proposals pass or fail."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<matplotlib.axes._subplots.AxesSubplot at 0x7fe4c76d6b10>"
|
||
]
|
||
},
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"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": [
|
||
"df.plot('timestep',['funds', 'candidate_funds'])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Funds represent the total available funds, whereas candidate funds represent total funds requested by candidate proposals."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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M6QGgB+CfkEhERET/XCQzByJSSUTKhf8uDqAtgFkARgM4I7zaxQAGRDE+IiJKLSISyU9cRTVzUB3A2LAlsACYoKqDRORwAL1F5H0AAwFcl9uGBhS/1oxV399/4Dc5lUtNOufYGRcA8Ocdp7vbfXLKt2bs6U3+Qy51DjBj/Wr2dHNrLD7HjN03YZ6be3TVkmbsoY9mmbGRN7Vyt9uidAMzphtzbIK3gxQrbcY+XpxTY7+/dNpa0YxNtkMAgCnLl5ixrgceb8Y++t1vrNjmge/NWPlHzjBjADDy8FfN2PIVZggAcF7Wa2bs0wX2/nfHdfbjDwC3bFpmxprs45epzly11Yydf+03bu4PM+xaurad/dOU2rfe14xNmWY/7xs7NnO365U6Lqnlv+AW1LGf22Lf2+9pACjilMcenlnBzS329Adm7JCGdiPS0w8q5263++AcJ3wBACtv8+/P5R//7sZhf5xQHovqnIMpAPZR1eII+paXCEsZByI492ABgMtU1f4EISIiyiMsZUwWycyBBn2i/1bKqKrfA4j1VAsREdGeLrJqBRFJF5FpAJYDGK6q9lw8ERFRPorzzIGItBOROSIyV0RuyyG+v4iMFpHvRWSGiLTf3ccjNqWMIlJvV3MTSxmHZ67Jv0ESERFFKFwT6GUAJwI4BMC5InLITle7C8BHqnoYgHMAvLK7txt5V0ZVXYOgSqHdP8jpoaqNVbVx23T/5BgiIqL/sCYA5qrqr2GvoQ8AdNjpOgqgTPjvsgAW7+6NRtV4qRKA7eEaB9mljI9HMRYiIqIYnxy4H4CFCb//AeDIna5zH4AvReQaACUBHLe7NxpVKWNlAH3C6ZI0BNMhg0TkWgC3ANgXwAwRGayqXbwNtb/BLjksfkkLdxBLq9nlOsVm/WoPfvZ8bG6w83Pzlzte+dGMba29lzsmfL3ADJ3+kL+IZJEJdhnQoD/87m76m13X+UH9fczYygM/dLc7Ys52M1bh+P5ubstzqpmxcf0WmjEAKNLWLh/b2tY/glVvpP3cZ3W0y+wuGjjD3e6gYXbN4ZdX/eTmPv6t3YNszYNfuLkbrnnIjJ3atIoZy8QqrPzsFzPeqP+fZqxyVf+D9qa7dp4V/UvzeX7bvxOu2t+MXXFCVTd38AD7/TX5TrtcseL709ztbttod0Bc26iGm1tz9t1mbK4sd3OP6m2X9C5tcKCb++uczWbs+tbFzdjd/zvZ3W5GY7uMde9r7nFzJ95zohtv4kb/m0TkCgBXJFzUI1zo7584F0BvVX1aRJoBeEdE6qnm0qbUEdXOwc8AtiPYMRAA6eHlRwLYCGA2gEkAukYyulx4OwZEewpvx4BoT5NbV+78krjir2ERgMRvSjmtHnwZwkPzqjpBRIoBqIjghP9/JW4tm/siWOegPoDiANxZAyIioj3cZAC1ReQAESmC4ITDgTtd53cAbQBARA4GUAxALsuj+eK2zsGOLkoiMgnBHhIREVG+So/p+jqqmiEiVwMYhmCWvZeq/igiDwD4TlUHArgJwBsicgOCkxM7h39n/7XYtGxOXOdARAoDuBDG8smJx2hePOFQXNrQPjZNRET0XxZ+cR6802X3JPz7JwBH5eVtxnWdg1cAfKWqXxu5O0oZuWNARESUt+LUsrkdgJkici+ASgCujHZkRESUKtLjW8oYiTi1bJ4tIl0AnADg3N0pwSAiIqJ/T3bznIV/d6MijQGMxV+ljBNUtbWIZCEoccxCUNHwkqre5W0rI2u4eQcU/v6FOPtGhTKdWmvJZZ8qvYgf9+zOPlGmXWudVcgfU5Zzu4Wy7FhGLvU/Xu72XB7GwuKM2bmvgD+u9DR/wszbL01z3i4ZubzeCsm/n6hT50tNRpb/WHjPbVG1HyfN5TUjGf7tumNyWg2n5eN3liznOfIep7Rc3vNpzus8K5f3iDjb3pbpr09SOM1+jrz7k5tC3nOQ2+efw3v8AWDGKr/FTsOKd+Tb1/vavc4o+D+GAH659ONYTlnErWVzOVUtGl7eC39VNBAREVEBiVsp4zoAkKBnc3EEJRlERET5Kj3yTkPxEruWzSLyFoClCBZDejGq8REREaWq2JUyquolAKoAmAXg7JxyE1s2v9HDX1eeiIiI/pnYlTKGl2WKyAcImjC9lUPOjrWovRMSiYiIdkVcV0iMSpxKGeeISK3wMgFwKoIGTERERFSAYtOyGcAXAL4WkTIIyhunYxe6MnqlctjiFztoht2ydGPJ0k4iUHKzXWKkZfY2Y7mVJnmKeqV9AFZmrrJvd5t/u145VeWMkmZsQ1F//7IcSpixQmv9viCrShY1YxWK2I8xABTass6MrS/spmJzxnozViTNbmVbdr2dBwDrypQ1Y6U3bHRzV9g3i/JF7dbjAFB04xoztqqI8/7JACpstcO63X7/SAm/NflStV+r5Yv596dYmv2awlb/Pb8aa81YRbXf85sK+6/zEpvsludbSjjjBVAivZQZK/LnEjd3RUn7I7xwmv3+AfzXeRXnPb8xl/tTcqtdBp5Wsryb22Ce/b4FEPQZzCecOUgW1TkH2S2bFWHLZlXNUtWjVLU+gFEAOmRXL8SNt2NAtKfwdgyIaM8W1cxBdsvmDWGTpW9EZIiqTgwXSPK/dhAREeUhLp+cLJKZAw38bZ2D8DDDkwhORCQiIqIIxG2dg6sBDFRV90BbYiljjzcGe1clIiKifyiyUkZVzQTQMKxa6C8ixwA4E0CrXcjdUcqIjGEsZSQiot2SzqMKSSJfMFJV1wAYDaA1gFoA5orIfAT9FuZGOTYiIqJUFMnMgYhUArA9XAApe52Dx1V134TrbFDVWlGMj4iIUgtPSEwW1WGF6gDGSlBcn92yeZCI9AbQEsBaAMVFpKGqTnO35LRH/rH5nW7q9H5tzNi5ZeubsbkdX3K3+/7zR5mx75bateEA8MR9k83YM0/Z2wWA39fatWcDFvq3W26pXXc+/+5LzNjd475yt3vDYXXMWO0RM93cCu3a2UH169knrptuxprudYSbO2f9AjPWuExDM1b5o77udsee18KMlSlygJuratek957V383dt6S9SEKJpoPMWJVZXdzt1i1V14xlTRrt5pZ4e6oZm/mI/b4EgKuG26+b7ycudHPL17Tr7BcssevzJ/ed7263+V0NzFipmb+7uWPutO/vcU+NdXOr19vHjM1c5rSdB1Ajw16bYc1VZ5mxEqv9x/ic2T+ZsRsb1XRzD23UzI3bqy9QXotq5yC7ZXNiKWPTMHazqn4c0biIiCgFcRGkZLFq2RzFWIiIiChZ3EoZAeBhEZkhIs+KiL/+JxEREeW5uLVsvh1AXQBHACgP4NaccpPWOehhHzMlIiLaFelpEslPXMWqZbOqPhVevFVE3gLQ3cj5a50DHc3DEURERHkoVqWMIlJZVZeELZtPA+Cfzk5ERJQHuAhSsti0bA5LGUeFOw4CYBqAq3Lb0NVj3zNj75y5v5v74w3fmrFO3exSufe+edzdbv0FI8zYTR/bZXIA8PRrx5ix5wYvcnN//sheM+pKp7wSAEqpXQZZ7e43zdgnV/tlgXvfMNCMZRzi9189f/wwM7ZhW6abe+ZBdvvdJpP80r/L19llg02q2M/fHxXslswAsLiQ3ZpXt/ktm/de+JsZu6zBmW7uKQN6mbHPP+9kxir0GOVu95PnepqxjRNOcnMz7jjMjF034Hs3d+jLdqlcqX39dsJdj7N7upW4/DQz1qyy/TkDANsvv8CMjVg6wc09em0xM7Zosf86r3CGXQJbZr5dLgoAvS48xIz1WDbFjM1e7ZdIvj3bfv8UPsxvtf7n5U+58ZK9O7hxyjuxadkcXt4GwEAARQAcDuDSSEZHREQpheccJItVy2YABwOoBqCuqmaJiL+bSURERHkubuscdAVwnqpmhddbHsX4iIiIUlnc1jk4EMDZYZniEBGpbeTuKGX88fPZBTlsIiLaA6WLRPITV3Fb56AogC2q2hjAGwByPJNKVXuoamNVbXzoKfb67kRERPTPxWqdAwB/APg0DPUH8FZkAyMiopQR52/xUZDg8H8B3+jf1zn4EsDjAFoA+FlVe4lIKwBPqqpbKzevRX3zDpQbcYc7jrRur5mxDS+c6+Z2HTnOjA0sbZdLpTX2SwqLP/KqGetzmd0pEgDa7m93/Zu8zC9rWrbJLt286Hm7rAkA6jezS0anZtj7nwdjm7vdu5rZ56NeVKiam1uqz3Aztmhplpv74hV2J8k7j7C71R37sV3aBwBjzrjcjF33lV8q90jzk81Yy4/83FmzVpixRXd3dXPLfG133Uw76jgz1mW8XcIKAG9UrGrGrvlzsZs7dan9Wh13qP3cAYD+YpeiFvvGLgUGgJvPPNiM3d3ELrHr98tgd7uHVbJfy/WK+/dn6Aq7TLLdvi3d3DNH9DNjZYulm7HXV/qljGnHHGnGhmy1S3IBoG55/31ds8y1+fYX/MT+F0ayoN6Qju/Ecq8kqsMK1QEsEpHNAP4EkKGqgwAcA+DZ8PIvAdgFsxHydgxSjbdjQP9t3o5BqvF2DGjPkJ4WzU9cRTW07JbNxQGUBlBCRJqqanNVLRte/jmA3hGNj4iIKGVFsnOgAbNls4iUAXAsgM8iGB4REVFKi+yExHDp5CkAagF4OaFlMxD0VRipqusiGRwREaUUnpCYLG6ljNnOBfC+lZu4zsEHS1fn91CJiIhSStxKGWeKSEUATQB0dHJ2tGz2qhWIiIh2RZz7HEQhkpkDEakkIuXCf2e3bM5e6vAMAINUdUsUYyMiIkp1Uc0cVAcwVkTSEHRlnBC2bG4D4EkAy0XkGwCdVdUtPvbWMii/xW93+ng3uzxp/9LPmrGPbqzubhcti5qhSZvnuKlrq9q1/YV+2e7mtp1h15b3fmOWm3vCAUXMWP8+f5qx0564yd1ut+/stsurJtnbBYBGZ9otg/c6138ORl5tt77eXMYvvzytyRNmrOTJ9vO3sX0jd7u62q6xf652AzcXs+02xpVK2M8dALzY1143oEwT+/l5tLJd6w4AW6d/YcbuamK3ZAaARgO+NmNTjzrczUXdMmZIJ052U2+qaH/k/fl4dzPW8J133O0+3Nj+nvXDKv9927yy3bK51Rf2WgQA8MnA3+3g2ye6uW9PXmnGDty3sBkbeZfdMhsA0tImmrEP3vXXeXnoSbvdPQBg+LV+fDfwnINksSplBPAqgCaqeiCA9wDcFdH4iIiIUlbcujIqgOyvBWUB+EulERERUZ6LVSmjiHQBMDhcIXEdgKZRjY+IiFJHnFcrjELcShlvANBeVasiaLr0TE65iaWMfXqOLLhBExERpYA4lTKeCKBBwmJIHwIYauTsKGVcteV9ljISEdFu4QmJyeJUyjgLQFkROSi8WvZlREREVICimjmoDKBPeN5BGoCPwlLGywF8IiJZCLo1Xprbhjp9/rkZ+33dVjd34nl2m9W9t5zi5pZ88GkztqSdXULUxmnJDAAPnFbLjF23oaSbO+rzaWas3bkHurkLO7U1YyWfK2vGDuv3sbvdS+qXN2Mv3farm3v0QzXM2NvnHerm/lHdXGAT3393rJv72+t2GaR88YsZ02X+ap0b+vYxY5nb/TbS8ycsM2MvTbzKzR0yoJQZO+dbe7tYBvxazi4b3NqxkxlbfPTt7piKX2e3H19286du7kd322WSt4z32y5vuccuhfvt+HvM2Jz727jbXXjKnWbssRubublpRQ4wY0Nm2K3HAaBYd3tcU1f63WOXv2C/lv942273XLJLZXe7PTrb7833XrU/owDgsRJ2CWV+4yJIyaJqvDRDVQ8DcBiA7QhWRASAaQA2ASgJYBmAP6IYX268HQOiPYW3Y0BEe7aoz8+8DsmHDh4H8Kyq1kIwc3BZJKMiIiJKYZHtHIhIVQAnAXgz/F0QtGnOnqfug6A7IxERUb5KF4nkJ66inDl4DsAtALIPtlYAsEZVM8Lf/wCwXxQDIyIiSmVRVSucDGC5qk75l/k71jlYPMI/AYmIiCg36WnR/MRVVEM7CsCpIjIfwAcIDic8D6CciGRXUFQFsCinZFXtoaqNVbVxlePss/uJiIjon4uqWuF2Va2qqjUAnANglKqeD2A0gpbNAHAxgAFRjI+IiFILz6HTJBgAACAASURBVDlIFukKieE6B2/gr2ZLMwH0FpH3AQxEUM3gGnPy2WZs/Jqpbm65N+wWx/um2W1W/9zbrt0HgMIrcpzwAABkbvPbSN/81g9mrOszdntqANjY384tOXGhm1t0SG8zVu7O483Y6QeVc7d7w5vTzVilF+u6uX+eYLfWWPNgjotn7lDmrSPNWJEe9pgAYORtrc3YwzXXm7HHatitugHglkb7mLFCHVq5uY3u723Glhb2n4MsZw3RBZ0vN2OfLfzS3e6JYn98VL/MXscAAD7oOduMlW3i19Hf3s9um/3RK0vd3AV32+s6lBxstx//5ahH3e1Wm2S3+V6xbZWbO7furWas3SVV3dx1DVqZsYPvfNLNXTnNPt97c+XmZmxxW3/NB3R9xQxtO+t/bqpUquBvmwpM1Ec8rgMwOfwBgh2CugAWALhMVf1VjIiIiCjPRdmVMbuU8WEANwKAqn4fxqIaFhERpaB0/tlJEqdSRiIiIoqB/3wpY4+ew/J4dERElGrSRCL5iauoDitklzK2B1AMQBkReVdVL9iV5MSWzdg8gC2biYiI8lAkOweqejuA2wFARFoB6L6rOwZERER5jeccJIu0lHFnInItgvMQ9gUwQ0QGq2oXL+d//fqasSK5PNvfXXquGZtwygNmbBGAvb+040UyzBDW16vmjunpOiXs7Y71D6H8uXyTHbS79gIAynesbcZ01Rozdt5d493tXlPXLk0q2/pwN3ft/vYCV3u90t3NvWGSXaa6uY5fHtZttl0O17KN3S6456hv3e3eN9suo1s97SM395Pudllnepr/Nj61pt1C95cW9uN4KID2Vx5sxk87d5sZm9rBb6ldZ8YKM9atlV/KmDncXhW1eYeKbi6usj8vah1iv0lGbvQnKMve/LgZm3633/698g/dzNjW+/q5uTrRLundvnKzm9t2s9N183e71LREbq2Ne91nx9bYZd4AsK2n3667qP+RQXko0lLGcJ2DZxMuOhLARgCzAQwF0DWKceXG2zEg2lN4OwZEtGeLwzoHiS2b+yJY56A+gOIA3FkDIiKivJAm0fzEVWxaNgOAqg7WEIBJCPorEBERUQGK5ToHIlIYwIUIDi38TWIp4+rRv+bvKImIaI+XLtH8xFVc1zl4BcBXqvp1TsHErozlW9fMt3ESERGlotitcyAi9wKoBODKiMZGREQpJi3OJwBEIE4tmy8QkS4ATgBwrqpyWWUiIqIIxK1lcw8A2wGsFpGtAF5S1bu8bfww4XczlvXgFe7tl3jkJTO24YYjzNiIJX5tf0aWvV/Trqpfh31774lm7Lx5dl05AOxVZy8zdmprv3a8+Bj73A1x9qjnDb3G3e60tQvM2KZD7ZpzAFg8zm4NW75YETd3xgp7zYfGTnk3AEzf216LYtVdA8zYMw82cbd76BlHmbF527e4uR2xrxlbmuW/Lurcb6+hcNUTdmveX5raLX0BIKtIMTM2eZ5fz/7Eifa5xu/X8ddI2JZpv79er+C/Lu6oeKAZ+6HrB2Zs87d+4dS2bfaLKqNcdTf3gDdfM2Nbz2/p5mb9+ocZm9R/sZubdvdGM9ZtxM9mbOZF/qTuT6vsz7D/lbfX6wAA3bjdjVPBiXoRpOyWzdnvrHKqug4AROQZABuiGhgREaWOOJ8cGIW4lTJm7xgIgnUO2DeBiIiogEU5c5Bdylg68UIReQtAewA/AbgpgnEREVGK4fmIyWJXyqiqlwCogmDlxLON/B3rHOCn5fk7WCIiohQT1WGF7FLG+QA+AHCsiLybHVTVzPDyTjklJ65zgEP2LojxEhHRHoyLICWLTSkjgAtFpBaw45yDUxE0YCIiIqICFHW1QiIB0EdEyoT/no5d6MqYeWI9M6bfTXZzF99rt0qVdWvNWFsAafe8aca/GmS3OJ49/RJ3TOs+tls2Dxm5ys2t2ccuPXu+WDk397PmrczY7OOeMGNVmk9wtzv+bLvsc844+zYBYMA0+/5+/vF8N7feH+vN2KevHuPmZnwzy4x1Pc9ubf37t0vc7W7YZpedrVrvlyO+2+EQM9Zm8z5ubv+rjzRjVUrZ5a/T1k9Hh/7fmfHrj7Bn7dpfOtYd08vX1zdjR3ztt75+/4WZZqxWjzPcXGyyS0a//8Yu7Zs3324TDQCVDx9txjbMsEtyAeDMunZ58+Vr/NfUcefY77+zZ5zv5pZ6bYwZO6GpXc67dNNv7nbr/WC3Jl/YeI6bW/X8Fm6cCk5sWjarapaqHqWq9RHMJHTIrl6IG2/HgGhP4e0YEO1p0kQi+YmruLVshog0BmB/nSEiIqJ8Fat1DsKZhCcRlDgSEREVCJ6QmCxuLZuvBjBQVd0DbYmljD2G8ZxFIiKivBTJCYmJ6xyISKvwsioAzgTQKrd8Ve2BoA8DsgZ24SqKRES0W7gIUrLYtGwG8COArQDmBpWMKCEic1W1VkRjJCIiSkmxWedAVfdS1X1VtUZ4+SbuGBARERW8WLVsFpHeAFoCWAuguIg0VNVp3jYyl9u1yYXa2y1yAWDqiu/N2JVf2i1Ll6xwN4vGdx9sxhYutFsJA0CN+nbr11kbn3Jz/2h2qxmr1GI/N1dPPMiMdb2zkRkblebvX57+lt3GeOkhh7u5Tw//xIwtn2OvQwEAx19l35/1n/q11rdeeZIZ6/PCl2as5Ca/8rbrWfaaHC2qFHVz26y2WxFvPtRuQwwAs2bY5+VcN8quWT+9TgV3u8eeN8aM1fnhRTd3ylu9zFjLUm4q5n1kPz/lbh3u5r52o72+wnWrHzFjD/Sz214DwOoe9nN71Ra7xTQAtDn5KzN2+WP2ZwkADH/Nbm99zkF223kAWL7Ovt1mqzebsSrjp7vbnf/Q12ZsZh//86LqupJuPD+lx7isMApRL4K0c8tmALhZVT+OaDxEREQpL1aljERERFFIk2h+4ipupYwA8LCIzBCRZ0XEn2slIiKiPBe3ls23A6gL4AgA5QHkeBA9cZ2DN7/y1zwnIiLKDRdBSharls2qukQDWwG8BSDHs9kSWzZ3OYYFDURERHkpTqWMF4hIZWBHy+bTANjt14iIiChfRF2tsLO+IlIJQcvmaQCuyi1hyPVTzdjJFe32xwCQ0eUbMzbg50vNWPqXi93tXtnQbmX7eRm7lTAALLjgYjNW7A6/lLHTg3Zr3ilL/dvt03WkGTv7s9ZmrPzAX93tnt3Mbv1691k93NwVp9glep//Nt/Nnf7VcjNW97Cybm7aY3axzJxH7ZbAM1r459YeepK9L552kN9qeMnpt5mxZW/4p+bcWNNug3v6TU+asXOu8MvonhnSxYyt6ny/m/vatXYJ3rh9/duVSvbr4vzOfp3xXpvsssIDX37LjHU5soq73YeXZ5qxtEZnu7nlbrXLSS8+tJKbW6qIPS+dNWWMm/tptzpmbPpGp/ddFf+xOOBZO7fUvnaJMQBIpehKGXOpyk45sWnZHCoMIBNABoBjAbwbxbiIiIhSWdQzB9ktm8sAgKoenR0QkU8ADIhoXERElEK4CFKyWK5zICJlEMwcfFbQ4yIiIkp1cVznAAhORhypqjmuR5tYyjhs+5r8HCMREVHKids6B9nOBfC+lZ9YynhC4XL5MkYiIkodXCExWazWOQAAEamIYH2DLyIaGxERUUqL5IREVb0dwWqIEJFWALqr6gVh+AwAg1R1y65sq5BzD0pN8svs3naq+84ZZE1qAGhfFZ+9aXe6u3moXerY+Qi/092p5e2ujYVLFHZzv5xll3Fd3Liym7t+TEczdulHdhfDwy5r5m53W1aGGat4lN8p0lOvXrobX/WzfbhpzTu3u7ly46NmrPayrWZswMAT3e1W26+5Gbt8cB8399mtdqncT5X83O217K8np1xnl7Nh1WZU3auYGS78TH8zNvSRY9wx3VG8thkbdtDjbu4Dr9hdQl9qY5dIAsDPfy41Yy8ecqoZu3H2ZHe7mUs2mDFZ8YubO+s7+7Vac739egOAn77904ydcVNDN/eT0nZJaI2pdjPchb3ssmcAyLylkxnbkMth4Eob7ccRACSXjp27I86rFUYhDqWMbyBYLhki0gbAkwBaiMg3IhLL5Q+9HQOiPYW3Y0BEe7Y4lDImtmx+FUATVZ0lIt0A3AWgc0RjIyKiFBHn4/9RiFspo+KvHYWyAPylCImIiCjPRTlzkF3KWDrhsi4ABovIZgDrADSNYmBERESpLG6ljDcAaK+qVRF0ZXzGyN+xzsGQbVzngIiIdk+6SCQ/cRXVzEF2KWN7AMUAlBGRLwDUVdVvw+t8CGBoTsmq2gNADwAYXK6OFsB4iYiIUkZsWjYD6ACgrIhkt+1qi6DvAhERUb7iIkjJIq1WSChlLKOqGSLyAoDpEky1rAVwtJcPAO3ebWnGfnn9ezd3slMqP+osu+a50oTf3e0esF8RM7Z+9ko3d/t8u53EWxcc4uaePsNeuGH7N34r26LN7JbBC66w2y63f+Vzd7vLbr7ejB1e7Wc3d/rRh5uxN//nt13ueMFXZmzzQ6+7uYc0te8vMuz1BsYu82u0T9u/jBn7bc1mN/en19qYsYOK+cXfjYvZbXLfrXWPGWvbyWnbC2BDsdJmbFITew0EAFg67xYzdtyzR7i59U6211BY3yLHI5E7VB1ylhkr9GBvM7axkr8+ydeX1jdjy9fMcHNP+shux134kVfc3Ok9jzVjHy/1P94/LGy3Nf/0qZ/M2JHt/MfijkX2+/ryCye6uTUu9KvXF1zezY1T3olNKaOIpAG4HEADVf1ZRB4AcAwAfwURIiKi3cRFkJLFqZSxAoBtqpq92zkcgL3UFhEREeWLOHVlXAmgkIg0Dn8/A4Azv0tERET5ITaljKqqCE5OfFZEJgFYDyDHA7yJpYw9hvKcRSIi2j1pIpH87AoRaScic0RkroiYJ6mISCcR0YQv2f9anEoZ3w2bLx0NACJyPIAcz6RKLGXMGnQFSxmJiGiPFJ64/zKCCr4/AEwWkYGq+tNO1yuN4Dy+b/++lX8uNqWMqnqBiOwNACJSFMCtAF6LYnxERJRa0iWan13QBMBcVf1VVbcB+ABB6f/OHgTwOIBd6micm6irFXZ2c3jIIQ3Aq6o6KreECf9nl6zNGNTaze16qF2+p799Z8ayunfE+mp2yc2L0780YzccdrI7psKb7ee1fku77AzwyzoOa7evm7vW2U2s9/SnZmx9967udtdn2i2op15wuZv78kz7dp981y8P6/7THXbwoQ/c3Mevsbe9/f4sM3bOSz+62/3fvWvN2OSl293cYYfYJa6TJ9jlbACwuvW7ZqxCBfvT6btRa/DbF8eb8eLz7Um7Jk9c7Y7pi/kjzFi51n4r73Ib7bbL9b570s2tM9QuvT3/lLpmrGjl/d3tNnnsazOmmf7k5uhb7NLnxSvs1xsAfN9yiBmb41cc4qtnW5ixx+9uYMZ6NPI3vHdTu6XzAd9f6uZOq1rDje+JROQKAFckXNQjnB3Pth+AhQm//wHgyJ220QhANVX9QkRuzotxRbZzICLz8dd5BRnhxY8CaAigBoCTRKSPqtoNyyPi7RgQ7Sm8HQOiPc2uHv/Pa4mHyf+NcBmAZ5DHHYyjrFYAgNaq2lBVs0+euA3ASFWtDWBk+DsREVGqWoTkyr2q4WXZSgOoB2BM+KW7KYCBu3tSYtQ7BzvrAKBP+O8+AE6LcCxERERRmwygtogcICJFEJynNzA7qKprVbWiqtYIz+ObCOBUVbWPje+CKM85UABfiogCeD2cWtlHVZeE8aUA9olsdERElDKiOqyQm7C1wNUAhgFIB9BLVX8MVxH+TlUH+lv4d6LcOWihqovCCoXhIjI7MaiqGu44/E3iCRw3l98HHUqVy//REhERRUBVBwMYvNNlOZ6hrqqt8uI2I9s5UNVF4f+Xi0h/BOUay0SksqouEZHKAHLsDJJ4Ase46nW5zgEREe2WuM4cRCWqFRJLhgs2QERKAjgewEwEx1EuDq92MYABUYyPiIgolUmwanEB36hITQCz8dfyyKtUtaqIXALgRQAlAUwCcKKqrva2teXhDuYdWDlukRUCAEz7xm5x3O45u13wb69Pd7e7byP7VImlU5e5uRUPrWjGyj11mZurM6fZwQ32egMAIM3tmucBq6eYsTbV7Pa5ADBkvr1URa1y/iklRdLsia1DV/uvW53zmxmb/4TfNja9sL3PfFYXuxZ+XGZhd7t31yxuxh6t39bNlVueNmPbj6jp5p5Q1v5GNPx0u+58n+eedbf7u71sA4p1s1ueA8Adc+1zpbr9r5Gbu99UuyWwVCrv5m5+6xszVqSunXtBZXstAgB4vEUTM7b/1mJuboPBQ81YnQol3NzzD7bbap9co72bu3zzAjO2ZusqM1Yozel1D+DDn+0VVy462F4/AQD2/9X/fJQG9+fb1/tP510fySz06Qc+F8spi6hWSPwVwGIEizYUV9WqYWgigMMBjAXwf7ntGBAREVHei9UKiao6CwCEx36IiIgiE7dSRiIiogLHExKTRbkIUgtVbQTgRAD/JyL+wesEiS2be06en28DJCIiSkVxK2W0uygl5+4oZfROSCQiItoVnDlIFrdSRiIiIopYlKWM/cNfCwF4T1UfFpGOCEoZKwFYA2Caqp7gbUsXPmfegfkXveWOo2nH6mZsRA+7XAoAfhnQzozdN26xGZve7DB3u+U+GG3G/jzKLqMDgO+72q2im7a3SyQBYGsHu0fHU6XtNtJ3vP2Du93VT95oxkoV9le2TO92pxmbOW6Nm3vIhxeYMdmvtpubdvtzZmzr/+zXTHr9amYsN+MPsMscAaDFvnb78StH93VzH317jhlLe/1aN7dCd7uccckTV5mxikPs1zEAXL2XXw7nef7nDWZMivmTobc55aTHV7dLGbNy+ZxsV9UuRV1+rt9q/ZvP7aKsjovs90AwsAwz9PYKf2n9kQvt93WvP+xYegO/dBbF7LLP5Qf4ra87Dhzmxsef836+fb0fNP+mSGahT67xdCynLKIsZSwb3v52AB3DUHMAGwDMATAewNlRjC833o4B0Z7C2zEgoj1b1F0Zd27ZPBxAPVX9H4CfAdwe3dCIiIhSU9zWOUicF58I4IyoxkJERKkjLfLvyvES5aORvc7BlLDL4s4uBTAkp8TEUsYefcfn6yCJiIhSTaxaNqvqVwAgIncCyACQ45lWiaWM3gmJREREu4KljMkimzlIXOcAQeVCEwAQkc4ATgZwvkZRSkFERJTiIpk5CNc2SFPV9QnrHDwgIu0A3AKgpar6bQSJiIjyCGcOkkV1WGEfALNFJLFl81ARmQugKIBpIlIFQB9V7extaLguMWNt3/VbHA8ratdLj221nxnrtsifcDl5nR1bdvsAN7fo0ZXs4Ca79hgAFg450YytfflHNxfFipqhqxu0MWMbzs1yN/tjHbtOe/wnrdzc9CJ2LfwLz9ktpgGgVw+7De6koe+6uZlj7bUZ0u7tZcYG3PS9u91bXqpvxib2Wunmjrk/04y9PN/fjx5xfzMzdvyUSWYs8ym/xn7wkjFmrNF7s9zcYx6x19VYu82+rwBQfc5WMzblM7tVNwD0OcZ+f709dakZW3axX1U9dv9rzFjdX25zc7HEbrU+rNbDbmrzM6uYsbK3NnRzH2xWz4yllz7YjJ3waU93u72Ot1fC7/ez3y59/Emnu3EqOLFq2ayqtRCsdTATwO8AukcxPiIiolQWq1LG0LMIDi34X7GJiIjySJqwlDFRrEoZRaQDgEWqOj3CcREREaW0WJUyArgDwcmJrnBn4goAuO7R49H+PP/YGhERkYcnJCaLUyljSwAHAJguIvMBVAUwVUT2zSG3h6o2VtXG3DEgIiLKW7EqZVTVvROuMx9AY1X1T+MmIiLaTZw5SBZlKWN/CZ6M7JbNdu2Z45O5q8xY47f9pZXrn2i3Hi3S9hAzNrsqcOUI+7SIF8+xZzNKX9jcHdPSCXZ73Tequqm4cr/jzFjd+gvd3NlH2LkfzOlvxq5raLcSBoCj7rXb0b7S3m/rm/HLQ2as5kcfuLl9X1phxlbMO83N3VrBLmMtUtJuR5uWyzxc+WKFzdj4u45wc68bZr8uxp7tdwmtse1PM3bfkl/sxO8H4IJrvjHDba9sYMYK93vUHdO53eyeahtf9EsoL18+3IzNPecoN/fbU982Y5PG2SWSG7qVdLfbpLPzWfLFGDf3ktl2i/e3NrupWPu0XWJ5/2f++2vqMXaL8ZunvWfGvljtl5q+NHeqGetQ0/5cBYCtr9q3CwBFbznTjVPeiVXLZhG5T0QWicg0AGsQrpoYN96OAdGewtsxIKI9W9SljK1zOGzwrKo+FcloiIgoJfGwQjIWdhIREVGSWK1zELpaRGaISC8R2SunxMSWzbMGzS6Y0RIR0R4rTdIi+YmrKEfWQlUbATgRwP+JyDEAXgVwIICGAJYAeDqnxMRSxoNPrltgAyYiIkoFkZ1zkLjOgYj0B9BEVb/KjovIGwAGRTU+IiJKHWngOQeJRFUL/kb/vs7BcAAPAJiuGrRZFJEbABypque4G8scbt6BJVv98r2Xptsdwi6sW9vNnb/OLpXbcJDfFqJaVftF2OQDuyvZpnfsDnoAUOJ2O7fGoBFu7twMu8yuUNer3NwBvw40Yx3K2uVuADBhm91Fr1nRA+3EJfPd7W79bIoZW3djJzd35gF2J7zz77G71QHA4fuVNmMD1S6DTB/md80cfoddMlrz/C/cXK9E7+437E6RADD9R7vFaLFT/LK0r8+3q5PLlbXzRr3Xyt3uocfZJXotLq7u5k7+4HczVqWq3QUUACb2scfV5c0f3NzyNcubse9H2d0gv3rUL3FtVdWOVypulyoCwGkD3zJj/eZtdHOvrFfOjL06yV6apuid17vb1RGfuvG0417Mt7/g45fcW/B/DAE0r3x/LPdK4tayeb6I7BNepgB+jWZ4Pm/HIDfejsF/kbdjkBtvx+C/yNsx+C/ydgxy4+0Y/Bd5Owa58XYM/ou8HQPac0Syc6Cqv4rIYuy0AqKq1sj+t4g8DWBtBMMjIqIUw1LGZFGvc5AjCZZOPAvAsVGPhYiIKNVEuXOQXcqoAF5X1R4JsaMBLFNVZ21XIiKivBHnssIoxK2UMdu5AN63EhPXOejxhn8yFhEREf0zsSplBPCViBQCcDqAw53cHgCCmQanWoGIiGhX8JyDZJHMHIhISREpnf1vBC2bZ4bh4wDMVtU/ohgbERFRqotbKWNDAO8A2CIi3wHopqpucX+Jux8xYxta1HEHsaGkPelQZ7tdiD1t6wJ3u502v2DG+s/z13Uqc6Zd5/v9h23c3HOdWL9T7TbSAFDpzXFmbM1auw77lWl+WecT24aZsQ97222IAaBS28pmrHOTKm7uKa/Y2z5pax83d1Mnu/Rs9MP2ct1Nr/PXQBh6Vi0zNqulv/bC2sY9zVil4/3H4tIW9uu81Hp7DYSSm/xSxqyGjc3YipVD3NyKFexvadWb+a2GD2ltl4wW7+y3RF92lf2Z0K6nvVZBmzn+YzHgmqZmrNuIeW7uNZfYq7yen+5/RE/dz16T4/YX/Pf88DNOMWM/b7S/m11/dG93u9un2+26N5x/l5tb8Y3ubpwKTqxKGQE8AeAiVR0iIu3D31tFMUYiIkodPKyQLG6nZyqAMuG/ywJYHOFYiIiIUlLcShmvBzBMRJ5CsOPizxESERHlAZYyJotbKWNXADeoajUANwDI8UBrYiljxvecXCAiIspLke0cJJYyAsguZbwYQPYZef3Cy3LK3dGyudBh/slYREREuUkTieQnruJWyrgYQMvwascC4AqJREREBSzKUsb+QQsFFALwXljKuAHA8+FCSFsAXJHbhjZefJwZk31rurnPaaYZe3LeN25uurPDlz7Rbo9cq/Y+ZgwAhvZuZcbWrslwc9fWqGTGDr3Zb4V684V2e+R3ltiPxZhv/LLOQdfbp40svft7N3fJGWXM2MpnZri52+oWM2Mb7vfLqRZvskvP1o57yowV6WSXswHAEbfZrbwr9b3fza1wlf1aXlHfb1O8ok07M/bId84Ko00q45G37cf5tK++NGMDVj7kjinjU/uxeHl/+7kDgI5VDrBzt/rLo1zW127jvleNvcxYkXT/e1TzykeasYUzx7u5P3W+1IxdPcZ/37766GFm7I3jD3VzK6xabcYmbrdLlP1G0MBZg+wxf1LNfk8DwPi109x48xIdc7l1yiuRzByo6q8IZgh+AZAO4AIRaQbgJwCrAZQAsA4xbdns7RgQ7Sm8HQOiPU0aJJKfuIryhMTnAQxV1boAGgCYBeA2ACNVtTaAkeHvREREVIAiOawgImUBHAOgMwCo6jYA20SkA/5a9KgPgDEAbi34ERIRUSqJ88mBUYhq5uAAACsAvCUi34vIm+GJifuo6pLwOksRnJtAREREBSiqnYNCABoBeFVVDwOwETsdQlBVRbBQ0t8ktWz+cHK+D5aIiPZsaZIWyU9cRTWyPwD8oarfhr9/jGBnYZmIVAaA8P/Lc0pOXOfgirOPKJABExERpYqoqhWWAlgoItkt0togqFQYiGAhJIT/t+udiIiIKF9E2VvhDgATRKQEgO0AOiFYDOlOEbkfwAYAR+e2kbn7FjdjlYr5d69en4/MWGaG3eZ27uWXuNv97He75WydNu+6uVe0qmrGmhxu12EDQLH0kWasy6rNbu6hFUqYsd/XbzFjxfeyH38A+GTuKjP2fGe/pXZ6LfuUkyr7/ubmLitsj2tK3a5u7qJF9nN/8ketzdjIImvc7bZ50G6RW7XnK27ukuKlzNiq3tPd3GfK2Ot5dDn0EDP28CteE3Cg5N0P2sGW/poco1rvZ8bu6/6Vm7uizzFm7NjN/nv+l2v2N2Oj275txmp289sfV3n5LTPW+ugabu6YSjeasU9uttd0AIAPNm83Yz+XtFueA8DB/YaasdFntTdjD768g1j6NQAAIABJREFU1t3uooV2e+siB1dwc7c2+tCNY4m/Hsju4AmJyaI84NEFwC2qWgxABQCTADygqqVVtSiA+wFcG+H4iIiIUlKsShl3ulpJGCckEhER5SWJ8cmBUYjqsEJiKWMDAFMAXKeqG0XkYQAXAVgLwJ7DJSIionwRu1JGVb0zbNncF8DVOSUnljJ+0HtCQY2ZiIj2UGkR/RdXcStlTNQXwUmKf5NYynhO52b5OEwiIqLUE6tSRhGpnXC1DgBmF/jgiIiIUlyUpYzXAOgrIkUQdF+8BMCb4Q5DFoAFAK7KbSMnfDzOjE0es8zN7fXwUWZs9p92OU7Pn4bixCvscqsjRl5jxva91h9Tej271GpoPb+saUubF83Y1Hm5tHu+a6sZO7O2vdDUd0faeQDw+tE5Tv4AAK7EJ27ua4fWM2M3nbfRzf3lgq/NWPNB57i5LfvZueurVzFjbR8Z7G43rZC9L77l+Xvd3NVblpixvVvOd3Mf3miXsWb0su/rNnyN8WfVNeMzxthtfcsV7+2OSbPsc42HvXCCm9v1E7vN9KhRS93cw4+qaMbm9T3fjI1u/Y673VEV7TK4ejNfdXN/eXSqGVvzu18eO/LOY81Y+ec+d3PXVbBfj5vbPWfGVt++8yRvss/7/GzGZn54lpvbYrtddpvfeEJisigfjfnhTxEAdQDUVdVOqloPQdOlkwH4f3ki4u0YEO0pvB0DItqzRTlzkN2y+Yxw9qAEAIhINQSLIf0e4diIiCiFxLnPQRQieTQS1jnoCQTrHKhq9vzZswBuAdc4ICIiikSsWjaLSAcAi1TVXQ82sZRx3Vh/GV0iIqLcCNIi+YmrOK1zcB+Cfgv35JacWMpYpqV/kh4RERH9M3Fb5+AAANNFZD6AqgCmisi+0QyRiIgoNUVyQqKqLhWRhSJSR1XnIFjnYKqqtsm+TriD0FhVV0YxRiIiSh08ITGZqEZz3p+IHA1gAIIqhe0AOqnqlyJyDYD/A1ATwOuqai8aAODhyVead6BNNb9l6WF9Z5ixbXPt+uKnO9c2YwDwysTFZmzjCr8+v+sJB5qxFvvZbXsBoOkt9lLSmuk/zzUOLGrGFv5ut4V94+b/uds98qQxZiw93W+R+vRr9voKzarYLaYB4Ld19roOPT/+0c3N7GTf7m9H2M9Pepq/r93snRFmbNJAvzinVj37uW/RrJqbe+eR1c3Y4N/stQqeKFze3e720XPM2MOt93Zz7y9ttya/apn/WDTau7AZ63igv2LqqFIPm7Gzp9rrX0h9u000AHy3crwZa9vTXosFAFYdZbfNnl3PblsOANrKbhWdlsvfum07t7pLcOej9ntgwzZ/LYJR6fZnyYZh893c0red6MblgFvyra/ywg2vRfLHsFqpq2LZKzrKUsbsls1vZpcyikhrBCsjNlDVrSLif8IQERHlgTifHBiFWLVsFpGuAB5T1a3h5cujGB8REVEqi1UpI4CDABwtIt+KyFgRsee2iIiI8kiapEXyE1dxKmW8Lby8PICmAG4G8JGI/O14TOI6B5P7zyrAYRMREe354lbK+AeATzUwCUEDpr91Sklc5+CIjgcX2KCJiIhSQZxKGX8CMA9AawCjReQgBE2ZWMpIRET5il0Zk0VZytgQwJsIdgCyWzZvBNALQEMA2wB0V9VR3nbWbfvEvAMrj7vfHcMBfS6wx1fDP91But1uxvRF53a3bHC3mzXCafubleXmfnl4JTNWo4wdA4C6ZRqascWn32nG9j79IHe7SzvZJWDpF/Z0c4840W6PvOh7u4UxAOgrj5qxqm+84OZuWW/XePXuZHcqnLnSL1O9rWFHM7Yywy4pBIBP5trlcGWLpLu5Xd61D72tv/5iN/ch53Zv+tBeunyNUwoMAJVftMsGv8ha5OZm1Rtkxto9aLf5BoAT9y9mxu5vbpepljz2I3e7VabcZsZuG2e3xQaA7jfZj3GRr69wcw8cbZfljujyjZu7bqLdGnvhBvs9cH1h+30JAJNOfc+MHdPRL0CbMcp/H9SZOivfyv6WbOoVyR/DyiUuZSnjTuaHP/UQtmwGcH347wwE5x48g2BHIVa8HQOiPYW3Y0C0p0ljKWOSWLVsVtWzs4Mi8jSAtZGNjoiIKEXFap2DhLgAOAvAsVGMj4iIUgvPOUgWt3UOsh0NYJmq/pJTcmIp41tvDi+I8RIREaWMuK1zkO1cAO9byYmljJd0aZu/IyUiIkoxUZ1zkNM6B7cBgIgUAnA6gMMjGhsREaWYOK9WGIVIHg1VXQpgoYjUCS/KXucAAI4DMFtV/4hibERERKkuymqFOwBMEJEdLZvDtQ/eAbBFRL4D0C1cKfFfqdeighvfuGmLGdvrscfNWOHidstYAOg+4WMz9vR7P7i562C3Ii51aVM31yt3L3/lp26uPmy3OF728zoztu7UBu5212yw21dnTvWLUYa+aa8LcGgbvzR4wQa7tv/i+v6aDys32+tJtF9qv2WWl/H3tZdts9dmmLNmrptbu9xeZuyQ692lQCBOu+flxez7eu51filjr9ePNmPdVtttewGg6Tj7bT1um/+xtLq93Ur68aP+tqBqkpENTjVjHYZ9aMYe8peSQINe/c3Ykja5rJkyrp0Z0/l+e3G0OMoMtZ3b2k19f68HzdiTr9ptpPdp7T8Y43var4sWqza5uVU2RvcnSZDLk5xiopxHyW7ZXAxABQCTADwB4CJVrQbgnvB3IiIiKkCxKmUUEQVQJrxaWQD2104iIqI8wnMOkkU1h5NYytgAwBQA1yFYIXGYiDyFYFajeUTjIyIiSllxK2XsCuCG8LDCDQByXHyf6xwQERHln1xnDkRkPQCzIYWqlrFiDquUsQWCGQQA6IegMVNOt9kDQA/Ab7xERES0K4S9FZLkunOgqqUBQEQeBLAEQTWBADgfQOV/c6NOy+aaAFoCGINg6eQcV0gkIiKi/PNPzjk4VVUT69ZeFZHpCKoK/o1rAPQNmy5lt2weAOD5cCGkLQD8fqUASq9ZZcZaNN7Pzc2YYJe7rT6jpRlbX91vU1zpjufN2PYjarq5fZuVM2PNOw9wc9e9f7wZ2/DmuW7u3mn2Y9Xw3TPsxEy/XLTRiO/M2PhLarm585zYb5f0dXNrjn7ajD243S8bHHex3Tb741H2ipzn9LRbGAPAsm/t0sDGjfZxc5895wAzdmQVu1QRANZ37+pE7W9LzbrUMWMA0POE0WYs8x6/xPXrTHvCL/3URm7uhp4zzNidGf5EZptBdjvhEXXt+5t29M/udg8pXcSMTa/kfyMtljHfjNWp4Jdmbn7CbiU940a/9PmcrFfM2EXX2x/rF81a7m63fE271PS9h/3HscGjfsvtX93o7uEJicn+yaOxUUTOF5F0EUkTkfMRnCvwj4WLH/VGsHOShWCW4GIEswerAZQAsA75+1ogIiKiHPyTnYPzEHRKXBb+nBle9o+p6hxVbaiqDREsk7wJQH8E5x2MVNXaAEYiud8CERFRvhBJi+Tn/9k7z/Coqi0Mv4vekQ7SlSIIioKICiioYMderle9WFCx994bir2LFQsoIip2FKUINkBUqoB0qdJrgKz740xwglk7CUk4I1nv88yTZL5Ze/ZMZiY75+xvfalKjk8rqOosoFsBzOFQYIaqzhaRbsAhiev7Eu09uKEA7tNxHMdxHIOcuBWuV9WHROQpsnAtqOrleZzD6fydwFhDVTN6zC4EsjwRKyI9SOxHeL73mfQ4u2Mep+A4juM4TgY5OXKQsWvP3lm2nSQ2Ix4H3LStpqqa6Jj4D5KtjLq4j1sZHcdxnDxRxK2MmciJlfGjxNe+oduJyFOqelku7/9IYJyqLkr8vEhEaqnqAhGpBYS3xTqO4ziOk+/kZ/tkOx7M5gz+PqUAMJjItdAr8TXs3QOWlC9lakMOOzZYKzc8ZmrpJ3Q1tfKrV7OkjL3K/PAy20JU/e1wKuPwU8aZWuNfnwnW1nv4aVOrUzzcMuLXEUtMreYTp9mF61agU2zT4S3tbIvewDZbgnM6smwdU/trzaZgLWl2+lv7P+10RIASj9pWur41mwUqwxbJBm+eZ2obnv08WHtzqUBLkSdPD9auOP8uU6t4iZ2gN2Pf5ly9eqGpH/KD7TQ+d2o4TfDTyfbrbfEDtj0PYNomO0ly4YPDg7VDTtvD1KSZ/RGmzxzE6i12OmmL194wtU0HvB6c0x/z7IOf9W9oHKwtfZ79+9u/WLgNzfot9nvkpyG2RbzV6ycGx6W6bWX89uHw59C0CuXDYxcgqbw5MA5ieTZEpKmI/AqcDFwlIqtE5Eqi5kq3iMhG4Doiu2PKEVoYFDZCCwPn301oYVDYCC0MHGdnJJbgpURXxL0ARKQoMJ/IyrhcVa9NXH85cDlwURxzdBzHcQoP3gQpM/m5OJDtrNtqZdzm+rIEMh0cx3EcxykY8nNxYPcMDpNsZURE7gPOBlYCnfJhXo7jOI7j5IJsj6OIyEciMti6ZNxOVV/L7Z0nWRnfTRrnlkRk81vApUbd1sjm11+2+7s7juM4Tk4QisRySVVycuTg4QK8/22tjMm8BXwK3LGtkNznYPH61/3Ug+M4juPkIznpcxD2BuWNTFZGEWmsqhlel27AlAK8b8dxHMcBfEPitohqzv7xFpHGwANAc2BrcwFVDWcQZz1WU6JTCc2JkhgbEEU/dwCaApWBWkBLVZ0QGkun3mc+gPJvfR2cx+oL7D4Iy6rZ/vzN6RuD44b8stV+D1v/xtay12uVS1UM1jbcWMbUNvUP++jHn7aXqZ3/hf0rePEuOz4XoFptO8r2+PNtzznAkMFzTK1khZLB2gpdG5jaim5HBGsrjf7O1NZ3ONTUyl3+QHDcNaVsD/fIvvZjBeiw6CFTK7PC7hkAMKGTffBvn0PtyO3Nj98bHFenf29qVT8YGZ7TaNtHX/PxU4K1I4uvMLWFa1cGa6t2GGJqnf/oZWpzut0dHLduHzsSffrpwf5xNB79uKn93CLcV65MaXsfeJNXTw3Wzmxo/+4b/Gj3I3irjt1nAmDMojRTe/aDyaYGkHZVuIeC1Lt6eze+Z8um9C9iOQpdvEjXAntMeSE3GxJfJTrE/xjRRsHubGefBMvKqKqPi0hd4CVgE1G+guM4juMUKKl8/j8OcvNslFbVoURHG2ar6p3A0fkwh22tjI8B1+M2RsdxHMeJhdwcOdgo0fHyaSJyKdF/++XyYQ5brYyJyOb5qvqLSEoeaXEcx3GcnZ7cHDm4AihD1LWwNXAWUf7BdpNsZRSRMsDNRHsPsqvbamXs885PeZmC4ziO41BEisRyyQkicoSITBWR6SJyYxZ6SRF5J6H/ICIN8vp85PjIgapm/BVeIyLnAeVUNa8Nx7daGUWkJdAQyDhqUAcYJyJtVTXT3oNMkc2BDYmO4ziO828msS/vGeBwYB7wk4gMVtVJSTc7jyh+oJGInA48CAQS87Inx0cORKSfiFQQkbLABGCSiFyXlzsnycqoqr+panVVbaCqDYiehH23XRg4juM4Tn4jUiSWSw5oC0xX1T9UNQ14m8jqn0w3IMMWMxA4VPJ4bj43ew6aq+oqETkT+Ay4ERgL9N6eO04sMg4HLtye+gzmXfGeqa1+5dxg7crbB5paxUPqBmvTfrXtYz+9NcvUpmbzWnjx5QNN7YGnwnHPG9vtamrFG4ZtkG1+yaoPVcSvZ15gagPOy7KJ5VbadbHtlb8dbUcjA4w+yH48FQ74LFh7XeeaptZ96ohgbfd3p5pa+dYHmNrc3uHXW+lxdtuOjkXC7+MiRey3at33PwnWzrm/vamlVbftbOk/fM7sm2wL7EeP26/VhRUqBOf0Ye9Gpnb8UDu2HKDNxKWm1rC+HeEOsOj3gCVxs23BWzRrfXDc8S1fNrVZozoHa4scfLWp1WgSfh5rvdLTFpeFo8l3fWqoqS26atu/PX9TtsqDwXFPrGy/lh//IGxVfK/xC0H95I32c7UTUxuYm/TzPGB/6zaqullEVgJVAPvNkg252XNQXESKA8cDg1V1E9vpKEj0ORhF9GCGZ0Q2i8idIjJfRMYDK4hWTClHaGHgODsLoYWB4+xsiMZ0SdpDl7j0iPu5gNwdOXgBmAX8AowQkfrAdu05SPQ5aAX/iGzuDjymqgXZstlxHMdxUoLkPXQG84HkQ9l1EtdldZt5IlIMqAjYncZyQI6PHKjqk6paW1WP0ojZ5E9qohXZ7DiO4ziFnZ+AxiLSMOHwOx0YvM1tBvO3e/Bk4GvNaftjg9xsSKwoIo8mHfp4BCiblztPkCmyGbhURH4VkVdEpFI+jO84juM4YTQ9nkt201LdTJRQ/AUwGRigqhNF5G4ROS5xs5eBKiIyHbiaaE9gnsjNnoNXgNXAqYnLKqKWyttNFpHNzwG7E51yWAA8YtRtPUfz1tzt3m/hOI7jOCmPqn6qqk1UdXdVvS9x3e2qOjjx/QZVPUVVG6lqW1X9I6/3mZs9B7ur6klJP9+V2DiYFzJFNidHN4vIi8DHWRUln6OZe8S+3ufAcRzHyRs5+C++MJGbIwfrRWSrJ0pEDgLC/p7s2TayuVaSdgJRPwXHcRzHcXYguTlycDHQV0QqAgIsYzvbJ28T2dxcRJ4gapvcWkQOJYpsTgPeyG6sdYvt9Un6qDHB2np1i5val3fYbZkbd6gWHHfOUDvW9781bZ88wLDf7ajbTf3CT/fRQ+yo4QtO/SFY+9UQ2wu/y+gBptZ74U3BcYvVsWOM9yryYbB25hT7d/vdt4cHa8cVtftU6EQ7jhagKOtM7Z3Fo03tmIZdguPO3Nset8gdtuccoH4R279/yh5Vg7UU28WUtrS1Pfj6Ye3gsJeXaWpqI6qFI5tblLb7bsw/JRzlPe3wuaY27Nj3g7Xretp9N/Z9045WHrqHHbcN0GZEd1N7Z/UkUwNoeP22lvW/WTHI7rkBoD+PNbUZt4Qj61sdbsfSf9/WjghvvfiK4Lj1i9vvvUGVrg3WtlkajqguUPzIQSZy0z55PLC3iFRI/LzdrZOtyGYim2Qt4GhV3Sgi1bf3PhzHcRzH2T5y41aoIiJPAsOAb0TkCRGx26vlnGQr48VAL1XdCKCqi/NhfMdxHMdxckFu9hy8DSwBTiLyUS4B3smHOSRbGZsAHRKpUsNFZL98GN9xHMdxwqSolTEucrM4qKWq96jqzMTlXsA+aZUDsrAyFiPab9AOuA4YkFV4RLKV8Z2lK/IyBcdxHMdxtiE3GxKHJKIgM3annUzUlCEvZLIyEgVKDEp0dvpRRNKBqkRHKbaSbGWcum8ztzI6juM4eSM9df+Lj4PcHDm4AOhH5CLIiI28UERWi8j2bk7MZGUEPiDRkllEmgAlyEOqlOM4juM4uSc3boWwlyeXGJHNrwCviMgEogXIOdn1h67/k53RNGtN2LI2c/B0U6vaxbZ/wSa+vNmOlagROI+k348KzuniQzqY2qxVM4O1Qw87xtTSntgUrD394FNNTRfbz2PaLuEzS7V3teNb376yTbD2p0Ac+eF1wpbQd+falsNuR59kagANlyw3tVVpW0xtSIWwTavtkutMreYNdvwxwNgml5jaI32PDdamT7Oje4tVs+ORGwKPrrYjT47437OmdvSJdYJzGtJ3nqlVm3xHsLaI2LWTp4YPJDYd8aWpHbmb3a29SDHbPgnw1GI7Tn3lxvB/pKcV3WxqJ9y0V7C2wxX250nVEbcHa9cWtT/j1q7qbWolB9nvLYD0KrZddMCg8Pv2xLLNgnqBksLn/+MgN6cVSPRx7pj4cZiqZtnBMAfjNCXazJgR2bwbUZ+DYUAjYDOQDqzZnvELmtDCwHF2FkILA8dxdm5yvDgQkV7AfsBbiauuEJGDVDXcBScLApHNLwJ3qepnInIU8BBwSG7HdxzHcRxn+8nNkYOjgFaq0bEXEekL/AzkenGwDVv7HIiIAhUS11cE/szj2I7jOI6TPX5aIRO52ZAIkHySyu5/mjuS+xxcCfQWkbnAwxgLj2Qr48svfp5P03Acx3EcB3J35OB+4GcR+YYoW6EjecyMTupzkLEIuBi4SlXfE5FTiTKqD9u2LtnKuGHLJ25ldBzHcfKGHznIRI4WByJShGiDYDuifQcAN6jqwjze/7Z9Ds4BMlI93gVeyuP4juM4juPkkhwtDlQ1XUSuV9UBwOB8vP9t+xz8CRxM5FroDIS9iI7jOI6TH3gTpEzk5rTCVyJyLZEFcW3Glaq6LLd3Gohs7gV8nHAwbAC6ZTfWaZ/Y8Q6H1LP9tgAX7W77fCsUTTO1dc0GZTctk5IXhOOe13c50dRaFwu/eBcUtyOODysfPvvSf6Xtdy9btrSprWl5VXDctsMuMrUmd4TjdSse18jULp4TjjjuP9K24e3aw/azA5QrXtTUrnr3d1Nbf0E4OvnjpXZt+Q/D0bxVxvY0taI39AvW/rXejib//uBapnbFb2EncXr3PU3t4N3C7706155sahcNDTdeHTLO3qe8/1vhOJa6Fw4xtYdu2MfU0tuH46uHzl5rau/OsqO6ATZefKaplS1WwdQANu43y9Q+Khfuc3DyZ3bE+Ow77Uj0dZvtz0aA+pd/Zmr9H7Q/3wBG1rf7eQB0mDslqDv5R24WB6cBCmz7KbVbbu80ENk8kCiuebiInEvUJGlEbsd3HMdxHGf7yY1boTnwDPALMB54CrD/dcg5yZHNTfh7MfAlUQKk4ziO4xQsnsqYidwsDvoCzYAniRYGzRPX5ZVkK+NE/j6VcApQNx/GdxzHcRwnF+RmcdBCVc9X1W8SlwuAFnm58ywim88FeorIWKA8Ub5CVnVb+xzM+sI+j+s4juM4OcKPHGQiN4uDcSLSLuMHEdkfGJPH+89kZVTVKaraRVVbEx1NmJFVkar2UdU2qtqmQdcmeZyC4ziO4zjJ5GZDYmtgtIjMSfxcD5gqIr8Bqqrh+LCsyWRlFJHqqro40VfhVuD57RjTcRzHcXJHCv8XHweSTSLy3zcUqR/SExsKc37HUWTzHGA3VV2ZuO4KIMPLMgi4KbvI5mkrHzX1xhK2H8kNj5layfIlg7W/DV9qaq0Ot2OM33lyfnDcZnMvNrV0wi/e2vfZNq2N93QP1u6ydJEtLlxsSndtXhIc96gG9u+gQgnbIgkwb43tkm19Rzg2du1C21q218FhO2mf2/8wtf9cUt3UZt8bfo63pNvRvJuOfSZY2/Dz+01t6bm2BrBq7mpTK1ezbLB25lg7vrryuB6m1uGN4cFxrz7AtlBe/rkdMQ2w5ppTgnqI60Z+Y2ut7aOQjd78KThuiZ4XmNqIxSODte3H258ld1YOv+dvn2TbTTefY1skASRw4LiklDC1C4f1NzWAOuVsK/A1A8N/Jkrf+J+gLpXPtnPc88rK/vF02614RsE9pjyQ4yMHuf3jnwN6AAuBUYmjD92JGiz9B6hCFCdfHGPfQZyEFgaOs7MQWhg4jrNzk9vgpXxBRGoDlwNtVLUFUJTItfAg8JiqNgKWA+fFMT/HcRyncKG6JZZLqhLL4iBBMaC0iBQDygALiFomD0zofYHjY5qb4ziO4xRaYlkcqOp8okjmOUSLgpXAWGCFqmacmJ0HZHnCOtnK+PZr3+2IKTuO4zg7M+np8VxSlNy4FfINEalE1OyoIbCCqM/BETmtT45sDm1IdBzHcRwn98SyOAAOA2aq6hIAERkEHATsIiLFEkcP6hBlLjiO4zhOweJWxkzEtedgDtBORMqIiBDlK0wCvgEy4trOAT6MaX6O4ziOU2iJ5ciBqv4gIvOIHAlKtO/gMqAy8JqI9CeyNV6R3Vi7XDjQ1Ga9GPbMnnVCc1Pr0bKOqU1+1o6JBlh2vB2zOqWOHTcLMHn5XFP733vhuNJZ6zaZ2s9LfgnW7lfDjrrtOtLe1zF17srguJVL2evPGSts3z/A4/Uam9ouDcN9KBofWtPU5o0LW/RKT7FNMmn17FhfufS24Lj9+9j3u/tuYatzFdlgahuWbwzW7tq1oamV2L+eqVVdFY5sLlKmmal9fKrdZwKgbjk7NqX4H8OCtSs69TK13Z8O72E+cw+7T8WtoyeZ2ps9zwmOO7js5aZWJtzOA97ubEoPDQpHed91pd3zIfzughJFS5na0jNvNbU7+oT7eZS6xI7cOalbg2DtVWsmBvUulYOyk4/EaWVsAuyiqqWAn4ATiRYEewCzgfNUNfyp5ziO4zj5gWcrZCKuPQcZ911aRDYRWRn/VNWfAaIzDY7jOI7jxEFcpxXmi0iGlXE9MERV7d6/juM4jlOQpPB/8XEQ12mFZCvjrkBZEflvLuq39jl4fUYgE8BxHMdxnFwTl1thq5VRVTcRhSwdmNPi5Mjms3e3Q44cx3EcJ0f4noNMpJKVcXJMc3Ecx3EcJ4k4rYwDgXFEjpufgT4icjlwPVAT+FVEPlXV80NjVb3Nbqw4dtmc4DyO7zbK1D4Y0tHUDswmK+Phw+yjGVWPtaNqAS6of5Sppa35NVj7+ZV7mtqJgVhYgDrzXje1uZVsa+ZHJzUNjtu5TntTK3L1s8HaHz+zo27vfO+QYG3r6lVNLe3kvYO1Jd5639RqrRxhamWrheOPd/nDfk0d8uKYYO3Ilfbaed9Pegdr07+07b4Tr/zS1Pa40rZtArR/z37N/D5/VbD2pk52f7NnVtkR0wAzW5UztXrjbDsiQOcGlUxt2EQ7mnz6AROC43a/ybZmzpph21ABinQ52dTWNw6/LhZe8kZQD1HzBduym77J/o+21rKwxXXBM7bt89ljngrWlv+sa1B3dhxxuhVWABl/ZosBAuwN/AUsA34Hroxnao7jOE6hIoVzDuIg1SKbr1LVvVV1L6JTD5fGMT/HcRzHKcykWp+DVQCJfQilibonOo7jOE7BksKbA+MgZSKbM/ociMirwEKiTolZnqBKtjL2eTd8Ts5xHMdxnNyRcn0OVLV74rrJwGlZ1SdbGXuc0mYHzdrxXldRAAAgAElEQVRxHMfZaXErYyZSss+Bqm4B3gZOiml+juM4jlNoiWvPwdY+B0Ttkw8FxohII1WdnthzcBwQjiEEdOpMU+s8L2yJmr2X/fAPfOI3u/DoStS45xhTXrLFtmn9p2mX4JymLLdPk6xfsT5Yu0+13UztrOnhVMZZaUVNbd0wOynyimxsZ89d96mpfTioQ7D2hSdvNrXVNXsGazcHlr1Txodf9vVOs1PyBjZ52NQOOW6X4LjHPj7a1MpULhOsPe8L+/X444Zwkt19LW0r6g0nNzK1zfNWM33gdFMf9ZVtJnp1Wdh2e+uIBaY2bVL4NfXr5ytMrcTXtgbQ+Lf7TO3FP20La1HCFuRiJe3XVKWLDwjWTjvATnQ8vqedHBvdwE7VfOawJsHSGtV2N7XBd9g21vbl7PRXgOlLbNvt0c+fGqxd9N+ngzoDc9xI18kjqRbZ/JmIVCA6rVABqBLH/LIjtDBwnJ2F0MLAcXY6UvgQfxykVGSzqh4EdAc+BTZmuBccx3Ecx9lxpJSVUUSKAr2B/wAnxDg3x3EcpzDhTZAykWpWxkuBwapqn5B0HMdxHKdASSUr49nAKRi9Dbap/7vPwZCpBTtZx3EcZ+fHrYyZiOu0wlYrI4CIDALuIuqKOD0yK1BGRKar6j+2U6tqH6APQPqg7t5F0XEcx3HykVSKbH5UVWuqagNVbQCsy2ph4DiO4zhOwZJSVkYReQ04GFhJtFmxlaqOD421ebZtaGhQOnzI5tsvLzS1gbNsr+6dn34XHLfnQbVNbdWJ9wdrzz/TXg99+OqSYG29R5qZ2sc/2f0GAIpfcKKpjT3gcVPb0MyOzwVou/A2UzvzsTeDtc+t6mdqlSpJsLZNZ7vnQI9J84K1tcraW15uf+YgU1tz9HHBcUu+Y0cnL343fHqs6DX/MbVi68Kv8xa1HzS1H/6w44I7LQrHfN88c5yp3da2W7D2f/Ps1+N3DwwN1i5ZYh8sfOiVfYO1jzW7xdSq1ytpar90qBYcd9nZgaj1t4cFaydP3GxqE9q1CtamDfrJ1B6aH/79Nahgf8ad2zzQf274R8FxmxSze6ZU/yn82bn42XODeoGSwof44yClrIwJ+TpVbaWqRbNbGDiO4ziOk/+klJUxxrk4juM4hRm3MmYi1ayMAPeJyK8i8piI2Mf5HMdxHMcpEFLJyvhf4CaiqOb9gMrADUb9VivjS9/Z2QqO4ziOkyPSNZ5LipJSqYyqukAjNgKvAm2zKk6ObD7/gIY7cNqO4ziOs/OTSlbGySJSCyBx3fHAhJjm5ziO4ziFFlGN57CGiNwFnAZsBn4Gzgc+A6oBAowHLlLVNaFx9Jc7zAfw5aFvB+dw59O27Wn0yf8L1qZ/+5mpyR572FrFcPTrI5PtcZduCG+Ymbpso6m9d+Chwdqhe9hRtq+/GbDvpW0JjnvH9T+YWquDwxHHX7633NT+e7v9HAPc3GFXUzvo1G+CtUf2bGxqM6asNbUlDx4fHLdsMTs6ue6tLwdrP359salVrBi2dbY4uo6plbkunDB60TR7ff7kwbbdbdSCkcFxO5dvaWqDl9n2PID5a+zo8t4/2s8TwOENK5naE98uMrWSXfcMjqvz7dqFfcOR2v3vsD+H/vdY+P+jKSNsu2LNXy8K1u762Femtn7WSlP78a5wBHWXGRtssVPY4pr+avh9UOzcfuEXex7QaQ/E8sdQGt9UYI8pL8R15ABgBZDxl6UY0YLgUGAwUAJoDcRoerUJLQwcZ2chtDBwHGfnJhYrY6LPweVAc1VdLyIDgNOJFgh1gT1UNV1EqscxP8dxHKeQ4VbGTKRan4N7gf+oRq2qVDV8jNBxHMdxnHwn1foc7A6clrApfiYi9slfx3Ecx8kv3MqYiVTrc1AS2KCqbYAXgVeM+r8jmweO3VHTdhzHcZxCQSpFNh8IzCPqeQDwPlGvg3+QHNkccis4juM4jpN74locbO1zAKwncimMAVYBnYCZROmMv8c0P8dxHKcw4RsSM5FSkc3A58DFIvIMUBT4Nruxijz/ham1ejwcd3rTWXbk7NofbP9w/TolguMuLmVHQtQaE7ZBLrygh6mVuu2hYO2Ia+xeBkPX2vGsAB2usuOei+1p92ZY3MCOIQZoelsTU5swcFqwds85D5vaS9WvDdbe80JpU7ts9I3B2nEl00xNS1Qxta+nhaOGr3zHjmXeeEm438DMMW+ZWu1jdg/W7h5oJzH8DNtX/vwzdow3wPLz7PjxCzvWCNZOO6uDqR0zal2w9u4q9sHCqWnhj7Tire33yJNVRpjaU2OnB8ftfelvpnbSefZrBmDTPDuy+e5Lw/0V7ux7qqk9NzH83jy/lh23fn0H+/d3TvvBwXFLnlzV1NotDcdIj2jZIKg7O444rYwZkc0ZVsYTVfXApNu8B3wYx/wcx3GcQoYfOchEnE2QMqyMxdgmsllEKgCdgQ9impvjOI7jFFriOq0wX0QyrIzrgSFJkc0Q5SoMVdVVcczPcRzHKWSksK0wDlLNypjBGUD/QP1WKyOTvE+S4ziO4+QnKRXZDCAiVYmimj+xipMjm2nuHZYdx3EcJz9JNSsjwMnAx6oaiPZyHMdxnHzENyRmIk4r40BgHH9HNvdJyKcDvXI6Vuv29U3trUd+DdauqGcnZZa9r6epLQWGLR9j6pvemmRqn71q29kAKq992tTWHd82WHv973NN7dw9GwVrT93TjhPuffz7prbGdgwCMO4CO46241/LgrVrn8yyQSYAk77pFKx98tRhpvZum2rB2lMq7WNq60vZNtYOZ4WjoHv3PcTUVtcN/346nWXrP49aEqx9+wH7uapznm2dTQNKXXqXqf9FcVN75nrb2gegh9oxxgOODT+PLaZ2NbVercMx4LcMt+2ml5YrY2ob9rbteQAnnGNHQQ+/qXOwtvmQKaZ2e7fw8/h1jWGm1qVR2HJds1tdUzutiG1zLJLN8eZzjmlqai9WCFtcv6wRjoDvEr5rJx9JqchmEekMVAAeFpG+CSdDyhFaGDjOzkJoYeA4Ox2erZCJuDYkZkQ2t1HVFkQNj/4D9AVOT1w3Gzgnjvk5juM4TmEmlfocrAXSVDWjZfKXwElxTc5xHMcpRKSnx3NJUVImshkYABQTkTaJm50MZHlSLNnKuOTrGTtiyo7jOI5TaEiZPgfAmUSbER8TkR+B1fy9JyETyVbGap3DfeUdx3Ecx8kdKRXZrKpvAh0S13Uhyl9wHMdxnIIlhQ/xx0Fcew629jkQESHqczBZRKoDiEhJ4Abg+Zjm5ziO4ziFljj7HMzl78jm34AeRBbG7kAJYCZR/4Mg/2tZ2dRmvn9ssPbIXnZEa3raWlPb85qwD7vdMbZ/mFv2Dtb+9d+LTG10/YuDtbccanuI37/W9qQDLFxrxxRX/9a2tDX+I9y3YdO7403tg6vaBWv/O/EvU7s20McAoO13V5vaMW+FI2cP+vp7Uyvyjv07aNUl7OFeu9L20Rexn34AZjZraNee1T1YO2f256Z25scvmlr6tScEx5V6e5naqQ89Hqw9bqrdC+SKRnb/EYBHp682tfa1SwVr035baGpSqqipXdfFfqwAJ45fY2qP/74oWHv3BXYvg6PTwjHtRR+ye4H82L1lsHbmXaNMrVxj22L31wH2Zy7Ap9Ps/iVy5hHB2ppp4WjsgkQ1Hlth+NUeH3HtOWgB1AEqAeWINiTWJeqWeK+qlgBeBW6MY36O4ziOU5iJa89BM+AHVV0HICLDgROJNikekrhNX2AY0ekFx3Ecxyk4fM9BJuLaczAB6CAiVRL5CkcRHTmooaoLErdZCISP0zqO4ziOk+/E1edgMvAgMAT4HBjPNrZFjU4AZXkSKLnPwaiB9rlLx3Ecx3FyT2wdElX1ZVVtraodiTYm/g4sEpFaAImvi43arX0ODjq5+Y6btOM4jrNz4h0SMxHb4iDJtliPaL9BP2Awf+cpnAN8GM/sHMdxHKfwEmfq4XsiUgXYBFyiqitEpBcwQETOIwpeOjW7QeastiM+xy+xrXDZTq7GPUF982+Hm9qjB39lau0uDEfz/vaXbaNr90A4srnoSaeZ2rlFw7/q6z970tTWb7GtY0vPfTs47u7P2/EYR/4wK1h726YNprbf9GeCtUPn2b+Dxe3DtrTfzi5vahv3tq1l6z4+Kziuzrctbellw1HDm0b/YWpfHPFxsPb01c+a2huTXjO1o36dwCel7ejeoo9+YGrPXNQqOKfzvsryoCAAj/c7JFj78ucLTK1U+7ODtR8/eLOpdb7ItovOaVA9OO6sAbZtesPv9mMFOODQsqZWZuHM8P1+NcvUWs5YEaxt3KqiqU076ihTe7S6bfkEeLxaSVPbc9CAYO2EY8P28wIlhRMS4yDO4KWBQDrRAiXDkNsZqAnsDtyoqrZhNkZCCwPH2VkILQwcx9m5ieXIQaLPwQVAWyAN+FxEPiZyMZwIvBDHvBzHcZxCSgqf/4+DuI4cbO1zoKqbgeHAiao6WVXDLfccx3EcxylQUq3PQY5ItjL+8uHkApuk4ziO4xRG4spWmCwiGX0O1pJFn4Ns6vsAfQCuH9XDd5E4juM4ecNPK2Qi1focOI7jOI4TMxJbEpVIdVVdnOhzMARop6orEtow4FpVHZPdOKPq72E+gDJ2CB4AFb63ExBLn90vWDvum5WmdtTAw4K1cx+009Am/7ze1DrfGU5ZWzp0jqmNe6JDsHbXspVMrd0DXwRrX+5hWwP3OnFosLblRNuSWO/Fp03t2cNt2xlA4xM+NbWPXj4kWHvNdPt3UH/lqmDt7Eq2PaxIe9uKumXod8Fxi+xS2tR6lA4fdHt54ERTS7/jf8HaH7BTDFv1GRusXX/Df03t5I/tZMyvv5oRHDf9Stvu1mRUOMi1aODfoa4N7fcAwMtDbTvpqIHzg7V7f3eHqem8aaZ2wcLZwXHPOtv+LKm1a/h/v4rDrze1ym/YSZ4AY3rZaasbv7Xty/X/G7bdznjdtlACHFb3gQILMUz/6rJY/hgWOeyplAxmjNPK+J2IbCA6YjA00efgAxHZBHQEvhWR8F+WmAgtDLIjtDD4NxJaGGRHaGHwbyS0MPg3EloYZEdoYfBvJLQwyI7QwuDfSGhh4Ow8xGllXA9U5m8rYyPgWeBkVd2c2JPgOI7jOAWP7znIRKpZGYckfgb4HqgT0/wcx3Ecp9CSylbGc4HPdvjMHMdxHKeQk5JWRhG5BdgMvJVVvYj0AHoAXFe5Bt3KhfvSO47jOE4QP62QiZSzMorI/4BjgDPVsFIkRzb7wsBxHMdx8pfYUhm3sTKeCLQTkSOA64GDVXVdXHNzHMdxChmeypiJOCObvxOR2onvX05YGfsB5YA5CUvjB6oazGB96CnbO/5Bw93CM1hpH0b6cpRtVzz7lvC4E9u3NrU6S+34Y4Bq4+1418pLlwdr9zpjd1O7sUXYX7x00vGmNuy6zqbW7od5wXFXHVjT1GT8sGDtTLWjX19aY/ciADjlMDti97fdw7HZPVdOMbUpI+0+B2ceHbYyHrj4N1tsGU5AvOJ527+/tkyFYG3vx6+yxeX2a6r5HcOD4z7Zo6mpXZ9eKlj75pvTTa3J3nZkNsDCG+0eCVPPah6sva1WcVNrWaWEqfWrZPeZAJgx0/7DsneZ8NHNRzf+aWpHHPVtsLbagnNNrbHWCNZKcVsvM9uOF1/YpVZw3HVX2A70Gu9eEaxt8+rAoP7XdUHZyUdSzcrYQFVXJW5zORB+pzuO4zhOfuB7DjKRalbG5H/LygJ+nMdxHMdxdjApZ2UUkftEZC5wJnB7TPNzHMdxnEJLLIsDVZ0MZFgZPyfJyqiqt6hqXSIb46VZ1SdHNs/6wvOaHMdxnDySnh7PJUVJOStjEm8BWSZ4JFsZG3RtUtBTdRzHcZyUQ0Qqi8iXIjIt8dVMDxORCiIyT0TsRLskYlsciEj1xNcMK2M/EWmcdJNugL1t3HEcx3Hyi3SN55I3biQKLmwMDE38bHEPMCKnA8cZ2TwSqAJsAq5W1aEi8h7QFEgHZgMXqWowC3WP1042H8BPPywLzuGC4+qZWv/KtgUP4L2O75ta7V3tBM4DjwjbmtLv6WGLK5cEa4eXsm12I+cvDdZevJcd6fz13B9M7fgvbMsTwPcn2kd22k8Kp1uW/cq2/o37PJwYeMi59v3OnL0hWDvpBvu5aF3Cjore/E7YLnpyLdsq90GrVsFaStlWxw8bPBIsPeNy23q2/pZLgrVX/TzE1Hp9b7+mSnRqFhz3o6r27+CQXuHY5XLdbAvlw2H3Hpe+a0cgl2xtF//WKXyEstv7drr8zBqVg7XSyjZlDZXwe/7IR0ea2pC3w+/5o7vbr4sJN55gapvT04LjVrv6I1Or9OTlwdq1Nz8X1Ms++VXBRTZ/cF48kc3Hv7zdj0lEpgKHqOoCEakFDFPVf7xBRKQ1cB3Rafw2qprlKftM89reSeUDA4kWAcWAlgCqepKqtgD6EnVJ3Bjf9GxCCwPH2VkILQwcZ6fj37nnoIaqLkh8vxD4xwpXRIoAjwDX5mbgOPscXAC05e8+Bx+r6nQRqQt0AebEMTfHcRzH2VEkZwUl6KOqfZL0r4CsDmXfkvyDqqqIZHX0oyfwqarOE8n5QYq4OiRu7XMAICLDifYdPAQ8RtRC+cOY5uY4juM4O4TEQqBPQD/M0kRkkYjUSjqtkFWL3QOIWgf0JOpAXEJE1qhqaH9CbIuDCcB9IlKFqFPiUcAYEekGzFfVX3KzwnEcx3GcvKBb/pU99wYD5wC9El//8U+1qp6Z8X0i2LBNdgsDSK0+ByWBm8lB46PkPgcrhv1RoHN1HMdxnBSlF3C4iEwDDkv8jIi0EZGX8jJwbMFLqvoy8DKAiNwPLAKOBzKOGtQBxolIW1VduE3t1sMwIbeC4ziO4+SIf2Eqo6r+BRyaxfVjgPOzuP414LWcjJ1KfQ76qmp1VW2gqg2AecC+2y4MHMdxHMcpWOLsczADSI5sviRx/WXAJcBuwAuqelloHJ32gPkAKrxlR4cCrLzjJltLsz3C5d+2fbwAq08/1tS+W2D7oQGOsNNbkaJFg7WUK2Nre9refYAJLS62S+9vb2rL3w33qRr7me3TLlkyvK/krxFHmNoJhGNjWWFHY69ssXewtPzgT0zt3b3tfgNrNoVtSfd/b/eE+OD4NsHaKcvsF0aTSuHnYs0mu6dAo8tsu+Knd+8bHLdCCfvA4+VfzQ3WvnmM3Qfh2Oe/D9a+8Yjd+mTPZuH/dxo/fLipzW9r9xuof0Pf4Ljpva83tWHLw+/5Tvfan1NbDg/3iyj+zVRTS+sY7s1Q9MQzTW1Vz/tNrVilcBz35YfvamrPzw5HrS84tWNQr1vuogLbjLal39mx/DEs+p/XU3KDXapFNtcl6oy4t6puzDi64DiO4zjOjiPVrIxtgF6quhFAVbOyZTiO4ziOU4CkWmRzk8T1P4jIcBHZL6b5OY7jOIUITddYLqlKKlkZtxAdyagMtCPqAz1Asmh4kGxl7PP2jztu4o7jOI5TCEglK+M8YA9gkEa7JH8UkXSgKrBkm9qtVsbQhkTHcRzHyRH/ziZIBUZsiwMRqa6qi5OsjO2Igpg6Ad+ISBOgBBCOFnMcx3EcJ1+JbXEAvJdon7wJuERVV4jIK8ArIjKByMVwjmbntSxinxm5vOtuwdITPrLtSSMmhvdCThj9l6ntemwXUzu8ntkmG4A/zrjG1Bq3sm10AFWbVDW1l/d/K1h73j2NTe22+sXtumxsTQfftKep1Su+OVj78KZNtqgBDVjyoG0PK/5Gy2Btkb3s5+K43fYytRcm2BZIgD5dbGvZ+rZhq9x1N9o2u6ZVlgdrP11nO6WKPm9HhHcPjgpF73jR1L691X4PAFQrU9HUhl4atrN1LTna1FZccGKwdmYZO264wa8zTK3fFa2D424eNMjURjYJWIyBinXs5+Lzfe33NMAtu9rvzVnN7NcMQO1eT5vaU+fbr9Wb64StwHsttqPWH65ozxfgjG7PB3WGXhTW88KWPCck7lSkVGQz8AbQAthMtPfg0XimFia0MHAcx3GcfzupFtl8WtJtHgFWxjE/x3EcxynMpFqfg4cSPwtwKtA5pvk5juM4hYhUthXGQar1OcigA7BIVadlVZzZyvjDDpiu4ziO4xQeYjlyoKqTRSSjz8Fa/u5zkMEZQP9A/d9WxhkP+nLPcRzHyRtuZcxEbBsSVfVlVW2tqh2B5cDvACJSjOgUwztxzc1xHMdxCjOp1ucA4DBgiqrOi2tujuM4TiHD9xxkIs4+B9+JSHJk8woRaUVkZ9wgImOAnqoa7I88vartmz37lJHBCUycbPta75tnu7wrXNs0OO56sZ/Wt6d8GKw9L9DL4MVLwxG69Q62vf37HFMlWPvbKLvXVM0r7d4LU7/tGRx35jX2nD8pXT5Yu/uNX5naZ7eHveON+pxkatOzic1ucal9v1X3sV9Tx9wS9vY3+nOdqWnfo4O1Y9vsY2o9vvoyWFu0td3X4deOD5law/H3Bcdd8di1plbzTttDDzD7j42mtk+T0sHa1Rfbv9ujvhsVrL24VTVTu3yJHS9eb2PJ4LhN7/vJ1GY/fVCwdu48u2dHWs3we770AXeZ2qZJdr8OgDHn2v0KHn97nKmlhx8O9crb0fLND/86WNvuznBE9YLwXTv5SCynFbaJbC4HNE1ENj8EnK2qdYHbEz87juM4jrMDSTUrowIVErepCPwZz/Qcx3GcwoT6hsRMxLU4mADcl2ifvJ7IyjgGuBL4QkQeJjqqcWBM83Mcx3GcQkuqRTZfDFyVOK1wFYnUxm1J7nPw9mvf7aBZO47jODst6enxXFKUVLMyngNkJJi8S9ReOavaPqraRlXbnP6/A3bMhB3HcRynkBDb4kBEqie+ZlgZ+xHtMTg4cZPOQJYdEh3HcRwnX9mi8VxSFMkuEbnA7lhkJJAR2Xy1qg4VkfbAE0R7ITYQWRnHhsbpMui/5gP49c/VwTn0u3eKqXVaGI4OrXCnbfNasWcdU7uiWjiy9LjdKpvaUY98G6xd++itplZyUzgeWdfaSZMNBg02tc9O2j84bpuHPze1wRe3MzWAA58Zb2rFG9oxtwAlxs8ytZcvC9vDupe342o/bPqcqX32cYfguNOXrze1TyauCtZ+95y9Ti4VTs1m72m9Te3bP8OvqZ8WrTC1mxsHHu+i2cFxPym5zNSOXhK2DdYeO9nUhp0e9tlVKVXb1Covmm9q6x6xX8cA7dvaVuGxFcKW3aZL7ef47QcnBGurj73S1KrcG7ZNr/nDvt+q57YytV/b1DU1gMu+tuf8TZptcwQo+YMdmw2w5anRdv54Hkl77KRY/hiWuOq9AntMeaFAjxyIyCsislhEJiRdV1lEvgRqAvOBQxILg4ywpYqAABdntzCIi9DCwHF2FkILA8dxdm4K+rTCa8AR21x3IzBUVRsDQxM/AxwJNE5cegD2v2iO4ziOk49ousZySVUKdHGgqiOAbY8fdgP6Jr7vCxyfdP3rGvE9sIuI1CrI+TmO4ziO80/i2JBYQ1UzumAuBGokvq8NzE263bzEdf8g2co4b4jvWXQcx3HyiG9IzERsbgUAjXZD5vrZSbYy1ukS7h/uOI7jOE7uiKND4iIRqaWqCxKnDRYnrp8PJG+DrZO4znEcx3EKlhT+Lz4O4jhyMJio2RGJrx8mXX+2RLQDViadfnAcx3EcZwdRoEcORGQG0AAoIiLzgDuA54EfReRuYDXQJnHzGcA+QDrRXoRjc3IfX7S2/bjVZw8L1i5cFBDn/2ZKy2qE44+LtLPn9NO33wdrHzzoGFNLOyNYyuZnsuw2DcC0t2xvOMCC908wtZnVKpnaNROmB8ddc0wbU9vy8R/B2mLXnmJqJe9/NVirj99mahV6PR6sPe3mrqbWbcYVpta465PBce98wO7r8Psl4RiR0Ufuamq37HFYsDZ99Df2uCXXmtodjcM9A/b9+CNTGz8q3OegfE3b+/9arznB2jnP72dqW8rvGawdWcvuC9C5T3tTK31Eo+C4p1W3tSL72BHTAKf++K6pvfNKx2DtMavmmVrd23sEa6e1ucnU1h1kP4+t0sOff8OrLjS1O8tsCNbOeceOr3Z2LAV9WqE7sIbIhdACQEQeAnqrai8RuRG4CLiByNVwLJF7YbmqjinguTmO4zgOQErbCuMgZayMqrpYVX8i6pjoOI7jOE5MxLEh0bIyOo7jOE48bEndhMQ4+FdaGZP7HPTpFz6H7ziO4zhO7kglK2OOUdU+QB8Anf2wnyhyHMdx8oTvOchMKlkZHcdxHMdJAQo0sllE+gOHAFWBRURWxg+AAUA9YDZwqqouE5GawBigApGdcQ3QXFWDWbZDqjY1H0DnR2zLE4AUt9dGdVbYd7tk6pLguMVL27HMW9K2BGs3HLGXqc1q1zRY2+DrX0yt5uzwAZpFh9gxxjMb2talhmvDB58OGz3K1L6qVz9Y++s5g0yt+Lfdg7VdBtqnm+b/HG6f8X7f5abWbcqFdt2msAVv73OGmtqGVeF9uN/172xqt48MP575h9sWyvUvDjO1SpttmyPA+o52tHXxLycFaze22c3Uvr7mp2Btjep2wu3YgfbzBNBdbEso62ybXaXPw3NqvrsdtT58Zfgztm1ZW0/L5jz4mJl2DPg+DcJZ3sfsbluUHz7wVFPTSfZ7GuD9Mnaa52lPh01oR3WyXxcAHx73eoHFG6+/85hYDh2UvvPjlIxsLujTCuuBosDUJCtjZaI//hDtN8j4hexP5GxYCmwGrsxuYeA4juM4+YJ3SMxEKkU2DwX2VtVWwLnASwU8N8dxHMdxsqBAjxyo6ggRabDN1d2ITjVA1OdgGHCDqq5Juk1ZtsPF4DiO4zjbhW9IzEQqRTYjIieIyBTgE6KjB1mSbGX8dIN9fstxHMdxnNwTh5VxK6qqIqJJP+kaLtAAACAASURBVL8PvC8iHYF7gCybxidbGUMbEh3HcRwnJ6jvOchEHEcOFiX6G2D1OUi0Xd5NRKru6Mk5juM4TmEnjiMHGX0OepHU50BEGgEzEkcT9gVKAn9lN1jXU2w7zug7w7aZA8bfYWp/bglby0re+6Kprb/1MlPTDSuD494x3bbg3V22WbB25hN2KuO1Dx8QrE371LZBcoltD5tZdjMNl6cFx7a4Y8vSsH6XbcGr+oKdNAjw4Tl2GmS9Mw4N1k7q/5Sp/VHRXk8fUNy2oQI07mwnfa7uaSdQAjRfab8VTp8wNVj7YJuZpnbO1G2jT/5mFjDnV/v1WuROO6HythpFg3O6fJ1t6V3f305HBHilqZ0YGP4NwIo77bYqFY9vbGrLWtZl0kF2MmP9W4eY2tB35gfn9EmHiqZW7f17grWPjHvf1L5p3jZY+9d62948ajf7M6ztzXbqLMAJHVuYWtq1tkUSQHZtGdSdHUcqRTZfB5wvImlEVsf+WpBNGPJAaGFQ2NjehYGT+oQWBoWN0MLA2UnwDYmZKOjTCt2B/YCJqlpHVV8GLiSKbC4B9CaKbAboD3ymqqVVtayqnl/Ac3Mcx3EcJwtSxspYkPNwHMdxnCCeypiJlLIyAgeIyC8i8pmI2CcWHcdxHMcpMFIpsnkcUF9V9waeIspgyJLkPgdMynWoo+M4juM4AVLGyqiqqzK6JKrqp0Bxy8qoqn1UtY2qtqF59R01b8dxHGcnRdM1lkuqkjKRzSJSU0Qk8X3bxNyytTI6juM4jpO/pJKV8WTgmkR0sxIlOWa7rErvcbipdeo8I1j73T3PmVrDFvYRiT/vt2N7Adbd8oKplTp6j2Dtrb/Y66GHir8XrL36LHubRs9XpwVrX7vIjt+94NfZpvbHjba/G6DZvXavgtvanhSsLdKuhKnd/9uAYG27Qb+b2vz3PgrWHn2G3Tvj/Vr2a2a/QysEx/36pWNNbfpxdm8FgKmTN5ta1d/PCNZe+5X9PljR3/azH/nEW8FxP1870dQ2ZfMf0dRldtTwFeeE+5Mw0o5d1o1ha23RQEz7w7Xtj8Pjj3g9OO78IXaE+KLL7T4TALOP+trUdl23xtQAWnS2+32snhO2X5Y65k1Te//dg00trdvw4Lh7zexmakPnjg7WVqjyTFA/Ylm4p0ee8A6JmUglK+ObRBHPTVW1DNC1gOfmOI7jOE4WpJKV8T/AIFWdk6j1nYaO4zjODiGVz//HQSpZGZsAlURkmIiMFZGzY5ib4ziO4xR6UsnKWAxoDRxNdErhNhHJ8kR4spWxz3vjdsxkHcdxHKeQEEfw0iIRqaWqC7ZJZZwH/KWqa4G1IjIC2Bv4x86y5Mhm/fk2PxbkOI7j5AmPbM5MylgZE1/bi0gxESkD7A9MjmF+juM4jlOoKWgrY3+izYdVk6yMvYABInIeMBs4FUBVJ4vI58CvRKmML6nqhGzvZLMd/dr/6MOCpXNufMjU9ipd3i68rz97Nihlyssqi6mNvDxs/Ws5prepXVXCtvYB9C872NRO32g/TwBd69s2yCKN6pvaqr8+CY77VNN9TO2yEQODtTc9Mt7ULry4dbD2qzvtCOqOV4XtpMe1tCN0j9nPthQWO+us4Lj7v9rH1GacZFtJARY+Za+TX/htbrC286R5pjaguh0J/DpQYsLxpr7/nZ+a2pZTwnHBNeb8aWpn3WC/3gBW1KptavPOsm3EAC07VjO1h0ra79vGn1wZHHfDE/Z7r8mB4cez6eJAFHuZXYK1R712oKmtvcn+/QDs2rWmqa1Mt++34d3Ng+OO2rza1A662o6kB6g1MD6Tmm9IzExBn1ZYDxQl6lnQAkBEKhP98Ydov4Emrq8IdAQ2JeaVsnmxoYWB4+wshBYGjuPs3BT0aYXXgCO2ue5GYKiqNgaGJn4GuASYlMhWOAR4RETC/yo7juM4Tj6QvkVjuaQqBbo4UNURwLJtru5G1N+AxNeMf08UKJ9ooVwuUWcfw3Ucx3Ecp0CIw61g9Tl4mmiz4p9AeeA0VfWAbcdxHMfZwaRSn4OuwHhgV6AV8LSIZNmsPlOfg/d/3jGTdRzHcXZaPJUxMykT2UyUwzBII6YDM4Est5UnRzb3OMHeDe84juM4Tu5JpT4Hc4BDAUSkBtAU+GOHz85xHMcpdGh6eiyXVCWOyOZJwFMi8gDwI3Bk4ub3AMNF5KbEz4+r6tLs7uPUxbZfutqacHbT088das+9dg1T+6N5h+C40vMmU6vZonKwttTtD5vavPnh/ZkHzbX9xauG2HMCaHqrHUW8tmU9U9v7sU7Bcb9jkakt3xB+PPscZvuwP9jH/v0AHHGa/TyXWLkiWPvEbnYk8Aen2PG6Tbq1DI7bqFJpU+tUIXx4cfRsO8q2/fJwn4Prrxtlak3ftn9/t81fHhz3hzuPMrVa74Rjl0d1P8TUGm8Ke/uvm/KDqbUbsS5Y2/cFu5/Ht/PteOS/HuhragClq5cxtd/3axCsvWil/TwPSws/nhtrFDe19te3CtYODfRQKFLd/oz78x379QTw3G/27/6eS9oEa6V8uaDu7DgKekNid2AN8HpSn4NmRBkKLwDXqmqGm2EXYB1QgWjfwVcicrOqhrv3OI7jOE4e8fbJmdnhVkZVnayqU7O4eTfgbVXdqKozgelAuM2a4ziO4zj5TqxuhW2oDSQfH52XuM5xHMdxnB1IKi0OckyylfGPz/8R2ug4juM4ucKtjJlJpcXBfKBu0s91Etf9g2Qr425HhANrHMdxHMfJHXF0SLQYDPQTkUeJNiQ2JnIzOI7jOE6B4hsSMxNHZPMy4CmgGvCJiIxX1a6qOlFEBhBZHTcDl+TEqdBvznpTK9owbBt8rLo9/Lq1WR60iPjpbQ6oZY+9trxtEarTwI6FBZh3xZmmVu6Rd4O1Pc5oZGrDnng9WLt0rf1SeH2/LBtVAnDFgGxO63w/zZRqNwhb1i7av5aptZscttkddHBDU7vlorHB2sNbNzC1X3ezf383jJgUHHfgsbb1b+VhDwZr79ltgKndWrNpsLbJTfub2uh9qpvakUDaPu+Yetvp95va0mmfB+eUrvYH8cQiYQdzv4m23nP/ksHadRVtC2ybS0eaWtUBYSvw7RO+MLV7bn4vWHvvuXubmk4O/39Uq6x98PfRMQuDtaNG22Pffob9+ffDzdtm6WWm0/MjTO3mGy8J1o6scX1QP2KZ/Zpz8pc4IptPIYpj3hU4WlXHJK6vAhxGlLXwmqp+VsBz225CCwPH2VkILQwcZ2cjlc//x0Eckc0TgBOBbZeXG4DbgGsLeE6O4ziO4wRImT4HqrpWVb8lWiQ4juM4jhMTqeRWyDHJVsaXvp0R93Qcx3Gcfznp6RrLJVVJJbdCjlHVPkAfgE3PnZa6z67jOI7j/Av5Vy4OHMdxHCc/cStjZv6VpxUcx3Ecxyk44ohs3gO4MnHfX4jIGFXtKiJtgW+JFiwiIqcDHVU1aB6vL2mm9sE+tk8e4LJ3J5iarttkaiMfHhYc9/PvjjS15Q9/H6x9aKztET6k95Rg7Zxn7IjWDs2qBWtbVLbjamdOsXtJXFU0vL5cmGZHypZsUCdYu2ivvUxtUKVwX4Cv7rejeYssfTJYu3//V0xt1PnNTe2FZ+3XE0C5SatMbXXx8PNYq2xRU1t6/cBg7ZX/s/sg1Jq+wNTOqhzuyaEL7B4X6xrYcdsAx37zi6lddOZPwdoHfz7M1Da/nFWm298UEfsxzZ5ovwfWb5kdHPfypyaa2olPHBes3XtJuqnNqRO2TV+5ob6pXdgv/Loo0aqBqY2rb8fa7FXF7psBMKL/m6a28uolwdovPugY1MMdFvKGWxkzU9BHDroD+wETVbWOqr4MvAK0AIYDXVW1a+K2E4AyqlqMqHVyOuDBCY7jOI6zgynQIweqOkJEGmxz3WQA2WYFr6rrkn4sBfgyznEcx3FiIKU2JIrI/kRHFuoDZ6nq5pin5DiO4xQC/LRCZlJqQ6Kq/qCqexKdirhJREpldbvkPgfrRs7aoXN0HMdxnJ2dlFocZJA49bCGaG9CVvrWyOYyHRrs0Lk5juM4Ox+6RWO5pCopszgQkYYiUizxfX0iV8OsWCflOI7jOIWQlIlsBtoDN4rIJiKnQk9VDWe3Ag8esqupHfN22BK1+Lz/mdrAhcPtwhPrsl+NPbKZWdbok12D+olfvG1qD/58XrB26so/TK1qKdsKB9DvG9vS1uOYSqa2YEVwWEq0rWpq0qpNsPaF3+wI3TumhJ+LvUeOMbWf1LaOAZzRzH6831z2jal1WvJ4cNxSl95lajVOahCsndusm6nNfH63YO2Tl31galXL2dt6Hjl4F+bMtK3Cm78ab2o1NqwNzqm1lja18d8cHKy9e1fb7qY/nRysHbXA/v0d08O2BS5XO9oaoNnhtm36nY7huPQNJ9i/v/r3XhesXXvLfab2+gWNg7UHnmY/F20qZXlGF4Cn69ufFQBl3u1sav8tFbaX9/o+m4/8sNMxT2h6+HOhsFHQRw6SI5szrIzFiCKbixNFNncFUNU3EvsNjgOaAI0KeG7bzfYuDBzn30RoYeA4zs5NKkU2Z/Ao8FkBzslxHMdxnAAp0+cgcd3xwEwgfEzScRzHcfKRVN4cGAeptCGxHHADYJ+Y/fu2W62M3wyw25Y6juM4jpN7UqkJ0p3AY6q6JqujCskkRza/MeUSX+45juM4ecKbIGUmlRYH+wMni8hDwC5AuohsUNWnY56X4ziO4xQqUmZxoKodMr4XkTuBNb4wcBzHcXYE6X7kIBOiWnBPSHJkMzCff0Y2LwMyIpsbAJOBqUBNYKaqHpDtnax5z3wAZ377cbC0z6dzTa31vrtke9cWk5o0NDWpa0ehAhR/+F1TW1Pd7hkAsH6G3XTgj1GLgrWlStragk9OMLVDF4ZPAf31uN0v4tt72gZrh8xebWpPLQjb7D46/wdTO+Ecu48BwPOX7GNqLavYfvcDy4Qtrls+/9zUVnQLh9FW2VzC1AZVuzlYO/IL2xze87rRptb8wPDzdGpX20d/74PjgrV332z3uHilqR23DZD27remVuIIO7Yc4PFNf5pa8SL2a3nKsnDMy9Otu5jaLMLvvbu+/9nUXmkV/giUCnbfgL/UjggHWL3pL1PbpUQNU+s97svguKG/sa07BfrHAGnZuGf/o1PDHzh5YGanvWNZHTT85pcCe0x5oaCPHHQnaoP8uqq2ABCRZsBLwAvAtaqa3K1mhqqG392O4ziO4xQoKWVldBzHcZw4cCtjZlLGypigoYj8LCLDRaRD9jd3HMdxHCe/SaXFwQKgnqruA1wN9BORClndMLnPQZ9Xwue/HMdxHCc7NF1juaQqqeRW2AhsTHw/NrGZsQnwjwSd5D4HoQ2JjuM4juPknpRZHIhINWCZqm4Rkd2AxoAdM+g4juM4+YTvOchMKkU2dwTuTopsvkhVl2V3HwMq2Tau0wL2PIBL329vaoNaNzG1L+bMDI67aegMU0vb78Bg7Yb97PjWYqf/N1jbvG8fU5tx3eHBWorZkc717g+curny0OCwlc+xzSfrNoV9S09LOVNLT7dtWABHr3nS1L6ufUWwtv7EUab28oPtTG1E8bBlbV4N24747DX2fAGWPXq1qZ24/OFgbas0+/VavNJYU5t33/nBcaun2R8f6/edH6x9ZY+WpvZOg+eCte1WXGZqFS57J1h7X8ssz1QCcOGBts24SaXwR2W53s+b2vRJ4aiYqSfb1ucRrcK/24Nn9Da1fd+wbdEAE39ZaWplL+lkanfP2Rgct9iwqab2w5wzg7UNr7Dtvs6OJZUim98DzkjUlATuFxE7VNxxHMdxnAIhZSKbRaQY8CbREYM9iY44bCrg+TmO4ziOb0jchlTqc9AF+FVVf0ncLnzc2HEcx3GcAiGVrIxNABWRL0RknIhcb90w2cr4VbrdMthxHMdxcoIfOchMyrgViObSHtgPWAcMFZGxqjp02xsmWxkHFG+aus+u4ziO4/wLSaUjB/OAEaq6VFXXAZ8C+8Y8J8dxHKcQoFs0lkteEJHKIvKliExLfM0yLU1EHhKRiSIyWUSelBzkF6TSkYMvgOtFpAyQBhwMPJZdUc9ejUxtyVndgrUdb+5naru0P87Umjfdg5lH3W7qs9fY+yj/uO/G4Jx2mXSGqZ326gvB2ieumGhqRbvbaXQAU+8/xdT+HGzXtnmyRXDc2gNHmNrSeVWCtUV/sttcbO4cTkAs8sHbpnbwn88Ea3XA66Z27Yu2TWtu73CyYtFOfU3tiyvDz+MuJexEzmJX3hqsXfTgpaZWqkllu/DGN/nx3XmmfNYzdrLimefb70uALlVsS+FpC64J1jLJfl0Mv89OoAS4Yv5SU6tdzrbzVi0dNk6F7IrN9wunWy476nhTSy9XPlibPt5OqJy1b/NgLafY76ElJdNNbWPdcDpswzVrTK1ttYOCtV99/VZQPyyoFkpuBIaqai8RuTHx8w3JNxCRA4GDgL0SV31L9Pd1WGjgAj1ykOhyOAPYU0Tmich5ItI70cvgYOALEfkicfOjgCrAX8Bq4FCimOeUI7QwcJydhdDCwHGclKAbkPFfR18gq5WmAqWAEkRtAopDNjnipFBks6q+BbyVuE1L4ANVHV/A83Mcx3Ec0mPaHCgiPYAeSVf1Seyrywk1VHVB4vuFQI1tb6Cq34nIN0T5RQI8neEaDJFKVsZkzgDs48KO4ziOsxOQKSsoC0TkK6BmFtIt24yjIvKPFY6INAKaAXUSV30pIh1UdWRoXqm05yCZ04gOlziO4zhOgZNub7OIFVU1t1qIyCIRqaWqC0SkFrA4i5udAHyvqmsSNZ8BBwDBxUEquRUAEJH9gXWqOiFwm619DjZ8P2cHzs5xHMdxUobBwDmJ788BPsziNnOAg0WkmIgUJ9rvl+1phZRbHACnA/1DN1DVPqraRlXblGpXbwdNy3Ecx9lZSU+P55JHegGHi8g0IjNHLwARaSMiLyVuM5DIGPAb8Avwi6p+lN3AKXVaQUSKAKcCHeKei+M4juOkMomYgX9E4yY2+p+f+H4LcGFuxy7oyOYZQAOgSFJk8x7AlYn7/kJExqhq18Thjo+ASkRRzq+r6gPZ3Ue/UwL+8Plzg7WzenUxtUdHZnV0JuL1Ib2C4x7ywRum1ufwcF+nRhX3MbXuey0wNYAuF9c3tam3hz34Le+zH6+eUd3U1vQN7xutXNOOXS7W/YJgbZk/7jG1xUeH/ewrN9rRHOOK9AzWnjHvElOrUMTeSNuwQrj3QqVOdl+Hin+tC9YW+X975x5vVVXt8e9QEXkYmG95hCJh4keNEOWa+TbsFuIjNb2+SuvmIz/aFbKHr5tdMi2zJK96EzPEDB+koRKJJioCIogi6IdA8A0hvkAB+d0/xtyw9mavfc7ZnsPeB8b38+HD2WusMdecc60155hjzrHmuflbk/fou1NF3b8vfCJX9tU2+c7DB0ZXruOHOnfNlb3evXKebp01OVc2dMtuFXWvbZv/yfRz7lxUUbfnQ/nbV4+5at9c2WHd8rcwBjj25PzrLjny+Iq6J064K1d266z8bZUBtpj3bP51r72gom7n117Ola3YtnOu7KVdK39+5tG9O+TKZh09taLuVTfsU1Ee3zlYf9RNKCPwdWCppPbpQ0izzGyUpPktnMcgCIJgI6deFyTWinoKZRTQIW3d3A7/SuK7LZm/IAiCIAjWpZ7WHIzGwxdfB9oDF0haUtssBUEQBBsDdbxBYk2op2iF/sDHwE7AzsD3zWyXcidmQxnH3h4fUQyCIAiC5qSePAcnAQ9KWgm8ZWaPA/2AdXZZyX5RatyCoWHvBUEQBJ+IWHNQTD15DhYAhwCYWQdgP2B2TXMUBEEQBBshLb0r4yjgSaB3ZlfGo1NY4wA8ZLGwK+P1QEczex6YAtwiKT9OJwiCIAiClkHSBvUP+HY1staoW495irqovW495inKE3XRErrN+W/yrr1Vi3/rq3xN/VdP0wrNxberlLVG3XrMU6106zFPtdKtxzx9Et16zFOtdOsxT7XUDVqIelqQGARBEAQ1IRYkFrMheg6CIAiCIPgEbIiegxurlLVG3XrMU6106zFPtdKtxzx9Et16zFOtdOsxT7XUbTbCc1CMpUUfQRAEQbDR8uTOu9WkMxwwb3b+bm41JKYVgiAIgiAoYkOcVgiCIAiCJhHTCsW0euPAzHbDN2zqkg69CvxFaffHT5Buf0CSppjZ7sBAYLaksWXO/YOkUz/J9RqRn82BE4HXJI03s5OAfwNeAG6Uf3Y6CIIgCD4xrXrNgZkNBb4B3AG8kg53xTvROyQNa0B/N9yoeErS+5njtwG9cOPpb8C+wATgcGBb4KVsMsDBwMMAkgaVXOOL+KZSzwHvAC9IetfM2gE/APoCs4CfAacB90haWCavI1N+2gNLgY7A3cCh+H08LW1UdQzQDd/E6kXgdkmx9XUrw8y2k/RWlbpbS/pXc+epVmxo5Qnqk8e61WbNwQEL63PNQWs3Dl4E+pSOmtMo+3lJvSrojsQ3dnoB2Bs4X9KYJFuOd75tgTeArpkOfQlwF3AzINw4GIUbJAC/kNQ/pXMWcA5wD3AEsCvQRdIqM7sRWIZvVX0osFf6/wNgbkrzz5IWpbSelbSnmW2Ge0d2kvSxmRkwI+Xnq8A/gK8Az+BGxNHA2ZIeaUrdthT13umZWSfgYmAwsB1+j98CxgDDJC3N0RsHPI0bpw9Iuj0jGw5cAVwKrAYuAc4DjsWfv0uBN7PJpbQ+n/7uL+nBTP5+CeyDG5yLgSskLTazfsCd6RptgFOBX+FG5ChJc0vy3A/4Bf48XQz8HjdkXwTOx71lx6YyrcCfyxskjTCzTYDTM/KCMXoDsAdunC82s11TunsCc4AzgUUV6uJN4PIK5XkaGJKXr3L3JpX1AeCEVM5y9+g+SV/LqeMfAWc09ZoprYaei/3Ivz+bpvrqim9K93hGdg2wFfAaMAy/zwNSHV4E7Fbhmam6PBXKOUjSX8ysc947UnL+tqx9bv5ZMjhrU6ZN30bS4mry1ljCOCimtRsHs4EvS3q55PhngHGSelfQXQF8WtL7ZtYD76Rvk/RrM1smqX067xlJn8/oTQduxTvgiyRNN7N/Stql9HwzmwJ8RdKitJnUvyRtkWTTJPUtSVfAF4DD8IZsEN6wjAKG4kZMB3yTqs9IWmJmW+CGwCpg72QwtAfGSjrIzLoD9wEP0MQOL+VrY+v0HsK9QLdKeiOltwPu1RmMG3vrVBMwEfg1MAn4JrASOEnSR2Y2LdX3X/H7dxIwErg9pXkdML8kza64N0zA0sKzYmY34wbrTbiX6DJJnZJsAjAkTYV9NqW/NW7MHp/0RgF/kvSamU1O96gzcBVwgaTRZnYo8GfgQmB80u2Ae+h+nOp1R+DlJD8OeBd4DH9O+0jaMeXpr8DNku4xs4OAK4H3KtTFlZI+VaE8r+LGdrl8rcL3aCl3f+4HnsC9fuXuUfadL63jIfhzlFcXo8tcs3Ddhp6LrSrcn5txT+Fk4BTgUUkXpjy+l/LVCfgP4Bb8HTkCOBnoXOGZaag8o9K5XfB2Y6ikt1Nak3FjpLSc1wNn48/NhJTGXaVtS5qivQ7oAXTH267tgEdTPd4AbAFMwz+dPD/pFbWXLcGjXWpjHBz4ahgHzY6ZDQR+i7/wBVd8d3yEfi7e4JVVxRuwNdEaZtYRfzhnAd8BtpW0zMw2kbQ6ndMJmCCpr5l1xTuoN4FBkrqnc2YAB+GRIA9J6pe5xtvAhZJuMbNbgOslTU0N30hg0xKDoQ1wJD518jW8g9kUuAZfZ/FPfOQxGm+g+6VGZyvgb4Vrp4bkp5Tv8A7Fpzfy6mlj6/SOzTMqzUx4w1fuZT6w5Hn6EW5ADsKnpixjNC4oPC/p96vATNzYnJmOzZO0c/p7WqaepkvaO6P7IdAxeaMmSdovI5sJrMzoHoA/S8fgxlvPzHNbmqflktplfk+RtE/yGMwCVkjaMyOfJGk/M2sLvCupbVYvc96zwMcV6qKh8qyWtFdOvj6ucH/2A+aU1F32Hi3MGAelddxQXfTCO7dqngsq3J9ekrom2WbAcGCbdM7bmfyW1uEznmzuM9NQeRbj7cUk3HNxBt7GzU1p7wE8hL/7hTIfh7dDR+HG+zdwI3wi/m6OkbTczCYBp0maY76u6xz5lOhZwM+BAyQ9b2bHAf8DnCJpUukgrSUI46CYVm0cAKQHuj/FCxKnpBH0m8CXgbdL1XAX576SpmfS2gwfZZ4sadMy19oG2LHQgKdj/w7sL+mH6fd8fIRreAe4v6TXk/HxBG4RH4C/gH1xo2Yh8D1gRN4LkLwBnQFSB9gZ9zAskDTZzM4HvgU8ldL/eTJCtgVeLjQkZdKdgxtT1TZuG1qntwA3HG6V9GY6Z3vchf4T4POSsmtOCumsBNoWDMl07HTcxdsRN4T2Ssd/KunHJeU5Ejc2F+KGzQyt9Ua9gntVDPdc9FR6cVMdP4+P5r6Ej0Tvxrc/3wU3gotGXObu6sOBP+Cjzk7A1fjU2r1mdiBu8A2UNNHMBuGN+JeT/hzgfeD41GH0Ba6V9KUkX5T0r8Cn25bho/1DcI9Ntwp10VB5euKGYrl8fQTskXN/FqY898m5R73xEXW5Ov4A91Dm1cVK4Ogqn4slpe985v7cIalziexS3DvwBXyqpjMwNt2rqWbWCzeaP03+M5Mtz1H4tGO2PB+WGGAH4x8jOgU3UL6T7s9oSb9L58yTtHPJO90OH9ScCByIGxR9StLOnv+hkmc1/e6D3/uhwCWlz3FzE8ZBMa0+WiG9dJNyxPfjHcz0UoGZjcVHnNm0VgGnmtn/5lxrMd6pZ4/9FW8IC7975ORlNXCUpHlm9ilgZ7z+X8l0Qifk6CJpGd7IFn4vJePOlE+HjAc+B1wjaXY6vsjMJprZEMp3eAvxxu07eY2bZbwnAekQrQAACoFJREFUkq5MDfg/SJ1e5vQ/lKgvwUcev0qN86W4wVRgOzO7EG/APmVmVmjAgH8BY81sGPCgmf2atZ3EdKBPpuyPAY+Z2XmkTs/MjsA7PZnZ4Eyn97GZfTHT0C9Jaaw2M8Onc34APJrqSLh36C+4Nyrv2yD3pbyNz+RrhJm9AfwGGGNmHSW9X9IZ7oqPaF8Bvp7y9DfcnVzgJmDL9Pet+Ohxkbn35++4Qftd4LP4M9ULuBcf/d1WmlFJH6c6PRz3rKzGjejvmtkI3MA+G/hl6myexw3Pwlzx9fjUz4TUIW9GWnOT5LfgRtoovDNvi2+gcy/u8v5+hbp4EvcG5pVnd+CmTL6+mbnuH8m/P+fh0T1592hkA3X8y5xrXo/P+1f7XMwpVcjcn/vNbKDS1FuSXZ7ev31T2qtxL93FZrYn/syfhU9BFsozoqQ8DzdQnm+ZWSdJ76RrTjCzY0lGh9yDdzhwnrlXbyhr3+s1HZ2k5fhUx53mXtfBwBZm9pOUh2Pwd7ngJcXMdlDybiYPwqF4O94zp36bjQhlLKbVew6ChjGfZvgB7vLbLh0udHjD8KmFmZLWaajM7G5guKTxJccH4o3bKOAqZRYUJfmu+HqG49LvQcAPgR6SdkjHLi253PBkzOyAd1qlnd5CvJP4PfBHSSdSBjPbi7Wd3gUpjdPwTm8Y3lGsaRglvZgaxm9Ius48iqUrMEnFC6UG4tMk5SJcKsmOlPSA5UfHFOni7vGekp4rdA6N1S2VNUL3ZWCnCrIu5eohpTsAWKXGhfveJumU0uNJtiaiR9K4EtkBSTazVFaqy7rRQBfj61sK0UC7pfy9U0b+EPB0krXH35c82VAaH2X0vTxZQ3LzKZoTKB++PAJ35b+aZCcD++PP9I2SVppZT7wDzi4YvT3VT0HWDV+rkZWdhC8SnFSSn+7ATySdlTnWBfd49ZO0i5n9l6Sry5U1nd8Zbwd2xxdSD5P0XjIeTgX+IWlGiU4n4FxJV+al2xxM2LE2noODX69Pz0EYBxs5ZnaGpFuqkTdVNzXIhU5vvV23KTJ8tHUO5aNYFuLem6bKpuGj6XPrUPcDYHYTZdPwxaxHsjbctz/wCOXDfcFHzw+nv3dQfkRPd0ndkuzMJLs3ye4DjsnonpnK1thooN7AXjny7+Oj4qbKGooyeidP1pDcKocvHwBMBdrhRlGHVA+HwpqFv3nRS+NxQ6muI5vWNw/v0LsmneEhb8wJ4yCoP6xkTr4p8g1N18wW4A3tAJWPYlmOL1RtquwZvJGvJt1a6TYm3b2pLtz3WuVH9CxWWhNSRjYJ91RUGw3UVtLncuQfVtCtJGsoyuhC3ANRTnY33kHn6V4kaQ8rH768TFK7HNmMVOd50Utz8OnWcrIx+ILq3HDe9DtPPhyfkmpI9yhg+1K58kOFH5B0ZDlZcxHGQTGtfs1B0DDmK8TLioDtG5B3zZG3Vt2K6QLvFVzokuabh9+NNg+PpUqZAZu0Mt2G0l0lnxtfZmZzlT60JV+RPgfv4H7E2nDf5ZIeBTCzTcynujbBByiLku4HZkYF2Sqgku7yjGdohpn109pooJXASxXky6qUrcSjjFYD44BxVhxltHsF2dV4lESefDfzb7Z0wL0HnXDDq61XY66sDT5VsBk+ndAW9zogaUEyIPJkbfB1Ag8DB2nd6KY78Q49Tz4NDyNuSPfgMvIHzCwvVHjvMseblVhzUEx4DjYCrHLUxhN4eGSefC4+stlQdBtKdzYeblouiuUUPFqhqbKT8WiQatKtlW5D6U7FG/hqwn3nkx/RswQPdS0nm4ivzq82Gmg+HpZbTv5DfCFfU2UNRRkVReGUyNoDj1fQHQL8J+XDl1/Bp0nyQpuXkh+99CQ+NVJOdhewvfLDeecAVJCvkLR5lbqVQoX3UybKqCUYv11tPAeHvVWfnoMwDjYCzOz/gFskTSwjux1YXkE+F49L3lB0G0p3CD4qfqOMfDC+OK+psv3xxX3VpFsr3YbSnSrpozKyBsN980id5faS5jVFViq3nGigzLm58mpkZvZZSS/m5CtX1kj5TpAbvpwrS7p98Oil55SilzLpVpKNIz+c93DcOMuTD8G/V1CNbqVQ4YVKa1FaijAOignjIAiCIFiDNRzdRAX57/DooGp0Z+EGabmoqcGS7m2G4uUybpvaGAdHLA7jIAiCIGjFWB1GGTUXYRwUE8ZBEARB0CisDqOMmosHP10b42Dgkvo0DiJaIQiCIFiD1Wl0U6U8B81PGAdBEARBlu2pHN1UST4X/9JhS+i2KBHKWEwYB0EQBEGWSnvSPIJHCuXJ57egbrAeiTUHQRAEwUbP2M61WXPwlaWx5iAIgiAI6pKYVigmb5vRIAiCIAg2UsJzEARBEGz0hOegmPAcBMF6xswGm9numd9XmNlhFc7vZ2bXtUA+Ti98grfCOTdn81qi+9vmzlMQBPVBeA6CYD2SNi8ajK8InwUg6ZJKOpKm4hsdNTenA88Br1W49pktcN0gqDvCc1BMeA6CoImYWQ8zm21mI83sBTMbbWbtzewSM5tiZs+Z2Y1pa1zM7BEzu9bMpgJDgUHAL8xsupn1NLMRZnZcOncfM3vCzGaY2WQz29LMDjKz+5P8MjO7zcyeNLOXzOysdLyjmf3dzKaZ2UwzOyqT1xfM7CYze97MxplZu3S9fsDIlI+yO96lvPdLf59hZi+a2WRg/5at5SAIakkYB0FQHb2B4ZI+B7wLnA38VtI+kvYA2gFfzZy/uaR+kq7EN5m5SNLekuYWTjCzzYE/AedL2gvfZW95mWvvCRwCDAAuSVMDHwJHS+oLHAxcUzBOgF7A9ZL64Fv5HitpNO6NODnlo9x11mBmOwKX40bBF4F1phqCINhwCOMgCKpjoaTH099/xDvMg83sKTObiXfefTLn/6kRafYGXpc0BUDSu5JWlTlvjKTlkhYDE4D++FfkfpY+Pzse6MLaT87Oy3xY5mmgR2MLmWFf4BFJiyStaGR5gqDVsFq1+VevxJqDIKiO0tdawHCgn6SFZnYZsEVG/kELX/tkYFvgC5JWpq/NFa7/Uebcj3GvRhAEQS7hOQiC6uhuZgPS3ycBE9Pfi82sI3BcBd33gC3LHJ8D7Ghm+wCk9QblDPijzGwLM9saOAiYAnQC3kqGwcHAZxpRhrx8lOMp4EAz29rM2gBfb6ReELQKVq+uzb96JTwHQVAdc4BzzOz3eNTB74Ct8NX/b+Addh53ADeZ2ffIGBGSVpjZCcBv0gLB5fi6g1KexacTtgH+W9JrZjYSuC9NaUwFZjeiDCOAG8xsOTCg0roDSa8nb8iT+LqFdb5/HwTBhkPsrRAETcTMegD3p4WH6/valwHvS7p6fV87CDZk7mxTm70Vjl9Zn3srxLRCEARBEARFxLRCEDQRSfOB9e41SNe+rCXSNbN7gJ1LDg+V9FBLXC8IgvomjIMgCJB0dK3zEAS1pJ4XB9aCmFYIgiAIgqCI8BwEQRAEGz3hOSgmPAdBEARBEBQRoYxBEARBEBQRnoMgCIIgCIoI4yAIgiAIgiLCOAiCIAiCoIgwDoIgCIIgKCKMgyAIgiAIigjjIAiCIAiCIv4f19RnfqIItnUAAAAASUVORK5CYII=\n",
|
||
"text/plain": [
|
||
"<Figure size 612x792 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"affinities_plot(df)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"The above matrix represents participant affinities towards proposals, ranging from -1 to +1."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<matplotlib.legend.Legend at 0x7fe4a30e0c50>"
|
||
]
|
||
},
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"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": [
|
||
"df.plot(x='timestep',y=['candidate_count','active_count','completed_count', 'killed_count', 'failed_count'],\n",
|
||
" kind='area')\n",
|
||
"plt.title('Proposal Status')\n",
|
||
"plt.ylabel('count of proposals')\n",
|
||
"plt.legend(ncol = 3,loc='upper center', bbox_to_anchor=(0.5, -0.15))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"The above graph shows the number of various types of proposals at a range of timesteps. Ecosystems with more completed proposals will have higher overall agent sentiment than systems with more failed and killed proposals."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<matplotlib.legend.Legend at 0x7fe484aabd50>"
|
||
]
|
||
},
|
||
"execution_count": 24,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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91ANG+RgMhmOMWGuC1lC8V+dEwgnqy1uoLwvT0nTo3Kr9TbQ0xhHrcJfno+FonQssO8Goxp3kt5S3p/kCgtrHHaVkfYdRPgaD4ZihfHcjj/9hE4nYkY1S2pbJtKsYBZ9fsJOKMvAjFX8izPiaTeRGqtvTclurGdOwHZ9zuIK1K6+AkyYMpIhdYpSPwWA4Jijb0cDjf9qE4yjik147kjm2km77M7u/zWOqBGMN5IfLCMZDWE4cnx1jVNMeRjdsx1IHR/yopxkTgXwqJ5xGNGcMueEKAokwAFZWgEWBxv6VtRcY5WMwGEYEdsKhpSmW1vRVX97CM3duQ1VBFbUZhHFKL1Flwc4HKa7ZSCDZelh2JDiG0ilnkwzkU9S4+1AZdZhUuQZLkx3KWwX5+P1Dx8HZKB+DwTBsObitjg3PHKCxsrXDXEw6LL/rPTZcFmsW12xgSsXLVI87mda8iYjaZEUb8TlxLCeBPxFhatmLWMOlQZ3oN+UjIncC7weqVfWETnnfBn4NjFfVWnEDDv0euBhoBa5R1Q1e2auBH3un3qSq93jpbwfuBnKAJ4Gv67G2J7jBcIwSqo2w+h+72Lep1p3odxTLJ4iQ1jQGrtls6A93XCw7wZy9jxHOmwzqMPPA04MtUp/TnyOfu4E/AktTE0VkGnABcDAl+SJgnvd3OvBn4HQRGQPcACzBfWzWi8hyVW3wynweWIurfC4EnurH9hgMhgHEcZSqfSHKdjZQvrOB2pJw+zqaRNRGcYNrtpnZMvE0y4o1sejNO4lnFVI69RyaCmcf8iIYQkwtW0VOtI7tC65iwY4HB1ucfqHflI+qviQiM9Nk3Qp8D3gsJe1SYKk3clkjIqNEZBJwDrBSVesBRGQlcKGIvAAUquoaL30pcBlG+RgMI4KK3Y289NBOakvcyXI3grO6/wEEBMXphdNaVqyRxW/cRnasAcfyMaFmA6H86TQVzaItjk3t2BNpGDO47siBeIiZB1ZQO2YRk8tWj9gIOwM65yMilwJlqrqpU2jvKUBJynGpl9ZdemmadIPBMMyoL2+hrjzcfrx/Sy0711Zh+QTLLzhJPeoIzsFoA4s3/Z6seIgd869kzp5/U180lWCimYlVrwFgOTZTyl9iywlfoG7sCT1csSN54TKKa94A0s2/CLYv2P6n0v2k//iaN7CcOPVjj2f+rn/2So7hxIApHxHJBX6Ia3IbUETkOuA6gOnTpw909QaDoRON1a3s2VDNzteqDtvqGfFW+GegaALxEFPKXyYnUk1OpJZgrBFJMwHvtyOAsGP+FSzY+RB+O0awKYQitOROdBWCOgTsCCdu/TtvnPQVGkfP77H+YLSe2fseZ2LVawiKphmnyBFMNB2ceh4zDqzs9XnDiYEc+cwBZgFto56pwAYROQ0oA6allJ3qpZXhmt5S01/w0qemKZ8WVf0b8DeAJUuWDJMpR4NhZFFf3sJbr5Szf0sdjVWuW7BYh0Y37WjXTgNtiDdKmbXvCfx2lFiwiGj22A4mtFRUfDQVzWlXPO3XQclvrUitmnhWISdt/Qs7532MpD8HgEA8TH5LBXkt5WTFm9rL50RqEaBk6jmoBCgK7TlcWFUcy4djBXAkgFq+btumCIVN+wim1DMSGTDlo6pbgOK2YxHZDyzxvN2WA18VkWW4DgdNqlohIiuAX4rIaO+0C4DrVbVeREIi8g5ch4NPA38YqLYYDIbe0VDZwiP/u4541EZw3Z7dzdG6d30Wx6ag+QCjG3eR31LWrpXyWsrJb62kftQCaorfxqSKNeS1lDOqKU3n7zGpam2PcgqQFQ+RCOSzcPu9HfJsK4uW3Am05hbTtkS1sWgu0dxxTD/4LFmJcJorGrqiP12tH8QdtYwTkVLgBlW9o4viT+K6We/GdbW+FsBTMjcCr3vlft7mfAB8mUOu1k9hnA0MhiFJtCXBE7dvJhl3sDzvNO0hKoA/0crMA08yufxl/F6YmEj2GBxxuyzbn82O+VcwofJ1Fux8uE/lFSCQCFM99kRsXxBwR05Z8WbyWispCJcekSnN0JH+9Ha7qof8mSmfFfhKF+XuBO5Mk74O6N2soMFgGFAc22HF37cSqolg+SR9KBp1CCQOzfuMq9vCnL3LCSTCVE1YQmvuBLKiDRQ1HyQYc8PDBJIR5u98uN+UgADFdVv65doGFxPhwGAw9Auh2ghrHttL6faGwxWPKoWh/UyoXkdxzQaC8VCHc5sKZ7Fv1iVMP7iSiVWvYxh5GOVjMBj6DHWU0h0NbF5Vyv4ttaCHPNcsO8Hoxh2MrdvCuLqtZMcaccRP3ZiFtOZNxOe4jgAJfx6FjXtYsHPZILfG0J8Y5WMwGDLCsR2iLUnSRbFKxh32bKhm2+rydhObCCDu3jS+ZIRTNt5KQUsZtpVF/ejjKJv8brJijRTXbmJ83eaBb5BhUDHKx2A4BnAcpa40TPmuRhqqDo+Q3Bm1HSLhhPsXihMJx4lHeg4nIJa0729z6GIOi966m7yWCnbOvZyC0H7GNmw3CucYxygfg2GYo6qU7Whgywtl1Fe0pB2ZtIbi7ds8i2QWX1PwPJsFLHGVCqQ/Vzw5HFuxO7lOz9m7nHF1W9k1+0NML3mW7NjQ2VPGMHgY5WMwDACR5jjbVpdjJ/o2/L06yr4ttdSXtbhuzI6mj5Mp7jbKdiL9hmhpr53ywVFNHzmme+GYUr6aGSUrKZt0JhNqNhjFY2jHKB+DoZ8JN8R47Pcbaazs2dx1JHQ2daVVLgq2MzBrU3zJCJMqXmVq+UvkRmpoGDUPXzJCYfOBAanfMDwwysdg6EdCtREe+91Gwg2xw+dC+gh1Djd1DTiqFDQfZEr5aiZUr8PnxGkqnMmuye+mqGkPE2s2DLKAhqGGUT4GQx9StS/ErvVV7SaqPRuraQ3FXVNYNILfiyXjWD4cb/X8gKEOeS0V5LVW9lhU1CGQCBNIhMmKe/8TzfgTLUiaoZXlJMiJ1WNbWVQXn0I4fzLFVRuYt+fR/miJYQRglI/B0AfYCYfXntjHxhUHOkzIi4BlCVP3PMXsfY+3r8hXhPJJZ7Jn9gdIBvKPun5fMsKkyrXktlalzQ/GGhjVtIdAsnemP0VIBPJIBPKIBwpozS0G0m8JUDH5DPzxZiZVrWNS5ZreNsFwjGGUj8FwBFQfCFHylhtmUBV2r6uirqwFsXBn6j0NpI4yY/djzDz4DDVjTyKaMwZBCUYbmVzxCsU1G9g382LCeUe2HZWow7i6zUyqXIPfjpHw56YN658M5FIz7iSiOWPJioXwJ1rSXK3DlQHFn4ySlXRHPoXNB/F5cdbSUVz7xhG1wXBsYpSPwdALHNth3ZP7Wffk/g4T+2J5kZoTjhd/zM2cXvI808pWUT7xnWTFmphW+kL7ObFAAfFgIfN3H92GYY74qB6/mObCGYyr3Uwgfnh0ZcFhYtVrWN2FkDYYBhCjfAyGDKkrC7Pqvu1U7QuR31LGpLLVHJx2PrGccagDwZYajt9+H6ObdnU4r2TyWeS1VDKmaWeH9GCimWCimVD+VFqOcOQDYFsBxtdtYWL1uiO+hsEw0BjlYzB0ItIcp6kmArgmtco9Tex8vZLakjCWKLP3/YeZB1bgiI8pFS9TMvU8otljmLPnXyDCvhkXErCjACR92Yyu305R8/4u6ysMl1IYLu0y32AYifSofERkDlCqqjEROQc4CViqqma1mGHEUVsa5l+/2UA8kuyQ7sNmTNNu5r91P4FkKzvmXUFx1WtYwIwSd7vjhlHzqBl/ErP3Pt5ht0yDwXA4mYx8HgGWiMhc3K2oHwMewN38zWAYMTRWt7L89xtxIlEmVm8ExwaUMfXbmVC9HkGpHzWf0vHnMnfPv9on3yPB0dSNO5Gclmrm73pkcBthMAwTMlE+jqomReRDwB9U9Q8isrG/BTMYBpJwQ5Tlv3uDeHOEJa/9ktzW6va8SM54Dkx/L44vwLiazczf1dFBICfWwNSylwZaZINhWJOJ8kmIyFXA1cAHvLRA/4lkMPQ/dWVhVt65jeY6d27GTjpo0uaUdb8hGhxD3ejjKQyXAO4CyhkHV5qtkw2GPiT9arGOXAu8E/iFqu4TkVnAvT2dJCJ3iki1iGxNSftfEdkuIptF5F8iMiol73oR2S0iO0TkfSnpF3ppu0XkBynps0RkrZf+kIhkZdpow7HN/s21PHLLehorwkhzPVa4gZzmSk5e/xscy0dr7gSml73AqKY9jGraQ2HzQaN4DIY+pseRj6q+CXwt5Xgf8KsMrn038EdgaUraSuB6z4z3K+B64PsishC4ElgETAaeFZH53jl/At4LlAKvi8hyT6ZfAbeq6jIR+QvwWeDPGchlOEZpaYzx1qsVrF2+l6xEmNNfv5nsWEN7fnPeFGrHncSsA08NopQGw7FBl8pHRLbQzbYfqnpSdxdW1ZdEZGantGdSDtcAl3ufLwWWqWoM2Cciu4HTvLzdqrrXk2kZcKmIvAWcB3zcK3MP8FOM8hn2qCotjXHqK8K0hg6tprcTDtEWd3Ozzp5oPWEnHCr3hQh57tMF4RJO2fAbGkbNp2r8KfiTbrqozUyjeAyGAaG7kc/7+7nuzwAPeZ+n4CqjNkq9NICSTumnA2OBRlVNpilvGMLEI0k2PHOAmoNh4pEEsdYkdvLQqvtoOEE82s2OmdIW+CVzBLCSMYoiFUwse4XJFS9TMvVcclprmVH63JE2xWAwHAVdKh9V7bfNN0TkR0ASuL+/6uhU33XAdQDTp08fiCoNnVBH2fFaJa8+uudQlGfbRtTGwqEtHJnPjjMqWkt+6CA5LYeCZFpOnJxIPcF4E3470qu6LSdBwBvdxAMF7Jp7OZMqXqWgpayvmmcwDBka8mDDXGH9XOHg+JQ4f1aUh504Rx/Gtm/IZJHpO4A/AMcDWYAPaFHVwiOpUESuwR1Vna+H9vstA6alFJvqpdFFeh0wSkT83ugntfxhqOrfcNcosWTJEjNzfJSEG6I8/qfNxFoTGZ9jJxwizQkCTpSFO5Yxser17stbWUSDo2jbllPFRzyQTzh/Eo7VW98SIZozFisZIz9cypy9y7sNkGkwDBV2T4J18yz2F8OBYqE1u+dzIkH3NzM2pMwvPdTdqSi7Tq5hbH8J20sycbX+I64zwD+AJcCngfndntEFInIh8D3gbFVNje2+HHhARH6L63AwD3gN9314nudhV+bJ8XFVVRFZhTtntAzXDfyxI5HJ0Hs2rjxIXVnYnStJu2fz4YjaTK1ax7yd/yCaPZqDU89zPcjEArXb94hxLD9WMkpOtJ7cSDW+5KFIAbmt1Vjau/keg2G4Ul0E//NJH44Fk+phQamSnQDt4TeXH3UIJoTtU5TXFgi2zy2fH8gnOWfqQIieERnFdlPV3SLiU1UbuMtbZHp9d+eIyIPAOcA4ESkFbvDOCQIrxb2Ba1T1i6q6TUQeBt7ENcd9xasLEfkqsAJ3xHWnqm7zqvg+sExEbgI2Anf0ot2GIyQSjrPtxVImVKxl0falPZ+Qgm0FODD9AvyJZqaVrjLuywZDN9x3roXPgaufU1acIqw5XnCsTF72pNP/oUkmyqfVW0PzhojcAlSQwfogVb0qTXKXCkJVfwH8Ik36k8CTadL3csgjzjBAbH5mH7YNxdXrOTD9vb0yXyV9QaaUrSaQ7GkvGYOhfwhnw+2XWIxqgYvWOUyrHWyJ0rN9Cqw53uKyV2yWnWXRkjO0FcmRkIny+RSusvkq8E3cOZiP9KdQhqFJPJpk87MHGFe7jfLJ7+akrX8Z4u9WBsMhQjlw05U+SseBKDy72M+J+xzmlx0aI5y8x2F++aCKiQPc8x4fo5uV1qCMSMUDmS0ybfN6i4rIbcA0Vd3dv2IZhiKbH9lA3PGT21LJ5Mo1RvEYhg1NuXDjVT4qRsN1Tzs8+k6Lcc0OZWOFLbMOPcmPnS788GGHRQd7ZxJWIJQLmu5HoZCdgGAiM0PY6kXCnsnCNSsd7j1v5P7KMvF2ewH4oFd2PVAtIi+r6rf6WTbDECIZibHphQoKwzVEcsaRG6nu+SSDoR9R4L+LhKdOtYj30JM15UI0Cz6/wmHpeRbhXKFyrOCzlQWliuVA0oKaUfCrj1r8zwM2cyt6liHug5cXCk8tsdg/sXtFIfD909sAACAASURBVI7rMGD1sJlsNAtmVSqbZtLuLDASycTsVqSqIRH5HO4+PjeIyOb+FswwdKjdvJdnbltD1D+RoFay6K17BlskwzHO/mK48wIf26cJ06uVifXa7bBich0s3gv3nN9x/sT2CTtSHcBUKWyFX1zh4yuPO+TG3BFQKFcoGQ8l44SGgkPnl4+B5lxhWo3y4ZcdWrMPF0IFbMv9n7Ro9+zsCp8D0+os7j4vs3sxXMlE+fhFZBLwMeBH/SyPYRCINTTTXJp+5vXAqm28vsUCiihs2svUspewtJsIBAZDHxH3wabZwrbpwpvThQPFoCneXgWtyqefddgyA15f0HOM5LXHZVCpCKFcJT8Kt3zU1zFLleJGGNekWJ7+OHEfzKuA9XOUR8+QDJceZDqaGbmjHshM+fwc19X5ZVV9XURmA7t6OMcwhLFjCdb8+jH27k0SIY9EIK+b0rnkt5aSCORx0ta/kpUID5ichmMTBdbNE+4536J6tBBIKvPKlPdtcEcQAMGk4k8KD54jJPx93EmL0JqlnP6WQ3bc1TJ+G1qDQvk4YefUjnW+sghGuqLoDzJxOPgH7gLTtuO9GG+3YUv5K9t4/s4tNPmLyYuVkZssR1RRSf/m6IiPvJZyFu58YIAlNYw09hfD9qk9dNIC6+cKm2ZbTKlVrn3GYftU2D5NeHOGdCzYjzg+Ye3xRqH0J5k4HEzFDa9zppf0X+Drqlran4IZjozWijpW/3YFDU2H25VVoV7G49NcCpv2Mr5mIzNKnx8EKQ3HGvuL4Sef8hHL6rlDz40qV71gs3+8cNd7MzVlGYYbmZjd7gIeAD7qHX/SS3tvfwll6D1O0mbj7U+xfrOS8BWT11pJutjPhcn9tORO5G1bbm8Ptmkw9CeNufCry33kReHKlxy29hDbV4BHz7AyUlSG4Usmyme8qt6Vcny3iHyjvwQy9J7Q/gqe+MUq6n0TyYuW4bfCzN/1EIXN+w8rK6rGYcAwYCR88JuP+GjOhc89o/zlokxDxBhGOpkonzoR+STwoHd8FW5UacMQoOy/W3j67t3ErTEUNe6mIHSA+XsfHWyxDMcgCuyYCi+caNHoxe1vyBP2TRI+/5TNPedZRvEY2slE+XwGd87nVu/4ZeDafpPIkDFb717Jf19O4ncgO17H8dvvJTc6RINVHeO0Zrkr19sWQ2Yl4V1vKrmx7s8bLBR3sWMo1/1ryhVCuRDOASeN/ogHhDULhNLxQnZMmdRAu9X3k8/ZPH7ayIxPZjhyMg2v88EBkMWQIU7S5qWf/5Nt1ePJi1QRD+Rz6vqb8Tlmu4GhSHOOu2hx76SOne8/3q18fJXD2Vu050i93dC2yv7pt1uU99FmLbYFyV66MM+uUD6xyqGySHl9gUUs4KY/eI41olfqG46MTLzdZgO/B96B+y7zKvBNz+XaMMDE6kM88aPlVOhkihr34IjFu9cdFgzcMERozIMbr/RROQaue8rm9bmCCsQC7sr4P7/fx4q3K1Nrj2x7CUdg8ywhlCdMqVXO2qp90tGLQnbcwYdFUpSYzyEeEKJZruyHSasQzhPuP0fcPZoMhh7IxOz2APAn4EPe8ZW48z+n95dQhvQkWqI8/N0nCfknMqpxF6PrtzGrZOVgi3VMkrQg4YecTjtKlI6FtQukPcDkfxdZ1BfA55/W9phiqUyrcohmuSv4j5R55coJB5QNs2Hl4r50TU7dF8bXXcFhxZJIlFqfj/1ZgcEW5ZgmE+WTq6r3phzfJyLf7S+BDF3z+m1PEAoUU9i0l9n7/8Oopj2DLdIxyd4J8IcP+qgrgA+96nDJ64rfhsdPE5adZXUwVxW0Kp99RrnrPUIkTdyvkgkWPlsZfRSBI3ZOEdbPM2atTPh4UzPX1zcAsDonm/sLC3gjO9g+kmsRs65ooMhE+TwlIj/A3a5agSuAJ0VkDICq1vejfAaP1qp6tu4JUhA5yISaDUbxDAKOwH9OE5adbVHYCnPLlQfP8bFysas8dk0R3r7LYV45bJ7hdWcC//c+IR7oukOzfUJt0QA1YhCZH4+T4yibglmD0sF/uinEd+sbWZWTQ1KExbEof66q6VDmybxcvj9+rFFAA0Amyudj3v8vdEq/ElcZze5TiQxpeeV3K0j4xuO3sphWumqwxRnRJC0OhegXd3X++rkW6+YJFWOFU3c4zKwWHj1DmFDvBpksGwvXPuPw8kJh2dmCifXVkbnxOEvLq8hT5c2sAPcXFvB0Xh7xo3S9Hpe0OT0aJdBDpOi58QRXh5pZmZtDHOGS1lbiwLrsLPYGgtgCUxNJLm5p5am8XF7Iyz0quQw9k4m326wjubCI3Am8H6hW1RO8tDHAQ8BMYD/wMVVtEBHBdWq4GGgFrlHVDd45VwM/9i57k6re46W/HbgbyMHdZvvrqj08gcOUhu0H2VU3hqLmfcw4sGKwxRkxVIyGBm89imMJeyfAllnC9qly2Op6n60cX6JcsFF5dYHwj3e7+VVjAFXmlMMD5xx+ngFG2zZ/qKqh1bJYnpvLOyIRflFbz1camrh57GhWddPR5zkOp0RjLIlGmZVIUu3zUeH3kRDh3NYIp0RjGXsKPpWXi89xuCTiRvbIApZE4yyJHpq4C4lwfV0Da3KyiVrGcaI/ycTbLQB8CTjLS3oB+KuqJno49W7gj8DSlLQfAM+p6s2eKe8HwPeBi4B53t/pwJ+B0z1ldQOwBHeUtV5Elqtqg1fm88BaXOVzIfBUT+0ZylSv30ks1HpY+vp/bkGZQDQ4hnH12wZBspGFAk+eKiw9z+oQoh9gcp3rMRawwfH6nvwo1OUpm2cJ97wnjXIRYc+U7us8IRbjyw1NZHnvRzER7iss4NXcnD5o0dDFr8pvq2sZZzvcNHYMN9TW4QO2ZQUocJTbqmtZlZvD34sKiXnfxfikzWnRKKdGYhwfj+MHEkBJwM/iaIxRjrsb256AnzuKClERZsTj3Y41FZiSSHBSvPtuq0CVQtvmc00h/jh6VEZtnJhMckUozBmRSLsMNT4fPxo/lkbfyHHU6GsyMbv9GQgAt3vHn/LSPtfdSar6kojM7JR8KXCO9/keXEX2fS99qTdyWSMio7w9hM4BVrbNK4nISuBCb3fVQlVd46UvBS5jmCofO5Zg5Y8eZk94UhclplAU2smCXQ8PqFwjkaQFd1xg8dxiiyU7HYoblJZsVyEEbGHfROHZk+UwpXQ0ZrS3R6L8qaqGiCUc8LseVtMTCf5WVcOzuTn875jRlAcy+SkOcVRZFI9zWXML42w3hFOxbXNSLM6NY0fznfoG2vzLFsUTKPBmIMA7IlHObe0YZzABbAtmcX9hAa2WjymJOItjcaIivJGVRY3PYqyjfLYpdFRrpDojQLMI1zaGWJ6fx8GAK3Gu4zAnnmBuIkFx8lB4qvnxOOe1ukpnfXaQVhEEODMS5Zv1TdwwfsxRyTPatrERQr6RNwrL5Ik/VVXflnL8vIhsOsL6Jqhq2+a0lcAE7/MUoCSlXKmX1l16aZr0YUekNsTjP1pOtUymqHEXjpXO/dMit6WS/JYM9vUdASR8UDEGGvKlQ1o4B0I5pN0tsjsUiGW5UQYOFLvhXj6wxqFkHDx5ev/+qN8ZifD7qloq/T4eLijk2/X1WIADbM3K4sxIlHNKy2k5ChNPpd/H+uwg67KzORDwpwkn23t8CqMdm9G2Q5Hj4FOlu5msoCrvbWnl+HiCiAil/kNdy62ji/h4qLl9xNKGAAsTCZoFnszNpdkbJcRFKLKTnBKL8elQ82F1TrT7NzZhgSpRER4rrSDhOR7kdGHRb7Is7i/Mp9nyc2m4mWmeYmoR4cPhMI8U5LE5O3hEchTaNo+VVlDkOOzKCrAuO0i5P32XXePzsTYnm3rvHhbaDmdGIsxLGell+VqQIbQQPRPlY4vIHFXdA+2LTo/621dVFZEBmaMRkeuA6wCmT+8hpO4A0VrdwO7lr7Hx1SZa/BMoatzFpMq1TK58dbBF61eSFjy7WNg7QYgEoTUIyZRFkaFcV/F0FwNMVNMF7O66PBBMuGtycqPwmRUOK08RSsYf+WhmfizOJDvJizk57Z5RospVoTCXtLS073Q5Px5nfyDA4wX5fK++vr0jtYAT4nFCAutzcqg9ijfbKckkH2pu4eOhwd3ob2cgwO9GFzE+aXN+a6S9w/5SY4jsbqZjCxQubj3c3DyYqCor8vKIes9hq1hERSi0bSbYSbI9RSrAR5tbyO3UvjxVWkT4SV0DV0yegHME3nNfbAxR6Dg8kp/PrGSCDze3dKkE23gzK0CrZXFyNIYfSNLxp7KusRym9VqUfiET5fMdYJWI7MW91zM48thuVSIySVUrPLNatZdeRsdbMtVLK+OQma4t/QUvfWqa8mlR1b8BfwNYsmTJgCi88pe3sWvlVtqeFbWVeMwmHlNaIkK9FKNWgIAGyWsuY96ef1HYfGAgRBs0Ns0S7nqvRflYYVRYyYtCbgyykoe+ksl1cNI+yElAc7ZDzHNRthRsLzJAa7D3oV9syz0vEoSl50uvz2/DUuXaphBfaWgiALwRzOKWMaOp9/m4sbaOU6MxtmVlUe8pkyfz8yj3B/hmfUPaUUOhwrsiR7+1RbVl8UYwiwp/4Kg9yNpRJeg45ECHzrarqxfZNl9vaBoRfn45wCUtLUd1jaAqx8XjfKw5zLLCgl6dOzOe4MpQM4/l53FGNMKUpE2NJWwIBrHTKDK/Kq2Wj/F2kjzH4b7CAlp8PhZEo4xr++78OSzJ6qP4S31At8pHRHzA23AdARZ4yTtU9UjDIS4HrgZu9v4/lpL+VRFZhutw0OQpqBXAL0VktFfuAuB6Va0XkZCIvAPX4eDTuMFPBx07luDlm//N1rIi1BrfIc+XjOCzY/iTEYrie0gE8sgLl7Bg9z+H1d46jbnw9wstQrmZdzPxAOybKExoUD73tMOrx0HFGKG+AKJZgqZ9Mxxak7Wz4gn+p66eJdEYz+TmEBeLd0YiPFBRRVSEJHDzmNGc39LCuyPRAZWt2HEojsaAIRqp9BjEDzRZwtfqG9mWlcWWXpjfvtXQSEyECn+AKUlXCY53lPHRnr/fOsvi06HmNHNh2WANnd9Ut8pHVW0RuUpVbwU29+bCIvIg7qhlnIiU4nqt3Qw8LCKfBQ5waA3Rk7hu1rtxXa2v9eqvF5Ebgde9cj9PWdT6ZQ65Wj/FEHA2qN28lxW3raXRP4HC5r205hQzuXx1e35WMkx2rJFgrIGC5oNY6nRztaHLnRdYbJwjHFeS+SAykITL/+tQVyjc8b6ulM3gk+M4nByNMSblTX9BLM7ZkQizEklaRPjlmNFcHA5zcjxBAnf0U+vzsSEnh/9X30DeyPT4NxwBRY7SaFncV1HFA4X53DZ6FJEe5vdO9xwwbh9VyNVNTb2uc6wzPPoV6WlpjIjciuvt9hDQPg5tW4cz3FiyZImuW7euz6/buKuMR25+jaQEyG2ppLhqHdPLX+zzegabdXOFWz7q4yOrbZ45xaK5F6OfoYRPlStDzUz0Joh9wPHxOG+Lxujs8uEuRszmjewgAYWrQiHyjYIxZEgS2JGVxfHxOLU+HyVdOA20MSOZIC7Cq9nZfCR8dKa/DgQL4fK7YN57juh0EVmvqkv6SpxM5nxO9v7/PCVNgfP6SojhTqS2ieU3ryYhBWTFGjlp61/JSgzu5G9/EMmCO95nMbVGqSpi2CqeAtvhf2tqOTMSJSLSPiF7IOBnWWE+zT4/0+JxAl6OA5wSjXNGdGBNaYaRgR9YFI9T6fNR7veT7OFnszMQ4In8fH5eO7L37MwkwsG5AyHIcCXZGmX5Dx8n7C8mL1zKSVv/NiIVD8Cys9wIzdetVv560ZF7Z82NxzkuniDfcch3nA6hUfIdZYJtMzGZpDDFfNBkWTyfm8szebmUHcWamGmJBH+sqmFaIsmvxozmzEiE0bZbT646fDIUHhET5oahx0TbzthN/IwM5naGO5lEOBiLO1/zLtwRz2rcuZeRrZa7wEnaPPWdBylvdr1XHPGR9E+isHE38/c8QnascZAl7HsUeO5k4eklwoXbk8x4W4gVJVGCntKwRXghN4d7igraF+WlY7Rt8/X6Rj4UbulyYWBEhCqfj0q/j6pAoF0RTEkm+VZDI99qaGRfwE+8F3NGlkKOOuQ6SoHj0GJZ/GzcWL7R0MA4e3jYxw2GkUYmr5DLgJeAj3jHn8Cd/zkyw+EwZ/UvHmV/dDKFrfsQdRdsqVhML3uBwuaDgyxd3xPKgb9cbLFuvsXp4Sg35NRgJeDlnGxavYnTAsfhsuYwlzeHWZWbw+40+6RkO8qHwmFyHOWBwgJs3LAkWark6qGdPAUY662KT107ocDugJ8yfwALJdmL8YkCMUuIiRAVoTSQxU/q6giaaRuDYdDIRPlMUtUbU45vEpEr+kugocyuR1ezpXw0haG9jKnbyuyDQyfI5/6pDhWTe+5N7YSFk3C7+mgAaoqEmiJ3cWc6SsYLrTnw3boGzmyK8rexRUyNJzi/NdLuEQZQJ8LuYJBTojHOaU3vNv5adjYv5OXx2cZGinu5Sl2AuYkkcxNDZ4W2wWA4cjJRPs+IyJVAW2Cxy4Gh0+sOEA3bD7LqyQZy4q3EA3lDRvEosPJ8mztPzTqiVdR+RymOOoxJ2GlNYSfaDl8vb+C1QDZv5vv5Rn0jAjgqHVYbjlVlbDRKqwibgsG0AQgm2EmurzsmrbVscmZzR/Ii5lulnGFt4yTZi1+Myc/QNarw6+THmCbVXOF7YcRtMZSJ8vk88A2gbTdTH9AiIl/AjZJT2F/CDRWS0RhP/PplHIpQEd659uc9nzQAJHzK0o85rJgZ5NyGCIsOOrRmd39OkWUzLxBjmpUgF2WcbdOKUOfz4aR5uMOWxQNFhVzX2MhUzy15hzOVS+M3kk2cqVLLVKlhsbWbM62tLJQDnBIb+ZOlveE1ZwGfiX8XRVjunAlAkDjZxA8rGyTBjYG7eJ+v75cDGIYXTzun8if7MgDW6QJu8t9JtvS0mcAhmjSXkOYxzarpufAgkIm3W+/iQoxArKwAc2b52L21ghO3/X3QvKFsgbWn2RycDLWFwt7RFqU5Qa450ILT6uO6wrrMZFMowcfGYBZJEYptm0nJJHnO4eOVAsfhp7V1HUZFtycvxYfDedYbVDOK7TqNp5OnATCKZj7le5Yv+peTJ4croY3OHH6Z+ATjpYkbA3cyVpqP7GYME/5rn8DnE99mstTxSf9zPJg4h6lSQ74VI5hG+Wx05vKtxJd4Qn7ITKtqECQ2DAUimsVNiU+yQA4ygQb+aZ/Ndmc6X/X/Cz/dj5j360SedRbzunMcNj4WyT4u873MBwNb2iM5DwV6XGQ60jjSRabJ+np2n3seOkhv9XtmOtzxAWF3foBsx2FS0mZizObs3UmChQkutwamEy9xxnN2/Fau9q3gn/bZNJNLFgnmUMZkq5ZWcnjVWcR4Gvmu/yHe5dsCQESD/Nn+IP+0z2YcTYTIpYgWfh34C2f7OgbPiKuP/TqRHOJD9q2tRYO0kE2xHFqB7qhwj30B/7bPRL3XgO06nVlSwYd8L3Nz8koy2ZohlygzpZJHs27o1ZuuoWdsFf5qv5/ZUsH7rHV9YsqKq4+nnNN5yT4RTWO8DkiSt8ke3mG9ySypzKjO3yYu5zb7w/wycAc/THyGYhpoIYcWMtv/aZ6Ucra1iRB5bHVm8qbOxMLhv1cEmLL4wt42ERicRaaGASA8yua58x2ifkEd0BQbWGOe8MLkAONtmx+8EaYqy2Jidozj/HHmj44P6Gr7v9uX4MNhlLTQjOulECfAW8zkLWcmAFOoJk9ifC/5BXd5t0eAJJ/1PYkC/7bPJCgJrk78gLcndxAQt2CdFrFPJ5LETwGtPBf8DsUytNzXX7JP5DuJL1JLER+0XuGr/n8TJMF3El9grS7kRNnLGAkB8AF5lelWTcaKByCBjzd1Jr9IfoIbA3f3WztGAs2awyvOIs6wtlEg3cdHTKrFtxJfajd9vtvazE/99zDHymyrkhYNskunUq2HNpl7S6dzX/I91DCasTSRk2a03+Lk8BDucskxhMiTwxcrWzi8y9rKtb6nCZDkL/b7+YD1Cncl3wcI1YwhhyiXWK8SoHunmyyS7NBp3GFf5ClDZT4lzPFVcFA+MmT2nuly5CMis1R13wDL0+8MxZFP9bQk/99H/VRk+dKavnwoF1ZF8ZX7+cy4GooHKSZcrRZyZuw2PmC9yovOSdQwutvyi2Qfp1i7sbydM8YQ4r7k+SnnKYtkP7nEaIszUCgtTJVa/OJwd/ICLrbWclvWn/qzWRkT1QC3JK/gTvti5kgZJ1r7WGGfSowAWSTw4fBt/8OssE9lrS48qrqKaKaJAuZLCZZ3b0609vIB61XOsLZ1cFZIqI+nnNNYmryA3Tq5x2tPlVp+F/gTc63yo5Kxt0Q1gKAE5eg9FlXhX867+GXi49QyivE08oPAg3zIWt3+vKWSUB9fT3yVJ53T+ZrvEV5zjmOrziJGFgvlAD3t0VFPISVanDbvLGsTZ/s2szq5iB2abssWZaLUM17ciN/+NDvStBJktXMCcbIYSxNRsviK/zFuSV6Zwd3IjIJsP3+4ajHnLEjfjp4YyJHPP4G3i8hzqnp+X1Vo6MjOkxPcfEGQLFW+vSpG/qQoakHSkvYwHA4w2xfjzLHRXu1j09fcnXwfcfzM85XxT+fsHstv01lss2d1U0LYprPo0Ch10wFyiLLcOZOr7Od5p++toxG9V9RrAfUpU53bdAar7MW84LyNRgr4pG8lqnC//R4KaOEkaw+TqOdE3z5+k/hYxqaR7miigDmUMV2qASWBn6fs0/iHfQ5jaWKhdYA8ouQS42VnEVWMYbpU8T7rdexuooErwvPOyXw4/jP+HPgdZ/oGZlv2Js3lw/GfEdYcfhK4l0ustRmZn1Rhj05mjXM8O3QajmfWesuZzgadz0myh0/4nucJ+zS+nfgSS+UCFliHr7fb60xmnS7gO76HeMJ5B2/pDHKIcrK1m2yNIz38sKZILRdarxOUBDVOIVGyANdBZIPO48bEp7o9v1zH9/jbHUsTx1s7Oajj+aLvP9yW/HD3Jwxzuhv5bAT+AXwJuLVzvqr+tn9F6x8GYuSTBB76aCFvTT60QZYtStSnRCyIppiFIyLMiia5+FWHT8yoJt/qnXZp1SCP2WfwoH0e5Xpor45pUsN5vo2cZ21koRw4att2vRZwTuy3vNPaxnadzgGdeHQXzJB8WpkgDTyd9QMC0rc7WK5z5rffMweLrc5MVjsnsF1nHFZ2NM2cbW3iZN9e7k2ezx4deONFAS2caO3DhxLWbMLkENYc5kg55/re4BV7Iat0cXsH3RUBEoyTEDVaxHf9D3Oi7KVAIuQSbR9lWTiMkjCFtCLiKoEWsomSxTjPpJgpSbW4NvE91jgLmSUV7NRpnGVt4nO+J9tNSBOlgVlWZfs5UQ3wV/v93Jt8L7W4Zq5CWvB75fMkytW+lbxiH8/z+nYAjpMDxMgioodvXeATm2t8K/iHfTY7dYjsptYFAZKMIkwNo3ou3AuG2sinO+WzALgM1836L53zVfVnfSXEQNLfymfPjFyWXhbnrVyYHVVyk25HIEAwaZGVtIhZefjVYWJLNblxRWp8fH9CJT3tAeaosEVnsUcnU6bj2O9M4BnnVJrJZYGUcILs9ToeYZdOZqvO7vZ6xTTwcf9zfML3LOO76VBesRfy7cSXqKWIH/vv54bk1d0L2ocIDorF9f77+YL/iT65pq3CLckr+Kv9wQ7pWSQ4RXay2NpDjABJzzCQQ5Q9ziRe0RNopQdf9gFCcMglRj7uPEcVY3p5BWWGVPX4EhEgSREthMgh7r3tX+N7mv/x35vWvJWOnyU+xV32RfyPfym3Jz/IeGnkoE44bIR4mrzFx/3PEyTOL5KfoFSLOdfayNusvYQ1m23ODKpTTL0lWky8UwzyAEkmSfq1ZPVaQJguVlMfAwwb5ZNS4UWqOuh75fQV/aV86gv9PHFRDs/NdEOgX7y9gIgdZJn/e4eVDZAgiY9LrVe4NXA7Iq5ieVOnH/ZjAohqFquck3nCfgfljGtPH0OIM6ytvM23j2eTi1mrx3NoUluZTTkzrSrGS6j9jTGV3TqFtc7xZJHgXdYWcjzXXz82E6WOqVLLAZ3AHfZFzJAqvuz/D7ckrqCWol7evaOjkBbC5PAh67983f8vplvVPZ/UBS0a5OuJr/Css4QrreewcAj9/+3deXhV1bn48e+bOYEwRwQCBMh4wkyMIOBVVNQWFUUu9PIDr1erYlFbR1q9Wrl6QYutordVBLSoOBSlIlYQBVFBmWTOAIEiYxhMCIQhkOT9/bH3wUgznEwnJHk/z5MnZ6+z9tprZ++c9+y1116LJs6bCms1gf2cP7M91rbeso3OcpBgCgmVwrPdeIsQCgmkkADOaCBN5RRhcoas4vYsK+7F6MAlPB00k8BzAtApDebz4r4cUucc2aNRzCj6OeMCF7G2OIEtGgNABCe5LGD92XPuCE3ZWtyR3TgfjHGyh1uDFjK7cCgZpd5HMZVVH4NPc5yBRS91k5bhDCxa+VmOzgM1EXwOtg5hS5L7rU2Urd2K+LbtSY4HBNA9X+izLpiPWo9jt8SWWV4z8jlKUx4KepdihHeLLmNPGTc0wflGNyhgEykBW9lT3JpcjeQArdih7cijaaX3x6sjB+gUcIgD2vJsk3SBBpNNq7Pf/EcFLCVMCphdNLTUrqT+0Eu2k6EdKSKAKwO+o5n4Ps9JMQGc1FDyCWO7tmeftuHhoHdYWJTKei37GJnSteMw+2nD8ICv+aV7NXqKED4pSuX9okvJ5aePBl4esI5gPc2nenG55QrF9JQdXBSQyf7ilnysA/C1h6CpWH0MPu8Dm4G/ukljgV6qWi/vhlU3+OxtDk+PO8Oh4B8/hANU6ZsXREJmKIeaRvF+HuRZLQAAIABJREFUwHifymxFHjnuVUT/gDQGB2xiT3Gbf3mETHDa4JcW9621b+VhFNCEU2e314JjREkuUXKUdO3MNo2ule1WRnOOER+wlz3FbSoZBJUIKaAJJ2nBcYYFfcvzZ0Y0qiucmnYhP5B9zt8viEKuCPiOlMBtrCvsyimcey9HtAnfkVAX1TQlnG/Bx5fnfLqp6ogSy0+KyPqaqkB9cjoQpo8u4migMHJTE/LdFrKQk2fYFZrKy82GVaq8HJrTPyCdSwI2s6CoP38orLvxWk8RevbDAuAwzcnS6DrtXXeuPCJZXZxIM47TQio3Z9IpDeEwzUkjjK/O9KylGjYe2bSmIwcYHLj5bCeFUAr4R1F/FhWn1nHtTH3gS/A5KSKDVPVrABEZCJT/NFcFROQ3wO04H22bgFuBdjjTN7QG1gJjVfW0iIQCs4F+wA/AKFXd6ZbzW+A2oAi4V1VrdbTP10YGk9akiJvTmrIi8OdkaF/njWrcg/62OIlvi5NqpoKNxFGacFSb1HU1Gr3dtGVO0fk0YIupT3xpu7gL+D8R2SkiO4GXgDurukER6QDcC6SoanecgUpHA88Af1LVWCAXJ6jg/s510//k5kNEPO56ycA1wJ9FpOwHHKpp1pJn+az9KQYeDiZPo8iQvrW1KWOMafAqDD6qukFVewE9gZ6q2kdVN1a0XgWCgHARCQIigP3AEJwHW8G5vzTcfX0DP95vmgtcISLipr+jqgXuSAxZQK1c7584dZz3jnxEt5NK+3+e4YOAu2pjM8YY02j4fNdWVY+qauWeLiu9nL3AVGAXTtDJw2lmO6Kq3v7Ae+DsEEQdgN3uuoVu/tYl00tZp0ZFhDXh0d7Pkbi7JbOaTqmNTRhjTKPi94FFRaQlzlVLF+AIzigKVRtm1fdt3gHcAdCpU9WeGeiZeDl3iUIFw5kbY4ypWF08tHEl8E9VPaSqZ4APgIFAC7cZDiAa2Ou+3gt0BHDfb47T8eBseinr/ISqTlfVFFVNiYqKqun9McYYU0k+BR8RuURE/kNExnl/qrHNXUB/EYlw791cAaQBS3Gm6Aa4BfjQfT3fXcZ9f4k6DyfNB0aLSKiIdAHigFXVqJcxxhg/qbDZTUTeALoB6+HsWOCK0/250lR1pYjMBb7DGYNzHTAd+Bh4R0SectNmuqvMBN4QkSwgB6eHG6q6RUTewwlchcCvVLVmR540xhhTK3y555MCeLQGpzxV1SdwhuwpaQel9FZT1VPAyDLKeRp4uqbqZYwxxj98aXbbDPhn/HxjjDGNQplXPiLyEU7zWiSQJiKrgLNDOqvq9WWta4wxxpSnvGa3qX6rhTHGmEalzOCjqssAROQZVX2k5Hsi8gzO1ArGGGNMpflyz+eqUtKuremKGGOMaTzKu+czHrgb6CYiJcdyiwSW13bFjDHGNFzl3fOZA3wCTAYmlkg/pqo5tVorY4wxDVp593zyRCQf6KOq3/uxTsYYYxq4cu/5uCMGZIpI1UbjNMYYY0rhywgHLYEt7nM+x72J9pyPMcaYqvIl+Px3rdfCGGNMo1Jh8FHVZSLSFrjITVqlqgdrt1rGGGMasgqf8xGRf8eZqmAk8O/AShG5ufy1jDHGmLL50uz2KHCR92pHRKKAz4C5tVkxY4wxDZcvIxwEnNPM9oOP6xljjDGl8uXKZ6GILALedpdHAf+ovSoZY4xp6HzpcPCQiNwEDHKTpqvqvNqtljHGmIbMl2m0bwO+VNUP/FAfY4wxjYAv9246Aa+IyA4R+ZuI3CMivauzURFpISJzRSRDRNJFZICItBKRxSKyzf3d0s0rIjJNRLJEZKOI9C1Rzi1u/m0ickt16mSMMcZ/Kgw+qvqEqg4BkoGvgIeAtdXc7gvAQlVNBHoB6TiDl36uqnHA5/w4mOm1QJz7cwfwFwARaQU8AVwMpAJPeAOWMcaY85svz/k8JiKfAJ8CscCDQHRVNygizYFLgZkAqnpaVY8ANwB/dbP9FRjuvr4BmK2Ob4EWItIOuBpYrKo5qpoLLAauqWq9jDHG+I8vvd1uAgqBj3FmL/1GVQuqsc0uwCHgNRHphXMVdR/QVlX3u3mygbbu6w7A7hLr73HTyko3xhhznvOl2a0vcCXOKAdXAZtE5OtqbDMI6Av8RVX74AxWWnK+IFRVAa3GNn5CRO4QkTUisubQoUM1Vawxxpgq8qXZrTswBrgF5xmfvcCSamxzD7BHVVe6y3NxgtEBtzkN97f3wda9QMcS60e7aWWl/wtVna6qKaqaEhUVVY2qG2OMqQm+9HabgjN19jQgSVUvV9XHq7pBVc0GdotIgpt0BZAGzMcJcLi/P3RfzwfGub3e+gN5bvPcImCoiLR0OxoMddOMMcac53x5yHRYLWz3HuAtEQkBdgC34gTC99znir7HGcQUnNEUfgZkASfcvKhqjoj8D7DazTfJpvc2xpj6wZcOBzVOVdcDKaW8dUUpeRX4VRnlzAJm1WztjDHG1DYbINQYY4zfVSr4iEiAiDSrrcoYY4xpHHzp7TZHRJqJSBNgM5AmIg/VftWMMcY0VL5c+XhU9SjOiAOf4DwkOrZWa2WMMaZB8yX4BItIME7wma+qZ6jBB0CNMcY0Pr4En1eAnUAT4EsR6Qwcrc1KGWOMadh8ec5nGs4Dpl7fi8jltVclY4wxDV2ZwUdE7q9g3T/WcF2MMcY0EuVd+US6vxOAi3CGuQG4DmeQUWOMMaZKygw+qvokgIh8CfRV1WPu8u9xplcwxhhjqsSXDgdtgdMllk/z41w7xhhjTKX5MrbbbGCViMxzl4fz44yjxhhjTKX50tvtaRFZCAxyk25V1XW1Wy1jjDENma+jWq8H9nvzi0gnVd1Va7UyxhjToFUYfETkHuAJ4ABQBAjOCAc9a7dqxhhjGipfrnzuAxJU9YfarowxxpjGwZfebruBvNquiDHGmMbDlyufHcAXIvIxUOBNVFUb4cAYY0yV+HLlswtYDITgjHrg/akWEQkUkXUissBd7iIiK0UkS0TeFZEQNz3UXc5y348pUcZv3fRMEbm6unUyxhjjH750tX6ylrZ9H5AOeGdGfQb4k6q+IyIvA7cBf3F/56pqrIiMdvONEhEPMBpIBtoDn4lIvKoW1VJ9jTHG1BBfZjJdKiJLzv2pzkZFJBr4OTDDXRZgCDDXzfJXnIdZAW7gx4da5wJXuPlvAN5R1QJV/SeQBaRWp17GGGP8w5d7Pg+WeB0GjAAKq7nd54GH+bH5rjVwRFW95e4BOrivO+B0ekBVC0Ukz83fAfi2RJkl1zHGGHMe86XZbe05SctFpMqjWovIMOCgqq4VkcuqWk4lt3kHcAdAp06d/LFJY4wx5fDlIdNWJRYDgH5A82pscyBwvYj8DOdKqhnwAtBCRILcq59oYK+bfy/QEdgjIkHutn8oke5Vcp2fUNXpwHSAlJQUmwLcGGPqmC+93daW+PkGeACnE0CVqOpvVTVaVWNwOgwsUdUxwFLgZjfbLcCH7uv57jLu+0tUVd300W5vuC5AHDbPkDHG1AvlzWTaSVV3qWoXP9XlEeAdEXkKWAfMdNNnAm+ISBaQgxOwUNUtIvIekIZzD+pX1tPNGGPqh/Ka3f4O9AUQkfdVdURNb1xVvwC+cF/voJTeaqp6ChhZxvpPA0/XdL2MMcbUrvKa3aTE6661XRFjjDGNR3nBR8t4bYwxxlRLec1uvUTkKM4VULj7GndZVbVZ2asaY4wxZSsz+KhqoD8rYowxpvHwpau1McYYU6Ms+BhjjPE7Cz7GGGP8zoKPMcYYv7PgY4wxxu8s+BhjjPE7Cz7GGGP8zoKPMcYYv7PgY4wxxu8s+BhjjPE7Cz7GGGP8zoKPMcYYv7PgY4wxxu8s+BhjjPE7Cz7GGGP8zu/BR0Q6ishSEUkTkS0icp+b3kpEFovINvd3SzddRGSaiGSJyEYR6VuirFvc/NtE5BZ/74sxxpiqqYsrn0LgAVX1AP2BX4mIB5gIfK6qccDn7jLAtUCc+3MH8BdwghXwBHAxkAo84Q1Yxhhjzm9+Dz6qul9Vv3NfHwPSgQ7ADcBf3Wx/BYa7r28AZqvjW6CFiLQDrgYWq2qOquYCi4Fr/LgrxhhjqqhO7/mISAzQB1gJtFXV/e5b2UBb93UHYHeJ1fa4aWWll7adO0RkjYisOXToUI3V3xhjTNXUWfARkabA+8CvVfVoyfdUVQGtqW2p6nRVTVHVlKioqJoq1hhjTBXVSfARkWCcwPOWqn7gJh9wm9Nwfx900/cCHUusHu2mlZVujDHmPFcXvd0EmAmkq+ofS7w1H/D2WLsF+LBE+ji311t/IM9tnlsEDBWRlm5Hg6FumjHGmPNcUB1scyAwFtgkIuvdtN8BU4D3ROQ24Hvg3933/gH8DMgCTgC3Aqhqjoj8D7DazTdJVXP8swvGGGOqw+/BR1W/BqSMt68oJb8CvyqjrFnArJqrnTHGGH+wEQ6MMcb4nQUfY4wxfmfBxxhjjN9Z8DHGGON3FnyMMcb4nQUfY4wxfmfBxxhjjN9Z8DHGGON3FnyMMcb4nQUfY4wxfmfBxxhjjN9Z8DHGGON3FnyMMcb4nQUfY4wxfmfBxxhjjN9Z8DHGGON3FnyMMcb4nQUfY4wxflfvg4+IXCMimSKSJSIT67o+xhhjKlavg4+IBAL/B1wLeIBfiIinbmtljDGmIvU6+ACpQJaq7lDV08A7wA11XCdjjDEVCKrrClRTB2B3ieU9wMXnZhKRO4A73MV8EcmsxDbaAIcJCAwKierSC7S4yrWtJ4pO5ElgRHOt63r4W2Pc78a4z9A493vPiaNyxZSTWcUF+UerWETnmqxPfQ8+PlHV6cD0qqwrImtUNaWGq3ReE5E1hUcPNap9hsa5341xn6Fx7vf59llW35vd9gIdSyxHu2nGGGPOY/U9+KwG4kSki4iEAKOB+XVcJ2OMMRWo181uqlooIhOARUAgMEtVt9TwZqrUXFfPNcZ9hsa5341xn6Fx7vd5tc+i2qjuuRljjDkP1PdmN2OMMfWQBR9jjDF+Z8GnDI1l2B4R6SgiS0UkTUS2iMh9bnorEVksItvc3y3ruq41TUQCRWSdiCxwl7uIyEr3mL/rdmJpUESkhYjMFZEMEUkXkQEN/ViLyG/cc3uziLwtImEN8ViLyCwROSgim0uklXpsxTHN3f+NItLX3/W14FOKRjZsTyHwgKp6gP7Ar9x9nQh8rqpxwOfuckNzH5BeYvkZ4E+qGgvkArfVSa1q1wvAQlVNBHrh7H+DPdYi0gG4F0hR1e44HZNG0zCP9evANeeklXVsrwXi3J87gL/4qY5nWfApXaMZtkdV96vqd+7rYzgfRh1w9vevbra/AsPrpoa1Q0SigZ8DM9xlAYYAc90sDXGfmwOXAjMBVPW0qh6hgR9rnF694SISBEQA+2mAx1pVvwRyzkku69jeAMxWx7dACxFp55+aOiz4lK60YXs61FFd/EZEYoA+wEqgrarud9/KBtrWUbVqy/PAw4B3uKTWwBFVLXSXG+Ix7wIcAl5zmxtniEgTGvCxVtW9wFRgF07QyQPW0vCPtVdZx7bOP+Ms+BgARKQp8D7wa1X9ydhP6vTHbzB98kVkGHBQVdfWdV38LAjoC/xFVfsAxzmnia0BHuuWON/yuwDtgSb8a9NUo3C+HVsLPqVrVMP2iEgwTuB5S1U/cJMPeC/D3d8H66p+tWAgcL2I7MRpUh2Ccy+khds0Aw3zmO8B9qjqSnd5Lk4wasjH+krgn6p6SFXPAB/gHP+Gfqy9yjq2df4ZZ8GndI1m2B73XsdMIF1V/1jirfnALe7rW4AP/V232qKqv1XVaFWNwTm2S1R1DLAUuNnN1qD2GUBVs4HdIpLgJl0BpNGAjzVOc1t/EYlwz3XvPjfoY11CWcd2PjDO7fXWH8gr0TznFzbCQRlE5Gc49wW8w/Y8XcdVqhUiMgj4CtjEj/c/fodz3+c9oBPwPfDvqnruzcx6T0QuAx5U1WEi0hXnSqgVsA74f6paUJf1q2ki0hunk0UIsAO4FedLaIM91iLyJDAKp2fnOuB2nPsbDepYi8jbwGU408AcAJ4A/k4px9YNxC/hNEGeAG5V1TV+ra8FH2OMMf5mzW7GGGP8zoKPMcYYv7PgY4wxxu8s+BhjjPE7Cz7GGGP8zoKPMZXkjgx9t/u6vYjMrWidamyrt9vt35gGxYKPMZXXArgbQFX3qerNFeSvjt6ABR/T4NhzPsZUkoh4RznPBLYBSaraXUT+E2fU4CY4Q9VPxXmYcyxQAPzMfcCvG86UHVE4D/j9UlUzRGQkzoOBRTgDYF4JZAHhOEOfTAYWAC8C3YFg4Peq+qG77RuB5jgPUL6pqk/W8p/CmCoLqjiLMeYcE4HuqtrbHQl8QYn3uuOMDB6GEzgeUdU+IvInYBzOqBnTgbtUdZuIXAz8GWd8uceBq1V1r4i0UNXTIvI4zlw0EwBE5H9xhgP6LxFpAawSkc/cbae62z8BrBaRj/391LoxvrLgY0zNWurOi3RMRPKAj9z0TUBPd/TwS4C/OSOcABDq/l4OvC4i7+EMgFmaoTiDoj7oLofhDJ0CsFhVfwAQkQ+AQYAFH3NesuBjTM0qOT5YcYnlYpz/twCcuWR6n7uiqt7lXgn9HFgrIv1KKV+AEaqa+ZNEZ71z29CtTd2ct6zDgTGVdwyIrMqK7lxJ/3Tv7+COKtzLfd1NVVeq6uM4k751LGVbi4B73IEhEZE+Jd67SkRaiUg4zr2n5VWpozH+YMHHmEpym7aWi8hm4A9VKGIMcJuIbAC28OMU7X8QkU1uuSuADThD/3tEZL2IjAL+B6ejwUYR2eIue63CmZdpI/C+3e8x5zPr7WZMA+D2djvbMcGY851d+RhjjPE7u/Ixxhjjd3blY4wxxu8s+BhjjPE7Cz7GGGP8zoKPMcYYv7PgY4wxxu/q7fA6a9euvSAoKGgGzkCKFkSNMeb8UQxsLiwsvL1fv34HS8tQb4NPUFDQjAsvvDApKioqNyAgwPqLG2PMeaK4uFgOHTrkyc7OngFcX1qe+nzF0D0qKuqoBR5jjDm/BAQEaFRUVB5Oy1TpefxYn5oWYIHHGGPOT+7nc5kxpj4HH2OMMfVUvb3nc67ekz7tdeTEmRrbnxYRwYXrHx+6oabK89X999/fvmnTpkWTJk068Otf/7r9ZZdddmz48OHHSuZZsGBB5HPPPdd26dKlWWWVs2LFivDdu3eHjBo1Kq8q9bjuuuu6ZGZmho8ZM+bwE088UeoNQ19Nmzat9Zo1a5rMnj17V3XK8dWgdwb1yivIq7FzoXlo88KvR3/t93OhLCNGjIgZNmxY3q233ppbVp5p06a1vv7664/GxMSc8bXczMzMkGHDhsVt27ZtS1l56vN5MfOBL3udOl5YY+dFWJOgwtueu7Tc8+Kpp566YNasWVHdu3c/MX/+/H+e+/6XX34ZMWvWrNavv/767qr8PTp06NBjzZo16e3atSusyvYrw5fzozIaTPCpycBTG+VVxfPPP7+vquuuWbMmYs2aNU2qEnx27doVtGHDhia7du3aXNXt16WaDDy1UZ4/vPnmm2169+59sjLBpyL1/byoycDja3kzZ86M+uyzz7Z269at1ONw6aWXnrj00ktP1GS9KrP9umTNbtX00ksvtY6Pj/ckJCR4hg8f3mXOnDnNe/bsmZiUlOS55JJL4nfv3h0EzhXNyJEjY1JTUxOio6N7PPXUUxd4y3jkkUcujImJ6d6vX7+Ebdu2eadUZsSIETGvvfZaS4C5c+c269KlS7LH40maO3duC2+epUuXRvTu3TsxKSnJ06dPn8QNGzaEnjp1SiZPntz+o48+apmYmOh59dVXWx49ejRg5MiRMT169EhKSkryvPnmmy0ow5VXXhl/8ODBkMTERM/ChQubpqamJnz55ZcRAPv37w/q0KFDD3C+uQ4dOrTb4MGD4zp37tz9rrvuivaW8cILL7SOiYnp3qNHj6QVK1Y09abPmjWrZVxcXHJCQoInJSUloWaOwvnh3HMhMzMzpH///vHx8fGeAQMGxG/bti0EnOM6ZsyYTr169UqMjo7usWDBgsiRI0fGdO3aNXnEiBEx3vIiIiL63HbbbR1jY2OTBwwYEL9v375/+bD76quvIi666KKE5OTkpEGDBsV9//33wa+99lrLzZs3R4wbN65rYmKiJz8/X0rL510/ISHBk5CQ4PnjH/94wbnll2TnReX8x3/8R6c9e/aEXnvttXGPPvrohef+n4LTinH55ZfHnrvuvn37gq6++upu3bt3T+revXvSp59+2gQgOzs7cODAgXGxsbHJo0aN6lzewNAlt//kk09ecP/997d//PHH23rfj4uLS87MzAzJzMwM6dq1a/Lo0aM7x8bGJg8cODAuPz9foOzzY82aNWE9evRISkxM9MTHx3s2bdoUWlodymPBpxrWrFkTNnXq1HbLli3bmpmZmfbKK6/suuqqq/LXr1+fkZ6ennbzzTfnTJo06UJv/qysrLBly5ZtXb16dfrUqVPbFxQUyFdffRUxb968Vps2bUpbvHjxtg0bNjQ5dzsnTpyQCRMmxMyfPz9r8+bN6QcPHgz2vterV69Tq1evzkhPT0974okn9j788MPRYWFh+tvf/nbfddddl5uRkZH2y1/+Mvd3v/tdu8svv/zopk2b0r/66qvMxx57LPro0aOlHv+PPvooq2PHjgUZGRlp11xzTX55f4O0tLSIv//97zvS09O3zJ8/v2VWVlbw999/HzxlypT2K1asyFi9enXG1q1bw735p0yZ0u7TTz/dmpmZmbZw4cIymw3rm9LOhfHjx3caM2bMD1u3bk0bNWrUD+PHj+/ozZ+Xlxe0bt26jClTpuwePXp07EMPPXRg27ZtWzIyMsJXrFgRDnDy5MmAlJSU41lZWVsGDhx4bOLEie1LbrOgoEDuvffeTh9++OH2LVu2pN9yyy2HH3zwwQ633nprbvfu3U/Mnj17R0ZGRlpwcDCl5QO47bbbYp5//vldmZmZaRXto50XlTNnzpxdF1xwwZlly5ZtfeCBBw6e+39a3rp33nlnx/vvv//A5s2b0+fNm7f9rrvuigGYOHFi+wEDBuRnZWVtufHGG4/s378/xJftV9REumvXrrB77733YFZW1pbmzZsXzZ49uyWUfX68+OKLUXffffeBjIyMtI0bN6Z36dLltM9/GFe9a044nyxatKjZddddl+ttb23btm3RqlWrwocPHx596NCh4NOnTwd07NixwJt/6NChR8LDwzU8PLywVatWZ/bs2RO0dOnSpj/72c+OREZGFnvznLud9evXh0VHRxf06NGjAGDMmDE/zJgxIwogJycncNSoUV127twZJiJ65swZKa2uX3zxRbNFixa1mDZt2oXgfHBlZWWF9O3b91R1/gaDBg062rp16yKA2NjYU9u3bw89ePBgUP/+/Y+1b9++EOCmm27K2bp1axhASkpK/pgxY2JGjBiRO2bMmDLvWdQ3pZ0L69ata/LJJ59sBxg/fnzOk08+efYD5+c///mRgIAA+vbte6J169ZnUlNTTwLEx8ef3L59e+gll1xyMiAggNtvvz0H4L/+679+uOmmm37yDXnjxo2h27ZtCx8yZEg8QHFxMVFRUf/SvFJWvsOHDwceO3Ys8Nprr833bmPJkiXNa+LvYefFT/n6f+q1fPnyZtu2bTsbnPPz8wPz8vICvv3228gPPvggC2D06NF5d955Z1FN1K9Dhw4Fl1xyyUmAPn36nNi5c2doeefHgAEDjk+dOrXdnj17QkaPHp3r/WyqDAs+NWzChAmd7rvvvuwxY8bkLViwIHLSpElnv62GhoaevUYODAyksLCw3BPQF4888kiHf/u3fzu2ePHi7ZmZmSFDhgwptclCVZk7d25Wr169Kn+SBAVpUZFzjp84ceIndQ4JCSm5TxX+U82ZM2fXkiVLmsyfP795v379PGvXrk278MILa+QfqD4JCwtTcM6Dkn/DgICAMs8LkZ8mq6rExsaeXL9+fUZ52yor3+HDhwOrvAPYeVEZvv6feqkq3333XXpERESNPU4SFBSkxcXFZ5cLCgrOHpNzj9fJkyfLbRW76667cgYPHnx83rx5zYcNGxb34osvfn/99dcfK2+dc1mzWzVcffXVRz/66KOW2dnZgQAHDhwIPHbsWGCnTp3OALz++uutKypjyJAh+f/4xz9a5OfnS25ubsDixYv/5V5M7969T+3duzdky5YtoQDvvPNOK+97R48eDYyOjj4N8Morr7Txpjdr1qwoPz//7PG9/PLLjz733HNtvSff8uXLz36rqkjHjh0LVq1a1QTgrbfeallR/ksvvfT4ypUrI7OzswMLCgpk3rx5Z9fZsmVL6JAhQ44///zz+1q2bFm4Y8eOMpsN6pPSzoU+ffocnzFjRkuAV155pVVKSkq5TVXnKi4uxnvP7/XXX2+dmpr6k3/unj17nsrJyQn67LPPmoDzYbJmzZowgKZNmxbl5eUFlpevTZs2RZGRkUWLFi1q6m6jFZVg54Xvyvo/LcugQYOOTp48+ew9Fm9TbP/+/Y95P1fee++9ZkePHvX5C0RMTEzB+vXrmwB8/fXXEXv37i33Pk1550daWlpIUlJSwWOPPXbw6quvPrJ+/XqfP0+8GkzwaRERXGpXw9osLyUl5dQDDzywf/DgwYkJCQmeu+++u+Ojjz667xe/+EW35OTkpNatW1dYxqBBg07ceOONOd27d0++8sor43r27Hn83DwRERH64osvfj9s2LBYj8eT1KZNm7PlPvLII9m///3vo5OSkjyFhT9u7tprrz22devWcG+HgylTpuwrLCyUxMRET2xsbPJjjz3Wwde/xcSJEw/MnDkzKikpyXP48OEKr5Y7d+585pFHHtnXv3//pJSUlMT4+PizTXu/+c1vouPj4z1xcXHJF110UX7//v10PJiCAAAJ6klEQVRP+loPXzUPbV6j54Iv5ZV2Lrz88su73njjjTbx8fGet99+u/Wf//zn3ZXZbnh4ePGqVauaxMXFJX/55ZeRkydP3l/y/bCwMH3nnXe2T5w4MTohIcGTnJzsWbZsWVOAcePGHb7nnns6JyYmegoLCykr38yZM3fee++9nRITEz2qWqkr8fp2XoQ1CarR86Iy5ZX1f1qW6dOn7/7uu++axMfHe7p165b80ksvRQFMmTJl3/Lly5vGxsYmf/DBBy3btWvn872WcePG5ebm5gbGxsYmv/DCCxd07ty5wib3ss6PN998s1V8fHxyYmKiJz09PfzOO+/8wdd6eNXbabQ3bNiws1evXofruh7G1JaIiIg+J06cWFfX9TCmqjZs2NCmV69eMaW912CufIwxxtQf1uGgEXv//febPfrooz/p8tmxY8eCxYsXb6+rOpkf1dVVj50X9Ut2dnbgZZdd9i8dGL744ovM87nThjW7GWOMqRXW7GaMMea8YsHHGGOM31nwMcYY43cWfIwxxvhdw+nt9kyXXpzMqbn9CW9VyCP/rLE5XBYsWBAZGhpafNVVVx0HePbZZ6MiIiKKJ0yYUOmHs0qzbt26sF/84hddRYS5c+duT05OrvQwOiWlpqYmTJ06dXdtDvdeW7b2H9Cr6MiRGjsXAlu0KIz/9ptyz4XS5jopa66WknM2+VqHip75ufPOO6M///zz5ldccUXeK6+8ssfXckvjy3xRxlRXwwk+NRl4aqG8JUuWRDZt2rTIG3wefvjhQzVZ/t/+9rcW119/fe6zzz67v+LcDVtNBp7qlFfbc7WUNGfOnDa5ubnrg4Iazr+0adis2a2arrzyym7JyclJsbGxyVOnTm0Dztw7Ho8nKSEhwTNgwID4zMzMkNmzZ0e9/PLLbb1zoXjn1li3bl1Yjx49krzlZWZmhsTHx3ug9LlaSqvDu+++23z69OltX3/99aiLL744PjMzMyQuLi7Z+/7jjz/e9v77728PzhXN+PHjO/To0SMpJiam+8KFC5sC5Ofny7Bhw7p27do1+aqrrup26tQpASgsLGTEiBExcXFxyfHx8Z4nn3yy3DlfzNlxrzz//d//3ba0uVpK2rJlS+jgwYPjkpOTk/r165ewbt26MICMjIyQ3r17J8bHx3vuvffe9uWVMWTIkNgTJ04Edu/e3fPqq6+2LDkPFDhXTeBc0aSmpiZcc801Xbt06ZJ8/fXXd/GO9VfWfFEff/xx08TERE9iYqInKSnJk5uba58ZpkbY16Rqeuutt3a2bdu2KD8/X/r06eMZNWrUkQkTJsR88cUXGYmJiacPHDgQ2LZt26Jx48YdKtnU8umnnzYD6NOnz6kzZ85IRkZGSGJi4unZs2e3Gj58eK53rpaPP/44q3379oWvvvpqywcffLDD3/72t53n1mHUqFF5K1euPFt+ZmZmuYMyFhYWyqZNm9Lffffd5pMmTWp/zTXXbJ06deoF4eHhxTt27NiycuXK8IEDB3oAvvnmm4j9+/cHe5uTqjsSckO3YcOG0NGjR3ebNWvWP3/44Yegr7/+OrK8/Lfffnvn6dOnf9+jR4+CJUuWNBk/fnynb7/9duvdd9/d6fbbbz80YcKEHyZPnhxVXhlLlizJioiI6JORkZEGsHDhwjKnRUhPTw9fv379jpiYmDP9+vVLXLx4cdPBgwcfnzBhQszixYszk5OTC4YNG9bVm/+55567cNq0ad8PHTr0eF5eXkBERERxWWUbUxn2LaaannnmmbYJCQmefv36JWVnZwdPmzYtKjU19VhiYuJpcOZ1qaiM4cOH58yePbsVwLx581qOHTs2p+QcLImJiZ4//OEP7fbt21fqlU9ljRw5MhfgkksuOb5nz54QgK+//rrp2LFjfwC4+OKLT8bHx58ASExMLNi9e3foLbfc0nHu3LnNWrZsed4+MV3XcnJygoYPHx775ptv7hgwYECFA2Pm5eUFrFu3runIkSO7JSYmeu6+++7O3okCv/vuu6a//OUvcwCqMmhjWXr06HG8W7duZwIDA0lOTj6xffv2kJLzRQUEBDBmzJiz2+vfv3/+gw8+2PGpp5664PDhw4HBwTVyChpjwac6FixYELls2bLINWvWZGRmZqYlJSWd7NOnT6Xb+MeOHZv797//veXGjRtDRYQePXoUeOdgycjISMvIyEjbunVr2vLly7f5Ut6583acOnXqJ8fZO5dMUFAQRUVF5Y5kHBUVVbR58+a0yy+//NjLL78cNXr06JjK7l9jERkZWdS+ffvTS5cubVpxbigqKiIyMrLQe4wzMjLSduzYcbbDQkBAQJWGHyk5z05RUREl59Kp7JxS//u//5s9Y8aM70+ePBkwePDgRG+zoDHVZcGnGo4cORLYvHnzosjIyOJ169aFbdiwocmpU6cCVq1aFZmRkRECzrwu4HwwHTt2rNQmq+Tk5IKAgAAef/zx9jfeeGMOlD9XS0Wio6MLc3JygrKzswNPnjwpixYtqnB2ykGDBuW/9dZbrQBWr14dtnXr1giA/fv3BxUVFfGf//mfRyZPnrx306ZNEb7UoTEKDg7WTz75ZPvbb7/d+uWXX65wbpxWrVoVR0dHn541a1ZLcObv+eabb8IB+vbtm//qq6+2Anj11VcrnBeqpM6dO59eu3ZtBMCcOXNaVBRgypsvasuWLaGpqaknn3766eyePXse37x5swUfUyMaTvAJb1Wjc3X4Ut6IESPyCgsLpWvXrskPPfRQh169eh2/4IILCqdNm7bzxhtvjE1ISPDceOONXd28Rz7++OMW3g4H55Z100035Xz44Yetxo4dmwvlz9VSkdDQUH3ggQf2X3TRRUmDBw+Oj42NrXDejgcffPDg8ePHA7t27Zr86KOPdvB4PMcBdu7cGTxo0KCExMREz9ixY7tOmjSpWt14/SGwRYsaPRcqU16zZs2KFy1alPXSSy+1zcvLq/D/6+23397x2muvtUlISPDExcUlv//++y0A/vznP++aPn36BfHx8Z69e/dWqq3rnnvuObRixYrIhIQEz4oVK5qEh4eXe5+mvPminn322Qu8nU2Cg4P15ptvzqtMXYwpiw0saowxplbYwKLGGGPOK9bVup4ZO3Zsp9WrV/+k+W38+PEH7rvvvhrrEWXOT6tWrQofN25cl5JpISEhxRs3bsyoqzoZU1UWfOqZN954Y1dd18HUjdTU1JPeZ3mMqe/qc7NbcXFxcbm9eIwxxtQN9/O5zM4u9Tn4bD506FBzC0DGGHN+KS4ulkOHDjUHNpeVp942uxUWFt6enZ09Izs7uzv1O4gaY0xDUwxsLiwsvL2sDPW2q7Uxxpj6y64YjDHG+J0FH2OMMX5nwccYY4zfWfAxxhjjdxZ8jDHG+N3/B8Phf5n4AXU4AAAAAElFTkSuQmCC\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"df.plot(x='timestep',y=['candidate_funds','active_funds','completed_funds', 'killed_funds', 'failed_funds'], kind='area')\n",
|
||
"plt.title('Proposal Status weighted by funds requested')\n",
|
||
"plt.ylabel('Funds worth of proposals')\n",
|
||
"plt.legend(ncol = 3,loc='upper center', bbox_to_anchor=(0.5, -0.15))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"The above graph shows the amount of funds requested by various types of proposals at a range of timesteps."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"nets = df.network.values"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/home/aclarkdata/anaconda3/lib/python3.7/site-packages/networkx/drawing/nx_pylab.py:563: MatplotlibDeprecationWarning: \n",
|
||
"The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
|
||
" if not cb.iterable(width):\n",
|
||
"/home/aclarkdata/anaconda3/lib/python3.7/site-packages/networkx/drawing/nx_pylab.py:569: MatplotlibDeprecationWarning: \n",
|
||
"The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
|
||
" and cb.iterable(edge_color) \\\n",
|
||
"/home/aclarkdata/anaconda3/lib/python3.7/site-packages/networkx/drawing/nx_pylab.py:579: MatplotlibDeprecationWarning: \n",
|
||
"The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
|
||
" for c in edge_color]):\n",
|
||
"/home/aclarkdata/anaconda3/lib/python3.7/site-packages/networkx/drawing/nx_pylab.py:660: MatplotlibDeprecationWarning: \n",
|
||
"The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
|
||
" if cb.iterable(node_size): # many node sizes\n",
|
||
"/home/aclarkdata/repos/Aragon_Conviction_Voting/models/v3/model/parts/utils.py:356: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
|
||
" plt.tight_layout()\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"K = 0\n",
|
||
"N = 1\n",
|
||
"\n",
|
||
"snap_plot(nets[K:N], size_scale = 1/300,savefigs=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"On the left side are participants, with the right side of the graph being the proposals. With this graph, we can see the links between the participants and the proposals that they support. The percentage on the right hand are the the amount of the required amount to pass that has been fulfilled.\n",
|
||
"\n",
|
||
"You can move the K and N to different points within the 100 timesteps, 0 indexed, to see how the model evolves overtime. \n",
|
||
"\n",
|
||
"As you can see with the plot above at the start of the simulation, no proposals have been formally supported yet. Below we can see a many interactions between agents and proposals."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"snap_plot(nets[80:81], size_scale = 1/300,savefigs=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<matplotlib.axes._subplots.AxesSubplot at 0x7fe4a3e6c950>"
|
||
]
|
||
},
|
||
"execution_count": 28,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"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": [
|
||
"df.plot('timestep',['effective_supply'])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<matplotlib.axes._subplots.AxesSubplot at 0x7fe53888d1d0>"
|
||
]
|
||
},
|
||
"execution_count": 29,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"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": [
|
||
"df.plot('timestep',['participant_count'])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"As expected *effective_supply* is increasing with the arrival of new participants."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Conclusion\n",
|
||
"\n",
|
||
"We have created a conviction voting model that closely adheres to the 1Hive implementation. This notebook describes the use case, how the model works, and provides descriptions of how it fits together."
|
||
]
|
||
}
|
||
],
|
||
"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
|
||
}
|