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diff --git a/Methodology/graybox-system-modeling.jpeg b/Methodology/graybox-system-modeling.jpeg deleted file mode 100644 index bdf774d..0000000 Binary files a/Methodology/graybox-system-modeling.jpeg and /dev/null differ diff --git a/Methodology/methodology.md b/Methodology/methodology.md deleted file mode 100644 index f2df35d..0000000 --- a/Methodology/methodology.md +++ /dev/null @@ -1 +0,0 @@ -Todo: Write Descrition of design, design validation, development, verification, systems validation, monitoring and maintenaince methodology and identify the role simCAD tools play at each level of the process. Also, identify how to integrate system modeling with machine learning create robust decision and decision support systems. diff --git a/Methodology/v&v.png b/Methodology/v&v.png deleted file mode 100644 index 4e22579..0000000 Binary files a/Methodology/v&v.png and /dev/null differ diff --git a/Simulation.md b/Simulation.md deleted file mode 100644 index e0750ab..0000000 --- a/Simulation.md +++ /dev/null @@ -1,151 +0,0 @@ -# cadCAD Documentation - -## Introduction - -A blockchain is a distributed ledger with economic agents transacting in a network. The state of the network evolves with every new transaction, which can be a result of user behaviors, protocol-defined system mechanisms, or external processes. - -It is not uncommon today for blockchain projects to announce a set of rules for their network and make claims about their system level behvaior. However, the validity of those claims is hardly validated. Furthermore, it is difficult to know the potential system-level impact when the network is considering an upgrade to their system rules and prameters. - -To rigorously and reliably analyze, design, and improve cryptoeconomic networks, we are introducing this Computer Aided Design Engine where we define a cryptoeconomic network with its state and exogneous variables, model transactions as a result of agent behaviors, state mechanisms, and environmental processes. We can then run simulations with different initial states, mechanisms, environmental processes to understand and visualize network behavior under different conditions. - -## State Variables and Transitions - -We now define variables and different transition mechanisms that will be inputs to the simulation engine. - -- ***State variables*** are defined to capture the shape and property of the network, such as a vector or a dictionary that captures all user balances. -- ***Exogenous variables*** are variables that represent external input and signal. They are only affected by environmental processes and are not affected by system mechanisms. Nonetheless, exgoneous variables can be used as an input to a mechanism that impacts state variables. They can be considered as read-only variables to the system. -- ***Behaviors per transition*** model agent behaviors in reaction to state variables and exogenous variables. The resulted user action will become an input to state mechanisms. Note that user behaviors should not directly update value of state variables. -- ***State mechanisms per transition*** are system defined mechanisms that take user actions and other states as inputs and produce updates to the value of state variables. -- ***Exogenous state updates*** specify how exogenous variables evolve with time which can indirectly impact state variables through behavior and state mechanisms. -- ***Environmental processes*** model external changes that directly impact state or exogenous variables at specific timestamps or conditions. - -A state evolves to another state via state transition. Each transition is composed of behavior and state mechanisms as functions of state and exogenous variables. A flow of the state transition is as follows. - -Given some state and exogenous variables of the system at the onset of a state transition, agent behavior takes in these variables as input and return a set of agent actions. This models after agent behavior and reaction to a set of variables. Given these agent actions, state mechanism, as defined by the protocol, takes these actions, state, and exogenous variables as inputs and return a new set of state variables. - -## System Configuration File - -Simulation engine takes in system configuration files, e.g. `config.py`, where all the above variables and mechanisms are defined. The following import statements should be added at the beginning of the configuration files. -```python -from decimal import Decimal -import numpy as np -from datetime import timedelta - -from cadCAD import configs -from cadCAD.configuration import Configuration -from cadCAD.configuration.utils import exo_update_per_ts, proc_trigger, bound_norm_random, \ - ep_time_step -``` - -State variables and their initial values can be defined as follows. Note that `timestamp` is a required field for this iteration of cadCAD for `env_proc` to work. Future iterations will strive to make this more generic and timestamp optional. -```python -genesis_dict = { - 's1': Decimal(0.0), - 's2': Decimal(0.0), - 's3': Decimal(1.0), - 'timestamp': '2018-10-01 15:16:24' -} -``` - -Each potential transition and its state and behavior mechanisms can be defined in the following dictionary object. -```python -transitions = { - "m1": { - "behaviors": { - "b1": b1m1, - "b2": b2m1 - }, - "states": { - "s1": s1m1, - "s2": s2m1 - } - }, - "m2": {...} -} -``` -Every behavior per transition should return a dictionary as actions taken by the agents. They will then be aggregated through addition in this version of cadCAD. Some examples of behaviors per transition are as follows. More flexible and user-defined aggregation functions will be introduced in future iterations but no example is provided at this point. -```python -def b1m1(step, sL, s): - return {'param1': 1} - -def b1m2(step, sL, s): - return {'param1': 'a', 'param2': 2} - -def b1m3(step, sL, s): - return {'param1': ['c'], 'param2': np.array([10, 100])} -``` -State mechanism per transition on the other hand takes in the output of behavior mechanisms (`_input`) and returns a tuple of the name of the variable and the new value for the variable. Some examples of a state mechanism per transition are as follows. Note that each state mechanism is supposed to change one state variable at a time. Changes to multiple state variables should be done in separate mechanisms. -```python -def s1m1(step, sL, s, _input): - y = 's1' - x = _input['param1'] + 1 - return (y, x) - -def s1m2(step, sL, s, _input): - y = 's1' - x = _input['param1'] - return (y, x) -``` -Exogenous state update functions, for example `es3p1`, `es4p2` and `es5p2` below, update exogenous variables at every timestamp. Note that every timestamp is consist of all behaviors and state mechanisms in the order defined in `transitions` dictionary. If `exo_update_per_ts` is not used, exogenous state updates will be applied at every mechanism step (`m1`, `m2`, etc). Otherwise, exogenous state updates will only be applied once for every timestamp after all the mechanism steps are executed. -```python -exogenous_states = exo_update_per_ts( - { - "s3": es3p1, - "s4": es4p2, - "timestamp": es5p2 - } -) -``` -To model randomness, we should also define pseudorandom seeds in the configuration as follows. -```python -seed = { - 'z': np.random.RandomState(1), - 'a': np.random.RandomState(2), - 'b': np.random.RandomState(3), - 'c': np.random.RandomState(3) -} -``` -cadCAD currently supports generating random number from a normal distribution through `bound_norm_random` with `min` and `max` values specified. Examples of environmental processes with randomness are as follows. We also define timestamp format with `ts_format` and timestamp changes with `t_delta`. Users can define other distributions to update exogenous variables. -```python -proc_one_coef_A = 0.7 -proc_one_coef_B = 1.3 - -def es3p1(step, sL, s, _input): - y = 's3' - x = s['s3'] * bound_norm_random(seed['a'], proc_one_coef_A, proc_one_coef_B) - return (y, x) - -def es4p2(step, sL, s, _input): - y = 's4' - x = s['s4'] * bound_norm_random(seed['b'], proc_one_coef_A, proc_one_coef_B) - return (y, x) - -ts_format = '%Y-%m-%d %H:%M:%S' -t_delta = timedelta(days=0, minutes=0, seconds=1) -def es5p2(step, sL, s, _input): - y = 'timestamp' - x = ep_time_step(s, s['timestamp'], fromat_str=ts_format, _timedelta=t_delta) - return (y, x) -``` -User can also define specific external events such as market shocks at specific timestamps through `env_processes` with `proc_trigger`. An environmental process with no `proc_trigger` will be called at every timestamp. In the example below, it will return the value of `s3` at every timestamp. Logical event triggers, such as a big draw down in exogenous variables, will be supported in a later version of cadCAD. -```python -def env_a(x): - return x -def env_b(x): - return 10 - -env_processes = { - "s3": env_a, - "s4": proc_trigger('2018-10-01 15:16:25', env_b) -} -``` - -Lastly, we set the overall simulation configuration and initialize the `Configuration` class with the following. `T` denotes the time range and `N` refers to the number of simulation runs. Each run will start from the same initial states and run for `T` time range. Every transition is consist of behaviors, state mechanisms, exogenous updates, and potentially environmental processes. All of these happen within one time step in the simulation. -```python -sim_config = { - "N": 2, - "T": range(5) -} - -configs.append(Configuration(sim_config, state_dict, seed, exogenous_states, env_processes, mechanisms)) -``` diff --git a/demos/robot-marbles-network/robot-marbles-agents-advanced.ipynb b/demos/robot-marbles-network/robot-marbles-agents-advanced.ipynb deleted file mode 100644 index ea2b485..0000000 --- a/demos/robot-marbles-network/robot-marbles-agents-advanced.ipynb +++ /dev/null @@ -1,629 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# cadCAD Tutorials: The Robot and the Marbles, Networks Addition\n", - "In [Part 2](https://github.com/BlockScience/SimCAD-Tutorials/blob/master/demos/robot-marbles-part-2/robot-marbles-part-2.ipynb) we introduced the 'language' in which a system must be described in order for it to be interpretable by cadCAD and some of the basic concepts of the library:\n", - "* State Variables\n", - "* Timestep\n", - "* Policies\n", - "* State Update Functions\n", - "* Partial State Update Blocks\n", - "* Simulation Configuration Parameters\n", - "\n", - "In the previous example, we observed how two robotic arms acting in parallel could result in counterintuitive system level behavior despite the simplicity of the individual robotic arm policies. \n", - "In this notebook we'll introduce the concept of networks. This done by extending from two boxes of marbles to *n* boxes which are the nodes in our network. Furthermore, there are are going to be arms between some of the boxes but not others forming a network where the arms are the edges.\n", - "\n", - "__The robot and the marbles__ \n", - "* Picture a set of n boxes (`balls`) with an integer number of marbles in each; a network of robotic arms is capable of taking a marble from their one of their boxes and dropping it into the other one.\n", - "* Each robotic arm in the network only controls 2 boxes and they act by moving a marble from one box to the other.\n", - "* Each robotic arm is programmed to take one marble at a time from the box containing the largest number of marbles and drop it in the other box. It repeats that process until the boxes contain an equal number of marbles.\n", - "* For the purposes of our analysis of this system, suppose we are only interested in monitoring the number of marbles in only their two boxes." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from cadCAD.engine import ExecutionMode, ExecutionContext, Executor\n", - "from cadCAD.configuration import Configuration\n", - "import networkx as nx\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "%matplotlib inline\n", - "\n", - "T = 50 #iterations in our simulation\n", - "n=3 #number of boxes in our network\n", - "m= 2 #for barabasi graph type number of edges is (n-2)*m\n", - "\n", - "G = nx.barabasi_albert_graph(n, m)\n", - "k = len(G.edges)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "balls = np.zeros(n,)\n", - "\n", - "for node in G.nodes:\n", - " rv = np.random.randint(1,25)\n", - " G.nodes[node]['initial_balls'] = rv\n", - " balls[node] = rv" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "scale=100\n", - "nx.draw_kamada_kawai(G, node_size=balls*scale,labels=nx.get_node_attributes(G,'initial_balls'))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "initial_conditions = {'balls':balls, 'network':G}" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "#input the deltas along the edges and update the boxes\n", - "#mechanism: edge by node dimensional operator\n", - "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n", - "# We make the state update functions less \"intelligent\",\n", - "# ie. they simply add the number of marbles specified in _input \n", - "# (which, per the policy function definition, may be negative)\n", - "\n", - "\n", - "def update_balls(params, step, sL, s, _input):\n", - " \n", - " delta_balls = _input['delta']\n", - " new_balls = s['balls']\n", - " for e in G.edges:\n", - " move_ball = delta_balls[e]\n", - " src = e[0]\n", - " dst = e[1]\n", - " if (new_balls[src] >= move_ball) and (new_balls[dst] >= -move_ball):\n", - " new_balls[src] = new_balls[src]-move_ball\n", - " new_balls[dst] = new_balls[dst]+move_ball\n", - " \n", - " \n", - " key = 'balls'\n", - " value = new_balls\n", - " \n", - " return (key, value)\n", - "\n", - "def update_network(params, step, sL, s, _input):\n", - " \n", - " new_nodes = _input['nodes']\n", - " new_edges = _input['edges']\n", - " new_balls = _input['quantity']\n", - " \n", - " graph = s['network']\n", - " \n", - " for node in new_nodes:\n", - " graph.add_node(node)\n", - " graph.nodes['initial_balls'] = new_balls['quantity']['node']\n", - " graph.nodes[node]['strat'] = _input['node_strats'][node]\n", - " \n", - " for edge in new_edges:\n", - " graph.add_edge(edge[0], edge[1])\n", - " graph.edges[edge]['strat'] = _input['edge_strats'][edge]\n", - " \n", - " \n", - " key = 'network'\n", - " value = graph\n", - " \n", - " return (key, value)\n", - "\n", - "def update_network_balls(params, step, sL, s, _input):\n", - " \n", - " new_nodes = _input['nodes']\n", - " new_balls = _input['quantity']\n", - " \n", - " balls = np.zeros(len(s['balls']+len(new_nodes)))\n", - " \n", - " for node in s['network'].nodes:\n", - " balls = s['balls'][node]\n", - " \n", - " for node in new_nodes:\n", - " balls = new_balls[node]\n", - " \n", - " key = 'balls'\n", - " value = balls\n", - " \n", - " return (key, value)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# this time lets make three kinds of robots\n", - "\n", - "def greedy_robot(src_balls, dst_balls):\n", - " \n", - " #robot wishes to accumlate balls at its source\n", - " #takes half of its neighbors balls\n", - " if src_balls < dst_balls:\n", - " delta = -np.floor(dst_balls/2)\n", - " else:\n", - " delta = 0\n", - " \n", - " return delta\n", - "\n", - "def fair_robot(src_balls, dst_balls):\n", - " \n", - " #robot follows the simple balancing rule\n", - " delta = np.sign(src_balls-dst_balls)\n", - " \n", - " return delta\n", - "\n", - "\n", - "def giving_robot(src_balls, dst_balls):\n", - " \n", - " #robot wishes to gice away balls one at a time\n", - " if src_balls > 0:\n", - " delta = 1\n", - " else:\n", - " delta = 0\n", - " \n", - " return delta" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "#in the previous version the robots were assigned to the edges\n", - "#moving towards an agent based model formulation we assign the stratgies\n", - "#instead to the nodes\n", - "robot_strategies = [greedy_robot,fair_robot, giving_robot]\n", - "\n", - "for node in G.nodes:\n", - " nstrats = len(robot_strategies)\n", - " rv = np.random.randint(0,nstrats)\n", - " G.nodes[node]['strat'] = robot_strategies[rv]\n", - "\n", - "for e in G.edges:\n", - " owner_node = e[0]\n", - " G.edges[e]['strat'] = G.nodes[owner_node]['strat']" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "#Policy: node by edge dimensional operator\n", - "#input the states of the boxes output the deltas along the edges\n", - "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n", - "# We specify the robotic networks logic in a Policy Function\n", - "# unlike previous examples our policy controls a vector valued action, defined over the edges of our network\n", - "def robotic_network(params, step, sL, s):\n", - " \n", - " graph = s['network']\n", - " \n", - " \n", - " delta_balls = {}\n", - " for e in graph.edges:\n", - " src = e[0]\n", - " src_balls = s['balls'][src]\n", - " dst = e[1]\n", - " dst_balls = s['balls'][dst]\n", - " \n", - " #transfer balls according to specific robot strat\n", - " srat = graph.edges[e]['strat']\n", - " \n", - " delta_balls[e] = srat(src_balls,dst_balls)\n", - " print(delta_balls)\n", - " \n", - " return_dict = {'nodes': [],'edges': {}, 'quantity': {}, 'node_strats':{},'edge_strats':{},'delta': delta_balls}\n", - " print(return_dict)\n", - "\n", - " return(return_dict)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "def agent_arrival(params, step, sL, s):\n", - " \n", - " node= len(s['network'].nodes)\n", - " edge_list = s['network'].edges\n", - " \n", - " #choose a m random edges without replacement\n", - " #new = np.random.choose(edgelist,m) \n", - " new = [(0,0), (1,1)]#tester\n", - " \n", - " nodes = [node]\n", - " edges = [(node,new_node) for new_node in new]\n", - " \n", - " initial_balls = {node:np.random.randint(1,25) }\n", - " \n", - " rv = np.random.randint(0,nstrats)\n", - " node_strat = {node: robot_strategies[rv]}\n", - " \n", - " edge_strats = {e:node_strat for e in edges}\n", - "\n", - " return_dict = {'nodes': nodes,\n", - " 'edges': edges, \n", - " 'quantity':initial_balls, \n", - " 'node_strats':node_strat,\n", - " 'edge_strats':edge_strats,\n", - " 'delta': np.zeros(node+1)\n", - " } \n", - " print(return_dict)\n", - " return(return_dict)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n", - "# In the Partial State Update Blocks, the user specifies if state update functions will be run in series or in parallel\n", - "partial_state_update_blocks = [\n", - " { \n", - " 'policies': { # The following policy functions will be evaluated and their returns will be passed to the state update functions\n", - " 'p1': robotic_network\n", - " },\n", - " 'variables': { # The following state variables will be updated simultaneously\n", - " 'balls': update_balls\n", - " \n", - " }\n", - " },\n", - " {\n", - " 'policies': { # The following policy functions will be evaluated and their returns will be passed to the state update functions\n", - " 'p1': agent_arrival\n", - " },\n", - " 'variables': { # The following state variables will be updated simultaneously\n", - " 'network': update_network,\n", - " 'balls': update_network_balls\n", - " }\n", - " }\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n", - "# Settings of general simulation parameters, unrelated to the system itself\n", - "# `T` is a range with the number of discrete units of time the simulation will run for;\n", - "# `N` is the number of times the simulation will be run (Monte Carlo runs)\n", - "# In this example, we'll run the simulation once (N=1) and its duration will be of 10 timesteps\n", - "# We'll cover the `M` key in a future article. For now, let's leave it empty\n", - "simulation_parameters = {\n", - " 'T': range(T),\n", - " 'N': 1,\n", - " 'M': {}\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n", - "# The configurations above are then packaged into a `Configuration` object\n", - "config = Configuration(initial_state=initial_conditions, #dict containing variable names and initial values\n", - " partial_state_update_blocks=partial_state_update_blocks, #dict containing state update functions\n", - " sim_config=simulation_parameters #dict containing simulation parameters\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "single_proc: []\n", - "{(0, 2): -1.0}\n", - "{(0, 2): -1.0, (1, 2): -12.0}\n", - "{'nodes': [], 'edges': {}, 'quantity': {}, 'node_strats': {}, 'edge_strats': {}, 'delta': {(0, 2): -1.0, (1, 2): -12.0}}\n" - ] - }, - { - "ename": "TypeError", - "evalue": "unsupported operand type(s) for +: 'int' and 'dict'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mexec_context\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mExecutionContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexec_mode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msingle_proc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mexecutor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mExecutor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexec_context\u001b[0m\u001b[0;34m,\u001b[0m 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"exec_mode = ExecutionMode()\n", - "exec_context = ExecutionContext(exec_mode.single_proc)\n", - "executor = Executor(exec_context, [config]) # Pass the configuration object inside an array\n", - "raw_result, tensor = executor.main() # The `main()` method returns a tuple; its first elements contains the raw results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.DataFrame(raw_result)\n", - "balls_list = [b for b in df.balls]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(df.timestep.values, balls_list)\n", - "plt.xlabel('iteration')\n", - "plt.ylabel('number of balls')\n", - "plt.title('balls in each box')\n", - "plt.legend(['Box #'+str(node) for node in range(n)], ncol = 2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "end_state_balls = np.array([b for b in balls_list[-1]])\n", - "avg_balls = np.array([np.mean(b) for b in balls_list])\n", - "\n", - "for node in G.nodes:\n", - " G.nodes[node]['final_balls'] = end_state_balls[node]\n", - " G.nodes[node]['avg_balls'] = avg_balls[node]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cmap = plt.cm.jet\n", - "Nc = cmap.N\n", - "Ns = len(robot_strategies)\n", - "d = int(Nc/Ns)\n", - "\n", - "k = len(G.edges)\n", - "strat_color = []\n", - "for e in G.edges:\n", - " \n", - " for i in range(Ns):\n", - " if G.edges[e]['strat']==robot_strategies[i]:\n", - " color = cmap(i*d)\n", - " G.edges[e]['color'] = color\n", - " strat_color = strat_color+[color]\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'end_state_balls' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_kamada_kawai\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mend_state_balls\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_node_attributes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'final_balls'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medge_color\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstrat_color\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mend_state_balls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'end_state_balls' is not defined" - ] - } - ], - "source": [ - "nx.draw_kamada_kawai(G, node_size=end_state_balls*scale, labels=nx.get_node_attributes(G,'final_balls'), edge_color=strat_color)\n", - "print(end_state_balls)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'balls_list' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mnode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnodes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtau\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mrolling_avg_balls\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtau\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtau\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mrolling_avg_balls\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtau\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mballs_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtau\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'balls_list' is not defined" - ] - } - ], - "source": [ - "rolling_avg_balls = np.zeros((T+1, n))\n", - "for t in range(T+1):\n", - " for node in G.nodes:\n", - " for tau in range(t):\n", - " rolling_avg_balls[t,node] = (tau)/(tau+1)*rolling_avg_balls[t, node]+ 1/(tau+1)*balls_list[tau][node]" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(range(len(rolling_avg_balls)),rolling_avg_balls)\n", - "plt.xlabel('iteration')\n", - "plt.ylabel('number of balls')\n", - "plt.title('time average balls in each box')\n", - "plt.legend(['Box #'+str(node) for node in range(n)], ncol = 2)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'avg_balls' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnodes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'avg_balls'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrolling_avg_balls\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mnx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_kamada_kawai\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mavg_balls\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_node_attributes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'avg_balls'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mend_state_balls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'avg_balls' is not defined" - ] - } - ], - "source": [ - "for node in G.nodes:\n", - " G.nodes[node]['avg_balls'] = int(10*(rolling_avg_balls[node][-1]))/10\n", - "\n", - "nx.draw_kamada_kawai(G, node_size=avg_balls*scale, labels=nx.get_node_attributes(G,'avg_balls'))\n", - "print(end_state_balls)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cmap = plt.cm.jet\n", - "Nc = cmap.N\n", - "Nt = len(simulation_parameters['T'])\n", - "dN = int(Nc/Nt)\n", - "cmaplist = [cmap(i*dN) for i in range(Nt)]\n", - "\n", - "for t in simulation_parameters['T']:\n", - " state = np.array([b for b in balls_list[t]])\n", - " nx.draw_kamada_kawai(G, node_size=state*scale, alpha = .4/(t+1), node_color = cmaplist[t])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "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": "3.6.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/demos/robot-marbles-network/robot-marbles-agents-incentive-field.ipynb b/demos/robot-marbles-network/robot-marbles-agents-incentive-field.ipynb deleted file mode 100644 index fa336e5..0000000 --- a/demos/robot-marbles-network/robot-marbles-agents-incentive-field.ipynb +++ /dev/null @@ -1,854 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# cadCAD Tutorials: The Robot and the Marbles, Networks Addition\n", - "In [Part 2](https://github.com/BlockScience/SimCAD-Tutorials/blob/master/demos/robot-marbles-part-2/robot-marbles-part-2.ipynb) we introduced the 'language' in which a system must be described in order for it to be interpretable by cadCAD and some of the basic concepts of the library:\n", - "* State Variables\n", - "* Timestep\n", - "* Policies\n", - "* State Update Functions\n", - "* Partial State Update Blocks\n", - "* Simulation Configuration Parameters\n", - "\n", - "In the previous example, we observed how two robotic arms acting in parallel could result in counterintuitive system level behavior despite the simplicity of the individual robotic arm policies. \n", - "In this notebook we'll introduce the concept of networks. This done by extending from two boxes of marbles to *n* boxes which are the nodes in our network. Furthermore, there are are going to be arms between some of the boxes but not others forming a network where the arms are the edges.\n", - "\n", - "__The robot and the marbles__ \n", - "* Picture a set of n boxes (`balls`) with an integer number of marbles in each; a network of robotic arms is capable of taking a marble from their one of their boxes and dropping it into the other one.\n", - "* Each robotic arm in the network only controls 2 boxes and they act by moving a marble from one box to the other.\n", - "* Each robotic arm is programmed to take one marble at a time from the box containing the largest number of marbles and drop it in the other box. It repeats that process until the boxes contain an equal number of marbles.\n", - "* For the purposes of our analysis of this system, suppose we are only interested in monitoring the number of marbles in only their two boxes." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from cadCAD.engine import ExecutionMode, ExecutionContext, Executor\n", - "from cadCAD.configuration import Configuration\n", - "import networkx as nx\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "%matplotlib inline\n", - "\n", - "T = 50 #iterations in our simulation\n", - "n=7 #number of boxes in our network\n", - "m= 2 #for barabasi graph type number of edges is (n-2)*m\n", - "\n", - "g = nx.barabasi_albert_graph(n, m)\n", - "\n", - "G = g.to_directed()\n", - "for ed in g.edges:\n", - " G.remove_edge(ed[1],ed[0])\n", - "\n", - "k = len(G.edges)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def local_energy_function(node, G, balls):\n", - " \n", - " src_balls = balls[node]\n", - " \n", - " #barrier force field\n", - " v = -np.log(1+src_balls)\n", - " \n", - " #laplacian force field\n", - " nbrs = [e[1] for e in G.edges if e[0]==node]\n", - " for nbr in nbrs:\n", - " dst_balls = balls[nbr]\n", - " v= v+(src_balls - dst_balls)**2\n", - " \n", - " return v" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "balls = np.zeros(n,)\n", - "balance = np.zeros(n,)\n", - "energy = np.zeros(n,)\n", - "\n", - "for node in G.nodes:\n", - " rv = np.random.randint(1,25)\n", - " G.nodes[node]['initial_balls'] = rv\n", - " balls[node] = rv\n", - " \n", - "for node in G.nodes:\n", - " energy[node] = local_energy_function(node, G, balls)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "scale=100\n", - "nx.draw_kamada_kawai(G, node_size=balls*scale,labels=nx.get_node_attributes(G,'initial_balls'))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "initial_conditions = {'balls':balls, 'balance': balance, 'energy':energy}" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'balance': array([0., 0., 0., 0., 0., 0., 0.]),\n", - " 'balls': array([ 6., 3., 12., 6., 9., 17., 4.]),\n", - " 'energy': array([159.05408985, 79.61370564, 42.43505064, 128.05408985,\n", - " -2.30258509, 166.10962824, -1.60943791])}" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "initial_conditions" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "#input the deltas along the edges and update the boxes\n", - "#mechanism: edge by node dimensional operator\n", - "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n", - "# We make the state update functions less \"intelligent\",\n", - "# ie. they simply add the number of marbles specified in _input \n", - "# (which, per the policy function definition, may be negative)\n", - "def conserved_flow(delta_balls, s):\n", - " \n", - " new_balls = s['balls']\n", - " for e in G.edges:\n", - " move_ball = delta_balls[e]\n", - " src = e[0]\n", - " dst = e[1]\n", - " if (new_balls[src] >= move_ball) and (new_balls[dst] >= -move_ball):\n", - " new_balls[src] = new_balls[src]-move_ball\n", - " new_balls[dst] = new_balls[dst]+move_ball\n", - "\n", - " return new_balls\n", - "\n", - "\n", - "def update_balls(params, step, sL, s, _input):\n", - " \n", - " delta_balls = _input['delta']\n", - " new_balls = conserved_flow(delta_balls, s)\n", - " \n", - " key = 'balls'\n", - " value = new_balls\n", - " \n", - " return (key, value)\n", - "\n", - "def update_balance(params, step, sL, s, _input):\n", - " \n", - " delta_balls = _input['delta']\n", - " new_balls = conserved_flow(delta_balls, s)\n", - " for node in G.nodes:\n", - " new_local_energy = local_energy_function(node, G, new_balls)\n", - " balance[node] = balance[node]-(new_local_energy-s['energy'][node])\n", - " \n", - " \n", - " key = 'balance'\n", - " value = balance\n", - " \n", - " return (key, value)\n", - "\n", - "def update_energy(params, step, sL, s, _input):\n", - " \n", - " delta_balls = _input['delta']\n", - " new_balls = conserved_flow(delta_balls, s)\n", - " \n", - " for node in G.nodes:\n", - " energy[node] = local_energy_function(node, G, new_balls)\n", - " \n", - " \n", - " key = 'energy'\n", - " value = energy\n", - " \n", - " return (key, value)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# this time lets make three kinds of robots\n", - "def greedy_robot(src_balls, dst_balls):\n", - " \n", - " #robot wishes to accumlate balls at its source\n", - " #takes half of its neighbors balls\n", - " if src_balls < dst_balls:\n", - " delta = -np.floor(dst_balls/2)\n", - " else:\n", - " delta = 0\n", - " \n", - " return delta\n", - "\n", - "def fair_robot(src_balls, dst_balls):\n", - " \n", - " #robot follows the simple balancing rule\n", - " delta = np.sign(src_balls-dst_balls)\n", - " \n", - " return delta\n", - "\n", - "\n", - "def giving_robot(src_balls, dst_balls):\n", - " \n", - " #robot wishes to gice away balls one at a time\n", - " if src_balls > 1:\n", - " delta = 1\n", - " else:\n", - " delta = 0\n", - " \n", - " return delta" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "#in the previous version the robots were assigned to the edges\n", - "#moving towards an agent based model formulation we assign the stratgies\n", - "#instead to the nodes\n", - "robot_strategies = [greedy_robot,fair_robot, giving_robot]\n", - "\n", - "for node in G.nodes:\n", - " nstrats = len(robot_strategies)\n", - " rv = np.random.randint(0,nstrats)\n", - " G.nodes[node]['strat'] = robot_strategies[rv]\n", - "\n", - "for e in G.edges:\n", - " owner_node = e[0]\n", - " G.edges[e]['strat'] = G.nodes[owner_node]['strat']" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "#Policy: node by edge dimensional operator\n", - "#input the states of the boxes output the deltas along the edges\n", - "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n", - "# We specify the robotic networks logic in a Policy Function\n", - "# unlike previous examples our policy controls a vector valued action, defined over the edges of our network\n", - "def robotic_network(params, step, sL, s):\n", - " \n", - " delta_balls = {}\n", - " for e in G.edges:\n", - " src = e[0]\n", - " src_balls = s['balls'][src]\n", - " dst = e[1]\n", - " dst_balls = s['balls'][dst]\n", - " \n", - " #transfer balls according to specific robot strat\n", - " srat = G.edges[e]['strat']\n", - " \n", - " delta_balls[e] = srat(src_balls,dst_balls)\n", - "\n", - " return({'delta': delta_balls})" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n", - "# In the Partial State Update Blocks, the user specifies if state update functions will be run in series or in parallel\n", - "partial_state_update_blocks = [\n", - " { \n", - " 'policies': { # The following policy functions will be evaluated and their returns will be passed to the state update functions\n", - " 'robotic_network': robotic_network\n", - " },\n", - " 'variables': { # The following state variables will be updated simultaneously\n", - " 'balls': update_balls,\n", - " 'energy': update_energy,\n", - " 'balances': update_balance,\n", - " \n", - " }\n", - " }\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n", - "# Settings of general simulation parameters, unrelated to the system itself\n", - "# `T` is a range with the number of discrete units of time the simulation will run for;\n", - "# `N` is the number of times the simulation will be run (Monte Carlo runs)\n", - "# In this example, we'll run the simulation once (N=1) and its duration will be of 10 timesteps\n", - "# We'll cover the `M` key in a future article. For now, let's leave it empty\n", - "simulation_parameters = {\n", - " 'T': range(T),\n", - " 'N': 1,\n", - " 'M': {}\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n", - "# The configurations above are then packaged into a `Configuration` object\n", - "config = Configuration(initial_state=initial_conditions, #dict containing variable names and initial values\n", - " partial_state_update_blocks=partial_state_update_blocks, #dict containing state update functions\n", - " sim_config=simulation_parameters #dict containing simulation parameters\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "single_proc: []\n" - ] - } - ], - "source": [ - "exec_mode = ExecutionMode()\n", - "exec_context = ExecutionContext(exec_mode.single_proc)\n", - "executor = Executor(exec_context, [config]) # Pass the configuration object inside an array\n", - "raw_result, tensor = executor.main() # The `main()` method returns a tuple; its first elements contains the raw results\n", - "df = pd.DataFrame(raw_result)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "balls_list = [b for b in df.balls]\n", - "energy_list = [b for b in df.energy]\n", - "balance_list = [b for b in df.balance]\n", - "\n", - "total_energy = [sum(b) for b in df.energy]\n", - "total_balance = [sum(b) for b in df.balance]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(balance_list, energy_list, 'o-', alpha=.4)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.semilogy(balance_list, balls_list, 'o-', alpha=.4)\n", - "plt.legend(['Box #'+str(node)+\"[\"+str(G.nodes[node]['strat']).split(\" \")[1]+\"]\" for node in range(n)], ncol = 5,loc='upper center', bbox_to_anchor=(0.5, -0.15))" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(df.timestep.values, balls_list)\n", - "plt.xlabel('iteration')\n", - "plt.ylabel('Number of Marbles')\n", - "plt.title('Marbles in each box')\n", - "plt.legend(['Box #'+str(node)+\"[\"+str(G.nodes[node]['strat']).split(\" \")[1]+\"]\" for node in range(n)], ncol = 5,loc='upper center', bbox_to_anchor=(0.5, -0.15))" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.semilogy(df.timestep.values, energy_list)\n", - "plt.xlabel('iteration')\n", - "plt.ylabel('Local Engergy')\n", - "plt.title('Energy Levels')\n", - "plt.legend(['Box #'+str(node)+\"[\"+str(G.nodes[node]['strat']).split(\" \")[1]+\"]\" for node in range(n)], ncol = 5,loc='upper center', bbox_to_anchor=(0.5, -0.15))" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(df.timestep.values, balance_list)\n", - "plt.xlabel('iteration')\n", - "plt.ylabel('Net funds for each Robot')\n", - "plt.title('Net funds for each Robot')\n", - "plt.legend(['Box #'+str(node)+\"[\"+str(G.nodes[node]['strat']).split(\" \")[1]+\"]\" for node in range(n)], ncol = 5,loc='upper center', bbox_to_anchor=(0.5, -0.15))" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "end_state_balls = np.array([b for b in balls_list[-1]])\n", - "#avg_balls = np.array([np.mean(b) for b in balls_list])\n", - "\n", - "for node in G.nodes:\n", - " G.nodes[node]['final_balls'] = end_state_balls[node]\n", - " #G.nodes[node]['avg_balls'] = avg_balls[node]" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "cmap = plt.cm.jet\n", - "Nc = cmap.N\n", - "Ns = len(robot_strategies)\n", - "d = int(Nc/Ns)\n", - "\n", - "k = len(G.edges)\n", - "strat_color = []\n", - "for e in G.edges:\n", - " \n", - " for i in range(Ns):\n", - " if G.edges[e]['strat']==robot_strategies[i]:\n", - " color = cmap(i*d)\n", - " G.edges[e]['color'] = color\n", - " strat_color = strat_color+[color]\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "box #0 has 28.0 marbles: [greedy_robot]\n", - "box #1 has 9.0 marbles: [greedy_robot]\n", - "box #2 has 0.0 marbles: [fair_robot]\n", - "box #3 has 2.0 marbles: [giving_robot]\n", - "box #4 has 3.0 marbles: [giving_robot]\n", - "box #5 has 1.0 marbles: [giving_robot]\n", - "box #6 has 14.0 marbles: [greedy_robot]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "nx.draw_kamada_kawai(G, node_size=end_state_balls*scale, labels=nx.get_node_attributes(G,'final_balls'), edge_color=strat_color)\n", - "for node in G.nodes:\n", - " print(\"box #\"+str(node)+\" has \"+str(end_state_balls[node])+\" marbles: \"+\"[\"+str(G.nodes[node]['strat']).split(\" \")[1]+\"]\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "rolling_avg_balls = np.zeros((T+1, n))\n", - "for t in range(T+1):\n", - " for node in G.nodes:\n", - " for tau in range(t):\n", - " rolling_avg_balls[t,node] = (tau)/(tau+1)*rolling_avg_balls[t, node]+ 1/(tau+1)*balls_list[tau][node]" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(range(len(rolling_avg_balls)),rolling_avg_balls)\n", - "plt.xlabel('iteration')\n", - "plt.ylabel('number of balls')\n", - "plt.title('time average balls in each box')\n", - "plt.legend(['Box #'+str(node)+\"[\"+str(G.nodes[node]['strat']).split(\" \")[1]+\"]\" for node in range(n)], ncol = 5,loc='upper center', bbox_to_anchor=(0.5, -0.15))" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[27.56 8.88 2.3 1.12 3.24 1.3 12.6 ]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "avg_balls = np.zeros(n)\n", - "for node in G.nodes:\n", - " #store in graph\n", - " G.nodes[node]['avg_balls'] = int(10*(rolling_avg_balls[-1][node]))/10\n", - " #store as vector\n", - " avg_balls[node] = G.nodes[node]['avg_balls']\n", - " #need both for plotting\n", - "\n", - "nx.draw_kamada_kawai(G, node_size=avg_balls*scale, labels=nx.get_node_attributes(G,'avg_balls'))\n", - "print(rolling_avg_balls[-1])" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "cmap = plt.cm.jet\n", - "Nc = cmap.N\n", - "Nt = len(simulation_parameters['T'])\n", - "dN = int(Nc/Nt)\n", - "cmaplist = [cmap(i*dN) for i in range(Nt)]\n", - "\n", - "for t in simulation_parameters['T']:\n", - " state = np.array([b for b in balls_list[t]])\n", - " nx.draw_kamada_kawai(G, node_size=state*scale, alpha = .4/(t+1), node_color = cmaplist[t])" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[28. 9. 0. 2. 3. 1. 14.]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "nx.draw_kamada_kawai(G, node_size=avg_balls*scale, labels=nx.get_node_attributes(G,'avg_balls'), edge_color=strat_color)\n", - "print(end_state_balls)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([27.5, 8.8, 2.3, 1.1, 3.2, 1.2, 12.6])" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "avg_balls" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "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": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/demos/robot-marbles-network/robot-marbles-agents.ipynb b/demos/robot-marbles-network/robot-marbles-agents.ipynb deleted file mode 100644 index d116631..0000000 --- a/demos/robot-marbles-network/robot-marbles-agents.ipynb +++ /dev/null @@ -1,582 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# cadCAD Tutorials: The Robot and the Marbles, Networks Addition\n", - "In [Part 2](https://github.com/BlockScience/SimCAD-Tutorials/blob/master/demos/robot-marbles-part-2/robot-marbles-part-2.ipynb) we introduced the 'language' in which a system must be described in order for it to be interpretable by cadCAD and some of the basic concepts of the library:\n", - "* State Variables\n", - "* Timestep\n", - "* Policies\n", - "* State Update Functions\n", - "* Partial State Update Blocks\n", - "* Simulation Configuration Parameters\n", - "\n", - "In the previous example, we observed how two robotic arms acting in parallel could result in counterintuitive system level behavior despite the simplicity of the individual robotic arm policies. \n", - "In this notebook we'll introduce the concept of networks. This done by extending from two boxes of marbles to *n* boxes which are the nodes in our network. Furthermore, there are are going to be arms between some of the boxes but not others forming a network where the arms are the edges.\n", - "\n", - "__The robot and the marbles__ \n", - "* Picture a set of n boxes (`balls`) with an integer number of marbles in each; a network of robotic arms is capable of taking a marble from their one of their boxes and dropping it into the other one.\n", - "* Each robotic arm in the network only controls 2 boxes and they act by moving a marble from one box to the other.\n", - "* Each robotic arm is programmed to take one marble at a time from the box containing the largest number of marbles and drop it in the other box. It repeats that process until the boxes contain an equal number of marbles.\n", - "* For the purposes of our analysis of this system, suppose we are only interested in monitoring the number of marbles in only their two boxes." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from cadCAD.engine import ExecutionMode, ExecutionContext, Executor\n", - "from cadCAD.configuration import Configuration\n", - "import networkx as nx\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "%matplotlib inline\n", - "\n", - "T = 50 #iterations in our simulation\n", - "n=10 #number of boxes in our network\n", - "m= 2 #for barabasi graph type number of edges is (n-2)*m\n", - "\n", - "G = nx.barabasi_albert_graph(n, m)\n", - "k = len(G.edges)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "balls = np.zeros(n,)\n", - "\n", - "for node in G.nodes:\n", - " rv = np.random.randint(1,25)\n", - " G.nodes[node]['initial_balls'] = rv\n", - " balls[node] = rv" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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RtHfvXl544QVmz55N69atrfQNCb1Jt7YQLkhazhZy9ix07Ajly8OECXDlCty48d9gvp+EBEhJgd27oXdvKFECvvrqvt3fMTExtGrVihkzZtC2bVvLfR/C7kg4C+GCpOWcT5oGc+eq7un//U+FcUpK/o6ZkKDCfeRIaNgQjh2769OxsbEEBwfz9ddf07Fjx/ydS9g9CWchXJC0nPPh4kVo3hwGD4abN1U3tiUlJkJMjOrqnjYNNI0DBw7QsmVLpk6dSkhIiGXPJ+ySvDuFcEEyWzuPTp9WE7jOnVNjxdZiNkNSErz7Llf27OH5DRuYNGkSXbp0sd45hV2Rd6cQLki6tfPgzBnV3Xzp0r+Tu6wtKQmvBQvY/MwzVOnWzTbnFHZBurWFcEHSrZ1L166pFrMtg/kWX02jyp498NFHNj2v0JeEsxAuSFrOufTKK6or28bBnCkxESZNgp079Tm/sDkJZyFckLScc2HVqn9nZOspORm6dFF/Cqcn4SyEC5IJYTl09Sr06qUmZ9mDK1fg7bf1rkLYgISzEC5IurVz6LPP7CeYQbWaZ82CEyf0rkRYmVw625OLF+H33/9dl/fMGbWwgbs7GI1Qu7Zaj7dBA6hVCzw99a5YOCjp1s6B1FSYOVP/7ux7mc0wfboagxZOS96dejOZ1HjWhAmwdy94e6ur46zuoYyNhZ9/VmGdkQFhYfDmm2qVIiFyQVrOORARoVYCszdpafDdd/Dpp+r3hXBK0q2tF7NZrf5TooQK2d9+U2+6GzcevLhBUpJa5i8pSXVvBQbC44/D/v22q104PGk558CECWoFMHukaRAerncVwooknPXw11/QqBG8956a4JGQkLfjmEyq23vPHrX13EcfWXfVIuE0ZEJYNq5dg8OH9a7i/m7ehCVL9K5CWJGEs619/70aL963T927aAmaprrCJ02CmjVlsojIlnRrZ2PvXvDxydOXTgcaAF5AnzueXwD43fEwAm7A73mtMTo6r18pHICEsy2NGwdDhqguaWssZpCUpHayadAADh2y/PGF05Bu7WxEReV5l6lSwAdAv3ue7wHcvOPxNVABqJfXGi9dguvX8/rVws5JONvKpEkwdqz1b8swm9W9mU8/rbrPhciCtJyzsXWrmgOSB52ADkCxbF43FwhDtZ7zxGhUPXDCKUk428KaNWo82Fb3S2qauqJu2tS+7tEUdkNaztk4edK6hwe2ocI5zzIyID7eMgUJuyPhbG3XrkHPnrYPSbMZLl+GESNse17hEKTlnA0r39s8D2gMPJqfg5jNspSnE5NLZ2t79VXLTfzKreRk+OEH6N5d7agjxC2uNFtb0zSSk5NJTEzMfCQlJd318b2P1y9d4iEr1jQPeN8SB7LH+7CFRbjGu1MvO3bAL7/ou8JQcrJquf/1F7jleXRLODKzGQ4eVLN7d+yAuDi6nzpFqskEcXHqNrxGjSAoCEqV0qVEk8n0wLDMSaDe7zVJSUl4enri6+ub7cNoNOLr6wteXlb7XncAZ4HQ/B7IYMjzjHJh/yScrWn8ePvodrp0CTZvhmbN9K5E2NLVq+rWvUmT1L30mpbZi5PZKty6FbZvBz8/NQGqfn145x1o3VqtRHfLva3PnARlbgLVZDLdNyizehQsWJCSJUtm+7rbn891F/727bBpU57+2U23Hhm3HimoX7S3f9nOBUKAgnk6+h3c3aF48fweRdgpN02TfhGrOHcOype3n3V5W7aEtWv1rkJYUWbr88YNCkydSrFp09AA91z+DCYaDCQYDLz+8MNszcjIVeszu6C83+u8vLxw07lnx2w2c+LECWJiYig8dSpNt2/HIw+/HkcDH9/z3Khbz6cAJYEIoHn+ylVr6585Aw9ZswNe6EVaztby7bf21Y28dSucPatbt6XIW+szNy1Uk8lEHR8f5qemUj4jA0Mer7t9zWZ8zWYWXb1KcpcumCdOxPjQQ041gSwtLY2DBw8SExPDvn37iImJYf/+/fj7+1O3bl06lyqF5uOTp4mco289suINXMt72XcrWFCC2YlJy9la6taFmBi9q/hXwYIwY4aaHCbuK7uxz/wEqiVbn1m9xis6GrdWrVSgmM2W+Qfx8YHHHoMtW6BwYcsc08auXbvG/v377wriP//8k4oVKxIYGEhgYCB169alTp06PHQ77M6fh3Ll7KfnKyvPPQeRkXpXIaxEwtkazGa1QEAu39jTgTlAHNDt1t/v9THqqjwSeC43B3dzg0GD1FZzDuzO1qelxz0TExP/M/aZ125ai4195tTu3dC8uXXuDPD0hMqVYdcudZFnpzRNIz4+/q4QjomJ4cKFC9SuXfuuIK5ZsyY+2U2mevRR+Ptvm9Sea76+amOOwYP1rkRYiYSzNfz5p5pYk8tflD+jbjxfByTz33A+jlp56DLqVoxchTNAnTo2ac0/qPWZ30DNSeszP4FqD2OfuXbunNo29MYN653DywueeUYtqGMH/z4mk4k///zzP0Hs7u5O3bp1M0M4MDCQSpUq5e2i6Ouv4e239bsV8kF8fNT/u7+/3pUIK5ExZ2uIi7trpmtOdbr1ZzSQ1bo/rwETgEF5revoUeDf1qc1Wp55aX0WLFiQEiVK5DhQnWnsM980TW05au27AlJT4ddfYeFC6NHDuue6x82bN4mLi7srhA8ePEjp0qUzW8NvvfUWgYGBPPLII5Y7ca9eMHy45Y5nKR4e8OKLEsxOTsLZGhISLL6xxVLAE2idj2NkJCVRyM8v29ZnVmGaVXjeL3QdsvXpqBYuhJ07bbNVaGKiWlTnuefUPuRWcP78+f+0hk+dOkX16tUzW8O9e/emdu3aFLR2F3vBgtCtG8ybp7ZntRcFCsDQoXpXIaxMwtka0tMtunLPTdRqQuvzeRx3IHL9eoqXKEGhQoXw8/OTIHVkZrPtu11TU2HyZDXemQ9ms5njx4/fFcL79u0jNTU1szXcpk0bRo4cSdWqVSlQoICFvoFcGj0aFi+2n3D29ITgYKhdW+9KhJVJOFuDl5dFx+VGAb3I5zq8qAURXnr5ZW7evElCQgI3b94kIyMDPz8/ChYsiJ+f312PvD4ngW8jGzdad5w5K2lpatb/J5/keBWtlJQUDh48eFcQx8bGUrRo0czW8CuvvEJgYCBly5a1r5+dMmVgyhTVUrWHsWejEb77Tu8qhA1IOFtD0aJ5GnO+n42oMeivb318EegCvHPrkVPuhQpx4MCBu55LS0sjMTHxrsC+83HncxcvXuTEiRPZvi4jIyPLEJfAt7DPP4ebN21/Xk2DZcuga9f/fOrKlSvs378/M4j37dvHsWPHqFy5cmYQh4SEEBgYSJEiRWxfe14MGAA//qhmq+vZgjYaYc4cKJbdZpTCGchsbWuIj1e3nuRys/bby/59jArj71BXT9eBO0cUGwKTgVaAX25O8NRTalKPld0O/OxCPLvn73zObDZbNOwdPvDNZnU7TQ5+xh50i95GYDBwCmh063PlcnB6rVs3To0b959u6StXrlCnTp3MmdKBgYHUqFEDb2/vXH17dic+XnUlX72qz/mNRujYEebP1+f8wuYknK1B06BQITUxLBdGc/9l/+5UHphFLm+lMhjUmsmffZarmuxFbgM/J89lFfj57d739PS0TeAfPaoWuslBV+v9btG7BFRE/Sy1BT4EtgO/5eD0JwwGnipR4q5blgIDA6lYsSIGg5PuRBsXpy5wc/m+zjcfH2jcGFatUjO1hUuQcLaWxo1t0krNMX9/mDsXOnTQuxK7kZaWlqdW/IOeuzfwcxrsuQ78RYvg5ZdzFRQfoHpk5tz6+Ntbf9956+NE1IYY+4Cq2RxL8/DALSEBHL1FnFv79sGzz6qxflv86jQaoWlTNYxgxZ2yhP2RyzBr6dVLvZHtYRIJqBnkzfO91L5T8fT0pGjRohQtWtRix8wq8O8X7OfPn+f48ePZvk7TtP8Edp8rV3jp5k3yM4f5IFDnjo99US3pg2Qfzm6enmq3s4CAfFTggOrWVauxtWlDxpkzuOdy6CpXfHygd2+YOlVazC5I/setpUcP+7kX0d1d3a9px0svOgtbBX7RmTNxP3EiX623m8DD9zxXCMhRW9xgsO91p62pShUSo6KYV6ECL5vNuKelWfb4Pj5qHfNFi6BJE8seWzgMJx0csgO+vtCzp31c8Xp62s+Fgsi124FftmxZqlevTlBQEJWqV8eQzzsC/IB7b8S6QQ73GTab1c+Vixr58cfsaNMG999+g+rV1fs9v2PtPj5qmKB3bzWnQILZpUk4W9Pw4Wo1Hz25u0O9elCzpr51CMsqWTLf4701gP13fJyIWr+9Rk6+OC3NZW/p2b59O0uXLmXq1Kmqm/vAAdi0CUJC1P+Jr2/OD+burnq0iheHUaPg9Gn45pvcHUM4JTto1jmxypXh3XfVakp52BfWIry81D2awrnUq5fjLu3bt+hl3HqkoN74HYERQATQBvgEqE32482A2hfcaMx12Y4uKSmJfv368fXXX/87dOHmBkFBsGQJXL4MK1eqyaA7d8KxY6qHwWBQr9M0dWHj7Q2BgWqyV+PG0KxZ/lvewqnIbG1rM5nU/ZF//GGb2Z13km3lnFdGhvr/zcG472juf4veBtSGKif59z7n8jk4vRYaitvSpTku11kMHTqUCxcusGDBgpx9gcmk7pFOTlaTMn181G2WxYtbt1Dh8CScbeHgQWjUyLYzt7281NX8li1yRe6smjSB7dttftpENzc+LFIELSyM0NBQnnjiCee9t/kOv/76K126dCEuLo5iLtqlL2zH+d9R9qBGDfjlF9t1A3p6QqVKatECF/il6bJGjNBlBr7RaKT/2rUULlyYgQMHEhAQwODBg9m8eTMme9kgwsLu7M6WYBa2IC1nW9qwQS0CYs0WtLc3VK0Kmzer2zGE88rIUBPDLl2y3TkLFIBXXoFp0zKf+vPPP4mIiCAiIoLTp0/ToUMHQkJCaNasmX67SVnYsGHDOHfuHAsXLtS7FOEiJJxtLToaOnVSE0csPUnMaFTHnjFDZnu6ihkz1F0Bthoy8fWFQ4egbNksP33ixInMoD5y5Aht27YlJCSEFi1aOOz62jt27CA0NJS4uDgeeughvcsRLkLCWQ/JyfDee/Dtt+rv+XX79o0FC6Bly/wfTzgOTYMnn4SoKNWStiZfX7UT1quv5ujlp0+fZtmyZYSHhxMXF0erVq0IDQ0lODgYo4PM9E5OTqZOnTqMHz+eTp066V2OcCESznraswc+/BC2blUf53bFpYIF1ZjywIEq7AsVsnyNwv6dPKnmNViz9VygADRoADt25Gmv8nPnzrF8+XLCw8OJiori+eefJyQkhDZt2lDQjleuGz58OGfOnOGnn37SuxThYiSc7UF8PPzf/3Fp0iSKahoGo1FtBXjvsoC3VyFKTYVatVR3ZqdOLr1Sk7hlwwZo184yPTH38vBQa2hHR1tk4ZFLly6xYsUKIiIi+PXXX3n22WcJCQmhXbt2FLajeRI7d+4kJCREurOFLiSc7cSlS5eoWLEiF48exfPAAfWL8ORJ1Rry8FC7StWqBfXrq+UC7WFZUGFf1qyB0FDLzmXw9FTBvGOHmnxmYdeuXWPlypVERESwadMmnnrqKUJDQ2nfvr2ugZicnExgYCCfffYZISEhutUhXJeEs5346aefWLRoEStWrNC7FOHIoqPVMpKXLuU/pI1GCA6G2bNtMvM/ISGB1atXEx4ezvr162nQoAGhoaF07NiRkla4MHiQESNGcPr0aRYtWmTT8wpxm4SznejduzePP/44r+Zwso0Q95WSAh98AP/3f2qDitzumuTnp8aY58xRXeU6SEpKYu3atURERLB69Wpq1apFSEgInTp1okyZMlY9965du+jUqROxsbE8/PC9+3YJYRsSznbAbDZTqlQpdu7cSYUKFfQuRziLU6dUQM+YoT7Oah7Dbb6+aJrGsZQUCo0bR/EhQ9RSk3YgNTWVyMhIIiIi+OWXX6hcuTIhISGEhIRY/P2SnJxM3bp1GTt2LKGhoRY9thC5IeFsB2JiYujSpQtHjhzRuxThjNLS1JhxdLS6M+DwYRXUt3dECgqCp5+GRo14c9YsChYsyJgxY/SuOkvp6els3ryZ8PBwli81NNQmAAAgAElEQVRfTkBAAKGhoYSEhFClSpV8H//tt9/m77//ZsmSJRaoVoi8k3C2A+PHj+fs2bNqCzohdBQXF0fr1q35+++/cc/nftHWZjKZ+PXXXwkPD+fnn3+mWLFihISEEBoaSo0aNXDL5S1fv/32Gx06dCAuLk66s4XuZOFlO7B27VpayuIhwg7UqlWLkiVLEhkZqXcp2fLw8OCZZ55h+vTpxMfHM2PGDG7cuEGbNm2oWrUq77//Pnv37iUn7Y+UlBT69u3LtGnTJJiFXZCWs84SEhIoVaoU586dw1eW3BR2YMaMGWzcuJGlDrolpKZpREdHExERQXh4OBkZGZld30FBQVnuoPXOO+/w119/Oez3LJyPhLPOVqxYwfTp0x2ipSJcw7Vr1yhfvjzHjh1z+MU3NE0jNjaW8PBwIiIiSEhIoFOnToSEhPDUU0/h7u7O7t27ad++PbGxsRSXfZaFnZBubZ1Jl7awN4ULF6Zt27bMnz9f71Lyzc3NjTp16jBmzBgOHTrEunXreOihh3j99dcpXbo0L730El26dGHKlCkSzMKuSMtZR5qmUaFCBVauXEnNmjX1LkeITJs3b+aNN95g//79uZ5Y5SiOHTvGgAEDiI2NxWAwZG512bx5czxlSVyhM2k56+jo0aOkp6dTo0YNvUsR4i5NmzYlMTGR6OhovUuxmitXrvDHH39w+PBhoqOjqV69OmPHjqVkyZKEhYWxYsUKkq2xVrkQOSDhrKPbXdrO2jIRjstgMNC3b1++//57vUuxituzs7/66itKlChB+fLlGTZsGDt27CAuLo6goCC+/PJLHnnkEbp27crSpUtJtNWe2UIg3dq6atOmDX369KFz5856lyLEf8THx1O7dm3i4+MdZv/lnHrvvfc4cuQI4eHhD7w4vnDhQuZWl7t376Z58+aEhobywgsv4O/vb8OKhauRcNZJSkoKxYsX5+TJkxQpUkTvcoTIUqtWrejRowc9e/bUuxSLiYqK4oUXXiA2NpYSJUrk+OsuX77ML7/8QkREBNu2baNp06aZW10WLVrUihULVyTd2jrZvn07NWvWlGAWdq1///7Mnj1b7zIsJjU1lT59+vDll1/mKpgBihUrRt++ffnf//7H6dOn6dq1K7/88gvly5enZcuWfPvtt1y4cMFKlQtXIy1nnQwfPhx/f38++ugjvUsR4r5SU1MpU6YMu3btomLFinqXk28jR47k8OHDREREWGyux82bN1mzZg3h4eGsW7eOunXrZm51WapUKYucQ7geCWed1KxZk++//56goCC9SxHigYYOHYqvry9jx47Vu5R8iY6Opk2bNuzfv99q+0MnJyezfv16wsPDWbVqFdWrV8/cQats2bJWOadwThLOOjh9+jR169bl/Pnzdr+5gBBxcXG0atWKkydPOuzPa2pqKvXr1+f999+ne/fuNjlnWloaGzduJDw8nBUrVlChQoXMoK5UqZJNahCOS8acdbB+/XpatGjhsL/ohGupVasWpUqVYv369XqXkmdjxoyhcuXKdOvWzWbn9PT0pFWrVsyePZt//vmHzz77jL///punn36awMBAxo4dy+HDh21Wj3As0nLWQefOnXnhhRfo3bu33qUIkSOOvBmGLbqzcyMjI4MdO3ZkbnVZqFChzBZ17dq1Zd0DAUg425zJZOLhhx/m0KFDPPLII3qXI0SOXL9+nXLlynH06FGH2lIxNTWVBg0a8O6779KjRw+9y/kPs9nMnj17Mjfm8PDwyNyTun79+hLULky6tW1sz549lCtXToJZOJRChQrRrl07FixYoHcpuTJ27FgqVKhgs3Hm3DIYDDz++ON88cUX/PXXXyxatAiDwUD37t159NFHGTZsGDt37sRsNutdqrAxaTnb2EcffURaWhrjx4/XuxQhcmXLli0MGTKE2NhYh2jR7d27l1atWhETE+NwF8OapnHgwIHMPamvXr2audVl48aNZb6KC5CWs43JFpHCUTVt2pTk5GSH2AwjLS2NPn36MGnSJIcLZlBbXdaqVYvRo0dz4MABNm7cSMmSJRk2bBilSpXilVdeYf369aSnp+tdqrASaTnb0KVLl6hYsSIXL16ULemEQ/r00085ffo0M2bM0LuUB/roo4+IiYlhxYoVDtHKz42//vqLiIgIIiIiOHbsGG3btiU0NJTnnnsOLy8vvcsTFiLhbEM//fQTixYtYsWKFXqXIkSeOMJmGHv37iU4OJiYmBinX6Hr9OnT/Pzzz4SHh3PgwAHatGlDSEgIwcHB+Pj46F2eyAfp1rYh6dIWji4gIIDHH3+ciIgIvUvJUlpaGn379mXSpElOH8wAZcqU4Y033mD79u0cOnSIJ598kunTp/PII4/QpUsXFi9ezM2bN/UuU+SBtJxtxGw2U6pUKXbu3EmFChX0LkeIPIuIiGDatGls2bJF71L+Y9SoUezdu5dffvnF6bqzc+PixYusWLGCiIgIduzYQbNmzQgNDaVt27YUKlRI7/IsT9Pg5EnYtw+uXIGUFChQAPz8oEYNqF5dfexAJJxtJCYmhi5dunDkyBG9SxEiX9LS0ggICGDnzp12tQzlvn37aNmypUt0Z+fG1atXWblyJeHh4WzZsoXGjRsTEhJC+/btKVasmN7l5d3ly/D99xAeDgcPgtmsAthkUn83GOD2rPaUFKhQAVq0gEGDoFo1fWvPAQlnGxk/fjxnzpxh2rRpepciRL4NGzYMHx8fPv30U71LAdQFQ1BQEMOGDSMsLEzvcuxWQkICq1atIjw8nMjISIKCgggJCaFjx4653kJTN9HR8Pnn8Msv4OYGyck5/1oPDxXgNWvCO+9Ahw7/BridkXC2kWeffZbhw4fTpk0bvUsRIt8OHDhAcHCw3WyGMXr0aKKjo1m5cqVLd2fnRmJiImvXriUiIoLVq1dTp04dQkND6dSpE6VLl9a7vP+6cQOGDFEt5ZQU1TrODz8/ePRRWLIEqla1TI0WJOFsAwkJCZQqVYpz587h6+urdzlCWESjRo0YNWoUrVu31rWOmJgYWrRoQUxMjH2GigNISUkhMjKS8PBwVq5cSZUqVQgNDSUkJITy5cvrXR5s2ADdu0NCggpmSzEYwMsLPvoIRoywq1a0zNa2gU2bNtGoUSMJZuFU+vXrx/fff69rDenp6fTp04fPP/9cgjkfvL29adu2LXPnzuXcuXOMHj2aP/74g4YNG9KgQQPGjRun33yZ8eOhfXu4eNGywQyq9Z2cDGPGwPPP566L3Mqk5WwDgwYNokKFCgwfPlzvUoSwGHvYDOOTTz5h9+7d/O9//5PubCswmUxs27aNiIgIfv75Zx5++OHMjTmqV69u/X/zDz6AKVMgKcm65wHw8VFj0Vu2gB3cwy/hbGWaplGhQgVWrlxJzZo19S5HCIsKCwujbt26DB061Obn3r9/Py1atGDfvn3SarYBs9nMzp07M1cnMxqNmV3fgYGBlg/qL76AUaNsE8y3eXtDo0YQGan7rVcSzlZ25MgRmjVrxunTp+XKXjidrVu3MnjwYOLi4mz6852enk5QUBCvv/46ffv2tdl5haJpGlFRUZlbXQKZe1IHBQXl/2dh92549ll9upmNRjX+PHq07c99BwlnK5s2bRoxMTHMnj1b71KEsDhN06hcuTILFy4kKCjIZucdM2YMu3btYtWqVXLRqzNN09i/fz/h4eGEh4eTlJREp06dCA0N5cknn8RgyOXUppQUqFIFTp2yTsE54eOjLhBq1dKtBAlnK2vTpg19+vShc+fOepcihFV89tlnnDx5kpkzZ9rkfLGxsTRv3px9+/YREBBgk3OKnNE0jUOHDmVudXnx4sXMrS6bNGmCh4dH9gcZNgxmzrRtd/a93NzgsccgLk637m0JZytKSUmhePHinDx5kiJFiuhdjhBWcebMGWrVqmWTzTDS09Np1KgRr732Gv369bPquUT+HT16NDOoT506Rfv27QkJCaFZs2ZZ78wXHw+VK1t+VnZe+PrCtGmg07CJ3EplRb/++is1a9aUYBZOrXTp0jzxxBOEh4db/VwTJkygRIkSMs7sICpXrsy7775LdHQ0u3fvpmrVqnzyySc88sgj9O7dm5UrV5JyZxB/841aJ9seJCbCxIm6nV5azlY0fPhw/P39+eijj/QuRQirssVmGHFxcTRr1oy9e/dSpkwZq51HWF98fDzLli0jIiKCmJgYWrduTWj79nR85RXcrl/Xu7x/+frC5s3QsKHNTy3hbEW1atVi9uzZNp0oI4Qe0tLSKFOmDDt27LDKZhjp6ek8/vjjDBo0iP79+1v8+EI/58+fZ/ny5Zz7+muGxcZSUO+C7mQwQLduMH++zU8t4Wwl8fHxBAYGcv78ebtYe1gIa3vrrbfw9va2ymYYn376Kdu2bWPt2rUyO9tZDRyINnMmdve/W7Ik/POPzU8rY85Wsm7dOlq0aCHBLFxGv379mDNnDiaTyaLHPXDgAF9++SXfffedBLMz27Ej18E8HWgAeAF97nj+b8AN8LvjMSavdV2+DDp0tUs4W8m6desIDg7WuwwhbKZGjRqUKVOGdevWWeyYJpOJPn36MG7cOMqWLWux4wo7k5EBR4/m+stKAR8A95u3fw24eevxYV5r8/GBffvy+tV5loObzkSma9dg715179uNG+o+PHd39Z/30ENQrx7UqoXJw4MNGzbw1Vdf6V2xEDZ1ezMMS22NOnHiRIoWLSrjzM7uxAm113Jqaq6+rNOtP6OBeIsXdUtaGsTGwjPPWOsMWZJwfpDkZLXX55IlKpQvX1ZLu6WkqP+w28P1BoNak9XDA5KSMBUvzo+axiMHDkCJEurzQriArl278vbbb3PhwgWKFy+er2MdOHCAKVOm8Pvvv0t3trO7cUP9/rSwcqju7RbA58BDeTlIWhrcvGnJsnJEUiMrx4/DG29A8eLw2muwejWcOwfp6WrsITX17nvxzGbVir5xA0wmvM+epdW1a9CpEwQEwKRJcPWqft+PEDbi7+9P+/btmZ/P2a0mk4m+ffvy6aefSne2K0hLs+jhHgKigJPA70AC0COvBzObLV5fTkg43+mff6BVK7Vt2DffqKulPF4xGUB97T//qI28S5VSy9LZw8o3QlhR//79mT17Nvm5EeSLL76gcOHCvPTSSxasTNgtLy+LLj7ih5oo5gGUQE0cWw/cyMvBDAZVn41JtzaoH4r582HwYBWe6emWPf7tNWJnzoSICFi6FOTeZ+GkGjduTFpaGnv27KFRo0a5/vqDBw8yadIkoqOjpTvbidy4cYP4+HhOnz5915/x8fGYjx9n2Y0b+Frp3Ld/ivIU/15eUKiQBavJGQnnc+egVy/YtUst12ZNSUlqp5VnnoGBA2HcOF2uyISwJjc3N/r168fs2bNzHc63u7PHjh1LuXLlrFShsLTr169nBm1W4Xv69GkyMjIoU6YMAQEBmX82bNiQTp06EVCqFMYnnsh1z6Lp1iPj1iMFFWq/A4WBysBV4HXgGSBPEVugANSpk5evzBfXXoTk2DF46ik1Hmzp1nJ2fHygbl1Yv14tESeEEzlz5gw1a9YkPj4e31z8fE+YMIENGzawfv16aTXbAU3TuHHjxn+C9j8tX7M5M3DvDN87/164cOEH/5/Wr68m3ubCaODje54bBVQB3gcuAP6oCWETgZK5OvotHh7qTh0b/5523XA+fFgF8/XrasBfD15eULUqbN8OBe1q0Toh8u2FF16gc+fO9O7dO0evP3ToEE2bNiUqKory5ctbtziBpmmZLd4Hha+maZQpU+aB4VuoUKH8X0y9+SZMnWo/G1/cVqaMLntLu2Y4//03NGgAV67o/4Pg5QWBgbBli7odSwgn8fPPP/PVV1+xdevWbF9rMpl46qmn6Nu3LwMHDrRBdc5N0zSuXbv2n8C99+/Af7qa7/27RYI3J9auhS5dICHB+ufKKQ8P6NdPzReyMdcL55QU1Vo9fVq/FvO9fHygbVtYvFjvSoSwmNubYfz6669Urlz5ga+dOHEi69atIzIyEoOsC/BAdwbv/cZ34+PjcXNzu6vFm1X4+vv728/wgdkMjzwCFy7oXcm/fHzg99+hWjWbn9r1wvmNN+C779QCI/bEaIRFi1RIC+Ek3nrrLQpnZPBhhQpw6JAauytaFGrXhu7dwd+fw4cP06RJE+nORgXv1atXs51c5eHh8Z+gvTd8/f399f52cm/CBPj4Y/v5/dygAURF6XJq1wrn336DZs3s5z/+XkWKqAVQihTRuxIh8m/fPq6//z5ea9fi5eOD253vO19fMJsxd+5M9337aPrqq7z66qv61WoDt4M3u8lVHh4e2U6ucsjgzYnLl9UYrz38jvbzgzlzICREl9O7TjgnJ8Njj0G81VZgzT8vL2jXTi0XKoQj++YbGD5cDSM9YPgow2AgHfCcPx9Dt262q8/CNE3jypUr2U6u8vT0vG/glilThtKlSztv8ObU+PEwdqz1b219EHd31WretQt06vZ3nXCeNAk+/NA+rsgexGiEbdvUbQVCOKKvv4YRI/5dfCcnjEaYPRu6drVeXXmkaRqXL1/OdnKVp6dntpOrCspdGdnLyFC3mR48qN+8IF9fOHAAdBxmcY1wNpvVGtc6bJidawYDvPgiLFyodyVC5N5vv0Hz5rkL5tuMRjW+V7265eu6j9vBm11Xs7e39wO7mkuXLi3Ba0l//KF2+dOjMeXrCxMnwqBBtj/3HVwjnNevV+MGOuwskife3nD2rIw9C8fTpg2sWZPlLYo9gY1AImoxiLeBAXe+wN1drdb3ww8WKUXTNC5duvTA1m58fDw+Pj4PnFwVEBCAn5+fRWoSubBokbqNyZYBbTRChw5qOWedZ7G7Rjg/9xxs3Kh3FTlnNKoZi8OH612JEDn3zz/w6KP33ZP3IFAJ8AL+QC2nuAq4awDHx0ctqZvNuOvt4H3QrUTx8fEYjcb7ju8GBARQunRpCV579u23anESWwS00QgtWqj9D9zdrX++bDh/OF+8qGb/5XITb92VLQsnT+pdhRA5N3YsfPppjtZH/hMVzl8BXe78hK8v5s8/51JIyANvJTpz5gy+vr7ZTq7KzdKhwk4tXAgDBqifK2vFla+v6l39/nu7CGZwhXBetUrdT3kjT5uF6adAAbh0KdsWhBB2IyQEfv75gS8ZBMwBkoG6wDbU9n53mmEw8EGRIg+8lSggIACj0WiN70LYowMHoHNntYxmXuYz3I+npxpG/O47tTqZHXH+Xamioiz7n2krPj6wbx80bap3JcKBZGRkkJaWRlpaGqmpqZl/z+qR3edz+5rx0dFkd4/B18A0YBewBdXFfa8BnToxcOlSC//LCIdWsybExsJnn6mFSszm/PWGGgwqlJs2VfcyFy9usVItxfnDeetWMJnydYhU1BX/BuAKatzsM6AV8BvwIWqLMndUV91U4JF8nRH1g/f77xLOduLO0MtPmFk6EO99aJqGl5cXnp6emY97P87q8aDXeHl54e/vn+0xyo4dqzZxyYY78DQwH/gGtZ3fnTweesjy/4HC8RUoAKNGQf/+6j76r79Wt13lZi1uHx/VNd6uHbz1FgQFWa/efHL+cN6/P9+HMAFlgK1AWWA1apwsDrVX6MtAS9Q/5mtAX2Btfk+amqo2wxg2LL9Hsmtms1nXMMvp581ms8XC7s7X+Pn55fsYd37srud42fbtsHs3pKXl6OUm4Pg9z2ne3rjpsI6xcCABAWpuw+jRsHy5evz2m9ovwccnc5Z1ckoKboC3pkHhwurWrOBg6NkTihXT9VvICecec87IULuKWEFt1L6h9y7sthdoClhkX5UaNdRYSx6YzWbS09PtsnV35+czMjJyFESWDMS8vMbd3d1+NgiwVydPqk1lspgQdgHYBLwA+KB6oToBC4H2d7wuGRjUrh2dBgwgODiYAgUKWL9u4RxSUtTCJVeuQEoKy1at4uSlS7z57bdqPXcH49wt5+RkFc757Na+13ngCFAji89tu8/zeXHu5EmGdO6cp0A0mUxWCap7uzfzG4geHh4Sek7ieuHCXChenIqnTnHvvlJuqC7sgYAZKAd8yd3BjMGAoUMHnmjZkvHjxzNgwAC6detG7969CQwMlJ8T8WDe3netrHjz2jWi161zyGAGZw/ntDQ18G9B6UAPoDdQ9Z7PxQKfACssdK6CXl506dIlT6FaoEAB+WUmbMJkMjFr1ixGjx7NsHr1GH7x4n/uS30YNSz0QN7eeH3wAS/XrcvLL7/MsWPHmDdvHp06dcLPz4+wsDB69OhBqVKlrPWtCCdStGhRrly5oncZeac5s5s3Nc3DQ9PUFIB8PzJAexG0VqCl3fO5o6CVAm2ehc6lgaZVqqT3v6AQD7Ru3TqtRo0a2jPPPKPt3btXPTl2rKYZjbn7WTcaNW3mzCzPkZGRoW3dulXr16+fVrhwYa1ly5bawoULtcTERBt+p8LR7Ny5U2vUqJHeZeSZc4dzRobFgtIMWh/QngEt6Z7P/Q1aOdC+sWQwg6bVqqX3v6AQWTp06JDWqlUrrVKlStqyZcs0s9n87yfN5pwHtJubet3//V+OzpuYmKgtWLBAa9mypVakSBGtf//+2tatW7WMjAwrfafCUf3xxx9aJQdu4Fi2z9feGAxQooRFDvUqcBhYiZrQctsZoBkwGDWeZlE1LDV6LYRlXLp0iddee40mTZrw3HPPcfDgQTp06HD3EIqbG4wcqRYAat5cbYXq6Xn3gby91fOtW6uldXO4yYDRaKR79+6sXbuWAwcOUKVKFQYNGkSlSpUYNWoUx44ds+B3KxyZo3drO/dsbVAL8a9ena9DnATKoxZMuHOQfiZwDBgN3LtIYL632PDxgS++0H1nFCEA0tLSmD59OuPGjePFF19k9OjRPJTT+5FPnYJZs9RM2uvX1W0tgYFqScaSJfNdm6Zp7Nu3j3nz5vHTTz9RuXJlwsLC6NKlC4ULF8738YVjMplMeHt7k5aWhsHCc49swfnDeeJEtY9zDu+9tBv+/mo3rUaN9K5EuDBN01ixYgUjRozgscce4/PPP6e6Dbd0zK309HTWrl3L3LlziYyMJDg4mN69e/P888/jYaXbKoX9Kly4MCdOnKCIA+7w5/zhvGkTdOzoeGtre3iomn18sn+tEFYQExPD0KFDuXjxIpMnT+b555/Xu6RcuXLlCkuWLGHu3LmcOHGC7t2707t3b+rUqaN3acJGKlSoQGRkJBUrVtS7lFxzvLZ+bjVo4HitZoDHHpNgFrr4559/6N+/P8HBwbz44ovExMQ4XDCDGnMcOHAgu3btYtu2bRiNRtq1a0edOnWYPHky586d07tEYWVFixbl8uXLepeRJ84fzv7+ah1VRxpz8POTvZyFzSUnJzN27Fhq1qxJsWLF+PPPPxk4cKBTdAc/9thjjB07lhMnTvDll18SFxdHtWrVaNOmDYsXLybZFvsFC5tz5ElhDpRY+TB8uJod6ki6dtW7AuEiNE1j4cKFVK1alZiYGKKiopg4cSKFChXSuzSLMxgMPPvss/zwww/Ex8fTrVs3Zs+eTenSpXn55Zf59ddfcfaRPlci4WzvGjaEMmX0riJnChSAPn2kS1vYxK5du3jiiSeYPHkyP/74I+Hh4VSoUEHvsmzC19eXnj17sn79emJjY6lYsSIvv/wylSpV4uOPP+avv/7Su0SRT8WKFZNwtnvvvw++997wZIc8POCNN/SuQji5kydP0rVrVzp37sygQYPYs2cPTZo00bss3QQEBPDOO+9w8OBBFi9ezKVLl2jUqBFNmjRh1qxZXL9+Xe8SRR5Iy9kR9OwJVarY99iz0ajua65USe9KhJNKSEjg/fffp169elStWpU///yTsLAwh7wP1Brc3Nxo0KAB06ZN48yZM7z11lusXr2acuXK0a1bN9auXYvJwhvpCOuRcHYEBgMsXqxWJbJDGsDDD6t9SoWwsIyMDGbNmkWVKlWIj49n//79jB49Gl9H6E3SiaenJ+3bt+fnn3/m+PHjNG7cmFGjRlG2bFlGjBhBXFyc3iWKbMhsbUdRqRKMHWuX3dspwLYhQ+z24kE4rk2bNlG/fn3mzp3LihUrmDdvHgEBAXqX5VCKFSvGoEGD2L17N5s2baJAgQK0bt2aunXr8uWXX3L+/Hm9SxRZkJazI3nzTahZ075C0NeXK7160WvqVEaMGEF6erreFQkncOTIEdq3b8+AAQP44IMP2LZtGw0bNtS7LIdXtWpVPvvsM06ePMmkSZPYt28fVapUoW3btixdupSUlBS9SxS3yIQwR2IwwLp1UK6cmhmtN6MR2ren9Ny57N27l0OHDvHss88SHx+vd2XCQV25coWhQ4fy5JNP8tRTT3Ho0CFCQ0Nlf28LMxgMNGvWjLlz5xIfH0/nzp2ZOXMmpUuXZuDAgezcuVNuy9KZtJwdTaFCsH07lC37391ybMlohOBgmDcP3NwoVqwYK1eupE2bNjRs2JD169frV5twOOnp6UydOpWqVauSnJzMoUOHePvtt/F2tHv8HZCfnx9hYWFs2LCBffv2Ua5cOfr168djjz3GmDFj+Pvvv/Uu0SU5cjg7/9raD3L1KjzzDBw9CrZeIchohBdfVLv1ZDFTdsuWLfTo0YMBAwbw0Ucf4e7ubtv6hMPQNI1Vq1YxfPhwypYty6RJk6hVq5beZbk8TdOIiopi3rx5LF68mBo1atC7d29CQkLw9/fXuzyXkJ6ejo+Pj0PuTOXa4QyQkqLugZ4xwzYB7eGhViubNg1691Z7397HuXPn6N69OwaDgQULFlDCQntTC+cRFxfHsGHDOH36NJMmTaJ169bSfW2HUlNTWb16NXPnzmXLli288MILhIWF0bx5c7nwtjJ/f39OnTrlcNuHOtalhDV4e8PkybB5s+rmNhqtdy5fX3j6afjjD7UKWDa/REuWLElkZCRPPPEE9evXZ9u2bdarTfEqjDgAABO2SURBVDiU8+fP88orr9C8eXPatWtHXFwcbdq0kWC2U15eXnTs2JHly5dz7NgxGjVqxMiRIylbtmzm4ifCOhy1a1vC+bZGjeDPP2HIEBWifn6WO3bBglCiBPzf/6ktLEuXzvGXuru7M2bMGGbNmkWXLl2YMGECZrPZcrUJh5KSksKECROoUaMGvr6+/PnnnwwZMoQC9jC5UeTIQw89xJAhQ4iKiiIyMhI3NzdatmxJgwYNmDp1KhcvXtS7RKfiqDO2JZzv5O0N48fDhQswdarattHXF/LS7eTlpY7XrBksXQpnz2bbjf0gwcHBREVFsWLFCtq3b++QP2wi7zRNY8mSJVSrVo1du3axa9cuJk+e7JCbyIt/Va9enfHjx3Py5EnGjRtHVFQUlStXpn379kRERJCamqp3iQ7PUVvOMuacnagoWLUKtm6FmBg1Lu3tDZoGZrMK29uPpCS1yleDBtC0KYSEqFu2LCg9PZ13332XiIgIlixZQlBQkEWPL+xPVFQUQ4cO5ebNm0yePJlmzZrpXZKwooSEBCIiIpg3bx6xsbF06dKF3r17ExQUJMMWefDiiy/SsWNHujrYTn8Szrl17hzExkJCggpqd3cV1sWKQWCg2j/aBpYtW8Yrr7zChx9+yGuvvSZvWicUHx/Pe++9x8aNGxkzZgx9+vSRyUMu5uTJk8yfP5+5c+diMBgICwujZ8+elC1bVu/SHMarr75KzZo1GTx4sN6l5IqEswM7fvw4nTt3plKlSsyaNUtuz3ASiYmJTJw4kenTpzNw4EDeffddChYsqHdZQkeaprF7927mzp3LkiVLqFOnTuZtWX6WnB/jhEaOHIm3tzcffvih3qXkiow5O7CKFSuyc+dOihUrRv369dm/f7/eJYl8MJvNzJkzh8cee4wjR46wd+9ePv30UwlmgZubG48//jjffPMNZ8+eZfDgwURERBAQEJC5+ElGRobeZdolmRAmdOHt7c0333zDxx9/zHPPPcfs2bNlyUAHdHvd65kzZxIeHs5PP/1EOQvPVxDOwcvLi5CQEH755ReOHDlC/fr1eeeddyhfvjzvvfcehw8f1rtEu+KoE8IknJ1E9+7d2bZtG1OmTKFPnz4kJibqXZLIgePHjxMSEkKvXr0YPnw4O3fu5IknntC7LOEgihcvzhtvvMHvv//OmjVryMjIoHnz5gQFBTF9+nSH3S7RkiSche6qVavG7t27AWjUqJFcQdux69evM2LECIKCgqhXrx5//PEH3bp1k4l9Is9q1qzJxIkTOX36NGPGjGHXrl1UrFgxc/GTtLQ0vUvUhaPu6Szh7GR8fX2ZM2cOQ4cOpUmTJixcuFDvksQdTCYT33zzDVWqVOHq1ascOHCAkSNH4uPjo3dpwkm4u7vTsmVLFixYwKlTp2jbti1TpkyhdOnSvPbaa0RFRbnU0JejtpxltrYT279/P6GhoTz33HNMmTJFdifS2bp163jrrbcoXrw4kydPJjAwUO+ShAs5ceIEP/74I/PmzcPT0zPztqyAgAC9S7Oqc+fOUbt2bS5cuKB3Kbki4ezkbty4Qf/+/fnrr79YunQpFSpU0Lskl3Po0CGGDx/O0aNH+eKLL2jXrp10XwvdaJrGrl27mDt3LuHh4dSrV4+wsDA6deqEr6+v3uVZXFpaGr6+vqSlpTnU+066tZ2cv78/S5YsoU+fPjz++OMsW7ZM75JcxqVLlxg8eDBNmzalRYsWHDx4kPbt2zvULwjhfNzc3HjyySeZOXMmZ86c4aWXXmLx4sUEBATQp08fNm3a5FTr93sWKEBxLy9uHj+uFpG6fl2t7mjnpOXsQnbv3s2LL75ISEgI48ePl80SrCQtLY1p06Yxfvx4unbtyujRoylWrJjeZQnxQOfPn2fhwoXMmzePy5cv06tXL8LCwqhSpYrepeXOtWuwbh3s2gXbt8Phw6QlJ+Ph7Y3BzQ0yMtRyy1WqqF0Cn3gCWrZUSy/bEQlnF3PlyhXCwsK4cuUKixcvpkyZMnqX5DQ0TWP58uWMGDGCKlWq8MUXX1CtWjW9yxIi12JjY5k3bx4LFiygXLlyhIWF0bVrV4oWLap3afe3d6/a/jciAgoUgJs31R4I2fHzA5MJWrWC4cNVWNtB75aEswsym818/vnnTJkyhTlz5hAcHKx3SQ5v3759DB06lMuXLzNp0iSef/55vUsSIt9MJhORkZHMmzePNWvW0Lx5c8LCwmjVqhWenp56l6ccOAC9esGRI5CaqlrGeeHmBkaj2tL3xx9B702FNOGytm7dqpUuXVr74IMPNJPJpHc5Duns2bNa3759tRIlSmgzZszQ0tPT9S5JCKu4du2a9t1332mNGzfWHn74YW3IkCFadHS0Zjab9SkoPV3TPv5Y03x8NM3NTdNUO9kyDx8fTRs2TNOSk/X53jRNk5azizt//jzdu3dH0zQWLlxIyZIl9S7JISQlJTFp0iS+/PJLBgwYwPvvv0+hQoX0LksImzh+/Djz589n3rx5eHt707t3b3r06EHp0qVtU8CpUxAcDCdPqq16rcFoVOPQa9dC1arWOccDyGxtF1eiRAnWr1/P008/Tf369dm6daveJdk1s9nMggULqFq1KrGxsURFRTFhwgQJZuFSKlasyKhRozh27BgzZ87k6NGj1KpVi+eff/7/27v32CirPIzj36G0MIUpV7mIKCUgW5AKtNFCUqSrgLhAgAYIIMVFKYogAU2qsiCwhK0oN92kZhOI7XIJ1gglspGNIMGWSwEvdcW2glYFuUUaCp1CoZ3941SRSqEzvO/MS3k+CeEy877nkDA8c857zu+wfv16e8sHFxVBXJyZxrYrmMHc+8cfISHBPM8OMo2c5Tfbt29nypQpzJ49m7S0NBo10ne339uzZw9z5syhurqaFStWkJiYGOouiThGRUUFOTk5ZGVlsXfvXkaPHk1KSgoDBw607v+S77+H+HgoLa3fYi+reDywZw888EDQmlQ4yzWOHTvG+PHjadmyJVlZWdoCBJSUlPDyyy+Tl5fH0qVLmTRpkr64iNzAiRMn2LBhA5mZmZSVlf22Lat79+6B37SsDGJizF7lUOxTbt0avvkG2rULSnP6H0aucc8997Br1y5iYmKIi4v77SCNO1FZWRmvvPIKcXFxxMTEUFhYyOTJkxXMIjfRsWNHXnzxRQoKCtiyZQvl5eUkJiYyYMAA3nnnHUpLS/2/6axZcPZs6AqIXLgATz0VtBF72MKFCxcGpSW5bYSFhTFkyBCio6OZMGECERERPPzww3dMZauqqirWrFlDcnIy7dq14/3332fEiBHO2Toichvp0KEDQ4cOZfbs2XTq1ImcnBxmzZrFoUOHiIyMJDo6mrCwsBvf5OOPYcECqKgITqevp6oKTpyAbt2CMr2taW25oe+++45x48bRpUsX1qxZ0+AXPu3YsYO5c+cSFRXFypUriY+PD3WXRBqc0tJSsrOzyczM5MiRI0ycOJGUlBT69Onzx0FAeTlER8OZM6HpbG1RUXD0KLRta2szmp+TG+ratSu5ubm0b9+e+Ph4vvjii1B3yRbFxcWMHDmSadOmMX/+fHbv3q1gFrFJq1atSE1NJS8vj7y8PDweD2PGjCE2NpY333yTEydOXH3zunX2rsr2V2UlZGTY3oxGzlJvGzdu5IUXXmDp0qU888wztzbNXVIC+fmwbx/k5ppvxZWVpuxemzYwYIApo/fQQ2YaySZnz55l8eLFrFu3jrS0NGbNmqWjNUVCoLq6mtzcXDIzM/nggw9ISEggZfJkxs+bR6OSklB371pt2sCpU3Cz6fhboHAWvxQWFjJ27Fj69u1LRkaGf0fMVVbC5s3w+utQWAiNG9dd/9blulrzNjoa0tJg3DiwKDgvX75MRkYGS5YsITk5mUWLFtEuSKswReTGvF4vW7Zs4cDq1fw9P5/moe5QbR6PGdGPHGlbEwpn8ZvX62XGjBkcOHCA7OxsevbseeMLfD5Yv96stqyqgvPn/W+0ec3Hc9kyePbZgAvT+3w+PvzwQ1566SW6dOnC8uXLeSCIexdFxA9PPolvwwZcToypRx81C9VsonCWgK1du5a0tDRWrVrFpEmTrv+mkychJcVs4LeialCzZhAbCxs3wn33+XVpQUEBc+fO5fjx4yxfvpxhw4bdMSvQRW5L994LP/0U6l5cX4sW5nhKm2hBmARs6tSp7Nixg0WLFjF9+nQuXrx47RsOHDA1aXftsiaYwdwnP99sZfjkk3pdcurUKVJTUxk8eDCjRo2ioKCAJ554QsEs4mTl5WbrUgD+CcQDTYCnar3mBWYAbYEWwMBA+3fpEvz8c6BX35TCWW5JbGwsBw8epLS0lP79+3P06FHzwp49kJQE587B5cvWNlpVZZ5VDx9uitLX4eLFi6Snp9OrVy+aN29OYWEhM2fOJDw83Nr+iIj1vvzSHD4RgLuBvwFTr/NaKnAW+Kbm55WB9i8iAg4dCvTqm1I4yy2Liopi06ZNPP300/Tv35+PV62CoUOtGy3XxeuF5GSz4vt3fD4fmzZtIiYmhv3797N3715WrFhBq1at7O2PiFjn8GGzIDQAY4BRQO3iw0XAVuBfwF1AGBAXaP+8Xvj660CvvqnGtt1Z7igul4uZM2eS0K8fbR95hOorV4Lzzc/rhdGj4cgRaNaM/Px85syZg9frZe3atSQlJQWjFyJitfLygMO5LvuB+4DXgH8DHYGFQHIgN7tyxdYBiMJZLBW/bRu+iAhcFn+obujcOc6npvKcy8XOnTtZsmQJU6ZMuXlJQBFxrsuXLa9jfQz4HyaMfwb2An8BegIxgdyw9jobC2laW6xz+DCsXIkr2NV8KioI27CBxMaNKSoqYurUqQpmkdtdkyZg8SEzbiAc8zw6AngESAL+G+gNA3wmXh8KZ7FOeropNBICbpeL6adP4/F4QtK+iFisRQtTMdBCsVbeLCLC9NEmCmexxrlzkJ1tVlKHgMvnM1urbNzaICJB9OCDARcbugJcBKpqflys+bOBwL3AP2p+nwfsAoYG0ojbDX36BNS/+lA4izWysiyfgvKbzxeUgvQiEgQ9ewb8THcJZgo7HVhX8+slmCntHOA/mD3O04As4E+BNOL1Qr9+AfWvPhTOYo3Nm+t9ckxdBQL2AYOB1phtDmMBv0oQXLoEW7f6c4WIOFV4OHTtGtClCwFfrR8La17rhVkIVg4cBkYH2r/WraFly0CvvimFs1jDj6Mk6yoQUIopEFAC/AB4gL/624+iIqiu9vcqEXGixx+3/LmzJRo1gkGD7G3C1rvLneH0ab/OW62rQMAwzGg5CogEZmKeCfklPByKi/29SkSc6PnnbT2WMWBuN8yZY2sTCme5dcXFlh3l+Hu7MVNQfmnUSOEs0lB07w59+4a6F390993mrHkbKZzl1tmwr7kAWAy84e+FPp8t/RGREElLu3pkrBM0a2b6ZPPBOQpnuXUW/yM9gpniXg0kOqA/IhJCw4ebhWGh3g3yqzZtoK4jci3kkL+t3NYsrJLzA/AYMB+YHMgNXC7zPEhEGoawMHjvPVMxLNTcblPPwYbHeLUpnOXW9egBFRX1fntdBQKOA38GngeeDbQvVVXmDGkRaTh69IAFC8yUcqhERsJzz9n+rPlXLp/P4sricmdq2xZ++aVeb10ILKr1Z68BrprXan/8LvjTj6ZNzUkxTpkCExFrVFXBwIHw2We2HjhxXRER0K2bOb85CKNmUDiLVR57DHbsCHUvTDm9zz8PdS9ExA4XLsCAAWZHxqVLwWkzPBw6dYKDB83z5iDR8EKskZwc2iknMN9ox4wJbR9ExD7Nm0NuLvTuHZwRbJMmZjFafn5Qgxk0charnD8P7dv79ezZck2bQkmJ6YeINFwVFeb5b3a2fVsnIyNhyBB4911bT5+qi0bOYg2PByZOhMaNQ9O+y2U+SApmkYbP7TahmZMDd91l7Sj616Mg1683ZwaEIJhBI2ex0rffmmPeQjF6drvh008hLi74bYtI6Jw/D/PmwZo15kt6eXlg94mMNHX5J0yAN94I+jR2bRo5i3W6d4dXXw3+s2e3G1JTFcwidyKPB956y9T4X73arKp2uyEq6sa7Nlwuc21kJHTuDOnpcPIkrF0b8mAGjZzFaleumBXThw+bUprB0LmzbfW9ReQ24/OZtSeHDsH+/bB7Nxw7dnV1d5Mm0KEDJCZCQoL5Ut+tm+MqCyqcxXrFxWaj/rlz9rfVrJn58Nl46LmISLBpWlusd//9sHOnmTKyU2QkbNumYBaRBkcjZ7HPV19BUpJZsFFZad19w8PNM6WPPoL+/a27r4iIQ2jkLPbp3dtMcQ8fbt3hGJGRMGgQFBUpmEWkwdLIWYJj61azotrrNSNpf3k8Zg/122+b/dQOW7whImIlhbMET1UVbN8Oy5bBvn1m1aTXa1Z41xYWZhZ7VVaavdNpaTBiROiKnIiIBJHCWULjzBlTSP7AAbPa+tQpE8Th4abiT2KiWfEdH2+2PYiI3EEUziIiIg6jBWEiIiIOo3AWERFxGIWziIiIwyicRUREHEbhLCIi4jAKZxEREYdROIuIiDiMwllERMRhFM4iIiIOo3AWERFxGIWziIiIwyicRUREHEbhLCIi4jAKZxEREYdROIuIiDiMwllERMRhFM4iIiIOo3AWERFxGIWziIiIwyicRUREHEbhLCIi4jAKZxEREYdROIuIiDiMwllERMRhFM4iIiIOo3AWERFxmP8DVDKF50oBLT8AAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "scale=100\n", - "nx.draw_kamada_kawai(G, node_size=balls*scale,labels=nx.get_node_attributes(G,'initial_balls'))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "initial_conditions = {'balls':balls}" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "#input the deltas along the edges and update the boxes\n", - "#mechanism: edge by node dimensional operator\n", - "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n", - "# We make the state update functions less \"intelligent\",\n", - "# ie. they simply add the number of marbles specified in _input \n", - "# (which, per the policy function definition, may be negative)\n", - "\n", - "\n", - "def update_balls(params, step, sL, s, _input):\n", - " \n", - " delta_balls = _input['delta']\n", - " new_balls = s['balls']\n", - " for e in G.edges:\n", - " move_ball = delta_balls[e]\n", - " src = e[0]\n", - " dst = e[1]\n", - " if (new_balls[src] >= move_ball) and (new_balls[dst] >= -move_ball):\n", - " new_balls[src] = new_balls[src]-move_ball\n", - " new_balls[dst] = new_balls[dst]+move_ball\n", - " \n", - " \n", - " key = 'balls'\n", - " value = new_balls\n", - " \n", - " return (key, value)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# this time lets make three kinds of robots\n", - "def greedy_robot(src_balls, dst_balls):\n", - " \n", - " #robot wishes to accumlate balls at its source\n", - " #takes half of its neighbors balls\n", - " if src_balls < dst_balls:\n", - " delta = -np.floor(dst_balls/2)\n", - " else:\n", - " delta = 0\n", - " \n", - " return delta\n", - "\n", - "def fair_robot(src_balls, dst_balls):\n", - " \n", - " #robot follows the simple balancing rule\n", - " delta = np.sign(src_balls-dst_balls)\n", - " \n", - " return delta\n", - "\n", - "\n", - "def giving_robot(src_balls, dst_balls):\n", - " \n", - " #robot wishes to gice away balls one at a time\n", - " if src_balls > 0:\n", - " delta = 1\n", - " else:\n", - " delta = 0\n", - " \n", - " return delta" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "#in the previous version the robots were assigned to the edges\n", - "#moving towards an agent based model formulation we assign the stratgies\n", - "#instead to the nodes\n", - "robot_strategies = [greedy_robot,fair_robot, giving_robot]\n", - "\n", - "for node in G.nodes:\n", - " nstrats = len(robot_strategies)\n", - " rv = np.random.randint(0,nstrats)\n", - " G.nodes[node]['strat'] = robot_strategies[rv]\n", - "\n", - "for e in G.edges:\n", - " owner_node = e[0]\n", - " G.edges[e]['strat'] = G.nodes[owner_node]['strat']" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "#Policy: node by edge dimensional operator\n", - "#input the states of the boxes output the deltas along the edges\n", - "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n", - "# We specify the robotic networks logic in a Policy Function\n", - "# unlike previous examples our policy controls a vector valued action, defined over the edges of our network\n", - "def robotic_network(params, step, sL, s):\n", - " \n", - " delta_balls = {}\n", - " for e in G.edges:\n", - " src = e[0]\n", - " src_balls = s['balls'][src]\n", - " dst = e[1]\n", - " dst_balls = s['balls'][dst]\n", - " \n", - " #transfer balls according to specific robot strat\n", - " srat = G.edges[e]['strat']\n", - " \n", - " delta_balls[e] = srat(src_balls,dst_balls)\n", - "\n", - " return({'delta': delta_balls})" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n", - "# In the Partial State Update Blocks, the user specifies if state update functions will be run in series or in parallel\n", - "partial_state_update_blocks = [\n", - " { \n", - " 'policies': { # The following policy functions will be evaluated and their returns will be passed to the state update functions\n", - " 'robotic_network': robotic_network\n", - " },\n", - " 'variables': { # The following state variables will be updated simultaneously\n", - " 'balls': update_balls,\n", - " \n", - " }\n", - " }\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n", - "# Settings of general simulation parameters, unrelated to the system itself\n", - "# `T` is a range with the number of discrete units of time the simulation will run for;\n", - "# `N` is the number of times the simulation will be run (Monte Carlo runs)\n", - "# In this example, we'll run the simulation once (N=1) and its duration will be of 10 timesteps\n", - "# We'll cover the `M` key in a future article. For now, let's leave it empty\n", - "simulation_parameters = {\n", - " 'T': range(T),\n", - " 'N': 1,\n", - " 'M': {}\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n", - "# The configurations above are then packaged into a `Configuration` object\n", - "config = Configuration(initial_state=initial_conditions, #dict containing variable names and initial values\n", - " partial_state_update_blocks=partial_state_update_blocks, #dict containing state update functions\n", - " sim_config=simulation_parameters #dict containing simulation parameters\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "single_proc: []\n" - ] - } - ], - "source": [ - "exec_mode = ExecutionMode()\n", - "exec_context = ExecutionContext(exec_mode.single_proc)\n", - "executor = Executor(exec_context, [config]) # Pass the configuration object inside an array\n", - "raw_result, tensor = executor.main() # The `main()` method returns a tuple; its first elements contains the raw results\n", - "df = pd.DataFrame(raw_result)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "balls_list = [b for b in df.balls]" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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dW0rcsZchpQeGOy62KwqFQqGYhYVGUncAG6SUNgAhxGPAMeC7i+XYUuLRR4LLBkNKpBQKhWI5s9A6qUZgYoeOP1D3jr1ZYtweN4c7DmMd7EU6bTA2BCPdF9sthUKhUMzA+Wb3/UQI8WPADlQIIZ4VQvwGKAdGlsLBxaSir4LPvvZZ6tvKkS4bUpig8n8vtlsKhUKhmIHzbe4r8/49Cvxlwvq3F8WbJWZt9FoSghJo7TCTpC9AGqOh4i9w+b9dbNcUCoVCMQ3nm4L+nBBCDzwnpfzoEvm0ZAghuC71Oob7fw0xLqQpHpoOaAkUofEX2z2FQqFQnMN590lJKd1AjBDiXTNd/ESuS72OQJvEhR2PPgKQqslPoVAolikLze5rBA4IIV7GO10HgJTyqcVwainJjcqlweWH1TNGoNsAsXlQ8WfI/8LFdk2hUCgU57DQ7L524O/e94dMWJY9Qghi3UFYpAW3zQVrboOWwzDUerFdUygUCsU5LHQU9IcW25ELSYhDR4ewIu0uZO5tiDe/CxV/hcL/uNiuKRQKhWICCx1xIkYI8QMhxCtCiDfHl8V2bqnQjVjxGEFIAWFpEHeZ1uSnUCgUimXFQvukfgf8EbgJ+ALwCaBnsZxaSr5Z08KRz92NKzoOf0zojlVjzPkODDRCaTkY/S+2iwqFQjEja4JNPJy58mK7ccFYaJ9UlJTy14BTSrlXSvlpIH8R/VoypMMJgJ9RS04csg1BYLS2caz3YrmlUCgUimlYaCTl9P7tEELciJZI8a6Q9m+H+1H3w4eJufcJbLWSn278C4998Cn41Veg2wWf33exXVQoFAqFl4VGUt8VQoQBu4B7gP8GvrpoXi0hnmFtbkZ9iDb0YHNvI12jXZB3G3SchL5lPwShQqFQXDIsSKSklH+XUg5JKcullFdKKTdJKV9ebOeWAvewBQBdaBAAJo8/e5r2aCIF2jBJCoVCoVgWnFdzn3dw2RmRUn75nbmz9HhGNJHShwcBg6wypfFq46t8NPejkLRVE6kd91xcJxUKhUIBnH8k9QW02Xjb0QabPXrOsuwZj6T04Vrt8cbwDZzsOUnHSIcWTXWVQ0/NxXRRoVAoFF7OV6TigV8C1wEfA4zAy1LK56SUz831ZiHEM0KIbiFE+YR1Dwoh2oQQJ7zLDefp03nhsWh9UobIUADyQlYD8FrTa5D7PkCoJj+FQqFYJpyXSEkp+6SUP5dSXgl8EghHm1fqY/PcxbPA9dOs/6GUcr13eeV8fDpffJFUhCZSoSKE3KhcXm14VRsJPaVQFfYqFArFMmGhI05sBO4CPgr8g3k29Ukp9wH9CznmYuBwOGgaHsIRGYnOaED46ZE2F9elXkd5Xzktlhatya+nGroqF/XY/aMOekfs73g/LqeTgc72RfBo8Rjq7sJps83bfnRwgLHhoXnb28fGGO6df624x+2mv/38xmLsbWk6L/v+9lY8bve87Yd7u7GPjc3bfmx4iNHBgXnbO202hrq75m3v8bjpa22Ztz1AX2szUsp52w90tuNyOuc29GLp78U2Ov+5U60jFkYG5n87cTkcDHZ1ztteSklfa/O87cF7jTyeedsPdnXidMz/vtBaWX5e5/DPwPnOzPuQEOIocDewF9gspfyMlPKd3tH/QwhxytscGDHDsT8nhCgTQpT19CxscIu+vj7+7nTSlZSk7TNAj8fu5rrU6wDY3bgbcm8FoVv0aGrXiyf4wvPvvNvu6N//wnO77jyvG9hS4na5eP7rX2bvC8/M+z0vPfptXn7ye/O2f/OZp3nhG3fhds3vhnf0//7Ks7vuZKh7fj/mhuNlPHfPF2k4Xja3MTDU3cmzu+6k7O/zaxZ2u5y88I2v8uYzT8/LHuDlJ7/HS49+e972e194ht/e96V5Pyycen03z95z57zFubWynGd33UnNoQPzsh8dHOC5XXdy+C9/nJe9x+PmDw/cy+6nfzQve4BXfvIELz709XkL54EXX+C5e76I1Zs8NRdVxW/x7K476Thjnpd9V/0Znt11JxV735iXvW10hOfu/SIH/ue387KXUrL75z/i9f/+2bzs/1k430jqASAMWAc8ChzzistpIcSpBfrwNLAKWA90AE9OZySl/KWUcrOUcnNMTMyCDhQbG4vR46EvKgoAnb8e6XCTGJzIZdGX8VrjaxAcC6nbtX6p83hqnAtzp4UTLYNYHfN/+p6OlsrTuF0uag8fXCTP3hm9zY3YR0cxH9qP2+Wa276liZ7GetqqKxnu6Z7T3mm3UVtagnV4iKbTJ+blU9X+t5EeD+aS/fO2B6g6sHde9uaS/UiPh+p52jedPoF1eIja0pJ5PTUPdXfRVl1JT2P9vJ7k3S4X5pJiHNYx6o8fmZdPVfvfBikxlxTPz/7A2wBUe//ORc3hA7hdLqr3752XiLRXV2Hp7aHh+JF5RVNjQ4M0nTzOQEc7XXW1c9qPf14uh50zR0rmdQ7j34vqA/Mr8K8+qNnN93tUV3YYl91O9cFiPJ657wvdDXUMdnWQXVA0r/3/s3C+IpUGXI02Zt9NwM3eZfz1eSOl7JJSuqWUHuBXwOUL2c980Ov1RNvs9IRqmX3CX4/Hpn05rku9jqr+KpqGmyDv/dB3BjpPL8px7S43HcM2XB7JqdbBBe/H43HTUas91c335rLUtJmrALBZhmkpPzmnvblkPwihvT40t4g0HC/DabeBEJgPzn3O/e2t9DQ1aPbzuEZOh50zZYdBCOrKDuFyOOY+h4PFIAQ9TQ3zalYct3fabfOK1mrGr8s8z6G5/CS2Ecu8r9Fwbw/t5kqf/Vwi4nG7tYciIWg4cXRezZbj5zzY1UF3w9wF8tUlmr3b5aKu7PCc9jWHDyKlB4TQ3jsHbTVVjPT3zfsajY0/FAlBzaH9czbhyXHBF4KW8lOMDc39Ozcf3AdCMDrQT1v13I1R1Qf3odPrybi8YE7bfybON3GiabZlIQ4IISbO234bUD6T7WIQYxlmwN8fu92uRVJ2TaSuTb0WQEugWH0LCP2iNfm1Dlh9QdnR5oU30/W1tuCwjhGZsJLW6grtR3eRaa+pIigiEj9T4Jw3i/EfctLqNaxIz5jXzcJ8sJjAsHByt1/BmSNzi8j4zXHTDbfS3VA3Z/9d4/GjOG1WNt90Gw6rlYYTs4vIQEcb3Y11bLrh1nnd8FwOB2eOHCJ3+xUEhoXP75xLilmRnsnK1XnzEhHzwWL8TIGsvepaGo6X4bDOLiLjIrj5ptsY6GjTRH0WmstPYrUMs/mm23A7ndSVHZrV3tLfS5u5ko3X34xOr/dFGDOhieABMi8vIDRmhXbzngNzyT4iE1aSvmEzNSVzi4j5YDEGox/rr72B5vKTc/aJniktQXo8bL7pNkb6+2gzzy4iHbXVWHp72HzTbUjpoWaOlg6rZZim0ydYd80NGPz85/xeSCmpObSflLXrMQW/K6buWzQWOizSghBC/AEoAbKFEK1CiM8Aj09oLrySJR5eKbqnFykEbW1tCH+DT6TiguLYELuB3U27ISgK0ncuWpNfc59209DrBMeaFi5S7d6oZefHPgNSUnN4fv0DS0l7TRWJOXlkbN7KmSMls/Yb9TY3MtDeSnZhEdkFRXTV187aCeywWak/Xkbm1m3kbL8Ch3WMxlPHZ/XHXFLMypw8Nt5wq/b/HD9+c0kxptAwtn3wo5hCQudhr93gN95wK4nZuXNGOo0nj+GwjpGz/Qoyt26j/tgRHDbrjPaDnR101Z/xXqMd9Le30tvcOKO92+XkzJESMrbkk7vjKlxOB3VHS+c4h2JiU1ex5ZYPIHS6OUXBXLIfP5OJwg9+hJComDnPufbQAZCSddfeQMra9VokMsvvqKXyNGNDg+QU7iC7YLvWPOotFZmOkYF+WqsqfN8jS18P7bUz9xt5PJoIpm3YzNqrrkN6PJwpnb3Jz1yyj4j4BAo+8K8YjH5zNh2bDxajNxrJf/+/EpmwEnPJ7Nf0zJFDeNxu1l55Dekbt1BbenDWRJzOMzUM93STXbhj1v3+M3JBRUpKeYeUMl5KaZRSrpRS/lpK+TEp5Vop5WVSyluklB1L6UNEh7b71tZWdP56PPaz/SjXpV5H7UAt9YP1WpPfQCO0z35TnA/N/ZpIFWVGc6x58LwypCbSUVtNYFg4aRs2E5OcOq+n8qXE0t+LpbeHxKwcsgt3YB8dpenUzP1G5pJihNCRuXWbr119thte/dFSXA47OQVFJK9ZR0BwyKw31N6WJvpam8kuKCI0OoaErNXUzLJ/p81G3bFSsrYWYvDzI3NrIXXHSrXmxVnOISE7l9DoGLILi+hrbZ41+cBcUkxASCjJa9aRU1CEy2Gn/tjM/Ubj1yM7fztZWwsRQjfrDbLx5HHsY6NkFxaRmLWa4MioWa/pUHcXnWdqyC4sIjA0jJS16zHPIiJul5MzpQdZtTkfo58/WQXbaTx5HNvIzP1G1SXFxKSkEZmwkuzCHQz3dNN5ZuYC+ZqS/Rj9A0jbsJnsgiI8bjdnjswcrdV4RTC7oIhVm/PRG42zfs5tVRWMDg6QXVhETEoaEfGJs4rI6OAALRXlZBcU4WcKJG3jZmoPH5ix30h6PNQc2k/a+k34BwaSXVhEa1XFrJmH5pJiwlfEE5u2iuzCIsaGBmmpnLl7wVyyD73BwKrNW2e0+WflfLP73vD+/f7SuLO0SI8Hw8AAETodLS0tiAnNfQDXpFyDQPBq46uQcyPoDIvS5NfUN4bJqOfa3Dj6Rx009s0/FXki7TVVJGTlIIQgu3AH7TVV55Wavdh01FQDkJC1mpTL1uMfFDSjiEgpMR8sJmnNZQSGhhEaE0t8ZvasN1RzSTFBEZEk5uSiNxjIvLyAuqOlMyYfnBXBQgCyC4voaW6kr236VOv640dw2e1k5WuCmV2wA5fdTv2x6Zv8+lpb6G1u9Als1tZtXhGZ/hycDjt1R0vJvLwAvcFAQs5qgiIiZ324MJcUE5+ZTWhMLIFh4SStuQxzyb4ZRcRcUkxAUDApa9cjdDqy8rfTeOLojMkHPhEs2K6dQ8F2hro66ao/M6190+kT2EZHfPY5BUV43K4Zkw+Ge7vpqKn2XaNVm7eiNxhmFAW3y0VN6UFWbd6K0T+A2LRVhK+In/N7EZ2UQtTKZPwDA0lbv2nWfiNzSTEGf3/SN2zx/naKaKkonzFDttbb3zV+DtkFRYwODtBaWTGtfZu5kpGBfrIm2CPljJmQY8NDNJefJKtgO0II0jZsxugfMOM5S48H86EDpKzbSEBQ8IzX5Z+V8x5xQgixE7hFCLFBCLFx4rIUDi4mnrEx8HiINwVqIuWnwzNBpGIDY9m0YhO7G3cjTRGw6iptWvl32OTX3D9KcmQgm1O17PqjC2jyGxseYqCjnYQsbYSMLO9NY7YnyKWmvaYKg58/Manp6A1GMrYUcGaG5IPpMpOyC4roaaynv71tir19bIyGE0fJyt+G0Om89jtw2qw0Hp+ayu8Twbw1BIVr1zlr67ZZ+43MJcUEhUewMjcPgJW5eVq/0Qw31PGO8SyvCGrvXaNl+03zHWk4XobTZiXbK4I6nZ6s/G00nCibNvlgPOkju+Bsk052wXYGO6dPPnA5HNSVHSLj8gL0BqPXvmjW5ANzSTFxqzIJi40DIHNLITq9YcYbZE3JfvwDg0i5TPt5r1iVSVjsilntx/0ANAFdtxHzoQPTikhL+UlslmHf93lcRGbqN7L09dJurpz0PcoqKGJkhuQDj9tNzeGDpG+8HGOANvNBdv52pPTMmCFrLikmMjGJqKQUANI3bMHg70/NoZm/RwY/f1Zt0nK+olYmE52UMuM1qj18EOk5K4JGP39Wbd5KbWnJtBmy7TXVjPT1knOJZfWNc74i9S3g62hzRz2Fli4+vjyxuK4tPh6LVh+RGB6GzWZjwDMCbol0nf3xXJ96PfVD9dQO1kLe+xno6cHVMHtH8Vw094+RHBVIRkwwIQGGSSJlHbHMKxpq90Yt8Vk5AETEJRCbtmpeGXLnS09Tw7Tt45Y2M27n2Sim3VzNivQM9AZtnOKcgiIcViuNJ48BMNrThGNUy3IylxSj0+vJnJCZlJU/WWjtln7G+jXBqjt6GLfTOemGnZS3FlNomC9Bw+2wYmmv8flKvca5AAAgAElEQVQ80NE2yT44MoqVOXm+RAGP281Qq/Y07LCO0XBM6+/S6fTABBE5VubrNxpqrcDjdvuSPlauziM4Msp3jOyCIgbGMwoBS3sNbofVe877MYWGkZS3doL9DtxOJ/VHNREZ623Fbun3XSOArIJtPvvMywvR6fW+z9kxOshoj9a82HDyKA6rddINOz4zm5Dos/1GbqcdS5vWXzPQ2U53Q90k+4DgYFLXbcBcoiVoeDwehlq03CWX08mZI4fI2FKAwaiJoBCCrIKiSf1GQ62VeNwu3zmsSM8gPO5sPlROQREjfb2+7/BIRx0u26hmf2i/1qS2btOkayo9Z0XEOtCJbVArVxj/LLMmnMOqTZdryQdeEXFaRxjprgegpeI01uGhSTf46ORUolYmn71GLifDbV7f+vtora4gu6AI4c1CNQYEsGrj5dQc1vqNxq+Rx+PB43FTc+gA6Rs24xdgmnQO7eZKLH3aRKrDbdW+/lpzSTERCSuJSUmbZD8xQ3akuxGn1eKz1xuNpG+69Jr64Pyz+/4kpXwv8Lh3io6Jy1VL5OOiMT4kUmK0VmfVOaaJw8Ro6uqUq9EJHa82vIo741qeb9jIgd/9YsHHlFLS3D9GSmQgOp1gY3LEpOSJ3U//iD9869456yTaa6rQ6Q2sSM/wrcsuKKLzTM28i1bnw5myw/z2vi9xcs/k0al6qvdSWn0jJ/fcCWhNWV0NdSRkr/bZJK1ZR0BIKOaSYkZ7mig9dhNH3v4XXA4b5pL9JK9djykk1GcfEhVNYo6WfOCyjXKk+H2Ult6EdaAT88F9hETFkJCZ7bPX6fVkbS2k/lgp9rExju35NKWVN9Ffd0Rr6tPppqTnZhd4+42aG6l680HKam6hufR31B0txeV0+JqxJtqPJx80l/6OsppbqH7zIXpbmuhva5kkggCZWwu15IOSYvrrjlBaeRPH9nwa+9gY9cdKydq6DZ1e77NPyMwmOCqa6pJirAOdlJbdzJHi9+GyWzEfLCYxJ5eQyGifvSkklOS16zEfLMblsHHk7dspPX4Toz1NmA8Wa9vXrPPZCyHILiii6dRxrCMWTrz275RW30hP9V5flJN1zhN5Vv52LL09dNSaqXn7ccpqb6XhwC9pPHlM6++a5hpJj4fa0oO0HfsrZTU3U7Hnawx2ddJZVzuljid901b0RiPmkmKGmsspLb+Ro69/1FsDd5CMzVsx+Pn57KOTU4lIWIm5pBi7pZ/SQzdTWnIrjrFhzAeLiUlNJzIh0WfvF2DSsvwOHcDltFP2xocoPXkjlnYz5pJ9GANMpG7YNMmn7IIiX4bs6T1f5Yj5RjpP/0NLRpKS7MKiKfbW4SGaK05Rv/+nlNXeSl3xj2itrGBsaHDKNR1/f82h/XSWv8YR842c2vNlb7NhOdnepr5xUtdv8mXIWjrOcOTEjZS98SFcTjs1hw+Qtn4z/oGBXIosdD6ph4UQtwghnvAuNy22Y0uBX3ISqS/+kcSdOzCZTLRbNJGa2C8VbYpmS9wWdjfuZmjYhtOjp9rciZxHoep0dFvs2JwekqO0L9jG5Ahqui0M25zYRkZoOF7GSF8vbVXTt3eP026uYkXaKox+/r51Z5MPFieaGhseYs8vfwJMLkh0Wi1U198PUsdAwNu0lL1IV/0ZPG6Xr/kRQG8wkHV5IXVlhyg/fDcevR2bqYGK3Q8x3NM1bRFidkERvS1NlL/+TewBbbgNo5QfuIfGk8e0Nnudboq9y26n+rUfMWzSstgqq++j5nAxyWvWERgaNsk+05t8ULvvT3Tq/gAePfUDj1Nf+hbBkVEkZudOsk/MziU4IpL6w29SN/B98Ojp0P2eun1/Qgidr6lvnMDQMJLXrKP28H4qq+8FYNhUStVr/4nLbp9ysxM6HdnefqPyA7twG0axB7RR/tr9vqSP6a7RcE8X5bu/jc1Uj0dnp/zw3dQfPUzm1sJJIjhu73G7qX7tvxg07QWpo7r+fs4c3ktC1mpCoycXw2dsyUdvMFB34G+0eZ4Bj57G0R/RcGgPAcEhJK9dP8k+NjWdiPgE6g/vpbb7O+DR0+33V2rffsF3/Ilo/UabqS09QMXpXUjhYSTwFJWvPo59dHRKxtq40LZWllO+9z5cxkGcfj1UvPl1Os6Yp79G3uSDile/x1hgNVK4KT+2i9ojJazadPmk3w14m8ulxPzmb+jz/wd49Jhbv0Xdob3EJKcSlZg0yT51wyaMASYaDr1Ks/1p8OhpcfyC+pJXMPoHkL5x8yT7iPhEYlNXUXdoHzUtD4BHT7//a1S//utJ/V3jGIxGMrbkc+bIQSqO3o1H52Qs0EzFq99ldKB/yvfoUmKhY/c9CnwFqPQuX/GuW9boTCZMl12GMTKSlStX0j6ojXXmsU0WoOtTr6fZ0kz5Ge0mOOI00LZ/YQkU45l9yZGaSG1KiUBKONE8yJkjJXjcrjkzuNwuF111tSRk50xaHxa7griMrEUr7H3jmZ9jGxkhZ9tOOmqqGe7Vmliq9n4LR0AneSt+TIA1nbq+R+io0PqFErIm+5RVsJ301QZGAk+Q6v8VImxX0Gt6ibjEYDK25E85ZubWbSQkB9Fn+hvR9htZqf8Mw4ElpOeYpjzBAySuziM2IZp+0+8IHMslJ/Jx7KZmkjMHpv0hB4VHkLImF0foSxgckaxPeRaPzk5Q3GEyt26bIoJCp2UfBsUdRuocrE95FoMjEnvon0lZo/VZnUt2QRFJmf3YTS3kRD1O4FguA6bfE5ugRYpT7AuLSM8xMRx4iCT9Z4m230Bf4N9ITA72NYFOJGNzPisSgukz/YUI25Wk+H+ZkcATpK82THvDXpGewYqkeAYDf0+ANZ28FT/GEdBJXFr7tNfUPzCItPUb8US8jN4VxPrU5wHwi32bTK+ATbpGQpCVX0R44mnchhEuS/pv/GxxjAa9SFJWFqExsdOec2q2FavpDJlhDxFs3UBf4B+ITQgn5bL1U+xzCotIyQxkMPAt4vkwK1wfoD9wN0npQdOec9qGzcQmhNIX8EdCrfmsCvoGY4FVpOd4pk3bjkpMIiE9BUvg7/G3rmRt4i9w+Q0QlVI7JSoCrd8oY9MWdFGvITwG1qc+j/D4ISJfJX2jlvhwLlkF24lOrcPp18fahKfxtyZjCfodcWlan9WUa1RQRPpqwWhgBemB9xFmLaDP9CIrEkJZtXHJxjhY9iw0Bf1G4Bop5TNSymfQRja/cfHcWnqSkpLoswxgw4k8Z6iiq5OvRi/0lJm1m79AYn7rbws6TpM3ky8lSpsJeF1SGDqhJU+YS4oJWxFH5tZCag4fmLFOoqexHpfTMSlqGSe7oGheRatzUX1wHzUlxRTe/mEKP/gRQOsE7yx/jR6/l4l13kbc2uvJW/MEHr0Vu+slIuLjp0QuEZEmAjedxm8gi7TCL5C77TH0jmBWXtGK0aifclz/ACMJOxvRWyPJ2fEwmTt2YRxKIXRrBaHhQdN4Kkjb3oMUHrLzHiFx4/sw9WxFl3OS6Ojp5/BMyhnEFdxNYtBdRGUWEjb8ftxxtayImz4DbkX8KO64WsKHP0BUZiEJgXfhCu4iKWf6UQSionXock5i6sknccP7yM57BCncpG3vAcQU+9AwE6FbKzAOppKx425ydnwHvTWS+J0N+PlPPQejn56kK1vRO4LJ3fYo6YX/jl9/FoGbThMeZZpiL6UkLb8PqbeRkf4d4tZeT1DPTsg8RXT0FHMAElcN4AptZ4XuC0RlbCXSegeu6AbiEqevV4qJteJOqiK0/0ZisneQHPU13KZ+ki+bPu06KsoPw5qTBPRuIHnLHaze8BjCoyd1R9ekZq9xgoIDiCw0Y7Qkkr3jG+Rc8SCGkRXEFp3BFOg3xV6n15OyswPh9id362Mkb/0E/n1r8F93gsioqQICkLqxH7efhbSEB4hdfSXBvdciUyuIiZm+1i8+eQhXRDPRzk8SlbGVGPencUW0kJg6/fciJsaFTCsnuPc9xOZeTXrSA7iNo6Rtmv4aRUYFELD+FP59uaTkf5rVWx9DOANI3tmOTj/1Gl0qvJM6qYmPlGEzWi1TkryDzPbohib1SQFEBESQH59PY3M1Bj8/MhL8qK3rweOae8icc2nuG0UnIDFcu5mEBBjJjgvl5Jk2mk6fIDt/O9mFWnt3S8X0dRLtNVoRb/w5UQucTT54JzVTo4MDvPHrp4nLyGLLLR/wJWXUHdaaKvxs8eTseBCA8JR1JOo/gz2mnIw1oZP243G7qTx+LyCofTMMl9NBX2cvPfuzcAV3UL33u1OOXbX3AVymPjr3pTHcN6D15bwRjdQ5qTi8C885GWENB3+BPaIa67G1dLX1aZX4JSb0tnDOtHzb1yE/TnfVW1giXkfUraWjVfv8mmv9MPSl0a17hpHuxkn2I92NdOuewdCXRlOtlizQ2eZA1F2GJWIP3VVvTbJ3Wkeoa30QvS2CmpIApJR0tfZiPX4Z9ohqGg5O7s/0eDxUlN6L1LmofzPKO8L7IJ370nAF9lK191tTrlH12w/jCu6gZ38WfZ29uJwOat8KAwSVR++d8nDTUvo7bJEncZxeR1ebliFXWxaAYTSGxr5HcIxMvqn2nTnMUOjf0TXn0d6qZSm21BswdGfSG/B7X1LBONb+djqcP8MwlER9pSYYHW1WpHkdo1HFdJya3J/pdtioOXM/emcQ5uJAPG433W29WI6swRFWz5niqYPKVhy4D49xlIY34hgbGWFkYJDWt1bi9humsvj+Kfa1ex/HGdrMYEkuPW09SI+bmreDEW5/qiu/NqXIvPXoS4xFleKuXE9Hm9ZXXXfKH4MlnpbRH/oSNcYZbDrJQPCfMHRk09ak3TbbmgT69hwGgv/CYOPkekr7cC8tlicxjMRx5oTW1NjZasFVsQ5r1BFayl6cZO9xu6gsvw/hMWB+OwSPy0VvRy8DJatxhrZQs/ddWfWzKCxUpB4FjgshnhVCPIc2Vccji+fW0pOYmIgQgi7dENI2NYK5LvU6xJCNgKgIsgu2M+o00Pb2i9PsaXaa+seIDzPhZzh7qTelhDNcfQzp8ZBVUKTVSQSYfNlJ59JWU01IdMykDvVxQqNjSMjOXXAqupSSPb/6KS67nevv/KqvfyO7oIiYtAacfn3krHoUo+lsfUZM1h0Y+pNxJ73GWO/ZGqS6/T9lNLCCFc5PMtA7Qv3RUswl+2hrthNpvYZO3R/pqTnbrNlZvpsev78Rbb+F9mYtcaC29CB9PWPEuj6GxVRG06Hf+Owt7TU0WX9C8Ng6Wpv8MZcU01Frpq+ji3DXJ3EEtFO19yGfvWNsmOqG+zHaYxjqXYO5pBjriIXGUyfQWW9GCg8VR+72CaEmIHcjhQed9WYaT53AOmLROvz71mC0x1DdcD+OsbPRRfW+B3EEdBDu+iR9HVqhbHVJMa1NAQSPraPJ+hNfBiJA06FnsJjKiHV9jL6eMWpLSzCXFNPeYiXadgs9fi/TWb7bZ99jLqZT/yKR1mtpa7ZjLtlH/dFSBnpHiHV9ktHAcur2/9RnP9bTTP3Q4wRas2lvjsBcso/e5ka6m5sJcX4cp18flcXf9Nm77FaqzPdhcIUxNrCF2sMHcFjHqDt+BM/IexAeAxUn7vZl73k8HspL7sWjs+Nn/QCt1ZWM9PdhLimmrzsdf+tKatq+7ctYBDDvexSbqYko96cY6O6npfI05pJiWhoFoWNbaXX+iqHms6OhNR/5A4OmYmKd/0pv1xg1JQcwlxTT2T5GjPNf6A/YQ9uxsyPQ99eX0SafI9y6g9YmN+aSYhpPHmegd4gY96ewmmqp3fcDn711sJszPd8lwJpKT0eC9vl2d9JxphaT/Q7cBgsVB77us3c77VSU34PObcIxtJMzZYdw2KycOVKCfXA7OlcgFRX3Tsp8rdj/dVyGYYLsH6azXmvpqC4pprt9ha/J3DpwdtyC2n1PYjXVEOP6NIO9wzSePIa5pJjWRhfh1ito5wX66+Y3ePA/GwtNnPgDkA/82bsUSCn/ZzEdW2r8/PyIi1lBtxialDgxzlXJVxEyZsQS6CL9+k9iEG7Me/9x3sdp7h8jJWpyVs7G5AiSB2sIiokjNjVdq5PYdDm1hw/OUCdRNW1T3zjZBdtnLVqdjcp9b1JXdpjtd3x8UmdxTPTZpoqY7Mlt9J11tdS9EYXUuSg/pEU7Q62VtDh+TsjYJrKvu4ugiEitCfGQlpmUt/MxjI4oqs98Had1BMfIIDUt38LPlkDeVQ/7ilbNB4sJj4tn9TVfI2gsj8aRpxjprsfjdlNxbBdC6si7/AmyC4poPHGUU2+8it5gYPU1nyLacTPdhj/TXfkmAFX7HsDp30NO2iNk5O9gqLuLA//zPB63i8xtN5AS8CVGTCdpOKBFOw0HnmYk8CQpAV8is/C9eNwuDvzP8wx1d5G5dQc5qd/D6d9D1T4t2umufINuw1+JcdxMzns+gd5g4OTr/6DxxDGy87eTd/kTCKmj4tg9eNxuRrrqaRz5IUFja1h9zX2Ex2lFq+aDWtJH7pUP42eLp6blWzhGBnFaR6iu+wZGRxR5Ox/1Fq0eoPrgPoIjIll97VcJGdtEi+PnWhq4x0N56T1InYu89U/5ilaPvfo3hNCRfdUdrHB/kD7/f9B+Umu+rt77MHZTK1nxD5KRfyWjgwPs+92zuOx2MgquJS3sXsZMZs4U/ycAzaW/Zdh0iJXGz5K1430gJYf+/D/0NjeSuXUHq7O/j8s4SGWxdpPvqy2hg98TaXsPOdd+HqN/ABV736Cu7DCZlxeypvAJdB5/Kk7vwu1yMtbfRn3/Y5isGeRe8wAxqena96KkmPisHPKu/jYB1hRqux/GNtiN22Gjquo+9K4Q8rZ9n1Wb86ktPUhl8VsEBAWz+rr/INxaRJvnWQYajmsPIgfuwW0YJS/vCTLztQzZIy+/pP2WrvgAieLjDJr20lKmTTNSu+8H2Ez1rIr6BhkFV2MfG2XfC7/BNjpCRsHVZMR8E5upAfPexwBoPfonBgLeIp6PkHXl7QCU/e3PdNRUk5W/w9dkXnHwXjweD4ONJ2hzP0OYtZDsa75IQEgoVcVvUXtYK3JeU/R9DK4QKqvv9ZU3XEosuLlPStkhpXxZSvm/Usp35SxcK1eupFs3jMs6tRkv1C+UMKsfTbou9CERrEoMpKa+D895TFAG2rh954pUXoQg0dYO6et97fHZBUXYRiw0nzOS+HBvDyN9vbOK1FxFqzNh6evlrWd/SWJOHhvfe4tvvX24l2bLExhG4qk9PrX9v72mmlGrjiS/z2MJPErjwV9ScXIXwmNkTf6TGAxGsvK3UVd2WMtMKtiOX3A4WUkP4wjooHrfg1Tuux+nXx+rMx7DEKB1hg92dtBcfpLsgh3oDQbyNj2FFJKK0nuoK/4xo4GVpAZ/leDYdLLytaLVirdfJ3X9JvwDg1i94zsY7bFUN/0/Wo++RK/f34l1vZ/Y1Vf6ilZP7nmFsBVxrEjPIG3b5wm2rqPJ9hM6T/+DJtvPCLauI23b57Wi1RVxnNzzCjq9gYwtBcTmXkWs6/30+v2N1qMvUd30TYz2WFbvfNhXtFrx9ut43C6yC3YQHJtOavBXGQ2soK74x1Qc2aV9/pueRG/Qkh6aT59gsKuDrPztGE3B5GQ8pkU7++73RWlZSQ/jFxyujXww0E9d2WGy8rejNxhYk/8kwmOk8sQ9NBz8BRbTUZL9/53QxBxf0erpN3b7ipxzdj6AvzWJ2o6HaDvxMl36F4myv5f4dTeRvmEzBn9/Tu55haDwCBJX55F8+UcJtebT6vxvOk//g4bhJwkcyyGj6C6iViYRnZzKyT3/0Iqc87cRlZFPPB+mP+ANmkt/T1Xt1zA4w8ktetRXtFpV/BZOu43sgiJMkQmsivwGVtMZavc+TkXJPXh0dvLWPoneYCQ7fzsdtWatyDm/CL1fAKtX/wC3YYSKA/dh3vsINlMTmbEPEBAeS3ZBEfbRUWpKin1FzrnbHkfvCqKychfNpb9lyHSARN2nCU/d4EvCOLnnH8RlZBEWG0fmjnsxWVdR1/coHadeoU3+lnDrTpI2f4jktesICArm5J5XvEXOG1i56QNE2K6kgxfoOPl3zvR8jwBrGtk7v05odCzxWTmcev1V3+88PGUdK/WfYchUQtOhZ6mouAedK5C8wh9g9PMj63Ktj9o2YiG7oAj/0Ggy4x7EbmqZtsn8n50LOnbfciMpNRmXcNMz0Dtl29jQIDqXpNM4zKmeU2QX7sDqMtD85u8BaOgdZcg6+yR8I3YXfaMOkiIni9RYzXF0SOpDztY8jddJjGfqOa0jWNrNdNRq/QGJ3nokq8ONuXPypG1a0WoufbVvU/7Kz+a9FD/3EInJBra8Zy3tJ16m7dhfaTv2V04X34XLMESQ4w66GuoZ6Jg8IkR7TRXxmdms2v4lgsbW0GB/AquphvTwewmM1qKx8Xoig58/6d5K/Lg11xLjuJlu41/oC9hNnOdDRGdphasTU6nHs/RC4jNIMX2JkcCTNLt/Rqh1Cyn5nwLwDR2kHUuz9wsMJSf9EZx+PZgHv4afLYHVO7WJA8eLVkEbcUAIgU6nI2/LU1q00/0lLUrb8hQ6nU5Lg/b296Wu20BAsNbcuXrnt/GzJWAe/BpOvx5y0h/BaNJGpR4vGA2N0bIuAVLyP0WodQvN7p8xEniKlMAvERKfMclvnV7vG8opJms7ce4P0hewm27jX4hx3ELcGm2E/nRv0erEaxQYnUR62L2MBZpptD9J0Nga0rd9EThbtDrp8/A3adGOYYjqvrsxOqLI3aHd+MaLVrXPQyty1ul0rCn4gRbtdH8JKVys2fAUOr1h0jkkrV5DcESktm7HNwiwplJr+Rb2gDayEh/CPyRykn1gWDhJuVqRc9KWf9WiHX7DsKmUlcZ/Iyx5jfc8vZl5QviKnCPTN5EoPsGgqZgO3e+JtF1D4sb3+T4r/6CgSccyhceSGfMANlMTdaPfxWTNJHOH9sAQFruC+IzsSfZ6oz+5a57Ao7NR2fNl9K4Q1hRpfUJ6g5GMy7XPamKRc972xzC4wqjsvQuPYYy8vCfQG/0nfS8mFjln7LibwLFs6q2PYDM1kBF9P6aIuEmfrZ8pkNT1Wn1XwvqbibJdN6XJ/FLgkhap5GTtB9zWPzUQHOzU2ottQfBq46ukXv8JjDo3Nft209A7ynt/tI9HX6madf/jo5+nRE7OUqs5tB97cDSlA2ejlLN1EiU47TaOvvERjlTcSnf1IQx+/kQnpwLw07dquf5H+yhrnJwhlLamj8D8N+gKeGrei//6VwjdeYhG57epHtzlW4ZMJSTwcbKu+CAwuQ7LPjZGb3MTCVk56PR68jY9ic5tIty6naQtH/HZJWRmEx4XT+blBZMq8Vfv1Jq0Aqwp5Ow82zdiCg4hfeMWYlLSJqXnphV+nhDrJvSuYPK2PoHOmzIuhCC36Er8A4N8w9EAxOZcwQr37SB1vihtnNwdVyGEjpztV/jWBcemkhai3bDSQu4hODbVty1n+xUIoSN3x9k6dUNAEKszHgOpY4X7dmJzzu5r1eat+AcGkVt0hS9C1ul05G19Ar0rmBDrJlILP+ezj05OJSYljbQNWyZNv5BzxQMEWFPws8Wzeud3fOv9Akxkbi0kPC6e+MyzSTRJl3+EcOt2dG4TazY9NaluKnfHVRj8/ScVOUdlbCWBj4MUZCV/F7+gszlQuTu1c80tuvLsZxOZwKooLVkhJeA/CEk8W2CdU7gDnd7A6h1n7fV+AeTmPoHwGIh23ET8ZTf4tqWu24gpNIycbTsn+Zm37XEMznCCxnLJKPqKb334ijgSsnNJzrtsUp9s5s77MFkztabQHWerX/QGIzmFOwiOiJxU5Jy46f1E2q5BeIzkXfaEbxip8WukNxonpf+Hp1zGSuO/acda8QD+oTET7K+c9BfQop147YFopf6zhKeeTavPyt+Oweg36Zrq9AZy1z+Bzu1PhO0qVm6+3bdtZe4aQqJjyC7Y7hNB7XiPYHREUVfzOJcS4nxH5BZC6IBTUso1S+PS3GzevFmWlc1vqu/ZkFLyg4ceIyk8njvu+uSkbRV73+DV//ohbR9ayUlXLXtu38Or932QhrYR9hV+jcOtY6RHB/HmPVfMuP9Xyzv4wgvH+PuXtrMmUUuAHOnv4xd3fhLj5ut4qjeNo998D1HB2hNX/fEj/OWxh7j6w5voC3kB4TZiGImj4+RWPvTg95FSUvT4W7QOWEmNCuSVrxQR6Gegs3w3Fd13Ej7yHiKj51dXvbuykxNtI6SnxPOZ7WmTtumNJsJTLgPgDw/ci8Nm5RM/0Drnm06d4E/f+yYfuP87pK7TxnOzD/dgDIrwPV2PYxsZQe9nnFJI6bRaEEI3SUBAG5Xc43HjHzh5vdvlxGW1+J7Gz653YR8dmVK75PF4cAz3EhA+uV5HSsnoQP+kYY3GsQ52YwqfWt8z0t9HUETklDRp22A3fqHRPtEcZ2xoEP+g4Cm1RXZLPwZTyKSbI4B9bBSdTu8bV24cl20UKT2+KG0cp8OO2+H0RXa+c3a7cI4OTLqZgjZNhXV42Dee4XzP+byu0UA/QeER879Gw0P4mQIn3YABHCMD6P1M6P0mXwv72BhCaJHFRNwOK26nfZLIgjamodNumzS6CWjZp87R/inXSHo8jA4N+iLBeZ3zeV6j0cEBAkPDptTlab+dyCkF2dYRC0Y//0kjcYCWJBIYmTzluz1fhBBHpZSb57ZcPkxfWDILUkqPEOKkECJZSjn33NbLGCEEcfpIOkanjp031N0JQnDVmhvZU3I/x7qOkb39Kqp+/woxtf/HuqzbONkySP+og8igqf02cLZGamJz3/iwK+uKdsJfmjnWPMg1uSsASFm7nhWJ4fQHvkjI2CZWxLyPM/oH+P/snXd4HNXZt+/ZqrbqvcuqlmTJtoyNbVzBuDfAhFBCgPaE9CQAACAASURBVEAaed+8gQRCEgKpkEBCSD4wEAgQAgSDe5GLjKtsq9hWsWSr915XK23f+f5YaWV5ZRsLgw2a+7rmknQ0O3Pm7Mx55pzze54nKsH+PlDQ0EtDt547p4XzUV4Dz+86yy9uDrcLEMQQ0ha9OEKFdzFMFhuv7t1Hn8GMolzG9+6aiqeLctR9E2fN4dO3X6ezoR6/8Ai7HF4QCDkvXNGFD/0QF3akQ1zY8TrKXUb3Z5ErlMg1zh2IXKEY1blWJpON+hALgjBqxwKM2rEAF93/Yp3EaPUBnAyso9xtNF8wnAz4EEqV2snog/3NfLTvQSaTj2qg4Mqv+aL7j9K5wyXayHN0jxWVx+j1vFg4ILtBc/YTU6hUTp072KdVR2sjQSa76DVcrTa62HdwsWfnYokNfSd8pezLVWGs030hwBlBELIEQdg6tF3Nin1ZBKv90Jr76esbuc7T09qCxtefhTE34yJ3YXfNbsxT1qKSWZhuKeeppfbpllOXyLRb1zWAt5sSL9dhA3Du2BH8I6OZMTUZpVwYEWxWEASi5rQgWBUkZTyH3DMNWX0S/YF76KrKY3tBE0q5wC+WJ/PA7GjeOVZLftYTdpl43HOfyUABHK3ooFdv5gfz4zBZbew903rRfR2ijMG1sqbys/hHRF20c5WQkJC4mozVSD0LrAB+w8hI6F85gl3t89z19SPl2z2tzXgHBeOmdGNO+Bz21u7l8S3lqDVKtF0mUv0VKGTCJdNuDAWWHULb0e5IM+CilJMS6jUi2GzFob9g8qpGm5NCa0MrTWVnKf1UidziQUnpz8gqqmNufABerkp+tjiJRyIrGNDsI8ByJwEJzuFuLsb2wmY0Lgp+dHMcYd6u7Ci6eJ5JD18/exrzY4cRbTaay846hUKSkJCQ+KIYq5/UQaAGUA7+ngucvIr1+tII0vgiR+ZkpHpbW/AKsitxFkcvptPQSWn3KaJuXIDRJqfl0/+QEup5SSNV2zkwcqpvMM3AkHonI8qHgoYeTBa7r0SD9U28BmbSUGeXkzeVleLiE0x80NMYXWt5IGA7K9LtdZIZupgV8y70hfFf7W2f+XqNFit7SlpYnBKMWiFneVoIh8vb6R24uFIx8cY5dDXW26NSD/RfUg4vISEhcTUZa4DZh4GPgaGYL2HA5qtVqS8ThYuKAJnXCCNlMugZ6O3BO8guCQ2QT0a0qYiLqeSWe76Pi9zCuSP7mTpoZMxW52RuFquNxh79CB+pc8cOExgTi09wKGB36jVabJTUd3LmzE/tvhKz/0zC9NlU5efQeLaE0Pgkwqasgc55hMVkcoPKnrfozJEnsSr7qLM8xn/yW/n0bJtTHUbjUFkHfQYLK9Lsxm75pBDMVpHdZy7u6jYUSfzwB+8AzkFlJSQkJL4oxjrd90NgNqAFEEWxHBib3OQaI1PLCRS9aW5uxjIY7aG31d5hewUFY7RYeeqTc8gNKfQrTiIqlcRFe1PR0E9GkByD2UZps3MQzqYeA1ab6JCf97bZQ+acH8F5apR9kb3r5F8xuFYN+kqEkDhrDmajAYOuj9DEJGw2kT9VrEVm9KG69ilqj/+bbpcDhHIfd69eQUKQB098UkjPwOVjC+4obMLHTcnsOPs0Z1q4F5G+bmy/xJSfu7cPESmT0La34arxxHvQyEpISEh80YzVSBlFUXT0iIIgKIDPl2P9S8Bi6aOtffeIrcf1OH7etXh51dDYWANAz2ASQe+gEF7OKudsSx/fTl9Dr6mXnOYcEucuxmyT43bsPSb0V3Fs/wHKc7Ipz8nm7KdbKNz2Nyr3/oMfeh8hqOpDCrf9jaItLxCT6IGfTz8N+R/TkP8x1oodPB59EtHrY3wM8x2+EuETU3Hx9sWqciE0YSK5NV2c65Uhyh/H5NJExcAzuOgnkDDvZ6gVcv5y52S6+k08s9U5J5XNZqOlpQVRFDGYrewtaWVJajBK+bC/0fK0EI5WdNDVb/9KW1pasF4QtHTIuAbFJzlJjTs6OjCNkjK+uqOfDp1zhA6tVotO5xyBvKvfRGOPc9gXg8FAZ2enc7nZSkWb83EsFgutrc5iEKtNpKRJ65TqXRRFmpubRy0vadJitTnf2q2trY6XmvOpaNNhMDuH2erq6sJgMDiVN/boHe1+PjqdDq3W+eWnd8BMfZdz6nmj0UhHh7NTutFipby1z6ncarXS0uI8erbZRM409V60jS4M+AtwtkWLZZTZhLa2Nsxm52nkqnYd/Ubntuvu7kavd/7+W7UG2vuc76P+/n56epwjkPcZzNR09DuVm81m2tudlbxmq83JQR5GPjvnI4oXb6OWlpZR2+hcSx8mi3N5e3v7qM9ObWc/WsMobVdVNeqz8HVmrEbqoCAITwGugiAsAjYAY8tl8SViMDRTVPSDEVuV6hksk94hJfUANbX/BKB30JG3zuzCqwcqWZcRzg9mLMdd6U5mTSaRC+/GQ2nmVPZplrftRrfjn2x98Q9kvfZnmo2/pN39ZWyBrzF1+kdYAtbT7v4yxtiNeM3PpcbyG871PuHYJia8jczkiX/acGBUBAFTTBL6mGQ0AUFsL2zGRSljxvzbCTCtQrCqSEkd9mhPDfPi0YVxbD7dRGbxyBHRwYMHWb9+PXl5eRw410a/ycrySSNHQivSQrDaRDKLWzhz5gzr168nMzNzxD49gUmYBCVVqpGfbWlp4ZVXXuE///nPiIfTYrVx52vHuPefJzBahjvtgYEBXnvtNd54440RnbbeZOX2V7NZ/vJh2vqGy61WK++++y6vvvrqiE5YFEW+++98Fr90iIL6kR3Vtm3bePXVVykvLx9R/qfMsyx7+TAbT46MoJGdnc1rr71Gdnb2iPJNpxpZ9vJh/pQ5Mgp4eXk5r776Ktu2jbzlT9f3sPilQ3z33/kjOrDOzk5eeeUV/v3vf48w/m19Bpa/fJjbX81Gf166GIPBwBtvvMFrr73GwMCwQTJarHzj9WMs/dthGrqHy202G++//z6vvPLKCMMjiiL/99/TLPrrIY5XjezYMjMzWb9+PWfOjHyx+fv+Cpa/fIT3jteOKM/Ly+O1117j4MGDI8ozi5tZ8tJhfn3BC1JNTQ2vvPIKGzduHNEWZ1u0LHnpMA+8nYvtPOPf09PD+vXreeutt0YY/+5+Eyv+foTV/zgyotM2mUy89dZbrF+/foQy12K1cd+bOSx+6RCV7cMvMKIo8tFHH/HKK6/Q0NAwoq4/31jE4pcOOU2ZZ2VlsX79ek6fPj2i/M0j1Sx/+QivHaoaUV5YWMj69evZu3fviPKDZe0sfukQT3xSOKK8sbGRV155hf/+978j2qi6o58lLx3m3n+eGGH8dTodGzZscLrvvu6M1Ug9CbQDRcB3gZ3ALy/5iesAV9copt+wfcSWqniLqOzfMDAQhMFgl1n3tLagdnPnyR2VBHu68KuVyajlahZELCCrLgurXMZ9axK4L76E1rkPkpV4H/f88SUmr1Ygys1EKX5Lbf3TfHzkUWLd/0Gs8Cyx2T5MyvFgiu9fmRL9kWOL9P83v8z5Bb/Y3ei4UU+cOEGvwYQok3H2XBm7ipu5OSkId7WC1FtfZPaNR/GOSh9xbT9cEEdqmCe/2FRM5+DopampiUOHDqFQKNizZw878irwc1dx44SRPiHJIZ7E+Luz61Q1O3bsQKFQkJubS1WV/SHUGS08tauKdyLuZasp2lFPi8XCpk2bkMlk1NbWkpOT4zjm8aou2vuMnG3p4+WsYWOxY8cO9Ho9Wq2WPXv2OMqfzzxLdUc/A0YrT20sdpzj6NGjNDXZc2Vt3rzZYQg/zK3nYFk7CpnAYxsKHKOXs2fPUlBQgEKhYOvWrY4387yaLl4/XIVaIeOZbWdo7rWXt7W1sX//fhQKBfv376etzd5RNffq+fXWM6gVMl4/XEV+rT3Ch16vZ+vWrSgUCgoKCjh71m7ADGYrj310GoVM4GBZO//Nta9x2mw2Nm2yR+xubGzk6NGjgL3TfGpjEQNGK9Ud/Tx/niHcvXs3Wq0WvV7Pzp3DaS+GRvUmq42ffVzo6ORzcnKora1FJpOxefNmRye/taCJnUUtqBUyfvpxgWP0UlVVRW5uLgqFgh07djhGtcWNvfx9fzlqhYw/7DxLbad9NNLV1cWePXtQKBQcPnzY8X106oz8YlMxaoWM/5yo43C5fZRiNBrZvHkzCoWC0tJSiorsKWjMVhuPfVSAIEBOdRf/yq5xtMXWrVuxWCy0t7dz4MABxzX/eusZuvtNtGgN/G57iaM8KyuLzs5OzGYz27Ztc9wvrx2q4vTgS8vjGwoco+BTp05RXl6OXC5n06ZNjhHe3pJWPs5vQK2Q8eTGQoeAqL6+nuzsbBQKBZmZmfT22lOeVLT18afd51ArZPxlTxllg6NUrVbLzp07USgUHDt2jNpau5Hv1Zt54uNC1AoZm041OtZ+zWYzmzZtQi6XU1lZSX6+PYmo1Sby+IYCbKJIYUMvrx6odLTRtm3bMJlMLF/+lUrd97kZq7rPBrwD/Ba7HP0d8UpDV1wD5HI1Gs3EEZuHayIuukiwTkehaEGnK6entZkBF2+q2vv50x3pDkfXJdFL0Jq0HGs+hlvGOgIVnSwNbqPE5EFn/X76PU4SofoucXPvJkeWSpn7FKJnLCVa3kK0pZxAQy2+XUX4TshwbPFps/j+kqkcLu/gPyfqaG9vJysri4SEBDw8PDiad5oOnYnlg0IHmUw2qmOoUi7jxXWT6TNY+OXmYsdD4OHhwXe+8x0EmQxrdQ5LU4NQyC/IRisILJ8UjLLxFEajkQceeAA/Pz+2bNmCwWDgDztLaezRs+bGeOq7jRQ22B/YQ4cO0drayh133EF8fDz79u1zjHa2FzbhrpKzenIorx6o5FRdN8XFxZw5c4b58+cza9YsTp48SXl5OdmVHbydXcO3Z0XzsyWJ7CttZePJRlpaWjhw4AApKSmsWrWKhoYGjh07Rn3XAL/bXsKsWD9euy+DijYdf9lbRn9/P9u2bSM4OJj7778fnU5HZmYmAyYLj28oIMzblY0/mIXFKvLEJ0VYLBY2b96MWq3m4YcfRqVSsXnzZqxWq/3/VpGNP5hFmLcrj31UwIDJwq5du9DpdNx///0EBQWxbds2+vv7eXHPOSrb+3n9W9OYOcGP324vob5rgGPHjtHQ0MCqVatISUnhwIEDtLS08MnJRvaVtvGzJYl8e1Y0b2fXkF3ZQVlZGadOnWL27NnMmzfP0Wan6rodo/pnV6WQXdnJeydq6ejoYN++fcTHx3P77bfT0tLC4cOHadMaeHrLGaZEevPOg9Np6Nbzh52lGAwGtmzZgp+fHw888ABGo5Ht27djMFt47KMCfN1VbP7hbBRygZ9uKMRitbF582ZkMhnf+c53cHd3d3Tyv9hUTJ/BwobvzSQ2wJ2ffVyI1mBm79699PT0cM899xAeHs7OnTvRarX8Y38FZ5q0/O2uKdycFMifMs9S2a4jLy+Pqqoqli5dypQpUzh69Cj19fXsLGpma0ET/3NzPN+bF8tHeQ3sP9tKdXU1J06cYPr06dxyyy2UlZVx+vRpzrZoeWlfGcsnhfCnO9I4VdfD64eq6OnpITMzk+joaO666y46OzvZv38/3f0mfr6xiKRgDR88ciOdOhPPbDuDyWRi06ZNeHp68tBDD2Gz2diyZQtmi5XHPirAXSVny6Oz8XBR8NhHBZgsVrZu3YrNZuOhhx7C29ubzZs3YzKZ+M22Etp1Rt5/eAYpoZ78YlMRnTojn376KR0dHXzjG98gJiaGPXv20N3dzZtHqsiv7ea52yexMj2Ul/eXc6apl8LCQs6dO8fNN99MQMDoDsBfV+TPPPPMFX9IEITlwF7s6TqWA79+9tlnzz7zzDMVV7d6o/P6668/88gjj1x+x8+ApVOPvqgD88RQzGRis7lRnFXDOaMbM29eyLdnRzv2DfMI4/3S97GKVm6edD/kv42Pwsh+XSRTQ1/EzZDApJv/iiCT8cqBSsK8XVkzJQx2/hR8Y+xbQx5MfxjOW9eZFObFybpuNuTV4Vp/HKvFzL333ovBYOBcaQnVshB+f9tkxzrSxfD3UKOQy3g7uwbXjlLa66tYt24dUVFRVHZbMLeUMTHCn0kJE5w+q22qpK+2GJ/4DBbNnkZYWBjHjx+nsqmTV0/reWTuBH6yKJE3j1ThrlYwwc3E5s2bSU9PZ86cOURHR5Ofn09dXR0pk9J4cmMRC5MC+e2aVDafauRwaQOWskMEBgayevVqoqOjKS0tpbj4DK8WW/HxcOXVezKYFu3LscoOPjlZh7omGwG45557CAsLo7W1lfz8fDZUirQMwDsPTict3Ju2PiPvHKvBs/U0PZ1t3HvvvYSGhmKz2cjJyeFIg4WDdUZeu28aU6N88HRV8HZ2Dcr2Mtpqy1izZg0xMTH4+Phw4sQJzjTr+LBUz9Mrk1mUHMzEEE/ePFJNf2sdrWfzmDdvHunp6URERHDixAmqGtt4+ZSBe2ZE8uBNMcyI8eW947WcrW6gt+QwiYmJLFy4kJiYGE6dOkVZeSV/PWUiPcKH36+dxIwJvuwobCaruAFb+WF8vL25/fbbiYqKoqKigsLCQt4osaFSqnjj/mlkRPpwur6Hj3LrcKs/jsVs4r777iMsLIyuri5yc3PZViNS0W11tFG/0cLb2bVo2orobG3k7rvvJjw8HIVCQU5ODieaTOypNvCPu6dyQ4wvgRoX/pVdg9BeTlt1KStWrCA+Pp6AgACOHz/OuRYt7xTreXxxIivSQkmP8OatI9V0NtfTXnqCmTNnMm3aNCIjI8nJyaGyromXThpYMzmMRxfGMXOCHx/k1HO6ooH+0kPExMSwZMkSoqOjKSoqovTsOV4+bSE20JMX1qUzY4Iv+0pa2XG6HnnVEdzd3bnzzjuJjIykpqaG06dP885ZEZMo518PTGdyhDdlrX28f6IWj6ZcDAM67r33XsLCwtDpdOTk5LC7zkZRu5m3H7C3kdUm8nZ2De7tZ2hvrOWuu+4iIiICV1dXcnJyONmkZ1uFkRfWpTMr1p9IXzfeOlqDta2K1ooiFi9eTFJSEsHBwRw/fpzy5m7eKNDz6II4bs+IICPKh38draGpsYGOM9lkZGQwc+ZMoqOjyc3NpaKmjr+cNHFzUhA/XZzIzAl+bMhv4MS5BgxnDxEWFsaKFStGzWT8WXn22Webn3nmmdfHfIBrwFin+14EFoiiOF8UxXnAAuCvV69aXx4ytT1mVrhvMr29QbS27WCgsw1R48eTS0dKrZVyJQsjF7K/bj8m0QrJq9HUf8rjKR8iYiNl6ovI5HJEURzMI+UO7WXQWgwpt0HqbdBZbv/7PARB4Pnb05goNNPR2syyZcvQaDQkTUxGEG3cGmrDVeWcen00Hpk7gdkh0FpewMRJ6cTHxwNwvEdDq+BD5eljTgvsWq2W09mfopV7ckRrH6WFh4czbcZMmqtKucHHwE8WJeDlpmROfAC7Chodo7QlS5YA4OnpybJly2hoaOC/27PoGTCzIi0UTxclz90+iTBtCXqDkbVr1yKXy1EoFKxdu5Y+nY6I/jJeWJeGq0qOXCbwwrp0Em0NdHW0s3LlStzc3BAEgRUrViDKFHi2nuKXyxIJ97HL+59aNpGpmj6aa8q5ae48goLsYabmzp2LxscfQ1UO354RwsxYeyibe2dEsSBSSXvZKSYkTCQlJQWAlJQUJiQk0V52igWRSu6dYQ90OzPWj2/PCEFfmYPGN4A5c+wikuDgYGbPmUtzTTlTNTqeWmb3H4vwdeOXy5LQtJ5ClCkcHYubmxsrV66ks6ONRBr487o0ZDIBN5WCF+9MJ7L/HDpdP2vWrEGhUCCXy1mzZg16g5FQbQnP356Gp4vScb8ky5tpb21m6eD9ArB06VLkalfUjfn8bFE8sQH2KCSP3ZrIDT4GmqtKmTZjJuHh4fZrmzkTn8AQdBW5fCPdnwVJdpHu7VPDWBLrTmdZPmHRsUyebA+YGh8fz8TUdFrLC5gdYr/fACZHePP9myKx1eTgqvFm4UJ7oFp/f3/mL7yZlvpqJrt288xKe1sHerrwm9XJ+HYUYLHBqlWrEAQBFxcXVq1aRU93F7GWGl68Mx2lXIZaIeeFdelMMFbS29vLmjVrUKlUyGQyVq9ejdFsJbCriN+tScXXXYUgCPxuTSqTVB20NtZxy6Jb8fGxhyZatGgRandPZHV5/M/8aJJD7TH+Hl0Yx40BFloqikmbkkFMjD2m5bRp0wgKi6KnPJ9VSZ6sSLOvyy6dFMLaZG96y3MJCI3ghhtuACA6OprJGTfQUlHMDH8LP1pofwaTgj358cIJyOvyULlpuPVWe3R7b29vbl28mLamBiYp2/n92kkIgoCPu4o/rEklqLsYo9nCmjVrnOIgjgfGesVtoiieP2qqAj6bo851huBiD1/o7+pNd/cEbLZG3Hz1LJ+VirvaObThkpgl6Mw6jjYehZS11PinIPcrpbbhm2hC7ekZuvpN6IwWIn3d4MwmQIDk1TBxFQhyKN7odFyZoZdJsgaqrT6c6LHL1muNruhEFRHCZ1fzWC1mptoqGBBV7OkNRhRFdEYLn5a1E5Q6G4VC4ZjSgpHrAWGT55Jb202r1i5c2N/jT7fNlQyhCttg1tEVaSEE9lfQ0dHB6tWrcXUdjp02adIkJk6cSGXhCcLUJuYm2GXuXvpmIuU95JrDqNYNG9uyPgUFlhBi5Z24DQyr8RSGHlLkTVRY/cjvHT5+u0HgkCESf9kAwfoaR7lo0jOVGtpt7hzsGY6RpreI7OmPQi1YSTQOr4vZbFam2MoxoiBTG+xY27HZRDJ7QzCiYIqtHJttuI0SjeWoBSt7dJHoLcMz2wd7fWm3uTNVqMZ2XkK6EEMN/rIBDhkiaTcMv/nm97pSYfUjRdaEwjAs+HDtb2GCvIvT5hDKdcNhtGr6FeSZQ4mS9+Cpb3KUC4P3S43VhxPdwyGquoxwQB+Jj8xAhHF4Yd9mNjKVSrptruzvGY4mbrSI7NZFoBBEks1ljrUdm81Guq0MmyAnsy/MsbYjiiK7ewMZEFVMtVWMSMsea6rETTCzdyAKnWm4jQ73eNNs1TBZqEU0DavuAgbqCJbpOGoMp1k/3EZFWhdKLYEkyVpQ6YfvfRd9Ownyds5YgjnTO9xGDQMyjpvCCZVr8dOfJ4ow6kiX1dFg9eJoz3AsPK0J9g1EopEZiT6/jSxmMqikT1SztzfQ0RZmq0imLhwEgUmWshGZnFMsZSAI7NaFYz5PCLK3NxCt6EIGFdgsw+q9aEMVnjIj+/WR9BqH98/u1lBv9SJdVo9gHBaC+OgbCJNrOWEKp65//BkouEIjJQjCbYIg3IY9bt9OQRC+LQjC/diVfV/J3MZDIynMNgy2GxBFAe8JWianxI26/4yQGXirvcmsyaRP7kttXDOuXbH8oWKqY/G+blAiHOXnBmc2QtQs8AwBd3+ImWsvO28Jb2htxM3NFXVMBn/efY7Kdh07i5ppwo/e1vpRpbmjkZWVRV9vNzEZ88kq62JDfgNZpa0YLTaWZ0xg+fLlNDQ0OJRsJ0+epKKigkWLFrF6RhKiCDuLmtlb0sonp5sJmzwXk0HPrl32rMQTPU2kylvAfwJxcSPbSBAEFi1ZilGUsdC1BoUAvb297Ny5k7DwcPo0MTy+wb6A3ztg5slPihjwiScoKNixtjO0luap0SCLmMLvd5RS1zmA1Sby2EenaZP7E5eYzKFDhxyy8e3btyPaLAROmsM7x+s5WmEfKf5+RynlfTKSM2Zy7mwpxcX2EezBgwfp6mgn/oZ5ZNfoeOdYDQDvHKshu1ZHwg3z6epodyjZiouLOXe2lOSMmZT3yfjDYIqWoxUdvHu8nsDUOdgsZns9BqXahw4dIi4pmTa5v2MBv65zgN/vKEUeOQWNh4djbae/v5/t27cTFByM3jeeJz4upFdvpt9oX0vTaiYQNri209vbi9VqZdOmTbi6uKCeMI0X9pZR3tqHKIo88XEhTaI3CSlpZGdnU1dnjwG9a9cuzEYDYZPn8slp+/cL8Ofd5yjpEkmeNpvqqkpOnrQHjsnOzqa1uYnEG+aS36h3KNk25DWwv7yHmGnz6evtJisrC4Bz585RWHCaiZOnU6NX8/Sg2i+/tps3DlfhmzwLuUxgy5Yt2Gw2x9prTGw8XeqQwbUdGy29Bp7eUow1NBUfHx82b96M0WhEr9ezZcsW/P39MQVO5Bebi+nQGQcFKwX0uIYTGW1f2+nq6sJms6+lKRVyNAkz+H+fVlLcaJeNP7WpiHqzOxPTM8jPy6Oy0i5O2LNnDwO6PiKnzmdXSQdbC+wvBX/fX05Bq4mJN8ylsaGeEydOAJCbm0tDXS3JN9xEYZuFv+2zvwhtK2hiR3EbkVPnoe/XsXv3bmBIsJJDUtpU6s0e/HxjkUPO/vL+CtwTZqBSKhwCoe7ubvbs2UNkVDQ9bhE89tHpUd0bvu5c0ZrUs88++zMgEXv09H4genAbAFyeeeaZLVe9hqNwNdekRJMN3dEmxBhP3iyqI1pTjsa3l4SJj+Li7hywVS7IqdPW8WnNPqb0HMUq0zLlTCfvGOYyMzGUMG9Xcqq7yCxu4bF0C965f4HZ/wth9uRlWE1Q8AEkLgGNXQxx8OBBzpw5w2233caKmSl8mFvPieou8mu7mRwdiLK7Bn9/f0JCQi55LdXVdnXe9OnTuWvZAo5XdfJxfgMN3QOIIvxqeTJBQYG0t7eTn59PcLDdOERERLBs2TL8PNRkFrdQ0tzH1oImovzc+dt9M5EJdgWZj48P+/ftRWeBLGMcD9wU6zQ/frSqh/dOdRIrNiMIArm5ufT09HDfffcxLTaYt45Wo9Vb2FvSysn6Hv71wAympcaTk5NDV1cXzc3NlJWVsW7dOpbckMj7x+s4Xd9DZ7+Jj/MbeP72NFbMTuf06dNUVlYil8vJzs5m3KKNkwAAIABJREFU0aJF3LFwOjuLmsksbiFQo+bPu8/x3XmxPLxkGpWVlRQWFuLv78+uXbuYPHky31y5iMKGHj7MrSc51JOnNhUxJ96fZ+68kd7eXnJzcwkODrYbkKAg7r3zdowW+7pFbIAHv956hgCNmle/PRNXtYqcnBw8PT3Zt28fAN+69x4i/DX862gNrko5bxyuolNn4u2HZhIbGcrx48exWq0UFhbS1mZfS5s9MZy3jtbQojVworqTIxUdvPGtG5g9OYnc3FxaWlro7u7mzJkzrF27lpUzU/lvbh0nqrswWmy8e7yWZ1alcMe8qXbjeu4cLi4uHDx4kHnz5rFu0Wz2lbaxtaCJSF83frezhG/NjOLRFdOpra3l9OnTBAYGsn37diZOnMjda5ZS0a7jPydqSQn15Ocbi5ga5c0f75qJXq8nJyeHwMBAduzYgY+PD/d9807kMvu6aISPG7/bUYqbSsFrD8zEW+NBTk4Orq6uHDx4ELPZzLfuu5eEUF/eOlqDIAj850QttV0DvPPQTCbGRnL8+HEMBgNlZWXU19dzzz33sCAtmreP1lDd0U9ps5Y9Ja38456pLLwhlby8PBoaGtDr9Zw6dYpVq1ax5qbJfHKygYNl7ajkMl4/VMWTS5K4++YMSkpKKCkpQaPRkJWVxaxZs7hr6TwOl7ez8WQjcYH27/m2KeE8vmY6zc3NnDx50vHsTJgwgXtuX0Vzr553j9WSHOrFzzcVkRjsyYt334jNaiUnJwc/Pz927dqFu7s79919FxpXNW9n1xDk6cKfd59DBN56YCZB/vZ1UYVCQXZ2Njqdjm996z7SogJ462gNRouVuQljF0587dekRFF84BLbg19UJb9IZC72kdTuk41U613paI/GxdsE6ovPXi6OXsz9YiADbqUoTEvJlmtJ89zGf0u288nRLZRU2mdCw5oyQZDZp/qGSFoBMoVjyq/yXA2HDh0iJjwB5YAf2jItP0+NRF+pRaGzsHTGRLy9vZ38WS5kYEDPJxs2onH3YoJfOlWn2vlJUjgxemiv1LJ8UjAymWBX8i1fjlqt5oMPPgBg9erVjrnuFWkhlDZr6dWb+Mud6agUMubMmUNISAibNm2iq6uLpBkLaeyzkDdK3MIdRc30qoNISZ3EwYMHqaysZNGiRfj5+TFjgh8Pzo7h38dr2XiqkR8uiGNSuBdBQUEsWLCAkpISjh07xrRp04iLswe//dXKZE5Ud/F85lkWpwSxenIobm5urFq1ira2NrZt20ZkZCQzZszARSnnhTvTae7V86MPTpEQ5MH/LYp3rO2YzWY+/PBDNBoNS5YsQRAEnrs9DbVCzoNv56FWyHnu9jQEQWDJkiVoNBo+/PBDzGazYy3t/xbFkxDkwY8+OEVzr54X16XjopRz4403EhkZybZt22hra2PVqlW4ubmxZnIYi1OCeD7zLCequ3h6ZTKh3q7ExcWRkZFBdnY2paWlLFiwgKCgINLCvfnh/Fg2nmzkveN1PDQ7hukxvvj5+bFo0SIqKys5ePAgkyZNIjk5mQCNmt+tmURBQy/PbCthTrw/d0+PRK1Ws3r1arq6uti0aRMhISHMmTMHlULGi+vS6dWb+N57+UT6uvHk0iTH2g7ABx98gKurK8uXL0cQBH67OhUvVyUPvZNnz8F2RzoymcAtt9yCr68vH330EQMDA461tO/PjyUt3IvHNhRQ3dHPn9eloXFRkpGRQWxsLJmZmTQ1NbF8+XI0Gg2LU4K5bUoYL2eVc+BcO08uSSLG352oqChmzpxJXl4eBQUFzJkzh7CwMBKCNPzk1gQyz7Tw2qEqvjk9kvmJgXh5ebF06VLq6urYu3cviYmJpKWl4eWm5Lnb0yhr1fHkxiKmRfnw4E0xKJVK+7poXx8ff/wxAQEBLFiwALlM4MV16RgtVh5+N48ADzVPr0xGEARWrlyJQqHg/fffR6FQONbSfrkimWBPFx5+Nw+9ycqL69JRyGXMnz+fwMBAPvnkE7RaLWvXrkWlUvHArGimx/jy1KYizrb08dxtk/BxVzmmzLOysqitrWXJkiV4e3szNyGAe2ZE8s8j1eRekPD0685YY/fFCILwF0EQNn7VU3UIg9N9ZQ29rJqdSkdHBKJNoL1910U/k2h2Jz64DFPXBH7QuYvHgwIoDDvFwaZ/ULS7AEPBEUI9FChKN0P0TeBxXsQoN1+IXQhnNmM2m9nw0ScIFiXafD92v1HM7jeK6djTxOoBNff3qUl2cyMlJYWqqqoRjp0X8u/1H6Pr70NeF03Wv86x+41i8j4oZ4lWyV39aqZbh/PruLu7s3LlSkdnPLSgDLAyPRSlXODHtyQwMcS+oCyXy1m7di1KpZKZM2eydu4U1AoZ2wubRtTBEdEiJZgVy5fh6elJbGws06YN58D56eJEEoI8SAv34tEFw9OFs2bNIjIyEl9fXxYtWuQoX5cRzq3JQQRo1I4FZYCEhASmTp2KWq0esaA8NdKHHy6Is/ux3DkZtcL+/QYEBLBo0SJHZ+wymLsqyNOF36xOQS4T+O2aVII87eUuLi4O471o0SL8/e3rOGqFnBfXTUalkPHDBXFMibS3nUwmY82aNajVajIyMkhIsK9PCoLA79dOIkCj5tbkIO7ICHdc26233oqvry+RkZHMmjXLUf7ownjSwr1ICPLg8cXDebumTZtGbGwsnp6eLF261FG+PC2ENZND8XZT8vygkQWIiYnhxhtvdHTG8sHEesmhnvxkUSJKuV2k4qayr736+Pg4jPfKlStxH0zD7uuu4o+3pSGXCTy9MtkRNFmlUrFmzRrkcjkLFixwjPQVcrshdFPJeXB2DLNi/R1tsWrVKlxdXZk0aRKpqcN5U3+9MoUwb1duivPnWzOjHeULFy4kMDCQkJAQ5s6d6yh/eM4Epkf7Eu3nxi+WDwc8Tk9PZ+LEiSPucYAFiYHcPSMSjVrBC+vSkcvs5eHh4cyZM8fxIqMcTMQ4IcCDp5ZNRCETeP6ONEe6HY1G4xDCLFtmv8cBPF2U/OmOdBQygSeWJBEXaJ+FGRIIKRQKbrrpJiIiIgbvF4EX7khH46LgrhsiuHlikKONli9fjoeHB4mJiUyZMsVxbU8tm0i4jysv7D7HeOKKM/MCCIJQALyJ3ZnX4RI9GBH9C+dqZeYFu0d792+Ps98d7vv5bF74zTMkp39KYIiCWTM/dZrOslrMnNi9EqOimdCE9ci9AyD7ZSynPuR182N4YgBBxKLy5nemX8OKl2DaAyNPevp92Px9dk38OydKq0gLm8e85TeM2MVksLDzlSI8fNTM/lYY/3zzDVauXElGRobTNWRnnWTP4a1E+k5k9R3Ojn4HPiyjo0bLXb+ajlfAcMBbg8Hg6KzPp6vfhI+b0jnTqsGAWq1GEAS+/14+uTXdnHjqZscDn1ncwvfey+fdB6czNyEAk8mEQqFwUiQZzFZkgoBKMbLcarVitVpRXZCwzmYT0ZutTkIWURQxGo2jXkOfwYxmlESOF7vmq7n/UBudT7/RgqtSjkw2stxkMiGXyx0GxFFusWETRVyUI8ttNhsWi8WpjURRpN9kxeM6ayMPtcKpLYxGIyqVyql8wGRBJZc5+fGZzWYEQUBxQbZjs9WGxSo6KV9tNhtmsxm1emRyyCER0bW6L660jZRKpdOzc66lj2AvlxE56q6Er2Jm3rHKRQyiKL4siuKnoigeHNquas2+JH699Qz9iCyY4IdKLkPWr6WtPQaDoZ6+viKn/csP/Rm9azmxvk+SGDOTOJ844iZ/mwFLHJ6CHve+GFz7Q1GYeqgmyq7ou5Ck5dTJIjlRWoXLQDBzlkzDL9RjxBYywZsF9ybRUa+j4ZQBX1/fUaf8erv6yDq0GxUe3P3wGqfj+IV6sPiBZGRyGVnvlI4IRTPaQwM4JLwX4uLi4ihfkRZKh87Iieph9dX2wiZ83VXMGpR6D0mEnY6jlDsZKLCP2C7sfMH+1jma0nJIsjwao3UUQ9fwRe8/Wtu5qxVOBgrsbXShgQJQKWROBgrsI7bR2kgQBCcDNVR+LdtotLYYzYgDuKkUTgYKQKlUOhkosDuwj+aaIZPJnAwU2NviWt4XF9v/Ym002rOTGKwZs4H6qjJWI/U3QRB+LQjCTEEQpg5tV7VmXwJDHu1KVyU+cjkGXR9CXw/tHeEIgoLW1u0j9u+uPkWj7W289XOIvOGbjvIetxgyWYDK5IHMyx+D1QWFVc4WxXKMCmfxhUnmymb5SlQ2GaEuKfiHj55Rd8LkABJvDOZkZh0x4fFUV1c7BWb9z5sfY8XIqpWrcHF1fjABPHxcmPONeJoreincXz/qPlfKwqRA3FRythfaYwXqTVaySttYkho8akcjISEhMRbG2ptMAh4GnmM4K+8LV6tSXwbtfUZ+ubmYtHAvvL1csBmt9LQ2I9frsFjUKORptLbtdPhKWE0GSkoeQ27xIHn2nxzHsdlsbNm6FVFQ4tGbTK1qgCq/Ajx6Uum1qEfEpxti3759dJlVuPUkkxgvXtKDfM6d8bh5qugoVCKKIqWlpY7/Hdh1grb+WmJD0kjNSLjk9SbOCCY6zZ/jm6voanaOEH2luKrk3DwxiMziFixWG/vPtqE3Wx15qiQkJCSuBmM1UmuBCaIozhNFccHgtvBqVuyLRBRFfrGpCJ3Rwovr0pG5yBGNVnpaWxBMRtRqNX19SRiNzWi1pwA4d+iPGFxriQ/8Fa7ew0KI3NxcqqurmeCVjNoG7n3HqfY9hdLsRWxIEvn5+SOicVdXV5OTk0NcaBJKky9xygOXrKvaTcmC+5LobxFwV3s5pvw623o4dHwfajz5xgMrL3vNgiAw/55EFGoZWW+XYBsltcKVsiIthK5+E8eqOtle2IS/h5oZMX6f+7gSEhISQ4zVSBUA3lf6IUEQ3hIEoU0QhOLzynwFQdgrCEL54E+fSx3jarD5dCN7Slp5/NYE4oM0yNRybEYrvS3NCEBERDi1td4IgorW1h10lh+jmffxNdxC2NS1juN0dnayb98+YmNjGWgIJtS1hJXyo/jKWxlwb0TVHUNAQIAjGrfBYGDz5s34+vriqp2Aj1s3vvXvge3SDnpRKX6kzAlD7PSipqaGvr4+/vPWx9iwcNvta1GpP9sctbuXmnnfTKStto+Te+o+TxMCMC8hAA+1gv/m1rP/bBvLJgU7RBQSEhISV4OxGqkg4KwgCLuvUIL+NrDkgrIngSxRFOOBrMG/vzDsHu1nmBblw0M32eOOCerBkVRbC+7ePkRFRdPW1oe392xa23ZRWv4ECrM3yXP+6DjOkEe7XC7nhuR5GHQWAmIVzJKXkGzSUxVYTHtNP7cuWIZOp2PXrl3s2bMHrVbLkluW01KpIy7VBaG/FWqzL1ZdB7Nvj8NXbZcvv/evD+kyNJAUOYXESTFXdP3x04KIywgkd3s1HQ3OCQOvBBelnEXJ9nxXRovNEdNMQkJC4mrhLJn5bPx6LB8SRfGQIAjRFxSvBuYP/v4OcAB4Yoz1utz5eeKTQixWcYSvhMxFgc1ooVfXgldQiCP4piBMx2T6FFwg2f8fI1JkHDt2jPr6etauXUtz0QBKtZyJSxfAu78mxmzlgPcJUllMX4OMuXPnOkLszJ49G32LCkSIXzQD3nWD46+AzjmT7PmogKVzFLx12J3WrkZcBW/u+NayMbXD3G8m0Fjew75/lZCxJGpMx3AcS+1GiUnOgK+SaVGfbRCs7dTTWuWcdVZCQuLyuHurCI3/wiecrhvGZKSustw8SBTF5sHjNguCEDjaToIgPAI8AhAZGTmmE52s6+ZgWTvPrkoh2n84KKegkiMarPR0NBOZmk5YWBiCINBeFYDS3R1f2wJC0oadJ4cS5SUlJZGSksrb7x4lOs0frwkpVCriUSh96XNpxiNMTkVeG7f9bA7l5eXYbDYWLFjAlr8U4BfmgU+Ev12iXvghnNs5WpVHEAZMsH6XCrkHdyxMQKEc2zuGq4eKBfcmsWt9EXvevHQki8/CKlRYXZ1l0aMxoDXx8XN56PucU2NLSEhcnug0f8lIXQ5BEPqAIYcbFaAE+kVR9LxaFbsQURRfB14HuzPvWI6REeXL5h/OJi3Ma0S5oJYjmm30d3XhFRiMSqUiOCiYmuIGlrn9jZBHZzv2tVqtjkR5K1asoPFcD8Z+C3EZdtsa9pNPWaBv4y/b10CslvZDVnRdJh580B41Sq+10FLVy4xVgzmdVv0d5jz2ma/hm1Yzuve/i+epDXDjTeAytiaPSfPn/j/OwqS3XH7ny1BZ3MmJjysoyKpnyqKLv0CIosjBD85h1FtY9ePJeHiPLpmXkJC4OEr1Z0vb83VhrCMpzfl/C4KwBpg+xjq0CoIQMjiKCuELTvkxOcJZ7zEUv08hKPEOtkuoA62enLFW4LcmHbnL8CjhyJEjNDU1sW7dOjw8PDiRX4/KRU5Uil3V5uKmIdLVHVeFKy3B5XgwmcqTbWQsiQag8qQ9lFDctMEBo0IFAZeWj4+oK+C57i/w1mLY80tY9fKVNoEDdy817l6f31BkBLnRVt7DiS1VRKX64RviPup+5bmtVJ1qZ+baWCKSnLMLS0hISFzIVfG6FEVxMzBWCfpW4P7B3+8HvpRI6uczFL9PIVPjHRSMoaIH30YFFsFKj7vRsV9zczMHDx4kNTWVlJQUrBYb1afbiZkcgFw53JQyQUasVyzllhKCJ3hSnjdsd8vz2giI1OAdOBye6IqJmA6z/gdOvgPle8d+nKuEXd6ehFItv6i8vb/HyKEPywiK8WTyJUZbEhISEucz1gCzt5233SEIwnMMT/9d6nMfAMeAREEQGgRBeAi7Q/AiQRDKgUWDf3+pDOWUUspUeHoF0P1xGaHe9oCP9fX2CA3DOZ/cWLbMLlioL+nCODA81Xc+cT5xVHRXEJcRRGeDju6WfrQdetpqtKPuf8UseAoCJsLWH4HeORr5l42bp4p5dw/K23ePlLeLosin753FarZxy7eTRw0PJCEhITEaYx1JrTxvWwz0YVfpXRJRFL8pimKIKIpKURTDRVF8UxTFTlEUbxZFMX7w55ceh14YjHnmovbAdKQLa6+RiG+ko9FoHEbq4MGDtLa2OtKZA5Tnt6J2UxAx0XnqKs47jk5DJwGpahCgIr+Ninz7iOqqGCmFGta+Cv3tsOsLEUNeMXEZgcRPCyR3RzXt9cPZRUuzm6kt7uTGtbF4B32OEaSEhMS4Y6xrUg9cfq+vDkMjqVi/KQzktqKZF45LlBcRERHU19fT0NDAkSNHmDx5MomJ9tQJFrOV6oIO4qYGIh8lWGqsdywATWIdoXHeVOS3IVfICIz2xNPf1Wn/MRE6BeY8Dgefg4kr7ds1Zu5diTSW9ZD1dinrfj6N/l4jRzaUE5bgTdr88MsfQEJCQuI8rshICYLw9CX+LYqi+NvPWZ9rwtCaVKQsEUWQG56L7L5DERERlJSUsGHDBkeivCHqznRhNliHBRAXEOdtz5VU0VNBSsZNHPqwDIDZd4yeln7MzH3cLl/f9mOInGlPUX8NcfFQMv/eJHa+UkjO9mpaq7UgwsJvTUSQpvkkJCSukCsdSY0WmdQdeAjwA76aRkplHwmJiPjemYgwODIacurt7e3lvvvuGxFuvyKvFRd3JWGJo/srBLkF4aH0oKKnglVTb+Pwf8sQRYidehWm+s5HroS1r8Hr82DT92DSuqt7/DEQAyQlajiZWQvA/HsSr97oUUJCYlxxRUZKFMUXh34XBEED/C/wAPAh9kjoX0kMln4sNhPGWBuqsOG0GSEhIY4sorGxsY5ys8lKdVEnCdODkF8kLYUgCMR5x1HRU4Gbp4qoVD8sZhsa39HzynwugpLh5qftkvSKa6/2A7jJ5kaT/EV83bpInnHjta6OhITEV5QrXpMSBMEX+AlwD/YwRlNFUbz28rLPQZ+2ky11/2DFN34+olyhUPDjH//YKclcbVEnFqOV+MsIIGK9Y8mqy0IURZY8Mumq13sEs34EKbeBxfDFnuczoga+2VCIfOMvET5thsW/v9ZVkpCQ+ApypWtSfwZuwx75YZIoip8vQul1Ql9nBxbRjMbPeT1ntAyfFfmtuGqUhMZfOhB8nHccn5R/QqehE3/XL2GtyCvsiz/HFaDwi4X6Q3Ds/0HScoiada2rJCEh8RXjSiXojwGhwC+BJkEQtINbnyAIX9mIoX2d7QCjGqkLMRks1BZ1Ejs1ENllMtDG+QyLJ8Yti34L3pGw+ftg/Fq800hISHyJXJGREkVRJoqiqyiKGlEUPc/bNF9k3L4vGl1XBwqVGhcPzWX3rS3qxGK2EX8RVd/5DCn8KnsqP3cdv7KoPWDNK9BdC/vGFDxfQkJiHHNVwiJ91enr6EDj53/JNO5DlOe14ualIiT28jkf/Vz88FJ7Ud5dftl9v9ZE3wQ3fh9y/wlVB651bSQkJL5CSEYK+5rUZ5rq01uoO9NF3NTAz+TzM6TwG9cjqSFufhr84mHLo2Dovda1kZCQ+IogGSnsa1Iav4DL7ldd0I7VYiNuWtBnPvaQkRLFMWUX+fqgdIW160HbCLufuta1kZCQ+Iow1sy8XxusFgu6nm40/pcfSVXkt+HhoyY45rMvv8V6x9Jn7qN1oJVg9+DPU9WvPuHTYPb/wpG/gk80aKR08xISV4xXGEyYf61r8aUx7o1Uf3cXiOJlp/sM/WbqSrpIWxB+ReF9zhdPjHsjBTD/51D5Kez/3bWuiYTEV5OEpZKRGk9oHfLzS0/3VRe0Y7OKxGV89qk+GBnDb3bY7MvsPQ5QqOE7+0DbdK1rIiHx1UQ5vkKMjXsj1dfZAVzeR6oirw1PfxcCoy8vUz8fHxcffF18x7ev1IXIleATda1rISEh8RVg3Asn+jouP5Iy6MzUn+0mLiPwM8nULyTeO15S+ElISEiMAclIdXagcnVF7XbxZHyVp9oQbVc+1TdErHcsFT0V2ETntOoSEhISEhdn3BspXVfHZdejKvLb8ApwxT/C45L7XYxY71j0Fj3N/c1j+ryEhITEeGXcG6nLOfIOaE00nusmbtrYpvoA4n3igXEeHklCQkJiDEhG6jJGqupUG6II8VfgwHshQ6nkx314JAkJCYkrZFyr+yxmMwO9PZec7ivPa8Mn2A3fUPcxn8dT5UmgWyCHGg7hrb58zD8JCQmJixHiHsKssPGT9mZcGyndZeTn/b1Gmip6uGFZ9Jin+oaYHDCZPbV7ONl28nMdR0JCYnwzP3y+ZKTGC32XceStPNkGIlcUq+9iPD/3eX6q/+nnPo6EhMT4Ri13TsT6dWacG6nBkdRF4vZV5LXhF+aOb8jYp/qGUMgUUlgkCQkJiStkXAsnLhVtoq/LQHNlL3EZl09uKCEhISHxxTDOjVQ7Lh4alGoXp/9VnmwDGLMDr4SEhITE52ecG6mLy88r8tvwj/DAO+jikSgkJCQkJL5YxreR6mgf1UhpO/S0Vms/l2+UhISEhMTnZ3wbqa7OUY1URb59qi92qrQeJSEhIXEtGbdGymw0YND1jSo/r8hvIzBKg1fA+MrbIiEhIXG9MW6N1MWUfT1tA7TX9V0V3ygJCQkJic/H+DVSHaMbqaGpPkl6LiEhIXHtGb9G6iLRJiry2wie4InG11mWLiEhISHx5TKOjZR9JOVx3kiqu6Wfzgad5BslISEhcZ0wjo1UO25e3iiUSkdZRX4bCJKqT0JCQuJ6YRwbKWdH3vK8NkJivfDwGV8BHCUkJCSuV64bIyUIQo0gCEWCIJwWBCHviz7fhUaqs0lHd3O/5MArISEhcR1xvUVBXyCKYseXcaK+znYiU9Mdf1fktSEIMGHKxRMgSkhISEh8uVw3I6kvE+PAACa93jGSEkWRivw2QhO8cfeSpvokJCQkrheuJyMlAnsEQcgXBOGRC/8pCMIjgiDkCYKQ197e/rlONCQ/H1L2dTbq6GkdkFR9EhISEtcZ15ORmi2K4lRgKfBDQRDmnv9PURRfF0VxmiiK0wICPt+U3IXRJsrz2hBkArHSVJ+EhITEdcV1Y6REUWwa/NkGbAKmf1HnGhpJefoF2Kf68loJT/TGVaP6ok4pISEhITEGrgsjJQiCuyAImqHfgVuB4i/qfH2dHSAIuPv40l7Xh7bDIMXqk5CQkLgOuV7UfUHAJkEQwF6n90VRzPyiTtbX0YGHtw9yhYKKvDZkMoEJk6WpPgkJCYnrjevCSImiWAWkX3bHq0RfZzuaoam+/DYikn1xcVde/oMSEhISEl8q18V035fNkCNva7WWvi6DFPFcQkJC4jpl3BkpURTtRsrf3z7VpxCIkab6JCQkJK5Lxp2RMuj6sJiMePj6U3GyjchkP9Su18Wsp4SEhITEBYw7IzXkI2WxuNPfYyR+mjTVJyEhIXG9Mm6NVFeTgFwpIzrN/zKfkJCQkJC4VoxbI9VcYSEq1Q+VizTVJyEhIXG9Mg6NVDuCTI6+Xymp+iQkJCSuc8ahkepAqfZEqVYQPUma6pOQkJC4nhl/RqqjHavNjehJ/vz/9u49OIp6ywP490wmTwiBQEJiXsNjSDI8Io8LXBBRFt2gyKUQCOICgpZoXVzwxSpbFgVE0QKyUKBSkkVulHuNFSGgwnJTKgsIAjdiYMgLDElIyJM8TAgJmfRv/5ieMjeGhFkn3Z2e86myMt3TdP/OsWfO/Hp6fj9Pbw+1m8MYY6wLblekassrIaS+GM539THGmOa5VZESkoSm+pvw8OqHqJED1W4OY4yxbrhVkWqoq4WQ2jAoIhRGL77UxxhjWudWRaokuwQAEDUySuWWMMYYuxdu9SMhywNxCI/eD+8+3mo3hTHG2D1wqyIFAP2CAtRuAmOMsXvkVpf7GGOM9S5cpBhjjGkWFynGGGOaxUWKMcaYZnGRYowxpllcpBhjjGkWFynGGGOaRUIItdvgNCKqAlD0O3YxCEC1i5rTG7hbvADH7C44ZudECSGCXNnZn1hDAAARbklEQVSYntYri9TvRUT/EEJMULsdSnG3eAGO2V1wzPrHl/sYY4xpFhcpxhhjmuWuReojtRugMHeLF+CY3QXHrHNu+Z0UY4yx3sFde1KMMcZ6AS5SjDHGNMutihQRxRNRHhFdJaI31G5PTyCivURUSUTWdusCiSiDiK7Ifweo2UZXI6IIIvqOiHKI6DIRrZbX6zZuIvIhonNElCXHvEFeP4SIzsoxpxKRl9ptdSUi8iCiC0T0lbys93gLiegSEf1ERP+Q1+n2vO6M2xQpIvIA8D6AWQAsAJ4iIou6reoR+wDEd1j3BoBvhBBmAN/Iy3piA/CqECIWwGQAf5b/3+o57hYAM4QQcQDuBxBPRJMBvAfgv+SYawE8q2Ibe8JqADntlvUeLwA8LIS4v91vo/R8Xv+G2xQpABMBXBVCFAgh7gD4DMCfVG6TywkhTgCo6bD6TwD+Ij/+C4C5ijaqhwkhyoQQP8qPG2B/EwuDjuMWdo3yoqf8nwAwA0CavF5XMRNROIDHASTLywQdx9sF3Z7XnXGnIhUG4Hq75RJ5nTsYLIQoA+xv6ACCVW5PjyEiE4CxAM5C53HLl75+AlAJIAPAzwDqhBA2eRO9nePbAawFIMnLA6HveAH7B4+/E1EmET0vr9P1ed2RUe0GKIg6Wcf33+sIEfUF8AWANUKIX+wftPVLCNEG4H4i6g/gIIDYzjZTtlU9g4hmA6gUQmQS0UOO1Z1sqot425kqhLhBRMEAMogoV+0GKc2delIlACLaLYcDuKFSW5RWQUShACD/rVS5PS5HRJ6wF6j9QogD8mrdxw0AQog6AMdh/z6uPxE5Pnzq6RyfCmAOERXCfql+Buw9K73GCwAQQtyQ/1bC/kFkItzkvHZwpyJ1HoBZvhvIC8AiAIdVbpNSDgNYJj9eBuCQim1xOfm7if8GkCOESGr3lG7jJqIguQcFIvIFMBP27+K+AzBf3kw3MQsh3hRChAshTLC/dr8VQjwNncYLAETUh4j8HY8BPArACh2f151xqxEniOgx2D99eQDYK4R4W+UmuRwR/Q3AQ7AP518BYD2AdACfA4gEUAxggRCi480VvRYRPQDgJIBL+PX7inWwfy+ly7iJaAzsX5p7wP5h83MhxEYiGgp7TyMQwAUA/yaEaFGvpa4nX+57TQgxW8/xyrEdlBeNAP4qhHibiAZCp+d1Z9yqSDHGGOtd3OlyH2OMsV6GixRjjDHN4iLFGGNMs7hIMcYY0ywuUowxxjSLixRze0R0Wv5rIqLFLt73us6OxRi7N3wLOmOy9r+/ceLfeMjDE93t+UYhRF9XtI8xd8Q9Keb2iMgxmvi7AKbJc/e8LA/guoWIzhPRRSJaKW//kDx/1V9h/wExiChdHgT0smMgUCJ6F4CvvL/97Y9FdluIyCrPF5TQbt/HiSiNiHKJaD/pfRBCxrrgTgPMMtadN9CuJyUXm3ohxB+IyBvA90T0d3nbiQBGCSGuycsrhBA18hBF54noCyHEG0S0SghxfyfHmgf7PFBxsI8Ocp6ITsjPjQUwEvZx6L6Hfdy6U64PlzHt454UY3f3KICl8nQYZ2GfGsIsP3euXYECgH8noiwAP8A+kLEZXXsAwN+EEG1CiAoA/wvgD+32XSKEkAD8BMDkkmgY64W4J8XY3RGAl4QQx/5ppf27q1sdlmcC+KMQoomIjgPwuYd93037sefawK9T5sa4J8XYrxoA+LdbPgbgRXkaEBDRCHk06o4CANTKBSoG9ikzHFod/76DEwAS5O+9ggA8COCcS6JgTEf4Expjv7oIwCZfttsHYAfsl9p+lG9eqELnU3X/D4AXiOgigDzYL/k5fATgIhH9KE8t4XAQwB8BZME+Ud9aIUS5XOQYYzK+BZ0xxphm8eU+xhhjmsVFijHGmGZxkWKMMaZZXKQYY4xpFhcpxhhjmsVFijHGmGZxkWKMMaZZXf6YNzMzM9hoNCYDGAUuaIwxxlxLAmC12WzPjR8/vrKzDbosUkajMTkkJCQ2KCio1mAw8K9+GWOMuYwkSVRVVWUpLy9PBjCns2266x2NCgoK+oULFGOMMVczGAwiKCioHvardZ1v0/0+uEAxxhjrGXKNuWst0vz3TB4eHuNjYmIs0dHRFovFEpuRkdHZKNT/b0888cSQvLw8r40bNwZ/9NFHAxzrc3NzvcaMGRMTFRU16vHHHx/a3Nysm9lR1crpnDlzhphMplFms3nkggULTC0tLbrIqVr5dFi2bFmEn5/fWFceU21q5VSSJLz00kthJpNp1NChQ0cmJiYGu/K4alIrp4cOHfK3WCyxMTExlvHjx0dbrVZvZ/ar+SLl7e0t5ebmZufl5WVv2rSpdN26deGu3H9xcbF3dHT0nZMnT/o/8sgjjmnE8corr4SvWrWqoqioyBoQEGDbsWPHIFceV01q5fTpp5+uKSgosObl5V1ubm6m7du36yKnauUTAE6cOOFXX1+vu9kM1Mrpzp07B5aUlHj+/PPP1oKCgsvLly+vceVx1aRWTlevXh316aefXsvNzc1esGBBzfr160Od2a/mi1R79fX1HgEBATbA/oln5cqV4WazeeSIESMse/bsGQAAKSkp/adMmTJCkiQUFRV5mkymUcXFxb95Ec+ZM2fIsGHDRl67ds0nJibGcurUqX6zZs0yJyUlDZIkCWfOnPFfvnx5LQCsWLHi5pdfftlf2WiVoVROASAhIaHeYDDAYDBgwoQJt0pKSryUjbbnKZlPm82G119/PXzHjh0lykapLCVzmpycHLxp06YyDw8PAEBYWJhNwVAVo2ROAaCurs7DcdzQ0NBWZ9p6z5/AXk/Lisgvb/BzZufdGRHi37Rlftz1rrZpaWkxxMTEWFpaWqi6utrzyJEj+YA9gZcuXfLNycm5XFZWZpw4cWLso48+2rh06dK6L774YsC7774blJGREfDmm2/eiIyM/M2Jdvjw4WvJyckDrl+/7rV48eLaNWvWhB89erQAAMrKyoz+/v5tnp72uepMJtOdiooKl7+hvvX9WxFXa6+6NKfDBwxv2jR1k+Zy2uH4lJqaOjApKanLdjrrm5SciJrSRpfmMzCsb9O/LI3VZD43b94c/Nhjj9VFRUU59aJ3Rk1afkRr+S2X5tQzpE9T4PwRmszp9evXvT/55JMBX3/99YDAwEDb+++/Xzx69OiWjvv5PbJz/iPiVmO+S3Pap++IJkvse5rM6e7duwvnzZtn9vb2lvr27dt2/vz5HGdi03xPytFFvXbt2uWDBw9eWb58+RBJknDy5En/hQsX1hiNRkRERNgmTZrUeOrUKT8ASE5OLt6+fXuol5eXWLly5V276xcuXPAbN25cU2Zmpu+YMWOaHOs7m2OLiHRzA4kaOW1v2bJlkZMnT26Mj49v7Oz53kaNfBYWFnqmp6cPWLduXae/Lent1DpH79y5Qz4+PsJqteY8++yzVc8884yph0NVjFo5TUpKGnzgwIErFRUVFxcvXlz94osvRjjT7nvuSXXX41HCzJkzb9XW1hrLysqMXU3WWFhY6GkwGFBdXW1sa2uDo+vukJqaGrB+/fqw0tJSr4yMjICamhpPX1/ftuPHj/c7e/ZsfkhIiK2hocGjtbUVnp6eKCws9AoODnb5p9XuejxKUCqnju1effXV0OrqauOxY8d+dnUs3fV4lKBUPn/44Qe/oqIiH5PJNBoAmpubDZGRkaOKi4utroynux6PEpQ8RwcPHnxn8eLFtQCwZMmSulWrVplcHU93PR4lKJXTGzduGHNycnxnzJhxCwCWLl1aGx8fb3amrZrvSbV34cIFH0mSMHjwYNv06dMb0tLSAm02G27cuGE8d+5c32nTpt1qbW3F8uXLh+zbt6/AbDY3b9iwYXDH/SQkJNRbrdZss9ncnJ+fn202m29nZWVlO05Ug8GAyZMnN3z88ccDAGDv3r0DZ8+eXad0vEpQKqcAkJSUNOjbb78NSE9PL+h4suuFUvlctGhRfXV1dVZpaeml0tLSSz4+PpKrC5RWKHmOzpo1q+7o0aP+AHDkyBH/qKgol17q0wqlchoUFGRrbGz0uHjxojcAfPXVV/2GDx/e7ExbNX9XkOM6KmC/DPfhhx8WGo1GLFmypO706dN9Y2NjRxKR2LBhQ0lkZKTttddeC508eXJDfHx846RJk5rGjRsXO3fu3Ppx48b9U2JOnz7tZ7FYmpqbm6m1tZUCAwOl9s9v27atJCEhYVhiYmLYyJEjm1avXl2tZNw9Sa2crl27Nio0NLRlwoQJsQAwe/bs2q1bt5YpF3nPUCufeqZWTjdu3Fg+f/78IR988MFgPz8/ac+ePYUKht2j1Mipp6cnduzYUTR//vxhRISAgIC2ffv2XXOm3dRVVy8rK6swLi5ON2/OjDHGtCcrK2tQXFycqbPnetXlPsYYY+6FixRjjDHN4iLFGGNMs7hIMcYY0ywuUowxxjSLixRjjDHN0nyRUmt4+XfeeScoMjJyFBGNLysr0/zvyZyhVk4XLlwYFR0dbRkxYoQlPj5+aH19vebPv3uhVj6ffPJJU1hY2OiYmBhLTEyM5fTp076uPK6a1Mrp+PHjox35DA4OHjNz5sxhrjyumtTK6eHDh/0tFkus2WweOW/ePFNrq3OD92j+TUKt4eWnT5/emJGRkX/ffffdceXxtECtnO7evft6Xl5edn5+fnZ4ePid9957Txdz9ag5VUdiYmJJbm5udm5ubvaUKVNuu/K4alIrp5mZmXmOfI4dO/bW3LlzdTPSjBo5bWtrw/PPPz/ks88+K7hy5crlyMjIO7t27XJqih7NF6n2lBxefurUqbejo6N1V6A6UjKnjl+iS5KE27dvG4h0MefhP1F6CgR3oEZOa2trDWfOnPF3jOOnN0rltKKiwujl5SWNGTOmBQDi4+N/SU9Pd2rao3u/jJX+5whUZrt0eHkEW5ow931NDi+vhBvr/jOi5coVl+bU22xuuu+dtzWb0/nz55u+++67gOHDh9/evXu3S+dBOvbh9ojq60UuzeegiKimf31xjWbzuWHDhrDNmzeHTps2rWHXrl0lvr6+Lh2tPz09PaKystKlOQ0ODm6aO3euZnMKAPv37x8wZcqUX3piKKo1OcURubeaXZrTmD4+TdtjIzWXU0mSYLPZ6MSJE34PPvhgU2pq6oCysjKnpj3SfE9K7Wkl9EjNnKalpRVWVFRkmc3m5r179/5mKvTeSMUpEEoLCgqsWVlZObW1tR5vvfVWSE/HqhS1X/eff/554KJFi3QzKy+gTk4NBgNSUlIKXn755YjRo0fH+vv7tzk7uPS996S66fEoQelpJXpadz0eJaiRU6PRiKeeeqpm69atIatXr77pqli66/EoQcl8OiY79PX1FStWrLi5bdu234xS/Xt11+NRgtLnaHl5ucfFixf7LFy48GpPxNNdj0cJSuZ05syZtzIzM/MA4MCBA/2uXr3q40xbNd+Tak/JIfvdhVI5lSQJVqvV2/H40KFD/c1ms1ND9vcGSp6jRUVFnoA9nwcOHOgfGxurmxsn2lP6dZ+SkhI4Y8aMOj8/P91MdNqRkjktLS01AsDt27dpy5YtIS+88EKVM23V/K3Vag3Zn5iYGLxz586QmzdvesbFxVkefvjh+tTU1CIlY+8pauRUCIGlS5cOaWxsNAghKDY2tmnfvn2cz99xjiYkJAypqakxCiHIYrE0paSk6CKfgLrTn6SlpQWuXbu2108h05GK05+EZGRkBEiSRCtWrKicM2dOgzPt5qk6GGOMqYqn6mCMMdYrcZFijDGmWVykGGOMaVZ3RUqSJEl/wwIwxhjTBLnG3PVH090VKWtVVVUAFyrGGGOuJkkSVVVVBQCw3m2bLm9Bt9lsz5WXlyeXl5ePAl8aZIwx5loSAKvNZnvubht0eQs6Y4wxpibuHTHGGNMsLlKMMcY0i4sUY4wxzeIixRhjTLO4SDHGGNOs/wOtiJB8yM6V2wAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(df.timestep.values, balls_list)\n", - "plt.xlabel('iteration')\n", - "plt.ylabel('Number of Marbles')\n", - "plt.title('Marbles in each box')\n", - "plt.legend(['Box #'+str(node) for node in range(n)], ncol = 5,loc='upper center', bbox_to_anchor=(0.5, -0.15))" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "end_state_balls = np.array([b for b in balls_list[-1]])\n", - "#avg_balls = np.array([np.mean(b) for b in balls_list])\n", - "\n", - "for node in G.nodes:\n", - " G.nodes[node]['final_balls'] = end_state_balls[node]\n", - " #G.nodes[node]['avg_balls'] = avg_balls[node]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "cmap = plt.cm.jet\n", - "Nc = cmap.N\n", - "Ns = len(robot_strategies)\n", - "d = int(Nc/Ns)\n", - "\n", - "k = len(G.edges)\n", - "strat_color = []\n", - "for e in G.edges:\n", - " \n", - " for i in range(Ns):\n", - " if G.edges[e]['strat']==robot_strategies[i]:\n", - " color = cmap(i*d)\n", - " G.edges[e]['color'] = color\n", - " strat_color = strat_color+[color]\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[11. 8. 7. 25. 10. 15. 13. 12. 13. 16.]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "nx.draw_kamada_kawai(G, node_size=end_state_balls*scale, labels=nx.get_node_attributes(G,'final_balls'), edge_color=strat_color)\n", - "print(end_state_balls)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "rolling_avg_balls = np.zeros((T+1, n))\n", - "for t in range(T+1):\n", - " for node in G.nodes:\n", - " for tau in range(t):\n", - " rolling_avg_balls[t,node] = (tau)/(tau+1)*rolling_avg_balls[t, node]+ 1/(tau+1)*balls_list[tau][node]" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(range(len(rolling_avg_balls)),rolling_avg_balls)\n", - "plt.xlabel('iteration')\n", - "plt.ylabel('number of balls')\n", - "plt.title('time average balls in each box')\n", - "plt.legend(['Box #'+str(node) for node in range(n)], ncol = 2)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[11.58 8.94 7.64 24.78 9.74 13.44 13.3 11.42 13.16 16. ]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "avg_balls = np.zeros(n)\n", - "for node in G.nodes:\n", - " #store in graph\n", - " G.nodes[node]['avg_balls'] = int(10*(rolling_avg_balls[-1][node]))/10\n", - " #store as vector\n", - " avg_balls[node] = G.nodes[node]['avg_balls']\n", - " #need both for plotting\n", - "\n", - "nx.draw_kamada_kawai(G, node_size=avg_balls*scale, labels=nx.get_node_attributes(G,'avg_balls'))\n", - "print(rolling_avg_balls[-1])" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "cmap = plt.cm.jet\n", - "Nc = cmap.N\n", - "Nt = len(simulation_parameters['T'])\n", - "dN = int(Nc/Nt)\n", - "cmaplist = [cmap(i*dN) for i in range(Nt)]\n", - "\n", - "for t in simulation_parameters['T']:\n", - " state = np.array([b for b in balls_list[t]])\n", - " nx.draw_kamada_kawai(G, node_size=state*scale, alpha = .4/(t+1), node_color = cmaplist[t])" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[11. 8. 7. 25. 10. 15. 13. 12. 13. 16.]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "nx.draw_kamada_kawai(G, node_size=avg_balls*scale, labels=nx.get_node_attributes(G,'avg_balls'), edge_color=strat_color)\n", - "print(end_state_balls)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([11.5, 8.9, 7.6, 24.7, 9.7, 13.4, 13.2, 11.4, 13.1, 16. ])" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "avg_balls" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "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": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/demos/robot-marbles-network/robot-marbles-network-advanced.ipynb b/demos/robot-marbles-network/robot-marbles-network-advanced.ipynb deleted file mode 100644 index 51d3239..0000000 --- a/demos/robot-marbles-network/robot-marbles-network-advanced.ipynb +++ /dev/null @@ -1,509 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# cadCAD Tutorials: The Robot and the Marbles, Networks Addition\n", - "In [Part 2](https://github.com/BlockScience/SimCAD-Tutorials/blob/master/demos/robot-marbles-part-2/robot-marbles-part-2.ipynb) we introduced the 'language' in which a system must be described in order for it to be interpretable by cadCAD and some of the basic concepts of the library:\n", - "* State Variables\n", - "* Timestep\n", - "* Policies\n", - "* State Update Functions\n", - "* Partial State Update Blocks\n", - "* Simulation Configuration Parameters\n", - "\n", - "In the previous example, we observed how two robotic arms acting in parallel could result in counterintuitive system level behavior despite the simplicity of the individual robotic arm policies. \n", - "In this notebook we'll introduce the concept of networks. This done by extending from two boxes of marbles to *n* boxes which are the nodes in our network. Furthermore, there are are going to be arms between some of the boxes but not others forming a network where the arms are the edges.\n", - "\n", - "__The robot and the marbles__ \n", - "* Picture a set of n boxes (`balls`) with an integer number of marbles in each; a network of robotic arms is capable of taking a marble from their one of their boxes and dropping it into the other one.\n", - "* Each robotic arm in the network only controls 2 boxes and they act by moving a marble from one box to the other.\n", - "* Each robotic arm is programmed to take one marble at a time from the box containing the largest number of marbles and drop it in the other box. It repeats that process until the boxes contain an equal number of marbles.\n", - "* For the purposes of our analysis of this system, suppose we are only interested in monitoring the number of marbles in only their two boxes." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from cadCAD.engine import ExecutionMode, ExecutionContext, Executor\n", - "from cadCAD.configuration import Configuration\n", - "import networkx as nx\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "%matplotlib inline\n", - "\n", - "T = 50 #iterations in our simulation\n", - "n=10 #number of boxes in our network\n", - "m= 2 #for barabasi graph type number of edges is (n-2)*m\n", - "\n", - "G = nx.barabasi_albert_graph(n, m)\n", - "k = len(G.edges)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "balls = np.zeros(n,)\n", - "\n", - "for node in G.nodes:\n", - " rv = np.random.randint(1,25)\n", - " G.nodes[node]['initial_balls'] = rv\n", - " balls[node] = rv" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "scale=100\n", - "nx.draw_kamada_kawai(G, node_size=balls*scale,labels=nx.get_node_attributes(G,'initial_balls'))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "initial_conditions = {'balls':balls}" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "#input the deltas along the edges and update the boxes\n", - "#mechanism: edge by node dimensional operator\n", - "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n", - "# We make the state update functions less \"intelligent\",\n", - "# ie. they simply add the number of marbles specified in _input \n", - "# (which, per the policy function definition, may be negative)\n", - "\n", - "\n", - "def update_balls(params, step, sL, s, _input):\n", - " \n", - " delta_balls = _input['delta']\n", - " new_balls = s['balls']\n", - " for e in G.edges:\n", - " move_ball = delta_balls[e]\n", - " src = e[0]\n", - " dst = e[1]\n", - " if (new_balls[src] >= move_ball) and (new_balls[dst] >= -move_ball):\n", - " new_balls[src] = new_balls[src]-move_ball\n", - " new_balls[dst] = new_balls[dst]+move_ball\n", - " \n", - " \n", - " key = 'balls'\n", - " value = new_balls\n", - " \n", - " return (key, value)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# this time lets make three kinds of robots\n", - "\n", - "def greedy_robot(src_balls, dst_balls):\n", - " \n", - " #robot wishes to accumlate balls at its source\n", - " #takes half of its neighbors balls\n", - " if src_balls < dst_balls:\n", - " delta = -np.floor(dst_balls/2)\n", - " else:\n", - " delta = 0\n", - " \n", - " return delta\n", - "\n", - "def fair_robot(src_balls, dst_balls):\n", - " \n", - " #robot follows the simple balancing rule\n", - " delta = np.sign(src_balls-dst_balls)\n", - " \n", - " return delta\n", - "\n", - "\n", - "def giving_robot(src_balls, dst_balls):\n", - " \n", - " #robot wishes to gice away balls one at a time\n", - " if src_balls > 0:\n", - " delta = 1\n", - " else:\n", - " delta = 0\n", - " \n", - " return delta\n", - "\n", - "robot_strategies = [greedy_robot,fair_robot, giving_robot]\n", - "\n", - "for e in G.edges:\n", - " nstrats = len(robot_strategies)\n", - " rv = np.random.randint(0,nstrats)\n", - " G.edges[e]['strat'] = robot_strategies[rv]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "#Policy: node by edge dimensional operator\n", - "#input the states of the boxes output the deltas along the edges\n", - "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n", - "# We specify the robotic networks logic in a Policy Function\n", - "# unlike previous examples our policy controls a vector valued action, defined over the edges of our network\n", - "def robotic_network(params, step, sL, s):\n", - " \n", - " delta_balls = {}\n", - " for e in G.edges:\n", - " src = e[0]\n", - " src_balls = s['balls'][src]\n", - " dst = e[1]\n", - " dst_balls = s['balls'][dst]\n", - " \n", - " #transfer balls according to specific robot strat\n", - " srat = G.edges[e]['strat']\n", - " \n", - " delta_balls[e] = srat(src_balls,dst_balls)\n", - "\n", - " return({'delta': delta_balls})" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n", - "# In the Partial State Update Blocks, the user specifies if state update functions will be run in series or in parallel\n", - "partial_state_update_blocks = [\n", - " { \n", - " 'policies': { # The following policy functions will be evaluated and their returns will be passed to the state update functions\n", - " 'robotic_network': robotic_network\n", - " },\n", - " 'variables': { # The following state variables will be updated simultaneously\n", - " 'balls': update_balls,\n", - " \n", - " }\n", - " }\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n", - "# Settings of general simulation parameters, unrelated to the system itself\n", - "# `T` is a range with the number of discrete units of time the simulation will run for;\n", - "# `N` is the number of times the simulation will be run (Monte Carlo runs)\n", - "# In this example, we'll run the simulation once (N=1) and its duration will be of 10 timesteps\n", - "# We'll cover the `M` key in a future article. For now, let's leave it empty\n", - "simulation_parameters = {\n", - " 'T': range(T),\n", - " 'N': 1,\n", - " 'M': {}\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n", - "# The configurations above are then packaged into a `Configuration` object\n", - "config = Configuration(initial_state=initial_conditions, #dict containing variable names and initial values\n", - " partial_state_update_blocks=partial_state_update_blocks, #dict containing state update functions\n", - " sim_config=simulation_parameters #dict containing simulation parameters\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "single_proc: []\n" - ] - } - ], - "source": [ - "exec_mode = ExecutionMode()\n", - "exec_context = ExecutionContext(exec_mode.single_proc)\n", - "executor = Executor(exec_context, [config]) # Pass the configuration object inside an array\n", - "raw_result, tensor = executor.main() # The `main()` method returns a tuple; its first elements contains the raw results\n", - "df = pd.DataFrame(raw_result)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "balls_list = [b for b in df.balls]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(df.timestep.values, balls_list)\n", - "plt.xlabel('iteration')\n", - "plt.ylabel('number of balls')\n", - "plt.title('balls in each box')\n", - "plt.legend(['Box #'+str(node) for node in range(n)], ncol = 2)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "end_state_balls = np.array([b for b in balls_list[-1]])\n", - "avg_balls = np.array([np.mean(b) for b in balls_list])\n", - "\n", - "for node in G.nodes:\n", - " G.nodes[node]['final_balls'] = end_state_balls[node]\n", - " G.nodes[node]['avg_balls'] = avg_balls[node]" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "cmap = plt.cm.jet\n", - "Nc = cmap.N\n", - "Ns = len(robot_strategies)\n", - "d = int(Nc/Ns)\n", - "\n", - "k = len(G.edges)\n", - "strat_color = []\n", - "for e in G.edges:\n", - " \n", - " for i in range(Ns):\n", - " if G.edges[e]['strat']==robot_strategies[i]:\n", - " color = cmap(i*d)\n", - " G.edges[e]['color'] = color\n", - " strat_color = strat_color+[color]\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[19. 0. 16. 16. 7. 8. 58. 7. 3. 5.]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "nx.draw_kamada_kawai(G, node_size=end_state_balls*scale, labels=nx.get_node_attributes(G,'final_balls'), edge_color=strat_color)\n", - "print(end_state_balls)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "rolling_avg_balls = np.zeros((T+1, n))\n", - "for t in range(T+1):\n", - " for node in G.nodes:\n", - " for tau in range(t):\n", - " rolling_avg_balls[t,node] = (tau)/(tau+1)*rolling_avg_balls[t, node]+ 1/(tau+1)*balls_list[tau][node]" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(range(len(rolling_avg_balls)),rolling_avg_balls)\n", - "plt.xlabel('iteration')\n", - "plt.ylabel('number of balls')\n", - "plt.title('time average balls in each box')\n", - "plt.legend(['Box #'+str(node) for node in range(n)], ncol = 2)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[19. 0. 16. 16. 7. 8. 58. 7. 3. 5.]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for node in G.nodes:\n", - " G.nodes[node]['avg_balls'] = int(10*(rolling_avg_balls[node][-1]))/10\n", - "\n", - "nx.draw_kamada_kawai(G, node_size=avg_balls*scale, labels=nx.get_node_attributes(G,'avg_balls'))\n", - "print(end_state_balls)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "cmap = plt.cm.jet\n", - "Nc = cmap.N\n", - "Nt = len(simulation_parameters['T'])\n", - "dN = int(Nc/Nt)\n", - "cmaplist = [cmap(i*dN) for i in range(Nt)]\n", - "\n", - "for t in simulation_parameters['T']:\n", - " state = np.array([b for b in balls_list[t]])\n", - " nx.draw_kamada_kawai(G, node_size=state*scale, alpha = .4/(t+1), node_color = cmaplist[t])" - ] - } - ], - "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": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/demos/robot-marbles-network/robot-marbles-network.ipynb b/demos/robot-marbles-network/robot-marbles-network.ipynb deleted file mode 100644 index 95e91bf..0000000 --- a/demos/robot-marbles-network/robot-marbles-network.ipynb +++ /dev/null @@ -1,435 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# cadCAD Tutorials: The Robot and the Marbles, Networks Addition\n", - "In [Part 2](https://github.com/BlockScience/SimCAD-Tutorials/blob/master/demos/robot-marbles-part-2/robot-marbles-part-2.ipynb) we introduced the 'language' in which a system must be described in order for it to be interpretable by cadCAD and some of the basic concepts of the library:\n", - "* State Variables\n", - "* Timestep\n", - "* Policies\n", - "* State Update Functions\n", - "* Partial State Update Blocks\n", - "* Simulation Configuration Parameters\n", - "\n", - "In the previous example, we observed how two robotic arms acting in parallel could result in counterintuitive system level behavior despite the simplicity of the individual robotic arm policies. \n", - "In this notebook we'll introduce the concept of networks. This done by extending from two boxes of marbles to *n* boxes which are the nodes in our network. Furthermore, there are are going to be arms between some of the boxes but not others forming a network where the arms are the edges.\n", - "\n", - "__The robot and the marbles__ \n", - "* Picture a set of n boxes (`balls`) with an integer number of marbles in each; a network of robotic arms is capable of taking a marble from their one of their boxes and dropping it into the other one.\n", - "* Each robotic arm in the network only controls 2 boxes and they act by moving a marble from one box to the other.\n", - "* Each robotic arm is programmed to take one marble at a time from the box containing the largest number of marbles and drop it in the other box. It repeats that process until the boxes contain an equal number of marbles.\n", - "* For the purposes of our analysis of this system, suppose we are only interested in monitoring the number of marbles in only their two boxes." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from cadCAD.engine import ExecutionMode, ExecutionContext, Executor\n", - "from cadCAD.configuration import Configuration\n", - "import networkx as nx\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "%matplotlib inline\n", - "\n", - "T = 25 #iterations in our simulation\n", - "n=10 #number of boxes in our network\n", - "m= 2 #for barabasi graph type number of edges is (n-2)*m\n", - "\n", - "G = nx.barabasi_albert_graph(n, m)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "balls = np.zeros(n,)\n", - "\n", - "for node in G.nodes:\n", - " rv = np.random.randint(1,25)\n", - " G.nodes[node]['initial_balls'] = rv\n", - " balls[node] = rv" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "scale=100\n", - "nx.draw_kamada_kawai(G, node_size=balls*scale,labels=nx.get_node_attributes(G,'initial_balls'))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "initial_conditions = {'balls':balls}" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "#input the deltas along the edges and update the boxes\n", - "#mechanism: edge by node dimensional operator\n", - "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n", - "# We make the state update functions less \"intelligent\",\n", - "# ie. they simply add the number of marbles specified in _input \n", - "# (which, per the policy function definition, may be negative)\n", - "\n", - "\n", - "def update_balls(params, step, sL, s, _input):\n", - " \n", - " delta_balls = _input['delta']\n", - " new_balls = s['balls']\n", - " for e in G.edges:\n", - " move_ball = delta_balls[e]\n", - " src = e[0]\n", - " dst = e[1]\n", - " if (new_balls[src] >= move_ball) and (new_balls[dst] >= -move_ball):\n", - " new_balls[src] = new_balls[src]-move_ball\n", - " new_balls[dst] = new_balls[dst]+move_ball\n", - " \n", - " \n", - " key = 'balls'\n", - " value = new_balls\n", - " \n", - " return (key, value)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "#Policy: node by edge dimensional operator\n", - "#input the states of the boxes output the deltas along the edges\n", - "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n", - "# We specify the robotic networks logic in a Policy Function\n", - "# unlike previous examples our policy controls a vector valued action, defined over the edges of our network\n", - "def robotic_network(params, step, sL, s):\n", - " \n", - " delta_balls = {}\n", - " for e in G.edges:\n", - " src = e[0]\n", - " dst = e[1]\n", - " #transfer one ball across the edge in the direction of more balls to less\n", - " delta_balls[e] = np.sign(s['balls'][src]-s['balls'][dst])\n", - "\n", - " return({'delta': delta_balls})" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n", - "# In the Partial State Update Blocks, the user specifies if state update functions will be run in series or in parallel\n", - "partial_state_update_blocks = [\n", - " { \n", - " 'policies': { # The following policy functions will be evaluated and their returns will be passed to the state update functions\n", - " 'robotic_network': robotic_network\n", - " },\n", - " 'variables': { # The following state variables will be updated simultaneously\n", - " 'balls': update_balls,\n", - " \n", - " }\n", - " }\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n", - "# Settings of general simulation parameters, unrelated to the system itself\n", - "# `T` is a range with the number of discrete units of time the simulation will run for;\n", - "# `N` is the number of times the simulation will be run (Monte Carlo runs)\n", - "# In this example, we'll run the simulation once (N=1) and its duration will be of 10 timesteps\n", - "# We'll cover the `M` key in a future article. For now, let's leave it empty\n", - "simulation_parameters = {\n", - " 'T': range(T),\n", - " 'N': 1,\n", - " 'M': {}\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n", - "# The configurations above are then packaged into a `Configuration` object\n", - "config = Configuration(initial_state=initial_conditions, #dict containing variable names and initial values\n", - " partial_state_update_blocks=partial_state_update_blocks, #dict containing state update functions\n", - " sim_config=simulation_parameters #dict containing simulation parameters\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "single_proc: []\n" - ] - } - ], - "source": [ - "exec_mode = ExecutionMode()\n", - "exec_context = ExecutionContext(exec_mode.single_proc)\n", - "executor = Executor(exec_context, [config]) # Pass the configuration object inside an array\n", - "raw_result, tensor = executor.main() # The `main()` method returns a tuple; its first elements contains the raw results\n", - "df = pd.DataFrame(raw_result)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "balls_list = [b for b in df.balls]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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ZCY2ULI/z5fJWBOtuhbIq2P/Aog43VlfTcu+9JIeHmfziF/PbK1rqCJh9WJ0WMsnSWIXLhTPqnPFdqvSnolMlST+WjhFKhkpWfjhb9lLLqFTpZ9QM7pi7ZPcALn0Z5dJ2Rp0lax3nryHy5huFeHkrAqMFNn4Qjj4O8cU1x8qvvYa6j3+cwI5H8T98tr/AdmU9ZUo5o8/snefsS4/cC1yqSuJiVhAlf4FLdA2lvgfeuJeMzOCNe0llUiXJ41KXUSQVIZKKaIZFKlSSPC6GMjtfLm9FALD5bkjH4fAjCx+bpe5P/hjbNdcw+cUvkjh5EoC2W7eRVOMEB8ZKVdJl4WK1CEqdflJNEkgESppHqSqhiyUjAFdsadf/np3HpSqj6eUulcVeahldCJe/Ili9FWp7Fu0eAhAGAy333otSVqb1F8TjmKwWIlURHNEqYt7SWAwXm3AyTDStLRR3qSqC6S9VKV6wlJrK9w1cDjIqRR5SysvGoChlHqVuHV8Il78iEEJrFQy/suihpACmxgZavvJlEsePM/XlLwNQ+7YeDMLI8OMDpSrtReViPPwXy1Kc/Xup8MQ8SGTJ0p+erivqQpVLP+359HKX4j4Ek0HimficvJaSXLkDiUBJRiaVWkapTApv3DsnrzcLl78iANj0IUDAGw+e02kV27dT8/u/j/+nDxJ86imaru8jogZJH7k8WgSltqbh7EMfSoaIpWMlS3/276Wi1Nb09DzSMp2vLJaSUsvoYhoUoCnMUqZfimuY7pLTFcFyUdUKnW+F1x+Ac+xQbPjUJynbvImJz36O9NgY6TaolLV4j735hoCdK7kHst3RXpKHM5lJ4o17aXe0z8hvKXFGnbTaW0uaPmgyKoWylFLiirpKKqOp6BSNtkbMivmSlFEuj5yMSpHHVGQKu9lOlaWq5DLSFcFysvlu8J2B4VfP6TRhNrPqa/8MQjD2F39Jy80bkVIy/tTrpSnnRST3QG6o3VBSK2hj3cYZ+S0lU9EpWipaqCmrKUkFkSvzxrqNJSm/P+EnqSZLKiNn1EmjrZEGW0PJZeSJeciomSVNP56OE0gELpqMStmy3Fi3kVAqRDQVXfI8LoSVowh6bwdTObx+/zmfal69iuYvfYn4gQMkH3mQoMGDeUy55JexnIpO4TA7aHO04Yl5SKlLO7Qw90JdUXdFPr+lJvcCN9oaS/YCmxQTa6vXEk1HCSeXNvButoxKVck12BpKXsltqN1ARmbwxJc28C7nCrpYMiqlsizlNVwIK0cRWCqg73Y49Aikzt1X7XjPu6m6+y683/se1ESxKXbGX3yjBAW9eEx/+CVyySNnp1cQufyWElWquKPuklZyORk12hrz/5eSnIx6a3pRhFKySih3DaWSUbWlmtUVq/P/l5KcTDorO7EarZesjCwGCz1VPfn/byZWjiIA2HwXJIJw9InzOr3xr/8ay7p1iIf/g7SaxPfi4kchvRmZbk3D0lvsufHYnZWdVJgqlvzh98a9pGX6oiiCBlsDUAIZZcvcXN5MXVndkl9DNBUlnArPkNFSB97lZVReWhmVquWXVtO442cNCm/cu+St46noVEmfowtlQUUghLhBCFGe/f17Qoh/FkK0l75oJaBjOzhWw+s/Pa/TFYuFVV//OkosjC86SEXQTjKy9CNhLhazK7mlfsFyVpDD7ChJRZ17mXLX4Ev4SGaSS5pHqVsEzqgTgaDOVndRZJTIJJZ80rOLISOgZArfE/OgSjXfRyCRuKPuJc2j1DK6UBbTIvg2EBVCbAbuAYaAHy50khDiPiGEUwhxcNq2GiHEr4UQJ7Lf1edd8vNBUWDzh+DkMxCaPK8kLF2dNH3+c6SPPYdJsTD8xKUZU5BW03jinpIrggZbA0KIkvhecy2O6a2apbyGXKBUqWVUU1aDSTGVpJKbbk2XymLPWbs1ZTUYhbEkysxqtFJhqiipjErd8muwNWAz2UrSOr5QFqMI0lJrS94B/IuU8l8A+yLO+z5wy6xtfw08I6VcAzyT/X9x2Xw3SBUO/Py8k6i6806ar2wnmgoQ2/PmuqGLxR1zo0qVBlsD1ZZqTIqpZBUEcNFe4KXMI5TSYh8abY2UGctwmB0ll1Gp3CqlskZzgVKNtkYUoVBnW3r3Vs6FmTMonDHnkgbelVpGOYMil3ap3JgXwmIUQUgI8TfA7wFPCCEMgGmhk6SUO4HZ0TF3AD/I/v4BcOc5lHVpqFsDq7ZqU05cgK+05e8+TzBwiEoa8B5c3DoFUkr+/YWTjHgXP3TM/4uHiO7dt+jjdx54nKf3LOz6mm4p5l+wRTyck75xHnjmC6TSC/tQc1ZQLh931L2oF/i/Xvgnjo4svH7BVHQKgzBQW1Z7Torg5UNP8+RrCzZq8y2Oc1VmnpCb+5/5PPHkwm7D6RVEY3njogPvHnrpmxw4s/CqebNdQ7k8F2Lg2As8/up3FjwuN0T4XJVZMOLn/t98jkh84VFY05+jBlsDaTWNL+5b8Lwdr/wbewdfWfC485XRvpO7eOTlby14XDAZJJFJnPNzNOoe5Ye//Aj7T7224LEXymIUwYeABPAxKeUksAq49zzza5RSTgBkvxuKHSiE+CMhxG4hxG6Xa4kjCTffBc5DMHn+o36U8nJWffB6FKFw6DuPIxcxlPT4VJj//eRRHhgYXlQeabebic9/Hve//uuiyzUxci9R11cX7BCcbgXlvhfzcL74+n00iB+xZ/D5eY8rZAUtJnJ21H2S6sy32X144RfMGXVSa63FoBjOqUk/NPQ1Mr6voC6w0MxsGS22o/LF139Mo/gJ/UefWvDY2ZXc9HyL4Qs7qUz+C/uPLPxcOKNO7CY7NpONBuviZXTi5Ncxhu4lmZp/OofzltHBn9Oo3M9LBxeeDHK2QTE932JE40Gs0a9x+Pg3FpW+UTFSU1ZDlaVq0YF3h459k/L4PxOO+ec9brqiyX0v5h4cHX6RVWUvkVjgHiwFCyoCKeWklPKfpZQvZv8PSykXNqcuECnlf0gpt0opt9bX1y9t4hs/CAbzeXca52i+dTux+ATVSgevPfrQgsf3n9aGZx6ZWFxnXeDxxyGTIbp/PzK1sAUei7upKxvFbg7g8p+Y99hCD+diHv5MXJuGe9K1a/6yJwIk1eScSm6hF+D48E4ArHJhJT1d0TjMDsoMZQteQzodoc58CqsxzvDU/Hmcr4ziEc1Sd3vmD15MZBL4E/5zVgRHz2gycigHFqXwc+maDCZqymoWTF9VU9SYjmEypDkxNv+qaecro1BQs3ID/vnTV6WKM3buyvLY8CsYFJUq4xGknD/AzRl1Um+tRxEKQgjqbfULPqdSqlQZD6EIydGhlxdMH84qsUZbI+6Ye8HAO6+vn3jaQl/7tnmPWwqKKgIhREgIESzwCQkhznfYwZQQojmbfjOwPI4yWw2svQXe+Blc4Pzsjq4yqsz1HHroKcaPH5332P5TmjV8ZGJxcxUFdjyKMJmQ0SjxQwuvzHRi9OwDeXz4xXmPzVlB1WVaf/1ihhaqaopKg3aN6dj86zLMriDyltwCU/z6/VoFUWWeJB6f/2WcXskt1r11avxVDIqa/b2wjKZfQ4OtAU/cM++SlVJKKnLjI5L7zzl9WFhZOrMKpsLkJxw5s2AeuXRzeSwko3HXXswGbfTVyOT8rpXZlVyDrSE/t/98lKmaEjZm5o/Q98V9pNX0Octo3KWV22qM4vbNv+rYbBktplXjDRzFatSucdI9v1FU6D7n1oeYD2N6P6ORHuxlZfMetxQUVQRSSruU0lHgY5dSOs4zv0eBj2R/fwTYcZ7pXDib74aoGwafuaBkan7nLUg1RbdjE09886vEw4V9nlJK+k97MCqCyWAcb2T+YY7xY8dIHDlC7R/+IQCR1xb2E447d5HImAgk7Hh981tazqiTBmsDitAegUZb44KLcnj9BzAbErhiNdiVo6hqYt70c+nC4i05Y3o/rlgNAKfGF7a0zrWSG5l8mYyq4I1XEQzML1Nn1EmVpQqLwZJPX5Uq7ljxoYXh8AmsxhCuWA0O4ynS6eLyLGQpTt9eDJncl5fRidH5ldn0zujcNSyoLMc0ubtjNcTD8/dDOKNOzIqZSktlPv1cvsWIxcawm1y4YjVUmsZIJovLc7aM6qx1KEJZ8BqS0b15GQ2WQEaDI1qarlgNqdieBdMHqLfW59OH+e9zMuml0jRCTGyaN+2lYr4WQc18n4USFkI8AOwC1gkhRoUQHwO+DNwshDgB3Jz9vzysuRlsdec15cR0TM11CHWKdssaIl4fT//7Nwta1SddYdzhJLde0Qws7B4KPLIDTCaq/9vvYe7uJroIRZCK7uZUoJNTwbUYUvvnte4LVaIwv8V+auwlAHaOvxejksLvL27NzbaCaspqMAjDvBVEPD5OuXGK/ql3EktbGJsqbo1GU1FCqdCca1jIUoyGdjMUamUwsAGL+gZyns7rQpbi9GsrxOlsK2Pn+G0oQsXlLX7fZsuo3FROual83vRTKT8VyhD7XDcSSNhxuou7nzJqBk/Mc86VXCAwwFi4iaP+LVg5jKoWN1pylagQAlicjEYmNUWzc/w2QDNgijFbRkbFSG1Z7bzpZzJRypXjHPRegytauyijKFfuXF4LtY493n48sWoOeK6lQhkknS7eAsoPETZoY2wWE8A5mb2v5fbSu4Vg/j6CPcDu7Pfsz4LDFaSUd0spm6WUJinlainld6WUHinlTVLKNdnvpZ9zd7EYTHDF78CxJyF6YcWwbW3GYKrgrVe8lxMDr7D/6bmRy69m3UIffUsHML8ikOk0gcceo+Jt2zFWV2PbtpXY7j3IdHGXRCoVwCrO4E1tICauoMzgIRYr3ildVBHM84J5fa8yHm6kfdVtqFJwZqK4xZ5LJ2cFGRQDddb5hxbmXB6rG9/KoL+LZLS4pTXbUsz9dkVdRV/gTCZGGUeZjK1HNW3BrEQIh48VzaOQpTg974LnuF/FG6+ibdUdpFUDZ+Zp1eQrufLFV9Qe7wBCSBrqrmfQ34Oa2Fv0ej1xDxmZmVPJeePeooF3qprGrB5gOLyOMttWjEqSYLB4X8r5PEejU68QTtpoXfUB4mkzw/M8R7NdjLnf86Xv9+/FIDJUOrZxKrgGQ+r1ogo/tzjT7PTjmXjRwDspJSK1n5OBNVRVXYsiVPz+4lViIUWT216MoYmXSGRMdDRdXfSYpWQ+11CnlLIr+z3703VRSldqNt8FmSQceviCkqm683pkMkyDp4LOLVfzwg+/g/PMqRnH9J/20uiwcFVbFQ12C4fnUQSRl18m43ZTdac2uta2bRtqNEr8yJGi5/j9ryGERLFcha18KwAeb2FrUUpZtJIrZqWoahqReoOTgR7ec8VaxsLNRdPPpTPdCoKFfa8jEy8TSVnpa92CK9FHmRgu6jaYbSnmfifVJP5E4VEcgcA+FJEhY7ySysprAHDN06Fb7AUuJiMpJZn4Po77evitLV2cDrQRDBQPOMwFStlNZ8NyFmrVjEy+TDJjor1pK4HMBizCRTw+WrT808sNZxVnsSUrQ+FDGEWchLKJ2hrNGp1YwGI/10ouEd3DCX8377+qg0F/F9F53E/OqFOLT7DWzchjPhmNTr1CRlVoqr+GKJswKSEikcKDJ+aTUbFriEQHMSsBIlxBS/020qrCyDyt19nKMtc6nk9GkeBuTvo76VtVW/SYpWRRcw0JIaqFENcIIbbnPqUu2EWheTM09GnrFFwASpkFpSIAopGbfvujWO0OHv/GV0jGtfHgUkr6T3m4trMWIQS9zQ4OjxdXBIEdOzBUVVGxXROzbZv2QkYHirsZJlyvkMoYaa67io7GjYSS5UVdK+FUOB8olWOhSi4cPoxRxAjLK1jfZOe4bw0yeQC1yJwsuTnwp7PQCxwO7eaEr5velmqE5UoAfP7C11zMUpzvGpyeV1GloLZmGz3NPbhiNUVllFK1QKnp6VeXVWNUjEXTj0ZPYxI+vKk+1jc5OBVcg5I+RiZTOG5kKjLTrQILK0u/v5+TgQ56W2ox2TRr0Vdk5M1UJCuj8gIyihS+hpxyr3Rcw7qWVkZDzUU7Q6dHXuewGq3YzXYmI4Uj9+OJScyMMxFbz7pGO8PhtZjkaZLJwq3yqegUtWW1GBWCJn1VAAAgAElEQVTjjGuYT0Zu76sMhVazvqWJ8grNKPIWMVomo5P5NKenn8u7EL6sq8lavpX1LY0MBdvweIu7n3L3OUeudVws/VQqgFE9xZnQWloqS99RDIuba+gPgJ3Ar4C/z35/obTFukgIobUKRl8D9+AFJeV4Vy/CYCLx1AHe+6efxj85wW++8y2klJzxRHGGElzbpXWt9DY7OOkKk0zPba5mgkFCv3kGx223IcxmAEwNDZg7OubtJ3B7shXEqjr6VlVy3NdDKFTY0ipkBVkMlnkX5cg9/OUV2ygzGQiqGzCIBKFQ4cCv2RVELr9i6ScSToxylNPBNbTV2Giq20IiY8ZZpBLKvUTnYo1OunYxHFrF+pZmNrQ4OO7tIR7ZU9C14o66kcgZ16AIhQZr8WvwZytkk20rBkUQF5tQRAZ/oPAIq2IyKhZ4l0oFEelBBv09rGmw01a/gXDSVnQo7/nIaNy5i8lIA2ta2ultdnDM10M6/npBhT87UCrHfMrM79NaSMJ8FUIIVNMWbXuRjvtCMmq0NWrLYxZYsjKTiaMmD3Pc18P6JjtdTT14YtWMF5FRIRfjQjKacO7CF6+ks3Fd1ijqQSaPkMnMDQRMZpL4Er6C11BMEeRa92nDlhlGQilZTIvgk8A2YEhK+Q7gSmDp14pbLjZ9CIRywa2C8huvQI27SJxM0LphE9d98EMcefE5Dr3wDP2ntPiBazu1Zl5fi4NURjLonDvCKPjUU8hkkso7ZwZd27ZtI7pnDzIzd+xxOh1CTR3nuK+HvmYH3fUVDPp7UNRJYrGxOccXsqZz/4s9/FPuV5mK1NPV1AGAtTxrjfoKuz6KVXLhVLjgohw5qzZt3IKiCHqbaxj0d+IuYmk5o04qTBXYTLb8tvk64TKZBOn4AY57e+httrO62spQaB0KgYJug/ORkdPzKv6Eg7b6tQA4HFeTkUpeiRa6hkLpFwu8CwR2I4QkIjdhNir0tlRx3Ncz7z0wCi1QKsd8MpIyQzy8l2O+HnqbHdRVWJiM9aIQJxSaO3w5L6PyxcvI7X2VaMpKU3aRmbrqLSQzJrzz3OdCMsrtm00wuB+FFN7UBuxlJnpbKjnu6yYc3F1Q4RdzMU6/vulIKQkGX9Nk1OKg3GLEn9mIItIECij8Qooml0cxGXl9/aQyRmprthTcXwoWowjiUso4gBDCIqU8CqwrbbEuIvYm6H6ntp7xBSw0oygK5tUgypqI7D3OdR+8i9V9G3nmvm+z5/Wj1FVY6K4vB6CvWfMJF+onCDyyA3N3N2UbN8zYbrtmG2ooROLY3M5Nv383AhV3so8qmxmzUSGubM7um/uCnevDKWWGUHAPx3zd9DZrI4e7m1oZCzcV9LHPDpSanj4UfsH8vgHi6TLqa7QKordZs9jV5CCp1NzpBApVEHW2OgSiSAXxOoIUI5F1rKqyIoRAWrLWqH9uRXruMpL4fAOaomnRhlKua25gKNhacGTP7ECp6elDYRn5/P2kVSOVDu3e9jU7OObrhsw48fh4wWuos9XlhwiDFnhnMVgKXkMofARBhJOBNXTVac+qyaq56JbiOQKt5XrC30Vvsxa/sr65lpP+zvxAgdnM7svKpZ/bNxuffwApBdZyrdxrG+0c9/cgpI9o9GTBa3CYHZQZz7pgzAYz1ZbqgtcQi51BqB6O+3pY16S9x7aKLahS4JvnOTpXg+JkoIP1zXUF95eCxSiCUSFEFfAI8GshxA5g7lN3KbP5bgiMwNBLF5RM1QeuQ0qVwBP7URQD7/3TT2MyW7C+dD/XtdnzzbyO2nIsRmXOyKHk0BCxvXupvPOOOU3CfD9BAfeQ3z9ARjVgq9ic31Zf3Uc0bZv34ay3zYzYLtZcDYePgQxpze2sEutrdnDc10MwuGfOVA3FKoj5OuFcnlc57uuit1mzXpsryxiLrcte39xrLqQITErxyFm/vx8pBcayK/OyXV3XjS9eVdBiX+gFnm1dxmLDyIyTY75uNmSVZV+Lg2PeHqKRA2QyM90YuUCpojIqMIzX7ennVKCd9S3afau0mfClNcVZ7D7PLv98gXc5t41q3ILRoFUNXU1tTEQa8ZyjjAoF3iUSLjKpMxzz9dDXMk1Gvm6S8eOkUoEZx8fSMULJ0Dk9Rx7vqwyHVtHTpA3TLjMZiKO9F4VkVEjR5K6hUPq5ZyXKFdjMWr/F2qYmhoKtBQdPzCejQoF36XSIROxovnV/sVjMFBPvl1L6pZRfAD4HfJflmCyulKy/DSwObSK6C6CsqwVSE6S9VtR0BntNHVv/+yeojLvpHX4uf5zRoLC+yT5HEQR2PApCUHn77XPSNjU1YWptLRhY5vX1czrYzrqmsxV7b3Mlx7xdRR/OSkvlDCsIzg4tTM2Kts65bUJyI44yUzZ9rZJDRgmHZ0ZuzvfwT9+fI5l0k0yc4rivm96sohFCYLNtIq2azvkFLmgp+gYYj7TQ1diS39bXXMlRbw8eX/+cij0XKFVlqZqxvVjgXa5VMRXvpd6uBaCta9KUpSBFIDhz4sBzlVE6HSYaOcwxb0++VQZQU9lLPGPFX6CiLtRhn8ujYCXn78cTq6O1/uxyI7n77PfvmTNVQ941ZJ3r/y4UeJeT0XBoLe01mkuvu76Ck4E1CCT+wMw+rXOVkaomCAb3a4pmmoyaansIJisLymj2qKfpeRQ2KAYIpxw01KzNb+vNGkWh0BtkMjODLOdzMU7ffzZ9rXU/6O+hp6FiTv6lYrGjhq4SQvwZsAkYlVIu7eofy43JChvuhMM7IDl/aPxCWK+oRrFUEXxSe+iHbG3srdxM/MBLHNt1tsXR2+zgyEQwXwFJVSWwYwfl11+PqXHugwlaqyD22u4ZE9yl0xFCoYMcnVVB5Cz2ZGKEeGLmCI75KlGYO7TQ7x/An6ijpaYzv63ebsGV7APmjlpZ6AWe/fDnRgYd959tbgOsa67lVKBjjsWeC5Qq9AIX6qhU1ST+wF6OeLpnVBC5FziT9hCNzlxtbio6Rb2tfk7LrFjgnc/fTzRtp7ZqXf6cCouRuNiAlCJvbecoJqPastqCgXeBwB4gk/ff51jfXMUxbzfeRbYIcnnOTl9KFZ/vNY54u2c9R3aO+XqQaphQaK7Cnz1EePo1zb4PPv8ASbUMa3kfiqLJyGxUUCwbyUjTnIq6mIwqzBXYjLY56QeDB0Amsv1A0+9zJUc83XiLKPzFtgiklHh8/RzxdNHXXDkt/ayLTiYJBmdOK+KMOikzaFOYT6dYq8bvHyAjDWDaSJnJMKdcpWIxo4Y+jzZldC1QB3xPCPHZUhfsorP5bkhF4MhjF5RM9W+/FZmOEX75DKDFDxxtuZGm7rU8/e/fxD+lVcq9zQ580RSTQc1lENuzh9TYGJXvL97Ysm3bRiYQIHHi7AgnrYMqM8OazqV/1LsGYE4lNBUpbCkWeji1CmKAQ56uGS8XQGvdaryJpqKVXGP5zDxsJht2k73Aw99PSrUgjOvzzW3tGuwc8XYTDh8hlTrbeioUKJW/hvK5iiAYOoCUcY751sy4hnVNdk74e/JlmH0NxdKffo05fL4Bjs6qRAG6GpqYjLbOUZaFRvSANrSw1jo3ctbnH0CVBkLqOmrKzfnt2n3uIR47QyJx9pxcoFSha2iyNc0JvAtHjpPJBDjqnSmjjtpyzgSzz5F/7n0uVIkWr+T6OenvYn3zzPWo1jbVMhTqmNPyy8uovLDFPteg0GQ8HlvL6mprfntvs51jvjWkUi5isTP57Sk1pRkUBdJvLG+cE3gXj4+QSk5mlfHZd625sozJ2HpkgX6CnIyKGhQF7vNIqIM1TRevfwAW1yK4G9gmpfw7KeXfAdcBv1vaYi0DbddDVTvsP7cpJ2aPgDHYbSgmDzJdT9oXpP+0h63d9bzvU/cghOCJf/kKmXQq7yPNuYf8jzyCYrNhv+mmGekFooH83P+F+gk037fCWLSH9tpyUtEQqYCf6nIzaaWblGoraLHnXtZUJoMvpimjQhZ7JHKCdNrPMe9Zv25sQgtg6mtxcMjdhc//2gy3wexAKVfkrIwKWVp+3wDDoW7WtWj9A/GpSdRkKu9jB0lgmttgtqUYjCeIZWdnbbA1EEgEZgwtzCmqk4Ee1jRWkA6HSPq9lJkMlJd3EstUFX2BAdKZDJ5otKiMYrFREokxrcVRQEYH3F0EgvtnuA1mB0pNl1GhVo3f189EtIM1jZr7L+5ykkkmsh3GmjKbfp9nyyicTBJJJvPbZgfe5azx49kWRzoaIen1YDQoNNe0Ekg1zisjVVVxR4rLKJn0EImc4LCnK98qy8uo2cFBVxeh0KEZczPN7mtyR6Ko2dZwYRkN4Em00l7fghCCpNtFJhaf9hzN7CfwxDwzhghHkknCWRkVCrzLjc7S/PeVZGJxkm4XQgi6GppwJ1oLGhTTZeSaJaPp15BOhwkGD3DQrRkUaibD5K+eQi0wUnCpWYwiOANMdyZbgLnd75c6QmitgtM7IVA4UnM2T515ihseuIFHTz46Y3vF9i6E0cLo/S8w4o1xXVctlQ1NvPt//CmTJ0+w/1dPsD7rAjkyEUKNxQg99Svst9yCYjs7HNIZcfIP3/gHvvLjrwBgXr0KY0vzDEXg8w/gTHTSWV+PokpO/t0vOfbFxwHNbTAcXjPDkpsdKPXJF3dz9YuvE06mClpyuRcn53cd/Lf7cH7jBJ7+V+hrdnDE000mEyIUPhv1PN0KemlknM2vHuVHB7XRTrMVQSrlIxw5xuvOTvqaHcQmRhn/pzc4eu9/0tNQwXCoE1UaZ1Ry04ctSinZ/vwe7npuIJ8+gCs67QX29+NPrqaxqpEyk4EjX/wpJ//Xs6iZDL3NlQz6NRnl3XSzAqU+88pernr5EJ5YvOALnHv5c5Xo6R/8GNc3Bpl85td5H7tUEwSDZ+dmckad+UCp/ZMuNr96hG/tO1RQRplMlGDoAAdcWqss6fcy+tU9HPnH79JWY8ObaCMtrTPu82zf9LueeY33z5LR7PsczdRTVraKSquJI1/6Eaf/8SUyyUS+1eH3vzZjqobpMvpf/fu5ctdhRoKhfODdTBlpz2yuj2PkZ/+F6xsnGXvssbwbE2ZO1eCMOvPzL53w+tmy6wj37j5QUEaqmiIQ2MtBl6Zo0tEIQ1/u5/CXvkeDvYy0spqEWjWj9Tq7VXbbswPc+mxxGfn9/SRVOzG1lUaHhSP/eB9DXx4gHQ7R2+zggKuLQGDfjLmZprthv7H3IFt2HeGo26u1js32GYF9udZ9zugaf+wx0s+Vc+q7JZ/1f95J5/6PEOKbaIvSHBJCfF8I8T3gILDwskKXIpvvAqQ2lHQBRoIjfOGVL6Ci8qVXv8SpwNkpJezv2Yaa8JE5pokpFz+w9robaepZy8Hnf0OFxUhrjZXD41oAmRqJUHnnHfk0VKnyxSe+iC1pIzZxNlClfNs2oru1MdGZTIxg8A0OZisI1892U2FoocrUjvO5Z+lttrNvqoNo9BSJhFYxTg+USquSp1MKUZOFn50cptJSOWdRDr9/gLhaS0JtZHW1lcSgGYMwMvHLfXkfO8x0P02vIL53ehxVUfjJpDYEdHaTPl9BZJvbww88gdlgxehbjUlAe10NrkT3nPRBe4FfmvIwabGx22DFm0zNsUZVVRvffTRbSUeGTlEp1mA3NTLxy1/S22xn/1QHicRkfm6mYDJIPKNV+qqUPB5XSRhN/PjEUMHAO59/gLS044qtoru+gvDBFIow4Hp2kN5mOyd83UjEDGtxhoxOjaIqBh50hwrKKBDYh5Rpjng0S3Ho/ocpM1RQFulEJhKsaaxiPLp2RjzBdBm94Q1wpqycA6ZyxmOJOTKSUuL3DzDo19xCcZcTe3oN5cYaRn/xiFbJOTtJpwP5uZmSmWTeoJBS8kgoScpg5AcnhgsG3vn8/ajSwlCojfVNdvy7/ShCwfvyuBZg6e9EYpyhzGbIaHCYtMHAQ4FYXkauqCsfeBcKHSSjRrMuTDsjDz6E1VhJeaqbVMBPb3MlZ0Jr8PnP9hNMbzWdCkc5aqnghKWC48FwwVaNzz/AcHgt65urSIdD2OLdWI0Ohh58mN5mO4c93ahqPD83k5QSV9SVVzQ/90XJGAx87+RI/t7Mfo4kBgYDnZrruH+StJpk9ftvo9TM1yLITTj3MPAZ4DngeeBvgSdLXrLloKYT2t6iLVgzz8yDyUyST+/8NAZh4Ie3/pAyQxmffuHTeXeEoiiY6hKYrS1sVaN56x9gw/abcA+fwTV0WrOoJ4IEHnkEU0sLtq1b88fdd/A+IsNax7UpYSIY1FxItm3byHg8JE+dIhDYi5Qp3nB1cyNGkm8kOBM+hCozTD17VBsV48n5wLOjWqZZir9xegkbTQip8vMp35xF5rWx8f0MhdbT21xJ4PW9VBrbUaWKJdJOR6WZSKaWuNo8xy2huR9Unk+BkCpvKGZc2YraE/PkF+Xw+QdQsXAm0EZfcyXpMTuqVLEZqxl/9FH6mh0ccncTCh8inQ7n088FSv3ozDhCqmQUhftPj81p1YTCh8hkIuyb7KCv2cHIz55GEQZUqeLdNZbt6FtTUEaNtkZ2eYN4jRaEVHnYE8zLboay9A0wFl1HT6ODxOlBKpVO7RqSnTSaJQZjJeFM+wy3RM5SVKXk6VgGIVWOGcsYiSfnBN75/P1aBeHvoq/FQeKUEVWqlBkqGPmvh+hrcfD6VCfR6GB+bqbpldz3To4ipIoUgh+cHJ4jo0h0kFTKy97JdvpaHIz89FGMihlVqgT3B7JDPGf2pcxQNMEoE6YyhFR5zH/W9THboHAl1tJaU4nim8JBF6pUqVC7KU+EqLU78CV7CspISskvQwmEVBkylnEsHJsTeJc7L9cqCx9JokoVs2Jl6MGH6Wt2sG+yg0RiIj830wwZDQ5r77yUfH9wZM4w3nh8nHh8lL2TWst1+MGHMRtsqFIlejRNX8s0oyhbFn/Cn1+caTAS57RRk9GT4SRSygIy6sef6qbS5sCRjlCR6SYkTlJWP7cfZqmZb9K5H8z3KXnJlovNd4H7OIwVX3jl63u+zmHPYb54wxfZXL+Zf7jxHzjhO8G9r51dwbPqjqsRQuFPEqH8CAmAdTdsRzEYOfTCM/Q2OwiOjhPZtUuLHVC027HPuY9/2/NvtEXbMFVrIzKOndQssbPzDg1kLQiFgK+Ljft9hFKTjKaeIKCexhrvYF2NiaHQaiTW/Isy/QX+0ZkJylIJNo2eZL9qYCqRmvFwRqOnSKU87J1sp7fZzthj2lDUkP0gVmMlkzt2ZOeLWYffvxsp1Rluld+4A0QUI9eePoIqFH4+qllHGZnBE9eirf2+AXyptditNkzH9lNpaiVoPURaTeLrn6K32cHeyU6kzGRHzpwNlEpJ+E00TY9rjJpwgJ9P+uY06Wf7vtXJKkLpKQLyOOWpLtZVwESkkQxVeWU2vYL4welxTOkUV46c4CgmhrIWdU5ZxOMTxOLDvOHUrLiR/3oGRRgIOw5hMZQz8uBD9DY7OBVYSyCwN+82yMnoFX8Yn2LkmjPagj/3D03Oif71+wYIZ7pBsVHjOoPD0EHAfIhkJkroQJTeZgdvuLQRXbkRWFPRKRxmB0bFwpOhOO2eKRoDHh5yh6i11s4IvMu1to56e+hrtpMYshBN+wgoR7DLbjoNEbzxGpI0zXmONBmNoagZtg4dY0gxcSRbUeeOSaX8hMPHOOzpzrb6HsegmAg7DmFSLAw98Eh+OotQ6EB+SudcX9a+UJRJxcS2M8cQUuVHZ8bnysjfT1y2EU07aE15cIguAobDxNJB4iey83t5urMy6s+fa1JMVJmreNQXZZXfzWqfi8cCUSpM9hmBd7mRa4ey/vvoMZV4JkzAeJhKpYuWmItEpoKo2lFQRprBItl25ihOxcTuYHSGjLTW/YF8/MDwTx/BpJThuPLs6KRSsqjhoyuKDXeCsazoOgXPjzzPj4/8mA+v/zA3tWkdu29d/VY+uuGj/Oz4z/jVmV8BEGxrIRgYoZvqfAcXgLXCTvfV13DkpedZ32Dj7SN7QVXzsQOBRIB7dt5Db7oXoQp6r+slJVIcGdR88Ka2NowNDURfe01z28hu/jpTg0in2Tn5KH3XbMG2VlBmsKM89zQWoxlfev0cS67MXMfOaIoe5xh32C1IIfivSe+M5mruhXnD1cX6hnKMgVUE0sOs+f/uJqXGCez109tsZ+9kB+m0n3DkOL6Ej5Sq9Tf8eHgSazLO79XYqA/5eGDcNaOiTqWChMKHOZGtpCd+qc370/rbNxISJ6lQu+mtkJwMdACG/AuWsxSfdgeJCoW3keIK3xTHVMFkyojVaM1XED7/AElaCSYdrB45gMO0ClnrwrHZjtlgJfrEL6ktt+BK9uYtudz1Oyz1/DoUp8s9zgcdZSAlPx1zz5BR7py9kx3aaBtXHaHUBGv//CMkMmEiR1L0NjsYGGvX3AahA8TT2hTHjbZGfjQ0iTmd4sPVNpr9bn426aXeelZGmUycQPB1TgfXsK7JwcQjO1GEQvN7NxMxncJBF+tNEYaCbUhhnXGfG2wN7PSF8AsD16cjXBn2MiIVjkTTMwLvfP5+MqIOV6yO7sAQlYZ2kvZRqq9rxqiY8T70FK01Viai6/P9BLlza6z1POGL0OGZ5P+ptqGoKvePuvLKUnM7vQZI+sfa6W1ykB6zE0m7Wf9Xf0g07Sdx2kRvs53+sbaswt+rxSFE3TTYGrh/xIkhk+GuaiutXicPu4PUZqc3d0acqGoav38PI5F1dNWVM/WLX2MQRurf0UXCNoRD6WRNYorxcBOqqMwrvpyMXgtGmUJha8zPNfEALgz0B2ZW1H7/AFLYGQs3szbtplLpJF52moZ3rkERBiZ/8RutTyu8lkBgD6qayj+DddYGHnYFWO1zcne1DWMmzY+HtWfYHXeTzrovpUwxMN6muecGFWLpIKs/cHFCtnRFMJuySi3A7OAvID0zOGQyMslnX/4svTW9/OXWv5yx78+u/DOuqLuCL7zyBUZDo7x6ysMLqRBGax3h52eOLe57203EggGqvKd41/Buwj19mDs6kFLy+Zc/jzvm5jp5HTU1NVyx5grcZW7GR7RgbiEEtmuuIbxvgGBwP9K5hs0YmRTPEct4WHvHH9N21wdIZmJEDkRZ32xn0L+GSOQEyaQnHyi1MyBJCcENapwbuztoDHp5YNRJvfVs5Kzf149UanFG61l/+jXKjbWYWiOYq2oIKyexy242V6TZO9kBaNZ37sWxWhrZGYqzxjnK9htuYJ1zjBMpSVjRXuCp6FR2ojHJrtE2+hqsmMNtBDJDVG7cROXV1ZiUMmpeeY5kxkKcdXmrLGcp/nTMhS0R411NddxsMyKk5OeTvnxFLWUGv/81JmPrqbdbiO48iipVWj/wdlb/zvuJZ0LETkgtpsPTTTw+Riw2mn+B98XKiCG4Jh7iht51rPK7eHDCQ701G3inpjRlKSoYCa3iiokD2E1N0OjFaCsnZjlDpdLFFkuYg+7OrIwGziqaskZ+5YvQ7Rxj+3XX0uceZ0wFFzX56wwE9yFlkoHxdvoaylG8jQTTY9RdfwN1N7ZhUExYn3sRFQNhdf0cGT047saSSvLu+irebS/DoGb42YRnVkXdjzvZR4XFRPK5vQghaLntalre9z4iaS+pESt92VZHKuUlEjmRl9GxhIMAgitDHm7YdAVt3kl+MeWlzqoF3oVTYU2BCwunA21sjpyh0thKumoCxWwiWTGMw9DOlpST475OwIDf34837iUt09RYG3nUFaDTPc6NV1/FJr8TjxQMpavy1xkOHyaTCbNvsjPf6gunnTS88100vqtPW4v4uVcxGY34030zWn4NtgZ+NuHBlEnznuoK3l1TgSmT5sEJ94yWn8//Kv50H0aDAcPzu1CEgYZ3rqfxpncRSk0hndX0NTvYO9lBJhMlFDqYv8+jmWqcUnCFb4obt15Np3uCxz1BaqxNqFLFE/NkjRyFo55ONksXDkMnifIhDGbLOVZg58d8ncU/yn5/8qKU5M3E5g9DzAfHf5XflFbT3LPzHlKZFPe+7V7MBvOMU0wGE1/d/lUEgnt23sOuU04erGtCZpKEnp05P1Dnlqux2h1MPv8k7aEpDl1xIwD3H72fZ0ee5RPrPoFrzMXmzZtpq2zDXeYmHowTCmmdibZt24hVuFDVJN3ja3ijCgZO7aJ7VRlldasw2R1ETCdx0MU2R5Jdo62A1jGbC5R6cMJDdTTEDc31tLe3s3ZymMFkhrS5jUQmQSARwOcfwJ/eiCIEhgNe0mqK1g9pHVfV1zZiVMyse6MfT7wGDJrbIPfwH0rWkUZwZdhLfX09240qilR5OawNQHNGnZrbRpg56m3jhvH92IxVWDo05bv6zjuJpQOoZ0w0OiyMRtcRCh0gk4nijDqpsK7meX+EtVMjdHa0s7m9ldXeKX424aE+a8mFQloFccDVxYaGMiyRdoKZM9jX9WIwW4hbz1CpdPKWsgAvDedkpCmzaks1v5gKYI9H2d5QTVtbG+smRxhPq4SMbUgk7qhbizSVG5AolL0+gioztH5QaynWva0bRRho37ObcKoC1dCJz9+fr1xOZpqIA1f4p2hsbOQdNiNGNcNzAS2WYio6lbVeFfZPtbM9cJQKYwNKkzbss/E9txBOu5HjdjpryzkTXEskcpxk0osz6qTK2sKTniA9zlF62tvZ0NFGu3uShya91NuacUadRKOnSSbdHPV2s77ehtHfTCA9Qs3Wa1EMBtKOMSqN7dwoJ3lluA0gf58tBguPu+NYU0neUWNn9erVrHeO4lXBpbSevc/+fhJiPWlponLPMaSUrLrjLQA033olilCo3/sGiYyFlGEtPv9AXkZjchVBCb3ucVpaWniHw4olneJXvgyKUJiKTuUr9l2jbWxPnMZhXIVaM4ViMFC//e0EU+MIdwNrGys44e8hHh8lHh/XAuKszTzi9NPpGkZe7PgAACAASURBVGddexvrOjroco7xmNNPjVWTUTw7kGAwsIaeBjvCWUsoNUnDO96pvdD1ThzGFt4aPcXAePsMGQkET/sllkyad9jLaGlpoc89TkTCsNoyTUYDpA1riGesNO95HUUoNN58xeLqqyVgvhbB1UKIduD3s+sRnNNSlZc0XW+Hiiat0zjLt/Z/i33OfXzu+s/R7mgveNpq+2r+/oa/54D7AM9OfZ/Odc0InKixGjLRs+PaDUYj6298G6ePHiRqMvPrxis47DnM13Z/jbetfhtrYlrn5aZNm7T1crPxN0NDQ4CmCJLrTSAFHl8PIfMLxDNGNrzjlnwetW9pxaCY2H76IIdcqxCiDF+2krOXr2V3KMbayWE62tuprKzk6lQEg1Q5klkFwJhvH8mkk0H/GjZWG6jIdBHiJNYmbX/L7bcTTfsoc2od4YHMRvz+Aaay89C/EDRSHw1xTYO2BsPGttW0eaZ4wh3DILSRST7/ABlDL2nVRPWZGCk1Qftd2sgpxWwiUT6Mw9DBzeVB9k12ImWaSc8rRFIRxsQaMmgVxP/l7r3DJLmqu/9PVXXunk7TM9OTemZ20u5sDtIq7UpCCZQDSrw2yWCCwTbwYgy8Ar+AfzYmCIQfDBgDNgbDCoGERJJQWEkr7WpX0uY4OXaazrG6qu7vj5qd1WhmVvvwIPn38/d55p+e6lN1v133nHvOPfec1tZWOjo66I9NEK3p6I4185ML4Jnxdm7JHMFp8eLoOZOT3XjlKmRJYdPgccayTciKb34C+9wreDpdoDc2QVdHBy6Xi62yhs3QOaiaoZuZ7HFKpRHG8n10ehRc1RVkjRE83ebvF77qavK1GPbZehRZYlYdIJt9iXhxBoDnCk781TIXBL3IsszKjghdiWkeSeRxW/1zHO0Bay9lzUl4cBZdaETueovJkaKg+6fxWdu5zhpl70wnYJYcma3MkrCsQhXQH58gEonMcTROWheU7avnlTTAs2MRblEHcVtCWFrP5PK3XG828Fl5aph4OYhsaSIzx1HQ3cGjs1l6YuOs6OjAZrNxocOCU9d4qWK+tNH8CPn8MaZKK/HZFJxF0+vzrzUL/tVvvYisNokj14JTkYiVV5HLHSSWNzNrXih78dRULqxzYLFY6O3soDs+yW+SOQLO1rkFxQtIlgg51UvXqSkMYdB266XzY5Ca0tRZm7hFG+G5icgcR7vNxYJtgKIh6I+N09HRYRr82DglAVmbydFpL2v3ZISbxBh11mZoOFM+o+22N2EIg8ipafJqHZKlc35B4XeE+XUiS1d8kt6OCIqicJHPRV2typ6SWUIiXpgil9tPvLIKu0XGngmTq03RcMm2JbTM64OzGYJvAb8FVvIHtKr8/zUUC6y7HU79DopJnp9+nu8e+i639NzC9SuuP+tXr+q4ihu73krZ9QRN4RHcF7QhWV1kfr6woN3ARdsxhODkqgFeThf4+M6PE3AE+NxFn+PAgQN0dHQQCJiTqb6pHkM25g2BtaMDbVMj9nwbn9UkpOEXcFk0Oq9+x7z88FveQlFL4s8E0YUFw7pmfgJn7VuQEPTFJ4lEzImxsq2VrnScPUUXAoVkymwf+PxkhHcXj2OTnXjXn8l+khUFtW4Sn9LBNfYUQ9learUUmcJxdEsLh4saPTNjdHZ2ApheR2ycRE3H6buQ2eIU+fwRYtVVNFHCJ3ooyMPYQmfqJYWvWYcsyVwTG+eZsWZAYSa5E4BDtSaaKwXW1/uxWCw0NDSwqpTFKXSmlJXEy6YSVaztJEpeInGoGRUid52JuTZuv5x8bQZvvhmBjCqvmw/dlF0XYgB9cwoCoDfSzorENHsKNoRkZTZt1rjfO93Jn2sncSgeXH0Lp5QIJfBaW7nTPsPxdA+6XiSTO4iuBHmxoNMzM0rXqzjK6QZW7zaSxRlyuf3M1gZwChWv3k1ODONqO7MQabt5G0IILpqJsWeiEUm2MzO7E0MYHNdbCakV1nuc2O12/H4/awwVj6ExQg+ZaobZ9PMolnqGM0FWRkvoRo3IXWfSFQObtpDVxvGWIyDMEtjp9B7ipRia+2JqAvpjE/O/c3dHhO7oOLvzMobknCt9YbA/1sUHlSFclgC2joUF+JTmLB5LA++xTXAosQIhamSyL2HIdezJCbqjY3Qv4GiCsiGQ6i4hMRdizBlrkIWOt9pFTh/Fu/JM9d7226/CEDpro1mOxBuQFS+J1C7KWplB0YVfU1lnAY/Hg9vtZr1dIaCpnDI6UA2V+OyzyIqHA9FGNkXTptf31jMHP+t6V5IzRvCqXViFRtZYQybzIvHSDLJvGyVDLOCoq6OD7pkx9uYFhuwllX0Rw1A5nOzm3bYJ6qxN817fG4WzZQ3dJ4RYBXxviZaV/zNaVZ4N698GhkZy/w/55DOfpMvXxd+e/7fn9NXzvO9Er4TZmf4GlTd1IdQ8pZcXFuByjU/hKaukfHaM+geYzE/yT9v/iWKySCqVYsOGM7XIO3wdpBwpRkdHAcg9PUolNINlxEpBizGV1FjV34z8iniirChovmn81giXaJMkqqsoFE6QK8cYlXrpLufpCfpxOMxQTWdnJz3To2R1CdW5jlL+ABZrPQeiProKPnPj6vZbFoyh5brNSJLEXeUEuydNg6IXjyH5r0RG0BubmFei7e3tdKUTeIRByXUxonIKMDic6OYjjGGV7fg3LXQ0QxddYsbDq+0UVStWx0ry2RfRrG2MqzZWTAzPTy5JkuiJtNM3G+Ok1khNyKTTeylLa2k0CgSkHgrKEDb/q5zZphRea5ibtBGmyispV8YpVaaZUlbRoZbodjmoq6ub56g3OkbRgKpzM+X8QRTFze7JelbmHah6ichdCzlqv/UyDGFwbaHArvE2AGrFoxh1lyOAvldw1NLSQmchg1/o5JznIaojGEaVk6le/rc0jF1x4xlYGDP2rVlHVh+jXuvCMGRk21pymX3oSgNDqoue6ZF5QwOwoiNCX3ySk7UAhuwmnd5DTVlPnagSEL3kGMbZ3LbgHtb2Im5LPe8Uw4wX+qjVZqlVJklY19GiVemUjPlFS2dnJ32xcVQBVddWqoVDSJKVp0cb2ZSX0AyVyF03LZAfufNadKFxcV5l52gzIFMtHEJ1X4QO9EfPGOOGhgY6a2Ua9Bqz9o3olXE0Lc9wtpe/YBCnxYeje2HVU09XN1ljmKDRg03oGNZ1Zk0f2cdJtY6e6PgCjjo7OuiZGeOU6kFXAuZGsXUdFmEQ1LtNr6+nb8E9HL0Cp8XLh6QhBjO96HoBozJOyraJkFGjs1qksbFxnqP+2Dg6oHouoVY8Akg8PdrC9lzVNDR3XM0biXOpPvoBSZLWS5L0obm/dW/Eg/23o2kAI7yWT534Dwq1Al++9MsLmqCcDS+N5SH+p2hGlU/tuQe5LgdKGHXyTM5w7qGHaFd1Krkk9cphrmz+EzY3bWb//v1YLBYGBgbmr+30dRK1R0kkEqRPxIi/8BRCUbHtHueu/O8xhMzAdXcveo7Wmy5CCME7jQJHZ81SDR5nOznhpHN8cF6JgrnSak/F8CKouC9GqpxCWNezUp8lYDGzL169cRXcspWsNkGz3sXLUw5stiastXEKjq2sUks02q3U15uH6Ww2G+3NYVbnkiQtvVi1FJJk5dmxMKvUBkpahtabFyoIADmcoc7ayK3aMHljLXp5ENV9CQqCnvjkvIIAc4J1TgyiCgVn3RZ0Pcd4oZ+/rI1ikW0EtoYXyW+/7QoMoXOboXEwbm7o2hQvaeFlxeTwIo5aMklCGKie7SjVQRT7eppqeeqVHorWYax1C2sN1fWvIqeP0CR6GEpYsDu6sKqjVNwX06tVaBA64bD5XIqi0NHezsBslLjcgUUvABLPTbSyUfVR1Yu037HQ0ADYO6u4LAHeoZ4ira2mVhlF91yMhKDnFUr0NEcrJofRkbB4tqLVkkyXV/FBdRCb4sS7fvE73n7ndWhGjWs0hRfnwk8KCglC9EbH6OrsnK+l097eTmMhS4vQ0OouxVIdxuZcg1JWCcm9S+bFu9oi5MQQDVIvpYKBw7USqzpKzXMpHUaNhmqJ1lYzJCnLMp0dHayMTxKnGUWYpUVemO5gW81pen13L+bItdKCQ/HwfvUk8eoAWnUayXMhAome6dFFHPVMjyKQwH0hWnWSpDrAn9dO4lDqcPUt7hrWcectqEaZi1TX/KJIMVTiUgsrE9N0dnQgz6WHt7S00KBW6DJUap5LsaojOJz9ZHKCRqmHrDGMu+ONXWufS9G5vwR+BDTO/f1IkqQPv94P9v8FfK99Jc8rGp9Y+Q56A73n/L09wyk2t/Rzz4X3sC+2j+dXTSPJCun7zf64WjpN/qmdtG7ZiIFgxWgbYXEdtVqNI0eOsGrVKuz2M0q3w9tBwmGeDD76sz1Ums2ic7ZBCV8uToNbp3Hz4hWEf91GsvoY7XIvu0ZCSJKNonsbdgw64lMLXv5AIEDQ62VjKU3NuYmqIUhUB3hfLWpuXF05sEg+gKU1j8ca4i51GOwbSYpmqrKXFVPDdHR0LCi21dnZSdvwcXQsRJUBnO411M/GCFg7UT0TyDbrIvntt1+DLjRuMmA414uBQHVfxFq9gkfotLWdWb12dHQQzqVooEbFbcZXX5zpZJ3UQklL0XLDDYvke7p7yRojtCh97B3yICseiu5tWDDonBlbwJHb7aaxoYH1uSRV+2rKBqS11XxQnUCRrdRf3L4kR44eA6fFy3trJ9As60kLPxWlgb7YOJFIZF5BzHM0chyBTNy6Aaerj8pMhnprNyXbMBaXe5H8jrtuomZUuUo45g41CaqeS1hpqHiq5fnw32mOQsUcbdSoznF0ML6CrYSo6Hnabr9tkXxnuJU8QzQpfRwZEVisIbKuC5EQRCaGFnBkt9tpbWlhTSpKxdZDwbBQEGv5sDqEVXbg2+RfJB/As9qBXXHzAfUUZdaRMxxUrO2sSk7R1taG1Xrm3ejs7KR9zOwql3Kcj93RxuSYSqOlj4K8hNeHqaireolL8HMk2Y0Aqp5trEDFXy4sMvi+SpEeoVL1bEMAR5MruFTUoeplInfduki+1eenqAzTYOklNlnG7uggad8MSERGTyzgyGKx0N7ezsr4FBVLCxnDTUVex/vVU9gVD+5Vi+fB641zSR99D7BVCPEZIcRnMIvOvff1faz/frwcf5l/nt3LNcUyb00trku+HFJFlROxPBesqOfG7hu5sftGPlf9AVo5ijpmuqy5X/8aajW+1XqEZKNOX9TNiZkCJ0+epFKpLAgLAXTUdZC2pVGQma4kqPWN43b3MSt5yRg2Vm9evdSjAGDrqOC2BNgWO0LZ1s+Y9QLWazmshr7g5YQ5r2DkBEKyspuLOTrbTb+yglxtmobtly0pP3L39ehGjbcYMkl1gL3yJdiFStPkyILJdVp+KJciJIocsWyjyCreoyXMvPi3LN2Wz93RRU4M02bpZ/94E4dYT00JsDI2SWtr6wIF0djYiMvpZHMpQ8IyQEzuxhhMELR1UvNOIitLl/V19cs4lDpuzR5BtfUzZL2Y1Xoeu1ZbNIbOzk5aho6BJLOL7ZzK9LJGbqOgJQm/+S1Lc3TXzdSMCpcbHmbKq9gtbUcRGk0jJ5fkKFjK0yoKHLNupyz38351ysyLv6xnSfm2UAMFeZhmWz9HRzwcZzUVpYm1qSjhcBin80wlzkAggNfrZVN+loy1hwmpm/hgmZCtm7JjdNl0Re8G89zFO0uDqNZ+Tlm30W/kcavVJTkKDx4FIdjFpYzk+tgkhSlrWdpuWbxaB4jc8VaqeoELqGei2M8u6VJkodM0dGxJjrzVEiuMPCes26nI/by3OjTn9S1dwt3iqaNkHabR1svooMSw1EfBEmFDJkkwGJwP/4G5VxAKhVifiVO0tDFEP4PDNhqtfUt6facRvNA8d/G+6ggVuZ8T1u10ijzeSmlJjpqGjiILneek7UwWV3KR8FPVi0TueOPbvZyLIZCAV5a/0+c++x+L04e6mt0tfNa3EenQ/WCcWwXAF0bMI+9bu8xVyae3fpoObwf7rLuRHE0U9xwl++BDpNv9POua5MKrb8WlFkicOsqBAweoq6ujq6trgcxmTzM3ZS+nSfcR9+XIVw8g2zcy2VyHJAQrb37fss8TuesmNEPlasPOE7ULqEou1iTHaWpqWqAgwHw53ckYTcY0O7kMy6EZvNYwUtPiVpGn4Qy3kmOYVutKDs80spcLWK+fxGIYi17+SCSCLElsqQ5xQhpgTyZCr9JNtjZJ/daLlr2HZ5UNu+Jmy9BhnuBKXKKAd+j4IvmyLBOJROgcGwRJ5nGu5JaiuTfTcv15y3N05y2oeolLhI8n1fMoSj7WZ8bnleYr0dHRgTuXpt0Y42kuI380T9AWQffPLGtobP4gBWWIsK2PE+NBnudi1hinsOnaojG0trZisVjYUh5mXFrBc8UeBuQI+VqMxjddsaR8AN+mAFbZzmXjx3iCq7CJCv5TRxfJlyTJDHENnUAWOo9zDTenxpElhaY3rVxWfttbb6Gs5TifBp6ubiAtNbAxN4rb7Z4P/72SI1elRI8Y5hkuZeaERL3NzItfyusDUJwOyvZRGm09jA652MV2+o0hnEsYmqamJhwOB5uKI8SlFp6trGY9LZS0NC1LNHU6jdAlERTJwvWxUzzB1SiiRsMSHJ0eQ+jUUSxC5Qnpaq6dOYlFthK8sHVZ+c3XXU9Rm2UdrewqryIqtbG5OILdbp8P/71SvkOrscoY5Dm2MTzsodHeS8k2gsVTt8wdXj+ciyH4PrBHkqS/kyTp74DdmF3K/mBIkvQRSZKOSJJ0WJKk/5IkyfHa33pjIITgnl33kCwn+dKlX6Ju459AfgaGnzqn7+8ensVhlVnXZrrALquLL1/6ZX7YtRdh6KR+8iyVQ4f4ZV+Od615F9dd8w6wOWiKH+bUqVOsW7duQagAwJip8O7YzdidFirSILpeIlntJ+uyEMqXsEmLwwWn4WhoJMsgLbZ+nlZXExJxmlO7ln35JWC7eJphqY/OmjA3rm6/6qxj9q53YVOcJKJlqpKDS9RncbvdhEILa6rb7Xaam5vZWn4GSRi8lPRSZ21EackuI9lE+523UtWLrKKeA2ziIp5FEeoijwZMY+YqnGClOMIufTMrlF4ytXECm85fVr61zkvBMkzY2svT5V68IkNX6tllOWKOoykpQkupihCCtpsvOesYAlvDWGQb1WSOolTHttoz2Gy2RQrCYrHQ1tbGpsIzKEJj92wjflsboj6+rKEB89xFScuwSgqzjy2cz/NY9fyyHNkrE6xjP8+LLfTKXeRqM2fy4peAee5ijJCtm2fyXThFkZXZZ+h8xf7AaUQiESRJYpv+DEmpCX8+jyzJhK9Zv4x0E6HLe5AlBVsqS0YKsk1/BkVRFoT/4IzBX5d7Bpuo8my2nXpbJ2rdxFk5arrmzRRqCfqUdvaI89jEPmy1xLIcWWspNrOXPeI8+uU2itoszddeu6z80+cu6u2d7Mq2YhVV1uWeWRT+A9PgK4rCJfozZKUAcipjen3blk5Nf71xLpvFXwXeBaSANPAuIcTX/tAbSpLUCvwlsEUIsQZQgLv+UHl/bPz4+I95cuJJ/nrTX7MmtAb63gwOPxw4tzaWe0ZSbIoEsFnOUNsf7OdtV/wFs4XDCKMZXZJIbV/Nhzd+GKvNTv2a82i3ZBFCsH79wsliVDVSPz5GxabyTOuL+HzmQZvUi6OUsdKWyi/Zx/iV8G8OkHE6mbS0chFPU+eZXlLJBYNBgkG4VH4MhMGBjtWktSE8Xd1nld92+21U9DzHmjrxG1HWKM/R0RFZpCBgziWWDrPCOMwpbwRNaETuXDqkchoWl5ucZYiDkX40ycZ2nsDnS9Hevjgm39nZic8XYxs7ycj1jIUakFtfu1hu6JJ2CnYrY9Z2topn8bjGl+Sorq6OxkYXl8qPIYsaByOryGij+NacPYei5YYbKGopjoW7cBspNis7iUTaUJZQXJ2dnQSV46w0XmLQHUFlYV78UpBtVoqOMQ639aJKTrbzFF7v8krO5OgpCpKPwXAbWn1yCakLEb5qgIpFZtjezmbxPHX20SU5cjgcNDcHuVj5PRZR5nBbP9naJKGLzm4sw1dcRa4W5UhrD3ZR4ALlCVpbwwvCf68cg9c6yHqxhyFnO6oi0XLdliWknoGsKFR905xo6aIsedjGU/h88WUNvs8XZztPUZHcHGtZQdmzfHjxNFqu34oqwZCznbXGXvzWU0vKt1qttLU1s1V+AoeR42hLD/lanMarrjmr/NcLlte+BIQQLwHLV2H7w+7rlCSpBriA6T+i7D8Yp9Kn5g91vX3g7eaHFjusuc1sWFPJgeNMqEAtazzz05PUqmbYqKYb9A1W6Gm08NtvH1og28sAw+4jhBQ/tTf/Ex86HuDkF8wiahvUzczWa8i6lcS3hklwpqT1rxtlutCoWqv4T67Ft/EXqOUQ2b0zyJJCsvNtPPF4GWd04f1eCWH0sqcph5BkNuWO4PPFGHyqyNjTh9AyafTZFLbuFUhI+J1ZAmToTcV4tLWJywZtzHx7edmnUbCkOBRq4bL4YewNRWbjOX777UOIWo3q8DC2SATZ6SRdVQkNJNmQGuKBhnU86Z6m+Vch4Oz3MByt/LrVTmOhwAr3EDP+Rp74/lxJ5PFxJIcDa6NZqdIfSBCqDmG1GvyyWeGG7FrGX2MMwmhnf0MSXa5jY/oYPl+M0V0VZnYfQs9lqcUT2Lu7kSQJjyWNmyKrZqd5vKWdtxwzmDoHjqpylr2NEbYmXsbWWMHIZUyONM3kqK0N2eUiV63hWxlnY2qQIw3n85g3SsfOIOx8DY5cK/h1q41AqUK//RjJgI2nf2i2DlEnJ5EsFqzhMEIIAv4kbbVpnJLGwy0W7oitZvo1x1DP0fppVEsdG1Mn8PliTOxRmX3pEHo+Ty0aNTmSZRwijVMqsyY5wc6WXm49XGXiHDhS5SK7mr2sTRzF3lDBUk2ZHBk61cEhrC0tKB4PhVoNX0+cjelT7K3fzq/9CXpfbIcXX+N3dqzkkRYrnmqNNcohMoEenv+JWX68NjODEAJbi3nitz6QIqCfwKupPNJioX68n+hrjsHOoD9G0dbKxuQpfL44My+p/PbwIfRikdrUlMmRoqDoaexKlbWJcZ4Pr+aUMsrYd48ukrj5LZ00RF7fcNE5GYI/JoQQU5IkfRkYB8rAo0KIR199nSRJfw78ObAg6+H1xJMTT1Izavzfi/7vwtXs+rth37+ZPY03/en8xxPHUhzfHcXX6ESxyBQqGgFdwlUVpGOlRfIN4SBRLeOwOJErBmCWus5LKnlbjY2VMBbtTA50wi5xb58dq2HlPXvc+Mr1+LxxZuO9VEszWJ3dVBxdlAsqlSXudxoCeHi9jbVpDWdGoa4jRfJIAaNSRR2fAkPHok6jBAN4umfQNCtXjpT4ly0ye8Pd9J1F9mk8FjEnzwUjWWgAh2WadLREbWYao1hByo1ja29D8c0gywarJ3L8OiB4pD3AWydeW37CFmZ/wMI7jpYptfvx1sVIj5fQ83m0aBqQsFasyHYbgc4Y6XQzlxkqjzZbuHBCx5J97Xs8stpBT14nkBS4e7IkMjmKFR11fAJ0HUWdxFJfj7trBl1XuHykxD+fJ/Nsaw/rzoGjnR3t6LLERSN5aASndYr0dIlaLIqRLyNlxrC2t6PUxbBYNPqmC/h8gkfafLxt9LXlZ2317A4p3DlYoNgQxOuNkR4tYRSL1KbNaq/WihXZ6cTXHqeQDXJlqcqjrU62jzlwFF/7Hg/3u2gtGYRjKo6VRfKFFJWyRG18HKFpKNUJLA0NuCLTCCGxfbjE/q0ST7Z3s+UcOHo+0kVVkdg2XIQGcNqnSU+W0OJx9GwZKT2OtT2C4k5is1WIzJRpdBs83ObmXYOvLb9oqWNng8KN40VK3nq83jjpoRJGqUxtykwKsVZtyC4X3g0xSrl6rk5XebDLw5UjXtznMIZHuj3UVw06Z0pY66volQSpWYfJUa2GUpnA0tSIs9Vc/24bKrC3UeKxSAcXLSG/pr7+HcrecEMgSVIAuAnoAjLA/ZIk/YkQ4j9feZ0Q4jvAdwC2bNmyfHOAPyLGcmM0uhqpdy7c/KJtC9T3mOGhVxiC5GQBSZa4657zsVgVvvDIUX68O8fBz25d3Hj6xG/hv94BvdfA0OPQ/xa444cgSfzud79DPAcnxx/m8vtuxDt3uvaxsRjG8AwOu8KvLg/iH/0oF1lqpLJBDDlDw/ZerpKyxL/4RXr+4allm94fyJeY2neSPz1SJqMFaZMEV75XUPjov1A5egzn+nWUfr+Pzv/8IfsKUWZmmticreKsacy+uZW7V509bimE4Ou7j7F2KkNd3k6t5mBgW57mlweJ/dcX8Fx5BYXfP07wXe+ieKOd4REJLefj0miZZ1bU859vX4tTOXuU8ksjM0gjUbYlK6S8TbS1jXLNOxsZu/VD2Lq60GIx5ISLpv/8EnsOlshmG7gmX+WxFgeB9/VwfePSaYunMViqMLTnOH91vEymGCACXP5nNaqf/j6ll17Adf75FB/dReT73+MgCaanQ6zJqnirKvErW7l7TddZ5QN8b+8JuidTNOUVKhUPfVtzdAxOM/Nfn8JzxRUUHn8c/513ot7eyuAglPI+rpyp8HCHnx+9bS1ey9nDEt8cj2MMTXNZrELK1kQkcoyr3tvE+C13Y2lqwigWEVM6rT/9Ns8fyjA53c2VqSoPd7hxvbuXO5vPXjlmqqLy0eeP8J5TFXJ58wDZ9rerGP/wE/J7n8Rz8cUUHttJ2798k+OeFFNTQfpyGg2lKtOXtnD3+rOHGAF++tIpWqZm6cgbFIt+VmxI09uSZuqv/wbP5ZdT2LkTb/11iPeu58RJyOf9XDNV5UfdPq5862oaltmMPo1/n0qinZzkimiFjGgk0LWfK97fwORt9H4yfgAAIABJREFU70R2u0GW0UdytP/s33nuaILY6DreFCvzs+465Lev4O62hrPKT6oa/3vXYe4aLlMomBxddHcV5RsPkd39MJ5LL6Xw+JO0fvUrDDanmZry0V6A9nyZ0Qtb+MbmvrPKf71w1tknSZIiSdLv/8j3vBIYEUIkhBA14OfA8ikjbyDGcmN0ejsX/+N0G8uxXZAenf84OVnA3+TCMqf094yk2NjuX2wEslPw4PshvBbu+A+44jNw7GHY+110XefgwYModQ3Iusbhp58ATOW6I5rmPK+bbw50MFqVyAfMVtGZbBOWOgctF9+K6/zT/QmW3ye4P5rCKkHn9EmyuQZAYfrJb1He9yLNf/dZ2r7+daxNTYx95q9Q1QmK2TBpKUtXZpxHElmK2tlXJC/lSoxWVHqiR8koKrVSG+nELuJf/CLuS7fTdt99BN52N6nvf5/k6O8w9BayyPRPHaZoCH6XPPtmsTFXVbStkkFVk+QyTQhRZeSLHwRZpvXee2n58pdQx8cZ+/HfmZRnmwjNjuNFsCOaOqt8k6M0MrBi6ji5Yj1gJ/rcDyg++yxNn/okbV//GrbOTibu+Ril0gnK2WZScp7u2REeS+ZI17Szyj9WKHOoUKY3doyUpUKt2EomtYeZz38O1/nn03bf16l/z5+R+elPSZx4BGE0ktXs9E8eRgUejp+95ID5vqQI14rIlRjZbBOgM3LvhzFUldZ776X1q19BiycY/d4n5jnyZqYICu2cOHogljYPYE0eJVv2AR5iL/2E/GOP0fiRj9B639exr1rF1P/5W/KFg1QKLaSUIj2JU+xM5YlWa2eVP1ausjtbpDd+gpSljFpoIZd9kenP/B8c69bR9vWvEfrQX5B7+GHih38Owk9WddM3dRQD+EVs+ey209gRTdFgVHEUZuY4gpF/+Sh6Nkvr1+6l9d57MfJ5Rr75Ecxe2U3Yi1Ea9So7oq8t/8F4Gh3omzxMVnWBCJI4/AuyDz1E6AMfoO2+r+PcsIHpz95DLvMiaqGVlKVEb+wEe3MlhkvV17zH64GzGgJhdiQvSZL0x+yOMA5cIEmSSzLjL1cAx17jO28IxnJjyxaUY92dgAQHzrSxTE7mCbWZhaNylRpHprNsXfEqb0LX4IE/A02Ft/4ArA648MPQcxX87tMM7XucYrFIZGAD0/Ywh556HCEEBwtlTpYq3B4OcEW9lw+2NzDkuJpnjMupVRwUfY0MtPpxrFyJXFe37Iaxahj8PJZmu8/GcctRDMOKtdxEJv8ivltuwXfjjSheL61f/QpFv+kaK8kOpqUUdfYxSrrBr19DUf80msIpS8TkZ8nLFaRYE1Ujjuiso+Uf/xFJlmn8xCewru4jXzuBHAuhSwZj0i5a7VZ++hpK6IVskfGKSo9zljE5ipw2V995yxDNX/g8trZW3OefT+iDHyRbPoCiudFKfo5ZhrnIU+OJVI6EurwSMoTgZ9EUF/sdnLAcRggZayFMpvASdddcg//OO5Hdblq/di/l+gwgUJIRonIa2XECVQgeeg1FfX80jUWCWWU3abmAFG9GM7IYESstX/oSkqLQ8Fd/hWPDOnLqEeRECCHBkLSbXpf9NRX14UKZ48UKaxwZhqQppEw7CIkcxwl/5h7sK7pwrltH40c/SrZ6CFm3o+cbOaGMcb6rzK5MgYmKuqz804bmfK+TU8p+QMKSaSJbehn39m0E3/VOZLud1q9+hWpLGSFqyPE2YnKWiv0QBqYhORt+Fk0jAXnryySkHFK8DUNUUNt0Wr/6FSSbjdD73ofzgq1kKwdQUk2AxKC8l/V1ztdU1EOlCi/mSmxwFhmUJzCyLUiGhZx2hMa//QSOlStx9PfR9MlPktOPIhkKItvCoDzBBmeO/fkSJ4uVs95jRzTFGo+TQcUsxybPNpCtHsR53hZCH/wAktVK61e+TK1NoIsSUryFpJQja92HjLlo++/AuaSPVoBDkiT9myRJ953++0NvKITYA/wMc/P50NwzfOcPlffHQqaSIVPNLG8I/O3Qtc0MDwlBpVijkKrOG4IXR9MYAi7oepV7vfMfYfx5uP5eCM0dCJJluOVb4Axw4PH7cTqdnL9+Ncfq+inEpokOnWTHTAq7LHHjXEjjb7uaWCFO8gPp3RQliZwrTMhjR1IUXJs2LWsInkzlSdV0tntVppwFfIYLeaiBWoeg4VMfnb/OuX49yq2roSIRLqyjLNdo1MbpcNjOqoSqhsFD8QxvafCDZE6S+riZveH821uwzNWgke12vJ9/N1jBMzkAAmbtJW5r8rMzlSd2ltXijmgKtyLTLY+RVSo0qW3ICQdiexPeq8+cqK5///tQByxIowGajABxW5ELPSk0AQ/GllfUz2UKTFVrvMmrM+XM4zJsyENhtFZB6LMfnd8vcvT3Y/lfW0CDxuxmVEmnXp1hldtxVo40Q/BALMUV9V7swuy+FUqY6ayOj1yLtcksuSBZrfi+8D6EU+Aa70MWEnFniZsbPOzJFhktL79a3BFNYZMkVtunSFsqhGth5KgbfWsA/81nDigF3/kOauscSBNemo16ktYS57nNU+sPnGUML+dLDJaqXB2QmXYVsAkL1pF29JAg+Lm/nu+uZ+/qwvbuS8CAxvR56JKBv5Zki9fFjmhqvl/wqyGE4P5Yiov9HjxaGiEJGmdNb9f+/jdhm0shlRSFwOc/hOEV2Mc6sQiZGWeRa4M2DhfKHC2Ul5QPZ7y+Ta4ZZi1lwkYIeaoObZOHwN1nSrT477wDbXMd8nQdYa2RWUuFDc4ZFOnsivp4sczBfJkbQnaiziKKkHGOrcCoEwT+/kNIcxlH1tZW7B+8EoCG5PkICdx6ju2BOu6PpTDO0ib39cK5GIJfAfcAT7OwAukfDCHEZ4UQK4UQa4QQfyqE+O/xh16BsbxZ2XNZQwBmeCg9AhN7mJ00UxJPG4LdI7NYFYmNkcCZ64efgqe/DBv+F6y/c6Esd4jyDd/kuNrEWleS3rCXcW8vQrFwYOeT/CKe5up6H36ruY1TLZ3kL/kqwoDfr78Qq6xTrZq0uc4/D3VkBC2RWPTIO6IpQlYLrUTxqn78VYGvdDEokK8t7JNQDmexpFoJa6Yxs+RVbg8HeTZdYGqZ1eKjyRxZTeetTX68JQeyYdBSWoek2igFF6YkFp3jIKC5eAkuTRCqhLjCx1lXiyXd4JfxDNc3+DEys4BEg+bGmu2jXJ/BMM6EZCrqJLpbxVXcQqAqYzdcKKVJ1nmcZ1XUP42m8Fpkei2zBKpB/FWoL5vlF/L68VdxlMOSaqRFM5W3vaBzRzjIS7kSg6WlV4tPp/PEVI07wkHcJSsIQWulH7nsohSaXXBtwTYKQGNxOx5VEKqGuLiuisTySqhmCH4ey3B1yIuWiSEhU685seUGqASz6PoZ5ajWktS8JZz5jQRUKxZhQ89NcqHfzY5oellFvSOaxiFLrHPkCFbr8VUFodIcR5xcyFFTFiUboK1mKm9HAe4IBzlRrHBwGUX9QrbIaFnljuYgrqJicqR2oeS8lJsWGvGCbJZYaShux1uF+mqI81xFrJK07O9shhdTXBqsQ0tPI2OhvmbHnl9H1ZdH03Lz1+p6gao/jy23hvqaHRmFWnqGy4NefhZLoy/D0Wmv73xXmfpqCK8qCJW2mxwpQ6/iKIOcr6NNXQFC4Coq3B4OMFmpsTtTXFL+64lzOUfw78AOYPf/5J7FY7lzMASrbgSrC/b/mOScIaifMwR7hlOsb/PjtM3tDxTi8PM/h1AvXPulJcUdyfvQsbB+9iGsh35CZ0s9mYZ+fj0+Raqmc0f4jFFJZ/bQQILrdj3ATCDM3q5VTEyYNdvn+xjvW1gdPFXTeDSZ47amANGZSazCSmBqFF9+LQhpQcP5qpqkVBrGnduAUYhhUatYUoLbwwEEyyvqHdEUYZuV1U6VpkI9DbE4uq5iTXfM9ws+M4YXsBQbkGpOwhPjNFTqsWsJNp9ltfi7ZJaCbnBHOEAlVgYhsCYn8Re2ohsl8oUj89ee7j8QLJyPJzoKQGwyxh3NQQ4VyhxbQgkVNZ1fJbLc2BAgnpjEYTionxzFne9HMiwLmqlrWp58/jDO7Fq0UhJHqYQjbeG2psCcW780R/dHUwQsChd5LTTkfQQTSWpaCWtqBen0ngXjTmf2oJT92NQg4ckxgpUAWjnKtoCH+6PpJVeLT6ZyzNZMQ5ObMRWaPTFOsHAhQmhksy8v5qh4Aa45jpJTSe4IBxkuV3kptzhrpWoYPBhL85aQj3RqEo/moWFiDGexC0mzL/idDaNKNvcyjvQq1Goady6HN2PnhgYfdlla1pjdH03jUmTeXO/Bn3PhzWSoqVls6T4ymX0LDH4m8wJyzYW93ELj5Ci+mo98boor6708EEujGYs5Ou313REOkp4xn8ERGyNYuAA43U7ztPx9gEGweCGOuMlRZibNHeEgM9Uau9KLz6XoQvBANM2bgl6K+Sl8VR+NE2PYy2Fk1bOAI7Nz3gvY072otTy+dJpAzsPV9R7cinxO+zV/bJxL0bkbgP2YvQmQJGmDJEm/fL0f7I3GaHYURVJo87Qtf5HdYxqDIw+SnMjg9Npw++wUqxqHprJsXTEXFjIM+MX7oJKF238AtqVP/h44cIBQKERLpBd+9TG2B9Lst/fwcmQlAQSXB8+cWchkXqBWstJx4hjuiSkOtPfy4OgUAI6BAWSXa1F46KF4hpowlXnhgHk2ITqbo6YaWLLh+d6tp+UD+LIbGGOSplicxqSbiMPGBT73koo6oZrx99vCASanB3GKOvyzSRL6CbypjZQr41QqM3OUqGSzL+FIDJCujWBNp5GxMHr0MLeHgxwvVji8hKLeEU3R5rByod9DcFQnkE5zzEjgyZj1lV45wdKZPciqC0uhkePZPIqmoRyOc3NjAIvEkhPsV8ksJd3g9nCA9P65QmazGSq1EpZU20KOsi8CBr7sJqakEZpiMRqyXryyzmXBOn4WXezW5zSd3ySz3NQUIJ4cw6P5CSUTxPUT+DKbqNVmKZXM30YIg0xmH/ZkHxl1HJHOIEkyE4dMjsYrKi9kF68WfxpNUW+1cHnQi/9UhbpcjsNqGldmJQhp/rc1OXoBSbNhz0U4VMxhq1ZxHEtzfYMfpywtuV/z+9kcGU3n9nCQ2H7TQyqm0hS1FLbZzgXGMps7iGFU8WU3MyOGaIrHCZYDGGqWq+t9/DyWRn1FD2+Asm7wUDzNdQ0+8vkYgWqAxnicqHEKf3YLul6gUDg6x5EgndmDLdFNvhZFTZnewvSBo9weDpBQNXam87waO6IpPIrMNSEf7uN5nKUSh8oZnLluMJRXcbQHDBlXtoeDlSyuYhHPiSJX13vxWZQl36Nn0nmiao3bw0EmDxxBkiT0VJpcbQZ7opt05oX5+VMoHEfT8vgym4kZp2hIJPCrftLZGW5o8PNwIkNRf/1TRl+JcwkN/R1wPmaqJ0KI/Zipn/+jMJ4fp9XTilV5jcp/G+6Gapbk0MyZ/YGxNLoh2No1t1G862sw9AS8+R+gaemCcLOzs0xMTLBhwwakt/4rWJ28J/p5TtpCDHWsZFN0BItsxqaFMEinX6Aw6WQsXEJMHKexUuSbuJmpqkgWC84l9gl2zKQYcDtYZdSwHUnhLBa4b+AWUtIgnvQ6stn96LoZzsikX0DSrCiZFv7e3UcxYEe3uTn5o3/njnCQwVKVl/MLV4u/iKXRBdweDjD1r+bJ6wMr1/FcnYI7Y1YrPT3BcvlDGEYFb3Yto+4Mv1pjxvZLDzzFjSEvtiXc+mi1xs5UntubguR27cJQfMhSlc81XIxaEiiF+gVKKJ3ag2O2n4w2wn391+IppLElJbyxGa5YZrW4YyZFp9PGZgUs+yaxVcr888BtzFqGqMusJ184Sq2Wm+cIQ8aW6eSrri5STV4Mq4Oj//Yd7ggHmarWeC6zcLX4SDxDxRDcEQ4w8u0fgCRxsqeX33o9uNMmR6c9s0LxJJqWwZtZz5Qrxk/X3IhkGNQeeYFrA0uvFtM1jcfmvD71wH6EqMOmFfhiy6WUKjms2eaFHKX3YE+tIFub5BvdV+MuZbAU7NjGRrm2wc9D8QzVVynqHdEUTTYL21xW5GdPYqmpfHvgNhLWMbyZDZTLo1Src03e5wyzI93Ld93NRFsbEIqV49/5DneEA6RqOk+mFirq3yWz5HWDO8NBTn7rXxGywnhnGz/xNuBK989xZI6hUpmgWo3izW4gZp/ie6tuQdY0ePwwV9Q5CVoXK+qirvNIIsuNjX6kE8eRq26c1Rz3dlxBvhrDlu6Y70J2+p21ZSLkq0m+GrkCRzWHXHNjHDvKTY1+fpXIUnhVJt2OaBq/ReEqrwMeO4Cs63x/4Gaijmm82Q2oapxyeXTBWFzplTxQ52ekK4KQFU5923yPirrBbxNnT9D4Y+NcDIEmhHj1U73xuxmvM86aMfRKdG5Hr4uQSkqEWuf2B4ZnUWSJzR0BGN8DT3wBBm6Gze9aVsyBAwcAWLt2LXhb4OZvESyc5E1txzEUhciu31LKmbQXi6fQtAz5KTeDrUXs9gQfVSqowAcOj6ILgeu886ieGkRLm+GJU8UKL+dL5grl058m7Q+SD6l4gn4ONrlxp1chqJHL7QcgNfscjkwPOTFKXPJQvswMNw399AGuKprx4VdnZdwfTbOuzknL00+RnUyBUUM571L+pdaBlnYh1RzzSu70yto628l3XT14Bs7D0HPkagrih//B1SEvP49lqL1CUT8QS2MAt9jh0D/+I4aikL+4BySZrGMUT3oN6dQehNAplyepqjN4MquJ1pcQikKu303O52fo43/D7SEvcVXj6VesFicrKrsyBW5vChD9/OfIeAOU6koEW5p4vrEBd3olZgqhGXJLpZ7Hnu0gX5vkpPBRvdTMep78zeNclpzGa5EXrah3RFP0uOz0vLiX9IkJMHTERZfxn2oL5ayBXKmbN5anlah9tpsf+Xrx961GI0tOdlL69re4vsHPL+MZyvoZRf1QPIMqBLd5rBz+7GfRbDZyWztAksi6p/Fk1pLNvISuV1HVWUqlQTyZNST9aQxJJrcuSMnt4eTH/4bbgh6yms6jyTPx8qSq8fhsjtuagiS/+EWyLh8Ve55QZzuPNrTgTJ1W1Oazp9N7sOaaKVSSPK/XU7vUjJHHntnH+WPDhKyWRYp6RzRFq93K+uNHSb5kVi3Vtl/Gb9QGisU8lmJoXlGn594jx2wvv6zvoL6nG9WSJWv3kLn3Xm5uDPDbZJbsK9J5f52Y8/r8Lo5/6tNUnU5yG5sQkky6LkldZi35/BE0LY+mFchlD+HJrCFTFwNJIrelmarDwbFP/x9u9TopGwaPJM7sW+Q1nd8kMtzU6Cd73zfI2jyocpb63j5+EezAkeqd48h89nRqN0opSKlQ4iE1jHHJZSAMZvefZO3Rg7Q7bOeUqvrHxLkYgsOSJL0NUCRJ6pUk6RvAc6/zc72hEEKcuyGQZTKdb8cQCqEGc1WwZyTF2lYfbj1npor62uDG+8zzB0vAMAwOHjzIihUr8PnmMnP7rqZ63gcZa+6gtZonlJjm+C6zLePpSWYk7CTqLQR8WS7uirDt1AF250p8dTR6Zp9gziu4P5pCkeBNzz7J1L4Xqdls6J1uVjV7uZ8ujGRgbp/gBVQ1RakyhDu9itmIDYD1vf3UJJV4YyP5j32MNwfqeDCWnl8tns6Lv80uMXPPZ4i2NRNzzbKhrRPVgIpnBlemn9no0wDMxp/Bmm+hpMc4nJMYaPETD5WIhxuIfv0+biplma1pPJnKzf8mO6IptnhdOD5zDzGXC4Ggb6AHRZY41t2BK92PIUrkC8fme+9akx083LCOrpAbd7e5oTuZSLD+P76H/1Vu/QPRNAK45sA+Zp54grLLRSViZ6DZy4NaM7VZJ+gKqdRudL1EPn8Yd3qAXKvJwfruXipyiXhzmNmPfowbAh5+9YpzF6fz4m9zW4l+8pNE21uYdabY2N6NEFANJEzOY88ghCCV3IWlFKRcybA7Y2FVcx2z4RqzoXqi3/lXbkhFKegGv31FOu+OaIpVbgfB/+fzzMjm/lTHqi4cVpl9K3pxpPrmDP6B+Ti4PdXN79vX0uxzEOw1T4RPFov0ffM+mu3WBRw9GE+jCbh26CgzDz5E3usl3yazKuzlN6UAlYxAqtlJz+7GMGpk0ntxZwYohU3vcUNXLwUlR6ytmfjHP8YtfhePJnOk5hR1rFrjqVSeW30Oon/zcWJtLWRtGTZFzMNnpVAWd3qAdPJ5hNBJpZ5HVj2U81V+n3Mz0Owl2wIZv5/oj3/MdVPDVA3Bw4mFHEUcNtrv/RJTNTM7ram/jYDLys7O1dhTPSAJMpl9ZLMvgWTgSPXyTOdqvA4Lzf1mTatpQ6ftK19khXOhon44kaFsCK6PjhP94Q9JBwJkWgwGmr08kXVRypeQq3VzYzBIJ5/DnR6gHDJlbIqsIGPLEGtrYeYTn+BWr52n03lmqsun8/6xcS6G4MPAaqAK/BeQA/769XyoNxrxUpyyVj43QwAkfWY1zvrC05RVnYOTGbZ2BeCXH4Z8FG7/PjiWP3oxPj5OJpNZ1HdgfNuneNk7wDumfkJjextH5w6XJWeeQM1b6GnvQFNDWJ2zc40tJtkmqtw7GuPl9k4kh4PS3n1mXnwszXarhPiHvyd74YUA/y97bx4e11ne/X/OObNv2meRZEmWVzl2LDt2nMR2nNV27MTEZAcKCaW80AJvF3ZogECbQGhIaelGKaWUzUmcACGkOGmIE7KRxM4mefeMthnNKs1Is8+c3x9H52hGMyPLtpwXuH7f65pLy5y5z/Pc85x7f+6HBk8DK1odHI8mke1xjIkOQr5fMTauCAgx3M4zrb1YDRLnezoJmcP4uxeQOXKEq596nLF8gSciiqDeE4gqFRJf/Qppk4lJs5VJR5JVbUqCe7R3OeboMjJFP+m0n/HxA1hjy8muqEeWYYXHAY1QFHUkurtZ8tlP0KSbDn28MZHi8GSaHcf6mPzNbwj0nse4YZzO5nYWtVh5WmglH1b6r8SiLxAeeQoxayWbSXMgkmdFax3uVjcFoUB008VMfPe77MwneTw8Tjxf0MoVNxgldF/6AmMbNgBgd9vp8dgZiCahZQJzfBHhwScYmxIQukgnL3RfiF4SWL+gm6A5TKDNQ2ZwkCt+9iDJQpFfTAnqB6bq4jf+w31kCgXGbXZi1nEuWKB0HPX2rsEUW0qeMVIpL9Hw81jGeiguNZAtKIJE16RDFkTi551H+2c+TltJ6ONYMs2r8STXjniZ2PcEo+t7mdBN0OFsZ7nbwbPZevJho6LwI88TGd2PkDeQjRf4bUxkhcdBq7uVrJglcskGEnv2cO1krGzfxZ5AlJVGCfvnPsP4ugsAsDgtrGh1EJ7IIrRlsYwtIzz8vyQSbyALOQyRbg4sV8pjN3R0EbSECbW0kA2GuOxH/0muZN/F3imv79Lv/CvZRIJIQwMhc4T1U21l+letxxhdTFFIMTFxmMjI01hiyxC7ZBKZPD0eB8YWIwgCY2vX0PzpT7LEIGlJ6eF0lmdjE1w3FiTx058RumgdaSlNu6udFa0OXpwwkosJUJSIhp4lEnoWiiLZqMQLk2ZWtDpod7aTklKENlxA4tFHuS7i57mSfRcPBKJ0GyScn/4E472rQRAwNBtY4XGQzBYQFoI1tpxIYD+Tk0cpCElM0SUcOl8ps16/YAFhS5RIfT25yUku/bd/QkbZV/F2YS5VQ0lZlj+HsvHrclmWPyfL8uy7Kn7PMKeKoRKEx21IQp4G73/x6kCMXEHmhvwv4dCjcNUXoe2CWT//2muvYTAYWL68vP/7A6EEgixzXeDXnGc+weiJY4QGvEp+wG+h9YrbyKWbyDI61eHRw5UDh+i2GPnTI8OkL7qY5G9/y29iE4xkcly25wdIjY2Ee1cyqZvE3eSmx+MgV5ApXrYec3QpE7mjBI7/HKGgh7xAXzDNco8Dl9VJ2BQmkwPD7bez7F/+EadcYE8gOlUXH2NzZBTTy78l96cfBkBsFFnUYsMgibxqbKMYVpLdAwP/gSxlkcLt9C9SBERPqwObUwmtpf/oPciBAFsPvcGvpnbp7glEMSCz7t6/xbJtG5EChE1hXBYXPR4H/f44xiYJfbKFwOFHiQSfxxJbhv3CdobHUvR47HjsHiLGCP7mRozLl7P5n+4nXZT5eXCMA3GlLv6Kn+9FNJsZ23gJaTGNs8VJj0cZd3LzJZiiS0nJAwROPgJFkeKkxJuRLItabLRYGohZYuQLoPvgB1n4X9+lo5Bjj19JrD8QiHLhRAz7009R+OhHAAG5Qaa9wYzdqONAvo5CRCkkGPT9N0UpiT7SyfHVSlnmCo+DOlcdMjLJm26EsXG2HnhR26Wr1sVfdO/fYLl0MyFBImwK47Q4p3iUwNJpxJhoJ3DsF4QGn8I8vgTb6kaOhybp8Thw2VwKj+xWzKtXs+kbX6MgK/kftS7+qicfB0EgcfXV5IU8za5mjUeRTVswRheRlYKMeB8CoDiu541xgY5GC81WG0l7kmIRxI/8Ga0PPcCyXFrj0Z5AlPPTEzQ/9nPkj34EWRbI1mVx2S047UYOTuopRJXDcoZ9Pyavi2GIdDN8odKNtcdjp9HVSEEoMLlzJ3Iux9W/eUrbd6HshoZL/u5uzOvXEzAZlXVkddHjdnA4kMC23IZ5vJugdx/Bk7/CFO/CttTK4UBC4ZHVRdgUJqA3YL7oIi76+t0APBiI4ktleH5skq3P70dOp0nu2kWRIg3uBo1HQ+u3oI90k9eNM+xTcmmFqInXkiaabQZcDjP5ujzIAsWPfpTG/3mMNakED8xSzjvfmEvV0HpBEN4AXkfZWPaaIAizS7rfM3jjXoDq7SWqIDKUoLGxgBh8nRNvPM8q8SRLXrtH6SN08Z/N+tlsNstbb73FihUrMBgM2v8LU1b8Qlni68ldHWKCAAAgAElEQVQbWC6/iijAW8/tQdZlEGNmhhpWU8w2M1GIkswl6ezsJDQ4wLeWtjOWL/DlnTeROnKEHw8EsOWybPj1r2i992sM+UcJmUK4rC5WeBQr+ojJgxxpAClPOLEP8/hiPNvW0e+P0+Oxo5f0ZB2KxTO5bSvWVau44ul9PBmOszcYI5jNc/me/6bhXe8iWF9PQSxQ11yHXhJZ7LQpQqi+ESFvYmjg+wCIOTP9wUkcJh2tdSZaGlqY0E8wks3i/Iu/4PKf/DdZWVEyewNRNva9RmOdA/nDH0IuyIRMIU3IjYynqb/+GkzRpUwI/RSMY0iRdkbPuwig7AEei4zTdM89LDt+hM6xCD/xR/hJIIqxUGDjL39K61fvwRfwEzaFcVvd2gN8SK5DjtpBlBmN/hxTohPX5hX0++Os8DgQBAGhUQn/xTdegvXCC7ly36P8ZmyCvaMxfOksVzz4I+quv55wayuyIGNttiIIAss9dvr9cextTqSsneHhHwAgT5g5FMpg0IksbLbirHMyZhhjKJHA9dnPcPkDP6CIYoU+4I9w4fHDOCUR3cc/Tj6bV75ni/I9j6dyGHZuxxhdTErykTMG0Yc7mbzkSgpFmR6PA7fFTcgcYjKexPHlL9MVHqVndIQ9/ih7/DF0xSKX/uxBPF/5Ct7QqMIjm5set8Kj/kkd8piizPzBhzBMtNK0pltbRwC6RmUvTGzVKmxbtnDlo3s5kEjySHCM/sk0V/z0QexbtxJZrGy4NDYbte+w35+gYXk7+lQzI/49Co/iFvrHiwgCLHPbcdldRI1RBmJRPF/8Apft/QnClJL5yUiE1cM+FkwmsNz512QmM9qz0ONxkMkXyV+5HUNkIRn9CBn9MIboIuSrtpPKFRQeWd2ETCGyqSzmz36GtkyKtYMnecAf0UqGL3voR7jvvJOBsRhRYxS33c0Slw1JFOiLFSCuHLky7P8xulQjdYs99Afi9EytI2OzERmZSPdC7Nds5/JH9nAkmea1RO0NcvOJuYSGvgP8qSzLXbIsdwF/hnJYzR8MBuIDGCUjLmv1pm2lkGWZ8NAEzYs8IOppPfLf/KvpWwiWZrj+n2vmBVQcOnSIbDZbce6AasVvq7Pzi8IGkitvocsaYejozwHocK6hzx+nmFUOe/HFfXR1dVEoFKgbj/DFxW38xtbA93a8k1+ExtjywjO0ffBPmOjsJJPOaNZ0V5MVo06kzx/HZlfiw7KURwx7SC3t1dxtAEujhaJUxDc0RNt9f8e2V14gD3z20CCOyQkuLaRxfuqTeL1eYqYYTpsSk+/xOOgbidN5625MsUXIYg7DpJsF2y6bEhDK4ndZXARNQXw+H/Xvey+ruxewcGSQe44NE80XuXr/k7R94z4GpzbKZR1ZTDqTNr7jRQtE60FS4s26nIP+oFJeucLjwGlxamc9BwTw3PnXXPX0Pl6KJ3lwOMTGV19gwbtvI79qFRPxCcJmxZr21Jmot+jp88epdy2EooQs5ZEiC5Au3MRoPKONwVHvIK/P4xsYoPXee9n25gFk4FOHBzFnMlwRC+L+68/j9XpJmBK02Fs0HvX74yy4aTfG6GJkMYcu3UDrxRvoG4mzzGVHJ4m4LC5CphBDw0PYbriBFWtWs+LkUf7+xAgj2TxXP/0rWu+9l6GpIoFxyzj1xnptfEcmJBh3KDwSQEzZ6A+mpsZgL+PRcCaN5ytf5qpf/4o3J9N8b3CUC988wMJrdyBt2kg0HNWUcZ1FT1u9mb6ROE2LehDyRmQphz7SRf32HZyMTGpjaKxvJGPM4BsYwHPP3Ww93o9ULPCJQwPo83m2DiqtQnw+HylTipa6aR4dCyZwXv8ODJFuZCmHmLPiXLWavpE4C5utWAw6bQ6jgVGMW7ey+PItrD30Jv/mDXA8neWqp35F6z13MzyhVHSFTCGc5mnPrz+SRZhwgCiDWESI2zk0ltfWUZO5ibBJ2Rw5lEjQ+tV7uOqp/+FEOse/eP2sOfwWSy/diHnHNfhH/BqPTHqJ7mYr/f44rjVrkLI2ZDGHMboIzzt3cyQwoY2h2dFMypTC5/Phuesurh7xYcjn+InPP6s8mS/MRREkZFl+Rv1DluVngcpC3d9BZDIZhoaGTnmdL+6jw9GBLAv0jcR5Y2i85uuVQxFSiRzZeitjHVfSw/MkLQWOb/pzjo++zvETT836ev2NR3G7MzQ2JojH39BePxg8gV2C3S1hOu0D7F+wgyUrBCztcXKTEit2fJw+fxynuW16zFNxVK/Xy+2tTexstPNfO28grdPzjkSY5g99CJ9PCXupi1MniSx3K9Zo963vxpBQ6JmEFvqmNiOtmFqcLquLCesEXq8XQ3s7Gz/yYZYMnGBClrniwIssvPdrpAsFgsEgAWNAU6RK/DhDwlSHGFNKaqVIO47e9RwOJFjRqtB3WqbCT5kMwVCItnvu4Zo3XmFChob4ODu2Xo75/PPxer3kLXkaHA1l4+v3J2h0K1UrYs5M11XX0++P02Q14LQbaTI1MW4aBxFF2Vx/PTfUWRCKRSYFketGB3H++Z/j9XrLeCQIAj1uB33+BJ0334pxXOGzIdfE4WBSmyOA0+pk3DKO1+tF52xh3ac/wflH+pkoylx68CWW3PtV8no9IyMjCo8sLm0Ok9kCgbwOcVyZlz7SgfPyKzSPo5RHhXyBkZERPF++ix39rzOBgDU5ya4NF2DdcCFer5eiqYitzjblcag8itPSvRYAoaCnfcNW+vxxLAaJziYrNoONnCUHOoVHjq1beWeH0g59EoGdA8dwffrT2jpSQ09QosyuvwFDrAsAXaqBI6HkdB5oag4RUwSfz4dYV8f5X7yTdX2vM1GUufiNV+n526+A1crg4CBBU1Cjv6JVCWOeHMshTXXyNES7aN1xrWZNA7gsiucnyzKDg4O4//rz7DzRzwQChmyW3T2LsG3ZonzPetDb9eglPYudNvSSQL8/gXv1JiiKIAt4zruEfn8cnSgo14h6jA4jskHG5/Nh27yZ3auWYcqkmUBgx7E+3HfeyeDgoGIomsNlc+j3J3Bv24E+qvBIjNfjSxS0PJDKo1HTqELDbGb53X/DJa+/yt7hEJm3YU9BTUUgCMJaQRDWAi8JgvCvgiBcJgjCFkEQ/gn49Tkf2Tzg0Ucf5fvf/z6x2OxJF2/cS5eji/9+wceObz7Ddf/4bM3Xx/9dSax+5blj3B9uon+9Ht/aAt7Jr+H1fuCUL4/nOyxZuoeXX9nNb1++nt++fD37X76VxyMp1ud/Rej4rdx58depy3yM0TWj1HVOIMTsWD0LFWu6qRtQFIHZbMbtduP1ehEEgftWdOKZGKcjFGD7X/05giTh9XoRzSJJXZIWc7k1qq9rQIp6EPJGuq+5nX5/XHO3YWpxGkeJRCIkEgkc27fxzqxiVb2ndwXGhQtrCAjl8/3+OE1O5eQui9yOLzJJMlvQHuBSa9Tr9aJrbOSPrt+BLp9nx/BJXHfcTqFQYGBggLg1rtFvsRtpthnp98dZ+M4PoJtsQR/ppH7VGvr9Cc3jkESJBmsDskPWhH3vJ/+Kdb7jtIxFuf7PP4Kg1ys8MojE9fEyIXc4EEcwWZDG3AgFHQsvu4X+KWVZKoSG9cMkEgmi0Si2zZu5yaBUFb1rcQemZcsYHBykWCwSNAbL6Ks8ci1UwlnGrIdQIkNkMqvxUFUEKo8kh4P3vPtGjNkMWweO0f7h/6NUvfl8JG1JTdHYjDo6myz0B+J0XHsb+vE29NEOnBsvp98fZ5nbjjS1T6XF2kLOkdN4tOyv/oJN3iPUTSS46U8/gGg0KmtMEogZYyXKzM6J8CRZQUSfcEJRYsG6HTV5lE6nGR0dxbJ+PTfXWwC41d2AefVqRkZGyOVy+A3+aSFaso5aV10FsoB+0slErshgNFUmRKPGKIIoKN+lxcKtf/xerKkkl3mPsuhjH1GeGZ+PjCOD06rQN+hEFjsVo6jtyuswxDrQjy3Ac9Uu+v0JFrXYtE7CTquTtCON1+tFlmW6PvoRrvAexZJO8a473o1otWrPYcQY0XjU43EwPJYinilgSLkURdNzWQWP3FY3AUOAfD7PyMgI5lWruKWtmXGjiUf3Pc25xmznEfzdjL+/UPL778U+giuuuIIjR47w4IMP8v73v7/qsYD5Yp6hxBBXdlzJT54dZLnbzse3LqtJM/JyiPBzQf7qug7S/mMUCkZamj+JIMzFuVIiRy6XC0k3zfpHYkYyQybu6N7A+dYLuPuxfkQBPnVND5Mjx2k8bwvJbJ6T4Ul2rV7CwJhbS3B3dXXx8ssvk8/nqdPr2Ld+OZlsFoPbpQmIYn2RRnOjtlmux+Pgx78dJBBPs3rb3YydeBV7x1L6fv0yC5sUdxuUB2yffh9LWILP52PlypV89I53sfnQMdZdrhwv6fV6ESWRmDFW8gAri7vPH+fSd/4Fuv+x0fGu9/H44UjZ+y6Li7QujcFmwOv1cvHFF7Pwogv55aFjLNx4C4Io4h8aIpfLETAE6LH0aDzr8djpG4kjGYz0tH4Jvb2JfKHI4dEE77t4OunvsriYsE3gH/aTyWQwWq18Z9eVTE5OYl6g7CL3+XwIDQImnQmHQRnbilYH6VyRk+FJet/xVcJv7aehZz19rx/E5TDSaDVoPAoYA5zP+fh8Ppqamnj/e29lTd9RLrhsl8YjQRCImCIaj5a57YgC9I3E2b79j2EftL/nNp7xlgsIp8VJTsphrFeE8ZYtW3D3rubxI8dp23gjgiQRDAZJJpOEGkMafUDxakbiiJJET/eXkPRGZFmmzx/nutWtZTwat4yjH9IzMTGBzWbjH2/aSSw2hmNhp8YjXaMOBLTzOno8DgpFmSOjCXpv+jqBV36J68Lt9D38BnaTjvYGszaHkDmk0fF4PNz27ptY+tYRLthyg8YjKDcoSsOYN1z7TnJPxmm99XoO+hNl68hpcVIQC+gb9Bqdxp7l/NJwkuZLViIYDIyNjTE2NkZ0QbScRx47zxxVFO2Kni8iIyNKEn0jcS7qnm4g6bQ4iZqjmMNmYrEYjY2N3HfbOxgNhWla3K3NTd+gpyAWqij8BOtuu4/h3+xhwdW38v3H+jFIIt0tVo1+qcLv6Ojg2pt2M/n4k2y/avZjSucDNRWBLMuXn/O7n2M0NDSwa9cuHnjgAZ588km2lnSqVDEyMUJezmOQXfT549z1jvO4akXtXMGvnguRatQT6N/HokU+3O7rOX/V7Wc1zscHj7HQnOWqjksRBAGrw8UjB4a5t+kKWpqvBODAQAxZVhbWm8XO6Uqnzk5eeOEFhoeH6ezspNk9PfZwOMzk5CSJ1oRmocB0WKPfH+eK5d1YW5WF3B+Ic3779AEuTouSqNRPWc0rV65Ep9OxbuV0tZPP58PaYqUoFLXFX28x0Fpn0qyehdv+RLuf6m4DtFgUD0XXpGNgYIBisYgoiqxavlijrz7YJ8WTbLFMPxArWh1891kvuUIR5wXKOa9HRhNk80VtfuocRo2j2GU7AwMDLFmyBGdzIzQrD/n4+DixWIzM4owWFoJyr2bx6jYWbLlN+1sVQCr9hD6B0awI6rVr1yJJEutWlfPI1mQjL+a178Gkl+husdE3JdS6rv7jKfrKqVU9U3NoNDWiE3XIDUrYI5/Po9Pp6Fm6qIw+gFfystKysoxH/9MXYDKTp2W18jgPxZIk0vmKORwyHKKZZnw+H+eddx7N9XU01ysl0MlkktHRUeTFMk2GJvSiXqOv8uT89g46r/gj7W/VK1Ppp3QpLHYLXq+Xiy66CFEUWT+DR9Z6Kxkpo/GoNIwJ0HXl7Qr9171T39FULktvwa63k6/PM3JiRFH4RiNLFy0sow8wqBtkg2XDNI88Dva+Okx4IkPzSqVaKzaZJRBPl60jl8XFM7pnaKMNn89HY2Mj9XUO6uuUa7LZLMPDw4hdIhbJgs1g0+irPLl40UK6rn6/9vdStw391IFMTouTrJTF1mDTxqqXRN6z82reDsylaqheEISPCYJw33y0oX67cd5557Fu3Tqee+45jh49WvG+WjF0ZMiIXhK47vzWimtKER6aYNJxHFl+BUnK09Fx81mNb1Dd3epuLBFCDhKZPEOx6YqB/hIrqMvRxcn4SWRZ1g4nVxdPKdT/BQyBMitouVsVctOpnng6V+Zug7L4ZUGm3l1flX4qlSIQCCA2itr1KtTwUyn6/fEyd9ukM1FnrCPtSJNKpQgGg1XnUN9YT1qXLpvDCo+DbKHI8dBEGX313iqcFic+wYcoirPyqNQSBVjstKEThbI5ZPIFjgUnyui7LC4QoM5dV5V+LpdjeHgYfbNeG8/sPErQ3mDGYVKuFwWRFnMLk7ZJLWwwE16vF5vdRlSYae06kGU4FEiU0a/GI6/sRa/XV53DwIBypm/UXE5/QYMFq0EqW0fFosyhQKJC0QBYXVbFQ53RwkIN/5md5rLrS3lUWkbZ74/TYNHjchjL7hG3xLU8QTUemUwmBuXBinWk0iylX41HI/IIZrNZM05KMTQ0RLFYLAthQnkYU4Usy/SNxLXKq9I5G1uMDAwMUPgd7DX0GNCFcnbAvLShfruxbds2XC4XDz/8MPF4+YOnWtbP9IlcsdxJg9VQjQQA+WyBwJiPUNrL0qVhzOZO6hxrz2psag/4G13TnUZVa/Stkemx9vnHsRsVd7vD3kEim2AsM4bFYsHpdFZdnF6vF5vNxlBhqGxx2k16FjQqFR8qDmkCYvqQ7NLFGQqFmJwsb3imCoiUPYVVb8Wqn26u1+NxcDw0STo3vaD7SkoKS+8RMSsho5lCqFgsMjAwQL1b8VJmKhqgbA59I3EMkrKXoZT+WGEMt8ddk0dGo5FBygWEUSex2GnTEugAR0cnyE+VXc7kkdQoad5FKYaGhigUCmTrskiCRKNpOtzQ47EzPJZiPDl9FkPfyHgZffUeIeN0aKUUsqzkP5pbm0GYySOF16Vz6BtR8kCqMaDSz5HD3VabRzqdjhFppIxHoqgkpUu/A180OZUHmqavjalBMR5CM9qlBwIBstksxfqiNp7pOTiIJXME4tNbl/pmeBzqZ/x6P4IgVFVmXq8XV5urCo+qrKMaigABnG3OmvQFQcBv8JfRV+jYy76D6TxQ5Toq1BfI5XJVFf65xFwUgUmW5b+UZfm7v69tqPV6PTfeeCO5XI69e/eWWSS+uA+zZCMS1/HOtbN0HgVOHh4m4TiCx2lCEI7gce8uP+T+NKEeR3lxvZUO87R1s9ztQBRmWinTSdCuui5t7KDkCQYHB8usCFVAdHR2EM2UW3KgWELVrKAVnukd0WpSrVA31TJhxgPg9XqRJImwIVxJv1WJHx8dVSz2sWQW/3i5uw1TMfZigLq6ugohFAgEyGQyGJqn4/EquputGHRi2Rz6/HGWuKbdbZh+6JtamxgZGSGbLd+27/P5lP0YqVDFA1yTRyVzUBPwGUemJo8EQSBmjtFsbkYSp/NUmjUaUOimcwVOhifLrGl13v6cn5aWlgoeRSIRJd/RUmlNt9WbcZh0FXPoarJiNU5HhdV517nrCAaDFQrf5/PR3t7OaHq0Oo8C0xZ7tXVUZ6zDIBpI2pM1eQQQt8axG+yYdeZp+q3lFnu+UFQqz6rwKJAJ0NraWsGjeDxOLBbD5rJV8KjBasBTEsYEZR05pyz5UvoAVqdVyzfM5JHH42E0M1r1WTg6OkFuqkdUX5V1ZJSM1BvriVviVXl0rjEXRfB9QRD+RBAEjyAIjerrnI9sntHS0sLOnTvxer3s379f+78v7kNXdNJgMXD5MmfNz+fzeR7b93NAYMMGZdG73bvPakyvxJOcSGW42V3OTrNBomuq/him3O0Sa1rdAV2aJ5hpRUSjUSYmJmjwKJ5GpZXi4GRkkmRWqZeu5m7b9cpDOWYaQ6fTVTxgmoCosvh7Zrjc1awsdVzBZJCuri58Pl9ZCEC9X65OsZhL76GTRJa57GVhCVVZlkL9jKnZRLFYLAsbJBIJIpEIzjYn2WK26hxG4xmik1mNvkkv0tU07fnoJT2NpkaiumjVsIHP58PtdhPMBqsK0VIeHQ4kKMqz82imwlfvV2yotKYFQagIPylll5VeGSi5Gpj29ADS6TSBQIC2BW2MZ8ar8iiRng5j9vvjSKLAEte0VyYIguLVyCEcDkdVHjU1NREqVCrjmWFMb2SSTL5Y9XuOpCJ0dHYwPDxcpvDV+6mb/6rN4VTrSB2XSqNUUOdyOYaGhujo7CCUDFU1urKFIidCk2VzKQ0NqeMKFUI0NzdX9czOJeaiCLLAvcDzTIeFXp71E7+j6O3t5fzzz+fpp5+eTkKOexkbr2fX6lYMutrseOKJJ4jFQ9Qnl5HMPkF9/QbM5tk9iFNhz9RZv9e21Fe81zNlaQEMxpJMlpRdttpa0Qm6MkUA5YtTnV81a1qlL8uK8IHKBB+UPMDpEAsWLCijn06n8fv9dHZ2EkxWCrnORgsWg6QpANX1rvUAt3e0K5UvJWEDn89HQ0MDESLoRT0Nxoayz/ZM7c6VZZlgIk14IlNTEeTqcgiCUPaAqfOxOC01eaTyRv25zO3Qyi5VuCwugqkgnZ2dZTzK5/MMDQ1pPJpJv8VupMlqKKMPVLV2k/kkrjYX2WwWv396k5HP58NqtTImjdWcw+FAgkJRZiKTxxdJVhVAAGlrukLhDwwMIMsyDpej7Npp+tNJdfVnd7NVywOV3iOYqlT4xWJR88qq8UgLY2oGRWWOA5TvoCAXaPQ0UiwWy/YP+Xw+jEYjk6ZJ7dqZczgemiCdK5DNFzkWrG1QTBgnMJlMZTwaHh6mUCjQ1NpEXs7XXEd9/nGNR231Zuos5S3vnRYno8lRurq63vY8wVwUwV8Ci6d2Fi+cenWf64GdK+zcuZPGxkYeeughouNRRpMB8plGbrigtlA/fPgwL7zwAk36Lrq686RSXjzud57VONIF5azfHS312HWVZa0rPA4Goyni6ZwmRFVXUi/qabe3a4lum81WYUWoAiJpUNzxalYKKJZ6vlDkUKBy8aufU63R0dFRkkmFnrp5pqOzg3CyMjQkigLL3NOx0X5/QkuczaQvMy1oVEGqCoiuri5NQMwMw/V4HEQmswQTmZIkaLm1qz70kVwEj8dToSwNBgNZa7Yqj7QY+0hcK7tcMYN+KY86OzuJxWKMjysP/PDwMPl8vmwOpVAt9r4Sr8lmnC67LKUPaOEfdQ5q+K+rq4tQKkSDsQGDVJ7jUhuf+SKTHKrhlTWZmxAFkVAmRHt7ewWPJEmC+vKxqFjmtiMIlCn8autI9Wo6OzuZnJwkHFZKJUdHR8lkMjV5BIrl3D8yTV8vTVeezeSRrklXkSdQyzFDqRBGyaiVCGv0PQ7yRZljwQmOBSfIFeSKdWTT2zDrzARTQTo6OqoaXfomvTbXUkyHMZU1Wi1XNpNH2WyWQCBQcc25wlwUwVtA5fl1v6cwGo3ceOONJJNJHtj7AMjgNLWzqq16t9Dx8XEeeeQR3G43xlAH9o7fIIomnM7tZzWOfRHlrN+ZYSEVqqA+5E/Q748jCrDUNb14Oh3TJaRAmRWhCojOzk6CKaUSZ+YDpjY+6/fHNXd7piWqfk5dnDAdNvB6vYiiiLXFWtUKUuegWuwzyy5VqA9NypDCbrdrD1UwGCSdTte0FEt51OeP17SmLXoLNr1NU2bDw8PkploR+3zK7uxwOlw2FhVNNiMuh1Lx4R9PM57Kzcqjrq4uja7KI4CW1hYmchPV59Dq4MjoBPlCUevPI87wONTPxYU4TU1NGt1YLEYikZidR1qMPVE1xwGgE3U0m5q1OQQCAVKplDaXtrY2ItlIVR5ZDDoWToUxx5JZRqrkgUp5NNN7VefS3tFOOF1pUKjjVcOY/f44i532Cu9dzWdF81FtkyVMh/9KeTTToKi2js6bMQetJcoUj6LRqFZ4oob/xgrVvbLpMGacdK7AidBEzXUUTUdp62gr49Hbgdk2lKkoAAcFQXgKpRU1ALIsf+ycjeocw+PxsG3bNh577DGWNC6hd9mqqknfQqHAQw89RD6f55orr+MXr78JlmdwtmxDp7NVoTx37AlE8Rj1bGqoTqc0LNHnT9BdUnYJiiJ40f8iRbmIKIh0dnby8ssvEwgEsFgsxONxOjs72Z/cj0kyVVhB0/HjBH3+BA6jyBJLiv7+/rLrdjt2s9W0lWQyybZt20gmk/T391NXV8c111xDaCDE/SvupzHXWPHZ6xfCZa4W3nyrj7+80IrNqKu4pr5Yz/0r7qc4WuSyyy4jn8/T399PJpNh27Zt6PV63tf8PvSivuKzNlnm27s8WJMBVlllvnt9K37fcWZ2Z7ln2T3oRB12t52rrrqKQ4cOIYoia9euxWQykUvluH/F/cQGYowJ5UnAr1/dTKEoMzpwnG/v8tBim6wYx3bLdjYu2kgsFmP79u3kcjn6+/uxWCzs2LGDkcER7l9xP/VyfcVnt7XlWb/DRV9/Px9abcJikCquMRVN3L/ifgxhA5dccgm5XI6+vj5yuRzbtm3DYrFws3QzYqNY8VlRlvn3XR7s+RANBpnvvMPDuN/L+AwmfWHxFxARqXPUsW3bNo4ePYpOp2PlypWYTCbyiTz3r7if9Eiafn/5Pb68pYFsvoj32BG+vctDs61yHW0xbmHNsjWMBkfZvn072WyWXC6nhf9y+hxFuVihaKA8jNnvj7NpSXPFNernVEH90ksvafRBMZRG36jMZQF0Nlkx6yUlvyEIGHXleSAVmjJbMq3MenqUs8MvuOACgsnqRpcyBztP9gdr5oHUz8nIZKQMjY2NeL1eLrnkkorrzgXmoggemXr9QWH9+vU88OzjrIyuZFtbW9Vrnn76aQYGBti9ezeFSQO21teQmcDtObuwkHrW74cXOJFqVB25HEYaLHr6RhQrZW1neXy809FJupAmmAzitrrLrFGzWQkhdHV18eChB6taQaAszgdeGeGTiVEAACAASURBVOKt4XH+70WNeFoaaG5qKrs2kooQmAywtHEpY9ExZFmmqamJQCCAzWYDI0gJie66bsz68pDGZCbP8dAETocJOZ6mo9FCvaU8dJEv5jkcPYzb6sZUNDE+Pk5LSwuJRIJcLofT6eRQ9BANpgbcVnfFHKSpvjnpXBGDTqSrufIBNo+bKcpFuhxd2rj1ej2xWIzm5mbC2TCJbIJljZU7yv3jKcKJLC12I1IizXmtdRU5glg6xsjECIsbFpMYS5DP53E6nZpSlswSYlyky9GFdcb51alcgaOjCVwOE8TTtDWYabKWh8+KchExIuK0OLGiVK00NzczOTlJJpPB5XJxOHYYh8FBq61yH4xuNIFBEskXZQSBsvJaFda4lWwxS7ejm0AggNVqxWg0Eo1GaWxsJFaIMZYeY3nj8oq1FIynCcTTuBwmxHiaHo+jrHILYDwzzlBiiEX1i5gcnyQWizE4OIjP52PZsmWzClHVen7maJhgIlPVmm40NaITdASTQdZ0ruH5559neHhY2e2r1+PxeAi+GGRVy6qKz0pqGHMkrv2ukyqDJU6Lk1dHX8XtdmMwKLvh6+rqtPDfE8knEAVR23ldih6Pgz0vD7H/SEj7uxp9QMsT9PX1aZsszzXmch7B96q9zvnIzjFkGfYbB0lLGZ5/Yp/mCqs4ceIE+/fvp7e3l9WrVxMeTFDX9TwGg4vGhovP6t7TZ/3WLr4SBEE5OONkhOGxVMXin1k5ZLfbNStCVQYtLS01QwaguNzJbIFf9Y3S3WioUAKAtos0V8xhMBjI5XJkMopjaDAYyBenOn+KlTaF6sGMTdXJz0wgAkiChCAIGn1Qdmlms1kMBgNFuUhRLlalr9JMZgtk8sWq9NU55Io5RFFEp9Np9AVBQK9X3qtF36yXkJGVls46sUIJzOSR0WikUCiQTqeRZfmUPDLqRARB0HhkrjIHURCRBEmjP5NHMjKFYmFWHqVyBdK5QlX6Go8KCo8MBoNGH6a/Z52oq2pQlH7POlGsUAIzeWQymTCbzUxMTJBKpbT8AFRXBGoY8+EDw0Bl+E/lUbOluSL8pOYHRFGsWtSgQmkOF68ZwlTHFkwFEURByxOoIaiOjg6CySDNpuaq34NKc++BYawGiY5GS8U1pV5NZ2en1pvp7cBcdhafFAThxMzX2zG4c4mXvFHiop+BzhDxeJyf/exnWiXDxMQEe/fupbm5mR07dgAQ9g9h9byJx309glD9YZor9gRirLabWWY1zXpdj9uBN6KkZ2Yml2YqApjOE6j5AVEUGU1Wd4cVmsriPBmexCCJVR9ydVHni3lNCCUSStLLYDCQK+bKriuFJAoYdRKZfAFxyuWeCUEQ0Ik6RdDodIiiyOTkJMVisYy+KkhmwmSQyBaKyMiY9dWXs0pflmWMRiPZbJZMJoNer9eUUE36U0Iuk68tREt5pCqzufJIFARMOpFMvoAAmKoUDgDoJGUOkiQhSRLJZJJCoVCmaGrNwawXyRWKFGW5prLUiTpN6aoKP51Oo9frEUVxVh6ZS3hkmuU7gGkeCYJAOq1sEuvs7GQ0qQi8amtVDWOeDCtVP9WsafWzo8lRzGYzLpeLvr4+QqEQnZ2djGfGq5YIq+jxOIin88SSuVnp54t5YukYXV1dhMNh+vr6cDqdWK3WWY2u5SXP2nKPoyIPVDr3avmmc425+BzrgPVTr83AN4H/PpubTrWteFAQhEOCIPQLgnB2JvYZYO+rQ0jGMIu727niiivo7+/n5Zdfplgs8vDDD5NOp7npppu0Bzst70MQirg9Z7d3oG8ixZsTqZpJ4lKULshqJYVmnVmrHAJFEaTTacbGxujq6kKWZULJytpsFUtdSuMzoKoVB+WWnF6v/J7P5zUBMZulCGiCwaivrmjUe+SLeQRBUARbXhFsRqNxVmsaKBP+s3kEUC6o8/lpxabOoRqMOhFxatyzCVGY5pEgCFpPIEmSyBfzilUvVv+8Stegk6oKCHUOqkIxGo2nxaPScddSljO9GpjBo0JtHukkAd3UuM2GU/NIVfiFQoG6ujoaGhoIJoPoRF3ZzutSqIaQ22GqufvfZXExOqkoFLXKTft9FkUDlFWD1VIEM/MQoGx6VH+fTRHUmZXzG0rnMhP1xnoMooFgMkhdXR319fVv236CuYSGIiWvYVmW7weuOMv7/j3wuCzLy4HVQP8prp9XpLIFHnvrBII0yaL6hVxyySUsWrSIxx9/nEcffZTjx4+zfft2XC7li8+k8hibn0EqLsNmXXJW994TiKIXBK53NpzyWrX6otlmoMVeHjcWBZEOe0eZR6C6xOrvY5mxWa0gk17S4sUGqboAUoVXvpjXwgaAJiBmsxRh2lqsZU2DIiRyxRySJHHZZZdx1VVXsW3bNl588cVTewRTdEVBqLkPRBVC737XuxkZGeHb3/42P/3pT7XQk++kj+suv44lS5Zwyy23lG1GEgRBu0etOajhrVJlBmg/54tHqsBX6QqCgE6nq8kjSZLo7e1l44Z13HLNFl57+SWMc/BqVIWv3kuWZeX/M+jfdttteL1e/v7v/559jz6szeH2229n4cKF9Pb20tvby8GDBzVFOJNHpUK0xdyCWKOLr/osVKtIUqEmc0vp6nQ6Wltbtf/XMoqWuR3amVLLawjqUovd4/FofFKfu9m877I5eKpXKAqCQIulRVNa6p6Lmb2ZzgXmEhpaW/JaJwjCh4DqnJoDBEFwAJeinHyGLMtZWZbHZv/UmeH1p4Z4/N/eqPj/r/oCJGWF2Z0OJYSye/duzGYzr776KitWrOCCC6ZP4xw+cQBTwyANjuvOajz5osze0RhXNTloMpw6T7+oRTk4Y+ZGLxUzS0hV68pkMuFyuWaNu6pQrZ9aHoEoiJqgBiqE3GzWNEwL6lrWNEx7BGazmVdeeYUnnniCL3zhC3z2s589pbVrkESkKWE9m8cBcNJ7kkWLFvHiiy9y4YUXamGVb9z1DT780Q9z9OhRGhoa+M53vjNjDmLZz5kQBKHMYp+pLOfMI0Ptx1HlkRq6Ue+jKiCo5JHZbObgwYO8/tpr/MVnv8A/fPUuzbupRh/QcimqkDMYDOTlPDJyBf2TJ0/S1dXF008/zaZNm8rmcu+993Lw4EEOHjxIb2+vdo9Srwamhehs1jRMr9Na1jRMb7ybyE5ohzYtWLAAnU53ymfBZtTR2WhhQeN0w79q9EER+JIkaffo7OwklU+RyCZmPeVwLnNQS1RBUQTVejOdC8ylaqj0XII84AXOpuVmNxACvisIwmqUncr/V5blsgYngiB8EPggoDH8dJHPFjj+aoix0ST1runkzIOvDNHUME6a6XOKbTYbN998My+//DI7duwoEyqB0YeRCxIdC88uLPR0LEEwm+cm96m9AVAOzvjwZYtrJq86HZ08OfBkmcW5adMmMpmMlh+A2RXBLesX0GwzloUkvvTzt8qacKXzKeVcVUnZZVooFNDpFAWUzCfRCToM0nDNe2TyBVa31/Old6ys+r4anwbFgrNYLGSzWaWssJhDFEQ+9clP8ctf/hJBEPj85z/PLbfcwsMPP8y3vvUtfrD358RCQZZuWsv+/ftxu8uri97/vvfz0isvEQlG6O3t5ejRo9xxxx187GMf49b33sqLz77Ij3/0YwDe97738cUvfpEPf/jD2ucbLAYEaitLdQ6qQDabzeRy0yGWgz9+gInhUV7U1WhoKEMmX0QvidQqEMkX82QKWd7QmREEgTpPO5e99wOAIrwFQUCaJXcl5dI0T7XelmWZT37yk2X8vPGmG3niF0+w93t7+fWTvyaRSLB161aeffZZ6poVC1ZdY+9+97s5cOAAfr9f4+fxEyf4o/f/H1Z97MM1x1DKI5PJhF6vZ9kypVJrNDnKkoba3naPx8ENa9vZtbp6hR+UW+zd9d1s3LhRkx2qcFV7Q1XDBzZ3z3pYfLO5GVEQNVobNmzA6XRis9k0g2y2Z23Xag9D0STntVb3CNTP90X6AEXBuN1uLZdyLnFKRXAOziXQAWuBj8qy/KIgCH8PfBr46xn3/Tfg3wDWrVt3RgfhLNvg5oVHjnP4xQAbdimboQPjaX5zLMzGdRlenxRpt0/vKO7o6KhQOsVinlThf0iGVlPffOozjWfDnkCURr3EVU213duZ+Murl9Z8r9PRSUEuMJwY1hrRlXoyp3KHATYubmbj4uaKuu9SCIKoCWo1HKFABllGqBHXVmGcJfYN0wImlUqxZs0arX3F//7v/5Iv5nnyF09y8OBBXnvtNcLhMOvXr+fSSy9l9+7dPPTQQzzwX9/h8ccf50tf+lKFEgD44Q9+yH3fuY9EKMHtt93OJz7xCR544AEATgyfwO6wYzIoifv29naGh8uVmtWoK2vSVmsOybyS2NfpdDQ2TgvdIsXZmxMKSg5lNggIGj1BEDAaDJhMyphr5WlSqRS9vb1l/ATYu3dvVX5uvXYr+3+5n29961s8/vjjfPnLX8btdpPIKolv1SP4wQ9+wJ49exgcHOSGG24o46eKz33uc9x1111ceeWV3HPPPRiNRvSinlReqc6TJAmr1YrVqpTTBpNBNrVtqs1fSeTvbl5d832YXuejyVG667u5+urpXv6jyVEaTdOHM1XDey7qrPkeKPNvMjVpz9XSpUtZunSpNn6YXREsdtq575beWe/htDj59eCvkWWZhoYGPvShD816/XzhlIpAEAQjcANKK2rtelmW7zrDew4BQ7Isvzj194MoimDeYa030t7TyKEX/Fx47UIEUeCRg8MUZbA7xmiltWJL/kxEY8+CNIaYvvKsOo2O5/I8Hh7n3Z4mDPNUF6xWDg0kBjRFUIpgMoiAQLOlcgPObPjCdeeV/T0yMUI8G2d54/Ky/2fyGY6NHaPN1ka9qbJf0lyhChg1lAHw/PPP8973vpefPvtTDrx4gNtuuw1JknC5XGzZsoXf/va37Nq1i3/4h39g5cqVXHTRRdx2221V6QuCwOE3D3PllVfyxhtvaKEKgGxByQeUxr/P5HsurUwq/Xy+mKfnxl14rB4azWfeqzGVT3Fi7AQL7AtwGMsNiVo5iGr8fPPNN3n22Wer8rNncw9f/NoX2X7x9jJ+VstBHDhwgKuuuqqCnwB33303brebbDbLBz/4Qb761a9y5513ohN1FIoFbROkionsBMl8clYhOheUegQzMVvp6Oneoxr9uXjfc6WfLqSJZ+PUGWt7DvONuYSGfgqMo4RwMqe49pSQZTkgCMKgIAjLZFk+DFwJ9J0t3VpYfpGbff/Rx/DRMdqW1vPQK0Nc0NlAOD2kCdLZ4B/ZSyFjo85x6VmN4+ehcTJFeda9A6cLNazlHfdyaXvl+ILJoGIFzZKonAv0or7qA3yq+P3p0J+Jiy++mHA4TDAY1KzhahgeHlbCYKOjVTffPPbYY3z2s5/l+Inj7N+3n7HIGFarlSeeeIKnnnoKR6ODRDxBsVBEEiWGhoZobZ39cKJac5BlmYJcQCdM82O+eaQK5VLki/my1s3VoPIzFArVDH/oRT3Hho9V8LO0/FXl58mTJ3n00UcJhUJl/ARl5z4oeYA77riDr3/962VzyBfzZQbYXKzpueBUiqDahsQzucdgovLgm7l433NBaWXS26kI5mKatsuyfIssy1+TZfnv1NdZ3vejwA8EQXgd6AX+9izp1cTC3hb0JonDL/h5Y3ico8EJdq9pxRf3nVIR5HJxQuF9xAfW09I+t7h+LewJRFliMdJrn/2BPR3Um+qpM9aVJYxLcaoqhrmitKKkFKeq6Dld+nLJUdiHDh2iUChgq7excdNGfvKTn1AoFAiFQuzfv58LL7yQfD7PHXfcwQ9/+EN6enq47777Kmjv2LGDV155hWUrlvHos49y3nnnceDAAU1oFeQCF226iIceegiA733ve7zjHe844znU5NEsIYm5oHTjXSlkWZ51Q5wKlZ9NTU1ceumlVfkpFAU++aefrOBnaehJ5efKlSt54403KvgJaN1RZVnmkUceYeVKJTdUi0fzZU2bdWYcBodGrxSnSkbPFbU8gmAyiE1vKzuc6Uzpq/TeTszFTHlOEIRVsixXlt+cIWRZPoiyP+GcQ2+QWLzWybFXgjxjV9oQXLzUwFcPJ0+pCILBx5DlLOPei2neecaFUnhTGV4an+Rz3Z6zCi9Vw8zKoVIEk0Faradv3c5E2e7iEktuto1SpwO1tDCdSmthBlmW+ff/+HckSWLX9bvoe7WP1atXIwgCX/va13C73dx1111s3ryZzZs309vby/r169m5cyc9PT1l9A8cOMDKVSuZTE+Sy+VwOKZDK7lijs/c9Rk+/icf5/Of/zxr1qzhj//4j097DmU7Z5neKDhfPCrdeFeKgqw0GaxGX80RgMLP733ve0iSxO7du3n++ecr+PnJz3+StRetZdOmTWX8tLRZKsJCq1ev1voFlfITlGSy6nn09vbyL//yLxU8KsV8WdNQXVBnChlimdi8KAKXxUU8GyeVT5V5YfOpaFR6byfmsjo3AbcLgnASJTQkALIsy+ef05HNI5Zf7Kb/OT9vveTn6vNdRLNKMlANrdSCP7AXodBBNr6QBnfllvC5Yk8gigDc4Do7r6IaOu2dvBR4qep7wWSQ3pbZk1NzQelmoFKcaqPU6UAv6jkZO0mHYzpZn8wlOTl+EoNk4N577+Xee+8t+8ydd96p/W632zl06FBV2uvWrePeFfcSTAZ56tdPlb2XL+Tp7u7mpZeq83CumI1HAkJZuOhMoZaQzqSvvjcTtfrZC4JQlZ+f+tynCEwGyBfzZfw8NnYMgzhtAKxbt4516xQ7rvSQJxVqUnomavFovjwCKC+/VDHfikalWWpIjk7Oj/ddWqL6dmIuoaFrgCXAVuA64Nqpn7838CyqR+fQ0zUBN6xtYyCutFLurKvtESSTPsbHXyET3kyDx4Y0y6E1s6EoyzwQiHFpg51W0+yJ6TNBp0PZnq9WY6jIFDKMZcbmZXGWxnZLcaqNUqeD0r0KpfRL7382qDYHWZbJy5Ubpc4Es4WGZtt5fbr3eLt5BJArzM/3XLrxrhTBZBCHwYFJN3vLlbmgmkcwXzmIUhoz7zFfYViDZKDB2PC2ewRz2Vnsq/Z6OwY3XxBEgeE6gc68yNpmO764D4NowG2pnTwKBB4GBEJ962hpP/OW0y+OTzKYzs5578DpQlVmqnJTMZ+LXxREREGsau2ebchDRelmo1L6cPZhFZU+lFujs4VVThczN96pyBfz6KT545FamVRKH84dj07V9O90MHPjnYr5EqIwdeJdOlKmbObzWSgtUVVRKBYIp8Lz4nHAdM+ktxPnvr/p7wBik1keSyQQEDj+chBv3EuHo6NmSEOWi/gDD1Nnv4hEyEbTWSiCBwJRrJLINS3npgJADW/NzBPMpztcKz493x6BWplUSv9UG6VOhz6UW7vzaU2r9zjXPFIFcyl99b35oA9vP4/mq7QTFCFalIuEU+Ey+up780G/lCZANB2lIBfmVZn9znkEfwj4+esjhClSt8DG4RcC+MZ9dNhr71YeG3uZdHoIs6icQtZ8hoogWSjys+AY17bUY5XOXphVgzqPWopgvhbnTEtO7T8znx4BlAuhUzW0Ox1Ui0/PpzUNtb2a+ebRzDlIolSzR8/p4P8Vj+Yr0Qrl5ZcqRpOjVQ9nOhPYDDYsOksZ/fl+1v5/RXCO8NArQyx321lzaRuxQJKkX541P+AP7EWSrKQjSkKsuf3MKoYeD48zUShy8zkKC4FyFKPT4izrQgoli9M6P4tTJ+rIF8qFNMyvpVhKF+bXmlaF5dtp7aoezu8Lj6p5fueKR2p4K1fMEUlF5lWIAhWCutbhTGd6j5mKBubH+1bpRNNRbbPj24E/eEVwLJjgtaFxbrygncXrXIg6gcWja2tWDBUKKYLBX+Js2U50qICtwYjJdmYPwQOBKO0mPRfXn92xlqdCl6OrwiMYTY5i1pmx68+87LUUM+PT58JShEprdL4EECi1/Ofa2i0Nb/1e8kg89zxSN96BcgKejDxrs7bTQbWqm2AyOG/0AVxWVwX90nufLVQ6odS5bzan4g9eETz06jCSKLCrtxWjWYdjqcDiyAUssFQPDYVC+ygUJvB43kl4aOKM8wOBTI6nowlucjXW7Pg4X+hwdFQNDbksrnmzgnSiDhmlygbmb6NUKf3zXeezaf0mVq9ezdq1a3nphZfmTQAB/OUH/hKv18v999/Pj3/8Y62i51vf+haLFy9GEATC4fCpCc0yB5gWnufCmi6lr96jFo/UNtQqP5977rlT3mNmiara9K9aPk1tQ63yU4Usy3zuc59j6dKl9PT08M1vfrPmHObbmm4wNaATdVU9gvnCzBLVYDKIJEg1z1I4Xfy/2EvwB60ICkWZh18d5tIlzTjtSmlabkkYU96Kfqh6uMYf2IvJ1IbNegFjgSTNbWemCB4ajVFk9uMo5wtdji7GMmOMZ8a1/8334p8Zw5/PJCUopYVGs5F9z+/jtdde4yt/8xXu//L986oIhnxDuNpdPP3002zevFmL32/cuJEnnnii7DyHM8FMi32+rWlVIKv0i3Jx1iMq1V5Dr732GnfffTef+cxnTnmPmZVPs+U4SttQb968Wfv/f/7nfzI4OMihQ4fo7+/n1ltv1d6byaP5tqZFQcRpng7dyLI878+C0+IknAxrnt9ocpRmc/O87KdR6at03y7M31P2O4iHDgUYlorceL6T38SUDoov2cPoWm1kDk0wuTRRdn0uF2MoeoKLu65nLJCmWJRpXnD6oRVZltkTiLLOYaHbYjz1B84SpcdWnt+i7PMLJoOsca45M4K//DQEyjeSW+UCXfk0ep0RBB32YhZTIYdOb4FZegFpcK+Ca+6p+bYgCAhM15jHxmM46h1aKGFm2+TSNtT79u0jEAiwZcuWqm2o1bbJI/4R3nHpOxg8OcjOnTu5+f03857b38OaNWfIpxmo5REkfzHIuD85L/ew5FMUhDhjC1JYdiwA5uZxxONxGhoU42c2fn7jm9/gn/b8E0PDQ1xx+RV8/9Hvawc0qajWhnrnzp185CMf4QMf+AD//M//zA9/+EOt75PTOS2EZyak51sRqLRUurFMjFwxN28eh0o/L+eJpqM0m5vnteoJShLek2+fR/AHrQj+wTtK7sIWvh4fg4Pq2TcXKQduIsPB45UfEr7Jf5gsLPQqSuJMKoZen0hxeDLN15a2n/riecBMRXAurCA1xCTLMgjT7ZBnawh3ukin0my9ZCvFXBG/38+3934bvaiv2TZZbUOttk2u1YZabZt8+MRhNmzdwL/+7b/y0IMPcShyaF49jpnWbq6Y0zZRzRcEQShLtJbedyZOtw317t27+dGeH/Gj7/yIA8/8f+2de3hU5b3vP29mciUJiQkgEJKIIAgREAICupXSlqPoRsVaS/HIRj0qWq2e2srxefY+9dl22y1Vq/XUWi+tdYt4Aeq1uyqooAVCkKsGECGBmISEXCb3ZCbzO3/MhUkySSbJWrPCzPt5Hh4ma2Z+7/t715r1W+/t+9vNQw89RHpmejf7fclQf/PNN7z22mts3LiRESNG8NRTTzFxoifXQLChodiYWNLjjVtQMTJpJIdrDwPmBRo43ROobK5k/PDxhtkfHj/cn7IyXER0IHhyzrkUnnR0SgTxiy0PMMk+lYztFzBtYRbjZ5xOVPHN0d/ydMMs7j86jkcq3NjjYkgd0X+RuDcqaoiPUSwZOXBp5v6QlZyFTdn8K4d8T0EDvviDPLkrEYqrvyIzKZNRSaModxTjxm3oDyAhMYG3trzFxPSJ/P2Tv3PXHXdx9YGre5RNDlWGGjz6OPMvm8/XRV9zwbQLPMMq0mHoRGvXjXe+zWRp/3yuYWWUNZbR0N5A9lnn+ocC+xoagtBkqJcsWcLjv32cWTNmMXfuXH70ox9RVF0U1H5vMtRtbW0kJCRQWFjIhg0buPnmm9m6dau/jQJXJhm9ogc8N+qt3271PxD5jhlF4BP71IypVDZXMnf0XMPsK6UYNay7VIaZRHQgmJmZwszM00M7bR1tNNR9Rv70PDISElDbqrn4O54bmYibjua3eOgsuLM2i8fiWvjJ2OReE6oEo93tZsPJWhZlDCctNjzNG2uLZWzyWP+EsZGbyXx0XVrodDsNkQToitPtRES4cM6F1NbUUltd22vWqFBlqI8dO8Y777xDRWUFKckpfLz5Y37/5u8NDQTB2shI+3B6+aVb3P2apwlFhhqgsrwSFaM4efIk7R3t3VJUhiJDnZWVxXXXXQfAtddey8qVK7v5EDg0ZORNGjzXfYurhUZno+GT0dB5MrfZ2Uyjs9FwH8K9uziiJ4u7cqL+BIKQk5rD5LmjqTreQHVZIwBNTV/jdNYyPfN8Hpk4lsMpiq2T+3+j+7imgRpnh6l7B4IRuHLIjKcg6LyixMiNUoH4lhYeLDqIu8PNiMwRPcom90eGOi8vj937djNh8gQ+3vYx73/4PmDcRK6PwOWXZrRR4NCKLxF8KDuvQ5Ghdrlc3P6/bufRPzzKxEkT/e0ZGMxCkaG+5ppr/MNQn376qT+Ll4/ANjIjEATeqH3JmTISMwyzn5GQgU3ZONl80lDBvEDCvaksonsEXSlp8Nwoc1NzOWf2KD5ff4RD2yuYv3QCtXWehGnp6RexuDmJF4rbWJ8Ly2obmZ8e+jzB6xU1ZMbaWXDW4Hcx9ofc1Fx2ndyFiJh2cdpj7LS72w3fKOWjtaWV6xZcR7wtnnZ3O2t+v6ZX2eT+yFBPnz4dt9ONy+kiMTmx0/j6U089xaOPPkpFRQXTpk1j8eLFPP/88wPywR5jp9nZ7N95bXQbBa7e8vU4ehpW6a8Mta89Z8+fzbz8eVy54EqmXjKV8bM7D//1JUO9evVqli9fzhNPPEFycnK3trTH2P1pPSubK4MmVRoMgWP4lc2VZCRmGHoebDE2g6zNhgAAF9tJREFUMhIz/IEGjO1x+Oxtat7ULeOdWURXIPA+MWenZpMUF0fO1LM4vKOCudecS11tAfHxo0lIyKLi8Cmu2NWEY8Iw7vyqhI9mTyIzru+mqnW6+OBUPTePzSS2n0NKgyUnNYcWVwtVLVVUNlcSo2LITOxfisq+iI2JpcnZZPiySB8NrQ0cc3ikqCubK/32e5JN7o8MtU82ee17az2TuG6b34d77rmHe+65xxAfYmNicYnLtDYKnJDuq8fRXxlqX3seqT1C3LA4tu/ZTnlTebcy+pKhTktL47333uvVhw63R/CvxdViyk0UPEHGSEG7rmUEBgIzegTt7nYcbY5BpYENlagaGiqpLyEjIYOUOM+8waS5o2lytHOiqJraugLS0y7ybCoqbSS+A56dkkON08VPi47j7mVc1cdfK+twipimNNobgSuHKpsryUjIMGVYwi1u2t3d8/waZR86P+0ajd1m99s3KpdCJ/sxdkSE1o5W4Axto5jTbRRYppH2wSNvDcbfREckeRaA+G7UZgQC39CNmUNDEL69BFEVCIodxZ2SSeROyyA+yc6hXYU4ndWkpc8BoLq0keGZiVyYkcIvJ4xhU009z57oe7v36+U1TBmWQF7KwJPYDBR//uL6YtOegnw3HV/uA7NuEO0d7b1ulBoMvvFps+Y4zG6jwJSVZvoQaN/ooYmuy2yNvlYT7AkMjx/uDwRG9zjgdCCobK4kJTaFpFhjf/PBxPPMJKoCQdc8xfZYGxPyR1FdtR2A9DRPIDhV2ujfP7BybCaLM4fzq6NlfFHf1KPtr5ta2d3QHJadxMEYNWwU8bZ4Shwlpj0F+W46vpuc0U+j/pSVJj1NQ+enXbPsg3lt5FuZ1NbRhoic0W3kE1Uz64n9RMMJHG0O0+w3OBsoqS8xzT7oQGA4je2NVLdWd8tTPHnu2SRkHCSGDBITc2lvdeGoavFrDCmleHzyOM6Oj+X2L0twOF3BzPNGRQ02ZU46ylCIUTGMSxnnHxoytUfgbPGvmTejjBanOU/TPvtucdPuaje3R+BsMSyXQrAyzG4jgFZXq6n2zeoR+GweOHXANPu+J/YDpw6YYn9E4unhrXAQNYEgcMVQICNzUxg26mvaaiejlKL6W89Tf6C0RFqsnT9MyaWsrZ2fHTrRbR22W4Q3T9ayID2VkfHGP0GFSm5qLodrD+Noc5jSHfbdFDqkwzCxuWBl+JQpzXwaNXozWTD7dmX8sIqvjDO5jXwPEW5xkxafRrzNeBkWX5J5MC/QANS315vz0GWL5ayEs/QcgdGUODyBoGuPoLW1BFt8HdVHc6k/1UJ1aXBpifzhw1h9zmjerXLwl7LqTu99XttIWZvTkkniQHJScyhrKgPMufgDE6CYcYPoatfMp1Gz7PuGbgDDUlR2xfQ2soWvjcy4TrvaNWuOINhrI+mqcmom0RMI6ktQKMaljut0vLauAIDmykkc2lFBVWkj8Ul2ktO7P6XclT2S75yVwr8d+ZavGk8ni3+tooZUewz/I9OcdJShEhjkzLo4/Tc5E24QNpuNRfMXsXTBUq5feD07tu0w1P6yZcsoPV7Ky394mfc3vu+/oS5fvpxJkyaRl5fHzTffjNPp7MNS7/jaxqxgGdj2vZ2HgchQd7XZmw89yVD79nXMmDGDMWPGcM0113T7rs9uOAKBmUNDXV8bSTg3lUVNICiuL2b0sNHduqF1tQXExmYwYsxkDm2v4NQJz0RxsC59jFI8dX42aXYbt39ZTJOrgyZXB+9VOVgyIp1Em7XNmTs81//arIvT9wM2IxAkJiaytWArGz7ZwP3/dj8PPvigofaPHTvGxPET2bltJ7PmzvL7sHz5cg4ePMj+/ftpaWkZ8GYyH2a2UVf7vc3TDESGGvAMaXnFBHvzoScZ6q1bt7Jnzx727NnDvHnzWLp0afcyvHbNuk59dhPtiSTHGp8YKik2yW/XzGAWrkBg2YYypZQNKAS+FZGrzC6v64oh8Oy2rK3bQXraHEbOHcOml4qgqoVpC3tWDR0RF8v/m5LD9Xu+4f98XcrFaSm0uM1NRxkqRvUI/rPgPzlYE3xzVltHGy63i3hbfL9udJPPmswDcx7o83P+VTdNLSHJJvdHhrq8vJyZF87k8NeHuXPZndx3z33cftvtLF682P/ZOXPmUFpaGrJfvfngu2H/7W9/o6KiYlA2A3GLm4T0BOZ+J3Shs1BlqH3tWVNZw43/fCOffvopyV2GSfuSofbR0NDA5s2b+dOf/tStPuEaGjIyOVOwMhodjYalgw1mv7atlvaOduJscaaU4cPKncU/BYoA07UYRISS+hKuGt853rS2ltLWVk5a+m2MmjiCT189hKvd3af09CXpKdybM4onSk6yubqB3MQ4Zg8fZqYLIZEen05KXAod7g6S48xJj+l7UjTjx9XS0sIlsy+hobmBU5Wn+HizR7/GKBlqn2zyqntX8Zvnf8OUjCmdPud0Onn55Zd58sknB+WHLwCYNTTka/u+7A9EhtrXnhve3cBdD9xF1pjuD0V9yVD72LhxI9/97ne7SVAE1t3sQGCWfZ/to46jpvdqKpsryUoxV9LekkCglMoCrgR+Bfxvs8urbq2m0dnYaegEOK0vlHYRcQl2zr1wJId2VISUrP5nuWezra6R7Y4mVo49Oyx6IH2hlCInJYdGZ+Og7PT25F7dUk1FUwXj08aTaO+/RHdvJCYmsmv3Lg7VHOLYvmMhySb3R4baJ5ucd0Fe0I1Sd955J5deemmnYY6B0HVo6IorrhiUva64xd2jPHQgA5Gh9rXn9PzpXHXdVT3uvO5NhtrHq6++2qmHEIjZgSA93pNHwexAYFd2w1JUBrMPERwIgN8CvwB6vOMqpW4DbgPIzg6eXzhUjtcfB7qvGKqrLcBuT2PYME/SjJmX5xCbYCNjbN9P9/YYxTNTc3iqpJIVY43V9BkMK/JW0Ow0JhtWMFLjUmnraDNlyR94ds5mJmYy/tLxIckm90eG2iebnJiUyLYt2/js08/8n3vooYeoqqri2WefHbQPyXHJpCekGx4ofcSoGEYkjejX2HeoMtS+9qw9VUtGQnfFzlBkqAGqq6spKChg48aNQctJik0iKTaJC0ZeELIP/UEpxV0z7uKCTHPsA1w74VpyU3NN2U8DkJWSxbzR80zrWXZCRML6D7gK+L339QLg3b6+M2vWLBkMGw5vkLw/58nx+uOdjn/2+WWyd+/tg7IdKXz11VdWV0GGDRvmf11UVCQZGRnicrlk/fr1smjRInG5XFJZWSnZ2dlSXl4uTqdT8vPzZcuWLXLrrbfKmjVrgtp1uVwyf/58ERFZuHChOByOTu8/99xzMm/ePGlubjbPOQuwqj1FRJ555hm56aabeq3fULjmIh2gUEK4L1vRI7gYWKKUWgwkAKlKqf8SkRvNKrC4vhh7jJ3Rw0b7j7W2ltHaeoJx41aYVaymnwxUNjlUGeqeZJPvuOMOcnJymDdvHgBLly7tpGx6pmJVewKsW7eO1atXh8VPzeBREoKqpmmFK7UAuF/6WDWUn58vhYWFAy7n3o/v5ajjKG9f87b/WHnFX/nqq58xZ/Y7pKRM6eXb0UFRUVG3H7xGYyb6mjMfpdQuEcnv63NRsY8g2NLRutod2O2pJCdPsqhWGo1GMzSwNBCIyCd99QYGi1vcHK8/3k1jqLZuB2lps1EmiIJpNBrNmUTE9wgqmipod7d36hG0tZ2kpaWENK/stEaj0UQzER8IiuuLgc5LR2trffsHdCDQaDSaiA8EvjzFgUNDdXUF2GzJJCfrSWKNRqOJikCQZE/qlMi9tq6AtLRZxJgkCqbRaDRnEhEfCIrrPXmKfXICbe2naG7+hrS0iyyumaYrA5VNDpWeZJNvueUWpk+fzrRp0/jBD35AY+PgJDqGCla156ZNm5g5cyYzZszgkksu4ciRI4aWqzGeiA8EJY7OS0frvPkH9PzA0GOgssmh0pNs8hNPPMHevXvZt28f2dnZPP3004aWaxVWteeqVat45ZVX2LNnDz/+8Y95+OGHDS1XYzwRPTbi7HBS1lTGleOv9B+rqy3AZksiJSXPwpoNbSr+4z9oKwouQz1Q4s+fzNn9yC/QX9nkUGWog8km+3bGiggtLS2GCwgePvzvNDQWGWozJfl8zjvvX0P+fDjbUylFfb0nTaTD4WDMmDHGOa4xhYgOBCcaT+AWd+cVQ3U7GJ46k5hwCDlp+sVgZJP7I0MdTDZ55cqVvP/++0yZMoXHHnssLP6ajVXt+fzzz7N48WISExNJTU1l+/btYfNZM0BCESSy+t9ARec2l2yWvD/nyb7KfSIi0tZWLR9tGi9Hjz09IHuRzFAQAAsUSfvHP/4hU6ZMEbfbLffee6+88MIL/vduvPFGeeutt0REpKamRsaMGSNLly7t1fbq1avlo48+krffflsefvjhoJ9xuVyyatUqefHFFw3wxnqsas9rr71Wtm/fLiIijz76qNxyyy1BbQyFay7SYQiLzoUN39LR7FSPjHWdYyfgyT+gGdr0Vza5PzLUwWSTwTO5esMNN7BmzRpWrlxpil9WEa72rKqqYu/evVx0kec3dsMNN3D55Zeb6ptm8ET0ZHFxfTHp8ekMj/ckla+rLSAmJp7UVPM0yjXGcPDgQTo6OsjIyODSSy/ltddeo6Ojg6qqKrZs2cKcOXNwuVysXLmStWvXcv755/P44493s7N48WJ27dpFXl4e+/fvZ+rUqezevdsfBETEv6pFRHjnnXeYPHlyWH0NB+Fqz/T0dBwOB4cPHwbgww8/1MJyZwAR3SO4+8K7uX7S9f6/a+sKGD58JjEx5iRV0QwOK2STRYQVK1ZQX1+PiDB9+nSeeeaZsPptFla0p91u57nnnuO6664jJiaG9PR0XnzxxbD6rek/lspQh8pgZagBnE4HW7bO4pxzfsr4c+42qGaRg5YE1oQbfc2Zj5ah7kKdoxAQvX9Ao9FouhA9gaB2BzExcaSmBk+0rdFoNNFK1ASC2roCUlNnYDMp6bpGo9GcqURFIHC5Gmho+FLnH9BoNJogREUgqKsrBNx6fkCj0WiCECWBoAClYhk+fKbVVdFoNJohR1QEAs/8wAXYbIlWV0XTC1bJJvu4++67SU5ONrRMK7GqPTdv3szMmTPJy8tjxYoVuFwuQ8vVGE/EBwKXq4mGhv06/8AZgFWyyQCFhYXU1dUZWp7VWNGebrebFStWsG7dOg4cOEBOTg4vvfSSoeVqjCeidxYDOBxfINKh5wf6wdbXD3PqhLHJWTLHJfNPPzwv5M+HUza5o6ODn//856xdu5aNGzca6jfAv35dyoHGFkNt5iUn8u8Ts0L+fLja8+qrryY+Pp7zzvOc6+9///s88sgj3HLLLcY5rzGciA8EdXU7UMqm5wfOAKySTX766adZsmQJo0ePDpuv4cCK9hQRnE4nhYWF5Ofn8+abb3LixImw+q3pPxEfCGrrCkhJycNuj5yxX7Ppz5O7kfiGMgC2bdvGTTfdxIEDB/jss89YtmwZNpuNUaNGcdlll7Fz506WLFnC7373O/Ly8pg7dy7Lli3r0fbu3bv53ve+x/79+/36OwBlZWW88cYbfPLJJ6b51Z8ndyOxoj2VUqxbt4777ruPtrY2Fi1ahN0e8beZM56wnyGl1DjgL8DZgBv4o4g8aUZZHR0t1NfvY9y4fzHDvMZEwiWbvHv3bo4cOcKECRMAaG5uZsKECRGXZzecst7z5s1j69atAHzwwQd+JVLNECaUpAVG/gNGAzO9r1OAw8CU3r4z0MQ01dWfy0ebxktV1eYBfT+aGApJQgITqRQVFUlGRoa4XC5Zv369LFq0SFwul1RWVkp2draUl5eL0+mU/Px82bJli9x6662yZs2aoHZdLpfMnz9fREQWLlwoDocjpDqc6VjVnidPnhQRkdbWVlm4cKFs2rQpqJ2hcM1FOgzVxDQiUg6Ue183KKWKgLHAV0aX5UlUH0NaWp/ie5ohgBWyyZGMVe25Zs0a3n33XdxuN6tWrWLhwoVh81kzMCyVoVZK5QJbgDwRqe/y3m3AbQDZ2dmzSkpK+m2/rOx16hxfMOX8Xw++shGOlgTWhBt9zZnPkJehVkolA+uBe7sGAQAR+aOI5ItI/ogRIwZUxpgxP9RBQKPRaPrAkkCglIrFEwReEZENVtRBo9FoNB7CHgiUUgp4ASgSke5JUTWWYeUwoSa60Nfa0MKKHsHFwP8EFiql9nj/LbagHpoAEhISqK6u1j9QjemICNXV1SQkJFhdFY0XK1YNfQaocJer6Z2srCxKS0upqqqyuiqaKCAhIYGsLGs22mm6o7f8aQCIjY3lnHPOsboaGo3GAiJefVSj0Wg0vaMDgUaj0UQ5OhBoNBpNlGPpzuJQUUpVAf3fWuwhEzhlYHXOBLTP0YH2OToYjM85ItLnjtwzIhAMBqVUYShbrCMJ7XN0oH2ODsLhsx4a0mg0mihHBwKNRqOJcqIhEPzR6gpYgPY5OtA+Rwem+xzxcwQajUaj6Z1o6BFoNBqNphd0INBoNJooJ6IDgVLqcqXUIaXUEaXUaqvrEw6UUsVKqf1eVddCq+tjBkqpF5VSlUqpAwHHzlJKfaiU+tr7f7qVdTSaHnz+pVLq20hU8VVKjVNKfayUKlJKfamU+qn3eMSe5158Nv08R+wcgVLKBhwGvg+UAjuBZSJieG7koYRSqhjIF5GI3XSjlLoUaAT+IiJ53mOPAjUi8mtv0E8XkQesrKeR9ODzL4FGEfmNlXUzA6XUaGC0iHyhlEoBdgHXAP9ChJ7nXnz+ISaf50juEcwBjojIURFpB9YBV1tcJ40BiMgWoKbL4auBl7yvX8LzA4oYevA5YhGRchH5wvu6ASgCxhLB57kXn00nkgPBWOBEwN+lhKlRLUaAD5RSu5RSt1ldmTAySkTKwfODAkZaXJ9w8ROl1D7v0FHEDJMEopTKBS4EdhAl57mLz2DyeY7kQBAs+U1kjoN15mIRmQlcAdzlHVLQRCbPAOcCM4By4DFrq2M8SqlkPPnN7xWReqvrEw6C+Gz6eY7kQFAKjAv4Owsos6guYUNEyrz/VwIb8QyRRQMnvWOsvrHWSovrYzoiclJEOkTEDTxHhJ1rpVQsnhviKyKywXs4os9zMJ/DcZ4jORDsBCYqpc5RSsUBPwLetrhOpqKUGuadZEIpNQxYBBzo/VsRw9vACu/rFcBbFtYlLPhuiF6uJYLOtVJKAS8ARSLyeMBbEXuee/I5HOc5YlcNAXiXWf0WsAEvisivLK6SqSilxuPpBYAnDenaSPRZKfUqsACPPO9J4P8CfwVeB7KB48D1IhIxk6s9+LwAz3CBAMXA7b7x8zMdpdQlwFZgP+D2Hn4Qz5h5RJ7nXnxehsnnOaIDgUaj0Wj6JpKHhjQajUYTAjoQaDQaTZSjA4FGo9FEOToQaDQaTZSjA4FGo9FEOToQaKIKpdQ/vP/nKqV+bLDtB4OVpdEMdfTyUU1UopRaANwvIlf14zs2Eeno5f1GEUk2on4aTTjRPQJNVKGUavS+/DXwT1599/uUUjal1Bql1E6vuNft3s8v8GrEr8Wz0Qel1F+9on5f+oT9lFK/BhK99l4JLEt5WKOUOuDNFXFDgO1PlFJvKqUOKqVe8e4u1WjCit3qCmg0FrGagB6B94buEJHZSql44HOl1Afez84B8kTkmPfvm0WkRimVCOxUSq0XkdVKqZ+IyIwgZS3FszN0Op6dwTuVUlu8710ITMWjg/U5cDHwmfHuajQ9o3sEGo2HRcBNSqk9eGQMMoCJ3vcKAoIAwD1Kqb3AdjzChhPpnUuAV73CYSeBT4HZAbZLvYJie4BcQ7zRaPqB7hFoNB4UcLeI/L3TQc9cQlOXv78HzBORZqXUJ0BCCLZ7oi3gdQf6N6mxAN0j0EQrDUBKwN9/B1Z5ZYBRSp3nVXDtynCg1hsEJgNzA95z+r7fhS3ADd55iBHApUCBIV5oNAagnz400co+wOUd4vkz8CSeYZkvvBO2VQRPg/jfwB1KqX3AITzDQz7+COxTSn0hIssDjm8E5gF78ShI/kJEKryBRKOxHL18VKPRaKIcPTSk0Wg0UY4OBBqNRhPl6ECg0Wg0UY4OBBqNRhPl6ECg0Wg0UY4OBBqNRhPl6ECg0Wg0Uc7/BzJh5HSK+ou8AAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(df.timestep.values, balls_list)\n", - "plt.xlabel('iteration')\n", - "plt.ylabel('number of balls')\n", - "plt.title('balls in each box')\n", - "plt.legend(['Box #'+str(node) for node in range(n)], ncol = 2)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "end_state_balls = np.array([b for b in balls_list[-1]])\n", - "avg_balls = np.array([np.mean(b) for b in balls_list])\n", - "\n", - "for node in G.nodes:\n", - " G.nodes[node]['final_balls'] = end_state_balls[node]\n", - " G.nodes[node]['avg_balls'] = avg_balls[node]" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[10. 9. 4. 7. 8. 9. 9. 6. 10. 7.]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "nx.draw_kamada_kawai(G, node_size=end_state_balls*scale, labels=nx.get_node_attributes(G,'final_balls'))\n", - "print(end_state_balls)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "rolling_avg_balls = np.zeros((len(balls_list), n))\n", - "for t in range(T+1):\n", - " for node in G.nodes:\n", - " for tau in simulation_parameters['T'][:t+1]:\n", - " rolling_avg_balls[t,node] = (tau)/(tau+1)*rolling_avg_balls[t, node]+ 1/(tau+1)*balls_list[tau][node]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(range(len(rolling_avg_balls)),rolling_avg_balls)\n", - "plt.xlabel('iteration')\n", - "plt.ylabel('number of balls')\n", - "plt.title('time average balls in each box')\n", - "plt.legend(['Box #'+str(node) for node in range(n)], ncol = 2)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[10. 9. 4. 7. 8. 9. 9. 6. 10. 7.]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for node in G.nodes:\n", - " G.nodes[node]['avg_balls'] = int(10*(rolling_avg_balls[node][-1]))/10\n", - "\n", - "nx.draw_kamada_kawai(G, node_size=avg_balls*scale, labels=nx.get_node_attributes(G,'avg_balls'))\n", - "print(end_state_balls)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "cmap = plt.cm.jet\n", - "Nc = cmap.N\n", - "Nt = len(simulation_parameters['T'])\n", - "dN = int(Nc/Nt)\n", - "cmaplist = [cmap(i*dN) for i in range(Nt)]\n", - "\n", - "for t in simulation_parameters['T']:\n", - " state = np.array([b for b in balls_list[t]])\n", - " nx.draw_kamada_kawai(G, node_size=state*scale, alpha = .4/(t+1), node_color = cmaplist[t])" - ] - } - ], - "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": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/demos/sim_test.py b/demos/sim_test.py deleted file mode 100644 index ae58ba4..0000000 --- a/demos/sim_test.py +++ /dev/null @@ -1,37 +0,0 @@ -import pandas as pd -from tabulate import tabulate - -# The following imports NEED to be in the exact order -from SimCAD.engine import ExecutionMode, ExecutionContext, Executor -from simulations.validation import config1, config2 -from SimCAD import configs - -exec_mode = ExecutionMode() - - -print("Simulation Execution 1") -print() -first_config = [configs[0]] # from config1 -single_proc_ctx = ExecutionContext(context=exec_mode.single_proc) -run1 = Executor(exec_context=single_proc_ctx, configs=first_config) -run1_raw_result, tensor_field = run1.main() -result = pd.DataFrame(run1_raw_result) -print() -print("Tensor Field:") -print(tabulate(tensor_field, headers='keys', tablefmt='psql')) -print("Output:") -print(tabulate(result, headers='keys', tablefmt='psql')) -print() - -print("Simulation Execution 2: Pairwise Execution") -print() -multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc) -run2 = Executor(exec_context=multi_proc_ctx, configs=configs) -for raw_result, tensor_field in run2.main(): - result = pd.DataFrame(raw_result) - print() - print("Tensor Field:") - print(tabulate(tensor_field, headers='keys', tablefmt='psql')) - print("Output:") - print(tabulate(result, headers='keys', tablefmt='psql')) - print() \ No newline at end of file diff --git a/demos/test.ipynb b/demos/test.ipynb deleted file mode 100644 index e3c6800..0000000 --- a/demos/test.ipynb +++ /dev/null @@ -1,137 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "# The following imports NEED to be in the exact order\n", - "from SimCAD.engine import ExecutionMode, ExecutionContext, Executor\n", - "from simulations.validation import config1, config2\n", - "from SimCAD import configs\n", - "\n", - "exec_mode = ExecutionMode()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Simulation Execution 1\")\n", - "print()\n", - "first_config = [configs[0]] # from config1\n", - "single_proc_ctx = ExecutionContext(context=exec_mode.single_proc)\n", - "run1 = Executor(exec_context=single_proc_ctx, configs=first_config)\n", - "run1_raw_result, raw_tensor_field = run1.main()\n", - "result = pd.DataFrame(run1_raw_result)\n", - "tensor_field = pd.DataFrame(raw_tensor_field)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Tensor Field:\")\n", - "tensor_field" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Output:\")\n", - "result" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Simulation Execution 2: Pairwise Execution\")\n", - "print()\n", - "multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)\n", - "run2 = Executor(exec_context=multi_proc_ctx, configs=configs)\n", - "results = []\n", - "tensor_fields = []\n", - "for raw_result, raw_tensor_field in run2.main():\n", - " results.append(pd.DataFrame(raw_result))\n", - " tensor_fields.append(pd.DataFrame(raw_tensor_field))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "print(\"Tensor Field A:\")\n", - "tensor_fields[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Output A:\")\n", - "results[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Tensor Field B:\")\n", - "tensor_fields[1]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Output B:\")\n", - "results[1]" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -}