balkian
/
soil
mirror of https://github.com/gsi-upm/soil
1
0
Fork 0
Code Issues Releases Wiki Activity
You cannot select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
master
wip-1.0
mesa
dependabot/pip/docs/ipython-8.10.0
remove-history
exporters
visualization
history
TFG-David
test
models
0.13.7
0.13.4-fix5
0.10
0.10.1
0.11
0.11.3
0.12.0
0.13.0
0.13.1
0.13.3
0.13.4
0.13.4-fix1
0.13.4-fix2
0.13.4-fix3
0.13.4-fix4
0.13.4-fix6
0.13.6
0.14.0
0.14.4
0.14.6
0.14.7
0.14.8
0.14.9
0.15.1
0.15.2
0.20.0
0.20.1
0.20.2
0.20.2.post1
0.20.2.post2
0.20.2.post3
0.20.2.post4
0.20.3
0.20.4
0.20.5
0.20.6
0.20.7
0.20.8
0.30.0rc2
0.9.7
v1.0.0rc11
Branches Tags
${ item.name }
Create tag ${ searchTerm }
Create branch ${ searchTerm }
from 'a7c51742f6'
${ noResults }
soil/examples/tutorial/soil_tutorial.ipynb

3868 lines
725 KiB
Plaintext
Raw Blame History Unescape Escape

This file contains ambiguous Unicode characters!

This file contains ambiguous Unicode characters that may be confused with others in your current locale. If your use case is intentional and legitimate, you can safely ignore this warning. Use the Escape button to highlight these characters.

{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T12:41:48.007238Z",
"start_time": "2017-10-19T14:41:47.980725+02:00"
}
},
"source": [
"# Soil Tutorial"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-02T16:44:14.120953Z",
"start_time": "2017-07-02T18:44:14.117152+02:00"
}
},
"source": [
"## Introduction"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_style": "center",
"collapsed": true
},
"source": [
"This notebook is an introduction to the soil agent-based social network simulation framework.\n",
"In particular, we will focus on a specific use case: studying the propagation of news in a social network.\n",
"\n",
"The steps we will follow are:\n",
"\n",
"* Modelling the behavior of agents\n",
"* Running the simulation using different configurations\n",
"* Analysing the results of each simulation"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T13:38:48.052876Z",
"start_time": "2017-07-03T15:38:48.044762+02:00"
}
},
"source": [
"But before that, let's import the soil module and networkx."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:56:07.621891Z",
"start_time": "2017-10-19T17:56:06.963273+02:00"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"import soil\n",
"import networkx as nx\n",
" \n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"%pylab inline\n",
"# To display plots in the notebooed_"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T13:41:19.788717Z",
"start_time": "2017-07-03T15:41:19.785448+02:00"
}
},
"source": [
"## Basic concepts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are three main elements in a soil simulation:\n",
" \n",
"* The network topology. A simulation may use an existing NetworkX topology, or generate one on the fly\n",
"* Agents. There are two types: 1) network agents, which are linked to a node in the topology, and 2) environment agents, which are freely assigned to the environment.\n",
"* The environment. It assigns agents to nodes in the network, and stores the environment parameters (shared state for all agents).\n",
"\n",
"Soil is based on ``simpy``, which is an event-based network simulation library.\n",
"Soil provides several abstractions over events to make developing agents easier.\n",
"This means you can use events (timeouts, delays) in soil, but for the most part we will assume your models will be step-based.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-02T15:55:12.933978Z",
"start_time": "2017-07-02T17:55:12.930860+02:00"
}
},
"source": [
"## Modeling behaviour"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T13:49:31.269687Z",
"start_time": "2017-07-03T15:49:31.257850+02:00"
}
},
"source": [
"Our first step will be to model how every person in the social network reacts when it comes to news.\n",
"We will follow a very simple model (a finite state machine).\n",
"\n",
"There are two types of people, those who have heard about a newsworthy event (infected) or those who have not (neutral).\n",
"A neutral person may heard about the news either on the TV (with probability **prob_tv_spread**) or through their friends.\n",
"Once a person has heard the news, they will spread it to their friends (with a probability **prob_neighbor_spread**).\n",
"Some users do not have a TV, so they only rely on their friends.\n",
"\n",
"The spreading probabilities will change over time due to different factors.\n",
"We will represent this variance using an environment agent."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Network Agents"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T14:03:07.171127Z",
"start_time": "2017-07-03T16:03:07.165779+02:00"
}
},
"source": [
"A basic network agent in Soil should inherit from ``soil.agents.BaseAgent``, and define its behaviour in every step of the simulation by implementing a ``run(self)`` method.\n",
"The most important attributes of the agent are:\n",
"\n",
"* ``agent.state``, a dictionary with the state of the agent. ``agent.state['id']`` reflects the state id of the agent. That state id can be used to look for other networks in that specific state. The state can be access via the agent as well. For instance:\n",
"```py\n",
"a = soil.agents.BaseAgent(env=env)\n",
"a['hours_of_sleep'] = 10\n",
"print(a['hours_of_sleep'])\n",
"```\n",
" The state of the agent is stored in every step of the simulation:\n",
" ```py\n",
" print(a['hours_of_sleep', 10]) # hours of sleep before step #10\n",
" print(a[None, 0]) # whole state of the agent before step #0\n",
" ```\n",
"\n",
"* ``agent.env``, a reference to the environment. Most commonly used to get access to the environment parameters and the topology:\n",
" ```py\n",
" a.env.G.nodes() # Get all nodes ids in the topology\n",
" a.env['minimum_hours_of_sleep']\n",
"\n",
" ```\n",
"\n",
"Since our model is a finite state machine, we will be basing it on ``soil.agents.FSM``.\n",
"\n",
"With ``soil.agents.FSM``, we do not need to specify a ``step`` method.\n",
"Instead, we describe every step as a function.\n",
"To change to another state, a function may return the new state.\n",
"If no state is returned, the state remains unchanged.[\n",
"It will consist of two states, ``neutral`` (default) and ``infected``.\n",
"\n",
"Here's the code:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:56:08.659268Z",
"start_time": "2017-10-19T17:56:08.606261+02:00"
},
"collapsed": true
},
"outputs": [],
"source": [
"import random\n",
"\n",
"class NewsSpread(soil.agents.FSM):\n",
" @soil.agents.default_state\n",
" @soil.agents.state\n",
" def neutral(self):\n",
" r = random.random()\n",
" if self['has_tv'] and r < self.env['prob_tv_spread']:\n",
" return self.infected\n",
" return\n",
" \n",
" @soil.agents.state\n",
" def infected(self):\n",
" prob_infect = self.env['prob_neighbor_spread']\n",
" for neighbor in self.get_neighboring_agents(state_id=self.neutral.id):\n",
" r = random.random()\n",
" if r < prob_infect:\n",
" neighbor.state['id'] = self.infected.id\n",
" return\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-02T12:22:53.931963Z",
"start_time": "2017-07-02T14:22:53.928340+02:00"
}
},
"source": [
"### Environment agents"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Environment agents allow us to control the state of the environment.\n",
"In this case, we will use an environment agent to simulate a very viral event.\n",
"\n",
"When the event happens, the agent will modify the probability of spreading the rumor."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:56:09.116995Z",
"start_time": "2017-10-19T17:56:09.081481+02:00"
},
"collapsed": true
},
"outputs": [],
"source": [
"NEIGHBOR_FACTOR = 0.9\n",
"TV_FACTOR = 0.5\n",
"class NewsEnvironmentAgent(soil.agents.BaseAgent):\n",
" def step(self):\n",
" if self.now == self['event_time']:\n",
" self.env['prob_tv_spread'] = 1\n",
" self.env['prob_neighbor_spread'] = 1\n",
" elif self.now > self['event_time']:\n",
" self.env['prob_tv_spread'] = self.env['prob_tv_spread'] * TV_FACTOR\n",
" self.env['prob_neighbor_spread'] = self.env['prob_neighbor_spread'] * NEIGHBOR_FACTOR"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-02T11:23:18.052235Z",
"start_time": "2017-07-02T13:23:18.047452+02:00"
}
},
"source": [
"### Testing the agents"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-02T16:14:54.572431Z",
"start_time": "2017-07-02T18:14:54.564095+02:00"
}
},
"source": [
"Feel free to skip this section if this is your first time with soil."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Testing agents is not easy, and this is not a thorough testing process for agents.\n",
"Rather, this section is aimed to show you how to access internal pats of soil so you can test your agents."
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_style": "split"
},
"source": [
"First of all, let's check if our network agent has the states we would expect:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:56:11.367368Z",
"start_time": "2017-10-19T17:56:11.344245+02:00"
},
"cell_style": "split"
},
"outputs": [
{
"data": {
"text/plain": [
"{'infected': <function __main__.NewsSpread.infected>,\n",
" 'neutral': <function __main__.NewsSpread.neutral>}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"NewsSpread.states"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_style": "split"
},
"source": [
"Now, let's run a simulation on a simple network. It is comprised of three nodes:\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:56:12.472479Z",
"start_time": "2017-10-19T17:56:12.206162+02:00"
},
"cell_style": "split",
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAD8CAYAAACfF6SlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/EVEYuj/A/nJqW3BkOC1AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd79a7d5860>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"G = nx.Graph()\n",
"G.add_edge(0, 1)\n",
"G.add_edge(0, 2)\n",
"G.add_edge(2, 3)\n",
"G.add_node(4)\n",
"pos = nx.spring_layout(G)\n",
"nx.draw_networkx(G, pos, node_color='red')\n",
"nx.draw_networkx(G, pos, nodelist=[0], node_color='blue')"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T11:53:30.997756Z",
"start_time": "2017-07-03T13:53:30.989609+02:00"
},
"cell_style": "split"
},
"source": [
"Let's run a simple simulation that assigns a NewsSpread agent to all the nodes in that network.\n",
"Notice how node 0 is the only one with a TV."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:56:12.719473Z",
"start_time": "2017-10-19T17:56:12.653974+02:00"
},
"cell_style": "split"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:soil.utils:Trial: 0\n",
"INFO:soil.utils:\tRunning\n",
"INFO:soil.utils:Finished trial in 0.02695441246032715 seconds\n",
"INFO:soil.utils:NOT dumping results\n",
"INFO:soil.utils:Finished simulation in 0.03360605239868164 seconds\n"
]
}
],
"source": [
"env_params = {'prob_tv_spread': 0,\n",
" 'prob_neighbor_spread': 0}\n",
"\n",
"MAX_TIME = 100\n",
"EVENT_TIME = 10\n",
"\n",
"sim = soil.simulation.SoilSimulation(topology=G,\n",
" num_trials=1,\n",
" max_time=MAX_TIME,\n",
" environment_agents=[{'agent_type': NewsEnvironmentAgent,\n",
" 'state': {\n",
" 'event_time': EVENT_TIME\n",
" }}],\n",
" network_agents=[{'agent_type': NewsSpread,\n",
" 'weight': 1}],\n",
" states={0: {'has_tv': True}},\n",
" default_state={'has_tv': False},\n",
" environment_params=env_params)\n",
"env = sim.run_simulation()[0]"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_style": "split"
},
"source": [
"Now we can access the results of the simulation and compare them to our expected results"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:56:15.004439Z",
"start_time": "2017-10-19T17:56:14.904160+02:00"
},
"cell_style": "split",
"collapsed": true,
"scrolled": false
},
"outputs": [],
"source": [
"agents = list(env.network_agents)\n",
"\n",
"# Until the event, all agents are neutral\n",
"for t in range(10):\n",
" for a in agents:\n",
" assert a['id', t] == a.neutral.id\n",
"\n",
"# After the event, the node with a TV is infected, the rest are not\n",
"assert agents[0]['id', 11] == NewsSpread.infected.id\n",
"\n",
"for a in agents[1:4]:\n",
" assert a['id', 11] == NewsSpread.neutral.id\n",
"\n",
"# At the end, the agents connected to the infected one will probably be infected, too.\n",
"assert agents[1]['id', MAX_TIME] == NewsSpread.infected.id\n",
"assert agents[2]['id', MAX_TIME] == NewsSpread.infected.id\n",
"\n",
"# But the node with no friends should not be affected\n",
"assert agents[4]['id', MAX_TIME] == NewsSpread.neutral.id\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-02T16:41:09.110652Z",
"start_time": "2017-07-02T18:41:09.106966+02:00"
},
"cell_style": "split"
},
"source": [
"Lastly, let's see if the probabilities have decreased as expected:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:56:17.382019Z",
"start_time": "2017-10-19T17:56:17.340851+02:00"
},
"cell_style": "split",
"collapsed": true
},
"outputs": [],
"source": [
"assert abs(env.environment_params['prob_neighbor_spread'] - (NEIGHBOR_FACTOR**(MAX_TIME-1-10))) < 10e-4\n",
"assert abs(env.environment_params['prob_tv_spread'] - (TV_FACTOR**(MAX_TIME-1-10))) < 10e-6"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Running the simulation"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T11:20:28.566944Z",
"start_time": "2017-07-03T13:20:28.561052+02:00"
},
"cell_style": "split"
},
"source": [
"To run a simulation, we need a configuration.\n",
"Soil can load configurations from python dictionaries as well as JSON and YAML files.\n",
"For this demo, we will use a python dictionary:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:56:19.477080Z",
"start_time": "2017-10-19T17:56:19.386840+02:00"
},
"cell_style": "split",
"collapsed": true
},
"outputs": [],
"source": [
"config = {\n",
" 'name': 'ExampleSimulation',\n",
" 'max_time': 20,\n",
" 'interval': 1,\n",
" 'num_trials': 1,\n",
" 'network_params': {\n",
" 'generator': 'complete_graph',\n",
" 'n': 500,\n",
" },\n",
" 'network_agents': [\n",
" {\n",
" 'agent_type': NewsSpread,\n",
" 'weight': 1,\n",
" 'state': {\n",
" 'has_tv': False\n",
" }\n",
" },\n",
" {\n",
" 'agent_type': NewsSpread,\n",
" 'weight': 2,\n",
" 'state': {\n",
" 'has_tv': True\n",
" }\n",
" }\n",
" ],\n",
" 'environment_agents':[\n",
" {'agent_type': NewsEnvironmentAgent,\n",
" 'state': {\n",
" 'event_time': 10\n",
" }\n",
" }\n",
" ],\n",
" 'states': [ {'has_tv': True} ],\n",
" 'environment_params':{\n",
" 'prob_tv_spread': 0.01,\n",
" 'prob_neighbor_spread': 0.5\n",
" }\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T11:57:34.219618Z",
"start_time": "2017-07-03T13:57:34.213817+02:00"
},
"cell_style": "split"
},
"source": [
"Let's run our simulation:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:56:28.208300Z",
"start_time": "2017-10-19T17:56:20.515069+02:00"
},
"cell_style": "split"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:soil.utils:Using config(s): ExampleSimulation\n",
"INFO:soil.utils:Dumping results to soil_output/ExampleSimulation : False\n",
"INFO:soil.utils:Trial: 0\n",
"INFO:soil.utils:\tRunning\n",
"INFO:soil.utils:Finished trial in 5.869051456451416 seconds\n",
"INFO:soil.utils:NOT dumping results\n",
"INFO:soil.utils:Finished simulation in 6.9609293937683105 seconds\n"
]
}
],
"source": [
"soil.simulation.run_from_config(config, dump=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T12:03:32.183588Z",
"start_time": "2017-07-03T14:03:32.167797+02:00"
},
"cell_style": "split",
"collapsed": true
},
"source": [
"In real life, you probably want to run several simulations, varying some of the parameters so that you can compare and answer your research questions.\n",
"\n",
"For instance:\n",
" \n",
"* Does the outcome depend on the structure of our network? We will use different generation algorithms to compare them (Barabasi-Albert and Erdos-Renyi)\n",
"* How does neighbor spreading probability affect my simulation? We will try probability values in the range of [0, 0.4], in intervals of 0.1."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:57:10.690166Z",
"start_time": "2017-10-19T17:56:28.210104+02:00"
},
"cell_style": "split",
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:soil.utils:Using config(s): Spread_erdos_renyi_graph_prob_0.0\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.0 : True\n",
"INFO:soil.utils:Trial: 0\n",
"INFO:soil.utils:\tRunning\n",
"INFO:soil.utils:Finished trial in 1.2258412837982178 seconds\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.0\n",
"INFO:soil.utils:Finished simulation in 5.597268104553223 seconds\n",
"INFO:soil.utils:Using config(s): Spread_erdos_renyi_graph_prob_0.1\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.1 : True\n",
"INFO:soil.utils:Trial: 0\n",
"INFO:soil.utils:\tRunning\n",
"INFO:soil.utils:Finished trial in 1.3026399612426758 seconds\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.1\n",
"INFO:soil.utils:Finished simulation in 5.534018278121948 seconds\n",
"INFO:soil.utils:Using config(s): Spread_erdos_renyi_graph_prob_0.2\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.2 : True\n",
"INFO:soil.utils:Trial: 0\n",
"INFO:soil.utils:\tRunning\n",
"INFO:soil.utils:Finished trial in 1.4764575958251953 seconds\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.2\n",
"INFO:soil.utils:Finished simulation in 6.170421123504639 seconds\n",
"INFO:soil.utils:Using config(s): Spread_erdos_renyi_graph_prob_0.3\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.3 : True\n",
"INFO:soil.utils:Trial: 0\n",
"INFO:soil.utils:\tRunning\n",
"INFO:soil.utils:Finished trial in 1.5429913997650146 seconds\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.3\n",
"INFO:soil.utils:Finished simulation in 5.936013221740723 seconds\n",
"INFO:soil.utils:Using config(s): Spread_erdos_renyi_graph_prob_0.4\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.4 : True\n",
"INFO:soil.utils:Trial: 0\n",
"INFO:soil.utils:\tRunning\n",
"INFO:soil.utils:Finished trial in 1.4097135066986084 seconds\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.4\n",
"INFO:soil.utils:Finished simulation in 5.732810974121094 seconds\n",
"INFO:soil.utils:Using config(s): Spread_barabasi_albert_graph_prob_0.0\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.0 : True\n",
"INFO:soil.utils:Trial: 0\n",
"INFO:soil.utils:\tRunning\n",
"INFO:soil.utils:Finished trial in 0.751497745513916 seconds\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.0\n",
"INFO:soil.utils:Finished simulation in 2.3415369987487793 seconds\n",
"INFO:soil.utils:Using config(s): Spread_barabasi_albert_graph_prob_0.1\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.1 : True\n",
"INFO:soil.utils:Trial: 0\n",
"INFO:soil.utils:\tRunning\n",
"INFO:soil.utils:Finished trial in 0.8503265380859375 seconds\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.1\n",
"INFO:soil.utils:Finished simulation in 2.5671920776367188 seconds\n",
"INFO:soil.utils:Using config(s): Spread_barabasi_albert_graph_prob_0.2\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.2 : True\n",
"INFO:soil.utils:Trial: 0\n",
"INFO:soil.utils:\tRunning\n",
"INFO:soil.utils:Finished trial in 0.8511502742767334 seconds\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.2\n",
"INFO:soil.utils:Finished simulation in 2.55816912651062 seconds\n",
"INFO:soil.utils:Using config(s): Spread_barabasi_albert_graph_prob_0.3\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.3 : True\n",
"INFO:soil.utils:Trial: 0\n",
"INFO:soil.utils:\tRunning\n",
"INFO:soil.utils:Finished trial in 0.8982968330383301 seconds\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.3\n",
"INFO:soil.utils:Finished simulation in 2.6871559619903564 seconds\n",
"INFO:soil.utils:Using config(s): Spread_barabasi_albert_graph_prob_0.4\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.4 : True\n",
"INFO:soil.utils:Trial: 0\n",
"INFO:soil.utils:\tRunning\n",
"INFO:soil.utils:Finished trial in 0.9563727378845215 seconds\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.4\n",
"INFO:soil.utils:Finished simulation in 2.5253307819366455 seconds\n"
]
}
],
"source": [
"network_1 = {\n",
" 'generator': 'erdos_renyi_graph',\n",
" 'n': 500,\n",
" 'p': 0.1\n",
"}\n",
"network_2 = {\n",
" 'generator': 'barabasi_albert_graph',\n",
" 'n': 500,\n",
" 'm': 2\n",
"}\n",
"\n",
"\n",
"for net in [network_1, network_2]:\n",
" for i in range(5):\n",
" prob = i / 10\n",
" config['environment_params']['prob_neighbor_spread'] = prob\n",
" config['network_params'] = net\n",
" config['name'] = 'Spread_{}_prob_{}'.format(net['generator'], prob)\n",
" s = soil.simulation.run_from_config(config)"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T11:05:18.043194Z",
"start_time": "2017-07-03T13:05:18.034699+02:00"
},
"cell_style": "split"
},
"source": [
"The results are conveniently stored in pickle (simulation), csv and sqlite (history of agent and environment state) and gexf (dynamic network) format."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:57:28.534861Z",
"start_time": "2017-10-19T17:57:28.258984+02:00"
},
"cell_style": "split",
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[01;34msoil_output\u001b[00m\n",
"├── \u001b[01;34mSpread_barabasi_albert_graph_prob_0.0\u001b[00m\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.0.dumped.yml\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.0.simulation.pickle\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.backup1508409808.7944386.sqlite\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.backup1508428617.9811945.sqlite\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.db.sqlite\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.environment.csv\n",
"│   └── Spread_barabasi_albert_graph_prob_0.0_trial_0.gexf\n",
"├── \u001b[01;34mSpread_barabasi_albert_graph_prob_0.1\u001b[00m\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.1.dumped.yml\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.1.simulation.pickle\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.backup1508409810.9913027.sqlite\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.backup1508428620.3419535.sqlite\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.db.sqlite\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.environment.csv\n",
"│   └── Spread_barabasi_albert_graph_prob_0.1_trial_0.gexf\n",
"├── \u001b[01;34mSpread_barabasi_albert_graph_prob_0.2\u001b[00m\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.2.dumped.yml\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.2.simulation.pickle\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.backup1508409813.2012305.sqlite\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.backup1508428622.91827.sqlite\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.db.sqlite\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.environment.csv\n",
"│   └── Spread_barabasi_albert_graph_prob_0.2_trial_0.gexf\n",
"├── \u001b[01;34mSpread_barabasi_albert_graph_prob_0.3\u001b[00m\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.3.dumped.yml\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.3.simulation.pickle\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.backup1508409815.5177016.sqlite\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.backup1508428625.5117545.sqlite\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.db.sqlite\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.environment.csv\n",
"│   └── Spread_barabasi_albert_graph_prob_0.3_trial_0.gexf\n",
"├── \u001b[01;34mSpread_barabasi_albert_graph_prob_0.4\u001b[00m\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.4.dumped.yml\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.4.simulation.pickle\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.backup1508409818.1516452.sqlite\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.backup1508428628.1986933.sqlite\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.db.sqlite\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.environment.csv\n",
"│   └── Spread_barabasi_albert_graph_prob_0.4_trial_0.gexf\n",
"├── \u001b[01;34mSpread_erdos_renyi_graph_prob_0.0\u001b[00m\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.0.dumped.yml\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.0.simulation.pickle\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.backup1508409781.0791047.sqlite\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.backup1508428588.625598.sqlite\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.db.sqlite\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.environment.csv\n",
"│   └── Spread_erdos_renyi_graph_prob_0.0_trial_0.gexf\n",
"├── \u001b[01;34mSpread_erdos_renyi_graph_prob_0.1\u001b[00m\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.1.dumped.yml\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.1.simulation.pickle\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.backup1508409786.6177793.sqlite\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.backup1508428594.3783743.sqlite\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.db.sqlite\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.environment.csv\n",
"│   └── Spread_erdos_renyi_graph_prob_0.1_trial_0.gexf\n",
"├── \u001b[01;34mSpread_erdos_renyi_graph_prob_0.2\u001b[00m\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.2.dumped.yml\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.2.simulation.pickle\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.backup1508409791.9751768.sqlite\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.backup1508428600.041021.sqlite\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.db.sqlite\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.environment.csv\n",
"│   └── Spread_erdos_renyi_graph_prob_0.2_trial_0.gexf\n",
"├── \u001b[01;34mSpread_erdos_renyi_graph_prob_0.3\u001b[00m\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.3.dumped.yml\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.3.simulation.pickle\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.backup1508409797.606661.sqlite\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.backup1508428606.2884977.sqlite\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.db.sqlite\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.environment.csv\n",
"│   └── Spread_erdos_renyi_graph_prob_0.3_trial_0.gexf\n",
"└── \u001b[01;34mSpread_erdos_renyi_graph_prob_0.4\u001b[00m\n",
" ├── Spread_erdos_renyi_graph_prob_0.4.dumped.yml\n",
" ├── Spread_erdos_renyi_graph_prob_0.4.simulation.pickle\n",
" ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.backup1508409803.4306188.sqlite\n",
" ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.backup1508428612.3312593.sqlite\n",
" ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.db.sqlite\n",
" ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.environment.csv\n",
" └── Spread_erdos_renyi_graph_prob_0.4_trial_0.gexf\n",
"\n",
"10 directories, 70 files\n",
"2.5M\tsoil_output/Spread_barabasi_albert_graph_prob_0.0\n",
"2.5M\tsoil_output/Spread_barabasi_albert_graph_prob_0.1\n",
"2.5M\tsoil_output/Spread_barabasi_albert_graph_prob_0.2\n",
"2.5M\tsoil_output/Spread_barabasi_albert_graph_prob_0.3\n",
"2.5M\tsoil_output/Spread_barabasi_albert_graph_prob_0.4\n",
"3.6M\tsoil_output/Spread_erdos_renyi_graph_prob_0.0\n",
"3.7M\tsoil_output/Spread_erdos_renyi_graph_prob_0.1\n",
"3.7M\tsoil_output/Spread_erdos_renyi_graph_prob_0.2\n",
"3.7M\tsoil_output/Spread_erdos_renyi_graph_prob_0.3\n",
"3.7M\tsoil_output/Spread_erdos_renyi_graph_prob_0.4\n"
]
}
],
"source": [
"!tree soil_output\n",
"!du -xh soil_output/*"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-02T10:40:14.384177Z",
"start_time": "2017-07-02T12:40:14.381885+02:00"
}
},
"source": [
"## Analysing the results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Loading data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once the simulations are over, we can use soil to analyse the results.\n",
"\n",
"Soil allows you to load results for specific trials, or for a set of trials if you specify a pattern. The specific methods are:\n",
"\n",
"* `analysis.read_data(<directory pattern>)` to load all the results from a directory. e.g. `read_data('my_simulation/')`. For each trial it finds in each folder matching the pattern, it will return the dumped configuration for the simulation, the results of the trial, and the configuration itself. By default, it will try to load data from the sqlite database. \n",
"* `analysis.read_csv(<csv_file>)` to load all the results from a CSV file. e.g. `read_csv('my_simulation/my_simulation_trial0.environment.csv')`\n",
"* `analysis.read_sql(<sqlite_file>)` to load all the results from a sqlite database . e.g. `read_sql('my_simulation/my_simulation_trial0.db.sqlite')`"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T14:44:30.978223Z",
"start_time": "2017-07-03T16:44:30.971952+02:00"
}
},
"source": [
"Let's see it in action by loading the stored results into a pandas dataframe:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:57:43.662893Z",
"start_time": "2017-10-19T17:57:43.632252+02:00"
},
"cell_style": "center",
"collapsed": true
},
"outputs": [],
"source": [
"from soil.analysis import *"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:57:44.101253Z",
"start_time": "2017-10-19T17:57:44.039710+02:00"
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>agent_id</th>\n",
" <th>t_step</th>\n",
" <th>key</th>\n",
" <th>value</th>\n",
" <th>value_type</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>14</td>\n",
" <td>0</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>15</td>\n",
" <td>0</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>16</td>\n",
" <td>0</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>17</td>\n",
" <td>0</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>18</td>\n",
" <td>0</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>19</td>\n",
" <td>0</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>20</td>\n",
" <td>0</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>22</td>\n",
" <td>0</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>23</td>\n",
" <td>0</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>24</td>\n",
" <td>0</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>25</td>\n",
" <td>0</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>26</td>\n",
" <td>0</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>28</td>\n",
" <td>0</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>29</td>\n",
" <td>0</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21025</th>\n",
" <td>470</td>\n",
" <td>20</td>\n",
" <td>id</td>\n",
" <td>infected</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21027</th>\n",
" <td>471</td>\n",
" <td>20</td>\n",
" <td>id</td>\n",
" <td>infected</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21029</th>\n",
" <td>472</td>\n",
" <td>20</td>\n",
" <td>id</td>\n",
" <td>infected</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21031</th>\n",
" <td>473</td>\n",
" <td>20</td>\n",
" <td>id</td>\n",
" <td>infected</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21033</th>\n",
" <td>474</td>\n",
" <td>20</td>\n",
" <td>id</td>\n",
" <td>infected</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21035</th>\n",
" <td>475</td>\n",
" <td>20</td>\n",
" <td>id</td>\n",
" <td>infected</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21037</th>\n",
" <td>476</td>\n",
" <td>20</td>\n",
" <td>id</td>\n",
" <td>infected</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21039</th>\n",
" <td>477</td>\n",
" <td>20</td>\n",
" <td>id</td>\n",
" <td>infected</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21041</th>\n",
" <td>478</td>\n",
" <td>20</td>\n",
" <td>id</td>\n",
" <td>infected</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21043</th>\n",
" <td>479</td>\n",
" <td>20</td>\n",
" <td>id</td>\n",
" <td>infected</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21045</th>\n",
" <td>480</td>\n",
" <td>20</td>\n",
" <td>id</td>\n",
" <td>infected</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21047</th>\n",
" <td>481</td>\n",
" <td>20</td>\n",
" <td>id</td>\n",
" <td>infected</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21049</th>\n",
" <td>482</td>\n",
" <td>20</td>\n",
" <td>id</td>\n",
" <td>infected</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21051</th>\n",
" <td>483</td>\n",
" <td>20</td>\n",
" <td>id</td>\n",
" <td>infected</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21053</th>\n",
" <td>484</td>\n",
" <td>20</td>\n",
" <td>id</td>\n",
" <td>infected</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21055</th>\n",
" <td>485</td>\n",
" <td>20</td>\n",
" <td>id</td>\n",
" <td>infected</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21057</th>\n",
" <td>486</td>\n",
" <td>20</td>\n",
" <td>id</td>\n",
" <td>infected</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21059</th>\n",
" <td>487</td>\n",
" <td>20</td>\n",
" <td>id</td>\n",
" <td>infected</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21061</th>\n",
" <td>488</td>\n",
" <td>20</td>\n",
" <td>id</td>\n",
" <td>infected</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21063</th>\n",
" <td>489</td>\n",
" <td>20</td>\n",
" <td>id</td>\n",
" <td>infected</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21065</th>\n",
" <td>490</td>\n",
" <td>20</td>\n",
" <td>id</td>\n",
" <td>infected</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21067</th>\n",
" <td>491</td>\n",
" <td>20</td>\n",
" <td>id</td>\n",
" <td>infected</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21069</th>\n",
" <td>492</td>\n",
" <td>20</td>\n",
" <td>id</td>\n",
" <td>infected</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21071</th>\n",
" <td>493</td>\n",
" <td>20</td>\n",
" <td>id</td>\n",
" <td>infected</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21073</th>\n",
" <td>494</td>\n",
" <td>20</td>\n",
" <td>id</td>\n",
" <td>infected</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21075</th>\n",
" <td>495</td>\n",
" <td>20</td>\n",
" <td>id</td>\n",
" <td>infected</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21077</th>\n",
" <td>496</td>\n",
" <td>20</td>\n",
" <td>id</td>\n",
" <td>infected</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21079</th>\n",
" <td>497</td>\n",
" <td>20</td>\n",
" <td>id</td>\n",
" <td>infected</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21081</th>\n",
" <td>498</td>\n",
" <td>20</td>\n",
" <td>id</td>\n",
" <td>infected</td>\n",
" <td>str</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21083</th>\n",
" <td>499</td>\n",
" <td>20</td>\n",
" <td>id</td>\n",
" <td>infected</td>\n",
" <td>str</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10500 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" agent_id t_step key value value_type\n",
"5 0 0 id neutral str\n",
"7 1 0 id neutral str\n",
"9 2 0 id neutral str\n",
"11 3 0 id neutral str\n",
"13 4 0 id neutral str\n",
"15 5 0 id neutral str\n",
"17 6 0 id neutral str\n",
"19 7 0 id neutral str\n",
"21 8 0 id neutral str\n",
"23 9 0 id neutral str\n",
"25 10 0 id neutral str\n",
"27 11 0 id neutral str\n",
"29 12 0 id neutral str\n",
"31 13 0 id neutral str\n",
"33 14 0 id neutral str\n",
"35 15 0 id neutral str\n",
"37 16 0 id neutral str\n",
"39 17 0 id neutral str\n",
"41 18 0 id neutral str\n",
"43 19 0 id neutral str\n",
"45 20 0 id neutral str\n",
"47 21 0 id neutral str\n",
"49 22 0 id neutral str\n",
"51 23 0 id neutral str\n",
"53 24 0 id neutral str\n",
"55 25 0 id neutral str\n",
"57 26 0 id neutral str\n",
"59 27 0 id neutral str\n",
"61 28 0 id neutral str\n",
"63 29 0 id neutral str\n",
"... ... ... .. ... ...\n",
"21025 470 20 id infected str\n",
"21027 471 20 id infected str\n",
"21029 472 20 id infected str\n",
"21031 473 20 id infected str\n",
"21033 474 20 id infected str\n",
"21035 475 20 id infected str\n",
"21037 476 20 id infected str\n",
"21039 477 20 id infected str\n",
"21041 478 20 id infected str\n",
"21043 479 20 id infected str\n",
"21045 480 20 id infected str\n",
"21047 481 20 id infected str\n",
"21049 482 20 id infected str\n",
"21051 483 20 id infected str\n",
"21053 484 20 id infected str\n",
"21055 485 20 id infected str\n",
"21057 486 20 id infected str\n",
"21059 487 20 id infected str\n",
"21061 488 20 id infected str\n",
"21063 489 20 id infected str\n",
"21065 490 20 id infected str\n",
"21067 491 20 id infected str\n",
"21069 492 20 id infected str\n",
"21071 493 20 id infected str\n",
"21073 494 20 id infected str\n",
"21075 495 20 id infected str\n",
"21077 496 20 id infected str\n",
"21079 497 20 id infected str\n",
"21081 498 20 id infected str\n",
"21083 499 20 id infected str\n",
"\n",
"[10500 rows x 5 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = read_csv('soil_output/Spread_barabasi_albert_graph_prob_0.0/Spread_barabasi_albert_graph_prob_0.0_trial_0.environment.csv', keys=['id'])\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Soil can also process the data for us and return a dataframe with as many columns as there are attributes in the environment and the agent states:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:57:45.777794Z",
"start_time": "2017-10-19T17:57:45.698020+02:00"
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>id</th>\n",
" </tr>\n",
" <tr>\n",
" <th>t_step</th>\n",
" <th>agent_id</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"30\" valign=\"top\">0</th>\n",
" <th>0</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>106</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>107</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>108</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>110</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>111</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>113</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>114</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>117</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>119</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>121</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>122</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>123</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>124</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"30\" valign=\"top\">20</th>\n",
" <th>72</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10500 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" id\n",
"t_step agent_id \n",
"0 0 neutral\n",
" 1 neutral\n",
" 10 neutral\n",
" 100 neutral\n",
" 101 neutral\n",
" 102 neutral\n",
" 103 neutral\n",
" 104 neutral\n",
" 105 neutral\n",
" 106 neutral\n",
" 107 neutral\n",
" 108 neutral\n",
" 109 neutral\n",
" 11 neutral\n",
" 110 neutral\n",
" 111 neutral\n",
" 112 neutral\n",
" 113 neutral\n",
" 114 neutral\n",
" 115 neutral\n",
" 116 neutral\n",
" 117 neutral\n",
" 118 neutral\n",
" 119 neutral\n",
" 12 neutral\n",
" 120 neutral\n",
" 121 neutral\n",
" 122 neutral\n",
" 123 neutral\n",
" 124 neutral\n",
"... ...\n",
"20 72 infected\n",
" 73 infected\n",
" 74 infected\n",
" 75 infected\n",
" 76 infected\n",
" 77 infected\n",
" 78 infected\n",
" 79 infected\n",
" 8 infected\n",
" 80 infected\n",
" 81 infected\n",
" 82 infected\n",
" 83 infected\n",
" 84 infected\n",
" 85 infected\n",
" 86 infected\n",
" 87 infected\n",
" 88 infected\n",
" 89 infected\n",
" 9 infected\n",
" 90 infected\n",
" 91 infected\n",
" 92 infected\n",
" 93 infected\n",
" 94 infected\n",
" 95 infected\n",
" 96 infected\n",
" 97 infected\n",
" 98 infected\n",
" 99 infected\n",
"\n",
"[10500 rows x 1 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"env, agents = process(df)\n",
"agents"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-18T14:01:00.669671Z",
"start_time": "2017-10-18T16:01:00.635624+02:00"
}
},
"source": [
"The index of the results are the simulation step and the agent_id. Hence, we can access the state of the simulation at a given step: "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:57:47.132212Z",
"start_time": "2017-10-19T17:57:47.084737+02:00"
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" </tr>\n",
" <tr>\n",
" <th>agent_id</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>106</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>107</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>108</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>110</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>111</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>113</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>114</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>117</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>119</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>121</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>122</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>123</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>124</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>500 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" id\n",
"agent_id \n",
"0 neutral\n",
"1 neutral\n",
"10 neutral\n",
"100 neutral\n",
"101 neutral\n",
"102 neutral\n",
"103 neutral\n",
"104 neutral\n",
"105 neutral\n",
"106 neutral\n",
"107 neutral\n",
"108 neutral\n",
"109 neutral\n",
"11 neutral\n",
"110 neutral\n",
"111 neutral\n",
"112 neutral\n",
"113 neutral\n",
"114 neutral\n",
"115 neutral\n",
"116 neutral\n",
"117 neutral\n",
"118 neutral\n",
"119 neutral\n",
"12 neutral\n",
"120 neutral\n",
"121 neutral\n",
"122 neutral\n",
"123 neutral\n",
"124 neutral\n",
"... ...\n",
"72 neutral\n",
"73 neutral\n",
"74 neutral\n",
"75 neutral\n",
"76 neutral\n",
"77 neutral\n",
"78 neutral\n",
"79 neutral\n",
"8 neutral\n",
"80 neutral\n",
"81 neutral\n",
"82 neutral\n",
"83 neutral\n",
"84 neutral\n",
"85 neutral\n",
"86 neutral\n",
"87 neutral\n",
"88 neutral\n",
"89 neutral\n",
"9 neutral\n",
"90 neutral\n",
"91 neutral\n",
"92 neutral\n",
"93 neutral\n",
"94 neutral\n",
"95 neutral\n",
"96 neutral\n",
"97 neutral\n",
"98 neutral\n",
"99 neutral\n",
"\n",
"[500 rows x 1 columns]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agents.loc[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or, we can perform more complex tasks such as showing the agents that have changed their state between two simulation steps:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:57:48.168805Z",
"start_time": "2017-10-19T17:57:48.113961+02:00"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" </tr>\n",
" <tr>\n",
" <th>agent_id</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>140</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>164</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>170</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>310</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>455</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id\n",
"agent_id \n",
"140 neutral\n",
"164 neutral\n",
"170 neutral\n",
"310 neutral\n",
"455 neutral"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"changed = agents.loc[1]['id'] != agents.loc[0]['id']\n",
"agents.loc[0][changed]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To focus on specific agents, we can swap the levels of the index:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:57:49.046261Z",
"start_time": "2017-10-19T17:57:49.019721+02:00"
},
"collapsed": true
},
"outputs": [],
"source": [
"agents1 = agents.swaplevel()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:57:49.459500Z",
"start_time": "2017-10-19T17:57:49.420016+02:00"
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" </tr>\n",
" <tr>\n",
" <th>t_step</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id\n",
"t_step \n",
"0 neutral\n",
"1 neutral\n",
"2 neutral\n",
"3 neutral\n",
"4 neutral\n",
"5 neutral\n",
"6 neutral\n",
"7 neutral\n",
"8 neutral\n",
"9 neutral\n",
"10 neutral\n",
"11 infected\n",
"12 infected\n",
"13 infected\n",
"14 infected\n",
"15 infected\n",
"16 infected\n",
"17 infected\n",
"18 infected\n",
"19 infected\n",
"20 infected"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agents1.loc['0'].dropna(axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T10:35:40.140920Z",
"start_time": "2017-10-19T12:35:40.106265+02:00"
}
},
"source": [
"### Plotting data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you don't want to work with pandas, you can also use some pre-defined functions from soil to conveniently plot the results:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:57:52.271094Z",
"start_time": "2017-10-19T17:57:51.102434+02:00"
}
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEXCAYAAACgUUN5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niGoAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd79a1c60f0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEXCAYAAACgUUN5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niGoAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd79a1c62b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_all('soil_output/Spread_barabasi_albert_graph_prob_0.0/', get_count, 'id');"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:57:57.982007Z",
"start_time": "2017-10-19T17:57:52.273160+02:00"
},
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEXCAYAAACgUUN5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niGoAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd79a0b4940>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEXCAYAAACgUUN5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl8W9WZ8PHfY9mWvMlOvCUkIQ4QKNkIJiwtBUIJUCg0\nbIUwhJ3Ssry0L20H3naGUkrfoR0GKO07tOyEBggFCgyTthAKk7KThJCyZYNAnDix4yS2vG/n/eMe\nybIsyZItWZL9fD8ffSTde3T16Fq+j86595wjxhiUUkqNXVmpDkAppVRqaSJQSqkxThOBUkqNcZoI\nlFJqjNNEoJRSY5wmAqWUGuM0EYxiIvKwiNw6SJn5IlKTTjENYZv7ikiziLiGsY1++0FEtojIgsRE\nmL5E5BIReS0N4hgT+ztdaSJIABH5qoi8ISKNIrJbRF4XkcNTHddYYYz5whhTaIzpSXUskeiBLjlE\n5AQR+UREWkXkFRGZGqVslS3Tal+jfw9LE8EwiYgXeAH4DTAemAT8DOiIczsiIhn99xCR7FTHkG6S\nvU8yYZ8nK0YRKQOeAf4V539vFbAsykseB94DSoGfAE+JSHkyYss0GX3gSRMHAhhjHjfG9Bhj2owx\nLxpj1tlq9+si8htbW/hERE7wv1BEXhWRX4jI60ArsJ+IFIvIAyJSKyLbRORWf5OHiOwvIn8TkQYR\n2SUiS0WkJGh7h4rIGhHxicgywBPrhxCRH9ttbhGRC4KWf0NE3hORJhHZKiI3B62rEhEjIpeLyBfA\n3+zyP4rIDvuZV4rIzJC3KxORl2yc/xP8K05Efm3fp0lEVovIMUHrjhCRVXbdThG5IySOqAccEblU\nRD627/upiHxnkN1yuIh8JCJ7ROQhEQnsTxE5TUTWisheWxucE7Rui4jcICLrgBYReRzYF/gv24T1\nz4PEeZGIfG7/zv8aXJsQkZtF5CkR+YOINAGX2P3ypo2lVkR+KyK5QdszInKd/cy7ROTfQ390iMjt\n9nN+JiKnDLJf/N/dfxORd+zf+TkRGW/XRfpefFNEPrRxvioiB8e6vyM4C/jQGPNHY0w7cDNwiIh8\nKUy8BwLVwE/t/+jTwD+Aswf7rGOCMUZvw7gBXqABeAQ4BRgXtO4SoBv430AOcB7QCIy3618FvgBm\nAtm2zLPA74ECoAJ4B/iOLX8AcCLgBsqBlcBddl0u8HnQe50DdAG3DhL/fBvjHXa7xwEtwEFB62fj\n/GiYA+wEzrDrqgADLLHx5tnllwFFdnt3AWuD3u9hwAcca9f/GngtaP1inF9s2cAPgB2Ax657E7jQ\nPi4EjgqJI3uQz/oNYH9A7OdsBaqDPmdNUNktwAfAFJxfm6/79yXOAaUOOBJwARfb8u6g1661r80L\nWrYghu/TDKAZ+Kr9m95u/44L7Pqb7fMz7N8kDzgMOMrusyrgY+D7Qds0wCv2c+wLbACuCPqOdgHf\ntp/lKmA7IIPE+SqwDZhl//ZPA3+I9L3A+cHUgvP9zQH+GdgE5A62v6PE8GvgnpBlHwBnhyl7JvBx\nyLLfAr9J9TEkHW4pD2A03ICDcQ5wNTgH1eeBSvtP1u+fCufA7j+YvQrcErSuEqdJKS9o2fnAKxHe\n9wzgPfv42DDv9UYM/0zzbcwFQcueBP41Qvm7gDvtY/8//H5Rtl9iyxTb5w8DTwStLwR6gCkRXr8H\nOMQ+XonT7FYWUsYfR9REEGbbzwLfC9oPoYngu0HPTwU228f3AD8P2dZ64Lig114Wsn4LsSWCm4DH\ng57nA530TwQrB9nG94E/BT03wNeDnl8NvGwfXwJsCnk/A0wY5D1eBW4Lej7DxukK973Aab55Muh5\nFk4imT/Y/o4SwwPBMdhlrwOXhCl7IfBWyLJfAA/H850ZrTdtGkoAY8zHxphLjDGTcX4h7YNzwATY\nZuy3zvrcrvfbGvR4Ks6vpVpbfd6LUzuoABCRChF5wjYZNQF/AMrsa/eJ8F6x2GOMaQkXo4gcKc4J\ntnoRaQS+G/SeAz6DiLhE5DYR2Wxj3GJXlYUrb4xpBnYHvd8PbPNNo/38xUGvvRznl+UnIvKuiJwW\n4+fzx3aKiLwlzgn9vTgHm9DPEvZz0f/vNhX4gf9vZLc1hch/13jsQ//904pT44wUFyJyoIi8YJvj\nmoD/S5S/EQO/gztC3g+cBD2Y0G3mEOHvbN8v8H00xvTa9ZNijDGcZpwaeTAvTo1zOGXHHE0ECWaM\n+QTnV+8su2iSiEhQkX1xfrkHXhL0eCtOjaDMGFNib15jjL+N/d9s+TnGGC9OM4p/27UR3isW40Sk\nIEKMj+HUcKYYY4qB3wW9Z7jP8E/AQmABzkG8yi4Pfs0U/wMRKcRpCthuzwfcAJyL08RWgtOUJgDG\nmI3GmPNxEuMvcU72BccdkYi4cZovbgcq7baXh/kswaYEPQ7eJ1uBXwT9jUqMMfnGmMeDyocO6xvr\nML+1wOSguPNwmsqibese4BNguv1e/JiBnyvSZxmO0G12AbsixLkdJ4ECzsUR9vXbhhHjh8AhQdss\nwGn6+zBC2f1EpCho2SERyo45mgiGSUS+ZH/FTrbPp+A057xli1QA14lIjoh8C6cZaXm4bRljaoEX\ngf8QEa+IZIlzgvg4W6QI55fNXhGZBPwo6OVv4jTxXCci2SJyFnBEHB/lZyKSaw/GpwF/DHrP3caY\ndhE5AudAH00RTjJrwGlm+L9hypwqziW3ucDPgbeNMVvta7uBeiBbRG4i6FeciCwWkXL7a3KvXRzr\nJaO5OOck6oFue0L0pEFec42ITLYnQX9M3xUp9wHftbUlEZECcU6qF0XeFDuB/WKI8yngdBH5it0/\nPyN6sgJnvzUBzfZE6VVhyvxIRMbZ7+f3iH51TawWi8gMEckHbgGeMpEv4X0S+IY4l3vm4Jz/6cBp\nvvSLtL8j+RMwS0TOtieWbwLW2R9j/RhjNuCct/mpiHhE5Eycc15Px/5xRy9NBMPnwzlp+LaItOAk\ngA9wvugAbwPTcX4p/QI4xxgTWtUPdhHOQesjnPbxp4CJdt3PcE5UNgL/jXPpHADGmE6cqygusa87\nL3j9IHbY12wHluK01fr/ma4GbhERH84/2pODbGsJTrV+m/0Mb4Up8xjwU5wmocMA/1VKfwX+jHMy\n83Ognf7NBV8HPhSRZpwThYuMc7XIoIwxPuA6G/8enIT2/CAvewwnMX9qb7faba3CObn6W7utTTj7\nPZp/A/7FNiX9MEqcHwL/C3gCp3bgwzkxHe1y5B/az+PDSVLhDqDPAatxDob/jdO+PlyP4tR+d+Bc\noXZdpILGmPU4Ndjf4PwvnA6cbr+3fmH3d5Rt1uNc9fMLnL/DkcAi/3oR+Z2I/C7oJYuAebbsbTj/\ni/UxfM5RT/o3KatEEpFLcK7O+GqqY1GZyTad7cVp9vlsiNsw9vWbEhjXqzhXCd2fqG2q1NEagVJp\nRkROF5F82+Z9O8717ltSG5UazTQRjAHidBZrDnP7c6pjS7QIn7NZgjqmpZqIXBAhRv+Jy4U4zXTb\ncZoVF5kUVN3TYV+Ope9uKmnTkFJKjXFaI1BKqTFOE4FSSo1xaTFyYVlZmamqqkp1GEoplVFWr169\nyxgz7BFU0yIRVFVVsWrVqlSHoZRSGUVEYh1GJiptGlJKqTFOE4FSSo1xmgiUUmqM00SglFJjXEyJ\nQJyp8v4hztR8q+yy8eJMN7jR3o+zy0VE7haRTSKyTkSqk/kBlFJKDU88NYLjjTFzjTHz7PMbcWY5\nmg68bJ+DM13jdHu7EmesdKWUUmlqOE1DC3Hm6cXenxG0fIlxvAWUiMjEcBtQSimVerH2IzDAi3Y4\n298bY+7FmeWpFpwJVUSkwpadRP8x5GvsstpIG1+/w8d5v3+TSSV57GNvk8blManEwz4leeTnpkV3\nB6VGXmcLPHMlfBFuWgelEiPWI+zRxpjt9mD/kogMmAEoSLjZlAaMbCciV+I0HeHdZz+Mgbc/282O\npnZ6evsXL8nPCSQJ594TeDypJI+yQjdZWYNN4qRUhmlvgsfOha1vwyHnQ7Yn1RGptHNnQrYSUyIw\nxmy393Ui8iecKRB3ishEWxuYiDOLEjg1gOC5RycTZu5RW6u4F2DevHnmye9+GYDunl7qfB1s39vG\nNnvbvreN7Xvb2bq7lbc2N+Dr6O63rVxXFpPH5TG1NJ+ppQVMKytgamk+VaUFTB6XR7ZLL45SGaZt\nD/zhbKh9H855EGaemeqIVFoaoURgJ8fIMsb47OOTcOYnfR64GGfKt4txpsLDLr9WRJ7AmTqu0d+E\nFFNArqxA89C8CGWa2rucRLHHSRI1e9vYuruVLbtaefuz3bR29k2bmp0lNkkUUFWaT1VZAVWlTqKY\nMj6fHE0SKt20NMCjZ0D9J3DuEvjSN1IdkRrlYqkRVAJ/EhF/+ceMMX8RkXeBJ0XkcuAL4Fu2/HLg\nVJx5XFuBSxMdtNeTg3dCDl+a4B2wzhhDfXMHnze0smVXC1saWtjS0MrnDS2s/nwPzUG1CVeWMKnE\nX5PIp7TATUl+jr3lUpLXd+/Ny8GlzU8q2ZrrYMlC2P0pLHocpi9IdURqDEiLiWnmzZtnRmLQOWMM\nDS2dfN7QwpZdrf2SxOcNrTS2dUV9vdeTTUl+LuPycygOJIocSvKc58V5ORTkuihwZ1PgdpGfm01B\nbjb5bhcFudl4crKwCVWpgZq2wyPfhKZtcP7jsN/8VEek0pyIrA66pH/IxtTlOCJCWaGbskI3h00d\nP2B9T6+hqa2LvW1d7G3tZG9rF3vb7H1rF43+5W3O8y8aWtjb5iyPJZ+K4CQGmyzyc119icKdTUGu\ni9JCN1X2XEdVaQGVXrcmj7Fg7xdOEmjZBYufhqlfSXVEagwZU4lgMK4sYVxBLuMKcoGCmF/X22vw\ntXfT2NZFS2c3rZ3dtHT09L/v7KG1w97b5S0d3bR0drO7pZOtu1tp6eihoaWDrp6+rOLJyQqc06gq\nLaAq6ET4BK9Hr5YaDXZ/6iSB9ia46FmYPOwfeErFRRNBAmRlCcX5ORTn5wx7Wz29hu172/qarXY5\n95vrW3jlk3o6e3oDZd3ZWYErpYJPhE8el8fE4jxys/VEeNrbtREeOR262+Hi52GfuamOSI1BmgjS\njCtLmDLeuaLpmOn91/X0GnY0tfP5rhY+s+c1tuxy7lduqKejuy9JiEB5obtf34t+HfZK8ijJz9Fm\np1Ta+ZFzYhgDl/w3VM5MdURqjNJEkEH8VzlNKsnjKweU9VvX22vY6Wtny65Wp/+FvbR2e2MbH9c2\nseLjnf0SBUB+risoMXjYp7gvUVR43VQUuSl0Z2uySIba92HJGeDKhYv/C8oPTHVEagzTRDBKZGUJ\nE4udJqFwjDHsbulk+952tu1tZdvedttRz+m099H2RnY1dw54nScni/IiN+WFbue+yE15oYcKb/9l\nZYVubYqKVc1q+MOZkFvkNAeV7p/qiNQYp4lgjBARSgvdlBa6mT25OGyZ9q4eahudBFHv66De10Gd\nr9153NzBZ7taeOez3expDX+ZbUl+DhU2MZQWuAdcZjsuP5die7ltSX4uXk/22Ov1/cVb8IdzoKAU\nLnoexk1NdURKaSJQfTw5LqaVOUN0RNPZ3cuu5o5AsqhvDkkavg7W7t5LY1sXTe3RL60t8mQzLj+X\nkvwcivOCO/LlML4gN1AbqfB6KC9yU5Drytymqs9WwmOLwDvRSQLFk1IdkVKAJgI1BLnZfcOADGZA\n34y2Lhpbncd7wvTNqNnTxp7Wzoh9M/JyXIHmqIpAU1VQs1WRm4oiD6WFuek1fMimFfDEBTBuGlz0\nHBRVpjoipQI0EaikGk7fjL1tXQNqGsE1kE11zbyxuSFij/DxBbmBJFFR1D9ZOLUM53yHNy/JJ8TX\n/xmevAjKD4ILn3OahZRKI5oIVFrKyhLGF+QyviCXgyYURS3b0d3DrubO/ski5PzGO1taqPN10Bly\n5RQ4o9eWF7kpC1PL2Hd8PsdMLxt6ovj0f2DZYpgwx+kxnD+wR7tSqaaJQGU8d7YrcFltNMYYmtq7\nB9QsgpPG1t2tvPfFHhpaOgNNU89eczRzp5QMLbiPnoOcfKfHsCf8SXqlUk0TgRozRITiPOek9AEV\nhVHLdvf0svrzPZx371vU7GkdeiLw7YDiKZoEVFpLo7NpSqWPbFdWoElqZ1PH0Dfkq4WiCQmKSqnk\n0ESgVATFeTnkZmdR19Q+9I34dkDRxMQFpVQSaCJQKgIRodLrZudQE0FvDzTv1BqBSnuaCJSKorLI\nM/SmoZZdYHo0Eai0p4lAqSgqvR52+oZYI/DZqbq1aUilOU0ESkVR4XVTN9QagW+Hc6+JQKU5TQRK\nRVHp9dDc0U1zR3f8Lw7UCLRpSKU3TQRKRVHpdQMM7coh3w5AoLAisUEplWCaCJSKorLIAwyxL4Gv\nFgrKwTX8KUyVSiZNBEpFUeF1EkHdUE4Y+3Zos5DKCJoIlIrC3zQ0pL4Evlo9UawygiYCpaIodGeT\nn+saYtOQ1ghUZtBEoFQUTu9iT/w1gp4uaKnXGoHKCJoIlBpERdEQ+hI01wFGawQqI2giUGoQQ+pd\nrJ3JVAbRRKDUIPwDz5lwkyhHop3JVAbRRKDUICq9Htq7emlqj6N3sY4zpDKIJgKlBhHoSxDPCWPf\nDhAXFJQlKSqlEifmRCAiLhF5T0ResM+nicjbIrJRRJaJSK5d7rbPN9n1VckJXamRUVnk70sQxwlj\n3w4orIQsV5KiUipx4qkRfA/4OOj5L4E7jTHTgT3A5Xb55cAeY8wBwJ22nFIZq9LrH2YinhqBTlGp\nMkdMiUBEJgPfAO63zwX4GvCULfIIcIZ9vNA+x64/wZZXKiNV+HsXx3PlkE5RqTJIrDWCu4B/Bnrt\n81JgrzHGf/asBphkH08CtgLY9Y22fD8icqWIrBKRVfX19UMMX6nky8/NpsiTHV9fAq0RqAwyaCIQ\nkdOAOmPM6uDFYYqaGNb1LTDmXmPMPGPMvPLy8piCVSpV4upd3N0Bbbu1RqAyRnYMZY4GvikipwIe\nwItTQygRkWz7q38ysN2WrwGmADUikg0UA7sTHrlSIyiuSewDncm0RqAyw6A1AmPM/zHGTDbGVAGL\ngL8ZYy4AXgHOscUuBp6zj5+3z7Hr/2bi6omjVPqJaxJ77VWsMsxw+hHcAFwvIptwzgE8YJc/AJTa\n5dcDNw4vRKVSr8Lroc4XY+9i7VWsMkwsTUMBxphXgVft40+BI8KUaQe+lYDYlEoblV43XT2GPa1d\njC/IjV5YawQqw2jPYqViEFdfAl8tZOVA/vgkR6VUYmgiUCoGcc1U5u9DoN1nVIbQRKBUDCqK/OMN\nxXDCWPsQqAyjiUCpGFTEXSPQRKAyhyYCpWLgznYxLj8ntmEmdHgJlWE0ESgVI6d38SBNQ50t0NGo\nNQKVUTQRKBWjCq9n8DkJ9NJRlYE0ESgVo8oi9+A1Ah1eQmUgTQRKxajS66G+uYOe3ii9i3WKSpWB\nNBEoFaNKr5ueXkNDS5RagdYIVAbSRKBUjPrmLo6WCGohOw88xSMUlVLDp4lAqRjFNMyEvw+B9ipW\nGUQTgVIx6htmYpCmIT0/oDKMJgKlYlRW6EZksBqBDi+hMo8mAqVilOPKorTATV2k3sXGaI1AZSRN\nBErFwZmyMkLTUIcPulq0RqAyjiYCpeIQdRJ77VWsMpQmAqXiELVGoFNUqgyliUCpOFQUeWho6aCr\np3fgSq0RqAyliUCpOFR6PRgDu5rD1AoCNYLKkQ1KqWHSRKBUHKL2JfDtgNwicBeNcFRKDY8mAqXi\nELV3sfYhUBlKE4FScfBPWRl2XgKdolJlKE0ESsWhtMCNK0siNA3V6olilZE0ESgVB1eWUF7oHtg0\nFOhVrDUClXk0ESgVp0qvm52+kBpB2x7o6dAagcpImgiUilPYuYt1QhqVwTQRKBUnp3dxaCLQKSpV\n5tJEoFScKos87GntoqO7p2+h1ghUBtNEoFScKsNNWanjDKkMlj1YARHxACsBty3/lDHmpyIyDXgC\nGA+sAS40xnSKiBtYAhwGNADnGWO2JCl+pUZcoC+Br50p4/Odhb4d4CmBnLwURpZYXV1d1NTU0N4e\nZSIeNSI8Hg+TJ08mJycnKdsfNBEAHcDXjDHNIpIDvCYifwauB+40xjwhIr8DLgfusfd7jDEHiMgi\n4JfAeUmJXqkU6OtdHFIjGGXnB2pqaigqKqKqqgrROZhTxhhDQ0MDNTU1TJs2LSnvMWjTkHE026c5\n9maArwFP2eWPAGfYxwvtc+z6E0S/RWoU6WsaCvqlPAr7ELS3t1NaWqpJIMVEhNLS0qTWzGI6RyAi\nLhFZC9QBLwGbgb3GmG5bpAaYZB9PArYC2PWNQGkig1Yqlcbl55Djkv59CUbpFJWaBNJDsv8OMSUC\nY0yPMWYuMBk4Ajg4XDF7Hy5iE7pARK4UkVUisqq+vj7WeJVKORGhoihoprLeXmgefTWCkbRlyxZm\nzZo1Zt8/1eK6asgYsxd4FTgKKBER/zmGycB2+7gGmAJg1xcDu8Ns615jzDxjzLzy8vKhRa9UilR6\n3X1XDbU2QG/3qKwRqLFh0EQgIuUiUmIf5wELgI+BV4BzbLGLgefs4+ftc+z6vxljBtQIlMpk/eYu\n1ktHE+rTTz/l0EMP5e233+ZHP/oRhx9+OHPmzOH3v/89ABdeeCHPPfdcoPwFF1zA888/328b5513\nHsuXLw88v+SSS3j66afZsmULxxxzDNXV1VRXV/PGG28MeP+HH36Ya6+9NvD8tNNO49VXXwXgxRdf\n5Mtf/jLV1dV861vform5ecDrM1EsNYKJwCsisg54F3jJGPMCcANwvYhswjkH8IAt/wBQapdfD9yY\n+LCVSq3+iUCnqEyU9evXc/bZZ/PQQw/x/vvvU1xczLvvvsu7777Lfffdx2effcYVV1zBQw89BEBj\nYyNvvPEGp556ar/tLFq0iGXLlgHQ2dnJyy+/zKmnnkpFRQUvvfQSa9asYdmyZVx33XUxx7Zr1y5u\nvfVWVqxYwZo1a5g3bx533HFH4j58Cg16+agxZh1waJjln+KcLwhd3g58KyHRKZWmKrxumtq7aevs\nIU9rBAlRX1/PwoULefrpp5k5cya33nor69at46mnnIsTGxsb2bhxIyeddBLXXHMNdXV1PPPMM5x9\n9tlkZ/c/lJ1yyilcd911dHR08Je//IVjjz2WvLw8Ghsbufbaa1m7di0ul4sNGzbEHN9bb73FRx99\nxNFHHw04CebLX/5y4nZACsXSj0ApFaKyyF5C6mtnqr9GUKhzFQ9HcXExU6ZM4fXXX2fmzJkYY/jN\nb37DySefPKDshRdeyNKlS3niiSd48MEHB6z3eDzMnz+fv/71ryxbtozzzz8fgDvvvJPKykref/99\nent78Xg8A16bnZ1Nb29v4Ln/sk1jDCeeeCKPP/54oj5y2tAhJpQagn6dyny1kF8G2bkpjiqz5ebm\n8uyzz7JkyRIee+wxTj75ZO655x66uroA2LBhAy0tLYDT5n/XXXcBMHPmTAC2bdvGCSecENjeokWL\neOihh/j73/8eSCaNjY1MnDiRrKwsHn30UXp6eghVVVXF2rVr6e3tZevWrbzzzjsAHHXUUbz++uts\n2rQJgNbW1rhqFOlME4FSQ9A3iX37qO1DkAoFBQW88MILgV/uM2bMoLq6mlmzZvGd73yH7m6n61Jl\nZSUHH3wwl156aeC1tbW1/ZqITjrpJFauXMmCBQvIzXWS9NVXX80jjzzCUUcdxYYNGygoKBgQw9FH\nH820adOYPXs2P/zhD6murgagvLychx9+mPPPP585c+Zw1FFH8cknnyRzd4wYSYcLeubNm2dWrVqV\n6jCUilljWxeH/OxF/uUbB3PFR5dCQTksfmrwF2aQjz/+mIMPDtdlKPVaW1uZPXs2a9asobi4GIDf\n/va37Lvvvnzzm99McXTJEe7vISKrjTHzhrttPUeg1BB4Pdl4crKo83U4NYIJs1Md0pixYsUKLrvs\nMq6//vpAEgD6XfKp4qOJQKkhEBEqvR7qG5uhpU6bhkbQggUL+OKLL1Idxqii5wiUGqLKIg/te3eC\n6dVLR1VG00Sg1BBVeN2YJp2iUmU+TQRKDVGl10N2y07nidYIVAbTcwRKDVFFkZv2ngbn55TWCFQG\n0xqBUkNU6fVQIXswkuVcPqoSrq2tjeOOO46enh62b9/OOeecE7bc/PnzGclL0O+66y5aW1vjft0l\nl1wSGDJj0aJFbNy4MdGhDYkmAqWGqMLrppI9dHnKwKWV62R48MEHOeuss3C5XOyzzz6Bg2iqRUsE\n4Xorh3PVVVfxq1/9KpFhDZkmAqWGqNLroVL20JKrtYFkWbp0KQsXLgT6Tx7T1tbGokWLmDNnDued\ndx5tbW2Dbmv+/PnccMMNHHHEERx44IH8/e9/B5wDd7jhrl999VVOO+20wOuvvfZaHn74Ye6++262\nb9/O8ccfz/HHHw9AYWEhN910E0ceeSRvvvkmt9xyC4cffjizZs3iyiuvJFzH3WOOOYYVK1YEekun\nkv6MUWqIKr0eOmQvjdlTGZfqYJLsZ//1IR9tb0roNmfs4+Wnp8+MuL6zs5NPP/2UqqqqAevuuece\n8vPzWbduHevWrQsMAzGY7u5u3nnnHZYvX87PfvYzVqxYwQMPPBAY7rqjo4Ojjz6ak046KeI2rrvu\nOu644w5eeeUVysrKAGhpaWHWrFnccsstzmebMYObbroJcAbIe+GFFzj99NP7bScrK4sDDjiA999/\nn8MOOyym+JNFawRKDVGhO5tK2cMuGZ/qUEalXbt2UVJSEnbdypUrWbx4MQBz5sxhzpw5MW3zrLPO\nAuCwww5jy5YtgDPZzJIlS5g7dy5HHnkkDQ0Ncbfdu1wuzj777MDzV155hSOPPJLZs2fzt7/9jQ8/\n/DDs6yoqKti+fXvYdSNJawRKDVV3J6XSxJsm/MFqNIn2yz1Z8vLyAkNAhzOUCd3dbmewQJfLFWiS\niTTc9WuvvRZ2OOpwPB4PLpcrUO7qq69m1apVTJkyhZtvvjnia9vb28nLy4v7cySa1giUGqqWOgC2\ndhUPUlBu0AyjAAAZT0lEQVQNxbhx4+jp6Ql7ED322GNZunQpAB988AHr1q0LrLvooosCQ0fHItJw\n11OnTuWjjz6io6ODxsZGXn755cBrioqK8Pl8Ybfnj7esrIzm5uaoJ7g3bNgQGEY7lbRGoNRQ2Qlp\nPusoSnEgo9dJJ53Ea6+9xoIFC/otv+qqq7j00kuZM2cOc+fO5Ygj+iZLXLduHRMnxt6v44orrmDL\nli1UV1djjKG8vJxnn32WKVOmcO655zJnzhymT5/OoYf2TdR45ZVXcsoppzBx4kReeeWVftsrKSnh\n29/+NrNnz6aqqorDDz887Pvu3LmTvLy8uGJNFh2GWqmh+vi/YNliFnbfxrM//+6QmirSWToMQ/3e\ne+9xxx138Oijj8ZUvqmpicsvv5w//vGPSY5s+O688068Xi+XX355TOWTOQy1Ng0pNVS2RlDTXUxT\nW+ovARyNDj30UI4//viYr833er0ZkQTAqTlcfPHFqQ4D0ESg1ND5aumVbHZTxE5f5BOJanguu+yy\nwInY0eTSSy/tN6NaKmkiUGqofDvoyq/AkOVMWalUhtJEoNRQ+Wqh0Bl1dGdTR4qDUWroNBEoNVS+\nHWQX7wOgNQKV0TQRKDVUvlpcxRPxerKp00SgMpgmAqWGoqsd2vZA0QQqvR5tGkqSRA5DfdNNN7Fi\nxYqoZTo6OliwYAFz585l2bJlccW6ZcsWHnvssbheA+kxNLUmAqWGotm5dJSiiU4i0KuGkiKRw1Df\ncsstAzqmhXrvvffo6upi7dq1nHfeeXFtf6iJIFiqhqbWRKDUUPj8iWACFV43dVojSIpEDkMd/Mu7\nqqqKn/70p1RXVzN79mw++eQT6urqWLx4MWvXrmXu3Lls3ryZ1atXc9xxx3HYYYdx8sknU1vrzFG9\nadMmFixYwCGHHEJ1dTWbN2/mxhtv5O9//ztz587lzjvvjDi8tTGGa6+9lhkzZvCNb3yDurq6QIyp\nGpo6PS5iVSrT+Pomra/0ZlHna6e315CVNbp6Fwf8+UbY8Y/EbnPCbDjltoirkzEMdbCysjLWrFnD\nf/7nf3L77bdz//33c//993P77bfzwgsv0NXVxYUXXshzzz1HeXk5y5Yt4yc/+QkPPvggF1xwATfe\neCNnnnkm7e3t9Pb2cttttwVeC3DvvfeGHd76vffeY/369fzjH/9g586dzJgxg8suuwxI3dDUmgiU\nGoqgGkFlUSNdPYY9rZ2UFrpTG9coMtgw1Ndddx0Q3zDUwYKHpH7mmWcGrF+/fj0ffPABJ554IuBM\nYDNx4kR8Ph/btm3jzDPPBJyRR8N58cUXWbduXaAW0tjYyMaNG1m5ciXnn39+oLnra1/7Wr/X+Yem\n1kSgVLrz1YLLDXnjqPQ6zUI7mzpGbyKI8ss9WZIxDHWwcENSBzPGMHPmTN58881+y5uaYpugJ9Lw\n1suXL48aeyqGph70HIGITBGRV0TkYxH5UES+Z5ePF5GXRGSjvR9nl4uI3C0im0RknYjEX2dTKt35\ndkBRJYhQ4XV+EeoJ48QaqWGoIznooIOor68PJIKuri4+/PBDvF4vkydP5tlnnwWcK41aW1sHDE0d\naXjrY489lieeeIKenh5qa2sHjF6aiqGpYzlZ3A38wBhzMHAUcI2IzABuBF42xkwHXrbPAU4Bptvb\nlcA9CY9aqVTz1UKRM3xwpdf5Zal9CRLPPwx1qKuuuorm5mbmzJnDr371q2ENQx1Jbm4uTz31FDfc\ncAOHHHIIc+fO5Y033gDg0Ucf5e6772bOnDl85StfYceOHcyZM4fs7GwOOeQQ7rzzTq644gpmzJhB\ndXU1s2bN4jvf+Q7d3d2ceeaZTJ8+ndmzZ3PVVVdx3HHHBd4zZUNTG2PiugHPAScC64GJdtlEYL19\n/Hvg/KDygXKRbocddphRKqP8Zp4xyy40xhjT3tVtpt7wgvn1ig0pDiqxPvroo1SHYNasWWMWL14c\nc/nGxkZzzjnnJDGi5LrjjjvM/fffH3ZduL8HsMrEeQwPd4vr8lERqQIOBd4GKo0xtTaZ1AIVttgk\nYGvQy2rsstBtXSkiq0RkVX19fTxhKJV6vh2BGoE728X4glwdZiIJRvMw1OGkamjqmBOBiBQCTwPf\nN8ZEO1sS7izIgNlvjDH3GmPmGWPmlZeXxxqGUqnX0QwdTVA0IbCoositvYuTZLQOQx1OqoamjikR\niEgOThJYaozxX2e1U0Qm2vUTAX+viBpgStDLJwPbExOuUmmgeadzX9TXjlvp9VCnJ4tVhorlqiEB\nHgA+NsbcEbTqecBfh7kY59yBf/lF9uqho4BGfxOSUqNCoDNZX42g0uselU1DJg2mslXJ/zvEUgc5\nGrgQ+IeIrLXLfgzcBjwpIpcDXwDfsuuWA6cCm4BW4NKERqxUqvn6xhnyq/R6qPd10NNrcI2S3sUe\nj4eGhgZKS0tH3XzMmcQYQ0NDQ8SOa4kwaCIwxrxG+HZ/gBPClDfANcOMS6n0FaZGUOH10Gugobkj\n0K8g002ePJmamhr0Yo7U83g8TJ48OWnb157FSsXLtwNy8sHtDSyqLHL6EuxsGj2JICcnh2nTpqU6\nDDUCdPRRpeLlq3VqA0HNJZX+3sWj8DyBGv00ESgVr6A+BH6VOsyEymCaCJSKl79GEKSsMBcRncRe\nZSZNBErFw5iwNYJsVxZlhW4db0hlJE0ESsWjowm6WgfUCGD09iVQo58mAqXiEaYPgV9lkU5irzKT\nJgKl4hGmD4FfhQ4zoTKUJgKl4hGtRuB1s6u5k66e3hEOSqnh0USgVDz8NYLCygGr/JeQ1vu0eUhl\nFk0ESsXDt8PpUewuHLDKP1OZnjBWmUYTgVLxCNOHwK+iyN+7WGsEKrNoIlAqHr4dEROBv2lITxir\nTKOJQKl4BE1aH6q0IBdXlmjTkMo4mgiUilWgV3H4GkFWluiUlSojaSJQKlZte6CnM2KNAJy+BFoj\nUJlGE4FSsYrSmcyvsshNndYIVIbRRKBUrAKJIHKNoNLr0aGoVcbRRKBUrAK9iqPUCLxu9rZ20d7V\nM0JBKTV8mgiUilWgV3HkRFChvYtVBtJEoFSsfDsgbxzkRJ6TWKesVJlIE4FSsQozIU2ovmEmtEag\nMocmAqViFWV4Cb/KIq0RqMyjiUCpWMVQIyjJzyHXlaVXDqmMoolAqVj09kbtVewnIlR4tS+Byiya\nCJSKResuMD2D1gjA9iXQpiGVQTQRKBWLGHoV++kk9irTaCJQKhZRpqgMVVHk0aYhlVE0ESgVi7hq\nBB58Hd20dHQnOSilEkMTgVKx8NcIwsxVHMrfl6BOexerDDFoIhCRB0WkTkQ+CFo2XkReEpGN9n6c\nXS4icreIbBKRdSJSnczglRoxvlooKAdXzqBFtXexyjSx1AgeBr4esuxG4GVjzHTgZfsc4BRgur1d\nCdyTmDCVSrEYLh3100nsVaYZNBEYY1YCu0MWLwQesY8fAc4IWr7EON4CSkRk8LNrSqW7KFNUhvIP\nPKcnjFWmGOo5gkpjTC2Ava+wyycBW4PK1dhlSmW2OGoERe5s8nJcWiNQGSPRJ4slzDITtqDIlSKy\nSkRW1dfXJzgMpRKopxua62KuEYiI05dATxarDDHURLDT3+Rj7+vs8hpgSlC5ycD2cBswxtxrjJln\njJlXXl4+xDCUGgEtdYCJuUYAOnexyixDTQTPAxfbxxcDzwUtv8hePXQU0OhvQlIqY8UwRWWoSq+H\nOk0EKkPEcvno48CbwEEiUiMilwO3ASeKyEbgRPscYDnwKbAJuA+4OilRKzWSYpiiMlRlkZudTR0Y\nE7ZlVKm0kj1YAWPM+RFWnRCmrAGuGW5QSqWVIdYI2rp68HV04/UM3vdAqVTSnsVKDca3AyTL6VAW\nowp/72JtHlIZQBOBUoPx1TpDS2S5Yn5JX+9ivXJIpT9NBEoNJo4+BH46zITKJJoIlBpMDFNUhqoo\n0knsVebQRKDUYGKYtD5UgTubIne21ghURtBEoFQ03R3Q2hB3jQCcE8Z1Oom9ygCaCJSKpnmncx9n\njQD8cxdr05BKf5oIlIomjikqQ+kk9ipTaCJQKpo4pqgMVeF1U6e9i1UG0ESgVDTDqREUeejs6WVv\na1eCg1IqsTQRKBWNrxayciBvfNwvDfQl0BPGKs1pIlAqGn9nsqz4/1X6pqzUE8YqvWkiUCqaIfQh\n8NPexSpTaCJQKpohDC/hV16kA8+pzKCJQKlo4pi0PpQnx0VJfo42Dam0p4lAqUg6W6G9ccg1AnCu\nHNKmIZXuNBEoFUnz0C8d9avQSexVBhh0hjKlRq3eHmjZ5Qwj0e9W59zv/swpV1g55Leo9HrYuHNX\nggJWKjk0EajRxRjo8NmD+Y7+B/bmOufkr/956y4wvQO34S6GwgonAcy9AKYcMeRwvjShiKdW13Dn\nSxv4/oLpiMgwPpxSyaGJQGWG7k5oqYtwYA9Z1t028PVZOc6BvbACiifDpGqn7d9/wPevK6yEnLyE\nhX3p0dP4ZIePX7+8kY7uXm74+kGaDFTa0USgRl5PN7Tvhdbd0LYnzM0ub9nV98u+bU/4beWN7zuI\nTznSuS+a4CwrKO97nDcOUnAAdmUJvzp7Du7sLH73P5vp6O7hptNmaDJQaUUTgRoeY6CzeeCv85b6\n/gf1wG0vdDRF2aBAXolz4M4vhdL9YepXQn692/uCCsjOHbGPOlRZWcKtZ8wiNzuLh17fQmd3Lz9f\nOIusLE0GKj1oIlDh9XQ5B/PgNvXAfciyrtaBrxeXczD33wonQPnBfc/zx9vHJf3LuYuHNJxDuhMR\nbjptBu5sl60Z9PLLs+fg0mSg0oAmgrGot9c5yDfWQONWe6sJel7jzMoVTt64vl/lkw8Pal8P+qVe\nNAE8JaPygD4cIsINXz8IT04Wd63YSGd3L3ecewjZLt1PKrU0EYxGXe39D+oD7rdBT8i17bmFUDzF\nOZG6z6HOtfMDDvIVkO1OzWcaJUSE7y84kNzsLH71l/V09fTy60WHkputyUCljiaCTNXeCLs/7bs1\nBD1uqQspLM6BvWSKc5A/+PS+g77/3lOckpOpY9XV8w/Ane3i5y98ROcfVvP/LqjGk+NKdVhqjNJE\nkM7a9tiD+2fQsLn/gb81pJNS0T7OidUDT4ZxU6F4X3ugnwzefcCVk5rPoCK6/KvTcGdn8S/PfsC3\nl6zi3gvnkZeryUCNPE0Eydbd6VxV09kSdGsOWtbcf7lvpz3Ybx54yaR3MpTuBwefBuP3g/H7O/fj\nqiA3PyUfTw3P4qOmkpudxQ1Pr+PSh9/hgYsPp8Ct/5ZqZOk3bjA9Xc4lj9Gudw++NLLfwb4FeuOY\npjAnH/LLnIP9zDNDDvZTE9rRSaWPc+dNwZ2dxfVPvs9FD77DQ5cejtejNTg1csZuIuhqhz2f9TW1\n7NniXCkTfGBv3QOdvsjbkCzn6pjgyx9LpkBuEeQWBN0KY3hcAFnaLDBWLZw7iVxXFv/r8fdYfP/b\nLLnsCEry07+PhBodRnci6Gztf7APtLN/Bk3bANNX1lPi9ET1X/NeMaP/Ab7fNe/2Gni3Vy+RVAlz\nyuyJ/M6VxdVL1/BP973No5cfQWmhXqWlkk+MMYOXinejIl8Hfg24gPuNMbdFKz9v3jyzatWq+N/I\nGOeXe2NN3wG/YbNzoN/9Kfi29y+fX9rX1DJ+P+fk6vhpzuO8cfG/v1JJsHJDPd9esop9x+ez9NtH\nUlHkSXVIKk2JyGpjzLxhbyfRiUBEXMAG4ESgBngXON8Y81Gk10RMBD1d0LQ9pLNTcOenGqctPlhB\necjB3t6Pm+b8olcqA7y5uYHLH3mXCV4PS799JBOL9fyQGihRiSAZTUNHAJuMMZ8CiMgTwEIgYiKg\nvQneuS+k01ONM01g6DDB+WXOJZGlB8D+X7OXR05yftmPmwYebxI+klIj68v7l7LksiO45KF3Off3\nb/KLM2ZrPwOVNMlIBJOArUHPa4Ajo75i92ZY/kNw5ToH9eLJsN/8vuvgiyc718V799HLJNWYMa9q\nPEuvOJILH3ibix58J9XhqFEsGYkgXPfUAe1PInIlcCXA/lMmwA/WOs06evJVqYBDppSw4vrj2FjX\nPHhhNeZ89ZeJ2U4yEkENMCXo+WRge2ghY8y9wL3gnCOgaOjTASo1mlV4PVR49YSxSp5k/Px+F5gu\nItNEJBdYBDyfhPdRSimVAAmvERhjukXkWuCvOJePPmiM+TDR76OUUioxktKhzBizHFiejG0rpZRK\nLD0zq5RSY5wmAqWUGuM0ESil1BiniUAppca4pAw6F3cQIj5gfarjiEEZsGvQUqmncSZOJsQIGmei\nZUqcBxljioa7kXQZhnp9IgZOSjYRWaVxJk4mxJkJMYLGmWiZFGcitqNNQ0opNcZpIlBKqTEuXRLB\nvakOIEYaZ2JlQpyZECNonIk2puJMi5PFSimlUiddagRKKaVSRBOBUkqNcSOaCETk6yKyXkQ2iciN\nYda7RWSZXf+2iFSNZHw2hiki8oqIfCwiH4rI98KUmS8ijSKy1t5uGuk4bRxbROQfNoYBl5GJ4267\nP9eJSPUIx3dQ0D5aKyJNIvL9kDIp25ci8qCI1InIB0HLxovISyKy0d6Pi/Dai22ZjSJy8QjH+O8i\n8on9m/5JRMJOxj3Y92ME4rxZRLYF/W1PjfDaqMeFEYhzWVCMW0RkbYTXjuT+DHscStr30xgzIjec\nIak3A/sBucD7wIyQMlcDv7OPFwHLRiq+oBgmAtX2cRGwIUyc84EXRjq2MLFuAcqirD8V+DPOrHFH\nAW+nMFYXsAOYmi77EjgWqAY+CFr2K+BG+/hG4JdhXjce+NTej7OPx41gjCcB2fbxL8PFGMv3YwTi\nvBn4YQzfi6jHhWTHGbL+P4Cb0mB/hj0OJev7OZI1gsCk9saYTsA/qX2whcAj9vFTwAkiEm7qy6Qx\nxtQaY9bYxz7gY5x5mDPRQmCJcbwFlIjIxBTFcgKw2RjzeYrefwBjzEpgd8ji4O/gI8AZYV56MvCS\nMWa3MWYP8BLw9ZGK0RjzojGm2z59C2cWwJSKsC9jEctxIWGixWmPNecCjyfr/WMV5TiUlO/nSCaC\ncJPahx5gA2XsF70RKB2R6MKwTVOHAm+HWf1lEXlfRP4sIjNHNLA+BnhRRFbbOaBDxbLPR8oiIv+D\npcO+9Ks0xtSC888IVIQpk0779TKcWl84g30/RsK1tgnrwQjNGOm0L48BdhpjNkZYn5L9GXIcSsr3\ncyQTQSyT2sc08f1IEJFC4Gng+8aYppDVa3CaOA4BfgM8O9LxWUcbY6qBU4BrROTYkPVpsT/FmbL0\nm8Afw6xOl30Zj3TZrz8BuoGlEYoM9v1ItnuA/YG5QC1Os0uotNiX1vlErw2M+P4c5DgU8WVhlkXd\npyOZCGKZ1D5QRkSygWKGVt0cFhHJwdn5S40xz4SuN8Y0GWOa7ePlQI6IlI1wmBhjttv7OuBPONXs\nYLHs85FwCrDGGLMzdEW67MsgO/3NZ/a+LkyZlO9XewLwNOACYxuGQ8Xw/UgqY8xOY0yPMaYXuC/C\n+6d8X0LgeHMWsCxSmZHenxGOQ0n5fo5kIohlUvvnAf8Z7nOAv0X6kieLbSd8APjYGHNHhDIT/Ocu\nROQInP3YMHJRgogUiEiR/zHOCcQPQoo9D1wkjqOARn+1coRF/KWVDvsyRPB38GLguTBl/gqcJCLj\nbHPHSXbZiBCRrwM3AN80xrRGKBPL9yOpQs5HnRnh/WM5LoyEBcAnxpiacCtHen9GOQ4l5/s5EmfA\ng85mn4pz9nsz8BO77BacLzSAB6f5YBPwDrDfSMZnY/gqTjVqHbDW3k4Fvgt815a5FvgQ5wqHt4Cv\npCDO/ez7v29j8e/P4DgF+H92f/8DmJeCOPNxDuzFQcvSYl/iJKdaoAvnV9TlOOekXgY22vvxtuw8\n4P6g115mv6ebgEtHOMZNOG3A/u+n/0q7fYDl0b4fIxzno/Z7tw7nADYxNE77fMBxYSTjtMsf9n8n\ng8qmcn9GOg4l5fupQ0wopdQYpz2LlVJqjNNEoJRSY5wmAqWUGuM0ESil1BiniUCNCSJSIiJXD+F1\nP05GPEqlE71qSI0Jtpv+C8aYWXG+rtkYU5iUoJRKE1ojUGPFbcD+dgjhfw9dKSITRWSlXf+BiBwj\nIrcBeXbZUltusYi8Y5f9XkRcdnmziPyHiKwRkZdFpHxkP55SQ6c1AjUmDFYjEJEfAB5jzC/swT3f\nGOMLrhGIyME4wwCfZYzpEpH/BN4yxiwREQMsNsYsFWdOhQpjzLUj8dmUGq7sVAegVJp4F3jQju/y\nrDEm3OQkJwCHAe/aUTHy6BvrpZe+cWr+AAwYo0qpdKVNQ0oRGKf+WGAb8KiIXBSmmACPGGPm2ttB\nxpibI20ySaEqlXCaCNRY4cOZ6SksEZkK1Blj7sMZ7Ms/rWeXrSWAM7bLOSJSYV8z3r4OnP+lc+zj\nfwJeS3D8SiWNNg2pMcEY0yAir4szV+2fjTE/CikyH/iRiHQBzYC/RnAvsE5E1hhjLhCRf8GZnCQL\nZ+Cya4DPgRZgpoisxplQ6bzkfyqlEkNPFiuVAHqZqcpk2jSklFJjnNYI1JgiIrNxxskP1mGMOTIV\n8SiVDjQRKKXUGKdNQ0opNcZpIlBKqTFOE4FSSo1xmgiUUmqM00SglFJjnCYCpZQa4/4/tFbef8jw\niGoAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd79a0b4518>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEXCAYAAACgUUN5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd799958f28>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEXCAYAAACgUUN5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd799d1a518>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEXCAYAAACgUUN5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd799764be0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEXCAYAAACgUUN5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl4VeW58P/vnRkyMSSBTBgZJQkhhDCoVVAQZ1ERwToP\ntVWp7a/D0ff0Pdb6ek5tj0Vre46tdURRcB4oVgHBGSUEEmYCyBASkoCQgZD5+f2xVsIm7J3skD2F\n3J/r2tfee433WllZ917Ps9bziDEGpZRSvVeQvwNQSinlX5oIlFKql9NEoJRSvZwmAqWU6uU0ESil\nVC+niUAppXo5TQSnMRF5UUQe7WSaqSJSHEgxncIyh4hIjYgEd2MZJ+wHEdktItM9E2HgEpHbROSL\nAIijV+zvQKWJwANE5Aci8pWIVIrI9yLypYhM8HdcvYUxZq8xJsoY0+zvWFzRE513iMg0EdkqIrUi\nslJEznBjnikiYjz9g6Qn00TQTSISAywB/gIMAJKB3wH1XVyOiEiP/nuISIi/Ywg03t4nPWGfeytG\nEYkD3gb+A+t/Lw9Y3Mk8ocCfgW+8EVNP1aNPPAFiJIAx5jVjTLMx5pgx5mNjTKF92f2liPzFvlrY\nKiLTWmcUkVUi8p8i8iVQCwwVkVgReU5ESkVkv4g82lrkISLDROQTETkkIgdFZKGI9HNY3jgRyReR\nahFZDES4uxEi8u/2MneLyI0Owy8XkXUiUiUi+0TkYYdxafYvqztFZC/wiT38DRE5YG/zZyKS0W51\ncSKyzI7zU8dfcSLyZ3s9VSKyVkTOcxg3UUTy7HFlIjK/XRwdnnBE5HYR2WKvd5eI/LiT3TJBRDaL\nyGEReUFE2vaniFwhIutF5Ih9NZjlMG63iDwgIoXAURF5DRgCfGAXYf1bJ3HeIiJ77L/zfzheTYjI\nwyLypoi8IiJVwG32fvnajqVURP4qImEOyzMicr+9zQdF5L/b/+gQkcft7fxORC7tZL+0Hru/F5Fv\n7b/zeyIywB7n6ri4SkQ22XGuEpHR7u5vF64FNhlj3jDG1AEPA2NF5KwO5vkl8DGwtbNt7FWMMfrq\nxguIAQ4BLwGXAv0dxt0GNAH/HxAKzAEqgQH2+FXAXiADCLGneRf4OxAJJADfAj+2px8OXASEA/HA\nZ8CT9rgwYI/Duq4DGoFHO4l/qh3jfHu5U4CjwCiH8WOwfjRkAWXA1fa4NMAAC+x4+9jD7wCi7eU9\nCax3WN+LQDVwvj3+z8AXDuNvAgba++OXwAEgwh73NXCz/TkKmNwujpBOtvVyYBgg9nbWAjkO21ns\nMO1uYCOQivVr88vWfQnkAOXAJCAYuNWePtxh3vX2vH0chk1343hKB2qAH9h/08ftv+N0e/zD9ver\n7b9JH2A8MNneZ2nAFuDnDss0wEp7O4YA24G7HI7RRuBH9rbcA5QA0kmcq4D9QKb9t38LeMXVcYH1\ng+ko1vEbCvwbsAMI62x/dxDDn4Gn2w3bCMxyMf0Z9rZHYR2HHS6/N738HsDp8AJG2wdWMdZJ9X1g\nkP1PdsI/FdaJvfVktgp4xGHcIKwipT4Ow24AVrpY79XAOvvz+U7W9ZUb/0xT7ZgjHYa9DvyHi+mf\nBJ6wP7f+ww/tYPn97Gli7e8vAoscxkcBzUCqi/kPA2Ptz59hFbvFtZumNY4OE4GTZb8L/MxhP7RP\nBD9x+H4ZsNP+/DTw/9otaxswxWHeO9qN3417ieAh4DWH732BBk5MBJ91soyfA+84fDfAJQ7f7wVW\n2J9vA3a0W58BBneyjlXAYw7f0+04g50dF1jFN687fA/CSiRTO9vfHcTwnGMM9rAvgdtcTP8eMMfh\nONREYL+0aMgDjDFbjDG3GWNSsH4hJWGdMAH2G/vIs+2xx7fa5/D5DKxfS6X25fMRrKuDBAARSRCR\nRXaRURXwChBnz5vkYl3uOGyMOeosRhGZJFYlXIWIVAI/cVjnSdsgIsEi8piI7LRj3G2PinM2vTGm\nBvjeYX2/tItvKu3tj3WY906sX5ZbRWSNiFzh5va1xnapiKwWq0L/CNbJpv22ON0uTvy7nQH8svVv\nZC8rFdd/165I4sT9U4t1xekqLkRkpIgssYvjqoD/ooO/EScfgwfarQ+sBN2Z9ssMxcXf2V5f2/Fo\njGmxxye7GaMzNVhX5I5isK44TyAiVwLRxpgO6xB6K00EHmaM2Yr1ayPTHpQsIuIwyRCsX+5tszh8\n3od1RRBnjOlnv2KMMa1l7L+3p88yxsRgFaO0LrvUxbrc0V9EIl3E+CrWFU6qMSYW+JvDOp1tww+B\nmcB0rJN4mj3ccZ7U1g8iEoVVFFBi1wc8AFyPVcTWD6soTQCMMUXGmBuwEuMfgDfbxe2SiIRjFV88\nDgyyl73UybY4SnX47LhP9gH/6fA36meM6WuMec1h+vbN+rrbzG8pkOIQdx+sorKOlvU0Vpn3CPu4\n+HdO3i5X29Id7ZfZCBx0EWcJVgIFrJsj7Pn3dyPGTcBYh2VGYhX9bXIy7TQg106WB7CKaX8uIu91\nso5eQRNBN4nIWfav2BT7eypWcc5qe5IE4H4RCRWR2VjFSEudLcsYU4pVkfUnEYkRkSCxKoin2JNE\nY/0KOiIiycCvHWb/GquI534RCRGRa4GJXdiU34lImH0yvgJ4w2Gd3xtj6kRkItaJviPRWMnsEFYx\nw385meYysW65DQP+H/CNMWafPW8TUAGEiMhDOPziE5GbRCTe/jV5xB7s7i2jYVh1EhVAk10hOqOT\nee4TkRS7EvTfOX5Hyj+An9hXSyIikWJVqkd3sKwyYKgbcb4JXCki59j753d0nKzA2m9VQI1dUXqP\nk2l+LSL97ePzZ3Ryd42bbhKRdBHpCzwCvGlc38L7OnC5WLd7hmLV/9RjFV+2crW/XXkHyBSRWXbF\n8kNAof1jrL3/wLqazLZf72P9HW93a0tPc5oIuq8aq9LwGxE5ipUANmId6GDdpjYC65fSfwLXGWPa\nX+o7ugXrpLUZq3z8TSDRHvc7rIrKSuCfWLfOAWCMacC6i+I2e745juM7ccCepwRYiFVW2/rPdC/w\niIhUY/2jvd7JshZgXdbvt7dhtZNpXgV+i1UkNB5ovUvpI+BDrAq9PUAdJxYXXAJsEpEarIrCuca6\nW6RTxphq4H47/sNYCe39TmZ7FSsx77Jfj9rLysOqXP2rvawdWPu9I78H/q9dlPSrDuLcBPwUWIR1\ndVCNVTHd0e3Iv7K3pxrr5ObsBPoesBarEvufWOXr3fUy1tXvAaw71O53NaExZhvWFexfsP4XrgSu\ntI/bVk73dwfLrABmYf1fHcb6P5zbOl5E/iYif7OnrTbGHGh9AceAo8aY77uywacrObFIWXmSiNyG\ndXfGD/wdi+qZ7KKzI1jFPt+d4jKMPf8OD8a1CusuoWc9tUzlP3pFoFSAEZErRaSvXeb9OLCB45Xu\nSnmcJoJeQKyHxWqcvD70d2ye5mI7a8ThwTR/E5EbXcTYWsk5E6uYrgSrWHGu8cOleyDsy9507PqT\nFg0ppVQvp1cESinVy2kiUEqpXi4gWi6Mi4szaWlp/g5DKaV6lLVr1x40xsR3dzkBkQjS0tLIy8vz\ndxhKKdWjiIi7zch0SIuGlFKql9NEoJRSvZwmAqWU6uU0ESilVC/nViIQq6u8DWJ1zZdnDxsgVneD\nRfZ7f3u4iMhTIrJDRApFJMebG6CUUqp7unJFcIExJtsYk2t/fxCrl6MRwAr7O1jdNY6wX3djtZWu\nlFIqQHWnaGgmVj+92O9XOwxfYCyrgX4ikuhsAUoppfzP3ecIDPCx3Zzt340xz2D18lQKVocqIpJg\nT5vMiW3IF9vDSl0tfHtZNfe9mk96YgwZSTGkJ8WQEB3R5Y1Rp4mmBqg7AnVV0FADDUft99bPR9t9\ndvheX3Pid9Pi761RKuC5mwjONcaU2Cf7ZSLirAegVs56UzqpZTsRuRur6IjopKEUFh/hn4XHc0Vc\nVHhbUshIiiE9MYa0gZEEBXXWWZMKGE31cOyw9ar9/vjnE16Ow49Y7w017i0/OAzCIiEsyn63X30H\nWO+hfSEoIJ6ZVMpLnvDIUrrc+qiIPIzVXeKPgKn21UAisMoYM0pE/m5/fs2eflvrdK6WmZuba/Ly\n8qg81siW0io2l1SxqaSKzaVVFJVV09Rixdg3LJjRiVZSaE0QIwdFExEafEobrzzIGDiwAXYst14l\n66Cx1vX0QSHQp7/9GuDw2X717Q/hMQ4neYeTfXgUhEZCSJjvtk+pACQiax3qbU9Zpz+X7M4xgowx\n1fbnGVj9k74P3Ao8Zr+3dgL9PjBPRBZhdR1X2VEScBTbJ5TJQwcyeejxvrrrm5opKqths50gNpdU\n8c66/by82nqyOjhIGBYfyYS0AUwdlcA5wwYSGa6/An3i2GHYufL4yb+mzBo+eAzk3AKR8U5O8PZJ\nPywKRK/ulAoE7pwxBwHviPVPGwK8aoz5l4isAV4XkTuBvcBse/qlwGVY/bjW0s3OocNDgslMjiUz\nObZtWEuLYd/hWisxlFaxYX8l767bz8Jv9hIaLHZSiGfKyARGDopC9ITjGS0tULr++Im/eI1VBh8R\nC8MuhOEXwfBpED3Y35EqpbogIDqmaS0a6o6Gphbydn/Pp9srWLWtgm1l1QAkxkYwZWQ8U0fFc87w\nOGIiQj0Rcu9x9CDs/MQ++a+A2oPW8KRx9ol/OiSPh2C9ClPK1zxVNHTaJIL2SiuP8em2Cj7dXsEX\nRQeprm8iJEjIOaO/fbUQT3pijF4ttGcM7M+Hoo+gaJlV1o+BvgNh2DTrxD/sQojqdsu3Sqlu0kTQ\nBY3NLeTvOdx2tbC5tAqAhOhwpoyMZ8qoeM4bHk9s315+tVBVAv/8FWz7J0gQJOfCCLu4J3EcBGmL\nJEoFEk0E3VBeVcen262rhc+LDlJ5rJGwkCAW3z2ZcUP6+yyOgNHSAmufh2UPQ0sTTH0Acm61KnaV\nUgHLZ3cNnY4SYiKYnZvK7NxUmppbKCg+wu0vrOHFr3b3vkRQvhU++BnsWw1Dp8IVT8KAM/0dlVLK\nh3plInAUEhzE+DMGMDM7mdfz9lF5rJHYPr2giKipHj6fD5//ybov/+q/wdi5ekunUr2QFvrars9N\npb6phQ8KSvwdivftXQ1/Ow8+fQwyrob71kD2DZoElOqlNBHYMpNjOGtwNG/k7et84p6qrhKW/AKe\nvxgaj8GNb8KsZ/UOIKV6OU0ENhFhdm4qBcWVbDtQ7e9wPG/LEvifSbD2BZh8L9z7tXVHkFKq19NE\n4ODq7CRCg+X0uiqoKoXFN8HiG61nAe5aDpf83qoXUEopNBGcYGBUONNHD+KddftpbO7hzRe3tEDe\nC9ZVQNEymPZbuHuV9RSwUko50ETQzuzcFA4dbeCTreX+DuXUHSyCFy+HJT+HxCy45ys47xcQ3Avu\nhlJKdZkmgnbOHxFPQnR4zy0e+uqv8PQ5UL4Zrvor3PoBDBzm76iUUgFME0E7IcFBzBqfwsptFZRX\n1/k7nK45tBM+/o3VFtC8NZBzs94SqpTqlCYCJ2aPT6G5xfBO/n5/h9I1+QtAguHKP0NUQufTK6UU\nmgicGhofRe4Z/Xk9bx+B0BaTW5obYf2rMPIS7Q9AKdUlmghcmJ2bws6Ko+TvPeLvUNyz/SM4Wm71\nDKaUUl2gicCFy7OS6BMazJtre0ilcf5LEJ1o9ReglFJdoInAhajwEC7PSuSDglJqG5r8HU7HKout\nHsTG3aQ9hSmlukwTQQdmj0+hpr6JDzcc8HcoHVv/qtV38Lib/B2JUqoH0kTQgYlnDiBtYF/eCOTi\noZYWyH/Z6kugf5qfg1FK9USaCDrQ2hDd6l3fs+fQUX+H49yulVC51+pRTCmlToEmgk5cm5NMkMCb\na4v9HYpz+S9BnwFw1uX+jkQp1UNpIuhEYmwfzhsRz1tri2luCbBnCmoqYOtSGHsDhIT7OxqlVA+l\nicAN1+emUlJZx5c7Dvo7lBMVLoKWRn12QCnVLZoI3DA9PYF+fUN5PZAaojMG1r4EqZMg4Sx/R6OU\n6sE0EbghPCSYq7OT+XhzGUdqG/wdjmXvajhUpJXESqlu00Tgptm5KTQ0tfB+oHRun78AwqKtzueV\nUqobNBG4KSMplvTEmMAoHjp2BDa9A2Oug7BIf0ejlOrhNBF0wfW5KWzcX8Xmkir/BrLxTWg6BuO1\nWEgp1X2aCLpgZnYyYcFB/n/SeO1LMHgMJGb7Nw6l1GlBE0EX9I8M46L0Qby7bj8NTX7q3L5kPRwo\ntCqJtfcxpZQHuJ0IRCRYRNaJyBL7+5ki8o2IFInIYhEJs4eH29932OPTvBO6f8zOTeFwbSMrtpT5\nJ4D8BRASAWNm+2f9SqnTTleuCH4GbHH4/gfgCWPMCOAwcKc9/E7gsDFmOPCEPd1p47wR8QyOifBP\npXHDUdjwBqRfDX36+X79SqnTkluJQERSgMuBZ+3vAlwIvGlP8hLQeh/jTPs79vhp9vSnheAgYdb4\nZD7dXsGBSh93br/5Paiv0ieJlVIe5e4VwZPAvwGtBeMDgSPGmNYeW4qBZPtzMrAPwB5faU9/AhG5\nW0TyRCSvoqLiFMP3j9njU2kx8PY6HzdEl78ABg6HM87x7XqVUqe1ThOBiFwBlBtj1joOdjKpcWPc\n8QHGPGOMyTXG5MbHx7sVbKBIi4tkYtoA3sgr9l3n9hXbYO/X1tXA6XOBpZQKAO5cEZwLXCUiu4FF\nWEVCTwL9RKS1X8QUoPWR22IgFcAeHwt878GYA8Ls3BS+O3iUvD2HfbPC/AUQFGK1NKqUUh7UaSIw\nxvwfY0yKMSYNmAt8Yoy5EVgJXGdPdivwnv35ffs79vhPjM9+NvvOZWMSiQwL5g1fVBo31UPBazDq\nMohK8P76lFK9SneeI3gA+IWI7MCqA3jOHv4cMNAe/gvgwe6FGJgi7c7tlxSWcrTey53bb1sKtYe0\ngTmllFd0KREYY1YZY66wP+8yxkw0xgw3xsw2xtTbw+vs78Pt8bu8EXgguD43ldqGZv65odS7K8pf\nALGpMOwC765HKdUr6ZPF3TD+jP4MjYvkzTwv3j10eA/sXAnjboKgYO+tRynVa2ki6AYR4brcFL7d\n/T27Kmq8s5J1r1jv2Td6Z/lKqV5PE0E3zcpJ8V7n9i3NViIYPh36pXp++UophSaCbhsUE8HUUQm8\nle+Fzu13LIfqEn2SWCnlVZoIPGD2+BTKqur5rMjDT0jnL4DIeBh5iWeXq5RSDjQReMC00YMYEBnm\n2WcKqg/Atg8h+4cQEua55SqlVDuaCDwgLCSImdlJLNtc5rlnCta/CqYZxmmxkFLKuzQReMi5w+Jo\nbDZsPeCBbiyNsYqFzjgX4oZ3f3lKKdUBTQQekpkcC8DG/R5IBLs/h8Pf6ZPESimf0ETgIYNiwomL\nCmPj/sruLyx/AUTEQvpV3V+WUkp1QhOBh4gIGUmxbCzp5hVB7few+X3ImgOhfTwTnFJKdUATgQdl\nJsdQVFZNXWPzqS+k8HVortdnB5RSPqOJwIMykmJpajFsL6s+tQUYA/kvQdI4GDzGs8EppZQLmgg8\nKDOpmxXG+9dC+WatJFZK+ZQmAg9KHdCH6IgQNpacYoXxupchtC9kzvJsYEop1QFNBB4kImQmxbLp\nVO8c2vMVDJ0KETGeDEsppTqkicDDMpNj2HKgmsbmlq7NWF8DB4sgMds7gSmllAuaCDwsMzmWhqYW\ndpR3sX+Cso2AgcSxXolLKaVc0UTgYRltFcZdLB4qLbDeNREopXxME4GHnRkXSd+wYDZ19cGy0gKI\nTIDowd4JTCmlXNBE4GHBQUJ6YsypXREkjgUR7wSmlFIuaCLwgszkWDaXVrnfY1ljHZRv0WIhpZRf\naCLwgoykGGobmvnu4FH3ZijfZPU9oIlAKeUHmgi8oLVJ6k3uPlimFcVKKT/SROAFwxOiCAsJcr/C\nuLQAIvpBvyHeDUwppZzQROAFocFBjB4c7X6FsVYUK6X8SBOBl2Qkx7JxfyXGdFJh3NwIZZu0WEgp\n5TeaCLwkMymWqromig8f63jCiq3Q3KCJQCnlN5oIvCQz2Wo4rtPiobaKYm1jSCnlH5oIvGTkoGhC\ngqTzJqlLCyAsCgYM9U1gSinVTkhnE4hIBPAZEG5P/6Yx5rciciawCBgA5AM3G2MaRCQcWACMBw4B\nc4wxu70Uf8CKCA1mxKDozjupKS2AwVkQpDlZBZbGxkaKi4upq6vzdyi9XkREBCkpKYSGhnpl+Z0m\nAqAeuNAYUyMiocAXIvIh8AvgCWPMIhH5G3An8LT9ftgYM1xE5gJ/AOZ4JfoAl5kUwydbyzHGIM7u\nCGpphgMbtEcyFZCKi4uJjo4mLS3N+fGrfMIYw6FDhyguLubMM8/0yjo6/RlqLK1tKofaLwNcCLxp\nD38JuNr+PNP+jj1+mvTSoygzOZZDRxsoq6p3PsGhHdBYqxXFKiDV1dUxcOBATQJ+JiIMHDjQq1dm\nbpVHiEiwiKwHyoFlwE7giDGmyZ6kGEi2PycD+wDs8ZXAQE8G3VN0WmGsTxSrAKdJIDB4++/gViIw\nxjQbY7KBFGAiMNrZZPa7s4hPupleRO4WkTwRyauoqHA33h5ldGIMIriuMC4tgJAIiBvp28CUCjC7\nd+8mMzOz167f37pUQ2mMOQKsAiYD/USktY4hBSixPxcDqQD2+FjgeyfLesYYk2uMyY2Pjz+16ANc\n37AQhsVHua4wLi2AQRkQ7E5VjVJKeUeniUBE4kWkn/25DzAd2AKsBK6zJ7sVeM/+/L79HXv8J6bT\nx2tPX5lJMc4bnzMGSgu1WEipdnbt2sW4ceP45ptv+PWvf82ECRPIysri73//OwA333wz7733Xtv0\nN954I++///4Jy5gzZw5Lly5t+37bbbfx1ltvsXv3bs477zxycnLIycnhq6++Omn9L774IvPmzWv7\nfsUVV7Bq1SoAPv74Y84++2xycnKYPXs2NTVd7JI2QLlzRZAIrBSRQmANsMwYswR4APiFiOzAqgN4\nzp7+OWCgPfwXwIOeD7vnyEiKpbSyjoM17SqMD++G+kpNBEo52LZtG7NmzeKFF16goKCA2NhY1qxZ\nw5o1a/jHP/7Bd999x1133cULL7wAQGVlJV999RWXXXbZCcuZO3cuixcvBqChoYEVK1Zw2WWXkZCQ\nwLJly8jPz2fx4sXcf//9bsd28OBBHn30UZYvX05+fj65ubnMnz/fcxvvR52WSRhjCoFxTobvwqov\naD+8DpjtkehOAxl2hfGmkiqmjHQoAtOKYqVOUFFRwcyZM3nrrbfIyMjg0UcfpbCwkDfftG5OrKys\npKioiBkzZnDfffdRXl7O22+/zaxZswgJOfFUdumll3L//fdTX1/Pv/71L84//3z69OlDZWUl8+bN\nY/369QQHB7N9+3a341u9ejWbN2/m3HPPBawEc/bZZ3tuB/iRFk57mWNn9iclgqAQSEj3U2RKBZbY\n2FhSU1P58ssvycjIwBjDX/7yFy6++OKTpr355ptZuHAhixYt4vnnnz9pfEREBFOnTuWjjz5i8eLF\n3HDDDQA88cQTDBo0iIKCAlpaWoiIiDhp3pCQEFpaWtq+t962aYzhoosu4rXXXvPUJgcMfZzVy2L7\nhDJkQN+T6wlKCyBhNISE+ycwpQJMWFgY7777LgsWLODVV1/l4osv5umnn6axsRGA7du3c/So1evf\nbbfdxpNPPglARkYGAPv372fatGlty5s7dy4vvPACn3/+eVsyqaysJDExkaCgIF5++WWam5tPiiMt\nLY3169fT0tLCvn37+PbbbwGYPHkyX375JTt27ACgtra2S1cUgUwTgQ9kJseceOeQMcf7IFBKtYmM\njGTJkiVtv9zT09PJyckhMzOTH//4xzQ1WY8uDRo0iNGjR3P77be3zVtaWnpCEdGMGTP47LPPmD59\nOmFhYQDce++9vPTSS0yePJnt27cTGRl5UgznnnsuZ555JmPGjOFXv/oVOTk5AMTHx/Piiy9yww03\nkJWVxeTJk9m6das3d4fPSCDc0JObm2vy8vL8HYbX/M/KHfz3R9soeGgGsX1DoXI/PJEOlz0OE3/k\n7/CUcmrLli2MHu3skSH/q62tZcyYMeTn5xMbaxW//vWvf2XIkCFcddVVfo7OO5z9PURkrTEmt7vL\n1isCH2jrw7jULh7SimKlTtny5cs566yz+OlPf9qWBADmzZt32iYBb9PKYh/ISLLuHNpcUsU5w+Ks\nRCBB1sNkSqkumT59Onv37vV3GKcVvSLwgbiocBJjI463OVRaYDUrEXZy+aRSSvmaJgIfyUiKZWOJ\nXWGsFcVKqQCiicBHMpNj2FlRQ+33JVBdoolAKRUwNBH4SGZSLMbA/i3fWAM0ESilAoQmAh9pvXOo\n+ru11oDBY/wYjVI9w7Fjx5gyZQrNzc2UlJRw3XXXOZ1u6tSp+PIW9CeffJLa2touz3fbbbe1NZkx\nd+5cioqKPB3aKdFE4CODYsKJiwojpLzQ6qg+IrbzmZTq5Z5//nmuvfZagoODSUpKajuJ+ltHicDZ\n08rO3HPPPfzxj3/0ZFinTBOBj4gIGUmxxNds1WIhpdy0cOFCZs6cCZzYecyxY8eYO3cuWVlZzJkz\nh2PHjnW6rKlTp/LAAw8wceJERo4cyeeffw5YJ25nzV2vWrWKK664om3+efPm8eKLL/LUU09RUlLC\nBRdcwAUXXABAVFQUDz30EJMmTeLrr7/mkUceYcKECWRmZnL33Xfj7MHd8847j+XLl7c9Le1P+hyB\nD40fBIl7y2hMyCLU38Eo1QW/+2ATm0tcdLB0itKTYvjtla6fpWloaGDXrl2kpaWdNO7pp5+mb9++\nFBYWUlhY2NYMRGeampr49ttvWbp0Kb/73e9Yvnw5zz33XFtz1/X19Zx77rnMmDHD5TLuv/9+5s+f\nz8qVK4mLiwPg6NGjZGZm8sgjj1jblp7OQw89BFgN5C1ZsoQrr7zyhOUEBQUxfPhwCgoKGD9+vFvx\ne4teEfjQpIj9AOwLH+HnSJQKfAcPHqRfv35Ox3322WfcdNNNAGRlZZGVleXWMq+99loAxo8fz+7d\nuwGrs5lcYvqaAAAb8ElEQVQFCxaQnZ3NpEmTOHToUJfL7oODg5k1a1bb95UrVzJp0iTGjBnDJ598\nwqZNm5zOl5CQQElJidNxvqRXBD40yuwCYF3jEIb6ORaluqKjX+7e0qdPn7YmoJ05lQ7dw8Ot1n6D\ng4PbimRcNXf9xRdfOG2O2pmIiAiCg4Pbprv33nvJy8sjNTWVhx9+2OW8dXV19OnTp8vb4Wl6ReBD\nsUc2U8pA1h7U3a5UZ/r3709zc7PTk+j555/PwoULAdi4cSOFhYVt42655Za2pqPd4aq56zPOOIPN\nmzdTX19PZWUlK1asaJsnOjqa6upqp8trjTcuLo6ampoOK7i3b9/e1oy2P+kVgQ9JaQH7I0ayab+T\nPoyVUieZMWMGX3zxBdOnTz9h+D333MPtt99OVlYW2dnZTJx4vLPEwsJCEhMT3V7HXXfdxe7du8nJ\nycEYQ3x8PO+++y6pqalcf/31ZGVlMWLECMaNO95R4913382ll15KYmIiK1euPGF5/fr140c/+hFj\nxowhLS2NCRMmOF1vWVkZffr06VKs3qLNUPtKfTX8PpXPku/irj3T2PS7iwkN1isDFbgCoRnqdevW\nMX/+fF5++WW3pq+qquLOO+/kjTfe8HJk3ffEE08QExPDnXfe6db02gz16eDARsAQljqOhqYWdpTX\n+DsipQLeuHHjuOCCC9y+Nz8mJqZHJAGwrhxuvfVWf4cBaCLwHbsPgsFnTQI43hKpUqpDd9xxR1tF\n7Onk9ttvP6FHNX/SROArBwohMoEhqUOJDAtmk4fvyVZKqVOlicBX7Kang4KDSE+K0SsCpVTA0ETg\nC411UL6lrWmJjKRYNpdW0dzi/4p6pZTSROAL5ZvANDskghhqG5r57uBRPwemlFKaCHyjXWf1bZ3Z\nl2jxkFId8WQz1A899BDLly/vcJr6+nqmT59OdnY2ixcv7lKsu3fv5tVXX+3SPBAYTVNrIvCF0gKI\n6Af9hgAwPCGKsJAgrTBWqhOebIb6kUceOenBtPbWrVtHY2Mj69evZ86cOV1a/qkmAkf+appaE4Ev\ntPZRbLeNEhocxOjB0VphrFQnPNkMteMv77S0NH7729+Sk5PDmDFj2Lp1K+Xl5dx0002sX7+e7Oxs\ndu7cydq1a5kyZQrjx4/n4osvprS0FIAdO3Ywffp0xo4dS05ODjt37uTBBx/k888/Jzs7myeeeMJl\n89bGGObNm0d6ejqXX3455eXlbTH6q2nqwLiJ9XTW3Ahlm2DST04YnJEcy5KCEowxp9R4llI+9eGD\ncGCDZ5c5eAxc+pjL0d5ohtpRXFwc+fn5/O///i+PP/44zz77LM8++yyPP/44S5YsobGxkZtvvpn3\n3nuP+Ph4Fi9ezG9+8xuef/55brzxRh588EGuueYa6urqaGlp4bHHHmubF+CZZ55x2rz1unXr2LZt\nGxs2bKCsrIz09HTuuOMOwH9NU2si8LaKrdDccFJnNJlJsbz6zV6KDx8jdUBfPwWnVODqrBnq+++/\nH+haM9SOHJukfvvtt08av23bNjZu3MhFF10EWB3YJCYmUl1dzf79+7nmmmsAq+VRZz7++GMKCwvb\nrkIqKyspKiris88+44Ybbmgr7rrwwgtPmK+1aWpNBKeTtori7BMGZybHANYTxpoIVMDr4Je7t3ij\nGWpHzpqkdmSMISMjg6+//vqE4VVV7tXtuWreeunSpR3G7o+mqTutIxCRVBFZKSJbRGSTiPzMHj5A\nRJaJSJH93t8eLiLylIjsEJFCEen6NdvppLQAwqKsfoodjBwUTUiQsFHvHFLKKV81Q+3KqFGjqKio\naEsEjY2NbNq0iZiYGFJSUnj33XcB606j2trak5qmdtW89fnnn8+iRYtobm6mtLT0pNZL/dE0tTuV\nxU3AL40xo4HJwH0ikg48CKwwxowAVtjfAS4FRtivu4GnPR51T1JaAIOzIOjEXR0RGsyIQdFs3K93\nDinlSmsz1O3dc8891NTUkJWVxR//+MduNUPtSlhYGG+++SYPPPAAY8eOJTs7m6+++gqAl19+maee\neoqsrCzOOeccDhw4QFZWFiEhIYwdO5YnnniCu+66i/T0dHJycsjMzOTHP/4xTU1NXHPNNYwYMYIx\nY8Zwzz33MGXKlLZ1+q1pamNMl17Ae8BFwDYg0R6WCGyzP/8duMFh+rbpXL3Gjx9vTkvNTcY8OtiY\npQ84Hf2r19ebnEc+Ni0tLT4OTKnObd682d8hmPz8fHPTTTe5PX1lZaW57rrrvBiRd82fP988++yz\nTsc5+3sAeaaL53Bnry7dPioiacA44BtgkDGm1E4mpUCCPVkysM9htmJ7WPtl3S0ieSKSV1FR0ZUw\neo5DO6Cx9qSK4laZybEcOtpAWVW9jwNTqmc4nZuhdsZfTVO7nQhEJAp4C/i5Maaj8gxntSAnNapj\njHnGGJNrjMmNj493N4yepd0Txe05VhgrpZw7XZuhdsZfTVO7lQhEJBQrCSw0xrTeZ1UmIon2+ESg\n9amIYiDVYfYUoMQz4fYwpQUQEgFxI52OHp0YgwhaYayU8it37hoS4DlgizFmvsOo94HWa5hbseoO\nWoffYt89NBmobC1C6nVKC2BQBgQ7z/B9w0IYFh+lFcYqYJkA6MpWef/v4M4VwbnAzcCFIrLefl0G\nPAZcJCJFWJXHrTcaLwV2ATuAfwD3ej7sHqCl5XjTEh3ITIrRxudUQIqIiODQoUOaDPzMGMOhQ4dc\nPrjmCZ0WRhljvsB5uT/ANCfTG+C+bsbV8x3ZDfVVnSeC5FjeXV/CwZp64qLCfRObUm5ISUmhuLiY\n0/Zmjh4kIiKClJQUry1fnyz2lk4qiltlJLU2SV3FlJGnaaW56pFCQ0M588wz/R2G8gFtfdRbSgsg\nKAQS0jucLD1J7xxSSvmXJgJvKS2AhNEQ0nFxT2yfUM4Y2FfrCZRSfqOJwBuMcauiuFVmUqzeOaSU\n8htNBN5QtR9qD53U4qgrGckx7P2+lsraRi8HppRSJ9NE4A1uVhS3ymytMC7V4iGllO9pIvCG0gKQ\nIOthMjdk2BXGm7UPY6WUH2gi8IbSQqtZibBItyYfGBVOUmyE3jmklPILTQTe0IWK4lYZybFs1CsC\npZQfaCLwtJpyqC7pciLITIplZ0UNtQ0nd5mnlFLepInA00rtLvO6ekWQFIMxsKVUrwqUUr6licDT\nStdb74PHdGm2zGTrziF9nkAp5WuaCDyttMDqqD4itkuzDYoJJy4qTCuMlVI+p4nA006hohhARMhK\n6cfXuw7R0qLN/iqlfEcTgScdOwxH9pxSIgCYmZ1E8eFjfL3rkIcDU0op1zQReNIpVhS3ujhjMLF9\nQnnt270eDEoppTqmicCTWpuWGHxqiSAiNJhrxiXz8aYyvj/a4MHAlFLKNU0EnlRaALGpEDnwlBcx\nd2IqDc0tvJ1f7MHAlFLKNU0EnlRaAIOzurWIswbHkJ3aj8Vr9mlfsUopn9BE4Cn11XBoxynXDzia\nOyGVovIa8vce8UBgSinVMU0EnnJgI2A8kgiuHJtEZFgwi7TSWCnlA5oIPKV4jfXugUQQGR7ClWOT\nWFJYSnWddlajlPIuTQSesuF1KwnEJHpkcXMmpHKssZkPCko9sjyllHJFE4EnlBbAgQ0w7maPLTI7\ntR9nDY5m0RotHlJKeZcmAk9YtxCCwyFzlscWKSLMmZBKYXElm0q0/SGllPdoIuiuxjooXAxnXQ59\nB3h00deMSyYsJIjX1+zz6HKVUsqRJoLu2rYU6o7AuJs8vuh+fcO4NHMw76zbT11js8eXr5RSoImg\n+9YvhJgUGDrVK4ufMyGVqromPtyolcZKKe/QRNAdlcWwYwVk3wBBwV5ZxdlDB5I2sC+vfavFQ0op\n79BE0B0FrwEGsn/otVWICNdPSOXb775nV0WN19ajlOq9Ok0EIvK8iJSLyEaHYQNEZJmIFNnv/e3h\nIiJPicgOESkUkRxvBu9Xxlh3C6WdZ/VI5kXXjU8hOEhYnKdXBUopz3PniuBF4JJ2wx4EVhhjRgAr\n7O8AlwIj7NfdwNOeCTMA7fkKDn8H2Td6fVUJ0RFMOyuBt9YW09DU4vX1KaV6l04TgTHmM+D7doNn\nAi/Zn18CrnYYvsBYVgP9RMQzj9oGmnWvQFg0pF/lk9XNnZjKwZoGPtla5pP1KaV6j1OtIxhkjCkF\nsN8T7OHJgGP5RbE97PRSXw2b34XMayEs0iernDIygcExESzSZwqUUh7m6cpicTLMaaP6InK3iOSJ\nSF5FRYWHw/CyTe9AY61Xnh1wJThIuD43hU+3V7D/yDGfrVcpdfo71URQ1lrkY7+X28OLgVSH6VKA\nEmcLMMY8Y4zJNcbkxsfHn2IYfrLuFYgbCSkTfLra2bnWrn1DK42VUh50qongfeBW+/OtwHsOw2+x\n7x6aDFS2FiGdNg4Wwb5vrKsBcXYB5D2pA/ryg+FxvJFXTHOL9l6mlPIMd24ffQ34GhglIsUicifw\nGHCRiBQBF9nfAZYCu4AdwD+Ae70StT+tewUkGLLm+mX1cycMYf+RY3xe1MOK05RSASukswmMMTe4\nGDXNybQGuK+7QQWs5ibrIbIRMyB6kF9CuCh9EAMiw1i8Zh9TRyV0PoNSSnVCnyzuip0roKbMp5XE\n7YWFBDErJ5llm8uoqK73WxxKqdOHJoKuWPcy9I2DkRf7NYw5E1JpajG8nV/s1ziUUqcHTQTuOnoQ\ntv0Lxs6F4FC/hjI8IZoJaf1ZvGYfVmmcUkqdOk0E7ip8HVoafdKkhDvmTBjCroNH+fa79g99K6VU\n12gicIcx1t1CSTkwKN3f0QBw2ZjBRIeHsFifNFZKdZMmAneUrofyTX6tJG6vb1gIM8cl8c8NpVQe\na/R3OEqpHkwTgTvWvQIhER7tnN4T5k4YQn1TC++t3+/vUJRSPZgmgs401sGGN2D0ldCnn7+jOUFm\nciyZyTG89q1WGiulTp0mgs5sXQJ1lQFVLORozoQhbCmtYuP+Kn+HopTqoTQRdGbdKxA7BNLO93ck\nTs3MTiIiNIjX1uz1dyhKqR5KE0FHjuyDXausPomDAnNXxUSEcvmYJN5fX0JtQ5O/w1FK9UCBeXYL\nFD7onN4T5k5Mpaa+iX8Wnl4NvSqlfEMTgSstLVax0JlToP8Z/o6mQ7ln9GdYfKT2XqaUOiWaCFzZ\n8wUc2ROwlcSORIS5E4awds9hisqq/R2OUqqH0UTgyrqFEB5r3TbaA1ybk0xosOhVgVKqyzQROFNX\nCZvfgzGzILSPv6Nxy8CocGakD+bt/GLqm5r9HY5SqgfRRODMxreh6RhkB36xkKM5E1I5XNvIa9/o\nraRKKfdpInBm/UKIHw3JOf6OpEt+MDyO8Wf05+EPNvOTl9dSVlXn75CUUj2AJoL2KrZB8Rq/dE7f\nXUFBwqK7J/Nvl4xi5bZyps//lIXf7KFFO7pXSnVAE0F7616BoBDImuPvSE5JaHAQ904dzkc/P5/M\npFh+885G5j6zmh3lNf4OTSkVoDQROGpuhIJFMPISiIr3dzTdkhYXyas/msQfr8tiW1k1l/35c55a\nUURDU4u/Q1NKBRhNBI6KlsHR8h7x7IA7RITrc1NZ/ospzMgYxPxl27niL5+zds9hf4emlAogmggc\nrV8IkQkw/CJ/R+JR8dHh/PWHOTx/Wy41dU1c97ev+O17G6mp17aJlFKaCI6rKYftrZ3Th/g7Gq+4\n8KxBfPyLKdx6dhoLVu/hovmfsnxzmb/DUkr5mSaCVoWLoaXptCkWciUqPISHr8rgrXvOISYilLsW\n5HHfq/mUV+utpkr1VpoIwO6cfiGkTID4Uf6OxidyhvTng5/+gF/NGMmyzWVM/9OnLF6zV3s6U6oX\n6r2JoK4KtiyBD34GT46Bii0w7mZ/R+VTYSFBzLtwBB/+7DzOSozhgbc2cMM/VvPdwaP+Dk0p5UMS\nCL8Ac3NzTV5enndXYgyUbYIdy2DHCtj7tVUUFBYNQ6fAqEthbOB2QONtLS2GxXn7+K+lW6hvbOHs\nYQOZOiqeKSPjOTMuEulhD9cp1RuIyFpjTG63l3NaJ4JjR6wexlpP/tV2xy2DxsDwaTDiIkidBMGh\nnl93D1VeVcffPt3Fqm3l7LKvDIYM6MuUkfFMHRXP2cMG0jfs9KxMV6qn0UTgTEsLHCg8fuLf9y2Y\nZqs56WEXwPDp1ismsfvr6gX2Hqrl0+3lrNpWwVc7D3GssZmw4CAmnjmgLTEMT4jSqwWl/EQTAUBz\nExwqgtIC+5f/CuuBMIDEbOukP+IiSM49bW8J9ZX6pmbWfHe4LTEU2U1WJPfrw/l2Ujhn2ECiI/Tq\nSilfCehEICKXAH8GgoFnjTGPdTS9W4mgvsYq4z9QCAc2WO9lm6G53hrfpz8Mm2b/6p8GUQke2Rbl\n3P4jx/h0WwWfbi/nyx2HqKlvIiRIyE3rz5SRCUw8cwDxUeHE9g0lOjyEoCC9alDK0wI2EYhIMLAd\nuAgoBtYANxhjNrua56REUFMOpYUnnvQP7QTsWPv0h8FjYHCW/Rpj3fYZFOzRbVHuaWhqIX/vYVZt\nq+DT7RVsKa06YXyQQGyfUPr1DbPfQ+nv8Llf6zj7c/++YfTrG0p0RCjBmkCUcimQE8HZwMPGmIvt\n7/8HwBjze1fz5GYMN3l/vvn4ib/G4WnXfkNOPOEnZkFMco9rIro3KauqY0NxJUeONXKktoHKY40c\nrm3gSG0jlccaOVLbyJFj1vfquo6buQgPCSIyPIS+YcFE2e+t3yPDQugbbr1HuhgX0kvvAlO9w6Sh\nAz2SCLxRcJ4MOHacWwxM6nCOw9/BV09B/FlW8c7gMcdfffp5IUTlTYNiIhiUHuHWtE3NLVZysBNE\npZ0gDtc2UlPXRG1DE0cbmjha38zR+iZqG5qpqW+ivKqeow3W96P1TdRrq6pKnTJvJAJnP9VPuuwQ\nkbuBuwGGpibCv38HIeFeCEcFspDgIAZGhTMwqnt/+6bmFmobraRwtL7ZSiD1zbQEwM0QSnnLD/7g\nmeV4IxEUA6kO31OAkvYTGWOeAZ4Bq45Ak4DqjpDgIGKCg4jRu5aU6jJvFKCuAUaIyJkiEgbMBd73\nwnqUUkp5gMevCIwxTSIyD/gI6/bR540xmzy9HqWUUp7hlaesjDFLgaXeWLZSSinP0nvrlFKql9NE\noJRSvZwmAqWU6uU0ESilVC8XEK2Pikg1sM3fcbghDjjo7yDcoHF6Tk+IETROT+spcY4yxkR3dyGB\n0jbzNk+0l+FtIpKncXpOT4izJ8QIGqen9aQ4PbEcLRpSSqleThOBUkr1coGSCJ7xdwBu0jg9qyfE\n2RNiBI3T03pVnAFRWayUUsp/AuWKQCmllJ9oIlBKqV7Op4lARC4RkW0iskNEHnQyPlxEFtvjvxGR\nNF/GZ8eQKiIrRWSLiGwSkZ85mWaqiFSKyHr79ZCv47Tj2C0iG+wYTrqNTCxP2fuzUERyfBzfKId9\ntF5EqkTk5+2m8du+FJHnRaRcRDY6DBsgIstEpMh+7+9i3lvtaYpE5FYfx/jfIrLV/pu+IyJOu/Hr\n7PjwQZwPi8h+h7/tZS7m7fC84IM4FzvEuFtE1ruY15f70+l5yGvHpzHGJy+sJql3AkOBMKAASG83\nzb3A3+zPc4HFvorPIYZEIMf+HA1sdxLnVGCJr2NzEutuIK6D8ZcBH2L1GjcZ+MaPsQYDB4AzAmVf\nAucDOcBGh2F/BB60Pz8I/MHJfAOAXfZ7f/tzfx/GOAMIsT//wVmM7hwfPojzYeBXbhwXHZ4XvB1n\nu/F/Ah4KgP3p9DzkrePTl1cEE4EdxphdxpgGYBEws900M4GX7M9vAtNEfNtLvTGm1BiTb3+uBrZg\n9cPcE80EFhjLaqCfiCT6KZZpwE5jzB4/rf8kxpjPgO/bDXY8Bl8CrnYy68XAMmPM98aYw8Ay4BJf\nxWiM+dgY02R/XY3VC6BfudiX7nDnvOAxHcVpn2uuB17z1vrd1cF5yCvHpy8TgbNO7dufYNumsQ/0\nSmCgT6Jzwi6aGgd842T02SJSICIfikiGTwM7zgAfi8hauw/o9tzZ574yF9f/YIGwL1sNMsaUgvXP\nCCQ4mSaQ9usdWFd9znR2fPjCPLsI63kXxRiBtC/PA8qMMUUuxvtlf7Y7D3nl+PRlInCnU3u3Or73\nBRGJAt4Cfm6MqWo3Oh+riGMs8BfgXV/HZzvXGJMDXArcJyLntxsfEPtTrC5LrwLecDI6UPZlVwTK\nfv0N0AQsdDFJZ8eHtz0NDAOygVKsYpf2AmJf2m6g46sBn+/PTs5DLmdzMqzDferLROBOp/Zt04hI\nCBDLqV1udouIhGLt/IXGmLfbjzfGVBljauzPS4FQEYnzcZgYY0rs93LgHazLbEfu7HNfuBTIN8aU\ntR8RKPvSQVlr8Zn9Xu5kGr/vV7sC8ArgRmMXDLfnxvHhVcaYMmNMszGmBfiHi/X7fV9C2/nmWmCx\nq2l8vT9dnIe8cnz6MhG406n9+0BrDfd1wCeuDnJvscsJnwO2GGPmu5hmcGvdhYhMxNqPh3wXJYhI\npIhEt37GqkDc2G6y94FbxDIZqGy9rPQxl7+0AmFftuN4DN4KvOdkmo+AGSLS3y7umGEP8wkRuQR4\nALjKGFPrYhp3jg+valcfdY2L9btzXvCF6cBWY0yxs5G+3p8dnIe8c3z6ogbcoTb7Mqza753Ab+xh\nj2Ad0AARWMUHO4BvgaG+jM+O4QdYl1GFwHr7dRnwE+An9jTzgE1YdzisBs7xQ5xD7fUX2LG07k/H\nOAX4H3t/bwBy/RBnX6wTe6zDsIDYl1jJqRRoxPoVdSdWndQKoMh+H2BPmws86zDvHfZxugO43ccx\n7sAqA249PlvvtEsClnZ0fPg4zpft464Q6wSW2D5O+/tJ5wVfxmkPf7H1mHSY1p/709V5yCvHpzYx\noZRSvZw+WayUUr2cJgKllOrlNBEopVQvp4lAKaV6OU0EqlcQkX4icu8pzPfv3ohHqUCidw2pXsF+\nTH+JMSazi/PVGGOivBKUUgFCrwhUb/EYMMxuQvi/248UkUQR+cwev1FEzhORx4A+9rCF9nQ3ici3\n9rC/i0iwPbxGRP4kIvkiskJE4n27eUqdOr0iUL1CZ1cEIvJLIMIY85/2yb2vMaba8YpAREZjNQN8\nrTGmUUT+F1htjFkgIga4yRizUKw+FRKMMfN8sW1KdVeIvwNQKkCsAZ6323d51xjjrHOSacB4YI3d\nKkYfjrf10sLxdmpeAU5qo0qpQKVFQ0rR1k79+cB+4GURucXJZAK8ZIzJtl+jjDEPu1qkl0JVyuM0\nEajeohqrpyenROQMoNwY8w+sxr5au/VstK8SwGrb5ToRSbDnGWDPB9b/0nX25x8CX3g4fqW8RouG\nVK9gjDkkIl+K1Vfth8aYX7ebZCrwaxFpBGqA1iuCZ4BCEck3xtwoIv8Xq3OSIKyGy+4D9gBHgQwR\nWYvVodIc72+VUp6hlcVKeYDeZqp6Mi0aUkqpXk6vCFSvIiJjsNrJd1RvjJnkj3iUCgSaCJRSqpfT\noiGllOrlNBEopVQvp4lAKaV6OU0ESinVy2kiUEqpXk4TgVJK9XL/P23Y88YG+EI/AAAAAElFTkSu\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd79941eba8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEXCAYAAACgUUN5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd798e3eeb8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEXCAYAAACgUUN5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd798ea15c0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEXCAYAAACgUUN5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nK4YxWoQtWWgAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd798d70898>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEXCAYAAACgUUN5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl8FFW2wPHfyb6QjSwYCCSoqGwBQgTcURkUxEEFFBQE\nBXFUhvHN8vTN4qjPeeM4jtssOCiICwICKsjoqCgOKouyiYCsIUDYSSCQhOz3/VGV0IRO0oFOL+nz\n/Xz609219elKpU7fW7fuFWMMSimlAleQtwNQSinlXZoIlFIqwGkiUEqpAKeJQCmlApwmAqWUCnCa\nCJRSKsBpImjBRGSGiDzVyDL9RSTPl2I6i212EJEiEQk+h22cth9EJFdEBrgnQt8lIuNE5CsfiCMg\n9rev0kTgBiJypYgsE5FCESkQka9F5FJvxxUojDG7jTGtjDFV3o6lPnqiax4icr2IbBaREhFZIiLp\nDSz7vyLyvYhUisjjHgzT52kiOEciEgssAv4KtAbaAU8AZU3cjoiIX/89RCTE2zH4mubeJ/6wz5sr\nRhFJAt4Ffof1v7cKmNPAKtuB/wb+1Rzx+DO/PvH4iIsAjDGzjDFVxpiTxphPjDHr7WL31yLyV7u0\nsFlErq9ZUUS+EJE/iMjXQAlwvojEicg0EdkvIntF5KmaKg8RuUBEPheRfBE5IiIzRSTeYXu9RGSN\niJwQkTlAhKtfQkR+bW8zV0Tucph+k4isFZHjIrLH8ZeUiGSIiBGR8SKyG/jcnj5XRA7Y33mpiHSt\n83FJIvKpHed/HH/FiciL9uccF5HVInKVw7w+IrLKnndQRJ6rE0eDJxwRuUdEfrA/N0dE7m9kt1wq\nIptE5KiIvCYitftTRIaIyDoROWaXBjMd5uWKyCMish4oFpFZQAfgA7sK678bifNuEdll/51/51ia\nEJHHRWSeiLwlIseBcfZ+WW7Hsl9E/iYiYQ7bMyIy2f7OR0Tkz3V/dIjIs/b33CkigxrZLzXH7h9F\n5Bv777xARFrb8+o7Ln4sIhvtOL8Qkc6u7u963AZsNMbMNcaUAo8DPUTkEmcLG2NeN8Z8BJxo7PsF\nHGOMPs7hAcQC+cDrwCAgwWHeOKAS+C8gFLgDKARa2/O/AHYDXYEQe5n3gX8C0UAK8A1wv738hcCP\ngHAgGVgKvGDPCwN2OXzWcKACeKqR+PvbMT5nb/caoBi42GF+d6wfDZnAQeAWe14GYIA37Hgj7en3\nAjH29l4A1jl83gysf8Sr7fkvAl85zB8NJNr74xfAASDCnrccGGO/bgX0qxNHSCPf9SbgAkDs71kC\nZDl8zzyHZXOBDUB7rF+bX9fsSyALOAT0BYKBsfby4Q7rrrPXjXSYNsCF46kLUARcaf9Nn7X/jgPs\n+Y/b72+x/yaRQG+gn73PMoAfgIcdtmmAJfb36ABsBSY4HKMVwH32d3kA2AdII3F+AewFutl/+/nA\nW/UdF1g/mIqxjt9QrF/m24GwxvZ3AzG8CEypM20DMKyR9d4CHvf2ucOXHl4PoCU8gM5YJ7g8rJPq\nQqCN/U922j8V1om95mT2BfCkw7w2WFVKkQ7TRgFL6vncW4C19uurnXzWMhf+mfrbMUc7THsH+F09\ny78APG+/rvmHP7+B7cfby8TZ72cAsx3mtwKqgPb1rH8U6GG/XopV7ZZUZ5maOBpMBE62/T7wM4f9\nUDcR/MTh/WBgh/16CvC/dba1BbjGYd1768zPxbVE8Bgwy+F9FFDO6YlgaSPbeBh4z+G9AW50eP8g\n8Jn9ehywvc7nGeC8Rj7jC+Bph/dd7DiDnR0XWNU37zi8D8JKJP0b298NxDDNMQZ72tfAuEbW00RQ\n56FVQ25gjPnBGDPOGJOG9QupLdYJE2CvsY8+2y57fo09Dq/TsX4t7beLz8ewSgcpACKSIiKz7Sqj\n41gHdJK9btt6PssVR40xxc5iFJG+Yl2EOywihcBPHD7zjO8gIsEi8rSI7LBjzLVnJTlb3hhTBBQ4\nfN4v7OqbQvv7xzmsOx7rl+VmEflWRIa4+P1qYhskIivEuqB/DOtkU/e7OP1enP53Swd+UfM3srfV\nnvr/rk3RltP3TwlWibO+uBCRi0RkkV0ddxz4Pxr4G3HmMXigzueBlaAbU3ebodTzd7Y/r/Z4NMZU\n2/PbuRijM0VYJXJHsWjVT5NpInAzY8xmrF+93exJ7UREHBbpgPXLvXYVh9d7sEoEScaYePsRa4yp\nqWP/o718pjEmFqsapWbb++v5LFckiEh0PTG+jVXCaW+MiQNedvhMZ9/hTmAoMADrJJ5hT3dcp33N\nCxFphVUVsM++HvAIcDtWFVs8VlWaABhjthljRmElxj8B8+rEXS8RCceqvngWaGNv+0Mn38VRe4fX\njvtkD/AHh79RvDEmyhgzy2H5ut36utrN734gzSHuSKyqsoa2NQXYDHSyj4tfc+b3qu+7nIu626wA\njtQT5z6sBApYjSPs9feeQ4wbgR4O24zGqvrb6ELsyoEmgnMkIpfYv2LT7PftsapzVtiLpACTRSRU\nREZgVSN96Gxbxpj9wCfAX0QkVkSCxLpAfI29SAzWr6BjItIO+JXD6suxqngmi0iIiNwG9GnCV3lC\nRMLsk/EQYK7DZxYYY0pFpA/Wib4hMVjJLB+rmuH/nCwzWKwmt2HA/wIrjTF77HUrgcNAiIg8hsMv\nPhEZLSLJ9q/JY/ZkV5uMhmFdkzgMVNoXRAc2ss5DIpJmXwT9NadapLwC/MQuLYmIRIt1UT2mgW0d\nBM53Ic55wM0icrm9f56g4WQF1n47DhTZF0ofcLLMr0QkwT4+f0bDrWtcNVpEuohIFPAkMM/U34T3\nHeAmsZp7hmJd/ynDqr6sUd/+rs97QDcRGWZfWH4MWG//GDuD/T8YgXXeCxGRCDmHe09aEk0E5+4E\n1kXDlSJSjJUANmAd6AArgU5Yv5T+AAw3xtQt6ju6G+uktQmrfnwekGrPewLrQmUhVhO4d2tWMsaU\nY7WiGGevd4fj/EYcsNfZB8zEqqut+Wd6EHhSRE5g/aO908i23sAq1u+1v8MKJ8u8Dfweq0qoN1DT\nSulj4COsi5m7gFJOry64EdgoIkVYFwpHGqu1SKOMMSeAyXb8R7ES2sJGVnsbKzHn2I+n7G2twrq4\n+jd7W9ux9ntD/gj81q5K+mUDcW4EfgrMxiodnMC6MN1Qc+Rf2t/nBFaScnYCXQCsxrqI/S+s+vVz\n9SZW6fcAVgu1yfUtaIzZglWC/SvW/8LNwM32cVvD6f5uYJuHgWFY/1dHsf4PR9bMF5GXReRlh1Ve\nAU5i/VD7jf16TONfs+WT06uUlTuJyDis1hlXejsW5Z/sqrNjWNU+O89yG8Zef7sb4/oCq5XQq+7a\npvIeLREo5WNE5GYRibLrvJ8FvufURXel3E4TQQAQ62axIiePj7wdm7vV8z2LxOHGNG8TkbvqibHm\nIudQrGq6fVjViiONF4ruvrAvA+nY9SatGlJKqQCnJQKllApwmgiUUirA+UTPhUlJSSYjI8PbYSil\nlF9ZvXr1EWNM8rluxycSQUZGBqtWrfJ2GEop5VdExNVuZBqkVUNKKRXgNBEopVSA00SglFIBThOB\nUkoFOJcSgVhD5X0v1tB8q+xprcUabnCb/ZxgTxcReUlEtovIehHJas4voJRS6tw0pURwrTGmpzEm\n237/KNYoR52Az+z3YA3X2Ml+TMTqK10ppZSPOpeqoaFY4/RiP9/iMP0NY1kBxItIqrMNKKWU8j5X\n7yMwwCd2d7b/NMZMxRrlaT9YA6qISIq9bDtO70M+z562v76Nbz5wgkEvfkl8ZCjxUdYjLjLMeh0Z\nSnxUWO30eHt6RKiOJ6H8wPfz4N//A6ba25EoVS9XE8EVxph99sn+UxFxOgKQzdloSmf0bCciE7Gq\njohrez5pCZEUllSw43ARx0oqOFZSQXlV/f884SFBxEeFkhAVRlxkKEmtwkmOsR+Or2PCSYwOIyRY\nr4srL9j2CVSVQbfh3o5EtUjPu2UrLiUCY8w++/mQiLyHNQTiQRFJtUsDqVijKIFVAnAcezQNJ2OP\n2qWKqQDZ2dnmlbuz687nZEVVbVI4drKcwpIKjp2035eU104/WlLB5gPH+XJbGcdLK8+IXwQSo8NO\nTxZ2wkiJjahNHGkJkVrSUO5VkAOpPWDIc96ORLVIHkoE9uAYQcaYE/brgVjjky4ExgJP288L7FUW\nApNEZDbW0HGFNVVITSEiRIWFEBUWQtv4SJfXK62o4khRGYdOlHHY4VH7vqiMnMPFHD5RdkaJQwTa\nxkWSnhhFemI0HZOs54zEaNITozRJqKYryIHON3s7CqUa5EqJoA3wnojULP+2MebfIvIt8I6IjAd2\nAyPs5T8EBmON41oC3OP2qBsQERpMWkIUaQlRDS5njKHwZEVtojhwvJTdBSXsyi9h55Fi/r1hP0dL\nKk5bJzUugvTEKDISo8lIiibDThjpiVFEhflEt03Kl5w8BiX50NqVMeuV8p5Gz17GmBygh5Pp+cD1\nTqYb4CG3RNeMRMS+CB1GpzYxTpcpLKlgV0Exufkl5B4pJje/mF35JSz+4SBHispPW7ZNbLhdeoiy\nk0R0bcmiVbgmiYB01B5iWBOB8nF6hmpAXFQomVHxZKbFnzHveGkFu/NLapPDziPF5B4pZsmWwxxe\nlXfasskx4bWlh7qJIiYi1FNfR3laQY71rIlA+ThNBGcpNiKUbu3i6NYu7ox5xWWVtQkiN7+YXUdK\n2JlfzJfbDjNvddlpyyZGh5GRFF1b5dSzfTxXXJhEcJCzxlfKrxTYJYKEDK+GoVRjNBE0g+jwELq2\njaNr2zOTREl5JbvyS9iVb1U57covZueRYpbvyOfdNXsBOC82gtuy2jG8dxrnJ7fydPjKXQp2Qkwq\nhEV7OxKlGqSJwMOiwkLonBpL59TYM+adLK/iiy2HmLs6j5f/s4N/fLGD7PQEhvdO46bMVK1G8jcF\nOZDQ0dtRKNUoTQQ+JDIsmEHdUxnUPZVDx0t5b+1e5q7O49F3v+fxDzYyuFsqw3un0e/8RIK06sj3\nFeTAhQO8HYVSjdJE4KNSYiO4/5oLmHj1+azbc4y5q/P44Lt9vLt2L2kJkQzLSmN47zTat264mazy\nkvJiKDoArbVEoHyfJgIfJyL06pBArw4JPDakCx9vPMC81Xm89Pk2XvxsG/3Ob82I3u0Z1P08vZfB\nlxRo01HlP/TM4UciQoMZ2rMdQ3u2Y++xk7y3Jo95q/P4xdzv+P3CjdzUPZXh2Wlkpydg3wCovEWb\njio/oonAT7WLj2TSdZ146NoL+Tb3KHNX7eGD9fuYs2oPHZOiGd47jVt7tWtS9xzKjWoTgVYNKd8n\n1o3A3pWdnW1WrVrl7TD8XnFZJR9tOMDcVXtYubMAEbjywiRGZLdnYJc22leSJy2cDJv/Bf+9w9uR\nqBZMRFY7DBZ21rRE0IJEh4cwvLd1EXl3fgnz1uQxf3Uek2etJTYihJt7tGVEdnt6pMVp1VFzO7pT\nq4WU39BE0EJ1SIzi5z+6iIev78TynHzmrc5j/po8Zq7cTaeUVlbVUVY7UmIivB1qy1SwE9Kv8HYU\nSrlEE0ELFxQkXHFhEldcmMQTQ7vyr/X7mbtqD3/8aDPPfLyF/hclM7x3Gtd3bkNYiA7e4xYVpVCY\np9cHlN/QRBBAYiNCGdWnA6P6dGDH4SLmrc7j3TV5fLb5EAlRoQztaXVr4az/JNUEx3YBRquGlN/Q\nRBCgLkhuxSM3XsIvB17M0m2Hmbc6j7dX7mbGsly6pMbyp2GZdE/ThHBWtOmo8jOaCAJccJBw7cUp\nXHtxCsdKyln43T5e/mIHI/65jGdH9GBIZltvh+h/NBEoP6OVwqpWfFQYd1+WwcKfXkn3dnFMenst\nz32yhepq7zcx9isFORARB5EJ3o5EKZdoIlBnSGoVzlsT+jKidxovfb6dB2euoaS80tth+Y+CHKs0\noE10lZ/QRKCcCg8J5pnhmfz2ps58sukAw6YsZ++xk94Oyz/UJAKl/IQmAlUvEWHCVeczfdyl5BWU\nMPRvX7F6V4G3w/JtVRVwbI8mAuVXNBGoRvW/OIX3HrqcVuEhjJq6krmr9ng7JN91bDeYKk0Eyq9o\nIlAuuTAlhvcfuoJLOybwq3nr+cO/NlGlF5HPpN1PKz+kiUC5LD4qjBn39GHsZem88uVOJrz+LcdL\nK7wdlm+paTqqQ1QqP6KJQDVJaHAQTwztxh9u7caX245w2z+WkXuk2Nth+Y6CHAiNhlYp3o5EKZdp\nIlBn5a6+6bw5vi9HisoY+vevWbb9iLdD8g3adFT5IU0E6qxddkEiCx+6kpSYcMZM/4Y3l+d6OyTv\nK8jRzuaU39FEoM5Jh8Qo3n3wcvpflMzvFmzkt+9/T0VVtbfD8o7qKjiaqxeKld/RRKDOWUxEKFPv\nzub+a87nrRW7uXvaNxwtLvd2WJ5XmAfVFZoIlN/RRKDcIjhI+J9BnXnu9h6s3nWUH//9K9bsPurt\nsDxLO5tTfkoTgXKr27LSmH1/P6qrYcTLy3lx8TYqA6WqSBOB8lOaCJTbZXVI4KOHr+LmzFSeX7yV\nO6auYHd+ibfDan5Hd0JIBMSkejsSpZrE5UQgIsEislZEFtnvO4rIShHZJiJzRCTMnh5uv99uz89o\nntCVL4uNCOWFkb14cWRPth44weCXvmT+6jyMacF3IxfshIQMCNLfV8q/NOWI/Rnwg8P7PwHPG2M6\nAUeB8fb08cBRY8yFwPP2cipADe3Zjo8evoouqbH8Yu53TJq1lsKSFno3svY6qvyUS4lARNKAm4BX\n7fcCXAfMsxd5HbjFfj3Ufo89/3p7eRWg0hKimDWxH7+64WI+3nCAG19cyvId+d4Oy72qq60SgSYC\n5YdcLRG8APw3UHPVLxE4ZoypGa0kD2hnv24H7AGw5xfay59GRCaKyCoRWXX48OGzDF/5i+Ag4aFr\nL+TdBy8nIjSYO19dwR8/+oHyyhZyIbnoAFSe1JvJlF9qNBGIyBDgkDFmteNkJ4saF+admmDMVGNM\ntjEmOzk52aVglf/LTIvnX5OvZOSlHfjnf3K4bcrXbD9U5O2wzp22GFJ+zJUSwRXAj0UkF5iNVSX0\nAhAvIiH2MmnAPvt1HtAewJ4fB+hoJqpWVFgIf7ytO1PH9Gbv0ZMM+euXvLVil39fSNZEoPxYo4nA\nGPM/xpg0Y0wGMBL43BhzF7AEGG4vNhZYYL9eaL/Hnv+58ev/cNVcBnY9j48fvpo+HRP57fsbuO+N\nVRwpKvN2WGenIAeCQiE2zduRKNVk59LO7RHg5yKyHesawDR7+jQg0Z7+c+DRcwtRtWQpsRHMGHcp\nv7+5C0u3HeHGF5ayZMshb4fVdAU5kJAOwSGNL6uUjxFf+LGenZ1tVq1a5e0wlJdtPnCch2evY/OB\nE4y9LJ3/GdyZiNBgb4flmpevgpjz4K653o5EBRARWW2MyT7X7eidL8pnXHJeLO8/dAX3XtGR15fv\nYtiUZf5RVWSMfTOZthhS/kkTgfIpEaHBPHZzF6aNzWbH4SJGvLycPQU+3j1F8REoP6EXipXf0kSg\nfNL1ndswc0Jf8ovKGP7yMrYePOHtkOqnLYaUn9NEoHxW7/TWvPOTyzDG6sl09S4f7dZaE4Hyc5oI\nlE+75LxY5j9wOfFRoYx+dSX/2eqDd6EX5IAEQXwHb0ei1FnRRKB8XvvWUcz7yeVkJEUz4fVvWfjd\nvsZX8qSCHIhrDyFh3o5EqbOiiUD5heSYcObc349eHRL42ey1vLk819shnaK9jio/p4lA+Y3YiFDe\nuLcP11+Swu8WbOTFxdt8o1sKTQTKz2kiUH4lIjSYl0f3ZlhWGs8v3soTH2yiutqLyaCkAEqPaSJQ\nfk3vh1d+JyQ4iD8PzyQhKpRXv9rJsZJy/jyiB6HBXvhdc3Sn9azdTys/polA+aWgIOE3N3Wmdasw\nnvn3FgpPVvCPu3oTGebhLikKahKBlgiU/9KqIeW3RIQH+1/I/93anS+2HmbMtJWeHwaz5h6ChAzP\nfq5SbqSJQPm9O/t24O93ZrE+r5A7pi7n0PFSz314QQ7EtoPQSM99plJupolAtQiDu6cyfdyl7C4o\nYdjLy9iVX+yZD9YWQ6oF0ESgWowrOyXx9n39KCqtZNiU5Wzad7z5P7QgRy8UK7+niUC1KD3bxzP3\nJ5cRGizcMXU5q3KbcZTU0uNQfFhLBMrvaSJQLc6FKTHMe+BykluFM3b6N83XWd1RbTGkWgZNBKpF\nahcfydv39SM5Jpxx079h3Z5j7v8Q7XVUtRCaCFSLdV5cBLMm9iMhOowx01byfV6hez9Am46qFkIT\ngWrRUuMimTWxH3GRoYyetpINe92YDAp2QnQKhMe4b5tKeYEmAtXitYuPZNZ9/WgVHsLoaSvd15qo\nYKdWC6kWQROBCgjtW0cx675+RIYGM3raSrYccMPQl3oPgWohNBGogNEh0UoGocHCna+sYNu5jINc\nXgIn9mkiUC2CJgIVUDKSopl1Xz+CgoRRr6xk+6Gis9vQ0VzrWW8mUy2AJgIVcM5PbsWs+/oBcOcr\nK9h55Cy6o9Cmo6oF0USgAtKFKa14+76+VFUbRk1d0fS+iWoTgZYIlP/TRKAC1kVtYph5X1/KKqsY\nNXUFu/NLXF+5IAciW0NkQvMFqJSHaCJQAe2S82KZOaEfJRVVjHplBXsKXEwG2tmcakE0EaiA16Vt\nLG+N78uJ0grufHUFe4+dbHylo3oPgWo5NBEoBXRrF8dbE/pyrKSCO19Zwf7CBpJBZRkU5mkiUC1G\no2MWi0gEsBQIt5efZ4z5vYh0BGYDrYE1wBhjTLmIhANvAL2BfOAOY0xuM8WvlNtkpsXzxr19GDPt\nG+58ZSWzJ/ajTWzEmQse2w2musUngoqKCvLy8igt9eCIb8qpiIgI0tLSCA0NbZbtuzJ4fRlwnTGm\nSERCga9E5CPg58DzxpjZIvIyMB6YYj8fNcZcKCIjgT8BdzRL9Eq5Wa8OCbx+76XcPe0bRr2ygtkT\n+5ESUycZBEjT0by8PGJiYsjIyEBEvB1OwDLGkJ+fT15eHh07Ns91qUarhoyl5q6bUPthgOuAefb0\n14Fb7NdD7ffY868XPYqUH+md3poZ9/bhQGEpd76ykuKyytMXCJBEUFpaSmJioiYBLxMREhMTm7Vk\n5tI1AhEJFpF1wCHgU2AHcMwYU/Mfkge0s1+3A/YA2PMLgUR3Bq1Uc7s0ozV/GdGD7YeK+GZnnVHO\nCnIgPBaiWv5hrUnANzT338GlRGCMqTLG9ATSgD5AZ2eL2c/OIjZ1J4jIRBFZJSKrDh8+7Gq8SnlM\ndkZrgDNvNqtpOqonSbfJzc2lW7duAfv53takVkPGmGPAF0A/IF5Eaq4xpAH77Nd5QHsAe34ccMbA\nscaYqcaYbGNMdnJy8tlFr1QzSmoVRnRYMLl1bzTTXkdVC9NoIhCRZBGJt19HAgOAH4AlwHB7sbHA\nAvv1Qvs99vzPjTFnlAiU8nUiQofE6NNLBFUVVquhBL2ZrLnk5OTQq1cvVq5cya9+9SsuvfRSMjMz\n+ec//wnAmDFjWLBgQe3yd911FwsXLjxtG3fccQcffvhh7ftx48Yxf/58cnNzueqqq8jKyiIrK4tl\ny5ad8fkzZsxg0qRJte+HDBnCF198AcAnn3zCZZddRlZWFiNGjKCo6Cw7LfQxrpQIUoElIrIe+Bb4\n1BizCHgE+LmIbMe6BjDNXn4akGhP/znwqPvDVsozMhKj2OVYIijcA9WVWiJoJlu2bGHYsGG89tpr\nfPfdd8TFxfHtt9/y7bff8sorr7Bz504mTJjAa6+9BkBhYSHLli1j8ODBp21n5MiRzJkzB4Dy8nI+\n++wzBg8eTEpKCp9++ilr1qxhzpw5TJ482eXYjhw5wlNPPcXixYtZs2YN2dnZPPfcc+778l7UaPNR\nY8x6oJeT6TlY1wvqTi8FRrglOqW8LD0xmsU/HKSq2hAcJNaoZKCJoBkcPnyYoUOHMn/+fLp27cpT\nTz3F+vXrmTfPapxYWFjItm3bGDhwIA899BCHDh3i3XffZdiwYYSEnH4qGzRoEJMnT6asrIx///vf\nXH311URGRlJYWMikSZNYt24dwcHBbN261eX4VqxYwaZNm7jiiisAK8Fcdtll7tsBXuTKfQRKBayM\nxCgqqgz7jp2kfeuogGk66g1xcXG0b9+er7/+mq5du2KM4a9//Ss33HDDGcuOGTOGmTNnMnv2bKZP\nn37G/IiICPr378/HH3/MnDlzGDVqFADPP/88bdq04bvvvqO6upqIiDNvGAwJCaG6urr2fU2zTWMM\nP/rRj5g1a5a7vrLP0C4mlGpAemI0wKnqoYKdEBIJMed5MaqWKSwsjPfff5833niDt99+mxtuuIEp\nU6ZQUVEBwNatWykutq7XjBs3jhdeeAGArl27ArB3716uv/762u2NHDmS1157jS+//LI2mRQWFpKa\nmkpQUBBvvvkmVVVVZ8SRkZHBunXrqK6uZs+ePXzzzTcA9OvXj6+//prt27cDUFJS0qQShS/TRKBU\nAzKSogDIrblgXNNiSJuONovo6GgWLVpU+8u9S5cuZGVl0a1bN+6//34qK61bl9q0aUPnzp255557\natfdv3//aVVEAwcOZOnSpQwYMICwsDAAHnzwQV5//XX69evH1q1biY6OPiOGK664go4dO9K9e3d+\n+ctfkpWVBUBycjIzZsxg1KhRZGZm0q9fPzZv3tycu8NjxBca9GRnZ5tVq1Z5OwylzlBdbej82L+5\n+7J0fnNTF/hbH0jqBCNneju0ZvfDDz/QubOzW4a8r6SkhO7du7NmzRri4uIA+Nvf/kaHDh348Y9/\n7OXomoezv4eIrDbGZJ/rtrVEoFQDgoKE9MQo616C6irtftoHLF68mEsuuYSf/vSntUkAYNKkSS02\nCTQ3vVisVCPSa+4lOL4Pqso1EXjZgAED2L17t7fDaFG0RKBUI9JbW/cSVOfvsCZoIlAtjCYCpRqR\nnhRNWWU1x/fZLUR0iErVwmgiUKoRGYlWy6Hi/dsgOAxi2zWyhlL+RROBUo3IsO8lqC7IgYQMCAr2\nbkBKuZktIdkVAAAdyUlEQVQmAqUakRoXQWiwEH48V68PeNjJkye55pprqKqqYt++fQwfPtzpcv37\n98eTTdBfeOEFSkpKGl+wjnHjxtV2mTFy5Ei2bdvm7tDOiiYCpRoREhxE+/hI4k7qgPWeNn36dG67\n7TaCg4Np27Zt7UnU2xpKBM7uVnbmgQce4JlnnnFnWGdNE4FSLsiMLyXclGoi8LCZM2cydOhQ4PTB\nY06ePMnIkSPJzMzkjjvu4OTJk41uq3///jzyyCP06dOHiy66iC+//BKwTtzOurv+4osvGDJkSO36\nkyZNYsaMGbz00kvs27ePa6+9lmuvvRaAVq1a8dhjj9G3b1+WL1/Ok08+yaWXXkq3bt2YOHEizm7c\nveqqq1i8eHHt3dLepPcRKOWCzGhrbCWT0NHpEHwt3RMfbGTTvuNu3WaXtrH8/uau9c4vLy8nJyeH\njIyMM+ZNmTKFqKgo1q9fz/r162u7gWhMZWUl33zzDR9++CFPPPEEixcvZtq0abXdXZeVlXHFFVcw\ncODAercxefJknnvuOZYsWUJSUhIAxcXFdOvWjSeffNL6bl268NhjjwFWB3mLFi3i5ptvPm07QUFB\nXHjhhXz33Xf07t3bpfibi5YIlHLBRSGHACiISPNyJIHjyJEjxMfHO523dOlSRo8eDUBmZiaZmZku\nbfO2224DoHfv3uTm5gLWYDNvvPEGPXv2pG/fvuTn5ze57j44OJhhw4bVvl+yZAl9+/ale/fufP75\n52zcuNHpeikpKezbt8/pPE/SEoFSLkjjIBUmmJ0VrWn5Q9afqaFf7s0lMjKytgtoZ85mQPfw8HDA\nOnHXVMnU1931V1995bQ7amciIiIIDg6uXe7BBx9k1apVtG/fnscff7zedUtLS4mMjGzy93A3LREo\n5YKk8jz2miRyj5Z7O5SAkZCQQFVVldOT6NVXX83MmVbHfxs2bGD9+vW18+6+++7arqNdUV931+np\n6WzatImysjIKCwv57LPPateJiYnhxIkTTrdXE29SUhJFRUUNXuDeunVrbTfa3qQlAqVcEFW0m120\nOX38YtXsBg4cyFdffcWAAQNOm/7AAw9wzz33kJmZSc+ePenT59RgievXryc1NdXlz5gwYQK5ublk\nZWVhjCE5OZn333+f9u3bc/vtt5OZmUmnTp3o1evUQI0TJ05k0KBBpKamsmTJktO2Fx8fz3333Uf3\n7t3JyMjg0ksvdfq5Bw8eJDIyskmxNhfthlqpxhgDT6czv+py/nPBI7w06oyRW1skX+iGeu3atTz3\n3HO8+eabLi1//Phxxo8fz9y5c5s5snP3/PPPExsby/jx411aXruhVsqbSgqgrJCTrdK1ROBhvXr1\n4tprr3W5bX5sbKxfJAGwSg5jx471dhiAVg0p1Th7nGKT0JHc3KbfTarOzb333uvtEJqF4+hq3qYl\nAqUaYyeCiDadKDxZwbESvWCsWhZNBEo1piAHEBLaXQRgjVamVAuiiUCpxhTkQFx70lOsm5v0OoFq\naTQRKNWYghxo3ZH2raMQgdwjWiJQLYsmAqUaYyeCiNBgzouN0BKBB7mzG+rHHnuMxYsXN7hMWVkZ\nAwYMoGfPnsyZM6dJsebm5vL22283aR3wja6pNREo1ZCTR+FkQW2vo+mJUeRqIvAYd3ZD/eSTT55x\nY1pda9eupaKignXr1nHHHXc0aftnmwgceatrak0ESjWkYKf1bCeCjMRodhdo1ZCnuLMbasdf3hkZ\nGfz+978nKyuL7t27s3nzZg4dOsTo0aNZt24dPXv2ZMeOHaxevZprrrmG3r17c8MNN7B//34Atm/f\nzoABA+jRowdZWVns2LGDRx99lC+//JKePXvy/PPP19u9tTGGSZMm0aVLF2666SYOHTpUG6O3uqbW\n+wiUasjR0xNBemI0R4rKOVFaQUxEqBcD87CPHoUD37t3m+d1h0FP1zu7ObqhdpSUlMSaNWv4xz/+\nwbPPPsurr77Kq6++yrPPPsuiRYuoqKhgzJgxLFiwgOTkZObMmcNvfvMbpk+fzl133cWjjz7Krbfe\nSmlpKdXV1Tz99NO16wJMnTrVaffWa9euZcuWLXz//fccPHiQLl261N4r4a2uqTURKNUQ+x4CEjKA\nUwPZ78ovoVu7OC8FFRga64Z68uTJQNO6oXbk2CX1u+++e8b8LVu2sGHDBn70ox8B1gA2qampnDhx\ngr1793LrrbcCVs+jznzyySesX7++thRSWFjItm3bWLp0KaNGjaqt7rruuutOW6+ma2pNBEr5ioKd\nEJMKYdYA9un2QPYBlwga+OXeXJqjG2pHzrqkdmSMoWvXrixfvvy06cePuzZAT33dW3/44YcNxu6N\nrqkbvUYgIu1FZImI/CAiG0XkZ/b01iLyqYhss58T7OkiIi+JyHYRWS8iTS+zKeUrCnJOG54y3S4R\n6AXj5uepbqjrc/HFF3P48OHaRFBRUcHGjRuJjY0lLS2N999/H7BaGpWUlJzRNXV93VtfffXVzJ49\nm6qqKvbv339G76Xe6JralYvFlcAvjDGdgX7AQyLSBXgU+MwY0wn4zH4PMAjoZD8mAlPcHrVSnmI3\nHa0RHR5Ccky4NiH1kJpuqOt64IEHKCoqIjMzk2eeeeacuqGuT1hYGPPmzeORRx6hR48e9OzZk2XL\nlgHw5ptv8tJLL5GZmcnll1/OgQMHyMzMJCQkhB49evD8888zYcIEunTpQlZWFt26deP++++nsrKS\nW2+9lU6dOtG9e3ceeOABrrnmmtrP9FrX1MaYJj2ABcCPgC1Aqj0tFdhiv/4nMMph+drl6nv07t3b\nKOVzSk8Y8/tYY5Y+e9rk4VO+NiNeXualoDxn06ZN3g7BrFmzxowePdrl5QsLC83w4cObMaLm9dxz\nz5lXX33V6Txnfw9glWniOdzZo0nNR0UkA+gFrATaGGP228lkP5BiL9YO2OOwWp49re62JorIKhFZ\ndfjw4aaEoZRnFOywnhM6njY5PTFaSwQe0pK7oXbGW11Tu5wIRKQVMB942BjT0NUSZ1dBzhj9xhgz\n1RiTbYzJTk5OdjUMpTyjtBA+eBiCw6Dt6QPRpLeO4uDxMk6Wu3ZyUufm3nvvrR0PuKW75557CAnx\nfBselxKBiIRiJYGZxpiadlYHRSTVnp8K1NwVkQe0d1g9DdjnnnCV8oDS4/DWMKvd/O1vnHaNACA9\nyWo5pDeWqZbClVZDAkwDfjDGPOcwayFQU4YZi3XtoGb63XbroX5AYU0VklI+r+wEzBwO+9bCiBlw\n8aAzFskIoJZDxgeGslXN/3dwpQxyBTAG+F5E1tnTfg08DbwjIuOB3cAIe96HwGBgO1AC+M4wPEo1\npLwYZt4OeatgxGvQeYjTxdJb19xL0LITQUREBPn5+SQmJp5zm3119owx5Ofn13vjmjs0mgiMMV/h\nvN4f4HonyxvgoXOMSynPKi+Bt++APStg2DToMrTeReOiQkmICm3xA9SkpaWRl5eHNubwvoiICNLS\n0ppt+3pnsVIVJ2HWSNj1Ndw6Fbrd1ugqgdByKDQ0lI4dOza+oPJ72vuoCmwVpTD7Tti5FG6ZApkj\nGl8H6zqBDlCjWgpNBCpwVZbBnLtgxxIY+nfoMdLlVdMTo9lXeJKySm1CqvyfJgIVmCrLYM4Y2L4Y\nbn4Ret3VpNUzkqIwBvYUNN4PvlK+ThOBCjyV5TD3Htj2MQx5Hno3/U7OU72QtuzrBCowaCJQgaWq\nAubfC1v+BYOfhex7z2oz6a1PjUuglL/TRKACR1UlzJ8AP3wANz4Nfe476021jg4jJjxESwSqRdBE\noAJDVSW8NxE2vQ8D/wD9HjinzYkI6UlRLf5eAhUYNBGolq+6ChY8CBvmw4An4PJJbtlsINxLoAKD\nJgLVslVXw4JJsH4OXPc7uPJht206IzGKvKMnqaiqdts2lfIGTQSq5aquhg8mw3dvQ/9fw9W/dOvm\n0xOjqaw27DumTUiVf9NEoFqm6mr413/B2jfh6v+G/o+4/SMy7Cakep1A+TtNBKrlqSyHd++D1TPg\nql/Atb9ulo+p6Y5arxMof6edzqmWpbwY3rnbumN4wONw5X8120clx4QTGRqsfQ4pv6eJQLUcJQVW\nV9J7V1ndRvQe16wfJyKkJ0ZpiUD5PU0EqmU4vh/eug3yt1sjizUwnoA7pSdGseOwJgLl3/QagfJ/\n+Ttg+kA4thvumuexJADWBePd+SVUVeuQjsp/aSJQ/m3/dzD9BuvawNgP4PxrPPrx6YnRlFdVc+B4\nqUc/Vyl30kSg/Ffu1zBjCASHwz3/hnZZHg+htuXQEa0eUv5LE4HyT5s/tK4JxJwH4z+G5Iu8EkZ6\nkt5LoPyfJgLlf9a9DXNGQ0oXqyQQ13yDejcmNTaCsJAgbTmk/JomAuVflv0N3n8AOl4FYxdCdKJX\nwwkKEjq0jiJXE4HyY9p8VPkHY+Dz/4Uv/2K1CrrtFQgJ93ZUgHWdQAeoUf5MSwTK91VXwaKHrSTQ\nexwMf81nkgDUdEddgjHahFT5J00EyrdVlsG8e071GzTkBQgK9nZUp0lPjOJkRRWHT5R5OxSlzopW\nDSnfVXYCZt8FO/9jjSrmpgFl3C3doRfSlNgIL0ejVNNpiUD5puJ8eP3HkPsV3PKyzyYBOHUvgV4w\nVv5KSwTK9+xeaXUjXXQQRs6Eiwd5O6IGtYuPJCRItAmp8ltaIlC+o6oSlvwfvHaj9X7sIp9PAgAh\nwUGkJUTqTWXKb2mJQPmG/B3w7kSrC+keo2DQMxAR6+2oXKYD2St/1miJQESmi8ghEdngMK21iHwq\nItvs5wR7uojISyKyXUTWi4jnO39R/sUYWPsWvHwV5G+zmobe+rJfJQGw7yU4ok1IlX9ypWpoBnBj\nnWmPAp8ZYzoBn9nvAQYBnezHRGCKe8JULVJJgTWa2IKHrA7jHlgG3W7zdlRnJT0xmhNllRQUl3s7\nFKWarNFEYIxZChTUmTwUeN1+/Tpwi8P0N4xlBRAvIqnuCla1IDlfwJTLYctHMOAJuHuBV/sMOlcZ\nSTUth/Q6gfI/Z3uxuI0xZj+A/ZxiT28H7HFYLs+eppSlsgw+/g28MRTCWsGExXDlwz53k1hT1dxL\nsLtArxMo/+Pui8XiZJrTSlMRmYhVfUSHDh3cHIbySYd+gPn3wcHvIXs8DHwKwqK8HZVbpCVEIoIO\nZK/80tmWCA7WVPnYz4fs6XlAe4fl0oB9zjZgjJlqjMk2xmQnJyefZRjKLxgDK6fC1P5wYj+MmgND\nnmsxSQAgPCSYtnGR2nJI+aWzTQQLgbH267HAAofpd9uth/oBhTVVSCpAnTgIM0fAR7+CjKvgweVw\ncd22By1DRlKUXiNQfqnRqiERmQX0B5JEJA/4PfA08I6IjAd2AyPsxT8EBgPbgRLgnmaIWfmLLf+2\nWgSVF8HgZ+HSCSDOag9bhvTEaD76Xn/3KP/TaCIwxoyqZ9b1TpY1wEPnGpTyc+Ul8MlvYdU0aNMd\nhr0KKZd4O6pml5EYxdGSCgpLKoiLCvV2OEq5TO8sVu5VmAezRsGB9XD5T+G63/nU2AHNqabl0K6C\nYjKj4r0cjVKu00Sg3GfPN1a30ZWlcOc7cNEN3o7IozIcuqPOTNNEoPyHJgLlHutmwQeTIbYdjP0g\nIKqC6urQ2moFteuIthxS/kUTgTo31VWw+HFY9pLVKuj2NyCqtbej8orIsGDOi41gV4G2HFL+RROB\nOnulx2H+BNj2sdUi6ManITiwL5J2SIzSewmU39FEoM5OwU6YNRKObLOahva5z9sR+YSMxCiWbDns\n7TCUahJNBKrpdi61eg01Bsa8B+df4+2IfEZ6YjSHT+RRXFZJdLj+eyn/oCOUqaZZNR3evBWiU+C+\nzzUJ1FHTcmiX3mGs/IgmAuWaqgr41y9h0X/BBdfBhE8h8QJvR+Vz0u2B7PU6gfInWnZVjSspgLnj\nYOd/rJvEBjzh991GN5eaRKB9Dil/oolANezwVph1h3XH8C1ToOed3o7Ip8VEhJLUKkxLBMqvaCJQ\n9dv2Kcy71+oiYuwi6NDX2xH5hfTEaHI1ESg/otcI1JmMgeV/h7dvh/h0uG+JJoEmSE+MYrdWDSk/\noolAne7EQVgwCT7+NVwyBMZ/DPHtG19P1cpIjGZfYSmlFVXeDkUpl2jVkILKcuvu4LUzYdsnYKrg\nmkfgmkchSH8rNFXNBeM9BSV0ahPj5WiUapwmgkB24Hvr5P/9O1CSD63Og8snQc/RkHyRt6PzW+kO\nvZBqIlD+QBNBoCnOh+/nwrqZ1pgBwWFw8WDoeZd1f0CwHhLnKkPvJVB+Rv/rA0FVJez4DNa+BVs+\nguoKSO0Bg/4M3YcHbG+hzSU+Koy4yFBtOaT8hiaCluzwFuvkv34OFB2EqCToM9G6F+C8bt6OrkXL\nSIzSbiaU39BE0NKcPAYb5ltVP3tXQ1AIdLrBOvl3GgghYd6OMCCkJ0azds9Rb4ehlEs0EbQE1VWQ\n8wWsexs2L7KGikzpAgP/AJl3QKtkb0cYcDISo1i0fh/lldWEhWjLK+XbNBH4s/wd1sn/u1lwfC9E\nxEOvMdav/7a9QMTbEQas9MRoqg3sPXaSjknR3g5HqQZpIvA3ZSdg4/tW1c/u5SBBcMH1cMMf4KJB\nEBrh7QgVkJFU0/lcsSYC5fM0EfiD6mrY9bX163/TAqgohsROMOBxq+ontq23I1R11NxLsOtIMVzs\n5WCUaoQmAl92bDesm2X9+j+2C8JjreaevUZD2qVa9ePDEqPDiA4L1u6olV/QROBrykvghw+sk//O\npda0jlfDdb+1+v4Ji/JufMolIkJ6YrTeVKb8giYCb6muti7wFuRAwQ7rOT8Hcr+EsuOQkAHX/hp6\njIT4Dt6OVp2FjKQoNu8/4e0wlGqUJoLmVF1lDehSe7LfaZ/wd8DRXKgqO7VscDi07gidb7Za/XS4\nXDt883PpidF8uukglVXVhATr31L5Lk0E56qqEgr3nH6idzzZV1ecWjYk0jrZJ3WCi26A1udbj8QL\nIKatnvhbmIzEKCqqDPsLS2nfWqv0lO/SROCKqgrrwm3NSb7mRF+QY13Era48tWxotHVyT+kMl9x0\n6kTf+nyrd0892QeMU72QFmsiUD5NE0GNynLrpF73RF+QYyUB4zDISFgr68R+XnfoMvTUyT6hI8Sc\np615FGANUAOwK7+Eqzp5ORilGtAsiUBEbgReBIKBV40xTzfH5zTKGCgthKJDUHTAfj5oPU7YzzXT\nSvIBc2rd8FjrBN+2F3QbdupXfevzITpZT/aqUSkx4USEBmnLIeXz3J4IRCQY+DvwIyAP+FZEFhpj\nNp3zxo2x7qw9efTMR/Hh00/sNSd6xwuyNYLDoFUb65GQAe37nHpdc8KPStSTvTonQUFCeutovZdA\n+bzmKBH0AbYbY3IARGQ2MBSoPxGUHYfv5zk/wdd9ONbH1xWVZJ/gUyD9Auu5VRuruqbmdasUq08e\nPckrD+iQGMXWgyf4ZmeBt0NRql7NkQjaAXsc3ucBfRtcI38HzB9/6n1YDEQmQGS89dymq/2+gUdU\nIgSHNsPXUersXdwmhk83HeT2fy73dihK1as5EoGzn9rmjIVEJgITAS5ofx489J9TJ389oasW4qFr\nL+TyCxMxZ/wHKHXurvyTe7bTHIkgD2jv8D4N2Fd3IWPMVGAqQHZ2ttHB0lVLFBkWzOUXJHk7DKUa\n1ByN2r8FOolIRxEJA0YCC5vhc5RSSrmB20sExphKEZkEfIzVfHS6MWajuz9HKaWUezTLfQTGmA+B\nD5tj20oppdxL+ztQSqkAp4lAKaUCnCYCpZQKcJoIlFIqwInxgTtdROQEsMXbcbggCTji7SBcoHG6\njz/ECBqnu/lLnBcbY2LOdSO+0g31FmNMtreDaIyIrNI43ccf4vSHGEHjdDd/itMd29GqIaWUCnCa\nCJRSKsD5SiKY6u0AXKRxupc/xOkPMYLG6W4BFadPXCxWSinlPb5SIlBKKeUlmgiUUirAeTQRiMiN\nIrJFRLaLyKNO5oeLyBx7/koRyfBkfHYM7UVkiYj8ICIbReRnTpbpLyKFIrLOfjzm6TjtOHJF5Hs7\nhjOakYnlJXt/rheRLA/Hd7HDPlonIsdF5OE6y3htX4rIdBE5JCIbHKa1FpFPRWSb/ZxQz7pj7WW2\nichYD8f4ZxHZbP9N3xOR+HrWbfD48ECcj4vIXoe/7eB61m3wvOCBOOc4xJgrIuvqWdeT+9PpeajZ\njk9jjEceWF1S7wDOB8KA74AudZZ5EHjZfj0SmOOp+BxiSAWy7NcxwFYncfYHFnk6Niex5gJJDcwf\nDHyENWpcP2ClF2MNBg4A6b6yL4GrgSxgg8O0Z4BH7dePAn9ysl5rIMd+TrBfJ3gwxoFAiP36T85i\ndOX48ECcjwO/dOG4aPC80Nxx1pn/F+AxH9ifTs9DzXV8erJEUDuovTGmHKgZ1N7RUOB1+/U84HoR\nz44yb4zZb4xZY78+AfyANQ6zPxoKvGEsK4B4EUn1UizXAzuMMbu89PlnMMYsBeqOKu94DL4O3OJk\n1RuAT40xBcaYo8CnwI2eitEY84kxptJ+uwJrFECvqmdfusKV84LbNBSnfa65HZjVXJ/vqgbOQ81y\nfHoyETgb1L7uCbZ2GftALwQSPRKdE3bVVC9gpZPZl4nIdyLykYh09WhgpxjgExFZbY8BXZcr+9xT\nRlL/P5gv7MsabYwx+8H6ZwRSnCzjS/v1XqxSnzONHR+eMMmuwppeTzWGL+3Lq4CDxpht9cz3yv6s\ncx5qluPTk4nAlUHtXRr43hNEpBUwH3jYGHO8zuw1WFUcPYC/Au97Oj7bFcaYLGAQ8JCIXF1nvk/s\nT7GGLP0xMNfJbF/Zl03hK/v1N0AlMLOeRRo7PprbFOACoCewH6vapS6f2Je2UTRcGvD4/mzkPFTv\nak6mNbhPPZkIXBnUvnYZEQkB4ji74uY5EZFQrJ0/0xjzbt35xpjjxpgi+/WHQKiIeHyEcmPMPvv5\nEPAeVjHbkSv73BMGAWuMMQfrzvCVfengYE31mf18yMkyXt+v9gXAIcBdxq4YrsuF46NZGWMOGmOq\njDHVwCv1fL7X9yXUnm9uA+bUt4yn92c956FmOT49mQhcGdR+IVBzhXs48Hl9B3lzsesJpwE/GGOe\nq2eZ82quXYhIH6z9mO+5KEFEokUkpuY11gXEDXUWWwjcLZZ+QGFNsdLD6v2l5Qv7sg7HY3AssMDJ\nMh8DA0Ukwa7uGGhP8wgRuRF4BPixMaaknmVcOT6aVZ3rUbfW8/munBc8YQCw2RiT52ymp/dnA+eh\n5jk+PXEF3OFq9mCsq987gN/Y057EOqABIrCqD7YD3wDnezI+O4YrsYpR64F19mMw8BPgJ/Yyk4CN\nWC0cVgCXeyHO8+3P/86OpWZ/OsYpwN/t/f09kO2FOKOwTuxxDtN8Yl9iJaf9QAXWr6jxWNekPgO2\n2c+t7WWzgVcd1r3XPk63A/d4OMbtWHXANcdnTUu7tsCHDR0fHo7zTfu4W491AkutG6f9/ozzgifj\ntKfPqDkmHZb15v6s7zzULMendjGhlFIBTu8sVkqpAKeJQCmlApwmAqWUCnCaCJRSKsBpIlABQUTi\nReTBs1jv180Rj1K+RFsNqYBg36a/yBjTrYnrFRljWjVLUEr5CC0RqEDxNHCB3YXwn+vOFJFUEVlq\nz98gIleJyNNApD1tpr3caBH5xp72TxEJtqcXichfRGSNiHwmIsme/XpKnT0tEaiA0FiJQER+AUQY\nY/5gn9yjjDEnHEsEItIZqxvg24wxFSLyD2CFMeYNETHAaGPMTLHGVEgxxkzyxHdT6lyFeDsApXzE\nt8B0u3+X940xzgYnuR7oDXxr94oRyam+Xqo51U/NW8AZfVQp5au0akgpavupvxrYC7wpInc7WUyA\n140xPe3HxcaYx+vbZDOFqpTbaSJQgeIE1khPTolIOnDIGPMKVmdfNcN6VtilBLD6dhkuIin2Oq3t\n9cD6Xxpuv74T+MrN8SvVbLRqSAUEY0y+iHwt1li1HxljflVnkf7Ar0SkAigCakoEU4H1IrLGGHOX\niPwWa3CSIKyOyx4CdgHFQFcRWY01oNIdzf+tlHIPvVislBtoM1Plz7RqSCmlApyWCFRAEZHuWP3k\nOyozxvT1RjxK+QJNBEopFeC0akgppQKcJgKllApwmgiUUirAaSJQSqkAp4lAKaUCnCYCpZQKcP8P\nK4YxWoQtWWgAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd798d81e48>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_all('soil_output/Spread_barabasi*', get_count, 'id');"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:58:26.903783Z",
"start_time": "2017-10-19T17:57:57.983957+02:00"
},
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEXCAYAAABF40RQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nACn5i4gMQP8fEeHAAqu6cxAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd799674080>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEXCAYAAABF40RQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nACn5i4gMQP8fEeHAAqu6cxAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd7996d2eb8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEXCAYAAABF40RQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhJT8RUQGof8Puw/Kf+5ObzkAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd799841390>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEXCAYAAABF40RQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhJT8RUQGof8Puw/Kf+5ObzkAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd7998504a8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEXCAYAAABF40RQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd7997c82b0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEXCAYAAABF40RQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd7997c83c8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEXCAYAAABF40RQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxQn7eJiJ/QAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd799a9cf98>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEXCAYAAABF40RQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxQn7eJiJ/QAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd799a9c588>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEXCAYAAABF40RQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRKQPUvIXEemDlPxFRPqg/w8q2vnGLNBQ+wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd799c4a7b8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEXCAYAAABF40RQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRKQPUvIXEemDlPxFRPqg/w8q2vnGLNBQ+wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd799d51438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_all('soil_output/Spread_erdos*', get_value, 'prob_tv_spread');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Manually plotting with pandas"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T11:00:37.003972Z",
"start_time": "2017-10-19T13:00:36.983128+02:00"
}
},
"source": [
"Although the simplest way to visualize the results of a simulation is to use the built-in methods in the analysis module, sometimes the setup is more complicated and we need to explore the data a little further.\n",
"\n",
"For that, we can use native pandas over the results.\n",
"\n",
"Soil provides some convenience methods to simplify common operations:\n",
"\n",
"* `analysis.split_df` to separate a history dataframe into environment and agent parameters.\n",
"* `analysis.get_count` to get a dataframe with the value counts for different attributes during the simulation.\n",
"* `analysis.get_value` to get the evolution of the value of an attribute during the simulation.\n",
"\n",
"And, as we saw earlier, `analysis.process` can turn a dataframe in canonical form into a dataframe with a column per attribute.\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:59:15.791793Z",
"start_time": "2017-10-19T17:59:15.604960+02:00"
},
"collapsed": true
},
"outputs": [],
"source": [
"p = read_sql('soil_output/Spread_barabasi_albert_graph_prob_0.0/Spread_barabasi_albert_graph_prob_0.0_trial_0.db.sqlite')\n",
"env, agents = split_df(p);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's look at the evolution of agent parameters in the simulation"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:59:17.153282Z",
"start_time": "2017-10-19T17:59:16.830872+02:00"
}
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEKCAYAAAAfGVI8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd799c4f908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"res = agents.groupby(by=['t_step', 'key', 'value']).size().unstack(level=[1,2]).fillna(0)\n",
"res.plot();"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T11:10:36.086913Z",
"start_time": "2017-10-19T13:10:36.058547+02:00"
}
},
"source": [
"As we can see, `event_time` is cluttering our results, "
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:59:18.418348Z",
"start_time": "2017-10-19T17:59:18.143443+02:00"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd795b17b38>"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEKCAYAAAAfGVI8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqggqHK0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd796e1ff60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"del res['event_time']\n",
"res.plot()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:59:42.750011Z",
"start_time": "2017-10-19T17:59:42.649353+02:00"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>event_time</th>\n",
" <th>has_tv</th>\n",
" <th>id</th>\n",
" </tr>\n",
" <tr>\n",
" <th>t_step</th>\n",
" <th>agent_id</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"30\" valign=\"top\">0</th>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>106</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>107</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>108</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>110</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>111</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>113</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>114</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>117</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>119</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>121</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>122</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>123</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>124</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"30\" valign=\"top\">20</th>\n",
" <th>73</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NewsEnvironmentAgent</th>\n",
" <td>10</td>\n",
" <td>False</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10521 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" event_time has_tv id\n",
"t_step agent_id \n",
"0 0 0 True neutral\n",
" 1 0 False neutral\n",
" 10 0 True neutral\n",
" 100 0 True neutral\n",
" 101 0 True neutral\n",
" 102 0 False neutral\n",
" 103 0 True neutral\n",
" 104 0 True neutral\n",
" 105 0 False neutral\n",
" 106 0 False neutral\n",
" 107 0 True neutral\n",
" 108 0 True neutral\n",
" 109 0 False neutral\n",
" 11 0 True neutral\n",
" 110 0 False neutral\n",
" 111 0 False neutral\n",
" 112 0 True neutral\n",
" 113 0 True neutral\n",
" 114 0 True neutral\n",
" 115 0 True neutral\n",
" 116 0 False neutral\n",
" 117 0 True neutral\n",
" 118 0 True neutral\n",
" 119 0 False neutral\n",
" 12 0 False neutral\n",
" 120 0 False neutral\n",
" 121 0 True neutral\n",
" 122 0 True neutral\n",
" 123 0 True neutral\n",
" 124 0 False neutral\n",
"... ... ... ...\n",
"20 73 0 True infected\n",
" 74 0 True infected\n",
" 75 0 True infected\n",
" 76 0 True infected\n",
" 77 0 True infected\n",
" 78 0 True infected\n",
" 79 0 False infected\n",
" 8 0 False infected\n",
" 80 0 True infected\n",
" 81 0 False infected\n",
" 82 0 False infected\n",
" 83 0 True infected\n",
" 84 0 False infected\n",
" 85 0 True infected\n",
" 86 0 True infected\n",
" 87 0 True infected\n",
" 88 0 False infected\n",
" 89 0 False infected\n",
" 9 0 True infected\n",
" 90 0 True infected\n",
" 91 0 True infected\n",
" 92 0 True infected\n",
" 93 0 False infected\n",
" 94 0 True infected\n",
" 95 0 True infected\n",
" 96 0 True infected\n",
" 97 0 True infected\n",
" 98 0 False infected\n",
" 99 0 True infected\n",
" NewsEnvironmentAgent 10 False 0\n",
"\n",
"[10521 rows x 3 columns]"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"processed = process_one(agents);\n",
"processed"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Which is equivalent to:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:59:51.165806Z",
"start_time": "2017-10-19T17:59:50.886780+02:00"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd799c15748>"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEKCAYAAAAfGVI8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqggqHK0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd799d3fbe0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"get_count(agents, 'id', 'has_tv').plot()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:59:55.203641Z",
"start_time": "2017-10-19T17:59:54.950046+02:00"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd79a228c88>"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nbgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd79a1fe208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"get_value(agents, 'event_time').plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dealing with bigger data"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T16:00:18.148006Z",
"start_time": "2017-10-19T18:00:18.117654+02:00"
},
"collapsed": true
},
"outputs": [],
"source": [
"from soil import analysis"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T16:00:18.636440Z",
"start_time": "2017-10-19T18:00:18.504421+02:00"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"267M\t../rabbits/soil_output/rabbits_example/\r\n"
]
}
],
"source": [
"!du -xsh ../rabbits/soil_output/rabbits_example/"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T11:22:22.301765Z",
"start_time": "2017-10-19T13:22:22.281986+02:00"
}
},
"source": [
"If we tried to load the entire history, we would probably run out of memory. Hence, it is recommended that you also specify the attributes you are interested in."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T16:00:25.080582Z",
"start_time": "2017-10-19T18:00:19.594165+02:00"
},
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEXCAYAAABGeIg9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0SbFISIi2qQ4REREmxSHiIho8y9ffsz3DjLrngAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd799c54ba8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEXCAYAAABGeIg9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0SbFISIi2qQ4REREmxSHiIho8y9ffsz3DjLrngAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd799aa66d8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"p = analysis.plot_all('../rabbits/soil_output/rabbits_example/', analysis.get_count, 'id')"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T16:00:38.434367Z",
"start_time": "2017-10-19T18:00:33.645762+02:00"
},
"collapsed": true
},
"outputs": [],
"source": [
"df = analysis.read_sql('../rabbits/soil_output/rabbits_example/rabbits_example_trial_0.db.sqlite', keys=['id', 'rabbits_alive'])"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T16:00:39.160418Z",
"start_time": "2017-10-19T18:00:38.436153+02:00"
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd799b5b2b0>"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEKCAYAAAD5MJl4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl4VOXZ+PHvPZNMNkJISFgTCCj7IoTIIoJYFJCquP7E\nFUWLa1urttX2bbVV+2rrW6m20qos6ouCdaXWvoIYRVAEAmFfwhIgBMi+rzPz/P6YkxjIQEIyyUzg\n/lzXXHPOc55zzj0zydxztvuIMQallFKqPpu/A1BKKRV4NDkopZRqQJODUkqpBjQ5KKWUakCTg1JK\nqQY0OSillGpAk4NSSqkGNDkopZRqoNHkICILRCRbRLbVa/uTiOwSkS0i8qGIdKo37QkR2Ssiu0Vk\nar32aVbbXhF5vF57HxH5TkTSRWSpiDh8+QKVUkqdOWnsCmkRmQiUAm8aY4ZabVOAL4wxThF5HsAY\n80sRGQy8A4wGegCfA/2tRe0BLgcygfXAzcaYHSLyLvCBMWaJiPwd2GyMmddY4LGxsSYxMfGMX7BS\nSp3LUlNTc40xcY31C2qsgzFmlYgkntS2vN7oWuAGa3gGsMQYUwUcEJG9eBIFwF5jzH4AEVkCzBCR\nncAPgFusPm8ATwGNJofExEQ2bNjQWDellFL1iMjBpvTzxTGH2cB/rOGewOF60zKttlO1dwYKjTHO\nk9q9EpE5IrJBRDbk5OT4IHSllFLetCg5iMivASewuLbJSzfTjHavjDGvGmOSjTHJcXGNbhUppZRq\npkZ3K52KiMwCrgQmm+8PXGQCCfW6xQNZ1rC39lygk4gEWVsP9fsrpZTyk2YlBxGZBvwSuMQYU15v\n0jLgbRH5M54D0v2AdXi2EPqJSB/gCDATuMUYY0QkBc8xiyXALODj5r6YmpoaMjMzqaysbO4iVCsK\nDQ0lPj6e4OBgf4eilGpEo8lBRN4BJgGxIpIJPAk8AYQAK0QEYK0x5j5jzHbr7KMdeHY3PWiMcVnL\neQj4DLADC4wx261V/BJYIiLPAJuA+c19MZmZmURGRpKYmIgVlwoQxhjy8vLIzMykT58+/g5HKdWI\nRk9lDVTJycnm5LOVdu7cycCBAzUxBChjDLt27WLQoEH+DkWpc5aIpBpjkhvr1+xjDoFKE0Pg0s9G\nKf8xxuA+g20BLZ+hlFLngO1ZxSQ9vaLJ/TU5tFBGRgZDhw49Z9evlGofvt2XR1FFTZP7a3JQSqlz\nwDf7cukbF9Hk/pocfGj//v2MHDmS7777jp///OdceOGFDB8+nH/84x8A3H777Xz88fdn6t56660s\nW7bshGXcdNNNfPrpp3Xjd955J++//z4ZGRlMmDCBpKQkkpKS+Oabbxqsf9GiRTz00EN141deeSVf\nfvklAMuXL2fcuHEkJSVx4403Ulpa6suXrpQKYDUuN+sO5DP+vNgmz6PJwUd2797N9ddfz8KFC9m8\neTNRUVGsX7+e9evX89prr3HgwAHuueceFi5cCEBRURHffPMN06dPP2E5M2fOZOnSpQBUV1ezcuVK\npk+fTpcuXVixYgUbN25k6dKl/OQnP2lybLm5uTzzzDN8/vnnbNy4keTkZP785z/77sUrpQLakYIK\nyqpdDIuPavI8Z93ZSv6Qk5PDjBkzeP/99xkyZAjPPPMMW7Zs4b333gM8iSA9PZ0pU6bw4IMPkp2d\nzQcffMD1119PUNCJH8EVV1zBT37yE6qqqvi///s/Jk6cSFhYGEVFRTz00EOkpaVht9vZs2dPk+Nb\nu3YtO3bsYPz48YAn6YwbN853b4BSKqBlFlQAkBAd3uR5NDn4QFRUFAkJCaxZs4YhQ4ZgjOHll19m\n6tSpDfrefvvtLF68mCVLlrBgwYIG00NDQ5k0aRKfffYZS5cu5eabbwbgxRdfpGvXrmzevBm3201o\naGiDeYOCgnC73XXjtVeKG2O4/PLLeeedd3z1kpVS7ciRQk8hi/josCbPo7uVfMDhcPDRRx/x5ptv\n8vbbbzN16lTmzZtHTY3nzIA9e/ZQVlYGeI4hzJ07F4AhQ4YAcOTIESZPnly3vJkzZ7Jw4UK+/vrr\nugRTVFRE9+7dsdlsvPXWW7hcrgZxJCYmkpaWhtvt5vDhw6xbtw6AsWPHsmbNGvbu3QtAeXn5GW15\nKKXat305ZTjsNrpFNfxReSqaHHwkIiKCTz75pO4X/uDBg0lKSmLo0KHce++9OJ2equRdu3Zl0KBB\n3HXXXXXzHj169ITdS1OmTGHVqlVcdtllOByeG+M98MADvPHGG4wdO5Y9e/YQEdHwrIPx48fTp08f\nhg0bxmOPPUZSUhIAcXFxLFq0iJtvvpnhw4czduxYdu3a1Zpvh1IqgKw7kM/w+CiC7U3/yj/rymcE\nemmG8vJyhg0bxsaNG4mK8hwc+utf/0qvXr24+uqr/Rxd62sPn5FSZ5PyaifDn1rOjyb25ZfTBp67\n5TMC2eeff87s2bN55JFH6hIDcMLpp0op5Utphwpxug2jE2POaD5NDm3osssu49ChQ/4OQyl1DlmX\nkY8IJPWOPqP59JiDUkqdxdZn5DOwW0eiws7sPiqaHJRS6ixkjGHqi6tYszeP0YlnttUAmhyUUuqs\nlFlQwe7jJQBMGdLtjOfXYw5KKXUW2nakCICPHxzPBQmdznh+3XLwsYqKCi655BJcLhdZWVnccMMN\nXvtNmjSJk0/FbW2tWd5769at3Hnnna2ybKXUmTtW7KmQkBDT9JIZ9Wly8LEFCxZw3XXXYbfb6dGj\nR119pbOBt6uyaw0bNozMzEw9G0upAFFQVo0IZ3wgutZZu1vpd//azo6sYp8uc3CPjjx51ZDT9lm8\neDFvv/024PmlfuWVV7Jt2zYqKiq466672LFjB4MGDaKioqLR9U2aNIkxY8aQkpJCYWEh8+fPZ8KE\nCbhcLh5//HG+/PJLqqqqePDBB7n33nt54IEHmDZtGldffTXXXnst0dHRLFiwgPnz59dVhXU6ncya\nNYtNmzbRv39/3nzzTcLDw1m5ciWPPfYYTqeTCy+8kHnz5hESEkJiYiKzZ89m+fLlPPTQQ/z973/3\nGhPAVVddxZIlS/jFL37R8jdbKdUieWXVRIc7sNuad3te3XLwoerqavbv309iYmKDafPmzSM8PJwt\nW7bw61//mtTU1CYt0+l0sm7dOubOncvvfvc7AObPn++1JPjEiRP5+uuvAU+9ph07dgCwevXqui/w\n3bt3M2fOHLZs2ULHjh155ZVXqKys5M4772Tp0qVs3boVp9PJvHnz6mIIDQ1l9erVzJw585QxASQn\nJ9etXynlXwXl1USHN2+rAc7iLYfGfuG3htzcXDp18n7gZ9WqVXX3YBg+fDjDhw9v0jKvu+46AEaN\nGkVGRgbguXGPt5LgEyZMYO7cuezYsYPBgwdTUFDA0aNH+fbbb3nppZfIy8sjISGhrnT3bbfdxksv\nvcTll19Onz596N+/PwCzZs3ib3/7Gw8//DDguQFRYzEBdOnShaysrCa9LqVU68otrSYmwtHs+c/a\n5OAPYWFhdWWyvRE58827kJAQAOx2e13xvtOVBC8oKKi7D0R+fj7vvvsuHTp0IDIykry8vAYxiAiN\n1dc6ucift5jAUyI8LKzpJYGVUq0nI7eMi/s1/c5vJ9PdSj4UHR2Ny+XymiAmTpzI4sWLAdi2bRtb\ntmypm3bHHXfUldduitOVBB83bhxz585l4sSJTJgwgRdeeKFulxLAoUOH+PbbbwF45513uPjiixk4\ncCAZGRl1Jb3feustLrnkkjN89Z44WutsKKVU0xWV15BdUkX/rpHNXoYmBx+bMmUKq1evbtB+//33\nU1payvDhw/njH//I6NGj66Zt2bKF7t27N3kd99xzzylLgk+YMAGn08n5559PUlIS+fn5JySHQYMG\n8cYbbzB8+HDy8/O5//77CQ0NZeHChdx4440MGzYMm83Gfffdd8avPSUlhR/+8IdnPJ9SyrfSsz0X\nv/Xv2qH5CzHGnPYBLACygW312mKAFUC69RxttQvwErAX2AIk1ZtnltU/HZhVr30UsNWa5yWsMuKN\nPUaNGmVOtmPHjgZtbW3jxo3mtttua3L/oqIic8MNN7RiRG2jsrLSjBkzxtTU1Jy2XyB8Rkqd7Rav\nPWh6//ITcyivrME0YINpwndsU7YcFgHTTmp7HFhpjOkHrLTGAa4A+lmPOcA8ABGJAZ4ExgCjgSdF\npLbYxzyrb+18J6+rXRk5ciSXXnrpaa8JqK9jx47885//bOWoWt+hQ4d47rnnGtwTWynVtp5atp1f\nfbiVkCAbPTs1/xhgo8nBGLMKyD+peQbwhjX8BnBNvfY3rQS1FugkIt2BqcAKY0y+MaYAz9bGNGta\nR2PMt1ZGe7Pestqt2bNnY7fb/R1Gm+rXrx+TJk3ydxhKndP2Zpew6JsMAIb2jMLWzGscoPlnK3U1\nxhwFMMYcFZEuVntP4HC9fplW2+naM720eyUic/BsZdCrV69mhq6UUmenvdmlANw+tjd3X9ynRcvy\n9QFpb2nKNKPdK2PMq8aYZGNMclxcXDNDVEqps9OxIs+Zkj+9rB+JsQ3vM38mmpscjlu7hLCes632\nTCChXr94IKuR9ngv7Uoppc7Q8ZIqgu1CTHjzL36r1dzksAzP2UdYzx/Xa79DPMYCRdbup8+AKSIS\nbR2IngJ8Zk0rEZGx4rk66456y1JKKXUGDueX0yUytEXHGmo1mhxE5B3gW2CAiGSKyN3Ac8DlIpIO\nXG6NA3wK7MdzWuprwAMAxph84GlgvfX4vdUGcD/wujXPPuA/LX5VfuTLkt27du1ixIgRjBw5kn37\n9jU5hrlz51JeXl43Pn36dAoLCwHo0OH05z1XV1czceLEE658VkoFPpfbsGZvLhc2465v3jR6QNoY\nc/MpJk320tcAD55iOQvwXDNxcvsG4Ky5rNaXJbs/+ugjZsyYcUJxu8a4XC7mzp3LbbfdRni4p477\np59+2uT5HQ4HkydPZunSpdx6661nHLNSyj+OFlVQUF7D6D6dfbK8s/ek9P88Dse2+naZ3YbBFc+d\ntouvSnZ/+umnzJ07F7vdzqpVq0hJSeF///d/eemll6iurmbMmDG88sor2O12OnTowCOPPMJnn33G\nD3/4Q7Kysrj00kuJjY0lJSWFxMRENmzYQGzsiXVW/vSnP/Huu+9SVVXFtddeW5eErrnmGp544glN\nDkq1I1mFnoPR8dG+qW+m5TN8yJclu6dPn859993Hz372M1JSUti5cydLly5lzZo1pKWlYbfb62o1\nlZWVMXToUL777jt++9vf0qNHD1JSUkhJSTnl8pcvX056ejrr1q0jLS2N1NRUVq1aBcDQoUNZv359\n898IpVSbO1rk+cHZowUXvtV39m45NPILvzW0RsnuWitXriQ1NZULL7wQ8Bzb6NLFc3mJ3W7n+uuv\nP6PlLV++nOXLlzNy5EgASktLSU9PZ+LEidjtdhwOByUlJURGNr9wl1Kq7WTkeo4z9ugU6pPlnb3J\nwQ9ao2R3LWMMs2bN4r//+78bTAsNDT3jK7KNMTzxxBPce++9XqdXVVURGuqbPzKlVOtbl5HHwG6R\nhDt887Wuu5V8qDVLdk+ePJn33nuP7GzPJSX5+fkcPHjQa9/IyEhKSkpOu7ypU6eyYMECSks9V1Qe\nOXKkbtl5eXnExcURHNz8u0gppdrO4fxy1u7PZ9KALo13biJNDj7WWiW7Bw8ezDPPPMOUKVMYPnw4\nl19+OUePHvXad86cOVxxxRVceumlp43zlltuYdy4cQwbNowbbrihLqGkpKQwffr0prxcpVQAWLY5\nC5fbcPu43r5baFNKtwbiQ0t2t55rr73W7Nq1q1WWHQifkVJnm9teX2umzV3VpL74sGS3OgPtvWR3\ndXU111xzDQMGDPB3KEqpJjpeXEmCj05hraXJoRW055LdDoeDO+64w99hKKXOwPHiKrpF+fYEEk0O\nSinVjlXWuCiqqKFrR00OSimlLAdyywDfXRldS5ODUkq1Y5sOeYpqXhDv/QLc5tLkoJRS7djOo8VE\nhgbRu3O4T5erycHHfFmyu734wx/+UDesJb+ValsZeWX0iY1oUQUGbzQ5+JgvS3Y3RSB8CddPDvVL\nfiulWleNy82uYyX07tyyW4J6c9bWVnp+3fPsyt/l02UOjBnIL0f/8rR9fFWyGzxbFyNGjGDdunUU\nFxezYMECRo8ezVNPPUVWVhYZGRnExsby1ltv8fjjj/Pll19SVVXFgw8+yL333ovb7eahhx7iq6++\nok+fPrjdbmbPns0NN9xAYmIis2bN4l//+hc1NTX885//ZODAgaxbt46HH36YiooKwsLCWLhwIQMG\nDGDRokUsW7aM8vJy9u3bx7XXXssf//hHHn/8cSoqKhgxYgRDhgxh8eLFWvJbqTayfPtxckqqmNQ/\nzufLPmuTgz80tWT3li1bSEpKatIyy8rK+Oabb1i1ahWzZ89m27ZtAKSmprJ69WrCwsJ49dVXiYqK\nYv369VRVVTF+/HimTJlCamoqGRkZbN26lezsbAYNGsTs2bPrlh0bG8vGjRt55ZVXeOGFF3j99dcZ\nOHAgq1atIigoiM8//5xf/epXvP/++wCkpaWxadMmQkJCGDBgAD/+8Y957rnn+Otf/0paWlrdcrXk\nt1JtIyPPc6bS9GGnL7/THGdtcmjsF35raI2S3Tff7LkR38SJEykuLq673efVV19NWJjn1LXly5ez\nZcuWul1YRUVFpKens3r1am688UZsNhvdunVrUGvpuuuuA2DUqFF88MEHdfPOmjWL9PR0RISampq6\n/pMnTyYqKgrw1Ho6ePAgCQkJDWLWkt9KtY3MgnJiOzgIc/j+otuzNjn4Q2uU7D55ntrxiIjv9zEa\nY3j55ZeZOnXqCX3//e9/n3bZISEhgOfLvPbYxW9+8xsuvfRSPvzwQzIyMpg0aVKD/ifP442W/Faq\n9R3Or6Cnj27uczI9IO1DrVGyu/bA7urVq4mKiqr75V7f1KlTmTdvXt2v/D179lBWVsbFF1/M+++/\nj9vt5vjx43z55ZeNvoaioiJ69uwJwKJFixrtDxAcHHzCFoaW/Faq9bndhm1ZRQzs1rFVlq/Jwcd8\nXbI7Ojqaiy66iPvuu4/58+d77XPPPfcwePBgkpKSGDp0KPfeey9Op5Prr7+e+Pj4urYxY8Z4TS71\n/eIXv+CJJ55g/PjxTS4eOGfOHIYPH153AFpLfivV+vbnllFYXkNSb99e/FanKaVbA/FxLpTsvuSS\nS8z69etbFE9JSYkxxpjc3FzTt29fc/To0RYtrylOV/I7ED4jpc4GS9cfMr1/+YnZc6z4jOajiSW7\n9ZiDj9Uv2d2UyqytXbL7yiuvpLCwkOrqan7zm9/QrVu3VlsXaMlvpdrKpkMFdAwN4ry4Dq2yfE0O\nraD+6aIt0ZRjBG2xjDOhJb+VahsbDxYyslc0Nptvr4yupccclFKqnSmurGFPdglJvaJbbR0tSg4i\n8jMR2S4i20TkHREJFZE+IvKdiKSLyFIRcVh9Q6zxvdb0xHrLecJq3y0iU0+1PqWUUrD5cCHG0HoH\no2lBchCRnsBPgGRjzFDADswEngdeNMb0AwqAu61Z7gYKjDHnAy9a/RCRwdZ8Q4BpwCsi0j5vo6aU\nUm1g48FCRGBEQgAmB0sQECYiQUA4cBT4AVBbbe4N4BpreIY1jjV9sniu6JoBLDHGVBljDgB7ge/P\n81RKKVXHGEPK7mz6d4kkMrT1riVqdnIwxhwBXgAO4UkKRUAqUGiMqb10NhPoaQ33BA5b8zqt/p3r\nt3uZp91prZLdGRkZDB061CcxPvXUU7zwwgsAPPbYY3zxxRc+Wa5SqvWt3Z9P2uFCZozs0arraclu\npWg8v/r7AD2ACOAKL11N7SynmHaqdm/rnCMiG0RkQ05OzpkH3QbaumR3S9UWz1NKtQ+pB/MBuHV0\n71ZdT0tOZb0MOGCMyQEQkQ+Ai4BOIhJkbR3EA1lW/0wgAci0dkNFAfn12mvVn+cExphXgVcBkpOT\nvSaQWsf+8Aeqdvq2ZHfIoIF0+9WvTtvHlyW7U1NTmT17NuHh4Vx88cV17S6Xy2uJ7tLSUmbMmEFB\nQQE1NTU888wzzJgxA4Bnn32WN998k4SEBOLi4hg1ahQAvXv3Ji8vj2PHjrX6NRBKqZZLO1xI37gI\nosJbtzxNS445HALGiki4dexgMrADSAFq96XMAj62hpdZ41jTv7Cu1lsGzLTOZuoD9AO8FxoKcE0t\n2f3rX/+a1NTURpd311138dJLL/Htt9+e0D5//vy6Et3r16/ntdde48CBA4SGhvLhhx+yceNGUlJS\nePTRRzHGkJqaypIlS9i0aRMffPBBg3LaSUlJrFmzpkWvXSnVNrZkFvn8ftHeNHvLwRjznYi8B2wE\nnMAmPL/q/w0sEZFnrLbagkDzgbdEZC+eLYaZ1nK2i8i7eBKLE3jQGNO0oj6n0dgv/Nbgy5LdRUVF\nFBYWcskllwBw++2385///Ac4dYnu+Ph4fvWrX7Fq1SpsNhtHjhzh+PHjfP3111x77bWEh3vuMXv1\n1VefsK4uXbqQleV1Y00pFUCOFVWSXVLF8PjT10jzhRZdIW2MeRJ48qTm/Xg528gYUwnceIrlPAs8\n25JYAoEvS3YbY07Z35yiRPeiRYvIyckhNTWV4OBgEhMT6+I53borKyvr7g2hlApcWzI993Npi+Sg\nV0j7kC9Ldnfq1ImoqKi6Cq+188KpS3QXFRXRpUsXgoODSUlJ4eDBg3Xr/vDDD6moqKCkpIR//etf\nJ6xrz549PjsTSinVerYeKcJuEwZ3D/AtB9VQbcnuyy677IT2+++/n7vuuovhw4czYsSIJpXsXrhw\nYd0B6fpbCffccw8ZGRkkJSVhjCEuLo6PPvqIW2+9lauuuork5GRGjBjBwIEDAc8xhZtuuokRI0bQ\nu3dvJkyYULesmpoa9u7dS3Jysq/fCqWUj23OLKJflw6tcue3k4nnmHD7k5ycbE6+TmDnzp0MGjTI\nTxF5bNq0iT//+c+89dZbTepfXFzM3Xff3aqVWU+n9gD2008/3SbrC4TPSKn2yBhD0tMrmDK4G8/f\n0LTbDHsjIqnGmEZ/DepuJR+rX7K7KVq7ZHdjnE4njz76qN/Wr5RqmsyCCgrKaxjWBscbQHcrtQpf\nlexuCzfe6PUcAaVUgNmSWQTQJqexwlm45dBed5OdC/SzUar5tmQW4rDbGNAtsk3Wd1Ylh9DQUPLy\n8vRLKAAZY8jLyyM0NNTfoSjV7uzPKeXDTUcYkdAJR1DbfG2fVbuV4uPjyczMJFDrLp3rQkNDiY+P\n93cYSrU7sxetp6zKyS+mtd3td8+q5BAcHEyfPn38HYZSSvlMUUUNGXnl/HzqAJITY9psvWfVbiWl\nlDrb7M8pBaB/17Y51lBLk4NSSgWwrUc8ZykN0OSglFKq1ur0XOKjw0iIadv6Z5oclFIqgG3PKiap\nV/QZFe70BU0OSikVoEqrnBwprGizaxvqO6vOVlJKqbOB2234nxW7+Wz7cQCSekW3eQyaHJRSKsCs\n3JXN31L2ATA6MYaxfdvuFNZamhyUUirAfLr1KB1Cgvjv64Yxpm9Mmx9vAE0OSikVUEqrnKzYcZzp\nw7px1QU9/BaHHpBWSqkAsiwti9IqJzeP7uXXODQ5KKVUANlzvIQOIUGMSGib0tynoslBKaUCSEZe\nGb07h/vlOEN9mhyUUiqAHMorJ7FzhL/D0OSglFKBwhjDkcIKeka3bakMbzQ5KKVUgCgor6HK6aZ7\nlP9viqXJQSmlAkRWYQUA3aN0y0EppZSlNjn07NTOk4OIdBKR90Rkl4jsFJFxIhIjIitEJN16jrb6\nioi8JCJ7RWSLiCTVW84sq3+6iMxq6YtSSqn2qG7LoVP73630F+D/jDEDgQuAncDjwEpjTD9gpTUO\ncAXQz3rMAeYBiEgM8CQwBhgNPFmbUJRS6lxytKgSR5CNzhEOf4fS/OQgIh2BicB8AGNMtTGmEJgB\nvGF1ewO4xhqeAbxpPNYCnUSkOzAVWGGMyTfGFAArgGnNjUsppdqrwwXl9IgK9fs1DtCyLYe+QA6w\nUEQ2icjrIhIBdDXGHAWwnrtY/XsCh+vNn2m1naq9ARGZIyIbRGRDTk5OC0JXSqnAYoxh06FChvaM\n8ncoQMuSQxCQBMwzxowEyvh+F5I33lKhOU17w0ZjXjXGJBtjkuPi4s40XqWUClg5JVUcLapkVO/A\n2KvekuSQCWQaY76zxt/DkyyOW7uLsJ6z6/VPqDd/PJB1mnallDpnHMgtA+C8uA5+jsSj2cnBGHMM\nOCwiA6ymycAOYBlQe8bRLOBja3gZcId11tJYoMja7fQZMEVEoq0D0VOsNqWUOmcczC8HCIjSGdDy\n+zn8GFgsIg5gP3AXnoTzrojcDRwCbrT6fgpMB/YC5VZfjDH5IvI0sN7q93tjTH4L41JKqXZlzd5c\nIhx2egTAaazQwuRgjEkDkr1MmuylrwEePMVyFgALWhKLUkq1V5U1Lv6z7RgzL0wgyB4Y1yYHRhRK\nKXUOW77jONVON5f0D5wTbTQ5KKWUn72Sspfzu3Tg4n6x/g6ljiYHpZTyowO5Zew6VsLNo3sREmT3\ndzh1NDkopZQfLd9+DICpQ7r6OZITaXJQSik/StmdzcBukcRHh/s7lBNoclBKKT8pq3KSerAgoA5E\n19LkoJRSfvKfbceocRkmanJQSikF4HYbXlyxh8HdOzK6T4y/w2lAk4NSSvlB6qECjhRW8KOJfQgO\nkAvf6gu8iJRS6hzw6dajhAbbmDK4m79D8UqTg1JK+cGOrGIGd+9IREhLS9y1Dk0OSinlB+nZpfTr\nEunvME5Jk4NSSrWxw/nl5JdVM6i7JgellFKWb/blAnDR+YFTS+lkmhyUUqqNfbMvj9gODvp1CYy7\nvnmjyUEppdqQMYZv9+Ux7rxYRMTf4ZySJgellGpD+3JKyS6p4qLzOvs7lNPS5KCUUm3EGMOLK9IR\ngUkDAq9kRn2aHJRSqo1szyrm31uPctXwHnSPCvN3OKelyUEppdrI8h3HsQk8edVgf4fSKE0OSinV\nRlbuPM4Y++N3AAAfUElEQVSo3tF07hDi71AapclBKaXaQEW1i51HixnXN7APRNfS5KCUUm1gx9Fi\n3AaG9ozydyhNoslBKaXawHcH8gAY0auTnyNpmsAsB6iUUmeJKqeLfdllvP3dIYb06EiXyFB/h9Qk\nLd5yEBG7iGwSkU+s8T4i8p2IpIvIUhFxWO0h1vhea3pivWU8YbXvFpGpLY1JKaUCxexF65n+0tcc\nL67k6WuG+jucJvPFbqWfAjvrjT8PvGiM6QcUAHdb7XcDBcaY84EXrX6IyGBgJjAEmAa8IiJ2H8Sl\nlFJ+dTCvjDV780juHc0/77uIpF7R/g6pyVqUHEQkHvgh8Lo1LsAPgPesLm8A11jDM6xxrOmTrf4z\ngCXGmCpjzAFgLzC6JXEppZS/Hc4v50dvbkAEXrp5JCMS2sexhlotPeYwF/gFUFuUvDNQaIxxWuOZ\nQE9ruCdwGMAY4xSRIqt/T2BtvWXWn+cEIjIHmAPQq1evFoaulFK+V1HtYsGaA7ySshebCL+/egg9\nOgX21dDeNHvLQUSuBLKNMan1m710NY1MO908JzYa86oxJtkYkxwXF9h1SZRS56a/f7WPP322m/CQ\nIBbNHs3t4xL9HVKztGTLYTxwtYhMB0KBjni2JDqJSJC19RAPZFn9M4EEIFNEgoAoIL9ee6368yil\nVLuRWVDOX1amM7JXJz58YLy/w2mRZm85GGOeMMbEG2MS8RxQ/sIYcyuQAtxgdZsFfGwNL7PGsaZ/\nYYwxVvtM62ymPkA/YF1z41JKKX9Zuz8fgJkXJjTSM/C1xnUOvwSWiMgzwCZgvtU+H3hLRPbi2WKY\nCWCM2S4i7wI7ACfwoDHG1QpxKaVUqzqYV4bdJlyXFO/vUFrMJ8nBGPMl8KU1vB8vZxsZYyqBG08x\n/7PAs76IRSml/GV/bhk9O4URbG//xSf0CmmllPKRLZmFDOkemLWTjNNJ2Zo1Te7f/tObUkoFgJyS\nKg7nV5DUOzCvZyhdtYrD997X5P6aHJRSygc2HSoACNiroAvefgd7XGyT+2tyUEopH9h4qJBguwRk\nSe6KrVspW72amNvvaPI8mhyUUsoHNh4qYHCPKEKDA680XO68v2OLiiL6lluaPI8mB6WUaqHyaieb\nDxcyKgB3KVXu3EnpF18QM+sO7B0imjyfJgellGqh1em5VDndTB7Uxd+hNJA77+/YOnQg5rbbzmg+\nTQ5KKdVCn+88TmRoEKP7xPg7lBNUpadTsnw50bffhr1jxzOaV5ODUkq1gMttWLkzm0kDugTcxW+5\nf/8HtvBwYu5o+oHoWoH1SpRSqp1JO1xIXlk1lwXYLqWq/Qco/vRTom+5maDoMz8WoslBKaVaYPn2\nY9htwqT+gZUc8v7xDyQkhJi77mrW/JoclFKqmVxuw7sbDnPZoC5EhQf7O5w6VQcOUPTJJ0Tf9P8I\n6ty5WcvQ5KCUUs20L6eUgvIaLh/czd+hnCD7Ty9gCwmh8z33NHsZmhyUUqqZPt95HICRvQKnnlJF\nWhqlX3xB53vvJagFd8zU5KCUUs3gdhsWrclgQr9Yzovr4O9w6uT87RXs0dHE3H5m1zWcTJODUko1\nw2tf7ye7pIobkwPnrm9la9dS9vXXxMy+C1t4eIuWpclBKaWa4ZMtRxnUvSNXDuvu71AAcFdUkPX4\nEzgSE4m59dYWL0+Tg1JKnaHUg/lsPVLE5YO7YrOJv8MBoGDpUpzHjtH96d+3eKsBNDkopdQZeyVl\nH7EdHNw9vo+/QwHAVVpK3uvzCR8zhvALL/TJMjU5KKXUGTicX07K7mxuujAhYK5tyJ03D1duLl0e\ne9Rny9TkoJRSTWSM4fef7MARZOPWMb39HQ4A1YcOkf/mW0Rddx1hw4b5bLlBPluSUkqdpb7ak8OL\nK/aw42gx1U43T1wxkB6dwvwdFgA5L72M2O3E/fSnPl2uJgellDqNJesO8fgHWwG4+PxYrr6gBzcm\nx/s5Ko+KzZsp/uQTOs+ZQ3BX39Z20uSglFJerN2fx4odx5m/+gAAL9x4ATeMCoykAOAqKSHr17/G\nHhdL5zlzfL58TQ5KKXWSrMIK7ly4jsoaN9OHdeOpq4bQpWOov8OqY4zh6H/9huoDGfSa//oZ3f6z\nqZp9QFpEEkQkRUR2ish2Efmp1R4jIitEJN16jrbaRUReEpG9IrJFRJLqLWuW1T9dRGa1/GUppVTz\nvfxFOm43fP7IJbxy66iASgwABe+8Q8lnn9HlZw8TMXZsq6yjJWcrOYFHjTGDgLHAgyIyGHgcWGmM\n6QestMYBrgD6WY85wDzwJBPgSWAMMBp4sjahKKVUWzLGcNfCdbyz7jBXDu/O+V0Cp2ZSrfING8j+\n7+eImDiBmNmzW209zU4OxpijxpiN1nAJsBPoCcwA3rC6vQFcYw3PAN40HmuBTiLSHZgKrDDG5Btj\nCoAVwLTmxqWUUs31zb48UnbnEB0ezF0BcoFbfRVpaRyecy/BCQn0eP55xNZ6VyP45JiDiCQCI4Hv\ngK7GmKPgSSAiUnsIvSdwuN5smVbbqdq9rWcOnq0OevXq5YvQlVKqzqur9hPbIYQ1j19KSJDd3+Gc\noGLbdg79aA722Fh6LVzYrFt/nokWpx0R6QC8DzxsjCk+XVcvbeY07Q0bjXnVGJNsjEmOa0GdcqWU\nOtn/rj3IV3tyuGNc74BLDJV79nD47ruxR0bSe9FCn5+26k2LkoOIBONJDIuNMR9Yzcet3UVYz9lW\neyZQv7ZtPJB1mnallGoTxZU1zF99gPPiIrjvkvP8Hc4JXEVFZP74x4jDQa83FhHco0ebrLclZysJ\nMB/YaYz5c71Jy4DaM45mAR/Xa7/DOmtpLFBk7X76DJgiItHWgegpVptSSrU6Ywx3L1rP4fxynrhi\nEI6gwKkq5MzN5eCsO6nJOkrPuS/iSGi7e0e05JjDeOB2YKuIpFltvwKeA94VkbuBQ8CN1rRPgenA\nXqAcuAvAGJMvIk8D661+vzfG5LcgLqWUarK1+/NZn1HAM9cM5bLBXf0dTp3qzCMcuns2zuwcEubN\nI3zUqDZdf7OTgzFmNd6PFwBM9tLfAA+eYlkLgAXNjUUppZqjtMrJ8/+3iwiHneuTAufq58o9ezh8\nz49wV1XRe+ECwkaMaPMYAmf7SSml2tiz/97J1iNF/M//u4AwR2AchK5IS+Pg7XeAMfR+602/JAbQ\n5KCUOkelHS5kyfpD3HlRItOGBsatPkvXrOHg7LuxR0XR+523Ce3f32+xaG0lpdQ5pbTKyadbjvLs\npzvpGhnKw5f183dIAJR8/jmZP3uEkL596fX6awT5+XR9TQ5KqXPG9qwirvnbGmpchsHdO/LKrUlE\nhvr/bm6FH37E0f/6L8KGDiXh1X9gj4ryd0iaHJRSZ7fD+eW88U0GVU43H206Qo3LcOOoeJ6+Ziih\nwf49zmBcLvJen0/Oiy8ScdE44l9+GVuE7yusNocmB6XUWcvtNty/OJVtRzzFG+w24S8zRzBjhNcK\nPW3KVVrKkUceoWzV10ROnUqPP/0Rm8Ph77DqaHJQSp1VjDFsySxi9d5cNh8uZNuRYv54w3BGJ8aQ\nGBsYv8ord+7kyM9/TnXGQbo99SSdbroJz3XFgUOTg1LqrJFdXMmj/9zM1+m5AIQG25g1rjc3jooP\niC9f43SSN38BOX/9K/ZOUfR6/bVWux9DS2lyUEq1a7uPlfD4B1vYdKiwru26kT25c3wiw+M7+TGy\nE1WkpXHs6Weo3L6dyGnT6Pbkb1u9smpLaHJQSrU7brfh2/15ZBVW8MfPdlPtdDOwWyR9YiN4bOoA\nzosLnJv0uEpKOPbU7yj+97+xx8bS88U/EzltWkBsyZyOJgelVECrqHZR5XTx4oo9VDnddIkM4ePN\nWRzMKwcgKiyYt380hiE9/H/658nK1q3j2O9+T3VGBrEPPkjMXXe1yv2eT8kYsJJQzcFv2VCV0+RZ\nNTkopQJOUUUNcz/fQ8qubDLyygm2CzWu72/zMrh7R/50w3DiIkO4MDGGiJDA+iqrycri2DPPUvrF\nFwR160av+fOJGDumbYOoKqVw/uUsCK5iR7Cd7aac0jO4c1xgvaNKqXOaMYaMvHIeXprG9iNFTOwf\nx5Qh3XC5DZMHdmFEr07UOA0dw4ICcreMcTopWLyY7L+8BMYQ9+gjxNx+O7bQ0DaNw1lVwjfLH+PF\n4Hz2Ohz0ryoj2enkivhJ/JBtTVqGJgelVEA4nF/ONX9bQ15ZNQAv3HgBN4zyUik1cC4FOEH5+vUc\n+/3TVKWnEzFxAt1++ySO+Da+nqLoCFVr/8pDGR+wNtQBDgdPDLmHW4bNhpBIq1PTCmBrclBK+VV2\nSSVr9uYy78t95JVV87PL+jOkR8eAurfCqRink5KVX5D/1ptUbEgluEcP4v/6Mh0mT27bLRuXE5O2\nmH1fPcuz4YbU0FAeixjAD3/wHLEx5zdrkZoclFJtLrOgnPyyar5Oz+XlL9KprHHTKyac1+9IbhdJ\noeZ4NqUpKeS/8QbVBw4Q3KuXZxfSbbdhCwtr01hcVaVs+s+P+UPON6R3DsEhQTw7/vdcdd5VLVqu\nJgelVKsrrqzh31uOEmQTtmQW8c66QzjdngPMlw3qwr2XnEdSr2jstsA7jlDLOJ0UffQRBe8soXL7\ndgBC+vWj59wXiZw8GQluowJ+xoBxk1t4gI3bl/By+lIy7NDREc6DF8zhuv7X0yW8S4tXo8lBKeVz\n5dVOXlyxh4N55ezNLmV/btkJ06cO6cqMET3pFRPO0J6BdwpqrZrj2ZSu+orydespW7MGV34+IYMH\nEfezn9Hh0kmE9OvX+ruPnFUUvj+b9+yVmJBIqo5vJcVdyp4Qz8GXrgJ/GHAHPxj1ABHBvjtNVpOD\nUsqntmcV8ZN3NrEvp4y4yBC6dQzl9rG9uTapJ1FhwRRV1DAyoVNAnm0E4C4ro3TNGoo++pjSr74C\nlwt7TAwRF4+n4xVX0GHSpNaN/dB38NVz7Dy2kU+7JJDlqmC53XOQnjKwBRmGuEN4NGYUwxImcsHg\nmwhyhPs8DE0OSimfqKxx8cHGIzy5bBsOu43nrx/G/0tOCNgkUMuZm0vFlq2Ur1tH+fr1VO7a5UkI\ncbF0nj2bqKuvwnH++a33OtxuWPkUOTs/5svwUExRJtVi46XYjlRQRC8RRofHc9sF93JRpwG4YxIJ\nC2r94xqaHJRSzXY4v5y0w4VsOlTIPzccpqTKSd/YCN6ZM5auHdv23P6mMMZQc/Ag5WlpVGzeTPn6\n9VTv3QeAOByEXXABnef8iIjRowm/8EIk6Ay/Ip3VsHMZhHeGDl2g5Cj0uoia4iNUVRZQue8LjuTt\npDA8mpqcXRQUHmBHSAgFzjJWd4ygSsohNgaAQdH9efHi5+gZ45871WlyUEqdUnFlDZsOFZKakc+x\n4koiQ4NJPVgAeMpWfLs/j2qnG4CrLujBdSN7MrZvZ8Ic/r2JDngSgauggKrdu6lIS6MibTMVmzfj\nKvQU6LNFRBA2YgRRM2YQPmoUoYMHt+xitepyWPk7+O7vANQAb3eM5B+doiixn3Rlcon13NFBJEKX\njglM7jGGOcPn4DZuKp2VDIkdgk2afkWzr2lyUErVqaxxsXZ/Hm9/d4i8smrSj5dQXOkEICTIhjEQ\nFxlC37gIckurmDakG3df3IdO4cH07uy/eyWY6mqq9u+nctcuqtLTqdiURtWuXbjLy+v6OM4/jw6X\nTSbsggsIu+ACQs47D7E3ksRqKsBZBbnp4KwA44aIODiSCkWZVCDkVWRzIGsdUnKUxLJCwhMuZFO/\nS3glczl7agoZH9qdkVHnExoUSljHeDrFDSbGHkKHDt3pFNKJ2LBYgmyB91UceBEppdpc+vES1u7P\nY+GaDPbnlhEabKNrx1BG94nhxuQELkyMISbC/5cmG2Nw5eZSlZ5O1d69VO7aTeXOnVTt3Qs1NZ5O\nwcGEDR5M1PXX40hIwNG3L2HDh2Hv2PH0C3e7ofQ45O+HfV9Qs28l2/O2kxkURLUIexwO0h3BCGDD\nsN3hoLg2uQQDMRGeB8fhwLt0De/K3PFz+UGvHwT8cRdvNDkodQ4oqawh7XAhsR1CACivdrHuQD6l\nVTUUVzjrrjuIiXDw/PXDmDakO1HhbXTe/kmMy4UzN5eaI1lU799HzdFj1Bw7Ss2hw1Slp9ftFgKw\nd+5M6IABdJh1ByGDBhE6cCCO3r09xwpcNVB8xPNlf+QrOFQNdgeEdITSbHDXePrs/BeUHIPS4xyr\nzGNniIOvw8P5qkMHsnt0q1tXqC2Y/hE9sbtdOO1BTIgZRGJkPLHhcfSN7o/buNlftJ/CqkIuiLuA\npK5JBNv88x76QsAkBxGZBvwFsAOvG2Oe83NISvmUMd9XFa12uTlaWInbGAye65rAeK5vApwuw4Hc\nMo4WVRAXGUJcZAjGgNsY3NazMQa3G0qqajhSUEFuaTURIZ5fslU1bmpcbnLLqjlSUMHuYyVU1Lga\nxGS3CQJMHdqNn07ux3lxHXx2IZpxuXCXluIqKcVdXISrqPZRbD0X4ioqwl1UhKu4BHdpKc7cXJw5\nOZ5f8bVECIqNJbhnTyInX0LIwKGE9DufkPPOIyioHFzV4HZC+nLYtxazehNHc3ax31XKgeAgjgXZ\nKbbZKLHZqBDBKUKN9RLD3AabPYSjEWEcD4+gBM8xB4ctmIt7TuCy3pcxKGYQYcFhdAnv0uiXfXK3\nZJ+8d4FA6v/B+i0IETuwB7gcyATWAzcbY3acap7k5GSzYcOGNopQnQuM9cV7KL+cPcdLsIlgE7CJ\n4DaG/TllON2mrk2sZ5uAzSaINVxUUcOytCxKKp3YbYLdJlRUuzhWXNmq8Yt4koxNIDTYTrDdRliw\nnb5xEQTZbVwxtBvhwTaCBULFTUKkgz6dHJgaJ6amBlNTbT3XQFUl7iNbMG4bRhwYY8O4BVNV6XlU\nVuIuL8dVVIyzoBBnTh6u0lJMRSXu8gpc5RWYikZeb7AdCXdAhAMJEQhyY8IFOoViIoSaCBeVnW1U\nRgpV4qKy9DjV1SVUhkZSLTYqnRVU46ZKBANk2+0ccjg4GBRERb0EF2ILJioogo7BEYTaHQRjIzgo\nBGxBlDsrcImNrh26ExcWR2LHRC7ocgF9ovrQ0dHIbqh2SkRSjTGNZrFA2XIYDew1xuwHEJElwAzg\nlMmhKOcI/5r3BFD7q6v+wPdOTH51HRt09Z4jzQkTTF1bg15I/X4N5jlpzDRosdbjJQgDpsEKjZdB\nL0HVDXqZ33ibZiitdOG22jy/KRu+f+IlTMGAfD8m3mKq1/T9v65nHfV/qwqGMKlGcHMyafDBNRgg\nJMiGCLjcbnJKqnC5v58mGMRtsLlNXYxiPJ9ZZbWLGqfb08d6ObXrq23DGNx4frnXxizG87AZN2Ig\nzBhuQYgItoGxlmHAYaNuGWIgSKhbD8YTl7is2FyGIOPZt01NFeLGWpZYz9a42yDGYKvd/HAZxO0G\nt2d5GPGsww02N9hc37/3TiC94Sd0RiqDoTQcCjtAaShUdYDKaCgPgQqHUBbiaS8NFUrDhNKw2nGo\nCRY85/TUnLTUMi9rAqIcQOd6DZEEYSPEFoQB4kI7kxB9HqM69qZvVF/6RvWlT1QfYkJj2uU+f38L\nlOTQEzhcbzwTOO2dMUKOF3L+Xz5q1aBU+zegjdfnlnoPGxjxPNye73TPsM0arzcNwGXzPKrs3w/X\nLsdtk7r53ALGduJ6apf3/TwCNht2EYzN2qqwC9htGLvgtgtuu/Es1441LrjsYGyCKwhMSBju0HCC\nguyE2G247WCCgzDBdkywHXdIEAQHIQg2sWETm2frCc9wmNgID4mkh6MD0RHdsBuDzbixhXXCJnbr\nwK54HjY7tqAQRAS72LGJjVB7KA674/vnoFBC7CF1D4fdEZBn+ZwtAuWd9ZbWG/zsFJE5wByAXl2j\nyfiJl6qDIif9Cj156XKaftIgEmnYgCCYk36JiCe+Bv28BOi5a598P98JK5X6AyfG6v1lfD/FnCKO\n2tdRf35j7RY5MTQhwhFEsO3kd8bL8rzul27YVj++k7udsBFQ74NyGyiTcNzYPG0nv9cnn/tdN92G\nG0NBWXXdsuJjIwgPDqoXC4g9COx2z/tq96xDbNY3KDawe77kEEGs3UXYbJ71ivVs80zDakME7HZs\nNvuJ67JilZM+z/rvjVivsfYTqvv8attsQSfN8/3fVv15gmxBBElQ3bnx+mtZtUSgJIdMIKHeeDyQ\ndXInY8yrwKvgOeZwxQN/bJvolFLqHOO/y+9OtB7oJyJ9RMQBzASW+TkmpZQ6ZwXEloMxxikiDwGf\n4TmVdYExZrufw1JKqXNWQCQHAGPMp8Cn/o5DKaVU4OxWUkopFUA0OSillGpAk4NSSqkGNDkopZRq\nICBqKzWHiJQAu/0dRyNigVx/B9EIjdE3NEbf0Bh943Qx9jbGxDW2gIA5W6kZdjeleJQ/icgGjbHl\nNEbf0Bh941yJUXcrKaWUakCTg1JKqQbac3J41d8BNIHG6Bsao29ojL5xTsTYbg9IK6WUaj3tectB\nKaVUK9HkoJRSqoF2lxxEZJqI7BaRvSLyuB/jWCAi2SKyrV5bjIisEJF06znaahcRecmKeYuIJLVR\njAkikiIiO0Vku4j8NNDiFJFQEVknIputGH9ntfcRke+sGJdapdwRkRBrfK81PbG1Y6wXq11ENonI\nJ4EYo4hkiMhWEUkTkQ1WW8B81tZ6O4nIeyKyy/q7HBeAMQ6w3sPaR7GIPByAcf7M+p/ZJiLvWP9L\nvvubNMa0mweect77gL6AA9gMDPZTLBOBJGBbvbY/Ao9bw48Dz1vD04H/4LkV2FjguzaKsTuQZA1H\nAnuAwYEUp7WuDtZwMPCdte53gZlW+9+B+63hB4C/W8MzgaVt+Jk/ArwNfGKNB1SMQAYQe1JbwHzW\n1nrfAO6xhh1Ap0CL8aR47cAxoHcgxYnn1soHgLB6f4t3+vJvsk3faB+8IeOAz+qNPwE84cd4Ejkx\nOewGulvD3fFcqAfwD+Bmb/3aON6PgcsDNU4gHNiI5/7huUDQyZ87nnt+jLOGg6x+0gaxxQMrgR8A\nn1hfBIEWYwYNk0PAfNZAR+sLTQI1Ri8xTwHWBFqceJLDYSDG+hv7BJjqy7/J9rZbqfYNqZVptQWK\nrsaYowDWcxer3e9xW5uRI/H8Mg+oOK3dNWlANrACz9ZhoTHG6SWOuhit6UVA59aOEZgL/AJwW+Od\nAzBGAywXkVTx3G8dAuuz7gvkAAut3XOvi0hEgMV4spnAO9ZwwMRpjDkCvAAcAo7i+RtLxYd/k+0t\nOXi7Y3p7OBfXr3GLSAfgfeBhY0zx6bp6aWv1OI0xLmPMCDy/zkcDg04TR5vHKCJXAtnGmNT6zaeJ\nw1+f93hjTBJwBfCgiEw8TV9/xBiEZ1fsPGPMSKAMz+6ZU/H3/40DuBr4Z2NdvbS19t9kNDAD6AP0\nACLwfO6niuOMY2xvySETSKg3Hg9k+SkWb46LSHcA6znbavdb3CISjCcxLDbGfBCocQIYYwqBL/Hs\nt+0kIrW1v+rHURejNT0KyG/l0MYDV4tIBrAEz66luQEWI8aYLOs5G/gQT6INpM86E8g0xnxnjb+H\nJ1kEUoz1XQFsNMYct8YDKc7LgAPGmBxjTA3wAXARPvybbG/JYT3Qzzoi78CzybfMzzHVtwyYZQ3P\nwrOPv7b9DuushrFAUe3maWsSEQHmAzuNMX8OxDhFJE5EOlnDYXj+6HcCKcANp4ixNvYbgC+MtSO1\ntRhjnjDGxBtjEvH8zX1hjLk1kGIUkQgRiawdxrOvfBsB9FkbY44Bh0VkgNU0GdgRSDGe5Ga+36VU\nG0+gxHkIGCsi4db/ee176bu/ybY8uOOjAzHT8Zx1sw/4tR/jeAfPvr4aPFn5bjz78FYC6dZzjNVX\ngL9ZMW8FktsoxovxbDpuAdKsx/RAihMYDmyyYtwG/NZq7wusA/bi2awPsdpDrfG91vS+bfy5T+L7\ns5UCJkYrls3WY3vt/0YgfdbWekcAG6zP+yMgOtBitNYdDuQBUfXaAipO4HfALuv/5i0gxJd/k1o+\nQymlVAPtbbeSUkqpNqDJQSmlVAOaHJRSSjWgyUEppVQDmhyUUko1oMlBKS+s6qEPNGO+X7VGPEq1\nNT2VVSkvrFpUnxhjhp7hfKXGmA6tEpRSbUi3HJTy7jngPKue/59Onigi3UVklTV9m4hMEJHngDCr\nbbHV7zbx3K8iTUT+ISJ2q71URP5HRDaKyEoRiWvbl6fU6emWg1JeNLblICKPAqHGmGetL/xwY0xJ\n/S0HERmE5x4A1xljakTkFWCtMeZNETHAbcaYxSLyW6CLMeahtnhtSjVFUONdlFJerAcWWIUNPzLG\npHnpMxkYBaz3lL8hjO+LtbmBpdbw/+IpnKZUwNDdSko1gzFmFZ67AR4B3hKRO7x0E+ANY8wI6zHA\nGPPUqRbZSqEq1SyaHJTyrgTPrVW9EpHeeO7x8Bqeyre19w2usbYmwFOc7QYR6WLNE2PNB57/vdrq\nmbcAq30cv1ItoruVlPLCGJMnImtEZBvwH2PMz0/qMgn4uYjUAKVA7ZbDq8AWEdlojLlVRP4Lz93Z\nbHgq+D4IHMRzo5shIpKK565cN7X+q1Kq6fSAtFJ+oKe8qkCnu5WUUko1oFsOSp2GiAzDcyOV+qqM\nMWP8EY9SbUWTg1JKqQZ0t5JSSqkGNDkopZRqQJODUkqpBjQ5KKWUakCTg1JKqQb+P+iV3wxckdaY\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd79a2f8940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"states = analysis.get_count(df, 'id')\n",
"states.plot()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T16:00:39.515032Z",
"start_time": "2017-10-19T18:00:39.162240+02:00"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd796161cf8>"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEKCAYAAAD5MJl4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npAGFg4iINPD/AfaLVEq6LAp5AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd79593ef98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"alive = analysis.get_value(df, 'rabbits_alive', 'rabbits_alive', aggfunc='sum').apply(pd.to_numeric)\n",
"alive.plot()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T16:00:58.815038Z",
"start_time": "2017-10-19T18:00:58.566807+02:00"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/jfernando/.local/lib/python3.6/site-packages/pandas/core/reshape/merge.py:551: UserWarning: merging between different levels can give an unintended result (1 levels on the left, 2 on the right)\n",
" warnings.warn(msg, UserWarning)\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEKCAYAAAD5MJl4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/D/rW5PT2bI3LAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd799b45588>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"h = alive.join(states);\n",
"h.plot();"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T16:01:01.195253Z",
"start_time": "2017-10-19T18:01:01.142907+02:00"
},
"collapsed": true,
"scrolled": true
},
"outputs": [],
"source": [
"states[[('id','newborn'),('id','fertile'),('id', 'pregnant')]].sum(axis=1).sub(alive['rabbits_alive'], fill_value=0)"
]
}
],
"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": "3.6.2"
},
"toc": {
"colors": {
"hover_highlight": "#DAA520",
"navigate_num": "#000000",
"navigate_text": "#333333",
"running_highlight": "#FF0000",
"selected_highlight": "#FFD700",
"sidebar_border": "#EEEEEE",
"wrapper_background": "#FFFFFF"
},
"moveMenuLeft": true,
"nav_menu": {
"height": "31px",
"width": "252px"
},
"navigate_menu": true,
"number_sections": true,
"sideBar": true,
"threshold": 4,
"toc_cell": false,
"toc_position": {
"height": "867px",
"left": "0px",
"right": "1670px",
"top": "106px",
"width": "250px"
},
"toc_section_display": "block",
"toc_window_display": true,
"widenNotebook": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Reference in New Issue View Git Blame Copy Permalink