mirror of
https://github.com/gsi-upm/soil
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1913 lines
252 KiB
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1913 lines
252 KiB
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2017-07-02T16:44:14.120953Z",
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"start_time": "2017-07-02T18:44:14.117152+02:00"
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}
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},
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"source": [
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"# Introduction"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"cell_style": "center",
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"collapsed": true
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},
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"source": [
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"This notebook is an introduction to the soil agent-based social network simulation framework.\n",
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"In particular, we will focus on a specific use case: studying the propagation of news in a social network.\n",
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"\n",
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"The steps we will follow are:\n",
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"\n",
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"* Modelling the behavior of agents\n",
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"* Running the simulation using different configurations\n",
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"* Analysing the results of each simulation"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2017-07-03T13:38:48.052876Z",
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"start_time": "2017-07-03T15:38:48.044762+02:00"
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}
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},
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"source": [
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"But before that, let's import the soil module and networkx."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2017-07-03T14:42:51.679937Z",
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"start_time": "2017-07-03T16:42:51.185463+02:00"
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},
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import soil\n",
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"import networkx as nx"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2017-07-03T14:42:51.690373Z",
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"start_time": "2017-07-03T16:42:51.682644+02:00"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Populating the interactive namespace from numpy and matplotlib\n"
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]
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}
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],
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"source": [
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"%pylab inline\n",
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"# To display plots in the notebook"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2017-07-03T13:41:19.788717Z",
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"start_time": "2017-07-03T15:41:19.785448+02:00"
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}
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},
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"source": [
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"# Basic concepts"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"There are three main elements in a soil simulation:\n",
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" \n",
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"* The network topology. A simulation may use an existing NetworkX topology, or generate one on the fly\n",
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"* 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",
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"* The environment. It assigns agents to nodes in the network, and stores the environment parameters (shared state for all agents).\n",
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"\n",
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"Soil is based on ``simpy``, which is an event-based network simulation library.\n",
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"Soil provides several abstractions over events to make developing agents easier.\n",
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"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"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2017-07-02T15:55:12.933978Z",
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"start_time": "2017-07-02T17:55:12.930860+02:00"
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}
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},
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"source": [
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"# Modeling behaviour"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2017-07-03T13:49:31.269687Z",
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"start_time": "2017-07-03T15:49:31.257850+02:00"
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}
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},
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"source": [
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"Our first step will be to model how every person in the social network reacts when it comes to news.\n",
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"We will follow a very simple model (a finite state machine).\n",
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"\n",
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"There are two types of people, those who have heard about a newsworthy event (infected) or those who have not (neutral).\n",
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"A neutral person may heard about the news either on the TV (with probability **prob_tv_spread**) or through their friends.\n",
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"Once a person has heard the news, they will spread it to their friends (with a probability **prob_neighbor_spread**).\n",
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"Some users do not have a TV, so they only rely on their friends.\n",
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"\n",
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"The spreading probabilities will change over time due to different factors.\n",
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"We will represent this variance using an environment agent."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Network Agents"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2017-07-03T14:03:07.171127Z",
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"start_time": "2017-07-03T16:03:07.165779+02:00"
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}
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},
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"source": [
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"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",
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"The most important attributes of the agent are:\n",
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"\n",
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"* ``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",
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"```py\n",
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"a = soil.agents.BaseAgent(env=env)\n",
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"a['hours_of_sleep'] = 10\n",
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"print(a['hours_of_sleep'])\n",
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"```\n",
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" The state of the agent is stored in every step of the simulation:\n",
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" ```py\n",
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" print(a['hours_of_sleep', 10]) # hours of sleep before step #10\n",
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" print(a[None, 0]) # whole state of the agent before step #0\n",
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" ```\n",
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"\n",
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"* ``agent.env``, a reference to the environment. Most commonly used to get access to the environment parameters and the topology:\n",
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" ```py\n",
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" a.env.G.nodes() # Get all nodes ids in the topology\n",
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" a.env['minimum_hours_of_sleep']\n",
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"\n",
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" ```\n",
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"\n",
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"Since our model is a finite state machine, we will be basing it on ``soil.agents.FSM``.\n",
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"\n",
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"With ``soil.agents.FSM``, we do not need to specify a ``step`` method.\n",
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"Instead, we describe every step as a function.\n",
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"To change to another state, a function may return the new state.\n",
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"If no state is returned, the state remains unchanged.[\n",
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"It will consist of two states, ``neutral`` (default) and ``infected``.\n",
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"\n",
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"Here's the code:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2017-07-03T14:42:51.715535Z",
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"start_time": "2017-07-03T16:42:51.692301+02:00"
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},
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import random\n",
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"\n",
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"class NewsSpread(soil.agents.FSM):\n",
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" @soil.agents.default_state\n",
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" @soil.agents.state\n",
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" def neutral(self):\n",
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" r = random.random()\n",
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" if self['has_tv'] and r < self.env['prob_tv_spread']:\n",
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" return self.infected\n",
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" return\n",
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" \n",
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" @soil.agents.state\n",
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" def infected(self):\n",
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" prob_infect = self.env['prob_neighbor_spread']\n",
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" for neighbor in self.get_neighboring_agents(state_id=self.neutral.id):\n",
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" r = random.random()\n",
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" if r < prob_infect:\n",
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" neighbor.state['id'] = self.infected.id\n",
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" return\n",
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" "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2017-07-02T12:22:53.931963Z",
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"start_time": "2017-07-02T14:22:53.928340+02:00"
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}
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},
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"source": [
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"## Environment agents"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Environment agents allow us to control the state of the environment.\n",
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"In this case, we will use an environment agent to simulate a very viral event.\n",
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"\n",
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"When the event happens, the agent will modify the probability of spreading the rumor."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2017-07-03T14:42:51.727938Z",
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"start_time": "2017-07-03T16:42:51.717828+02:00"
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},
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"NEIGHBOR_FACTOR = 0.9\n",
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"TV_FACTOR = 0.5\n",
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"class NewsEnvironmentAgent(soil.agents.BaseAgent):\n",
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" def step(self):\n",
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" if self.now == self['event_time']:\n",
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" self.env['prob_tv_spread'] = 1\n",
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" self.env['prob_neighbor_spread'] = 1\n",
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" elif self.now > self['event_time']:\n",
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" self.env['prob_tv_spread'] = self.env['prob_tv_spread'] * TV_FACTOR\n",
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" self.env['prob_neighbor_spread'] = self.env['prob_neighbor_spread'] * NEIGHBOR_FACTOR"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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|||
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"ExecuteTime": {
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|||
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"end_time": "2017-07-02T11:23:18.052235Z",
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|||
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"start_time": "2017-07-02T13:23:18.047452+02:00"
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}
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|||
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},
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|||
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"source": [
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"## Testing the agents"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"ExecuteTime": {
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|||
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"end_time": "2017-07-02T16:14:54.572431Z",
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|||
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"start_time": "2017-07-02T18:14:54.564095+02:00"
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|||
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}
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|||
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},
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|||
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"source": [
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"Feel free to skip this section if this is your first time with soil."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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|
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"Testing agents is not easy, and this is not a thorough testing process for agents.\n",
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"Rather, this section is aimed to show you how to access internal pats of soil so you can test your agents."
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]
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},
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{
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|||
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"cell_type": "markdown",
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|||
|
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"metadata": {
|
|||
|
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"cell_style": "split"
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|||
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},
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|||
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"source": [
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|||
|
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"First of all, let's check if our network agent has the states we would expect:"
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]
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|||
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},
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{
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|||
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"cell_type": "code",
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|||
|
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"execution_count": 5,
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|||
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"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2017-07-03T14:42:51.816465Z",
|
|||
|
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"start_time": "2017-07-03T16:42:51.811222+02:00"
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|||
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},
|
|||
|
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"cell_style": "split"
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|||
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},
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|||
|
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"outputs": [
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|||
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{
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|||
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"data": {
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|||
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"text/plain": [
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|||
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"{'infected': <function __main__.NewsSpread.infected>,\n",
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" 'neutral': <function __main__.NewsSpread.neutral>}"
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|||
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]
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|||
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|
},
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|||
|
|
"execution_count": 5,
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|||
|
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"metadata": {},
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|||
|
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"output_type": "execute_result"
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|
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}
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|||
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],
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|||
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"source": [
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|||
|
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"NewsSpread.states"
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|||
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]
|
|||
|
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},
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|||
|
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{
|
|||
|
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"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": 6,
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2017-07-03T14:42:52.106636Z",
|
|||
|
|
"start_time": "2017-07-03T16:42:51.904738+02:00"
|
|||
|
|
},
|
|||
|
|
"cell_style": "split",
|
|||
|
|
"scrolled": false
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkwiJKtZwD8+dmIqKoGt
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.figure.Figure at 0x7fd08276d978>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"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": 7,
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2017-07-03T14:42:52.136477Z",
|
|||
|
|
"start_time": "2017-07-03T16:42:52.108729+02:00"
|
|||
|
|
},
|
|||
|
|
"cell_style": "split"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Trial: 0\n",
|
|||
|
|
"\tRunning\n",
|
|||
|
|
"Finished trial in 0.014928102493286133 seconds\n",
|
|||
|
|
"Finished simulation in 0.015764951705932617 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": 8,
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2017-07-03T14:42:52.160856Z",
|
|||
|
|
"start_time": "2017-07-03T16:42:52.138976+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": 9,
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2017-07-03T14:42:52.193918Z",
|
|||
|
|
"start_time": "2017-07-03T16:42:52.163476+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": 10,
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2017-07-03T14:42:52.219072Z",
|
|||
|
|
"start_time": "2017-07-03T16:42:52.196203+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",
|
|||
|
|
" '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": 11,
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2017-07-03T14:42:55.366288Z",
|
|||
|
|
"start_time": "2017-07-03T16:42:52.295584+02:00"
|
|||
|
|
},
|
|||
|
|
"cell_style": "split"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Using config(s): ExampleSimulation\n",
|
|||
|
|
"Trial: 0\n",
|
|||
|
|
"\tRunning\n",
|
|||
|
|
"Finished trial in 1.4140360355377197 seconds\n",
|
|||
|
|
"Finished simulation in 2.4056642055511475 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": 12,
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2017-07-03T14:43:15.488799Z",
|
|||
|
|
"start_time": "2017-07-03T16:42:55.368021+02:00"
|
|||
|
|
},
|
|||
|
|
"cell_style": "split",
|
|||
|
|
"scrolled": true
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Using config(s): Spread_erdos_renyi_graph_prob_0.0\n",
|
|||
|
|
"Trial: 0\n",
|
|||
|
|
"\tRunning\n",
|
|||
|
|
"Finished trial in 0.2691483497619629 seconds\n",
|
|||
|
|
"Finished simulation in 0.3650345802307129 seconds\n",
|
|||
|
|
"Using config(s): Spread_erdos_renyi_graph_prob_0.1\n",
|
|||
|
|
"Trial: 0\n",
|
|||
|
|
"\tRunning\n",
|
|||
|
|
"Finished trial in 0.34261059761047363 seconds\n",
|
|||
|
|
"Finished simulation in 0.44017767906188965 seconds\n",
|
|||
|
|
"Using config(s): Spread_erdos_renyi_graph_prob_0.2\n",
|
|||
|
|
"Trial: 0\n",
|
|||
|
|
"\tRunning\n",
|
|||
|
|
"Finished trial in 0.34417223930358887 seconds\n",
|
|||
|
|
"Finished simulation in 0.4550771713256836 seconds\n",
|
|||
|
|
"Using config(s): Spread_erdos_renyi_graph_prob_0.3\n",
|
|||
|
|
"Trial: 0\n",
|
|||
|
|
"\tRunning\n",
|
|||
|
|
"Finished trial in 0.3237779140472412 seconds\n",
|
|||
|
|
"Finished simulation in 0.42307496070861816 seconds\n",
|
|||
|
|
"Using config(s): Spread_erdos_renyi_graph_prob_0.4\n",
|
|||
|
|
"Trial: 0\n",
|
|||
|
|
"\tRunning\n",
|
|||
|
|
"Finished trial in 0.3507683277130127 seconds\n",
|
|||
|
|
"Finished simulation in 0.45061564445495605 seconds\n",
|
|||
|
|
"Using config(s): Spread_barabasi_albert_graph_prob_0.0\n",
|
|||
|
|
"Trial: 0\n",
|
|||
|
|
"\tRunning\n",
|
|||
|
|
"Finished trial in 0.19115304946899414 seconds\n",
|
|||
|
|
"Finished simulation in 0.20927715301513672 seconds\n",
|
|||
|
|
"Using config(s): Spread_barabasi_albert_graph_prob_0.1\n",
|
|||
|
|
"Trial: 0\n",
|
|||
|
|
"\tRunning\n",
|
|||
|
|
"Finished trial in 0.22086191177368164 seconds\n",
|
|||
|
|
"Finished simulation in 0.2390913963317871 seconds\n",
|
|||
|
|
"Using config(s): Spread_barabasi_albert_graph_prob_0.2\n",
|
|||
|
|
"Trial: 0\n",
|
|||
|
|
"\tRunning\n",
|
|||
|
|
"Finished trial in 0.21225976943969727 seconds\n",
|
|||
|
|
"Finished simulation in 0.23252630233764648 seconds\n",
|
|||
|
|
"Using config(s): Spread_barabasi_albert_graph_prob_0.3\n",
|
|||
|
|
"Trial: 0\n",
|
|||
|
|
"\tRunning\n",
|
|||
|
|
"Finished trial in 0.2853121757507324 seconds\n",
|
|||
|
|
"Finished simulation in 0.30568504333496094 seconds\n",
|
|||
|
|
"Using config(s): Spread_barabasi_albert_graph_prob_0.4\n",
|
|||
|
|
"Trial: 0\n",
|
|||
|
|
"\tRunning\n",
|
|||
|
|
"Finished trial in 0.21434736251831055 seconds\n",
|
|||
|
|
"Finished simulation in 0.23370599746704102 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 (history of agent and environment state) and gexf format."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 13,
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2017-07-03T14:43:15.721720Z",
|
|||
|
|
"start_time": "2017-07-03T16:43:15.490854+02:00"
|
|||
|
|
},
|
|||
|
|
"cell_style": "split",
|
|||
|
|
"scrolled": true
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"\u001b[01;34msoil_output\u001b[00m\n",
|
|||
|
|
"├── \u001b[01;34mSim_prob_0\u001b[00m\n",
|
|||
|
|
"│ ├── Sim_prob_0.dumped.yml\n",
|
|||
|
|
"│ ├── Sim_prob_0.simulation.pickle\n",
|
|||
|
|
"│ ├── Sim_prob_0_trial_0.environment.csv\n",
|
|||
|
|
"│ └── Sim_prob_0_trial_0.gexf\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.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.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.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.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.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.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.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.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.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.environment.csv\n",
|
|||
|
|
" └── Spread_erdos_renyi_graph_prob_0.4_trial_0.gexf\n",
|
|||
|
|
"\n",
|
|||
|
|
"11 directories, 44 files\n",
|
|||
|
|
"1.8M\tsoil_output/Sim_prob_0\n",
|
|||
|
|
"652K\tsoil_output/Spread_barabasi_albert_graph_prob_0.0\n",
|
|||
|
|
"684K\tsoil_output/Spread_barabasi_albert_graph_prob_0.1\n",
|
|||
|
|
"692K\tsoil_output/Spread_barabasi_albert_graph_prob_0.2\n",
|
|||
|
|
"692K\tsoil_output/Spread_barabasi_albert_graph_prob_0.3\n",
|
|||
|
|
"688K\tsoil_output/Spread_barabasi_albert_graph_prob_0.4\n",
|
|||
|
|
"1.8M\tsoil_output/Spread_erdos_renyi_graph_prob_0.0\n",
|
|||
|
|
"1.9M\tsoil_output/Spread_erdos_renyi_graph_prob_0.1\n",
|
|||
|
|
"1.9M\tsoil_output/Spread_erdos_renyi_graph_prob_0.2\n",
|
|||
|
|
"1.9M\tsoil_output/Spread_erdos_renyi_graph_prob_0.3\n",
|
|||
|
|
"1.9M\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": [
|
|||
|
|
"Once the simulations are over, we can use soil to analyse the results."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2017-07-03T14:44:30.978223Z",
|
|||
|
|
"start_time": "2017-07-03T16:44:30.971952+02:00"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"First, let's load the stored results into a pandas dataframe."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 14,
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2017-07-03T14:43:15.987794Z",
|
|||
|
|
"start_time": "2017-07-03T16:43:15.724519+02:00"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Populating the interactive namespace from numpy and matplotlib\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/usr/lib/python3.6/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['random']\n",
|
|||
|
|
"`%matplotlib` prevents importing * from pylab and numpy\n",
|
|||
|
|
" \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"%pylab inline\n",
|
|||
|
|
"from soil import analysis"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 15,
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2017-07-03T14:43:16.590910Z",
|
|||
|
|
"start_time": "2017-07-03T16:43:15.990320+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>tstep</th>\n",
|
|||
|
|
" <th>attribute</th>\n",
|
|||
|
|
" <th>value</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>0</th>\n",
|
|||
|
|
" <td>env</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>prob_tv_spread</td>\n",
|
|||
|
|
" <td>0.01</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1</th>\n",
|
|||
|
|
" <td>env</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>prob_neighbor_spread</td>\n",
|
|||
|
|
" <td>0.1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2</th>\n",
|
|||
|
|
" <td>env</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>prob_tv_spread</td>\n",
|
|||
|
|
" <td>0.01</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>3</th>\n",
|
|||
|
|
" <td>env</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>prob_neighbor_spread</td>\n",
|
|||
|
|
" <td>0.1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>4</th>\n",
|
|||
|
|
" <td>env</td>\n",
|
|||
|
|
" <td>2</td>\n",
|
|||
|
|
" <td>prob_tv_spread</td>\n",
|
|||
|
|
" <td>0.01</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>5</th>\n",
|
|||
|
|
" <td>env</td>\n",
|
|||
|
|
" <td>2</td>\n",
|
|||
|
|
" <td>prob_neighbor_spread</td>\n",
|
|||
|
|
" <td>0.1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>6</th>\n",
|
|||
|
|
" <td>env</td>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" <td>prob_tv_spread</td>\n",
|
|||
|
|
" <td>0.01</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>7</th>\n",
|
|||
|
|
" <td>env</td>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" <td>prob_neighbor_spread</td>\n",
|
|||
|
|
" <td>0.1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>8</th>\n",
|
|||
|
|
" <td>env</td>\n",
|
|||
|
|
" <td>4</td>\n",
|
|||
|
|
" <td>prob_tv_spread</td>\n",
|
|||
|
|
" <td>0.01</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>9</th>\n",
|
|||
|
|
" <td>env</td>\n",
|
|||
|
|
" <td>4</td>\n",
|
|||
|
|
" <td>prob_neighbor_spread</td>\n",
|
|||
|
|
" <td>0.1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>10</th>\n",
|
|||
|
|
" <td>env</td>\n",
|
|||
|
|
" <td>5</td>\n",
|
|||
|
|
" <td>prob_tv_spread</td>\n",
|
|||
|
|
" <td>0.01</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>11</th>\n",
|
|||
|
|
" <td>env</td>\n",
|
|||
|
|
" <td>5</td>\n",
|
|||
|
|
" <td>prob_neighbor_spread</td>\n",
|
|||
|
|
" <td>0.1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>12</th>\n",
|
|||
|
|
" <td>env</td>\n",
|
|||
|
|
" <td>6</td>\n",
|
|||
|
|
" <td>prob_tv_spread</td>\n",
|
|||
|
|
" <td>0.01</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>13</th>\n",
|
|||
|
|
" <td>env</td>\n",
|
|||
|
|
" <td>6</td>\n",
|
|||
|
|
" <td>prob_neighbor_spread</td>\n",
|
|||
|
|
" <td>0.1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>14</th>\n",
|
|||
|
|
" <td>env</td>\n",
|
|||
|
|
" <td>7</td>\n",
|
|||
|
|
" <td>prob_tv_spread</td>\n",
|
|||
|
|
" <td>0.01</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>15</th>\n",
|
|||
|
|
" <td>env</td>\n",
|
|||
|
|
" <td>7</td>\n",
|
|||
|
|
" <td>prob_neighbor_spread</td>\n",
|
|||
|
|
" <td>0.1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>16</th>\n",
|
|||
|
|
" <td>env</td>\n",
|
|||
|
|
" <td>8</td>\n",
|
|||
|
|
" <td>prob_tv_spread</td>\n",
|
|||
|
|
" <td>0.01</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>17</th>\n",
|
|||
|
|
" <td>env</td>\n",
|
|||
|
|
" <td>8</td>\n",
|
|||
|
|
" <td>prob_neighbor_spread</td>\n",
|
|||
|
|
" <td>0.1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>18</th>\n",
|
|||
|
|
" <td>env</td>\n",
|
|||
|
|
" <td>9</td>\n",
|
|||
|
|
" <td>prob_tv_spread</td>\n",
|
|||
|
|
" <td>0.01</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>19</th>\n",
|
|||
|
|
" <td>env</td>\n",
|
|||
|
|
" <td>9</td>\n",
|
|||
|
|
" <td>prob_neighbor_spread</td>\n",
|
|||
|
|
" <td>0.1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>20</th>\n",
|
|||
|
|
" <td>env</td>\n",
|
|||
|
|
" <td>10</td>\n",
|
|||
|
|
" <td>prob_tv_spread</td>\n",
|
|||
|
|
" <td>0.01</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21</th>\n",
|
|||
|
|
" <td>env</td>\n",
|
|||
|
|
" <td>10</td>\n",
|
|||
|
|
" <td>prob_neighbor_spread</td>\n",
|
|||
|
|
" <td>0.1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>22</th>\n",
|
|||
|
|
" <td>env</td>\n",
|
|||
|
|
" <td>11</td>\n",
|
|||
|
|
" <td>prob_tv_spread</td>\n",
|
|||
|
|
" <td>0.01</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>23</th>\n",
|
|||
|
|
" <td>env</td>\n",
|
|||
|
|
" <td>11</td>\n",
|
|||
|
|
" <td>prob_neighbor_spread</td>\n",
|
|||
|
|
" <td>0.1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>24</th>\n",
|
|||
|
|
" <td>env</td>\n",
|
|||
|
|
" <td>12</td>\n",
|
|||
|
|
" <td>prob_tv_spread</td>\n",
|
|||
|
|
" <td>0.01</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>25</th>\n",
|
|||
|
|
" <td>env</td>\n",
|
|||
|
|
" <td>12</td>\n",
|
|||
|
|
" <td>prob_neighbor_spread</td>\n",
|
|||
|
|
" <td>0.1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>26</th>\n",
|
|||
|
|
" <td>env</td>\n",
|
|||
|
|
" <td>13</td>\n",
|
|||
|
|
" <td>prob_tv_spread</td>\n",
|
|||
|
|
" <td>0.01</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>27</th>\n",
|
|||
|
|
" <td>env</td>\n",
|
|||
|
|
" <td>13</td>\n",
|
|||
|
|
" <td>prob_neighbor_spread</td>\n",
|
|||
|
|
" <td>0.1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>28</th>\n",
|
|||
|
|
" <td>env</td>\n",
|
|||
|
|
" <td>14</td>\n",
|
|||
|
|
" <td>prob_tv_spread</td>\n",
|
|||
|
|
" <td>0.01</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>29</th>\n",
|
|||
|
|
" <td>env</td>\n",
|
|||
|
|
" <td>14</td>\n",
|
|||
|
|
" <td>prob_neighbor_spread</td>\n",
|
|||
|
|
" <td>0.1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>...</th>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21012</th>\n",
|
|||
|
|
" <td>499</td>\n",
|
|||
|
|
" <td>6</td>\n",
|
|||
|
|
" <td>has_tv</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21013</th>\n",
|
|||
|
|
" <td>499</td>\n",
|
|||
|
|
" <td>6</td>\n",
|
|||
|
|
" <td>id</td>\n",
|
|||
|
|
" <td>neutral</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21014</th>\n",
|
|||
|
|
" <td>499</td>\n",
|
|||
|
|
" <td>7</td>\n",
|
|||
|
|
" <td>has_tv</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21015</th>\n",
|
|||
|
|
" <td>499</td>\n",
|
|||
|
|
" <td>7</td>\n",
|
|||
|
|
" <td>id</td>\n",
|
|||
|
|
" <td>neutral</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21016</th>\n",
|
|||
|
|
" <td>499</td>\n",
|
|||
|
|
" <td>8</td>\n",
|
|||
|
|
" <td>has_tv</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21017</th>\n",
|
|||
|
|
" <td>499</td>\n",
|
|||
|
|
" <td>8</td>\n",
|
|||
|
|
" <td>id</td>\n",
|
|||
|
|
" <td>neutral</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21018</th>\n",
|
|||
|
|
" <td>499</td>\n",
|
|||
|
|
" <td>9</td>\n",
|
|||
|
|
" <td>has_tv</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21019</th>\n",
|
|||
|
|
" <td>499</td>\n",
|
|||
|
|
" <td>9</td>\n",
|
|||
|
|
" <td>id</td>\n",
|
|||
|
|
" <td>neutral</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21020</th>\n",
|
|||
|
|
" <td>499</td>\n",
|
|||
|
|
" <td>10</td>\n",
|
|||
|
|
" <td>has_tv</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21021</th>\n",
|
|||
|
|
" <td>499</td>\n",
|
|||
|
|
" <td>10</td>\n",
|
|||
|
|
" <td>id</td>\n",
|
|||
|
|
" <td>neutral</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21022</th>\n",
|
|||
|
|
" <td>499</td>\n",
|
|||
|
|
" <td>11</td>\n",
|
|||
|
|
" <td>has_tv</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21023</th>\n",
|
|||
|
|
" <td>499</td>\n",
|
|||
|
|
" <td>11</td>\n",
|
|||
|
|
" <td>id</td>\n",
|
|||
|
|
" <td>neutral</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21024</th>\n",
|
|||
|
|
" <td>499</td>\n",
|
|||
|
|
" <td>12</td>\n",
|
|||
|
|
" <td>has_tv</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21025</th>\n",
|
|||
|
|
" <td>499</td>\n",
|
|||
|
|
" <td>12</td>\n",
|
|||
|
|
" <td>id</td>\n",
|
|||
|
|
" <td>neutral</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21026</th>\n",
|
|||
|
|
" <td>499</td>\n",
|
|||
|
|
" <td>13</td>\n",
|
|||
|
|
" <td>has_tv</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21027</th>\n",
|
|||
|
|
" <td>499</td>\n",
|
|||
|
|
" <td>13</td>\n",
|
|||
|
|
" <td>id</td>\n",
|
|||
|
|
" <td>neutral</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21028</th>\n",
|
|||
|
|
" <td>499</td>\n",
|
|||
|
|
" <td>14</td>\n",
|
|||
|
|
" <td>has_tv</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21029</th>\n",
|
|||
|
|
" <td>499</td>\n",
|
|||
|
|
" <td>14</td>\n",
|
|||
|
|
" <td>id</td>\n",
|
|||
|
|
" <td>neutral</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21030</th>\n",
|
|||
|
|
" <td>499</td>\n",
|
|||
|
|
" <td>15</td>\n",
|
|||
|
|
" <td>has_tv</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21031</th>\n",
|
|||
|
|
" <td>499</td>\n",
|
|||
|
|
" <td>15</td>\n",
|
|||
|
|
" <td>id</td>\n",
|
|||
|
|
" <td>neutral</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21032</th>\n",
|
|||
|
|
" <td>499</td>\n",
|
|||
|
|
" <td>16</td>\n",
|
|||
|
|
" <td>has_tv</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21033</th>\n",
|
|||
|
|
" <td>499</td>\n",
|
|||
|
|
" <td>16</td>\n",
|
|||
|
|
" <td>id</td>\n",
|
|||
|
|
" <td>neutral</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21034</th>\n",
|
|||
|
|
" <td>499</td>\n",
|
|||
|
|
" <td>17</td>\n",
|
|||
|
|
" <td>has_tv</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21035</th>\n",
|
|||
|
|
" <td>499</td>\n",
|
|||
|
|
" <td>17</td>\n",
|
|||
|
|
" <td>id</td>\n",
|
|||
|
|
" <td>neutral</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21036</th>\n",
|
|||
|
|
" <td>499</td>\n",
|
|||
|
|
" <td>18</td>\n",
|
|||
|
|
" <td>has_tv</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21037</th>\n",
|
|||
|
|
" <td>499</td>\n",
|
|||
|
|
" <td>18</td>\n",
|
|||
|
|
" <td>id</td>\n",
|
|||
|
|
" <td>neutral</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21038</th>\n",
|
|||
|
|
" <td>499</td>\n",
|
|||
|
|
" <td>19</td>\n",
|
|||
|
|
" <td>has_tv</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21039</th>\n",
|
|||
|
|
" <td>499</td>\n",
|
|||
|
|
" <td>19</td>\n",
|
|||
|
|
" <td>id</td>\n",
|
|||
|
|
" <td>neutral</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21040</th>\n",
|
|||
|
|
" <td>499</td>\n",
|
|||
|
|
" <td>20</td>\n",
|
|||
|
|
" <td>has_tv</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21041</th>\n",
|
|||
|
|
" <td>499</td>\n",
|
|||
|
|
" <td>20</td>\n",
|
|||
|
|
" <td>id</td>\n",
|
|||
|
|
" <td>infected</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"<p>21042 rows × 4 columns</p>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" agent_id tstep attribute value\n",
|
|||
|
|
"0 env 0 prob_tv_spread 0.01\n",
|
|||
|
|
"1 env 0 prob_neighbor_spread 0.1\n",
|
|||
|
|
"2 env 1 prob_tv_spread 0.01\n",
|
|||
|
|
"3 env 1 prob_neighbor_spread 0.1\n",
|
|||
|
|
"4 env 2 prob_tv_spread 0.01\n",
|
|||
|
|
"5 env 2 prob_neighbor_spread 0.1\n",
|
|||
|
|
"6 env 3 prob_tv_spread 0.01\n",
|
|||
|
|
"7 env 3 prob_neighbor_spread 0.1\n",
|
|||
|
|
"8 env 4 prob_tv_spread 0.01\n",
|
|||
|
|
"9 env 4 prob_neighbor_spread 0.1\n",
|
|||
|
|
"10 env 5 prob_tv_spread 0.01\n",
|
|||
|
|
"11 env 5 prob_neighbor_spread 0.1\n",
|
|||
|
|
"12 env 6 prob_tv_spread 0.01\n",
|
|||
|
|
"13 env 6 prob_neighbor_spread 0.1\n",
|
|||
|
|
"14 env 7 prob_tv_spread 0.01\n",
|
|||
|
|
"15 env 7 prob_neighbor_spread 0.1\n",
|
|||
|
|
"16 env 8 prob_tv_spread 0.01\n",
|
|||
|
|
"17 env 8 prob_neighbor_spread 0.1\n",
|
|||
|
|
"18 env 9 prob_tv_spread 0.01\n",
|
|||
|
|
"19 env 9 prob_neighbor_spread 0.1\n",
|
|||
|
|
"20 env 10 prob_tv_spread 0.01\n",
|
|||
|
|
"21 env 10 prob_neighbor_spread 0.1\n",
|
|||
|
|
"22 env 11 prob_tv_spread 0.01\n",
|
|||
|
|
"23 env 11 prob_neighbor_spread 0.1\n",
|
|||
|
|
"24 env 12 prob_tv_spread 0.01\n",
|
|||
|
|
"25 env 12 prob_neighbor_spread 0.1\n",
|
|||
|
|
"26 env 13 prob_tv_spread 0.01\n",
|
|||
|
|
"27 env 13 prob_neighbor_spread 0.1\n",
|
|||
|
|
"28 env 14 prob_tv_spread 0.01\n",
|
|||
|
|
"29 env 14 prob_neighbor_spread 0.1\n",
|
|||
|
|
"... ... ... ... ...\n",
|
|||
|
|
"21012 499 6 has_tv True\n",
|
|||
|
|
"21013 499 6 id neutral\n",
|
|||
|
|
"21014 499 7 has_tv True\n",
|
|||
|
|
"21015 499 7 id neutral\n",
|
|||
|
|
"21016 499 8 has_tv True\n",
|
|||
|
|
"21017 499 8 id neutral\n",
|
|||
|
|
"21018 499 9 has_tv True\n",
|
|||
|
|
"21019 499 9 id neutral\n",
|
|||
|
|
"21020 499 10 has_tv True\n",
|
|||
|
|
"21021 499 10 id neutral\n",
|
|||
|
|
"21022 499 11 has_tv True\n",
|
|||
|
|
"21023 499 11 id neutral\n",
|
|||
|
|
"21024 499 12 has_tv True\n",
|
|||
|
|
"21025 499 12 id neutral\n",
|
|||
|
|
"21026 499 13 has_tv True\n",
|
|||
|
|
"21027 499 13 id neutral\n",
|
|||
|
|
"21028 499 14 has_tv True\n",
|
|||
|
|
"21029 499 14 id neutral\n",
|
|||
|
|
"21030 499 15 has_tv True\n",
|
|||
|
|
"21031 499 15 id neutral\n",
|
|||
|
|
"21032 499 16 has_tv True\n",
|
|||
|
|
"21033 499 16 id neutral\n",
|
|||
|
|
"21034 499 17 has_tv True\n",
|
|||
|
|
"21035 499 17 id neutral\n",
|
|||
|
|
"21036 499 18 has_tv True\n",
|
|||
|
|
"21037 499 18 id neutral\n",
|
|||
|
|
"21038 499 19 has_tv True\n",
|
|||
|
|
"21039 499 19 id neutral\n",
|
|||
|
|
"21040 499 20 has_tv True\n",
|
|||
|
|
"21041 499 20 id infected\n",
|
|||
|
|
"\n",
|
|||
|
|
"[21042 rows x 4 columns]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 15,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"config_file, df, config = list(analysis.get_data('soil_output/Spread_barabasi*prob_0.1*', process=False))[0]\n",
|
|||
|
|
"df"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 16,
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2017-07-03T14:43:17.192030Z",
|
|||
|
|
"start_time": "2017-07-03T16:43:16.601046+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>value</th>\n",
|
|||
|
|
" <th>0.01</th>\n",
|
|||
|
|
" <th>0.1</th>\n",
|
|||
|
|
" <th>False</th>\n",
|
|||
|
|
" <th>True</th>\n",
|
|||
|
|
" <th>infected</th>\n",
|
|||
|
|
" <th>neutral</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>tstep</th>\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>0</th>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>163.0</td>\n",
|
|||
|
|
" <td>337.0</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>500.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1</th>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>163.0</td>\n",
|
|||
|
|
" <td>337.0</td>\n",
|
|||
|
|
" <td>3.0</td>\n",
|
|||
|
|
" <td>497.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2</th>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>163.0</td>\n",
|
|||
|
|
" <td>337.0</td>\n",
|
|||
|
|
" <td>6.0</td>\n",
|
|||
|
|
" <td>494.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>3</th>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>163.0</td>\n",
|
|||
|
|
" <td>337.0</td>\n",
|
|||
|
|
" <td>12.0</td>\n",
|
|||
|
|
" <td>488.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>4</th>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>163.0</td>\n",
|
|||
|
|
" <td>337.0</td>\n",
|
|||
|
|
" <td>23.0</td>\n",
|
|||
|
|
" <td>477.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>5</th>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>163.0</td>\n",
|
|||
|
|
" <td>337.0</td>\n",
|
|||
|
|
" <td>36.0</td>\n",
|
|||
|
|
" <td>464.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>6</th>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>163.0</td>\n",
|
|||
|
|
" <td>337.0</td>\n",
|
|||
|
|
" <td>53.0</td>\n",
|
|||
|
|
" <td>447.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>7</th>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>163.0</td>\n",
|
|||
|
|
" <td>337.0</td>\n",
|
|||
|
|
" <td>79.0</td>\n",
|
|||
|
|
" <td>421.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>8</th>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>163.0</td>\n",
|
|||
|
|
" <td>337.0</td>\n",
|
|||
|
|
" <td>119.0</td>\n",
|
|||
|
|
" <td>381.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>9</th>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>163.0</td>\n",
|
|||
|
|
" <td>337.0</td>\n",
|
|||
|
|
" <td>164.0</td>\n",
|
|||
|
|
" <td>336.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>10</th>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>163.0</td>\n",
|
|||
|
|
" <td>337.0</td>\n",
|
|||
|
|
" <td>204.0</td>\n",
|
|||
|
|
" <td>296.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>11</th>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>163.0</td>\n",
|
|||
|
|
" <td>337.0</td>\n",
|
|||
|
|
" <td>254.0</td>\n",
|
|||
|
|
" <td>246.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>12</th>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>163.0</td>\n",
|
|||
|
|
" <td>337.0</td>\n",
|
|||
|
|
" <td>293.0</td>\n",
|
|||
|
|
" <td>207.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>13</th>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>163.0</td>\n",
|
|||
|
|
" <td>337.0</td>\n",
|
|||
|
|
" <td>336.0</td>\n",
|
|||
|
|
" <td>164.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>14</th>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>163.0</td>\n",
|
|||
|
|
" <td>337.0</td>\n",
|
|||
|
|
" <td>365.0</td>\n",
|
|||
|
|
" <td>135.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>15</th>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>163.0</td>\n",
|
|||
|
|
" <td>337.0</td>\n",
|
|||
|
|
" <td>391.0</td>\n",
|
|||
|
|
" <td>109.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>16</th>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>163.0</td>\n",
|
|||
|
|
" <td>337.0</td>\n",
|
|||
|
|
" <td>407.0</td>\n",
|
|||
|
|
" <td>93.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>17</th>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>163.0</td>\n",
|
|||
|
|
" <td>337.0</td>\n",
|
|||
|
|
" <td>424.0</td>\n",
|
|||
|
|
" <td>76.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>18</th>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>163.0</td>\n",
|
|||
|
|
" <td>337.0</td>\n",
|
|||
|
|
" <td>442.0</td>\n",
|
|||
|
|
" <td>58.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>19</th>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>163.0</td>\n",
|
|||
|
|
" <td>337.0</td>\n",
|
|||
|
|
" <td>452.0</td>\n",
|
|||
|
|
" <td>48.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>20</th>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>163.0</td>\n",
|
|||
|
|
" <td>337.0</td>\n",
|
|||
|
|
" <td>464.0</td>\n",
|
|||
|
|
" <td>36.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
"value 0.01 0.1 False True infected neutral\n",
|
|||
|
|
"tstep \n",
|
|||
|
|
"0 1.0 1.0 163.0 337.0 0.0 500.0\n",
|
|||
|
|
"1 1.0 1.0 163.0 337.0 3.0 497.0\n",
|
|||
|
|
"2 1.0 1.0 163.0 337.0 6.0 494.0\n",
|
|||
|
|
"3 1.0 1.0 163.0 337.0 12.0 488.0\n",
|
|||
|
|
"4 1.0 1.0 163.0 337.0 23.0 477.0\n",
|
|||
|
|
"5 1.0 1.0 163.0 337.0 36.0 464.0\n",
|
|||
|
|
"6 1.0 1.0 163.0 337.0 53.0 447.0\n",
|
|||
|
|
"7 1.0 1.0 163.0 337.0 79.0 421.0\n",
|
|||
|
|
"8 1.0 1.0 163.0 337.0 119.0 381.0\n",
|
|||
|
|
"9 1.0 1.0 163.0 337.0 164.0 336.0\n",
|
|||
|
|
"10 1.0 1.0 163.0 337.0 204.0 296.0\n",
|
|||
|
|
"11 1.0 1.0 163.0 337.0 254.0 246.0\n",
|
|||
|
|
"12 1.0 1.0 163.0 337.0 293.0 207.0\n",
|
|||
|
|
"13 1.0 1.0 163.0 337.0 336.0 164.0\n",
|
|||
|
|
"14 1.0 1.0 163.0 337.0 365.0 135.0\n",
|
|||
|
|
"15 1.0 1.0 163.0 337.0 391.0 109.0\n",
|
|||
|
|
"16 1.0 1.0 163.0 337.0 407.0 93.0\n",
|
|||
|
|
"17 1.0 1.0 163.0 337.0 424.0 76.0\n",
|
|||
|
|
"18 1.0 1.0 163.0 337.0 442.0 58.0\n",
|
|||
|
|
"19 1.0 1.0 163.0 337.0 452.0 48.0\n",
|
|||
|
|
"20 1.0 1.0 163.0 337.0 464.0 36.0"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 16,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"list(analysis.get_data('soil_output/Spread_barabasi*prob_0.1*', process=True))[0][1]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"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": 17,
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2017-07-03T14:43:20.937324Z",
|
|||
|
|
"start_time": "2017-07-03T16:43:17.193845+02:00"
|
|||
|
|
},
|
|||
|
|
"scrolled": true
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
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|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1VBxuO7oSbyuoZG6+uN
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.figure.Figure at 0x7fd081e8d860>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"analysis.plot_all('soil_output/Spread_barabasi*', attributes=['id'])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 18,
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2017-07-03T14:43:49.238790Z",
|
|||
|
|
"start_time": "2017-07-03T16:43:20.939175+02:00"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ns4genUdY/lkzu43oUXO
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.figure.Figure at 0x7fd081c96978>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
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"metadata": {},
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{
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"data": {
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|||
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