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WIP soil

Browse Source 
* Pandas integration
* Improved environment
* Logging and data dumps
* Tests
* Added Finite State Machine models
* Rewritten ipython notebook and documentation
This commit is contained in:
J. Fernando Sánchez 2017-06-20 17:45:43 +02:00
parent 764177c634
commit e1be3a730e
110 changed files with 32706 additions and 57437 deletions
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__pycache__/
.idea/
.ipynb_checkpoints/
*.png
*.gexf
.*
results
soil_output
docs/_build*
build/*
dist/*

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include requirements.txt
include test-requirements.txt
include README.rst
graft soil

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#[Soil](https://github.com/gsi-upm/soil)
The purpose of Soil (SOcial network sImuLator) is provding an Agent-based Social Simulator written in Python for Social Networks.
In order to see quickly how to use Soil, you can follow the following [tutorial](https://github.com/gsi-upm/soil/blob/master/soil_tutorial.ipynb).
# [Soil](https://github.com/gsi-upm/soil)
Soil is an extensible and user-friendly Agent-based Social Simulator for Social Networks.
Learn how to run your own simulations with our [documentation](http://soilsim.readthedocs.io).
Follow our [tutorial](notebooks/soil_tutorial.ipynb) to develop your own agent models.
@Copyright GSI - Universidad Politécnica de Madrid 2017

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import soil
soil.main()
import pdb

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# Sphinx build info version 1
# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
config: d241ad8149638a17f61def88be87f400
tags: 645f666f9bcd5a90fca523b33c5a78b7

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.. Soil documentation master file, created by
sphinx-quickstart on Tue Apr 25 12:48:56 2017.
You can adapt this file completely to your liking, but it should at least
contain the root `toctree` directive.
Welcome to Soil's documentation!
================================
Soil is an Agent-based Social Simulator in Python for modelling and simulation of Social Networks.
.. toctree::
:maxdepth: 2
:caption: Learn more about soil:
installation
usage
models
.. Indices and tables
==================
* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`

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Installation
------------
The latest version can be installed through GitLab.
.. code:: bash
git clone https://lab.cluster.gsi.dit.upm.es/soil/soil.git

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Developing new models
---------------------
This document describes how to develop a new analysis model.
What is a model?
================
A model defines the behaviour of the agents with a view to assessing their effects on the system as a whole.
In practice, a model consists of at least two parts:
* Python module: the actual code that describes the behaviour.
* Setting up the variables in the Settings JSON file.
This separation allows us to run the simulation with different agents.
Models Code
===========
All the models are imported to the main file. The initialization look like this:
.. code:: python
import settings
networkStatus = {} # Dict that will contain the status of every agent in the network
sentimentCorrelationNodeArray = []
for x in range(0, settings.network_params["number_of_nodes"]):
sentimentCorrelationNodeArray.append({'id': x})
# Initialize agent states. Let's assume everyone is normal.
init_states = [{'id': 0, } for _ in range(settings.network_params["number_of_nodes"])]
# add keys as as necessary, but "id" must always refer to that state category
A new model have to inherit the BaseBehaviour class which is in the same module.
There are two basics methods:
* __init__
* step: used to define the behaviour over time.
Variable Initialization
=======================
The different parameters of the model have to be initialize in the Simulation Settings JSON file which will be
passed as a parameter to the simulation.
.. code:: json
{
"agent": ["SISaModel","ControlModelM2"],
"neutral_discontent_spon_prob": 0.04,
"neutral_discontent_infected_prob": 0.04,
"neutral_content_spon_prob": 0.18,
"neutral_content_infected_prob": 0.02,
"discontent_neutral": 0.13,
"discontent_content": 0.07,
"variance_d_c": 0.02,
"content_discontent": 0.009,
"variance_c_d": 0.003,
"content_neutral": 0.088,
"standard_variance": 0.055,
"prob_neutral_making_denier": 0.035,
"prob_infect": 0.075,
"prob_cured_healing_infected": 0.035,
"prob_cured_vaccinate_neutral": 0.035,
"prob_vaccinated_healing_infected": 0.035,
"prob_vaccinated_vaccinate_neutral": 0.035,
"prob_generate_anti_rumor": 0.035
}
In this file you will also define the models you are going to simulate. You can simulate as many models as you want.
The simulation returns one result for each model, executing each model separately. For the usage, see :doc:`usage`.
Example Model
=============
In this section, we will implement a Sentiment Correlation Model.
The class would look like this:
.. code:: python
from ..BaseBehaviour import *
from .. import sentimentCorrelationNodeArray
class SentimentCorrelationModel(BaseBehaviour):
def __init__(self, environment=None, agent_id=0, state=()):
super().__init__(environment=environment, agent_id=agent_id, state=state)
self.outside_effects_prob = environment.environment_params['outside_effects_prob']
self.anger_prob = environment.environment_params['anger_prob']
self.joy_prob = environment.environment_params['joy_prob']
self.sadness_prob = environment.environment_params['sadness_prob']
self.disgust_prob = environment.environment_params['disgust_prob']
self.time_awareness = []
for i in range(4): # In this model we have 4 sentiments
self.time_awareness.append(0) # 0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
sentimentCorrelationNodeArray[self.id][self.env.now] = 0
def step(self, now):
self.behaviour() # Method which define the behaviour
super().step(now)
The variables will be modified by the user, so you have to include them in the Simulation Settings JSON file.

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Usage
-----
First of all, you need to install the package. See :doc:`installation` for installation instructions.
Simulation Settings
===================
Once installed, before running a simulation, you need to configure it.
* In the Settings JSON file you will find the configuration of the network.
.. code:: python
{
"network_type": 1,
"number_of_nodes": 1000,
"max_time": 50,
"num_trials": 1,
"timeout": 2
}
* In the Settings JSON file, you will also find the configuration of the models.
Network Types
=============
There are three types of network implemented, but you could add more.
.. code:: python
if settings.network_type == 0:
G = nx.complete_graph(settings.number_of_nodes)
if settings.network_type == 1:
G = nx.barabasi_albert_graph(settings.number_of_nodes, 10)
if settings.network_type == 2:
G = nx.margulis_gabber_galil_graph(settings.number_of_nodes, None)
# More types of networks can be added here
Models Settings
===============
After having configured the simulation, the next step is setting up the variables of the models.
For this, you will need to modify the Settings JSON file again.
.. code:: json
{
"agent": ["SISaModel","ControlModelM2"],
"neutral_discontent_spon_prob": 0.04,
"neutral_discontent_infected_prob": 0.04,
"neutral_content_spon_prob": 0.18,
"neutral_content_infected_prob": 0.02,
"discontent_neutral": 0.13,
"discontent_content": 0.07,
"variance_d_c": 0.02,
"content_discontent": 0.009,
"variance_c_d": 0.003,
"content_neutral": 0.088,
"standard_variance": 0.055,
"prob_neutral_making_denier": 0.035,
"prob_infect": 0.075,
"prob_cured_healing_infected": 0.035,
"prob_cured_vaccinate_neutral": 0.035,
"prob_vaccinated_healing_infected": 0.035,
"prob_vaccinated_vaccinate_neutral": 0.035,
"prob_generate_anti_rumor": 0.035
}
In this file you will define the different models you are going to simulate. You can simulate as many models
as you want. Each model will be simulated separately.
After setting up the models, you have to initialize the parameters of each one. You will find the parameters needed
in the documentation of each model.
Parameter validation will fail if a required parameter without a default has not been provided.
Running the Simulation
======================
After setting all the configuration, you will be able to run the simulation. All you need to do is execute:
.. code:: bash
python3 soil.py
The simulation will return a dynamic graph .gexf file which could be visualized with
`Gephi <https://gephi.org/users/download/>`__.
It will also return one .png picture for each model simulated.

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ul.keywordmatches li.goodmatch a {
font-weight: bold;
}
/* -- index page ------------------------------------------------------------ */
table.contentstable {
width: 90%;
margin-left: auto;
margin-right: auto;
}
table.contentstable p.biglink {
line-height: 150%;
}
a.biglink {
font-size: 1.3em;
}
span.linkdescr {
font-style: italic;
padding-top: 5px;
font-size: 90%;
}
/* -- general index --------------------------------------------------------- */
table.indextable {
width: 100%;
}
table.indextable td {
text-align: left;
vertical-align: top;
}
table.indextable ul {
margin-top: 0;
margin-bottom: 0;
list-style-type: none;
}
table.indextable > tbody > tr > td > ul {
padding-left: 0em;
}
table.indextable tr.pcap {
height: 10px;
}
table.indextable tr.cap {
margin-top: 10px;
background-color: #f2f2f2;
}
img.toggler {
margin-right: 3px;
margin-top: 3px;
cursor: pointer;
}
div.modindex-jumpbox {
border-top: 1px solid #ddd;
border-bottom: 1px solid #ddd;
margin: 1em 0 1em 0;
padding: 0.4em;
}
div.genindex-jumpbox {
border-top: 1px solid #ddd;
border-bottom: 1px solid #ddd;
margin: 1em 0 1em 0;
padding: 0.4em;
}
/* -- domain module index --------------------------------------------------- */
table.modindextable td {
padding: 2px;
border-collapse: collapse;
}
/* -- general body styles --------------------------------------------------- */
div.body p, div.body dd, div.body li, div.body blockquote {
-moz-hyphens: auto;
-ms-hyphens: auto;
-webkit-hyphens: auto;
hyphens: auto;
}
a.headerlink {
visibility: hidden;
}
h1:hover > a.headerlink,
h2:hover > a.headerlink,
h3:hover > a.headerlink,
h4:hover > a.headerlink,
h5:hover > a.headerlink,
h6:hover > a.headerlink,
dt:hover > a.headerlink,
caption:hover > a.headerlink,
p.caption:hover > a.headerlink,
div.code-block-caption:hover > a.headerlink {
visibility: visible;
}
div.body p.caption {
text-align: inherit;
}
div.body td {
text-align: left;
}
.first {
margin-top: 0 !important;
}
p.rubric {
margin-top: 30px;
font-weight: bold;
}
img.align-left, .figure.align-left, object.align-left {
clear: left;
float: left;
margin-right: 1em;
}
img.align-right, .figure.align-right, object.align-right {
clear: right;
float: right;
margin-left: 1em;
}
img.align-center, .figure.align-center, object.align-center {
display: block;
margin-left: auto;
margin-right: auto;
}
.align-left {
text-align: left;
}
.align-center {
text-align: center;
}
.align-right {
text-align: right;
}
/* -- sidebars -------------------------------------------------------------- */
div.sidebar {
margin: 0 0 0.5em 1em;
border: 1px solid #ddb;
padding: 7px 7px 0 7px;
background-color: #ffe;
width: 40%;
float: right;
}
p.sidebar-title {
font-weight: bold;
}
/* -- topics ---------------------------------------------------------------- */
div.topic {
border: 1px solid #ccc;
padding: 7px 7px 0 7px;
margin: 10px 0 10px 0;
}
p.topic-title {
font-size: 1.1em;
font-weight: bold;
margin-top: 10px;
}
/* -- admonitions ----------------------------------------------------------- */
div.admonition {
margin-top: 10px;
margin-bottom: 10px;
padding: 7px;
}
div.admonition dt {
font-weight: bold;
}
div.admonition dl {
margin-bottom: 0;
}
p.admonition-title {
margin: 0px 10px 5px 0px;
font-weight: bold;
}
div.body p.centered {
text-align: center;
margin-top: 25px;
}
/* -- tables ---------------------------------------------------------------- */
table.docutils {
border: 0;
border-collapse: collapse;
}
table caption span.caption-number {
font-style: italic;
}
table caption span.caption-text {
}
table.docutils td, table.docutils th {
padding: 1px 8px 1px 5px;
border-top: 0;
border-left: 0;
border-right: 0;
border-bottom: 1px solid #aaa;
}
table.footnote td, table.footnote th {
border: 0 !important;
}
th {
text-align: left;
padding-right: 5px;
}
table.citation {
border-left: solid 1px gray;
margin-left: 1px;
}
table.citation td {
border-bottom: none;
}
/* -- figures --------------------------------------------------------------- */
div.figure {
margin: 0.5em;
padding: 0.5em;
}
div.figure p.caption {
padding: 0.3em;
}
div.figure p.caption span.caption-number {
font-style: italic;
}
div.figure p.caption span.caption-text {
}
/* -- field list styles ----------------------------------------------------- */
table.field-list td, table.field-list th {
border: 0 !important;
}
.field-list ul {
margin: 0;
padding-left: 1em;
}
.field-list p {
margin: 0;
}
/* -- other body styles ----------------------------------------------------- */
ol.arabic {
list-style: decimal;
}
ol.loweralpha {
list-style: lower-alpha;
}
ol.upperalpha {
list-style: upper-alpha;
}
ol.lowerroman {
list-style: lower-roman;
}
ol.upperroman {
list-style: upper-roman;
}
dl {
margin-bottom: 15px;
}
dd p {
margin-top: 0px;
}
dd ul, dd table {
margin-bottom: 10px;
}
dd {
margin-top: 3px;
margin-bottom: 10px;
margin-left: 30px;
}
dt:target, .highlighted {
background-color: #fbe54e;
}
dl.glossary dt {
font-weight: bold;
font-size: 1.1em;
}
.optional {
font-size: 1.3em;
}
.sig-paren {
font-size: larger;
}
.versionmodified {
font-style: italic;
}
.system-message {
background-color: #fda;
padding: 5px;
border: 3px solid red;
}
.footnote:target {
background-color: #ffa;
}
.line-block {
display: block;
margin-top: 1em;
margin-bottom: 1em;
}
.line-block .line-block {
margin-top: 0;
margin-bottom: 0;
margin-left: 1.5em;
}
.guilabel, .menuselection {
font-family: sans-serif;
}
.accelerator {
text-decoration: underline;
}
.classifier {
font-style: oblique;
}
abbr, acronym {
border-bottom: dotted 1px;
cursor: help;
}
/* -- code displays --------------------------------------------------------- */
pre {
overflow: auto;
overflow-y: hidden; /* fixes display issues on Chrome browsers */
}
span.pre {
-moz-hyphens: none;
-ms-hyphens: none;
-webkit-hyphens: none;
hyphens: none;
}
td.linenos pre {
padding: 5px 0px;
border: 0;
background-color: transparent;
color: #aaa;
}
table.highlighttable {
margin-left: 0.5em;
}
table.highlighttable td {
padding: 0 0.5em 0 0.5em;
}
div.code-block-caption {
padding: 2px 5px;
font-size: small;
}
div.code-block-caption code {
background-color: transparent;
}
div.code-block-caption + div > div.highlight > pre {
margin-top: 0;
}
div.code-block-caption span.caption-number {
padding: 0.1em 0.3em;
font-style: italic;
}
div.code-block-caption span.caption-text {
}
div.literal-block-wrapper {
padding: 1em 1em 0;
}
div.literal-block-wrapper div.highlight {
margin: 0;
}
code.descname {
background-color: transparent;
font-weight: bold;
font-size: 1.2em;
}
code.descclassname {
background-color: transparent;
}
code.xref, a code {
background-color: transparent;
font-weight: bold;
}
h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
background-color: transparent;
}
.viewcode-link {
float: right;
}
.viewcode-back {
float: right;
font-family: sans-serif;
}
div.viewcode-block:target {
margin: -1px -10px;
padding: 0 10px;
}
/* -- math display ---------------------------------------------------------- */
img.math {
vertical-align: middle;
}
div.body div.math p {
text-align: center;
}
span.eqno {
float: right;
}
span.eqno a.headerlink {
position: relative;
left: 0px;
z-index: 1;
}
div.math:hover a.headerlink {
visibility: visible;
}
/* -- printout stylesheet --------------------------------------------------- */
@media print {
div.document,
div.documentwrapper,
div.bodywrapper {
margin: 0 !important;
width: 100%;
}
div.sphinxsidebar,
div.related,
div.footer,
#top-link {
display: none;
}
}

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/*
* doctools.js
* ~~~~~~~~~~~
*
* Sphinx JavaScript utilities for all documentation.
*
* :copyright: Copyright 2007-2016 by the Sphinx team, see AUTHORS.
* :license: BSD, see LICENSE for details.
*
*/
/**
* select a different prefix for underscore
*/
$u = _.noConflict();
/**
* make the code below compatible with browsers without
* an installed firebug like debugger
if (!window.console || !console.firebug) {
var names = ["log", "debug", "info", "warn", "error", "assert", "dir",
"dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace",
"profile", "profileEnd"];
window.console = {};
for (var i = 0; i < names.length; ++i)
window.console[names[i]] = function() {};
}
*/
/**
* small helper function to urldecode strings
*/
jQuery.urldecode = function(x) {
return decodeURIComponent(x).replace(/\+/g, ' ');
};
/**
* small helper function to urlencode strings
*/
jQuery.urlencode = encodeURIComponent;
/**
* This function returns the parsed url parameters of the
* current request. Multiple values per key are supported,
* it will always return arrays of strings for the value parts.
*/
jQuery.getQueryParameters = function(s) {
if (typeof s == 'undefined')
s = document.location.search;
var parts = s.substr(s.indexOf('?') + 1).split('&');
var result = {};
for (var i = 0; i < parts.length; i++) {
var tmp = parts[i].split('=', 2);
var key = jQuery.urldecode(tmp[0]);
var value = jQuery.urldecode(tmp[1]);
if (key in result)
result[key].push(value);
else
result[key] = [value];
}
return result;
};
/**
* highlight a given string on a jquery object by wrapping it in
* span elements with the given class name.
*/
jQuery.fn.highlightText = function(text, className) {
function highlight(node) {
if (node.nodeType == 3) {
var val = node.nodeValue;
var pos = val.toLowerCase().indexOf(text);
if (pos >= 0 && !jQuery(node.parentNode).hasClass(className)) {
var span = document.createElement("span");
span.className = className;
span.appendChild(document.createTextNode(val.substr(pos, text.length)));
node.parentNode.insertBefore(span, node.parentNode.insertBefore(
document.createTextNode(val.substr(pos + text.length)),
node.nextSibling));
node.nodeValue = val.substr(0, pos);
}
}
else if (!jQuery(node).is("button, select, textarea")) {
jQuery.each(node.childNodes, function() {
highlight(this);
});
}
}
return this.each(function() {
highlight(this);
});
};
/*
* backward compatibility for jQuery.browser
* This will be supported until firefox bug is fixed.
*/
if (!jQuery.browser) {
jQuery.uaMatch = function(ua) {
ua = ua.toLowerCase();
var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
/(webkit)[ \/]([\w.]+)/.exec(ua) ||
/(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
/(msie) ([\w.]+)/.exec(ua) ||
ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
[];
return {
browser: match[ 1 ] || "",
version: match[ 2 ] || "0"
};
};
jQuery.browser = {};
jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
}
/**
* Small JavaScript module for the documentation.
*/
var Documentation = {
init : function() {
this.fixFirefoxAnchorBug();
this.highlightSearchWords();
this.initIndexTable();
},
/**
* i18n support
*/
TRANSLATIONS : {},
PLURAL_EXPR : function(n) { return n == 1 ? 0 : 1; },
LOCALE : 'unknown',
// gettext and ngettext don't access this so that the functions
// can safely bound to a different name (_ = Documentation.gettext)
gettext : function(string) {
var translated = Documentation.TRANSLATIONS[string];
if (typeof translated == 'undefined')
return string;
return (typeof translated == 'string') ? translated : translated[0];
},
ngettext : function(singular, plural, n) {
var translated = Documentation.TRANSLATIONS[singular];
if (typeof translated == 'undefined')
return (n == 1) ? singular : plural;
return translated[Documentation.PLURALEXPR(n)];
},
addTranslations : function(catalog) {
for (var key in catalog.messages)
this.TRANSLATIONS[key] = catalog.messages[key];
this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')');
this.LOCALE = catalog.locale;
},
/**
* add context elements like header anchor links
*/
addContextElements : function() {
$('div[id] > :header:first').each(function() {
$('<a class="headerlink">\u00B6</a>').
attr('href', '#' + this.id).
attr('title', _('Permalink to this headline')).
appendTo(this);
});
$('dt[id]').each(function() {
$('<a class="headerlink">\u00B6</a>').
attr('href', '#' + this.id).
attr('title', _('Permalink to this definition')).
appendTo(this);
});
},
/**
* workaround a firefox stupidity
* see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075
*/
fixFirefoxAnchorBug : function() {
if (document.location.hash)
window.setTimeout(function() {
document.location.href += '';
}, 10);
},
/**
* highlight the search words provided in the url in the text
*/
highlightSearchWords : function() {
var params = $.getQueryParameters();
var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : [];
if (terms.length) {
var body = $('div.body');
if (!body.length) {
body = $('body');
}
window.setTimeout(function() {
$.each(terms, function() {
body.highlightText(this.toLowerCase(), 'highlighted');
});
}, 10);
$('<p class="highlight-link"><a href="javascript:Documentation.' +
'hideSearchWords()">' + _('Hide Search Matches') + '</a></p>')
.appendTo($('#searchbox'));
}
},
/**
* init the domain index toggle buttons
*/
initIndexTable : function() {
var togglers = $('img.toggler').click(function() {
var src = $(this).attr('src');
var idnum = $(this).attr('id').substr(7);
$('tr.cg-' + idnum).toggle();
if (src.substr(-9) == 'minus.png')
$(this).attr('src', src.substr(0, src.length-9) + 'plus.png');
else
$(this).attr('src', src.substr(0, src.length-8) + 'minus.png');
}).css('display', '');
if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) {
togglers.click();
}
},
/**
* helper function to hide the search marks again
*/
hideSearchWords : function() {
$('#searchbox .highlight-link').fadeOut(300);
$('span.highlighted').removeClass('highlighted');
},
/**
* make the url absolute
*/
makeURL : function(relativeURL) {
return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL;
},
/**
* get the current relative url
*/
getCurrentURL : function() {
var path = document.location.pathname;
var parts = path.split(/\//);
$.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() {
if (this == '..')
parts.pop();
});
var url = parts.join('/');
return path.substring(url.lastIndexOf('/') + 1, path.length - 1);
},
initOnKeyListeners: function() {
$(document).keyup(function(event) {
var activeElementType = document.activeElement.tagName;
// don't navigate when in search box or textarea
if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT') {
switch (event.keyCode) {
case 37: // left
var prevHref = $('link[rel="prev"]').prop('href');
if (prevHref) {
window.location.href = prevHref;
return false;
}
case 39: // right
var nextHref = $('link[rel="next"]').prop('href');
if (nextHref) {
window.location.href = nextHref;
return false;
}
}
}
});
}
};
// quick alias for translations
_ = Documentation.gettext;
$(document).ready(function() {
Documentation.init();
});

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.highlight .hll { background-color: #ffffcc }
.highlight { background: #eeffcc; }
.highlight .c { color: #408090; font-style: italic } /* Comment */
.highlight .err { border: 1px solid #FF0000 } /* Error */
.highlight .k { color: #007020; font-weight: bold } /* Keyword */
.highlight .o { color: #666666 } /* Operator */
.highlight .ch { color: #408090; font-style: italic } /* Comment.Hashbang */
.highlight .cm { color: #408090; font-style: italic } /* Comment.Multiline */
.highlight .cp { color: #007020 } /* Comment.Preproc */
.highlight .cpf { color: #408090; font-style: italic } /* Comment.PreprocFile */
.highlight .c1 { color: #408090; font-style: italic } /* Comment.Single */
.highlight .cs { color: #408090; background-color: #fff0f0 } /* Comment.Special */
.highlight .gd { color: #A00000 } /* Generic.Deleted */
.highlight .ge { font-style: italic } /* Generic.Emph */
.highlight .gr { color: #FF0000 } /* Generic.Error */
.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
.highlight .gi { color: #00A000 } /* Generic.Inserted */
.highlight .go { color: #333333 } /* Generic.Output */
.highlight .gp { color: #c65d09; font-weight: bold } /* Generic.Prompt */
.highlight .gs { font-weight: bold } /* Generic.Strong */
.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
.highlight .gt { color: #0044DD } /* Generic.Traceback */
.highlight .kc { color: #007020; font-weight: bold } /* Keyword.Constant */
.highlight .kd { color: #007020; font-weight: bold } /* Keyword.Declaration */
.highlight .kn { color: #007020; font-weight: bold } /* Keyword.Namespace */
.highlight .kp { color: #007020 } /* Keyword.Pseudo */
.highlight .kr { color: #007020; font-weight: bold } /* Keyword.Reserved */
.highlight .kt { color: #902000 } /* Keyword.Type */
.highlight .m { color: #208050 } /* Literal.Number */
.highlight .s { color: #4070a0 } /* Literal.String */
.highlight .na { color: #4070a0 } /* Name.Attribute */
.highlight .nb { color: #007020 } /* Name.Builtin */
.highlight .nc { color: #0e84b5; font-weight: bold } /* Name.Class */
.highlight .no { color: #60add5 } /* Name.Constant */
.highlight .nd { color: #555555; font-weight: bold } /* Name.Decorator */
.highlight .ni { color: #d55537; font-weight: bold } /* Name.Entity */
.highlight .ne { color: #007020 } /* Name.Exception */
.highlight .nf { color: #06287e } /* Name.Function */
.highlight .nl { color: #002070; font-weight: bold } /* Name.Label */
.highlight .nn { color: #0e84b5; font-weight: bold } /* Name.Namespace */
.highlight .nt { color: #062873; font-weight: bold } /* Name.Tag */
.highlight .nv { color: #bb60d5 } /* Name.Variable */
.highlight .ow { color: #007020; font-weight: bold } /* Operator.Word */
.highlight .w { color: #bbbbbb } /* Text.Whitespace */
.highlight .mb { color: #208050 } /* Literal.Number.Bin */
.highlight .mf { color: #208050 } /* Literal.Number.Float */
.highlight .mh { color: #208050 } /* Literal.Number.Hex */
.highlight .mi { color: #208050 } /* Literal.Number.Integer */
.highlight .mo { color: #208050 } /* Literal.Number.Oct */
.highlight .sb { color: #4070a0 } /* Literal.String.Backtick */
.highlight .sc { color: #4070a0 } /* Literal.String.Char */
.highlight .sd { color: #4070a0; font-style: italic } /* Literal.String.Doc */
.highlight .s2 { color: #4070a0 } /* Literal.String.Double */
.highlight .se { color: #4070a0; font-weight: bold } /* Literal.String.Escape */
.highlight .sh { color: #4070a0 } /* Literal.String.Heredoc */
.highlight .si { color: #70a0d0; font-style: italic } /* Literal.String.Interpol */
.highlight .sx { color: #c65d09 } /* Literal.String.Other */
.highlight .sr { color: #235388 } /* Literal.String.Regex */
.highlight .s1 { color: #4070a0 } /* Literal.String.Single */
.highlight .ss { color: #517918 } /* Literal.String.Symbol */
.highlight .bp { color: #007020 } /* Name.Builtin.Pseudo */
.highlight .vc { color: #bb60d5 } /* Name.Variable.Class */
.highlight .vg { color: #bb60d5 } /* Name.Variable.Global */
.highlight .vi { color: #bb60d5 } /* Name.Variable.Instance */
.highlight .il { color: #208050 } /* Literal.Number.Integer.Long */

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/*
* searchtools.js_t
* ~~~~~~~~~~~~~~~~
*
* Sphinx JavaScript utilities for the full-text search.
*
* :copyright: Copyright 2007-2016 by the Sphinx team, see AUTHORS.
* :license: BSD, see LICENSE for details.
*
*/
/* Non-minified version JS is _stemmer.js if file is provided */
/**
* Porter Stemmer
*/
var Stemmer = function() {
var step2list = {
ational: 'ate',
tional: 'tion',
enci: 'ence',
anci: 'ance',
izer: 'ize',
bli: 'ble',
alli: 'al',
entli: 'ent',
eli: 'e',
ousli: 'ous',
ization: 'ize',
ation: 'ate',
ator: 'ate',
alism: 'al',
iveness: 'ive',
fulness: 'ful',
ousness: 'ous',
aliti: 'al',
iviti: 'ive',
biliti: 'ble',
logi: 'log'
};
var step3list = {
icate: 'ic',
ative: '',
alize: 'al',
iciti: 'ic',
ical: 'ic',
ful: '',
ness: ''
};
var c = "[^aeiou]"; // consonant
var v = "[aeiouy]"; // vowel
var C = c + "[^aeiouy]*"; // consonant sequence
var V = v + "[aeiou]*"; // vowel sequence
var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0
var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1
var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1
var s_v = "^(" + C + ")?" + v; // vowel in stem
this.stemWord = function (w) {
var stem;
var suffix;
var firstch;
var origword = w;
if (w.length < 3)
return w;
var re;
var re2;
var re3;
var re4;
firstch = w.substr(0,1);
if (firstch == "y")
w = firstch.toUpperCase() + w.substr(1);
// Step 1a
re = /^(.+?)(ss|i)es$/;
re2 = /^(.+?)([^s])s$/;
if (re.test(w))
w = w.replace(re,"$1$2");
else if (re2.test(w))
w = w.replace(re2,"$1$2");
// Step 1b
re = /^(.+?)eed$/;
re2 = /^(.+?)(ed|ing)$/;
if (re.test(w)) {
var fp = re.exec(w);
re = new RegExp(mgr0);
if (re.test(fp[1])) {
re = /.$/;
w = w.replace(re,"");
}
}
else if (re2.test(w)) {
var fp = re2.exec(w);
stem = fp[1];
re2 = new RegExp(s_v);
if (re2.test(stem)) {
w = stem;
re2 = /(at|bl|iz)$/;
re3 = new RegExp("([^aeiouylsz])\\1$");
re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
if (re2.test(w))
w = w + "e";
else if (re3.test(w)) {
re = /.$/;
w = w.replace(re,"");
}
else if (re4.test(w))
w = w + "e";
}
}
// Step 1c
re = /^(.+?)y$/;
if (re.test(w)) {
var fp = re.exec(w);
stem = fp[1];
re = new RegExp(s_v);
if (re.test(stem))
w = stem + "i";
}
// Step 2
re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
if (re.test(w)) {
var fp = re.exec(w);
stem = fp[1];
suffix = fp[2];
re = new RegExp(mgr0);
if (re.test(stem))
w = stem + step2list[suffix];
}
// Step 3
re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
if (re.test(w)) {
var fp = re.exec(w);
stem = fp[1];
suffix = fp[2];
re = new RegExp(mgr0);
if (re.test(stem))
w = stem + step3list[suffix];
}
// Step 4
re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
re2 = /^(.+?)(s|t)(ion)$/;
if (re.test(w)) {
var fp = re.exec(w);
stem = fp[1];
re = new RegExp(mgr1);
if (re.test(stem))
w = stem;
}
else if (re2.test(w)) {
var fp = re2.exec(w);
stem = fp[1] + fp[2];
re2 = new RegExp(mgr1);
if (re2.test(stem))
w = stem;
}
// Step 5
re = /^(.+?)e$/;
if (re.test(w)) {
var fp = re.exec(w);
stem = fp[1];
re = new RegExp(mgr1);
re2 = new RegExp(meq1);
re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
w = stem;
}
re = /ll$/;
re2 = new RegExp(mgr1);
if (re.test(w) && re2.test(w)) {
re = /.$/;
w = w.replace(re,"");
}
// and turn initial Y back to y
if (firstch == "y")
w = firstch.toLowerCase() + w.substr(1);
return w;
}
}
/**
* Simple result scoring code.
*/
var Scorer = {
// Implement the following function to further tweak the score for each result
// The function takes a result array [filename, title, anchor, descr, score]
// and returns the new score.
/*
score: function(result) {
return result[4];
},
*/
// query matches the full name of an object
objNameMatch: 11,
// or matches in the last dotted part of the object name
objPartialMatch: 6,
// Additive scores depending on the priority of the object
objPrio: {0: 15, // used to be importantResults
1: 5, // used to be objectResults
2: -5}, // used to be unimportantResults
// Used when the priority is not in the mapping.
objPrioDefault: 0,
// query found in title
title: 15,
// query found in terms
term: 5
};
var splitChars = (function() {
var result = {};
var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648,
1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702,
2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971,
2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345,
3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761,
3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823,
4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125,
8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695,
11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587,
43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141];
var i, j, start, end;
for (i = 0; i < singles.length; i++) {
result[singles[i]] = true;
}
var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709],
[722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161],
[1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568],
[1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807],
[1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047],
[2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383],
[2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450],
[2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547],
[2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673],
[2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820],
[2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946],
[2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023],
[3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173],
[3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332],
[3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481],
[3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718],
[3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791],
[3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095],
[4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205],
[4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687],
[4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968],
[4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869],
[5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102],
[6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271],
[6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592],
[6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822],
[6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167],
[7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959],
[7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143],
[8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318],
[8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483],
[8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101],
[10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567],
[11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292],
[12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444],
[12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783],
[12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311],
[19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511],
[42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774],
[42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071],
[43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263],
[43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519],
[43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647],
[43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967],
[44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295],
[57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274],
[64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007],
[65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381],
[65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]];
for (i = 0; i < ranges.length; i++) {
start = ranges[i][0];
end = ranges[i][1];
for (j = start; j <= end; j++) {
result[j] = true;
}
}
return result;
})();
function splitQuery(query) {
var result = [];
var start = -1;
for (var i = 0; i < query.length; i++) {
if (splitChars[query.charCodeAt(i)]) {
if (start !== -1) {
result.push(query.slice(start, i));
start = -1;
}
} else if (start === -1) {
start = i;
}
}
if (start !== -1) {
result.push(query.slice(start));
}
return result;
}
/**
* Search Module
*/
var Search = {
_index : null,
_queued_query : null,
_pulse_status : -1,
init : function() {
var params = $.getQueryParameters();
if (params.q) {
var query = params.q[0];
$('input[name="q"]')[0].value = query;
this.performSearch(query);
}
},
loadIndex : function(url) {
$.ajax({type: "GET", url: url, data: null,
dataType: "script", cache: true,
complete: function(jqxhr, textstatus) {
if (textstatus != "success") {
document.getElementById("searchindexloader").src = url;
}
}});
},
setIndex : function(index) {
var q;
this._index = index;
if ((q = this._queued_query) !== null) {
this._queued_query = null;
Search.query(q);
}
},
hasIndex : function() {
return this._index !== null;
},
deferQuery : function(query) {
this._queued_query = query;
},
stopPulse : function() {
this._pulse_status = 0;
},
startPulse : function() {
if (this._pulse_status >= 0)
return;
function pulse() {
var i;
Search._pulse_status = (Search._pulse_status + 1) % 4;
var dotString = '';
for (i = 0; i < Search._pulse_status; i++)
dotString += '.';
Search.dots.text(dotString);
if (Search._pulse_status > -1)
window.setTimeout(pulse, 500);
}
pulse();
},
/**
* perform a search for something (or wait until index is loaded)
*/
performSearch : function(query) {
// create the required interface elements
this.out = $('#search-results');
this.title = $('<h2>' + _('Searching') + '</h2>').appendTo(this.out);
this.dots = $('<span></span>').appendTo(this.title);
this.status = $('<p style="display: none"></p>').appendTo(this.out);
this.output = $('<ul class="search"/>').appendTo(this.out);
$('#search-progress').text(_('Preparing search...'));
this.startPulse();
// index already loaded, the browser was quick!
if (this.hasIndex())
this.query(query);
else
this.deferQuery(query);
},
/**
* execute search (requires search index to be loaded)
*/
query : function(query) {
var i;
var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"];
// stem the searchterms and add them to the correct list
var stemmer = new Stemmer();
var searchterms = [];
var excluded = [];
var hlterms = [];
var tmp = splitQuery(query);
var objectterms = [];
for (i = 0; i < tmp.length; i++) {
if (tmp[i] !== "") {
objectterms.push(tmp[i].toLowerCase());
}
if ($u.indexOf(stopwords, tmp[i].toLowerCase()) != -1 || tmp[i].match(/^\d+$/) ||
tmp[i] === "") {
// skip this "word"
continue;
}
// stem the word
var word = stemmer.stemWord(tmp[i].toLowerCase());
// prevent stemmer from cutting word smaller than two chars
if(word.length < 3 && tmp[i].length >= 3) {
word = tmp[i];
}
var toAppend;
// select the correct list
if (word[0] == '-') {
toAppend = excluded;
word = word.substr(1);
}
else {
toAppend = searchterms;
hlterms.push(tmp[i].toLowerCase());
}
// only add if not already in the list
if (!$u.contains(toAppend, word))
toAppend.push(word);
}
var highlightstring = '?highlight=' + $.urlencode(hlterms.join(" "));
// console.debug('SEARCH: searching for:');
// console.info('required: ', searchterms);
// console.info('excluded: ', excluded);
// prepare search
var terms = this._index.terms;
var titleterms = this._index.titleterms;
// array of [filename, title, anchor, descr, score]
var results = [];
$('#search-progress').empty();
// lookup as object
for (i = 0; i < objectterms.length; i++) {
var others = [].concat(objectterms.slice(0, i),
objectterms.slice(i+1, objectterms.length));
results = results.concat(this.performObjectSearch(objectterms[i], others));
}
// lookup as search terms in fulltext
results = results.concat(this.performTermsSearch(searchterms, excluded, terms, titleterms));
// let the scorer override scores with a custom scoring function
if (Scorer.score) {
for (i = 0; i < results.length; i++)
results[i][4] = Scorer.score(results[i]);
}
// now sort the results by score (in opposite order of appearance, since the
// display function below uses pop() to retrieve items) and then
// alphabetically
results.sort(function(a, b) {
var left = a[4];
var right = b[4];
if (left > right) {
return 1;
} else if (left < right) {
return -1;
} else {
// same score: sort alphabetically
left = a[1].toLowerCase();
right = b[1].toLowerCase();
return (left > right) ? -1 : ((left < right) ? 1 : 0);
}
});
// for debugging
//Search.lastresults = results.slice(); // a copy
//console.info('search results:', Search.lastresults);
// print the results
var resultCount = results.length;
function displayNextItem() {
// results left, load the summary and display it
if (results.length) {
var item = results.pop();
var listItem = $('<li style="display:none"></li>');
if (DOCUMENTATION_OPTIONS.FILE_SUFFIX === '') {
// dirhtml builder
var dirname = item[0] + '/';
if (dirname.match(/\/index\/$/)) {
dirname = dirname.substring(0, dirname.length-6);
} else if (dirname == 'index/') {
dirname = '';
}
listItem.append($('<a/>').attr('href',
DOCUMENTATION_OPTIONS.URL_ROOT + dirname +
highlightstring + item[2]).html(item[1]));
} else {
// normal html builders
listItem.append($('<a/>').attr('href',
item[0] + DOCUMENTATION_OPTIONS.FILE_SUFFIX +
highlightstring + item[2]).html(item[1]));
}
if (item[3]) {
listItem.append($('<span> (' + item[3] + ')</span>'));
Search.output.append(listItem);
listItem.slideDown(5, function() {
displayNextItem();
});
} else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) {
var suffix = DOCUMENTATION_OPTIONS.SOURCELINK_SUFFIX;
$.ajax({url: DOCUMENTATION_OPTIONS.URL_ROOT + '_sources/' + item[5] + (item[5].slice(-suffix.length) === suffix ? '' : suffix),
dataType: "text",
complete: function(jqxhr, textstatus) {
var data = jqxhr.responseText;
if (data !== '' && data !== undefined) {
listItem.append(Search.makeSearchSummary(data, searchterms, hlterms));
}
Search.output.append(listItem);
listItem.slideDown(5, function() {
displayNextItem();
});
}});
} else {
// no source available, just display title
Search.output.append(listItem);
listItem.slideDown(5, function() {
displayNextItem();
});
}
}
// search finished, update title and status message
else {
Search.stopPulse();
Search.title.text(_('Search Results'));
if (!resultCount)
Search.status.text(_('Your search did not match any documents. Please make sure that all words are spelled correctly and that you\'ve selected enough categories.'));
else
Search.status.text(_('Search finished, found %s page(s) matching the search query.').replace('%s', resultCount));
Search.status.fadeIn(500);
}
}
displayNextItem();
},
/**
* search for object names
*/
performObjectSearch : function(object, otherterms) {
var filenames = this._index.filenames;
var docnames = this._index.docnames;
var objects = this._index.objects;
var objnames = this._index.objnames;
var titles = this._index.titles;
var i;
var results = [];
for (var prefix in objects) {
for (var name in objects[prefix]) {
var fullname = (prefix ? prefix + '.' : '') + name;
if (fullname.toLowerCase().indexOf(object) > -1) {
var score = 0;
var parts = fullname.split('.');
// check for different match types: exact matches of full name or
// "last name" (i.e. last dotted part)
if (fullname == object || parts[parts.length - 1] == object) {
score += Scorer.objNameMatch;
// matches in last name
} else if (parts[parts.length - 1].indexOf(object) > -1) {
score += Scorer.objPartialMatch;
}
var match = objects[prefix][name];
var objname = objnames[match[1]][2];
var title = titles[match[0]];
// If more than one term searched for, we require other words to be
// found in the name/title/description
if (otherterms.length > 0) {
var haystack = (prefix + ' ' + name + ' ' +
objname + ' ' + title).toLowerCase();
var allfound = true;
for (i = 0; i < otherterms.length; i++) {
if (haystack.indexOf(otherterms[i]) == -1) {
allfound = false;
break;
}
}
if (!allfound) {
continue;
}
}
var descr = objname + _(', in ') + title;
var anchor = match[3];
if (anchor === '')
anchor = fullname;
else if (anchor == '-')
anchor = objnames[match[1]][1] + '-' + fullname;
// add custom score for some objects according to scorer
if (Scorer.objPrio.hasOwnProperty(match[2])) {
score += Scorer.objPrio[match[2]];
} else {
score += Scorer.objPrioDefault;
}
results.push([docnames[match[0]], fullname, '#'+anchor, descr, score, filenames[match[0]]]);
}
}
}
return results;
},
/**
* search for full-text terms in the index
*/
performTermsSearch : function(searchterms, excluded, terms, titleterms) {
var docnames = this._index.docnames;
var filenames = this._index.filenames;
var titles = this._index.titles;
var i, j, file;
var fileMap = {};
var scoreMap = {};
var results = [];
// perform the search on the required terms
for (i = 0; i < searchterms.length; i++) {
var word = searchterms[i];
var files = [];
var _o = [
{files: terms[word], score: Scorer.term},
{files: titleterms[word], score: Scorer.title}
];
// no match but word was a required one
if ($u.every(_o, function(o){return o.files === undefined;})) {
break;
}
// found search word in contents
$u.each(_o, function(o) {
var _files = o.files;
if (_files === undefined)
return
if (_files.length === undefined)
_files = [_files];
files = files.concat(_files);
// set score for the word in each file to Scorer.term
for (j = 0; j < _files.length; j++) {
file = _files[j];
if (!(file in scoreMap))
scoreMap[file] = {}
scoreMap[file][word] = o.score;
}
});
// create the mapping
for (j = 0; j < files.length; j++) {
file = files[j];
if (file in fileMap)
fileMap[file].push(word);
else
fileMap[file] = [word];
}
}
// now check if the files don't contain excluded terms
for (file in fileMap) {
var valid = true;
// check if all requirements are matched
if (fileMap[file].length != searchterms.length)
continue;
// ensure that none of the excluded terms is in the search result
for (i = 0; i < excluded.length; i++) {
if (terms[excluded[i]] == file ||
titleterms[excluded[i]] == file ||
$u.contains(terms[excluded[i]] || [], file) ||
$u.contains(titleterms[excluded[i]] || [], file)) {
valid = false;
break;
}
}
// if we have still a valid result we can add it to the result list
if (valid) {
// select one (max) score for the file.
// for better ranking, we should calculate ranking by using words statistics like basic tf-idf...
var score = $u.max($u.map(fileMap[file], function(w){return scoreMap[file][w]}));
results.push([docnames[file], titles[file], '', null, score, filenames[file]]);
}
}
return results;
},
/**
* helper function to return a node containing the
* search summary for a given text. keywords is a list
* of stemmed words, hlwords is the list of normal, unstemmed
* words. the first one is used to find the occurrence, the
* latter for highlighting it.
*/
makeSearchSummary : function(text, keywords, hlwords) {
var textLower = text.toLowerCase();
var start = 0;
$.each(keywords, function() {
var i = textLower.indexOf(this.toLowerCase());
if (i > -1)
start = i;
});
start = Math.max(start - 120, 0);
var excerpt = ((start > 0) ? '...' : '') +
$.trim(text.substr(start, 240)) +
((start + 240 - text.length) ? '...' : '');
var rv = $('<div class="context"></div>').text(excerpt);
$.each(hlwords, function() {
rv = rv.highlightText(this, 'highlighted');
});
return rv;
}
};
$(document).ready(function() {
Search.init();
});

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docs/_build/html/_static/underscore-1.3.1.js vendored
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@ -1,999 +0,0 @@
// Underscore.js 1.3.1
// (c) 2009-2012 Jeremy Ashkenas, DocumentCloud Inc.
// Underscore is freely distributable under the MIT license.
// Portions of Underscore are inspired or borrowed from Prototype,
// Oliver Steele's Functional, and John Resig's Micro-Templating.
// For all details and documentation:
// http://documentcloud.github.com/underscore
(function() {
// Baseline setup
// --------------
// Establish the root object, `window` in the browser, or `global` on the server.
var root = this;
// Save the previous value of the `_` variable.
var previousUnderscore = root._;
// Establish the object that gets returned to break out of a loop iteration.
var breaker = {};
// Save bytes in the minified (but not gzipped) version:
var ArrayProto = Array.prototype, ObjProto = Object.prototype, FuncProto = Function.prototype;
// Create quick reference variables for speed access to core prototypes.
var slice = ArrayProto.slice,
unshift = ArrayProto.unshift,
toString = ObjProto.toString,
hasOwnProperty = ObjProto.hasOwnProperty;
// All **ECMAScript 5** native function implementations that we hope to use
// are declared here.
var
nativeForEach = ArrayProto.forEach,
nativeMap = ArrayProto.map,
nativeReduce = ArrayProto.reduce,
nativeReduceRight = ArrayProto.reduceRight,
nativeFilter = ArrayProto.filter,
nativeEvery = ArrayProto.every,
nativeSome = ArrayProto.some,
nativeIndexOf = ArrayProto.indexOf,
nativeLastIndexOf = ArrayProto.lastIndexOf,
nativeIsArray = Array.isArray,
nativeKeys = Object.keys,
nativeBind = FuncProto.bind;
// Create a safe reference to the Underscore object for use below.
var _ = function(obj) { return new wrapper(obj); };
// Export the Underscore object for **Node.js**, with
// backwards-compatibility for the old `require()` API. If we're in
// the browser, add `_` as a global object via a string identifier,
// for Closure Compiler "advanced" mode.
if (typeof exports !== 'undefined') {
if (typeof module !== 'undefined' && module.exports) {
exports = module.exports = _;
}
exports._ = _;
} else {
root['_'] = _;
}
// Current version.
_.VERSION = '1.3.1';
// Collection Functions
// --------------------
// The cornerstone, an `each` implementation, aka `forEach`.
// Handles objects with the built-in `forEach`, arrays, and raw objects.
// Delegates to **ECMAScript 5**'s native `forEach` if available.
var each = _.each = _.forEach = function(obj, iterator, context) {
if (obj == null) return;
if (nativeForEach && obj.forEach === nativeForEach) {
obj.forEach(iterator, context);
} else if (obj.length === +obj.length) {
for (var i = 0, l = obj.length; i < l; i++) {
if (i in obj && iterator.call(context, obj[i], i, obj) === breaker) return;
}
} else {
for (var key in obj) {
if (_.has(obj, key)) {
if (iterator.call(context, obj[key], key, obj) === breaker) return;
}
}
}
};
// Return the results of applying the iterator to each element.
// Delegates to **ECMAScript 5**'s native `map` if available.
_.map = _.collect = function(obj, iterator, context) {
var results = [];
if (obj == null) return results;
if (nativeMap && obj.map === nativeMap) return obj.map(iterator, context);
each(obj, function(value, index, list) {
results[results.length] = iterator.call(context, value, index, list);
});
if (obj.length === +obj.length) results.length = obj.length;
return results;
};
// **Reduce** builds up a single result from a list of values, aka `inject`,
// or `foldl`. Delegates to **ECMAScript 5**'s native `reduce` if available.
_.reduce = _.foldl = _.inject = function(obj, iterator, memo, context) {
var initial = arguments.length > 2;
if (obj == null) obj = [];
if (nativeReduce && obj.reduce === nativeReduce) {
if (context) iterator = _.bind(iterator, context);
return initial ? obj.reduce(iterator, memo) : obj.reduce(iterator);
}
each(obj, function(value, index, list) {
if (!initial) {
memo = value;
initial = true;
} else {
memo = iterator.call(context, memo, value, index, list);
}
});
if (!initial) throw new TypeError('Reduce of empty array with no initial value');
return memo;
};
// The right-associative version of reduce, also known as `foldr`.
// Delegates to **ECMAScript 5**'s native `reduceRight` if available.
_.reduceRight = _.foldr = function(obj, iterator, memo, context) {
var initial = arguments.length > 2;
if (obj == null) obj = [];
if (nativeReduceRight && obj.reduceRight === nativeReduceRight) {
if (context) iterator = _.bind(iterator, context);
return initial ? obj.reduceRight(iterator, memo) : obj.reduceRight(iterator);
}
var reversed = _.toArray(obj).reverse();
if (context && !initial) iterator = _.bind(iterator, context);
return initial ? _.reduce(reversed, iterator, memo, context) : _.reduce(reversed, iterator);
};
// Return the first value which passes a truth test. Aliased as `detect`.
_.find = _.detect = function(obj, iterator, context) {
var result;
any(obj, function(value, index, list) {
if (iterator.call(context, value, index, list)) {
result = value;
return true;
}
});
return result;
};
// Return all the elements that pass a truth test.
// Delegates to **ECMAScript 5**'s native `filter` if available.
// Aliased as `select`.
_.filter = _.select = function(obj, iterator, context) {
var results = [];
if (obj == null) return results;
if (nativeFilter && obj.filter === nativeFilter) return obj.filter(iterator, context);
each(obj, function(value, index, list) {
if (iterator.call(context, value, index, list)) results[results.length] = value;
});
return results;
};
// Return all the elements for which a truth test fails.
_.reject = function(obj, iterator, context) {
var results = [];
if (obj == null) return results;
each(obj, function(value, index, list) {
if (!iterator.call(context, value, index, list)) results[results.length] = value;
});
return results;
};
// Determine whether all of the elements match a truth test.
// Delegates to **ECMAScript 5**'s native `every` if available.
// Aliased as `all`.
_.every = _.all = function(obj, iterator, context) {
var result = true;
if (obj == null) return result;
if (nativeEvery && obj.every === nativeEvery) return obj.every(iterator, context);
each(obj, function(value, index, list) {
if (!(result = result && iterator.call(context, value, index, list))) return breaker;
});
return result;
};
// Determine if at least one element in the object matches a truth test.
// Delegates to **ECMAScript 5**'s native `some` if available.
// Aliased as `any`.
var any = _.some = _.any = function(obj, iterator, context) {
iterator || (iterator = _.identity);
var result = false;
if (obj == null) return result;
if (nativeSome && obj.some === nativeSome) return obj.some(iterator, context);
each(obj, function(value, index, list) {
if (result || (result = iterator.call(context, value, index, list))) return breaker;
});
return !!result;
};
// Determine if a given value is included in the array or object using `===`.
// Aliased as `contains`.
_.include = _.contains = function(obj, target) {
var found = false;
if (obj == null) return found;
if (nativeIndexOf && obj.indexOf === nativeIndexOf) return obj.indexOf(target) != -1;
found = any(obj, function(value) {
return value === target;
});
return found;
};
// Invoke a method (with arguments) on every item in a collection.
_.invoke = function(obj, method) {
var args = slice.call(arguments, 2);
return _.map(obj, function(value) {
return (_.isFunction(method) ? method || value : value[method]).apply(value, args);
});
};
// Convenience version of a common use case of `map`: fetching a property.
_.pluck = function(obj, key) {
return _.map(obj, function(value){ return value[key]; });
};
// Return the maximum element or (element-based computation).
_.max = function(obj, iterator, context) {
if (!iterator && _.isArray(obj)) return Math.max.apply(Math, obj);
if (!iterator && _.isEmpty(obj)) return -Infinity;
var result = {computed : -Infinity};
each(obj, function(value, index, list) {
var computed = iterator ? iterator.call(context, value, index, list) : value;
computed >= result.computed && (result = {value : value, computed : computed});
});
return result.value;
};
// Return the minimum element (or element-based computation).
_.min = function(obj, iterator, context) {
if (!iterator && _.isArray(obj)) return Math.min.apply(Math, obj);
if (!iterator && _.isEmpty(obj)) return Infinity;
var result = {computed : Infinity};
each(obj, function(value, index, list) {
var computed = iterator ? iterator.call(context, value, index, list) : value;
computed < result.computed && (result = {value : value, computed : computed});
});
return result.value;
};
// Shuffle an array.
_.shuffle = function(obj) {
var shuffled = [], rand;
each(obj, function(value, index, list) {
if (index == 0) {
shuffled[0] = value;
} else {
rand = Math.floor(Math.random() * (index + 1));
shuffled[index] = shuffled[rand];
shuffled[rand] = value;
}
});
return shuffled;
};
// Sort the object's values by a criterion produced by an iterator.
_.sortBy = function(obj, iterator, context) {
return _.pluck(_.map(obj, function(value, index, list) {
return {
value : value,
criteria : iterator.call(context, value, index, list)
};
}).sort(function(left, right) {
var a = left.criteria, b = right.criteria;
return a < b ? -1 : a > b ? 1 : 0;
}), 'value');
};
// Groups the object's values by a criterion. Pass either a string attribute
// to group by, or a function that returns the criterion.
_.groupBy = function(obj, val) {
var result = {};
var iterator = _.isFunction(val) ? val : function(obj) { return obj[val]; };
each(obj, function(value, index) {
var key = iterator(value, index);
(result[key] || (result[key] = [])).push(value);
});
return result;
};
// Use a comparator function to figure out at what index an object should
// be inserted so as to maintain order. Uses binary search.
_.sortedIndex = function(array, obj, iterator) {
iterator || (iterator = _.identity);
var low = 0, high = array.length;
while (low < high) {
var mid = (low + high) >> 1;
iterator(array[mid]) < iterator(obj) ? low = mid + 1 : high = mid;
}
return low;
};
// Safely convert anything iterable into a real, live array.
_.toArray = function(iterable) {
if (!iterable) return [];
if (iterable.toArray) return iterable.toArray();
if (_.isArray(iterable)) return slice.call(iterable);
if (_.isArguments(iterable)) return slice.call(iterable);
return _.values(iterable);
};
// Return the number of elements in an object.
_.size = function(obj) {
return _.toArray(obj).length;
};
// Array Functions
// ---------------
// Get the first element of an array. Passing **n** will return the first N
// values in the array. Aliased as `head`. The **guard** check allows it to work
// with `_.map`.
_.first = _.head = function(array, n, guard) {
return (n != null) && !guard ? slice.call(array, 0, n) : array[0];
};
// Returns everything but the last entry of the array. Especcialy useful on
// the arguments object. Passing **n** will return all the values in
// the array, excluding the last N. The **guard** check allows it to work with
// `_.map`.
_.initial = function(array, n, guard) {
return slice.call(array, 0, array.length - ((n == null) || guard ? 1 : n));
};
// Get the last element of an array. Passing **n** will return the last N
// values in the array. The **guard** check allows it to work with `_.map`.
_.last = function(array, n, guard) {
if ((n != null) && !guard) {
return slice.call(array, Math.max(array.length - n, 0));
} else {
return array[array.length - 1];
}
};
// Returns everything but the first entry of the array. Aliased as `tail`.
// Especially useful on the arguments object. Passing an **index** will return
// the rest of the values in the array from that index onward. The **guard**
// check allows it to work with `_.map`.
_.rest = _.tail = function(array, index, guard) {
return slice.call(array, (index == null) || guard ? 1 : index);
};
// Trim out all falsy values from an array.
_.compact = function(array) {
return _.filter(array, function(value){ return !!value; });
};
// Return a completely flattened version of an array.
_.flatten = function(array, shallow) {
return _.reduce(array, function(memo, value) {
if (_.isArray(value)) return memo.concat(shallow ? value : _.flatten(value));
memo[memo.length] = value;
return memo;
}, []);
};
// Return a version of the array that does not contain the specified value(s).
_.without = function(array) {
return _.difference(array, slice.call(arguments, 1));
};
// Produce a duplicate-free version of the array. If the array has already
// been sorted, you have the option of using a faster algorithm.
// Aliased as `unique`.
_.uniq = _.unique = function(array, isSorted, iterator) {
var initial = iterator ? _.map(array, iterator) : array;
var result = [];
_.reduce(initial, function(memo, el, i) {
if (0 == i || (isSorted === true ? _.last(memo) != el : !_.include(memo, el))) {
memo[memo.length] = el;
result[result.length] = array[i];
}
return memo;
}, []);
return result;
};
// Produce an array that contains the union: each distinct element from all of
// the passed-in arrays.
_.union = function() {
return _.uniq(_.flatten(arguments, true));
};
// Produce an array that contains every item shared between all the
// passed-in arrays. (Aliased as "intersect" for back-compat.)
_.intersection = _.intersect = function(array) {
var rest = slice.call(arguments, 1);
return _.filter(_.uniq(array), function(item) {
return _.every(rest, function(other) {
return _.indexOf(other, item) >= 0;
});
});
};
// Take the difference between one array and a number of other arrays.
// Only the elements present in just the first array will remain.
_.difference = function(array) {
var rest = _.flatten(slice.call(arguments, 1));
return _.filter(array, function(value){ return !_.include(rest, value); });
};
// Zip together multiple lists into a single array -- elements that share
// an index go together.
_.zip = function() {
var args = slice.call(arguments);
var length = _.max(_.pluck(args, 'length'));
var results = new Array(length);
for (var i = 0; i < length; i++) results[i] = _.pluck(args, "" + i);
return results;
};
// If the browser doesn't supply us with indexOf (I'm looking at you, **MSIE**),
// we need this function. Return the position of the first occurrence of an
// item in an array, or -1 if the item is not included in the array.
// Delegates to **ECMAScript 5**'s native `indexOf` if available.
// If the array is large and already in sort order, pass `true`
// for **isSorted** to use binary search.
_.indexOf = function(array, item, isSorted) {
if (array == null) return -1;
var i, l;
if (isSorted) {
i = _.sortedIndex(array, item);
return array[i] === item ? i : -1;
}
if (nativeIndexOf && array.indexOf === nativeIndexOf) return array.indexOf(item);
for (i = 0, l = array.length; i < l; i++) if (i in array && array[i] === item) return i;
return -1;
};
// Delegates to **ECMAScript 5**'s native `lastIndexOf` if available.
_.lastIndexOf = function(array, item) {
if (array == null) return -1;
if (nativeLastIndexOf && array.lastIndexOf === nativeLastIndexOf) return array.lastIndexOf(item);
var i = array.length;
while (i--) if (i in array && array[i] === item) return i;
return -1;
};
// Generate an integer Array containing an arithmetic progression. A port of
// the native Python `range()` function. See
// [the Python documentation](http://docs.python.org/library/functions.html#range).
_.range = function(start, stop, step) {
if (arguments.length <= 1) {
stop = start || 0;
start = 0;
}
step = arguments[2] || 1;
var len = Math.max(Math.ceil((stop - start) / step), 0);
var idx = 0;
var range = new Array(len);
while(idx < len) {
range[idx++] = start;
start += step;
}
return range;
};
// Function (ahem) Functions
// ------------------
// Reusable constructor function for prototype setting.
var ctor = function(){};
// Create a function bound to a given object (assigning `this`, and arguments,
// optionally). Binding with arguments is also known as `curry`.
// Delegates to **ECMAScript 5**'s native `Function.bind` if available.
// We check for `func.bind` first, to fail fast when `func` is undefined.
_.bind = function bind(func, context) {
var bound, args;
if (func.bind === nativeBind && nativeBind) return nativeBind.apply(func, slice.call(arguments, 1));
if (!_.isFunction(func)) throw new TypeError;
args = slice.call(arguments, 2);
return bound = function() {
if (!(this instanceof bound)) return func.apply(context, args.concat(slice.call(arguments)));
ctor.prototype = func.prototype;
var self = new ctor;
var result = func.apply(self, args.concat(slice.call(arguments)));
if (Object(result) === result) return result;
return self;
};
};
// Bind all of an object's methods to that object. Useful for ensuring that
// all callbacks defined on an object belong to it.
_.bindAll = function(obj) {
var funcs = slice.call(arguments, 1);
if (funcs.length == 0) funcs = _.functions(obj);
each(funcs, function(f) { obj[f] = _.bind(obj[f], obj); });
return obj;
};
// Memoize an expensive function by storing its results.
_.memoize = function(func, hasher) {
var memo = {};
hasher || (hasher = _.identity);
return function() {
var key = hasher.apply(this, arguments);
return _.has(memo, key) ? memo[key] : (memo[key] = func.apply(this, arguments));
};
};
// Delays a function for the given number of milliseconds, and then calls
// it with the arguments supplied.
_.delay = function(func, wait) {
var args = slice.call(arguments, 2);
return setTimeout(function(){ return func.apply(func, args); }, wait);
};
// Defers a function, scheduling it to run after the current call stack has
// cleared.
_.defer = function(func) {
return _.delay.apply(_, [func, 1].concat(slice.call(arguments, 1)));
};
// Returns a function, that, when invoked, will only be triggered at most once
// during a given window of time.
_.throttle = function(func, wait) {
var context, args, timeout, throttling, more;
var whenDone = _.debounce(function(){ more = throttling = false; }, wait);
return function() {
context = this; args = arguments;
var later = function() {
timeout = null;
if (more) func.apply(context, args);
whenDone();
};
if (!timeout) timeout = setTimeout(later, wait);
if (throttling) {
more = true;
} else {
func.apply(context, args);
}
whenDone();
throttling = true;
};
};
// Returns a function, that, as long as it continues to be invoked, will not
// be triggered. The function will be called after it stops being called for
// N milliseconds.
_.debounce = function(func, wait) {
var timeout;
return function() {
var context = this, args = arguments;
var later = function() {
timeout = null;
func.apply(context, args);
};
clearTimeout(timeout);
timeout = setTimeout(later, wait);
};
};
// Returns a function that will be executed at most one time, no matter how
// often you call it. Useful for lazy initialization.
_.once = function(func) {
var ran = false, memo;
return function() {
if (ran) return memo;
ran = true;
return memo = func.apply(this, arguments);
};
};
// Returns the first function passed as an argument to the second,
// allowing you to adjust arguments, run code before and after, and
// conditionally execute the original function.
_.wrap = function(func, wrapper) {
return function() {
var args = [func].concat(slice.call(arguments, 0));
return wrapper.apply(this, args);
};
};
// Returns a function that is the composition of a list of functions, each
// consuming the return value of the function that follows.
_.compose = function() {
var funcs = arguments;
return function() {
var args = arguments;
for (var i = funcs.length - 1; i >= 0; i--) {
args = [funcs[i].apply(this, args)];
}
return args[0];
};
};
// Returns a function that will only be executed after being called N times.
_.after = function(times, func) {
if (times <= 0) return func();
return function() {
if (--times < 1) { return func.apply(this, arguments); }
};
};
// Object Functions
// ----------------
// Retrieve the names of an object's properties.
// Delegates to **ECMAScript 5**'s native `Object.keys`
_.keys = nativeKeys || function(obj) {
if (obj !== Object(obj)) throw new TypeError('Invalid object');
var keys = [];
for (var key in obj) if (_.has(obj, key)) keys[keys.length] = key;
return keys;
};
// Retrieve the values of an object's properties.
_.values = function(obj) {
return _.map(obj, _.identity);
};
// Return a sorted list of the function names available on the object.
// Aliased as `methods`
_.functions = _.methods = function(obj) {
var names = [];
for (var key in obj) {
if (_.isFunction(obj[key])) names.push(key);
}
return names.sort();
};
// Extend a given object with all the properties in passed-in object(s).
_.extend = function(obj) {
each(slice.call(arguments, 1), function(source) {
for (var prop in source) {
obj[prop] = source[prop];
}
});
return obj;
};
// Fill in a given object with default properties.
_.defaults = function(obj) {
each(slice.call(arguments, 1), function(source) {
for (var prop in source) {
if (obj[prop] == null) obj[prop] = source[prop];
}
});
return obj;
};
// Create a (shallow-cloned) duplicate of an object.
_.clone = function(obj) {
if (!_.isObject(obj)) return obj;
return _.isArray(obj) ? obj.slice() : _.extend({}, obj);
};
// Invokes interceptor with the obj, and then returns obj.
// The primary purpose of this method is to "tap into" a method chain, in
// order to perform operations on intermediate results within the chain.
_.tap = function(obj, interceptor) {
interceptor(obj);
return obj;
};
// Internal recursive comparison function.
function eq(a, b, stack) {
// Identical objects are equal. `0 === -0`, but they aren't identical.
// See the Harmony `egal` proposal: http://wiki.ecmascript.org/doku.php?id=harmony:egal.
if (a === b) return a !== 0 || 1 / a == 1 / b;
// A strict comparison is necessary because `null == undefined`.
if (a == null || b == null) return a === b;
// Unwrap any wrapped objects.
if (a._chain) a = a._wrapped;
if (b._chain) b = b._wrapped;
// Invoke a custom `isEqual` method if one is provided.
if (a.isEqual && _.isFunction(a.isEqual)) return a.isEqual(b);
if (b.isEqual && _.isFunction(b.isEqual)) return b.isEqual(a);
// Compare `[[Class]]` names.
var className = toString.call(a);
if (className != toString.call(b)) return false;
switch (className) {
// Strings, numbers, dates, and booleans are compared by value.
case '[object String]':
// Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
// equivalent to `new String("5")`.
return a == String(b);
case '[object Number]':
// `NaN`s are equivalent, but non-reflexive. An `egal` comparison is performed for
// other numeric values.
return a != +a ? b != +b : (a == 0 ? 1 / a == 1 / b : a == +b);
case '[object Date]':
case '[object Boolean]':
// Coerce dates and booleans to numeric primitive values. Dates are compared by their
// millisecond representations. Note that invalid dates with millisecond representations
// of `NaN` are not equivalent.
return +a == +b;
// RegExps are compared by their source patterns and flags.
case '[object RegExp]':
return a.source == b.source &&
a.global == b.global &&
a.multiline == b.multiline &&
a.ignoreCase == b.ignoreCase;
}
if (typeof a != 'object' || typeof b != 'object') return false;
// Assume equality for cyclic structures. The algorithm for detecting cyclic
// structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.
var length = stack.length;
while (length--) {
// Linear search. Performance is inversely proportional to the number of
// unique nested structures.
if (stack[length] == a) return true;
}
// Add the first object to the stack of traversed objects.
stack.push(a);
var size = 0, result = true;
// Recursively compare objects and arrays.
if (className == '[object Array]') {
// Compare array lengths to determine if a deep comparison is necessary.
size = a.length;
result = size == b.length;
if (result) {
// Deep compare the contents, ignoring non-numeric properties.
while (size--) {
// Ensure commutative equality for sparse arrays.
if (!(result = size in a == size in b && eq(a[size], b[size], stack))) break;
}
}
} else {
// Objects with different constructors are not equivalent.
if ('constructor' in a != 'constructor' in b || a.constructor != b.constructor) return false;
// Deep compare objects.
for (var key in a) {
if (_.has(a, key)) {
// Count the expected number of properties.
size++;
// Deep compare each member.
if (!(result = _.has(b, key) && eq(a[key], b[key], stack))) break;
}
}
// Ensure that both objects contain the same number of properties.
if (result) {
for (key in b) {
if (_.has(b, key) && !(size--)) break;
}
result = !size;
}
}
// Remove the first object from the stack of traversed objects.
stack.pop();
return result;
}
// Perform a deep comparison to check if two objects are equal.
_.isEqual = function(a, b) {
return eq(a, b, []);
};
// Is a given array, string, or object empty?
// An "empty" object has no enumerable own-properties.
_.isEmpty = function(obj) {
if (_.isArray(obj) || _.isString(obj)) return obj.length === 0;
for (var key in obj) if (_.has(obj, key)) return false;
return true;
};
// Is a given value a DOM element?
_.isElement = function(obj) {
return !!(obj && obj.nodeType == 1);
};
// Is a given value an array?
// Delegates to ECMA5's native Array.isArray
_.isArray = nativeIsArray || function(obj) {
return toString.call(obj) == '[object Array]';
};
// Is a given variable an object?
_.isObject = function(obj) {
return obj === Object(obj);
};
// Is a given variable an arguments object?
_.isArguments = function(obj) {
return toString.call(obj) == '[object Arguments]';
};
if (!_.isArguments(arguments)) {
_.isArguments = function(obj) {
return !!(obj && _.has(obj, 'callee'));
};
}
// Is a given value a function?
_.isFunction = function(obj) {
return toString.call(obj) == '[object Function]';
};
// Is a given value a string?
_.isString = function(obj) {
return toString.call(obj) == '[object String]';
};
// Is a given value a number?
_.isNumber = function(obj) {
return toString.call(obj) == '[object Number]';
};
// Is the given value `NaN`?
_.isNaN = function(obj) {
// `NaN` is the only value for which `===` is not reflexive.
return obj !== obj;
};
// Is a given value a boolean?
_.isBoolean = function(obj) {
return obj === true || obj === false || toString.call(obj) == '[object Boolean]';
};
// Is a given value a date?
_.isDate = function(obj) {
return toString.call(obj) == '[object Date]';
};
// Is the given value a regular expression?
_.isRegExp = function(obj) {
return toString.call(obj) == '[object RegExp]';
};
// Is a given value equal to null?
_.isNull = function(obj) {
return obj === null;
};
// Is a given variable undefined?
_.isUndefined = function(obj) {
return obj === void 0;
};
// Has own property?
_.has = function(obj, key) {
return hasOwnProperty.call(obj, key);
};
// Utility Functions
// -----------------
// Run Underscore.js in *noConflict* mode, returning the `_` variable to its
// previous owner. Returns a reference to the Underscore object.
_.noConflict = function() {
root._ = previousUnderscore;
return this;
};
// Keep the identity function around for default iterators.
_.identity = function(value) {
return value;
};
// Run a function **n** times.
_.times = function (n, iterator, context) {
for (var i = 0; i < n; i++) iterator.call(context, i);
};
// Escape a string for HTML interpolation.
_.escape = function(string) {
return (''+string).replace(/&/g, '&amp;').replace(/</g, '&lt;').replace(/>/g, '&gt;').replace(/"/g, '&quot;').replace(/'/g, '&#x27;').replace(/\//g,'&#x2F;');
};
// Add your own custom functions to the Underscore object, ensuring that
// they're correctly added to the OOP wrapper as well.
_.mixin = function(obj) {
each(_.functions(obj), function(name){
addToWrapper(name, _[name] = obj[name]);
});
};
// Generate a unique integer id (unique within the entire client session).
// Useful for temporary DOM ids.
var idCounter = 0;
_.uniqueId = function(prefix) {
var id = idCounter++;
return prefix ? prefix + id : id;
};
// By default, Underscore uses ERB-style template delimiters, change the
// following template settings to use alternative delimiters.
_.templateSettings = {
evaluate : /<%([\s\S]+?)%>/g,
interpolate : /<%=([\s\S]+?)%>/g,
escape : /<%-([\s\S]+?)%>/g
};
// When customizing `templateSettings`, if you don't want to define an
// interpolation, evaluation or escaping regex, we need one that is
// guaranteed not to match.
var noMatch = /.^/;
// Within an interpolation, evaluation, or escaping, remove HTML escaping
// that had been previously added.
var unescape = function(code) {
return code.replace(/\\\\/g, '\\').replace(/\\'/g, "'");
};
// JavaScript micro-templating, similar to John Resig's implementation.
// Underscore templating handles arbitrary delimiters, preserves whitespace,
// and correctly escapes quotes within interpolated code.
_.template = function(str, data) {
var c = _.templateSettings;
var tmpl = 'var __p=[],print=function(){__p.push.apply(__p,arguments);};' +
'with(obj||{}){__p.push(\'' +
str.replace(/\\/g, '\\\\')
.replace(/'/g, "\\'")
.replace(c.escape || noMatch, function(match, code) {
return "',_.escape(" + unescape(code) + "),'";
})
.replace(c.interpolate || noMatch, function(match, code) {
return "'," + unescape(code) + ",'";
})
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return "');" + unescape(code).replace(/[\r\n\t]/g, ' ') + ";__p.push('";
})
.replace(/\r/g, '\\r')
.replace(/\n/g, '\\n')
.replace(/\t/g, '\\t')
+ "');}return __p.join('');";
var func = new Function('obj', '_', tmpl);
if (data) return func(data, _);
return function(data) {
return func.call(this, data, _);
};
};
// Add a "chain" function, which will delegate to the wrapper.
_.chain = function(obj) {
return _(obj).chain();
};
// The OOP Wrapper
// ---------------
// If Underscore is called as a function, it returns a wrapped object that
// can be used OO-style. This wrapper holds altered versions of all the
// underscore functions. Wrapped objects may be chained.
var wrapper = function(obj) { this._wrapped = obj; };
// Expose `wrapper.prototype` as `_.prototype`
_.prototype = wrapper.prototype;
// Helper function to continue chaining intermediate results.
var result = function(obj, chain) {
return chain ? _(obj).chain() : obj;
};
// A method to easily add functions to the OOP wrapper.
var addToWrapper = function(name, func) {
wrapper.prototype[name] = function() {
var args = slice.call(arguments);
unshift.call(args, this._wrapped);
return result(func.apply(_, args), this._chain);
};
};
// Add all of the Underscore functions to the wrapper object.
_.mixin(_);
// Add all mutator Array functions to the wrapper.
each(['pop', 'push', 'reverse', 'shift', 'sort', 'splice', 'unshift'], function(name) {
var method = ArrayProto[name];
wrapper.prototype[name] = function() {
var wrapped = this._wrapped;
method.apply(wrapped, arguments);
var length = wrapped.length;
if ((name == 'shift' || name == 'splice') && length === 0) delete wrapped[0];
return result(wrapped, this._chain);
};
});
// Add all accessor Array functions to the wrapper.
each(['concat', 'join', 'slice'], function(name) {
var method = ArrayProto[name];
wrapper.prototype[name] = function() {
return result(method.apply(this._wrapped, arguments), this._chain);
};
});
// Start chaining a wrapped Underscore object.
wrapper.prototype.chain = function() {
this._chain = true;
return this;
};
// Extracts the result from a wrapped and chained object.
wrapper.prototype.value = function() {
return this._wrapped;
};
}).call(this);

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@ -1,31 +0,0 @@
// Underscore.js 1.3.1
// (c) 2009-2012 Jeremy Ashkenas, DocumentCloud Inc.
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function(){var a=i.call(arguments);H.call(a,this._wrapped);return v(c.apply(b,a),this._chain)}};b.mixin(b);j("pop,push,reverse,shift,sort,splice,unshift".split(","),function(a){var b=k[a];m.prototype[a]=function(){var d=this._wrapped;b.apply(d,arguments);var e=d.length;(a=="shift"||a=="splice")&&e===0&&delete d[0];return v(d,this._chain)}});j(["concat","join","slice"],function(a){var b=k[a];m.prototype[a]=function(){return v(b.apply(this._wrapped,arguments),this._chain)}});m.prototype.chain=function(){this._chain=
true;return this};m.prototype.value=function(){return this._wrapped}}).call(this);

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/*
* websupport.js
* ~~~~~~~~~~~~~
*
* sphinx.websupport utilities for all documentation.
*
* :copyright: Copyright 2007-2016 by the Sphinx team, see AUTHORS.
* :license: BSD, see LICENSE for details.
*
*/
(function($) {
$.fn.autogrow = function() {
return this.each(function() {
var textarea = this;
$.fn.autogrow.resize(textarea);
$(textarea)
.focus(function() {
textarea.interval = setInterval(function() {
$.fn.autogrow.resize(textarea);
}, 500);
})
.blur(function() {
clearInterval(textarea.interval);
});
});
};
$.fn.autogrow.resize = function(textarea) {
var lineHeight = parseInt($(textarea).css('line-height'), 10);
var lines = textarea.value.split('\n');
var columns = textarea.cols;
var lineCount = 0;
$.each(lines, function() {
lineCount += Math.ceil(this.length / columns) || 1;
});
var height = lineHeight * (lineCount + 1);
$(textarea).css('height', height);
};
})(jQuery);
(function($) {
var comp, by;
function init() {
initEvents();
initComparator();
}
function initEvents() {
$(document).on("click", 'a.comment-close', function(event) {
event.preventDefault();
hide($(this).attr('id').substring(2));
});
$(document).on("click", 'a.vote', function(event) {
event.preventDefault();
handleVote($(this));
});
$(document).on("click", 'a.reply', function(event) {
event.preventDefault();
openReply($(this).attr('id').substring(2));
});
$(document).on("click", 'a.close-reply', function(event) {
event.preventDefault();
closeReply($(this).attr('id').substring(2));
});
$(document).on("click", 'a.sort-option', function(event) {
event.preventDefault();
handleReSort($(this));
});
$(document).on("click", 'a.show-proposal', function(event) {
event.preventDefault();
showProposal($(this).attr('id').substring(2));
});
$(document).on("click", 'a.hide-proposal', function(event) {
event.preventDefault();
hideProposal($(this).attr('id').substring(2));
});
$(document).on("click", 'a.show-propose-change', function(event) {
event.preventDefault();
showProposeChange($(this).attr('id').substring(2));
});
$(document).on("click", 'a.hide-propose-change', function(event) {
event.preventDefault();
hideProposeChange($(this).attr('id').substring(2));
});
$(document).on("click", 'a.accept-comment', function(event) {
event.preventDefault();
acceptComment($(this).attr('id').substring(2));
});
$(document).on("click", 'a.delete-comment', function(event) {
event.preventDefault();
deleteComment($(this).attr('id').substring(2));
});
$(document).on("click", 'a.comment-markup', function(event) {
event.preventDefault();
toggleCommentMarkupBox($(this).attr('id').substring(2));
});
}
/**
* Set comp, which is a comparator function used for sorting and
* inserting comments into the list.
*/
function setComparator() {
// If the first three letters are "asc", sort in ascending order
// and remove the prefix.
if (by.substring(0,3) == 'asc') {
var i = by.substring(3);
comp = function(a, b) { return a[i] - b[i]; };
} else {
// Otherwise sort in descending order.
comp = function(a, b) { return b[by] - a[by]; };
}
// Reset link styles and format the selected sort option.
$('a.sel').attr('href', '#').removeClass('sel');
$('a.by' + by).removeAttr('href').addClass('sel');
}
/**
* Create a comp function. If the user has preferences stored in
* the sortBy cookie, use those, otherwise use the default.
*/
function initComparator() {
by = 'rating'; // Default to sort by rating.
// If the sortBy cookie is set, use that instead.
if (document.cookie.length > 0) {
var start = document.cookie.indexOf('sortBy=');
if (start != -1) {
start = start + 7;
var end = document.cookie.indexOf(";", start);
if (end == -1) {
end = document.cookie.length;
by = unescape(document.cookie.substring(start, end));
}
}
}
setComparator();
}
/**
* Show a comment div.
*/
function show(id) {
$('#ao' + id).hide();
$('#ah' + id).show();
var context = $.extend({id: id}, opts);
var popup = $(renderTemplate(popupTemplate, context)).hide();
popup.find('textarea[name="proposal"]').hide();
popup.find('a.by' + by).addClass('sel');
var form = popup.find('#cf' + id);
form.submit(function(event) {
event.preventDefault();
addComment(form);
});
$('#s' + id).after(popup);
popup.slideDown('fast', function() {
getComments(id);
});
}
/**
* Hide a comment div.
*/
function hide(id) {
$('#ah' + id).hide();
$('#ao' + id).show();
var div = $('#sc' + id);
div.slideUp('fast', function() {
div.remove();
});
}
/**
* Perform an ajax request to get comments for a node
* and insert the comments into the comments tree.
*/
function getComments(id) {
$.ajax({
type: 'GET',
url: opts.getCommentsURL,
data: {node: id},
success: function(data, textStatus, request) {
var ul = $('#cl' + id);
var speed = 100;
$('#cf' + id)
.find('textarea[name="proposal"]')
.data('source', data.source);
if (data.comments.length === 0) {
ul.html('<li>No comments yet.</li>');
ul.data('empty', true);
} else {
// If there are comments, sort them and put them in the list.
var comments = sortComments(data.comments);
speed = data.comments.length * 100;
appendComments(comments, ul);
ul.data('empty', false);
}
$('#cn' + id).slideUp(speed + 200);
ul.slideDown(speed);
},
error: function(request, textStatus, error) {
showError('Oops, there was a problem retrieving the comments.');
},
dataType: 'json'
});
}
/**
* Add a comment via ajax and insert the comment into the comment tree.
*/
function addComment(form) {
var node_id = form.find('input[name="node"]').val();
var parent_id = form.find('input[name="parent"]').val();
var text = form.find('textarea[name="comment"]').val();
var proposal = form.find('textarea[name="proposal"]').val();
if (text == '') {
showError('Please enter a comment.');
return;
}
// Disable the form that is being submitted.
form.find('textarea,input').attr('disabled', 'disabled');
// Send the comment to the server.
$.ajax({
type: "POST",
url: opts.addCommentURL,
dataType: 'json',
data: {
node: node_id,
parent: parent_id,
text: text,
proposal: proposal
},
success: function(data, textStatus, error) {
// Reset the form.
if (node_id) {
hideProposeChange(node_id);
}
form.find('textarea')
.val('')
.add(form.find('input'))
.removeAttr('disabled');
var ul = $('#cl' + (node_id || parent_id));
if (ul.data('empty')) {
$(ul).empty();
ul.data('empty', false);
}
insertComment(data.comment);
var ao = $('#ao' + node_id);
ao.find('img').attr({'src': opts.commentBrightImage});
if (node_id) {
// if this was a "root" comment, remove the commenting box
// (the user can get it back by reopening the comment popup)
$('#ca' + node_id).slideUp();
}
},
error: function(request, textStatus, error) {
form.find('textarea,input').removeAttr('disabled');
showError('Oops, there was a problem adding the comment.');
}
});
}
/**
* Recursively append comments to the main comment list and children
* lists, creating the comment tree.
*/
function appendComments(comments, ul) {
$.each(comments, function() {
var div = createCommentDiv(this);
ul.append($(document.createElement('li')).html(div));
appendComments(this.children, div.find('ul.comment-children'));
// To avoid stagnating data, don't store the comments children in data.
this.children = null;
div.data('comment', this);
});
}
/**
* After adding a new comment, it must be inserted in the correct
* location in the comment tree.
*/
function insertComment(comment) {
var div = createCommentDiv(comment);
// To avoid stagnating data, don't store the comments children in data.
comment.children = null;
div.data('comment', comment);
var ul = $('#cl' + (comment.node || comment.parent));
var siblings = getChildren(ul);
var li = $(document.createElement('li'));
li.hide();
// Determine where in the parents children list to insert this comment.
for(i=0; i < siblings.length; i++) {
if (comp(comment, siblings[i]) <= 0) {
$('#cd' + siblings[i].id)
.parent()
.before(li.html(div));
li.slideDown('fast');
return;
}
}
// If we get here, this comment rates lower than all the others,
// or it is the only comment in the list.
ul.append(li.html(div));
li.slideDown('fast');
}
function acceptComment(id) {
$.ajax({
type: 'POST',
url: opts.acceptCommentURL,
data: {id: id},
success: function(data, textStatus, request) {
$('#cm' + id).fadeOut('fast');
$('#cd' + id).removeClass('moderate');
},
error: function(request, textStatus, error) {
showError('Oops, there was a problem accepting the comment.');
}
});
}
function deleteComment(id) {
$.ajax({
type: 'POST',
url: opts.deleteCommentURL,
data: {id: id},
success: function(data, textStatus, request) {
var div = $('#cd' + id);
if (data == 'delete') {
// Moderator mode: remove the comment and all children immediately
div.slideUp('fast', function() {
div.remove();
});
return;
}
// User mode: only mark the comment as deleted
div
.find('span.user-id:first')
.text('[deleted]').end()
.find('div.comment-text:first')
.text('[deleted]').end()
.find('#cm' + id + ', #dc' + id + ', #ac' + id + ', #rc' + id +
', #sp' + id + ', #hp' + id + ', #cr' + id + ', #rl' + id)
.remove();
var comment = div.data('comment');
comment.username = '[deleted]';
comment.text = '[deleted]';
div.data('comment', comment);
},
error: function(request, textStatus, error) {
showError('Oops, there was a problem deleting the comment.');
}
});
}
function showProposal(id) {
$('#sp' + id).hide();
$('#hp' + id).show();
$('#pr' + id).slideDown('fast');
}
function hideProposal(id) {
$('#hp' + id).hide();
$('#sp' + id).show();
$('#pr' + id).slideUp('fast');
}
function showProposeChange(id) {
$('#pc' + id).hide();
$('#hc' + id).show();
var textarea = $('#pt' + id);
textarea.val(textarea.data('source'));
$.fn.autogrow.resize(textarea[0]);
textarea.slideDown('fast');
}
function hideProposeChange(id) {
$('#hc' + id).hide();
$('#pc' + id).show();
var textarea = $('#pt' + id);
textarea.val('').removeAttr('disabled');
textarea.slideUp('fast');
}
function toggleCommentMarkupBox(id) {
$('#mb' + id).toggle();
}
/** Handle when the user clicks on a sort by link. */
function handleReSort(link) {
var classes = link.attr('class').split(/\s+/);
for (var i=0; i<classes.length; i++) {
if (classes[i] != 'sort-option') {
by = classes[i].substring(2);
}
}
setComparator();
// Save/update the sortBy cookie.
var expiration = new Date();
expiration.setDate(expiration.getDate() + 365);
document.cookie= 'sortBy=' + escape(by) +
';expires=' + expiration.toUTCString();
$('ul.comment-ul').each(function(index, ul) {
var comments = getChildren($(ul), true);
comments = sortComments(comments);
appendComments(comments, $(ul).empty());
});
}
/**
* Function to process a vote when a user clicks an arrow.
*/
function handleVote(link) {
if (!opts.voting) {
showError("You'll need to login to vote.");
return;
}
var id = link.attr('id');
if (!id) {
// Didn't click on one of the voting arrows.
return;
}
// If it is an unvote, the new vote value is 0,
// Otherwise it's 1 for an upvote, or -1 for a downvote.
var value = 0;
if (id.charAt(1) != 'u') {
value = id.charAt(0) == 'u' ? 1 : -1;
}
// The data to be sent to the server.
var d = {
comment_id: id.substring(2),
value: value
};
// Swap the vote and unvote links.
link.hide();
$('#' + id.charAt(0) + (id.charAt(1) == 'u' ? 'v' : 'u') + d.comment_id)
.show();
// The div the comment is displayed in.
var div = $('div#cd' + d.comment_id);
var data = div.data('comment');
// If this is not an unvote, and the other vote arrow has
// already been pressed, unpress it.
if ((d.value !== 0) && (data.vote === d.value * -1)) {
$('#' + (d.value == 1 ? 'd' : 'u') + 'u' + d.comment_id).hide();
$('#' + (d.value == 1 ? 'd' : 'u') + 'v' + d.comment_id).show();
}
// Update the comments rating in the local data.
data.rating += (data.vote === 0) ? d.value : (d.value - data.vote);
data.vote = d.value;
div.data('comment', data);
// Change the rating text.
div.find('.rating:first')
.text(data.rating + ' point' + (data.rating == 1 ? '' : 's'));
// Send the vote information to the server.
$.ajax({
type: "POST",
url: opts.processVoteURL,
data: d,
error: function(request, textStatus, error) {
showError('Oops, there was a problem casting that vote.');
}
});
}
/**
* Open a reply form used to reply to an existing comment.
*/
function openReply(id) {
// Swap out the reply link for the hide link
$('#rl' + id).hide();
$('#cr' + id).show();
// Add the reply li to the children ul.
var div = $(renderTemplate(replyTemplate, {id: id})).hide();
$('#cl' + id)
.prepend(div)
// Setup the submit handler for the reply form.
.find('#rf' + id)
.submit(function(event) {
event.preventDefault();
addComment($('#rf' + id));
closeReply(id);
})
.find('input[type=button]')
.click(function() {
closeReply(id);
});
div.slideDown('fast', function() {
$('#rf' + id).find('textarea').focus();
});
}
/**
* Close the reply form opened with openReply.
*/
function closeReply(id) {
// Remove the reply div from the DOM.
$('#rd' + id).slideUp('fast', function() {
$(this).remove();
});
// Swap out the hide link for the reply link
$('#cr' + id).hide();
$('#rl' + id).show();
}
/**
* Recursively sort a tree of comments using the comp comparator.
*/
function sortComments(comments) {
comments.sort(comp);
$.each(comments, function() {
this.children = sortComments(this.children);
});
return comments;
}
/**
* Get the children comments from a ul. If recursive is true,
* recursively include childrens' children.
*/
function getChildren(ul, recursive) {
var children = [];
ul.children().children("[id^='cd']")
.each(function() {
var comment = $(this).data('comment');
if (recursive)
comment.children = getChildren($(this).find('#cl' + comment.id), true);
children.push(comment);
});
return children;
}
/** Create a div to display a comment in. */
function createCommentDiv(comment) {
if (!comment.displayed && !opts.moderator) {
return $('<div class="moderate">Thank you! Your comment will show up '
+ 'once it is has been approved by a moderator.</div>');
}
// Prettify the comment rating.
comment.pretty_rating = comment.rating + ' point' +
(comment.rating == 1 ? '' : 's');
// Make a class (for displaying not yet moderated comments differently)
comment.css_class = comment.displayed ? '' : ' moderate';
// Create a div for this comment.
var context = $.extend({}, opts, comment);
var div = $(renderTemplate(commentTemplate, context));
// If the user has voted on this comment, highlight the correct arrow.
if (comment.vote) {
var direction = (comment.vote == 1) ? 'u' : 'd';
div.find('#' + direction + 'v' + comment.id).hide();
div.find('#' + direction + 'u' + comment.id).show();
}
if (opts.moderator || comment.text != '[deleted]') {
div.find('a.reply').show();
if (comment.proposal_diff)
div.find('#sp' + comment.id).show();
if (opts.moderator && !comment.displayed)
div.find('#cm' + comment.id).show();
if (opts.moderator || (opts.username == comment.username))
div.find('#dc' + comment.id).show();
}
return div;
}
/**
* A simple template renderer. Placeholders such as <%id%> are replaced
* by context['id'] with items being escaped. Placeholders such as <#id#>
* are not escaped.
*/
function renderTemplate(template, context) {
var esc = $(document.createElement('div'));
function handle(ph, escape) {
var cur = context;
$.each(ph.split('.'), function() {
cur = cur[this];
});
return escape ? esc.text(cur || "").html() : cur;
}
return template.replace(/<([%#])([\w\.]*)\1>/g, function() {
return handle(arguments[2], arguments[1] == '%' ? true : false);
});
}
/** Flash an error message briefly. */
function showError(message) {
$(document.createElement('div')).attr({'class': 'popup-error'})
.append($(document.createElement('div'))
.attr({'class': 'error-message'}).text(message))
.appendTo('body')
.fadeIn("slow")
.delay(2000)
.fadeOut("slow");
}
/** Add a link the user uses to open the comments popup. */
$.fn.comment = function() {
return this.each(function() {
var id = $(this).attr('id').substring(1);
var count = COMMENT_METADATA[id];
var title = count + ' comment' + (count == 1 ? '' : 's');
var image = count > 0 ? opts.commentBrightImage : opts.commentImage;
var addcls = count == 0 ? ' nocomment' : '';
$(this)
.append(
$(document.createElement('a')).attr({
href: '#',
'class': 'sphinx-comment-open' + addcls,
id: 'ao' + id
})
.append($(document.createElement('img')).attr({
src: image,
alt: 'comment',
title: title
}))
.click(function(event) {
event.preventDefault();
show($(this).attr('id').substring(2));
})
)
.append(
$(document.createElement('a')).attr({
href: '#',
'class': 'sphinx-comment-close hidden',
id: 'ah' + id
})
.append($(document.createElement('img')).attr({
src: opts.closeCommentImage,
alt: 'close',
title: 'close'
}))
.click(function(event) {
event.preventDefault();
hide($(this).attr('id').substring(2));
})
);
});
};
var opts = {
processVoteURL: '/_process_vote',
addCommentURL: '/_add_comment',
getCommentsURL: '/_get_comments',
acceptCommentURL: '/_accept_comment',
deleteCommentURL: '/_delete_comment',
commentImage: '/static/_static/comment.png',
closeCommentImage: '/static/_static/comment-close.png',
loadingImage: '/static/_static/ajax-loader.gif',
commentBrightImage: '/static/_static/comment-bright.png',
upArrow: '/static/_static/up.png',
downArrow: '/static/_static/down.png',
upArrowPressed: '/static/_static/up-pressed.png',
downArrowPressed: '/static/_static/down-pressed.png',
voting: false,
moderator: false
};
if (typeof COMMENT_OPTIONS != "undefined") {
opts = jQuery.extend(opts, COMMENT_OPTIONS);
}
var popupTemplate = '\
<div class="sphinx-comments" id="sc<%id%>">\
<p class="sort-options">\
Sort by:\
<a href="#" class="sort-option byrating">best rated</a>\
<a href="#" class="sort-option byascage">newest</a>\
<a href="#" class="sort-option byage">oldest</a>\
</p>\
<div class="comment-header">Comments</div>\
<div class="comment-loading" id="cn<%id%>">\
loading comments... <img src="<%loadingImage%>" alt="" /></div>\
<ul id="cl<%id%>" class="comment-ul"></ul>\
<div id="ca<%id%>">\
<p class="add-a-comment">Add a comment\
(<a href="#" class="comment-markup" id="ab<%id%>">markup</a>):</p>\
<div class="comment-markup-box" id="mb<%id%>">\
reStructured text markup: <i>*emph*</i>, <b>**strong**</b>, \
<code>``code``</code>, \
code blocks: <code>::</code> and an indented block after blank line</div>\
<form method="post" id="cf<%id%>" class="comment-form" action="">\
<textarea name="comment" cols="80"></textarea>\
<p class="propose-button">\
<a href="#" id="pc<%id%>" class="show-propose-change">\
Propose a change &#9657;\
</a>\
<a href="#" id="hc<%id%>" class="hide-propose-change">\
Propose a change &#9663;\
</a>\
</p>\
<textarea name="proposal" id="pt<%id%>" cols="80"\
spellcheck="false"></textarea>\
<input type="submit" value="Add comment" />\
<input type="hidden" name="node" value="<%id%>" />\
<input type="hidden" name="parent" value="" />\
</form>\
</div>\
</div>';
var commentTemplate = '\
<div id="cd<%id%>" class="sphinx-comment<%css_class%>">\
<div class="vote">\
<div class="arrow">\
<a href="#" id="uv<%id%>" class="vote" title="vote up">\
<img src="<%upArrow%>" />\
</a>\
<a href="#" id="uu<%id%>" class="un vote" title="vote up">\
<img src="<%upArrowPressed%>" />\
</a>\
</div>\
<div class="arrow">\
<a href="#" id="dv<%id%>" class="vote" title="vote down">\
<img src="<%downArrow%>" id="da<%id%>" />\
</a>\
<a href="#" id="du<%id%>" class="un vote" title="vote down">\
<img src="<%downArrowPressed%>" />\
</a>\
</div>\
</div>\
<div class="comment-content">\
<p class="tagline comment">\
<span class="user-id"><%username%></span>\
<span class="rating"><%pretty_rating%></span>\
<span class="delta"><%time.delta%></span>\
</p>\
<div class="comment-text comment"><#text#></div>\
<p class="comment-opts comment">\
<a href="#" class="reply hidden" id="rl<%id%>">reply &#9657;</a>\
<a href="#" class="close-reply" id="cr<%id%>">reply &#9663;</a>\
<a href="#" id="sp<%id%>" class="show-proposal">proposal &#9657;</a>\
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<p>Soil is an Agent-based Social Simulator in Python for modelling and simulation of Social Networks.</p>
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<p>The latest version can be installed through GitLab.</p>
<div class="code bash highlight-default"><div class="highlight"><pre><span></span><span class="n">git</span> <span class="n">clone</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">lab</span><span class="o">.</span><span class="n">cluster</span><span class="o">.</span><span class="n">gsi</span><span class="o">.</span><span class="n">dit</span><span class="o">.</span><span class="n">upm</span><span class="o">.</span><span class="n">es</span><span class="o">/</span><span class="n">soil</span><span class="o">/</span><span class="n">soil</span><span class="o">.</span><span class="n">git</span>
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<h1>Developing new models<a class="headerlink" href="#developing-new-models" title="Permalink to this headline"></a></h1>
<p>This document describes how to develop a new analysis model.</p>
<div class="section" id="what-is-a-model">
<h2>What is a model?<a class="headerlink" href="#what-is-a-model" title="Permalink to this headline"></a></h2>
<p>A model defines the behaviour of the agents with a view to assessing their effects on the system as a whole.
In practice, a model consists of at least two parts:</p>
<ul class="simple">
<li>Python module: the actual code that describes the behaviour.</li>
<li>Setting up the variables in the Settings JSON file.</li>
</ul>
<p>This separation allows us to run the simulation with different agents.</p>
</div>
<div class="section" id="models-code">
<h2>Models Code<a class="headerlink" href="#models-code" title="Permalink to this headline"></a></h2>
<p>All the models are imported to the main file. The initialization look like this:</p>
<div class="code python highlight-default"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">settings</span>
<span class="n">networkStatus</span> <span class="o">=</span> <span class="p">{}</span> <span class="c1"># Dict that will contain the status of every agent in the network</span>
<span class="n">sentimentCorrelationNodeArray</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">settings</span><span class="o">.</span><span class="n">network_params</span><span class="p">[</span><span class="s2">&quot;number_of_nodes&quot;</span><span class="p">]):</span>
<span class="n">sentimentCorrelationNodeArray</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="s1">&#39;id&#39;</span><span class="p">:</span> <span class="n">x</span><span class="p">})</span>
<span class="c1"># Initialize agent states. Let&#39;s assume everyone is normal.</span>
<span class="n">init_states</span> <span class="o">=</span> <span class="p">[{</span><span class="s1">&#39;id&#39;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="p">}</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">settings</span><span class="o">.</span><span class="n">network_params</span><span class="p">[</span><span class="s2">&quot;number_of_nodes&quot;</span><span class="p">])]</span>
<span class="c1"># add keys as as necessary, but &quot;id&quot; must always refer to that state category</span>
</pre></div>
</div>
<p>A new model have to inherit the BaseBehaviour class which is in the same module.
There are two basics methods:</p>
<ul class="simple">
<li>__init__</li>
<li>step: used to define the behaviour over time.</li>
</ul>
</div>
<div class="section" id="variable-initialization">
<h2>Variable Initialization<a class="headerlink" href="#variable-initialization" title="Permalink to this headline"></a></h2>
<p>The different parameters of the model have to be initialize in the Simulation Settings JSON file which will be
passed as a parameter to the simulation.</p>
<div class="code json highlight-default"><div class="highlight"><pre><span></span><span class="p">{</span>
<span class="s2">&quot;agent&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;SISaModel&quot;</span><span class="p">,</span><span class="s2">&quot;ControlModelM2&quot;</span><span class="p">],</span>
<span class="s2">&quot;neutral_discontent_spon_prob&quot;</span><span class="p">:</span> <span class="mf">0.04</span><span class="p">,</span>
<span class="s2">&quot;neutral_discontent_infected_prob&quot;</span><span class="p">:</span> <span class="mf">0.04</span><span class="p">,</span>
<span class="s2">&quot;neutral_content_spon_prob&quot;</span><span class="p">:</span> <span class="mf">0.18</span><span class="p">,</span>
<span class="s2">&quot;neutral_content_infected_prob&quot;</span><span class="p">:</span> <span class="mf">0.02</span><span class="p">,</span>
<span class="s2">&quot;discontent_neutral&quot;</span><span class="p">:</span> <span class="mf">0.13</span><span class="p">,</span>
<span class="s2">&quot;discontent_content&quot;</span><span class="p">:</span> <span class="mf">0.07</span><span class="p">,</span>
<span class="s2">&quot;variance_d_c&quot;</span><span class="p">:</span> <span class="mf">0.02</span><span class="p">,</span>
<span class="s2">&quot;content_discontent&quot;</span><span class="p">:</span> <span class="mf">0.009</span><span class="p">,</span>
<span class="s2">&quot;variance_c_d&quot;</span><span class="p">:</span> <span class="mf">0.003</span><span class="p">,</span>
<span class="s2">&quot;content_neutral&quot;</span><span class="p">:</span> <span class="mf">0.088</span><span class="p">,</span>
<span class="s2">&quot;standard_variance&quot;</span><span class="p">:</span> <span class="mf">0.055</span><span class="p">,</span>
<span class="s2">&quot;prob_neutral_making_denier&quot;</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
<span class="s2">&quot;prob_infect&quot;</span><span class="p">:</span> <span class="mf">0.075</span><span class="p">,</span>
<span class="s2">&quot;prob_cured_healing_infected&quot;</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
<span class="s2">&quot;prob_cured_vaccinate_neutral&quot;</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
<span class="s2">&quot;prob_vaccinated_healing_infected&quot;</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
<span class="s2">&quot;prob_vaccinated_vaccinate_neutral&quot;</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
<span class="s2">&quot;prob_generate_anti_rumor&quot;</span><span class="p">:</span> <span class="mf">0.035</span>
<span class="p">}</span>
</pre></div>
</div>
<p>In this file you will also define the models you are going to simulate. You can simulate as many models as you want.
The simulation returns one result for each model, executing each model separately. For the usage, see <a class="reference internal" href="usage.html"><span class="doc">Usage</span></a>.</p>
</div>
<div class="section" id="example-model">
<h2>Example Model<a class="headerlink" href="#example-model" title="Permalink to this headline"></a></h2>
<p>In this section, we will implement a Sentiment Correlation Model.</p>
<p>The class would look like this:</p>
<div class="code python highlight-default"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">..BaseBehaviour</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">..</span> <span class="k">import</span> <span class="n">sentimentCorrelationNodeArray</span>
<span class="k">class</span> <span class="nc">SentimentCorrelationModel</span><span class="p">(</span><span class="n">BaseBehaviour</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">environment</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">agent_id</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">state</span><span class="o">=</span><span class="p">()):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">__init__</span><span class="p">(</span><span class="n">environment</span><span class="o">=</span><span class="n">environment</span><span class="p">,</span> <span class="n">agent_id</span><span class="o">=</span><span class="n">agent_id</span><span class="p">,</span> <span class="n">state</span><span class="o">=</span><span class="n">state</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">outside_effects_prob</span> <span class="o">=</span> <span class="n">environment</span><span class="o">.</span><span class="n">environment_params</span><span class="p">[</span><span class="s1">&#39;outside_effects_prob&#39;</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">anger_prob</span> <span class="o">=</span> <span class="n">environment</span><span class="o">.</span><span class="n">environment_params</span><span class="p">[</span><span class="s1">&#39;anger_prob&#39;</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">joy_prob</span> <span class="o">=</span> <span class="n">environment</span><span class="o">.</span><span class="n">environment_params</span><span class="p">[</span><span class="s1">&#39;joy_prob&#39;</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sadness_prob</span> <span class="o">=</span> <span class="n">environment</span><span class="o">.</span><span class="n">environment_params</span><span class="p">[</span><span class="s1">&#39;sadness_prob&#39;</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">disgust_prob</span> <span class="o">=</span> <span class="n">environment</span><span class="o">.</span><span class="n">environment_params</span><span class="p">[</span><span class="s1">&#39;disgust_prob&#39;</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">time_awareness</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">):</span> <span class="c1"># In this model we have 4 sentiments</span>
<span class="bp">self</span><span class="o">.</span><span class="n">time_awareness</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="c1"># 0-&gt; Anger, 1-&gt; joy, 2-&gt;sadness, 3 -&gt; disgust</span>
<span class="n">sentimentCorrelationNodeArray</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">id</span><span class="p">][</span><span class="bp">self</span><span class="o">.</span><span class="n">env</span><span class="o">.</span><span class="n">now</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">def</span> <span class="nf">step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">now</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">behaviour</span><span class="p">()</span> <span class="c1"># Method which define the behaviour</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="n">now</span><span class="p">)</span>
</pre></div>
</div>
<p>The variables will be modified by the user, so you have to include them in the Simulation Settings JSON file.</p>
</div>
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</div>
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<h3><a href="index.html">Table Of Contents</a></h3>
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<li><a class="reference internal" href="#">Developing new models</a><ul>
<li><a class="reference internal" href="#what-is-a-model">What is a model?</a></li>
<li><a class="reference internal" href="#models-code">Models Code</a></li>
<li><a class="reference internal" href="#variable-initialization">Variable Initialization</a></li>
<li><a class="reference internal" href="#example-model">Example Model</a></li>
</ul>
</li>
</ul>
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<h3>Related Topics</h3>
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<li><a href="index.html">Documentation overview</a><ul>
<li>Previous: <a href="usage.html" title="previous chapter">Usage</a></li>
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<div class="section" id="usage">
<h1>Usage<a class="headerlink" href="#usage" title="Permalink to this headline"></a></h1>
<p>First of all, you need to install the package. See <a class="reference internal" href="installation.html"><span class="doc">Installation</span></a> for installation instructions.</p>
<div class="section" id="simulation-settings">
<h2>Simulation Settings<a class="headerlink" href="#simulation-settings" title="Permalink to this headline"></a></h2>
<p>Once installed, before running a simulation, you need to configure it.</p>
<ul>
<li><p class="first">In the Settings JSON file you will find the configuration of the network.</p>
<div class="code python highlight-default"><div class="highlight"><pre><span></span><span class="p">{</span>
<span class="s2">&quot;network_type&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
<span class="s2">&quot;number_of_nodes&quot;</span><span class="p">:</span> <span class="mi">1000</span><span class="p">,</span>
<span class="s2">&quot;max_time&quot;</span><span class="p">:</span> <span class="mi">50</span><span class="p">,</span>
<span class="s2">&quot;num_trials&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
<span class="s2">&quot;timeout&quot;</span><span class="p">:</span> <span class="mi">2</span>
<span class="p">}</span>
</pre></div>
</div>
</li>
<li><p class="first">In the Settings JSON file, you will also find the configuration of the models.</p>
</li>
</ul>
</div>
<div class="section" id="network-types">
<h2>Network Types<a class="headerlink" href="#network-types" title="Permalink to this headline"></a></h2>
<p>There are three types of network implemented, but you could add more.</p>
<div class="code python highlight-default"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="n">settings</span><span class="o">.</span><span class="n">network_type</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">complete_graph</span><span class="p">(</span><span class="n">settings</span><span class="o">.</span><span class="n">number_of_nodes</span><span class="p">)</span>
<span class="k">if</span> <span class="n">settings</span><span class="o">.</span><span class="n">network_type</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">barabasi_albert_graph</span><span class="p">(</span><span class="n">settings</span><span class="o">.</span><span class="n">number_of_nodes</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="k">if</span> <span class="n">settings</span><span class="o">.</span><span class="n">network_type</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
<span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">margulis_gabber_galil_graph</span><span class="p">(</span><span class="n">settings</span><span class="o">.</span><span class="n">number_of_nodes</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="c1"># More types of networks can be added here</span>
</pre></div>
</div>
</div>
<div class="section" id="models-settings">
<h2>Models Settings<a class="headerlink" href="#models-settings" title="Permalink to this headline"></a></h2>
<p>After having configured the simulation, the next step is setting up the variables of the models.
For this, you will need to modify the Settings JSON file again.</p>
<div class="code json highlight-default"><div class="highlight"><pre><span></span><span class="p">{</span>
<span class="s2">&quot;agent&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;SISaModel&quot;</span><span class="p">,</span><span class="s2">&quot;ControlModelM2&quot;</span><span class="p">],</span>
<span class="s2">&quot;neutral_discontent_spon_prob&quot;</span><span class="p">:</span> <span class="mf">0.04</span><span class="p">,</span>
<span class="s2">&quot;neutral_discontent_infected_prob&quot;</span><span class="p">:</span> <span class="mf">0.04</span><span class="p">,</span>
<span class="s2">&quot;neutral_content_spon_prob&quot;</span><span class="p">:</span> <span class="mf">0.18</span><span class="p">,</span>
<span class="s2">&quot;neutral_content_infected_prob&quot;</span><span class="p">:</span> <span class="mf">0.02</span><span class="p">,</span>
<span class="s2">&quot;discontent_neutral&quot;</span><span class="p">:</span> <span class="mf">0.13</span><span class="p">,</span>
<span class="s2">&quot;discontent_content&quot;</span><span class="p">:</span> <span class="mf">0.07</span><span class="p">,</span>
<span class="s2">&quot;variance_d_c&quot;</span><span class="p">:</span> <span class="mf">0.02</span><span class="p">,</span>
<span class="s2">&quot;content_discontent&quot;</span><span class="p">:</span> <span class="mf">0.009</span><span class="p">,</span>
<span class="s2">&quot;variance_c_d&quot;</span><span class="p">:</span> <span class="mf">0.003</span><span class="p">,</span>
<span class="s2">&quot;content_neutral&quot;</span><span class="p">:</span> <span class="mf">0.088</span><span class="p">,</span>
<span class="s2">&quot;standard_variance&quot;</span><span class="p">:</span> <span class="mf">0.055</span><span class="p">,</span>
<span class="s2">&quot;prob_neutral_making_denier&quot;</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
<span class="s2">&quot;prob_infect&quot;</span><span class="p">:</span> <span class="mf">0.075</span><span class="p">,</span>
<span class="s2">&quot;prob_cured_healing_infected&quot;</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
<span class="s2">&quot;prob_cured_vaccinate_neutral&quot;</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
<span class="s2">&quot;prob_vaccinated_healing_infected&quot;</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
<span class="s2">&quot;prob_vaccinated_vaccinate_neutral&quot;</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
<span class="s2">&quot;prob_generate_anti_rumor&quot;</span><span class="p">:</span> <span class="mf">0.035</span>
<span class="p">}</span>
</pre></div>
</div>
<p>In this file you will define the different models you are going to simulate. You can simulate as many models
as you want. Each model will be simulated separately.</p>
<p>After setting up the models, you have to initialize the parameters of each one. You will find the parameters needed
in the documentation of each model.</p>
<p>Parameter validation will fail if a required parameter without a default has not been provided.</p>
</div>
<div class="section" id="running-the-simulation">
<h2>Running the Simulation<a class="headerlink" href="#running-the-simulation" title="Permalink to this headline"></a></h2>
<p>After setting all the configuration, you will be able to run the simulation. All you need to do is execute:</p>
<div class="code bash highlight-default"><div class="highlight"><pre><span></span><span class="n">python3</span> <span class="n">soil</span><span class="o">.</span><span class="n">py</span>
</pre></div>
</div>
<p>The simulation will return a dynamic graph .gexf file which could be visualized with
<a class="reference external" href="https://gephi.org/users/download/">Gephi</a>.</p>
<p>It will also return one .png picture for each model simulated.</p>
</div>
</div>
</div>
</div>
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<h3><a href="index.html">Table Of Contents</a></h3>
<ul>
<li><a class="reference internal" href="#">Usage</a><ul>
<li><a class="reference internal" href="#simulation-settings">Simulation Settings</a></li>
<li><a class="reference internal" href="#network-types">Network Types</a></li>
<li><a class="reference internal" href="#models-settings">Models Settings</a></li>
<li><a class="reference internal" href="#running-the-simulation">Running the Simulation</a></li>
</ul>
</li>
</ul>
<div class="relations">
<h3>Related Topics</h3>
<ul>
<li><a href="index.html">Documentation overview</a><ul>
<li>Previous: <a href="installation.html" title="previous chapter">Installation</a></li>
<li>Next: <a href="models.html" title="next chapter">Developing new models</a></li>
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4
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@ -13,8 +13,8 @@ Soil is an Agent-based Social Simulator in Python for modelling and simulation o
:caption: Learn more about soil:
installation
usage
models
quickstart
Tutorial - Spreading news

21
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Installation
------------
The latest version can be installed through GitLab.
The easiest way to install Soil is through pip:
.. code:: bash
git clone https://lab.cluster.gsi.dit.upm.es/soil/soil.git
pip install soil
Now test that it worked by running the command line tool
.. code:: bash
soil --help
Or using soil programmatically:
.. code:: python
import soil
print(soil.__version__)
The latest version can be installed through `GitLab <https://lab.cluster.gsi.dit.upm.es/soil/soil.git>`_.

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Quickstart
----------
This section shows how to run simulations from simulation configuration files.
First of all, you need to install the package (See :doc:`installation`)
Simulation configuration files are ``json`` or ``yaml`` files that define all the parameters of a simulation.
Here's an example (``example.yml``).
.. code:: yaml
---
name: MyExampleSimulation
max_time: 50
num_trials: 3
timeout: 2
network_params:
network_type: barabasi_albert_graph
n: 100
m: 2
agent_distribution:
- agent_type: SISaModel
weight: 1
state:
id: content
- agent_type: SISaModel
weight: 1
state:
id: discontent
- agent_type: SISaModel
weight: 8
state:
id: neutral
environment_params:
prob_infect: 0.075
Now run the simulation with the command line tool:
.. code:: bash
soil example.yml
Once the simulation finishes, its results will be stored in a folder named ``MyExampleSimulation``.
Four types of objects are saved by default: a pickle of the simulation, a ``YAML`` representation of the simulation (to re-launch it), for every trial, a csv file with the content of the state of every network node and the environment parameters at every step of the simulation as well as the network in gephi format (``gexf``).
.. code::
soil_output
├── Sim_prob_0
│   ├── Sim_prob_0.dumped.yml
│   ├── Sim_prob_0.simulation.pickle
│   ├── Sim_prob_0_trial_0.environment.csv
│   └── Sim_prob_0_trial_0.gexf
This example configuration will run three trials of a simulation containing a randomly generated network.
The 100 nodes in the network will be SISaModel agents, 10% of them will start in the content state, 10% in the discontent state, and the remaining 80% in the neutral state.
All agents will have access to the environment, which only contains one variable, ``prob_infected``.
The state of the agents will be updated every 2 seconds (``timeout``).
Network
=======
The network topology for the simulation can be loaded from an existing network file or generated with one of the random network generation methods from networkx.
Loading a network
#################
To load an existing network, specify its path in the configuration:
.. code:: yaml
---
network_params:
path: /tmp/mynetwork.gexf
Soil will try to guess what networkx method to use to read the file based on its extension.
However, we only test using ``gexf`` files.
Generating a random network
###########################
To generate a random network using one of networkx's built-in methods, specify the `graph generation algorithm <https://networkx.github.io/documentation/development/reference/generators.html>`_ and other parameters.
For example, the following configuration is equivalent to :code:`nx.complete_graph(100)`:
.. code:: yaml
network_params:
network_type: complete_graph
n: 100
Environment
============
The environment is the place where the shared state of the simulation is stored.
For instance, the probability of certain events.
The configuration file may specify the initial value of the environment parameters:
.. code:: yaml
environment_params:
daily_probability_of_earthquake: 0.001
number_of_earthquakes: 0
Agents
======
Agents are a way of modelling behavior.
Agents can be characterized with two variables: an agent type (``agent_type``) and its state.
Only one agent is executed at a time (generally, every ``timeout`` seconds), and it has access to its state and the environment parameters.
Through the environment, it can access the network topology and the state of other agents.
There are three three types of agents according to how they are added to the simulation: network agents, environment agent, and other agents.
Network Agents
##############
Network agents are attached to a node in the topology.
The configuration file allows you to specify how agents will be mapped to topology nodes.
The simplest way is to specify a single type of agent.
Hence, every node in the network will have an associated agent of that type.
.. code:: yaml
agent_type: SISaModel
It is also possible to add more than one type of agent to the simulation, and to control the ratio of each type (``weight``).
For instance, with following configuration, it is five times more likely for a node to be assigned a CounterModel type than a SISaModel type.
.. code:: yaml
agent_distribution:
- agent_type: SISaModel
weight: 1
- agent_type: CounterModel
weight: 5
In addition to agent type, you may also add a custom initial state to the distribution.
This is very useful to add the same agent type with different states.
e.g., to populate the network with SISaModel, roughly 10% of them with a discontent state:
.. code:: yaml
agent_distribution:
- agent_type: SISaModel
weight: 9
state:
id: neutral
- agent_type: SISaModel
weight: 1
state:
id: discontent
Lastly, the configuration may include initial state for one or more nodes.
For instance, to add a state for the two nodes in this configuration:
.. code:: yaml
agent_type: SISaModel
network:
network_type: complete_graph
n: 2
states:
- id: content
- id: discontent
Or to add state only to specific nodes (by ``id``).
For example, to apply special skills to Linux Torvalds in a simulation:
.. literalinclude:: ../examples/torvalds.yml
:language: yaml
Environment Agents
##################
In addition to network agents, more agents can be added to the simulation.
These agens are programmed in much the same way as network agents, the only difference is that they will not be assigned to network nodes.
.. code::
environment_agents:
- agent_type: MyAgent
state:
mood: happy
- agent_type: DummyAgent
Visualizing the results
=======================
The simulation will return a dynamic graph .gexf file which could be visualized with
`Gephi <https://gephi.org/users/download/>`__.

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Usage
-----
First of all, you need to install the package. See :doc:`installation` for installation instructions.
Simulation Settings
===================
Once installed, before running a simulation, you need to configure it.
* In the Settings JSON file you will find the configuration of the network.
.. code:: python
{
"network_type": 1,
"number_of_nodes": 1000,
"max_time": 50,
"num_trials": 1,
"timeout": 2
}
* In the Settings JSON file, you will also find the configuration of the models.
Network Types
=============
There are three types of network implemented, but you could add more.
.. code:: python
if settings.network_type == 0:
G = nx.complete_graph(settings.number_of_nodes)
if settings.network_type == 1:
G = nx.barabasi_albert_graph(settings.number_of_nodes, 10)
if settings.network_type == 2:
G = nx.margulis_gabber_galil_graph(settings.number_of_nodes, None)
# More types of networks can be added here
Models Settings
===============
After having configured the simulation, the next step is setting up the variables of the models.
For this, you will need to modify the Settings JSON file again.
.. code:: json
{
"agent": ["SISaModel","ControlModelM2"],
"neutral_discontent_spon_prob": 0.04,
"neutral_discontent_infected_prob": 0.04,
"neutral_content_spon_prob": 0.18,
"neutral_content_infected_prob": 0.02,
"discontent_neutral": 0.13,
"discontent_content": 0.07,
"variance_d_c": 0.02,
"content_discontent": 0.009,
"variance_c_d": 0.003,
"content_neutral": 0.088,
"standard_variance": 0.055,
"prob_neutral_making_denier": 0.035,
"prob_infect": 0.075,
"prob_cured_healing_infected": 0.035,
"prob_cured_vaccinate_neutral": 0.035,
"prob_vaccinated_healing_infected": 0.035,
"prob_vaccinated_vaccinate_neutral": 0.035,
"prob_generate_anti_rumor": 0.035
}
In this file you will define the different models you are going to simulate. You can simulate as many models
as you want. Each model will be simulated separately.
After setting up the models, you have to initialize the parameters of each one. You will find the parameters needed
in the documentation of each model.
Parameter validation will fail if a required parameter without a default has not been provided.
Running the Simulation
======================
After setting all the configuration, you will be able to run the simulation. All you need to do is execute:
.. code:: bash
python3 soil.py
The simulation will return a dynamic graph .gexf file which could be visualized with
`Gephi <https://gephi.org/users/download/>`__.
It will also return one .png picture for each model simulated.

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---
name: simple
dir_path: "/tmp/"
num_trials: 3
max_time: 100
interval: 1
network_params:
generator: complete_graph
n: 10
network_agents:
- agent_type: CounterModel
weight: 1
state:
id: 0
- agent_type: AggregatedCounter
weight: 0.2
environment_agents: []
environment_params:
am_i_complete: true
default_state:
incidents: 0
states:
- name: 'The first node'
- name: 'The second node'

17
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default_state: {}
environment_agents: []
environment_params: {prob_neighbor_spread: 0.0, prob_tv_spread: 0.01}
interval: 1
max_time: 20
name: Sim_prob_0
network_agents:
- agent_type: NewsSpread
state: {has_tv: false}
weight: 1
- agent_type: NewsSpread
state: {has_tv: true}
weight: 2
network_params: {generator: erdos_renyi_graph, n: 500, p: 0.1}
num_trials: 1
states:
- {has_tv: true}

20
examples/custom-agents/agent.py Normal file
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@ -0,0 +1,20 @@
import soil
import random
class NewsSpread(soil.agents.FSM):
@soil.agents.default_state
@soil.agents.state
def neutral(self):
r = random.random()
if self['has_tv'] and r < self.env['prob_tv_spread']:
return self.infected
return
@soil.agents.state
def infected(self):
prob_infect = self.env['prob_neighbor_spread']
for neighbor in self.get_neighboring_agents(state_id=self.neutral.id):
r = random.random()
if r < prob_infect:
neighbor.state['id'] = self.infected.id
return

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examples/torvalds.edgelist Normal file
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@ -0,0 +1,2 @@
balkian Torvalds {}
anonymous Torvalds {}

14
examples/torvalds.yml Normal file
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---
name: torvalds_example
max_time: 1
interval: 2
agent_type: CounterModel
default_state:
skill_level: 'beginner'
network_params:
path: 'torvalds.edgelist'
states:
Torvalds:
skill_level: 'God'
balkian:
skill_level: 'developer'

38
models/BaseBehaviour/BaseBehaviour.py
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@ -1,38 +0,0 @@
import settings
from nxsim import BaseNetworkAgent
from .. import networkStatus
class BaseBehaviour(BaseNetworkAgent):
def __init__(self, environment=None, agent_id=0, state=()):
super().__init__(environment=environment, agent_id=agent_id, state=state)
self._attrs = {}
@property
def attrs(self):
now = self.env.now
if now not in self._attrs:
self._attrs[now] = {}
return self._attrs[now]
@attrs.setter
def attrs(self, value):
self._attrs[self.env.now] = value
def run(self):
while True:
self.step(self.env.now)
yield self.env.timeout(settings.network_params["timeout"])
def step(self, now):
networkStatus['agent_%s'% self.id] = self.to_json()
def to_json(self):
final = {}
for stamp, attrs in self._attrs.items():
for a in attrs:
if a not in final:
final[a] = {}
final[a][stamp] = attrs[a]
return final

1
models/BaseBehaviour/__init__.py
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@ -1 +0,0 @@
from .BaseBehaviour import BaseBehaviour

46
models/BassModel/BassModel.py
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@ -1,46 +0,0 @@
import random
from ..BaseBehaviour import *
from .. import sentimentCorrelationNodeArray
class BassModel(BaseBehaviour):
"""
Settings:
innovation_prob
imitation_prob
"""
def __init__(self, environment=None, agent_id=0, state=()):
super().__init__(environment=environment, agent_id=agent_id, state=state)
self.innovation_prob = environment.environment_params['innovation_prob']
self.imitation_prob = environment.environment_params['imitation_prob']
sentimentCorrelationNodeArray[self.id][self.env.now] = 0
def step(self, now):
self.behaviour()
super().step(now)
def behaviour(self):
# Outside effects
if random.random() < self.innovation_prob:
if self.state['id'] == 0:
self.state['id'] = 1
sentimentCorrelationNodeArray[self.id][self.env.now] = 1
else:
pass
self.attrs['status'] = self.state['id']
return
# Imitation effects
if self.state['id'] == 0:
aware_neighbors = self.get_neighboring_agents(state_id=1)
num_neighbors_aware = len(aware_neighbors)
if random.random() < (self.imitation_prob*num_neighbors_aware):
self.state['id'] = 1
sentimentCorrelationNodeArray[self.id][self.env.now] = 1
else:
pass
self.attrs['status'] = self.state['id']

1
models/BassModel/__init__.py
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@ -1 +0,0 @@
from .BassModel import BassModel

1
models/BigMarketModel/__init__.py
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@ -1 +0,0 @@
from .BigMarketModel import BigMarketModel

1
models/IndependentCascadeModel/__init__.py
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@ -1 +0,0 @@
from .IndependentCascadeModel import IndependentCascadeModel

112
models/ModelM2/SpreadModelM2.py
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@ -1,112 +0,0 @@
import settings
import random
import numpy as np
from ..BaseBehaviour import *
from .. import init_states
class SpreadModelM2(BaseBehaviour):
"""
Settings:
prob_neutral_making_denier
prob_infect
prob_cured_healing_infected
prob_cured_vaccinate_neutral
prob_vaccinated_healing_infected
prob_vaccinated_vaccinate_neutral
prob_generate_anti_rumor
"""
init_states[random.randint(0, settings.network_params["number_of_nodes"])] = {'id': 1}
init_states[random.randint(0, settings.network_params["number_of_nodes"])] = {'id': 1}
def __init__(self, environment=None, agent_id=0, state=()):
super().__init__(environment=environment, agent_id=agent_id, state=state)
self.prob_neutral_making_denier = np.random.normal(environment.environment_params['prob_neutral_making_denier'],
environment.environment_params['standard_variance'])
self.prob_infect = np.random.normal(environment.environment_params['prob_infect'],
environment.environment_params['standard_variance'])
self.prob_cured_healing_infected = np.random.normal(environment.environment_params['prob_cured_healing_infected'],
environment.environment_params['standard_variance'])
self.prob_cured_vaccinate_neutral = np.random.normal(environment.environment_params['prob_cured_vaccinate_neutral'],
environment.environment_params['standard_variance'])
self.prob_vaccinated_healing_infected = np.random.normal(environment.environment_params['prob_vaccinated_healing_infected'],
environment.environment_params['standard_variance'])
self.prob_vaccinated_vaccinate_neutral = np.random.normal(environment.environment_params['prob_vaccinated_vaccinate_neutral'],
environment.environment_params['standard_variance'])
self.prob_generate_anti_rumor = np.random.normal(environment.environment_params['prob_generate_anti_rumor'],
environment.environment_params['standard_variance'])
def step(self, now):
if self.state['id'] == 0: # Neutral
self.neutral_behaviour()
elif self.state['id'] == 1: # Infected
self.infected_behaviour()
elif self.state['id'] == 2: # Cured
self.cured_behaviour()
elif self.state['id'] == 3: # Vaccinated
self.vaccinated_behaviour()
self.attrs['status'] = self.state['id']
super().step(now)
def neutral_behaviour(self):
# Infected
infected_neighbors = self.get_neighboring_agents(state_id=1)
if len(infected_neighbors) > 0:
if random.random() < self.prob_neutral_making_denier:
self.state['id'] = 3 # Vaccinated making denier
def infected_behaviour(self):
# Neutral
neutral_neighbors = self.get_neighboring_agents(state_id=0)
for neighbor in neutral_neighbors:
if random.random() < self.prob_infect:
neighbor.state['id'] = 1 # Infected
def cured_behaviour(self):
# Vaccinate
neutral_neighbors = self.get_neighboring_agents(state_id=0)
for neighbor in neutral_neighbors:
if random.random() < self.prob_cured_vaccinate_neutral:
neighbor.state['id'] = 3 # Vaccinated
# Cure
infected_neighbors = self.get_neighboring_agents(state_id=1)
for neighbor in infected_neighbors:
if random.random() < self.prob_cured_healing_infected:
neighbor.state['id'] = 2 # Cured
def vaccinated_behaviour(self):
# Cure
infected_neighbors = self.get_neighboring_agents(state_id=1)
for neighbor in infected_neighbors:
if random.random() < self.prob_cured_healing_infected:
neighbor.state['id'] = 2 # Cured
# Vaccinate
neutral_neighbors = self.get_neighboring_agents(state_id=0)
for neighbor in neutral_neighbors:
if random.random() < self.prob_cured_vaccinate_neutral:
neighbor.state['id'] = 3 # Vaccinated
# Generate anti-rumor
infected_neighbors_2 = self.get_neighboring_agents(state_id=1)
for neighbor in infected_neighbors_2:
if random.random() < self.prob_generate_anti_rumor:
neighbor.state['id'] = 2 # Cured

2
models/ModelM2/__init__.py
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@ -1,2 +0,0 @@
from .ControlModelM2 import ControlModelM2
from .SpreadModelM2 import SpreadModelM2

1
models/SISaModel/__init__.py
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@ -1 +0,0 @@
from .SISaModel import SISaModel

1
models/SentimentCorrelationModel/__init__.py
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@ -1 +0,0 @@
from .SentimentCorrelationModel import SentimentCorrelationModel

8
models/__init__.py
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@ -1,8 +0,0 @@
from .models import *
from .BaseBehaviour import *
from .BassModel import *
from .BigMarketModel import *
from .IndependentCascadeModel import *
from .ModelM2 import *
from .SentimentCorrelationModel import *
from .SISaModel import *

10
models/models.py
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@ -1,10 +0,0 @@
import settings
networkStatus = {} # Dict that will contain the status of every agent in the network
sentimentCorrelationNodeArray = []
for x in range(0, settings.network_params["number_of_nodes"]):
sentimentCorrelationNodeArray.append({'id': x})
# Initialize agent states. Let's assume everyone is normal.
init_states = [{'id': 0, } for _ in range(settings.network_params["number_of_nodes"])]
# add keys as as necessary, but "id" must always refer to that state category

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3
requirements.txt
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@ -2,4 +2,5 @@ nxsim
simpy
networkx
numpy
matplotlib
matplotlib
pyyaml

62
settings.json
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@ -1,62 +0,0 @@
[
{
"network_type": 1,
"number_of_nodes": 1000,
"max_time": 50,
"num_trials": 1,
"timeout": 2
},
{
"agent": ["SISaModel","ControlModelM2"],
"bite_prob": 0.01,
"heal_prob": 0.01,
"innovation_prob": 0.001,
"imitation_prob": 0.005,
"outside_effects_prob": 0.2,
"anger_prob": 0.06,
"joy_prob": 0.05,
"sadness_prob": 0.02,
"disgust_prob": 0.02,
"enterprises": ["BBVA", "Santander", "Bankia"],
"tweet_probability_users": 0.44,
"tweet_relevant_probability": 0.25,
"tweet_probability_about": [0.15, 0.15, 0.15],
"sentiment_about": [0, 0, 0],
"tweet_probability_enterprises": [0.3, 0.3, 0.3],
"neutral_discontent_spon_prob": 0.04,
"neutral_discontent_infected_prob": 0.04,
"neutral_content_spon_prob": 0.18,
"neutral_content_infected_prob": 0.02,
"discontent_neutral": 0.13,
"discontent_content": 0.07,
"variance_d_c": 0.02,
"content_discontent": 0.009,
"variance_c_d": 0.003,
"content_neutral": 0.088,
"standard_variance": 0.055,
"prob_neutral_making_denier": 0.035,
"prob_infect": 0.075,
"prob_cured_healing_infected": 0.035,
"prob_cured_vaccinate_neutral": 0.035,
"prob_vaccinated_healing_infected": 0.035,
"prob_vaccinated_vaccinate_neutral": 0.035,
"prob_generate_anti_rumor": 0.035
}
]

67
settings.py
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@ -1,67 +0,0 @@
# General configuration
import json
with open('settings.json', 'r') as f:
settings = json.load(f)
network_params = settings[0]
environment_params = settings[1]
'''
environment_params = {
# Zombie model
'bite_prob': 0.01, # 0-1
'heal_prob': 0.01, # 0-1
# Bass model
'innovation_prob': 0.001,
'imitation_prob': 0.005,
# Sentiment Correlation model
'outside_effects_prob': 0.2,
'anger_prob': 0.06,
'joy_prob': 0.05,
'sadness_prob': 0.02,
'disgust_prob': 0.02,
# Big Market model
## Names
'enterprises': ["BBVA", "Santander", "Bankia"],
## Users
'tweet_probability_users': 0.44,
'tweet_relevant_probability': 0.25,
'tweet_probability_about': [0.15, 0.15, 0.15],
'sentiment_about': [0, 0, 0], # Default values
## Enterprises
'tweet_probability_enterprises': [0.3, 0.3, 0.3],
# SISa
'neutral_discontent_spon_prob': 0.04,
'neutral_discontent_infected_prob': 0.04,
'neutral_content_spon_prob': 0.18,
'neutral_content_infected_prob': 0.02,
'discontent_neutral': 0.13,
'discontent_content': 0.07,
'variance_d_c': 0.02,
'content_discontent': 0.009,
'variance_c_d': 0.003,
'content_neutral': 0.088,
'standard_variance': 0.055,
# Spread Model M2 and Control Model M2
'prob_neutral_making_denier': 0.035,
'prob_infect': 0.075,
'prob_cured_healing_infected': 0.035,
'prob_cured_vaccinate_neutral': 0.035,
'prob_vaccinated_healing_infected': 0.035,
'prob_vaccinated_vaccinate_neutral': 0.035,
'prob_generate_anti_rumor': 0.035
}
'''

39
setup.py Normal file
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@ -0,0 +1,39 @@
import pip
from setuptools import setup
# parse_requirements() returns generator of pip.req.InstallRequirement objects
from pip.req import parse_requirements
from soil import __version__
try:
install_reqs = parse_requirements(
"requirements.txt", session=pip.download.PipSession())
test_reqs = parse_requirements(
"test-requirements.txt", session=pip.download.PipSession())
except AttributeError:
install_reqs = parse_requirements("requirements.txt")
test_reqs = parse_requirements("test-requirements.txt")
install_reqs = [str(ir.req) for ir in install_reqs]
test_reqs = [str(ir.req) for ir in test_reqs]
setup(
name='soil',
packages=['soil'], # this must be the same as the name above
version=__version__,
description=('An Agent-Based Social Simulator for Social Networks'),
author='J. Fernando Sanchez',
author_email='jf.sanchez@upm.es',
url='https://github.com/gsi-upm/soil', # use the URL to the github repo
download_url='https://github.com/gsi-upm/soil/archive/{}.tar.gz'.format(
__version__),
keywords=['agent', 'social', 'simulator'],
classifiers=[],
install_requires=install_reqs,
tests_require=test_reqs,
setup_requires=['pytest-runner', ],
include_package_data=True,
entry_points={
'console_scripts':
['soil = soil.__init__:main']
})

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sim_01/log.0.state.pickled
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63
simulation.yml Normal file
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---
name: ControlModelM2_sim
max_time: 50
num_trials: 1
timeout: 2
network_params:
generator: barabasi_albert_graph
n: 100
m: 2
agent_distribution:
- agent_type: ControlModelM2
weight: 0.1
state:
id: 1
- agent_type: ControlModelM2
weight: 0.9
state:
id: 0
environment_params:
prob_neutral_making_denier: 0.035
prob_infect: 0.075
prob_cured_healing_infected: 0.035
prob_cured_vaccinate_neutral: 0.035
prob_vaccinated_healing_infected: 0.035
prob_vaccinated_vaccinate_neutral: 0.035
prob_generate_anti_rumor: 0.035
standard_variance: 0.055
---
name: SISA_sm
max_time: 50
num_trials: 2
timeout: 2
network_params:
generator: erdos_renyi_graph
n: 10000
p: 0.05
#other_agents:
# - agent_type: DrawingAgent
agent_distribution:
- agent_type: SISaModel
weight: 1
state:
id: content
- agent_type: SISaModel
weight: 1
state:
id: neutral
- agent_type: SISaModel
weight: 1
state:
id: discontent
environment_params:
neutral_discontent_spon_prob: 0.04
neutral_discontent_infected_prob: 0.04
neutral_content_spon_prob: 0.18
neutral_content_infected_prob: 0.02
discontent_neutral: 0.13
discontent_content: 0.07
variance_d_c: 0.02
content_discontent: 0.009
variance_c_d: 0.003
content_neutral: 0.088
standard_variance: 0.055

144
soil.py
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from models import *
from nxsim import NetworkSimulation
# import numpy
from matplotlib import pyplot as plt
import networkx as nx
import settings
import models
import math
import json
#################
# Visualization #
#################
def visualization(graph_name):
for x in range(0, settings.network_params["number_of_nodes"]):
attributes = {}
spells = []
for attribute in models.networkStatus["agent_%s" % x]:
if attribute == 'visible':
lastvisible = False
laststep = 0
for t_step in models.networkStatus["agent_%s" % x][attribute]:
nowvisible = models.networkStatus["agent_%s" % x][attribute][t_step]
if nowvisible and not lastvisible:
laststep = t_step
if not nowvisible and lastvisible:
spells.append((laststep, t_step))
lastvisible = nowvisible
if lastvisible:
spells.append((laststep, None))
else:
emotionStatusAux = []
for t_step in models.networkStatus["agent_%s" % x][attribute]:
prec = 2
output = math.floor(models.networkStatus["agent_%s" % x][attribute][t_step] * (10 ** prec)) / (10 ** prec) # 2 decimals
emotionStatusAux.append((output, t_step, t_step + settings.network_params["timeout"]))
attributes[attribute] = emotionStatusAux
if spells:
G.add_node(x, attributes, spells=spells)
else:
G.add_node(x, attributes)
print("Done!")
with open('data.txt', 'w') as outfile:
json.dump(models.networkStatus, outfile, sort_keys=True, indent=4, separators=(',', ': '))
nx.write_gexf(G, graph_name+".gexf", version="1.2draft")
###########
# Results #
###########
def results(model_name):
x_values = []
infected_values = []
neutral_values = []
cured_values = []
vaccinated_values = []
attribute_plot = 'status'
for time in range(0, settings.network_params["max_time"]):
value_infectados = 0
value_neutral = 0
value_cured = 0
value_vaccinated = 0
real_time = time * settings.network_params["timeout"]
activity = False
for x in range(0, settings.network_params["number_of_nodes"]):
if attribute_plot in models.networkStatus["agent_%s" % x]:
if real_time in models.networkStatus["agent_%s" % x][attribute_plot]:
if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 1: ## Infected
value_infectados += 1
activity = True
if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 0: ## Neutral
value_neutral += 1
activity = True
if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 2: ## Cured
value_cured += 1
activity = True
if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 3: ## Vaccinated
value_vaccinated += 1
activity = True
if activity:
x_values.append(real_time)
infected_values.append(value_infectados)
neutral_values.append(value_neutral)
cured_values.append(value_cured)
vaccinated_values.append(value_vaccinated)
activity = False
fig1 = plt.figure()
ax1 = fig1.add_subplot(111)
infected_line = ax1.plot(x_values, infected_values, label='Infected')
neutral_line = ax1.plot(x_values, neutral_values, label='Neutral')
cured_line = ax1.plot(x_values, cured_values, label='Cured')
vaccinated_line = ax1.plot(x_values, vaccinated_values, label='Vaccinated')
ax1.legend()
fig1.savefig(model_name + '.png')
# plt.show()
####################
# Network creation #
####################
if settings.network_params["network_type"] == 0:
G = nx.complete_graph(settings.network_params["number_of_nodes"])
if settings.network_params["network_type"] == 1:
G = nx.barabasi_albert_graph(settings.network_params["number_of_nodes"], 10)
if settings.network_params["network_type"] == 2:
G = nx.margulis_gabber_galil_graph(settings.network_params["number_of_nodes"], None)
# More types of networks can be added here
##############
# Simulation #
##############
agents = settings.environment_params['agent']
print("Using Agent(s): {agents}".format(agents=agents))
if len(agents) > 1:
for agent in agents:
sim = NetworkSimulation(topology=G, states=init_states, agent_type=locals()[agent], max_time=settings.network_params["max_time"],
num_trials=settings.network_params["num_trials"], logging_interval=1.0, **settings.environment_params)
sim.run_simulation()
print(str(agent))
results(str(agent))
visualization(str(agent))
else:
agent = agents[0]
sim = NetworkSimulation(topology=G, states=init_states, agent_type=locals()[agent], max_time=settings.network_params["max_time"],
num_trials=settings.network_params["num_trials"], logging_interval=1.0, **settings.environment_params)
sim.run_simulation()
results(str(agent))
visualization(str(agent))

394
soil.py~
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from nxsim import NetworkSimulation
from nxsim import BaseNetworkAgent
from nxsim import BaseLoggingAgent
from random import randint
from matplotlib import pyplot as plt
import random
import numpy as np
import networkx as nx
import settings
settings.init()
if settings.network_type == 0:
G = nx.complete_graph(settings.number_of_nodes)
if settings.network_type == 1:
G = nx.barabasi_albert_graph(settings.number_of_nodes,3)
if settings.network_type == 2:
G = nx.margulis_gabber_galil_graph(settings.number_of_nodes, None)
myList=[]
networkStatus=[]
for x in range(0, settings.number_of_nodes):
networkStatus.append({'id':x})
# # Just like subclassing a process in SimPy
# class MyAgent(BaseNetworkAgent):
# def __init__(self, environment=None, agent_id=0, state=()): # Make sure to have these three keyword arguments
# super().__init__(environment=environment, agent_id=agent_id, state=state)
# # Add your own attributes here
# def run(self):
# # Add your behaviors here
class SentimentCorrelationModel(BaseNetworkAgent):
def __init__(self, environment=None, agent_id=0, state=()):
super().__init__(environment=environment, agent_id=agent_id, state=state)
self.outside_effects_prob = settings.outside_effects_prob
self.anger_prob = settings.anger_prob
self.joy_prob = settings.joy_prob
self.sadness_prob = settings.sadness_prob
self.disgust_prob = settings.disgust_prob
self.time_awareness=[]
for i in range(4):
self.time_awareness.append(0) #0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
networkStatus[self.id][self.env.now]=0
def run(self):
while True:
if self.env.now > 10:
G.add_node(205)
G.add_edge(205,0)
angry_neighbors_1_time_step=[]
joyful_neighbors_1_time_step=[]
sad_neighbors_1_time_step=[]
disgusted_neighbors_1_time_step=[]
angry_neighbors = self.get_neighboring_agents(state_id=1)
for x in angry_neighbors:
if x.time_awareness[0] > (self.env.now-500):
angry_neighbors_1_time_step.append(x)
num_neighbors_angry = len(angry_neighbors_1_time_step)
joyful_neighbors = self.get_neighboring_agents(state_id=2)
for x in joyful_neighbors:
if x.time_awareness[1] > (self.env.now-500):
joyful_neighbors_1_time_step.append(x)
num_neighbors_joyful = len(joyful_neighbors_1_time_step)
sad_neighbors = self.get_neighboring_agents(state_id=3)
for x in sad_neighbors:
if x.time_awareness[2] > (self.env.now-500):
sad_neighbors_1_time_step.append(x)
num_neighbors_sad = len(sad_neighbors_1_time_step)
disgusted_neighbors = self.get_neighboring_agents(state_id=4)
for x in disgusted_neighbors:
if x.time_awareness[3] > (self.env.now-500):
disgusted_neighbors_1_time_step.append(x)
num_neighbors_disgusted = len(disgusted_neighbors_1_time_step)
# #Outside effects. Asignamos un estado aleatorio
# if random.random() < settings.outside_effects_prob:
# if self.state['id'] == 0:
# self.state['id'] = random.randint(1,4)
# myList.append(self.id)
# networkStatus[self.id][self.env.now]=self.state['id'] #Almaceno cuando se ha infectado para la red dinamica
# self.time_awareness = self.env.now #Para saber cuando se han contagiado
# yield self.env.timeout(settings.timeout)
# else:
# yield self.env.timeout(settings.timeout)
# #Imitation effects-Joy
# if random.random() < (settings.joy_prob*(num_neighbors_joyful)/10):
# myList.append(self.id)
# self.state['id'] = 2
# networkStatus[self.id][self.env.now]=2
# yield self.env.timeout(settings.timeout)
# #Imitation effects-Sadness
# if random.random() < (settings.sadness_prob*(num_neighbors_sad)/10):
# myList.append(self.id)
# self.state['id'] = 3
# networkStatus[self.id][self.env.now]=3
# yield self.env.timeout(settings.timeout)
# #Imitation effects-Disgust
# if random.random() < (settings.disgust_prob*(num_neighbors_disgusted)/10):
# myList.append(self.id)
# self.state['id'] = 4
# networkStatus[self.id][self.env.now]=4
# yield self.env.timeout(settings.timeout)
# #Imitation effects-Anger
# if random.random() < (settings.anger_prob*(num_neighbors_angry)/10):
# myList.append(self.id)
# self.state['id'] = 1
# networkStatus[self.id][self.env.now]=1
# yield self.env.timeout(settings.timeout)
# yield self.env.timeout(settings.timeout)
###########################################
anger_prob= settings.anger_prob+(len(angry_neighbors_1_time_step)*settings.anger_prob)
print("anger_prob " + str(anger_prob))
joy_prob= settings.joy_prob+(len(joyful_neighbors_1_time_step)*settings.joy_prob)
print("joy_prob " + str(joy_prob))
sadness_prob = settings.sadness_prob+(len(sad_neighbors_1_time_step)*settings.sadness_prob)
print("sadness_prob "+ str(sadness_prob))
disgust_prob = settings.disgust_prob+(len(disgusted_neighbors_1_time_step)*settings.disgust_prob)
print("disgust_prob " + str(disgust_prob))
outside_effects_prob= settings.outside_effects_prob
print("outside_effects_prob " + str(outside_effects_prob))
num = random.random()
if(num<outside_effects_prob):
self.state['id'] = random.randint(1,4)
myList.append(self.id)
networkStatus[self.id][self.env.now]=self.state['id'] #Almaceno cuando se ha infectado para la red dinamica
self.time_awareness[self.state['id']-1] = self.env.now
yield self.env.timeout(settings.timeout)
if(num<anger_prob):
myList.append(self.id)
self.state['id'] = 1
networkStatus[self.id][self.env.now]=1
self.time_awareness[self.state['id']-1] = self.env.now
elif (num<joy_prob+anger_prob and num>anger_prob):
myList.append(self.id)
self.state['id'] = 2
networkStatus[self.id][self.env.now]=2
self.time_awareness[self.state['id']-1] = self.env.now
elif (num<sadness_prob+anger_prob+joy_prob and num>joy_prob+anger_prob):
myList.append(self.id)
self.state['id'] = 3
networkStatus[self.id][self.env.now]=3
self.time_awareness[self.state['id']-1] = self.env.now
elif (num<disgust_prob+sadness_prob+anger_prob+joy_prob and num>sadness_prob+anger_prob+joy_prob):
myList.append(self.id)
self.state['id'] = 4
networkStatus[self.id][self.env.now]=4
self.time_awareness[self.state['id']-1] = self.env.now
yield self.env.timeout(settings.timeout)
# anger_propagation = settings.anger_prob*num_neighbors_angry/10
# joy_propagation = anger_propagation + (settings.joy_prob*num_neighbors_joyful/10)
# sadness_propagation = joy_propagation + (settings.sadness_prob*num_neighbors_sad/10)
# disgust_propagation = sadness_propagation + (settings.disgust_prob*num_neighbors_disgusted/10)
# outside_effects_propagation = disgust_propagation + settings.outside_effects_prob
# if (num<anger_propagation):
# if(self.state['id'] !=0):
# myList.append(self.id)
# self.state['id'] = 1
# networkStatus[self.id][self.env.now]=1
# yield self.env.timeout(settings.timeout)
# if (num<joy_propagation):
# if(self.state['id'] !=0):
# myList.append(self.id)
# self.state['id'] = 2
# networkStatus[self.id][self.env.now]=2
# yield self.env.timeout(settings.timeout)
# if(num<sadness_propagation):
# if(self.state['id'] !=0):
# myList.append(self.id)
# self.state['id'] = 3
# networkStatus[self.id][self.env.now]=3
# yield self.env.timeout(settings.timeout)
# # if(num<disgust_propagation):
# # if(self.state['id'] !=0):
# # myList.append(self.id)
# # self.state['id'] = 4
# # networkStatus[self.id][self.env.now]=4
# # yield self.env.timeout(settings.timeout)
# if(num <outside_effects_propagation):
# if self.state['id'] == 0:
# self.state['id'] = random.randint(1,4)
# myList.append(self.id)
# networkStatus[self.id][self.env.now]=self.state['id'] #Almaceno cuando se ha infectado para la red dinamica
# self.time_awareness = self.env.now #Para saber cuando se han contagiado
# yield self.env.timeout(settings.timeout)
# else:
# yield self.env.timeout(settings.timeout)
# else:
# yield self.env.timeout(settings.timeout)
class BassModel(BaseNetworkAgent):
def __init__(self, environment=None, agent_id=0, state=()):
super().__init__(environment=environment, agent_id=agent_id, state=state)
self.innovation_prob = settings.innovation_prob
self.imitation_prob = settings.imitation_prob
networkStatus[self.id][self.env.now]=0
def run(self):
while True:
#Outside effects
if random.random() < settings.innovation_prob:
if self.state['id'] == 0:
self.state['id'] = 1
myList.append(self.id)
networkStatus[self.id][self.env.now]=1
yield self.env.timeout(settings.timeout)
else:
yield self.env.timeout(settings.timeout)
#Imitation effects
if self.state['id'] == 0:
aware_neighbors = self.get_neighboring_agents(state_id=1)
num_neighbors_aware = len(aware_neighbors)
if random.random() < (settings.imitation_prob*num_neighbors_aware):
myList.append(self.id)
self.state['id'] = 1
networkStatus[self.id][self.env.now]=1
yield self.env.timeout(settings.timeout)
else:
yield self.env.timeout(settings.timeout)
class IndependentCascadeModel(BaseNetworkAgent):
def __init__(self, environment=None, agent_id=0, state=()):
super().__init__(environment=environment, agent_id=agent_id, state=state)
self.innovation_prob = settings.innovation_prob
self.imitation_prob = settings.imitation_prob
self.time_awareness = 0
networkStatus[self.id][self.env.now]=0
def run(self):
while True:
aware_neighbors_1_time_step=[]
#Outside effects
if random.random() < settings.innovation_prob:
if self.state['id'] == 0:
self.state['id'] = 1
myList.append(self.id)
networkStatus[self.id][self.env.now]=1
self.time_awareness = self.env.now #Para saber cuando se han contagiado
yield self.env.timeout(settings.timeout)
else:
yield self.env.timeout(settings.timeout)
#Imitation effects
if self.state['id'] == 0:
aware_neighbors = self.get_neighboring_agents(state_id=1)
for x in aware_neighbors:
if x.time_awareness == (self.env.now-1):
aware_neighbors_1_time_step.append(x)
num_neighbors_aware = len(aware_neighbors_1_time_step)
if random.random() < (settings.imitation_prob*num_neighbors_aware):
myList.append(self.id)
self.state['id'] = 1
networkStatus[self.id][self.env.now]=1
yield self.env.timeout(settings.timeout)
else:
yield self.env.timeout(settings.timeout)
class ZombieOutbreak(BaseNetworkAgent):
def __init__(self, environment=None, agent_id=0, state=()):
super().__init__(environment=environment, agent_id=agent_id, state=state)
self.bite_prob = settings.bite_prob
networkStatus[self.id][self.env.now]=0
def run(self):
while True:
if random.random() < settings.heal_prob:
if self.state['id'] == 1:
self.zombify()
yield self.env.timeout(settings.timeout)
else:
yield self.env.timeout(settings.timeout)
else:
if self.state['id'] == 1:
print("Soy el zombie " + str(self.id) + " y me voy a curar porque el num aleatorio ha sido " + str(num))
networkStatus[self.id][self.env.now]=0
if self.id in myList:
myList.remove(self.id)
self.state['id'] = 0
yield self.env.timeout(settings.timeout)
else:
yield self.env.timeout(settings.timeout)
def zombify(self):
normal_neighbors = self.get_neighboring_agents(state_id=0)
for neighbor in normal_neighbors:
if random.random() < self.bite_prob:
print("Soy el zombie " + str(self.id) + " y voy a contagiar a " + str(neighbor.id))
neighbor.state['id'] = 1 # zombie
myList.append(neighbor.id)
networkStatus[self.id][self.env.now]=1
networkStatus[neighbor.id][self.env.now]=1
print(self.env.now, "Soy el zombie: "+ str(self.id), "Mi vecino es: "+ str(neighbor.id), sep='\t')
break
# Initialize agent states. Let's assume everyone is normal.
init_states = [{'id': 0, } for _ in range(settings.number_of_nodes)] # add keys as as necessary, but "id" must always refer to that state category
# Seed a zombie
#init_states[5] = {'id': 1}
#init_states[3] = {'id': 1}
sim = NetworkSimulation(topology=G, states=init_states, agent_type=SentimentCorrelationModel,
max_time=settings.max_time, num_trials=settings.num_trials, logging_interval=1.0)
sim.run_simulation()
myList = sorted(myList, key=int)
#print("Los zombies son: " + str(myList))
trial = BaseLoggingAgent.open_trial_state_history(dir_path='sim_01', trial_id=0)
zombie_census = [sum([1 for node_id, state in g.items() if state['id'] == 1]) for t,g in trial.items()]
#for x in range(len(myList)):
# G.node[myList[x]]['viz'] = {'color': {'r': 255, 'g': 0, 'b': 0, 'a': 0}}
#G.node[1]['viz'] = {'color': {'r': 255, 'g': 0, 'b': 0, 'a': 0}}
#lista = nx.nodes(G)
#print('Nodos: ' + str(lista))
for x in range(0, settings.number_of_nodes):
networkStatusAux=[]
for tiempo in networkStatus[x]:
if tiempo != 'id':
networkStatusAux.append((networkStatus[x][tiempo],tiempo,None))
G.add_node(x, zombie= networkStatusAux)
#print(networkStatus)
nx.write_gexf(G,"test.gexf", version="1.2draft")
plt.plot(zombie_census)
plt.draw() # pyplot draw()
plt.savefig("zombie.png")
#print(networkStatus)
#nx.draw(G)
#plt.show()
#plt.savefig("path.png")

42
soil/__init__.py Normal file
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@ -0,0 +1,42 @@
import importlib
import sys
import os
__version__ = "0.9.2"
try:
basestring
except NameError:
basestring = str
from . import agents
from . import simulation
from . import environment
from . import utils
from . import settings
def main():
import argparse
from . import simulation
parser = argparse.ArgumentParser(description='Run a SOIL simulation')
parser.add_argument('file', type=str,
nargs="?",
default='simulation.yml',
help='python module containing the simulation configuration.')
parser.add_argument('--module', '-m', type=str,
help='file containing the code of any custom agents.')
args = parser.parse_args()
if args.module:
sys.path.append(os.getcwd())
importlib.import_module(args.module)
print('Loading config file: {}'.format(args.file))
simulation.run_from_config(args.file)
if __name__ == '__main__':
main()

123
soil/agents/BaseBehaviour.py Normal file
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@ -0,0 +1,123 @@
import nxsim
from collections import OrderedDict
from copy import deepcopy
import json
from functools import wraps
class BaseAgent(nxsim.BaseAgent):
"""
A special simpy BaseAgent that keeps track of its state history.
"""
def __init__(self, *args, **kwargs):
self._history = OrderedDict()
self._neighbors = None
super().__init__(*args, **kwargs)
self._history[None] = deepcopy(self.state)
@property
def now(self):
try:
return self.env.now
except AttributeError:
# No environment
return None
def run(self):
while True:
res = self.step()
self._history[self.env.now] = deepcopy(self.state)
yield res or self.env.timeout(self.env.interval)
def step(self):
pass
def to_json(self):
return json.dumps(self._history)
class NetworkAgent(BaseAgent, nxsim.BaseNetworkAgent):
def count_agents(self, state_id=None, limit_neighbors=False):
if limit_neighbors:
agents = self.global_topology.neighbors(self.id)
else:
agents = self.global_topology.nodes()
count = 0
for agent in agents:
if state_id and state_id != self.global_topology.node[agent]['agent'].state['id']:
continue
count += 1
return count
def count_neighboring_agents(self, state_id=None):
return self.count_agents(state_id, limit_neighbors=True)
def state(func):
@wraps(func)
def func_wrapper(self):
when = None
next_state = func(self)
try:
next_state, when = next_state
except TypeError:
pass
if next_state:
try:
self.state['id'] = next_state.id
except AttributeError:
raise NotImplemented('State id %s is not valid.' % next_state)
return when
func_wrapper.id = func.__name__
func_wrapper.is_default = False
return func_wrapper
def default_state(func):
func.is_default = True
return func
class MetaFSM(type):
def __init__(cls, name, bases, nmspc):
super(MetaFSM, cls).__init__(name, bases, nmspc)
states = {}
# Re-use states from inherited classes
default_state = None
for i in bases:
if isinstance(i, MetaFSM):
for state_id, state in i.states.items():
if state.is_default:
default_state = state
states[state_id] = state
# Add new states
for name, func in nmspc.items():
if hasattr(func, 'id'):
if func.is_default:
default_state = func
states[func.id] = func
cls.default_state = default_state
cls.states = states
class FSM(BaseAgent, metaclass=MetaFSM):
def __init__(self, *args, **kwargs):
super(FSM, self).__init__(*args, **kwargs)
if 'id' not in self.state:
self.state['id'] = self.default_state.id
def step(self):
if 'id' in self.state:
next_state = self.state['id']
elif self.default_state:
next_state = self.default_state.id
else:
raise Exception('{} has no valid state id or default state'.format(self))
if next_state not in self.states:
raise Exception('{} is not a valid id for {}'.format(next_state, self))
self.states[next_state](self)

40
soil/agents/BassModel.py Normal file
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@ -0,0 +1,40 @@
import random
from . import NetworkAgent
class BassModel(NetworkAgent):
"""
Settings:
innovation_prob
imitation_prob
"""
def __init__(self, environment, agent_id, state):
super().__init__(environment=environment, agent_id=agent_id, state=state)
env_params = environment.environment_params
self.state['sentimentCorrelation'] = 0
def step(self):
self.behaviour()
def behaviour(self):
# Outside effects
if random.random() < self.state_params['innovation_prob']:
if self.state['id'] == 0:
self.state['id'] = 1
self.state['sentimentCorrelation'] = 1
else:
pass
return
# Imitation effects
if self.state['id'] == 0:
aware_neighbors = self.get_neighboring_agents(state_id=1)
num_neighbors_aware = len(aware_neighbors)
if random.random() < (self.state_params['imitation_prob']*num_neighbors_aware):
self.state['id'] = 1
self.state['sentimentCorrelation'] = 1
else:
pass

10
models/BigMarketModel/BigMarketModel.py → soil/agents/BigMarketModel.py
View File

@ -1,8 +1,8 @@
import random
from ..BaseBehaviour import *
from . import NetworkAgent
class BigMarketModel(BaseBehaviour):
class BigMarketModel(NetworkAgent):
"""
Settings:
Names:
@ -37,7 +37,7 @@ class BigMarketModel(BaseBehaviour):
self.tweet_probability_about = environment.environment_params['tweet_probability_about'] # List
self.sentiment_about = environment.environment_params['sentiment_about'] # List
def step(self, now):
def step(self):
if self.id < self.number_of_enterprises: # Enterprise
self.enterpriseBehaviour()
@ -46,11 +46,9 @@ class BigMarketModel(BaseBehaviour):
for i in range(self.number_of_enterprises): # So that it never is set to 0 if there are not changes (logs)
self.attrs['sentiment_enterprise_%s'% self.enterprises[i]] = self.sentiment_about[i]
super().step(now)
def enterpriseBehaviour(self):
if random.random()< self.tweet_probability: # Tweets
if random.random() < self.tweet_probability: # Tweets
aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises) # Nodes neighbour users
for x in aware_neighbors:
if random.uniform(0,10) < 5:

31
soil/agents/CounterModel.py Normal file
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@ -0,0 +1,31 @@
from . import NetworkAgent
class CounterModel(NetworkAgent):
"""
Dummy behaviour. It counts the number of nodes in the network and neighbors
in each step and adds it to its state.
"""
def step(self):
# Outside effects
total = len(self.get_all_agents())
neighbors = len(self.get_neighboring_agents())
self.state['times'] = self.state.get('times', 0) + 1
self.state['neighbors'] = neighbors
self.state['total'] = total
class AggregatedCounter(NetworkAgent):
"""
Dummy behaviour. It counts the number of nodes in the network and neighbors
in each step and adds it to its state.
"""
def step(self):
# Outside effects
total = len(self.get_all_agents())
neighbors = len(self.get_neighboring_agents())
self.state['times'] = self.state.get('times', 0) + 1
self.state['neighbors'] = self.state.get('neighbors', 0) + neighbors
self.state['total'] = self.state.get('total', 0) + total

18
soil/agents/DrawingAgent.py Normal file
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@ -0,0 +1,18 @@
from . import BaseAgent
import os.path
import matplotlib
import matplotlib.pyplot as plt
import networkx as nx
class DrawingAgent(BaseAgent):
"""
Agent that draws the state of the network.
"""
def step(self):
# Outside effects
f = plt.figure()
nx.draw(self.env.G, node_size=10, width=0.2, pos=nx.spring_layout(self.env.G, scale=100), ax=f.add_subplot(111))
f.savefig(os.path.join(self.env.sim().dir_path, "graph-"+str(self.env.now)+".png"))

22
models/IndependentCascadeModel/IndependentCascadeModel.py → soil/agents/IndependentCascadeModel.py
View File

@ -1,9 +1,8 @@
import random
from ..BaseBehaviour import *
from .. import sentimentCorrelationNodeArray
from . import BaseAgent
class IndependentCascadeModel(BaseBehaviour):
class IndependentCascadeModel(BaseAgent):
"""
Settings:
innovation_prob
@ -15,12 +14,11 @@ class IndependentCascadeModel(BaseBehaviour):
super().__init__(environment=environment, agent_id=agent_id, state=state)
self.innovation_prob = environment.environment_params['innovation_prob']
self.imitation_prob = environment.environment_params['imitation_prob']
self.time_awareness = 0
sentimentCorrelationNodeArray[self.id][self.env.now] = 0
self.state['time_awareness'] = 0
self.state['sentimentCorrelation'] = 0
def step(self, now):
def step(self):
self.behaviour()
super().step(now)
def behaviour(self):
aware_neighbors_1_time_step = []
@ -28,26 +26,24 @@ class IndependentCascadeModel(BaseBehaviour):
if random.random() < self.innovation_prob:
if self.state['id'] == 0:
self.state['id'] = 1
sentimentCorrelationNodeArray[self.id][self.env.now] = 1
self.time_awareness = self.env.now # To know when they have been infected
self.state['sentimentCorrelation'] = 1
self.state['time_awareness'] = self.env.now # To know when they have been infected
else:
pass
self.attrs['status'] = self.state['id']
return
# Imitation effects
if self.state['id'] == 0:
aware_neighbors = self.get_neighboring_agents(state_id=1)
for x in aware_neighbors:
if x.time_awareness == (self.env.now-1):
if x.state['time_awareness'] == (self.env.now-1):
aware_neighbors_1_time_step.append(x)
num_neighbors_aware = len(aware_neighbors_1_time_step)
if random.random() < (self.imitation_prob*num_neighbors_aware):
self.state['id'] = 1
sentimentCorrelationNodeArray[self.id][self.env.now] = 1
self.state['sentimentCorrelation'] = 1
else:
pass
self.attrs['status'] = self.state['id']
return

151
models/ModelM2/ControlModelM2.py → soil/agents/ModelM2.py
View File

@ -1,36 +1,26 @@
import settings
import random
import numpy as np
from ..BaseBehaviour import *
from .. import init_states
from . import NetworkAgent
class ControlModelM2(BaseBehaviour):
class SpreadModelM2(NetworkAgent):
"""
Settings:
prob_neutral_making_denier
prob_infect
prob_cured_healing_infected
prob_cured_vaccinate_neutral
prob_vaccinated_healing_infected
prob_vaccinated_vaccinate_neutral
prob_generate_anti_rumor
"""
# Init infected
init_states[random.randint(0, settings.network_params["number_of_nodes"]-1)] = {'id': 1}
init_states[random.randint(0, settings.network_params["number_of_nodes"]-1)] = {'id': 1}
# Init beacons
init_states[random.randint(0, settings.network_params["number_of_nodes"]-1)] = {'id': 4}
init_states[random.randint(0, settings.network_params["number_of_nodes"]-1)] = {'id': 4}
def __init__(self, environment=None, agent_id=0, state=()):
super().__init__(environment=environment, agent_id=agent_id, state=state)
@ -52,7 +42,7 @@ class ControlModelM2(BaseBehaviour):
self.prob_generate_anti_rumor = np.random.normal(environment.environment_params['prob_generate_anti_rumor'],
environment.environment_params['standard_variance'])
def step(self, now):
def step(self):
if self.state['id'] == 0: # Neutral
self.neutral_behaviour()
@ -62,16 +52,8 @@ class ControlModelM2(BaseBehaviour):
self.cured_behaviour()
elif self.state['id'] == 3: # Vaccinated
self.vaccinated_behaviour()
elif self.state['id'] == 4: # Beacon-off
self.beacon_off_behaviour()
elif self.state['id'] == 5: # Beacon-on
self.beacon_on_behaviour()
self.attrs['status'] = self.state['id']
super().step(now)
def neutral_behaviour(self):
self.attrs['visible'] = False
# Infected
infected_neighbors = self.get_neighboring_agents(state_id=1)
@ -86,11 +68,9 @@ class ControlModelM2(BaseBehaviour):
for neighbor in neutral_neighbors:
if random.random() < self.prob_infect:
neighbor.state['id'] = 1 # Infected
self.attrs['visible'] = False
def cured_behaviour(self):
self.attrs['visible'] = True
# Vaccinate
neutral_neighbors = self.get_neighboring_agents(state_id=0)
for neighbor in neutral_neighbors:
@ -104,7 +84,116 @@ class ControlModelM2(BaseBehaviour):
neighbor.state['id'] = 2 # Cured
def vaccinated_behaviour(self):
self.attrs['visible'] = True
# Cure
infected_neighbors = self.get_neighboring_agents(state_id=1)
for neighbor in infected_neighbors:
if random.random() < self.prob_cured_healing_infected:
neighbor.state['id'] = 2 # Cured
# Vaccinate
neutral_neighbors = self.get_neighboring_agents(state_id=0)
for neighbor in neutral_neighbors:
if random.random() < self.prob_cured_vaccinate_neutral:
neighbor.state['id'] = 3 # Vaccinated
# Generate anti-rumor
infected_neighbors_2 = self.get_neighboring_agents(state_id=1)
for neighbor in infected_neighbors_2:
if random.random() < self.prob_generate_anti_rumor:
neighbor.state['id'] = 2 # Cured
class ControlModelM2(NetworkAgent):
"""
Settings:
prob_neutral_making_denier
prob_infect
prob_cured_healing_infected
prob_cured_vaccinate_neutral
prob_vaccinated_healing_infected
prob_vaccinated_vaccinate_neutral
prob_generate_anti_rumor
"""
def __init__(self, environment=None, agent_id=0, state=()):
super().__init__(environment=environment, agent_id=agent_id, state=state)
self.prob_neutral_making_denier = np.random.normal(environment.environment_params['prob_neutral_making_denier'],
environment.environment_params['standard_variance'])
self.prob_infect = np.random.normal(environment.environment_params['prob_infect'],
environment.environment_params['standard_variance'])
self.prob_cured_healing_infected = np.random.normal(environment.environment_params['prob_cured_healing_infected'],
environment.environment_params['standard_variance'])
self.prob_cured_vaccinate_neutral = np.random.normal(environment.environment_params['prob_cured_vaccinate_neutral'],
environment.environment_params['standard_variance'])
self.prob_vaccinated_healing_infected = np.random.normal(environment.environment_params['prob_vaccinated_healing_infected'],
environment.environment_params['standard_variance'])
self.prob_vaccinated_vaccinate_neutral = np.random.normal(environment.environment_params['prob_vaccinated_vaccinate_neutral'],
environment.environment_params['standard_variance'])
self.prob_generate_anti_rumor = np.random.normal(environment.environment_params['prob_generate_anti_rumor'],
environment.environment_params['standard_variance'])
def step(self):
if self.state['id'] == 0: # Neutral
self.neutral_behaviour()
elif self.state['id'] == 1: # Infected
self.infected_behaviour()
elif self.state['id'] == 2: # Cured
self.cured_behaviour()
elif self.state['id'] == 3: # Vaccinated
self.vaccinated_behaviour()
elif self.state['id'] == 4: # Beacon-off
self.beacon_off_behaviour()
elif self.state['id'] == 5: # Beacon-on
self.beacon_on_behaviour()
def neutral_behaviour(self):
self.state['visible'] = False
# Infected
infected_neighbors = self.get_neighboring_agents(state_id=1)
if len(infected_neighbors) > 0:
if random.random() < self.prob_neutral_making_denier:
self.state['id'] = 3 # Vaccinated making denier
def infected_behaviour(self):
# Neutral
neutral_neighbors = self.get_neighboring_agents(state_id=0)
for neighbor in neutral_neighbors:
if random.random() < self.prob_infect:
neighbor.state['id'] = 1 # Infected
self.state['visible'] = False
def cured_behaviour(self):
self.state['visible'] = True
# Vaccinate
neutral_neighbors = self.get_neighboring_agents(state_id=0)
for neighbor in neutral_neighbors:
if random.random() < self.prob_cured_vaccinate_neutral:
neighbor.state['id'] = 3 # Vaccinated
# Cure
infected_neighbors = self.get_neighboring_agents(state_id=1)
for neighbor in infected_neighbors:
if random.random() < self.prob_cured_healing_infected:
neighbor.state['id'] = 2 # Cured
def vaccinated_behaviour(self):
self.state['visible'] = True
# Cure
infected_neighbors = self.get_neighboring_agents(state_id=1)
@ -125,13 +214,13 @@ class ControlModelM2(BaseBehaviour):
neighbor.state['id'] = 2 # Cured
def beacon_off_behaviour(self):
self.attrs['visible'] = False
self.state['visible'] = False
infected_neighbors = self.get_neighboring_agents(state_id=1)
if len(infected_neighbors) > 0:
self.state['id'] == 5 # Beacon on
def beacon_on_behaviour(self):
self.attrs['visible'] = False
self.state['visible'] = False
# Cure (M2 feature added)
infected_neighbors = self.get_neighboring_agents(state_id=1)
for neighbor in infected_neighbors:

59
models/SISaModel/SISaModel.py → soil/agents/SISaModel.py
View File

@ -1,9 +1,9 @@
import random
import numpy as np
from models.BaseBehaviour import *
from . import FSM, state
class SISaModel(BaseBehaviour):
class SISaModel(FSM):
"""
Settings:
neutral_discontent_spon_prob
@ -51,48 +51,43 @@ class SISaModel(BaseBehaviour):
self.content_neutral = np.random.normal(environment.environment_params['content_neutral'],
environment.environment_params['standard_variance'])
def step(self, now):
if self.state['id'] == 0:
self.neutral_behaviour()
if self.state['id'] == 1:
self.discontent_behaviour()
if self.state['id'] == 2:
self.content_behaviour()
self.attrs['status'] = self.state['id']
super().step(now)
def neutral_behaviour(self):
@state
def neutral(self):
# Spontaneous effects
if random.random() < self.neutral_discontent_spon_prob:
self.state['id'] = 1
return self.discontent
if random.random() < self.neutral_content_spon_prob:
self.state['id'] = 2
return self.content
# Infected
discontent_neighbors = self.get_neighboring_agents(state_id=1)
if random.random() < len(discontent_neighbors) * self.neutral_discontent_infected_prob:
self.state['id'] = 1
content_neighbors = self.get_neighboring_agents(state_id=2)
if random.random() < len(content_neighbors) * self.neutral_content_infected_prob:
self.state['id'] = 2
discontent_neighbors = self.count_neighboring_agents(state_id=self.discontent)
if random.random() < discontent_neighbors * self.neutral_discontent_infected_prob:
return self.discontent
content_neighbors = self.count_neighboring_agents(state_id=self.content.id)
if random.random() < content_neighbors * self.neutral_content_infected_prob:
return self.content
return self.neutral
def discontent_behaviour(self):
@state
def discontent(self):
# Healing
if random.random() < self.discontent_neutral:
self.state['id'] = 0
return self.neutral
# Superinfected
content_neighbors = self.get_neighboring_agents(state_id=2)
if random.random() < len(content_neighbors) * self.discontent_content:
self.state['id'] = 2
content_neighbors = self.count_neighboring_agents(state_id=self.content.id)
if random.random() < content_neighbors * self.discontent_content:
return self.content
return self.discontent
def content_behaviour(self):
@state
def content(self):
# Healing
if random.random() < self.content_neutral:
self.state['id'] = 0
return self.neutral
# Superinfected
discontent_neighbors = self.get_neighboring_agents(state_id=1)
if random.random() < len(discontent_neighbors) * self.content_discontent:
self.state['id'] = 1
discontent_neighbors = self.count_neighboring_agents(state_id=self.discontent.id)
if random.random() < discontent_neighbors * self.content_discontent:
self.discontent
return self.content

48
models/SentimentCorrelationModel/SentimentCorrelationModel.py → soil/agents/SentimentCorrelationModel.py
View File

@ -1,9 +1,8 @@
import random
from ..BaseBehaviour import *
from .. import sentimentCorrelationNodeArray
from . import NetworkAgent
class SentimentCorrelationModel(BaseBehaviour):
class SentimentCorrelationModel(NetworkAgent):
"""
Settings:
outside_effects_prob
@ -24,14 +23,13 @@ class SentimentCorrelationModel(BaseBehaviour):
self.joy_prob = environment.environment_params['joy_prob']
self.sadness_prob = environment.environment_params['sadness_prob']
self.disgust_prob = environment.environment_params['disgust_prob']
self.time_awareness = []
self.state['time_awareness'] = []
for i in range(4): # In this model we have 4 sentiments
self.time_awareness.append(0) # 0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
sentimentCorrelationNodeArray[self.id][self.env.now] = 0
self.state['time_awareness'].append(0) # 0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
self.state['sentimentCorrelation'] = 0
def step(self, now):
def step(self):
self.behaviour()
super().step(now)
def behaviour(self):
@ -42,25 +40,25 @@ class SentimentCorrelationModel(BaseBehaviour):
angry_neighbors = self.get_neighboring_agents(state_id=1)
for x in angry_neighbors:
if x.time_awareness[0] > (self.env.now-500):
if x.state['time_awareness'][0] > (self.env.now-500):
angry_neighbors_1_time_step.append(x)
num_neighbors_angry = len(angry_neighbors_1_time_step)
joyful_neighbors = self.get_neighboring_agents(state_id=2)
for x in joyful_neighbors:
if x.time_awareness[1] > (self.env.now-500):
if x.state['time_awareness'][1] > (self.env.now-500):
joyful_neighbors_1_time_step.append(x)
num_neighbors_joyful = len(joyful_neighbors_1_time_step)
sad_neighbors = self.get_neighboring_agents(state_id=3)
for x in sad_neighbors:
if x.time_awareness[2] > (self.env.now-500):
if x.state['time_awareness'][2] > (self.env.now-500):
sad_neighbors_1_time_step.append(x)
num_neighbors_sad = len(sad_neighbors_1_time_step)
disgusted_neighbors = self.get_neighboring_agents(state_id=4)
for x in disgusted_neighbors:
if x.time_awareness[3] > (self.env.now-500):
if x.state['time_awareness'][3] > (self.env.now-500):
disgusted_neighbors_1_time_step.append(x)
num_neighbors_disgusted = len(disgusted_neighbors_1_time_step)
@ -73,32 +71,32 @@ class SentimentCorrelationModel(BaseBehaviour):
num = random.random()
if num<outside_effects_prob:
self.state['id'] = random.randint(1,4)
self.state['id'] = random.randint(1, 4)
sentimentCorrelationNodeArray[self.id][self.env.now]=self.state['id'] # It is stored when it has been infected for the dynamic network
self.time_awareness[self.state['id']-1] = self.env.now
self.attrs['sentiment'] = self.state['id']
self.state['sentimentCorrelation'] = self.state['id'] # It is stored when it has been infected for the dynamic network
self.state['time_awareness'][self.state['id']-1] = self.env.now
self.state['sentiment'] = self.state['id']
if(num<anger_prob):
self.state['id'] = 1
sentimentCorrelationNodeArray[self.id][self.env.now]=1
self.time_awareness[self.state['id']-1] = self.env.now
self.state['sentimentCorrelation'] = 1
self.state['time_awareness'][self.state['id']-1] = self.env.now
elif (num<joy_prob+anger_prob and num>anger_prob):
self.state['id'] = 2
sentimentCorrelationNodeArray[self.id][self.env.now]=2
self.time_awareness[self.state['id']-1] = self.env.now
self.state['sentimentCorrelation'] = 2
self.state['time_awareness'][self.state['id']-1] = self.env.now
elif (num<sadness_prob+anger_prob+joy_prob and num>joy_prob+anger_prob):
self.state['id'] = 3
sentimentCorrelationNodeArray[self.id][self.env.now]=3
self.time_awareness[self.state['id']-1] = self.env.now
self.state['sentimentCorrelation'] = 3
self.state['time_awareness'][self.state['id']-1] = self.env.now
elif (num<disgust_prob+sadness_prob+anger_prob+joy_prob and num>sadness_prob+anger_prob+joy_prob):
self.state['id'] = 4
sentimentCorrelationNodeArray[self.id][self.env.now]=4
self.time_awareness[self.state['id']-1] = self.env.now
self.state['sentimentCorrelation'] = 4
self.state['time_awareness'][self.state['id']-1] = self.env.now
self.attrs['sentiment'] = self.state['id']
self.state['sentiment'] = self.state['id']

166
soil/agents/__init__.py Normal file
View File

@ -0,0 +1,166 @@
# networkStatus = {} # Dict that will contain the status of every agent in the network
# sentimentCorrelationNodeArray = []
# for x in range(0, settings.network_params["number_of_nodes"]):
# sentimentCorrelationNodeArray.append({'id': x})
# Initialize agent states. Let's assume everyone is normal.
import nxsim
from collections import OrderedDict
from copy import deepcopy
import json
from functools import wraps
agent_types = {}
class MetaAgent(type):
def __init__(cls, name, bases, nmspc):
super(MetaAgent, cls).__init__(name, bases, nmspc)
agent_types[name] = cls
class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent):
"""
A special simpy BaseAgent that keeps track of its state history.
"""
def __init__(self, *args, **kwargs):
self._history = OrderedDict()
self._neighbors = None
super().__init__(*args, **kwargs)
def __getitem__(self, key):
if isinstance(key, tuple):
k, t_step = key
if k is not None:
if t_step is not None:
return self._history[t_step][k]
else:
return {tt: tv.get(k, None) for tt, tv in self._history.items()}
else:
return self._history[t_step]
return self.state[key]
def __setitem__(self, key, value):
self.state[key] = value
def save_state(self):
self._history[self.now] = deepcopy(self.state)
@property
def now(self):
try:
return self.env.now
except AttributeError:
# No environment
return None
def run(self):
while True:
res = self.step()
yield res or self.env.timeout(self.env.interval)
def step(self):
pass
def to_json(self):
return json.dumps(self._history)
class NetworkAgent(BaseAgent, nxsim.BaseNetworkAgent):
def count_agents(self, state_id=None, limit_neighbors=False):
if limit_neighbors:
agents = self.global_topology.neighbors(self.id)
else:
agents = self.global_topology.nodes()
count = 0
for agent in agents:
if state_id and state_id != self.global_topology.node[agent]['agent'].state['id']:
continue
count += 1
return count
def count_neighboring_agents(self, state_id=None):
return self.count_agents(state_id, limit_neighbors=True)
def state(func):
@wraps(func)
def func_wrapper(self):
when = None
next_state = func(self)
try:
next_state, when = next_state
except TypeError:
pass
if next_state:
try:
self.state['id'] = next_state.id
except AttributeError:
raise NotImplemented('State id %s is not valid.' % next_state)
return when
func_wrapper.id = func.__name__
func_wrapper.is_default = False
return func_wrapper
def default_state(func):
func.is_default = True
return func
class MetaFSM(MetaAgent):
def __init__(cls, name, bases, nmspc):
super(MetaFSM, cls).__init__(name, bases, nmspc)
states = {}
# Re-use states from inherited classes
default_state = None
for i in bases:
if isinstance(i, MetaFSM):
for state_id, state in i.states.items():
if state.is_default:
default_state = state
states[state_id] = state
# Add new states
for name, func in nmspc.items():
if hasattr(func, 'id'):
if func.is_default:
default_state = func
states[func.id] = func
cls.default_state = default_state
cls.states = states
class FSM(BaseAgent, metaclass=MetaFSM):
def __init__(self, *args, **kwargs):
super(FSM, self).__init__(*args, **kwargs)
if 'id' not in self.state:
self.state['id'] = self.default_state.id
def step(self):
if 'id' in self.state:
next_state = self.state['id']
elif self.default_state:
next_state = self.default_state.id
else:
raise Exception('{} has no valid state id or default state'.format(self))
if next_state not in self.states:
raise Exception('{} is not a valid id for {}'.format(next_state, self))
self.states[next_state](self)
from .BassModel import *
from .BigMarketModel import *
from .IndependentCascadeModel import *
from .ModelM2 import *
from .SentimentCorrelationModel import *
from .SISaModel import *
from .CounterModel import *
from .DrawingAgent import *

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