├── .gitignore
├── README.md
├── build_index.py
├── build_knowledge.py
├── build_wordvec.py
├── crawl_wiki.py
├── create_pos_patterns.py
├── graphic
    ├── graph.png
    ├── index-0.png
    ├── index-1.png
    ├── index-2.png
    └── vor.png
├── html
    ├── graph-index.html
    ├── graph-universe.html
    └── sigma.min.js
├── jslib
    └── knowledge.js
├── package.json
├── pos-patterns
├── pos-stopwords
├── pylib
    ├── jobmq
    │   └── rabbit.py
    ├── knowledge
    │   ├── __init__.py
    │   ├── datasource.py
    │   └── graph.py
    ├── spider
    │   ├── __init__.py
    │   ├── crawler.py
    │   └── wiki.py
    └── text
    │   ├── __init__.py
    │   ├── cleanser.py
    │   ├── intent.py
    │   ├── pos_tree.py
    │   ├── structure.py
    │   └── texthasher.py
├── repl_word2vec.py
├── requirements.txt
├── test
    ├── __pycache__
    │   └── crawler.cpython-34.pyc
    └── crawler.py
└── visualise.js


/.gitignore:
--------------------------------------------------------------------------------
1 | html/graph-data.js
2 | html/graph-index.js
3 | mine.txt
4 | models/
5 | node_modules/
6 | pylib/knowledge/__pycache__/
7 | pylib/spider/__pycache__/
8 | pylib/text/__pycache__/


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
  1 | # Project vör : Open Knowledge modeling
  2 | 
  3 | ---
  4 | 
  5 | ![Network](graphic/index-0.png)
  6 | ![Network](graphic/vor.png)
  7 | 
  8 | ---
  9 | 
 10 | ## Synopsis
 11 | 
 12 | The project is initiated as a dirty hack for crawling and modeling 
 13 | a large volume of open knowledge out there in Wikipedia. Thus, we 
 14 | have a "nearly" complete graph of those knowledge, also obtain an 
 15 | ability to traverse the relations between knowledge topics.
 16 | 
 17 | ---
 18 | 
 19 | ## Infrastructure / Prerequisites
 20 | 
 21 | To build and run the knowledge graph engine with vör, 
 22 | you need the following software for the infrastructure.
 23 | 
 24 | - [x] [OrientDB](http://orientdb.com/download/)
 25 | - [x] [MongoDB](https://www.mongodb.com/download-center#community)
 26 | 
 27 | ---
 28 | 
 29 | ## Setup
 30 | 
 31 | Install python 3.x requirements by:
 32 | 
 33 | ```bash
 34 |   $ pip3 install -r -U requirements.txt
 35 | ```
 36 | 
 37 | Install Node.js modules required by the graph visualiser. 
 38 | You may ignore these steps if you are not interested in 
 39 | visualisation.
 40 | 
 41 | ```bash
 42 |   $ npm install
 43 | ```
 44 | 
 45 | Other than registered NPM packages, you also need to [install Sigma.js 
 46 | for visualisation](https://github.com/jacomyal/sigma.js/wiki#getting-started). The module is not bundled within this repository.
 47 | 
 48 | ---
 49 | 
 50 | ## 1) Download (crawl) wikipedia pages
 51 | 
 52 | Execute:
 53 | 
 54 | ```bash
 55 |   $ python3 crawl_wiki.py --verbose 
 56 | ```
 57 | 
 58 | The script continuously and endlessly crawls the knowledge topic 
 59 | from Wikipedia starting from the seeding page. You may change 
 60 | the initial topic within the script to what best suits you. 
 61 | To stop the process, just terminate is fine. It won't leave 
 62 | anything at dirty stat so you can re-execute the script again 
 63 | at any time.
 64 | 
 65 | >**[NOTE]** The script keeps continuously crawling 
 66 | and downloading the related knowledge through link traveral. 
 67 | **The script never ends unless you terminate it.**
 68 | 
 69 | ---
 70 | 
 71 | ## 2) Build the knowledge graph
 72 | 
 73 | Execute:
 74 | 
 75 | ```bash
 76 |   $ python3 build_knowledge.py --verbose --root {PASSWORD} --limit {NUM}
 77 | ```
 78 | 
 79 | Where `{PASSWORD}` represents your `root` password of **OrientDB**.
 80 | And `{NUM}` represents the number of wikipedia topics to process.
 81 | 
 82 | What the script does is simply imports the entire raw hefty text 
 83 | knowledge from **MongoDB** to **OrientDB** as a big graph. 
 84 | The output graph in OrientDB is built from the following components:
 85 | 
 86 | - [1] **Vertices** : Represent topic / keyword
 87 | - [2] **Edges** : Represent relations between topic-keyword or keyword-keyword.
 88 | 
 89 | > **[NOTE]** The script processes the entire data in the collection 
 90 | all the way to the end. This will definitely take large amount of 
 91 | time if you have large data in your collection.
 92 | 
 93 | ---
 94 | 
 95 | ## 3) Visualise the knowledge graph
 96 | 
 97 | Execute:
 98 | 
 99 | ```bash
100 |   $ node visualise {PASSWORD}
101 | ```
102 | 
103 | Where `{PASSWORD}` is your OrientDB root's password. The script 
104 | downloads the graph data from OrientDB, renders it with appropriate 
105 | visual figure. After it's done, you can view the graphs as follows.
106 | 
107 | - [1] Universe of topics graph [`html/graph-universe.html`].
108 | - [2] Index graph [`html/graph-index.html`.]
109 | 
110 | 
111 | ---
112 | 
113 | ## 4) Build Word2Vec model over the crawled data
114 | 
115 | Execute:
116 | 
117 | ```bash
118 |   $ python3 build_wordvec.py --limit {LIMIT} --out {PATH_TO_MODEL}
119 | ``` 
120 | 
121 | There should be sufficient amount of the downloaded wikipedia 
122 | in MongoDB which is done by running `crawl_wiki.py`. The output 
123 | is a binary file.
124 | 
125 | ---
126 | 
127 | ## 5) Create topic index
128 | 
129 | Execute:
130 | 
131 | ```bash
132 |   $ python3 build_index.py --limit {LIMIT} --root {PASSWORD}
133 | ```
134 | 
135 | The script generates another OrientDB collection `vorindex` 
136 | which contains all invert-index of the topics and their 
137 | corresponding keywords. Weights of the edges are calculated 
138 | by how frequent the word appear in each of the topics.
139 | 
140 | ![Network](graphic/index-1.png)
141 | ![Network](graphic/index-2.png)
142 | 
143 | ---
144 | 
145 | ## Licence
146 | 
147 | The project is licenced under [GNU 3 public licence](https://www.gnu.org/licenses/gpl-3.0.en.html). All third party libraries are redistributed 
148 | under their own licences.
149 | 
150 | ---
151 | 


--------------------------------------------------------------------------------
/build_index.py:
--------------------------------------------------------------------------------
 1 | """
 2 | Knowledge index maker
 3 | @author Tao PR (github.com/starcolon)
 4 | """
 5 | 
 6 | import numpy as np
 7 | import os
 8 | import sys
 9 | import argparse
10 | import word2vec
11 | from termcolor import colored
12 | from collections import Counter
13 | from pylib.knowledge.graph import Knowledge
14 | from pylib.knowledge.datasource import MineDB
15 | from nltk.tokenize.punkt import PunktSentenceTokenizer
16 | 
17 | arguments = argparse.ArgumentParser()
18 | arguments.add_argument('--verbose', dest='verbose', action='store_true', help='Turn verbose output on.')
19 | arguments.add_argument('--limit', type=int, default=100, help='Maximum number of topics we want to build index')
20 | arguments.add_argument('--root', type=str, default=None, help='Supply the OrientDB password for root account.')
21 | arguments.add_argument('--modelpath', type=str, default='./models/word2vec.bin', help='Path of the word2vec binary model.')
22 | args = vars(arguments.parse_args(sys.argv[1:]))
23 | 
24 | def collect_wordbag(kb, model):
25 |   print(colored('Iterating through topics...','cyan'))
26 |   n = 0
27 |   for topic in kb:
28 |     n += 1
29 |     kws = list(kb.keywords_in_topic(topic.title, with_edge_count=True))
30 |     
31 |     # Frequency of [w] in the current topic
32 |     cnt = Counter([kw.w for kw in kws])
33 |     # Normalise with global frequency
34 |     for word in kws:
35 |       cnt[word.w] /= word.freq
36 |     # Normalise topic counter
37 |     norm = np.linalg.norm(list(cnt.values()))
38 |     cnt0 = {k:v/norm for k,v in cnt.items()}
39 | 
40 |     # Generate similar words with word2vec
41 |     cnt = {}
42 |     for word, freq in cnt0.items():
43 |       cnt[word] = freq
44 |       try:
45 |         indexes, metrics = model.cosine(word)
46 |         synnonyms = model.generate_response(indexes, metrics).tolist()
47 |         for syn, confidence in synnonyms:
48 |           if confidence < 0.85: break
49 |           cnt[syn] = confidence * freq
50 |       except:
51 |         pass
52 | 
53 |     yield (n,topic,cnt)
54 |     if n>=args['limit']: break
55 | 
56 | def load_word2vec_model(path):
57 |   if not os.path.isfile(model_path):
58 |     print(colored('[ERROR] word2vec model does not exist.','red'))
59 |     raise RuntimeError('Model does not exist')
60 |   print(colored('[Model] loading binary model.','cyan'))
61 |   return word2vec.WordVectors.from_binary(model_path, encoding='ISO-8859-1')
62 | 
63 | def add_to_index(index,bag):
64 |   print('------------------------------------')
65 |   n, topic, cnt = bag
66 |   print('...Constructing : {}'.format(colored(topic.title,'magenta')))
67 |   print('...#{} {}'.format(n, cnt))
68 | 
69 |   words, weights = zip(*[(w,weight) for w,weight in cnt.items()])
70 |   index.add(topic.title, words, weights, verbose=False)
71 | 
72 | if __name__ == '__main__':
73 |   # Load word2vec model
74 |   model_path = os.path.realpath(args['modelpath'])
75 |   model = load_word2vec_model(model_path)
76 | 
77 |   # Initialise a knowledge database
78 |   print(colored('Initialising knowledge graph database...','cyan'))
79 |   kb = Knowledge('localhost','vor','root',args['root'])
80 |   
81 |   # Collect topic wordbag
82 |   wb = collect_wordbag(kb, model)
83 | 
84 |   # Create knowledge index
85 |   index = Knowledge('localhost','vorindex','root',args['root'])
86 |   index.clear()
87 |   for bag in wb:
88 |     add_to_index(index, bag)
89 | 
90 |   print(colored('[DONE] all process ended','green'))


--------------------------------------------------------------------------------
/build_knowledge.py:
--------------------------------------------------------------------------------
  1 | """
  2 | Knowledge graph builder
  3 | @author TaoPR (github.com/starcolon)
  4 | ---
  5 | Process the entire bulk of the crawled dataset (MongoDB)
  6 | and potentially create a knowledge graph (OrientDB)
  7 | """
  8 | 
  9 | import re
 10 | import sys
 11 | import argparse
 12 | from termcolor import colored
 13 | from nltk.tokenize.punkt import PunktSentenceTokenizer
 14 | from pylib.knowledge.graph import Knowledge
 15 | from pylib.text import structure as TextStructure
 16 | from pylib.text.pos_tree import PatternCapture
 17 | from pylib.knowledge.datasource import MineDB
 18 | from pybloom_live import ScalableBloomFilter
 19 | 
 20 | arguments = argparse.ArgumentParser()
 21 | arguments.add_argument('--verbose', dest='verbose', action='store_true', help='Turn verbose output on.')
 22 | arguments.add_argument('--start', type=int, default=0, help='Starting index of the crawling record to annotate.')
 23 | arguments.add_argument('--root', type=str, default=None, help='Supply the OrientDB password for root account.')
 24 | arguments.add_argument('--limit', type=int, default=100, help='Maximum number of topics we want to import')
 25 | args = vars(arguments.parse_args(sys.argv[1:]))
 26 | 
 27 | """
 28 | Initialise a lazy connection to the crawling record collection
 29 | """
 30 | def init_crawl_collection():
 31 |   crawl_collection = MineDB('localhost','vor','crawl')
 32 |   return crawl_collection
 33 | 
 34 | def iter_topic(crawl_collection,start):
 35 |   
 36 |   # Prepare a naive sentence tokeniser utility
 37 |   pst = PunktSentenceTokenizer()
 38 |   
 39 |   n = 0
 40 |   
 41 |   for wiki in crawl_collection.query({'downloaded': True},field=None,skip=start):
 42 |     
 43 |     # Skip empty content or the added one
 44 |     if wiki['content'] is None or 'added_to_graph' in wiki:
 45 |       continue
 46 | 
 47 |     m = 0
 48 |     content = wiki['content']
 49 |     
 50 |     if args['verbose']:
 51 |       print(colored('[Extracting wiki] : ','cyan'), content['title'])
 52 |     
 53 |     # A wiki page may probably comprise of multiple content
 54 |     for c in content['contents']:
 55 |       # Explode a long topic into list of sentences
 56 |       sentences = pst.sentences_from_text(c)
 57 |       for s in sentences:
 58 |         m += 1
 59 |         yield (content['title'],s.split(' '))
 60 | 
 61 |     # After all sentences are processed,
 62 |     # mark the current wiki record as 'processed'
 63 |     crit = {'_id': wiki['_id']}
 64 |     crawl_collection.update(crit, {'$set':{'added_to_graph':True}})
 65 | 
 66 |     n += 1
 67 |     if args['verbose']:
 68 |       print(content['title'] + " processed with {0} nodes.".format(m))
 69 |       print(colored("{0} wiki documents processed so far...".format(n),'blue'))
 70 | 
 71 | """
 72 | Remove stopwords & ensure text encoder
 73 | """
 74 | def ensure_viable(ns,stopwords):
 75 |   def clean(a):
 76 |     # Strip non-alphanumeric symbols (unicode symbols reserved)
 77 |     a = re.sub("[\x00-\x2F\x3A-\x40\x5B-\x60\x7B-\x7F\(\)]+", "", a)
 78 |     for s in stopwords:
 79 |       a.replace(s,'')
 80 |     return a.strip()
 81 |   ns = set(clean(n) for n in ns)
 82 |   ns = [n for n in ns if len(n)>2]
 83 |   return list(ns)
 84 |   
 85 | 
 86 | 
 87 | if __name__ == '__main__':
 88 | 
 89 |   # Initialise a knowledge database
 90 |   print(colored('Initialising knowledge graph database...','cyan'))
 91 |   kb = Knowledge('localhost','vor','root',args['root'])
 92 |   kb.clear()
 93 | 
 94 |   # Load existing pos patterns
 95 |   print(colored('Loading POS patterns...','cyan'))
 96 |   patterns = PatternCapture()
 97 |   patterns.load('./pos-patterns')
 98 | 
 99 |   # Load list of stopwords
100 |   print(colored('Loading stopwords...','cyan'))
101 |   stopwords = []
102 |   with open('./pos-stopwords') as f:
103 |     stopwords = list(f.readlines())
104 | 
105 |   # Initialise a crawling dataset connection
106 |   print(colored('Initialising wikipedia crawling collection...','cyan'))
107 |   crawl_collection = init_crawl_collection()
108 | 
109 |   # Iterate through the crawling database
110 |   n = 0
111 |   print(colored('Iterating over crawling database...','cyan'))
112 |   bf = ScalableBloomFilter(mode=ScalableBloomFilter.SMALL_SET_GROWTH)
113 |   for topic,sentence in iter_topic(crawl_collection,args['start']):
114 |     
115 |     # Clean topic string
116 |     topic = topic.replace("'",'').replace('\n','')
117 | 
118 |     # Check if the number of processed topic exceed the limit?
119 |     if topic not in bf:
120 |       bf.add(topic)
121 |       if len(bf) > args['limit']:
122 |         print(colored('[Topics limit reached] ... BYE','cyan'))
123 |         sys.exit(0)
124 | 
125 |     # Break the sentence into knowledge nodes
126 |     pos      = TextStructure.pos_tag(sentence)
127 |     kb_nodes = patterns.capture(pos)  
128 | 
129 |     # Clean up each of the nodes
130 |     # a) Remove stopwords
131 |     # b) Remove duplicates
132 |     # c) Ensure supported encoding
133 |     kb_nodes = ensure_viable(kb_nodes, stopwords)
134 | 
135 |     if args['verbose']:
136 |       print(kb_nodes)
137 | 
138 |     # Create a set of knowledge links
139 |     kb.add(topic,kb_nodes,None,args['verbose'])
140 | 
141 |     n += 1
142 |     if n%100 == 0 and n>0:
143 |       print('... {} topics done so far.'.format(n))
144 |   
145 | 
146 | 


--------------------------------------------------------------------------------
/build_wordvec.py:
--------------------------------------------------------------------------------
  1 | """
  2 | Create word vector space from the crawled dataset
  3 | @author TaoPR (github.com/starcolon)
  4 | """
  5 | 
  6 | import os
  7 | import sys
  8 | import codecs
  9 | import argparse
 10 | import word2vec
 11 | from termcolor import colored
 12 | from nltk.tokenize.punkt import PunktSentenceTokenizer
 13 | from pylib.knowledge.datasource import MineDB
 14 | from pylib.text.cleanser import *
 15 | 
 16 | arguments = argparse.ArgumentParser()
 17 | arguments.add_argument('--verbose', dest='verbose', action='store_true', help='Turn verbose output on.')
 18 | arguments.add_argument('--limit', type=int, default=100, help='Maximum number of topics we want to import')
 19 | arguments.add_argument('--out', type=str, default='./models/word2vec.bin', help='Output path of the word2vec binary model.')
 20 | args = vars(arguments.parse_args(sys.argv[1:]))
 21 | 
 22 | def model_from_crawl_collection(mineDB, output_path):
 23 |   # Dump sentences out of the DB
 24 |   print(colored('Exporting crawled data to text file...','cyan'))
 25 |   text_path = export_crawl_to_text(mineDB)
 26 |   print(colored('[Done]','green'))
 27 | 
 28 |   # Train word2vec model
 29 |   print(colored('Training word2vec...','cyan'))
 30 |   model = create_model(text_path, output_path)
 31 |   print(colored('[Done]','green'))
 32 |   print('Word2Vec model is saved at : {}'.format(output_path))
 33 | 
 34 |   return model
 35 | 
 36 | def export_crawl_to_text(mineDB):
 37 |   
 38 |   # Prepare a naive sentence tokeniser utility
 39 |   pst = PunktSentenceTokenizer()
 40 | 
 41 |   text_path = os.path.realpath('./mine.txt')
 42 | 
 43 |   with codecs.open(text_path, 'w', 'utf-8') as f:
 44 |     m = 0
 45 |     for wiki in mineDB.query({'downloaded': True},field=None):
 46 |       
 47 |       # Skip empty content or the added one
 48 |       if wiki['content'] is None or 'added_to_graph' in wiki:
 49 |         continue
 50 | 
 51 |       content = wiki['content']
 52 | 
 53 |       # A wiki page may probably comprise of multiple content
 54 |       for c in content['contents']:
 55 |         # Explode content into sentences
 56 |         sentences = pst.sentences_from_text(c)
 57 |         print('... content #{} ==> {} sentences extracted.'.format(m, len(sentences)))
 58 | 
 59 |         for s in sentences:
 60 |           # Cleanse the sentence
 61 |           s_ = cleanse(s)
 62 |           # Filter out noise by length
 63 |           if len(s_)<5 or len(s_.split(' '))<3:
 64 |             continue
 65 |           f.write(s_.lower() + '\n')
 66 | 
 67 |       m += 1
 68 | 
 69 |       if m>=args['limit']:
 70 |         print(colored('[Ending] Maximum number of topics reached.','yellow'))
 71 |         break
 72 | 
 73 |   return text_path
 74 | 
 75 | def create_model(input_path, output_path):
 76 |   word2vec.word2vec(\
 77 |     input_path, \
 78 |     output_path, \
 79 |     size=10, binary=1, verbose=True)
 80 |   assert(os.path.isfile(output_path))
 81 |   #return word2vec.load(output_path)
 82 |   return word2vec.WordVectors.from_binary(output_path, encoding='ISO-8859-1')
 83 | 
 84 | def repl(model):
 85 |   while True:
 86 |     w = input('Enter a word to try: ')
 87 |     indexes, metrics = model.cosine(w)
 88 |     print('... Similar words : {}', model.vocab[indexes])
 89 | 
 90 | if __name__ == '__main__':
 91 |   mineDB = crawl_collection = MineDB('localhost','vor','crawl')
 92 |   model = model_from_crawl_collection(mineDB, os.path.realpath(args['out']))
 93 | 
 94 |   # Examine the model properties
 95 |   if model is None:
 96 |     print(colored('[ERROR] Model is empty.','red'))
 97 |   else:
 98 |     print(colored('[Word2Vec model spec]','cyan'))
 99 |     print('... Model shape : {}'.format(model.vectors.shape))
100 |     # Execute a playground REPL
101 |     print(colored('[Word2Vec REPL]:','cyan'))
102 |     repl(model)


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/crawl_wiki.py:
--------------------------------------------------------------------------------
  1 | """
  2 | Wikipedia crawler service (downloader)
  3 | @author TaoPR (github.com/starcolon)
  4 | ---
  5 | The service runs in background and keeps crawling raw knowledge data.
  6 | The downloaded data from Wikipedia is senquentially pushed to 
  7 | the specified RabbitMQ.
  8 | """
  9 | 
 10 | import sys
 11 | import asyncio
 12 | import argparse
 13 | from functools import reduce
 14 | from termcolor import colored
 15 | from pylib.spider import wiki as Wiki
 16 | from pylib.knowledge.datasource import MineDB
 17 | 
 18 | arguments = argparse.ArgumentParser()
 19 | arguments.add_argument('--verbose', dest='verbose', action='store_true', help='Turn verbose output on.')
 20 | arguments.add_argument('--depth', type=int, default=4, help='Indicate maximum depth of crawling level')
 21 | args = vars(arguments.parse_args(sys.argv[1:]))
 22 | 
 23 | """
 24 | Initialise a lazy connection to the crawling record collection
 25 | """
 26 | def init_crawl_collection():
 27 |   crawl_collection = MineDB('localhost','vor','crawl')
 28 |   return crawl_collection
 29 | 
 30 | """
 31 | Save the content of the wikipedia page
 32 | """
 33 | def save_content(crawl_collection,title,content):
 34 |   if crawl_collection.count({'title': title})>0:
 35 |     crawl_collection.update(
 36 |       {'title': title},
 37 |       {'$set':{'downloaded':True, 'content': content}}
 38 |     )
 39 |   else:
 40 |     crawl_collection.insert({'title': title, 'downloaded': True, 'content':content})
 41 | 
 42 | """
 43 | Check whether a wiki has been downloaded
 44 | """
 45 | def is_downloaded(crawl_collection,title):
 46 |   return crawl_collection.count({'title': title, 'downloaded': True})>0
 47 | 
 48 | """
 49 | Mark a wiki page as downloaded
 50 | """
 51 | def mark_as_downloaded(crawl_collection,title):
 52 |   if crawl_collection.count({'title': title})>0:
 53 |     crawl_collection.update({'title': title},{'$set':{'downloaded':True}})
 54 |   else:
 55 |     crawl_collection.insert({'title': title, 'downloaded': True, 'content':None})
 56 | 
 57 | 
 58 | def list_crawl_pending(crawl_collection,max_samples):
 59 | 
 60 |   n = 0
 61 | 
 62 |   # Major pending list
 63 |   majors = [t['title'] for t in crawl_collection.query({'downloaded': False})]
 64 | 
 65 |   # If fresh new crawl, no pending downloads,
 66 |   # initialise with a seed
 67 |   if len(majors)==0:
 68 |     print(colored('Fresh new crawling...','yellow'))
 69 |     yield '/wiki/Graph_theory'
 70 | 
 71 |   for m in majors:
 72 |     n += 1
 73 |     yield m
 74 | 
 75 |   # List rels of downloaded major pages
 76 |   # which are not yet downloaded
 77 |   for t in crawl_collection.query({'downloaded': True}):
 78 |     content = t['content']
 79 |     rels    = content['rels']
 80 |     for r in rels:
 81 |       # Skip the downloaded links
 82 |       if not is_downloaded(crawl_collection,r):
 83 |         # Short circuit if maximum number of samples to generate exceeded
 84 |         if n>max_samples:
 85 |           return
 86 |         n += 1
 87 |         yield r
 88 | 
 89 | """
 90 | Add a fresh new wiki page as yet to be downloaded
 91 | """
 92 | def add_pending(crawl_collection,title):
 93 |   if crawl_collection.count({'title': title})==0:
 94 |     crawl_collection.insert({'title': title, 'downloaded': False, 'content':None})
 95 | 
 96 | """
 97 | Execute a crawling subprocess on the destination wiki page title
 98 | """
 99 | @asyncio.coroutine
100 | def crawl(crawl_collection,title,depth,verbose):
101 |   loop = asyncio.get_event_loop()
102 | 
103 |   # Crawl the content
104 |   add_pending(crawl_collection, title)
105 | 
106 |   # Skip if downloaded or depth recursion exceeded
107 |   if depth>0 and not is_downloaded(crawl_collection, title):
108 |     content = Wiki.download_wiki('https://en.wikipedia.org' + title, verbose)
109 |     
110 |     # Store the downloaded content in MongoDB
111 |     save_content(crawl_collection, title, content)
112 | 
113 |     # Now recursively download the related links
114 |     subtasks = []
115 |     for rel in content['rels']:
116 |       subtasks.append(asyncio.async(
117 |         crawl(crawl_collection, rel, depth-1, verbose)
118 |       ))
119 | 
120 |     loop.run_until_complete(asyncio.wait(subtasks))
121 | 
122 | 
123 | 
124 | if __name__ == '__main__':
125 | 
126 |   depth = args['depth']
127 |   print(colored('# Max depth to run: ','cyan'), depth)
128 |   loop  = asyncio.get_event_loop()
129 | 
130 |   # Prepare the crawling record handler
131 |   crawl_collection = init_crawl_collection()
132 | 
133 |   # Load all list of pending wiki pages
134 |   pendings = list_crawl_pending(crawl_collection,max_samples=32)
135 | 
136 |   # For each of the pending list, spawns a new crawler subprocess
137 |   # to download those data
138 |   tasks = []
139 |   for title in pendings:
140 |     print(colored('Pending for crawling: ','green'), title)
141 |     tasks.append(asyncio.async(
142 |       crawl(crawl_collection, title, depth, args['verbose'])
143 |     ))
144 | 
145 |   # Wait until all top-level async crawling tasks end
146 |   loop.run_until_complete(asyncio.wait(tasks))
147 |   loop.close()
148 | 
149 | 
150 | 


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/create_pos_patterns.py:
--------------------------------------------------------------------------------
  1 | """
  2 | Make pos patterns which represent knowledge node
  3 | from the downloaded wikipedia topics.
  4 | @author TaoPR (github.com/starcolon)
  5 | """
  6 | 
  7 | import sys
  8 | import argparse
  9 | from termcolor import colored
 10 | from pylib.text import structure as TextStructure
 11 | from pylib.text.pos_tree import PatternCapture
 12 | from pylib.knowledge.datasource import MineDB
 13 | from nltk.tokenize.punkt import PunktSentenceTokenizer
 14 | 
 15 | arguments = argparse.ArgumentParser()
 16 | arguments.add_argument('--verbose', dest='verbose', action='store_true', help='Turn verbose output on.')
 17 | arguments.add_argument('--start', type=int, default=0, help='Starting index of the crawling record to annotate.')
 18 | args = vars(arguments.parse_args(sys.argv[1:]))
 19 | 
 20 | """
 21 | Initialise a lazy connection to the crawling record collection
 22 | """
 23 | def init_crawl_collection():
 24 |   crawl_collection = MineDB('localhost','vor','crawl')
 25 |   return crawl_collection
 26 | 
 27 | """
 28 | Iterate through the unannotated recordset in the crawled collection,
 29 | and generate each of the sentence from the topic.
 30 | """
 31 | def raw_records(crawl_collection,start):
 32 | 
 33 |   # Prepare a naive sentence tokeniser utility
 34 |   pst = PunktSentenceTokenizer()
 35 | 
 36 |   for rec in crawl_collection.query({'downloaded': True},field=None,skip=start):
 37 |     _id     = rec['_id']
 38 |     if rec['content'] is None:
 39 |       continue
 40 |     content = rec['content']['contents']
 41 |     # A wiki page may probably comprise of multiple content
 42 |     for c in content:
 43 |       # Explode a long topic into list of sentences
 44 |       sentences = pst.sentences_from_text(c)
 45 |       for s in sentences:
 46 |         yield (_id,s)
 47 | 
 48 | 
 49 | """
 50 | Prompt the user to annotate the given text sentence
 51 | """
 52 | def cli_annotate(crawl_collection):
 53 |   # Load existing pos patterns
 54 |   patterns = PatternCapture()
 55 |   patterns.load('./pos-patterns')
 56 | 
 57 |   print(colored('Existing patterns :','green'))
 58 |   print(patterns.join(' , '))
 59 | 
 60 |   def annotate(_id,text):
 61 |     # Analyse the POS structure of the sentence
 62 |     tokens = text.split(' ')
 63 |     pos    = TextStructure.pos_tag(tokens)
 64 |     TextStructure.tag_with_color(tokens)
 65 | 
 66 |     # Test POS pattern parsing and show the result
 67 |     print(patterns.capture(pos))
 68 | 
 69 |     # Extract the pure list of POS
 70 |     pos_ = [tag for t,tag in pos]
 71 | 
 72 |     # POS token sample form: NN-JJ,NN,NN-NNS
 73 |     nodes = input(colored("POS token patterns: ","cyan"))
 74 | 
 75 |     if len(nodes)>0:
 76 |       # Add patterns to the registry if not yet
 77 |       nodes = [n.strip() for n in nodes.split(',')]
 78 |       for n in nodes:
 79 |         if n not in patterns:
 80 |           print(colored('New pattern added: ','green'), n)
 81 |           patterns.append(n)
 82 | 
 83 |       # Save right away
 84 |       patterns.save('./pos-patterns')
 85 |       print("Patterns saved!")
 86 | 
 87 |   return annotate
 88 | 
 89 | if __name__ == '__main__':
 90 | 
 91 |   # Prepare a connection to the crawled dataset
 92 |   # and the annotation collection respectively
 93 |   crawl_collection = init_crawl_collection()
 94 | 
 95 |   # Make an annotator function
 96 |   annotate = cli_annotate(crawl_collection)
 97 | 
 98 |   # Iterate through each unannotated sentence
 99 |   [annotate(_id,t) for (_id,t) in raw_records(crawl_collection,args['start'])]
100 | 


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/graphic/graph.png:
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https://raw.githubusercontent.com/tao-pr/vor-knowledge-graph/7c7140b682796b997871751090ac3a1b3ea36eee/graphic/graph.png


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/graphic/index-0.png:
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https://raw.githubusercontent.com/tao-pr/vor-knowledge-graph/7c7140b682796b997871751090ac3a1b3ea36eee/graphic/index-0.png


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/graphic/index-1.png:
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https://raw.githubusercontent.com/tao-pr/vor-knowledge-graph/7c7140b682796b997871751090ac3a1b3ea36eee/graphic/index-1.png


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/graphic/index-2.png:
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https://raw.githubusercontent.com/tao-pr/vor-knowledge-graph/7c7140b682796b997871751090ac3a1b3ea36eee/graphic/index-2.png


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/graphic/vor.png:
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https://raw.githubusercontent.com/tao-pr/vor-knowledge-graph/7c7140b682796b997871751090ac3a1b3ea36eee/graphic/vor.png


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/html/graph-index.html:
--------------------------------------------------------------------------------
 1 | <!DOCTYPE html>
 2 | <html lang="en-US" style="width:100%;height:100%">
 3 | <head>
 4 |   <meta charset="UTF-8">
 5 |   <script type="text/javascript" src="./sigma.min.js" ></script>
 6 |   <script type="text/javascript" src="./graph-index.js" ></script>
 7 |   <script type="text/javascript" src="//code.jquery.com/jquery-1.11.3.min.js"></script>
 8 |   <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/Chart.js/1.0.2/Chart.min.js"></script>
 9 |   <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/vis/4.12.0/vis.min.js"></script>
10 |   <script src="//cdnjs.cloudflare.com/ajax/libs/d3/3.4.4/d3.min.js"></script>
11 |   <script type="text/javascript">
12 | 
13 |   function draw(graphData){
14 |     var options = {
15 |       nodes:{
16 |         shape: 'dot',
17 |         scaling:{
18 |           min: 20,
19 |           max: 100,
20 |           label:{
21 |             min: 10,
22 |             max: 16
23 |           }
24 |         }
25 |       },
26 |       edges:{
27 |         scaling:{
28 |           min:1,
29 |           max:8,
30 |           label:{
31 |             min:12,
32 |             max:24
33 |           }
34 |         }
35 |       },
36 |       interaction:{
37 |         hover: true
38 |       }
39 |     }
40 | 
41 |     var graph = new vis.Network(
42 |       document.getElementById('graph'),
43 |       graphData,
44 |       options
45 |     );
46 |   }
47 | 
48 |   // Render
49 |   document.addEventListener("DOMContentLoaded", function(event) { 
50 |       
51 |       console.log('INITIALISING');
52 |       var g = getGraph();
53 |       var vis = {
54 |         edges: g.edges,
55 |         nodes: g.nodes
56 |       }
57 | 
58 |       console.log('Drawing...')
59 |       draw(vis);
60 |       console.log('[done]')
61 |   })
62 | 
63 |   </script>
64 |   <body style="width:100%;height:100%">
65 |     <div id="graph" style="width:100%;height:100%">
66 |   </body>
67 | </head>
68 | </html>


--------------------------------------------------------------------------------
/html/graph-universe.html:
--------------------------------------------------------------------------------
 1 | <!DOCTYPE html>
 2 | <html lang="en-US">
 3 | <head>
 4 |   <meta charset="UTF-8">
 5 |   <script type="text/javascript" src="./sigma.min.js" ></script>
 6 |   <script type="text/javascript" src="./graph-data.js" ></script>
 7 |   <script type="text/javascript">
 8 | 
 9 |     document.addEventListener("DOMContentLoaded", function(event) {
10 |       console.log('INITIALISING');
11 |       var g = getGraph();
12 | 
13 |       // For the sake of simplicity,
14 |       // Filter only 60 topics, 1000 keywords
15 |       var topic   = (n) => n.type == 'TOPIC';
16 |       var keyword = (n) => n.type == 'KEYWORD';
17 |       g.nodes = g.nodes.filter(topic).slice(0,60)
18 |         .concat(g.nodes.filter(keyword).slice(0,1000))
19 | 
20 |       g.edges = g.edges.filter((e) => 
21 |         g.nodes.filter((n) => n.id == e.source).length>0 &&
22 |         g.nodes.filter((n) => n.id == e.target).length>0
23 |       )
24 | 
25 |       var S = new sigma({ graph: g, container: 'container'});
26 |       S.settings({
27 |         edgeColor:        'default',
28 |         defaultEdgeColor: '#FFE1E1'
29 |       });
30 | 
31 |       // Bind node selection event
32 |       window['selected'] = [];
33 |       S.bind('clickNode', function(e) {
34 |         console.log(e.type, e.data.node.label, e.data.captor);
35 |       });
36 | 
37 |       // Draw the graph
38 |       console.log('REDRAWING CANVAS');
39 |       console.log(g.nodes.length, ' vertices to render');
40 |       console.log(g.edges.length, ' edges to render');
41 |       S.refresh();
42 |     });
43 | 
44 |   </script>
45 | </head>
46 | <body style="width:100%;height:100%;margin:0;padding:0">
47 |   <div id="container" style="width:100%;height:100%;margin:0;padding:0">
48 |     <!-- -->
49 |   </div>
50 | </body>
51 | </html>


--------------------------------------------------------------------------------
/html/sigma.min.js:
--------------------------------------------------------------------------------
1 | /* sigma.js - A JavaScript library dedicated to graph drawing. - Version: 1.1.0 - Author: Alexis Jacomy, Sciences-Po Médialab - License: MIT */
2 | (function(a){"use strict";var b={},c=function(a){var d,e,f,g,h;c.classes.dispatcher.extend(this);var i=this,j=a||{};if("string"==typeof j||j instanceof HTMLElement?j={renderers:[j]}:"[object Array]"===Object.prototype.toString.call(j)&&(j={renderers:j}),g=j.renderers||j.renderer||j.container,j.renderers&&0!==j.renderers.length||("string"==typeof g||g instanceof HTMLElement||"object"==typeof g&&"container"in g)&&(j.renderers=[g]),j.id){if(b[j.id])throw'sigma: Instance "'+j.id+'" already exists.';Object.defineProperty(this,"id",{value:j.id})}else{for(h=0;b[h];)h++;Object.defineProperty(this,"id",{value:""+h})}for(b[this.id]=this,this.settings=new c.classes.configurable(c.settings,j.settings||{}),Object.defineProperty(this,"graph",{value:new c.classes.graph(this.settings),configurable:!0}),Object.defineProperty(this,"middlewares",{value:[],configurable:!0}),Object.defineProperty(this,"cameras",{value:{},configurable:!0}),Object.defineProperty(this,"renderers",{value:{},configurable:!0}),Object.defineProperty(this,"renderersPerCamera",{value:{},configurable:!0}),Object.defineProperty(this,"cameraFrames",{value:{},configurable:!0}),Object.defineProperty(this,"camera",{get:function(){return this.cameras[0]}}),Object.defineProperty(this,"events",{value:["click","rightClick","clickStage","doubleClickStage","rightClickStage","clickNode","clickNodes","doubleClickNode","doubleClickNodes","rightClickNode","rightClickNodes","overNode","overNodes","outNode","outNodes","downNode","downNodes","upNode","upNodes"],configurable:!0}),this._handler=function(a){var b,c={};for(b in 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undefined.";var b,c,d=this.renderersPerCamera[a.id];for(c=d.length,b=c-1;b>=0;b--)this.killRenderer(d[b]);return delete this.renderersPerCamera[a.id],delete this.cameraFrames[a.id],delete this.cameras[a.id],a.kill&&a.kill(),this},c.prototype.addRenderer=function(a){var b,d,e,f,g=a||{};if("string"==typeof g?g={container:document.getElementById(g)}:g instanceof HTMLElement&&(g={container:g}),"string"==typeof g.container&&(g.container=document.getElementById(g.container)),"id"in g)b=g.id;else{for(b=0;this.renderers[""+b];)b++;b=""+b}if(this.renderers[b])throw'sigma.addRenderer: The renderer "'+b+'" already exists.';if(d="function"==typeof g.type?g.type:c.renderers[g.type],d=d||c.renderers.def,e="camera"in g?g.camera instanceof c.classes.camera?g.camera:this.cameras[g.camera]||this.addCamera(g.camera):this.addCamera(),this.cameras[e.id]!==e)throw"sigma.addRenderer: The camera is not properly referenced.";return f=new d(this.graph,e,this.settings,g),this.renderers[b]=f,Object.defineProperty(f,"id",{value:b}),f.bind&&f.bind(["click","rightClick","clickStage","doubleClickStage","rightClickStage","clickNode","clickNodes","clickEdge","clickEdges","doubleClickNode","doubleClickNodes","doubleClickEdge","doubleClickEdges","rightClickNode","rightClickNodes","rightClickEdge","rightClickEdges","overNode","overNodes","overEdge","overEdges","outNode","outNodes","outEdge","outEdges","downNode","downNodes","downEdge","downEdges","upNode","upNodes","upEdge","upEdges"],this._handler),this.renderersPerCamera[e.id].push(f),f},c.prototype.killRenderer=function(a){if(a="string"==typeof a?this.renderers[a]:a,!a)throw"sigma.killRenderer: The renderer is undefined.";var b=this.renderersPerCamera[a.camera.id],c=b.indexOf(a);return c>=0&&b.splice(c,1),a.kill&&a.kill(),delete this.renderers[a.id],this},c.prototype.refresh=function(b){var d,e,f,g,h,i,j=0;for(b=b||{},g=this.middlewares||[],d=0,e=g.length;e>d;d++)g[d].call(this,0===d?"":"tmp"+j+":",d===e-1?"ready:":"tmp"+ ++j+":");for(f in this.cameras)h=this.cameras[f],h.settings("autoRescale")&&this.renderersPerCamera[h.id]&&this.renderersPerCamera[h.id].length?c.middlewares.rescale.call(this,g.length?"ready:":"",h.readPrefix,{width:this.renderersPerCamera[h.id][0].width,height:this.renderersPerCamera[h.id][0].height}):c.middlewares.copy.call(this,g.length?"ready:":"",h.readPrefix),b.skipIndexation||(i=c.utils.getBoundaries(this.graph,h.readPrefix),h.quadtree.index(this.graph.nodes(),{prefix:h.readPrefix,bounds:{x:i.minX,y:i.minY,width:i.maxX-i.minX,height:i.maxY-i.minY}}),h.edgequadtree!==a&&h.settings("drawEdges")&&h.settings("enableEdgeHovering")&&h.edgequadtree.index(this.graph,{prefix:h.readPrefix,bounds:{x:i.minX,y:i.minY,width:i.maxX-i.minX,height:i.maxY-i.minY}}));for(g=Object.keys(this.renderers),d=0,e=g.length;e>d;d++)if(this.renderers[g[d]].process)if(this.settings("skipErrors"))try{this.renderers[g[d]].process()}catch(k){console.log('Warning: The renderer "'+g[d]+'" crashed on ".process()"')}else 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exists.';var b,c=a.id,d=Object.create(null);if(this.settings("clone"))for(b in a)"id"!==b&&(d[b]=a[b]);else d=a;return this.settings("immutable")?Object.defineProperty(d,"id",{value:c,enumerable:!0}):d.id=c,this.inNeighborsIndex[c]=Object.create(null),this.outNeighborsIndex[c]=Object.create(null),this.allNeighborsIndex[c]=Object.create(null),this.inNeighborsCount[c]=0,this.outNeighborsCount[c]=0,this.allNeighborsCount[c]=0,this.nodesArray.push(d),this.nodesIndex[d.id]=d,this}),j.addMethod("addEdge",function(a){if(Object(a)!==a||1!==arguments.length)throw"addEdge: Wrong arguments.";if("string"!=typeof a.id&&"number"!=typeof a.id)throw"The edge must have a string or number id.";if("string"!=typeof a.source&&"number"!=typeof a.source||!this.nodesIndex[a.source])throw"The edge source must have an existing node id.";if("string"!=typeof a.target&&"number"!=typeof a.target||!this.nodesIndex[a.target])throw"The edge target must have an existing node id.";if(this.edgesIndex[a.id])throw'The edge "'+a.id+'" already exists.';var b,c=Object.create(null);if(this.settings("clone"))for(b in a)"id"!==b&&"source"!==b&&"target"!==b&&(c[b]=a[b]);else c=a;return this.settings("immutable")?(Object.defineProperty(c,"id",{value:a.id,enumerable:!0}),Object.defineProperty(c,"source",{value:a.source,enumerable:!0}),Object.defineProperty(c,"target",{value:a.target,enumerable:!0})):(c.id=a.id,c.source=a.source,c.target=a.target),this.edgesArray.push(c),this.edgesIndex[c.id]=c,this.inNeighborsIndex[c.target][c.source]||(this.inNeighborsIndex[c.target][c.source]=Object.create(null)),this.inNeighborsIndex[c.target][c.source][c.id]=c,this.outNeighborsIndex[c.source][c.target]||(this.outNeighborsIndex[c.source][c.target]=Object.create(null)),this.outNeighborsIndex[c.source][c.target][c.id]=c,this.allNeighborsIndex[c.source][c.target]||(this.allNeighborsIndex[c.source][c.target]=Object.create(null)),this.allNeighborsIndex[c.source][c.target][c.id]=c,c.target!==c.source&&(this.allNeighborsIndex[c.target][c.source]||(this.allNeighborsIndex[c.target][c.source]=Object.create(null)),this.allNeighborsIndex[c.target][c.source][c.id]=c),this.inNeighborsCount[c.target]++,this.outNeighborsCount[c.source]++,this.allNeighborsCount[c.target]++,this.allNeighborsCount[c.source]++,this}),j.addMethod("dropNode",function(a){if("string"!=typeof a&&"number"!=typeof a||1!==arguments.length)throw"dropNode: Wrong arguments.";if(!this.nodesIndex[a])throw'The node "'+a+'" does not exist.';var b,c,d;for(delete this.nodesIndex[a],b=0,d=this.nodesArray.length;d>b;b++)if(this.nodesArray[b].id===a){this.nodesArray.splice(b,1);break}for(b=this.edgesArray.length-1;b>=0;b--)(this.edgesArray[b].source===a||this.edgesArray[b].target===a)&&this.dropEdge(this.edgesArray[b].id);delete this.inNeighborsIndex[a],delete this.outNeighborsIndex[a],delete this.allNeighborsIndex[a],delete this.inNeighborsCount[a],delete this.outNeighborsCount[a],delete this.allNeighborsCount[a];for(c in this.nodesIndex)delete this.inNeighborsIndex[c][a],delete this.outNeighborsIndex[c][a],delete this.allNeighborsIndex[c][a];return this}),j.addMethod("dropEdge",function(a){if("string"!=typeof a&&"number"!=typeof a||1!==arguments.length)throw"dropEdge: Wrong arguments.";if(!this.edgesIndex[a])throw'The edge "'+a+'" does not exist.';var b,c,d;for(d=this.edgesIndex[a],delete this.edgesIndex[a],b=0,c=this.edgesArray.length;c>b;b++)if(this.edgesArray[b].id===a){this.edgesArray.splice(b,1);break}return delete this.inNeighborsIndex[d.target][d.source][d.id],Object.keys(this.inNeighborsIndex[d.target][d.source]).length||delete this.inNeighborsIndex[d.target][d.source],delete this.outNeighborsIndex[d.source][d.target][d.id],Object.keys(this.outNeighborsIndex[d.source][d.target]).length||delete this.outNeighborsIndex[d.source][d.target],delete this.allNeighborsIndex[d.source][d.target][d.id],Object.keys(this.allNeighborsIndex[d.source][d.target]).length||delete this.allNeighborsIndex[d.source][d.target],d.target!==d.source&&(delete this.allNeighborsIndex[d.target][d.source][d.id],Object.keys(this.allNeighborsIndex[d.target][d.source]).length||delete this.allNeighborsIndex[d.target][d.source]),this.inNeighborsCount[d.target]--,this.outNeighborsCount[d.source]--,this.allNeighborsCount[d.source]--,this.allNeighborsCount[d.target]--,this}),j.addMethod("kill",function(){this.nodesArray.length=0,this.edgesArray.length=0,delete this.nodesArray,delete this.edgesArray,delete this.nodesIndex,delete this.edgesIndex,delete this.inNeighborsIndex,delete this.outNeighborsIndex,delete this.allNeighborsIndex,delete this.inNeighborsCount,delete this.outNeighborsCount,delete this.allNeighborsCount
3 | }),j.addMethod("clear",function(){return this.nodesArray.length=0,this.edgesArray.length=0,b(this.nodesIndex),b(this.edgesIndex),b(this.nodesIndex),b(this.inNeighborsIndex),b(this.outNeighborsIndex),b(this.allNeighborsIndex),b(this.inNeighborsCount),b(this.outNeighborsCount),b(this.allNeighborsCount),this}),j.addMethod("read",function(a){var b,c,d;for(c=a.nodes||[],b=0,d=c.length;d>b;b++)this.addNode(c[b]);for(c=a.edges||[],b=0,d=c.length;d>b;b++)this.addEdge(c[b]);return this}),j.addMethod("nodes",function(a){if(!arguments.length)return this.nodesArray.slice(0);if(1===arguments.length&&("string"==typeof a||"number"==typeof a))return this.nodesIndex[a];if(1===arguments.length&&"[object Array]"===Object.prototype.toString.call(a)){var b,c,d=[];for(b=0,c=a.length;c>b;b++){if("string"!=typeof a[b]&&"number"!=typeof a[b])throw"nodes: Wrong arguments.";d.push(this.nodesIndex[a[b]])}return d}throw"nodes: Wrong arguments."}),j.addMethod("degree",function(a,b){if(b={"in":this.inNeighborsCount,out:this.outNeighborsCount}[b||""]||this.allNeighborsCount,"string"==typeof a||"number"==typeof a)return b[a];if("[object Array]"===Object.prototype.toString.call(a)){var c,d,e=[];for(c=0,d=a.length;d>c;c++){if("string"!=typeof a[c]&&"number"!=typeof a[c])throw"degree: Wrong arguments.";e.push(b[a[c]])}return e}throw"degree: Wrong arguments."}),j.addMethod("edges",function(a){if(!arguments.length)return this.edgesArray.slice(0);if(1===arguments.length&&("string"==typeof a||"number"==typeof a))return this.edgesIndex[a];if(1===arguments.length&&"[object Array]"===Object.prototype.toString.call(a)){var b,c,d=[];for(b=0,c=a.length;c>b;b++){if("string"!=typeof a[b]&&"number"!=typeof a[b])throw"edges: Wrong arguments.";d.push(this.edgesIndex[a[b]])}return d}throw"edges: Wrong arguments."}),"undefined"!=typeof sigma?(sigma.classes=sigma.classes||Object.create(null),sigma.classes.graph=j):"undefined"!=typeof exports?("undefined"!=typeof module&&module.exports&&(exports=module.exports=j),exports.graph=j):this.graph=j}.call(this),function(a){"use strict";if("undefined"==typeof sigma)throw"sigma is not declared";sigma.utils.pkg("sigma.classes"),sigma.classes.camera=function(a,b,c,d){sigma.classes.dispatcher.extend(this),Object.defineProperty(this,"graph",{value:b}),Object.defineProperty(this,"id",{value:a}),Object.defineProperty(this,"readPrefix",{value:"read_cam"+a+":"}),Object.defineProperty(this,"prefix",{value:"cam"+a+":"}),this.x=0,this.y=0,this.ratio=1,this.angle=0,this.isAnimated=!1,this.settings="object"==typeof d&&d?c.embedObject(d):c},sigma.classes.camera.prototype.goTo=function(b){if(!this.settings("enableCamera"))return this;var c,d,e=b||{},f=["x","y","ratio","angle"];for(c=0,d=f.length;d>c;c++)if(e[f[c]]!==a){if("number"!=typeof e[f[c]]||isNaN(e[f[c]]))throw'Value for "'+f[c]+'" is not a number.';this[f[c]]=e[f[c]]}return this.dispatchEvent("coordinatesUpdated"),this},sigma.classes.camera.prototype.applyView=function(b,c,d){d=d||{},c=c!==a?c:this.prefix,b=b!==a?b:this.readPrefix;var e,f,g,h=d.nodes||this.graph.nodes(),i=d.edges||this.graph.edges(),j=Math.cos(this.angle),k=Math.sin(this.angle),l=Math.pow(this.ratio,this.settings("nodesPowRatio")),m=Math.pow(this.ratio,this.settings("edgesPowRatio")),n=(d.width||0)/2,o=(d.height||0)/2;for(e=0,f=h.length;f>e;e++)g=h[e],g[c+"x"]=(((g[b+"x"]||0)-this.x)*j+((g[b+"y"]||0)-this.y)*k)/this.ratio+n,g[c+"y"]=(((g[b+"y"]||0)-this.y)*j-((g[b+"x"]||0)-this.x)*k)/this.ratio+o,g[c+"size"]=(g[b+"size"]||0)/l;for(e=0,f=i.length;f>e;e++)i[e][c+"size"]=(i[e][b+"size"]||0)/m;return this},sigma.classes.camera.prototype.graphPosition=function(a,b,c){var d=0,e=0,f=Math.cos(this.angle),g=Math.sin(this.angle);return c||(d=-(this.x*f+this.y*g)/this.ratio,e=-(this.y*f-this.x*g)/this.ratio),{x:(a*f+b*g)/this.ratio+d,y:(b*f-a*g)/this.ratio+e}},sigma.classes.camera.prototype.cameraPosition=function(a,b,c){var d=0,e=0,f=Math.cos(this.angle),g=Math.sin(this.angle);return c||(d=-(this.x*f+this.y*g)/this.ratio,e=-(this.y*f-this.x*g)/this.ratio),{x:((a-d)*f-(b-e)*g)*this.ratio,y:((b-e)*f+(a-d)*g)*this.ratio}},sigma.classes.camera.prototype.getMatrix=function(){var a=sigma.utils.matrices.scale(1/this.ratio),b=sigma.utils.matrices.rotation(this.angle),c=sigma.utils.matrices.translation(-this.x,-this.y),d=sigma.utils.matrices.multiply(c,sigma.utils.matrices.multiply(b,a));return d},sigma.classes.camera.prototype.getRectangle=function(a,b){var c=this.cameraPosition(a,0,!0),d=this.cameraPosition(0,b,!0),e=this.cameraPosition(a/2,b/2,!0),f=this.cameraPosition(a/4,0,!0).x,g=this.cameraPosition(0,b/4,!0).y;return{x1:this.x-e.x-f,y1:this.y-e.y-g,x2:this.x-e.x+f+c.x,y2:this.y-e.y-g+c.y,height:Math.sqrt(Math.pow(d.x,2)+Math.pow(d.y+2*g,2))}}}.call(this),function(a){"use strict";function b(a,b){var c=b.x+b.width/2,d=b.y+b.height/2,e=a.y<d,f=a.x<c;return e?f?0:1:f?2:3}function c(a,b){for(var c=[],d=0;4>d;d++)a.x2>=b[d][0].x&&a.x1<=b[d][1].x&&a.y1+a.height>=b[d][0].y&&a.y1<=b[d][2].y&&c.push(d);return c}function d(a,b){for(var c=[],d=0;4>d;d++)j.collision(a,b[d])&&c.push(d);return c}function e(a,b){var c,d,e=b.level+1,f=Math.round(b.bounds.width/2),g=Math.round(b.bounds.height/2),h=Math.round(b.bounds.x),j=Math.round(b.bounds.y);switch(a){case 0:c=h,d=j;break;case 1:c=h+f,d=j;break;case 2:c=h,d=j+g;break;case 3:c=h+f,d=j+g}return i({x:c,y:d,width:f,height:g},e,b.maxElements,b.maxLevel)}function f(b,d,g){if(g.level<g.maxLevel)for(var h=c(d,g.corners),i=0,j=h.length;j>i;i++)g.nodes[h[i]]===a&&(g.nodes[h[i]]=e(h[i],g)),f(b,d,g.nodes[h[i]]);else g.elements.push(b)}function g(c,d){if(d.level<d.maxLevel){var e=b(c,d.bounds);return d.nodes[e]!==a?g(c,d.nodes[e]):[]}return d.elements}function h(b,c,d,e){if(e=e||{},c.level<c.maxLevel)for(var f=d(b,c.corners),g=0,i=f.length;i>g;g++)c.nodes[f[g]]!==a&&h(b,c.nodes[f[g]],d,e);else for(var j=0,k=c.elements.length;k>j;j++)e[c.elements[j].id]===a&&(e[c.elements[j].id]=c.elements[j]);return e}function i(a,b,c,d){return{level:b||0,bounds:a,corners:j.splitSquare(a),maxElements:c||20,maxLevel:d||4,elements:[],nodes:[]}}var j={pointToSquare:function(a){return{x1:a.x-a.size,y1:a.y-a.size,x2:a.x+a.size,y2:a.y-a.size,height:2*a.size}},isAxisAligned:function(a){return a.x1===a.x2||a.y1===a.y2},axisAlignedTopPoints:function(a){return a.y1===a.y2&&a.x1<a.x2?a:a.x1===a.x2&&a.y2>a.y1?{x1:a.x1-a.height,y1:a.y1,x2:a.x1,y2:a.y1,height:a.height}:a.x1===a.x2&&a.y2<a.y1?{x1:a.x1,y1:a.y2,x2:a.x2+a.height,y2:a.y2,height:a.height}:{x1:a.x2,y1:a.y1-a.height,x2:a.x1,y2:a.y1-a.height,height:a.height}},lowerLeftCoor:function(a){var b=Math.sqrt(Math.pow(a.x2-a.x1,2)+Math.pow(a.y2-a.y1,2));return{x:a.x1-(a.y2-a.y1)*a.height/b,y:a.y1+(a.x2-a.x1)*a.height/b}},lowerRightCoor:function(a,b){return{x:b.x-a.x1+a.x2,y:b.y-a.y1+a.y2}},rectangleCorners:function(a){var b=this.lowerLeftCoor(a),c=this.lowerRightCoor(a,b);return[{x:a.x1,y:a.y1},{x:a.x2,y:a.y2},{x:b.x,y:b.y},{x:c.x,y:c.y}]},splitSquare:function(a){return[[{x:a.x,y:a.y},{x:a.x+a.width/2,y:a.y},{x:a.x,y:a.y+a.height/2},{x:a.x+a.width/2,y:a.y+a.height/2}],[{x:a.x+a.width/2,y:a.y},{x:a.x+a.width,y:a.y},{x:a.x+a.width/2,y:a.y+a.height/2},{x:a.x+a.width,y:a.y+a.height/2}],[{x:a.x,y:a.y+a.height/2},{x:a.x+a.width/2,y:a.y+a.height/2},{x:a.x,y:a.y+a.height},{x:a.x+a.width/2,y:a.y+a.height}],[{x:a.x+a.width/2,y:a.y+a.height/2},{x:a.x+a.width,y:a.y+a.height/2},{x:a.x+a.width/2,y:a.y+a.height},{x:a.x+a.width,y:a.y+a.height}]]},axis:function(a,b){return[{x:a[1].x-a[0].x,y:a[1].y-a[0].y},{x:a[1].x-a[3].x,y:a[1].y-a[3].y},{x:b[0].x-b[2].x,y:b[0].y-b[2].y},{x:b[0].x-b[1].x,y:b[0].y-b[1].y}]},projection:function(a,b){var c=(a.x*b.x+a.y*b.y)/(Math.pow(b.x,2)+Math.pow(b.y,2));return{x:c*b.x,y:c*b.y}},axisCollision:function(a,b,c){for(var d=[],e=[],f=0;4>f;f++){var g=this.projection(b[f],a),h=this.projection(c[f],a);d.push(g.x*a.x+g.y*a.y),e.push(h.x*a.x+h.y*a.y)}var i=Math.max.apply(Math,d),j=Math.max.apply(Math,e),k=Math.min.apply(Math,d),l=Math.min.apply(Math,e);return i>=l&&j>=k},collision:function(a,b){for(var c=this.axis(a,b),d=!0,e=0;4>e;e++)d=d&&this.axisCollision(c[e],a,b);return d}},k=function(){this._geom=j,this._tree=null,this._cache={query:!1,result:!1}};k.prototype.index=function(a,b){if(!b.bounds)throw"sigma.classes.quad.index: bounds information not given.";var c=b.prefix||"";this._tree=i(b.bounds,0,b.maxElements,b.maxLevel);for(var d=0,e=a.length;e>d;d++)f(a[d],j.pointToSquare({x:a[d][c+"x"],y:a[d][c+"y"],size:a[d][c+"size"]}),this._tree);return this._cache={query:!1,result:!1},this._tree},k.prototype.point=function(a,b){return this._tree?g({x:a,y:b},this._tree)||[]:[]},k.prototype.area=function(a){var b,e,f=JSON.stringify(a);if(this._cache.query===f)return this._cache.result;j.isAxisAligned(a)?(b=c,e=j.axisAlignedTopPoints(a)):(b=d,e=j.rectangleCorners(a));var g=this._tree?h(e,this._tree,b):[],i=[];for(var k in g)i.push(g[k]);return this._cache.query=f,this._cache.result=i,i},"undefined"!=typeof this.sigma?(this.sigma.classes=this.sigma.classes||{},this.sigma.classes.quad=k):"undefined"!=typeof exports?("undefined"!=typeof module&&module.exports&&(exports=module.exports=k),exports.quad=k):this.quad=k}.call(this),function(a){"use strict";function b(a,b){var c=b.x+b.width/2,d=b.y+b.height/2,e=a.y<d,f=a.x<c;return e?f?0:1:f?2:3}function c(a,b){for(var c=[],d=0;4>d;d++)a.x2>=b[d][0].x&&a.x1<=b[d][1].x&&a.y1+a.height>=b[d][0].y&&a.y1<=b[d][2].y&&c.push(d);return c}function d(a,b){for(var c=[],d=0;4>d;d++)j.collision(a,b[d])&&c.push(d);return c}function e(a,b){var c,d,e=b.level+1,f=Math.round(b.bounds.width/2),g=Math.round(b.bounds.height/2),h=Math.round(b.bounds.x),j=Math.round(b.bounds.y);switch(a){case 0:c=h,d=j;break;case 1:c=h+f,d=j;break;case 2:c=h,d=j+g;break;case 3:c=h+f,d=j+g}return i({x:c,y:d,width:f,height:g},e,b.maxElements,b.maxLevel)}function f(b,d,g){if(g.level<g.maxLevel)for(var h=c(d,g.corners),i=0,j=h.length;j>i;i++)g.nodes[h[i]]===a&&(g.nodes[h[i]]=e(h[i],g)),f(b,d,g.nodes[h[i]]);else g.elements.push(b)}function g(c,d){if(d.level<d.maxLevel){var e=b(c,d.bounds);return d.nodes[e]!==a?g(c,d.nodes[e]):[]}return d.elements}function h(b,c,d,e){if(e=e||{},c.level<c.maxLevel)for(var f=d(b,c.corners),g=0,i=f.length;i>g;g++)c.nodes[f[g]]!==a&&h(b,c.nodes[f[g]],d,e);else for(var j=0,k=c.elements.length;k>j;j++)e[c.elements[j].id]===a&&(e[c.elements[j].id]=c.elements[j]);return e}function i(a,b,c,d){return{level:b||0,bounds:a,corners:j.splitSquare(a),maxElements:c||40,maxLevel:d||8,elements:[],nodes:[]}}var j={pointToSquare:function(a){return{x1:a.x-a.size,y1:a.y-a.size,x2:a.x+a.size,y2:a.y-a.size,height:2*a.size}},lineToSquare:function(a){return a.y1<a.y2?a.x1<a.x2?{x1:a.x1-a.size,y1:a.y1-a.size,x2:a.x2+a.size,y2:a.y1-a.size,height:a.y2-a.y1+2*a.size}:{x1:a.x2-a.size,y1:a.y1-a.size,x2:a.x1+a.size,y2:a.y1-a.size,height:a.y2-a.y1+2*a.size}:a.x1<a.x2?{x1:a.x1-a.size,y1:a.y2-a.size,x2:a.x2+a.size,y2:a.y2-a.size,height:a.y1-a.y2+2*a.size}:{x1:a.x2-a.size,y1:a.y2-a.size,x2:a.x1+a.size,y2:a.y2-a.size,height:a.y1-a.y2+2*a.size}},quadraticCurveToSquare:function(a,b){var c=sigma.utils.getPointOnQuadraticCurve(.5,a.x1,a.y1,a.x2,a.y2,b.x,b.y),d=Math.min(a.x1,a.x2,c.x),e=Math.max(a.x1,a.x2,c.x),f=Math.min(a.y1,a.y2,c.y),g=Math.max(a.y1,a.y2,c.y);return{x1:d-a.size,y1:f-a.size,x2:e+a.size,y2:f-a.size,height:g-f+2*a.size}},selfLoopToSquare:function(a){var b=sigma.utils.getSelfLoopControlPoints(a.x,a.y,a.size),c=Math.min(a.x,b.x1,b.x2),d=Math.max(a.x,b.x1,b.x2),e=Math.min(a.y,b.y1,b.y2),f=Math.max(a.y,b.y1,b.y2);return{x1:c-a.size,y1:e-a.size,x2:d+a.size,y2:e-a.size,height:f-e+2*a.size}},isAxisAligned:function(a){return a.x1===a.x2||a.y1===a.y2},axisAlignedTopPoints:function(a){return a.y1===a.y2&&a.x1<a.x2?a:a.x1===a.x2&&a.y2>a.y1?{x1:a.x1-a.height,y1:a.y1,x2:a.x1,y2:a.y1,height:a.height}:a.x1===a.x2&&a.y2<a.y1?{x1:a.x1,y1:a.y2,x2:a.x2+a.height,y2:a.y2,height:a.height}:{x1:a.x2,y1:a.y1-a.height,x2:a.x1,y2:a.y1-a.height,height:a.height}},lowerLeftCoor:function(a){var b=Math.sqrt(Math.pow(a.x2-a.x1,2)+Math.pow(a.y2-a.y1,2));return{x:a.x1-(a.y2-a.y1)*a.height/b,y:a.y1+(a.x2-a.x1)*a.height/b}},lowerRightCoor:function(a,b){return{x:b.x-a.x1+a.x2,y:b.y-a.y1+a.y2}},rectangleCorners:function(a){var b=this.lowerLeftCoor(a),c=this.lowerRightCoor(a,b);return[{x:a.x1,y:a.y1},{x:a.x2,y:a.y2},{x:b.x,y:b.y},{x:c.x,y:c.y}]},splitSquare:function(a){return[[{x:a.x,y:a.y},{x:a.x+a.width/2,y:a.y},{x:a.x,y:a.y+a.height/2},{x:a.x+a.width/2,y:a.y+a.height/2}],[{x:a.x+a.width/2,y:a.y},{x:a.x+a.width,y:a.y},{x:a.x+a.width/2,y:a.y+a.height/2},{x:a.x+a.width,y:a.y+a.height/2}],[{x:a.x,y:a.y+a.height/2},{x:a.x+a.width/2,y:a.y+a.height/2},{x:a.x,y:a.y+a.height},{x:a.x+a.width/2,y:a.y+a.height}],[{x:a.x+a.width/2,y:a.y+a.height/2},{x:a.x+a.width,y:a.y+a.height/2},{x:a.x+a.width/2,y:a.y+a.height},{x:a.x+a.width,y:a.y+a.height}]]},axis:function(a,b){return[{x:a[1].x-a[0].x,y:a[1].y-a[0].y},{x:a[1].x-a[3].x,y:a[1].y-a[3].y},{x:b[0].x-b[2].x,y:b[0].y-b[2].y},{x:b[0].x-b[1].x,y:b[0].y-b[1].y}]},projection:function(a,b){var c=(a.x*b.x+a.y*b.y)/(Math.pow(b.x,2)+Math.pow(b.y,2));return{x:c*b.x,y:c*b.y}},axisCollision:function(a,b,c){for(var d=[],e=[],f=0;4>f;f++){var g=this.projection(b[f],a),h=this.projection(c[f],a);d.push(g.x*a.x+g.y*a.y),e.push(h.x*a.x+h.y*a.y)}var i=Math.max.apply(Math,d),j=Math.max.apply(Math,e),k=Math.min.apply(Math,d),l=Math.min.apply(Math,e);return i>=l&&j>=k},collision:function(a,b){for(var c=this.axis(a,b),d=!0,e=0;4>e;e++)d=d&&this.axisCollision(c[e],a,b);return d}},k=function(){this._geom=j,this._tree=null,this._cache={query:!1,result:!1},this._enabled=!0};k.prototype.index=function(a,b){if(!this._enabled)return this._tree;if(!b.bounds)throw"sigma.classes.edgequad.index: bounds information not given.";var c,d,e,g,h,k=b.prefix||"";this._tree=i(b.bounds,0,b.maxElements,b.maxLevel);for(var l=a.edges(),m=0,n=l.length;n>m;m++)d=a.nodes(l[m].source),e=a.nodes(l[m].target),h={x1:d[k+"x"],y1:d[k+"y"],x2:e[k+"x"],y2:e[k+"y"],size:l[m][k+"size"]||0},"curve"===l[m].type||"curvedArrow"===l[m].type?d.id===e.id?(g={x:d[k+"x"],y:d[k+"y"],size:d[k+"size"]||0},f(l[m],j.selfLoopToSquare(g),this._tree)):(c=sigma.utils.getQuadraticControlPoint(h.x1,h.y1,h.x2,h.y2),f(l[m],j.quadraticCurveToSquare(h,c),this._tree)):f(l[m],j.lineToSquare(h),this._tree);return this._cache={query:!1,result:!1},this._tree},k.prototype.point=function(a,b){return this._enabled&&this._tree?g({x:a,y:b},this._tree)||[]:[]},k.prototype.area=function(a){if(!this._enabled)return[];var b,e,f=JSON.stringify(a);if(this._cache.query===f)return this._cache.result;j.isAxisAligned(a)?(b=c,e=j.axisAlignedTopPoints(a)):(b=d,e=j.rectangleCorners(a));var g=this._tree?h(e,this._tree,b):[],i=[];for(var k in g)i.push(g[k]);return this._cache.query=f,this._cache.result=i,i},"undefined"!=typeof this.sigma?(this.sigma.classes=this.sigma.classes||{},this.sigma.classes.edgequad=k):"undefined"!=typeof exports?("undefined"!=typeof module&&module.exports&&(exports=module.exports=k),exports.edgequad=k):this.edgequad=k}.call(this),function(){"use strict";if("undefined"==typeof sigma)throw"sigma is not declared";sigma.utils.pkg("sigma.captors"),sigma.captors.mouse=function(a,b,c){function d(a){var b,c,d;return y("mouseEnabled")&&(v.dispatchEvent("mousemove",sigma.utils.mouseCoords(a)),q)?(r=!0,s=!0,u&&clearTimeout(u),u=setTimeout(function(){r=!1},y("dragTimeout")),sigma.misc.animation.killAll(x),x.isMoving=!0,d=x.cameraPosition(sigma.utils.getX(a)-o,sigma.utils.getY(a)-p,!0),b=k-d.x,c=l-d.y,(b!==x.x||c!==x.y)&&(m=x.x,n=x.y,x.goTo({x:b,y:c})),a.preventDefault?a.preventDefault():a.returnValue=!1,a.stopPropagation(),!1):void 0}function e(a){if(y("mouseEnabled")&&q){q=!1,u&&clearTimeout(u),x.isMoving=!1;var b=sigma.utils.getX(a),c=sigma.utils.getY(a);r?(sigma.misc.animation.killAll(x),sigma.misc.animation.camera(x,{x:x.x+y("mouseInertiaRatio")*(x.x-m),y:x.y+y("mouseInertiaRatio")*(x.y-n)},{easing:"quadraticOut",duration:y("mouseInertiaDuration")})):(o!==b||p!==c)&&x.goTo({x:x.x,y:x.y}),v.dispatchEvent("mouseup",sigma.utils.mouseCoords(a)),r=!1}}function f(a){if(y("mouseEnabled"))switch(k=x.x,l=x.y,m=x.x,n=x.y,o=sigma.utils.getX(a),p=sigma.utils.getY(a),s=!1,t=(new Date).getTime(),a.which){case 2:break;case 3:v.dispatchEvent("rightclick",sigma.utils.mouseCoords(a,o,p));break;default:q=!0,v.dispatchEvent("mousedown",sigma.utils.mouseCoords(a,o,p))}}function g(){y("mouseEnabled")&&v.dispatchEvent("mouseout")}function h(a){if(y("mouseEnabled")){var b=sigma.utils.mouseCoords(a);b.isDragging=(new Date).getTime()-t>100&&s,v.dispatchEvent("click",b)}return a.preventDefault?a.preventDefault():a.returnValue=!1,a.stopPropagation(),!1}function i(a){var b,c,d;return y("mouseEnabled")?(c=1/y("doubleClickZoomingRatio"),v.dispatchEvent("doubleclick",sigma.utils.mouseCoords(a,o,p)),y("doubleClickEnabled")&&(b=x.cameraPosition(sigma.utils.getX(a)-sigma.utils.getCenter(a).x,sigma.utils.getY(a)-sigma.utils.getCenter(a).y,!0),d={duration:y("doubleClickZoomDuration")},sigma.utils.zoomTo(x,b.x,b.y,c,d)),a.preventDefault?a.preventDefault():a.returnValue=!1,a.stopPropagation(),!1):void 0}function j(a){var b,c,d;return y("mouseEnabled")&&y("mouseWheelEnabled")?(c=sigma.utils.getDelta(a)>0?1/y("zoomingRatio"):y("zoomingRatio"),b=x.cameraPosition(sigma.utils.getX(a)-sigma.utils.getCenter(a).x,sigma.utils.getY(a)-sigma.utils.getCenter(a).y,!0),d={duration:y("mouseZoomDuration")},sigma.utils.zoomTo(x,b.x,b.y,c,d),a.preventDefault?a.preventDefault():a.returnValue=!1,a.stopPropagation(),!1):void 0}var k,l,m,n,o,p,q,r,s,t,u,v=this,w=a,x=b,y=c;sigma.classes.dispatcher.extend(this),sigma.utils.doubleClick(w,"click",i),w.addEventListener("DOMMouseScroll",j,!1),w.addEventListener("mousewheel",j,!1),w.addEventListener("mousemove",d,!1),w.addEventListener("mousedown",f,!1),w.addEventListener("click",h,!1),w.addEventListener("mouseout",g,!1),document.addEventListener("mouseup",e,!1),this.kill=function(){sigma.utils.unbindDoubleClick(w,"click"),w.removeEventListener("DOMMouseScroll",j),w.removeEventListener("mousewheel",j),w.removeEventListener("mousemove",d),w.removeEventListener("mousedown",f),w.removeEventListener("click",h),w.removeEventListener("mouseout",g),document.removeEventListener("mouseup",e)}}}.call(this),function(){"use strict";if("undefined"==typeof sigma)throw"sigma is not declared";sigma.utils.pkg("sigma.captors"),sigma.captors.touch=function(a,b,c){function d(a){var b=sigma.utils.getOffset(B);return{x:a.pageX-b.left,y:a.pageY-b.top}}function e(a){if(D("touchEnabled")){var b,c,e,f,g,h;switch(E=a.touches,E.length){case 1:C.isMoving=!0,w=1,i=C.x,j=C.y,m=C.x,n=C.y,g=d(E[0]),q=g.x,r=g.y;break;case 2:return C.isMoving=!0,w=2,g=d(E[0]),h=d(E[1]),b=g.x,e=g.y,c=h.x,f=h.y,m=C.x,n=C.y,k=C.angle,l=C.ratio,i=C.x,j=C.y,q=b,r=e,s=c,t=f,u=Math.atan2(t-r,s-q),v=Math.sqrt((t-r)*(t-r)+(s-q)*(s-q)),a.preventDefault(),!1}}}function f(a){if(D("touchEnabled")){E=a.touches;var b=D("touchInertiaRatio");switch(z&&(x=!1,clearTimeout(z)),w){case 2:if(1===a.touches.length){e(a),a.preventDefault();break}case 1:C.isMoving=!1,A.dispatchEvent("stopDrag"),x&&(y=!1,sigma.misc.animation.camera(C,{x:C.x+b*(C.x-m),y:C.y+b*(C.y-n)},{easing:"quadraticOut",duration:D("touchInertiaDuration")})),x=!1,w=0}}}function g(a){if(!y&&D("touchEnabled")){var b,c,e,f,g,h,B,F,G,H,I,J,K,L,M,N,O;switch(E=a.touches,x=!0,z&&clearTimeout(z),z=setTimeout(function(){x=!1},D("dragTimeout")),w){case 1:F=d(E[0]),b=F.x,e=F.y,H=C.cameraPosition(b-q,e-r,!0),L=i-H.x,M=j-H.y,(L!==C.x||M!==C.y)&&(m=C.x,n=C.y,C.goTo({x:L,y:M}),A.dispatchEvent("mousemove",sigma.utils.mouseCoords(a,F.x,F.y)),A.dispatchEvent("drag"));break;case 2:F=d(E[0]),G=d(E[1]),b=F.x,e=F.y,c=G.x,f=G.y,I=C.cameraPosition((q+s)/2-sigma.utils.getCenter(a).x,(r+t)/2-sigma.utils.getCenter(a).y,!0),B=C.cameraPosition((b+c)/2-sigma.utils.getCenter(a).x,(e+f)/2-sigma.utils.getCenter(a).y,!0),J=Math.atan2(f-e,c-b)-u,K=Math.sqrt((f-e)*(f-e)+(c-b)*(c-b))/v,b=I.x,e=I.y,N=l/K,b*=K,e*=K,O=k-J,g=Math.cos(-J),h=Math.sin(-J),c=b*g+e*h,f=e*g-b*h,b=c,e=f,L=b-B.x+i,M=e-B.y+j,(N!==C.ratio||O!==C.angle||L!==C.x||M!==C.y)&&(m=C.x,n=C.y,o=C.angle,p=C.ratio,C.goTo({x:L,y:M,angle:O,ratio:N}),A.dispatchEvent("drag"))}return a.preventDefault(),!1}}function h(a){var b,c,e;return a.touches&&1===a.touches.length&&D("touchEnabled")?(y=!0,c=1/D("doubleClickZoomingRatio"),b=d(a.touches[0]),A.dispatchEvent("doubleclick",sigma.utils.mouseCoords(a,b.x,b.y)),D("doubleClickEnabled")&&(b=C.cameraPosition(b.x-sigma.utils.getCenter(a).x,b.y-sigma.utils.getCenter(a).y,!0),e={duration:D("doubleClickZoomDuration"),onComplete:function(){y=!1}},sigma.utils.zoomTo(C,b.x,b.y,c,e)),a.preventDefault?a.preventDefault():a.returnValue=!1,a.stopPropagation(),!1):void 0}var i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,A=this,B=a,C=b,D=c,E=[];sigma.classes.dispatcher.extend(this),sigma.utils.doubleClick(B,"touchstart",h),B.addEventListener("touchstart",e,!1),B.addEventListener("touchend",f,!1),B.addEventListener("touchcancel",f,!1),B.addEventListener("touchleave",f,!1),B.addEventListener("touchmove",g,!1),this.kill=function(){sigma.utils.unbindDoubleClick(B,"touchstart"),B.addEventListener("touchstart",e),B.addEventListener("touchend",f),B.addEventListener("touchcancel",f),B.addEventListener("touchleave",f),B.addEventListener("touchmove",g)}}}.call(this),function(a){"use strict";if("undefined"==typeof sigma)throw"sigma is not declared";if("undefined"==typeof conrad)throw"conrad is not declared";sigma.utils.pkg("sigma.renderers"),sigma.renderers.canvas=function(a,b,c,d){if("object"!=typeof d)throw"sigma.renderers.canvas: Wrong arguments.";if(!(d.container instanceof HTMLElement))throw"Container not found.";var e,f,g,h;for(sigma.classes.dispatcher.extend(this),Object.defineProperty(this,"conradId",{value:sigma.utils.id()}),this.graph=a,this.camera=b,this.contexts={},this.domElements={},this.options=d,this.container=this.options.container,this.settings="object"==typeof d.settings&&d.settings?c.embedObjects(d.settings):c,this.nodesOnScreen=[],this.edgesOnScreen=[],this.jobs={},this.options.prefix="renderer"+this.conradId+":",this.settings("batchEdgesDrawing")?(this.initDOM("canvas","edges"),this.initDOM("canvas","scene"),this.contexts.nodes=this.contexts.scene,this.contexts.labels=this.contexts.scene):(this.initDOM("canvas","scene"),this.contexts.edges=this.contexts.scene,this.contexts.nodes=this.contexts.scene,this.contexts.labels=this.contexts.scene),this.initDOM("canvas","mouse"),this.contexts.hover=this.contexts.mouse,this.captors=[],g=this.options.captors||[sigma.captors.mouse,sigma.captors.touch],e=0,f=g.length;f>e;e++)h="function"==typeof g[e]?g[e]:sigma.captors[g[e]],this.captors.push(new h(this.domElements.mouse,this.camera,this.settings));sigma.misc.bindEvents.call(this,this.options.prefix),sigma.misc.drawHovers.call(this,this.options.prefix),this.resize(!1)},sigma.renderers.canvas.prototype.render=function(b){b=b||{};var c,d,e,f,g,h,i,j,k,l,m,n,o,p={},q=this.graph,r=this.graph.nodes,s=(this.options.prefix||"",this.settings(b,"drawEdges")),t=this.settings(b,"drawNodes"),u=this.settings(b,"drawLabels"),v=this.settings(b,"drawEdgeLabels"),w=this.settings.embedObjects(b,{prefix:this.options.prefix});this.resize(!1),this.settings(b,"hideEdgesOnMove")&&(this.camera.isAnimated||this.camera.isMoving)&&(s=!1),this.camera.applyView(a,this.options.prefix,{width:this.width,height:this.height}),this.clear();for(e in this.jobs)conrad.hasJob(e)&&conrad.killJob(e);for(this.edgesOnScreen=[],this.nodesOnScreen=this.camera.quadtree.area(this.camera.getRectangle(this.width,this.height)),c=this.nodesOnScreen,d=0,f=c.length;f>d;d++)p[c[d].id]=c[d];if(s){for(c=q.edges(),d=0,f=c.length;f>d;d++)g=c[d],!p[g.source]&&!p[g.target]||g.hidden||r(g.source).hidden||r(g.target).hidden||this.edgesOnScreen.push(g);if(this.settings(b,"batchEdgesDrawing"))h="edges_"+this.conradId,n=w("canvasEdgesBatchSize"),l=this.edgesOnScreen,f=l.length,k=0,i=Math.min(l.length,k+n),j=function(){for(o=this.contexts.edges.globalCompositeOperation,this.contexts.edges.globalCompositeOperation="destination-over",m=sigma.canvas.edges,d=k;i>d;d++)g=l[d],(m[g.type||this.settings(b,"defaultEdgeType")]||m.def)(g,q.nodes(g.source),q.nodes(g.target),this.contexts.edges,w);if(v)for(m=sigma.canvas.edges.labels,d=k;i>d;d++)g=l[d],g.hidden||(m[g.type||this.settings(b,"defaultEdgeType")]||m.def)(g,q.nodes(g.source),q.nodes(g.target),this.contexts.labels,w);return this.contexts.edges.globalCompositeOperation=o,i===l.length?(delete this.jobs[h],!1):(k=i+1,i=Math.min(l.length,k+n),!0)},this.jobs[h]=j,conrad.addJob(h,j.bind(this));else{for(m=sigma.canvas.edges,c=this.edgesOnScreen,d=0,f=c.length;f>d;d++)g=c[d],(m[g.type||this.settings(b,"defaultEdgeType")]||m.def)(g,q.nodes(g.source),q.nodes(g.target),this.contexts.edges,w);if(v)for(m=sigma.canvas.edges.labels,c=this.edgesOnScreen,d=0,f=c.length;f>d;d++)c[d].hidden||(m[c[d].type||this.settings(b,"defaultEdgeType")]||m.def)(c[d],q.nodes(c[d].source),q.nodes(c[d].target),this.contexts.labels,w)}}if(t)for(m=sigma.canvas.nodes,c=this.nodesOnScreen,d=0,f=c.length;f>d;d++)c[d].hidden||(m[c[d].type||this.settings(b,"defaultNodeType")]||m.def)(c[d],this.contexts.nodes,w);if(u)for(m=sigma.canvas.labels,c=this.nodesOnScreen,d=0,f=c.length;f>d;d++)c[d].hidden||(m[c[d].type||this.settings(b,"defaultNodeType")]||m.def)(c[d],this.contexts.labels,w);return this.dispatchEvent("render"),this},sigma.renderers.canvas.prototype.initDOM=function(a,b){var c=document.createElement(a);c.style.position="absolute",c.setAttribute("class","sigma-"+b),this.domElements[b]=c,this.container.appendChild(c),"canvas"===a.toLowerCase()&&(this.contexts[b]=c.getContext("2d"))},sigma.renderers.canvas.prototype.resize=function(b,c){var d,e=this.width,f=this.height,g=sigma.utils.getPixelRatio();if(b!==a&&c!==a?(this.width=b,this.height=c):(this.width=this.container.offsetWidth,this.height=this.container.offsetHeight,b=this.width,c=this.height),e!==this.width||f!==this.height)for(d in this.domElements)this.domElements[d].style.width=b+"px",this.domElements[d].style.height=c+"px","canvas"===this.domElements[d].tagName.toLowerCase()&&(this.domElements[d].setAttribute("width",b*g+"px"),this.domElements[d].setAttribute("height",c*g+"px"),1!==g&&this.contexts[d].scale(g,g));return this},sigma.renderers.canvas.prototype.clear=function(){for(var a in this.contexts)this.contexts[a].clearRect(0,0,this.width,this.height);return this},sigma.renderers.canvas.prototype.kill=function(){for(var a,b;b=this.captors.pop();)b.kill();delete this.captors;for(a in this.domElements)this.domElements[a].parentNode.removeChild(this.domElements[a]),delete this.domElements[a],delete this.contexts[a];delete this.domElements,delete this.contexts},sigma.utils.pkg("sigma.canvas.nodes"),sigma.utils.pkg("sigma.canvas.edges"),sigma.utils.pkg("sigma.canvas.labels")}.call(this),function(a){"use strict";if("undefined"==typeof sigma)throw"sigma is not declared";sigma.utils.pkg("sigma.renderers"),sigma.renderers.webgl=function(a,b,c,d){if("object"!=typeof d)throw"sigma.renderers.webgl: Wrong arguments.";if(!(d.container instanceof HTMLElement))throw"Container not found.";var e,f,g,h;for(sigma.classes.dispatcher.extend(this),this.jobs={},Object.defineProperty(this,"conradId",{value:sigma.utils.id()}),this.graph=a,this.camera=b,this.contexts={},this.domElements={},this.options=d,this.container=this.options.container,this.settings="object"==typeof d.settings&&d.settings?c.embedObjects(d.settings):c,this.options.prefix=this.camera.readPrefix,Object.defineProperty(this,"nodePrograms",{value:{}}),Object.defineProperty(this,"edgePrograms",{value:{}}),Object.defineProperty(this,"nodeFloatArrays",{value:{}}),Object.defineProperty(this,"edgeFloatArrays",{value:{}}),Object.defineProperty(this,"edgeIndicesArrays",{value:{}}),this.settings(d,"batchEdgesDrawing")?(this.initDOM("canvas","edges",!0),this.initDOM("canvas","nodes",!0)):(this.initDOM("canvas","scene",!0),this.contexts.nodes=this.contexts.scene,this.contexts.edges=this.contexts.scene),this.initDOM("canvas","labels"),this.initDOM("canvas","mouse"),this.contexts.hover=this.contexts.mouse,this.captors=[],g=this.options.captors||[sigma.captors.mouse,sigma.captors.touch],e=0,f=g.length;f>e;e++)h="function"==typeof g[e]?g[e]:sigma.captors[g[e]],this.captors.push(new h(this.domElements.mouse,this.camera,this.settings));sigma.misc.bindEvents.call(this,this.camera.prefix),sigma.misc.drawHovers.call(this,this.camera.prefix),this.resize()},sigma.renderers.webgl.prototype.process=function(){var a,b,c,d,e,f,g=this.graph,h=sigma.utils.extend(h,this.options),i=this.settings(h,"defaultEdgeType"),j=this.settings(h,"defaultNodeType");for(d in this.nodeFloatArrays)delete this.nodeFloatArrays[d];for(d in this.edgeFloatArrays)delete this.edgeFloatArrays[d];for(d in this.edgeIndicesArrays)delete this.edgeIndicesArrays[d];for(a=g.edges(),b=0,c=a.length;c>b;b++)e=a[b].type||i,d=e&&sigma.webgl.edges[e]?e:"def",this.edgeFloatArrays[d]||(this.edgeFloatArrays[d]={edges:[]}),this.edgeFloatArrays[d].edges.push(a[b]);for(a=g.nodes(),b=0,c=a.length;c>b;b++)e=a[b].type||j,d=e&&sigma.webgl.nodes[e]?e:"def",this.nodeFloatArrays[d]||(this.nodeFloatArrays[d]={nodes:[]}),this.nodeFloatArrays[d].nodes.push(a[b]);for(d in this.edgeFloatArrays){for(f=sigma.webgl.edges[d],a=this.edgeFloatArrays[d].edges,this.edgeFloatArrays[d].array=new Float32Array(a.length*f.POINTS*f.ATTRIBUTES),b=0,c=a.length;c>b;b++)a[b].hidden||g.nodes(a[b].source).hidden||g.nodes(a[b].target).hidden||f.addEdge(a[b],g.nodes(a[b].source),g.nodes(a[b].target),this.edgeFloatArrays[d].array,b*f.POINTS*f.ATTRIBUTES,h.prefix,this.settings);"function"==typeof f.computeIndices&&(this.edgeIndicesArrays[d]=f.computeIndices(this.edgeFloatArrays[d].array))}for(d in this.nodeFloatArrays)for(f=sigma.webgl.nodes[d],a=this.nodeFloatArrays[d].nodes,this.nodeFloatArrays[d].array=new Float32Array(a.length*f.POINTS*f.ATTRIBUTES),b=0,c=a.length;c>b;b++)this.nodeFloatArrays[d].array||(this.nodeFloatArrays[d].array=new Float32Array(a.length*f.POINTS*f.ATTRIBUTES)),a[b].hidden||f.addNode(a[b],this.nodeFloatArrays[d].array,b*f.POINTS*f.ATTRIBUTES,h.prefix,this.settings);return this},sigma.renderers.webgl.prototype.render=function(b){var c,d,e,f,g,h,i=this,j=(this.graph,this.contexts.nodes),k=this.contexts.edges,l=this.camera.getMatrix(),m=sigma.utils.extend(b,this.options),n=this.settings(m,"drawLabels"),o=this.settings(m,"drawEdges"),p=this.settings(m,"drawNodes");this.resize(!1),this.settings(m,"hideEdgesOnMove")&&(this.camera.isAnimated||this.camera.isMoving)&&(o=!1),this.clear(),l=sigma.utils.matrices.multiply(l,sigma.utils.matrices.translation(this.width/2,this.height/2));for(f in this.jobs)conrad.hasJob(f)&&conrad.killJob(f);if(o)if(this.settings(m,"batchEdgesDrawing"))(function(){var a,b,c,d,e,f,g,h,i,j;c="edges_"+this.conradId,j=this.settings(m,"webglEdgesBatchSize"),a=Object.keys(this.edgeFloatArrays),a.length&&(b=0,i=sigma.webgl.edges[a[b]],e=this.edgeFloatArrays[a[b]].array,h=this.edgeIndicesArrays[a[b]],g=0,f=Math.min(g+j*i.POINTS,e.length/i.ATTRIBUTES),d=function(){return this.edgePrograms[a[b]]||(this.edgePrograms[a[b]]=i.initProgram(k)),f>g&&(k.useProgram(this.edgePrograms[a[b]]),i.render(k,this.edgePrograms[a[b]],e,{settings:this.settings,matrix:l,width:this.width,height:this.height,ratio:this.camera.ratio,scalingRatio:this.settings(m,"webglOversamplingRatio"),start:g,count:f-g,indicesData:h})),f>=e.length/i.ATTRIBUTES&&b===a.length-1?(delete this.jobs[c],!1):(f>=e.length/i.ATTRIBUTES?(b++,e=this.edgeFloatArrays[a[b]].array,i=sigma.webgl.edges[a[b]],g=0,f=Math.min(g+j*i.POINTS,e.length/i.ATTRIBUTES)):(g=f,f=Math.min(g+j*i.POINTS,e.length/i.ATTRIBUTES)),!0)},this.jobs[c]=d,conrad.addJob(c,d.bind(this)))}).call(this);else for(f in this.edgeFloatArrays)h=sigma.webgl.edges[f],this.edgePrograms[f]||(this.edgePrograms[f]=h.initProgram(k)),this.edgeFloatArrays[f]&&(k.useProgram(this.edgePrograms[f]),h.render(k,this.edgePrograms[f],this.edgeFloatArrays[f].array,{settings:this.settings,matrix:l,width:this.width,height:this.height,ratio:this.camera.ratio,scalingRatio:this.settings(m,"webglOversamplingRatio"),indicesData:this.edgeIndicesArrays[f]}));
4 | if(p){j.blendFunc(j.SRC_ALPHA,j.ONE_MINUS_SRC_ALPHA),j.enable(j.BLEND);for(f in this.nodeFloatArrays)h=sigma.webgl.nodes[f],this.nodePrograms[f]||(this.nodePrograms[f]=h.initProgram(j)),this.nodeFloatArrays[f]&&(j.useProgram(this.nodePrograms[f]),h.render(j,this.nodePrograms[f],this.nodeFloatArrays[f].array,{settings:this.settings,matrix:l,width:this.width,height:this.height,ratio:this.camera.ratio,scalingRatio:this.settings(m,"webglOversamplingRatio")}))}if(n)for(c=this.camera.quadtree.area(this.camera.getRectangle(this.width,this.height)),this.camera.applyView(a,a,{nodes:c,edges:[],width:this.width,height:this.height}),g=function(a){return i.settings({prefix:i.camera.prefix},a)},d=0,e=c.length;e>d;d++)c[d].hidden||(sigma.canvas.labels[c[d].type||this.settings(m,"defaultNodeType")]||sigma.canvas.labels.def)(c[d],this.contexts.labels,g);return this.dispatchEvent("render"),this},sigma.renderers.webgl.prototype.initDOM=function(a,b,c){var d=document.createElement(a),e=this;d.style.position="absolute",d.setAttribute("class","sigma-"+b),this.domElements[b]=d,this.container.appendChild(d),"canvas"===a.toLowerCase()&&(this.contexts[b]=d.getContext(c?"experimental-webgl":"2d",{preserveDrawingBuffer:!0}),c&&(d.addEventListener("webglcontextlost",function(a){a.preventDefault()},!1),d.addEventListener("webglcontextrestored",function(){e.render()},!1)))},sigma.renderers.webgl.prototype.resize=function(b,c){var d,e=this.width,f=this.height,g=sigma.utils.getPixelRatio();if(b!==a&&c!==a?(this.width=b,this.height=c):(this.width=this.container.offsetWidth,this.height=this.container.offsetHeight,b=this.width,c=this.height),e!==this.width||f!==this.height)for(d in this.domElements)this.domElements[d].style.width=b+"px",this.domElements[d].style.height=c+"px","canvas"===this.domElements[d].tagName.toLowerCase()&&(this.contexts[d]&&this.contexts[d].scale?(this.domElements[d].setAttribute("width",b*g+"px"),this.domElements[d].setAttribute("height",c*g+"px"),1!==g&&this.contexts[d].scale(g,g)):(this.domElements[d].setAttribute("width",b*this.settings("webglOversamplingRatio")+"px"),this.domElements[d].setAttribute("height",c*this.settings("webglOversamplingRatio")+"px")));for(d in this.contexts)this.contexts[d]&&this.contexts[d].viewport&&this.contexts[d].viewport(0,0,this.width*this.settings("webglOversamplingRatio"),this.height*this.settings("webglOversamplingRatio"));return this},sigma.renderers.webgl.prototype.clear=function(){return this.contexts.labels.clearRect(0,0,this.width,this.height),this.contexts.nodes.clear(this.contexts.nodes.COLOR_BUFFER_BIT),this.contexts.edges.clear(this.contexts.edges.COLOR_BUFFER_BIT),this},sigma.renderers.webgl.prototype.kill=function(){for(var a,b;b=this.captors.pop();)b.kill();delete this.captors;for(a in this.domElements)this.domElements[a].parentNode.removeChild(this.domElements[a]),delete this.domElements[a],delete this.contexts[a];delete this.domElements,delete this.contexts},sigma.utils.pkg("sigma.webgl.nodes"),sigma.utils.pkg("sigma.webgl.edges"),sigma.utils.pkg("sigma.canvas.labels")}.call(this),function(a){"use strict";if("undefined"==typeof sigma)throw"sigma is not declared";if("undefined"==typeof conrad)throw"conrad is not declared";sigma.utils.pkg("sigma.renderers"),sigma.renderers.svg=function(a,b,c,d){if("object"!=typeof d)throw"sigma.renderers.svg: Wrong arguments.";if(!(d.container instanceof HTMLElement))throw"Container not found.";var e,f,g,h,i=this;for(sigma.classes.dispatcher.extend(this),this.graph=a,this.camera=b,this.domElements={graph:null,groups:{},nodes:{},edges:{},labels:{},hovers:{}},this.measurementCanvas=null,this.options=d,this.container=this.options.container,this.settings="object"==typeof d.settings&&d.settings?c.embedObjects(d.settings):c,this.settings("freeStyle",!!this.options.freeStyle),this.settings("xmlns","http://www.w3.org/2000/svg"),this.nodesOnScreen=[],this.edgesOnScreen=[],this.options.prefix="renderer"+sigma.utils.id()+":",this.initDOM("svg"),this.captors=[],g=this.options.captors||[sigma.captors.mouse,sigma.captors.touch],e=0,f=g.length;f>e;e++)h="function"==typeof g[e]?g[e]:sigma.captors[g[e]],this.captors.push(new h(this.domElements.graph,this.camera,this.settings));window.addEventListener("resize",function(){i.resize()}),sigma.misc.bindDOMEvents.call(this,this.domElements.graph),this.bindHovers(this.options.prefix),this.resize(!1)},sigma.renderers.svg.prototype.render=function(b){b=b||{};var c,d,e,f,g,h,i,j,k,l={},m=this.graph,n=this.graph.nodes,o=(this.options.prefix||"",this.settings(b,"drawEdges")),p=this.settings(b,"drawNodes"),q=(this.settings(b,"drawLabels"),this.settings.embedObjects(b,{prefix:this.options.prefix,forceLabels:this.options.forceLabels}));for(this.settings(b,"hideEdgesOnMove")&&(this.camera.isAnimated||this.camera.isMoving)&&(o=!1),this.camera.applyView(a,this.options.prefix,{width:this.width,height:this.height}),this.hideDOMElements(this.domElements.nodes),this.hideDOMElements(this.domElements.edges),this.hideDOMElements(this.domElements.labels),this.edgesOnScreen=[],this.nodesOnScreen=this.camera.quadtree.area(this.camera.getRectangle(this.width,this.height)),c=this.nodesOnScreen,d=0,f=c.length;f>d;d++)l[c[d].id]=c[d];for(c=m.edges(),d=0,f=c.length;f>d;d++)g=c[d],!l[g.source]&&!l[g.target]||g.hidden||n(g.source).hidden||n(g.target).hidden||this.edgesOnScreen.push(g);if(j=sigma.svg.nodes,k=sigma.svg.labels,p)for(c=this.nodesOnScreen,d=0,f=c.length;f>d;d++)c[d].hidden||this.domElements.nodes[c[d].id]||(e=(j[c[d].type]||j.def).create(c[d],q),this.domElements.nodes[c[d].id]=e,this.domElements.groups.nodes.appendChild(e),e=(k[c[d].type]||k.def).create(c[d],q),this.domElements.labels[c[d].id]=e,this.domElements.groups.labels.appendChild(e));if(p)for(c=this.nodesOnScreen,d=0,f=c.length;f>d;d++)c[d].hidden||((j[c[d].type]||j.def).update(c[d],this.domElements.nodes[c[d].id],q),(k[c[d].type]||k.def).update(c[d],this.domElements.labels[c[d].id],q));if(j=sigma.svg.edges,o)for(c=this.edgesOnScreen,d=0,f=c.length;f>d;d++)this.domElements.edges[c[d].id]||(h=n(c[d].source),i=n(c[d].target),e=(j[c[d].type]||j.def).create(c[d],h,i,q),this.domElements.edges[c[d].id]=e,this.domElements.groups.edges.appendChild(e));if(o)for(c=this.edgesOnScreen,d=0,f=c.length;f>d;d++)h=n(c[d].source),i=n(c[d].target),(j[c[d].type]||j.def).update(c[d],this.domElements.edges[c[d].id],h,i,q);return this.dispatchEvent("render"),this},sigma.renderers.svg.prototype.initDOM=function(a){var b,c,d,e=document.createElementNS(this.settings("xmlns"),a),f=this.settings("classPrefix");e.style.position="absolute",e.setAttribute("class",f+"-svg"),e.setAttribute("xmlns",this.settings("xmlns")),e.setAttribute("xmlns:xlink","http://www.w3.org/1999/xlink"),e.setAttribute("version","1.1");var g=document.createElement("canvas");g.setAttribute("class",f+"-measurement-canvas"),this.domElements.graph=this.container.appendChild(e);var h=["edges","nodes","labels","hovers"];for(d=0,c=h.length;c>d;d++)b=document.createElementNS(this.settings("xmlns"),"g"),b.setAttributeNS(null,"id",f+"-group-"+h[d]),b.setAttributeNS(null,"class",f+"-group"),this.domElements.groups[h[d]]=this.domElements.graph.appendChild(b);this.container.appendChild(g),this.measurementCanvas=g.getContext("2d")},sigma.renderers.svg.prototype.hideDOMElements=function(a){var b,c;for(c in a)b=a[c],sigma.svg.utils.hide(b);return this},sigma.renderers.svg.prototype.bindHovers=function(a){function b(b){var c=b.data.node,d=g.settings.embedObjects({prefix:a});if(d("enableHovering")){var h=(f[c.type]||f.def).create(c,g.domElements.nodes[c.id],g.measurementCanvas,d);g.domElements.hovers[c.id]=h,g.domElements.groups.hovers.appendChild(h),e=c}}function c(b){var c=b.data.node,d=g.settings.embedObjects({prefix:a});d("enableHovering")&&(g.domElements.groups.hovers.removeChild(g.domElements.hovers[c.id]),e=null,delete g.domElements.hovers[c.id],g.domElements.groups.nodes.appendChild(g.domElements.nodes[c.id]))}function d(){if(e){var b=g.settings.embedObjects({prefix:a});g.domElements.groups.hovers.removeChild(g.domElements.hovers[e.id]),delete g.domElements.hovers[e.id];var c=(f[e.type]||f.def).create(e,g.domElements.nodes[e.id],g.measurementCanvas,b);g.domElements.hovers[e.id]=c,g.domElements.groups.hovers.appendChild(c)}}var e,f=sigma.svg.hovers,g=this;this.bind("overNode",b),this.bind("outNode",c),this.bind("render",d)},sigma.renderers.svg.prototype.resize=function(b,c){var d=this.width,e=this.height,f=1;return b!==a&&c!==a?(this.width=b,this.height=c):(this.width=this.container.offsetWidth,this.height=this.container.offsetHeight,b=this.width,c=this.height),(d!==this.width||e!==this.height)&&(this.domElements.graph.style.width=b+"px",this.domElements.graph.style.height=c+"px","svg"===this.domElements.graph.tagName.toLowerCase()&&(this.domElements.graph.setAttribute("width",b*f),this.domElements.graph.setAttribute("height",c*f))),this},sigma.utils.pkg("sigma.svg.nodes"),sigma.utils.pkg("sigma.svg.edges"),sigma.utils.pkg("sigma.svg.labels")}.call(this),function(a){"use strict";if("undefined"==typeof sigma)throw"sigma is not declared";sigma.utils.pkg("sigma.renderers");var b,c=!!a.WebGLRenderingContext;if(c){b=document.createElement("canvas");try{c=!(!b.getContext("webgl")&&!b.getContext("experimental-webgl"))}catch(d){c=!1}}sigma.renderers.def=c?sigma.renderers.webgl:sigma.renderers.canvas}(this),function(){"use strict";sigma.utils.pkg("sigma.webgl.nodes"),sigma.webgl.nodes.def={POINTS:3,ATTRIBUTES:5,addNode:function(a,b,c,d,e){var f=sigma.utils.floatColor(a.color||e("defaultNodeColor"));b[c++]=a[d+"x"],b[c++]=a[d+"y"],b[c++]=a[d+"size"],b[c++]=f,b[c++]=0,b[c++]=a[d+"x"],b[c++]=a[d+"y"],b[c++]=a[d+"size"],b[c++]=f,b[c++]=2*Math.PI/3,b[c++]=a[d+"x"],b[c++]=a[d+"y"],b[c++]=a[d+"size"],b[c++]=f,b[c++]=4*Math.PI/3},render:function(a,b,c,d){var e,f=a.getAttribLocation(b,"a_position"),g=a.getAttribLocation(b,"a_size"),h=a.getAttribLocation(b,"a_color"),i=a.getAttribLocation(b,"a_angle"),j=a.getUniformLocation(b,"u_resolution"),k=a.getUniformLocation(b,"u_matrix"),l=a.getUniformLocation(b,"u_ratio"),m=a.getUniformLocation(b,"u_scale");e=a.createBuffer(),a.bindBuffer(a.ARRAY_BUFFER,e),a.bufferData(a.ARRAY_BUFFER,c,a.DYNAMIC_DRAW),a.uniform2f(j,d.width,d.height),a.uniform1f(l,1/Math.pow(d.ratio,d.settings("nodesPowRatio"))),a.uniform1f(m,d.scalingRatio),a.uniformMatrix3fv(k,!1,d.matrix),a.enableVertexAttribArray(f),a.enableVertexAttribArray(g),a.enableVertexAttribArray(h),a.enableVertexAttribArray(i),a.vertexAttribPointer(f,2,a.FLOAT,!1,this.ATTRIBUTES*Float32Array.BYTES_PER_ELEMENT,0),a.vertexAttribPointer(g,1,a.FLOAT,!1,this.ATTRIBUTES*Float32Array.BYTES_PER_ELEMENT,8),a.vertexAttribPointer(h,1,a.FLOAT,!1,this.ATTRIBUTES*Float32Array.BYTES_PER_ELEMENT,12),a.vertexAttribPointer(i,1,a.FLOAT,!1,this.ATTRIBUTES*Float32Array.BYTES_PER_ELEMENT,16),a.drawArrays(a.TRIANGLES,d.start||0,d.count||c.length/this.ATTRIBUTES)},initProgram:function(a){var b,c,d;return b=sigma.utils.loadShader(a,["attribute vec2 a_position;","attribute float a_size;","attribute float a_color;","attribute float a_angle;","uniform vec2 u_resolution;","uniform float u_ratio;","uniform float u_scale;","uniform mat3 u_matrix;","varying vec4 color;","varying vec2 center;","varying float radius;","void main() {","radius = a_size * u_ratio;","vec2 position = (u_matrix * vec3(a_position, 1)).xy;","center = position * u_scale;","center = vec2(center.x, u_scale * u_resolution.y - center.y);","position = position +","2.0 * radius * vec2(cos(a_angle), sin(a_angle));","position = (position / u_resolution * 2.0 - 1.0) * vec2(1, -1);","radius = radius * u_scale;","gl_Position = vec4(position, 0, 1);","float c = a_color;","color.b = mod(c, 256.0); c = floor(c / 256.0);","color.g = mod(c, 256.0); c = floor(c / 256.0);","color.r = mod(c, 256.0); c = floor(c / 256.0); color /= 255.0;","color.a = 1.0;","}"].join("\n"),a.VERTEX_SHADER),c=sigma.utils.loadShader(a,["precision mediump float;","varying vec4 color;","varying vec2 center;","varying float radius;","void main(void) {","vec4 color0 = vec4(0.0, 0.0, 0.0, 0.0);","vec2 m = gl_FragCoord.xy - center;","float diff = radius - sqrt(m.x * m.x + m.y * m.y);","if (diff > 0.0)","gl_FragColor = color;","else","gl_FragColor = color0;","}"].join("\n"),a.FRAGMENT_SHADER),d=sigma.utils.loadProgram(a,[b,c])}}}(),function(){"use strict";sigma.utils.pkg("sigma.webgl.nodes"),sigma.webgl.nodes.fast={POINTS:1,ATTRIBUTES:4,addNode:function(a,b,c,d,e){b[c++]=a[d+"x"],b[c++]=a[d+"y"],b[c++]=a[d+"size"],b[c++]=sigma.utils.floatColor(a.color||e("defaultNodeColor"))},render:function(a,b,c,d){var e,f=a.getAttribLocation(b,"a_position"),g=a.getAttribLocation(b,"a_size"),h=a.getAttribLocation(b,"a_color"),i=a.getUniformLocation(b,"u_resolution"),j=a.getUniformLocation(b,"u_matrix"),k=a.getUniformLocation(b,"u_ratio"),l=a.getUniformLocation(b,"u_scale");e=a.createBuffer(),a.bindBuffer(a.ARRAY_BUFFER,e),a.bufferData(a.ARRAY_BUFFER,c,a.DYNAMIC_DRAW),a.uniform2f(i,d.width,d.height),a.uniform1f(k,1/Math.pow(d.ratio,d.settings("nodesPowRatio"))),a.uniform1f(l,d.scalingRatio),a.uniformMatrix3fv(j,!1,d.matrix),a.enableVertexAttribArray(f),a.enableVertexAttribArray(g),a.enableVertexAttribArray(h),a.vertexAttribPointer(f,2,a.FLOAT,!1,this.ATTRIBUTES*Float32Array.BYTES_PER_ELEMENT,0),a.vertexAttribPointer(g,1,a.FLOAT,!1,this.ATTRIBUTES*Float32Array.BYTES_PER_ELEMENT,8),a.vertexAttribPointer(h,1,a.FLOAT,!1,this.ATTRIBUTES*Float32Array.BYTES_PER_ELEMENT,12),a.drawArrays(a.POINTS,d.start||0,d.count||c.length/this.ATTRIBUTES)},initProgram:function(a){var b,c,d;return b=sigma.utils.loadShader(a,["attribute vec2 a_position;","attribute float a_size;","attribute float a_color;","uniform vec2 u_resolution;","uniform float u_ratio;","uniform float u_scale;","uniform mat3 u_matrix;","varying vec4 color;","void main() {","gl_Position = vec4(","((u_matrix * vec3(a_position, 1)).xy /","u_resolution * 2.0 - 1.0) * vec2(1, -1),","0,","1",");","gl_PointSize = a_size * u_ratio * u_scale * 2.0;","float c = a_color;","color.b = mod(c, 256.0); c = floor(c / 256.0);","color.g = mod(c, 256.0); c = floor(c / 256.0);","color.r = mod(c, 256.0); c = floor(c / 256.0); color /= 255.0;","color.a = 1.0;","}"].join("\n"),a.VERTEX_SHADER),c=sigma.utils.loadShader(a,["precision mediump float;","varying vec4 color;","void main(void) {","float border = 0.01;","float radius = 0.5;","vec4 color0 = vec4(0.0, 0.0, 0.0, 0.0);","vec2 m = gl_PointCoord - vec2(0.5, 0.5);","float dist = radius - sqrt(m.x * m.x + m.y * m.y);","float t = 0.0;","if (dist > border)","t = 1.0;","else if (dist > 0.0)","t = dist / border;","gl_FragColor = mix(color0, color, t);","}"].join("\n"),a.FRAGMENT_SHADER),d=sigma.utils.loadProgram(a,[b,c])}}}(),function(){"use strict";sigma.utils.pkg("sigma.webgl.edges"),sigma.webgl.edges.def={POINTS:6,ATTRIBUTES:7,addEdge:function(a,b,c,d,e,f,g){var h=(a[f+"size"]||1)/2,i=b[f+"x"],j=b[f+"y"],k=c[f+"x"],l=c[f+"y"],m=a.color;if(!m)switch(g("edgeColor")){case"source":m=b.color||g("defaultNodeColor");break;case"target":m=c.color||g("defaultNodeColor");break;default:m=g("defaultEdgeColor")}m=sigma.utils.floatColor(m),d[e++]=i,d[e++]=j,d[e++]=k,d[e++]=l,d[e++]=h,d[e++]=0,d[e++]=m,d[e++]=k,d[e++]=l,d[e++]=i,d[e++]=j,d[e++]=h,d[e++]=1,d[e++]=m,d[e++]=k,d[e++]=l,d[e++]=i,d[e++]=j,d[e++]=h,d[e++]=0,d[e++]=m,d[e++]=k,d[e++]=l,d[e++]=i,d[e++]=j,d[e++]=h,d[e++]=0,d[e++]=m,d[e++]=i,d[e++]=j,d[e++]=k,d[e++]=l,d[e++]=h,d[e++]=1,d[e++]=m,d[e++]=i,d[e++]=j,d[e++]=k,d[e++]=l,d[e++]=h,d[e++]=0,d[e++]=m},render:function(a,b,c,d){var e,f=a.getAttribLocation(b,"a_color"),g=a.getAttribLocation(b,"a_position1"),h=a.getAttribLocation(b,"a_position2"),i=a.getAttribLocation(b,"a_thickness"),j=a.getAttribLocation(b,"a_minus"),k=a.getUniformLocation(b,"u_resolution"),l=a.getUniformLocation(b,"u_matrix"),m=a.getUniformLocation(b,"u_matrixHalfPi"),n=a.getUniformLocation(b,"u_matrixHalfPiMinus"),o=a.getUniformLocation(b,"u_ratio"),p=a.getUniformLocation(b,"u_scale");e=a.createBuffer(),a.bindBuffer(a.ARRAY_BUFFER,e),a.bufferData(a.ARRAY_BUFFER,c,a.STATIC_DRAW),a.uniform2f(k,d.width,d.height),a.uniform1f(o,d.ratio/Math.pow(d.ratio,d.settings("edgesPowRatio"))),a.uniform1f(p,d.scalingRatio),a.uniformMatrix3fv(l,!1,d.matrix),a.uniformMatrix2fv(m,!1,sigma.utils.matrices.rotation(Math.PI/2,!0)),a.uniformMatrix2fv(n,!1,sigma.utils.matrices.rotation(-Math.PI/2,!0)),a.enableVertexAttribArray(f),a.enableVertexAttribArray(g),a.enableVertexAttribArray(h),a.enableVertexAttribArray(i),a.enableVertexAttribArray(j),a.vertexAttribPointer(g,2,a.FLOAT,!1,this.ATTRIBUTES*Float32Array.BYTES_PER_ELEMENT,0),a.vertexAttribPointer(h,2,a.FLOAT,!1,this.ATTRIBUTES*Float32Array.BYTES_PER_ELEMENT,8),a.vertexAttribPointer(i,1,a.FLOAT,!1,this.ATTRIBUTES*Float32Array.BYTES_PER_ELEMENT,16),a.vertexAttribPointer(j,1,a.FLOAT,!1,this.ATTRIBUTES*Float32Array.BYTES_PER_ELEMENT,20),a.vertexAttribPointer(f,1,a.FLOAT,!1,this.ATTRIBUTES*Float32Array.BYTES_PER_ELEMENT,24),a.drawArrays(a.TRIANGLES,d.start||0,d.count||c.length/this.ATTRIBUTES)},initProgram:function(a){var b,c,d;return b=sigma.utils.loadShader(a,["attribute vec2 a_position1;","attribute vec2 a_position2;","attribute float a_thickness;","attribute float a_minus;","attribute float a_color;","uniform vec2 u_resolution;","uniform float u_ratio;","uniform float u_scale;","uniform mat3 u_matrix;","uniform mat2 u_matrixHalfPi;","uniform mat2 u_matrixHalfPiMinus;","varying vec4 color;","void main() {","vec2 position = a_thickness * u_ratio *","normalize(a_position2 - a_position1);","mat2 matrix = a_minus * u_matrixHalfPiMinus +","(1.0 - a_minus) * u_matrixHalfPi;","position = matrix * position + a_position1;","gl_Position = vec4(","((u_matrix * vec3(position, 1)).xy /","u_resolution * 2.0 - 1.0) * vec2(1, -1),","0,","1",");","float c = a_color;","color.b = mod(c, 256.0); c = floor(c / 256.0);","color.g = mod(c, 256.0); c = floor(c / 256.0);","color.r = mod(c, 256.0); c = floor(c / 256.0); color /= 255.0;","color.a = 1.0;","}"].join("\n"),a.VERTEX_SHADER),c=sigma.utils.loadShader(a,["precision mediump float;","varying vec4 color;","void main(void) {","gl_FragColor = color;","}"].join("\n"),a.FRAGMENT_SHADER),d=sigma.utils.loadProgram(a,[b,c])}}}(),function(){"use strict";sigma.utils.pkg("sigma.webgl.edges"),sigma.webgl.edges.fast={POINTS:2,ATTRIBUTES:3,addEdge:function(a,b,c,d,e,f,g){var h=((a[f+"size"]||1)/2,b[f+"x"]),i=b[f+"y"],j=c[f+"x"],k=c[f+"y"],l=a.color;if(!l)switch(g("edgeColor")){case"source":l=b.color||g("defaultNodeColor");break;case"target":l=c.color||g("defaultNodeColor");break;default:l=g("defaultEdgeColor")}l=sigma.utils.floatColor(l),d[e++]=h,d[e++]=i,d[e++]=l,d[e++]=j,d[e++]=k,d[e++]=l},render:function(a,b,c,d){var e,f=a.getAttribLocation(b,"a_color"),g=a.getAttribLocation(b,"a_position"),h=a.getUniformLocation(b,"u_resolution"),i=a.getUniformLocation(b,"u_matrix");e=a.createBuffer(),a.bindBuffer(a.ARRAY_BUFFER,e),a.bufferData(a.ARRAY_BUFFER,c,a.DYNAMIC_DRAW),a.uniform2f(h,d.width,d.height),a.uniformMatrix3fv(i,!1,d.matrix),a.enableVertexAttribArray(g),a.enableVertexAttribArray(f),a.vertexAttribPointer(g,2,a.FLOAT,!1,this.ATTRIBUTES*Float32Array.BYTES_PER_ELEMENT,0),a.vertexAttribPointer(f,1,a.FLOAT,!1,this.ATTRIBUTES*Float32Array.BYTES_PER_ELEMENT,8),a.lineWidth(3),a.drawArrays(a.LINES,d.start||0,d.count||c.length/this.ATTRIBUTES)},initProgram:function(a){var b,c,d;return b=sigma.utils.loadShader(a,["attribute vec2 a_position;","attribute float a_color;","uniform vec2 u_resolution;","uniform mat3 u_matrix;","varying vec4 color;","void main() {","gl_Position = vec4(","((u_matrix * vec3(a_position, 1)).xy /","u_resolution * 2.0 - 1.0) * vec2(1, -1),","0,","1",");","float c = a_color;","color.b = mod(c, 256.0); c = floor(c / 256.0);","color.g = mod(c, 256.0); c = floor(c / 256.0);","color.r = mod(c, 256.0); c = floor(c / 256.0); color /= 255.0;","color.a = 1.0;","}"].join("\n"),a.VERTEX_SHADER),c=sigma.utils.loadShader(a,["precision mediump float;","varying vec4 color;","void main(void) {","gl_FragColor = color;","}"].join("\n"),a.FRAGMENT_SHADER),d=sigma.utils.loadProgram(a,[b,c])}}}(),function(){"use strict";sigma.utils.pkg("sigma.webgl.edges"),sigma.webgl.edges.arrow={POINTS:9,ATTRIBUTES:11,addEdge:function(a,b,c,d,e,f,g){var h=(a[f+"size"]||1)/2,i=b[f+"x"],j=b[f+"y"],k=c[f+"x"],l=c[f+"y"],m=c[f+"size"],n=a.color;if(!n)switch(g("edgeColor")){case"source":n=b.color||g("defaultNodeColor");break;case"target":n=c.color||g("defaultNodeColor");break;default:n=g("defaultEdgeColor")}n=sigma.utils.floatColor(n),d[e++]=i,d[e++]=j,d[e++]=k,d[e++]=l,d[e++]=h,d[e++]=m,d[e++]=0,d[e++]=0,d[e++]=0,d[e++]=0,d[e++]=n,d[e++]=k,d[e++]=l,d[e++]=i,d[e++]=j,d[e++]=h,d[e++]=m,d[e++]=1,d[e++]=1,d[e++]=0,d[e++]=0,d[e++]=n,d[e++]=k,d[e++]=l,d[e++]=i,d[e++]=j,d[e++]=h,d[e++]=m,d[e++]=1,d[e++]=0,d[e++]=0,d[e++]=0,d[e++]=n,d[e++]=k,d[e++]=l,d[e++]=i,d[e++]=j,d[e++]=h,d[e++]=m,d[e++]=1,d[e++]=0,d[e++]=0,d[e++]=0,d[e++]=n,d[e++]=i,d[e++]=j,d[e++]=k,d[e++]=l,d[e++]=h,d[e++]=m,d[e++]=0,d[e++]=1,d[e++]=0,d[e++]=0,d[e++]=n,d[e++]=i,d[e++]=j,d[e++]=k,d[e++]=l,d[e++]=h,d[e++]=m,d[e++]=0,d[e++]=0,d[e++]=0,d[e++]=0,d[e++]=n,d[e++]=k,d[e++]=l,d[e++]=i,d[e++]=j,d[e++]=h,d[e++]=m,d[e++]=1,d[e++]=0,d[e++]=1,d[e++]=-1,d[e++]=n,d[e++]=k,d[e++]=l,d[e++]=i,d[e++]=j,d[e++]=h,d[e++]=m,d[e++]=1,d[e++]=0,d[e++]=1,d[e++]=0,d[e++]=n,d[e++]=k,d[e++]=l,d[e++]=i,d[e++]=j,d[e++]=h,d[e++]=m,d[e++]=1,d[e++]=0,d[e++]=1,d[e++]=1,d[e++]=n},render:function(a,b,c,d){var e,f=a.getAttribLocation(b,"a_pos1"),g=a.getAttribLocation(b,"a_pos2"),h=a.getAttribLocation(b,"a_thickness"),i=a.getAttribLocation(b,"a_tSize"),j=a.getAttribLocation(b,"a_delay"),k=a.getAttribLocation(b,"a_minus"),l=a.getAttribLocation(b,"a_head"),m=a.getAttribLocation(b,"a_headPosition"),n=a.getAttribLocation(b,"a_color"),o=a.getUniformLocation(b,"u_resolution"),p=a.getUniformLocation(b,"u_matrix"),q=a.getUniformLocation(b,"u_matrixHalfPi"),r=a.getUniformLocation(b,"u_matrixHalfPiMinus"),s=a.getUniformLocation(b,"u_ratio"),t=a.getUniformLocation(b,"u_nodeRatio"),u=a.getUniformLocation(b,"u_arrowHead"),v=a.getUniformLocation(b,"u_scale");e=a.createBuffer(),a.bindBuffer(a.ARRAY_BUFFER,e),a.bufferData(a.ARRAY_BUFFER,c,a.STATIC_DRAW),a.uniform2f(o,d.width,d.height),a.uniform1f(s,d.ratio/Math.pow(d.ratio,d.settings("edgesPowRatio"))),a.uniform1f(t,Math.pow(d.ratio,d.settings("nodesPowRatio"))/d.ratio),a.uniform1f(u,5),a.uniform1f(v,d.scalingRatio),a.uniformMatrix3fv(p,!1,d.matrix),a.uniformMatrix2fv(q,!1,sigma.utils.matrices.rotation(Math.PI/2,!0)),a.uniformMatrix2fv(r,!1,sigma.utils.matrices.rotation(-Math.PI/2,!0)),a.enableVertexAttribArray(f),a.enableVertexAttribArray(g),a.enableVertexAttribArray(h),a.enableVertexAttribArray(i),a.enableVertexAttribArray(j),a.enableVertexAttribArray(k),a.enableVertexAttribArray(l),a.enableVertexAttribArray(m),a.enableVertexAttribArray(n),a.vertexAttribPointer(f,2,a.FLOAT,!1,this.ATTRIBUTES*Float32Array.BYTES_PER_ELEMENT,0),a.vertexAttribPointer(g,2,a.FLOAT,!1,this.ATTRIBUTES*Float32Array.BYTES_PER_ELEMENT,8),a.vertexAttribPointer(h,1,a.FLOAT,!1,this.ATTRIBUTES*Float32Array.BYTES_PER_ELEMENT,16),a.vertexAttribPointer(i,1,a.FLOAT,!1,this.ATTRIBUTES*Float32Array.BYTES_PER_ELEMENT,20),a.vertexAttribPointer(j,1,a.FLOAT,!1,this.ATTRIBUTES*Float32Array.BYTES_PER_ELEMENT,24),a.vertexAttribPointer(k,1,a.FLOAT,!1,this.ATTRIBUTES*Float32Array.BYTES_PER_ELEMENT,28),a.vertexAttribPointer(l,1,a.FLOAT,!1,this.ATTRIBUTES*Float32Array.BYTES_PER_ELEMENT,32),a.vertexAttribPointer(m,1,a.FLOAT,!1,this.ATTRIBUTES*Float32Array.BYTES_PER_ELEMENT,36),a.vertexAttribPointer(n,1,a.FLOAT,!1,this.ATTRIBUTES*Float32Array.BYTES_PER_ELEMENT,40),a.drawArrays(a.TRIANGLES,d.start||0,d.count||c.length/this.ATTRIBUTES)},initProgram:function(a){var b,c,d;return b=sigma.utils.loadShader(a,["attribute vec2 a_pos1;","attribute vec2 a_pos2;","attribute float a_thickness;","attribute float a_tSize;","attribute float a_delay;","attribute float a_minus;","attribute float a_head;","attribute float a_headPosition;","attribute float a_color;","uniform vec2 u_resolution;","uniform float u_ratio;","uniform float u_nodeRatio;","uniform float u_arrowHead;","uniform float u_scale;","uniform mat3 u_matrix;","uniform mat2 u_matrixHalfPi;","uniform mat2 u_matrixHalfPiMinus;","varying vec4 color;","void main() {","vec2 pos = normalize(a_pos2 - a_pos1);","mat2 matrix = (1.0 - a_head) *","(","a_minus * u_matrixHalfPiMinus +","(1.0 - a_minus) * u_matrixHalfPi",") + a_head * (","a_headPosition * u_matrixHalfPiMinus * 0.6 +","(a_headPosition * a_headPosition - 1.0) * mat2(1.0)",");","pos = a_pos1 + (","(1.0 - a_head) * a_thickness * u_ratio * matrix * pos +","a_head * u_arrowHead * a_thickness * u_ratio * matrix * pos +","a_delay * pos * (","a_tSize / u_nodeRatio +","u_arrowHead * a_thickness * u_ratio",")",");","gl_Position = vec4(","((u_matrix * vec3(pos, 1)).xy /","u_resolution * 2.0 - 1.0) * vec2(1, -1),","0,","1",");","float c = a_color;","color.b = mod(c, 256.0); c = floor(c / 256.0);","color.g = mod(c, 256.0); c = floor(c / 256.0);","color.r = mod(c, 256.0); c = floor(c / 256.0); color /= 255.0;","color.a = 1.0;","}"].join("\n"),a.VERTEX_SHADER),c=sigma.utils.loadShader(a,["precision mediump float;","varying vec4 color;","void main(void) {","gl_FragColor = color;","}"].join("\n"),a.FRAGMENT_SHADER),d=sigma.utils.loadProgram(a,[b,c])}}}(),function(){"use strict";if("undefined"==typeof sigma)throw"sigma is not declared";sigma.utils.pkg("sigma.canvas.labels"),sigma.canvas.labels.def=function(a,b,c){var d,e=c("prefix")||"",f=a[e+"size"];f<c("labelThreshold")||a.label&&"string"==typeof a.label&&(d="fixed"===c("labelSize")?c("defaultLabelSize"):c("labelSizeRatio")*f,b.font=(c("fontStyle")?c("fontStyle")+" ":"")+d+"px "+c("font"),b.fillStyle="node"===c("labelColor")?a.color||c("defaultNodeColor"):c("defaultLabelColor"),b.fillText(a.label,Math.round(a[e+"x"]+f+3),Math.round(a[e+"y"]+d/3)))}}.call(this),function(){"use strict";if("undefined"==typeof sigma)throw"sigma is not declared";sigma.utils.pkg("sigma.canvas.hovers"),sigma.canvas.hovers.def=function(a,b,c){var d,e,f,g,h,i=c("hoverFontStyle")||c("fontStyle"),j=c("prefix")||"",k=a[j+"size"],l="fixed"===c("labelSize")?c("defaultLabelSize"):c("labelSizeRatio")*k;b.font=(i?i+" ":"")+l+"px "+(c("hoverFont")||c("font")),b.beginPath(),b.fillStyle="node"===c("labelHoverBGColor")?a.color||c("defaultNodeColor"):c("defaultHoverLabelBGColor"),a.label&&c("labelHoverShadow")&&(b.shadowOffsetX=0,b.shadowOffsetY=0,b.shadowBlur=8,b.shadowColor=c("labelHoverShadowColor")),a.label&&"string"==typeof a.label&&(d=Math.round(a[j+"x"]-l/2-2),e=Math.round(a[j+"y"]-l/2-2),f=Math.round(b.measureText(a.label).width+l/2+k+7),g=Math.round(l+4),h=Math.round(l/2+2),b.moveTo(d,e+h),b.arcTo(d,e,d+h,e,h),b.lineTo(d+f,e),b.lineTo(d+f,e+g),b.lineTo(d+h,e+g),b.arcTo(d,e+g,d,e+g-h,h),b.lineTo(d,e+h),b.closePath(),b.fill(),b.shadowOffsetX=0,b.shadowOffsetY=0,b.shadowBlur=0),c("borderSize")>0&&(b.beginPath(),b.fillStyle="node"===c("nodeBorderColor")?a.color||c("defaultNodeColor"):c("defaultNodeBorderColor"),b.arc(a[j+"x"],a[j+"y"],k+c("borderSize"),0,2*Math.PI,!0),b.closePath(),b.fill());var m=sigma.canvas.nodes[a.type]||sigma.canvas.nodes.def;m(a,b,c),a.label&&"string"==typeof a.label&&(b.fillStyle="node"===c("labelHoverColor")?a.color||c("defaultNodeColor"):c("defaultLabelHoverColor"),b.fillText(a.label,Math.round(a[j+"x"]+k+3),Math.round(a[j+"y"]+l/3)))}}.call(this),function(){"use strict";sigma.utils.pkg("sigma.canvas.nodes"),sigma.canvas.nodes.def=function(a,b,c){var d=c("prefix")||"";b.fillStyle=a.color||c("defaultNodeColor"),b.beginPath(),b.arc(a[d+"x"],a[d+"y"],a[d+"size"],0,2*Math.PI,!0),b.closePath(),b.fill()}}(),function(){"use strict";sigma.utils.pkg("sigma.canvas.edges"),sigma.canvas.edges.def=function(a,b,c,d,e){var f=a.color,g=e("prefix")||"",h=a[g+"size"]||1,i=e("edgeColor"),j=e("defaultNodeColor"),k=e("defaultEdgeColor");if(!f)switch(i){case"source":f=b.color||j;break;case"target":f=c.color||j;break;default:f=k}d.strokeStyle=f,d.lineWidth=h,d.beginPath(),d.moveTo(b[g+"x"],b[g+"y"]),d.lineTo(c[g+"x"],c[g+"y"]),d.stroke()}}(),function(){"use strict";sigma.utils.pkg("sigma.canvas.edges"),sigma.canvas.edges.curve=function(a,b,c,d,e){var f=a.color,g=e("prefix")||"",h=a[g+"size"]||1,i=e("edgeColor"),j=e("defaultNodeColor"),k=e("defaultEdgeColor"),l={},m=b[g+"size"],n=b[g+"x"],o=b[g+"y"],p=c[g+"x"],q=c[g+"y"];if(l=b.id===c.id?sigma.utils.getSelfLoopControlPoints(n,o,m):sigma.utils.getQuadraticControlPoint(n,o,p,q),!f)switch(i){case"source":f=b.color||j;break;case"target":f=c.color||j;break;default:f=k}d.strokeStyle=f,d.lineWidth=h,d.beginPath(),d.moveTo(n,o),b.id===c.id?d.bezierCurveTo(l.x1,l.y1,l.x2,l.y2,p,q):d.quadraticCurveTo(l.x,l.y,p,q),d.stroke()}}(),function(){"use strict";sigma.utils.pkg("sigma.canvas.edges"),sigma.canvas.edges.arrow=function(a,b,c,d,e){var f=a.color,g=e("prefix")||"",h=e("edgeColor"),i=e("defaultNodeColor"),j=e("defaultEdgeColor"),k=a[g+"size"]||1,l=c[g+"size"],m=b[g+"x"],n=b[g+"y"],o=c[g+"x"],p=c[g+"y"],q=Math.max(2.5*k,e("minArrowSize")),r=Math.sqrt(Math.pow(o-m,2)+Math.pow(p-n,2)),s=m+(o-m)*(r-q-l)/r,t=n+(p-n)*(r-q-l)/r,u=(o-m)*q/r,v=(p-n)*q/r;if(!f)switch(h){case"source":f=b.color||i;break;case"target":f=c.color||i;break;default:f=j}d.strokeStyle=f,d.lineWidth=k,d.beginPath(),d.moveTo(m,n),d.lineTo(s,t),d.stroke(),d.fillStyle=f,d.beginPath(),d.moveTo(s+u,t+v),d.lineTo(s+.6*v,t-.6*u),d.lineTo(s-.6*v,t+.6*u),d.lineTo(s+u,t+v),d.closePath(),d.fill()}}(),function(){"use strict";sigma.utils.pkg("sigma.canvas.edges"),sigma.canvas.edges.curvedArrow=function(a,b,c,d,e){var f,g,h,i,j,k=a.color,l=e("prefix")||"",m=e("edgeColor"),n=e("defaultNodeColor"),o=e("defaultEdgeColor"),p={},q=a[l+"size"]||1,r=c[l+"size"],s=b[l+"x"],t=b[l+"y"],u=c[l+"x"],v=c[l+"y"],w=Math.max(2.5*q,e("minArrowSize"));if(p=b.id===c.id?sigma.utils.getSelfLoopControlPoints(s,t,r):sigma.utils.getQuadraticControlPoint(s,t,u,v),b.id===c.id?(f=Math.sqrt(Math.pow(u-p.x1,2)+Math.pow(v-p.y1,2)),g=p.x1+(u-p.x1)*(f-w-r)/f,h=p.y1+(v-p.y1)*(f-w-r)/f,i=(u-p.x1)*w/f,j=(v-p.y1)*w/f):(f=Math.sqrt(Math.pow(u-p.x,2)+Math.pow(v-p.y,2)),g=p.x+(u-p.x)*(f-w-r)/f,h=p.y+(v-p.y)*(f-w-r)/f,i=(u-p.x)*w/f,j=(v-p.y)*w/f),!k)switch(m){case"source":k=b.color||n;break;case"target":k=c.color||n;break;default:k=o}d.strokeStyle=k,d.lineWidth=q,d.beginPath(),d.moveTo(s,t),b.id===c.id?d.bezierCurveTo(p.x2,p.y2,p.x1,p.y1,g,h):d.quadraticCurveTo(p.x,p.y,g,h),d.stroke(),d.fillStyle=k,d.beginPath(),d.moveTo(g+i,h+j),d.lineTo(g+.6*j,h-.6*i),d.lineTo(g-.6*j,h+.6*i),d.lineTo(g+i,h+j),d.closePath(),d.fill()}}(),function(){"use 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f=a.color,g=e("prefix")||"",h=e("edgeHoverSizeRatio")*(a[g+"size"]||1),i=e("edgeColor"),j=e("defaultNodeColor"),k=e("defaultEdgeColor"),l={},m=b[g+"size"],n=b[g+"x"],o=b[g+"y"],p=c[g+"x"],q=c[g+"y"];if(l=b.id===c.id?sigma.utils.getSelfLoopControlPoints(n,o,m):sigma.utils.getQuadraticControlPoint(n,o,p,q),!f)switch(i){case"source":f=b.color||j;break;case"target":f=c.color||j;break;default:f=k}f="edge"===e("edgeHoverColor")?a.hover_color||f:a.hover_color||e("defaultEdgeHoverColor")||f,d.strokeStyle=f,d.lineWidth=h,d.beginPath(),d.moveTo(n,o),b.id===c.id?d.bezierCurveTo(l.x1,l.y1,l.x2,l.y2,p,q):d.quadraticCurveTo(l.x,l.y,p,q),d.stroke()}}(),function(){"use strict";sigma.utils.pkg("sigma.canvas.edgehovers"),sigma.canvas.edgehovers.arrow=function(a,b,c,d,e){var f=a.color,g=e("prefix")||"",h=e("edgeColor"),i=e("defaultNodeColor"),j=e("defaultEdgeColor"),k=a[g+"size"]||1,l=c[g+"size"],m=b[g+"x"],n=b[g+"y"],o=c[g+"x"],p=c[g+"y"];
5 | k=a.hover?e("edgeHoverSizeRatio")*k:k;var q=2.5*k,r=Math.sqrt(Math.pow(o-m,2)+Math.pow(p-n,2)),s=m+(o-m)*(r-q-l)/r,t=n+(p-n)*(r-q-l)/r,u=(o-m)*q/r,v=(p-n)*q/r;if(!f)switch(h){case"source":f=b.color||i;break;case"target":f=c.color||i;break;default:f=j}f="edge"===e("edgeHoverColor")?a.hover_color||f:a.hover_color||e("defaultEdgeHoverColor")||f,d.strokeStyle=f,d.lineWidth=k,d.beginPath(),d.moveTo(m,n),d.lineTo(s,t),d.stroke(),d.fillStyle=f,d.beginPath(),d.moveTo(s+u,t+v),d.lineTo(s+.6*v,t-.6*u),d.lineTo(s-.6*v,t+.6*u),d.lineTo(s+u,t+v),d.closePath(),d.fill()}}(),function(){"use strict";sigma.utils.pkg("sigma.canvas.edgehovers"),sigma.canvas.edgehovers.curvedArrow=function(a,b,c,d,e){var f,g,h,i,j,k,l=a.color,m=e("prefix")||"",n=e("edgeColor"),o=e("defaultNodeColor"),p=e("defaultEdgeColor"),q={},r=e("edgeHoverSizeRatio")*(a[m+"size"]||1),s=c[m+"size"],t=b[m+"x"],u=b[m+"y"],v=c[m+"x"],w=c[m+"y"];if(q=b.id===c.id?sigma.utils.getSelfLoopControlPoints(t,u,s):sigma.utils.getQuadraticControlPoint(t,u,v,w),b.id===c.id?(f=Math.sqrt(Math.pow(v-q.x1,2)+Math.pow(w-q.y1,2)),g=2.5*r,h=q.x1+(v-q.x1)*(f-g-s)/f,i=q.y1+(w-q.y1)*(f-g-s)/f,j=(v-q.x1)*g/f,k=(w-q.y1)*g/f):(f=Math.sqrt(Math.pow(v-q.x,2)+Math.pow(w-q.y,2)),g=2.5*r,h=q.x+(v-q.x)*(f-g-s)/f,i=q.y+(w-q.y)*(f-g-s)/f,j=(v-q.x)*g/f,k=(w-q.y)*g/f),!l)switch(n){case"source":l=b.color||o;break;case"target":l=c.color||o;break;default:l=p}l="edge"===e("edgeHoverColor")?a.hover_color||l:a.hover_color||e("defaultEdgeHoverColor")||l,d.strokeStyle=l,d.lineWidth=r,d.beginPath(),d.moveTo(t,u),b.id===c.id?d.bezierCurveTo(q.x2,q.y2,q.x1,q.y1,h,i):d.quadraticCurveTo(q.x,q.y,h,i),d.stroke(),d.fillStyle=l,d.beginPath(),d.moveTo(h+j,i+k),d.lineTo(h+.6*k,i-.6*j),d.lineTo(h-.6*k,i+.6*j),d.lineTo(h+j,i+k),d.closePath(),d.fill()}}(),function(){"use 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b.setAttributeNS(null,"cx",a[d+"x"]),b.setAttributeNS(null,"cy",a[d+"y"]),b.setAttributeNS(null,"r",a[d+"size"]),c("freeStyle")||b.setAttributeNS(null,"fill",a.color||c("defaultNodeColor")),b.style.display="",this}}}(),function(){"use strict";sigma.utils.pkg("sigma.svg.edges"),sigma.svg.edges.def={create:function(a,b,c,d){var e=a.color,f=(d("prefix")||"",d("edgeColor")),g=d("defaultNodeColor"),h=d("defaultEdgeColor");if(!e)switch(f){case"source":e=b.color||g;break;case"target":e=c.color||g;break;default:e=h}var i=document.createElementNS(d("xmlns"),"line");return i.setAttributeNS(null,"data-edge-id",a.id),i.setAttributeNS(null,"class",d("classPrefix")+"-edge"),i.setAttributeNS(null,"stroke",e),i},update:function(a,b,c,d,e){var f=e("prefix")||"";return b.setAttributeNS(null,"stroke-width",a[f+"size"]||1),b.setAttributeNS(null,"x1",c[f+"x"]),b.setAttributeNS(null,"y1",c[f+"y"]),b.setAttributeNS(null,"x2",d[f+"x"]),b.setAttributeNS(null,"y2",d[f+"y"]),b.style.display="",this}}}(),function(){"use strict";sigma.utils.pkg("sigma.svg.edges"),sigma.svg.edges.curve={create:function(a,b,c,d){var e=a.color,f=(d("prefix")||"",d("edgeColor")),g=d("defaultNodeColor"),h=d("defaultEdgeColor");if(!e)switch(f){case"source":e=b.color||g;break;case"target":e=c.color||g;break;default:e=h}var i=document.createElementNS(d("xmlns"),"path");return i.setAttributeNS(null,"data-edge-id",a.id),i.setAttributeNS(null,"class",d("classPrefix")+"-edge"),i.setAttributeNS(null,"stroke",e),i},update:function(a,b,c,d,e){var f=e("prefix")||"";b.setAttributeNS(null,"stroke-width",a[f+"size"]||1);var g=(c[f+"x"]+d[f+"x"])/2+(d[f+"y"]-c[f+"y"])/4,h=(c[f+"y"]+d[f+"y"])/2+(c[f+"x"]-d[f+"x"])/4,i="M"+c[f+"x"]+","+c[f+"y"]+" Q"+g+","+h+" "+d[f+"x"]+","+d[f+"y"];return b.setAttributeNS(null,"d",i),b.setAttributeNS(null,"fill","none"),b.style.display="",this}}}(),function(){"use strict";if("undefined"==typeof sigma)throw"sigma is not declared";sigma.utils.pkg("sigma.svg.labels"),sigma.svg.labels.def={create:function(a,b){var c=b("prefix")||"",d=a[c+"size"],e=document.createElementNS(b("xmlns"),"text"),f="fixed"===b("labelSize")?b("defaultLabelSize"):b("labelSizeRatio")*d,g="node"===b("labelColor")?a.color||b("defaultNodeColor"):b("defaultLabelColor");return e.setAttributeNS(null,"data-label-target",a.id),e.setAttributeNS(null,"class",b("classPrefix")+"-label"),e.setAttributeNS(null,"font-size",f),e.setAttributeNS(null,"font-family",b("font")),e.setAttributeNS(null,"fill",g),e.innerHTML=a.label,e.textContent=a.label,e},update:function(a,b,c){var d=c("prefix")||"",e=a[d+"size"],f="fixed"===c("labelSize")?c("defaultLabelSize"):c("labelSizeRatio")*e;return!c("forceLabels")&&e<c("labelThreshold")||"string"!=typeof a.label?void 0:(b.setAttributeNS(null,"x",Math.round(a[d+"x"]+e+3)),b.setAttributeNS(null,"y",Math.round(a[d+"y"]+f/3)),b.style.display="",this)}}}.call(this),function(){"use strict";if("undefined"==typeof sigma)throw"sigma is not declared";sigma.utils.pkg("sigma.svg.hovers"),sigma.svg.hovers.def={create:function(a,b,c,d){var e,f,g,h,i,j=(d("hoverFontStyle")||d("fontStyle"),d("prefix")||""),k=a[j+"size"],l="fixed"===d("labelSize")?d("defaultLabelSize"):d("labelSizeRatio")*k,m="node"===d("labelHoverColor")?a.color||d("defaultNodeColor"):d("defaultLabelHoverColor"),n=document.createElementNS(d("xmlns"),"g"),o=document.createElementNS(d("xmlns"),"rect"),p=document.createElementNS(d("xmlns"),"circle"),q=document.createElementNS(d("xmlns"),"text");return n.setAttributeNS(null,"class",d("classPrefix")+"-hover"),n.setAttributeNS(null,"data-node-id",a.id),"string"==typeof a.label&&(q.innerHTML=a.label,q.textContent=a.label,q.setAttributeNS(null,"class",d("classPrefix")+"-hover-label"),q.setAttributeNS(null,"font-size",l),q.setAttributeNS(null,"font-family",d("font")),q.setAttributeNS(null,"fill",m),q.setAttributeNS(null,"x",Math.round(a[j+"x"]+k+3)),q.setAttributeNS(null,"y",Math.round(a[j+"y"]+l/3)),e=Math.round(a[j+"x"]-l/2-2),f=Math.round(a[j+"y"]-l/2-2),g=Math.round(c.measureText(a.label).width+l/2+k+9),h=Math.round(l+4),i=Math.round(l/2+2),p.setAttributeNS(null,"class",d("classPrefix")+"-hover-area"),p.setAttributeNS(null,"fill","#fff"),p.setAttributeNS(null,"cx",a[j+"x"]),p.setAttributeNS(null,"cy",a[j+"y"]),p.setAttributeNS(null,"r",i),o.setAttributeNS(null,"class",d("classPrefix")+"-hover-area"),o.setAttributeNS(null,"fill","#fff"),o.setAttributeNS(null,"x",a[j+"x"]+i/4),o.setAttributeNS(null,"y",a[j+"y"]-i),o.setAttributeNS(null,"width",g),o.setAttributeNS(null,"height",h)),n.appendChild(p),n.appendChild(o),n.appendChild(q),n.appendChild(b),n}}}.call(this),function(){"use strict";if("undefined"==typeof sigma)throw"sigma is not declared";sigma.utils.pkg("sigma.middlewares"),sigma.utils.pkg("sigma.utils"),sigma.middlewares.rescale=function(a,b,c){var d,e,f,g,h,i,j,k,l=this.graph.nodes(),m=this.graph.edges(),n=this.settings.embedObjects(c||{}),o=n("bounds")||sigma.utils.getBoundaries(this.graph,a,!0),p=o.minX,q=o.minY,r=o.maxX,s=o.maxY,t=o.sizeMax,u=o.weightMax,v=n("width")||1,w=n("height")||1,x=n("autoRescale"),y={nodePosition:1,nodeSize:1,edgeSize:1};for(x instanceof Array||(x=["nodePosition","nodeSize","edgeSize"]),d=0,e=x.length;e>d;d++)if(!y[x[d]])throw new Error('The rescale setting "'+x[d]+'" is not recognized.');var z=~x.indexOf("nodePosition"),A=~x.indexOf("nodeSize"),B=~x.indexOf("edgeSize");for(j="outside"===n("scalingMode")?Math.max(v/Math.max(r-p,1),w/Math.max(s-q,1)):Math.min(v/Math.max(r-p,1),w/Math.max(s-q,1)),k=(n("rescaleIgnoreSize")?0:(n("maxNodeSize")||t)/j)+(n("sideMargin")||0),r+=k,p-=k,s+=k,q-=k,j="outside"===n("scalingMode")?Math.max(v/Math.max(r-p,1),w/Math.max(s-q,1)):Math.min(v/Math.max(r-p,1),w/Math.max(s-q,1)),n("maxNodeSize")||n("minNodeSize")?n("maxNodeSize")===n("minNodeSize")?(f=0,g=+n("maxNodeSize")):(f=(n("maxNodeSize")-n("minNodeSize"))/t,g=+n("minNodeSize")):(f=1,g=0),n("maxEdgeSize")||n("minEdgeSize")?n("maxEdgeSize")===n("minEdgeSize")?(h=0,i=+n("minEdgeSize")):(h=(n("maxEdgeSize")-n("minEdgeSize"))/u,i=+n("minEdgeSize")):(h=1,i=0),d=0,e=m.length;e>d;d++)m[d][b+"size"]=m[d][a+"size"]*(B?h:1)+(B?i:0);for(d=0,e=l.length;e>d;d++)l[d][b+"size"]=l[d][a+"size"]*(A?f:1)+(A?g:0),l[d][b+"x"]=(l[d][a+"x"]-(r+p)/2)*(z?j:1),l[d][b+"y"]=(l[d][a+"y"]-(s+q)/2)*(z?j:1)},sigma.utils.getBoundaries=function(a,b,c){var d,e,f=a.edges(),g=a.nodes(),h=-1/0,i=-1/0,j=1/0,k=1/0,l=-1/0,m=-1/0;if(c)for(d=0,e=f.length;e>d;d++)h=Math.max(f[d][b+"size"],h);for(d=0,e=g.length;e>d;d++)i=Math.max(g[d][b+"size"],i),l=Math.max(g[d][b+"x"],l),j=Math.min(g[d][b+"x"],j),m=Math.max(g[d][b+"y"],m),k=Math.min(g[d][b+"y"],k);return h=h||1,i=i||1,{weightMax:h,sizeMax:i,minX:j,minY:k,maxX:l,maxY:m}}}.call(this),function(){"use strict";if("undefined"==typeof sigma)throw"sigma is not declared";sigma.utils.pkg("sigma.middlewares"),sigma.middlewares.copy=function(a,b){var c,d,e;if(b+""!=a+""){for(e=this.graph.nodes(),c=0,d=e.length;d>c;c++)e[c][b+"x"]=e[c][a+"x"],e[c][b+"y"]=e[c][a+"y"],e[c][b+"size"]=e[c][a+"size"];for(e=this.graph.edges(),c=0,d=e.length;d>c;c++)e[c][b+"size"]=e[c][a+"size"]}}}.call(this),function(a){"use strict";if("undefined"==typeof sigma)throw"sigma is not declared";sigma.utils.pkg("sigma.misc.animation.running");var b=function(){var a=0;return function(){return""+ ++a}}();sigma.misc.animation.camera=function(c,d,e){if(!(c instanceof sigma.classes.camera&&"object"==typeof d&&d))throw"animation.camera: Wrong arguments.";if("number"!=typeof d.x&&"number"!=typeof d.y&&"number"!=typeof d.ratio&&"number"!=typeof d.angle)throw"There must be at least one valid coordinate in the given val.";var f,g,h,i,j,k,l=e||{},m=sigma.utils.dateNow();return k={x:c.x,y:c.y,ratio:c.ratio,angle:c.angle},j=l.duration,i="function"!=typeof l.easing?sigma.utils.easings[l.easing||"quadraticInOut"]:l.easing,f=function(){var b,e=l.duration?(sigma.utils.dateNow()-m)/l.duration:1;e>=1?(c.isAnimated=!1,c.goTo({x:d.x!==a?d.x:k.x,y:d.y!==a?d.y:k.y,ratio:d.ratio!==a?d.ratio:k.ratio,angle:d.angle!==a?d.angle:k.angle}),cancelAnimationFrame(g),delete sigma.misc.animation.running[g],"function"==typeof l.onComplete&&l.onComplete()):(b=i(e),c.isAnimated=!0,c.goTo({x:d.x!==a?k.x+(d.x-k.x)*b:k.x,y:d.y!==a?k.y+(d.y-k.y)*b:k.y,ratio:d.ratio!==a?k.ratio+(d.ratio-k.ratio)*b:k.ratio,angle:d.angle!==a?k.angle+(d.angle-k.angle)*b:k.angle}),"function"==typeof l.onNewFrame&&l.onNewFrame(),h.frameId=requestAnimationFrame(f))},g=b(),h={frameId:requestAnimationFrame(f),target:c,type:"camera",options:l,fn:f},sigma.misc.animation.running[g]=h,g},sigma.misc.animation.kill=function(a){if(1!==arguments.length||"number"!=typeof a)throw"animation.kill: Wrong arguments.";var b=sigma.misc.animation.running[a];return b&&(cancelAnimationFrame(a),delete sigma.misc.animation.running[b.frameId],"camera"===b.type&&(b.target.isAnimated=!1),"function"==typeof(b.options||{}).onComplete&&b.options.onComplete()),this},sigma.misc.animation.killAll=function(a){var b,c,d=0,e="string"==typeof a?a:null,f="object"==typeof a?a:null,g=sigma.misc.animation.running;for(c in g)e&&g[c].type!==e||f&&g[c].target!==f||(b=sigma.misc.animation.running[c],cancelAnimationFrame(b.frameId),delete sigma.misc.animation.running[c],"camera"===b.type&&(b.target.isAnimated=!1),d++,"function"==typeof(b.options||{}).onComplete&&b.options.onComplete());return d},sigma.misc.animation.has=function(a){var b,c="string"==typeof a?a:null,d="object"==typeof a?a:null,e=sigma.misc.animation.running;for(b in e)if(!(c&&e[b].type!==c||d&&e[b].target!==d))return!0;return!1}}.call(this),function(a){"use strict";if("undefined"==typeof sigma)throw"sigma is not declared";sigma.utils.pkg("sigma.misc"),sigma.misc.bindEvents=function(b){function c(a){a&&(h="x"in a.data?a.data.x:h,i="y"in a.data?a.data.y:i);var 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z=j.camera.quadtree.area(j.camera.getRectangle(j.width,j.height));for(l=z,f=0,k=l.length;k>f;f++)t[l[f].id]=l[f]}if(j.camera.edgequadtree!==a&&(y=j.camera.edgequadtree.point(x.x,x.y)),y.length)for(f=0,k=y.length;k>f;f++)m=y[f],o=j.graph.nodes(m.source),p=j.graph.nodes(m.target),n=m[b+"size"]||m["read_"+b+"size"],!m.hidden&&!o.hidden&&!p.hidden&&(!e||t[m.source]||t[m.target])&&sigma.utils.getDistance(o[b+"x"],o[b+"y"],v,w)>o[b+"size"]&&sigma.utils.getDistance(p[b+"x"],p[b+"y"],v,w)>p[b+"size"]&&("curve"==m.type||"curvedArrow"==m.type?o.id===p.id?(q=sigma.utils.getSelfLoopControlPoints(o[b+"x"],o[b+"y"],o[b+"size"]),sigma.utils.isPointOnBezierCurve(v,w,o[b+"x"],o[b+"y"],p[b+"x"],p[b+"y"],q.x1,q.y1,q.x2,q.y2,Math.max(n,s))&&d(u,m)):(q=sigma.utils.getQuadraticControlPoint(o[b+"x"],o[b+"y"],p[b+"x"],p[b+"y"]),sigma.utils.isPointOnQuadraticCurve(v,w,o[b+"x"],o[b+"y"],p[b+"x"],p[b+"y"],q.x,q.y,Math.max(n,s))&&d(u,m)):sigma.utils.isPointOnSegment(v,w,o[b+"x"],o[b+"y"],p[b+"x"],p[b+"y"],Math.max(n,s))&&d(u,m));return u}function e(a){function b(a){j.settings("eventsEnabled")&&(j.dispatchEvent("click",a.data),i=c(a),k=d(a),i.length?(j.dispatchEvent("clickNode",{node:i[0],captor:a.data}),j.dispatchEvent("clickNodes",{node:i,captor:a.data})):k.length?(j.dispatchEvent("clickEdge",{edge:k[0],captor:a.data}),j.dispatchEvent("clickEdges",{edge:k,captor:a.data})):j.dispatchEvent("clickStage",{captor:a.data}))}function e(a){j.settings("eventsEnabled")&&(j.dispatchEvent("doubleClick",a.data),i=c(a),k=d(a),i.length?(j.dispatchEvent("doubleClickNode",{node:i[0],captor:a.data}),j.dispatchEvent("doubleClickNodes",{node:i,captor:a.data})):k.length?(j.dispatchEvent("doubleClickEdge",{edge:k[0],captor:a.data}),j.dispatchEvent("doubleClickEdges",{edge:k,captor:a.data})):j.dispatchEvent("doubleClickStage",{captor:a.data}))}function f(a){j.settings("eventsEnabled")&&(j.dispatchEvent("rightClick",a.data),i=c(a),k=d(a),i.length?(j.dispatchEvent("rightClickNode",{node:i[0],captor:a.data}),j.dispatchEvent("rightClickNodes",{node:i,captor:a.data})):k.length?(j.dispatchEvent("rightClickEdge",{edge:k[0],captor:a.data}),j.dispatchEvent("rightClickEdges",{edge:k,captor:a.data})):j.dispatchEvent("rightClickStage",{captor:a.data}))}function g(a){if(j.settings("eventsEnabled")){var b,c,d,e,f=[],g=[];for(b in l)f.push(l[b]);for(l={},c=0,d=f.length;d>c;c++)j.dispatchEvent("outNode",{node:f[c],captor:a.data});for(f.length&&j.dispatchEvent("outNodes",{nodes:f,captor:a.data}),m={},c=0,e=g.length;e>c;c++)j.dispatchEvent("outEdge",{edge:g[c],captor:a.data});g.length&&j.dispatchEvent("outEdges",{edges:g,captor:a.data})}}function h(a){if(j.settings("eventsEnabled")){i=c(a),k=d(a);var b,e,f,g,h=[],n=[],o={},p=i.length,q=[],r=[],s={},t=k.length;for(b=0;p>b;b++)f=i[b],o[f.id]=f,l[f.id]||(n.push(f),l[f.id]=f);for(e in l)o[e]||(h.push(l[e]),delete l[e]);for(b=0,p=n.length;p>b;b++)j.dispatchEvent("overNode",{node:n[b],captor:a.data});for(b=0,p=h.length;p>b;b++)j.dispatchEvent("outNode",{node:h[b],captor:a.data});for(n.length&&j.dispatchEvent("overNodes",{nodes:n,captor:a.data}),h.length&&j.dispatchEvent("outNodes",{nodes:h,captor:a.data}),b=0;t>b;b++)g=k[b],s[g.id]=g,m[g.id]||(r.push(g),m[g.id]=g);for(e in m)s[e]||(q.push(m[e]),delete m[e]);for(b=0,t=r.length;t>b;b++)j.dispatchEvent("overEdge",{edge:r[b],captor:a.data});for(b=0,t=q.length;t>b;b++)j.dispatchEvent("outEdge",{edge:q[b],captor:a.data});r.length&&j.dispatchEvent("overEdges",{edges:r,captor:a.data}),q.length&&j.dispatchEvent("outEdges",{edges:q,captor:a.data})}}var i,k,l={},m={};a.bind("click",b),a.bind("mousedown",h),a.bind("mouseup",h),a.bind("mousemove",h),a.bind("mouseout",g),a.bind("doubleclick",e),a.bind("rightclick",f),j.bind("render",h)}var f,g,h,i,j=this;for(f=0,g=this.captors.length;g>f;f++)e(this.captors[f])}}.call(this),function(){"use strict";if("undefined"==typeof sigma)throw"sigma is not declared";sigma.utils.pkg("sigma.misc"),sigma.misc.bindDOMEvents=function(a){function b(a){this.attr=function(b){return a.getAttributeNS(null,b)},this.tag=a.tagName,this.class=this.attr("class"),this.id=this.attr("id"),this.isNode=function(){return!!~this.class.indexOf(g.settings("classPrefix")+"-node")},this.isEdge=function(){return!!~this.class.indexOf(g.settings("classPrefix")+"-edge")},this.isHover=function(){return!!~this.class.indexOf(g.settings("classPrefix")+"-hover")}}function c(a){if(g.settings("eventsEnabled")){g.dispatchEvent("click",a);var c=new b(a.target);c.isNode()?g.dispatchEvent("clickNode",{node:h.nodes(c.attr("data-node-id"))}):g.dispatchEvent("clickStage"),a.preventDefault(),a.stopPropagation()}}function d(a){if(g.settings("eventsEnabled")){g.dispatchEvent("doubleClick",a);var c=new b(a.target);c.isNode()?g.dispatchEvent("doubleClickNode",{node:h.nodes(c.attr("data-node-id"))}):g.dispatchEvent("doubleClickStage"),a.preventDefault(),a.stopPropagation()}}function e(a){var c=a.toElement||a.target;if(g.settings("eventsEnabled")&&c){var d=new b(c);if(d.isNode())g.dispatchEvent("overNode",{node:h.nodes(d.attr("data-node-id"))});else if(d.isEdge()){var e=h.edges(d.attr("data-edge-id"));g.dispatchEvent("overEdge",{edge:e,source:h.nodes(e.source),target:h.nodes(e.target)})}}}function f(a){var c=a.fromElement||a.originalTarget;if(g.settings("eventsEnabled")){var d=new b(c);if(d.isNode())g.dispatchEvent("outNode",{node:h.nodes(d.attr("data-node-id"))});else if(d.isEdge()){var e=h.edges(d.attr("data-edge-id"));g.dispatchEvent("outEdge",{edge:e,source:h.nodes(e.source),target:h.nodes(e.target)})}}}var g=this,h=this.graph;a.addEventListener("click",c,!1),sigma.utils.doubleClick(a,"click",d),a.addEventListener("touchstart",c,!1),sigma.utils.doubleClick(a,"touchstart",d),a.addEventListener("mouseover",e,!0),a.addEventListener("mouseout",f,!0)}}.call(this),function(){"use strict";if("undefined"==typeof sigma)throw"sigma is not declared";sigma.utils.pkg("sigma.misc"),sigma.misc.drawHovers=function(a){function b(){var b,f,g,h,i,j=c.contexts.hover.canvas,k=c.settings("defaultNodeType"),l=c.settings("defaultEdgeType"),m=sigma.canvas.hovers,n=sigma.canvas.edgehovers,o=sigma.canvas.extremities,p=c.settings.embedObjects({prefix:a});if(c.contexts.hover.clearRect(0,0,j.width,j.height),p("enableHovering")&&p("singleHover")&&Object.keys(d).length&&(h=d[Object.keys(d)[0]],(m[h.type]||m[k]||m.def)(h,c.contexts.hover,p)),p("enableHovering")&&!p("singleHover"))for(b in d)(m[d[b].type]||m[k]||m.def)(d[b],c.contexts.hover,p);if(p("enableEdgeHovering")&&p("singleHover")&&Object.keys(e).length&&(i=e[Object.keys(e)[0]],f=c.graph.nodes(i.source),g=c.graph.nodes(i.target),i.hidden||((n[i.type]||n[l]||n.def)(i,f,g,c.contexts.hover,p),p("edgeHoverExtremities")?(o[i.type]||o.def)(i,f,g,c.contexts.hover,p):((sigma.canvas.nodes[f.type]||sigma.canvas.nodes.def)(f,c.contexts.hover,p),(sigma.canvas.nodes[g.type]||sigma.canvas.nodes.def)(g,c.contexts.hover,p)))),p("enableEdgeHovering")&&!p("singleHover"))for(b in e)i=e[b],f=c.graph.nodes(i.source),g=c.graph.nodes(i.target),i.hidden||((n[i.type]||n[l]||n.def)(i,f,g,c.contexts.hover,p),p("edgeHoverExtremities")?(o[i.type]||o.def)(i,f,g,c.contexts.hover,p):((sigma.canvas.nodes[f.type]||sigma.canvas.nodes.def)(f,c.contexts.hover,p),(sigma.canvas.nodes[g.type]||sigma.canvas.nodes.def)(g,c.contexts.hover,p)))}var c=this,d={},e={};this.bind("overNode",function(a){var c=a.data.node;c.hidden||(d[c.id]=c,b())}),this.bind("outNode",function(a){delete d[a.data.node.id],b()}),this.bind("overEdge",function(a){var c=a.data.edge;c.hidden||(e[c.id]=c,b())}),this.bind("outEdge",function(a){delete e[a.data.edge.id],b()}),this.bind("render",function(){b()})}}.call(this);


--------------------------------------------------------------------------------
/jslib/knowledge.js:
--------------------------------------------------------------------------------
  1 | /**
  2 |  * Knowledge graph query
  3 |  * @author TaoPR (github.com/starcolon)
  4 |  */
  5 | 
  6 | var Knw = {}
  7 | var ODatabase = require('orientjs').ODatabase;
  8 | var Promise = require('bluebird');
  9 | 
 10 | /**
 11 |  * Make a connection to the OrientDB
 12 |  * @param {string} database name
 13 |  * @param {string} username
 14 |  * @param {string} password
 15 |  * @param {bool} force reconnect?
 16 |  * @return {Promise} which wraps a [db] variable
 17 |  */
 18 | Knw.connect = function(db,usrname,psw,reconnect=false){
 19 |   // Do nothing if connected
 20 |   if (Knw.db && !reconnect)
 21 |     return Promise.resolve(Knw.db);
 22 | 
 23 |   Knw.db = new ODatabase({
 24 |     host:       'localhost',
 25 |     port:       2424,
 26 |     username:   usrname,
 27 |     password:   psw,
 28 |     name:       db
 29 |   });
 30 | 
 31 |   return Knw.db.open();
 32 | }
 33 | 
 34 | 
 35 | /**
 36 |  * List all vertices where condition is met
 37 |  * @param {Object} must satisfy OrientDB query format
 38 |  */
 39 | Knw.nodes = function(condition){
 40 |   if (condition)
 41 |     return Knw.db.select().from('V').where(condition).all();
 42 |   return Knw.db.select().from('V').all();
 43 | }
 44 | 
 45 | Knw.allTopics = () => Knw.nodes({'@class': 'TOPIC'})
 46 | Knw.allKeywords = () => Knw.nodes({'@class': 'KEYWORD'})
 47 | 
 48 | /**
 49 |  * List all edges where condition is met
 50 |  * @param {Object} must satisfy OrientDB query format
 51 |  */
 52 | Knw.edges = function(condition){
 53 |   if (condition)
 54 |     return Knw.db.select().from('E').where(condition).all();
 55 |   return Knw.db.select().from('E').all();
 56 | }
 57 | 
 58 | /**
 59 |  * List all outbound edges from the specified node
 60 |  * @param {Object} node object
 61 |  * @return {Promise}
 62 |  */
 63 | Knw.getOutE = function(limit){
 64 |   return function(node){
 65 |     var linked = {'out': node.id}
 66 |     var output = (node.type=='TOPIC') ? 
 67 |       Knw.db.select().from('has').where(linked) :
 68 |       Knw.db.select().from('rel').where(linked);
 69 | 
 70 |     if (limit){
 71 |       return output.limit(limit).all();
 72 |     }
 73 |     else
 74 |       return output.all();
 75 |   }
 76 | }
 77 | 
 78 | /**
 79 |  * List all outbound index edges from the specified node
 80 |  * @param {Object} node object
 81 |  * @return {Promise}
 82 |  */
 83 | Knw.getInboundIndex = function(limit){
 84 |   return function(node){
 85 | 
 86 |     var linked = {'in': node.id}
 87 |     var output = Knw.db
 88 |       .select().from('e')
 89 |       .where(linked)
 90 |       .order('weight desc')
 91 | 
 92 |     if (limit){
 93 |       return output.limit(limit).all();
 94 |     }
 95 |     else
 96 |       return output.all();
 97 |   }
 98 | }
 99 | 
100 | module.exports = Knw;


--------------------------------------------------------------------------------
/package.json:
--------------------------------------------------------------------------------
 1 | {
 2 |   "name": "vor-visualiser",
 3 |   "version": "0.0.1",
 4 |   "description": "A knowledge graph visualiser module",
 5 |   "main": "visualise.js",
 6 |   "directories": {
 7 |     "test": "test"
 8 |   },
 9 |   "scripts": {
10 |     "test": "echo \"Error: no test specified\" && exit 1"
11 |   },
12 |   "repository": {
13 |     "type": "git",
14 |     "url": "git+https://github.com/starcolon/vor-knowledge-graph.git"
15 |   },
16 |   "keywords": [
17 |     "vor",
18 |     "graph",
19 |     "knowledge",
20 |     "visualise",
21 |     "visualize",
22 |     "visualizer",
23 |     "visualiser",
24 |     "render",
25 |     "d3"
26 |   ],
27 |   "author": "TaoPR (StarColon)",
28 |   "license": "ISC",
29 |   "bugs": {
30 |     "url": "https://github.com/starcolon/vor-knowledge-graph/issues"
31 |   },
32 |   "homepage": "https://github.com/starcolon/vor-knowledge-graph#readme",
33 |   "dependencies": {
34 |     "bluebird": "^3.4.1",
35 |     "orientjs": "^2.2.2",
36 |     "sigma": "^1.1.0"
37 |   }
38 | }
39 | 


--------------------------------------------------------------------------------
/pos-patterns:
--------------------------------------------------------------------------------
 1 | NN-NN
 2 | NN
 3 | NNS
 4 | NNP
 5 | JJ
 6 | CD
 7 | JJ-JJ
 8 | NN-JJ-JJ
 9 | NNP-NN
10 | JJ-NN
11 | JJ-NNS
12 | JJ-NN-NN
13 | 


--------------------------------------------------------------------------------
/pos-stopwords:
--------------------------------------------------------------------------------
1 | the
2 | i.e.
3 | e.g.
4 | etc.
5 | such
6 | other
7 | words
8 | way
9 | respectively


--------------------------------------------------------------------------------
/pylib/jobmq/rabbit.py:
--------------------------------------------------------------------------------
 1 | """
 2 | RabbitMQ job pipe
 3 | @author TaoPR (github.com/starcolon)
 4 | """
 5 | 
 6 | import pika
 7 | import signal
 8 | import time
 9 | 
10 | class JobMQ:
11 |   def __init__(server_addr,q):
12 |     conn    = pika.BlockingConnection(pika.ConnectionParameters(server_addr))
13 |     channel = conn.channel()
14 |     channel.queue_declare(queue=q)
15 | 
16 |     self.conn    = conn
17 |     self.channel = channel
18 |     self.q_name  = q
19 | 
20 |   def publish(job):
21 |     self.channel.basic_publish(
22 |       exchange='',
23 |       routing_key=self.q_name,
24 |       body=job)
25 | 
26 |   def next():
27 |     pass
28 | 
29 |   # Message generator
30 | def iter(transformation=lambda x:x):
31 |   TIMEOUT = 5 # seconds
32 |   # Start the awaiting signal
33 |   try:
34 |     def __timeout(signum,frame):
35 |       raise StopIteration
36 | 
37 |     signal.signal(signal.SIGALRM,__timeout)
38 |     signal.alarm(TIMEOUT)
39 |     for methodframe, prop, body in self.channel.consume(self.q_name):
40 |       signal.alarm(0)
41 |       job = transformation(body.decode('utf-8'))
42 |       
43 |       yield job
44 |       self.channel.basic_ack(methodframe.delivery_tag)
45 |       
46 |       # Startover a new timer
47 |       signal.alarm(TIMEOUT)
48 | 
49 |   except StopIteration as e:
50 |     signal.alarm(0) # Cancel the timer
51 |     print(colored('--Timeout, no further message--','yellow'))
52 |     raise
53 |     
54 |   except Exception as e:
55 |     signal.alarm(0) 
56 |     print(colored('--Exception broke the iteration--','yellow'))
57 |     raise
58 | 
59 | def end():
60 |   print('Ending MQ # ',self.q_name)
61 |   try:
62 |     self.conn.close()
63 |   except pika.exceptions.ConnectionClosed:
64 |     print(colored('MQ appeared offline','red'))
65 | 
66 | 


--------------------------------------------------------------------------------
/pylib/knowledge/__init__.py:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/tao-pr/vor-knowledge-graph/7c7140b682796b997871751090ac3a1b3ea36eee/pylib/knowledge/__init__.py


--------------------------------------------------------------------------------
/pylib/knowledge/datasource.py:
--------------------------------------------------------------------------------
 1 | """
 2 | Mining data source access
 3 | @author TaoPR (github.com/starcolon)
 4 | """
 5 | from pymongo import MongoClient
 6 | from pymongo import InsertOne
 7 | 
 8 | class MineDB:
 9 | 
10 |   def __init__(self,host,db,coll):
11 |     addr       = "mongodb://{0}:27017/".format(host)
12 |     self.mongo = MongoClient(addr)
13 |     self.db    = self.mongo[db]
14 |     self.src   = self.db[coll]
15 | 
16 |   def count(self,conditions={}):
17 |     return self.src.count(conditions)
18 | 
19 |   def query(self,conditions={},field=None,skip=0):
20 |     query = self.src.find(conditions) if skip==0 else self.src.find(filter=conditions,skip=skip)
21 |     for n in query:
22 |       # No field name specified, generate the entire record
23 |       if field is None:
24 |         yield n
25 |       # Generate the specified field
26 |       else:
27 |         yield n[field]
28 | 
29 |   def update(self,criteria,updater):
30 |     self.src.update_one(criteria,updater)
31 | 
32 |   def insert(self,record):
33 |     self.src.insert_one(record)
34 | 
35 |   def insert_many(self,records):
36 |     new_records = [InsertOne(r) for r in records]
37 |     self.src.bulk_write(new_records)
38 | 


--------------------------------------------------------------------------------
/pylib/knowledge/graph.py:
--------------------------------------------------------------------------------
  1 | """
  2 | Knowledge graph
  3 | @author TaoPR (github.com/starcolon)
  4 | """
  5 | 
  6 | import pyorient
  7 | from pyorient.exceptions import PyOrientSchemaException
  8 | import numpy as np
  9 | import os.path
 10 | import json
 11 | from termcolor import colored
 12 | 
 13 | class Knowledge:
 14 | 
 15 |   """
 16 |   Create a new knowledge graph connection.
 17 |   The constructor itself is idemponent which means it creates 
 18 |   a new database and fill in the structure if it doesn't exist.
 19 |   Otherwise, it just open the existing connection.
 20 |   """
 21 |   def __init__(self,host,dbname,usrname,psw):
 22 |     self.orient    = pyorient.OrientDB(host,2424)
 23 |     self.__session = self.orient.connect(usrname,psw)
 24 |     if self.orient.db_exists(dbname):
 25 |       print(colored('Connecting OrientDB: {0}'.format(dbname),'green'))
 26 |       self.orient.db_open(dbname,usrname,psw)
 27 |     else:
 28 |       print(colored('Creating OrientDB: {0}'.format(dbname),'magenta'))
 29 |       self.orient.db_create(
 30 |         dbname,
 31 |         pyorient.DB_TYPE_GRAPH
 32 |       )
 33 |       self.orient.db_open(dbname,usrname,psw)
 34 | 
 35 |     # Make sure {Edge} and {Vertex} classes are recognised by the DB
 36 |     self.__prepare_classes()
 37 | 
 38 | 
 39 |   """
 40 |   Create initial classes (Edge + Vertex)
 41 |   """
 42 |   def __prepare_classes(self):
 43 |     try:
 44 |       self.orient.command('create class TOPIC extends V')
 45 |       self.orient.command('create class KEYWORD extends V')
 46 |       self.orient.command('create class REL extends E')
 47 |       self.orient.command('create class HAS extends E')
 48 |     except PyOrientSchemaException as e:
 49 |       print(colored('[WARNING] Preparing graph schema','yellow'))
 50 |       print(colored(e,'yellow'))
 51 |     
 52 | 
 53 |   """
 54 |   Permanently remove all edges and vertices
 55 |   """
 56 |   def clear(self):
 57 |     self.orient.command('delete vertex TOPIC')
 58 |     self.orient.command('delete vertex KEYWORD')
 59 |     self.orient.command('delete edge') # May this be redundant?
 60 |     print(colored('[Graph clearance] done','yellow'))
 61 | 
 62 |   """
 63 |   Add a set of new knowledge links
 64 |   @param {str} topic
 65 |   @param {list} of {str} words
 66 |   @param {float} weight of the link
 67 |   @param {bool} verbose
 68 |   """
 69 |   def add(self,topic,words,weights,verbose):
 70 | 
 71 |     if verbose: print(colored('Adding : ','green'), topic, ' ===> ', words)
 72 | 
 73 |     # Escape some unwanted characters from topic and words
 74 |     unwanted = "'"
 75 |     topic = topic.replace(unwanted," ")
 76 |     words = map(lambda w: w.replace(unwanted, " "), words)
 77 |     weights = iter(weights) if weights is not None else None
 78 | 
 79 |     # Add a new topic if not exist
 80 |     queryTopic = "select from TOPIC where title='{0}'".format(topic)
 81 |     if len(self.orient.command(queryTopic))==0:
 82 |       if verbose: print(colored('New topic added: ','green'), topic)
 83 |       self.orient.command("create vertex TOPIC set title='{0}'"
 84 |         .format(topic))
 85 | 
 86 |     # Add new words which don't exist
 87 |     for w in words:
 88 |       queryWord = "select from KEYWORD where w='{0}'".format(w)
 89 |       if len(self.orient.command(queryWord))==0:
 90 |         if verbose: print(colored('New word added: ','green'), w)
 91 |         self.orient.command("create vertex KEYWORD set w='{0}'".format(w))
 92 | 
 93 |       # Add a link from {topic} => {word}
 94 |       if verbose: print(colored('New link [{0}] HAS => [{1}]'.format(topic,w),'green'))
 95 |       
 96 |       # If [weight] is specified,
 97 |       # Create an inverted-index edge from
 98 |       # [keyword] => [topic]
 99 |       if weights is None:
100 |         # General relation
101 |         self.orient.command("create edge HAS from ({0}) to ({1})"\
102 |           .format(queryTopic, queryWord))
103 |       else:
104 |         # Invert-index
105 |         weight = next(weights)
106 |         self.orient.command("create edge INDEX from ({0}) to ({1}) SET weight={2}"\
107 |           .format(queryWord, queryTopic, weight))
108 | 
109 |     # Add links between words
110 |     for w in words:
111 |       # Add a link to sibling words (words which co-exist in same sentence)
112 |       siblings = (u for u in words if not w == u)
113 |       for s in siblings:
114 |         querySib = "select from KEYWORD where w='{0}'".format(s)
115 |         self.orient.command("create edge REL from ({0}) to ({1})".
116 |           format(queryWord,querySib))
117 | 
118 |   """
119 |   {Generator} Enumurate keywords by strength of connections
120 |   """
121 |   def top_keywords(self):
122 |     query = "select w, in().size() as cnt from keyword order by cnt desc"
123 |     for k in self.orient.query(query):
124 |       yield k
125 | 
126 |   """
127 |   {Generator} Enumurate all topics
128 |   """
129 |   def __iter__(self):
130 |     query = "select from topic"
131 |     for k in self.orient.query(query):
132 |       yield k
133 | 
134 |   """
135 |   {Generator} Enumerate all keywords in a topic
136 |   @param {str} topic title
137 |   """
138 |   def keywords_in_topic(self, topic, with_edge_count=False):
139 |     subquery = "select expand(out()) from topic where title = '{0}'".format(topic)
140 |     query = "select w from ({0})".format(subquery) \
141 |       if not with_edge_count \
142 |       else "select w, in().size() as freq from (select expand(out()) from topic where title = '{}')".format(topic)
143 |     for k in self.orient.query(query):
144 |       yield k
145 | 
146 |   """
147 |   {Generator} Enumerate all topics which the given keyword
148 |   belong to
149 |   @param {str} keyword to query
150 |   """
151 |   def topics_which_have(self,w):
152 |     query = "select in().title from KEYWORD where w='{}'".format(w)
153 |     for k in self.orient.query(query):
154 |       yield k
155 | 
156 | 


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/pylib/spider/__init__.py:
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https://raw.githubusercontent.com/tao-pr/vor-knowledge-graph/7c7140b682796b997871751090ac3a1b3ea36eee/pylib/spider/__init__.py


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/pylib/spider/crawler.py:
--------------------------------------------------------------------------------
 1 | """
 2 | Website crawler with specific reader
 3 | @author TaoPR (github.com/starcolon)
 4 | """
 5 | 
 6 | import urllib.request
 7 | from htmldom import htmldom
 8 | from termcolor import colored
 9 | 
10 | """
11 | Download and scrape the content from the given URL
12 | @param {string} URL locator of the website to scrape
13 | @param {list} of tuples: (field name, selector function)
14 | @param {bool} turn on/off verbose output
15 | @return {object} scraped content
16 | """
17 | def download_page(url,selectors,verbose=False):
18 |   # Download the entire page HTML, processed as a DOM tree
19 |   print(colored('Fetching: ','green') + colored(url,'cyan'))
20 |   page = htmldom.HtmlDom(url).createDom()
21 | 
22 |   # Apply selector functions in order to create 
23 |   # a content package
24 |   content = {}
25 |   for tup in selectors:
26 |     field, selector = tup
27 |     if verbose:
28 |       print(colored('   Mapping : ','green'), field)
29 |     content[field]  = selector(page)
30 | 
31 |   return content


--------------------------------------------------------------------------------
/pylib/spider/wiki.py:
--------------------------------------------------------------------------------
 1 | """
 2 | Wikipedia single page crawler and scraper
 3 | @author TaoPR (github.com/starcolon)
 4 | """
 5 | 
 6 | import re
 7 | from termcolor import colored
 8 | from . import crawler
 9 | 
10 | def download_wiki(url,verbose=False):
11 |   selector = [
12 |     ('title', wiki_title),
13 |     ('contents', wiki_contents),
14 |     ('rels', wiki_rels)
15 |   ]
16 |   content  = crawler.download_page(url,selector,verbose)
17 | 
18 |   if verbose:
19 |     print(colored('[Downloaded : {0}]'.format(url),'cyan'))
20 |     print(colored('  title : ','cyan'), content['title'])
21 |     print(colored('  rels  : ','cyan'), len(content['rels']))
22 | 
23 |   return content
24 | 
25 | def wiki_title(page):
26 |   return page.find('h1#firstHeading').text()
27 | 
28 | """
29 | Scrape the main article content from the specified 
30 | wikipedia page
31 | """
32 | def wiki_contents(page):
33 |   contents   = []
34 |   paragraphs = page.find('#bodyContent p')
35 |   for p in paragraphs:
36 |     contents.append(p.text())
37 | 
38 |   return contents
39 | 
40 | """
41 | Scrape all related links (to other wiki pages)
42 | from the current wikipedia page
43 | """
44 | def wiki_rels(page):
45 |   links = []
46 |   for li in page.find('ul li a'):
47 | 
48 |     href = li.attr('href')
49 |     
50 |     # Skip unuseable or unwanted links
51 |     if href[0]=='#' or \
52 |     ':' in href or \
53 |     '//' in href or \
54 |     'index.php' in href or \
55 |     'Main_Page' in href or \
56 |     re.search('\/+\w{2,5}.wiki[m|p]edia.org\/*.*', href):
57 |       continue
58 | 
59 |     links.append(href)
60 | 
61 |   return links


--------------------------------------------------------------------------------
/pylib/text/__init__.py:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/tao-pr/vor-knowledge-graph/7c7140b682796b997871751090ac3a1b3ea36eee/pylib/text/__init__.py


--------------------------------------------------------------------------------
/pylib/text/cleanser.py:
--------------------------------------------------------------------------------
 1 | """
 2 | Text cleanser
 3 | @author TaoPR (github.com/starcolon)
 4 | """
 5 | 
 6 | import re
 7 | 
 8 | """
 9 | Remove unwanted tokens from the given text
10 | """
11 | def cleanse(txt):
12 |   txt_ = txt
13 |   for p in patterns():
14 |     txt_ = re.sub(p, ' ', txt_)
15 |   return txt_
16 | 
17 | def patterns():
18 |   return [ \
19 |     r'(',\
20 |     r')',\
21 |     r',',\
22 |     r'{.+}',\
23 |     r'<.*>',\
24 |     r'&#.+;',\
25 |     r'\.($| )',\
26 |     r'\n',\
27 |     r'[\d+]',
28 |     r' \w{1,2}\.',
29 |     r'\[.*\]',
30 |     r'title=\"\w*\"',
31 |     r'\(\)']
32 | 
33 | 


--------------------------------------------------------------------------------
/pylib/text/intent.py:
--------------------------------------------------------------------------------
 1 | """
 2 | Sentence intent classifier
 3 | @author TaoPR (github.com/starcolon)
 4 | """
 5 | import numpy as np
 6 | import os.path
 7 | import pickle
 8 | import json
 9 | from termcolor import colored
10 | from sklearn.cluster import KMeans
11 | from sklearn.preprocessing import LabelEncoder
12 | 
13 | """
14 | Create a new instance of intent classifier, label encoder included.
15 | @param {list} distinct list of intent labels (string)
16 | @param {string} classification method
17 | @return {object} classifier object along with the label encoder
18 | """
19 | def new(intent_labels=[],method='kmeans'):
20 |   
21 |   # Classification methods
22 |   methods = {
23 |     'kmeans': KMeans(n_clusters = len(intent_labels))
24 |   }
25 | 
26 |   # Label encoders
27 |   encoder = LabelEncoder()
28 |   encoder.fit(intent_labels)
29 | 
30 |   return {
31 |     'clf':     methods[method],
32 |     'encoder': encoder
33 |   }
34 | 
35 | 
36 | def save(operations,path):
37 |   with open(path,'wb+') as f:
38 |     pickle.dump(operations,f)
39 | 
40 | def load(path):
41 |   with open(path,'rb') as f:
42 |     return pickle.load(f)
43 | 
44 | """
45 | Load the existing operations or create new if not exist
46 | """
47 | def safe_load(path):
48 |   if os.path.isfile(path): 
49 |     print(colored('Text intent classifier loaded.','cyan'))
50 |     return load(path)
51 |   else: 
52 |     print(colored('Text intent classifier created...','yellow'))
53 |     return new()
54 | 
55 | # Classify multiple vectors at a time using 
56 | # the specified trained operations
57 | def classify(opr):
58 |   def classify_us(vectors):
59 |     vs   = opr['clf'].predict(vectors)
60 |     return opr['encoder'].inverse_transform(vs)
61 |   return classify_us
62 | 
63 | def train(opr):
64 |   def fit(vectors,labels):
65 |     # Make labels numeric
66 |     numeric_labels = opr['encoder'].transform(labels)
67 |     opr['clf'].fit(vectors,numeric_labels)
68 |   return fit
69 | 
70 | 


--------------------------------------------------------------------------------
/pylib/text/pos_tree.py:
--------------------------------------------------------------------------------
 1 | """
 2 | POS pattern tree
 3 | @author TaoPR (github.com/starcolon)
 4 | ---
 5 | Pattern tree is built from a list of POS patterns.
 6 | It is employed as a sentence parser which captures 
 7 | and build a mini knowledge graph.
 8 | """
 9 | 
10 | from collections import deque
11 | 
12 | class PatternCapture:
13 |   def __init__(self):
14 |     self.__patterns = []
15 | 
16 |   """
17 |   Read in the list of POS structure from a text file
18 |   """
19 |   def load(self,path):
20 |     with open(path,'r') as f:
21 |       self.__patterns = set([p.replace('\n','') for p in f.readlines()])
22 | 
23 |   """
24 |   Save a list of POS structure to a text file
25 |   """
26 |   def save(self,path):
27 |     with open(path,'w') as f:
28 |       for p in self.__patterns:
29 |         f.write(p+"\n")
30 | 
31 |   def append(self,p):
32 |     self.__patterns.append(p)
33 | 
34 |   def join(self,delim):
35 |     return delim.join(self.__patterns)
36 | 
37 |   """
38 |   Capture the keyword tree of the given sentence
39 |   Sample output:
40 |     [
41 |       ['Ammonia','Phosphate'],
42 |       ['Heated',['Potassiam','Nitrate'],'Solution']
43 |     ]
44 |   @param {list} of tuples of (word,tag)
45 |   @return {list} represents a captured tree
46 |   """
47 |   def capture(self,pos_sentence):
48 |     pos_deq = deque(pos_sentence)
49 |     tree    = []
50 | 
51 |     # Iterate through the sentence POS tags (greedy capture)
52 |     bichain, bichain_tag = deque(),deque()
53 |     trichain, trichain_tag = deque(),deque()
54 |     
55 |     while len(pos_deq)>0:
56 |       t,tag = pos_deq.popleft()
57 | 
58 |       # Accumulate the current chain
59 |       bichain.append(t)
60 |       bichain_tag.append(tag)
61 |       trichain.append(t)
62 |       trichain_tag.append(tag)
63 | 
64 |       # Check if the individual tag matches any of the patterns?
65 |       if tag in self.__patterns:
66 |         tree.append(t)
67 | 
68 |       # Check if current bichain matches any patterns?
69 |       if len(bichain)==2:
70 |         if '-'.join(bichain_tag) in self.__patterns:
71 |           tree.append(' '.join(bichain))
72 |         # Clean up for the next iteration
73 |         bichain.popleft()
74 |         bichain_tag.popleft()
75 | 
76 |       # Check if current trichain matches any patterns?
77 |       if len(trichain)==3:
78 |         if '-'.join(trichain_tag) in self.__patterns:
79 |           tree.append(' '.join(trichain))
80 |         # Clean up for the next iteration
81 |         trichain.popleft()
82 |         trichain_tag.popleft()
83 | 
84 |     return tree
85 | 
86 | 
87 | 


--------------------------------------------------------------------------------
/pylib/text/structure.py:
--------------------------------------------------------------------------------
 1 | """
 2 | Sentence pattern semantic extraction module
 3 | @author TaoPR (github.com/starcolon)
 4 | """
 5 | 
 6 | import numpy as np
 7 | import os.path
 8 | import pickle
 9 | import json
10 | from collections import deque
11 | from termcolor import colored
12 | from textblob import TextBlob
13 | 
14 | 
15 | """
16 | Extract part of speech structure of the given text
17 | @param {list} of tokenised words
18 | """
19 | def pos_tag(words):
20 |   def generate(t):
21 |     tags = TextBlob(t).tags
22 |     if len(tags)==0:
23 |       return None
24 |     return tags[0]
25 | 
26 |   blobs = [generate(t) for t in words]
27 |   return [b for b in blobs if b is not None]
28 | 
29 | def tag_with_color(words):
30 |   pos    = pos_tag(words)
31 |   tokens = ' | '.join([colored(tag,'yellow') + ':' + t for t,tag in pos])
32 |   print(tokens)
33 | 
34 | 
35 | 
36 | 


--------------------------------------------------------------------------------
/pylib/text/texthasher.py:
--------------------------------------------------------------------------------
  1 | """
  2 | Text hashing (vectoriser)
  3 | @author TaoPR (github.com/starcolon)
  4 | """
  5 | 
  6 | import numpy as np
  7 | import os.path
  8 | import pickle
  9 | import json
 10 | from termcolor import colored
 11 | from sklearn.feature_extraction.text import TfidfVectorizer
 12 | from sklearn.feature_extraction.text import HashingVectorizer
 13 | from sklearn.feature_selection import SelectKBest, chi2
 14 | from sklearn.linear_model import RidgeClassifier
 15 | from sklearn.neighbors import NearestCentroid
 16 | from sklearn.preprocessing import Normalizer
 17 | from sklearn.pipeline import make_pipeline
 18 | from sklearn.decomposition import SparsePCA
 19 | from sklearn.decomposition import TruncatedSVD
 20 | from sklearn.decomposition import LatentDirichletAllocation
 21 | 
 22 | decomposers = {
 23 |   'LDA': lambda n: 
 24 |     LatentDirichletAllocation( # TFIDF --> Topic term
 25 |       n_topics=n,
 26 |       max_iter=15
 27 |     ),
 28 |   'PCA': lambda n:
 29 |     SparsePCA( # NOTE: PCA is supposed to perform slow, so we limit num of iterations
 30 |       n_components=n,
 31 |       alpha=1,
 32 |       max_iter=10
 33 |     ),
 34 |   'SVD': lambda n:
 35 |     TruncatedSVD(n) # SVD may introduce some estimation losses
 36 | }
 37 | 
 38 | # Create a text process pipeline (vectorizer)
 39 | def new(n_components=None,stop_words=[],decomposition='SVD'):
 40 | 
 41 |   # Word stemming (English)
 42 |   # TAOTODO:
 43 | 
 44 |   # Prepare vectoriser engines
 45 |   idf = TfidfVectorizer(
 46 |     ngram_range=(1,3), #Unigram,bigram,& trigram
 47 |     stop_words=stop_words
 48 |   )
 49 | 
 50 |   # Prepare normaliser
 51 |   norm = Normalizer(norm='l2') # Cosine similarity 
 52 | 
 53 |   # Prepare dimentionality reducer
 54 |   if n_components:
 55 |     reducer = decomposers[decomposition](n_components)
 56 |     return [idf,reducer,norm]
 57 |   else:
 58 |     return [idf,norm]
 59 | 
 60 | 
 61 | def save(operations,path):
 62 |   with open(path,'wb+') as f:
 63 |     pickle.dump(operations,f)
 64 | 
 65 | def load(path):
 66 |   with open(path,'rb') as f:
 67 |     return pickle.load(f)
 68 | 
 69 | """
 70 | Load the transformer pipeline object
 71 | from the physical file,
 72 | or initialise a new object if the file doesn't exist
 73 | """
 74 | def safe_load(path,n_components,stop_words,decomposition):
 75 |   if os.path.isfile(path): 
 76 |     print(colored('Text hasher loaded.','cyan'))
 77 |     return load(path)
 78 |   else: 
 79 |     print(colored('Text hasher created...','yellow'))
 80 |     return new(n_components,stop_words,decomposition)
 81 | 
 82 | 
 83 | def hash(operations,learn=False,verbose=True):
 84 |   # @param {iterable} of string
 85 |   def hash_me(dataset):
 86 |     x = dataset
 87 | 
 88 |     if learn:
 89 |       for i in range(len(operations)): 
 90 |         verbose and print(colored('Processing ... #{0} : {1}'.format(i,type(operations[i])),'grey'))
 91 |         x = operations[i].fit_transform(x)
 92 |     else:
 93 |       for i in range(len(operations)): 
 94 |         x = operations[i].transform(x)
 95 | 
 96 |     return x
 97 |   return hash_me
 98 | 
 99 | 
100 | 
101 | 


--------------------------------------------------------------------------------
/repl_word2vec.py:
--------------------------------------------------------------------------------
 1 | """
 2 | Play with word2vec model
 3 | @author TaoPR (github.com/starcolon)
 4 | """
 5 | 
 6 | import os
 7 | import sys
 8 | import argparse
 9 | import word2vec
10 | from termcolor import colored
11 | 
12 | arguments = argparse.ArgumentParser()
13 | arguments.add_argument('--modelpath', type=str, default='./models/word2vec.bin', help='Path of the word2vec binary model.')
14 | args = vars(arguments.parse_args(sys.argv[1:]))
15 | 
16 | def repl(model):
17 |   print(colored('[Model] Loaded:','cyan'))
18 |   print('... Model shape : {}'.format(model.vectors.shape))
19 |   print('... Clusters    : {}'.format(model.clusters))
20 |   while True:
21 |     w = input('Enter word to test : ')
22 |     try:
23 |       indexes, metrics = model.cosine(w)
24 |       print('... indexes  : {}'.format(indexes))
25 |       print('... metrics  : {}'.format(metrics))
26 |       print('... similar  : {}'.format(model.vocab[indexes]))
27 |       print('... response : ')
28 |       print(model.generate_response(indexes, metrics).tolist())
29 |     except Exception:
30 |       print(colored('... Vocab not recognised by the model.','red'))
31 | 
32 | 
33 | if __name__ == '__main__':
34 |   # Load the word2vec model
35 |   model_path = os.path.realpath(args['modelpath'])
36 |   if not os.path.isfile(model_path):
37 |     print(colored('[ERROR] word2vec model does not exist.','red'))
38 |     raise RuntimeError('Model does not exist')
39 |   print(colored('[Model] loading binary model.','cyan'))
40 |   model = word2vec.WordVectors.from_binary(model_path, encoding='ISO-8859-1')
41 |   repl(model)


--------------------------------------------------------------------------------
/requirements.txt:
--------------------------------------------------------------------------------
 1 | htmldom == 2.0
 2 | nltk == 3.6.6
 3 | numpy == 1.22.0
 4 | pika == 0.10.0
 5 | pybloom_live == 2.1.0
 6 | pymongo == 3.2.2
 7 | pyorient == 1.5.4
 8 | termcolor == 1.1.0
 9 | textblob == 0.11.0
10 | word2vec == 0.9.1
11 | scikit_learn == 0.18.1
12 | 


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/test/__pycache__/crawler.cpython-34.pyc:
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https://raw.githubusercontent.com/tao-pr/vor-knowledge-graph/7c7140b682796b997871751090ac3a1b3ea36eee/test/__pycache__/crawler.cpython-34.pyc


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/test/crawler.py:
--------------------------------------------------------------------------------
 1 | """
 2 | Spider / crawler test suite
 3 | """
 4 | 
 5 | import re
 6 | from pprint import pprint
 7 | from pylib.spider import wiki
 8 | 
 9 | def test_wiki_crawl():
10 |   wiki_url = 'https://en.wikipedia.org/wiki/Celery'
11 |   content  = wiki.download_wiki(wiki_url,verbose=True)
12 | 
13 |   celery_first_content = ["Celery",
14 |     "(Apium graveolens",
15 |     "), a marshland plant variety",
16 |     "in the family Apiaceae",
17 |     ", has been cultivated as a vegetable",
18 |     "since antiquity. Depending on location and cultivar, either its stalks, leaves, or hypocotyl",
19 |     "are eaten and used in cooking."
20 |   ]
21 | 
22 |   assert content['title'] == 'Celery'
23 |   assert len(content['contents']) > 0
24 |   assert content['contents'][0] == "\n".join(celery_first_content)
25 |   assert content['rels'][:3] == [
26 |     '/wiki/Microgram',
27 |     '/wiki/Milligram',
28 |     '/wiki/International_unit'
29 |   ]


--------------------------------------------------------------------------------
/visualise.js:
--------------------------------------------------------------------------------
  1 | /**
  2 |  * Knowledge graph visualiser module
  3 |  * @author TaoPR (github.com/starcolon)
  4 |  */
  5 | 
  6 | var args    = process.argv.slice(2);
  7 | var colors  = require('colors');
  8 | var fs      = require('fs');
  9 | var Promise = require('bluebird');
 10 | 
 11 | 
 12 | // Validate the arguments initially
 13 | (function validateArgs(args){
 14 |   if (args.length==0){
 15 |     console.error('[ERROR] You need to supply OrientDB root database like:'.red);
 16 |     console.error('$ node visualise MYPASSWORD'.red);
 17 |     process.exit(1);
 18 |   }
 19 | })(args);
 20 | 
 21 | const usrname = 'root';
 22 | var password = args[0];
 23 | 
 24 | var indexGraphMapper = function(KB){
 25 |   return function(nodes){
 26 |     var nodeHash = new Set();
 27 |     var nodes = nodes.map(n => {
 28 |       nodeHash.add(n['@rid'].toString());
 29 |       return {
 30 |         id:    n['@rid'],
 31 |         type:  n['@class'],
 32 |         label: n['@class']=='TOPIC' ? n.title : n.w,
 33 |         color: n['@class']=='TOPIC' ? '#F00000' : '#FFAAAA',
 34 |         value: n['@class']=='TOPIC' ? 5 : 3,
 35 |         borderWidth: n['@class']=='TOPIC' ? 3 : 1
 36 |       }
 37 |     })
 38 | 
 39 |     nodeHash = Array.from(nodeHash)
 40 | 
 41 |     var topicOnly   = (n) => n.type == 'TOPIC';
 42 |     var keywordOnly = (n) => n.type == 'KEYWORD';
 43 |     var allKeywords = nodes.filter(keywordOnly);
 44 |     var allTopics   = nodes.filter(topicOnly);
 45 | 
 46 |     // Enumurate index between [nodes] <==> [keywords]
 47 |     console.log('Enurating edges...');
 48 |     var edges = [];
 49 |     var collectEdges = (iter) => iter.then((es) => {
 50 |       es.forEach(e => {
 51 | 
 52 |         var _in = e['in'].toString();
 53 |         var _out = e['out'].toString();
 54 | 
 55 |         edges.push({
 56 |           from:  nodeHash.indexOf(_in),
 57 |           to:    nodeHash.indexOf(_out),
 58 |           value: e['weight']
 59 |         });
 60 |       })
 61 |     });
 62 | 
 63 |     // Prepare edge enumuration jobs
 64 |     var jobs = allTopics
 65 |       .map(KB.getInboundIndex(42)) 
 66 |       .map(collectEdges);
 67 | 
 68 |     return Promise
 69 |       .all(jobs).then(() => [nodes,edges])
 70 |       .then((p) => {
 71 | 
 72 |         var [nodes, edges] = p;
 73 | 
 74 |         console.log('Remapping nodes...')
 75 |         nodes = nodes.map( n => {
 76 |           n.id = nodeHash.indexOf(n.id.toString()); // @rid => integer
 77 |           return n
 78 |         })
 79 | 
 80 |         // Remove unlinked nodes from the graph
 81 |         var allInboundNodes = new Set(edges.map(e => e.to));
 82 |         var allOutboundNodes = new Set(edges.map(e => e.from));
 83 |         nodes = nodes.filter(n => 
 84 |           allOutboundNodes.has(n.id) ||
 85 |           allInboundNodes.has(n.id));
 86 | 
 87 |         var graph = { nodes: nodes, edges: edges }; 
 88 |         var sgraph = JSON.stringify(graph);
 89 | 
 90 |         return [graph,sgraph]
 91 |       });
 92 |   }
 93 | }
 94 |   
 95 | var circularGraphMapper = function(KB){
 96 |   return function(nodes){
 97 |     // Reduce the form of nodes so they become renderable.
 98 |     var L = nodes.filter((n) => n['@class']=='TOPIC').length;
 99 |     var degreeStep = 2*Math.PI/L;
100 |     var i = -1;
101 | 
102 |     nodes = nodes.map((n) => {
103 |       i++;
104 |       var randomDegree = Math.random()*2*Math.PI;
105 |       var randomR = Math.random()*0.8;
106 |       var x = randomR * Math.cos(randomDegree);
107 |       var y = randomR * Math.sin(randomDegree);
108 | 
109 |       return {
110 |         id:    n['@rid'],
111 |         type:  n['@class'],
112 |         label: n['@class']=='TOPIC' ? n.title : n.w,
113 |         x:     n['@class']=='TOPIC' ? Math.cos(degreeStep*i) : x,
114 |         y:     n['@class']=='TOPIC' ? Math.sin(degreeStep*i) : y, 
115 |         size:  n['@class']=='TOPIC' ? 10 : 1,
116 |         color: n['@class']=='TOPIC' ? '#F00000' : '#FFAAAA'
117 |       }
118 |     })
119 | 
120 |     // Take outbound edges of those underlying nodes
121 |     console.log('Enumerating edges...');
122 |     var edges = [];
123 |     var collectEdges = (node) => node.then((e) => {
124 |       edges.push(e);
125 |     });
126 |     var topic = (n) => n.type=='TOPIC';
127 | 
128 |     // Prepare edge enumuration jobs
129 |     var jobs = nodes
130 |       .filter(topic)
131 |       .map(KB.getOutE(100))
132 |       .map(collectEdges);
133 | 
134 |     // Make sure edges of all underlying nodes are processed.
135 |     return Promise
136 |       .all(jobs).then(() => [nodes,edges])
137 |       .then((p) => {
138 | 
139 |         console.log('Transforming nodes & edges ...')
140 |         var [nodes, edges] = p;
141 | 
142 |         // Flatten edges
143 |         edges = edges.reduce((a,b) => a.concat(b), []);
144 | 
145 |         // Make all edges renderable
146 |         edges = edges.map((e) => {
147 |           return {
148 |             id:     Math.random()*10000,
149 |             source: e.out,
150 |             target: e.in,
151 |             type:   'curve'
152 |           }
153 |         })
154 | 
155 |         var graph = { nodes: nodes, edges: edges }; 
156 |         var sgraph = JSON.stringify(graph);
157 | 
158 |         return [graph,sgraph]
159 |       })
160 |   }
161 | }
162 | 
163 | function saveToJSON(outputPath){
164 |   return function (graphArray){
165 |     
166 |     var graph = graphArray[0];
167 |     var sgraph = graphArray[1];
168 | 
169 |     return new Promise((done,reject) => {
170 |       console.log('Initialising I/O ...');
171 |       var content = `function getGraph(){ return ${sgraph} }`;
172 |       fs.writeFile(`./HTML/${outputPath}`,content,(err) => {
173 |         console.log('Serialising graph to JS ...'.green);
174 |         console.log(`   ${graph.nodes.length} nodes`);
175 |         console.log(`   ${graph.edges.length} links`);
176 |         if (err){
177 |           console.error('Serialisation failed...'.red);
178 |           console.error(err);
179 |           return reject();
180 |         }
181 |         else{
182 |           console.log('Graph HTML is ready in ./HTML/'.green);
183 |           return done();
184 |         }
185 |       })
186 |     })
187 |   }
188 | }
189 | 
190 | //-------------------------------
191 | // [OrientDB data] => [JSON data]
192 | // mapping strategies
193 | //-------------------------------
194 | const dataMapping = [
195 |   {
196 |     'name':  'vor',
197 |     'mapper': circularGraphMapper,
198 |     'output': 'graph-data.js'
199 |   },
200 |   {
201 |     'name': 'vorindex',
202 |     'mapper': indexGraphMapper,
203 |     'output': 'graph-index.js'
204 |   }
205 | ]
206 | 
207 | /**
208 |  * Map [OrientDB data] => [renderable JSON]
209 |  * by the predefined mapping strategies
210 |  */
211 | 
212 | Promise.mapSeries(dataMapping, (db) => {
213 |   
214 |   console.log('================================'.cyan)
215 |   console.log('[Datasource] Processing : '.cyan, db)
216 |   console.log('================================'.cyan)
217 | 
218 |   var KB      = require('./jslib/knowledge.js');
219 |   var reconnect = true
220 |   var mapToJSON = db.mapper;
221 |   var outputPath = db.output;
222 | 
223 |   return KB
224 |     .connect(db.name,usrname,password,reconnect)
225 |     .catch((e) => {
226 |       console.error(`[ERROR] connection to OrientDB [${db.name}] failed.`.red);
227 |       console.error(e);
228 |       process.exit(1);
229 |     }) 
230 |     .then((g) => {
231 |       console.log(`[Connected] to OrientDB [${db.name}].`.green);
232 | 
233 |       // Read all nodes in
234 |       var nodes = KB.nodes();
235 |       console.log('All nodes retrieved...')
236 | 
237 |       // Fulfill the values and go on
238 |       return Promise.resolve(nodes)
239 |     })
240 |     .then(mapToJSON(KB))
241 |     .then(saveToJSON(outputPath))
242 |   })
243 | .then((_) => process.exit())


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