├── VERSION ├── texttk ├── texttk.pyc ├── __init__.pyc ├── __init__.py └── texttk.py ├── .gitignore ├── setup.py ├── demo.py ├── README.rst └── LICENSE /VERSION: -------------------------------------------------------------------------------- 1 | 0.1 -------------------------------------------------------------------------------- /texttk/texttk.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fmpr/texttk/HEAD/texttk/texttk.pyc -------------------------------------------------------------------------------- /texttk/__init__.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fmpr/texttk/HEAD/texttk/__init__.pyc -------------------------------------------------------------------------------- /texttk/__init__.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # Copyright (C) 2016, Filipe Rodrigues 3 | 4 | from texttk import * -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | stanford-ner-2015-12-09 2 | stanford-ner-2015-12-09/* 3 | .DS_Store 4 | *.txt 5 | dist/* 6 | dist 7 | build/* 8 | build 9 | debug/* 10 | debug 11 | texttk.egg-info/* 12 | texttk.egg-info 13 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # Copyright (C) 2016, Filipe Rodrigues 3 | # 4 | # Permission is hereby granted, free of charge, to any person obtaining a copy 5 | # of this software and associated documentation files (the "Software"), to deal 6 | # in the Software without restriction, including without limitation the rights 7 | # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 8 | # copies of the Software, and to permit persons to whom the Software is 9 | # furnished to do so, subject to the following conditions: 10 | # 11 | # The above copyright notice and this permission notice shall be included in 12 | # all copies or substantial portions of the Software. 13 | # 14 | # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 15 | # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 16 | # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 17 | # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 18 | # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 19 | # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN 20 | # THE SOFTWARE. 21 | 22 | from __future__ import division 23 | try: 24 | import setuptools 25 | except: 26 | print ''' 27 | setuptools not found. 28 | 29 | On linux, the package is often called python-setuptools''' 30 | from sys import exit 31 | exit(1) 32 | long_description = file('README.rst').read() 33 | 34 | packages = filter(lambda p: p.startswith('texttk'), setuptools.find_packages()) 35 | print packages 36 | 37 | setuptools.setup(name = 'texttk', 38 | version = '0.1', 39 | description = 'Text Preprocessing ToolKit', 40 | long_description = long_description, 41 | author = u'Filipe Rodrigues', 42 | author_email = 'fmpr@dei.uc.pt', 43 | url = 'http://amilab.dei.uc.pt/fmpr/', 44 | license = 'Academic Free License', 45 | install_requires = ['nltk','sklearn','HTMLParser'], 46 | packages = packages, 47 | ) 48 | 49 | 50 | -------------------------------------------------------------------------------- /demo.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | 4 | import texttk 5 | import csv, codecs 6 | 7 | # load data from the csv file 8 | def read_csv_utf8(filename, delimiter=',', is_header=True): 9 | rows, header = [], "" 10 | rownum = 0 11 | corpus = [] 12 | with open(filename, "r") as f: 13 | reader = csv.reader(f, delimiter=delimiter) 14 | for row in reader: 15 | if is_header and rownum == 0: header = row 16 | elif row: corpus.append(row[14].decode("utf-8")) 17 | rownum += 1 18 | if rownum > 1000: 19 | break 20 | return corpus 21 | 22 | # load corpus from directory 23 | def read_utf8_corpus(self, directory): 24 | print "reading utf8 corpus from %s" % (directory,) 25 | corpus = [] 26 | for fname in os.listdir(directory): 27 | f = codecs.open(directory + fname, "r", "utf-8") 28 | f.readline() 29 | doc = "" 30 | for line in f: 31 | splt = line.split(",") 32 | doc += splt[0] + " " + splt[1] + " " 33 | f.close() 34 | corpus.append(doc) 35 | if len(corpus) > 10: 36 | break 37 | return corpus 38 | 39 | def demo(): 40 | # load corpus 41 | #corpus = read_utf8_corpus("/Users/fmpr/Downloads/events_search_results/query_search_results/") 42 | corpus = read_csv_utf8("/Users/fmpr/code/ioracle/data/sg_events_join_venues_26052015_whole_island_utf8.csv") 43 | 44 | # debug text preprocessing 45 | for d in xrange(len(corpus)): 46 | fw = codecs.open("debug/%d_original.txt" % (d,), "w", "utf-8") 47 | fw.write(corpus[d]) 48 | fw.close() 49 | 50 | ignore_list = ["singapor", "sg", "expo", "sale", "event", "exhibit", "fair", "com", "asia"] 51 | stanford_ner_path = "/Users/fmpr/code/texttk/stanford-ner-2015-12-09/" 52 | tp = texttk.TextPreprocesser(decode_error='strict', strip_accents='unicode', ignore_list=ignore_list, lowercase=True, \ 53 | remove_html=True, join_urls=True, use_bigrams=True, use_ner=True, stanford_ner_path=stanford_ner_path, \ 54 | use_lemmatizer=False, max_df=0.95, min_df=1, max_features=None) 55 | 56 | corpus = tp.preprocess_corpus(corpus) 57 | 58 | # debug text preprocessing 59 | for d in xrange(len(corpus)): 60 | fw = codecs.open("debug/%d_cleaned.txt" % (d,), "w", "utf-8") 61 | fw.write(corpus[d]) 62 | fw.close() 63 | 64 | dtm, vocab = tp.convert_to_bag_of_words(corpus) 65 | 66 | # debug text preprocessing 67 | for d in xrange(len(corpus)): 68 | fw = codecs.open("debug/%d_preproc.txt" % (d,), "w", "utf-8") 69 | for n in dtm[d].nonzero()[1]: 70 | fw.write(vocab[n] + " ") 71 | fw.close() 72 | 73 | if __name__ == "__main__": 74 | demo() 75 | 76 | -------------------------------------------------------------------------------- /README.rst: -------------------------------------------------------------------------------- 1 | ============================================== 2 | texttk -- Text Preprocessing in Python 3 | ============================================== 4 | 5 | texttk is a Python library for *text preprocessing* of large corpora, that can be used for *topic modelling*, *text classification*, *document clustering*, *information retrieval*, etc. 6 | 7 | Features 8 | --------- 9 | 10 | * Removes stopwords, punctuation, HTML tags, accents, rare words, very frequent words, etc. 11 | * Applies stemmer or lemmatizer 12 | * Finds the most relevant bigrams 13 | * Performs named entity recognition 14 | * Tokenizes text 15 | * Generates bag-of-words representations 16 | * Identifies and joins compound words 17 | * Splits documents in sentences 18 | 19 | Default preprocessing pipeline 20 | --------- 21 | 22 | * unescape characters 23 | * remove HTML tags 24 | * strip accents 25 | * join URLs 26 | * join compound words 27 | * space out punctuation 28 | * split documents in sentences 29 | * tag sentences using Stanford NER 30 | * merge named entities as single tokens 31 | * remove punctuation 32 | * convert to lowercase 33 | * find top-100 bigrams using chi-squared 34 | * merge bigrams as single tokens 35 | * tokenize documents 36 | * remove stopwords 37 | * apply stemmer/lemmatizer 38 | * remove rare words 39 | * remove very frequent words 40 | * convert corpus to bag-of-words format 41 | 42 | Basic Usage 43 | ------------ 44 | 45 | Basic preprocessing of a corpus and transformation to bag-of-words format:: 46 | 47 | import texttk 48 | corpus = ... # a list of strings 49 | tp = texttk.TextPreprocesser(decode_error='strict', strip_accents='unicode', ignore_list=[], \ 50 | lowercase=True, remove_html=True, join_urls=True, use_bigrams=True, \ 51 | use_ner=True, stanford_ner_path="", use_lemmatizer=False, \ 52 | max_df=0.95, min_df=1, max_features=None) 53 | corpus = tp.preprocess_corpus(corpus) 54 | transformed_corpus, vocab = tp.convert_to_bag_of_words(corpus) 55 | 56 | Installation 57 | ------------ 58 | 59 | This software depends on `nltk`, `sklearn` and `HTMLParser`. 60 | You must have them installed prior to installing `texttk`. 61 | If you wish to use NER, `texttk` also requires access to the Stanford NER Java library. 62 | 63 | The simple way to install `texttk` is:: 64 | 65 | python setup.py install 66 | 67 | 68 | Copyright (c) 2016 Filipe Rodrigues 69 | 70 | This program is free software. You can redistribute it and/or modify it under the terms of the GNU General Public License, version 3, as published by the Free Software Foundation. 71 | 72 | This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. 73 | 74 | Appropriate reference to this software should be made when describing research in which it played a substantive role, so that it may be replicated and verified by others. 75 | 76 | 77 | -------------------------------------------------------------------------------- /texttk/texttk.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | 4 | import re, os 5 | import unicodedata 6 | import codecs 7 | import HTMLParser 8 | import nltk 9 | import csv 10 | from nltk.corpus import stopwords 11 | from nltk.tokenize.punkt import PunktSentenceTokenizer 12 | from nltk.stem.snowball import SnowballStemmer 13 | from nltk.stem import WordNetLemmatizer 14 | from nltk import word_tokenize 15 | from nltk.collocations import * 16 | from nltk.tag import StanfordNERTagger 17 | from sklearn.feature_extraction.text import CountVectorizer 18 | 19 | # general purpose functions 20 | def strip_accents_unicode(text): 21 | return ''.join([c for c in unicodedata.normalize('NFKD', text) if not unicodedata.combining(c)]) 22 | 23 | def remove_html(text): 24 | return re.sub(r'( ?\.+ )+', ' . ', re.sub(r'<[^>]*>', ' . ', text)) 25 | 26 | def join_urls(text, url_pattern): 27 | m = re.search(url_pattern, text) 28 | while m: 29 | text = re.sub(url_pattern, m.group(3).replace("http://","").replace(".",""), text) 30 | m = re.search(url_pattern, text) 31 | return text 32 | 33 | def join_compound_words(text, compound_pattern): 34 | m = re.search(compound_pattern, text) 35 | while m: 36 | text = re.sub(m.group(0), m.group(0).replace("-",""), text) 37 | m = re.search(compound_pattern, text) 38 | return text 39 | 40 | def space_out_punctuation(text): 41 | text = re.sub(r',\s', ' , ', text) 42 | text = re.sub(r'\.\.\.\s', ' ... ', text) 43 | text = re.sub(r'\.\s', ' . ', text) 44 | text = re.sub(r';\s', ' ; ', text) 45 | text = re.sub(r':\s', ' : ', text) 46 | text = re.sub(r'\?\s', ' ? ', text) 47 | text = re.sub(r'!\s', ' ! ', text) 48 | text = re.sub(r'"', ' " ', text) 49 | text = re.sub(r'\'', ' \' ', text) 50 | text = re.sub(r'\s\(', ' ( ', text) 51 | text = re.sub(r'\)\s', ' ) ', text) 52 | text = re.sub(r'\s\[', ' [ ', text) 53 | text = re.sub(r'\]\s', ' ] ', text) 54 | text = re.sub(r'-', ' - ', text) 55 | text = re.sub(r'_', ' _ ', text) 56 | text = re.sub(r'\n', ' ', text) 57 | text = re.sub(r'\r', ' ', text) 58 | text = re.sub(r'\s+', ' ', text) 59 | return text 60 | 61 | class CustomTokenizer(object): 62 | def __init__(self, tokenizer, stemmer, token_pattern, numeric_pattern): 63 | self.tokenizer = tokenizer 64 | self.stemmer = stemmer 65 | self.token_pattern = token_pattern 66 | self.numeric_pattern = numeric_pattern 67 | def __call__(self, doc): 68 | tokens = [] 69 | for t in self.tokenizer(doc): 70 | if self.token_pattern.match(t) and not self.numeric_pattern.match(t): 71 | while "_" in t: 72 | splt = t.split("_") 73 | t = ''.join(splt[1:]) 74 | tokens.append(self.stemmer(splt[0])) 75 | tokens.append(self.stemmer(t)) 76 | return tokens 77 | #return [self.stemmer(t) for t in self.tokenizer(doc) \ 78 | # if self.token_pattern.match(t) and not self.numeric_pattern.match(t)] 79 | 80 | class TextPreprocesser(object): 81 | def __init__(self, decode_error='strict', strip_accents='unicode', ignore_list=[], lowercase=True, \ 82 | remove_html=True, join_urls=True, use_bigrams=True, use_ner=True, stanford_ner_path="", \ 83 | use_lemmatizer=False, max_df=0.95, min_df=1, max_features=None): 84 | self.stanford_ner_path = stanford_ner_path # path to stanford NER 85 | self.decode_error = decode_error # options: {‘strict’, ‘ignore’, ‘replace’} 86 | self.strip_accents = strip_accents # options: {‘ascii’, ‘unicode’, None} 87 | self.ignore_list = ignore_list 88 | self.lowercase = lowercase 89 | self.remove_html = remove_html 90 | self.join_urls = join_urls 91 | self.use_bigrams = use_bigrams 92 | self.use_ner = use_ner 93 | self.use_lemmatizer = use_lemmatizer # use lemmatizer instead of stemmer? 94 | self.max_df = max_df # maximum document frequency 95 | self.min_df = min_df # remove terms that occur in less than min_df documents 96 | self.max_features = max_features # keep only top-N words according to tf across corpus 97 | 98 | self.sentence_splitter = PunktSentenceTokenizer().tokenize # Punkt sentence splitter 99 | self.stemmer = SnowballStemmer("english").stem # Snowball stemmer 100 | self.lemmatizer = WordNetLemmatizer().lemmatize # WordNet lemmatizer 101 | self.base_tokenizer = CountVectorizer().build_tokenizer() # sklearn tokenizer works the best, I think... 102 | self.stop_words = stopwords.words("english") # nltk list of 128 stopwords 103 | self.token_pattern = re.compile(r'(?u)\b(\w*[a-zA-Z_]\w+|\w+[a-zA-Z_]\w*)\b') # default value was r'(?u)\b\w\w+\b' 104 | self.numeric_pattern = re.compile(r'^[0-9]+$') # number regex 105 | self.url_pattern = re.compile(r'((http://)?(www\..*?\.\w+).*?)\s') 106 | self.compound_pattern = re.compile(r'\w+(\-\w+)+') 107 | if self.use_lemmatizer: 108 | self.tokenizer = CustomTokenizer(self.base_tokenizer, self.lemmatizer, self.token_pattern, self.numeric_pattern) 109 | else: 110 | self.tokenizer = CustomTokenizer(self.base_tokenizer, self.stemmer, self.token_pattern, self.numeric_pattern) 111 | 112 | def find_nbest_bigrams(self, corpus, n, metric, min_freq): 113 | print "finding top-%d bigrams using %s..." % (n, metric) 114 | alltokens = [] 115 | simplerTokenizer = CustomTokenizer(self.base_tokenizer, lambda x: x, re.compile(".*"), re.compile("^$")) 116 | for doc in corpus: 117 | for token in [t for t in simplerTokenizer(doc)]: 118 | alltokens.append(token) 119 | bigram_measures = nltk.collocations.BigramAssocMeasures() 120 | finder = BigramCollocationFinder.from_words(alltokens) 121 | finder.apply_freq_filter(min_freq) # bigrams must appear at least 5 times 122 | if metric.lower() == "pmi": 123 | best_bigrams = finder.nbest(bigram_measures.pmi, n) # doctest: +NORMALIZE_WHITESPACE 124 | elif metric.lower() == "chi_sq": 125 | best_bigrams = finder.nbest(bigram_measures.chi_sq, n) # doctest: +NORMALIZE_WHITESPACE 126 | else: 127 | raise Exception("Unknown metric for bigram finder") 128 | return best_bigrams 129 | 130 | def remove_punctuation(self, text): 131 | if not hasattr(self, 'simplerTokenizer'): 132 | self.simplerTokenizer = CustomTokenizer(self.base_tokenizer, lambda x: x, self.token_pattern, self.numeric_pattern) 133 | tokens = self.simplerTokenizer(text) 134 | return ' '.join(tokens) 135 | 136 | def tag_corpus_ner(self, corpus): 137 | if not hasattr(self, 'stanford_ner'): 138 | self.stanford_ner = StanfordNERTagger(self.stanford_ner_path+"classifiers/english.all.3class.distsim.crf.ser.gz", 139 | self.stanford_ner_path+"stanford-ner.jar") 140 | self.stanford_ner._stanford_jar = self.stanford_ner_path+"stanford-ner.jar:"+self.stanford_ner_path+"lib/*" 141 | 142 | print "splitting sentences in corpus (for NER)..." 143 | corpus_sentences = [] 144 | sentence_to_doc_map = {} 145 | sent_no = 0 146 | for d in xrange(len(corpus)): 147 | for sent in self.sentence_splitter(corpus[d]): 148 | corpus_sentences.append(sent) 149 | sentence_to_doc_map[sent_no] = d 150 | sent_no += 1 151 | tokenized_sentences = [] 152 | for sent in corpus_sentences: 153 | tokenized_sentences.append([t for t in re.split(r'\s+', sent) if len(t) > 0]) 154 | #tokenized_sentences = [re.split(r'\s+', sent) for sent in corpus_sentences] 155 | 156 | print "tagging sentences with Stanford NER..." 157 | tagged_sentences = self.stanford_ner.tag_sents(tokenized_sentences) 158 | 159 | # process NER output 160 | tagged_corpus = [] 161 | current_doc_no = 0 162 | current_doc = [] 163 | for i in xrange(len(tagged_sentences)): 164 | doc_no = sentence_to_doc_map[i] 165 | if doc_no == current_doc_no: 166 | current_doc += tagged_sentences[i] 167 | else: 168 | tagged_corpus.append(current_doc) 169 | current_doc = [] 170 | current_doc_no = doc_no 171 | tagged_corpus.append(current_doc) 172 | 173 | # get dictionary of named entities per document 174 | named_entities = [] 175 | for tagged_doc in tagged_corpus: 176 | tags = {} 177 | current_ne = [] 178 | for token, tag in tagged_doc: 179 | if current_ne: 180 | if tag == "O" or (tag != "O" and tag != current_ne[-1][1]): 181 | tags[' '.join([t for t,_ in current_ne])] = current_ne[0][1] 182 | current_ne = [] 183 | if tag != "O": 184 | current_ne.append((token, tag)) 185 | if current_ne: 186 | tags[' '.join([t for t,_ in current_ne])] = current_ne[0][1] 187 | named_entities.append(tags) 188 | 189 | return tagged_corpus, named_entities 190 | 191 | def preprocess_corpus(self, corpus): 192 | print "preprocessing corpus..." 193 | print "corpus size:", len(corpus) 194 | 195 | # first pass over the corpus: prepare for NER 196 | print "first pass over the corpus...\n\tunescape characters" 197 | if self.remove_html: print "\tremove html" 198 | if self.strip_accents: print "\tstrip accents" 199 | if self.join_urls: print "\tjoin URLs" 200 | print "\tjoin compound words\n\tspace out punctuation" 201 | for d in xrange(len(corpus)): 202 | corpus[d] = HTMLParser.HTMLParser().unescape(corpus[d])+" " 203 | if self.remove_html: 204 | corpus[d] = remove_html(corpus[d]) 205 | if self.strip_accents == 'unicode': 206 | corpus[d] = strip_accents_unicode(corpus[d]) 207 | if self.join_urls: 208 | corpus[d] = join_urls(corpus[d], self.url_pattern) 209 | corpus[d] = join_compound_words(corpus[d], self.compound_pattern) 210 | corpus[d] = space_out_punctuation(corpus[d]) 211 | 212 | if self.use_ner: 213 | tagged_corpus, named_entities = self.tag_corpus_ner(corpus) 214 | 215 | # debug NER 216 | fw = codecs.open("debug_NER.txt", "w", "utf-8") 217 | for tags in named_entities: 218 | fw.write(unicode(tags.items())+"\n") 219 | fw.close() 220 | 221 | print "merging named entities as single tokens..." 222 | for d in xrange(len(corpus)): 223 | tags = named_entities[d] 224 | for ne in tags: 225 | corpus[d] = corpus[d].replace(ne, re.sub(r'\s+', '', ne)) 226 | 227 | # second pass over the corpus: remove punctuation and convert to lowercase 228 | # (these were useful above for NER, but now can be removed) 229 | print "second pass over the corpus..." 230 | print "\tremove punctuation" 231 | if self.lowercase: print "\tconvert to lowercase" 232 | simplerTokenizer = CustomTokenizer(self.base_tokenizer, lambda x: x, self.token_pattern, self.numeric_pattern) 233 | for d in xrange(len(corpus)): 234 | corpus[d] = self.remove_punctuation(corpus[d]) 235 | if self.lowercase: 236 | corpus[d] = corpus[d].lower() 237 | 238 | if self.use_bigrams: 239 | # find top N bigrams 240 | #best_bigrams = self.find_nbest_bigrams(corpus, 100, "pmi", 10) 241 | best_bigrams = self.find_nbest_bigrams(corpus, 100, "chi_sq", 10) 242 | 243 | # debug bigrams 244 | fw = codecs.open("debug_bigrams.txt", "w", "utf-8") 245 | for w1, w2 in best_bigrams: 246 | fw.write(w1+" "+w2+"\n") 247 | fw.close() 248 | 249 | print "merging bigrams as single tokens..." 250 | for d in xrange(len(corpus)): 251 | for w1, w2 in best_bigrams: 252 | corpus[d] = corpus[d].replace(w1+" "+w2, w1+w2) 253 | 254 | return corpus 255 | 256 | def convert_to_bag_of_words(self, corpus): 257 | print "converting corpus to bag-of-words format..." 258 | print "\ttokenize documents\n\tremove stopwords" 259 | print "\tapply lemmatizer" if self.use_lemmatizer else "\tapply stemmer" 260 | print "\tremove rare words\n\tremove very frequent words" 261 | vectorizer = CountVectorizer(input='content', 262 | decode_error=self.decode_error, 263 | strip_accents=self.strip_accents, 264 | tokenizer=self.tokenizer, 265 | stop_words=self.stop_words+self.ignore_list, 266 | lowercase=self.lowercase, 267 | max_df=self.max_df, 268 | min_df=self.min_df, 269 | max_features=self.max_features 270 | ) 271 | dtm = vectorizer.fit_transform(corpus) # a sparse matrix 272 | vocab = vectorizer.get_feature_names() # a list 273 | print "vocabulary size:", len(vocab) 274 | 275 | # debug vocabulary 276 | fw = codecs.open("vocabulary.txt", "w", "utf-8") 277 | for word in vocab: 278 | fw.write(word+"\n") 279 | fw.close() 280 | 281 | return dtm, vocab 282 | 283 | 284 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | {one line to give the program's name and a brief idea of what it does.} 635 | Copyright (C) {year} {name of author} 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | {project} Copyright (C) {year} {fullname} 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------