def highlight_text(args, parser):
"""Outputs the result of highlighting a text."""
tokenizer = utils.get_tokenizer(args)
corpus = utils.get_corpus(args)
output_dir = os.path.abspath(args.output)
if os.path.exists(output_dir):
parser.exit(status=3,
message='Output directory already exists, '
'aborting.\n')
os.makedirs(output_dir, exist_ok=True)
if args.ngrams:
if args.label is None or len(args.label) != len(args.ngrams):
parser.error('There must be as many labels as there are files '
'of n-grams')
report = tacl.NgramHighlightReport(corpus, tokenizer)
ngrams = []
for ngram_file in args.ngrams:
ngrams.append(utils.get_ngrams(ngram_file))
minus_ngrams = []
if args.minus_ngrams:
minus_ngrams = utils.get_ngrams(args.minus_ngrams)
report.generate(args.output, args.base_name, ngrams, args.label,
minus_ngrams)
else:
report = tacl.ResultsHighlightReport(corpus, tokenizer)
report.generate(args.output, args.base_name, args.results)
def search_texts(args, parser):
"""Searches texts for presence of n-grams."""
store = utils.get_data_store(args)
corpus = utils.get_corpus(args)
catalogue = utils.get_catalogue(args)
store.validate(corpus, catalogue)
ngrams = utils.get_ngrams(args.ngrams)
store.search(catalogue, ngrams, sys.stdout)
def search_texts(args, parser):
"""Searches texts for presence of n-grams."""
store = utils.get_data_store(args)
corpus = utils.get_corpus(args)
catalogue = tacl.Catalogue()
if args.catalogue:
catalogue.load(args.catalogue)
store.validate(corpus, catalogue)
ngrams = utils.get_ngrams(args.ngrams)
store.search(catalogue, ngrams, sys.stdout)
def highlight_text(args, parser):
"""Outputs the result of highlighting a text."""
tokenizer = utils.get_tokenizer(args)
corpus = utils.get_corpus(args)
output_dir = os.path.abspath(args.output)
if os.path.exists(output_dir):
parser.exit(status=3, message="Output directory already exists, " "aborting.\n")
os.makedirs(output_dir, exist_ok=True)
if args.ngrams:
if args.label is None or len(args.label) != len(args.ngrams):
parser.error("There must be as many labels as there are files " "of n-grams")
report = tacl.NgramHighlightReport(corpus, tokenizer)
ngrams = []
for ngram_file in args.ngrams:
ngrams.append(utils.get_ngrams(ngram_file))
minus_ngrams = []
if args.minus_ngrams:
minus_ngrams = utils.get_ngrams(args.minus_ngrams)
report.generate(args.output, args.base_name, ngrams, args.label, minus_ngrams)
else:
report = tacl.ResultsHighlightReport(corpus, tokenizer)
report.generate(args.output, args.base_name, args.results)
