def fetch_relevance(hit, path, q, kwic=True, samples=3):
length = 400
text_snippet = []
if len(hit.bytes) >= samples:
byte_sample = sample(hit.bytes, samples)
else:
byte_sample = hit.bytes
for byte in byte_sample:
byte = [int(byte)]
bytes, byte_start = adjust_bytes(byte, length)
conc_text = f.get_text(hit, byte_start, length, path)
conc_start, conc_middle, conc_end = chunkifier(conc_text, bytes, highlight=True, kwic=kwic)
conc_start = clean_text(conc_start, kwic=kwic)
conc_end = clean_text(conc_end, kwic=kwic)
if kwic:
conc_middle = clean_text(conc_middle, notag=False, kwic=kwic)
conc_text = (conc_start + conc_middle + conc_end).decode('utf-8', 'ignore')
conc_text = align_text(conc_text, 1)
else:
conc_text = (conc_start + conc_middle + conc_end).decode('utf-8', 'ignore')
text_snippet.append(conc_text)
if kwic:
text = '<br>\n'.join(text_snippet)
else:
text = '... '.join(text_snippet)
return text
def fetch_relevance(hit, path, q, kwic=True, samples=3):
length = 400
text_snippet = []
if len(hit.bytes) >= samples:
byte_sample = sample(hit.bytes, samples)
else:
byte_sample = hit.bytes
for byte in byte_sample:
byte = [int(byte)]
bytes, byte_start = adjust_bytes(byte, length)
conc_text = f.get_text(hit, byte_start, length, path)
conc_start, conc_middle, conc_end = chunkifier(conc_text, bytes, highlight=True, kwic=kwic)
conc_start = clean_text(conc_start, kwic=kwic)
conc_end = clean_text(conc_end, kwic=kwic)
if kwic:
conc_middle = clean_text(conc_middle, notag=False, kwic=kwic)
conc_text = (conc_start + conc_middle + conc_end).decode('utf-8', 'ignore')
conc_text = align_text(conc_text, 1)
else:
conc_text = (conc_start + conc_middle + conc_end).decode('utf-8', 'ignore')
text_snippet.append(conc_text)
if kwic:
text = '<br>\n'.join(text_snippet)
else:
text = '... '.join(text_snippet)
return text
def fetch_collocation(results, path, q, filter_words=100):
within_x_words = q['word_num']
## set up filtering of most frequent 100 terms ##
filter_list_path = path + '/data/frequencies/word_frequencies'
filter_words_file = open(filter_list_path)
line_count = 0
filter_list = set([])
for line in filter_words_file:
line_count += 1
word = line.split()[0]
filter_list.add(word.decode('utf-8', 'ignore'))
if line_count > filter_words:
break
## start going though hits ##
left_collocates = {}
right_collocates = {}
all_collocates = {}
count = 0
for hit in results:
## get my chunk of text ##
bytes, byte_start = adjust_bytes(hit.bytes, 400)
conc_text = f.get_text(hit, byte_start, 400, path)
conc_left, conc_middle, conc_right = chunkifier(conc_text, bytes)
left_words = tokenize(conc_left, filter_list, within_x_words, 'left')
right_words = tokenize(conc_right, filter_list, within_x_words, 'right')
for l_word in left_words:
if l_word == q['q']:
continue
if l_word not in left_collocates:
left_collocates[l_word] = 0
left_collocates[l_word] += 1
if l_word not in all_collocates:
all_collocates[l_word] = 0
all_collocates[l_word] += 1
for r_word in right_words:
if r_word == q['q']:
continue
if r_word not in right_collocates:
right_collocates[r_word] = 0
if r_word not in all_collocates:
all_collocates[r_word] = 0
right_collocates[r_word] += 1
all_collocates[r_word] += 1
left_out = sorted(left_collocates.items(), key=lambda x: x[1], reverse=True)[:100]
right_out = sorted(right_collocates.items(), key=lambda x: x[1], reverse=True)[:100]
all_out = sorted(all_collocates.items(), key=lambda x: x[1], reverse=True)[:100]
tuple_out = zip(all_out, left_out, right_out)
return tuple_out
def fetch_collocation(results, path, q, word_filter=True, filter_num=200, full_report=True):
within_x_words = q['word_num']
## set up filtering of most frequent 200 terms ##
filter_list = set([])
if word_filter:
filter_list_path = path + '/data/frequencies/word_frequencies'
filter_words_file = open(filter_list_path)
line_count = 0
for line in filter_words_file:
line_count += 1
word = line.split()[0]
filter_list.add(word.decode('utf-8', 'ignore'))
if line_count > filter_num:
break
## start going though hits ##
left_collocates = defaultdict(int)
right_collocates = defaultdict(int)
all_collocates = defaultdict(int)
count = 0
if not full_report:
q['colloc_start'] = None
q['colloc_end'] = None
for hit in results[q['colloc_start']:q['colloc_end']]:
## get my chunk of text ##
bytes, byte_start = adjust_bytes(hit.bytes, 400)
conc_text = f.get_text(hit, byte_start, 400, path)
conc_left, conc_middle, conc_right = chunkifier(conc_text, bytes)
left_words = tokenize(conc_left, filter_list, within_x_words, 'left')
right_words = tokenize(conc_right, filter_list, within_x_words, 'right')
query_words = set([w.decode('utf-8') for w in q['q'].split('|')])
for l_word in left_words:
if l_word in query_words:
continue
left_collocates[l_word] += 1
all_collocates[l_word] += 1
for r_word in right_words:
if r_word in query_words:
continue
right_collocates[r_word] += 1
all_collocates[r_word] += 1
if full_report:
return dict(all_collocates), dict(left_collocates), dict(right_collocates)
else:
return sorted(all_collocates.items(), key=lambda x: x[1], reverse=True)
def fetch_relevance(hit, path, q, samples=10):
length = 75
text_snippet = []
byte_sample = hit.bytes[:samples]
for byte in byte_sample:
byte = [int(byte)]
bytes, byte_start = adjust_bytes(byte, length)
conc_text = f.get_text(hit, byte_start, length, path)
conc_start, conc_middle, conc_end = chunkifier(conc_text, bytes, highlight=True)
conc_start = clean_text(conc_start)
conc_end = clean_text(conc_end)
conc_text = (conc_start + conc_middle + conc_end).decode('utf-8', 'ignore')
text_snippet.append(conc_text)
text = ' ... '.join(text_snippet)
return text
def fetch_colloc_concordance(results, path, q, filter_words=100):
within_x_words = q['word_num']
direction = q['direction']
collocate = q['collocate'].decode('utf-8', 'ignore')
collocate_num = q['collocate_num']
## set up filtering of most frequent 100 terms ##
filter_list_path = path + '/data/frequencies/word_frequencies'
filter_words_file = open(filter_list_path)
line_count = 0
filter_list = set([])
for line in filter_words_file:
line_count += 1
word = line.split()[0]
filter_list.add(word.decode('utf-8', 'ignore'))
if line_count > filter_words:
break
new_hitlist = []
for hit in results:
## get my chunk of text ##
bytes, byte_start = adjust_bytes(hit.bytes, 400)
conc_text = f.get_text(hit, byte_start, 400, path)
conc_left, conc_middle, conc_right = chunkifier(conc_text, bytes)
if direction =='left':
words = tokenize(conc_left, filter_list, within_x_words, direction)
elif direction == 'right':
words = tokenize(conc_right, filter_list, within_x_words, direction)
else:
words = tokenize(conc_left, filter_list, within_x_words, 'left')
words.extend(tokenize(conc_right, filter_list, within_x_words, 'right'))
if collocate in set(words):
count = words.count(collocate)
hit.collocate_num = count
new_hitlist.append(hit)
if len(new_hitlist) > (q["start"] + q["results_per_page"]):
break
return collocation_hitlist(new_hitlist, collocate_num)
def fetch_colloc_concordance(results, path, q, filter_words=100):
within_x_words = q['word_num']
direction = q['direction']
collocate = q['collocate']
collocate_num = q['collocate_num']
## set up filtering of most frequent 100 terms ##
filter_list_path = path + '/data/frequencies/word_frequencies'
filter_words_file = open(filter_list_path)
line_count = 0
filter_list = set([])
for line in filter_words_file:
line_count += 1
word = line.split()[0]
filter_list.add(word.decode('utf-8', 'ignore'))
if line_count > filter_words:
break
new_hitlist = []
for hit in results:
## get my chunk of text ##
bytes, byte_start = adjust_bytes(hit.bytes, 400)
conc_text = f.get_text(hit, byte_start, 400, path)
conc_left, conc_middle, conc_right = chunkifier(conc_text, bytes)
if direction =='left':
words = tokenize(conc_left, filter_list, within_x_words, direction)
elif direction == 'right':
words = tokenize(conc_right, filter_list, within_x_words, direction)
else:
words = tokenize(conc_left, filter_list, within_x_words, 'left')
words.extend(tokenize(conc_right, filter_list, within_x_words, 'right'))
if collocate in set(words):
count = words.count(collocate)
hit.collocate_num = count
new_hitlist.append(hit)
return collocation_hitlist(new_hitlist, collocate_num)
def fetch_collocation(results, path, q, filter_words=100):
within_x_words = q['word_num']
## set up filtering of most frequent 100 terms ##
filter_list_path = path + '/data/frequencies/word_frequencies'
filter_words_file = open(filter_list_path)
line_count = 0
filter_list = set([])
for line in filter_words_file:
line_count += 1
word = line.split()[0]
filter_list.add(word.decode('utf-8', 'ignore'))
if line_count > filter_words:
break
## start going though hits ##
left_collocates = {}
right_collocates = {}
all_collocates = {}
count = 0
for hit in results:
## get my chunk of text ##
bytes, byte_start = adjust_bytes(hit.bytes, 400)
conc_text = f.get_text(hit, byte_start, 400, path)
conc_left, conc_middle, conc_right = chunkifier(conc_text, bytes)
left_words = tokenize(conc_left, filter_list, within_x_words, 'left')
if not sum([len(i) for i in left_words]):
count += 1
right_words = tokenize(conc_right, filter_list, within_x_words,
'right')
for l_word in left_words:
if l_word == q['q']:
continue
if l_word not in left_collocates:
left_collocates[l_word] = 0
left_collocates[l_word] += 1
if l_word not in all_collocates:
all_collocates[l_word] = 0
all_collocates[l_word] += 1
for r_word in right_words:
if r_word == q['q']:
continue
if r_word not in right_collocates:
right_collocates[r_word] = 0
if r_word not in all_collocates:
all_collocates[r_word] = 0
right_collocates[r_word] += 1
all_collocates[r_word] += 1
left_out = sorted(left_collocates.items(),
key=lambda x: x[1],
reverse=True)
right_out = sorted(right_collocates.items(),
key=lambda x: x[1],
reverse=True)
all_out = sorted(all_collocates.items(), key=lambda x: x[1], reverse=True)
tuple_out = zip(all_out, left_out, right_out)
print >> sys.stderr, "COUNT", count
return tuple_out