def extract_from_file(filename, num_process):
import utils
global LOGGER
LOGGER = utils.get_logger()
dataset_path = u'../datasets/wiki'
# get processed words
processed_words = get_target_words(dataset_path)
jobs = dd(list)
for line in codecs.open(filename, encoding='utf-8'):
line = line.split()
target_word, page_title, offset = line[:3]
if target_word not in processed_words:
jobs[target_word].append(dict(word=target_word, page_title=page_title, offset=offset, fetch_links=True))
LOGGER.info("Total {} of jobs available. Num of consumer = {}".format(len(jobs), num_process))
if num_process > 1:
pool = Pool(num_process)
pool.map(extract_instances_for_word, jobs.values())
else:
# for v in jobs.values():
for v in [jobs['milk']]:
extract_instances_for_word(v)
LOGGER.info("Done.")
def extract_from_file(filename, num_process, dataset_path, fetch_links=True):
import utils
global LOGGER
LOGGER = utils.get_logger()
# get processed words
processed_words = get_target_words(dataset_path)
jobs = dd(list)
for line in codecs.open(filename, encoding='utf-8'):
line = line.split()
target_word, page_title, offset = line[:3]
if target_word not in processed_words:
jobs[target_word].append(dict(word=target_word, page_title=page_title, offset=offset,
fetch_links=fetch_links))
LOGGER.info("Total {} of jobs available. Num of consumer = {}".format(len(jobs), num_process))
if num_process > 1:
pool = Pool(num_process)
func = partial(extract_instances_for_word, wiki_dir=dataset_path)
pool.map(func, jobs.values())
else:
for v in jobs.values():
# for v in [jobs['milk']]:
extract_instances_for_word(v, dataset_path)
LOGGER.info("Done.")
def get_sense_counts(wiki_dir):
words = get_target_words(wiki_dir)
word_sense_dict = dd(list)
for word in words:
fn = os.path.join(wiki_dir, "%s.clean.txt" % word)
for line in codecs.open(fn, encoding='utf8'):
line = line.strip().split('\t')
try:
sense = line[2]
word_sense_dict[word].append(sense)
except IndexError:
print "IndexError for %s - %s" % (word, line)
word_sense_count = dict()
for w, s in word_sense_dict.iteritems():
word_sense_count[w] = Counter(s)
return word_sense_count
import numpy as np
import matplotlib.pyplot as plt
import utils
patient = 83
# session = 2
# block_type = 'syntactic' # syntactic/pragmatic
SOA = 600 # in msec
for session in [3]:
# for block_type in ['pragmatic', 'syntactic']:
for block_type in ['pragmatic']:
#############
# LOAD DATA #
#############
target_words = utils.get_target_words(patient, session, block_type)
path2data = f'../data/patient_{patient}_s{session}'
TrialInfo, cherries = utils.get_data(path2data, block_type)
assert len(
TrialInfo['word_strings']) == cherries[1]['trial_data'].shape[
0] # check that there's unit activity for each word
####################
# GENERATE RASTERS #
####################
num_target_words = len(target_words)
times = np.arange(-1000, 2000)
linelength = 0.3
for unit in cherries.keys():
# PREPARE FIGURE