def build_full_link_set_dictionary(in_tokenized_sentences, in_language = DEFAULT_LANGUAGE):
stopped_sentences = common.stop(in_tokenized_sentences, in_language)
full_link_set = link_lexemes.extract_full_link_set(stopped_sentences, True)
result = collections.defaultdict(lambda: 0)
for lexeme in full_link_set:
result[lexeme[0]] = lexeme[1]
return result
def build_stopped_freq_dictionary(in_tokenized_sentences, in_language = DEFAULT_LANGUAGE):
result = collections.defaultdict(lambda: 0)
stopped_sentences = common.stop(in_tokenized_sentences, in_language)
for sentence in stopped_sentences:
for token in sentence:
result[token.lower()] += 1
return result
def deploy_tomcat_apps(apps='',ext='war',version='release',silent=1,profile=''):
get_tomcat_apps(apps=apps,version=version,ext=ext)
put_tomcat_apps(apps=apps,ext=ext)
# Formating a list of services
applications=apps.split(' ')
services=list()
for app in applications:
services.append('tomcat7-'+app)
services=' '.join(services)
common.start(services=services,silent=1)
common.stop(services=services,silent=1)
install_tomcat_apps(apps=apps,ext=ext)
get_profile_app(apps=apps,ext='tar.gz',version='',profile=profile)
put_profile_app(profiles=profile,ext='tar.gz')
install_profile_app(apps=apps,profile=profile,ext='tar.gz')
common.start(services=services,silent=1)
def deploy_tomcat_apps(apps='',
ext='war',
version='release',
silent=1,
profile=''):
get_tomcat_apps(apps=apps, version=version, ext=ext)
put_tomcat_apps(apps=apps, ext=ext)
# Formating a list of services
applications = apps.split(' ')
services = list()
for app in applications:
services.append('tomcat7-' + app)
services = ' '.join(services)
common.start(services=services, silent=1)
common.stop(services=services, silent=1)
install_tomcat_apps(apps=apps, ext=ext)
get_profile_app(apps=apps, ext='tar.gz', version='', profile=profile)
put_profile_app(profiles=profile, ext='tar.gz')
install_profile_app(apps=apps, profile=profile, ext='tar.gz')
common.start(services=services, silent=1)
def build_associative_power_dictionary(in_tokenized_sentences, in_language = DEFAULT_LANGUAGE):
stopped_sentences = common.stop(in_tokenized_sentences, in_language)
filtered_sentences = []
# we need filtered sentences 1) not to contain stop words, 2) not to contain 1-freq. words
for sentence in stopped_sentences:
filtered_sentence = [word for word in sentence]
filtered_sentences.append(filtered_sentence)
associative_power_dict = collections.defaultdict(lambda: set([]))
for sentence in filtered_sentences:
for index in xrange(len(sentence)):
word = sentence[index]
if index != 0:
associative_power_dict[word].add(sentence[index - 1])
if index != len(sentence) - 1:
associative_power_dict[word].add(sentence[index + 1])
result_dict = {word: len(context) for (word, context) in associative_power_dict.iteritems()}
return result_dict
players[CONTROL]].mean(
axis=1)
BehaviouralResults.MT[:,
CONTROL] = mat['MT_original'][:, common.
players[CONTROL]].mean(
axis=1)
movement_offset = TimePoint(
seconds=(BehaviouralResults.RT.mean(axis=1).mean() +
BehaviouralResults.MT.mean(axis=1).mean()) / 1000,
fs=common.FS.eye)
# 50% of action time
print(movement_offset.seconds * .5)
common.stop()
## extract data
start = time.time()
H = np.empty([
len(y_edges) - 1,
len(x_edges) - 1, common.Number.movement_epochs, common.Number.subjects
])
H[:] = np.nan
gaze = np.empty([
common.Number.frames1, common.Number.axes, common.Number.trials,
common.Number.movement_epochs, common.Number.subjects
])
def stopKiller(self):
common.stop()
self.refreshTrayMenu()
def handle_noargs(self, **options):
common.stop()
common.start()
def onRunClicked(self):
if common.isRunning():
common.stop()
else:
common.run()
self.refreshRunButtonIcon()
