def google(self, menuItem, pid):
try:
webbrowser.open("https://google.com/search?q=Mac process '%s'" % process.get_name(pid))
log.log("Google process %d (%s)" % (pid, process.get_name(pid)))
except:
error.error("Error in menu callback")
finally:
self.handle_action()
def suspend_process(pid, manual=False):
name = process.get_name(pid)
if process.suspend_pid(pid):
suspended_tasks.add((pid, name))
if manual:
set_suspend_preference(name, True)
else:
set_suspend_preference(name, False)
def resume_process(pid, manual=False):
name = process.get_name(pid)
if manual or (pid,name) in suspended_tasks:
if process.resume_pid(pid):
for pid, suspended_name in list(suspended_tasks):
if name == suspended_name:
suspended_tasks.remove((pid, name))
if manual:
set_suspend_preference(name, "")
def suspend_process(pid, manual=False, battery=False):
name = process.get_name(pid)
if manual:
set_suspend_preference(name, SUSPEND_ON_BATTERY if battery else SUSPEND_ALWAYS)
if battery and not process.on_battery():
return
if process.suspend_pid(pid):
suspended_tasks.add((pid, name))
else:
set_suspend_preference(name, "")
def menu_item_for_process(self, p, resumable=False, suspendable=False):
if not p:
return None
name = process.get_name(p.pid)
cpu = process.cpu(p.pid)
percent = max(0 if resumable else 1, int(100 * cpu))
if p.pid != utils.get_current_app_pid() and not resumable and percent < IDLE_PROCESS_PERCENT_CPU:
return None
item = rumps.MenuItem("%s - %d%%" % (name, percent))
item.icon = self.get_icon(percent)
item.percent = percent
item.pid = p.pid
item.add(rumps.MenuItem(TITLE_GOOGLE, callback=functools.partial(self.google, pid=p.pid)))
if resumable:
item.add(rumps.MenuItem(TITLE_RESUME, callback=functools.partial(self.resume, pid=p.pid)))
elif suspendable:
item.add(rumps.MenuItem(TITLE_SUSPEND, callback=functools.partial(self.suspend, pid=p.pid)))
item.add(rumps.MenuItem(TITLE_TERMINATE, callback=functools.partial(self.terminate, pid=p.pid)))
return item
def get_suspend_preference(pid):
return preferences.get("suspend - %s" % process.get_name(pid))
# similar_true_positive_count = {"PER": 0, "RAN": 0, "ORG": 0, "TIT": 0, "ROL": 0, "LOC": 0}
# similar_false_positive_count = {"PER": 0, "RAN": 0, "ORG": 0, "TIT": 0, "ROL": 0, "LOC": 0}
# similar_false_negative_count = {"PER": 0, "RAN": 0, "ORG": 0, "TIT": 0, "ROL": 0, "LOC": 0}
for line in test_lines:
sentence = line.strip()
id, s_position = dataset_sentences[sentence]
print('=================================================')
print("From file with id: ", id)
print("Sentence: ")
pp.pprint(sentence)
print('\n-------------- Ground truth --------------')
ground_truth_names = []
ground_truth_tags = []
for label in dataset_labels[id][s_position]:
gt_name = process.get_name(sentence, label)
ground_truth_names.append(tokenizer(gt_name))
ground_truth_tags.append(label_mapping[label[1]])
print(ground_truth_names[-1], "||||", label[1], "||||", label[2])
# print(ground_truth_names)
print('\n-------------- Predicted labels --------------')
pred_names = []
pred_tags = []
for name_position in sentence_pred_tags[sentence].keys():
pred_name = sentence[name_position[0]:name_position[1]]
pred_names.append(tokenizer(pred_name))
# pred_names.append(pred_name)
pred_tags.append(sentence_pred_tags[sentence][name_position])
print(pred_names[-1], "||||", inv_label_mapping[pred_tags[-1]],
"||||", name_position)