def _filter_bags(self, filter_by="trial"):
"""Get relevant bags for parsing for each trial"""
if filter_by == "trial":
bags_dict = {1: [], 2: [], 3: []}
elif filter_by == "participant":
bags_dict = {}
for root, dirs, filenames in os.walk(args.bag_path):
for p in dirs:
if p in ["ABORTED", "PILOTS"]:
continue
try:
excluded_trials = set(self.exclude[p])
except KeyError:
excluded_trials = set(())
bags = list(participant_bags(args.bag_path, p))
for i in (TRIALS - excluded_trials):
if filter_by == "trial":
bags_dict[i].append(bags[i - 1])
elif filter_by == "participant":
if p not in bags_dict.keys():
bags_dict[p] = []
bags_dict[p].append(bags[i - 1])
break # Dont want to loop through ABORTED or PILOT dirs
return bags_dict
def participant_to_session(path, participant, instructions):
trials = []
bags = participant_bags(path, participant)
for b, instr in zip(bags, instructions):
print('\nLoading: {} ({}: {})'.format(participant, instr,
os.path.split(b.filename)[-1]))
pairer = parse_bag(b)
pairs = list(pairer.get_associations())
trials.append(
Trial(instruction=instr,
pairs=pairs,
initial_time=b.get_start_time()))
print("Total: {} actions found with {} non-empty utterances.".format(
len(pairs), sum([len(u) > 0 for a, u in pairs])))
try:
next(bags)
except StopIteration:
pass
else:
raise ValueError('Too many bag files for {} (expected {})'.format(
participant, len(bags)))
return Session(trials)
guess_participant_id,
bag_summary, parse_bag)
parser = argparse.ArgumentParser(
"Displays the bag as a log of relevant information")
parser.add_argument('path',
help='path to the experiment files',
default=os.path.curdir)
parser.add_argument('participant', help='id of the participant')
if __name__ == "__main__":
args = parser.parse_args()
participant = guess_participant_id(args.path, args.participant)
for b in participant_bags(args.path, participant):
print('\nNew Session: {} ({})\n'.format(args.participant,
b.filename.split('/')[-1]))
print('Topics:')
types, topics = b.get_type_and_topic_info()
for t in topics:
print('- {}: {} messages of type {}'.format(
t, topics[t].message_count, topics[t].msg_type))
bag_summary(b)
pairer = parse_bag(b)
print('\nAssociation:')
pairs = list(pairer.get_associations())
for a, u in pairs:
print(a[0], ' | '.join([uu[0] for uu in u]))
print("Total: {} actions found with {} non-empty utterances.".format(
len(pairs), sum([len(u) > 0 for a, u in pairs])))
i = 0
for m in bag.read_messages():
if m.topic == TOPIC:
model.predict(cntxt, m.message.utter, plot=True)
cntxt.append(m.message.action)
i += 1
plt.tight_layout()
path = os.path.join(fig_path, "sample_{}_{}".format(
m.message.result, i))
plt.savefig(path + ".pdf", format="pdf")
plt.clf()
def plot_all_trials(bags):
for i, b in enumerate(bags):
plot_trial(i + 1, b)
args = parser.parse_args()
data_path = args.bag_path
BAGS = list(participant_bags(data_path, args.participant))
trial = args.trial
plot_all_trials(BAGS)
#plot_trial(trial, BAGS[trial])