def load_sensor_data_and_labels_by_name(record_dir,
type_name='accel',
inference=False):
print(f'\nProcessing record dir： {record_dir}')
if type_name == 'accel':
suffix = ACC_SUFFIX
elif type_name == 'gyro':
suffix = GYRO_SUFFIX
elif type_name == 'magnet':
suffix = MAGNET_SUFFIX
else:
raise ValueError(f'Unsupport type name: {type_name}')
# Load data and split ts out
data_file = record_dir / f'{record_dir.name}-{suffix}'
if not data_file.exists():
Log.e(f'Record has no {type_name} data, do nothing')
return None, None, None
data_all = pd.read_csv(data_file)
data_all = data_all[:-1] # Remove the last line, maybe one broken line
data_ts, data = split_ts_and_data(data_all)
# Fitler outliner
print(f'Origing raw data shape: {data.shape}')
exception_point_process(data.T[0], inplace=True)
exception_point_process(data.T[1], inplace=True)
exception_point_process(data.T[2], inplace=True)
# Low pass
data_lp = signal.filtfilt(FILTER_B, FILTER_A, data, axis=0)
data = np.hstack((data, data_lp))
# To get the data labels
label_file = record_dir / f'{record_dir.name}-{LABEL_SUFFIX}'
if label_file.exists():
labels_ts = load_label_result(label_file)
labels = label_convert_ts2index(labels_ts, data_ts)
else:
if inference:
Log.i('Label file not exists, Using activity type name Infer.')
activity_type_name = get_activity_type_name_by_file_name(
record_dir.name)
is_daily = activity_type_name in LABEL_DAILY
is_other_sports = activity_type_name in LABEL_OTHER_SPORTS
if is_daily or is_other_sports:
if is_daily:
activity_type_name_as = 'DailyActivity'
else:
activity_type_name_as = 'OtherSports'
activity_type = LABEL_ITEMS_INDEX_DICT.get(
activity_type_name_as)
print(
f'Treat {activity_type_name} as {activity_type_name_as}: {activity_type}'
)
labels = [(activity_type, 0, data.shape[0])]
else:
labels = None
else:
Log.w(
'Label file not exists, Inference by activity type name disabled. Skipped!'
)
labels = None
return data_ts, data, labels
