"""

This function walks through directories in train, and reads touch.csv and audio.wav files.

It invokes create_feature_vector function to extract features, extracts labels and writes them to

corresponding csv files.

:return:

"""

top_level_train_dirs = os.listdir(TRAIN_DATA_DIR)

top_level_train_dirs = [x for x in top_level_train_dirs if not x.startswith('.')]

# Remove prep data files if exist

try:

os.remove(TRAIN_FEATURES_FILE)

os.remove(TRAIN_LABELS_FILE)

except OSError:

pass

# Write header to the csv file that should contain feature matrix

with open(TRAIN_FEATURES_FILE, 'wb') as write_header:

writer = csv.writer(write_header)

writer.writerow(['x', 'y', 'major', 'minor', 'pressure', 'orientation', 'signal energy',

'signal energy entropy', 'spectral centroid', 'spectral spread', 'spectral entropy',

'spectral roll off', 'mode'])

# Walk through directories in train directory and extract features from files,

# along with label from directory names

for dir_name in top_level_train_dirs:

mode = dir_name.split('-')[0]

timestamped_dirs = os.listdir(os.path.join(TRAIN_DATA_DIR, dir_name))

timestamped_dirs = [x for x in timestamped_dirs if not x.startswith('.')]

for folder_name in timestamped_dirs:

split_details = folder_name.split('-')

label = split_details[1]

# timestamp = split_details[0]

audio_file = os.path.join(TRAIN_DATA_DIR, dir_name, folder_name, 'audio.wav')

touch_file = os.path.join(TRAIN_DATA_DIR, dir_name, folder_name, 'touch.csv')

touch_features = pd.read_csv(touch_file, sep=',', skiprows=1, header=None)

(fs, frame) = sciw.read(audio_file, mmap=False)

feature_matrix = create_feature_vector(touch_features, frame, fs, mode)

with open(TRAIN_FEATURES_FILE, 'a') as f_handle:

np.savetxt(f_handle, feature_matrix, delimiter=',')

with open(TRAIN_LABELS_FILE, 'a') as f_handle: