def smoothening(X_raw, normalize=True, window_size=300, downsample=1):
if len(X_raw.shape) > 1:
wg = utils.window_generator_ND(X_raw, window_size=window_size, downsample=downsample)
else:
wg = utils.window_generator_1D(X_raw, window_size=window_size, downsample=downsample)
smoothened = []
for windowed_data in wg:
if normalize:
windowed_data = utils.normalize(windowed_data)
if len(X_raw.shape) > 1:
smoothened.append( np.mean(windowed_data, axis=0).tolist() )
else:
smoothened.appened( np.mean(windowed_data) )
return np.array(smoothened)
def preprocess_sample(X_raw, normalize=True, filters=utils.FREQUENCY_BANDS.keys(), window_size=300, downsample=1, shuffle=True):
if normalize:
X_raw = utils.normalize(X_raw)
if len(X_raw.shape) > 1:
wg = utils.window_generator_ND(X_raw, window_size=window_size, downsample=downsample)
else:
wg = utils.window_generator_1D(X_raw, window_size=window_size, downsample=downsample)
features_extracted = []
for windowed_data in wg:
data_point = []
for filter_name in filters:
low, high = utils.FREQUENCY_BANDS[filter_name]
if len(X_raw.shape) > 1:
data_point.extend(np.mean(utils.butter_apply(windowed_data, low, high), axis=0).tolist())
else:
data_point.append( np.mean( utils.butter_apply(windowed_data, low, high) ) )
features_extracted.append(data_point)
return np.array(features_extracted)