def dae_generator(subjects, batch_size):
# intialize tracking and saving items
threat_zone_examples = []
start_time = timer()
labels = get_subject_labels()
print_shape = True
while True:
for i in range(0, len(subjects), batch_size):
y_batch = []
x_batch = []
for subject in subjects[i:i + batch_size]:
images = tsa.read_data(INPUT_FOLDER + '/' + subject + '.aps')
# transpose so that the slice is the first dimension shape(16, 660, 512)
images = images.transpose()
images = np.reshape(images, (16, 660, 512, 1))
for image in images:
y_batch.append(image)
y_batch = np.array(y_batch)
noise_factor = 0.2
noisy_images = images + noise_factor * np.random.normal(
loc=0.0, scale=1.0, size=images.shape)
for image in noisy_images:
x_batch.append(image)
x_batch = np.array(x_batch)
yield np.array(y_batch), np.array(y_batch)
def make_submission():
model = MVCNN()
sub_file = open("labels/stage1_submission_1.csv", "w")
sub_file.write("Id,Probability\n")
model.load_weights(MVCNN_PATH)
example_sub = "labels/stage1_sample_submission.csv"
df = pd.read_csv(example_sub)
# Separate the zone and subject id into a df
df['Subject'], df['Zone'] = df['Id'].str.split('_', 1).str
#TODO: convert zone to correct int here
df = df[['Subject', 'Zone', 'Probability']]
subjects = df['Subject'].unique()
get_train_test_file_list()
"""
print("hmm we tryin")
gen = generator(TRAIN_SUBJECT_LIST, 1)
x, y = next(gen)
print (x['0'].shape)
model.predict(x)
print ("ok, here")
"""
prog = 0
for subject in subjects:
images = tsa.read_data(INPUT_FOLDER + '/' + subject + '.aps')
images = images.transpose()
images_to_predict = {}
image_array = []
for i in range(0, len(images)):
images_to_predict[str(i)] = []
images_to_predict[str(i)].append(
np.reshape(images[i], (660, 512, 1)))
images_to_predict[str(i)] = np.array(images_to_predict[str(i)])
image_array.append(np.array(images_to_predict[str(i)]))
def pred_gen():
while True:
yield images_to_predict
prediction = model.predict_generator(pred_gen(), steps=1)
prediction = prediction[0]
print(prediction)
for i in range(0, len(prediction)):
sub_file.write(
str(subject) + "_" + "Zone" + str(i + 1) + "," +
str(prediction[i]) + "\n")
print(str(prog))
prog = prog + 1
sub_file.close()
def readData(file):
print(file)
imgs1 = read_data(file)
# print("原始16个不同视图的图片的shape: {}".format(imgs.shape))
# ----------------------------------------------------
# 图片的shape（高，宽）与（宽，高）对图片本身来说是否一样？？
# (512,660,16) --> (16, 660, 512)
imgs1 = imgs1.transpose()
# print("转换过的不同视图的图片的shape: {}".format(imgs.shape))
return imgs1
def generator(subjects, batch_size):
# intialize tracking and saving items
threat_zone_examples = []
start_time = timer()
labels = get_subject_labels()
print_shape = True
while True:
for i in range(0, len(subjects), batch_size):
y_batch = []
features = {}
for j in range(0, 16):
features[str(j)] = []
for subject in subjects[i:i + batch_size]:
images = tsa.read_data(INPUT_FOLDER + '/' + subject + '.aps')
# transpose so that the slice is the first dimension shape(16, 620, 512)
images = images.transpose()
for j in range(0, 16):
fake_rgb = np.array([images[j], images[j], images[j]])
image = fake_rgb.transpose()
"""if print_shape:
print ("Shape of re-transposed image:")
print (image.shape)
print_shape = False
"""
#resized_image = images[j] #scipy.ndimage.zoom(images[j], (0.5, 0.5))
#features[str(j)].append(np.reshape(resized_image, (660, 512, 1)))# (330, 256, 1)))
features[str(j)].append(image) # (330, 256, 1)))
# get label
y = np.zeros((17))
threat_list = labels.loc[labels['Subject'] == subject.split(
".")[0]]
threat_iter = threat_list.iterrows()
while True:
threat = next(threat_iter, None)
if threat is None:
break
threat = threat[1]
if threat['Probability'] is 1:
zone = threat['Zone']
zone = int(zone[4:])
y[zone - 1] = 1
"""
y = np.array(tsa.get_subject_zone_label(THREAT_ZONE,
tsa.get_subject_labels(STAGE1_LABELS, subject)))
np.reshape(y, (2, 1))
"""
y_batch.append(y)
for j in range(0, 16):
features[str(j)] = np.array(features[str(j)])
#features2.append(np.array(features[j]))
yield features, np.array(y_batch)
def big_im_generator(subjects, batch_size):
for_vgg = False
# intialize tracking and saving items
threat_zone_examples = []
start_time = timer()
labels = get_subject_labels()
print_shape = True
while True:
for i in range(0, len(subjects), batch_size):
y_batch = []
x_batch = []
for subject in subjects[i:i + batch_size]:
images = tsa.read_data(INPUT_FOLDER + '/' + subject + '.aps')
# transpose so that the slice is the first dimension shape(16, 620, 512)
images = images.transpose()
x_images = []
if for_vgg:
for j in range(0, 16):
image_to_use = scipy.ndimage.zoom(
images[j], (0.75, 0.75))
fake_rgb = np.array(
[image_to_use, image_to_use, image_to_use])
image = fake_rgb.transpose()
x_images.append(image)
else:
for j in range(0, 16):
image_to_use = scipy.ndimage.zoom(
images[j], (0.5, 0.5))
image_to_use = image_to_use.reshape(
len(image_to_use), len(image_to_use[0]), 1)
x_images.append(image_to_use)
x = np.hstack(np.array(x_images))
#x = x.reshape((495, 6144, 1))
# get label
y = np.zeros((17))
threat_list = labels.loc[labels['Subject'] == subject.split(
".")[0]]
threat_iter = threat_list.iterrows()
while True:
threat = next(threat_iter, None)
if threat is None:
break
threat = threat[1]
if threat['Probability'] is 1:
zone = threat['Zone']
zone = int(zone[4:])
y[zone - 1] = 1
y_batch.append(y)
x_batch.append(x)
yield np.array(x_batch), np.array(y_batch)
def preprocess_tsa_data(type='labels'):
"""Preprocess all a3d files for training and persist to disk."""
scans = get_labels(type)
crop_log = {}
for subject_id in tqdm(scans.subject_id.unique()):
image = tsa.read_data(path.join(path_a3d, subject_id + '.a3d'))
image = image.transpose(2, 0, 1) # axis are now height (top) x width (side) x depth (front)
image = tsa.convert_to_grayscale(image)
cropped_image = crop_image(image)
resized_image = resize(cropped_image, (IMAGE_DIM, IMAGE_DIM, IMAGE_DIM), mode='constant')
np.save(path.join(path_cache, subject_id + '.npy'), resized_image)
crop_log[subject_id] = cropped_image.shape
# save a cross section for validating cropping
save_image(
path.join(path_plots, subject_id + '.png'),
tsa.convert_to_grayscale(resized_image)[:, :, np.floor(resized_image.shape[2] / 2).astype(int) - 5],
)
with open(path.join(path_logs, 'crop_log_{}.json'.format(type)), 'w') as f:
json.dump(crop_log, f, indent=4)
def preprocess_lb_plates_for_tz(tz, flip_tz):
print("prep lb for zone - " + str(tz))
TOP_DIR = "stage_2_plates/" + str(tz) + "/full_size/ims/"
#TOP_DIR = "lb_plates/" + str(tz) + "/full_size/ims/"
get_lb_file_list()
#list existing files so we can skip those
files_list = os.listdir(TOP_DIR)
already_prepped_subs = []
for sub_file in files_list:
already_prepped_subs.append(sub_file.split(".")[0])
if flip_tz:
flip_list = [False, True]
else:
flip_list = [False]
for should_flip in flip_list:
print("advance sf")
for subject in LB_SUBJECT_LIST:
sub_name = subject
if should_flip:
sub_name = sub_name + "_Zone" + str(flip_tz)
else:
sub_name = sub_name + "_Zone" + str(tz)
print("name: " + str(sub_name))
if sub_name not in already_prepped_subs:
#Try - Keep running even if one file is troublesome (corrupted or missing data)
try:
images = tsa.read_data(INPUT_FOLDER + '/' + subject +
'.aps')
images = images.transpose()
if tz == 9:
images_to_use = images
elif tz == 5:
images_to_use = switch_chest_back(
images) if should_flip else images
else:
images_to_use = flip_and_switch(
images) if should_flip else images
cropped_ims = []
for i in range(0, len(images_to_use)):
if tz in []:
#if tz in [9, 15, 3, 13]:
cropped_ims.append(
crop_and_resize_2D(images_to_use[i]))
else:
if i == 3 or i == 5 or i == 11 or i == 13:
cropped_ims.append(
crop_and_resize_2D(images_to_use[i],
x_resize_ratio=0.75))
elif i == 4 or i == 12:
cropped_ims.append(
crop_and_resize_2D(images_to_use[i],
x_resize_ratio=0.5))
else:
cropped_ims.append(
crop_and_resize_2D(images_to_use[i]))
pre_stack = get_zone_fs(tz, cropped_ims)
stack = np.hstack(pre_stack)
path = TOP_DIR + sub_name + ".jpg"
scipy.misc.imsave(path, stack)
except:
print("Failed!!!")
print("next!")
def preprocess_plates(tz, flip_tz):
print("preprocess plates for tz: " + str(tz) + " and: " + str(flip_tz))
TOP_DIR = "plates/" + str(tz) + "/full_size/"
TRAIN_DIR_NAME = "train/"
TEST_DIR_NAME = "test/"
THREAT_DIR_NAME = "threats/"
NON_THREAT_DIR_NAME = "non_threats/"
FLIP_NAME = "flip_"
DIRS = [
TOP_DIR + TRAIN_DIR_NAME + THREAT_DIR_NAME,
TOP_DIR + TRAIN_DIR_NAME + NON_THREAT_DIR_NAME,
TOP_DIR + TEST_DIR_NAME + THREAT_DIR_NAME,
TOP_DIR + TEST_DIR_NAME + NON_THREAT_DIR_NAME
]
get_train_test_file_list()
labels = get_subject_labels()
#list existing files so we can skip those
files_list = []
for directory in DIRS:
files_list = files_list + os.listdir(directory)
already_prepped_subs = []
for sub_file in files_list:
already_prepped_subs.append(sub_file.split(".")[0])
for already_prepped in already_prepped_subs[:5]:
print("ap: " + already_prepped)
for should_flip in [False, True]:
print("advance sf")
for sub_list in [TEST_SUBJECT_LIST, TRAIN_SUBJECT_LIST]:
print("advance sub list")
folder = "train"
if sub_list is TEST_SUBJECT_LIST:
folder = "test"
for subject in sub_list:
sub_name = subject
if should_flip:
sub_name = FLIP_NAME + subject
print("name: " + str(sub_name))
if sub_name in already_prepped_subs:
print("already has... " + str(sub_name))
else:
#Try - Keep running even if one file is troublesome (corrupted or missing data)
try:
images = tsa.read_data(INPUT_FOLDER + '/' + subject +
'.aps')
images = images.transpose()
if tz == 5:
images_to_use = switch_chest_back(
images) if should_flip else images
else:
images_to_use = flip_and_switch(
images) if should_flip else images
cropped_ims = []
for i in range(0, len(images_to_use)):
if i == 3 or i == 5 or i == 11 or i == 13:
cropped_ims.append(
crop_and_resize_2D(images_to_use[i],
x_resize_ratio=0.75))
elif i == 4 or i == 12:
cropped_ims.append(
crop_and_resize_2D(images_to_use[i],
x_resize_ratio=0.5))
else:
cropped_ims.append(
crop_and_resize_2D(images_to_use[i]))
pre_stack = get_zone_fs(tz, cropped_ims)
stack = np.hstack(pre_stack)
path = TOP_DIR + folder + "/"
threat_prob_0 = get_threat_prob(tz, labels, subject)
threat_prob_1 = get_threat_prob(
flip_tz, labels, subject)
"""threat_prob_9 = get_threat_prob(9, labels, subject)
if threat_prob_9 == True:
path = path + THREAT_DIR_NAME
else:
path = path + NON_THREAT_DIR_NAME"""
if threat_prob_0 == 1 and threat_prob_1 == 1:
path = path + THREAT_DIR_NAME
elif should_flip and threat_prob_1 == 1:
path = path + THREAT_DIR_NAME
elif not should_flip and threat_prob_0 == 1:
path = path + THREAT_DIR_NAME
else:
path = path + NON_THREAT_DIR_NAME
scipy.misc.imsave(path + sub_name + ".jpg", stack)
except:
#time.sleep(5)
print("Failed!!!")
def preprocess_tsa_data(): # Preprocessing all subjects with labels
# Type 1: get a list of all subjects for whom there is data
#SUBJECT_LIST = [os.path.splitext(subject)[0] for subject in os.listdir(INPUT_FOLDER)]
# Type 2: get a list of subjects for small bore test purposes
#SUBJECT_LIST = ['00360f79fd6e02781457eda48f85da90','0043db5e8c819bffc15261b1f1ac5e42',
# '0050492f92e22eed3474ae3a6fc907fa','006ec59fa59dd80a64c85347eef810c7',
# '0097503ee9fa0606559c56458b281a08','011516ab0eca7cad7f5257672ddde70e']
# METHOD 3: get a list of all subjects for which there are labels (1200 subjects)
df = pd.read_csv(STAGE1_LABELS)
df['Subject'], df['Zone'] = df['Id'].str.split('_', 1).str
SUBJECT_LIST = df['Subject'].unique()
# intialize tracking and saving items
batch_num = 1
threat_zone_examples = []
start_time = timer()
for subject in SUBJECT_LIST: # for each subject
# read in the images
print('t+> {:5.3f} |Reading images for subject #: {}'.format(
timer() - start_time, subject))
images = tsa.read_data(INPUT_FOLDER + '/' + subject + '.aps')
# transpose so that the slice is the first dimension shape(16, 620, 512)
images = images.transpose()
# for each threat zone, loop through each image, mask off the zone and then crop it
for tz_num, threat_zone_x_crop_dims in enumerate(
zip(tsa.zone_slice_list, tsa.zone_crop_list)):
threat_zone = threat_zone_x_crop_dims[0]
crop_dims = threat_zone_x_crop_dims[1]
# get label
label = np.array(
tsa.get_subject_zone_label(
tz_num, tsa.get_subject_labels(STAGE1_LABELS, subject)))
for img_num, img in enumerate(images):
print('Threat Zone:Image -> {}:{}'.format(tz_num, img_num))
print('Threat Zone Label -> {}'.format(label))
if threat_zone[
img_num] is not None: # If there is a threat zone observable
# correct the orientation of the image
print('-> reorienting base image')
base_img = np.flipud(img)
print('-> shape {}|mean={}'.format(base_img.shape,
base_img.mean()))
# convert to grayscale
print('-> converting to grayscale')
rescaled_img = tsa.convert_to_grayscale(base_img)
print('-> shape {}|mean={}'.format(rescaled_img.shape,
rescaled_img.mean()))
# spread the spectrum to improve contrast
print('-> spreading spectrum')
#high_contrast_img = tsa.spread_spectrum(rescaled_img)
high_contrast_img = spread_spectrum(rescaled_img)
print('-> shape {}|mean={}'.format(
high_contrast_img.shape, high_contrast_img.mean()))
# get the masked image
print('-> masking image')
masked_img = tsa.roi(high_contrast_img,
threat_zone[img_num])
print('-> shape {}|mean={}'.format(masked_img.shape,
masked_img.mean()))
# crop the image
print('-> cropping image')
cropped_img = tsa.crop(masked_img, crop_dims[img_num])
print('-> shape {}|mean={}'.format(cropped_img.shape,
cropped_img.mean()))
# normalize the image
print('-> normalizing image')
normalized_img = tsa.normalize(cropped_img)
print('-> shape {}|mean={}'.format(normalized_img.shape,
normalized_img.mean()))
# zero center the image
print('-> zero centering')
zero_centered_img = tsa.zero_center(normalized_img)
print('-> shape {}|mean={}'.format(
zero_centered_img.shape, zero_centered_img.mean()))
# append the features and labels to this threat zone's example array
print('-> appending example to threat zone {}'.format(
tz_num))
threat_zone_examples.append([[tz_num], zero_centered_img,
label])
print('-> shape {:d}:{:d}:{:d}:{:d}:{:d}:{:d}'.format(
len(threat_zone_examples),
len(threat_zone_examples[0]),
len(threat_zone_examples[0][0]),
len(threat_zone_examples[0][1][0]),
len(threat_zone_examples[0][1][1]),
len(threat_zone_examples[0][2])))
else:
print('-> No view of tz:{} in img:{}. Skipping to next...'.
format(tz_num, img_num))
# This writes the the data once there is a full minibatch completed
if ((len(threat_zone_examples) %
(BATCH_SIZE * EXAMPLES_PER_SUBJECT)) == 0):
for tz_num, tz in enumerate(tsa.zone_slice_list):
tz_examples_to_save = []
# write out the batch and reset
print(' -> writing: ' + PREPROCESSED_DATA_FOLDER +
'preprocessed_TSA_scans-tz{}-{}-{}-b{}.npy'.format(
tz_num + 1, len(threat_zone_examples[0][1][0]),
len(threat_zone_examples[0][1][1]), batch_num))
# get this tz's examples
tz_examples = [
example for example in threat_zone_examples
if example[0] == [tz_num]
]
# drop unused columns
tz_examples_to_save.append(
[[features_label[1], features_label[2]]
for features_label in tz_examples])
np.save(
PREPROCESSED_DATA_FOLDER +
'preprocessed_TSA_scans-tz{}-{}-{}-b{}.npy'.format(
tz_num + 1, len(threat_zone_examples[0][1][0]),
len(threat_zone_examples[0][1][1]), batch_num),
tz_examples_to_save)
del tz_examples_to_save
#reset for next batch
del threat_zone_examples
threat_zone_examples = []
batch_num += 1
# we may run out of subjects before we finish a batch, so we write out the last batch stub
if (len(threat_zone_examples) > 0):
for tz_num, tz in enumerate(tsa.zone_slice_list):
tz_examples_to_save = []
# write out the batch and reset
print(' -> writing: ' + PREPROCESSED_DATA_FOLDER +
'preprocessed_TSA_scans-tz{}-{}-{}-b{}.npy'.format(
tz_num + 1, len(threat_zone_examples[0][1][0]),
len(threat_zone_examples[0][1][1]), batch_num))
# get this tz's examples
tz_examples = [
example for example in threat_zone_examples
if example[0] == [tz_num]
]
# drop unused columns
tz_examples_to_save.append([[features_label[1], features_label[2]]
for features_label in tz_examples])
#save batch
np.save(
PREPROCESSED_DATA_FOLDER +
'preprocessed_TSA_scans-tz{}-{}-{}-b{}.npy'.format(
tz_num + 1, len(threat_zone_examples[0][1][0]),
len(threat_zone_examples[0][1][1]), batch_num),
tz_examples_to_save)
def preprocess_tsa_test_data():
SUBJECT_LIST = sub_list[0:100]
# intialize tracking and saving items
batch_num = 1
threat_zone_examples = []
start_time = timer()
for subject in SUBJECT_LIST:
# read in the images
print('t+> {:5.3f} |Reading images for subject #: {}'.format(
timer() - start_time, subject))
images = tsa.read_data(INPUT_FOLDER + '/' + subject + '.aps')
# transpose so that the slice is the first dimension shape(16, 620, 512)
images = images.transpose()
# for each threat zone, loop through each image, mask off the zone and then crop it
for tz_num, threat_zone_x_crop_dims in enumerate(
zip(tsa.zone_slice_list, tsa.zone_crop_list)):
threat_zone = threat_zone_x_crop_dims[0]
crop_dims = threat_zone_x_crop_dims[1]
# get label
label = np.array(
tsa.get_subject_zone_label(
tz_num, tsa.get_subject_labels(STAGE1_NEW_LABELS,
subject)))
for img_num, img in enumerate(images):
print('Threat Zone:Image -> {}:{}'.format(tz_num, img_num))
print('Threat Zone Label -> {}'.format(label))
if threat_zone[img_num] is not None:
# correct the orientation of the image
print('-> reorienting base image')
base_img = np.flipud(img)
print('-> shape {}|mean={}'.format(base_img.shape,
base_img.mean()))
# convert to grayscale
print('-> converting to grayscale')
rescaled_img = tsa.convert_to_grayscale(base_img)
print('-> shape {}|mean={}'.format(rescaled_img.shape,
rescaled_img.mean()))
# spread the spectrum to improve contrast
print('-> spreading spectrum')
#high_contrast_img = tsa.spread_spectrum(rescaled_img)
high_contrast_img = spread_spectrum(rescaled_img)
print('-> shape {}|mean={}'.format(
high_contrast_img.shape, high_contrast_img.mean()))
# get the masked image
print('-> masking image')
masked_img = tsa.roi(high_contrast_img,
threat_zone[img_num])
print('-> shape {}|mean={}'.format(masked_img.shape,
masked_img.mean()))
# crop the image
print('-> cropping image')
cropped_img = tsa.crop(masked_img, crop_dims[img_num])
print('-> shape {}|mean={}'.format(cropped_img.shape,
cropped_img.mean()))
# normalize the image
print('-> normalizing image')
normalized_img = tsa.normalize(cropped_img)
print('-> shape {}|mean={}'.format(normalized_img.shape,
normalized_img.mean()))
# zero center the image
print('-> zero centering')
zero_centered_img = tsa.zero_center(normalized_img)
#high_contrast_img = tsa.spread_spectrum(rescaled_img)
print('-> shape {}|mean={}'.format(
zero_centered_img.shape, zero_centered_img.mean()))
# append the features and labels to this threat zone's example array
print('-> appending example to threat zone {}'.format(
tz_num))
threat_zone_examples.append([[tz_num], zero_centered_img,
label])
print('-> shape {:d}:{:d}:{:d}:{:d}:{:d}:{:d}'.format(
len(threat_zone_examples),
len(threat_zone_examples[0]),
len(threat_zone_examples[0][0]),
len(threat_zone_examples[0][1][0]),
len(threat_zone_examples[0][1][1]),
len(threat_zone_examples[0][2])))
else:
print('-> No view of tz:{} in img:{}. Skipping to next...'.
format(tz_num, img_num))
if ((len(threat_zone_examples) %
(BATCH_SIZE * EXAMPLES_PER_SUBJECT)) == 0):
for tz_num, tz in enumerate(tsa.zone_slice_list):
tz_examples_to_save = []
# write out the batch and reset
print(' -> writing: ' + PREPROCESSED_NEW_DATA_FOLDER +
'preprocessed_new_TSA_scans-tz{}-{}-{}-b{}.npy'.format(
tz_num + 1, len(threat_zone_examples[0][1][0]),
len(threat_zone_examples[0][1][1]), batch_num))
# get this tz's examples
tz_examples = [
example for example in threat_zone_examples
if example[0] == [tz_num]
]
# drop unused columns
tz_examples_to_save.append(
[[features_label[1], features_label[2]]
for features_label in tz_examples])
np.save(
PREPROCESSED_NEW_DATA_FOLDER +
'preprocessed_new_TSA_scans-tz{}-{}-{}-b{}.npy'.format(
tz_num + 1, len(threat_zone_examples[0][1][0]),
len(threat_zone_examples[0][1][1]), batch_num),
tz_examples_to_save)
del tz_examples_to_save
#reset for next batch
del threat_zone_examples
threat_zone_examples = []
batch_num += 1
if (len(threat_zone_examples) > 0):
for tz_num, tz in enumerate(tsa.zone_slice_list):
tz_examples_to_save = []
# write out the batch and reset
print(' -> writing: ' + PREPROCESSED_NEW_DATA_FOLDER +
'preprocessed_new_TSA_scans-tz{}-{}-{}-b{}.npy'.format(
tz_num + 1, len(threat_zone_examples[0][1][0]),
len(threat_zone_examples[0][1][1]), batch_num))
# get this tz's examples
tz_examples = [
example for example in threat_zone_examples
if example[0] == [tz_num]
]
# drop unused columns
tz_examples_to_save.append([[features_label[1], features_label[2]]
for features_label in tz_examples])
#save batch
np.save(
PREPROCESSED_NEW_DATA_FOLDER +
'preprocessed_new_TSA_scans-tz{}-{}-{}-b{}.npy'.format(
tz_num + 1, len(threat_zone_examples[0][1][0]),
len(threat_zone_examples[0][1][1]), batch_num),
tz_examples_to_save)
def load_animate_scan(path, *args, **kwargs):
"""Load a 3D image from from disk and animate."""
image = tsa.read_data(path)
return animate_scan(image, *args, **kwargs)
def animate_aps(subject_id):
"""Animate aps file by subject ID."""
aps = tsa.read_data(path.join(path_aps, subject_id + '.aps'))
aps = aps.transpose(1, 0, 2)
return animate_scan(aps)
