def concatenate_runs(subj_num):
"""
Concatenates data from all runs for a particular subject and saves that
data into a designated file. If file already exists, uses that cached file
instead.
Parameters
----------
subj_num : int
Returns
-------
None
"""
npy_file_name = dp.get_concatenated_path(subj_num)
if not exists(npy_file_name) or not USE_CACHED_DATA:
run_data = []
for j in range(NUM_RUNS):
img = nib.load(dp.get_raw_path(subj_num, j + 1))
data = img.get_data()
if j == 0:
run_data.append(data[..., :-4])
elif j >= 1 and j <= NUM_RUNS - 2:
run_data.append(data[..., 4:-4])
else:
run_data.append(data[..., 4:])
concatenated_run_data = np.concatenate(run_data, axis=3)
np.save(npy_file_name, concatenated_run_data)
print("Saved {0}".format(npy_file_name))
else:
print("Using cached version of {0}".format(npy_file_name))
def test_get_concatenated_path():
paths = [dp.get_concatenated_path(subject) for subject in SUBJECTS]
assert paths[0] == '{0}/data/processed/sub1_rcds.npy'.format(REPO_HOME_PATH)
assert paths[1] == '{0}/data/processed/sub2_rcds.npy'.format(REPO_HOME_PATH)
assert paths[2] == '{0}/data/processed/sub3_rcds.npy'.format(REPO_HOME_PATH)
assert paths[3] == '{0}/data/processed/sub4_rcds.npy'.format(REPO_HOME_PATH)
assert paths[4] == '{0}/data/processed/sub5_rcds.npy'.format(REPO_HOME_PATH)
def gaussian_smooth_subj(subj_num, fwhm_mm):
"""
Retrieves the absolute path to the smoothed data for a particular subject
and full width half maximum smoothed version. Loads the data and saves it
in the retrieved path. If file already exists, then uses cached version
instead.
Parameters
----------
subj_num : int
fwhm_mm : int
Returns
-------
None
"""
smoothed_data_path = dp.get_smoothed_path(subj_num, fwhm_mm)
if not exists(smoothed_data_path) or not USE_CACHED_DATA:
data = np.load(dp.get_concatenated_path(subj_num)).astype(np.float32)
smoothed_data = apply_gaussian_smooth(data, fwhm_mm)
del data
np.save(smoothed_data_path, smoothed_data)
print("Saved {0}".format(smoothed_data_path))
else:
print("Using cached version of {0}".format(smoothed_data_path))
def concatenate_runs(subj_num):
"""
Concatenates data from all runs for a particular subject and saves that
data into a designated file. If file already exists, uses that cached file
instead.
Parameters
----------
subj_num : int
Returns
-------
None
"""
npy_file_name = dp.get_concatenated_path(subj_num)
if not exists(npy_file_name) or not USE_CACHED_DATA:
run_data = []
for j in range(NUM_RUNS):
img = nib.load(dp.get_raw_path(subj_num, j + 1))
data = img.get_data()
if j == 0:
run_data.append(data[..., :-4])
elif j >= 1 and j <= NUM_RUNS - 2:
run_data.append(data[..., 4:-4])
else:
run_data.append(data[..., 4:])
concatenated_run_data = np.concatenate(run_data, axis=3)
np.save(npy_file_name, concatenated_run_data)
print('Saved {0}'.format(npy_file_name))
else:
print('Using cached version of {0}'.format(npy_file_name))
def gaussian_smooth_subj(subj_num, fwhm_mm):
"""
Retrieves the absolute path to the smoothed data for a particular subject
and full width half maximum smoothed version. Loads the data and saves it
in the retrieved path. If file already exists, then uses cached version
instead.
Parameters
----------
subj_num : int
fwhm_mm : int
Returns
-------
None
"""
smoothed_data_path = dp.get_smoothed_path(subj_num, fwhm_mm)
if not exists(smoothed_data_path) or not USE_CACHED_DATA:
data = np.load(dp.get_concatenated_path(subj_num)).astype(np.float32)
smoothed_data = apply_gaussian_smooth(data, fwhm_mm)
del data
np.save(smoothed_data_path, smoothed_data)
print('Saved {0}'.format(smoothed_data_path))
else:
print('Using cached version of {0}'.format(smoothed_data_path))
def get_brain_mask():
brain_mask_path = dp.get_brain_mask_path()
if not exists(brain_mask_path):
data = np.load(dp.get_concatenated_path(1))
mean_vol = np.mean(data, axis=-1)
in_brain_mask = mean_vol > 150
np.save(brain_mask_path, in_brain_mask)
return np.load(brain_mask_path)
def plot_vol_mean_histogram(subj_num):
data = np.load(dp.get_concatenated_path(subj_num))[...,
NUM_OFFSET_VOLUMES:]
mean_vol = np.mean(data, axis=-1)
plt.hist(np.ravel(mean_vol), bins=100)
plot_path = '{0}/figures/subj_{1}_vol_mean_histogram.png'.format(
REPO_HOME_PATH, subj_num)
plt.savefig(plot_path)
print('Saved {0}'.format(plot_path))
def gaussian_smooth_subj(subj_num, fwhm_mm):
smoothed_data_path = dp.get_smoothed_path(subj_num, fwhm_mm)
if not exists(smoothed_data_path) or not USE_CACHED_DATA:
data = np.load(dp.get_concatenated_path(subj_num)).astype(np.float32)
smoothed_data = apply_gaussian_smooth(data, fwhm_mm)
del data
np.save(smoothed_data_path, smoothed_data)
print('Saved {0}'.format(smoothed_data_path))
else:
print('Using cached version of {0}'.format(smoothed_data_path))
def test_get_concatenated_path():
paths = [dp.get_concatenated_path(subject) for subject in SUBJECTS]
assert paths[0] == '{0}/data/processed/sub1_rcds.npy'.format(
REPO_HOME_PATH)
assert paths[1] == '{0}/data/processed/sub2_rcds.npy'.format(
REPO_HOME_PATH)
assert paths[2] == '{0}/data/processed/sub3_rcds.npy'.format(
REPO_HOME_PATH)
assert paths[3] == '{0}/data/processed/sub4_rcds.npy'.format(
REPO_HOME_PATH)
assert paths[4] == '{0}/data/processed/sub5_rcds.npy'.format(
REPO_HOME_PATH)
def concatenate_runs(subj_num):
npy_file_name = dp.get_concatenated_path(subj_num)
if not exists(npy_file_name) or not USE_CACHED_DATA:
run_data = []
for j in range(NUM_RUNS):
img = nib.load(dp.get_raw_path(subj_num, j + 1))
data = img.get_data()
if j == 0:
run_data.append(data[..., :-4])
elif j >= 1 and j <= NUM_RUNS - 2:
run_data.append(data[..., 4:-4])
else:
run_data.append(data[..., 4:])
concatenated_run_data = np.concatenate(run_data, axis=3)
np.save(npy_file_name, concatenated_run_data)
print('Saved {0}'.format(npy_file_name))
else:
print('Using cached version of {0}'.format(npy_file_name))
def get_brain_mask():
"""
Retrieves the path to the brain mask and loads that data
Parameters
----------
None
Returns
-------
brain_mask : array
"""
brain_mask_path = dp.get_brain_mask_path()
if not exists(brain_mask_path):
data = np.load(dp.get_concatenated_path(1))
mean_vol = np.mean(data, axis=-1)
in_brain_mask = mean_vol > 150
np.save(brain_mask_path, in_brain_mask)
return np.load(brain_mask_path)
def plot_vol_mean_histogram(subj_num):
"""
Plots the mean volume for a particular subject and saves that histogram
into the figures folder
Parameters
----------
subj_num : int
Returns
-------
None
"""
data = np.load(dp.get_concatenated_path(subj_num))[...,
NUM_OFFSET_VOLUMES:]
mean_vol = np.mean(data, axis=-1)
plt.hist(np.ravel(mean_vol), bins=100)
plot_path = '{0}/figures/subj_{1}_vol_mean_histogram.png'.format(
REPO_HOME_PATH, subj_num)
plt.savefig(plot_path)
print('Saved {0}'.format(plot_path))
