def correlation_map_without_convoluation(data, cond_filename):
n_trs = data.shape[-1] + 5
time_course = events2neural_rounded(cond_filename, TR, n_trs)
time_course=time_course[5:]
correlations = np.zeros(data.shape[:-1])
for i in general_utils.vol_index_iter(data.shape[:-1]):
vox_values = data[i]
correlations[i] = np.corrcoef(time_course, vox_values)[1, 0]
def correlation_map_without_convoluation(data, cond_filename):
n_trs = data.shape[-1] + 5
time_course = events2neural_rounded(cond_filename, TR, n_trs)
time_course = time_course[5:]
correlations = np.zeros(data.shape[:-1])
for i in general_utils.vol_index_iter(data.shape[:-1]):
vox_values = data[i]
correlations[i] = np.corrcoef(time_course, vox_values)[1, 0]
def correlation_map_without_convoluation(data, cond_filename):
"""
Generate a cross-correlation per voxel between BOLD signals and a square wave
baseline function, which represents the boolean array of the on-off time
course. Assume that the first 5 images are already dropped.
"""
n_trs = data.shape[-1] + 5
time_course = events2neural_rounded(cond_filename, TR, n_trs)[5:]
correlations = np.zeros(data.shape[:-1])
for i in gu.vol_index_iter(data.shape[:-1]):
vox_values = data[i]
r = np.corrcoef(time_course, vox_values)[1, 0]
if np.isnan(r):
r = 0
correlations[i] = r
return correlations
def correlation_map_without_convoluation_linear(data, cond_filename):
"""
This is different from correlation_map_without_convoluation in that it accepts a 2d data
(n_samples, n_time_slices) so that it is suitable for working with
brain masks.
"""
n_trs = data.shape[-1] + 5
time_course = events2neural_rounded(cond_filename, TR, n_trs)[5:]
correlations = np.zeros(data.shape[:-1])
for i in range(data.shape[0]):
vox_values = data[i]
r = np.corrcoef(time_course, vox_values)[1, 0]
if np.isnan(r):
r = 0
correlations[i] = r
return correlations
def correlation_map_without_convoluation(data, cond_filename):
"""
Generate a cross-correlation per voxel between BOLD signals and a square wave
baseline function, which represents the boolean array of the on-off time
course. Assume that the first 5 images are already dropped.
"""
n_trs = data.shape[-1] + 5
time_course = events2neural_rounded(cond_filename, TR, n_trs)[5:]
correlations = np.zeros(data.shape[:-1])
for i in gu.vol_index_iter(data.shape[:-1]):
vox_values = data[i]
r = np.corrcoef(time_course, vox_values)[1, 0]
if np.isnan(r):
r = 0
correlations[i] = r
return correlations
def correlation_map_without_convoluation_linear(data, cond_filename):
"""
This is different from correlation_map_without_convoluation in that it accepts a 2d data
(n_samples, n_time_slices) so that it is suitable for working with
brain masks.
"""
n_trs = data.shape[-1] + 5
time_course = events2neural_rounded(cond_filename, TR, n_trs)[5:]
correlations = np.zeros(data.shape[:-1])
for i in range(data.shape[0]):
vox_values = data[i]
r = np.corrcoef(time_course, vox_values)[1, 0]
if np.isnan(r):
r = 0
correlations[i] = r
return correlations
def correlation_map_without_convoluation_linear(data, cond_filename):
"""
This is different from correlation_map_without_convoluation in that it accepts a 2d data
(n_samples, n_time_slices) and compute the correlations based on the square-wave time course
using the given condition file.
"""
n_trs = data.shape[-1] + 5
time_course = events2neural_rounded(cond_filename, TR, n_trs)
time_course = time_course[5:]
correlations = np.zeros(data.shape[:-1])
for i in range(data.shape[0]):
vox_values = data[i]
r = np.corrcoef(time_course, vox_values)[1, 0]
if np.isnan(r):
r = 0
correlations[i] = r
return correlations
def correlation_map_without_convoluation_linear(data, cond_filename):
"""
This is different from correlation_map_without_convoluation in that it accepts a 2d data
(n_samples, n_time_slices) and compute the correlations based on the square-wave time course
using the given condition file.
"""
n_trs = data.shape[-1] + 5
time_course = events2neural_rounded(cond_filename, TR, n_trs)
time_course = time_course[5:]
correlations = np.zeros(data.shape[:-1])
for i in range(data.shape[0]):
vox_values = data[i]
r = np.corrcoef(time_course, vox_values)[1, 0]
if np.isnan(r):
r = 0
correlations[i] = r
return correlations
ext_outlier = outliers_utils.extend_diff_outliers(rms_outlier)
mask = np.ones(data.shape[-1])
mask[ext_outlier] = 0
mask = np.array(mask, dtype=bool)
data_rem=data[..., mask]
print("7.After dropping the extended RMS outliers, now the data has the shape %r.") %str(data_rem.shape)
#basic statistics
np.amin(data_rem) #0
np.amax(data_rem) #1550
np.mean(data_rem) #137.08845107920848
#get the correlation matrix w/ outliers
TR=2.5
n_trs = img.shape[-1]
time_course = events2neural_rounded(cond_filename, 2.5, n_trs)
plt.plot(time_course)
plt.title("time_course")
plt.savefig("results/time_course.png")
plt.show()
print("8.The time_course is plotted and saved as 'time_course.png'")
time_course=time_course[5:]
correlations = np.zeros(data.shape[:-1])
for i in range(data.shape[0]):
for j in range(data.shape[1]):
for k in range(data.shape[2]):
vox_values = data[i, j, k]
correlations[i, j, k] = np.corrcoef(time_course, vox_values)[1, 0]
plt.imshow(correlations[:, :, 18], cmap='gray')
plt.colorbar()
plt.savefig("results/correlation_middle.png")
mask[ext_outlier] = 0
mask = np.array(mask, dtype=bool)
data_rem = data[..., mask]
print(
"7.After dropping the extended RMS outliers, now the data has the shape %r."
) % str(data_rem.shape)
#basic statistics
np.amin(data_rem) #0
np.amax(data_rem) #1550
np.mean(data_rem) #137.08845107920848
#get the correlation matrix w/ outliers
TR = 2.5
n_trs = img.shape[-1]
time_course = events2neural_rounded(cond_filename, 2.5, n_trs)
plt.plot(time_course)
plt.title("time_course")
plt.savefig("results/time_course.png")
plt.show()
print("8.The time_course is plotted and saved as 'time_course.png'")
time_course = time_course[5:]
correlations = np.zeros(data.shape[:-1])
for i in range(data.shape[0]):
for j in range(data.shape[1]):
for k in range(data.shape[2]):
vox_values = data[i, j, k]
correlations[i, j, k] = np.corrcoef(time_course, vox_values)[1, 0]
plt.imshow(correlations[:, :, 18], cmap='gray')
plt.colorbar()
plt.savefig("results/correlation_middle.png")
