def faster_correlation(fname, tr):
# Load the ds114_sub009_t2r1.nii image
img = nib.load(fname + ".nii")
# Get the number of volumes in ds114_sub009_t2r1.nii
n_trs = img.shape[-1]
# Time between 3D volumes in seconds
TR = tr
# Get on-off timecourse
time_course = events2neural(fname + "_cond.txt", TR, n_trs)
# Drop the first 4 volumes, and the first 4 on-off values
data = img.get_data()
data = data[..., 4:]
time_course = time_course[4:]
# Calculate the number of voxels (number of elements in one volume)
n_voxels = np.prod(data.shape[:-1])
# Reshape 4D array to 2D array n_voxels by n_volumes
data_2d = np.reshape(data, (n_voxels, data.shape[-1]))
# Transpose 2D array to give n_volumes, n_voxels array
data_2d_T = data_2d.T
# Calculate 1D vector length n_voxels of correlation coefficients
correlation_1d = pearson.pearson_2d(time_course, data_2d_T)
# Reshape the correlations array back to 3D
correlation_3d = correlation_1d.reshape(data.shape[:-1])
return correlation_3d
def test_pearson_2d():
# Test pearson_2d routine
x = np.random.rand(22)
Y = np.random.normal(size=(22, 12))
# Does routine give same answers as np.corrcoef?
expected = np.corrcoef(x, Y.T)[0, 1:]
actual = pearson.pearson_2d(x, Y)
# Did you, gentle user, forget to return the value?
if actual is None:
raise RuntimeError("function returned None")
assert_almost_equal(expected, actual)
def test_pearson_2d():
# Test pearson_2d routine
x = np.random.rand(22)
Y = np.random.normal(size=(22, 12))
# Does routine give same answers as np.corrcoef?
expected = np.corrcoef(x, Y.T)[0, 1:]
actual = pearson.pearson_2d(x, Y)
# Did you, gentle user, forget to return the value?
if actual is None:
raise RuntimeError("function returned None")
assert_almost_equal(expected, actual)
