def inter_train_correlation(train, arr):
tmp = np.zeros([arr.shape[0], arr.shape[1], arr.shape[-1]])
for t2 in range(arr.shape[-1]):
a = norm_difference(arr[:, :, :, train], arr[:, :, :, t2], plot=False)
tmp[:, :, t2] = a.mean(axis=-1)
return tmp
def intra_train_correlation(train, arr, method=norm_difference):
tmp = np.zeros([arr.shape[0], arr.shape[1], arr.shape[-2], arr.shape[-2]])
for p1 in range(arr.shape[-2]):
for p2 in range(arr.shape[-2]):
tmp[:, :, p1, p2] = norm_difference(arr[:, :, p1, train],
arr[:, :, p2, train],
plot=False)
return tmp.mean(axis=-1).mean(axis=-1)
def positional_pulse_correlation(position, arr):
tmp = np.zeros([arr.shape[0], arr.shape[1], arr.shape[-1], arr.shape[-1]])
for t1 in range(arr.shape[-1]):
for t2 in range(arr.shape[-1]):
tmp[:, :, t1, t2] = norm_difference(arr[:, :, position, t1],
arr[:, :, position, t2],
plot=False)
return tmp.mean(axis=-1)
def sequential_pulse_correlation(position, arr):
tmp = np.zeros([arr.shape[0], arr.shape[1], arr.shape[2], arr.shape[-1]])
for t in range(arr.shape[-1]):
for p in range(arr.shape[-2]):
tmp[:, :, p, t] = norm_difference(arr[:, :, position, t],
arr[:, :, p, t],
plot=False)
tmp = tmp.mean(axis=-1)
return tmp
def correlation(arr1, arr2):
corr = 1 - norm_difference(arr1, arr2, plot=False).mean()
return corr