def __compute_zscore(data: np.ndarray, axis: int = -1, clip: float = 3.0):
dims = data.ndim
data[data == np.inf] = np.nan
data[data == -np.inf] = np.nan
if axis is None:
length = 1
else:
if axis < 0:
axis += dims
length = data.shape[axis]
for i in range(length):
_slice = [i if j == axis else slice(None) for j in range(dims)]
sliced = data.__getitem__(_slice)
extremes = find_extreme_values(sliced)
score = (sliced - np.nanmedian(sliced[~extremes])) / np.nanstd(sliced[~extremes])
score = np.clip(score, -clip, clip)
epsilon = 1e-4
total_err = 1.0
avg = np.nanmean(score[~extremes])
sd = np.nanstd(score[~extremes])
z = (score - avg) / sd
trial = 0
while total_err > epsilon and trial < 3:
if (abs(z) >= clip).any():
z = np.clip(z, -clip, clip)
avg = np.nanmean(z)
std = np.nanstd(z)
total_err = abs(avg) + abs(std - 1)
z = (z - avg) / std
trial += 1
data.__setitem__(_slice, z)
return data
