dwi = asarray(dwi, "float")
b0 = data[..., where_b0].mean(-1)
b0 = b0[..., None]
b0 = asarray(b0, "float")
maximum(dwi, min_signal, dwi)
maximum(b0, min_signal, b0)
dwi /= b0
minimum(dwi, 1, dwi)
return dwi
def gfa(samples):
gfa of some function from a set of samples os that function
After Change
out.clip(min_signal, out=out)
b0 = out[..., where_b0].mean(-1)
b0 = b0.reshape(b0.shape + (1,))
out /= b0
return out
def gfa(samples):
