def normalize_z_max(spectrum: Spectrum, in_place: bool = False):
mu = np.mean(spectrum.y)
sig = np.std(spectrum.y)
if not in_place:
spectrum = spectrum.copy()
spectrum.y = np.array([(y - mu) / sig for y in spectrum.y])
spectrum.y /= np.max(spectrum.y)
return spectrum
def normalize_z(spectrum: Spectrum, in_place: bool = False):
"""
Normalize by treating the spectrum as a normal distribution and finding the
'z-score' associated with each point
:param spectrum:
:param in_place:
:return:
"""
mu = np.mean(spectrum.y)
sig = np.std(spectrum.y)
if not in_place:
spectrum = spectrum.copy()
spectrum.y = np.array([(y - mu) / sig for y in spectrum.y])
return spectrum