"""
Signal normalisation
====================

"""

#%%
import hyperspy.api as hs
import numpy as np

#%%
#
# Default normalisation
# ---------------------
#
# Create two unique Signal1D for example

s = hs.data.luminescence_signal()
 
s_shifted = np.roll(s.data, 200)
s_shifted = hs.signals.Signal1D(s_shifted)

s1 = (s/0.8 + hs.signals.Signal1D(s_shifted/4)) + 500
s2 = s + hs.signals.Signal1D(s_shifted/2)

#%%
# Plot un-normalised signals

hs.plot.plot_spectra([s1,s2])

#%%
# Plot both signals with default normalisation, of min and max intensity values to 0 and 1 

hs.plot.plot_spectra([s1, s2], normalise = True)

#%%
#
# Normalising to signal range
# ---------------------------
#
# In the case that we want to normalise our signals to the background noise, we can create a custom normalisation function. The mean intensity over the index range 5 - 100 is specified to normalise the signals to

def normalise_mean_range(signal):
    data = signal.data
    
    normalise_range = signal.isig[5:100].data
    
    scale_factor = 1 / normalise_range.mean()
    
    return data * scale_factor
    
#%%
# Plot signals with custom normalisation function passed as parameter  
hs.plot.plot_spectra([s1,s2], normalise = normalise_mean_range)

#%%
#
# Normalising to specified signal value
# -------------------------------------
#
# In the case that we want to normalise our signals to the second weaker peak in our spectra, we adjust our function to normalise the signals to the intensity at energy position 4.5 eV

def normalise_energy_value(signal):
    data = signal.data
    
    normalise_value = signal.isig[4.5].data
    
    scale = 1 / normalise_value
    
    return data * scale

#%%
# Plot signals with custom normalisation function passed as parameter  
hs.plot.plot_spectra([s1,s2], normalise = normalise_energy_value)