nyq = 0.5*fs normal_cutoff = cutoff/nyq b, a = signal.butter(order, normal_cutoff, btype = 'low', analog = False) return b, a def butter_lowpass_filtfilt(data, cutoff, fs, order=5): b, a = butter_lowpass(cutoff, fs, order = order) y = signal.filtfilt(b, a, data) return y cutoff = 15000 fs = 200000 Li2p1hn = Li2p1.copy() Li2p1hn2 = Li2p1.copy() for x_ind in range(len(x)): Li2p1hn2[int(t_start/avt):int(t_end/avt),0,x_ind] = butter_lowpass_filtfilt(Li2p1noise[int(t_start/avt):int(t_end/avt),0,x_ind],cutoff,fs) Li2p1hn[int(t_start/avt):int(t_end/avt),0,x_ind] = smooth(Li2p1noise[int(t_start/avt):int(t_end/avt),0,x_ind],window_len=smoothlen,window='hanning') # smoothing in x-direction for low resolution not necessary! # Li2p1xs = Li2p1.copy() # for t_ind in range(timestep): # Li2p1xs[t_ind,0,:] = smooth(Li2p1hn[t_ind,0,:],window_len=smoothlenx,window='hanning') # only use the new noise and smoothed data if selected Li2p1blockraw=Li2p1noise.copy() if SmoothTime: Li2p1blocksmooth = Li2p1hn2.copy() #Meshgrids if not RealCase: X,Y=np.meshgrid(x_ori,y) # transform x,y-axis in matrix for contourf-plot of 2D-plots if BlockCase:
for y_ind in range(stepsize): # + 2 safty distance
noiLi = np.random.normal(0,np.mean(Li2p1[:,y_ind,x_ind])/SNR,timestep)
for t_ind in range(timestep):
Li2p1[t_ind,y_ind,x_ind] = Li2p1[t_ind,y_ind,x_ind]+noiLi[t_ind]
Li2p1noise=Li2p1.copy()
# smoothing noisy data
if Smooth:
Li2p1hn = Li2p1.copy()
# Li2p1hm = Li2p1.copy()
# Li2p1ba = Li2p1.copy()
# Li2p1bl = Li2p1.copy()
smoothlen = 71
for t_ind in range(timestep):
for y_ind in range(stepsize):
Li2p1hn[t_ind,y_ind,:] = smooth(Li2p1[t_ind,y_ind,:],window_len=smoothlen,window='hanning')
# Li2p1hm[t_ind,shift*2,:] = smooth(Li2p1[t_ind,shift*2,:],window_len=smoothlen,window='hamming')
# Li2p1ba[t_ind,shift*2,:] = smooth(Li2p1[t_ind,shift*2,:],window_len=smoothlen,window='bartlett')
# Li2p1bl[t_ind,shift*2,:] = smooth(Li2p1[t_ind,shift*2,:],window_len=smoothlen,window='blackman')
Li2p1 = Li2p1hn.copy()
# use the block-emission data for the block case:
# Li2p1 = block_func(Li2p1,timestep,mn,b,avy,shift,shift2) # if original block-function should be used
Li2p1_beam, Li2p1_block, x_block, blur = dect_func(Li2p1,timestep,mn,b,avy,shift,shift2,x) # if detector reduced block function should be used
# Calculate index and position of Reference detector on xaxis (indices are counted backwards x[len(x)-1]<0!)
print('Reference detector has to be shifted to a position available for beam evaluation!')
countx=0
for g in range (0,len(x_block)):