def baseline_subtraction(x,y, fstgo):
print('The channel with the minimum counts in each segment will be a point on a segmented linearly interpolated background.')
min_indices, min_x_list, min_y_list, x_split_list, y_split_list = min_spectrum_split(x, y, fstgo)
interp_function = sp.interpolate.interp1d(min_x_list, min_y_list, kind = 'linear', fill_value = 'extrapolate')
plt.plot(x,y)
plt.plot(x, interp_function(x))
plt.plot(x, y - interp_function(x))
plt.show()
redo = ad.y_or_n('Would you like to reselect the boundaries?')
if redo == 'y':
return baseline_subtraction(x,y)
return( y - interp_function(x))
directory = os.getcwd() + '/spectra'
fileno = ad.listfiles(directory)
fileselect = ad.inputfileselector(fileno)
f = ad.openfilefromlist(fileselect, directory)
x, yinit = ad.file_reader(directory + '/' + f)
spe = ad.spectrum_plotter(x, yinit)
spe.show()
'''
###########################################Baseline Subtraction##################################################
'''
base_flag = ad.y_or_n(
"Does this spectrum need baseline subtraction to fid the peaks.\nThe subtraction will not be applied to the fit. Instead, there will be a linear background applied to each fitting region."
)
if base_flag == 'y':
ybase = sig.baseline_subtraction(x, yinit)
else:
ybase = yinit
'''
###########################################Contaminant Deletion##################################################
'''
cont_del = ad.y_or_n("Would you like to remove any contaminants?")
if cont_del == 'y':
yclipfit, yclipfind = sig.contaminant_clipper(x, yinit, ybase)
else:
fileno = ad.listfiles(directory)
fileselect = ad.inputfileselector(fileno)
f = ad.openfilefromlist(fileselect, directory)
x, yinit = ad.file_reader(directory + '/' + f)
spe = ad.spectrum_plotter(x, yinit)
spe.show()
#yinit = sig.baseline_subtraction(x,yinit, fstgo)
'''
###########################################Contaminant Deletion##################################################
'''
cont_del = ad.y_or_n("Would you like to remove any contaminants?")
if cont_del == 'y':
xlist, ylist, yclip = sig.contaminant_clipper(x, yinit, yinit, split=True)
else:
xlist, ylist = [[x], [yinit]]
yclip = yinit
for x2, y2 in zip(xlist, ylist):
plt.plot(x2, y2)
plt.show()
'''
##########################################Threshold set and Peak Region Detection###########################################################
'''
rlist, betalist = [], []
def contaminant_clipper(x, yinit, ybase, split = False):
'''
clips bits out of the spectrum after prompting the user to do so
'''
yclipfit = yinit #clip things out of this yclip variable so yinit doesn't change
yclipfind = ybase
if split:
xlist, ylist = [[],[]]
borders = [0, len(x)]
while True:
#set a buffer such that it can be reinstated to the last one if the user changes their minddef contaminant_c_split(x, y)
yclipfitbuffer = yclipfit
yclipfindbuffer = yclipfind
#plot what the current spectrum is
clip_spectrum = ad.spectrum_plotter(x, yclipfind)
clip_spectrum.show()
#get the user to input a valid set of bounds in which to delete the contaminant
while True:
lb = input('input a lower bound on the contaminant in channels')
ub = input('input an upper bound on the contaminant in channels')
if np.chararray.isdigit(ub + lb): #has to all be digits
lb = int(lb)
ub = int(ub)
if (lb > min(x) and ub > lb and ub < max(x)): #lower and upper bounds have to be in the spectrum, upper bound has to be bigger than lower
break
print('Invalid bounds, try again')
#get which indices x is less than the lower bound and greater than the upper bound set
x21 = np.where(x < lb)[0]
x22 = np.where(x > ub)[0]
#get y arrays above and below the contaminant
y21fit = yclipfit[x21]
y22fit = yclipfit[x22]
y21find = yclipfind[x21]
y22find = yclipfind[x22]
#how many channels does the contaminant have
length_difference = len(x) - len(x21) - len(x22)
#get an array of 0s to replace the contaminant ys with
y20 = np.zeros(length_difference)
#now to stick together the arrays
#need the ys below the contaminant, then the zeroes where the contaminant was, and then the ys above the contaminant
ynewfit = np.append(y21fit, y20)
ynewfit = np.append(ynewfit, y22fit)
ynewfind = np.append(y21find, y20)
ynewfind = np.append(ynewfind, y22find)
#now replace the old ys with the new one
yclipfit = ynewfit
yclipfind = ynewfind
#now plot that and ask if they're happy with it
clip_spectrum = ad.spectrum_plotter(x, yclipfind)
clip_spectrum.show()
confirmclip = ad.y_or_n('Are you happy with this removal?')
#what to do now? if they aren't happy, ask if they want another go. if they think there aren't any contaminants, return to yinit
#if there still are, then they can have another go
if confirmclip == 'n':
cancel = ad.y_or_n('Are you sure there are any contaminants?')
if cancel == 'n':
yclipfit = yinit
yclipfind = ybase
if split: xlist, ylist = [[x],[yinit]]
break
if cancel == 'y':
yclipfit = yclipfitbuffer
yclipfind = yclipfindbuffer
continue
else: #all good, so can continue
if split:
borders.append(int(max(np.intersect1d(x, x[x21], return_indices = True)[1])))
borders.append(int(min(np.intersect1d(x, x[x22], return_indices = True)[1])))
else:
pass
#do they want to get rid of more contaminants? continue loop if they do, don't if they don't
moreclip = ad.y_or_n('Would you like to remove any more contaminants?')
if moreclip == 'y':
continue
else:
break
if split:
borders = np.sort(borders)
for i, border in enumerate(borders):
if ((i % 2 == 0) and (i is not len(borders))):
ylist.append(yinit[border:borders[i+1]])
xlist.append(x[border:borders[i+1]])
return(xlist, ylist, yclipfind)
return yclipfit, yclipfind