this cell takes the average spectrum from a map and
performs a baseline subtraction; prepatory step for gaussian fits
this is also a useful step to check the arpls parameters and how they'll
construct a baseline
'''
import sys
sys.path.append(r'C:\Users\triton\xrays\python')
from baseline_algorithms import arpls
from scipy import optimize
# construct and subtract baseline from average spectrum
#for Au side spectra, 0hr
baseline_arpls = arpls(z_average, 1e5, 0.01)
zaverage_arpls = z_average - baseline_arpls
# check subtraction
plt.plot(energy, z_average)
plt.plot(energy, zaverage_arpls)
#%%
'''
this cell fits average spectrum of Au side
with single gaussian and plot the results
'''
def gaussian_fit(x, *pars):
offset = pars[0]
def multi_gaussian(x, *pars):
offset = pars[0]
g1 = gaussian2(x, pars[1], pars[2], pars[3])
g2 = gaussian2(x, pars[4], pars[5], pars[6])
return g1 + g2 + offset
import sys
sys.path.append(r'C:\Users\triton\xrays\python')
from baseline_algorithms import arpls
from scipy import optimize
# construct and subtract baseline from average spectrum
# for glass side spectra
baseline_arpls = arpls(z_average, 1e7, 0.1)
zaverage_arpls = z_average - baseline_arpls
# check subtraction
plt.plot(energy, z_average)
plt.plot(energy, zaverage_arpls)
#%%
'''
this cell fits average spectrum of glass side
with single gaussian and plot the results
'''
def gaussian_plot(x, A, x0, sig):
return A * np.exp(-(x - x0)**2 / (2 * sig**2))
y = np.shape(spectra)[0]
x = np.shape(spectra)[1]
spectra_ravel = spectra.reshape((x*y),z)
if aborted_map == 1:
spectra_ravel = spectra.copy()
# construct array to store gaussian fit parameters
# the no. of rows (4) correspond to the number of optimization parameters
# from the optimize.curve_fit function: offset, A, x0, sig
# the no. of columns correspond to the number of pixels or spectra measured
pixels = np.shape(spectra_ravel)[0]
stored_gauss_params = np.zeros((pixels,7))
for pix, spectrum in enumerate(spectra_ravel):
# construct and subtract baseline from pixel spectrum
baseline_arpls = arpls(spectrum, 1e6, 0.01)
spectrum_arpls = spectrum - baseline_arpls
# fit gaussian to background-subtracted spectrum
popt, pcov = optimize.curve_fit(multi_gaussian_fit, energy, spectrum_arpls, guess)
# store gaussian fit values; popt needs to be transposed
# since optimize.curve_fit outputs as (4,1) array
stored_gauss_params[pix,:] = popt.T
end = time.time()
t = end-start
t_str = "%.3f" % t
message = 'time to complete: {i}s'.format(i=str(t_str))
print(message)
#%%
SAVE = 0
if SAVE == 1: