wvl = data["lambda"]
background = data["spectra_" + str(background_spectra)]
lamp_spectra = lamp_data["spectra_0"]
peak_guesses = [540+i*20 for i in range(nbr_particles)]
#peak_guesses.reverse()
peak_positions = []
for i in range(0,nbr_particles):
spectra = data[f'spectra_{i}']
if not i == background_spectra:
print(i)
# Don't plot background
corrected_spectra = (spectra - background)/lamp_spectra
#norm_spectra = spectra/spectra.max()
print(peak_guesses[i])
peak_pos, fwhm = lorentzian_fit(wvl, corrected_spectra, 780, True)
peak_positions.append(peak_pos)
delta_y = np.ones(len(wvl))*(i)
ax.plot(wvl, delta_y, corrected_spectra, label=f'Particle {i+1}', alpha=0.7)
#ax.set_title(f'Corrected spectra for file {filename}')
ax.tick_params(axis='both', which='major', pad=0)
ax.set_xlabel(r'Wavelength [nm]')
ax.set_ylabel("Particle")
ax.set_zlabel(r'Intensity [arb. units]')
plt.locator_params(axis='z', nbins=5)
plt.grid()
#plt.legend(loc="upper left")
plt.tight_layout()
# Pick out background spectrum
sample_bground = sample_data["spectra_0"]
# Calculate average spectra
measurements = 0
avg_spectra = spectra_dict[sample]["avg_spectra"]
for measurement in sample_data:
if not (measurement == "spectra_0") and not (measurement == "lambda"):
# Skip background measurement
avg_spectra += sample_data[measurement]
measurements += 1
print(f'Number of measurements: {measurements} for sample: {sample}')
avg_spectra /= measurements
# Renormalize, by removing background and then dividing by lamp_spectra
avg_spectra = (avg_spectra - sample_bground) / lamp_spectra
# calc peak_pos and fwhm
peak_pos, fwhm = lorentzian_fit(wvl, avg_spectra, 720, False)
print(peak_pos)
# Save results
spectra_dict[sample]["wvl"] = wvl
spectra_dict[sample]["avg_spectra"] = avg_spectra
spectra_dict[sample]["peak_pos"] = peak_pos
# Iterate through and plot all spectras. Also append all peak pos together to a vector and plot that
peak_vector = []
for i, sample in enumerate(spectra_dict):
wvl = spectra_dict[sample]["wvl"]
avg_spectra = spectra_dict[sample]["avg_spectra"]
peak_vector.append(spectra_dict[sample]["peak_pos"])
ax.plot(wvl,
avg_spectra,
color=spectra_dict[sample]["color"],
#print(measurements)
peak_guess = 750 # About 750 nm is a good guess for Pd
# Loop over all measurements at different times t
peak_positions = [[] for i in range(nbr_particles)]
for iter, measurement_df in tqdm(enumerate(measurements)):
wvl = measurement_df.iloc[:, 0]
b_ground = measurement_df.iloc[:, 1]
# Find peak position and FWHM for each particle
for measurement_nbr in measurement_df:
if measurement_nbr in samples_to_plot:
# The first two columns are always wavelength and background
spectra = measurement_df.iloc[:, measurement_nbr]
corr_spectra = (spectra - b_ground) / lamp_spectra
peak_pos, fwhm = lorentzian_fit(wvl, corr_spectra, peak_guess,
False)
peak_positions[measurement_nbr].append(fwhm)
# Take only every 30:th point from the gas_data measurements
t = np.array([a for idx, a in enumerate(gas_data[0]) if idx % 30 == 0])
g = np.array([a for idx, a in enumerate(gas_data[1]) if idx % 30 == 0])
# Remove the extra measurements from gas measurement
t = t[:len(measurements)]
g = g[:len(measurements)]
# Plot
fig, ax1 = plt.subplots()
#for particle_peak_pos in peak_positions:
#t = np.arange(0,119)*30
for smpl in samples_to_plot:
# Plot delta FWHM relative to smallest FWHM for each sample (remove offset)
sample_series = np.array(peak_positions[smpl])