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This commit is contained in:
François Boulogne 2025-06-04 13:20:13 +02:00
parent d5fae56eb4
commit 1800675fd3
5 changed files with 107 additions and 109 deletions
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0
optifik/auto.py → old/auto.py
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2
optifik/analysis.py
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@ -26,7 +26,7 @@ def plot_spectrum(wavelengths, intensities, title=''):
def finds_peak(wavelengths, intensities, min_peak_prominence, min_peak_distance=10, plot=None): def finds_peak(wavelengths, intensities, min_peak_prominence, min_peak_distance=10, plot=None):
""" """
Detect minima and maxima Detect minima and maxima.
Parameters Parameters
---------- ----------

178
optifik/fft.py
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@ -71,12 +71,10 @@ def thickness_from_fft(wavelengths, intensities,
idx_max_fft = np.argmax(abs(intensities_fft)) idx_max_fft = np.argmax(abs(intensities_fft))
freq_max = inverse_wavelengths_fft[idx_max_fft] freq_max = inverse_wavelengths_fft[idx_max_fft]
thickness_fft = freq_max / 2. thickness_fft = freq_max / 2.
plt.figure(figsize=(10, 6),dpi =600)
if plot: if plot:
plt.figure(figsize=(10, 6),dpi =600)
plt.loglog(inverse_wavelengths_fft, np.abs(intensities_fft)) plt.loglog(inverse_wavelengths_fft, np.abs(intensities_fft))
plt.loglog(freq_max, np.abs(intensities_fft[idx_max_fft]), 'o') plt.loglog(freq_max, np.abs(intensities_fft[idx_max_fft]), 'o')
plt.xlabel('Frequency') plt.xlabel('Frequency')
@ -86,90 +84,90 @@ def thickness_from_fft(wavelengths, intensities,
return OptimizeResult(thickness=thickness_fft,) return OptimizeResult(thickness=thickness_fft,)
def Prominence_from_fft(wavelengths, intensities, refractive_index, #def Prominence_from_fft(wavelengths, intensities, refractive_index,
num_half_space=None, plot=None): # num_half_space=None, plot=None):
if num_half_space is None: # if num_half_space is None:
num_half_space = len(wavelengths) # num_half_space = len(wavelengths)
#
# # # 1. Spectre original # # # # 1. Spectre original
# if plot: # # if plot:
# plt.figure(figsize=(10, 6), dpi=150) # # plt.figure(figsize=(10, 6), dpi=150)
# plt.plot(1/wavelengths, intensities, label='Spectre original') # # plt.plot(1/wavelengths, intensities, label='Spectre original')
# plt.xlabel('1/Longueur d\'onde (nm-1)') # # plt.xlabel('1/Longueur d\'onde (nm-1)')
# plt.ylabel('Intensité') # # plt.ylabel('Intensité')
# plt.legend() # # plt.legend()
# plt.show() # # plt.show()
#
#
# 2. Conversion lambda → k = n(λ) / λ # # 2. Conversion lambda → k = n(λ) / λ
k_vals = refractive_index / wavelengths # k_vals = refractive_index / wavelengths
f_interp = interp1d(k_vals, intensities, kind='linear', fill_value="extrapolate") # f_interp = interp1d(k_vals, intensities, kind='linear', fill_value="extrapolate")
#
# 3. Axe k uniforme + interpolation # # 3. Axe k uniforme + interpolation
k_linspace = np.linspace(k_vals[0], k_vals[-1], 2 * num_half_space) # k_linspace = np.linspace(k_vals[0], k_vals[-1], 2 * num_half_space)
intensities_k = f_interp(k_linspace) # intensities_k = f_interp(k_linspace)
#
# 4. FFT # # 4. FFT
delta_k = (k_vals[-1] - k_vals[0]) / (2 * num_half_space) # delta_k = (k_vals[-1] - k_vals[0]) / (2 * num_half_space)
fft_vals = fft(intensities_k) # fft_vals = fft(intensities_k)
freqs = fftfreq(2 * num_half_space, delta_k) # freqs = fftfreq(2 * num_half_space, delta_k)
#
# 5. Pic FFT # # 5. Pic FFT
freqs_pos = freqs[freqs > 0] # freqs_pos = freqs[freqs > 0]
abs_fft_pos = np.abs(fft_vals[freqs > 0]) # abs_fft_pos = np.abs(fft_vals[freqs > 0])
idx_max = np.argmax(abs_fft_pos) # idx_max = np.argmax(abs_fft_pos)
F_max = freqs_pos[idx_max] # F_max = freqs_pos[idx_max]
#
if plot: # if plot:
plt.figure(figsize=(10, 6), dpi=150) # plt.figure(figsize=(10, 6), dpi=150)
plt.plot(freqs_pos, abs_fft_pos, label='|FFT|') # plt.plot(freqs_pos, abs_fft_pos, label='|FFT|')
plt.axvline(F_max, color='r', linestyle='--', label='Pic principal') # plt.axvline(F_max, color='r', linestyle='--', label='Pic principal')
plt.xlabel('Distance optique [nm]') # plt.xlabel('Distance optique [nm]')
plt.ylabel(r'FFT($I^*$)') # plt.ylabel(r'FFT($I^*$)')
plt.xscale('log') # plt.xscale('log')
plt.yscale('log') # plt.yscale('log')
plt.legend() # plt.legend()
plt.show() # plt.show()
#
# 6. Filtrage (garde hautes fréquences) # # 6. Filtrage (garde hautes fréquences)
cutoff_HF = 2 * F_max # cutoff_HF = 2 * F_max
#
mask_HF = np.abs(freqs) >= cutoff_HF # mask_HF = np.abs(freqs) >= cutoff_HF
fft_filtered_HF = np.zeros_like(fft_vals, dtype=complex) # fft_filtered_HF = np.zeros_like(fft_vals, dtype=complex)
fft_filtered_HF[mask_HF] = fft_vals[mask_HF] # fft_filtered_HF[mask_HF] = fft_vals[mask_HF]
#
# 7. Filtrage (garde basses fréquences) # # 7. Filtrage (garde basses fréquences)
cutoff_BF = 10 * F_max # cutoff_BF = 10 * F_max
mask_BF = np.abs(freqs) <= cutoff_BF # mask_BF = np.abs(freqs) <= cutoff_BF
fft_filtered_BF = np.zeros_like(fft_vals, dtype=complex) # fft_filtered_BF = np.zeros_like(fft_vals, dtype=complex)
fft_filtered_BF[mask_BF] = fft_vals[mask_BF] # fft_filtered_BF[mask_BF] = fft_vals[mask_BF]
#
#
# 8. Reconstruction # # 8. Reconstruction
signal_filtered_HF = np.real(ifft(fft_filtered_HF)) # signal_filtered_HF = np.real(ifft(fft_filtered_HF))
signal_filtered_BF = np.real(ifft(fft_filtered_BF)) # signal_filtered_BF = np.real(ifft(fft_filtered_BF))
lambda_reconstructed = np.interp(k_linspace, k_vals[::-1], wavelengths[::-1]) # lambda_reconstructed = np.interp(k_linspace, k_vals[::-1], wavelengths[::-1])
#
# Masque dans la plage [550, 700] nm # # Masque dans la plage [550, 700] nm
mask_Cam_Sensitivity = (lambda_reconstructed >= 550) & (lambda_reconstructed <= 700) # mask_Cam_Sensitivity = (lambda_reconstructed >= 550) & (lambda_reconstructed <= 700)
#
# 9. Affichage reconstruction # # 9. Affichage reconstruction
if plot: # if plot:
plt.figure(figsize=(10, 6), dpi=150) # plt.figure(figsize=(10, 6), dpi=150)
plt.plot(lambda_reconstructed, intensities_k, '--', label='Original interpolé') # plt.plot(lambda_reconstructed, intensities_k, '--', label='Original interpolé')
plt.plot(lambda_reconstructed, signal_filtered_HF,'--', color='gray') # plt.plot(lambda_reconstructed, signal_filtered_HF,'--', color='gray')
#
plt.plot(lambda_reconstructed[mask_Cam_Sensitivity], signal_filtered_HF[mask_Cam_Sensitivity], # plt.plot(lambda_reconstructed[mask_Cam_Sensitivity], signal_filtered_HF[mask_Cam_Sensitivity],
color='orange', label='Spectre filtré HF') # color='orange', label='Spectre filtré HF')
plt.plot(lambda_reconstructed, signal_filtered_BF, # plt.plot(lambda_reconstructed, signal_filtered_BF,
color='red', label='Spectre filtré BF') # color='red', label='Spectre filtré BF')
#
plt.xlabel('Wavelength (nm)') # plt.xlabel('Wavelength (nm)')
plt.ylabel('Intensity') # plt.ylabel('Intensity')
plt.legend() # plt.legend()
plt.show() # plt.show()
#
max_amplitude = np.max(np.abs(signal_filtered_HF[mask_Cam_Sensitivity])) # max_amplitude = np.max(np.abs(signal_filtered_HF[mask_Cam_Sensitivity]))
#
return max_amplitude, signal_filtered_BF, lambda_reconstructed # return max_amplitude, signal_filtered_BF, lambda_reconstructed
#

10
optifik/scheludko.py
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@ -29,10 +29,10 @@ def thickness_scheludko_at_order(wavelengths,
Wavelength values in nm. Wavelength values in nm.
intensities : array intensities : array
Intensity values. Intensity values.
interference_order: TYPE interference_order : int
DESCRIPTION. Interference order.
refractive_index : TYPE refractive_index : array_like (or float)
DESCRIPTION. Refractive index.
Imin : TYPE, optional Imin : TYPE, optional
DESCRIPTION. The default is None. DESCRIPTION. The default is None.
@ -240,7 +240,7 @@ def thickness_from_scheludko(wavelengths,
plt.xlabel(r'$\lambda$ ($ \mathrm{nm} $)') plt.xlabel(r'$\lambda$ ($ \mathrm{nm} $)')
return OptimizeResult(thickness=fitted_h ,) return OptimizeResult(thickness=fitted_h,)
def thickness_for_order0(wavelengths, def thickness_for_order0(wavelengths,