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clean up + fix api

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François Boulogne 2025-06-20 14:03:02 +02:00
parent a9df27b7c7
commit 9353913cf3
1 changed files with 1 additions and 90 deletions
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91
optifik/fft.py
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@ -27,7 +27,7 @@ def thickness_from_fft(wavelengths, intensities,
num_half_space : scalar, optional
Number of points to compute FFT's half space.
If `None`, default corresponds to `10*len(wavelengths)`.
debug : boolean, optional
plot : boolean, optional
Show plot of the transformed signal and the peak detection.
Returns
@ -77,92 +77,3 @@ def thickness_from_fft(wavelengths, intensities,
plt.title(f'Thickness={thickness_fft:.2f}')
return OptimizeResult(thickness=thickness_fft,)
#def Prominence_from_fft(wavelengths, intensities, refractive_index,
# num_half_space=None, plot=None):
# if num_half_space is None:
# num_half_space = len(wavelengths)
#
# # # # 1. Spectre original
# # if plot:
# # plt.figure()
# # plt.plot(1/wavelengths, intensities, label='Spectre original')
# # plt.xlabel('1/Longueur d\'onde (nm-1)')
# # plt.ylabel('Intensité')
# # plt.legend()
# # plt.show()
#
#
# # 2. Conversion lambda → k = n(λ) / λ
# k_vals = refractive_index / wavelengths
# f_interp = interp1d(k_vals, intensities, kind='linear', fill_value="extrapolate")
#
# # 3. Axe k uniforme + interpolation
# k_linspace = np.linspace(k_vals[0], k_vals[-1], 2 * num_half_space)
# intensities_k = f_interp(k_linspace)
#
# # 4. FFT
# delta_k = (k_vals[-1] - k_vals[0]) / (2 * num_half_space)
# fft_vals = fft(intensities_k)
# freqs = fftfreq(2 * num_half_space, delta_k)
#
# # 5. Pic FFT
# freqs_pos = freqs[freqs > 0]
# abs_fft_pos = np.abs(fft_vals[freqs > 0])
# idx_max = np.argmax(abs_fft_pos)
# F_max = freqs_pos[idx_max]
#
# if plot:
# plt.figure()
# plt.plot(freqs_pos, abs_fft_pos, label='|FFT|')
# plt.axvline(F_max, color='r', linestyle='--', label='Pic principal')
# plt.xlabel('Distance optique [nm]')
# plt.ylabel(r'FFT($I^*$)')
# plt.xscale('log')
# plt.yscale('log')
# plt.legend()
# plt.show()
#
# # 6. Filtrage (garde hautes fréquences)
# cutoff_HF = 2 * F_max
#
# mask_HF = np.abs(freqs) >= cutoff_HF
# fft_filtered_HF = np.zeros_like(fft_vals, dtype=complex)
# fft_filtered_HF[mask_HF] = fft_vals[mask_HF]
#
# # 7. Filtrage (garde basses fréquences)
# cutoff_BF = 10 * F_max
# mask_BF = np.abs(freqs) <= cutoff_BF
# fft_filtered_BF = np.zeros_like(fft_vals, dtype=complex)
# fft_filtered_BF[mask_BF] = fft_vals[mask_BF]
#
#
# # 8. Reconstruction
# signal_filtered_HF = np.real(ifft(fft_filtered_HF))
# signal_filtered_BF = np.real(ifft(fft_filtered_BF))
# lambda_reconstructed = np.interp(k_linspace, k_vals[::-1], wavelengths[::-1])
#
# # Masque dans la plage [550, 700] nm
# mask_Cam_Sensitivity = (lambda_reconstructed >= 550) & (lambda_reconstructed <= 700)
#
# # 9. Affichage reconstruction
# if plot:
# plt.figure()
# plt.plot(lambda_reconstructed, intensities_k, '--', label='Original interpolé')
# plt.plot(lambda_reconstructed, signal_filtered_HF,'--', color='gray')
#
# plt.plot(lambda_reconstructed[mask_Cam_Sensitivity], signal_filtered_HF[mask_Cam_Sensitivity],
# color='orange', label='Spectre filtré HF')
# plt.plot(lambda_reconstructed, signal_filtered_BF,
# color='red', label='Spectre filtré BF')
#
# plt.xlabel('Wavelength (nm)')
# plt.ylabel('Intensity')
# plt.legend()
# plt.show()
#
# max_amplitude = np.max(np.abs(signal_filtered_HF[mask_Cam_Sensitivity]))
#
# return max_amplitude, signal_filtered_BF, lambda_reconstructed
#