fixes
This commit is contained in:
François Boulogne
parent
d5fae56eb4
commit
1800675fd3
5 changed files with 107 additions and 109 deletions
178
optifik/fft.py
178
optifik/fft.py
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@ -71,12 +71,10 @@ def thickness_from_fft(wavelengths, intensities,
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idx_max_fft = np.argmax(abs(intensities_fft))
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freq_max = inverse_wavelengths_fft[idx_max_fft]
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thickness_fft = freq_max / 2.
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plt.figure(figsize=(10, 6),dpi =600)
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if plot:
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plt.figure(figsize=(10, 6),dpi =600)
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plt.loglog(inverse_wavelengths_fft, np.abs(intensities_fft))
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plt.loglog(freq_max, np.abs(intensities_fft[idx_max_fft]), 'o')
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plt.xlabel('Frequency')
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@ -86,90 +84,90 @@ def thickness_from_fft(wavelengths, intensities,
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return OptimizeResult(thickness=thickness_fft,)
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def Prominence_from_fft(wavelengths, intensities, refractive_index,
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num_half_space=None, plot=None):
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if num_half_space is None:
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num_half_space = len(wavelengths)
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# # # 1. Spectre original
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# if plot:
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# plt.figure(figsize=(10, 6), dpi=150)
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# plt.plot(1/wavelengths, intensities, label='Spectre original')
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# plt.xlabel('1/Longueur d\'onde (nm-1)')
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# plt.ylabel('Intensité')
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# plt.legend()
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# plt.show()
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# 2. Conversion lambda → k = n(λ) / λ
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k_vals = refractive_index / wavelengths
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f_interp = interp1d(k_vals, intensities, kind='linear', fill_value="extrapolate")
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# 3. Axe k uniforme + interpolation
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k_linspace = np.linspace(k_vals[0], k_vals[-1], 2 * num_half_space)
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intensities_k = f_interp(k_linspace)
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# 4. FFT
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delta_k = (k_vals[-1] - k_vals[0]) / (2 * num_half_space)
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fft_vals = fft(intensities_k)
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freqs = fftfreq(2 * num_half_space, delta_k)
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# 5. Pic FFT
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freqs_pos = freqs[freqs > 0]
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abs_fft_pos = np.abs(fft_vals[freqs > 0])
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idx_max = np.argmax(abs_fft_pos)
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F_max = freqs_pos[idx_max]
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if plot:
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plt.figure(figsize=(10, 6), dpi=150)
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plt.plot(freqs_pos, abs_fft_pos, label='|FFT|')
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plt.axvline(F_max, color='r', linestyle='--', label='Pic principal')
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plt.xlabel('Distance optique [nm]')
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plt.ylabel(r'FFT($I^*$)')
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plt.xscale('log')
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plt.yscale('log')
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plt.legend()
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plt.show()
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# 6. Filtrage (garde hautes fréquences)
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cutoff_HF = 2 * F_max
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mask_HF = np.abs(freqs) >= cutoff_HF
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fft_filtered_HF = np.zeros_like(fft_vals, dtype=complex)
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fft_filtered_HF[mask_HF] = fft_vals[mask_HF]
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# 7. Filtrage (garde basses fréquences)
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cutoff_BF = 10 * F_max
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mask_BF = np.abs(freqs) <= cutoff_BF
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fft_filtered_BF = np.zeros_like(fft_vals, dtype=complex)
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fft_filtered_BF[mask_BF] = fft_vals[mask_BF]
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# 8. Reconstruction
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signal_filtered_HF = np.real(ifft(fft_filtered_HF))
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signal_filtered_BF = np.real(ifft(fft_filtered_BF))
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lambda_reconstructed = np.interp(k_linspace, k_vals[::-1], wavelengths[::-1])
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# Masque dans la plage [550, 700] nm
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mask_Cam_Sensitivity = (lambda_reconstructed >= 550) & (lambda_reconstructed <= 700)
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# 9. Affichage reconstruction
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if plot:
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plt.figure(figsize=(10, 6), dpi=150)
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plt.plot(lambda_reconstructed, intensities_k, '--', label='Original interpolé')
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plt.plot(lambda_reconstructed, signal_filtered_HF,'--', color='gray')
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plt.plot(lambda_reconstructed[mask_Cam_Sensitivity], signal_filtered_HF[mask_Cam_Sensitivity],
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color='orange', label='Spectre filtré HF')
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plt.plot(lambda_reconstructed, signal_filtered_BF,
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color='red', label='Spectre filtré BF')
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plt.xlabel('Wavelength (nm)')
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plt.ylabel('Intensity')
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plt.legend()
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plt.show()
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max_amplitude = np.max(np.abs(signal_filtered_HF[mask_Cam_Sensitivity]))
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return max_amplitude, signal_filtered_BF, lambda_reconstructed
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#def Prominence_from_fft(wavelengths, intensities, refractive_index,
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# num_half_space=None, plot=None):
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# if num_half_space is None:
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# num_half_space = len(wavelengths)
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#
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# # # # 1. Spectre original
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# # if plot:
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# # plt.figure(figsize=(10, 6), dpi=150)
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# # plt.plot(1/wavelengths, intensities, label='Spectre original')
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# # plt.xlabel('1/Longueur d\'onde (nm-1)')
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# # plt.ylabel('Intensité')
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# # plt.legend()
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# # plt.show()
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#
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#
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# # 2. Conversion lambda → k = n(λ) / λ
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# k_vals = refractive_index / wavelengths
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# f_interp = interp1d(k_vals, intensities, kind='linear', fill_value="extrapolate")
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#
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# # 3. Axe k uniforme + interpolation
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# k_linspace = np.linspace(k_vals[0], k_vals[-1], 2 * num_half_space)
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# intensities_k = f_interp(k_linspace)
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#
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# # 4. FFT
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# delta_k = (k_vals[-1] - k_vals[0]) / (2 * num_half_space)
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# fft_vals = fft(intensities_k)
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# freqs = fftfreq(2 * num_half_space, delta_k)
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#
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# # 5. Pic FFT
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# freqs_pos = freqs[freqs > 0]
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# abs_fft_pos = np.abs(fft_vals[freqs > 0])
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# idx_max = np.argmax(abs_fft_pos)
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# F_max = freqs_pos[idx_max]
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#
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# if plot:
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# plt.figure(figsize=(10, 6), dpi=150)
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# plt.plot(freqs_pos, abs_fft_pos, label='|FFT|')
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# plt.axvline(F_max, color='r', linestyle='--', label='Pic principal')
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# plt.xlabel('Distance optique [nm]')
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# plt.ylabel(r'FFT($I^*$)')
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# plt.xscale('log')
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# plt.yscale('log')
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# plt.legend()
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# plt.show()
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#
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# # 6. Filtrage (garde hautes fréquences)
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# cutoff_HF = 2 * F_max
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#
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# mask_HF = np.abs(freqs) >= cutoff_HF
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# fft_filtered_HF = np.zeros_like(fft_vals, dtype=complex)
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# fft_filtered_HF[mask_HF] = fft_vals[mask_HF]
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#
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# # 7. Filtrage (garde basses fréquences)
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# cutoff_BF = 10 * F_max
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# mask_BF = np.abs(freqs) <= cutoff_BF
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# fft_filtered_BF = np.zeros_like(fft_vals, dtype=complex)
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# fft_filtered_BF[mask_BF] = fft_vals[mask_BF]
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#
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#
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# # 8. Reconstruction
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# signal_filtered_HF = np.real(ifft(fft_filtered_HF))
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# signal_filtered_BF = np.real(ifft(fft_filtered_BF))
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# lambda_reconstructed = np.interp(k_linspace, k_vals[::-1], wavelengths[::-1])
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#
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# # Masque dans la plage [550, 700] nm
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# mask_Cam_Sensitivity = (lambda_reconstructed >= 550) & (lambda_reconstructed <= 700)
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#
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# # 9. Affichage reconstruction
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# if plot:
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# plt.figure(figsize=(10, 6), dpi=150)
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# plt.plot(lambda_reconstructed, intensities_k, '--', label='Original interpolé')
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# plt.plot(lambda_reconstructed, signal_filtered_HF,'--', color='gray')
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#
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# plt.plot(lambda_reconstructed[mask_Cam_Sensitivity], signal_filtered_HF[mask_Cam_Sensitivity],
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# color='orange', label='Spectre filtré HF')
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# plt.plot(lambda_reconstructed, signal_filtered_BF,
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# color='red', label='Spectre filtré BF')
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#
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# plt.xlabel('Wavelength (nm)')
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# plt.ylabel('Intensity')
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# plt.legend()
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# plt.show()
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#
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# max_amplitude = np.max(np.abs(signal_filtered_HF[mask_Cam_Sensitivity]))
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#
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# return max_amplitude, signal_filtered_BF, lambda_reconstructed
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#
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