fixes
This commit is contained in:
François Boulogne
parent
d5fae56eb4
commit
1800675fd3
5 changed files with 107 additions and 109 deletions
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@ -26,7 +26,7 @@ def plot_spectrum(wavelengths, intensities, title=''):
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def finds_peak(wavelengths, intensities, min_peak_prominence, min_peak_distance=10, plot=None):
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"""
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Detect minima and maxima
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Detect minima and maxima.
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Parameters
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----------
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@ -1,88 +0,0 @@
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import os.path
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from .analysis import *
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from .io import load_spectrum
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def auto(spectrum_file, plot=None):
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spectre_file = spectrum_file
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##### Affichage du spectre brut et récupération des Intesités brutes#####
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lambdas, raw_intensities = load_spectrum(spectre_file, lambda_min=450)
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##### Affichage du spectre lissé #####
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#smoothed_intensities, intensities, lambdas = Data_Smoothed(spectre_file)
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smoothed_intensities = smooth_intensities(raw_intensities)
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##### Indice Optique en fonction de Lambda #####
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indice = 1.324188 + 3102.060378 / (lambdas**2)
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##### Determination de la prominence associé #####
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prominence, signal, wavelength = Prominence_from_fft(lambdas,
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smoothed_intensities,
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refractive_index=indice,
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plot=plot)
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prominence = 0.03
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##### Find Peak #####
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peaks_min, peaks_max = finds_peak(lambdas, smoothed_intensities,
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min_peak_prominence=prominence,
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plot=False)
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##### Epaisseur selon la methode #####
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#thickness_FFT = thickness_from_fft(lambdas,smoothed_intensities,refractive_index=1.33)
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total_extrema = len(peaks_max) + len(peaks_min)
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if total_extrema > 15 and total_extrema > 4:
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print('Apply method FFT')
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result = thickness_from_fft(lambdas, smoothed_intensities,
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refractive_index=indice,
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plot=plot)
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print(f'thickness: {result.thickness:.2f} nm')
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if total_extrema <= 15 and total_extrema > 4:
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print('Apply method minmax')
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result = thickness_from_minmax(lambdas, smoothed_intensities,
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refractive_index=indice,
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min_peak_prominence=prominence,
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plot=plot)
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print(f'thickness: {result.thickness:.2f} nm')
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if total_extrema <= 4 and total_extrema >= 2: #& 2peak minimum:
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print('Apply method Scheludko')
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result = thickness_from_scheludko(lambdas, smoothed_intensities,
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refractive_index=indice,
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min_peak_prominence=prominence,
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plot=plot)
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print(f'thickness: {result.thickness:.2f} nm')
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if total_extrema <= 4 and len(peaks_max) == 1 and len(peaks_min) == 0 : #dans l'ordre zéro !
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print('Apply method ordre0')
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result = thickness_for_order0(lambdas, smoothed_intensities,
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refractive_index=indice,
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min_peak_prominence=prominence,
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plot=plot)
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print(f'thickness: {result.thickness:.2f} nm')
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if total_extrema <= 4 and len(peaks_max) == 0 and (len(peaks_min) == 1 or len(peaks_min) == 0):
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#& 1peak min ou zéro:
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thickness = None
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print('Zone Ombre')
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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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@ -29,10 +29,10 @@ def thickness_scheludko_at_order(wavelengths,
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Wavelength values in nm.
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intensities : array
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Intensity values.
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interference_order: TYPE
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DESCRIPTION.
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refractive_index : TYPE
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DESCRIPTION.
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interference_order : int
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Interference order.
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refractive_index : array_like (or float)
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Refractive index.
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Imin : TYPE, optional
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DESCRIPTION. The default is None.
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@ -203,7 +203,7 @@ def thickness_from_scheludko(wavelengths,
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min_ecart = ecart
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best_m = m
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best_h = h_values
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if plot:
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plt.plot(wavelengths_masked, h_values,'.', markersize=3, label=f"Épaisseur du film (Scheludko, m={m})")
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@ -216,9 +216,9 @@ def thickness_from_scheludko(wavelengths,
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# DeltaVrai = (intensities_masked -np.min(intensities_masked))/(np.max(intensities_masked) -np.min(intensities_masked))
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# DeltaVrai = (intensities_raw_masked -np.min(intensities_raw_masked))/(np.max(intensities_raw_masked) -np.min(intensities_raw_masked))
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DeltaScheludko = Delta(wavelengths_masked,
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np.mean(best_h),
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best_m,
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DeltaScheludko = Delta(wavelengths_masked,
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np.mean(best_h),
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best_m,
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r_index_masked)
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@ -228,7 +228,7 @@ def thickness_from_scheludko(wavelengths,
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if plot:
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Delta_values = Delta(wavelengths_masked, fitted_h, best_m, r_index_masked)
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plt.figure(figsize=(10, 6), dpi=300)
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plt.plot(wavelengths_masked, DeltaVrai,
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'bo-', markersize=2, label=r'$\mathrm{{Smoothed}}\ \mathrm{{Data}}$')
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@ -240,7 +240,7 @@ def thickness_from_scheludko(wavelengths,
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plt.xlabel(r'$\lambda$ ($ \mathrm{nm} $)')
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return OptimizeResult(thickness=fitted_h ,)
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return OptimizeResult(thickness=fitted_h,)
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def thickness_for_order0(wavelengths,
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@ -249,11 +249,11 @@ def thickness_for_order0(wavelengths,
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min_peak_prominence,
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plot=None):
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# TODO :
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# TODO :
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# Load "trou"
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File_I_min = 'tests/spectre_trou/000043641.xy'
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wavelengths_I_min, intensities_I_min = load_spectrum(File_I_min, lambda_min=450)
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r_index = refractive_index
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peaks_min, peaks_max = finds_peak(wavelengths, intensities,
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@ -287,7 +287,7 @@ def thickness_for_order0(wavelengths,
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plt.figure(figsize=(10, 6), dpi=300)
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plt.plot(wavelengths_masked, h_values, label=r"Épaisseur du film (Scheludko, m=0)")
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best_m = m
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best_h = h_values
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@ -311,8 +311,8 @@ def thickness_for_order0(wavelengths,
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if plot:
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Delta_values = Delta(wavelengths_masked, fitted_h, best_m, r_index_masked)
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plt.plot(wavelengths_masked, Delta_values,
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'go-', markersize=2,
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plt.plot(wavelengths_masked, Delta_values,
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'go-', markersize=2,
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label=rf'$\mathrm{{Fit}}\ (h = {fitted_h:.1f}\pm {np.sqrt(pcov[0][0]):.1f} \ \mathrm{{nm}})$')
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plt.legend()
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plt.ylabel(r'$\Delta$')
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