init doc
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François Boulogne
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1800675fd3
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3e5d232e36
50 changed files with 4194 additions and 105 deletions
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@ -46,8 +46,6 @@ def finds_peak(wavelengths, intensities, min_peak_prominence, min_peak_distance=
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(peaks_min, peaks_max)
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"""
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peaks_max, _ = find_peaks(intensities, prominence=min_peak_prominence, distance=min_peak_distance)
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peaks_min, _ = find_peaks(-intensities, prominence=min_peak_prominence, distance=min_peak_distance)
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@ -59,10 +57,11 @@ def finds_peak(wavelengths, intensities, min_peak_prominence, min_peak_distance=
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plt.xlabel(r'$\lambda$ (nm)')
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plt.ylabel(r'$I^*$')
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plt.legend()
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import inspect
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plt.title(inspect.currentframe().f_code.co_name)
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plt.tight_layout()
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plt.show()
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return peaks_min, peaks_max
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@ -74,7 +74,7 @@ def thickness_from_fft(wavelengths, intensities,
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thickness_fft = freq_max / 2.
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if plot:
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plt.figure(figsize=(10, 6),dpi =600)
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plt.figure(figsize=(10, 6), dpi=300)
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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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@ -116,6 +116,8 @@ def thickness_from_minmax(wavelengths,
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ax.legend()
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ax.set_title(f'Thickness = {thickness_minmax:.2f} nm')
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import inspect
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plt.title(inspect.currentframe().f_code.co_name)
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plt.tight_layout()
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plt.show()
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@ -140,6 +142,8 @@ def thickness_from_minmax(wavelengths,
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ax.legend()
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ax.set_title(f'Thickness = {thickness_minmax:.2f} nm')
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import inspect
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plt.title(inspect.currentframe().f_code.co_name)
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plt.tight_layout()
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plt.show()
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@ -165,7 +165,17 @@ def thickness_from_scheludko(wavelengths,
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peaks_min, peaks_max = finds_peak(wavelengths, intensities,
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min_peak_prominence=min_peak_prominence,
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plot=False)
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plot=plot)
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failure, message = False, ''
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if len(peaks_min) == 0:
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message += 'Failed to detect at least one minimum. '
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failure = True
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if len(peaks_max) == 0:
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message += 'Failed to detect at least one maximum. '
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failure = True
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if failure:
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raise RuntimeError(message)
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# Get the last oscillation peaks
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lambda_min = wavelengths[peaks_min[-1]]
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@ -181,9 +191,9 @@ def thickness_from_scheludko(wavelengths,
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r_index_masked = r_index[mask]
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intensities_masked = intensities[mask]
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min_ecart = np.inf
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min_difference = np.inf
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best_m = None
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best_h = None
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best_h_values = None
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if plot:
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plt.figure(figsize=(10, 6), dpi=300)
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@ -195,19 +205,21 @@ def thickness_from_scheludko(wavelengths,
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intensities_masked,
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m, r_index_masked)
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ecart = np.max(h_values) - np.min(h_values)
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difference = np.max(h_values) - np.min(h_values)
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print(f"Écart pour m={m} : {ecart:.3f} nm")
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print(f"h-difference for m={m}: {difference:.1f} nm")
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if ecart < min_ecart:
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min_ecart = ecart
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if difference < min_difference:
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min_difference = difference
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best_m = m
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best_h = h_values
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best_h_values = 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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print(f"Optimized: m={best_m}")
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# Delta
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num = intensities_masked - np.min(intensities_masked)
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denom = np.max(intensities_masked) - np.min(intensities_masked)
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@ -217,14 +229,15 @@ def thickness_from_scheludko(wavelengths,
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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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np.mean(best_h_values),
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best_m,
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r_index_masked)
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xdata = (wavelengths_masked, r_index_masked)
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popt, pcov = curve_fit(lambda x, h: Delta_fit(x, h, m), xdata, DeltaVrai, p0=[np.mean(best_h)])
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popt, pcov = curve_fit(lambda x, h: Delta_fit(x, h, m), xdata, DeltaVrai, p0=[np.mean(best_h_values)])
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fitted_h = popt[0]
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std_err = np.sqrt(pcov[0][0])
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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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@ -232,15 +245,22 @@ def thickness_from_scheludko(wavelengths,
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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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# Scheludko
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label = rf'$\mathrm{{Scheludko}}\ (h = {np.mean(best_h_values):.1f} \pm {np.std(best_h_values):.1f}\ \mathrm{{nm}})$'
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plt.plot(wavelengths_masked, DeltaScheludko,
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'go-', markersize=2, label = rf'$\mathrm{{Scheludko}}\ (h = {np.mean(best_h):.1f} \pm {np.std(best_h):.1f}\ \mathrm{{nm}})$')
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plt.plot(wavelengths_masked, Delta_values, 'ro-',markersize=2, label=rf'$\mathrm{{Fit}}\ (h = {fitted_h:.1f}\pm {np.sqrt(pcov[0][0]):.1f} \ \mathrm{{nm}})$')
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'go-', markersize=2, label=label)
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# Fit
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label = rf'$\mathrm{{Fit}}\ (h = {fitted_h:.1f}\pm {std_err:.1f} \ \mathrm{{nm}})$'
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plt.plot(wavelengths_masked, Delta_values, 'ro-', markersize=2, label=label)
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plt.legend()
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plt.ylabel(r'$\Delta$')
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plt.xlabel(r'$\lambda$ ($ \mathrm{nm} $)')
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import inspect
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plt.title(inspect.currentframe().f_code.co_name)
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return OptimizeResult(thickness=fitted_h,)
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return OptimizeResult(thickness=fitted_h, stderr=std_err)
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def thickness_for_order0(wavelengths,
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@ -258,9 +278,11 @@ def thickness_for_order0(wavelengths,
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peaks_min, peaks_max = finds_peak(wavelengths, intensities,
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min_peak_prominence=min_peak_prominence,
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plot=False)
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plot=plot)
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if len(peaks_max) != 1:
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raise RuntimeError('Failed to detect a single maximum peak.')
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lambda_unique = wavelengths[peaks_max[0]]
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@ -272,50 +294,54 @@ def thickness_for_order0(wavelengths,
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intensities_masked = intensities[mask]
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intensities_I_min_masked =intensities_I_min[mask]
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# best_m = None
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# best_h = None
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m = 0
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h_values = thickness_scheludko_at_order(wavelengths_masked,
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intensities_masked,
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0,
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r_index_masked,
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Imin=intensities_I_min_masked)
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# We assume to be at order zero.
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best_m = 0
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best_h_values = thickness_scheludko_at_order(wavelengths_masked,
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intensities_masked,
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best_m,
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r_index_masked,
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Imin=intensities_I_min_masked)
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if plot:
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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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plt.plot(wavelengths_masked, best_h_values, label=r"Épaisseur du film (Scheludko, m=0)")
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plt.ylabel(r'$h$ ($\mathrm{{nm}}$)')
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plt.xlabel(r'$\lambda$ (nm)')
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import inspect
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plt.title(inspect.currentframe().f_code.co_name)
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# Delta
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num = intensities_masked - np.min(intensities_I_min_masked)
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denom = np.max(intensities_masked) - np.min(intensities_I_min_masked)
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DeltaVrai = num / denom
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DeltaScheludko = Delta(wavelengths_masked, np.mean(best_h), best_m, r_index_masked)
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#print(np.mean(best_h),np.std(best_h))
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if plot:
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plt.figure(figsize=(10, 6), dpi=300)
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plt.plot(wavelengths_masked,DeltaVrai,'bo-', markersize=2,label=r'$\mathrm{{Raw}}\ \mathrm{{Data}}$')
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plt.plot(wavelengths_masked,DeltaScheludko,'ro-', markersize=2,label = rf'$\mathrm{{Scheludko}}\ (h = {np.mean(best_h):.1f} \pm {np.std(best_h):.1f}\ \mathrm{{nm}})$')
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DeltaScheludko = Delta(wavelengths_masked, np.mean(best_h_values), best_m, r_index_masked)
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#print(np.mean(best_h_values),np.std(best_h_values))
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xdata = (wavelengths_masked, r_index_masked)
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popt, pcov = curve_fit(lambda x, h: Delta_fit(x, h, m), xdata, DeltaVrai, p0=[np.mean(best_h)])
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popt, pcov = curve_fit(lambda x, h: Delta_fit(x, h, best_m), xdata, DeltaVrai, p0=[np.mean(best_h_values)])
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fitted_h = popt[0]
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std_err = np.sqrt(pcov[0][0])
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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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label=rf'$\mathrm{{Fit}}\ (h = {fitted_h:.1f}\pm {np.sqrt(pcov[0][0]):.1f} \ \mathrm{{nm}})$')
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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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# Scheludko
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label = rf'$\mathrm{{Scheludko}}\ (h = {np.mean(best_h_values):.1f} \pm {np.std(best_h_values):.1f}\ \mathrm{{nm}})$'
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plt.plot(wavelengths_masked, DeltaScheludko,
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'go-', markersize=2, label=label)
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# Fit
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label = rf'$\mathrm{{Fit}}\ (h = {fitted_h:.1f}\pm {std_err:.1f} \ \mathrm{{nm}})$'
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plt.plot(wavelengths_masked, Delta_values, 'ro-', markersize=2, label=label)
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plt.legend()
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plt.ylabel(r'$\Delta$')
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plt.xlabel(r'$\lambda$ (nm)')
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plt.xlabel(r'$\lambda$ ($ \mathrm{nm} $)')
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import inspect
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plt.title(inspect.currentframe().f_code.co_name)
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return OptimizeResult(thickness=fitted_h,)
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return OptimizeResult(thickness=fitted_h, stderr=std_err)
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