clean up
This commit is contained in:
François Boulogne
parent
157fcdc102
commit
5fe2ef7a67
2 changed files with 61 additions and 107 deletions
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@ -160,8 +160,8 @@ def get_default_start_stop_wavelengths(wavelengths,
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def thickness_from_scheludko(wavelengths,
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intensities,
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refractive_index,
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wavelength_start,
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wavelength_stop,
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wavelength_start=None,
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wavelength_stop=None,
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interference_order=None,
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plot=None):
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"""
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@ -187,14 +187,20 @@ def thickness_from_scheludko(wavelengths,
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if plot:
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setup_matplotlib()
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# A bit extreme...
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max_tested_order = 12
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if interference_order is None or interference_order > 0:
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if wavelength_stop is None or wavelength_start is None:
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raise ValueError('wavelength_start and wavelength_stop must be passed for interference_order != 0.')
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else:
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if wavelength_start > wavelength_stop:
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raise ValueError('wavelength_start and wavelength_stop are swapped.')
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r_index = refractive_index
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# Handle the interference order
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if interference_order is None:
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# Guess the order...
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# A bit extreme...
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max_tested_order = 12
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# mask input data
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mask = (wavelengths >= wavelength_start) & (wavelengths <= wavelength_stop)
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@ -204,8 +210,7 @@ def thickness_from_scheludko(wavelengths,
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min_difference = np.inf
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best_m = None
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best_h_values = None
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thickness_values = None
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if plot:
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plt.figure()
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@ -224,51 +229,85 @@ def thickness_from_scheludko(wavelengths,
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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_values = h_values
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interference_order = m
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thickness_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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else:
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elif interference_order == 0:
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File_I_min = 'tests/spectre_trou/000043641.xy'
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_, intensities_I_min = load_spectrum(File_I_min, lambda_min=450)
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min_peak_prominence = 0.02
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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=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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# keep rhs from the maximum
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mask = wavelengths >= lambda_unique
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wavelengths_masked = wavelengths[mask]
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r_index_masked = r_index[mask]
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intensities_masked = intensities[mask]
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intensities_I_min_masked =intensities_I_min[mask]
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interference_order = 0
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thickness_values = _thicknesses_scheludko_at_order(wavelengths_masked,
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intensities_masked,
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interference_order,
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r_index_masked,
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Imin=intensities_I_min_masked)
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elif interference_order > 0:
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h_values = _thicknesses_scheludko_at_order(wavelengths_masked,
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intensities_masked,
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interference_order,
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r_index_masked)
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best_m = interference_order
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best_h_values = h_values
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thickness_values = h_values
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else:
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raise ValueError('interference_order must be >= 0.')
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# Compute the thickness for the selected order
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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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if interference_order == 0:
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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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else:
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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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Delta_from_data = num / denom
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# Delta_from_data = (intensities_masked -np.min(intensities_masked))/(np.max(intensities_masked) -np.min(intensities_masked))
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# Delta_from_data = (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_values),
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best_m,
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np.mean(thickness_values),
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interference_order,
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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, Delta_from_data, p0=[np.mean(best_h_values)])
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popt, pcov = curve_fit(lambda x, h: _Delta_fit(x, h, interference_order), xdata, Delta_from_data, p0=[np.mean(thickness_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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Delta_values = _Delta(wavelengths_masked, fitted_h, interference_order, r_index_masked)
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plt.figure()
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plt.plot(wavelengths_masked, Delta_from_data,
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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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label = rf'$\mathrm{{Scheludko}}\ (h = {np.mean(thickness_values):.1f} \pm {np.std(thickness_values):.1f}\ \mathrm{{nm}})$'
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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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@ -283,88 +322,3 @@ def thickness_from_scheludko(wavelengths,
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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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intensities,
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refractive_index,
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min_peak_prominence,
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plot=None):
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if plot:
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setup_matplotlib()
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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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min_peak_prominence=min_peak_prominence,
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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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# On crée le masque pour ne garder que les wavelengths superieures a wavelengths unique
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mask = wavelengths >= lambda_unique
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wavelengths_masked = wavelengths[mask]
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r_index_masked = r_index[mask]
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intensities_masked = intensities[mask]
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intensities_I_min_masked =intensities_I_min[mask]
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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 = _thicknesses_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()
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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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Delta_from_data = num / denom
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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, best_m), xdata, Delta_from_data, 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.figure()
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plt.plot(wavelengths_masked, Delta_from_data,
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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'$\lambda$ ($ \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, stderr=std_err)
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